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)
