/*********************************************************************** 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.Associative_Classification.ClassifierFuzzyFCRA; import java.util.*; /** * This class contains the representation of a itemset * * @author Jesus Alcal� (University of Granada) 09/02/2010 * @version 1.0 * @since JDK1.5 */ public class Itemset { ArrayList<Item> itemset; int clas; double support, supportRule; /** * <p> * Default Constructor * </p> */ public Itemset() { } /** * <p> * Parameters Constructor * </p> * @param clas int Associated output of the Itemset */ public Itemset(int clas) { this.itemset = new ArrayList<Item> (); this.clas = clas; this.support = 0; this.supportRule = 0; } /** * <p> * Clone function * </p> */ 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 = this.support; d.supportRule = this.supportRule; return (d); } /** * <p> * Function to add an item to our itemset * </p> * @param item Item Element to be added */ public void add (Item item) { this.itemset.add(item); } /** * <p> * It returns the item located in the given position of the itemset * </p> * @param pos int Position of the requested item into the itemset * @return Item The requested item of the itemset */ public Item get (int pos) { return (this.itemset.get(pos)); } /** * <p> * Function to remove the item located in the given position * </p> * @param pos int Position of the requested item into the itemset * @return Item The removed item of the itemset */ public Item remove (int pos) { return (this.itemset.remove(pos)); } /** * <p> * It returns the size of the itemset (the number of items it has) * </p> * @return int Number of items the itemset stores */ public int size () { return (this.itemset.size()); } /** * <p> * It returns the support of the antecedent of the itemset * </p> * @return double Support of the antecedent of the itemset */ public double getSupport() { return (this.support); } /** * <p> * It returns the support of the itemset for its related output class * </p> * @return double Support of the itemset for its related output class */ public double getSupportClass() { return (this.supportRule); } /** * <p> * It returns the output class of the itemset * </p> * @return int output class of the itemset */ public int getClas() { return (this.clas); } /** * <p> * Function which sets the itemset's output class * </p> * @param clas int Itemset's output class */ public void setClas(int clas) { this.clas = clas; } /** * <p> * Function to check if an itemset is equal to another given * </p> * @param a Itemset Itemset to compare with ours * @return boolean true = they are equal, false = they aren't. */ public boolean isEqual(Itemset a) { int i; Item item; if (this.itemset.size() != a.size()) return (false); for (i=0; i < this.itemset.size(); i++) { item = this.itemset.get(i); if (!item.isEqual(a.get(i))) return (false); } if (this.clas != a.getClas()) return (false); return (true); } /** * <p> * It computes the support, rule support, hits, misses and PER of our itemset for a given dataset * </p> * @param train Given training dataset to be able to calculate supports */ public void calculateSupports(myDataset train) { int i; double degree; this.support = 0.0; this.supportRule = 0.0; for (i = 0; i < train.size(); i++) { degree = this.degree(train.getExampleFGTTFS(i)); this.support += degree; if (train.getOutputAsInteger(i) == this.clas) this.supportRule += degree; } this.support /= train.getnData(); this.supportRule /= train.getnData(); } private double degree(double[][] ejemplo) { return (degreeProduct(ejemplo)); } private double degreeProduct(double[][] example) { double degree; Item item; degree = 1.0; for (int i = 0; i < itemset.size(); i++) { item = itemset.get(i); degree *= example[item.getVariable()][item.getValue()]; } return (degree); } }