Package weka.experiment

Examples of weka.experiment.Stats

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    m_attStats[i] = new AttributeStats();
    if (m_clusterInstances.attribute(i).isNominal()) {
      m_attStats[i].nominalCounts =
        new int [m_clusterInstances.attribute(i).numValues()];
    } else {
      m_attStats[i].numericStats = new Stats();
    }
  }
      }
      for (int i = 0; i < m_numAttributes; i++) {
  if (!updateInstance.isMissing(i)) {
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    double error;
    int evaluationCount = 0;
    raceSet: while (numRaces > 0) {
      boolean won = false;
      for (int i=0;i<numRaces;i++) {
        raceStats[i][0] = new Stats();
        raceStats[i][1] = new Stats();
      }

      // keep an eye on how many test instances have been randomly sampled
      int sampleCount = 0;
      // run the current set of races
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    /** do we ignore the base set or not? */
    int startPt = m_rankingRequested ? 1 : 0;

    for (int i=0;i<raceSets.length;i++) {
      individualStats[i] = new Stats();
      for (int j=i+1;j<raceSets.length;j++) {
        testers[i][j] = new PairedStats(m_sigLevel);
      }
    }
   
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    m_attStats[i] = new AttributeStats();
    if (m_clusterInstances.attribute(i).isNominal()) {
      m_attStats[i].nominalCounts =
        new int [m_clusterInstances.attribute(i).numValues()];
    } else {
      m_attStats[i].numericStats = new Stats();
    }
  }
      }
      for (int i = 0; i < m_numAttributes; i++) {
  if (!updateInstance.isMissing(i)) {
View Full Code Here
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    m_attStats[i] = new AttributeStats();
    if (m_clusterInstances.attribute(i).isNominal()) {
      m_attStats[i].nominalCounts =
        new int [m_clusterInstances.attribute(i).numValues()];
    } else {
      m_attStats[i].numericStats = new Stats();
    }
  }
      }
      for (int i = 0; i < m_numAttributes; i++) {
  if (!updateInstance.isMissing(i)) {
View Full Code Here
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Related Classes of weka.experiment.Stats

  • weka.attributeSelection.RaceSearch
  • weka.clusterers.Cobweb$CNode
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