Package org.apache.mahout.classifier

Examples of org.apache.mahout.classifier.ClassifierResult

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  }

  public void testResults() throws Exception {
    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);
    System.out.println("Result: " + result);
  }
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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());

    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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  }

  public void testResults() throws Exception {
    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);
    System.out.println("Result: " + result);
  }
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          for (Map.Entry<String, List<String>> stringListEntry : document
              .entrySet()) {
            List<String> strings = stringListEntry.getValue();
            TimingStatistics.Call call = operationStats.newCall();
            TimingStatistics.Call outercall = totalStatistics.newCall();
            ClassifierResult classifiedLabel = classifier.classifyDocument(
                strings.toArray(new String[strings.size()]), params
                    .get("defaultCat"));
            call.end();
            outercall.end();
            boolean correct = resultAnalyzer.addInstance(correctLabel,
                classifiedLabel);
            if (verbose) {
              // We have one document per line
              log.info("Line Number: " + lineNum + " Line(30): "
                  + (line.length() > 30 ? line.substring(0, 30) : line)
                  + " Expected Label: " + correctLabel + " Classified Label: "
                  + classifiedLabel.getLabel() + " Correct: " + correct);
            }
            // log.info("{} {}", correctLabel, classifiedLabel);

          }
          lineNum++;
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  @Override
  public ClassifierResult classifyDocument(String[] document,
      Datastore datastore, String defaultCategory)
      throws InvalidDatastoreException {
    ClassifierResult result = new ClassifierResult(defaultCategory);
    double max = Double.MAX_VALUE;
    Collection<String> categories = datastore.getKeys("labelWeight");   

    for (String category : categories) {
      double prob = documentWeight(datastore, category, document);
      if (prob < max) {
        max = prob;
        result.setLabel(category);
      }
    }
    result.setScore(max);
    return result;
  }
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    PriorityQueue<ClassifierResult> pq =
        new PriorityQueue<ClassifierResult>(numResults, new ByScoreLabelResultComparator());
    for (String category : categories){
      double prob = documentWeight(datastore, category, document);
      if (prob > 0.0) {
        pq.add(new ClassifierResult(category, prob));
        if (pq.size() > numResults) {
          pq.remove();
        }
      }
    }

    if (pq.isEmpty()) {
      return new ClassifierResult[] { new ClassifierResult(defaultCategory, 0.0) };
    } else {
      List<ClassifierResult> result = new ArrayList<ClassifierResult>(pq.size());
      while (pq.isEmpty() == false) {
        result.add(pq.remove());
      }
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  @Override
  public ClassifierResult classifyDocument(String[] document,
      Datastore datastore, String defaultCategory)
      throws InvalidDatastoreException {
    ClassifierResult result = new ClassifierResult(defaultCategory);
    double max = Double.MIN_VALUE;
    Collection<String> categories = datastore.getKeys("labelWeight");

    for (String category : categories) {
      double prob = documentWeight(datastore, category, document);
      if (max < prob) {
        max = prob;
        result.setLabel(category);
      }
    }
    result.setScore(max);
    return result;
  }
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    PriorityQueue<ClassifierResult> pq =
        new PriorityQueue<ClassifierResult>(numResults, new ByScoreLabelResultComparator());
    for (String category : categories) {
      double prob = documentWeight(datastore, category, document);
      if (prob > 0.0) {
        pq.add(new ClassifierResult(category, prob));
        if (pq.size() > numResults) {
          pq.remove();
        }
      }
    }

    if (pq.isEmpty()) {
      return new ClassifierResult[] { new ClassifierResult(defaultCategory, 0.0) };
    } else {
      List<ClassifierResult> result = new ArrayList<ClassifierResult>(pq.size());
      while (pq.isEmpty() == false) {
        result.add(pq.remove());
      }
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  @Override
  public ClassifierResult classifyDocument(String[] document,
                                           Datastore datastore,
                                           String defaultCategory) throws InvalidDatastoreException {
    ClassifierResult result = new ClassifierResult(defaultCategory);
    double max = Double.MAX_VALUE;
    Collection<String> categories = datastore.getKeys("labelWeight");
   
    for (String category : categories) {
      double prob = documentWeight(datastore, category, document);
      if (prob < max) {
        max = prob;
        result.setLabel(category);
      }
    }
    result.setScore(max);
    return result;
  }
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    PriorityQueue<ClassifierResult> pq = new PriorityQueue<ClassifierResult>(numResults,
        new ByScoreLabelResultComparator());
    for (String category : categories) {
      double prob = documentWeight(datastore, category, document);
      if (prob > 0.0) {
        pq.add(new ClassifierResult(category, prob));
        if (pq.size() > numResults) {
          pq.remove();
        }
      }
    }
   
    if (pq.isEmpty()) {
      return new ClassifierResult[] {new ClassifierResult(defaultCategory, 0.0)};
    } else {
      List<ClassifierResult> result = new ArrayList<ClassifierResult>(pq.size());
      while (!pq.isEmpty()) {
        result.add(pq.remove());
      }
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