/***********************************************************************
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.UnsupervisedLearning.AssociationRules.FuzzyRuleLearning.FuzzyApriori;
/**
* <p>
* @author Written by Alvaro Lopez
* @version 1.1
* @since JDK1.6
* </p>
*/
import java.io.IOException;
import keel.Dataset.*;
public class myDataset {
/**
* <p>
* It contains the methods to read a Dataset for the Association Rules Mining problem
* </p>
*/
public static final int NOMINAL = 0;
public static final int INTEGER = 1;
public static final int REAL = 2;
private double[][] trueTransactions = null; //true transactions array
private boolean[][] missing = null; //possible missing values
private double[] emax; //max value of an attribute
private double[] emin; //min value of an attribute
private int nTrans; // Number of transactions
private int nInputs; // Number of inputs
private int nOutputs; // Number of outputs
private int nVars; // Number of variables
private int nFuzzyRegionsForNumericAttributes; //Number of fuzzy regions for numeric attributes
private double[][][] fuzzyTransactions = null; //fuzzy transactions array
private FuzzyRegion[][] fuzzyAttributes = null; //fuzzy attributes array
private int nLabels[] = null; //number of labels for each attribute
private InstanceSet IS; //The whole instance set
/**
* <p>
* Initialize a new set of instances
* </p>
* @param nFuzzyRegionsForNumericAttributes The number of fuzzy regions with which numeric attributes are evaluated
*/
public myDataset(int nFuzzyRegionsForNumericAttributes) {
IS = new InstanceSet();
this.nFuzzyRegionsForNumericAttributes = (nFuzzyRegionsForNumericAttributes > 0) ? nFuzzyRegionsForNumericAttributes : 1;
}
/**
* Outputs an array of transactions with their corresponding attribute values.
* @return double[][] an array of transactions with their corresponding attribute values
*/
public double[][] getTrueTransactions() {
return trueTransactions;
}
/**
* Outputs the fuzzy attribute values for the specified transaction.
* @param idTrans The ID of the specified transaction
* @return double[][] an array of fuzzy attribute values also depending on the number of fuzzy regions
*/
public double[][] getFuzzyTransaction(int idTrans) {
return fuzzyTransactions[idTrans];
}
/**
* Outputs the fuzzy regions of the specified attribute.
* @param idAttr The ID of the specified attribute
* @return FuzzyRegion[] an array of fuzzy regions
*/
public FuzzyRegion[] getFuzzyAttribute(int idAttr) {
return fuzzyAttributes[idAttr];
}
/**
* Outputs the number of labels for all attributes in the dataset.
* @return int[] an array of values representing the number of labels for each attribute
*/
public int[] getNLabelsOfAttributes() {
return nLabels;
}
/**
* It returns an array with the maximum values of the attributes
* @return double[] an array with the maximum values of the attributes
*/
public double[] getemax() {
return emax;
}
/**
* It returns an array with the minimum values of the attributes
* @return double[] an array with the minimum values of the attributes
*/
public double[] getemin() {
return emin;
}
/**
* It returns the upper bound of the variable
* @param variable Id of the attribute
* @return double the upper bound of the variable
*/
public double getMax(int variable) {
return emax[variable];
}
/**
* It returns the lower bound of the variable
* @param variable Id of the attribute
* @return double the lower bound of the variable
*/
public double getMin(int variable) {
return emin[variable];
}
/**
* It gets the size of the data-set
* @return int the number of transactions in the data-set
*/
public int getnTrans() {
return nTrans;
}
/**
* It gets the number of variables of the data-set
* @return int the number of variables of the data-set
*/
public int getnVars() {
return nVars;
}
/**
* This function checks if the attribute value is missing
* @param i int Example id
* @param j int Variable id
* @return boolean True is the value is missing, else it returns false
*/
public boolean isMissing(int i, int j) {
return missing[i][j];
}
/**
* It reads the whole input data-set and it stores each transaction in
* local array
* @param datasetFile String name of the file containing the data-set
* @throws IOException If there occurs any problem with the reading of the data-set
*/
public void readDataSet(String datasetFile) throws
IOException {
int i, j, k;
try {
// Load in memory a data-set that contains a Frequent Items Mining problem
IS.readSet(datasetFile, true);
this.nTrans = IS.getNumInstances();
this.nInputs = Attributes.getInputNumAttributes();
this.nOutputs = Attributes.getOutputNumAttributes();
this.nVars = this.nInputs + this.nOutputs;
// Initialize and fill our own tables
this.trueTransactions = new double[nTrans][nVars];
this.fuzzyTransactions = new double[nTrans][nVars][];
this.fuzzyAttributes = new FuzzyRegion[nVars][];
this.nLabels = new int[nVars];
missing = new boolean[nTrans][nVars];
// Maximum and minimum of attributes
emax = new double[nVars];
emin = new double[nVars];
for (i = 0; i < nVars; i++) {
if ( getAttributeType(i) != myDataset.NOMINAL ) {
emax[i] = getMaxValue(i);
emin[i] = getMinValue(i);
}
else {
emin[i] = 0;
emax[i] = getNumNominalValues(i) - 1;
}
}
this.buildFuzzyRegions();
// All values are casted into double/integer
for (i=0; i < nTrans; i++) {
Instance inst = IS.getInstance(i);
for (j=0; j < nInputs; j++) {
trueTransactions[i][j] = IS.getInputNumericValue(i, j);
this.transformIntoFuzzySet(i, j);
missing[i][j] = inst.getInputMissingValues(j);
if (missing[i][j]) {
trueTransactions[i][j] = emin[j] - 1;
}
}
for (k=0; k < nOutputs; k++, j++) {
trueTransactions[i][j] = IS.getOutputNumericValue(i, k);
this.transformIntoFuzzySet(i, j);
}
}
}
catch (Exception e) {
System.out.println("DBG: Exception in readSet");
e.printStackTrace();
}
}
private void buildFuzzyRegions() {
int id_attr, id_label;
double rank, mark, value;
String str_label;
for (id_attr=0; id_attr < this.nVars; id_attr++) {
rank = Math.abs(emax[id_attr] - emin[id_attr]);
this.nLabels[id_attr] = (getAttributeType(id_attr) == myDataset.NOMINAL) ? ((int) rank) + 1 : this.nFuzzyRegionsForNumericAttributes;
this.fuzzyAttributes[id_attr] = new FuzzyRegion[ this.nLabels[id_attr] ];
mark = rank / (this.nLabels[id_attr] - 1.0);
for (id_label=0; id_label < this.nLabels[id_attr]; id_label++) {
this.fuzzyAttributes[id_attr][id_label] = new FuzzyRegion();
value = emin[id_attr] + mark * (id_label - 1);
this.fuzzyAttributes[id_attr][id_label].setX0( this.setValue(value, emax[id_attr]) );
value = emin[id_attr] + mark * id_label;
this.fuzzyAttributes[id_attr][id_label].setX1( this.setValue(value, emax[id_attr]) );
value = emin[id_attr] + mark * (id_label + 1);
this.fuzzyAttributes[id_attr][id_label].setX3( this.setValue(value, emax[id_attr]) );
this.fuzzyAttributes[id_attr][id_label].setY(1.0);
str_label = (getAttributeType(id_attr) == myDataset.NOMINAL) ? new String( getNominalValue(id_attr, id_label) ) : new String("LABEL_" + id_label);
this.fuzzyAttributes[id_attr][id_label].setLabel(str_label);
}
}
}
private double setValue(double val, double tope) {
if (val > -1E-4 && val < 1E-4) return 0.0;
if (val > tope - 1E-4 && val < tope + 1E-4) return tope;
return val;
}
private void transformIntoFuzzySet(int id_trans, int id_attr) {
int id_label;
this.fuzzyTransactions[id_trans][id_attr] = new double[ this.nLabels[id_attr] ];
for (id_label=0; id_label < this.nLabels[id_attr]; id_label++) {
this.fuzzyTransactions[id_trans][id_attr][id_label] = this.fuzzyAttributes[id_attr][id_label].getFuzzyValue(this.trueTransactions[id_trans][id_attr]);
}
}
/**
* It checks if the data-set has any real value
* @return boolean True if it has some real values, else false.
*/
public boolean hasRealAttributes() {
return Attributes.hasRealAttributes();
}
/**
* It checks if the data-set has any numerical value (real or integer)
* @return boolean True if it has some numerical values, else false.
*/
public boolean hasNumericalAttributes() {
return (Attributes.hasIntegerAttributes() ||
Attributes.hasRealAttributes());
}
/**
* It checks if the data-set has any missing value
* @return boolean True if it has some missing values, else false.
*/
public boolean hasMissingAttributes() {
return (this.sizeWithoutMissing() < this.getnTrans());
}
/**
* It return the size of the data-set without having account the missing values
* @return int the size of the data-set without having account the missing values
*/
public int sizeWithoutMissing() {
int tam = 0;
for (int i = 0; i < nTrans; i++) {
int j;
for (j = 1; (j < nVars) && (!isMissing(i, j)); j++) {
;
}
if (j == nVars) {
tam++;
}
}
return tam;
}
/**
* It returns an array indicating the position of the missing values on a specific example
* @param pos int Id of the example
* @return boolean[] an array indicating the position of the missing values on the example
*/
public boolean [] getMissing(int pos){
return this.missing[pos];
}
/**
* It returns the name of the attribute in "id_attr"
* @param id_attr int Id of the attribute
* @return String the name of the attribute
*/
public String getAttributeName(int id_attr) {
if (id_attr < this.nInputs) return ( Attributes.getInputAttribute(id_attr).getName() );
else return ( Attributes.getOutputAttribute(id_attr - this.nInputs).getName() );
}
/**
* It returns the type of the attribute in "id_attr"
* @param id_attr int Id of the attribute
* @return int the type of the attribute
*/
public int getAttributeType(int id_attr) {
if (id_attr < this.nInputs) return ( Attributes.getInputAttribute(id_attr).getType() );
else return ( Attributes.getOutputAttribute(id_attr - this.nInputs).getType() );
}
public String getAttributeTypeString(int id_attr) {
if (this.getAttributeType(id_attr) == myDataset.NOMINAL) return ("NOMINAL");
else if (this.getAttributeType(id_attr) == myDataset.INTEGER) return ("INTEGER");
else return ("REAL");
}
public boolean isNominal(int id_attr) {
if (id_attr < this.nInputs) {
if (Attributes.getInputAttribute(id_attr).getType() == myDataset.NOMINAL) return (true);
else return (false);
}
else {
if (Attributes.getOutputAttribute(id_attr - this.nInputs).getType() == myDataset.NOMINAL) return (true);
else return (false);
}
}
/**
* It returns the nominal value "id_val" within the attribute "id_attr"
* @param id_attr int Id of the attribute
* @param id_val int Id of the nominal value within the attribute
* @return String the nominal value
*/
public String getNominalValue(int id_attr, int id_val) {
if (id_attr < this.nInputs) return ( Attributes.getInputAttribute(id_attr).getNominalValue(id_val) );
else return ( Attributes.getOutputAttribute(id_attr - this.nInputs).getNominalValue(id_val) );
}
private double getMaxValue(int id_attr) {
if (id_attr < this.nInputs) return ( Attributes.getInputAttribute(id_attr).getMaxAttribute() );
else return ( Attributes.getOutputAttribute(id_attr - this.nInputs).getMaxAttribute() );
}
private double getMinValue(int id_attr) {
if (id_attr < this.nInputs) return ( Attributes.getInputAttribute(id_attr).getMinAttribute() );
else return ( Attributes.getOutputAttribute(id_attr - this.nInputs).getMinAttribute() );
}
private int getNumNominalValues(int id_attr) {
if (id_attr < this.nInputs) return ( Attributes.getInputAttribute(id_attr).getNumNominalValues() );
else return ( Attributes.getOutputAttribute(id_attr - this.nInputs).getNumNominalValues() );
}
}