spreadsubsample.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

   * displaying in the explorer/experimenter gui
   */
  public String maxCountTipText() {
    return "The maximum count for any class value (0 = unlimited).";
  }
  
  /**
   * Sets the value for the max count
   *
   * @param maxcount the new max count
   */
  public void setMaxCount(double maxcount) {

    m_MaxCount = (int)maxcount;
  }

  /**
   * Gets the value for the max count
   *
   * @return the max count
   */    
  public double getMaxCount() {

    return m_MaxCount;
  }
    
  /**
   * Returns the tip text for this property
   *
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String randomSeedTipText() {
    return "Sets the random number seed for subsampling.";
  }
  
  /**
   * Gets the random number seed.
   *
   * @return the random number seed.
   */
  public int getRandomSeed() {

    return m_RandomSeed;
  }
  
  /**
   * Sets the random number seed.
   *
   * @param newSeed the new random number seed.
   */
  public void setRandomSeed(int newSeed) {

    m_RandomSeed = newSeed;
  }

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

    // attributes
    result.enableAllAttributes();
    result.enable(Capability.MISSING_VALUES);
    
    // class
    result.enable(Capability.NOMINAL_CLASS);
    
    return result;
  }
  
  /**
   * Sets the format of the input instances.
   *
   * @param instanceInfo an Instances object containing the input 
   * instance structure (any instances contained in the object are 
   * ignored - only the structure is required).
   * @return true if the outputFormat may be collected immediately
   * @throws UnassignedClassException if no class attribute has been set.
   * @throws UnsupportedClassTypeException if the class attribute
   * is not nominal. 
   */
  public boolean setInputFormat(Instances instanceInfo) 
       throws Exception {

    super.setInputFormat(instanceInfo);
    setOutputFormat(instanceInfo);
    return true;
  }

  /**
   * Input an instance for filtering. Filter requires all
   * training instances be read before producing output.
   *
   * @param instance the input instance
   * @return true if the filtered instance may now be
   * collected with output().
   * @throws IllegalStateException if no input structure has been defined 
   */
  public boolean input(Instance instance) {

    if (getInputFormat() == null) {
      throw new IllegalStateException("No input instance format defined");
    }
    if (m_NewBatch) {
      resetQueue();
      m_NewBatch = false;
    }
    if (isFirstBatchDone()) {
      push(instance);
      return true;
    } else {
      bufferInput(instance);
      return false;
    }
  }

  /**
   * Signify that this batch of input to the filter is finished. 
   * If the filter requires all instances prior to filtering,
   * output() may now be called to retrieve the filtered instances.
   *
   * @return true if there are instances pending output
   * @throws IllegalStateException if no input structure has been defined
   */
  public boolean batchFinished() {

    if (getInputFormat() == null) {
      throw new IllegalStateException("No input instance format defined");
    }

    if (!isFirstBatchDone()) {
      // Do the subsample, and clear the input instances.
      createSubsample();
    }

    flushInput();
    m_NewBatch = true;
    m_FirstBatchDone = true;
    return (numPendingOutput() != 0);
  }


  /**
   * Creates a subsample of the current set of input instances. The output
   * instances are pushed onto the output queue for collection.
   */
  private void createSubsample() {

    int classI = getInputFormat().classIndex();
    // Sort according to class attribute.
    getInputFormat().sort(classI);
    // Determine where each class starts in the sorted dataset
    int [] classIndices = getClassIndices();

    // Get the existing class distribution
    int [] counts = new int [getInputFormat().numClasses()];
    double [] weights = new double [getInputFormat().numClasses()];
    int min = -1;
    for (int i = 0; i < getInputFormat().numInstances(); i++) {
      Instance current = getInputFormat().instance(i);
      if (current.classIsMissing() == false) {
        counts[(int)current.classValue()]++;
        weights[(int)current.classValue()]+= current.weight();
      }
    }

    // Convert from total weight to average weight
    for (int i = 0; i < counts.length; i++) {
      if (counts[i] > 0) {
        weights[i] = weights[i] / counts[i];
      }
      /*
      System.err.println("Class:" + i + " " + getInputFormat().classAttribute().value(i)
                         + " Count:" + counts[i]
                         + " Total:" + weights[i] * counts[i]
                         + " Avg:" + weights[i]);
      */
    }
    
    // find the class with the minimum number of instances
    for (int i = 0; i < counts.length; i++) {
      if ( (min < 0) && (counts[i] > 0) ) {
        min = counts[i];
      } else if ((counts[i] < min) && (counts[i] > 0)) {
        min = counts[i];
      }
    }

    if (min < 0) { 
	System.err.println("SpreadSubsample: *warning* none of the classes have any values in them.");
	return;
    }

    // determine the new distribution 
    int [] new_counts = new int [getInputFormat().numClasses()];
    for (int i = 0; i < counts.length; i++) {
      new_counts[i] = (int)Math.abs(Math.min(counts[i],
                                             min * m_DistributionSpread));
      if (m_DistributionSpread == 0) {
        new_counts[i] = counts[i];
      }

      if (m_MaxCount > 0) {
        new_counts[i] = Math.min(new_counts[i], m_MaxCount);
      }
    }

    // Sample without replacement
    Random random = new Random(m_RandomSeed);
    Hashtable t = new Hashtable();
    for (int j = 0; j < new_counts.length; j++) {
      double newWeight = 1.0;
      if (m_AdjustWeights && (new_counts[j] > 0)) {
        newWeight = weights[j] * counts[j] / new_counts[j];
        /*
        System.err.println("Class:" + j + " " + getInputFormat().classAttribute().value(j) 
                           + " Count:" + counts[j]
                           + " Total:" + weights[j] * counts[j]
                           + " Avg:" + weights[j]
                           + " NewCount:" + new_counts[j]
                           + " NewAvg:" + newWeight);
        */
      }
      for (int k = 0; k < new_counts[j]; k++) {
        boolean ok = false;
        do {
	  int index = classIndices[j] + (Math.abs(random.nextInt()) 
                                         % (classIndices[j + 1] - classIndices[j])) ;
	  // Have we used this instance before?
          if (t.get("" + index) == null) {
            // if not, add it to the hashtable and use it
            t.put("" + index, "");
            ok = true;
	    if(index >= 0) {
              Instance newInst = (Instance)getInputFormat().instance(index).copy();
              if (m_AdjustWeights) {
                newInst.setWeight(newWeight);
              }
              push(newInst);
            }
          }
        } while (!ok);
      }
    }
  }

  /**
   * Creates an index containing the position where each class starts in 
   * the getInputFormat(). m_InputFormat must be sorted on the class attribute.
   * 
   * @return the positions
   */
  private int[] getClassIndices() {

    // Create an index of where each class value starts
    int [] classIndices = new int [getInputFormat().numClasses() + 1];
    int currentClass = 0;
    classIndices[currentClass] = 0;
    for (int i = 0; i < getInputFormat().numInstances(); i++) {
      Instance current = getInputFormat().instance(i);
      if (current.classIsMissing()) {
        for (int j = currentClass + 1; j < classIndices.length; j++) {
          classIndices[j] = i;
        }
        break;
      } else if (current.classValue() != currentClass) {
        for (int j = currentClass + 1; j <= current.classValue(); j++) {
          classIndices[j] = i;
        }          
        currentClass = (int) current.classValue();
      }
    }
    if (currentClass <= getInputFormat().numClasses()) {
      for (int j = currentClass + 1; j < classIndices.length; j++) {
        classIndices[j] = getInputFormat().numInstances();
      }
    }
    return classIndices;
  }


  /**
   * Main method for testing this class.
   *
   * @param argv should contain arguments to the filter: 
   * use -h for help
   */
  public static void main(String [] argv) {
    runFilter(new SpreadSubsample(), argv);
  }
}