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_k_multiple_equals_k_no_grad_multiple_given(self): N = 10 X = np.random.randn(N) me = GaussianQuadraticTest(self.grad_log_normal) K = me.k_multiple(X) for i in range(N): for j in range(N): k = me.k(X[i], X[j]) assert_almost_equal(K[i, j], k)
def test_k_multiple_equals_k_grad_multiple_given(self): def fun(self, X): return -X N = 10 X = np.random.randn(N) me = GaussianQuadraticTest(self.grad_log_normal, grad_log_prob_multiple=fun) K = me.k_multiple(X) for i in range(N): for j in range(N): k = me.k(X[i], X[j]) assert_almost_equal(K[i, j], k)