Package org.apache.mahout.classifier

Examples of org.apache.mahout.classifier.ClassifierResult

31
32
33
34
35
36
37
38
39
40
41
42
43
44
    TopK<ClassifierResult> topResults =
        new TopK<ClassifierResult>(numResults, ClassifierResult.COMPARE_BY_SCORE_AND_LABEL);
    for (String category : categories) {
      double prob = documentWeight(datastore, category, document);
      if (prob > 0.0) {
        topResults.offer(new ClassifierResult(category, prob));
      }
    }
    if (topResults.isEmpty()) {
      return new ClassifierResult[] { new ClassifierResult(defaultCategory, 0.0) };
    } else {
      return topResults.retrieve().toArray(new ClassifierResult[topResults.size()]);
    }
  }
View Full Code Here
85
86
87
88
89
90
91
92
93
94
95
96
97
   
    for (String[] entry : ClassifierData.DATA) {
      List<String> document = new NGrams(entry[1], params.getGramSize()).generateNGramsWithoutLabel();
      assertEquals(3, classifier.classifyDocument(document.toArray(new String[document.size()]),
        params.get("defaultCat"), 100).length);
      ClassifierResult result = classifier.classifyDocument(document.toArray(new String[document.size()]), params
          .get("defaultCat"));
      assertEquals(entry[0], result.getLabel());
      resultAnalyzer.addInstance(entry[0], result);
    }
    int[][] matrix = resultAnalyzer.getConfusionMatrix().getConfusionMatrix();
    for (int i = 0; i < 3; i++) {
      for (int j = 0; j < 3; j++) {
View Full Code Here
135
136
137
138
139
140
141
142
143
144
145
146
147
    ResultAnalyzer resultAnalyzer = new ResultAnalyzer(classifier.getLabels(), params.get("defaultCat"));
    for (String[] entry : ClassifierData.DATA) {
      List<String> document = new NGrams(entry[1], params.getGramSize()).generateNGramsWithoutLabel();
      assertEquals(3, classifier.classifyDocument(document.toArray(new String[document.size()]),
        params.get("defaultCat"), 100).length);
      ClassifierResult result = classifier.classifyDocument(document.toArray(new String[document.size()]), params
          .get("defaultCat"));
      assertEquals(entry[0], result.getLabel());
      resultAnalyzer.addInstance(entry[0], result);
    }
    int[][] matrix = resultAnalyzer.getConfusionMatrix().getConfusionMatrix();
    for (int i = 0; i < 3; i++) {
      for (int j = 0; j < 3; j++) {
View Full Code Here
85
86
87
88
89
90
91
92
93
94
95
      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());
  }
View Full Code Here
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
  @Test
  public void test() 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);
    assertEquals(result + " is not equal to e", "e", result.getLabel());
   
    document = new String[] {"dd"};
    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());
  }
View Full Code Here
91
92
93
94
95
96
97
98
  @Test
  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);
  }
View Full Code Here
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
  @Test
  public void test() 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);
    assertEquals(result + " is not equal to e", "e", result.getLabel());
   
    document = new String[] {"dd"};
    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());
  }
View Full Code Here
97
98
99
100
101
102
103
104
  @Test
  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);
  }
View Full Code Here
261
262
263
264
265
266
267
268
269
270
271
        ofile.writeChar('\n');
      }

      if (analyzer != null) {
        analyzer.addInstance(dataset.getLabel(instance.getLabel()),
                             new ClassifierResult(dataset.getLabel(prediction), 1.0));
      }
    }

    scanner.close();
    input.close();
View Full Code Here
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
 
  @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;
  }
View Full Code Here
TOP

Related Classes of org.apache.mahout.classifier.ClassifierResult

  • com.tamingtext.classifier.bayes.BayesUpdateRequestProcessor
  • com.tamingtext.classifier.maxent.TestMaxent
  • com.tamingtext.classifier.mlt.TestMoreLikeThis
  • org.apache.mahout.classifier.bayes.AbstractBayesAlgorithm
  • org.apache.mahout.classifier.bayes.algorithm.BayesAlgorithm
  • org.apache.mahout.classifier.bayes.algorithm.CBayesAlgorithm
  • org.apache.mahout.classifier.bayes.BayesAlgorithm
  • org.apache.mahout.classifier.bayes.BayesClassifier
  • org.apache.mahout.classifier.bayes.BayesClassifier$ClassifierResultPriorityQueue
  • org.apache.mahout.classifier.bayes.BayesClassifierSelfTest
    • Copyright © 2018 www.massapicom. All rights reserved.
    All source code are property of their respective owners. Java is a trademark of Sun Microsystems, Inc and owned by ORACLE Inc. Contact coftware#gmail.com.