def test_mixin_composition():
# Check composed expressions as parameters
a = theano.shared(0.0)
b = theano.shared(-1.0)
mu = a + b - 1.0
sigma = T.abs_(a * b)
p = Normal(mu=mu, sigma=sigma)
assert a in p.parameters_
assert b in p.parameters_
# Compose parameters with observed variables
a = theano.shared(1.0)
b = theano.shared(0.0)
y = T.dmatrix(name="y")
p = Normal(mu=a * y + b)
assert len(p.parameters_) == 3
assert a in p.parameters_
assert b in p.parameters_
assert p.sigma in p.parameters_
assert p.mu not in p.parameters_
assert len(p.observeds_) == 1
assert y in p.observeds_
# Check signatures
data_X = np.random.rand(10, 1)
data_y = np.random.rand(10, 1)
p.pdf(X=data_X, y=data_y)
p.cdf(X=data_X, y=data_y)
p.rvs(10, y=data_y)
# Check error
a = theano.shared(1.0)
b = theano.shared(0.0)
y = T.dmatrix() # y must be named
assert_raises(ValueError, Normal, mu=a * y + b)
def test_mixin_composition():
# Check composed expressions as parameters
a = theano.shared(0.0)
b = theano.shared(-1.0)
mu = a + b - 1.0
sigma = T.abs_(a * b)
p = Normal(mu=mu, sigma=sigma)
assert a in p.parameters_
assert b in p.parameters_
# Compose parameters with observed variables
a = theano.shared(1.0)
b = theano.shared(0.0)
y = T.dmatrix(name="y")
p = Normal(mu=a * y + b)
assert len(p.parameters_) == 3
assert a in p.parameters_
assert b in p.parameters_
assert p.sigma in p.parameters_
assert p.mu not in p.parameters_
assert len(p.observeds_) == 1
assert y in p.observeds_
# Check signatures
data_X = np.random.rand(10, 1)
data_y = np.random.rand(10, 1)
p.pdf(X=data_X, y=data_y)
p.cdf(X=data_X, y=data_y)
p.rvs(10, y=data_y)
# Check error
a = theano.shared(1.0)
b = theano.shared(0.0)
y = T.dmatrix() # y must be named
assert_raises(ValueError, Normal, mu=a * y + b)
def check_normal(mu, sigma):
rng = check_random_state(1)
p_carl = Normal(mu=mu, sigma=sigma)
p_scipy = st.norm(loc=mu, scale=sigma)
X = rng.rand(50, 1)
assert_array_almost_equal(p_carl.pdf(X), p_scipy.pdf(X.ravel()))
assert_array_almost_equal(p_carl.cdf(X), p_scipy.cdf(X.ravel()))
assert_array_almost_equal(-np.log(p_carl.pdf(X)), p_carl.nll(X))
def check_normal(mu, sigma):
rng = check_random_state(1)
p_carl = Normal(mu=mu, sigma=sigma)
p_scipy = st.norm(loc=mu, scale=sigma)
X = rng.rand(50, 1)
assert_array_almost_equal(p_carl.pdf(X),
p_scipy.pdf(X.ravel()))
assert_array_almost_equal(p_carl.cdf(X),
p_scipy.cdf(X.ravel()))
assert_array_almost_equal(-np.log(p_carl.pdf(X)),
p_carl.nll(X))
