replacing the deprecated append method with the concat method in pandas

adding a check for the ndarray data type
2024-08-14 12:08:26 +04:00 · 2024-08-14 11:57:24 +04:00 · 2024-08-14 11:55:54 +04:00 · 2024-08-08 12:53:02 +04:00 · 2024-08-08 12:00:51 +04:00 · 2024-08-08 11:59:33 +04:00
28 changed files with 258 additions and 64 deletions
--- a/.gitignore
+++ b/.gitignore
@ -0,0 +1,57 @@
+# 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/
--- a/2
+++ b/2
@ -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/CMeans.py
+pyFTS/partitioners/KMeans.py
 pyFTS/partitioners/Entropy.py
 pyFTS/partitioners/FCM.py
 pyFTS/partitioners/Grid.py
--- a/README.md
+++ b/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), [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])
+ - 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])
 
 Check out the jupyter notebook on [notebooks/Partitioners.ipynb](https://github.com/PYFTS/notebooks/blob/master/Partitioners.ipynb) for sample codes.
 
--- a/docs/build/doctrees/environment.pickle
+++ b/docs/build/doctrees/environment.pickle
--- a/docs/build/doctrees/pyFTS.partitioners.doctree
+++ b/docs/build/doctrees/pyFTS.partitioners.doctree
--- a/docs/build/html/_modules/index.html
+++ b/docs/build/html/_modules/index.html
@ -94,7 +94,7 @@
 <li><a href="pyFTS/models/seasonal/common.html">pyFTS.models.seasonal.common</a></li>
 <li><a href="pyFTS/models/seasonal/partitioner.html">pyFTS.models.seasonal.partitioner</a></li>
 <li><a href="pyFTS/partitioners/CMeans.html">pyFTS.partitioners.CMeans</a></li>
-<li><a href="pyFTS/partitioners/ClassPartitioner.html">pyFTS.partitioners.ClassPartitioner</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>
--- a/docs/build/html/_modules/pyFTS/partitioners/Class.html
+++ b/docs/build/html/_modules/pyFTS/partitioners/Class.html
@ -0,0 +1,126 @@
+
+
+<!doctype html>
+
+<html>
+  <head>
+    <meta charset="utf-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+    <title>pyFTS.partitioners.Class &#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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+    <div class="related" role="navigation" aria-label="related navigation">
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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" accesskey="U">Module code</a> &#187;</li>
+        <li class="nav-item nav-item-this"><a href="">pyFTS.partitioners.Class</a></li> 
+      </ul>
+    </div>  
+
+    <div class="document">
+      <div class="documentwrapper">
+        <div class="bodywrapper">
+          <div class="body" role="main">
+            
+  <h1>Source code for pyFTS.partitioners.Class</h1><div class="highlight"><pre>
+<span></span><span class="sd">&quot;&quot;&quot;Class Partitioner with Singleton Fuzzy Sets&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">import</span> <span class="nn">random</span> <span class="k">as</span> <span class="nn">rnd</span>
+<span class="kn">import</span> <span class="nn">functools</span><span class="o">,</span> <span class="nn">operator</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">Membership</span>
+<span class="kn">from</span> <span class="nn">pyFTS.partitioners</span> <span class="kn">import</span> <span class="n">partitioner</span>
+
+
+<div class="viewcode-block" id="ClassPartitioner"><a class="viewcode-back" href="../../../pyFTS.partitioners.html#pyFTS.partitioners.Class.ClassPartitioner">[docs]</a><span class="k">class</span> <span class="nc">ClassPartitioner</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;Class Partitioner: Given a dictionary with class/values pairs, create singleton fuzzy sets for each class&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="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Class Partitioner</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="nb">super</span><span class="p">(</span><span class="n">ClassPartitioner</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;Class&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="bp">self</span><span class="o">.</span><span class="n">ordered_sets</span> <span class="o">=</span> <span class="p">[]</span>
+
+    <span class="bp">self</span><span class="o">.</span><span class="n">min</span> <span class="o">=</span> <span class="mi">0</span>
+    <span class="bp">self</span><span class="o">.</span><span class="n">max</span> <span class="o">=</span> <span class="mi">0</span>
+    <span class="bp">self</span><span class="o">.</span><span class="n">partitions</span> <span class="o">=</span> <span class="mi">0</span>
+
+    <span class="n">classes</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;classes&quot;</span><span class="p">,</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">classes</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">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">min</span><span class="p">,</span> <span class="n">v</span><span class="p">])</span>
+      <span class="bp">self</span><span class="o">.</span><span class="n">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">max</span><span class="p">,</span> <span class="n">v</span><span class="p">])</span>
+      <span class="bp">self</span><span class="o">.</span><span class="n">partitions</span> <span class="o">+=</span> <span class="mi">1</span>
+      <span class="bp">self</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">FuzzySet</span><span class="o">.</span><span class="n">FuzzySet</span><span class="p">(</span><span class="n">k</span><span class="p">,</span> <span class="n">Membership</span><span class="o">.</span><span class="n">singleton</span><span class="p">,</span> <span class="p">[</span><span class="n">v</span><span class="p">],</span> <span class="n">v</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">ordered_sets</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">k</span><span class="p">)</span>
+
+<div class="viewcode-block" id="ClassPartitioner.build"><a class="viewcode-back" href="../../../pyFTS.partitioners.html#pyFTS.partitioners.Class.ClassPartitioner.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="n">data</span> <span class="p">:</span> <span class="nb">list</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"/>
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+<script>$('#searchbox').show(0);</script>
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+    <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.partitioners.Class</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>
--- a/docs/build/html/_sources/pyFTS.partitioners.rst.txt
+++ b/docs/build/html/_sources/pyFTS.partitioners.rst.txt
@ -20,10 +20,10 @@ pyFTS.partitioners.partitioner module
    :undoc-members:
    :show-inheritance:

-pyFTS.partitioners.ClassPartitioner module
-----------------------------------
+pyFTS.partitioners.Class module
+-------------------------------

-.. automodule:: pyFTS.partitioners.ClassPartitioner
+.. automodule:: pyFTS.partitioners.Class
    :members:
    :undoc-members:
    :show-inheritance:
--- a/docs/build/html/genindex.html
+++ b/docs/build/html/genindex.html
@ -159,7 +159,7 @@
 </li>
        <li><a href="pyFTS.models.seasonal.html#pyFTS.models.seasonal.partitioner.TimeGridPartitioner.build">(pyFTS.models.seasonal.partitioner.TimeGridPartitioner method)</a>
 </li>
-        <li><a href="pyFTS.partitioners.html#pyFTS.partitioners.ClassPartitioner.ClassPartitioner.build">(pyFTS.partitioners.ClassPartitioner.ClassPartitioner method)</a>
+        <li><a href="pyFTS.partitioners.html#pyFTS.partitioners.Class.ClassPartitioner.build">(pyFTS.partitioners.Class.ClassPartitioner method)</a>
 </li>
        <li><a href="pyFTS.partitioners.html#pyFTS.partitioners.CMeans.CMeansPartitioner.build">(pyFTS.partitioners.CMeans.CMeansPartitioner method)</a>
 </li>
@ -216,7 +216,7 @@
      </ul></li>
  </ul></td>
  <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="pyFTS.partitioners.html#pyFTS.partitioners.ClassPartitioner.ClassPartitioner">ClassPartitioner (class in pyFTS.partitioners.ClassPartitioner)</a>
+      <li><a href="pyFTS.partitioners.html#pyFTS.partitioners.Class.ClassPartitioner">ClassPartitioner (class in pyFTS.partitioners.Class)</a>
 </li>
      <li><a href="pyFTS.common.html#pyFTS.common.SortedCollection.SortedCollection.clear">clear() (pyFTS.common.SortedCollection.SortedCollection method)</a>
 </li>
@ -876,7 +876,7 @@
 </li>
        <li><a href="pyFTS.partitioners.html#module-pyFTS.partitioners">pyFTS.partitioners</a>
 </li>
-        <li><a href="pyFTS.partitioners.html#module-pyFTS.partitioners.ClassPartitioner">pyFTS.partitioners.ClassPartitioner</a>
+        <li><a href="pyFTS.partitioners.html#module-pyFTS.partitioners.Class">pyFTS.partitioners.Class</a>
 </li>
        <li><a href="pyFTS.partitioners.html#module-pyFTS.partitioners.CMeans">pyFTS.partitioners.CMeans</a>
 </li>
@ -1468,10 +1468,10 @@
 </li>
      </ul></li>
      <li>
-    pyFTS.partitioners.ClassPartitioner
+    pyFTS.partitioners.Class

      <ul>
-        <li><a href="pyFTS.partitioners.html#module-pyFTS.partitioners.ClassPartitioner">module</a>
+        <li><a href="pyFTS.partitioners.html#module-pyFTS.partitioners.Class">module</a>
 </li>
      </ul></li>
      <li>
--- a/docs/build/html/objects.inv
+++ b/docs/build/html/objects.inv
--- a/docs/build/html/py-modindex.html
+++ b/docs/build/html/py-modindex.html
@ -389,7 +389,7 @@
     <tr class="cg-1">
       <td></td>
       <td>&#160;&#160;&#160;
-       <a href="pyFTS.partitioners.html#module-pyFTS.partitioners.ClassPartitioner"><code class="xref">pyFTS.partitioners.ClassPartitioner</code></a></td><td>
+       <a href="pyFTS.partitioners.html#module-pyFTS.partitioners.Class"><code class="xref">pyFTS.partitioners.Class</code></a></td><td>
       <em></em></td></tr>
     <tr class="cg-1">
       <td></td>
--- a/docs/build/html/pyFTS.html
+++ b/docs/build/html/pyFTS.html
@ -221,7 +221,7 @@
 <li class="toctree-l2"><a class="reference internal" href="pyFTS.partitioners.html#module-pyFTS.partitioners">Module contents</a></li>
 <li class="toctree-l2"><a class="reference internal" href="pyFTS.partitioners.html#submodules">Submodules</a></li>
 <li class="toctree-l2"><a class="reference internal" href="pyFTS.partitioners.html#module-pyFTS.partitioners.partitioner">pyFTS.partitioners.partitioner module</a></li>
-<li class="toctree-l2"><a class="reference internal" href="pyFTS.partitioners.html#pyfts-partitioners-classpartitioner-module">pyFTS.partitioners.ClassPartitioner module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="pyFTS.partitioners.html#module-pyFTS.partitioners.Class">pyFTS.partitioners.Class module</a></li>
 <li class="toctree-l2"><a class="reference internal" href="pyFTS.partitioners.html#module-pyFTS.partitioners.CMeans">pyFTS.partitioners.CMeans module</a></li>
 <li class="toctree-l2"><a class="reference internal" href="pyFTS.partitioners.html#module-pyFTS.partitioners.Entropy">pyFTS.partitioners.Entropy module</a></li>
 <li class="toctree-l2"><a class="reference internal" href="pyFTS.partitioners.html#module-pyFTS.partitioners.FCM">pyFTS.partitioners.FCM module</a></li>
--- a/docs/build/html/pyFTS.partitioners.html
+++ b/docs/build/html/pyFTS.partitioners.html
@ -299,17 +299,17 @@ overlapped fuzzy sets.</p>
 </dd></dl>

 </section>
-<section id="pyfts-partitioners-classpartitioner-module">
-<h2>pyFTS.partitioners.ClassPartitioner module<a class="headerlink" href="#pyfts-partitioners-classpartitioner-module" title="Permalink to this headline">¶</a></h2>
-<span class="target" id="module-pyFTS.partitioners.ClassPartitioner"></span><p>Class Partitioner with Singleton Fuzzy Sets</p>
+<section id="module-pyFTS.partitioners.Class">
+<span id="pyfts-partitioners-class-module"></span><h2>pyFTS.partitioners.Class module<a class="headerlink" href="#module-pyFTS.partitioners.Class" title="Permalink to this headline">¶</a></h2>
+<p>Class Partitioner with Singleton Fuzzy Sets</p>
 <dl class="py class">
-<dt class="sig sig-object py" id="pyFTS.partitioners.ClassPartitioner.ClassPartitioner">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">pyFTS.partitioners.ClassPartitioner.</span></span><span class="sig-name descname"><span class="pre">ClassPartitioner</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/partitioners/ClassPartitioner.html#ClassPartitioner"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.partitioners.ClassPartitioner.ClassPartitioner" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="pyFTS.partitioners.Class.ClassPartitioner">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">pyFTS.partitioners.Class.</span></span><span class="sig-name descname"><span class="pre">ClassPartitioner</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/partitioners/Class.html#ClassPartitioner"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.partitioners.Class.ClassPartitioner" title="Permalink to this definition">¶</a></dt>
 <dd><p>Bases: <a class="reference internal" href="#pyFTS.partitioners.partitioner.Partitioner" title="pyFTS.partitioners.partitioner.Partitioner"><code class="xref py py-class docutils literal notranslate"><span class="pre">pyFTS.partitioners.partitioner.Partitioner</span></code></a></p>
 <p>Class Partitioner: Given a dictionary with class/values pairs, create singleton fuzzy sets for each class</p>
 <dl class="py method">
-<dt class="sig sig-object py" id="pyFTS.partitioners.ClassPartitioner.ClassPartitioner.build">
-<span class="sig-name descname"><span class="pre">build</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><span class="pre">list</span></a></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/partitioners/ClassPartitioner.html#ClassPartitioner.build"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.partitioners.ClassPartitioner.ClassPartitioner.build" title="Permalink to this definition">¶</a></dt>
+<dt class="sig sig-object py" id="pyFTS.partitioners.Class.ClassPartitioner.build">
+<span class="sig-name descname"><span class="pre">build</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.11)"><span class="pre">list</span></a></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/pyFTS/partitioners/Class.html#ClassPartitioner.build"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pyFTS.partitioners.Class.ClassPartitioner.build" title="Permalink to this definition">¶</a></dt>
 <dd><p>Perform the partitioning of the Universe of Discourse</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
@ -604,7 +604,7 @@ Comput. Math. Appl., vol. 56, no. 12, pp. 3052–3063, Dec. 2008. DOI: 10.1016/j
 <li><a class="reference internal" href="#module-pyFTS.partitioners">Module contents</a></li>
 <li><a class="reference internal" href="#submodules">Submodules</a></li>
 <li><a class="reference internal" href="#module-pyFTS.partitioners.partitioner">pyFTS.partitioners.partitioner module</a></li>
-<li><a class="reference internal" href="#pyfts-partitioners-classpartitioner-module">pyFTS.partitioners.ClassPartitioner module</a></li>
+<li><a class="reference internal" href="#module-pyFTS.partitioners.Class">pyFTS.partitioners.Class module</a></li>
 <li><a class="reference internal" href="#module-pyFTS.partitioners.CMeans">pyFTS.partitioners.CMeans module</a></li>
 <li><a class="reference internal" href="#module-pyFTS.partitioners.Entropy">pyFTS.partitioners.Entropy module</a></li>
 <li><a class="reference internal" href="#module-pyFTS.partitioners.FCM">pyFTS.partitioners.FCM module</a></li>
--- a/docs/build/html/searchindex.js
+++ b/docs/build/html/searchindex.js
--- a/docs/pyFTS.partitioners.rst
+++ b/docs/pyFTS.partitioners.rst
@ -20,18 +20,18 @@ pyFTS.partitioners.partitioner module
    :undoc-members:
    :show-inheritance:

-pyFTS.partitioners.ClassPartitioner module
-----------------------------------
+pyFTS.partitioners.Class module
+-------------------------------

-.. automodule:: pyFTS.partitioners.ClassPartitioner
+.. automodule:: pyFTS.partitioners.Class
    :members:
    :undoc-members:
    :show-inheritance:

-pyFTS.partitioners.CMeans module
+pyFTS.partitioners.KMeans module
 --------------------------------

-.. automodule:: pyFTS.partitioners.CMeans
+.. automodule:: pyFTS.partitioners.KMeans
    :members:
    :undoc-members:
    :show-inheritance:
--- a/pyFTS/common/Activations.py
+++ b/pyFTS/common/Activations.py
@ -6,14 +6,14 @@ import numpy as np
 import math
 from pyFTS import *

-def scale(dist : dict) -> dict:
+def scale(dist : dict, weights : dict) -> dict:
  norm = np.sum([v for v in dist.values()])
-  return {k : (v / norm) for k,v in dist.items() }
+  return {k : ((v * weights[k]) / norm) for k,v in dist.items() }

-def softmax(dist : dict) -> dict:
+def softmax(dist : dict, weights : dict) -> dict:
  norm = np.sum([np.exp(v) for v in dist.values()])
-  return {k : (np.exp(v) / norm) for k,v in dist.items() }
+  return {k : (np.exp(v * weights[k]) / norm) for k,v in dist.items() }

-def argmax(dist : dict) -> str:
-  mx = np.max([v for v in dist.values()])
-  return [k for k,v in dist.items() if v == mx ][0]
+def argmax(dist : dict, weights : dict) -> str:
+  mx = np.max([v * weights[k] for k,v in dist.items()])
+  return [k for k,v in dist.items() if v * weights[k] == mx  ][0]
--- a/pyFTS/common/transformations/trend.py
+++ b/pyFTS/common/transformations/trend.py
@ -1,5 +1,5 @@
 from pyFTS.common.transformations.transformation import Transformation 
-from pandas import datetime
+# from pandas import datetime
 from sklearn.linear_model import LinearRegression
 import numpy as np
 import pandas as pd
--- a/pyFTS/models/multivariate/cmvfts.py
+++ b/pyFTS/models/multivariate/cmvfts.py
@ -146,7 +146,7 @@ class ClusteredMVFTS(mvfts.MVFTS):

            new_data_point[self.target_variable.data_label] = tmp.expected_value()

-            sample = sample.append(new_data_point, ignore_index=True)
+            sample = pd.concat([sample, pd.DataFrame([new_data_point])], ignore_index=True)

        return ret[-steps:]

@ -199,7 +199,7 @@ class ClusteredMVFTS(mvfts.MVFTS):
        for k in np.arange(0, steps):
            sample = ret.iloc[k:self.order+k]
            tmp = self.forecast_multivariate(sample, **kwargs)
-            ret = ret.append(tmp, ignore_index=True)
+            ret = pd.concat([ret, pd.DataFrame([tmp])], ignore_index=True)

        return ret

--- a/pyFTS/models/multivariate/mvfts.py
+++ b/pyFTS/models/multivariate/mvfts.py
@ -211,7 +211,7 @@ class MVFTS(fts.FTS):

            new_data_point[self.target_variable.data_label] = tmp

-            ndata = ndata.append(new_data_point, ignore_index=True)
+            ndata = pd.concat([ndata, pd.DataFrame([new_data_point])], ignore_index=True)

        return ret[-steps:]

@ -307,8 +307,8 @@ class MVFTS(fts.FTS):
            new_data_point_lo[self.target_variable.data_label] = min(tmp_lo)
            new_data_point_up[self.target_variable.data_label] = max(tmp_up)

-            lo = lo.append(new_data_point_lo, ignore_index=True)
-            up = up.append(new_data_point_up, ignore_index=True)
+            lo = pd.concat([lo, pd.DataFrame([new_data_point_lo])], ignore_index=True)
+            up = pd.concat([up, pd.DataFrame([new_data_point_up])], ignore_index=True)

        return ret[-steps:]

--- a/pyFTS/models/multivariate/wmvfts.py
+++ b/pyFTS/models/multivariate/wmvfts.py
@ -69,6 +69,8 @@ class WeightedMVFTS(mvfts.MVFTS):
        self.shortname = "WeightedMVFTS"
        self.name = "Weighted Multivariate FTS"
        self.has_classification = True
+        self.class_weights : dict = kwargs.get("class_weights", {})
+        

    def generate_flrg(self, flrs):
        for flr in flrs:
@ -80,6 +82,8 @@ class WeightedMVFTS(mvfts.MVFTS):
            self.flrgs[flrg.get_key()].append_rhs(flr.RHS)

    def classify(self, data, **kwargs):
+        if len(self.class_weights) == 0:
+            self.class_weights = {k : 1.0 for k in self.target_variable.partitioner.sets.keys()}
        ret = []
        ndata = self.apply_transformations(data)
        activation = kwargs.get('activation', Activations.scale)
@ -98,7 +102,7 @@ class WeightedMVFTS(mvfts.MVFTS):
                    for k,v in _flrg.RHS.items():
                        classification[k] += (v / _flrg.count) * mb
                
-            classification = activation(classification)
+            classification = activation(classification, self.class_weights)

            ret.append(classification)

--- a/pyFTS/partitioners/ClassPartitioner.py
+++ b/pyFTS/partitioners/ClassPartitioner.py
--- a/pyFTS/partitioners/FCM.py
+++ b/pyFTS/partitioners/FCM.py
@ -36,7 +36,7 @@ def fuzzy_cmeans(k, data, size, m, deltadist=0.001):
    centroids = [data[rnd.randint(0, data_length - 1)] for kk in range(0, k)]

    # Membership table
-    membership_table = np.zeros((k, data_length)) #[[0 for kk in range(0, k)] for xx in range(0, data_length)]
+    membership_table = np.zeros((data_length, k))

    mean_change = 1000

@ -50,12 +50,12 @@ def fuzzy_cmeans(k, data, size, m, deltadist=0.001):
        inst_count = 0
        for instance in data:

-            dist_groups = np.zeros(k) #[0 for xx in range(0, k)]
+            dist_groups = np.zeros(k)

            for group_count, group in enumerate(centroids):
                dist_groups[group_count] = fuzzy_distance(group, instance)

-            dist_groups_total = functools.reduce(operator.add, [xk for xk in dist_groups])
+            # dist_groups_total = functools.reduce(operator.add, [xk for xk in dist_groups])

            for grp in range(0, k):
                if dist_groups[grp] == 0:
--- a/pyFTS/partitioners/Huarng.py
+++ b/pyFTS/partitioners/Huarng.py
@ -19,13 +19,13 @@ class HuarngPartitioner(partitioner.Partitioner):
    def build(self, data):
        diff = Transformations.Differential(1)
        data2 = diff.apply(data)
-        davg = np.abs( np.mean(data2) / 2 )
+        divs = np.abs( np.mean(data2) / 2 )

-        if davg <= 1.0:
+        if divs <= 1.0:
            base = 0.1
-        elif 1 < davg <= 10:
+        elif 1 < divs <= 10:
            base = 1.0
-        elif 10 < davg <= 100:
+        elif 10 < divs <= 100:
            base = 10
        else:
            base = 100
--- a/pyFTS/partitioners/KMeans.py
+++ b/pyFTS/partitioners/KMeans.py
@ -14,15 +14,15 @@ def distance(x, y):
    return math.sqrt(tmp)


-def c_means(k, dados, tam):
-    # Inicializa as centróides escolhendo elementos aleatórios dos conjuntos
+def k_means(k, dados, tam):
+    # Инициализирует центроиды, выбирая случайные элементы из множества
    centroides = [dados[rnd.randint(0, len(dados)-1)] for kk in range(0, k)]

    grupos = [-1 for x in range(0, len(dados))]

    it_semmodificacao = 0

-    # para cada instância
+    # для каждого экземпляра
    iteracoes = 0
    while iteracoes < 1000 and it_semmodificacao < 10:
        inst_count = 0
@ -31,7 +31,7 @@ def c_means(k, dados, tam):

        for instancia in dados:

-            # verifica a distância para cada centroide
+            # проверяет расстояние до каждого центроида
            grupo_count = 0
            dist = 10000

@ -41,7 +41,7 @@ def c_means(k, dados, tam):
                tmp = distance(instancia, grupo)
                if tmp < dist:
                    dist = tmp
-                    # associa a a centroide de menor distância à instância
+                    # ассоциирует центроид с наименьшим расстоянием до экземпляра
                    grupos[inst_count] = grupo_count
                grupo_count = grupo_count + 1

@ -55,7 +55,7 @@ def c_means(k, dados, tam):
        else:
            it_semmodificacao = 0

-        # atualiza cada centroide com base nos valores médios de todas as instâncias à ela associadas
+        # обновляет каждый центроид на основе средних значений всех связанных с ним экземпляров
        grupo_count = 0
        for grupo in centroides:
            total_inst = functools.reduce(operator.add, [1 for xx in grupos if xx == grupo_count], 0)
@ -77,21 +77,21 @@ def c_means(k, dados, tam):
    return centroides


-class CMeansPartitioner(partitioner.Partitioner):
+class KMeansPartitioner(partitioner.Partitioner):
    def __init__(self, **kwargs):
-        super(CMeansPartitioner, self).__init__(name="CMeans", **kwargs)
+        super(KMeansPartitioner, self).__init__(name="KMeans", **kwargs)

    def build(self, data):
        sets = {}

        kwargs = {'type': self.type, 'variable': self.variable}

-        centroides = c_means(self.partitions, data, 1)
+        centroides = k_means(self.partitions, data, 1)
        centroides.append(self.max)
        centroides.append(self.min)
        centroides = list(set(centroides))
        centroides.sort()
-        for c in np.arange(1, len(centroides) - 1):
+        for c in range(1, len(centroides) - 1):
            _name = self.get_name(c)
            sets[_name] = FuzzySet.FuzzySet(_name, Membership.trimf,
                                 [round(centroides[c - 1], 3), round(centroides[c], 3), round(centroides[c + 1], 3)],
--- a/pyFTS/partitioners/Util.py
+++ b/pyFTS/partitioners/Util.py
@ -18,19 +18,20 @@ all_methods = [Grid.GridPartitioner, Entropy.EntropyPartitioner, FCM.FCMPartitio
 mfs = [Membership.trimf, Membership.gaussmf, Membership.trapmf]


-def plot_sets(data, sets: dict, titles : list, size=[12, 10], save=False, file=None, axis=None):
+def plot_sets(sets: dict, titles : list, size=[12, 10], save=False, file=None, axis=None):
    """
    Plot all fuzzy sets in a Partitioner
    
    """
    num = len(sets)
+    num_cols_plot = 1

    if axis is None:
-        fig, axes = plt.subplots(nrows=num, ncols=1,figsize=size)
-    for k in np.arange(0,num):
+        fig, axes = plt.subplots(nrows=num, ncols=num_cols_plot, figsize=size, squeeze=False)
+    for k in range(num):
        ticks = []
        x = []
-        ax = axes[k] if axis is None else axis
+        ax = axes[k, num_cols_plot-1] if axis is None else axis
        ax.set_title(titles[k])
        ax.set_ylim([0, 1.1])
        for key in sets[k].keys():
@ -54,7 +55,7 @@ def plot_sets(data, sets: dict, titles : list, size=[12, 10], save=False, file=N
        Util.show_and_save_image(fig, file, save)


-def plot_partitioners(data, objs, tam=[12, 10], save=False, file=None, axis=None):
+def plot_partitioners(objs, tam=[12, 10], save=False, file=None, axis=None):
    sets = [k.sets for k in objs]
    titles = [k.name for k in objs]
    plot_sets(sets, titles, tam, save, file, axis)
--- a/pyFTS/partitioners/partitioner.py
+++ b/pyFTS/partitioners/partitioner.py
@ -1,6 +1,7 @@
 from pyFTS.common import FuzzySet, Membership
 import numpy as np
 from scipy.spatial import KDTree
+import warnings


 class Partitioner(object):
@ -46,6 +47,9 @@ class Partitioner(object):

            data = kwargs.get('data',[None])
            
+            if isinstance(data, np.ndarray) and len(data.shape) > 1:
+                warnings.warn(f"An ndarray of dimension greater than 1 is used. shape.len(): {len(data.shape)}")
+
            if self.indexer is not None:
                ndata = self.indexer.get_data(data)
            else:
--- a/pyFTS/tests/cmsfts.py
+++ b/pyFTS/tests/cmsfts.py
@ -11,7 +11,7 @@ from mpl_toolkits.mplot3d import Axes3D
 import datetime

 import pandas as pd
-from pyFTS.partitioners import Grid, CMeans, FCM, Entropy
+from pyFTS.partitioners import Grid, KMeans, FCM, Entropy
 from pyFTS.common import FLR, FuzzySet, Membership, Transformations, Util, fts
 from pyFTS import sfts
 from pyFTS.models import msfts
--- a/setup.py
+++ b/setup.py
@ -25,6 +25,8 @@ setuptools.setup(
        'Programming Language :: Python :: 3.5',
        'Programming Language :: Python :: 3.6',
        'Programming Language :: Python :: 3.8',
+        'Programming Language :: Python :: 3.10',
+        'Programming Language :: Python :: 3.11',
        'Intended Audience :: Science/Research',
        'Intended Audience :: Developers',
        'Intended Audience :: Education',
Author	SHA1	Message	Date
sam	4b348ad818	replacing the deprecated append method with the concat method in pandas	2024-08-14 12:08:26 +04:00
sam	eac7a83265	replacing the deprecated append method with the concat method in pandas	2024-08-14 11:57:24 +04:00
sam	ccc0196f56	adding a check for the ndarray data type	2024-08-14 11:55:54 +04:00
sam	3ec1b4c5ce	#3 rename variable	2024-08-08 12:53:02 +04:00
sam	a8fb849bc8	#2 Index order is mixed up (transposed array)	2024-08-08 12:00:51 +04:00
sam	8be936e383	return work code	2024-08-08 11:59:33 +04:00
sam	e1f72797ea	Warning: An ndarray of dimension greater than 1 is used	2024-08-08 11:59:14 +04:00
sam	e55ef29351	Repair: Use of a single partitioned	2024-08-08 11:58:38 +04:00
sam	619c6ecd15	rename KMeans	2024-08-07 11:19:01 +04:00
sam	fac2aa5ca8	add python 3.11 support	2024-08-07 11:18:21 +04:00
sam	0329e7b83f	copy .gitignore to root	2024-08-07 10:30:46 +04:00
sam	e9d5f7629f	Delete an unnecessary variable "data"	2024-08-07 10:26:48 +04:00
sam	78e63aaa63	Update portugalian on russian	2024-08-07 10:26:07 +04:00
sam	c24ebe6b81	remove np.array in cMeans	2024-08-06 15:18:31 +04:00
Anton Skalkin	47e78bc066	remove "from pandas import datetime"	2024-08-06 13:19:08 +04:00
Petrônio Cândido de Lima e Silva	4e0ee7cdd9	Update wmvfts.py	2023-05-26 14:44:46 -03:00
Petrônio Cândido de Lima e Silva	afc9b219ce	Update wmvfts.py - class_weights	2023-05-26 14:24:41 -03:00
Petrônio Cândido de Lima e Silva	560dcfacf6	Update Activations.py - weights	2023-05-26 14:21:12 -03:00
Petrônio Cândido de Lima e Silva	b8cfea5278	Update wmvfts.py - class_weights	2023-05-26 14:13:41 -03:00
Petrônio Cândido	02f6022a53	Refactoring and documentation updates	2023-05-24 20:18:52 -03:00