Java Code Examples for weka.classifiers.trees.J48#classifyInstance()

The following examples show how to use weka.classifiers.trees.J48#classifyInstance() . You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may check out the related API usage on the sidebar.
Example 1
Source File: LoadModel.java    From Hands-On-Artificial-Intelligence-with-Java-for-Beginners with MIT License 6 votes vote down vote up 
/**
 * @param args the command line arguments
 */
public static void main(String[] args) {
    // TODO code application logic here
    try{
        J48 mytree = (J48) weka.core.SerializationHelper.read("/Users/admin/Documents/NetBeansProjects/LoadModel/myDT.model");
        
        DataSource src1 = new DataSource("/Users/admin/Documents/NetBeansProjects/LoadModel/segment-test.arff");
        Instances tdt = src1.getDataSet();
        tdt.setClassIndex(tdt.numAttributes() - 1);
        
        System.out.println("ActualClass \t ActualValue \t PredictedValue \t PredictedClass");
        for (int i = 0; i < tdt.numInstances(); i++) {
            String act = tdt.instance(i).stringValue(tdt.instance(i).numAttributes() - 1);
            double actual = tdt.instance(i).classValue();
            Instance inst = tdt.instance(i);
            double predict = mytree.classifyInstance(inst);
            String pred = inst.toString(inst.numAttributes() - 1);
            System.out.println(act + " \t\t " + actual + " \t\t " + predict + " \t\t " + pred);
        }
    }
    catch(Exception e){
        System.out.println("Error!!!!\n" + e.getMessage());
    }
}
Example 2
Source File: BookDecisionTree.java    From Java-for-Data-Science with MIT License 6 votes vote down vote up 
public static void main(String[] args) {
    try {
        BookDecisionTree decisionTree = new BookDecisionTree("books.arff");
        J48 tree = decisionTree.performTraining();
        System.out.println(tree.toString());
        
        Instance testInstance = decisionTree.
                getTestInstance("Leather", "yes", "historical");
        int result = (int) tree.classifyInstance(testInstance);
        String results = decisionTree.trainingData.attribute(3).value(result);
        System.out.println(
                "Test with: " + testInstance + "  Result: " + results);

        testInstance = decisionTree.
                getTestInstance("Paperback", "no", "historical");
        result = (int) tree.classifyInstance(testInstance);
        results = decisionTree.trainingData.attribute(3).value(result);
        System.out.println(
                "Test with: " + testInstance + "  Result: " + results);
    } catch (Exception ex) {
        ex.printStackTrace();
    }
}
Example 3
Source File: MakingPredictions.java    From Hands-On-Artificial-Intelligence-with-Java-for-Beginners with MIT License 5 votes vote down vote up 
/**
 * @param args the command line arguments
 */
public static void main(String[] args) { 
    // TODO code application logic here
    try {
        DataSource src = new DataSource("/Users/admin/Documents/NetBeansProjects/MakingPredictions/segment-challenge.arff");
        Instances dt = src.getDataSet();
        dt.setClassIndex(dt.numAttributes() - 1);

        String[] options = new String[4];
        options[0] = "-C";
        options[1] = "0.1";
        options[2] = "-M";
        options[3] = "2";
        J48 mytree = new J48();
        mytree.setOptions(options);
        mytree.buildClassifier(dt);

        DataSource src1 = new DataSource("/Users/admin/Documents/NetBeansProjects/MakingPredictions/segment-test.arff");
        Instances tdt = src1.getDataSet();
        tdt.setClassIndex(tdt.numAttributes()-1);
         
        System.out.println("ActualClass \t ActualValue \t PredictedValue \t PredictedClass");
        for (int i = 0; i < tdt.numInstances(); i++)
        {
            String act = tdt.instance(i).stringValue(tdt.instance(i).numAttributes()-1);
            double actual = tdt.instance(i).classValue();
            Instance inst = tdt.instance(i);
            double predict = mytree.classifyInstance(inst);
            String pred = inst.toString(inst .numAttributes()-1);
            System.out.println(act + " \t\t " + actual + " \t\t " + predict + " \t\t " + pred);
        }
        
    } 
    catch (Exception e) {
        System.out.println(e.getCause());
    }
}
Example 4
Source File: TestWekaJ48.java    From Java-Data-Analysis with MIT License 5 votes vote down vote up 
public static void main(String[] args) throws Exception {
    DataSource source = new DataSource("data/AnonFruit.arff");
    Instances instances = source.getDataSet();
    instances.setClassIndex(3);  // target attribute: (Sweet)
    
    J48 j48 = new J48();  // an extension of ID3
    j48.setOptions(new String[]{"-U"});  // use unpruned tree
    j48.buildClassifier(instances);

    for (Instance instance : instances) {
        double prediction = j48.classifyInstance(instance);
        System.out.printf("%4.0f%4.0f%n", 
                instance.classValue(), prediction);
    }
}