def build_net(cpts):
states = dict()
for name, cpt in cpts.items():
states[name] = State(cpt, name=name)
model = BayesianNetwork('Poker Game')
model.add_states(*list(states.values()))
for name, parents, _ in sheets:
for parent in parents:
print(states[parent])
model.add_transition(states[parent], states[name])
model.bake()
return model
def setup_huge_monty():
# Build the huge monty hall huge_monty_network. This is an example I made
# up with which may not exactly flow logically, but tests a varied type of
# tables ensures heterogeneous types of data work together.
global huge_monty_network, huge_monty_friend, huge_monty_guest, huge_monty
global huge_monty_remaining, huge_monty_randomize, huge_monty_prize
# Huge_Monty_Friend
huge_monty_friend = DiscreteDistribution({True: 0.5, False: 0.5})
# Huge_Monty_Guest emisisons are completely random
huge_monty_guest = ConditionalProbabilityTable(
[[True, 'A', 0.50],
[True, 'B', 0.25],
[True, 'C', 0.25],
[False, 'A', 0.0],
[False, 'B', 0.7],
[False, 'C', 0.3]], [huge_monty_friend])
# Number of huge_monty_remaining cars
huge_monty_remaining = DiscreteDistribution({0: 0.1, 1: 0.7, 2: 0.2, })
# Whether they huge_monty_randomize is dependent on the numnber of
# huge_monty_remaining cars
huge_monty_randomize = ConditionalProbabilityTable(
[[0, True, 0.05],
[0, False, 0.95],
[1, True, 0.8],
[1, False, 0.2],
[2, True, 0.5],
[2, False, 0.5]], [huge_monty_remaining])
# Where the huge_monty_prize is depends on if they huge_monty_randomize or
# not and also the huge_monty_guests huge_monty_friend
huge_monty_prize = ConditionalProbabilityTable(
[[True, True, 'A', 0.3],
[True, True, 'B', 0.4],
[True, True, 'C', 0.3],
[True, False, 'A', 0.2],
[True, False, 'B', 0.4],
[True, False, 'C', 0.4],
[False, True, 'A', 0.1],
[False, True, 'B', 0.9],
[False, True, 'C', 0.0],
[False, False, 'A', 0.0],
[False, False, 'B', 0.4],
[False, False, 'C', 0.6]], [huge_monty_randomize, huge_monty_friend])
# Monty is dependent on both the huge_monty_guest and the huge_monty_prize.
huge_monty = ConditionalProbabilityTable(
[['A', 'A', 'A', 0.0],
['A', 'A', 'B', 0.5],
['A', 'A', 'C', 0.5],
['A', 'B', 'A', 0.0],
['A', 'B', 'B', 0.0],
['A', 'B', 'C', 1.0],
['A', 'C', 'A', 0.0],
['A', 'C', 'B', 1.0],
['A', 'C', 'C', 0.0],
['B', 'A', 'A', 0.0],
['B', 'A', 'B', 0.0],
['B', 'A', 'C', 1.0],
['B', 'B', 'A', 0.5],
['B', 'B', 'B', 0.0],
['B', 'B', 'C', 0.5],
['B', 'C', 'A', 1.0],
['B', 'C', 'B', 0.0],
['B', 'C', 'C', 0.0],
['C', 'A', 'A', 0.0],
['C', 'A', 'B', 1.0],
['C', 'A', 'C', 0.0],
['C', 'B', 'A', 1.0],
['C', 'B', 'B', 0.0],
['C', 'B', 'C', 0.0],
['C', 'C', 'A', 0.5],
['C', 'C', 'B', 0.5],
['C', 'C', 'C', 0.0]], [huge_monty_guest, huge_monty_prize])
# Make the states
s0 = State(huge_monty_friend, name="huge_monty_friend")
s1 = State(huge_monty_guest, name="huge_monty_guest")
s2 = State(huge_monty_prize, name="huge_monty_prize")
s3 = State(huge_monty, name="huge_monty")
s4 = State(huge_monty_remaining, name="huge_monty_remaining")
s5 = State(huge_monty_randomize, name="huge_monty_randomize")
# Make the bayes net, add the states, and the conditional dependencies.
huge_monty_network = BayesianNetwork("test")
huge_monty_network.add_nodes(s0, s1, s2, s3, s4, s5)
huge_monty_network.add_transition(s0, s1)
huge_monty_network.add_transition(s1, s3)
huge_monty_network.add_transition(s2, s3)
huge_monty_network.add_transition(s4, s5)
huge_monty_network.add_transition(s5, s2)
huge_monty_network.add_transition(s0, s2)
huge_monty_network.bake()
def setup_huge_monty():
# Build the huge monty hall huge_monty_network. This is an example I made
# up with which may not exactly flow logically, but tests a varied type of
# tables ensures heterogeneous types of data work together.
global huge_monty_network, huge_monty_friend, huge_monty_guest, huge_monty
global huge_monty_remaining, huge_monty_randomize, huge_monty_prize
# Huge_Monty_Friend
huge_monty_friend = DiscreteDistribution({True: 0.5, False: 0.5})
# Huge_Monty_Guest emisisons are completely random
huge_monty_guest = ConditionalProbabilityTable(
[[True, 'A', 0.50],
[True, 'B', 0.25],
[True, 'C', 0.25],
[False, 'A', 0.0],
[False, 'B', 0.7],
[False, 'C', 0.3]], [huge_monty_friend])
# Number of huge_monty_remaining cars
huge_monty_remaining = DiscreteDistribution({0: 0.1, 1: 0.7, 2: 0.2, })
# Whether they huge_monty_randomize is dependent on the numnber of
# huge_monty_remaining cars
huge_monty_randomize = ConditionalProbabilityTable(
[[0, True, 0.05],
[0, False, 0.95],
[1, True, 0.8],
[1, False, 0.2],
[2, True, 0.5],
[2, False, 0.5]], [huge_monty_remaining])
# Where the huge_monty_prize is depends on if they huge_monty_randomize or
# not and also the huge_monty_guests huge_monty_friend
huge_monty_prize = ConditionalProbabilityTable(
[[True, True, 'A', 0.3],
[True, True, 'B', 0.4],
[True, True, 'C', 0.3],
[True, False, 'A', 0.2],
[True, False, 'B', 0.4],
[True, False, 'C', 0.4],
[False, True, 'A', 0.1],
[False, True, 'B', 0.9],
[False, True, 'C', 0.0],
[False, False, 'A', 0.0],
[False, False, 'B', 0.4],
[False, False, 'C', 0.6]], [huge_monty_randomize, huge_monty_friend])
# Monty is dependent on both the huge_monty_guest and the huge_monty_prize.
huge_monty = ConditionalProbabilityTable(
[['A', 'A', 'A', 0.0],
['A', 'A', 'B', 0.5],
['A', 'A', 'C', 0.5],
['A', 'B', 'A', 0.0],
['A', 'B', 'B', 0.0],
['A', 'B', 'C', 1.0],
['A', 'C', 'A', 0.0],
['A', 'C', 'B', 1.0],
['A', 'C', 'C', 0.0],
['B', 'A', 'A', 0.0],
['B', 'A', 'B', 0.0],
['B', 'A', 'C', 1.0],
['B', 'B', 'A', 0.5],
['B', 'B', 'B', 0.0],
['B', 'B', 'C', 0.5],
['B', 'C', 'A', 1.0],
['B', 'C', 'B', 0.0],
['B', 'C', 'C', 0.0],
['C', 'A', 'A', 0.0],
['C', 'A', 'B', 1.0],
['C', 'A', 'C', 0.0],
['C', 'B', 'A', 1.0],
['C', 'B', 'B', 0.0],
['C', 'B', 'C', 0.0],
['C', 'C', 'A', 0.5],
['C', 'C', 'B', 0.5],
['C', 'C', 'C', 0.0]], [huge_monty_guest, huge_monty_prize])
# Make the states
s0 = State(huge_monty_friend, name="huge_monty_friend")
s1 = State(huge_monty_guest, name="huge_monty_guest")
s2 = State(huge_monty_prize, name="huge_monty_prize")
s3 = State(huge_monty, name="huge_monty")
s4 = State(huge_monty_remaining, name="huge_monty_remaining")
s5 = State(huge_monty_randomize, name="huge_monty_randomize")
# Make the bayes net, add the states, and the conditional dependencies.
huge_monty_network = BayesianNetwork("test")
huge_monty_network.add_nodes(s0, s1, s2, s3, s4, s5)
huge_monty_network.add_transition(s0, s1)
huge_monty_network.add_transition(s1, s3)
huge_monty_network.add_transition(s2, s3)
huge_monty_network.add_transition(s4, s5)
huge_monty_network.add_transition(s5, s2)
huge_monty_network.add_transition(s0, s2)
huge_monty_network.bake()
