如何修复“在...中遇到溢出”类型的各种RuntimeWarnings在Scipy中使用最小二乘最小化？

from sklearn.preprocessing import StandardScaler

scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)

from scipy.optimize import least_squares

initial_guess = [1.0, 1.0]  # 根据实际情况调整
result = least_squares(fun, initial_guess, args=(X_scaled, y))

from sklearn.linear_model import Ridge

ridge = Ridge(alpha=1.0)
ridge.fit(X_scaled, y)

result = least_squares(fun, initial_guess, method='lm', args=(X_scaled, y))

import numpy as np
from scipy.optimize import least_squares
from sklearn.preprocessing import StandardScaler

# 示例数据
X = np.array([[1, 2], [3, 4], [5, 6]])
y = np.array([1, 2, 3])

# 标准化数据
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)

# 定义目标函数
def fun(params, X, y):
    return X @ params - y

# 初始参数
initial_guess = [1.0, 1.0]

# 使用最小二乘法进行优化
result = least_squares(fun, initial_guess, method='lm', args=(X_scaled, y))

print("优化结果:", result.x)