def test_transform_after_fit_no_labels(solver):
rng = np.random.mtrand.RandomState(36)
X = rng.randn(7, 5)
Y = rng.randn(5, 3)
X_new = rng.randn(15, 5)
model = CMF(n_components=2, solver=solver, x_init='svd', y_init='svd',
U_non_negative=False, V_non_negative=False, Z_non_negative=False,
random_state=0, max_iter=100)
U_ft, V_ft, Z_ft = model.fit_transform(X, Y)
U_t, V_t, Z_t = model.transform(X_new, None)
assert_array_equal(V_t, V_ft)
def test_transform_after_fit(solver):
rng = np.random.mtrand.RandomState(36)
X = rng.randn(7, 5)
Y = rng.randn(5, 3)
fit_model = CMF(n_components=2, solver=solver, x_init='random', y_init='random',
U_non_negative=False, V_non_negative=False, Z_non_negative=False,
random_state=0, max_iter=100)
fit_transform_model = clone(fit_model)
fit_model.fit(X, Y)
U_f, V_f, Z_f = fit_model.transform(X, Y)
U_ft, V_ft, Z_ft = fit_transform_model.fit_transform(X, Y)
# the initalizations will differ, so the results may also differ slightly
assert_array_almost_equal(U_f, U_ft, decimal=2)
assert_array_almost_equal(V_f, V_ft, decimal=2)
assert_array_almost_equal(Z_f, Z_ft, decimal=2)