def test_compute_b_matches_sym(): sigma = 1. X = np.random.randn(10, 2) R = incomplete_cholesky_gaussian(X, sigma, eta=0.1)["R"] x = gaussian_low_rank.compute_b(X, X, R.T, R.T, sigma) y = develop_gaussian_low_rank.compute_b_sym(X, R.T, sigma) assert_allclose(x, y)
def test_compute_b_matches_sym(): sigma = 1. X = np.random.randn(10, 2) R = incomplete_cholesky_gaussian(X, sigma, eta=0.1)["R"] x = gaussian_low_rank.compute_b(X, X, R.T, R.T, sigma) y = develop_gaussian_low_rank.compute_b_sym(X, R.T, sigma) assert_allclose(x, y)
def test_compute_b_sym_matches_full(): sigma = 1. Z = np.random.randn(100, 2) low_rank_dim = int(len(Z) * .9) K = gaussian_kernel(Z, sigma=sigma) R = incomplete_cholesky_gaussian(Z, sigma, eta=low_rank_dim)["R"] x = develop_gaussian.compute_b_sym(Z, K, sigma) y = develop_gaussian_low_rank.compute_b_sym(Z, R.T, sigma) assert_allclose(x, y, atol=5e-1)
def test_compute_b_sym_matches_full(): sigma = 1. Z = np.random.randn(100, 2) low_rank_dim = int(len(Z) * .9) K = gaussian_kernel(Z, sigma=sigma) R = incomplete_cholesky_gaussian(Z, sigma, eta=low_rank_dim)["R"] x = develop_gaussian.compute_b_sym(Z, K, sigma) y = develop_gaussian_low_rank.compute_b_sym(Z, R.T, sigma) assert_allclose(x, y, atol=5e-1)
def test_objective_sym_optimum(): sigma = 1. lmbda = 1. Z = np.random.randn(100, 2) L = incomplete_cholesky_gaussian(Z, sigma, eta=0.1)["R"].T a = develop_gaussian_low_rank.fit_sym(Z, sigma, lmbda, L) b = develop_gaussian_low_rank.compute_b_sym(Z, L, sigma) J_opt = develop_gaussian_low_rank.objective_sym(Z, sigma, lmbda, a, L, b) for _ in range(10): a_random = np.random.randn(len(Z)) J = develop_gaussian_low_rank.objective_sym(Z, sigma, lmbda, a_random, L) assert J >= J_opt
def test_objective_sym_optimum(): sigma = 1. lmbda = 1. Z = np.random.randn(100, 2) L = incomplete_cholesky_gaussian(Z, sigma, eta=0.1)["R"].T a = develop_gaussian_low_rank.fit_sym(Z, sigma, lmbda, L) b = develop_gaussian_low_rank.compute_b_sym(Z, L, sigma) J_opt = develop_gaussian_low_rank.objective_sym(Z, sigma, lmbda, a, L, b) for _ in range(10): a_random = np.random.randn(len(Z)) J = develop_gaussian_low_rank.objective_sym(Z, sigma, lmbda, a_random, L) assert J >= J_opt