content: /*
   *    This program is free software; you can redistribute it and/or modify
   *    it under the terms of the GNU General Public License as published by
   *    the Free Software Foundation; either version 2 of the License, or
   *    (at your option) any later version.
   *
   *    This program is distributed in the hope that it will be useful,
   *    but WITHOUT ANY WARRANTY; without even the implied warranty of
   *    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
   *    GNU General Public License for more details.
   *
   *    You should have received a copy of the GNU General Public License
   *    along with this program; if not, write to the Free Software
   *    Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA.
   */
  
  /*
   *    SimpleKMeans.java
   *    Copyright (C) 2000-2010 University of Waikato, Hamilton, New Zealand
   *
   */
  package weka.clusterers;
  
  import weka.classifiers.rules.DecisionTableHashKey;
  import weka.core.Attribute;
  import weka.core.Capabilities;
  import weka.core.DistanceFunction;
  import weka.core.EuclideanDistance;
  import weka.core.Instance;
  import weka.core.DenseInstance;
  import weka.core.Instances;
  import weka.core.ManhattanDistance;
  import weka.core.Option;
  import weka.core.RevisionUtils;
  import weka.core.TechnicalInformation;
  import weka.core.TechnicalInformation.Field;
  import weka.core.TechnicalInformation.Type;
  import weka.core.TechnicalInformationHandler;
  import weka.core.Utils;
  import weka.core.WeightedInstancesHandler;
  import weka.core.Capabilities.Capability;
  import weka.filters.Filter;
  import weka.filters.unsupervised.attribute.ReplaceMissingValues;
  
  import java.util.Enumeration;
  import java.util.HashMap;
  import java.util.Random;
  import java.util.Vector;
  
  /**
   <!-- globalinfo-start -->
   * Cluster data using the k means algorithm. Can use either the Euclidean distance (default) or the Manhattan distance. If the Manhattan distance is used, then centroids are computed as the component-wise median rather than mean. For more information see:<br/>
   * <br/>
   * D. Arthur, S. Vassilvitskii: k-means++: the advantages of carefull seeding. In: Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms, 1027-1035, 2007.
   * <p/>
   <!-- globalinfo-end -->
   *
   <!-- technical-bibtex-start -->
   * BibTeX:
   * <pre>
   * &#64;inproceedings{Arthur2007,
   *    author = {D. Arthur and S. Vassilvitskii},
   *    booktitle = {Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms},
   *    pages = {1027-1035},
   *    title = {k-means++: the advantages of carefull seeding},
   *    year = {2007}
   * }
   * </pre>
   * <p/>
   <!-- technical-bibtex-end -->
   *
   <!-- options-start -->
   * Valid options are: <p/>
   * 
   * <pre> -N &lt;num&gt;
   *  number of clusters.
   *  (default 2).</pre>
   * 
   * <pre> -P
   *  Initialize using the k-means++ method.
   * </pre>
   * 
   * <pre> -V
   *  Display std. deviations for centroids.
   * </pre>
   * 
   * <pre> -M
   *  Replace missing values with mean/mode.
   * </pre>
   * 
   * <pre> -A &lt;classname and options&gt;
   *  Distance function to use.
   *  (default: weka.core.EuclideanDistance)</pre>
   * 
   * <pre> -I &lt;num&gt;
   *  Maximum number of iterations.
   * </pre>
   * 
   * <pre> -O
   *  Preserve order of instances.
   * </pre>
   * 
   * <pre> -fast
   *  Enables faster distance calculations, using cut-off values.
   *  Disables the calculation/output of squared errors/distances.
   * </pre>
   * 
   * <pre> -S &lt;num&gt;
   *  Random number seed.
   *  (default 10)</pre>
   * 
   <!-- options-end -->
   *
   * @author Mark Hall (mhall@cs.waikato.ac.nz)
   * @author Eibe Frank (eibe@cs.waikato.ac.nz)
   * @version $Revision: 7282 $
   * @see RandomizableClusterer
   */
  public class SimpleKMeans
    extends RandomizableClusterer 
    implements NumberOfClustersRequestable, WeightedInstancesHandler,
      TechnicalInformationHandler {
  
    /** for serialization. */
    static final long serialVersionUID = -3235809600124455376L;
    
    /**
     * replace missing values in training instances.
     */
    private ReplaceMissingValues m_ReplaceMissingFilter;
  
    /**
     * number of clusters to generate.
     */
    private int m_NumClusters = 2;
  
    /**
     * holds the cluster centroids.
     */
    private Instances m_ClusterCentroids;
  
    /**
     * Holds the standard deviations of the numeric attributes in each cluster.
     */
    private Instances m_ClusterStdDevs;
  
    
    /**
     * For each cluster, holds the frequency counts for the values of each 
     * nominal attribute.
     */
    private int[][][] m_ClusterNominalCounts;
    private int[][] m_ClusterMissingCounts;
    
    /**
     * Stats on the full data set for comparison purposes.
     * In case the attribute is numeric the value is the mean if is 
     * being used the Euclidian distance or the median if Manhattan distance
     * and if the attribute is nominal then it's mode is saved.
     */
    private double[] m_FullMeansOrMediansOrModes;
    private double[] m_FullStdDevs;
    private int[][] m_FullNominalCounts;
    private int[] m_FullMissingCounts;
  
    /**
     * Display standard deviations for numeric atts.
     */
    private boolean m_displayStdDevs;
  
    /**
     * Replace missing values globally?
     */
    private boolean m_dontReplaceMissing = false;
  
    /**
     * The number of instances in each cluster.
     */
    private int[] m_ClusterSizes;
  
    /**
     * Maximum number of iterations to be executed.
     */
    private int m_MaxIterations = 500;
  
    /**
     * Keep track of the number of iterations completed before convergence.
     */
    private int m_Iterations = 0;
  
    /**
     * Holds the squared errors for all clusters.
     */
    private double[] m_squaredErrors;
  
    /** the distance function used. */
    protected DistanceFunction m_DistanceFunction = new EuclideanDistance();
  
    /**
     * Preserve order of instances.
     */
    private boolean m_PreserveOrder = false;
  	
    /**
     * Assignments obtained.
     */
    protected int[] m_Assignments = null;
  	
    /** whether to use fast calculation of distances (using a cut-off). */
    protected boolean m_FastDistanceCalc = false;
    
    /** Whether to initialize cluster centers using the k-means++ method */
    protected boolean m_initializeWithKMeansPlusPlus = false;
    
    /**
     * the default constructor.
     */
    public SimpleKMeans() {
      super();
      
      m_SeedDefault = 10;
      setSeed(m_SeedDefault);
    }
    
    public TechnicalInformation getTechnicalInformation() {
      TechnicalInformation result;
      
      result = new TechnicalInformation(Type.INPROCEEDINGS);
      result.setValue(Field.AUTHOR, "D. Arthur and S. Vassilvitskii");
      result.setValue(Field.TITLE, "k-means++: the advantages of carefull seeding");
      result.setValue(Field.BOOKTITLE, "Proceedings of the eighteenth annual " +
      		"ACM-SIAM symposium on Discrete algorithms");
      result.setValue(Field.YEAR, "2007");
      result.setValue(Field.PAGES, "1027-1035");
      
      return result;
    }
    
    /**
     * Returns a string describing this clusterer.
     * @return a description of the evaluator suitable for
     * displaying in the explorer/experimenter gui
     */
    public String globalInfo() {
      return "Cluster data using the k means algorithm. Can use either "
        + "the Euclidean distance (default) or the Manhattan distance."
        + " If the Manhattan distance is used, then centroids are computed "
        + "as the component-wise median rather than mean."
        + " For more information see:\n\n" 
        + getTechnicalInformation().toString();
    }
  
    /**
     * Returns default capabilities of the clusterer.
     *
     * @return      the capabilities of this clusterer
     */
    public Capabilities getCapabilities() {
      Capabilities result = super.getCapabilities();
      result.disableAll();
      result.enable(Capability.NO_CLASS);
  
      // attributes
      result.enable(Capability.NOMINAL_ATTRIBUTES);
      result.enable(Capability.NUMERIC_ATTRIBUTES);
      result.enable(Capability.MISSING_VALUES);
  
      return result;
    }
  
    /**
     * Generates a clusterer. Has to initialize all fields of the clusterer
     * that are not being set via options.
     *
     * @param data set of instances serving as training data 
     * @throws Exception if the clusterer has not been 
     * generated successfully
     */
    public void buildClusterer(Instances data) throws Exception {
  
      // can clusterer handle the data?
      getCapabilities().testWithFail(data);
  
      m_Iterations = 0;
  
      m_ReplaceMissingFilter = new ReplaceMissingValues();
      Instances instances = new Instances(data);
  				
      instances.setClassIndex(-1);
      if (!m_dontReplaceMissing) {
        m_ReplaceMissingFilter.setInputFormat(instances);
        instances = Filter.useFilter(instances, m_ReplaceMissingFilter);
      }
  
      m_FullMissingCounts = new int[instances.numAttributes()];
      if (m_displayStdDevs) {
        m_FullStdDevs = new double[instances.numAttributes()];
      }
      m_FullNominalCounts = new int[instances.numAttributes()][0];
  		
      m_FullMeansOrMediansOrModes = moveCentroid(0, instances, false);
      for (int i = 0; i < instances.numAttributes(); i++) {
        m_FullMissingCounts[i] = instances.attributeStats(i).missingCount;
        if (instances.attribute(i).isNumeric()) {
          if (m_displayStdDevs) {
            m_FullStdDevs[i] = Math.sqrt(instances.variance(i));
          }
          if (m_FullMissingCounts[i] == instances.numInstances()) {
            m_FullMeansOrMediansOrModes[i] = Double.NaN; // mark missing as mean
          }
        } else {
          m_FullNominalCounts[i] = instances.attributeStats(i).nominalCounts;
          if (m_FullMissingCounts[i] 
              > m_FullNominalCounts[i][Utils.maxIndex(m_FullNominalCounts[i])]) {
            m_FullMeansOrMediansOrModes[i] = -1; // mark missing as most common value
          }
        }
      }
  
      m_ClusterCentroids = new Instances(instances, m_NumClusters);
      int[] clusterAssignments = new int [instances.numInstances()];
  
      if (m_PreserveOrder)
        m_Assignments = clusterAssignments;
  		
      m_DistanceFunction.setInstances(instances);
      
      Random RandomO = new Random(getSeed());
      int instIndex;
      HashMap initC = new HashMap();
      DecisionTableHashKey hk = null;
  
      Instances initInstances = null;
      if (m_PreserveOrder)
        initInstances = new Instances(instances);
      else
        initInstances = instances;
  
      if (m_initializeWithKMeansPlusPlus) {
        kMeansPlusPlusInit(initInstances);
      } else {
        for (int j = initInstances.numInstances() - 1; j >= 0; j--) {
          instIndex = RandomO.nextInt(j+1);
          hk = new DecisionTableHashKey(initInstances.instance(instIndex),
              initInstances.numAttributes(), true);
          if (!initC.containsKey(hk)) {
            m_ClusterCentroids.add(initInstances.instance(instIndex));
            initC.put(hk, null);
          }
          initInstances.swap(j, instIndex);
  
          if (m_ClusterCentroids.numInstances() == m_NumClusters) {
            break;
          }
        }
      }
  
      m_NumClusters = m_ClusterCentroids.numInstances();
      
      //removing reference
      initInstances = null;
  		
      int i;
      boolean converged = false;
      int emptyClusterCount;
      Instances[] tempI = new Instances[m_NumClusters];
      m_squaredErrors = new double [m_NumClusters];
      m_ClusterNominalCounts = new int [m_NumClusters][instances.numAttributes()][0];
      m_ClusterMissingCounts = new int[m_NumClusters][instances.numAttributes()];
      while (!converged) {
        emptyClusterCount = 0;
        m_Iterations++;
        converged = true;
        for (i = 0; i < instances.numInstances(); i++) {
  	Instance toCluster = instances.instance(i);
  	int newC = clusterProcessedInstance(toCluster, false, true);
  	if (newC != clusterAssignments[i]) {
  	  converged = false;
  	}
  	clusterAssignments[i] = newC;
        }
        
        // update centroids
        m_ClusterCentroids = new Instances(instances, m_NumClusters);
        for (i = 0; i < m_NumClusters; i++) {
  	tempI[i] = new Instances(instances, 0);
        }
        for (i = 0; i < instances.numInstances(); i++) {
  	tempI[clusterAssignments[i]].add(instances.instance(i));
        }
        for (i = 0; i < m_NumClusters; i++) {
  	if (tempI[i].numInstances() == 0) {
  	  // empty cluster
  	  emptyClusterCount++;
  	} else {
            moveCentroid( i, tempI[i], true  );					
  	}
        }
  
        if (emptyClusterCount > 0) {
  	m_NumClusters -= emptyClusterCount;
          if (converged) {
            Instances[] t = new Instances[m_NumClusters];
            int index = 0;
            for (int k = 0; k < tempI.length; k++) {
              if (tempI[k].numInstances() > 0) {
                t[index++] = tempI[k];
              }
            }
            tempI = t;
          } else {
            tempI = new Instances[m_NumClusters];
          }
        }
  			
        if (m_Iterations == m_MaxIterations)
          converged = true;
  			
        if (!converged) {
  	m_ClusterNominalCounts = new int [m_NumClusters][instances.numAttributes()][0];
        }
      }
  
      // calculate errors
      if (!m_FastDistanceCalc) {
        for (i = 0; i < instances.numInstances(); i++) {
  	clusterProcessedInstance(instances.instance(i), true, false);
        }
      }
      
      if (m_displayStdDevs) {
        m_ClusterStdDevs = new Instances(instances, m_NumClusters);
      }
      m_ClusterSizes = new int [m_NumClusters];
      for (i = 0; i < m_NumClusters; i++) {
        if (m_displayStdDevs) {
          double[] vals2 = new double[instances.numAttributes()];
          for (int j = 0; j < instances.numAttributes(); j++) {
            if (instances.attribute(j).isNumeric()) {
              vals2[j] = Math.sqrt(tempI[i].variance(j));
            } else {
              vals2[j] = Utils.missingValue();
            }	
          }    
          m_ClusterStdDevs.add(new DenseInstance(1.0, vals2));
        }
        m_ClusterSizes[i] = tempI[i].numInstances();
      }
    }
    
    protected void kMeansPlusPlusInit(Instances data) throws Exception {
      Random randomO = new Random(getSeed());
      HashMap<DecisionTableHashKey, String> initC = new HashMap<DecisionTableHashKey, String>();
      
      // choose initial center uniformly at random
      int index = randomO.nextInt(data.numInstances());
      m_ClusterCentroids.add(data.instance(index));
      DecisionTableHashKey hk = new DecisionTableHashKey(data.instance(index),
          data.numAttributes(), true);
      initC.put(hk, null);
      
      int iteration = 0;
      int remainingInstances = data.numInstances() - 1;
      if (m_NumClusters > 1) {
        // proceed with selecting the rest
  
        // distances to the initial randomly chose center
        double[] distances = new double[data.numInstances()];
        double[] cumProbs = new double[data.numInstances()];
        for (int i = 0; i < data.numInstances(); i++) {
          distances[i] = 
            m_DistanceFunction.distance(data.instance(i), 
                m_ClusterCentroids.instance(iteration));
        }
  
        // now choose the remaining cluster centers
        for (int i = 1; i < m_NumClusters; i++) {
  
          // distances converted to probabilities
          double[] weights = new double[data.numInstances()];
          System.arraycopy(distances, 0, weights, 0, distances.length);
          Utils.normalize(weights);
  
          double sumOfProbs = 0;
          for (int k = 0; k < data.numInstances(); k++) {
            sumOfProbs += weights[k];
            cumProbs[k] = sumOfProbs;
          }
  
          cumProbs[data.numInstances() - 1] = 1.0; // make sure there are no rounding issues
  
          // choose a random instance
          double prob = randomO.nextDouble();
          for (int k = 0; k < cumProbs.length; k++) {
            if (prob < cumProbs[k]) {
              Instance candidateCenter = data.instance(k);
              hk = new DecisionTableHashKey(candidateCenter, data.numAttributes(), true);
              if (!initC.containsKey(hk)) {
                initC.put(hk, null);
                m_ClusterCentroids.add(candidateCenter);
              } else {
                // we shouldn't get here because any instance that is a duplicate of
                // an already chosen cluster center should have zero distance (and hence
                // zero probability of getting chosen) to that center.
                System.err.println("We shouldn't get here....");
              }
              remainingInstances--;
              break;
            }
          }
          iteration++;
  
          if (remainingInstances == 0) {
            break;
          }
          
          // prepare to choose the next cluster center.
          // check distances against the new cluster center to see if it is closer
          for (int k = 0; k < data.numInstances(); k++) {
            if (distances[k] > 0) {
              double newDist = m_DistanceFunction.distance(data.instance(k), 
                  m_ClusterCentroids.instance(iteration));
              if (newDist < distances[k]) {
                distances[k] = newDist;
              }
            }
          }        
        }
      }
    }
  
  
    /**
     * Move the centroid to it's new coordinates. Generate the centroid coordinates based 
     * on it's  members (objects assigned to the cluster of the centroid) and the distance 
     * function being used.
     * @param centroidIndex index of the centroid which the coordinates will be computed
     * @param members the objects that are assigned to the cluster of this centroid
     * @param updateClusterInfo if the method is supposed to update the m_Cluster arrays
     * @return the centroid coordinates
     */
    protected double[] moveCentroid(int centroidIndex, Instances members, boolean updateClusterInfo) {
      double[] vals = new double[members.numAttributes()];
  		
      //used only for Manhattan Distance
      Instances sortedMembers = null;
      int middle = 0;
      boolean dataIsEven = false;
  		
      if (m_DistanceFunction instanceof ManhattanDistance) {
        middle = (members.numInstances()-1)/2;
        dataIsEven = ((members.numInstances()%2)==0);
        if (m_PreserveOrder) {
          sortedMembers = members;
        }else{
          sortedMembers = new Instances(members);
        }
      }
  		
      for (int j = 0; j < members.numAttributes(); j++) {						
  			
        //in case of Euclidian distance the centroid is the mean point
        //in case of Manhattan distance the centroid is the median point
        //in both cases, if the attribute is nominal, the centroid is the mode
        if (m_DistanceFunction instanceof EuclideanDistance ||
           members.attribute(j).isNominal())
          {													
            vals[j] = members.meanOrMode(j);
          }else if (m_DistanceFunction instanceof ManhattanDistance) {
          //singleton special case
          if (members.numInstances() == 1) {
            vals[j] = members.instance(0).value(j);
          }else{
            sortedMembers.kthSmallestValue(j, middle+1);
            vals[j] = sortedMembers.instance(middle).value(j);
            if ( dataIsEven ) {						
              sortedMembers.kthSmallestValue(j, middle+2);						
              vals[j] = (vals[j]+sortedMembers.instance(middle+1).value(j))/2;
            }
          }
        }	
  			
        if (updateClusterInfo) {
          m_ClusterMissingCounts[centroidIndex][j] = members.attributeStats(j).missingCount;
          m_ClusterNominalCounts[centroidIndex][j] = members.attributeStats(j).nominalCounts;
          if (members.attribute(j).isNominal()) {
            if (m_ClusterMissingCounts[centroidIndex][j] >  
                m_ClusterNominalCounts[centroidIndex][j][Utils.maxIndex(m_ClusterNominalCounts[centroidIndex][j])]) 
              {
                vals[j] = Utils.missingValue(); // mark mode as missing
              }
          } else {
            if (m_ClusterMissingCounts[centroidIndex][j] == members.numInstances()) {
              vals[j] = Utils.missingValue(); // mark mean as missing
            }
          }
        }
      }
      if (updateClusterInfo)
        m_ClusterCentroids.add(new DenseInstance(1.0, vals));
      return vals;
    }
  	
    /**
     * clusters an instance that has been through the filters.
     *
     * @param instance the instance to assign a cluster to
     * @param updateErrors if true, update the within clusters sum of errors
     * @param useFastDistCalc whether to use the fast distance calculation or not
     * @return a cluster number
     */
    private int clusterProcessedInstance(Instance instance, boolean updateErrors, boolean useFastDistCalc) {
      double minDist = Integer.MAX_VALUE;
      int bestCluster = 0;
      for (int i = 0; i < m_NumClusters; i++) {
        double dist;
        if (useFastDistCalc)
  	dist = m_DistanceFunction.distance(instance, m_ClusterCentroids.instance(i), minDist);
        else
  	dist = m_DistanceFunction.distance(instance, m_ClusterCentroids.instance(i));
        if (dist < minDist) {
  	minDist = dist;
  	bestCluster = i;
        }
      }
      if (updateErrors) {
        if (m_DistanceFunction instanceof EuclideanDistance) {
          //Euclidean distance to Squared Euclidean distance
          minDist *= minDist;
        }
        m_squaredErrors[bestCluster] += minDist;
      }
      return bestCluster;
    }
  
    /**
     * Classifies a given instance.
     *
     * @param instance the instance to be assigned to a cluster
     * @return the number of the assigned cluster as an interger
     * if the class is enumerated, otherwise the predicted value
     * @throws Exception if instance could not be classified
     * successfully
     */
    public int clusterInstance(Instance instance) throws Exception {
      Instance inst = null;
      if (!m_dontReplaceMissing) {
        m_ReplaceMissingFilter.input(instance);
        m_ReplaceMissingFilter.batchFinished();
        inst = m_ReplaceMissingFilter.output();
      } else {
        inst = instance;
      }
  
      return clusterProcessedInstance(inst, false, true);
    }
  
    /**
     * Returns the number of clusters.
     *
     * @return the number of clusters generated for a training dataset.
     * @throws Exception if number of clusters could not be returned
     * successfully
     */
    public int numberOfClusters() throws Exception {
      return m_NumClusters;
    }
  
    /**
     * Returns an enumeration describing the available options.
     *
     * @return an enumeration of all the available options.
     */
    public Enumeration listOptions() {
      Vector result = new Vector();
  
      result.addElement(new Option(
                                   "\tnumber of clusters.\n"
                                   + "\t(default 2).", 
                                   "N", 1, "-N <num>"));
      
      result.addElement(new Option(
          "\tInitialize using the k-means++ method.\n", 
          "P", 0, "-P"));
      
      result.addElement(new Option(
                                   "\tDisplay std. deviations for centroids.\n", 
                                   "V", 0, "-V"));
      result.addElement(new Option(
                                   "\tReplace missing values with mean/mode.\n", 
                                   "M", 0, "-M"));
  
      result.add(new Option(
                            "\tDistance function to use.\n"
                            + "\t(default: weka.core.EuclideanDistance)",
                            "A", 1,"-A <classname and options>"));
  		
      result.add(new Option(
                            "\tMaximum number of iterations.\n",
                            "I",1,"-I <num>"));
  
      result.addElement(new Option(
                                   "\tPreserve order of instances.\n", 
                                   "O", 0, "-O"));
  
      result.addElement(new Option(
                                   "\tEnables faster distance calculations, using cut-off values.\n"
                                   + "\tDisables the calculation/output of squared errors/distances.\n", 
                                   "fast", 0, "-fast"));
  		
      Enumeration en = super.listOptions();
      while (en.hasMoreElements())
        result.addElement(en.nextElement());
  
      return  result.elements();
    }
  
    /**
     * Returns the tip text for this property.
     * @return tip text for this property suitable for
     * displaying in the explorer/experimenter gui
     */
    public String numClustersTipText() {
      return "set number of clusters";
    }
  
    /**
     * set the number of clusters to generate.
     *
     * @param n the number of clusters to generate
     * @throws Exception if number of clusters is negative
     */
    public void setNumClusters(int n) throws Exception {
      if (n <= 0) {
        throw new Exception("Number of clusters must be > 0");
      }
      m_NumClusters = n;
    }
  
    /**
     * gets the number of clusters to generate.
     *
     * @return the number of clusters to generate
     */
    public int getNumClusters() {
      return m_NumClusters;
    }
    
    /**
     * Returns the tip text for this property.
     * @return tip text for this property suitable for
     * displaying in the explorer/experimenter gui
     */
    public String initializeUsingKMeansPlusPlusMethodTipText() {
      return "Initialize cluster centers using the probabilistic "
        + " farthest first method of the k-means++ algorithm";
    }
    
    /**
     * Set whether to initialize using the probabilistic farthest
     * first like method of the k-means++ algorithm (rather than
     * the standard random selection of initial cluster centers).
     * 
     * @param k true if the k-means++ method is to be used to select
     * initial cluster centers.
     */
    public void setInitializeUsingKMeansPlusPlusMethod(boolean k) {
      m_initializeWithKMeansPlusPlus = k;
    }
    
    /**
     * Get whether to initialize using the probabilistic farthest
     * first like method of the k-means++ algorithm (rather than
     * the standard random selection of initial cluster centers).
     * 
     * @return true if the k-means++ method is to be used to select
     * initial cluster centers.
     */
    public boolean getInitializeUsingKMeansPlusPlusMethod() {
      return m_initializeWithKMeansPlusPlus;
    }
  
    /**
     * Returns the tip text for this property.
     * @return tip text for this property suitable for
     * displaying in the explorer/experimenter gui
     */
    public String maxIterationsTipText() {
      return "set maximum number of iterations";
    }
  
    /**
     * set the maximum number of iterations to be executed.
     *
     * @param n the maximum number of iterations
     * @throws Exception if maximum number of iteration is smaller than 1
     */
    public void setMaxIterations(int n) throws Exception {
      if (n <= 0) {
        throw new Exception("Maximum number of iterations must be > 0");
      }
      m_MaxIterations = n;
    }
  
    /**
     * gets the number of maximum iterations to be executed.
     *
     * @return the number of clusters to generate
     */
    public int getMaxIterations() {
      return m_MaxIterations;
    }
  	
  
    /**
     * Returns the tip text for this property.
     * @return tip text for this property suitable for
     * displaying in the explorer/experimenter gui
     */
    public String displayStdDevsTipText() {
      return "Display std deviations of numeric attributes "
        + "and counts of nominal attributes.";
    }
  
    /**
     * Sets whether standard deviations and nominal count.
     * Should be displayed in the clustering output.
     *
     * @param stdD true if std. devs and counts should be 
     * displayed
     */
    public void setDisplayStdDevs(boolean stdD) {
      m_displayStdDevs = stdD;
    }
  
    /**
     * Gets whether standard deviations and nominal count.
     * Should be displayed in the clustering output.
     *
     * @return true if std. devs and counts should be 
     * displayed
     */
    public boolean getDisplayStdDevs() {
      return m_displayStdDevs;
    }
  
    /**
     * Returns the tip text for this property.
     * @return tip text for this property suitable for
     * displaying in the explorer/experimenter gui
     */
    public String dontReplaceMissingValuesTipText() {
      return "Replace missing values globally with mean/mode.";
    }
  
    /**
     * Sets whether missing values are to be replaced.
     *
     * @param r true if missing values are to be
     * replaced
     */
    public void setDontReplaceMissingValues(boolean r) {
      m_dontReplaceMissing = r;
    }
  
    /**
     * Gets whether missing values are to be replaced.
     *
     * @return true if missing values are to be
     * replaced
     */
    public boolean getDontReplaceMissingValues() {
      return m_dontReplaceMissing;
    }
  
    /**
     * Returns the tip text for this property.
     * 
     * @return 		tip text for this property suitable for
     *         		displaying in the explorer/experimenter gui
     */
    public String distanceFunctionTipText() {
      return "The distance function to use for instances comparison " +
        "(default: weka.core.EuclideanDistance). ";
    }
  
    /**
     * returns the distance function currently in use.
     * 
     * @return the distance function
     */
    public DistanceFunction getDistanceFunction() {
      return m_DistanceFunction;
    }
  
    /**
     * sets the distance function to use for instance comparison.
     * 
     * @param df the new distance function to use
     * @throws Exception if instances cannot be processed
     */
    public void setDistanceFunction(DistanceFunction df) throws Exception {
      if (!(df instanceof EuclideanDistance) && 
  	!(df instanceof ManhattanDistance))      {
        throw new Exception("SimpleKMeans currently only supports the Euclidean and Manhattan distances.");
      }
      m_DistanceFunction = df;
    }	
  
    /**
     * Returns the tip text for this property.
     * @return tip text for this property suitable for
     * displaying in the explorer/experimenter gui
     */
    public String preserveInstancesOrderTipText() {
      return "Preserve order of instances.";
    }
  
    /**
     * Sets whether order of instances must be preserved.
     *
     * @param r true if missing values are to be
     * replaced
     */
    public void setPreserveInstancesOrder(boolean r) {
      m_PreserveOrder = r;
    }
  
    /**
     * Gets whether order of instances must be preserved.
     *
     * @return true if missing values are to be
     * replaced
     */
    public boolean getPreserveInstancesOrder() {
      return m_PreserveOrder;
    }
  
    /**
     * Returns the tip text for this property.
     * 
     * @return 		tip text for this property suitable for
     * 			displaying in the explorer/experimenter gui
     */
    public String fastDistanceCalcTipText() {
      return 
          "Uses cut-off values for speeding up distance calculation, but "
        + "suppresses also the calculation and output of the within cluster sum "
        + "of squared errors/sum of distances.";
    }
  
    /**
     * Sets whether to use faster distance calculation.
     *
     * @param value 	true if faster calculation to be used
     */
    public void setFastDistanceCalc(boolean value) {
      m_FastDistanceCalc = value;
    }
  
    /**
     * Gets whether to use faster distance calculation.
     *
     * @return 		true if faster calculation is used
     */
    public boolean getFastDistanceCalc() {
      return m_FastDistanceCalc;
    }
  	
    /**
     * Parses a given list of options. <p/>
     * 
     <!-- options-start -->
     * Valid options are: <p/>
     * 
     * <pre> -N &lt;num&gt;
     *  number of clusters.
     *  (default 2).</pre>
     * 
     * <pre> -P
     *  Initialize using the k-means++ method.
     * </pre>
     * 
     * <pre> -V
     *  Display std. deviations for centroids.
     * </pre>
     * 
     * <pre> -M
     *  Replace missing values with mean/mode.
     * </pre>
     * 
     * <pre> -A &lt;classname and options&gt;
     *  Distance function to use.
     *  (default: weka.core.EuclideanDistance)</pre>
     * 
     * <pre> -I &lt;num&gt;
     *  Maximum number of iterations.
     * </pre>
     * 
     * <pre> -O
     *  Preserve order of instances.
     * </pre>
     * 
     * <pre> -fast
     *  Enables faster distance calculations, using cut-off values.
     *  Disables the calculation/output of squared errors/distances.
     * </pre>
     * 
     * <pre> -S &lt;num&gt;
     *  Random number seed.
     *  (default 10)</pre>
     * 
     <!-- options-end -->
     *
     * @param options the list of options as an array of strings
     * @throws Exception if an option is not supported
     */
    public void setOptions (String[] options)
      throws Exception {
  
      m_displayStdDevs = Utils.getFlag("V", options);
      m_dontReplaceMissing = Utils.getFlag("M", options);
      m_initializeWithKMeansPlusPlus = Utils.getFlag('P', options);
  
      String optionString = Utils.getOption('N', options);
  
      if (optionString.length() != 0) {
        setNumClusters(Integer.parseInt(optionString));
      }
      
      optionString = Utils.getOption("I", options);
      if (optionString.length() != 0) {
        setMaxIterations(Integer.parseInt(optionString));
      }
  		
      String distFunctionClass = Utils.getOption('A', options);
      if (distFunctionClass.length() != 0) {
        String distFunctionClassSpec[] = Utils.splitOptions(distFunctionClass);
        if (distFunctionClassSpec.length == 0) { 
          throw new Exception("Invalid DistanceFunction specification string."); 
        }
        String className = distFunctionClassSpec[0];
        distFunctionClassSpec[0] = "";
  
        setDistanceFunction( (DistanceFunction)
                             Utils.forName( DistanceFunction.class, 
                                            className, distFunctionClassSpec) );
      }
      else {
        setDistanceFunction(new EuclideanDistance());
      }
  		
      m_PreserveOrder = Utils.getFlag("O", options);
  
      m_FastDistanceCalc = Utils.getFlag("fast", options);
      
      super.setOptions(options);
    }
  
    /**
     * Gets the current settings of SimpleKMeans.
     *
     * @return an array of strings suitable for passing to setOptions()
     */
    public String[] getOptions () {
      int       	i;
      Vector    	result;
      String[]  	options;
  
      result = new Vector();
      
      if (m_initializeWithKMeansPlusPlus) {
        result.add("-P");
      }
  
      if (m_displayStdDevs) {
        result.add("-V");
      }
  
      if (m_dontReplaceMissing) {
        result.add("-M");
      }
  
      result.add("-N");
      result.add("" + getNumClusters());
  
      result.add("-A");
      result.add((m_DistanceFunction.getClass().getName() + " " +
                  Utils.joinOptions(m_DistanceFunction.getOptions())).trim());
  		
      result.add("-I");
      result.add(""+ getMaxIterations());
  
      if (m_PreserveOrder) {
        result.add("-O");
      }
  		
      if (m_FastDistanceCalc) {
        result.add("-fast");
      }
      
      options = super.getOptions();
      for (i = 0; i < options.length; i++)
        result.add(options[i]);
  
      return (String[]) result.toArray(new String[result.size()]);	  
    }
  
    /**
     * return a string describing this clusterer.
     *
     * @return a description of the clusterer as a string
     */
    public String toString() {
      if (m_ClusterCentroids == null) {
        return "No clusterer built yet!";
      }
  
      int maxWidth = 0;
      int maxAttWidth = 0;
      boolean containsNumeric = false;
      for (int i = 0; i < m_NumClusters; i++) {
        for (int j = 0 ;j < m_ClusterCentroids.numAttributes(); j++) {
          if (m_ClusterCentroids.attribute(j).name().length() > maxAttWidth) {
            maxAttWidth = m_ClusterCentroids.attribute(j).name().length();
          }
  	if (m_ClusterCentroids.attribute(j).isNumeric()) {
            containsNumeric = true;
  	  double width = Math.log(Math.abs(m_ClusterCentroids.instance(i).value(j))) /
  	    Math.log(10.0);
            //          System.err.println(m_ClusterCentroids.instance(i).value(j)+" "+width);
            if (width < 0) {
              width = 1;
            }
            // decimal + # decimal places + 1
  	  width += 6.0;
  	  if ((int)width > maxWidth) {
  	    maxWidth = (int)width;
  	  }
  	}
        }
      }
  
      for (int i = 0; i < m_ClusterCentroids.numAttributes(); i++) {
        if (m_ClusterCentroids.attribute(i).isNominal()) {
          Attribute a = m_ClusterCentroids.attribute(i);
          for (int j = 0; j < m_ClusterCentroids.numInstances(); j++) {
            String val = a.value((int)m_ClusterCentroids.instance(j).value(i));
            if (val.length() > maxWidth) {
              maxWidth = val.length();
            }
          }
          for (int j = 0; j < a.numValues(); j++) {
            String val = a.value(j) + " ";
            if (val.length() > maxAttWidth) {
              maxAttWidth = val.length();
            }
          }
        }
      }
  
      if (m_displayStdDevs) {
        // check for maximum width of maximum frequency count
        for (int i = 0; i < m_ClusterCentroids.numAttributes(); i++) {
          if (m_ClusterCentroids.attribute(i).isNominal()) {
            int maxV = Utils.maxIndex(m_FullNominalCounts[i]);
            /*          int percent = (int)((double)m_FullNominalCounts[i][maxV] /
                        Utils.sum(m_ClusterSizes) * 100.0); */
            int percent = 6; // max percent width (100%)
            String nomV = "" + m_FullNominalCounts[i][maxV];
            //            + " (" + percent + "%)";
            if (nomV.length() + percent > maxWidth) {
              maxWidth = nomV.length() + 1;
            }
          }
        }
      }
  
      // check for size of cluster sizes
      for (int i = 0; i < m_ClusterSizes.length; i++) {
        String size = "(" + m_ClusterSizes[i] + ")";
        if (size.length() > maxWidth) {
          maxWidth = size.length();
        }
      }
      
      if (m_displayStdDevs && maxAttWidth < "missing".length()) {
        maxAttWidth = "missing".length();
      }
      
      String plusMinus = "+/-";
      maxAttWidth += 2;
      if (m_displayStdDevs && containsNumeric) {
        maxWidth += plusMinus.length();
      }
      if (maxAttWidth < "Attribute".length() + 2) {
        maxAttWidth = "Attribute".length() + 2;
      }
  
      if (maxWidth < "Full Data".length()) {
        maxWidth = "Full Data".length() + 1;
      }
  
      if (maxWidth < "missing".length()) {
        maxWidth = "missing".length() + 1;
      }
      
      StringBuffer temp = new StringBuffer();
      temp.append("\nkMeans\n======\n");
      temp.append("\nNumber of iterations: " + m_Iterations);
  		
      if (!m_FastDistanceCalc) {
        temp.append("\n");
        if (m_DistanceFunction instanceof EuclideanDistance) {
  	temp.append("Within cluster sum of squared errors: " + Utils.sum(m_squaredErrors));
        }else{
  	temp.append("Sum of within cluster distances: " + Utils.sum(m_squaredErrors));
        }
      }
  		
      if (!m_dontReplaceMissing) {
        temp.append("\nMissing values globally replaced with mean/mode");
      }
  
      temp.append("\n\nCluster centroids:\n");
      temp.append(pad("Cluster#", " ", (maxAttWidth + (maxWidth * 2 + 2)) - "Cluster#".length(), true));
  
      temp.append("\n");
      temp.append(pad("Attribute", " ", maxAttWidth - "Attribute".length(), false));
  
      
      temp.append(pad("Full Data", " ", maxWidth + 1 - "Full Data".length(), true));
  
      // cluster numbers
      for (int i = 0; i < m_NumClusters; i++) {
        String clustNum = "" + i;
        temp.append(pad(clustNum, " ", maxWidth + 1 - clustNum.length(), true));
      }
      temp.append("\n");
  
      // cluster sizes
      String cSize = "(" + Utils.sum(m_ClusterSizes) + ")";
      temp.append(pad(cSize, " ", maxAttWidth + maxWidth + 1 - cSize.length(), true));
      for (int i = 0; i < m_NumClusters; i++) {
        cSize = "(" + m_ClusterSizes[i] + ")";
        temp.append(pad(cSize, " ",maxWidth + 1 - cSize.length(), true));
      }
      temp.append("\n");
  
      temp.append(pad("", "=", maxAttWidth + 
                      (maxWidth * (m_ClusterCentroids.numInstances()+1) 
                       + m_ClusterCentroids.numInstances() + 1), true));
      temp.append("\n");
  
      for (int i = 0; i < m_ClusterCentroids.numAttributes(); i++) {
        String attName = m_ClusterCentroids.attribute(i).name();
        temp.append(attName);
        for (int j = 0; j < maxAttWidth - attName.length(); j++) {
          temp.append(" ");
        }
  
        String strVal;
        String valMeanMode;
        // full data
        if (m_ClusterCentroids.attribute(i).isNominal()) {
          if (m_FullMeansOrMediansOrModes[i] == -1) { // missing
            valMeanMode = pad("missing", " ", maxWidth + 1 - "missing".length(), true);
          } else {
            valMeanMode = 
              pad((strVal = m_ClusterCentroids.attribute(i).value((int)m_FullMeansOrMediansOrModes[i])),
                  " ", maxWidth + 1 - strVal.length(), true);
          }
        } else {
          if (Double.isNaN(m_FullMeansOrMediansOrModes[i])) {
            valMeanMode = pad("missing", " ", maxWidth + 1 - "missing".length(), true);
          } else {
            valMeanMode =  pad((strVal = Utils.doubleToString(m_FullMeansOrMediansOrModes[i],
                                                              maxWidth,4).trim()), 
                               " ", maxWidth + 1 - strVal.length(), true);
          }
        }
        temp.append(valMeanMode);
  
        for (int j = 0; j < m_NumClusters; j++) {
          if (m_ClusterCentroids.attribute(i).isNominal()) {
            if (m_ClusterCentroids.instance(j).isMissing(i)) {
              valMeanMode = pad("missing", " ", maxWidth + 1 - "missing".length(), true);
            } else {
              valMeanMode = 
                pad((strVal = m_ClusterCentroids.attribute(i).value((int)m_ClusterCentroids.instance(j).value(i))),
                    " ", maxWidth + 1 - strVal.length(), true);
            }
          } else {
            if (m_ClusterCentroids.instance(j).isMissing(i)) {
              valMeanMode = pad("missing", " ", maxWidth + 1 - "missing".length(), true);
            } else {
              valMeanMode = pad((strVal = Utils.doubleToString(m_ClusterCentroids.instance(j).value(i),
                                                               maxWidth,4).trim()), 
                                " ", maxWidth + 1 - strVal.length(), true);
            }
          }
          temp.append(valMeanMode);
        }
        temp.append("\n");
  
        if (m_displayStdDevs) {
          // Std devs/max nominal
          String stdDevVal = "";
  
          if (m_ClusterCentroids.attribute(i).isNominal()) {
            // Do the values of the nominal attribute
            Attribute a = m_ClusterCentroids.attribute(i);
            for (int j = 0; j < a.numValues(); j++) {
              // full data
              String val = "  " + a.value(j);
              temp.append(pad(val, " ", maxAttWidth + 1 - val.length(), false));
              int count = m_FullNominalCounts[i][j];
              int percent = (int)((double)m_FullNominalCounts[i][j] /
                                  Utils.sum(m_ClusterSizes) * 100.0);
              String percentS = "" + percent + "%)";
              percentS = pad(percentS, " ", 5 - percentS.length(), true);
              stdDevVal = "" + count + " (" + percentS;
              stdDevVal = 
                pad(stdDevVal, " ", maxWidth + 1 - stdDevVal.length(), true);
              temp.append(stdDevVal);
  
              // Clusters
              for (int k = 0; k < m_NumClusters; k++) {
                count = m_ClusterNominalCounts[k][i][j];
                percent = (int)((double)m_ClusterNominalCounts[k][i][j] /
                                m_ClusterSizes[k] * 100.0);
                percentS = "" + percent + "%)";
                percentS = pad(percentS, " ", 5 - percentS.length(), true);
                stdDevVal = "" + count + " (" + percentS;
                stdDevVal = 
                  pad(stdDevVal, " ", maxWidth + 1 - stdDevVal.length(), true);
                temp.append(stdDevVal);
              }
              temp.append("\n");
            }
            // missing (if any)
            if (m_FullMissingCounts[i] > 0) {
              // Full data
              temp.append(pad("  missing", " ", maxAttWidth + 1 - "  missing".length(), false));
              int count = m_FullMissingCounts[i];
              int percent = (int)((double)m_FullMissingCounts[i] /
                                  Utils.sum(m_ClusterSizes) * 100.0);
              String percentS = "" + percent + "%)";
              percentS = pad(percentS, " ", 5 - percentS.length(), true);
              stdDevVal = "" + count + " (" + percentS;
              stdDevVal = 
                pad(stdDevVal, " ", maxWidth + 1 - stdDevVal.length(), true);
              temp.append(stdDevVal);
             
              // Clusters
              for (int k = 0; k < m_NumClusters; k++) {
                count = m_ClusterMissingCounts[k][i];
                percent = (int)((double)m_ClusterMissingCounts[k][i] /
                                m_ClusterSizes[k] * 100.0);
                percentS = "" + percent + "%)";
                percentS = pad(percentS, " ", 5 - percentS.length(), true);
                stdDevVal = "" + count + " (" + percentS;
                stdDevVal = 
                  pad(stdDevVal, " ", maxWidth + 1 - stdDevVal.length(), true);
                temp.append(stdDevVal);
              }
  
              temp.append("\n");
            }
  
            temp.append("\n");
          } else {
            // Full data
            if (Double.isNaN(m_FullMeansOrMediansOrModes[i])) {
              stdDevVal = pad("--", " ", maxAttWidth + maxWidth + 1 - 2, true);
            } else {
              stdDevVal = pad((strVal = plusMinus 
                               + Utils.doubleToString(m_FullStdDevs[i],
                                                      maxWidth,4).trim()), 
                              " ", maxWidth + maxAttWidth + 1 - strVal.length(), true);
            }
            temp.append(stdDevVal);
  
            // Clusters
            for (int j = 0; j < m_NumClusters; j++) {
              if (m_ClusterCentroids.instance(j).isMissing(i)) {
                stdDevVal = pad("--", " ", maxWidth + 1 - 2, true);
              } else {
                stdDevVal = 
                  pad((strVal = plusMinus 
                       + Utils.doubleToString(m_ClusterStdDevs.instance(j).value(i),
                                              maxWidth,4).trim()), 
                      " ", maxWidth + 1 - strVal.length(), true);
              }
              temp.append(stdDevVal);
            }
            temp.append("\n\n");
          }
        }
      }
  
      temp.append("\n\n");
      return temp.toString();
    }
  
    private String pad(String source, String padChar, 
                       int length, boolean leftPad) {
      StringBuffer temp = new StringBuffer();
  
      if (leftPad) {
        for (int i = 0; i< length; i++) {
          temp.append(padChar);
        }
        temp.append(source);
      } else {
        temp.append(source);
        for (int i = 0; i< length; i++) {
          temp.append(padChar);
        }
      }
      return temp.toString();
    }
  
    /**
     * Gets the the cluster centroids.
     * 
     * @return		the cluster centroids
     */
    public Instances getClusterCentroids() {
      return m_ClusterCentroids;
    }
  
    /**
     * Gets the standard deviations of the numeric attributes in each cluster.
     * 
     * @return		the standard deviations of the numeric attributes 
     * 			in each cluster
     */
    public Instances getClusterStandardDevs() {
      return m_ClusterStdDevs;
    }
  
    /**
     * Returns for each cluster the frequency counts for the values of each 
     * nominal attribute.
     * 
     * @return		the counts
     */
    public int[][][] getClusterNominalCounts() {
      return m_ClusterNominalCounts;
    }
  
    /**
     * Gets the squared error for all clusters.
     * 
     * @return		the squared error, NaN if fast distance calculation is
     * 			used
     * @see		#m_FastDistanceCalc
     */
    public double getSquaredError() {
      if (m_FastDistanceCalc)
        return Double.NaN;
      else
        return Utils.sum(m_squaredErrors);
    }
  
    /**
     * Gets the number of instances in each cluster.
     * 
     * @return		The number of instances in each cluster
     */
    public int[] getClusterSizes() {
      return m_ClusterSizes;
    }
    
    /**
     * Gets the assignments for each instance.
     * @return Array of indexes of the centroid assigned to each instance
     * @throws Exception if order of instances wasn't preserved or no assignments were made
     */
    public int[] getAssignments() throws Exception{
      if (!m_PreserveOrder) {
        throw new Exception("The assignments are only available when order of instances is preserved (-O)");
      }
      if (m_Assignments == null) {
        throw new Exception("No assignments made.");
      }
      return m_Assignments;
    }
  	
    /**
     * Returns the revision string.
     * 
     * @return		the revision
     */
    public String getRevision() {
      return RevisionUtils.extract("$Revision: 7282 $");
    }
  
    /**
     * Main method for executing this class.
     *
     * @param args 	use -h to list all parameters
     */
    public static void main (String[] args) {
      runClusterer(new SimpleKMeans(), args);
    }
  }
  
  