def test_grad_exponential_squared(self):
data = helper.load_test_npz('test_points_10_2.npz')
X1 = data['X']
X2 = X1.copy()
dC = rs.grad_exponential_squared(X1, X2, 1.0, np.array([1.0,1.0]))
if self.writeData:
np.savez('data/test_grad_exp_cov.npz', dC=dC)
data = helper.load_test_npz('test_grad_exp_cov.npz')
np.testing.assert_almost_equal(dC, data['dC'])
def test_grad_exponential_squared_fd(self):
np.random.seed(42)
X1 = np.random.normal(size=(10, 2))
X2 = X1.copy()
C0 = rs.exponential_squared(X1, X2, 1.0, np.array([1.0, 1.0]))
dC = rs.grad_exponential_squared(X1, X2, 1.0, np.array([1.0, 1.0]))
e = 1e-6
X2p1 = X2.copy()
X2p1[:, 0] += e
C1 = rs.exponential_squared(X1, X2p1, 1.0, np.array([1.0, 1.0]))
dC1 = (C1 - C0) / e
np.testing.assert_array_almost_equal(dC[:, :, 0], dC1, decimal=5)
X2p2 = X2.copy()
X2p2[:, 1] += e
C2 = rs.exponential_squared(X1, X2p2, 1.0, np.array([1.0, 1.0]))
dC2 = (C2 - C0) / e
np.testing.assert_array_almost_equal(dC[:, :, 1], dC2, decimal=5)
def test_grad_exponential_squared_fd(self):
np.random.seed(42)
X1 = np.random.normal(size=(10, 2))
X2 = X1.copy()
C0 = rs.exponential_squared(X1, X2, 1.0, np.array([1.0, 1.0]))
dC = rs.grad_exponential_squared(X1, X2, 1.0, np.array([1.0, 1.0]))
e = 1e-6
X2p1 = X2.copy()
X2p1[:, 0] += e
C1 = rs.exponential_squared(X1, X2p1, 1.0, np.array([1.0, 1.0]))
dC1 = (C1 - C0) / e
np.testing.assert_array_almost_equal(dC[:, :, 0], dC1, decimal=5)
X2p2 = X2.copy()
X2p2[:, 1] += e
C2 = rs.exponential_squared(X1, X2p2, 1.0, np.array([1.0, 1.0]))
dC2 = (C2 - C0) / e
np.testing.assert_array_almost_equal(dC[:, :, 1], dC2, decimal=5)
def test_grad_exponential_squared_fd(self):
data = helper.load_test_npz('test_points_10_2.npz')
X1 = data['X']
X2 = X1.copy()
C0 = rs.exponential_squared(X1, X2, 1.0, np.array([1.0,1.0]))
dC = rs.grad_exponential_squared(X1, X2, 1.0, np.array([1.0,1.0]))
e = 1e-6
X2p1 = X2.copy()
X2p1[:,0] += e
C1 = rs.exponential_squared(X1, X2p1, 1.0, np.array([1.0,1.0]))
dC1 = (C1 - C0)/e
np.testing.assert_array_almost_equal(dC[:,:,0], dC1, decimal=5)
X2p2 = X2.copy()
X2p2[:,1] += e
C2 = rs.exponential_squared(X1, X2p2, 1.0, np.array([1.0,1.0]))
dC2 = (C2 - C0)/e
np.testing.assert_array_almost_equal(dC[:,:,1], dC2, decimal=5)
def test_grad_exponential_squared(self):
np.random.seed(42)
X1 = np.random.normal(size=(10, 2))
X2 = X1.copy()
dC = rs.grad_exponential_squared(X1, X2, 1.0, np.array([1.0, 1.0]))
def test_grad_exponential_squared(self):
np.random.seed(42)
X1 = np.random.normal(size=(10, 2))
X2 = X1.copy()
dC = rs.grad_exponential_squared(X1, X2, 1.0, np.array([1.0, 1.0]))