def to_markov_model(self):
"""
Converts the factor graph into markov model.
A markov model contains nodes as random variables and edge between
two nodes imply interaction between them.
Examples
--------
>>> from pgmpy.models import FactorGraph
>>> from pgmpy.factors.discrete import DiscreteFactor
>>> G = FactorGraph()
>>> G.add_nodes_from(['a', 'b', 'c'])
>>> phi1 = DiscreteFactor(['a', 'b'], [2, 2], np.random.rand(4))
>>> phi2 = DiscreteFactor(['b', 'c'], [2, 2], np.random.rand(4))
>>> G.add_factors(phi1, phi2)
>>> G.add_nodes_from([phi1, phi2])
>>> G.add_edges_from([('a', phi1), ('b', phi1),
... ('b', phi2), ('c', phi2)])
>>> mm = G.to_markov_model()
"""
mm = MarkovModel()
variable_nodes = self.get_variable_nodes()
if len(set(self.nodes()) - set(variable_nodes)) != len(self.factors):
raise ValueError('Factors not associated with all the factor nodes.')
mm.add_nodes_from(variable_nodes)
for factor in self.factors:
scope = factor.scope()
mm.add_edges_from(itertools.combinations(scope, 2))
mm.add_factors(factor)
return mm
def to_markov_model(self):
"""
Converts the factor graph into markov model.
A markov model contains nodes as random variables and edge between
two nodes imply interaction between them.
Examples
--------
>>> from pgmpy.models import FactorGraph
>>> from pgmpy.factors.discrete import DiscreteFactor
>>> G = FactorGraph()
>>> G.add_nodes_from(['a', 'b', 'c'])
>>> phi1 = DiscreteFactor(['a', 'b'], [2, 2], np.random.rand(4))
>>> phi2 = DiscreteFactor(['b', 'c'], [2, 2], np.random.rand(4))
>>> G.add_factors(phi1, phi2)
>>> G.add_nodes_from([phi1, phi2])
>>> G.add_edges_from([('a', phi1), ('b', phi1),
... ('b', phi2), ('c', phi2)])
>>> mm = G.to_markov_model()
"""
mm = MarkovModel()
variable_nodes = self.get_variable_nodes()
if len(set(self.nodes()) - set(variable_nodes)) != len(self.factors):
raise ValueError('Factors not associated with all the factor nodes.')
mm.add_nodes_from(variable_nodes)
for factor in self.factors:
scope = factor.scope()
mm.add_edges_from(itertools.combinations(scope, 2))
mm.add_factors(factor)
return mm
def to_markov_model(self):
"""
Converts bayesian model to markov model. The markov model created would
be the moral graph of the bayesian model.
Examples
--------
>>> from pgmpy.models import BayesianModel
>>> G = BayesianModel([('diff', 'grade'), ('intel', 'grade'),
... ('intel', 'SAT'), ('grade', 'letter')])
>>> mm = G.to_markov_model()
>>> mm.nodes()
['diff', 'grade', 'intel', 'SAT', 'letter']
>>> mm.edges()
[('diff', 'intel'), ('diff', 'grade'), ('intel', 'grade'),
('intel', 'SAT'), ('grade', 'letter')]
"""
moral_graph = self.moralize()
mm = MarkovModel(moral_graph.edges())
mm.add_factors(*[cpd.to_factor() for cpd in self.cpds])
return mm
def to_markov_model(self):
"""
Converts bayesian model to markov model. The markov model created would
be the moral graph of the bayesian model.
Examples
--------
>>> from pgmpy.models import BayesianModel
>>> G = BayesianModel([('diff', 'grade'), ('intel', 'grade'),
... ('intel', 'SAT'), ('grade', 'letter')])
>>> mm = G.to_markov_model()
>>> mm.nodes()
['diff', 'grade', 'intel', 'SAT', 'letter']
>>> mm.edges()
[('diff', 'intel'), ('diff', 'grade'), ('intel', 'grade'),
('intel', 'SAT'), ('grade', 'letter')]
"""
moral_graph = self.moralize()
mm = MarkovModel(moral_graph.edges())
mm.add_factors(*[cpd.to_factor() for cpd in self.cpds])
return mm