/***********************************************************************
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 org.core.Files;
/**
* Fuzzy Data Base
*
* @author Written by Jesus Alcal� (University of Granada) 09/02/2010
* @version 1.0
* @since JDK1.5
*/
public class DataBase {
int n_variables, partitions;
int[] nLabels;
Fuzzy[][] dataBase;
String names[];
public DataBase() {
}
/**
* <p>
* Parameters Constructor
* </p>
* @param nLabels int It is the number of membership functions for each real/integer variable
* @param train myDataset It contains the train data set with the whole information to execute the algorithm
*/
public DataBase(int nLabels, myDataset train) {
double mark, value, rank, labels;
double[][] ranks = train.devuelveRangos();
this.n_variables = train.getnInputs();
this.names = (train.names()).clone();
this.nLabels = new int[this.n_variables];
this.dataBase = new Fuzzy[this.n_variables][];
for (int i = 0; i < this.n_variables; i++) {
rank = Math.abs(ranks[i][1] - ranks[i][0]);
if (train.isNominal(i)) this.nLabels[i] = ((int) rank) + 1;
else this.nLabels[i] = nLabels;
this.dataBase[i] = new Fuzzy[this.nLabels[i]];
mark = rank / (this.nLabels[i] - 1.0);
for (int j = 0; j < this.nLabels[i]; j++) {
this.dataBase[i][j] = new Fuzzy();
value = ranks[i][0] + mark * (j - 1);
this.dataBase[i][j].x0 = this.setValue(value, ranks[i][0], ranks[i][1]);
value = ranks[i][0] + mark * j;
this.dataBase[i][j].x1 = this.setValue(value, ranks[i][0], ranks[i][1]);
value = ranks[i][0] + mark * (j + 1);
this.dataBase[i][j].x3 = this.setValue(value, ranks[i][0], ranks[i][1]);
this.dataBase[i][j].y = 1;
this.dataBase[i][j].name = new String("L_" + j + "(" + this.nLabels[i] + ")");
}
}
}
private double setValue(double val, double min, double tope) {
if (val > min -1E-4 && val < min + 1E-4) return (min);
if (val > tope - 1E-4 && val < tope + 1E-4) return (tope);
return (val);
}
/**
* <p>
* It returns the number of input attributes in the examples
* </p>
* @return int The number of input attributes
*/
public int numVariables() {
return (this.n_variables);
}
/**
* <p>
* It returns the number of different labels that a specific input attribute can hold
* </p>
* @param variable int The input attribute which we want to know the number of different labels it can have
* @return int The number of labels
*/
public int numLabels(int variable) {
return (this.nLabels[variable]);
}
/**
* <p>
* It return the whole array of number of labels for every attribute
* </p>
* @return int[] the whole array of number of labels for every attribute
*/
public int[] getnLabels() {
return (this.nLabels);
}
/**
* <p>
* It checks if the value of a specific label in a specific attribute matchs with a given value
* </p>
* @param variable int Attribute which we are going to check
* @param label int Attribute's label we are going to check
* @param value int Value to be compared
* @return int 1 = they match, 0 = they don't.
*/
public double matching(int variable, int label, double value) {
if ((variable < 0) || (label < 0)) return (1); // Don't care
else return (this.dataBase[variable][label].Fuzzifica(value));
}
/**
* <p>
* It prints the values of the fuzzy label of the given variable
* </p>
*
* @param var Attribute which we are going to check
* @param label Attribute's label we are going to check
* @return the values of the fuzzy label of the given variable
*/
public String print_triangle(int var, int label) {
String cadena = new String("");
Fuzzy d = this.dataBase[var][label];
cadena = d.name + ": \t" + d.x0 + "\t" + d.x1 + "\t" + d.x3 + "\n";
return cadena;
}
/**
* <p>
* It prints an attribute with its label in a string way
* </p>
* @param var int Attribute to be printed
* @param label int Attribute's label to be printed
* @return String A string which represents the "string format" of the given input
*/
public String print(int var, int label) {
return (this.dataBase[var][label].getName());
}
/**
* <p>
* It prints the whole database
* </p>
* @return String The whole database
*/
public String printString() {
String string = new String("@Using Triangular Membership Functions as antecedent fuzzy sets");
for (int i = 0; i < this.n_variables; i++) {
string += "\n\n@Number of Labels in Variable " + (i+1) + ": " + this.nLabels[i];
string += "\n" + this.names[i] + ":\n";
for (int j = 0; j < this.nLabels[i]; j++) {
string += this.dataBase[i][j].name + ": (" + this.dataBase[i][j].x0 + "," + this.dataBase[i][j].x1 + "," + this.dataBase[i][j].x3 + ")\n";
}
}
return string;
}
/**
* <p>
* It stores the data base in a given file
* </p>
* @param filename String Name for the database file
*/
public void saveFile(String filename) {
String stringOut = new String("");
stringOut = printString();
Files.writeFile(filename, stringOut);
}
}