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2
MANIFEST
2
MANIFEST
@ -104,7 +104,7 @@ pyFTS/models/seasonal/common.py
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pyFTS/models/seasonal/msfts.py
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pyFTS/models/seasonal/partitioner.py
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pyFTS/models/seasonal/sfts.py
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pyFTS/partitioners/KMeans.py
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pyFTS/partitioners/CMeans.py
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pyFTS/partitioners/Entropy.py
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pyFTS/partitioners/FCM.py
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pyFTS/partitioners/Grid.py
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@ -38,7 +38,7 @@ Fuzzy Time Series (FTS) are non parametric methods for time series forecasting b
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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:
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- the number of intervals
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- which fuzzy membership function (on [pyFTS.common.Membership](https://github.com/PYFTS/pyFTS/blob/master/pyFTS/common/Membership.py))
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- 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])
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- 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])
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Check out the jupyter notebook on [notebooks/Partitioners.ipynb](https://github.com/PYFTS/notebooks/blob/master/Partitioners.ipynb) for sample codes.
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<li><a href="pyFTS/models/seasonal/common.html">pyFTS.models.seasonal.common</a></li>
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<li><a href="pyFTS/models/seasonal/partitioner.html">pyFTS.models.seasonal.partitioner</a></li>
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<li><a href="pyFTS/partitioners/CMeans.html">pyFTS.partitioners.CMeans</a></li>
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<li><a href="pyFTS/partitioners/Class.html">pyFTS.partitioners.Class</a></li>
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<li><a href="pyFTS/partitioners/ClassPartitioner.html">pyFTS.partitioners.ClassPartitioner</a></li>
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<li><a href="pyFTS/partitioners/Grid.html">pyFTS.partitioners.Grid</a></li>
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@ -1,126 +0,0 @@
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<h1>Source code for pyFTS.partitioners.Class</h1><div class="highlight"><pre>
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<span></span><span class="sd">"""Class Partitioner with Singleton Fuzzy Sets"""</span>
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<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
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<span class="kn">import</span> <span class="nn">math</span>
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<span class="kn">import</span> <span class="nn">random</span> <span class="k">as</span> <span class="nn">rnd</span>
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<span class="kn">import</span> <span class="nn">functools</span><span class="o">,</span> <span class="nn">operator</span>
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<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>
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<span class="kn">from</span> <span class="nn">pyFTS.partitioners</span> <span class="kn">import</span> <span class="n">partitioner</span>
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<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>
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<span class="w"> </span><span class="sd">"""Class Partitioner: Given a dictionary with class/values pairs, create singleton fuzzy sets for each class"""</span>
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<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>
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<span class="w"> </span><span class="sd">"""</span>
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<span class="sd"> Class Partitioner</span>
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<span class="sd"> """</span>
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<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">"Class"</span><span class="p">,</span> <span class="n">preprocess</span> <span class="o">=</span> <span class="kc">False</span><span class="p">)</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">ordered_sets</span> <span class="o">=</span> <span class="p">[]</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">min</span> <span class="o">=</span> <span class="mi">0</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">max</span> <span class="o">=</span> <span class="mi">0</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">partitions</span> <span class="o">=</span> <span class="mi">0</span>
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<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">"classes"</span><span class="p">,</span> <span class="p">{})</span>
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<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>
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<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>
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<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>
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<span class="bp">self</span><span class="o">.</span><span class="n">partitions</span> <span class="o">+=</span> <span class="mi">1</span>
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<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>
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<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>
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<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>
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<span class="k">pass</span></div></div>
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</pre></div>
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@ -20,10 +20,10 @@ pyFTS.partitioners.partitioner module
|
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:undoc-members:
|
||||
:show-inheritance:
|
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|
||||
pyFTS.partitioners.Class module
|
||||
-------------------------------
|
||||
pyFTS.partitioners.ClassPartitioner module
|
||||
-----------------------------------
|
||||
|
||||
.. automodule:: pyFTS.partitioners.Class
|
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.. automodule:: pyFTS.partitioners.ClassPartitioner
|
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:members:
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:show-inheritance:
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10
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@ -159,7 +159,7 @@
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</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.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><a href="pyFTS.partitioners.html#pyFTS.partitioners.CMeans.CMeansPartitioner.build">(pyFTS.partitioners.CMeans.CMeansPartitioner method)</a>
|
||||
</li>
|
||||
@ -216,7 +216,7 @@
|
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</ul></li>
|
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</ul></td>
|
||||
<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><a href="pyFTS.common.html#pyFTS.common.SortedCollection.SortedCollection.clear">clear() (pyFTS.common.SortedCollection.SortedCollection method)</a>
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@ -876,7 +876,7 @@
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<li><a href="pyFTS.partitioners.html#module-pyFTS.partitioners">pyFTS.partitioners</a>
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<li><a href="pyFTS.partitioners.html#module-pyFTS.partitioners.Class">pyFTS.partitioners.Class</a>
|
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<li><a href="pyFTS.partitioners.html#module-pyFTS.partitioners.ClassPartitioner">pyFTS.partitioners.ClassPartitioner</a>
|
||||
</li>
|
||||
<li><a href="pyFTS.partitioners.html#module-pyFTS.partitioners.CMeans">pyFTS.partitioners.CMeans</a>
|
||||
</li>
|
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@ -1468,10 +1468,10 @@
|
||||
</li>
|
||||
</ul></li>
|
||||
<li>
|
||||
pyFTS.partitioners.Class
|
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pyFTS.partitioners.ClassPartitioner
|
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|
||||
<ul>
|
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<li><a href="pyFTS.partitioners.html#module-pyFTS.partitioners.Class">module</a>
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<li><a href="pyFTS.partitioners.html#module-pyFTS.partitioners.ClassPartitioner">module</a>
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|
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<tr class="cg-1">
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<td></td>
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<td>   
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<a href="pyFTS.partitioners.html#module-pyFTS.partitioners.Class"><code class="xref">pyFTS.partitioners.Class</code></a></td><td>
|
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<a href="pyFTS.partitioners.html#module-pyFTS.partitioners.ClassPartitioner"><code class="xref">pyFTS.partitioners.ClassPartitioner</code></a></td><td>
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<li class="toctree-l2"><a class="reference internal" href="pyFTS.partitioners.html#module-pyFTS.partitioners">Module contents</a></li>
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<li class="toctree-l2"><a class="reference internal" href="pyFTS.partitioners.html#submodules">Submodules</a></li>
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<li class="toctree-l2"><a class="reference internal" href="pyFTS.partitioners.html#module-pyFTS.partitioners.partitioner">pyFTS.partitioners.partitioner module</a></li>
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<li class="toctree-l2"><a class="reference internal" href="pyFTS.partitioners.html#module-pyFTS.partitioners.Class">pyFTS.partitioners.Class module</a></li>
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<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.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>
|
||||
|
||||
16
docs/build/html/pyFTS.partitioners.html
vendored
16
docs/build/html/pyFTS.partitioners.html
vendored
@ -299,17 +299,17 @@ overlapped fuzzy sets.</p>
|
||||
</dd></dl>
|
||||
|
||||
</section>
|
||||
<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>
|
||||
<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>
|
||||
<dl class="py class">
|
||||
<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>
|
||||
<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>
|
||||
<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.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>
|
||||
<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>
|
||||
<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="#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.Entropy">pyFTS.partitioners.Entropy 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
2
docs/build/html/searchindex.js
vendored
File diff suppressed because one or more lines are too long
@ -20,18 +20,18 @@ pyFTS.partitioners.partitioner module
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
pyFTS.partitioners.Class module
|
||||
-------------------------------
|
||||
pyFTS.partitioners.ClassPartitioner module
|
||||
-----------------------------------
|
||||
|
||||
.. automodule:: pyFTS.partitioners.Class
|
||||
.. automodule:: pyFTS.partitioners.ClassPartitioner
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
pyFTS.partitioners.KMeans module
|
||||
pyFTS.partitioners.CMeans module
|
||||
--------------------------------
|
||||
|
||||
.. automodule:: pyFTS.partitioners.KMeans
|
||||
.. automodule:: pyFTS.partitioners.CMeans
|
||||
:members:
|
||||
:undoc-members:
|
||||
:show-inheritance:
|
||||
|
||||
@ -6,14 +6,14 @@ import numpy as np
|
||||
import math
|
||||
from pyFTS import *
|
||||
|
||||
def scale(dist : dict, weights : dict) -> dict:
|
||||
def scale(dist : dict) -> dict:
|
||||
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()])
|
||||
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:
|
||||
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]
|
||||
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]
|
||||
@ -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
|
||||
|
||||
@ -146,7 +146,7 @@ class ClusteredMVFTS(mvfts.MVFTS):
|
||||
|
||||
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:]
|
||||
|
||||
@ -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 = pd.concat([ret, pd.DataFrame([tmp])], ignore_index=True)
|
||||
ret = ret.append(tmp, ignore_index=True)
|
||||
|
||||
return ret
|
||||
|
||||
|
||||
@ -211,7 +211,7 @@ class MVFTS(fts.FTS):
|
||||
|
||||
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:]
|
||||
|
||||
@ -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 = 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)
|
||||
lo = lo.append(new_data_point_lo, ignore_index=True)
|
||||
up = up.append(new_data_point_up, ignore_index=True)
|
||||
|
||||
return ret[-steps:]
|
||||
|
||||
|
||||
@ -69,8 +69,6 @@ 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:
|
||||
@ -82,8 +80,6 @@ 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)
|
||||
@ -102,7 +98,7 @@ class WeightedMVFTS(mvfts.MVFTS):
|
||||
for k,v in _flrg.RHS.items():
|
||||
classification[k] += (v / _flrg.count) * mb
|
||||
|
||||
classification = activation(classification, self.class_weights)
|
||||
classification = activation(classification)
|
||||
|
||||
ret.append(classification)
|
||||
|
||||
|
||||
@ -14,15 +14,15 @@ def distance(x, y):
|
||||
return math.sqrt(tmp)
|
||||
|
||||
|
||||
def k_means(k, dados, tam):
|
||||
# Инициализирует центроиды, выбирая случайные элементы из множества
|
||||
def c_means(k, dados, tam):
|
||||
# Inicializa as centróides escolhendo elementos aleatórios dos conjuntos
|
||||
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
|
||||
|
||||
# для каждого экземпляра
|
||||
# para cada instância
|
||||
iteracoes = 0
|
||||
while iteracoes < 1000 and it_semmodificacao < 10:
|
||||
inst_count = 0
|
||||
@ -31,7 +31,7 @@ def k_means(k, dados, tam):
|
||||
|
||||
for instancia in dados:
|
||||
|
||||
# проверяет расстояние до каждого центроида
|
||||
# verifica a distância para cada centroide
|
||||
grupo_count = 0
|
||||
dist = 10000
|
||||
|
||||
@ -41,7 +41,7 @@ def k_means(k, dados, tam):
|
||||
tmp = distance(instancia, grupo)
|
||||
if tmp < dist:
|
||||
dist = tmp
|
||||
# ассоциирует центроид с наименьшим расстоянием до экземпляра
|
||||
# associa a a centroide de menor distância à instância
|
||||
grupos[inst_count] = grupo_count
|
||||
grupo_count = grupo_count + 1
|
||||
|
||||
@ -55,7 +55,7 @@ def k_means(k, dados, tam):
|
||||
else:
|
||||
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
|
||||
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 k_means(k, dados, tam):
|
||||
return centroides
|
||||
|
||||
|
||||
class KMeansPartitioner(partitioner.Partitioner):
|
||||
class CMeansPartitioner(partitioner.Partitioner):
|
||||
def __init__(self, **kwargs):
|
||||
super(KMeansPartitioner, self).__init__(name="KMeans", **kwargs)
|
||||
super(CMeansPartitioner, self).__init__(name="CMeans", **kwargs)
|
||||
|
||||
def build(self, data):
|
||||
sets = {}
|
||||
|
||||
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.min)
|
||||
centroides = list(set(centroides))
|
||||
centroides.sort()
|
||||
for c in range(1, len(centroides) - 1):
|
||||
for c in np.arange(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)],
|
||||
@ -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((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
|
||||
|
||||
@ -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)
|
||||
dist_groups = np.zeros(k) #[0 for xx in range(0, 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:
|
||||
|
||||
@ -19,13 +19,13 @@ class HuarngPartitioner(partitioner.Partitioner):
|
||||
def build(self, data):
|
||||
diff = Transformations.Differential(1)
|
||||
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
|
||||
elif 1 < divs <= 10:
|
||||
elif 1 < davg <= 10:
|
||||
base = 1.0
|
||||
elif 10 < divs <= 100:
|
||||
elif 10 < davg <= 100:
|
||||
base = 10
|
||||
else:
|
||||
base = 100
|
||||
|
||||
@ -18,20 +18,19 @@ all_methods = [Grid.GridPartitioner, Entropy.EntropyPartitioner, FCM.FCMPartitio
|
||||
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
|
||||
|
||||
"""
|
||||
num = len(sets)
|
||||
num_cols_plot = 1
|
||||
|
||||
if axis is None:
|
||||
fig, axes = plt.subplots(nrows=num, ncols=num_cols_plot, figsize=size, squeeze=False)
|
||||
for k in range(num):
|
||||
fig, axes = plt.subplots(nrows=num, ncols=1,figsize=size)
|
||||
for k in np.arange(0,num):
|
||||
ticks = []
|
||||
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_ylim([0, 1.1])
|
||||
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)
|
||||
|
||||
|
||||
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]
|
||||
titles = [k.name for k in objs]
|
||||
plot_sets(sets, titles, tam, save, file, axis)
|
||||
|
||||
@ -1,7 +1,6 @@
|
||||
from pyFTS.common import FuzzySet, Membership
|
||||
import numpy as np
|
||||
from scipy.spatial import KDTree
|
||||
import warnings
|
||||
|
||||
|
||||
class Partitioner(object):
|
||||
@ -47,9 +46,6 @@ 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:
|
||||
|
||||
@ -11,7 +11,7 @@ from mpl_toolkits.mplot3d import Axes3D
|
||||
import datetime
|
||||
|
||||
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 import sfts
|
||||
from pyFTS.models import msfts
|
||||
|
||||
2
setup.py
2
setup.py
@ -25,8 +25,6 @@ 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',
|
||||
|
||||
Loading…
Reference in New Issue
Block a user