def test_lmvnpdf_spherical():
n_features, n_components, n_obs = 2, 3, 10
mu = rng.randint(10) * rng.rand(n_components, n_features)
spherecv = rng.rand(n_components, 1) ** 2 + 1
obs = rng.randint(10) * rng.rand(n_obs, n_features)
cv = np.tile(spherecv, (n_features, 1))
reference = _naive_lmvnpdf_diag(obs, mu, cv)
lpr = mixture.lmvnpdf(obs, mu, spherecv, 'spherical')
assert_array_almost_equal(lpr, reference)
def test_lmvnpdf_spherical():
n_features, n_states, n_obs = 2, 3, 10
mu = np.random.randint(10) * np.random.rand(n_states, n_features)
spherecv = np.random.rand(n_states, 1)**2 + 1
obs = np.random.randint(10) * np.random.rand(n_obs, n_features)
cv = np.tile(spherecv, (n_features, 1))
reference = _naive_lmvnpdf_diag(obs, mu, cv)
lpr = mixture.lmvnpdf(obs, mu, spherecv, 'spherical')
assert_array_almost_equal(lpr, reference)
def test_lmvnpdf_full():
n_features, n_components, n_obs = 2, 3, 10
mu = rng.randint(10) * rng.rand(n_components, n_features)
cv = (rng.rand(n_components, n_features) + 1.0) ** 2
obs = rng.randint(10) * rng.rand(n_obs, n_features)
fullcv = np.array([np.diag(x) for x in cv])
reference = _naive_lmvnpdf_diag(obs, mu, cv)
lpr = mixture.lmvnpdf(obs, mu, fullcv, 'full')
assert_array_almost_equal(lpr, reference)
def test_lmvnpdf_full():
n_features, n_states, n_obs = 2, 3, 10
mu = np.random.randint(10) * np.random.rand(n_states, n_features)
cv = (np.random.rand(n_states, n_features) + 1.0)**2
obs = np.random.randint(10) * np.random.rand(n_obs, n_features)
fullcv = np.array([np.diag(x) for x in cv])
reference = _naive_lmvnpdf_diag(obs, mu, cv)
lpr = mixture.lmvnpdf(obs, mu, fullcv, 'full')
assert_array_almost_equal(lpr, reference)
def test_lmvnpdf_diag():
"""
test a slow and naive implementation of lmvnpdf and
compare it to the vectorized version (mixture.lmvnpdf) to test
for correctness
"""
n_features, n_components, n_obs = 2, 3, 10
mu = rng.randint(10) * rng.rand(n_components, n_features)
cv = (rng.rand(n_components, n_features) + 1.0) ** 2
obs = rng.randint(10) * rng.rand(n_obs, n_features)
ref = _naive_lmvnpdf_diag(obs, mu, cv)
lpr = mixture.lmvnpdf(obs, mu, cv, 'diag')
assert_array_almost_equal(lpr, ref)
def test_lmvnpdf_diag():
"""
test a slow and naive implementation of lmvnpdf and
compare it to the vectorized version (mixture.lmvnpdf) to test
for correctness
"""
n_features, n_states, n_obs = 2, 3, 10
mu = np.random.randint(10) * np.random.rand(n_states, n_features)
cv = (np.random.rand(n_states, n_features) + 1.0)**2
obs = np.random.randint(10) * np.random.rand(n_obs, n_features)
ref = _naive_lmvnpdf_diag(obs, mu, cv)
lpr = mixture.lmvnpdf(obs, mu, cv, 'diag')
assert_array_almost_equal(lpr, ref)
