/*******************************************************************************
* Copyright (c) 2011 Michael Ruflin, Andr� Locher, Claudia von Bastian.
*
* This file is part of Tatool.
*
* Tatool is free software: you can redistribute it and/or modify
* it under the terms of the GNU Lesser General Public License as published
* by the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* Tatool 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 Lesser General Public License for more details.
*
* You should have received a copy of the GNU Lesser General Public License
* along with Tatool. If not, see <http://www.gnu.org/licenses/>.
******************************************************************************/
package ch.tatool.core.element.handler.score;
import java.util.HashMap;
import java.util.List;
import ch.tatool.core.data.DoubleProperty;
import ch.tatool.core.data.IntegerProperty;
import ch.tatool.core.data.Misc;
import ch.tatool.core.data.Points;
import ch.tatool.core.element.CompoundElement;
import ch.tatool.core.element.CompoundSelector;
import ch.tatool.core.element.handler.score.AbstractPointsAndLevelHandler;
import ch.tatool.data.Module;
import ch.tatool.data.Trial;
import ch.tatool.element.Executable;
import ch.tatool.element.Node;
import ch.tatool.exec.ExecutionContext;
import ch.tatool.exec.ExecutionOutcome;
/**
* Default implementation of a score and level handler. Checks for the
* performance (% correct) after n (sampleSize) trials and decides on level up or down depending on the
* maxThreshold and minThreshold parameters.
*
* if performance is >= maxThreshold then do a level-up
* if performance is <= minThreshold then do a level-down
*
* @author Andre Locher
*/
public class DefaultPointsAndLevelHandler extends AbstractPointsAndLevelHandler {
private int sampleSize = 3;
private double maxThreshold = 80;
private double minThreshold = 60;
private double performance = 0;
public static final String PROPERTY_LEVEL_COUNTER = "levelCounter";
public static final String PROPERTY_LEVEL_PERFORMANCE = "levelPerformance";
public static final String PROPERTY_LEVEL_TOTALPOINTS = "levelTotalPoints";
public static final String PROPERTY_LEVEL_MAXPOINTS = "levelMaxPoints";
private static IntegerProperty trialCounterProperty = new IntegerProperty(
PROPERTY_LEVEL_COUNTER);
private static DoubleProperty performanceProperty = new DoubleProperty(
PROPERTY_LEVEL_PERFORMANCE);
private double totalScore = 0;
private double maxScore = 0;
private int trialCounter;
// statistics data
private HashMap<Integer, Integer> performanceData;
private HashMap<Integer, Integer> levelData;
public DefaultPointsAndLevelHandler() {
super("level-handler");
}
/**
* Initializes the algorithm with the values of the DB at session start
*/
protected void initializeHandler(ExecutionContext context) {
totalScore = 0;
maxScore = 0;
// session data
performanceData = new HashMap<Integer, Integer>();
levelData = new HashMap<Integer, Integer>();
trialCounter = 0;
}
/**
* Initializes the algorithm with the values of the DB.
*/
public void initializeAlgorithm(ExecutionContext event) {
Module module = event.getExecutionData().getModule();
// get the counters from the module
trialCounter = trialCounterProperty.getValue(module, this, 0);
}
@Override
protected int checkLevelChange(ExecutionContext context, int currentLevel) {
List<Trial> trials = context.getExecutionData().getTrials();
int oldLevel = currentLevel;
int newLevel = oldLevel;
Executable executable = context.getActiveExecutable();
// loop through all trials
for (int i = 0; i < trials.size(); i++) {
Trial trial = trials.get(i);
totalScore += Points.getPointsProperty().getValue(trial, trial.getParentId(), 0);
maxScore += Points.getMaxPointsProperty().getValue(trial, trial.getParentId(), 0);
if (trials.isEmpty()) return currentLevel;
String trialOutcome = Misc.getOutcomeProperty().getValue(trial, executable);
// only do calculation if trial is finished and compound element is done
if (trialOutcome != null
&& trialOutcome.equals(ExecutionOutcome.FINISHED)
&& isCompoundDone(context)) {
initializeAlgorithm(context);
if (trialCounter >= (sampleSize - 1)) {
performance = (totalScore / maxScore) * 100;
// level up counter
if (performance >= maxThreshold) {
newLevel = changeLevel(context, oldLevel, 1);
// level down counter
} else if (performance <= minThreshold && oldLevel > 1) {
newLevel = changeLevel(context, oldLevel, -1);
}
// save data to trial
performanceProperty.setValue(trial, this, performance);
performanceData.put(trialCounter, (int) performance);
levelData.put(trialCounter, newLevel);
totalScore = 0;
maxScore = 0;
performance = 0;
trialCounter = 0;
trialCounterProperty.setValue(trial, this, trialCounter);
trialCounterProperty.setValue(context.getExecutionData()
.getModule(), this, trialCounter);
} else {
trialCounter++;
}
trialCounterProperty.setValue(trial, this, trialCounter);
trialCounterProperty.setValue(context.getExecutionData()
.getModule(), this, trialCounter);
}
}
return newLevel;
}
/**
* Changes level and adapts all parameters of the adaptive algorithm.
*
* @return the new level
*/
private int changeLevel(ExecutionContext event, int oldLevel, int addition) {
int newLevel;
newLevel = oldLevel + addition;
return newLevel;
}
/**
* Checks whether the current trial is complete. The algorithm only gets
* triggered if a compound element is finished
*
* @return whether the trial is complete
*/
private boolean isCompoundDone(ExecutionContext context) {
Node currElement = context.getActiveElement();
boolean isDone = true;
while (this.getParent() != null) {
// CompoundElement
if (currElement instanceof CompoundElement) {
CompoundElement comp = (CompoundElement) currElement;
for (Object handler : comp.getHandlers()) {
if (handler instanceof CompoundSelector) {
CompoundSelector selector = (CompoundSelector) handler;
isDone = selector.isDone();
}
}
}
if (this.getParent().getId().equals(currElement.getId())) {
return isDone;
}
if (currElement.getParent() != null) {
currElement = currElement.getParent();
} else {
return true;
}
}
return isDone;
}
public HashMap<Integer, Integer> getPerformanceData() {
return performanceData;
}
public HashMap<Integer, Integer> getLevelData() {
return levelData;
}
public double getMaxThreshold() {
return maxThreshold;
}
public void setMaxThreshold(double maxThreshold) {
this.maxThreshold = maxThreshold;
}
public double getMinThreshold() {
return minThreshold;
}
public void setMinThreshold(double minThreshold) {
this.minThreshold = minThreshold;
}
public int getSampleSize() {
return sampleSize;
}
public void setSampleSize(int sampleSize) {
this.sampleSize = sampleSize;
}
}