def test_sample():
rng = np.random.RandomState(0)
rand_data = RandomData(rng, scale=7, n_components=3)
n_features, n_components = rand_data.n_features, rand_data.n_components
for covar_type in COVARIANCE_TYPE:
X = rand_data.X[covar_type]
gmm = GaussianMixture(n_components=n_components,
covariance_type=covar_type,
random_state=rng)
# To sample we need that GaussianMixture is fitted
assert_raise_message(NotFittedError, "This GaussianMixture instance "
"is not fitted", gmm.sample, 0)
gmm.fit(X)
assert_raise_message(ValueError, "Invalid value for 'n_samples",
gmm.sample, 0)
# Just to make sure the class samples correctly
n_samples = 20000
X_s, y_s = gmm.sample(n_samples)
for k in range(n_components):
if covar_type == 'full':
assert_array_almost_equal(gmm.covariances_[k],
np.cov(X_s[y_s == k].T),
decimal=1)
elif covar_type == 'tied':
assert_array_almost_equal(gmm.covariances_,
np.cov(X_s[y_s == k].T),
decimal=1)
elif covar_type == 'diag':
assert_array_almost_equal(gmm.covariances_[k],
np.diag(np.cov(X_s[y_s == k].T)),
decimal=1)
else:
assert_array_almost_equal(gmm.covariances_[k],
np.var(X_s[y_s == k] -
gmm.means_[k]),
decimal=1)
means_s = np.array(
[np.mean(X_s[y_s == k], 0) for k in range(n_components)])
assert_array_almost_equal(gmm.means_, means_s, decimal=1)
# Check shapes of sampled data, see
# https://github.com/scikit-learn/scikit-learn/issues/7701
assert_equal(X_s.shape, (n_samples, n_features))
for sample_size in range(1, 100):
X_s, _ = gmm.sample(sample_size)
assert_equal(X_s.shape, (sample_size, n_features))
def test_sample():
rng = np.random.RandomState(0)
rand_data = RandomData(rng, scale=7, n_components=3)
n_features, n_components = rand_data.n_features, rand_data.n_components
for covar_type in COVARIANCE_TYPE:
X = rand_data.X[covar_type]
gmm = GaussianMixture(n_components=n_components,
covariance_type=covar_type, random_state=rng)
# To sample we need that GaussianMixture is fitted
assert_raise_message(NotFittedError, "This GaussianMixture instance "
"is not fitted", gmm.sample, 0)
gmm.fit(X)
assert_raise_message(ValueError, "Invalid value for 'n_samples",
gmm.sample, 0)
# Just to make sure the class samples correctly
n_samples = 20000
X_s, y_s = gmm.sample(n_samples)
for k in range(n_components):
if covar_type == 'full':
assert_array_almost_equal(gmm.covariances_[k],
np.cov(X_s[y_s == k].T), decimal=1)
elif covar_type == 'tied':
assert_array_almost_equal(gmm.covariances_,
np.cov(X_s[y_s == k].T), decimal=1)
elif covar_type == 'diag':
assert_array_almost_equal(gmm.covariances_[k],
np.diag(np.cov(X_s[y_s == k].T)),
decimal=1)
else:
assert_array_almost_equal(
gmm.covariances_[k], np.var(X_s[y_s == k] - gmm.means_[k]),
decimal=1)
means_s = np.array([np.mean(X_s[y_s == k], 0)
for k in range(n_components)])
assert_array_almost_equal(gmm.means_, means_s, decimal=1)
# Check shapes of sampled data, see
# https://github.com/scikit-learn/scikit-learn/issues/7701
assert_equal(X_s.shape, (n_samples, n_features))
for sample_size in range(1, 100):
X_s, _ = gmm.sample(sample_size)
assert_equal(X_s.shape, (sample_size, n_features))
def test_sample():
rng = np.random.RandomState(0)
rand_data = RandomData(rng, scale=7)
n_features, n_components = rand_data.n_features, rand_data.n_components
for covar_type in COVARIANCE_TYPE:
X = rand_data.X[covar_type]
gmm = GaussianMixture(n_components=n_components,
covariance_type=covar_type, random_state=rng)
# To sample we need that GaussianMixture is fitted
assert_raise_message(NotFittedError, "This GaussianMixture instance "
"is not fitted", gmm.sample, 0)
gmm.fit(X)
assert_raise_message(ValueError, "Invalid value for 'n_samples",
gmm.sample, 0)
# Just to make sure the class samples correctly
X_s, y_s = gmm.sample(20000)
for k in range(n_features):
if covar_type == 'full':
assert_array_almost_equal(gmm.covariances_[k],
np.cov(X_s[y_s == k].T), decimal=1)
elif covar_type == 'tied':
assert_array_almost_equal(gmm.covariances_,
np.cov(X_s[y_s == k].T), decimal=1)
elif covar_type == 'diag':
assert_array_almost_equal(gmm.covariances_[k],
np.diag(np.cov(X_s[y_s == k].T)),
decimal=1)
else:
assert_array_almost_equal(
gmm.covariances_[k], np.var(X_s[y_s == k] - gmm.means_[k]),
decimal=1)
means_s = np.array([np.mean(X_s[y_s == k], 0)
for k in range(n_features)])
assert_array_almost_equal(gmm.means_, means_s, decimal=1)
def wemd_from_pred_samples(y_pred, ):
gmm = GMM(covariance_type="diag")
gmm = gmm.fit(y_pred)
y_s, _ = gmm.sample(len(y_pred))
return wemd_from_samples(y_s, y_pred)
