Classify weka Instance - Java Machine Learning AI

Java examples for Machine Learning AI:weka

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Description

Classify weka Instance

Demo Code

/*// w  w w  . j  a  v  a  2  s .co  m
 *  How to use WEKA API in Java 
 *  Copyright (C) 2014 
 *  @author Dr Noureddin M. Sadawi (noureddin.sadawi@gmail.com)
 *  
 *  This program is free software: you can redistribute it and/or modify
 *  it as you wish ... 
 *  I ask you only, as a professional courtesy, to cite my name, web page 
 *  and my YouTube Channel!
 *  
 */

package weka.api;

//import required classes
import weka.core.Instance;
import weka.core.Instances;
import weka.core.converters.ConverterUtils.DataSource;
import weka.classifiers.bayes.NaiveBayes;
import weka.classifiers.functions.SMOreg;

public class ClassifyInstance {
    public static void main(String args[]) throws Exception {
        //load training dataset
        DataSource source = new DataSource(
                "/home/likewise-open/ACADEMIC/csstnns/Desktop/qdb1.arff");
        Instances trainDataset = source.getDataSet();
        //set class index to the last attribute
        trainDataset.setClassIndex(trainDataset.numAttributes() - 1);

        //build model
        SMOreg smo = new SMOreg();
        smo.buildClassifier(trainDataset);
        //output model
        System.out.println(smo);

        //load new dataset
        DataSource source1 = new DataSource(
                "/home/likewise-open/ACADEMIC/csstnns/Desktop/qdb-unknown.arff");
        Instances testDataset = source1.getDataSet();
        //set class index to the last attribute
        testDataset.setClassIndex(testDataset.numAttributes() - 1);

        //loop through the new dataset and make predictions
        System.out.println("===================");
        System.out.println("Actual Class, SMO Predicted");
        for (int i = 0; i < testDataset.numInstances(); i++) {
            //get class double value for current instance
            double actualValue = testDataset.instance(i).classValue();

            //get Instance object of current instance
            Instance newInst = testDataset.instance(i);
            //call classifyInstance, which returns a double value for the class
            double predSMO = smo.classifyInstance(newInst);

            System.out.println(actualValue + ", " + predSMO);
        }

        /*
        //load training dataset
        DataSource source = new DataSource("/home/likewise-open/ACADEMIC/csstnns/Desktop/iris.arff");
        Instances trainDataset = source.getDataSet();  
        //set class index to the last attribute
        trainDataset.setClassIndex(trainDataset.numAttributes()-1);
        //get number of classes
        int numClasses = trainDataset.numClasses();
        //print out class values in the training dataset
        for(int i = 0; i < numClasses; i++){
          //get class string value using the class index
          String classValue = trainDataset.classAttribute().value(i);
          System.out.println("Class Value "+i+" is " + classValue);
        }
        //create and build the classifier
        NaiveBayes nb = new NaiveBayes();
        nb.buildClassifier(trainDataset);

        //load new dataset
        DataSource source1 = new DataSource("/home/likewise-open/ACADEMIC/csstnns/Desktop/iris-unknown.arff");
        Instances testDataset = source1.getDataSet();  
        //set class index to the last attribute
        testDataset.setClassIndex(testDataset.numAttributes()-1);

        //loop through the new dataset and make predictions
        System.out.println("===================");
        System.out.println("Actual Class, NB Predicted");
        for (int i = 0; i < testDataset.numInstances(); i++) {
          //get class double value for current instance
          double actualClass = testDataset.instance(i).classValue();
          //get class string value using the class index using the class's int value
          String actual = testDataset.classAttribute().value((int) actualClass);
          //get Instance object of current instance
          Instance newInst = testDataset.instance(i);
          //call classifyInstance, which returns a double value for the class
          double predNB = nb.classifyInstance(newInst);
          //use this value to get string value of the predicted class
          String predString = testDataset.classAttribute().value((int) predNB);
          System.out.println(actual+", "+predString);
        }
         */
    }
}

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