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]])))