问题描述
我如何使用scipy.optimize中的minimumsq函数将一条直线和一个平方与下面的数据集拟合?我知道如何使用polyfit来做到这一点.但是我需要使用minimumsq函数.
How would i fit a straight line and a quadratic to the data set below using the leastsq function from scipy.optimize? I know how to use polyfit to do it. But i need to use leastsq function.
以下是x和y数据集:
x: 1.0,2.5,3.5,4.0,1.1,1.8,2.2,3.7
y: 6.008,15.722,27.130,33.772,5.257,9.549,11.098,28.828
有人可以帮我吗?
推荐答案
minimumsq()方法找到最小化误差函数(yExperimental与yFit之差)的参数集.我使用元组来传递参数和lambda函数以进行线性和二次拟合.
The leastsq() method finds the set of parameters that minimize the error function ( difference between yExperimental and yFit).I used a tuple to pass the parameters and lambda functions for the linear and quadratic fits.
leastsq从第一个猜测(参数的初始元组)开始，并尝试最小化误差函数.最后，如果minimumsq成功，它将返回最适合数据的参数列表. (我打印看到了).我希望它能起作用最好的问候
leastsq starts from a first guess ( initial Tuple of parameters) and tries to minimize the error function. At the end, if leastsq succeeds, it returns the list of parameters that best fit the data. ( I printed to see it).I hope it worksbest regards
from scipy.optimize import leastsq
import numpy as np
import matplotlib.pyplot as plt
def main():
# data provided
x=np.array([1.0,2.5,3.5,4.0,1.1,1.8,2.2,3.7])
y=np.array([6.008,15.722,27.130,33.772,5.257,9.549,11.098,28.828])
# here, create lambda functions for Line, Quadratic fit
# tpl is a tuple that contains the parameters of the fit
funcLine=lambda tpl,x : tpl[0]*x+tpl[1]
funcQuad=lambda tpl,x : tpl[0]*x**2+tpl[1]*x+tpl[2]
# func is going to be a placeholder for funcLine,funcQuad or whatever
# function we would like to fit
func=funcLine
# ErrorFunc is the diference between the func and the y "experimental" data
ErrorFunc=lambda tpl,x,y: func(tpl,x)-y
#tplInitial contains the "first guess" of the parameters
tplInitial1=(1.0,2.0)
# leastsq finds the set of parameters in the tuple tpl that minimizes
# ErrorFunc=yfit-yExperimental
tplFinal1,success=leastsq(ErrorFunc,tplInitial1[:],args=(x,y))
print " linear fit ",tplFinal1
xx1=np.linspace(x.min(),x.max(),50)
yy1=func(tplFinal1,xx1)
#------------------------------------------------
# now the quadratic fit
#-------------------------------------------------
func=funcQuad
tplInitial2=(1.0,2.0,3.0)
tplFinal2,success=leastsq(ErrorFunc,tplInitial2[:],args=(x,y))
print "quadratic fit" ,tplFinal2
xx2=xx1
yy2=func(tplFinal2,xx2)
plt.plot(xx1,yy1,'r-',x,y,'bo',xx2,yy2,'g-')
plt.show()
if __name__=="__main__":
main()
这篇关于如何在python中使用scipy.optimize中的scipy.optimize的Minimumsq函数以将直线和二次线都拟合到数据集x和y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！