def __init__(self, gamma, beta, nugget, kernelName, k_lambda, xTrain,
yTrain):
"""
Create a new GaussianProcess Object
gamma: Hyperparameter
beta: Hyperparameter
k_lambda: Hyperparameter
nugget: The noise hyperparameter
kernelName: The name of the covariance kernel
xTrain: Numpy array containing x training values
yTrain: Numpy array containing y training values
"""
self.xTrain = xTrain
self.yTrain = yTrain
self.k_lambda = k_lambda
self.beta = beta
self.gamma = gamma
self.nugget = nugget
self.kernelName = kernelName
# Setup the regressor as if gp.fit had been called
# See https://github.com/scikit-learn/scikit-learn/master/sklearn/gaussian_process/gpr.py
kernel = self._getKernel()
gp = GaussianProcessRegressor(kernel=kernel, n_restarts_optimizer=0)
gp.K = kernel(xTrain)
gp.X_train_ = xTrain
gp.y_train_ = yTrain
gp.L_ = cholesky(gp.K, lower=True)
gp.alpha_ = cho_solve((gp.L_, True), yTrain)
gp.fit(xTrain, yTrain)
gp.kernel_ = kernel
self.gp = gp
self.kernel = kernel
# Calculate the matrix inverses once. Save time later
# This is only used for own own implimentation of the scoring engine
self.L_inv = solve_triangular(self.L_.T, np.eye(self.L_.shape[0]))
self.K_inv = L_inv.dot(L_inv.T)
