Package Bio :: Package NeuralNetwork :: Package Gene :: Module Schema :: Class GeneticAlgorithmFinder
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Class GeneticAlgorithmFinder

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object --+
         |
        GeneticAlgorithmFinder

Find schemas using a genetic algorithm approach.

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.

The 'default' finder searches for ambiguous DNA elements. This
can be overridden easily by creating a GeneticAlgorithmFinder
with a different alphabet.

Instance Methods [hide private]
 
__init__(self, alphabet=SchemaDNAAlphabet())
Initialize a finder to get schemas using Genetic Algorithms.
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_set_up_genetic_algorithm(self)
Overrideable function to set up the genetic algorithm parameters.
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find_schemas(self, fitness, num_schemas)
Find the given number of unique schemas using a genetic algorithm
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Inherited from object: __delattr__, __format__, __getattribute__, __hash__, __new__, __reduce__, __reduce_ex__, __repr__, __setattr__, __sizeof__, __str__, __subclasshook__

Properties [hide private]

Inherited from object: __class__

Method Details [hide private]

__init__(self, alphabet=SchemaDNAAlphabet())
(Constructor)

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Initialize a finder to get schemas using Genetic Algorithms.

Arguments:

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.

Overrides: object.__init__

_set_up_genetic_algorithm(self)

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Overrideable function to set up the genetic algorithm parameters.

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.

find_schemas(self, fitness, num_schemas)

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Find the given number of unique schemas using a genetic algorithm

Arguments:

o fitness - A callable object (ie. function) which will evaluate
the fitness of a motif.

o num_schemas - The number of unique schemas with good fitness
that we want to generate.