gpr.py 文件源码

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)