python（scipy）曲线拟合的奇怪结果 [英] Strange result with python's (scipy) curve fitting

问题描述

这是我的代码（这是较大代码的示例）：

This is my code (it's an example of a larger piece of code):

from scipy.optimize import curve_fit

def func(x, a, b):
   return a + b*x

xlist = [10, 30, 50, 70, 90, 110, 130, 150, 170, 190, 210, 230]
ylist = [0.0074999999999999997, 0.011875, 0.0057812499999999999, 0.0036458333333333334, 0.0020312500000000001, 0.0013125000000000001, 0.00098958333333333342, 0.00089285714285714283, 0.00074218750000000001, 0.00093749999999999997, 0.00071874999999999999, 0.00088068181818181821]

popt, pcov = curve_fit(func, xlist, ylist)

print(popt[0], popt[1])

如您所见，我正在尝试使用非常简单的 a + b * x 函数进行非常简单的拟合。问题是这会返回值：

As you can see I'm attempting a very simple fit with a very simple a + b*x function. The issue is that this returns the values:

(-119.99689110581872, 1.0)

对于 a = popt [0] 和 b = popt [1 ] ，但与zunzun.com的拟合度却给出了更为合理的值：

for a=popt[0]and b=popt[1] but the same fit with zunzun.com gives much more reasonable values:

a = 7.8372289537296004E-03
b = -3.9402329475524466E-05

完全相同的功能。

我在这里做什么错了？

以下警告称这可能是错误。我应该这样报告还是应该这样？

Warren below stated this might be a bug. Should I report it as such or is this the expected behaviour?

推荐答案

如果您更改<$ c $中的值，则可以使用c> xlist 到一个numpy数组：

It works if you change the values in xlist to a numpy array:

In [38]: popt, pcov = curve_fit(func, array(xlist, dtype=float), ylist)

In [39]: popt
Out[39]: array([  7.83722896e-03,  -3.94023294e-05])

起初，这看起来像是个错误，但实际情况是底层代码采用了参数 xdata 并将其未更改传递给您的函数。在您的示例中，这意味着在表达式 a + b * x 中， x 是Python列表。 那表示 b * x 不在进行所需的计算。

At first this looks like a bug, but what's happening is that the underlying code takes the argument xdata and passes it unchanged to your function. In your example, this means in the expression a + b*x, x is a Python list. That means that b*x is not doing the calculation that you want.

因此，为了使 func 的定义起作用，参数 xdata 必须是一个numpy数组。或者，您可以这样定义 func ：

So to make your definition of func work, the argument xdata must be a numpy array. Or, you could define func this way:

def func(x, a, b):
    return a + b*np.asarray(x)

这篇关于python（scipy）曲线拟合的奇怪结果的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！