multilayerperceptron.java

Java 编写的多种数据挖掘算法 包括聚类、分类、预处理等

		  selection(tmp, 
			    (e.getModifiers() & MouseEvent.CTRL_MASK) == MouseEvent.CTRL_MASK
			    , true);
		  return;
		}
	      }
	      for (int noa = 0; noa < m_numClasses; noa++) {
		if (m_outputs[noa].onUnit(g, x, y, w, h)) {
		  tmp.addElement(m_outputs[noa]);
		  selection(tmp,
			    (e.getModifiers() & MouseEvent.CTRL_MASK) == MouseEvent.CTRL_MASK
			    , true);
		  return;
		}
	      }
	      for (int noa = 0; noa < m_neuralNodes.length; noa++) {
		if (m_neuralNodes[noa].onUnit(g, x, y, w, h)) {
		  tmp.addElement(m_neuralNodes[noa]);
		  selection(tmp,
			    (e.getModifiers() & MouseEvent.CTRL_MASK) == MouseEvent.CTRL_MASK
			    , true);
		  return;
		}

	      }
	      NeuralNode temp = new NeuralNode(String.valueOf(m_nextId), 
					       m_random, m_sigmoidUnit);
	      m_nextId++;
	      temp.setX((double)e.getX() / w);
	      temp.setY((double)e.getY() / h);
	      tmp.addElement(temp);
	      addNode(temp);
	      selection(tmp, (e.getModifiers() & MouseEvent.CTRL_MASK) == MouseEvent.CTRL_MASK
			, true);
	    }
	    else {
	      //then right click
	      Graphics g = NodePanel.this.getGraphics();
	      int x = e.getX();
	      int y = e.getY();
	      int w = NodePanel.this.getWidth();
	      int h = NodePanel.this.getHeight();
	      FastVector tmp = new FastVector(4);
	      for (int noa = 0; noa < m_numAttributes; noa++) {
		if (m_inputs[noa].onUnit(g, x, y, w, h)) {
		  tmp.addElement(m_inputs[noa]);
		  selection(tmp, 
			    (e.getModifiers() & MouseEvent.CTRL_MASK) == MouseEvent.CTRL_MASK
			    , false);
		  return;
		}
		
		
	      }
	      for (int noa = 0; noa < m_numClasses; noa++) {
		if (m_outputs[noa].onUnit(g, x, y, w, h)) {
		  tmp.addElement(m_outputs[noa]);
		  selection(tmp,
			    (e.getModifiers() & MouseEvent.CTRL_MASK) == MouseEvent.CTRL_MASK
			    , false);
		  return;
		}
	      }
	      for (int noa = 0; noa < m_neuralNodes.length; noa++) {
		if (m_neuralNodes[noa].onUnit(g, x, y, w, h)) {
		  tmp.addElement(m_neuralNodes[noa]);
		  selection(tmp,
			    (e.getModifiers() & MouseEvent.CTRL_MASK) == MouseEvent.CTRL_MASK
			    , false);
		  return;
		}
	      }
	      selection(null, (e.getModifiers() & MouseEvent.CTRL_MASK) == MouseEvent.CTRL_MASK
			, false);
	    }
	  }
	});
    }
    
    
    /**
     * This function gets called when the user has clicked something
     * It will amend the current selection or connect the current selection
     * to the new selection.
     * Or if nothing was selected and the right button was used it will 
     * delete the node.
     * @param v The units that were selected.
     * @param ctrl True if ctrl was held down.
     * @param left True if it was the left mouse button.
     */
    private void selection(FastVector v, boolean ctrl, boolean left) {
      
      if (v == null) {
	//then unselect all.
	m_selected.removeAllElements();
	repaint();
	return;
      }
      

      //then exclusive or the new selection with the current one.
      if ((ctrl || m_selected.size() == 0) && left) {
	boolean removed = false;
	for (int noa = 0; noa < v.size(); noa++) {
	  removed = false;
	  for (int nob = 0; nob < m_selected.size(); nob++) {
	    if (v.elementAt(noa) == m_selected.elementAt(nob)) {
	      //then remove that element
	      m_selected.removeElementAt(nob);
	      removed = true;
	      break;
	    }
	  }
	  if (!removed) {
	    m_selected.addElement(v.elementAt(noa));
	  }
	}
	repaint();
	return;
      }

      
      if (left) {
	//then connect the current selection to the new one.
	for (int noa = 0; noa < m_selected.size(); noa++) {
	  for (int nob = 0; nob < v.size(); nob++) {
	    NeuralConnection
	      .connect((NeuralConnection)m_selected.elementAt(noa)
		       , (NeuralConnection)v.elementAt(nob));
	  }
	}
      }
      else if (m_selected.size() > 0) {
	//then disconnect the current selection from the new one.
	
	for (int noa = 0; noa < m_selected.size(); noa++) {
	  for (int nob = 0; nob < v.size(); nob++) {
	    NeuralConnection
	      .disconnect((NeuralConnection)m_selected.elementAt(noa)
			  , (NeuralConnection)v.elementAt(nob));
	    
	    NeuralConnection
	      .disconnect((NeuralConnection)v.elementAt(nob)
			  , (NeuralConnection)m_selected.elementAt(noa));
	    
	  }
	}
      }
      else {
	//then remove the selected node. (it was right clicked while 
	//no other units were selected
	for (int noa = 0; noa < v.size(); noa++) {
	  ((NeuralConnection)v.elementAt(noa)).removeAllInputs();
	  ((NeuralConnection)v.elementAt(noa)).removeAllOutputs();
	  removeNode((NeuralConnection)v.elementAt(noa));
	}
      }
      repaint();
    }

    /**
     * This will paint the nodes ontot the panel.
     * @param g The graphics context.
     */
    public void paintComponent(Graphics g) {

      super.paintComponent(g);
      int x = getWidth();
      int y = getHeight();
      if (25 * m_numAttributes > 25 * m_numClasses && 
	  25 * m_numAttributes > y) {
	setSize(x, 25 * m_numAttributes);
      }
      else if (25 * m_numClasses > y) {
	setSize(x, 25 * m_numClasses);
      }
      else {
	setSize(x, y);
      }

      y = getHeight();
      for (int noa = 0; noa < m_numAttributes; noa++) {
	m_inputs[noa].drawInputLines(g, x, y);
      }
      for (int noa = 0; noa < m_numClasses; noa++) {
	m_outputs[noa].drawInputLines(g, x, y);
	m_outputs[noa].drawOutputLines(g, x, y);
      }
      for (int noa = 0; noa < m_neuralNodes.length; noa++) {
	m_neuralNodes[noa].drawInputLines(g, x, y);
      }
      for (int noa = 0; noa < m_numAttributes; noa++) {
	m_inputs[noa].drawNode(g, x, y);
      }
      for (int noa = 0; noa < m_numClasses; noa++) {
	m_outputs[noa].drawNode(g, x, y);
      }
      for (int noa = 0; noa < m_neuralNodes.length; noa++) {
	m_neuralNodes[noa].drawNode(g, x, y);
      }

      for (int noa = 0; noa < m_selected.size(); noa++) {
	((NeuralConnection)m_selected.elementAt(noa)).drawHighlight(g, x, y);
      }
    }
  }





  /** 
   * This provides the basic controls for working with the neuralnetwork
   * @author Malcolm Ware ([e-mail])
   * @version $Revision: 1.4 $
   */
  class ControlPanel 
    extends JPanel {
    
    /** for serialization */
    static final long serialVersionUID = 7393543302294142271L;
    
    /** The start stop button. */
    public JButton m_startStop;
    
    /** The button to accept the network (even if it hasn't done all epochs. */
    public JButton m_acceptButton;
    
    /** A label to state the number of epochs processed so far. */
    public JPanel m_epochsLabel;
    
    /** A label to state the total number of epochs to be processed. */
    public JLabel m_totalEpochsLabel;
    
    /** A text field to allow the changing of the total number of epochs. */
    public JTextField m_changeEpochs;
    
    /** A label to state the learning rate. */
    public JLabel m_learningLabel;
    
    /** A label to state the momentum. */
    public JLabel m_momentumLabel;
    
    /** A text field to allow the changing of the learning rate. */
    public JTextField m_changeLearning;
    
    /** A text field to allow the changing of the momentum. */
    public JTextField m_changeMomentum;
    
    /** A label to state roughly the accuracy of the network.(because the
	accuracy is calculated per epoch, but the network is changing 
	throughout each epoch train).
    */
    public JPanel m_errorLabel;
    
    /** The constructor. */
    public ControlPanel() { 
      setBorder(BorderFactory.createTitledBorder("Controls"));
      
      m_totalEpochsLabel = new JLabel("Num Of Epochs  ");
      m_epochsLabel = new JPanel(){ 
	  public void paintComponent(Graphics g) {
	    super.paintComponent(g);
	    g.setColor(m_controlPanel.m_totalEpochsLabel.getForeground());
	    g.drawString("Epoch  " + m_epoch, 0, 10);
	  }
	};
      m_epochsLabel.setFont(m_totalEpochsLabel.getFont());
      
      m_changeEpochs = new JTextField();
      m_changeEpochs.setText("" + m_numEpochs);
      m_errorLabel = new JPanel(){
	  public void paintComponent(Graphics g) {
	    super.paintComponent(g);
	    g.setColor(m_controlPanel.m_totalEpochsLabel.getForeground());
	    if (m_valSize == 0) {
	      g.drawString("Error per Epoch = " + 
			   Utils.doubleToString(m_error, 7), 0, 10);
	    }
	    else {
	      g.drawString("Validation Error per Epoch = "
			   + Utils.doubleToString(m_error, 7), 0, 10);
	    }
	  }
	};
      m_errorLabel.setFont(m_epochsLabel.getFont());
      
      m_learningLabel = new JLabel("Learning Rate = ");
      m_momentumLabel = new JLabel("Momentum = ");
      m_changeLearning = new JTextField();
      m_changeMomentum = new JTextField();
      m_changeLearning.setText("" + m_learningRate);
      m_changeMomentum.setText("" + m_momentum);
      setLayout(new BorderLayout(15, 10));

      m_stopIt = true;
      m_accepted = false;
      m_startStop = new JButton("Start");
      m_startStop.setActionCommand("Start");
      
      m_acceptButton = new JButton("Accept");
      m_acceptButton.setActionCommand("Accept");
      
      JPanel buttons = new JPanel();
      buttons.setLayout(new BoxLayout(buttons, BoxLayout.Y_AXIS));
      buttons.add(m_startStop);
      buttons.add(m_acceptButton);
      add(buttons, BorderLayout.WEST);
      JPanel data = new JPanel();
      data.setLayout(new BoxLayout(data, BoxLayout.Y_AXIS));
      
      Box ab = new Box(BoxLayout.X_AXIS);
      ab.add(m_epochsLabel);
      data.add(ab);
      
      ab = new Box(BoxLayout.X_AXIS);
      Component b = Box.createGlue();
      ab.add(m_totalEpochsLabel);
      ab.add(m_changeEpochs);
      m_changeEpochs.setMaximumSize(new Dimension(200, 20));
      ab.add(b);
      data.add(ab);
      
      ab = new Box(BoxLayout.X_AXIS);
      ab.add(m_errorLabel);
      data.add(ab);
      
      add(data, BorderLayout.CENTER);
      
      data = new JPanel();
      data.setLayout(new BoxLayout(data, BoxLayout.Y_AXIS));
      ab = new Box(BoxLayout.X_AXIS);
      b = Box.createGlue();
      ab.add(m_learningLabel);
      ab.add(m_changeLearning);
      m_changeLearning.setMaximumSize(new Dimension(200, 20));
      ab.add(b);
      data.add(ab);
      
      ab = new Box(BoxLayout.X_AXIS);
      b = Box.createGlue();
      ab.add(m_momentumLabel);
      ab.add(m_changeMomentum);
      m_changeMomentum.setMaximumSize(new Dimension(200, 20));
      ab.add(b);
      data.add(ab);
      
      add(data, BorderLayout.EAST);
      
      m_startStop.addActionListener(new ActionListener() {
	  public void actionPerformed(ActionEvent e) {
	    if (e.getActionCommand().equals("Start")) {
	      m_stopIt = false;
	      m_startStop.setText("Stop");
	      m_startStop.setActionCommand("Stop");
	      int n = Integer.valueOf(m_changeEpochs.getText()).intValue();
	      
	      m_numEpochs = n;
	      m_changeEpochs.setText("" + m_numEpochs);
	      
	      double m=Double.valueOf(m_changeLearning.getText()).
		doubleValue();
	      setLearningRate(m);
	      m_changeLearning.setText("" + m_learningRate);
	      
	      m = Double.valueOf(m_changeMomentum.getText()).doubleValue();
	      setMomentum(m);
	      m_changeMomentum.setText("" + m_momentum);
	      
	      blocker(false);
	    }
	    else if (e.getActionCommand().equals("Stop")) {
	      m_stopIt = true;
	      m_startStop.setText("Start");
	      m_startStop.setActionCommand("Start");
	    }
	  }
	});
      
      m_acceptButton.addActionListener(new ActionListener() {
	  public void actionPerformed(ActionEvent e) {
	    m_accepted = true;
	    blocker(false);
	  }
	});
      
      m_changeEpochs.addActionListener(new ActionListener() {
	  public void actionPerformed(ActionEvent e) {
	    int n = Integer.valueOf(m_changeEpochs.getText()).intValue();
	    if (n > 0) {
	      m_numEpochs = n;
	      blocker(false);
	    }
	  }
	});
    }
  }
  

    
  /** The training instances. */
  private Instances m_instances;
  
  /** The current instance running through the network. */
  private Instance m_currentInstance;
  
  /** A flag to say that it's a numeric class. */
  private boolean m_numeric;

  /** The ranges for all the attributes. */
  private double[] m_attributeRanges;

  /** The base values for all the attributes. */
  private double[] m_attributeBases;

  /** The output units.(only feeds the errors, does no calcs) */
  private NeuralEnd[] m_outputs;

  /** The input units.(only feeds the inputs does no calcs) */
  private NeuralEnd[] m_inputs;

  /** All the nodes that actually comprise the logical neural net. */
  private NeuralConnection[] m_neuralNodes;

  /** The number of classes. */
  private int m_numClasses = 0;
  
  /** The number of attributes. */
  private int m_numAttributes = 0; //note the number doesn't include the class.
  
  /** The panel the nodes are displayed on. */
  private NodePanel m_nodePanel;
  
  /** The control panel. */
  private ControlPanel m_controlPanel;

  /** The next id number available for default naming. */
  private int m_nextId;
   
  /** A Vector list of the units currently selected. */
  private FastVector m_selected;

  /** A Vector list of the graphers. */
  private FastVector m_graphers;