/*
 * JOONE - Java Object Oriented Neural Engine
 * http://joone.sourceforge.net
 *
 * XORMemory.java
 */

import org.joone.engine.*;
import org.joone.engine.learning.*;
import org.joone.io.*;
import org.joone.net.*;

/**
 * Sample class to demonstrate the use of the MemoryInputSynapse
 * @author Josï¿?Rodriguez
 */

public class XORMemory implements NeuralNetListener {
    private MemoryOutputSynapse memOut;     //Variables defined for the entire class
    private FileOutputSynapse fileOut;
    
    /**
     * @param args the command line arguments
     */
    public static void main(String args[]) {
        XORMemory   xor = new XORMemory();
        xor.Go();
    }
    
    /**
     * Go() sets up the XOR network and trains it.
     */
    public void Go() {
        SigmoidLayer	input = new SigmoidLayer(); // First, creates the three Layers
        SigmoidLayer	hidden = new SigmoidLayer();
        SigmoidLayer	output = new SigmoidLayer();
        
        input.setLayerName("input");
        hidden.setLayerName("hidden");
        output.setLayerName("output");
        
        input.setRows(2);                // Set their dimensions
        hidden.setRows(3);
        output.setRows(1);
        
        // Now create the two Synapses
        FullSynapse synapse_IH = new FullSynapse(); /* input -> hidden conn. */
        FullSynapse synapse_HO = new FullSynapse(); /* hidden -> output conn. */
        
        synapse_IH.setName("IH");
        synapse_HO.setName("HO");
        
        input.addOutputSynapse(synapse_IH); // Connect the input layer whit the hidden layer
        hidden.addInputSynapse(synapse_IH);
        hidden.addOutputSynapse(synapse_HO);// Connect the hidden layer whit the output layer
        output.addInputSynapse(synapse_HO);
        
        FileInputSynapse inputStream = new FileInputSynapse(); //USE WITH FILE INPUT
        inputStream.setFirstCol(1);
        inputStream.setLastCol(2);
        inputStream.setFileName("xor.txt");
        input.addInputSynapse(inputStream);
        
        TeachingSynapse trainer = new TeachingSynapse();
        FileInputSynapse samples = new FileInputSynapse();
        samples.setFileName("xor.txt");
        trainer.setDesired(samples);
        samples.setFirstCol(3);
        samples.setLastCol(3);
        
        // Connects the Teacher to the last layer of the net
        output.addOutputSynapse(trainer);  
        
        memOut = new MemoryOutputSynapse();         //OUTPUT TO ARRAY STUFF
        memOut.setBuffered(false);
        output.addOutputSynapse(memOut);
        
        NeuralNet nnet = new NeuralNet();          /*Use the NeuralNet object*/
        nnet.addLayer(input, NeuralNet.INPUT_LAYER);
        nnet.addLayer(hidden, NeuralNet.HIDDEN_LAYER);
        nnet.addLayer(output, NeuralNet.OUTPUT_LAYER);
        nnet.setTeacher(trainer);
        
        Monitor monitor = nnet.getMonitor();
        monitor.setLearningRate(0.8);
        monitor.setMomentum(0.3);
        monitor.setTrainingPatterns(4);
        monitor.setTotCicles(20000);
        monitor.setLearning(true);
        monitor.addNeuralNetListener(this);
        
        nnet.start();
        nnet.getMonitor().Go();
    }
    
    /** * Called after training.     */
    public void netStopped(NeuralNetEvent e) {
        System.out.println("Training finished");
        netFinished();     //Go to the method that deals with all post-training stuff
    }
    
    /** * Called after each cycle. */
    public void cicleTerminated(NeuralNetEvent e) {
        Monitor mon = (Monitor) e.getSource();
        long	c = mon.getCurrentCicle();
        long	cl = c / 1000;
        if ((cl * 1000) == c) {          // Print the results every 1000 cycles
            System.out.println(c + " cycles remaining - Error = " + mon.getGlobalError());
        }
    }
    
    /** * Called before training starts.  */
    public void netStarted(NeuralNetEvent e) {System.out.println("Training...");}
    public void errorChanged(NeuralNetEvent e) {}
    public void netStoppedError(NeuralNetEvent e,String error) { }
    
    public void netFinished(){
        System.out.println("\nPrinting output results");
        for(int i=0; i < 4; ++i){
            double[] pattern = memOut.getNextPattern();
            System.out.println("Output Pattern #" + (i+1) + " = " + pattern[0]);
        }
        System.exit(0);
    }
}