Example usage for weka.core Instances attribute

List of usage examples for weka.core Instances attribute

  1. HOME
  2. Java
  3. weka
  4. weka.core.*
  5. Instances
  6. attribute

Introduction

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

Prototype

publicAttribute attribute(String name)

Source Link

Document

Returns an attribute given its name.

Usage

From source file:demo.Demo.java

License:Open Source License

/**
 * @param args//from w  w w . ja va 2  s.co m
 * @throws IOException 
 */
public static void main(String[] args) throws IOException {

    CSVLoader loader = new CSVLoader();
    System.out.println("Downloading dataset...");
    URL oracle = new URL("http://repository.seasr.org/Datasets/UCI/csv/mushroom.csv");
    File csvFile = File.createTempFile("data-", ".csv");
    BufferedReader in = new BufferedReader(new InputStreamReader(oracle.openStream()));
    PrintWriter out = new PrintWriter(new BufferedOutputStream(new FileOutputStream(csvFile)));
    String inputLine;
    while ((inputLine = in.readLine()) != null) {
        out.println(inputLine);
    }
    in.close();
    out.close();
    System.out.println("Dataset written to: " + csvFile.getAbsolutePath());

    loader.setFile(csvFile);
    loader.setNominalAttributes("first-last");
    Instances instances = loader.getDataSet();
    String[] variablesNames = new String[instances.numAttributes()];
    for (int i = 0; i < variablesNames.length; i++) {
        variablesNames[i] = instances.attribute(i).name();
    }

    ChordalysisModelling modeller = new ChordalysisModelling(0.05);

    System.out.println("Learning...");
    modeller.buildModel(instances);
    DecomposableModel bestModel = modeller.getModel();
    bestModel.display(variablesNames);
    System.out.println("The model selected is:");
    System.out.println(bestModel.toString(variablesNames));
    bestModel.display(variablesNames);

}

From source file:demo.DemoInference.java

License:Open Source License

/**
 * @param args/*from www.j  a  va  2  s  . c  o m*/
 * @throws IOException 
 */
public static void main(String[] args) throws IOException {

    CSVLoader loader = new CSVLoader();
    System.out.println("Downloading dataset...");
    URL oracle = new URL("http://repository.seasr.org/Datasets/UCI/csv/mushroom.csv");
    File csvFile = File.createTempFile("data-", ".csv");
    BufferedReader in = new BufferedReader(new InputStreamReader(oracle.openStream()));
    PrintWriter out = new PrintWriter(new BufferedOutputStream(new FileOutputStream(csvFile)));
    String inputLine;
    while ((inputLine = in.readLine()) != null) {
        out.println(inputLine);
    }
    in.close();
    out.close();
    System.out.println("Dataset written to: " + csvFile.getAbsolutePath());

    loader.setFile(csvFile);
    loader.setNominalAttributes("first-last");
    Instances instances = loader.getDataSet();
    String[] variablesNames = new String[instances.numAttributes()];
    String[][] outcomes = new String[instances.numAttributes()][];
    for (int i = 0; i < variablesNames.length; i++) {
        variablesNames[i] = instances.attribute(i).name();
        outcomes[i] = new String[instances.attribute(i).numValues() + 1];//+1 for missing
        for (int j = 0; j < outcomes[i].length - 1; j++) {
            outcomes[i][j] = instances.attribute(i).value(j);
        }
        outcomes[i][outcomes[i].length - 1] = "missing";
        System.out.println("Dom(" + variablesNames[i] + ") = " + Arrays.toString(outcomes[i]));

    }

    ChordalysisModelling modeller = new ChordalysisModelling(0.05);

    System.out.println("Learning...");
    modeller.buildModel(instances);
    DecomposableModel bestModel = modeller.getModel();
    //      bestModel.display(variablesNames);
    System.out.println("The model selected is:");
    System.out.println(bestModel.toString(variablesNames));

    Inference inference = new Inference(bestModel, variablesNames, outcomes);
    inference.setProbabilities(modeller.getLattice());
    String targetVariable = "population";
    System.out.println("initial beliefs on " + targetVariable + " "
            + Arrays.toString(inference.getBelief(targetVariable)));

    System.out.println("adding evidence poisonous and convex shape");
    inference.addEvidence("class", "e");
    inference.addEvidence("cap-shape", "x");
    inference.recordEvidence();

    System.out.println(
            "beliefs on " + targetVariable + " " + Arrays.toString(inference.getBelief(targetVariable)));

    inference.clearEvidences();
    System.out.println("reset beliefs");
    System.out.println(
            "reset beliefs on " + targetVariable + " " + Arrays.toString(inference.getBelief(targetVariable)));

}

From source file:demo.Run.java

License:Open Source License

/**
 * @param args// ww w  .  ja  va 2s .  co  m
 */
public static void main(String[] args) {
    if (args.length != 4) {
        System.out.println("Usage:\tjava -Xmx1g -jar Chordalysis.jar dataFile pvalue imageOutputFile useGUI?");
        System.out.println("Example:\tjava -Xmx1g -jar Chordalysis.jar dataset.csv 0.05 graph.png false");
        System.out.println("\nNote:\t'1g' means that you authorize 1GB of memory. "
                + "\nNote:\tIt should be adjusted depending upon the size of your data set (mostly required to load the data set).");
        return;
    }
    System.out.println();
    CSVLoader loader = new CSVLoader();
    File csvFile = new File(args[0]);
    if (!csvFile.exists()) {
        System.out.println("The file doesn't exist");
        return;
    } else {
        System.out.println("Info:\tUsing the dataset file " + csvFile.getAbsolutePath());
    }

    double pValue = Double.valueOf(args[1]);
    if (pValue <= 0 || 1 <= pValue) {
        System.out.println("The p-value should be between 0 and 1 excluded. ");
        return;
    } else {
        System.out.println("Info:\tUsing p=" + pValue);
    }

    File outPutFile = new File(args[2]);
    String[] splitted = outPutFile.getName().split("\\.");
    if (splitted.length < 2) {
        System.out.println(
                "The image output file should declare an extension among \".jpg\", \".png\" or \".gif\"");
        return;
    }
    String extension = splitted[splitted.length - 1];
    if (!extension.equals("jpg") && !extension.equals("png") && !extension.equals("gif")) {
        System.out.println(
                "The format for the graphical representation of the model should be either jpg, png or gif. ");
        return;
    } else {
        System.out.println("Info:\tExporting result as a " + extension + " file");
    }

    boolean gui = Boolean.parseBoolean(args[3]);

    if (gui) {
        System.out.println("Info:\tUsing a graphical user interface");
    } else {
        System.out.println("Info:\tNot using a graphical user interface");
    }

    try {
        loader.setFile(csvFile);
        loader.setNominalAttributes("first-last");
        Instances instances = loader.getDataSet();
        String[] variablesNames = new String[instances.numAttributes()];
        for (int i = 0; i < variablesNames.length; i++) {
            variablesNames[i] = instances.attribute(i).name();
        }
        long start = System.currentTimeMillis();

        ChordalysisModelling modeller = new ChordalysisModelling(pValue);
        modeller.buildModel(instances);
        DecomposableModel bestModel = modeller.getModel();

        if (gui)
            bestModel.display(variablesNames);
        System.out
                .println("The model selected is: (selected in " + (System.currentTimeMillis() - start) + "ms)");
        System.out.println(bestModel.toString(variablesNames));
        ImageIO.write(bestModel.getImage(variablesNames), extension, outPutFile);
    } catch (IOException e) {
        System.out.println("I/O error while loading csv file");
        e.printStackTrace();
    }
}

From source file:demo.RunDot.java

License:Open Source License

/**
 * @param args/*from   w  w  w.  j  av  a 2 s  .  c  om*/
 */
public static void main(String[] args) {
    if (args.length != 3) {
        System.out.println("Usage:\tjava -Xmx1g -jar Chordalysis.jar dataFile pvalue dotOutputFile");
        System.out.println("Example:\tjava -Xmx1g -jar Chordalysis.jar dataset.csv 0.05 graph.dot");
        System.out.println("\nNote:\t'1g' means that you authorize 1GB of memory. "
                + "\nNote:\tIt should be adjusted depending upon the size of your data set (mostly required to load the data set).");
        return;
    }
    System.out.println();
    CSVLoader loader = new CSVLoader();
    File csvFile = new File(args[0]);
    if (!csvFile.exists()) {
        System.out.println("The file doesn't exist");
        return;
    } else {
        System.out.println("Info:\tUsing the dataset file " + csvFile.getAbsolutePath());
    }

    double pValue = Double.valueOf(args[1]);
    if (pValue <= 0 || 1 <= pValue) {
        System.out.println("The p-value should be between 0 and 1 excluded. ");
        return;
    } else {
        System.out.println("Info:\tUsing p=" + pValue);
    }

    File outPutFile = new File(args[2]);
    String[] splitted = outPutFile.getName().split("\\.");
    if (splitted.length < 2) {
        System.out.println("The image output file should declare a \".dot\" extension");
        return;
    }

    try {
        loader.setFile(csvFile);
        loader.setNominalAttributes("first-last");
        Instances instances = loader.getDataSet();
        String[] variablesNames = new String[instances.numAttributes()];
        for (int i = 0; i < variablesNames.length; i++) {
            variablesNames[i] = instances.attribute(i).name();
        }
        long start = System.currentTimeMillis();

        ChordalysisModelling modeller = new ChordalysisModelling(pValue);
        modeller.buildModel(instances);
        DecomposableModel bestModel = modeller.getModel();

        System.out
                .println("The model selected is: (selected in " + (System.currentTimeMillis() - start) + "ms)");
        System.out.println(bestModel.toString(variablesNames));
        bestModel.exportDOT(outPutFile, variablesNames);
        System.out.println(
                "DOT file exported - note that the variables with no neighbors won't be included in the graph");
    } catch (IOException e) {
        System.out.println("I/O error while loading csv file");
        e.printStackTrace();
    }
}

From source file:demo.RunGUI.java

License:Open Source License

/**
 * @param args/*www  .  ja  va 2s .  co m*/
 */
public static void main(String[] args) {
    JFileChooser chooser = new JFileChooser();
    FileNameExtensionFilter filter = new FileNameExtensionFilter("CSV file", "csv");
    chooser.setFileFilter(filter);
    int returnVal = chooser.showOpenDialog(null);
    if (returnVal == JFileChooser.APPROVE_OPTION) {
        System.out.println("You chose to open this file: " + chooser.getSelectedFile().getName());
    }
    CSVLoader loader = new CSVLoader();
    File csvFile = chooser.getSelectedFile();
    if (!csvFile.exists()) {
        System.out.println("The file doesn't exist");
        return;
    }

    double pValue = Double.valueOf(JOptionPane.showInputDialog("Desired p-value ]0,1[", 0.05));
    if (pValue <= 0 || 1 <= pValue) {
        System.out.println("The p-value should be between 0 and 1 excluded. ");
        return;
    }
    try {
        loader.setFile(csvFile);
        loader.setNominalAttributes("first-last");
        Instances instances = loader.getDataSet();
        String[] variablesNames = new String[instances.numAttributes()];
        for (int i = 0; i < variablesNames.length; i++) {
            variablesNames[i] = instances.attribute(i).name();
        }

        ChordalysisModelling modeller = new ChordalysisModelling(pValue);
        modeller.buildModel(instances);
        DecomposableModel bestModel = modeller.getModel();
        System.out.println("The model selected is:");
        System.out.println(bestModel.toString(variablesNames));
        bestModel.display(variablesNames);
    } catch (IOException e) {
        System.out.println("I/O error while loading csv file");
        e.printStackTrace();
    }

}

From source file:demo.RunGUIProof.java

License:Open Source License

/**
 * @param args/*from w w w .jav a 2 s  . c  o  m*/
 */
public static void main(String[] args) {
    JOptionPane.showMessageDialog(null, introductionMessage, "Chordalysis", JOptionPane.INFORMATION_MESSAGE);

    int result = JOptionPane.showOptionDialog(null, new JTextArea(agreeCitation), "Reference",
            JOptionPane.YES_NO_OPTION, JOptionPane.QUESTION_MESSAGE, null, null, null);
    if (result == JOptionPane.NO_OPTION || result == JOptionPane.CLOSED_OPTION) {
        JOptionPane.showMessageDialog(null,
                "Chordalysis will now stop, because you do not want to reference its source. ", "Chordalysis",
                JOptionPane.WARNING_MESSAGE);
        System.exit(0);
    }

    JFileChooser chooser = new JFileChooser();
    FileNameExtensionFilter filter = new FileNameExtensionFilter("CSV file", "csv");
    chooser.setFileFilter(filter);
    int returnVal = chooser.showOpenDialog(null);
    File csvFile = null;
    if (returnVal == JFileChooser.APPROVE_OPTION) {
        csvFile = chooser.getSelectedFile();
        System.out.println("You chose to open: " + csvFile);
    } else {
        JOptionPane.showMessageDialog(null, noFileSelectedMessage, "Chordalysis", JOptionPane.ERROR_MESSAGE);
        return;
    }
    CSVLoader loader = new CSVLoader();
    if (!csvFile.exists()) {
        JOptionPane.showMessageDialog(null, noFileMessage, "Chordalysis", JOptionPane.INFORMATION_MESSAGE);
        return;
    }
    double pValue = -1;
    while (pValue <= 0 || 1 <= pValue) {
        pValue = Double.valueOf(JOptionPane.showInputDialog("Desired p-value (between 0 and 1)", 0.05));
        if (pValue <= 0 || 1 <= pValue) {
            JOptionPane.showMessageDialog(null, incorrectPValueMessage, "Chordalysis",
                    JOptionPane.WARNING_MESSAGE);
        }
    }

    filter = new FileNameExtensionFilter("PNG or DOT or CSV file or DNE file", "png", "dot", "csv", "dne");
    chooser = new JFileChooser();
    chooser.setFileFilter(filter);
    chooser.setDialogTitle("Where to save the graph?");
    chooser.setSelectedFile(new File(csvFile.getAbsolutePath() + ".png"));
    returnVal = chooser.showSaveDialog(null);
    File graphFile = null;
    if (returnVal == JFileChooser.APPROVE_OPTION) {
        graphFile = chooser.getSelectedFile();
        System.out.println("You chose to save the graph to: " + graphFile.getAbsolutePath());
    } else {
        JOptionPane.showMessageDialog(null, noFileSelectedMessage, "Chordalysis", JOptionPane.ERROR_MESSAGE);
        return;
    }

    try {
        loader.setFile(csvFile);

        returnVal = JOptionPane.showConfirmDialog(null, "Are all of your attribute nominal?", "Chordalysis",
                JOptionPane.YES_NO_OPTION);
        if (returnVal == JOptionPane.YES_OPTION) {
            loader.setNominalAttributes("first-last");
        }

        Instances instances = loader.getDataSet();

        String cols = "";
        for (int i = 0; i < instances.numAttributes(); i++) {
            Attribute att = instances.attribute(i);
            if (!att.isNominal()) {
                cols += (i + 1) + ",";
            }
        }
        if (!cols.isEmpty()) {
            cols = cols.substring(0, cols.length() - 1);
            String message = "Some atributes are not nominal (number " + cols
                    + "), please wait during discretization. ";
            JOptionPane.showMessageDialog(null, message, "Chordalysis", JOptionPane.INFORMATION_MESSAGE);
            Discretize discretizer = new Discretize(cols);
            discretizer.setUseEqualFrequency(true);
            discretizer.setBins(3);
            discretizer.setIgnoreClass(true);
            discretizer.setInputFormat(instances);
            instances = Filter.useFilter(instances, discretizer);
            JOptionPane.showMessageDialog(null, "Discretization is now finished.", "Chordalysis",
                    JOptionPane.INFORMATION_MESSAGE);
        }

        String[] variablesNames = new String[instances.numAttributes()];
        String[][] outcomes = new String[instances.numAttributes()][];
        for (int i = 0; i < variablesNames.length; i++) {
            variablesNames[i] = instances.attribute(i).name();
            outcomes[i] = new String[instances.attribute(i).numValues()];
            for (int j = 0; j < outcomes[i].length; j++) {
                outcomes[i][j] = instances.attribute(i).value(j);
            }
        }

        ChordalysisModelling modeller = new ChordalysisModelling(pValue);
        modeller.buildModel(instances);
        DecomposableModel bestModel = modeller.getModel();
        JOptionPane.showMessageDialog(null, new JTextArea("Chordalysis has now finished analysing your data. "
                + "\nIf you found something useful, please reference Chordalysis as"
                + "\n\t- F. Petitjean, G.I. Webb and A. Nicholson, Scaling log-linear analysis to high-dimensional data, ICDM 2013"
                + "\n\t- F. Petitjean and G.I. Webb, Scaling log-linear analysis to datasets with thousands of variables, SDM 2015"
                + "\n\nYou can find the output file at: '" + graphFile.getAbsolutePath() + "'"), "Citation",
                JOptionPane.INFORMATION_MESSAGE);
        System.out.println("The model selected is:");
        System.out.println(bestModel.toString(variablesNames));
        if (graphFile.getName().endsWith("dot")) {
            bestModel.exportDOT(graphFile, variablesNames);
        } else if (graphFile.getName().endsWith("png")) {
            ImageIO.write(bestModel.getImage(variablesNames), "png", graphFile);
        } else if (graphFile.getName().endsWith("dne")) {
            bestModel.exportBNNetica(graphFile, variablesNames, outcomes);
            bestModel.exportDOT(new File(graphFile.getAbsolutePath() + ".dot"), variablesNames);
            ImageIO.write(bestModel.getImage(variablesNames), "png",
                    new File(graphFile.getAbsolutePath() + ".png"));
            bestModel.saveAssociations(variablesNames, new File(graphFile.getAbsolutePath() + ".csv"));
        } else {
            bestModel.saveAssociations(variablesNames, graphFile);
        }

    } catch (IOException e) {
        JOptionPane.showMessageDialog(null,
                "The file '" + csvFile.getAbsolutePath() + "'\ncannot be read properly.",
                "Error while reading file", JOptionPane.ERROR_MESSAGE);
        System.out.println("I/O error while loading csv file");
        e.printStackTrace();
    } catch (Exception e) {
        JOptionPane.showMessageDialog(null, "Error:" + e.getMessage(), "Chordalysis",
                JOptionPane.ERROR_MESSAGE);
        e.printStackTrace();
    }

}

From source file:detplagiasi.TextDirectoryToArff.java

License:Open Source License

public Instances createDataset(String directoryPath) throws Exception {

    FastVector atts = new FastVector(2);
    atts.addElement(new Attribute("filename", (FastVector) null));
    atts.addElement(new Attribute("contents", (FastVector) null));

    /*//w ww  . java  2s . c  o m
    ArrayList atts = new ArrayList(2);
    atts.addElement(new Attribute("filename", (ArrayList) null));
    atts.addElement(new Attribute("contents", (ArrayList) null));
     */
    Instances data = new Instances("text_files_in_" + directoryPath, atts, 0);
    File dir = new File(directoryPath);
    String[] files = dir.list();
    //create file a untuk menampung name file dari instance yang terkait
    //FileWriter fstream = new FileWriter(directoryPath+"\\cluster detail.txt");
    BufferedWriter out = null;
    out = new BufferedWriter(new FileWriter(directoryPath + "\\cluster detail.txt"));

    for (int i = 0; i < files.length; i++) {
        if (files[i].endsWith(".txt")) {
            out.write("file ke " + (i + 1) + ": " + files[i]);
            System.out.println("processed files:" + files[i]);
            fileName[i] = files[i];
            out.write("file ke " + (i + 1) + ": " + files[i]);
            try {
                double[] newInst = new double[2];
                newInst[0] = (double) data.attribute(0).addStringValue(files[i]);
                File txt = new File(directoryPath + File.separator + files[i]);

                System.out.println("TDTARFF: " + txt.getCanonicalPath());

                InputStreamReader is;
                is = new InputStreamReader(new FileInputStream(txt));
                StringBuffer txtStr = new StringBuffer();
                int c;
                while ((c = is.read()) != -1) {
                    txtStr.append((char) c);
                }
                newInst[1] = (double) data.attribute(1).addStringValue(txtStr.toString());
                try {
                    out.write("file ke " + (i + 1) + ": " + files[i]);
                    System.out.println("success");
                } catch (Exception d) {
                    System.err.println(d.getLocalizedMessage());
                }
                //input pada file a nama file dari instance
                //data.add(new Instance(1.0, newInst));
                data.add(new Instance(1.0, newInst));
                //data.renameAttributeValue(data.attribute("att_name_in_data2"),"att_value_in_data2","att_value_in_data1");
            } catch (Exception e) {
                System.err.println("failed to convert file: " + directoryPath + File.separator + files[i]);
            }
        }
    }
    return data;
}

From source file:dewaweebtreeclassifier.veranda.VerandaTree.java

/**
 * //  w  ww.  j a va 2 s  .c o  m
 * @param data 
 */
public void buildTree(Instances data) {
    // exit if there is no data left in the dataset
    if (data.numInstances() == 0) {
        mChild = null;
        return;
    }

    double[] informationGains = new double[data.numAttributes()];
    Enumeration enumAttrs = data.enumerateAttributes();
    while (enumAttrs.hasMoreElements()) {
        Attribute attr = (Attribute) enumAttrs.nextElement();
        informationGains[attr.index()] = computeGain(data, attr);
    }
    int maxIdx = Utils.maxIndex(informationGains);

    if (Utils.eq(informationGains[maxIdx], 0)) {
        mClassDistribution = new int[data.numClasses()];
        Enumeration enumInst = data.enumerateInstances();
        while (enumInst.hasMoreElements()) {
            Instance instance = (Instance) enumInst.nextElement();
            mClassDistribution[(int) instance.classValue()]++;
        }
        mClassValue = Utils.maxIndex(mClassDistribution);
    } else {
        mSplitAttribute = data.attribute(maxIdx);
        Instances[] splitInstances = splitInstancesOnAttribute(data, mSplitAttribute);
        mChild = new VerandaTree[mSplitAttribute.numValues()];
        for (int i = 0; i < mChild.length; i++) {
            mChild[i] = new VerandaTree();
            mChild[i].buildTree(splitInstances[i]);
        }
    }
}

From source file:distributed.core.DistributedUtils.java

License:Open Source License

public static NumericStats getNumericAttributeStatsSparse(Instances denormalized, int attIndex) {
    NumericStats ns = new NumericStats(denormalized.attribute(attIndex).name());

    for (int j = 0; j < denormalized.numInstances(); j++) {
        double value = denormalized.instance(j).value(attIndex);

        if (Utils.isMissingValue(value) || value == 0) {
            ns.getStats()[ArffSummaryNumericMetric.MISSING.ordinal()]++;
        } else {/*  w  w w  .j  a  va 2  s.c  o m*/
            ns.getStats()[ArffSummaryNumericMetric.COUNT.ordinal()]++;
            ns.getStats()[ArffSummaryNumericMetric.SUM.ordinal()] += value;
            ns.getStats()[ArffSummaryNumericMetric.SUMSQ.ordinal()] += value * value;
            if (Double.isNaN(ns.getStats()[ArffSummaryNumericMetric.MIN.ordinal()])) {
                ns.getStats()[ArffSummaryNumericMetric.MIN
                        .ordinal()] = ns.getStats()[ArffSummaryNumericMetric.MAX.ordinal()] = value;
            } else if (value < ns.getStats()[ArffSummaryNumericMetric.MIN.ordinal()]) {
                ns.getStats()[ArffSummaryNumericMetric.MIN.ordinal()] = value;
            } else if (value > ns.getStats()[ArffSummaryNumericMetric.MAX.ordinal()]) {
                ns.getStats()[ArffSummaryNumericMetric.MAX.ordinal()] = value;
            }
        }
    }

    ns.computeDerived();

    return ns;
}

From source file:distributed.core.DistributedUtils.java

License:Open Source License

public static Instances makeHeaderWithSummaryAtts(Instances denormalized, boolean treatZerosAsMissing) {
    Instances header = new Instances(denormalized, 0);

    for (int i = 0; i < denormalized.numAttributes(); i++) {
        AttributeStats stats = denormalized.attributeStats(i);
        if (denormalized.attribute(i).isNumeric()) {
            NumericStats ns = new NumericStats(denormalized.attribute(i).name());
            if (!treatZerosAsMissing) {
                ns.getStats()[ArffSummaryNumericMetric.MIN.ordinal()] = stats.numericStats.min;
                ns.getStats()[ArffSummaryNumericMetric.MAX.ordinal()] = stats.numericStats.max;
                ns.getStats()[ArffSummaryNumericMetric.COUNT.ordinal()] = stats.numericStats.count;
                ns.getStats()[ArffSummaryNumericMetric.SUM.ordinal()] = stats.numericStats.sum;
                ns.getStats()[ArffSummaryNumericMetric.SUMSQ.ordinal()] = stats.numericStats.sumSq;
                ns.getStats()[ArffSummaryNumericMetric.MISSING.ordinal()] = stats.missingCount;

                ns.computeDerived();/*  www .  j a  va 2  s.c om*/
            } else {
                ns = getNumericAttributeStatsSparse(denormalized, i);
            }

            Attribute newAtt = ns.makeAttribute();
            header.insertAttributeAt(newAtt, header.numAttributes());
        } else if (denormalized.attribute(i).isNominal()) {
            NominalStats nom = new NominalStats(denormalized.attribute(i).name());
            nom.setNumMissing(stats.missingCount);

            double[] labelFreqs = stats.nominalWeights;
            for (int j = 0; j < denormalized.attribute(i).numValues(); j++) {
                nom.add(denormalized.attribute(i).value(j), labelFreqs[j]);
            }

            Attribute newAtt = nom.makeAttribute();
            header.insertAttributeAt(newAtt, header.numAttributes());
        }
    }

    return header;
}