def setUp(self):
cNode = Node.BayesNode(0, 2, name="cloudy")
sNode = Node.BayesNode(1, 2, name="sprinkler")
rNode = Node.BayesNode(2, 2, name="rain")
wNode = Node.BayesNode(3, 2, name="wetgrass")
#cloudy
cNode.add_child(sNode)
cNode.add_child(rNode)
#sprinkler
sNode.add_parent(cNode)
sNode.add_child(wNode)
#rain
rNode.add_parent(cNode)
rNode.add_child(wNode)
#wetgrass
wNode.add_parent(sNode)
wNode.add_parent(rNode)
self.nodes = [cNode, sNode, rNode, wNode]
#create distributions
#cloudy distribution
cDistribution = Distribution.DiscreteDistribution(cNode)
index = cDistribution.generate_index([], [])
cDistribution[index] = 0.5
cNode.set_dist(cDistribution)
#sprinkler
dist = zeros([cNode.size(), sNode.size()], type=Float32)
dist[0, ] = 0.5
dist[1, ] = [0.9, 0.1]
sDistribution = Distribution.ConditionalDiscreteDistribution(
nodes=[cNode, sNode], table=dist)
sNode.set_dist(sDistribution)
#rain
dist = zeros([cNode.size(), rNode.size()], type=Float32)
dist[0, ] = [0.8, 0.2]
dist[1, ] = [0.2, 0.8]
rDistribution = Distribution.ConditionalDiscreteDistribution(
nodes=[cNode, rNode], table=dist)
rNode.set_dist(rDistribution)
#wetgrass
dist = zeros([sNode.size(), rNode.size(), wNode.size()], type=Float32)
dist[0, 0, ] = [1.0, 0.0]
dist[1, 0, ] = [0.1, 0.9]
dist[0, 1, ] = [0.1, 0.9]
dist[1, 1, ] = [0.01, 0.99]
wgDistribution = Distribution.ConditionalDiscreteDistribution(
nodes=[sNode, rNode, wNode], table=dist)
wNode.set_dist(wgDistribution)
#create bayes net
self.bnet = Graph.BayesNet(self.nodes)
self.engine = Inference.InferenceEngine(self.bnet)
def __init__(self, name, variables):
self.name = name
self.variables = set(variables)
self.potential = Distribution.Potential(self.variables)
self.visited = False
self.neighbors_separator = set()
self.num_var = len(self.variables)
def setUp(self):
cNode = Node.BayesNode(0, 2, index=0, name="cloudy")
sNode = Node.BayesNode(1, 2, index=1, name="sprinkler")
rNode = Node.BayesNode(2, 2, index=2, name="rain")
wNode = Node.BayesNode(3, 2, index=3, name="wetgrass")
self.nodes = [cNode, sNode, rNode, wNode]
self.potential = Distribution.Potential(self.nodes, default=1)
def testBasicDiv(self):
assert all(self.potential.table == 1)
other = Distribution.Potential(self.nodes, default=2)
new = self.potential / other
assert all(new.table == 0.5)
assert all(self.potential.table == 1)
self.potential /= other
assert all(new.table == 0.5)
def reinit_potential(self):
self.potential = Distribution.Potential(self.var)
def __init__(self, name, cx, cy):
self.name = name
self.var = cx.variables.intersection(cy.variables)
self.potential = Distribution.Potential(self.var)
self.visited = False
self.neighbors = [cx, cy]
def testBasicMultiply(self):
new = self.potential * Distribution.Potential(self.nodes[1:3],
default=3)
# This just checks their nodesets are equal
assert (len(new.nodes) == len(self.potential.nodes))
def testPotentialEQ(self):
new = Distribution.Potential(self.nodes)
assert (self.potential == new)