如何在python中创建N个指数函数来拟合数据

import numpy as np
from scipy.optimize import curve_fit
import matplotlib.pyplot as plt

# 定义指数函数模型
def exponential_func(x, a, b, c):
    return a * np.exp(b * x) + c

# 生成模拟数据
x_data = np.linspace(0, 10, 100)
y_data = exponential_func(x_data, 2, 0.5, 1) + np.random.normal(0, 0.2, 100)

# 定义拟合函数
def fit_exponential(x, *params):
    num_functions = len(params) // 3
    result = np.zeros_like(x)
    for i in range(num_functions):
        a = params[i*3]
        b = params[i*3+1]
        c = params[i*3+2]
        result += exponential_func(x, a, b, c)
    return result

# 初始参数猜测
initial_guess = [1, 1, 1] * N  # N为指数函数的个数

# 进行拟合
params, params_covariance = curve_fit(fit_exponential, x_data, y_data, p0=initial_guess)

# 绘制拟合结果
plt.scatter(x_data, y_data, label='Data')
plt.plot(x_data, fit_exponential(x_data, *params), 'r-', label='Fit')
plt.legend()
plt.show()