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_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_g1k_multiple_equals_g1k(self):
N = 10
X = np.random.randn(N)
me = GaussianQuadraticTest(self.grad_log_normal)
G1K = me.g1k_multiple(X)
for i in range(N):
for j in range(N):
g1k = me.g1k(X[i], X[j])
assert_almost_equal(G1K[i, j], g1k)
def test_g1k_multiple_equals_g1k(self):
N = 10
X = np.random.randn(N)
me = GaussianQuadraticTest(self.grad_log_normal)
G1K = me.g1k_multiple(X)
for i in range(N):
for j in range(N):
g1k = me.g1k(X[i], X[j])
assert_almost_equal(G1K[i, j], g1k)
