Itemset.java

/***********************************************************************

	This file is part of KEEL-software, the Data Mining tool for regression, 
	classification, clustering, pattern mining and so on.

	Copyright (C) 2004-2010
	
	F. Herrera (herrera@decsai.ugr.es)
    L. S�nchez (luciano@uniovi.es)
    J. Alcal�-Fdez (jalcala@decsai.ugr.es)
    S. Garc�a (sglopez@ujaen.es)
    A. Fern�ndez (alberto.fernandez@ujaen.es)
    J. Luengo (julianlm@decsai.ugr.es)

	This program is free software: you can redistribute it and/or modify
	it under the terms of the GNU General Public License as published by
	the Free Software Foundation, either version 3 of the License, or
	(at your option) any later version.

	This program is distributed in the hope that it will be useful,
	but WITHOUT ANY WARRANTY; without even the implied warranty of
	MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
	GNU General Public License for more details.

	You should have received a copy of the GNU General Public License
	along with this program.  If not, see http://www.gnu.org/licenses/
  
**********************************************************************/

package keel.Algorithms.Fuzzy_Rule_Learning.Genetic.IVTURS;

import java.util.*;

/**
 * <p>Title: Itemset</p>
 * <p>Description: This class contains the representation of a itemset</p>
 * <p>Copyright: Copyright KEEL (c) 2007</p>
 * <p>Company: KEEL </p>
 * @author Jesus Alcal� (University of Granada) 09/02/2011
 * @author Modified by Jose Antonio Sanz (University of Navarra) 19/12/2011
 * @author Modified by Alberto Fernandez (University of Jaen) 24/10/2013
 * @version 1.2
 * @since JDK1.6
 */
public class Itemset {
	ArrayList<Item> itemset;
	int clas;
	double support[], supportRule[];

	public Itemset() {
	}

	/**
	 * <p>
	 * Builder
	 * </p>
	 * @param clas Class
	 * @return Return a itemset for the class clas
	 */
	public Itemset(int clas) {
		this.itemset = new ArrayList<Item> ();
		this.clas = clas;
		this.support = new double[2];
		this.support[0] = this.support[1] = 0;
		this.supportRule = new double[2];
		this.supportRule[0] = this.supportRule[1] = 0;
	}

	/**
	 * <p>
	 * Clone
	 * </p>
	 * @return Return a copy of the itemset
	 */
	public Itemset clone() {
		Itemset d = new Itemset(this.clas);
		for (int i=0; i < this.itemset.size(); i++)  d.add((itemset.get(i)).clone());

		d.clas = this.clas;
		d.support[0] = this.support[0];
		d.support[1] = this.support[1];
		d.supportRule[0] = this.supportRule[0];
		d.supportRule[1] = this.supportRule[1];

		return (d);
	}

	public void add (Item item) {
		this.itemset.add(item);
	}

	public Item get (int pos) {
		return (this.itemset.get(pos));
	}

	public Item remove (int pos) {
		return (this.itemset.remove(pos));
	}

	public int size () {
		return (this.itemset.size());
	}

	public double[] getSupport() {
		return (this.support);
	}

	public double[] getSupportClass() {
		return (this.supportRule);
	}

	public int getClas() {
		return (this.clas);
	}

	public void setClas(int clas) {
		this.clas = clas;
	}


	public boolean isEqual(Itemset a) {
		int i;
		Item item;

		if (this.itemset.size() != a.size())  return (false);
		if (this.clas != a.getClas())  return (false);

		for (i=0; i < this.itemset.size(); i++) {
			item = this.itemset.get(i);
			if (!item.isEqual(a.get(i)))  return (false);
		}

		return (true);
	}


	public void calculateSupports(DataBase dataBase, myDataset train) {
		int i;
		double degree[] = new double[2];

		this.support[0] = this.support[1] = 0.0;
		this.supportRule[0] = this.supportRule[1] = 0.0;

		for (i = 0; i < train.size(); i++) {
			degree = this.degree(dataBase, train.getExample(i));
			//degree[1] = this.degree(dataBase, train.getExample(i))[1];
			this.support[0] += degree[0];
			this.support[1] += degree[1];
			if (train.getOutputAsInteger(i) == this.clas){
				this.supportRule[0] += degree[0];
				this.supportRule[1] += degree[1];
			}
		}

		this.support[0] /= train.getnData();
		this.support[1] /= train.getnData();
		this.supportRule[0] /= train.getnData();
		this.supportRule[1] /= train.getnData();
	}


	public double[] degree(DataBase dataBase, double[] ejemplo) {
		return (degreeProduct(dataBase, ejemplo));
	}


	private double[] degreeProduct(DataBase dataBase, double[] example) {
		double degree[] = new double[2];
		double var1[] = new double[2];
		Item item;

		degree[0] = 1.0;
		degree[1] = 1.0;
		for (int i = 0; i < itemset.size() && degree[0] > 0.0; i++) {
			item = itemset.get(i);
			var1 = dataBase.matching(item.getVariable(), item.getValue(), example[item.getVariable()]);
			degree[0] *= var1[0];
			degree[1] *= var1[1];
		}
		return (degree);
	}

}