tld.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

      pThisParam[1] = b;  // b
      pThisParam[2] = w;  // w
      pThisParam[3] = m;  // m

      // Negative examplars: first run
      a = (maxVarsN[x]>ZERO) ? maxVarsN[x]:1.0; 
      if (varMeanN[x]<=ZERO)   varMeanN[x] = ZERO; // modified by LinDong (09/2005)
      b = a/varMeanN[x]+2.0; // a/(b-2) = E(\sigma^2)
      w = meanVarN[x]/varMeanN[x]; // E[var(\mu)] = w*E[\sigma^2]	    
      if(w<=ZERO) w=1.0;

      m = nSumVal[x]; 	  
      nThisParam[0] = a;    // a
      nThisParam[1] = b;  // b
      nThisParam[2] = w;  // w
      nThisParam[3] = m;  // m

      // Bound constraints
      bounds[0][0] = ZERO; // a > 0
      bounds[0][1] = 2.0+ZERO;  // b > 2 
      bounds[0][2] = ZERO; // w > 0
      bounds[0][3] = Double.NaN;

      for(int t=0; t<4; t++){
        bounds[1][t] = Double.NaN;
        m_ParamsP[4*x+t] = pThisParam[t];	
        m_ParamsN[4*x+t] = nThisParam[t];
      }
      double pminVal=Double.MAX_VALUE, nminVal=Double.MAX_VALUE;
      Random whichEx = new Random(m_Seed); 
      TLD_Optm pOp=null, nOp=null;	
      boolean isRunValid = true;
      double[] sumP=new double[pnum], meanP=new double[pnum],
        varP=new double[pnum];
      double[] sumN=new double[nnum], meanN=new double[nnum],
        varN=new double[nnum];

      // One dimension
      for(int p=0; p<pnum; p++){
        sumP[p] = m_SumP[p][x];
        meanP[p] = m_MeanP[p][x];
        varP[p] = m_VarianceP[p][x];
      }
      for(int q=0; q<nnum; q++){
        sumN[q] = m_SumN[q][x];
        meanN[q] = m_MeanN[q][x];
        varN[q] = m_VarianceN[q][x];
      }

      for(int y=0; y<m_Run;){
	if (getDebug())
	  System.err.println("\n\n!!!!!!!!!!!!!!!!!!!!!!???Run #"+y);
        double thisMin;

        if (getDebug())
          System.err.println("\nPositive exemplars");
        pOp = new TLD_Optm();
        pOp.setNum(sumP);
        pOp.setSSquare(varP);
        pOp.setXBar(meanP);

        pThisParam = pOp.findArgmin(pThisParam, bounds);
        while(pThisParam==null){
          pThisParam = pOp.getVarbValues();		    
          if (getDebug())
            System.err.println("!!! 200 iterations finished, not enough!");
          pThisParam = pOp.findArgmin(pThisParam, bounds);
        }	

        thisMin = pOp.getMinFunction();
        if(!Double.isNaN(thisMin) && (thisMin<pminVal)){
          pminVal = thisMin;
          for(int z=0; z<4; z++)
            m_ParamsP[4*x+z] = pThisParam[z];
        }

        if(Double.isNaN(thisMin)){
          pThisParam = new double[4];
          isRunValid =false;
        }

        if (getDebug())
          System.err.println("\nNegative exemplars");
        nOp = new TLD_Optm();
        nOp.setNum(sumN);
        nOp.setSSquare(varN);
        nOp.setXBar(meanN);

        nThisParam = nOp.findArgmin(nThisParam, bounds);
        while(nThisParam==null){
          nThisParam = nOp.getVarbValues();
          if (getDebug())
            System.err.println("!!! 200 iterations finished, not enough!");
          nThisParam = nOp.findArgmin(nThisParam, bounds);
        }	
        thisMin = nOp.getMinFunction();
        if(!Double.isNaN(thisMin) && (thisMin<nminVal)){
          nminVal = thisMin;
          for(int z=0; z<4; z++)
            m_ParamsN[4*x+z] = nThisParam[z];     
        }

        if(Double.isNaN(thisMin)){
          nThisParam = new double[4];
          isRunValid =false;
        }

        if(!isRunValid){ y--; isRunValid=true; } 		

        if(++y<m_Run){
          // Change the initial parameters and restart	   	    
          int pone = whichEx.nextInt(pnum), // Randomly pick one pos. exmpl.
              none = whichEx.nextInt(nnum);

          // Positive exemplars: next run 
          while((m_SumP[pone][x]<=1.0)||Double.isNaN(m_MeanP[pone][x]))
            pone = whichEx.nextInt(pnum);

          a = m_VarianceP[pone][x]/(m_SumP[pone][x]-1.0); 		
          if(a<=ZERO) a=m_ParamsN[4*x]; // Change to negative params
          m = m_MeanP[pone][x];
          double sq = (m-m_ParamsP[4*x+3])*(m-m_ParamsP[4*x+3]);

          b = a*m_ParamsP[4*x+2]/sq+2.0; // b=a/Var+2, assuming Var=Sq/w'
          if((b<=ZERO) || Double.isNaN(b) || Double.isInfinite(b))
            b=m_ParamsN[4*x+1];

          w = sq*(m_ParamsP[4*x+1]-2.0)/m_ParamsP[4*x];//w=Sq/Var, assuming Var=a'/(b'-2)
          if((w<=ZERO) || Double.isNaN(w) || Double.isInfinite(w))
            w=m_ParamsN[4*x+2];

          pThisParam[0] = a;    // a
          pThisParam[1] = b;  // b
          pThisParam[2] = w;  // w
          pThisParam[3] = m;  // m	    

          // Negative exemplars: next run 
          while((m_SumN[none][x]<=1.0)||Double.isNaN(m_MeanN[none][x]))
            none = whichEx.nextInt(nnum);	    

          a = m_VarianceN[none][x]/(m_SumN[none][x]-1.0);	
          if(a<=ZERO) a=m_ParamsP[4*x];       
          m = m_MeanN[none][x];
          sq = (m-m_ParamsN[4*x+3])*(m-m_ParamsN[4*x+3]);

          b = a*m_ParamsN[4*x+2]/sq+2.0; // b=a/Var+2, assuming Var=Sq/w'
          if((b<=ZERO) || Double.isNaN(b) || Double.isInfinite(b))
            b=m_ParamsP[4*x+1];

          w = sq*(m_ParamsN[4*x+1]-2.0)/m_ParamsN[4*x];//w=Sq/Var, assuming Var=a'/(b'-2)
          if((w<=ZERO) || Double.isNaN(w) || Double.isInfinite(w))
            w=m_ParamsP[4*x+2];

          nThisParam[0] = a;    // a
          nThisParam[1] = b;  // b
          nThisParam[2] = w;  // w
          nThisParam[3] = m;  // m	    		
        }
      }	    	    	    
    }

    for (int x=0, y=0; x<m_Dimension; x++, y++){
      //if((x==exs.classIndex()) || (x==exs.idIndex()))
      //y++;
      a=m_ParamsP[4*x]; b=m_ParamsP[4*x+1]; 
      w=m_ParamsP[4*x+2]; m=m_ParamsP[4*x+3];
      if (getDebug())
	System.err.println("\n\n???Positive: ( "+exs.attribute(1).relation().attribute(y)+
          "): a="+a+", b="+b+", w="+w+", m="+m);

      a=m_ParamsN[4*x]; b=m_ParamsN[4*x+1]; 
      w=m_ParamsN[4*x+2]; m=m_ParamsN[4*x+3];
      if (getDebug())
	System.err.println("???Negative: ("+exs.attribute(1).relation().attribute(y)+
          "): a="+a+", b="+b+", w="+w+", m="+m);
    }

    if(m_UseEmpiricalCutOff){	
      // Find the empirical cut-off
      double[] pLogOdds=new double[pnum], nLogOdds=new double[nnum];  
      for(int p=0; p<pnum; p++)
        pLogOdds[p] = 
          likelihoodRatio(m_SumP[p], m_MeanP[p], m_VarianceP[p]);

      for(int q=0; q<nnum; q++)
        nLogOdds[q] = 
          likelihoodRatio(m_SumN[q], m_MeanN[q], m_VarianceN[q]);

      // Update m_Cutoff
      findCutOff(pLogOdds, nLogOdds);
    }
    else
      m_Cutoff = -Math.log((double)pnum/(double)nnum);

    if (getDebug())
      System.err.println("???Cut-off="+m_Cutoff);
  }        

  /**
   *
   * @param ex the given test exemplar
   * @return the classification 
   * @throws Exception if the exemplar could not be classified
   * successfully
   */
  public double classifyInstance(Instance ex)throws Exception{
    //Exemplar ex = new Exemplar(e);
    Instances exi = ex.relationalValue(1);
    double[] n = new double[m_Dimension];
    double [] xBar = new double[m_Dimension];
    double [] sSq = new double[m_Dimension];
    for (int i=0; i<exi.numAttributes() ; i++){
      xBar[i] = exi.meanOrMode(i);
      sSq[i] = exi.variance(i);
    }

    for (int w=0, t=0; w < m_Dimension; w++, t++){
      //if((t==m_ClassIndex) || (t==m_IdIndex))
      //t++;	
      for(int u=0;u<exi.numInstances();u++)
        if(!exi.instance(u).isMissing(t))
          n[w] += exi.instance(u).weight();

      sSq[w] = sSq[w]*(n[w]-1.0);
      if(sSq[w] <= 0.0)
        sSq[w] = 0.0;
    }

    double logOdds = likelihoodRatio(n, xBar, sSq);
    return (logOdds > m_Cutoff) ? 1 : 0 ;
  }

  private double likelihoodRatio(double[] n, double[] xBar, double[] sSq){	
    double LLP = 0.0, LLN = 0.0;

    for (int x=0; x<m_Dimension; x++){
      if(Double.isNaN(xBar[x])) continue; // All missing values

      int halfN = ((int)n[x])/2;	
      //Log-likelihood for positive 
      double a=m_ParamsP[4*x], b=m_ParamsP[4*x+1], 
             w=m_ParamsP[4*x+2], m=m_ParamsP[4*x+3];
      LLP += 0.5*b*Math.log(a) + 0.5*(b+n[x]-1.0)*Math.log(1.0+n[x]*w)
        - 0.5*(b+n[x])*Math.log((1.0+n[x]*w)*(a+sSq[x])+
            n[x]*(xBar[x]-m)*(xBar[x]-m))
        - 0.5*n[x]*Math.log(Math.PI);
      for(int y=1; y<=halfN; y++)
        LLP += Math.log(b/2.0+n[x]/2.0-(double)y);

      if(n[x]/2.0 > halfN) // n is odd
        LLP += TLD_Optm.diffLnGamma(b/2.0);

      //Log-likelihood for negative 
      a=m_ParamsN[4*x];
      b=m_ParamsN[4*x+1]; 
      w=m_ParamsN[4*x+2];
      m=m_ParamsN[4*x+3];
      LLN += 0.5*b*Math.log(a) + 0.5*(b+n[x]-1.0)*Math.log(1.0+n[x]*w)
        - 0.5*(b+n[x])*Math.log((1.0+n[x]*w)*(a+sSq[x])+
            n[x]*(xBar[x]-m)*(xBar[x]-m))
        - 0.5*n[x]*Math.log(Math.PI);
      for(int y=1; y<=halfN; y++)
        LLN += Math.log(b/2.0+n[x]/2.0-(double)y);	

      if(n[x]/2.0 > halfN) // n is odd
        LLN += TLD_Optm.diffLnGamma(b/2.0);   
    }

    return LLP - LLN;
  }

  private void findCutOff(double[] pos, double[] neg){
    int[] pOrder = Utils.sort(pos),
      nOrder = Utils.sort(neg);
    /*
       System.err.println("\n\n???Positive: ");
       for(int t=0; t<pOrder.length; t++)
       System.err.print(t+":"+Utils.doubleToString(pos[pOrder[t]],0,2)+" ");
       System.err.println("\n\n???Negative: ");
       for(int t=0; t<nOrder.length; t++)
       System.err.print(t+":"+Utils.doubleToString(neg[nOrder[t]],0,2)+" ");
       */
    int pNum = pos.length, nNum = neg.length, count, p=0, n=0;	
    double fstAccu=0.0, sndAccu=(double)pNum, split; 
    double maxAccu = 0, minDistTo0 = Double.MAX_VALUE;

    // Skip continuous negatives	
    for(;(n<nNum)&&(pos[pOrder[0]]>=neg[nOrder[n]]); n++, fstAccu++);

    if(n>=nNum){ // totally seperate
      m_Cutoff = (neg[nOrder[nNum-1]]+pos[pOrder[0]])/2.0;	
      //m_Cutoff = neg[nOrder[nNum-1]];
      return;  
    }	

    count=n;
    while((p<pNum)&&(n<nNum)){
      // Compare the next in the two lists
      if(pos[pOrder[p]]>=neg[nOrder[n]]){ // Neg has less log-odds
        fstAccu += 1.0;    
        split=neg[nOrder[n]];
        n++;	 
      }
      else{
        sndAccu -= 1.0;
        split=pos[pOrder[p]];
        p++;
      }	    	  
      count++;
      if((fstAccu+sndAccu > maxAccu) 
          || ((fstAccu+sndAccu == maxAccu) && (Math.abs(split)<minDistTo0))){
        maxAccu = fstAccu+sndAccu;
        m_Cutoff = split;
        minDistTo0 = Math.abs(split);
      }	    
    }		
  }

  /**
   * Returns an enumeration describing the available options
   *
   * @return an enumeration of all the available options
   */
  public Enumeration listOptions() {
    Vector result = new Vector();
    
    result.addElement(new Option(
          "\tSet whether or not use empirical\n"
          + "\tlog-odds cut-off instead of 0",
          "C", 0, "-C"));
    
    result.addElement(new Option(
          "\tSet the number of multiple runs \n"
          + "\tneeded for searching the MLE.",
          "R", 1, "-R <numOfRuns>"));
    
    Enumeration enu = super.listOptions();
    while (enu.hasMoreElements()) {
      result.addElement(enu.nextElement());
    }

    return result.elements();
  }

  /**
   * Parses a given list of options. <p/>
   * 
   <!-- options-start -->
   * Valid options are: <p/>
   * 
   * <pre> -C
   *  Set whether or not use empirical
   *  log-odds cut-off instead of 0</pre>
   * 
   * <pre> -R &lt;numOfRuns&gt;
   *  Set the number of multiple runs 
   *  needed for searching the MLE.</pre>
   * 
   * <pre> -S &lt;num&gt;
   *  Random number seed.
   *  (default 1)</pre>
   * 
   * <pre> -D
   *  If set, classifier is run in debug mode and
   *  may output additional info to the console</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{
    setDebug(Utils.getFlag('D', options));

    setUsingCutOff(Utils.getFlag('C', options));

    String runString = Utils.getOption('R', options);
    if (runString.length() != 0) 
      setNumRuns(Integer.parseInt(runString));
    else