geneticengine.algorithms.gp.operators.weight_learning
=====================================================

.. py:module:: geneticengine.algorithms.gp.operators.weight_learning


Classes
-------

.. autoapisummary::

   geneticengine.algorithms.gp.operators.weight_learning.WeightLearningStep
   geneticengine.algorithms.gp.operators.weight_learning.ConditionalWeightLearningStep


Module Contents
---------------

.. py:class:: WeightLearningStep(learning_rate = 0.01)

   Bases: :py:obj:`geneticengine.algorithms.gp.structure.GeneticStep`


   Applies weight learning to the grammar with a given learning rate.


   .. py:attribute:: learning_rate
      :value: 0.01



   .. py:method:: wrap(representation, genotype)


   .. py:method:: count_productions(individual, g)


   .. py:method:: compute_production_probabilities(individuals, g)

      Calculates production probabilities by aggregating counts from a list of individuals.



   .. py:method:: iterate(problem, evaluator, representation, random, population, target_size, generation)


.. py:class:: ConditionalWeightLearningStep(fitness_threshold = 0.5, weight_learning_rate = 0.01)

   Bases: :py:obj:`geneticengine.algorithms.gp.structure.GeneticStep`


   A genetic step that applies weight learning using only individuals
   from the Pareto front that are above a specified fitness threshold.

   :param fitness_threshold: The minimum average fitness an individual
                             must have to be included in the weight learning process.
   :type fitness_threshold: float
   :param weight_learning_rate: The learning rate for the grammar update.
   :type weight_learning_rate: float


   .. py:attribute:: fitness_threshold
      :value: 0.5



   .. py:attribute:: internal_weight_learning_step


   .. py:method:: iterate(problem, evaluator, representation, random, population, target_size, generation)

      The main iteration logic for the conditional step.