EDAspy.optimization.custom.probabilistic_models package
Submodules
EDAspy.optimization.custom.probabilistic_models.gaussian_bayesian_network module
- class EDAspy.optimization.custom.probabilistic_models.gaussian_bayesian_network.GBN(variables: list)[source]
Bases:
ProbabilisticModelThis probabilistic model is Gaussian Bayesian Network. All the relationships between the variables in the model are defined to be linearly Gaussian, and the variables distributions are assumed to be Gaussian. This is a very common approach when facing to continuous data as it is relatively easy and fast to learn a Gaussian distributions between variables. This implementation uses Pybnesian library [1].
References
[1]: Atienza, D., Bielza, C., & Larrañaga, P. (2022). PyBNesian: an extensible Python package for Bayesian networks. Neurocomputing, 504, 204-209.
- learn(dataset: array)[source]
Learn a Gaussian Bayesian network from the dataset passed as argument.
- Parameters
dataset – dataset from which learn the GBN.
- print_structure() list[source]
Prints the arcs between the nodes that represent the variables in the dataset. This function must be used after the learning process.
- Returns
list of arcs between variables
- Return type
list
- sample(size: int) array[source]
Samples the Gaussian Bayesian network several times defined by the user. The dataset is returned as a numpy matrix. The sampling process is implemented using probabilistic logic sampling.
- Parameters
size – number of samplings of the Gaussian Bayesian network.
- Returns
array with the dataset sampled.
- Return type
np.array
EDAspy.optimization.custom.probabilistic_models.multivariate_gaussian module
- class EDAspy.optimization.custom.probabilistic_models.multivariate_gaussian.MultiGauss(variables: list, lower_bound: float, upper_bound: float)[source]
Bases:
ProbabilisticModelThis class implements all the code needed to learn and sample multivariate Gaussian distributions defined by a vector of means and a covariance matrix among the variables. This is a simpler approach compared to Gaussian Bayesian networks, as multivariate Gaussian distributions do not identify conditional dependeces between the variables.
EDAspy.optimization.custom.probabilistic_models.univariate_binary module
- class EDAspy.optimization.custom.probabilistic_models.univariate_binary.UniBin(variables: list, upper_bound: float, lower_bound: float)[source]
Bases:
ProbabilisticModelThis is the simplest probabilistic model implemented in this package. This is used for binary EDAs where all the solutions are binary. The implementation involves a vector of independent probabilities [0, 1]. When sampling, a random float is sampled [0, 1]. If the float is below the probability, then the sampling is a 1. Thus, the probabilities show probabilities of a sampling being 1.
EDAspy.optimization.custom.probabilistic_models.univariate_gaussian module
- class EDAspy.optimization.custom.probabilistic_models.univariate_gaussian.UniGauss(variables: list, lower_bound: float)[source]
Bases:
ProbabilisticModelThis class implements the univariate Gaussians. With this implementation we are updating N univariate Gaussians in each iteration. When a dataset is given, each column is updated independently. The implementation involves a matrix with two rows, in which the first row are the means and the second one, are the standard deviations.