.gitignore

@@ -1,57 +0,0 @@
-# Byte-compiled / optimized / DLL files
-__pycache__/
-*.py[cod]
-
-# C extensions
-*.so
-
-# Distribution / packaging
-.Python
-env/
-build/
-develop-eggs/
-dist/
-downloads/
-eggs/
-.eggs/
-lib/
-lib64/
-parts/
-sdist/
-var/
-*.egg-info/
-.installed.cfg
-*.egg
-
-# PyInstaller
-#  Usually these files are written by a python script from a template
-#  before PyInstaller builds the exe, so as to inject date/other infos into it.
-*.manifest
-*.spec
-
-# Installer logs
-pip-log.txt
-pip-delete-this-directory.txt
-
-# Unit test / coverage reports
-htmlcov/
-.tox/
-.coverage
-.coverage.*
-.cache
-nosetests.xml
-coverage.xml
-*,cover
-
-# Translations
-*.mo
-*.pot
-
-# Django stuff:
-*.log
-
-# Sphinx documentation
-docs/_build/
-
-# PyBuilder
-target/

.zenodo.json

@@ -62,7 +62,7 @@
             "name": "Hossein Javedani Sadaei"
         }
     ], 
-    "version": "pkg1.7", 
+    "version": "pkg1.6", 
     "upload_type": "software", 
     "keywords": [
         "data science", 

CITATION.cff

@@ -1,10 +0,0 @@
-cff-version: 1.1.0
-message: "If you use this software, please cite it as below."
-authors:
-  - family-names: Silva
-    given-names: Petrônio C. L. 
-    orcid: https://orcid.org/0000-0002-1202-2552/
-title: PYFTS/pyFTS: Stable version 1.7
-doi: 10.5281/zenodo.597359
-version: pkg1.7
-date-released: 2023-03-20

MANIFEST

@@ -104,7 +104,7 @@ pyFTS/models/seasonal/common.py
 pyFTS/models/seasonal/msfts.py
 pyFTS/models/seasonal/partitioner.py
 pyFTS/models/seasonal/sfts.py
-pyFTS/partitioners/KMeans.py
+pyFTS/partitioners/CMeans.py
 pyFTS/partitioners/Entropy.py
 pyFTS/partitioners/FCM.py
 pyFTS/partitioners/Grid.py

README.md

@@ -38,7 +38,7 @@ Fuzzy Time Series (FTS) are non parametric methods for time series forecasting b
 2. **Universe of Discourse Partitioning**: This is the most important step. Here, the range of values of the numerical time series *Y(t)* will be splited in overlapped intervals and for each interval will be created a Fuzzy Set. This step is performed by pyFTS.partition module and its classes (for instance GridPartitioner, EntropyPartitioner, etc). The main parameters are:
  - the number of intervals
  - which fuzzy membership function (on [pyFTS.common.Membership](https://github.com/PYFTS/pyFTS/blob/master/pyFTS/common/Membership.py))
- - partition scheme ([GridPartitioner](https://github.com/PYFTS/pyFTS/blob/master/pyFTS/partitioners/Grid.py), [EntropyPartitioner](https://github.com/PYFTS/pyFTS/blob/master/pyFTS/partitioners/Entropy.py)[3], [FCMPartitioner](https://github.com/PYFTS/pyFTS/blob/master/pyFTS/partitioners/FCM.py), [KMeansPartitioner](https://github.com/PYFTS/pyFTS/blob/master/pyFTS/partitioners/KMeans.py), [HuarngPartitioner](https://github.com/PYFTS/pyFTS/blob/master/pyFTS/partitioners/Huarng.py)[4])
+ - partition scheme ([GridPartitioner](https://github.com/PYFTS/pyFTS/blob/master/pyFTS/partitioners/Grid.py), [EntropyPartitioner](https://github.com/PYFTS/pyFTS/blob/master/pyFTS/partitioners/Entropy.py)[3], [FCMPartitioner](https://github.com/PYFTS/pyFTS/blob/master/pyFTS/partitioners/FCM.py), [CMeansPartitioner](https://github.com/PYFTS/pyFTS/blob/master/pyFTS/partitioners/CMeans.py), [HuarngPartitioner](https://github.com/PYFTS/pyFTS/blob/master/pyFTS/partitioners/Huarng.py)[4])
  
  Check out the jupyter notebook on [notebooks/Partitioners.ipynb](https://github.com/PYFTS/notebooks/blob/master/Partitioners.ipynb) for sample codes.
  

docs/build/html/.buildinfo

@@ -1,4 +1,4 @@
 # Sphinx build info version 1
 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
-config: e1454eaeb1089d326a83742cdfb66db8
+config: 32d60ea8a7e69a10f6b65d4fe3722cb4
 tags: 645f666f9bcd5a90fca523b33c5a78b7

docs/build/html/_modules/index.html

@@ -42,13 +42,24 @@
           <div class="body" role="main">
             
   <h1>All modules for which code is available</h1>
-<ul><li><a href="pyFTS/common/Activations.html">pyFTS.common.Activations</a></li>
+<ul><li><a href="pyFTS/benchmarks/BSTS.html">pyFTS.benchmarks.BSTS</a></li>
+<li><a href="pyFTS/benchmarks/Measures.html">pyFTS.benchmarks.Measures</a></li>
+<li><a href="pyFTS/benchmarks/ResidualAnalysis.html">pyFTS.benchmarks.ResidualAnalysis</a></li>
+<li><a href="pyFTS/benchmarks/Tests.html">pyFTS.benchmarks.Tests</a></li>
+<li><a href="pyFTS/benchmarks/Util.html">pyFTS.benchmarks.Util</a></li>
+<li><a href="pyFTS/benchmarks/arima.html">pyFTS.benchmarks.arima</a></li>
+<li><a href="pyFTS/benchmarks/benchmarks.html">pyFTS.benchmarks.benchmarks</a></li>
+<li><a href="pyFTS/benchmarks/knn.html">pyFTS.benchmarks.knn</a></li>
+<li><a href="pyFTS/benchmarks/naive.html">pyFTS.benchmarks.naive</a></li>
+<li><a href="pyFTS/benchmarks/quantreg.html">pyFTS.benchmarks.quantreg</a></li>
 <li><a href="pyFTS/common/Composite.html">pyFTS.common.Composite</a></li>
 <li><a href="pyFTS/common/FLR.html">pyFTS.common.FLR</a></li>
 <li><a href="pyFTS/common/FuzzySet.html">pyFTS.common.FuzzySet</a></li>
 <li><a href="pyFTS/common/Membership.html">pyFTS.common.Membership</a></li>
 <li><a href="pyFTS/common/SortedCollection.html">pyFTS.common.SortedCollection</a></li>
+<li><a href="pyFTS/common/Util.html">pyFTS.common.Util</a></li>
 <li><a href="pyFTS/common/flrg.html">pyFTS.common.flrg</a></li>
+<li><a href="pyFTS/common/fts.html">pyFTS.common.fts</a></li>
 <li><a href="pyFTS/common/transformations/boxcox.html">pyFTS.common.transformations.boxcox</a></li>
 <li><a href="pyFTS/common/transformations/differential.html">pyFTS.common.transformations.differential</a></li>
 <li><a href="pyFTS/common/transformations/normalization.html">pyFTS.common.transformations.normalization</a></li>
@@ -57,6 +68,7 @@
 <li><a href="pyFTS/common/transformations/smoothing.html">pyFTS.common.transformations.smoothing</a></li>
 <li><a href="pyFTS/common/transformations/som.html">pyFTS.common.transformations.som</a></li>
 <li><a href="pyFTS/common/transformations/transformation.html">pyFTS.common.transformations.transformation</a></li>
+<li><a href="pyFTS/common/transformations/trend.html">pyFTS.common.transformations.trend</a></li>
 <li><a href="pyFTS/common/tree.html">pyFTS.common.tree</a></li>
 <li><a href="pyFTS/data/AirPassengers.html">pyFTS.data.AirPassengers</a></li>
 <li><a href="pyFTS/data/Bitcoin.html">pyFTS.data.Bitcoin</a></li>
@@ -80,28 +92,60 @@
 <li><a href="pyFTS/data/mackey_glass.html">pyFTS.data.mackey_glass</a></li>
 <li><a href="pyFTS/data/rossler.html">pyFTS.data.rossler</a></li>
 <li><a href="pyFTS/data/sunspots.html">pyFTS.data.sunspots</a></li>
+<li><a href="pyFTS/hyperparam/Evolutionary.html">pyFTS.hyperparam.Evolutionary</a></li>
+<li><a href="pyFTS/hyperparam/GridSearch.html">pyFTS.hyperparam.GridSearch</a></li>
 <li><a href="pyFTS/hyperparam/Util.html">pyFTS.hyperparam.Util</a></li>
+<li><a href="pyFTS/models/chen.html">pyFTS.models.chen</a></li>
+<li><a href="pyFTS/models/cheng.html">pyFTS.models.cheng</a></li>
+<li><a href="pyFTS/models/ensemble/ensemble.html">pyFTS.models.ensemble.ensemble</a></li>
+<li><a href="pyFTS/models/ensemble/multiseasonal.html">pyFTS.models.ensemble.multiseasonal</a></li>
+<li><a href="pyFTS/models/hofts.html">pyFTS.models.hofts</a></li>
+<li><a href="pyFTS/models/hwang.html">pyFTS.models.hwang</a></li>
+<li><a href="pyFTS/models/ifts.html">pyFTS.models.ifts</a></li>
+<li><a href="pyFTS/models/incremental/IncrementalEnsemble.html">pyFTS.models.incremental.IncrementalEnsemble</a></li>
+<li><a href="pyFTS/models/incremental/TimeVariant.html">pyFTS.models.incremental.TimeVariant</a></li>
+<li><a href="pyFTS/models/ismailefendi.html">pyFTS.models.ismailefendi</a></li>
 <li><a href="pyFTS/models/multivariate/FLR.html">pyFTS.models.multivariate.FLR</a></li>
+<li><a href="pyFTS/models/multivariate/cmvfts.html">pyFTS.models.multivariate.cmvfts</a></li>
 <li><a href="pyFTS/models/multivariate/common.html">pyFTS.models.multivariate.common</a></li>
 <li><a href="pyFTS/models/multivariate/flrg.html">pyFTS.models.multivariate.flrg</a></li>
+<li><a href="pyFTS/models/multivariate/granular.html">pyFTS.models.multivariate.granular</a></li>
 <li><a href="pyFTS/models/multivariate/grid.html">pyFTS.models.multivariate.grid</a></li>
+<li><a href="pyFTS/models/multivariate/mvfts.html">pyFTS.models.multivariate.mvfts</a></li>
 <li><a href="pyFTS/models/multivariate/partitioner.html">pyFTS.models.multivariate.partitioner</a></li>
+<li><a href="pyFTS/models/multivariate/variable.html">pyFTS.models.multivariate.variable</a></li>
+<li><a href="pyFTS/models/multivariate/wmvfts.html">pyFTS.models.multivariate.wmvfts</a></li>
 <li><a href="pyFTS/models/nonstationary/common.html">pyFTS.models.nonstationary.common</a></li>
+<li><a href="pyFTS/models/nonstationary/cvfts.html">pyFTS.models.nonstationary.cvfts</a></li>
 <li><a href="pyFTS/models/nonstationary/flrg.html">pyFTS.models.nonstationary.flrg</a></li>
+<li><a href="pyFTS/models/nonstationary/honsfts.html">pyFTS.models.nonstationary.honsfts</a></li>
+<li><a href="pyFTS/models/nonstationary/nsfts.html">pyFTS.models.nonstationary.nsfts</a></li>
 <li><a href="pyFTS/models/nonstationary/partitioners.html">pyFTS.models.nonstationary.partitioners</a></li>
 <li><a href="pyFTS/models/nonstationary/perturbation.html">pyFTS.models.nonstationary.perturbation</a></li>
+<li><a href="pyFTS/models/nonstationary/util.html">pyFTS.models.nonstationary.util</a></li>
+<li><a href="pyFTS/models/pwfts.html">pyFTS.models.pwfts</a></li>
+<li><a href="pyFTS/models/sadaei.html">pyFTS.models.sadaei</a></li>
 <li><a href="pyFTS/models/seasonal/SeasonalIndexer.html">pyFTS.models.seasonal.SeasonalIndexer</a></li>
+<li><a href="pyFTS/models/seasonal/cmsfts.html">pyFTS.models.seasonal.cmsfts</a></li>
 <li><a href="pyFTS/models/seasonal/common.html">pyFTS.models.seasonal.common</a></li>
+<li><a href="pyFTS/models/seasonal/msfts.html">pyFTS.models.seasonal.msfts</a></li>
 <li><a href="pyFTS/models/seasonal/partitioner.html">pyFTS.models.seasonal.partitioner</a></li>
+<li><a href="pyFTS/models/seasonal/sfts.html">pyFTS.models.seasonal.sfts</a></li>
+<li><a href="pyFTS/models/song.html">pyFTS.models.song</a></li>
+<li><a href="pyFTS/models/yu.html">pyFTS.models.yu</a></li>
 <li><a href="pyFTS/partitioners/CMeans.html">pyFTS.partitioners.CMeans</a></li>
-<li><a href="pyFTS/partitioners/Class.html">pyFTS.partitioners.Class</a></li>
 <li><a href="pyFTS/partitioners/Entropy.html">pyFTS.partitioners.Entropy</a></li>
 <li><a href="pyFTS/partitioners/FCM.html">pyFTS.partitioners.FCM</a></li>
 <li><a href="pyFTS/partitioners/Grid.html">pyFTS.partitioners.Grid</a></li>
+<li><a href="pyFTS/partitioners/Huarng.html">pyFTS.partitioners.Huarng</a></li>
 <li><a href="pyFTS/partitioners/Simple.html">pyFTS.partitioners.Simple</a></li>
 <li><a href="pyFTS/partitioners/Singleton.html">pyFTS.partitioners.Singleton</a></li>
 <li><a href="pyFTS/partitioners/SubClust.html">pyFTS.partitioners.SubClust</a></li>
+<li><a href="pyFTS/partitioners/Util.html">pyFTS.partitioners.Util</a></li>
+<li><a href="pyFTS/partitioners/parallel_util.html">pyFTS.partitioners.parallel_util</a></li>
 <li><a href="pyFTS/partitioners/partitioner.html">pyFTS.partitioners.partitioner</a></li>
+<li><a href="pyFTS/probabilistic/ProbabilityDistribution.html">pyFTS.probabilistic.ProbabilityDistribution</a></li>
+<li><a href="pyFTS/probabilistic/kde.html">pyFTS.probabilistic.kde</a></li>
 </ul>
 
             <div class="clearer"></div>
@@ -142,7 +186,7 @@
     </div>
     <div class="footer" role="contentinfo">
         &#169; Copyright 2022, Machine Intelligence and Data Science Laboratory - UFMG - Brazil.
-      Created using <a href="https://www.sphinx-doc.org/">Sphinx</a> 4.3.2.
+      Created using <a href="https://www.sphinx-doc.org/">Sphinx</a> 4.5.0.
     </div>
   </body>
 </html>

docs/build/html/_modules/pyFTS/benchmarks/BSTS.html

@@ -0,0 +1,211 @@
+
+
+<!doctype html>
+
+<html>
+  <head>
+    <meta charset="utf-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+    <title>pyFTS.benchmarks.BSTS &#8212; pyFTS 1.7 documentation</title>
+    <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css" />
+    <link rel="stylesheet" type="text/css" href="../../../_static/bizstyle.css" />
+    
+    <script data-url_root="../../../" id="documentation_options" src="../../../_static/documentation_options.js"></script>
+    <script src="../../../_static/jquery.js"></script>
+    <script src="../../../_static/underscore.js"></script>
+    <script src="../../../_static/doctools.js"></script>
+    <script src="../../../_static/bizstyle.js"></script>
+    <link rel="index" title="Index" href="../../../genindex.html" />
+    <link rel="search" title="Search" href="../../../search.html" />
+    <meta name="viewport" content="width=device-width,initial-scale=1.0" />
+    <!--[if lt IE 9]>
+    <script src="_static/css3-mediaqueries.js"></script>
+    <![endif]-->
+  </head><body>
+    <div class="related" role="navigation" aria-label="related navigation">
+      <h3>Navigation</h3>
+      <ul>
+        <li class="right" style="margin-right: 10px">
+          <a href="../../../genindex.html" title="General Index"
+             accesskey="I">index</a></li>
+        <li class="right" >
+          <a href="../../../py-modindex.html" title="Python Module Index"
+             >modules</a> |</li>
+        <li class="nav-item nav-item-0"><a href="../../../index.html">pyFTS 1.7 documentation</a> &#187;</li>
+          <li class="nav-item nav-item-1"><a href="../../index.html" accesskey="U">Module code</a> &#187;</li>
+        <li class="nav-item nav-item-this"><a href="">pyFTS.benchmarks.BSTS</a></li> 
+      </ul>
+    </div>  
+
+    <div class="document">
+      <div class="documentwrapper">
+        <div class="bodywrapper">
+          <div class="body" role="main">
+            
+  <h1>Source code for pyFTS.benchmarks.BSTS</h1><div class="highlight"><pre>
+<span></span><span class="ch">#!/usr/bin/python</span>
+<span class="c1"># -*- coding: utf8 -*-</span>
+
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">import</span> <span class="nn">scipy.stats</span> <span class="k">as</span> <span class="nn">st</span>
+<span class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="k">import</span> <span class="n">SortedCollection</span><span class="p">,</span> <span class="n">fts</span>
+<span class="kn">from</span> <span class="nn">pyFTS.probabilistic</span> <span class="k">import</span> <span class="n">ProbabilityDistribution</span>
+
+
+<div class="viewcode-block" id="ARIMA"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.BSTS.ARIMA">[docs]</a><span class="k">class</span> <span class="nc">ARIMA</span><span class="p">(</span><span class="n">fts</span><span class="o">.</span><span class="n">FTS</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Façade for statsmodels.tsa.arima_model</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">ARIMA</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s2">&quot;BSTS&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">detail</span> <span class="o">=</span> <span class="s2">&quot;Bayesian Structural Time Series&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">is_high_order</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">has_point_forecasting</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">has_interval_forecasting</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">has_probability_forecasting</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">uod_clip</span> <span class="o">=</span> <span class="kc">False</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">model</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">model_fit</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">trained_data</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">p</span> <span class="o">=</span> <span class="mi">1</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">d</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">q</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">benchmark_only</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">min_order</span> <span class="o">=</span> <span class="mi">1</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">alpha</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;alpha&quot;</span><span class="p">,</span> <span class="mf">0.05</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">order</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;order&quot;</span><span class="p">,</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">))</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_decompose_order</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">model</span> <span class="o">=</span> <span class="kc">None</span>
+
+    <span class="k">def</span> <span class="nf">_decompose_order</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">order</span><span class="p">):</span>
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">order</span><span class="p">,</span> <span class="p">(</span><span class="nb">tuple</span><span class="p">,</span> <span class="nb">set</span><span class="p">,</span> <span class="nb">list</span><span class="p">)):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">p</span> <span class="o">=</span> <span class="n">order</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">d</span> <span class="o">=</span> <span class="n">order</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">q</span> <span class="o">=</span> <span class="n">order</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">order</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">p</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">q</span> <span class="o">+</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">q</span> <span class="o">-</span> <span class="mi">1</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">q</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="k">else</span> <span class="mi">0</span><span class="p">)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">d</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">transformations</span><span class="p">)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">shortname</span> <span class="o">=</span> <span class="s2">&quot;BSTS(</span><span class="si">{}</span><span class="s2">,</span><span class="si">{}</span><span class="s2">,</span><span class="si">{}</span><span class="s2">)-</span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">p</span><span class="p">,</span><span class="bp">self</span><span class="o">.</span><span class="n">d</span><span class="p">,</span><span class="bp">self</span><span class="o">.</span><span class="n">q</span><span class="p">,</span><span class="bp">self</span><span class="o">.</span><span class="n">alpha</span><span class="p">)</span>
+
+<div class="viewcode-block" id="ARIMA.train"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.BSTS.ARIMA.train">[docs]</a>    <span class="k">def</span> <span class="nf">train</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="kn">import</span> <span class="nn">pyflux</span> <span class="k">as</span> <span class="nn">pf</span>
+
+        <span class="k">if</span> <span class="s1">&#39;order&#39;</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">:</span>
+            <span class="n">order</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">&#39;order&#39;</span><span class="p">)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_decompose_order</span><span class="p">(</span><span class="n">order</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">indexer</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">indexer</span><span class="o">.</span><span class="n">get_data</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+
+        <span class="k">try</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">model</span> <span class="o">=</span>  <span class="n">pf</span><span class="o">.</span><span class="n">ARIMA</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">data</span><span class="p">,</span> <span class="n">ar</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">p</span><span class="p">,</span> <span class="n">ma</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">q</span><span class="p">,</span> <span class="n">integ</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">d</span><span class="p">,</span> <span class="n">family</span><span class="o">=</span><span class="n">pf</span><span class="o">.</span><span class="n">Normal</span><span class="p">())</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">model_fit</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="s1">&#39;M-H&#39;</span><span class="p">,</span> <span class="n">nsims</span><span class="o">=</span><span class="mi">20000</span><span class="p">)</span>
+        <span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">ex</span><span class="p">:</span>
+            <span class="nb">print</span><span class="p">(</span><span class="n">ex</span><span class="p">)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">model_fit</span> <span class="o">=</span> <span class="kc">None</span></div>
+
+<div class="viewcode-block" id="ARIMA.inference"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.BSTS.ARIMA.inference">[docs]</a>    <span class="k">def</span> <span class="nf">inference</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">steps</span><span class="p">):</span>
+        <span class="n">t_z</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">transform_z</span><span class="p">()</span>
+        <span class="n">mu</span><span class="p">,</span> <span class="n">Y</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">_model</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">latent_variables</span><span class="o">.</span><span class="n">get_z_values</span><span class="p">())</span>
+        <span class="n">date_index</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">shift_dates</span><span class="p">(</span><span class="n">steps</span><span class="p">)</span>
+        <span class="n">sim_vector</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">_sim_prediction</span><span class="p">(</span><span class="n">mu</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">steps</span><span class="p">,</span> <span class="n">t_z</span><span class="p">,</span> <span class="mi">1000</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">sim_vector</span></div>
+
+<div class="viewcode-block" id="ARIMA.forecast"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.BSTS.ARIMA.forecast">[docs]</a>    <span class="k">def</span> <span class="nf">forecast</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ndata</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">()</span></div>
+
+<div class="viewcode-block" id="ARIMA.forecast_ahead"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.BSTS.ARIMA.forecast_ahead">[docs]</a>    <span class="k">def</span> <span class="nf">forecast_ahead</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">steps</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">steps</span><span class="p">,</span> <span class="n">intervals</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span></div>
+
+<div class="viewcode-block" id="ARIMA.forecast_interval"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.BSTS.ARIMA.forecast_interval">[docs]</a>    <span class="k">def</span> <span class="nf">forecast_interval</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">()</span></div>
+
+<div class="viewcode-block" id="ARIMA.forecast_ahead_interval"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.BSTS.ARIMA.forecast_ahead_interval">[docs]</a>    <span class="k">def</span> <span class="nf">forecast_ahead_interval</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ndata</span><span class="p">,</span> <span class="n">steps</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="n">sim_vector</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">inference</span><span class="p">(</span><span class="n">steps</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="s1">&#39;alpha&#39;</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">:</span>
+            <span class="n">alpha</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;alpha&#39;</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">alpha</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">alpha</span>
+
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="k">for</span> <span class="n">ct</span><span class="p">,</span> <span class="n">sample</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">sim_vector</span><span class="p">):</span>
+            <span class="n">i</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">percentile</span><span class="p">(</span><span class="n">sample</span><span class="p">,</span> <span class="p">[</span><span class="n">alpha</span><span class="o">*</span><span class="mi">100</span><span class="p">,</span> <span class="p">(</span><span class="mi">1</span><span class="o">-</span><span class="n">alpha</span><span class="p">)</span><span class="o">*</span><span class="mi">100</span><span class="p">])</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
+            <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">ret</span></div>
+
+<div class="viewcode-block" id="ARIMA.forecast_distribution"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.BSTS.ARIMA.forecast_distribution">[docs]</a>    <span class="k">def</span> <span class="nf">forecast_distribution</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="kn">import</span> <span class="nn">pyflux</span> <span class="k">as</span> <span class="nn">pf</span>
+
+        <span class="n">sim_vector</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">inference</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
+
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="k">for</span> <span class="n">ct</span><span class="p">,</span> <span class="n">sample</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">sim_vector</span><span class="p">):</span>
+            <span class="n">pd</span> <span class="o">=</span> <span class="n">ProbabilityDistribution</span><span class="o">.</span><span class="n">ProbabilityDistribution</span><span class="p">(</span><span class="nb">type</span><span class="o">=</span><span class="s1">&#39;histogram&#39;</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="n">sample</span><span class="p">,</span> <span class="n">nbins</span><span class="o">=</span><span class="mi">500</span><span class="p">)</span>
+            <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">pd</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">ret</span></div>
+
+
+<div class="viewcode-block" id="ARIMA.forecast_ahead_distribution"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.BSTS.ARIMA.forecast_ahead_distribution">[docs]</a>    <span class="k">def</span> <span class="nf">forecast_ahead_distribution</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">steps</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+
+        <span class="n">sim_vector</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">inference</span><span class="p">(</span><span class="n">steps</span><span class="p">)</span>
+
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="k">for</span> <span class="n">ct</span><span class="p">,</span> <span class="n">sample</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">sim_vector</span><span class="p">):</span>
+            <span class="n">pd</span> <span class="o">=</span> <span class="n">ProbabilityDistribution</span><span class="o">.</span><span class="n">ProbabilityDistribution</span><span class="p">(</span><span class="nb">type</span><span class="o">=</span><span class="s1">&#39;histogram&#39;</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="n">sample</span><span class="p">,</span> <span class="n">nbins</span><span class="o">=</span><span class="mi">500</span><span class="p">)</span>
+            <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">pd</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">ret</span></div></div>
+</pre></div>
+
+            <div class="clearer"></div>
+          </div>
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docs/build/html/_modules/pyFTS/benchmarks/Measures.html

@@ -0,0 +1,666 @@
+
+
+<!doctype html>
+
+<html>
+  <head>
+    <meta charset="utf-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+    <title>pyFTS.benchmarks.Measures &#8212; pyFTS 1.7 documentation</title>
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+          <li class="nav-item nav-item-1"><a href="../../index.html" accesskey="U">Module code</a> &#187;</li>
+        <li class="nav-item nav-item-this"><a href="">pyFTS.benchmarks.Measures</a></li> 
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+          <div class="body" role="main">
+            
+  <h1>Source code for pyFTS.benchmarks.Measures</h1><div class="highlight"><pre>
+<span></span><span class="c1"># -*- coding: utf8 -*-</span>
+
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">pyFTS module for common benchmark metrics</span>
+<span class="sd">&quot;&quot;&quot;</span>
+
+<span class="kn">import</span> <span class="nn">time</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="k">import</span> <span class="n">FuzzySet</span><span class="p">,</span> <span class="n">SortedCollection</span>
+<span class="kn">from</span> <span class="nn">pyFTS.probabilistic</span> <span class="k">import</span> <span class="n">ProbabilityDistribution</span>
+
+
+<div class="viewcode-block" id="acf"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Measures.acf">[docs]</a><span class="k">def</span> <span class="nf">acf</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">k</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Autocorrelation function estimative</span>
+
+<span class="sd">    :param data: </span>
+<span class="sd">    :param k: </span>
+<span class="sd">    :return: </span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">mu</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nanmean</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+    <span class="n">sigma</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">var</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+    <span class="n">n</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+    <span class="n">s</span> <span class="o">=</span> <span class="mi">0</span>
+    <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">n</span> <span class="o">-</span> <span class="n">k</span><span class="p">):</span>
+        <span class="n">s</span> <span class="o">+=</span> <span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">t</span><span class="p">]</span> <span class="o">-</span> <span class="n">mu</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">t</span> <span class="o">+</span> <span class="n">k</span><span class="p">]</span> <span class="o">-</span> <span class="n">mu</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="mi">1</span> <span class="o">/</span> <span class="p">((</span><span class="n">n</span> <span class="o">-</span> <span class="n">k</span><span class="p">)</span> <span class="o">*</span> <span class="n">sigma</span><span class="p">)</span> <span class="o">*</span> <span class="n">s</span></div>
+
+
+<div class="viewcode-block" id="rmse"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Measures.rmse">[docs]</a><span class="k">def</span> <span class="nf">rmse</span><span class="p">(</span><span class="n">targets</span><span class="p">,</span> <span class="n">forecasts</span><span class="p">,</span> <span class="n">order</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">offset</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Root Mean Squared Error</span>
+
+<span class="sd">    :param targets: array of targets</span>
+<span class="sd">    :param forecasts: array of forecasts</span>
+<span class="sd">    :param order: model order</span>
+<span class="sd">    :param offset: forecast offset related to target. </span>
+<span class="sd">    :return: </span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">targets</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
+        <span class="n">targets</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">targets</span><span class="p">)</span>
+    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">forecasts</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
+        <span class="n">forecasts</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">forecasts</span><span class="p">)</span>
+    
+    <span class="k">if</span> <span class="n">offset</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">nanmean</span><span class="p">((</span><span class="n">targets</span><span class="p">[</span><span class="n">order</span><span class="p">:]</span> <span class="o">-</span> <span class="n">forecasts</span><span class="p">[:])</span> <span class="o">**</span> <span class="mi">2</span><span class="p">))</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">nanmean</span><span class="p">((</span><span class="n">targets</span><span class="p">[</span><span class="n">order</span><span class="o">+</span><span class="n">offset</span><span class="p">:]</span> <span class="o">-</span> <span class="n">forecasts</span><span class="p">[:</span><span class="o">-</span><span class="n">offset</span><span class="p">])</span> <span class="o">**</span> <span class="mi">2</span><span class="p">))</span></div>
+
+
+<div class="viewcode-block" id="nmrse"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Measures.nmrse">[docs]</a><span class="k">def</span> <span class="nf">nmrse</span><span class="p">(</span><span class="n">targets</span><span class="p">,</span> <span class="n">forecasts</span><span class="p">,</span> <span class="n">order</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">offset</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot; Normalized Root Mean Squared Error &quot;&quot;&quot;</span>
+    <span class="k">return</span> <span class="n">rmse</span><span class="p">(</span><span class="n">targets</span><span class="p">,</span> <span class="n">forecasts</span><span class="p">,</span> <span class="n">order</span><span class="p">,</span> <span class="n">offset</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">targets</span><span class="p">)</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">targets</span><span class="p">))</span> <span class="c1">## normalizing in targets because on forecasts might explode to inf (when model predict a line)</span></div>
+
+
+    
+<div class="viewcode-block" id="rmse_interval"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Measures.rmse_interval">[docs]</a><span class="k">def</span> <span class="nf">rmse_interval</span><span class="p">(</span><span class="n">targets</span><span class="p">,</span> <span class="n">forecasts</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Root Mean Squared Error</span>
+
+<span class="sd">    :param targets: </span>
+<span class="sd">    :param forecasts: </span>
+<span class="sd">    :return: </span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">fmean</span> <span class="o">=</span> <span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">i</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">forecasts</span><span class="p">]</span>
+    <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">nanmean</span><span class="p">((</span><span class="n">fmean</span> <span class="o">-</span> <span class="n">targets</span><span class="p">)</span> <span class="o">**</span> <span class="mi">2</span><span class="p">))</span></div>
+
+
+<div class="viewcode-block" id="mape"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Measures.mape">[docs]</a><span class="k">def</span> <span class="nf">mape</span><span class="p">(</span><span class="n">targets</span><span class="p">,</span> <span class="n">forecasts</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Mean Average Percentual Error</span>
+
+<span class="sd">    :param targets: </span>
+<span class="sd">    :param forecasts: </span>
+<span class="sd">    :return: </span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">targets</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
+        <span class="n">targets</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">targets</span><span class="p">)</span>
+    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">forecasts</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
+        <span class="n">forecasts</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">forecasts</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">nanmean</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">divide</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">subtract</span><span class="p">(</span><span class="n">targets</span><span class="p">,</span> <span class="n">forecasts</span><span class="p">),</span> <span class="n">targets</span><span class="p">)))</span> <span class="o">*</span> <span class="mi">100</span></div>
+
+
+<div class="viewcode-block" id="smape"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Measures.smape">[docs]</a><span class="k">def</span> <span class="nf">smape</span><span class="p">(</span><span class="n">targets</span><span class="p">,</span> <span class="n">forecasts</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="mi">2</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Symmetric Mean Average Percentual Error</span>
+
+<span class="sd">    :param targets: </span>
+<span class="sd">    :param forecasts: </span>
+<span class="sd">    :param type: </span>
+<span class="sd">    :return: </span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">targets</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
+        <span class="n">targets</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">targets</span><span class="p">)</span>
+    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">forecasts</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
+        <span class="n">forecasts</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">forecasts</span><span class="p">)</span>
+    <span class="k">if</span> <span class="nb">type</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">nanmean</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">forecasts</span> <span class="o">-</span> <span class="n">targets</span><span class="p">)</span> <span class="o">/</span> <span class="p">((</span><span class="n">forecasts</span> <span class="o">+</span> <span class="n">targets</span><span class="p">)</span> <span class="o">/</span> <span class="mi">2</span><span class="p">))</span>
+    <span class="k">elif</span> <span class="nb">type</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">nanmean</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">forecasts</span> <span class="o">-</span> <span class="n">targets</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">forecasts</span><span class="p">)</span> <span class="o">+</span> <span class="nb">abs</span><span class="p">(</span><span class="n">targets</span><span class="p">)))</span> <span class="o">*</span> <span class="mi">100</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">forecasts</span> <span class="o">-</span> <span class="n">targets</span><span class="p">))</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">forecasts</span> <span class="o">+</span> <span class="n">targets</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="mape_interval"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Measures.mape_interval">[docs]</a><span class="k">def</span> <span class="nf">mape_interval</span><span class="p">(</span><span class="n">targets</span><span class="p">,</span> <span class="n">forecasts</span><span class="p">):</span>
+    <span class="n">fmean</span> <span class="o">=</span> <span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">i</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">forecasts</span><span class="p">]</span>
+    <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="nb">abs</span><span class="p">(</span><span class="n">fmean</span> <span class="o">-</span> <span class="n">targets</span><span class="p">)</span> <span class="o">/</span> <span class="n">fmean</span><span class="p">)</span> <span class="o">*</span> <span class="mi">100</span></div>
+
+
+<div class="viewcode-block" id="UStatistic"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Measures.UStatistic">[docs]</a><span class="k">def</span> <span class="nf">UStatistic</span><span class="p">(</span><span class="n">targets</span><span class="p">,</span> <span class="n">forecasts</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Theil&#39;s U Statistic</span>
+
+<span class="sd">    :param targets: </span>
+<span class="sd">    :param forecasts: </span>
+<span class="sd">    :return: </span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">forecasts</span><span class="p">,</span> <span class="p">(</span><span class="nb">list</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">)):</span>
+        <span class="n">forecasts</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">forecasts</span><span class="p">])</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">forecasts</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">forecasts</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">targets</span><span class="p">,</span> <span class="p">(</span><span class="nb">list</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">)):</span>
+        <span class="n">targets</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">targets</span><span class="p">])</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">targets</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">targets</span><span class="p">)</span>
+
+    <span class="n">l</span> <span class="o">=</span> <span class="n">forecasts</span><span class="o">.</span><span class="n">size</span>
+    <span class="n">l</span> <span class="o">=</span> <span class="mi">2</span> <span class="k">if</span> <span class="n">l</span> <span class="o">==</span> <span class="mi">1</span> <span class="k">else</span> <span class="n">l</span>
+
+    <span class="n">naive</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">y</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">l</span> <span class="o">-</span> <span class="mi">1</span><span class="p">):</span>
+        <span class="n">y</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">subtract</span><span class="p">(</span><span class="n">forecasts</span><span class="p">[</span><span class="n">k</span><span class="p">],</span> <span class="n">targets</span><span class="p">[</span><span class="n">k</span><span class="p">])</span> <span class="o">**</span> <span class="mi">2</span><span class="p">)</span>
+        <span class="n">naive</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">subtract</span><span class="p">(</span><span class="n">targets</span><span class="p">[</span><span class="n">k</span> <span class="o">+</span> <span class="mi">1</span><span class="p">],</span> <span class="n">targets</span><span class="p">[</span><span class="n">k</span><span class="p">])</span> <span class="o">**</span> <span class="mi">2</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">divide</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">y</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">naive</span><span class="p">)))</span></div>
+
+
+<div class="viewcode-block" id="TheilsInequality"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Measures.TheilsInequality">[docs]</a><span class="k">def</span> <span class="nf">TheilsInequality</span><span class="p">(</span><span class="n">targets</span><span class="p">,</span> <span class="n">forecasts</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Theil’s Inequality Coefficient</span>
+
+<span class="sd">    :param targets: </span>
+<span class="sd">    :param forecasts: </span>
+<span class="sd">    :return: </span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">res</span> <span class="o">=</span> <span class="n">targets</span> <span class="o">-</span> <span class="n">forecasts</span>
+    <span class="n">t</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">res</span><span class="p">)</span>
+    <span class="n">us</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">([</span><span class="n">u</span> <span class="o">**</span> <span class="mi">2</span> <span class="k">for</span> <span class="n">u</span> <span class="ow">in</span> <span class="n">res</span><span class="p">]))</span>
+    <span class="n">ys</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">([</span><span class="n">y</span> <span class="o">**</span> <span class="mi">2</span> <span class="k">for</span> <span class="n">y</span> <span class="ow">in</span> <span class="n">targets</span><span class="p">]))</span>
+    <span class="n">fs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">([</span><span class="n">f</span> <span class="o">**</span> <span class="mi">2</span> <span class="k">for</span> <span class="n">f</span> <span class="ow">in</span> <span class="n">forecasts</span><span class="p">]))</span>
+    <span class="k">return</span> <span class="n">us</span> <span class="o">/</span> <span class="p">(</span><span class="n">ys</span> <span class="o">+</span> <span class="n">fs</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="sharpness"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Measures.sharpness">[docs]</a><span class="k">def</span> <span class="nf">sharpness</span><span class="p">(</span><span class="n">forecasts</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;Sharpness - Mean size of the intervals&quot;&quot;&quot;</span>
+    <span class="n">tmp</span> <span class="o">=</span> <span class="p">[</span><span class="n">i</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">i</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">forecasts</span><span class="p">]</span>
+    <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="resolution"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Measures.resolution">[docs]</a><span class="k">def</span> <span class="nf">resolution</span><span class="p">(</span><span class="n">forecasts</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;Resolution - Standard deviation of the intervals&quot;&quot;&quot;</span>
+    <span class="n">shp</span> <span class="o">=</span> <span class="n">sharpness</span><span class="p">(</span><span class="n">forecasts</span><span class="p">)</span>
+    <span class="n">tmp</span> <span class="o">=</span> <span class="p">[</span><span class="nb">abs</span><span class="p">((</span><span class="n">i</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">i</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="o">-</span> <span class="n">shp</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">forecasts</span><span class="p">]</span>
+    <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="coverage"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Measures.coverage">[docs]</a><span class="k">def</span> <span class="nf">coverage</span><span class="p">(</span><span class="n">targets</span><span class="p">,</span> <span class="n">forecasts</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;Percent of target values that fall inside forecasted interval&quot;&quot;&quot;</span>
+
+    <span class="n">preds</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">forecasts</span><span class="p">)):</span>
+        <span class="k">if</span> <span class="n">targets</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">&gt;=</span> <span class="n">forecasts</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="ow">and</span> <span class="n">targets</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="n">forecasts</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">1</span><span class="p">]:</span>
+            <span class="n">preds</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">preds</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">nanmean</span><span class="p">(</span><span class="n">preds</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="pinball"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Measures.pinball">[docs]</a><span class="k">def</span> <span class="nf">pinball</span><span class="p">(</span><span class="n">tau</span><span class="p">,</span> <span class="n">target</span><span class="p">,</span> <span class="n">forecast</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Pinball loss function. Measure the distance of forecast to the tau-quantile of the target</span>
+
+<span class="sd">    :param tau: quantile value in the range (0,1)</span>
+<span class="sd">    :param target: </span>
+<span class="sd">    :param forecast: </span>
+<span class="sd">    :return: float, distance of forecast to the tau-quantile of the target</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">if</span> <span class="n">target</span> <span class="o">&gt;=</span> <span class="n">forecast</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">subtract</span><span class="p">(</span><span class="n">target</span><span class="p">,</span> <span class="n">forecast</span><span class="p">)</span> <span class="o">*</span> <span class="n">tau</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">subtract</span><span class="p">(</span><span class="n">forecast</span><span class="p">,</span> <span class="n">target</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">tau</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="pinball_mean"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Measures.pinball_mean">[docs]</a><span class="k">def</span> <span class="nf">pinball_mean</span><span class="p">(</span><span class="n">tau</span><span class="p">,</span> <span class="n">targets</span><span class="p">,</span> <span class="n">forecasts</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Mean pinball loss value of the forecast for a given tau-quantile of the targets</span>
+
+<span class="sd">    :param tau: quantile value in the range (0,1)</span>
+<span class="sd">    :param targets: list of target values</span>
+<span class="sd">    :param forecasts: list of prediction intervals</span>
+<span class="sd">    :return: float, the pinball loss mean for tau quantile</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">if</span> <span class="n">tau</span> <span class="o">&lt;=</span> <span class="mf">0.5</span><span class="p">:</span>
+        <span class="n">preds</span> <span class="o">=</span> <span class="p">[</span><span class="n">pinball</span><span class="p">(</span><span class="n">tau</span><span class="p">,</span> <span class="n">targets</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">forecasts</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">0</span><span class="p">])</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">forecasts</span><span class="p">))]</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">preds</span> <span class="o">=</span> <span class="p">[</span><span class="n">pinball</span><span class="p">(</span><span class="n">tau</span><span class="p">,</span> <span class="n">targets</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">forecasts</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">1</span><span class="p">])</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">forecasts</span><span class="p">))]</span>
+    <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">nanmean</span><span class="p">(</span><span class="n">preds</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="winkler_score"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Measures.winkler_score">[docs]</a><span class="k">def</span> <span class="nf">winkler_score</span><span class="p">(</span><span class="n">tau</span><span class="p">,</span> <span class="n">target</span><span class="p">,</span> <span class="n">forecast</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    R. L. Winkler, A Decision-Theoretic Approach to Interval Estimation, J. Am. Stat. Assoc. 67 (337) (1972) 187–191. doi:10.2307/2284720.</span>
+
+<span class="sd">    :param tau:</span>
+<span class="sd">    :param target:</span>
+<span class="sd">    :param forecast:</span>
+<span class="sd">    :return:</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">delta</span> <span class="o">=</span> <span class="n">forecast</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">forecast</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+    <span class="k">if</span> <span class="n">forecast</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="n">target</span> <span class="o">&lt;=</span> <span class="n">forecast</span><span class="p">[</span><span class="mi">1</span><span class="p">]:</span>
+        <span class="k">return</span> <span class="n">delta</span>
+    <span class="k">elif</span> <span class="n">forecast</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">&gt;</span> <span class="n">target</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">delta</span> <span class="o">+</span> <span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="p">(</span><span class="n">forecast</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">target</span><span class="p">))</span> <span class="o">/</span> <span class="n">tau</span>
+    <span class="k">elif</span> <span class="n">forecast</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">target</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">delta</span> <span class="o">+</span> <span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="p">(</span><span class="n">target</span> <span class="o">-</span> <span class="n">forecast</span><span class="p">[</span><span class="mi">1</span><span class="p">]))</span> <span class="o">/</span> <span class="n">tau</span></div>
+
+
+<div class="viewcode-block" id="winkler_mean"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Measures.winkler_mean">[docs]</a><span class="k">def</span> <span class="nf">winkler_mean</span><span class="p">(</span><span class="n">tau</span><span class="p">,</span> <span class="n">targets</span><span class="p">,</span> <span class="n">forecasts</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Mean Winkler score value of the forecast for a given tau-quantile of the targets</span>
+
+<span class="sd">    :param tau: quantile value in the range (0,1)</span>
+<span class="sd">    :param targets: list of target values</span>
+<span class="sd">    :param forecasts: list of prediction intervals</span>
+<span class="sd">    :return: float, the Winkler score mean for tau quantile</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">preds</span> <span class="o">=</span> <span class="p">[</span><span class="n">winkler_score</span><span class="p">(</span><span class="n">tau</span><span class="p">,</span> <span class="n">targets</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">forecasts</span><span class="p">[</span><span class="n">i</span><span class="p">])</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">forecasts</span><span class="p">))]</span>
+
+    <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">nanmean</span><span class="p">(</span><span class="n">preds</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="brier_score"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Measures.brier_score">[docs]</a><span class="k">def</span> <span class="nf">brier_score</span><span class="p">(</span><span class="n">targets</span><span class="p">,</span> <span class="n">densities</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Brier Score for probabilistic forecasts.</span>
+<span class="sd">    Brier (1950). &quot;Verification of Forecasts Expressed in Terms of Probability&quot;. Monthly Weather Review. 78: 1–3.</span>
+
+<span class="sd">    :param targets: a list with the target values</span>
+<span class="sd">    :param densities: a list with pyFTS.probabil objectsistic.ProbabilityDistribution</span>
+<span class="sd">    :return: float</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">densities</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
+        <span class="n">densities</span> <span class="o">=</span> <span class="p">[</span><span class="n">densities</span><span class="p">]</span>
+        <span class="n">targets</span> <span class="o">=</span> <span class="p">[</span><span class="n">targets</span><span class="p">]</span>
+
+    <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+
+    <span class="k">for</span> <span class="n">ct</span><span class="p">,</span> <span class="n">d</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">densities</span><span class="p">):</span>
+        <span class="k">try</span><span class="p">:</span>
+            <span class="n">v</span> <span class="o">=</span> <span class="n">d</span><span class="o">.</span><span class="n">bin_index</span><span class="o">.</span><span class="n">find_le</span><span class="p">(</span><span class="n">targets</span><span class="p">[</span><span class="n">ct</span><span class="p">])</span>
+
+            <span class="n">score</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">([</span><span class="n">d</span><span class="o">.</span><span class="n">density</span><span class="p">(</span><span class="n">k</span><span class="p">)</span> <span class="o">**</span> <span class="mi">2</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">d</span><span class="o">.</span><span class="n">bins</span> <span class="k">if</span> <span class="n">k</span> <span class="o">!=</span> <span class="n">v</span><span class="p">])</span>
+            <span class="n">score</span> <span class="o">+=</span> <span class="p">(</span><span class="n">d</span><span class="o">.</span><span class="n">density</span><span class="p">(</span><span class="n">v</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> <span class="o">**</span> <span class="mi">2</span>
+            <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">score</span><span class="p">)</span>
+        <span class="k">except</span> <span class="ne">ValueError</span> <span class="k">as</span> <span class="n">ex</span><span class="p">:</span>
+            <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">([</span><span class="n">d</span><span class="o">.</span><span class="n">density</span><span class="p">(</span><span class="n">k</span><span class="p">)</span> <span class="o">**</span> <span class="mi">2</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">d</span><span class="o">.</span><span class="n">bins</span><span class="p">]))</span>
+    <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">ret</span><span class="p">)</span> <span class="o">/</span> <span class="nb">len</span><span class="p">(</span><span class="n">ret</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="logarithm_score"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Measures.logarithm_score">[docs]</a><span class="k">def</span> <span class="nf">logarithm_score</span><span class="p">(</span><span class="n">targets</span><span class="p">,</span> <span class="n">densities</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Logarithm Score for probabilistic forecasts.</span>
+<span class="sd">    Good IJ (1952).  “Rational Decisions.”Journal of the Royal Statistical Society B,14(1),107–114. URLhttps://www.jstor.org/stable/2984087.</span>
+
+<span class="sd">    :param targets: a list with the target values</span>
+<span class="sd">    :param densities: a list with pyFTS.probabil objectsistic.ProbabilityDistribution</span>
+<span class="sd">    :return: float</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">_ls</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="mf">0.0</span><span class="p">)</span>
+    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">densities</span><span class="p">,</span> <span class="n">ProbabilityDistribution</span><span class="o">.</span><span class="n">ProbabilityDistribution</span><span class="p">):</span>
+        <span class="n">densities</span> <span class="o">=</span> <span class="p">[</span><span class="n">densities</span><span class="p">]</span>
+        <span class="n">targets</span> <span class="o">=</span> <span class="p">[</span><span class="n">targets</span><span class="p">]</span>
+
+    <span class="n">n</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">densities</span><span class="p">)</span>
+    <span class="k">for</span> <span class="n">ct</span><span class="p">,</span> <span class="n">df</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">densities</span><span class="p">):</span>
+        <span class="n">_ls</span> <span class="o">+=</span> <span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">density</span><span class="p">(</span><span class="n">targets</span><span class="p">[</span><span class="n">ct</span><span class="p">]))</span>
+
+    <span class="k">return</span> <span class="n">_ls</span> <span class="o">/</span> <span class="n">n</span></div>
+
+
+<div class="viewcode-block" id="crps"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Measures.crps">[docs]</a><span class="k">def</span> <span class="nf">crps</span><span class="p">(</span><span class="n">targets</span><span class="p">,</span> <span class="n">densities</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Continuous Ranked Probability Score</span>
+
+<span class="sd">    :param targets: a list with the target values</span>
+<span class="sd">    :param densities: a list with pyFTS.probabil objectsistic.ProbabilityDistribution</span>
+<span class="sd">    :return: float</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">_crps</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="mf">0.0</span><span class="p">)</span>
+    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">densities</span><span class="p">,</span> <span class="n">ProbabilityDistribution</span><span class="o">.</span><span class="n">ProbabilityDistribution</span><span class="p">):</span>
+        <span class="n">densities</span> <span class="o">=</span> <span class="p">[</span><span class="n">densities</span><span class="p">]</span>
+        <span class="n">targets</span> <span class="o">=</span> <span class="p">[</span><span class="n">targets</span><span class="p">]</span>
+
+    <span class="n">n</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">densities</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">n</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
+
+    <span class="k">for</span> <span class="n">ct</span><span class="p">,</span> <span class="n">df</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">densities</span><span class="p">):</span>
+        <span class="n">_crps</span> <span class="o">+=</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">([(</span><span class="n">df</span><span class="o">.</span><span class="n">cumulative</span><span class="p">(</span><span class="nb">bin</span><span class="p">)</span> <span class="o">-</span> <span class="p">(</span><span class="mi">1</span> <span class="k">if</span> <span class="nb">bin</span> <span class="o">&gt;=</span> <span class="n">targets</span><span class="p">[</span><span class="n">ct</span><span class="p">]</span> <span class="k">else</span> <span class="mi">0</span><span class="p">))</span> <span class="o">**</span> <span class="mi">2</span> <span class="k">for</span> <span class="nb">bin</span> <span class="ow">in</span> <span class="n">df</span><span class="o">.</span><span class="n">bins</span><span class="p">])</span>
+
+    <span class="k">return</span> <span class="n">_crps</span> <span class="o">/</span> <span class="n">n</span></div>
+
+
+<div class="viewcode-block" id="get_point_statistics"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Measures.get_point_statistics">[docs]</a><span class="k">def</span> <span class="nf">get_point_statistics</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Condensate all measures for point forecasters</span>
+
+<span class="sd">    :param data: test data</span>
+<span class="sd">    :param model: FTS model with point forecasting capability</span>
+<span class="sd">    :param kwargs:</span>
+<span class="sd">    :return: a list with the RMSE, SMAPE and U Statistic</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">steps_ahead</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;steps_ahead&#39;</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+    <span class="n">kwargs</span><span class="p">[</span><span class="s1">&#39;type&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;point&#39;</span>
+
+    <span class="n">indexer</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;indexer&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">indexer</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">ndata</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">indexer</span><span class="o">.</span><span class="n">get_data</span><span class="p">(</span><span class="n">data</span><span class="p">))</span>
+    <span class="k">elif</span> <span class="n">model</span><span class="o">.</span><span class="n">is_multivariate</span><span class="p">:</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">):</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Multivariate data must be a Pandas DataFrame!&quot;</span><span class="p">)</span>
+        <span class="n">ndata</span> <span class="o">=</span> <span class="n">data</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">ndata</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+
+    <span class="n">ret</span> <span class="o">=</span> <span class="nb">list</span><span class="p">()</span>
+
+    <span class="k">if</span> <span class="n">steps_ahead</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+        <span class="n">forecasts</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">ndata</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">model</span><span class="o">.</span><span class="n">is_multivariate</span> <span class="ow">and</span> <span class="n">model</span><span class="o">.</span><span class="n">has_seasonality</span><span class="p">:</span>
+            <span class="n">ndata</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">indexer</span><span class="o">.</span><span class="n">get_data</span><span class="p">(</span><span class="n">ndata</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="n">model</span><span class="o">.</span><span class="n">is_multivariate</span><span class="p">:</span>
+            <span class="n">ndata</span> <span class="o">=</span> <span class="n">ndata</span><span class="p">[</span><span class="n">model</span><span class="o">.</span><span class="n">target_variable</span><span class="o">.</span><span class="n">data_label</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
+
+        <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">forecasts</span><span class="p">,</span> <span class="p">(</span><span class="nb">list</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">)):</span>
+            <span class="n">forecasts</span> <span class="o">=</span> <span class="p">[</span><span class="n">forecasts</span><span class="p">]</span>
+
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">forecasts</span><span class="p">)</span> <span class="o">!=</span> <span class="nb">len</span><span class="p">(</span><span class="n">ndata</span><span class="p">)</span> <span class="o">-</span> <span class="n">model</span><span class="o">.</span><span class="n">max_lag</span><span class="p">:</span>
+            <span class="n">forecasts</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">forecasts</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">forecasts</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">forecasts</span><span class="p">)</span>
+
+        <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">rmse</span><span class="p">(</span><span class="n">ndata</span><span class="p">[</span><span class="n">model</span><span class="o">.</span><span class="n">max_lag</span><span class="p">:],</span> <span class="n">forecasts</span><span class="p">),</span> <span class="mi">2</span><span class="p">))</span>
+        <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">mape</span><span class="p">(</span><span class="n">ndata</span><span class="p">[</span><span class="n">model</span><span class="o">.</span><span class="n">max_lag</span><span class="p">:],</span> <span class="n">forecasts</span><span class="p">),</span> <span class="mi">2</span><span class="p">))</span>
+        <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">UStatistic</span><span class="p">(</span><span class="n">ndata</span><span class="p">[</span><span class="n">model</span><span class="o">.</span><span class="n">max_lag</span><span class="p">:],</span> <span class="n">forecasts</span><span class="p">),</span> <span class="mi">2</span><span class="p">))</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">steps_ahead_sampler</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;steps_ahead_sampler&#39;</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+        <span class="n">nforecasts</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">order</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">ndata</span><span class="p">)</span> <span class="o">-</span> <span class="n">steps_ahead</span><span class="p">,</span> <span class="n">steps_ahead_sampler</span><span class="p">):</span>
+            <span class="n">sample</span> <span class="o">=</span> <span class="n">ndata</span><span class="p">[</span><span class="n">k</span> <span class="o">-</span> <span class="n">model</span><span class="o">.</span><span class="n">order</span><span class="p">:</span> <span class="n">k</span><span class="p">]</span>
+            <span class="n">tmp</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">sample</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+            <span class="n">nforecasts</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">tmp</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
+
+        <span class="n">start</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">max_lag</span> <span class="o">+</span> <span class="n">steps_ahead</span> <span class="o">-</span> <span class="mi">1</span>
+        <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">rmse</span><span class="p">(</span><span class="n">ndata</span><span class="p">[</span><span class="n">start</span><span class="p">:</span><span class="o">-</span><span class="mi">1</span><span class="p">:</span><span class="n">steps_ahead_sampler</span><span class="p">],</span> <span class="n">nforecasts</span><span class="p">),</span> <span class="mi">2</span><span class="p">))</span>
+        <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">mape</span><span class="p">(</span><span class="n">ndata</span><span class="p">[</span><span class="n">start</span><span class="p">:</span><span class="o">-</span><span class="mi">1</span><span class="p">:</span><span class="n">steps_ahead_sampler</span><span class="p">],</span> <span class="n">nforecasts</span><span class="p">),</span> <span class="mi">2</span><span class="p">))</span>
+        <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">UStatistic</span><span class="p">(</span><span class="n">ndata</span><span class="p">[</span><span class="n">start</span><span class="p">:</span><span class="o">-</span><span class="mi">1</span><span class="p">:</span><span class="n">steps_ahead_sampler</span><span class="p">],</span> <span class="n">nforecasts</span><span class="p">),</span> <span class="mi">2</span><span class="p">))</span>
+
+    <span class="k">return</span> <span class="n">ret</span></div>
+
+
+<div class="viewcode-block" id="get_point_ahead_statistics"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Measures.get_point_ahead_statistics">[docs]</a><span class="k">def</span> <span class="nf">get_point_ahead_statistics</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">forecasts</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Condensate all measures for point forecasters</span>
+
+<span class="sd">    :param data: test data</span>
+<span class="sd">    :param model: FTS model with point forecasting capability</span>
+<span class="sd">    :param kwargs:</span>
+<span class="sd">    :return: a list with the RMSE, SMAPE and U Statistic</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">l</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">forecasts</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)</span> <span class="o">!=</span> <span class="n">l</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s2">&quot;Data and intervals have different lenghts!&quot;</span><span class="p">)</span>
+
+    <span class="n">lags</span> <span class="o">=</span> <span class="p">{}</span>
+
+    <span class="k">for</span> <span class="n">lag</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">l</span><span class="p">):</span>
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">{}</span>
+        <span class="n">datum</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">lag</span><span class="p">]</span>
+        <span class="n">forecast</span> <span class="o">=</span> <span class="n">forecasts</span><span class="p">[</span><span class="n">lag</span><span class="p">]</span>
+        <span class="n">ret</span><span class="p">[</span><span class="s1">&#39;steps&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">lag</span>
+        <span class="n">ret</span><span class="p">[</span><span class="s1">&#39;method&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;&#39;</span>
+        <span class="n">ret</span><span class="p">[</span><span class="s1">&#39;rmse&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">rmse</span><span class="p">(</span><span class="n">datum</span><span class="p">,</span> <span class="n">forecast</span><span class="p">)</span>
+        <span class="n">ret</span><span class="p">[</span><span class="s1">&#39;mape&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">mape</span><span class="p">(</span><span class="n">datum</span><span class="p">,</span> <span class="n">forecast</span><span class="p">)</span>
+        <span class="n">sample</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">lag</span><span class="o">-</span><span class="mi">1</span><span class="p">:</span><span class="n">lag</span><span class="o">+</span><span class="mi">1</span><span class="p">]</span> <span class="k">if</span> <span class="n">lag</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="k">else</span> <span class="p">[</span><span class="n">datum</span><span class="p">,</span> <span class="n">datum</span><span class="p">]</span>
+        <span class="n">ret</span><span class="p">[</span><span class="s1">&#39;u&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">UStatistic</span><span class="p">(</span><span class="n">sample</span><span class="p">,</span> <span class="n">forecast</span><span class="p">)</span>
+        <span class="n">lags</span><span class="p">[</span><span class="n">lag</span><span class="p">]</span> <span class="o">=</span> <span class="n">ret</span>
+
+    <span class="k">return</span> <span class="n">lags</span></div>
+
+
+<div class="viewcode-block" id="get_interval_statistics"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Measures.get_interval_statistics">[docs]</a><span class="k">def</span> <span class="nf">get_interval_statistics</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Condensate all measures for point interval forecasters</span>
+
+<span class="sd">    :param data: test data</span>
+<span class="sd">    :param model: FTS model with interval forecasting capability</span>
+<span class="sd">    :param kwargs:</span>
+<span class="sd">    :return: a list with the sharpness, resolution, coverage, .05 pinball mean,</span>
+<span class="sd">            .25 pinball mean, .75 pinball mean and .95 pinball mean.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">steps_ahead</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;steps_ahead&#39;</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+    <span class="n">kwargs</span><span class="p">[</span><span class="s1">&#39;type&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;interval&#39;</span>
+
+    <span class="n">ret</span> <span class="o">=</span> <span class="nb">list</span><span class="p">()</span>
+
+    <span class="k">if</span> <span class="n">steps_ahead</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+        <span class="n">forecasts</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">sharpness</span><span class="p">(</span><span class="n">forecasts</span><span class="p">),</span> <span class="mi">2</span><span class="p">))</span>
+        <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">resolution</span><span class="p">(</span><span class="n">forecasts</span><span class="p">),</span> <span class="mi">2</span><span class="p">))</span>
+        <span class="k">if</span> <span class="n">model</span><span class="o">.</span><span class="n">is_multivariate</span><span class="p">:</span>
+            <span class="n">data</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">model</span><span class="o">.</span><span class="n">target_variable</span><span class="o">.</span><span class="n">data_label</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
+        <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">coverage</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">model</span><span class="o">.</span><span class="n">max_lag</span><span class="p">:],</span> <span class="n">forecasts</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">]),</span> <span class="mi">2</span><span class="p">))</span>
+        <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">pinball_mean</span><span class="p">(</span><span class="mf">0.05</span><span class="p">,</span> <span class="n">data</span><span class="p">[</span><span class="n">model</span><span class="o">.</span><span class="n">max_lag</span><span class="p">:],</span> <span class="n">forecasts</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">]),</span> <span class="mi">2</span><span class="p">))</span>
+        <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">pinball_mean</span><span class="p">(</span><span class="mf">0.25</span><span class="p">,</span> <span class="n">data</span><span class="p">[</span><span class="n">model</span><span class="o">.</span><span class="n">max_lag</span><span class="p">:],</span> <span class="n">forecasts</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">]),</span> <span class="mi">2</span><span class="p">))</span>
+        <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">pinball_mean</span><span class="p">(</span><span class="mf">0.75</span><span class="p">,</span> <span class="n">data</span><span class="p">[</span><span class="n">model</span><span class="o">.</span><span class="n">max_lag</span><span class="p">:],</span> <span class="n">forecasts</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">]),</span> <span class="mi">2</span><span class="p">))</span>
+        <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">pinball_mean</span><span class="p">(</span><span class="mf">0.95</span><span class="p">,</span> <span class="n">data</span><span class="p">[</span><span class="n">model</span><span class="o">.</span><span class="n">max_lag</span><span class="p">:],</span> <span class="n">forecasts</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">]),</span> <span class="mi">2</span><span class="p">))</span>
+        <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">winkler_mean</span><span class="p">(</span><span class="mf">0.05</span><span class="p">,</span> <span class="n">data</span><span class="p">[</span><span class="n">model</span><span class="o">.</span><span class="n">max_lag</span><span class="p">:],</span> <span class="n">forecasts</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">]),</span> <span class="mi">2</span><span class="p">))</span>
+        <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">winkler_mean</span><span class="p">(</span><span class="mf">0.25</span><span class="p">,</span> <span class="n">data</span><span class="p">[</span><span class="n">model</span><span class="o">.</span><span class="n">max_lag</span><span class="p">:],</span> <span class="n">forecasts</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">]),</span> <span class="mi">2</span><span class="p">))</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">forecasts</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">order</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)</span> <span class="o">-</span> <span class="n">steps_ahead</span><span class="p">):</span>
+            <span class="n">sample</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">k</span> <span class="o">-</span> <span class="n">model</span><span class="o">.</span><span class="n">order</span><span class="p">:</span> <span class="n">k</span><span class="p">]</span>
+            <span class="n">tmp</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">sample</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+            <span class="n">forecasts</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">tmp</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
+
+        <span class="n">start</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">max_lag</span> <span class="o">+</span> <span class="n">steps_ahead</span> <span class="o">-</span> <span class="mi">1</span>
+        <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">sharpness</span><span class="p">(</span><span class="n">forecasts</span><span class="p">),</span> <span class="mi">2</span><span class="p">))</span>
+        <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">resolution</span><span class="p">(</span><span class="n">forecasts</span><span class="p">),</span> <span class="mi">2</span><span class="p">))</span>
+        <span class="k">if</span> <span class="n">model</span><span class="o">.</span><span class="n">is_multivariate</span><span class="p">:</span>
+            <span class="n">data</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">model</span><span class="o">.</span><span class="n">target_variable</span><span class="o">.</span><span class="n">data_label</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
+        <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">coverage</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">model</span><span class="o">.</span><span class="n">max_lag</span><span class="p">:],</span> <span class="n">forecasts</span><span class="p">),</span> <span class="mi">2</span><span class="p">))</span>
+        <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">pinball_mean</span><span class="p">(</span><span class="mf">0.05</span><span class="p">,</span> <span class="n">data</span><span class="p">[</span><span class="n">start</span><span class="p">:],</span> <span class="n">forecasts</span><span class="p">),</span> <span class="mi">2</span><span class="p">))</span>
+        <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">pinball_mean</span><span class="p">(</span><span class="mf">0.25</span><span class="p">,</span> <span class="n">data</span><span class="p">[</span><span class="n">start</span><span class="p">:],</span> <span class="n">forecasts</span><span class="p">),</span> <span class="mi">2</span><span class="p">))</span>
+        <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">pinball_mean</span><span class="p">(</span><span class="mf">0.75</span><span class="p">,</span> <span class="n">data</span><span class="p">[</span><span class="n">start</span><span class="p">:],</span> <span class="n">forecasts</span><span class="p">),</span> <span class="mi">2</span><span class="p">))</span>
+        <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">pinball_mean</span><span class="p">(</span><span class="mf">0.95</span><span class="p">,</span> <span class="n">data</span><span class="p">[</span><span class="n">start</span><span class="p">:],</span> <span class="n">forecasts</span><span class="p">),</span> <span class="mi">2</span><span class="p">))</span>
+        <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">winkler_mean</span><span class="p">(</span><span class="mf">0.05</span><span class="p">,</span> <span class="n">data</span><span class="p">[</span><span class="n">start</span><span class="p">:],</span> <span class="n">forecasts</span><span class="p">),</span> <span class="mi">2</span><span class="p">))</span>
+        <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">winkler_mean</span><span class="p">(</span><span class="mf">0.25</span><span class="p">,</span> <span class="n">data</span><span class="p">[</span><span class="n">start</span><span class="p">:],</span> <span class="n">forecasts</span><span class="p">),</span> <span class="mi">2</span><span class="p">))</span>
+    <span class="k">return</span> <span class="n">ret</span></div>
+
+
+<div class="viewcode-block" id="get_interval_ahead_statistics"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Measures.get_interval_ahead_statistics">[docs]</a><span class="k">def</span> <span class="nf">get_interval_ahead_statistics</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">intervals</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Condensate all measures for point interval forecasters</span>
+
+<span class="sd">    :param data: test data</span>
+<span class="sd">    :param intervals: predicted intervals for each datapoint</span>
+<span class="sd">    :param kwargs:</span>
+<span class="sd">    :return: a list with the sharpness, resolution, coverage, .05 pinball mean,</span>
+<span class="sd">            .25 pinball mean, .75 pinball mean and .95 pinball mean.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">l</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">intervals</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)</span> <span class="o">!=</span> <span class="n">l</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s2">&quot;Data and intervals have different lenghts!&quot;</span><span class="p">)</span>
+
+    <span class="n">lags</span> <span class="o">=</span> <span class="p">{}</span>
+
+    <span class="k">for</span> <span class="n">lag</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">l</span><span class="p">):</span>
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">{}</span>
+        <span class="n">datum</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">lag</span><span class="p">]</span>
+        <span class="n">interval</span> <span class="o">=</span> <span class="n">intervals</span><span class="p">[</span><span class="n">lag</span><span class="p">]</span>
+        <span class="n">ret</span><span class="p">[</span><span class="s1">&#39;steps&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">lag</span>
+        <span class="n">ret</span><span class="p">[</span><span class="s1">&#39;method&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;&#39;</span>
+        <span class="n">ret</span><span class="p">[</span><span class="s1">&#39;sharpness&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">round</span><span class="p">(</span><span class="n">interval</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">interval</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="mi">2</span><span class="p">)</span>
+        <span class="n">ret</span><span class="p">[</span><span class="s1">&#39;coverage&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span> <span class="k">if</span> <span class="n">interval</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="n">datum</span> <span class="o">&lt;=</span> <span class="n">interval</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">else</span> <span class="mi">0</span>
+        <span class="n">ret</span><span class="p">[</span><span class="s1">&#39;pinball05&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">round</span><span class="p">(</span><span class="n">pinball</span><span class="p">(</span><span class="mf">0.05</span><span class="p">,</span> <span class="n">datum</span><span class="p">,</span> <span class="n">interval</span><span class="p">[</span><span class="mi">0</span><span class="p">]),</span> <span class="mi">2</span><span class="p">)</span>
+        <span class="n">ret</span><span class="p">[</span><span class="s1">&#39;pinball25&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">round</span><span class="p">(</span><span class="n">pinball</span><span class="p">(</span><span class="mf">0.25</span><span class="p">,</span> <span class="n">datum</span><span class="p">,</span> <span class="n">interval</span><span class="p">[</span><span class="mi">0</span><span class="p">]),</span> <span class="mi">2</span><span class="p">)</span>
+        <span class="n">ret</span><span class="p">[</span><span class="s1">&#39;pinball75&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">round</span><span class="p">(</span><span class="n">pinball</span><span class="p">(</span><span class="mf">0.75</span><span class="p">,</span> <span class="n">datum</span><span class="p">,</span> <span class="n">interval</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span> <span class="mi">2</span><span class="p">)</span>
+        <span class="n">ret</span><span class="p">[</span><span class="s1">&#39;pinball95&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">round</span><span class="p">(</span><span class="n">pinball</span><span class="p">(</span><span class="mf">0.95</span><span class="p">,</span> <span class="n">datum</span><span class="p">,</span> <span class="n">interval</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span> <span class="mi">2</span><span class="p">)</span>
+        <span class="n">ret</span><span class="p">[</span><span class="s1">&#39;winkler05&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">round</span><span class="p">(</span><span class="n">winkler_score</span><span class="p">(</span><span class="mf">0.05</span><span class="p">,</span> <span class="n">datum</span><span class="p">,</span> <span class="n">interval</span><span class="p">),</span> <span class="mi">2</span><span class="p">)</span>
+        <span class="n">ret</span><span class="p">[</span><span class="s1">&#39;winkler25&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">round</span><span class="p">(</span><span class="n">winkler_score</span><span class="p">(</span><span class="mf">0.25</span><span class="p">,</span> <span class="n">datum</span><span class="p">,</span> <span class="n">interval</span><span class="p">),</span> <span class="mi">2</span><span class="p">)</span>
+        <span class="n">lags</span><span class="p">[</span><span class="n">lag</span><span class="p">]</span> <span class="o">=</span> <span class="n">ret</span>
+
+    <span class="k">return</span> <span class="n">lags</span></div>
+
+
+<div class="viewcode-block" id="get_distribution_statistics"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Measures.get_distribution_statistics">[docs]</a><span class="k">def</span> <span class="nf">get_distribution_statistics</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Get CRPS statistic and time for a forecasting model</span>
+
+<span class="sd">    :param data: test data</span>
+<span class="sd">    :param model: FTS model with probabilistic forecasting capability</span>
+<span class="sd">    :param kwargs:</span>
+<span class="sd">    :return: a list with the CRPS and execution time</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">steps_ahead</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;steps_ahead&#39;</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+    <span class="n">kwargs</span><span class="p">[</span><span class="s1">&#39;type&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;distribution&#39;</span>
+
+    <span class="n">ret</span> <span class="o">=</span> <span class="nb">list</span><span class="p">()</span>
+
+    <span class="k">if</span> <span class="n">steps_ahead</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+        <span class="n">_s1</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
+        <span class="n">forecasts</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="n">_e1</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
+        <span class="k">if</span> <span class="n">model</span><span class="o">.</span><span class="n">is_multivariate</span><span class="p">:</span>
+            <span class="n">data</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">model</span><span class="o">.</span><span class="n">target_variable</span><span class="o">.</span><span class="n">data_label</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
+        <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">crps</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">model</span><span class="o">.</span><span class="n">max_lag</span><span class="p">:],</span> <span class="n">forecasts</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">]),</span> <span class="mi">3</span><span class="p">))</span>
+        <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">_e1</span> <span class="o">-</span> <span class="n">_s1</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
+        <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">brier_score</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">model</span><span class="o">.</span><span class="n">max_lag</span><span class="p">:],</span> <span class="n">forecasts</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">]),</span> <span class="mi">3</span><span class="p">))</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">skip</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;steps_ahead_sampler&#39;</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+        <span class="n">forecasts</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="n">_s1</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">max_lag</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)</span> <span class="o">-</span> <span class="n">steps_ahead</span><span class="p">,</span> <span class="n">skip</span><span class="p">):</span>
+            <span class="n">sample</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">k</span> <span class="o">-</span> <span class="n">model</span><span class="o">.</span><span class="n">max_lag</span><span class="p">:</span> <span class="n">k</span><span class="p">]</span>
+            <span class="n">tmp</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">sample</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+            <span class="n">forecasts</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">tmp</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
+        <span class="n">_e1</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
+
+        <span class="n">start</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">max_lag</span> <span class="o">+</span> <span class="n">steps_ahead</span>
+        <span class="k">if</span> <span class="n">model</span><span class="o">.</span><span class="n">is_multivariate</span><span class="p">:</span>
+            <span class="n">data</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">model</span><span class="o">.</span><span class="n">target_variable</span><span class="o">.</span><span class="n">data_label</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
+        <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">crps</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">start</span><span class="p">:</span><span class="o">-</span><span class="mi">1</span><span class="p">:</span><span class="n">skip</span><span class="p">],</span> <span class="n">forecasts</span><span class="p">),</span> <span class="mi">3</span><span class="p">))</span>
+        <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">_e1</span> <span class="o">-</span> <span class="n">_s1</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
+        <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">brier_score</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">start</span><span class="p">:</span><span class="o">-</span><span class="mi">1</span><span class="p">:</span><span class="n">skip</span><span class="p">],</span> <span class="n">forecasts</span><span class="p">),</span> <span class="mi">3</span><span class="p">))</span>
+    <span class="k">return</span> <span class="n">ret</span></div>
+
+
+<div class="viewcode-block" id="get_distribution_ahead_statistics"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Measures.get_distribution_ahead_statistics">[docs]</a><span class="k">def</span> <span class="nf">get_distribution_ahead_statistics</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">distributions</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Get CRPS statistic and time for a forecasting model</span>
+
+<span class="sd">    :param data: test data</span>
+<span class="sd">    :param model: FTS model with probabilistic forecasting capability</span>
+<span class="sd">    :param kwargs:</span>
+<span class="sd">    :return: a list with the CRPS and execution time</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">l</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">distributions</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)</span> <span class="o">!=</span> <span class="n">l</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s2">&quot;Data and distributions have different lenghts!&quot;</span><span class="p">)</span>
+
+    <span class="n">lags</span> <span class="o">=</span> <span class="p">{}</span>
+
+    <span class="k">for</span> <span class="n">lag</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">l</span><span class="p">):</span>
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">{}</span>
+        <span class="n">datum</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">lag</span><span class="p">]</span>
+        <span class="n">dist</span> <span class="o">=</span> <span class="n">distributions</span><span class="p">[</span><span class="n">lag</span><span class="p">]</span>
+        <span class="n">ret</span><span class="p">[</span><span class="s1">&#39;steps&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">lag</span>
+        <span class="n">ret</span><span class="p">[</span><span class="s1">&#39;method&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;&#39;</span>
+        <span class="n">ret</span><span class="p">[</span><span class="s1">&#39;crps&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">round</span><span class="p">(</span><span class="n">crps</span><span class="p">(</span><span class="n">datum</span><span class="p">,</span> <span class="n">dist</span><span class="p">),</span> <span class="mi">3</span><span class="p">)</span>
+        <span class="n">ret</span><span class="p">[</span><span class="s1">&#39;brier&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">round</span><span class="p">(</span><span class="n">brier_score</span><span class="p">(</span><span class="n">datum</span><span class="p">,</span> <span class="n">dist</span><span class="p">),</span> <span class="mi">3</span><span class="p">)</span>
+        <span class="n">ret</span><span class="p">[</span><span class="s1">&#39;log&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">round</span><span class="p">(</span><span class="n">logarithm_score</span><span class="p">(</span><span class="n">datum</span><span class="p">,</span> <span class="n">dist</span><span class="p">),</span> <span class="mi">3</span><span class="p">)</span>
+        <span class="n">lags</span><span class="p">[</span><span class="n">lag</span><span class="p">]</span> <span class="o">=</span> <span class="n">ret</span>
+
+    <span class="k">return</span> <span class="n">lags</span></div>
+</pre></div>
+
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docs/build/html/_modules/pyFTS/benchmarks/ResidualAnalysis.html

@@ -0,0 +1,212 @@
+
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+<!doctype html>
+
+<html>
+  <head>
+    <meta charset="utf-8" />
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+        <li class="nav-item nav-item-this"><a href="">pyFTS.benchmarks.ResidualAnalysis</a></li> 
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+            
+  <h1>Source code for pyFTS.benchmarks.ResidualAnalysis</h1><div class="highlight"><pre>
+<span></span><span class="ch">#!/usr/bin/python</span>
+<span class="c1"># -*- coding: utf8 -*-</span>
+
+<span class="sd">&quot;&quot;&quot;Residual Analysis methods&quot;&quot;&quot;</span>
+
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">import</span> <span class="nn">matplotlib</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="k">import</span> <span class="n">Transformations</span><span class="p">,</span><span class="n">Util</span>
+<span class="kn">from</span> <span class="nn">pyFTS.benchmarks</span> <span class="k">import</span> <span class="n">Measures</span>
+<span class="kn">from</span> <span class="nn">scipy</span> <span class="k">import</span> <span class="n">stats</span>
+
+
+<div class="viewcode-block" id="residuals"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.ResidualAnalysis.residuals">[docs]</a><span class="k">def</span> <span class="nf">residuals</span><span class="p">(</span><span class="n">targets</span><span class="p">,</span> <span class="n">forecasts</span><span class="p">,</span> <span class="n">order</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;First order residuals&quot;&quot;&quot;</span>
+    <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">targets</span><span class="p">[</span><span class="n">order</span><span class="p">:])</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">forecasts</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span></div>
+
+
+<div class="viewcode-block" id="ljung_box_test"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.ResidualAnalysis.ljung_box_test">[docs]</a><span class="k">def</span> <span class="nf">ljung_box_test</span><span class="p">(</span><span class="n">residuals</span><span class="p">,</span> <span class="n">lags</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">],</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">):</span>
+    <span class="kn">from</span> <span class="nn">statsmodels.stats.diagnostic</span> <span class="k">import</span> <span class="n">acorr_ljungbox</span>
+    <span class="kn">from</span> <span class="nn">scipy.stats</span> <span class="k">import</span> <span class="n">chi2</span>
+    
+    <span class="n">stat</span><span class="p">,</span> <span class="n">pval</span> <span class="o">=</span> <span class="n">acorr_ljungbox</span><span class="p">(</span><span class="n">residuals</span><span class="p">,</span> <span class="n">lags</span><span class="o">=</span><span class="n">lags</span><span class="p">)</span>
+    
+    <span class="n">rows</span> <span class="o">=</span> <span class="p">[]</span>
+
+    <span class="k">for</span> <span class="n">ct</span><span class="p">,</span> <span class="n">Q</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">stat</span><span class="p">):</span>
+      <span class="n">lag</span> <span class="o">=</span> <span class="n">ct</span><span class="o">+</span><span class="mi">1</span>
+      <span class="n">p_value</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">-</span> <span class="n">chi2</span><span class="o">.</span><span class="n">cdf</span><span class="p">(</span><span class="n">Q</span><span class="p">,</span> <span class="n">df</span><span class="o">=</span><span class="n">lag</span><span class="p">)</span>
+      <span class="n">critical_value</span> <span class="o">=</span> <span class="n">chi2</span><span class="o">.</span><span class="n">ppf</span><span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">alpha</span><span class="p">,</span> <span class="n">df</span><span class="o">=</span><span class="n">lag</span><span class="p">)</span>
+      <span class="n">rows</span><span class="o">.</span><span class="n">append</span><span class="p">([</span><span class="n">lag</span><span class="p">,</span> <span class="n">Q</span><span class="p">,</span> <span class="n">p_value</span><span class="p">,</span> <span class="n">critical_value</span><span class="p">,</span> <span class="s1">&#39;H0 accepted&#39;</span> <span class="k">if</span> <span class="n">Q</span> <span class="o">&gt;</span> <span class="n">critical_value</span> <span class="k">else</span> <span class="s1">&#39;H0 rejected&#39;</span><span class="p">])</span>
+        
+    <span class="k">return</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">rows</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;Lag&#39;</span><span class="p">,</span><span class="s1">&#39;Statistic&#39;</span><span class="p">,</span><span class="s1">&#39;p-Value&#39;</span><span class="p">,</span><span class="s1">&#39;Critical Value&#39;</span><span class="p">,</span> <span class="s1">&#39;Result&#39;</span><span class="p">])</span></div>
+ 
+
+<div class="viewcode-block" id="compare_residuals"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.ResidualAnalysis.compare_residuals">[docs]</a><span class="k">def</span> <span class="nf">compare_residuals</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">models</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=.</span><span class="mi">05</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Compare residual&#39;s statistics of several models</span>
+
+<span class="sd">    :param data: test data</span>
+<span class="sd">    :param models: </span>
+<span class="sd">    :return: a Pandas dataframe with the Box-Ljung statistic for each model</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="kn">from</span> <span class="nn">statsmodels.stats.diagnostic</span> <span class="k">import</span> <span class="n">acorr_ljungbox</span>
+    <span class="n">rows</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">columns</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;Model&quot;</span><span class="p">,</span><span class="s2">&quot;Order&quot;</span><span class="p">,</span><span class="s2">&quot;AVG&quot;</span><span class="p">,</span><span class="s2">&quot;STD&quot;</span><span class="p">,</span><span class="s2">&quot;Box-Ljung&quot;</span><span class="p">,</span><span class="s2">&quot;p-value&quot;</span><span class="p">,</span><span class="s2">&quot;Result&quot;</span><span class="p">]</span>
+    <span class="k">for</span> <span class="n">mfts</span> <span class="ow">in</span> <span class="n">models</span><span class="p">:</span>
+        <span class="n">forecasts</span> <span class="o">=</span> <span class="n">mfts</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+        <span class="n">res</span> <span class="o">=</span> <span class="n">residuals</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">forecasts</span><span class="p">,</span> <span class="n">mfts</span><span class="o">.</span><span class="n">order</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span>
+        <span class="n">mu</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">res</span><span class="p">)</span>
+        <span class="n">sig</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">std</span><span class="p">(</span><span class="n">res</span><span class="p">)</span>
+        <span class="n">row</span> <span class="o">=</span> <span class="p">[</span><span class="n">mfts</span><span class="o">.</span><span class="n">shortname</span><span class="p">,</span> <span class="n">mfts</span><span class="o">.</span><span class="n">order</span><span class="p">,</span> <span class="n">mu</span><span class="p">,</span> <span class="n">sig</span><span class="p">]</span>
+        <span class="n">stat</span><span class="p">,</span> <span class="n">pval</span> <span class="o">=</span> <span class="n">acorr_ljungbox</span><span class="p">(</span><span class="n">res</span><span class="p">)</span>
+        <span class="n">test</span> <span class="o">=</span> <span class="s1">&#39;H0 Accepted&#39;</span> <span class="k">if</span> <span class="n">pval</span> <span class="o">&gt;</span> <span class="n">alpha</span> <span class="k">else</span> <span class="s1">&#39;H0 Rejected&#39;</span>
+        <span class="n">row</span><span class="o">.</span><span class="n">extend</span><span class="p">([</span><span class="n">stat</span><span class="p">,</span> <span class="n">pval</span><span class="p">,</span> <span class="n">test</span><span class="p">])</span>
+        <span class="n">rows</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">row</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">rows</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="n">columns</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="plot_residuals_by_model"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.ResidualAnalysis.plot_residuals_by_model">[docs]</a><span class="k">def</span> <span class="nf">plot_residuals_by_model</span><span class="p">(</span><span class="n">targets</span><span class="p">,</span> <span class="n">models</span><span class="p">,</span> <span class="n">tam</span><span class="o">=</span><span class="p">[</span><span class="mi">8</span><span class="p">,</span> <span class="mi">8</span><span class="p">],</span> <span class="n">save</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">file</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+    <span class="kn">import</span> <span class="nn">scipy</span> <span class="k">as</span> <span class="nn">sp</span>
+    
+    <span class="n">fig</span><span class="p">,</span> <span class="n">axes</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">nrows</span><span class="o">=</span><span class="nb">len</span><span class="p">(</span><span class="n">models</span><span class="p">),</span> <span class="n">ncols</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="n">tam</span><span class="p">)</span>
+
+    <span class="k">for</span> <span class="n">c</span><span class="p">,</span> <span class="n">mfts</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">models</span><span class="p">,</span> <span class="n">start</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">models</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
+            <span class="n">ax</span> <span class="o">=</span> <span class="n">axes</span><span class="p">[</span><span class="n">c</span><span class="p">]</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">ax</span> <span class="o">=</span> <span class="n">axes</span>
+        <span class="n">forecasts</span> <span class="o">=</span> <span class="n">mfts</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">targets</span><span class="p">)</span>
+        <span class="n">res</span> <span class="o">=</span> <span class="n">residuals</span><span class="p">(</span><span class="n">targets</span><span class="p">,</span> <span class="n">forecasts</span><span class="p">,</span> <span class="n">mfts</span><span class="o">.</span><span class="n">order</span><span class="p">)</span>
+        <span class="n">mu</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">res</span><span class="p">)</span>
+        <span class="n">sig</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">std</span><span class="p">(</span><span class="n">res</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">c</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span> <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;Residuals&quot;</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="s1">&#39;large&#39;</span><span class="p">)</span>
+        <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="n">mfts</span><span class="o">.</span><span class="n">shortname</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="s1">&#39;large&#39;</span><span class="p">)</span>
+        <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39; &#39;</span><span class="p">)</span>
+        <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">res</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">c</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span> <span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;Autocorrelation&quot;</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="s1">&#39;large&#39;</span><span class="p">)</span>
+        <span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;ACS&#39;</span><span class="p">)</span>
+        <span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;Lag&#39;</span><span class="p">)</span>
+        <span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">acorr</span><span class="p">(</span><span class="n">res</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">c</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span> <span class="n">ax</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;Histogram&quot;</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="s1">&#39;large&#39;</span><span class="p">)</span>
+        <span class="n">ax</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;Freq&#39;</span><span class="p">)</span>
+        <span class="n">ax</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;Bins&#39;</span><span class="p">)</span>
+        <span class="n">ax</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span><span class="n">res</span><span class="p">)</span>
+        
+        <span class="k">if</span> <span class="n">c</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span> <span class="n">ax</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;QQ Plot&quot;</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="s1">&#39;large&#39;</span><span class="p">)</span>
+        
+        <span class="n">_</span><span class="p">,</span> <span class="p">(</span><span class="n">__</span><span class="p">,</span> <span class="n">___</span><span class="p">,</span> <span class="n">r</span><span class="p">)</span> <span class="o">=</span> <span class="n">sp</span><span class="o">.</span><span class="n">stats</span><span class="o">.</span><span class="n">probplot</span><span class="p">(</span><span class="n">res</span><span class="p">,</span> <span class="n">plot</span><span class="o">=</span><span class="n">ax</span><span class="p">[</span><span class="mi">3</span><span class="p">],</span> <span class="n">fit</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+    <span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
+
+    <span class="n">Util</span><span class="o">.</span><span class="n">show_and_save_image</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="n">file</span><span class="p">,</span> <span class="n">save</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="single_plot_residuals"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.ResidualAnalysis.single_plot_residuals">[docs]</a><span class="k">def</span> <span class="nf">single_plot_residuals</span><span class="p">(</span><span class="n">res</span><span class="p">,</span> <span class="n">order</span><span class="p">,</span> <span class="n">tam</span><span class="o">=</span><span class="p">[</span><span class="mi">10</span><span class="p">,</span> <span class="mi">7</span><span class="p">],</span> <span class="n">save</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">file</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+    <span class="kn">import</span> <span class="nn">scipy</span> <span class="k">as</span> <span class="nn">sp</span>
+    
+    <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">nrows</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">ncols</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="n">tam</span><span class="p">)</span>
+
+    <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;Residuals&quot;</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="s1">&#39;large&#39;</span><span class="p">)</span>
+    <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">res</span><span class="p">)</span>
+
+    <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;Autocorrelation&quot;</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="s1">&#39;large&#39;</span><span class="p">)</span>
+    <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;ACF&#39;</span><span class="p">)</span>
+    <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;Lag&#39;</span><span class="p">)</span>
+    <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">acorr</span><span class="p">(</span><span class="n">res</span><span class="p">)</span>
+
+    <span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;Histogram&quot;</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="s1">&#39;large&#39;</span><span class="p">)</span>
+    <span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;Freq&#39;</span><span class="p">)</span>
+    <span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;Bins&#39;</span><span class="p">)</span>
+    <span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span><span class="n">res</span><span class="p">)</span>
+    
+    <span class="n">_</span><span class="p">,</span> <span class="p">(</span><span class="n">__</span><span class="p">,</span> <span class="n">___</span><span class="p">,</span> <span class="n">r</span><span class="p">)</span> <span class="o">=</span> <span class="n">sp</span><span class="o">.</span><span class="n">stats</span><span class="o">.</span><span class="n">probplot</span><span class="p">(</span><span class="n">res</span><span class="p">,</span> <span class="n">plot</span><span class="o">=</span><span class="n">ax</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">1</span><span class="p">],</span> <span class="n">fit</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+    <span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
+
+    <span class="n">Util</span><span class="o">.</span><span class="n">show_and_save_image</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="n">file</span><span class="p">,</span> <span class="n">save</span><span class="p">)</span></div>
+</pre></div>
+
+            <div class="clearer"></div>
+          </div>
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+      <div class="sphinxsidebar" role="navigation" aria-label="main navigation">
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docs/build/html/_modules/pyFTS/benchmarks/Tests.html

@@ -0,0 +1,257 @@
+
+
+<!doctype html>
+
+<html>
+  <head>
+    <meta charset="utf-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+    <title>pyFTS.benchmarks.Tests &#8212; pyFTS 1.7 documentation</title>
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+        <li class="nav-item nav-item-this"><a href="">pyFTS.benchmarks.Tests</a></li> 
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+          <div class="body" role="main">
+            
+  <h1>Source code for pyFTS.benchmarks.Tests</h1><div class="highlight"><pre>
+<span></span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+
+<span class="kn">from</span> <span class="nn">pyFTS.benchmarks.Measures</span> <span class="k">import</span> <span class="n">acf</span>
+
+
+<div class="viewcode-block" id="BoxPierceStatistic"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Tests.BoxPierceStatistic">[docs]</a><span class="k">def</span> <span class="nf">BoxPierceStatistic</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">h</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Q Statistic for Box-Pierce test</span>
+
+<span class="sd">    :param data:</span>
+<span class="sd">    :param h:</span>
+<span class="sd">    :return:</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">n</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+    <span class="n">s</span> <span class="o">=</span> <span class="mi">0</span>
+    <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">h</span> <span class="o">+</span> <span class="mi">1</span><span class="p">):</span>
+        <span class="n">r</span> <span class="o">=</span> <span class="n">acf</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">k</span><span class="p">)</span>
+        <span class="n">s</span> <span class="o">+=</span> <span class="n">r</span> <span class="o">**</span> <span class="mi">2</span>
+    <span class="k">return</span> <span class="n">n</span> <span class="o">*</span> <span class="n">s</span></div>
+
+
+<div class="viewcode-block" id="BoxLjungStatistic"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Tests.BoxLjungStatistic">[docs]</a><span class="k">def</span> <span class="nf">BoxLjungStatistic</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">h</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Q Statistic for Ljung–Box test</span>
+
+<span class="sd">    :param data:</span>
+<span class="sd">    :param h:</span>
+<span class="sd">    :return:</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">n</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+    <span class="n">s</span> <span class="o">=</span> <span class="mi">0</span>
+    <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">h</span> <span class="o">+</span> <span class="mi">1</span><span class="p">):</span>
+        <span class="n">r</span> <span class="o">=</span> <span class="n">acf</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">k</span><span class="p">)</span>
+        <span class="n">s</span> <span class="o">+=</span> <span class="n">r</span> <span class="o">**</span> <span class="mi">2</span> <span class="o">/</span> <span class="p">(</span><span class="n">n</span> <span class="o">-</span> <span class="n">k</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">n</span> <span class="o">*</span> <span class="p">(</span><span class="n">n</span> <span class="o">-</span> <span class="mi">2</span><span class="p">)</span> <span class="o">*</span> <span class="n">s</span></div>
+
+
+<div class="viewcode-block" id="format_experiment_table"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Tests.format_experiment_table">[docs]</a><span class="k">def</span> <span class="nf">format_experiment_table</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">exclude</span><span class="o">=</span><span class="p">[],</span> <span class="n">replace</span><span class="o">=</span><span class="p">{},</span> <span class="n">csv</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">std</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
+    <span class="n">rows</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">columns</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">datasets</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">Dataset</span><span class="o">.</span><span class="n">unique</span><span class="p">()</span>
+    <span class="n">models</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">Model</span><span class="o">.</span><span class="n">unique</span><span class="p">()</span>
+    <span class="k">for</span> <span class="n">model</span> <span class="ow">in</span> <span class="n">models</span><span class="p">:</span>
+        <span class="n">test</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">any</span><span class="p">([</span><span class="n">model</span><span class="o">.</span><span class="n">rfind</span><span class="p">(</span><span class="n">k</span><span class="p">)</span> <span class="o">!=</span> <span class="o">-</span><span class="mi">1</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">exclude</span><span class="p">])</span> <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">exclude</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="k">else</span> <span class="kc">False</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="n">test</span><span class="p">:</span>
+            <span class="n">columns</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
+
+    <span class="k">for</span> <span class="n">dataset</span> <span class="ow">in</span> <span class="n">datasets</span><span class="p">:</span>
+        <span class="n">row</span> <span class="o">=</span> <span class="p">[</span><span class="n">dataset</span><span class="p">]</span>
+        <span class="k">if</span> <span class="n">std</span><span class="p">:</span>
+            <span class="n">row_std</span> <span class="o">=</span> <span class="p">[</span><span class="n">dataset</span><span class="p">]</span>
+        <span class="k">for</span> <span class="n">model</span> <span class="ow">in</span> <span class="n">columns</span><span class="p">:</span>
+            <span class="n">avg</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nanmin</span><span class="p">(</span><span class="n">df</span><span class="p">[(</span><span class="n">df</span><span class="o">.</span><span class="n">Dataset</span> <span class="o">==</span> <span class="n">dataset</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">Model</span> <span class="o">==</span> <span class="n">model</span><span class="p">)][</span><span class="s2">&quot;AVG&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">)</span>
+            <span class="n">row</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">avg</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
+            <span class="k">if</span> <span class="n">std</span><span class="p">:</span>
+                <span class="n">_std</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nanmin</span><span class="p">(</span><span class="n">df</span><span class="p">[(</span><span class="n">df</span><span class="o">.</span><span class="n">Dataset</span> <span class="o">==</span> <span class="n">dataset</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">Model</span> <span class="o">==</span> <span class="n">model</span><span class="p">)][</span><span class="s2">&quot;STD&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">)</span>
+                <span class="n">row_std</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s2">&quot;(&quot;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">_std</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span> <span class="o">+</span> <span class="s2">&quot;)&quot;</span><span class="p">)</span>
+
+        <span class="n">rows</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">row</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">std</span><span class="p">:</span>
+            <span class="n">rows</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">row_std</span><span class="p">)</span>
+
+    <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">columns</span><span class="p">)):</span>
+        <span class="k">if</span> <span class="n">columns</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="ow">in</span> <span class="n">replace</span><span class="p">:</span>
+            <span class="n">columns</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="n">replace</span><span class="p">[</span><span class="n">columns</span><span class="p">[</span><span class="n">k</span><span class="p">]]</span>
+
+    <span class="n">columns</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="s2">&quot;dataset&quot;</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">csv</span><span class="p">:</span>
+        <span class="n">header</span> <span class="o">=</span> <span class="s2">&quot;&quot;</span>
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">columns</span><span class="p">)):</span>
+            <span class="k">if</span> <span class="n">k</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+                <span class="n">header</span> <span class="o">+=</span> <span class="s2">&quot;,&quot;</span>
+            <span class="n">header</span> <span class="o">+=</span> <span class="n">columns</span><span class="p">[</span><span class="n">k</span><span class="p">]</span>
+
+        <span class="n">body</span> <span class="o">=</span> <span class="s2">&quot;&quot;</span>
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">rows</span><span class="p">)):</span>
+            <span class="n">row</span> <span class="o">=</span> <span class="s2">&quot;&quot;</span>
+            <span class="k">for</span> <span class="n">w</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">rows</span><span class="p">[</span><span class="n">k</span><span class="p">])):</span>
+                <span class="k">if</span> <span class="n">w</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+                    <span class="n">row</span> <span class="o">+=</span> <span class="s2">&quot;,&quot;</span>
+                <span class="n">row</span> <span class="o">+=</span> <span class="nb">str</span><span class="p">(</span><span class="n">rows</span><span class="p">[</span><span class="n">k</span><span class="p">][</span><span class="n">w</span><span class="p">])</span>
+            <span class="n">body</span> <span class="o">+=</span> <span class="s1">&#39;</span><span class="se">\n</span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">row</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">header</span> <span class="o">+</span> <span class="n">body</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">ret</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">rows</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="n">columns</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">ret</span></div>
+
+
+<div class="viewcode-block" id="test_mean_equality"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Tests.test_mean_equality">[docs]</a><span class="k">def</span> <span class="nf">test_mean_equality</span><span class="p">(</span><span class="n">tests</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=.</span><span class="mi">05</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">&#39;friedman&#39;</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Test for the equality of the means, with alpha confidence level.</span>
+
+<span class="sd">    H_0: There&#39;s no significant difference between the means</span>
+<span class="sd">    H_1: There is at least one significant difference between the means</span>
+
+<span class="sd">    :param tests:</span>
+<span class="sd">    :param alpha:</span>
+<span class="sd">    :param method:</span>
+<span class="sd">    :return:</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="kn">from</span> <span class="nn">stac.stac</span> <span class="k">import</span> <span class="n">nonparametric_tests</span> <span class="k">as</span> <span class="n">npt</span>
+
+    <span class="n">methods</span> <span class="o">=</span> <span class="n">tests</span><span class="o">.</span><span class="n">columns</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span>
+
+    <span class="n">values</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">methods</span><span class="p">:</span>
+        <span class="n">values</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">tests</span><span class="p">[</span><span class="n">k</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">method</span><span class="o">==</span><span class="s1">&#39;quade&#39;</span><span class="p">:</span>
+        <span class="n">f_value</span><span class="p">,</span> <span class="n">p_value</span><span class="p">,</span> <span class="n">rankings</span><span class="p">,</span> <span class="n">pivots</span> <span class="o">=</span> <span class="n">npt</span><span class="o">.</span><span class="n">quade_test</span><span class="p">(</span><span class="o">*</span><span class="n">values</span><span class="p">)</span>
+    <span class="k">elif</span> <span class="n">method</span><span class="o">==</span><span class="s1">&#39;friedman&#39;</span><span class="p">:</span>
+        <span class="n">f_value</span><span class="p">,</span> <span class="n">p_value</span><span class="p">,</span> <span class="n">rankings</span><span class="p">,</span> <span class="n">pivots</span> <span class="o">=</span> <span class="n">npt</span><span class="o">.</span><span class="n">friedman_aligned_ranks_test</span><span class="p">(</span><span class="o">*</span><span class="n">values</span><span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s1">&#39;Unknown test method!&#39;</span><span class="p">)</span>
+
+    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;F-Value: </span><span class="si">{}</span><span class="s2"> </span><span class="se">\t</span><span class="s2">p-Value: </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">f_value</span><span class="p">,</span> <span class="n">p_value</span><span class="p">))</span>
+
+    <span class="k">if</span> <span class="n">p_value</span> <span class="o">&lt;</span> <span class="n">alpha</span><span class="p">:</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">H0 is rejected!</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">H0 is accepted!</span><span class="se">\n</span><span class="s2">&quot;</span><span class="p">)</span>
+
+    <span class="n">post_hoc</span> <span class="o">=</span> <span class="p">{}</span>
+    <span class="n">rows</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">methods</span><span class="p">)):</span>
+        <span class="n">rows</span><span class="o">.</span><span class="n">append</span><span class="p">([</span><span class="n">methods</span><span class="p">[</span><span class="n">k</span><span class="p">],</span> <span class="n">rankings</span><span class="p">[</span><span class="n">k</span><span class="p">]])</span>
+        <span class="n">post_hoc</span><span class="p">[</span><span class="n">methods</span><span class="p">[</span><span class="n">k</span><span class="p">]]</span> <span class="o">=</span> <span class="n">pivots</span><span class="p">[</span><span class="n">k</span><span class="p">]</span>
+
+    <span class="k">return</span> <span class="p">[</span><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">rows</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;METHOD&#39;</span><span class="p">,</span> <span class="s1">&#39;RANK&#39;</span><span class="p">])</span><span class="o">.</span><span class="n">sort_values</span><span class="p">([</span><span class="s1">&#39;RANK&#39;</span><span class="p">]),</span> <span class="n">post_hoc</span><span class="p">]</span></div>
+
+
+<div class="viewcode-block" id="post_hoc_tests"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.Tests.post_hoc_tests">[docs]</a><span class="k">def</span> <span class="nf">post_hoc_tests</span><span class="p">(</span><span class="n">post_hoc</span><span class="p">,</span> <span class="n">control_method</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=.</span><span class="mi">05</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">&#39;finner&#39;</span><span class="p">):</span>
+    <span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">    Finner paired post-hoc test with NSFTS as control method.</span>
+
+<span class="sd">    $H_0$: There is no significant difference between the means</span>
+
+<span class="sd">    $H_1$: There is a significant difference between the means</span>
+
+<span class="sd">    :param post_hoc:</span>
+<span class="sd">    :param control_method:</span>
+<span class="sd">    :param alpha:</span>
+<span class="sd">    :param method:</span>
+<span class="sd">    :return:</span>
+<span class="sd">    &#39;&#39;&#39;</span>
+    <span class="kn">from</span> <span class="nn">stac.stac</span> <span class="k">import</span> <span class="n">nonparametric_tests</span> <span class="k">as</span> <span class="n">npt</span>
+
+    <span class="k">if</span> <span class="n">method</span> <span class="o">==</span> <span class="s1">&#39;bonferroni_dunn&#39;</span><span class="p">:</span>
+        <span class="n">comparisons</span><span class="p">,</span> <span class="n">z_values</span><span class="p">,</span> <span class="n">p_values</span><span class="p">,</span> <span class="n">adj_p_values</span> <span class="o">=</span> <span class="n">npt</span><span class="o">.</span><span class="n">bonferroni_dunn_test</span><span class="p">(</span><span class="n">post_hoc</span><span class="p">,</span><span class="n">control_method</span><span class="p">)</span>
+    <span class="k">elif</span> <span class="n">method</span> <span class="o">==</span> <span class="s1">&#39;holm&#39;</span><span class="p">:</span>
+        <span class="n">comparisons</span><span class="p">,</span> <span class="n">z_values</span><span class="p">,</span> <span class="n">p_values</span><span class="p">,</span> <span class="n">adj_p_values</span> <span class="o">=</span> <span class="n">npt</span><span class="o">.</span><span class="n">holm_test</span><span class="p">(</span><span class="n">post_hoc</span><span class="p">,</span><span class="n">control_method</span><span class="p">)</span>
+    <span class="k">elif</span>  <span class="n">method</span> <span class="o">==</span> <span class="s1">&#39;finner&#39;</span><span class="p">:</span>
+        <span class="n">comparisons</span><span class="p">,</span> <span class="n">z_values</span><span class="p">,</span> <span class="n">p_values</span><span class="p">,</span> <span class="n">adj_p_values</span> <span class="o">=</span> <span class="n">npt</span><span class="o">.</span><span class="n">finner_test</span><span class="p">(</span><span class="n">post_hoc</span><span class="p">,</span> <span class="n">control_method</span><span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s1">&#39;Unknown test method!&#39;</span><span class="p">)</span>
+
+    <span class="n">rows</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">comparisons</span><span class="p">)):</span>
+        <span class="n">test</span> <span class="o">=</span> <span class="s1">&#39;H0 Accepted&#39;</span> <span class="k">if</span> <span class="n">adj_p_values</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">&gt;</span> <span class="n">alpha</span> <span class="k">else</span> <span class="s1">&#39;H0 Rejected&#39;</span>
+        <span class="n">rows</span><span class="o">.</span><span class="n">append</span><span class="p">([</span><span class="n">comparisons</span><span class="p">[</span><span class="n">k</span><span class="p">],</span> <span class="n">z_values</span><span class="p">[</span><span class="n">k</span><span class="p">],</span> <span class="n">p_values</span><span class="p">[</span><span class="n">k</span><span class="p">],</span> <span class="n">adj_p_values</span><span class="p">[</span><span class="n">k</span><span class="p">],</span> <span class="n">test</span><span class="p">])</span>
+
+    <span class="k">return</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">rows</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;COMPARISON&#39;</span><span class="p">,</span> <span class="s1">&#39;Z-VALUE&#39;</span><span class="p">,</span> <span class="s1">&#39;P-VALUE&#39;</span><span class="p">,</span> <span class="s1">&#39;ADJUSTED P-VALUE&#39;</span><span class="p">,</span> <span class="s1">&#39;Result&#39;</span><span class="p">])</span></div>
+
+</pre></div>
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+  <h1>Source code for pyFTS.benchmarks.arima</h1><div class="highlight"><pre>
+<span></span><span class="ch">#!/usr/bin/python</span>
+<span class="c1"># -*- coding: utf8 -*-</span>
+
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">from</span> <span class="nn">statsmodels.tsa.arima_model</span> <span class="k">import</span> <span class="n">ARIMA</span> <span class="k">as</span> <span class="n">stats_arima</span>
+<span class="kn">import</span> <span class="nn">scipy.stats</span> <span class="k">as</span> <span class="nn">st</span>
+<span class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="k">import</span> <span class="n">SortedCollection</span><span class="p">,</span> <span class="n">fts</span>
+<span class="kn">from</span> <span class="nn">pyFTS.probabilistic</span> <span class="k">import</span> <span class="n">ProbabilityDistribution</span>
+
+
+<div class="viewcode-block" id="ARIMA"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.arima.ARIMA">[docs]</a><span class="k">class</span> <span class="nc">ARIMA</span><span class="p">(</span><span class="n">fts</span><span class="o">.</span><span class="n">FTS</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Façade for statsmodels.tsa.arima_model</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">ARIMA</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s2">&quot;ARIMA&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">detail</span> <span class="o">=</span> <span class="s2">&quot;Auto Regressive Integrated Moving Average&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">is_high_order</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">has_point_forecasting</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">has_interval_forecasting</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">has_probability_forecasting</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">uod_clip</span> <span class="o">=</span> <span class="kc">False</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">model</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">model_fit</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">trained_data</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">p</span> <span class="o">=</span> <span class="mi">1</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">d</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">q</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">benchmark_only</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">min_order</span> <span class="o">=</span> <span class="mi">1</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">alpha</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;alpha&quot;</span><span class="p">,</span> <span class="mf">0.05</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">order</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;order&quot;</span><span class="p">,</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">))</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_decompose_order</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">_decompose_order</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">order</span><span class="p">):</span>
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">order</span><span class="p">,</span> <span class="p">(</span><span class="nb">tuple</span><span class="p">,</span> <span class="nb">set</span><span class="p">,</span> <span class="nb">list</span><span class="p">)):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">p</span> <span class="o">=</span> <span class="n">order</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">d</span> <span class="o">=</span> <span class="n">order</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">q</span> <span class="o">=</span> <span class="n">order</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">order</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">p</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">q</span> <span class="o">+</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">q</span> <span class="o">-</span> <span class="mi">1</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">q</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="k">else</span> <span class="mi">0</span><span class="p">)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">d</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">transformations</span><span class="p">)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">shortname</span> <span class="o">=</span> <span class="s2">&quot;ARIMA(</span><span class="si">{}</span><span class="s2">,</span><span class="si">{}</span><span class="s2">,</span><span class="si">{}</span><span class="s2">)-</span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">p</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">d</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">q</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">alpha</span><span class="p">)</span>
+
+<div class="viewcode-block" id="ARIMA.train"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.arima.ARIMA.train">[docs]</a>    <span class="k">def</span> <span class="nf">train</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+
+        <span class="k">if</span> <span class="s1">&#39;order&#39;</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">:</span>
+            <span class="n">order</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">&#39;order&#39;</span><span class="p">)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">_decompose_order</span><span class="p">(</span><span class="n">order</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">indexer</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">indexer</span><span class="o">.</span><span class="n">get_data</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+
+        <span class="k">try</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">model</span> <span class="o">=</span>  <span class="n">stats_arima</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">order</span><span class="o">=</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">p</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">d</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">q</span><span class="p">))</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">model_fit</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">disp</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+        <span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">ex</span><span class="p">:</span>
+            <span class="nb">print</span><span class="p">(</span><span class="n">ex</span><span class="p">)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">model_fit</span> <span class="o">=</span> <span class="kc">None</span></div>
+
+<div class="viewcode-block" id="ARIMA.ar"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.arima.ARIMA.ar">[docs]</a>    <span class="k">def</span> <span class="nf">ar</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">):</span>
+        <span class="k">return</span> <span class="n">data</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">model_fit</span><span class="o">.</span><span class="n">arparams</span><span class="p">)</span></div>
+
+<div class="viewcode-block" id="ARIMA.ma"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.arima.ARIMA.ma">[docs]</a>    <span class="k">def</span> <span class="nf">ma</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">):</span>
+        <span class="k">return</span> <span class="n">data</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">model_fit</span><span class="o">.</span><span class="n">maparams</span><span class="p">)</span></div>
+
+<div class="viewcode-block" id="ARIMA.forecast"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.arima.ARIMA.forecast">[docs]</a>    <span class="k">def</span> <span class="nf">forecast</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ndata</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">model_fit</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
+
+        <span class="n">ndata</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">ndata</span><span class="p">)</span>
+
+        <span class="n">l</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">ndata</span><span class="p">)</span>
+
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="n">ar</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="bp">self</span><span class="o">.</span><span class="n">ar</span><span class="p">(</span><span class="n">ndata</span><span class="p">[</span><span class="n">k</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">p</span><span class="p">:</span> <span class="n">k</span><span class="p">])</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">p</span><span class="p">,</span> <span class="n">l</span><span class="o">+</span><span class="mi">1</span><span class="p">)])</span> <span class="c1">#+1 to forecast one step ahead given all available lags</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">q</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="n">residuals</span> <span class="o">=</span> <span class="n">ndata</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">p</span><span class="o">-</span><span class="mi">1</span><span class="p">:]</span> <span class="o">-</span> <span class="n">ar</span>
+
+            <span class="n">ma</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="bp">self</span><span class="o">.</span><span class="n">ma</span><span class="p">(</span><span class="n">residuals</span><span class="p">[</span><span class="n">k</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">q</span><span class="p">:</span> <span class="n">k</span><span class="p">])</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">q</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">residuals</span><span class="p">)</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)])</span>
+
+            <span class="n">ret</span> <span class="o">=</span> <span class="n">ar</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">q</span> <span class="o">-</span> <span class="mi">1</span><span class="p">:]</span> <span class="o">+</span> <span class="n">ma</span>
+            <span class="n">ret</span> <span class="o">=</span> <span class="n">ret</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">q</span><span class="p">:]</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">ret</span> <span class="o">=</span> <span class="n">ar</span>
+
+        <span class="c1">#ret = self.apply_inverse_transformations(ret, params=[data[self.order - 1:]])        nforecasts = np.array(forecasts)</span>
+
+        <span class="k">return</span> <span class="n">ret</span></div>
+
+<div class="viewcode-block" id="ARIMA.forecast_interval"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.arima.ARIMA.forecast_interval">[docs]</a>    <span class="k">def</span> <span class="nf">forecast_interval</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">model_fit</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
+
+        <span class="k">if</span> <span class="s1">&#39;alpha&#39;</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">:</span>
+            <span class="n">alpha</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;alpha&#39;</span><span class="p">,</span><span class="mf">0.05</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">alpha</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">alpha</span>
+
+        <span class="n">sigma</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">model_fit</span><span class="o">.</span><span class="n">sigma2</span><span class="p">)</span>
+
+        <span class="n">l</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">,</span> <span class="n">l</span><span class="o">+</span><span class="mi">1</span><span class="p">):</span>
+            <span class="n">tmp</span> <span class="o">=</span> <span class="p">[]</span>
+
+            <span class="n">sample</span> <span class="o">=</span> <span class="p">[</span><span class="n">data</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">k</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">,</span> <span class="n">k</span><span class="p">)]</span>
+
+            <span class="n">mean</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecast</span><span class="p">(</span><span class="n">sample</span><span class="p">)</span>
+
+            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">mean</span><span class="p">,(</span><span class="nb">list</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">)):</span>
+                <span class="n">mean</span> <span class="o">=</span> <span class="n">mean</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+
+            <span class="n">tmp</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">mean</span> <span class="o">+</span> <span class="n">st</span><span class="o">.</span><span class="n">norm</span><span class="o">.</span><span class="n">ppf</span><span class="p">(</span><span class="n">alpha</span><span class="p">)</span> <span class="o">*</span> <span class="n">sigma</span><span class="p">)</span>
+            <span class="n">tmp</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">mean</span> <span class="o">+</span> <span class="n">st</span><span class="o">.</span><span class="n">norm</span><span class="o">.</span><span class="n">ppf</span><span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">alpha</span><span class="p">)</span> <span class="o">*</span> <span class="n">sigma</span><span class="p">)</span>
+
+            <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">ret</span></div>
+
+<div class="viewcode-block" id="ARIMA.forecast_ahead_interval"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.arima.ARIMA.forecast_ahead_interval">[docs]</a>    <span class="k">def</span> <span class="nf">forecast_ahead_interval</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ndata</span><span class="p">,</span> <span class="n">steps</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">model_fit</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
+
+        <span class="k">if</span> <span class="s1">&#39;alpha&#39;</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">:</span>
+            <span class="n">alpha</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;alpha&#39;</span><span class="p">,</span><span class="mf">0.05</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">alpha</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">alpha</span>
+
+        <span class="n">smoothing</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;smoothing&quot;</span><span class="p">,</span><span class="mf">0.5</span><span class="p">)</span>
+
+        <span class="n">sigma</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">model_fit</span><span class="o">.</span><span class="n">sigma2</span><span class="p">)</span>
+
+        <span class="n">l</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">ndata</span><span class="p">)</span>
+
+        <span class="n">nmeans</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecast_ahead</span><span class="p">(</span><span class="n">ndata</span><span class="p">,</span> <span class="n">steps</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">steps</span><span class="p">):</span>
+            <span class="n">tmp</span> <span class="o">=</span> <span class="p">[]</span>
+
+            <span class="n">hsigma</span> <span class="o">=</span> <span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="n">k</span><span class="o">*</span><span class="n">smoothing</span><span class="p">)</span><span class="o">*</span><span class="n">sigma</span>
+
+            <span class="n">tmp</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">nmeans</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">+</span> <span class="n">st</span><span class="o">.</span><span class="n">norm</span><span class="o">.</span><span class="n">ppf</span><span class="p">(</span><span class="n">alpha</span><span class="p">)</span> <span class="o">*</span> <span class="n">hsigma</span><span class="p">)</span>
+            <span class="n">tmp</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">nmeans</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">+</span> <span class="n">st</span><span class="o">.</span><span class="n">norm</span><span class="o">.</span><span class="n">ppf</span><span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">alpha</span><span class="p">)</span> <span class="o">*</span> <span class="n">hsigma</span><span class="p">)</span>
+
+            <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">ret</span><span class="p">[</span><span class="o">-</span><span class="n">steps</span><span class="p">:]</span></div>
+
+<div class="viewcode-block" id="ARIMA.forecast_distribution"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.arima.ARIMA.forecast_distribution">[docs]</a>    <span class="k">def</span> <span class="nf">forecast_distribution</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+
+        <span class="n">sigma</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">model_fit</span><span class="o">.</span><span class="n">sigma2</span><span class="p">)</span>
+
+        <span class="n">l</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">,</span> <span class="n">l</span> <span class="o">+</span> <span class="mi">1</span><span class="p">):</span>
+            <span class="n">sample</span> <span class="o">=</span> <span class="p">[</span><span class="n">data</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">k</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">,</span> <span class="n">k</span><span class="p">)]</span>
+
+            <span class="n">mean</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecast</span><span class="p">(</span><span class="n">sample</span><span class="p">)</span>
+
+            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">mean</span><span class="p">,</span> <span class="p">(</span><span class="nb">list</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">)):</span>
+                <span class="n">mean</span> <span class="o">=</span> <span class="n">mean</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+
+            <span class="n">dist</span> <span class="o">=</span> <span class="n">ProbabilityDistribution</span><span class="o">.</span><span class="n">ProbabilityDistribution</span><span class="p">(</span><span class="nb">type</span><span class="o">=</span><span class="s2">&quot;histogram&quot;</span><span class="p">,</span> <span class="n">uod</span><span class="o">=</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">original_min</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">original_max</span><span class="p">])</span>
+            <span class="n">intervals</span> <span class="o">=</span> <span class="p">[]</span>
+            <span class="k">for</span> <span class="n">alpha</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mf">0.05</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.05</span><span class="p">):</span>
+
+                <span class="n">qt1</span> <span class="o">=</span> <span class="n">mean</span> <span class="o">+</span> <span class="n">st</span><span class="o">.</span><span class="n">norm</span><span class="o">.</span><span class="n">ppf</span><span class="p">(</span><span class="n">alpha</span><span class="p">)</span> <span class="o">*</span> <span class="n">sigma</span>
+                <span class="n">qt2</span> <span class="o">=</span> <span class="n">mean</span> <span class="o">+</span> <span class="n">st</span><span class="o">.</span><span class="n">norm</span><span class="o">.</span><span class="n">ppf</span><span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">alpha</span><span class="p">)</span> <span class="o">*</span> <span class="n">sigma</span>
+
+                <span class="n">intervals</span><span class="o">.</span><span class="n">append</span><span class="p">([</span><span class="n">qt1</span><span class="p">,</span> <span class="n">qt2</span><span class="p">])</span>
+
+            <span class="n">dist</span><span class="o">.</span><span class="n">append_interval</span><span class="p">(</span><span class="n">intervals</span><span class="p">)</span>
+
+            <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">dist</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">ret</span></div>
+
+
+<div class="viewcode-block" id="ARIMA.forecast_ahead_distribution"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.arima.ARIMA.forecast_ahead_distribution">[docs]</a>    <span class="k">def</span> <span class="nf">forecast_ahead_distribution</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">steps</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="n">smoothing</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;smoothing&quot;</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">)</span>
+
+        <span class="n">sigma</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">model_fit</span><span class="o">.</span><span class="n">sigma2</span><span class="p">)</span>
+
+        <span class="n">l</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="n">nmeans</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecast_ahead</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">steps</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">steps</span><span class="p">):</span>
+            <span class="n">dist</span> <span class="o">=</span> <span class="n">ProbabilityDistribution</span><span class="o">.</span><span class="n">ProbabilityDistribution</span><span class="p">(</span><span class="nb">type</span><span class="o">=</span><span class="s2">&quot;histogram&quot;</span><span class="p">,</span>
+                                                                   <span class="n">uod</span><span class="o">=</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">original_min</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">original_max</span><span class="p">])</span>
+            <span class="n">intervals</span> <span class="o">=</span> <span class="p">[]</span>
+            <span class="k">for</span> <span class="n">alpha</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mf">0.05</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.05</span><span class="p">):</span>
+                <span class="n">tmp</span> <span class="o">=</span> <span class="p">[]</span>
+
+                <span class="n">hsigma</span> <span class="o">=</span> <span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="n">k</span> <span class="o">*</span> <span class="n">smoothing</span><span class="p">)</span> <span class="o">*</span> <span class="n">sigma</span>
+
+                <span class="n">tmp</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">nmeans</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">+</span> <span class="n">st</span><span class="o">.</span><span class="n">norm</span><span class="o">.</span><span class="n">ppf</span><span class="p">(</span><span class="n">alpha</span><span class="p">)</span> <span class="o">*</span> <span class="n">hsigma</span><span class="p">)</span>
+                <span class="n">tmp</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">nmeans</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">+</span> <span class="n">st</span><span class="o">.</span><span class="n">norm</span><span class="o">.</span><span class="n">ppf</span><span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">alpha</span><span class="p">)</span> <span class="o">*</span> <span class="n">hsigma</span><span class="p">)</span>
+
+                <span class="n">intervals</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span>
+
+            <span class="n">dist</span><span class="o">.</span><span class="n">append_interval</span><span class="p">(</span><span class="n">intervals</span><span class="p">)</span>
+
+            <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">dist</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">ret</span><span class="p">[</span><span class="o">-</span><span class="n">steps</span><span class="p">:]</span></div></div>
+
+</pre></div>
+
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+        <li class="nav-item nav-item-this"><a href="">pyFTS.benchmarks.knn</a></li> 
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+  <h1>Source code for pyFTS.benchmarks.knn</h1><div class="highlight"><pre>
+<span></span><span class="ch">#!/usr/bin/python</span>
+<span class="c1"># -*- coding: utf8 -*-</span>
+
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">statsmodels.tsa.tsatools</span> <span class="k">import</span> <span class="n">lagmat</span>
+<span class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="k">import</span> <span class="n">fts</span>
+<span class="kn">from</span> <span class="nn">pyFTS.probabilistic</span> <span class="k">import</span> <span class="n">ProbabilityDistribution</span>
+<span class="kn">from</span> <span class="nn">sklearn.neighbors</span> <span class="k">import</span> <span class="n">KDTree</span>
+<span class="kn">from</span> <span class="nn">itertools</span> <span class="k">import</span> <span class="n">product</span>
+<span class="kn">from</span> <span class="nn">pyFTS.models.ensemble.ensemble</span> <span class="k">import</span> <span class="n">sampler</span>
+
+<div class="viewcode-block" id="KNearestNeighbors"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.knn.KNearestNeighbors">[docs]</a><span class="k">class</span> <span class="nc">KNearestNeighbors</span><span class="p">(</span><span class="n">fts</span><span class="o">.</span><span class="n">FTS</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    A façade for sklearn.neighbors</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">KNearestNeighbors</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s2">&quot;kNN&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">shortname</span> <span class="o">=</span> <span class="s2">&quot;kNN&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">detail</span> <span class="o">=</span> <span class="s2">&quot;K-Nearest Neighbors&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">uod_clip</span> <span class="o">=</span> <span class="kc">False</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">is_high_order</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">has_point_forecasting</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">has_interval_forecasting</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">has_probability_forecasting</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">benchmark_only</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">min_order</span> <span class="o">=</span> <span class="mi">1</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">alpha</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;alpha&quot;</span><span class="p">,</span> <span class="mf">0.05</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">lag</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">k</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;k&quot;</span><span class="p">,</span> <span class="mi">30</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">uod</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">kdtree</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">values</span> <span class="o">=</span> <span class="kc">None</span>
+
+    <span class="k">def</span> <span class="nf">_prepare_x</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">):</span>
+        <span class="n">l</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+        <span class="n">X</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="k">if</span> <span class="n">l</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">:</span>
+            <span class="n">l</span> <span class="o">+=</span> <span class="mi">1</span>
+
+        <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">,</span> <span class="n">l</span><span class="p">):</span>
+            <span class="n">X</span><span class="o">.</span><span class="n">append</span><span class="p">([</span><span class="n">data</span><span class="p">[</span><span class="n">t</span> <span class="o">-</span> <span class="n">k</span> <span class="o">-</span> <span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">)])</span>
+
+        <span class="k">return</span> <span class="n">X</span>
+
+    <span class="k">def</span> <span class="nf">_prepare_xy</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">):</span>
+        <span class="n">l</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+        <span class="n">X</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="n">Y</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">,</span> <span class="n">l</span><span class="p">):</span>
+            <span class="n">X</span><span class="o">.</span><span class="n">append</span><span class="p">([</span><span class="n">data</span><span class="p">[</span><span class="n">t</span> <span class="o">-</span> <span class="n">k</span> <span class="o">-</span> <span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">)])</span>
+            <span class="n">Y</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">t</span><span class="p">])</span>
+
+        <span class="k">return</span> <span class="p">(</span><span class="n">X</span><span class="p">,</span><span class="n">Y</span><span class="p">)</span>
+
+<div class="viewcode-block" id="KNearestNeighbors.train"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.knn.KNearestNeighbors.train">[docs]</a>    <span class="k">def</span> <span class="nf">train</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="n">X</span><span class="p">,</span><span class="n">Y</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_prepare_xy</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">kdtree</span> <span class="o">=</span> <span class="n">KDTree</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">X</span><span class="p">))</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">values</span> <span class="o">=</span> <span class="n">Y</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">shortname</span> <span class="o">=</span> <span class="s2">&quot;kNN(</span><span class="si">{}</span><span class="s2">)-</span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">alpha</span><span class="p">)</span></div>
+
+<div class="viewcode-block" id="KNearestNeighbors.knn"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.knn.KNearestNeighbors.knn">[docs]</a>    <span class="k">def</span> <span class="nf">knn</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sample</span><span class="p">):</span>
+        <span class="n">X</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_prepare_x</span><span class="p">(</span><span class="n">sample</span><span class="p">)</span>
+        <span class="n">_</span><span class="p">,</span> <span class="n">ix</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">kdtree</span><span class="o">.</span><span class="n">query</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">X</span><span class="p">),</span> <span class="bp">self</span><span class="o">.</span><span class="n">k</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">values</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">ix</span><span class="o">.</span><span class="n">flatten</span><span class="p">()</span> <span class="p">]</span></div>
+
+<div class="viewcode-block" id="KNearestNeighbors.forecast"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.knn.KNearestNeighbors.forecast">[docs]</a>    <span class="k">def</span> <span class="nf">forecast</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="n">l</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">,</span> <span class="n">l</span><span class="o">+</span><span class="p">(</span><span class="mi">1</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span> <span class="o">==</span> <span class="n">l</span> <span class="k">else</span> <span class="mi">0</span><span class="p">)):</span>
+
+            <span class="n">sample</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">k</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span> <span class="p">:</span> <span class="n">k</span><span class="p">]</span>
+
+            <span class="n">forecasts</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">knn</span><span class="p">(</span><span class="n">sample</span><span class="p">)</span>
+
+            <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">nanmean</span><span class="p">(</span><span class="n">forecasts</span><span class="p">))</span>
+
+        <span class="k">return</span> <span class="n">ret</span></div>
+
+<div class="viewcode-block" id="KNearestNeighbors.forecast_interval"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.knn.KNearestNeighbors.forecast_interval">[docs]</a>    <span class="k">def</span> <span class="nf">forecast_interval</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+
+        <span class="n">alpha</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;alpha&#39;</span><span class="p">,</span><span class="bp">self</span><span class="o">.</span><span class="n">alpha</span><span class="p">)</span>
+
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)):</span>
+
+            <span class="n">sample</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">k</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span> <span class="p">:</span> <span class="n">k</span><span class="p">]</span>
+
+            <span class="n">forecasts</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">knn</span><span class="p">(</span><span class="n">sample</span><span class="p">)</span>
+
+            <span class="n">i</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">percentile</span><span class="p">(</span><span class="n">forecasts</span><span class="p">,</span> <span class="p">[</span><span class="n">alpha</span><span class="o">*</span><span class="mi">100</span><span class="p">,</span> <span class="p">(</span><span class="mi">1</span><span class="o">-</span><span class="n">alpha</span><span class="p">)</span><span class="o">*</span><span class="mi">100</span><span class="p">])</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
+            <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">ret</span></div>
+
+<div class="viewcode-block" id="KNearestNeighbors.forecast_ahead_interval"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.knn.KNearestNeighbors.forecast_ahead_interval">[docs]</a>    <span class="k">def</span> <span class="nf">forecast_ahead_interval</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">steps</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="n">alpha</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;alpha&#39;</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">alpha</span><span class="p">)</span>
+
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="n">start</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;start&#39;</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">)</span>
+
+        <span class="n">sample</span> <span class="o">=</span> <span class="p">[[</span><span class="n">k</span><span class="p">]</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">data</span><span class="p">[</span><span class="n">start</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">:</span> <span class="n">start</span><span class="p">]]</span>
+
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">,</span> <span class="n">steps</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">):</span>
+            <span class="n">forecasts</span> <span class="o">=</span> <span class="p">[]</span>
+
+            <span class="n">lags</span> <span class="o">=</span> <span class="p">[</span><span class="n">sample</span><span class="p">[</span><span class="n">k</span> <span class="o">-</span> <span class="n">i</span> <span class="o">-</span> <span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">)]</span>
+
+            <span class="c1"># Trace the possible paths</span>
+            <span class="k">for</span> <span class="n">path</span> <span class="ow">in</span> <span class="n">product</span><span class="p">(</span><span class="o">*</span><span class="n">lags</span><span class="p">):</span>
+                <span class="n">forecasts</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">knn</span><span class="p">(</span><span class="n">path</span><span class="p">))</span>
+
+            <span class="n">sample</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">sampler</span><span class="p">(</span><span class="n">forecasts</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="o">.</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">),</span> <span class="n">bounds</span><span class="o">=</span><span class="kc">True</span><span class="p">))</span>
+
+            <span class="n">interval</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">percentile</span><span class="p">(</span><span class="n">forecasts</span><span class="p">,</span> <span class="p">[</span><span class="n">alpha</span><span class="o">*</span><span class="mi">100</span><span class="p">,</span> <span class="p">(</span><span class="mi">1</span><span class="o">-</span><span class="n">alpha</span><span class="p">)</span><span class="o">*</span><span class="mi">100</span><span class="p">])</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
+
+            <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">interval</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">ret</span></div>
+
+<div class="viewcode-block" id="KNearestNeighbors.forecast_distribution"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.knn.KNearestNeighbors.forecast_distribution">[docs]</a>    <span class="k">def</span> <span class="nf">forecast_distribution</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="n">smooth</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;smooth&quot;</span><span class="p">,</span> <span class="s2">&quot;histogram&quot;</span><span class="p">)</span>
+
+        <span class="n">uod</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_UoD</span><span class="p">()</span>
+
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)):</span>
+
+            <span class="n">sample</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">k</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span> <span class="p">:</span> <span class="n">k</span><span class="p">]</span>
+
+            <span class="n">forecasts</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">knn</span><span class="p">(</span><span class="n">sample</span><span class="p">)</span>
+
+            <span class="n">dist</span> <span class="o">=</span> <span class="n">ProbabilityDistribution</span><span class="o">.</span><span class="n">ProbabilityDistribution</span><span class="p">(</span><span class="n">smooth</span><span class="p">,</span> <span class="n">uod</span><span class="o">=</span><span class="n">uod</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="n">forecasts</span><span class="p">,</span>
+                                                                   <span class="n">name</span><span class="o">=</span><span class="s2">&quot;&quot;</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+            <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">dist</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">ret</span></div>
+
+<div class="viewcode-block" id="KNearestNeighbors.forecast_ahead_distribution"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.knn.KNearestNeighbors.forecast_ahead_distribution">[docs]</a>    <span class="k">def</span> <span class="nf">forecast_ahead_distribution</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">steps</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="n">smooth</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;smooth&quot;</span><span class="p">,</span> <span class="s2">&quot;histogram&quot;</span><span class="p">)</span>
+
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="n">start</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;start&#39;</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">)</span>
+
+        <span class="n">uod</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_UoD</span><span class="p">()</span>
+
+        <span class="n">sample</span> <span class="o">=</span> <span class="p">[[</span><span class="n">k</span><span class="p">]</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">data</span><span class="p">[</span><span class="n">start</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">:</span> <span class="n">start</span><span class="p">]]</span>
+
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">,</span> <span class="n">steps</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">):</span>
+            <span class="n">forecasts</span> <span class="o">=</span> <span class="p">[]</span>
+
+            <span class="n">lags</span> <span class="o">=</span> <span class="p">[</span><span class="n">sample</span><span class="p">[</span><span class="n">k</span> <span class="o">-</span> <span class="n">i</span> <span class="o">-</span> <span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">)]</span>
+
+            <span class="c1"># Trace the possible paths</span>
+            <span class="k">for</span> <span class="n">path</span> <span class="ow">in</span> <span class="n">product</span><span class="p">(</span><span class="o">*</span><span class="n">lags</span><span class="p">):</span>
+                <span class="n">forecasts</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">knn</span><span class="p">(</span><span class="n">path</span><span class="p">))</span>
+
+            <span class="n">dist</span> <span class="o">=</span> <span class="n">ProbabilityDistribution</span><span class="o">.</span><span class="n">ProbabilityDistribution</span><span class="p">(</span><span class="n">smooth</span><span class="p">,</span> <span class="n">uod</span><span class="o">=</span><span class="n">uod</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="n">forecasts</span><span class="p">,</span>
+                                                                   <span class="n">name</span><span class="o">=</span><span class="s2">&quot;&quot;</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+            <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">dist</span><span class="p">)</span>
+
+            <span class="n">sample</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">sampler</span><span class="p">(</span><span class="n">forecasts</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="o">.</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">),</span> <span class="n">bounds</span><span class="o">=</span><span class="kc">True</span><span class="p">))</span>
+
+        <span class="k">return</span> <span class="n">ret</span></div></div>
+
+
+</pre></div>
+
+            <div class="clearer"></div>
+          </div>
+        </div>
+      </div>
+      <div class="sphinxsidebar" role="navigation" aria-label="main navigation">
+        <div class="sphinxsidebarwrapper">
+            <p class="logo"><a href="../../../index.html">
+              <img class="logo" src="../../../_static/logo_heading2.png" alt="Logo"/>
+            </a></p>
+<div id="searchbox" style="display: none" role="search">
+  <h3 id="searchlabel">Quick search</h3>
+    <div class="searchformwrapper">
+    <form class="search" action="../../../search.html" method="get">
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+    </form>
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+</div>
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+      <ul>
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+             >index</a></li>
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+          <a href="../../../py-modindex.html" title="Python Module Index"
+             >modules</a> |</li>
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+        <li class="nav-item nav-item-this"><a href="">pyFTS.benchmarks.knn</a></li> 
+      </ul>
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+    <div class="footer" role="contentinfo">
+        &#169; Copyright 2022, Machine Intelligence and Data Science Laboratory - UFMG - Brazil.
+      Created using <a href="https://www.sphinx-doc.org/">Sphinx</a> 4.5.0.
+    </div>
+  </body>
+</html>

docs/build/html/_modules/pyFTS/benchmarks/naive.html

@@ -0,0 +1,109 @@
+
+
+<!doctype html>
+
+<html>
+  <head>
+    <meta charset="utf-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+    <title>pyFTS.benchmarks.naive &#8212; pyFTS 1.7 documentation</title>
+    <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css" />
+    <link rel="stylesheet" type="text/css" href="../../../_static/bizstyle.css" />
+    
+    <script data-url_root="../../../" id="documentation_options" src="../../../_static/documentation_options.js"></script>
+    <script src="../../../_static/jquery.js"></script>
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+    <![endif]-->
+  </head><body>
+    <div class="related" role="navigation" aria-label="related navigation">
+      <h3>Navigation</h3>
+      <ul>
+        <li class="right" style="margin-right: 10px">
+          <a href="../../../genindex.html" title="General Index"
+             accesskey="I">index</a></li>
+        <li class="right" >
+          <a href="../../../py-modindex.html" title="Python Module Index"
+             >modules</a> |</li>
+        <li class="nav-item nav-item-0"><a href="../../../index.html">pyFTS 1.7 documentation</a> &#187;</li>
+          <li class="nav-item nav-item-1"><a href="../../index.html" accesskey="U">Module code</a> &#187;</li>
+        <li class="nav-item nav-item-this"><a href="">pyFTS.benchmarks.naive</a></li> 
+      </ul>
+    </div>  
+
+    <div class="document">
+      <div class="documentwrapper">
+        <div class="bodywrapper">
+          <div class="body" role="main">
+            
+  <h1>Source code for pyFTS.benchmarks.naive</h1><div class="highlight"><pre>
+<span></span><span class="ch">#!/usr/bin/python</span>
+<span class="c1"># -*- coding: utf8 -*-</span>
+
+<span class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="k">import</span> <span class="n">fts</span>
+
+
+<div class="viewcode-block" id="Naive"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.naive.Naive">[docs]</a><span class="k">class</span> <span class="nc">Naive</span><span class="p">(</span><span class="n">fts</span><span class="o">.</span><span class="n">FTS</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;Naïve Forecasting method&quot;&quot;&quot;</span>
+    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">Naive</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">order</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s2">&quot;Naive&quot;</span><span class="p">,</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s2">&quot;Naïve Model&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">detail</span> <span class="o">=</span> <span class="s2">&quot;Naïve Model&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">benchmark_only</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">is_high_order</span> <span class="o">=</span> <span class="kc">False</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">uod_clip</span> <span class="o">=</span> <span class="kc">False</span>
+
+<div class="viewcode-block" id="Naive.forecast"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.naive.Naive.forecast">[docs]</a>    <span class="k">def</span> <span class="nf">forecast</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="k">return</span> <span class="n">data</span></div></div>
+
+</pre></div>
+
+            <div class="clearer"></div>
+          </div>
+        </div>
+      </div>
+      <div class="sphinxsidebar" role="navigation" aria-label="main navigation">
+        <div class="sphinxsidebarwrapper">
+            <p class="logo"><a href="../../../index.html">
+              <img class="logo" src="../../../_static/logo_heading2.png" alt="Logo"/>
+            </a></p>
+<div id="searchbox" style="display: none" role="search">
+  <h3 id="searchlabel">Quick search</h3>
+    <div class="searchformwrapper">
+    <form class="search" action="../../../search.html" method="get">
+      <input type="text" name="q" aria-labelledby="searchlabel" autocomplete="off" autocorrect="off" autocapitalize="off" spellcheck="false"/>
+      <input type="submit" value="Go" />
+    </form>
+    </div>
+</div>
+<script>$('#searchbox').show(0);</script>
+        </div>
+      </div>
+      <div class="clearer"></div>
+    </div>
+    <div class="related" role="navigation" aria-label="related navigation">
+      <h3>Navigation</h3>
+      <ul>
+        <li class="right" style="margin-right: 10px">
+          <a href="../../../genindex.html" title="General Index"
+             >index</a></li>
+        <li class="right" >
+          <a href="../../../py-modindex.html" title="Python Module Index"
+             >modules</a> |</li>
+        <li class="nav-item nav-item-0"><a href="../../../index.html">pyFTS 1.7 documentation</a> &#187;</li>
+          <li class="nav-item nav-item-1"><a href="../../index.html" >Module code</a> &#187;</li>
+        <li class="nav-item nav-item-this"><a href="">pyFTS.benchmarks.naive</a></li> 
+      </ul>
+    </div>
+    <div class="footer" role="contentinfo">
+        &#169; Copyright 2022, Machine Intelligence and Data Science Laboratory - UFMG - Brazil.
+      Created using <a href="https://www.sphinx-doc.org/">Sphinx</a> 4.5.0.
+    </div>
+  </body>
+</html>

docs/build/html/_modules/pyFTS/benchmarks/quantreg.html

@@ -0,0 +1,258 @@
+
+
+<!doctype html>
+
+<html>
+  <head>
+    <meta charset="utf-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+    <title>pyFTS.benchmarks.quantreg &#8212; pyFTS 1.7 documentation</title>
+    <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css" />
+    <link rel="stylesheet" type="text/css" href="../../../_static/bizstyle.css" />
+    
+    <script data-url_root="../../../" id="documentation_options" src="../../../_static/documentation_options.js"></script>
+    <script src="../../../_static/jquery.js"></script>
+    <script src="../../../_static/underscore.js"></script>
+    <script src="../../../_static/doctools.js"></script>
+    <script src="../../../_static/bizstyle.js"></script>
+    <link rel="index" title="Index" href="../../../genindex.html" />
+    <link rel="search" title="Search" href="../../../search.html" />
+    <meta name="viewport" content="width=device-width,initial-scale=1.0" />
+    <!--[if lt IE 9]>
+    <script src="_static/css3-mediaqueries.js"></script>
+    <![endif]-->
+  </head><body>
+    <div class="related" role="navigation" aria-label="related navigation">
+      <h3>Navigation</h3>
+      <ul>
+        <li class="right" style="margin-right: 10px">
+          <a href="../../../genindex.html" title="General Index"
+             accesskey="I">index</a></li>
+        <li class="right" >
+          <a href="../../../py-modindex.html" title="Python Module Index"
+             >modules</a> |</li>
+        <li class="nav-item nav-item-0"><a href="../../../index.html">pyFTS 1.7 documentation</a> &#187;</li>
+          <li class="nav-item nav-item-1"><a href="../../index.html" accesskey="U">Module code</a> &#187;</li>
+        <li class="nav-item nav-item-this"><a href="">pyFTS.benchmarks.quantreg</a></li> 
+      </ul>
+    </div>  
+
+    <div class="document">
+      <div class="documentwrapper">
+        <div class="bodywrapper">
+          <div class="body" role="main">
+            
+  <h1>Source code for pyFTS.benchmarks.quantreg</h1><div class="highlight"><pre>
+<span></span><span class="ch">#!/usr/bin/python</span>
+<span class="c1"># -*- coding: utf8 -*-</span>
+
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">from</span> <span class="nn">statsmodels.regression.quantile_regression</span> <span class="k">import</span> <span class="n">QuantReg</span>
+<span class="kn">from</span> <span class="nn">statsmodels.tsa.tsatools</span> <span class="k">import</span> <span class="n">lagmat</span>
+<span class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="k">import</span> <span class="n">SortedCollection</span><span class="p">,</span> <span class="n">fts</span>
+<span class="kn">from</span> <span class="nn">pyFTS.probabilistic</span> <span class="k">import</span> <span class="n">ProbabilityDistribution</span>
+
+
+<div class="viewcode-block" id="QuantileRegression"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.quantreg.QuantileRegression">[docs]</a><span class="k">class</span> <span class="nc">QuantileRegression</span><span class="p">(</span><span class="n">fts</span><span class="o">.</span><span class="n">FTS</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;Façade for statsmodels.regression.quantile_regression&quot;&quot;&quot;</span>
+    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">QuantileRegression</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s2">&quot;QAR&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">detail</span> <span class="o">=</span> <span class="s2">&quot;Quantile Regression&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">is_high_order</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">has_point_forecasting</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">has_interval_forecasting</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">has_probability_forecasting</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">uod_clip</span> <span class="o">=</span> <span class="kc">False</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">benchmark_only</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">min_order</span> <span class="o">=</span> <span class="mi">1</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">alpha</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;alpha&quot;</span><span class="p">,</span> <span class="mf">0.05</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">dist</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;dist&quot;</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">upper_qt</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">mean_qt</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">lower_qt</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">dist_qt</span> <span class="o">=</span> <span class="kc">None</span>
+
+<div class="viewcode-block" id="QuantileRegression.train"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.quantreg.QuantileRegression.train">[docs]</a>    <span class="k">def</span> <span class="nf">train</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">indexer</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">):</span>
+            <span class="n">data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">indexer</span><span class="o">.</span><span class="n">get_data</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+
+        <span class="n">lagdata</span><span class="p">,</span> <span class="n">ndata</span> <span class="o">=</span> <span class="n">lagmat</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">maxlag</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">,</span> <span class="n">trim</span><span class="o">=</span><span class="s2">&quot;both&quot;</span><span class="p">,</span> <span class="n">original</span><span class="o">=</span><span class="s1">&#39;sep&#39;</span><span class="p">)</span>
+
+        <span class="n">mqt</span> <span class="o">=</span> <span class="n">QuantReg</span><span class="p">(</span><span class="n">ndata</span><span class="p">,</span> <span class="n">lagdata</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="mf">0.5</span><span class="p">)</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">alpha</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">uqt</span> <span class="o">=</span> <span class="n">QuantReg</span><span class="p">(</span><span class="n">ndata</span><span class="p">,</span> <span class="n">lagdata</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">alpha</span><span class="p">)</span>
+            <span class="n">lqt</span> <span class="o">=</span> <span class="n">QuantReg</span><span class="p">(</span><span class="n">ndata</span><span class="p">,</span> <span class="n">lagdata</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">alpha</span><span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">mean_qt</span> <span class="o">=</span> <span class="p">[</span><span class="n">k</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">mqt</span><span class="o">.</span><span class="n">params</span><span class="p">]</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">alpha</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">upper_qt</span> <span class="o">=</span> <span class="p">[</span><span class="n">k</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">uqt</span><span class="o">.</span><span class="n">params</span><span class="p">]</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">lower_qt</span> <span class="o">=</span> <span class="p">[</span><span class="n">k</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">lqt</span><span class="o">.</span><span class="n">params</span><span class="p">]</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">dist</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">dist_qt</span> <span class="o">=</span> <span class="p">[]</span>
+            <span class="k">for</span> <span class="n">alpha</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mf">0.05</span><span class="p">,</span><span class="mf">0.5</span><span class="p">,</span><span class="mf">0.05</span><span class="p">):</span>
+                <span class="n">lqt</span> <span class="o">=</span> <span class="n">QuantReg</span><span class="p">(</span><span class="n">ndata</span><span class="p">,</span> <span class="n">lagdata</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">alpha</span><span class="p">)</span>
+                <span class="n">uqt</span> <span class="o">=</span> <span class="n">QuantReg</span><span class="p">(</span><span class="n">ndata</span><span class="p">,</span> <span class="n">lagdata</span><span class="p">)</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">alpha</span><span class="p">)</span>
+                <span class="n">lo_qt</span> <span class="o">=</span> <span class="p">[</span><span class="n">k</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">lqt</span><span class="o">.</span><span class="n">params</span><span class="p">]</span>
+                <span class="n">up_qt</span> <span class="o">=</span> <span class="p">[</span><span class="n">k</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">uqt</span><span class="o">.</span><span class="n">params</span><span class="p">]</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">dist_qt</span><span class="o">.</span><span class="n">append</span><span class="p">([</span><span class="n">lo_qt</span><span class="p">,</span> <span class="n">up_qt</span><span class="p">])</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">shortname</span> <span class="o">=</span> <span class="s2">&quot;QAR(</span><span class="si">{}</span><span class="s2">)-</span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">,</span><span class="bp">self</span><span class="o">.</span><span class="n">alpha</span><span class="p">)</span></div>
+
+<div class="viewcode-block" id="QuantileRegression.linearmodel"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.quantreg.QuantileRegression.linearmodel">[docs]</a>    <span class="k">def</span> <span class="nf">linearmodel</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="n">data</span><span class="p">,</span><span class="n">params</span><span class="p">):</span>
+        <span class="c1">#return params[0] + sum([ data[k] * params[k+1] for k in np.arange(0, self.order) ])</span>
+        <span class="k">return</span> <span class="nb">sum</span><span class="p">([</span><span class="n">data</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">*</span> <span class="n">params</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">)])</span></div>
+
+<div class="viewcode-block" id="QuantileRegression.point_to_interval"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.quantreg.QuantileRegression.point_to_interval">[docs]</a>    <span class="k">def</span> <span class="nf">point_to_interval</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">lo_params</span><span class="p">,</span> <span class="n">up_params</span><span class="p">):</span>
+        <span class="n">lo</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">linearmodel</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">lo_params</span><span class="p">)</span>
+        <span class="n">up</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">linearmodel</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">up_params</span><span class="p">)</span>
+        <span class="k">return</span> <span class="p">[</span><span class="n">lo</span><span class="p">,</span> <span class="n">up</span><span class="p">]</span></div>
+
+<div class="viewcode-block" id="QuantileRegression.interval_to_interval"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.quantreg.QuantileRegression.interval_to_interval">[docs]</a>    <span class="k">def</span> <span class="nf">interval_to_interval</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">lo_params</span><span class="p">,</span> <span class="n">up_params</span><span class="p">):</span>
+        <span class="n">lo</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">linearmodel</span><span class="p">([</span><span class="n">k</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">data</span><span class="p">],</span> <span class="n">lo_params</span><span class="p">)</span>
+        <span class="n">up</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">linearmodel</span><span class="p">([</span><span class="n">k</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">data</span><span class="p">],</span> <span class="n">up_params</span><span class="p">)</span>
+        <span class="k">return</span> <span class="p">[</span><span class="n">lo</span><span class="p">,</span> <span class="n">up</span><span class="p">]</span></div>
+
+<div class="viewcode-block" id="QuantileRegression.forecast"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.quantreg.QuantileRegression.forecast">[docs]</a>    <span class="k">def</span> <span class="nf">forecast</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ndata</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+
+        <span class="n">l</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">ndata</span><span class="p">)</span>
+
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">,</span> <span class="n">l</span><span class="o">+</span><span class="mi">1</span><span class="p">):</span>   <span class="c1">#+1 to forecast one step ahead given all available lags</span>
+            <span class="n">sample</span> <span class="o">=</span> <span class="n">ndata</span><span class="p">[</span><span class="n">k</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span> <span class="p">:</span> <span class="n">k</span><span class="p">]</span>
+
+            <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">linearmodel</span><span class="p">(</span><span class="n">sample</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">mean_qt</span><span class="p">))</span>
+
+        <span class="k">return</span> <span class="n">ret</span></div>
+
+<div class="viewcode-block" id="QuantileRegression.forecast_interval"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.quantreg.QuantileRegression.forecast_interval">[docs]</a>    <span class="k">def</span> <span class="nf">forecast_interval</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ndata</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+
+        <span class="n">l</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">ndata</span><span class="p">)</span>
+
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span> <span class="p">,</span> <span class="n">l</span><span class="p">):</span>
+            <span class="n">sample</span> <span class="o">=</span> <span class="n">ndata</span><span class="p">[</span><span class="n">k</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">:</span> <span class="n">k</span><span class="p">]</span>
+            <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">point_to_interval</span><span class="p">(</span><span class="n">sample</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">lower_qt</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">upper_qt</span><span class="p">))</span>
+
+        <span class="k">return</span> <span class="n">ret</span></div>
+
+<div class="viewcode-block" id="QuantileRegression.forecast_ahead_interval"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.quantreg.QuantileRegression.forecast_ahead_interval">[docs]</a>    <span class="k">def</span> <span class="nf">forecast_ahead_interval</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ndata</span><span class="p">,</span> <span class="n">steps</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+
+        <span class="n">smoothing</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;smoothing&quot;</span><span class="p">,</span> <span class="mf">0.01</span><span class="p">)</span>
+
+        <span class="n">l</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">ndata</span><span class="p">)</span>
+
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="n">nmeans</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecast_ahead</span><span class="p">(</span><span class="n">ndata</span><span class="p">,</span> <span class="n">steps</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">):</span>
+            <span class="n">nmeans</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="n">k</span><span class="p">,</span><span class="n">ndata</span><span class="p">[</span><span class="n">k</span><span class="p">])</span>
+
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">,</span> <span class="n">steps</span><span class="o">+</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">):</span>
+            <span class="n">intl</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">point_to_interval</span><span class="p">(</span><span class="n">nmeans</span><span class="p">[</span><span class="n">k</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">:</span> <span class="n">k</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">lower_qt</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">upper_qt</span><span class="p">)</span>
+
+            <span class="n">tmpk</span> <span class="o">=</span> <span class="n">k</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span>
+
+            <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">([</span><span class="n">intl</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">*</span><span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="p">(</span><span class="n">tmpk</span><span class="o">*</span><span class="n">smoothing</span><span class="p">)),</span> <span class="n">intl</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">*</span><span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="p">(</span><span class="n">tmpk</span><span class="o">*</span><span class="n">smoothing</span><span class="p">))])</span>
+
+        <span class="k">return</span> <span class="n">ret</span><span class="p">[</span><span class="o">-</span><span class="n">steps</span><span class="p">:]</span></div>
+
+<div class="viewcode-block" id="QuantileRegression.forecast_distribution"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.quantreg.QuantileRegression.forecast_distribution">[docs]</a>    <span class="k">def</span> <span class="nf">forecast_distribution</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ndata</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="n">l</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">ndata</span><span class="p">)</span>
+
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">,</span> <span class="n">l</span> <span class="o">+</span> <span class="mi">1</span><span class="p">):</span>
+            <span class="n">dist</span> <span class="o">=</span> <span class="n">ProbabilityDistribution</span><span class="o">.</span><span class="n">ProbabilityDistribution</span><span class="p">(</span><span class="nb">type</span><span class="o">=</span><span class="s2">&quot;histogram&quot;</span><span class="p">,</span>
+                                                                   <span class="n">uod</span><span class="o">=</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">original_min</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">original_max</span><span class="p">])</span>
+            <span class="n">intervals</span> <span class="o">=</span> <span class="p">[]</span>
+            <span class="k">for</span> <span class="n">qt</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">dist_qt</span><span class="p">:</span>
+                <span class="n">sample</span> <span class="o">=</span> <span class="n">ndata</span><span class="p">[</span><span class="n">k</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">:</span> <span class="n">k</span><span class="p">]</span>
+                <span class="n">intl</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">point_to_interval</span><span class="p">(</span><span class="n">sample</span><span class="p">,</span> <span class="n">qt</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">qt</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+                <span class="n">intervals</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">intl</span><span class="p">)</span>
+
+            <span class="n">dist</span><span class="o">.</span><span class="n">append_interval</span><span class="p">(</span><span class="n">intervals</span><span class="p">)</span>
+
+            <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">dist</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">ret</span></div>
+
+<div class="viewcode-block" id="QuantileRegression.forecast_ahead_distribution"><a class="viewcode-back" href="../../../pyFTS.benchmarks.html#pyFTS.benchmarks.quantreg.QuantileRegression.forecast_ahead_distribution">[docs]</a>    <span class="k">def</span> <span class="nf">forecast_ahead_distribution</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ndata</span><span class="p">,</span> <span class="n">steps</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="n">smoothing</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;smoothing&quot;</span><span class="p">,</span> <span class="mf">0.01</span><span class="p">)</span>
+
+        <span class="n">l</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">ndata</span><span class="p">)</span>
+
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="n">nmeans</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecast_ahead</span><span class="p">(</span><span class="n">ndata</span><span class="p">,</span> <span class="n">steps</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">):</span>
+            <span class="n">nmeans</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="n">ndata</span><span class="p">[</span><span class="n">k</span><span class="p">])</span>
+
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">,</span> <span class="n">steps</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">):</span>
+            <span class="n">dist</span> <span class="o">=</span> <span class="n">ProbabilityDistribution</span><span class="o">.</span><span class="n">ProbabilityDistribution</span><span class="p">(</span><span class="nb">type</span><span class="o">=</span><span class="s2">&quot;histogram&quot;</span><span class="p">,</span>
+                                                                   <span class="n">uod</span><span class="o">=</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">original_min</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">original_max</span><span class="p">])</span>
+
+            <span class="n">intervals</span> <span class="o">=</span> <span class="p">[[</span><span class="n">nmeans</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">],</span> <span class="n">nmeans</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">]]]</span>
+            <span class="k">for</span> <span class="n">qt</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">dist_qt</span><span class="p">:</span>
+                <span class="n">intl1</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">point_to_interval</span><span class="p">(</span><span class="n">nmeans</span><span class="p">[</span><span class="n">k</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">:</span> <span class="n">k</span><span class="p">],</span> <span class="n">qt</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">qt</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+                <span class="n">tmpk</span> <span class="o">=</span> <span class="n">k</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span>
+                <span class="n">intl2</span> <span class="o">=</span> <span class="p">[</span><span class="n">intl1</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="p">(</span><span class="n">tmpk</span> <span class="o">*</span> <span class="n">smoothing</span><span class="p">)),</span> <span class="n">intl1</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="p">(</span><span class="n">tmpk</span> <span class="o">*</span> <span class="n">smoothing</span><span class="p">))]</span>
+                <span class="n">intervals</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">intl2</span><span class="p">)</span>
+            <span class="n">dist</span><span class="o">.</span><span class="n">append_interval</span><span class="p">(</span><span class="n">intervals</span><span class="p">)</span>
+
+            <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">dist</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">ret</span></div></div>
+
+</pre></div>
+
+            <div class="clearer"></div>
+          </div>
+        </div>
+      </div>
+      <div class="sphinxsidebar" role="navigation" aria-label="main navigation">
+        <div class="sphinxsidebarwrapper">
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+             >modules</a> |</li>
+        <li class="nav-item nav-item-0"><a href="../../../index.html">pyFTS 1.7 documentation</a> &#187;</li>
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+      </ul>
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+        &#169; Copyright 2022, Machine Intelligence and Data Science Laboratory - UFMG - Brazil.
+      Created using <a href="https://www.sphinx-doc.org/">Sphinx</a> 4.5.0.
+    </div>
+  </body>
+</html>

docs/build/html/_modules/pyFTS/common/Activations.html

@@ -1,109 +0,0 @@
-
-
-<!doctype html>
-
-<html>
-  <head>
-    <meta charset="utf-8" />
-    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-    <title>pyFTS.common.Activations &#8212; pyFTS 1.7 documentation</title>
-    <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css" />
-    <link rel="stylesheet" type="text/css" href="../../../_static/bizstyle.css" />
-    
-    <script data-url_root="../../../" id="documentation_options" src="../../../_static/documentation_options.js"></script>
-    <script src="../../../_static/jquery.js"></script>
-    <script src="../../../_static/underscore.js"></script>
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-    <script src="../../../_static/bizstyle.js"></script>
-    <link rel="index" title="Index" href="../../../genindex.html" />
-    <link rel="search" title="Search" href="../../../search.html" />
-    <meta name="viewport" content="width=device-width,initial-scale=1.0" />
-    <!--[if lt IE 9]>
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-    <![endif]-->
-  </head><body>
-    <div class="related" role="navigation" aria-label="related navigation">
-      <h3>Navigation</h3>
-      <ul>
-        <li class="right" style="margin-right: 10px">
-          <a href="../../../genindex.html" title="General Index"
-             accesskey="I">index</a></li>
-        <li class="right" >
-          <a href="../../../py-modindex.html" title="Python Module Index"
-             >modules</a> |</li>
-        <li class="nav-item nav-item-0"><a href="../../../index.html">pyFTS 1.7 documentation</a> &#187;</li>
-          <li class="nav-item nav-item-1"><a href="../../index.html" accesskey="U">Module code</a> &#187;</li>
-        <li class="nav-item nav-item-this"><a href="">pyFTS.common.Activations</a></li> 
-      </ul>
-    </div>  
-
-    <div class="document">
-      <div class="documentwrapper">
-        <div class="bodywrapper">
-          <div class="body" role="main">
-            
-  <h1>Source code for pyFTS.common.Activations</h1><div class="highlight"><pre>
-<span></span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">Activation functions for Time Series Classification</span>
-<span class="sd">&quot;&quot;&quot;</span>
-
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">math</span>
-<span class="kn">from</span> <span class="nn">pyFTS</span> <span class="kn">import</span> <span class="o">*</span>
-
-<div class="viewcode-block" id="scale"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.Activations.scale">[docs]</a><span class="k">def</span> <span class="nf">scale</span><span class="p">(</span><span class="n">dist</span> <span class="p">:</span> <span class="nb">dict</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">dict</span><span class="p">:</span>
-  <span class="n">norm</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">([</span><span class="n">v</span> <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">dist</span><span class="o">.</span><span class="n">values</span><span class="p">()])</span>
-  <span class="k">return</span> <span class="p">{</span><span class="n">k</span> <span class="p">:</span> <span class="p">(</span><span class="n">v</span> <span class="o">/</span> <span class="n">norm</span><span class="p">)</span> <span class="k">for</span> <span class="n">k</span><span class="p">,</span><span class="n">v</span> <span class="ow">in</span> <span class="n">dist</span><span class="o">.</span><span class="n">items</span><span class="p">()</span> <span class="p">}</span></div>
-
-<div class="viewcode-block" id="softmax"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.Activations.softmax">[docs]</a><span class="k">def</span> <span class="nf">softmax</span><span class="p">(</span><span class="n">dist</span> <span class="p">:</span> <span class="nb">dict</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">dict</span><span class="p">:</span>
-  <span class="n">norm</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">([</span><span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="n">v</span><span class="p">)</span> <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">dist</span><span class="o">.</span><span class="n">values</span><span class="p">()])</span>
-  <span class="k">return</span> <span class="p">{</span><span class="n">k</span> <span class="p">:</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="n">v</span><span class="p">)</span> <span class="o">/</span> <span class="n">norm</span><span class="p">)</span> <span class="k">for</span> <span class="n">k</span><span class="p">,</span><span class="n">v</span> <span class="ow">in</span> <span class="n">dist</span><span class="o">.</span><span class="n">items</span><span class="p">()</span> <span class="p">}</span></div>
-
-<div class="viewcode-block" id="argmax"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.Activations.argmax">[docs]</a><span class="k">def</span> <span class="nf">argmax</span><span class="p">(</span><span class="n">dist</span> <span class="p">:</span> <span class="nb">dict</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
-  <span class="n">mx</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">([</span><span class="n">v</span> <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">dist</span><span class="o">.</span><span class="n">values</span><span class="p">()])</span>
-  <span class="k">return</span> <span class="p">[</span><span class="n">k</span> <span class="k">for</span> <span class="n">k</span><span class="p">,</span><span class="n">v</span> <span class="ow">in</span> <span class="n">dist</span><span class="o">.</span><span class="n">items</span><span class="p">()</span> <span class="k">if</span> <span class="n">v</span> <span class="o">==</span> <span class="n">mx</span> <span class="p">][</span><span class="mi">0</span><span class="p">]</span></div>
-</pre></div>
-
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-          </div>
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-              <img class="logo" src="../../../_static/logo_heading2.png" alt="Logo"/>
-            </a></p>
-<div id="searchbox" style="display: none" role="search">
-  <h3 id="searchlabel">Quick search</h3>
-    <div class="searchformwrapper">
-    <form class="search" action="../../../search.html" method="get">
-      <input type="text" name="q" aria-labelledby="searchlabel" autocomplete="off" autocorrect="off" autocapitalize="off" spellcheck="false"/>
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-    </form>
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-<script>$('#searchbox').show(0);</script>
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-    </div>
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-      <h3>Navigation</h3>
-      <ul>
-        <li class="right" style="margin-right: 10px">
-          <a href="../../../genindex.html" title="General Index"
-             >index</a></li>
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-          <a href="../../../py-modindex.html" title="Python Module Index"
-             >modules</a> |</li>
-        <li class="nav-item nav-item-0"><a href="../../../index.html">pyFTS 1.7 documentation</a> &#187;</li>
-          <li class="nav-item nav-item-1"><a href="../../index.html" >Module code</a> &#187;</li>
-        <li class="nav-item nav-item-this"><a href="">pyFTS.common.Activations</a></li> 
-      </ul>
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-    <div class="footer" role="contentinfo">
-        &#169; Copyright 2022, Machine Intelligence and Data Science Laboratory - UFMG - Brazil.
-      Created using <a href="https://www.sphinx-doc.org/">Sphinx</a> 4.3.2.
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-  </body>
-</html>

docs/build/html/_modules/pyFTS/common/Composite.html

@@ -48,16 +48,16 @@
 <span class="sd">&quot;&quot;&quot;</span>
 
 <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">from</span> <span class="nn">pyFTS</span> <span class="kn">import</span> <span class="o">*</span>
+<span class="kn">from</span> <span class="nn">pyFTS</span> <span class="k">import</span> <span class="o">*</span>
-<span class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="kn">import</span> <span class="n">Membership</span><span class="p">,</span> <span class="n">FuzzySet</span>
+<span class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="k">import</span> <span class="n">Membership</span><span class="p">,</span> <span class="n">FuzzySet</span>
 
 
 <div class="viewcode-block" id="FuzzySet"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.Composite.FuzzySet">[docs]</a><span class="k">class</span> <span class="nc">FuzzySet</span><span class="p">(</span><span class="n">FuzzySet</span><span class="o">.</span><span class="n">FuzzySet</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+    <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Composite Fuzzy Set</span>
 <span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">name</span><span class="p">,</span> <span class="n">superset</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">name</span><span class="p">,</span> <span class="n">superset</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+        <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Create an empty composite fuzzy set</span>
 <span class="sd">        :param name: fuzzy set name</span>
 <span class="sd">        &quot;&quot;&quot;</span>
@@ -77,7 +77,7 @@
 
 
 <div class="viewcode-block" id="FuzzySet.membership"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.Composite.FuzzySet.membership">[docs]</a>    <span class="k">def</span> <span class="nf">membership</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+        <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Calculate the membership value of a given input</span>
 
 <span class="sd">        :param x: input value</span>
@@ -92,7 +92,7 @@
         <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">x</span><span class="p">)</span></div>
 
 <div class="viewcode-block" id="FuzzySet.append"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.Composite.FuzzySet.append">[docs]</a>    <span class="k">def</span> <span class="nf">append</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">mf</span><span class="p">,</span> <span class="n">parameters</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+        <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Adds a new function to composition</span>
 
 <span class="sd">        :param mf:</span>
@@ -103,7 +103,7 @@
         <span class="bp">self</span><span class="o">.</span><span class="n">parameters</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">parameters</span><span class="p">)</span></div>
 
 <div class="viewcode-block" id="FuzzySet.append_set"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.Composite.FuzzySet.append_set">[docs]</a>    <span class="k">def</span> <span class="nf">append_set</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">set</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+        <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Adds a new function to composition</span>
 
 <span class="sd">        :param mf:</span>
@@ -122,7 +122,7 @@
             <span class="bp">self</span><span class="o">.</span><span class="n">centroid</span> <span class="o">=</span> <span class="nb">set</span><span class="o">.</span><span class="n">centroid</span></div>
 
 
-    <span class="k">def</span> <span class="fm">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+    <span class="k">def</span> <span class="nf">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="n">tmp</span> <span class="o">=</span> <span class="nb">str</span><span class="p">([</span><span class="nb">str</span><span class="p">(</span><span class="n">k</span><span class="p">)</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">sets</span><span class="p">])</span>
         <span class="k">return</span> <span class="s2">&quot;</span><span class="si">{}</span><span class="s2">: </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">name</span><span class="p">,</span> <span class="n">tmp</span><span class="p">)</span></div>
 
@@ -168,7 +168,7 @@
     </div>
     <div class="footer" role="contentinfo">
         &#169; Copyright 2022, Machine Intelligence and Data Science Laboratory - UFMG - Brazil.
-      Created using <a href="https://www.sphinx-doc.org/">Sphinx</a> 4.3.2.
+      Created using <a href="https://www.sphinx-doc.org/">Sphinx</a> 4.5.0.
     </div>
   </body>
 </html>

docs/build/html/_modules/pyFTS/common/FLR.html

@@ -48,44 +48,44 @@
 <span class="sd">&quot;&quot;&quot;</span>
 
 <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="kn">import</span> <span class="n">FuzzySet</span>
+<span class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="k">import</span> <span class="n">FuzzySet</span>
 
 
 <div class="viewcode-block" id="FLR"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.FLR.FLR">[docs]</a><span class="k">class</span> <span class="nc">FLR</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+    <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Fuzzy Logical Relationship</span>
 
 <span class="sd">    Represents a temporal transition of the fuzzy set LHS on time t for the fuzzy set RHS on time t+1.</span>
 <span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">LHS</span><span class="p">,</span> <span class="n">RHS</span><span class="p">):</span>
+    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">LHS</span><span class="p">,</span> <span class="n">RHS</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+        <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Creates a Fuzzy Logical Relationship</span>
 <span class="sd">        &quot;&quot;&quot;</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">LHS</span> <span class="o">=</span> <span class="n">LHS</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Left Hand Side fuzzy set&quot;&quot;&quot;</span>
+        <span class="sd">&quot;&quot;&quot;Left Hand Side fuzzy set&quot;&quot;&quot;</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span> <span class="o">=</span> <span class="n">RHS</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Right Hand Side fuzzy set&quot;&quot;&quot;</span>
+        <span class="sd">&quot;&quot;&quot;Right Hand Side fuzzy set&quot;&quot;&quot;</span>
 
-    <span class="k">def</span> <span class="fm">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+    <span class="k">def</span> <span class="nf">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="k">return</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">LHS</span><span class="p">)</span> <span class="o">+</span> <span class="s2">&quot; -&gt; &quot;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">)</span></div>
 
 
 <div class="viewcode-block" id="IndexedFLR"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.FLR.IndexedFLR">[docs]</a><span class="k">class</span> <span class="nc">IndexedFLR</span><span class="p">(</span><span class="n">FLR</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Season Indexed Fuzzy Logical Relationship&quot;&quot;&quot;</span>
+    <span class="sd">&quot;&quot;&quot;Season Indexed Fuzzy Logical Relationship&quot;&quot;&quot;</span>
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">index</span><span class="p">,</span> <span class="n">LHS</span><span class="p">,</span> <span class="n">RHS</span><span class="p">):</span>
+    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">index</span><span class="p">,</span> <span class="n">LHS</span><span class="p">,</span> <span class="n">RHS</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+        <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Create a Season Indexed Fuzzy Logical Relationship</span>
 <span class="sd">        &quot;&quot;&quot;</span>
         <span class="nb">super</span><span class="p">(</span><span class="n">IndexedFLR</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">LHS</span><span class="p">,</span> <span class="n">RHS</span><span class="p">)</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">index</span> <span class="o">=</span> <span class="n">index</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;seasonal index&quot;&quot;&quot;</span>
+        <span class="sd">&quot;&quot;&quot;seasonal index&quot;&quot;&quot;</span>
 
-    <span class="k">def</span> <span class="fm">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+    <span class="k">def</span> <span class="nf">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="k">return</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">index</span><span class="p">)</span> <span class="o">+</span> <span class="s2">&quot;: &quot;</span><span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">LHS</span><span class="p">)</span> <span class="o">+</span> <span class="s2">&quot; -&gt; &quot;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">)</span></div>
 
 
 <div class="viewcode-block" id="generate_high_order_recurrent_flr"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.FLR.generate_high_order_recurrent_flr">[docs]</a><span class="k">def</span> <span class="nf">generate_high_order_recurrent_flr</span><span class="p">(</span><span class="n">fuzzyData</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+    <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Create a ordered FLR set from a list of fuzzy sets with recurrence</span>
 
 <span class="sd">    :param fuzzyData: ordered list of fuzzy sets</span>
@@ -107,7 +107,7 @@
 
 
 <div class="viewcode-block" id="generate_recurrent_flrs"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.FLR.generate_recurrent_flrs">[docs]</a><span class="k">def</span> <span class="nf">generate_recurrent_flrs</span><span class="p">(</span><span class="n">fuzzyData</span><span class="p">,</span> <span class="n">steps</span> <span class="o">=</span> <span class="mi">1</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+    <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Create a ordered FLR set from a list of fuzzy sets with recurrence</span>
 
 <span class="sd">    :param fuzzyData: ordered list of fuzzy sets</span>
@@ -127,7 +127,7 @@
 
 
 <div class="viewcode-block" id="generate_non_recurrent_flrs"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.FLR.generate_non_recurrent_flrs">[docs]</a><span class="k">def</span> <span class="nf">generate_non_recurrent_flrs</span><span class="p">(</span><span class="n">fuzzyData</span><span class="p">,</span> <span class="n">steps</span> <span class="o">=</span> <span class="mi">1</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+    <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Create a ordered FLR set from a list of fuzzy sets without recurrence</span>
 
 <span class="sd">    :param fuzzyData: ordered list of fuzzy sets</span>
@@ -141,7 +141,7 @@
 
 
 <div class="viewcode-block" id="generate_indexed_flrs"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.FLR.generate_indexed_flrs">[docs]</a><span class="k">def</span> <span class="nf">generate_indexed_flrs</span><span class="p">(</span><span class="n">sets</span><span class="p">,</span> <span class="n">indexer</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">transformation</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">alpha_cut</span><span class="o">=</span><span class="mf">0.0</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+    <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Create a season-indexed ordered FLR set from a list of fuzzy sets with recurrence</span>
 
 <span class="sd">    :param sets: fuzzy sets</span>
@@ -204,7 +204,7 @@
     </div>
     <div class="footer" role="contentinfo">
         &#169; Copyright 2022, Machine Intelligence and Data Science Laboratory - UFMG - Brazil.
-      Created using <a href="https://www.sphinx-doc.org/">Sphinx</a> 4.3.2.
+      Created using <a href="https://www.sphinx-doc.org/">Sphinx</a> 4.5.0.
     </div>
   </body>
 </html>

docs/build/html/_modules/pyFTS/common/FuzzySet.html

@@ -44,35 +44,35 @@
             
   <h1>Source code for pyFTS.common.FuzzySet</h1><div class="highlight"><pre>
 <span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">from</span> <span class="nn">pyFTS</span> <span class="kn">import</span> <span class="o">*</span>
+<span class="kn">from</span> <span class="nn">pyFTS</span> <span class="k">import</span> <span class="o">*</span>
-<span class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="kn">import</span> <span class="n">Membership</span>
+<span class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="k">import</span> <span class="n">Membership</span>
 
 
 <div class="viewcode-block" id="FuzzySet"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.FuzzySet.FuzzySet">[docs]</a><span class="k">class</span> <span class="nc">FuzzySet</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+    <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Fuzzy Set</span>
 <span class="sd">    &quot;&quot;&quot;</span>
 
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">name</span> <span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">mf</span><span class="p">,</span> <span class="n">parameters</span> <span class="p">:</span> <span class="nb">list</span><span class="p">,</span> <span class="n">centroid</span> <span class="p">:</span> <span class="nb">float</span><span class="p">,</span> <span class="n">alpha</span> <span class="p">:</span> <span class="nb">float</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">name</span> <span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">mf</span><span class="p">,</span> <span class="n">parameters</span> <span class="p">:</span> <span class="nb">list</span><span class="p">,</span> <span class="n">centroid</span> <span class="p">:</span> <span class="nb">float</span><span class="p">,</span> <span class="n">alpha</span> <span class="p">:</span> <span class="nb">float</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+        <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Create a Fuzzy Set</span>
 <span class="sd">        &quot;&quot;&quot;</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="n">name</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;The fuzzy set name&quot;&quot;&quot;</span>
+        <span class="sd">&quot;&quot;&quot;The fuzzy set name&quot;&quot;&quot;</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">mf</span>  <span class="o">=</span> <span class="n">mf</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;The membership function&quot;&quot;&quot;</span>
+        <span class="sd">&quot;&quot;&quot;The membership function&quot;&quot;&quot;</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">parameters</span> <span class="p">:</span> <span class="nb">list</span> <span class="o">=</span> <span class="n">parameters</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;The parameters of the membership function&quot;&quot;&quot;</span>
+        <span class="sd">&quot;&quot;&quot;The parameters of the membership function&quot;&quot;&quot;</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">centroid</span> <span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="n">centroid</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;The fuzzy set center of mass (or midpoint)&quot;&quot;&quot;</span>
+        <span class="sd">&quot;&quot;&quot;The fuzzy set center of mass (or midpoint)&quot;&quot;&quot;</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">alpha</span> <span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="n">alpha</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;The alpha cut value&quot;&quot;&quot;</span>
+        <span class="sd">&quot;&quot;&quot;The alpha cut value&quot;&quot;&quot;</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">type</span> <span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;type&#39;</span><span class="p">,</span> <span class="s1">&#39;common&#39;</span><span class="p">)</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;The fuzzy set type (common, composite, nonstationary, etc)&quot;&quot;&quot;</span>
+        <span class="sd">&quot;&quot;&quot;The fuzzy set type (common, composite, nonstationary, etc)&quot;&quot;&quot;</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">variable</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;variable&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;In multivariate time series, indicate for which variable this fuzzy set belogs&quot;&quot;&quot;</span>
+        <span class="sd">&quot;&quot;&quot;In multivariate time series, indicate for which variable this fuzzy set belogs&quot;&quot;&quot;</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">Z</span> <span class="o">=</span> <span class="kc">None</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Partition function in respect to the membership function&quot;&quot;&quot;</span>
+        <span class="sd">&quot;&quot;&quot;Partition function in respect to the membership function&quot;&quot;&quot;</span>
 
         <span class="k">if</span> <span class="n">parameters</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
             <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">mf</span> <span class="o">==</span> <span class="n">Membership</span><span class="o">.</span><span class="n">gaussmf</span><span class="p">:</span>
@@ -89,7 +89,7 @@
         <span class="bp">self</span><span class="o">.</span><span class="n">metadata</span> <span class="o">=</span> <span class="p">{}</span>
 
 <div class="viewcode-block" id="FuzzySet.transform"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.FuzzySet.FuzzySet.transform">[docs]</a>    <span class="k">def</span> <span class="nf">transform</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+        <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Preprocess the data point for non native types</span>
 
 <span class="sd">        :param x:</span>
@@ -99,7 +99,7 @@
         <span class="k">return</span> <span class="n">x</span></div>
 
 <div class="viewcode-block" id="FuzzySet.membership"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.FuzzySet.FuzzySet.membership">[docs]</a>    <span class="k">def</span> <span class="nf">membership</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+        <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Calculate the membership value of a given input</span>
 
 <span class="sd">        :param x: input value </span>
@@ -108,7 +108,7 @@
         <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">mf</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">x</span><span class="p">),</span> <span class="bp">self</span><span class="o">.</span><span class="n">parameters</span><span class="p">)</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">alpha</span></div>
 
 <div class="viewcode-block" id="FuzzySet.partition_function"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.FuzzySet.FuzzySet.partition_function">[docs]</a>    <span class="k">def</span> <span class="nf">partition_function</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">uod</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">nbins</span><span class="o">=</span><span class="mi">100</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+        <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Calculate the partition function over the membership function.</span>
 
 <span class="sd">        :param uod:</span>
@@ -122,12 +122,12 @@
 
         <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">Z</span></div>
 
-    <span class="k">def</span> <span class="fm">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+    <span class="k">def</span> <span class="nf">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">+</span> <span class="s2">&quot;: &quot;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">mf</span><span class="o">.</span><span class="vm">__name__</span><span class="p">)</span> <span class="o">+</span> <span class="s2">&quot;(&quot;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">parameters</span><span class="p">)</span> <span class="o">+</span> <span class="s2">&quot;)&quot;</span></div>
 
 
 <span class="k">def</span> <span class="nf">__binary_search</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">fuzzy_sets</span><span class="p">,</span> <span class="n">ordered_sets</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+    <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Search for elegible fuzzy sets to fuzzyfy x</span>
 
 <span class="sd">    :param x: input value to be fuzzyfied</span>
@@ -160,7 +160,7 @@
 
 
 <div class="viewcode-block" id="fuzzyfy"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.FuzzySet.fuzzyfy">[docs]</a><span class="k">def</span> <span class="nf">fuzzyfy</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">partitioner</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+    <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    A general method for fuzzyfication.</span>
 
 <span class="sd">    :param data: input value to be fuzzyfied</span>
@@ -170,7 +170,6 @@
 <span class="sd">    :keyword method: the fuzzyfication method (fuzzy: all fuzzy memberships, maximum: only the maximum membership)</span>
 <span class="sd">    :keyword mode: the fuzzyfication mode (sets: return the fuzzy sets names, vector: return a vector with the membership</span>
 <span class="sd">    values for all fuzzy sets, both: return a list with tuples (fuzzy set, membership value) )</span>
-<span class="sd">    </span>
 <span class="sd">    :returns a list with the fuzzyfied values, depending on the mode</span>
 
 <span class="sd">    &quot;&quot;&quot;</span>
@@ -205,7 +204,7 @@
 
 
 <div class="viewcode-block" id="set_ordered"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.FuzzySet.set_ordered">[docs]</a><span class="k">def</span> <span class="nf">set_ordered</span><span class="p">(</span><span class="n">fuzzy_sets</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+    <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Order a fuzzy set list by their centroids</span>
 
 <span class="sd">    :param fuzzy_sets: a dictionary where the key is the fuzzy set name and the value is the fuzzy set object.</span>
@@ -217,7 +216,7 @@
 
 
 <div class="viewcode-block" id="fuzzyfy_instance"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.FuzzySet.fuzzyfy_instance">[docs]</a><span class="k">def</span> <span class="nf">fuzzyfy_instance</span><span class="p">(</span><span class="n">inst</span><span class="p">,</span> <span class="n">fuzzy_sets</span> <span class="p">:</span> <span class="nb">dict</span> <span class="p">,</span> <span class="n">ordered_sets</span> <span class="p">:</span> <span class="nb">list</span> <span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+    <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Calculate the membership values for a data point given fuzzy sets</span>
 
 <span class="sd">    :param inst: data point</span>
@@ -238,7 +237,7 @@
 
 
 <div class="viewcode-block" id="fuzzyfy_instances"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.FuzzySet.fuzzyfy_instances">[docs]</a><span class="k">def</span> <span class="nf">fuzzyfy_instances</span><span class="p">(</span><span class="n">data</span> <span class="p">:</span> <span class="nb">list</span><span class="p">,</span> <span class="n">fuzzy_sets</span> <span class="p">:</span> <span class="nb">dict</span><span class="p">,</span> <span class="n">ordered_sets</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+    <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Calculate the membership values for a data point given fuzzy sets</span>
 
 <span class="sd">    :param inst: data point</span>
@@ -256,7 +255,7 @@
 
 
 <div class="viewcode-block" id="get_fuzzysets"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.FuzzySet.get_fuzzysets">[docs]</a><span class="k">def</span> <span class="nf">get_fuzzysets</span><span class="p">(</span><span class="n">inst</span><span class="p">,</span> <span class="n">fuzzy_sets</span> <span class="p">:</span> <span class="nb">dict</span><span class="p">,</span> <span class="n">ordered_sets</span> <span class="p">:</span> <span class="nb">list</span> <span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">alpha_cut</span> <span class="p">:</span> <span class="nb">float</span> <span class="o">=</span><span class="mf">0.0</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+    <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Return the fuzzy sets which membership value for a inst is greater than the alpha_cut</span>
 
 <span class="sd">    :param inst: data point</span>
@@ -279,7 +278,7 @@
 
 
 <div class="viewcode-block" id="get_maximum_membership_fuzzyset"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.FuzzySet.get_maximum_membership_fuzzyset">[docs]</a><span class="k">def</span> <span class="nf">get_maximum_membership_fuzzyset</span><span class="p">(</span><span class="n">inst</span><span class="p">,</span> <span class="n">fuzzy_sets</span><span class="p">,</span> <span class="n">ordered_sets</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">FuzzySet</span><span class="p">:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+    <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Fuzzify a data point, returning the fuzzy set with maximum membership value</span>
 
 <span class="sd">    :param inst: data point</span>
@@ -295,7 +294,7 @@
 
 
 <div class="viewcode-block" id="get_maximum_membership_fuzzyset_index"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.FuzzySet.get_maximum_membership_fuzzyset_index">[docs]</a><span class="k">def</span> <span class="nf">get_maximum_membership_fuzzyset_index</span><span class="p">(</span><span class="n">inst</span><span class="p">,</span> <span class="n">fuzzy_sets</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">int</span><span class="p">:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+    <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Fuzzify a data point, returning the fuzzy set with maximum membership value</span>
 
 <span class="sd">    :param inst: data point</span>
@@ -395,7 +394,7 @@
     </div>
     <div class="footer" role="contentinfo">
         &#169; Copyright 2022, Machine Intelligence and Data Science Laboratory - UFMG - Brazil.
-      Created using <a href="https://www.sphinx-doc.org/">Sphinx</a> 4.3.2.
+      Created using <a href="https://www.sphinx-doc.org/">Sphinx</a> 4.5.0.
     </div>
   </body>
 </html>

docs/build/html/_modules/pyFTS/common/Membership.html

@@ -49,11 +49,11 @@
 
 <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
 <span class="kn">import</span> <span class="nn">math</span>
-<span class="kn">from</span> <span class="nn">pyFTS</span> <span class="kn">import</span> <span class="o">*</span>
+<span class="kn">from</span> <span class="nn">pyFTS</span> <span class="k">import</span> <span class="o">*</span>
 
 
 <div class="viewcode-block" id="trimf"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.Membership.trimf">[docs]</a><span class="k">def</span> <span class="nf">trimf</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">parameters</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+    <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Triangular fuzzy membership function</span>
 
 <span class="sd">    :param x: data point</span>
@@ -73,7 +73,7 @@
 
 
 <div class="viewcode-block" id="trapmf"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.Membership.trapmf">[docs]</a><span class="k">def</span> <span class="nf">trapmf</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">parameters</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+    <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Trapezoidal fuzzy membership function</span>
 
 <span class="sd">    :param x: data point</span>
@@ -94,7 +94,7 @@
 
 
 <div class="viewcode-block" id="gaussmf"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.Membership.gaussmf">[docs]</a><span class="k">def</span> <span class="nf">gaussmf</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">parameters</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+    <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Gaussian fuzzy membership function</span>
 
 <span class="sd">    :param x: data point</span>
@@ -106,7 +106,7 @@
 
 
 <div class="viewcode-block" id="bellmf"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.Membership.bellmf">[docs]</a><span class="k">def</span> <span class="nf">bellmf</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">parameters</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+    <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Bell shaped membership function</span>
 
 <span class="sd">    :param x:</span>
@@ -118,7 +118,7 @@
 
 
 <div class="viewcode-block" id="sigmf"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.Membership.sigmf">[docs]</a><span class="k">def</span> <span class="nf">sigmf</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">parameters</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+    <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Sigmoid / Logistic membership function</span>
 
 <span class="sd">    :param x:</span>
@@ -129,7 +129,7 @@
 
 
 <div class="viewcode-block" id="singleton"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.Membership.singleton">[docs]</a><span class="k">def</span> <span class="nf">singleton</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">parameters</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+    <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Singleton membership function, a single value fuzzy function</span>
 
 <span class="sd">    :param x:</span>
@@ -178,7 +178,7 @@
     </div>
     <div class="footer" role="contentinfo">
         &#169; Copyright 2022, Machine Intelligence and Data Science Laboratory - UFMG - Brazil.
-      Created using <a href="https://www.sphinx-doc.org/">Sphinx</a> 4.3.2.
+      Created using <a href="https://www.sphinx-doc.org/">Sphinx</a> 4.5.0.
     </div>
   </body>
 </html>

docs/build/html/_modules/pyFTS/common/SortedCollection.html

@@ -43,7 +43,7 @@
           <div class="body" role="main">
             
   <h1>Source code for pyFTS.common.SortedCollection</h1><div class="highlight"><pre>
-<span></span><span class="kn">from</span> <span class="nn">bisect</span> <span class="kn">import</span> <span class="n">bisect_left</span><span class="p">,</span> <span class="n">bisect_right</span>
+<span></span><span class="kn">from</span> <span class="nn">bisect</span> <span class="k">import</span> <span class="n">bisect_left</span><span class="p">,</span> <span class="n">bisect_right</span>
 
 <span class="c1">#</span>
 <span class="c1"># Original Source Code: https://code.activestate.com/recipes/577197-sortedcollection/</span>
@@ -51,7 +51,7 @@
 
 
 <div class="viewcode-block" id="SortedCollection"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.SortedCollection.SortedCollection">[docs]</a><span class="k">class</span> <span class="nc">SortedCollection</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&#39;&#39;&#39;Sequence sorted by a key function.</span>
+    <span class="sd">&#39;&#39;&#39;Sequence sorted by a key function.</span>
 
 <span class="sd">    SortedCollection() is much easier to work with than using bisect() directly.</span>
 <span class="sd">    It supports key functions like those use in sorted(), min(), and max().</span>
@@ -119,7 +119,7 @@
 
 <span class="sd">    &#39;&#39;&#39;</span>
 
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">iterable</span><span class="o">=</span><span class="p">(),</span> <span class="n">key</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">iterable</span><span class="o">=</span><span class="p">(),</span> <span class="n">key</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_given_key</span> <span class="o">=</span> <span class="n">key</span>
         <span class="n">key</span> <span class="o">=</span> <span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span><span class="p">)</span> <span class="k">if</span> <span class="n">key</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">key</span>
         <span class="n">decorated</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">((</span><span class="n">key</span><span class="p">(</span><span class="n">item</span><span class="p">),</span> <span class="n">item</span><span class="p">)</span> <span class="k">for</span> <span class="n">item</span> <span class="ow">in</span> <span class="n">iterable</span><span class="p">)</span>
@@ -145,19 +145,19 @@
 <div class="viewcode-block" id="SortedCollection.copy"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.SortedCollection.SortedCollection.copy">[docs]</a>    <span class="k">def</span> <span class="nf">copy</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_key</span><span class="p">)</span></div>
 
-    <span class="k">def</span> <span class="fm">__len__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+    <span class="k">def</span> <span class="nf">__len__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_items</span><span class="p">)</span>
 
-    <span class="k">def</span> <span class="fm">__getitem__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">i</span><span class="p">):</span>
+    <span class="k">def</span> <span class="nf">__getitem__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">i</span><span class="p">):</span>
         <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_items</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
 
-    <span class="k">def</span> <span class="fm">__iter__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+    <span class="k">def</span> <span class="nf">__iter__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="k">return</span> <span class="nb">iter</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_items</span><span class="p">)</span>
 
-    <span class="k">def</span> <span class="fm">__reversed__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+    <span class="k">def</span> <span class="nf">__reversed__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="k">return</span> <span class="nb">reversed</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_items</span><span class="p">)</span>
 
-    <span class="k">def</span> <span class="fm">__repr__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+    <span class="k">def</span> <span class="nf">__repr__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="k">return</span> <span class="s1">&#39;</span><span class="si">%s</span><span class="s1">(</span><span class="si">%r</span><span class="s1">, key=</span><span class="si">%s</span><span class="s1">)&#39;</span> <span class="o">%</span> <span class="p">(</span>
             <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="p">,</span>
             <span class="bp">self</span><span class="o">.</span><span class="n">_items</span><span class="p">,</span>
@@ -167,7 +167,7 @@
     <span class="k">def</span> <span class="nf">__reduce__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="p">,</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_items</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_given_key</span><span class="p">)</span>
 
-    <span class="k">def</span> <span class="fm">__contains__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">item</span><span class="p">):</span>
+    <span class="k">def</span> <span class="nf">__contains__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">item</span><span class="p">):</span>
         <span class="n">k</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_key</span><span class="p">(</span><span class="n">item</span><span class="p">)</span>
         <span class="n">i</span> <span class="o">=</span> <span class="n">bisect_left</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_keys</span><span class="p">,</span> <span class="n">k</span><span class="p">)</span>
         <span class="n">j</span> <span class="o">=</span> <span class="n">bisect_right</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_keys</span><span class="p">,</span> <span class="n">k</span><span class="p">)</span>
@@ -311,7 +311,7 @@
     </div>
     <div class="footer" role="contentinfo">
         &#169; Copyright 2022, Machine Intelligence and Data Science Laboratory - UFMG - Brazil.
-      Created using <a href="https://www.sphinx-doc.org/">Sphinx</a> 4.3.2.
+      Created using <a href="https://www.sphinx-doc.org/">Sphinx</a> 4.5.0.
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   </body>
 </html>

docs/build/html/_modules/pyFTS/common/Util.html

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+        <li class="nav-item nav-item-this"><a href="">pyFTS.common.Util</a></li> 
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+            
+  <h1>Source code for pyFTS.common.Util</h1><div class="highlight"><pre>
+<span></span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">Common facilities for pyFTS</span>
+<span class="sd">&quot;&quot;&quot;</span>
+
+<span class="kn">import</span> <span class="nn">time</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">import</span> <span class="nn">matplotlib.cm</span> <span class="k">as</span> <span class="nn">cmx</span>
+<span class="kn">import</span> <span class="nn">matplotlib.colors</span> <span class="k">as</span> <span class="nn">pltcolors</span>
+<span class="kn">from</span> <span class="nn">pyFTS.probabilistic</span> <span class="k">import</span> <span class="n">ProbabilityDistribution</span>
+<span class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="k">import</span> <span class="n">Transformations</span>
+
+
+<div class="viewcode-block" id="plot_compared_intervals_ahead"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.Util.plot_compared_intervals_ahead">[docs]</a><span class="k">def</span> <span class="nf">plot_compared_intervals_ahead</span><span class="p">(</span><span class="n">original</span><span class="p">,</span> <span class="n">models</span><span class="p">,</span> <span class="n">colors</span><span class="p">,</span> <span class="n">distributions</span><span class="p">,</span> <span class="n">time_from</span><span class="p">,</span> <span class="n">time_to</span><span class="p">,</span> <span class="n">intervals</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
+                               <span class="n">save</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">file</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">tam</span><span class="o">=</span><span class="p">[</span><span class="mi">20</span><span class="p">,</span> <span class="mi">5</span><span class="p">],</span> <span class="n">resolution</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                               <span class="n">cmap</span><span class="o">=</span><span class="s1">&#39;Blues&#39;</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mf">1.5</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Plot the forecasts of several one step ahead models, by point or by interval</span>
+
+<span class="sd">    :param original: Original time series data (list)</span>
+<span class="sd">    :param models: List of models to compare</span>
+<span class="sd">    :param colors: List of models colors</span>
+<span class="sd">    :param distributions: True to plot a distribution</span>
+<span class="sd">    :param time_from: index of data poit to start the ahead forecasting</span>
+<span class="sd">    :param time_to: number of steps ahead to forecast</span>
+<span class="sd">    :param interpol: Fill space between distribution plots</span>
+<span class="sd">    :param save: Save the picture on file</span>
+<span class="sd">    :param file: Filename to save the picture</span>
+<span class="sd">    :param tam: Size of the picture</span>
+<span class="sd">    :param resolution:</span>
+<span class="sd">    :param cmap: Color map to be used on distribution plot</span>
+<span class="sd">    :param option: Distribution type to be passed for models</span>
+<span class="sd">    :return:</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="n">tam</span><span class="p">)</span>
+    <span class="n">ax</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span><span class="mi">111</span><span class="p">)</span>
+
+    <span class="n">cm</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">get_cmap</span><span class="p">(</span><span class="n">cmap</span><span class="p">)</span>
+    <span class="n">cNorm</span> <span class="o">=</span> <span class="n">pltcolors</span><span class="o">.</span><span class="n">Normalize</span><span class="p">(</span><span class="n">vmin</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">vmax</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+    <span class="n">scalarMap</span> <span class="o">=</span> <span class="n">cmx</span><span class="o">.</span><span class="n">ScalarMappable</span><span class="p">(</span><span class="n">norm</span><span class="o">=</span><span class="n">cNorm</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">cm</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">resolution</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span> <span class="n">resolution</span> <span class="o">=</span> <span class="p">(</span><span class="nb">max</span><span class="p">(</span><span class="n">original</span><span class="p">)</span> <span class="o">-</span> <span class="nb">min</span><span class="p">(</span><span class="n">original</span><span class="p">))</span> <span class="o">/</span> <span class="mi">100</span>
+
+    <span class="n">mi</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">ma</span> <span class="o">=</span> <span class="p">[]</span>
+
+    <span class="k">for</span> <span class="n">count</span><span class="p">,</span> <span class="n">fts</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">models</span><span class="p">,</span> <span class="n">start</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">fts</span><span class="o">.</span><span class="n">has_probability_forecasting</span> <span class="ow">and</span> <span class="n">distributions</span><span class="p">[</span><span class="n">count</span><span class="p">]:</span>
+            <span class="n">density</span> <span class="o">=</span> <span class="n">fts</span><span class="o">.</span><span class="n">forecast_ahead_distribution</span><span class="p">(</span><span class="n">original</span><span class="p">[</span><span class="n">time_from</span> <span class="o">-</span> <span class="n">fts</span><span class="o">.</span><span class="n">order</span><span class="p">:</span><span class="n">time_from</span><span class="p">],</span> <span class="n">time_to</span><span class="p">,</span>
+                                                      <span class="n">resolution</span><span class="o">=</span><span class="n">resolution</span><span class="p">)</span>
+
+            <span class="c1">#plot_density_scatter(ax, cmap, density, fig, resolution, time_from, time_to)</span>
+            <span class="n">plot_density_rectange</span><span class="p">(</span><span class="n">ax</span><span class="p">,</span> <span class="n">cm</span><span class="p">,</span> <span class="n">density</span><span class="p">,</span> <span class="n">fig</span><span class="p">,</span> <span class="n">resolution</span><span class="p">,</span> <span class="n">time_from</span><span class="p">,</span> <span class="n">time_to</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">fts</span><span class="o">.</span><span class="n">has_interval_forecasting</span> <span class="ow">and</span> <span class="n">intervals</span><span class="p">:</span>
+            <span class="n">forecasts</span> <span class="o">=</span> <span class="n">fts</span><span class="o">.</span><span class="n">forecast_ahead_interval</span><span class="p">(</span><span class="n">original</span><span class="p">[</span><span class="n">time_from</span> <span class="o">-</span> <span class="n">fts</span><span class="o">.</span><span class="n">order</span><span class="p">:</span><span class="n">time_from</span><span class="p">],</span> <span class="n">time_to</span><span class="p">)</span>
+            <span class="n">lower</span> <span class="o">=</span> <span class="p">[</span><span class="n">kk</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">kk</span> <span class="ow">in</span> <span class="n">forecasts</span><span class="p">]</span>
+            <span class="n">upper</span> <span class="o">=</span> <span class="p">[</span><span class="n">kk</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">kk</span> <span class="ow">in</span> <span class="n">forecasts</span><span class="p">]</span>
+            <span class="n">mi</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">min</span><span class="p">(</span><span class="n">lower</span><span class="p">))</span>
+            <span class="n">ma</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">max</span><span class="p">(</span><span class="n">upper</span><span class="p">))</span>
+            <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">time_from</span> <span class="o">-</span> <span class="n">fts</span><span class="o">.</span><span class="n">order</span><span class="p">):</span>
+                <span class="n">lower</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+                <span class="n">upper</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+            <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">lower</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="n">colors</span><span class="p">[</span><span class="n">count</span><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="n">fts</span><span class="o">.</span><span class="n">shortname</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="n">linewidth</span><span class="p">)</span>
+            <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">upper</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="n">colors</span><span class="p">[</span><span class="n">count</span><span class="p">],</span> <span class="n">linewidth</span><span class="o">=</span><span class="n">linewidth</span><span class="o">*</span><span class="mf">1.5</span><span class="p">)</span>
+
+    <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">original</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;black&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;Original&quot;</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="n">linewidth</span><span class="o">*</span><span class="mf">1.5</span><span class="p">)</span>
+    <span class="n">handles0</span><span class="p">,</span> <span class="n">labels0</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">get_legend_handles_labels</span><span class="p">()</span>
+    <span class="k">if</span> <span class="kc">True</span> <span class="ow">in</span> <span class="n">distributions</span><span class="p">:</span>
+        <span class="n">lgd</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">handles0</span><span class="p">,</span> <span class="n">labels0</span><span class="p">,</span> <span class="n">loc</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">lgd</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">handles0</span><span class="p">,</span> <span class="n">labels0</span><span class="p">,</span> <span class="n">loc</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">bbox_to_anchor</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
+    <span class="n">_mi</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="n">mi</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">_mi</span> <span class="o">&lt;</span> <span class="mi">0</span><span class="p">:</span>
+        <span class="n">_mi</span> <span class="o">*=</span> <span class="mf">1.1</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">_mi</span> <span class="o">*=</span> <span class="mf">0.9</span>
+    <span class="n">_ma</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">ma</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">_ma</span> <span class="o">&lt;</span> <span class="mi">0</span><span class="p">:</span>
+        <span class="n">_ma</span> <span class="o">*=</span> <span class="mf">0.9</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">_ma</span> <span class="o">*=</span> <span class="mf">1.1</span>
+
+    <span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">([</span><span class="n">_mi</span><span class="p">,</span> <span class="n">_ma</span><span class="p">])</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;F(T)&#39;</span><span class="p">)</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;T&#39;</span><span class="p">)</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">original</span><span class="p">)])</span>
+
+    <span class="n">show_and_save_image</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="n">file</span><span class="p">,</span> <span class="n">save</span><span class="p">,</span> <span class="n">lgd</span><span class="o">=</span><span class="n">lgd</span><span class="p">)</span></div>
+
+
+
+<div class="viewcode-block" id="plot_density_rectange"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.Util.plot_density_rectange">[docs]</a><span class="k">def</span> <span class="nf">plot_density_rectange</span><span class="p">(</span><span class="n">ax</span><span class="p">,</span> <span class="n">cmap</span><span class="p">,</span> <span class="n">density</span><span class="p">,</span> <span class="n">fig</span><span class="p">,</span> <span class="n">resolution</span><span class="p">,</span> <span class="n">time_from</span><span class="p">,</span> <span class="n">time_to</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Auxiliar function to plot_compared_intervals_ahead</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="kn">from</span> <span class="nn">matplotlib.patches</span> <span class="k">import</span> <span class="n">Rectangle</span>
+    <span class="kn">from</span> <span class="nn">matplotlib.collections</span> <span class="k">import</span> <span class="n">PatchCollection</span>
+    <span class="n">patches</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">colors</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">density</span><span class="o">.</span><span class="n">index</span><span class="p">:</span>
+        <span class="k">for</span> <span class="n">y</span> <span class="ow">in</span> <span class="n">density</span><span class="o">.</span><span class="n">columns</span><span class="p">:</span>
+            <span class="n">s</span> <span class="o">=</span> <span class="n">Rectangle</span><span class="p">((</span><span class="n">time_from</span> <span class="o">+</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">),</span> <span class="mi">1</span><span class="p">,</span> <span class="n">resolution</span><span class="p">,</span> <span class="n">fill</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">lw</span> <span class="o">=</span> <span class="mi">0</span><span class="p">)</span>
+            <span class="n">patches</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">s</span><span class="p">)</span>
+            <span class="n">colors</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">density</span><span class="p">[</span><span class="n">y</span><span class="p">][</span><span class="n">x</span><span class="p">]</span><span class="o">*</span><span class="mi">5</span><span class="p">)</span>
+    <span class="n">pc</span> <span class="o">=</span> <span class="n">PatchCollection</span><span class="p">(</span><span class="n">patches</span><span class="o">=</span><span class="n">patches</span><span class="p">,</span> <span class="n">match_original</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+    <span class="n">pc</span><span class="o">.</span><span class="n">set_clim</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span>
+    <span class="n">pc</span><span class="o">.</span><span class="n">set_cmap</span><span class="p">(</span><span class="n">cmap</span><span class="p">)</span>
+    <span class="n">pc</span><span class="o">.</span><span class="n">set_array</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">colors</span><span class="p">))</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">add_collection</span><span class="p">(</span><span class="n">pc</span><span class="p">)</span>
+    <span class="n">cb</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">colorbar</span><span class="p">(</span><span class="n">pc</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">)</span>
+    <span class="n">cb</span><span class="o">.</span><span class="n">set_label</span><span class="p">(</span><span class="s1">&#39;Density&#39;</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="plot_probability_distributions"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.Util.plot_probability_distributions">[docs]</a><span class="k">def</span> <span class="nf">plot_probability_distributions</span><span class="p">(</span><span class="n">pmfs</span><span class="p">,</span> <span class="n">lcolors</span><span class="p">,</span> <span class="n">tam</span><span class="o">=</span><span class="p">[</span><span class="mi">15</span><span class="p">,</span> <span class="mi">7</span><span class="p">]):</span>
+    <span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="n">tam</span><span class="p">)</span>
+    <span class="n">ax</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span><span class="mi">111</span><span class="p">)</span>
+
+    <span class="k">for</span> <span class="n">k</span><span class="p">,</span><span class="n">m</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">pmfs</span><span class="p">,</span><span class="n">start</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
+        <span class="n">m</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ax</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="n">lcolors</span><span class="p">[</span><span class="n">k</span><span class="p">])</span>
+
+    <span class="n">handles0</span><span class="p">,</span> <span class="n">labels0</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">get_legend_handles_labels</span><span class="p">()</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">handles0</span><span class="p">,</span> <span class="n">labels0</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="plot_distribution"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.Util.plot_distribution">[docs]</a><span class="k">def</span> <span class="nf">plot_distribution</span><span class="p">(</span><span class="n">ax</span><span class="p">,</span> <span class="n">cmap</span><span class="p">,</span> <span class="n">probabilitydist</span><span class="p">,</span> <span class="n">fig</span><span class="p">,</span> <span class="n">time_from</span><span class="p">,</span> <span class="n">reference_data</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Plot forecasted ProbabilityDistribution objects on a matplotlib axis</span>
+
+<span class="sd">    :param ax: matplotlib axis</span>
+<span class="sd">    :param cmap: matplotlib colormap name</span>
+<span class="sd">    :param probabilitydist: list of ProbabilityDistribution objects</span>
+<span class="sd">    :param fig: matplotlib figure</span>
+<span class="sd">    :param time_from: starting time (on x axis) to begin the plots</span>
+<span class="sd">    :param reference_data:</span>
+<span class="sd">    :return:</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="kn">from</span> <span class="nn">matplotlib.patches</span> <span class="k">import</span> <span class="n">Rectangle</span>
+    <span class="kn">from</span> <span class="nn">matplotlib.collections</span> <span class="k">import</span> <span class="n">PatchCollection</span>
+    <span class="n">patches</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">colors</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="k">for</span> <span class="n">ct</span><span class="p">,</span> <span class="n">dt</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">probabilitydist</span><span class="p">):</span>
+        <span class="n">disp</span> <span class="o">=</span> <span class="mf">0.0</span>
+        <span class="k">if</span> <span class="n">reference_data</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">disp</span> <span class="o">=</span> <span class="n">reference_data</span><span class="p">[</span><span class="n">time_from</span><span class="o">+</span><span class="n">ct</span><span class="p">]</span>
+
+        <span class="k">for</span> <span class="n">y</span> <span class="ow">in</span> <span class="n">dt</span><span class="o">.</span><span class="n">bins</span><span class="p">:</span>
+            <span class="n">s</span> <span class="o">=</span> <span class="n">Rectangle</span><span class="p">((</span><span class="n">time_from</span><span class="o">+</span><span class="n">ct</span><span class="p">,</span> <span class="n">y</span><span class="o">+</span><span class="n">disp</span><span class="p">),</span> <span class="mi">1</span><span class="p">,</span> <span class="n">dt</span><span class="o">.</span><span class="n">resolution</span><span class="p">,</span> <span class="n">fill</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">lw</span> <span class="o">=</span> <span class="mi">0</span><span class="p">)</span>
+            <span class="n">patches</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">s</span><span class="p">)</span>
+            <span class="n">colors</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">dt</span><span class="o">.</span><span class="n">density</span><span class="p">(</span><span class="n">y</span><span class="p">))</span>
+    <span class="n">scale</span> <span class="o">=</span> <span class="n">Transformations</span><span class="o">.</span><span class="n">Scale</span><span class="p">()</span>
+    <span class="n">colors</span> <span class="o">=</span> <span class="n">scale</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">colors</span><span class="p">)</span>
+    <span class="n">pc</span> <span class="o">=</span> <span class="n">PatchCollection</span><span class="p">(</span><span class="n">patches</span><span class="o">=</span><span class="n">patches</span><span class="p">,</span> <span class="n">match_original</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+    <span class="n">pc</span><span class="o">.</span><span class="n">set_clim</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span>
+    <span class="n">pc</span><span class="o">.</span><span class="n">set_cmap</span><span class="p">(</span><span class="n">cmap</span><span class="p">)</span>
+    <span class="n">pc</span><span class="o">.</span><span class="n">set_array</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">colors</span><span class="p">))</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">add_collection</span><span class="p">(</span><span class="n">pc</span><span class="p">)</span>
+    <span class="n">cb</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">colorbar</span><span class="p">(</span><span class="n">pc</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">)</span>
+    <span class="n">cb</span><span class="o">.</span><span class="n">set_label</span><span class="p">(</span><span class="s1">&#39;Density&#39;</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="plot_distribution2"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.Util.plot_distribution2">[docs]</a><span class="k">def</span> <span class="nf">plot_distribution2</span><span class="p">(</span><span class="n">probabilitydist</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Plot distributions in y-axis over the time (x-axis)</span>
+
+<span class="sd">    :param probabilitydist: the forecasted probability distributions to plot</span>
+<span class="sd">    :param data: the original test sample</span>
+<span class="sd">    :keyword start_at: the time index (inside of data) to start to plot the probability distributions</span>
+<span class="sd">    :keyword ax: a matplotlib axis. If no value was provided a new axis is created.</span>
+<span class="sd">    :keyword cmap: a matplotlib colormap name, the default value is &#39;Blues&#39;</span>
+<span class="sd">    :keyword quantiles: the list of quantiles intervals to plot, e. g. [.05, .25, .75, .95]</span>
+<span class="sd">    :keyword median: a boolean value indicating if the median value will be plot.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="kn">import</span> <span class="nn">matplotlib.colorbar</span> <span class="k">as</span> <span class="nn">cbar</span>
+    <span class="kn">import</span> <span class="nn">matplotlib.cm</span> <span class="k">as</span> <span class="nn">cm</span>
+
+    <span class="n">ax</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;ax&#39;</span><span class="p">,</span><span class="kc">None</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">ax</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">nrows</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">ncols</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">[</span><span class="mi">15</span><span class="p">,</span> <span class="mi">5</span><span class="p">])</span>
+
+    <span class="n">l</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">probabilitydist</span><span class="p">)</span>
+
+    <span class="n">cmap</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;cmap&#39;</span><span class="p">,</span><span class="s1">&#39;Blues&#39;</span><span class="p">)</span>
+    <span class="n">cmap</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">get_cmap</span><span class="p">(</span><span class="n">cmap</span><span class="p">)</span>
+
+    <span class="n">start_at</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;start_at&#39;</span><span class="p">,</span><span class="mi">0</span><span class="p">)</span>
+
+    <span class="n">x</span> <span class="o">=</span> <span class="p">[</span><span class="n">k</span> <span class="o">+</span> <span class="n">start_at</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">l</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)]</span>
+
+    <span class="n">qt</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;quantiles&#39;</span><span class="p">,</span><span class="kc">None</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">qt</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">qt</span> <span class="o">=</span> <span class="p">[</span><span class="nb">round</span><span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="o">.</span><span class="mi">05</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="o">.</span><span class="mi">05</span><span class="p">)]</span>
+        <span class="n">qt</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="o">.</span><span class="mi">01</span><span class="p">)</span>
+        <span class="n">qt</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="o">.</span><span class="mi">99</span><span class="p">)</span>
+
+    <span class="n">lq</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">qt</span><span class="p">)</span>
+
+    <span class="n">normal</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">Normalize</span><span class="p">(</span><span class="nb">min</span><span class="p">(</span><span class="n">qt</span><span class="p">),</span> <span class="nb">max</span><span class="p">(</span><span class="n">qt</span><span class="p">))</span>
+    <span class="n">scalarMap</span> <span class="o">=</span> <span class="n">cm</span><span class="o">.</span><span class="n">ScalarMappable</span><span class="p">(</span><span class="n">norm</span><span class="o">=</span><span class="n">normal</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">cmap</span><span class="p">)</span>
+
+    <span class="k">for</span> <span class="n">ct</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="nb">int</span><span class="p">(</span><span class="n">lq</span> <span class="o">/</span> <span class="mi">2</span><span class="p">)</span> <span class="o">+</span> <span class="mi">1</span><span class="p">):</span>
+        <span class="k">try</span><span class="p">:</span>
+            <span class="n">y</span> <span class="o">=</span> <span class="p">[[</span><span class="n">data</span><span class="p">[</span><span class="n">start_at</span><span class="p">],</span> <span class="n">data</span><span class="p">[</span><span class="n">start_at</span><span class="p">]]]</span>
+            <span class="k">for</span> <span class="n">pd</span> <span class="ow">in</span> <span class="n">probabilitydist</span><span class="p">:</span>
+                <span class="n">qts</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">quantile</span><span class="p">([</span><span class="n">qt</span><span class="p">[</span><span class="n">ct</span> <span class="o">-</span> <span class="mi">1</span><span class="p">],</span> <span class="n">qt</span><span class="p">[</span><span class="o">-</span><span class="n">ct</span><span class="p">]])</span>
+                <span class="n">y</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">qts</span><span class="p">)</span>
+
+            <span class="n">ax</span><span class="o">.</span><span class="n">fill_between</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="p">[</span><span class="n">k</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">y</span><span class="p">],</span> <span class="p">[</span><span class="n">k</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">y</span><span class="p">],</span>
+                            <span class="n">facecolor</span><span class="o">=</span><span class="n">scalarMap</span><span class="o">.</span><span class="n">to_rgba</span><span class="p">(</span><span class="n">ct</span> <span class="o">/</span> <span class="n">lq</span><span class="p">))</span>
+        <span class="k">except</span><span class="p">:</span>
+            <span class="k">pass</span>
+
+    <span class="k">if</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;median&#39;</span><span class="p">,</span><span class="kc">True</span><span class="p">):</span>
+        <span class="n">y</span> <span class="o">=</span> <span class="p">[</span><span class="n">data</span><span class="p">[</span><span class="n">start_at</span><span class="p">]]</span>
+        <span class="k">for</span> <span class="n">pd</span> <span class="ow">in</span> <span class="n">probabilitydist</span><span class="p">:</span>
+            <span class="n">qts</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">quantile</span><span class="p">([</span><span class="o">.</span><span class="mi">5</span><span class="p">])</span>
+            <span class="n">y</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">qts</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+
+        <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;red&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Median&#39;</span><span class="p">)</span>
+
+    <span class="n">cax</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">cbar</span><span class="o">.</span><span class="n">make_axes</span><span class="p">(</span><span class="n">ax</span><span class="p">)</span>
+    <span class="n">cb</span> <span class="o">=</span> <span class="n">cbar</span><span class="o">.</span><span class="n">ColorbarBase</span><span class="p">(</span><span class="n">cax</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">cmap</span><span class="p">,</span> <span class="n">norm</span><span class="o">=</span><span class="n">normal</span><span class="p">)</span>
+    <span class="n">cb</span><span class="o">.</span><span class="n">set_label</span><span class="p">(</span><span class="s1">&#39;Density&#39;</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="plot_distribution_tiled"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.Util.plot_distribution_tiled">[docs]</a><span class="k">def</span> <span class="nf">plot_distribution_tiled</span><span class="p">(</span><span class="n">distributions</span><span class="p">,</span><span class="n">data</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span><span class="n">rows</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span><span class="n">cols</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span><span class="n">index</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span><span class="n">axis</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span><span class="n">size</span><span class="o">=</span><span class="p">[</span><span class="mi">10</span><span class="p">,</span><span class="mi">20</span><span class="p">]):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Plot one distribution individually in each axis, with probability in y-axis and UoD on x-axis</span>
+
+<span class="sd">    :param distributions:</span>
+<span class="sd">    :param data:</span>
+<span class="sd">    :param rows:</span>
+<span class="sd">    :param cols:</span>
+<span class="sd">    :param index:</span>
+<span class="sd">    :param axis:</span>
+<span class="sd">    :param size:</span>
+<span class="sd">    :return:</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">if</span> <span class="n">axis</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">fig</span><span class="p">,</span> <span class="n">axis</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">nrows</span><span class="o">=</span><span class="n">rows</span><span class="p">,</span> <span class="n">ncols</span><span class="o">=</span><span class="n">cols</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="n">size</span><span class="p">)</span>
+
+    <span class="k">for</span> <span class="n">ct</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">rows</span><span class="o">*</span><span class="n">cols</span><span class="p">):</span>
+        <span class="n">col</span> <span class="o">=</span> <span class="n">ct</span> <span class="o">%</span> <span class="n">cols</span>
+        <span class="n">row</span> <span class="o">=</span> <span class="n">ct</span> <span class="o">//</span> <span class="n">cols</span>
+        <span class="k">if</span> <span class="n">index</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">ix</span> <span class="o">=</span> <span class="n">ct</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">ix</span> <span class="o">=</span><span class="n">index</span><span class="p">[</span><span class="n">ct</span><span class="p">]</span>
+        <span class="n">forecast</span> <span class="o">=</span> <span class="n">distributions</span><span class="p">[</span><span class="n">ix</span><span class="p">]</span>
+        <span class="n">forecast</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="n">axis</span><span class="p">[</span><span class="n">row</span><span class="p">][</span><span class="n">col</span><span class="p">])</span>
+        <span class="k">if</span> <span class="n">data</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">axis</span><span class="p">[</span><span class="n">row</span><span class="p">][</span><span class="n">col</span><span class="p">]</span><span class="o">.</span><span class="n">axvline</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">ix</span><span class="p">])</span>
+        <span class="n">axis</span><span class="p">[</span><span class="n">row</span><span class="p">][</span><span class="n">col</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;t+</span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">ix</span><span class="p">))</span>
+        <span class="n">axis</span><span class="p">[</span><span class="n">row</span><span class="p">][</span><span class="n">col</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="kc">None</span><span class="p">)</span>
+
+    <span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span></div>
+
+
+<div class="viewcode-block" id="plot_interval"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.Util.plot_interval">[docs]</a><span class="k">def</span> <span class="nf">plot_interval</span><span class="p">(</span><span class="n">axis</span><span class="p">,</span> <span class="n">intervals</span><span class="p">,</span> <span class="n">order</span><span class="p">,</span> <span class="n">label</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;red&#39;</span><span class="p">,</span> <span class="n">typeonlegend</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">ls</span><span class="o">=</span><span class="s1">&#39;-&#39;</span><span class="p">,</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Plot forecasted intervals on matplotlib</span>
+
+<span class="sd">    :param axis: matplotlib axis</span>
+<span class="sd">    :param intervals: list of forecasted intervals</span>
+<span class="sd">    :param order: order of the model that create the forecasts</span>
+<span class="sd">    :param label: figure label</span>
+<span class="sd">    :param color: matplotlib color name</span>
+<span class="sd">    :param typeonlegend:</span>
+<span class="sd">    :param ls: matplotlib line style</span>
+<span class="sd">    :param linewidth: matplotlib width</span>
+<span class="sd">    :return:</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">lower</span> <span class="o">=</span> <span class="p">[</span><span class="n">kk</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">kk</span> <span class="ow">in</span> <span class="n">intervals</span><span class="p">]</span>
+    <span class="n">upper</span> <span class="o">=</span> <span class="p">[</span><span class="n">kk</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">kk</span> <span class="ow">in</span> <span class="n">intervals</span><span class="p">]</span>
+    <span class="n">mi</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="n">lower</span><span class="p">)</span> <span class="o">*</span> <span class="mf">0.95</span>
+    <span class="n">ma</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">upper</span><span class="p">)</span> <span class="o">*</span> <span class="mf">1.05</span>
+    <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">order</span><span class="o">+</span><span class="mi">1</span><span class="p">):</span>
+        <span class="n">lower</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+        <span class="n">upper</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">typeonlegend</span><span class="p">:</span> <span class="n">label</span> <span class="o">+=</span> <span class="s2">&quot; (Interval)&quot;</span>
+    <span class="n">axis</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">lower</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="n">color</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="n">label</span><span class="p">,</span> <span class="n">ls</span><span class="o">=</span><span class="n">ls</span><span class="p">,</span><span class="n">linewidth</span><span class="o">=</span><span class="n">linewidth</span><span class="p">)</span>
+    <span class="n">axis</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">upper</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="n">color</span><span class="p">,</span> <span class="n">ls</span><span class="o">=</span><span class="n">ls</span><span class="p">,</span><span class="n">linewidth</span><span class="o">=</span><span class="n">linewidth</span><span class="p">)</span>
+    <span class="k">return</span> <span class="p">[</span><span class="n">mi</span><span class="p">,</span> <span class="n">ma</span><span class="p">]</span></div>
+
+
+<div class="viewcode-block" id="plot_interval2"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.Util.plot_interval2">[docs]</a><span class="k">def</span> <span class="nf">plot_interval2</span><span class="p">(</span><span class="n">intervals</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Plot forecasted intervals on matplotlib</span>
+
+<span class="sd">    :param intervals: list of forecasted intervals</span>
+<span class="sd">    :param data: the original test sample</span>
+<span class="sd">    :keyword start_at: the time index (inside of data) to start to plot the intervals</span>
+<span class="sd">    :keyword label: figure label</span>
+<span class="sd">    :keyword color: matplotlib color name</span>
+<span class="sd">    :keyword typeonlegend:</span>
+<span class="sd">    :keyword ls: matplotlib line style</span>
+<span class="sd">    :keyword linewidth: matplotlib width</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">l</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">intervals</span><span class="p">)</span>
+
+    <span class="n">nintervals</span> <span class="o">=</span> <span class="n">intervals</span>
+
+    <span class="n">start_at</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;start_at&#39;</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+
+    <span class="n">ax</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;ax&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">ax</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">nrows</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">ncols</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">[</span><span class="mi">15</span><span class="p">,</span> <span class="mi">5</span><span class="p">])</span>
+
+    <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">start_at</span><span class="p">):</span>
+        <span class="n">nintervals</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="p">[</span><span class="kc">None</span><span class="p">,</span><span class="kc">None</span><span class="p">])</span>
+
+    <span class="n">nintervals</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="n">start_at</span><span class="p">,</span> <span class="p">[</span><span class="n">data</span><span class="p">[</span><span class="n">start_at</span><span class="p">],</span> <span class="n">data</span><span class="p">[</span><span class="n">start_at</span><span class="p">]])</span>
+
+    <span class="n">lower</span> <span class="o">=</span> <span class="p">[</span><span class="n">kk</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">kk</span> <span class="ow">in</span> <span class="n">nintervals</span><span class="p">]</span>
+    <span class="n">upper</span> <span class="o">=</span> <span class="p">[</span><span class="n">kk</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">kk</span> <span class="ow">in</span> <span class="n">nintervals</span><span class="p">]</span>
+
+    <span class="n">typeonlegend</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;typeonlegend&#39;</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</span>
+    <span class="n">color</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;color&#39;</span><span class="p">,</span> <span class="s1">&#39;red&#39;</span><span class="p">)</span>
+    <span class="n">label</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;label&#39;</span><span class="p">,</span><span class="s1">&#39;&#39;</span><span class="p">)</span>
+    <span class="n">linewidth</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;linewidth&#39;</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+
+    <span class="n">ls</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;ls&#39;</span><span class="p">,</span><span class="s1">&#39;-&#39;</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">typeonlegend</span><span class="p">:</span> <span class="n">label</span> <span class="o">+=</span> <span class="s2">&quot; (Interval)&quot;</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">lower</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="n">color</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="n">label</span><span class="p">,</span> <span class="n">ls</span><span class="o">=</span><span class="n">ls</span><span class="p">,</span><span class="n">linewidth</span><span class="o">=</span><span class="n">linewidth</span><span class="p">)</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">upper</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="n">color</span><span class="p">,</span> <span class="n">ls</span><span class="o">=</span><span class="n">ls</span><span class="p">,</span><span class="n">linewidth</span><span class="o">=</span><span class="n">linewidth</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="plot_rules"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.Util.plot_rules">[docs]</a><span class="k">def</span> <span class="nf">plot_rules</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="p">[</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">rules_by_axis</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Plot the FLRG rules of a FTS model on a matplotlib axis</span>
+
+<span class="sd">    :param model: FTS model</span>
+<span class="sd">    :param size: figure size</span>
+<span class="sd">    :param axis: matplotlib axis</span>
+<span class="sd">    :param rules_by_axis: number of rules plotted by column</span>
+<span class="sd">    :param columns: number of columns</span>
+<span class="sd">    :return:</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">if</span> <span class="n">axis</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">rules_by_axis</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">rows</span> <span class="o">=</span> <span class="mi">1</span>
+    <span class="k">elif</span> <span class="n">axis</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">rules_by_axis</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">rows</span> <span class="o">=</span> <span class="p">(((</span><span class="nb">len</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">flrgs</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span><span class="o">//</span><span class="n">rules_by_axis</span><span class="p">))</span> <span class="o">//</span> <span class="n">columns</span><span class="p">)</span><span class="o">+</span><span class="mi">1</span>
+
+    <span class="n">fig</span><span class="p">,</span> <span class="n">axis</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">nrows</span><span class="o">=</span><span class="n">rows</span><span class="p">,</span> <span class="n">ncols</span><span class="o">=</span><span class="n">columns</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="n">size</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">rules_by_axis</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">draw_sets_on_axis</span><span class="p">(</span><span class="n">axis</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="n">size</span><span class="p">)</span>
+
+    <span class="n">_lhs</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">ordered_sets</span> <span class="k">if</span> <span class="ow">not</span> <span class="n">model</span><span class="o">.</span><span class="n">is_high_order</span> <span class="k">else</span> <span class="n">model</span><span class="o">.</span><span class="n">flrgs</span><span class="o">.</span><span class="n">keys</span><span class="p">()</span>
+
+    <span class="k">for</span> <span class="n">ct</span><span class="p">,</span> <span class="n">key</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">_lhs</span><span class="p">):</span>
+
+        <span class="n">xticks</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="n">xtickslabels</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="k">if</span> <span class="n">rules_by_axis</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">ax</span> <span class="o">=</span> <span class="n">axis</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">colcount</span> <span class="o">=</span> <span class="p">(</span><span class="n">ct</span> <span class="o">//</span> <span class="n">rules_by_axis</span><span class="p">)</span> <span class="o">%</span> <span class="n">columns</span>
+            <span class="n">rowcount</span> <span class="o">=</span> <span class="p">(</span><span class="n">ct</span> <span class="o">//</span> <span class="n">rules_by_axis</span><span class="p">)</span> <span class="o">//</span> <span class="n">columns</span>
+
+            <span class="k">if</span> <span class="n">rows</span> <span class="o">&gt;</span> <span class="mi">1</span> <span class="ow">and</span> <span class="n">columns</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
+                <span class="n">ax</span> <span class="o">=</span> <span class="n">axis</span><span class="p">[</span><span class="n">rowcount</span><span class="p">,</span> <span class="n">colcount</span><span class="p">]</span>
+            <span class="k">elif</span> <span class="n">columns</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
+                <span class="n">ax</span> <span class="o">=</span> <span class="n">axis</span><span class="p">[</span><span class="n">rowcount</span><span class="p">]</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">ax</span> <span class="o">=</span> <span class="n">axis</span>
+
+            <span class="k">if</span> <span class="n">ct</span> <span class="o">%</span> <span class="n">rules_by_axis</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+                <span class="n">draw_sets_on_axis</span><span class="p">(</span><span class="n">ax</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="n">size</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="ow">not</span> <span class="n">model</span><span class="o">.</span><span class="n">is_high_order</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">model</span><span class="o">.</span><span class="n">flrgs</span><span class="p">:</span>
+                <span class="n">x</span> <span class="o">=</span> <span class="p">(</span><span class="n">ct</span> <span class="o">%</span> <span class="n">rules_by_axis</span><span class="p">)</span> <span class="o">+</span> <span class="mi">1</span>
+                <span class="n">flrg</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">key</span><span class="p">]</span>
+                <span class="n">y</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">key</span><span class="p">]</span><span class="o">.</span><span class="n">centroid</span>
+                <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">([</span><span class="n">x</span><span class="p">],[</span><span class="n">y</span><span class="p">],</span><span class="s1">&#39;o&#39;</span><span class="p">)</span>
+                <span class="n">xticks</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+                <span class="n">xtickslabels</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">key</span><span class="p">)</span>
+                <span class="k">for</span> <span class="n">rhs</span> <span class="ow">in</span> <span class="n">flrg</span><span class="o">.</span><span class="n">RHS</span><span class="p">:</span>
+                    <span class="n">dest</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">rhs</span><span class="p">]</span><span class="o">.</span><span class="n">centroid</span>
+                    <span class="n">ax</span><span class="o">.</span><span class="n">arrow</span><span class="p">(</span><span class="n">x</span><span class="o">+.</span><span class="mi">1</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="mf">0.8</span><span class="p">,</span> <span class="n">dest</span> <span class="o">-</span> <span class="n">y</span><span class="p">,</span> <span class="c1">#length_includes_head=True,</span>
+                               <span class="n">head_width</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">head_length</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="s1">&#39;full&#39;</span><span class="p">,</span> <span class="n">overhang</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
+                               <span class="n">fc</span><span class="o">=</span><span class="s1">&#39;k&#39;</span><span class="p">,</span> <span class="n">ec</span><span class="o">=</span><span class="s1">&#39;k&#39;</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">flrg</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">key</span><span class="p">]</span>
+            <span class="n">x</span> <span class="o">=</span> <span class="p">(</span><span class="n">ct</span><span class="o">%</span><span class="n">rules_by_axis</span><span class="p">)</span><span class="o">*</span><span class="n">model</span><span class="o">.</span><span class="n">order</span> <span class="o">+</span> <span class="mi">1</span>
+            <span class="k">for</span> <span class="n">ct2</span><span class="p">,</span> <span class="n">lhs</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">flrg</span><span class="o">.</span><span class="n">LHS</span><span class="p">):</span>
+                <span class="n">y</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">lhs</span><span class="p">]</span><span class="o">.</span><span class="n">centroid</span>
+                <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">([</span><span class="n">x</span><span class="o">+</span><span class="n">ct2</span><span class="p">],</span> <span class="p">[</span><span class="n">y</span><span class="p">],</span> <span class="s1">&#39;o&#39;</span><span class="p">)</span>
+                <span class="n">xticks</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">x</span><span class="o">+</span><span class="n">ct2</span><span class="p">)</span>
+                <span class="n">xtickslabels</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">lhs</span><span class="p">)</span>
+            <span class="k">for</span> <span class="n">ct2</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">model</span><span class="o">.</span><span class="n">order</span><span class="p">):</span>
+                <span class="n">fs1</span> <span class="o">=</span> <span class="n">flrg</span><span class="o">.</span><span class="n">LHS</span><span class="p">[</span><span class="n">ct2</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
+                <span class="n">fs2</span> <span class="o">=</span> <span class="n">flrg</span><span class="o">.</span><span class="n">LHS</span><span class="p">[</span><span class="n">ct2</span><span class="p">]</span>
+                <span class="n">y</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">fs1</span><span class="p">]</span><span class="o">.</span><span class="n">centroid</span>
+                <span class="n">dest</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">fs2</span><span class="p">]</span><span class="o">.</span><span class="n">centroid</span>
+                <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">([</span><span class="n">x</span><span class="o">+</span><span class="n">ct2</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="n">x</span><span class="o">+</span><span class="n">ct2</span><span class="p">],</span> <span class="p">[</span><span class="n">y</span><span class="p">,</span><span class="n">dest</span><span class="p">],</span><span class="s1">&#39;-&#39;</span><span class="p">)</span>
+
+            <span class="n">y</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">flrg</span><span class="o">.</span><span class="n">LHS</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]]</span><span class="o">.</span><span class="n">centroid</span>
+            <span class="k">for</span> <span class="n">rhs</span> <span class="ow">in</span> <span class="n">flrg</span><span class="o">.</span><span class="n">RHS</span><span class="p">:</span>
+                <span class="n">dest</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">rhs</span><span class="p">]</span><span class="o">.</span><span class="n">centroid</span>
+                <span class="n">ax</span><span class="o">.</span><span class="n">arrow</span><span class="p">(</span><span class="n">x</span> <span class="o">+</span> <span class="n">model</span><span class="o">.</span><span class="n">order</span> <span class="o">-</span><span class="mi">1</span> <span class="o">+</span> <span class="o">.</span><span class="mi">1</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="mf">0.8</span><span class="p">,</span> <span class="n">dest</span> <span class="o">-</span> <span class="n">y</span><span class="p">,</span>  <span class="c1"># length_includes_head=True,</span>
+                           <span class="n">head_width</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">head_length</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">shape</span><span class="o">=</span><span class="s1">&#39;full&#39;</span><span class="p">,</span> <span class="n">overhang</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
+                           <span class="n">fc</span><span class="o">=</span><span class="s1">&#39;k&#39;</span><span class="p">,</span> <span class="n">ec</span><span class="o">=</span><span class="s1">&#39;k&#39;</span><span class="p">)</span>
+
+
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_xticks</span><span class="p">(</span><span class="n">xticks</span><span class="p">)</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_xticklabels</span><span class="p">(</span><span class="n">xtickslabels</span><span class="p">)</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span><span class="n">rules_by_axis</span><span class="o">*</span><span class="n">model</span><span class="o">.</span><span class="n">order</span><span class="o">+</span><span class="mi">1</span><span class="p">])</span>
+
+    <span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span></div>
+
+
+<div class="viewcode-block" id="draw_sets_on_axis"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.Util.draw_sets_on_axis">[docs]</a><span class="k">def</span> <span class="nf">draw_sets_on_axis</span><span class="p">(</span><span class="n">axis</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="n">size</span><span class="p">):</span>
+    <span class="k">if</span> <span class="n">axis</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">fig</span><span class="p">,</span> <span class="n">axis</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">nrows</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">ncols</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="n">size</span><span class="p">)</span>
+    <span class="k">for</span> <span class="n">ct</span><span class="p">,</span> <span class="n">key</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">ordered_sets</span><span class="p">):</span>
+        <span class="n">fs</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">key</span><span class="p">]</span>
+        <span class="n">axis</span><span class="o">.</span><span class="n">plot</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">fs</span><span class="o">.</span><span class="n">parameters</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="n">fs</span><span class="o">.</span><span class="n">name</span><span class="p">)</span>
+        <span class="n">axis</span><span class="o">.</span><span class="n">axhline</span><span class="p">(</span><span class="n">fs</span><span class="o">.</span><span class="n">centroid</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="s2">&quot;lightgray&quot;</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
+    <span class="n">axis</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">ordered_sets</span><span class="p">)])</span>
+    <span class="n">axis</span><span class="o">.</span><span class="n">set_xticks</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">ordered_sets</span><span class="p">)))</span>
+    <span class="n">tmp</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;&#39;</span><span class="p">]</span>
+    <span class="n">tmp</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">ordered_sets</span><span class="p">)</span>
+    <span class="n">axis</span><span class="o">.</span><span class="n">set_xticklabels</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span>
+    <span class="n">axis</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">([</span><span class="n">model</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">min</span><span class="p">,</span> <span class="n">model</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">max</span><span class="p">])</span>
+    <span class="n">axis</span><span class="o">.</span><span class="n">set_yticks</span><span class="p">([</span><span class="n">model</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">k</span><span class="p">]</span><span class="o">.</span><span class="n">centroid</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">model</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">ordered_sets</span><span class="p">])</span>
+    <span class="n">axis</span><span class="o">.</span><span class="n">set_yticklabels</span><span class="p">([</span><span class="nb">str</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">k</span><span class="p">]</span><span class="o">.</span><span class="n">centroid</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span> <span class="o">+</span> <span class="s2">&quot; - &quot;</span> <span class="o">+</span> <span class="n">k</span>
+                          <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">model</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">ordered_sets</span><span class="p">])</span></div>
+
+
+<span class="n">current_milli_time</span> <span class="o">=</span> <span class="k">lambda</span><span class="p">:</span> <span class="nb">int</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span> <span class="o">*</span> <span class="mi">1000</span><span class="p">))</span>
+
+
+<div class="viewcode-block" id="uniquefilename"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.Util.uniquefilename">[docs]</a><span class="k">def</span> <span class="nf">uniquefilename</span><span class="p">(</span><span class="n">name</span><span class="p">):</span>
+    <span class="k">if</span> <span class="s1">&#39;.&#39;</span> <span class="ow">in</span> <span class="n">name</span><span class="p">:</span>
+        <span class="n">tmp</span> <span class="o">=</span> <span class="n">name</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;.&#39;</span><span class="p">)</span>
+        <span class="k">return</span>  <span class="n">tmp</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">current_milli_time</span><span class="p">())</span> <span class="o">+</span> <span class="s1">&#39;.&#39;</span> <span class="o">+</span> <span class="n">tmp</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">name</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">current_milli_time</span><span class="p">())</span></div>
+
+
+<div class="viewcode-block" id="show_and_save_image"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.Util.show_and_save_image">[docs]</a><span class="k">def</span> <span class="nf">show_and_save_image</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="n">file</span><span class="p">,</span> <span class="n">flag</span><span class="p">,</span> <span class="n">lgd</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Show and image and save on file</span>
+
+<span class="sd">    :param fig: Matplotlib Figure object</span>
+<span class="sd">    :param file: filename to save the picture</span>
+<span class="sd">    :param flag: if True the image will be saved</span>
+<span class="sd">    :param lgd: legend</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+    <span class="k">if</span> <span class="n">flag</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">lgd</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">fig</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="n">file</span><span class="p">,</span> <span class="n">additional_artists</span><span class="o">=</span><span class="n">lgd</span><span class="p">,</span><span class="n">bbox_inches</span><span class="o">=</span><span class="s1">&#39;tight&#39;</span><span class="p">)</span>  <span class="c1">#bbox_extra_artists=(lgd,), )</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">fig</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="n">file</span><span class="p">)</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">close</span><span class="p">(</span><span class="n">fig</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="enumerate2"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.Util.enumerate2">[docs]</a><span class="k">def</span> <span class="nf">enumerate2</span><span class="p">(</span><span class="n">xs</span><span class="p">,</span> <span class="n">start</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">step</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
+    <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">xs</span><span class="p">:</span>
+        <span class="k">yield</span> <span class="p">(</span><span class="n">start</span><span class="p">,</span> <span class="n">x</span><span class="p">)</span>
+        <span class="n">start</span> <span class="o">+=</span> <span class="n">step</span></div>
+
+
+<div class="viewcode-block" id="sliding_window"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.Util.sliding_window">[docs]</a><span class="k">def</span> <span class="nf">sliding_window</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">windowsize</span><span class="p">,</span> <span class="n">train</span><span class="o">=</span><span class="mf">0.8</span><span class="p">,</span> <span class="n">inc</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Sliding window method of cross validation for time series</span>
+
+<span class="sd">    :param data: the entire dataset</span>
+<span class="sd">    :param windowsize: window size</span>
+<span class="sd">    :param train: percentual of the window size will be used for training the models</span>
+<span class="sd">    :param inc: percentual of data used for slide the window</span>
+<span class="sd">    :return: window count, training set, test set</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">multivariate</span> <span class="o">=</span> <span class="kc">True</span> <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">)</span> <span class="k">else</span> <span class="kc">False</span>
+
+    <span class="n">l</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)</span> <span class="k">if</span> <span class="ow">not</span> <span class="n">multivariate</span> <span class="k">else</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">index</span><span class="p">)</span>
+    <span class="n">ttrain</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">windowsize</span> <span class="o">*</span> <span class="n">train</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span>
+    <span class="n">ic</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">windowsize</span> <span class="o">*</span> <span class="n">inc</span><span class="p">,</span> <span class="mi">0</span><span class="p">))</span>
+
+    <span class="n">progressbar</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;progress&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+
+    <span class="n">rng</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="n">l</span><span class="o">-</span><span class="n">windowsize</span><span class="o">+</span><span class="n">ic</span><span class="p">,</span><span class="n">ic</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">progressbar</span><span class="p">:</span>
+        <span class="kn">from</span> <span class="nn">tqdm</span> <span class="k">import</span> <span class="n">tqdm</span>
+        <span class="n">rng</span> <span class="o">=</span> <span class="n">tqdm</span><span class="p">(</span><span class="n">rng</span><span class="p">)</span>
+
+    <span class="k">for</span> <span class="n">count</span> <span class="ow">in</span> <span class="n">rng</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">count</span> <span class="o">+</span> <span class="n">windowsize</span> <span class="o">&gt;</span> <span class="n">l</span><span class="p">:</span>
+            <span class="n">_end</span> <span class="o">=</span> <span class="n">l</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">_end</span> <span class="o">=</span> <span class="n">count</span> <span class="o">+</span> <span class="n">windowsize</span>
+        <span class="k">if</span> <span class="n">multivariate</span><span class="p">:</span>
+            <span class="k">yield</span> <span class="p">(</span><span class="n">count</span><span class="p">,</span> <span class="n">data</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">count</span><span class="p">:</span> <span class="n">count</span> <span class="o">+</span> <span class="n">ttrain</span><span class="p">],</span> <span class="n">data</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">count</span> <span class="o">+</span> <span class="n">ttrain</span><span class="p">:</span> <span class="n">_end</span><span class="p">])</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">yield</span> <span class="p">(</span><span class="n">count</span><span class="p">,</span>  <span class="n">data</span><span class="p">[</span><span class="n">count</span> <span class="p">:</span> <span class="n">count</span> <span class="o">+</span> <span class="n">ttrain</span><span class="p">],</span> <span class="n">data</span><span class="p">[</span><span class="n">count</span> <span class="o">+</span> <span class="n">ttrain</span> <span class="p">:</span> <span class="n">_end</span><span class="p">]</span>  <span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="persist_obj"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.Util.persist_obj">[docs]</a><span class="k">def</span> <span class="nf">persist_obj</span><span class="p">(</span><span class="n">obj</span><span class="p">,</span> <span class="n">file</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Persist an object on filesystem. This function depends on Dill package</span>
+
+<span class="sd">    :param obj: object on memory</span>
+<span class="sd">    :param file: file name to store the object</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="kn">import</span> <span class="nn">dill</span>
+    <span class="k">try</span><span class="p">:</span>
+        <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">file</span><span class="p">,</span> <span class="s1">&#39;wb&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">_file</span><span class="p">:</span>
+            <span class="n">dill</span><span class="o">.</span><span class="n">dump</span><span class="p">(</span><span class="n">obj</span><span class="p">,</span> <span class="n">_file</span><span class="p">)</span>
+    <span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">ex</span><span class="p">:</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;File </span><span class="si">{}</span><span class="s2"> could not be saved due exception </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">file</span><span class="p">,</span> <span class="n">ex</span><span class="p">))</span></div>
+
+
+<div class="viewcode-block" id="load_obj"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.Util.load_obj">[docs]</a><span class="k">def</span> <span class="nf">load_obj</span><span class="p">(</span><span class="n">file</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Load to memory an object stored filesystem. This function depends on Dill package</span>
+
+<span class="sd">    :param file: file name where the object is stored</span>
+<span class="sd">    :return: object</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="kn">import</span> <span class="nn">dill</span>
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">file</span><span class="p">,</span> <span class="s1">&#39;rb&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">_file</span><span class="p">:</span>
+        <span class="n">obj</span> <span class="o">=</span> <span class="n">dill</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">_file</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">obj</span></div>
+
+
+<div class="viewcode-block" id="persist_env"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.Util.persist_env">[docs]</a><span class="k">def</span> <span class="nf">persist_env</span><span class="p">(</span><span class="n">file</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Persist an entire environment on file. This function depends on Dill package</span>
+
+<span class="sd">    :param file: file name to store the environment</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="kn">import</span> <span class="nn">dill</span>
+    <span class="n">dill</span><span class="o">.</span><span class="n">dump_session</span><span class="p">(</span><span class="n">file</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="load_env"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.Util.load_env">[docs]</a><span class="k">def</span> <span class="nf">load_env</span><span class="p">(</span><span class="n">file</span><span class="p">):</span>
+    <span class="kn">import</span> <span class="nn">dill</span>
+    <span class="n">dill</span><span class="o">.</span><span class="n">load_session</span><span class="p">(</span><span class="n">file</span><span class="p">)</span></div>
+
+
+
+</pre></div>
+
+            <div class="clearer"></div>
+          </div>
+        </div>
+      </div>
+      <div class="sphinxsidebar" role="navigation" aria-label="main navigation">
+        <div class="sphinxsidebarwrapper">
+            <p class="logo"><a href="../../../index.html">
+              <img class="logo" src="../../../_static/logo_heading2.png" alt="Logo"/>
+            </a></p>
+<div id="searchbox" style="display: none" role="search">
+  <h3 id="searchlabel">Quick search</h3>
+    <div class="searchformwrapper">
+    <form class="search" action="../../../search.html" method="get">
+      <input type="text" name="q" aria-labelledby="searchlabel" autocomplete="off" autocorrect="off" autocapitalize="off" spellcheck="false"/>
+      <input type="submit" value="Go" />
+    </form>
+    </div>
+</div>
+<script>$('#searchbox').show(0);</script>
+        </div>
+      </div>
+      <div class="clearer"></div>
+    </div>
+    <div class="related" role="navigation" aria-label="related navigation">
+      <h3>Navigation</h3>
+      <ul>
+        <li class="right" style="margin-right: 10px">
+          <a href="../../../genindex.html" title="General Index"
+             >index</a></li>
+        <li class="right" >
+          <a href="../../../py-modindex.html" title="Python Module Index"
+             >modules</a> |</li>
+        <li class="nav-item nav-item-0"><a href="../../../index.html">pyFTS 1.7 documentation</a> &#187;</li>
+          <li class="nav-item nav-item-1"><a href="../../index.html" >Module code</a> &#187;</li>
+        <li class="nav-item nav-item-this"><a href="">pyFTS.common.Util</a></li> 
+      </ul>
+    </div>
+    <div class="footer" role="contentinfo">
+        &#169; Copyright 2022, Machine Intelligence and Data Science Laboratory - UFMG - Brazil.
+      Created using <a href="https://www.sphinx-doc.org/">Sphinx</a> 4.5.0.
+    </div>
+  </body>
+</html>

docs/build/html/_modules/pyFTS/common/flrg.html

@@ -47,20 +47,20 @@
 
 
 <div class="viewcode-block" id="FLRG"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.flrg.FLRG">[docs]</a><span class="k">class</span> <span class="nc">FLRG</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+    <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Fuzzy Logical Relationship Group</span>
 
 <span class="sd">    Group a set of FLR&#39;s with the same LHS. Represents the temporal patterns for time t+1 (the RHS fuzzy sets)</span>
 <span class="sd">    when the LHS pattern is identified on time t.</span>
 <span class="sd">    &quot;&quot;&quot;</span>
 
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">order</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">order</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">LHS</span> <span class="o">=</span> <span class="kc">None</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Left Hand Side of the rule&quot;&quot;&quot;</span>
+        <span class="sd">&quot;&quot;&quot;Left Hand Side of the rule&quot;&quot;&quot;</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span> <span class="o">=</span> <span class="kc">None</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Right Hand Side of the rule&quot;&quot;&quot;</span>
+        <span class="sd">&quot;&quot;&quot;Right Hand Side of the rule&quot;&quot;&quot;</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">order</span> <span class="o">=</span> <span class="n">order</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Number of lags on LHS&quot;&quot;&quot;</span>
+        <span class="sd">&quot;&quot;&quot;Number of lags on LHS&quot;&quot;&quot;</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">midpoint</span> <span class="o">=</span> <span class="kc">None</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">lower</span> <span class="o">=</span> <span class="kc">None</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">upper</span> <span class="o">=</span> <span class="kc">None</span>
@@ -70,7 +70,7 @@
         <span class="k">pass</span></div>
 
 <div class="viewcode-block" id="FLRG.get_key"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.flrg.FLRG.get_key">[docs]</a>    <span class="k">def</span> <span class="nf">get_key</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Returns a unique identifier for this FLRG&quot;&quot;&quot;</span>
+        <span class="sd">&quot;&quot;&quot;Returns a unique identifier for this FLRG&quot;&quot;&quot;</span>
         <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">key</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
             <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">LHS</span><span class="p">,</span> <span class="p">(</span><span class="nb">list</span><span class="p">,</span> <span class="nb">set</span><span class="p">)):</span>
                 <span class="n">names</span> <span class="o">=</span> <span class="p">[</span><span class="n">c</span> <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">LHS</span><span class="p">]</span>
@@ -88,7 +88,7 @@
         <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">key</span></div>
 
 <div class="viewcode-block" id="FLRG.get_membership"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.flrg.FLRG.get_membership">[docs]</a>    <span class="k">def</span> <span class="nf">get_membership</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">sets</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+        <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Returns the membership value of the FLRG for the input data</span>
 
 <span class="sd">        :param data: input data</span>
@@ -104,7 +104,7 @@
         <span class="k">return</span> <span class="n">ret</span></div>
 
 <div class="viewcode-block" id="FLRG.get_midpoint"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.flrg.FLRG.get_midpoint">[docs]</a>    <span class="k">def</span> <span class="nf">get_midpoint</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sets</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+        <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Returns the midpoint value for the RHS fuzzy sets</span>
 
 <span class="sd">        :param sets: fuzzy sets</span>
@@ -121,7 +121,7 @@
             <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">sets</span><span class="p">[</span><span class="n">s</span><span class="p">]</span><span class="o">.</span><span class="n">centroid</span> <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="o">.</span><span class="n">keys</span><span class="p">()])</span></div>
 
 <div class="viewcode-block" id="FLRG.get_lower"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.flrg.FLRG.get_lower">[docs]</a>    <span class="k">def</span> <span class="nf">get_lower</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sets</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+        <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Returns the lower bound value for the RHS fuzzy sets</span>
 
 <span class="sd">        :param sets: fuzzy sets</span>
@@ -135,7 +135,7 @@
         <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">lower</span></div>
 
 <div class="viewcode-block" id="FLRG.get_upper"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.flrg.FLRG.get_upper">[docs]</a>    <span class="k">def</span> <span class="nf">get_upper</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sets</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+        <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Returns the upper bound value for the RHS fuzzy sets</span>
 
 <span class="sd">        :param sets: fuzzy sets</span>
@@ -148,7 +148,7 @@
                 <span class="bp">self</span><span class="o">.</span><span class="n">upper</span> <span class="o">=</span> <span class="nb">max</span><span class="p">([</span><span class="n">sets</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">[</span><span class="n">s</span><span class="p">]]</span><span class="o">.</span><span class="n">upper</span> <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="o">.</span><span class="n">keys</span><span class="p">()])</span>
         <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">upper</span></div>
 
-    <span class="k">def</span> <span class="fm">__len__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+    <span class="k">def</span> <span class="nf">__len__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">)</span>
 
 <div class="viewcode-block" id="FLRG.reset_calculated_values"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.flrg.FLRG.reset_calculated_values">[docs]</a>    <span class="k">def</span> <span class="nf">reset_calculated_values</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
@@ -198,7 +198,7 @@
     </div>
     <div class="footer" role="contentinfo">
         &#169; Copyright 2022, Machine Intelligence and Data Science Laboratory - UFMG - Brazil.
-      Created using <a href="https://www.sphinx-doc.org/">Sphinx</a> 4.3.2.
+      Created using <a href="https://www.sphinx-doc.org/">Sphinx</a> 4.5.0.
     </div>
   </body>
 </html>

docs/build/html/_modules/pyFTS/common/fts.html

@@ -0,0 +1,744 @@
+
+
+<!doctype html>
+
+<html>
+  <head>
+    <meta charset="utf-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+    <title>pyFTS.common.fts &#8212; pyFTS 1.7 documentation</title>
+    <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css" />
+    <link rel="stylesheet" type="text/css" href="../../../_static/bizstyle.css" />
+    
+    <script data-url_root="../../../" id="documentation_options" src="../../../_static/documentation_options.js"></script>
+    <script src="../../../_static/jquery.js"></script>
+    <script src="../../../_static/underscore.js"></script>
+    <script src="../../../_static/doctools.js"></script>
+    <script src="../../../_static/bizstyle.js"></script>
+    <link rel="index" title="Index" href="../../../genindex.html" />
+    <link rel="search" title="Search" href="../../../search.html" />
+    <meta name="viewport" content="width=device-width,initial-scale=1.0" />
+    <!--[if lt IE 9]>
+    <script src="_static/css3-mediaqueries.js"></script>
+    <![endif]-->
+  </head><body>
+    <div class="related" role="navigation" aria-label="related navigation">
+      <h3>Navigation</h3>
+      <ul>
+        <li class="right" style="margin-right: 10px">
+          <a href="../../../genindex.html" title="General Index"
+             accesskey="I">index</a></li>
+        <li class="right" >
+          <a href="../../../py-modindex.html" title="Python Module Index"
+             >modules</a> |</li>
+        <li class="nav-item nav-item-0"><a href="../../../index.html">pyFTS 1.7 documentation</a> &#187;</li>
+          <li class="nav-item nav-item-1"><a href="../../index.html" accesskey="U">Module code</a> &#187;</li>
+        <li class="nav-item nav-item-this"><a href="">pyFTS.common.fts</a></li> 
+      </ul>
+    </div>  
+
+    <div class="document">
+      <div class="documentwrapper">
+        <div class="bodywrapper">
+          <div class="body" role="main">
+            
+  <h1>Source code for pyFTS.common.fts</h1><div class="highlight"><pre>
+<span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="k">import</span> <span class="n">FuzzySet</span><span class="p">,</span> <span class="n">Util</span>
+<span class="kn">from</span> <span class="nn">pyFTS.common.transformations</span> <span class="k">import</span> <span class="n">transformation</span>
+<span class="kn">from</span> <span class="nn">pyFTS.partitioners</span> <span class="k">import</span> <span class="n">partitioner</span>
+<span class="kn">from</span> <span class="nn">pyFTS.probabilistic</span> <span class="k">import</span> <span class="n">ProbabilityDistribution</span>
+
+
+<div class="viewcode-block" id="FTS"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.fts.FTS">[docs]</a><span class="k">class</span> <span class="nc">FTS</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Fuzzy Time Series object model</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Create a Fuzzy Time Series model</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">:</span> <span class="nb">dict</span> <span class="o">=</span> <span class="p">{}</span>
+        <span class="sd">&quot;&quot;&quot;The list of Fuzzy Logical Relationship Groups - FLRG&quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">order</span> <span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;order&#39;</span><span class="p">,</span><span class="mi">1</span><span class="p">)</span>
+        <span class="sd">&quot;&quot;&quot;A integer with the model order (number of past lags are used on forecasting)&quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">shortname</span> <span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;name&#39;</span><span class="p">,</span><span class="s2">&quot;&quot;</span><span class="p">)</span>
+        <span class="sd">&quot;&quot;&quot;A string with a short name or alias for the model&quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;name&#39;</span><span class="p">,</span><span class="s2">&quot;&quot;</span><span class="p">)</span>
+        <span class="sd">&quot;&quot;&quot;A string with the model name&quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">detail</span> <span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;name&#39;</span><span class="p">,</span><span class="s2">&quot;&quot;</span><span class="p">)</span>
+        <span class="sd">&quot;&quot;&quot;A string with the model detailed information&quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">is_wrapper</span> <span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span>
+        <span class="sd">&quot;&quot;&quot;Indicates that this model is a wrapper for other(s) method(s)&quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">is_high_order</span> <span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span>
+        <span class="sd">&quot;&quot;&quot;A boolean value indicating if the model support orders greater than 1, default: False&quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">min_order</span> <span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">1</span>
+        <span class="sd">&quot;&quot;&quot;In high order models, this integer value indicates the minimal order supported for the model, default: 1&quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">has_seasonality</span> <span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span>
+        <span class="sd">&quot;&quot;&quot;A boolean value indicating if the model supports seasonal indexers, default: False&quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">has_point_forecasting</span> <span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="sd">&quot;&quot;&quot;A boolean value indicating if the model supports point forecasting, default: True&quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">has_interval_forecasting</span> <span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span>
+        <span class="sd">&quot;&quot;&quot;A boolean value indicating if the model supports interval forecasting, default: False&quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">has_probability_forecasting</span> <span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span>
+        <span class="sd">&quot;&quot;&quot;A boolean value indicating if the model support probabilistic forecasting, default: False&quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">is_multivariate</span> <span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span>
+        <span class="sd">&quot;&quot;&quot;A boolean value indicating if the model support multivariate time series (Pandas DataFrame), default: False&quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">is_clustered</span> <span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span>
+        <span class="sd">&quot;&quot;&quot;A boolean value indicating if the model support multivariate time series (Pandas DataFrame), but works like </span>
+<span class="sd">        a monovariate method, default: False&quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">dump</span> <span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">transformations</span> <span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="n">transformation</span><span class="o">.</span><span class="n">Transformation</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="sd">&quot;&quot;&quot;A list with the data transformations (common.Transformations) applied on model pre and post processing, default: []&quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">transformations_param</span> <span class="p">:</span> <span class="nb">list</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="sd">&quot;&quot;&quot;A list with the specific parameters for each data transformation&quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">original_max</span> <span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">0.0</span>
+        <span class="sd">&quot;&quot;&quot;A float with the upper limit of the Universe of Discourse, the maximal value found on training data&quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">original_min</span> <span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">0.0</span>
+        <span class="sd">&quot;&quot;&quot;A float with the lower limit of the Universe of Discourse, the minimal value found on training data&quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span> <span class="p">:</span> <span class="n">partitioner</span><span class="o">.</span><span class="n">Partitioner</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;partitioner&quot;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+        <span class="sd">&quot;&quot;&quot;A pyFTS.partitioners.Partitioner object with the Universe of Discourse partitioner used on the model. This is a mandatory dependecy. &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span> <span class="o">!=</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">sets</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">auto_update</span> <span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span>
+        <span class="sd">&quot;&quot;&quot;A boolean value indicating that model is incremental&quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">benchmark_only</span> <span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span>
+        <span class="sd">&quot;&quot;&quot;A boolean value indicating a façade for external (non-FTS) model used on benchmarks or ensembles.&quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">indexer</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;indexer&quot;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+        <span class="sd">&quot;&quot;&quot;An pyFTS.models.seasonal.Indexer object for indexing the time series data&quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">uod_clip</span> <span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;uod_clip&quot;</span><span class="p">,</span> <span class="kc">True</span><span class="p">)</span>
+        <span class="sd">&quot;&quot;&quot;Flag indicating if the test data will be clipped inside the training Universe of Discourse&quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">alpha_cut</span> <span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;alpha_cut&quot;</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">)</span>
+        <span class="sd">&quot;&quot;&quot;A float with the minimal membership to be considered on fuzzyfication process&quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">lags</span> <span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;lags&quot;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+        <span class="sd">&quot;&quot;&quot;The list of lag indexes for high order models&quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span> <span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span>
+        <span class="sd">&quot;&quot;&quot;A integer indicating the largest lag used by the model. This value also indicates the minimum number of past lags </span>
+<span class="sd">        needed to forecast a single step ahead&quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">log</span> <span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">([],</span><span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;Datetime&quot;</span><span class="p">,</span><span class="s2">&quot;Operation&quot;</span><span class="p">,</span><span class="s2">&quot;Value&quot;</span><span class="p">])</span>
+        <span class="sd">&quot;&quot;&quot;&quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">is_time_variant</span> <span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span>
+        <span class="sd">&quot;&quot;&quot;A boolean value indicating if this model is time variant&quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">standard_horizon</span> <span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;standard_horizon&quot;</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+        <span class="sd">&quot;&quot;&quot;Standard forecasting horizon (Default: 1)&quot;&quot;&quot;</span>
+        
+
+<div class="viewcode-block" id="FTS.fuzzy"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.fts.FTS.fuzzy">[docs]</a>    <span class="k">def</span> <span class="nf">fuzzy</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">dict</span><span class="p">:</span>
+        <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Fuzzify a data point</span>
+
+<span class="sd">        :param data: data point</span>
+<span class="sd">        :return: maximum membership fuzzy set</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">best</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;fuzzyset&quot;</span><span class="p">:</span> <span class="s2">&quot;&quot;</span><span class="p">,</span> <span class="s2">&quot;membership&quot;</span><span class="p">:</span> <span class="mf">0.0</span><span class="p">}</span>
+
+        <span class="k">for</span> <span class="n">f</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">:</span>
+            <span class="n">fset</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">f</span><span class="p">]</span>
+            <span class="k">if</span> <span class="n">best</span><span class="p">[</span><span class="s2">&quot;membership&quot;</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="n">fset</span><span class="o">.</span><span class="n">membership</span><span class="p">(</span><span class="n">data</span><span class="p">):</span>
+                <span class="n">best</span><span class="p">[</span><span class="s2">&quot;fuzzyset&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">fset</span><span class="o">.</span><span class="n">name</span>
+                <span class="n">best</span><span class="p">[</span><span class="s2">&quot;membership&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">fset</span><span class="o">.</span><span class="n">membership</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">best</span></div>
+
+<div class="viewcode-block" id="FTS.clip_uod"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.fts.FTS.clip_uod">[docs]</a>    <span class="k">def</span> <span class="nf">clip_uod</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ndata</span><span class="p">):</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">uod_clip</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">ndata</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">clip</span><span class="p">(</span><span class="n">ndata</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">min</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">max</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">uod_clip</span><span class="p">:</span>
+            <span class="n">ndata</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">clip</span><span class="p">(</span><span class="n">ndata</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">original_min</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">original_max</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">ndata</span></div>
+
+<div class="viewcode-block" id="FTS.predict"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.fts.FTS.predict">[docs]</a>    <span class="k">def</span> <span class="nf">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Forecast using trained model</span>
+
+<span class="sd">        :param data: time series with minimal length to the order of the model</span>
+
+<span class="sd">        :keyword type: the forecasting type, one of these values: point(default), interval, distribution or multivariate.</span>
+<span class="sd">        :keyword steps_ahead: The forecasting path H, i. e., tell the model to forecast from t+1 to t+H.</span>
+<span class="sd">        :keyword step_to: The forecasting step H, i. e., tell the model to forecast to t+H for each input sample </span>
+<span class="sd">        :keyword start_at: in the multi step forecasting, the index of the data where to start forecasting (default value: 0)</span>
+<span class="sd">        :keyword distributed: boolean, indicate if the forecasting procedure will be distributed in a dispy cluster (default value: False)</span>
+<span class="sd">        :keyword nodes: a list with the dispy cluster nodes addresses</span>
+<span class="sd">        :keyword explain: try to explain, step by step, the one-step-ahead point forecasting result given the input data. (default value: False)</span>
+<span class="sd">        :keyword generators: for multivariate methods on multi step ahead forecasting, generators is a dict where the keys</span>
+<span class="sd">                            are the dataframe columun names (except the target_variable) and the values are lambda functions that</span>
+<span class="sd">                            accept one value (the actual value of the variable) and return the next value or trained FTS</span>
+<span class="sd">                            models that accept the actual values and forecast new ones.</span>
+
+<span class="sd">        :return: a numpy array with the forecasted data</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="kn">import</span> <span class="nn">copy</span>
+
+        <span class="n">kw</span> <span class="o">=</span> <span class="n">copy</span><span class="o">.</span><span class="n">deepcopy</span><span class="p">(</span><span class="n">kwargs</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">is_multivariate</span><span class="p">:</span>
+            <span class="n">ndata</span> <span class="o">=</span> <span class="n">data</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">ndata</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">apply_transformations</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+
+        <span class="n">ndata</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">clip_uod</span><span class="p">(</span><span class="n">ndata</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="s1">&#39;distributed&#39;</span> <span class="ow">in</span> <span class="n">kw</span><span class="p">:</span>
+            <span class="n">distributed</span> <span class="o">=</span> <span class="n">kw</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">&#39;distributed&#39;</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">distributed</span> <span class="o">=</span> <span class="kc">False</span>
+
+        <span class="k">if</span> <span class="s1">&#39;type&#39;</span> <span class="ow">in</span> <span class="n">kw</span><span class="p">:</span>
+            <span class="nb">type</span> <span class="o">=</span> <span class="n">kw</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s2">&quot;type&quot;</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="nb">type</span> <span class="o">=</span> <span class="s1">&#39;point&#39;</span>
+
+        <span class="k">if</span> <span class="n">distributed</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">distributed</span> <span class="o">==</span> <span class="kc">False</span><span class="p">:</span>
+
+            <span class="n">steps_ahead</span> <span class="o">=</span> <span class="n">kw</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;steps_ahead&quot;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+
+            <span class="n">step_to</span> <span class="o">=</span> <span class="n">kw</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;step_to&quot;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+
+            <span class="k">if</span> <span class="p">(</span><span class="n">steps_ahead</span> <span class="o">==</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">step_to</span> <span class="o">==</span> <span class="kc">None</span><span class="p">)</span> <span class="ow">or</span> <span class="p">(</span><span class="n">steps_ahead</span> <span class="o">==</span> <span class="mi">1</span> <span class="ow">or</span> <span class="n">step_to</span> <span class="o">==</span><span class="mi">1</span><span class="p">):</span>
+                <span class="k">if</span> <span class="nb">type</span> <span class="o">==</span> <span class="s1">&#39;point&#39;</span><span class="p">:</span>
+                    <span class="n">ret</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecast</span><span class="p">(</span><span class="n">ndata</span><span class="p">,</span> <span class="o">**</span><span class="n">kw</span><span class="p">)</span>
+                <span class="k">elif</span> <span class="nb">type</span> <span class="o">==</span> <span class="s1">&#39;interval&#39;</span><span class="p">:</span>
+                    <span class="n">ret</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecast_interval</span><span class="p">(</span><span class="n">ndata</span><span class="p">,</span> <span class="o">**</span><span class="n">kw</span><span class="p">)</span>
+                <span class="k">elif</span> <span class="nb">type</span> <span class="o">==</span> <span class="s1">&#39;distribution&#39;</span><span class="p">:</span>
+                    <span class="n">ret</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecast_distribution</span><span class="p">(</span><span class="n">ndata</span><span class="p">,</span> <span class="o">**</span><span class="n">kw</span><span class="p">)</span>
+                <span class="k">elif</span> <span class="nb">type</span> <span class="o">==</span> <span class="s1">&#39;multivariate&#39;</span><span class="p">:</span>
+                    <span class="n">ret</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecast_multivariate</span><span class="p">(</span><span class="n">ndata</span><span class="p">,</span> <span class="o">**</span><span class="n">kw</span><span class="p">)</span>
+            <span class="k">elif</span> <span class="n">step_to</span> <span class="o">==</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">steps_ahead</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
+                <span class="k">if</span> <span class="nb">type</span> <span class="o">==</span> <span class="s1">&#39;point&#39;</span><span class="p">:</span>
+                    <span class="n">ret</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecast_ahead</span><span class="p">(</span><span class="n">ndata</span><span class="p">,</span> <span class="n">steps_ahead</span><span class="p">,</span> <span class="o">**</span><span class="n">kw</span><span class="p">)</span>
+                <span class="k">elif</span> <span class="nb">type</span> <span class="o">==</span> <span class="s1">&#39;interval&#39;</span><span class="p">:</span>
+                    <span class="n">ret</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecast_ahead_interval</span><span class="p">(</span><span class="n">ndata</span><span class="p">,</span> <span class="n">steps_ahead</span><span class="p">,</span> <span class="o">**</span><span class="n">kw</span><span class="p">)</span>
+                <span class="k">elif</span> <span class="nb">type</span> <span class="o">==</span> <span class="s1">&#39;distribution&#39;</span><span class="p">:</span>
+                    <span class="n">ret</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecast_ahead_distribution</span><span class="p">(</span><span class="n">ndata</span><span class="p">,</span> <span class="n">steps_ahead</span><span class="p">,</span> <span class="o">**</span><span class="n">kw</span><span class="p">)</span>
+                <span class="k">elif</span> <span class="nb">type</span> <span class="o">==</span> <span class="s1">&#39;multivariate&#39;</span><span class="p">:</span>
+                    <span class="n">ret</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecast_ahead_multivariate</span><span class="p">(</span><span class="n">ndata</span><span class="p">,</span> <span class="n">steps_ahead</span><span class="p">,</span> <span class="o">**</span><span class="n">kw</span><span class="p">)</span>
+            <span class="k">elif</span> <span class="n">step_to</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
+                <span class="k">if</span> <span class="nb">type</span> <span class="o">==</span> <span class="s1">&#39;point&#39;</span><span class="p">:</span>
+                    <span class="n">ret</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecast_step</span><span class="p">(</span><span class="n">ndata</span><span class="p">,</span> <span class="n">step_to</span><span class="p">,</span> <span class="o">**</span><span class="n">kw</span><span class="p">)</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s1">&#39;This model only perform point step ahead forecasts!&#39;</span><span class="p">)</span>
+
+            <span class="k">if</span> <span class="ow">not</span> <span class="p">[</span><span class="s1">&#39;point&#39;</span><span class="p">,</span> <span class="s1">&#39;interval&#39;</span><span class="p">,</span> <span class="s1">&#39;distribution&#39;</span><span class="p">,</span> <span class="s1">&#39;multivariate&#39;</span><span class="p">]</span><span class="o">.</span><span class="fm">__contains__</span><span class="p">(</span><span class="nb">type</span><span class="p">):</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;The argument </span><span class="se">\&#39;</span><span class="s1">type</span><span class="se">\&#39;</span><span class="s1"> has an unknown value.&#39;</span><span class="p">)</span>
+
+        <span class="k">else</span><span class="p">:</span>
+
+            <span class="k">if</span> <span class="n">distributed</span> <span class="o">==</span> <span class="s1">&#39;dispy&#39;</span><span class="p">:</span>
+                <span class="kn">from</span> <span class="nn">pyFTS.distributed</span> <span class="k">import</span> <span class="n">dispy</span>
+
+                <span class="n">nodes</span> <span class="o">=</span> <span class="n">kw</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s2">&quot;nodes&quot;</span><span class="p">,</span> <span class="p">[</span><span class="s1">&#39;127.0.0.1&#39;</span><span class="p">])</span>
+                <span class="n">num_batches</span> <span class="o">=</span> <span class="n">kw</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">&#39;num_batches&#39;</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span>
+
+                <span class="n">ret</span> <span class="o">=</span> <span class="n">dispy</span><span class="o">.</span><span class="n">distributed_predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">kw</span><span class="p">,</span> <span class="n">nodes</span><span class="p">,</span> <span class="n">ndata</span><span class="p">,</span> <span class="n">num_batches</span><span class="p">,</span> <span class="o">**</span><span class="n">kw</span><span class="p">)</span>
+
+            <span class="k">elif</span> <span class="n">distributed</span> <span class="o">==</span> <span class="s1">&#39;spark&#39;</span><span class="p">:</span>
+                <span class="kn">from</span> <span class="nn">pyFTS.distributed</span> <span class="k">import</span> <span class="n">spark</span>
+
+                <span class="n">ret</span> <span class="o">=</span> <span class="n">spark</span><span class="o">.</span><span class="n">distributed_predict</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">ndata</span><span class="p">,</span> <span class="n">model</span><span class="o">=</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kw</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">is_multivariate</span><span class="p">:</span>
+            <span class="n">kw</span><span class="p">[</span><span class="s1">&#39;type&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">type</span>
+            <span class="n">ret</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">apply_inverse_transformations</span><span class="p">(</span><span class="n">ret</span><span class="p">,</span> <span class="n">params</span><span class="o">=</span><span class="p">[</span><span class="n">data</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span> <span class="o">-</span> <span class="mi">1</span><span class="p">:]],</span> <span class="o">**</span><span class="n">kw</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="s1">&#39;statistics&#39;</span> <span class="ow">in</span> <span class="n">kw</span><span class="p">:</span>
+            <span class="n">kwargs</span><span class="p">[</span><span class="s1">&#39;statistics&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">kw</span><span class="p">[</span><span class="s1">&#39;statistics&#39;</span><span class="p">]</span>
+
+        <span class="k">return</span> <span class="n">ret</span></div>
+
+<div class="viewcode-block" id="FTS.forecast"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.fts.FTS.forecast">[docs]</a>    <span class="k">def</span> <span class="nf">forecast</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">:</span>
+        <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Point forecast one step ahead</span>
+
+<span class="sd">        :param data: time series data with the minimal length equal to the max_lag of the model</span>
+<span class="sd">        :param kwargs: model specific parameters</span>
+<span class="sd">        :return: a list with the forecasted values</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s1">&#39;This model do not perform one step ahead point forecasts!&#39;</span><span class="p">)</span></div>
+
+<div class="viewcode-block" id="FTS.forecast_interval"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.fts.FTS.forecast_interval">[docs]</a>    <span class="k">def</span> <span class="nf">forecast_interval</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">:</span>
+        <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Interval forecast one step ahead</span>
+
+<span class="sd">        :param data: time series data with the minimal length equal to the max_lag of the model</span>
+<span class="sd">        :param kwargs: model specific parameters</span>
+<span class="sd">        :return: a list with the prediction intervals</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s1">&#39;This model do not perform one step ahead interval forecasts!&#39;</span><span class="p">)</span></div>
+
+<div class="viewcode-block" id="FTS.forecast_distribution"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.fts.FTS.forecast_distribution">[docs]</a>    <span class="k">def</span> <span class="nf">forecast_distribution</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">:</span>
+        <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Probabilistic forecast one step ahead</span>
+
+<span class="sd">        :param data: time series data with the minimal length equal to the max_lag of the model</span>
+<span class="sd">        :param kwargs: model specific parameters</span>
+<span class="sd">        :return: a list with probabilistic.ProbabilityDistribution objects representing the forecasted Probability Distributions</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s1">&#39;This model do not perform one step ahead distribution forecasts!&#39;</span><span class="p">)</span></div>
+
+<div class="viewcode-block" id="FTS.forecast_multivariate"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.fts.FTS.forecast_multivariate">[docs]</a>    <span class="k">def</span> <span class="nf">forecast_multivariate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">:</span>
+        <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Multivariate forecast one step ahead</span>
+
+<span class="sd">        :param data: Pandas dataframe with one column for each variable and with the minimal length equal to the max_lag of the model</span>
+<span class="sd">        :param kwargs: model specific parameters</span>
+<span class="sd">        :return: a Pandas Dataframe object representing the forecasted values for each variable</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s1">&#39;This model do not perform one step ahead multivariate forecasts!&#39;</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="FTS.forecast_ahead"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.fts.FTS.forecast_ahead">[docs]</a>    <span class="k">def</span> <span class="nf">forecast_ahead</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">steps</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">:</span>
+        <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Point forecast from 1 to H steps ahead, where H is given by the steps parameter</span>
+
+<span class="sd">        :param data: time series data with the minimal length equal to the max_lag of the model</span>
+<span class="sd">        :param steps: the number of steps ahead to forecast (default: 1)</span>
+<span class="sd">        :keyword start_at: in the multi step forecasting, the index of the data where to start forecasting (default: 0)</span>
+<span class="sd">        :return: a list with the forecasted values</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)</span> <span class="o">&lt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">data</span>
+
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
+            <span class="n">data</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
+
+        <span class="n">start</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;start_at&#39;</span><span class="p">,</span><span class="mi">0</span><span class="p">)</span>
+
+        <span class="n">ret</span> <span class="o">=</span> <span class="n">data</span><span class="p">[:</span><span class="n">start</span><span class="o">+</span><span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">]</span>
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">start</span><span class="o">+</span><span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">,</span> <span class="n">steps</span><span class="o">+</span><span class="n">start</span><span class="o">+</span><span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">):</span>
+            <span class="n">tmp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecast</span><span class="p">(</span><span class="n">ret</span><span class="p">[</span><span class="n">k</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">:</span><span class="n">k</span><span class="p">],</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">tmp</span><span class="p">,(</span><span class="nb">list</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">)):</span>
+                <span class="n">tmp</span> <span class="o">=</span> <span class="n">tmp</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
+
+            <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span>
+            <span class="n">data</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">ret</span><span class="p">[</span><span class="o">-</span><span class="n">steps</span><span class="p">:]</span></div>
+
+<div class="viewcode-block" id="FTS.forecast_ahead_interval"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.fts.FTS.forecast_ahead_interval">[docs]</a>    <span class="k">def</span> <span class="nf">forecast_ahead_interval</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">steps</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">:</span>
+        <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Interval forecast from 1 to H steps ahead, where H is given by the steps parameter</span>
+
+<span class="sd">        :param data: time series data with the minimal length equal to the max_lag of the model</span>
+<span class="sd">        :param steps: the number of steps ahead to forecast</span>
+<span class="sd">        :keyword start_at: in the multi step forecasting, the index of the data where to start forecasting (default: 0)</span>
+<span class="sd">        :return: a list with the forecasted intervals</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s1">&#39;This model do not perform multi step ahead interval forecasts!&#39;</span><span class="p">)</span></div>
+
+<div class="viewcode-block" id="FTS.forecast_ahead_distribution"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.fts.FTS.forecast_ahead_distribution">[docs]</a>    <span class="k">def</span> <span class="nf">forecast_ahead_distribution</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">steps</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">:</span>
+        <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Probabilistic forecast from 1 to H steps ahead, where H is given by the steps parameter</span>
+
+<span class="sd">        :param data: time series data with the minimal length equal to the max_lag of the model</span>
+<span class="sd">        :param steps: the number of steps ahead to forecast</span>
+<span class="sd">        :keyword start_at: in the multi step forecasting, the index of the data where to start forecasting (default: 0)</span>
+<span class="sd">        :return: a list with the forecasted Probability Distributions</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s1">&#39;This model do not perform multi step ahead distribution forecasts!&#39;</span><span class="p">)</span></div>
+
+<div class="viewcode-block" id="FTS.forecast_ahead_multivariate"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.fts.FTS.forecast_ahead_multivariate">[docs]</a>    <span class="k">def</span> <span class="nf">forecast_ahead_multivariate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">steps</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">:</span>
+        <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Multivariate forecast n step ahead</span>
+
+<span class="sd">        :param data: Pandas dataframe with one column for each variable and with the minimal length equal to the max_lag of the model</span>
+<span class="sd">        :param steps: the number of steps ahead to forecast</span>
+<span class="sd">        :keyword start_at: in the multi step forecasting, the index of the data where to start forecasting (default: 0)</span>
+<span class="sd">        :return: a Pandas Dataframe object representing the forecasted values for each variable</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s1">&#39;This model do not perform one step ahead multivariate forecasts!&#39;</span><span class="p">)</span></div>
+
+<div class="viewcode-block" id="FTS.forecast_step"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.fts.FTS.forecast_step">[docs]</a>    <span class="k">def</span> <span class="nf">forecast_step</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">step</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">:</span>
+        <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Point forecast for H steps ahead, where H is given by the step parameter</span>
+
+<span class="sd">        :param data: time series data with the minimal length equal to the max_lag of the model</span>
+<span class="sd">        :param step: the forecasting horizon (default: 1)</span>
+<span class="sd">        :keyword start_at: in the multi step forecasting, the index of the data where to start forecasting (default: 0)</span>
+<span class="sd">        :return: a list with the forecasted values</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="n">l</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="k">if</span> <span class="n">l</span> <span class="o">&lt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">data</span>
+
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
+            <span class="n">data</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
+
+        <span class="n">start</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;start_at&#39;</span><span class="p">,</span><span class="mi">0</span><span class="p">)</span>
+
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">start</span><span class="o">+</span><span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">,</span> <span class="n">l</span><span class="o">+</span><span class="mi">1</span><span class="p">):</span>
+            <span class="n">sample</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">k</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">:</span><span class="n">k</span><span class="p">]</span>
+            <span class="n">tmp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecast_ahead</span><span class="p">(</span><span class="n">sample</span><span class="p">,</span> <span class="n">step</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">tmp</span><span class="p">,(</span><span class="nb">list</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">)):</span>
+                <span class="n">tmp</span> <span class="o">=</span> <span class="n">tmp</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
+
+            <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">ret</span></div>
+
+<div class="viewcode-block" id="FTS.train"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.fts.FTS.train">[docs]</a>    <span class="k">def</span> <span class="nf">train</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Method specific parameter fitting</span>
+
+<span class="sd">        :param data: training time series data</span>
+<span class="sd">        :param kwargs: Method specific parameters</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">pass</span></div>
+
+<div class="viewcode-block" id="FTS.fit"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.fts.FTS.fit">[docs]</a>    <span class="k">def</span> <span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ndata</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Fit the model&#39;s parameters based on the training data.</span>
+
+<span class="sd">        :param ndata: training time series data</span>
+<span class="sd">        :param kwargs:</span>
+
+<span class="sd">        :keyword num_batches: split the training data in num_batches to save memory during the training process</span>
+<span class="sd">        :keyword save_model: save final model on disk</span>
+<span class="sd">        :keyword batch_save: save the model between each batch</span>
+<span class="sd">        :keyword file_path: path to save the model</span>
+<span class="sd">        :keyword distributed: boolean, indicate if the training procedure will be distributed in a dispy cluster</span>
+<span class="sd">        :keyword nodes: a list with the dispy cluster nodes addresses</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="kn">import</span> <span class="nn">datetime</span><span class="o">,</span> <span class="nn">copy</span>
+
+        <span class="n">kw</span> <span class="o">=</span> <span class="n">copy</span><span class="o">.</span><span class="n">deepcopy</span><span class="p">(</span><span class="n">kwargs</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">is_multivariate</span><span class="p">:</span>
+            <span class="n">data</span> <span class="o">=</span> <span class="n">ndata</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">apply_transformations</span><span class="p">(</span><span class="n">ndata</span><span class="p">)</span>
+
+            <span class="bp">self</span><span class="o">.</span><span class="n">original_min</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nanmin</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">original_max</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nanmax</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="s1">&#39;partitioner&#39;</span> <span class="ow">in</span> <span class="n">kw</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span> <span class="o">=</span> <span class="n">kw</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">&#39;partitioner&#39;</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">is_multivariate</span> <span class="ow">and</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">is_wrapper</span> <span class="ow">and</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">benchmark_only</span><span class="p">:</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s2">&quot;Fuzzy sets were not provided for the model. Use &#39;partitioner&#39; parameter. &quot;</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="s1">&#39;order&#39;</span> <span class="ow">in</span> <span class="n">kw</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">order</span> <span class="o">=</span> <span class="n">kw</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">&#39;order&#39;</span><span class="p">)</span>
+
+        <span class="n">dump</span> <span class="o">=</span> <span class="n">kw</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;dump&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+
+        <span class="n">num_batches</span> <span class="o">=</span> <span class="n">kw</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">&#39;num_batches&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+
+        <span class="n">save</span> <span class="o">=</span> <span class="n">kw</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;save_model&#39;</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</span>  <span class="c1"># save model on disk</span>
+
+        <span class="n">batch_save</span> <span class="o">=</span> <span class="n">kw</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;batch_save&#39;</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</span> <span class="c1">#save model between batches</span>
+
+        <span class="n">file_path</span> <span class="o">=</span> <span class="n">kw</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;file_path&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+
+        <span class="n">distributed</span> <span class="o">=</span> <span class="n">kw</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">&#39;distributed&#39;</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">distributed</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">distributed</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">num_batches</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="n">num_batches</span> <span class="o">=</span> <span class="mi">10</span>
+
+            <span class="k">if</span> <span class="n">distributed</span> <span class="o">==</span> <span class="s1">&#39;dispy&#39;</span><span class="p">:</span>
+                <span class="kn">from</span> <span class="nn">pyFTS.distributed</span> <span class="k">import</span> <span class="n">dispy</span>
+                <span class="n">nodes</span> <span class="o">=</span> <span class="n">kw</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">&#39;nodes&#39;</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</span>
+                <span class="n">train_method</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;train_method&#39;</span><span class="p">,</span> <span class="n">dispy</span><span class="o">.</span><span class="n">simple_model_train</span><span class="p">)</span>
+                <span class="n">dispy</span><span class="o">.</span><span class="n">distributed_train</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">train_method</span><span class="p">,</span> <span class="n">nodes</span><span class="p">,</span> <span class="nb">type</span><span class="p">(</span><span class="bp">self</span><span class="p">),</span> <span class="n">data</span><span class="p">,</span> <span class="n">num_batches</span><span class="p">,</span> <span class="p">{},</span>
+                                       <span class="o">**</span><span class="n">kw</span><span class="p">)</span>
+            <span class="k">elif</span> <span class="n">distributed</span> <span class="o">==</span> <span class="s1">&#39;spark&#39;</span><span class="p">:</span>
+                <span class="kn">from</span> <span class="nn">pyFTS.distributed</span> <span class="k">import</span> <span class="n">spark</span>
+                <span class="n">url</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;url&#39;</span><span class="p">,</span> <span class="s1">&#39;spark://127.0.0.1:7077&#39;</span><span class="p">)</span>
+                <span class="n">app</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;app&#39;</span><span class="p">,</span> <span class="s1">&#39;pyFTS&#39;</span><span class="p">)</span>
+
+                <span class="n">spark</span><span class="o">.</span><span class="n">distributed_train</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">url</span><span class="o">=</span><span class="n">url</span><span class="p">,</span> <span class="n">app</span><span class="o">=</span><span class="n">app</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+
+            <span class="k">if</span> <span class="n">dump</span> <span class="o">==</span> <span class="s1">&#39;time&#39;</span><span class="p">:</span>
+                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;[{0: %H:%M:%S}] Start training&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">datetime</span><span class="o">.</span><span class="n">datetime</span><span class="o">.</span><span class="n">now</span><span class="p">()))</span>
+
+            <span class="k">if</span> <span class="n">num_batches</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">is_wrapper</span><span class="p">:</span>
+                <span class="n">n</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+                <span class="n">batch_size</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">n</span> <span class="o">/</span> <span class="n">num_batches</span><span class="p">)</span>
+                <span class="n">bcount</span> <span class="o">=</span> <span class="mi">1</span>
+
+                <span class="n">rng</span> <span class="o">=</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">,</span> <span class="n">n</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">)</span>
+
+                <span class="k">if</span> <span class="n">dump</span> <span class="o">==</span> <span class="s1">&#39;tqdm&#39;</span><span class="p">:</span>
+                    <span class="kn">from</span> <span class="nn">tqdm</span> <span class="k">import</span> <span class="n">tqdm</span>
+
+                    <span class="n">rng</span> <span class="o">=</span> <span class="n">tqdm</span><span class="p">(</span><span class="n">rng</span><span class="p">)</span>
+
+                <span class="k">for</span> <span class="n">ct</span> <span class="ow">in</span> <span class="n">rng</span><span class="p">:</span>
+                    <span class="k">if</span> <span class="n">dump</span> <span class="o">==</span> <span class="s1">&#39;time&#39;</span><span class="p">:</span>
+                        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;[{0: %H:%M:%S}] Starting batch &quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">datetime</span><span class="o">.</span><span class="n">datetime</span><span class="o">.</span><span class="n">now</span><span class="p">())</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">bcount</span><span class="p">))</span>
+                    <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">is_multivariate</span><span class="p">:</span>
+                        <span class="n">mdata</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">ct</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">:</span><span class="n">ct</span> <span class="o">+</span> <span class="n">batch_size</span><span class="p">]</span>
+                    <span class="k">else</span><span class="p">:</span>
+                        <span class="n">mdata</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">ct</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span> <span class="p">:</span> <span class="n">ct</span> <span class="o">+</span> <span class="n">batch_size</span><span class="p">]</span>
+
+                    <span class="bp">self</span><span class="o">.</span><span class="n">train</span><span class="p">(</span><span class="n">mdata</span><span class="p">,</span> <span class="o">**</span><span class="n">kw</span><span class="p">)</span>
+
+                    <span class="k">if</span> <span class="n">batch_save</span><span class="p">:</span>
+                        <span class="n">Util</span><span class="o">.</span><span class="n">persist_obj</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="n">file_path</span><span class="p">)</span>
+
+                    <span class="k">if</span> <span class="n">dump</span> <span class="o">==</span> <span class="s1">&#39;time&#39;</span><span class="p">:</span>
+                        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;[{0: %H:%M:%S}] Finish batch &quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">datetime</span><span class="o">.</span><span class="n">datetime</span><span class="o">.</span><span class="n">now</span><span class="p">())</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">bcount</span><span class="p">))</span>
+
+                    <span class="n">bcount</span> <span class="o">+=</span> <span class="mi">1</span>
+
+            <span class="k">else</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">train</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kw</span><span class="p">)</span>
+
+            <span class="k">if</span> <span class="n">dump</span> <span class="o">==</span> <span class="s1">&#39;time&#39;</span><span class="p">:</span>
+                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;[{0: %H:%M:%S}] Finish training&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">datetime</span><span class="o">.</span><span class="n">datetime</span><span class="o">.</span><span class="n">now</span><span class="p">()))</span>
+
+        <span class="k">if</span> <span class="n">save</span><span class="p">:</span>
+            <span class="n">Util</span><span class="o">.</span><span class="n">persist_obj</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">file_path</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="FTS.clone_parameters"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.fts.FTS.clone_parameters">[docs]</a>    <span class="k">def</span> <span class="nf">clone_parameters</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">model</span><span class="p">):</span>
+        <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Import the parameters values from other model</span>
+
+<span class="sd">        :param model: a model to clone the parameters</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">order</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">order</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">partitioner</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">lags</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">lags</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">shortname</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">shortname</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">name</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">detail</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">detail</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">is_high_order</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">is_high_order</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">min_order</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">min_order</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">has_seasonality</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">has_seasonality</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">has_point_forecasting</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">has_point_forecasting</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">has_interval_forecasting</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">has_interval_forecasting</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">has_probability_forecasting</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">has_probability_forecasting</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">is_multivariate</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">is_multivariate</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">dump</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">dump</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">transformations</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">transformations</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">transformations_param</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">transformations_param</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">original_max</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">original_max</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">original_min</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">original_min</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">auto_update</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">auto_update</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">benchmark_only</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">benchmark_only</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">indexer</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">indexer</span></div>
+
+<div class="viewcode-block" id="FTS.append_rule"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.fts.FTS.append_rule">[docs]</a>    <span class="k">def</span> <span class="nf">append_rule</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">flrg</span><span class="p">):</span>
+        <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Append FLRG rule to the model</span>
+
+<span class="sd">        :param flrg: rule</span>
+<span class="sd">        :return:</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="k">if</span> <span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()]</span> <span class="o">=</span> <span class="n">flrg</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">flrg</span><span class="o">.</span><span class="n">RHS</span><span class="p">,</span> <span class="p">(</span><span class="nb">list</span><span class="p">,</span> <span class="nb">set</span><span class="p">)):</span>
+                <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">flrg</span><span class="o">.</span><span class="n">RHS</span><span class="p">:</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()]</span><span class="o">.</span><span class="n">append_rhs</span><span class="p">(</span><span class="n">k</span><span class="p">)</span>
+            <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">flrg</span><span class="o">.</span><span class="n">RHS</span><span class="p">,</span> <span class="nb">dict</span><span class="p">):</span>
+                <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">value</span> <span class="ow">in</span> <span class="n">flrg</span><span class="o">.</span><span class="n">RHS</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()]</span><span class="o">.</span><span class="n">append_rhs</span><span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="n">count</span><span class="o">=</span><span class="n">value</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()]</span><span class="o">.</span><span class="n">append_rhs</span><span class="p">(</span><span class="n">flrg</span><span class="o">.</span><span class="n">RHS</span><span class="p">)</span></div>
+
+<div class="viewcode-block" id="FTS.merge"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.fts.FTS.merge">[docs]</a>    <span class="k">def</span> <span class="nf">merge</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">model</span><span class="p">):</span>
+        <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Merge the FLRG rules from other model</span>
+
+<span class="sd">        :param model: source model</span>
+<span class="sd">        :return:</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">flrg</span> <span class="ow">in</span> <span class="n">model</span><span class="o">.</span><span class="n">flrgs</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">append_rule</span><span class="p">(</span><span class="n">flrg</span><span class="p">)</span></div>
+
+<div class="viewcode-block" id="FTS.append_transformation"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.fts.FTS.append_transformation">[docs]</a>    <span class="k">def</span> <span class="nf">append_transformation</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">transformation</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">transformation</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">transformations</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">transformation</span><span class="p">)</span></div>
+
+<div class="viewcode-block" id="FTS.apply_transformations"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.fts.FTS.apply_transformations">[docs]</a>    <span class="k">def</span> <span class="nf">apply_transformations</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">params</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">updateUoD</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Apply the data transformations for data preprocessing</span>
+
+<span class="sd">        :param data: input data</span>
+<span class="sd">        :param params: transformation parameters</span>
+<span class="sd">        :param updateUoD:</span>
+<span class="sd">        :param kwargs:</span>
+<span class="sd">        :return: preprocessed data</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="n">ndata</span> <span class="o">=</span> <span class="n">data</span>
+        <span class="k">if</span> <span class="n">updateUoD</span><span class="p">:</span>
+            <span class="k">if</span> <span class="nb">min</span><span class="p">(</span><span class="n">data</span><span class="p">)</span> <span class="o">&lt;</span> <span class="mi">0</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">original_min</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="n">data</span><span class="p">)</span> <span class="o">*</span> <span class="mf">1.1</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">original_min</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="n">data</span><span class="p">)</span> <span class="o">*</span> <span class="mf">0.9</span>
+
+            <span class="k">if</span> <span class="nb">max</span><span class="p">(</span><span class="n">data</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">original_max</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">data</span><span class="p">)</span> <span class="o">*</span> <span class="mf">1.1</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">original_max</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">data</span><span class="p">)</span> <span class="o">*</span> <span class="mf">0.9</span>
+
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">transformations</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">params</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="n">params</span> <span class="o">=</span> <span class="p">[</span> <span class="kc">None</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">transformations</span><span class="p">]</span>
+
+            <span class="k">for</span> <span class="n">c</span><span class="p">,</span> <span class="n">t</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">transformations</span><span class="p">,</span> <span class="n">start</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
+                <span class="n">ndata</span> <span class="o">=</span> <span class="n">t</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">ndata</span><span class="p">,</span> <span class="n">params</span><span class="p">[</span><span class="n">c</span><span class="p">],</span> <span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">ndata</span></div>
+
+<div class="viewcode-block" id="FTS.apply_inverse_transformations"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.fts.FTS.apply_inverse_transformations">[docs]</a>    <span class="k">def</span> <span class="nf">apply_inverse_transformations</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">params</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Apply the data transformations for data postprocessing</span>
+
+<span class="sd">        :param data: input data</span>
+<span class="sd">        :param params: transformation parameters</span>
+<span class="sd">        :param updateUoD:</span>
+<span class="sd">        :param kwargs:</span>
+<span class="sd">        :return: postprocessed data</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">transformations</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">params</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="n">params</span> <span class="o">=</span> <span class="p">[</span><span class="kc">None</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">transformations</span><span class="p">]</span>
+
+            <span class="k">for</span> <span class="n">c</span><span class="p">,</span> <span class="n">t</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="nb">reversed</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">transformations</span><span class="p">),</span> <span class="n">start</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
+                <span class="n">ndata</span> <span class="o">=</span> <span class="n">t</span><span class="o">.</span><span class="n">inverse</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">params</span><span class="p">[</span><span class="n">c</span><span class="p">],</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+            <span class="k">return</span> <span class="n">ndata</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">data</span></div>
+
+<div class="viewcode-block" id="FTS.get_UoD"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.fts.FTS.get_UoD">[docs]</a>    <span class="k">def</span> <span class="nf">get_UoD</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">set</span><span class="p">:</span>
+        <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Returns the interval of the known bounds of the universe of discourse (UoD), i. e.,</span>
+<span class="sd">        the known minimum and maximum values of the time series.</span>
+
+<span class="sd">        :return: A set with the lower and the upper bounds of the UoD</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">return</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">min</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">max</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">return</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">original_min</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">original_max</span><span class="p">)</span></div>
+        
+<div class="viewcode-block" id="FTS.offset"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.fts.FTS.offset">[docs]</a>    <span class="k">def</span> <span class="nf">offset</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">int</span><span class="p">:</span>
+        <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Returns the number of lags to skip in the input test data in order to synchronize it with</span>
+<span class="sd">        the forecasted values given by the predict function. This is necessary due to the order of the</span>
+<span class="sd">        model, among other parameters.</span>
+
+<span class="sd">        :return: An integer with the number of lags to skip</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">is_high_order</span><span class="p">:</span>
+            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">return</span> <span class="mi">1</span></div>
+
+    <span class="k">def</span> <span class="nf">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        String representation of the model</span>
+
+<span class="sd">        :return: a string containing the name of the model and the learned rules</span>
+<span class="sd">        (if the model was already trained)</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="n">tmp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">+</span> <span class="s2">&quot;:</span><span class="se">\n</span><span class="s2">&quot;</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">type</span> <span class="o">==</span> <span class="s1">&#39;common&#39;</span><span class="p">:</span>
+            <span class="k">for</span> <span class="n">r</span> <span class="ow">in</span> <span class="nb">sorted</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">,</span> <span class="n">key</span><span class="o">=</span><span class="k">lambda</span> <span class="n">key</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">key</span><span class="p">]</span><span class="o">.</span><span class="n">get_midpoint</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">)):</span>
+                <span class="n">tmp</span> <span class="o">=</span> <span class="s2">&quot;</span><span class="si">{0}{1}</span><span class="se">\n</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">tmp</span><span class="p">,</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">r</span><span class="p">]))</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">for</span> <span class="n">r</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">:</span>
+                <span class="n">tmp</span> <span class="o">=</span> <span class="s2">&quot;</span><span class="si">{0}{1}</span><span class="se">\n</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">tmp</span><span class="p">,</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">r</span><span class="p">]))</span>
+        <span class="k">return</span> <span class="n">tmp</span>
+
+    <span class="k">def</span> <span class="nf">__len__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        The length (number of rules) of the model</span>
+
+<span class="sd">        :return: number of rules</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">)</span>
+
+<div class="viewcode-block" id="FTS.len_total"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.fts.FTS.len_total">[docs]</a>    <span class="k">def</span> <span class="nf">len_total</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Total length of the model, adding the number of terms in all rules</span>
+
+<span class="sd">        :return:</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">return</span> <span class="nb">sum</span><span class="p">([</span><span class="nb">len</span><span class="p">(</span><span class="n">k</span><span class="p">)</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">])</span></div>
+
+<div class="viewcode-block" id="FTS.reset_calculated_values"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.fts.FTS.reset_calculated_values">[docs]</a>    <span class="k">def</span> <span class="nf">reset_calculated_values</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Reset all pre-calculated values on the FLRG&#39;s</span>
+
+<span class="sd">        :return:</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+        <span class="k">for</span> <span class="n">flrg</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flrg</span><span class="p">]</span><span class="o">.</span><span class="n">reset_calculated_values</span><span class="p">()</span></div>
+
+<div class="viewcode-block" id="FTS.append_log"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.fts.FTS.append_log">[docs]</a>    <span class="k">def</span> <span class="nf">append_log</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="n">operation</span><span class="p">,</span> <span class="n">value</span><span class="p">):</span>
+        <span class="k">pass</span></div></div>
+
+
+
+
+
+
+</pre></div>
+
+            <div class="clearer"></div>
+          </div>
+        </div>
+      </div>
+      <div class="sphinxsidebar" role="navigation" aria-label="main navigation">
+        <div class="sphinxsidebarwrapper">
+            <p class="logo"><a href="../../../index.html">
+              <img class="logo" src="../../../_static/logo_heading2.png" alt="Logo"/>
+            </a></p>
+<div id="searchbox" style="display: none" role="search">
+  <h3 id="searchlabel">Quick search</h3>
+    <div class="searchformwrapper">
+    <form class="search" action="../../../search.html" method="get">
+      <input type="text" name="q" aria-labelledby="searchlabel" autocomplete="off" autocorrect="off" autocapitalize="off" spellcheck="false"/>
+      <input type="submit" value="Go" />
+    </form>
+    </div>
+</div>
+<script>$('#searchbox').show(0);</script>
+        </div>
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+    </div>
+    <div class="related" role="navigation" aria-label="related navigation">
+      <h3>Navigation</h3>
+      <ul>
+        <li class="right" style="margin-right: 10px">
+          <a href="../../../genindex.html" title="General Index"
+             >index</a></li>
+        <li class="right" >
+          <a href="../../../py-modindex.html" title="Python Module Index"
+             >modules</a> |</li>
+        <li class="nav-item nav-item-0"><a href="../../../index.html">pyFTS 1.7 documentation</a> &#187;</li>
+          <li class="nav-item nav-item-1"><a href="../../index.html" >Module code</a> &#187;</li>
+        <li class="nav-item nav-item-this"><a href="">pyFTS.common.fts</a></li> 
+      </ul>
+    </div>
+    <div class="footer" role="contentinfo">
+        &#169; Copyright 2022, Machine Intelligence and Data Science Laboratory - UFMG - Brazil.
+      Created using <a href="https://www.sphinx-doc.org/">Sphinx</a> 4.5.0.
+    </div>
+  </body>
+</html>

docs/build/html/_modules/pyFTS/common/transformations/boxcox.html

@@ -1,122 +0,0 @@
-
-
-<!doctype html>
-
-<html>
-  <head>
-    <meta charset="utf-8" />
-    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-    <title>pyFTS.common.transformations.boxcox &#8212; pyFTS 1.7 documentation</title>
-    <link rel="stylesheet" type="text/css" href="../../../../_static/pygments.css" />
-    <link rel="stylesheet" type="text/css" href="../../../../_static/bizstyle.css" />
-    
-    <script data-url_root="../../../../" id="documentation_options" src="../../../../_static/documentation_options.js"></script>
-    <script src="../../../../_static/jquery.js"></script>
-    <script src="../../../../_static/underscore.js"></script>
-    <script src="../../../../_static/doctools.js"></script>
-    <script src="../../../../_static/bizstyle.js"></script>
-    <link rel="index" title="Index" href="../../../../genindex.html" />
-    <link rel="search" title="Search" href="../../../../search.html" />
-    <meta name="viewport" content="width=device-width,initial-scale=1.0" />
-    <!--[if lt IE 9]>
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-  </head><body>
-    <div class="related" role="navigation" aria-label="related navigation">
-      <h3>Navigation</h3>
-      <ul>
-        <li class="right" style="margin-right: 10px">
-          <a href="../../../../genindex.html" title="General Index"
-             accesskey="I">index</a></li>
-        <li class="right" >
-          <a href="../../../../py-modindex.html" title="Python Module Index"
-             >modules</a> |</li>
-        <li class="nav-item nav-item-0"><a href="../../../../index.html">pyFTS 1.7 documentation</a> &#187;</li>
-          <li class="nav-item nav-item-1"><a href="../../../index.html" accesskey="U">Module code</a> &#187;</li>
-        <li class="nav-item nav-item-this"><a href="">pyFTS.common.transformations.boxcox</a></li> 
-      </ul>
-    </div>  
-
-    <div class="document">
-      <div class="documentwrapper">
-        <div class="bodywrapper">
-          <div class="body" role="main">
-            
-  <h1>Source code for pyFTS.common.transformations.boxcox</h1><div class="highlight"><pre>
-<span></span><span class="kn">from</span> <span class="nn">pyFTS.common.transformations.transformation</span> <span class="kn">import</span> <span class="n">Transformation</span> 
-
-
-<div class="viewcode-block" id="BoxCox"><a class="viewcode-back" href="../../../../pyFTS.common.transformations.html#pyFTS.common.transformations.boxcox.BoxCox">[docs]</a><span class="k">class</span> <span class="nc">BoxCox</span><span class="p">(</span><span class="n">Transformation</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Box-Cox power transformation</span>
-
-<span class="sd">    y&#39;(t) = log( y(t) )</span>
-<span class="sd">    y(t) = exp( y&#39;(t) )</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">plambda</span><span class="p">):</span>
-        <span class="nb">super</span><span class="p">(</span><span class="n">BoxCox</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">plambda</span> <span class="o">=</span> <span class="n">plambda</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s1">&#39;BoxCox&#39;</span>
-
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">parameters</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">plambda</span>
-
-<div class="viewcode-block" id="BoxCox.apply"><a class="viewcode-back" href="../../../../pyFTS.common.transformations.html#pyFTS.common.transformations.boxcox.BoxCox.apply">[docs]</a>    <span class="k">def</span> <span class="nf">apply</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">param</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">plambda</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="n">modified</span> <span class="o">=</span> <span class="p">[(</span><span class="n">dat</span> <span class="o">**</span> <span class="bp">self</span><span class="o">.</span><span class="n">plambda</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">plambda</span> <span class="k">for</span> <span class="n">dat</span> <span class="ow">in</span> <span class="n">data</span><span class="p">]</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">modified</span> <span class="o">=</span> <span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">dat</span><span class="p">)</span> <span class="k">for</span> <span class="n">dat</span> <span class="ow">in</span> <span class="n">data</span><span class="p">]</span>
-        <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">modified</span><span class="p">)</span></div>
-
-<div class="viewcode-block" id="BoxCox.inverse"><a class="viewcode-back" href="../../../../pyFTS.common.transformations.html#pyFTS.common.transformations.boxcox.BoxCox.inverse">[docs]</a>    <span class="k">def</span> <span class="nf">inverse</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">param</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">plambda</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="n">modified</span> <span class="o">=</span> <span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">dat</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">plambda</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="p">)</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">plambda</span> <span class="k">for</span> <span class="n">dat</span> <span class="ow">in</span> <span class="n">data</span><span class="p">]</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">modified</span> <span class="o">=</span> <span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="n">dat</span><span class="p">)</span> <span class="k">for</span> <span class="n">dat</span> <span class="ow">in</span> <span class="n">data</span><span class="p">]</span>
-        <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">modified</span><span class="p">)</span></div></div>
-</pre></div>
-
-            <div class="clearer"></div>
-          </div>
-        </div>
-      </div>
-      <div class="sphinxsidebar" role="navigation" aria-label="main navigation">
-        <div class="sphinxsidebarwrapper">
-            <p class="logo"><a href="../../../../index.html">
-              <img class="logo" src="../../../../_static/logo_heading2.png" alt="Logo"/>
-            </a></p>
-<div id="searchbox" style="display: none" role="search">
-  <h3 id="searchlabel">Quick search</h3>
-    <div class="searchformwrapper">
-    <form class="search" action="../../../../search.html" method="get">
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-        <li class="right" style="margin-right: 10px">
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-             >index</a></li>
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-          <a href="../../../../py-modindex.html" title="Python Module Index"
-             >modules</a> |</li>
-        <li class="nav-item nav-item-0"><a href="../../../../index.html">pyFTS 1.7 documentation</a> &#187;</li>
-          <li class="nav-item nav-item-1"><a href="../../../index.html" >Module code</a> &#187;</li>
-        <li class="nav-item nav-item-this"><a href="">pyFTS.common.transformations.boxcox</a></li> 
-      </ul>
-    </div>
-    <div class="footer" role="contentinfo">
-        &#169; Copyright 2022, Machine Intelligence and Data Science Laboratory - UFMG - Brazil.
-      Created using <a href="https://www.sphinx-doc.org/">Sphinx</a> 4.3.2.
-    </div>
-  </body>
-</html>

docs/build/html/_modules/pyFTS/common/transformations/differential.html

@@ -1,171 +0,0 @@
-
-
-<!doctype html>
-
-<html>
-  <head>
-    <meta charset="utf-8" />
-    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-    <title>pyFTS.common.transformations.differential &#8212; pyFTS 1.7 documentation</title>
-    <link rel="stylesheet" type="text/css" href="../../../../_static/pygments.css" />
-    <link rel="stylesheet" type="text/css" href="../../../../_static/bizstyle.css" />
-    
-    <script data-url_root="../../../../" id="documentation_options" src="../../../../_static/documentation_options.js"></script>
-    <script src="../../../../_static/jquery.js"></script>
-    <script src="../../../../_static/underscore.js"></script>
-    <script src="../../../../_static/doctools.js"></script>
-    <script src="../../../../_static/bizstyle.js"></script>
-    <link rel="index" title="Index" href="../../../../genindex.html" />
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-    <meta name="viewport" content="width=device-width,initial-scale=1.0" />
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-  </head><body>
-    <div class="related" role="navigation" aria-label="related navigation">
-      <h3>Navigation</h3>
-      <ul>
-        <li class="right" style="margin-right: 10px">
-          <a href="../../../../genindex.html" title="General Index"
-             accesskey="I">index</a></li>
-        <li class="right" >
-          <a href="../../../../py-modindex.html" title="Python Module Index"
-             >modules</a> |</li>
-        <li class="nav-item nav-item-0"><a href="../../../../index.html">pyFTS 1.7 documentation</a> &#187;</li>
-          <li class="nav-item nav-item-1"><a href="../../../index.html" accesskey="U">Module code</a> &#187;</li>
-        <li class="nav-item nav-item-this"><a href="">pyFTS.common.transformations.differential</a></li> 
-      </ul>
-    </div>  
-
-    <div class="document">
-      <div class="documentwrapper">
-        <div class="bodywrapper">
-          <div class="body" role="main">
-            
-  <h1>Source code for pyFTS.common.transformations.differential</h1><div class="highlight"><pre>
-<span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">from</span> <span class="nn">pyFTS.common.transformations.transformation</span> <span class="kn">import</span> <span class="n">Transformation</span> 
-
-
-<div class="viewcode-block" id="Differential"><a class="viewcode-back" href="../../../../pyFTS.common.transformations.html#pyFTS.common.transformations.differential.Differential">[docs]</a><span class="k">class</span> <span class="nc">Differential</span><span class="p">(</span><span class="n">Transformation</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Differentiation data transform</span>
-
-<span class="sd">    y&#39;(t) = y(t) - y(t-1)</span>
-<span class="sd">    y(t) =  y(t-1)  + y&#39;(t)</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">lag</span><span class="p">):</span>
-        <span class="nb">super</span><span class="p">(</span><span class="n">Differential</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">lag</span> <span class="o">=</span> <span class="n">lag</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">minimal_length</span> <span class="o">=</span> <span class="mi">2</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s1">&#39;Diff&#39;</span>
-
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">parameters</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">lag</span>
-
-<div class="viewcode-block" id="Differential.apply"><a class="viewcode-back" href="../../../../pyFTS.common.transformations.html#pyFTS.common.transformations.differential.Differential.apply">[docs]</a>    <span class="k">def</span> <span class="nf">apply</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">param</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-        <span class="k">if</span> <span class="n">param</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">lag</span> <span class="o">=</span> <span class="n">param</span>
-
-        <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="p">(</span><span class="nb">list</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">generic</span><span class="p">)):</span>
-            <span class="n">data</span> <span class="o">=</span> <span class="p">[</span><span class="n">data</span><span class="p">]</span>
-
-        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">generic</span><span class="p">)):</span>
-            <span class="n">data</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
-
-        <span class="n">n</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
-        <span class="n">diff</span> <span class="o">=</span> <span class="p">[</span><span class="n">data</span><span class="p">[</span><span class="n">t</span><span class="p">]</span> <span class="o">-</span> <span class="n">data</span><span class="p">[</span><span class="n">t</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">lag</span><span class="p">]</span> <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">lag</span><span class="p">,</span> <span class="n">n</span><span class="p">)]</span>
-        <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">lag</span><span class="p">):</span> <span class="n">diff</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">diff</span></div>
-
-<div class="viewcode-block" id="Differential.inverse"><a class="viewcode-back" href="../../../../pyFTS.common.transformations.html#pyFTS.common.transformations.differential.Differential.inverse">[docs]</a>    <span class="k">def</span> <span class="nf">inverse</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">param</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-
-        <span class="nb">type</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;type&quot;</span><span class="p">,</span><span class="s2">&quot;point&quot;</span><span class="p">)</span>
-        <span class="n">steps_ahead</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;steps_ahead&quot;</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">generic</span><span class="p">)):</span>
-            <span class="n">data</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
-
-        <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
-            <span class="n">data</span> <span class="o">=</span> <span class="p">[</span><span class="n">data</span><span class="p">]</span>
-
-        <span class="n">n</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
-
-<span class="c1">#        print(n)</span>
-<span class="c1">#        print(len(param))</span>
-
-        <span class="k">if</span> <span class="n">steps_ahead</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
-            <span class="k">if</span> <span class="nb">type</span> <span class="o">==</span> <span class="s2">&quot;point&quot;</span><span class="p">:</span>
-                <span class="n">inc</span> <span class="o">=</span> <span class="p">[</span><span class="n">data</span><span class="p">[</span><span class="n">t</span><span class="p">]</span> <span class="o">+</span> <span class="n">param</span><span class="p">[</span><span class="n">t</span><span class="p">]</span> <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">n</span><span class="p">)]</span>
-            <span class="k">elif</span> <span class="nb">type</span> <span class="o">==</span> <span class="s2">&quot;interval&quot;</span><span class="p">:</span>
-                <span class="n">inc</span> <span class="o">=</span> <span class="p">[[</span><span class="n">data</span><span class="p">[</span><span class="n">t</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">param</span><span class="p">[</span><span class="n">t</span><span class="p">],</span> <span class="n">data</span><span class="p">[</span><span class="n">t</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">param</span><span class="p">[</span><span class="n">t</span><span class="p">]]</span> <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">n</span><span class="p">)]</span>
-            <span class="k">elif</span> <span class="nb">type</span> <span class="o">==</span> <span class="s2">&quot;distribution&quot;</span><span class="p">:</span>
-                <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">n</span><span class="p">):</span>
-                    <span class="n">data</span><span class="p">[</span><span class="n">t</span><span class="p">]</span><span class="o">.</span><span class="n">differential_offset</span><span class="p">(</span><span class="n">param</span><span class="p">[</span><span class="n">t</span><span class="p">])</span>
-                <span class="n">inc</span> <span class="o">=</span> <span class="n">data</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="k">if</span> <span class="nb">type</span> <span class="o">==</span> <span class="s2">&quot;point&quot;</span><span class="p">:</span>
-                <span class="n">inc</span> <span class="o">=</span> <span class="p">[</span><span class="n">data</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">param</span><span class="p">[</span><span class="mi">0</span><span class="p">]]</span>
-                <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">steps_ahead</span><span class="p">):</span>
-                    <span class="n">inc</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">t</span><span class="p">]</span> <span class="o">+</span> <span class="n">inc</span><span class="p">[</span><span class="n">t</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
-            <span class="k">elif</span> <span class="nb">type</span> <span class="o">==</span> <span class="s2">&quot;interval&quot;</span><span class="p">:</span>
-                <span class="n">inc</span> <span class="o">=</span> <span class="p">[[</span><span class="n">data</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">param</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">data</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">param</span><span class="p">[</span><span class="mi">0</span><span class="p">]]]</span>
-                <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">steps_ahead</span><span class="p">):</span>
-                    <span class="n">inc</span><span class="o">.</span><span class="n">append</span><span class="p">([</span><span class="n">data</span><span class="p">[</span><span class="n">t</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">nanmean</span><span class="p">(</span><span class="n">inc</span><span class="p">[</span><span class="n">t</span><span class="o">-</span><span class="mi">1</span><span class="p">]),</span> <span class="n">data</span><span class="p">[</span><span class="n">t</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">nanmean</span><span class="p">(</span><span class="n">inc</span><span class="p">[</span><span class="n">t</span><span class="o">-</span><span class="mi">1</span><span class="p">])])</span>
-            <span class="k">elif</span> <span class="nb">type</span> <span class="o">==</span> <span class="s2">&quot;distribution&quot;</span><span class="p">:</span>
-                <span class="n">data</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">differential_offset</span><span class="p">(</span><span class="n">param</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-                <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">steps_ahead</span><span class="p">):</span>
-                    <span class="n">ex</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">t</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">expected_value</span><span class="p">()</span>
-                    <span class="n">data</span><span class="p">[</span><span class="n">t</span><span class="p">]</span><span class="o">.</span><span class="n">differential_offset</span><span class="p">(</span><span class="n">ex</span><span class="p">)</span>
-                <span class="n">inc</span> <span class="o">=</span> <span class="n">data</span>
-
-        <span class="k">if</span> <span class="n">n</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
-            <span class="k">return</span> <span class="n">inc</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="k">return</span> <span class="n">inc</span></div></div>
-</pre></div>
-
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docs/build/html/_modules/pyFTS/common/transformations/normalization.html

@@ -1,113 +0,0 @@
-
-
-<!doctype html>
-
-<html>
-  <head>
-    <meta charset="utf-8" />
-    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-    <title>pyFTS.common.transformations.normalization &#8212; pyFTS 1.7 documentation</title>
-    <link rel="stylesheet" type="text/css" href="../../../../_static/pygments.css" />
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-             >modules</a> |</li>
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-        <li class="nav-item nav-item-this"><a href="">pyFTS.common.transformations.normalization</a></li> 
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-            
-  <h1>Source code for pyFTS.common.transformations.normalization</h1><div class="highlight"><pre>
-<span></span><span class="kn">from</span> <span class="nn">pyFTS.common.transformations.transformation</span> <span class="kn">import</span> <span class="n">Transformation</span> 
-
-
-<div class="viewcode-block" id="Normalization"><a class="viewcode-back" href="../../../../pyFTS.common.transformations.html#pyFTS.common.transformations.normalization.Normalization">[docs]</a><span class="k">class</span> <span class="nc">Normalization</span><span class="p">(</span><span class="n">Transformation</span><span class="p">):</span>
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-        <span class="nb">super</span><span class="p">(</span><span class="n">Normalization</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s1">&#39;Normalization&#39;</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">mu</span> <span class="o">=</span> <span class="mi">0</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">sigma</span> <span class="o">=</span> <span class="mi">0</span>
-        
-
-<div class="viewcode-block" id="Normalization.train"><a class="viewcode-back" href="../../../../pyFTS.common.transformations.html#pyFTS.common.transformations.normalization.Normalization.train">[docs]</a>    <span class="k">def</span> <span class="nf">train</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">mu</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">sigma</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">std</span><span class="p">(</span><span class="n">data</span><span class="p">)</span></div>
-
-<div class="viewcode-block" id="Normalization.apply"><a class="viewcode-back" href="../../../../pyFTS.common.transformations.html#pyFTS.common.transformations.normalization.Normalization.apply">[docs]</a>    <span class="k">def</span> <span class="nf">apply</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">param</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-        <span class="n">modified</span> <span class="o">=</span> <span class="p">(</span><span class="n">data</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">mu</span><span class="p">)</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">sigma</span>
-        <span class="k">return</span> <span class="n">modified</span></div>
-
-<div class="viewcode-block" id="Normalization.inverse"><a class="viewcode-back" href="../../../../pyFTS.common.transformations.html#pyFTS.common.transformations.normalization.Normalization.inverse">[docs]</a>    <span class="k">def</span> <span class="nf">inverse</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">param</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-        <span class="n">modified</span> <span class="o">=</span> <span class="p">(</span><span class="n">data</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">sigma</span><span class="p">)</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">mu</span>
-        <span class="k">return</span> <span class="n">modified</span></div></div>
-</pre></div>
-
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docs/build/html/_modules/pyFTS/common/transformations/roi.html

@@ -1,114 +0,0 @@
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-
-<html>
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-    <title>pyFTS.common.transformations.roi &#8212; pyFTS 1.7 documentation</title>
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-  <h1>Source code for pyFTS.common.transformations.roi</h1><div class="highlight"><pre>
-<span></span><span class="kn">from</span> <span class="nn">pyFTS.common.transformations.transformation</span> <span class="kn">import</span> <span class="n">Transformation</span> 
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-
-
-<div class="viewcode-block" id="ROI"><a class="viewcode-back" href="../../../../pyFTS.common.transformations.html#pyFTS.common.transformations.roi.ROI">[docs]</a><span class="k">class</span> <span class="nc">ROI</span><span class="p">(</span><span class="n">Transformation</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Return of Investment (ROI) transformation. Retrieved from Sadaei and Lee (2014) - Multilayer Stock</span>
-<span class="sd">    Forecasting Model Using Fuzzy Time Series</span>
-
-<span class="sd">    y&#39;(t) = ( y(t) - y(t-1) ) / y(t-1)</span>
-<span class="sd">    y(t) = ( y(t-1) * y&#39;(t) ) + y(t-1)</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-        <span class="nb">super</span><span class="p">(</span><span class="n">ROI</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s1">&#39;ROI&#39;</span>
-
-<div class="viewcode-block" id="ROI.apply"><a class="viewcode-back" href="../../../../pyFTS.common.transformations.html#pyFTS.common.transformations.roi.ROI.apply">[docs]</a>    <span class="k">def</span> <span class="nf">apply</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">param</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-        <span class="n">modified</span> <span class="o">=</span> <span class="p">[(</span><span class="n">data</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">-</span> <span class="n">data</span><span class="p">[</span><span class="n">i</span> <span class="o">-</span> <span class="mi">1</span><span class="p">])</span> <span class="o">/</span> <span class="n">data</span><span class="p">[</span><span class="n">i</span> <span class="o">-</span> <span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">))]</span>
-        <span class="n">modified</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mf">.0</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">modified</span></div>
-
-<div class="viewcode-block" id="ROI.inverse"><a class="viewcode-back" href="../../../../pyFTS.common.transformations.html#pyFTS.common.transformations.roi.ROI.inverse">[docs]</a>    <span class="k">def</span> <span class="nf">inverse</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">param</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-        <span class="n">modified</span> <span class="o">=</span> <span class="p">[(</span><span class="n">param</span><span class="p">[</span><span class="n">i</span> <span class="o">-</span> <span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="n">data</span><span class="p">[</span><span class="n">i</span><span class="p">])</span> <span class="o">+</span> <span class="n">param</span><span class="p">[</span><span class="n">i</span> <span class="o">-</span> <span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">))]</span>
-        <span class="k">return</span> <span class="n">modified</span></div></div>
-</pre></div>
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docs/build/html/_modules/pyFTS/common/transformations/scale.html

@@ -1,136 +0,0 @@
-
-
-<!doctype html>
-
-<html>
-  <head>
-    <meta charset="utf-8" />
-    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
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-    <link rel="stylesheet" type="text/css" href="../../../../_static/pygments.css" />
-    <link rel="stylesheet" type="text/css" href="../../../../_static/bizstyle.css" />
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-          <a href="../../../../py-modindex.html" title="Python Module Index"
-             >modules</a> |</li>
-        <li class="nav-item nav-item-0"><a href="../../../../index.html">pyFTS 1.7 documentation</a> &#187;</li>
-          <li class="nav-item nav-item-1"><a href="../../../index.html" accesskey="U">Module code</a> &#187;</li>
-        <li class="nav-item nav-item-this"><a href="">pyFTS.common.transformations.scale</a></li> 
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-            
-  <h1>Source code for pyFTS.common.transformations.scale</h1><div class="highlight"><pre>
-<span></span><span class="kn">from</span> <span class="nn">pyFTS.common.transformations.transformation</span> <span class="kn">import</span> <span class="n">Transformation</span> 
-
-
-<div class="viewcode-block" id="Scale"><a class="viewcode-back" href="../../../../pyFTS.common.transformations.html#pyFTS.common.transformations.scale.Scale">[docs]</a><span class="k">class</span> <span class="nc">Scale</span><span class="p">(</span><span class="n">Transformation</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Scale data inside a interval [min, max]</span>
-
-<span class="sd">    </span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">min</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="nb">max</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
-        <span class="nb">super</span><span class="p">(</span><span class="n">Scale</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">data_max</span> <span class="o">=</span> <span class="kc">None</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">data_min</span> <span class="o">=</span> <span class="kc">None</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">transf_max</span> <span class="o">=</span> <span class="nb">max</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">transf_min</span> <span class="o">=</span> <span class="nb">min</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s1">&#39;Scale&#39;</span>
-
-    <span class="nd">@property</span>
-    <span class="k">def</span> <span class="nf">parameters</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="k">return</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">transf_max</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">transf_min</span><span class="p">]</span>
-
-<div class="viewcode-block" id="Scale.apply"><a class="viewcode-back" href="../../../../pyFTS.common.transformations.html#pyFTS.common.transformations.scale.Scale.apply">[docs]</a>    <span class="k">def</span> <span class="nf">apply</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">param</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span><span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">data_max</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">data_max</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nanmax</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">data_min</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nanmin</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
-        <span class="n">data_range</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">data_max</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">data_min</span>
-        <span class="n">transf_range</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">transf_max</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">transf_min</span>
-        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
-            <span class="n">tmp</span> <span class="o">=</span> <span class="p">[(</span><span class="n">k</span> <span class="o">+</span> <span class="p">(</span><span class="o">-</span><span class="mi">1</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">data_min</span><span class="p">))</span> <span class="o">/</span> <span class="n">data_range</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">data</span><span class="p">]</span>
-            <span class="n">tmp2</span> <span class="o">=</span> <span class="p">[</span> <span class="p">(</span><span class="n">k</span> <span class="o">*</span> <span class="n">transf_range</span><span class="p">)</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">transf_min</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">tmp</span><span class="p">]</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">tmp</span> <span class="o">=</span> <span class="p">(</span><span class="n">data</span> <span class="o">+</span> <span class="p">(</span><span class="o">-</span><span class="mi">1</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">data_min</span><span class="p">))</span> <span class="o">/</span> <span class="n">data_range</span>
-            <span class="n">tmp2</span> <span class="o">=</span> <span class="p">(</span><span class="n">tmp</span> <span class="o">*</span> <span class="n">transf_range</span><span class="p">)</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">transf_min</span>
-
-        <span class="k">return</span>  <span class="n">tmp2</span></div>
-
-<div class="viewcode-block" id="Scale.inverse"><a class="viewcode-back" href="../../../../pyFTS.common.transformations.html#pyFTS.common.transformations.scale.Scale.inverse">[docs]</a>    <span class="k">def</span> <span class="nf">inverse</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">param</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-        <span class="n">data_range</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">data_max</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">data_min</span>
-        <span class="n">transf_range</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">transf_max</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">transf_min</span>
-        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
-            <span class="n">tmp2</span> <span class="o">=</span> <span class="p">[(</span><span class="n">k</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">transf_min</span><span class="p">)</span> <span class="o">/</span> <span class="n">transf_range</span>   <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">data</span><span class="p">]</span>
-            <span class="n">tmp</span> <span class="o">=</span> <span class="p">[(</span><span class="n">k</span> <span class="o">*</span> <span class="n">data_range</span><span class="p">)</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">data_min</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">tmp2</span><span class="p">]</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">tmp2</span> <span class="o">=</span> <span class="p">(</span><span class="n">data</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">transf_min</span><span class="p">)</span> <span class="o">/</span> <span class="n">transf_range</span>
-            <span class="n">tmp</span> <span class="o">=</span> <span class="p">(</span><span class="n">tmp2</span> <span class="o">*</span> <span class="n">data_range</span><span class="p">)</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">data_min</span>
-        <span class="k">return</span> <span class="n">tmp</span></div></div>
-</pre></div>
-
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-  <h1>Source code for pyFTS.common.transformations.smoothing</h1><div class="highlight"><pre>
-<span></span><span class="kn">from</span> <span class="nn">pyFTS.common.transformations.transformation</span> <span class="kn">import</span> <span class="n">Transformation</span> 
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-
-<div class="viewcode-block" id="MovingAverage"><a class="viewcode-back" href="../../../../pyFTS.common.transformations.html#pyFTS.common.transformations.smoothing.MovingAverage">[docs]</a><span class="k">class</span> <span class="nc">MovingAverage</span><span class="p">(</span><span class="n">Transformation</span><span class="p">):</span>
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-        <span class="nb">super</span><span class="p">(</span><span class="n">MovingAverage</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s1">&#39;Moving Average Smoothing&#39;</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">steps</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;steps&#39;</span><span class="p">,</span><span class="mi">2</span><span class="p">)</span>
-
-<div class="viewcode-block" id="MovingAverage.apply"><a class="viewcode-back" href="../../../../pyFTS.common.transformations.html#pyFTS.common.transformations.smoothing.MovingAverage.apply">[docs]</a>    <span class="k">def</span> <span class="nf">apply</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">param</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-        <span class="n">steps</span> <span class="o">=</span> <span class="n">param</span> <span class="k">if</span> <span class="n">param</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="bp">self</span><span class="o">.</span><span class="n">steps</span>
-        <span class="n">ma</span> <span class="o">=</span> <span class="p">[</span><span class="n">k</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">data</span><span class="p">[:</span><span class="n">steps</span><span class="p">]]</span>
-        <span class="n">ma</span><span class="o">.</span><span class="n">extend</span><span class="p">([</span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">k</span><span class="o">-</span><span class="n">steps</span><span class="p">:</span><span class="n">k</span><span class="p">])</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">steps</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">))])</span>
-        <span class="k">return</span> <span class="n">ma</span></div>
-
-<div class="viewcode-block" id="MovingAverage.inverse"><a class="viewcode-back" href="../../../../pyFTS.common.transformations.html#pyFTS.common.transformations.smoothing.MovingAverage.inverse">[docs]</a>    <span class="k">def</span> <span class="nf">inverse</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">param</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-        <span class="k">return</span> <span class="n">data</span></div></div>
-
-
-<div class="viewcode-block" id="ExponentialSmoothing"><a class="viewcode-back" href="../../../../pyFTS.common.transformations.html#pyFTS.common.transformations.smoothing.ExponentialSmoothing">[docs]</a><span class="k">class</span> <span class="nc">ExponentialSmoothing</span><span class="p">(</span><span class="n">Transformation</span><span class="p">):</span>
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-        <span class="nb">super</span><span class="p">(</span><span class="n">ExponentialSmoothing</span><span class="p">,</span><span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s1">&#39;Exponential Moving Average Smoothing&#39;</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">steps</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;steps&#39;</span><span class="p">,</span><span class="mi">2</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">beta</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;beta&#39;</span><span class="p">,</span><span class="mf">.5</span><span class="p">)</span>
-
-<div class="viewcode-block" id="ExponentialSmoothing.apply"><a class="viewcode-back" href="../../../../pyFTS.common.transformations.html#pyFTS.common.transformations.smoothing.ExponentialSmoothing.apply">[docs]</a>    <span class="k">def</span> <span class="nf">apply</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">param</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-        <span class="n">steps</span> <span class="o">=</span> <span class="n">param</span> <span class="k">if</span> <span class="n">param</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="bp">self</span><span class="o">.</span><span class="n">steps</span>
-        <span class="n">beta</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;beta&#39;</span><span class="p">,</span><span class="kc">None</span><span class="p">)</span>
-        <span class="n">beta</span> <span class="o">=</span> <span class="n">beta</span> <span class="k">if</span> <span class="n">beta</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="bp">self</span><span class="o">.</span><span class="n">beta</span>
-        <span class="n">mm</span> <span class="o">=</span> <span class="p">[</span><span class="n">k</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">data</span><span class="p">[:</span><span class="n">steps</span><span class="p">]]</span>
-        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">steps</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)):</span>
-            <span class="n">ret</span> <span class="o">=</span> <span class="mi">0</span>
-            <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="n">steps</span><span class="p">):</span>
-                <span class="n">ret</span> <span class="o">+=</span> <span class="p">(</span> <span class="n">beta</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">beta</span><span class="p">)</span> <span class="o">**</span> <span class="n">k</span> <span class="p">)</span> <span class="o">*</span> <span class="n">data</span><span class="p">[</span><span class="n">i</span> <span class="o">-</span> <span class="n">k</span><span class="p">]</span>
-            <span class="n">mm</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">ret</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">mm</span></div>
-
-<div class="viewcode-block" id="ExponentialSmoothing.inverse"><a class="viewcode-back" href="../../../../pyFTS.common.transformations.html#pyFTS.common.transformations.smoothing.ExponentialSmoothing.inverse">[docs]</a>    <span class="k">def</span> <span class="nf">inverse</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">param</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-        <span class="k">return</span> <span class="n">data</span></div></div>
-
-
-<div class="viewcode-block" id="AveragePooling"><a class="viewcode-back" href="../../../../pyFTS.common.transformations.html#pyFTS.common.transformations.smoothing.AveragePooling">[docs]</a><span class="k">class</span> <span class="nc">AveragePooling</span><span class="p">(</span><span class="n">Transformation</span><span class="p">):</span>
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-        <span class="nb">super</span><span class="p">(</span><span class="n">AveragePooling</span><span class="p">,</span><span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s1">&#39;Exponential Average Smoothing&#39;</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">kernel</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;kernel&#39;</span><span class="p">,</span><span class="mi">5</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">stride</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;stride&#39;</span><span class="p">,</span><span class="mi">1</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">padding</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;padding&#39;</span><span class="p">,</span><span class="s1">&#39;same&#39;</span><span class="p">)</span>
-
-<div class="viewcode-block" id="AveragePooling.apply"><a class="viewcode-back" href="../../../../pyFTS.common.transformations.html#pyFTS.common.transformations.smoothing.AveragePooling.apply">[docs]</a>    <span class="k">def</span> <span class="nf">apply</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">):</span>
-        <span class="n">result</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">padding</span> <span class="o">==</span> <span class="s1">&#39;same&#39;</span><span class="p">:</span>
-            <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">kernel</span><span class="o">/</span><span class="mi">2</span><span class="p">),</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)</span><span class="o">+</span><span class="nb">int</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">kernel</span><span class="o">/</span><span class="mi">2</span><span class="p">),</span> <span class="bp">self</span><span class="o">.</span><span class="n">stride</span><span class="p">):</span>
-                <span class="n">result</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span><span class="n">i</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">kernel</span><span class="p">]):</span><span class="n">np</span><span class="o">.</span><span class="n">min</span><span class="p">([</span><span class="n">i</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)])]))</span>
-
-        <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">padding</span> <span class="o">==</span> <span class="s1">&#39;valid&#39;</span><span class="p">:</span>
-            <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">kernel</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">),</span> <span class="bp">self</span><span class="o">.</span><span class="n">stride</span><span class="p">):</span>
-                <span class="n">result</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">i</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">kernel</span><span class="p">:</span><span class="n">i</span><span class="p">]))</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Invalid padding schema&#39;</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">result</span></div>
-
-<div class="viewcode-block" id="AveragePooling.inverse"><a class="viewcode-back" href="../../../../pyFTS.common.transformations.html#pyFTS.common.transformations.smoothing.AveragePooling.inverse">[docs]</a>    <span class="k">def</span> <span class="nf">inverse</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">param</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-        <span class="k">return</span> <span class="n">data</span></div></div>
-
-
-<div class="viewcode-block" id="MaxPooling"><a class="viewcode-back" href="../../../../pyFTS.common.transformations.html#pyFTS.common.transformations.smoothing.MaxPooling">[docs]</a><span class="k">class</span> <span class="nc">MaxPooling</span><span class="p">(</span><span class="n">Transformation</span><span class="p">):</span>
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-        <span class="nb">super</span><span class="p">(</span><span class="n">MaxPooling</span><span class="p">,</span><span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s1">&#39;Exponential Average Smoothing&#39;</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">kernel</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;kernel&#39;</span><span class="p">,</span><span class="mi">5</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">stride</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;stride&#39;</span><span class="p">,</span><span class="mi">1</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">padding</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;padding&#39;</span><span class="p">,</span><span class="s1">&#39;same&#39;</span><span class="p">)</span>
-
-<div class="viewcode-block" id="MaxPooling.apply"><a class="viewcode-back" href="../../../../pyFTS.common.transformations.html#pyFTS.common.transformations.smoothing.MaxPooling.apply">[docs]</a>    <span class="k">def</span> <span class="nf">apply</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">):</span>
-        <span class="n">result</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">padding</span> <span class="o">==</span> <span class="s1">&#39;same&#39;</span><span class="p">:</span>
-            <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">kernel</span><span class="o">/</span><span class="mi">2</span><span class="p">),</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)</span><span class="o">+</span><span class="nb">int</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">kernel</span><span class="o">/</span><span class="mi">2</span><span class="p">),</span> <span class="bp">self</span><span class="o">.</span><span class="n">stride</span><span class="p">):</span>
-                <span class="n">result</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span><span class="n">i</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">kernel</span><span class="p">]):</span><span class="n">np</span><span class="o">.</span><span class="n">min</span><span class="p">([</span><span class="n">i</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)])]))</span>
-
-        <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">padding</span> <span class="o">==</span> <span class="s1">&#39;valid&#39;</span><span class="p">:</span>
-            <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">kernel</span> <span class="o">-</span> <span class="mi">1</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">),</span> <span class="bp">self</span><span class="o">.</span><span class="n">stride</span><span class="p">):</span>
-                <span class="n">result</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">i</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">kernel</span><span class="p">:</span><span class="n">i</span><span class="p">]))</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">&#39;Invalid padding schema&#39;</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">result</span></div>
-
-<div class="viewcode-block" id="MaxPooling.inverse"><a class="viewcode-back" href="../../../../pyFTS.common.transformations.html#pyFTS.common.transformations.smoothing.MaxPooling.inverse">[docs]</a>    <span class="k">def</span> <span class="nf">inverse</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">param</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-        <span class="k">return</span> <span class="n">data</span></div></div>
-</pre></div>
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-  <h1>Source code for pyFTS.common.transformations.som</h1><div class="highlight"><pre>
-<span></span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">Kohonen Self Organizing Maps for Fuzzy Time Series</span>
-<span class="sd">&quot;&quot;&quot;</span>
-<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
-<span class="c1">#from pyFTS.models.multivariate import wmvfts</span>
-<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Tuple</span>
-<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">List</span>
-<span class="kn">from</span> <span class="nn">pyFTS.common.transformations.transformation</span> <span class="kn">import</span> <span class="n">Transformation</span>
-
-
-<div class="viewcode-block" id="SOMTransformation"><a class="viewcode-back" href="../../../../pyFTS.common.transformations.html#pyFTS.common.transformations.som.SOMTransformation">[docs]</a><span class="k">class</span> <span class="nc">SOMTransformation</span><span class="p">(</span><span class="n">Transformation</span><span class="p">):</span>
-    
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
-                 <span class="n">grid_dimension</span><span class="p">:</span> <span class="n">Tuple</span><span class="p">,</span>
-                 <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-
-        <span class="kn">import</span> <span class="nn">SimpSOM</span> <span class="k">as</span> <span class="nn">sps</span>
-
-        <span class="c1"># SOM attributes</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">load_file</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;loadFile&#39;</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">net</span><span class="p">:</span> <span class="n">sps</span><span class="o">.</span><span class="n">somNet</span> <span class="o">=</span> <span class="kc">None</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span> <span class="o">=</span> <span class="kc">None</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">grid_dimension</span><span class="p">:</span> <span class="n">Tuple</span> <span class="o">=</span> <span class="n">grid_dimension</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">pbc</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;PBC&#39;</span><span class="p">,</span> <span class="kc">True</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">is_multivariate</span> <span class="o">=</span> <span class="kc">True</span>
-
-        <span class="c1"># debug attributes</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s1">&#39;Kohonen Self Organizing Maps FTS&#39;</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">shortname</span> <span class="o">=</span> <span class="s1">&#39;SOM-FTS&#39;</span>
-
-<div class="viewcode-block" id="SOMTransformation.apply"><a class="viewcode-back" href="../../../../pyFTS.common.transformations.html#pyFTS.common.transformations.som.SOMTransformation.apply">[docs]</a>    <span class="k">def</span> <span class="nf">apply</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
-              <span class="n">data</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">,</span>
-              <span class="n">param</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-              <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Transform a M-dimensional dataset into a 3-dimensional dataset, where one dimension is the endogen variable</span>
-<span class="sd">        If endogen_variable = None, the last column will be the endogen_variable.</span>
-
-<span class="sd">        Args:</span>
-<span class="sd">            data (pd.DataFrame): M-Dimensional dataset</span>
-<span class="sd">            endogen_variable (str):  column of dataset</span>
-<span class="sd">            names (Tuple): names for new columns created by SOM Transformation.</span>
-<span class="sd">            param:</span>
-<span class="sd">            **kwargs: params of SOM&#39;s train process</span>
-<span class="sd">                percentage_train (float). Percentage of dataset that will be used for train SOM network. default: 0.7</span>
-<span class="sd">                leaning_rate (float): leaning rate of SOM network. default: 0.01</span>
-<span class="sd">                epochs: epochs of SOM network. default: 10000</span>
-<span class="sd">        </span>
-<span class="sd">        Returns:</span>
-
-<span class="sd">        &quot;&quot;&quot;</span>
-
-        <span class="n">endogen_variable</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;endogen_variable&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
-        <span class="n">names</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;names&#39;</span><span class="p">,</span> <span class="p">(</span><span class="s1">&#39;x&#39;</span><span class="p">,</span> <span class="s1">&#39;y&#39;</span><span class="p">))</span>
-
-        <span class="k">if</span> <span class="n">endogen_variable</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">data</span><span class="o">.</span><span class="n">columns</span><span class="p">:</span>
-            <span class="n">endogen_variable</span> <span class="o">=</span> <span class="kc">None</span>
-        <span class="n">cols</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">columns</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="k">if</span> <span class="n">endogen_variable</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="p">[</span><span class="n">col</span> <span class="k">for</span> <span class="n">col</span> <span class="ow">in</span> <span class="n">data</span><span class="o">.</span><span class="n">columns</span> <span class="k">if</span>
-                                                                   <span class="n">col</span> <span class="o">!=</span> <span class="n">endogen_variable</span><span class="p">]</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">net</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-            <span class="n">train</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">cols</span><span class="p">]</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">train</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">train</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
-        <span class="n">new_data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">net</span><span class="o">.</span><span class="n">project</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">cols</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">)</span>
-        <span class="n">new_data</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">new_data</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="n">names</span><span class="p">)</span>
-        <span class="n">endogen</span> <span class="o">=</span> <span class="n">endogen_variable</span> <span class="k">if</span> <span class="n">endogen_variable</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">data</span><span class="o">.</span><span class="n">columns</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
-        <span class="n">new_data</span><span class="p">[</span><span class="n">endogen</span><span class="p">]</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">endogen</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
-        <span class="k">return</span> <span class="n">new_data</span></div>
-
-    <span class="k">def</span> <span class="fm">__repr__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="n">status</span> <span class="o">=</span> <span class="s2">&quot;is trained&quot;</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">net</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="s2">&quot;not trained&quot;</span>
-        <span class="k">return</span> <span class="sa">f</span><span class="s1">&#39;</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">name</span><span class="si">}</span><span class="s1">-</span><span class="si">{</span><span class="n">status</span><span class="si">}</span><span class="s1">&#39;</span>
-
-    <span class="k">def</span> <span class="fm">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">name</span>
-
-    <span class="k">def</span> <span class="fm">__del__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="k">del</span> <span class="bp">self</span><span class="o">.</span><span class="n">net</span>
-
-<div class="viewcode-block" id="SOMTransformation.train"><a class="viewcode-back" href="../../../../pyFTS.common.transformations.html#pyFTS.common.transformations.som.SOMTransformation.train">[docs]</a>    <span class="k">def</span> <span class="nf">train</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
-              <span class="n">data</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">,</span>
-              <span class="n">percentage_train</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">.7</span><span class="p">,</span>
-              <span class="n">leaning_rate</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">0.01</span><span class="p">,</span>
-              <span class="n">epochs</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">10000</span><span class="p">):</span>
-        <span class="n">data</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">dropna</span><span class="p">()</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">data</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">values</span>
-        <span class="n">limit</span> <span class="o">=</span> <span class="nb">round</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="p">)</span> <span class="o">*</span> <span class="n">percentage_train</span><span class="p">)</span>
-        <span class="n">train</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="p">[:</span><span class="n">limit</span><span class="p">]</span>
-        <span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">grid_dimension</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">net</span> <span class="o">=</span> <span class="n">sps</span><span class="o">.</span><span class="n">somNet</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">train</span><span class="p">,</span> <span class="n">PBC</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">pbc</span><span class="p">,</span>
-                              <span class="n">loadFile</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">load_file</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">net</span><span class="o">.</span><span class="n">train</span><span class="p">(</span><span class="n">startLearnRate</span><span class="o">=</span><span class="n">leaning_rate</span><span class="p">,</span>
-                       <span class="n">epochs</span><span class="o">=</span><span class="n">epochs</span><span class="p">)</span></div>
-
-<div class="viewcode-block" id="SOMTransformation.save_net"><a class="viewcode-back" href="../../../../pyFTS.common.transformations.html#pyFTS.common.transformations.som.SOMTransformation.save_net">[docs]</a>    <span class="k">def</span> <span class="nf">save_net</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
-                 <span class="n">filename</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;SomNet trained&quot;</span><span class="p">):</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">net</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">filename</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">load_file</span> <span class="o">=</span> <span class="n">filename</span></div>
-
-<div class="viewcode-block" id="SOMTransformation.show_grid"><a class="viewcode-back" href="../../../../pyFTS.common.transformations.html#pyFTS.common.transformations.som.SOMTransformation.show_grid">[docs]</a>    <span class="k">def</span> <span class="nf">show_grid</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span>
-                  <span class="n">graph_type</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s1">&#39;nodes_graph&#39;</span><span class="p">,</span>
-                  <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-        <span class="k">if</span> <span class="n">graph_type</span> <span class="o">==</span> <span class="s1">&#39;nodes_graph&#39;</span><span class="p">:</span>
-            <span class="n">colnum</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;colnum&#39;</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">net</span><span class="o">.</span><span class="n">nodes_graph</span><span class="p">(</span><span class="n">colnum</span><span class="o">=</span><span class="n">colnum</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">net</span><span class="o">.</span><span class="n">diff_graph</span><span class="p">()</span></div></div>
-</pre></div>
-
-            <div class="clearer"></div>
-          </div>
-        </div>
-      </div>
-      <div class="sphinxsidebar" role="navigation" aria-label="main navigation">
-        <div class="sphinxsidebarwrapper">
-            <p class="logo"><a href="../../../../index.html">
-              <img class="logo" src="../../../../_static/logo_heading2.png" alt="Logo"/>
-            </a></p>
-<div id="searchbox" style="display: none" role="search">
-  <h3 id="searchlabel">Quick search</h3>
-    <div class="searchformwrapper">
-    <form class="search" action="../../../../search.html" method="get">
-      <input type="text" name="q" aria-labelledby="searchlabel" autocomplete="off" autocorrect="off" autocapitalize="off" spellcheck="false"/>
-      <input type="submit" value="Go" />
-    </form>
-    </div>
-</div>
-<script>$('#searchbox').show(0);</script>
-        </div>
-      </div>
-      <div class="clearer"></div>
-    </div>
-    <div class="related" role="navigation" aria-label="related navigation">
-      <h3>Navigation</h3>
-      <ul>
-        <li class="right" style="margin-right: 10px">
-          <a href="../../../../genindex.html" title="General Index"
-             >index</a></li>
-        <li class="right" >
-          <a href="../../../../py-modindex.html" title="Python Module Index"
-             >modules</a> |</li>
-        <li class="nav-item nav-item-0"><a href="../../../../index.html">pyFTS 1.7 documentation</a> &#187;</li>
-          <li class="nav-item nav-item-1"><a href="../../../index.html" >Module code</a> &#187;</li>
-        <li class="nav-item nav-item-this"><a href="">pyFTS.common.transformations.som</a></li> 
-      </ul>
-    </div>
-    <div class="footer" role="contentinfo">
-        &#169; Copyright 2022, Machine Intelligence and Data Science Laboratory - UFMG - Brazil.
-      Created using <a href="https://www.sphinx-doc.org/">Sphinx</a> 4.3.2.
-    </div>
-  </body>
-</html>

docs/build/html/_modules/pyFTS/common/transformations/transformation.html

@@ -1,126 +0,0 @@
-
-
-<!doctype html>
-
-<html>
-  <head>
-    <meta charset="utf-8" />
-    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-    <title>pyFTS.common.transformations.transformation &#8212; pyFTS 1.7 documentation</title>
-    <link rel="stylesheet" type="text/css" href="../../../../_static/pygments.css" />
-    <link rel="stylesheet" type="text/css" href="../../../../_static/bizstyle.css" />
-    
-    <script data-url_root="../../../../" id="documentation_options" src="../../../../_static/documentation_options.js"></script>
-    <script src="../../../../_static/jquery.js"></script>
-    <script src="../../../../_static/underscore.js"></script>
-    <script src="../../../../_static/doctools.js"></script>
-    <script src="../../../../_static/bizstyle.js"></script>
-    <link rel="index" title="Index" href="../../../../genindex.html" />
-    <link rel="search" title="Search" href="../../../../search.html" />
-    <meta name="viewport" content="width=device-width,initial-scale=1.0" />
-    <!--[if lt IE 9]>
-    <script src="_static/css3-mediaqueries.js"></script>
-    <![endif]-->
-  </head><body>
-    <div class="related" role="navigation" aria-label="related navigation">
-      <h3>Navigation</h3>
-      <ul>
-        <li class="right" style="margin-right: 10px">
-          <a href="../../../../genindex.html" title="General Index"
-             accesskey="I">index</a></li>
-        <li class="right" >
-          <a href="../../../../py-modindex.html" title="Python Module Index"
-             >modules</a> |</li>
-        <li class="nav-item nav-item-0"><a href="../../../../index.html">pyFTS 1.7 documentation</a> &#187;</li>
-          <li class="nav-item nav-item-1"><a href="../../../index.html" accesskey="U">Module code</a> &#187;</li>
-        <li class="nav-item nav-item-this"><a href="">pyFTS.common.transformations.transformation</a></li> 
-      </ul>
-    </div>  
-
-    <div class="document">
-      <div class="documentwrapper">
-        <div class="bodywrapper">
-          <div class="body" role="main">
-            
-  <h1>Source code for pyFTS.common.transformations.transformation</h1><div class="highlight"><pre>
-<span></span>
-<div class="viewcode-block" id="Transformation"><a class="viewcode-back" href="../../../../pyFTS.common.transformations.html#pyFTS.common.transformations.transformation.Transformation">[docs]</a><span class="k">class</span> <span class="nc">Transformation</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Data transformation used on pre and post processing of the FTS</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">is_invertible</span> <span class="o">=</span> <span class="kc">True</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">is_multivariate</span> <span class="o">=</span> <span class="kc">False</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;detemine if this transformation can be applied to multivariate data&quot;&quot;&quot;</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">minimal_length</span> <span class="o">=</span> <span class="mi">1</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s1">&#39;&#39;</span>
-
-<div class="viewcode-block" id="Transformation.apply"><a class="viewcode-back" href="../../../../pyFTS.common.transformations.html#pyFTS.common.transformations.transformation.Transformation.apply">[docs]</a>    <span class="k">def</span> <span class="nf">apply</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">param</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">        Apply the transformation on input data</span>
-
-<span class="sd">        :param data: input data</span>
-<span class="sd">        :param param:</span>
-<span class="sd">        :param kwargs:</span>
-<span class="sd">        :return: numpy array with transformed data</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">pass</span></div>
-
-<div class="viewcode-block" id="Transformation.inverse"><a class="viewcode-back" href="../../../../pyFTS.common.transformations.html#pyFTS.common.transformations.transformation.Transformation.inverse">[docs]</a>    <span class="k">def</span> <span class="nf">inverse</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="n">data</span><span class="p">,</span> <span class="n">param</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-
-<span class="sd">        :param data: transformed data</span>
-<span class="sd">        :param param:</span>
-<span class="sd">        :param kwargs:</span>
-<span class="sd">        :return: numpy array with inverse transformed data</span>
-<span class="sd">        &quot;&quot;&quot;</span>
-        <span class="k">pass</span></div>
-
-    <span class="k">def</span> <span class="fm">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">name</span></div>
-</pre></div>
-
-            <div class="clearer"></div>
-          </div>
-        </div>
-      </div>
-      <div class="sphinxsidebar" role="navigation" aria-label="main navigation">
-        <div class="sphinxsidebarwrapper">
-            <p class="logo"><a href="../../../../index.html">
-              <img class="logo" src="../../../../_static/logo_heading2.png" alt="Logo"/>
-            </a></p>
-<div id="searchbox" style="display: none" role="search">
-  <h3 id="searchlabel">Quick search</h3>
-    <div class="searchformwrapper">
-    <form class="search" action="../../../../search.html" method="get">
-      <input type="text" name="q" aria-labelledby="searchlabel" autocomplete="off" autocorrect="off" autocapitalize="off" spellcheck="false"/>
-      <input type="submit" value="Go" />
-    </form>
-    </div>
-</div>
-<script>$('#searchbox').show(0);</script>
-        </div>
-      </div>
-      <div class="clearer"></div>
-    </div>
-    <div class="related" role="navigation" aria-label="related navigation">
-      <h3>Navigation</h3>
-      <ul>
-        <li class="right" style="margin-right: 10px">
-          <a href="../../../../genindex.html" title="General Index"
-             >index</a></li>
-        <li class="right" >
-          <a href="../../../../py-modindex.html" title="Python Module Index"
-             >modules</a> |</li>
-        <li class="nav-item nav-item-0"><a href="../../../../index.html">pyFTS 1.7 documentation</a> &#187;</li>
-          <li class="nav-item nav-item-1"><a href="../../../index.html" >Module code</a> &#187;</li>
-        <li class="nav-item nav-item-this"><a href="">pyFTS.common.transformations.transformation</a></li> 
-      </ul>
-    </div>
-    <div class="footer" role="contentinfo">
-        &#169; Copyright 2022, Machine Intelligence and Data Science Laboratory - UFMG - Brazil.
-      Created using <a href="https://www.sphinx-doc.org/">Sphinx</a> 4.3.2.
-    </div>
-  </body>
-</html>

docs/build/html/_modules/pyFTS/common/tree.html

@@ -47,17 +47,17 @@
 <span class="sd">Tree data structure</span>
 <span class="sd">&quot;&quot;&quot;</span>
 
-<span class="kn">from</span> <span class="nn">pyFTS</span> <span class="kn">import</span> <span class="o">*</span>
+<span class="kn">from</span> <span class="nn">pyFTS</span> <span class="k">import</span> <span class="o">*</span>
-<span class="kn">from</span> <span class="nn">functools</span> <span class="kn">import</span> <span class="n">reduce</span>
+<span class="kn">from</span> <span class="nn">functools</span> <span class="k">import</span> <span class="n">reduce</span>
 <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
 
 
 <div class="viewcode-block" id="FLRGTreeNode"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.tree.FLRGTreeNode">[docs]</a><span class="k">class</span> <span class="nc">FLRGTreeNode</span><span class="p">:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+    <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Tree node for</span>
 <span class="sd">    &quot;&quot;&quot;</span>
 
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">):</span>
+    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">):</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">isRoot</span> <span class="o">=</span> <span class="kc">False</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">children</span> <span class="o">=</span> <span class="p">[]</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">value</span> <span class="o">=</span> <span class="n">value</span>
@@ -86,13 +86,13 @@
             <span class="n">tmp</span> <span class="o">=</span> <span class="n">tmp</span> <span class="o">+</span> <span class="s2">&quot;</span><span class="se">\n</span><span class="s2">&quot;</span> <span class="o">+</span> <span class="n">child</span><span class="o">.</span><span class="n">getStr</span><span class="p">(</span><span class="n">k</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span>
         <span class="k">return</span> <span class="n">tmp</span></div>
 
-    <span class="k">def</span> <span class="fm">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+    <span class="k">def</span> <span class="nf">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">getStr</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span></div>
 
 
 <div class="viewcode-block" id="FLRGTree"><a class="viewcode-back" href="../../../pyFTS.common.html#pyFTS.common.tree.FLRGTree">[docs]</a><span class="k">class</span> <span class="nc">FLRGTree</span><span class="p">:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Represents a FLRG set with a tree structure&quot;&quot;&quot;</span>
+    <span class="sd">&quot;&quot;&quot;Represents a FLRG set with a tree structure&quot;&quot;&quot;</span>
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">root</span> <span class="o">=</span> <span class="n">FLRGTreeNode</span><span class="p">(</span><span class="kc">None</span><span class="p">)</span></div>
 
 
@@ -157,7 +157,7 @@
     </div>
     <div class="footer" role="contentinfo">
         &#169; Copyright 2022, Machine Intelligence and Data Science Laboratory - UFMG - Brazil.
-      Created using <a href="https://www.sphinx-doc.org/">Sphinx</a> 4.3.2.
+      Created using <a href="https://www.sphinx-doc.org/">Sphinx</a> 4.5.0.
     </div>
   </body>
 </html>

docs/build/html/_modules/pyFTS/data/AirPassengers.html

@@ -49,13 +49,13 @@
 <span class="sd">Source: Hyndman, R.J., Time Series Data Library, http://www-personal.buseco.monash.edu.au/~hyndman/TSDL/.</span>
 <span class="sd">&quot;&quot;&quot;</span>
 
-<span class="kn">from</span> <span class="nn">pyFTS.data</span> <span class="kn">import</span> <span class="n">common</span>
+<span class="kn">from</span> <span class="nn">pyFTS.data</span> <span class="k">import</span> <span class="n">common</span>
 <span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
 <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
 
 
 <div class="viewcode-block" id="get_data"><a class="viewcode-back" href="../../../pyFTS.data.html#pyFTS.data.AirPassengers.get_data">[docs]</a><span class="k">def</span> <span class="nf">get_data</span><span class="p">()</span> <span class="o">-&gt;</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+    <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Get a simple univariate time series data.</span>
 
 <span class="sd">    :return: numpy array</span>
@@ -65,7 +65,7 @@
     <span class="k">return</span> <span class="n">dat</span></div>
 
 <div class="viewcode-block" id="get_dataframe"><a class="viewcode-back" href="../../../pyFTS.data.html#pyFTS.data.AirPassengers.get_dataframe">[docs]</a><span class="k">def</span> <span class="nf">get_dataframe</span><span class="p">()</span> <span class="o">-&gt;</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+    <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Get the complete multivariate time series data.</span>
 
 <span class="sd">    :return: Pandas DataFrame</span>
@@ -116,7 +116,7 @@
     </div>
     <div class="footer" role="contentinfo">
         &#169; Copyright 2022, Machine Intelligence and Data Science Laboratory - UFMG - Brazil.
-      Created using <a href="https://www.sphinx-doc.org/">Sphinx</a> 4.3.2.
+      Created using <a href="https://www.sphinx-doc.org/">Sphinx</a> 4.5.0.
     </div>
   </body>
 </html>

docs/build/html/_modules/pyFTS/data/Bitcoin.html

@@ -52,13 +52,13 @@
 <span class="sd">&quot;&quot;&quot;</span>
 
 
-<span class="kn">from</span> <span class="nn">pyFTS.data</span> <span class="kn">import</span> <span class="n">common</span>
+<span class="kn">from</span> <span class="nn">pyFTS.data</span> <span class="k">import</span> <span class="n">common</span>
 <span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
 <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
 
 
 <div class="viewcode-block" id="get_data"><a class="viewcode-back" href="../../../pyFTS.data.html#pyFTS.data.Bitcoin.get_data">[docs]</a><span class="k">def</span> <span class="nf">get_data</span><span class="p">(</span><span class="n">field</span><span class="p">:</span><span class="nb">str</span><span class="o">=</span><span class="s1">&#39;AVG&#39;</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+    <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Get the univariate time series data.</span>
 
 <span class="sd">    :param field: dataset field to load</span>
@@ -69,7 +69,7 @@
 
 
 <div class="viewcode-block" id="get_dataframe"><a class="viewcode-back" href="../../../pyFTS.data.html#pyFTS.data.Bitcoin.get_dataframe">[docs]</a><span class="k">def</span> <span class="nf">get_dataframe</span><span class="p">()</span> <span class="o">-&gt;</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+    <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Get the complete multivariate time series data.</span>
 
 <span class="sd">    :return: Pandas DataFrame</span>
@@ -120,7 +120,7 @@
     </div>
     <div class="footer" role="contentinfo">
         &#169; Copyright 2022, Machine Intelligence and Data Science Laboratory - UFMG - Brazil.
-      Created using <a href="https://www.sphinx-doc.org/">Sphinx</a> 4.3.2.
+      Created using <a href="https://www.sphinx-doc.org/">Sphinx</a> 4.5.0.
     </div>
   </body>
 </html>

docs/build/html/_modules/pyFTS/data/DowJones.html

@@ -52,13 +52,13 @@
 <span class="sd">&quot;&quot;&quot;</span>
 
 
-<span class="kn">from</span> <span class="nn">pyFTS.data</span> <span class="kn">import</span> <span class="n">common</span>
+<span class="kn">from</span> <span class="nn">pyFTS.data</span> <span class="k">import</span> <span class="n">common</span>
 <span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
 <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
 
 
 <div class="viewcode-block" id="get_data"><a class="viewcode-back" href="../../../pyFTS.data.html#pyFTS.data.DowJones.get_data">[docs]</a><span class="k">def</span> <span class="nf">get_data</span><span class="p">(</span><span class="n">field</span><span class="p">:</span><span class="nb">str</span><span class="o">=</span><span class="s1">&#39;AVG&#39;</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+    <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Get the univariate time series data.</span>
 
 <span class="sd">    :param field: dataset field to load</span>
@@ -69,7 +69,7 @@
 
 
 <div class="viewcode-block" id="get_dataframe"><a class="viewcode-back" href="../../../pyFTS.data.html#pyFTS.data.DowJones.get_dataframe">[docs]</a><span class="k">def</span> <span class="nf">get_dataframe</span><span class="p">()</span> <span class="o">-&gt;</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+    <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Get the complete multivariate time series data.</span>
 
 <span class="sd">    :return: Pandas DataFrame</span>
@@ -120,7 +120,7 @@
     </div>
     <div class="footer" role="contentinfo">
         &#169; Copyright 2022, Machine Intelligence and Data Science Laboratory - UFMG - Brazil.
-      Created using <a href="https://www.sphinx-doc.org/">Sphinx</a> 4.3.2.
+      Created using <a href="https://www.sphinx-doc.org/">Sphinx</a> 4.5.0.
     </div>
   </body>
 </html>

docs/build/html/_modules/pyFTS/data/EURGBP.html

@@ -50,13 +50,13 @@
 <span class="sd">&quot;&quot;&quot;</span>
 
 
-<span class="kn">from</span> <span class="nn">pyFTS.data</span> <span class="kn">import</span> <span class="n">common</span>
+<span class="kn">from</span> <span class="nn">pyFTS.data</span> <span class="k">import</span> <span class="n">common</span>
 <span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
 <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
 
 
 <div class="viewcode-block" id="get_data"><a class="viewcode-back" href="../../../pyFTS.data.html#pyFTS.data.EURGBP.get_data">[docs]</a><span class="k">def</span> <span class="nf">get_data</span><span class="p">(</span><span class="n">field</span><span class="p">:</span><span class="nb">str</span><span class="o">=</span><span class="s1">&#39;avg&#39;</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+    <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Get the univariate time series data.</span>
 
 <span class="sd">    :param field: dataset field to load</span>
@@ -67,7 +67,7 @@
 
 
 <div class="viewcode-block" id="get_dataframe"><a class="viewcode-back" href="../../../pyFTS.data.html#pyFTS.data.EURGBP.get_dataframe">[docs]</a><span class="k">def</span> <span class="nf">get_dataframe</span><span class="p">()</span> <span class="o">-&gt;</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+    <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Get the complete multivariate time series data.</span>
 
 <span class="sd">    :return: Pandas DataFrame</span>
@@ -118,7 +118,7 @@
     </div>
     <div class="footer" role="contentinfo">
         &#169; Copyright 2022, Machine Intelligence and Data Science Laboratory - UFMG - Brazil.
-      Created using <a href="https://www.sphinx-doc.org/">Sphinx</a> 4.3.2.
+      Created using <a href="https://www.sphinx-doc.org/">Sphinx</a> 4.5.0.
     </div>
   </body>
 </html>

docs/build/html/_modules/pyFTS/data/EURUSD.html

@@ -50,13 +50,13 @@
 <span class="sd">&quot;&quot;&quot;</span>
 
 
-<span class="kn">from</span> <span class="nn">pyFTS.data</span> <span class="kn">import</span> <span class="n">common</span>
+<span class="kn">from</span> <span class="nn">pyFTS.data</span> <span class="k">import</span> <span class="n">common</span>
 <span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
 <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
 
 
 <div class="viewcode-block" id="get_data"><a class="viewcode-back" href="../../../pyFTS.data.html#pyFTS.data.EURUSD.get_data">[docs]</a><span class="k">def</span> <span class="nf">get_data</span><span class="p">(</span><span class="n">field</span><span class="p">:</span> <span class="nb">str</span><span class="o">=</span><span class="s1">&#39;avg&#39;</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+    <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Get the univariate time series data.</span>
 
 <span class="sd">    :param field: dataset field to load</span>
@@ -67,7 +67,7 @@
 
 
 <div class="viewcode-block" id="get_dataframe"><a class="viewcode-back" href="../../../pyFTS.data.html#pyFTS.data.EURUSD.get_dataframe">[docs]</a><span class="k">def</span> <span class="nf">get_dataframe</span><span class="p">()</span> <span class="o">-&gt;</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+    <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Get the complete multivariate time series data.</span>
 
 <span class="sd">    :return: Pandas DataFrame</span>
@@ -118,7 +118,7 @@
     </div>
     <div class="footer" role="contentinfo">
         &#169; Copyright 2022, Machine Intelligence and Data Science Laboratory - UFMG - Brazil.
-      Created using <a href="https://www.sphinx-doc.org/">Sphinx</a> 4.3.2.
+      Created using <a href="https://www.sphinx-doc.org/">Sphinx</a> 4.5.0.
     </div>
   </body>
 </html>

docs/build/html/_modules/pyFTS/data/Enrollments.html

@@ -47,13 +47,13 @@
 <span class="sd">Yearly University of Alabama enrollments from 1971 to 1992.</span>
 <span class="sd">&quot;&quot;&quot;</span>
 
-<span class="kn">from</span> <span class="nn">pyFTS.data</span> <span class="kn">import</span> <span class="n">common</span>
+<span class="kn">from</span> <span class="nn">pyFTS.data</span> <span class="k">import</span> <span class="n">common</span>
 <span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
 <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
 
 
 <div class="viewcode-block" id="get_data"><a class="viewcode-back" href="../../../pyFTS.data.html#pyFTS.data.Enrollments.get_data">[docs]</a><span class="k">def</span> <span class="nf">get_data</span><span class="p">()</span> <span class="o">-&gt;</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+    <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Get a simple univariate time series data.</span>
 
 <span class="sd">    :return: numpy array</span>
@@ -109,7 +109,7 @@
     </div>
     <div class="footer" role="contentinfo">
         &#169; Copyright 2022, Machine Intelligence and Data Science Laboratory - UFMG - Brazil.
-      Created using <a href="https://www.sphinx-doc.org/">Sphinx</a> 4.3.2.
+      Created using <a href="https://www.sphinx-doc.org/">Sphinx</a> 4.5.0.
     </div>
   </body>
 </html>

docs/build/html/_modules/pyFTS/data/Ethereum.html

@@ -52,13 +52,13 @@
 <span class="sd">&quot;&quot;&quot;</span>
 
 
-<span class="kn">from</span> <span class="nn">pyFTS.data</span> <span class="kn">import</span> <span class="n">common</span>
+<span class="kn">from</span> <span class="nn">pyFTS.data</span> <span class="k">import</span> <span class="n">common</span>
 <span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
 <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
 
 
 <div class="viewcode-block" id="get_data"><a class="viewcode-back" href="../../../pyFTS.data.html#pyFTS.data.Ethereum.get_data">[docs]</a><span class="k">def</span> <span class="nf">get_data</span><span class="p">(</span><span class="n">field</span><span class="p">:</span> <span class="nb">str</span><span class="o">=</span><span class="s1">&#39;AVG&#39;</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+    <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Get the univariate time series data.</span>
 
 <span class="sd">    :param field: dataset field to load</span>
@@ -69,7 +69,7 @@
 
 
 <div class="viewcode-block" id="get_dataframe"><a class="viewcode-back" href="../../../pyFTS.data.html#pyFTS.data.Ethereum.get_dataframe">[docs]</a><span class="k">def</span> <span class="nf">get_dataframe</span><span class="p">()</span> <span class="o">-&gt;</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+    <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Get the complete multivariate time series data.</span>
 
 <span class="sd">    :return: Pandas DataFrame</span>
@@ -120,7 +120,7 @@
     </div>
     <div class="footer" role="contentinfo">
         &#169; Copyright 2022, Machine Intelligence and Data Science Laboratory - UFMG - Brazil.
-      Created using <a href="https://www.sphinx-doc.org/">Sphinx</a> 4.3.2.
+      Created using <a href="https://www.sphinx-doc.org/">Sphinx</a> 4.5.0.
     </div>
   </body>
 </html>

docs/build/html/_modules/pyFTS/data/GBPUSD.html

@@ -50,13 +50,13 @@
 <span class="sd">&quot;&quot;&quot;</span>
 
 
-<span class="kn">from</span> <span class="nn">pyFTS.data</span> <span class="kn">import</span> <span class="n">common</span>
+<span class="kn">from</span> <span class="nn">pyFTS.data</span> <span class="k">import</span> <span class="n">common</span>
 <span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
 <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
 
 
 <div class="viewcode-block" id="get_data"><a class="viewcode-back" href="../../../pyFTS.data.html#pyFTS.data.GBPUSD.get_data">[docs]</a><span class="k">def</span> <span class="nf">get_data</span><span class="p">(</span><span class="n">field</span><span class="p">:</span> <span class="nb">str</span><span class="o">=</span><span class="s1">&#39;avg&#39;</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+    <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Get the univariate time series data.</span>
 
 <span class="sd">    :param field: dataset field to load</span>
@@ -67,7 +67,7 @@
 
 
 <div class="viewcode-block" id="get_dataframe"><a class="viewcode-back" href="../../../pyFTS.data.html#pyFTS.data.GBPUSD.get_dataframe">[docs]</a><span class="k">def</span> <span class="nf">get_dataframe</span><span class="p">()</span> <span class="o">-&gt;</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+    <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Get the complete multivariate time series data.</span>
 
 <span class="sd">    :return: Pandas DataFrame</span>
@@ -118,7 +118,7 @@
     </div>
     <div class="footer" role="contentinfo">
         &#169; Copyright 2022, Machine Intelligence and Data Science Laboratory - UFMG - Brazil.
-      Created using <a href="https://www.sphinx-doc.org/">Sphinx</a> 4.3.2.
+      Created using <a href="https://www.sphinx-doc.org/">Sphinx</a> 4.5.0.
     </div>
   </body>
 </html>

docs/build/html/_modules/pyFTS/data/INMET.html

@@ -51,12 +51,12 @@
 <span class="sd">Source: http://www.inmet.gov.br</span>
 <span class="sd">&quot;&quot;&quot;</span>
 
-<span class="kn">from</span> <span class="nn">pyFTS.data</span> <span class="kn">import</span> <span class="n">common</span>
+<span class="kn">from</span> <span class="nn">pyFTS.data</span> <span class="k">import</span> <span class="n">common</span>
 <span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
 
 
 <div class="viewcode-block" id="get_dataframe"><a class="viewcode-back" href="../../../pyFTS.data.html#pyFTS.data.INMET.get_dataframe">[docs]</a><span class="k">def</span> <span class="nf">get_dataframe</span><span class="p">()</span> <span class="o">-&gt;</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+    <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Get the complete multivariate time series data.</span>
 
 <span class="sd">    :return: Pandas DataFrame</span>
@@ -107,7 +107,7 @@
     </div>
     <div class="footer" role="contentinfo">
         &#169; Copyright 2022, Machine Intelligence and Data Science Laboratory - UFMG - Brazil.
-      Created using <a href="https://www.sphinx-doc.org/">Sphinx</a> 4.3.2.
+      Created using <a href="https://www.sphinx-doc.org/">Sphinx</a> 4.5.0.
     </div>
   </body>
 </html>

docs/build/html/_modules/pyFTS/data/Malaysia.html

@@ -48,13 +48,13 @@
 <span class="sd">&quot;&quot;&quot;</span>
 
 
-<span class="kn">from</span> <span class="nn">pyFTS.data</span> <span class="kn">import</span> <span class="n">common</span>
+<span class="kn">from</span> <span class="nn">pyFTS.data</span> <span class="k">import</span> <span class="n">common</span>
 <span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
 <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
 
 
 <div class="viewcode-block" id="get_data"><a class="viewcode-back" href="../../../pyFTS.data.html#pyFTS.data.Malaysia.get_data">[docs]</a><span class="k">def</span> <span class="nf">get_data</span><span class="p">(</span><span class="n">field</span><span class="p">:</span> <span class="nb">str</span><span class="o">=</span><span class="s1">&#39;load&#39;</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+    <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Get the univariate time series data.</span>
 
 <span class="sd">    :param field: dataset field to load</span>
@@ -65,7 +65,7 @@
 
 
 <div class="viewcode-block" id="get_dataframe"><a class="viewcode-back" href="../../../pyFTS.data.html#pyFTS.data.Malaysia.get_dataframe">[docs]</a><span class="k">def</span> <span class="nf">get_dataframe</span><span class="p">()</span> <span class="o">-&gt;</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+    <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Get the complete multivariate time series data.</span>
 
 <span class="sd">    :return: Pandas DataFrame</span>
@@ -117,7 +117,7 @@
     </div>
     <div class="footer" role="contentinfo">
         &#169; Copyright 2022, Machine Intelligence and Data Science Laboratory - UFMG - Brazil.
-      Created using <a href="https://www.sphinx-doc.org/">Sphinx</a> 4.3.2.
+      Created using <a href="https://www.sphinx-doc.org/">Sphinx</a> 4.5.0.
     </div>
   </body>
 </html>

docs/build/html/_modules/pyFTS/data/NASDAQ.html

@@ -51,13 +51,13 @@
 <span class="sd">Source: http://www.nasdaq.com/aspx/flashquotes.aspx?symbol=IXIC&amp;selected=IXIC</span>
 <span class="sd">&quot;&quot;&quot;</span>
 
-<span class="kn">from</span> <span class="nn">pyFTS.data</span> <span class="kn">import</span> <span class="n">common</span>
+<span class="kn">from</span> <span class="nn">pyFTS.data</span> <span class="k">import</span> <span class="n">common</span>
 <span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
 <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
 
 
 <div class="viewcode-block" id="get_data"><a class="viewcode-back" href="../../../pyFTS.data.html#pyFTS.data.NASDAQ.get_data">[docs]</a><span class="k">def</span> <span class="nf">get_data</span><span class="p">(</span><span class="n">field</span><span class="p">:</span> <span class="nb">str</span><span class="o">=</span><span class="s2">&quot;avg&quot;</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+    <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Get a simple univariate time series data.</span>
 
 <span class="sd">    :param field: the dataset field name to extract</span>
@@ -69,7 +69,7 @@
 
 
 <div class="viewcode-block" id="get_dataframe"><a class="viewcode-back" href="../../../pyFTS.data.html#pyFTS.data.NASDAQ.get_dataframe">[docs]</a><span class="k">def</span> <span class="nf">get_dataframe</span><span class="p">()</span> <span class="o">-&gt;</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+    <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Get the complete multivariate time series data.</span>
 
 <span class="sd">    :return: Pandas DataFrame</span>
@@ -120,7 +120,7 @@
     </div>
     <div class="footer" role="contentinfo">
         &#169; Copyright 2022, Machine Intelligence and Data Science Laboratory - UFMG - Brazil.
-      Created using <a href="https://www.sphinx-doc.org/">Sphinx</a> 4.3.2.
+      Created using <a href="https://www.sphinx-doc.org/">Sphinx</a> 4.5.0.
     </div>
   </body>
 </html>

docs/build/html/_modules/pyFTS/data/SONDA.html

@@ -51,13 +51,13 @@
 <span class="sd">Source: http://sonda.ccst.inpe.br/</span>
 
 <span class="sd">&quot;&quot;&quot;</span>
-<span class="kn">from</span> <span class="nn">pyFTS.data</span> <span class="kn">import</span> <span class="n">common</span>
+<span class="kn">from</span> <span class="nn">pyFTS.data</span> <span class="k">import</span> <span class="n">common</span>
 <span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
 <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
 
 
 <div class="viewcode-block" id="get_data"><a class="viewcode-back" href="../../../pyFTS.data.html#pyFTS.data.SONDA.get_data">[docs]</a><span class="k">def</span> <span class="nf">get_data</span><span class="p">(</span><span class="n">field</span><span class="p">:</span><span class="nb">str</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+    <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Get a simple univariate time series data.</span>
 
 <span class="sd">    :param field: the dataset field name to extract</span>
@@ -69,7 +69,7 @@
 
 
 <div class="viewcode-block" id="get_dataframe"><a class="viewcode-back" href="../../../pyFTS.data.html#pyFTS.data.SONDA.get_dataframe">[docs]</a><span class="k">def</span> <span class="nf">get_dataframe</span><span class="p">()</span> <span class="o">-&gt;</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+    <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Get the complete multivariate time series data.</span>
 
 <span class="sd">    :return: Pandas DataFrame</span>
@@ -121,7 +121,7 @@
     </div>
     <div class="footer" role="contentinfo">
         &#169; Copyright 2022, Machine Intelligence and Data Science Laboratory - UFMG - Brazil.
-      Created using <a href="https://www.sphinx-doc.org/">Sphinx</a> 4.3.2.
+      Created using <a href="https://www.sphinx-doc.org/">Sphinx</a> 4.5.0.
     </div>
   </body>
 </html>

docs/build/html/_modules/pyFTS/data/SP500.html

@@ -51,13 +51,13 @@
 <span class="sd">Source: https://finance.yahoo.com/quote/%5EGSPC/history?p=%5EGSPC</span>
 <span class="sd">&quot;&quot;&quot;</span>
 
-<span class="kn">from</span> <span class="nn">pyFTS.data</span> <span class="kn">import</span> <span class="n">common</span>
+<span class="kn">from</span> <span class="nn">pyFTS.data</span> <span class="k">import</span> <span class="n">common</span>
 <span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
 <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
 
 
 <div class="viewcode-block" id="get_data"><a class="viewcode-back" href="../../../pyFTS.data.html#pyFTS.data.SP500.get_data">[docs]</a><span class="k">def</span> <span class="nf">get_data</span><span class="p">()</span> <span class="o">-&gt;</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+    <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Get the univariate time series data.</span>
 
 <span class="sd">    :return: numpy array</span>
@@ -67,7 +67,7 @@
 
 
 <div class="viewcode-block" id="get_dataframe"><a class="viewcode-back" href="../../../pyFTS.data.html#pyFTS.data.SP500.get_dataframe">[docs]</a><span class="k">def</span> <span class="nf">get_dataframe</span><span class="p">()</span> <span class="o">-&gt;</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+    <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Get the complete multivariate time series data.</span>
 
 <span class="sd">    :return: Pandas DataFrame</span>
@@ -118,7 +118,7 @@
     </div>
     <div class="footer" role="contentinfo">
         &#169; Copyright 2022, Machine Intelligence and Data Science Laboratory - UFMG - Brazil.
-      Created using <a href="https://www.sphinx-doc.org/">Sphinx</a> 4.3.2.
+      Created using <a href="https://www.sphinx-doc.org/">Sphinx</a> 4.5.0.
     </div>
   </body>
 </html>

docs/build/html/_modules/pyFTS/data/TAIEX.html

@@ -50,13 +50,13 @@
 
 <span class="sd">Source: http://www.twse.com.tw/en/products/indices/Index_Series.php</span>
 <span class="sd">&quot;&quot;&quot;</span>
-<span class="kn">from</span> <span class="nn">pyFTS.data</span> <span class="kn">import</span> <span class="n">common</span>
+<span class="kn">from</span> <span class="nn">pyFTS.data</span> <span class="k">import</span> <span class="n">common</span>
 <span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
 <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
 
 
 <div class="viewcode-block" id="get_data"><a class="viewcode-back" href="../../../pyFTS.data.html#pyFTS.data.TAIEX.get_data">[docs]</a><span class="k">def</span> <span class="nf">get_data</span><span class="p">()</span> <span class="o">-&gt;</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+    <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Get the univariate time series data.</span>
 
 <span class="sd">    :return: numpy array</span>
@@ -67,7 +67,7 @@
 
 
 <div class="viewcode-block" id="get_dataframe"><a class="viewcode-back" href="../../../pyFTS.data.html#pyFTS.data.TAIEX.get_dataframe">[docs]</a><span class="k">def</span> <span class="nf">get_dataframe</span><span class="p">()</span> <span class="o">-&gt;</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+    <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Get the complete multivariate time series data.</span>
 
 <span class="sd">    :return: Pandas DataFrame</span>
@@ -119,7 +119,7 @@
     </div>
     <div class="footer" role="contentinfo">
         &#169; Copyright 2022, Machine Intelligence and Data Science Laboratory - UFMG - Brazil.
-      Created using <a href="https://www.sphinx-doc.org/">Sphinx</a> 4.3.2.
+      Created using <a href="https://www.sphinx-doc.org/">Sphinx</a> 4.5.0.
     </div>
   </body>
 </html>

docs/build/html/_modules/pyFTS/data/artificial.html

@@ -51,18 +51,18 @@
 
 
 <div class="viewcode-block" id="SignalEmulator"><a class="viewcode-back" href="../../../pyFTS.data.html#pyFTS.data.artificial.SignalEmulator">[docs]</a><span class="k">class</span> <span class="nc">SignalEmulator</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+    <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Emulate a complex signal built from several additive and non-additive components</span>
 <span class="sd">    &quot;&quot;&quot;</span>
 
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
         <span class="nb">super</span><span class="p">(</span><span class="n">SignalEmulator</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
 
         <span class="bp">self</span><span class="o">.</span><span class="n">components</span> <span class="o">=</span> <span class="p">[]</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;Components of the signal&quot;&quot;&quot;</span>
+        <span class="sd">&quot;&quot;&quot;Components of the signal&quot;&quot;&quot;</span>
 
 <div class="viewcode-block" id="SignalEmulator.stationary_gaussian"><a class="viewcode-back" href="../../../pyFTS.data.html#pyFTS.data.artificial.SignalEmulator.stationary_gaussian">[docs]</a>    <span class="k">def</span> <span class="nf">stationary_gaussian</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">mu</span><span class="p">:</span><span class="nb">float</span><span class="p">,</span> <span class="n">sigma</span><span class="p">:</span><span class="nb">float</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+        <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Creates a continuous Gaussian signal with mean mu and variance sigma.</span>
 
 <span class="sd">        :param mu: mean</span>
@@ -82,7 +82,7 @@
         <span class="k">return</span> <span class="bp">self</span></div>
 
 <div class="viewcode-block" id="SignalEmulator.incremental_gaussian"><a class="viewcode-back" href="../../../pyFTS.data.html#pyFTS.data.artificial.SignalEmulator.incremental_gaussian">[docs]</a>    <span class="k">def</span> <span class="nf">incremental_gaussian</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">mu</span><span class="p">:</span><span class="nb">float</span><span class="p">,</span> <span class="n">sigma</span><span class="p">:</span><span class="nb">float</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+        <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Creates an additive gaussian interference on a previous signal</span>
 
 <span class="sd">        :param mu: increment on mean</span>
@@ -100,7 +100,7 @@
         <span class="k">return</span> <span class="bp">self</span></div>
 
 <div class="viewcode-block" id="SignalEmulator.periodic_gaussian"><a class="viewcode-back" href="../../../pyFTS.data.html#pyFTS.data.artificial.SignalEmulator.periodic_gaussian">[docs]</a>    <span class="k">def</span> <span class="nf">periodic_gaussian</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">type</span><span class="p">:</span><span class="nb">str</span><span class="p">,</span> <span class="n">period</span><span class="p">:</span><span class="nb">int</span><span class="p">,</span> <span class="n">mu_min</span><span class="p">:</span><span class="nb">float</span><span class="p">,</span> <span class="n">sigma_min</span><span class="p">:</span><span class="nb">float</span><span class="p">,</span> <span class="n">mu_max</span><span class="p">:</span><span class="nb">float</span><span class="p">,</span> <span class="n">sigma_max</span><span class="p">:</span><span class="nb">float</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+        <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Creates an additive periodic gaussian interference on a previous signal</span>
 
 <span class="sd">        :param type: &#39;linear&#39; or &#39;sinoidal&#39;</span>
@@ -121,7 +121,7 @@
         <span class="k">return</span> <span class="bp">self</span></div>
 
 <div class="viewcode-block" id="SignalEmulator.blip"><a class="viewcode-back" href="../../../pyFTS.data.html#pyFTS.data.artificial.SignalEmulator.blip">[docs]</a>    <span class="k">def</span> <span class="nf">blip</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+        <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Creates an outlier greater than the maximum or lower then the minimum previous values of the signal,</span>
 <span class="sd">        and insert it on a random location of the signal.</span>
 
@@ -133,7 +133,7 @@
         <span class="k">return</span> <span class="bp">self</span></div>
 
 <div class="viewcode-block" id="SignalEmulator.run"><a class="viewcode-back" href="../../../pyFTS.data.html#pyFTS.data.artificial.SignalEmulator.run">[docs]</a>    <span class="k">def</span> <span class="nf">run</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+        <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Render the signal</span>
 
 <span class="sd">        :return: a list of float values</span>
@@ -180,7 +180,7 @@
                     <span class="n">_mn</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">_mn</span><span class="p">,</span> <span class="n">vmin</span><span class="p">)</span> <span class="k">if</span> <span class="n">vmin</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="k">else</span> <span class="nb">min</span><span class="p">(</span><span class="n">_mn</span><span class="p">,</span> <span class="n">vmin</span><span class="p">)</span>
 
                 <span class="n">start</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">signal</span><span class="p">))</span>
-                <span class="n">tmp</span> <span class="o">=</span> <span class="p">[</span><span class="n">_mx</span><span class="p">]</span> <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">rand</span><span class="p">()</span> <span class="o">&gt;=</span> <span class="mf">.5</span> <span class="k">else</span> <span class="p">[</span><span class="o">-</span><span class="n">_mn</span><span class="p">]</span>
+                <span class="n">tmp</span> <span class="o">=</span> <span class="p">[</span><span class="n">_mx</span><span class="p">]</span> <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">rand</span><span class="p">()</span> <span class="o">&gt;=</span> <span class="o">.</span><span class="mi">5</span> <span class="k">else</span> <span class="p">[</span><span class="o">-</span><span class="n">_mn</span><span class="p">]</span>
 
             <span class="n">last_num</span> <span class="o">=</span> <span class="n">num</span>
             <span class="n">last_it</span> <span class="o">=</span> <span class="n">it</span>
@@ -193,7 +193,7 @@
 
 
 <div class="viewcode-block" id="generate_gaussian_linear"><a class="viewcode-back" href="../../../pyFTS.data.html#pyFTS.data.artificial.generate_gaussian_linear">[docs]</a><span class="k">def</span> <span class="nf">generate_gaussian_linear</span><span class="p">(</span><span class="n">mu_ini</span><span class="p">,</span> <span class="n">sigma_ini</span><span class="p">,</span> <span class="n">mu_inc</span><span class="p">,</span> <span class="n">sigma_inc</span><span class="p">,</span> <span class="n">it</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">num</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">vmin</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">vmax</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+    <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Generate data sampled from Gaussian distribution, with constant or linear changing parameters</span>
 
 <span class="sd">    :param mu_ini: Initial mean</span>
@@ -222,7 +222,7 @@
 
 
 <div class="viewcode-block" id="generate_linear_periodic_gaussian"><a class="viewcode-back" href="../../../pyFTS.data.html#pyFTS.data.artificial.generate_linear_periodic_gaussian">[docs]</a><span class="k">def</span> <span class="nf">generate_linear_periodic_gaussian</span><span class="p">(</span><span class="n">period</span><span class="p">,</span> <span class="n">mu_min</span><span class="p">,</span> <span class="n">sigma_min</span><span class="p">,</span> <span class="n">mu_max</span><span class="p">,</span> <span class="n">sigma_max</span><span class="p">,</span> <span class="n">it</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">num</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">vmin</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">vmax</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+    <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Generates a periodic linear variation on mean and variance</span>
 
 <span class="sd">    :param period: the period of recurrence</span>
@@ -267,7 +267,7 @@
 
 
 <div class="viewcode-block" id="generate_sinoidal_periodic_gaussian"><a class="viewcode-back" href="../../../pyFTS.data.html#pyFTS.data.artificial.generate_sinoidal_periodic_gaussian">[docs]</a><span class="k">def</span> <span class="nf">generate_sinoidal_periodic_gaussian</span><span class="p">(</span><span class="n">period</span><span class="p">,</span> <span class="n">mu_min</span><span class="p">,</span> <span class="n">sigma_min</span><span class="p">,</span> <span class="n">mu_max</span><span class="p">,</span> <span class="n">sigma_max</span><span class="p">,</span> <span class="n">it</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">num</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">vmin</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">vmax</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+    <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Generates a periodic sinoidal variation on mean and variance</span>
 
 <span class="sd">    :param period: the period of recurrence</span>
@@ -304,7 +304,7 @@
 
 
 <div class="viewcode-block" id="generate_uniform_linear"><a class="viewcode-back" href="../../../pyFTS.data.html#pyFTS.data.artificial.generate_uniform_linear">[docs]</a><span class="k">def</span> <span class="nf">generate_uniform_linear</span><span class="p">(</span><span class="n">min_ini</span><span class="p">,</span> <span class="n">max_ini</span><span class="p">,</span> <span class="n">min_inc</span><span class="p">,</span> <span class="n">max_inc</span><span class="p">,</span> <span class="n">it</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">num</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">vmin</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">vmax</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+    <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Generate data sampled from Uniform distribution, with constant or  linear changing bounds</span>
 
 <span class="sd">    :param mu_ini: Initial mean</span>
@@ -333,7 +333,7 @@
 
 
 <div class="viewcode-block" id="white_noise"><a class="viewcode-back" href="../../../pyFTS.data.html#pyFTS.data.artificial.white_noise">[docs]</a><span class="k">def</span> <span class="nf">white_noise</span><span class="p">(</span><span class="n">n</span><span class="o">=</span><span class="mi">500</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+    <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Simple Gaussian noise signal</span>
 <span class="sd">    :param n: number of samples</span>
 <span class="sd">    :return:</span>
@@ -342,7 +342,7 @@
 
 
 <div class="viewcode-block" id="random_walk"><a class="viewcode-back" href="../../../pyFTS.data.html#pyFTS.data.artificial.random_walk">[docs]</a><span class="k">def</span> <span class="nf">random_walk</span><span class="p">(</span><span class="n">n</span><span class="o">=</span><span class="mi">500</span><span class="p">,</span> <span class="nb">type</span><span class="o">=</span><span class="s1">&#39;gaussian&#39;</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+    <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Simple random walk</span>
 
 <span class="sd">    :param n: number of samples</span>
@@ -424,7 +424,7 @@
     </div>
     <div class="footer" role="contentinfo">
         &#169; Copyright 2022, Machine Intelligence and Data Science Laboratory - UFMG - Brazil.
-      Created using <a href="https://www.sphinx-doc.org/">Sphinx</a> 4.3.2.
+      Created using <a href="https://www.sphinx-doc.org/">Sphinx</a> 4.5.0.
     </div>
   </body>
 </html>

docs/build/html/_modules/pyFTS/data/common.html

@@ -48,12 +48,12 @@
 <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
 <span class="kn">import</span> <span class="nn">os</span>
 <span class="kn">import</span> <span class="nn">pkg_resources</span>
-<span class="kn">from</span> <span class="nn">pathlib</span> <span class="kn">import</span> <span class="n">Path</span>
+<span class="kn">from</span> <span class="nn">pathlib</span> <span class="k">import</span> <span class="n">Path</span>
-<span class="kn">from</span> <span class="nn">urllib</span> <span class="kn">import</span> <span class="n">request</span>
+<span class="kn">from</span> <span class="nn">urllib</span> <span class="k">import</span> <span class="n">request</span>
 
 
 <div class="viewcode-block" id="get_dataframe"><a class="viewcode-back" href="../../../pyFTS.data.html#pyFTS.data.common.get_dataframe">[docs]</a><span class="k">def</span> <span class="nf">get_dataframe</span><span class="p">(</span><span class="n">filename</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">url</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">sep</span><span class="p">:</span><span class="nb">str</span><span class="o">=</span><span class="s2">&quot;;&quot;</span><span class="p">,</span> <span class="n">compression</span><span class="p">:</span><span class="nb">str</span><span class="o">=</span><span class="s1">&#39;infer&#39;</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+    <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    This method check if filename already exists, read the file and return its data.</span>
 <span class="sd">    If the file don&#39;t already exists, it will be downloaded and decompressed.</span>
 
@@ -114,7 +114,7 @@
     </div>
     <div class="footer" role="contentinfo">
         &#169; Copyright 2022, Machine Intelligence and Data Science Laboratory - UFMG - Brazil.
-      Created using <a href="https://www.sphinx-doc.org/">Sphinx</a> 4.3.2.
+      Created using <a href="https://www.sphinx-doc.org/">Sphinx</a> 4.5.0.
     </div>
   </body>
 </html>

docs/build/html/_modules/pyFTS/data/henon.html

@@ -55,7 +55,7 @@
 
 
 <div class="viewcode-block" id="get_data"><a class="viewcode-back" href="../../../pyFTS.data.html#pyFTS.data.henon.get_data">[docs]</a><span class="k">def</span> <span class="nf">get_data</span><span class="p">(</span><span class="n">var</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">a</span><span class="p">:</span><span class="nb">float</span><span class="o">=</span><span class="mf">1.4</span><span class="p">,</span> <span class="n">b</span><span class="p">:</span><span class="nb">float</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">initial_values</span><span class="p">:</span> <span class="nb">list</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">iterations</span><span class="p">:</span><span class="nb">int</span><span class="o">=</span><span class="mi">1000</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+    <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Get a simple univariate time series data.</span>
 
 <span class="sd">    :param var: the dataset field name to extract</span>
@@ -65,7 +65,7 @@
 
 
 <div class="viewcode-block" id="get_dataframe"><a class="viewcode-back" href="../../../pyFTS.data.html#pyFTS.data.henon.get_dataframe">[docs]</a><span class="k">def</span> <span class="nf">get_dataframe</span><span class="p">(</span><span class="n">a</span><span class="p">:</span><span class="nb">float</span><span class="o">=</span><span class="mf">1.4</span><span class="p">,</span> <span class="n">b</span><span class="p">:</span><span class="nb">float</span><span class="o">=</span><span class="mf">0.3</span><span class="p">,</span> <span class="n">initial_values</span><span class="p">:</span> <span class="nb">list</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">iterations</span><span class="p">:</span><span class="nb">int</span><span class="o">=</span><span class="mi">1000</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">:</span>
-<span class="w">    </span><span class="sd">&#39;&#39;&#39;</span>
+    <span class="sd">&#39;&#39;&#39;</span>
 <span class="sd">    Return a dataframe with the bivariate Henon Map time series (x, y).</span>
 
 <span class="sd">    :param a: Equation coefficient</span>
@@ -124,7 +124,7 @@
     </div>
     <div class="footer" role="contentinfo">
         &#169; Copyright 2022, Machine Intelligence and Data Science Laboratory - UFMG - Brazil.
-      Created using <a href="https://www.sphinx-doc.org/">Sphinx</a> 4.3.2.
+      Created using <a href="https://www.sphinx-doc.org/">Sphinx</a> 4.5.0.
     </div>
   </body>
 </html>

docs/build/html/_modules/pyFTS/data/logistic_map.html

@@ -54,7 +54,7 @@
 
 
 <div class="viewcode-block" id="get_data"><a class="viewcode-back" href="../../../pyFTS.data.html#pyFTS.data.logistic_map.get_data">[docs]</a><span class="k">def</span> <span class="nf">get_data</span><span class="p">(</span><span class="n">r</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mi">4</span><span class="p">,</span> <span class="n">initial_value</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">0.3</span><span class="p">,</span> <span class="n">iterations</span><span class="p">:</span> <span class="nb">int</span><span class="o">=</span><span class="mi">100</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">:</span>
-<span class="w">    </span><span class="sd">&#39;&#39;&#39;</span>
+    <span class="sd">&#39;&#39;&#39;</span>
 <span class="sd">    Return a list with the logistic map chaotic time series.</span>
 
 <span class="sd">    :param r: Equation coefficient</span>
@@ -108,7 +108,7 @@
     </div>
     <div class="footer" role="contentinfo">
         &#169; Copyright 2022, Machine Intelligence and Data Science Laboratory - UFMG - Brazil.
-      Created using <a href="https://www.sphinx-doc.org/">Sphinx</a> 4.3.2.
+      Created using <a href="https://www.sphinx-doc.org/">Sphinx</a> 4.5.0.
     </div>
   </body>
 </html>

docs/build/html/_modules/pyFTS/data/lorentz.html

@@ -58,7 +58,7 @@
 
 <div class="viewcode-block" id="get_data"><a class="viewcode-back" href="../../../pyFTS.data.html#pyFTS.data.lorentz.get_data">[docs]</a><span class="k">def</span> <span class="nf">get_data</span><span class="p">(</span><span class="n">var</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">a</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">10.0</span><span class="p">,</span> <span class="n">b</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">28.0</span><span class="p">,</span> <span class="n">c</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">8.0</span> <span class="o">/</span> <span class="mf">3.0</span><span class="p">,</span> <span class="n">dt</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">0.01</span><span class="p">,</span>
                   <span class="n">initial_values</span><span class="p">:</span> <span class="nb">list</span> <span class="o">=</span> <span class="p">[</span><span class="mf">0.1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">iterations</span><span class="p">:</span> <span class="nb">int</span><span class="o">=</span><span class="mi">1000</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+    <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Get a simple univariate time series data.</span>
 
 <span class="sd">        :param var: the dataset field name to extract</span>
@@ -69,7 +69,7 @@
 
 <div class="viewcode-block" id="get_dataframe"><a class="viewcode-back" href="../../../pyFTS.data.html#pyFTS.data.lorentz.get_dataframe">[docs]</a><span class="k">def</span> <span class="nf">get_dataframe</span><span class="p">(</span><span class="n">a</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">10.0</span><span class="p">,</span> <span class="n">b</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">28.0</span><span class="p">,</span> <span class="n">c</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">8.0</span> <span class="o">/</span> <span class="mf">3.0</span><span class="p">,</span> <span class="n">dt</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">0.01</span><span class="p">,</span>
                   <span class="n">initial_values</span><span class="p">:</span> <span class="nb">list</span> <span class="o">=</span> <span class="p">[</span><span class="mf">0.1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">iterations</span><span class="p">:</span> <span class="nb">int</span><span class="o">=</span><span class="mi">1000</span><span class="p">)</span><span class="o">-&gt;</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">:</span>
-<span class="w">    </span><span class="sd">&#39;&#39;&#39;</span>
+    <span class="sd">&#39;&#39;&#39;</span>
 <span class="sd">    Return a dataframe with the multivariate Lorenz Map time series (x, y, z).</span>
 
 <span class="sd">    :param a: Equation coefficient. Default value: 10</span>
@@ -135,7 +135,7 @@
     </div>
     <div class="footer" role="contentinfo">
         &#169; Copyright 2022, Machine Intelligence and Data Science Laboratory - UFMG - Brazil.
-      Created using <a href="https://www.sphinx-doc.org/">Sphinx</a> 4.3.2.
+      Created using <a href="https://www.sphinx-doc.org/">Sphinx</a> 4.5.0.
     </div>
   </body>
 </html>

docs/build/html/_modules/pyFTS/data/mackey_glass.html

@@ -54,7 +54,7 @@
 
 
 <div class="viewcode-block" id="get_data"><a class="viewcode-back" href="../../../pyFTS.data.html#pyFTS.data.mackey_glass.get_data">[docs]</a><span class="k">def</span> <span class="nf">get_data</span><span class="p">(</span><span class="n">b</span><span class="p">:</span> <span class="nb">float</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">c</span><span class="p">:</span> <span class="nb">float</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span> <span class="n">tau</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span><span class="mi">17</span><span class="p">,</span> <span class="n">initial_values</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mf">0.5</span><span class="p">,</span><span class="mf">1.5</span><span class="p">,</span> <span class="mi">18</span><span class="p">),</span> <span class="n">iterations</span><span class="p">:</span> <span class="nb">int</span><span class="o">=</span><span class="mi">1000</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">:</span>
-<span class="w">    </span><span class="sd">&#39;&#39;&#39;</span>
+    <span class="sd">&#39;&#39;&#39;</span>
 <span class="sd">    Return a list with the Mackey-Glass chaotic time series.</span>
 
 <span class="sd">    :param b: Equation coefficient</span>
@@ -111,7 +111,7 @@
     </div>
     <div class="footer" role="contentinfo">
         &#169; Copyright 2022, Machine Intelligence and Data Science Laboratory - UFMG - Brazil.
-      Created using <a href="https://www.sphinx-doc.org/">Sphinx</a> 4.3.2.
+      Created using <a href="https://www.sphinx-doc.org/">Sphinx</a> 4.5.0.
     </div>
   </body>
 </html>

docs/build/html/_modules/pyFTS/data/rossler.html

@@ -58,7 +58,7 @@
 
 <div class="viewcode-block" id="get_data"><a class="viewcode-back" href="../../../pyFTS.data.html#pyFTS.data.rossler.get_data">[docs]</a><span class="k">def</span> <span class="nf">get_data</span><span class="p">(</span><span class="n">var</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">a</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">0.2</span><span class="p">,</span> <span class="n">b</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">0.2</span><span class="p">,</span> <span class="n">c</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">5.7</span><span class="p">,</span> <span class="n">dt</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">0.01</span><span class="p">,</span>
                   <span class="n">initial_values</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span> <span class="o">=</span> <span class="p">[</span><span class="mf">0.001</span><span class="p">,</span> <span class="mf">0.001</span><span class="p">,</span> <span class="mf">0.001</span><span class="p">],</span> <span class="n">iterations</span><span class="p">:</span> <span class="nb">int</span><span class="o">=</span><span class="mi">5000</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+    <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Get a simple univariate time series data.</span>
 
 <span class="sd">    :param var: the dataset field name to extract</span>
@@ -69,7 +69,7 @@
 
 <div class="viewcode-block" id="get_dataframe"><a class="viewcode-back" href="../../../pyFTS.data.html#pyFTS.data.rossler.get_dataframe">[docs]</a><span class="k">def</span> <span class="nf">get_dataframe</span><span class="p">(</span><span class="n">a</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">0.2</span><span class="p">,</span> <span class="n">b</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">0.2</span><span class="p">,</span> <span class="n">c</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">5.7</span><span class="p">,</span> <span class="n">dt</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">0.01</span><span class="p">,</span>
                   <span class="n">initial_values</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span> <span class="o">=</span> <span class="p">[</span><span class="mf">0.001</span><span class="p">,</span> <span class="mf">0.001</span><span class="p">,</span> <span class="mf">0.001</span><span class="p">],</span> <span class="n">iterations</span><span class="p">:</span> <span class="nb">int</span><span class="o">=</span><span class="mi">5000</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">:</span>
-<span class="w">    </span><span class="sd">&#39;&#39;&#39;</span>
+    <span class="sd">&#39;&#39;&#39;</span>
 <span class="sd">    Return a dataframe with the multivariate Rössler Map time series (x, y, z).</span>
 
 <span class="sd">    :param a: Equation coefficient. Default value: 0.2</span>
@@ -135,7 +135,7 @@
     </div>
     <div class="footer" role="contentinfo">
         &#169; Copyright 2022, Machine Intelligence and Data Science Laboratory - UFMG - Brazil.
-      Created using <a href="https://www.sphinx-doc.org/">Sphinx</a> 4.3.2.
+      Created using <a href="https://www.sphinx-doc.org/">Sphinx</a> 4.5.0.
     </div>
   </body>
 </html>

docs/build/html/_modules/pyFTS/data/sunspots.html

@@ -49,12 +49,12 @@
 <span class="sd">Source: https://www.esrl.noaa.gov/psd/gcos_wgsp/Timeseries/SUNSPOT/</span>
 <span class="sd">&quot;&quot;&quot;</span>
 
-<span class="kn">from</span> <span class="nn">pyFTS.data</span> <span class="kn">import</span> <span class="n">common</span>
+<span class="kn">from</span> <span class="nn">pyFTS.data</span> <span class="k">import</span> <span class="n">common</span>
 <span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
 <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
 
 <div class="viewcode-block" id="get_data"><a class="viewcode-back" href="../../../pyFTS.data.html#pyFTS.data.sunspots.get_data">[docs]</a><span class="k">def</span> <span class="nf">get_data</span><span class="p">()</span> <span class="o">-&gt;</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+    <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Get a simple univariate time series data.</span>
 
 <span class="sd">    :return: numpy array</span>
@@ -64,7 +64,7 @@
     <span class="k">return</span> <span class="n">dat</span></div>
 
 <div class="viewcode-block" id="get_dataframe"><a class="viewcode-back" href="../../../pyFTS.data.html#pyFTS.data.sunspots.get_dataframe">[docs]</a><span class="k">def</span> <span class="nf">get_dataframe</span><span class="p">()</span> <span class="o">-&gt;</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">:</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+    <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Get the complete multivariate time series data.</span>
 
 <span class="sd">    :return: Pandas DataFrame</span>
@@ -115,7 +115,7 @@
     </div>
     <div class="footer" role="contentinfo">
         &#169; Copyright 2022, Machine Intelligence and Data Science Laboratory - UFMG - Brazil.
-      Created using <a href="https://www.sphinx-doc.org/">Sphinx</a> 4.3.2.
+      Created using <a href="https://www.sphinx-doc.org/">Sphinx</a> 4.5.0.
     </div>
   </body>
 </html>

docs/build/html/_modules/pyFTS/distributed/dispy.html

@@ -0,0 +1,271 @@
+
+
+<!doctype html>
+
+<html>
+  <head>
+    <meta charset="utf-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0"><script type="text/javascript">
+
+      var _gaq = _gaq || [];
+      _gaq.push(['_setAccount', 'UA-55120145-3']);
+      _gaq.push(['_trackPageview']);
+
+      (function() {
+        var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true;
+        ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js';
+        var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s);
+      })();
+    </script>
+    <title>pyFTS.distributed.dispy &#8212; pyFTS 1.6 documentation</title>
+    <link rel="stylesheet" href="../../../_static/bizstyle.css" type="text/css" />
+    <link rel="stylesheet" href="../../../_static/pygments.css" type="text/css" />
+    
+    <script id="documentation_options" data-url_root="../../../" src="../../../_static/documentation_options.js"></script>
+    <script src="../../../_static/jquery.js"></script>
+    <script src="../../../_static/underscore.js"></script>
+    <script src="../../../_static/doctools.js"></script>
+    <script src="../../../_static/language_data.js"></script>
+    <script src="../../../_static/bizstyle.js"></script>
+    <link rel="index" title="Index" href="../../../genindex.html" />
+    <link rel="search" title="Search" href="../../../search.html" />
+    <meta name="viewport" content="width=device-width,initial-scale=1.0">
+    <!--[if lt IE 9]>
+    <script src="_static/css3-mediaqueries.js"></script>
+    <![endif]-->
+  </head><body>
+    <div class="related" role="navigation" aria-label="related navigation">
+      <h3>Navigation</h3>
+      <ul>
+        <li class="right" style="margin-right: 10px">
+          <a href="../../../genindex.html" title="General Index"
+             accesskey="I">index</a></li>
+        <li class="right" >
+          <a href="../../../py-modindex.html" title="Python Module Index"
+             >modules</a> |</li>
+        <li class="nav-item nav-item-0"><a href="../../../index.html">pyFTS 1.6 documentation</a> &#187;</li>
+          <li class="nav-item nav-item-1"><a href="../../index.html" accesskey="U">Module code</a> &#187;</li>
+        <li class="nav-item nav-item-this"><a href="">pyFTS.distributed.dispy</a></li> 
+      </ul>
+    </div>  
+
+    <div class="document">
+      <div class="documentwrapper">
+        <div class="bodywrapper">
+          <div class="body" role="main">
+            
+  <h1>Source code for pyFTS.distributed.dispy</h1><div class="highlight"><pre>
+<span></span><span class="kn">import</span> <span class="nn">dispy</span> <span class="k">as</span> <span class="nn">dispy</span><span class="o">,</span> <span class="nn">dispy.httpd</span><span class="o">,</span> <span class="nn">logging</span>
+<span class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="kn">import</span> <span class="n">Util</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+
+
+<div class="viewcode-block" id="start_dispy_cluster"><a class="viewcode-back" href="../../../pyFTS.distributed.html#pyFTS.distributed.dispy.start_dispy_cluster">[docs]</a><span class="k">def</span> <span class="nf">start_dispy_cluster</span><span class="p">(</span><span class="n">method</span><span class="p">,</span> <span class="n">nodes</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Start a new Dispy cluster on &#39;nodes&#39; to execute the method &#39;method&#39;</span>
+
+<span class="sd">    :param method: function to be executed on each cluster node</span>
+<span class="sd">    :param nodes: list of node names or IP&#39;s.</span>
+<span class="sd">    :return: the dispy cluster instance and the http_server for monitoring</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">cluster</span> <span class="o">=</span> <span class="n">dispy</span><span class="o">.</span><span class="n">JobCluster</span><span class="p">(</span><span class="n">method</span><span class="p">,</span> <span class="n">nodes</span><span class="o">=</span><span class="n">nodes</span><span class="p">,</span> <span class="n">loglevel</span><span class="o">=</span><span class="n">logging</span><span class="o">.</span><span class="n">DEBUG</span><span class="p">,</span> <span class="n">ping_interval</span><span class="o">=</span><span class="mi">1000</span><span class="p">)</span>
+
+    <span class="n">http_server</span> <span class="o">=</span> <span class="n">dispy</span><span class="o">.</span><span class="n">httpd</span><span class="o">.</span><span class="n">DispyHTTPServer</span><span class="p">(</span><span class="n">cluster</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">cluster</span><span class="p">,</span> <span class="n">http_server</span></div>
+
+
+<div class="viewcode-block" id="stop_dispy_cluster"><a class="viewcode-back" href="../../../pyFTS.distributed.html#pyFTS.distributed.dispy.stop_dispy_cluster">[docs]</a><span class="k">def</span> <span class="nf">stop_dispy_cluster</span><span class="p">(</span><span class="n">cluster</span><span class="p">,</span> <span class="n">http_server</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Stop a dispy cluster and http_server</span>
+
+<span class="sd">    :param cluster:</span>
+<span class="sd">    :param http_server:</span>
+<span class="sd">    :return:</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="c1">#cluster.wait()  # wait for all jobs to finish</span>
+
+    <span class="n">cluster</span><span class="o">.</span><span class="n">print_status</span><span class="p">()</span>
+
+    <span class="n">http_server</span><span class="o">.</span><span class="n">shutdown</span><span class="p">()</span>  <span class="c1"># this waits until browser gets all updates</span>
+    <span class="n">cluster</span><span class="o">.</span><span class="n">close</span><span class="p">()</span></div>
+
+
+<div class="viewcode-block" id="get_number_of_cpus"><a class="viewcode-back" href="../../../pyFTS.distributed.html#pyFTS.distributed.dispy.get_number_of_cpus">[docs]</a><span class="k">def</span> <span class="nf">get_number_of_cpus</span><span class="p">(</span><span class="n">cluster</span><span class="p">):</span>
+    <span class="n">cpus</span> <span class="o">=</span> <span class="mi">0</span>
+    <span class="k">for</span> <span class="n">dispy_node</span> <span class="ow">in</span> <span class="n">cluster</span><span class="o">.</span><span class="n">status</span><span class="p">()</span><span class="o">.</span><span class="n">nodes</span><span class="p">:</span>
+        <span class="n">cpus</span> <span class="o">+=</span> <span class="n">dispy_node</span><span class="o">.</span><span class="n">cpus</span>
+
+    <span class="k">return</span> <span class="n">cpus</span></div>
+
+
+<div class="viewcode-block" id="simple_model_train"><a class="viewcode-back" href="../../../pyFTS.distributed.html#pyFTS.distributed.dispy.simple_model_train">[docs]</a><span class="k">def</span> <span class="nf">simple_model_train</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">parameters</span><span class="p">):</span>
+    <span class="kn">import</span> <span class="nn">time</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Cluster function that receives a FTS instance &#39;model&#39; and train using the &#39;data&#39; and &#39;parameters&#39;</span>
+
+<span class="sd">    :param model: a FTS instance</span>
+<span class="sd">    :param data: training dataset</span>
+<span class="sd">    :param parameters: parameters for the training process</span>
+<span class="sd">    :return: the trained model</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">_start</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
+    <span class="n">model</span><span class="o">.</span><span class="n">train</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">parameters</span><span class="p">)</span>
+    <span class="n">_end</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
+    <span class="n">model</span><span class="o">.</span><span class="vm">__dict__</span><span class="p">[</span><span class="s1">&#39;training_time&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">_end</span> <span class="o">-</span> <span class="n">_start</span>
+    <span class="k">return</span> <span class="n">model</span></div>
+
+
+<div class="viewcode-block" id="distributed_train"><a class="viewcode-back" href="../../../pyFTS.distributed.html#pyFTS.distributed.dispy.distributed_train">[docs]</a><span class="k">def</span> <span class="nf">distributed_train</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">train_method</span><span class="p">,</span> <span class="n">nodes</span><span class="p">,</span> <span class="n">fts_method</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">num_batches</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span>
+                      <span class="n">train_parameters</span><span class="o">=</span><span class="p">{},</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+    <span class="kn">import</span> <span class="nn">dispy</span><span class="o">,</span> <span class="nn">dispy.httpd</span><span class="o">,</span> <span class="nn">datetime</span>
+
+    <span class="n">batch_save</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;batch_save&#39;</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</span>  <span class="c1"># save model between batches</span>
+
+    <span class="n">batch_save_interval</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;batch_save_interval&#39;</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+
+    <span class="n">file_path</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;file_path&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+
+    <span class="n">cluster</span><span class="p">,</span> <span class="n">http_server</span> <span class="o">=</span> <span class="n">start_dispy_cluster</span><span class="p">(</span><span class="n">train_method</span><span class="p">,</span> <span class="n">nodes</span><span class="p">)</span>
+
+    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;[{0: %H:%M:%S}] Distrituted Train Started with </span><span class="si">{1}</span><span class="s2"> CPU&#39;s&quot;</span>
+          <span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">datetime</span><span class="o">.</span><span class="n">datetime</span><span class="o">.</span><span class="n">now</span><span class="p">(),</span> <span class="n">get_number_of_cpus</span><span class="p">(</span><span class="n">cluster</span><span class="p">)))</span>
+
+    <span class="n">jobs</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">n</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+    <span class="n">batch_size</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">n</span> <span class="o">/</span> <span class="n">num_batches</span><span class="p">)</span>
+    <span class="n">bcount</span> <span class="o">=</span> <span class="mi">1</span>
+    <span class="k">for</span> <span class="n">ct</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">order</span><span class="p">,</span> <span class="n">n</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">model</span><span class="o">.</span><span class="n">is_multivariate</span><span class="p">:</span>
+            <span class="n">ndata</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">ct</span> <span class="o">-</span> <span class="n">model</span><span class="o">.</span><span class="n">order</span><span class="p">:</span><span class="n">ct</span> <span class="o">+</span> <span class="n">batch_size</span><span class="p">]</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">ndata</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">ct</span> <span class="o">-</span> <span class="n">model</span><span class="o">.</span><span class="n">order</span><span class="p">:</span> <span class="n">ct</span> <span class="o">+</span> <span class="n">batch_size</span><span class="p">]</span>
+
+        <span class="n">tmp_model</span> <span class="o">=</span> <span class="n">fts_method</span><span class="p">()</span>
+
+        <span class="n">tmp_model</span><span class="o">.</span><span class="n">clone_parameters</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
+
+        <span class="n">job</span> <span class="o">=</span> <span class="n">cluster</span><span class="o">.</span><span class="n">submit</span><span class="p">(</span><span class="n">tmp_model</span><span class="p">,</span> <span class="n">ndata</span><span class="p">,</span> <span class="n">train_parameters</span><span class="p">)</span>
+        <span class="n">job</span><span class="o">.</span><span class="n">id</span> <span class="o">=</span> <span class="n">bcount</span>  <span class="c1"># associate an ID to identify jobs (if needed later)</span>
+        <span class="n">jobs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">job</span><span class="p">)</span>
+
+        <span class="n">bcount</span> <span class="o">+=</span> <span class="mi">1</span>
+
+    <span class="k">for</span> <span class="n">job</span> <span class="ow">in</span> <span class="n">jobs</span><span class="p">:</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;[{0: %H:%M:%S}] Processing batch &quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">datetime</span><span class="o">.</span><span class="n">datetime</span><span class="o">.</span><span class="n">now</span><span class="p">())</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">job</span><span class="o">.</span><span class="n">id</span><span class="p">))</span>
+        <span class="n">tmp</span> <span class="o">=</span> <span class="n">job</span><span class="p">()</span>
+        <span class="k">if</span> <span class="n">job</span><span class="o">.</span><span class="n">status</span> <span class="o">==</span> <span class="n">dispy</span><span class="o">.</span><span class="n">DispyJob</span><span class="o">.</span><span class="n">Finished</span> <span class="ow">and</span> <span class="n">tmp</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">model</span><span class="o">.</span><span class="n">merge</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span>
+            <span class="k">if</span> <span class="s1">&#39;training_time&#39;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">model</span><span class="o">.</span><span class="vm">__dict__</span><span class="p">:</span>
+                <span class="n">model</span><span class="o">.</span><span class="vm">__dict__</span><span class="p">[</span><span class="s1">&#39;training_time&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
+            <span class="n">model</span><span class="o">.</span><span class="vm">__dict__</span><span class="p">[</span><span class="s1">&#39;training_time&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">tmp</span><span class="o">.</span><span class="vm">__dict__</span><span class="p">[</span><span class="s1">&#39;training_time&#39;</span><span class="p">])</span>
+
+            <span class="k">if</span> <span class="n">batch_save</span> <span class="ow">and</span> <span class="p">(</span><span class="n">job</span><span class="o">.</span><span class="n">id</span> <span class="o">%</span> <span class="n">batch_save_interval</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+                <span class="n">Util</span><span class="o">.</span><span class="n">persist_obj</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">file_path</span><span class="p">)</span>
+
+        <span class="k">else</span><span class="p">:</span>
+            <span class="nb">print</span><span class="p">(</span><span class="n">job</span><span class="o">.</span><span class="n">exception</span><span class="p">)</span>
+            <span class="nb">print</span><span class="p">(</span><span class="n">job</span><span class="o">.</span><span class="n">stdout</span><span class="p">)</span>
+
+        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;[{0: %H:%M:%S}] Finished batch &quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">datetime</span><span class="o">.</span><span class="n">datetime</span><span class="o">.</span><span class="n">now</span><span class="p">())</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">job</span><span class="o">.</span><span class="n">id</span><span class="p">))</span>
+
+    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;[{0: %H:%M:%S}] Distrituted Train Finished&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">datetime</span><span class="o">.</span><span class="n">datetime</span><span class="o">.</span><span class="n">now</span><span class="p">()))</span>
+
+    <span class="n">stop_dispy_cluster</span><span class="p">(</span><span class="n">cluster</span><span class="p">,</span> <span class="n">http_server</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">model</span></div>
+
+
+<div class="viewcode-block" id="simple_model_predict"><a class="viewcode-back" href="../../../pyFTS.distributed.html#pyFTS.distributed.dispy.simple_model_predict">[docs]</a><span class="k">def</span> <span class="nf">simple_model_predict</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">parameters</span><span class="p">):</span>
+    <span class="kn">import</span> <span class="nn">time</span>
+    <span class="n">_start</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
+    <span class="n">forecasts</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">parameters</span><span class="p">)</span>
+    <span class="n">_stop</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
+    <span class="k">return</span> <span class="n">forecasts</span><span class="p">,</span> <span class="n">_stop</span> <span class="o">-</span> <span class="n">_start</span></div>
+
+
+<div class="viewcode-block" id="distributed_predict"><a class="viewcode-back" href="../../../pyFTS.distributed.html#pyFTS.distributed.dispy.distributed_predict">[docs]</a><span class="k">def</span> <span class="nf">distributed_predict</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">parameters</span><span class="p">,</span> <span class="n">nodes</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">num_batches</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+    <span class="kn">import</span> <span class="nn">dispy</span><span class="o">,</span> <span class="nn">dispy.httpd</span>
+
+    <span class="n">cluster</span><span class="p">,</span> <span class="n">http_server</span> <span class="o">=</span> <span class="n">start_dispy_cluster</span><span class="p">(</span><span class="n">simple_model_predict</span><span class="p">,</span> <span class="n">nodes</span><span class="p">)</span>
+
+    <span class="n">jobs</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">n</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+    <span class="n">batch_size</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">n</span> <span class="o">/</span> <span class="n">num_batches</span><span class="p">)</span>
+    <span class="n">bcount</span> <span class="o">=</span> <span class="mi">1</span>
+    <span class="k">for</span> <span class="n">ct</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">order</span><span class="p">,</span> <span class="n">n</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">model</span><span class="o">.</span><span class="n">is_multivariate</span><span class="p">:</span>
+            <span class="n">ndata</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">ct</span> <span class="o">-</span> <span class="n">model</span><span class="o">.</span><span class="n">order</span><span class="p">:</span><span class="n">ct</span> <span class="o">+</span> <span class="n">batch_size</span><span class="p">]</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">ndata</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">ct</span> <span class="o">-</span> <span class="n">model</span><span class="o">.</span><span class="n">order</span><span class="p">:</span> <span class="n">ct</span> <span class="o">+</span> <span class="n">batch_size</span><span class="p">]</span>
+
+        <span class="n">job</span> <span class="o">=</span> <span class="n">cluster</span><span class="o">.</span><span class="n">submit</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">ndata</span><span class="p">,</span> <span class="n">parameters</span><span class="p">)</span>
+        <span class="n">job</span><span class="o">.</span><span class="n">id</span> <span class="o">=</span> <span class="n">bcount</span>  <span class="c1"># associate an ID to identify jobs (if needed later)</span>
+        <span class="n">jobs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">job</span><span class="p">)</span>
+
+        <span class="n">bcount</span> <span class="o">+=</span> <span class="mi">1</span>
+
+    <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+
+    <span class="k">for</span> <span class="n">job</span> <span class="ow">in</span> <span class="n">jobs</span><span class="p">:</span>
+        <span class="n">tmp</span> <span class="o">=</span> <span class="n">job</span><span class="p">()</span>
+        <span class="k">if</span> <span class="n">job</span><span class="o">.</span><span class="n">status</span> <span class="o">==</span> <span class="n">dispy</span><span class="o">.</span><span class="n">DispyJob</span><span class="o">.</span><span class="n">Finished</span> <span class="ow">and</span> <span class="n">tmp</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">job</span><span class="o">.</span><span class="n">id</span> <span class="o">&lt;</span> <span class="n">batch_size</span><span class="p">:</span>
+                <span class="n">ret</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">tmp</span><span class="p">[</span><span class="mi">0</span><span class="p">][:</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">ret</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">tmp</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+
+            <span class="k">if</span> <span class="s1">&#39;forecasting_time&#39;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">model</span><span class="o">.</span><span class="vm">__dict__</span><span class="p">:</span>
+                <span class="n">model</span><span class="o">.</span><span class="vm">__dict__</span><span class="p">[</span><span class="s1">&#39;forecasting_time&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
+            <span class="n">model</span><span class="o">.</span><span class="vm">__dict__</span><span class="p">[</span><span class="s1">&#39;forecasting_time&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">tmp</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+
+        <span class="k">else</span><span class="p">:</span>
+            <span class="nb">print</span><span class="p">(</span><span class="n">job</span><span class="o">.</span><span class="n">exception</span><span class="p">)</span>
+            <span class="nb">print</span><span class="p">(</span><span class="n">job</span><span class="o">.</span><span class="n">stdout</span><span class="p">)</span>
+
+    <span class="n">stop_dispy_cluster</span><span class="p">(</span><span class="n">cluster</span><span class="p">,</span> <span class="n">http_server</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">ret</span></div>
+</pre></div>
+
+            <div class="clearer"></div>
+          </div>
+        </div>
+      </div>
+      <div class="sphinxsidebar" role="navigation" aria-label="main navigation">
+        <div class="sphinxsidebarwrapper">
+<div id="searchbox" style="display: none" role="search">
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+    <div class="searchformwrapper">
+    <form class="search" action="../../../search.html" method="get">
+      <input type="text" name="q" aria-labelledby="searchlabel" />
+      <input type="submit" value="Go" />
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+      <h3>Navigation</h3>
+      <ul>
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+          <a href="../../../genindex.html" title="General Index"
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+  <h1>Source code for pyFTS.hyperparam.Evolutionary</h1><div class="highlight"><pre>
+<span></span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">Distributed Evolutionary Hyperparameter Optimization (DEHO) for MVFTS</span>
+<span class="sd">&quot;&quot;&quot;</span>
+
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">import</span> <span class="nn">math</span>
+<span class="kn">import</span> <span class="nn">time</span>
+<span class="kn">from</span> <span class="nn">functools</span> <span class="k">import</span> <span class="n">reduce</span>
+<span class="kn">from</span> <span class="nn">operator</span> <span class="k">import</span> <span class="n">itemgetter</span>
+
+<span class="kn">import</span> <span class="nn">random</span>
+<span class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="k">import</span> <span class="n">Util</span>
+<span class="kn">from</span> <span class="nn">pyFTS.benchmarks</span> <span class="k">import</span> <span class="n">Measures</span>
+<span class="kn">from</span> <span class="nn">pyFTS.partitioners</span> <span class="k">import</span> <span class="n">Grid</span><span class="p">,</span> <span class="n">Entropy</span>  <span class="c1"># , Huarng</span>
+<span class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="k">import</span> <span class="n">Membership</span>
+<span class="kn">from</span> <span class="nn">pyFTS.models</span> <span class="k">import</span> <span class="n">hofts</span><span class="p">,</span> <span class="n">ifts</span><span class="p">,</span> <span class="n">pwfts</span>
+<span class="kn">from</span> <span class="nn">pyFTS.hyperparam</span> <span class="k">import</span> <span class="n">Util</span> <span class="k">as</span> <span class="n">hUtil</span>
+
+
+<span class="n">__measures</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;f1&#39;</span><span class="p">,</span> <span class="s1">&#39;f2&#39;</span><span class="p">,</span> <span class="s1">&#39;rmse&#39;</span><span class="p">,</span> <span class="s1">&#39;size&#39;</span><span class="p">]</span>
+
+
+<div class="viewcode-block" id="genotype"><a class="viewcode-back" href="../../../pyFTS.hyperparam.html#pyFTS.hyperparam.Evolutionary.genotype">[docs]</a><span class="k">def</span> <span class="nf">genotype</span><span class="p">(</span><span class="n">mf</span><span class="p">,</span> <span class="n">npart</span><span class="p">,</span> <span class="n">partitioner</span><span class="p">,</span> <span class="n">order</span><span class="p">,</span> <span class="n">alpha</span><span class="p">,</span> <span class="n">lags</span><span class="p">,</span> <span class="n">f1</span><span class="p">,</span> <span class="n">f2</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Create the individual genotype</span>
+
+<span class="sd">    :param mf: membership function</span>
+<span class="sd">    :param npart: number of partitions</span>
+<span class="sd">    :param partitioner: partitioner method</span>
+<span class="sd">    :param order: model order</span>
+<span class="sd">    :param alpha: alpha-cut</span>
+<span class="sd">    :param lags: array with lag indexes</span>
+<span class="sd">    :param f1: accuracy fitness value</span>
+<span class="sd">    :param f2: parsimony fitness value</span>
+<span class="sd">    :return: the genotype, a dictionary with all hyperparameters</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">ind</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="n">mf</span><span class="o">=</span><span class="n">mf</span><span class="p">,</span> <span class="n">npart</span><span class="o">=</span><span class="n">npart</span><span class="p">,</span> <span class="n">partitioner</span><span class="o">=</span><span class="n">partitioner</span><span class="p">,</span> <span class="n">order</span><span class="o">=</span><span class="n">order</span><span class="p">,</span>
+               <span class="n">alpha</span><span class="o">=</span><span class="n">alpha</span><span class="p">,</span> <span class="n">lags</span><span class="o">=</span><span class="n">lags</span><span class="p">,</span> <span class="n">f1</span><span class="o">=</span><span class="n">f1</span><span class="p">,</span> <span class="n">f2</span><span class="o">=</span><span class="n">f2</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">ind</span></div>
+
+
+<div class="viewcode-block" id="random_genotype"><a class="viewcode-back" href="../../../pyFTS.hyperparam.html#pyFTS.hyperparam.Evolutionary.random_genotype">[docs]</a><span class="k">def</span> <span class="nf">random_genotype</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Create random genotype</span>
+
+<span class="sd">    :return: the genotype, a dictionary with all hyperparameters</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">order</span> <span class="o">=</span> <span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
+    <span class="n">lags</span> <span class="o">=</span> <span class="p">[</span><span class="n">k</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">order</span><span class="o">+</span><span class="mi">1</span><span class="p">)]</span>
+    <span class="k">return</span> <span class="n">genotype</span><span class="p">(</span>
+        <span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">4</span><span class="p">),</span>
+        <span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">100</span><span class="p">),</span>
+        <span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span>
+        <span class="n">order</span><span class="p">,</span>
+        <span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="o">.</span><span class="mi">5</span><span class="p">),</span>
+        <span class="n">lags</span><span class="p">,</span>
+        <span class="kc">None</span><span class="p">,</span>
+        <span class="kc">None</span>
+    <span class="p">)</span></div>
+
+
+<span class="c1">#</span>
+<div class="viewcode-block" id="initial_population"><a class="viewcode-back" href="../../../pyFTS.hyperparam.html#pyFTS.hyperparam.Evolutionary.initial_population">[docs]</a><span class="k">def</span> <span class="nf">initial_population</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Create a random population of size n</span>
+
+<span class="sd">    :param n: the size of the population</span>
+<span class="sd">    :return: a list with n random individuals</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">create_random_individual</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;random_individual&#39;</span><span class="p">,</span> <span class="n">random_genotype</span><span class="p">)</span>
+
+    <span class="n">pop</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n</span><span class="p">):</span>
+        <span class="n">pop</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">create_random_individual</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">))</span>
+    <span class="k">return</span> <span class="n">pop</span></div>
+
+
+<div class="viewcode-block" id="phenotype"><a class="viewcode-back" href="../../../pyFTS.hyperparam.html#pyFTS.hyperparam.Evolutionary.phenotype">[docs]</a><span class="k">def</span> <span class="nf">phenotype</span><span class="p">(</span><span class="n">individual</span><span class="p">,</span> <span class="n">train</span><span class="p">,</span> <span class="n">fts_method</span><span class="p">,</span> <span class="n">parameters</span><span class="o">=</span><span class="p">{},</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Instantiate the genotype, creating a fitted model with the genotype hyperparameters</span>
+
+<span class="sd">    :param individual: a genotype</span>
+<span class="sd">    :param train: the training dataset</span>
+<span class="sd">    :param fts_method: the FTS method </span>
+<span class="sd">    :param parameters: dict with model specific arguments for fit method.</span>
+<span class="sd">    :return: a fitted FTS model</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="kn">from</span> <span class="nn">pyFTS.models</span> <span class="k">import</span> <span class="n">hofts</span><span class="p">,</span> <span class="n">ifts</span><span class="p">,</span> <span class="n">pwfts</span>
+
+    <span class="k">if</span> <span class="n">individual</span><span class="p">[</span><span class="s1">&#39;mf&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+        <span class="n">mf</span> <span class="o">=</span> <span class="n">Membership</span><span class="o">.</span><span class="n">trimf</span>
+    <span class="k">elif</span> <span class="n">individual</span><span class="p">[</span><span class="s1">&#39;mf&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
+        <span class="n">mf</span> <span class="o">=</span> <span class="n">Membership</span><span class="o">.</span><span class="n">trapmf</span>
+    <span class="k">elif</span> <span class="n">individual</span><span class="p">[</span><span class="s1">&#39;mf&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="mi">3</span> <span class="ow">and</span> <span class="n">individual</span><span class="p">[</span><span class="s1">&#39;partitioner&#39;</span><span class="p">]</span> <span class="o">!=</span> <span class="mi">2</span><span class="p">:</span>
+        <span class="n">mf</span> <span class="o">=</span> <span class="n">Membership</span><span class="o">.</span><span class="n">gaussmf</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">mf</span> <span class="o">=</span> <span class="n">Membership</span><span class="o">.</span><span class="n">trimf</span>
+
+    <span class="k">if</span> <span class="n">individual</span><span class="p">[</span><span class="s1">&#39;partitioner&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+        <span class="n">partitioner</span> <span class="o">=</span> <span class="n">Grid</span><span class="o">.</span><span class="n">GridPartitioner</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">train</span><span class="p">,</span> <span class="n">npart</span><span class="o">=</span><span class="n">individual</span><span class="p">[</span><span class="s1">&#39;npart&#39;</span><span class="p">],</span> <span class="n">func</span><span class="o">=</span><span class="n">mf</span><span class="p">)</span>
+    <span class="k">elif</span> <span class="n">individual</span><span class="p">[</span><span class="s1">&#39;partitioner&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
+        <span class="n">partitioner</span> <span class="o">=</span> <span class="n">Entropy</span><span class="o">.</span><span class="n">EntropyPartitioner</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">train</span><span class="p">,</span> <span class="n">npart</span><span class="o">=</span><span class="n">individual</span><span class="p">[</span><span class="s1">&#39;npart&#39;</span><span class="p">],</span> <span class="n">func</span><span class="o">=</span><span class="n">mf</span><span class="p">)</span>
+
+    <span class="n">model</span> <span class="o">=</span> <span class="n">fts_method</span><span class="p">(</span><span class="n">partitioner</span><span class="o">=</span><span class="n">partitioner</span><span class="p">,</span>
+                                       <span class="n">lags</span><span class="o">=</span><span class="n">individual</span><span class="p">[</span><span class="s1">&#39;lags&#39;</span><span class="p">],</span>
+                                       <span class="n">alpha_cut</span><span class="o">=</span><span class="n">individual</span><span class="p">[</span><span class="s1">&#39;alpha&#39;</span><span class="p">],</span>
+                                       <span class="n">order</span><span class="o">=</span><span class="n">individual</span><span class="p">[</span><span class="s1">&#39;order&#39;</span><span class="p">])</span>
+
+    <span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">train</span><span class="p">,</span> <span class="o">**</span><span class="n">parameters</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">model</span></div>
+
+
+<div class="viewcode-block" id="evaluate"><a class="viewcode-back" href="../../../pyFTS.hyperparam.html#pyFTS.hyperparam.Evolutionary.evaluate">[docs]</a><span class="k">def</span> <span class="nf">evaluate</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">individual</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Evaluate an individual using a sliding window cross validation over the dataset.</span>
+
+<span class="sd">    :param dataset: Evaluation dataset</span>
+<span class="sd">    :param individual: genotype to be tested</span>
+<span class="sd">    :param window_size: The length of scrolling window for train/test on dataset</span>
+<span class="sd">    :param train_rate: The train/test split ([0,1])</span>
+<span class="sd">    :param increment_rate: The increment of the scrolling window, relative to the window_size ([0,1])</span>
+<span class="sd">    :param parameters: dict with model specific arguments for fit method.</span>
+<span class="sd">    :return: a tuple (len_lags, rmse) with the parsimony fitness value and the accuracy fitness value</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="kn">from</span> <span class="nn">pyFTS.models</span> <span class="k">import</span> <span class="n">hofts</span><span class="p">,</span> <span class="n">ifts</span><span class="p">,</span> <span class="n">pwfts</span>
+    <span class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="k">import</span> <span class="n">Util</span>
+    <span class="kn">from</span> <span class="nn">pyFTS.benchmarks</span> <span class="k">import</span> <span class="n">Measures</span>
+    <span class="kn">from</span> <span class="nn">pyFTS.hyperparam.Evolutionary</span> <span class="k">import</span> <span class="n">phenotype</span><span class="p">,</span> <span class="n">__measures</span>
+    <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+
+    <span class="n">window_size</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;window_size&#39;</span><span class="p">,</span> <span class="mi">800</span><span class="p">)</span>
+    <span class="n">train_rate</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;train_rate&#39;</span><span class="p">,</span> <span class="o">.</span><span class="mi">8</span><span class="p">)</span>
+    <span class="n">increment_rate</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;increment_rate&#39;</span><span class="p">,</span> <span class="o">.</span><span class="mi">2</span><span class="p">)</span>
+    <span class="n">fts_method</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;fts_method&#39;</span><span class="p">,</span> <span class="n">hofts</span><span class="o">.</span><span class="n">WeightedHighOrderFTS</span><span class="p">)</span>
+    <span class="n">parameters</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;parameters&#39;</span><span class="p">,{})</span>
+
+    <span class="k">if</span> <span class="n">individual</span><span class="p">[</span><span class="s1">&#39;f1&#39;</span><span class="p">]</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">individual</span><span class="p">[</span><span class="s1">&#39;f2&#39;</span><span class="p">]</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="k">return</span> <span class="p">{</span> <span class="n">key</span><span class="p">:</span> <span class="n">individual</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">__measures</span> <span class="p">}</span>
+
+    <span class="n">errors</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">lengths</span> <span class="o">=</span> <span class="p">[]</span>
+
+    <span class="k">for</span> <span class="n">count</span><span class="p">,</span> <span class="n">train</span><span class="p">,</span> <span class="n">test</span> <span class="ow">in</span> <span class="n">Util</span><span class="o">.</span><span class="n">sliding_window</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">window_size</span><span class="p">,</span> <span class="n">train</span><span class="o">=</span><span class="n">train_rate</span><span class="p">,</span> <span class="n">inc</span><span class="o">=</span><span class="n">increment_rate</span><span class="p">):</span>
+
+        <span class="k">try</span><span class="p">:</span>
+
+            <span class="n">model</span> <span class="o">=</span> <span class="n">phenotype</span><span class="p">(</span><span class="n">individual</span><span class="p">,</span> <span class="n">train</span><span class="p">,</span> <span class="n">fts_method</span><span class="o">=</span><span class="n">fts_method</span><span class="p">,</span> <span class="n">parameters</span><span class="o">=</span><span class="n">parameters</span><span class="p">)</span>
+
+            <span class="n">forecasts</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">test</span><span class="p">)</span>
+
+            <span class="n">rmse</span> <span class="o">=</span> <span class="n">Measures</span><span class="o">.</span><span class="n">rmse</span><span class="p">(</span><span class="n">test</span><span class="p">[</span><span class="n">model</span><span class="o">.</span><span class="n">max_lag</span><span class="p">:],</span> <span class="n">forecasts</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
+            <span class="n">lengths</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">model</span><span class="p">))</span>
+
+            <span class="n">errors</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">rmse</span><span class="p">)</span>
+
+        <span class="k">except</span><span class="p">:</span>
+            <span class="n">lengths</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">)</span>
+            <span class="n">errors</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">)</span>
+
+    <span class="k">try</span><span class="p">:</span>
+        <span class="n">_lags</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">lags</span><span class="p">)</span> <span class="o">*</span> <span class="mi">100</span>
+
+        <span class="n">_rmse</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nanmean</span><span class="p">(</span><span class="n">errors</span><span class="p">)</span>
+        <span class="n">_len</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nanmean</span><span class="p">(</span><span class="n">lengths</span><span class="p">)</span>
+
+        <span class="n">f1</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">([</span><span class="o">.</span><span class="mi">6</span> <span class="o">*</span> <span class="n">_rmse</span><span class="p">,</span> <span class="o">.</span><span class="mi">4</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">nanstd</span><span class="p">(</span><span class="n">errors</span><span class="p">)])</span>
+        <span class="n">f2</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">([</span><span class="o">.</span><span class="mi">4</span> <span class="o">*</span> <span class="n">_len</span><span class="p">,</span> <span class="o">.</span><span class="mi">6</span> <span class="o">*</span> <span class="n">_lags</span><span class="p">])</span>
+
+        <span class="k">return</span> <span class="p">{</span><span class="s1">&#39;f1&#39;</span><span class="p">:</span> <span class="n">f1</span><span class="p">,</span> <span class="s1">&#39;f2&#39;</span><span class="p">:</span> <span class="n">f2</span><span class="p">,</span> <span class="s1">&#39;rmse&#39;</span><span class="p">:</span> <span class="n">_rmse</span><span class="p">,</span> <span class="s1">&#39;size&#39;</span><span class="p">:</span> <span class="n">_len</span> <span class="p">}</span>
+    <span class="k">except</span><span class="p">:</span>
+        <span class="k">return</span> <span class="p">{</span><span class="s1">&#39;f1&#39;</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">inf</span><span class="p">,</span> <span class="s1">&#39;f2&#39;</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">inf</span><span class="p">,</span> <span class="s1">&#39;rmse&#39;</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">inf</span><span class="p">,</span> <span class="s1">&#39;size&#39;</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">inf</span><span class="p">}</span></div>
+
+
+<div class="viewcode-block" id="tournament"><a class="viewcode-back" href="../../../pyFTS.hyperparam.html#pyFTS.hyperparam.Evolutionary.tournament">[docs]</a><span class="k">def</span> <span class="nf">tournament</span><span class="p">(</span><span class="n">population</span><span class="p">,</span> <span class="n">objective</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Simple tournament selection strategy.</span>
+
+<span class="sd">    :param population: the population</span>
+<span class="sd">    :param objective: the objective to be considered on tournament</span>
+<span class="sd">    :return:</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">n</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">population</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span>
+
+    <span class="n">r1</span> <span class="o">=</span> <span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">n</span><span class="p">)</span> <span class="k">if</span> <span class="n">n</span> <span class="o">&gt;</span> <span class="mi">2</span> <span class="k">else</span> <span class="mi">0</span>
+    <span class="n">r2</span> <span class="o">=</span> <span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">n</span><span class="p">)</span> <span class="k">if</span> <span class="n">n</span> <span class="o">&gt;</span> <span class="mi">2</span> <span class="k">else</span> <span class="mi">1</span>
+    <span class="n">ix</span> <span class="o">=</span> <span class="n">r1</span> <span class="k">if</span> <span class="n">population</span><span class="p">[</span><span class="n">r1</span><span class="p">][</span><span class="n">objective</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">population</span><span class="p">[</span><span class="n">r2</span><span class="p">][</span><span class="n">objective</span><span class="p">]</span> <span class="k">else</span> <span class="n">r2</span>
+    <span class="k">return</span> <span class="n">population</span><span class="p">[</span><span class="n">ix</span><span class="p">]</span></div>
+
+
+<div class="viewcode-block" id="double_tournament"><a class="viewcode-back" href="../../../pyFTS.hyperparam.html#pyFTS.hyperparam.Evolutionary.double_tournament">[docs]</a><span class="k">def</span> <span class="nf">double_tournament</span><span class="p">(</span><span class="n">population</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Double tournament selection strategy.</span>
+
+<span class="sd">    :param population:</span>
+<span class="sd">    :return:</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">ancestor1</span> <span class="o">=</span> <span class="n">tournament</span><span class="p">(</span><span class="n">population</span><span class="p">,</span> <span class="s1">&#39;f1&#39;</span><span class="p">)</span>
+    <span class="n">ancestor2</span> <span class="o">=</span> <span class="n">tournament</span><span class="p">(</span><span class="n">population</span><span class="p">,</span> <span class="s1">&#39;f1&#39;</span><span class="p">)</span>
+
+    <span class="n">selected</span> <span class="o">=</span> <span class="n">tournament</span><span class="p">([</span><span class="n">ancestor1</span><span class="p">,</span> <span class="n">ancestor2</span><span class="p">],</span> <span class="s1">&#39;f2&#39;</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">selected</span></div>
+
+
+<div class="viewcode-block" id="lag_crossover2"><a class="viewcode-back" href="../../../pyFTS.hyperparam.html#pyFTS.hyperparam.Evolutionary.lag_crossover2">[docs]</a><span class="k">def</span> <span class="nf">lag_crossover2</span><span class="p">(</span><span class="n">best</span><span class="p">,</span> <span class="n">worst</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Cross over two lag genes</span>
+
+<span class="sd">    :param best: best genotype</span>
+<span class="sd">    :param worst: worst genotype</span>
+<span class="sd">    :return: a tuple (order, lags)</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">order</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="o">.</span><span class="mi">7</span> <span class="o">*</span> <span class="n">best</span><span class="p">[</span><span class="s1">&#39;order&#39;</span><span class="p">]</span> <span class="o">+</span> <span class="o">.</span><span class="mi">3</span> <span class="o">*</span> <span class="n">worst</span><span class="p">[</span><span class="s1">&#39;order&#39;</span><span class="p">]))</span>
+    <span class="n">lags</span> <span class="o">=</span> <span class="p">[]</span>
+
+    <span class="n">min_order</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="n">best</span><span class="p">[</span><span class="s1">&#39;order&#39;</span><span class="p">],</span> <span class="n">worst</span><span class="p">[</span><span class="s1">&#39;order&#39;</span><span class="p">])</span>
+
+    <span class="n">max_order</span> <span class="o">=</span> <span class="n">best</span> <span class="k">if</span> <span class="n">best</span><span class="p">[</span><span class="s1">&#39;order&#39;</span><span class="p">]</span> <span class="o">&gt;</span> <span class="n">min_order</span> <span class="k">else</span> <span class="n">worst</span>
+
+    <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">order</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">k</span> <span class="o">&lt;</span> <span class="n">min_order</span><span class="p">:</span>
+            <span class="n">lags</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="o">.</span><span class="mi">7</span> <span class="o">*</span> <span class="n">best</span><span class="p">[</span><span class="s1">&#39;lags&#39;</span><span class="p">][</span><span class="n">k</span><span class="p">]</span> <span class="o">+</span> <span class="o">.</span><span class="mi">3</span> <span class="o">*</span> <span class="n">worst</span><span class="p">[</span><span class="s1">&#39;lags&#39;</span><span class="p">][</span><span class="n">k</span><span class="p">])))</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">lags</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">max_order</span><span class="p">[</span><span class="s1">&#39;lags&#39;</span><span class="p">][</span><span class="n">k</span><span class="p">])</span>
+
+    <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">order</span><span class="p">):</span>
+        <span class="k">while</span> <span class="n">lags</span><span class="p">[</span><span class="n">k</span> <span class="o">-</span> <span class="mi">1</span><span class="p">]</span> <span class="o">&gt;=</span> <span class="n">lags</span><span class="p">[</span><span class="n">k</span><span class="p">]:</span>
+            <span class="n">lags</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">+=</span> <span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">order</span><span class="p">,</span> <span class="n">lags</span></div>
+
+
+<div class="viewcode-block" id="crossover"><a class="viewcode-back" href="../../../pyFTS.hyperparam.html#pyFTS.hyperparam.Evolutionary.crossover">[docs]</a><span class="k">def</span> <span class="nf">crossover</span><span class="p">(</span><span class="n">population</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Crossover operation between two parents</span>
+
+<span class="sd">    :param population: the original population</span>
+<span class="sd">    :return: a genotype</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="kn">import</span> <span class="nn">random</span>
+
+    <span class="n">n</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">population</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span>
+
+    <span class="n">r1</span><span class="p">,</span> <span class="n">r2</span> <span class="o">=</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span>
+    <span class="k">while</span> <span class="n">r1</span> <span class="o">==</span> <span class="n">r2</span><span class="p">:</span>
+        <span class="n">r1</span> <span class="o">=</span> <span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">n</span><span class="p">)</span>
+        <span class="n">r2</span> <span class="o">=</span> <span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">n</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">population</span><span class="p">[</span><span class="n">r1</span><span class="p">][</span><span class="s1">&#39;f1&#39;</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">population</span><span class="p">[</span><span class="n">r2</span><span class="p">][</span><span class="s1">&#39;f1&#39;</span><span class="p">]:</span>
+        <span class="n">best</span> <span class="o">=</span> <span class="n">population</span><span class="p">[</span><span class="n">r1</span><span class="p">]</span>
+        <span class="n">worst</span> <span class="o">=</span> <span class="n">population</span><span class="p">[</span><span class="n">r2</span><span class="p">]</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">best</span> <span class="o">=</span> <span class="n">population</span><span class="p">[</span><span class="n">r2</span><span class="p">]</span>
+        <span class="n">worst</span> <span class="o">=</span> <span class="n">population</span><span class="p">[</span><span class="n">r1</span><span class="p">]</span>
+
+    <span class="n">npart</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="o">.</span><span class="mi">7</span> <span class="o">*</span> <span class="n">best</span><span class="p">[</span><span class="s1">&#39;npart&#39;</span><span class="p">]</span> <span class="o">+</span> <span class="o">.</span><span class="mi">3</span> <span class="o">*</span> <span class="n">worst</span><span class="p">[</span><span class="s1">&#39;npart&#39;</span><span class="p">]))</span>
+    <span class="n">alpha</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="o">.</span><span class="mi">7</span> <span class="o">*</span> <span class="n">best</span><span class="p">[</span><span class="s1">&#39;alpha&#39;</span><span class="p">]</span> <span class="o">+</span> <span class="o">.</span><span class="mi">3</span> <span class="o">*</span> <span class="n">worst</span><span class="p">[</span><span class="s1">&#39;alpha&#39;</span><span class="p">])</span>
+
+    <span class="n">rnd</span> <span class="o">=</span> <span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+    <span class="n">mf</span> <span class="o">=</span> <span class="n">best</span><span class="p">[</span><span class="s1">&#39;mf&#39;</span><span class="p">]</span> <span class="k">if</span> <span class="n">rnd</span> <span class="o">&lt;</span> <span class="o">.</span><span class="mi">7</span> <span class="k">else</span> <span class="n">worst</span><span class="p">[</span><span class="s1">&#39;mf&#39;</span><span class="p">]</span>
+
+    <span class="n">rnd</span> <span class="o">=</span> <span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+    <span class="n">partitioner</span> <span class="o">=</span> <span class="n">best</span><span class="p">[</span><span class="s1">&#39;partitioner&#39;</span><span class="p">]</span> <span class="k">if</span> <span class="n">rnd</span> <span class="o">&lt;</span> <span class="o">.</span><span class="mi">7</span> <span class="k">else</span> <span class="n">worst</span><span class="p">[</span><span class="s1">&#39;partitioner&#39;</span><span class="p">]</span>
+
+    <span class="n">order</span><span class="p">,</span> <span class="n">lags</span> <span class="o">=</span> <span class="n">lag_crossover2</span><span class="p">(</span><span class="n">best</span><span class="p">,</span> <span class="n">worst</span><span class="p">)</span>
+
+    <span class="n">descendent</span> <span class="o">=</span> <span class="n">genotype</span><span class="p">(</span><span class="n">mf</span><span class="p">,</span> <span class="n">npart</span><span class="p">,</span> <span class="n">partitioner</span><span class="p">,</span> <span class="n">order</span><span class="p">,</span> <span class="n">alpha</span><span class="p">,</span> <span class="n">lags</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">descendent</span></div>
+
+
+<div class="viewcode-block" id="mutation_lags"><a class="viewcode-back" href="../../../pyFTS.hyperparam.html#pyFTS.hyperparam.Evolutionary.mutation_lags">[docs]</a><span class="k">def</span> <span class="nf">mutation_lags</span><span class="p">(</span><span class="n">lags</span><span class="p">,</span> <span class="n">order</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Mutation operation for lags gene</span>
+
+<span class="sd">    :param lags:</span>
+<span class="sd">    :param order:</span>
+<span class="sd">    :return:</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">try</span><span class="p">:</span>
+        <span class="n">l</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">lags</span><span class="p">)</span>
+        <span class="n">new</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">for</span> <span class="n">lag</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">order</span><span class="p">):</span>
+            <span class="k">if</span> <span class="n">lag</span> <span class="o">&lt;</span> <span class="n">l</span><span class="p">:</span>
+                <span class="n">new</span><span class="o">.</span><span class="n">append</span><span class="p">(</span> <span class="nb">min</span><span class="p">(</span><span class="mi">50</span><span class="p">,</span> <span class="nb">max</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="nb">int</span><span class="p">(</span><span class="n">lags</span><span class="p">[</span><span class="n">lag</span><span class="p">]</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="o">-</span><span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">))))</span> <span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">new</span><span class="o">.</span><span class="n">append</span><span class="p">(</span> <span class="n">new</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span> <span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">order</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
+            <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">order</span><span class="p">):</span>
+                <span class="k">while</span> <span class="n">new</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="n">new</span><span class="p">[</span><span class="n">k</span> <span class="o">-</span> <span class="mi">1</span><span class="p">]:</span>
+                    <span class="n">new</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">new</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
+
+        <span class="k">return</span> <span class="n">new</span>
+    <span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">ex</span><span class="p">:</span>
+        <span class="nb">print</span><span class="p">(</span><span class="n">lags</span><span class="p">,</span> <span class="n">order</span><span class="p">,</span> <span class="n">new</span><span class="p">,</span> <span class="n">lag</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="mutation"><a class="viewcode-back" href="../../../pyFTS.hyperparam.html#pyFTS.hyperparam.Evolutionary.mutation">[docs]</a><span class="k">def</span> <span class="nf">mutation</span><span class="p">(</span><span class="n">individual</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Mutation operator</span>
+
+<span class="sd">    :param individual: an individual genotype</span>
+<span class="sd">    :param pmut: individual probability o</span>
+<span class="sd">    :return:</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">individual</span><span class="p">[</span><span class="s1">&#39;npart&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="mi">50</span><span class="p">,</span> <span class="nb">max</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="nb">int</span><span class="p">(</span><span class="n">individual</span><span class="p">[</span><span class="s1">&#39;npart&#39;</span><span class="p">]</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">normal</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">4</span><span class="p">))))</span>
+    <span class="n">individual</span><span class="p">[</span><span class="s1">&#39;alpha&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="o">.</span><span class="mi">5</span><span class="p">,</span> <span class="nb">max</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">individual</span><span class="p">[</span><span class="s1">&#39;alpha&#39;</span><span class="p">]</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">normal</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="o">.</span><span class="mi">5</span><span class="p">)))</span>
+    <span class="n">individual</span><span class="p">[</span><span class="s1">&#39;mf&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
+    <span class="n">individual</span><span class="p">[</span><span class="s1">&#39;partitioner&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
+    <span class="n">individual</span><span class="p">[</span><span class="s1">&#39;order&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="nb">max</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="nb">int</span><span class="p">(</span><span class="n">individual</span><span class="p">[</span><span class="s1">&#39;order&#39;</span><span class="p">]</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">normal</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">))))</span>
+    <span class="c1"># Chama a função mutation_lags</span>
+    <span class="n">individual</span><span class="p">[</span><span class="s1">&#39;lags&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">mutation_lags</span><span class="p">(</span> <span class="n">individual</span><span class="p">[</span><span class="s1">&#39;lags&#39;</span><span class="p">],</span>  <span class="n">individual</span><span class="p">[</span><span class="s1">&#39;order&#39;</span><span class="p">])</span>
+
+    <span class="n">individual</span><span class="p">[</span><span class="s1">&#39;f1&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span>
+    <span class="n">individual</span><span class="p">[</span><span class="s1">&#39;f2&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span>
+
+    <span class="k">return</span> <span class="n">individual</span></div>
+
+
+<div class="viewcode-block" id="elitism"><a class="viewcode-back" href="../../../pyFTS.hyperparam.html#pyFTS.hyperparam.Evolutionary.elitism">[docs]</a><span class="k">def</span> <span class="nf">elitism</span><span class="p">(</span><span class="n">population</span><span class="p">,</span> <span class="n">new_population</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Elitism operation, always select the best individual of the population and discard the worst</span>
+
+<span class="sd">    :param population:</span>
+<span class="sd">    :param new_population:</span>
+<span class="sd">    :return:</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">population</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">population</span><span class="p">,</span> <span class="n">key</span><span class="o">=</span><span class="n">itemgetter</span><span class="p">(</span><span class="s1">&#39;f1&#39;</span><span class="p">))</span>
+    <span class="n">best</span> <span class="o">=</span> <span class="n">population</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+
+    <span class="n">new_population</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">new_population</span><span class="p">,</span> <span class="n">key</span><span class="o">=</span><span class="n">itemgetter</span><span class="p">(</span><span class="s1">&#39;f1&#39;</span><span class="p">))</span>
+    <span class="k">if</span> <span class="n">new_population</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="s2">&quot;f1&quot;</span><span class="p">]</span> <span class="o">&gt;</span> <span class="n">best</span><span class="p">[</span><span class="s2">&quot;f1&quot;</span><span class="p">]:</span>
+        <span class="n">new_population</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="n">best</span><span class="p">)</span>
+    <span class="k">elif</span> <span class="n">new_population</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="s2">&quot;f1&quot;</span><span class="p">]</span> <span class="o">==</span> <span class="n">best</span><span class="p">[</span><span class="s2">&quot;f1&quot;</span><span class="p">]</span> <span class="ow">and</span> <span class="n">new_population</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="s2">&quot;f2&quot;</span><span class="p">]</span> <span class="o">&gt;</span> <span class="n">best</span><span class="p">[</span><span class="s2">&quot;f2&quot;</span><span class="p">]:</span>
+        <span class="n">new_population</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">best</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">new_population</span></div>
+
+
+<div class="viewcode-block" id="GeneticAlgorithm"><a class="viewcode-back" href="../../../pyFTS.hyperparam.html#pyFTS.hyperparam.Evolutionary.GeneticAlgorithm">[docs]</a><span class="k">def</span> <span class="nf">GeneticAlgorithm</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Genetic algoritm for Distributed Evolutionary Hyperparameter Optimization (DEHO)</span>
+
+<span class="sd">    :param dataset: The time series to optimize the FTS</span>
+<span class="sd">    :keyword ngen: An integer value with the maximum number of generations, default value: 30</span>
+<span class="sd">    :keyword mgen: An integer value with the maximum number of generations without improvement to stop, default value 7</span>
+<span class="sd">    :keyword npop: An integer value with the population size, default value: 20</span>
+<span class="sd">    :keyword pcross: A float value between 0 and 1 with the probability of crossover, default: .5</span>
+<span class="sd">    :keyword psel: A float value between 0 and 1 with the probability of selection, default: .5</span>
+<span class="sd">    :keyword pmut: A float value between 0 and 1 with the probability of mutation, default: .3</span>
+<span class="sd">    :keyword fts_method: The FTS method to optimize</span>
+<span class="sd">    :keyword parameters: dict with model specific arguments for fts_method</span>
+<span class="sd">    :keyword elitism: A boolean value indicating if the best individual must always survive to next population</span>
+<span class="sd">    :keyword initial_operator: a function that receives npop and return a random population with size npop</span>
+<span class="sd">    :keyword evalutation_operator: a function that receives a dataset and an individual and return its fitness</span>
+<span class="sd">    :keyword selection_operator: a function that receives the whole population and return a selected individual</span>
+<span class="sd">    :keyword crossover_operator: a function that receives the whole population and return a descendent individual</span>
+<span class="sd">    :keyword mutation_operator: a function that receives one individual and return a changed individual</span>
+<span class="sd">    :keyword window_size: An integer value with the the length of scrolling window for train/test on dataset</span>
+<span class="sd">    :keyword train_rate: A float value between 0 and 1 with the train/test split ([0,1])</span>
+<span class="sd">    :keyword increment_rate: A float value between 0 and 1 with the the increment of the scrolling window,</span>
+<span class="sd">             relative to the window_size ([0,1])</span>
+<span class="sd">    :keyword collect_statistics: A boolean value indicating to collect statistics for each generation</span>
+<span class="sd">    :keyword distributed: A value indicating it the execution will be local and sequential (distributed=False),</span>
+<span class="sd">             or parallel and distributed (distributed=&#39;dispy&#39; or distributed=&#39;spark&#39;)</span>
+<span class="sd">    :keyword cluster: If distributed=&#39;dispy&#39; the list of cluster nodes, else if distributed=&#39;spark&#39; it is the master node</span>
+<span class="sd">    :return: the best genotype</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">statistics</span> <span class="o">=</span> <span class="p">[]</span>
+
+    <span class="n">ngen</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;ngen&#39;</span><span class="p">,</span><span class="mi">30</span><span class="p">)</span>
+    <span class="n">mgen</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;mgen&#39;</span><span class="p">,</span> <span class="mi">7</span><span class="p">)</span>
+    <span class="n">npop</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;npop&#39;</span><span class="p">,</span><span class="mi">20</span><span class="p">)</span>
+    <span class="n">psel</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;psel&#39;</span><span class="p">,</span> <span class="o">.</span><span class="mi">5</span><span class="p">)</span>
+    <span class="n">pcross</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;pcross&#39;</span><span class="p">,</span><span class="o">.</span><span class="mi">5</span><span class="p">)</span>
+    <span class="n">pmut</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;pmut&#39;</span><span class="p">,</span><span class="o">.</span><span class="mi">3</span><span class="p">)</span>
+    <span class="n">distributed</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;distributed&#39;</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</span>
+
+    <span class="n">initial_operator</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;initial_operator&#39;</span><span class="p">,</span> <span class="n">initial_population</span><span class="p">)</span>
+    <span class="n">evaluation_operator</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;evaluation_operator&#39;</span><span class="p">,</span> <span class="n">evaluate</span><span class="p">)</span>
+    <span class="n">selection_operator</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;selection_operator&#39;</span><span class="p">,</span> <span class="n">double_tournament</span><span class="p">)</span>
+    <span class="n">crossover_operator</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;crossover_operator&#39;</span><span class="p">,</span> <span class="n">crossover</span><span class="p">)</span>
+    <span class="n">mutation_operator</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;mutation_operator&#39;</span><span class="p">,</span> <span class="n">mutation</span><span class="p">)</span>
+
+    <span class="n">_elitism</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;elitism&#39;</span><span class="p">,</span> <span class="kc">True</span><span class="p">)</span>
+
+    <span class="n">elitism_operator</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;elitism_operator&#39;</span><span class="p">,</span> <span class="n">elitism</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">distributed</span> <span class="o">==</span> <span class="s1">&#39;dispy&#39;</span><span class="p">:</span>
+        <span class="n">cluster</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">&#39;cluster&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+
+    <span class="n">collect_statistics</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;collect_statistics&#39;</span><span class="p">,</span> <span class="kc">True</span><span class="p">)</span>
+
+    <span class="n">no_improvement_count</span> <span class="o">=</span> <span class="mi">0</span>
+
+    <span class="n">new_population</span> <span class="o">=</span> <span class="p">[]</span>
+
+    <span class="n">population</span> <span class="o">=</span> <span class="n">initial_operator</span><span class="p">(</span><span class="n">npop</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+    <span class="n">last_best</span> <span class="o">=</span> <span class="n">population</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+    <span class="n">best</span> <span class="o">=</span> <span class="n">population</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+
+    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Evaluating initial population </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()))</span>
+    <span class="k">if</span> <span class="ow">not</span> <span class="n">distributed</span><span class="p">:</span>
+        <span class="k">for</span> <span class="n">individual</span> <span class="ow">in</span> <span class="n">population</span><span class="p">:</span>
+            <span class="n">ret</span> <span class="o">=</span> <span class="n">evaluation_operator</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">individual</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+            <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">__measures</span><span class="p">:</span>
+                <span class="n">individual</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="n">ret</span><span class="p">[</span><span class="n">key</span><span class="p">]</span>
+    <span class="k">elif</span> <span class="n">distributed</span><span class="o">==</span><span class="s1">&#39;dispy&#39;</span><span class="p">:</span>
+        <span class="kn">from</span> <span class="nn">pyFTS.distributed</span> <span class="k">import</span> <span class="n">dispy</span> <span class="k">as</span> <span class="n">dUtil</span>
+        <span class="kn">import</span> <span class="nn">dispy</span>
+        <span class="n">jobs</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">for</span> <span class="n">ct</span><span class="p">,</span> <span class="n">individual</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">population</span><span class="p">):</span>
+            <span class="n">job</span> <span class="o">=</span> <span class="n">cluster</span><span class="o">.</span><span class="n">submit</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">individual</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+            <span class="n">job</span><span class="o">.</span><span class="n">id</span> <span class="o">=</span> <span class="n">ct</span>
+            <span class="n">jobs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">job</span><span class="p">)</span>
+        <span class="k">for</span> <span class="n">job</span> <span class="ow">in</span> <span class="n">jobs</span><span class="p">:</span>
+            <span class="n">result</span> <span class="o">=</span> <span class="n">job</span><span class="p">()</span>
+            <span class="k">if</span> <span class="n">job</span><span class="o">.</span><span class="n">status</span> <span class="o">==</span> <span class="n">dispy</span><span class="o">.</span><span class="n">DispyJob</span><span class="o">.</span><span class="n">Finished</span> <span class="ow">and</span> <span class="n">result</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">__measures</span><span class="p">:</span>
+                    <span class="n">population</span><span class="p">[</span><span class="n">job</span><span class="o">.</span><span class="n">id</span><span class="p">][</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="n">result</span><span class="p">[</span><span class="n">key</span><span class="p">]</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="nb">print</span><span class="p">(</span><span class="n">job</span><span class="o">.</span><span class="n">exception</span><span class="p">)</span>
+                <span class="nb">print</span><span class="p">(</span><span class="n">job</span><span class="o">.</span><span class="n">stdout</span><span class="p">)</span>
+
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">ngen</span><span class="p">):</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;GENERATION </span><span class="si">{}</span><span class="s2"> </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()))</span>
+
+        <span class="n">generation_statistics</span> <span class="o">=</span> <span class="p">{}</span>
+
+        <span class="c1"># Selection</span>
+        <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">npop</span> <span class="o">*</span> <span class="n">psel</span><span class="p">)):</span>
+            <span class="n">new_population</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">selection_operator</span><span class="p">(</span><span class="n">population</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">))</span>
+
+        <span class="c1"># Crossover</span>
+        <span class="n">new</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">npop</span> <span class="o">*</span> <span class="n">pcross</span><span class="p">)):</span>
+            <span class="n">new</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">crossover_operator</span><span class="p">(</span><span class="n">new_population</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">))</span>
+
+        <span class="n">new_population</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">new</span><span class="p">)</span>
+
+        <span class="c1"># Mutation</span>
+        <span class="k">for</span> <span class="n">ct</span><span class="p">,</span> <span class="n">individual</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">new_population</span><span class="p">):</span>
+            <span class="n">rnd</span> <span class="o">=</span> <span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+            <span class="k">if</span> <span class="n">rnd</span> <span class="o">&lt;</span> <span class="n">pmut</span><span class="p">:</span>
+                <span class="n">new_population</span><span class="p">[</span><span class="n">ct</span><span class="p">]</span> <span class="o">=</span> <span class="n">mutation_operator</span><span class="p">(</span><span class="n">individual</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+        <span class="c1"># Evaluation</span>
+        <span class="k">if</span> <span class="n">collect_statistics</span><span class="p">:</span>
+            <span class="n">stats</span> <span class="o">=</span> <span class="p">{}</span>
+            <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">__measures</span><span class="p">:</span>
+                <span class="n">stats</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="k">if</span> <span class="ow">not</span> <span class="n">distributed</span><span class="p">:</span>
+            <span class="k">for</span> <span class="n">individual</span> <span class="ow">in</span> <span class="n">new_population</span><span class="p">:</span>
+                <span class="n">ret</span> <span class="o">=</span> <span class="n">evaluation_operator</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">individual</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+                <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">__measures</span><span class="p">:</span>
+                    <span class="n">individual</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="n">ret</span><span class="p">[</span><span class="n">key</span><span class="p">]</span>
+                    <span class="k">if</span> <span class="n">collect_statistics</span><span class="p">:</span> <span class="n">stats</span><span class="p">[</span><span class="n">key</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">ret</span><span class="p">[</span><span class="n">key</span><span class="p">])</span>
+
+        <span class="k">elif</span> <span class="n">distributed</span> <span class="o">==</span> <span class="s1">&#39;dispy&#39;</span><span class="p">:</span>
+            <span class="n">jobs</span> <span class="o">=</span> <span class="p">[]</span>
+
+            <span class="k">for</span> <span class="n">ct</span><span class="p">,</span> <span class="n">individual</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">new_population</span><span class="p">):</span>
+                <span class="n">job</span> <span class="o">=</span> <span class="n">cluster</span><span class="o">.</span><span class="n">submit</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">individual</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+                <span class="n">job</span><span class="o">.</span><span class="n">id</span> <span class="o">=</span> <span class="n">ct</span>
+                <span class="n">jobs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">job</span><span class="p">)</span>
+            <span class="k">for</span> <span class="n">job</span> <span class="ow">in</span> <span class="n">jobs</span><span class="p">:</span>
+                <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;job id </span><span class="si">{}</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">job</span><span class="o">.</span><span class="n">id</span><span class="p">))</span>
+                <span class="n">result</span> <span class="o">=</span> <span class="n">job</span><span class="p">()</span>
+                <span class="k">if</span> <span class="n">job</span><span class="o">.</span><span class="n">status</span> <span class="o">==</span> <span class="n">dispy</span><span class="o">.</span><span class="n">DispyJob</span><span class="o">.</span><span class="n">Finished</span> <span class="ow">and</span> <span class="n">result</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                    <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">__measures</span><span class="p">:</span>
+                        <span class="n">new_population</span><span class="p">[</span><span class="n">job</span><span class="o">.</span><span class="n">id</span><span class="p">][</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="n">result</span><span class="p">[</span><span class="n">key</span><span class="p">]</span>
+                        <span class="k">if</span> <span class="n">collect_statistics</span><span class="p">:</span> <span class="n">stats</span><span class="p">[</span><span class="n">key</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">result</span><span class="p">[</span><span class="n">key</span><span class="p">])</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="nb">print</span><span class="p">(</span><span class="n">job</span><span class="o">.</span><span class="n">exception</span><span class="p">)</span>
+                    <span class="nb">print</span><span class="p">(</span><span class="n">job</span><span class="o">.</span><span class="n">stdout</span><span class="p">)</span>
+
+
+        <span class="k">if</span> <span class="n">collect_statistics</span><span class="p">:</span>
+            <span class="n">mean_stats</span> <span class="o">=</span> <span class="p">{</span><span class="n">key</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">nanmedian</span><span class="p">(</span><span class="n">stats</span><span class="p">[</span><span class="n">key</span><span class="p">])</span> <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">__measures</span> <span class="p">}</span>
+
+            <span class="n">generation_statistics</span><span class="p">[</span><span class="s1">&#39;population&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">mean_stats</span>
+
+        <span class="c1"># Elitism</span>
+        <span class="k">if</span> <span class="n">_elitism</span><span class="p">:</span>
+            <span class="n">population</span> <span class="o">=</span> <span class="n">elitism_operator</span><span class="p">(</span><span class="n">population</span><span class="p">,</span> <span class="n">new_population</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+        <span class="n">population</span> <span class="o">=</span> <span class="n">population</span><span class="p">[:</span><span class="n">npop</span><span class="p">]</span>
+
+        <span class="n">new_population</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="n">last_best</span> <span class="o">=</span> <span class="n">best</span>
+
+        <span class="n">best</span> <span class="o">=</span> <span class="n">population</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+
+        <span class="k">if</span> <span class="n">collect_statistics</span><span class="p">:</span>
+            <span class="n">generation_statistics</span><span class="p">[</span><span class="s1">&#39;best&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="p">{</span><span class="n">key</span><span class="p">:</span> <span class="n">best</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">__measures</span> <span class="p">}</span>
+
+            <span class="n">statistics</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">generation_statistics</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">last_best</span><span class="p">[</span><span class="s1">&#39;f1&#39;</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="n">best</span><span class="p">[</span><span class="s1">&#39;f1&#39;</span><span class="p">]</span> <span class="ow">and</span> <span class="n">last_best</span><span class="p">[</span><span class="s1">&#39;f2&#39;</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="n">best</span><span class="p">[</span><span class="s1">&#39;f2&#39;</span><span class="p">]:</span>
+            <span class="n">no_improvement_count</span> <span class="o">+=</span> <span class="mi">1</span>
+            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;WITHOUT IMPROVEMENT </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">no_improvement_count</span><span class="p">))</span>
+            <span class="n">pmut</span> <span class="o">+=</span> <span class="o">.</span><span class="mi">05</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">no_improvement_count</span> <span class="o">=</span> <span class="mi">0</span>
+            <span class="n">pcross</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;pcross&#39;</span><span class="p">,</span> <span class="o">.</span><span class="mi">5</span><span class="p">)</span>
+            <span class="n">pmut</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;pmut&#39;</span><span class="p">,</span> <span class="o">.</span><span class="mi">3</span><span class="p">)</span>
+            <span class="nb">print</span><span class="p">(</span><span class="n">best</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">no_improvement_count</span> <span class="o">==</span> <span class="n">mgen</span><span class="p">:</span>
+            <span class="k">break</span>
+
+    <span class="k">return</span> <span class="n">best</span><span class="p">,</span> <span class="n">statistics</span></div>
+
+
+<div class="viewcode-block" id="process_experiment"><a class="viewcode-back" href="../../../pyFTS.hyperparam.html#pyFTS.hyperparam.Evolutionary.process_experiment">[docs]</a><span class="k">def</span> <span class="nf">process_experiment</span><span class="p">(</span><span class="n">fts_method</span><span class="p">,</span> <span class="n">result</span><span class="p">,</span> <span class="n">datasetname</span><span class="p">,</span> <span class="n">conn</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Persist the results of an DEHO execution in sqlite database (best hyperparameters) and json file (generation statistics)</span>
+
+<span class="sd">    :param fts_method:</span>
+<span class="sd">    :param result:</span>
+<span class="sd">    :param datasetname:</span>
+<span class="sd">    :param conn:</span>
+<span class="sd">    :return:</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">log_result</span><span class="p">(</span><span class="n">conn</span><span class="p">,</span> <span class="n">datasetname</span><span class="p">,</span> <span class="n">fts_method</span><span class="p">,</span> <span class="n">result</span><span class="p">[</span><span class="s1">&#39;individual&#39;</span><span class="p">])</span>
+    <span class="n">persist_statistics</span><span class="p">(</span><span class="n">datasetname</span><span class="p">,</span> <span class="n">result</span><span class="p">[</span><span class="s1">&#39;statistics&#39;</span><span class="p">])</span>
+    <span class="k">return</span> <span class="n">result</span><span class="p">[</span><span class="s1">&#39;individual&#39;</span><span class="p">]</span></div>
+
+
+<div class="viewcode-block" id="persist_statistics"><a class="viewcode-back" href="../../../pyFTS.hyperparam.html#pyFTS.hyperparam.Evolutionary.persist_statistics">[docs]</a><span class="k">def</span> <span class="nf">persist_statistics</span><span class="p">(</span><span class="n">datasetname</span><span class="p">,</span> <span class="n">statistics</span><span class="p">):</span>
+    <span class="kn">import</span> <span class="nn">json</span>
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="s1">&#39;statistics_</span><span class="si">{}</span><span class="s1">.json&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">datasetname</span><span class="p">),</span> <span class="s1">&#39;w&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">file</span><span class="p">:</span>
+        <span class="n">file</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="n">json</span><span class="o">.</span><span class="n">dumps</span><span class="p">(</span><span class="n">statistics</span><span class="p">))</span></div>
+
+
+<div class="viewcode-block" id="log_result"><a class="viewcode-back" href="../../../pyFTS.hyperparam.html#pyFTS.hyperparam.Evolutionary.log_result">[docs]</a><span class="k">def</span> <span class="nf">log_result</span><span class="p">(</span><span class="n">conn</span><span class="p">,</span> <span class="n">datasetname</span><span class="p">,</span> <span class="n">fts_method</span><span class="p">,</span> <span class="n">result</span><span class="p">):</span>
+    <span class="n">metrics</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;rmse&#39;</span><span class="p">,</span> <span class="s1">&#39;size&#39;</span><span class="p">,</span> <span class="s1">&#39;time&#39;</span><span class="p">]</span>
+    <span class="k">for</span> <span class="n">metric</span> <span class="ow">in</span> <span class="n">metrics</span><span class="p">:</span>
+        <span class="n">record</span> <span class="o">=</span> <span class="p">(</span><span class="n">datasetname</span><span class="p">,</span> <span class="s1">&#39;Evolutive&#39;</span><span class="p">,</span> <span class="n">fts_method</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="n">result</span><span class="p">[</span><span class="s1">&#39;mf&#39;</span><span class="p">],</span>
+                  <span class="n">result</span><span class="p">[</span><span class="s1">&#39;order&#39;</span><span class="p">],</span> <span class="n">result</span><span class="p">[</span><span class="s1">&#39;partitioner&#39;</span><span class="p">],</span> <span class="n">result</span><span class="p">[</span><span class="s1">&#39;npart&#39;</span><span class="p">],</span>
+                  <span class="n">result</span><span class="p">[</span><span class="s1">&#39;alpha&#39;</span><span class="p">],</span> <span class="nb">str</span><span class="p">(</span><span class="n">result</span><span class="p">[</span><span class="s1">&#39;lags&#39;</span><span class="p">]),</span> <span class="n">metric</span><span class="p">,</span> <span class="n">result</span><span class="p">[</span><span class="n">metric</span><span class="p">])</span>
+
+        <span class="nb">print</span><span class="p">(</span><span class="n">record</span><span class="p">)</span>
+
+        <span class="n">hUtil</span><span class="o">.</span><span class="n">insert_hyperparam</span><span class="p">(</span><span class="n">record</span><span class="p">,</span> <span class="n">conn</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="execute"><a class="viewcode-back" href="../../../pyFTS.hyperparam.html#pyFTS.hyperparam.Evolutionary.execute">[docs]</a><span class="k">def</span> <span class="nf">execute</span><span class="p">(</span><span class="n">datasetname</span><span class="p">,</span> <span class="n">dataset</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Batch execution of Distributed Evolutionary Hyperparameter Optimization (DEHO) for monovariate methods</span>
+
+<span class="sd">    :param datasetname:</span>
+<span class="sd">    :param dataset: The time series to optimize the FTS</span>
+<span class="sd">    :keyword file:</span>
+<span class="sd">    :keyword experiments:</span>
+<span class="sd">    :keyword distributed:</span>
+<span class="sd">    :keyword ngen: An integer value with the maximum number of generations, default value: 30</span>
+<span class="sd">    :keyword mgen: An integer value with the maximum number of generations without improvement to stop, default value 7</span>
+<span class="sd">    :keyword npop: An integer value with the population size, default value: 20</span>
+<span class="sd">    :keyword pcross: A float value between 0 and 1 with the probability of crossover, default: .5</span>
+<span class="sd">    :keyword psel: A float value between 0 and 1 with the probability of selection, default: .5</span>
+<span class="sd">    :keyword pmut: A float value between 0 and 1 with the probability of mutation, default: .3</span>
+<span class="sd">    :keyword fts_method: The FTS method to optimize</span>
+<span class="sd">    :keyword parameters: dict with model specific arguments for fts_method</span>
+<span class="sd">    :keyword elitism: A boolean value indicating if the best individual must always survive to next population</span>
+<span class="sd">    :keyword initial_operator: a function that receives npop and return a random population with size npop</span>
+<span class="sd">    :keyword random_individual: create an random genotype</span>
+<span class="sd">    :keyword evalutation_operator: a function that receives a dataset and an individual and return its fitness</span>
+<span class="sd">    :keyword selection_operator: a function that receives the whole population and return a selected individual</span>
+<span class="sd">    :keyword crossover_operator: a function that receives the whole population and return a descendent individual</span>
+<span class="sd">    :keyword mutation_operator: a function that receives one individual and return a changed individual</span>
+<span class="sd">    :keyword window_size: An integer value with the the length of scrolling window for train/test on dataset</span>
+<span class="sd">    :keyword train_rate: A float value between 0 and 1 with the train/test split ([0,1])</span>
+<span class="sd">    :keyword increment_rate: A float value between 0 and 1 with the the increment of the scrolling window,</span>
+<span class="sd">             relative to the window_size ([0,1])</span>
+<span class="sd">    :keyword collect_statistics: A boolean value indicating to collect statistics for each generation</span>
+<span class="sd">    :keyword distributed: A value indicating it the execution will be local and sequential (distributed=False),</span>
+<span class="sd">             or parallel and distributed (distributed=&#39;dispy&#39; or distributed=&#39;spark&#39;)</span>
+<span class="sd">    :keyword cluster: If distributed=&#39;dispy&#39; the list of cluster nodes, else if distributed=&#39;spark&#39; it is the master node</span>
+<span class="sd">    :return: the best genotype</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">file</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;file&#39;</span><span class="p">,</span> <span class="s1">&#39;hyperparam.db&#39;</span><span class="p">)</span>
+
+    <span class="n">conn</span> <span class="o">=</span> <span class="n">hUtil</span><span class="o">.</span><span class="n">open_hyperparam_db</span><span class="p">(</span><span class="n">file</span><span class="p">)</span>
+
+    <span class="n">experiments</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;experiments&#39;</span><span class="p">,</span> <span class="mi">30</span><span class="p">)</span>
+
+    <span class="n">distributed</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;distributed&#39;</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</span>
+
+    <span class="n">fts_method</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;fts_method&#39;</span><span class="p">,</span> <span class="n">hofts</span><span class="o">.</span><span class="n">WeightedHighOrderFTS</span><span class="p">)</span>
+    <span class="n">shortname</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="n">fts_method</span><span class="o">.</span><span class="vm">__module__</span><span class="p">)</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;.&#39;</span><span class="p">)[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
+
+    <span class="k">if</span> <span class="n">distributed</span> <span class="o">==</span> <span class="s1">&#39;dispy&#39;</span><span class="p">:</span>
+        <span class="kn">from</span> <span class="nn">pyFTS.distributed</span> <span class="k">import</span> <span class="n">dispy</span> <span class="k">as</span> <span class="n">dUtil</span>
+        <span class="n">nodes</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;nodes&#39;</span><span class="p">,</span> <span class="p">[</span><span class="s1">&#39;127.0.0.1&#39;</span><span class="p">])</span>
+        <span class="n">cluster</span><span class="p">,</span> <span class="n">http_server</span> <span class="o">=</span> <span class="n">dUtil</span><span class="o">.</span><span class="n">start_dispy_cluster</span><span class="p">(</span><span class="n">evaluate</span><span class="p">,</span> <span class="n">nodes</span><span class="o">=</span><span class="n">nodes</span><span class="p">)</span>
+        <span class="n">kwargs</span><span class="p">[</span><span class="s1">&#39;cluster&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">cluster</span>
+
+    <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">experiments</span><span class="p">):</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Experiment </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">i</span><span class="p">))</span>
+
+        <span class="n">start</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
+        <span class="n">ret</span><span class="p">,</span> <span class="n">statistics</span> <span class="o">=</span> <span class="n">GeneticAlgorithm</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="n">end</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
+        <span class="n">ret</span><span class="p">[</span><span class="s1">&#39;time&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">end</span> <span class="o">-</span> <span class="n">start</span>
+        <span class="n">experiment</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;individual&#39;</span><span class="p">:</span> <span class="n">ret</span><span class="p">,</span> <span class="s1">&#39;statistics&#39;</span><span class="p">:</span> <span class="n">statistics</span><span class="p">}</span>
+
+        <span class="n">ret</span> <span class="o">=</span> <span class="n">process_experiment</span><span class="p">(</span><span class="n">shortname</span><span class="p">,</span> <span class="n">experiment</span><span class="p">,</span> <span class="n">datasetname</span><span class="p">,</span> <span class="n">conn</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">distributed</span> <span class="o">==</span> <span class="s1">&#39;dispy&#39;</span><span class="p">:</span>
+        <span class="n">dUtil</span><span class="o">.</span><span class="n">stop_dispy_cluster</span><span class="p">(</span><span class="n">cluster</span><span class="p">,</span> <span class="n">http_server</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">ret</span></div>
+</pre></div>
+
+            <div class="clearer"></div>
+          </div>
+        </div>
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docs/build/html/_modules/pyFTS/hyperparam/GridSearch.html

@@ -0,0 +1,263 @@
+
+
+<!doctype html>
+
+<html>
+  <head>
+    <meta charset="utf-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+    <title>pyFTS.hyperparam.GridSearch &#8212; pyFTS 1.7 documentation</title>
+    <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css" />
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+          <a href="../../../py-modindex.html" title="Python Module Index"
+             >modules</a> |</li>
+        <li class="nav-item nav-item-0"><a href="../../../index.html">pyFTS 1.7 documentation</a> &#187;</li>
+          <li class="nav-item nav-item-1"><a href="../../index.html" accesskey="U">Module code</a> &#187;</li>
+        <li class="nav-item nav-item-this"><a href="">pyFTS.hyperparam.GridSearch</a></li> 
+      </ul>
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+
+    <div class="document">
+      <div class="documentwrapper">
+        <div class="bodywrapper">
+          <div class="body" role="main">
+            
+  <h1>Source code for pyFTS.hyperparam.GridSearch</h1><div class="highlight"><pre>
+<span></span>
+<span class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="k">import</span> <span class="n">Util</span><span class="p">,</span> <span class="n">Membership</span>
+<span class="kn">from</span> <span class="nn">pyFTS.models</span> <span class="k">import</span> <span class="n">hofts</span>
+<span class="kn">from</span> <span class="nn">pyFTS.partitioners</span> <span class="k">import</span> <span class="n">Grid</span><span class="p">,</span> <span class="n">Entropy</span>
+<span class="kn">from</span> <span class="nn">pyFTS.benchmarks</span> <span class="k">import</span> <span class="n">Measures</span>
+<span class="kn">from</span> <span class="nn">pyFTS.hyperparam</span> <span class="k">import</span> <span class="n">Util</span> <span class="k">as</span> <span class="n">hUtil</span>
+
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">itertools</span> <span class="k">import</span> <span class="n">product</span>
+
+
+<div class="viewcode-block" id="dict_individual"><a class="viewcode-back" href="../../../pyFTS.hyperparam.html#pyFTS.hyperparam.GridSearch.dict_individual">[docs]</a><span class="k">def</span> <span class="nf">dict_individual</span><span class="p">(</span><span class="n">mf</span><span class="p">,</span> <span class="n">partitioner</span><span class="p">,</span> <span class="n">partitions</span><span class="p">,</span> <span class="n">order</span><span class="p">,</span> <span class="n">lags</span><span class="p">,</span> <span class="n">alpha_cut</span><span class="p">):</span>
+    <span class="k">return</span> <span class="p">{</span>
+        <span class="s1">&#39;mf&#39;</span><span class="p">:</span> <span class="n">mf</span><span class="p">,</span>
+        <span class="s1">&#39;partitioner&#39;</span><span class="p">:</span> <span class="n">partitioner</span><span class="p">,</span>
+        <span class="s1">&#39;npart&#39;</span><span class="p">:</span> <span class="n">partitions</span><span class="p">,</span>
+        <span class="s1">&#39;alpha&#39;</span><span class="p">:</span> <span class="n">alpha_cut</span><span class="p">,</span>
+        <span class="s1">&#39;order&#39;</span><span class="p">:</span> <span class="n">order</span><span class="p">,</span>
+        <span class="s1">&#39;lags&#39;</span><span class="p">:</span> <span class="n">lags</span>
+    <span class="p">}</span></div>
+
+
+<div class="viewcode-block" id="cluster_method"><a class="viewcode-back" href="../../../pyFTS.hyperparam.html#pyFTS.hyperparam.GridSearch.cluster_method">[docs]</a><span class="k">def</span> <span class="nf">cluster_method</span><span class="p">(</span><span class="n">individual</span><span class="p">,</span> <span class="n">dataset</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+    <span class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="k">import</span> <span class="n">Util</span><span class="p">,</span> <span class="n">Membership</span>
+    <span class="kn">from</span> <span class="nn">pyFTS.models</span> <span class="k">import</span> <span class="n">hofts</span>
+    <span class="kn">from</span> <span class="nn">pyFTS.partitioners</span> <span class="k">import</span> <span class="n">Grid</span><span class="p">,</span> <span class="n">Entropy</span>
+    <span class="kn">from</span> <span class="nn">pyFTS.benchmarks</span> <span class="k">import</span> <span class="n">Measures</span>
+    <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+
+    <span class="k">if</span> <span class="n">individual</span><span class="p">[</span><span class="s1">&#39;mf&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+        <span class="n">mf</span> <span class="o">=</span> <span class="n">Membership</span><span class="o">.</span><span class="n">trimf</span>
+    <span class="k">elif</span> <span class="n">individual</span><span class="p">[</span><span class="s1">&#39;mf&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
+        <span class="n">mf</span> <span class="o">=</span> <span class="n">Membership</span><span class="o">.</span><span class="n">trapmf</span>
+    <span class="k">elif</span> <span class="n">individual</span><span class="p">[</span><span class="s1">&#39;mf&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="mi">3</span> <span class="ow">and</span> <span class="n">individual</span><span class="p">[</span><span class="s1">&#39;partitioner&#39;</span><span class="p">]</span> <span class="o">!=</span> <span class="mi">2</span><span class="p">:</span>
+        <span class="n">mf</span> <span class="o">=</span> <span class="n">Membership</span><span class="o">.</span><span class="n">gaussmf</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">mf</span> <span class="o">=</span> <span class="n">Membership</span><span class="o">.</span><span class="n">trimf</span>
+
+    <span class="n">window_size</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;window_size&#39;</span><span class="p">,</span> <span class="mi">800</span><span class="p">)</span>
+    <span class="n">train_rate</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;train_rate&#39;</span><span class="p">,</span> <span class="o">.</span><span class="mi">8</span><span class="p">)</span>
+    <span class="n">increment_rate</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;increment_rate&#39;</span><span class="p">,</span> <span class="o">.</span><span class="mi">2</span><span class="p">)</span>
+    <span class="n">parameters</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;parameters&#39;</span><span class="p">,</span> <span class="p">{})</span>
+
+    <span class="n">errors</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">sizes</span> <span class="o">=</span> <span class="p">[]</span>
+
+    <span class="k">for</span> <span class="n">count</span><span class="p">,</span> <span class="n">train</span><span class="p">,</span> <span class="n">test</span> <span class="ow">in</span> <span class="n">Util</span><span class="o">.</span><span class="n">sliding_window</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">window_size</span><span class="p">,</span> <span class="n">train</span><span class="o">=</span><span class="n">train_rate</span><span class="p">,</span> <span class="n">inc</span><span class="o">=</span><span class="n">increment_rate</span><span class="p">):</span>
+
+        <span class="k">if</span> <span class="n">individual</span><span class="p">[</span><span class="s1">&#39;partitioner&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+            <span class="n">partitioner</span> <span class="o">=</span> <span class="n">Grid</span><span class="o">.</span><span class="n">GridPartitioner</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">train</span><span class="p">,</span> <span class="n">npart</span><span class="o">=</span><span class="n">individual</span><span class="p">[</span><span class="s1">&#39;npart&#39;</span><span class="p">],</span> <span class="n">func</span><span class="o">=</span><span class="n">mf</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="n">individual</span><span class="p">[</span><span class="s1">&#39;partitioner&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
+            <span class="n">npart</span> <span class="o">=</span> <span class="n">individual</span><span class="p">[</span><span class="s1">&#39;npart&#39;</span><span class="p">]</span> <span class="k">if</span> <span class="n">individual</span><span class="p">[</span><span class="s1">&#39;npart&#39;</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mi">10</span> <span class="k">else</span> <span class="mi">10</span>
+            <span class="n">partitioner</span> <span class="o">=</span> <span class="n">Entropy</span><span class="o">.</span><span class="n">EntropyPartitioner</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">train</span><span class="p">,</span> <span class="n">npart</span><span class="o">=</span><span class="n">npart</span><span class="p">,</span> <span class="n">func</span><span class="o">=</span><span class="n">mf</span><span class="p">)</span>
+
+        <span class="n">model</span> <span class="o">=</span> <span class="n">hofts</span><span class="o">.</span><span class="n">WeightedHighOrderFTS</span><span class="p">(</span><span class="n">partitioner</span><span class="o">=</span><span class="n">partitioner</span><span class="p">,</span>
+                                   <span class="n">lags</span><span class="o">=</span><span class="n">individual</span><span class="p">[</span><span class="s1">&#39;lags&#39;</span><span class="p">],</span>
+                                   <span class="n">alpha_cut</span><span class="o">=</span><span class="n">individual</span><span class="p">[</span><span class="s1">&#39;alpha&#39;</span><span class="p">],</span>
+                                   <span class="n">order</span><span class="o">=</span><span class="n">individual</span><span class="p">[</span><span class="s1">&#39;order&#39;</span><span class="p">])</span>
+        <span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">train</span><span class="p">)</span>
+
+        <span class="n">forecasts</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">test</span><span class="p">)</span>
+
+        <span class="c1">#rmse, mape, u = Measures.get_point_statistics(test, model)</span>
+        <span class="n">rmse</span> <span class="o">=</span> <span class="n">Measures</span><span class="o">.</span><span class="n">rmse</span><span class="p">(</span><span class="n">test</span><span class="p">[</span><span class="n">model</span><span class="o">.</span><span class="n">max_lag</span><span class="p">:],</span> <span class="n">forecasts</span><span class="p">)</span>
+
+        <span class="n">size</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
+
+        <span class="n">errors</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">rmse</span><span class="p">)</span>
+        <span class="n">sizes</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">size</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="p">{</span><span class="s1">&#39;parameters&#39;</span><span class="p">:</span> <span class="n">individual</span><span class="p">,</span> <span class="s1">&#39;rmse&#39;</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">nanmean</span><span class="p">(</span><span class="n">errors</span><span class="p">),</span> <span class="s1">&#39;size&#39;</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">nanmean</span><span class="p">(</span><span class="n">size</span><span class="p">)}</span></div>
+
+
+<div class="viewcode-block" id="process_jobs"><a class="viewcode-back" href="../../../pyFTS.hyperparam.html#pyFTS.hyperparam.GridSearch.process_jobs">[docs]</a><span class="k">def</span> <span class="nf">process_jobs</span><span class="p">(</span><span class="n">jobs</span><span class="p">,</span> <span class="n">datasetname</span><span class="p">,</span> <span class="n">conn</span><span class="p">):</span>
+    <span class="kn">from</span> <span class="nn">pyFTS.distributed</span> <span class="k">import</span> <span class="n">dispy</span> <span class="k">as</span> <span class="n">dUtil</span>
+    <span class="kn">import</span> <span class="nn">dispy</span>
+    <span class="k">for</span> <span class="n">ct</span><span class="p">,</span> <span class="n">job</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">jobs</span><span class="p">):</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Processing job </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">ct</span><span class="p">))</span>
+        <span class="n">result</span> <span class="o">=</span> <span class="n">job</span><span class="p">()</span>
+        <span class="k">if</span> <span class="n">job</span><span class="o">.</span><span class="n">status</span> <span class="o">==</span> <span class="n">dispy</span><span class="o">.</span><span class="n">DispyJob</span><span class="o">.</span><span class="n">Finished</span> <span class="ow">and</span> <span class="n">result</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Processing result of </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">result</span><span class="p">))</span>
+            
+            <span class="n">metrics</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;rmse&#39;</span><span class="p">:</span> <span class="n">result</span><span class="p">[</span><span class="s1">&#39;rmse&#39;</span><span class="p">],</span> <span class="s1">&#39;size&#39;</span><span class="p">:</span> <span class="n">result</span><span class="p">[</span><span class="s1">&#39;size&#39;</span><span class="p">]}</span>
+            
+            <span class="k">for</span> <span class="n">metric</span> <span class="ow">in</span> <span class="n">metrics</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
+
+                <span class="n">param</span> <span class="o">=</span> <span class="n">result</span><span class="p">[</span><span class="s1">&#39;parameters&#39;</span><span class="p">]</span>
+
+                <span class="n">record</span> <span class="o">=</span> <span class="p">(</span><span class="n">datasetname</span><span class="p">,</span> <span class="s1">&#39;GridSearch&#39;</span><span class="p">,</span> <span class="s1">&#39;WHOFTS&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="n">param</span><span class="p">[</span><span class="s1">&#39;mf&#39;</span><span class="p">],</span>
+                          <span class="n">param</span><span class="p">[</span><span class="s1">&#39;order&#39;</span><span class="p">],</span> <span class="n">param</span><span class="p">[</span><span class="s1">&#39;partitioner&#39;</span><span class="p">],</span> <span class="n">param</span><span class="p">[</span><span class="s1">&#39;npart&#39;</span><span class="p">],</span>
+                          <span class="n">param</span><span class="p">[</span><span class="s1">&#39;alpha&#39;</span><span class="p">],</span> <span class="nb">str</span><span class="p">(</span><span class="n">param</span><span class="p">[</span><span class="s1">&#39;lags&#39;</span><span class="p">]),</span> <span class="n">metric</span><span class="p">,</span> <span class="n">metrics</span><span class="p">[</span><span class="n">metric</span><span class="p">])</span>
+
+
+                <span class="n">hUtil</span><span class="o">.</span><span class="n">insert_hyperparam</span><span class="p">(</span><span class="n">record</span><span class="p">,</span> <span class="n">conn</span><span class="p">)</span>
+
+        <span class="k">else</span><span class="p">:</span>
+            <span class="nb">print</span><span class="p">(</span><span class="n">job</span><span class="o">.</span><span class="n">exception</span><span class="p">)</span>
+            <span class="nb">print</span><span class="p">(</span><span class="n">job</span><span class="o">.</span><span class="n">stdout</span><span class="p">)</span></div>
+    
+
+<div class="viewcode-block" id="execute"><a class="viewcode-back" href="../../../pyFTS.hyperparam.html#pyFTS.hyperparam.GridSearch.execute">[docs]</a><span class="k">def</span> <span class="nf">execute</span><span class="p">(</span><span class="n">hyperparams</span><span class="p">,</span> <span class="n">datasetname</span><span class="p">,</span> <span class="n">dataset</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+    <span class="kn">from</span> <span class="nn">pyFTS.distributed</span> <span class="k">import</span> <span class="n">dispy</span> <span class="k">as</span> <span class="n">dUtil</span>
+    <span class="kn">import</span> <span class="nn">dispy</span>
+
+    <span class="n">nodes</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;nodes&#39;</span><span class="p">,[</span><span class="s1">&#39;127.0.0.1&#39;</span><span class="p">])</span>
+
+    <span class="n">individuals</span> <span class="o">=</span> <span class="p">[]</span>
+
+    <span class="k">if</span> <span class="s1">&#39;lags&#39;</span> <span class="ow">in</span> <span class="n">hyperparams</span><span class="p">:</span>
+        <span class="n">lags</span> <span class="o">=</span> <span class="n">hyperparams</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">&#39;lags&#39;</span><span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">lags</span> <span class="o">=</span> <span class="p">[</span><span class="n">k</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">50</span><span class="p">)]</span>
+
+    <span class="n">keys_sorted</span> <span class="o">=</span> <span class="p">[</span><span class="n">k</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">hyperparams</span><span class="o">.</span><span class="n">keys</span><span class="p">())]</span>
+
+    <span class="n">index</span> <span class="o">=</span> <span class="p">{}</span>
+    <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">keys_sorted</span><span class="p">)):</span>
+        <span class="n">index</span><span class="p">[</span><span class="n">keys_sorted</span><span class="p">[</span><span class="n">k</span><span class="p">]]</span> <span class="o">=</span> <span class="n">k</span>
+        
+    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Evaluation order: </span><span class="se">\n</span><span class="s2"> </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">index</span><span class="p">))</span>
+
+    <span class="n">hp_values</span> <span class="o">=</span> <span class="p">[</span>
+        <span class="p">[</span><span class="n">v</span> <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">hyperparams</span><span class="p">[</span><span class="n">hp</span><span class="p">]]</span>
+        <span class="k">for</span> <span class="n">hp</span> <span class="ow">in</span> <span class="n">keys_sorted</span>
+    <span class="p">]</span>
+    
+    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Evaluation values: </span><span class="se">\n</span><span class="s2"> </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">hp_values</span><span class="p">))</span>
+    
+    <span class="n">cluster</span><span class="p">,</span> <span class="n">http_server</span> <span class="o">=</span> <span class="n">dUtil</span><span class="o">.</span><span class="n">start_dispy_cluster</span><span class="p">(</span><span class="n">cluster_method</span><span class="p">,</span> <span class="n">nodes</span><span class="o">=</span><span class="n">nodes</span><span class="p">)</span>
+    <span class="n">file</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;file&#39;</span><span class="p">,</span> <span class="s1">&#39;hyperparam.db&#39;</span><span class="p">)</span>
+
+    <span class="n">conn</span> <span class="o">=</span> <span class="n">hUtil</span><span class="o">.</span><span class="n">open_hyperparam_db</span><span class="p">(</span><span class="n">file</span><span class="p">)</span>
+
+    <span class="k">for</span> <span class="n">instance</span> <span class="ow">in</span> <span class="n">product</span><span class="p">(</span><span class="o">*</span><span class="n">hp_values</span><span class="p">):</span>
+        <span class="n">partitions</span> <span class="o">=</span> <span class="n">instance</span><span class="p">[</span><span class="n">index</span><span class="p">[</span><span class="s1">&#39;partitions&#39;</span><span class="p">]]</span>
+        <span class="n">partitioner</span> <span class="o">=</span> <span class="n">instance</span><span class="p">[</span><span class="n">index</span><span class="p">[</span><span class="s1">&#39;partitioner&#39;</span><span class="p">]]</span>
+        <span class="n">mf</span> <span class="o">=</span> <span class="n">instance</span><span class="p">[</span><span class="n">index</span><span class="p">[</span><span class="s1">&#39;mf&#39;</span><span class="p">]]</span>
+        <span class="n">alpha_cut</span> <span class="o">=</span> <span class="n">instance</span><span class="p">[</span><span class="n">index</span><span class="p">[</span><span class="s1">&#39;alpha&#39;</span><span class="p">]]</span>
+        <span class="n">order</span> <span class="o">=</span> <span class="n">instance</span><span class="p">[</span><span class="n">index</span><span class="p">[</span><span class="s1">&#39;order&#39;</span><span class="p">]]</span>
+        <span class="n">count</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="k">for</span> <span class="n">lag1</span> <span class="ow">in</span> <span class="n">lags</span><span class="p">:</span> <span class="c1"># o é o lag1</span>
+            <span class="n">_lags</span> <span class="o">=</span> <span class="p">[</span><span class="n">lag1</span><span class="p">]</span>
+            <span class="n">count</span> <span class="o">+=</span> <span class="mi">1</span>
+            <span class="k">if</span> <span class="n">order</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
+                <span class="k">for</span> <span class="n">lag2</span> <span class="ow">in</span> <span class="n">lags</span><span class="p">:</span>  <span class="c1"># o é o lag1</span>
+                    <span class="n">_lags2</span> <span class="o">=</span> <span class="p">[</span><span class="n">lag1</span><span class="p">,</span> <span class="n">lag1</span><span class="o">+</span><span class="n">lag2</span><span class="p">]</span>
+                    <span class="n">count</span> <span class="o">+=</span> <span class="mi">1</span>
+                    <span class="k">if</span> <span class="n">order</span> <span class="o">&gt;</span> <span class="mi">2</span><span class="p">:</span>
+                        <span class="k">for</span> <span class="n">lag3</span> <span class="ow">in</span> <span class="n">lags</span><span class="p">:</span>  <span class="c1"># o é o lag1</span>
+                            <span class="n">count</span> <span class="o">+=</span> <span class="mi">1</span>
+                            <span class="n">_lags3</span> <span class="o">=</span> <span class="p">[</span><span class="n">lag1</span><span class="p">,</span> <span class="n">lag1</span> <span class="o">+</span> <span class="n">lag2</span><span class="p">,</span> <span class="n">lag1</span> <span class="o">+</span> <span class="n">lag2</span><span class="o">+</span><span class="n">lag3</span> <span class="p">]</span>
+                            <span class="n">individuals</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">dict_individual</span><span class="p">(</span><span class="n">mf</span><span class="p">,</span> <span class="n">partitioner</span><span class="p">,</span> <span class="n">partitions</span><span class="p">,</span> <span class="n">order</span><span class="p">,</span> <span class="n">_lags3</span><span class="p">,</span> <span class="n">alpha_cut</span><span class="p">))</span>
+                    <span class="k">else</span><span class="p">:</span>
+                        <span class="n">individuals</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
+                            <span class="n">dict_individual</span><span class="p">(</span><span class="n">mf</span><span class="p">,</span> <span class="n">partitioner</span><span class="p">,</span> <span class="n">partitions</span><span class="p">,</span> <span class="n">order</span><span class="p">,</span> <span class="n">_lags2</span><span class="p">,</span> <span class="n">alpha_cut</span><span class="p">))</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">individuals</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">dict_individual</span><span class="p">(</span><span class="n">mf</span><span class="p">,</span> <span class="n">partitioner</span><span class="p">,</span> <span class="n">partitions</span><span class="p">,</span> <span class="n">order</span><span class="p">,</span> <span class="n">_lags</span><span class="p">,</span> <span class="n">alpha_cut</span><span class="p">))</span>
+                
+            <span class="k">if</span> <span class="n">count</span> <span class="o">&gt;</span> <span class="mi">10</span><span class="p">:</span>
+                <span class="n">jobs</span> <span class="o">=</span> <span class="p">[]</span>
+
+                <span class="k">for</span> <span class="n">ind</span> <span class="ow">in</span> <span class="n">individuals</span><span class="p">:</span>
+                    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Testing individual </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">ind</span><span class="p">))</span>
+                    <span class="n">job</span> <span class="o">=</span> <span class="n">cluster</span><span class="o">.</span><span class="n">submit</span><span class="p">(</span><span class="n">ind</span><span class="p">,</span> <span class="n">dataset</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+                    <span class="n">jobs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">job</span><span class="p">)</span>
+                    
+                <span class="n">process_jobs</span><span class="p">(</span><span class="n">jobs</span><span class="p">,</span> <span class="n">datasetname</span><span class="p">,</span> <span class="n">conn</span><span class="p">)</span>
+                
+                <span class="n">count</span> <span class="o">=</span> <span class="mi">0</span>
+                
+                <span class="n">individuals</span> <span class="o">=</span> <span class="p">[]</span>
+
+    <span class="n">dUtil</span><span class="o">.</span><span class="n">stop_dispy_cluster</span><span class="p">(</span><span class="n">cluster</span><span class="p">,</span> <span class="n">http_server</span><span class="p">)</span></div>
+</pre></div>
+
+            <div class="clearer"></div>
+          </div>
+        </div>
+      </div>
+      <div class="sphinxsidebar" role="navigation" aria-label="main navigation">
+        <div class="sphinxsidebarwrapper">
+            <p class="logo"><a href="../../../index.html">
+              <img class="logo" src="../../../_static/logo_heading2.png" alt="Logo"/>
+            </a></p>
+<div id="searchbox" style="display: none" role="search">
+  <h3 id="searchlabel">Quick search</h3>
+    <div class="searchformwrapper">
+    <form class="search" action="../../../search.html" method="get">
+      <input type="text" name="q" aria-labelledby="searchlabel" autocomplete="off" autocorrect="off" autocapitalize="off" spellcheck="false"/>
+      <input type="submit" value="Go" />
+    </form>
+    </div>
+</div>
+<script>$('#searchbox').show(0);</script>
+        </div>
+      </div>
+      <div class="clearer"></div>
+    </div>
+    <div class="related" role="navigation" aria-label="related navigation">
+      <h3>Navigation</h3>
+      <ul>
+        <li class="right" style="margin-right: 10px">
+          <a href="../../../genindex.html" title="General Index"
+             >index</a></li>
+        <li class="right" >
+          <a href="../../../py-modindex.html" title="Python Module Index"
+             >modules</a> |</li>
+        <li class="nav-item nav-item-0"><a href="../../../index.html">pyFTS 1.7 documentation</a> &#187;</li>
+          <li class="nav-item nav-item-1"><a href="../../index.html" >Module code</a> &#187;</li>
+        <li class="nav-item nav-item-this"><a href="">pyFTS.hyperparam.GridSearch</a></li> 
+      </ul>
+    </div>
+    <div class="footer" role="contentinfo">
+        &#169; Copyright 2022, Machine Intelligence and Data Science Laboratory - UFMG - Brazil.
+      Created using <a href="https://www.sphinx-doc.org/">Sphinx</a> 4.5.0.
+    </div>
+  </body>
+</html>

docs/build/html/_modules/pyFTS/hyperparam/Util.html

@@ -51,7 +51,7 @@
 
 
 <div class="viewcode-block" id="open_hyperparam_db"><a class="viewcode-back" href="../../../pyFTS.hyperparam.html#pyFTS.hyperparam.Util.open_hyperparam_db">[docs]</a><span class="k">def</span> <span class="nf">open_hyperparam_db</span><span class="p">(</span><span class="n">name</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+    <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Open a connection with a Sqlite database designed to store benchmark results.</span>
 
 <span class="sd">    :param name: database filenem</span>
@@ -68,7 +68,7 @@
 
 
 <div class="viewcode-block" id="create_hyperparam_tables"><a class="viewcode-back" href="../../../pyFTS.hyperparam.html#pyFTS.hyperparam.Util.create_hyperparam_tables">[docs]</a><span class="k">def</span> <span class="nf">create_hyperparam_tables</span><span class="p">(</span><span class="n">conn</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+    <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Create a sqlite3 table designed to store benchmark results.</span>
 
 <span class="sd">    :param conn: a sqlite3 database connection</span>
@@ -85,7 +85,7 @@
 
 
 <div class="viewcode-block" id="insert_hyperparam"><a class="viewcode-back" href="../../../pyFTS.hyperparam.html#pyFTS.hyperparam.Util.insert_hyperparam">[docs]</a><span class="k">def</span> <span class="nf">insert_hyperparam</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">conn</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+    <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Insert benchmark data on database</span>
 
 <span class="sd">    :param data: a tuple with the benchmark data with format:</span>
@@ -154,7 +154,7 @@
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docs/build/html/_modules/pyFTS/models/chen.html

@@ -0,0 +1,180 @@
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+          <li class="nav-item nav-item-1"><a href="../../index.html" accesskey="U">Module code</a> &#187;</li>
+        <li class="nav-item nav-item-this"><a href="">pyFTS.models.chen</a></li> 
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+            
+  <h1>Source code for pyFTS.models.chen</h1><div class="highlight"><pre>
+<span></span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">First Order Conventional Fuzzy Time Series by Chen (1996)</span>
+
+<span class="sd">S.-M. Chen, “Forecasting enrollments based on fuzzy time series,” Fuzzy Sets Syst., vol. 81, no. 3, pp. 311–319, 1996.</span>
+<span class="sd">&quot;&quot;&quot;</span>
+
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="k">import</span> <span class="n">FuzzySet</span><span class="p">,</span> <span class="n">FLR</span><span class="p">,</span> <span class="n">fts</span><span class="p">,</span> <span class="n">flrg</span>
+
+
+<div class="viewcode-block" id="ConventionalFLRG"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.chen.ConventionalFLRG">[docs]</a><span class="k">class</span> <span class="nc">ConventionalFLRG</span><span class="p">(</span><span class="n">flrg</span><span class="o">.</span><span class="n">FLRG</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;First Order Conventional Fuzzy Logical Relationship Group&quot;&quot;&quot;</span>
+    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">LHS</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">ConventionalFLRG</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">LHS</span> <span class="o">=</span> <span class="n">LHS</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span> <span class="o">=</span> <span class="nb">set</span><span class="p">()</span>
+
+<div class="viewcode-block" id="ConventionalFLRG.get_key"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.chen.ConventionalFLRG.get_key">[docs]</a>    <span class="k">def</span> <span class="nf">get_key</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sets</span><span class="p">):</span>
+        <span class="k">return</span> <span class="n">sets</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">LHS</span><span class="p">]</span><span class="o">.</span><span class="n">name</span></div>
+
+<div class="viewcode-block" id="ConventionalFLRG.append_rhs"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.chen.ConventionalFLRG.append_rhs">[docs]</a>    <span class="k">def</span> <span class="nf">append_rhs</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">c</span><span class="p">)</span></div>
+
+    <span class="k">def</span> <span class="nf">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="n">tmp</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">LHS</span><span class="p">)</span> <span class="o">+</span> <span class="s2">&quot; -&gt; &quot;</span>
+        <span class="n">tmp2</span> <span class="o">=</span> <span class="s2">&quot;&quot;</span>
+        <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="nb">sorted</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">,</span> <span class="n">key</span><span class="o">=</span><span class="k">lambda</span> <span class="n">s</span><span class="p">:</span> <span class="n">s</span><span class="p">):</span>
+            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">tmp2</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+                <span class="n">tmp2</span> <span class="o">=</span> <span class="n">tmp2</span> <span class="o">+</span> <span class="s2">&quot;,&quot;</span>
+            <span class="n">tmp2</span> <span class="o">=</span> <span class="n">tmp2</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">c</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">tmp</span> <span class="o">+</span> <span class="n">tmp2</span></div>
+
+
+<div class="viewcode-block" id="ConventionalFTS"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.chen.ConventionalFTS">[docs]</a><span class="k">class</span> <span class="nc">ConventionalFTS</span><span class="p">(</span><span class="n">fts</span><span class="o">.</span><span class="n">FTS</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;Conventional Fuzzy Time Series&quot;&quot;&quot;</span>
+    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">ConventionalFTS</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">order</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s2">&quot;Conventional FTS&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">detail</span> <span class="o">=</span> <span class="s2">&quot;Chen&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">shortname</span> <span class="o">=</span> <span class="s2">&quot;CFTS&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span> <span class="o">=</span> <span class="p">{}</span>
+
+<div class="viewcode-block" id="ConventionalFTS.generate_flrg"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.chen.ConventionalFTS.generate_flrg">[docs]</a>    <span class="k">def</span> <span class="nf">generate_flrg</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">flrs</span><span class="p">):</span>
+        <span class="k">for</span> <span class="n">flr</span> <span class="ow">in</span> <span class="n">flrs</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">flr</span><span class="o">.</span><span class="n">LHS</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flr</span><span class="o">.</span><span class="n">LHS</span><span class="p">]</span><span class="o">.</span><span class="n">append_rhs</span><span class="p">(</span><span class="n">flr</span><span class="o">.</span><span class="n">RHS</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flr</span><span class="o">.</span><span class="n">LHS</span><span class="p">]</span> <span class="o">=</span> <span class="n">ConventionalFLRG</span><span class="p">(</span><span class="n">flr</span><span class="o">.</span><span class="n">LHS</span><span class="p">)</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flr</span><span class="o">.</span><span class="n">LHS</span><span class="p">]</span><span class="o">.</span><span class="n">append_rhs</span><span class="p">(</span><span class="n">flr</span><span class="o">.</span><span class="n">RHS</span><span class="p">)</span></div>
+
+<div class="viewcode-block" id="ConventionalFTS.train"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.chen.ConventionalFTS.train">[docs]</a>    <span class="k">def</span> <span class="nf">train</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+
+        <span class="n">tmpdata</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">fuzzyfy</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">&#39;maximum&#39;</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;sets&#39;</span><span class="p">)</span>
+        <span class="n">flrs</span> <span class="o">=</span> <span class="n">FLR</span><span class="o">.</span><span class="n">generate_non_recurrent_flrs</span><span class="p">(</span><span class="n">tmpdata</span><span class="p">,</span> <span class="n">steps</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">standard_horizon</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">generate_flrg</span><span class="p">(</span><span class="n">flrs</span><span class="p">)</span></div>
+
+<div class="viewcode-block" id="ConventionalFTS.forecast"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.chen.ConventionalFTS.forecast">[docs]</a>    <span class="k">def</span> <span class="nf">forecast</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ndata</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+
+        <span class="n">explain</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;explain&#39;</span><span class="p">,</span><span class="kc">False</span><span class="p">)</span>
+
+        <span class="n">l</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">ndata</span><span class="p">)</span> <span class="k">if</span> <span class="ow">not</span> <span class="n">explain</span> <span class="k">else</span> <span class="mi">1</span>
+
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">l</span><span class="p">):</span>
+
+            <span class="n">actual</span> <span class="o">=</span> <span class="n">FuzzySet</span><span class="o">.</span><span class="n">get_maximum_membership_fuzzyset</span><span class="p">(</span><span class="n">ndata</span><span class="p">[</span><span class="n">k</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">)</span>
+
+            <span class="k">if</span> <span class="n">explain</span><span class="p">:</span>
+                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Fuzzyfication:</span><span class="se">\n\n</span><span class="s2"> </span><span class="si">{}</span><span class="s2"> -&gt; </span><span class="si">{}</span><span class="s2"> </span><span class="se">\n</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">ndata</span><span class="p">[</span><span class="n">k</span><span class="p">],</span> <span class="n">actual</span><span class="o">.</span><span class="n">name</span><span class="p">))</span>
+
+            <span class="k">if</span> <span class="n">actual</span><span class="o">.</span><span class="n">name</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">:</span>
+                <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">actual</span><span class="o">.</span><span class="n">centroid</span><span class="p">)</span>
+
+                <span class="k">if</span> <span class="n">explain</span><span class="p">:</span>
+                    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Rules:</span><span class="se">\n\n</span><span class="s2"> </span><span class="si">{}</span><span class="s2"> -&gt; </span><span class="si">{}</span><span class="s2"> (Naïve)</span><span class="se">\t</span><span class="s2"> Midpoint: </span><span class="si">{}</span><span class="s2">  </span><span class="se">\n\n</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">actual</span><span class="o">.</span><span class="n">name</span><span class="p">,</span> <span class="n">actual</span><span class="o">.</span><span class="n">name</span><span class="p">,</span><span class="n">actual</span><span class="o">.</span><span class="n">centroid</span><span class="p">))</span>
+
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">_flrg</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">actual</span><span class="o">.</span><span class="n">name</span><span class="p">]</span>
+
+                <span class="n">mp</span> <span class="o">=</span> <span class="n">_flrg</span><span class="o">.</span><span class="n">get_midpoint</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">)</span>
+
+                <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">mp</span><span class="p">)</span>
+
+                <span class="k">if</span> <span class="n">explain</span><span class="p">:</span>
+                    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Rules:</span><span class="se">\n\n</span><span class="s2"> </span><span class="si">{}</span><span class="s2"> </span><span class="se">\t</span><span class="s2"> Midpoint: </span><span class="si">{}</span><span class="s2"> </span><span class="se">\n</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">_flrg</span><span class="p">),</span> <span class="n">mp</span><span class="p">))</span>
+
+                    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Deffuzyfied value: </span><span class="si">{}</span><span class="s2"> </span><span class="se">\n</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">mp</span><span class="p">))</span>
+
+        <span class="k">return</span> <span class="n">ret</span></div></div>
+</pre></div>
+
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+        <li class="nav-item nav-item-this"><a href="">pyFTS.models.chen</a></li> 
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+        &#169; Copyright 2022, Machine Intelligence and Data Science Laboratory - UFMG - Brazil.
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docs/build/html/_modules/pyFTS/models/cheng.html

@@ -0,0 +1,151 @@
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+
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+  <h1>Source code for pyFTS.models.cheng</h1><div class="highlight"><pre>
+<span></span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">Trend Weighted Fuzzy Time Series by Cheng, Chen and Wu (2009)</span>
+
+<span class="sd">C.-H. Cheng, Y.-S. Chen, and Y.-L. Wu, “Forecasting innovation diffusion of products using trend-weighted fuzzy time-series model,” </span>
+<span class="sd">Expert Syst. Appl., vol. 36, no. 2, pp. 1826–1832, 2009.</span>
+<span class="sd">&quot;&quot;&quot;</span>
+
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="k">import</span> <span class="n">FuzzySet</span><span class="p">,</span> <span class="n">FLR</span><span class="p">,</span> <span class="n">fts</span>
+<span class="kn">from</span> <span class="nn">pyFTS.models</span> <span class="k">import</span> <span class="n">yu</span>
+
+
+<div class="viewcode-block" id="TrendWeightedFLRG"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.cheng.TrendWeightedFLRG">[docs]</a><span class="k">class</span> <span class="nc">TrendWeightedFLRG</span><span class="p">(</span><span class="n">yu</span><span class="o">.</span><span class="n">WeightedFLRG</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    First Order Trend Weighted Fuzzy Logical Relationship Group</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">LHS</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">TrendWeightedFLRG</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">LHS</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">w</span> <span class="o">=</span> <span class="kc">None</span>
+
+<div class="viewcode-block" id="TrendWeightedFLRG.weights"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.cheng.TrendWeightedFLRG.weights">[docs]</a>    <span class="k">def</span> <span class="nf">weights</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sets</span><span class="p">):</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">w</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">count_nochange</span> <span class="o">=</span> <span class="mf">0.0</span>
+            <span class="n">count_up</span> <span class="o">=</span> <span class="mf">0.0</span>
+            <span class="n">count_down</span> <span class="o">=</span> <span class="mf">0.0</span>
+            <span class="n">weights</span> <span class="o">=</span> <span class="p">[]</span>
+
+            <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">:</span>
+                <span class="n">tmp</span> <span class="o">=</span> <span class="mi">0</span>
+                <span class="k">if</span> <span class="n">sets</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">LHS</span><span class="p">]</span><span class="o">.</span><span class="n">centroid</span> <span class="o">==</span> <span class="n">sets</span><span class="p">[</span><span class="n">c</span><span class="p">]</span><span class="o">.</span><span class="n">centroid</span><span class="p">:</span>
+                    <span class="n">count_nochange</span> <span class="o">+=</span> <span class="mf">1.0</span>
+                    <span class="n">tmp</span> <span class="o">=</span> <span class="n">count_nochange</span>
+                <span class="k">elif</span> <span class="n">sets</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">LHS</span><span class="p">]</span><span class="o">.</span><span class="n">centroid</span> <span class="o">&gt;</span> <span class="n">sets</span><span class="p">[</span><span class="n">c</span><span class="p">]</span><span class="o">.</span><span class="n">centroid</span><span class="p">:</span>
+                    <span class="n">count_down</span> <span class="o">+=</span> <span class="mf">1.0</span>
+                    <span class="n">tmp</span> <span class="o">=</span> <span class="n">count_down</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">count_up</span> <span class="o">+=</span> <span class="mf">1.0</span>
+                    <span class="n">tmp</span> <span class="o">=</span> <span class="n">count_up</span>
+                <span class="n">weights</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span>
+
+            <span class="n">tot</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span><span class="n">weights</span><span class="p">)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">w</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">k</span> <span class="o">/</span> <span class="n">tot</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">weights</span><span class="p">])</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">w</span></div></div>
+
+
+<div class="viewcode-block" id="TrendWeightedFTS"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.cheng.TrendWeightedFTS">[docs]</a><span class="k">class</span> <span class="nc">TrendWeightedFTS</span><span class="p">(</span><span class="n">yu</span><span class="o">.</span><span class="n">WeightedFTS</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;First Order Trend Weighted Fuzzy Time Series&quot;&quot;&quot;</span>
+    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">TrendWeightedFTS</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">shortname</span> <span class="o">=</span> <span class="s2">&quot;TWFTS&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s2">&quot;Trend Weighted FTS&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">detail</span> <span class="o">=</span> <span class="s2">&quot;Cheng&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">is_high_order</span> <span class="o">=</span> <span class="kc">False</span>
+
+<div class="viewcode-block" id="TrendWeightedFTS.generate_FLRG"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.cheng.TrendWeightedFTS.generate_FLRG">[docs]</a>    <span class="k">def</span> <span class="nf">generate_FLRG</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">flrs</span><span class="p">):</span>
+        <span class="k">for</span> <span class="n">flr</span> <span class="ow">in</span> <span class="n">flrs</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">flr</span><span class="o">.</span><span class="n">LHS</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flr</span><span class="o">.</span><span class="n">LHS</span><span class="p">]</span><span class="o">.</span><span class="n">append_rhs</span><span class="p">(</span><span class="n">flr</span><span class="o">.</span><span class="n">RHS</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flr</span><span class="o">.</span><span class="n">LHS</span><span class="p">]</span> <span class="o">=</span> <span class="n">TrendWeightedFLRG</span><span class="p">(</span><span class="n">flr</span><span class="o">.</span><span class="n">LHS</span><span class="p">)</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flr</span><span class="o">.</span><span class="n">LHS</span><span class="p">]</span><span class="o">.</span><span class="n">append_rhs</span><span class="p">(</span><span class="n">flr</span><span class="o">.</span><span class="n">RHS</span><span class="p">)</span></div></div>
+</pre></div>
+
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docs/build/html/_modules/pyFTS/models/ensemble/ensemble.html

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+  <h1>Source code for pyFTS.models.ensemble.ensemble</h1><div class="highlight"><pre>
+<span></span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">EnsembleFTS wraps several FTS methods to ensemble their forecasts, providing point,</span>
+<span class="sd">interval and probabilistic forecasting.</span>
+
+<span class="sd">Silva, P. C. L et al. Probabilistic Forecasting with Seasonal Ensemble Fuzzy Time-Series</span>
+<span class="sd">XIII Brazilian Congress on Computational Intelligence, 2017. Rio de Janeiro, Brazil.</span>
+<span class="sd">&quot;&quot;&quot;</span>
+
+
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="k">import</span> <span class="n">SortedCollection</span><span class="p">,</span> <span class="n">fts</span><span class="p">,</span> <span class="n">tree</span>
+<span class="kn">from</span> <span class="nn">pyFTS.models</span> <span class="k">import</span> <span class="n">chen</span><span class="p">,</span> <span class="n">cheng</span><span class="p">,</span> <span class="n">hofts</span><span class="p">,</span> <span class="n">hwang</span><span class="p">,</span> <span class="n">ismailefendi</span><span class="p">,</span> <span class="n">sadaei</span><span class="p">,</span> <span class="n">song</span><span class="p">,</span> <span class="n">yu</span>
+<span class="kn">from</span> <span class="nn">pyFTS.probabilistic</span> <span class="k">import</span> <span class="n">ProbabilityDistribution</span>
+<span class="kn">from</span> <span class="nn">pyFTS.partitioners</span> <span class="k">import</span> <span class="n">Grid</span>
+<span class="kn">import</span> <span class="nn">scipy.stats</span> <span class="k">as</span> <span class="nn">st</span>
+<span class="kn">from</span> <span class="nn">itertools</span> <span class="k">import</span> <span class="n">product</span>
+
+
+<div class="viewcode-block" id="sampler"><a class="viewcode-back" href="../../../../pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.sampler">[docs]</a><span class="k">def</span> <span class="nf">sampler</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">quantiles</span><span class="p">,</span> <span class="n">bounds</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
+    <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="k">for</span> <span class="n">qt</span> <span class="ow">in</span> <span class="n">quantiles</span><span class="p">:</span>
+        <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">nanpercentile</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">q</span><span class="o">=</span><span class="n">qt</span> <span class="o">*</span> <span class="mi">100</span><span class="p">))</span>
+    <span class="k">if</span> <span class="n">bounds</span><span class="p">:</span>
+        <span class="n">ret</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">min</span><span class="p">(</span><span class="n">data</span><span class="p">))</span>
+        <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">max</span><span class="p">(</span><span class="n">data</span><span class="p">))</span>
+    <span class="k">return</span> <span class="n">ret</span></div>
+
+
+<div class="viewcode-block" id="EnsembleFTS"><a class="viewcode-back" href="../../../../pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.EnsembleFTS">[docs]</a><span class="k">class</span> <span class="nc">EnsembleFTS</span><span class="p">(</span><span class="n">fts</span><span class="o">.</span><span class="n">FTS</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Ensemble FTS</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">EnsembleFTS</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">shortname</span> <span class="o">=</span> <span class="s2">&quot;EnsembleFTS&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s2">&quot;Ensemble FTS&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span> <span class="o">=</span> <span class="p">{}</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">is_wrapper</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">has_point_forecasting</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">has_interval_forecasting</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">has_probability_forecasting</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">is_high_order</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">models</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="sd">&quot;&quot;&quot;A list of FTS models, the ensemble components&quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">parameters</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="sd">&quot;&quot;&quot;A list with the parameters for each component model&quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">alpha</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;alpha&quot;</span><span class="p">,</span> <span class="mf">0.05</span><span class="p">)</span>
+        <span class="sd">&quot;&quot;&quot;The quantiles &quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">point_method</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;point_method&#39;</span><span class="p">,</span> <span class="s1">&#39;mean&#39;</span><span class="p">)</span>
+        <span class="sd">&quot;&quot;&quot;The method used to mix the several model&#39;s forecasts into a unique point forecast. Options: mean, median, quantile, exponential&quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">interval_method</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;interval_method&#39;</span><span class="p">,</span> <span class="s1">&#39;quantile&#39;</span><span class="p">)</span>
+        <span class="sd">&quot;&quot;&quot;The method used to mix the several model&#39;s forecasts into a interval forecast. Options: quantile, extremum, normal&quot;&quot;&quot;</span>
+
+<div class="viewcode-block" id="EnsembleFTS.append_model"><a class="viewcode-back" href="../../../../pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.EnsembleFTS.append_model">[docs]</a>    <span class="k">def</span> <span class="nf">append_model</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">model</span><span class="p">):</span>
+        <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Append a new trained model to the ensemble</span>
+
+<span class="sd">        :param model: FTS model</span>
+
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">models</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">model</span><span class="o">.</span><span class="n">order</span> <span class="o">&gt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">order</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">order</span>
+
+        <span class="k">if</span> <span class="n">model</span><span class="o">.</span><span class="n">is_multivariate</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">is_multivariate</span> <span class="o">=</span> <span class="kc">True</span>
+
+        <span class="k">if</span> <span class="n">model</span><span class="o">.</span><span class="n">has_seasonality</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">has_seasonality</span> <span class="o">=</span> <span class="kc">True</span>
+
+        <span class="k">if</span> <span class="n">model</span><span class="o">.</span><span class="n">original_min</span> <span class="o">&lt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">original_min</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">original_min</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">original_min</span>
+        <span class="k">elif</span> <span class="n">model</span><span class="o">.</span><span class="n">original_max</span> <span class="o">&gt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">original_max</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">original_max</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">original_max</span></div>
+
+
+<div class="viewcode-block" id="EnsembleFTS.get_UoD"><a class="viewcode-back" href="../../../../pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.EnsembleFTS.get_UoD">[docs]</a>    <span class="k">def</span> <span class="nf">get_UoD</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">original_min</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">original_max</span><span class="p">]</span></div>
+
+<div class="viewcode-block" id="EnsembleFTS.train"><a class="viewcode-back" href="../../../../pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.EnsembleFTS.train">[docs]</a>    <span class="k">def</span> <span class="nf">train</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="k">pass</span></div>
+
+<div class="viewcode-block" id="EnsembleFTS.get_models_forecasts"><a class="viewcode-back" href="../../../../pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.EnsembleFTS.get_models_forecasts">[docs]</a>    <span class="k">def</span> <span class="nf">get_models_forecasts</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="n">data</span><span class="p">):</span>
+        <span class="n">tmp</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">for</span> <span class="n">model</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">models</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">model</span><span class="o">.</span><span class="n">is_multivariate</span> <span class="ow">or</span> <span class="n">model</span><span class="o">.</span><span class="n">has_seasonality</span><span class="p">:</span>
+                <span class="n">forecast</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">forecast</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+
+                <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">)</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">indexer</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                    <span class="n">data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">indexer</span><span class="o">.</span><span class="n">get_data</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+
+                <span class="n">sample</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="o">-</span><span class="n">model</span><span class="o">.</span><span class="n">order</span><span class="p">:]</span>
+                <span class="n">forecast</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">sample</span><span class="p">)</span>
+                <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">forecast</span><span class="p">,</span> <span class="p">(</span><span class="nb">list</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">))</span> <span class="ow">and</span> <span class="nb">len</span><span class="p">(</span><span class="n">forecast</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+                    <span class="n">forecast</span> <span class="o">=</span> <span class="n">forecast</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
+                <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">forecast</span><span class="p">,</span> <span class="p">(</span><span class="nb">list</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">))</span> <span class="ow">and</span> <span class="nb">len</span><span class="p">(</span><span class="n">forecast</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+                    <span class="n">forecast</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
+            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">forecast</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
+                <span class="n">tmp</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">forecast</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">tmp</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">forecast</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">tmp</span></div>
+
+<div class="viewcode-block" id="EnsembleFTS.get_point"><a class="viewcode-back" href="../../../../pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.EnsembleFTS.get_point">[docs]</a>    <span class="k">def</span> <span class="nf">get_point</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="n">forecasts</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">point_method</span> <span class="o">==</span> <span class="s1">&#39;mean&#39;</span><span class="p">:</span>
+            <span class="n">ret</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nanmean</span><span class="p">(</span><span class="n">forecasts</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">point_method</span> <span class="o">==</span> <span class="s1">&#39;median&#39;</span><span class="p">:</span>
+            <span class="n">ret</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nanpercentile</span><span class="p">(</span><span class="n">forecasts</span><span class="p">,</span> <span class="mi">50</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">point_method</span> <span class="o">==</span> <span class="s1">&#39;quantile&#39;</span><span class="p">:</span>
+            <span class="n">alpha</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;alpha&quot;</span><span class="p">,</span><span class="mf">0.05</span><span class="p">)</span>
+            <span class="n">ret</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nanpercentile</span><span class="p">(</span><span class="n">forecasts</span><span class="p">,</span> <span class="n">alpha</span><span class="o">*</span><span class="mi">100</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">point_method</span> <span class="o">==</span> <span class="s1">&#39;exponential&#39;</span><span class="p">:</span>
+            <span class="n">l</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">models</span><span class="p">)</span>
+            <span class="k">if</span> <span class="n">l</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+                <span class="k">return</span> <span class="n">forecasts</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+            <span class="n">w</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="p">(</span><span class="n">l</span> <span class="o">-</span> <span class="n">k</span><span class="p">))</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">l</span><span class="p">)])</span>
+            <span class="n">w</span> <span class="o">=</span> <span class="n">w</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">w</span><span class="p">)</span>
+            <span class="n">ret</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">([</span><span class="n">w</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">*</span> <span class="n">forecasts</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">l</span><span class="p">)])</span>
+
+        <span class="k">return</span> <span class="n">ret</span></div>
+
+<div class="viewcode-block" id="EnsembleFTS.get_interval"><a class="viewcode-back" href="../../../../pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.EnsembleFTS.get_interval">[docs]</a>    <span class="k">def</span> <span class="nf">get_interval</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">forecasts</span><span class="p">):</span>
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">interval_method</span> <span class="o">==</span> <span class="s1">&#39;extremum&#39;</span><span class="p">:</span>
+            <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">([</span><span class="nb">min</span><span class="p">(</span><span class="n">forecasts</span><span class="p">),</span> <span class="nb">max</span><span class="p">(</span><span class="n">forecasts</span><span class="p">)])</span>
+        <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">interval_method</span> <span class="o">==</span> <span class="s1">&#39;quantile&#39;</span><span class="p">:</span>
+            <span class="n">qt_lo</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nanpercentile</span><span class="p">(</span><span class="n">forecasts</span><span class="p">,</span> <span class="n">q</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">alpha</span> <span class="o">*</span> <span class="mi">100</span><span class="p">)</span>
+            <span class="n">qt_up</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nanpercentile</span><span class="p">(</span><span class="n">forecasts</span><span class="p">,</span> <span class="n">q</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">alpha</span><span class="p">)</span> <span class="o">*</span> <span class="mi">100</span><span class="p">)</span>
+            <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">([</span><span class="n">qt_lo</span><span class="p">,</span> <span class="n">qt_up</span><span class="p">])</span>
+        <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">interval_method</span> <span class="o">==</span> <span class="s1">&#39;normal&#39;</span><span class="p">:</span>
+            <span class="n">mu</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nanmean</span><span class="p">(</span><span class="n">forecasts</span><span class="p">)</span>
+            <span class="n">sigma</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">nanvar</span><span class="p">(</span><span class="n">forecasts</span><span class="p">))</span>
+            <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">mu</span> <span class="o">+</span> <span class="n">st</span><span class="o">.</span><span class="n">norm</span><span class="o">.</span><span class="n">ppf</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">alpha</span><span class="p">)</span> <span class="o">*</span> <span class="n">sigma</span><span class="p">)</span>
+            <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">mu</span> <span class="o">+</span> <span class="n">st</span><span class="o">.</span><span class="n">norm</span><span class="o">.</span><span class="n">ppf</span><span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">alpha</span><span class="p">)</span> <span class="o">*</span> <span class="n">sigma</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">ret</span></div>
+
+<div class="viewcode-block" id="EnsembleFTS.get_distribution_interquantile"><a class="viewcode-back" href="../../../../pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.EnsembleFTS.get_distribution_interquantile">[docs]</a>    <span class="k">def</span> <span class="nf">get_distribution_interquantile</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="n">forecasts</span><span class="p">,</span> <span class="n">alpha</span><span class="p">):</span>
+        <span class="n">size</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">forecasts</span><span class="p">)</span>
+        <span class="n">qt_lower</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="n">size</span> <span class="o">*</span> <span class="n">alpha</span><span class="p">))</span> <span class="o">-</span> <span class="mi">1</span>
+        <span class="n">qt_upper</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="n">size</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span><span class="o">-</span> <span class="n">alpha</span><span class="p">)))</span> <span class="o">-</span> <span class="mi">1</span>
+
+        <span class="n">ret</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">forecasts</span><span class="p">)[</span><span class="n">qt_lower</span> <span class="p">:</span> <span class="n">qt_upper</span><span class="p">]</span>
+
+        <span class="k">return</span> <span class="n">ret</span></div>
+
+<div class="viewcode-block" id="EnsembleFTS.forecast"><a class="viewcode-back" href="../../../../pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.EnsembleFTS.forecast">[docs]</a>    <span class="k">def</span> <span class="nf">forecast</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+
+        <span class="k">if</span> <span class="s2">&quot;method&quot;</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">point_method</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;method&#39;</span><span class="p">,</span><span class="s1">&#39;mean&#39;</span><span class="p">)</span>
+
+        <span class="n">l</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">,</span> <span class="n">l</span><span class="o">+</span><span class="mi">1</span><span class="p">):</span>
+            <span class="n">sample</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">k</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span> <span class="p">:</span> <span class="n">k</span><span class="p">]</span>
+            <span class="n">tmp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_models_forecasts</span><span class="p">(</span><span class="n">sample</span><span class="p">)</span>
+            <span class="n">point</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_point</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span>
+            <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">point</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">ret</span></div>
+
+<div class="viewcode-block" id="EnsembleFTS.forecast_interval"><a class="viewcode-back" href="../../../../pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.EnsembleFTS.forecast_interval">[docs]</a>    <span class="k">def</span> <span class="nf">forecast_interval</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+
+        <span class="k">if</span> <span class="s2">&quot;method&quot;</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">interval_method</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;method&#39;</span><span class="p">,</span><span class="s1">&#39;quantile&#39;</span><span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">alpha</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;alpha&#39;</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">alpha</span><span class="p">)</span>
+
+        <span class="n">l</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">,</span> <span class="n">l</span><span class="o">+</span><span class="mi">1</span><span class="p">):</span>
+            <span class="n">sample</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">k</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span> <span class="p">:</span> <span class="n">k</span><span class="p">]</span>
+            <span class="n">tmp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_models_forecasts</span><span class="p">(</span><span class="n">sample</span><span class="p">)</span>
+            <span class="n">interval</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_interval</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span>
+            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">interval</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+                <span class="n">interval</span> <span class="o">=</span> <span class="n">interval</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
+            <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">interval</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">ret</span></div>
+
+<div class="viewcode-block" id="EnsembleFTS.forecast_ahead_interval"><a class="viewcode-back" href="../../../../pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.EnsembleFTS.forecast_ahead_interval">[docs]</a>    <span class="k">def</span> <span class="nf">forecast_ahead_interval</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">steps</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+
+        <span class="k">if</span> <span class="s1">&#39;method&#39;</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">interval_method</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;method&#39;</span><span class="p">,</span><span class="s1">&#39;quantile&#39;</span><span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">alpha</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;alpha&#39;</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">alpha</span><span class="p">)</span>
+
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="n">start</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;start_at&#39;</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">)</span>
+
+        <span class="n">sample</span> <span class="o">=</span> <span class="p">[[</span><span class="n">k</span><span class="p">]</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">data</span><span class="p">[</span><span class="n">start</span><span class="p">:</span> <span class="n">start</span><span class="o">+</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">]]</span>
+
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">,</span> <span class="n">steps</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">):</span>
+            <span class="n">forecasts</span> <span class="o">=</span> <span class="p">[]</span>
+
+            <span class="n">lags</span> <span class="o">=</span> <span class="p">[]</span>
+            <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">):</span>
+                <span class="n">lags</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">sample</span><span class="p">[</span><span class="n">i</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">])</span>
+
+            <span class="c1"># Trace the possible paths</span>
+            <span class="k">for</span> <span class="n">path</span> <span class="ow">in</span> <span class="n">product</span><span class="p">(</span><span class="o">*</span><span class="n">lags</span><span class="p">):</span>
+                <span class="n">forecasts</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_models_forecasts</span><span class="p">(</span><span class="n">path</span><span class="p">))</span>
+
+            <span class="n">sample</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">sampler</span><span class="p">(</span><span class="n">forecasts</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="o">.</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">),</span> <span class="n">bounds</span><span class="o">=</span><span class="kc">True</span><span class="p">))</span>
+
+            <span class="n">interval</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_interval</span><span class="p">(</span><span class="n">forecasts</span><span class="p">)</span>
+
+            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">interval</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+                <span class="n">interval</span> <span class="o">=</span> <span class="n">interval</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+
+            <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">interval</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">ret</span><span class="p">[</span><span class="o">-</span><span class="n">steps</span><span class="p">:]</span></div>
+
+<div class="viewcode-block" id="EnsembleFTS.forecast_distribution"><a class="viewcode-back" href="../../../../pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.EnsembleFTS.forecast_distribution">[docs]</a>    <span class="k">def</span> <span class="nf">forecast_distribution</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="n">smooth</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;smooth&quot;</span><span class="p">,</span> <span class="s2">&quot;KDE&quot;</span><span class="p">)</span>
+        <span class="n">alpha</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;alpha&quot;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+
+        <span class="n">uod</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_UoD</span><span class="p">()</span>
+
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)):</span>
+
+            <span class="n">sample</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">k</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span> <span class="p">:</span> <span class="n">k</span><span class="p">]</span>
+
+            <span class="n">forecasts</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_models_forecasts</span><span class="p">(</span><span class="n">sample</span><span class="p">)</span>
+
+            <span class="k">if</span> <span class="n">alpha</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="n">forecasts</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ravel</span><span class="p">(</span><span class="n">forecasts</span><span class="p">)</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">forecasts</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_distribution_interquantile</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ravel</span><span class="p">(</span><span class="n">forecasts</span><span class="p">)</span><span class="o">.</span><span class="n">tolist</span><span class="p">(),</span> <span class="n">alpha</span><span class="p">)</span>
+
+            <span class="n">dist</span> <span class="o">=</span> <span class="n">ProbabilityDistribution</span><span class="o">.</span><span class="n">ProbabilityDistribution</span><span class="p">(</span><span class="n">smooth</span><span class="p">,</span> <span class="n">uod</span><span class="o">=</span><span class="n">uod</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="n">forecasts</span><span class="p">,</span>
+                                                                   <span class="n">name</span><span class="o">=</span><span class="s2">&quot;&quot;</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+            <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">dist</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">ret</span></div>
+
+<div class="viewcode-block" id="EnsembleFTS.forecast_ahead_distribution"><a class="viewcode-back" href="../../../../pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.EnsembleFTS.forecast_ahead_distribution">[docs]</a>    <span class="k">def</span> <span class="nf">forecast_ahead_distribution</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">steps</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="k">if</span> <span class="s1">&#39;method&#39;</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">point_method</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;method&#39;</span><span class="p">,</span><span class="s1">&#39;mean&#39;</span><span class="p">)</span>
+
+        <span class="n">smooth</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;smooth&quot;</span><span class="p">,</span> <span class="s2">&quot;histogram&quot;</span><span class="p">)</span>
+        <span class="n">alpha</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;alpha&quot;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="n">start</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;start_at&#39;</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">)</span>
+
+        <span class="n">uod</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_UoD</span><span class="p">()</span>
+
+        <span class="n">sample</span> <span class="o">=</span> <span class="p">[[</span><span class="n">k</span><span class="p">]</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">data</span><span class="p">[</span><span class="n">start</span><span class="p">:</span> <span class="n">start</span><span class="o">+</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">]]</span>
+
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">,</span> <span class="n">steps</span><span class="o">+</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">):</span>
+            <span class="n">forecasts</span> <span class="o">=</span> <span class="p">[]</span>
+
+            <span class="n">lags</span> <span class="o">=</span> <span class="p">[]</span>
+            <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">):</span>
+                <span class="n">lags</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">sample</span><span class="p">[</span><span class="n">i</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">])</span>
+
+            <span class="c1"># Trace the possible paths</span>
+            <span class="k">for</span> <span class="n">path</span> <span class="ow">in</span> <span class="n">product</span><span class="p">(</span><span class="o">*</span><span class="n">lags</span><span class="p">):</span>
+                <span class="n">forecasts</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_models_forecasts</span><span class="p">(</span><span class="n">path</span><span class="p">))</span>
+
+            <span class="n">sample</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">sampler</span><span class="p">(</span><span class="n">forecasts</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="o">.</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">),</span> <span class="n">bounds</span><span class="o">=</span><span class="kc">True</span><span class="p">))</span>
+
+            <span class="k">if</span> <span class="n">alpha</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="n">forecasts</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ravel</span><span class="p">(</span><span class="n">forecasts</span><span class="p">)</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">forecasts</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_distribution_interquantile</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ravel</span><span class="p">(</span><span class="n">forecasts</span><span class="p">)</span><span class="o">.</span><span class="n">tolist</span><span class="p">(),</span> <span class="n">alpha</span><span class="p">)</span>
+
+            <span class="n">dist</span> <span class="o">=</span> <span class="n">ProbabilityDistribution</span><span class="o">.</span><span class="n">ProbabilityDistribution</span><span class="p">(</span><span class="n">smooth</span><span class="p">,</span> <span class="n">uod</span><span class="o">=</span><span class="n">uod</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="n">forecasts</span><span class="p">,</span>
+                                                                   <span class="n">name</span><span class="o">=</span><span class="s2">&quot;&quot;</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+            <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">dist</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">ret</span><span class="p">[</span><span class="o">-</span><span class="n">steps</span><span class="p">:]</span></div></div>
+
+
+<div class="viewcode-block" id="SimpleEnsembleFTS"><a class="viewcode-back" href="../../../../pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.SimpleEnsembleFTS">[docs]</a><span class="k">class</span> <span class="nc">SimpleEnsembleFTS</span><span class="p">(</span><span class="n">EnsembleFTS</span><span class="p">):</span>
+    <span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">    An homogeneous FTS method ensemble with variations on partitionings and orders.</span>
+<span class="sd">    &#39;&#39;&#39;</span>
+    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">SimpleEnsembleFTS</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">method</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;fts_method&#39;</span><span class="p">,</span> <span class="n">hofts</span><span class="o">.</span><span class="n">WeightedHighOrderFTS</span><span class="p">)</span>
+        <span class="sd">&quot;&quot;&quot;FTS method class that will be used on internal models&quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">partitioner_method</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;partitioner_method&#39;</span><span class="p">,</span> <span class="n">Grid</span><span class="o">.</span><span class="n">GridPartitioner</span><span class="p">)</span>
+        <span class="sd">&quot;&quot;&quot;UoD partitioner class that will be used on internal methods&quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">partitions</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;partitions&#39;</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span><span class="mi">35</span><span class="p">,</span><span class="mi">10</span><span class="p">))</span>
+        <span class="sd">&quot;&quot;&quot;Possible variations of number of partitions on internal models&quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">orders</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;orders&#39;</span><span class="p">,</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">])</span>
+        <span class="sd">&quot;&quot;&quot;Possible variations of order on internal models&quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">uod_clip</span> <span class="o">=</span> <span class="kc">False</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">shortname</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;name&#39;</span><span class="p">,</span> <span class="s1">&#39;EnsembleFTS-&#39;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">method</span><span class="o">.</span><span class="vm">__module__</span><span class="p">)</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;.&#39;</span><span class="p">)[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
+
+<div class="viewcode-block" id="SimpleEnsembleFTS.train"><a class="viewcode-back" href="../../../../pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.SimpleEnsembleFTS.train">[docs]</a>    <span class="k">def</span> <span class="nf">train</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitions</span><span class="p">:</span>
+            <span class="n">fs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner_method</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">data</span><span class="p">,</span> <span class="n">npart</span><span class="o">=</span><span class="n">k</span><span class="p">)</span>
+
+            <span class="k">for</span> <span class="n">order</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">orders</span><span class="p">:</span>
+                <span class="n">tmp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">method</span><span class="p">(</span><span class="n">partitioner</span><span class="o">=</span><span class="n">fs</span><span class="p">,</span> <span class="n">order</span><span class="o">=</span><span class="n">order</span><span class="p">)</span>
+
+                <span class="n">tmp</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+
+                <span class="bp">self</span><span class="o">.</span><span class="n">append_model</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span></div></div>
+
+
+<div class="viewcode-block" id="AllMethodEnsembleFTS"><a class="viewcode-back" href="../../../../pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.AllMethodEnsembleFTS">[docs]</a><span class="k">class</span> <span class="nc">AllMethodEnsembleFTS</span><span class="p">(</span><span class="n">EnsembleFTS</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Creates an EnsembleFTS with all point forecast methods, sharing the same partitioner</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">AllMethodEnsembleFTS</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">min_order</span> <span class="o">=</span> <span class="mi">3</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">shortname</span> <span class="o">=</span><span class="s2">&quot;Ensemble FTS&quot;</span>
+
+<div class="viewcode-block" id="AllMethodEnsembleFTS.set_transformations"><a class="viewcode-back" href="../../../../pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.AllMethodEnsembleFTS.set_transformations">[docs]</a>    <span class="k">def</span> <span class="nf">set_transformations</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">model</span><span class="p">):</span>
+        <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">transformations</span><span class="p">:</span>
+            <span class="n">model</span><span class="o">.</span><span class="n">append_transformation</span><span class="p">(</span><span class="n">t</span><span class="p">)</span></div>
+
+<div class="viewcode-block" id="AllMethodEnsembleFTS.train"><a class="viewcode-back" href="../../../../pyFTS.models.ensemble.html#pyFTS.models.ensemble.ensemble.AllMethodEnsembleFTS.train">[docs]</a>    <span class="k">def</span> <span class="nf">train</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="n">fo_methods</span> <span class="o">=</span> <span class="p">[</span><span class="n">song</span><span class="o">.</span><span class="n">ConventionalFTS</span><span class="p">,</span> <span class="n">chen</span><span class="o">.</span><span class="n">ConventionalFTS</span><span class="p">,</span> <span class="n">yu</span><span class="o">.</span><span class="n">WeightedFTS</span><span class="p">,</span> <span class="n">cheng</span><span class="o">.</span><span class="n">TrendWeightedFTS</span><span class="p">,</span>
+                      <span class="n">sadaei</span><span class="o">.</span><span class="n">ExponentialyWeightedFTS</span><span class="p">,</span> <span class="n">ismailefendi</span><span class="o">.</span><span class="n">ImprovedWeightedFTS</span><span class="p">]</span>
+
+        <span class="n">ho_methods</span> <span class="o">=</span> <span class="p">[</span><span class="n">hofts</span><span class="o">.</span><span class="n">HighOrderFTS</span><span class="p">,</span> <span class="n">hwang</span><span class="o">.</span><span class="n">HighOrderFTS</span><span class="p">]</span>
+
+        <span class="k">for</span> <span class="n">method</span> <span class="ow">in</span> <span class="n">fo_methods</span><span class="p">:</span>
+            <span class="n">model</span> <span class="o">=</span> <span class="n">method</span><span class="p">(</span><span class="n">partitioner</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="p">)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">set_transformations</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
+            <span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">append_model</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
+
+        <span class="k">for</span> <span class="n">method</span> <span class="ow">in</span> <span class="n">ho_methods</span><span class="p">:</span>
+            <span class="k">for</span> <span class="n">o</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="o">+</span><span class="mi">1</span><span class="p">):</span>
+                <span class="n">model</span> <span class="o">=</span> <span class="n">method</span><span class="p">(</span><span class="n">partitioner</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="p">)</span>
+                <span class="k">if</span> <span class="n">model</span><span class="o">.</span><span class="n">min_order</span> <span class="o">&gt;=</span> <span class="n">o</span><span class="p">:</span>
+                    <span class="n">model</span><span class="o">.</span><span class="n">order</span> <span class="o">=</span> <span class="n">o</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">set_transformations</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
+                    <span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">append_model</span><span class="p">(</span><span class="n">model</span><span class="p">)</span></div></div>
+
+
+
+
+</pre></div>
+
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+          </div>
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+
+
+<!doctype html>
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+  <head>
+    <meta charset="utf-8" />
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+        <li class="nav-item nav-item-this"><a href="">pyFTS.models.ensemble.multiseasonal</a></li> 
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+            
+  <h1>Source code for pyFTS.models.ensemble.multiseasonal</h1><div class="highlight"><pre>
+<span></span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">Silva, P. C. L et al. Probabilistic Forecasting with Seasonal Ensemble Fuzzy Time-Series</span>
+<span class="sd">XIII Brazilian Congress on Computational Intelligence, 2017. Rio de Janeiro, Brazil.</span>
+<span class="sd">&quot;&quot;&quot;</span>
+
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="k">import</span> <span class="n">Util</span> <span class="k">as</span> <span class="n">cUtil</span>
+<span class="kn">from</span> <span class="nn">pyFTS.models.ensemble</span> <span class="k">import</span> <span class="n">ensemble</span>
+<span class="kn">from</span> <span class="nn">pyFTS.models.seasonal</span> <span class="k">import</span> <span class="n">cmsfts</span>
+<span class="kn">from</span> <span class="nn">pyFTS.probabilistic</span> <span class="k">import</span> <span class="n">ProbabilityDistribution</span>
+<span class="kn">from</span> <span class="nn">copy</span> <span class="k">import</span> <span class="n">deepcopy</span>
+<span class="kn">from</span> <span class="nn">joblib</span> <span class="k">import</span> <span class="n">Parallel</span><span class="p">,</span> <span class="n">delayed</span>
+<span class="kn">import</span> <span class="nn">multiprocessing</span>
+
+
+<div class="viewcode-block" id="train_individual_model"><a class="viewcode-back" href="../../../../pyFTS.models.ensemble.html#pyFTS.models.ensemble.multiseasonal.train_individual_model">[docs]</a><span class="k">def</span> <span class="nf">train_individual_model</span><span class="p">(</span><span class="n">partitioner</span><span class="p">,</span> <span class="n">train_data</span><span class="p">,</span> <span class="n">indexer</span><span class="p">):</span>
+    <span class="n">pttr</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="n">partitioner</span><span class="o">.</span><span class="vm">__module__</span><span class="p">)</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;.&#39;</span><span class="p">)[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
+    <span class="n">diff</span> <span class="o">=</span> <span class="s2">&quot;_diff&quot;</span> <span class="k">if</span> <span class="n">partitioner</span><span class="o">.</span><span class="n">transformation</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="s2">&quot;&quot;</span>
+    <span class="n">_key</span> <span class="o">=</span> <span class="s2">&quot;msfts_&quot;</span> <span class="o">+</span> <span class="n">pttr</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">partitioner</span><span class="o">.</span><span class="n">partitions</span><span class="p">)</span> <span class="o">+</span> <span class="n">diff</span> <span class="o">+</span> <span class="s2">&quot;_&quot;</span> <span class="o">+</span> <span class="n">indexer</span><span class="o">.</span><span class="n">name</span>
+
+    <span class="nb">print</span><span class="p">(</span><span class="n">_key</span><span class="p">)</span>
+
+    <span class="n">model</span> <span class="o">=</span> <span class="n">cmsfts</span><span class="o">.</span><span class="n">ContextualMultiSeasonalFTS</span><span class="p">(</span><span class="n">_key</span><span class="p">,</span> <span class="n">indexer</span><span class="o">=</span><span class="n">indexer</span><span class="p">)</span>
+    <span class="n">model</span><span class="o">.</span><span class="n">append_transformation</span><span class="p">(</span><span class="n">partitioner</span><span class="o">.</span><span class="n">transformation</span><span class="p">)</span>
+    <span class="n">model</span><span class="o">.</span><span class="n">train</span><span class="p">(</span><span class="n">train_data</span><span class="p">,</span> <span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">,</span> <span class="n">order</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+
+    <span class="n">cUtil</span><span class="o">.</span><span class="n">persist_obj</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="s2">&quot;models/&quot;</span><span class="o">+</span><span class="n">_key</span><span class="o">+</span><span class="s2">&quot;.pkl&quot;</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">model</span></div>
+
+
+<div class="viewcode-block" id="SeasonalEnsembleFTS"><a class="viewcode-back" href="../../../../pyFTS.models.ensemble.html#pyFTS.models.ensemble.multiseasonal.SeasonalEnsembleFTS">[docs]</a><span class="k">class</span> <span class="nc">SeasonalEnsembleFTS</span><span class="p">(</span><span class="n">ensemble</span><span class="o">.</span><span class="n">EnsembleFTS</span><span class="p">):</span>
+    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">name</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">SeasonalEnsembleFTS</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s2">&quot;Seasonal Ensemble FTS&quot;</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">min_order</span> <span class="o">=</span> <span class="mi">1</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">indexers</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">partitioners</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">is_multivariate</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">has_seasonality</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">has_probability_forecasting</span> <span class="o">=</span> <span class="kc">True</span>
+
+<div class="viewcode-block" id="SeasonalEnsembleFTS.update_uod"><a class="viewcode-back" href="../../../../pyFTS.models.ensemble.html#pyFTS.models.ensemble.multiseasonal.SeasonalEnsembleFTS.update_uod">[docs]</a>    <span class="k">def</span> <span class="nf">update_uod</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">original_max</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">indexer</span><span class="o">.</span><span class="n">get_data</span><span class="p">(</span><span class="n">data</span><span class="p">))</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">original_min</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">indexer</span><span class="o">.</span><span class="n">get_data</span><span class="p">(</span><span class="n">data</span><span class="p">))</span></div>
+
+<div class="viewcode-block" id="SeasonalEnsembleFTS.train"><a class="viewcode-back" href="../../../../pyFTS.models.ensemble.html#pyFTS.models.ensemble.multiseasonal.SeasonalEnsembleFTS.train">[docs]</a>    <span class="k">def</span> <span class="nf">train</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">original_max</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">indexer</span><span class="o">.</span><span class="n">get_data</span><span class="p">(</span><span class="n">data</span><span class="p">))</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">original_min</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">indexer</span><span class="o">.</span><span class="n">get_data</span><span class="p">(</span><span class="n">data</span><span class="p">))</span>
+
+        <span class="n">num_cores</span> <span class="o">=</span> <span class="n">multiprocessing</span><span class="o">.</span><span class="n">cpu_count</span><span class="p">()</span>
+
+        <span class="n">pool</span> <span class="o">=</span> <span class="p">{}</span>
+        <span class="n">count</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="k">for</span> <span class="n">ix</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">indexers</span><span class="p">:</span>
+            <span class="k">for</span> <span class="n">pt</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioners</span><span class="p">:</span>
+                <span class="n">pool</span><span class="p">[</span><span class="n">count</span><span class="p">]</span> <span class="o">=</span> <span class="p">{</span><span class="s1">&#39;ix&#39;</span><span class="p">:</span> <span class="n">ix</span><span class="p">,</span> <span class="s1">&#39;pt&#39;</span><span class="p">:</span> <span class="n">pt</span><span class="p">}</span>
+                <span class="n">count</span> <span class="o">+=</span> <span class="mi">1</span>
+
+        <span class="n">results</span> <span class="o">=</span> <span class="n">Parallel</span><span class="p">(</span><span class="n">n_jobs</span><span class="o">=</span><span class="n">num_cores</span><span class="p">)(</span>
+            <span class="n">delayed</span><span class="p">(</span><span class="n">train_individual_model</span><span class="p">)(</span><span class="n">deepcopy</span><span class="p">(</span><span class="n">pool</span><span class="p">[</span><span class="n">m</span><span class="p">][</span><span class="s1">&#39;pt&#39;</span><span class="p">]),</span> <span class="n">data</span><span class="p">,</span> <span class="n">deepcopy</span><span class="p">(</span><span class="n">pool</span><span class="p">[</span><span class="n">m</span><span class="p">][</span><span class="s1">&#39;ix&#39;</span><span class="p">]))</span>
+            <span class="k">for</span> <span class="n">m</span> <span class="ow">in</span> <span class="n">pool</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>
+
+        <span class="k">for</span> <span class="n">tmp</span> <span class="ow">in</span> <span class="n">results</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">append_model</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span>
+
+        <span class="n">cUtil</span><span class="o">.</span><span class="n">persist_obj</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">&quot;models/&quot;</span><span class="o">+</span><span class="bp">self</span><span class="o">.</span><span class="n">name</span><span class="o">+</span><span class="s2">&quot;.pkl&quot;</span><span class="p">)</span></div>
+
+<div class="viewcode-block" id="SeasonalEnsembleFTS.forecast_distribution"><a class="viewcode-back" href="../../../../pyFTS.models.ensemble.html#pyFTS.models.ensemble.multiseasonal.SeasonalEnsembleFTS.forecast_distribution">[docs]</a>    <span class="k">def</span> <span class="nf">forecast_distribution</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="n">smooth</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;smooth&quot;</span><span class="p">,</span> <span class="s2">&quot;KDE&quot;</span><span class="p">)</span>
+        <span class="n">alpha</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;alpha&quot;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+
+        <span class="n">uod</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_UoD</span><span class="p">()</span>
+
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">data</span><span class="o">.</span><span class="n">index</span><span class="p">:</span>
+
+            <span class="n">tmp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_models_forecasts</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">ix</span><span class="p">[</span><span class="n">k</span><span class="p">])</span>
+
+            <span class="k">if</span> <span class="n">alpha</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="n">tmp</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ravel</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">tmp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_distribution_interquantile</span><span class="p">(</span> <span class="n">np</span><span class="o">.</span><span class="n">ravel</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span><span class="o">.</span><span class="n">tolist</span><span class="p">(),</span> <span class="n">alpha</span><span class="p">)</span>
+
+            <span class="n">name</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">indexer</span><span class="o">.</span><span class="n">get_index</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">ix</span><span class="p">[</span><span class="n">k</span><span class="p">]))</span>
+
+            <span class="n">dist</span> <span class="o">=</span> <span class="n">ProbabilityDistribution</span><span class="o">.</span><span class="n">ProbabilityDistribution</span><span class="p">(</span><span class="n">smooth</span><span class="p">,</span> <span class="n">uod</span><span class="o">=</span><span class="n">uod</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="n">tmp</span><span class="p">,</span>
+                                                                   <span class="n">name</span><span class="o">=</span><span class="n">name</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+            <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">dist</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">ret</span></div></div>
+</pre></div>
+
+            <div class="clearer"></div>
+          </div>
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docs/build/html/_modules/pyFTS/models/hofts.html

@@ -0,0 +1,386 @@
+
+
+<!doctype html>
+
+<html>
+  <head>
+    <meta charset="utf-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
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+        <li class="nav-item nav-item-this"><a href="">pyFTS.models.hofts</a></li> 
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+            
+  <h1>Source code for pyFTS.models.hofts</h1><div class="highlight"><pre>
+<span></span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">High Order FTS</span>
+
+<span class="sd">Severiano, S. A. Jr; Silva, P. C. L.; Sadaei, H. J.; Guimarães, F. G. Very Short-term Solar Forecasting</span>
+<span class="sd">using Fuzzy Time Series. 2017 IEEE International Conference on Fuzzy Systems. DOI10.1109/FUZZ-IEEE.2017.8015732</span>
+<span class="sd">&quot;&quot;&quot;</span>
+
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="k">import</span> <span class="n">FuzzySet</span><span class="p">,</span> <span class="n">FLR</span><span class="p">,</span> <span class="n">fts</span><span class="p">,</span> <span class="n">flrg</span>
+<span class="kn">from</span> <span class="nn">itertools</span> <span class="k">import</span> <span class="n">product</span>
+
+
+<div class="viewcode-block" id="HighOrderFLRG"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.hofts.HighOrderFLRG">[docs]</a><span class="k">class</span> <span class="nc">HighOrderFLRG</span><span class="p">(</span><span class="n">flrg</span><span class="o">.</span><span class="n">FLRG</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;Conventional High Order Fuzzy Logical Relationship Group&quot;&quot;&quot;</span>
+    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">order</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">HighOrderFLRG</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">order</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">LHS</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span> <span class="o">=</span> <span class="p">{}</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">strlhs</span> <span class="o">=</span> <span class="s2">&quot;&quot;</span>
+
+<div class="viewcode-block" id="HighOrderFLRG.append_rhs"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.hofts.HighOrderFLRG.append_rhs">[docs]</a>    <span class="k">def</span> <span class="nf">append_rhs</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">c</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">=</span> <span class="n">c</span></div>
+
+<div class="viewcode-block" id="HighOrderFLRG.append_lhs"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.hofts.HighOrderFLRG.append_lhs">[docs]</a>    <span class="k">def</span> <span class="nf">append_lhs</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">c</span><span class="p">):</span>
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">c</span><span class="p">,(</span><span class="nb">tuple</span><span class="p">,</span><span class="nb">list</span><span class="p">)):</span>
+            <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">c</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">LHS</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">k</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">LHS</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">c</span><span class="p">)</span></div>
+
+    <span class="k">def</span> <span class="nf">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="n">tmp</span> <span class="o">=</span> <span class="s2">&quot;&quot;</span>
+        <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="nb">sorted</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">):</span>
+            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+                <span class="n">tmp</span> <span class="o">=</span> <span class="n">tmp</span> <span class="o">+</span> <span class="s2">&quot;,&quot;</span>
+            <span class="n">tmp</span> <span class="o">=</span> <span class="n">tmp</span> <span class="o">+</span> <span class="n">c</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_key</span><span class="p">()</span> <span class="o">+</span> <span class="s2">&quot; -&gt; &quot;</span> <span class="o">+</span> <span class="n">tmp</span>
+
+
+    <span class="k">def</span> <span class="nf">__len__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="WeightedHighOrderFLRG"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.hofts.WeightedHighOrderFLRG">[docs]</a><span class="k">class</span> <span class="nc">WeightedHighOrderFLRG</span><span class="p">(</span><span class="n">flrg</span><span class="o">.</span><span class="n">FLRG</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;Weighted High Order Fuzzy Logical Relationship Group&quot;&quot;&quot;</span>
+
+    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">order</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">WeightedHighOrderFLRG</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">order</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">LHS</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span> <span class="o">=</span> <span class="p">{}</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">count</span> <span class="o">=</span> <span class="mf">0.0</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">strlhs</span> <span class="o">=</span> <span class="s2">&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">w</span> <span class="o">=</span> <span class="kc">None</span>
+
+<div class="viewcode-block" id="WeightedHighOrderFLRG.append_rhs"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.hofts.WeightedHighOrderFLRG.append_rhs">[docs]</a>    <span class="k">def</span> <span class="nf">append_rhs</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">fset</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="n">count</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;count&#39;</span><span class="p">,</span><span class="mf">1.0</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">fset</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">[</span><span class="n">fset</span><span class="p">]</span> <span class="o">=</span> <span class="n">count</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">[</span><span class="n">fset</span><span class="p">]</span> <span class="o">+=</span> <span class="n">count</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">count</span> <span class="o">+=</span> <span class="n">count</span></div>
+
+<div class="viewcode-block" id="WeightedHighOrderFLRG.append_lhs"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.hofts.WeightedHighOrderFLRG.append_lhs">[docs]</a>    <span class="k">def</span> <span class="nf">append_lhs</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">c</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">LHS</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">c</span><span class="p">)</span></div>
+
+<div class="viewcode-block" id="WeightedHighOrderFLRG.weights"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.hofts.WeightedHighOrderFLRG.weights">[docs]</a>    <span class="k">def</span> <span class="nf">weights</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">w</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">w</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">count</span> <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="o">.</span><span class="n">keys</span><span class="p">()])</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">w</span></div>
+
+<div class="viewcode-block" id="WeightedHighOrderFLRG.get_midpoint"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.hofts.WeightedHighOrderFLRG.get_midpoint">[docs]</a>    <span class="k">def</span> <span class="nf">get_midpoint</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sets</span><span class="p">):</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">midpoint</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">mp</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">sets</span><span class="p">[</span><span class="n">c</span><span class="p">]</span><span class="o">.</span><span class="n">centroid</span> <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="o">.</span><span class="n">keys</span><span class="p">()])</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">midpoint</span> <span class="o">=</span> <span class="n">mp</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">weights</span><span class="p">())</span>
+
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">midpoint</span></div>
+
+<div class="viewcode-block" id="WeightedHighOrderFLRG.get_lower"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.hofts.WeightedHighOrderFLRG.get_lower">[docs]</a>    <span class="k">def</span> <span class="nf">get_lower</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sets</span><span class="p">):</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">lower</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">lw</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">sets</span><span class="p">[</span><span class="n">s</span><span class="p">]</span><span class="o">.</span><span class="n">lower</span> <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="o">.</span><span class="n">keys</span><span class="p">()])</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">lower</span> <span class="o">=</span> <span class="n">lw</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">weights</span><span class="p">())</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">lower</span></div>
+
+<div class="viewcode-block" id="WeightedHighOrderFLRG.get_upper"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.hofts.WeightedHighOrderFLRG.get_upper">[docs]</a>    <span class="k">def</span> <span class="nf">get_upper</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sets</span><span class="p">):</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">upper</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">up</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">sets</span><span class="p">[</span><span class="n">s</span><span class="p">]</span><span class="o">.</span><span class="n">upper</span> <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="o">.</span><span class="n">keys</span><span class="p">()])</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">upper</span> <span class="o">=</span> <span class="n">up</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">weights</span><span class="p">())</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">upper</span></div>
+
+    <span class="k">def</span> <span class="nf">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="n">_str</span> <span class="o">=</span> <span class="s2">&quot;&quot;</span>
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
+            <span class="n">_str</span> <span class="o">+=</span> <span class="s2">&quot;, &quot;</span> <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">_str</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="k">else</span> <span class="s2">&quot;&quot;</span>
+            <span class="n">_str</span> <span class="o">+=</span> <span class="n">k</span> <span class="o">+</span> <span class="s2">&quot; (&quot;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">count</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span> <span class="o">+</span> <span class="s2">&quot;)&quot;</span>
+
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_key</span><span class="p">()</span> <span class="o">+</span> <span class="s2">&quot; -&gt; &quot;</span> <span class="o">+</span> <span class="n">_str</span>
+
+    <span class="k">def</span> <span class="nf">__len__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="HighOrderFTS"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.hofts.HighOrderFTS">[docs]</a><span class="k">class</span> <span class="nc">HighOrderFTS</span><span class="p">(</span><span class="n">fts</span><span class="o">.</span><span class="n">FTS</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;Conventional High Order Fuzzy Time Series&quot;&quot;&quot;</span>
+    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">HighOrderFTS</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s2">&quot;High Order FTS&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">shortname</span> <span class="o">=</span> <span class="s2">&quot;HOFTS&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">detail</span> <span class="o">=</span> <span class="s2">&quot;Severiano, Silva, Sadaei and Guimarães&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">is_high_order</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">min_order</span> <span class="o">=</span> <span class="mi">1</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;order&quot;</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">min_order</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">configure_lags</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+<div class="viewcode-block" id="HighOrderFTS.configure_lags"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.hofts.HighOrderFTS.configure_lags">[docs]</a>    <span class="k">def</span> <span class="nf">configure_lags</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="k">if</span> <span class="s2">&quot;order&quot;</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">order</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;order&quot;</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">min_order</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="s2">&quot;lags&quot;</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">lags</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;lags&quot;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">lags</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">lags</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">lags</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span></div>
+
+<div class="viewcode-block" id="HighOrderFTS.generate_lhs_flrg"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.hofts.HighOrderFTS.generate_lhs_flrg">[docs]</a>    <span class="k">def</span> <span class="nf">generate_lhs_flrg</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sample</span><span class="p">,</span> <span class="n">explain</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
+
+        <span class="n">nsample</span> <span class="o">=</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">fuzzyfy</span><span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s2">&quot;sets&quot;</span><span class="p">,</span> <span class="n">alpha_cut</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">alpha_cut</span><span class="p">)</span>
+                   <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">sample</span><span class="p">]</span>
+
+        <span class="k">if</span> <span class="n">explain</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">append_log</span><span class="p">(</span><span class="s2">&quot;Fuzzyfication&quot;</span><span class="p">,</span><span class="s2">&quot;</span><span class="si">{}</span><span class="s2"> -&gt; </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">sample</span><span class="p">,</span> <span class="n">nsample</span><span class="p">))</span>
+
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">generate_lhs_flrg_fuzzyfied</span><span class="p">(</span><span class="n">nsample</span><span class="p">,</span> <span class="n">explain</span><span class="p">)</span></div>
+
+<div class="viewcode-block" id="HighOrderFTS.generate_lhs_flrg_fuzzyfied"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.hofts.HighOrderFTS.generate_lhs_flrg_fuzzyfied">[docs]</a>    <span class="k">def</span> <span class="nf">generate_lhs_flrg_fuzzyfied</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sample</span><span class="p">,</span> <span class="n">explain</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
+        <span class="n">lags</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="n">flrgs</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="k">for</span> <span class="n">ct</span><span class="p">,</span> <span class="n">o</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">lags</span><span class="p">):</span>
+            <span class="n">lhs</span> <span class="o">=</span> <span class="n">sample</span><span class="p">[</span><span class="n">o</span> <span class="o">-</span> <span class="mi">1</span><span class="p">]</span>
+            <span class="n">lags</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">lhs</span><span class="p">)</span>
+
+            <span class="k">if</span> <span class="n">explain</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">append_log</span><span class="p">(</span><span class="s2">&quot;Ordering Lags&quot;</span><span class="p">,</span> <span class="s2">&quot;Lag </span><span class="si">{}</span><span class="s2"> Value </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">o</span><span class="p">,</span> <span class="n">lhs</span><span class="p">))</span>
+
+        <span class="c1"># Trace the possible paths</span>
+        <span class="k">for</span> <span class="n">path</span> <span class="ow">in</span> <span class="n">product</span><span class="p">(</span><span class="o">*</span><span class="n">lags</span><span class="p">):</span>
+            <span class="n">flrg</span> <span class="o">=</span> <span class="n">HighOrderFLRG</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">)</span>
+
+            <span class="k">for</span> <span class="n">lhs</span> <span class="ow">in</span> <span class="n">path</span><span class="p">:</span>
+                <span class="n">flrg</span><span class="o">.</span><span class="n">append_lhs</span><span class="p">(</span><span class="n">lhs</span><span class="p">)</span>
+
+            <span class="n">flrgs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">flrg</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">flrgs</span></div>
+
+<div class="viewcode-block" id="HighOrderFTS.generate_flrg"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.hofts.HighOrderFTS.generate_flrg">[docs]</a>    <span class="k">def</span> <span class="nf">generate_flrg</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">):</span>
+        <span class="n">_tmp_steps</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">standard_horizon</span> <span class="o">-</span> <span class="mi">1</span>
+        <span class="n">l</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">,</span> <span class="n">l</span> <span class="o">-</span> <span class="n">_tmp_steps</span><span class="p">):</span>
+
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">dump</span><span class="p">:</span> <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;FLR: &quot;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">k</span><span class="p">))</span>
+
+            <span class="n">sample</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">k</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">:</span> <span class="n">k</span><span class="p">]</span>
+
+            <span class="n">rhs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">fuzzyfy</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">k</span> <span class="o">+</span> <span class="n">_tmp_steps</span><span class="p">],</span> <span class="n">mode</span><span class="o">=</span><span class="s2">&quot;sets&quot;</span><span class="p">,</span> <span class="n">alpha_cut</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">alpha_cut</span><span class="p">)</span>
+
+            <span class="n">flrgs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">generate_lhs_flrg</span><span class="p">(</span><span class="n">sample</span><span class="p">)</span>
+
+            <span class="k">for</span> <span class="n">flrg</span> <span class="ow">in</span> <span class="n">flrgs</span><span class="p">:</span>
+                <span class="k">if</span> <span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">:</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()]</span> <span class="o">=</span> <span class="n">flrg</span><span class="p">;</span>
+
+                <span class="k">for</span> <span class="n">st</span> <span class="ow">in</span> <span class="n">rhs</span><span class="p">:</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()]</span><span class="o">.</span><span class="n">append_rhs</span><span class="p">(</span><span class="n">st</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="HighOrderFTS.generate_flrg_fuzzyfied"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.hofts.HighOrderFTS.generate_flrg_fuzzyfied">[docs]</a>    <span class="k">def</span> <span class="nf">generate_flrg_fuzzyfied</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">):</span>
+        <span class="n">_tmp_steps</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">standard_horizon</span> <span class="o">-</span> <span class="mi">1</span>
+        <span class="n">l</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">,</span> <span class="n">l</span> <span class="o">-</span> <span class="n">_tmp_steps</span><span class="p">):</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">dump</span><span class="p">:</span> <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;FLR: &quot;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">k</span><span class="p">))</span>
+
+            <span class="n">sample</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">k</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">:</span> <span class="n">k</span><span class="p">]</span>
+
+            <span class="n">rhs</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">k</span> <span class="o">+</span> <span class="n">_tmp_steps</span><span class="p">]</span>
+
+            <span class="n">flrgs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">generate_lhs_flrg_fuzzyfied</span><span class="p">(</span><span class="n">sample</span><span class="p">)</span>
+
+            <span class="k">for</span> <span class="n">flrg</span> <span class="ow">in</span> <span class="n">flrgs</span><span class="p">:</span>
+
+                <span class="k">if</span> <span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">:</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()]</span> <span class="o">=</span> <span class="n">flrg</span>
+
+                <span class="k">for</span> <span class="n">st</span> <span class="ow">in</span> <span class="n">rhs</span><span class="p">:</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()]</span><span class="o">.</span><span class="n">append_rhs</span><span class="p">(</span><span class="n">st</span><span class="p">)</span></div>
+
+<div class="viewcode-block" id="HighOrderFTS.train"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.hofts.HighOrderFTS.train">[docs]</a>    <span class="k">def</span> <span class="nf">train</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">configure_lags</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;fuzzyfied&#39;</span><span class="p">,</span><span class="kc">False</span><span class="p">):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">generate_flrg</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">generate_flrg_fuzzyfied</span><span class="p">(</span><span class="n">data</span><span class="p">)</span></div>
+
+<div class="viewcode-block" id="HighOrderFTS.forecast"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.hofts.HighOrderFTS.forecast">[docs]</a>    <span class="k">def</span> <span class="nf">forecast</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ndata</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+
+        <span class="n">explain</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;explain&#39;</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</span>
+
+        <span class="n">fuzzyfied</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;fuzzyfied&#39;</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</span>
+
+        <span class="n">mode</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;mode&#39;</span><span class="p">,</span> <span class="s1">&#39;mean&#39;</span><span class="p">)</span>
+
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="n">l</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">ndata</span><span class="p">)</span> <span class="k">if</span> <span class="ow">not</span> <span class="n">explain</span> <span class="k">else</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span> <span class="o">+</span> <span class="mi">1</span>
+
+        <span class="k">if</span> <span class="n">l</span> <span class="o">&lt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">ndata</span>
+        <span class="k">elif</span> <span class="n">l</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">:</span>
+            <span class="n">l</span> <span class="o">+=</span> <span class="mi">1</span>
+
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">,</span> <span class="n">l</span><span class="p">):</span>
+
+            <span class="n">sample</span> <span class="o">=</span> <span class="n">ndata</span><span class="p">[</span><span class="n">k</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">:</span> <span class="n">k</span><span class="p">]</span>
+
+            <span class="k">if</span> <span class="ow">not</span> <span class="n">fuzzyfied</span><span class="p">:</span>
+                <span class="n">flrgs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">generate_lhs_flrg</span><span class="p">(</span><span class="n">sample</span><span class="p">,</span> <span class="n">explain</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">flrgs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">generate_lhs_flrg_fuzzyfied</span><span class="p">(</span><span class="n">sample</span><span class="p">,</span> <span class="n">explain</span><span class="p">)</span>
+
+            <span class="n">midpoints</span> <span class="o">=</span> <span class="p">[]</span>
+            <span class="n">memberships</span> <span class="o">=</span> <span class="p">[]</span>
+            <span class="k">for</span> <span class="n">flrg</span> <span class="ow">in</span> <span class="n">flrgs</span><span class="p">:</span>
+
+                <span class="k">if</span> <span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">:</span>
+                    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">flrg</span><span class="o">.</span><span class="n">LHS</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+                        <span class="n">mp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">flrg</span><span class="o">.</span><span class="n">LHS</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]]</span><span class="o">.</span><span class="n">centroid</span>
+                        <span class="n">mv</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">flrg</span><span class="o">.</span><span class="n">LHS</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]]</span><span class="o">.</span><span class="n">membership</span><span class="p">(</span><span class="n">sample</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span> <span class="k">if</span> <span class="ow">not</span> <span class="n">fuzzyfied</span> <span class="k">else</span> <span class="kc">None</span>
+                        <span class="n">midpoints</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">mp</span><span class="p">)</span>
+                        <span class="n">memberships</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">mv</span><span class="p">)</span>
+
+                        <span class="k">if</span> <span class="n">explain</span><span class="p">:</span>
+                            <span class="bp">self</span><span class="o">.</span><span class="n">append_log</span><span class="p">(</span><span class="s2">&quot;Rule Matching&quot;</span><span class="p">,</span> <span class="s2">&quot;</span><span class="si">{}</span><span class="s2"> -&gt; </span><span class="si">{}</span><span class="s2"> (Naïve) Midpoint: </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">flrg</span><span class="o">.</span><span class="n">LHS</span><span class="p">),</span> <span class="n">flrg</span><span class="o">.</span><span class="n">LHS</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span>
+                                                                                            <span class="n">mp</span><span class="p">))</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">flrg</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()]</span>
+                    <span class="n">mp</span> <span class="o">=</span> <span class="n">flrg</span><span class="o">.</span><span class="n">get_midpoint</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">)</span>
+                    <span class="n">mv</span> <span class="o">=</span> <span class="n">flrg</span><span class="o">.</span><span class="n">get_membership</span><span class="p">(</span><span class="n">sample</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">)</span> <span class="k">if</span> <span class="ow">not</span> <span class="n">fuzzyfied</span> <span class="k">else</span> <span class="kc">None</span>
+                    <span class="n">midpoints</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">mp</span><span class="p">)</span>
+                    <span class="n">memberships</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">mv</span><span class="p">)</span>
+
+                    <span class="k">if</span> <span class="n">explain</span><span class="p">:</span>
+                        <span class="bp">self</span><span class="o">.</span><span class="n">append_log</span><span class="p">(</span><span class="s2">&quot;Rule Matching&quot;</span><span class="p">,</span> <span class="s2">&quot;</span><span class="si">{}</span><span class="s2">, Midpoint: </span><span class="si">{}</span><span class="s2"> Membership: </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">(),</span> <span class="n">mp</span><span class="p">,</span> <span class="n">mv</span><span class="p">))</span>
+
+            <span class="k">if</span> <span class="n">mode</span> <span class="o">==</span> <span class="s2">&quot;mean&quot;</span> <span class="ow">or</span> <span class="n">fuzzyfied</span><span class="p">:</span>
+                <span class="n">final</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nanmean</span><span class="p">(</span><span class="n">midpoints</span><span class="p">)</span>
+                <span class="k">if</span> <span class="n">explain</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">append_log</span><span class="p">(</span><span class="s2">&quot;Deffuzyfication&quot;</span><span class="p">,</span> <span class="s2">&quot;By Mean: </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">final</span><span class="p">))</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">final</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">midpoints</span><span class="p">,</span> <span class="n">memberships</span><span class="p">)</span><span class="o">/</span><span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">memberships</span><span class="p">)</span>
+                <span class="k">if</span> <span class="n">explain</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">append_log</span><span class="p">(</span><span class="s2">&quot;Deffuzyfication&quot;</span><span class="p">,</span> <span class="s2">&quot;By Memberships: </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">final</span><span class="p">))</span>
+
+            <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">final</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">ret</span></div></div>
+
+
+<div class="viewcode-block" id="WeightedHighOrderFTS"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.hofts.WeightedHighOrderFTS">[docs]</a><span class="k">class</span> <span class="nc">WeightedHighOrderFTS</span><span class="p">(</span><span class="n">HighOrderFTS</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;Weighted High Order Fuzzy Time Series&quot;&quot;&quot;</span>
+    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">WeightedHighOrderFTS</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s2">&quot;Weighted High Order FTS&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">shortname</span> <span class="o">=</span> <span class="s2">&quot;WHOFTS&quot;</span>
+
+<div class="viewcode-block" id="WeightedHighOrderFTS.generate_lhs_flrg_fuzzyfied"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.hofts.WeightedHighOrderFTS.generate_lhs_flrg_fuzzyfied">[docs]</a>    <span class="k">def</span> <span class="nf">generate_lhs_flrg_fuzzyfied</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sample</span><span class="p">,</span> <span class="n">explain</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
+        <span class="n">lags</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="n">flrgs</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="k">for</span> <span class="n">ct</span><span class="p">,</span> <span class="n">o</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">lags</span><span class="p">):</span>
+            <span class="n">lags</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">sample</span><span class="p">[</span><span class="n">o</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
+
+            <span class="k">if</span> <span class="n">explain</span><span class="p">:</span>
+                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\t</span><span class="s2"> (Lag </span><span class="si">{}</span><span class="s2">) </span><span class="si">{}</span><span class="s2"> </span><span class="se">\n</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">o</span><span class="p">,</span> <span class="n">sample</span><span class="p">[</span><span class="n">o</span><span class="o">-</span><span class="mi">1</span><span class="p">]))</span>
+
+        <span class="c1"># Trace the possible paths</span>
+        <span class="k">for</span> <span class="n">path</span> <span class="ow">in</span> <span class="n">product</span><span class="p">(</span><span class="o">*</span><span class="n">lags</span><span class="p">):</span>
+            <span class="n">flrg</span> <span class="o">=</span> <span class="n">WeightedHighOrderFLRG</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">)</span>
+
+            <span class="k">for</span> <span class="n">lhs</span> <span class="ow">in</span> <span class="n">path</span><span class="p">:</span>
+                <span class="n">flrg</span><span class="o">.</span><span class="n">append_lhs</span><span class="p">(</span><span class="n">lhs</span><span class="p">)</span>
+
+            <span class="n">flrgs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">flrg</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">flrgs</span></div></div>
+</pre></div>
+
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docs/build/html/_modules/pyFTS/models/hwang.html

@@ -0,0 +1,151 @@
+
+
+<!doctype html>
+
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+            
+  <h1>Source code for pyFTS.models.hwang</h1><div class="highlight"><pre>
+<span></span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">High Order Fuzzy Time Series by Hwang, Chen and Lee (1998)</span>
+
+<span class="sd">Jeng-Ren Hwang, Shyi-Ming Chen, and Chia-Hoang Lee, “Handling forecasting problems using fuzzy time series,” </span>
+<span class="sd">Fuzzy Sets Syst., no. 100, pp. 217–228, 1998.</span>
+<span class="sd">&quot;&quot;&quot;</span>
+
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="k">import</span> <span class="n">FuzzySet</span><span class="p">,</span> <span class="n">FLR</span><span class="p">,</span> <span class="n">fts</span>
+
+
+<div class="viewcode-block" id="HighOrderFTS"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.hwang.HighOrderFTS">[docs]</a><span class="k">class</span> <span class="nc">HighOrderFTS</span><span class="p">(</span><span class="n">fts</span><span class="o">.</span><span class="n">FTS</span><span class="p">):</span>
+    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">HighOrderFTS</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">is_high_order</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">min_order</span> <span class="o">=</span> <span class="mi">2</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s2">&quot;Hwang High Order FTS&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">shortname</span> <span class="o">=</span> <span class="s2">&quot;Hwang&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">detail</span> <span class="o">=</span> <span class="s2">&quot;Hwang&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">configure_lags</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+<div class="viewcode-block" id="HighOrderFTS.configure_lags"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.hwang.HighOrderFTS.configure_lags">[docs]</a>    <span class="k">def</span> <span class="nf">configure_lags</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="k">if</span> <span class="s2">&quot;order&quot;</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">order</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;order&quot;</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span></div>
+
+<div class="viewcode-block" id="HighOrderFTS.forecast"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.hwang.HighOrderFTS.forecast">[docs]</a>    <span class="k">def</span> <span class="nf">forecast</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ndata</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+
+        <span class="n">l</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="p">)</span>
+
+        <span class="n">cn</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mf">0.0</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">l</span><span class="p">)])</span>
+        <span class="n">ow</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mf">0.0</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">l</span><span class="p">)]</span> <span class="k">for</span> <span class="n">z</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)])</span>
+        <span class="n">rn</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mf">0.0</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">l</span><span class="p">)]</span> <span class="k">for</span> <span class="n">z</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)])</span>
+        <span class="n">ft</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mf">0.0</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">l</span><span class="p">)])</span>
+
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">ndata</span><span class="p">)):</span>
+
+            <span class="k">for</span> <span class="n">ix</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">l</span><span class="p">):</span>
+                <span class="n">s</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">ordered_sets</span><span class="p">[</span><span class="n">ix</span><span class="p">]</span>
+                <span class="n">cn</span><span class="p">[</span><span class="n">ix</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">s</span><span class="p">]</span><span class="o">.</span><span class="n">membership</span><span class="p">(</span> <span class="n">FuzzySet</span><span class="o">.</span><span class="n">grant_bounds</span><span class="p">(</span><span class="n">ndata</span><span class="p">[</span><span class="n">t</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">ordered_sets</span><span class="p">))</span>
+                <span class="k">for</span> <span class="n">w</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="o">-</span><span class="mi">1</span><span class="p">):</span>
+                    <span class="n">ow</span><span class="p">[</span><span class="n">w</span><span class="p">,</span> <span class="n">ix</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">s</span><span class="p">]</span><span class="o">.</span><span class="n">membership</span><span class="p">(</span><span class="n">FuzzySet</span><span class="o">.</span><span class="n">grant_bounds</span><span class="p">(</span><span class="n">ndata</span><span class="p">[</span><span class="n">t</span> <span class="o">-</span> <span class="n">w</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">ordered_sets</span><span class="p">))</span>
+                    <span class="n">rn</span><span class="p">[</span><span class="n">w</span><span class="p">,</span> <span class="n">ix</span><span class="p">]</span> <span class="o">=</span> <span class="n">ow</span><span class="p">[</span><span class="n">w</span><span class="p">,</span> <span class="n">ix</span><span class="p">]</span> <span class="o">*</span> <span class="n">cn</span><span class="p">[</span><span class="n">ix</span><span class="p">]</span>
+                    <span class="n">ft</span><span class="p">[</span><span class="n">ix</span><span class="p">]</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">ft</span><span class="p">[</span><span class="n">ix</span><span class="p">],</span> <span class="n">rn</span><span class="p">[</span><span class="n">w</span><span class="p">,</span> <span class="n">ix</span><span class="p">])</span>
+            <span class="n">mft</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">ft</span><span class="p">)</span>
+            <span class="n">out</span> <span class="o">=</span> <span class="mf">0.0</span>
+            <span class="n">count</span> <span class="o">=</span> <span class="mf">0.0</span>
+            <span class="k">for</span> <span class="n">ix</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">l</span><span class="p">):</span>
+                <span class="n">s</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">ordered_sets</span><span class="p">[</span><span class="n">ix</span><span class="p">]</span>
+                <span class="k">if</span> <span class="n">ft</span><span class="p">[</span><span class="n">ix</span><span class="p">]</span> <span class="o">==</span> <span class="n">mft</span><span class="p">:</span>
+                    <span class="n">out</span> <span class="o">=</span> <span class="n">out</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">s</span><span class="p">]</span><span class="o">.</span><span class="n">centroid</span>
+                    <span class="n">count</span> <span class="o">+=</span> <span class="mf">1.0</span>
+            <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">out</span> <span class="o">/</span> <span class="n">count</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">ret</span></div>
+
+<div class="viewcode-block" id="HighOrderFTS.train"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.hwang.HighOrderFTS.train">[docs]</a>    <span class="k">def</span> <span class="nf">train</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">configure_lags</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div></div>
+</pre></div>
+
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+        <li class="nav-item nav-item-this"><a href="">pyFTS.models.hwang</a></li> 
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docs/build/html/_modules/pyFTS/models/ifts.html

@@ -0,0 +1,276 @@
+
+
+<!doctype html>
+
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+    <meta charset="utf-8" />
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+    <title>pyFTS.models.ifts &#8212; pyFTS 1.7 documentation</title>
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+        <li class="nav-item nav-item-this"><a href="">pyFTS.models.ifts</a></li> 
+      </ul>
+    </div>  
+
+    <div class="document">
+      <div class="documentwrapper">
+        <div class="bodywrapper">
+          <div class="body" role="main">
+            
+  <h1>Source code for pyFTS.models.ifts</h1><div class="highlight"><pre>
+<span></span><span class="ch">#!/usr/bin/python</span>
+<span class="c1"># -*- coding: utf8 -*-</span>
+
+<span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">High Order Interval Fuzzy Time Series</span>
+
+<span class="sd">SILVA, Petrônio CL; SADAEI, Hossein Javedani; GUIMARÃES, Frederico Gadelha. Interval Forecasting with Fuzzy Time Series.</span>
+<span class="sd">In: Computational Intelligence (SSCI), 2016 IEEE Symposium Series on. IEEE, 2016. p. 1-8.</span>
+<span class="sd">&quot;&quot;&quot;</span>
+
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="k">import</span> <span class="n">FuzzySet</span><span class="p">,</span> <span class="n">FLR</span><span class="p">,</span> <span class="n">fts</span><span class="p">,</span> <span class="n">tree</span>
+<span class="kn">from</span> <span class="nn">pyFTS.models</span> <span class="k">import</span> <span class="n">hofts</span>
+
+
+<div class="viewcode-block" id="IntervalFTS"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.ifts.IntervalFTS">[docs]</a><span class="k">class</span> <span class="nc">IntervalFTS</span><span class="p">(</span><span class="n">hofts</span><span class="o">.</span><span class="n">HighOrderFTS</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    High Order Interval Fuzzy Time Series</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">IntervalFTS</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">shortname</span> <span class="o">=</span> <span class="s2">&quot;IFTS&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s2">&quot;Interval FTS&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">detail</span> <span class="o">=</span> <span class="s2">&quot;Silva, P.; Guimarães, F.; Sadaei, H. (2016)&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span> <span class="o">=</span> <span class="p">{}</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">has_point_forecasting</span> <span class="o">=</span> <span class="kc">False</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">has_interval_forecasting</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">is_high_order</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">min_order</span> <span class="o">=</span> <span class="mi">1</span>
+
+<div class="viewcode-block" id="IntervalFTS.get_upper"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.ifts.IntervalFTS.get_upper">[docs]</a>    <span class="k">def</span> <span class="nf">get_upper</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">flrg</span><span class="p">):</span>
+        <span class="n">ret</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">flrg</span><span class="o">.</span><span class="n">LHS</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">:</span>
+                <span class="n">tmp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()]</span>
+                <span class="n">ret</span> <span class="o">=</span> <span class="n">tmp</span><span class="o">.</span><span class="n">get_upper</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">ret</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">flrg</span><span class="o">.</span><span class="n">LHS</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]]</span><span class="o">.</span><span class="n">upper</span>
+        <span class="k">return</span> <span class="n">ret</span></div>
+
+<div class="viewcode-block" id="IntervalFTS.get_lower"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.ifts.IntervalFTS.get_lower">[docs]</a>    <span class="k">def</span> <span class="nf">get_lower</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">flrg</span><span class="p">):</span>
+        <span class="n">ret</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">flrg</span><span class="o">.</span><span class="n">LHS</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">:</span>
+                <span class="n">tmp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()]</span>
+                <span class="n">ret</span> <span class="o">=</span> <span class="n">tmp</span><span class="o">.</span><span class="n">get_lower</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">ret</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">flrg</span><span class="o">.</span><span class="n">LHS</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]]</span><span class="o">.</span><span class="n">lower</span>
+        <span class="k">return</span> <span class="n">ret</span></div>
+
+<div class="viewcode-block" id="IntervalFTS.get_sequence_membership"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.ifts.IntervalFTS.get_sequence_membership">[docs]</a>    <span class="k">def</span> <span class="nf">get_sequence_membership</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">fuzzySets</span><span class="p">):</span>
+        <span class="n">mb</span> <span class="o">=</span> <span class="p">[</span><span class="n">fuzzySets</span><span class="p">[</span><span class="n">k</span><span class="p">]</span><span class="o">.</span><span class="n">membership</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">k</span><span class="p">])</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">))]</span>
+        <span class="k">return</span> <span class="n">mb</span></div>
+
+<div class="viewcode-block" id="IntervalFTS.forecast_interval"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.ifts.IntervalFTS.forecast_interval">[docs]</a>    <span class="k">def</span> <span class="nf">forecast_interval</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ndata</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="n">l</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">ndata</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">l</span> <span class="o">&lt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">ndata</span>
+
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">,</span> <span class="n">l</span><span class="o">+</span><span class="mi">1</span><span class="p">):</span>
+
+            <span class="n">sample</span> <span class="o">=</span> <span class="n">ndata</span><span class="p">[</span><span class="n">k</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">:</span> <span class="n">k</span><span class="p">]</span>
+
+            <span class="n">flrgs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">generate_lhs_flrg</span><span class="p">(</span><span class="n">sample</span><span class="p">)</span>
+
+            <span class="n">up</span> <span class="o">=</span> <span class="p">[]</span>
+            <span class="n">lo</span> <span class="o">=</span> <span class="p">[]</span>
+            <span class="n">affected_flrgs_memberships</span> <span class="o">=</span> <span class="p">[]</span>
+
+            <span class="k">for</span> <span class="n">flrg</span> <span class="ow">in</span> <span class="n">flrgs</span><span class="p">:</span>
+                <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">flrg</span><span class="o">.</span><span class="n">LHS</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+
+                    <span class="n">mv</span> <span class="o">=</span> <span class="n">flrg</span><span class="o">.</span><span class="n">get_membership</span><span class="p">(</span><span class="n">sample</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">)</span>
+                    <span class="n">up</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">mv</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_upper</span><span class="p">(</span><span class="n">flrg</span><span class="p">))</span>
+                    <span class="n">lo</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">mv</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_lower</span><span class="p">(</span><span class="n">flrg</span><span class="p">))</span>
+                    <span class="n">affected_flrgs_memberships</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">mv</span><span class="p">)</span>
+
+            <span class="c1"># gerar o intervalo</span>
+            <span class="n">norm</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">affected_flrgs_memberships</span><span class="p">)</span>
+            <span class="n">lo_</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">lo</span><span class="p">)</span> <span class="o">/</span> <span class="n">norm</span>
+            <span class="n">up_</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">up</span><span class="p">)</span> <span class="o">/</span> <span class="n">norm</span>
+            <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">([</span><span class="n">lo_</span><span class="p">,</span> <span class="n">up_</span><span class="p">])</span>
+
+        <span class="k">return</span> <span class="n">ret</span></div>
+
+<div class="viewcode-block" id="IntervalFTS.forecast_ahead_interval"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.ifts.IntervalFTS.forecast_ahead_interval">[docs]</a>    <span class="k">def</span> <span class="nf">forecast_ahead_interval</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">steps</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="n">start</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;start_at&#39;</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">[[</span><span class="n">x</span><span class="p">,</span> <span class="n">x</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">data</span><span class="p">[</span><span class="n">start</span><span class="p">:</span><span class="n">start</span><span class="o">+</span><span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">]]</span>
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span> <span class="o">+</span> <span class="n">steps</span><span class="p">):</span>
+            <span class="n">interval_lower</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">clip_uod</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">forecast_interval</span><span class="p">([</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">ret</span><span class="p">[</span><span class="n">k</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">:</span> <span class="n">k</span><span class="p">]])[</span><span class="mi">0</span><span class="p">])</span>
+            <span class="n">interval_upper</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">clip_uod</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">forecast_interval</span><span class="p">([</span><span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">ret</span><span class="p">[</span><span class="n">k</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">:</span> <span class="n">k</span><span class="p">]])[</span><span class="mi">0</span><span class="p">])</span>
+            <span class="n">interval</span> <span class="o">=</span> <span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">nanmin</span><span class="p">(</span><span class="n">interval_lower</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">nanmax</span><span class="p">(</span><span class="n">interval_upper</span><span class="p">)]</span>
+            <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">interval</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">ret</span><span class="p">[</span><span class="o">-</span><span class="n">steps</span><span class="p">:]</span></div></div>
+
+
+<div class="viewcode-block" id="WeightedIntervalFTS"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.ifts.WeightedIntervalFTS">[docs]</a><span class="k">class</span> <span class="nc">WeightedIntervalFTS</span><span class="p">(</span><span class="n">hofts</span><span class="o">.</span><span class="n">WeightedHighOrderFTS</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Weighted High Order Interval Fuzzy Time Series</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">WeightedIntervalFTS</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">shortname</span> <span class="o">=</span> <span class="s2">&quot;WIFTS&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s2">&quot;Weighted Interval FTS&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">detail</span> <span class="o">=</span> <span class="s2">&quot;Silva, P.; Guimarães, F.; Sadaei, H. (2016)&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span> <span class="o">=</span> <span class="p">{}</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">has_point_forecasting</span> <span class="o">=</span> <span class="kc">False</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">has_interval_forecasting</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">is_high_order</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">min_order</span> <span class="o">=</span> <span class="mi">1</span>
+
+<div class="viewcode-block" id="WeightedIntervalFTS.get_upper"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.ifts.WeightedIntervalFTS.get_upper">[docs]</a>    <span class="k">def</span> <span class="nf">get_upper</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">flrg</span><span class="p">):</span>
+        <span class="n">ret</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">flrg</span><span class="o">.</span><span class="n">LHS</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">:</span>
+                <span class="n">tmp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()]</span>
+                <span class="n">ret</span> <span class="o">=</span> <span class="n">tmp</span><span class="o">.</span><span class="n">get_upper</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">ret</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">flrg</span><span class="o">.</span><span class="n">LHS</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]]</span><span class="o">.</span><span class="n">upper</span>
+        <span class="k">return</span> <span class="n">ret</span></div>
+
+<div class="viewcode-block" id="WeightedIntervalFTS.get_lower"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.ifts.WeightedIntervalFTS.get_lower">[docs]</a>    <span class="k">def</span> <span class="nf">get_lower</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">flrg</span><span class="p">):</span>
+        <span class="n">ret</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">flrg</span><span class="o">.</span><span class="n">LHS</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">:</span>
+                <span class="n">tmp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()]</span>
+                <span class="n">ret</span> <span class="o">=</span> <span class="n">tmp</span><span class="o">.</span><span class="n">get_lower</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">ret</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">flrg</span><span class="o">.</span><span class="n">LHS</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]]</span><span class="o">.</span><span class="n">lower</span>
+        <span class="k">return</span> <span class="n">ret</span></div>
+
+<div class="viewcode-block" id="WeightedIntervalFTS.get_sequence_membership"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.ifts.WeightedIntervalFTS.get_sequence_membership">[docs]</a>    <span class="k">def</span> <span class="nf">get_sequence_membership</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">fuzzySets</span><span class="p">):</span>
+        <span class="n">mb</span> <span class="o">=</span> <span class="p">[</span><span class="n">fuzzySets</span><span class="p">[</span><span class="n">k</span><span class="p">]</span><span class="o">.</span><span class="n">membership</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">k</span><span class="p">])</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">))]</span>
+        <span class="k">return</span> <span class="n">mb</span></div>
+
+<div class="viewcode-block" id="WeightedIntervalFTS.forecast_interval"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.ifts.WeightedIntervalFTS.forecast_interval">[docs]</a>    <span class="k">def</span> <span class="nf">forecast_interval</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ndata</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="n">l</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">ndata</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">l</span> <span class="o">&lt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">ndata</span>
+
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">,</span> <span class="n">l</span><span class="o">+</span><span class="mi">1</span><span class="p">):</span>
+
+            <span class="n">sample</span> <span class="o">=</span> <span class="n">ndata</span><span class="p">[</span><span class="n">k</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">:</span> <span class="n">k</span><span class="p">]</span>
+
+            <span class="n">flrgs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">generate_lhs_flrg</span><span class="p">(</span><span class="n">sample</span><span class="p">)</span>
+
+            <span class="n">up</span> <span class="o">=</span> <span class="p">[]</span>
+            <span class="n">lo</span> <span class="o">=</span> <span class="p">[]</span>
+            <span class="n">affected_flrgs_memberships</span> <span class="o">=</span> <span class="p">[]</span>
+
+            <span class="k">for</span> <span class="n">flrg</span> <span class="ow">in</span> <span class="n">flrgs</span><span class="p">:</span>
+                <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">flrg</span><span class="o">.</span><span class="n">LHS</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+
+                    <span class="n">mv</span> <span class="o">=</span> <span class="n">flrg</span><span class="o">.</span><span class="n">get_membership</span><span class="p">(</span><span class="n">sample</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">)</span>
+                    <span class="n">up</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">mv</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_upper</span><span class="p">(</span><span class="n">flrg</span><span class="p">))</span>
+                    <span class="n">lo</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">mv</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_lower</span><span class="p">(</span><span class="n">flrg</span><span class="p">))</span>
+                    <span class="n">affected_flrgs_memberships</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">mv</span><span class="p">)</span>
+
+            <span class="c1"># gerar o intervalo</span>
+            <span class="n">norm</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">affected_flrgs_memberships</span><span class="p">)</span>
+            <span class="n">lo_</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">lo</span><span class="p">)</span> <span class="o">/</span> <span class="n">norm</span>
+            <span class="n">up_</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">up</span><span class="p">)</span> <span class="o">/</span> <span class="n">norm</span>
+            <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">([</span><span class="n">lo_</span><span class="p">,</span> <span class="n">up_</span><span class="p">])</span>
+
+        <span class="k">return</span> <span class="n">ret</span></div>
+
+<div class="viewcode-block" id="WeightedIntervalFTS.forecast_ahead_interval"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.ifts.WeightedIntervalFTS.forecast_ahead_interval">[docs]</a>    <span class="k">def</span> <span class="nf">forecast_ahead_interval</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">steps</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="n">start</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;start_at&#39;</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">[[</span><span class="n">x</span><span class="p">,</span> <span class="n">x</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">data</span><span class="p">[</span><span class="n">start</span><span class="p">:</span><span class="n">start</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">]]</span>
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span> <span class="o">+</span> <span class="n">steps</span><span class="p">):</span>
+            <span class="n">interval_lower</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">clip_uod</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">forecast_interval</span><span class="p">([</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">ret</span><span class="p">[</span><span class="n">k</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">:</span> <span class="n">k</span><span class="p">]])[</span><span class="mi">0</span><span class="p">])</span>
+            <span class="n">interval_upper</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">clip_uod</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">forecast_interval</span><span class="p">([</span><span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">ret</span><span class="p">[</span><span class="n">k</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">:</span> <span class="n">k</span><span class="p">]])[</span><span class="mi">0</span><span class="p">])</span>
+            <span class="n">interval</span> <span class="o">=</span> <span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">nanmin</span><span class="p">(</span><span class="n">interval_lower</span><span class="p">),</span> <span class="n">np</span><span class="o">.</span><span class="n">nanmax</span><span class="p">(</span><span class="n">interval_upper</span><span class="p">)]</span>
+            <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">interval</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">ret</span><span class="p">[</span><span class="o">-</span><span class="n">steps</span><span class="p">:]</span></div></div>
+
+
+</pre></div>
+
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docs/build/html/_modules/pyFTS/models/incremental/IncrementalEnsemble.html

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+        <li class="nav-item nav-item-this"><a href="">pyFTS.models.incremental.IncrementalEnsemble</a></li> 
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+            
+  <h1>Source code for pyFTS.models.incremental.IncrementalEnsemble</h1><div class="highlight"><pre>
+<span></span><span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">Time Variant/Incremental Ensemble of FTS methods</span>
+<span class="sd">&#39;&#39;&#39;</span>
+
+
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="k">import</span> <span class="n">FuzzySet</span><span class="p">,</span> <span class="n">FLR</span><span class="p">,</span> <span class="n">fts</span><span class="p">,</span> <span class="n">flrg</span>
+<span class="kn">from</span> <span class="nn">pyFTS.partitioners</span> <span class="k">import</span> <span class="n">Grid</span>
+<span class="kn">from</span> <span class="nn">pyFTS.models</span> <span class="k">import</span> <span class="n">hofts</span>
+<span class="kn">from</span> <span class="nn">pyFTS.models.ensemble</span> <span class="k">import</span> <span class="n">ensemble</span>
+
+
+<div class="viewcode-block" id="IncrementalEnsembleFTS"><a class="viewcode-back" href="../../../../pyFTS.models.incremental.html#pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS">[docs]</a><span class="k">class</span> <span class="nc">IncrementalEnsembleFTS</span><span class="p">(</span><span class="n">ensemble</span><span class="o">.</span><span class="n">EnsembleFTS</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Time Variant/Incremental Ensemble of FTS methods</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">IncrementalEnsembleFTS</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">shortname</span> <span class="o">=</span> <span class="s2">&quot;IncrementalEnsembleFTS&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s2">&quot;Incremental Ensemble FTS&quot;</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">order</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;order&#39;</span><span class="p">,</span><span class="mi">1</span><span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">partitioner_method</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;partitioner_method&#39;</span><span class="p">,</span> <span class="n">Grid</span><span class="o">.</span><span class="n">GridPartitioner</span><span class="p">)</span>
+        <span class="sd">&quot;&quot;&quot;The partitioner method to be called when a new model is build&quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">partitioner_params</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;partitioner_params&#39;</span><span class="p">,</span> <span class="p">{</span><span class="s1">&#39;npart&#39;</span><span class="p">:</span> <span class="mi">10</span><span class="p">})</span>
+        <span class="sd">&quot;&quot;&quot;The partitioner method parameters&quot;&quot;&quot;</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">fts_method</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;fts_method&#39;</span><span class="p">,</span> <span class="n">hofts</span><span class="o">.</span><span class="n">WeightedHighOrderFTS</span><span class="p">)</span>
+        <span class="sd">&quot;&quot;&quot;The FTS method to be called when a new model is build&quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">fts_params</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;fts_params&#39;</span><span class="p">,</span> <span class="p">{})</span>
+        <span class="sd">&quot;&quot;&quot;The FTS method specific parameters&quot;&quot;&quot;</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">window_length</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;window_length&#39;</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span>
+        <span class="sd">&quot;&quot;&quot;The memory window length&quot;&quot;&quot;</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">batch_size</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;batch_size&#39;</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span>
+        <span class="sd">&quot;&quot;&quot;The batch interval between each retraining&quot;&quot;&quot;</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">num_models</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;num_models&#39;</span><span class="p">,</span> <span class="mi">5</span><span class="p">)</span>
+        <span class="sd">&quot;&quot;&quot;The number of models to hold in the ensemble&quot;&quot;&quot;</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">point_method</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;point_method&#39;</span><span class="p">,</span> <span class="s1">&#39;exponential&#39;</span><span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">is_high_order</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">uod_clip</span> <span class="o">=</span> <span class="kc">False</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">window_length</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span>
+
+<div class="viewcode-block" id="IncrementalEnsembleFTS.offset"><a class="viewcode-back" href="../../../../pyFTS.models.incremental.html#pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS.offset">[docs]</a>    <span class="k">def</span> <span class="nf">offset</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span></div>
+
+<div class="viewcode-block" id="IncrementalEnsembleFTS.train"><a class="viewcode-back" href="../../../../pyFTS.models.incremental.html#pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS.train">[docs]</a>    <span class="k">def</span> <span class="nf">train</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+
+        <span class="n">partitioner</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner_method</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner_params</span><span class="p">)</span>
+        <span class="n">model</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">fts_method</span><span class="p">(</span><span class="n">partitioner</span><span class="o">=</span><span class="n">partitioner</span><span class="p">,</span> <span class="o">**</span><span class="bp">self</span><span class="o">.</span><span class="n">fts_params</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">model</span><span class="o">.</span><span class="n">is_high_order</span><span class="p">:</span>
+            <span class="n">model</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">fts_method</span><span class="p">(</span><span class="n">partitioner</span><span class="o">=</span><span class="n">partitioner</span><span class="p">,</span> <span class="n">order</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">,</span> <span class="o">**</span><span class="bp">self</span><span class="o">.</span><span class="n">fts_params</span><span class="p">)</span>
+        <span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">append_model</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">models</span><span class="p">)</span> <span class="o">&gt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_models</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">models</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span></div>
+
+<div class="viewcode-block" id="IncrementalEnsembleFTS.forecast"><a class="viewcode-back" href="../../../../pyFTS.models.incremental.html#pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS.forecast">[docs]</a>    <span class="k">def</span> <span class="nf">forecast</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="n">l</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+        <span class="n">no_update</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;no_update&#39;</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">no_update</span><span class="p">:</span>
+            <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+            <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">,</span> <span class="n">l</span><span class="o">+</span><span class="mi">1</span><span class="p">):</span>
+                <span class="n">sample</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">k</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">:</span> <span class="n">k</span><span class="p">]</span>
+                <span class="n">tmp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_models_forecasts</span><span class="p">(</span><span class="n">sample</span><span class="p">)</span>
+                <span class="n">point</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_point</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span>
+                <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">point</span><span class="p">)</span>
+            <span class="k">return</span> <span class="n">ret</span>
+
+        <span class="n">data_window</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">,</span> <span class="n">l</span><span class="p">):</span>
+
+            <span class="n">k2</span> <span class="o">=</span> <span class="n">k</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span>
+
+            <span class="n">data_window</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">k2</span><span class="p">])</span>
+
+            <span class="k">if</span> <span class="n">k2</span> <span class="o">&gt;=</span> <span class="bp">self</span><span class="o">.</span><span class="n">window_length</span><span class="p">:</span>
+                <span class="n">data_window</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
+
+            <span class="k">if</span> <span class="n">k</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">batch_size</span> <span class="o">==</span> <span class="mi">0</span> <span class="ow">and</span> <span class="n">k2</span> <span class="o">&gt;=</span> <span class="bp">self</span><span class="o">.</span><span class="n">window_length</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">train</span><span class="p">(</span><span class="n">data_window</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">models</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+                <span class="n">sample</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">k2</span><span class="p">:</span> <span class="n">k</span><span class="p">]</span>
+                <span class="n">tmp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_models_forecasts</span><span class="p">(</span><span class="n">sample</span><span class="p">)</span>
+                <span class="n">point</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_point</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span>
+                <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">point</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">ret</span></div>
+
+<div class="viewcode-block" id="IncrementalEnsembleFTS.forecast_ahead"><a class="viewcode-back" href="../../../../pyFTS.models.incremental.html#pyFTS.models.incremental.IncrementalEnsemble.IncrementalEnsembleFTS.forecast_ahead">[docs]</a>    <span class="k">def</span> <span class="nf">forecast_ahead</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">steps</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)</span> <span class="o">&lt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">data</span>
+
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
+            <span class="n">data</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
+
+        <span class="n">start</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;start_at&#39;</span><span class="p">,</span><span class="mi">0</span><span class="p">)</span>
+
+        <span class="n">ret</span> <span class="o">=</span> <span class="n">data</span><span class="p">[:</span><span class="n">start</span><span class="o">+</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">]</span>
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">start</span><span class="o">+</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">,</span> <span class="n">steps</span><span class="o">+</span><span class="n">start</span><span class="o">+</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">):</span>
+            <span class="n">tmp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecast</span><span class="p">(</span><span class="n">ret</span><span class="p">[</span><span class="n">k</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">:</span><span class="n">k</span><span class="p">],</span> <span class="n">no_update</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">tmp</span><span class="p">,(</span><span class="nb">list</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">)):</span>
+                <span class="n">tmp</span> <span class="o">=</span> <span class="n">tmp</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
+
+            <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span>
+            <span class="n">data</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">ret</span><span class="p">[</span><span class="o">-</span><span class="n">steps</span><span class="p">:]</span></div></div>
+
+
+
+
+
+
+</pre></div>
+
+            <div class="clearer"></div>
+          </div>
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docs/build/html/_modules/pyFTS/models/incremental/TimeVariant.html

@@ -0,0 +1,206 @@
+
+
+<!doctype html>
+
+<html>
+  <head>
+    <meta charset="utf-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
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+             >modules</a> |</li>
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+          <li class="nav-item nav-item-1"><a href="../../../index.html" accesskey="U">Module code</a> &#187;</li>
+        <li class="nav-item nav-item-this"><a href="">pyFTS.models.incremental.TimeVariant</a></li> 
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+        <div class="bodywrapper">
+          <div class="body" role="main">
+            
+  <h1>Source code for pyFTS.models.incremental.TimeVariant</h1><div class="highlight"><pre>
+<span></span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">Meta model that wraps another FTS method and continously retrain it using a data window with</span>
+<span class="sd">the most recent data</span>
+<span class="sd">&quot;&quot;&quot;</span>
+
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="k">import</span> <span class="n">FuzzySet</span><span class="p">,</span> <span class="n">FLR</span><span class="p">,</span> <span class="n">fts</span><span class="p">,</span> <span class="n">flrg</span>
+<span class="kn">from</span> <span class="nn">pyFTS.partitioners</span> <span class="k">import</span> <span class="n">Grid</span>
+
+
+<div class="viewcode-block" id="Retrainer"><a class="viewcode-back" href="../../../../pyFTS.models.incremental.html#pyFTS.models.incremental.TimeVariant.Retrainer">[docs]</a><span class="k">class</span> <span class="nc">Retrainer</span><span class="p">(</span><span class="n">fts</span><span class="o">.</span><span class="n">FTS</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Meta model for incremental/online learning that retrain its internal model after</span>
+<span class="sd">    data windows controlled by the parameter &#39;batch_size&#39;, using as the training data a</span>
+<span class="sd">    window of recent lags, whose size is controlled by the parameter &#39;window_length&#39;.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">Retrainer</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">partitioner_method</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;partitioner_method&#39;</span><span class="p">,</span> <span class="n">Grid</span><span class="o">.</span><span class="n">GridPartitioner</span><span class="p">)</span>
+        <span class="sd">&quot;&quot;&quot;The partitioner method to be called when a new model is build&quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">partitioner_params</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;partitioner_params&#39;</span><span class="p">,</span> <span class="p">{</span><span class="s1">&#39;npart&#39;</span><span class="p">:</span> <span class="mi">10</span><span class="p">})</span>
+        <span class="sd">&quot;&quot;&quot;The partitioner method parameters&quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="sd">&quot;&quot;&quot;The most recent trained partitioner&quot;&quot;&quot;</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">fts_method</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;fts_method&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+        <span class="sd">&quot;&quot;&quot;The FTS method to be called when a new model is build&quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">fts_params</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;fts_params&#39;</span><span class="p">,</span> <span class="p">{})</span>
+        <span class="sd">&quot;&quot;&quot;The FTS method specific parameters&quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">model</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="sd">&quot;&quot;&quot;The most recent trained model&quot;&quot;&quot;</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">window_length</span>  <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;window_length&#39;</span><span class="p">,</span><span class="mi">100</span><span class="p">)</span>
+        <span class="sd">&quot;&quot;&quot;The memory window length&quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">auto_update</span> <span class="o">=</span> <span class="kc">False</span>
+        <span class="sd">&quot;&quot;&quot;If true the model is updated at each time and not recreated&quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">batch_size</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;batch_size&#39;</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span>
+        <span class="sd">&quot;&quot;&quot;The batch interval between each retraining&quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">is_high_order</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">is_time_variant</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">uod_clip</span> <span class="o">=</span> <span class="kc">False</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">window_length</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">is_wrapper</span> <span class="o">=</span> <span class="kc">True</span>
+
+<div class="viewcode-block" id="Retrainer.train"><a class="viewcode-back" href="../../../../pyFTS.models.incremental.html#pyFTS.models.incremental.TimeVariant.Retrainer.train">[docs]</a>    <span class="k">def</span> <span class="nf">train</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner_method</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner_params</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">model</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">fts_method</span><span class="p">(</span><span class="n">partitioner</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="p">,</span> <span class="o">**</span><span class="bp">self</span><span class="o">.</span><span class="n">fts_params</span><span class="p">)</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">is_high_order</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">order</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">fts_method</span><span class="p">(</span><span class="n">partitioner</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="p">,</span>
+                                                            <span class="n">order</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">,</span> <span class="o">**</span><span class="bp">self</span><span class="o">.</span><span class="n">fts_params</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">shortname</span> <span class="o">=</span> <span class="s2">&quot;TimeVariant - &quot;</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">shortname</span></div>
+
+<div class="viewcode-block" id="Retrainer.forecast"><a class="viewcode-back" href="../../../../pyFTS.models.incremental.html#pyFTS.models.incremental.TimeVariant.Retrainer.forecast">[docs]</a>    <span class="k">def</span> <span class="nf">forecast</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="n">l</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+
+        <span class="n">no_update</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;no_update&#39;</span><span class="p">,</span><span class="kc">False</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">no_update</span><span class="p">:</span>
+            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+        <span class="n">horizon</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">window_length</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span>
+
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">horizon</span><span class="p">,</span> <span class="n">l</span><span class="o">+</span><span class="mi">1</span><span class="p">):</span>
+            <span class="n">_train</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">k</span> <span class="o">-</span> <span class="n">horizon</span><span class="p">:</span> <span class="n">k</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">]</span>
+            <span class="n">_test</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">k</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">:</span> <span class="n">k</span><span class="p">]</span>
+
+            <span class="k">if</span> <span class="n">k</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">batch_size</span> <span class="o">==</span> <span class="mi">0</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">auto_update</span><span class="p">:</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">train</span><span class="p">(</span><span class="n">_train</span><span class="p">)</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">train</span><span class="p">(</span><span class="n">_train</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+            <span class="n">ret</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">_test</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">))</span>
+
+        <span class="k">return</span> <span class="n">ret</span></div>
+
+<div class="viewcode-block" id="Retrainer.forecast_ahead"><a class="viewcode-back" href="../../../../pyFTS.models.incremental.html#pyFTS.models.incremental.TimeVariant.Retrainer.forecast_ahead">[docs]</a>    <span class="k">def</span> <span class="nf">forecast_ahead</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">steps</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)</span> <span class="o">&lt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">data</span>
+
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
+            <span class="n">data</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
+
+        <span class="n">start</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;start_at&#39;</span><span class="p">,</span><span class="mi">0</span><span class="p">)</span>
+
+        <span class="n">ret</span> <span class="o">=</span> <span class="n">data</span><span class="p">[:</span><span class="n">start</span><span class="o">+</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">]</span>
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">start</span><span class="o">+</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">,</span> <span class="n">steps</span><span class="o">+</span><span class="n">start</span><span class="o">+</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">):</span>
+            <span class="n">tmp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecast</span><span class="p">(</span><span class="n">ret</span><span class="p">[</span><span class="n">k</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">:</span><span class="n">k</span><span class="p">],</span> <span class="n">no_update</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">tmp</span><span class="p">,(</span><span class="nb">list</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">)):</span>
+                <span class="n">tmp</span> <span class="o">=</span> <span class="n">tmp</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
+
+            <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span>
+            <span class="n">data</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">ret</span><span class="p">[</span><span class="o">-</span><span class="n">steps</span><span class="p">:]</span></div>
+
+<div class="viewcode-block" id="Retrainer.offset"><a class="viewcode-back" href="../../../../pyFTS.models.incremental.html#pyFTS.models.incremental.TimeVariant.Retrainer.offset">[docs]</a>    <span class="k">def</span> <span class="nf">offset</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span></div>
+
+    <span class="k">def</span> <span class="nf">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="sd">&quot;&quot;&quot;String representation of the model&quot;&quot;&quot;</span>
+
+        <span class="k">return</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">__len__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        The length (number of rules) of the model</span>
+
+<span class="sd">        :return: number of rules</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="p">)</span></div>
+</pre></div>
+
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docs/build/html/_modules/pyFTS/models/ismailefendi.html

@@ -0,0 +1,202 @@
+
+
+<!doctype html>
+
+<html>
+  <head>
+    <meta charset="utf-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
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+          <li class="nav-item nav-item-1"><a href="../../index.html" accesskey="U">Module code</a> &#187;</li>
+        <li class="nav-item nav-item-this"><a href="">pyFTS.models.ismailefendi</a></li> 
+      </ul>
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+
+    <div class="document">
+      <div class="documentwrapper">
+        <div class="bodywrapper">
+          <div class="body" role="main">
+            
+  <h1>Source code for pyFTS.models.ismailefendi</h1><div class="highlight"><pre>
+<span></span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">First Order Improved Weighted Fuzzy Time Series by Efendi, Ismail and Deris (2013)</span>
+
+<span class="sd">R. Efendi, Z. Ismail, and M. M. Deris, “Improved weight Fuzzy Time Series as used in the exchange rates forecasting of </span>
+<span class="sd">US Dollar to Ringgit Malaysia,” Int. J. Comput. Intell. Appl., vol. 12, no. 1, p. 1350005, 2013.</span>
+<span class="sd">&quot;&quot;&quot;</span>
+
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="k">import</span> <span class="n">FuzzySet</span><span class="p">,</span> <span class="n">FLR</span><span class="p">,</span> <span class="n">fts</span><span class="p">,</span> <span class="n">flrg</span>
+
+
+<div class="viewcode-block" id="ImprovedWeightedFLRG"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.ismailefendi.ImprovedWeightedFLRG">[docs]</a><span class="k">class</span> <span class="nc">ImprovedWeightedFLRG</span><span class="p">(</span><span class="n">flrg</span><span class="o">.</span><span class="n">FLRG</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;First Order Improved Weighted Fuzzy Logical Relationship Group&quot;&quot;&quot;</span>
+    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">LHS</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">ImprovedWeightedFLRG</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">LHS</span> <span class="o">=</span> <span class="n">LHS</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span> <span class="o">=</span> <span class="p">{}</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">rhs_counts</span> <span class="o">=</span> <span class="p">{}</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">count</span> <span class="o">=</span> <span class="mf">0.0</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">w</span> <span class="o">=</span> <span class="kc">None</span>
+
+<div class="viewcode-block" id="ImprovedWeightedFLRG.append_rhs"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.ismailefendi.ImprovedWeightedFLRG.append_rhs">[docs]</a>    <span class="k">def</span> <span class="nf">append_rhs</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="n">count</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;count&#39;</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">c</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">=</span> <span class="n">c</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">rhs_counts</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">=</span> <span class="n">count</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">rhs_counts</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">+=</span> <span class="n">count</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">count</span> <span class="o">+=</span> <span class="n">count</span></div>
+
+<div class="viewcode-block" id="ImprovedWeightedFLRG.weights"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.ismailefendi.ImprovedWeightedFLRG.weights">[docs]</a>    <span class="k">def</span> <span class="nf">weights</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">w</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">w</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="bp">self</span><span class="o">.</span><span class="n">rhs_counts</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">count</span> <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="o">.</span><span class="n">keys</span><span class="p">()])</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">w</span></div>
+
+    <span class="k">def</span> <span class="nf">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="n">tmp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">LHS</span> <span class="o">+</span> <span class="s2">&quot; -&gt; &quot;</span>
+        <span class="n">tmp2</span> <span class="o">=</span> <span class="s2">&quot;&quot;</span>
+        <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="nb">sorted</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="o">.</span><span class="n">keys</span><span class="p">()):</span>
+            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">tmp2</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+                <span class="n">tmp2</span> <span class="o">=</span> <span class="n">tmp2</span> <span class="o">+</span> <span class="s2">&quot;,&quot;</span>
+            <span class="n">tmp2</span> <span class="o">=</span> <span class="n">tmp2</span> <span class="o">+</span> <span class="n">c</span> <span class="o">+</span> <span class="s2">&quot;(&quot;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">rhs_counts</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">count</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span> <span class="o">+</span> <span class="s2">&quot;)&quot;</span>
+        <span class="k">return</span> <span class="n">tmp</span> <span class="o">+</span> <span class="n">tmp2</span>
+
+    <span class="k">def</span> <span class="nf">__len__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="ImprovedWeightedFTS"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.ismailefendi.ImprovedWeightedFTS">[docs]</a><span class="k">class</span> <span class="nc">ImprovedWeightedFTS</span><span class="p">(</span><span class="n">fts</span><span class="o">.</span><span class="n">FTS</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;First Order Improved Weighted Fuzzy Time Series&quot;&quot;&quot;</span>
+    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">ImprovedWeightedFTS</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">order</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s2">&quot;IWFTS&quot;</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s2">&quot;Improved Weighted FTS&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">detail</span> <span class="o">=</span> <span class="s2">&quot;Ismail &amp; Efendi&quot;</span>
+
+<div class="viewcode-block" id="ImprovedWeightedFTS.generate_flrg"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.ismailefendi.ImprovedWeightedFTS.generate_flrg">[docs]</a>    <span class="k">def</span> <span class="nf">generate_flrg</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">flrs</span><span class="p">):</span>
+        <span class="k">for</span> <span class="n">flr</span> <span class="ow">in</span> <span class="n">flrs</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">flr</span><span class="o">.</span><span class="n">LHS</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flr</span><span class="o">.</span><span class="n">LHS</span><span class="p">]</span><span class="o">.</span><span class="n">append_rhs</span><span class="p">(</span><span class="n">flr</span><span class="o">.</span><span class="n">RHS</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flr</span><span class="o">.</span><span class="n">LHS</span><span class="p">]</span> <span class="o">=</span> <span class="n">ImprovedWeightedFLRG</span><span class="p">(</span><span class="n">flr</span><span class="o">.</span><span class="n">LHS</span><span class="p">)</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flr</span><span class="o">.</span><span class="n">LHS</span><span class="p">]</span><span class="o">.</span><span class="n">append_rhs</span><span class="p">(</span><span class="n">flr</span><span class="o">.</span><span class="n">RHS</span><span class="p">)</span></div>
+
+<div class="viewcode-block" id="ImprovedWeightedFTS.train"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.ismailefendi.ImprovedWeightedFTS.train">[docs]</a>    <span class="k">def</span> <span class="nf">train</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ndata</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+
+        <span class="n">tmpdata</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">fuzzyfy</span><span class="p">(</span><span class="n">ndata</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">&#39;maximum&#39;</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;sets&#39;</span><span class="p">)</span>
+        <span class="n">flrs</span> <span class="o">=</span> <span class="n">FLR</span><span class="o">.</span><span class="n">generate_recurrent_flrs</span><span class="p">(</span><span class="n">tmpdata</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">generate_flrg</span><span class="p">(</span><span class="n">flrs</span><span class="p">)</span></div>
+
+<div class="viewcode-block" id="ImprovedWeightedFTS.forecast"><a class="viewcode-back" href="../../../pyFTS.models.html#pyFTS.models.ismailefendi.ImprovedWeightedFTS.forecast">[docs]</a>    <span class="k">def</span> <span class="nf">forecast</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ndata</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+
+        <span class="n">explain</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;explain&#39;</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">ordered_sets</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">ordered_sets</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">ordered_sets</span> <span class="o">=</span> <span class="n">FuzzySet</span><span class="o">.</span><span class="n">set_ordered</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">)</span>
+
+        <span class="n">ndata</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">ndata</span><span class="p">)</span>
+
+        <span class="n">l</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">ndata</span><span class="p">)</span> <span class="k">if</span> <span class="ow">not</span> <span class="n">explain</span> <span class="k">else</span> <span class="mi">1</span>
+
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">l</span><span class="p">):</span>
+
+            <span class="n">actual</span> <span class="o">=</span> <span class="n">FuzzySet</span><span class="o">.</span><span class="n">get_maximum_membership_fuzzyset</span><span class="p">(</span><span class="n">ndata</span><span class="p">[</span><span class="n">k</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">,</span> <span class="n">ordered_sets</span><span class="p">)</span>
+
+            <span class="k">if</span> <span class="n">explain</span><span class="p">:</span>
+                <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Fuzzyfication:</span><span class="se">\n\n</span><span class="s2"> </span><span class="si">{}</span><span class="s2"> -&gt; </span><span class="si">{}</span><span class="s2"> </span><span class="se">\n</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">ndata</span><span class="p">[</span><span class="n">k</span><span class="p">],</span> <span class="n">actual</span><span class="o">.</span><span class="n">name</span><span class="p">))</span>
+
+            <span class="k">if</span> <span class="n">actual</span><span class="o">.</span><span class="n">name</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">:</span>
+                <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">actual</span><span class="o">.</span><span class="n">centroid</span><span class="p">)</span>
+
+                <span class="k">if</span> <span class="n">explain</span><span class="p">:</span>
+                    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Rules:</span><span class="se">\n\n</span><span class="s2"> </span><span class="si">{}</span><span class="s2"> -&gt; </span><span class="si">{}</span><span class="s2"> (Naïve)</span><span class="se">\t</span><span class="s2"> Midpoint: </span><span class="si">{}</span><span class="s2">  </span><span class="se">\n\n</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">actual</span><span class="o">.</span><span class="n">name</span><span class="p">,</span> <span class="n">actual</span><span class="o">.</span><span class="n">name</span><span class="p">,</span><span class="n">actual</span><span class="o">.</span><span class="n">centroid</span><span class="p">))</span>
+
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">flrg</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">actual</span><span class="o">.</span><span class="n">name</span><span class="p">]</span>
+                <span class="n">mp</span> <span class="o">=</span> <span class="n">flrg</span><span class="o">.</span><span class="n">get_midpoints</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">)</span>
+
+                <span class="n">final</span> <span class="o">=</span> <span class="n">mp</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">flrg</span><span class="o">.</span><span class="n">weights</span><span class="p">())</span>
+
+                <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">final</span><span class="p">)</span>
+
+                <span class="k">if</span> <span class="n">explain</span><span class="p">:</span>
+                    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Rules:</span><span class="se">\n\n</span><span class="s2"> </span><span class="si">{}</span><span class="s2"> </span><span class="se">\n\n</span><span class="s2"> &quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">flrg</span><span class="p">)))</span>
+                    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Midpoints: </span><span class="se">\n\n</span><span class="s2"> </span><span class="si">{}</span><span class="se">\n\n</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">mp</span><span class="p">))</span>
+
+                    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Deffuzyfied value: </span><span class="si">{}</span><span class="s2"> </span><span class="se">\n</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">final</span><span class="p">))</span>
+
+        <span class="k">return</span> <span class="n">ret</span></div></div>
+</pre></div>
+
+            <div class="clearer"></div>
+          </div>
+        </div>
+      </div>
+      <div class="sphinxsidebar" role="navigation" aria-label="main navigation">
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+            <p class="logo"><a href="../../../index.html">
+              <img class="logo" src="../../../_static/logo_heading2.png" alt="Logo"/>
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+<div id="searchbox" style="display: none" role="search">
+  <h3 id="searchlabel">Quick search</h3>
+    <div class="searchformwrapper">
+    <form class="search" action="../../../search.html" method="get">
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+        <li class="nav-item nav-item-0"><a href="../../../index.html">pyFTS 1.7 documentation</a> &#187;</li>
+          <li class="nav-item nav-item-1"><a href="../../index.html" >Module code</a> &#187;</li>
+        <li class="nav-item nav-item-this"><a href="">pyFTS.models.ismailefendi</a></li> 
+      </ul>
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+    <div class="footer" role="contentinfo">
+        &#169; Copyright 2022, Machine Intelligence and Data Science Laboratory - UFMG - Brazil.
+      Created using <a href="https://www.sphinx-doc.org/">Sphinx</a> 4.5.0.
+    </div>
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+</html>

docs/build/html/_modules/pyFTS/models/multivariate/FLR.html

@@ -46,10 +46,10 @@
 <span></span>
 
 <div class="viewcode-block" id="FLR"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.FLR.FLR">[docs]</a><span class="k">class</span> <span class="nc">FLR</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;Multivariate Fuzzy Logical Relationship&quot;&quot;&quot;</span>
+    <span class="sd">&quot;&quot;&quot;Multivariate Fuzzy Logical Relationship&quot;&quot;&quot;</span>
 
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+        <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Creates a Fuzzy Logical Relationship</span>
 <span class="sd">        :param LHS: Left Hand Side fuzzy set</span>
 <span class="sd">        :param RHS: Right Hand Side fuzzy set</span>
@@ -63,7 +63,7 @@
 <div class="viewcode-block" id="FLR.set_rhs"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.FLR.FLR.set_rhs">[docs]</a>    <span class="k">def</span> <span class="nf">set_rhs</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">set</span><span class="p">):</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span> <span class="o">=</span> <span class="nb">set</span></div>
 
-    <span class="k">def</span> <span class="fm">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+    <span class="k">def</span> <span class="nf">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="k">return</span> <span class="s2">&quot;</span><span class="si">{}</span><span class="s2"> -&gt; </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">([</span><span class="n">k</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">LHS</span><span class="o">.</span><span class="n">values</span><span class="p">()],</span> <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">)</span></div>
 
 
@@ -109,7 +109,7 @@
     </div>
     <div class="footer" role="contentinfo">
         &#169; Copyright 2022, Machine Intelligence and Data Science Laboratory - UFMG - Brazil.
-      Created using <a href="https://www.sphinx-doc.org/">Sphinx</a> 4.3.2.
+      Created using <a href="https://www.sphinx-doc.org/">Sphinx</a> 4.5.0.
     </div>
   </body>
 </html>

docs/build/html/_modules/pyFTS/models/multivariate/cmvfts.html

@@ -0,0 +1,307 @@
+
+
+<!doctype html>
+
+<html>
+  <head>
+    <meta charset="utf-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+    <title>pyFTS.models.multivariate.cmvfts &#8212; pyFTS 1.7 documentation</title>
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+        <li class="nav-item nav-item-0"><a href="../../../../index.html">pyFTS 1.7 documentation</a> &#187;</li>
+          <li class="nav-item nav-item-1"><a href="../../../index.html" accesskey="U">Module code</a> &#187;</li>
+        <li class="nav-item nav-item-this"><a href="">pyFTS.models.multivariate.cmvfts</a></li> 
+      </ul>
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+      <div class="documentwrapper">
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+            
+  <h1>Source code for pyFTS.models.multivariate.cmvfts</h1><div class="highlight"><pre>
+<span></span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="k">import</span> <span class="n">FuzzySet</span><span class="p">,</span> <span class="n">FLR</span><span class="p">,</span> <span class="n">fts</span><span class="p">,</span> <span class="n">flrg</span>
+<span class="kn">from</span> <span class="nn">pyFTS.models</span> <span class="k">import</span> <span class="n">hofts</span>
+<span class="kn">from</span> <span class="nn">pyFTS.models.multivariate</span> <span class="k">import</span> <span class="n">mvfts</span><span class="p">,</span> <span class="n">grid</span><span class="p">,</span> <span class="n">common</span>
+<span class="kn">from</span> <span class="nn">types</span> <span class="k">import</span> <span class="n">LambdaType</span>
+
+
+<div class="viewcode-block" id="ClusteredMVFTS"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.cmvfts.ClusteredMVFTS">[docs]</a><span class="k">class</span> <span class="nc">ClusteredMVFTS</span><span class="p">(</span><span class="n">mvfts</span><span class="o">.</span><span class="n">MVFTS</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Meta model for high order, clustered multivariate FTS</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">ClusteredMVFTS</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">fts_method</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;fts_method&#39;</span><span class="p">,</span> <span class="n">hofts</span><span class="o">.</span><span class="n">WeightedHighOrderFTS</span><span class="p">)</span>
+        <span class="sd">&quot;&quot;&quot;The FTS method to be called when a new model is build&quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">fts_params</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;fts_params&#39;</span><span class="p">,</span> <span class="p">{})</span>
+        <span class="sd">&quot;&quot;&quot;The FTS method specific parameters&quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">model</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="sd">&quot;&quot;&quot;The most recent trained model&quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">knn</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;knn&#39;</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">is_high_order</span> <span class="o">=</span> <span class="kc">True</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">is_clustered</span> <span class="o">=</span> <span class="kc">True</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">order</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;order&quot;</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">lags</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;lags&quot;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">alpha_cut</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;alpha_cut&#39;</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">shortname</span> <span class="o">=</span> <span class="s2">&quot;ClusteredMVFTS&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s2">&quot;Clustered Multivariate FTS&quot;</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">pre_fuzzyfy</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;pre_fuzzyfy&#39;</span><span class="p">,</span> <span class="kc">True</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">fuzzyfy_mode</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;fuzzyfy_mode&#39;</span><span class="p">,</span> <span class="s1">&#39;sets&#39;</span><span class="p">)</span>
+
+<div class="viewcode-block" id="ClusteredMVFTS.fuzzyfy"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.fuzzyfy">[docs]</a>    <span class="k">def</span> <span class="nf">fuzzyfy</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="n">data</span><span class="p">):</span>
+        <span class="n">ndata</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">for</span> <span class="n">index</span><span class="p">,</span> <span class="n">row</span> <span class="ow">in</span> <span class="n">data</span><span class="o">.</span><span class="n">iterrows</span><span class="p">()</span> <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">)</span> <span class="k">else</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">data</span><span class="p">):</span>
+            <span class="n">data_point</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">format_data</span><span class="p">(</span><span class="n">row</span><span class="p">)</span>
+            <span class="n">ndata</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">fuzzyfy</span><span class="p">(</span><span class="n">data_point</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">fuzzyfy_mode</span><span class="p">))</span>
+
+        <span class="k">return</span> <span class="n">ndata</span></div>
+
+<div class="viewcode-block" id="ClusteredMVFTS.train"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.train">[docs]</a>    <span class="k">def</span> <span class="nf">train</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">fts_params</span><span class="p">[</span><span class="s1">&#39;order&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">model</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">fts_method</span><span class="p">(</span><span class="n">partitioner</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="p">,</span> <span class="o">**</span><span class="bp">self</span><span class="o">.</span><span class="n">fts_params</span><span class="p">)</span>
+
+        <span class="n">ndata</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">apply_transformations</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+
+        <span class="n">ndata</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">check_data</span><span class="p">(</span><span class="n">ndata</span><span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">train</span><span class="p">(</span><span class="n">ndata</span><span class="p">,</span> <span class="n">fuzzyfied</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">pre_fuzzyfy</span><span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">prune</span><span class="p">()</span></div>
+
+<div class="viewcode-block" id="ClusteredMVFTS.check_data"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.check_data">[docs]</a>    <span class="k">def</span> <span class="nf">check_data</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">):</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">pre_fuzzyfy</span><span class="p">:</span>
+            <span class="n">ndata</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">fuzzyfy</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">ndata</span> <span class="o">=</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">format_data</span><span class="p">(</span><span class="n">k</span><span class="p">)</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">data</span><span class="o">.</span><span class="n">to_dict</span><span class="p">(</span><span class="s1">&#39;records&#39;</span><span class="p">)]</span>
+
+        <span class="k">return</span> <span class="n">ndata</span></div>
+
+<div class="viewcode-block" id="ClusteredMVFTS.forecast"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.forecast">[docs]</a>    <span class="k">def</span> <span class="nf">forecast</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+
+        <span class="n">ndata1</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">apply_transformations</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+
+        <span class="n">ndata</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">check_data</span><span class="p">(</span><span class="n">ndata1</span><span class="p">)</span>
+
+        <span class="n">pre_fuzz</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;pre_fuzzyfy&#39;</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">pre_fuzzyfy</span><span class="p">)</span>
+
+        <span class="n">ret</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">forecast</span><span class="p">(</span><span class="n">ndata</span><span class="p">,</span> <span class="n">fuzzyfied</span><span class="o">=</span><span class="n">pre_fuzz</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+        <span class="n">ret</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span><span class="o">.</span><span class="n">apply_inverse_transformations</span><span class="p">(</span><span class="n">ret</span><span class="p">,</span>
+                                                                 <span class="n">params</span><span class="o">=</span><span class="n">data</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span><span class="o">.</span><span class="n">data_label</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">ret</span></div>
+
+<div class="viewcode-block" id="ClusteredMVFTS.forecast_interval"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.forecast_interval">[docs]</a>    <span class="k">def</span> <span class="nf">forecast_interval</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+
+        <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">has_interval_forecasting</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s2">&quot;The internal method does not support interval forecasting!&quot;</span><span class="p">)</span>
+
+        <span class="n">data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">check_data</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+
+        <span class="n">pre_fuzz</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;pre_fuzzyfy&#39;</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">pre_fuzzyfy</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">forecast_interval</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">fuzzyfied</span><span class="o">=</span><span class="n">pre_fuzz</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
+
+
+
+<div class="viewcode-block" id="ClusteredMVFTS.forecast_distribution"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.forecast_distribution">[docs]</a>    <span class="k">def</span> <span class="nf">forecast_distribution</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+
+        <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">has_probability_forecasting</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s2">&quot;The internal method does not support probabilistic forecasting!&quot;</span><span class="p">)</span>
+
+        <span class="n">data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">check_data</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+
+        <span class="n">pre_fuzz</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;pre_fuzzyfy&#39;</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">pre_fuzzyfy</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">forecast_distribution</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">fuzzyfied</span><span class="o">=</span><span class="n">pre_fuzz</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div>
+
+<div class="viewcode-block" id="ClusteredMVFTS.forecast_ahead_distribution"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.forecast_ahead_distribution">[docs]</a>    <span class="k">def</span> <span class="nf">forecast_ahead_distribution</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">steps</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+
+        <span class="n">generators</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;generators&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">generators</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s1">&#39;You must provide parameter </span><span class="se">\&#39;</span><span class="s1">generators</span><span class="se">\&#39;</span><span class="s1">! generators is a dict where the keys&#39;</span> <span class="o">+</span>
+                            <span class="s1">&#39; are the dataframe column names (except the target_variable) and the values are &#39;</span> <span class="o">+</span>
+                            <span class="s1">&#39;lambda functions that accept one value (the actual value of the variable) &#39;</span>
+                            <span class="s1">&#39; and return the next value or trained FTS models that accept the actual values and &#39;</span>
+                            <span class="s1">&#39;forecast new ones.&#39;</span><span class="p">)</span>
+
+        <span class="n">ndata</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">apply_transformations</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+
+        <span class="n">start</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;start_at&#39;</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
+
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="n">sample</span> <span class="o">=</span> <span class="n">ndata</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">start</span><span class="p">:</span> <span class="n">start</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">]</span>
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">steps</span><span class="p">):</span>
+            <span class="n">tmp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecast_distribution</span><span class="p">(</span><span class="n">sample</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">:],</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
+
+            <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span>
+
+            <span class="n">new_data_point</span> <span class="o">=</span> <span class="p">{}</span>
+
+            <span class="k">for</span> <span class="n">data_label</span> <span class="ow">in</span> <span class="n">generators</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
+                <span class="k">if</span> <span class="n">data_label</span> <span class="o">!=</span> <span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span><span class="o">.</span><span class="n">data_label</span><span class="p">:</span>
+                    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">generators</span><span class="p">[</span><span class="n">data_label</span><span class="p">],</span> <span class="n">LambdaType</span><span class="p">):</span>
+                        <span class="n">last_data_point</span> <span class="o">=</span> <span class="n">sample</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
+                        <span class="n">new_data_point</span><span class="p">[</span><span class="n">data_label</span><span class="p">]</span> <span class="o">=</span> <span class="n">generators</span><span class="p">[</span><span class="n">data_label</span><span class="p">](</span><span class="n">last_data_point</span><span class="p">[</span><span class="n">data_label</span><span class="p">])</span>
+
+                    <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">generators</span><span class="p">[</span><span class="n">data_label</span><span class="p">],</span> <span class="n">fts</span><span class="o">.</span><span class="n">FTS</span><span class="p">):</span>
+                        <span class="n">gen_model</span> <span class="o">=</span> <span class="n">generators</span><span class="p">[</span><span class="n">data_label</span><span class="p">]</span>
+                        <span class="n">last_data_point</span> <span class="o">=</span> <span class="n">sample</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="o">-</span><span class="n">gen_model</span><span class="o">.</span><span class="n">order</span><span class="p">:]</span>
+
+                        <span class="k">if</span> <span class="ow">not</span> <span class="n">gen_model</span><span class="o">.</span><span class="n">is_multivariate</span><span class="p">:</span>
+                            <span class="n">last_data_point</span> <span class="o">=</span> <span class="n">last_data_point</span><span class="p">[</span><span class="n">data_label</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
+
+                        <span class="n">new_data_point</span><span class="p">[</span><span class="n">data_label</span><span class="p">]</span> <span class="o">=</span> <span class="n">gen_model</span><span class="o">.</span><span class="n">forecast</span><span class="p">(</span><span class="n">last_data_point</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
+
+            <span class="n">new_data_point</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span><span class="o">.</span><span class="n">data_label</span><span class="p">]</span> <span class="o">=</span> <span class="n">tmp</span><span class="o">.</span><span class="n">expected_value</span><span class="p">()</span>
+
+            <span class="n">sample</span> <span class="o">=</span> <span class="n">sample</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">new_data_point</span><span class="p">,</span> <span class="n">ignore_index</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">ret</span><span class="p">[</span><span class="o">-</span><span class="n">steps</span><span class="p">:]</span></div>
+
+<div class="viewcode-block" id="ClusteredMVFTS.forecast_multivariate"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.forecast_multivariate">[docs]</a>    <span class="k">def</span> <span class="nf">forecast_multivariate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+
+        <span class="n">ndata</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">check_data</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+
+        <span class="n">generators</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;generators&#39;</span><span class="p">,</span> <span class="p">{})</span>
+
+        <span class="n">already_processed_cols</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">{}</span>
+
+        <span class="n">ret</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span><span class="o">.</span><span class="n">data_label</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">forecast</span><span class="p">(</span><span class="n">ndata</span><span class="p">,</span> <span class="n">fuzzyfied</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">pre_fuzzyfy</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+        <span class="k">for</span> <span class="n">var</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">explanatory_variables</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">var</span><span class="o">.</span><span class="n">data_label</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">already_processed_cols</span><span class="p">:</span>
+                <span class="k">if</span> <span class="n">var</span><span class="o">.</span><span class="n">data_label</span> <span class="ow">in</span> <span class="n">generators</span><span class="p">:</span>
+                    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">generators</span><span class="p">[</span><span class="n">var</span><span class="o">.</span><span class="n">data_label</span><span class="p">],</span> <span class="n">LambdaType</span><span class="p">):</span>
+                        <span class="n">fx</span> <span class="o">=</span> <span class="n">generators</span><span class="p">[</span><span class="n">var</span><span class="o">.</span><span class="n">data_label</span><span class="p">]</span>
+                        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">var</span><span class="o">.</span><span class="n">data_label</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">)</span> <span class="o">&gt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">:</span>
+                            <span class="n">ret</span><span class="p">[</span><span class="n">var</span><span class="o">.</span><span class="n">data_label</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="n">fx</span><span class="p">(</span><span class="n">k</span><span class="p">)</span> <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">data</span><span class="p">[</span><span class="n">var</span><span class="o">.</span><span class="n">data_label</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="p">:]]</span>
+                        <span class="k">else</span><span class="p">:</span>
+                            <span class="n">ret</span><span class="p">[</span><span class="n">var</span><span class="o">.</span><span class="n">data_label</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="n">fx</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">var</span><span class="o">.</span><span class="n">data_label</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])]</span>
+                    <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">generators</span><span class="p">[</span><span class="n">var</span><span class="o">.</span><span class="n">data_label</span><span class="p">],</span> <span class="n">fts</span><span class="o">.</span><span class="n">FTS</span><span class="p">):</span>
+                        <span class="n">model</span> <span class="o">=</span> <span class="n">generators</span><span class="p">[</span><span class="n">var</span><span class="o">.</span><span class="n">data_label</span><span class="p">]</span>
+                        <span class="k">if</span> <span class="ow">not</span> <span class="n">model</span><span class="o">.</span><span class="n">is_multivariate</span><span class="p">:</span>
+                            <span class="n">ret</span><span class="p">[</span><span class="n">var</span><span class="o">.</span><span class="n">data_label</span><span class="p">]</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">forecast</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">var</span><span class="o">.</span><span class="n">data_label</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">)</span>
+                        <span class="k">else</span><span class="p">:</span>
+                            <span class="n">ret</span><span class="p">[</span><span class="n">var</span><span class="o">.</span><span class="n">data_label</span><span class="p">]</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">forecast</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+                <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span><span class="o">.</span><span class="n">name</span> <span class="o">!=</span> <span class="n">var</span><span class="o">.</span><span class="n">name</span><span class="p">:</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span> <span class="o">=</span> <span class="n">var</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">change_target_variable</span><span class="p">(</span><span class="n">var</span><span class="p">)</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">partitioner</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">reset_calculated_values</span><span class="p">()</span>
+                    <span class="n">ret</span><span class="p">[</span><span class="n">var</span><span class="o">.</span><span class="n">data_label</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="o">.</span><span class="n">forecast</span><span class="p">(</span><span class="n">ndata</span><span class="p">,</span> <span class="n">fuzzyfied</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">pre_fuzzyfy</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+                <span class="n">already_processed_cols</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">var</span><span class="o">.</span><span class="n">data_label</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">ret</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="n">ret</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span></div>
+
+<div class="viewcode-block" id="ClusteredMVFTS.forecast_ahead_multivariate"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.cmvfts.ClusteredMVFTS.forecast_ahead_multivariate">[docs]</a>    <span class="k">def</span> <span class="nf">forecast_ahead_multivariate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">steps</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+
+        <span class="n">ndata</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">apply_transformations</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+
+        <span class="n">start</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;start_at&#39;</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
+
+        <span class="n">ret</span> <span class="o">=</span> <span class="n">ndata</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">start</span><span class="p">:</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="o">+</span><span class="n">start</span><span class="p">]</span>
+
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">steps</span><span class="p">):</span>
+            <span class="n">sample</span> <span class="o">=</span> <span class="n">ret</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">k</span><span class="p">:</span><span class="bp">self</span><span class="o">.</span><span class="n">order</span><span class="o">+</span><span class="n">k</span><span class="p">]</span>
+            <span class="n">tmp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecast_multivariate</span><span class="p">(</span><span class="n">sample</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+            <span class="n">ret</span> <span class="o">=</span> <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">tmp</span><span class="p">,</span> <span class="n">ignore_index</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">ret</span></div>
+
+    <span class="k">def</span> <span class="nf">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="sd">&quot;&quot;&quot;String representation of the model&quot;&quot;&quot;</span>
+        <span class="k">return</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">__len__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        The length (number of rules) of the model</span>
+
+<span class="sd">        :return: number of rules</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="p">)</span></div>
+
+</pre></div>
+
+            <div class="clearer"></div>
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+             >index</a></li>
+        <li class="right" >
+          <a href="../../../../py-modindex.html" title="Python Module Index"
+             >modules</a> |</li>
+        <li class="nav-item nav-item-0"><a href="../../../../index.html">pyFTS 1.7 documentation</a> &#187;</li>
+          <li class="nav-item nav-item-1"><a href="../../../index.html" >Module code</a> &#187;</li>
+        <li class="nav-item nav-item-this"><a href="">pyFTS.models.multivariate.cmvfts</a></li> 
+      </ul>
+    </div>
+    <div class="footer" role="contentinfo">
+        &#169; Copyright 2022, Machine Intelligence and Data Science Laboratory - UFMG - Brazil.
+      Created using <a href="https://www.sphinx-doc.org/">Sphinx</a> 4.5.0.
+    </div>
+  </body>
+</html>

docs/build/html/_modules/pyFTS/models/multivariate/common.html

@@ -45,15 +45,15 @@
   <h1>Source code for pyFTS.models.multivariate.common</h1><div class="highlight"><pre>
 <span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
 <span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
-<span class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="kn">import</span> <span class="n">FuzzySet</span><span class="p">,</span> <span class="n">Composite</span>
+<span class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="k">import</span> <span class="n">FuzzySet</span><span class="p">,</span> <span class="n">Composite</span>
 
 
 <div class="viewcode-block" id="MultivariateFuzzySet"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.common.MultivariateFuzzySet">[docs]</a><span class="k">class</span> <span class="nc">MultivariateFuzzySet</span><span class="p">(</span><span class="n">Composite</span><span class="o">.</span><span class="n">FuzzySet</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+    <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Multivariate Composite Fuzzy Set</span>
 <span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+        <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Create an empty composite fuzzy set</span>
 <span class="sd">        :param name: fuzzy set name</span>
 <span class="sd">        &quot;&quot;&quot;</span>
@@ -62,7 +62,7 @@
         <span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;target_variable&#39;</span><span class="p">,</span><span class="kc">None</span><span class="p">)</span>
 
 <div class="viewcode-block" id="MultivariateFuzzySet.append_set"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.common.MultivariateFuzzySet.append_set">[docs]</a>    <span class="k">def</span> <span class="nf">append_set</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">variable</span><span class="p">,</span> <span class="nb">set</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+        <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Appends a new fuzzy set from a new variable</span>
 
 <span class="sd">        :param variable: an multivariate.variable instance</span>
@@ -161,7 +161,7 @@
     </div>
     <div class="footer" role="contentinfo">
         &#169; Copyright 2022, Machine Intelligence and Data Science Laboratory - UFMG - Brazil.
-      Created using <a href="https://www.sphinx-doc.org/">Sphinx</a> 4.3.2.
+      Created using <a href="https://www.sphinx-doc.org/">Sphinx</a> 4.5.0.
     </div>
   </body>
 </html>

docs/build/html/_modules/pyFTS/models/multivariate/flrg.html

@@ -45,15 +45,15 @@
   <h1>Source code for pyFTS.models.multivariate.flrg</h1><div class="highlight"><pre>
 <span></span>
 <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="kn">import</span> <span class="n">flrg</span> <span class="k">as</span> <span class="n">flg</span>
+<span class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="k">import</span> <span class="n">flrg</span> <span class="k">as</span> <span class="n">flg</span>
 
 
 <div class="viewcode-block" id="FLRG"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.flrg.FLRG">[docs]</a><span class="k">class</span> <span class="nc">FLRG</span><span class="p">(</span><span class="n">flg</span><span class="o">.</span><span class="n">FLRG</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+    <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Multivariate Fuzzy Logical Rule Group</span>
 <span class="sd">    &quot;&quot;&quot;</span>
 
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
         <span class="nb">super</span><span class="p">(</span><span class="n">FLRG</span><span class="p">,</span><span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">order</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;order&#39;</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">LHS</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;lhs&#39;</span><span class="p">,</span> <span class="p">{})</span>
@@ -90,7 +90,7 @@
 
         <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">upper</span></div>
 
-    <span class="k">def</span> <span class="fm">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+    <span class="k">def</span> <span class="nf">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="n">_str</span> <span class="o">=</span> <span class="s2">&quot;&quot;</span>
         <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">:</span>
             <span class="n">_str</span> <span class="o">+=</span> <span class="s2">&quot;,&quot;</span> <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">_str</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="k">else</span> <span class="s2">&quot;&quot;</span>
@@ -138,7 +138,7 @@
     </div>
     <div class="footer" role="contentinfo">
         &#169; Copyright 2022, Machine Intelligence and Data Science Laboratory - UFMG - Brazil.
-      Created using <a href="https://www.sphinx-doc.org/">Sphinx</a> 4.3.2.
+      Created using <a href="https://www.sphinx-doc.org/">Sphinx</a> 4.5.0.
     </div>
   </body>
 </html>

docs/build/html/_modules/pyFTS/models/multivariate/granular.html

@@ -0,0 +1,118 @@
+
+
+<!doctype html>
+
+<html>
+  <head>
+    <meta charset="utf-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+    <title>pyFTS.models.multivariate.granular &#8212; pyFTS 1.7 documentation</title>
+    <link rel="stylesheet" type="text/css" href="../../../../_static/pygments.css" />
+    <link rel="stylesheet" type="text/css" href="../../../../_static/bizstyle.css" />
+    
+    <script data-url_root="../../../../" id="documentation_options" src="../../../../_static/documentation_options.js"></script>
+    <script src="../../../../_static/jquery.js"></script>
+    <script src="../../../../_static/underscore.js"></script>
+    <script src="../../../../_static/doctools.js"></script>
+    <script src="../../../../_static/bizstyle.js"></script>
+    <link rel="index" title="Index" href="../../../../genindex.html" />
+    <link rel="search" title="Search" href="../../../../search.html" />
+    <meta name="viewport" content="width=device-width,initial-scale=1.0" />
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+    <script src="_static/css3-mediaqueries.js"></script>
+    <![endif]-->
+  </head><body>
+    <div class="related" role="navigation" aria-label="related navigation">
+      <h3>Navigation</h3>
+      <ul>
+        <li class="right" style="margin-right: 10px">
+          <a href="../../../../genindex.html" title="General Index"
+             accesskey="I">index</a></li>
+        <li class="right" >
+          <a href="../../../../py-modindex.html" title="Python Module Index"
+             >modules</a> |</li>
+        <li class="nav-item nav-item-0"><a href="../../../../index.html">pyFTS 1.7 documentation</a> &#187;</li>
+          <li class="nav-item nav-item-1"><a href="../../../index.html" accesskey="U">Module code</a> &#187;</li>
+        <li class="nav-item nav-item-this"><a href="">pyFTS.models.multivariate.granular</a></li> 
+      </ul>
+    </div>  
+
+    <div class="document">
+      <div class="documentwrapper">
+        <div class="bodywrapper">
+          <div class="body" role="main">
+            
+  <h1>Source code for pyFTS.models.multivariate.granular</h1><div class="highlight"><pre>
+<span></span><span class="kn">from</span> <span class="nn">pyFTS.models.multivariate</span> <span class="k">import</span> <span class="n">cmvfts</span><span class="p">,</span> <span class="n">grid</span>
+<span class="kn">from</span> <span class="nn">pyFTS.models</span> <span class="k">import</span> <span class="n">hofts</span>
+
+
+<div class="viewcode-block" id="GranularWMVFTS"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.granular.GranularWMVFTS">[docs]</a><span class="k">class</span> <span class="nc">GranularWMVFTS</span><span class="p">(</span><span class="n">cmvfts</span><span class="o">.</span><span class="n">ClusteredMVFTS</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Granular multivariate weighted high order FTS</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">GranularWMVFTS</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">fts_method</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;fts_method&#39;</span><span class="p">,</span> <span class="n">hofts</span><span class="o">.</span><span class="n">WeightedHighOrderFTS</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">model</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="sd">&quot;&quot;&quot;The most recent trained model&quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">knn</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;knn&#39;</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">order</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;order&quot;</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">shortname</span> <span class="o">=</span> <span class="s2">&quot;FIG-FTS&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s2">&quot;Fuzzy Information Granular FTS&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">mode</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;mode&#39;</span><span class="p">,</span><span class="s1">&#39;sets&#39;</span><span class="p">)</span>
+
+<div class="viewcode-block" id="GranularWMVFTS.train"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.granular.GranularWMVFTS.train">[docs]</a>    <span class="k">def</span> <span class="nf">train</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span> <span class="o">=</span> <span class="n">grid</span><span class="o">.</span><span class="n">IncrementalGridCluster</span><span class="p">(</span>
+            <span class="n">explanatory_variables</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">explanatory_variables</span><span class="p">,</span>
+            <span class="n">target_variable</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span><span class="p">,</span>
+            <span class="n">neighbors</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">knn</span><span class="p">)</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">GranularWMVFTS</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="n">train</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">mode</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></div></div>
+
+</pre></div>
+
+            <div class="clearer"></div>
+          </div>
+        </div>
+      </div>
+      <div class="sphinxsidebar" role="navigation" aria-label="main navigation">
+        <div class="sphinxsidebarwrapper">
+            <p class="logo"><a href="../../../../index.html">
+              <img class="logo" src="../../../../_static/logo_heading2.png" alt="Logo"/>
+            </a></p>
+<div id="searchbox" style="display: none" role="search">
+  <h3 id="searchlabel">Quick search</h3>
+    <div class="searchformwrapper">
+    <form class="search" action="../../../../search.html" method="get">
+      <input type="text" name="q" aria-labelledby="searchlabel" autocomplete="off" autocorrect="off" autocapitalize="off" spellcheck="false"/>
+      <input type="submit" value="Go" />
+    </form>
+    </div>
+</div>
+<script>$('#searchbox').show(0);</script>
+        </div>
+      </div>
+      <div class="clearer"></div>
+    </div>
+    <div class="related" role="navigation" aria-label="related navigation">
+      <h3>Navigation</h3>
+      <ul>
+        <li class="right" style="margin-right: 10px">
+          <a href="../../../../genindex.html" title="General Index"
+             >index</a></li>
+        <li class="right" >
+          <a href="../../../../py-modindex.html" title="Python Module Index"
+             >modules</a> |</li>
+        <li class="nav-item nav-item-0"><a href="../../../../index.html">pyFTS 1.7 documentation</a> &#187;</li>
+          <li class="nav-item nav-item-1"><a href="../../../index.html" >Module code</a> &#187;</li>
+        <li class="nav-item nav-item-this"><a href="">pyFTS.models.multivariate.granular</a></li> 
+      </ul>
+    </div>
+    <div class="footer" role="contentinfo">
+        &#169; Copyright 2022, Machine Intelligence and Data Science Laboratory - UFMG - Brazil.
+      Created using <a href="https://www.sphinx-doc.org/">Sphinx</a> 4.5.0.
+    </div>
+  </body>
+</html>

docs/build/html/_modules/pyFTS/models/multivariate/grid.html

@@ -43,20 +43,20 @@
           <div class="body" role="main">
             
   <h1>Source code for pyFTS.models.multivariate.grid</h1><div class="highlight"><pre>
-<span></span><span class="kn">from</span> <span class="nn">pyFTS.models.multivariate</span> <span class="kn">import</span> <span class="n">partitioner</span>
+<span></span><span class="kn">from</span> <span class="nn">pyFTS.models.multivariate</span> <span class="k">import</span> <span class="n">partitioner</span>
-<span class="kn">from</span> <span class="nn">pyFTS.models.multivariate.common</span> <span class="kn">import</span> <span class="n">MultivariateFuzzySet</span><span class="p">,</span> <span class="n">fuzzyfy_instance_clustered</span>
+<span class="kn">from</span> <span class="nn">pyFTS.models.multivariate.common</span> <span class="k">import</span> <span class="n">MultivariateFuzzySet</span><span class="p">,</span> <span class="n">fuzzyfy_instance_clustered</span>
-<span class="kn">from</span> <span class="nn">itertools</span> <span class="kn">import</span> <span class="n">product</span>
+<span class="kn">from</span> <span class="nn">itertools</span> <span class="k">import</span> <span class="n">product</span>
-<span class="kn">from</span> <span class="nn">scipy.spatial</span> <span class="kn">import</span> <span class="n">KDTree</span>
+<span class="kn">from</span> <span class="nn">scipy.spatial</span> <span class="k">import</span> <span class="n">KDTree</span>
 <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
 <span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
 
 
 <div class="viewcode-block" id="GridCluster"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.grid.GridCluster">[docs]</a><span class="k">class</span> <span class="nc">GridCluster</span><span class="p">(</span><span class="n">partitioner</span><span class="o">.</span><span class="n">MultivariatePartitioner</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+    <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    A cartesian product of all fuzzy sets of all variables</span>
 <span class="sd">    &quot;&quot;&quot;</span>
 
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
         <span class="nb">super</span><span class="p">(</span><span class="n">GridCluster</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">name</span><span class="o">=</span><span class="s2">&quot;GridCluster&quot;</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">build</span><span class="p">(</span><span class="kc">None</span><span class="p">)</span>
@@ -106,12 +106,12 @@
 
 
 <div class="viewcode-block" id="IncrementalGridCluster"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.grid.IncrementalGridCluster">[docs]</a><span class="k">class</span> <span class="nc">IncrementalGridCluster</span><span class="p">(</span><span class="n">partitioner</span><span class="o">.</span><span class="n">MultivariatePartitioner</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+    <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Create combinations of fuzzy sets of the variables on demand, incrementally increasing the</span>
 <span class="sd">    multivariate fuzzy set base.</span>
 <span class="sd">    &quot;&quot;&quot;</span>
 
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
         <span class="nb">super</span><span class="p">(</span><span class="n">IncrementalGridCluster</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">name</span><span class="o">=</span><span class="s2">&quot;IncrementalGridCluster&quot;</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">build</span><span class="p">(</span><span class="kc">None</span><span class="p">)</span>
@@ -222,7 +222,7 @@
     </div>
     <div class="footer" role="contentinfo">
         &#169; Copyright 2022, Machine Intelligence and Data Science Laboratory - UFMG - Brazil.
-      Created using <a href="https://www.sphinx-doc.org/">Sphinx</a> 4.3.2.
+      Created using <a href="https://www.sphinx-doc.org/">Sphinx</a> 4.5.0.
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   </body>
 </html>

docs/build/html/_modules/pyFTS/models/multivariate/mvfts.html

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+          <li class="nav-item nav-item-1"><a href="../../../index.html" accesskey="U">Module code</a> &#187;</li>
+        <li class="nav-item nav-item-this"><a href="">pyFTS.models.multivariate.mvfts</a></li> 
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+            
+  <h1>Source code for pyFTS.models.multivariate.mvfts</h1><div class="highlight"><pre>
+<span></span><span class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="k">import</span> <span class="n">fts</span><span class="p">,</span> <span class="n">FuzzySet</span><span class="p">,</span> <span class="n">FLR</span><span class="p">,</span> <span class="n">Membership</span>
+<span class="kn">from</span> <span class="nn">pyFTS.partitioners</span> <span class="k">import</span> <span class="n">Grid</span>
+<span class="kn">from</span> <span class="nn">pyFTS.models.multivariate</span> <span class="k">import</span> <span class="n">FLR</span> <span class="k">as</span> <span class="n">MVFLR</span><span class="p">,</span> <span class="n">common</span><span class="p">,</span> <span class="n">flrg</span> <span class="k">as</span> <span class="n">mvflrg</span>
+<span class="kn">from</span> <span class="nn">itertools</span> <span class="k">import</span> <span class="n">product</span>
+<span class="kn">from</span> <span class="nn">types</span> <span class="k">import</span> <span class="n">LambdaType</span>
+<span class="kn">from</span> <span class="nn">copy</span> <span class="k">import</span> <span class="n">deepcopy</span>
+
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+
+
+<div class="viewcode-block" id="product_dict"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.mvfts.product_dict">[docs]</a><span class="k">def</span> <span class="nf">product_dict</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Code by Seth Johnson</span>
+<span class="sd">    :param kwargs:</span>
+<span class="sd">    :return:</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">keys</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">keys</span><span class="p">()</span>
+    <span class="n">vals</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">values</span><span class="p">()</span>
+    <span class="k">for</span> <span class="n">instance</span> <span class="ow">in</span> <span class="n">product</span><span class="p">(</span><span class="o">*</span><span class="n">vals</span><span class="p">):</span>
+        <span class="k">yield</span> <span class="nb">dict</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">keys</span><span class="p">,</span> <span class="n">instance</span><span class="p">))</span></div>
+
+
+<div class="viewcode-block" id="MVFTS"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.mvfts.MVFTS">[docs]</a><span class="k">class</span> <span class="nc">MVFTS</span><span class="p">(</span><span class="n">fts</span><span class="o">.</span><span class="n">FTS</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Multivariate extension of Chen&#39;s ConventionalFTS method</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">MVFTS</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">explanatory_variables</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;explanatory_variables&#39;</span><span class="p">,[])</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;target_variable&#39;</span><span class="p">,</span><span class="kc">None</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span> <span class="o">=</span> <span class="p">{}</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">is_multivariate</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">shortname</span> <span class="o">=</span> <span class="s2">&quot;MVFTS&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s2">&quot;Multivariate FTS&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">uod_clip</span> <span class="o">=</span> <span class="kc">False</span>
+
+<div class="viewcode-block" id="MVFTS.append_transformation"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.mvfts.MVFTS.append_transformation">[docs]</a>    <span class="k">def</span> <span class="nf">append_transformation</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">transformation</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="n">transformation</span><span class="o">.</span><span class="n">is_multivariate</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s1">&#39;The transformation is not multivariate&#39;</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">transformations</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">transformation</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">transformations_param</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">kwargs</span><span class="p">)</span></div>
+
+<div class="viewcode-block" id="MVFTS.append_variable"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.mvfts.MVFTS.append_variable">[docs]</a>    <span class="k">def</span> <span class="nf">append_variable</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">var</span><span class="p">):</span>
+        <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Append a new endogenous variable to the model</span>
+
+<span class="sd">        :param var: variable object</span>
+<span class="sd">        :return:</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">explanatory_variables</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">var</span><span class="p">)</span></div>
+
+<div class="viewcode-block" id="MVFTS.format_data"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.mvfts.MVFTS.format_data">[docs]</a>    <span class="k">def</span> <span class="nf">format_data</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">):</span>
+        <span class="n">ndata</span> <span class="o">=</span> <span class="p">{}</span>
+        <span class="k">for</span> <span class="n">var</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">explanatory_variables</span><span class="p">:</span>
+            <span class="n">ndata</span><span class="p">[</span><span class="n">var</span><span class="o">.</span><span class="n">name</span><span class="p">]</span> <span class="o">=</span> <span class="n">var</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">extractor</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="n">var</span><span class="o">.</span><span class="n">data_label</span><span class="p">])</span>
+
+        <span class="k">return</span> <span class="n">ndata</span></div>
+
+<div class="viewcode-block" id="MVFTS.apply_transformations"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.mvfts.MVFTS.apply_transformations">[docs]</a>    <span class="k">def</span> <span class="nf">apply_transformations</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">params</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">updateUoD</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="n">ndata</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="n">deep</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+        <span class="k">for</span> <span class="n">ct</span><span class="p">,</span> <span class="n">transformation</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">transformations</span><span class="p">):</span>
+            <span class="n">ndata</span> <span class="o">=</span> <span class="n">transformation</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">ndata</span><span class="p">,</span> <span class="o">**</span><span class="bp">self</span><span class="o">.</span><span class="n">transformations_param</span><span class="p">[</span><span class="n">ct</span><span class="p">])</span>
+
+        <span class="k">for</span> <span class="n">var</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">explanatory_variables</span><span class="p">:</span>
+            <span class="k">try</span><span class="p">:</span>
+                <span class="n">values</span> <span class="o">=</span> <span class="n">ndata</span><span class="p">[</span><span class="n">var</span><span class="o">.</span><span class="n">data_label</span><span class="p">]</span><span class="o">.</span><span class="n">values</span> <span class="c1">#if isinstance(ndata, pd.DataFrame) else ndata[var.data_label]</span>
+                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">uod_clip</span> <span class="ow">and</span> <span class="n">var</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">type</span> <span class="o">==</span> <span class="s1">&#39;common&#39;</span><span class="p">:</span>
+                    <span class="n">ndata</span><span class="p">[</span><span class="n">var</span><span class="o">.</span><span class="n">data_label</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">clip</span><span class="p">(</span><span class="n">values</span><span class="p">,</span>
+                                                    <span class="n">var</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">min</span><span class="p">,</span> <span class="n">var</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">max</span><span class="p">)</span>
+
+                <span class="n">ndata</span><span class="p">[</span><span class="n">var</span><span class="o">.</span><span class="n">data_label</span><span class="p">]</span> <span class="o">=</span> <span class="n">var</span><span class="o">.</span><span class="n">apply_transformations</span><span class="p">(</span><span class="n">values</span><span class="p">)</span>
+            <span class="k">except</span><span class="p">:</span>
+                <span class="k">pass</span>
+
+        <span class="k">return</span> <span class="n">ndata</span></div>
+
+<div class="viewcode-block" id="MVFTS.generate_lhs_flrs"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.mvfts.MVFTS.generate_lhs_flrs">[docs]</a>    <span class="k">def</span> <span class="nf">generate_lhs_flrs</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">):</span>
+        <span class="n">flrs</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="n">lags</span> <span class="o">=</span> <span class="p">{}</span>
+        <span class="k">for</span> <span class="n">vc</span><span class="p">,</span> <span class="n">var</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">explanatory_variables</span><span class="p">):</span>
+            <span class="n">data_point</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="n">var</span><span class="o">.</span><span class="n">name</span><span class="p">]</span>
+            <span class="n">lags</span><span class="p">[</span><span class="n">var</span><span class="o">.</span><span class="n">name</span><span class="p">]</span> <span class="o">=</span> <span class="n">common</span><span class="o">.</span><span class="n">fuzzyfy_instance</span><span class="p">(</span><span class="n">data_point</span><span class="p">,</span> <span class="n">var</span><span class="p">,</span> <span class="n">tuples</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+
+        <span class="k">for</span> <span class="n">path</span> <span class="ow">in</span> <span class="n">product_dict</span><span class="p">(</span><span class="o">**</span><span class="n">lags</span><span class="p">):</span>
+            <span class="n">flr</span> <span class="o">=</span> <span class="n">MVFLR</span><span class="o">.</span><span class="n">FLR</span><span class="p">()</span>
+
+            <span class="n">flr</span><span class="o">.</span><span class="n">LHS</span> <span class="o">=</span> <span class="n">path</span>
+
+            <span class="c1">#for var, fset in path.items():</span>
+            <span class="c1">#    flr.set_lhs(var, fset)</span>
+
+            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">flr</span><span class="o">.</span><span class="n">LHS</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">explanatory_variables</span><span class="p">):</span>
+                <span class="n">flrs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">flr</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">flrs</span></div>
+
+<div class="viewcode-block" id="MVFTS.generate_flrs"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.mvfts.MVFTS.generate_flrs">[docs]</a>    <span class="k">def</span> <span class="nf">generate_flrs</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">):</span>
+        <span class="n">flrs</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">for</span> <span class="n">ct</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">index</span><span class="p">)):</span>
+            <span class="n">ix</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">index</span><span class="p">[</span><span class="n">ct</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
+            <span class="n">data_point</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">format_data</span><span class="p">(</span> <span class="n">data</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">ix</span><span class="p">]</span> <span class="p">)</span>
+
+            <span class="n">tmp_flrs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">generate_lhs_flrs</span><span class="p">(</span><span class="n">data_point</span><span class="p">)</span>
+
+            <span class="n">target_ix</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">index</span><span class="p">[</span><span class="n">ct</span><span class="p">]</span>
+            <span class="n">target_point</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span><span class="o">.</span><span class="n">data_label</span><span class="p">][</span><span class="n">target_ix</span><span class="p">]</span>
+            <span class="n">target</span> <span class="o">=</span> <span class="n">common</span><span class="o">.</span><span class="n">fuzzyfy_instance</span><span class="p">(</span><span class="n">target_point</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span><span class="p">)</span>
+
+            <span class="k">for</span> <span class="n">flr</span> <span class="ow">in</span> <span class="n">tmp_flrs</span><span class="p">:</span>
+                <span class="k">for</span> <span class="n">v</span><span class="p">,</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">target</span><span class="p">:</span>
+                    <span class="n">new_flr</span> <span class="o">=</span> <span class="n">deepcopy</span><span class="p">(</span><span class="n">flr</span><span class="p">)</span>
+                    <span class="n">new_flr</span><span class="o">.</span><span class="n">set_rhs</span><span class="p">(</span><span class="n">s</span><span class="p">)</span>
+                    <span class="n">flrs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">new_flr</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">flrs</span></div>
+
+<div class="viewcode-block" id="MVFTS.generate_flrg"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.mvfts.MVFTS.generate_flrg">[docs]</a>    <span class="k">def</span> <span class="nf">generate_flrg</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">flrs</span><span class="p">):</span>
+        <span class="k">for</span> <span class="n">flr</span> <span class="ow">in</span> <span class="n">flrs</span><span class="p">:</span>
+            <span class="n">flrg</span> <span class="o">=</span> <span class="n">mvflrg</span><span class="o">.</span><span class="n">FLRG</span><span class="p">(</span><span class="n">lhs</span><span class="o">=</span><span class="n">flr</span><span class="o">.</span><span class="n">LHS</span><span class="p">)</span>
+
+            <span class="k">if</span> <span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()]</span> <span class="o">=</span> <span class="n">flrg</span>
+
+            <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()]</span><span class="o">.</span><span class="n">append_rhs</span><span class="p">(</span><span class="n">flr</span><span class="o">.</span><span class="n">RHS</span><span class="p">)</span></div>
+
+<div class="viewcode-block" id="MVFTS.train"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.mvfts.MVFTS.train">[docs]</a>    <span class="k">def</span> <span class="nf">train</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+
+        <span class="n">ndata</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">apply_transformations</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+
+        <span class="n">flrs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">generate_flrs</span><span class="p">(</span><span class="n">ndata</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">generate_flrg</span><span class="p">(</span><span class="n">flrs</span><span class="p">)</span></div>
+
+<div class="viewcode-block" id="MVFTS.forecast"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.mvfts.MVFTS.forecast">[docs]</a>    <span class="k">def</span> <span class="nf">forecast</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="n">ndata</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">apply_transformations</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+        <span class="n">c</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="k">for</span> <span class="n">index</span><span class="p">,</span> <span class="n">row</span> <span class="ow">in</span> <span class="n">ndata</span><span class="o">.</span><span class="n">iterrows</span><span class="p">()</span> <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">ndata</span><span class="p">,</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">)</span> <span class="k">else</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">ndata</span><span class="p">):</span>
+            <span class="n">data_point</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">format_data</span><span class="p">(</span><span class="n">row</span><span class="p">)</span>
+            <span class="n">flrs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">generate_lhs_flrs</span><span class="p">(</span><span class="n">data_point</span><span class="p">)</span>
+            <span class="n">mvs</span> <span class="o">=</span> <span class="p">[]</span>
+            <span class="n">mps</span> <span class="o">=</span> <span class="p">[]</span>
+            <span class="k">for</span> <span class="n">flr</span> <span class="ow">in</span> <span class="n">flrs</span><span class="p">:</span>
+                <span class="n">flrg</span> <span class="o">=</span> <span class="n">mvflrg</span><span class="o">.</span><span class="n">FLRG</span><span class="p">(</span><span class="n">lhs</span><span class="o">=</span><span class="n">flr</span><span class="o">.</span><span class="n">LHS</span><span class="p">)</span>
+                <span class="k">if</span> <span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">:</span>
+                    <span class="c1">#Naïve approach is applied when no rules were found</span>
+                    <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span><span class="o">.</span><span class="n">name</span> <span class="ow">in</span> <span class="n">flrg</span><span class="o">.</span><span class="n">LHS</span><span class="p">:</span>
+                        <span class="n">fs</span> <span class="o">=</span> <span class="n">flrg</span><span class="o">.</span><span class="n">LHS</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span><span class="o">.</span><span class="n">name</span><span class="p">]</span>
+                        <span class="n">fset</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">fs</span><span class="p">]</span>
+                        <span class="n">mp</span> <span class="o">=</span> <span class="n">fset</span><span class="o">.</span><span class="n">centroid</span>
+                        <span class="n">mv</span> <span class="o">=</span> <span class="n">fset</span><span class="o">.</span><span class="n">membership</span><span class="p">(</span><span class="n">data_point</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span><span class="o">.</span><span class="n">name</span><span class="p">])</span>
+                        <span class="n">mvs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">mv</span><span class="p">)</span>
+                        <span class="n">mps</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">mp</span><span class="p">)</span>
+                    <span class="k">else</span><span class="p">:</span>
+                        <span class="n">mvs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="mf">0.</span><span class="p">)</span>
+                        <span class="n">mps</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="mf">0.</span><span class="p">)</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">_flrg</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()]</span>
+                    <span class="n">mvs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">_flrg</span><span class="o">.</span><span class="n">get_membership</span><span class="p">(</span><span class="n">data_point</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">explanatory_variables</span><span class="p">))</span>
+                    <span class="n">mps</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">_flrg</span><span class="o">.</span><span class="n">get_midpoint</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">))</span>
+
+            <span class="n">mv</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">mvs</span><span class="p">)</span>
+            <span class="n">mp</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">mps</span><span class="p">)</span>
+
+            <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">mv</span><span class="p">,</span><span class="n">mp</span><span class="o">.</span><span class="n">T</span><span class="p">)</span><span class="o">/</span><span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">mv</span><span class="p">))</span>
+
+        <span class="n">ret</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span><span class="o">.</span><span class="n">apply_inverse_transformations</span><span class="p">(</span><span class="n">ret</span><span class="p">,</span>
+                                                           <span class="n">params</span><span class="o">=</span><span class="n">data</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span><span class="o">.</span><span class="n">data_label</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">ret</span></div>
+
+<div class="viewcode-block" id="MVFTS.forecast_ahead"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.mvfts.MVFTS.forecast_ahead">[docs]</a>    <span class="k">def</span> <span class="nf">forecast_ahead</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">steps</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="n">generators</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;generators&#39;</span><span class="p">,</span><span class="kc">None</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">generators</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s1">&#39;You must provide parameter </span><span class="se">\&#39;</span><span class="s1">generators</span><span class="se">\&#39;</span><span class="s1">! generators is a dict where the keys&#39;</span> <span class="o">+</span>
+                            <span class="s1">&#39; are the dataframe column names (except the target_variable) and the values are &#39;</span> <span class="o">+</span>
+                            <span class="s1">&#39;lambda functions that accept one value (the actual value of the variable) &#39;</span>
+                            <span class="s1">&#39; and return the next value or trained FTS models that accept the actual values and &#39;</span>
+                            <span class="s1">&#39;forecast new ones.&#39;</span><span class="p">)</span>
+
+        <span class="n">ndata</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">apply_transformations</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+
+        <span class="n">start</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;start_at&#39;</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
+
+        <span class="n">ndata</span> <span class="o">=</span> <span class="n">ndata</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">start</span><span class="p">:</span> <span class="n">start</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">]</span>
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">steps</span><span class="p">):</span>
+            <span class="n">sample</span> <span class="o">=</span> <span class="n">ndata</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">:]</span>
+            <span class="n">tmp</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecast</span><span class="p">(</span><span class="n">sample</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">tmp</span><span class="p">,</span> <span class="p">(</span><span class="nb">list</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">)):</span>
+                <span class="n">tmp</span> <span class="o">=</span> <span class="n">tmp</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
+
+            <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">tmp</span><span class="p">)</span>
+
+            <span class="n">new_data_point</span> <span class="o">=</span> <span class="p">{}</span>
+
+            <span class="k">for</span> <span class="n">data_label</span> <span class="ow">in</span> <span class="n">generators</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
+                <span class="k">if</span> <span class="n">data_label</span> <span class="o">!=</span> <span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span><span class="o">.</span><span class="n">data_label</span><span class="p">:</span>
+                    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">generators</span><span class="p">[</span><span class="n">data_label</span><span class="p">],</span> <span class="n">LambdaType</span><span class="p">):</span>
+                        <span class="n">last_data_point</span> <span class="o">=</span> <span class="n">ndata</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">ndata</span><span class="o">.</span><span class="n">index</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]]</span>
+                        <span class="n">new_data_point</span><span class="p">[</span><span class="n">data_label</span><span class="p">]</span> <span class="o">=</span> <span class="n">generators</span><span class="p">[</span><span class="n">data_label</span><span class="p">](</span><span class="n">last_data_point</span><span class="p">[</span><span class="n">data_label</span><span class="p">])</span>
+                    <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">generators</span><span class="p">[</span><span class="n">data_label</span><span class="p">],</span> <span class="n">fts</span><span class="o">.</span><span class="n">FTS</span><span class="p">):</span>
+                        <span class="n">gen_model</span> <span class="o">=</span> <span class="n">generators</span><span class="p">[</span><span class="n">data_label</span><span class="p">]</span>
+                        <span class="n">last_data_point</span> <span class="o">=</span> <span class="n">sample</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="o">-</span><span class="n">gen_model</span><span class="o">.</span><span class="n">order</span><span class="p">:]</span>
+
+                        <span class="k">if</span> <span class="ow">not</span> <span class="n">gen_model</span><span class="o">.</span><span class="n">is_multivariate</span><span class="p">:</span>
+                            <span class="n">last_data_point</span> <span class="o">=</span> <span class="n">last_data_point</span><span class="p">[</span><span class="n">data_label</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
+
+                        <span class="n">new_data_point</span><span class="p">[</span><span class="n">data_label</span><span class="p">]</span> <span class="o">=</span> <span class="n">gen_model</span><span class="o">.</span><span class="n">forecast</span><span class="p">(</span><span class="n">last_data_point</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
+
+            <span class="n">new_data_point</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span><span class="o">.</span><span class="n">data_label</span><span class="p">]</span> <span class="o">=</span> <span class="n">tmp</span>
+
+            <span class="n">ndata</span> <span class="o">=</span> <span class="n">ndata</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">new_data_point</span><span class="p">,</span> <span class="n">ignore_index</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">ret</span><span class="p">[</span><span class="o">-</span><span class="n">steps</span><span class="p">:]</span></div>
+
+<div class="viewcode-block" id="MVFTS.forecast_interval"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.mvfts.MVFTS.forecast_interval">[docs]</a>    <span class="k">def</span> <span class="nf">forecast_interval</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="n">ndata</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">apply_transformations</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+        <span class="n">c</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="k">for</span> <span class="n">index</span><span class="p">,</span> <span class="n">row</span> <span class="ow">in</span> <span class="n">ndata</span><span class="o">.</span><span class="n">iterrows</span><span class="p">()</span> <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">ndata</span><span class="p">,</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">)</span> <span class="k">else</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">ndata</span><span class="p">):</span>
+            <span class="n">data_point</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">format_data</span><span class="p">(</span><span class="n">row</span><span class="p">)</span>
+            <span class="n">flrs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">generate_lhs_flrs</span><span class="p">(</span><span class="n">data_point</span><span class="p">)</span>
+            <span class="n">mvs</span> <span class="o">=</span> <span class="p">[]</span>
+            <span class="n">ups</span> <span class="o">=</span> <span class="p">[]</span>
+            <span class="n">los</span> <span class="o">=</span> <span class="p">[]</span>
+            <span class="k">for</span> <span class="n">flr</span> <span class="ow">in</span> <span class="n">flrs</span><span class="p">:</span>
+                <span class="n">flrg</span> <span class="o">=</span> <span class="n">mvflrg</span><span class="o">.</span><span class="n">FLRG</span><span class="p">(</span><span class="n">lhs</span><span class="o">=</span><span class="n">flr</span><span class="o">.</span><span class="n">LHS</span><span class="p">)</span>
+                <span class="k">if</span> <span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">:</span>
+                    <span class="c1">#Naïve approach is applied when no rules were found</span>
+                    <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span><span class="o">.</span><span class="n">name</span> <span class="ow">in</span> <span class="n">flrg</span><span class="o">.</span><span class="n">LHS</span><span class="p">:</span>
+                        <span class="n">fs</span> <span class="o">=</span> <span class="n">flrg</span><span class="o">.</span><span class="n">LHS</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span><span class="o">.</span><span class="n">name</span><span class="p">]</span>
+                        <span class="n">fset</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">[</span><span class="n">fs</span><span class="p">]</span>
+                        <span class="n">up</span> <span class="o">=</span> <span class="n">fset</span><span class="o">.</span><span class="n">upper</span>
+                        <span class="n">lo</span> <span class="o">=</span> <span class="n">fset</span><span class="o">.</span><span class="n">lower</span>
+                        <span class="n">mv</span> <span class="o">=</span> <span class="n">fset</span><span class="o">.</span><span class="n">membership</span><span class="p">(</span><span class="n">data_point</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span><span class="o">.</span><span class="n">name</span><span class="p">])</span>
+                        <span class="n">mvs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">mv</span><span class="p">)</span>
+                        <span class="n">ups</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">up</span><span class="p">)</span>
+                        <span class="n">los</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">lo</span><span class="p">)</span>
+                    <span class="k">else</span><span class="p">:</span>
+                        <span class="n">mvs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="mf">0.</span><span class="p">)</span>
+                        <span class="n">ups</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="mf">0.</span><span class="p">)</span>
+                        <span class="n">los</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="mf">0.</span><span class="p">)</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">_flrg</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()]</span>
+                    <span class="n">mvs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">_flrg</span><span class="o">.</span><span class="n">get_membership</span><span class="p">(</span><span class="n">data_point</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">explanatory_variables</span><span class="p">))</span>
+                    <span class="n">ups</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">_flrg</span><span class="o">.</span><span class="n">get_upper</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">))</span>
+                    <span class="n">los</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">_flrg</span><span class="o">.</span><span class="n">get_lower</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">sets</span><span class="p">))</span>
+
+            <span class="n">mv</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">mvs</span><span class="p">)</span>
+            <span class="n">up</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">mv</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">ups</span><span class="p">)</span><span class="o">.</span><span class="n">T</span><span class="p">)</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">mv</span><span class="p">)</span>
+            <span class="n">lo</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">mv</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">los</span><span class="p">)</span><span class="o">.</span><span class="n">T</span><span class="p">)</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">mv</span><span class="p">)</span>
+
+            <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">([</span><span class="n">lo</span><span class="p">,</span> <span class="n">up</span><span class="p">])</span>
+
+        <span class="n">ret</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span><span class="o">.</span><span class="n">apply_inverse_transformations</span><span class="p">(</span><span class="n">ret</span><span class="p">,</span>
+                                                           <span class="n">params</span><span class="o">=</span><span class="n">data</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span><span class="o">.</span><span class="n">data_label</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">ret</span></div>
+
+<div class="viewcode-block" id="MVFTS.forecast_ahead_interval"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.mvfts.MVFTS.forecast_ahead_interval">[docs]</a>    <span class="k">def</span> <span class="nf">forecast_ahead_interval</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">steps</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="n">generators</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;generators&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">generators</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">Exception</span><span class="p">(</span><span class="s1">&#39;You must provide parameter </span><span class="se">\&#39;</span><span class="s1">generators</span><span class="se">\&#39;</span><span class="s1">! generators is a dict where the keys&#39;</span> <span class="o">+</span>
+                            <span class="s1">&#39; are the dataframe column names (except the target_variable) and the values are &#39;</span> <span class="o">+</span>
+                            <span class="s1">&#39;lambda functions that accept one value (the actual value of the variable) &#39;</span>
+                            <span class="s1">&#39; and return the next value or trained FTS models that accept the actual values and &#39;</span>
+                            <span class="s1">&#39;forecast new ones.&#39;</span><span class="p">)</span>
+
+        <span class="n">ndata</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">apply_transformations</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+
+        <span class="n">start</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;start_at&#39;</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
+
+        <span class="n">ret</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="n">ix</span> <span class="o">=</span> <span class="n">ndata</span><span class="o">.</span><span class="n">index</span><span class="p">[</span><span class="n">start</span><span class="p">:</span> <span class="n">start</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">]</span>
+        <span class="n">lo</span> <span class="o">=</span> <span class="n">ndata</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">ix</span><span class="p">]</span> <span class="c1">#[ndata.loc[k] for k in ix]</span>
+        <span class="n">up</span> <span class="o">=</span> <span class="n">ndata</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">ix</span><span class="p">]</span> <span class="c1">#[ndata.loc[k] for k in ix]</span>
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">steps</span><span class="p">):</span>
+            <span class="n">tmp_lo</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecast_interval</span><span class="p">(</span><span class="n">lo</span><span class="p">[</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">:],</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
+            <span class="n">tmp_up</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">forecast_interval</span><span class="p">(</span><span class="n">up</span><span class="p">[</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">max_lag</span><span class="p">:],</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
+
+            <span class="n">ret</span><span class="o">.</span><span class="n">append</span><span class="p">([</span><span class="nb">min</span><span class="p">(</span><span class="n">tmp_lo</span><span class="p">),</span> <span class="nb">max</span><span class="p">(</span><span class="n">tmp_up</span><span class="p">)])</span>
+
+            <span class="n">new_data_point_lo</span> <span class="o">=</span> <span class="p">{}</span>
+            <span class="n">new_data_point_up</span> <span class="o">=</span> <span class="p">{}</span>
+
+            <span class="k">for</span> <span class="n">data_label</span> <span class="ow">in</span> <span class="n">generators</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
+                <span class="k">if</span> <span class="n">data_label</span> <span class="o">!=</span> <span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span><span class="o">.</span><span class="n">data_label</span><span class="p">:</span>
+                    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">generators</span><span class="p">[</span><span class="n">data_label</span><span class="p">],</span> <span class="n">LambdaType</span><span class="p">):</span>
+                        <span class="n">last_data_point_lo</span> <span class="o">=</span> <span class="n">lo</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">lo</span><span class="o">.</span><span class="n">index</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]]</span>
+                        <span class="n">new_data_point_lo</span><span class="p">[</span><span class="n">data_label</span><span class="p">]</span> <span class="o">=</span> <span class="n">generators</span><span class="p">[</span><span class="n">data_label</span><span class="p">](</span><span class="n">last_data_point_lo</span><span class="p">[</span><span class="n">data_label</span><span class="p">])</span>
+                        <span class="n">last_data_point_up</span> <span class="o">=</span> <span class="n">up</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">up</span><span class="o">.</span><span class="n">index</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]]</span>
+                        <span class="n">new_data_point_up</span><span class="p">[</span><span class="n">data_label</span><span class="p">]</span> <span class="o">=</span> <span class="n">generators</span><span class="p">[</span><span class="n">data_label</span><span class="p">](</span><span class="n">last_data_point_up</span><span class="p">[</span><span class="n">data_label</span><span class="p">])</span>
+                    <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">generators</span><span class="p">[</span><span class="n">data_label</span><span class="p">],</span> <span class="n">fts</span><span class="o">.</span><span class="n">FTS</span><span class="p">):</span>
+                        <span class="n">model</span> <span class="o">=</span> <span class="n">generators</span><span class="p">[</span><span class="n">data_label</span><span class="p">]</span>
+                        <span class="n">last_data_point_lo</span> <span class="o">=</span> <span class="n">lo</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">lo</span><span class="o">.</span><span class="n">index</span><span class="p">[</span><span class="o">-</span><span class="n">model</span><span class="o">.</span><span class="n">order</span><span class="p">:]]</span>
+                        <span class="n">last_data_point_up</span> <span class="o">=</span> <span class="n">up</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">up</span><span class="o">.</span><span class="n">index</span><span class="p">[</span><span class="o">-</span><span class="n">model</span><span class="o">.</span><span class="n">order</span><span class="p">:]]</span>
+
+                        <span class="k">if</span> <span class="ow">not</span> <span class="n">model</span><span class="o">.</span><span class="n">is_multivariate</span><span class="p">:</span>
+                            <span class="n">last_data_point_lo</span> <span class="o">=</span> <span class="n">last_data_point_lo</span><span class="p">[</span><span class="n">data_label</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
+                            <span class="n">last_data_point_up</span> <span class="o">=</span> <span class="n">last_data_point_up</span><span class="p">[</span><span class="n">data_label</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
+
+                        <span class="n">new_data_point_lo</span><span class="p">[</span><span class="n">data_label</span><span class="p">]</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">forecast</span><span class="p">(</span><span class="n">last_data_point_lo</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
+                        <span class="n">new_data_point_up</span><span class="p">[</span><span class="n">data_label</span><span class="p">]</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">forecast</span><span class="p">(</span><span class="n">last_data_point_up</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
+
+            <span class="n">new_data_point_lo</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span><span class="o">.</span><span class="n">data_label</span><span class="p">]</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="n">tmp_lo</span><span class="p">)</span>
+            <span class="n">new_data_point_up</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span><span class="o">.</span><span class="n">data_label</span><span class="p">]</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">tmp_up</span><span class="p">)</span>
+
+            <span class="n">lo</span> <span class="o">=</span> <span class="n">lo</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">new_data_point_lo</span><span class="p">,</span> <span class="n">ignore_index</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+            <span class="n">up</span> <span class="o">=</span> <span class="n">up</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">new_data_point_up</span><span class="p">,</span> <span class="n">ignore_index</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">ret</span><span class="p">[</span><span class="o">-</span><span class="n">steps</span><span class="p">:]</span></div>
+
+<div class="viewcode-block" id="MVFTS.clone_parameters"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.mvfts.MVFTS.clone_parameters">[docs]</a>    <span class="k">def</span> <span class="nf">clone_parameters</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">model</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">MVFTS</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="n">clone_parameters</span><span class="p">(</span><span class="n">model</span><span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">explanatory_variables</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">explanatory_variables</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">target_variable</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">target_variable</span></div>
+
+    <span class="k">def</span> <span class="nf">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="n">_str</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">+</span> <span class="s2">&quot;:</span><span class="se">\n</span><span class="s2">&quot;</span>
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
+            <span class="n">_str</span> <span class="o">+=</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">k</span><span class="p">])</span> <span class="o">+</span> <span class="s2">&quot;</span><span class="se">\n</span><span class="s2">&quot;</span>
+
+        <span class="k">return</span> <span class="n">_str</span></div>
+
+
+</pre></div>
+
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+             >modules</a> |</li>
+        <li class="nav-item nav-item-0"><a href="../../../../index.html">pyFTS 1.7 documentation</a> &#187;</li>
+          <li class="nav-item nav-item-1"><a href="../../../index.html" >Module code</a> &#187;</li>
+        <li class="nav-item nav-item-this"><a href="">pyFTS.models.multivariate.mvfts</a></li> 
+      </ul>
+    </div>
+    <div class="footer" role="contentinfo">
+        &#169; Copyright 2022, Machine Intelligence and Data Science Laboratory - UFMG - Brazil.
+      Created using <a href="https://www.sphinx-doc.org/">Sphinx</a> 4.5.0.
+    </div>
+  </body>
+</html>

docs/build/html/_modules/pyFTS/models/multivariate/partitioner.html

@@ -43,20 +43,20 @@
           <div class="body" role="main">
             
   <h1>Source code for pyFTS.models.multivariate.partitioner</h1><div class="highlight"><pre>
-<span></span><span class="kn">from</span> <span class="nn">pyFTS.partitioners</span> <span class="kn">import</span> <span class="n">partitioner</span>
+<span></span><span class="kn">from</span> <span class="nn">pyFTS.partitioners</span> <span class="k">import</span> <span class="n">partitioner</span>
-<span class="kn">from</span> <span class="nn">pyFTS.models.multivariate.common</span> <span class="kn">import</span> <span class="n">MultivariateFuzzySet</span><span class="p">,</span> <span class="n">fuzzyfy_instance_clustered</span>
+<span class="kn">from</span> <span class="nn">pyFTS.models.multivariate.common</span> <span class="k">import</span> <span class="n">MultivariateFuzzySet</span><span class="p">,</span> <span class="n">fuzzyfy_instance_clustered</span>
-<span class="kn">from</span> <span class="nn">itertools</span> <span class="kn">import</span> <span class="n">product</span>
+<span class="kn">from</span> <span class="nn">itertools</span> <span class="k">import</span> <span class="n">product</span>
-<span class="kn">from</span> <span class="nn">scipy.spatial</span> <span class="kn">import</span> <span class="n">KDTree</span>
+<span class="kn">from</span> <span class="nn">scipy.spatial</span> <span class="k">import</span> <span class="n">KDTree</span>
 <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
 <span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
 
 
 <div class="viewcode-block" id="MultivariatePartitioner"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.partitioner.MultivariatePartitioner">[docs]</a><span class="k">class</span> <span class="nc">MultivariatePartitioner</span><span class="p">(</span><span class="n">partitioner</span><span class="o">.</span><span class="n">Partitioner</span><span class="p">):</span>
-<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+    <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Base class for partitioners which use the MultivariateFuzzySet</span>
 <span class="sd">    &quot;&quot;&quot;</span>
 
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
         <span class="nb">super</span><span class="p">(</span><span class="n">MultivariatePartitioner</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s2">&quot;MultivariatePartitioner&quot;</span><span class="p">,</span> <span class="n">preprocess</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
 
         <span class="bp">self</span><span class="o">.</span><span class="n">type</span> <span class="o">=</span> <span class="s1">&#39;multivariate&#39;</span>
@@ -103,7 +103,7 @@
         <span class="bp">self</span><span class="o">.</span><span class="n">build_index</span><span class="p">()</span></div>
 
 <div class="viewcode-block" id="MultivariatePartitioner.search"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.partitioner.MultivariatePartitioner.search">[docs]</a>    <span class="k">def</span> <span class="nf">search</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+        <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Perform a search for the nearest fuzzy sets of the point &#39;data&#39;. This function were designed to work with several</span>
 <span class="sd">        overlapped fuzzy sets.</span>
 
@@ -204,7 +204,7 @@
     </div>
     <div class="footer" role="contentinfo">
         &#169; Copyright 2022, Machine Intelligence and Data Science Laboratory - UFMG - Brazil.
-      Created using <a href="https://www.sphinx-doc.org/">Sphinx</a> 4.3.2.
+      Created using <a href="https://www.sphinx-doc.org/">Sphinx</a> 4.5.0.
     </div>
   </body>
 </html>

docs/build/html/_modules/pyFTS/models/multivariate/variable.html

@@ -0,0 +1,172 @@
+
+
+<!doctype html>
+
+<html>
+  <head>
+    <meta charset="utf-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+    <title>pyFTS.models.multivariate.variable &#8212; pyFTS 1.7 documentation</title>
+    <link rel="stylesheet" type="text/css" href="../../../../_static/pygments.css" />
+    <link rel="stylesheet" type="text/css" href="../../../../_static/bizstyle.css" />
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+          <a href="../../../../genindex.html" title="General Index"
+             accesskey="I">index</a></li>
+        <li class="right" >
+          <a href="../../../../py-modindex.html" title="Python Module Index"
+             >modules</a> |</li>
+        <li class="nav-item nav-item-0"><a href="../../../../index.html">pyFTS 1.7 documentation</a> &#187;</li>
+          <li class="nav-item nav-item-1"><a href="../../../index.html" accesskey="U">Module code</a> &#187;</li>
+        <li class="nav-item nav-item-this"><a href="">pyFTS.models.multivariate.variable</a></li> 
+      </ul>
+    </div>  
+
+    <div class="document">
+      <div class="documentwrapper">
+        <div class="bodywrapper">
+          <div class="body" role="main">
+            
+  <h1>Source code for pyFTS.models.multivariate.variable</h1><div class="highlight"><pre>
+<span></span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="k">import</span> <span class="n">fts</span><span class="p">,</span> <span class="n">FuzzySet</span><span class="p">,</span> <span class="n">FLR</span><span class="p">,</span> <span class="n">Membership</span><span class="p">,</span> <span class="n">tree</span>
+<span class="kn">from</span> <span class="nn">pyFTS.partitioners</span> <span class="k">import</span> <span class="n">Grid</span>
+<span class="kn">from</span> <span class="nn">pyFTS.models.multivariate</span> <span class="k">import</span> <span class="n">FLR</span> <span class="k">as</span> <span class="n">MVFLR</span>
+
+
+<div class="viewcode-block" id="Variable"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.variable.Variable">[docs]</a><span class="k">class</span> <span class="nc">Variable</span><span class="p">:</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    A variable of a fuzzy time series multivariate model. Each variable contains its own</span>
+<span class="sd">    transformations and partitioners.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">name</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="sd">&quot;&quot;&quot;</span>
+
+<span class="sd">        :param name:</span>
+<span class="sd">        :param \**kwargs: See below</span>
+
+<span class="sd">        :Keyword Arguments:</span>
+<span class="sd">            * *alias* -- Alternative name for the variable</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="n">name</span>
+        <span class="sd">&quot;&quot;&quot;A string with the name of the variable&quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">alias</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;alias&#39;</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">name</span><span class="p">)</span>
+        <span class="sd">&quot;&quot;&quot;A string with the alias of the variable&quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">data_label</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;data_label&#39;</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">name</span><span class="p">)</span>
+        <span class="sd">&quot;&quot;&quot;A string with the column name on DataFrame&quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">type</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;type&#39;</span><span class="p">,</span> <span class="s1">&#39;common&#39;</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">data_type</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;data_type&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+        <span class="sd">&quot;&quot;&quot;The type of the data column on Pandas Dataframe&quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">mask</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;mask&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+        <span class="sd">&quot;&quot;&quot;The mask for format the data column on Pandas Dataframe&quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">transformation</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;transformation&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+        <span class="sd">&quot;&quot;&quot;Pre processing transformation for the variable&quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">transformation_params</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;transformation_params&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="sd">&quot;&quot;&quot;UoD partitioner for the variable data&quot;&quot;&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">alpha_cut</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;alpha_cut&#39;</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">)</span>
+        <span class="sd">&quot;&quot;&quot;Minimal membership value to be considered on fuzzyfication process&quot;&quot;&quot;</span>
+
+
+        <span class="k">if</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;data&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">build</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+<div class="viewcode-block" id="Variable.build"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.variable.Variable.build">[docs]</a>    <span class="k">def</span> <span class="nf">build</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="sd">&quot;&quot;&quot;</span>
+
+<span class="sd">        :param kwargs:</span>
+<span class="sd">        :return:</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">fs</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">&#39;partitioner&#39;</span><span class="p">,</span> <span class="n">Grid</span><span class="o">.</span><span class="n">GridPartitioner</span><span class="p">)</span>
+        <span class="n">mf</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">&#39;func&#39;</span><span class="p">,</span> <span class="n">Membership</span><span class="o">.</span><span class="n">trimf</span><span class="p">)</span>
+        <span class="n">np</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">&#39;npart&#39;</span><span class="p">,</span> <span class="mi">10</span><span class="p">)</span>
+        <span class="n">data</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;data&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+        <span class="n">kw</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">&#39;partitioner_specific&#39;</span><span class="p">,</span> <span class="p">{})</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span> <span class="o">=</span> <span class="n">fs</span><span class="p">(</span><span class="n">data</span><span class="o">=</span><span class="n">data</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">data_label</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span> <span class="n">npart</span><span class="o">=</span><span class="n">np</span><span class="p">,</span> <span class="n">func</span><span class="o">=</span><span class="n">mf</span><span class="p">,</span>
+                              <span class="n">transformation</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">transformation</span><span class="p">,</span> <span class="n">prefix</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">alias</span><span class="p">,</span>
+                              <span class="n">variable</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">name</span><span class="p">,</span> <span class="o">**</span><span class="n">kw</span><span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">+</span> <span class="s2">&quot; &quot;</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">partitioner</span><span class="o">.</span><span class="n">name</span></div>
+
+<div class="viewcode-block" id="Variable.apply_transformations"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.variable.Variable.apply_transformations">[docs]</a>    <span class="k">def</span> <span class="nf">apply_transformations</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+
+        <span class="k">if</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;params&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">transformation_params</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;params&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">transformation</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">transformation</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">transformation_params</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">data</span></div>
+
+<div class="viewcode-block" id="Variable.apply_inverse_transformations"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.variable.Variable.apply_inverse_transformations">[docs]</a>    <span class="k">def</span> <span class="nf">apply_inverse_transformations</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+
+        <span class="k">if</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;params&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">transformation_params</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;params&#39;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">transformation</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">transformation</span><span class="o">.</span><span class="n">inverse</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">transformation_params</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">data</span></div>
+
+    <span class="k">def</span> <span class="nf">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">name</span></div>
+</pre></div>
+
+            <div class="clearer"></div>
+          </div>
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+            <p class="logo"><a href="../../../../index.html">
+              <img class="logo" src="../../../../_static/logo_heading2.png" alt="Logo"/>
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+            
+  <h1>Source code for pyFTS.models.multivariate.wmvfts</h1><div class="highlight"><pre>
+<span></span><span class="kn">from</span> <span class="nn">pyFTS.common</span> <span class="k">import</span> <span class="n">fts</span><span class="p">,</span> <span class="n">FuzzySet</span><span class="p">,</span> <span class="n">FLR</span><span class="p">,</span> <span class="n">Membership</span><span class="p">,</span> <span class="n">tree</span>
+<span class="kn">from</span> <span class="nn">pyFTS.partitioners</span> <span class="k">import</span> <span class="n">Grid</span>
+<span class="kn">from</span> <span class="nn">pyFTS.models.multivariate</span> <span class="k">import</span> <span class="n">mvfts</span><span class="p">,</span> <span class="n">FLR</span> <span class="k">as</span> <span class="n">MVFLR</span><span class="p">,</span> <span class="n">common</span><span class="p">,</span> <span class="n">flrg</span> <span class="k">as</span> <span class="n">mvflrg</span>
+
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+
+
+<div class="viewcode-block" id="WeightedFLRG"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.wmvfts.WeightedFLRG">[docs]</a><span class="k">class</span> <span class="nc">WeightedFLRG</span><span class="p">(</span><span class="n">mvflrg</span><span class="o">.</span><span class="n">FLRG</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Weighted Multivariate Fuzzy Logical Rule Group</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">WeightedFLRG</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">order</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;order&#39;</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">LHS</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;lhs&#39;</span><span class="p">,</span> <span class="p">{})</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span> <span class="o">=</span> <span class="p">{}</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">count</span> <span class="o">=</span> <span class="mf">0.0</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">w</span> <span class="o">=</span> <span class="kc">None</span>
+
+<div class="viewcode-block" id="WeightedFLRG.append_rhs"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.wmvfts.WeightedFLRG.append_rhs">[docs]</a>    <span class="k">def</span> <span class="nf">append_rhs</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">fset</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="n">count</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">&#39;count&#39;</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">fset</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">[</span><span class="n">fset</span><span class="p">]</span> <span class="o">=</span> <span class="n">count</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">[</span><span class="n">fset</span><span class="p">]</span> <span class="o">+=</span> <span class="n">count</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">count</span> <span class="o">+=</span> <span class="n">count</span></div>
+
+<div class="viewcode-block" id="WeightedFLRG.weights"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.wmvfts.WeightedFLRG.weights">[docs]</a>    <span class="k">def</span> <span class="nf">weights</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">w</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">w</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">count</span> <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="o">.</span><span class="n">keys</span><span class="p">()])</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">w</span></div>
+
+<div class="viewcode-block" id="WeightedFLRG.get_midpoint"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.wmvfts.WeightedFLRG.get_midpoint">[docs]</a>    <span class="k">def</span> <span class="nf">get_midpoint</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sets</span><span class="p">):</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">midpoint</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">mp</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">sets</span><span class="p">[</span><span class="n">c</span><span class="p">]</span><span class="o">.</span><span class="n">centroid</span> <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="o">.</span><span class="n">keys</span><span class="p">()])</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">midpoint</span> <span class="o">=</span> <span class="n">mp</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">weights</span><span class="p">())</span>
+
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">midpoint</span></div>
+
+<div class="viewcode-block" id="WeightedFLRG.get_lower"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.wmvfts.WeightedFLRG.get_lower">[docs]</a>    <span class="k">def</span> <span class="nf">get_lower</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sets</span><span class="p">):</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">lower</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">lw</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">sets</span><span class="p">[</span><span class="n">s</span><span class="p">]</span><span class="o">.</span><span class="n">lower</span> <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="o">.</span><span class="n">keys</span><span class="p">()])</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">lower</span> <span class="o">=</span> <span class="n">lw</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">weights</span><span class="p">())</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">lower</span></div>
+
+<div class="viewcode-block" id="WeightedFLRG.get_upper"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.wmvfts.WeightedFLRG.get_upper">[docs]</a>    <span class="k">def</span> <span class="nf">get_upper</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sets</span><span class="p">):</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">upper</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">up</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">sets</span><span class="p">[</span><span class="n">s</span><span class="p">]</span><span class="o">.</span><span class="n">upper</span> <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="o">.</span><span class="n">keys</span><span class="p">()])</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">upper</span> <span class="o">=</span> <span class="n">up</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">weights</span><span class="p">())</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">upper</span></div>
+
+    <span class="k">def</span> <span class="nf">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="n">_str</span> <span class="o">=</span> <span class="s2">&quot;&quot;</span>
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
+            <span class="n">_str</span> <span class="o">+=</span> <span class="s2">&quot;, &quot;</span> <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">_str</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="k">else</span> <span class="s2">&quot;&quot;</span>
+            <span class="n">_str</span> <span class="o">+=</span> <span class="n">k</span> <span class="o">+</span> <span class="s2">&quot; (&quot;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span> <span class="bp">self</span><span class="o">.</span><span class="n">RHS</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">count</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span> <span class="o">+</span> <span class="s2">&quot;)&quot;</span>
+
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_key</span><span class="p">()</span> <span class="o">+</span> <span class="s2">&quot; -&gt; &quot;</span> <span class="o">+</span> <span class="n">_str</span></div>
+
+
+<div class="viewcode-block" id="WeightedMVFTS"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.wmvfts.WeightedMVFTS">[docs]</a><span class="k">class</span> <span class="nc">WeightedMVFTS</span><span class="p">(</span><span class="n">mvfts</span><span class="o">.</span><span class="n">MVFTS</span><span class="p">):</span>
+    <span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Weighted Multivariate FTS</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">WeightedMVFTS</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">order</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">shortname</span> <span class="o">=</span> <span class="s2">&quot;WeightedMVFTS&quot;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">=</span> <span class="s2">&quot;Weighted Multivariate FTS&quot;</span>
+
+<div class="viewcode-block" id="WeightedMVFTS.generate_flrg"><a class="viewcode-back" href="../../../../pyFTS.models.multivariate.html#pyFTS.models.multivariate.wmvfts.WeightedMVFTS.generate_flrg">[docs]</a>    <span class="k">def</span> <span class="nf">generate_flrg</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">flrs</span><span class="p">):</span>
+        <span class="k">for</span> <span class="n">flr</span> <span class="ow">in</span> <span class="n">flrs</span><span class="p">:</span>
+            <span class="n">flrg</span> <span class="o">=</span> <span class="n">WeightedFLRG</span><span class="p">(</span><span class="n">lhs</span><span class="o">=</span><span class="n">flr</span><span class="o">.</span><span class="n">LHS</span><span class="p">)</span>
+
+            <span class="k">if</span> <span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()</span> <span class="ow">not</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()]</span> <span class="o">=</span> <span class="n">flrg</span>
+
+            <span class="bp">self</span><span class="o">.</span><span class="n">flrgs</span><span class="p">[</span><span class="n">flrg</span><span class="o">.</span><span class="n">get_key</span><span class="p">()]</span><span class="o">.</span><span class="n">append_rhs</span><span class="p">(</span><span class="n">flr</span><span class="o">.</span><span class="n">RHS</span><span class="p">)</span></div></div>
+</pre></div>
+
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+        <li class="nav-item nav-item-this"><a href="">pyFTS.models.multivariate.wmvfts</a></li> 
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