如何使用scipy优化来找到3个参数和数据点列表的最小卡方？

import numpy as np
from scipy.optimize import minimize

def chi_square(params, data_points):
    # 解析参数
    param1, param2, param3 = params
    
    # 计算模型预测值
    predicted_values = param1 * data_points[:, 0] + param2 * data_points[:, 1] + param3
    
    # 计算卡方值
    chi_square_value = np.sum((data_points[:, 2] - predicted_values) ** 2 / predicted_values)
    
    return chi_square_value

initial_params = [1, 1, 1]  # 初始参数值
data_points = np.array([[x1, x2, y1], [x3, x4, y2], ...])  # 数据点列表

result = minimize(chi_square, initial_params, args=(data_points,))

optimized_params = result.x  # 优化后的参数值