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
