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_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(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, [])
assert isinstance(steps, BinaryGibbsMetropolis)
with Model() as model:
Categorical('y', np.array([0.25, 0.70, 0.05]))
steps = assign_step_methods(model, [])
assert isinstance(steps, CategoricalGibbsMetropolis)
def test_normal_nograd_op(self):
"""Test normal distribution without an implemented gradient is assigned slice method"""
with Model() as model:
x = Normal('x', 0, 1)
# a custom Theano Op that does not have a grad:
is_64 = theano.config.floatX == "float64"
itypes = [tt.dscalar] if is_64 else [tt.fscalar]
otypes = [tt.dscalar] if is_64 else [tt.fscalar]
@theano.as_op(itypes, otypes)
def kill_grad(x):
return x
data = np.random.normal(size=(100,))
Normal("y", mu=kill_grad(x), sd=1, observed=data.astype(theano.config.floatX))
steps = assign_step_methods(model, [])
assert isinstance(steps, Slice)
def test_normal_nograd_op(self):
"""Test normal distribution without an implemented gradient is assigned slice method"""
with Model() as model:
x = Normal('x', 0, 1)
# a custom Theano Op that does not have a grad:
is_64 = theano.config.floatX == "float64"
itypes = [tt.dscalar] if is_64 else [tt.fscalar]
otypes = [tt.dscalar] if is_64 else [tt.fscalar]
@theano.as_op(itypes, otypes)
def kill_grad(x):
return x
data = np.random.normal(size=(100,))
Normal("y", mu=kill_grad(x), sd=1, observed=data.astype(theano.config.floatX))
steps = assign_step_methods(model, [])
assert isinstance(steps, Slice)
def test_binomial(self):
"""Test binomial distribution is assigned metropolis method."""
with Model() as model:
Binomial('x', 10, 0.5)
steps = assign_step_methods(model, [])
self.assertIsInstance(steps, Metropolis)
def test_normal(self):
"""Test normal distribution is assigned NUTS method"""
with Model() as model:
Normal('x', 0, 1)
steps = assign_step_methods(model, [])
self.assertIsInstance(steps, NUTS)
def test_bernoulli(self):
"""Test bernoulli distribution is assigned binary gibbs metropolis method"""
with Model() as model:
Bernoulli('x', 0.5)
steps = assign_step_methods(model, [])
self.assertIsInstance(steps, BinaryGibbsMetropolis)
def test_binomial(self):
"""Test binomial distribution is assigned metropolis method."""
with Model() as model:
Binomial('x', 10, 0.5)
steps = assign_step_methods(model, [])
assert isinstance(steps, Metropolis)
def test_normal(self):
"""Test normal distribution is assigned NUTS method"""
with Model() as model:
Normal('x', 0, 1)
steps = assign_step_methods(model, [])
assert isinstance(steps, NUTS)
def test_bernoulli(self):
"""Test bernoulli distribution is assigned binary gibbs metropolis method"""
with Model() as model:
Bernoulli('x', 0.5)
steps = assign_step_methods(model, [])
assert isinstance(steps, BinaryGibbsMetropolis)
