def test_binary(self):
m = bayesnet.BayesNetMap()
with pytest.raises(KeyError):
m.get_net("a")
m.add_net("a", self.net_1)
assert m.get_net("a") == self.net_1
with pytest.raises(KeyError):
m.get_net("b")
with pytest.raises(AssertionError):
m.add_net("a", self.net_2)
m.add_net("b", self.net_2)
assert m.get_net("a") == self.net_1
assert m.get_net("b") == self.net_2
with pytest.raises(KeyError):
m.get_net("c")
with pytest.raises(AssertionError):
m.add_net("c", sprinkler_net.get(self.rng))
m.add_net("c", self.net_1)
m.add_net("d", self.net_2)
assert m.get_net("a") == self.net_1
assert m.get_net("b") == self.net_2
assert m.get_net("c") == self.net_1
assert m.get_net("d") == self.net_2
assert len(m.items()) == 4
assert sorted(m.items())[0] == ("a", self.net_1)
assert sorted(m.keys()) == ["a", "b", "c", "d"]
def setup(self):
"""Set up random stream and network."""
rng = utils.RandomState(seed=0)
self.net = sprinkler_net.get(rng)
self.rain_node = self.net.find_node("Rain")
self.sprinkler_node = self.net.find_node("Sprinkler")
self.grass_node = self.net.find_node("Grass")
self.n = 10000
def test_marg_counter():
rng = utils.RandomState(seed=0)
net = sprinkler_net.get(rng)
counter = marg.MarginalCounter(net)
with pytest.raises(AssertionError):
counter.marginals()
value_lists = [[0, 0, 0], [0, 0, 1], [0, 1, 1]]
for values in value_lists:
world = random_world.RandomWorld([0, 1, 2], values)
counter.observe(world)
assert counter.num_observations == 3
marginals = counter.marginals()
np.testing.assert_almost_equal(marginals[0][0], 1.0)
np.testing.assert_almost_equal(marginals[0][1], 0.0)
np.testing.assert_almost_equal(marginals[1][0], 2 / 3)
np.testing.assert_almost_equal(marginals[1][1], 1 / 3)
np.testing.assert_almost_equal(marginals[2][0], 1 / 3)
np.testing.assert_almost_equal(marginals[2][1], 2 / 3)
def test_gibbs_learner():
"""Verify that Gibbs learner makes same predictions as enumerator."""
num_samples = 10000
rng = utils.RandomState(seed=0)
net = sprinkler_net.get(rng)
for node in net.nodes_by_index:
learner = learn.GibbsLearner(node, rng)
learner.finalize()
for markov_blanket_values in utils.lexicographic_combinations(
[[0, 1]] * len(node.markov_blanket)):
world = random_world.RandomWorld(
[n.index for n in node.markov_blanket],
markov_blanket_values)
enumerator = exact_inference.Enumerator(net, world)
probabilities = enumerator.marginalize_node(node)
check_scorer(
lambda v: learner.log_probability(
markov_blanket_values, v), probabilities)
check_sampler(
lambda: learner.sample(
markov_blanket_values), probabilities, num_samples)
def setup(self): self.rng = utils.RandomState(seed=0) self.net = sprinkler_net.get(self.rng)
def setup(self): """Set up random stream and sprinkler network.""" self.rng = utils.RandomState(seed=0) self.net = sprinkler_net.get(self.rng)
def test_sprinkler_net(proposal_size, evidence_index):
rng = utils.RandomState(seed=0)
net = sprinkler_net.get(rng)
evidence = sprinkler_net.evidence(evidence_index)
run_test(rng, net, evidence, proposal_size=proposal_size)
