ClassifierInstanceFactory.java

// Distributed Decision making system framework 
// Copyright (c) 2014, Jordi Coll Corbilla
// All rights reserved.
//
// Redistribution and use in source and binary forms, with or without
// modification, are permitted provided that the following conditions are met:
//
// - Redistributions of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
// - Redistributions in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
// - Neither the name of this library nor the names of its contributors may be
// used to endorse or promote products derived from this software without
// specific prior written permission.
//
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// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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package ddm.factory.classifiers;

import java.util.Random;

/**
 * 
 * @author jordi Corbilla
 * Factory that handles the list of classifiers available in the system
 */
public class ClassifierInstanceFactory {

	/**
	 * Method that builds the classifier according to a type
	 * @param type
	 * @return ClassifierInstance
	 */
	public static ClassifierInstance buildClassifier(ClassifierType type) {
		ClassifierInstance classifier = null;
		switch (type) {
		case J48DecisionTree:
			classifier = new J48DecisionTreeClassifier();
			break;
		case KNearestNeighbour:
			classifier = new KNearestNeighbourClassifier();
			break;
		case NeuronalNetwork:
			classifier = new NeuronalNetworkClassifier();
			break;
		default:
			break;
		}
		return classifier;
	}

	/**
	 * Method that returns a random instance
	 * @return ClassifierInstance
	 */
	public static ClassifierInstance giveMeAClassifier() {
		Random randomGenerator = new Random();
		int randomInt = randomGenerator.nextInt(2);
		ClassifierInstance classifier = null;
		switch (randomInt) {
		case 0:
			classifier = new J48DecisionTreeClassifier();
			break;
		case 1:
			classifier = new KNearestNeighbourClassifier();
			break;
		case 2:
			classifier = new NeuronalNetworkClassifier();
			break;
		}
		return classifier;
	}
}