class EvidenceSelect(variables, features, balance, interval_evidence_count = 10,subgraph_limit_num,k_hop)[source]
the class for selecting evidence, select evidential variables for latent variables, including two methods for selecting evidential variables, and allow users to define their own methods Parameters:
- variables – One of the input data of gml, the variable of the factor graph
- features – One of the input data of gml, the feature of factor graph
- interval_evidence_limit – When dividing interval sampling evidence, the number of evidences sampled in each interval
- subgraph_limit_num – Maximum number of variables allowed in the subgraph
- each_feature_evidence_limit – When sampling randomly, the number of evidence for each single factor sampling
This class currently provides the following methods：
- evidence_select(var_id)[source]
Function: Provide a unified evidence selection method, which can be used to construct factor graphs containing parameterized single factor, non-parameterized single factor, and double factor. In this case, call this function. Parameters：
· var_id - the id of latent variable
Return：connected_var_set, connected_edge_set, connected_feature_set
Return type：set