Python 曲线拟合库，允许我为参数分配界限 [英] Python curve fit library that allows me to assign bounds to parameters

问题描述

我希望能够执行拟合，允许我将任意曲线函数拟合到数据，并允许我设置参数的任意边界，例如我想拟合函数:

I'd like to be able to perform fits that allows me to fit an arbitrary curve function to data, and allows me to set arbitrary bounds on parameters, for example I want to fit function:

f(x) = a1(x-a2)^a3cdotexp(-a4*x^a5)

然后说:

• a2 在以下范围内:(-1, 1)
• a3 和 a5 为正

• a2 is in following range: (-1, 1)
• a3 and a5 are positive

有很好的 scipy curve_fit 函数，但它没有t 允许指定参数范围.还有很好的 http://code.google.com/p/pyminuit/ 库可以进行通用最小化，并且它允许设置参数的界限，但在我的情况下它没有覆盖.

There is nice scipy curve_fit function, but it doesn't allow to specify parameter bounds. There also is nice http://code.google.com/p/pyminuit/ library that does generic minimalization, and it allows to set bounds on parameters, but in my case it did not coverge.

推荐答案

注意:SciPy 0.17 版本中的新内容

Note: New in version 0.17 of SciPy

假设您想将一个模型拟合到如下所示的数据中:

Let's suppose you want to fit a model to the data which looks like this:

y=a*t**alpha+b

以及对 alpha 的约束

and with the constraint on alpha

0<alpha<2

而其他参数 a 和 b 保持空闲.那么我们应该以如下方式使用curve_fit的bounds选项:

while other parameters a and b remains free. Then we should use the bounds option of curve_fit in the following fashion:

import numpy as np
from scipy.optimize import curve_fit
def func(t, a,alpha,b):
     return a*t**alpha+b
param_bounds=([-np.inf,0,-np.inf],[np.inf,2,np.inf])
popt, pcov = curve_fit(func, xdata, ydata,bounds=param_bounds)

来源是这里.

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