# Licensed under the Apache License, Version 2.0 (the "License");
# based on http://www.heatonresearch.com/encog/

# This example shows how to construct an Encog neural
# network to predict the output from the XOR operator.  This example
# uses backpropagation to train the neural network.


from org.encog.ml.data.basic import BasicMLDataSet 
from org.encog.neural.networks import BasicNetwork 
from org.encog.neural.networks.layers import BasicLayer 
from org.encog.neural.networks.training.propagation.resilient import ResilientPropagation 
from org.encog.engine.network.activation  import ActivationSigmoid

XOR_INPUT =  [ [ 0.0, 0.0 ], [1.0, 0.0], [0.0, 1.0], [1.0, 1.0]]
XOR_IDEAL =  [ [ 0.0 ], [1.0], [1.0], [0.0]]
 
network = BasicNetwork()
network.addLayer(BasicLayer(None,True,2))
network.addLayer(BasicLayer(ActivationSigmoid(),True,3))
network.addLayer(BasicLayer(ActivationSigmoid(),False,1))
network.getStructure().finalizeStructure()
network.reset()

# train
trainingSet = BasicMLDataSet(XOR_INPUT, XOR_IDEAL)
train = ResilientPropagation(network, trainingSet)        # train network

# iterations
epoch=1
error=1
while (error > 0.01):
        train.iteration()
        error=train.getError()
        if (epoch%10==0): print "Epoch #", epoch ," Error:",error
        epoch+=1

print "-> Test Trained Neural Network"

for pair in trainingSet:
            output = network.compute(pair.getInput())
            print pair.getInput().getData(0), "," , pair.getInput().getData(1), ", actual=", output.getData(0), ",ideal=",pair.getIdeal().getData(0)