def test_check_weights():
rng = np.random.RandomState(0)
rand_data = RandomData(rng)
n_components = rand_data.n_components
X = rand_data.X['full']
g = GaussianMixture(n_components=n_components)
# Check bad shape
weights_bad_shape = rng.rand(n_components, 1)
g.weights_init = weights_bad_shape
assert_raise_message(ValueError,
"The parameter 'weights' should have the shape of "
"(%d,), "
"but got %s" % (n_components,
str(weights_bad_shape.shape)),
g.fit, X)
# Check bad range
weights_bad_range = rng.rand(n_components) + 1
g.weights_init = weights_bad_range
assert_raise_message(ValueError,
"The parameter 'weights' should be in the range "
"[0, 1], but got max value %.5f, min value %.5f"
% (np.min(weights_bad_range),
np.max(weights_bad_range)),
g.fit, X)
# Check bad normalization
weights_bad_norm = rng.rand(n_components)
weights_bad_norm = weights_bad_norm / (weights_bad_norm.sum() + 1)
g.weights_init = weights_bad_norm
assert_raise_message(ValueError,
"The parameter 'weights' should be normalized, "
"but got sum(weights) = %.5f"
% np.sum(weights_bad_norm),
g.fit, X)
# Check good weights matrix
weights = rand_data.weights
g = GaussianMixture(weights_init=weights, n_components=n_components)
g.fit(X)
assert_array_equal(weights, g.weights_init)
