def test_singular_values(setup):
# Check that the TruncatedSVD output has the correct singular values
# 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)
rpca = TruncatedSVD(n_components=3,
algorithm='randomized',
random_state=rng)
X_rpca = rpca.fit_transform(X)
X_rpca /= mt.sqrt(mt.sum(X_rpca**2.0, axis=0))
X_rpca[:, 0] *= 3.142
X_rpca[:, 1] *= 2.718
X_hat_rpca = mt.dot(X_rpca, rpca.components_)
rpca.fit(X_hat_rpca)
assert_array_almost_equal(rpca.singular_values_.to_numpy(),
[3.142, 2.718, 1.0], 14)
def test_too_many_components(setup):
for n_components in (n_features, n_features + 1):
tsvd = TruncatedSVD(n_components=n_components, algorithm='randomized')
with pytest.raises(ValueError):
tsvd.fit(X)
def test_too_many_components(self):
for n_components in (self.n_features, self.n_features + 1):
tsvd = TruncatedSVD(n_components=n_components,
algorithm='randomized')
with self.assertRaises(ValueError):
tsvd.fit(self.X)