class DynamicBayesNet(BayesNet):
''' This is the implementation of a dynamic Bayesian network (also called
temporal Bayesian network).
Definition: DBN is a pair (B0, TwoTBN), where B0 is a BN over X(0),
representing the initial distribution over states, and TwoTBN is a
2-TBN for the process.
See Koller, Friedman - "Probabilistic Graphical Models" (p. 204)
Properties: Markov property, stationary, directed, discrete,
acyclic (within a slice)
'''
def __init__(self):
super(DynamicBayesNet, self).__init__()
self._B0 = BayesNet()
self._twoTBN = TwoTBN()
@property
def B0(self):
''' Get the Bayesian network representing the initial distribution.'''
return self._B0
@B0.setter
def B0(self, value):
''' Set the Bayesian network representing the initial distribution.'''
if isinstance(value, BayesNet):
if not value.is_valid():
raise Exception("BayesNet is not valid.")
self._B0 = value
else:
raise Exception("Can only set 'BayesNet' and its subclasses as " +
"B0 of a DBN.")
@property
def twoTBN(self):
''' Get the 2-time-slice Bayesian network.'''
return self._twoTBN
@twoTBN.setter
def twoTBN(self, value):
''' Set the 2-time-slice Bayesian network.'''
if isinstance(value, TwoTBN):
if not value.is_valid():
raise Exception("BayesNet is not valid.")
self._twoTBN = value
else:
raise Exception("Can only set 'TwoTBN' and its subclasses as " +
"twoTBN of a DBN.")
def is_valid(self):
'''Check if graph structure is valid. And if there is a same-named
inital node in towTBN for every node in BO.
Returns true if graph is directed and acyclic, false otherwiese'''
for node in self._B0.get_nodes():
if not self._twoTBN.has_initial_node_by_name(node.name):
print("Node with name " + str(node.name) +
" not found in TwoTBN!")
return False;
return super(DynamicBayesNet, self).is_valid()
#!/usr/bin/env python
# -*- coding: utf-8 -*-
from primo.core import BayesNet
from primo.core import DynamicBayesNet
from primo.core import TwoTBN
from primo.reasoning import DiscreteNode
import primo.reasoning.particlebased.ParticleFilterDBN as pf
import numpy
#Construct some simple DynmaicBayesianNetwork
B0 = BayesNet()
dbn = DynamicBayesNet()
twoTBN = TwoTBN()
weather0_init = DiscreteNode("Weather0", ["Sun", "Rain"])
weather0 = DiscreteNode("Weather0", ["Sun", "Rain"])
weather = DiscreteNode("Weather", ["Sun", "Rain"])
ice_cream_eaten = DiscreteNode("Ice Cream Eaten", [True, False])
B0.add_node(weather0_init)
twoTBN.add_node(weather0, True)
twoTBN.add_node(weather)
twoTBN.add_node(ice_cream_eaten)
twoTBN.add_edge(weather, ice_cream_eaten)
twoTBN.add_edge(weather0, weather);
cpt_weather0_init = numpy.array([.6, .4])
weather0_init.set_probability_table(cpt_weather0_init, [weather0_init])
def __init__(self):
super(DynamicBayesNet, self).__init__()
self._B0 = BayesNet()
self._twoTBN = TwoTBN()
