Package org.apache.mahout.classifier

Examples of org.apache.mahout.classifier.ConfusionMatrix.addInstance()

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    line = in.readLine();
    while (line != null) {
      String[] pieces = line.split(",");        
      String trueValue = pieces[0];
      String estimatedValue = pieces[1];
      x2.addInstance(trueValue, estimatedValue);     
      line = in.readLine();
    }
    System.out.printf("%s\n\n", x2.toString());
  }
}
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    ConfusionMatrix matrix = new ConfusionMatrix(confusionMatrix.keySet(), defaultLabel);
    for (Map.Entry<String,Map<String,Integer>> correctLabelSet : confusionMatrix.entrySet()) {
      Map<String,Integer> rowMatrix = correctLabelSet.getValue();
      for (Map.Entry<String,Integer> classifiedLabelSet : rowMatrix.entrySet()) {
        matrix.addInstance(correctLabelSet.getKey(), classifiedLabelSet.getKey());
        matrix.putCount(correctLabelSet.getKey(), classifiedLabelSet.getKey(), classifiedLabelSet.getValue());
      }
    }
    return matrix;
   
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    ConfusionMatrix matrix = new ConfusionMatrix(confusionMatrix.keySet(), defaultLabel);
    for (Map.Entry<String,Map<String,Integer>> correctLabelSet : confusionMatrix.entrySet()) {
      Map<String,Integer> rowMatrix = correctLabelSet.getValue();
      for (Map.Entry<String,Integer> classifiedLabelSet : rowMatrix.entrySet()) {
        matrix.addInstance(correctLabelSet.getKey(), classifiedLabelSet.getKey());
        matrix.putCount(correctLabelSet.getKey(), classifiedLabelSet.getKey(), classifiedLabelSet.getValue());
      }
    }
    return matrix;
  }
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        int target = csv.processLine(line, v);
        double likelihood = learner.logLikelihood(target, v);
        double score = learner.classifyFull(v).maxValue();
       
        slh.add(likelihood);
        cm.addInstance(csv.getTargetString(line), csv.getTargetLabel(target));       
       
        if (showScores) {
          output.printf(Locale.ENGLISH, "%8d, %.12f, %.13f, %.13f\n", target,
              score, learner.logLikelihood(target, v), slh.getMean());
        }
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    ConfusionMatrix matrix = new ConfusionMatrix(confusionMatrix.keySet(), defaultLabel);
    for (Map.Entry<String,Map<String,Integer>> correctLabelSet : confusionMatrix.entrySet()) {
      Map<String,Integer> rowMatrix = correctLabelSet.getValue();
      for (Map.Entry<String,Integer> classifiedLabelSet : rowMatrix.entrySet()) {
        matrix.addInstance(correctLabelSet.getKey(), classifiedLabelSet.getKey());
        matrix.putCount(correctLabelSet.getKey(), classifiedLabelSet.getKey(), classifiedLabelSet.getValue());
      }
    }
    return matrix;
   
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        int target = csv.processLine(line, v);
        double likelihood = learner.logLikelihood(target, v);
        double score = learner.classifyFull(v).maxValue();

        slh.add(likelihood);
        cm.addInstance(csv.getTargetString(line), csv.getTargetLabel(target));       

        if (showScores) {
          output.printf(Locale.ENGLISH, "%8d, %.12f, %.13f, %.13f%n", target,
              score, learner.logLikelihood(target, v), slh.getMean());
        }
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    for(Map.Entry<String, Map<String, Integer>> correctLabelSet:confusionMatrix.entrySet())
    {
      Map<String, Integer> rowMatrix = correctLabelSet.getValue();
      for(Map.Entry<String, Integer> classifiedLabelSet : rowMatrix.entrySet())
      {
        matrix.addInstance(correctLabelSet.getKey(), classifiedLabelSet.getKey());
        matrix.putCount(correctLabelSet.getKey(), classifiedLabelSet.getKey(), classifiedLabelSet.getValue());
      }
    }
    return matrix;
   
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    ConfusionMatrix matrix = new ConfusionMatrix(confusionMatrix.keySet(), defaultLabel);
    for (Map.Entry<String,Map<String,Integer>> correctLabelSet : confusionMatrix.entrySet()) {
      Map<String,Integer> rowMatrix = correctLabelSet.getValue();
      for (Map.Entry<String,Integer> classifiedLabelSet : rowMatrix.entrySet()) {
        matrix.addInstance(correctLabelSet.getKey(), classifiedLabelSet.getKey());
        matrix.putCount(correctLabelSet.getKey(), classifiedLabelSet.getKey(), classifiedLabelSet.getValue());
      }
    }
    return matrix;
   
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        int target = csv.processLine(line, v);
        double likelihood = learner.logLikelihood(target, v);
        double score = learner.classifyFull(v).maxValue();
       
        slh.add(likelihood);
        cm.addInstance(csv.getTargetString(line), csv.getTargetLabel(target));       
       
        if (showScores) {
          output.printf(Locale.ENGLISH, "%8d, %.12f, %.13f, %.13f\n", target,
              score, learner.logLikelihood(target, v), slh.getMean());
        }
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