def test_incomplete_cholesky_new_point(): kernel = lambda X, Y = None : gaussian_kernel(X, Y, sigma=200.) X = np.random.randn(1000, 10) low_rank_dim = 15 temp = incomplete_cholesky(X, kernel, eta=low_rank_dim) R, I, nu = (temp["R"], temp["I"], temp["nu"]) # construct train-train kernel matrix approximation using one by one calls for i in range(low_rank_dim): r = incomplete_cholesky_new_point(X, X[i], kernel, I, R, nu) assert_allclose(r, R[:,i], atol=1e-1)
def test_incomplete_cholesky_new_point(): kernel = lambda X, Y=None: gaussian_kernel(X, Y, sigma=200.) X = np.random.randn(1000, 10) low_rank_dim = 15 temp = incomplete_cholesky(X, kernel, eta=low_rank_dim) R, I, nu = (temp["R"], temp["I"], temp["nu"]) # construct train-train kernel matrix approximation using one by one calls for i in range(low_rank_dim): r = incomplete_cholesky_new_point(X, X[i], kernel, I, R, nu) assert_allclose(r, R[:, i], atol=1e-1)
def test_incomplete_cholesky_new_points_euqals_new_point(): kernel = lambda X, Y = None : gaussian_kernel(X, Y, sigma=200.) X = np.random.randn(1000, 10) low_rank_dim = 15 temp = incomplete_cholesky(X, kernel, eta=low_rank_dim) R, I, nu = (temp["R"], temp["I"], temp["nu"]) R_test_full = incomplete_cholesky_new_points(X, X, kernel, I, R, nu) # construct train-train kernel matrix approximation using one by one calls R_test = np.zeros(R.shape) for i in range(low_rank_dim): R_test[:, i] = incomplete_cholesky_new_point(X, X[i], kernel, I, R, nu) assert_allclose(R_test[:, i], R_test_full[:, i], atol=0.001)
def test_incomplete_cholesky_new_points_euqals_new_point(): kernel = lambda X, Y=None: gaussian_kernel(X, Y, sigma=200.) X = np.random.randn(1000, 10) low_rank_dim = 15 temp = incomplete_cholesky(X, kernel, eta=low_rank_dim) R, I, nu = (temp["R"], temp["I"], temp["nu"]) R_test_full = incomplete_cholesky_new_points(X, X, kernel, I, R, nu) # construct train-train kernel matrix approximation using one by one calls R_test = np.zeros(R.shape) for i in range(low_rank_dim): R_test[:, i] = incomplete_cholesky_new_point(X, X[i], kernel, I, R, nu) assert_allclose(R_test[:, i], R_test_full[:, i], atol=0.001)