Example usage for weka.core Instances numInstances

Introduction

In this page you can find the example usage for weka.core Instances numInstances.

Prototype

publicint numInstances() 

Source Link

Document

Returns the number of instances in the dataset.

Usage

From source file:adams.gui.visualization.instances.instancestable.AbstractPlotRow.java

License:Open Source License

/**
 * Hook method for checks before attempting the plot.
 *
 * @param table   the source table//from  w  w w.jav a  2s . com
 * @param data   the instances to use as basis
 * @param actRow   the actual row in the instances
 * @param selRow    the selected row in the table
 * @return      null if passed, otherwise error message
 */
protected String check(InstancesTable table, Instances data, int actRow, int selRow) {
    if (table == null)
        return "No source table available!";
    if (data == null)
        return "No instances available!";
    if (actRow < 0)
        return "Negative row index!";
    if (actRow >= data.numInstances())
        return "Row index too large: " + (actRow + 1) + " > " + data.numInstances();
    return null;
}

---

From source file:adams.gui.visualization.instances.instancestable.AbstractProcessCell.java

License:Open Source License

/**
 * Hook method for checks before attempting processing.
 *
 * @param table   the source table/*  www. j  a v a  2s  .co m*/
 * @param data   the instances to use as basis
 * @param actRow      the row in the instances
 * @param selRow    the selected row in the table
 * @param column   the column in the instances
 * @return      null if passed, otherwise error message
 */
protected String check(InstancesTable table, Instances data, int actRow, int selRow, int column) {
    if (table == null)
        return "No source table available!";
    if (data == null)
        return "No instances available!";
    if (actRow < 0)
        return "Negative row index!";
    if (actRow >= data.numInstances())
        return "Row index too large: " + (actRow + 1) + " > " + data.numInstances();
    if (column < 0)
        return "Negative column index!";
    if (column >= data.numAttributes())
        return "Column index too large: " + (column + 1) + " > " + data.numAttributes();
    return null;
}

---

From source file:adams.gui.visualization.instances.instancestable.JFreeChart.java

License:Open Source License

/**
 * Allows the user to generate a plot from either a row or a column.
 *
 * @param data   the instances to use/*from  w w  w  .  j  a v  a2 s  .  c  o  m*/
 * @param isColumn   whether the to use column or row
 * @param index   the index of the row/column
 */
protected void plot(final InstancesTable table, final Instances data, final boolean isColumn, int index) {
    final List<Double> list;
    List<Double> tmp;
    GenericObjectEditorDialog setup;
    int i;
    final String title;
    SwingWorker worker;
    adams.flow.sink.JFreeChartPlot last;
    int numPoints;
    String newPoints;
    int col;
    int row;
    Object value;
    final SpreadSheet sheet;
    Row srow;
    boolean sorted;
    boolean asc;

    numPoints = isColumn ? data.numInstances() : data.numAttributes();
    if (numPoints > MAX_POINTS) {
        newPoints = GUIHelper.showInputDialog(null,
                "More than " + MAX_POINTS + " data points to plot - enter sample size:", "" + numPoints);
        if (newPoints == null)
            return;
        if (!Utils.isInteger(newPoints))
            return;
        if (Integer.parseInt(newPoints) != numPoints)
            numPoints = Integer.parseInt(newPoints);
        else
            numPoints = -1;
    } else {
        numPoints = -1;
    }

    // let user customize plot
    if (GUIHelper.getParentDialog(table) != null)
        setup = new GenericObjectEditorDialog(GUIHelper.getParentDialog(table), ModalityType.DOCUMENT_MODAL);
    else
        setup = new GenericObjectEditorDialog(GUIHelper.getParentFrame(table), true);
    setup.setDefaultCloseOperation(GenericObjectEditorDialog.DISPOSE_ON_CLOSE);
    setup.getGOEEditor().setClassType(Actor.class);
    setup.getGOEEditor().setCanChangeClassInDialog(false);
    last = (adams.flow.sink.JFreeChartPlot) table.getLastSetup(getClass(), true, !isColumn);
    if (last == null)
        last = new adams.flow.sink.JFreeChartPlot();
    setup.setCurrent(last);
    setup.setLocationRelativeTo(GUIHelper.getParentComponent(table));
    setup.setVisible(true);
    if (setup.getResult() != GenericObjectEditorDialog.APPROVE_OPTION)
        return;
    last = (adams.flow.sink.JFreeChartPlot) setup.getCurrent();
    table.addLastSetup(getClass(), true, !isColumn, last);

    // get data from instances
    tmp = new ArrayList<>();
    sorted = false;
    asc = table.isAscending();
    if (isColumn) {
        col = index + 1;
        sorted = (table.getSortColumn() == col);
        for (i = 0; i < table.getRowCount(); i++) {
            value = table.getValueAt(i, col);
            if ((value != null) && (Utils.isDouble(value.toString())))
                tmp.add(Utils.toDouble(value.toString()));
        }
    } else {
        row = index;
        for (i = 0; i < data.numAttributes(); i++) {
            if (data.attribute(i).isNumeric() && !data.instance(row).isMissing(i))
                tmp.add(data.instance(row).value(i));
        }
    }

    if (numPoints > -1) {
        numPoints = Math.min(numPoints, tmp.size());
        Collections.shuffle(tmp, new Random(1));
        list = tmp.subList(0, numPoints);
        if (sorted) {
            Collections.sort(list);
            if (!asc)
                Collections.reverse(list);
        }
    } else {
        list = tmp;
    }

    // create new spreadsheet
    sheet = new DefaultSpreadSheet();
    sheet.getHeaderRow().addCell("x").setContentAsString(isColumn ? "Row" : "Column");
    sheet.getHeaderRow().addCell("y")
            .setContentAsString(isColumn ? data.attribute(index).name() : ("Row " + (index + 1)));
    for (i = 0; i < list.size(); i++) {
        srow = sheet.addRow();
        srow.addCell("x").setContent((double) i + 1.0);
        srow.addCell("y").setContent(list.get(i));
    }

    // generate plot
    if (isColumn)
        title = "Column " + (index + 1) + "/" + data.attribute(index).name();
    else
        title = "Row " + (index + 1);
    last.getChart().setTitle(sheet.getColumnName(1));

    worker = new SwingWorker() {
        @Override
        protected Object doInBackground() throws Exception {
            Flow flow = new Flow();
            flow.setDefaultCloseOperation(BaseFrame.DISPOSE_ON_CLOSE);

            StorageValue sv = new StorageValue();
            sv.setStorageName(new StorageName("values"));
            flow.add(sv);

            Object last = table.getLastSetup(JFreeChart.this.getClass(), true, !isColumn);
            adams.flow.sink.JFreeChartPlot plot = (adams.flow.sink.JFreeChartPlot) ((adams.flow.sink.JFreeChartPlot) last)
                    .shallowCopy();
            plot.setShortTitle(true);
            plot.setName(title);
            plot.setX(-2);
            plot.setY(-2);
            flow.add(plot);

            flow.setUp();
            flow.getStorage().put(new StorageName("values"), sheet);
            flow.execute();
            flow.wrapUp();
            return null;
        }
    };
    worker.execute();
}

---

From source file:adams.gui.visualization.instances.instancestable.SimplePlot.java

License:Open Source License

/**
 * Allows the user to generate a plot from either a row or a column.
 *
 * @param data   the instances to use/*ww w. ja v a2 s  . c o m*/
 * @param isColumn   whether the to use column or row
 * @param index   the index of the row/column
 */
protected void plot(final InstancesTable table, final Instances data, final boolean isColumn, int index) {
    final List<Double> list;
    List<Double> tmp;
    GenericObjectEditorDialog setup;
    int i;
    final String title;
    SwingWorker worker;
    adams.flow.sink.SimplePlot last;
    int numPoints;
    String newPoints;
    int col;
    int row;
    Object value;
    boolean sorted;
    boolean asc;

    numPoints = isColumn ? data.numInstances() : data.numAttributes();
    if (numPoints > MAX_POINTS) {
        newPoints = GUIHelper.showInputDialog(null,
                "More than " + MAX_POINTS + " data points to plot - enter sample size:", "" + numPoints);
        if (newPoints == null)
            return;
        if (!Utils.isInteger(newPoints))
            return;
        if (Integer.parseInt(newPoints) != numPoints)
            numPoints = Integer.parseInt(newPoints);
        else
            numPoints = -1;
    } else {
        numPoints = -1;
    }

    // let user customize plot
    if (GUIHelper.getParentDialog(table) != null)
        setup = new GenericObjectEditorDialog(GUIHelper.getParentDialog(table), ModalityType.DOCUMENT_MODAL);
    else
        setup = new GenericObjectEditorDialog(GUIHelper.getParentFrame(table), true);
    setup.setDefaultCloseOperation(GenericObjectEditorDialog.DISPOSE_ON_CLOSE);
    setup.getGOEEditor().setClassType(Actor.class);
    setup.getGOEEditor().setCanChangeClassInDialog(false);
    last = (adams.flow.sink.SimplePlot) table.getLastSetup(getClass(), true, !isColumn);
    if (last == null) {
        last = new adams.flow.sink.SimplePlot();
        last.setNoToolTips(true);
        last.setMouseClickAction(new ViewDataClickAction());
    }
    setup.setCurrent(last);
    setup.setLocationRelativeTo(GUIHelper.getParentComponent(table));
    setup.setVisible(true);
    if (setup.getResult() != GenericObjectEditorDialog.APPROVE_OPTION)
        return;
    last = (adams.flow.sink.SimplePlot) setup.getCurrent();
    table.addLastSetup(getClass(), true, !isColumn, last);

    // get data from instances
    tmp = new ArrayList<>();
    sorted = false;
    asc = table.isAscending();
    if (isColumn) {
        col = index + 1;
        sorted = (table.getSortColumn() == col);
        for (i = 0; i < table.getRowCount(); i++) {
            value = table.getValueAt(i, col);
            if ((value != null) && (Utils.isDouble(value.toString())))
                tmp.add(Utils.toDouble(value.toString()));
        }
    } else {
        row = index;
        for (i = 0; i < data.numAttributes(); i++) {
            if (data.attribute(i).isNumeric() && !data.instance(row).isMissing(i))
                tmp.add(data.instance(row).value(i));
        }
    }

    if (numPoints > -1) {
        numPoints = Math.min(numPoints, tmp.size());
        Collections.shuffle(tmp, new Random(1));
        list = tmp.subList(0, numPoints);
        if (sorted) {
            Collections.sort(list);
            if (!asc)
                Collections.reverse(list);
        }
    } else {
        list = tmp;
    }

    // generate plot
    if (isColumn)
        title = "Column " + (index + 1) + "/" + data.attribute(index).name();
    else
        title = "Row " + (index + 1);

    worker = new SwingWorker() {
        @Override
        protected Object doInBackground() throws Exception {
            Flow flow = new Flow();
            flow.setDefaultCloseOperation(BaseFrame.DISPOSE_ON_CLOSE);

            StorageValue sv = new StorageValue();
            sv.setStorageName(new StorageName("values"));
            flow.add(sv);

            ArrayToSequence a2s = new ArrayToSequence();
            flow.add(a2s);

            MakePlotContainer mpc = new MakePlotContainer();
            mpc.setPlotName(title);
            flow.add(mpc);

            Object last = table.getLastSetup(SimplePlot.this.getClass(), true, !isColumn);
            adams.flow.sink.SimplePlot plot = (adams.flow.sink.SimplePlot) ((adams.flow.sink.SimplePlot) last)
                    .shallowCopy();
            plot.setShortTitle(true);
            plot.setShowSidePanel(false);
            plot.setName(title);
            plot.setX(-2);
            plot.setY(-2);
            flow.add(plot);

            flow.setUp();
            flow.getStorage().put(new StorageName("values"), list.toArray(new Double[list.size()]));
            flow.execute();
            flow.wrapUp();
            return null;
        }
    };
    worker.execute();
}

---

From source file:adams.opt.optimise.genetic.fitnessfunctions.AttributeSelection.java

License:Open Source License

/**
 * Callback for best measure so far//from w w  w . ja  v  a 2 s  .c  o m
 */
@Override
public void newBest(double val, OptData opd) {
    int cnt = 0;
    int[] weights = getWeights(opd);
    Instances newInstances = new Instances(getInstances());
    for (int i = 0; i < getInstances().numInstances(); i++) {
        Instance in = newInstances.instance(i);
        cnt = 0;
        for (int a = 0; a < getInstances().numAttributes(); a++) {
            if (a == getInstances().classIndex())
                continue;
            if (weights[cnt++] == 0) {
                in.setValue(a, 0);
            } else {
                in.setValue(a, in.value(a));
            }
        }
    }
    try {
        File file = new File(getOutputDirectory().getAbsolutePath() + File.separator
                + Double.toString(getMeasure().adjust(val)) + ".arff");
        file.createNewFile();
        Writer writer = new BufferedWriter(new FileWriter(file));
        Instances header = new Instances(newInstances, 0);

        // remove filter setup
        Remove remove = new Remove();
        remove.setAttributeIndices(getRemoveAsString(weights));
        remove.setInvertSelection(true);

        header.setRelationName(OptionUtils.getCommandLine(remove));

        writer.write(header.toString());
        writer.write("\n");
        for (int i = 0; i < newInstances.numInstances(); i++) {
            writer.write(newInstances.instance(i).toString());
            writer.write("\n");
        }
        writer.flush();
        writer.close();
    } catch (Exception e) {
        e.printStackTrace();
    }
}

---

From source file:adaptedClusteringAlgorithms.MyFarthestFirst.java

License:Open Source License

/**
 * Generates a clusterer. Has to initialize all fields of the clusterer
 * that are not being set via options.//w  w  w  .j  a v  a 2s .  com
 *
 * @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 {

    if (!SESAME.SESAME_GUI)
        MyFirstClusterer.weka_gui = true;

    // can clusterer handle the data?
    getCapabilities().testWithFail(data);

    //long start = System.currentTimeMillis();

    m_ReplaceMissingFilter = new ReplaceMissingValues();
    // Missing values replacement is not required so this modification is made
    /*m_ReplaceMissingFilter.setInputFormat(data);
    m_instances = Filter.useFilter(data, m_ReplaceMissingFilter);*/
    Instances m_instances = new Instances(data);

    // To use semantic measurers through DistanceFunction interface
    m_DistanceFunction.setInstances(m_instances);

    initMinMax(m_instances);

    m_ClusterCentroids = new Instances(m_instances, m_NumClusters);

    int n = m_instances.numInstances();
    Random r = new Random(getSeed());
    boolean[] selected = new boolean[n];
    double[] minDistance = new double[n];

    for (int i = 0; i < n; i++)
        minDistance[i] = Double.MAX_VALUE;

    int firstI = r.nextInt(n);
    m_ClusterCentroids.add(m_instances.instance(firstI));
    selected[firstI] = true;

    updateMinDistance(minDistance, selected, m_instances, m_instances.instance(firstI));

    if (m_NumClusters > n)
        m_NumClusters = n;

    for (int i = 1; i < m_NumClusters; i++) {
        int nextI = farthestAway(minDistance, selected);
        m_ClusterCentroids.add(m_instances.instance(nextI));
        selected[nextI] = true;
        updateMinDistance(minDistance, selected, m_instances, m_instances.instance(nextI));
    }

    m_instances = new Instances(m_instances, 0);
    //long end = System.currentTimeMillis();
    //System.out.println("Clustering Time = " + (end-start));

    // Save memory!!
    m_DistanceFunction.clean();

    if (!SESAME.SESAME_GUI)
        MyFirstClusterer.weka_gui = true;
}

---

From source file:adaptedClusteringAlgorithms.MyFarthestFirst.java

License:Open Source License

protected void initMinMax(Instances data) {
    m_Min = new double[data.numAttributes()];
    m_Max = new double[data.numAttributes()];
    for (int i = 0; i < data.numAttributes(); i++) {
        m_Min[i] = m_Max[i] = Double.NaN;
    }//from  w  ww.  j a  va  2  s .  c  o m

    for (int i = 0; i < data.numInstances(); i++) {
        updateMinMax(data.instance(i));
    }
}

---

From source file:adaptedClusteringAlgorithms.MySimpleKMeans.java

License:Open Source License

/**
 * Generates a clusterer. Has to initialize all fields of the clusterer that
 * are not being set via options./*from   w  w  w.ja  v a  2  s  . co  m*/
 * 
 * @param data set of instances serving as training data
 * @throws Exception if the clusterer has not been generated successfully
 */
@Override
public void buildClusterer(Instances data) throws Exception {

    if (!SESAME.SESAME_GUI)
        MyFirstClusterer.weka_gui = true;

    // 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;
    }

    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, 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 (m_Iterations == m_MaxIterations) {
            converged = 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];

                        for (i = 0; i < tempI[k].numAttributes(); i++) {
                            m_ClusterNominalCounts[index][i] = m_ClusterNominalCounts[k][i];
                        }
                        index++;
                    }
                }
                tempI = t;
            } else {
                tempI = new Instances[m_NumClusters];
            }
        }

        if (!converged) {
            m_squaredErrors = new double[m_NumClusters];
            m_ClusterNominalCounts = new int[m_NumClusters][instances.numAttributes()][0];
        }
    }

    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] = Instance.missingValue();
                }
            }
            m_ClusterStdDevs.add(new Instance(1.0, vals2));
        }
        m_ClusterSizes[i] = tempI[i].numInstances();
    }

    // Save memory!!
    m_DistanceFunction.clean();

    if (!SESAME.SESAME_GUI)
        MyFirstClusterer.weka_gui = true;
}

---

From source file:adaptedClusteringAlgorithms.MySimpleKMeans.java

License:Open Source License

/**
 * 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.
 * /*from  ww w  .  j a v a2s.  c o  m*/
 * @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()];

    for (int j = 0; j < members.numAttributes(); j++) {

        // The centroid is the mean point. If the attribute is nominal, the centroid is the mode
        if (m_DistanceFunction instanceof ChEBIInd || m_DistanceFunction instanceof ChEBIDir
                || m_DistanceFunction instanceof GOInd || m_DistanceFunction instanceof GODir
                || m_DistanceFunction instanceof GOChEBIInd || m_DistanceFunction instanceof GOChEBIDir
                || m_DistanceFunction instanceof CalculusInd || m_DistanceFunction instanceof CalculusDir
                || members.attribute(j).isNominal()) {
            vals[j] = members.meanOrMode(j);
        }

        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] = Instance.missingValue(); // mark mode as missing
                }
            } else {
                if (m_ClusterMissingCounts[centroidIndex][j] == members.numInstances()) {
                    vals[j] = Instance.missingValue(); // mark mean as missing
                }
            }
        }
    }
    if (updateClusterInfo) {
        m_ClusterCentroids.add(new Instance(1.0, vals));
    }
    return vals;
}

---

From source file:agnes.MyAgnes.java

public void buildClusterer(Instances data) {
    distanceCounter = new EuclideanDistance(data);
    ArrayList<ArrayList<Instance>> currentClusters = new ArrayList<>();
    for (int i = 0; i < data.numInstances(); i++) {
        currentClusters.add(new ArrayList<>());
        currentClusters.get(i).add(data.instance(i));
        instanceID.put(data.instance(i), i);
    }/*from www. ja  v a 2  s .c  om*/
    addNewClusterHierarchy(currentClusters);
}

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