def sample_global(self, global_state, variable, allowed_values):
'''
This method can be used to sample from the density according to a global
state containing values for all other variables that this node belongs to.
@param global_state: A Dict (node -> value) that must hold a value for
all variables that this density depends on. Except for the variable
that it is directly associated with.
@param variable: The variable that this density is directly associated
with.
@param allowed_values: A list of values that are allowed to be sampled.
This is necessary because external evidence may restrict the value
range.
@returns: The sampled value.
'''
node_value_pairs=[(node,global_state[node]) for node in self.variables if node != variable]
probabilities=[self.get_probability(node_value_pairs+[(variable,value)]) for value in allowed_values]
idx=weighted_random(probabilities)
return allowed_values[idx]
def weighted_sample(network, evidence = {}):
'''
Each nonevidence variable is sampled according to the conditional
distribution given the values already sampled for the variable's parents,
while a weight isaccumulated based on the likelihood for each evidence
variable.
See "Artificial Intelligence: A Modern Approach (Third edition)" by
Stuart Russell and Peter Norvig (p. 534)
Keyword arguments:
network -- a Bayesian network
evidence -- a dict of observed values
Returns a new sample as tuple of state and weight (state, w).
'''
w = 1.0
state = {}
if not isinstance(network, primo.networks.BayesianNetwork):
raise Exception("The given network is not an instance of BayesNet.")
nodes = network.get_nodes_in_topological_sort()
for node in nodes:
#reduce this node's cpd
parents = network.get_parents(node)
if parents:
evidence_tmp = [(parent, state[parent]) for parent in parents]
reduced_cpd = node.get_cpd_reduced(evidence_tmp)
else:
reduced_cpd = node.get_cpd()
# (re-)calulate weight
if node in evidence:
w *= reduced_cpd.get_table()[node.get_value_range().index(evidence[node])]
state[node] = node.get_value_range().index(evidence[node])
else:
# sample state
new_state = weighted_random(reduced_cpd.get_table())
state[node] = node.get_value_range()[new_state]
return (state, w)
def weighted_sample(network, evidence = {}):
'''
Each nonevidence variable is sampled according to the conditional
distribution given the values already sampled for the variable's parents,
while a weight isaccumulated based on the likelihood for each evidence
variable.
See "Artificial Intelligence: A Modern Approach (Third edition)" by
Stuart Russell and Peter Norvig (p. 534)
Keyword arguments:
network -- a Bayesian network
evidence -- a dict of observed values
Returns a new sample as tuple of state and weight (state, w).
'''
w = 1.0
state = {}
if not isinstance(network, primo.networks.BayesianNetwork):
raise Exception("The given network is not an instance of BayesNet.")
nodes = network.get_nodes_in_topological_sort()
for node in nodes:
#reduce this node's cpd
parents = network.get_parents(node)
if parents:
evidence_tmp = [(parent, state[parent]) for parent in parents]
reduced_cpd = node.get_cpd_reduced(evidence_tmp)
else:
reduced_cpd = node.get_cpd()
# (re-)calulate weight
if node in evidence:
w *= reduced_cpd.get_table()[node.get_value_range().index(evidence[node])]
state[node] = node.get_value_range().index(evidence[node])
else:
# sample state
new_state = weighted_random(reduced_cpd.get_table())
state[node] = node.get_value_range()[new_state]
return (state, w)
def transition(self, network, state, extern_evidence):
'''
Does one single state transition.
@param network: A BayesNet.
@param state: The current state of the BayesNet. Given as a Dict from
RandomNode to Value
@param extern_evidence: Evidence that is given by the user. This is a
Dict from Node to Evidence.
'''
nodes = network.get_nodes([])
nodes_to_resample = [
n for n in nodes if not n in extern_evidence.keys()
or extern_evidence[n].get_unique_value() == None
]
for node in nodes_to_resample:
parents = network.get_parents(node)
if parents:
evidence = [(parent, state[parent]) for parent in parents]
reduced_cpd = node.get_cpd_reduced(evidence)
else:
reduced_cpd = node.get_cpd()
#reduce the children's cpds
children = network.get_children(node)
for child in children:
#reduce this node's cpd
parents = network.get_parents(child)
evidence = [(parent, state[parent]) for parent in parents
if parent != node]
evidence.append((child, state[child]))
reduced_child_cpd = child.get_cpd_reduced(evidence)
reduced_cpd = reduced_cpd.multiplication(reduced_child_cpd)
new_state = weighted_random(reduced_cpd.get_table())
state[node] = node.get_value_range()[new_state]
return state
def sample_global(self, global_state, variable, allowed_values):
'''
This method can be used to sample from the density according to a global
state containing values for all other variables that this node belongs to.
@param global_state: A Dict (node -> value) that must hold a value for
all variables that this density depends on. Except for the variable
that it is directly associated with.
@param variable: The variable that this density is directly associated
with.
@param allowed_values: A list of values that are allowed to be sampled.
This is necessary because external evidence may restrict the value
range.
@returns: The sampled value.
'''
node_value_pairs = [(node, global_state[node])
for node in self.variables if node != variable]
probabilities = [
self.get_probability(node_value_pairs + [(variable, value)])
for value in allowed_values
]
idx = weighted_random(probabilities)
return allowed_values[idx]
def transition(self, network, state, extern_evidence):
'''
Does one single state transition.
@param network: A BayesNet.
@param state: The current state of the BayesNet. Given as a Dict from
RandomNode to Value
@param extern_evidence: Evidence that is given by the user. This is a
Dict from Node to Evidence.
'''
nodes = network.get_nodes([])
nodes_to_resample=[n for n in nodes if not n in extern_evidence.keys() or extern_evidence[n].get_unique_value() == None]
for node in nodes_to_resample:
parents=network.get_parents(node)
if parents:
evidence=[(parent,state[parent]) for parent in parents]
reduced_cpd = node.get_cpd_reduced(evidence)
else:
reduced_cpd = node.get_cpd()
#reduce the children's cpds
children = network.get_children(node)
for child in children:
#reduce this node's cpd
parents=network.get_parents(child)
evidence=[(parent,state[parent]) for parent in parents if parent != node]
evidence.append((child,state[child]))
reduced_child_cpd = child.get_cpd_reduced(evidence)
reduced_cpd = reduced_cpd.multiplication(reduced_child_cpd)
new_state=weighted_random(reduced_cpd.get_table())
state[node]=node.get_value_range()[new_state]
return state
