def test_simple_model_lte():
command_switch = Sequence(
Sample(Y, beta(a=2, b=3)),
Switch(Y, binspace(0, 1, 5),
lambda i: Sample(X, bernoulli(p=i.right))))
model_switch = command_switch.interpret()
command_ifelse = Sequence(
Sample(Y, beta(a=2, b=3)),
IfElse(
Y <= 0,
Sample(X, bernoulli(p=0)),
Y <= 0.25,
Sample(X, bernoulli(p=.25)),
Y <= 0.50,
Sample(X, bernoulli(p=.50)),
Y <= 0.75,
Sample(X, bernoulli(p=.75)),
Y <= 1,
Sample(X, bernoulli(p=1)),
))
model_ifelse = command_ifelse.interpret()
grid = [float(x) for x in linspace(0, 1, 5)]
for model in [model_switch, model_ifelse]:
symbols = model.get_symbols()
assert symbols == {X, Y}
assert allclose(
model.logprob(X << {1}),
logsumexp([
model.logprob((il < Y) <= ih) + log(ih)
for il, ih in zip(grid[:-1], grid[1:])
]))
def test_error_linspace():
with pytest.raises(AssertionError):
# Switch cases do not sum to one.
command = Sequence(
Sample(Y, beta(a=2, b=3)),
Switch(Y, linspace(0, .5, 5), lambda i: Sample(X, bernoulli(p=i))))
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 test_condition_prob_zero():
with pytest.raises(Exception):
Sequence(Sample(Y, {
'a': .1,
'b': .1,
'c': .8
}), Condition(Y << {'d'})).interpret()
with pytest.raises(Exception):
Sequence(Sample(Y, beta(a=1, b=1)), Condition(Y > 1)).interpret()
def test_condition_real_continuous():
command = Sequence(Sample(Y, beta(a=1, b=1)), Condition(Y < .5))
model = command.interpret()
assert allclose(model.prob(Y < .5), 1)
assert allclose(model.prob(Y > .5), 0)