def check_mv_normal(mu, sigma):
p = MultivariateNormal(mu=mu, sigma=sigma)
X = p.rvs(20000, random_state=0)
assert np.mean(mu - X.mean(axis=0)) < 0.02
assert np.mean(sigma - np.cov(X.T)) < 0.02
assert p.ndim == len(mu)
def check_mv_normal(mu, sigma):
p = MultivariateNormal(mu=mu, sigma=sigma)
X = p.rvs(20000, random_state=0)
assert np.mean(mu - X.mean(axis=0)) < 0.02
assert np.mean(sigma - np.cov(X.T)) < 0.02
assert p.ndim == len(mu)
def test_mv_mixture():
p1 = MultivariateNormal(mu=np.array([0.0, 0.0]), sigma=np.eye(2))
p2 = MultivariateNormal(mu=np.array([2.0, 2.0]), sigma=0.5 * np.eye(2))
m = Mixture(components=[p1, p2])
assert m.ndim == 2
X = m.rvs(100)
assert X.shape == (100, 2)
assert_raises(ValueError, Mixture, components=[p1, Normal()])
def test_linear_transform_2d():
p0 = MultivariateNormal(mu=np.array([0.0, 1.0]), sigma=np.eye(2))
pt = LinearTransform(p0, A=np.array([[1.0, 0], [0.0, 0.5]]))
X0 = p0.rvs(10, random_state=0)
Xt = pt.rvs(10, random_state=0)
assert X0.shape == Xt.shape
assert_array_equal(p0.pdf(X0), pt.pdf(Xt))
assert_array_equal(p0.nll(X0), pt.nll(Xt))
def test_linear_transform_2d():
p0 = MultivariateNormal(mu=np.array([0., 1.]), sigma=np.eye(2))
pt = LinearTransform(p0, A=np.array([[1.0, 0], [0., 0.5]]))
X0 = p0.rvs(10, random_state=0)
Xt = pt.rvs(10, random_state=0)
assert X0.shape == Xt.shape
assert_array_equal(p0.pdf(X0), pt.pdf(Xt))
assert_array_equal(p0.nll(X0), pt.nll(Xt))
