Incremental update. Removing some experimental classes after commit.

data/gogame.cfg

@@ -1,10 +1,10 @@
-PlayerOne=RANDOM
+PlayerOne=ROOT_PAR
 PlayerTwo=RANDOM
 GUIDelay=1000 	//1 second
 BoardSize=9
 Komi=6.5
-NumGames=1000 	//Games for each color per player 
+NumGames=1 	//Games for each color per player 
-TurnTime=1000 	//seconds per player per turn
+TurnTime=2000 	//seconds per player per turn
-SpectatorBoardShown=false;
+SpectatorBoardShown=true
-WhiteMoveLogged=false;
+WhiteMoveLogged=false
-BlackMoveLogged=false;
+BlackMoveLogged=false

src/net/woodyfolsom/msproj/StandAloneGame.java

@@ -29,7 +29,7 @@ public class StandAloneGame {
 		HUMAN, HUMAN_GUI, ROOT_PAR, UCT, RANDOM, RAVE
 	};
 
-	public static void main(String[] args) {
+	public static void main(String[] args) throws IOException {
 		try {
 			GameSettings gameSettings = GameSettings
 					.createGameSetings("data/gogame.cfg");
@@ -42,6 +42,8 @@ public class StandAloneGame {
 					gameSettings.getNumGames(), gameSettings.getTurnTime(),
 					gameSettings.isSpectatorBoardShown(),
 					gameSettings.isBlackMoveLogged(), gameSettings.isWhiteMoveLogged());
+			System.out.println("Press <Enter> or CTRL-C to exit");
+			System.in.read(new byte[80]);
 		} catch (IOException ioe) {
 			ioe.printStackTrace();
 			System.exit(EXIT_IO_EXCEPTION);

src/net/woodyfolsom/msproj/ann/DoublePair.java

@@ -3,12 +3,7 @@ package net.woodyfolsom.msproj.ann;
 import org.encog.ml.data.basic.BasicMLData;
 
 public class DoublePair extends BasicMLData {
-	// private final double x;
-	// private final double y;
 
-	/**
-	 * 
-	 */
 	private static final long serialVersionUID = 1L;
 
 	public DoublePair(double x, double y) {

src/net/woodyfolsom/msproj/ann/FusekiLearner.java

@@ -1,5 +0,0 @@
-package net.woodyfolsom.msproj.ann;
-
-public class FusekiLearner {
-
-}

src/net/woodyfolsom/msproj/ann/GradientWorker.java

@@ -1,4 +1,5 @@
 package net.woodyfolsom.msproj.ann;
+
 /*
  * Class copied verbatim from Encog framework due to dependency on Propagation
  * implementation.
@@ -6,7 +7,7 @@ package net.woodyfolsom.msproj.ann;
  * Encog(tm) Core v3.2 - Java Version
  * http://www.heatonresearch.com/encog/
  * http://code.google.com/p/encog-java/
- 
+
  * Copyright 2008-2012 Heaton Research, Inc.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
@@ -26,12 +27,12 @@ package net.woodyfolsom.msproj.ann;
  * http://www.heatonresearch.com/copyright
  */
 
+import java.util.ArrayList;
 import java.util.List;
 import java.util.Set;
 
 import org.encog.engine.network.activation.ActivationFunction;
 import org.encog.ml.data.MLDataPair;
-import org.encog.ml.data.MLDataSet;
 import org.encog.ml.data.basic.BasicMLDataPair;
 import org.encog.neural.error.ErrorFunction;
 import org.encog.neural.flat.FlatNetwork;
@@ -42,7 +43,7 @@ public class GradientWorker implements EngineTask {
 
 	private final FlatNetwork network;
 	private final ErrorCalculation errorCalculation = new ErrorCalculation();
-	private final double[] actual;
+	private final List<double[]> actuals;
 	private final double[] layerDelta;
 	private final int[] layerCounts;
 	private final int[] layerFeedCounts;
@@ -52,30 +53,29 @@ public class GradientWorker implements EngineTask {
 	private final double[] layerSums;
 	private final double[] gradients;
 	private final double[] weights;
-	private final MLDataPair pair;
+	private final MLDataPair pairPrototype;
 	private final Set<List<MLDataPair>> training;
-	private final int low;
+	//private final int low;
-	private final int high;
+	//private final int high;
 	private final TemporalDifferenceLearning owner;
 	private double[] flatSpot;
 	private final ErrorFunction errorFunction;
-	
+
 	public GradientWorker(final FlatNetwork theNetwork,
 			final TemporalDifferenceLearning theOwner,
-			final Set<List<MLDataPair>> theTraining, final int theLow, 
+			final Set<List<MLDataPair>> theTraining, final int theLow,
-			final int theHigh, final double[] flatSpot, 
+			final int theHigh, final double[] flatSpot, ErrorFunction ef) {
-			ErrorFunction ef) {
 		this.network = theNetwork;
 		this.training = theTraining;
-		this.low = theLow;
+		//this.low = theLow;
-		this.high = theHigh;
+		//this.high = theHigh;
 		this.owner = theOwner;
 		this.flatSpot = flatSpot;
 		this.errorFunction = ef;
 
 		this.layerDelta = new double[network.getLayerOutput().length];
 		this.gradients = new double[network.getWeights().length];
-		this.actual = new double[network.getOutputCount()];
+		this.actuals = new ArrayList<double[]>();
 
 		this.weights = network.getWeights();
 		this.layerIndex = network.getLayerIndex();
@@ -85,8 +85,8 @@ public class GradientWorker implements EngineTask {
 		this.layerSums = network.getLayerSums();
 		this.layerFeedCounts = network.getLayerFeedCounts();
 
-		this.pair = BasicMLDataPair.createPair(network.getInputCount(), network
+		this.pairPrototype = BasicMLDataPair.createPair(
-				.getOutputCount());
+				network.getInputCount(), network.getOutputCount());
 	}
 
 	public FlatNetwork getNetwork() {
@@ -97,22 +97,48 @@ public class GradientWorker implements EngineTask {
 		return this.weights;
 	}
 
-	private void process(final double[] input, final double[] ideal, double s) {
+	private void process(List<MLDataPair> trainingSequence) {
-		this.network.compute(input, this.actual);
+		actuals.clear();
 
-		this.errorCalculation.updateError(this.actual, ideal, s);
+		for (int trainingIdx = 0; trainingIdx < trainingSequence.size(); trainingIdx++) {
-		this.errorFunction.calculateError(ideal, actual, this.layerDelta);
+			MLDataPair mlDataPair = trainingSequence.get(trainingIdx);
+			MLDataPair dataPairCopy = this.pairPrototype;
+			dataPairCopy.setInputArray(mlDataPair.getInputArray());
+			if (dataPairCopy.getIdealArray() != null) {
+				dataPairCopy.setIdealArray(mlDataPair.getIdealArray());
+			}
 
-		for (int i = 0; i < this.actual.length; i++) {
+			double[] input = dataPairCopy.getInputArray();
+			double[] ideal = dataPairCopy.getIdealArray();
+			double significance = dataPairCopy.getSignificance();
 
-			this.layerDelta[i] = ((this.network.getActivationFunctions()[0]
+			actuals.add(trainingIdx, new double[ideal.length]);
-					.derivativeFunction(this.layerSums[i],this.layerOutput[i]) + this.flatSpot[0]))
+			this.network.compute(input, actuals.get(trainingIdx));
-					* (this.layerDelta[i] * s);
+
-		}
+			// For now, only calculate deltas for the final data pair
+			// For final TDL algorithm, deltas won't be used at all, instead the
+			// List of Actual vectors will.
+			if (trainingIdx < trainingSequence.size() - 1) {
+				continue;
+			}
+
+			this.errorCalculation.updateError(actuals.get(trainingIdx), ideal,
+					significance);
+			this.errorFunction.calculateError(ideal, actuals.get(trainingIdx),
+					this.layerDelta);
+
+			for (int i = 0; i < actuals.get(trainingIdx).length; i++) {
+				this.layerDelta[i] = ((this.network.getActivationFunctions()[0]
+						.derivativeFunction(this.layerSums[i],
+								this.layerOutput[i]) + this.flatSpot[0]))
+						* (this.layerDelta[i] * significance);
+			}
+
+			for (int i = this.network.getBeginTraining(); i < this.network
+					.getEndTraining(); i++) {
+				processLevel(i);
+			}
 
-		for (int i = this.network.getBeginTraining(); i < this.network
-				.getEndTraining(); i++) {
-			processLevel(i);
 		}
 	}
 
@@ -142,7 +168,8 @@ public class GradientWorker implements EngineTask {
 			}
 
 			this.layerDelta[yi] = sum
-					* (activation.derivativeFunction(this.layerSums[yi],this.layerOutput[yi])+currentFlatSpot);
+					* (activation.derivativeFunction(this.layerSums[yi],
+							this.layerOutput[yi]) + currentFlatSpot);
 			yi++;
 		}
 	}
@@ -150,17 +177,11 @@ public class GradientWorker implements EngineTask {
 	public final void run() {
 		try {
 			this.errorCalculation.reset();
-			//for (int i = this.low; i <= this.high; i++) {
+
 			for (List<MLDataPair> trainingSequence : training) {
-				MLDataPair mldp = trainingSequence.get(trainingSequence.size()-1);
+				process(trainingSequence);
-				this.pair.setInputArray(mldp.getInputArray());
-				if (this.pair.getIdealArray() != null) {
-					this.pair.setIdealArray(mldp.getIdealArray());
-				}
-				//this.training.getRecord(i, this.pair);
-				process(this.pair.getInputArray(), this.pair.getIdealArray(),pair.getSignificance());
 			}
-			//}
+
 			final double error = this.errorCalculation.calculate();
 			this.owner.report(this.gradients, error, null);
 			EngineArray.fill(this.gradients, 0);

src/net/woodyfolsom/msproj/ann/JosekiLearner.java

@@ -1,5 +0,0 @@
-package net.woodyfolsom.msproj.ann;
-
-public class JosekiLearner {
-
-}

src/net/woodyfolsom/msproj/ann/ShapeLearner.java

@@ -1,5 +0,0 @@
-package net.woodyfolsom.msproj.ann;
-
-public class ShapeLearner {
-
-}

src/net/woodyfolsom/msproj/ann/TemporalDifferenceLearning.java

@@ -73,7 +73,7 @@ public class TemporalDifferenceLearning implements MLTrain, Momentum,
 
 	// BasicTraining
 	private final List<Strategy> strategies = new ArrayList<Strategy>();
-	private Set<List<MLDataPair>> training;
+	//private Set<List<MLDataPair>> training;
 	private double error;
 	private int iteration;
 	private TrainingImplementationType implementationType;
@@ -104,7 +104,7 @@ public class TemporalDifferenceLearning implements MLTrain, Momentum,
 		initBasicTraining(TrainingImplementationType.Iterative);
 		this.network = network;
 		this.currentFlatNetwork = network.getFlat();
-		setTraining(training);
+		//setTraining(training);
 
 		this.gradients = new double[this.currentFlatNetwork.getWeights().length];
 		this.lastGradient = new double[this.currentFlatNetwork.getWeights().length];
@@ -181,8 +181,10 @@ public class TemporalDifferenceLearning implements MLTrain, Momentum,
 
 	public double updateWeight(final double[] gradients,
 			final double[] lastGradient, final int index) {
+		
 		final double delta = (gradients[index] * this.learningRate)
 				+ (this.lastDelta[index] * this.momentum);
+		
 		this.lastDelta[index] = delta;
 
 		System.out.println("Updating weights for connection: " + index
@@ -474,7 +476,8 @@ public class TemporalDifferenceLearning implements MLTrain, Momentum,
 	}
 
 	public void setTraining(final Set<List<MLDataPair>> training) {
-		this.training = training;
+		//this.training = training;
+		throw new UnsupportedOperationException();
 	}
 
 	public TrainingImplementationType getImplementationType() {

src/net/woodyfolsom/msproj/ann/WinFilter.java

@@ -61,9 +61,13 @@ public class WinFilter extends AbstractNeuralNetFilter implements
 
 	@Override
 	public void learn(MLDataSet trainingData) {
-		throw new UnsupportedOperationException("This filter learns a Set<List<MLData>>, not an MLDataSet");
+		throw new UnsupportedOperationException("This filter learns a Set<List<MLDataPair>>, not an MLDataSet");
 	}
 	
+	/**
+	 * Method is necessary because with temporal difference learning, some of the MLDataPairs are related by being a sequence
+	 * of moves within a particular game.
+	 */
 	@Override
 	public void learn(Set<List<MLDataPair>> trainingSet) {