在curve_fit（scipy）期间将边界应用于特定变量会导致错误 [英] Applying bounds to specific variable during curve_fit (scipy) leads to an error

问题描述

我正在尝试在曲线拟合过程中将边界应用于某些参数，但尝试这样做时会收到以下错误消息：

I am trying to apply bounds onto some of the parameters during curve fitting but I'm getting the following error message when I tried to do so:

ValueError：太多的值无法解包

绑定命令中的每个2元组不是对应于x0，k，失效，sigmoidscaled中的猜测在我的情况下分别起作用（即也对应于p0）？

Doesn't each 2-tuple in the bound command corresponds to x0, k, lapse, guess in the sigmoidscaled function in my case respectively (Ie. corresponding to p0 too)?

然后我尝试玩耍，希望通过将绑定命令简化为以下内容来摆脱太多的值，以试图弄清它的工作原理：

I then tried playing around in hopes of trying to figure out how it works by reducing the bound command to the following to rid the 'too many values':

bounds =（（-np.inf，np.inf），（0,1））

bounds=((-np.inf,np.inf), (0,1))

然后我得到以下错误消息：

Then I get the error message of:

ValueError：边界和 x0 。

我在这里怎么了？

import pylab
from scipy.optimize import curve_fit
from matplotlib.pyplot import *

n = 20 #20 trials
ydata = [0/n, 9.0/n, 9.0/n, 14.0/n, 17.0/n] #Divided by n to fit to a plot of y =1
xdata = np.array([ 1.0, 2.0, 3.0, 4.0, 5.0])


#The scaled sigmoid function
def sigmoidscaled(x, x0, k, lapse, guess):
    F = (1 + np.exp(-k*(x-x0))) 
    z = guess + (1-guess-lapse)/F
    return z

p0=[1,1,0,0] 
popt, pcov = curve_fit(sigmoidscaled, xdata, ydata, p0, bounds=((-np.inf,np.inf), (-np.inf,np.inf), (0,1), (0,1))

#Start and End of x-axis, in spaces of n. The higher the n, the smoother the curve.
x = np.linspace(1,5,20)
#The sigmoid values along the y-axis, generated in relation to the x values and the 50% point.
y = sigmoidscaled(x, *popt)

pylab.plot(xdata, ydata, 'o', label='Psychometric Raw', color = 'blue')
pylab.plot(x,y, label='Psychometric Fit', color = 'blue')
#y axis range.
pylab.ylim(0, 1)
#Replace x-axis numbers as labels and y-axis numbers as percentage
xticks([1., 2., 3., 4., 5.], ['C1','CN2','N3','CN4','S5'])
yticks([0.0, 0.2, 0.4, 0.6, 0.8, 1.0], ['0%','20%','40%','60%','80%','100%'])
pylab.legend(loc='best')
xlabel('Conditions')
ylabel('% perceived more sin like')
pylab.show() 

推荐答案

问题行是：

popt, pcov = curve_fit(sigmoidscaled, xdata, ydata, p0, bounds=((-np.inf,np.inf), (-np.inf,np.inf), (0,1), (0,1))

来自文档，边界必须是2个元数组，例如数组。因此，而不是指定每个点的上下限，您需要在第一个类似数组的数组中指定每个点的下限，然后是第二个类似数组的数组，如下所示：

From the documentation, bounds needs to be a 2-tuple of array likes. So instead of specifying the lower and upper bound of each point, you need to specify the lower bound of each point in the first array-like followed by the upper bound of each point in the second array-like, like this:

popt, pcov = curve_fit(sigmoidscaled, xdata, ydata, p0, bounds=((-np.inf, -np.inf, 0, 0), (np.inf, np.inf, 1, 1)))

此更改后，剧情就弹出了！

After this change, the plot popped right up!

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