def test_from_scope_variables_with_fn(self):
""""""
A = NumCatRVariable(
"A",
input_data={"outcome-values": [True, False]},
marginal_distribution=lambda x: 0.6 if x else 0.4,
)
B = NumCatRVariable(
"B",
input_data={"outcome-values": [True, False]},
marginal_distribution=lambda x: 0.62 if x else 0.38,
)
def phi_ab(scope_product):
ss = set(scope_product)
if ss == set([("A", True), ("B", True)]):
return 0.9
elif ss == set([("A", True), ("B", False)]):
return 0.1
elif ss == set([("A", False), ("B", True)]):
return 0.2
elif ss == set([("A", False), ("B", False)]):
return 0.8
else:
raise ValueError("unknown argument")
f = Factor.from_scope_variables_with_fn(svars=set([A, B]), fn=phi_ab)
query = set([("A", True), ("B", True)])
ff = f.phi(query)
self.assertEqual(round(ff, 2), 0.9)
def setUp(self):
""""""
idata = {
"rain": {
"outcome-values": [True, False]
},
"sprink": {
"outcome-values": [True, False]
},
"wet": {
"outcome-values": [True, False]
},
"road": {
"outcome-values": [True, False]
},
"winter": {
"outcome-values": [True, False]
},
"earthquake": {
"outcome-values": [True, False]
},
"burglary": {
"outcome-values": [True, False]
},
"alarm": {
"outcome-values": [True, False]
},
}
self.rain = NumCatRVariable(
input_data=idata["rain"],
node_id="rain",
marginal_distribution=lambda x: 0.2 if x is True else 0.8,
)
self.sprink = NumCatRVariable(
node_id="sprink",
input_data=idata["sprink"],
marginal_distribution=lambda x: 0.6 if x is True else 0.4,
)
self.wet = NumCatRVariable(
node_id="wet",
input_data=idata["wet"],
marginal_distribution=lambda x: 0.7 if x is True else 0.3,
)
self.rain_wet = Edge(
edge_id="rain_wet",
start_node=self.rain,
end_node=self.wet,
edge_type=EdgeType.DIRECTED,
)
self.rain_sprink = Edge(
edge_id="rain_sprink",
start_node=self.rain,
end_node=self.sprink,
edge_type=EdgeType.DIRECTED,
)
self.sprink_wet = Edge(
edge_id="sprink_wet",
start_node=self.sprink,
end_node=self.wet,
edge_type=EdgeType.DIRECTED,
)
def sprink_rain_factor(scope_product):
""""""
sfs = set(scope_product)
if sfs == set([("rain", True), ("sprink", True)]):
return 0.01
elif sfs == set([("rain", True), ("sprink", False)]):
return 0.99
elif sfs == set([("rain", False), ("sprink", True)]):
return 0.4
elif sfs == set([("rain", False), ("sprink", False)]):
return 0.6
else:
raise ValueError("unknown product")
self.rain_sprink_f = Factor.from_scope_variables_with_fn(
svars=set([self.rain, self.sprink]), fn=sprink_rain_factor)
def grass_wet_factor(scope_product):
""""""
sfs = set(scope_product)
if sfs == set([("rain", False), ("sprink", False), ("wet", True)]):
return 0.0
elif sfs == set([("rain", False), ("sprink", False),
("wet", False)]):
return 1.0
elif sfs == set([("rain", False), ("sprink", True),
("wet", True)]):
return 0.8
elif sfs == set([("rain", False), ("sprink", True),
("wet", False)]):
return 0.2
elif sfs == set([("rain", True), ("sprink", False),
("wet", True)]):
return 0.9
elif sfs == set([("rain", True), ("sprink", False),
("wet", False)]):
return 0.1
elif sfs == set([("rain", True), ("sprink", True), ("wet", True)]):
return 0.99
elif sfs == set([("rain", True), ("sprink", True),
("wet", False)]):
return 0.01
else:
raise ValueError("unknown product")
self.grass_wet_f = Factor.from_scope_variables_with_fn(
svars=set([self.rain, self.sprink, self.wet]), fn=grass_wet_factor)
self.bayes = BayesianNetwork(
gid="b",
nodes=set([self.rain, self.sprink, self.wet]),
edges=set([self.rain_wet, self.rain_sprink, self.sprink_wet]),
factors=set([self.grass_wet_f, self.rain_sprink_f]),
)
#
# Darwiche 2009, p. 30
#
# Earthquake Burglary
# \ /
# \ /
# Alarm
#
self.EarthquakeN = NumCatRVariable(
input_data=idata["earthquake"],
node_id="EarthquakeN",
marginal_distribution=lambda x: 0.1 if x is True else 0.9,
)
self.BurglaryN = NumCatRVariable(
input_data=idata["burglary"],
node_id="BurglaryN",
marginal_distribution=lambda x: 0.2 if x is True else 0.8,
)
self.AlarmN = NumCatRVariable(
input_data=idata["alarm"],
node_id="AlarmN",
marginal_distribution=lambda x: 0.2442 if x is True else 0.7558,
)
self.burglar_alarm = Edge(
edge_id="burglar_alarm",
start_node=self.BurglaryN,
end_node=self.AlarmN,
edge_type=EdgeType.DIRECTED,
)
self.earthquake_alarm = Edge(
edge_id="earthquake_alarm",
start_node=self.EarthquakeN,
end_node=self.AlarmN,
edge_type=EdgeType.DIRECTED,
)
idata = {"outcome-values": [True, False]}
self.C = NumCatRVariable(node_id="C",
input_data=idata,
marginal_distribution=lambda x: 0.5)
self.E = NumCatRVariable(node_id="E",
input_data=idata,
marginal_distribution=lambda x: 0.5)
self.F = NumCatRVariable(node_id="F",
input_data=idata,
marginal_distribution=lambda x: 0.5)
self.D = NumCatRVariable(node_id="D",
input_data=idata,
marginal_distribution=lambda x: 0.5)
self.CE = Edge(
edge_id="CE",
start_node=self.C,
end_node=self.E,
edge_type=EdgeType.DIRECTED,
)
self.ED = Edge(
edge_id="ED",
start_node=self.E,
end_node=self.D,
edge_type=EdgeType.DIRECTED,
)
self.EF = Edge(
edge_id="EF",
start_node=self.E,
end_node=self.F,
edge_type=EdgeType.DIRECTED,
)
def phi_c(scope_product):
ss = set(scope_product)
if ss == set([("C", True)]):
return 0.8
elif ss == set([("C", False)]):
return 0.2
else:
raise ValueError("scope product unknown")
def phi_ec(scope_product):
ss = set(scope_product)
if ss == set([("C", True), ("E", True)]):
return 0.9
elif ss == set([("C", True), ("E", False)]):
return 0.1
elif ss == set([("C", False), ("E", True)]):
return 0.7
elif ss == set([("C", False), ("E", False)]):
return 0.3
else:
raise ValueError("scope product unknown")
def phi_fe(scope_product):
ss = set(scope_product)
if ss == set([("E", True), ("F", True)]):
return 0.9
elif ss == set([("E", True), ("F", False)]):
return 0.1
elif ss == set([("E", False), ("F", True)]):
return 0.5
elif ss == set([("E", False), ("F", False)]):
return 0.5
else:
raise ValueError("scope product unknown")
def phi_de(scope_product):
ss = set(scope_product)
if ss == set([("E", True), ("D", True)]):
return 0.7
elif ss == set([("E", True), ("D", False)]):
return 0.3
elif ss == set([("E", False), ("D", True)]):
return 0.4
elif ss == set([("E", False), ("D", False)]):
return 0.6
else:
raise ValueError("scope product unknown")
self.CE_f = Factor(gid="CE_f",
scope_vars=set([self.C, self.E]),
factor_fn=phi_ec)
self.C_f = Factor(gid="C_f", scope_vars=set([self.C]), factor_fn=phi_c)
self.FE_f = Factor(gid="FE_f",
scope_vars=set([self.F, self.E]),
factor_fn=phi_fe)
self.DE_f = Factor(gid="DE_f",
scope_vars=set([self.D, self.E]),
factor_fn=phi_de)
self.bayes_n = BayesianNetwork(
gid="ba",
nodes=set([self.C, self.E, self.D, self.F]),
edges=set([self.EF, self.CE, self.ED]),
factors=set([self.C_f, self.DE_f, self.CE_f, self.FE_f]),
)
