Updated docs.

Author: vrettasm

docs/_build/doctrees/environment.pickle

@@ -230,7 +230,7 @@ <h1>Source code for pygenalgo.engines.island_model_ga</h1><div class="highlight"
         <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">epochs</span><span class="p">):</span>
 
             <span class="c1"># Update current iteration in the selection operator.</span>
-            <span class="c1"># Currently this is used only from Boltzmann Selector.</span>
+            <span class="c1"># Currently, this is used only from Boltzmann Selector.</span>
             <span class="n">sel_op</span><span class="o">.</span><span class="n">iter</span> <span class="o">=</span> <span class="n">i</span>
 
             <span class="c1"># SELECT the parents.</span>
@@ -259,16 +259,16 @@ <h1>Source code for pygenalgo.engines.island_model_ga</h1><div class="highlight"
             <span class="c1"># Check if &#39;elitism&#39; is enabled.</span>
             <span class="k">if</span> <span class="n">elitism</span><span class="p">:</span>
 
-                <span class="c1"># Find the individual chromosome with the highest fitness value</span>
-                <span class="c1"># (from the old population).</span>
+                <span class="c1"># Find the individual chromosome with the highest fitness</span>
+                <span class="c1"># value (from the old subpopulation of the current island).</span>
                 <span class="n">best_chromosome</span> <span class="o">=</span> <span class="nb">max</span><span class="p">((</span><span class="n">p</span> <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">island</span><span class="o">.</span><span class="n">population</span> <span class="k">if</span> <span class="ow">not</span> <span class="n">isnan</span><span class="p">(</span><span class="n">p</span><span class="o">.</span><span class="n">fitness</span><span class="p">)),</span>
                                       <span class="n">key</span><span class="o">=</span><span class="k">lambda</span> <span class="n">c</span><span class="p">:</span> <span class="n">c</span><span class="o">.</span><span class="n">fitness</span><span class="p">,</span> <span class="n">default</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
 
                 <span class="c1"># Select randomly a position.</span>
                 <span class="n">locus</span> <span class="o">=</span> <span class="n">rnd_gen</span><span class="o">.</span><span class="n">integers</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">N</span><span class="p">)</span>
 
                 <span class="c1"># Replace the chromosome with the previous best.</span>
-                <span class="n">population_i</span><span class="p">[</span><span class="n">locus</span><span class="p">]</span> <span class="o">=</span> <span class="n">best_chromosome</span><span class="o">.</span><span class="n">clone</span><span class="p">()</span>
+                <span class="n">population_i</span><span class="p">[</span><span class="n">locus</span><span class="p">]</span> <span class="o">=</span> <span class="n">best_chromosome</span>
             <span class="c1"># _end_if_</span>
 
             <span class="c1"># EVALUATE the i-th population.</span>

docs/_build/doctrees/pygenalgo.engines.doctree

@@ -243,7 +243,7 @@ <h1>Source code for pygenalgo.engines.standard_ga</h1><div class="highlight"><pr
         <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">epochs</span><span class="p">):</span>
 
             <span class="c1"># Update current iteration in the selection operator.</span>
-            <span class="c1"># Currently this is used only from Boltzmann Selector.</span>
+            <span class="c1"># Currently, this is used only from Boltzmann Selector.</span>
             <span class="bp">self</span><span class="o">.</span><span class="n">_select_op</span><span class="o">.</span><span class="n">iter</span> <span class="o">=</span> <span class="n">i</span>
 
             <span class="c1"># SELECT the parents.</span>
@@ -271,22 +271,18 @@ <h1>Source code for pygenalgo.engines.standard_ga</h1><div class="highlight"><pr
 
                 <span class="c1"># Print only if there were corrections,</span>
                 <span class="c1"># to avoid cluttering the screen.</span>
-                <span class="k">if</span> <span class="n">total_corrections</span><span class="p">:</span>
+                <span class="k">if</span> <span class="n">verbose</span> <span class="ow">and</span> <span class="n">total_corrections</span><span class="p">:</span>
                     <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;&gt; </span><span class="si">{</span><span class="n">total_corrections</span><span class="si">}</span><span class="s2"> correction(s) took place at epoch: </span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s2">.&quot;</span><span class="p">)</span>
                 <span class="c1"># _end_if_</span>
             <span class="c1"># _end_if_</span>
 
             <span class="c1"># Check if &#39;elitism&#39; is enabled.</span>
             <span class="k">if</span> <span class="n">elitism</span><span class="p">:</span>
-                <span class="c1"># Find the individual chromosome with the highest</span>
-                <span class="c1"># fitness value (from the previous population).</span>
-                <span class="n">best_chromosome</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">best_chromosome</span><span class="p">()</span>
-
                 <span class="c1"># Select randomly a position.</span>
                 <span class="n">locus</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">rng_GA</span><span class="o">.</span><span class="n">integers</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">N</span><span class="p">)</span>
 
                 <span class="c1"># Replace the chromosome with the previous best.</span>
-                <span class="n">population_i</span><span class="p">[</span><span class="n">locus</span><span class="p">]</span> <span class="o">=</span> <span class="n">best_chromosome</span><span class="o">.</span><span class="n">clone</span><span class="p">()</span>
+                <span class="n">population_i</span><span class="p">[</span><span class="n">locus</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">best_chromosome</span><span class="p">()</span>
             <span class="c1"># _end_if_</span>
 
             <span class="c1"># Calculate the new fitness values.</span>

docs/_build/doctrees/pygenalgo.genome.doctree

@@ -253,21 +253,55 @@ <h1>Source code for pygenalgo.genome.chromosome</h1><div class="highlight"><pre>
             <span class="c1"># _end_if_</span>
 
             <span class="c1"># Compute the dissimilarities in their genomes.</span>
-            <span class="k">return</span> <span class="nb">sum</span><span class="p">([</span><span class="n">k</span> <span class="o">!=</span> <span class="n">l</span> <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">l</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_genome</span><span class="p">,</span> <span class="n">other</span><span class="o">.</span><span class="n">_genome</span><span class="p">,</span> <span class="n">strict</span><span class="o">=</span><span class="kc">True</span><span class="p">)])</span>
+            <span class="k">return</span> <span class="p">[</span><span class="n">k</span> <span class="o">!=</span> <span class="n">l</span> <span class="k">for</span> <span class="n">k</span><span class="p">,</span> <span class="n">l</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_genome</span><span class="p">,</span> <span class="n">other</span><span class="o">.</span><span class="n">_genome</span><span class="p">,</span>
+                                           <span class="n">strict</span><span class="o">=</span><span class="kc">True</span><span class="p">)]</span><span class="o">.</span><span class="n">count</span><span class="p">(</span><span class="kc">True</span><span class="p">)</span>
         <span class="k">else</span><span class="p">:</span>
             <span class="k">raise</span> <span class="bp">NotImplemented</span></div>
 
         <span class="c1"># _end_if_</span>
 
     <span class="c1"># _end_def_</span>
 
+    <span class="k">def</span> <span class="nf">__deepcopy__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">memo</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        This custom method overrides the default deepcopy method</span>
+<span class="sd">        and is used when we call the &quot;clone&quot; method of the class.</span>
+
+<span class="sd">        :param memo: dictionary of objects already copied during</span>
+<span class="sd">        the current copying pass.</span>
+
+<span class="sd">        :return: a new identical &quot;clone&quot; of the self object.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="c1"># Get the class of the object.</span>
+        <span class="bp">cls</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span>
+
+        <span class="c1"># Create a new instance.</span>
+        <span class="n">new_object</span> <span class="o">=</span> <span class="bp">cls</span><span class="o">.</span><span class="fm">__new__</span><span class="p">(</span><span class="bp">cls</span><span class="p">)</span>
+
+        <span class="c1"># Don&#39;t copy self reference.</span>
+        <span class="n">memo</span><span class="p">[</span><span class="nb">id</span><span class="p">(</span><span class="bp">self</span><span class="p">)]</span> <span class="o">=</span> <span class="n">new_object</span>
+
+        <span class="c1"># Don&#39;t copy the cache.</span>
+        <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">&quot;_cache&quot;</span><span class="p">):</span>
+            <span class="n">memo</span><span class="p">[</span><span class="nb">id</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="p">)]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_cache</span><span class="o">.</span><span class="fm">__new__</span><span class="p">(</span><span class="nb">dict</span><span class="p">)</span>
+        <span class="c1"># _end_if_</span>
+
+        <span class="c1"># Deep copy all other attributes.</span>
+        <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">value</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__dict__</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+            <span class="nb">setattr</span><span class="p">(</span><span class="n">new_object</span><span class="p">,</span> <span class="n">key</span><span class="p">,</span> <span class="n">deepcopy</span><span class="p">(</span><span class="n">value</span><span class="p">,</span> <span class="n">memo</span><span class="p">))</span>
+        <span class="c1"># _end_for_</span>
+
+        <span class="c1"># Return identical instance.</span>
+        <span class="k">return</span> <span class="n">new_object</span>
+    <span class="c1"># _end_def_</span>
+
 <div class="viewcode-block" id="Chromosome.clone">
 <a class="viewcode-back" href="../../../pygenalgo.genome.html#pygenalgo.genome.chromosome.Chromosome.clone">[docs]</a>
     <span class="k">def</span> <span class="nf">clone</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Makes a duplicate of the self object.</span>
 
-<span class="sd">        :return: a &#39;deep-copy&#39; of the object.</span>
+<span class="sd">        :return: a &quot;deep-copy&quot; of the object.</span>
 <span class="sd">        &quot;&quot;&quot;</span>
         <span class="k">return</span> <span class="n">deepcopy</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span></div>
 </div>