如何在python中使用MLE拟合双指数分布？

import numpy as np
from scipy.optimize import minimize

def double_exponential(x, mu, sigma1, sigma2):
    return (np.exp(-np.abs(x - mu) / sigma1) / (2 * sigma1)) * (x < mu) + (np.exp(-np.abs(x - mu) / sigma2) / (2 * sigma2)) * (x >= mu)

def neg_log_likelihood(params, data):
    mu, sigma1, sigma2 = params
    return -np.sum(np.log(double_exponential(data, mu, sigma1, sigma2)))

data = np.array([1.2, 2.5, 3.7, 4.1, 5.8, 6.3, 7.9, 8.6, 9.2])

initial_params = [np.mean(data), np.std(data), np.std(data)]
result = minimize(neg_log_likelihood, initial_params, args=(data,))
mu_hat, sigma1_hat, sigma2_hat = result.x

print("拟合参数：")
print("mu_hat =", mu_hat)
print("sigma1_hat =", sigma1_hat)
print("sigma2_hat =", sigma2_hat)