:py:mod:`ontolearn.ea_initialization`
=====================================

.. py:module:: ontolearn.ea_initialization

.. autoapi-nested-parse::

   Initialization for evolutionary algorithms.



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

Classes
~~~~~~~

.. autoapisummary::

   ontolearn.ea_initialization.RandomInitMethod
   ontolearn.ea_initialization.AbstractEAInitialization
   ontolearn.ea_initialization.EARandomInitialization
   ontolearn.ea_initialization.PropObjPair
   ontolearn.ea_initialization.EARandomWalkInitialization




Attributes
~~~~~~~~~~

.. autoapisummary::

   ontolearn.ea_initialization.Property
   ontolearn.ea_initialization.Object


.. py:class:: RandomInitMethod


   Bases: :py:obj:`enum.Enum`

   Generic enumeration.

   Derive from this class to define new enumerations.

   .. py:attribute:: GROW
      :type: Final

      

   .. py:attribute:: FULL
      :type: Final

      

   .. py:attribute:: RAMPED_HALF_HALF
      :type: Final

      


.. py:class:: AbstractEAInitialization


   Abstract base class for initialization methods for evolutionary algorithms.



   .. py:attribute:: __slots__
      :value: ()

      

   .. py:method:: get_population(container: Callable, pset: deap.gp.PrimitiveSetTyped, population_size: int = 0) -> List[ontolearn.ea_utils.Tree]
      :abstractmethod:


   .. py:method:: get_expression(pset: deap.gp.PrimitiveSetTyped) -> ontolearn.ea_utils.Tree
      :abstractmethod:



.. py:class:: EARandomInitialization(min_height: int = 3, max_height: int = 6, method: RandomInitMethod = RandomInitMethod.RAMPED_HALF_HALF)


   Bases: :py:obj:`AbstractEAInitialization`

   Rnndom initialization methods for evolutionary algorithms.



   .. py:attribute:: __slots__
      :value: ('min_height', 'max_height', 'method')

      

   .. py:attribute:: min_height
      :type: int

      

   .. py:attribute:: max_height
      :type: int

      

   .. py:attribute:: method
      :type: RandomInitMethod

      

   .. py:method:: get_population(container: Callable, pset: deap.gp.PrimitiveSetTyped, population_size: int = 0) -> List[ontolearn.ea_utils.Tree]


   .. py:method:: get_expression(pset: deap.gp.PrimitiveSetTyped, type_: type = None) -> ontolearn.ea_utils.Tree



.. py:data:: Property

   

.. py:data:: Object

   

.. py:class:: PropObjPair


   .. py:attribute:: property_
      :type: Property

      

   .. py:attribute:: object_
      :type: Object

      


.. py:class:: EARandomWalkInitialization(max_t: int = 2, jump_pr: float = 0.5)


   Bases: :py:obj:`AbstractEAInitialization`

   Random walk initialization for description logic learning.



   .. py:attribute:: __slots__
      :value: ('max_t', 'jump_pr', 'type_counts', 'dp_to_prim_type', 'dp_splits', 'kb')

      

   .. py:attribute:: connection_pr
      :type: float
      :value: 0.5

      

   .. py:attribute:: max_t
      :type: int

      

   .. py:attribute:: jump_pr
      :type: float

      

   .. py:attribute:: type_counts
      :type: Dict[owlapy.class_expression.OWLClass, int]

      

   .. py:attribute:: dp_to_prim_type
      :type: Dict[owlapy.owl_property.OWLDataProperty, Any]

      

   .. py:attribute:: dp_splits
      :type: Dict[owlapy.owl_property.OWLDataProperty, List[owlapy.owl_literal.OWLLiteral]]

      

   .. py:attribute:: kb
      :type: ontolearn.knowledge_base.KnowledgeBase

      

   .. py:method:: get_population(container: Callable, pset: deap.gp.PrimitiveSetTyped, population_size: int = 0, pos: List[owlapy.owl_individual.OWLNamedIndividual] = None, dp_to_prim_type: Dict[owlapy.owl_property.OWLDataProperty, Any] = None, dp_splits: Dict[owlapy.owl_property.OWLDataProperty, List[owlapy.owl_literal.OWLLiteral]] = None, kb: ontolearn.knowledge_base.KnowledgeBase = None) -> List[ontolearn.ea_utils.Tree]


   .. py:method:: get_expression(pset: deap.gp.PrimitiveSetTyped, ind: owlapy.owl_individual.OWLNamedIndividual = None) -> ontolearn.ea_utils.Tree