/**
* 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 2 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, write to the Free Software
* Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA.
*/
package org.cspoker.ai.bots.bot.gametree.mcts.strategies.backpropagation;
import org.cspoker.ai.bots.bot.gametree.mcts.nodes.DecisionNode;
import org.cspoker.ai.bots.bot.gametree.mcts.nodes.INode;
import org.cspoker.ai.bots.bot.gametree.mcts.nodes.OpponentNode;
import org.cspoker.ai.bots.util.Gaussian;
import com.google.common.collect.ImmutableList;
public abstract class MaxDistributionPlusBackPropStrategy implements BackPropagationStrategy{
private static final Gaussian startGaussian = new Gaussian(0, 0);
private MaxDistributionPlusBackPropStrategy() {
}
public static class Factory implements BackPropagationStrategy.Factory{
@Override
public DecisionStrategy createForDecisionNode(DecisionNode node) {
return new DecisionStrategy(node);
}
@Override
public OpponentStrategy createForOpponentNode(OpponentNode node) {
return new OpponentStrategy(node);
}
}
private static class OpponentStrategy extends MaxDistributionPlusBackPropStrategy{
private final OpponentNode node;
private int nbSamples = 0;
private Gaussian EVGaussian = startGaussian;
public OpponentStrategy(OpponentNode node) {
this.node = node;
}
@Override
public double getEV() {
return EVGaussian.mean;
}
@Override
public int getNbSamples() {
return nbSamples;
}
@Override
public double getStdDev() {
return Math.sqrt(getVariance());
}
@Override
public double getVariance() {
throw new UnsupportedOperationException();
}
@Override
public double getEVStdDev() {
return EVGaussian.getStdDev();
}
@Override
public double getEVVar() {
return EVGaussian.variance;
}
@Override
public int getNbSamplesInMean() {
throw new UnsupportedOperationException();
}
@Override
public void onBackPropagate(double value) {
++this.nbSamples;
ImmutableList<INode> children = node.getChildren();
double[] probabilities = node.getProbabilities();
double EV = 0;
double EVVar = 0;
double totalWeight = 0;
for (int i = 0; i < probabilities.length; i++) {
INode child = children.get(i);
double childWeight = probabilities[i];
if (childWeight>0) {
double childEV = child.getEV();
EV += childWeight * childEV;
totalWeight += childWeight;
double childVariance = child.getEVVar();
EVVar += childWeight * (childVariance );//+ childEV * childEV);
}
}
EV /= totalWeight;
EVVar /= totalWeight;
//EVVar -= EV*EV;
if(EVVar<0){
if(EVVar<-0.001){
throw new IllegalStateException("Rounding error is too big.");
}
EVVar = 0;
}
this.EVGaussian = new Gaussian(EV,EVVar);
}
}
private static class DecisionStrategy extends MaxDistributionPlusBackPropStrategy{
private final DecisionNode node;
private int nbSamples = 0;
private Gaussian EVGaussian = startGaussian;
public DecisionStrategy(DecisionNode node) {
this.node = node;
}
@Override
public double getEV() {
return EVGaussian.mean;
}
@Override
public int getNbSamples() {
return nbSamples;
}
@Override
public double getStdDev() {
throw new UnsupportedOperationException();
}
@Override
public double getVariance() {
throw new UnsupportedOperationException();
}
@Override
public double getEVStdDev() {
return EVGaussian.getStdDev();
}
@Override
public double getEVVar() {
return EVGaussian.variance;
}
@Override
public int getNbSamplesInMean() {
throw new UnsupportedOperationException();
}
@Override
public void onBackPropagate(double value) {
++this.nbSamples;
ImmutableList<INode> children = node.getChildren();
Gaussian[] gaussians = new Gaussian[children.size()];
for (int i = 0; i < children.size(); i++) {
INode child = children.get(i);
gaussians[i] = new Gaussian(child.getEV(),child.getEVVar());
}
EVGaussian = Gaussian.maxOf(gaussians);
}
}
}