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<a href="Bio-module.html">Package Bio</a> ::
<a href="Bio.NeuralNetwork-module.html">Package NeuralNetwork</a> ::
<a href="Bio.NeuralNetwork.Gene-module.html">Package Gene</a> ::
<a href="Bio.NeuralNetwork.Gene.Schema-module.html">Module Schema</a> ::
Class GeneticAlgorithmFinder
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<h1 class="epydoc">Class GeneticAlgorithmFinder</h1><p class="nomargin-top"><span class="codelink"><a href="Bio.NeuralNetwork.Gene.Schema-pysrc.html#GeneticAlgorithmFinder">source code</a></span></p>
<p>Find schemas using a genetic algorithm approach.</p>
<p>This approach to finding schema uses Genetic Algorithms to evolve a
set of schema and find the best schema for a specific set of records.</p>
<p>The 'default' finder searches for ambiguous DNA elements. This can be
overridden easily by creating a GeneticAlgorithmFinder with a different
alphabet.</p>
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<td><span class="summary-sig"><a href="Bio.NeuralNetwork.Gene.Schema.GeneticAlgorithmFinder-class.html#__init__" class="summary-sig-name">__init__</a>(<span class="summary-sig-arg">self</span>,
<span class="summary-sig-arg">alphabet</span>=<span class="summary-sig-default">SchemaDNAAlphabet()</span>)</span><br />
Initialize a finder to get schemas using Genetic Algorithms.</td>
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<td><span class="summary-sig"><a href="Bio.NeuralNetwork.Gene.Schema.GeneticAlgorithmFinder-class.html#_set_up_genetic_algorithm" class="summary-sig-name" onclick="show_private();">_set_up_genetic_algorithm</a>(<span class="summary-sig-arg">self</span>)</span><br />
Overrideable function to set up the genetic algorithm parameters.</td>
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<td><span class="summary-sig"><a href="Bio.NeuralNetwork.Gene.Schema.GeneticAlgorithmFinder-class.html#find_schemas" class="summary-sig-name">find_schemas</a>(<span class="summary-sig-arg">self</span>,
<span class="summary-sig-arg">fitness</span>,
<span class="summary-sig-arg">num_schemas</span>)</span><br />
Find the given number of unique schemas using a genetic algorithm</td>
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<a name="__init__"></a>
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<h3 class="epydoc"><span class="sig"><span class="sig-name">__init__</span>(<span class="sig-arg">self</span>,
<span class="sig-arg">alphabet</span>=<span class="sig-default">SchemaDNAAlphabet()</span>)</span>
<br /><em class="fname">(Constructor)</em>
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<p>Initialize a finder to get schemas using Genetic Algorithms.</p>
<p>Arguments:</p>
<p>o alphabet -- The alphabet which specifies the contents of the schemas
we'll be generating. This alphabet must contain the attribute
'alphabet_matches', which is a dictionary specifying the potential
ambiguities of each letter in the alphabet. These ambiguities will be
used in building up the schema.</p>
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<a name="_set_up_genetic_algorithm"></a>
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<h3 class="epydoc"><span class="sig"><span class="sig-name">_set_up_genetic_algorithm</span>(<span class="sig-arg">self</span>)</span>
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<p>Overrideable function to set up the genetic algorithm parameters.</p>
<p>This functions sole job is to set up the different genetic algorithm
functionality. Since this can be quite complicated, this allows
cusotmizablity of all of the parameters. If you want to customize
specially, you can inherit from this class and override this
function.</p>
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<a name="find_schemas"></a>
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<h3 class="epydoc"><span class="sig"><span class="sig-name">find_schemas</span>(<span class="sig-arg">self</span>,
<span class="sig-arg">fitness</span>,
<span class="sig-arg">num_schemas</span>)</span>
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><span class="codelink"><a href="Bio.NeuralNetwork.Gene.Schema-pysrc.html#GeneticAlgorithmFinder.find_schemas">source code</a></span>
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<p>Find the given number of unique schemas using a genetic algorithm</p>
<p>Arguments:</p>
<p>o fitness - A callable object (ie. function) which will evaluate the
fitness of a motif.</p>
<p>o num_schemas - The number of unique schemas with good fitness that we
want to generate.</p>
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