# Path: pyBN/utils/class_equivalence.py
# def are_class_equivalent(x,y):
# 	"""
# 	Check whether two Bayesian networks belong
# 	to the same equivalence class.
# 	"""
# 	are_equivalent = True
# 
# 	if set(list(x.nodes())) != set(list(y.nodes())):
# 		are_equivalent = False
# 	else:
# 		for rv in x.nodes():
# 			rv_x_neighbors = set(x.parents(rv)) + set(y.children(rv))
# 			rv_y_neighbors = set(y.parents(rv)) + set(y.children(rv))
# 			if rv_x_neighbors != rv_y_neighbors:
# 				are_equivalent =  False
# 				break
# 	return are_equivalent
# 
# Path: pyBN/utils/graph.py
# def topsort(edge_dict, root=None):
# 	"""
# 	List of nodes in topological sort order from edge dict
# 	where key = rv and value = list of rv's children
# 	"""
# 	queue = []
# 	if root is not None:
# 		queue = [root]
# 	else:
# 		for rv in edge_dict.keys():
# 			prior=True
# 			for p in edge_dict.keys():
# 				if rv in edge_dict[p]:
# 					prior=False
# 			if prior==True:
# 				queue.append(rv)
# 	
# 	visited = []
# 	while queue:
# 		vertex = queue.pop(0)
# 		if vertex not in visited:
# 			visited.append(vertex)
# 			for nbr in edge_dict[vertex]:
# 				queue.append(nbr)
# 			#queue.extend(edge_dict[vertex]) # add all vertex's children
# 	return visited


from copy import copy, deepcopy
from pyBN.utils.class_equivalence import are_class_equivalent
from pyBN.utils.graph import topsort
import numpy as np
            import pyBN.io.read as ior

                    'values', 
                    'cpt'

        *V* : a dict        

        Notes
        -----
        
        """
        if file is not None:
            bn = ior.read_bn(file)
            self.V = bn.V
            self.E = bn.E
            self.F = bn.F        
        else:
            if E is not None:
                #assert (value_dict is not None), 'Must set values if E is set.'
                self.set_structure(E, value_dict)
            else:
                self.V = []
                self.E = {}
                self.F = {}

    def __eq__(self, y):
        """
        Tests whether two Bayesian Networks are
        equivalent - i.e. they contain the same
        node/edge structure, and equality of
        conditional probabilities.
        """

# Path: pyBN/utils/class_equivalence.py
# def are_class_equivalent(x,y):
# 	"""
# 	Check whether two Bayesian networks belong
# 	to the same equivalence class.
# 	"""
# 	are_equivalent = True
# 
# 	if set(list(x.nodes())) != set(list(y.nodes())):
# 		are_equivalent = False
# 	else:
# 		for rv in x.nodes():
# 			rv_x_neighbors = set(x.parents(rv)) + set(y.children(rv))
# 			rv_y_neighbors = set(y.parents(rv)) + set(y.children(rv))
# 			if rv_x_neighbors != rv_y_neighbors:
# 				are_equivalent =  False
# 				break
# 	return are_equivalent
# 
# Path: pyBN/utils/graph.py
# def topsort(edge_dict, root=None):
# 	"""
# 	List of nodes in topological sort order from edge dict
# 	where key = rv and value = list of rv's children
# 	"""
# 	queue = []
# 	if root is not None:
# 		queue = [root]
# 	else:
# 		for rv in edge_dict.keys():
# 			prior=True
# 			for p in edge_dict.keys():
# 				if rv in edge_dict[p]:
# 					prior=False
# 			if prior==True:
# 				queue.append(rv)
# 	
# 	visited = []
# 	while queue:
# 		vertex = queue.pop(0)
# 		if vertex not in visited:
# 			visited.append(vertex)
# 			for nbr in edge_dict[vertex]:
# 				queue.append(nbr)
# 			#queue.extend(edge_dict[vertex]) # add all vertex's children
# 	return visited


# Path: pyBN/classes/bayesnet.py
from copy import copy, deepcopy
from pyBN.utils.class_equivalence import are_class_equivalent
from pyBN.utils.graph import topsort
import numpy as np
            import pyBN.io.read as ior

                    'values', 
                    'cpt'

        *V* : a dict        

        Notes
        -----
        
        """
        if file is not None:
            bn = ior.read_bn(file)
            self.V = bn.V
            self.E = bn.E
            self.F = bn.F        
        else:
            if E is not None:
                #assert (value_dict is not None), 'Must set values if E is set.'
                self.set_structure(E, value_dict)
            else:
                self.V = []
                self.E = {}
                self.F = {}

    def __eq__(self, y):
        """
        Tests whether two Bayesian Networks are
        equivalent - i.e. they contain the same
        node/edge structure, and equality of
        conditional probabilities.
        """

        return are_class_equivalent(self, y)
