Package cc.mallet.types

Examples of cc.mallet.types.LabelVector

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      }
    if (outputAlphabet != null) {
      labelings = new LabelVector[latticeLength];
      for (int ip = latticeLength - 2; ip >= 0; ip--) {
        assert (Math.abs(1.0 - MatrixOps.sum(outputCounts[ip])) < 0.000001);
        labelings[ip] = new LabelVector(outputAlphabet,
            outputCounts[ip]);
      }
    }
  }
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      }
      MatrixOps.expNormalize(scores);
  
      entropy += Maths.getEntropy(scores);

      LabelVector lv = new LabelVector((LabelAlphabet)data.getTargetAlphabet(), scores);
      Instance instance = new Instance(data.get(ii).getData(),lv,null,null);
      dataLabeled.add(instance);
    }
   
    // train supervised
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    double[] scores = new double[numClasses];
    //getClassificationScores (instance, scores);
    getClassificationScores(instance, scores);
    // Create and return a Classification object
    return new Classification (instance, this,
        new LabelVector (getLabelAlphabet(),
            scores));
  }
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    double[] scores = new double[numClasses];
    //getClassificationScores (instance, scores);
    getClassificationScores(instance, scores);
    // Create and return a Classification object
    return new Classification (instance, this,
        new LabelVector (getLabelAlphabet(),
            scores));
  }
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      }
    if (outputAlphabet != null) {
      labelings = new LabelVector[latticeLength];
      for (int ip = latticeLength-2; ip >= 0; ip--) {
        assert (Math.abs(1.0-MatrixOps.sum (outputCounts[ip])) < 0.000001);;
        labelings[ip] = new LabelVector (outputAlphabet, outputCounts[ip]);
      }
    }
   
    if (logger.isLoggable (Level.FINE)) {
      logger.fine("Lattice:");
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      }
    if (outputAlphabet != null) {
      labelings = new LabelVector[latticeLength];
      for (int ip = latticeLength-2; ip >= 0; ip--) {
        assert (Math.abs(1.0-MatrixOps.sum (outputCounts[ip])) < 0.000001);;
        labelings[ip] = new LabelVector (outputAlphabet, outputCounts[ip]);
      }
    }

  }
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      }
    if (outputAlphabet != null) {
      labelings = new LabelVector[latticeLength];
      for (int ip = latticeLength-2; ip >= 0; ip--) {
        assert (Math.abs(1.0-MatrixOps.sum (outputCounts[ip])) < 0.000001);;
        labelings[ip] = new LabelVector (outputAlphabet, outputCounts[ip]);
      }
    }
  }
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    double[] scores = new double[numClasses];
    //getClassificationScores (instance, scores);
    getClassificationScoresWithTemperature (instance, DEFAULT_TEMPERATURE, scores);
    // Create and return a Classification object
    return new Classification (instance, this,
        new LabelVector (getLabelAlphabet(),
            scores));
  }
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Related Classes of cc.mallet.types.LabelVector

  • cc.mallet.classify.BalancedWinnow
  • cc.mallet.classify.Classification
  • cc.mallet.classify.ClassifierEnsemble
  • cc.mallet.classify.ConfidencePredictingClassifier
  • cc.mallet.classify.evaluate.AccuracyCoverage$ClassificationComparator
  • cc.mallet.classify.evaluate.ConfusionMatrix
  • cc.mallet.classify.MaxEnt
  • cc.mallet.classify.MaxEntPRTrainer
  • cc.mallet.classify.MCMaxEnt
  • cc.mallet.classify.PRAuxClassifier
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