高斯过程处理大型数据集时返回值为 0 的问题

这是我在网上搜的高斯过程的代码，挺容易理解的

import numpy as np
from scipy.optimize import minimize

class GPR:

    def __init__(self, optimize=True):
        self.is_fit = False
        self.train_X, self.train_y = None, None
        self.params = {
   "l": 0.5, "sigma": 0.2}
        self.optimize = optimize

    def fit(self, train_X, train_y):
        # store train data
        self.train_X = train_X
        self.train_y = train_y
        self.is_fit = True

        # hyper parameters optimization
        def negative_log_likelihood_loss(params):
            self.params["l"], self.params["sigma"] = params[0], params[1]
            Kyy = self.kernel(self.train_X, self.train_X) + 1e-8 * np.eye(len(self.train_X))
            loss = 0.5 * self.train_y.T.dot(np.linalg.inv(Kyy)).dot(self.train_y) + 0.5 * np.linalg.slogdet(Kyy)[1]
            return loss.ravel()

        if self.optimize:
            res = minimize(negative_log_likelihood_loss, [self.params["l"], self.params["sigma"]],
                           bounds=((1e-4, 1e4), (1e-4, 1e4)),
                           method='L-BFGS-B')
            self.params