Changelog

v0.3.4

Improvements on the code that checks that a matrix positive definite.
A bug affecting the learning of conditional Bayesian networks with MMHC has been fixed. This bug also affected DMMHC.
Fixed a bug that affected the type of the parameter bn_type of MMHC.estimate, MMHC.estimate_conditional and DMMHC.estimate.

Adds support for pyarrow 5.0.0 in the PyPi wheels.
Added Arguments.args to access the args and kwargs for a node.
Added BayesianNetworkBase.underlying_node_type to get the underlying node type of a node given some data.
Improves the fitting of hybrid factors. Now, an specific discrete configuration can be left unfitted if the base continuous factor raises SingularCovarianceData.
Improves the LinearGaussianCPD fit when the covariance matrix of the data is singular.
Improves the NormalReferenceRule, ScottsBandwidth, and UCV estimation when the covariance of the data is singular.
Fixes a bug loading an heterogeneous Bayesian network from a file.
Introduces a check that a needed category exists in discrete data.
Assignment now supports integer numbers converting them automatically to float.
Fix a bug in GreedyHillClimbing that caused the return of Bayesian networks with UnknownFactorType.
Reduces memory usage when fitting and printing an hybrid Factor.
Fixes a precision bug in GreedyHillClimbing.
Improves CrossValidation parameter checking.

Fixed a bug in the UCV bandwidth selector that may cause segmentation fault.
Added some checks to ensure that the categorical data is of type string.
Fixed the GreedyHillClimbing iteration counter, which was begin increased twice per iteration.
Added a default parameter value for include_cpd in BayesianNetworkBase.save and DynamicBayesianNetworkBase.save.
Added more checks to detect ill-conditioned regression problems. The BIC score returns -infinity for ill-conditioned regression problems.

Fixed the build process to support CMake versions older than 3.13.
Fixed a bug that might raise an error with a call to FactorType.new_factor with *args and **kwargs arguments . This bug was only reproducible if the library was compiled with gcc.
Added CMake as prerequisite to compile the library in the docs.

Removed all the submodules to simplify the imports. Now, all the classes are accessible directly from the pybnesian root module.
Added a ProductKDE class that implements KDE with diagonal bandwidth matrix.
Added an abstract class BandwidthSelector to implement bandwidth selection for KDE and ProductKDE. Three concrete implementations of bandwidth selection are included: ScottsBandwidth, NormalReferenceRule and UCV.
Added Arguments, Args and Kwargs to store a set of arguments to be used to create new factors through FactorType.new_factor. The Arguments are accepted by BayesianNetworkBase.fit and the constructors of CVLikelihood, HoldoutLikelihood and ValidatedLikelihood.

An error related to the processing of categorical data with too many categories has been corrected.
Removed -march=native flag in the build script to avoid the use of instruction sets not available on some CPUs.

Added conditional linear Gaussian networks (CLGNetworkType, CLGNetwork, ConditionalCLGNetwork and DynamicCLGNetwork).
Implemented ChiSquare (and DynamicChiSquare) indepencence test.
Implemented MutualInformation (and DynamicMutualInformation) indepencence test. This independence test is valid for hybrid data.
Implemented BDe (Bayesian Dirichlet equivalent) score (and DynamicBDe).
Added UnknownFactorType as default FactorType for Bayesian networks when the node type could not be deduced.
Added Assignment class to represent the assignment of values to variables.

API changes: