/***********************************************************************
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/
**********************************************************************/
/**
* <p>
* @author Written by Albert Orriols (La Salle, Ram�n Llull University - Barcelona) 28/03/2004
* @author Modified by Xavi Sol� (La Salle, Ram�n Llull University - Barcelona) 03/12/2008
* @version 1.1
* @since JDK1.2
* </p>
*/
package keel.Algorithms.Genetic_Rule_Learning.XCS;
import keel.Algorithms.Genetic_Rule_Learning.XCS.KeelParser.Config;
import java.lang.*;
import java.io.*;
import java.util.*;
public class RMPEnvironment extends SSEnvironment {
/**
* <p>
* RMPEnvironment. It's the environment for Real Multiplexer Environment
* </p>
*/
///////////////////////////////////////
// attributes
/**
* <p>
* Represents the number of actions that can take the condition.
* </p>
*
*/
private int numActions;
/**
* <p>
* Represents the length of the condition.
* </p>
*
*/
private int condLength;
/**
* <p>
* Indicates if the classification has been correct.
* </p>
*
*/
private boolean isCorrect;
/**
* <p>
* Represents the maximum payoff that a classifier can get.
* </p>
*
*/
private double maxPayOff;
/**
* <p>
* Represents the minimum payoff that a classifier can get.
* </p>
*
*/
private double minPayOff;
/**
* <p>
* Represents the current state of the environment.
* </p>
*
*/
private double[] currentState;
/**
* <p>
* The number of bits that indicate the position.
* </p>
*
*/
private int positionBits;
/**
* <p>
* It indicates if the classification has been performed. In mux problem it's true
* in every step.
*
*/
private boolean classExecuted;
///////////////////////////////////////
// operations
/**
* <p>
* It's the constructor of the class. It initializes the environment.
* </p>
* <p>
*/
public RMPEnvironment() {
//super();
condLength = Config.clLength;
numActions = 2;
double i = 0;
for(i=1.0; (int) i+Math.pow(2.,i)<=condLength; i++);//calculates the position bits in this problem
positionBits=(int)(i-1);
classExecuted = false;
isCorrect = false;
currentState = new double[condLength];
maxPayOff = 1000;
minPayOff = 0;
System.out.println ("The bits number of the position is: "+positionBits);
} // end MPEnvironment
/**
* <p>
* Determines if the classification was good
* </p>
* <p>
*
* @return a boolean that indicates if the last classification was good.
* </p>
*/
public boolean wasCorrect() {
return isCorrect;
} // end wasCorrect
/**
* <p>
* This function returns the reward given when applying the action to the
* environment.
* </p>
* <p>
*
* @return a double with the reward
* </p>
* <p>
* @param action is the action chosen to do.
* </p>
*/
public double makeAction(int action) {
int position = 0;
double value;
for (int i=0; i<positionBits; i++){
if (currentState[positionBits-i-1] >= 0.5)
position += (int)Math.pow(2.0, positionBits -1 -i);
}
int act = 0;
if (currentState[position] >= 0.5) act = 1;
if ( act == action){
isCorrect = true;
classExecuted = true;
return maxPayOff;
}
isCorrect = false;
classExecuted = true;
return minPayOff;
} // end makeAction
/**
* <p>
* The function returns the current state.
* </p>
* <p>
*
* @return a float[] with the current state.
* </p>
*/
public double[] getCurrentState() {
return currentState;
} // end getCurrentSate
/**
* <p>
* Creates a new state of the problem. XCS has to decide the
* action to do.
* </p>
* <p>
*
* @return a float[] with the new situation.
* </p>
*/
public double[] newState() {
for (int i=0; i<condLength; i++){
currentState[i] = Config.rand();;
}
classExecuted = false;
return currentState;
} // end newState
/**
* <p>
* Returns the class of the environmental state. It is
* used by UCS (supervised learning).
* </p>
* <p>
* @return an integer with the class associated with the current environmental
* state.
* </p>
*/
public int getEnvironmentClass(){
int position = 0;
double value;
for (int i=0; i<positionBits; i++){
if (currentState[positionBits-i-1] >= 0.5)
position += (int)Math.pow(2.0, positionBits -1 -i);
}
int act = 0;
if (currentState[position] >= 0.5) act = 1;
return act;
} //end getClass
} // end RMPEnvironment