def setup_titanic():
# Build a model of the titanic disaster
global titanic_network, passenger, gender, tclass
# Passengers on the Titanic either survive or perish
passenger = DiscreteDistribution({'survive': 0.6, 'perish': 0.4})
# Gender, given survival data
gender = ConditionalProbabilityTable(
[['survive', 'male', 0.0], ['survive', 'female', 1.0],
['perish', 'male', 1.0], ['perish', 'female', 0.0]], [passenger])
# Class of travel, given survival data
tclass = ConditionalProbabilityTable(
[['survive', 'first', 0.0], ['survive', 'second', 1.0],
['survive', 'third', 0.0], ['perish', 'first', 1.0],
['perish', 'second', 0.0], ['perish', 'third', 0.0]], [passenger])
# State objects hold both the distribution, and a high level name.
s1 = State(passenger, name="passenger")
s2 = State(gender, name="gender")
s3 = State(tclass, name="class")
# Create the Bayesian network object with a useful name
titanic_network = BayesianNetwork("Titanic Disaster")
# Add the three nodes to the network
titanic_network.add_nodes(s1, s2, s3)
# Add transitions which represent conditional dependencies, where the
# second node is conditionally dependent on the first node (Monty is
# dependent on both guest and prize)
titanic_network.add_edge(s1, s2)
titanic_network.add_edge(s1, s3)
titanic_network.bake()
def get_bayesnet(self):
door_lock = DiscreteDistribution({'d1': 0.7, 'd2': 0.3})
clock_alarm = DiscreteDistribution( { 'a1' : 0.8, 'a2' : 0.2} )
light = ConditionalProbabilityTable(
[[ 'd1', 'a1', 'l1', 0.96 ],
['d1', 'a1', 'l2', 0.04 ],
[ 'd1', 'a2', 'l1', 0.89 ],
[ 'd1', 'a2', 'l2', 0.11 ],
[ 'd2', 'a1', 'l1', 0.96 ],
[ 'd2', 'a1', 'l2', 0.04 ],
[ 'd2', 'a2', 'l1', 0.89 ],
[ 'd2', 'a2', 'l2', 0.11 ]], [door_lock, clock_alarm])
coffee_maker = ConditionalProbabilityTable(
[[ 'a1', 'c1', 0.92 ],
[ 'a1', 'c2', 0.08 ],
[ 'a2', 'c1', 0.03 ],
[ 'a2', 'c2', 0.97 ]], [clock_alarm] )
s_door_lock = State(door_lock, name="door_lock")
s_clock_alarm = State(clock_alarm, name="clock_alarm")
s_light = State(light, name="light")
s_coffee_maker = State(coffee_maker, name="coffee_maker")
network = BayesianNetwork("User_pref")
network.add_nodes(s_door_lock, s_clock_alarm, s_light, s_coffee_maker)
network.add_edge(s_door_lock,s_light)
network.add_edge(s_clock_alarm,s_coffee_maker)
network.add_edge(s_clock_alarm,s_light)
network.bake()
return network
def setup_monty():
# Build a model of the Monty Hall Problem
global monty_network, monty_index, prize_index, guest_index
random.seed(0)
# Friends emissions are completely random
guest = DiscreteDistribution({'A': 1. / 3, 'B': 1. / 3, 'C': 1. / 3})
# The actual prize is independent of the other distributions
prize = DiscreteDistribution({'A': 1. / 3, 'B': 1. / 3, 'C': 1. / 3})
# Monty is dependent on both the guest and the prize.
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]], [guest, prize])
# Make the states
s1 = State(guest, name="guest")
s2 = State(prize, name="prize")
s3 = State(monty, name="monty")
# Make the bayes net, add the states, and the conditional dependencies.
monty_network = BayesianNetwork("test")
monty_network.add_nodes(s1, s2, s3)
monty_network.add_edge(s1, s3)
monty_network.add_edge(s2, s3)
monty_network.bake()
monty_index = monty_network.states.index(s3)
prize_index = monty_network.states.index(s2)
guest_index = monty_network.states.index(s1)
def setup_monty():
# Build a model of the Monty Hall Problem
global monty_network, monty_index, prize_index, guest_index
random.seed(0)
# Friends emissions are completely random
guest = DiscreteDistribution({'A': 1. / 3, 'B': 1. / 3, 'C': 1. / 3})
# The actual prize is independent of the other distributions
prize = DiscreteDistribution({'A': 1. / 3, 'B': 1. / 3, 'C': 1. / 3})
# Monty is dependent on both the guest and the prize.
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]], [guest, prize])
# Make the states
s1 = State(guest, name="guest")
s2 = State(prize, name="prize")
s3 = State(monty, name="monty")
# Make the bayes net, add the states, and the conditional dependencies.
monty_network = BayesianNetwork("test")
monty_network.add_nodes(s1, s2, s3)
monty_network.add_edge(s1, s3)
monty_network.add_edge(s2, s3)
monty_network.bake()
monty_index = monty_network.states.index(s3)
prize_index = monty_network.states.index(s2)
guest_index = monty_network.states.index(s1)
def __init__(self):
Pollution = DiscreteDistribution({'F': 0.9, 'T': 0.1})
Smoker = DiscreteDistribution({'T': 0.3, 'F': 0.7})
print(Smoker)
Cancer = ConditionalProbabilityTable([
['T', 'T', 'T', 0.05],
['T', 'T', 'F', 0.95],
['T', 'F', 'T', 0.02],
['T', 'F', 'F', 0.98],
['F', 'T', 'T', 0.03],
['F', 'T', 'F', 0.97],
['F', 'F', 'T', 0.001],
['F', 'F', 'F', 0.999],
], [Pollution, Smoker])
print(Cancer)
XRay = ConditionalProbabilityTable([
['T', 'T', 0.9],
['T', 'F', 0.1],
['F', 'T', 0.2],
['F', 'F', 0.8],
], [Cancer])
Dyspnoea = ConditionalProbabilityTable([
['T', 'T', 0.65],
['T', 'F', 0.35],
['F', 'T', 0.3],
['F', 'F', 0.7],
], [Cancer])
s1 = Node(Pollution, name="Pollution")
s2 = Node(Smoker, name="Smoker")
s3 = Node(Cancer, name="Cancer")
s4 = Node(XRay, name="XRay")
s5 = Node(Dyspnoea, name="Dyspnoea")
model = BayesianNetwork("Lung Cancer")
model.add_states(s1, s2, s3, s4, s5)
model.add_edge(s1, s3)
model.add_edge(s2, s3)
model.add_edge(s3, s4)
model.add_edge(s3, s5)
model.bake()
self.model = model
meta = []
name_mapper = ["Pollution", "Smoker", "Cancer", "XRay", "Dyspnoea"]
for i in range(self.model.node_count()):
meta.append({
"name": name_mapper[i],
"type": "categorical",
"size": 2,
"i2s": ['T', 'F']
})
self.meta = meta
def __init__(self):
A = DiscreteDistribution({'1': 1. / 3, '2': 1. / 3, '3': 1. / 3})
B = ConditionalProbabilityTable([
['1', '1', 0.5],
['1', '2', 0.5],
['1', '3', 0],
['2', '1', 0],
['2', '2', 0.5],
['2', '3', 0.5],
['3', '1', 0.5],
['3', '2', 0],
['3', '3', 0.5],
], [A])
C = ConditionalProbabilityTable([
['1', '4', 0.5],
['1', '5', 0.5],
['1', '6', 0],
['2', '4', 0],
['2', '5', 0.5],
['2', '6', 0.5],
['3', '4', 0.5],
['3', '5', 0],
['3', '6', 0.5],
], [A])
s1 = Node(A, name="A")
s2 = Node(B, name="B")
s3 = Node(C, name="C")
model = BayesianNetwork("tree")
model.add_states(s1, s2, s3)
model.add_edge(s1, s2)
model.add_edge(s1, s3)
model.bake()
self.model = model
meta = []
for i in range(self.model.node_count() - 1):
meta.append({
"name": chr(ord('A') + i),
"type": "categorical",
"size": 3,
"i2s": ['1', '2', '3']
})
meta.append({
"name": "C",
"type": "categorical",
"size": 3,
"i2s": ['4', '5', '6']
})
self.meta = meta
def test_io_fit():
d1 = DiscreteDistribution({True: 0.6, False: 0.4})
d2 = ConditionalProbabilityTable([
[True, 'A', 0.2],
[True, 'B', 0.8],
[False, 'A', 0.3],
[False, 'B', 0.7]], [d1])
d3 = ConditionalProbabilityTable([
['A', 0, 0.3],
['A', 1, 0.7],
['B', 0, 0.8],
['B', 1, 0.2]], [d2])
n1 = Node(d1)
n2 = Node(d2)
n3 = Node(d3)
model1 = BayesianNetwork()
model1.add_nodes(n1, n2, n3)
model1.add_edge(n1, n2)
model1.add_edge(n2, n3)
model1.bake()
model1.fit(X, weights=weights)
d1 = DiscreteDistribution({True: 0.2, False: 0.8})
d2 = ConditionalProbabilityTable([
[True, 'A', 0.7],
[True, 'B', 0.2],
[False, 'A', 0.4],
[False, 'B', 0.6]], [d1])
d3 = ConditionalProbabilityTable([
['A', 0, 0.9],
['A', 1, 0.1],
['B', 0, 0.0],
['B', 1, 1.0]], [d2])
n1 = Node(d1)
n2 = Node(d2)
n3 = Node(d3)
model2 = BayesianNetwork()
model2.add_nodes(n1, n2, n3)
model2.add_edge(n1, n2)
model2.add_edge(n2, n3)
model2.bake()
model2.fit(data_generator)
logp1 = model1.log_probability(X)
logp2 = model2.log_probability(X)
assert_array_almost_equal(logp1, logp2)
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 __init__(self):
Rain = DiscreteDistribution({'T': 0.2, 'F': 0.8})
Sprinkler = ConditionalProbabilityTable([
['F', 'T', 0.4],
['F', 'F', 0.6],
['T', 'T', 0.1],
['T', 'F', 0.9],
], [Rain])
Wet = ConditionalProbabilityTable([
['F', 'F', 'T', 0.01],
['F', 'F', 'F', 0.99],
['F', 'T', 'T', 0.8],
['F', 'T', 'F', 0.2],
['T', 'F', 'T', 0.9],
['T', 'F', 'F', 0.1],
['T', 'T', 'T', 0.99],
['T', 'T', 'F', 0.01],
], [Sprinkler, Rain])
s1 = Node(Rain, name="Rain")
s2 = Node(Sprinkler, name="Sprinkler")
s3 = Node(Wet, name="Wet")
model = BayesianNetwork("Simple fully connected")
model.add_states(s1, s2, s3)
model.add_edge(s1, s2)
model.add_edge(s1, s3)
model.add_edge(s2, s3)
model.bake()
self.model = model
meta = []
for i in range(self.model.node_count()):
meta.append({
"name": None,
"type": "categorical",
"size": 2,
"i2s": ['T', 'F']
})
meta[0]['name'] = 'Rain'
meta[1]['name'] = 'Sprinkler'
meta[2]['name'] = 'Wet'
self.meta = meta
def setup_titanic():
# Build a model of the titanic disaster
global titanic_network, passenger, gender, tclass
# Passengers on the Titanic either survive or perish
passenger = DiscreteDistribution({'survive': 0.6, 'perish': 0.4})
# Gender, given survival data
gender = ConditionalProbabilityTable(
[['survive', 'male', 0.0],
['survive', 'female', 1.0],
['perish', 'male', 1.0],
['perish', 'female', 0.0]], [passenger])
# Class of travel, given survival data
tclass = ConditionalProbabilityTable(
[['survive', 'first', 0.0],
['survive', 'second', 1.0],
['survive', 'third', 0.0],
['perish', 'first', 1.0],
['perish', 'second', 0.0],
['perish', 'third', 0.0]], [passenger])
# State objects hold both the distribution, and a high level name.
s1 = State(passenger, name="passenger")
s2 = State(gender, name="gender")
s3 = State(tclass, name="class")
# Create the Bayesian network object with a useful name
titanic_network = BayesianNetwork("Titanic Disaster")
# Add the three nodes to the network
titanic_network.add_nodes(s1, s2, s3)
# Add transitions which represent conditional dependencies, where the
# second node is conditionally dependent on the first node (Monty is
# dependent on both guest and prize)
titanic_network.add_edge(s1, s2)
titanic_network.add_edge(s1, s3)
titanic_network.bake()
def pomegranate_User_pref():
door_lock = DiscreteDistribution({'True': 0.7, 'False': 0.3})
thermostate = ConditionalProbabilityTable(
[['True', 'True', 0.2], ['True', 'False', 0.8],
['False', 'True', 0.01], ['False', 'False', 0.99]], [door_lock])
clock_alarm = DiscreteDistribution({'True': 0.8, 'False': 0.2})
light = ConditionalProbabilityTable(
[['True', 'True', 'True', 0.96], ['True', 'True', 'False', 0.04],
['True', 'False', 'True', 0.89], ['True', 'False', 'False', 0.11],
['False', 'True', 'True', 0.96], ['False', 'True', 'False', 0.04],
['False', 'False', 'True', 0.89], ['False', 'False', 'False', 0.11]],
[door_lock, clock_alarm])
coffee_maker = ConditionalProbabilityTable(
[['True', 'True', 0.92], ['True', 'False', 0.08],
['False', 'True', 0.03], ['False', 'False', 0.97]], [clock_alarm])
window = ConditionalProbabilityTable(
[['True', 'True', 0.885], ['True', 'False', 0.115],
['False', 'True', 0.04], ['False', 'False', 0.96]], [thermostate])
s0_door_lock = State(door_lock, name="door_lock")
s1_clock_alarm = State(clock_alarm, name="clock_alarm")
s2_light = State(light, name="light")
s3_coffee_maker = State(coffee_maker, name="coffee_maker")
s4_thermostate = State(thermostate, name="thermostate")
s5_window = State(window, name="Window")
network = BayesianNetwork("User_pref")
network.add_nodes(s0_door_lock, s1_clock_alarm, s2_light, s3_coffee_maker,
s4_thermostate, s5_window)
network.add_edge(s0_door_lock, s2_light)
network.add_edge(s0_door_lock, s4_thermostate)
network.add_edge(s1_clock_alarm, s3_coffee_maker)
network.add_edge(s1_clock_alarm, s2_light)
network.add_edge(s4_thermostate, s5_window)
network.bake()
return network
def __get_bayesian_network_model(
self,
symptom_distributions: List,
symptom_states: List,
file_name: str,
disease_name: str,
):
disease_conditional_distribution = list()
for (s1, s2, s3, s4, s5, d, p) in get_from_csv(file_name):
disease_conditional_distribution.append(
[s1, s2, s3, s4, s5, d, float(p)])
disease_distribution = ConditionalProbabilityTable(
disease_conditional_distribution,
symptom_distributions,
)
disease = Node(disease_distribution, name=disease_name)
model = BayesianNetwork(disease_name)
model.add_state(disease)
for symptom_state in symptom_states:
model.add_state(symptom_state)
model.add_edge(symptom_state, disease)
model.bake()
return model
from pomegranate import DiscreteDistribution
from pomegranate import ConditionalProbabilityTable
from pomegranate import BayesianNetwork
from pomegranate import Node
guest = DiscreteDistribution({'A': 1. / 3, 'B': 1. / 3, 'C': 1. / 3})
prize = DiscreteDistribution({'A': 1. / 3, 'B': 1. / 3, 'C': 1. / 3})
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]],
[guest, prize])
s1 = Node(guest, name="guest")
s2 = Node(prize, name="prize")
s3 = Node(monty, name="monty")
model = BayesianNetwork("Monty Hall Problem")
model.add_states(s1, s2, s3)
model.add_edge(s1, s3)
model.add_edge(s2, s3)
model.bake()
def __init__(self, filename):
with open(filename) as f:
bif = f.read()
vars = re.findall(r"variable[^\{]+{[^\}]+}", bif)
probs = re.findall(r"probability[^\{]+{[^\}]+}", bif)
var_nodes = {}
var_index_to_name = []
edges = []
self.meta = []
todo = set()
for v, p in zip(vars, probs):
m = re.search(r"variable\s+([^\{\s]+)\s+", v)
v_name = m.group(1)
m = re.search(r"type\s+discrete\s+\[\s*(\d+)\s*\]\s*\{([^\}]+)\}",
v)
v_opts_n = int(m.group(1))
v_opts = m.group(2).replace(',', ' ').split()
assert v_opts_n == len(v_opts)
# print(v_name, v_opts_n, v_opts)
m = re.search(r"probability\s*\(([^)]+)\)", p)
cond = m.group(1).replace('|', ' ').replace(',', ' ').split()
assert cond[0] == v_name
# print(cond)
self.meta.append({
"name": v_name,
"type": "categorical",
"size": v_opts_n,
"i2s": v_opts
})
if len(cond) == 1:
m = re.search(r"table([e\-\d\.\s,]*);", p)
margin_p = m.group(1).replace(',', ' ').split()
margin_p = [float(x) for x in margin_p]
assert abs(sum(margin_p) - 1) < 1e-6
assert len(margin_p) == v_opts_n
margin_p = dict(zip(v_opts, margin_p))
var_index_to_name.append(v_name)
tmp = DiscreteDistribution(margin_p)
# print(tmp)
var_nodes[v_name] = tmp
else:
m_iter = re.finditer(r"\(([^)]*)\)([\s\d\.,\-e]+);", p)
cond_p_table = []
for m in m_iter:
cond_values = m.group(1).replace(',', ' ').split()
cond_p = m.group(2).replace(',', ' ').split()
cond_p = [float(x) for x in cond_p]
assert len(cond_values) == len(cond) - 1
assert len(cond_p) == v_opts_n
assert abs(sum(cond_p) - 1) < 1e-6
for opt, opt_p in zip(v_opts, cond_p):
cond_p_table.append(cond_values + [opt, opt_p])
var_index_to_name.append(v_name)
tmp = (cond_p_table, cond)
# print(tmp)
var_nodes[v_name] = tmp
for x in cond[1:]:
edges.append((x, v_name))
todo.add(v_name)
while len(todo) > 0:
# print(todo)
for v_name in todo:
# print(v_name, type(var_nodes[v_name]))
cond_p_table, cond = var_nodes[v_name]
flag = True
for y in cond[1:]:
if y in todo:
flag = False
break
if flag:
cond_t = [var_nodes[x] for x in cond[1:]]
var_nodes[v_name] = ConditionalProbabilityTable(
cond_p_table, cond_t)
todo.remove(v_name)
break
for x in var_index_to_name:
var_nodes[x] = Node(var_nodes[x], name=x)
var_nodes_list = [var_nodes[x] for x in var_index_to_name]
# print(var_nodes_list)
model = BayesianNetwork("tmp")
model.add_states(*var_nodes_list)
for edge in edges:
model.add_edge(var_nodes[edge[0]], var_nodes[edge[1]])
model.bake()
# print(model.to_json())
self.model = model
network = BayesianNetwork("Monty hall problem")
network.add_states(*states.values())
network.add_edge(states["Peer_Pressure"],states["Smoking"])
network.add_edge(states["Anxiety"],states["Smoking"])
network.add_edge(states["Smoking"],states["Yellow_Fingers"])
network.add_edge(states["Genetics"],states["Lung_cancer"])
network.add_edge(states["Smoking"],states["Lung_cancer"])
network.add_edge(states["Genetics"],states["Attention_Disorder"])
network.add_edge(states['Lung_cancer'], states["Coughing"])
network.add_edge(states['Allergy'], states["Coughing"])
network.add_edge(states['Coughing'], states["Fatigue"])
network.add_edge(states['Lung_cancer'], states["Fatigue"])
network.add_edge(states["Fatigue"], states["Car_Accident"])
network.add_edge(states["Attention_Disorder"], states["Car_Accident"])
import ast
network.bake()
beliefs = network.predict_proba({"Genetics":"T"},max_iterations=100000)
# print(beliefs)
# beliefs = map(str, beliefs)
# for state, belief in zip(network.states, beliefs):
# if hasattr(belief,"parameters"):
# print(state.name,belief.parameters)
# exit()
# network.add_edge(s1, s3)
# network.add_edge(s2, s3)
#
# beliefs = network.predict_proba({"guest": "A", "monty": "B"})
# beliefs = map(str, beliefs)
# for state, belief in zip(network.states, beliefs):
# print(state.name, belief)
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()
