从scipy.optimize.leastsq()输出所有猜测

import numpy as np
from scipy.optimize import leastsq

# 定义线性模型
def model(params, x):
    a, b = params
    return a * x + b

# 定义误差函数
def error(params, x, y):
    return model(params, x) - y

# 实验数据
x_data = np.array([1, 2, 3, 4, 5])
y_data = np.array([2.3, 3.5, 4.8, 6.1, 7.3])

# 初始猜测
initial_guess = [1, 1]

# 执行最小二乘优化
params_fit, success = leastsq(error, initial_guess, args=(x_data, y_data))

print("Optimized Parameters:", params_fit)

import numpy as np
from scipy.optimize import leastsq

# 定义线性模型
def model(params, x):
    a, b = params
    return a * x + b

# 定义误差函数
def error(params, x, y):
    return model(params, x) - y

# 实验数据
x_data = np.array([1, 2, 3, 4, 5])
y_data = np.array([2.3, 3.5, 4.8, 6.1, 7.3])

# 初始猜测
initial_guess = [1, 1]

# 存储所有猜测的列表
all_guesses = []

# 自定义回调函数
def callback(params):
    all_guesses.append(params.copy())

# 执行最小二乘优化，并传入回调函数
params_fit, success = leastsq(error, initial_guess, args=(x_data, y_data), full_output=True, ftol=1e-15, xtol=1e-15, gtol=1e-15, maxfev=1000, epsfcn=None, factor=100, diag=None, warning=False, callback=callback)

print("Optimized Parameters:", params_fit)
print("All Guesses:", all_guesses)