Example usage for weka.filters.supervised.attribute NominalToBinary NominalToBinary

Introduction

In this page you can find the example usage for weka.filters.supervised.attribute NominalToBinary NominalToBinary.

Prototype

NominalToBinary

Source Link

Usage

From source file:ann.ANNOptions.java

public ANNOptions() {
    normalize = new Normalize();
    ntb = new NominalToBinary();
    output = new ArrayList<Neuron>();
    layer = new ArrayList<List<Neuron>>();
    layerNeuron = new ArrayList<Integer>();

    weightOpt = 2;//from ww w. j a v a 2s  . c om
    topologyOpt = 4;
    activationFunctionOpt = 1;
    hiddenLayer = 1;
    layerNeuron.add(3);
    maxIteration = 1000;
    momentum = 0.2;
    learningRate = 0.3;
    threshold = 0.01;
}

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From source file:ann.SingleLayerPerceptron.java

@Override
public void buildClassifier(Instances data) throws Exception {
    // can classifier handle the data?
    getCapabilities().testWithFail(data);
    annOptions = new ANNOptions();
    annOptions = annOptions.loadConfiguration();
    output = new ArrayList<Neuron>();
    normalize = new Normalize();
    ntb = new NominalToBinary();
    output = annOptions.output;//www. j ava2  s.co  m

    // remove instances with missing class
    data = new Instances(data);
    data.deleteWithMissingClass();

    //nominal to binary filter
    ntb.setInputFormat(data);
    data = new Instances(Filter.useFilter(data, ntb));

    //normalize filter
    normalize.setInputFormat(data);
    data = new Instances(Filter.useFilter(data, normalize));

    // do main function
    doPerceptron(data);
}

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From source file:CGLSMethod.LinearRegression.java

License:Open Source License

/**
 * Builds a regression model for the given data.
 *
 * @param data the training data to be used for generating the
 * linear regression function//from   w  ww  . ja  v a  2  s  . c o  m
 * @throws Exception if the classifier could not be built successfully
 */
public void buildClassifier(Instances data) throws Exception {

    // Preprocess instances
    if (!m_checksTurnedOff) {
        m_TransformFilter = new NominalToBinary();
        m_TransformFilter.setInputFormat(data);
        data = Filter.useFilter(data, m_TransformFilter);
        m_MissingFilter = new ReplaceMissingValues();
        m_MissingFilter.setInputFormat(data);
        data = Filter.useFilter(data, m_MissingFilter);
        data.deleteWithMissingClass();
    } else {
        m_TransformFilter = null;
        m_MissingFilter = null;
    }

    m_ClassIndex = data.classIndex();
    m_TransformedData = data;

    // Turn all attributes on for a start
    m_SelectedAttributes = new boolean[data.numAttributes()];
    for (int i = 0; i < data.numAttributes(); i++) {
        if (i != m_ClassIndex) {
            m_SelectedAttributes[i] = true;
        }
    }
    m_Coefficients = null;

    // Compute means and standard deviations
    m_Means = new double[data.numAttributes()];
    m_StdDevs = new double[data.numAttributes()];
    for (int j = 0; j < data.numAttributes(); j++) {
        if (j != data.classIndex()) {
            m_Means[j] = data.meanOrMode(j);
            m_StdDevs[j] = Math.sqrt(data.variance(j));
            if (m_StdDevs[j] == 0) {
                m_SelectedAttributes[j] = false;
            }
        }
    }

    m_ClassStdDev = Math.sqrt(data.variance(m_TransformedData.classIndex()));
    m_ClassMean = data.meanOrMode(m_TransformedData.classIndex());

    // Perform the regression
    findBestModel();

    // Save memory
    m_TransformedData = new Instances(data, 0);
}

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From source file:Classifier.supervised.LinearRegression.java

License:Open Source License

/**
 * Builds a regression model for the given data.
 *
 * @param data the training data to be used for generating the
 * linear regression function//from   ww w  .  j  a  v a2s . c om
 * @throws Exception if the classifier could not be built successfully
 */
public void buildClassifier(Instances data) throws Exception {
    m_ModelBuilt = false;

    if (!m_checksTurnedOff) {
        // can classifier handle the data?
        getCapabilities().testWithFail(data);

        // remove instances with missing class
        data = new Instances(data);
        data.deleteWithMissingClass();
    }

    // Preprocess instances
    if (!m_checksTurnedOff) {
        m_TransformFilter = new NominalToBinary();
        m_TransformFilter.setInputFormat(data);
        data = Filter.useFilter(data, m_TransformFilter);
        m_MissingFilter = new ReplaceMissingValues();
        m_MissingFilter.setInputFormat(data);
        data = Filter.useFilter(data, m_MissingFilter);
        data.deleteWithMissingClass();
    } else {
        m_TransformFilter = null;
        m_MissingFilter = null;
    }

    m_ClassIndex = data.classIndex();
    m_TransformedData = data;

    // Turn all attributes on for a start
    m_SelectedAttributes = new boolean[data.numAttributes()];
    for (int i = 0; i < data.numAttributes(); i++) {
        if (i != m_ClassIndex) {
            m_SelectedAttributes[i] = true;
        }
    }
    m_Coefficients = null;

    // Compute means and standard deviations
    m_Means = new double[data.numAttributes()];
    m_StdDevs = new double[data.numAttributes()];
    for (int j = 0; j < data.numAttributes(); j++) {
        if (j != data.classIndex()) {
            m_Means[j] = data.meanOrMode(j);
            m_StdDevs[j] = Math.sqrt(data.variance(j));
            if (m_StdDevs[j] == 0) {
                m_SelectedAttributes[j] = false;
            }
        }
    }

    m_ClassStdDev = Math.sqrt(data.variance(m_TransformedData.classIndex()));
    m_ClassMean = data.meanOrMode(m_TransformedData.classIndex());

    // Perform the regression
    findBestModel();

    // Save memory
    if (m_Minimal) {
        m_TransformedData = null;
        m_Means = null;
        m_StdDevs = null;
    } else {
        m_TransformedData = new Instances(data, 0);
    }

    m_ModelBuilt = true;
}

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From source file:com.mycompany.tubesann.MyANN.java

public static Instances setNominalToBinary(Instances instances) {
    NominalToBinary ntb = new NominalToBinary();
    Instances newInstances = null;/*from  w  w  w .  j ava2 s.c  o  m*/
    try {
        ntb.setInputFormat(instances);
        newInstances = new Instances(Filter.useFilter(instances, ntb));
    } catch (Exception e) {
        e.printStackTrace();
    }

    return newInstances;
}

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From source file:ml.ann.MainPTR.java

public static void main(String[] args) throws FileNotFoundException, IOException, Exception {
    boolean randomWeight;
    double weightawal = 0.0;
    double learningRate = 0.0001;
    double threshold = 0.00;
    double momentum = 0.00;
    int maxEpoch = 100000;
    int nCrossValidate = 2;

    m_nominalToBinaryFilter = new NominalToBinary();
    m_normalize = new Normalize();

    Scanner in = new Scanner(System.in);
    System.out.println("Lokasi file: ");

    String filepath = in.nextLine();
    filepath = "test-arffs/iris.arff";
    System.out.println("--- Algoritma ---");
    System.out.println("1. Perceptron Training Rule");
    System.out.println("2. Delta Rule Incremental");
    System.out.println("3. Delta Rule Batch");
    System.out.println("Pilihan Algoritma (1/2/3) : ");
    int choice = in.nextInt();
    String temp = in.nextLine();//from  ww w.j av a  2 s  . c o m

    System.out.println("Apakah Anda ingin memasukkan nilai weight awal? (YES/NO)");
    String isRandom = in.nextLine();
    System.out.println("Apakah Anda ingin memasukkan konfigurasi? (YES/NO)");
    String config = in.nextLine();

    if (config.equalsIgnoreCase("yes")) {
        System.out.print("Masukkan nilai learning rate: ");
        learningRate = in.nextDouble();
        System.out.print("Masukkan nilai threshold: ");
        threshold = in.nextDouble();
        System.out.print("Masukkan nilai momentum: ");
        momentum = in.nextDouble();
        System.out.print("Masukkan jumlah epoch: ");
        threshold = in.nextInt();
        System.out.print("Masukkan jumlah folds untuk crossvalidate: ");
        nCrossValidate = in.nextInt();
    }

    randomWeight = isRandom.equalsIgnoreCase("yes");

    if (randomWeight) {
        System.out.print("Masukkan nilai weight awal: ");
        weightawal = Double.valueOf(in.nextLine());
    }

    //print config
    if (isRandom.equalsIgnoreCase("yes")) {
        System.out.print("isRandom | ");
    } else {
        System.out.print("Weight " + weightawal + " | ");
    }

    System.out.print("L.rate " + learningRate + " | ");
    System.out.print("Max Epoch " + maxEpoch + " | ");
    System.out.print("Threshold " + threshold + " | ");
    System.out.print("Momentum " + momentum + " | ");
    System.out.print("Folds " + nCrossValidate + " | ");
    System.out.println();

    FileReader trainreader = new FileReader(filepath);
    Instances train = new Instances(trainreader);
    train.setClassIndex(train.numAttributes() - 1);

    m_nominalToBinaryFilter.setInputFormat(train);
    train = new Instances(Filter.useFilter(train, m_nominalToBinaryFilter));

    m_normalize.setInputFormat(train);
    train = new Instances(Filter.useFilter(train, m_normalize));

    MultiClassPTR tempMulti = new MultiClassPTR(choice, randomWeight, learningRate, maxEpoch, threshold);
    tempMulti.buildClassifier(train);

    Evaluation eval = new Evaluation(new Instances(train));
    eval.evaluateModel(tempMulti, train);
    System.out.println(eval.toSummaryString());
    System.out.println(eval.toClassDetailsString());
    System.out.println(eval.toMatrixString());
}

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From source file:ml.ann.SinglePTR.java

@Override
public double classifyInstance(Instance instnc) throws Exception {
    m_nominalToBinaryFilter = new NominalToBinary();//To change body of generated methods, choose Tools | Templates.
    return 0;//from w  ww .jav  a2s  .c  om
}

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