def test_assign_step_methods():
with Model() as model:
x = Bernoulli('x', 0.5)
steps = assign_step_methods(model, [])
assert isinstance(steps, BinaryMetropolis)
with Model() as model:
x = Normal('x', 0, 1)
steps = assign_step_methods(model, [])
assert isinstance(steps, NUTS)
with Model() as model:
x = Categorical('x', np.array([0.25, 0.75]))
steps = assign_step_methods(model, [])
assert isinstance(steps, BinaryMetropolis)
with Model() as model:
x = Categorical('x', np.array([0.25, 0.70, 0.05]))
steps = assign_step_methods(model, [])
assert isinstance(steps, Metropolis)
with Model() as model:
x = Binomial('x', 10, 0.5)
steps = assign_step_methods(model, [])
assert isinstance(steps, Metropolis)
def test_categorical(self):
"""Test categorical distribution is assigned categorical gibbs metropolis method"""
with Model() as model:
Categorical('x', np.array([0.25, 0.75]))
steps = assign_step_methods(model, [])
self.assertIsInstance(steps, BinaryGibbsMetropolis)
with Model() as model:
Categorical('y', np.array([0.25, 0.70, 0.05]))
steps = assign_step_methods(model, [])
self.assertIsInstance(steps, CategoricalGibbsMetropolis)
def test_categorical_mode():
model = Model()
with model:
x = Categorical("x", p=np.eye(4), shape=4)
assert np.allclose(model.test_point["x"], np.arange(4))
