<s> from itertools import combinations <EOL> from collections import defaultdict <EOL> import numpy as np <EOL> import networkx as nx <EOL> from pgmpy . factors import TabularCPD <EOL> from pgmpy . base import DirectedGraph , UndirectedGraph <EOL> class DynamicBayesianNetwork ( DirectedGraph ) : <EOL> def __init__ ( self , ebunch = None ) : <EOL> """<STR_LIT>""" <EOL> super ( DynamicBayesianNetwork , self ) . __init__ ( ) <EOL> if ebunch : <EOL> self . add_edges_from ( ebunch ) <EOL> self . cpds = [ ] <EOL> self . cardinalities = defaultdict ( int ) <EOL> def add_node ( self , node , ** attr ) : <EOL> """<STR_LIT>""" <EOL> super ( DynamicBayesianNetwork , self ) . add_node ( ( node , <NUM_LIT:0> ) , ** attr ) <EOL> def add_nodes_from ( self , nodes , ** attr ) : <EOL> """<STR_LIT>""" <EOL> for node in nodes : <EOL> self . add_node ( node ) <EOL> def nodes ( self ) : <EOL> """<STR_LIT>""" <EOL> return list ( set ( [ node for node , timeslice in <EOL> super ( DynamicBayesianNetwork , self ) . nodes ( ) ] ) ) <EOL> def add_edge ( self , start , end , ** kwargs ) : <EOL> """<STR_LIT>""" <EOL> try : <EOL> if len ( start ) != <NUM_LIT:2> or len ( end ) != <NUM_LIT:2> : <EOL> raise ValueError ( '<STR_LIT>' ) <EOL> elif not isinstance ( start [ <NUM_LIT:1> ] , int ) or not isinstance ( end [ <NUM_LIT:1> ] , int ) : <EOL> raise ValueError ( '<STR_LIT>' ) <EOL> elif start [ <NUM_LIT:1> ] == end [ <NUM_LIT:1> ] : <EOL> start = ( start [ <NUM_LIT:0> ] , <NUM_LIT:0> ) <EOL> end = ( end [ <NUM_LIT:0> ] , <NUM_LIT:0> ) <EOL> elif start [ <NUM_LIT:1> ] == end [ <NUM_LIT:1> ] - <NUM_LIT:1> : <EOL> start = ( start [ <NUM_LIT:0> ] , <NUM_LIT:0> ) <EOL> end = ( end [ <NUM_LIT:0> ] , <NUM_LIT:1> ) <EOL> elif start [ <NUM_LIT:1> ] > end [ <NUM_LIT:1> ] : <EOL> raise NotImplementedError ( '<STR_LIT>' ) <EOL> elif start [ <NUM_LIT:1> ] != end [ <NUM_LIT:1> ] : <EOL> raise ValueError ( "<STR_LIT>" ) <EOL> except TypeError : <EOL> raise ValueError ( '<STR_LIT>' ) <EOL> if start == end : <EOL> raise ValueError ( '<STR_LIT>' ) <EOL> elif start in super ( DynamicBayesianNetwork , self ) . nodes ( ) and end in super ( DynamicBayesianNetwork , self ) . nodes ( ) and nx . has_path ( self , end , start ) : <EOL> raise ValueError ( '<STR_LIT>' . format ( <EOL> start = str ( start ) , end = str ( end ) ) ) <EOL> super ( DynamicBayesianNetwork , self ) . add_edge ( start , end , ** kwargs ) <EOL> if start [ <NUM_LIT:1> ] == end [ <NUM_LIT:1> ] : <EOL> super ( DynamicBayesianNetwork , self ) . add_edge ( ( start [ <NUM_LIT:0> ] , <NUM_LIT:1> - start [ <NUM_LIT:1> ] ) , ( end [ <NUM_LIT:0> ] , <NUM_LIT:1> - end [ <NUM_LIT:1> ] ) ) <EOL> else : <EOL> super ( DynamicBayesianNetwork , self ) . add_node ( ( end [ <NUM_LIT:0> ] , <NUM_LIT:1> - end [ <NUM_LIT:1> ] ) ) <EOL> def add_edges_from ( self , ebunch , ** kwargs ) : <EOL> """<STR_LIT>""" <EOL> for edge in ebunch : <EOL> self . add_edge ( edge [ <NUM_LIT:0> ] , edge [ <NUM_LIT:1> ] ) <EOL> def get_intra_edges ( self , time_slice = <NUM_LIT:0> ) : <EOL> """<STR_LIT>""" <EOL> if not isinstance ( time_slice , int ) or time_slice < <NUM_LIT:0> : <EOL> raise ValueError ( "<STR_LIT>" ) <EOL> return [ tuple ( ( x [ <NUM_LIT:0> ] , time_slice ) for x in edge ) for edge in self . edges ( ) if edge [ <NUM_LIT:0> ] [ <NUM_LIT:1> ] == edge [ <NUM_LIT:1> ] [ <NUM_LIT:1> ] == <NUM_LIT:0> ] <EOL> def get_inter_edges ( self ) : <EOL> """<STR_LIT>""" <EOL> return [ edge for edge in self . edges ( ) if edge [ <NUM_LIT:0> ] [ <NUM_LIT:1> ] != edge [ <NUM_LIT:1> ] [ <NUM_LIT:1> ] ] <EOL> def get_interface_nodes ( self , time_slice = <NUM_LIT:0> ) : <EOL> """<STR_LIT>""" <EOL> if not isinstance ( time_slice , int ) or time_slice < <NUM_LIT:0> : <EOL> raise ValueError ( "<STR_LIT>" ) <EOL> return [ ( edge [ <NUM_LIT:0> ] [ <NUM_LIT:0> ] , time_slice ) for edge in self . get_inter_edges ( ) ] <EOL> def get_slice_nodes ( self , time_slice = <NUM_LIT:0> ) : <EOL> """<STR_LIT>""" <EOL> if not isinstance ( time_slice , int ) or time_slice < <NUM_LIT:0> : <EOL> raise ValueError ( "<STR_LIT>" ) <EOL> return [ ( node , time_slice ) for node in self . nodes ( ) ] <EOL> def add_cpds ( self , * cpds ) : <EOL> """<STR_LIT>""" <EOL> for cpd in cpds : <EOL> if not isinstance ( cpd , TabularCPD ) : <EOL> raise ValueError ( '<STR_LIT>' ) <EOL> if set ( cpd . variables ) - set ( cpd . variables ) . intersection ( set ( <EOL> super ( DynamicBayesianNetwork , self ) . nodes ( ) ) ) : <EOL> raise ValueError ( '<STR_LIT>' , cpd ) <EOL> self . cpds . extend ( cpds ) <EOL> def get_cpds ( self , node = None , time_slice = <NUM_LIT:0> ) : <EOL> """<STR_LIT>""" <EOL> if node : <EOL> if node not in super ( DynamicBayesianNetwork , self ) . nodes ( ) : <EOL> raise ValueError ( '<STR_LIT>' ) <EOL> else : <EOL> for cpd in self . cpds : <EOL> if cpd . variable == node : <EOL> return cpd <EOL> else : <EOL> return [ cpd for cpd in self . cpds if set ( list ( cpd . variables ) ) . issubset ( self . get_slice_nodes ( time_slice ) ) ] <EOL> def remove_cpds ( self , * cpds ) : <EOL> """<STR_LIT>""" <EOL> for cpd in cpds : <EOL> if isinstance ( cpd , ( tuple , list ) ) : <EOL> cpd = self . get_cpds ( cpd ) <EOL> self . cpds . remove ( cpd ) <EOL> def check_model ( self ) : <EOL> """<STR_LIT>""" <EOL> for node in super ( DynamicBayesianNetwork , self ) . nodes ( ) : <EOL> cpd = self . get_cpds ( node = node ) <EOL> if isinstance ( cpd , TabularCPD ) : <EOL> evidence = cpd . variables [ : <NUM_LIT:0> : - <NUM_LIT:1> ] <EOL> evidence_card = cpd . cardinality [ : <NUM_LIT:0> : - <NUM_LIT:1> ] <EOL> parents = self . get_parents ( node ) <EOL> if set ( evidence ) != set ( parents if parents else [ ] ) : <EOL> raise ValueError ( "<STR_LIT>" <EOL> "<STR_LIT>" . format ( node = node ) ) <EOL> if not np . allclose ( cpd . to_factor ( ) . marginalize ( [ node ] , inplace = False ) . values . flatten ( '<STR_LIT:C>' ) , <EOL> np . ones ( np . product ( evidence_card ) ) , <EOL> atol = <NUM_LIT> ) : <EOL> raise ValueError ( '<STR_LIT>' <EOL> '<STR_LIT>' . format ( node = node ) ) <EOL> return True <EOL> def initialize_initial_state ( self ) : <EOL> """<STR_LIT>""" <EOL> for cpd in self . cpds : <EOL> temp_var = ( cpd . variable [ <NUM_LIT:0> ] , <NUM_LIT:1> - cpd . variable [ <NUM_LIT:1> ] ) <EOL> parents = self . get_parents ( temp_var ) <EOL> if not any ( x . variable == temp_var for x in self . cpds ) : <EOL> if all ( x [ <NUM_LIT:1> ] == parents [ <NUM_LIT:0> ] [ <NUM_LIT:1> ] for x in parents ) : <EOL> if parents : <EOL> evidence_card = cpd . cardinality [ : <NUM_LIT:0> : - <NUM_LIT:1> ] <EOL> new_cpd = TabularCPD ( temp_var , cpd . variable_card , <EOL> cpd . values . reshape ( cpd . variable_card , np . prod ( evidence_card ) ) , <EOL> parents , evidence_card ) <EOL> else : <EOL> if cpd . get_evidence ( ) : <EOL> initial_cpd = cpd . marginalize ( cpd . get_evidence ( ) , inplace = False ) <EOL> new_cpd = TabularCPD ( temp_var , cpd . variable_card , np . reshape ( initial_cpd . values , ( - <NUM_LIT:1> , <NUM_LIT:2> ) ) ) <EOL> else : <EOL> new_cpd = TabularCPD ( temp_var , cpd . variable_card , np . reshape ( cpd . values , ( - <NUM_LIT:1> , <NUM_LIT:2> ) ) ) <EOL> self . add_cpds ( new_cpd ) <EOL> self . check_model ( ) <EOL> def moralize ( self ) : <EOL> """<STR_LIT>""" <EOL> moral_graph = self . to_undirected ( ) <EOL> for node in super ( DynamicBayesianNetwork , self ) . nodes ( ) : <EOL> moral_graph . add_edges_from ( combinations ( <EOL> self . get_parents ( node ) , <NUM_LIT:2> ) ) <EOL> return moral_graph <EOL> def copy ( self ) : <EOL> """<STR_LIT>""" <EOL> dbn = DynamicBayesianNetwork ( ) <EOL> dbn . add_nodes_from ( self . nodes ( ) ) <EOL> dbn . add_edges_from ( self . edges ( ) ) <EOL> cpd_copy = [ cpd . copy ( ) for cpd in self . get_cpds ( ) ] <EOL> dbn . add_cpds ( * cpd_copy ) <EOL> return dbn </s>
