def main():
"""
The main function
"""
# create the network
graph = BNet( 'Water Sprinkler Bayesian Network' )
b,c = [graph.add_v( BVertex( nm, False, 1 ) ) for nm in 'b c'.split()]
a = graph.add_v(BVertex('a',True,2))
for start, end in [( a, b ), ( b, c )]:
graph.add_e( DirEdge( len( graph.e ), start, end ) )
print graph
# finalize the bayesian network once all edges have been added
graph.init_distributions()
# fill in the parameters
a.distribution.set_parameters([0.7,0.3])
b.distribution.set_parameters(mu=[2.0,0.0], sigma=[1.0,1.0])
c.distribution.set_parameters(mu=2.0, sigma=1.0, wi=1.0)
case = graph.sample(1) #---TODO: correct this bug...
print case
# NOTE : for the moment only MCMCEngine can work for continuous networks
engine = MCMCEngine(graph)
res = engine.marginalise_all()
for res in res.values():
print res
def eval(self, evidences, node):
"""
This use MCMC engine to compute the marginal on a given node
based on the given evidence
"""
#TODO: node could be a list if more than 1 node needs to be guessed
ie = MCMCEngine(self._network)
for e in evidences:
ie.set_obs(e)
return ie.marginalise(node).convert_to_cpt()
def main():
"""
Simply the main function. We watn to be clean
"""
# create the network
graph = BNet( 'Water Sprinkler Bayesian Network' )
node_a, node_b, node_c = [graph.add_v(BVertex(nm, False, 1))
for nm in 'a b c'.split()]
for start, end in [(node_a, node_b), (node_b, node_c)]:
graph.add_e(DirEdge(len(graph.e), start, end ))
print graph
# finalize the bayesian network once all edges have been added
graph.init_distributions()
# fill in the parameters
node_a.distribution.set_parameters(mu=1.0, sigma=0.5)
node_b.distribution.set_parameters(mu=2.0, sigma=1.0, wi=2.0)
node_c.distribution.set_parameters(mu=2.0, sigma=1.0, wi=1.0)
# NOTE : for the moment only MCMCEngine can work for continuous networks
engine = MCMCEngine(graph)
res = engine.marginalise_all()
for res in res.values():
print res,'\n'
