multilayerperceptron.java

Java 编写的多种数据挖掘算法 包括聚类、分类、预处理等

						    JOptionPane.YES_NO_OPTION);
	    
	    if (well == 0) {
	      m_win.setDefaultCloseOperation(JFrame.DISPOSE_ON_CLOSE);
	      m_accepted = true;
	      blocker(false);
	    }
	    else {
	      m_win.setDefaultCloseOperation(JFrame.DO_NOTHING_ON_CLOSE);
	    }
	    m_stopIt = k;
	  }
	});
      
      m_win.getContentPane().setLayout(new BorderLayout());
      m_win.setTitle("Neural Network");
      m_nodePanel = new NodePanel();

      JScrollPane sp = new JScrollPane(m_nodePanel,
				       JScrollPane.VERTICAL_SCROLLBAR_ALWAYS, 
				       JScrollPane.HORIZONTAL_SCROLLBAR_NEVER);
      m_controlPanel = new ControlPanel();
           
      m_win.getContentPane().add(sp, BorderLayout.CENTER);
      m_win.getContentPane().add(m_controlPanel, BorderLayout.SOUTH);
      m_win.setSize(640, 480);
      m_win.setVisible(true);
    }
   
    //This sets up the initial state of the gui
    if (m_gui) {
      blocker(true);
      m_controlPanel.m_changeEpochs.setEnabled(false);
      m_controlPanel.m_changeLearning.setEnabled(false);
      m_controlPanel.m_changeMomentum.setEnabled(false);
    } 
    
    //For silly situations in which the network gets accepted before training
    //commenses
    if (m_numeric) {
      setEndsToLinear();
    }
    if (m_accepted) {
      m_win.dispose();
      m_controlPanel = null;
      m_nodePanel = null;
      m_instances = new Instances(m_instances, 0);
      return;
    }

    //connections done.
    double right = 0;
    double driftOff = 0;
    double lastRight = Double.POSITIVE_INFINITY;
    double tempRate;
    double totalWeight = 0;
    double totalValWeight = 0;
    double origRate = m_learningRate; //only used for when reset
    
    //ensure that at least 1 instance is trained through.
    if (numInVal == m_instances.numInstances()) {
      numInVal--;
    }
    if (numInVal < 0) {
      numInVal = 0;
    }
    for (int noa = numInVal; noa < m_instances.numInstances(); noa++) {
      if (!m_instances.instance(noa).classIsMissing()) {
	totalWeight += m_instances.instance(noa).weight();
      }
    }
    if (m_valSize != 0) {
      for (int noa = 0; noa < valSet.numInstances(); noa++) {
	if (!valSet.instance(noa).classIsMissing()) {
	  totalValWeight += valSet.instance(noa).weight();
	}
      }
    }
    m_stopped = false;
     

    for (int noa = 1; noa < m_numEpochs + 1; noa++) {
      right = 0;
      for (int nob = numInVal; nob < m_instances.numInstances(); nob++) {
	m_currentInstance = m_instances.instance(nob);
	
	if (!m_currentInstance.classIsMissing()) {
	   
	  //this is where the network updating (and training occurs, for the
	  //training set
	  resetNetwork();
	  calculateOutputs();
	  tempRate = m_learningRate * m_currentInstance.weight();  
	  if (m_decay) {
	    tempRate /= noa;
	  }

	  right += (calculateErrors() / m_instances.numClasses()) *
	    m_currentInstance.weight();
	  updateNetworkWeights(tempRate, m_momentum);
	  
	}
	
      }
      right /= totalWeight;
      if (Double.isInfinite(right) || Double.isNaN(right)) {
	if (!m_reset) {
	  m_instances = null;
	  throw new Exception("Network cannot train. Try restarting with a" +
			      " smaller learning rate.");
	}
	else {
	  //reset the network
	  m_learningRate /= 2;
	  buildClassifier(i);
	  m_learningRate = origRate;
	  m_instances = new Instances(m_instances, 0);	  
	  return;
	}
      }

      ////////////////////////do validation testing if applicable
      if (m_valSize != 0) {
	right = 0;
	for (int nob = 0; nob < valSet.numInstances(); nob++) {
	  m_currentInstance = valSet.instance(nob);
	  if (!m_currentInstance.classIsMissing()) {
	    //this is where the network updating occurs, for the validation set
	    resetNetwork();
	    calculateOutputs();
	    right += (calculateErrors() / valSet.numClasses()) 
	      * m_currentInstance.weight();
	    //note 'right' could be calculated here just using
	    //the calculate output values. This would be faster.
	    //be less modular
	  }
	  
	}
	
	if (right < lastRight) {
	  driftOff = 0;
	}
	else {
	  driftOff++;
	}
	lastRight = right;
	if (driftOff > m_driftThreshold || noa + 1 >= m_numEpochs) {
	  m_accepted = true;
	}
	right /= totalValWeight;
      }
      m_epoch = noa;
      m_error = right;
      //shows what the neuralnet is upto if a gui exists. 
      updateDisplay();
      //This junction controls what state the gui is in at the end of each
      //epoch, Such as if it is paused, if it is resumable etc...
      if (m_gui) {
	while ((m_stopIt || (m_epoch >= m_numEpochs && m_valSize == 0)) && 
		!m_accepted) {
	  m_stopIt = true;
	  m_stopped = true;
	  if (m_epoch >= m_numEpochs && m_valSize == 0) {
	    
	    m_controlPanel.m_startStop.setEnabled(false);
	  }
	  else {
	    m_controlPanel.m_startStop.setEnabled(true);
	  }
	  m_controlPanel.m_startStop.setText("Start");
	  m_controlPanel.m_startStop.setActionCommand("Start");
	  m_controlPanel.m_changeEpochs.setEnabled(true);
	  m_controlPanel.m_changeLearning.setEnabled(true);
	  m_controlPanel.m_changeMomentum.setEnabled(true);
	  
	  blocker(true);
	  if (m_numeric) {
	    setEndsToLinear();
	  }
	}
	m_controlPanel.m_changeEpochs.setEnabled(false);
	m_controlPanel.m_changeLearning.setEnabled(false);
	m_controlPanel.m_changeMomentum.setEnabled(false);
	
	m_stopped = false;
	//if the network has been accepted stop the training loop
	if (m_accepted) {
	  m_win.dispose();
	  m_controlPanel = null;
	  m_nodePanel = null;
	  m_instances = new Instances(m_instances, 0);
	  return;
	}
      }
      if (m_accepted) {
	m_instances = new Instances(m_instances, 0);
	return;
      }
    }
    if (m_gui) {
      m_win.dispose();
      m_controlPanel = null;
      m_nodePanel = null;
    }
    m_instances = new Instances(m_instances, 0);  
  }

  /**
   * Call this function to predict the class of an instance once a 
   * classification model has been built with the buildClassifier call.
   * @param i The instance to classify.
   * @return A double array filled with the probabilities of each class type.
   * @throws if can't classify instance.
   */
  public double[] distributionForInstance(Instance i) throws Exception {

    
    if (m_useNomToBin) {
      m_nominalToBinaryFilter.input(i);
      m_currentInstance = m_nominalToBinaryFilter.output();
    }
    else {
      m_currentInstance = i;
    }
    
    if (m_normalizeAttributes) {
      for (int noa = 0; noa < m_instances.numAttributes(); noa++) {
	if (noa != m_instances.classIndex()) {
	  if (m_attributeRanges[noa] != 0) {
	    m_currentInstance.setValue(noa, (m_currentInstance.value(noa) - 
					     m_attributeBases[noa]) / 
				       m_attributeRanges[noa]);
	  }
	  else {
	    m_currentInstance.setValue(noa, m_currentInstance.value(noa) -
				       m_attributeBases[noa]);
	  }
	}
      }
    }
    resetNetwork();
    
    //since all the output values are needed.
    //They are calculated manually here and the values collected.
    double[] theArray = new double[m_numClasses];
    for (int noa = 0; noa < m_numClasses; noa++) {
      theArray[noa] = m_outputs[noa].outputValue(true);
    }
    if (m_instances.classAttribute().isNumeric()) {
      return theArray;
    }
    
    //now normalize the array
    double count = 0;
    for (int noa = 0; noa < m_numClasses; noa++) {
      count += theArray[noa];
    }
    if (count <= 0) {
      return null;
    }
    for (int noa = 0; noa < m_numClasses; noa++) {
      theArray[noa] /= count;
    }
    return theArray;
  }
  


  /**
   * Returns an enumeration describing the available options.
   *
   * @return an enumeration of all the available options.
   */
  public Enumeration listOptions() {
    
    Vector newVector = new Vector(14);

    newVector.addElement(new Option(
	      "\tLearning Rate for the backpropagation algorithm.\n"
	      +"\t(Value should be between 0 - 1, Default = 0.3).",
	      "L", 1, "-L <learning rate>"));
    newVector.addElement(new Option(
	      "\tMomentum Rate for the backpropagation algorithm.\n"
	      +"\t(Value should be between 0 - 1, Default = 0.2).",
	      "M", 1, "-M <momentum>"));
    newVector.addElement(new Option(
	      "\tNumber of epochs to train through.\n"
	      +"\t(Default = 500).",
	      "N", 1,"-N <number of epochs>"));
    newVector.addElement(new Option(
	      "\tPercentage size of validation set to use to terminate" +
	      " training (if this is non zero it can pre-empt num of epochs.\n"
	      +"\t(Value should be between 0 - 100, Default = 0).",
	      "V", 1, "-V <percentage size of validation set>"));
    newVector.addElement(new Option(
	      "\tThe value used to seed the random number generator" +
	      "\t(Value should be >= 0 and and a long, Default = 0).",
	      "S", 1, "-S <seed>"));
    newVector.addElement(new Option(
	      "\tThe consequetive number of errors allowed for validation" +
	      " testing before the netwrok terminates." +
	      "\t(Value should be > 0, Default = 20).",
	      "E", 1, "-E <threshold for number of consequetive errors>"));
    newVector.addElement(new Option(
              "\tGUI will be opened.\n"
	      +"\t(Use this to bring up a GUI).",
	      "G", 0,"-G"));
    newVector.addElement(new Option(
              "\tAutocreation of the network connections will NOT be done.\n"
	      +"\t(This will be ignored if -G is NOT set)",
	      "A", 0,"-A"));
    newVector.addElement(new Option(
              "\tA NominalToBinary filter will NOT automatically be used.\n"
	      +"\t(Set this to not use a NominalToBinary filter).",
	      "B", 0,"-B"));
    newVector.addElement(new Option(
	      "\tThe hidden layers to be created for the network.\n"
	      +"\t(Value should be a list of comma seperated Natural numbers" +
	      " or the letters 'a' = (attribs + classes) / 2, 'i'" +
	      " = attribs, 'o' = classes, 't' = attribs .+ classes)" +
	      " For wildcard values" +
	      ",Default = a).",
	      "H", 1, "-H <comma seperated numbers for nodes on each layer>"));
    newVector.addElement(new Option(
              "\tNormalizing a numeric class will NOT be done.\n"
	      +"\t(Set this to not normalize the class if it's numeric).",
	      "C", 0,"-C"));
    newVector.addElement(new Option(
              "\tNormalizing the attributes will NOT be done.\n"
	      +"\t(Set this to not normalize the attributes).",
	      "I", 0,"-I"));
    newVector.addElement(new Option(
              "\tReseting the network will NOT be allowed.\n"
	      +"\t(Set this to not allow the network to reset).",
	      "R", 0,"-R"));
    newVector.addElement(new Option(
              "\tLearning rate decay will occur.\n"
	      +"\t(Set this to cause the learning rate to decay).",
	      "D", 0,"-D"));
    
    
    return newVector.elements();
  }

  /**
   * Parses a given list of options. <p/>
   *
   <!-- options-start -->
   * Valid options are: <p/>
   * 
   * <pre> -L &lt;learning rate&gt;
   *  Learning Rate for the backpropagation algorithm.
   *  (Value should be between 0 - 1, Default = 0.3).</pre>
   * 
   * <pre> -M &lt;momentum&gt;
   *  Momentum Rate for the backpropagation algorithm.
   *  (Value should be between 0 - 1, Default = 0.2).</pre>
   * 
   * <pre> -N &lt;number of epochs&gt;
   *  Number of epochs to train through.
   *  (Default = 500).</pre>
   * 
   * <pre> -V &lt;percentage size of validation set&gt;
   *  Percentage size of validation set to use to terminate training (if this is non zero it can pre-empt num of epochs.
   *  (Value should be between 0 - 100, Default = 0).</pre>
   * 
   * <pre> -S &lt;seed&gt;
   *  The value used to seed the random number generator (Value should be &gt;= 0 and and a long, Default = 0).</pre>
   * 
   * <pre> -E &lt;threshold for number of consequetive errors&gt;
   *  The consequetive number of errors allowed for validation testing before the netwrok terminates. (Value should be &gt; 0, Default = 20).</pre>
   * 
   * <pre> -G
   *  GUI will be opened.
   *  (Use this to bring up a GUI).</pre>
   * 
   * <pre> -A
   *  Autocreation of the network connections will NOT be done.
   *  (This will be ignored if -G is NOT set)</pre>
   * 
   * <pre> -B
   *  A NominalToBinary filter will NOT automatically be used.
   *  (Set this to not use a NominalToBinary filter).</pre>
   * 
   * <pre> -H &lt;comma seperated numbers for nodes on each layer&gt;
   *  The hidden layers to be created for the network.
   *  (Value should be a list of comma seperated Natural numbers or the letters 'a' = (attribs + classes) / 2, 'i' = attribs, 'o' = classes, 't' = attribs .+ classes) For wildcard values,Default = a).</pre>
   * 
   * <pre> -C
   *  Normalizing a numeric class will NOT be done.
   *  (Set this to not normalize the class if it's numeric).</pre>
   * 
   * <pre> -I
   *  Normalizing the attributes will NOT be done.
   *  (Set this to not normalize the attributes).</pre>
   * 
   * <pre> -R
   *  Reseting the network will NOT be allowed.
   *  (Set this to not allow the network to reset).</pre>
   * 
   * <pre> -D
   *  Learning rate decay will occur.
   *  (Set this to cause the learning rate to decay).</pre>
   * 
   <!-- options-end -->
   *
   * @param options the list of options as an array of strings
   * @throws Exception if an option is not supported
   */
  public void setOptions(String[] options) throws Exception {
    //the defaults can be found here!!!!
    String learningString = Utils.getOption('L', options);
    if (learningString.length() != 0) {
      setLearningRate((new Double(learningString)).doubleValue());
    } else {
      setLearningRate(0.3);
    }
    String momentumString = Utils.getOption('M', options);
    if (momentumString.length() != 0) {
      setMomentum((new Double(momentumString)).doubleValue());
    } else {
      setMomentum(0.2);
    }
    String epochsString = Utils.getOption('N', options);
    if (epochsString.length() != 0) {
      setTrainingTime(Integer.parseInt(epochsString));
    } else {
      setTrainingTime(500);
    }
    String valSizeString = Utils.getOption('V', options);
    if (valSizeString.length() != 0) {
      setValidationSetSize(Integer.parseInt(valSizeString));
    } else {
      setValidationSetSize(0);
    }
    String seedString = Utils.getOption('S', options);
    if (seedString.length() != 0) {
      setRandomSeed(Long.parseLong(seedString));
    } else {
      setRandomSeed(0);
    }
    String thresholdString = Utils.getOption('E', options);
    if (thresholdString.length() !=