EDAspy.optimization package

Subpackages

• EDAspy.optimization.custom package
  • Subpackages
    • EDAspy.optimization.custom.initialization_models package
      • Submodules
      • EDAspy.optimization.custom.initialization_models.multi_gauss_gininit module
      • EDAspy.optimization.custom.initialization_models.uni_bin_geninit module
      • EDAspy.optimization.custom.initialization_models.uni_gauss_geninit module
      • EDAspy.optimization.custom.initialization_models.uniform_geninit module
      • Module contents
    • EDAspy.optimization.custom.probabilistic_models package
      • Submodules
      • EDAspy.optimization.custom.probabilistic_models.gaussian_bayesian_network module
      • EDAspy.optimization.custom.probabilistic_models.multivariate_gaussian module
      • EDAspy.optimization.custom.probabilistic_models.univariate_binary module
      • EDAspy.optimization.custom.probabilistic_models.univariate_gaussian module
      • Module contents
  • Submodules
  • EDAspy.optimization.custom.eda_custom module
  • Module contents
• EDAspy.optimization.multivariate package
  • Submodules
  • EDAspy.optimization.multivariate.egna module
  • EDAspy.optimization.multivariate.emna module
  • Module contents
• EDAspy.optimization.univariate package
  • Submodules
  • EDAspy.optimization.univariate.umda_binary module
  • EDAspy.optimization.univariate.umda_continuous module
  • Module contents

Submodules

EDAspy.optimization.eda module

class EDAspy.optimization.eda.EDA(size_gen: int, max_iter: int, dead_iter: int, n_variables: int, alpha: float = 0.5, elite_factor: float = 0.4, disp: bool = True)

Bases: ABC

Abstract class which defines the general performance of the algorithms. The baseline of the EDA approach is defined in this object. The specific configurations is defined in the class of each specific algorithm.

export_settings() → dict

Export the configuration of the algorithm to an object to be loaded in other execution. :return: dict

property init

minimize(cost_function: callable, output_runtime: bool = True)

Parameters

• cost_function – cost function to be optimized and accepts an array as argument.

• output_runtime – true if information during runtime is desired.

Returns

EdaResult object

Return type

EdaResult

property pm

EDAspy.optimization.eda_result module

class EDAspy.optimization.eda_result.EdaResult(best_ind: array, best_cost: float, n_fev: int, history: list, settings: dict)

Bases: object

Object used to encapsulate the result and information of the EDA during the execution

Module contents