Example usage for weka.core Instances numAttributes

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:FFNN.MultiplePerceptron.java

public MultiplePerceptron(int itt, double learn, int numHLayer, Instances i) {
    listNodeHidden = new ArrayList<>();//inisialisasis listNodeHidden
    listNodeOutput = new ArrayList<>();
    itteration = itt;/*w ww  .  ja  v a2s  .c o  m*/
    learningRate = learn;
    numHiddenLayer = numHLayer;
    for (int hiddenLayer = 0; hiddenLayer < numHiddenLayer + 1; hiddenLayer++) {//buat neuron untuk hidden layer
        //ditambah 1 untuk neuron bias
        listNodeHidden.add(new Node(i.numAttributes()));

    }

    for (int numInstance = 0; numInstance < i.numClasses(); numInstance++) {//buat neuron untuk output
        listNodeOutput.add(new Node(listNodeHidden.size()));
    }
    target = new ArrayList<>();
    instancesToDouble = new double[i.numInstances()];
    for (int numIns = 0; numIns < i.numInstances(); numIns++) {
        instancesToDouble[numIns] = i.instance(numIns).toDoubleArray()[i.classIndex()];
    }
}

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From source file:FFNN.MultiplePerceptron.java

@Override
public void buildClassifier(Instances i) {
    //iterasi/*  w  ww .  j a  v  a  2s  .c  o m*/
    for (int itt = 0; itt < itteration; itt++) {
        //            System.out.println("Iterasi ke "+ itt);
        for (int indexInstance = 0; indexInstance < i.numInstances(); indexInstance++) {
            ArrayList<Double> listInput = new ArrayList<>();

            //mengisi nilai listInput dengan nilai di instances
            listInput.add(1.0);//ini bias input
            for (int index = 0; index < i.numAttributes() - 1; index++)
                listInput.add(i.get(indexInstance).value(index));

            ArrayList<Double> listOutputHidden = new ArrayList<>();
            listOutputHidden.add(1.0);//input bias
            //                System.out.println();
            //                System.out.println("Hidden layer");
            listNodeHidden.get(0).setValue(1.0);//bias gak boleh ganti output
            //menghitung output hidden layer
            for (int index = 1; index < listNodeHidden.size(); index++) {//output bias tidak boleh ganti
                double value = listNodeHidden.get(index).output(listInput);
                listNodeHidden.get(index).setValue(value);
                listOutputHidden.add(value);
                //                    System.out.println("neuron "+index+" "+value);
            }

            //                System.out.println();
            //                System.out.println("Output layer");
            //menghitung output output layer
            for (int index = 0; index < listNodeOutput.size(); index++) {
                double value = listNodeOutput.get(index).output(listOutputHidden);
                listNodeOutput.get(index).setValue(value);
                //                    System.out.print(value+" ");

            }

            //            System.out.println(listNodeHidden.get(1).getWeightFromList(0));   
            calculateError(indexInstance);

            updateBobot(i.instance(indexInstance));
        }
    }
    for (int idx = 0; idx < listNodeHidden.size(); idx++) {
        System.out.println("Hidden value " + listNodeHidden.get(idx).getValue());
        System.out.println("Hidden error " + listNodeHidden.get(idx).getError());
        for (int idx2 = 0; idx2 < listNodeHidden.get(idx).getWeightSize(); idx2++)
            System.out.println("Hidden weight" + listNodeHidden.get(idx).getWeightFromList(idx2));
    }
    System.out.println();
    for (int idx = 0; idx < listNodeOutput.size(); idx++) {
        System.out.println("Output value " + listNodeOutput.get(idx).getValue());
        System.out.println("Output error " + listNodeOutput.get(idx).getError());
        for (int idx2 = 0; idx2 < listNodeOutput.get(idx).getWeightSize(); idx2++)
            System.out.println("Output weight" + listNodeOutput.get(idx).getWeightFromList(idx2));
    }
}

---

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  ww.  jav a2s .c om*/
    //        
    //        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  v  a  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   ww  w.j a  v a  2 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:fr.loria.synalp.jtrans.phonetiseur.Classifieurs.java

License:Open Source License

private Instances loadInstances(String partialFilename) throws IOException {
    final String filename = repertoireFichiersARFF + partialFilename + ".arff";

    // This may be overkill, but since Weka doesn't specify what charset
    // it will use, make sure we read the file as UTF-8.
    ArffLoader loader = new ArffLoader() {
        {//w  w w . j a  v  a2  s . c  o m
            m_sourceReader = new InputStreamReader(new FileInputStream(filename), "UTF-8");
        }
    };
    Instances i = loader.getStructure();
    i.setClassIndex(i.numAttributes() - 1);
    return i;
}

---

From source file:fr.loria.synalp.jtrans.phonetiseur.Classifieurs.java

License:Open Source License

private void entrainerClassifieurSimpleOuDoublePhoneme(String repertoireFichiersARFF) throws Exception {
    Instances instances;

    DataSource source = new DataSource(
            repertoireFichiersARFF + Configuration.NOM_FICHIER_ARFF_SIMPLE_OU_DOUBLE_PHONEME + ".arff");
    instances = source.getDataSet();/*ww  w  .  j ava  2 s.  c o  m*/

    // On definit la sortie (dernier attibut)
    instances.setClassIndex(instances.numAttributes() - 1);

    // On ne garde certains attributs
    instances = appliquerFiltre(filtreSimpleOuDoublePhoneme, instances);

    // On lance l'apprentissage
    classifieurSimpleOuDoublePhoneme = new J48();
    classifieurSimpleOuDoublePhoneme.buildClassifier(instances);
}

---

From source file:fr.loria.synalp.jtrans.phonetiseur.Classifieurs.java

License:Open Source License

private void entrainerClassifieurDoublePhoneme1er(String repertoireFichiersARFF) throws Exception {
    Instances instances;

    DataSource source = new DataSource(
            repertoireFichiersARFF + Configuration.NOM_FICHIER_ARFF_1er_DOUBLE_PHONEME + ".arff");
    instances = source.getDataSet();/*  www. j av a2s. c  om*/

    // On definit la sortie (dernier attibut)
    instances.setClassIndex(instances.numAttributes() - 1);

    // On ne garde certains attributs
    instances = appliquerFiltre(filtreDoublePhoneme1er, instances);

    // On lance l'apprentissage
    classifieurDoublePhoneme1er = new J48();
    classifieurDoublePhoneme1er.buildClassifier(instances);

}

---

From source file:fr.loria.synalp.jtrans.phonetiseur.Classifieurs.java

License:Open Source License

private void entrainerClassifieurDoublePhoneme2eme(String repertoireFichiersARFF) throws Exception {
    Instances instances;

    DataSource source = new DataSource(
            repertoireFichiersARFF + Configuration.NOM_FICHIER_ARFF_2eme_DOUBLE_PHONEME + ".arff");
    instances = source.getDataSet();/*www .  j  a v a 2s .com*/

    // On definit la sortie (dernier attibut)
    instances.setClassIndex(instances.numAttributes() - 1);

    // On ne garde certains attributs
    instances = appliquerFiltre(filtreDoublePhoneme2eme, instances);

    // On lance l'apprentissage
    classifieurDoublePhoneme2eme = new J48();
    classifieurDoublePhoneme2eme.buildClassifier(instances);
}

---

From source file:fr.loria.synalp.jtrans.phonetiseur.Classifieurs.java

License:Open Source License

private void entrainerClassifieurSimplesPhonemes(String repertoireFichiersARFF) throws Exception {
    Instances instances;
    DataSource source = null;//  w ww.  j  a  v  a 2  s  . c o  m

    tClassifieurSimplePhoneme = new J48[lexique.getNbGraphemes()];

    for (int i = 0; i < lexique.getNbGraphemes(); i++) {
        String graphemeCourant = lexique.getGraphemeFromIndice(i);

        try {
            source = new DataSource(repertoireFichiersARFF + Configuration.NOM_FICHIER_ARFF_SIMPLE_PHONEME + "_"
                    + graphemeCourant + ".arff");
        } catch (Exception e) {
            // Fichier introuvable
            System.out.println("Pas de fichier " + repertoireFichiersARFF
                    + Configuration.NOM_FICHIER_ARFF_SIMPLE_PHONEME + "_" + graphemeCourant + ".arff");
            source = null;
        }

        if (source != null) {
            System.out.println(
                    "        * " + graphemeCourant + " (" + (i + 1) + "/" + lexique.getNbGraphemes() + ")");
            instances = source.getDataSet();

            // On definit la sortie (dernier attibut)
            instances.setClassIndex(instances.numAttributes() - 1);

            // On ne garde certains attributs
            instances = appliquerFiltre(filtreSimplePhoneme, instances);

            // On lance l'apprentissage
            tClassifieurSimplePhoneme[i] = new J48();
            tClassifieurSimplePhoneme[i].buildClassifier(instances);
            System.gc();
        }

    }
}

---

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