Fitting piecewise function in Python

Question

I\'m trying to fit a piecewise defined function to a data set in Python. I\'ve searched for quite a while now, but I haven\'t found an answer whether it is possible or not.<

Answer 1

Couldn't you simply replace fitfunc with

def fitfunc2(x, p):
    return np.abs(x-p)

which then produces something like

>>> x = np.arange(1,10)
>>> y = fitfunc2(x,6) + 0.1*np.random.randn(len(x))
>>> 
>>> so.curve_fit(fitfunc2, x, y) 
(array([ 5.98273313]), array([[ 0.00101859]]))

Using a switch function and/or building blocks like where to replace branches, this should scale up to more complicated expressions without needing to call vectorize.

[PS: the errfunc in your least squares example doesn't need to be a lambda. You could write

def errfunc(p, x, y):
    return array_fitfunc(p, x) - y

instead, if you liked.]