def test_score_matching_matches_sym():
sigma = 1.
lmbda = 1.
Z = np.random.randn(100, 2)
a_sym = develop_gaussian.fit_sym(Z, sigma, lmbda)
a = gaussian.fit(Z, Z, sigma, lmbda)
assert_allclose(a, a_sym)
def test_score_matching_matches_sym():
sigma = 1.
lmbda = 1.
Z = np.random.randn(100, 2)
a_sym = develop_gaussian.fit_sym(Z, sigma, lmbda)
a = gaussian.fit(Z, Z, sigma, lmbda)
assert_allclose(a, a_sym)
def test_score_matching_objective_matches_sym():
sigma = 1.
lmbda = 1.
Z = np.random.randn(100, 2)
K = gaussian_kernel(Z, sigma=sigma)
J_sym = develop_gaussian.fit_sym(Z, sigma, lmbda, K)
J = gaussian.fit(Z, Z, sigma, lmbda, K)
assert_allclose(J, J_sym)
def test_score_matching_objective_matches_sym():
sigma = 1.
lmbda = 1.
Z = np.random.randn(100, 2)
K = gaussian_kernel(Z, sigma=sigma)
J_sym = develop_gaussian.fit_sym(Z, sigma, lmbda, K)
J = gaussian.fit(Z, Z, sigma, lmbda, K)
assert_allclose(J, J_sym)
