import java.awt.*;import javax.swing.*;import java.awt.event.*;/** * Hopfield * Copyright 2005 by Jeff Heaton(jeff@jeffheaton.com) * * Example program from Chapter 2 * 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 Hopfield extends JFrame implements ActionListener {  /**   * The number of neurons in this neural network.   */  public static final int NETWORK_SIZE = 4;  /**   * The weight matrix for the four fully connected   * neurons.   */  JTextField weightMatrix[][] =    new JTextField[NETWORK_SIZE][NETWORK_SIZE];  /**   * The input pattern, used to either train   * or run the neural network. When the network   * is being trained, this is the training   * data. When the neural network is to be ran   * this is the input pattern.   */  JComboBox input[] = new JComboBox[NETWORK_SIZE];  /**   * The output from each of the four neurons.   */  JTextField output[] = new JTextField[NETWORK_SIZE];  /**   * The clear button. Used to clear the weight   * matrix.   */  JButton btnClear = new JButton("Clear");  /**   * The train button. Used to train the   * neural network.   */  JButton btnTrain = new JButton("Train");  /**   * The run button. Used to run the neural   * network.   */  JButton btnRun = new JButton("Run");  /**   * Constructor, create all of the components and position   * the JFrame to the center of the screen.   */  public Hopfield()  {    setTitle("Hopfield Neural Network");    // create connections panel    JPanel connections = new JPanel();    connections.setLayout(      new GridLayout(NETWORK_SIZE,NETWORK_SIZE) );    for ( int row=0;row<NETWORK_SIZE;row++ ) {      for ( int col=0;col<NETWORK_SIZE;col++ ) {        weightMatrix[row][col] = new JTextField(3);        weightMatrix[row][col].setText("0");        connections.add(weightMatrix[row][col]);      }    }    Container content = getContentPane();    GridBagLayout gridbag = new GridBagLayout();    GridBagConstraints c = new GridBagConstraints();    content.setLayout(gridbag);    c.fill = GridBagConstraints.NONE;    c.weightx = 1.0;    // Weight matrix label    c.gridwidth = GridBagConstraints.REMAINDER; //end row    c.anchor = GridBagConstraints.NORTHWEST;    content.add(      new JLabel("Weight Matrix for the Hopfield Neural Network:"),c);    // Weight matrix    c.anchor = GridBagConstraints.CENTER;    c.gridwidth = GridBagConstraints.REMAINDER; //end row    content.add(connections,c);    c.gridwidth = 1;    // Input pattern label    c.anchor = GridBagConstraints.NORTHWEST;    c.gridwidth = GridBagConstraints.REMAINDER; //end row    content.add(      new JLabel("Click \"Train\" to train the following pattern:"),c);    // Input pattern    String options[] = { "0","1"};    JPanel inputPanel = new JPanel();    inputPanel.setLayout(new FlowLayout());    for ( int i=0;i<NETWORK_SIZE;i++ ) {      input[i] = new JComboBox(options);      inputPanel.add(input[i]);    }    c.gridwidth = GridBagConstraints.REMAINDER; //end row    c.anchor = GridBagConstraints.CENTER;    content.add(inputPanel,c);    // Output pattern label    c.anchor = GridBagConstraints.NORTHWEST;    c.gridwidth = GridBagConstraints.REMAINDER; //end row    content.add(      new JLabel("Click \"Run\" to see output pattern:"),c);    // Output pattern    JPanel outputPanel = new JPanel();    outputPanel.setLayout(new FlowLayout());    for ( int i=0;i<NETWORK_SIZE;i++ ) {      output[i] = new JTextField(3);      output[i].setEditable(false);      outputPanel.add(output[i]);    }    c.gridwidth = GridBagConstraints.REMAINDER; //end row    c.anchor = GridBagConstraints.CENTER;    content.add(outputPanel,c);    // Buttons    JPanel buttonPanel = new JPanel();    btnClear = new JButton("Clear");    btnTrain = new JButton("Train");    btnRun = new JButton("Run");    btnClear.addActionListener(this);    btnTrain.addActionListener(this);    btnRun.addActionListener(this);    buttonPanel.setLayout(new FlowLayout());    buttonPanel.add(btnClear);    buttonPanel.add(btnTrain);    buttonPanel.add(btnRun);    content.add(buttonPanel,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);  }  /**   * Used to dispatch events from the buttons   * to the handler methods.   *   * @param e The event   */  public void actionPerformed(ActionEvent e)  {    if ( e.getSource()==btnRun )      run();    else if ( e.getSource()==btnClear )      clear();    else if ( e.getSource()==btnTrain )      train();  }  /**   * Called when the neural network is to be ran against   * the input.   */  protected void run()  {    boolean pattern[] = new boolean[NETWORK_SIZE];    int wt[][] = new int[NETWORK_SIZE][NETWORK_SIZE];    for ( int row=0;row<NETWORK_SIZE;row++ )      for ( int col=0;col<NETWORK_SIZE;col++ )        wt[row][col]=Integer.parseInt(weightMatrix[row][col].getText());    for ( int row=0;row<NETWORK_SIZE;row++ ) {      int i = input[row].getSelectedIndex();      if ( i==0 )        pattern[row] = false;      else        pattern[row] = true;    }    Layer net = new Layer(wt);    net.activation(pattern);    for ( int row=0;row<NETWORK_SIZE;row++ ) {      if ( net.output[row] )        output[row].setText("1");      else        output[row].setText("0");      if ( net.output[row]==pattern[row] )        output[row].setBackground(java.awt.Color.green);      else        output[row].setBackground(java.awt.Color.red);    }  }  /**   * Called to clear the weight matrix.   */  protected void clear()  {    for ( int row=0;row<NETWORK_SIZE;row++ )      for ( int col=0;col<NETWORK_SIZE;col++ )        weightMatrix[row][col].setText("0");  }  /**   * Called to train the weight matrix based on the   * current input pattern.   */  protected void train()  {    int work[][] = new int[NETWORK_SIZE][NETWORK_SIZE];    int bi[] = new int[NETWORK_SIZE];    for ( int x=0;x<NETWORK_SIZE;x++ ) {      if ( input[x].getSelectedIndex()==0 )        bi[x] = -1;      else        bi[x] = 1;    }    for ( int row=0;row<NETWORK_SIZE;row++ )      for ( int col=0;col<NETWORK_SIZE;col++ ) {        work[row][col] = bi[row]*bi[col];      }    for ( int x=0;x<NETWORK_SIZE;x++ )      work[x][x] -=1;    for ( int row=0;row<NETWORK_SIZE;row++ )      for ( int col=0;col<NETWORK_SIZE;col++ ) {        int i = Integer.parseInt(weightMatrix[row][col].getText());        weightMatrix[row][col].setText( "" + (i+work[row][col]));      }  }  /**   * Main program entry point, display the   * frame.   *   * @param args Command line arguments are not used   */  static public void main(String args[])  {    JFrame f = new Hopfield();    f.show();  }}