Examples of weka.classifiers.Classifier.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

                instanceToClassify.setValue(tempEntry.getKey(), tempEntry.getValue());
            }
            instanceToClassify.setDataset(structure);
            instanceToClassify.setClassMissing();
            try {
                return cModel.classifyInstance(instanceToClassify);
            } catch (Exception ex) {
                Logger.getLogger(WekaWrapper.class.getName()).log(Level.SEVERE, null, ex);
                System.out.println(ex.toString());
                return -2;
            }

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        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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        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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        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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        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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        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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    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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      classifier.buildClassifier(data);
      
      // record predictions
      result = new double[data.numInstances()];
      for (i = 0; i < result.length; i++)
  result[i] = classifier.classifyInstance(data.instance(i));
      
      // save
      SerializationHelper.write(MODEL_FILENAME, classifier);
    }
    catch (Exception e) {

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    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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    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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