/*
 * 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.
 */

/*
 * HierarchicalClusterer.java
 * Copyright (C) 2009 University of Waikato, Hamilton, New Zealand
 */

package weka.clusterers;

import java.io.Serializable;
import java.text.DecimalFormat;
import java.text.NumberFormat;
import java.util.Comparator;
import java.util.Enumeration;
import java.util.Locale;
import java.util.PriorityQueue;
import java.util.Vector;

import weka.core.Capabilities;
import weka.core.CapabilitiesHandler;
import weka.core.DistanceFunction;
import weka.core.Drawable;
import weka.core.EuclideanDistance;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.Option;
import weka.core.OptionHandler;
import weka.core.RevisionUtils;
import weka.core.SelectedTag;
import weka.core.Tag;
import weka.core.Utils;
import weka.core.Capabilities.Capability;

/**
<!-- globalinfo-start -->
* Hierarchical clustering class.
* Implements a number of classic hierarchical clustering methods.
<!-- globalinfo-end -->
* 
<!-- options-start -->
* Valid options are: <p/>
* 
* <pre> -N
*  number of clusters
* </pre>
* 
* 
* <pre> -L
*  Link type (Single, Complete, Average, Mean, Centroid, Ward, Adjusted complete, Neighbor Joining)
*  [SINGLE|COMPLETE|AVERAGE|MEAN|CENTROID|WARD|ADJCOMLPETE|NEIGHBOR_JOINING]
* </pre>
* 
* <pre> -A
* Distance function to use. (default: weka.core.EuclideanDistance)
* </pre>
*
* <pre> -P
* Print hierarchy in Newick format, which can be used for display in other programs.
* </pre>
*  
* <pre> -D
* If set, classifier is run in debug mode and may output additional info to the console.
* </pre>
* 
* <pre> -B
* \If set, distance is interpreted as branch length, otherwise it is node height.
* </pre>
* 
*<!-- options-end -->
*
* 
* @author Remco Bouckaert (rrb@xm.co.nz, remco@cs.waikato.ac.nz)
* @author Eibe Frank (eibe@cs.waikato.ac.nz)
* @version $Revision: 11330 $
*/
public class HierarchicalClusterer extends AbstractClusterer implements OptionHandler, CapabilitiesHandler, Drawable {
  private static final long serialVersionUID = 1L;

  /** Whether the classifier is run in debug mode. */
  protected boolean m_bDebug = false;

  /** Whether the distance represent node height (if false) or branch length (if true). */
  protected boolean m_bDistanceIsBranchLength = false;

  /** training data **/
  Instances m_instances;

  /** number of clusters desired in clustering **/
  int m_nNumClusters = 2;
  public void setNumClusters(int nClusters) {m_nNumClusters = Math.max(1,nClusters);}
  public int getNumClusters() {return m_nNumClusters;}

  /** distance function used for comparing members of a cluster **/
  protected DistanceFunction m_DistanceFunction = new EuclideanDistance();
  public DistanceFunction getDistanceFunction() {return m_DistanceFunction;}
  public void setDistanceFunction(DistanceFunction distanceFunction) {m_DistanceFunction = distanceFunction;}

  /** used for priority queue for efficient retrieval of pair of clusters to merge**/
  class Tuple {
    public Tuple(double d, int i, int j, int nSize1, int nSize2) {
      m_fDist = d;
      m_iCluster1 = i;
      m_iCluster2 = j;
      m_nClusterSize1 = nSize1;
      m_nClusterSize2 = nSize2;
    }
    double m_fDist;
    int m_iCluster1;
    int m_iCluster2;
    int m_nClusterSize1;
    int m_nClusterSize2;
  }
  /** comparator used by priority queue**/
  class TupleComparator implements Comparator<Tuple> {
    public int compare(Tuple o1, Tuple o2) {
      if (o1.m_fDist < o2.m_fDist) {
        return -1;
      } else if (o1.m_fDist == o2.m_fDist) {
        return 0;
      }
      return 1;
    }
  }

  /** the various link types */
  final static int SINGLE = 0;
  final static int COMPLETE = 1;
  final static int AVERAGE = 2;
  final static int MEAN = 3;
  final static int CENTROID = 4;
  final static int WARD = 5;
  final static int ADJCOMLPETE = 6;
  final static int NEIGHBOR_JOINING = 7;
  public static final Tag[] TAGS_LINK_TYPE = {
    new Tag(SINGLE, "SINGLE"),
    new Tag(COMPLETE, "COMPLETE"),
    new Tag(AVERAGE, "AVERAGE"),
    new Tag(MEAN, "MEAN"),
    new Tag(CENTROID, "CENTROID"),
    new Tag(WARD, "WARD"),
    new Tag(ADJCOMLPETE,"ADJCOMLPETE"),
    new Tag(NEIGHBOR_JOINING,"NEIGHBOR_JOINING")
  };

  /**
   * Holds the Link type used calculate distance between clusters
   */
  int m_nLinkType = SINGLE;

  boolean m_bPrintNewick = true;;
  public boolean getPrintNewick() {return m_bPrintNewick;}
  public void setPrintNewick(boolean bPrintNewick) {m_bPrintNewick = bPrintNewick;}

  public void setLinkType(SelectedTag newLinkType) {
    if (newLinkType.getTags() == TAGS_LINK_TYPE) {
      m_nLinkType = newLinkType.getSelectedTag().getID();
    }
  }

  public SelectedTag getLinkType() {
    return new SelectedTag(m_nLinkType, TAGS_LINK_TYPE);
  }

  /** class representing node in cluster hierarchy **/
  class Node implements Serializable {
    Node m_left;
    Node m_right;
    Node m_parent;
    int m_iLeftInstance;
    int m_iRightInstance;
    double m_fLeftLength = 0;
    double m_fRightLength = 0;
    double m_fHeight = 0;
    public String toString(int attIndex) {
      NumberFormat nf = NumberFormat.getNumberInstance(new Locale("en","US"));
      DecimalFormat myFormatter = (DecimalFormat)nf;
      myFormatter.applyPattern("#.#####");

      if (m_left == null) {
        if (m_right == null) {
          return "(" + m_instances.instance(m_iLeftInstance).stringValue(attIndex) + ":" + myFormatter.format(m_fLeftLength) + "," +
          m_instances.instance(m_iRightInstance).stringValue(attIndex) +":" + myFormatter.format(m_fRightLength) + ")";
        } else {
          return "(" + m_instances.instance(m_iLeftInstance).stringValue(attIndex) + ":" + myFormatter.format(m_fLeftLength) + "," +
          m_right.toString(attIndex) + ":" + myFormatter.format(m_fRightLength) + ")";
        }
      } else {
        if (m_right == null) {
          return "(" + m_left.toString(attIndex) + ":" + myFormatter.format(m_fLeftLength) + "," +
          m_instances.instance(m_iRightInstance).stringValue(attIndex) + ":" + myFormatter.format(m_fRightLength) + ")";
        } else {
          return "(" + m_left.toString(attIndex) + ":" + myFormatter.format(m_fLeftLength) + "," +m_right.toString(attIndex) + ":" + myFormatter.format(m_fRightLength) + ")";
        }
      }
    }
    public String toString2(int attIndex) {
      NumberFormat nf = NumberFormat.getNumberInstance(new Locale("en","US"));
      DecimalFormat myFormatter = (DecimalFormat)nf;
      myFormatter.applyPattern("#.#####");

      if (m_left == null) {
        if (m_right == null) {
          return "(" + m_instances.instance(m_iLeftInstance).value(attIndex) + ":" + myFormatter.format(m_fLeftLength) + "," +
          m_instances.instance(m_iRightInstance).value(attIndex) +":" + myFormatter.format(m_fRightLength) + ")";
        } else {
          return "(" + m_instances.instance(m_iLeftInstance).value(attIndex) + ":" + myFormatter.format(m_fLeftLength) + "," +
          m_right.toString2(attIndex) + ":" + myFormatter.format(m_fRightLength) + ")";
        }
      } else {
        if (m_right == null) {
          return "(" + m_left.toString2(attIndex) + ":" + myFormatter.format(m_fLeftLength) + "," +
          m_instances.instance(m_iRightInstance).value(attIndex) + ":" + myFormatter.format(m_fRightLength) + ")";
        } else {
          return "(" + m_left.toString2(attIndex) + ":" + myFormatter.format(m_fLeftLength) + "," +m_right.toString2(attIndex) + ":" + myFormatter.format(m_fRightLength) + ")";
        }
      }
    }
    void setHeight(double fHeight1, double fHeight2) {
      m_fHeight = fHeight1;
      if (m_left == null) {
        m_fLeftLength = fHeight1;
      } else {
        m_fLeftLength = fHeight1 - m_left.m_fHeight;
      }
      if (m_right == null) {
        m_fRightLength = fHeight2;
      } else {
        m_fRightLength = fHeight2 - m_right.m_fHeight;
      }
    }
    void setLength(double fLength1, double fLength2) {
      m_fLeftLength = fLength1;
      m_fRightLength = fLength2;
      m_fHeight = fLength1;
      if (m_left != null) {
        m_fHeight += m_left.m_fHeight;
      }
    }
  }
  Node [] m_clusters;
  int [] m_nClusterNr;


  @Override
  public void buildClusterer(Instances data) throws Exception {
    //		/System.err.println("Method " + m_nLinkType);
    m_instances = data;
    int nInstances = m_instances.numInstances();
    if (nInstances == 0) {
      return;
    }
    m_DistanceFunction.setInstances(m_instances);
    // use array of integer vectors to store cluster indices,
    // starting with one cluster per instance
    Vector<Integer> [] nClusterID = new Vector[data.numInstances()];
    for (int i = 0; i < data.numInstances(); i++) {
      nClusterID[i] = new Vector<Integer>();
      nClusterID[i].add(i);
    }
    // calculate distance matrix
    int nClusters = data.numInstances();

    // used for keeping track of hierarchy
    Node [] clusterNodes = new Node[nInstances];
    if (m_nLinkType == NEIGHBOR_JOINING) {
      neighborJoining(nClusters, nClusterID, clusterNodes);
    } else {
      doLinkClustering(nClusters, nClusterID, clusterNodes);
    }

    // move all clusters in m_nClusterID array
    // & collect hierarchy
    int iCurrent = 0;
    m_clusters = new Node[m_nNumClusters];
    m_nClusterNr = new int[nInstances];
    for (int i = 0; i < nInstances; i++) {
      if (nClusterID[i].size() > 0) {
        for (int j = 0; j < nClusterID[i].size(); j++) {
          m_nClusterNr[nClusterID[i].elementAt(j)] = iCurrent;
        }
        m_clusters[iCurrent] = clusterNodes[i];
        iCurrent++;
      }
    }

  } // buildClusterer

  /** use neighbor joining algorithm for clustering
   * This is roughly based on the RapidNJ simple implementation and runs at O(n^3)
   * More efficient implementations exist, see RapidNJ (or my GPU implementation :-))
   * @param nClusters
   * @param nClusterID
   * @param clusterNodes
   */
  void neighborJoining(int nClusters, Vector<Integer>[] nClusterID, Node [] clusterNodes) {
    int n = m_instances.numInstances();

    double [][] fDist = new double[nClusters][nClusters];
    for (int i = 0; i < nClusters; i++) {
      fDist[i][i] = 0;
      for (int j = i+1; j < nClusters; j++) {
        fDist[i][j] = getDistance0(nClusterID[i], nClusterID[j]);
        fDist[j][i] = fDist[i][j];
      }
    }

    double [] fSeparationSums = new double [n];
    double [] fSeparations = new double [n];
    int [] nNextActive = new int[n];

    //calculate initial separation rows
    for(int i = 0; i < n; i++){
      double fSum = 0;
      for(int j = 0; j < n; j++){
        fSum += fDist[i][j];
      }
      fSeparationSums[i] = fSum;
      fSeparations[i] = fSum / (nClusters - 2);
      nNextActive[i] = i +1;
    }

    while (nClusters > 2) {
      // find minimum
      int iMin1 = -1;
      int iMin2 = -1;
      double fMin = Double.MAX_VALUE;
      if (m_bDebug) {
        for (int i = 0; i < n; i++) {
          if(nClusterID[i].size() > 0){
            double [] fRow = fDist[i];
            double fSep1 = fSeparations[i];
            for(int j = 0; j < n; j++){
              if(nClusterID[j].size() > 0 && i != j){
                double fSep2 = fSeparations[j];
                double fVal = fRow[j] - fSep1 - fSep2;

                if(fVal < fMin){
                  // new minimum
                  iMin1 = i;
                  iMin2 = j;
                  fMin = fVal;
                }
              }
            }
          }
        }
      } else {
        int i = 0;
        while (i < n) {
          double fSep1 = fSeparations[i];
          double [] fRow = fDist[i];
          int j = nNextActive[i];
          while (j < n) {
            double fSep2 = fSeparations[j];
            double fVal = fRow[j] - fSep1 - fSep2;
            if(fVal < fMin){
              // new minimum
              iMin1 = i;
              iMin2 = j;
              fMin = fVal;
            }
            j = nNextActive[j];
          }
          i = nNextActive[i];
        }		
      }
      // record distance
      double fMinDistance = fDist[iMin1][iMin2];
      nClusters--;
      double fSep1 = fSeparations[iMin1];
      double fSep2 = fSeparations[iMin2];
      double fDist1 = (0.5 * fMinDistance) + (0.5 * (fSep1 - fSep2));
      double fDist2 = (0.5 * fMinDistance) + (0.5 * (fSep2 - fSep1));
      if (nClusters > 2) {
        // update separations  & distance
        double fNewSeparationSum = 0;
        double fMutualDistance = fDist[iMin1][iMin2];
        double[] fRow1 = fDist[iMin1];
        double[] fRow2 = fDist[iMin2];
        for(int i = 0; i < n; i++) {
          if(i == iMin1 || i == iMin2 || nClusterID[i].size() == 0) {
            fRow1[i] = 0;
          } else {
            double fVal1 = fRow1[i];
            double fVal2 = fRow2[i];
            double fDistance = (fVal1 + fVal2 - fMutualDistance) / 2.0;
            fNewSeparationSum += fDistance;
            // update the separationsum of cluster i.
            fSeparationSums[i] += (fDistance - fVal1 - fVal2);
            fSeparations[i] = fSeparationSums[i] / (nClusters -2);
            fRow1[i] = fDistance;
            fDist[i][iMin1] = fDistance;
          }
        }
        fSeparationSums[iMin1] = fNewSeparationSum;
        fSeparations[iMin1] = fNewSeparationSum / (nClusters - 2);
        fSeparationSums[iMin2] = 0;
        merge(iMin1, iMin2, fDist1, fDist2, nClusterID, clusterNodes);
        int iPrev = iMin2;
        // since iMin1 < iMin2 we havenActiveRows[0] >= 0, so the next loop should be save
        while (nClusterID[iPrev].size() == 0) {
          iPrev--;
        }
        nNextActive[iPrev] = nNextActive[iMin2];
      } else {
        merge(iMin1, iMin2, fDist1, fDist2, nClusterID, clusterNodes);
        break;
      }
    }

    for (int i = 0; i < n; i++) {
      if (nClusterID[i].size() > 0) {
        for (int j = i+1; j < n; j++) {
          if (nClusterID[j].size() > 0) {
            double fDist1 = fDist[i][j];
            if(nClusterID[i].size() == 1) {
              merge(i,j,fDist1,0,nClusterID, clusterNodes);
            } else if (nClusterID[j].size() == 1) {
              merge(i,j,0,fDist1,nClusterID, clusterNodes);
            } else {
              merge(i,j,fDist1/2.0,fDist1/2.0,nClusterID, clusterNodes);
            }
            break;
          }
        }
      }
    }
  } // neighborJoining

  /** Perform clustering using a link method
   * This implementation uses a priority queue resulting in a O(n^2 log(n)) algorithm
   * @param nClusters number of clusters
   * @param nClusterID 
   * @param clusterNodes 
   */
  void doLinkClustering(int nClusters, Vector<Integer>[] nClusterID, Node [] clusterNodes) {
    int nInstances = m_instances.numInstances();
    PriorityQueue<Tuple> queue = new PriorityQueue<Tuple>(nClusters*nClusters/2, new TupleComparator());
    double [][] fDistance0 = new double[nClusters][nClusters];
    double [][] fClusterDistance = null;
    if (m_bDebug) {
      fClusterDistance = new double[nClusters][nClusters];
    }
    for (int i = 0; i < nClusters; i++) {
      fDistance0[i][i] = 0;
      for (int j = i+1; j < nClusters; j++) {
        fDistance0[i][j] = getDistance0(nClusterID[i], nClusterID[j]);
        fDistance0[j][i] = fDistance0[i][j];
        queue.add(new Tuple(fDistance0[i][j], i, j, 1, 1));
        if (m_bDebug) {
          fClusterDistance[i][j] = fDistance0[i][j];
          fClusterDistance[j][i] = fDistance0[i][j];
        }
      }
    }
    while (nClusters > m_nNumClusters) {
      int iMin1 = -1;
      int iMin2 = -1;
      // find closest two clusters
      if (m_bDebug) {
        /* simple but inefficient implementation */
        double fMinDistance = Double.MAX_VALUE;
        for (int i = 0; i < nInstances; i++) {
          if (nClusterID[i].size()>0) {
            for (int j = i+1; j < nInstances; j++) {
              if (nClusterID[j].size()>0) {
                double fDist = fClusterDistance[i][j];
                if (fDist < fMinDistance) {
                  fMinDistance = fDist;
                  iMin1 = i;
                  iMin2 = j;
                }
              }
            }
          }
        }
        merge(iMin1, iMin2, fMinDistance, fMinDistance, nClusterID, clusterNodes);
      } else {
        // use priority queue to find next best pair to cluster
        Tuple t;
        do {
          t = queue.poll();
        } while (t!=null && (nClusterID[t.m_iCluster1].size() != t.m_nClusterSize1 || nClusterID[t.m_iCluster2].size() != t.m_nClusterSize2));
        iMin1 = t.m_iCluster1;
        iMin2 = t.m_iCluster2;
        merge(iMin1, iMin2, t.m_fDist, t.m_fDist, nClusterID, clusterNodes);
      }
      // merge  clusters

      // update distances & queue
      for (int i = 0; i < nInstances; i++) {
        if (i != iMin1 && nClusterID[i].size()!=0) {
          int i1 = Math.min(iMin1,i);
          int i2 = Math.max(iMin1,i);
          double fDistance = getDistance(fDistance0, nClusterID[i1], nClusterID[i2]);
          if (m_bDebug) {
            fClusterDistance[i1][i2] = fDistance;
            fClusterDistance[i2][i1] = fDistance;
          }
          queue.add(new Tuple(fDistance, i1, i2, nClusterID[i1].size(), nClusterID[i2].size()));
        }
      }

      nClusters--;
    }
  } // doLinkClustering

  void merge(int iMin1, int iMin2, double fDist1, double fDist2, Vector<Integer>[] nClusterID, Node [] clusterNodes) {
    if (m_bDebug) {
      System.err.println("Merging " + iMin1 + " " + iMin2 + " " + fDist1 + " " + fDist2);
    }
    if (iMin1 > iMin2) {
      int h = iMin1; iMin1 = iMin2; iMin2 = h;
      double f = fDist1; fDist1 = fDist2; fDist2 = f;
    }
    nClusterID[iMin1].addAll(nClusterID[iMin2]);
    nClusterID[iMin2].removeAllElements();

    // track hierarchy
    Node node = new Node();
    if (clusterNodes[iMin1] == null) {
      node.m_iLeftInstance = iMin1;
    } else {
      node.m_left = clusterNodes[iMin1];
      clusterNodes[iMin1].m_parent = node;
    }
    if (clusterNodes[iMin2] == null) {
      node.m_iRightInstance = iMin2;
    } else {
      node.m_right = clusterNodes[iMin2];
      clusterNodes[iMin2].m_parent = node;
    }
    if (m_bDistanceIsBranchLength) {
      node.setLength(fDist1, fDist2);
    } else {
      node.setHeight(fDist1, fDist2);
    }
    clusterNodes[iMin1] = node;
  } // merge

  /** calculate distance the first time when setting up the distance matrix **/
  double getDistance0(Vector<Integer> cluster1, Vector<Integer> cluster2) {
    double fBestDist = Double.MAX_VALUE;
    switch (m_nLinkType) {
    case SINGLE:
    case NEIGHBOR_JOINING:
    case CENTROID:
    case COMPLETE:
    case ADJCOMLPETE:
    case AVERAGE:
    case MEAN:
      // set up two instances for distance function
      Instance instance1 = (Instance) m_instances.instance(cluster1.elementAt(0)).copy();
      Instance instance2 = (Instance) m_instances.instance(cluster2.elementAt(0)).copy();
      fBestDist = m_DistanceFunction.distance(instance1, instance2);
      break;
    case WARD:
    {
      // finds the distance of the change in caused by merging the cluster.
      // The information of a cluster is calculated as the error sum of squares of the
      // centroids of the cluster and its members.
      double ESS1 = calcESS(cluster1);
      double ESS2 = calcESS(cluster2);
      Vector<Integer> merged = new Vector<Integer>();
      merged.addAll(cluster1);
      merged.addAll(cluster2);
      double ESS = calcESS(merged);
      fBestDist = ESS * merged.size() - ESS1 * cluster1.size() - ESS2 * cluster2.size();
    }
    break;
    }
    return fBestDist;
  } // getDistance0

  /** calculate the distance between two clusters 
   * @param cluster1 list of indices of instances in the first cluster
   * @param cluster2 dito for second cluster
   * @return distance between clusters based on link type
   */
  double getDistance(double [][] fDistance, Vector<Integer> cluster1, Vector<Integer> cluster2) {
    double fBestDist = Double.MAX_VALUE;
    switch (m_nLinkType) {
    case SINGLE:
      // find single link distance aka minimum link, which is the closest distance between
      // any item in cluster1 and any item in cluster2
      fBestDist = Double.MAX_VALUE;
      for (int i = 0; i < cluster1.size(); i++) {
        int i1 = cluster1.elementAt(i);
        for (int j = 0; j < cluster2.size(); j++) {
          int i2  = cluster2.elementAt(j);
          double fDist = fDistance[i1][i2];
          if (fBestDist > fDist) {
            fBestDist = fDist;
          }
        }
      }
      break;
    case COMPLETE:
    case ADJCOMLPETE:
      // find complete link distance aka maximum link, which is the largest distance between
      // any item in cluster1 and any item in cluster2
      fBestDist = 0;
      for (int i = 0; i < cluster1.size(); i++) {
        int i1 = cluster1.elementAt(i);
        for (int j = 0; j < cluster2.size(); j++) {
          int i2 = cluster2.elementAt(j);
          double fDist = fDistance[i1][i2];
          if (fBestDist < fDist) {
            fBestDist = fDist;
          }
        }
      }
      if (m_nLinkType == COMPLETE) {
        break;
      }
      // calculate adjustment, which is the largest within cluster distance
      double fMaxDist = 0;
      for (int i = 0; i < cluster1.size(); i++) {
        int i1 = cluster1.elementAt(i);
        for (int j = i+1; j < cluster1.size(); j++) {
          int i2 = cluster1.elementAt(j);
          double fDist = fDistance[i1][i2];
          if (fMaxDist < fDist) {
            fMaxDist = fDist;
          }
        }
      }
      for (int i = 0; i < cluster2.size(); i++) {
        int i1 = cluster2.elementAt(i);
        for (int j = i+1; j < cluster2.size(); j++) {
          int i2 = cluster2.elementAt(j);
          double fDist = fDistance[i1][i2];
          if (fMaxDist < fDist) {
            fMaxDist = fDist;
          }
        }
      }
      fBestDist -= fMaxDist;
      break;
    case AVERAGE:
      // finds average distance between the elements of the two clusters
      fBestDist = 0;
      for (int i = 0; i < cluster1.size(); i++) {
        int i1 = cluster1.elementAt(i);
        for (int j = 0; j < cluster2.size(); j++) {
          int i2 = cluster2.elementAt(j);
          fBestDist += fDistance[i1][i2];
        }
      }
      fBestDist /= (cluster1.size() * cluster2.size());
      break;
    case MEAN: 
    {
      // calculates the mean distance of a merged cluster (akak Group-average agglomerative clustering)
      Vector<Integer> merged = new Vector<Integer>();
      merged.addAll(cluster1);
      merged.addAll(cluster2);
      fBestDist = 0;
      for (int i = 0; i < merged.size(); i++) {
        int i1 = merged.elementAt(i);
        for (int j = i+1; j < merged.size(); j++) {
          int i2 = merged.elementAt(j);
          fBestDist += fDistance[i1][i2];
        }
      }
      int n = merged.size();
      fBestDist /= (n*(n-1.0)/2.0);
    }
    break;
    case CENTROID:
      // finds the distance of the centroids of the clusters
      double [] fValues1 = new double[m_instances.numAttributes()];
      for (int i = 0; i < cluster1.size(); i++) {
        Instance instance = m_instances.instance(cluster1.elementAt(i));
        for (int j = 0; j < m_instances.numAttributes(); j++) {
          fValues1[j] += instance.value(j);
        }
      }
      double [] fValues2 = new double[m_instances.numAttributes()];
      for (int i = 0; i < cluster2.size(); i++) {
        Instance instance = m_instances.instance(cluster2.elementAt(i));
        for (int j = 0; j < m_instances.numAttributes(); j++) {
          fValues2[j] += instance.value(j);
        }
      }
      for (int j = 0; j < m_instances.numAttributes(); j++) {
        fValues1[j] /= cluster1.size();
        fValues2[j] /= cluster2.size();
      }
      // set up two instances for distance function
      Instance instance1 = (Instance) m_instances.instance(0).copy();
      Instance instance2 = (Instance) m_instances.instance(0).copy();
      for (int j = 0; j < m_instances.numAttributes(); j++) {
        instance1.setValue(j, fValues1[j]);
        instance2.setValue(j, fValues2[j]);
      }
      fBestDist = m_DistanceFunction.distance(instance1, instance2);
      break;
    case WARD:
    {
      // finds the distance of the change in caused by merging the cluster.
      // The information of a cluster is calculated as the error sum of squares of the
      // centroids of the cluster and its members.
      double ESS1 = calcESS(cluster1);
      double ESS2 = calcESS(cluster2);
      Vector<Integer> merged = new Vector<Integer>();
      merged.addAll(cluster1);
      merged.addAll(cluster2);
      double ESS = calcESS(merged);
      fBestDist = ESS * merged.size() - ESS1 * cluster1.size() - ESS2 * cluster2.size();
    }
    break;
    }
    return fBestDist;
  } // getDistance

  /** calculated error sum-of-squares for instances wrt centroid **/
  double calcESS(Vector<Integer> cluster) {
    double [] fValues1 = new double[m_instances.numAttributes()];
    for (int i = 0; i < cluster.size(); i++) {
      Instance instance = m_instances.instance(cluster.elementAt(i));
      for (int j = 0; j < m_instances.numAttributes(); j++) {
        fValues1[j] += instance.value(j);
      }
    }
    for (int j = 0; j < m_instances.numAttributes(); j++) {
      fValues1[j] /= cluster.size();
    }
    // set up two instances for distance function
    Instance centroid = (Instance) m_instances.instance(cluster.elementAt(0)).copy();
    for (int j = 0; j < m_instances.numAttributes(); j++) {
      centroid.setValue(j, fValues1[j]);
    }
    double fESS = 0;
    for (int i = 0; i < cluster.size(); i++) {
      Instance instance = m_instances.instance(cluster.elementAt(i));
      fESS += m_DistanceFunction.distance(centroid, instance);
    }
    return fESS / cluster.size(); 
  } // calcESS

  @Override
  /** instances are assigned a cluster by finding the instance in the training data 
   * with the closest distance to the instance to be clustered. The cluster index of
   * the training data point is taken as the cluster index.
   */
  public int clusterInstance(Instance instance) throws Exception {
    if (m_instances.numInstances() == 0) {
      return 0;
    }
    double fBestDist = Double.MAX_VALUE;
    int iBestInstance = -1;
    for (int i = 0; i < m_instances.numInstances(); i++) {
      double fDist = m_DistanceFunction.distance(instance, m_instances.instance(i));
      if (fDist < fBestDist) {
        fBestDist = fDist;
        iBestInstance = i;
      }
    }
    return m_nClusterNr[iBestInstance];
  }

  @Override
  /** create distribution with all clusters having zero probability, except the
   * cluster the instance is assigned to.
   */
  public double[] distributionForInstance(Instance instance) throws Exception {
    if (numberOfClusters() == 0) {
      double [] p = new double[1];
      p[0] = 1;
      return p;
    }
    double [] p = new double[numberOfClusters()];
    p[clusterInstance(instance)] = 1.0;
    return p;
  }

  @Override
  public Capabilities getCapabilities() {
    Capabilities result = new Capabilities(this);
    result.disableAll();
    result.enable(Capability.NO_CLASS);

    // attributes
    result.enable(Capability.NOMINAL_ATTRIBUTES);
    result.enable(Capability.NUMERIC_ATTRIBUTES);
    result.enable(Capability.DATE_ATTRIBUTES);
    result.enable(Capability.MISSING_VALUES);
    result.enable(Capability.STRING_ATTRIBUTES);

    // other
    result.setMinimumNumberInstances(0);
    return result;
  }

  @Override
  public int numberOfClusters() throws Exception {
    return Math.min(m_nNumClusters, m_instances.numInstances());
  }

  /**
   * Returns an enumeration describing the available options.
   *
   * @return an enumeration of all the available options.
   */
  public Enumeration listOptions() {

    Vector newVector = new Vector(8);
    newVector.addElement(new Option(
        "\tIf set, classifier is run in debug mode and\n"
        + "\tmay output additional info to the console",
        "D", 0, "-D"));
    newVector.addElement(new Option(
        "\tIf set, distance is interpreted as branch length\n"
        + "\totherwise it is node height.",
        "B", 0, "-B"));

    newVector.addElement(new Option(
        "\tnumber of clusters",
        "N", 1,"-N <Nr Of Clusters>"));
    newVector.addElement(new Option(
        "\tFlag to indicate the cluster should be printed in Newick format.",
        "P", 0,"-P"));
    newVector.addElement(
        new Option(
            "Link type (Single, Complete, Average, Mean, Centroid, Ward, Adjusted complete, Neighbor joining)", "L", 1,
        "-L [SINGLE|COMPLETE|AVERAGE|MEAN|CENTROID|WARD|ADJCOMLPETE|NEIGHBOR_JOINING]"));
    newVector.add(new Option(
        "\tDistance function to use.\n"
        + "\t(default: weka.core.EuclideanDistance)",
        "A", 1,"-A <classname and options>"));
    return newVector.elements();
  }

  /**
   * Parses a given list of options. <p/>
   *
	   <!-- options-start -->
   * Valid options are: <p/>
   * 
	   <!-- 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 {
    m_bPrintNewick = Utils.getFlag('P', options);

    String optionString = Utils.getOption('N', options); 
    if (optionString.length() != 0) {
      Integer temp = new Integer(optionString);
      setNumClusters(temp);
    }
    else {
      setNumClusters(2);
    }

    setDebug(Utils.getFlag('D', options));
    setDistanceIsBranchLength(Utils.getFlag('B', options));

    String sLinkType = Utils.getOption('L', options);


    if (sLinkType.compareTo("SINGLE") == 0) {setLinkType(new SelectedTag(SINGLE, TAGS_LINK_TYPE));}
    if (sLinkType.compareTo("COMPLETE") == 0) {setLinkType(new SelectedTag(COMPLETE, TAGS_LINK_TYPE));}
    if (sLinkType.compareTo("AVERAGE") == 0) {setLinkType(new SelectedTag(AVERAGE, TAGS_LINK_TYPE));}
    if (sLinkType.compareTo("MEAN") == 0) {setLinkType(new SelectedTag(MEAN, TAGS_LINK_TYPE));}
    if (sLinkType.compareTo("CENTROID") == 0) {setLinkType(new SelectedTag(CENTROID, TAGS_LINK_TYPE));}
    if (sLinkType.compareTo("WARD") == 0) {setLinkType(new SelectedTag(WARD, TAGS_LINK_TYPE));}
    if (sLinkType.compareTo("ADJCOMLPETE") == 0) {setLinkType(new SelectedTag(ADJCOMLPETE, TAGS_LINK_TYPE));}
    if (sLinkType.compareTo("NEIGHBOR_JOINING") == 0) {setLinkType(new SelectedTag(NEIGHBOR_JOINING, TAGS_LINK_TYPE));}

    String nnSearchClass = Utils.getOption('A', options);
    if(nnSearchClass.length() != 0) {
      String nnSearchClassSpec[] = Utils.splitOptions(nnSearchClass);
      if(nnSearchClassSpec.length == 0) { 
        throw new Exception("Invalid DistanceFunction specification string."); 
      }
      String className = nnSearchClassSpec[0];
      nnSearchClassSpec[0] = "";

      setDistanceFunction( (DistanceFunction)
          Utils.forName( DistanceFunction.class, 
              className, nnSearchClassSpec) );
    }
    else {
      setDistanceFunction(new EuclideanDistance());
    }

    Utils.checkForRemainingOptions(options);
  }

  /**
   * Gets the current settings of the clusterer.
   *
   * @return an array of strings suitable for passing to setOptions()
   */
  public String [] getOptions() {

    String [] options = new String [14];
    int current = 0;

    options[current++] = "-N";
    options[current++] = "" + getNumClusters();

    options[current++] = "-L";
    switch (m_nLinkType) {
    case (SINGLE) :options[current++] = "SINGLE";break;
    case (COMPLETE) :options[current++] = "COMPLETE";break;
    case (AVERAGE) :options[current++] = "AVERAGE";break;
    case (MEAN) :options[current++] = "MEAN";break;
    case (CENTROID) :options[current++] = "CENTROID";break;
    case (WARD) :options[current++] = "WARD";break;
    case (ADJCOMLPETE) :options[current++] = "ADJCOMLPETE";break;
    case (NEIGHBOR_JOINING) :options[current++] = "NEIGHBOR_JOINING";break;
    }
    if (m_bPrintNewick) {
      options[current++] = "-P";
    }
    if (getDebug()) {
      options[current++] = "-D";
    }
    if (getDistanceIsBranchLength()) {
      options[current++] = "-B";
    }

    options[current++] = "-A";
    options[current++] = (m_DistanceFunction.getClass().getName() + " " +
        Utils.joinOptions(m_DistanceFunction.getOptions())).trim();

    while (current < options.length) {
      options[current++] = "";
    }

    return options;
  }
  public String toString() {
    StringBuffer buf = new StringBuffer();
    int attIndex = m_instances.classIndex();
    if (attIndex < 0) {
      // try find a string, or last attribute otherwise
      attIndex = 0;
      while (attIndex < m_instances.numAttributes()-1) {
        if (m_instances.attribute(attIndex).isString()) {
          break;
        }
        attIndex++;
      }
    }
    try {
      if (m_bPrintNewick && (numberOfClusters() > 0)) {
        for (int i = 0; i < m_clusters.length; i++) {
          if (m_clusters[i] != null) {
            buf.append("Cluster " + i + "\n");
            if (m_instances.attribute(attIndex).isString()) {
              buf.append(m_clusters[i].toString(attIndex));
            } else {
              buf.append(m_clusters[i].toString2(attIndex));
            }
            buf.append("\n\n");
          }
        }
      }
    } catch (Exception e) {
      e.printStackTrace();
    }
    return buf.toString();
  }
  /**
   * Set debugging mode.
   *
   * @param debug true if debug output should be printed
   */
  public void setDebug(boolean debug) {

    m_bDebug = debug;
  }

  /**
   * Get whether debugging is turned on.
   *
   * @return true if debugging output is on
   */
  public boolean getDebug() {

    return m_bDebug;
  }

  public boolean getDistanceIsBranchLength() {return m_bDistanceIsBranchLength;}

  public void setDistanceIsBranchLength(boolean bDistanceIsHeight) {m_bDistanceIsBranchLength = bDistanceIsHeight;}

  public String distanceIsBranchLengthTipText() {
    return "If set to false, the distance between clusters is interpreted " +
    "as the height of the node linking the clusters. This is appropriate for " +
    "example for single link clustering. However, for neighbor joining, the " +
    "distance is better interpreted as branch length. Set this flag to " +
    "get the latter interpretation.";
  }
  /**
   * Returns the tip text for this property
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String debugTipText() {
    return "If set to true, classifier may output additional info to " +
    "the console.";
  }
  /**
   * @return a string to describe the NumClusters
   */
  public String numClustersTipText() {
    return "Sets the number of clusters. " +
    "If a single hierarchy is desired, set this to 1.";
  }

  /**
   * @return a string to describe the print Newick flag
   */
  public String printNewickTipText() {
    return "Flag to indicate whether the cluster should be print in Newick format." +
    " This can be useful for display in other programs. However, for large datasets" +
    " a lot of text may be produced, which may not be a nuisance when the Newick format" +
    " is not required";
  }

  /**
   * @return a string to describe the distance function
   */
  public String distanceFunctionTipText() {
    return "Sets the distance function, which measures the distance between two individual. " +
    "instances (or possibly the distance between an instance and the centroid of a cluster" +
    "depending on the Link type).";
  }

  /**
   * @return a string to describe the Link type
   */
  public String linkTypeTipText() {
    return "Sets the method used to measure the distance between two clusters.\n" +
    "SINGLE:\n" +
    " find single link distance aka minimum link, which is the closest distance between" +
    " any item in cluster1 and any item in cluster2\n" +
    "COMPLETE:\n" +
    " find complete link distance aka maximum link, which is the largest distance between" +
    " any item in cluster1 and any item in cluster2\n" +
    "ADJCOMLPETE:\n" +
    " as COMPLETE, but with adjustment, which is the largest within cluster distance\n" +
    "AVERAGE:\n" +
    " finds average distance between the elements of the two clusters\n" +
    "MEAN: \n" +
    " calculates the mean distance of a merged cluster (akak Group-average agglomerative clustering)\n" +
    "CENTROID:\n" +
    " finds the distance of the centroids of the clusters\n" +
    "WARD:\n" +
    " finds the distance of the change in caused by merging the cluster." +
    " The information of a cluster is calculated as the error sum of squares of the" +
    " centroids of the cluster and its members.\n" +
    "NEIGHBOR_JOINING\n" +
    " use neighbor joining algorithm."
    ;
  }

  /**
   * This will return a string describing the clusterer.
   * @return The string.
   */
  public String globalInfo() {
    return 
    "Hierarchical clustering class.\n" +
    "Implements a number of classic agglomorative (i.e. bottom up) hierarchical clustering methods" +
    "based on .";
  }

  public static void main(String [] argv) {
    runClusterer(new HierarchicalClusterer(), argv);
  }

  public String graph() throws Exception {
    if (numberOfClusters() == 0) {
      return "Newick:(no,clusters)";
    }
    int attIndex = m_instances.classIndex();
    if (attIndex < 0) {
      // try find a string, or last attribute otherwise
      attIndex = 0;
      while (attIndex < m_instances.numAttributes()-1) {
        if (m_instances.attribute(attIndex).isString()) {
          break;
        }
        attIndex++;
      }
    }
    String sNewick = null;
    if (m_instances.attribute(attIndex).isString()) {
      sNewick = m_clusters[0].toString(attIndex);
    } else {
      sNewick = m_clusters[0].toString2(attIndex);
    }
    return "Newick:" + sNewick;
  }

  public int graphType() {
    return Drawable.Newick;
  }
  
  /**
   * Returns the revision string.
   * 
   * @return		the revision
   */
  public String getRevision() {
    return RevisionUtils.extract("$Revision: 11330 $");
  }
} // class HierarchicalClusterer
