scipy.optimize curve_fit返回错误的值（取决于机器） [英] scipy.optimize curve_fit returns wrong value (depends on the machine)
问题描述
我用scipy.optimize curve_fit编写了一些代码。它可以在我的计算机上完美运行：
I wrote some code with scipy.optimize curve_fit. It works perfectly on my computer:
带有Service Pack 1的Windows 7 Home Premium，64位
Dell Studio 1558
Intel Core i3 cpu M330@2.13 GHz 2.13GHz，3.86 GB RAM
Python 2.7.3（默认值，2012年4月10日，23：24：47）[MSC v.1500 64位（AMD64）]
IPython 0.13.1
Windows 7 Home Premium with Service Pack 1, 64bit Dell Studio 1558 Intel Core i3 cpu M330@2.13GHz 2.13GHz, 3.86 GB of RAM Python 2.7.3 (default, Apr 10 2012, 23:24:47)[MSC v.1500 64 bit (AMD64)] IPython 0.13.1
然后我将脚本移动到另一台计算机（COMP2）：
Microsoft Windows XP Professional版本2002 Service Pack 3，AMD Athlon（tm）II X4 620
处理器2.61 GHz，3.25 GB RAM，物理地址扩展
Python 2.7.5 | 32位| （默认值，2013年6月14日，18：15：12）[MSC v.1500 32位（英特尔）]
Ipython 1.0.dev
Then I moved the script to another machine (COMP2): Microsoft Windows XP Professional Version 2002 Service Pack 3, AMD Athlon(tm) II X4 620 Processor 2.61 GHz, 3.25 GB of RAM, Physical Address Extension Python 2.7.5 |32 bit | (default, Jun 14 2013,18:15:12) [MSC v.1500 32 bit (Intel)] Ipython 1.0.dev
真的很糟糕我的代码很长，但是我准备了一个包含所有情况的自包含示例。
And the fits are really bad. My code was rather long but I prepared self containing example which reproduces all situation.
我读到有时xdata和ydata是array（，dtype = float）会有所帮助，但这不是我的情况（我已经尝试过）
I read that sometimes helps when xdata and ydata are array( , dtype=float) but this is not my case (I've tried)
我什至在Windows 7 32bit（我的朋友的机器）上使用Python 2.7.3 32bit尝试了此代码，有效-我不知道为什么拟合结果如此不可预测，以及如何强制此代码在具有COMP2的计算机上正常工作。
I've even tried this code with Python 2.7.3 32bit on Windows 7 32bit ( my firends' machine) and it worked - so I have no idea why fit results are so unpredictable and how I can force this code working properly on computer with COMP2.
此处是自包含的示例：
from string import*
from numpy import *
from matplotlib.pylab import *
from scipy.optimize import curve_fit
from sys import exit
nm_range=[574.14200000000005, 574.154, 574.16499999999996, 574.17700000000002, 574.18799999999999, 574.19899999999996, 574.21100000000001, 574.22199999999998, 574.23400000000004, 574.245]
data_for_fit=[859.0, 997.0, 1699.0, 2604.0, 2013.0, 1964.0, 2435.0, 1550.0, 949.0, 841.0]
guess=[574.1861428571428, 574.2155714285715, 1302.0, 1302.0, 0.0035019999999983615, 859.0]
def f_double_gauss(x,x0,x1,A0,A1,sigma,c):
return A0*exp(-(x-x0)**2/(2.*sigma**2)) + A1*exp(-(x-x1)**2/(2.*sigma**2)) + c
popt,pcov=curve_fit(f_double_gauss,nm_range,data_for_fit,guess,maxfev=10000)
print guess
print popt
fig=figure("If fit of gauss or double gauss is good")
ax=fig.add_subplot(1,1,1)
pdata,=plot(nm_range,data_for_fit,"bo-")
guessed=[]
for i in nm_range:
guessed.append(f_double_gauss(i,guess[0],guess[1],guess[2],guess[3],guess[4],guess[5]))
pfit,=plot(nm_range,f_double_gauss(nm_range,popt[0],popt[1],popt[2],popt[3],popt[4],popt[5]),"k-")
pguess,=plot(nm_range,guessed,"y")
ax.set_title("Anizo fit"+" : data, init guess & fit")
ax.set_xlabel("wavelenght [nm]")
ax.set_ylabel("PL intensity")
legend([pdata,pguess,pfit],["data","guess","fit"])
show()
不良匹配的输出：
[574.1861428571428,574.2155714285715,1302,1302.0,0.0035019999999983615,859.0]
[5.69174152e+02 8.66516577e+04 -9.27629569e+04 1.59887720e+09 7.56288801e-03 1.59110000e+03]
输出良好适合：
[574.1861428571428,574.2155714285715,1302,1302.0,0.0035019999999983615,859.0]
[ 5.74177150e+02 5.74209188e+02 1.74187044e+03 1.58646166e+03 1.0068462e-02 8.57450661e+02]
好身材图片：
https://docs.google.com / file / d / 0B6GA05-W4ZzzdTIxa3U3Rl92MU0 / edit？usp = sharing
不良图片
https://docs.google.com/file/d/0B6GA05-W4ZzzRlk4eWlER01WejQ/edit?usp=sharing
推荐答案
好的，问题出在与当前scipy相连的优化库上。
Ok, the problem is with optimize library attached to current scipy.
当我复制 _minpack.pyd 和 minpack.py 从附加到EPD 7.3-2的文件中，并代替当前的 _minpack.pyd 和 minpack.py 文件适合。
When I copied _minpack.pyd and minpack.py from files attached to EPD 7.3-2 and put instead of current _minpack.pyd and minpack.py files the fit is perfect.
我将将该错误报告给scipy。
I will report that bug to scipy.
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