misvm.java

代码是一个分类器的实现,其中使用了部分weka的源代码。可以将项目导入eclipse运行

   *
   * @param value	the kernel
   */
  public void setKernel(Kernel value) {
    m_kernel = value;
  }

  /**
   * Returns the tip text for this property
   *
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String filterTypeTipText() {
    return "The filter type for transforming the training data.";
  }

  /**
   * Sets how the training data will be transformed. Should be one of
   * FILTER_NORMALIZE, FILTER_STANDARDIZE, FILTER_NONE.
   *
   * @param newType the new filtering mode
   */
  public void setFilterType(SelectedTag newType) {

    if (newType.getTags() == TAGS_FILTER) {
      m_filterType = newType.getSelectedTag().getID();
    }
  }

  /**
   * Gets how the training data will be transformed. Will be one of
   * FILTER_NORMALIZE, FILTER_STANDARDIZE, FILTER_NONE.
   *
   * @return the filtering mode
   */
  public SelectedTag getFilterType() {

    return new SelectedTag(m_filterType, TAGS_FILTER);
  }

  /**
   * Returns the tip text for this property
   *
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String cTipText() {
    return "The value for C.";
  }

  /**
   * Get the value of C.
   *
   * @return Value of C.
   */
  public double getC() {

    return m_C;
  }

  /**
   * Set the value of C.
   *
   * @param v  Value to assign to C.
   */
  public void setC(double v) {
    m_C = v;
  }

  /**
   * Returns the tip text for this property
   *
   * @return 		tip text for this property suitable for
   * 			displaying in the explorer/experimenter gui
   */
  public String maxIterationsTipText() {
    return "The maximum number of iterations to perform.";
  }

  /**
   * Gets the maximum number of iterations.
   *
   * @return 		the maximum number of iterations.
   */
  public int getMaxIterations() {
    return m_MaxIterations;
  }

  /**
   * Sets the maximum number of iterations.
   *
   * @param value	the maximum number of iterations.
   */
  public void setMaxIterations(int value) {
    if (value < 1)
      System.out.println(
	  "At least 1 iteration is necessary (provided: " + value + ")!");
    else
      m_MaxIterations = value;
  }

  /**
   * adapted version of SMO
   */
  private class SVM extends SMO {
    
    /** for serialization */
    static final long serialVersionUID = -8325638229658828931L;
    
    /**
     * Constructor
     */
    protected SVM (){
      super();
    }

    /**
     * Computes SVM output for given instance.
     *
     * @param index the instance for which output is to be computed
     * @param inst the instance 
     * @return the output of the SVM for the given instance
     * @throws Exception in case of an error
     */
    protected double output(int index, Instance inst) throws Exception {
      double output = 0;
      output = m_classifiers[0][1].SVMOutput(index, inst);
      return output;
    }
  }

  /**
   * Returns default capabilities of the classifier.
   *
   * @return      the capabilities of this classifier
   */
  public Capabilities getCapabilities() {
    Capabilities result = super.getCapabilities();

    // attributes
    result.enable(Capability.NOMINAL_ATTRIBUTES);
    result.enable(Capability.RELATIONAL_ATTRIBUTES);
    result.enable(Capability.MISSING_VALUES);

    // class
    result.disableAllClasses();
    result.disableAllClassDependencies();
    result.enable(Capability.BINARY_CLASS);
    result.enable(Capability.MISSING_CLASS_VALUES);
    
    // other
    result.enable(Capability.ONLY_MULTIINSTANCE);
    
    return result;
  }

  /**
   * Returns the capabilities of this multi-instance classifier for the
   * relational data.
   *
   * @return            the capabilities of this object
   * @see               Capabilities
   */
  public Capabilities getMultiInstanceCapabilities() {
    SVM 		classifier;
    Capabilities 	result;

    classifier = null;
    result     = null;
    
    try {
      classifier = new SVM();
      classifier.setKernel(Kernel.makeCopy(getKernel()));
      result = classifier.getCapabilities();
      result.setOwner(this);
    }
    catch (Exception e) {
      e.printStackTrace();
    }
    
    // class
    result.disableAllClasses();
    result.enable(Capability.NO_CLASS);
    
    return result;
  }

  /**
   * Builds the classifier
   *
   * @param train the training data to be used for generating the
   * boosted classifier.
   * @throws Exception if the classifier could not be built successfully
   */
  public void buildClassifier(Instances train) throws Exception {
    // can classifier handle the data?
    getCapabilities().testWithFail(train);

    // remove instances with missing class
    train = new Instances(train);
    train.deleteWithMissingClass();
    
    int numBags = train.numInstances(); //number of bags
    int []bagSize= new int [numBags];   
    int classes [] = new int [numBags];

    Vector instLabels = new Vector();  //store the class label assigned to each single instance
    Vector pre_instLabels=new Vector();

    for(int h=0; h<numBags; h++)  {//h_th bag 
      classes[h] = (int) train.instance(h).classValue();  
      bagSize[h]=train.instance(h).relationalValue(1).numInstances();
      for (int i=0; i<bagSize[h];i++)
        instLabels.addElement(new Double(classes[h]));	       	  
    }

    // convert the training dataset into single-instance dataset
    m_ConvertToProp.setWeightMethod(
        new SelectedTag(
          MultiInstanceToPropositional.WEIGHTMETHOD_1, 
          MultiInstanceToPropositional.TAGS_WEIGHTMETHOD)); 
    m_ConvertToProp.setInputFormat(train);
    train = Filter.useFilter( train, m_ConvertToProp);
    train.deleteAttributeAt(0); //remove the bagIndex attribute;

    if (m_filterType == FILTER_STANDARDIZE)  
      m_Filter = new Standardize();
    else if (m_filterType == FILTER_NORMALIZE)
      m_Filter = new Normalize();
    else 
      m_Filter = null;

    if (m_Filter!=null) {
      m_Filter.setInputFormat(train);
      train = Filter.useFilter(train, m_Filter);
    }	

    if (m_Debug) {
      System.out.println("\nIteration History..." );
    }

    if (getDebug())
      System.out.println("\nstart building model ...");

    int index;
    double sum, max_output; 
    Vector max_index = new Vector();
    Instance inst=null;

    int loopNum=0;
    do {
      loopNum++;
      index=-1;
      if (m_Debug)
        System.out.println("=====================loop: "+loopNum);

      //store the previous label information
      pre_instLabels=(Vector)instLabels.clone();   

      // set the proper SMO options in order to build a SVM model
      m_SVM = new SVM();
      m_SVM.setC(getC());
      m_SVM.setKernel(Kernel.makeCopy(getKernel()));
      // SVM model do not normalize / standardize the input dataset as the the dataset has already been processed  
      m_SVM.setFilterType(new SelectedTag(FILTER_NONE, TAGS_FILTER));  

      m_SVM.buildClassifier(train); 

      for(int h=0; h<numBags; h++)  {//h_th bag
        if (classes[h]==1) { //positive bag
          if (m_Debug)
            System.out.println("--------------- "+h+" ----------------");
          sum=0;

          //compute outputs f=(w,x)+b for all instance in positive bags
          for (int i=0; i<bagSize[h]; i++){
            index ++; 

            inst=train.instance(index); 
            double output =m_SVM.output(-1, inst); //System.out.println(output); 
            if (output<=0){
              if (inst.classValue()==1.0) {	
                train.instance(index).setClassValue(0.0);
                instLabels.set(index, new Double(0.0));

                if (m_Debug)
                  System.out.println( index+ "- changed to 0");
              }
            }
            else { 
              if (inst.classValue()==0.0) {
                train.instance(index).setClassValue(1.0);
                instLabels.set(index, new Double(1.0));

                if (m_Debug)
                  System.out.println(index+ "+ changed to 1");
              }
            }
            sum += train.instance(index).classValue();  
          }

          /* if class value of all instances in a positive bag 
             are changed to 0.0, find the instance with max SVMOutput value 
             and assign the class value 1.0 to it.
             */
          if (sum==0){ 
            //find the instance with max SVMOutput value  
            max_output=-Double.MAX_VALUE;
            max_index.clear();
            for (int j=index-bagSize[h]+1; j<index+1; j++){
              inst=train.instance(j);
              double output = m_SVM.output(-1, inst);
              if(max_output<output) {
                max_output=output;
                max_index.clear();
                max_index.add(new Integer(j));
              }
              else if(max_output==output) 
                max_index.add(new Integer(j));
            }

            //assign the class value 1.0 to the instances with max SVMOutput
            for (int vecIndex=0; vecIndex<max_index.size(); vecIndex ++) {
              Integer i =(Integer)max_index.get(vecIndex);
              train.instance(i.intValue()).setClassValue(1.0);
              instLabels.set(i.intValue(), new Double(1.0));

              if (m_Debug)
                System.out.println("##change to 1 ###outpput: "+max_output+" max_index: "+i+ " bag: "+h);
            }

          }
        }else   //negative bags
          index += bagSize[h];
      }
    }while(!instLabels.equals(pre_instLabels) && loopNum < m_MaxIterations);

    if (getDebug())
      System.out.println("finish building model.");
  }

  /**
   * Computes the distribution for a given exemplar
   *
   * @param exmp the exemplar for which distribution is computed
   * @return the distribution
   * @throws Exception if the distribution can't be computed successfully
   */
  public double[] distributionForInstance(Instance exmp)
    throws Exception {

    double sum=0;
    double classValue;
    double[] distribution = new double[2];

    Instances testData = new Instances(exmp.dataset(), 0);
    testData.add(exmp);

    // convert the training dataset into single-instance dataset
    testData = Filter.useFilter(testData, m_ConvertToProp);	
    testData.deleteAttributeAt(0); //remove the bagIndex attribute	

    if (m_Filter != null)	
      testData = Filter.useFilter(testData, m_Filter); 

    for(int j = 0; j < testData.numInstances(); j++){
      Instance inst = testData.instance(j);
      double output = m_SVM.output(-1, inst); 
      if (output <= 0)
        classValue = 0.0;
      else
        classValue = 1.0;
      sum += classValue;
    }
    if (sum == 0)
      distribution[0] = 1.0;
    else 
      distribution[0] = 0.0;
    distribution [1] = 1.0 - distribution[0];

    return distribution;
  }

  /**
   * Main method for testing this class.
   *
   * @param argv should contain the command line arguments to the
   * scheme (see Evaluation)
   */
  public static void main(String[] argv) {
    runClassifier(new MISVM(), argv);
  }
}