def test_gaussian_mixture_estimate_log_prob_resp():
# test whether responsibilities are normalized
rng = np.random.RandomState(0)
rand_data = RandomData(rng, scale=5)
n_samples = rand_data.n_samples
n_features = rand_data.n_features
n_components = rand_data.n_components
X = rng.rand(n_samples, n_features)
for covar_type in COVARIANCE_TYPE:
weights = rand_data.weights
means = rand_data.means
precisions = rand_data.precisions[covar_type]
g = GaussianMixture(n_components=n_components,
random_state=rng,
weights_init=weights,
means_init=means,
precisions_init=precisions,
covariance_type=covar_type)
g.fit(X)
resp = g.predict_proba(X)
assert_array_almost_equal(resp.sum(axis=1), np.ones(n_samples))
assert_array_equal(g.weights_init, weights)
assert_array_equal(g.means_init, means)
assert_array_equal(g.precisions_init, precisions)
def test_gaussian_mixture_estimate_log_prob_resp():
# test whether responsibilities are normalized
rng = np.random.RandomState(0)
rand_data = RandomData(rng, scale=5)
n_samples = rand_data.n_samples
n_features = rand_data.n_features
n_components = rand_data.n_components
X = rng.rand(n_samples, n_features)
for covar_type in COVARIANCE_TYPE:
weights = rand_data.weights
means = rand_data.means
precisions = rand_data.precisions[covar_type]
g = GaussianMixture(n_components=n_components, random_state=rng,
weights_init=weights, means_init=means,
precisions_init=precisions,
covariance_type=covar_type)
g.fit(X)
resp = g.predict_proba(X)
assert_array_almost_equal(resp.sum(axis=1), np.ones(n_samples))
assert_array_equal(g.weights_init, weights)
assert_array_equal(g.means_init, means)
assert_array_equal(g.precisions_init, precisions)
def test_gaussian_mixture_predict_predict_proba():
rng = np.random.RandomState(0)
rand_data = RandomData(rng)
for covar_type in COVARIANCE_TYPE:
X = rand_data.X[covar_type]
Y = rand_data.Y
g = GaussianMixture(n_components=rand_data.n_components,
random_state=rng, weights_init=rand_data.weights,
means_init=rand_data.means,
precisions_init=rand_data.precisions[covar_type],
covariance_type=covar_type)
# Check a warning message arrive if we don't do fit
assert_raise_message(NotFittedError,
"This GaussianMixture instance is not fitted "
"yet. Call 'fit' with appropriate arguments "
"before using this method.", g.predict, X)
g.fit(X)
Y_pred = g.predict(X)
Y_pred_proba = g.predict_proba(X).argmax(axis=1)
assert_array_equal(Y_pred, Y_pred_proba)
assert_greater(adjusted_rand_score(Y, Y_pred), .95)
def test_gaussian_mixture_predict_predict_proba():
rng = np.random.RandomState(0)
rand_data = RandomData(rng)
for covar_type in COVARIANCE_TYPE:
X = rand_data.X[covar_type]
Y = rand_data.Y
g = GaussianMixture(n_components=rand_data.n_components,
random_state=rng, weights_init=rand_data.weights,
means_init=rand_data.means,
precisions_init=rand_data.precisions[covar_type],
covariance_type=covar_type)
# Check a warning message arrive if we don't do fit
assert_raise_message(NotFittedError,
"This GaussianMixture instance is not fitted "
"yet. Call 'fit' with appropriate arguments "
"before using this method.", g.predict, X)
g.fit(X)
Y_pred = g.predict(X)
Y_pred_proba = g.predict_proba(X).argmax(axis=1)
assert_array_equal(Y_pred, Y_pred_proba)
assert_greater(adjusted_rand_score(Y, Y_pred), .95)
