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)
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)
def test_gk_multiple_equals_gk(self): N = 10 X = np.random.randn(N) me = GaussianQuadraticTest(self.grad_log_normal) GK = me.gk_multiple(X) for i in range(N): for j in range(N): gk = me.gk(X[i], X[j]) assert_almost_equal(GK[i, j], gk)
def test_gk_multiple_equals_gk(self): N = 10 X = np.random.randn(N) me = GaussianQuadraticTest(self.grad_log_normal) GK = me.gk_multiple(X) for i in range(N): for j in range(N): gk = me.gk(X[i], X[j]) assert_almost_equal(GK[i, j], gk)