o }o™iã@sNddlmZddlZddlmZddlZerddlmZGdd„dej ƒZ dS)é)Ú annotationsN)Ú TYPE_CHECKING)ÚStudyc@s>eZdZdZddd„Zeejddd„ƒƒZejddd„ƒZ dS)Ú BaseCrossoveraæBase class for crossovers. A crossover operation is used by :class:`~optuna.samplers.NSGAIISampler` to create new parameter combination from parameters of ``n`` parent individuals. .. note:: Concrete implementations of this class are expected to only accept parameters from numerical distributions. At the moment, only crossover operation for categorical parameters (uniform crossover) is built-in into :class:`~optuna.samplers.NSGAIISampler`. ÚreturnÚstrcCs|jjS)N)Ú __class__Ú__name__©Úself©r ú\/home/ubuntu/.local/lib/python3.10/site-packages/optuna/samplers/nsgaii/_crossovers/_base.pyÚ__str__szBaseCrossover.__str__ÚintcCót‚)z;Number of parent individuals required to perform crossover.©ÚNotImplementedErrorr r r r Ú n_parentsszBaseCrossover.n_parentsÚparents_paramsú np.ndarrayÚrngúnp.random.RandomStateÚstudyrÚsearch_space_boundscCr)aaPerform crossover of selected parent individuals. This method is called in :func:`~optuna.samplers.NSGAIISampler.sample_relative`. Args: parents_params: A ``numpy.ndarray`` with dimensions ``num_parents x num_parameters``. Represents a parameter space for each parent individual. This space is continuous for numerical parameters. rng: An instance of ``numpy.random.RandomState``. study: Target study object. search_space_bounds: A ``numpy.ndarray`` with dimensions ``len_search_space x 2`` representing numerical distribution bounds constructed from transformed search space. Returns: A 1-dimensional ``numpy.ndarray`` containing new parameter combination. r)r rrrrr r r Ú crossover#szBaseCrossover.crossoverN)rr)rr) rrrrrrrrrr) r Ú __module__Ú __qualname__Ú__doc__rÚpropertyÚabcÚabstractmethodrrr r r r r s r) Ú __future__rrÚtypingrÚnumpyÚnpÚ optuna.studyrÚABCrr r r r Ús