用粒子群优化高斯过程回归预测python代码

以下是一个用粒子群优化高斯过程回归进行预测的Python代码，仅供参考：

import numpy as np
from sklearn.gaussian_process import GaussianProcessRegressor
from sklearn.gaussian_process.kernels import RBF, ConstantKernel as C
from pyswarm import pso

# 定义高斯过程回归模型
ker = C(1.0, (1e-3, 1e3)) * RBF(10, (1e-2, 1e2))
gp = GaussianProcessRegressor(kernel=ker, n_restarts_optimizer=9, alpha=0.1)

# 粒子群优化
def optimize(x, y):
    def fun(params):
        gp.set_params(alpha=params[0], kernel__k1__constant_value=params[1], kernel__k2__length_scale=params[2])
        return -gp.log_marginal_likelihood([params[0], params[1], params[2]], y)
    lb = [1e-6, 1e-6, 1e-6]
    ub = [1e6, 50, 50]
    xopt, fopt = pso(fun, lb, ub, swarmsize=10, maxiter=20)
    return xopt

# 加载数据
X = np.array([1, 2, 3, 4, 5]).reshape(-1, 1)
y = np.array([2.3, 3.1, 4.1, 4.8, 4.4])

# 用 PSO 进行粒子群优化
alpha, const, length = optimize(X, y)

# 训练高斯过程回归模型并进行预测
gp.set_params(alpha=alpha, kernel__k1__constant_value=const, kernel__k2__length_scale=length)
gp.fit(X, y)
X_test = np.array([6, 7, 8]).reshape(-1, 1)
y_pred, sigma = gp.predict(X_test, return_std=True)

print(y_pred)   # 预测的输出值
print(sigma)    # 预测的标准差