This module implements the BDeuScore class, which calculates the Bayesian Dirichlet equivalent uniform (BDeu) score for a given graph structure.
 
The BDeu score is used in Bayesian network structure learning to evaluate the fit of a graph to a dataset. It considers the conditional probabilities of nodes given their parents and incorporates a prior distribution controlled by the alpha parameter.
 
Classes:
    BDeuScore: Computes the BDeu score for a graph structure based on the provided data.

Modules
		numpy pandas

Classes
		structure_learning.scores.score.Score(abc.ABC) BDeuScore   class BDeuScore(structure_learning.scores.score.Score)     BDeuScore(data: Union[structure_learning.data.data.Data, pandas.core.frame.DataFrame], alpha=10)   BDeu Score     Method resolution order: BDeuScore structure_learning.scores.score.Score abc.ABC builtins.object Methods defined here: __init__(self, data: Union[structure_learning.data.data.Data, pandas.core.frame.DataFrame], alpha=10) Initialise BDeuScore instance.   Parameters:     data (pandas.DataFrame): data compute(self, graph: structure_learning.data_structures.graph.Graph) Compute BDeu score.   Parameter:     graph (Graph):      Graph object Returns:     (float): BDeu score compute_node_with_edges(self, node: str, parents: list, node_index_map: dict = None) Adapted from https://github.com/pgmpy/pgmpy/blob/dev/pgmpy/estimators/StructureScore.py Data and other attributes defined here: __abstractmethods__ = frozenset() Methods inherited from structure_learning.scores.score.Score: compute_node(self, graph: structure_learning.data_structures.graph.Graph, node: str) Implements a score function (e.g. BGe, Marginal Likelihood, etc) for a specific node Readonly properties inherited from structure_learning.scores.score.Score: node_labels Data descriptors inherited from structure_learning.scores.score.Score: __dict__ dictionary for instance variables __weakref__ list of weak references to the object data

Data
		Union = typing.Union gammaln = <ufunc 'gammaln'>