conjunctiverule.java

wekaUT是 university texas austin 开发的基于weka的半指导学习(semi supervised learning)的分类器

    private boolean isIn;
	
    /* Constructor for nominal class */
    public NominalAntd(Attribute a, double[] unc){ 
      super(a, unc);
      int bag = att.numValues();
      stats = new double[bag][m_NumClasses];
      coverage = new double[bag];
      isIn = true;
    }   
	
    /* Constructor for numeric class */
    public NominalAntd(Attribute a, double sq, double vl, double wts){ 
      super(a, sq, vl, wts);
      int bag = att.numValues();	    
      stats = null;
      coverage = new double[bag];
      isIn = true;
    }
	
    /**
     * Implements the splitData function.  
     * This procedure is to split the data into bags according 
     * to the nominal attribute value
     * the data with missing values are stored in the last bag.
     * The infoGain for each bag is also calculated.  
     * 
     * @param data the data to be split
     * @param defInfo the default information for data
     * @return the array of data after split
     */
    public Instances[] splitData(Instances data, double defInfo){
      int bag = att.numValues();
      Instances[] splitData = new Instances[bag+1];
      double[] wSq = new double[bag];
      double[] wVl = new double[bag];
      double totalWS=0, totalWV=0, msingWS=0, msingWV=0, sum=data.sumOfWeights();
      double[] all = new double[m_NumClasses];
      double[] missing = new double[m_NumClasses];	   
	    
      for(int w=0; w < m_NumClasses; w++)
	all[w] = missing[w] = 0;

      for(int x=0; x<bag; x++){
	coverage[x] = wSq[x] = wVl[x] = 0;
	if(stats != null)
	  for(int y=0; y < m_NumClasses; y++)
	    stats[x][y] = 0;		
	splitData[x] = new Instances(data, data.numInstances());
      }
      splitData[bag] = new Instances(data, data.numInstances());
	    
      // Record the statistics of data
      for(int x=0; x<data.numInstances(); x++){
	Instance inst=data.instance(x);
	if(!inst.isMissing(att)){
	  int v = (int)inst.value(att);
	  splitData[v].add(inst);
	  coverage[v] += inst.weight();
	  if(m_ClassAttribute.isNominal()){ // Nominal class			
	    stats[v][(int)inst.classValue()] += inst.weight();
	    all[(int)inst.classValue()] += inst.weight();	    
	  }
	  else{                             // Numeric class
	    wSq[v] += inst.weight() * inst.classValue() * inst.classValue();
	    wVl[v] += inst.weight() * inst.classValue();
	    totalWS += inst.weight() * inst.classValue() * inst.classValue();
	    totalWV += inst.weight() * inst.classValue();
	  }
	}
	else{
	  splitData[bag].add(inst);
	  if(m_ClassAttribute.isNominal()){ // Nominal class
	    all[(int)inst.classValue()] += inst.weight();
	    missing[(int)inst.classValue()] += inst.weight();
	  }
	  else{                            // Numeric class
	    totalWS += inst.weight() * inst.classValue() * inst.classValue();
	    totalWV += inst.weight() * inst.classValue();
	    msingWS += inst.weight() * inst.classValue() * inst.classValue();
	    msingWV += inst.weight() * inst.classValue();		 
	  }
	}
      }
	    
      // The total weights of the whole grow data
      double whole;
      if(m_ClassAttribute.isNominal())
	whole = sum + Utils.sum(uncover);
      else
	whole = sum + uncoverSum;
	  
      // Find the split  
      double minEntrp=Double.MAX_VALUE;
      maxInfoGain = 0;
	    
      // Check if >=2 splits have more than the minimal data
      int count=0;
      for(int x=0; x<bag; x++)
	if(Utils.grOrEq(coverage[x], m_MinNo))		    
	  ++count;
	    
      if(count < 2){ // Don't split
	maxInfoGain = 0;
	inform = defInfo;
	value = Double.NaN;
	return null;
      }
	    
      for(int x=0; x<bag; x++){		
	double t = coverage[x], entrp, infoGain;

	if(Utils.sm(t, m_MinNo))
	  continue;
		
	if(m_ClassAttribute.isNominal()){ // Nominal class	   
	  double[] other = new double[m_NumClasses];
	  for(int y=0; y < m_NumClasses; y++)
	    other[y] = all[y] - stats[x][y] + uncover[y]; 
	  double otherCover = whole - t;	
		    
	  // Entropies of data covered and uncovered 	
	  entrp = entropy(stats[x], t);
	  double uncEntp = entropy(other, otherCover);
		    
	  // Weighted average
	  infoGain = defInfo - (entrp*t + uncEntp*otherCover)/whole;		   
	}	
	else{                             // Numeric class
	  double weight = (whole - t);
	  entrp = wtMeanSqErr(wSq[x], wVl[x], t)/t;
	  infoGain = defInfo - (entrp * t) - 
	    wtMeanSqErr((totalWS-wSq[x]+uncoverWtSq),
			(totalWV-wVl[x]+uncoverWtVl), 
			weight);		  
	}   		
		
	// Test the exclusive expression
	boolean isWithin =true;		
	if(m_IsExclude){
	  double infoGain2, entrp2;
	  if(m_ClassAttribute.isNominal()){ // Nominal class	
	    double[] other2 = new double[m_NumClasses];
	    double[] notIn = new double[m_NumClasses];
	    for(int y=0; y < m_NumClasses; y++){
	      other2[y] = stats[x][y] + missing[y] + uncover[y];
	      notIn[y] = all[y] - stats[x][y] - missing[y];
	    } 
			
	    double msSum = Utils.sum(missing);
	    double otherCover2 = t + msSum + Utils.sum(uncover);
			
	    entrp2 = entropy(notIn, (sum-t-msSum));
	    double uncEntp2 = entropy(other2, otherCover2);
	    infoGain2 = defInfo - 
	      (entrp2*(sum-t-msSum) + uncEntp2*otherCover2)/whole;
	  }
	  else{                             // Numeric class
	    double msWts = splitData[bag].sumOfWeights();
	    double weight2 = t + uncoverSum + msWts;
			
	    entrp2 = wtMeanSqErr((totalWS-wSq[x]-msingWS),
				 (totalWV-wVl[x]-msingWV),(sum-t-msWts))
	      /(sum-t-msWts);
	    infoGain2 = defInfo - entrp2 * (sum-t-msWts) -
	      wtMeanSqErr((wSq[x]+uncoverWtSq+msingWS),
			  (wVl[x]+uncoverWtVl+msingWV), 
			  weight2);
	  }
		    
	  // Use the exclusive expression?
	  if (Utils.gr(infoGain2, infoGain) ||
	      (Utils.eq(infoGain2, infoGain) && Utils.sm(entrp2, entrp))){
	    infoGain = infoGain2;
	    entrp = entrp2;
	    isWithin =false;
	  }
	}
		
	// Test this split
	if (Utils.gr(infoGain, maxInfoGain) ||
	    (Utils.eq(infoGain, maxInfoGain) && Utils.sm(entrp, minEntrp))){
	  value = (double)x;
	  maxInfoGain = infoGain;
	  inform = maxInfoGain - defInfo;
	  minEntrp = entrp;
	  isIn = isWithin;
	}		
      }
	    
      return splitData;
    }
	
    /**
     * Whether the instance is covered by this antecedent
     * 
     * @param inst the instance in question
     * @return the boolean value indicating whether the instance is covered 
     *         by this antecedent
     */
    public boolean isCover(Instance inst){	  
      boolean isCover=false;
      if(!inst.isMissing(att)){
	if(isIn){
	  if(Utils.eq(inst.value(att), value))
	    isCover=true;
	}
	else if(!Utils.eq(inst.value(att), value))
	  isCover=true;
      }
      return isCover;
    }
	
    /**
     * Whether the expression is "att = value" or att != value"
     * for this nominal attribute.  True if in the former expression, 
     * otherwise the latter
     * 
     * @return the boolean value
     */
    public boolean isIn(){	 
      return isIn;
    }
	
    /**
     * Prints this antecedent
     *
     * @return a textual description of this antecedent
     */
    public String toString() {
      String symbol = isIn ? " = " : " != ";	    
      return (att.name() + symbol + att.value((int)value));
    } 
  }
    
  /**
   * Returns an enumeration describing the available options
   * Valid options are: <p>
   *
   * -N number <br>
   * Set number of folds for REP. One fold is
   * used as the pruning set. (Default: 3) <p>
   *
   * -R <br>
   * Set if NOT randomize the data before split to growing and 
   * pruning data. If NOT set, the seed of randomization is 
   * specified by the -S option. (Default: randomize) <p>
   * 
   * -S <br>
   * Seed of randomization. (Default: 1)<p>
   *
   * -E <br>
   * Set whether consider the exclusive expressions for nominal
   * attribute split. (Default: false) <p>
   *
   * -M number <br>
   * Set the minimal weights of instances within a split.
   * (Default: 2) <p>
   *
   * -P number <br>
   * Set the number of antecedents allowed in the rule if pre-pruning
   * is used.  If this value is other than -1, then pre-pruning will be
   * used, otherwise the rule uses REP. (Default: -1) <p>
   *
   * @return an enumeration of all the available options
   */
  public Enumeration listOptions() {
    Vector newVector = new Vector(6);
	
    newVector.addElement(new Option("\tSet number of folds for REP\n" +
				    "\tOne fold is used as pruning set.\n" +
				    "\t(default 3)","N", 1, "-N <number of folds>"));
	
    newVector.addElement(new Option("\tSet if NOT uses randomization\n" +
				    "\t(default:use randomization)","R", 0, "-R"));

    newVector.addElement(new Option("\tSet whether consider the exclusive\n" +
				    "\texpressions for nominal attributes\n"+
				    "\t(default false)","E", 0, "-E"));
	
    newVector.addElement(new Option("\tSet the minimal weights of instances\n" +
				    "\twithin a split.\n" +
				    "\t(default 2.0)","M", 1, "-M <min. weights>"));
    
    newVector.addElement(new Option("\tSet number of antecedents for pre-pruning\n" +
				    "\tif -1, then REP is used\n" +
				    "\t(default -1)","P", 1, "-P <number of antecedents>"));
    
    newVector.addElement(new Option("\tSet the seed of randomization\n" +
				    "\t(default 1)","S", 1, "-S <seed>"));
    
    return newVector.elements();
  }
    
  /**
   * Parses a given list of options.
   *
   * @param options the list of options as an array of strings
   * @exception Exception if an option is not supported
   */
  public void setOptions(String[] options) throws Exception {
	
    String numFoldsString = Utils.getOption('N', options);
    if (numFoldsString.length() != 0) 
      m_Folds = Integer.parseInt(numFoldsString);
    else 
      m_Folds = 3;

    String minNoString = Utils.getOption('M', options);
    if (minNoString.length() != 0) 
      m_MinNo = Double.parseDouble(minNoString);
    else 
      m_MinNo = 2.0;
	
    String seedString = Utils.getOption('S', options);
    if (seedString.length() != 0) 
      m_Seed = Integer.parseInt(seedString);
    else 
      m_Seed = 1;
	
    String numAntdsString = Utils.getOption('P', options);
    if (numAntdsString.length() != 0) 
      m_NumAntds = Integer.parseInt(numAntdsString);
    else 
      m_NumAntds = -1;
	
    m_IsRandomized = (!Utils.getFlag('R', options));
    m_IsExclude = Utils.getFlag('E', options);	
  }
    
  /**
   * Gets the current settings of the Classifier.
   *
   * @return an array of strings suitable for passing to setOptions
   */
  public String [] getOptions() {
	
    String [] options = new String [10];
    int current = 0;
    options[current++] = "-N"; options[current++] = "" + m_Folds;
    options[current++] = "-M"; options[current++] = "" + m_MinNo;
    options[current++] = "-P"; options[current++] = "" + m_NumAntds;
    options[current++] = "-S"; options[current++] = "" + m_Seed;

    if(!m_IsRandomized)
      options[current++] = "-R";
    if(m_IsExclude)
      options[current++] = "-E";
	
    while (current < options.length) 
      options[current++] = "";
    return options;
  }
    
  /** The access functions for parameters */
  public void setFolds(int folds){  m_Folds = folds; }
  public int getFolds(){ return m_Folds; }
  public void setSeed(long s){ m_Seed = s; }
  public long getSeed(){ return m_Seed; }
  public boolean getRandomized(){ return m_IsRandomized;}
  public void setRandomized(boolean r){ m_IsRandomized = r;}
  public boolean getExclusive(){ return m_IsExclude;}
  public void setExclusive(boolean e){ m_IsExclude = e;}
  public void setMinNo(double m){  m_MinNo = m; }
  public double getMinNo(){ return m_MinNo; }
  public void setNumAntds(int n){  m_NumAntds = n; }
  public int getNumAntds(){ return m_NumAntds; }
    
  /**
   * Builds a single rule learner with REP dealing with nominal classes or
   * numeric classes.
   * For nominal classes, this rule learner predicts a distribution on
   * the classes.
   * For numeric classes, this learner predicts a single value.
   *
   * @param instances the training data
   * @exception Exception if classifier can't be built successfully
   */
  public void buildClassifier(Instances instances) throws Exception {
    if (instances.checkForStringAttributes())
      throw new UnsupportedAttributeTypeException("Cannot handle string attributes!");
	 
    Instances data = new Instances(instances);

    if(data.numInstances() == 0)
	throw new Exception("No training data!");
    data.deleteWithMissingClass();
    
    if(data.numInstances() == 0)
	throw new Exception("Not training data without missing class values.");

    if(data.numInstances() < m_Folds)
      throw new Exception("Not enough data for REP.");

    m_ClassAttribute = data.classAttribute();
    if(m_ClassAttribute.isNominal())
      m_NumClasses = m_ClassAttribute.numValues();
    else
      m_NumClasses = 1;
	
    m_Antds = new FastVector();
    m_DefDstr = new double[m_NumClasses];
    m_Cnsqt = new double[m_NumClasses];
    m_Targets = new FastVector();	    
    m_Random = new Random(m_Seed);
    
    if(m_IsRandomized){  // Randomize the data	
	data.randomize(m_Random);
    }
    
    if(m_NumAntds != -1){
      grow(data);
    }
    else{
      // Split data into Grow and Prune	   
      data.stratify(m_Folds);
	
      Instances growData=data.trainCV(m_Folds, m_Folds-1);
      Instances pruneData=data.testCV(m_Folds, m_Folds-1);

      grow(growData);      // Build this rule  
      prune(pruneData);    // Prune this rule		  	  
    }
	
    if(m_ClassAttribute.isNominal()){			   
      Utils.normalize(m_Cnsqt);
      if(Utils.gr(Utils.sum(m_DefDstr), 0))
	Utils.normalize(m_DefDstr);
    }	
  }
    
  /**
   * Computes class distribution for the given instance.
   *
   * @param instance the instance for which distribution is to be computed
   * @return the class distribution for the given instance
   */
  public double[] distributionForInstance(Instance instance) throws Exception {
      if(instance == null)
	  throw new Exception("Testing instance is NULL!");
	
    if (isCover(instance))		
      return m_Cnsqt;
    else
      return m_DefDstr;
  }
 
  /**
   * Whether the instance covered by this rule
   * 
   * @param inst the instance in question
   * @return the boolean value indicating whether the instance is covered by this rule
   */
  public boolean isCover(Instance datum){
    boolean isCover=true;

    for(int i=0; i<m_Antds.size(); i++){
      Antd antd = (Antd)m_Antds.elementAt(i);
      if(!antd.isCover(datum)){
	isCover = false;
	break;
      }
    }