class Gaussian_Process_Regression:
'''Performs GPR (used as approximation architecture)'''
def __init__(self):
length_scale = 1
self.kernel = RBF(length_scale) * (1 / sqrt(2 * pi *
(length_scale**2)))
self.gp = GaussianProcessRegressor(kernel=self.kernel,
alpha=1e-2,
n_restarts_optimizer=6,
normalize_y=True)
def fit(self, training_data, target_values):
'''Returns the prediction function of the GPR'''
self.gp.fit(training_data, target_values)
return self
def predict(self, test_point):
return self.gp.predict(test_point)
#TODO: DELETE? (implemented in gpr.py (direct modification of sklearn files))
def jacobian(self, test_point):
'''Returns the jacobian of the mean of the predictive distribution wrt to the decisions of the testpoint'''
return self.gp.jacobian_RBF(test_point)[4:]
