def test_k_multiple_equals_k_no_dim(self):
N = 10
X = np.random.randn(N,1)
me = GaussianQuadraticTest(self.grad_log_normal)
K1 = me.k_multiple_dim(X)
K2 =me.k_multiple(X[:,0])
np.testing.assert_almost_equal(K1, K2)
def test_k_multiple_equals_k_no_dim(self):
N = 10
X = np.random.randn(N, 1)
me = GaussianQuadraticTest(self.grad_log_normal)
K1 = me.k_multiple_dim(X)
K2 = me.k_multiple(X[:, 0])
np.testing.assert_almost_equal(K1, K2)
def test_g1k_multiple_dim(self):
N = 10
X = np.random.randn(N,1)
me = GaussianQuadraticTest(self.grad_log_normal)
K = me.k_multiple_dim(X)
g1k_alt = me.g1k_multiple_dim(X,K,0)
g1k_orig = me.g1k_multiple(X[:,0])
np.testing.assert_almost_equal(g1k_alt, g1k_orig)
def test_gk_multiple_dim(self):
N = 10
X = np.random.randn(N, 1)
me = GaussianQuadraticTest(self.grad_log_normal)
K = me.k_multiple_dim(X)
gk_alt = me.gk_multiple_dim(X, K, 0)
gk_orig = me.gk_multiple(X[:, 0])
np.testing.assert_almost_equal(gk_alt, gk_orig)
