所以尝试使用scipy.odr进行线性回归。然而,它失败了。 scipy.odr之前曾为我工作过,而且我的代码中没有任何错误。我能想到的唯一原因是坡度可能太小但我不知道这会怎么打扰scipy。 谢谢你的帮助。
守则:
#!/usr/bin/env python3 -i
# -*- coding: iso-8859-1 -*-
import matplotlib.pyplot as plt
import numpy as np
from scipy.odr import *
fig = plt.figure()
ax1 = fig.add_subplot(111)
x = np.linspace(0,1E15,10)
y = 1E-15*x-2
ax1.set_xlim(-0.05E15,1.1E15)
ax1.set_ylim(-2.1, -0.7)
ax1.plot(x, y, 'o')
# Fit using odr
def f(B, x):
return B[0]*x + B[1]
linear = Model(f)
mydata = RealData(x, y)
myodr = ODR(mydata, linear, beta0=[1., 2.])
myoutput = myodr.run()
myoutput.pprint()
a, b = myoutput.beta
sa, sb = myoutput.sd_beta
xp = np.linspace(ax1.get_xlim()[0], ax1.get_xlim()[1], 1000)
yp = a*xp+b
ax1.plot(xp,yp)
plt.show()
这是结果终端输出:
Beta: [ -4.84615388e-15 2.00000000e+00]
Beta Std Error: [ 8.14077323e-16 0.00000000e+00]
Beta Covariance: [[ 1.46153845e-31 0.00000000e+00]
[ 0.00000000e+00 0.00000000e+00]]
Residual Variance: 4.534412955465587
Inverse Condition #: 1.0
Reason(s) for Halting:
Problem is not full rank at solution
Parameter convergence
这就是结果图:
编辑:我的odr-regression代码来自http://docs.scipy.org/doc/scipy/reference/odr.html
答案 0 :(得分:1)
import numpy as np
import matplotlib.pyplot as plt
fig = plt.figure()
ax1 = fig.add_subplot(111)
x = np.linspace(0,1E15,10)
y = 1E-15*x-2
ax1.set_xlim(-0.05E15,1.1E15)
ax1.set_ylim(-2.1, -0.7)
ax1.plot(x, y, 'o')
# Fit using odr
def f(B, x):
return B[0]*x + B[1]
sx = np.std(x)
sy = np.std(y)
linear = Model(f)
mydata = RealData(x=x,y=y, sx=sx, sy=sy)
myodr = ODR(mydata, linear, beta0=[1.00000000e-15, 2.])
myoutput = myodr.run()
myoutput.pprint()
a, b = myoutput.beta
sa, sb = myoutput.sd_beta
xp = np.linspace(min(x), max(x), 1000)
yp = a*xp+b
ax1.plot(xp,yp)
plt.show()