高斯过程回归模型完整源代码python，不调用

以下是一个基于numpy实现的高斯过程回归模型的完整源代码：

import numpy as np

class GaussianProcessRegressor:
    def __init__(self, kernel, noise=0.1):
        self.kernel = kernel
        self.noise = noise
        self.X = None
        self.y = None
        self.K = None
        self.L = None
        self.alpha = None
    
    def fit(self, X, y):
        self.X = X
        self.y = y
        self.K = self.kernel(X, X) + self.noise**2 * np.eye(X.shape[0])
        self.L = np.linalg.cholesky(self.K)
        self.alpha = np.linalg.solve(self.L.T, np.linalg.solve(self.L, y))
    
    def predict(self, X_star):
        K_star = self.kernel(self.X, X_star)
        f_star_mean = K_star.T.dot(self.alpha)
        v = np.linalg.solve(self.L, K_star)
        f_star_cov = self.kernel(X_star, X_star) - v.T.dot(v)
        return f_star_mean, f_star_cov
    
    def kernel(self, X1, X2):
        pass

class RBFKernel:
    def __init__(self, length_scale=1.0, sigma_f=1.0):
        self.length_scale = length_scale
        self.sigma_f = sigma_f
    
    def __call__(self, X1, X2):
        dists = np.sum(X1**2, axis=1).reshape(-1, 1) + np.sum(X2**2, axis=1) - 2 * X1.dot(X2.T)
        return self.sigma_f**2 * np.exp(-0.5 * dists / self.length_scale**2)

其中，`GaussianProcessRegressor`类实现了高斯过程回归模型的训练和预测，`RBFKernel`类实现了高斯核函数。在使用时，可以先创建一个`RBFKernel`实例，然后将其作为参数传入`GaussianProcessRegressor`中，如下所示：

kernel = RBFKernel(length_scale=1, sigma_f=1)
gp = GaussianProcessRegressor(kernel=kernel, noise=0.1)
gp.fit(X_train, y_train)
y_pred, y_pred_var = gp.predict(X_test)