def test_evaluate_hessian_respect_point(self):
point = np.array([[4.5, 7.5]])
inputs = np.array([[5.0, 6.0], [8.0, 9.0]])
params = np.array([1.0, 5.0])
result = Matern52.evaluate_hessian_respect_point(
params, point, inputs, 2)
dh = 0.00001
finite_diff = FiniteDifferences.second_order_central(
lambda x: Matern52.evaluate_cross_cov_defined_by_params(
params, x.reshape((1, len(x))), inputs, 2), point[0, :],
np.array([dh]))
for i in xrange(2):
for j in xrange(2):
print i, j
npt.assert_almost_equal(
finite_diff[i, j],
np.array([[result[0, i, j], result[1, i, j]]]),
decimal=5)
def test_evaluate_hessian_respect_point(self):
type_kernel = [MATERN52_NAME]
training_data = {
"evaluations":
[42.2851784656, 72.3121248508, 1.0113231069, 30.9309246906, 15.5288331909],
"points": [
[42.2851784656], [72.3121248508], [1.0113231069], [30.9309246906], [15.5288331909]],
"var_noise": []}
dimensions = [1]
gp = GPFittingGaussian(type_kernel, training_data, dimensions)
value = gp.evaluate_hessian_cross_cov_respect_point(np.array([[40.0]]),
np.array([[39.0], [38.0]]),
np.array([1.0]))
value_2 = Matern52.evaluate_hessian_respect_point(np.array([1.0]),
np.array([[40.0]]),
np.array([[39.0], [38.0]]), 1)
assert np.all(value == value_2)