Regression with a multi-variable function

Question

I have this code to fit a function with only one variable (x):

from scipy.optimize import curve_fit

def func(x, s, k, L,A):
    return A + (L * (1/(1+((x/k)**(-s)))))

init_vals = [0.4,4, 100,50]
# fit your data and getting fit parameters
popt, pcov = curve_fit(func, xdata, ydata, p0=init_vals, bounds=([0,0.1, 1,0], [10,10, 1000,1000]))

But now I need to fit this one:

def func(x, s, k, L,A):
    return A + (L * (1/(1+(((b1*x1+b2*x2+b3*x3)/k)**(-s)))))

Where x is now f(x1,x2,x3)

Answer 1

Should it be like this?

def func(x, s, k, L,A):
    return A + (L * (1/(1+(((b1*x[0]+b2*x[1]+b3*x[2])/k)**(-s)))))

and in this case xdata has to be (3,n) shaped array.