List of stratify() Examples

Examples of stratify()

etc.aloe.processes.CrossValidationPrep.stratify()
Stratifies a set of instances according to its class values if the class attribute is nominal (so that afterwards a stratified cross-validation can be performed). @param instances @param numFolds the number of folds in the cross-validation
weka.core.Instances.stratify()
Stratifies a set of instances according to its class values if the class attribute is nominal (so that afterwards a stratified cross-validation can be performed). @param numFolds the number of folds in the cross-validation @throws UnassignedClassException if the class is not set

Examples of weka.core.Instances.stratify()

    newData.deleteWithMissingClass();
    
    Random random = new Random(m_Seed);
    newData.randomize(random);
    if (newData.classAttribute().isNominal() && (m_NumXValFolds > 1)) {
      newData.stratify(m_NumXValFolds);
    }
    Instances train = newData;               // train on all data by default
    Instances test = newData;               // test on training data by default
    Classifier bestClassifier = null;
    int bestIndex = -1;

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Examples of weka.core.Instances.stratify()

    else{


      data.randomize(m_Random);


      // Split data into Grow and Prune     
      data.stratify(m_Folds);
  
      Instances growData=data.trainCV(m_Folds, m_Folds-1, m_Random);
      Instances pruneData=data.testCV(m_Folds, m_Folds-1);


      grow(growData);      // Build this rule

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Examples of weka.core.Instances.stratify()

      m_Antds = new FastVector();  
      
      /* Split data into Grow and Prune*/
      m_Random = new Random(m_Seed);
      data.randomize(m_Random);
      data.stratify(m_Folds);
      Instances growData=data.trainCV(m_Folds, m_Folds-1, m_Random);
      Instances pruneData=data.testCV(m_Folds, m_Folds-1);
      
      grow(growData);      // Build this rule

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Examples of weka.core.Instances.stratify()

    FastVector predictions = new FastVector();
    Instances runInstances = new Instances(data);
    Random random = new Random(m_Seed);
    runInstances.randomize(random);
    if (runInstances.classAttribute().isNominal() && (numFolds > 1)) {
      runInstances.stratify(numFolds);
    }
    int inst = 0;
    for (int fold = 0; fold < numFolds; fold++) {
      Instances train = runInstances.trainCV(numFolds, fold, random);
      Instances test = runInstances.testCV(numFolds, fold);

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Examples of weka.core.Instances.stratify()

    // Randomize on a copy of the original dataset
    Instances runInstances = new Instances(m_Instances);
    Random random = new Random(run);
    runInstances.randomize(random);
    if (runInstances.classAttribute().isNominal()) {
      runInstances.stratify(m_NumFolds);
    }
    for (int fold = 0; fold < m_NumFolds; fold++) {
      // Add in some fields to the key like run and fold number, dataset name
      Object [] seKey = m_SplitEvaluator.getKey();
      Object [] key = new Object [seKey.length + 3];

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Examples of weka.core.Instances.stratify()

    Random random = new Random(m_seed);
    Instances dataCopy = new Instances(data);
    dataCopy.randomize(random);


    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);

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Examples of weka.core.Instances.stratify()

       m_BestClassifierOptions = m_InitOptions;
       return;
    }


    if (trainData.classAttribute().isNominal()) {
      trainData.stratify(m_NumFolds);
    }
    m_BestClassifierOptions = null;
    
    // Set up m_ClassifierOptions -- take getOptions() and remove
    // those being optimised.

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Examples of weka.core.Instances.stratify()


    // Randomize on a copy of the original dataset
    Instances runInstances = new Instances(m_Instances);
    runInstances.randomize(new Random(run));
    if (runInstances.classAttribute().isNominal()) {
      runInstances.stratify(m_StepSize);
    }


    // Tell the resultproducer to send results to us
    m_ResultProducer.setResultListener(this);

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Examples of weka.core.Instances.stratify()


    Random random = new Random(m_Seed);
    Instances cvData = new Instances(data);
    cvData.randomize(random);
    cvData = new Instances(cvData,0,(int)(cvData.numInstances()*m_SizePer)-1);
    cvData.stratify(m_numFoldsPruning);


    Instances[] train = new Instances[m_numFoldsPruning];
    Instances[] test = new Instances[m_numFoldsPruning];
    FastVector[] parallelBFElements = new FastVector [m_numFoldsPruning];
    BFTree[] m_roots = new BFTree[m_numFoldsPruning];

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Examples of weka.core.Instances.stratify()


    Random random = new Random(m_Seed);
    Instances cvData = new Instances(data);
    cvData.randomize(random);
    cvData = new Instances(cvData,0,(int)(cvData.numInstances()*m_SizePer)-1);
    cvData.stratify(m_numFoldsPruning);


    double[][] alphas = new double[m_numFoldsPruning][];
    double[][] errors = new double[m_numFoldsPruning][];


    // calculate errors and alphas for each fold

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