def test_join():
p = Join(components=[Normal(mu=0), Normal(mu=1), Normal(mu=2)])
assert p.ndim == 3
assert len(p.parameters_) == 6
X = p.rvs(10000, random_state=1)
assert X.shape == (10000, 3)
assert np.abs(np.mean(X[:, 0]) - 0.) < 0.05
assert np.abs(np.mean(X[:, 1]) - 1.) < 0.05
assert np.abs(np.mean(X[:, 2]) - 2.) < 0.05
assert_array_almost_equal(-np.log(p.pdf(X)), p.nnlf(X))
def test_join():
p = Join(components=[Normal(mu=0, random_state=0),
Normal(mu=1, random_state=1),
Normal(mu=2, random_state=2)], random_state=0)
assert p.ndim == 3
assert len(p.parameters_) == 6
X = p.rvs(10000)
assert X.shape == (10000, 3)
assert np.abs(np.mean(X[:, 0]) - 0.) < 0.05
assert np.abs(np.mean(X[:, 1]) - 1.) < 0.05
assert np.abs(np.mean(X[:, 2]) - 2.) < 0.05
assert_array_almost_equal(-np.log(p.pdf(X)), p.nnlf(X))
def test_join_non_theano():
h0 = Histogram(interpolation="linear", bins=30)
h1 = Histogram(interpolation="linear", bins=30)
h2 = Histogram(interpolation="linear", bins=30)
h0.fit(Normal(mu=0, random_state=0).rvs(10000))
h1.fit(Normal(mu=1, random_state=1).rvs(10000))
h2.fit(Normal(mu=2, random_state=2).rvs(10000))
p = Join(components=[h0, h1, h2], random_state=0)
assert p.ndim == 3
assert len(p.parameters_) == 0
X = p.rvs(10000)
assert X.shape == (10000, 3)
assert np.abs(np.mean(X[:, 0]) - 0.) < 0.05
assert np.abs(np.mean(X[:, 1]) - 1.) < 0.05
assert np.abs(np.mean(X[:, 2]) - 2.) < 0.05
assert_array_almost_equal(-np.log(p.pdf(X)), p.nnlf(X))