Merge pull request #3 from VicentePerezSoloviev/devel

packaging

docs/build/html/_modules/index.html

@@ -32,6 +32,7 @@
 
   <h1>All modules for which code is available</h1>
 <ul><li><a href="EDApy/optimization/multivariate/EDA_multivariate.html">EDApy.optimization.multivariate.EDA_multivariate</a></li>
+<li><a href="EDApy/optimization/multivariate/EDA_multivariate_gaussian.html">EDApy.optimization.multivariate.EDA_multivariate_gaussian</a></li>
 <li><a href="EDApy/optimization/univariate/continuous.html">EDApy.optimization.univariate.continuous</a></li>
 <li><a href="EDApy/optimization/univariate/discrete.html">EDApy.optimization.univariate.discrete</a></li>
 <li><a href="EDApy/prueba.html">EDApy.prueba</a></li>

docs/build/html/_sources/index.rst.txt

@@ -42,12 +42,18 @@ To use both univariate EDAs there is no need to install R.
 Documentation for the code
 ##########################
 
-EDA multivariate
-****************
+EDA multivariate with evidences
+********************************
 .. autoclass:: EDApy.optimization.multivariate.EDA_multivariate
 .. autoclass:: EDApy.optimization.multivariate.EDA_multivariate.EDAgbn
    :members:
 
+EDA multivariate with no evidences
+***********************************
+.. autoclass:: EDApy.optimization.multivariate.EDA_multivariate_gaussian
+.. autoclass:: EDApy.optimization.multivariate.EDA_multivariate_gaussian.EDA_multivariate_gaussian
+   :members:
+
 EDA discrete
 ************
 .. autoclass:: EDApy.optimization.univariate.EDA_discrete

docs/build/html/genindex.html

@@ -65,9 +65,11 @@ <h2 id="B">B</h2>
 <h2 id="C">C</h2>
 <table style="width: 100%" class="indextable genindextable"><tr>
   <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="index.html#EDApy.optimization.univariate.continuous.UMDAc.check_generation">check_generation() (EDApy.optimization.univariate.continuous.UMDAc method)</a>
+      <li><a href="index.html#EDApy.optimization.multivariate.EDA_multivariate_gaussian.EDA_multivariate_gaussian.check_generation">check_generation() (EDApy.optimization.multivariate.EDA_multivariate_gaussian.EDA_multivariate_gaussian method)</a>
 
       <ul>
+        <li><a href="index.html#EDApy.optimization.univariate.continuous.UMDAc.check_generation">(EDApy.optimization.univariate.continuous.UMDAc method)</a>
+</li>
         <li><a href="index.html#EDApy.optimization.univariate.discrete.UMDAd.check_generation">(EDApy.optimization.univariate.discrete.UMDAd method)</a>
 </li>
       </ul></li>
@@ -88,11 +90,17 @@ <h2 id="E">E</h2>
       <li><a href="index.html#EDApy.optimization.univariate.EDA_continuous">EDA_continuous (in module EDApy.optimization.univariate)</a>
 </li>
       <li><a href="index.html#EDApy.optimization.univariate.EDA_discrete">EDA_discrete (in module EDApy.optimization.univariate)</a>
+</li>
+      <li><a href="index.html#EDApy.optimization.multivariate.EDA_multivariate">EDA_multivariate (in module EDApy.optimization.multivariate)</a>
 </li>
   </ul></td>
   <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="index.html#EDApy.optimization.multivariate.EDA_multivariate">EDA_multivariate (in module EDApy.optimization.multivariate)</a>
+      <li><a href="index.html#EDApy.optimization.multivariate.EDA_multivariate_gaussian">EDA_multivariate_gaussian (class in EDApy.optimization.multivariate)</a>
+
+      <ul>
+        <li><a href="index.html#EDApy.optimization.multivariate.EDA_multivariate_gaussian.EDA_multivariate_gaussian">(class in EDApy.optimization.multivariate.EDA_multivariate_gaussian)</a>
 </li>
+      </ul></li>
       <li><a href="index.html#EDApy.optimization.multivariate.EDA_multivariate.EDAgbn">EDAgbn (class in EDApy.optimization.multivariate.EDA_multivariate)</a>
 </li>
   </ul></td>
@@ -124,6 +132,8 @@ <h2 id="N">N</h2>
       <li><a href="index.html#EDApy.optimization.multivariate.EDA_multivariate.EDAgbn.new_generation">new_generation() (EDApy.optimization.multivariate.EDA_multivariate.EDAgbn method)</a>
 
       <ul>
+        <li><a href="index.html#EDApy.optimization.multivariate.EDA_multivariate_gaussian.EDA_multivariate_gaussian.new_generation">(EDApy.optimization.multivariate.EDA_multivariate_gaussian.EDA_multivariate_gaussian method)</a>
+</li>
         <li><a href="index.html#EDApy.optimization.univariate.continuous.UMDAc.new_generation">(EDApy.optimization.univariate.continuous.UMDAc method)</a>
 </li>
         <li><a href="index.html#EDApy.optimization.univariate.discrete.UMDAd.new_generation">(EDApy.optimization.univariate.discrete.UMDAd method)</a>
@@ -138,6 +148,8 @@ <h2 id="R">R</h2>
       <li><a href="index.html#EDApy.optimization.multivariate.EDA_multivariate.EDAgbn.run">run() (EDApy.optimization.multivariate.EDA_multivariate.EDAgbn method)</a>
 
       <ul>
+        <li><a href="index.html#EDApy.optimization.multivariate.EDA_multivariate_gaussian.EDA_multivariate_gaussian.run">(EDApy.optimization.multivariate.EDA_multivariate_gaussian.EDA_multivariate_gaussian method)</a>
+</li>
         <li><a href="index.html#EDApy.optimization.univariate.continuous.UMDAc.run">(EDApy.optimization.univariate.continuous.UMDAc method)</a>
 </li>
         <li><a href="index.html#EDApy.optimization.univariate.discrete.UMDAd.run">(EDApy.optimization.univariate.discrete.UMDAd method)</a>
@@ -165,8 +177,12 @@ <h2 id="T">T</h2>
 </li>
   </ul></td>
   <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="index.html#EDApy.optimization.univariate.continuous.UMDAc.truncation">truncation() (EDApy.optimization.univariate.continuous.UMDAc method)</a>
+      <li><a href="index.html#EDApy.optimization.multivariate.EDA_multivariate_gaussian.EDA_multivariate_gaussian.truncation">truncation() (EDApy.optimization.multivariate.EDA_multivariate_gaussian.EDA_multivariate_gaussian method)</a>
+
+      <ul>
+        <li><a href="index.html#EDApy.optimization.univariate.continuous.UMDAc.truncation">(EDApy.optimization.univariate.continuous.UMDAc method)</a>
 </li>
+      </ul></li>
   </ul></td>
 </tr></table>
 
@@ -179,9 +195,11 @@ <h2 id="U">U</h2>
 </li>
   </ul></td>
   <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="index.html#EDApy.optimization.univariate.continuous.UMDAc.update_vector">update_vector() (EDApy.optimization.univariate.continuous.UMDAc method)</a>
+      <li><a href="index.html#EDApy.optimization.multivariate.EDA_multivariate_gaussian.EDA_multivariate_gaussian.update_vector">update_vector() (EDApy.optimization.multivariate.EDA_multivariate_gaussian.EDA_multivariate_gaussian method)</a>
 
       <ul>
+        <li><a href="index.html#EDApy.optimization.univariate.continuous.UMDAc.update_vector">(EDApy.optimization.univariate.continuous.UMDAc method)</a>
+</li>
         <li><a href="index.html#EDApy.optimization.univariate.discrete.UMDAd.update_vector">(EDApy.optimization.univariate.discrete.UMDAd method)</a>
 </li>
       </ul></li>

docs/build/html/index.html

@@ -63,8 +63,8 @@ <h2>Requirements<a class="headerlink" href="#requirements" title="Permalink to t
 </div>
 <div class="section" id="documentation-for-the-code">
 <h2>Documentation for the code<a class="headerlink" href="#documentation-for-the-code" title="Permalink to this headline">¶</a></h2>
-<div class="section" id="eda-multivariate">
-<h3>EDA multivariate<a class="headerlink" href="#eda-multivariate" title="Permalink to this headline">¶</a></h3>
+<div class="section" id="eda-multivariate-with-evidences">
+<h3>EDA multivariate with evidences<a class="headerlink" href="#eda-multivariate-with-evidences" title="Permalink to this headline">¶</a></h3>
 <dl class="py attribute">
 <dt id="EDApy.optimization.multivariate.EDA_multivariate">
 <code class="sig-prename descclassname">EDApy.optimization.multivariate.</code><code class="sig-name descname">EDA_multivariate</code><a class="headerlink" href="#EDApy.optimization.multivariate.EDA_multivariate" title="Permalink to this definition">¶</a></dt>
@@ -237,6 +237,99 @@ <h3>EDA multivariate<a class="headerlink" href="#eda-multivariate" title="Permal
 
 </dd></dl>
 
+</div>
+<div class="section" id="eda-multivariate-with-no-evidences">
+<h3>EDA multivariate with no evidences<a class="headerlink" href="#eda-multivariate-with-no-evidences" title="Permalink to this headline">¶</a></h3>
+<dl class="py class">
+<dt id="EDApy.optimization.multivariate.EDA_multivariate_gaussian">
+<em class="property">class </em><code class="sig-prename descclassname">EDApy.optimization.multivariate.</code><code class="sig-name descname">EDA_multivariate_gaussian</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">SIZE_GEN</span></em>, <em class="sig-param"><span class="n">MAX_ITER</span></em>, <em class="sig-param"><span class="n">DEAD_ITER</span></em>, <em class="sig-param"><span class="n">ALPHA</span></em>, <em class="sig-param"><span class="n">aim</span></em>, <em class="sig-param"><span class="n">cost_function</span></em>, <em class="sig-param"><span class="n">mus</span></em>, <em class="sig-param"><span class="n">sigma</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/EDApy/optimization/multivariate/EDA_multivariate_gaussian.html#EDA_multivariate_gaussian"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#EDApy.optimization.multivariate.EDA_multivariate_gaussian" title="Permalink to this definition">¶</a></dt>
+<dd><p>Multivariate Estimation of Distribution algorithm continuous.
+New individuals are sampled from a multivariate normal distribution. Evidences are not allowed</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>SIZE_GEN</strong> (<em>int</em>) – total size of the generations in the execution of the algorithm</p></li>
+<li><p><strong>MAX_ITER</strong> (<em>int</em>) – total number of iterations in case that optimum is not yet found. If reached, the optimum found is returned</p></li>
+<li><p><strong>DEAD_ITER</strong> (<em>int</em>) – total number of iteration with no better solution found. If reached, the optimum found is returned</p></li>
+<li><p><strong>ALPHA</strong> (<em>float</em><em> [</em><em>0-1</em><em>]</em>) – percentage of the generation tu take, in order to sample from them. The best individuals selection</p></li>
+<li><p><strong>aim</strong> (<em>'minimize'</em><em> or </em><em>'maximize'.</em>) – Represents the optimization aim.</p></li>
+<li><p><strong>cost_function</strong> (<em>callable function which receives a dictionary as input and returns a numeric</em>) – a callable function implemented by the user, to optimize.</p></li>
+<li><p><strong>mus</strong> (<em>pandas dataframe</em><em> (</em><em>one row</em><em>)</em>) – pandas dataframe with initial mus of the multivariate gaussian</p></li>
+<li><p><strong>sigma</strong> (<em>pandas dataframe</em><em> (</em><em>one row</em><em>)</em>) – pandas dataframe with the sigmas of the variable (diagonal of covariance matrix)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Raises</dt>
+<dd class="field-even"><p><strong>Exception</strong> – cost function is not callable</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py class">
+<dt id="EDApy.optimization.multivariate.EDA_multivariate_gaussian.EDA_multivariate_gaussian">
+<em class="property">class </em><code class="sig-prename descclassname">EDApy.optimization.multivariate.EDA_multivariate_gaussian.</code><code class="sig-name descname">EDA_multivariate_gaussian</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">SIZE_GEN</span></em>, <em class="sig-param"><span class="n">MAX_ITER</span></em>, <em class="sig-param"><span class="n">DEAD_ITER</span></em>, <em class="sig-param"><span class="n">ALPHA</span></em>, <em class="sig-param"><span class="n">aim</span></em>, <em class="sig-param"><span class="n">cost_function</span></em>, <em class="sig-param"><span class="n">mus</span></em>, <em class="sig-param"><span class="n">sigma</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/EDApy/optimization/multivariate/EDA_multivariate_gaussian.html#EDA_multivariate_gaussian"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#EDApy.optimization.multivariate.EDA_multivariate_gaussian.EDA_multivariate_gaussian" title="Permalink to this definition">¶</a></dt>
+<dd><p>Multivariate Estimation of Distribution algorithm continuous.
+New individuals are sampled from a multivariate normal distribution. Evidences are not allowed</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>SIZE_GEN</strong> (<em>int</em>) – total size of the generations in the execution of the algorithm</p></li>
+<li><p><strong>MAX_ITER</strong> (<em>int</em>) – total number of iterations in case that optimum is not yet found. If reached, the optimum found is returned</p></li>
+<li><p><strong>DEAD_ITER</strong> (<em>int</em>) – total number of iteration with no better solution found. If reached, the optimum found is returned</p></li>
+<li><p><strong>ALPHA</strong> (<em>float</em><em> [</em><em>0-1</em><em>]</em>) – percentage of the generation tu take, in order to sample from them. The best individuals selection</p></li>
+<li><p><strong>aim</strong> (<em>'minimize'</em><em> or </em><em>'maximize'.</em>) – Represents the optimization aim.</p></li>
+<li><p><strong>cost_function</strong> (<em>callable function which receives a dictionary as input and returns a numeric</em>) – a callable function implemented by the user, to optimize.</p></li>
+<li><p><strong>mus</strong> (<em>pandas dataframe</em><em> (</em><em>one row</em><em>)</em>) – pandas dataframe with initial mus of the multivariate gaussian</p></li>
+<li><p><strong>sigma</strong> (<em>pandas dataframe</em><em> (</em><em>one row</em><em>)</em>) – pandas dataframe with the sigmas of the variable (diagonal of covariance matrix)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Raises</dt>
+<dd class="field-even"><p><strong>Exception</strong> – cost function is not callable</p>
+</dd>
+</dl>
+<dl class="py method">
+<dt id="EDApy.optimization.multivariate.EDA_multivariate_gaussian.EDA_multivariate_gaussian.check_generation">
+<code class="sig-name descname">check_generation</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/EDApy/optimization/multivariate/EDA_multivariate_gaussian.html#EDA_multivariate_gaussian.check_generation"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#EDApy.optimization.multivariate.EDA_multivariate_gaussian.EDA_multivariate_gaussian.check_generation" title="Permalink to this definition">¶</a></dt>
+<dd><p>Check the cost of each individual in the cost function implemented by the user</p>
+</dd></dl>
+
+<dl class="py method">
+<dt id="EDApy.optimization.multivariate.EDA_multivariate_gaussian.EDA_multivariate_gaussian.new_generation">
+<code class="sig-name descname">new_generation</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/EDApy/optimization/multivariate/EDA_multivariate_gaussian.html#EDA_multivariate_gaussian.new_generation"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#EDApy.optimization.multivariate.EDA_multivariate_gaussian.EDA_multivariate_gaussian.new_generation" title="Permalink to this definition">¶</a></dt>
+<dd><p>Build a new generation sampled from the vector of probabilities. Updates the generation pandas dataframe</p>
+</dd></dl>
+
+<dl class="py method">
+<dt id="EDApy.optimization.multivariate.EDA_multivariate_gaussian.EDA_multivariate_gaussian.run">
+<code class="sig-name descname">run</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">output</span><span class="o">=</span><span class="default_value">True</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/EDApy/optimization/multivariate/EDA_multivariate_gaussian.html#EDA_multivariate_gaussian.run"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#EDApy.optimization.multivariate.EDA_multivariate_gaussian.EDA_multivariate_gaussian.run" title="Permalink to this definition">¶</a></dt>
+<dd><p>Run method to execute the EDA algorithm</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>output</strong> (<em>bool</em>) – True if wanted to print each iteration</p>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>best cost, best individual, history of costs along execution</p>
+</dd>
+<dt class="field-odd">Return type</dt>
+<dd class="field-odd"><p>float, pandas dataframe, list</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt id="EDApy.optimization.multivariate.EDA_multivariate_gaussian.EDA_multivariate_gaussian.truncation">
+<code class="sig-name descname">truncation</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/EDApy/optimization/multivariate/EDA_multivariate_gaussian.html#EDA_multivariate_gaussian.truncation"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#EDApy.optimization.multivariate.EDA_multivariate_gaussian.EDA_multivariate_gaussian.truncation" title="Permalink to this definition">¶</a></dt>
+<dd><p>Selection of the best individuals of the actual generation. Updates the generation by selecting the best individuals</p>
+</dd></dl>
+
+<dl class="py method">
+<dt id="EDApy.optimization.multivariate.EDA_multivariate_gaussian.EDA_multivariate_gaussian.update_vector">
+<code class="sig-name descname">update_vector</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/EDApy/optimization/multivariate/EDA_multivariate_gaussian.html#EDA_multivariate_gaussian.update_vector"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#EDApy.optimization.multivariate.EDA_multivariate_gaussian.EDA_multivariate_gaussian.update_vector" title="Permalink to this definition">¶</a></dt>
+<dd><p>From the best individuals update the vector of normal distributions in order to the next
+generation can sample from it. Update the vector of normal distributions</p>
+</dd></dl>
+
+</dd></dl>
+
 </div>
 <div class="section" id="eda-discrete">
 <h3>EDA discrete<a class="headerlink" href="#eda-discrete" title="Permalink to this headline">¶</a></h3>

docs/build/html/objects.inv

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