/*
 * #%L
 * gitools-core
 * %%
 * Copyright (C) 2013 Universitat Pompeu Fabra - Biomedical Genomics group
 * %%
 * 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/gpl-3.0.html>.
 * #L%
 */
package org.gitools.core.clustering.method.value;

import org.jetbrains.annotations.NotNull;
import weka.clusterers.AbstractClusterer;
import weka.core.*;
import weka.core.Capabilities.Capability;

import java.io.Serializable;
import java.text.DecimalFormat;
import java.text.NumberFormat;
import java.util.*;

/**
 * <!-- globalinfo-start -->
 * Hierarchical clustering class.
 * Implements a number of classic hierarchical clustering methods.
 * <!-- globalinfo-end -->
 * <p/>
 * <!-- options-start -->
 * Valid options are: <p/>
 * <p/>
 * <pre> -N
 *  number of clusters
 * </pre>
 * <p/>
 * <p/>
 * <pre> -L
 *  Link type (Single, Complete, Average, Mean, Centroid, Ward, Adjusted complete, Neighbor Joining)
 *  [SINGLE|COMPLETE|AVERAGE|MEAN|CENTROID|WARD|ADJCOMLPETE|NEIGHBOR_JOINING]
 * </pre>
 * <p/>
 * <pre> -A
 * Distance function to use. (default: weka.core.EuclideanDistance)
 * </pre>
 * <p/>
 * <pre> -P
 * Print hierarchy in Newick format, which can be used for display in other programs.
 * </pre>
 * <p/>
 * <pre> -D
 * If set, classifier is run in debug mode and may output additional info to the console.
 * </pre>
 * <p/>
 * <pre> -B
 * \If set, distance is interpreted as branch length, otherwise it is node height.
 * </pre>
 * <p/>
 * <!-- options-end -->
 *
 * @author Remco Bouckaert (rrb@xm.co.nz, remco@cs.waikato.ac.nz)
 * @author Eibe Frank (eibe@cs.waikato.ac.nz)
 * @version $Revision: 6587 $
 * @noinspection ALL
 */
public class WekaHierarchicalClusterer extends AbstractClusterer implements OptionHandler, CapabilitiesHandler, Drawable {
    private static final long serialVersionUID = 1L;

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

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

    /**
     * training data *
     */
    private Instances m_instances;

    /**
     * number of clusters desired in clustering *
     */
    private int m_nNumClusters = 2;

    public void setNumClusters(int nClusters) {
        m_nNumClusters = Math.max(1, nClusters);
    }

    int getNumClusters() {
        return m_nNumClusters;
    }

    /**
     * distance function used for comparing members of a cluster *
     */
    private 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;
        }

        final double m_fDist;
        final int m_iCluster1;
        final int m_iCluster2;
        final int m_nClusterSize1;
        final int m_nClusterSize2;
    }

    /**
     * comparator used by priority queue*
     */
    private class TupleComparator implements Comparator<Tuple> {
        public int compare(@NotNull Tuple o1, @NotNull 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
     */
    private final static int SINGLE = 0;
    private final static int COMPLETE = 1;
    private final static int AVERAGE = 2;
    private final static int MEAN = 3;
    private final static int CENTROID = 4;
    private final static int WARD = 5;
    private final static int ADJCOMLPETE = 6;
    private 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, "ADJCOMPLETE"), new Tag(NEIGHBOR_JOINING, "NEIGHBOR_JOINING")};

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

    private boolean m_bPrintNewick = true;

    public boolean getPrintNewick() {
        return m_bPrintNewick;
    }

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

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

    @NotNull
    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;

        @NotNull
        public String toString(int attIndex) {
            DecimalFormat myFormatter = new DecimalFormat("#.#####");

            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) + ")";
                }
            }
        }

        @NotNull
        public String toString2(int attIndex) {

            Locale locale = Locale.US;
            DecimalFormat myFormatter = new DecimalFormat("#.#####;#");
            myFormatter.setNegativePrefix("");

            NumberFormat f = myFormatter.getNumberInstance(locale);

            if (m_left == null) {
                if (m_right == null) {
                    return "(" + m_iLeftInstance + ":" + f.format(m_fLeftLength) + "," +
                            m_iRightInstance + ":" + f.format(m_fRightLength) + ")";
                } else {
                    return "(" + m_iLeftInstance + ":" + f.format(m_fLeftLength) + "," +
                            m_right.toString2(attIndex) + ":" + f.format(m_fRightLength) + ")";
                }
            } else {
                if (m_right == null) {
                    return "(" + m_left.toString2(attIndex) + ":" + f.format(m_fLeftLength) + "," +
                            m_iRightInstance + ":" + f.format(m_fRightLength) + ")";
                } else {
                    return "(" + m_left.toString2(attIndex) + ":" + f.format(m_fLeftLength) + "," +
                            m_right.toString2(attIndex) + ":" + f.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;
            }
        }
    }

    private Node[] m_clusters;
    private int[] m_nClusterNr;


    @Override
    public void buildClusterer(@NotNull 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();

        long size = (long) nClusters * nClusters / 2;
        if (size > Integer.MAX_VALUE) {
            throw new OutOfMemoryError();
        }

        PriorityQueue<Tuple> queue = new PriorityQueue<Tuple>((int) size, 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(@NotNull Vector<Integer> cluster1, @NotNull 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, @NotNull Vector<Integer> cluster1, @NotNull 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(@NotNull 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];
    }

    @NotNull
    @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;
    }

    @NotNull
    @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/>
     * <p/>
     * <!-- options-start -->
     * Valid options are: <p/>
     * <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()
     */
    @NotNull
    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;
    }

    @NotNull
    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
     */
    void setDebug(boolean debug) {

        m_bDebug = debug;
    }

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

        return m_bDebug;
    }

    boolean getDistanceIsBranchLength() {
        return m_bDistanceIsBranchLength;
    }

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

    @NotNull
    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
     */
    @NotNull
    public String debugTipText() {
        return "If set to true, classifier may output additional info to " + "the console.";
    }

    /**
     * @return a string to describe the NumClusters
     */
    @NotNull
    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
     */
    @NotNull
    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
     */
    @NotNull
    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
     * @noinspection UnusedDeclaration
     */
    @NotNull
    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.
     */
    @NotNull
    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 WekaHierarchicalClusterer(), argv);
    }

    @Override
    public String graph() throws Exception {
        if (numberOfClusters() == 0) {
            return "";
        }
        int attIndex = m_instances.classIndex();
        String sNewick;
        if (attIndex < 0) {
            sNewick = m_clusters[0].toString2(attIndex);
        } else {
            sNewick = m_clusters[0].toString(attIndex);
        }
        return sNewick;
    }

    @Override
    public int graphType() {
        return Drawable.Newick;
    }

    /**
     * Returns the revision string.
     *
     * @return the revision
     */
    public String getRevision() {
        return RevisionUtils.extract("$Revision: 6587 $");
    }
} // class HierarchicalClusterer
