def model() -> Model:
KeanuRandom.set_default_random_seed(1)
model = thermometers.model()
model.thermometer_one.observe(22.0)
model.thermometer_two.observe(20.0)
return model
def test_iter_with_live_plot(net: BayesNet) -> None:
KeanuRandom.set_default_random_seed(1)
_, ax = plt.subplots(3, 1, squeeze=False)
samples = generate_samples(net=net,
sample_from=net.get_latent_vertices(),
live_plot=True,
refresh_every=5,
ax=ax)
for sample in islice(samples, 5):
pass
reorder_subplots(ax)
assert len(ax) == 3
assert all(len(ax[i][0].get_lines()) == 1 for i in range(3))
assert np.allclose(
ax[0][0].get_lines()[0].get_ydata(),
[0.49147822, 0.49147822, 0.49147822, 0.20033212, 0.20033212])
assert np.allclose(
ax[1][0].get_lines()[0].get_ydata(),
[0.87268333, 1.10409369, 1.10409369, 1.10409369, 0.69098161])
assert np.allclose(
ax[2][0].get_lines()[0].get_ydata(),
[-14.46166855, -14.46166855, 0.32305686, 0.32305686, 0.32305686])
def test_sample_with_plot(net: BayesNet) -> None:
KeanuRandom.set_default_random_seed(1)
_, ax = plt.subplots(3, 1, squeeze=False)
sample(net=net, sample_from=net.get_latent_vertices(), draws=5, plot=True, ax=ax)
reorder_subplots(ax)
assert len(ax) == 3
assert all(len(ax[i][0].get_lines()) == 1 for i in range(3))
assert all(len(ax[i][0].get_lines()[0].get_ydata()) == 5 for i in range(3))
def model() -> Model:
KeanuRandom.set_default_random_seed(1)
with Model() as m:
m.a = Gaussian(0., 50.)
m.b = Gaussian(0., 50.)
m.c = m.a + m.b
m.d = Gaussian(m.c, 1.)
m.d.observe(20.0)
return m
def test_iter_with_live_plot(net: BayesNet) -> None:
KeanuRandom.set_default_random_seed(1)
_, ax = plt.subplots(3, 1, squeeze=False)
samples = generate_samples(net=net, sample_from=net.get_latent_vertices(), live_plot=True, refresh_every=5, ax=ax)
for sample in islice(samples, 5):
pass
reorder_subplots(ax)
assert len(ax) == 3
assert all(len(ax[i][0].get_lines()) == 1 for i in range(3))
assert all(len(ax[i][0].get_lines()[0].get_ydata() == 5) for i in range(3))
def test_coalmining() -> None:
KeanuRandom.set_default_random_seed(1)
coal_mining = CoalMining()
model = coal_mining.model()
model.disasters.observe(coal_mining.training_data())
net = BayesNet(model.switchpoint.get_connected_graph())
samples = sample(net=net, sample_from=net.get_latent_vertices(), draws=50000, drop=10000, down_sample_interval=5)
vertex_samples: List[numpy_types] = samples["switchpoint"]
vertex_samples_primitive: List[List[primitive_types]] = list(map(
lambda a: a.tolist(), vertex_samples)) # because you can't concatenate 0-d arrays
vertex_samples_concatentated: np.ndarray = np.array(vertex_samples_primitive)
switch_year = np.argmax(np.bincount(vertex_samples_concatentated))
assert switch_year == 1890
def test_probe_for_non_zero_probability_from_bayes_net() -> None:
gamma = Gamma(1., 1.)
poisson = Poisson(gamma)
net = BayesNet([poisson, gamma])
assert not gamma.has_value()
assert not poisson.has_value()
net.probe_for_non_zero_probability(100, KeanuRandom())
assert gamma.has_value()
assert poisson.has_value()
def set_starting_state(model: Model) -> None:
KeanuRandom.set_default_random_seed(1)
model.temperature.set_value(model.temperature.sample())
model.thermometer_one.set_value(model.thermometer_one.sample())
model.thermometer_two.set_value(model.thermometer_two.sample())
def set_starting_state(model: Model) -> None:
KeanuRandom.set_default_random_seed(1)
model.uniform.set_value(model.uniform.sample())
def test_default_keanu_random() -> None:
keanu_random = KeanuRandom()
random = keanu_random.next_double()
assert type(random) == float
assert 0 <= random < 1
def test_seeded_keanu_random() -> None:
keanu_random = KeanuRandom(1)
random = keanu_random.next_double()
assert type(random) == float
assert random == 0.1129943035738381
