Package org.apache.mahout.vectorizer.encoders

Examples of org.apache.mahout.vectorizer.encoders.Dictionary.values()

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    dict.intern("qrz");

    assertEquals("[a, d, c, b, qrz]", dict.values().toString());

    Dictionary dict2 = Dictionary.fromList(dict.values());
    assertEquals("[a, d, c, b, qrz]", dict2.values().toString());

  }
}
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      asfDictionary.intern(next.getFirst().toString());
      numItems++;
    }

    System.out.printf("%d test files\n", numItems);
    ResultAnalyzer ra = new ResultAnalyzer(asfDictionary.values(), "DEFAULT");
    iter = new SequenceFileDirIterator<Text, VectorWritable>(new Path(base.toString()), PathType.LIST, testFilter,
            null, true, conf);
    while (iter.hasNext()) {
      Pair<Text, VectorWritable> next = iter.next();
      String ng = next.getFirst().toString();
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      int actual = asfDictionary.intern(ng);
      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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      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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        newsGroups.intern(newsgroup.getName());
        files.addAll(Arrays.asList(newsgroup.listFiles()));
      }
    }
    System.out.printf("%d test files\n", files.size());
    ResultAnalyzer ra = new ResultAnalyzer(newsGroups.values(), "DEFAULT");
    for (File file : files) {
      String ng = file.getParentFile().getName();

      int actual = newsGroups.intern(ng);
      NewsgroupHelper helper = new NewsgroupHelper();
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      Vector input = helper.encodeFeatureVector(file, actual, 0, overallCounts); //no leak type ensures this is a normal vector
      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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      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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