List of trainCV() Examples

Examples of trainCV()

weka.core.Instances.trainCV()
Creates the training set for one fold of a cross-validation on the dataset. @param numFolds the number of folds in the cross-validation. Mustbe greater than 1. @param numFold 0 for the first fold, 1 for the second, ... @return the training set @throws IllegalArgumentException if the number of folds is less than 2or greater than the number of instances.

Examples of weka.core.Instances.trainCV()

    m_InitOptions = ((OptionHandler)m_Classifier).getOptions();
    m_BestPerformance = -99;
    m_NumAttributes = trainData.numAttributes();
    Random random = new Random(m_Seed);
    trainData.randomize(random);
    m_TrainFoldSize = trainData.trainCV(m_NumFolds, 0).numInstances();


    // Check whether there are any parameters to optimize
    if (m_CVParams.size() == 0) {
       m_Classifier.buildClassifier(trainData);
       m_BestClassifierOptions = m_InitOptions;

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Examples of weka.core.Instances.trainCV()

    double[][][] totalSubsetWeights =
      new double[m_numFoldsPruning][data.numAttributes()][2];
    FastVector[] nodeInfo = new FastVector[m_numFoldsPruning];


    for (int i = 0; i < m_numFoldsPruning; i++) {
      train[i] = cvData.trainCV(m_numFoldsPruning, i);
      test[i] = cvData.testCV(m_numFoldsPruning, i);
      parallelBFElements[i] = new FastVector();
      m_roots[i] = new BFTree();


      // calculate sorted indices, weights, initial class counts and total weights for each training data

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Examples of weka.core.Instances.trainCV()

    if (dataCopy.classAttribute().isNominal()) {
      dataCopy.stratify(m_numFolds);
    }


    for (int f = 0; f < m_numFolds; f++) {
      trainData[f] = dataCopy.trainCV(m_numFolds, f, random);
      testData[f] = dataCopy.testCV(m_numFolds, f);
    }


    LFSMethods LSF = new LFSMethods();

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Examples of weka.core.Instances.trainCV()

    if (dataCopy.classAttribute().isNominal()) {
      dataCopy.stratify(m_numFolds);
    }


    for (int f = 0; f < m_numFolds; f++) {
      trainData[f] = dataCopy.trainCV(m_numFolds, f, random);
      testData[f] = dataCopy.testCV(m_numFolds, f);
    }


    LFSMethods LSF = new LFSMethods();

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Examples of weka.core.Instances.trainCV()

    m_InitOptions = ((OptionHandler)m_Classifier).getOptions();
    m_BestPerformance = -99;
    m_NumAttributes = trainData.numAttributes();
    Random random = new Random(m_Seed);
    trainData.randomize(random);
    m_TrainFoldSize = trainData.trainCV(m_NumFolds, 0).numInstances();


    // Check whether there are any parameters to optimize
    if (m_CVParams.size() == 0) {
       m_Classifier.buildClassifier(trainData);
       m_BestClassifierOptions = m_InitOptions;

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Examples of weka.core.Instances.trainCV()

    double[][][] totalSubsetWeights =
      new double[m_numFoldsPruning][data.numAttributes()][2];
    FastVector[] nodeInfo = new FastVector[m_numFoldsPruning];


    for (int i = 0; i < m_numFoldsPruning; i++) {
      train[i] = cvData.trainCV(m_numFoldsPruning, i);
      test[i] = cvData.testCV(m_numFoldsPruning, i);
      parallelBFElements[i] = new FastVector();
      m_roots[i] = new BFTree();


      // calculate sorted indices, weights, initial class counts and total weights for each training data

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Examples of weka.core.Instances.trainCV()

      data.randomize(random);
      data.stratify(numFolds);
      
      // Make sure that both subsets contain at least one positive instance
      for (int subsetIndex = 0; subsetIndex < numFolds; subsetIndex++) {
        trainData = data.trainCV(numFolds, subsetIndex, random);
        evalData = data.testCV(numFolds, subsetIndex);
        if (checkForInstance(trainData) && checkForInstance(evalData)) {
          break;
        }
      }

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Examples of weka.core.Instances.trainCV()

    m_InitOptions = ((OptionHandler)m_Classifier).getOptions();
    m_BestPerformance = -99;
    m_NumAttributes = trainData.numAttributes();
    Random random = new Random(m_Seed);
    trainData.randomize(random);
    m_TrainFoldSize = trainData.trainCV(m_NumFolds, 0).numInstances();


    // Check whether there are any parameters to optimize
    if (m_CVParams.size() == 0) {
       m_Classifier.buildClassifier(trainData);
       m_BestClassifierOptions = m_InitOptions;

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