Example usage for weka.core Instances numAttributes

List of usage examples for weka.core Instances numAttributes

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Introduction

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

Prototype


publicint numAttributes()

Source Link

Document

Returns the number of attributes.

Usage

From source file:ANN_Single.SinglelayerPerceptron.java

public static void main(String[] args) throws Exception {
    ConverterUtils.DataSource source = new ConverterUtils.DataSource(
            ("D:\\Program Files\\Weka-3-8\\data\\diabetes.arff"));
    Instances train = source.getDataSet();
    Normalize nm = new Normalize();
    nm.setInputFormat(train);/* w w  w .j a v a 2  s .  co  m*/
    train = Filter.useFilter(train, nm);
    train.setClassIndex(train.numAttributes() - 1);
    System.out.println();
    //                System.out.println(i + " "+0.8);
    SinglelayerPerceptron slp = new SinglelayerPerceptron(train, 0.1, 5000);
    slp.buildClassifier(train);
    Evaluation eval = new Evaluation(train);
    //                eval.crossValidateModel(slp, train, 10, new Random(1));
    eval.evaluateModel(slp, train);
    System.out.println(eval.toSummaryString());
    System.out.print(eval.toMatrixString());
}

From source file:ANN_single2.MultilayerPerceptron.java

public MultilayerPerceptron(Instances i, int numHide, double rate, double thres) {
    learningRate = rate;/*from   www.  j  a  v  a2 s  .c om*/
    threshold = thres;
    numHiden = numHide;
    //inisialisasi array hidden
    listHidden = new ArrayList<>();
    for (int idx = 0; idx < numHiden; idx++) {
        listHidden.add(new Node(i.numAttributes())); //1 untuk bias
    }

    //inialisasi array output
    listOutput = new ArrayList<>();
    for (int idx = 0; idx < i.numClasses(); idx++) {
        listOutput.add(new Node(listHidden.size()));
    }
}

From source file:ANN_single2.MultilayerPerceptron.java

public static void main(String[] args) throws Exception {
    ConverterUtils.DataSource source = new ConverterUtils.DataSource(
            ("D:\\Program Files\\Weka-3-8\\data\\Team.arff"));
    Instances train = source.getDataSet();
    Normalize nm = new Normalize();
    nm.setInputFormat(train);//from w w w . java 2s .com
    train = Filter.useFilter(train, nm);
    train.setClassIndex(train.numAttributes() - 1);
    MultilayerPerceptron slp = new MultilayerPerceptron(train, 13, 0.1, 0.5);
    //        slp.buildClassifier(train);
    Evaluation eval = new Evaluation(train);
    eval.crossValidateModel(slp, train, 10, new Random(1));
    //        eval.evaluateModel(slp, train);
    System.out.println(eval.toSummaryString());
    System.out.println(eval.toMatrixString());
}

From source file:ANN_single2.SinglelayerPerceptron.java

@Override
public void buildClassifier(Instances i) {
    listOutput = new ArrayList<>();
    for (int idx = 0; idx < i.numClasses(); idx++) {
        listOutput.add(new Node(i.numAttributes()));
    }//  w w w.  ja v a2s.com

    //mengubah class menjadi numeric (diambil indexnya)
    listDoubleinstance = new double[i.numInstances()];
    for (int numIns = 0; numIns < i.numInstances(); numIns++) {
        listDoubleinstance[numIns] = i.instance(numIns).toDoubleArray()[i.classIndex()];
    }

    double error = 0;
    for (int iter = 0; iter < itteration; iter++) {
        double errorThres = 0;
        for (int idxInstance = 0; idxInstance < i.numInstances(); idxInstance++) {

            //buat list input
            ArrayList<Double> listInput = new ArrayList<>();
            listInput.add(1.0); //ini bias
            for (int idx = 0; idx < i.numAttributes() - 1; idx++) {
                listInput.add(i.get(idxInstance).value(idx));
            }

            //Hitung output rumus = sigmoid dari sigma
            for (int idxOut = 0; idxOut < listOutput.size(); idxOut++) {
                output(listInput, idxOut);
            }

            //Hitung error
            calculateError(idxInstance);
            //update bobot
            updateBobot(listInput);

        }
        for (int idxOut = 0; idxOut < listOutput.size(); idxOut++) {
            errorThres += Math.pow(listOutput.get(idxOut).getError(), 2) / 2;
        }
        if (errorThres <= threshold)
            break;
        //            System.out.println(errorThres);
    }
    //        fold++;
    //        for (int idx =0; idx < i.numInstances(); idx++) {
    //            for (int idxOut=0; idxOut < listOutput.size(); idxOut++) {
    //                error += Math.pow(listOutput.get(idxOut).getError(), 2)/2;
    //            }
    //        }
    //        System.out.println("Fold " + fold);
    //        System.out.println("error " + error);
}

From source file:ANN_single2.SinglelayerPerceptron.java

public static void main(String[] args) throws Exception {
    ConverterUtils.DataSource source = new ConverterUtils.DataSource(
            ("D:\\Program Files\\Weka-3-8\\data\\Team.arff"));
    Instances train = source.getDataSet();
    Normalize nm = new Normalize();
    nm.setInputFormat(train);//w w w  . j  a va 2 s.  c  om
    train = Filter.useFilter(train, nm);
    train.setClassIndex(train.numAttributes() - 1);
    for (int i = 100; i < 3000; i += 100) {
        for (double j = 0.01; j < 1; j += 0.01) {
            System.out.println(i + " " + j);
            SinglelayerPerceptron slp = new SinglelayerPerceptron(i, j, 0.00);
            slp.buildClassifier(train);
            Evaluation eval = new Evaluation(train);
            //                eval.crossValidateModel(slp, train,10, new Random(1));
            eval.evaluateModel(slp, train);
            System.out.println(eval.toSummaryString());
            System.out.println(eval.toMatrixString());
        }
    }
}

From source file:ap.mavenproject1.HelloWeka.java

public static void main(String args[]) {
    Instances data = null;
    ArffLoader loader = new ArffLoader();
    try {/* w  w  w . j  av a 2  s  . c o  m*/

        loader.setFile(new File("C:\\Users\\USER\\Desktop\\data.arff"));

        data = loader.getDataSet();
        data.setClassIndex(data.numAttributes() - 1);

    } catch (IOException ex) {
        Logger.getLogger(HelloWeka.class.getName()).log(Level.SEVERE, null, ex);
    }

    Apriori apriori = new Apriori();
    try {
        NumericToNominal numericToNominal = new NumericToNominal();
        numericToNominal.setInputFormat(data);

        Instances nominalData = Filter.useFilter(data, numericToNominal);
        apriori.buildAssociations(nominalData);
        FastVector[] allTheRules;
        allTheRules = apriori.getAllTheRules();
        for (int i = 0; i < allTheRules.length; i++) {
            System.out.println(allTheRules[i]);
        }
        //             BufferedWriter writer = new BufferedWriter(new FileWriter("./output.arff"));
        //                writer.write(nominalData.toString());
        //                writer.flush();
        //                writer.close();

    } catch (Exception ex) {
        Logger.getLogger(HelloWeka.class.getName()).log(Level.SEVERE, null, ex);
    }

}

From source file:app.RunApp.java

License:Open Source License

/**
 * Generates TableModel for attributes/*from   www .j ava2  s.c  o  m*/
 * 
 * @param jtable Table
 * @param dataset Multi-label dataset
 * @return Generated TableModel
 */
private TableModel attributesTableModel(JTable jtable, MultiLabelInstances dataset) {
    DefaultTableModel tableModel = new DefaultTableModel() {
        @Override
        public boolean isCellEditable(int row, int column) {
            //This causes all cells to be not editable
            return false;
        }
    };

    tableModel.addColumn("Attribute");

    Object[] row = new Object[1];

    Instances instances = dataset.getDataSet();

    int numLabels = dataset.getNumLabels();

    int numAttributes = instances.numAttributes() - numLabels;

    Attribute att;
    for (int i = 0; i < numAttributes; i++) {
        att = instances.attribute(i);
        if (att.isNumeric()) {
            row[0] = att.name();
            tableModel.addRow(row);
        }
    }

    jtable.setModel(tableModel);

    return jtable.getModel();
}

From source file:arffcreator.arffFrame.java

private void createActionPerformed(java.awt.event.ActionEvent evt) {//GEN-FIRST:event_createActionPerformed
    // TODO add your handling code here:

    FastVector atts;/*from  w  w  w . ja  v  a  2 s  .  c  om*/
    FastVector attsRel;
    FastVector attVals;
    FastVector attValsRel;
    Instances data;
    Instances dataRel;
    double[] vals;
    double[] valsRel;
    int i;

    // 1. set up attributes
    atts = new FastVector();
    // - numeric
    atts.addElement(new Attribute("att1"));
    // - nominal
    attVals = new FastVector();
    for (i = 0; i < 5; i++)
        attVals.addElement("val" + (i + 1));
    atts.addElement(new Attribute("att2", attVals));
    // - string
    atts.addElement(new Attribute("att3", (FastVector) null));
    // - date
    atts.addElement(new Attribute("att4", "yyyy-MM-dd"));
    // - relational
    attsRel = new FastVector();
    // -- numeric
    attsRel.addElement(new Attribute("att5.1"));
    // -- nominal
    attValsRel = new FastVector();
    for (i = 0; i < 5; i++)
        attValsRel.addElement("val5." + (i + 1));
    attsRel.addElement(new Attribute("att5.2", attValsRel));
    dataRel = new Instances("att5", attsRel, 0);
    atts.addElement(new Attribute("att5", dataRel, 0));

    // 2. create Instances object
    data = new Instances("MyRelation", atts, 0);

    // 3. fill with data
    // first instance
    vals = new double[data.numAttributes()];
    // - numeric
    vals[0] = Math.PI;
    // - nominal
    vals[1] = attVals.indexOf("val3");
    // - string
    vals[2] = data.attribute(2).addStringValue("This is a string!");
    try {
        // - date
        vals[3] = data.attribute(3).parseDate("2015-07-30");
    } catch (ParseException ex) {
        Logger.getLogger(arffFrame.class.getName()).log(Level.SEVERE, null, ex);
    }
    // - relational
    dataRel = new Instances(data.attribute(4).relation(), 0);
    // -- first instance
    valsRel = new double[2];
    valsRel[0] = Math.PI + 1;
    valsRel[1] = attValsRel.indexOf("val5.3");
    dataRel.add(new Instance(1.0, valsRel));
    // -- second instance
    valsRel = new double[2];
    valsRel[0] = Math.PI + 2;
    valsRel[1] = attValsRel.indexOf("val5.2");
    dataRel.add(new Instance(1.0, valsRel));
    vals[4] = data.attribute(4).addRelation(dataRel);
    // add
    data.add(new Instance(1.0, vals));

    // second instance
    vals = new double[data.numAttributes()]; // important: needs NEW array!
    // - numeric
    vals[0] = Math.E;
    // - nominal
    vals[1] = attVals.indexOf("val1");
    // - string
    vals[2] = data.attribute(2).addStringValue("And another one!");
    try {
        // - date
        vals[3] = data.attribute(3).parseDate("2015-07-30");
    } catch (ParseException ex) {
        Logger.getLogger(arffFrame.class.getName()).log(Level.SEVERE, null, ex);
    }
    // - relational
    dataRel = new Instances(data.attribute(4).relation(), 0);
    // -- first instance
    valsRel = new double[2];
    valsRel[0] = Math.E + 1;
    valsRel[1] = attValsRel.indexOf("val5.4");
    dataRel.add(new Instance(1.0, valsRel));
    // -- second instance
    valsRel = new double[2];
    valsRel[0] = Math.E + 2;
    valsRel[1] = attValsRel.indexOf("val5.1");
    dataRel.add(new Instance(1.0, valsRel));
    vals[4] = data.attribute(4).addRelation(dataRel);
    // add
    data.add(new Instance(1.0, vals));

    // 4. output data

    textArea.append(data.toString());

    dataset = data.toString();

}

From source file:asap.CrossValidation.java

/**
 *
 * @param dataInput/*from w  ww.  j  a  va2  s  .co m*/
 * @param classIndex
 * @param removeIndices
 * @param cls
 * @param seed
 * @param folds
 * @param modelOutputFile
 * @return
 * @throws Exception
 */
public static String performCrossValidation(String dataInput, String classIndex, String removeIndices,
        AbstractClassifier cls, int seed, int folds, String modelOutputFile) throws Exception {

    PerformanceCounters.startTimer("cross-validation ST");

    PerformanceCounters.startTimer("cross-validation init ST");

    // loads data and set class index
    Instances data = DataSource.read(dataInput);
    String clsIndex = classIndex;

    switch (clsIndex) {
    case "first":
        data.setClassIndex(0);
        break;
    case "last":
        data.setClassIndex(data.numAttributes() - 1);
        break;
    default:
        try {
            data.setClassIndex(Integer.parseInt(clsIndex) - 1);
        } catch (NumberFormatException e) {
            data.setClassIndex(data.attribute(clsIndex).index());
        }
        break;
    }

    Remove removeFilter = new Remove();
    removeFilter.setAttributeIndices(removeIndices);
    removeFilter.setInputFormat(data);
    data = Filter.useFilter(data, removeFilter);

    // randomize data
    Random rand = new Random(seed);
    Instances randData = new Instances(data);
    randData.randomize(rand);
    if (randData.classAttribute().isNominal()) {
        randData.stratify(folds);
    }

    // perform cross-validation and add predictions
    Evaluation eval = new Evaluation(randData);
    Instances trainSets[] = new Instances[folds];
    Instances testSets[] = new Instances[folds];
    Classifier foldCls[] = new Classifier[folds];

    for (int n = 0; n < folds; n++) {
        trainSets[n] = randData.trainCV(folds, n);
        testSets[n] = randData.testCV(folds, n);
        foldCls[n] = AbstractClassifier.makeCopy(cls);
    }

    PerformanceCounters.stopTimer("cross-validation init ST");
    PerformanceCounters.startTimer("cross-validation folds+train ST");
    //paralelize!!:--------------------------------------------------------------
    for (int n = 0; n < folds; n++) {
        Instances train = trainSets[n];
        Instances test = testSets[n];

        // the above code is used by the StratifiedRemoveFolds filter, the
        // code below by the Explorer/Experimenter:
        // Instances train = randData.trainCV(folds, n, rand);
        // build and evaluate classifier
        Classifier clsCopy = foldCls[n];
        clsCopy.buildClassifier(train);
        eval.evaluateModel(clsCopy, test);
    }

    cls.buildClassifier(data);
    //until here!-----------------------------------------------------------------

    PerformanceCounters.stopTimer("cross-validation folds+train ST");
    PerformanceCounters.startTimer("cross-validation post ST");
    // output evaluation
    String out = "\n" + "=== Setup ===\n" + "Classifier: " + cls.getClass().getName() + " "
            + Utils.joinOptions(cls.getOptions()) + "\n" + "Dataset: " + data.relationName() + "\n" + "Folds: "
            + folds + "\n" + "Seed: " + seed + "\n" + "\n"
            + eval.toSummaryString("=== " + folds + "-fold Cross-validation ===", false) + "\n";

    if (!modelOutputFile.isEmpty()) {
        SerializationHelper.write(modelOutputFile, cls);
    }

    PerformanceCounters.stopTimer("cross-validation post ST");
    PerformanceCounters.stopTimer("cross-validation ST");

    return out;
}

From source file:asap.CrossValidation.java

/**
 *
 * @param dataInput/*from  w w w .j  a v  a2s.co  m*/
 * @param classIndex
 * @param removeIndices
 * @param cls
 * @param seed
 * @param folds
 * @param modelOutputFile
 * @return
 * @throws Exception
 */
public static String performCrossValidationMT(String dataInput, String classIndex, String removeIndices,
        AbstractClassifier cls, int seed, int folds, String modelOutputFile) throws Exception {

    PerformanceCounters.startTimer("cross-validation MT");

    PerformanceCounters.startTimer("cross-validation init MT");

    // loads data and set class index
    Instances data = DataSource.read(dataInput);
    String clsIndex = classIndex;

    switch (clsIndex) {
    case "first":
        data.setClassIndex(0);
        break;
    case "last":
        data.setClassIndex(data.numAttributes() - 1);
        break;
    default:
        try {
            data.setClassIndex(Integer.parseInt(clsIndex) - 1);
        } catch (NumberFormatException e) {
            data.setClassIndex(data.attribute(clsIndex).index());
        }
        break;
    }

    Remove removeFilter = new Remove();
    removeFilter.setAttributeIndices(removeIndices);
    removeFilter.setInputFormat(data);
    data = Filter.useFilter(data, removeFilter);

    // randomize data
    Random rand = new Random(seed);
    Instances randData = new Instances(data);
    randData.randomize(rand);
    if (randData.classAttribute().isNominal()) {
        randData.stratify(folds);
    }

    // perform cross-validation and add predictions
    Evaluation eval = new Evaluation(randData);
    List<Thread> foldThreads = (List<Thread>) Collections.synchronizedList(new LinkedList<Thread>());

    List<FoldSet> foldSets = (List<FoldSet>) Collections.synchronizedList(new LinkedList<FoldSet>());

    for (int n = 0; n < folds; n++) {
        foldSets.add(new FoldSet(randData.trainCV(folds, n), randData.testCV(folds, n),
                AbstractClassifier.makeCopy(cls)));

        if (n < Config.getNumThreads() - 1) {
            Thread foldThread = new Thread(new CrossValidationFoldThread(n, foldSets, eval));
            foldThreads.add(foldThread);
        }
    }

    PerformanceCounters.stopTimer("cross-validation init MT");
    PerformanceCounters.startTimer("cross-validation folds+train MT");
    //paralelize!!:--------------------------------------------------------------
    if (Config.getNumThreads() > 1) {
        for (Thread foldThread : foldThreads) {
            foldThread.start();
        }
    } else {
        //use the current thread to run the cross-validation instead of using the Thread instance created here:
        new CrossValidationFoldThread(0, foldSets, eval).run();
    }

    cls.buildClassifier(data);

    for (Thread foldThread : foldThreads) {
        foldThread.join();
    }

    //until here!-----------------------------------------------------------------
    PerformanceCounters.stopTimer("cross-validation folds+train MT");
    PerformanceCounters.startTimer("cross-validation post MT");
    // evaluation for output:
    String out = "\n" + "=== Setup ===\n" + "Classifier: " + cls.getClass().getName() + " "
            + Utils.joinOptions(cls.getOptions()) + "\n" + "Dataset: " + data.relationName() + "\n" + "Folds: "
            + folds + "\n" + "Seed: " + seed + "\n" + "\n"
            + eval.toSummaryString("=== " + folds + "-fold Cross-validation ===", false) + "\n";

    if (!modelOutputFile.isEmpty()) {
        SerializationHelper.write(modelOutputFile, cls);
    }

    PerformanceCounters.stopTimer("cross-validation post MT");
    PerformanceCounters.stopTimer("cross-validation MT");
    return out;
}