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)