def test_evaluate_grad_cross_cov_respect_point(self):
value = self.gp.evaluate_grad_cross_cov_respect_point(np.array([[40.0]]),
np.array([[39.0], [38.0]]),
np.array([1.0, 1.0]))
value_2 = ScaledKernel.evaluate_grad_respect_point(np.array([1.0, 1.0]),
np.array([[40.0]]),
np.array([[39.0], [38.0]]), 1,
*([MATERN52_NAME],))
assert np.all(value == value_2)
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_grad_cross_cov_respect_point(np.array([[40.0]]),
np.array([[39.0], [38.0]]),
np.array([1.0]))
value_2 = Matern52.evaluate_grad_respect_point(np.array([1.0]),
np.array([[40.0]]),
np.array([[39.0], [38.0]]), 1)
assert np.all(value == value_2)
def test_evaluate_grad_respect_point(self):
result = Matern52.evaluate_grad_respect_point(np.array([5.0]),
np.array([[1]]),
np.array([[4], [5]]), 1)
kernel = Matern52.define_kernel_from_array(1, np.array([5.0]))
assert np.all(result == kernel.grad_respect_point(
np.array([[1]]), np.array([[4], [5]])))