This version solves several bugs related to the number of function evaluations counter.
This version adds the Latin Hypercube sampling as a new initializer.
All the continuous optimization algorithms are now initialized using Uniform sampling.
This version adds the Discrete Bayesian network probabilistic model.
This version adds the Estimation of Bayesian network algorithm for categorical variables.
This version adds the categorical variant of the UMDA approach.
This version adds the categorical data initializer.
This version solves several issues related to the documentations.
This version allows to use different upper and lower bounds in each of the dimensions of the algorithm.
This version removes old versions of discrete EDAs related to time series selection. Now can be implemented with new and more efficient versions.
This version implements the SPEDA algorithm to allow dependencies between variables that fit Gaussian distributions and KDE nodes.
This version implements the multivariate version of KEDA, which shares all the characteristics with the SPEDA approach, with the exception that all the nodes have to be estimated with KDE. Gaussian nodes are forbidden.
This version implements a function to plot the BN structure learnt in the EDA implementations.
This version enforces the tests to avoid bugs in the algorithms.
This version implements the possibility of settings white and black boxes to set the mandatory or forbidden arcs in the BN structure learnt in each iteration.
This version solves several bugs present in v1.0.2.
This version implements the parallelization for all the EDAs.
This version allows initialize the algorithm from a custom set of samples.
This version implements the multivariate and univariate KEDA algorithms, where variables are estimated using KDE.
This version solves a bug in the EGNA optimizer related to the Gaussian Bayesian network structure learning.
This version solves a bug in the UMDAd optimizer related to the limits of the std in each variable.
This version implies a change in the way of using the EDAs.
All EDAs extend an abstract class so, all EDAs have the some outline and the same minimize function.
The cost function is now used only for the minimize function, so it is easier to be used.
The probabilistic models and initialization models are treated separately from the EDA implementations so the user is able to decide whether to use a probabilistic model or other in the EDAs custom implementation.
Th user is able to export and read the configuration of and EDA in order to re-use the same implementation in the future.
All the EDA implementations have their own name according to the state-of-the-art of EDAs.
More tests have been added.
Documentation has been redone.
Deprecation warning to TimeSeries selector. This class will be formatted. in following versions.
The structure in the package has been removed and also the names.
The implementation of EGAN with evidences has been removed to avoid having rpy2 as a dependency.
Time series transformations selection was added as a new functionality of the package.
Added a notebooks section to show some real use cases of EDAspy. (3 implementations)
Added documentation to readdocs.
First operative version 4 EDAs implemented.
univariate EDA discrete.
Univariate EDA continuous.
Multivariate continuous EDA with evidences
Multivariate continuous EDA with no evidences gaussian distribution.