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:ffnn.FFNNTubesAI.java

public static Instances filterNominalNumeric(Instances i) {
    NominalToBinary filter = new NominalToBinary();
    Instances temp_instances = new Instances(i);
    if (temp_instances.classIndex() > -1) { //Jika ada classs index
        temp_instances.setClassIndex(-1); //Unset
    }// w w w .ja v a  2 s . c o  m
    try {
        filter.setInputFormat(temp_instances);
        temp_instances = Filter.useFilter(temp_instances, filter);
    } catch (Exception ex) {
        Logger.getLogger(FFNN.class.getName()).log(Level.SEVERE, null, ex);
    }
    return temp_instances;
}

From source file:FFNN.MultiplePerceptron.java

public static void main(String args[]) throws Exception {
    //        System.out.println("input jumlah layer 0/1 :");
    //        Scanner input = new Scanner(System.in);
    //        int layer = input.nextInt();
    //        System.out.println("input learning rate");
    //        double rate = input.nextDouble();
    //        int hidden = 0;
    //        if(layer==1){
    //            System.out.println("input jumlah neuron di hidden layer");
    //            hidden = input.nextInt();
    //        }/*from   w  w  w .j a v  a2 s  . c  o m*/
    //        
    //        System.out.print("Masukkan nama file : ");
    //        String filename = input.next();
    ConverterUtils.DataSource source = new ConverterUtils.DataSource(
            ("D:\\Program Files\\Weka-3-8\\data\\iris.arff"));
    Instances train = source.getDataSet();
    //        Normalize nm = new Normalize();
    //        nm.setInputFormat(train);
    //        train = Filter.useFilter(train, nm);
    for (int i = 0; i < train.numAttributes(); i++)
        System.out.println(i + ". " + train.attribute(i).name());
    System.out.print("Masukkan indeks kelas : ");
    //int classIdx = input.nextInt();
    train.setClassIndex(train.numAttributes() - 1);
    MultiplePerceptron mlp = new MultiplePerceptron(10000, 1, 13, train);
    mlp.buildClassifier(train);
    Evaluation eval = new Evaluation(train);
    eval.evaluateModel(mlp, train);
    System.out.println(eval.toSummaryString());
    //        System.out.println(eval.toMatrixString());
}

From source file:ffnn.TucilWeka.java

public static Instances readDataSet(String filepath) {
    //Membaca dataset

    Instances data = null;
    try {/*from   w w w .j  a  va  2 s  . c om*/
        data = DataSource.read(filepath);
    } catch (Exception ex) {
        Logger.getLogger(TucilWeka.class.getName()).log(Level.SEVERE, null, ex);
    }
    data.setClassIndex(data.numAttributes() - 1);
    return data;
}

From source file:ffnn.TucilWeka.java

public static Instances createInstances(int max) {
    //List of Attributes dan List of Class untuk Header
    //Jumlah attributes: 4 jika tanpa class, 5 jika dengan class
    ArrayList<Attribute> attrs = new ArrayList<Attribute>(5);
    ArrayList<String> classVal = new ArrayList<String>();

    //Menambahkkan class yang mungkin ke List
    classVal.add("Iris-setosa");
    classVal.add("Iris-versicolor");
    classVal.add("Iris-virginica");

    //Menambahkan attributes ke List
    Attribute sepallength = new Attribute("sepallength");
    attrs.add(sepallength); //Numeric Attributes
    Attribute sepalwidth = new Attribute("sepalwidth");
    attrs.add(sepalwidth); //Numeric Attributes
    Attribute petallength = new Attribute("petallength");
    attrs.add(petallength); //Numeric Attributes
    Attribute petalwidth = new Attribute("petalwidth");
    attrs.add(petalwidth); //Numeric Attributes
    Attribute classValue = new Attribute("@@class@@", classVal);
    attrs.add(classValue); //String Attributes

    //Pembuatan//from  w w  w.ja va2 s.  c  om
    //Constructor dengan param Nama, List of Attribute, size
    Instances dataRaw = new Instances("irisNew", attrs, 0); //Instances kosong
    dataRaw.setClassIndex(dataRaw.numAttributes() - 1);
    Scanner scan = new Scanner(System.in);

    for (int i = 0; i < max; i++) {
        //Weka mennyimpan instance sebagai double
        double temp;
        Instance inst = new DenseInstance(dataRaw.numAttributes());
        System.out.println("Sepallength:");
        temp = scan.nextDouble();
        inst.setValue(sepallength, temp);
        System.out.println("Sepalwidth:");
        temp = scan.nextDouble();
        inst.setValue(sepalwidth, temp);
        System.out.println("Petallegth:");
        temp = scan.nextDouble();
        inst.setValue(petallength, temp);
        System.out.println("Petalwidth:");
        temp = scan.nextDouble();
        inst.setValue(petalwidth, temp);

        //System.out.println("Masukan kelima:");
        //temp = scan.nextDouble();
        //0 -> setosa, 1 -> vesicolor, 2-> virginica
        //instS.setValue(classValue, temp); //tidak dibutuhkan sebenarnya

        //Menambahkan instance ke instances
        dataRaw.add(inst);
    }
    return dataRaw;
}

From source file:FinalMineria.Reconocimiento.java

/**
 * Processes requests for both HTTP <code>GET</code> and <code>POST</code>
 * methods.//  www  . j a va 2s. co  m
 *
 * @param request servlet request
 * @param response servlet response
 * @throws ServletException if a servlet-specific error occurs
 * @throws IOException if an I/O error occurs
 */
protected void processRequest(HttpServletRequest request, HttpServletResponse response)
        throws ServletException, IOException, Exception {

    String accion = request.getParameter("accion");
    BufferedReader br = null;
    String ruta = request.getServletContext().getRealPath("/Recursos");
    br = new BufferedReader(new FileReader(ruta + "/nombres.txt"));
    linea = br.readLine();
    br.close();
    if ("Detener".equals(accion)) {
        grabar.finish();
        try {
            Thread.sleep(4000);
        } catch (InterruptedException ex) {
            Logger.getLogger(GrabarAudio.class.getName()).log(Level.SEVERE, null, ex);
        }
        String comando = "cmd /c " + request.getServletContext().getRealPath("/Recursos/OpenSmile")
                + "\\SMILExtract_Release.exe -C " + request.getServletContext().getRealPath("/Recursos/config")
                + "\\IS12_speaker_trait.conf -I " + request.getServletContext().getRealPath("/Recursos/audios")
                + "\\prueba.wav -O " + request.getServletContext().getRealPath("/Recursos/arff")
                + "\\prueba.arff -classes {" + linea + "} -classlabel ?";
        Process proceso = Runtime.getRuntime().exec(comando);
        proceso.waitFor();
        Instances prueba, conocimiento;
        try (BufferedReader archivoBase = new BufferedReader(new FileReader(
                request.getServletContext().getRealPath("/Recursos/arff") + "\\baseDatos.arff"))) {
            conocimiento = new Instances(archivoBase);
        }
        try (BufferedReader archivoPrueba = new BufferedReader(
                new FileReader(request.getServletContext().getRealPath("/Recursos/arff") + "\\prueba.arff"))) {
            prueba = new Instances(archivoPrueba);
        }

        conocimiento.deleteStringAttributes();
        conocimiento.setClassIndex(981);
        prueba.deleteStringAttributes();
        prueba.setClassIndex(981);
        Classifier clasificadorModelo = (Classifier) new NaiveBayes();
        clasificadorModelo.buildClassifier(conocimiento);
        double valorP = clasificadorModelo.classifyInstance(prueba.instance(prueba.numInstances() - 1));
        String prediccion = prueba.classAttribute().value((int) valorP);
        System.out.println(prediccion);
        request.setAttribute("prediccion", prediccion);
        RequestDispatcher dispatcher = request.getRequestDispatcher("./Hablante.jsp");
        dispatcher.forward(request, response);
    } else if ("Grabar".equals(accion)) {
        ruta = request.getServletContext().getRealPath("/Recursos/audios");
        grabar = new Grabador(ruta + "\\" + "prueba");
        Thread stopper = new Thread(new Runnable() {
            public void run() {
                try {
                    Thread.sleep(tiempo);
                } catch (InterruptedException ex) {
                    ex.printStackTrace();
                }
                grabar.finish();
            }
        });

        stopper.start();

        // start recording
        grabar.start();
        response.sendRedirect("./grabar.jsp");
    }
}

From source file:fk.stardust.localizer.machinelearn.WekaFaultLocalizer.java

License:Open Source License

@Override
public Ranking<T> localize(final ISpectra<T> spectra) {

    // == 1. Create Weka training instance

    final List<INode<T>> nodes = new ArrayList<>(spectra.getNodes());

    // nominal true/false values
    final List<String> tf = new ArrayList<String>();
    tf.add("t");//from  w ww  . jav a 2  s.  co  m
    tf.add("f");

    // create an attribute for each component
    final Map<INode<T>, Attribute> attributeMap = new HashMap<INode<T>, Attribute>();
    final ArrayList<Attribute> attributeList = new ArrayList<Attribute>(); // NOCS: Weka needs ArrayList..
    for (final INode<T> node : nodes) {
        final Attribute attribute = new Attribute(node.toString(), tf);
        attributeList.add(attribute);
        attributeMap.put(node, attribute);
    }

    // create class attribute (trace success)
    final Attribute successAttribute = new Attribute("success", tf);
    attributeList.add(successAttribute);

    // create weka training instance
    final Instances trainingSet = new Instances("TraceInfoInstances", attributeList, 1);
    trainingSet.setClassIndex(attributeList.size() - 1);

    // == 2. add traces to training set

    // add an instance for each trace
    for (final ITrace<T> trace : spectra.getTraces()) {
        final Instance instance = new DenseInstance(nodes.size() + 1);
        instance.setDataset(trainingSet);
        for (final INode<T> node : nodes) {
            instance.setValue(attributeMap.get(node), trace.isInvolved(node) ? "t" : "f");
        }
        instance.setValue(successAttribute, trace.isSuccessful() ? "t" : "f");
        trainingSet.add(instance);
    }

    // == 3. use prediction to localize faults

    // build classifier
    try {
        final Classifier classifier = this.buildClassifier(this.classifierName, this.classifierOptions,
                trainingSet);
        final Ranking<T> ranking = new Ranking<>();

        System.out.println("begin classifying");
        int classified = 0;

        final Instance instance = new DenseInstance(nodes.size() + 1);
        instance.setDataset(trainingSet);
        for (final INode<T> node : nodes) {
            instance.setValue(attributeMap.get(node), "f");
        }
        instance.setValue(successAttribute, "f");

        for (final INode<T> node : nodes) {
            classified++;
            if (classified % 1000 == 0) {
                System.out.println(String.format("Classified %d nodes.", classified));
            }

            // contain only the current node in the network
            instance.setValue(attributeMap.get(node), "t");

            // predict with which probability this setup leads to a failing network
            final double[] distribution = classifier.distributionForInstance(instance);
            ranking.rank(node, distribution[1]);

            // reset involvment for node
            instance.setValue(attributeMap.get(node), "f");
        }
        return ranking;
    } catch (final Exception e) { // NOCS: Weka throws only raw exceptions
        throw new RuntimeException(e);
    }
}

From source file:focusedCrawler.link.classifier.LinkClassifierFactoryImpl.java

License:Open Source License

public static LinkClassifier createLinkClassifierImpl(String[] attributes, String[] classValues,
        Classifier classifier, String className, int levels) throws IOException {
    LinkClassifier linkClassifier = null;
    LinkNeighborhoodWrapper wrapper = loadWrapper(attributes, stoplist);
    weka.core.FastVector vectorAtt = new weka.core.FastVector();
    for (int i = 0; i < attributes.length; i++) {
        vectorAtt.addElement(new weka.core.Attribute(attributes[i]));
    }//from ww  w.j  a  v  a 2  s .com
    weka.core.FastVector classAtt = new weka.core.FastVector();
    for (int i = 0; i < classValues.length; i++) {
        classAtt.addElement(classValues[i]);
    }
    vectorAtt.addElement(new weka.core.Attribute("class", classAtt));
    Instances insts = new Instances("link_classification", vectorAtt, 1);
    insts.setClassIndex(attributes.length);
    if (className.indexOf("LinkClassifierImpl") != -1) {
        LNClassifier lnClassifier = new LNClassifier(classifier, insts, wrapper, attributes);
        linkClassifier = new LinkClassifierImpl(lnClassifier);
    }
    if (className.indexOf("LinkClassifierAuthority") != -1) {
        linkClassifier = new LinkClassifierAuthority(classifier, insts, wrapper, attributes);
    }
    if (className.indexOf("LinkClassifierHub") != -1) {
        linkClassifier = new LinkClassifierHub(classifier, insts, wrapper, attributes);
    }
    return linkClassifier;
}

From source file:focusedCrawler.target.classifier.WekaTargetClassifier.java

License:Open Source License

public static TargetClassifier create(String modelFile, String featureFile, double relevanceThreshold,
        String stopwordsFile) throws IOException {
    try {/*  w  w w.  java2s .  co m*/
        ParameterFile featureConfig = new ParameterFile(featureFile);

        InputStream is = new FileInputStream(modelFile);
        ObjectInputStream objectInputStream = new ObjectInputStream(is);
        Classifier classifier = (Classifier) objectInputStream.readObject();
        is.close();

        String[] attributes = featureConfig.getParam("ATTRIBUTES", " ");
        weka.core.FastVector vectorAtt = new weka.core.FastVector();
        for (int i = 0; i < attributes.length; i++) {
            vectorAtt.addElement(new weka.core.Attribute(attributes[i]));
        }
        String[] classValues = featureConfig.getParam("CLASS_VALUES", " ");
        weka.core.FastVector classAtt = new weka.core.FastVector();
        for (int i = 0; i < classValues.length; i++) {
            classAtt.addElement(classValues[i]);
        }
        vectorAtt.addElement(new weka.core.Attribute("class", classAtt));
        Instances insts = new Instances("target_classification", vectorAtt, 1);
        insts.setClassIndex(attributes.length);

        StopList stoplist = new StopListArquivo(stopwordsFile);

        return new WekaTargetClassifier(classifier, relevanceThreshold, insts, attributes, stoplist);

    } catch (FileNotFoundException e) {
        throw new IllegalArgumentException("Could not find file: " + modelFile, e);
    } catch (ClassNotFoundException e) {
        throw new IllegalArgumentException("Could not deserialize classifier from file:" + modelFile, e);
    } catch (IOException e) {
        throw new IllegalArgumentException("Could not load classifier.", e);
    }
}

From source file:focusedCrawler.target.EMClassifier.java

License:Open Source License

private void runDevSet(String model, String developData, String outputDir)
        throws IOException, ClassNotFoundException, TargetClassifierException {
    InputStream is = new FileInputStream(model);
    ObjectInputStream objectInputStream = new ObjectInputStream(is);
    Classifier classifier = (Classifier) objectInputStream.readObject();
    weka.core.FastVector vectorAtt = new weka.core.FastVector();
    for (int i = 0; i < attributes.length; i++) {
        vectorAtt.addElement(new weka.core.Attribute(attributes[i]));
    }/*  w w  w  . j av a 2  s .c  o  m*/
    String[] classValues = new String[] { "S", "NS" };
    weka.core.FastVector classAtt = new weka.core.FastVector();
    for (int i = 0; i < classValues.length; i++) {
        classAtt.addElement(classValues[i]);
    }
    vectorAtt.addElement(new weka.core.Attribute("class", classAtt));
    Instances insts = new Instances("target_classification", vectorAtt, 1);
    insts.setClassIndex(attributes.length);
    TargetClassifier targetClassifier = new TargetClassifierImpl(classifier, insts, attributes, stoplist);
    File[] list = new File(developData).listFiles();
    int total = 0;
    System.out.println("TOTAL:" + list.length);
    Vector<VSMElement> posDistances = new Vector<VSMElement>();
    Vector<VSMElement> negDistances = new Vector<VSMElement>();
    for (int i = 0; i < list.length; i++) {
        StringBuffer content = new StringBuffer();
        BufferedReader input = new BufferedReader(new FileReader(list[i]));
        for (String line = input.readLine(); line != null; line = input.readLine()) {
            content.append(line);
            content.append("\n");
        }
        String src = content.toString();
        boolean isTarget = targetClassifier.classify(new Page(null, src));
        double[] prob = targetClassifier.distributionForInstance((new Page(null, src)));
        if (isTarget) {
            posDistances.add(new VSMElement(list[i].toString(), prob[0]));
            FileOutputStream fout = new FileOutputStream(outputDir + File.separator + list[i].getName(), false);
            DataOutputStream dout = new DataOutputStream(fout);
            dout.writeBytes(src);
            dout.close();
        } else {
            negDistances.add(new VSMElement(list[i].toString(), prob[1]));
            FileOutputStream fout = new FileOutputStream(
                    outputDir + "_non" + File.separator + list[i].getName(), false);
            DataOutputStream dout = new DataOutputStream(fout);
            dout.writeBytes(src);
            dout.close();
            total++;
        }
    }
    Collections.sort(posDistances, new VSMElementComparator());
    Collections.sort(negDistances, new VSMElementComparator());
    if (posDistances.size() < 1 && negDistances.size() < 1) {
        System.exit(0);
    }

    for (int i = 0; i < posDistances.size() && i < 10; i++) {
        VSMElement elem = posDistances.elementAt(i);
        String fileName = elem.getWord();
        System.out.println(elem.getWeight() + " " + fileName);
        Runtime.getRuntime().exec("mv " + fileName + " " + rootDir + File.separator + "trainData_form"
                + File.separator + "positive" + File.separator);
        posCount++;
    }
    System.out.println("--------");
    for (int i = 0; i < negDistances.size() && i < 10; i++) {
        VSMElement elem = negDistances.elementAt(i);
        String fileName = elem.getWord();
        System.out.println(elem.getWeight() + " " + fileName);
        Runtime.getRuntime().exec("mv " + fileName + " " + rootDir + File.separator + "trainData_form"
                + File.separator + "negative" + File.separator);
        negCount++;
    }

    System.out.println("NON:" + total);
}

From source file:focusedCrawler.target.PEBL.java

License:Open Source License

private void runDevSet(String model) throws IOException, ClassNotFoundException, TargetClassifierException {
    InputStream is = new FileInputStream(model);
    ObjectInputStream objectInputStream = new ObjectInputStream(is);
    Classifier classifier = (Classifier) objectInputStream.readObject();
    weka.core.FastVector vectorAtt = new weka.core.FastVector();
    for (int i = 0; i < attributes.length; i++) {
        vectorAtt.addElement(new weka.core.Attribute(attributes[i]));
    }//from   w  w w  . ja  v  a2  s . c om
    String[] classValues = new String[] { "S", "NS" };
    weka.core.FastVector classAtt = new weka.core.FastVector();
    for (int i = 0; i < classValues.length; i++) {
        classAtt.addElement(classValues[i]);
    }
    vectorAtt.addElement(new weka.core.Attribute("class", classAtt));
    Instances insts = new Instances("target_classification", vectorAtt, 1);
    insts.setClassIndex(attributes.length);
    TargetClassifier targetClassifier = new TargetClassifierImpl(classifier, insts, attributes, stoplist);
    File[] list = new File(unlabelDir).listFiles();
    int total = 0;
    System.out.println("TOTAL:" + list.length);
    //        Vector<VSMElement> posDistances = new Vector<VSMElement>();
    //        Vector<VSMElement> negDistances = new Vector<VSMElement>();
    for (int i = 0; i < list.length; i++) {
        StringBuffer content = new StringBuffer();
        BufferedReader input = new BufferedReader(new FileReader(list[i]));
        for (String line = input.readLine(); line != null; line = input.readLine()) {
            content.append(line);
            content.append("\n");
        }
        String src = content.toString();
        boolean isTarget = targetClassifier.classify(new Page(null, src));
        if (!isTarget) {
            Runtime.getRuntime().exec("mv " + list[i].toString() + " " + trainingData + File.separator
                    + "negative" + File.separator);
            total++;
        }
    }
    if (total == 0) {
        System.exit(0);
    }
}