fitting multivariate curve_fit in python

user_3133865 0
Viewed 39983

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?

curve-fitting scipy python
2 Answers

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 (e.g. dogbox or trf).

user_84737650
Related