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
- EDAspy.optimization.custom.initialization_models package
- Submodules
- EDAspy.optimization.custom.eda_custom module
- Module contents
- Subpackages
- EDAspy.optimization.multivariate package
- EDAspy.optimization.univariate package
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)[source]
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 [source]
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)[source]
- 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
- property pm