lwl.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

    return options;
  }
  
  /**
   * Returns the tip text for this property
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String KNNTipText() {
    return "How many neighbours are used to determine the width of the "
      + "weighting function (<= 0 means all neighbours).";
  }

  /**
   * Sets the number of neighbours used for kernel bandwidth setting.
   * The bandwidth is taken as the distance to the kth neighbour.
   *
   * @param knn the number of neighbours included inside the kernel
   * bandwidth, or 0 to specify using all neighbors.
   */
  public void setKNN(int knn) {

    m_kNN = knn;
    if (knn <= 0) {
      m_kNN = 0;
      m_UseAllK = true;
    } else {
      m_UseAllK = false;
    }
  }

  /**
   * Gets the number of neighbours used for kernel bandwidth setting.
   * The bandwidth is taken as the distance to the kth neighbour.
   *
   * @return the number of neighbours included inside the kernel
   * bandwidth, or 0 for all neighbours
   */
  public int getKNN() {

    return m_kNN;
  }

  /**
   * Returns the tip text for this property
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String weightingKernelTipText() {
    return "Determines weighting function. [0 = Linear, 1 = Epnechnikov,"+
	   "2 = Tricube, 3 = Inverse, 4 = Gaussian and 5 = Constant. "+
	   "(default 0 = Linear)].";
  }

  /**
   * Sets the kernel weighting method to use. Must be one of LINEAR, 
   * EPANECHNIKOV,  TRICUBE, INVERSE, GAUSS or CONSTANT, other values
   * are ignored.
   *
   * @param kernel the new kernel method to use. Must be one of LINEAR,
   * EPANECHNIKOV,  TRICUBE, INVERSE, GAUSS or CONSTANT.
   */
  public void setWeightingKernel(int kernel) {

    if ((kernel != LINEAR)
	&& (kernel != EPANECHNIKOV)
	&& (kernel != TRICUBE)
	&& (kernel != INVERSE)
	&& (kernel != GAUSS)
	&& (kernel != CONSTANT)) {
      return;
    }
    m_WeightKernel = kernel;
  }

  /**
   * Gets the kernel weighting method to use.
   *
   * @return the new kernel method to use. Will be one of LINEAR,
   * EPANECHNIKOV,  TRICUBE, INVERSE, GAUSS or CONSTANT.
   */
  public int getWeightingKernel() {

    return m_WeightKernel;
  }

  /**
   * Returns the tip text for this property
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String nearestNeighbourSearchAlgorithmTipText() {
    return "The nearest neighbour search algorithm to use (Default: LinearNN).";
  }
  
  /**
   * Returns the current nearestNeighbourSearch algorithm in use.
   * @return the NearestNeighbourSearch algorithm currently in use.
   */
  public NearestNeighbourSearch getNearestNeighbourSearchAlgorithm() {
    return m_NNSearch;
  }
  
  /**
   * Sets the nearestNeighbourSearch algorithm to be used for finding nearest
   * neighbour(s).
   * @param nearestNeighbourSearchAlgorithm - The NearestNeighbourSearch class.
   */
  public void setNearestNeighbourSearchAlgorithm(NearestNeighbourSearch nearestNeighbourSearchAlgorithm) {
    m_NNSearch = nearestNeighbourSearchAlgorithm;
  }

  /**
   * Returns default capabilities of the classifier.
   *
   * @return      the capabilities of this classifier
   */
  public Capabilities getCapabilities() {
    Capabilities      result;
    
    if (m_Classifier != null)
      result = m_Classifier.getCapabilities();
    else
      result = super.getCapabilities();
    
    result.setMinimumNumberInstances(0);
    
    // set dependencies
    for (Capability cap: Capability.values())
      result.enableDependency(cap);
    
    return result;
  }
  
  /**
   * Generates the classifier.
   *
   * @param instances set of instances serving as training data 
   * @throws Exception if the classifier has not been generated successfully
   */
  public void buildClassifier(Instances instances) throws Exception {

    if (!(m_Classifier instanceof WeightedInstancesHandler)) {
      throw new IllegalArgumentException("Classifier must be a "
					 + "WeightedInstancesHandler!");
    }

    // can classifier handle the data?
    getCapabilities().testWithFail(instances);

    // remove instances with missing class
    instances = new Instances(instances);
    instances.deleteWithMissingClass();
    
    m_Train = new Instances(instances, 0, instances.numInstances());

    m_NNSearch.setInstances(m_Train);
  }

  /**
   * Adds the supplied instance to the training set
   *
   * @param instance the instance to add
   * @throws Exception if instance could not be incorporated
   * successfully
   */
  public void updateClassifier(Instance instance) throws Exception {

    if (m_Train.numInstances() == 0) {
      throw new Exception("No training instances!");
    }
    else if (m_Train.equalHeaders(instance.dataset()) == false) {
      throw new Exception("Incompatible instance types");
    }
    if (!instance.classIsMissing()) {
      m_NNSearch.update(instance);
      m_Train.add(instance);
    }
  }
  
  /**
   * Calculates the class membership probabilities for the given test instance.
   *
   * @param instance the instance to be classified
   * @return preedicted class probability distribution
   * @throws Exception if distribution can't be computed successfully
   */
  public double[] distributionForInstance(Instance instance) throws Exception {
    
    if (m_Train.numInstances() == 0) {
      throw new Exception("No training instances!");
    }
    
    m_NNSearch.addInstanceInfo(instance);
    
    int k = m_Train.numInstances();
    if( (!m_UseAllK && (m_kNN < k)) &&
       !(m_WeightKernel==INVERSE ||
         m_WeightKernel==GAUSS) ) {
      k = m_kNN;
    }
    
    Instances neighbours = m_NNSearch.kNearestNeighbours(instance, k);
    double distances[] = m_NNSearch.getDistances();

    if (m_Debug) {
      System.out.println("Kept " + neighbours.numInstances() + " out of " + 
                         m_Train.numInstances() + " instances");
    }

    if (m_Debug) {
      System.out.println("Instance Distances");
      for (int i = 0; i < distances.length; i++) {
	System.out.println("" + distances[i]);
      }
    }

    // Determine the bandwidth
    double bandwidth = distances[k-1];

    // Check for bandwidth zero
    if (bandwidth <= 0) {
      //if the kth distance is zero than give all instances the same weight
      for(int i=0; i < distances.length; i++)
        distances[i] = 1;
    } else {
      // Rescale the distances by the bandwidth
      for (int i = 0; i < distances.length; i++)
        distances[i] = distances[i] / bandwidth;
    }
    
    // Pass the distances through a weighting kernel
    for (int i = 0; i < distances.length; i++) {
      switch (m_WeightKernel) {
        case LINEAR:
          distances[i] = 1.0001 - distances[i];
          break;
        case EPANECHNIKOV:
          distances[i] = 3/4D*(1.0001 - distances[i]*distances[i]);
          break;
        case TRICUBE:
          distances[i] = Math.pow( (1.0001 - Math.pow(distances[i], 3)), 3 );
          break;
        case CONSTANT:
          //System.err.println("using constant kernel");
          distances[i] = 1;
          break;
        case INVERSE:
          distances[i] = 1.0 / (1.0 + distances[i]);
          break;
        case GAUSS:
          distances[i] = Math.exp(-distances[i] * distances[i]);
          break;
      }
    }

    if (m_Debug) {
      System.out.println("Instance Weights");
      for (int i = 0; i < distances.length; i++) {
	System.out.println("" + distances[i]);
      }
    }
    
    // Set the weights on the training data
    double sumOfWeights = 0, newSumOfWeights = 0;
    for (int i = 0; i < distances.length; i++) {
      double weight = distances[i];
      Instance inst = (Instance) neighbours.instance(i);
      sumOfWeights += inst.weight();
      newSumOfWeights += inst.weight() * weight;
      inst.setWeight(inst.weight() * weight);
      //weightedTrain.add(newInst);
    }
    
    // Rescale weights
    for (int i = 0; i < neighbours.numInstances(); i++) {
      Instance inst = neighbours.instance(i);
      inst.setWeight(inst.weight() * sumOfWeights / newSumOfWeights);
    }

    // Create a weighted classifier
    m_Classifier.buildClassifier(neighbours);

    if (m_Debug) {
      System.out.println("Classifying test instance: " + instance);
      System.out.println("Built base classifier:\n" 
			 + m_Classifier.toString());
    }

    // Return the classifier's predictions
    return m_Classifier.distributionForInstance(instance);
  }
 
  /**
   * Returns a description of this classifier.
   *
   * @return a description of this classifier as a string.
   */
  public String toString() {

    if (m_Train == null) {
      return "Locally weighted learning: No model built yet.";
    }
    String result = "Locally weighted learning\n"
      + "===========================\n";

    result += "Using classifier: " + m_Classifier.getClass().getName() + "\n";

    switch (m_WeightKernel) {
    case LINEAR:
      result += "Using linear weighting kernels\n";
      break;
    case EPANECHNIKOV:
      result += "Using epanechnikov weighting kernels\n";
      break;
    case TRICUBE:
      result += "Using tricube weighting kernels\n";
      break;
    case INVERSE:
      result += "Using inverse-distance weighting kernels\n";
      break;
    case GAUSS:
      result += "Using gaussian weighting kernels\n";
      break;
    case CONSTANT:
      result += "Using constant weighting kernels\n";
      break;
    }
    result += "Using " + (m_UseAllK ? "all" : "" + m_kNN) + " neighbours";
    return result;
  }
  
  /**
   * Main method for testing this class.
   *
   * @param argv the options
   */
  public static void main(String [] argv) {
    runClassifier(new LWL(), argv);
  }
}