# Code from Chapter 5 of Machine Learning: An Algorithmic Perspective (2nd Edition)
# by Stephen Marsland (http://stephenmonika.net)

# You are free to use, change, or redistribute the code in any way you wish for
# non-commercial purposes, but please maintain the name of the original author.
# This code comes with no warranty of any kind.

# Stephen Marsland, 2008, 2014

import pylab as pl
import numpy as np

x = np.arange(-3,10,0.05)
y = 2.5 * np.exp(-(x)**2/9) + 3.2 * np.exp(-(x-0.5)**2/4) + np.random.normal(0.0, 1.0, len(x))
nParam = 2
A = np.zeros((len(x),nParam), float)
A[:,0] = np.exp(-(x)**2/9)
A[:,1] = np.exp(-(x*0.5)**2/4)
(p, residuals, rank, s) = np.linalg.lstsq(A,y)

print p
pl.ion()
pl.plot(x,y,'.')
pl.plot(x,p[0]*A[:,0]+p[1]*A[:,1],'x')

pl.show()