问题
I would like to perform a multidimensional ODR with scipy.odr
. I read the API documentation, it says that multi-dimensionality is possible, but I cannot make it work. I cannot find working example on the internet and API is really crude and give no hints how to proceed.
Here is my MWE:
import numpy as np
import scipy.odr
def linfit(beta, x):
return beta[0]*x[:,0] + beta[1]*x[:,1] + beta[2]
n = 1000
t = np.linspace(0, 1, n)
x = np.full((n, 2), float('nan'))
x[:,0] = 2.5*np.sin(2*np.pi*6*t)+4
x[:,1] = 0.5*np.sin(2*np.pi*7*t + np.pi/3)+2
e = 0.25*np.random.randn(n)
y = 3*x[:,0] + 4*x[:,1] + 5 + e
print(x.shape)
print(y.shape)
linmod = scipy.odr.Model(linfit)
data = scipy.odr.Data(x, y)
odrfit = scipy.odr.ODR(data, linmod, beta0=[1., 1., 1.])
odrres = odrfit.run()
odrres.pprint()
It raises the following exception:
scipy.odr.odrpack.odr_error: number of observations do not match
Which seems to be related to my matrix shapes, but I do not know how must I shape it properly. Does anyone know?
回答1:
Firstly, in my experience scipy.odr uses mostly arrays, not matrices. The library seems to make a large amount of size checks along the way and getting it to work with multiple variables seems to be quite troublesome.
This is the workflow how I usually get it to work (and worked at least on python 2.7):
import numpy as np
import scipy.odr
n = 1000
t = np.linspace(0, 1, n)
def linfit(beta, x):
return beta[0]*x[0] + beta[1]*x[1] + beta[2] #notice changed indices for x
x1 = 2.5*np.sin(2*np.pi*6*t)+4
x2 = 0.5*np.sin(2*np.pi*7*t + np.pi/3)+2
x = np.row_stack( (x1, x2) ) #odr doesn't seem to work with column_stack
e = 0.25*np.random.randn(n)
y = 3*x[0] + 4*x[1] + 5 + e #indices changed
linmod = scipy.odr.Model(linfit)
data = scipy.odr.Data(x, y)
odrfit = scipy.odr.ODR(data, linmod, beta0=[1., 1., 1.])
odrres = odrfit.run()
odrres.pprint()
So using identical (1D?) arrays, using row_stack and adressing by single index number seems to work.
来源:https://stackoverflow.com/questions/35041266/scipy-odr-multiple-variable-regression