def create_timeslice(self, state):
for node_x in self.__initial_nodes:
for node_y in state:
if node_x.name == node_y.name:
cpd = ProbabilityTable()
cpd.add_variable(node_x)
node_x.set_cpd(cpd)
node_x.set_probability(1., [(node_x, state[node_y])])
return self
class DiscreteNode(RandomNode):
'''#TODO: write doc'''
def __init__(self, name, value_range):
super(DiscreteNode, self).__init__(name)
self.value_range = value_range
self.cpd = ProbabilityTable()
self.cpd.add_variable(self)
def announce_parent(self, node):
self.cpd.add_variable(node)
def __str__(self):
return self.name # + "\n" + str(self.cpd)
def set_probability(self, value, node_value_pairs):
self.cpd.set_probability(value, node_value_pairs)
def set_probability_table(self, table, nodes):
self.cpd.set_probability_table(table, nodes)
def get_cpd_reduced(self, evidence):
return self.cpd.reduction(evidence)
def get_value_range(self):
return self.value_range
def set_cpd(self, cpd):
self.cpd = cpd
def get_cpd(self):
return self.cpd
def is_valid(self):
return self.cpd.is_normalized_as_cpt(self)
def calculate_marginal(self,variables):
''' If evidence is set, then this methods calculates the posterior marginal.
With an empty evidence this is automatically the prior marginal.'''
if not self.graph.graph['messagesValid']:
self.calculate_messages()
resPT = ProbabilityTable.get_neutral_multiplication_PT()
for f in self.graph.nodes():
if f.get_node() in variables:
resPT = resPT.multiplication(self.calculate_marginal_forOne(f))
resPT = resPT.normalize_as_jpt()
return resPT
def calculate_messages(self):
''' Calculates the messages and stores the intermediate results.'''
self.pull_phase(self.rootNode,self.graph)
self.push_phase(self.rootNode,self.graph,ProbabilityTable.get_neutral_multiplication_PT())
self.graph.graph['messagesValid'] = True
burglary.set_probability_table(burglary_cpt, [burglary])
alarm_cpt=numpy.array([[0.8,0.15,0.05],[0.05,0.9,0.05]])
alarm.set_probability_table(alarm_cpt, [burglary,alarm])
#Construct a Markov Chain by sampling states from this Network
transition_model = GibbsTransitionModel()
mcs = MarkovChainSampler()
initial_state={burglary:"Safe",alarm:"Silent"}
chain = mcs.generateMarkovChain(bn, 5000, transition_model, initial_state)
#for c in chain:
# print c
pt = ProbabilityTable()
pt.add_variable(burglary)
pt.add_variable(alarm)
pt.to_jpt_by_states(chain)
print "----joint-probability----"
print pt
print "----burglary----"
print pt.marginalization(alarm)
print "----alarm----"
print pt.division(burglary.get_cpd())
bn.draw()
def __init__(self, name, value_range):
super(DiscreteNode, self).__init__(name)
self.value_range = value_range
self.cpd = ProbabilityTable()
self.cpd.add_variable(self)
