AdaptiveRandomForest-master/src/main/java/moa/classifiers/lazy/neighboursearch/LinearNNSearch.java
/*
 *   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 3 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, see <http://www.gnu.org/licenses/>.
 */

/*
 *    LinearNNSearch.java
 *    Copyright (C) 1999-2012 University of Waikato
 */

package moa.classifiers.lazy.neighboursearch;


import com.yahoo.labs.samoa.instances.Instance;
import com.yahoo.labs.samoa.instances.Instances;
//import weka.core.Option;
//import weka.core.RevisionUtils;
//import weka.core.Utils;

/**
 <!-- globalinfo-start -->
 * Class implementing the brute force search algorithm for nearest neighbour search.
 * <p/>
 <!-- globalinfo-end -->
 * 
 <!-- options-start -->
 * Valid options are: <p/>
 * 
 * <pre> -S
 *  Skip identical instances (distances equal to zero).
 * </pre>
 * 
 <!-- options-end -->
 *
 * @author Ashraf M. Kibriya (amk14[at-the-rate]cs[dot]waikato[dot]ac[dot]nz)
 * @version $Revision: 8034 $
 */
public class LinearNNSearch
  extends NearestNeighbourSearch {

  /** for serialization. */
  private static final long serialVersionUID = 1915484723703917241L;

  /** Array holding the distances of the nearest neighbours. It is filled up
   *  both by nearestNeighbour() and kNearestNeighbours(). 
   */
  protected double[] m_Distances;
    
  /** Whether to skip instances from the neighbours that are identical to the query instance. */
  protected boolean m_SkipIdentical = false;

  /**
   * Constructor. Needs setInstances(Instances) 
   * to be called before the class is usable.
   */
  public LinearNNSearch() {
    super();
  }
  
  /**
   * Constructor that uses the supplied set of 
   * instances.
   * 
   * @param insts	the instances to use
   */
  public LinearNNSearch(Instances insts) {
    super(insts);
    m_DistanceFunction.setInstances(insts);
  }
  
  /**
   * Returns a string describing this nearest neighbour search algorithm.
   * 
   * @return 		a description of the algorithm for displaying in the 
   * 			explorer/experimenter gui
   */
  public String globalInfo() {
    return 
        "Class implementing the brute force search algorithm for nearest "
      + "neighbour search.";  
  }
  
  

  /**
   * Returns the tip text for this property.
   * 
   * @return 		tip text for this property suitable for
   * 			displaying in the explorer/experimenter gui
   */
  public String skipIdenticalTipText() {
    return "Whether to skip identical instances (with distance 0 to the target)";
  }
  
  /**
   * Sets the property to skip identical instances (with distance zero from 
   * the target) from the set of neighbours returned.
   * 
   * @param skip 	if true, identical intances are skipped
   */
  public void setSkipIdentical(boolean skip) {
    m_SkipIdentical = skip;
  }
  
  /**
   * Gets whether if identical instances are skipped from the neighbourhood.
   * 
   * @return 		true if identical instances are skipped
   */
  public boolean getSkipIdentical() {
    return m_SkipIdentical;
  }

  
  /** 
   * Returns the nearest instance in the current neighbourhood to the supplied
   * instance.
   *  
   * @param target 	The instance to find the nearest neighbour for.
   * @return		the nearest instance
   * @throws Exception 	if the nearest neighbour could not be found.
   */
  public Instance nearestNeighbour(Instance target) throws Exception {
    return (kNearestNeighbours(target, 1)).instance(0);
  }
  
  /**
   * Returns k nearest instances in the current neighbourhood to the supplied
   * instance.
   *  
   * @param target 	The instance to find the k nearest neighbours for.
   * @param kNN		The number of nearest neighbours to find.
   * @return		the k nearest neighbors
   * @throws Exception  if the neighbours could not be found.
   */
  public Instances kNearestNeighbours(Instance target, int kNN) throws Exception {
  
    //debug
    boolean print=false;
 
    MyHeap heap = new MyHeap(kNN);
    double distance; int firstkNN=0;
    for(int i=0; i<m_Instances.numInstances(); i++) {
      if(target == m_Instances.instance(i)) //for hold-one-out cross-validation
        continue;
      if(firstkNN<kNN) {
        if(print)
          System.out.println("K(a): "+(heap.size()+heap.noOfKthNearest()));
        distance = m_DistanceFunction.distance(target, m_Instances.instance(i), Double.POSITIVE_INFINITY);
        if(distance == 0.0 && m_SkipIdentical)
          if(i<m_Instances.numInstances()-1)
            continue;
          else
            heap.put(i, distance);
        heap.put(i, distance);
        firstkNN++;
      }
      else {
        MyHeapElement temp = heap.peek();
        if(print)
          System.out.println("K(b): "+(heap.size()+heap.noOfKthNearest()));
        distance = m_DistanceFunction.distance(target, m_Instances.instance(i), temp.distance);
        if(distance == 0.0 && m_SkipIdentical)
          continue;
        if(distance < temp.distance) {
          heap.putBySubstitute(i, distance);
        }
        else if(distance == temp.distance) {
          heap.putKthNearest(i, distance);
        }

      }
    }
    
    Instances neighbours = new Instances(m_Instances, (heap.size()+heap.noOfKthNearest()));
    m_Distances = new double[heap.size()+heap.noOfKthNearest()];
    int [] indices = new int[heap.size()+heap.noOfKthNearest()];
    int i=1; MyHeapElement h;
    while(heap.noOfKthNearest()>0) {
      h = heap.getKthNearest();
      indices[indices.length-i] = h.index;
      m_Distances[indices.length-i] = h.distance;
      i++;
    }
    while(heap.size()>0) {
      h = heap.get();
      indices[indices.length-i] = h.index;
      m_Distances[indices.length-i] = h.distance;
      i++;
    }
    
    m_DistanceFunction.postProcessDistances(m_Distances);
    
    for(int k=0; k<indices.length; k++) {
      neighbours.add(m_Instances.instance(indices[k]));
    }
    
    return neighbours;    
  }
  
  /** 
   * Returns the distances of the k nearest neighbours. The kNearestNeighbours
   * or nearestNeighbour must always be called before calling this function. If
   * this function is called before calling either the kNearestNeighbours or 
   * the nearestNeighbour, then it throws an exception. If, however, if either
   * of the nearestNeighbour functions are called at any point in the 
   * past then no exception is thrown and the distances of the training set from
   * the last supplied target instance (to either one of the nearestNeighbour 
   * functions) is/are returned.
   *
   * @return 		array containing the distances of the 
   * 			nearestNeighbours. The length and ordering of the 
   * 			array is the same as that of the instances returned 
   * 			by nearestNeighbour functions.
   * @throws Exception 	if called before calling kNearestNeighbours
   *            	or nearestNeighbours.
   */
  public double[] getDistances() throws Exception {
    if(m_Distances==null)
      throw new Exception("No distances available. Please call either "+
                          "kNearestNeighbours or nearestNeighbours first.");
    return m_Distances;    
  }

  /** 
   * Sets the instances comprising the current neighbourhood.
   * 
   * @param insts 	The set of instances on which the nearest neighbour 
   * 			search is carried out. Usually this set is the 
   * 			training set. 
   * @throws Exception	if setting of instances fails
   */
  public void setInstances(Instances insts) throws Exception {
    m_Instances = insts;
    m_DistanceFunction.setInstances(insts);
  }
  
  /** 
   * Updates the LinearNNSearch to cater for the new added instance. This 
   * implementation only updates the ranges of the DistanceFunction class, 
   * since our set of instances is passed by reference and should already have 
   * the newly added instance.
   * 
   * @param ins 	The instance to add. Usually this is the instance that 
   * 			is added to our neighbourhood i.e. the training 
   * 			instances.
   * @throws Exception	if the given instances are null
   */
  public void update(Instance ins) throws Exception {
    if(m_Instances==null)
      throw new Exception("No instances supplied yet. Cannot update without"+
                          "supplying a set of instances first.");
    m_DistanceFunction.update(ins);
  }
  
  /** 
   * Adds the given instance info. This implementation updates the range
   * datastructures of the DistanceFunction class.
   * 
   * @param ins 	The instance to add the information of. Usually this is
   * 			the test instance supplied to update the range of 
   * 			attributes in the  distance function.
   */
  public void addInstanceInfo(Instance ins) {
    if(m_Instances!=null)
      try{ update(ins); }
      catch(Exception ex) { ex.printStackTrace(); }
  }
  

}
