ann xor

/**
 * Network
 * Copyright 2005 by Jeff Heaton([e-mail])
 *
 * Example program from Chapter 3
 * Programming Neural Networks in Java
 * http://www.heatonresearch.com/articles/series/1/
 *
 * This software is copyrighted. You may use it in programs
 * of your own, without restriction, but you may not
 * publish the source code without the author's permission.
 * For more information on distributing this code, please
 * visit:
 *    http://www.heatonresearch.com/hr_legal.php
 *
 * @author Jeff Heaton
 * @version 1.1
 */

public class Network {

  /**
   * The global error for the training.
   */
  protected double globalError;


  /**
   * The number of input neurons.
   */
  protected int inputCount;

  /**
   * The number of hidden neurons.
   */
  protected int hiddenCount;

  /**
   * The number of output neurons
   */
  protected int outputCount;

  /**
   * The total number of neurons in the network.
   */
  protected int neuronCount;

  /**
   * The number of weights in the network.
   */
  protected int weightCount;

  /**
   * The learning rate.
   */
  protected double learnRate;

  /**
   * The outputs from the various levels.
   */
  protected double fire[];

  /**
   * The weight matrix this, along with the thresholds can be
   * thought of as the "memory" of the neural network.
   */
  protected double matrix[];

  /**
   * The errors from the last calculation.
   */
  protected double error[];

  /**
   * Accumulates matrix delta's for training.
   */
  protected double accMatrixDelta[];

  /**
   * The thresholds, this value, along with the weight matrix
   * can be thought of as the memory of the neural network.
   */
  protected double thresholds[];

  /**
   * The changes that should be applied to the weight
   * matrix.
   */
  protected double matrixDelta[];

  /**
   * The accumulation of the threshold deltas.
   */
  protected double accThresholdDelta[];

  /**
   * The threshold deltas.
   */
  protected double thresholdDelta[];

  /**
   * The momentum for training.
   */
  protected double momentum;

  /**
   * The changes in the errors.
   */
  protected double errorDelta[];


  /**
   * Construct the neural network.
   *
   * @param inputCount The number of input neurons.
   * @param hiddenCount The number of hidden neurons
   * @param outputCount The number of output neurons
   * @param learnRate The learning rate to be used when training.
   * @param momentum The momentum to be used when training.
   */
  public Network(int inputCount,
                 int hiddenCount,
                 int outputCount,
                 double learnRate,
                 double momentum) {

    this.learnRate = learnRate;
    this.momentum = momentum;

    this.inputCount = inputCount;
    this.hiddenCount = hiddenCount;
    this.outputCount = outputCount;
    neuronCount = inputCount + hiddenCount + outputCount;
    weightCount = (inputCount * hiddenCount) + (hiddenCount * outputCount);

    fire        = new double[neuronCount];
    matrix      = new double[weightCount];
    matrixDelta = new double[weightCount];
    thresholds  = new double[neuronCount];
    errorDelta  = new double[neuronCount];
    error       = new double[neuronCount];
    accThresholdDelta = new double[neuronCount];
    accMatrixDelta = new double[weightCount];
    thresholdDelta = new double[neuronCount];

    reset();
  }



  /**
   * Returns the root mean square error for a complet training set.
   *
   * @param len The length of a complete training set.
   * @return The current error for the neural network.
   */
  public double getError(int len) {
    double err = Math.sqrt(globalError / (len * outputCount));
    globalError = 0;  // clear the accumulator
    return err;

  }

  /**
   * The threshold method. You may wish to override this class to provide other
   * threshold methods.
   *
   * @param sum The activation from the neuron.
   * @return The activation applied to the threshold method.
   */
  public double threshold(double sum) {
    return 1.0 / (1 + Math.exp(-1.0 * sum));
  }

  /**
   * Compute the output for a given input to the neural network.
   *
   * @param input The input provide to the neural network.
   * @return The results from the output neurons.
   */
  public double []computeOutputs(double input[]) {
    int i, j;
    final int hiddenIndex = inputCount;
    final int outIndex = inputCount + hiddenCount;

    for (i = 0; i < inputCount; i++) {
      fire[i] = input[i];
    }

    // first layer
    int inx = 0;

    for (i = hiddenIndex; i < outIndex; i++) {
      double sum = thresholds[i];

      for (j = 0; j < inputCount; j++) {
        sum += fire[j] * matrix[inx++];
      }
      fire[i] = threshold(sum);
    }

    // hidden layer

    double result[] = new double[outputCount];

    for (i = outIndex; i < neuronCount; i++) {
      double sum = thresholds[i];

      for (j = hiddenIndex; j < outIndex; j++) {
        sum += fire[j] * matrix[inx++];
      }
      fire[i] = threshold(sum);
      result[i-outIndex] = fire[i];
    }

    return result;
  }


  /**
   * Calculate the error for the recogntion just done.
   *
   * @param ideal What the output neurons should have yielded.
   */
  public void calcError(double ideal[]) {
    int i, j;
    final int hiddenIndex = inputCount;
    final int outputIndex = inputCount + hiddenCount;

    // clear hidden layer errors
    for (i = inputCount; i < neuronCount; i++) {
      error[i] = 0;
    }

    // layer errors and deltas for output layer
    for (i = outputIndex; i < neuronCount; i++) {
      error[i] = ideal[i - outputIndex] - fire[i];
      globalError += error[i] * error[i];
      errorDelta[i] = error[i] * fire[i] * (1 - fire[i]);
    }

    // hidden layer errors
    int winx = inputCount * hiddenCount;

    for (i = outputIndex; i < neuronCount; i++) {
      for (j = hiddenIndex; j < outputIndex; j++) {
        accMatrixDelta[winx] += errorDelta[i] * fire[j];
        error[j] += matrix[winx] * errorDelta[i];
        winx++;
      }
      accThresholdDelta[i] += errorDelta[i];
    }

    // hidden layer deltas
    for (i = hiddenIndex; i < outputIndex; i++) {
      errorDelta[i] = error[i] * fire[i] * (1 - fire[i]);
    }

    // input layer errors
    winx = 0;  // offset into weight array
    for (i = hiddenIndex; i < outputIndex; i++) {
      for (j = 0; j < hiddenIndex; j++) {
        accMatrixDelta[winx] += errorDelta[i] * fire[j];
        error[j] += matrix[winx] * errorDelta[i];
        winx++;
      }
      accThresholdDelta[i] += errorDelta[i];
    }
  }

  /**
   * Modify the weight matrix and thresholds based on the last call to
   * calcError.
   */
  public void learn() {
    int i;

    // process the matrix
    for (i = 0; i < matrix.length; i++) {
      matrixDelta[i] = (learnRate * accMatrixDelta[i]) + (momentum * matrixDelta[i]);
      matrix[i] += matrixDelta[i];
      accMatrixDelta[i] = 0;
    }

    // process the thresholds
    for (i = inputCount; i < neuronCount; i++) {
      thresholdDelta[i] = learnRate * accThresholdDelta[i] + (momentum * thresholdDelta[i]);
      thresholds[i] += thresholdDelta[i];
      accThresholdDelta[i] = 0;
    }
  }

  /**
   * Reset the weight matrix and the thresholds.
   */
  public void reset() {
    int i;

    for (i = 0; i < neuronCount; i++) {
      thresholds[i] = 0.5 - (Math.random());
      thresholdDelta[i] = 0;
      accThresholdDelta[i] = 0;
    }
    for (i = 0; i < matrix.length; i++) {
      matrix[i] = 0.5 - (Math.random());
      matrixDelta[i] = 0;
      accMatrixDelta[i] = 0;
    }
  }
}

import javax.swing.*;
import java.awt.*;
import java.awt.event.*;
import java.text.*;

/**
 * XorExample
 * Copyright 2005 by Jeff Heaton([e-mail])
 *
 * Example program from Chapter 3
 * Programming Neural Networks in Java
 * http://www.heatonresearch.com/articles/series/1/
 *
 * This software is copyrighted. You may use it in programs
 * of your own, without restriction, but you may not
 * publish the source code without the author's permission.
 * For more information on distributing this code, please
 * visit:
 *    http://www.heatonresearch.com/hr_legal.php
 *
 * @author Jeff Heaton
 * @version 1.1
 */
public class XorExample extends JFrame implements
ActionListener,Runnable {

  /**
   * The train button.
   */
  JButton btnTrain;

  /**
   * The run button.
   */
  JButton btnRun;

  /**
   * The quit button.
   */
  JButton btnQuit;

  /**
   * The status line.
   */
  JLabel status;

  /**
   * The background worker thread.
   */
  protected Thread worker = null;

/**
 * The number of input neurons.
 */
  protected final static int NUM_INPUT = 2;

/**
 * The number of output neurons.
 */
  protected final static int NUM_OUTPUT = 1;

/**
 * The number of hidden neurons.
 */
  protected final static int NUM_HIDDEN = 3;

/**
 * The learning rate.
 */
  protected final static double RATE = 0.5;

/**
 * The learning momentum.
 */
  protected final static double MOMENTUM = 0.7;


  /**
   * The training data that the user enters.
   * This represents the inputs and expected
   * outputs for the XOR problem.
   */
  protected JTextField data[][] = new JTextField[4][4];

  /**
   * The neural network.
   */
  protected Network network;



  /**
   * Constructor. Setup the components.
   */
  public XorExample()
  {
    setTitle("XOR Solution");
    network = new Network(
                         NUM_INPUT,
                         NUM_HIDDEN,
                         NUM_OUTPUT,
                         RATE,
                         MOMENTUM);

    Container content = getContentPane();

    GridBagLayout gridbag = new GridBagLayout();
    GridBagConstraints c = new GridBagConstraints();
    content.setLayout(gridbag);

    c.fill = GridBagConstraints.NONE;
    c.weightx = 1.0;

    // Training input label
    c.gridwidth = GridBagConstraints.REMAINDER; //end row
    c.anchor = GridBagConstraints.NORTHWEST;
    content.add(
               new JLabel(
                         "Enter training data:"),c);

    JPanel grid = new JPanel();
    grid.setLayout(new GridLayout(5,4));
    grid.add(new JLabel("IN1"));
    grid.add(new JLabel("IN2"));
    grid.add(new JLabel("Expected OUT   "));
    grid.add(new JLabel("Actual OUT"));

    for ( int i=0;i<4;i++ ) {
      int x = (i&1);
      int y = (i&2)>>1;
      grid.add(data[i][0] = new JTextField(""+y));
      grid.add(data[i][1] = new JTextField(""+x));
      grid.add(data[i][2] = new JTextField(""+(x^y)));
      grid.add(data[i][3] = new JTextField("??"));
      data[i][0].setEditable(false);
      data[i][1].setEditable(false);
      data[i][3].setEditable(false);
    }

    content.add(grid,c);

    // the button panel
    JPanel buttonPanel = new JPanel(new FlowLayout());
    buttonPanel.add(btnTrain = new JButton("Train"));
    buttonPanel.add(btnRun = new JButton("Run"));
    buttonPanel.add(btnQuit = new JButton("Quit"));
    btnTrain.addActionListener(this);
    btnRun.addActionListener(this);
    btnQuit.addActionListener(this);

    // Add the button panel
    c.gridwidth = GridBagConstraints.REMAINDER; //end row
    c.anchor = GridBagConstraints.CENTER;
    content.add(buttonPanel,c);

    // Training input label
    c.gridwidth = GridBagConstraints.REMAINDER; //end row
    c.anchor = GridBagConstraints.NORTHWEST;
    content.add(
               status = new JLabel("Click train to begin training..."),c);

    // adjust size and position
    pack();
    Toolkit toolkit = Toolkit.getDefaultToolkit();
    Dimension d = toolkit.getScreenSize();
    setLocation(
               (int)(d.width-this.getSize().getWidth())/2,
               (int)(d.height-this.getSize().getHeight())/2 );
    setDefaultCloseOperation(WindowConstants.DISPOSE_ON_CLOSE);
    setResizable(false);

    btnRun.setEnabled(false);
  }

  /**
   * The main function, just display the JFrame.
   *
   * @param args No arguments are used.
   */
  public static void main(String args[])
  {
    (new XorExample()).show(true);
  }

  /**
   * Called when the user clicks one of the three
   * buttons.
   *
   * @param e The event.
   */
  public void actionPerformed(ActionEvent e)
  {
    if ( e.getSource()==btnQuit )
      System.exit(0);
    else if ( e.getSource()==btnTrain )
      train();
    else if ( e.getSource()==btnRun )
      evaluate();
  }

  /**
   * Called when the user clicks the run button.
   */
  protected void evaluate()
  {
    double xorData[][] = getGrid();
    int update=0;

    for (int i=0;i<4;i++) {
      NumberFormat nf = NumberFormat.getInstance();
      double d[] = network.computeOutputs(xorData[i]);
      data[i][3].setText(nf.format(d[0]));
    }

  }


  /**
  * Called when the user clicks the train button.
  */
  protected void train()
  {
    if ( worker != null )
      worker = null;
    worker = new Thread(this);
    worker.setPriority(Thread.MIN_PRIORITY);
    worker.start();
  }

  /**
  * The thread worker, used for training
  */
  public void run()
  {
    double xorData[][] = getGrid();
    double xorIdeal[][] = getIdeal();
    int update=0;

    int max = 10000;
    for (int i=0;i<max;i++) {
      for (int j=0;j<xorData.length;j++) {
        network.computeOutputs(xorData[j]);
        network.calcError(xorIdeal[j]);
        network.learn();
      }


      update++;
      if (update==100) {
        status.setText( "Cycles Left:" + (max-i) + ",Error:" + network.getError(xorData.length) );
        update=0;
      }
    }
    btnRun.setEnabled(true);
  }


  /**
   * Called to generate an array of doubles based on
   * the training data that the user has entered.
   *
   * @return An array of doubles
   */
  double [][]getGrid()
  {
    double array[][] = new double[4][2];

    for ( int i=0;i<4;i++ ) {
      array[i][0] =
      Float.parseFloat(data[i][0].getText());
      array[i][1] =
      Float.parseFloat(data[i][1].getText());
    }

    return array;
  }

  /**
   * Called to the the ideal values that that the neural network
   * should return for each of the grid training values.
   *
   * @return The ideal results.
   */
  double [][]getIdeal()
  {
    double array[][] = new double[4][1];

    for ( int i=0;i<4;i++ ) {
      array[i][0] =
      Float.parseFloat(data[i][2].getText());
    }

    return array;
  }


}