def test_safe_compute_error(): A = np.abs(random_state.randn(10, 10)) A[:, 2 * np.arange(5)] = 0 A_sparse = csc_matrix(A) W, H = nmf._initialize_nmf(A, 5, init='random', random_state=0) error = nmf._safe_compute_error(A, W, H) error_sparse = nmf._safe_compute_error(A_sparse, W, H) assert_almost_equal(error, error_sparse)
def test_safe_compute_error(): A = np.abs(random_state.randn(10, 10)) A[:, 2 * np.arange(5)] = 0 A_sparse = csc_matrix(A) W, H = nmf._initialize_nmf(A, 5, init='random', random_state=0) error = nmf._safe_compute_error(A, W, H) error_sparse = nmf._safe_compute_error(A_sparse, W, H) assert_almost_equal(error, error_sparse)
def test_sparse_input(): # Test that sparse matrices are accepted as input from scipy.sparse import csc_matrix A = np.abs(random_state.randn(10, 10)) A[:, 2 * np.arange(5)] = 0 A_sparse = csc_matrix(A) for solver in ('proj-grad', 'coordinate', 'greedy'): est1 = nmf.NMF(solver=solver, n_components=5, init='random', random_state=0, tol=1e-2) est2 = clone(est1) W1 = est1.fit_transform(A) W2 = est2.fit_transform(A_sparse) H1 = est1.components_ H2 = est2.components_ assert_array_almost_equal(est2.reconstruction_err_, nmf._safe_compute_error(A, W2, H2)) assert_array_almost_equal(W1, W2) assert_array_almost_equal(H1, H2)