def test_singular_values_expected(solver):
# Set the singular values and see what we get back
rng = np.random.RandomState(0)
n_samples = 100
n_features = 110
X = rng.randn(n_samples, n_features)
pca = TruncatedSVD(n_components=3, algorithm=solver, random_state=rng)
X_pca = pca.fit_transform(X)
X_pca /= np.sqrt(np.sum(X_pca**2.0, axis=0))
X_pca[:, 0] *= 3.142
X_pca[:, 1] *= 2.718
X_hat_pca = np.dot(X_pca, pca.components_)
pca.fit(X_hat_pca)
assert_allclose(pca.singular_values_, [3.142, 2.718, 1.0], rtol=1e-14)
def test_too_many_components(algorithm, X_sparse):
n_features = X_sparse.shape[1]
for n_components in (n_features, n_features + 1):
tsvd = TruncatedSVD(n_components=n_components, algorithm=algorithm)
with pytest.raises(ValueError):
tsvd.fit(X_sparse)