Example usage for weka.classifiers.functions LibSVM setNormalize

Introduction

In this page you can find the example usage for weka.classifiers.functions LibSVM setNormalize.

Prototype

public void setNormalize(boolean value) 

Source Link

Document

whether to normalize input data.

Usage

From source file:Tubes.Classification.java

public static void main(String[] args) throws FileNotFoundException, IOException, Exception {

    StringToWordVector filter = new StringToWordVector();

    File training = new File(classTrain);
    File testing = new File(classTest);

    BufferedReader readTrain = new BufferedReader(new FileReader(training));
    BufferedReader readTest = new BufferedReader(new FileReader(testing));

    Instances dataTrain = new Instances(readTrain);
    Instances dataTest = new Instances(readTest);

    filter.setInputFormat(dataTrain);// w w w .j a  v a2  s  .  c o  m
    dataTrain = Filter.useFilter(dataTrain, filter);

    dataTrain.setClassIndex(dataTrain.numAttributes() - 1);
    dataTest.setClassIndex(dataTest.numAttributes() - 1);

    Classification classify = new Classification();
    NaiveBayes bayes = new NaiveBayes();
    //        RandomForest rf = new RandomForest();
    //        BayesNet bayesNet = new BayesNet();
    LibSVM libSVM = new LibSVM();
    System.out.println("==========================Naive Bayes Evaluation===========================");
    Evaluation eval = classify.runClassifier(bayes, dataTrain, dataTest);
    System.out.println(eval.toSummaryString() + "\n");
    System.out.println(eval.toClassDetailsString() + "\n");
    System.out.println(eval.toMatrixString() + "\n");
    System.out.println("===========================================================================");
    //
    //        ====System.out.println("==============================Random Forest================================");
    //        Evaluation eval2 = classify.runClassifier(rf, dataTrain, dataTest);
    //        System.out.println(eval2.toSummaryString() + "\n");
    //        System.out.println(eval2.toClassDetailsString() + "\n");
    //        System.out.println(eval2.toMatrixString() + "\n");
    //        System.out.println("=======================================================================");
    //
    //        System.out.println("==============================Bayesian Network================================");
    //        Evaluation eval3 = classify.runClassifier(bayesNet, dataTrain, dataTest);
    //        System.out.println(eval3.toSummaryString() + "\n");
    //        System.out.println(eval3.toClassDetailsString() + "\n");
    //        System.out.println(eval3.toMatrixString() + "\n");
    //        System.out.println("===========================================================================");

    System.out.println("==============================LibSVM================================");
    libSVM.setCacheSize(512); // MB
    libSVM.setNormalize(true);
    libSVM.setShrinking(true);
    libSVM.setKernelType(new SelectedTag(LibSVM.KERNELTYPE_LINEAR, LibSVM.TAGS_KERNELTYPE));
    libSVM.setDegree(3);
    libSVM.setSVMType(new SelectedTag(LibSVM.SVMTYPE_C_SVC, LibSVM.TAGS_SVMTYPE));
    Evaluation eval4 = classify.runClassifier(libSVM, dataTrain, dataTest);
    System.out.println(eval4.toSummaryString() + "\n");
    System.out.println(eval4.toClassDetailsString() + "\n");
    System.out.println(eval4.toMatrixString() + "\n");
    System.out.println("===========================================================================");
}

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