# 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
import pylab as pl
import numpy as np
import pcn
import cPickle, gzip

# Read the dataset in (code from sheet)
f = gzip.open('mnist.pkl.gz','rb')
tset, vset, teset = cPickle.load(f)
f.close()

nread = 200
# Just use the first few images
train_in = tset[0][:nread,:]

# This is a little bit of work -- 1 of N encoding
# Make sure you understand how it does it
train_tgt = np.zeros((nread,10))
for i in range(nread):
    train_tgt[i,tset[1][i]] = 1

test_in = teset[0][:nread,:]
test_tgt = np.zeros((nread,10))
for i in range(nread):
    test_tgt[i,teset[1][i]] = 1

# Train a Perceptron on training set
p = pcn.pcn(train_in, train_tgt)
p.pcntrain(train_in, train_tgt,0.25,100)

# This isn't really good practice since it's on the training data, 
# but it does show that it is learning.
p.confmat(train_in,train_tgt)

# Now test it
p.confmat(test_in,test_tgt)