使用scipy.optimize.curve_fit时如何传递参数以适合函数 [英] How to pass parameter to fit function when using scipy.optimize.curve_fit

问题描述

我正在尝试使用scipy.optimize.curve_fit拟合一些数据。
我的拟合函数是：

I am trying to fit some data that I have using scipy.optimize.curve_fit. My fit function is:

def fitfun(x, a):
    return np.exp(a*(x - b))

我要定义的是 a 作为拟合参数， b 作为根据我要拟合的数据而变化的参数。这意味着对于一组数据，我希望适合该函数： np.exp（a *（x-10））而对于另一组数据，我希望适合函数 np.exp（a *（x-20））。原则上，我希望将参数b作为任何值传递。

What i want is to define a as the fitting parameter, and b as a parameter that changes depending on the data I want to fit. This means that for one set of data I would want to fit the function: np.exp(a*(x - 10)) while for another set I would like to fit the function np.exp(a*(x - 20)). In principle, I would like the parameter b to be passed in as any value.

我目前对curve_fit的称呼是：

The way I am currently calling curve_fit is:

coeffs, coeffs_cov = curve_fit(fitfun, xdata, ydata)

但是我想要的是这样的：

But what I would like would be something like this:

b=10     
coeffs, coeffs_cov = curve_fit(fitfun(b), xdata, ydata)
b=20     
coeffs2, coeffs_cov2 = curve_fit(fitfun(b), xdata, ydata)

这样我就得到了两种情况下的系数a（b = 10和b = 20）。

So that I get the coefficient a for both cases (b=10 and b=20).

我是python的新手，所以即使我也无法使用它试图阅读文档。任何帮助将不胜感激。

I am new to python so I cannot make it work, even though I have tried to read the documentation. Any help would be greatly appreciated.

推荐答案

我不知道这是否是正确的工作方式，但是我通常将函数包装在一个类中，以便可以从 self 访问参数。然后，您的示例如下所示：

I don't know if this is the "proper" way of doing things, but I usually wrap my function in a class, so that I can access parameters from self. Your example would then look like:

class fitClass:

    def __init__(self):
        pass

    def fitfun(self, x, a):
        return np.exp(a*(x - self.b))

inst = fitClass()

inst.b = 10
coeffs, coeffs_cov = curve_fit(inst.fitfun, xdata, ydata)

inst.b = 20
coeffs, coeffs_cov = curve_fit(inst.fitfun, xdata, ydata)

这种方法避免使用全局参数，通常认为是邪恶的。

This approach avoids using global parameters, which are generally considered evil.

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