幂律数据拟合不正确

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

# 假设我们有一组幂律分布的数据
data = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])  # 示例数据
x_data = np.log(data)
y_data = np.log(np.histogram(data, bins=len(data))[0])

# 定义幂律函数
def power_law(x, a, alpha):
    return a * x ** (-alpha)

# 使用curve_fit进行拟合
params, covariance = curve_fit(power_law, x_data, y_data)

# 提取拟合参数
a_fit, alpha_fit = params

# 绘制拟合结果
plt.scatter(x_data, y_data, label='Data')
x_fit = np.linspace(min(x_data), max(x_data), 100)
y_fit = power_law(x_fit, a_fit, alpha_fit)
plt.plot(x_fit, y_fit, label='Fit', color='red')
plt.xlabel('log(x)')
plt.ylabel('log(y)')
plt.legend()
plt.show()

print(f"拟合参数: a = {a_fit}, alpha = {alpha_fit}")