content: /*
   *    This program is free software; you can redistribute it and/or modify
   *    it under the terms of the GNU General Public License as published by
   *    the Free Software Foundation; either version 2 of the License, or
   *    (at your option) any later version.
   *
   *    This program is distributed in the hope that it will be useful,
   *    but WITHOUT ANY WARRANTY; without even the implied warranty of
   *    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
   *    GNU General Public License for more details.
   *
   *    You should have received a copy of the GNU General Public License
   *    along with this program; if not, write to the Free Software
   *    Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA.
   */
  
  /*
   *    LWL.java
   *    Copyright (C) 1999, 2002, 2003 University of Waikato, Hamilton, New Zealand
   *
   */
  
  package weka.classifiers.lazy;
  
  import weka.classifiers.Classifier;
  import weka.classifiers.AbstractClassifier;
  import weka.classifiers.SingleClassifierEnhancer;
  import weka.classifiers.UpdateableClassifier;
  import weka.core.Capabilities;
  import weka.core.Instance;
  import weka.core.Instances;
  import weka.core.neighboursearch.LinearNNSearch;
  import weka.core.neighboursearch.NearestNeighbourSearch;
  import weka.core.Option;
  import weka.core.RevisionUtils;
  import weka.core.TechnicalInformation;
  import weka.core.TechnicalInformationHandler;
  import weka.core.Utils;
  import weka.core.WeightedInstancesHandler;
  import weka.core.Capabilities.Capability;
  import weka.core.TechnicalInformation.Field;
  import weka.core.TechnicalInformation.Type;
  
  import java.util.Enumeration;
  import java.util.Vector;
  
  /**
   <!-- globalinfo-start -->
   * Locally weighted learning. Uses an instance-based algorithm to assign instance weights which are then used by a specified WeightedInstancesHandler.<br/>
   * Can do classification (e.g. using naive Bayes) or regression (e.g. using linear regression).<br/>
   * <br/>
   * For more info, see<br/>
   * <br/>
   * Eibe Frank, Mark Hall, Bernhard Pfahringer: Locally Weighted Naive Bayes. In: 19th Conference in Uncertainty in Artificial Intelligence, 249-256, 2003.<br/>
   * <br/>
   * C. Atkeson, A. Moore, S. Schaal (1996). Locally weighted learning. AI Review..
   * <p/>
   <!-- globalinfo-end -->
   *
   <!-- technical-bibtex-start -->
   * BibTeX:
   * <pre>
   * &#64;inproceedings{Frank2003,
   *    author = {Eibe Frank and Mark Hall and Bernhard Pfahringer},
   *    booktitle = {19th Conference in Uncertainty in Artificial Intelligence},
   *    pages = {249-256},
   *    publisher = {Morgan Kaufmann},
   *    title = {Locally Weighted Naive Bayes},
   *    year = {2003}
   * }
   * 
   * &#64;article{Atkeson1996,
   *    author = {C. Atkeson and A. Moore and S. Schaal},
   *    journal = {AI Review},
   *    title = {Locally weighted learning},
   *    year = {1996}
   * }
   * </pre>
   * <p/>
   <!-- technical-bibtex-end -->
   *
   <!-- options-start -->
   * Valid options are: <p/>
   * 
   * <pre> -A
   *  The nearest neighbour search algorithm to use (default: weka.core.neighboursearch.LinearNNSearch).
   * </pre>
   * 
   * <pre> -K &lt;number of neighbours&gt;
   *  Set the number of neighbours used to set the kernel bandwidth.
   *  (default all)</pre>
   * 
   * <pre> -U &lt;number of weighting method&gt;
   *  Set the weighting kernel shape to use. 0=Linear, 1=Epanechnikov,
   *  2=Tricube, 3=Inverse, 4=Gaussian.
   *  (default 0 = Linear)</pre>
   * 
   * <pre> -D
   *  If set, classifier is run in debug mode and
   *  may output additional info to the console</pre>
   * 
   * <pre> -W
   *  Full name of base classifier.
   *  (default: weka.classifiers.trees.DecisionStump)</pre>
   * 
   * <pre> 
   * Options specific to classifier weka.classifiers.trees.DecisionStump:
   * </pre>
   * 
   * <pre> -D
   *  If set, classifier is run in debug mode and
   *  may output additional info to the console</pre>
   * 
   <!-- options-end -->
   *
   * @author Len Trigg (trigg@cs.waikato.ac.nz)
   * @author Eibe Frank (eibe@cs.waikato.ac.nz)
   * @author Ashraf M. Kibriya (amk14[at-the-rate]cs[dot]waikato[dot]ac[dot]nz)
   * @version $Revision: 6055 $ 
   */
  public class LWL 
    extends SingleClassifierEnhancer
    implements UpdateableClassifier, WeightedInstancesHandler, 
               TechnicalInformationHandler {
  
    /** for serialization. */
    static final long serialVersionUID = 1979797405383665815L;
  
    /** The training instances used for classification. */
    protected Instances m_Train;
      
    /** The number of neighbours used to select the kernel bandwidth. */
    protected int m_kNN = -1;
  
    /** The weighting kernel method currently selected. */
    protected int m_WeightKernel = LINEAR;
  
    /** True if m_kNN should be set to all instances. */
    protected boolean m_UseAllK = true;
    
    /** The nearest neighbour search algorithm to use. 
     * (Default: weka.core.neighboursearch.LinearNNSearch) 
     */
    protected NearestNeighbourSearch m_NNSearch =  new LinearNNSearch();
    
    /** The available kernel weighting methods. */
    public static final int LINEAR       = 0;
    public static final int EPANECHNIKOV = 1;
    public static final int TRICUBE      = 2;  
    public static final int INVERSE      = 3;
    public static final int GAUSS        = 4;
    public static final int CONSTANT     = 5;
  
    /** a ZeroR model in case no model can be built from the data. */
    protected Classifier m_ZeroR;
      
    /**
     * Returns a string describing classifier.
     * @return a description suitable for
     * displaying in the explorer/experimenter gui
     */
    public String globalInfo() {
      return 
          "Locally weighted learning. Uses an instance-based algorithm to "
        + "assign instance weights which are then used by a specified "
        + "WeightedInstancesHandler.\n"
        + "Can do classification (e.g. using naive Bayes) or regression "
        + "(e.g. using linear regression).\n\n"
        + "For more info, see\n\n"
        + getTechnicalInformation().toString();
    }
  
    /**
     * Returns an instance of a TechnicalInformation object, containing 
     * detailed information about the technical background of this class,
     * e.g., paper reference or book this class is based on.
     * 
     * @return the technical information about this class
     */
    public TechnicalInformation getTechnicalInformation() {
      TechnicalInformation 	result;
      TechnicalInformation 	additional;
      
      result = new TechnicalInformation(Type.INPROCEEDINGS);
      result.setValue(Field.AUTHOR, "Eibe Frank and Mark Hall and Bernhard Pfahringer");
      result.setValue(Field.YEAR, "2003");
      result.setValue(Field.TITLE, "Locally Weighted Naive Bayes");
      result.setValue(Field.BOOKTITLE, "19th Conference in Uncertainty in Artificial Intelligence");
      result.setValue(Field.PAGES, "249-256");
      result.setValue(Field.PUBLISHER, "Morgan Kaufmann");
      
      additional = result.add(Type.ARTICLE);
      additional.setValue(Field.AUTHOR, "C. Atkeson and A. Moore and S. Schaal");
      additional.setValue(Field.YEAR, "1996");
      additional.setValue(Field.TITLE, "Locally weighted learning");
      additional.setValue(Field.JOURNAL, "AI Review");
      
      return result;
    }
      
    /**
     * Constructor.
     */
    public LWL() {    
      m_Classifier = new weka.classifiers.trees.DecisionStump();
    }
  
    /**
     * String describing default classifier.
     * 
     * @return the default classifier classname
     */
    protected String defaultClassifierString() {
      
      return "weka.classifiers.trees.DecisionStump";
    }
  
    /**
     * Returns an enumeration of the additional measure names 
     * produced by the neighbour search algorithm.
     * @return an enumeration of the measure names
     */
    public Enumeration enumerateMeasures() {
      return m_NNSearch.enumerateMeasures();
    }
    
    /**
     * Returns the value of the named measure from the 
     * neighbour search algorithm.
     * @param additionalMeasureName the name of the measure to query for its value
     * @return the value of the named measure
     * @throws IllegalArgumentException if the named measure is not supported
     */
    public double getMeasure(String additionalMeasureName) {
      return m_NNSearch.getMeasure(additionalMeasureName);
    }
  
    /**
     * Returns an enumeration describing the available options.
     *
     * @return an enumeration of all the available options.
     */
    public Enumeration listOptions() {
      
      Vector newVector = new Vector(3);
      newVector.addElement(new Option("\tThe nearest neighbour search " +
                                      "algorithm to use " +
                                      "(default: weka.core.neighboursearch.LinearNNSearch).\n",
                                      "A", 0, "-A"));
      newVector.addElement(new Option("\tSet the number of neighbours used to set"
  				    +" the kernel bandwidth.\n"
  				    +"\t(default all)",
  				    "K", 1, "-K <number of neighbours>"));
      newVector.addElement(new Option("\tSet the weighting kernel shape to use."
  				    +" 0=Linear, 1=Epanechnikov,\n"
  				    +"\t2=Tricube, 3=Inverse, 4=Gaussian.\n"
  				    +"\t(default 0 = Linear)",
  				    "U", 1,"-U <number of weighting method>"));
      
      Enumeration enu = super.listOptions();
      while (enu.hasMoreElements()) {
        newVector.addElement(enu.nextElement());
      }
  
      return newVector.elements();
    }
  
    /**
     * Parses a given list of options. <p/>
     *
     <!-- options-start -->
     * Valid options are: <p/>
     * 
     * <pre> -A
     *  The nearest neighbour search algorithm to use (default: weka.core.neighboursearch.LinearNNSearch).
     * </pre>
     * 
     * <pre> -K &lt;number of neighbours&gt;
     *  Set the number of neighbours used to set the kernel bandwidth.
     *  (default all)</pre>
     * 
     * <pre> -U &lt;number of weighting method&gt;
     *  Set the weighting kernel shape to use. 0=Linear, 1=Epanechnikov,
     *  2=Tricube, 3=Inverse, 4=Gaussian.
     *  (default 0 = Linear)</pre>
     * 
     * <pre> -D
     *  If set, classifier is run in debug mode and
     *  may output additional info to the console</pre>
     * 
     * <pre> -W
     *  Full name of base classifier.
     *  (default: weka.classifiers.trees.DecisionStump)</pre>
     * 
     * <pre> 
     * Options specific to classifier weka.classifiers.trees.DecisionStump:
     * </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 {
  
      String knnString = Utils.getOption('K', options);
      if (knnString.length() != 0) {
        setKNN(Integer.parseInt(knnString));
      } else {
        setKNN(-1);
      }
  
      String weightString = Utils.getOption('U', options);
      if (weightString.length() != 0) {
        setWeightingKernel(Integer.parseInt(weightString));
      } else {
        setWeightingKernel(LINEAR);
      }
      
      String nnSearchClass = Utils.getOption('A', options);
      if(nnSearchClass.length() != 0) {
        String nnSearchClassSpec[] = Utils.splitOptions(nnSearchClass);
        if(nnSearchClassSpec.length == 0) { 
          throw new Exception("Invalid NearestNeighbourSearch algorithm " +
                              "specification string."); 
        }
        String className = nnSearchClassSpec[0];
        nnSearchClassSpec[0] = "";
  
        setNearestNeighbourSearchAlgorithm( (NearestNeighbourSearch)
                    Utils.forName( NearestNeighbourSearch.class, 
                                   className, 
                                   nnSearchClassSpec)
                                          );
      }
      else 
        this.setNearestNeighbourSearchAlgorithm(new LinearNNSearch());
  
      super.setOptions(options);
    }
  
    /**
     * Gets the current settings of the classifier.
     *
     * @return an array of strings suitable for passing to setOptions
     */
    public String [] getOptions() {
  
      String [] superOptions = super.getOptions();
      String [] options = new String [superOptions.length + 6];
  
      int current = 0;
  
      options[current++] = "-U"; options[current++] = "" + getWeightingKernel();
      if ( (getKNN() == 0) && m_UseAllK) {
        options[current++] = "-K"; options[current++] = "-1";
      }
      else {
        options[current++] = "-K"; options[current++] = "" + getKNN();
      }
      options[current++] = "-A";
      options[current++] = m_NNSearch.getClass().getName()+" "+Utils.joinOptions(m_NNSearch.getOptions()); 
  
      System.arraycopy(superOptions, 0, options, current,
                       superOptions.length);
  
      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();
      
      // only class? -> build ZeroR model
      if (instances.numAttributes() == 1) {
        System.err.println(
  	  "Cannot build model (only class attribute present in data!), "
  	  + "using ZeroR model instead!");
        m_ZeroR = new weka.classifiers.rules.ZeroR();
        m_ZeroR.buildClassifier(instances);
        return;
      }
      else {
        m_ZeroR = null;
      }
      
      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 == null) {
        throw new Exception("No training instance structure set!");
      }
      else if (m_Train.equalHeaders(instance.dataset()) == false) {
        throw new Exception("Incompatible instance types\n" + m_Train.equalHeadersMsg(instance.dataset()));
      }
      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 {
      
      // default model?
      if (m_ZeroR != null) {
        return m_ZeroR.distributionForInstance(instance);
      }
      
      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("Test Instance: "+instance);
        System.out.println("For "+k+" kept " + neighbours.numInstances() + " out of " + 
                           m_Train.numInstances() + " instances.");
      }
      
      //IF LinearNN has skipped so much that <k neighbours are remaining.
      if(k>distances.length)
        k = distances.length;
  
      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() {
  
      // only ZeroR model?
      if (m_ZeroR != null) {
        StringBuffer buf = new StringBuffer();
        buf.append(this.getClass().getName().replaceAll(".*\\.", "") + "\n");
        buf.append(this.getClass().getName().replaceAll(".*\\.", "").replaceAll(".", "=") + "\n\n");
        buf.append("Warning: No model could be built, hence ZeroR model is used:\n\n");
        buf.append(m_ZeroR.toString());
        return buf.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;
    }
    
    /**
     * Returns the revision string.
     * 
     * @return		the revision
     */
    public String getRevision() {
      return RevisionUtils.extract("$Revision: 6055 $");
    }
    
    /**
     * Main method for testing this class.
     *
     * @param argv the options
     */
    public static void main(String [] argv) {
      runClassifier(new LWL(), argv);
    }
  }
  