Package org.apache.mahout.classifier

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

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      Vector result = classifier.classifyFull(next.getSecond().get());
      int cat = result.maxValueIndex();
      double score = result.maxValue();
      double ll = classifier.logLikelihood(actual, next.getSecond().get());
      ClassifierResult cr = new ClassifierResult(asfDictionary.values().get(cat), score, ll);
      ra.addInstance(asfDictionary.values().get(actual), cr);

    }
    output.printf("%s\n\n", ra.toString());
  }
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          regressionAnalyzer.setInstances(results);
          log.info("{}", regressionAnalyzer);
        } else {
          ResultAnalyzer analyzer = new ResultAnalyzer(Arrays.asList(dataset.labels()), "unknown");
          for (double[] res : results) {
            analyzer.addInstance(dataset.getLabelString(res[0]),
              new ClassifierResult(dataset.getLabelString(res[1]), 1.0));
          }
          log.info("{}", analyzer);
        }
      }
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        regressionAnalyzer.setInstances(resList.toArray(results));
        log.info("{}", regressionAnalyzer);
      } else {
        ResultAnalyzer analyzer = new ResultAnalyzer(Arrays.asList(dataset.labels()), "unknown");
        for (double[] r : resList) {
          analyzer.addInstance(dataset.getLabelString(r[0]),
            new ClassifierResult(dataset.getLabelString(r[1]), 1.0));
        }
        log.info("{}", analyzer);
      }
    }
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      Vector result = classifier.classifyFull(input);
      int cat = result.maxValueIndex();
      double score = result.maxValue();
      double ll = classifier.logLikelihood(actual, input);
      ClassifierResult cr = new ClassifierResult(newsGroups.values().get(cat), score, ll);
      ra.addInstance(newsGroups.values().get(actual), cr);

    }
    output.printf("%s\n\n", ra.toString());
  }
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