def model_exposed():
return ExposedSumSPE(
spe_weights=(Nationality >> choice({
'India': 0.5,
'USA': 0.5
})),
children={
# American student.
'USA':
ExposedSumSPE(spe_weights=(Perfect >> choice({
'True': 0.01,
'False': 0.99
})),
children={
'False': GPA >> uniform(loc=0, scale=4),
'True': GPA >> atomic(loc=4),
}),
# Indian student.
'India':
ExposedSumSPE(spe_weights=(Perfect >> choice({
'True': 0.01,
'False': 0.99
})),
children={
'False': GPA >> uniform(loc=0, scale=10),
'True': GPA >> atomic(loc=10),
})
},
)
def model_perfect_nested():
Nationality = Id('Nationality')
Perfect = Id('Perfect')
GPA = Id('GPA')
command = Sequence(
Sample(Nationality, choice({
'India': 0.5,
'USA': 0.5
})),
IfElse(
Nationality << {'India'},
Sequence(
Sample(Perfect, choice({
'True': 0.01,
'False': 0.99
})),
IfElse(Perfect << {'True'}, Sample(GPA, atomic(loc=10)), True,
Sample(GPA, uniform(scale=10)))),
Nationality << {'USA'},
Sequence(
Sample(Perfect, choice({
'True': 0.01,
'False': 0.99
})),
IfElse(
Perfect << {'True'},
Sample(GPA, atomic(loc=4)),
True,
Sample(GPA, uniform(scale=4)),
))))
return command.interpret()
def get_command_beta():
return Sequence(
Sample(simAll, beta(a=2, b=3)),
For(
0, 5, lambda k: Switch(
simAll, binspace(0, 1, ns), lambda i: Sequence(
Sample(sim[k], bernoulli(p=i.right)),
Sample(p1[k], uniform()),
IfElse(
sim[k] << {1},
Sequence(
Transform(p2[k], p1[k]),
Switch(
p1[k], binspace(0, 1, ns), lambda j: Sequence(
Sample(clickA[k], bernoulli(p=i.right)),
Sample(clickB[k], bernoulli(p=i.right))))),
True,
Sequence(
Sample(p2[k], uniform()),
Switch(
p1[k], binspace(0, 1, ns), lambda j: Sample(
clickA[k], bernoulli(p=j.right))),
Switch(
p2[k], binspace(0, 1, ns), lambda j: Sample(
clickB[k], bernoulli(p=j.right)))))))))
def model_no_latents():
return \
0.5 * ( # American student
0.99 * (GPA >> uniform(loc=0, scale=4)) | \
0.01 * (GPA >> atomic(loc=4))) | \
0.5 * ( # Indian student
0.99 * (GPA >> uniform(loc=0, scale=10)) | \
0.01 * (GPA >> atomic(loc=10)))
def model_ifelse_exhuastive():
command = Sequence(
Sample(Nationality, choice({
'India': 0.5,
'USA': 0.5
})), Sample(Perfect, choice({
'True': 0.01,
'False': 0.99
})),
IfElse((Nationality << {'India'}) & (Perfect << {'False'}),
Sample(GPA, uniform(loc=0, scale=10)),
(Nationality << {'India'}) & (Perfect << {'True'}),
Sample(GPA, atomic(loc=10)),
(Nationality << {'USA'}) & (Perfect << {'False'}),
Sample(GPA, uniform(loc=0, scale=4)),
(Nationality << {'USA'}) & (Perfect << {'True'}),
Sample(GPA, atomic(loc=4))))
return command.interpret()