sam/pyFTS
1
0
Fork 0
Code Issues 3 Pull Requests Actions Packages Projects Releases Wiki Activity

Compare commits

base: sam:master
Branches Tags
sam:master
sam:petroniocandido-patch-1
sam:classification
sam:release1.7
sam:autoencoders
sam:feature/som_fts
sam:pkg1.7
sam:pkg1.6
sam:pkg1.4
sam:docSphinx
sam:v4.0
sam:pkg1.2.3
sam:pkg1.2.2
sam:pkg1.2.1
sam:pkg1.1.1
sam:pkg1.1
sam:pkg1.0
sam:v3.0
sam:v2.0
sam:v1.0
..
compare: sam:classification
Branches Tags
sam:master
sam:petroniocandido-patch-1
sam:classification
sam:release1.7
sam:autoencoders
sam:feature/som_fts
sam:pkg1.7
sam:pkg1.6
sam:pkg1.4
sam:docSphinx
sam:v4.0
sam:pkg1.2.3
sam:pkg1.2.2
sam:pkg1.2.1
sam:pkg1.1.1
sam:pkg1.1
sam:pkg1.0
sam:v3.0
sam:v2.0
sam:v1.0

No commits in common. "master" and "classification" have entirely different histories.
master ... classifica

28 changed files with 64 additions and 258 deletions
Show Stats Expand all files Collapse all files

57
.gitignore vendored
View File

@ -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/

2
MANIFEST
View File

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

2
README.md
View File

@ -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: 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 - the number of intervals
- which fuzzy membership function (on [pyFTS.common.Membership](https://github.com/PYFTS/pyFTS/blob/master/pyFTS/common/Membership.py)) - 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. Check out the jupyter notebook on [notebooks/Partitioners.ipynb](https://github.com/PYFTS/notebooks/blob/master/Partitioners.ipynb) for sample codes.

BIN
docs/build/doctrees/environment.pickle vendored
View File

Binary file not shown.

BIN
docs/build/doctrees/pyFTS.partitioners.doctree vendored
View File

Binary file not shown.

2
docs/build/html/_modules/index.html vendored
View File

@ -94,7 +94,7 @@
<li><a href="pyFTS/models/seasonal/common.html">pyFTS.models.seasonal.common</a></li> <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/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/CMeans.html">pyFTS.partitioners.CMeans</a></li>
<li><a href="pyFTS/partitioners/Class.html">pyFTS.partitioners.Class</a></li> <li><a href="pyFTS/partitioners/ClassPartitioner.html">pyFTS.partitioners.ClassPartitioner</a></li>
<li><a href="pyFTS/partitioners/Entropy.html">pyFTS.partitioners.Entropy</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/FCM.html">pyFTS.partitioners.FCM</a></li>
<li><a href="pyFTS/partitioners/Grid.html">pyFTS.partitioners.Grid</a></li> <li><a href="pyFTS/partitioners/Grid.html">pyFTS.partitioners.Grid</a></li>

126
docs/build/html/_modules/pyFTS/partitioners/Class.html vendored
View File

@ -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.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" />
<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.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"/>
</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.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>

6
docs/build/html/_sources/pyFTS.partitioners.rst.txt vendored
View File

@ -20,10 +20,10 @@ pyFTS.partitioners.partitioner module
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
pyFTS.partitioners.Class module pyFTS.partitioners.ClassPartitioner module
------------------------------- -----------------------------------
.. automodule:: pyFTS.partitioners.Class .. automodule:: pyFTS.partitioners.ClassPartitioner
:members: :members:
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:

10
docs/build/html/genindex.html vendored
View File

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

BIN
docs/build/html/objects.inv vendored
View File

Binary file not shown.

2
docs/build/html/py-modindex.html vendored
View File

@ -389,7 +389,7 @@
<tr class="cg-1"> <tr class="cg-1">
<td></td> <td></td>
<td>&#160;&#160;&#160; <td>&#160;&#160;&#160;
<a href="pyFTS.partitioners.html#module-pyFTS.partitioners.Class"><code class="xref">pyFTS.partitioners.Class</code></a></td><td> <a href="pyFTS.partitioners.html#module-pyFTS.partitioners.ClassPartitioner"><code class="xref">pyFTS.partitioners.ClassPartitioner</code></a></td><td>
<em></em></td></tr> <em></em></td></tr>
<tr class="cg-1"> <tr class="cg-1">
<td></td> <td></td>

2
docs/build/html/pyFTS.html vendored
View File

@ -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#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#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#module-pyFTS.partitioners.partitioner">pyFTS.partitioners.partitioner 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#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.CMeans">pyFTS.partitioners.CMeans 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.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> <li class="toctree-l2"><a class="reference internal" href="pyFTS.partitioners.html#module-pyFTS.partitioners.FCM">pyFTS.partitioners.FCM module</a></li>

16
docs/build/html/pyFTS.partitioners.html vendored
View File

@ -299,17 +299,17 @@ overlapped fuzzy sets.</p>
</dd></dl> </dd></dl>
</section> </section>
<section id="module-pyFTS.partitioners.Class"> <section id="pyfts-partitioners-classpartitioner-module">
<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> <h2>pyFTS.partitioners.ClassPartitioner module<a class="headerlink" href="#pyfts-partitioners-classpartitioner-module" title="Permalink to this headline"></a></h2>
<p>Class Partitioner with Singleton Fuzzy Sets</p> <span class="target" id="module-pyFTS.partitioners.ClassPartitioner"></span><p>Class Partitioner with Singleton Fuzzy Sets</p>
<dl class="py class"> <dl class="py class">
<dt class="sig sig-object py" id="pyFTS.partitioners.Class.ClassPartitioner"> <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.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> <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>
<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> <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> <p>Class Partitioner: Given a dictionary with class/values pairs, create singleton fuzzy sets for each class</p>
<dl class="py method"> <dl class="py method">
<dt class="sig sig-object py" id="pyFTS.partitioners.Class.ClassPartitioner.build"> <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/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> <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>
<dd><p>Perform the partitioning of the Universe of Discourse</p> <dd><p>Perform the partitioning of the Universe of Discourse</p>
<dl class="field-list simple"> <dl class="field-list simple">
<dt class="field-odd">Parameters</dt> <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="#module-pyFTS.partitioners">Module contents</a></li>
<li><a class="reference internal" href="#submodules">Submodules</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="#module-pyFTS.partitioners.partitioner">pyFTS.partitioners.partitioner 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="#pyfts-partitioners-classpartitioner-module">pyFTS.partitioners.ClassPartitioner 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.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.Entropy">pyFTS.partitioners.Entropy module</a></li>
<li><a class="reference internal" href="#module-pyFTS.partitioners.FCM">pyFTS.partitioners.FCM module</a></li> <li><a class="reference internal" href="#module-pyFTS.partitioners.FCM">pyFTS.partitioners.FCM module</a></li>

2
docs/build/html/searchindex.js vendored
View File

File diff suppressed because one or more lines are too long

10
docs/pyFTS.partitioners.rst
View File

@ -20,18 +20,18 @@ pyFTS.partitioners.partitioner module
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
pyFTS.partitioners.Class module pyFTS.partitioners.ClassPartitioner module
------------------------------- -----------------------------------
.. automodule:: pyFTS.partitioners.Class .. automodule:: pyFTS.partitioners.ClassPartitioner
:members: :members:
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:
pyFTS.partitioners.KMeans module pyFTS.partitioners.CMeans module
-------------------------------- --------------------------------
.. automodule:: pyFTS.partitioners.KMeans .. automodule:: pyFTS.partitioners.CMeans
:members: :members:
:undoc-members: :undoc-members:
:show-inheritance: :show-inheritance:

14
pyFTS/common/Activations.py
View File

@ -6,14 +6,14 @@ import numpy as np
import math import math
from pyFTS import * from pyFTS import *
def scale(dist : dict, weights : dict) -> dict: def scale(dist : dict) -> dict:
norm = np.sum([v for v in dist.values()]) norm = np.sum([v for v in dist.values()])
return {k : ((v * weights[k]) / norm) for k,v in dist.items() } return {k : (v / norm) for k,v in dist.items() }
def softmax(dist : dict, weights : dict) -> dict: def softmax(dist : dict) -> dict:
norm = np.sum([np.exp(v) for v in dist.values()]) norm = np.sum([np.exp(v) for v in dist.values()])
return {k : (np.exp(v * weights[k]) / norm) for k,v in dist.items() } return {k : (np.exp(v) / norm) for k,v in dist.items() }
def argmax(dist : dict, weights : dict) -> str: def argmax(dist : dict) -> str:
mx = np.max([v * weights[k] for k,v in dist.items()]) mx = np.max([v for v in dist.values()])
return [k for k,v in dist.items() if v * weights[k] == mx ][0] return [k for k,v in dist.items() if v == mx ][0]

2
pyFTS/common/transformations/trend.py
View File

@ -1,5 +1,5 @@
from pyFTS.common.transformations.transformation import Transformation from pyFTS.common.transformations.transformation import Transformation
# from pandas import datetime from pandas import datetime
from sklearn.linear_model import LinearRegression from sklearn.linear_model import LinearRegression
import numpy as np import numpy as np
import pandas as pd import pandas as pd

4
pyFTS/models/multivariate/cmvfts.py
View File

@ -146,7 +146,7 @@ class ClusteredMVFTS(mvfts.MVFTS):
new_data_point[self.target_variable.data_label] = tmp.expected_value() new_data_point[self.target_variable.data_label] = tmp.expected_value()
sample = pd.concat([sample, pd.DataFrame([new_data_point])], ignore_index=True) sample = sample.append(new_data_point, ignore_index=True)
return ret[-steps:] return ret[-steps:]
@ -199,7 +199,7 @@ class ClusteredMVFTS(mvfts.MVFTS):
for k in np.arange(0, steps): for k in np.arange(0, steps):
sample = ret.iloc[k:self.order+k] sample = ret.iloc[k:self.order+k]
tmp = self.forecast_multivariate(sample, **kwargs) tmp = self.forecast_multivariate(sample, **kwargs)
ret = pd.concat([ret, pd.DataFrame([tmp])], ignore_index=True) ret = ret.append(tmp, ignore_index=True)
return ret return ret

6
pyFTS/models/multivariate/mvfts.py
View File

@ -211,7 +211,7 @@ class MVFTS(fts.FTS):
new_data_point[self.target_variable.data_label] = tmp new_data_point[self.target_variable.data_label] = tmp
ndata = pd.concat([ndata, pd.DataFrame([new_data_point])], ignore_index=True) ndata = ndata.append(new_data_point, ignore_index=True)
return ret[-steps:] 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_lo[self.target_variable.data_label] = min(tmp_lo)
new_data_point_up[self.target_variable.data_label] = max(tmp_up) new_data_point_up[self.target_variable.data_label] = max(tmp_up)
lo = pd.concat([lo, pd.DataFrame([new_data_point_lo])], ignore_index=True) lo = lo.append(new_data_point_lo, ignore_index=True)
up = pd.concat([up, pd.DataFrame([new_data_point_up])], ignore_index=True) up = up.append(new_data_point_up, ignore_index=True)
return ret[-steps:] return ret[-steps:]

6
pyFTS/models/multivariate/wmvfts.py
View File

@ -69,8 +69,6 @@ class WeightedMVFTS(mvfts.MVFTS):
self.shortname = "WeightedMVFTS" self.shortname = "WeightedMVFTS"
self.name = "Weighted Multivariate FTS" self.name = "Weighted Multivariate FTS"
self.has_classification = True self.has_classification = True
self.class_weights : dict = kwargs.get("class_weights", {})
def generate_flrg(self, flrs): def generate_flrg(self, flrs):
for flr in flrs: for flr in flrs:
@ -82,8 +80,6 @@ class WeightedMVFTS(mvfts.MVFTS):
self.flrgs[flrg.get_key()].append_rhs(flr.RHS) self.flrgs[flrg.get_key()].append_rhs(flr.RHS)
def classify(self, data, **kwargs): 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 = [] ret = []
ndata = self.apply_transformations(data) ndata = self.apply_transformations(data)
activation = kwargs.get('activation', Activations.scale) activation = kwargs.get('activation', Activations.scale)
@ -102,7 +98,7 @@ class WeightedMVFTS(mvfts.MVFTS):
for k,v in _flrg.RHS.items(): for k,v in _flrg.RHS.items():
classification[k] += (v / _flrg.count) * mb classification[k] += (v / _flrg.count) * mb
classification = activation(classification, self.class_weights) classification = activation(classification)
ret.append(classification) ret.append(classification)

20
pyFTS/partitioners/KMeans.py → pyFTS/partitioners/CMeans.py
View File

@ -14,15 +14,15 @@ def distance(x, y):
return math.sqrt(tmp) return math.sqrt(tmp)
def k_means(k, dados, tam): def c_means(k, dados, tam):
# Инициализирует центроиды, выбирая случайные элементы из множества # Inicializa as centróides escolhendo elementos aleatórios dos conjuntos
centroides = [dados[rnd.randint(0, len(dados)-1)] for kk in range(0, k)] centroides = [dados[rnd.randint(0, len(dados)-1)] for kk in range(0, k)]
grupos = [-1 for x in range(0, len(dados))] grupos = [-1 for x in range(0, len(dados))]
it_semmodificacao = 0 it_semmodificacao = 0
# для каждого экземпляра # para cada instância
iteracoes = 0 iteracoes = 0
while iteracoes < 1000 and it_semmodificacao < 10: while iteracoes < 1000 and it_semmodificacao < 10:
inst_count = 0 inst_count = 0
@ -31,7 +31,7 @@ def k_means(k, dados, tam):
for instancia in dados: for instancia in dados:
# проверяет расстояние до каждого центроида # verifica a distância para cada centroide
grupo_count = 0 grupo_count = 0
dist = 10000 dist = 10000
@ -41,7 +41,7 @@ def k_means(k, dados, tam):
tmp = distance(instancia, grupo) tmp = distance(instancia, grupo)
if tmp < dist: if tmp < dist:
dist = tmp dist = tmp
# ассоциирует центроид с наименьшим расстоянием до экземпляра # associa a a centroide de menor distância à instância
grupos[inst_count] = grupo_count grupos[inst_count] = grupo_count
grupo_count = grupo_count + 1 grupo_count = grupo_count + 1
@ -55,7 +55,7 @@ def k_means(k, dados, tam):
else: else:
it_semmodificacao = 0 it_semmodificacao = 0
# обновляет каждый центроид на основе средних значений всех связанных с ним экземпляров # atualiza cada centroide com base nos valores médios de todas as instâncias à ela associadas
grupo_count = 0 grupo_count = 0
for grupo in centroides: for grupo in centroides:
total_inst = functools.reduce(operator.add, [1 for xx in grupos if xx == grupo_count], 0) total_inst = functools.reduce(operator.add, [1 for xx in grupos if xx == grupo_count], 0)
@ -77,21 +77,21 @@ def k_means(k, dados, tam):
return centroides return centroides
class KMeansPartitioner(partitioner.Partitioner): class CMeansPartitioner(partitioner.Partitioner):
def __init__(self, **kwargs): def __init__(self, **kwargs):
super(KMeansPartitioner, self).__init__(name="KMeans", **kwargs) super(CMeansPartitioner, self).__init__(name="CMeans", **kwargs)
def build(self, data): def build(self, data):
sets = {} sets = {}
kwargs = {'type': self.type, 'variable': self.variable} kwargs = {'type': self.type, 'variable': self.variable}
centroides = k_means(self.partitions, data, 1) centroides = c_means(self.partitions, data, 1)
centroides.append(self.max) centroides.append(self.max)
centroides.append(self.min) centroides.append(self.min)
centroides = list(set(centroides)) centroides = list(set(centroides))
centroides.sort() centroides.sort()
for c in range(1, len(centroides) - 1): for c in np.arange(1, len(centroides) - 1):
_name = self.get_name(c) _name = self.get_name(c)
sets[_name] = FuzzySet.FuzzySet(_name, Membership.trimf, sets[_name] = FuzzySet.FuzzySet(_name, Membership.trimf,
[round(centroides[c - 1], 3), round(centroides[c], 3), round(centroides[c + 1], 3)], [round(centroides[c - 1], 3), round(centroides[c], 3), round(centroides[c + 1], 3)],

0
pyFTS/partitioners/Class.py → pyFTS/partitioners/ClassPartitioner.py
View File

6
pyFTS/partitioners/FCM.py
View File

@ -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)] centroids = [data[rnd.randint(0, data_length - 1)] for kk in range(0, k)]
# Membership table # Membership table
membership_table = np.zeros((data_length, k)) membership_table = np.zeros((k, data_length)) #[[0 for kk in range(0, k)] for xx in range(0, data_length)]
mean_change = 1000 mean_change = 1000
@ -50,12 +50,12 @@ def fuzzy_cmeans(k, data, size, m, deltadist=0.001):
inst_count = 0 inst_count = 0
for instance in data: for instance in data:
dist_groups = np.zeros(k) dist_groups = np.zeros(k) #[0 for xx in range(0, k)]
for group_count, group in enumerate(centroids): for group_count, group in enumerate(centroids):
dist_groups[group_count] = fuzzy_distance(group, instance) 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): for grp in range(0, k):
if dist_groups[grp] == 0: if dist_groups[grp] == 0:

8
pyFTS/partitioners/Huarng.py
View File

@ -19,13 +19,13 @@ class HuarngPartitioner(partitioner.Partitioner):
def build(self, data): def build(self, data):
diff = Transformations.Differential(1) diff = Transformations.Differential(1)
data2 = diff.apply(data) data2 = diff.apply(data)
divs = np.abs( np.mean(data2) / 2 ) davg = np.abs( np.mean(data2) / 2 )
if divs <= 1.0: if davg <= 1.0:
base = 0.1 base = 0.1
elif 1 < divs <= 10: elif 1 < davg <= 10:
base = 1.0 base = 1.0
elif 10 < divs <= 100: elif 10 < davg <= 100:
base = 10 base = 10
else: else:
base = 100 base = 100

11
pyFTS/partitioners/Util.py
View File

@ -18,20 +18,19 @@ all_methods = [Grid.GridPartitioner, Entropy.EntropyPartitioner, FCM.FCMPartitio
mfs = [Membership.trimf, Membership.gaussmf, Membership.trapmf] mfs = [Membership.trimf, Membership.gaussmf, Membership.trapmf]
def plot_sets(sets: dict, titles : list, size=[12, 10], save=False, file=None, axis=None): def plot_sets(data, sets: dict, titles : list, size=[12, 10], save=False, file=None, axis=None):
""" """
Plot all fuzzy sets in a Partitioner Plot all fuzzy sets in a Partitioner
""" """
num = len(sets) num = len(sets)
num_cols_plot = 1
if axis is None: if axis is None:
fig, axes = plt.subplots(nrows=num, ncols=num_cols_plot, figsize=size, squeeze=False) fig, axes = plt.subplots(nrows=num, ncols=1,figsize=size)
for k in range(num): for k in np.arange(0,num):
ticks = [] ticks = []
x = [] x = []
ax = axes[k, num_cols_plot-1] if axis is None else axis ax = axes[k] if axis is None else axis
ax.set_title(titles[k]) ax.set_title(titles[k])
ax.set_ylim([0, 1.1]) ax.set_ylim([0, 1.1])
for key in sets[k].keys(): for key in sets[k].keys():
@ -55,7 +54,7 @@ def plot_sets(sets: dict, titles : list, size=[12, 10], save=False, file=None, a
Util.show_and_save_image(fig, file, save) Util.show_and_save_image(fig, file, save)
def plot_partitioners(objs, tam=[12, 10], save=False, file=None, axis=None): def plot_partitioners(data, objs, tam=[12, 10], save=False, file=None, axis=None):
sets = [k.sets for k in objs] sets = [k.sets for k in objs]
titles = [k.name for k in objs] titles = [k.name for k in objs]
plot_sets(sets, titles, tam, save, file, axis) plot_sets(sets, titles, tam, save, file, axis)

4
pyFTS/partitioners/partitioner.py
View File

@ -1,7 +1,6 @@
from pyFTS.common import FuzzySet, Membership from pyFTS.common import FuzzySet, Membership
import numpy as np import numpy as np
from scipy.spatial import KDTree from scipy.spatial import KDTree
import warnings
class Partitioner(object): class Partitioner(object):
@ -46,9 +45,6 @@ class Partitioner(object):
if kwargs.get('preprocess',True): if kwargs.get('preprocess',True):
data = kwargs.get('data',[None]) 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: if self.indexer is not None:
ndata = self.indexer.get_data(data) ndata = self.indexer.get_data(data)

2
pyFTS/tests/cmsfts.py
View File

@ -11,7 +11,7 @@ from mpl_toolkits.mplot3d import Axes3D
import datetime import datetime
import pandas as pd import pandas as pd
from pyFTS.partitioners import Grid, KMeans, FCM, Entropy from pyFTS.partitioners import Grid, CMeans, FCM, Entropy
from pyFTS.common import FLR, FuzzySet, Membership, Transformations, Util, fts from pyFTS.common import FLR, FuzzySet, Membership, Transformations, Util, fts
from pyFTS import sfts from pyFTS import sfts
from pyFTS.models import msfts from pyFTS.models import msfts

2
setup.py
View File

@ -25,8 +25,6 @@ setuptools.setup(
'Programming Language :: Python :: 3.5', 'Programming Language :: Python :: 3.5',
'Programming Language :: Python :: 3.6', 'Programming Language :: Python :: 3.6',
'Programming Language :: Python :: 3.8', 'Programming Language :: Python :: 3.8',
'Programming Language :: Python :: 3.10',
'Programming Language :: Python :: 3.11',
'Intended Audience :: Science/Research', 'Intended Audience :: Science/Research',
'Intended Audience :: Developers', 'Intended Audience :: Developers',
'Intended Audience :: Education', 'Intended Audience :: Education',