在python中拟合多元curve_fit

Question

I'm trying to fit a simple function to two arrays of independent data in python. I understand that I need to bunch the data for my independent variables into one array, but something still seems to be wrong with the way I'm passing variables when I try to do the fit. (There are a couple previous posts related to this one, but they haven't been much help.)

import numpy as np
import matplotlib.pyplot as plt
from scipy.optimize import curve_fit

def fitFunc(x_3d, a, b, c, d):
    return a + b*x_3d[0,:] + c*x_3d[1,:] + d*x_3d[0,:]*x_3d[1,:]

x_3d = np.array([[1,2,3],[4,5,6]])

p0 = [5.11, 3.9, 5.3, 2]

fitParams, fitCovariances = curve_fit(fitFunc, x_3d[:2,:], x_3d[2,:], p0)
print ' fit coefficients:\n', fitParams

The error I get reads,

raise TypeError('Improper input: N=%s must not exceed M=%s' % (n, m)) 
TypeError: Improper input: N=4 must not exceed M=3

What is M the length of? Is N the length of p0 ? What am I doing wrong here?

Answer 1

N and M are defined in the help for the function. N is the number of data points and M is the number of parameters. Your error therefore basically means you need at least as many data points as you have parameters, which makes perfect sense.

This code works for me:

import numpy as np
import matplotlib.pyplot as plt
from scipy.optimize import curve_fit

def fitFunc(x, a, b, c, d):
    return a + b*x[0] + c*x[1] + d*x[0]*x[1]

x_3d = np.array([[1,2,3,4,6],[4,5,6,7,8]])

p0 = [5.11, 3.9, 5.3, 2]

fitParams, fitCovariances = curve_fit(fitFunc, x_3d, x_3d[1,:], p0)
print ' fit coefficients:\n', fitParams

I have included more data. I have also changed fitFunc to be written in a form that scans as only being a function of a single x - the fitter will handle calling this for all the data points. The code as you posted also referenced x_3d[2,:] , which was causing an error.

Answer 2

The default curve_fit method needs you to have fewer parameters for the fitted function fitFunc than data points. I had the same problem fitting a function that took 15 parameters in total and I had only 13 data points. The solution is to use another method (eg dogbox or trf ).