List of numInstances() Examples

Examples of numInstances()

net.kuujo.vertigo.component.ModuleContext.numInstances()
net.kuujo.vertigo.component.VerticleContext.numInstances()
weka.classifiers.Evaluation.numInstances()
Gets the number of test instances that had a known class value (actually the sum of the weights of test instances with known class value). @return the number of test instances with known class
weka.core.Instances.numInstances()
Returns the number of instances in the dataset. @return the number of instances in the dataset as an integer

Examples of weka.classifiers.Evaluation.numInstances()

    m_result = eval.toSummaryString();
    // The results stored are all per instance -- can be multiplied by the
    // number of instances to get absolute numbers
    int current = 0;
    result[current++] = new Double(train.numInstances());
    result[current++] = new Double(eval.numInstances());


    result[current++] = new Double(eval.meanAbsoluteError());
    result[current++] = new Double(eval.rootMeanSquaredError());
    result[current++] = new Double(eval.relativeAbsoluteError());
    result[current++] = new Double(eval.rootRelativeSquaredError());

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Examples of weka.core.Instances.numInstances()

      m_removeFilter = new Remove();
      m_removeFilter.setAttributeIndicesArray(finalAttsToDelete);
      m_removeFilter.setInvertSelection(false);
      m_removeFilter.setInputFormat(toFilter);
      
      for (int i = 0; i < toFilter.numInstances(); i++) {
  m_removeFilter.input(toFilter.instance(i));
      }
      m_removeFilter.batchFinished();


      Instance processed;

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Examples of weka.core.Instances.numInstances()

          testSetPresent = true;
          Instances tmpInst = trainSource.getDataSet(actualClassIndex);
          if (!preserveOrder)
            tmpInst.randomize(new Random(seed));
          int trainSize = 
            (int) Math.round(tmpInst.numInstances() * splitPercentage / 100);
          int testSize  = tmpInst.numInstances() - trainSize;
          Instances trainInst = new Instances(tmpInst, 0, trainSize);
          Instances testInst  = new Instances(tmpInst, trainSize, testSize);
          trainSource = new DataSource(trainInst);
          testSource  = new DataSource(testInst);

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Examples of weka.core.Instances.numInstances()

          Instances tmpInst = trainSource.getDataSet(actualClassIndex);
          if (!preserveOrder)
            tmpInst.randomize(new Random(seed));
          int trainSize = 
            (int) Math.round(tmpInst.numInstances() * splitPercentage / 100);
          int testSize  = tmpInst.numInstances() - trainSize;
          Instances trainInst = new Instances(tmpInst, 0, trainSize);
          Instances testInst  = new Instances(tmpInst, trainSize, testSize);
          trainSource = new DataSource(trainInst);
          testSource  = new DataSource(testInst);
          template = test = testSource.getStructure();

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Examples of weka.core.Instances.numInstances()

        return;
      // The dataset may be large, so to make things easier we'll
      // output an instance at a time (rather than having to convert
      // the entire dataset to one large string)
      System.out.println(new Instances(aha, 0));
      for (int i = 0; i < aha.numInstances(); i++) {
  System.out.println(aha.instance(i));
      }
    } catch(Exception e) {
      e.printStackTrace();
      System.err.println(e.getMessage());

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Examples of weka.core.Instances.numInstances()

        pd.setPlotName(textTitle + " (class: "
                        +ce.getTestSet().getDataSet().
                          classAttribute().value(0) + ")");
        pd.setPlotNameHTML(htmlTitle);
        boolean [] connectPoints = 
          new boolean [result.numInstances()];
        for (int jj = 1; jj < connectPoints.length; jj++) {
          connectPoints[jj] = true;
        }
        pd.setConnectPoints(connectPoints);
        ThresholdDataEvent rde =

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Examples of weka.core.Instances.numInstances()

          notifyBatchClassifierListeners(ce);          
        } else {
          // if headers do not match check to see if it's
          // just the class that is different and that
          // all class values are missing
          if (testSet.numInstances() > 0) {
            if (testSet.classIndex() == m_trainingSet.classIndex() && 
                testSet.attributeStats(testSet.classIndex()).missingCount ==
                testSet.numInstances()) {
              // now check the other attributes against the training
              // structure

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Examples of weka.core.Instances.numInstances()

          // just the class that is different and that
          // all class values are missing
          if (testSet.numInstances() > 0) {
            if (testSet.classIndex() == m_trainingSet.classIndex() && 
                testSet.attributeStats(testSet.classIndex()).missingCount ==
                testSet.numInstances()) {
              // now check the other attributes against the training
              // structure
              boolean ok = true;
              for (int i = 0; i < testSet.numAttributes(); i++) {
                if (i != testSet.classIndex()) {

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Examples of weka.core.Instances.numInstances()


        case 2: // Percent split
        m_Log.statusMessage("Randomizing instances...");
        inst.randomize(new Random(1));
        trainInst.randomize(new Random(1));
        int trainSize = trainInst.numInstances() * percent / 100;
        int testSize = trainInst.numInstances() - trainSize;
        Instances train = new Instances(trainInst, 0, trainSize);
        Instances test = new Instances(trainInst, trainSize, testSize);
        Instances testVis = new Instances(inst, trainSize, testSize);
        m_Log.statusMessage("Building model on training split...");

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Examples of weka.core.Instances.numInstances()

        case 2: // Percent split
        m_Log.statusMessage("Randomizing instances...");
        inst.randomize(new Random(1));
        trainInst.randomize(new Random(1));
        int trainSize = trainInst.numInstances() * percent / 100;
        int testSize = trainInst.numInstances() - trainSize;
        Instances train = new Instances(trainInst, 0, trainSize);
        Instances test = new Instances(trainInst, trainSize, testSize);
        Instances testVis = new Instances(inst, trainSize, testSize);
        m_Log.statusMessage("Building model on training split...");
        clusterer.buildClusterer(train);

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