List of classifyInstance() Examples

Examples of classifyInstance()

org.mitre.jcarafe.jarafe.JarafeMEDecoder.classifyInstance()
weka.classifiers.Classifier.classifyInstance()
Classifies the given test instance. The instance has to belong to a dataset when it's being classified. Note that a classifier MUST implement either this or distributionForInstance(). @param instance the instance to be classified @return the predicted most likely class for the instance or Instance.missingValue() if no prediction is made @exception Exception if an error occurred during the prediction
weka.classifiers.meta.FilteredClassifier.classifyInstance()
weka.classifiers.trees.J48.classifyInstance()
Classifies an instance. @param instance the instance to classify @return the classification for the instance @throws Exception if instance can't be classified successfully

Examples of weka.classifiers.Classifier.classifyInstance()

    Instance metaInstance;
    int i = 0;
    for (int k = 0; k < m_Classifiers.length; k++) {
      Classifier classifier = getClassifier(k);
      if (m_BaseFormat.classAttribute().isNumeric()) {
  values[i++] = classifier.classifyInstance(instance);
      } else {
  double[] dist = classifier.distributionForInstance(instance);
  for (int j = 0; j < dist.length; j++) {
    values[i++] = dist[j];
  }

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Examples of weka.classifiers.Classifier.classifyInstance()

    Instance metaInstance;
    int i = 0;
    for (int k = 0; k < m_Classifiers.length; k++) {
      Classifier classifier = getClassifier(k);
      if (m_BaseFormat.classAttribute().isNumeric()) {
  values[i++] = classifier.classifyInstance(instance);
      } else {
  double[] dist = classifier.distributionForInstance(instance);
  for (int j = 0; j < dist.length; j++) {
    values[i++] = dist[j];
  }

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Examples of weka.classifiers.meta.FilteredClassifier.classifyInstance()

        fc.setClassifier(j48);
        // train using stock_training_data.arff:
        fc.buildClassifier(training_data);
        // test using stock_testing_data.arff:
        for (int i = 0; i < testing_data.numInstances(); i++) {
          double pred = fc.classifyInstance(testing_data.instance(i));
          System.out.print("given value: " + testing_data.classAttribute().value((int)testing_data.instance(i).classValue()));
          System.out.println(". predicted value: " + testing_data.classAttribute().value((int) pred));
        }


  }

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Examples of weka.classifiers.trees.J48.classifyInstance()

      }
      String[] options = weka.core.Utils.splitOptions("-p 0");
      J48 cls = (J48)weka.core.SerializationHelper.read(modelfile);
      cls.setOptions(options);
      for(int i = 0; i < data.numInstances(); i++){
        double pred = cls.classifyInstance(data.instance(i));
        ClusterClass clusClass = ClusterClass.valueOf(
            data.classAttribute().value((int)pred).toUpperCase());
        if(!retval.containsKey(clusClass)){
          retval.put(clusClass, new ArrayList<StoredDomainCluster>());
        }

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Examples of weka.classifiers.trees.J48.classifyInstance()

        dataset.add(createInstance(dataset, "rain", "true", 71, 91, "no"));
        dataset.setClassIndex(4);
        classifier.buildClassifier(dataset);
        System.out.println(classifier);
        
        double result = classifier.classifyInstance(createInstance(dataset, "sunny", "false", 85, 85, Null.getValue()));
        assertEquals(1.0, result, .1);
        result = classifier.classifyInstance(createInstance(dataset, "sunny", "true", 80, 90, Null.getValue()));
        assertEquals(1.0, result, .1);
        result = classifier.classifyInstance(createInstance(dataset, "overcast", "false", 83, 78, "yes"));
        assertEquals(0.0, result, .1);

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Examples of weka.classifiers.trees.J48.classifyInstance()

        classifier.buildClassifier(dataset);
        System.out.println(classifier);
        
        double result = classifier.classifyInstance(createInstance(dataset, "sunny", "false", 85, 85, Null.getValue()));
        assertEquals(1.0, result, .1);
        result = classifier.classifyInstance(createInstance(dataset, "sunny", "true", 80, 90, Null.getValue()));
        assertEquals(1.0, result, .1);
        result = classifier.classifyInstance(createInstance(dataset, "overcast", "false", 83, 78, "yes"));
        assertEquals(0.0, result, .1);
        result = classifier.classifyInstance(createInstance(dataset, "rain", "false", 70, 96, "yes"));
        assertEquals(0.0, result, .1);

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Examples of weka.classifiers.trees.J48.classifyInstance()

        
        double result = classifier.classifyInstance(createInstance(dataset, "sunny", "false", 85, 85, Null.getValue()));
        assertEquals(1.0, result, .1);
        result = classifier.classifyInstance(createInstance(dataset, "sunny", "true", 80, 90, Null.getValue()));
        assertEquals(1.0, result, .1);
        result = classifier.classifyInstance(createInstance(dataset, "overcast", "false", 83, 78, "yes"));
        assertEquals(0.0, result, .1);
        result = classifier.classifyInstance(createInstance(dataset, "rain", "false", 70, 96, "yes"));
        assertEquals(0.0, result, .1);
        result = classifier.classifyInstance(createInstance(dataset, "rain", "false", 68, 80, "yes"));
        assertEquals(0.0, result, .1);

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Examples of weka.classifiers.trees.J48.classifyInstance()

        assertEquals(1.0, result, .1);
        result = classifier.classifyInstance(createInstance(dataset, "sunny", "true", 80, 90, Null.getValue()));
        assertEquals(1.0, result, .1);
        result = classifier.classifyInstance(createInstance(dataset, "overcast", "false", 83, 78, "yes"));
        assertEquals(0.0, result, .1);
        result = classifier.classifyInstance(createInstance(dataset, "rain", "false", 70, 96, "yes"));
        assertEquals(0.0, result, .1);
        result = classifier.classifyInstance(createInstance(dataset, "rain", "false", 68, 80, "yes"));
        assertEquals(0.0, result, .1);
    }

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Examples of weka.classifiers.trees.J48.classifyInstance()

        assertEquals(1.0, result, .1);
        result = classifier.classifyInstance(createInstance(dataset, "overcast", "false", 83, 78, "yes"));
        assertEquals(0.0, result, .1);
        result = classifier.classifyInstance(createInstance(dataset, "rain", "false", 70, 96, "yes"));
        assertEquals(0.0, result, .1);
        result = classifier.classifyInstance(createInstance(dataset, "rain", "false", 68, 80, "yes"));
        assertEquals(0.0, result, .1);
    }
    
    private Instance createInstance(Instances dataset, Object... parameters)
    {

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