<s> from collections import defaultdict <EOL> from itertools import chain <EOL> from pgmpy . models import BayesianModel <EOL> from pgmpy . models import MarkovModel <EOL> from pgmpy . models import FactorGraph <EOL> from pgmpy . models import JunctionTree <EOL> from pgmpy . models import DynamicBayesianNetwork <EOL> from pgmpy . utils import StateNameInit <EOL> class Inference ( object ) : <EOL> """<STR_LIT>""" <EOL> @ StateNameInit ( ) <EOL> def __init__ ( self , model ) : <EOL> self . model = model <EOL> model . check_model ( ) <EOL> if isinstance ( model , JunctionTree ) : <EOL> self . variables = set ( chain ( * model . nodes ( ) ) ) <EOL> else : <EOL> self . variables = model . nodes ( ) <EOL> self . cardinality = { } <EOL> self . factors = defaultdict ( list ) <EOL> if isinstance ( model , BayesianModel ) : <EOL> for node in model . nodes ( ) : <EOL> cpd = model . get_cpds ( node ) <EOL> cpd_as_factor = cpd . to_factor ( ) <EOL> self . cardinality [ node ] = cpd . variable_card <EOL> for var in cpd . variables : <EOL> self . factors [ var ] . append ( cpd_as_factor ) <EOL> elif isinstance ( model , ( MarkovModel , FactorGraph , JunctionTree ) ) : <EOL> self . cardinality = model . get_cardinality ( ) <EOL> for factor in model . get_factors ( ) : <EOL> for var in factor . variables : <EOL> self . factors [ var ] . append ( factor ) <EOL> elif isinstance ( model , DynamicBayesianNetwork ) : <EOL> self . start_bayesian_model = BayesianModel ( model . get_intra_edges ( <NUM_LIT:0> ) ) <EOL> self . start_bayesian_model . add_cpds ( * model . get_cpds ( time_slice = <NUM_LIT:0> ) ) <EOL> cpd_inter = [ model . get_cpds ( node ) for node in model . get_interface_nodes ( <NUM_LIT:1> ) ] <EOL> self . interface_nodes = model . get_interface_nodes ( <NUM_LIT:0> ) <EOL> self . one_and_half_model = BayesianModel ( model . get_inter_edges ( ) + model . get_intra_edges ( <NUM_LIT:1> ) ) <EOL> self . one_and_half_model . add_cpds ( * ( model . get_cpds ( time_slice = <NUM_LIT:1> ) + cpd_inter ) ) </s>
