# Code from Chapter 3 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

# Demonstration of the Perceptron and Linear Regressor on the basic logic functions

import numpy as np
inputs = np.array([[0,0],[0,1],[1,0],[1,1]])
# AND data
ANDtargets = np.array([[0],[0],[0],[1]])
# OR data
ORtargets = np.array([[0],[1],[1],[1]])
# XOR data
XORtargets = np.array([[0],[1],[1],[0]])
import pcn_logic_eg

print "AND logic function"
pAND = pcn_logic_eg.pcn(inputs,ANDtargets)
pAND.pcntrain(inputs,ANDtargets,0.25,6)

print "OR logic function"
pOR = pcn_logic_eg.pcn(inputs,ORtargets)
pOR.pcntrain(inputs,ORtargets,0.25,6)

print "XOR logic function"
pXOR = pcn_logic_eg.pcn(inputs,XORtargets)
pXOR.pcntrain(inputs,XORtargets,0.25,6)