Examples of cc.mallet.classify.Classification

cc.mallet.classify.Classification
The result of classifying a single instance. Contains the instance, the classifier used, and the labeling the classifier produced. Also has methods for comparing the correct (true) label contained in the target field of the instance with the one produced by the classifier.

    // as features 
    FeatureVector[] fva = new FeatureVector[fvs.size()];


    for (int i = 0; i < ilist.size(); i++) {
      Instance inst = ilist.get(i);
      Classification c = m_tokenClassifiers.classify(inst, ! m_inProduction);
      LabelVector lv = c.getLabelVector();
      AugmentableFeatureVector afv1 = (AugmentableFeatureVector) inst.getData();
      int[] indices = afv1.getIndices();
      AugmentableFeatureVector afv2 = new AugmentableFeatureVector(m_dataAlphabet, 
          indices, afv1.getValues(), indices.length + m_predRanks2add.length);

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    int numCorrect = 0;
//    System.out.println("NumTrials="+numTrials);
    for(int i= classifications.size()-1; 
        i >= classifications.size()-numTrials; i--)
    {
      Classification temp = (Classification)classifications.get(i); 
      if(temp.bestLabelIsCorrect())
        numCorrect++;
    }
//    System.out.println("Accuracy at cov "+cov+" is "+
    //(double)numCorrect/numTrials);
    return((double)numCorrect/numTrials);

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  {
    FeatureVector fv = (FeatureVector) instance.getData ();
    assert (instancePipe == null || fv.getAlphabet () == this.instancePipe.getDataAlphabet ());
    
    Node leaf = getLeaf(m_root, fv);
    return new Classification (instance, this, leaf.getGainRatio().getBaseLabelDistribution());
  }

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Related Classes of cc.mallet.classify.Classification

cc.mallet.classify.C45

cc.mallet.classify.evaluate.AccuracyCoverage

cc.mallet.pipe.AddClassifierTokenPredictions

cc.mallet.types.FeatureVector

cc.mallet.types.Instance

cc.mallet.types.Labeling

cc.mallet.types.LabelVector

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