pybrain implementation of M. Tim Jones AI Application Programming chapter 5 sample code

backprop.py

# -*- coding: utf-8 -*-
from pybrain.tools.shortcuts import buildNetwork

from pybrain.datasets.supervised import SupervisedDataSet
from pybrain.supervised.trainers.backprop import BackpropTrainer

from pybrain.structure.modules.softmax import SoftmaxLayer
from pybrain.structure.modules.tanhlayer import TanhLayer

actions = ("Attack", "Run", "Wander", "Hide")
inputs = ('Health', 'Knife', 'Gun', 'Enemy')


samples = (

    ((2.0, 0.0, 0.0, 0.0), (0.0, 0.0, 1.0, 0.0)),
    ((2.0, 0.0, 0.0, 1.0), (0.0, 0.0, 1e0, 0.0)),
    ((2.0, 0.0, 1.0, 1.0), (1.0, 0.0, 0.0, 0.0)),
    ((2.0, 0.0, 1.0, 2.0), (1.0, 0.0, 0.0, 0.0)),
    ((2.0, 1.0, 0.0, 2.0), (0.0, 0.0, 0.0, 1.0)),
    ((2.0, 1.0, 0.0, 1.0), (1.0, 0.0, 0.0, 0.0)),

    ((1.0, 0.0, 0.0, 0.0), (0.0, 0.0, 1.0, 0.0)),
    ((1.0, 0.0, 0.0, 1.0), (0.0, 0.0, 0.0, 1.0)),
    ((1.0, 0.0, 1.0, 1.0), (1.0, 0.0, 0.0, 0.0)),
    ((1.0, 0.0, 1.0, 2.0), (0.0, 0.0, 0.0, 1.0)),
    ((1.0, 1.0, 0.0, 2.0), (0.0, 0.0, 0.0, 1.0)),
    ((1.0, 1.0, 0.0, 1.0), (0.0, 0.0, 0.0, 1.0)),

    ((0.0, 0.0, 0.0, 0.0), (0.0, 0.0, 1.0, 0.0)),
    ((0.0, 0.0, 0.0, 1.0), (0.0, 0.0, 0.0, 1.0)),
    ((0.0, 0.0, 1.0, 1.0), (0.0, 0.0, 0.0, 1.0)),
    ((0.0, 0.0, 1.0, 2.0), (0.0, 1.0, 0.0, 0.0)),
    ((0.0, 1.0, 0.0, 2.0), (0.0, 1.0, 0.0, 0.0)),
    ((0.0, 1.0, 0.0, 1.0), (0.0, 0.0, 0.0, 1.0)),
)

test_data = (

    (2.0, 1.0, 1.0, 1.0,),
    (1.0, 1.0, 1.0, 2.0,),
    (0.0, 0.0, 0.0, 0.0,),
    (0.0, 1.0, 1.0, 1.0,),
    (2.0, 0.0, 1.0, 3.0,),
    (2.0, 1.0, 0.0, 3.0,),
    (0.0, 1.0, 0.0, 3.0,),
)

'''
Health = 2 Knife = 1 Gun = 1 Enemy = 1 Wander
Health = 1 Knife = 1 Gun = 1 Enemy = 2 Hide
Health = 0 Knife = 0 Gun = 0 Enemy = 0 Wander
Health = 0 Knife = 1 Gun = 1 Enemy = 1 Hide
Health = 2 Knife = 0 Gun = 1 Enemy = 3 Hide
Health = 2 Knife = 1 Gun = 0 Enemy = 3 Hide
Health = 0 Knife = 1 Gun = 0 Enemy = 3 Run

'''

net = buildNetwork(4, 3, 4, bias=True)
ds = SupervisedDataSet(4, 4)
for sample in samples:
    ds.addSample(*sample)

trainer = BackpropTrainer(net, ds, learningrate = 0.1, momentum = 0., )


for _ in range(100):
    trainer.trainEpochs(5)

for row in test_data:
    print ' '.join(['%s = %s' % (inputs[i], int(x)) for i, x in enumerate(row)]),
    res = net.activate(row)
    res = list(res)
    #print res, res.index(max(res))
    print actions[res.index(max(res))]