def test_fit_sym_time(): sigma = 1. lmbda = 1. N = 20000 Z = np.random.randn(N, 2) R = incomplete_cholesky_gaussian(Z, sigma, eta=0.1)["R"] develop_gaussian_low_rank.fit_sym(Z, sigma, lmbda, L=R.T, cg_tol=1e-1)
def test_fit_sym_time(): sigma = 1. lmbda = 1. N = 20000 Z = np.random.randn(N, 2) R = incomplete_cholesky_gaussian(Z, sigma, eta=0.1)["R"] develop_gaussian_low_rank.fit_sym(Z, sigma, lmbda, L=R.T, cg_tol=1e-1)
def test_fit_sym_matches_full(): sigma = 1. lmbda = 1. Z = np.random.randn(100, 2) a = develop_gaussian.fit_sym(Z, sigma, lmbda) R = incomplete_cholesky_gaussian(Z, sigma, eta=0.1)["R"] a_cholesky_cg = develop_gaussian_low_rank.fit_sym(Z, sigma, lmbda, L=R.T) assert_allclose(a, a_cholesky_cg, atol=3)
def test_fit_sym_matches_full(): sigma = 1. lmbda = 1. Z = np.random.randn(100, 2) a = develop_gaussian.fit_sym(Z, sigma, lmbda) R = incomplete_cholesky_gaussian(Z, sigma, eta=0.1)["R"] a_cholesky_cg = develop_gaussian_low_rank.fit_sym(Z, sigma, lmbda, L=R.T) assert_allclose(a, a_cholesky_cg, atol=3)
def test_objective_sym_matches_full(): sigma = 1. lmbda = 1. Z = np.random.randn(100, 2) K = gaussian_kernel(Z, sigma=sigma) a_opt = develop_gaussian.fit_sym(Z, sigma, lmbda, K) J_opt = develop_gaussian.objective_sym(Z, sigma, lmbda, a_opt, K) L = incomplete_cholesky_gaussian(Z, sigma, eta=0.01)["R"].T a_opt_chol = develop_gaussian_low_rank.fit_sym(Z, sigma, lmbda, L) J_opt_chol = develop_gaussian_low_rank.objective_sym(Z, sigma, lmbda, a_opt_chol, L) assert_almost_equal(J_opt, J_opt_chol, delta=2.)
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_matches_full(): sigma = 1. lmbda = 1. Z = np.random.randn(100, 2) K = gaussian_kernel(Z, sigma=sigma) a_opt = develop_gaussian.fit_sym(Z, sigma, lmbda, K) J_opt = develop_gaussian.objective_sym(Z, sigma, lmbda, a_opt, K) L = incomplete_cholesky_gaussian(Z, sigma, eta=0.01)["R"].T a_opt_chol = develop_gaussian_low_rank.fit_sym(Z, sigma, lmbda, L) J_opt_chol = develop_gaussian_low_rank.objective_sym( Z, sigma, lmbda, a_opt_chol, L) assert_almost_equal(J_opt, J_opt_chol, delta=2.)
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_fit_matches_sym(): sigma = 1. lmbda = 1. Z = np.random.randn(100, 2) low_rank_dim = int(len(Z) * .9) kernel = lambda X, Y: gaussian_kernel(X, Y, sigma=sigma) temp = incomplete_cholesky(Z, kernel, eta=low_rank_dim) I, R, nu = (temp["I"], temp["R"], temp["nu"]) R_test = incomplete_cholesky_new_points(Z, Z, kernel, I, R, nu) a = gaussian_low_rank.fit(Z, Z, sigma, lmbda, R.T, R_test.T) a_sym = develop_gaussian_low_rank.fit_sym(Z, sigma, lmbda, R.T) assert_allclose(a, a_sym, atol=1e-5)
def test_fit_matches_sym(): sigma = 1. lmbda = 1. Z = np.random.randn(100, 2) low_rank_dim = int(len(Z) * .9) kernel = lambda X, Y: gaussian_kernel(X, Y, sigma=sigma) temp = incomplete_cholesky(Z, kernel, eta=low_rank_dim) I, R, nu = (temp["I"], temp["R"], temp["nu"]) R_test = incomplete_cholesky_new_points(Z, Z, kernel, I, R, nu) a = gaussian_low_rank.fit(Z, Z, sigma, lmbda, R.T, R_test.T) a_sym = develop_gaussian_low_rank.fit_sym(Z, sigma, lmbda, R.T) assert_allclose(a, a_sym)