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_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)
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)