Package org.apache.mahout.classifier

Examples of org.apache.mahout.classifier.ClassifierResult

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  }

  public void testResults() throws Exception {
    BayesClassifier classifier = new BayesClassifier();
    String[] document = {"aa", "ff"};
    ClassifierResult result = classifier.classify(model, document, "unknown");
    assertNotNull("category is null and it shouldn't be", result);
    System.out.println("Result: " + result);
  }
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  }

  public void test() {
    BayesClassifier classifier = new BayesClassifier();
    String[] document = {"aa", "ff"};
    ClassifierResult result = classifier.classify(model, document, "unknown");
    assertNotNull("category is null and it shouldn't be", result);
    assertEquals(result + " is not equal to e", "e", result.getLabel());

    document = new String[]{"ff"};
    result = classifier.classify(model, document, "unknown");
    assertNotNull("category is null and it shouldn't be", result);
    assertEquals(result + " is not equal to d", "d", result.getLabel());

    document = new String[]{"cc"};
    result = classifier.classify(model, document, "unknown");
    assertNotNull("category is null and it shouldn't be", result);
    assertEquals(result + " is not equal to d", "d", result.getLabel());
  }
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  }

  public void testResults() throws Exception {
    BayesClassifier classifier = new BayesClassifier();
    String[] document = {"aa", "ff"};
    ClassifierResult result = classifier.classify(model, document, "unknown");
    assertNotNull("category is null and it shouldn't be", result);
    System.out.println("Result: " + result);
  }
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          bestScore = element.get();
          bestIdx = element.index();
        }
      }
      if (bestIdx != Integer.MIN_VALUE) {
        ClassifierResult classifierResult = new ClassifierResult(labelMap.get(bestIdx), bestScore);
        analyzer.addInstance(pair.getFirst().toString(), classifierResult);
      }
    }
  }
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          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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        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 input = helper.encodeFeatureVector(file, actual, 0, overallCounts);
      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.println(ra);
  }
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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.println(ra);
  }
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    //builder.ensureCapacity(32);// make sure we have a reasonably size buffer to
                               // begin with
    List<String> ngrams  = new NGrams(value.toString(), gramSize).generateNGramsWithoutLabel();
   
    try {
      ClassifierResult result = classifier.classifyDocument( ngrams
          .toArray(new String[ngrams.size()]), defaultCategory);
    
      String correctLabel = label;
      String classifiedLabel = result.getLabel();
     
      StringTuple outputTuple = new StringTuple(BayesConstants.CLASSIFIER_TUPLE);
      outputTuple.add(correctLabel);
      outputTuple.add(classifiedLabel);
     
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  }

  public void test() throws InvalidDatastoreException {
    ClassifierContext classifier = new ClassifierContext(algorithm, store);
    String[] document = { "aa", "ff" };
    ClassifierResult result = classifier.classifyDocument(document, "unknown");
    assertNotNull("category is null and it shouldn't be", result);
    assertEquals(result + " is not equal to e", "e", result.getLabel());

    document = new String[] { "ff" };
    result = classifier.classifyDocument(document, "unknown");
    assertNotNull("category is null and it shouldn't be", result);
    assertEquals(result + " is not equal to d", "d", result.getLabel());// GSI:
                                                                        // was
                                                                        // unknown,
                                                                        // but
                                                                        // we
                                                                        // now
                                                                        // just
                                                                        // pick
                                                                        // the
                                                                        // first
                                                                        // cat

    document = new String[] { "cc" };
    result = classifier.classifyDocument(document, "unknown");
    assertNotNull("category is null and it shouldn't be", result);
    assertEquals(result + " is not equal to d", "d", result.getLabel());
  }
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