Python curve_fit with multiple independent variables

Question

Python\'s curve_fit calculates the best-fit parameters for a function with a single independent variable, but is there a way, using curve_fit or something else,

Answer 1

Yes. We can pass multiple variables for curve_fit. I have written a piece of code:

import numpy as np
x = np.random.randn(2,100)
w = np.array([1.5,0.5]).reshape(1,2)
esp = np.random.randn(1,100)
y = np.dot(w,x)+esp
y = y.reshape(100,)

In the above code I have generated x a 2D data set in shape of (2,100) i.e, there are two variables with 100 data points. I have fit the dependent variable y with independent variables x with some noise.

def model_func(x,w1,w2,b):
  w = np.array([w1,w2]).reshape(1,2)
  b = np.array([b]).reshape(1,1)
  y_p = np.dot(w,x)+b
  return y_p.reshape(100,)

We have defined a model function that establishes relation between y & x.
Note: The shape of output of the model function or predicted y should be (length of x,)

popt, pcov = curve_fit(model_func,x,y)

The popt is an 1D numpy array containing predicted parameters. In our case there are 3 parameters.