def test_sparse_formats(fmt, X_sparse):
n_samples = X_sparse.shape[0]
Xfmt = (X_sparse.toarray() if fmt == "dense" else getattr(
X_sparse, "to" + fmt)())
tsvd = TruncatedSVD(n_components=11)
Xtrans = tsvd.fit_transform(Xfmt)
assert Xtrans.shape == (n_samples, 11)
Xtrans = tsvd.transform(Xfmt)
assert Xtrans.shape == (n_samples, 11)
def test_singular_values_consistency(solver):
# Check that the TruncatedSVD output has the correct singular values
rng = np.random.RandomState(0)
n_samples, n_features = 100, 80
X = rng.randn(n_samples, n_features)
pca = TruncatedSVD(n_components=2, algorithm=solver,
random_state=rng).fit(X)
# Compare to the Frobenius norm
X_pca = pca.transform(X)
assert_allclose(np.sum(pca.singular_values_**2.0),
np.linalg.norm(X_pca, "fro")**2.0,
rtol=1e-2)
# Compare to the 2-norms of the score vectors
assert_allclose(pca.singular_values_,
np.sqrt(np.sum(X_pca**2.0, axis=0)),
rtol=1e-2)