Example usage for weka.core Instances setClassIndex

List of usage examples for weka.core Instances setClassIndex

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Introduction

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

Prototype

public void setClassIndex(int classIndex)

Source Link

Document

Sets the class index of the set.

Usage

From source file:lu.lippmann.cdb.datasetview.tabs.RegressionTreeTabView.java

License:Open Source License

/**
 * {@inheritDoc}//from www  .ja v a  2s  .co  m
 */
@SuppressWarnings("unchecked")
@Override
public void update0(final Instances dataSet) throws Exception {
    this.panel.removeAll();

    //final Object[] attrNames=WekaDataStatsUtil.getNumericAttributesNames(dataSet).toArray();
    final Object[] attrNames = WekaDataStatsUtil.getAttributeNames(dataSet).toArray();
    final JComboBox xCombo = new JComboBox(attrNames);
    xCombo.setBorder(new TitledBorder("Attribute to evaluate"));

    final JXPanel comboPanel = new JXPanel();
    comboPanel.setLayout(new GridLayout(1, 2));
    comboPanel.add(xCombo);
    final JXButton jxb = new JXButton("Compute");
    comboPanel.add(jxb);
    this.panel.add(comboPanel, BorderLayout.NORTH);

    jxb.addActionListener(new ActionListener() {
        @Override
        public void actionPerformed(ActionEvent e) {
            try {
                if (gv != null)
                    panel.remove((Component) gv);

                dataSet.setClassIndex(xCombo.getSelectedIndex());

                final REPTree rt = new REPTree();
                rt.setNoPruning(true);
                //rt.setMaxDepth(3);
                rt.buildClassifier(dataSet);

                /*final M5P rt=new M5P();
                rt.buildClassifier(dataSet);*/

                final Evaluation eval = new Evaluation(dataSet);
                double[] d = eval.evaluateModel(rt, dataSet);
                System.out.println("PREDICTED -> " + FormatterUtil.buildStringFromArrayOfDoubles(d));
                System.out.println(eval.errorRate());
                System.out.println(eval.sizeOfPredictedRegions());
                System.out.println(eval.toSummaryString("", true));

                final GraphWithOperations gwo = GraphUtil
                        .buildGraphWithOperationsFromWekaRegressionString(rt.graph());
                final DecisionTree dt = new DecisionTree(gwo, eval.errorRate());

                gv = DecisionTreeToGraphViewHelper.buildGraphView(dt, eventPublisher, commandDispatcher);
                gv.addMetaInfo("Size=" + dt.getSize(), "");
                gv.addMetaInfo("Depth=" + dt.getDepth(), "");

                gv.addMetaInfo("MAE=" + FormatterUtil.DECIMAL_FORMAT.format(eval.meanAbsoluteError()) + "", "");
                gv.addMetaInfo("RMSE=" + FormatterUtil.DECIMAL_FORMAT.format(eval.rootMeanSquaredError()) + "",
                        "");

                final JCheckBox toggleDecisionTreeDetails = new JCheckBox("Toggle details");
                toggleDecisionTreeDetails.addActionListener(new ActionListener() {
                    @Override
                    public void actionPerformed(ActionEvent e) {
                        if (!tweakedGraph) {
                            final Object[] mapRep = WekaDataStatsUtil
                                    .buildNodeAndEdgeRepartitionMap(dt.getGraphWithOperations(), dataSet);
                            gv.updateVertexShapeTransformer((Map<CNode, Map<Object, Integer>>) mapRep[0]);
                            gv.updateEdgeShapeRenderer((Map<CEdge, Float>) mapRep[1]);
                        } else {
                            gv.resetVertexAndEdgeShape();
                        }
                        tweakedGraph = !tweakedGraph;
                    }
                });
                gv.addMetaInfoComponent(toggleDecisionTreeDetails);

                /*final JButton openInEditorButton = new JButton("Open in editor");
                openInEditorButton.addActionListener(new ActionListener() {
                   @Override
                   public void actionPerformed(ActionEvent e) {
                       GraphUtil.importDecisionTreeInEditor(dtFactory, dataSet, applicationContext, eventPublisher, commandDispatcher);
                   }
                });
                this.gv.addMetaInfoComponent(openInEditorButton);*/

                final JButton showTextButton = new JButton("In text");
                showTextButton.addActionListener(new ActionListener() {
                    @Override
                    public void actionPerformed(ActionEvent e) {
                        JOptionPane.showMessageDialog(null, graphDsl.getDslString(dt.getGraphWithOperations()));
                    }
                });
                gv.addMetaInfoComponent(showTextButton);

                panel.add(gv.asComponent(), BorderLayout.CENTER);
            } catch (Exception e1) {
                e1.printStackTrace();
                panel.add(new JXLabel("Error during computation: " + e1.getMessage()), BorderLayout.CENTER);
            }

        }
    });
}

From source file:lu.lippmann.cdb.datasetview.tabs.SymmetricalUncertaintyCorrelationTabView.java

License:Open Source License

/**
 * {@inheritDoc}//w  w  w  .ja va 2s. com
 */
@Override
public void update0(final Instances dataSet) throws Exception {
    this.panel.removeAll();

    final Object[] attrNames = WekaDataStatsUtil.getAttributeNames(dataSet).toArray();
    final JComboBox xCombo = new JComboBox(attrNames);
    xCombo.setBorder(new TitledBorder("Attribute to evaluate"));

    final JXPanel comboPanel = new JXPanel();
    comboPanel.setLayout(new GridLayout(1, 2));
    comboPanel.add(xCombo);
    final JXButton jxb = new JXButton("Compute");
    comboPanel.add(jxb);
    this.panel.add(comboPanel, BorderLayout.NORTH);

    jxb.addActionListener(new ActionListener() {
        @Override
        public void actionPerformed(ActionEvent e) {
            try {
                final Instances newds = new Instances(dataSet);
                newds.setClassIndex(xCombo.getSelectedIndex());

                final Map<Attribute, Double> mapCoefficients = WekaDataStatsUtil
                        .computeSymmetricUncertaintyCorrelation(newds);

                final JXPanel tblpanel = new JXPanel();
                tblpanel.setLayout(new BorderLayout());
                tblpanel.setBorder(new TitledBorder("Coefficients"));
                final JXTable tbl = new JXTable();
                final SingleColumnTableModel mdl = new SingleColumnTableModel("Coeffs");
                final java.util.List<String> l = new ArrayList<String>();
                for (final Map.Entry<Attribute, Double> entry : mapCoefficients.entrySet()) {
                    l.add(entry.getKey().name() + " -> " + entry.getValue());
                }
                mdl.setData(l);
                tbl.setModel(mdl);
                tbl.setEditable(true);
                tbl.setShowHorizontalLines(false);
                tbl.setShowVerticalLines(false);
                tbl.setVisibleRowCount(5);
                tbl.setAutoResizeMode(JTable.AUTO_RESIZE_OFF);
                tblpanel.add(tbl, BorderLayout.CENTER);
                panel.add(tblpanel, BorderLayout.CENTER);
            } catch (Exception e1) {
                e1.printStackTrace();
                panel.add(new JXLabel("Error during computation: " + e1.getMessage()), BorderLayout.CENTER);
            }

        }
    });
}

From source file:lu.lippmann.cdb.datasetview.tabs.TimeSeriesSimilarityPanel.java

License:Open Source License

private Instances buildFeatureDS(final Instances dataSet) throws Exception {
    final int numAttributes = dataSet.numAttributes();

    final java.util.List<String> namesOfFeaturesToConsider = new ArrayList<String>();
    namesOfFeaturesToConsider.addAll(WekaDataStatsUtil.getAttributeNames(dataSet));
    namesOfFeaturesToConsider.removeAll(WekaDataStatsUtil.getDateAttributeNames(dataSet));

    final double[][] simMatrix = new double[numAttributes][numAttributes];
    for (int i = 0; i < numAttributes; i++) {
        final double[] arrayI = dataSet.attributeToDoubleArray(i);
        if (this.withNormalization)
            MathsUtil.normalize(arrayI);
        simMatrix[i][i] = 0d;/* w w  w .  ja v a  2 s .  co  m*/
        ;
        for (int j = i + 1; j < numAttributes; j++) {
            final double[] arrayJ = dataSet.attributeToDoubleArray(j);
            if (this.withNormalization)
                MathsUtil.normalize(arrayJ);
            simMatrix[i][j] = new DynamicTimeWarping(arrayI, arrayJ).getDistance();
            //System.out.println(i+" "+j);
        }

    }
    for (int i = 0; i < numAttributes; i++) {
        for (int j = 0; j < i + 1; j++) {
            simMatrix[i][j] = simMatrix[j][i];
        }
    }

    /*for (int i=0;i<numAttributes;i++)
    {
       System.out.println(i+" -> "+FormatterUtil.buildStringFromArrayOfDoubles(simMatrix[i]));
    }*/

    final ArrayList<Attribute> attrs = new ArrayList<Attribute>(numAttributes + 1);
    for (int i = 0; i < numAttributes; i++) {
        attrs.add(new Attribute(dataSet.attribute(i).name() + "-feat"));
    }
    attrs.add(new Attribute(FEATUREDESC_ATTRNAME, namesOfFeaturesToConsider));
    final Instances ds = new Instances("featuresComparisonDs", attrs, 0);
    ds.setClassIndex(attrs.size() - 1);

    for (int i = 0; i < simMatrix.length; i++) {
        final DenseInstance di = new DenseInstance(1.0d, ArraysUtil.concat(simMatrix[i], new double[] { 0d }));
        di.setDataset(ds);
        di.setValue(simMatrix.length, dataSet.attribute(i).name());
        ds.add(di);
    }
    return ds;
}

From source file:lu.lippmann.cdb.datasetview.tabs.UnsupervisedFeatureEvaluationTabView.java

License:Open Source License

/**
 * {@inheritDoc}//  w  w  w . j  a  v  a  2  s .  c  o m
 */
@Override
public void update0(final Instances dataSet) throws Exception {
    Instances preparedDataSet = new Instances(dataSet);
    preparedDataSet.setClassIndex(-1);

    preparedDataSet = WekaTimeSeriesUtil.buildDataSetWithoutDates(preparedDataSet);
    preparedDataSet = WekaDataProcessingUtil.buildNumerizedDataSet(preparedDataSet);
    preparedDataSet = WekaDataProcessingUtil.buildNormalizedDataSet(preparedDataSet);

    final Instances newds;
    if (this.combo.getSelectedIndex() == 1) {
        newds = buildDerivatedDatasetForBestFeatures(preparedDataSet, this.slider.getValue());
    } else {
        newds = buildDerivatedDatasetForFeaturesClusters(preparedDataSet, this.slider.getValue());
    }

    if (this.mdsView != null)
        this.jxp.remove(this.mdsView);

    final int limitForCollapsing = 1000;
    final boolean normalized = false; //TODO checkbox
    final MDSResult mdsResult = ClassicMDS.doMDS(newds, MDSDistancesEnum.EUCLIDEAN, 2, limitForCollapsing, true,
            normalized);
    this.mdsView = MDSViewBuilder.buildMDSViewFromDataSet(newds, mdsResult, limitForCollapsing,
            new Listener<Instances>() {
                @Override
                public void onAction(final Instances parameter) {
                }
            }, FEATUREDESC_ATTRNAME);

    this.jxp.add(this.mdsView, BorderLayout.CENTER);

    if (this.cl != null)
        this.slider.removeChangeListener(this.cl);
    this.cl = new ChangeListener() {
        @Override
        public void stateChanged(final ChangeEvent e) {
            if (!slider.getValueIsAdjusting()) {
                try {
                    update0(dataSet);
                } catch (Exception e1) {
                    e1.printStackTrace();
                }
            }
        }
    };
    this.slider.addChangeListener(this.cl);

    if (this.al != null)
        this.combo.removeActionListener(this.al);
    this.al = new ActionListener() {
        @Override
        public void actionPerformed(ActionEvent e) {
            try {
                update0(dataSet);
            } catch (Exception e1) {
                e1.printStackTrace();
            }
        }
    };
    this.combo.addActionListener(this.al);

    this.jxp.updateUI();
}

From source file:lu.lippmann.cdb.datasetview.tabs.UnsupervisedFeatureEvaluationTabView.java

License:Open Source License

private static Instances buildDerivatedDataset(final Instances dataSet, final List<String> possibleValues,
        final List<Integer> valueForEachFeature) throws Exception {
    final int numInstances = dataSet.numInstances();
    final ArrayList<Attribute> attrs = new ArrayList<Attribute>(numInstances + 2);
    attrs.add(new Attribute(FEATUREDESC_ATTRNAME, (java.util.List<String>) null));
    for (int i = 0; i < numInstances; i++) {
        attrs.add(new Attribute(i + "_eval"));
    }//from  w w w .j av a  2  s .co m
    attrs.add(new Attribute("__", possibleValues));

    final Instances newds = new Instances("unsupervisedFeaturesEval", attrs, 0);
    final int numAttributes = dataSet.numAttributes();
    for (int j = 0; j < numAttributes; j++) {
        double[] val = ArraysUtil.concat(dataSet.attributeToDoubleArray(j), new double[] { 0.0d });
        val = ArraysUtil.concat(new double[] { 0.0d }, val);
        newds.add(new DenseInstance(1.0d, val));
    }
    for (int j = 0; j < numAttributes; j++) {
        newds.instance(j).setValue(0, dataSet.attribute(j).name());
        newds.instance(j).setValue(numInstances + 1, possibleValues.get(valueForEachFeature.get(j)));
    }
    newds.setClassIndex(numInstances + 1);
    return newds;
}

From source file:lu.lippmann.cdb.ext.hydviga.data.StationsDataProvider.java

License:Open Source License

private Instances getDataSetForMap(final Collection<String> sel, final Collection<String> usable) {
    final Instances ds = new Instances("ds", new ArrayList<Attribute>(), 0);
    ds.insertAttributeAt(new Attribute("name", new ArrayList<String>(this.coordinatesMap.keySet())),
            ds.numAttributes());//  w  w  w  .  j a  v a2  s .  c o  m
    ds.insertAttributeAt(new Attribute("x"), ds.numAttributes());
    ds.insertAttributeAt(new Attribute("y"), ds.numAttributes());
    ds.insertAttributeAt(
            new Attribute("status",
                    Arrays.asList(new String[] { SELECTED_STATUS, USABLE_STATUS, NOT_USABLE_STATUS })),
            ds.numAttributes());
    ds.setClassIndex(ds.numAttributes() - 1);

    final Set<String> coordSelected = new HashSet<String>();
    for (final String ssel : sel) {
        final String coordsKey = coordinatesMap.get(ssel)[0] + "-" + coordinatesMap.get(ssel)[1];
        coordSelected.add(coordsKey);
    }
    final Set<String> coordUsable = new HashSet<String>();
    for (final String uu : usable) {
        final String coordsKey = coordinatesMap.get(uu)[0] + "-" + coordinatesMap.get(uu)[1];
        coordUsable.add(coordsKey);
    }

    final Set<String> coordAlreadyLoaded = new HashSet<String>();
    for (final Map.Entry<String, double[]> entry : this.coordinatesMap.entrySet()) {
        final String coordsKey = entry.getValue()[0] + "-" + entry.getValue()[1];
        if (coordAlreadyLoaded.contains(coordsKey))
            continue;
        final Instance inst = new DenseInstance(1.0d, new double[] { 0d, 0d, 0d, 0d });
        inst.setDataset(ds);
        inst.setValue(0, entry.getKey());
        inst.setValue(1, entry.getValue()[0]);
        inst.setValue(2, entry.getValue()[1]);
        //System.out.println(sel+" "+entry.getKey());
        inst.setValue(3, (coordSelected.contains(coordsKey)) ? SELECTED_STATUS
                : ((coordUsable.contains(coordsKey)) ? USABLE_STATUS : NOT_USABLE_STATUS));
        ds.add(inst);
        coordAlreadyLoaded.add(coordsKey);
    }

    return ds;
}

From source file:lu.lippmann.cdb.ext.hydviga.gaps.GapFillerClassifier.java

License:Open Source License

/**
 * {@inheritDoc}// w ww . ja  v  a  2 s .c o  m
 */
@Override
Instances fillGaps0(final Instances ds) throws Exception {
    final Instances newds = WekaDataProcessingUtil.buildDataSetWithoutConstantAttributes(ds);

    final int attrWithMissingIdx = WekaDataStatsUtil.getFirstAttributeWithMissingValue(newds);
    if (attrWithMissingIdx == -1)
        throw new IllegalStateException();

    final Instances trainingSet = new Instances(newds, 0);
    for (int i = 0; i < newds.numInstances(); i++) {
        if (!newds.instance(i).hasMissingValue())
            trainingSet.add(newds.instance(i));
    }
    //System.out.println(trainingSet);      
    trainingSet.setClassIndex(attrWithMissingIdx);

    //System.out.println("Training (size="+trainingSet.numInstances()+") ...");      
    this.classifier.buildClassifier(trainingSet);
    //System.out.println("... trained!");

    newds.setClassIndex(attrWithMissingIdx);
    for (int i = 0; i < newds.numInstances(); i++) {
        if (newds.instance(i).isMissing(attrWithMissingIdx)) {
            final Instance newrecord = new DenseInstance(newds.instance(i));
            newrecord.setDataset(newds);
            final double newval = this.classifier.classifyInstance(newrecord);
            newds.instance(i).setValue(attrWithMissingIdx, newval);
        }
    }

    //System.out.println("initial -> "+ds.toSummaryString());
    //System.out.println("corrected -> "+newds.toSummaryString());

    this.model = this.classifier.toString();

    return newds;
}

From source file:lu.lippmann.cdb.ext.hydviga.ui.GapsUIUtil.java

License:Open Source License

public static ChartPanel buildGapChartPanelWithCorrection(final Instances pdataSet, final int dateIdx,
        final Attribute attr, final int gapsize, final int position, final GapFiller gapFiller,
        final java.util.Collection<String> attrs) throws Exception {
    final Instances dataSetWithTheGap = new Instances(pdataSet);
    for (int i = position; i < position + gapsize; i++)
        dataSetWithTheGap.instance(i).setMissing(attr);

    int[] arr = new int[] { attr.index(), dateIdx };
    for (final String sss : attrs) {
        arr = ArraysUtil.concat(arr, new int[] { dataSetWithTheGap.attribute(sss).index() });
    }/*from  w  ww. java 2s .c o m*/

    Instances filteredDsWithTheGap = WekaDataProcessingUtil.buildFilteredByAttributesDataSet(dataSetWithTheGap,
            arr);
    filteredDsWithTheGap = WekaDataProcessingUtil.buildFilteredDataSet(filteredDsWithTheGap, 0,
            filteredDsWithTheGap.numAttributes() - 1,
            Math.max(0, position - GapsUtil.VALUES_BEFORE_AND_AFTER_RATIO * gapsize),
            Math.min(position + gapsize + GapsUtil.VALUES_BEFORE_AND_AFTER_RATIO * gapsize,
                    filteredDsWithTheGap.numInstances() - 1));

    final Instances completedds = gapFiller.fillGaps(filteredDsWithTheGap);
    final Instances diff = WekaTimeSeriesUtil.buildDiff(filteredDsWithTheGap, completedds);

    Instances filteredDsWithoutTheGap = WekaDataProcessingUtil.buildFilteredByAttributesDataSet(pdataSet, arr);
    filteredDsWithoutTheGap = WekaDataProcessingUtil.buildFilteredDataSet(filteredDsWithoutTheGap, 0,
            filteredDsWithoutTheGap.numAttributes() - 1,
            Math.max(0, position - GapsUtil.VALUES_BEFORE_AND_AFTER_RATIO * gapsize),
            Math.min(position + gapsize + GapsUtil.VALUES_BEFORE_AND_AFTER_RATIO * gapsize,
                    filteredDsWithoutTheGap.numInstances() - 1));

    diff.insertAttributeAt(new Attribute(attr.name() + "_orig"), diff.numAttributes());
    for (int i = 0; i < filteredDsWithoutTheGap.numInstances(); i++) {
        diff.instance(i).setValue(diff.numAttributes() - 1,
                filteredDsWithoutTheGap.instance(i).value(filteredDsWithoutTheGap.attribute(attr.name())));
    }
    //System.out.println(attr.name()+"\n"+diff.toSummaryString());

    final java.util.List<String> toRemove = new java.util.ArrayList<String>();
    for (int j = 0; j < diff.numAttributes(); j++) {
        final String consideredAttrName = diff.attribute(j).name();
        if (!consideredAttrName.contains("timestamp") && !consideredAttrName.contains(attr.name()))
            toRemove.add(consideredAttrName);
    }
    diff.setClassIndex(-1);
    for (final String ssss : toRemove)
        diff.deleteAttributeAt(diff.attribute(ssss).index());
    //System.out.println(attr.name()+"\n"+diff.toSummaryString());

    final ChartPanel cp = TimeSeriesChartUtil.buildChartPanelForAllAttributes(diff, false,
            WekaDataStatsUtil.getFirstDateAttributeIdx(diff), null);

    final XYPlot xyp = (XYPlot) cp.getChart().getPlot();
    xyp.getDomainAxis().setLabel("");
    xyp.getRangeAxis().setLabel("");

    final Marker gapBeginMarker = new ValueMarker(
            dataSetWithTheGap.instance(Math.max(0, position - 1)).value(dateIdx));
    gapBeginMarker.setPaint(Color.RED);
    gapBeginMarker.setLabel("Gap begin");
    gapBeginMarker.setLabelAnchor(RectangleAnchor.TOP_LEFT);
    gapBeginMarker.setLabelTextAnchor(TextAnchor.TOP_RIGHT);
    cp.getChart().getXYPlot().addDomainMarker(gapBeginMarker);

    final Marker gapEndMarker = new ValueMarker(dataSetWithTheGap
            .instance(Math.min(dataSetWithTheGap.numInstances() - 1, position + gapsize)).value(dateIdx));
    gapEndMarker.setPaint(Color.RED);
    gapEndMarker.setLabel("Gap end");
    gapEndMarker.setLabelAnchor(RectangleAnchor.TOP_RIGHT);
    gapEndMarker.setLabelTextAnchor(TextAnchor.TOP_LEFT);
    cp.getChart().getXYPlot().addDomainMarker(gapEndMarker);

    addExportPopupMenu(diff, cp);

    return cp;
}

From source file:lu.lippmann.cdb.ext.hydviga.ui.HydroDatasetView.java

License:Open Source License

public HydroDatasetView setDataSet(final Instances pdataSet) {
    if (pdataSet.classIndex() != -1 && !pdataSet.classAttribute().isNominal())
        pdataSet.setClassIndex(-1);

    if (this.initialDataSet == null) {
        this.initialDataSet = pdataSet;
        this.initialCompleteness = new CompletenessComputer(this.initialDataSet);
        this.dataCompletenessProgressBar.setMaximum(pdataSet.numInstances() * pdataSet.numAttributes());
        reinitDataCompleteness();//from w  ww .  j av  a2  s  .  c o  m
    }

    this.dataSet = pdataSet;

    if (!filtered)
        this.notFilteredDataSet = pdataSet;

    //updateClassSelectionMenu();
    this.supervisedTransformPane.setVisible(pdataSet.classIndex() != -1);

    for (final TabView tv : tabViews) {
        tv.update(dataSet);
    }

    try {
        updateFiltersPane(dataSet);
    } catch (Exception e) {
        eventPublisher.publish(new ErrorOccuredEvent("Error when updating filters", e));
    }

    updateTooltipShowingDatasetDimensions();

    return this;
}

From source file:lu.lippmann.cdb.lab.beta.shih.Shih2010.java

License:Open Source License

/**
 * //from www  .j a  v  a 2s . c o  m
 * @return
 */
public Instances getModifiedInstances() {

    //Copy attribute list (and change categorical by numerical)
    final ArrayList<Attribute> lAttrs = new ArrayList<Attribute>();
    for (int i = 0; i < instances.numAttributes(); i++) {
        Attribute attr = instances.attribute(i);
        if (attr.isNumeric() || attr.index() == instances.classIndex()) {
            lAttrs.add(attr);
        } else {
            Attribute newAttr = new Attribute(attr.name());
            lAttrs.add(newAttr);
        }
    }

    //Build new instance
    final Instances newInstances = new Instances("Shih instance", lAttrs, instances.numInstances());
    newInstances.setClassIndex(instances.classIndex());
    for (int i = 0; i < instances.numInstances(); i++) {
        final Instance instance = instances.instance(i);
        final Instance cpyInstance = (Instance) instance.copy();
        for (int j = 0; j < instance.numAttributes(); j++) {
            Attribute attribute = instance.attribute(j);
            int k = 0;
            if (attribute.index() == instances.classIndex()) {
                //The class index is nominal
                cpyInstance.setValue(attribute, instance.stringValue(j));
            } else if (!attribute.isNumeric()) {
                String elt = attribute.value((int) instance.value(j));
                cpyInstance.setValue(attribute, F.get(new TupleSI(elt, j)));
            } else {
                if (maxNum[k] > 1) {
                    cpyInstance.setValue(attribute, instance.value(j) / maxNum[k]);
                }
                k++;
            }
        }
        newInstances.add(cpyInstance);
    }

    if (ignoreClass && instances.classIndex() != -1) {
        newInstances.deleteAttributeAt(instances.classIndex());
    }
    return newInstances;
}