Examples of weka.classifiers.evaluation.NominalPrediction

weka.classifiers.evaluation.NominalPrediction
Encapsulates an evaluatable nominal prediction: the predicted probability distribution plus the actual class value. @author Len Trigg (len@reeltwo.com) @version $Revision: 1.12 $

      }
      updateStatsForClassifier(dist, instance);
      if (storePredictions) {
        if (m_Predictions == null)
          m_Predictions = new FastVector();
        m_Predictions.addElement(new NominalPrediction(instance.classValue(), dist,
                                                       instance.weight()));
      }
    } else {
      pred = dist[0];
      updateStatsForPredictor(pred, instance);

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      pred = Utils.maxIndex(dist);
      if (dist[(int)pred] <= 0) {
  pred = Instance.missingValue();
      }
      updateStatsForClassifier(dist, instance);
      m_Predictions.addElement(new NominalPrediction(instance.classValue(), dist, 
    instance.weight()));
    } else {
      pred = classifier.classifyInstance(classMissing);
      updateStatsForPredictor(pred, instance);
    }

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      pred = Utils.maxIndex(dist);
      if (dist[(int)pred] <= 0) {
  pred = Instance.missingValue();
      }
      updateStatsForClassifier(dist, instance);
      m_Predictions.addElement(new NominalPrediction(instance.classValue(), dist, 
    instance.weight()));
    } else {
      pred = dist[0];
      updateStatsForPredictor(pred, instance);
    }

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    result = useClassifier();
    assertTrue(result.size() != 0);
    double minp = 0;
    double maxp = 0;
    for (int i = 0; i < result.size(); i++) {
      NominalPrediction p = (NominalPrediction)result.elementAt(i);
      double prob = p.distribution()[cind];
      if ((i == 0) || (prob < minp)) minp = prob;
      if ((i == 0) || (prob > maxp)) maxp = prob;
    }
    assertTrue("Upper limit shouldn't increase", maxp <= 1.0);
    assertTrue("Lower limit shouldn'd decrease", minp >= 0.25);

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      pred = Utils.maxIndex(dist);
      if (dist[(int) pred] <= 0) {
        pred = Instance.missingValue();
      }
      updateStatsForClassifier(dist, instance);
      m_Predictions.addElement(new NominalPrediction(instance.classValue(), dist,
          instance.weight()));
    } else {
      pred = classifier.classifyInstance(classMissing);
      updateStatsForPredictor(pred, instance);
    }

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      pred = Utils.maxIndex(dist);
      if (dist[(int) pred] <= 0) {
        pred = Instance.missingValue();
      }
      updateStatsForClassifier(dist, instance);
      m_Predictions.addElement(new NominalPrediction(instance.classValue(), dist,
          instance.weight()));
    } else {
      pred = dist[0];
      updateStatsForPredictor(pred, instance);
    }

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      }
      updateStatsForClassifier(dist, instance);
      if (storePredictions && !m_DiscardPredictions) {
        if (m_Predictions == null)
          m_Predictions = new FastVector();
        m_Predictions.addElement(new NominalPrediction(instance.classValue(), dist,
                                                       instance.weight()));
      }
    } else {
      pred = dist[0];
      updateStatsForPredictor(pred, instance);

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Related Classes of weka.classifiers.evaluation.NominalPrediction

weka.classifiers.Evaluation

weka.classifiers.meta.ThresholdSelectorTest

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