如何编写包装器以修复函数中的任意参数

问题描述

我想编写一个曲线拟合脚本，使我可以修复以下形式的函数的参数:

I would like to write a curve-fitting script that allows me to fix parameters of a function of the form:

def func(x, *p):
    assert len(p) % 2 == 0
    fval = 0
    for j in xrange(0, len(p), 2):
        fval += p[j]*np.exp(-p[j+1]*t)
    return fval

例如，假设我要p = [p1，p2，p3，p4]，并且我希望p2和p3为常数A和B(从4参数拟合到2参数拟合).我知道functools.partial不允许我这样做，这就是为什么我要编写自己的包装器.但是我这样做有点麻烦.这是我到目前为止的内容:

For example, let's say I want p = [p1, p2, p3, p4], and I want p2 and p3 to be constant A and B (going from a 4-parameter fit to a 2-parameter fit). I understand that functools.partial doesn't let me do this which is why I want to write my own wrapper. But I am having a bit of trouble doing so. This is what I have so far:

def fix_params(f, t, pars, fix_pars):
    # fix_pars = ((ind1, A), (ind2, B))
    new_pars = [None]*(len(pars) + len(fix_pars))
    for ind, fix in fix_pars:
        new_pars[ind] = fix
    for par in pars:
        for j, npar in enumerate(new_pars):
            if npar == None:
                new_pars[j] = par
                break
    assert None not in new_pars
    return f(t, *new_pars)

我认为与此有关的问题是，scipy.optimize.curve_fit不能与通过此类包装程序传递的函数一起很好地工作.我应该如何解决这个问题?

The problem with this I think is that, scipy.optimize.curve_fit won't work well with a function passed through this kind of wrapper. How should I get around this?

推荐答案

所以我认为我有一些可行的方法.也许有办法对此进行改进.

So I think I have something workable. Maybe there is a way to improve on this.

这是我的代码(没有所有异常处理):

Here is my code (without all the exception handling):

def func(x, *p):
    fval = 0
    for j in xrange(0, len(p), 2):
        fval += p[j]*np.exp(-p[j+1]*x)
    return fval

def fix_params(f, fix_pars):
    # fix_pars = ((1, A), (2, B))
    def new_func(x, *pars):
        new_pars = [None]*(len(pars) + len(fix_pars))
        for j, fp in fix_pars:
            new_pars[j] = fp
        for par in pars:
            for j, npar in enumerate(new_pars):
                if npar is None:
                    new_pars[j] = par
                    break
        return f(x, *new_pars)
    return new_func

p1 = [1, 0.5, 0.1, 1.2]
pfix = ((1, 0.5), (2, 0.1))
p2 = [1, 1.2]

new_func = fix_params(func, pfix)

x = np.arange(10)
dat1 = func(x, *p1)
dat2 = new_func(x, *p2)

if (dat1==dat2).all()
    print "ALL GOOD"

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