Implementing temporal difference learning based heavily on Encog framework.

Not functional yet - incremental update.

.classpath

@@ -7,7 +7,6 @@
 	<classpathentry kind="lib" path="lib/log4j-1.2.16.jar"/>
 	<classpathentry kind="lib" path="lib/kgsGtp.jar"/>
 	<classpathentry kind="lib" path="lib/antlrworks-1.4.3.jar"/>
-	<classpathentry kind="lib" path="lib/neuroph-2.6.jar"/>
-	<classpathentry kind="lib" path="lib/encog-engine-2.5.0.jar"/>
+	<classpathentry kind="lib" path="lib/encog-java-core.jar" sourcepath="lib/encog-java-core-sources.jar"/>
 	<classpathentry kind="output" path="bin"/>
 </classpath>

build.xml

@@ -23,7 +23,7 @@
 
 	<target name="compile" depends="init" description="compile the source ">
 		<!-- Compile the java code from ${src} into ${build} -->
-		<javac srcdir="${src}" destdir="${build}" classpathref="build.classpath" debug="true" source="1.6" target="1.6"/>
+		<javac includeantruntime="false" srcdir="${src}" destdir="${build}" classpathref="build.classpath" debug="true"/>
 	</target>
 
 	<target name="compile-test" depends="compile">
@@ -33,9 +33,25 @@
 	</target>
 
 	<target name="copy-resources">
-		<copy todir="${dist}">
+		<copy todir="${dist}/data">
 			<fileset dir="data" />
 		</copy>
+		<copy todir="${build}/net/woodyfolsom/msproj/gui">
+			<fileset dir="${src}/net/woodyfolsom/msproj/gui">
+				<exclude name="**/*.java"/>
+			</fileset>
+		</copy>
+		<copy todir="${build}/net/woodyfolsom/msproj/sfx">
+			<fileset dir="${src}/net/woodyfolsom/msproj/sfx">
+				<exclude name="**/*.java"/>
+			</fileset>
+		</copy>
+	</target>
+	
+	<target name="copy-libs">
+		<copy todir="${dist}/lib">
+			<fileset dir="lib" />
+		</copy>
 	</target>
 
 	<target name="clean" description="clean up">
@@ -44,12 +60,12 @@
 		<delete dir="${dist}" />
 	</target>
 
-	<target name="dist" depends="compile,copy-resources" description="generate the distribution">
+	<target name="dist" depends="compile,copy-resources,copy-libs" description="generate the distribution">
 		<jar jarfile="${dist}/GoGame.jar">
 			<fileset dir="${build}" excludes="**/*Test.class" />
 			<manifest>
-				<attribute name="Main-Class" value="net.woodyfolsom.msproj.GoGame" />
-				<attribute name="Class-Path" value="kgsGtp.jar log4j-1.2.16.jar"/>
+				<attribute name="Main-Class" value="net.woodyfolsom.msproj.StandAloneGame" />
+				<attribute name="Class-Path" value="lib/kgsGtp.jar lib/log4j-1.2.16.jar lib/antlrworks-1.4.3.jar lib/encog-engine-2.5.0.jar lib/neuroph-2.6.jar"/>
 			</manifest>
 		</jar>
 	</target>

data/gogame.cfg

@@ -1,7 +1,10 @@
-PlayerOne=ROOT_PAR
+PlayerOne=RANDOM
 PlayerTwo=RANDOM
-GUIDelay=2000 	//1 second
+GUIDelay=1000 	//1 second
 BoardSize=9
 Komi=6.5
-NumGames=10 	//Games for each player 
-TurnTime=2000 	//seconds per player per turn
+NumGames=1000 	//Games for each color per player 
+TurnTime=1000 	//seconds per player per turn
+SpectatorBoardShown=false;
+WhiteMoveLogged=false;
+BlackMoveLogged=false;

src/net/woodyfolsom/msproj/GameRecord.java

@@ -23,6 +23,16 @@ public class GameRecord {
 		moves.add(Action.NONE);
 	}
 	
+	public GameRecord(GameRecord that) {
+		for(GameState gameState : that.gameStates) {
+			gameStates.add(new GameState(gameState));
+		}
+		//initial 'move' of Action.NONE allows for a game that starts with a board setup
+		for (Action action : that.moves) {
+			moves.add(action);
+		}
+	}
+	
 	/**
 	 * Adds a comment for the current turn.
 	 * @param comment

src/net/woodyfolsom/msproj/GameSettings.java

@@ -13,6 +13,9 @@ public class GameSettings {
 	private int boardSize = 9;
 	private double komi = 6.5;
 	private int numGames = 10;
+	private boolean spectatorBoardShown = false;
+	private boolean whiteMoveLogged = true;
+	private boolean blackMoveLogged = true;
 	
 	private GameSettings() {	
 	}
@@ -49,6 +52,12 @@ public class GameSettings {
 				gameSettings.setNumGames(Integer.parseInt(value));
 			} else if ("Komi".equals(name)) {
 				gameSettings.setKomi(Double.parseDouble(value));
+			} else if ("SpectatorBoardShown".equals(name)) {
+				gameSettings.setSpectatorBoardShown(Boolean.parseBoolean(value));
+			} else if ("WhiteMoveLogged".equals(name)) {
+				gameSettings.setWhiteMoveLogged(Boolean.parseBoolean(value));
+			} else if ("BlackMoveLogged".equals(name)) {
+				gameSettings.setBlackMoveLogged(Boolean.parseBoolean(value));
 			} else {
 				System.out.println("Ignoring game settings property with unrecognized name: " + name);
 			}
@@ -127,4 +136,29 @@ public class GameSettings {
 		sb.append(", GUIDelay=" + guiDelay);
 		return sb.toString();
 	}
+
+	public boolean isSpectatorBoardShown() {
+		return spectatorBoardShown;
+	}
+
+	private void setSpectatorBoardShown(boolean spectatorBoardShown) {
+		this.spectatorBoardShown = spectatorBoardShown;
+	}
+
+	public boolean isWhiteMoveLogged() {
+		return whiteMoveLogged;
+	}
+
+	private void setWhiteMoveLogged(boolean whiteMoveLogged) {
+		this.whiteMoveLogged = whiteMoveLogged;
+	}
+
+	public boolean isBlackMoveLogged() {
+		return blackMoveLogged;
+	}
+
+	private void setBlackMoveLogged(boolean blackMoveLogged) {
+		this.blackMoveLogged = blackMoveLogged;
+	}
+	
 }

src/net/woodyfolsom/msproj/Referee.java

@@ -4,8 +4,6 @@ import java.io.File;
 import java.io.FileOutputStream;
 import java.io.IOException;
 import java.io.InputStream;
-import java.text.DateFormat;
-import java.text.SimpleDateFormat;
 
 import net.woodyfolsom.msproj.gui.Goban;
 import net.woodyfolsom.msproj.policy.HumanGuiInput;
@@ -65,10 +63,11 @@ public class Referee {
 		return gameRecord;
 	}
 
-	public GameResult play(GameConfig gameConfig, int gameNo) {
+	public GameResult play(GameConfig gameConfig, int gameNo,
+			boolean showSpectatorBoard, boolean logGameRecord) {
 		GameRecord gameRecord = new GameRecord(gameConfig);
 
-		System.out.println("Game started.");
+		//System.out.println("Game started.");
 
 		GameState initialGameState = gameRecord.getGameState(gameRecord
 				.getNumTurns());
@@ -78,20 +77,21 @@ public class Referee {
 		whitePolicy.setState(initialGameState);
 
 		Goban spectatorBoard;
-		if (blackPolicy instanceof HumanGuiInput
-				|| whitePolicy instanceof HumanGuiInput) {
+		if (blackPolicy instanceof HumanGuiInput || whitePolicy instanceof HumanGuiInput) {
 			System.out.println("Human is controlling the game board GUI.");
 			spectatorBoard = null;
-		} else {
+		} else if (showSpectatorBoard){
 			System.out.println("Starting game board GUI in spectator mode.");
-			spectatorBoard = new Goban(gameConfig, null);
+			spectatorBoard = new Goban(gameConfig, null, "Game #" + gameNo);
+		} else { // else showing spectator board is disabled
+			spectatorBoard = null;
 		}
-
+		
 		try {
 			while (!gameRecord.isFinished()) {
 				GameState gameState = gameRecord.getGameState(gameRecord
 						.getNumTurns());
-				//System.out.println(gameState);
+				// System.out.println(gameState);
 
 				Player playerToMove = gameRecord.getPlayerToMove();
 				Policy policy = getPolicy(playerToMove);
@@ -120,8 +120,9 @@ public class Referee {
 
 		System.out.println("Game over.  Result: " + result);
 
-		//DateFormat dateFormat = new SimpleDateFormat("yyMMddHHmmssZ");
+		// DateFormat dateFormat = new SimpleDateFormat("yyMMddHHmmssZ");
 
+		if (logGameRecord) {
 		try {
 
 			// File sgfFile = new File("gogame-" + dateFormat.format(new Date())
@@ -143,8 +144,9 @@ public class Referee {
 			System.out.println("Unable to save game file due to IOException: "
 					+ ioe.getMessage());
 		}
-
-		System.out.println("Game finished.");
+		}
+		
+		//System.out.println("Game finished.");
 
 		return result;
 	}

src/net/woodyfolsom/msproj/StandAloneGame.java

@@ -19,8 +19,9 @@ import net.woodyfolsom.msproj.policy.RandomMovePolicy;
 import net.woodyfolsom.msproj.policy.RootParallelization;
 
 public class StandAloneGame {
-	private static final int EXIT_NOMINAL = 0;
-	private static final int EXIT_IO_EXCEPTION = 1;
+	public static final int EXIT_USER_QUIT = 1;
+	public static final int EXIT_NOMINAL = 0;
+	public static final int EXIT_IO_EXCEPTION = -1;
 	
 	private int gameNo = 0;
 	
@@ -38,7 +39,9 @@ public class StandAloneGame {
 					parsePlayerType(gameSettings.getPlayerOne()),
 					parsePlayerType(gameSettings.getPlayerTwo()),
 					gameSettings.getBoardSize(), gameSettings.getKomi(),
-					gameSettings.getNumGames(), gameSettings.getTurnTime());
+					gameSettings.getNumGames(), gameSettings.getTurnTime(),
+					gameSettings.isSpectatorBoardShown(),
+					gameSettings.isBlackMoveLogged(), gameSettings.isWhiteMoveLogged());
 		} catch (IOException ioe) {
 			ioe.printStackTrace();
 			System.exit(EXIT_IO_EXCEPTION);
@@ -65,7 +68,8 @@ public class StandAloneGame {
 	}
 
 	public void playGame(PLAYER_TYPE playerType1, PLAYER_TYPE playerType2,
-			int size, double komi, int rounds, long turnLength) {
+			int size, double komi, int rounds, long turnLength, boolean showSpectatorBoard,
+			boolean blackMoveLogged, boolean whiteMoveLogged) {
 
 		long startTime = System.currentTimeMillis();
 
@@ -74,32 +78,31 @@ public class StandAloneGame {
 
 		Referee referee = new Referee();
 		referee.setPolicy(Player.BLACK,
-				getPolicy(playerType1, gameConfig, Player.BLACK, turnLength));
+				getPolicy(playerType1, gameConfig, Player.BLACK, turnLength, blackMoveLogged));
 		referee.setPolicy(Player.WHITE,
-				getPolicy(playerType2, gameConfig, Player.WHITE, turnLength));
+				getPolicy(playerType2, gameConfig, Player.WHITE, turnLength, whiteMoveLogged));
 
 		List<GameResult> round1results = new ArrayList<GameResult>();
 		
+		boolean logGameRecords = rounds <= 50;
 		for (int round = 0; round < rounds; round++) {
 			gameNo++;
-			round1results.add(referee.play(gameConfig, gameNo));
+			round1results.add(referee.play(gameConfig, gameNo, showSpectatorBoard, logGameRecords));
 		}
 
 		List<GameResult> round2results = new ArrayList<GameResult>();
 
 		referee.setPolicy(Player.BLACK,
-				getPolicy(playerType2, gameConfig, Player.BLACK, turnLength));
+				getPolicy(playerType2, gameConfig, Player.BLACK, turnLength, blackMoveLogged));
 		referee.setPolicy(Player.WHITE,
-				getPolicy(playerType1, gameConfig, Player.WHITE, turnLength));
+				getPolicy(playerType1, gameConfig, Player.WHITE, turnLength, whiteMoveLogged));
 		for (int round = 0; round < rounds; round++) {
 			gameNo++;
-			round2results.add(referee.play(gameConfig, gameNo));
+			round2results.add(referee.play(gameConfig, gameNo, showSpectatorBoard, logGameRecords));
 		}
 
 		long endTime = System.currentTimeMillis();
-
 		DateFormat dateFormat = new SimpleDateFormat("yyMMddHHmmss");
-
 		try {
 
 			File txtFile = new File("gotournament-"
@@ -107,14 +110,16 @@ public class StandAloneGame {
 			FileWriter writer = new FileWriter(txtFile);
 
 			try {
+				if (!logGameRecords) {
+					System.out.println("Each player is set to play more than 50 rounds as each color; omitting individual game .sgf log file output.");
+				}
+				
 				logResults(writer, round1results, playerType1.toString(),
-						playerType2.toString());
+							playerType2.toString());
 				logResults(writer, round2results, playerType2.toString(),
-						playerType1.toString());
-
+							playerType1.toString());
 				writer.write("Elapsed Time: " + (endTime - startTime) / 1000.0
 						+ " seconds.");
-
 				System.out.println("Game tournament saved as "
 						+ txtFile.getAbsolutePath());
 			} finally {
@@ -149,19 +154,19 @@ public class StandAloneGame {
 	}
 
 	private Policy getPolicy(PLAYER_TYPE playerType, GameConfig gameConfig,
-			Player player, long turnLength) {
+			Player player, long turnLength, boolean moveLogged) {
 		switch (playerType) {
 		case HUMAN:
 			return new HumanKeyboardInput();
 		case HUMAN_GUI:
-			return new HumanGuiInput(new Goban(gameConfig, player));
+			return new HumanGuiInput(new Goban(gameConfig, player,""));
 		case ROOT_PAR:
 			return new RootParallelization(4, turnLength);
 		case UCT:
 			return new MonteCarloUCT(new RandomMovePolicy(), turnLength);
 		case RANDOM:
 			RandomMovePolicy randomMovePolicy = new RandomMovePolicy();
-			randomMovePolicy.setLogging(true);
+			randomMovePolicy.setLogging(moveLogged);
 			return randomMovePolicy;
 		case RAVE:
 			return new MonteCarloAMAF(new RandomMovePolicy(), turnLength);

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

@@ -0,0 +1,54 @@
+package net.woodyfolsom.msproj.ann;
+
+import java.io.File;
+import java.io.FileInputStream;
+import java.io.FileOutputStream;
+import java.io.IOException;
+
+import org.encog.neural.networks.BasicNetwork;
+import org.encog.neural.networks.PersistBasicNetwork;
+
+public abstract class AbstractNeuralNetFilter implements NeuralNetFilter {
+	protected BasicNetwork neuralNetwork;
+	protected int actualTrainingEpochs = 0;
+	protected int maxTrainingEpochs = 1000;
+	
+	public int getActualTrainingEpochs() {
+		return actualTrainingEpochs;
+	}
+	
+	public int getMaxTrainingEpochs() {
+		return maxTrainingEpochs;
+	}
+	
+	@Override
+	public BasicNetwork getNeuralNetwork() {
+		return neuralNetwork;
+	}
+	
+	public void load(String filename) throws IOException {
+		FileInputStream fis = new FileInputStream(new File(filename));
+		neuralNetwork = (BasicNetwork) new PersistBasicNetwork().read(fis);
+		fis.close();
+	}
+	
+	@Override
+	public void reset() {
+		neuralNetwork.reset();
+	}
+	
+	@Override
+	public void reset(int seed) {
+		neuralNetwork.reset(seed);
+	}
+	
+	public void save(String filename) throws IOException {
+		FileOutputStream fos = new FileOutputStream(new File(filename));
+		new PersistBasicNetwork().save(fos, getNeuralNetwork());
+		fos.close();	
+	}
+	
+	public void setMaxTrainingEpochs(int max) {
+		this.maxTrainingEpochs = max;
+	}
+}

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

@@ -0,0 +1,17 @@
+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) {
+		super(new double[] { x, y });
+	}
+}

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

@@ -0,0 +1,95 @@
+package net.woodyfolsom.msproj.ann;
+
+import org.encog.mathutil.error.ErrorCalculationMode;
+
+/*
+ Initial erison of this class was a verbatim copy from Encog framework.
+ */
+
+public class ErrorCalculation {
+
+	private static ErrorCalculationMode mode = ErrorCalculationMode.MSE;
+
+	public static ErrorCalculationMode getMode() {
+		return ErrorCalculation.mode;
+	}
+
+	public static void setMode(final ErrorCalculationMode theMode) {
+		ErrorCalculation.mode = theMode;
+	}
+
+	private double globalError;
+
+	private int setSize;
+
+	public final double calculate() {
+		if (this.setSize == 0) {
+			return 0;
+		}
+
+		switch (ErrorCalculation.getMode()) {
+		case RMS:
+			return calculateRMS();
+		case MSE:
+			return calculateMSE();
+		case ESS:
+			return calculateESS();
+		default:
+			return calculateMSE();
+		}
+
+	}
+
+	public final double calculateMSE() {
+		if (this.setSize == 0) {
+			return 0;
+		}
+		final double err = this.globalError / this.setSize;
+		return err;
+
+	}
+
+	public final double calculateESS() {
+		if (this.setSize == 0) {
+			return 0;
+		}
+		final double err = this.globalError / 2;
+		return err;
+
+	}
+
+	public final double calculateRMS() {
+		if (this.setSize == 0) {
+			return 0;
+		}
+		final double err = Math.sqrt(this.globalError / this.setSize);
+		return err;
+	}
+
+	public final void reset() {
+		this.globalError = 0;
+		this.setSize = 0;
+	}
+
+	public final void updateError(final double actual, final double ideal) {
+
+		double delta = ideal - actual;
+
+		this.globalError += delta * delta;
+
+		this.setSize++;
+
+	}
+
+	public final void updateError(final double[] actual, final double[] ideal,
+			final double significance) {
+		for (int i = 0; i < actual.length; i++) {
+			double delta = (ideal[i] - actual[i]) * significance;
+
+			this.globalError += delta * delta;
+		}
+
+		this.setSize += ideal.length;
+	}
+
+}

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

@@ -0,0 +1,25 @@
+package net.woodyfolsom.msproj.ann;
+
+import net.woodyfolsom.msproj.GameState;
+
+import org.encog.ml.data.basic.BasicMLData;
+
+public class GameStateMLData extends BasicMLData {
+
+	/**
+	 * 
+	 */
+	private static final long serialVersionUID = 1L;
+
+	private GameState gameState;
+	
+	public GameStateMLData(double[] d, GameState gameState) {
+		super(d);
+		// TODO Auto-generated constructor stub
+		this.gameState = gameState;
+	}
+
+	public GameState getGameState() {
+		return gameState;
+	}
+}

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

@@ -0,0 +1,121 @@
+package net.woodyfolsom.msproj.ann;
+
+import net.woodyfolsom.msproj.GameResult;
+import net.woodyfolsom.msproj.GameState;
+import net.woodyfolsom.msproj.Player;
+
+import org.encog.ml.data.MLData;
+import org.encog.ml.data.MLDataPair;
+import org.encog.ml.data.basic.BasicMLData;
+import org.encog.ml.data.basic.BasicMLDataPair;
+import org.encog.util.kmeans.Centroid;
+
+public class GameStateMLDataPair implements MLDataPair {
+	//private final String[] inputs = { "BlackScore", "WhiteScore" };
+	//private final String[] outputs = { "BlackWins", "WhiteWins" };
+
+	private BasicMLDataPair mlDataPairDelegate;
+	private GameState gameState;
+
+	public GameStateMLDataPair(GameState gameState) {
+		this.gameState = gameState;
+		mlDataPairDelegate = new BasicMLDataPair(
+				new GameStateMLData(createInput(), gameState), new BasicMLData(createIdeal()));
+	}
+
+	public GameStateMLDataPair(GameStateMLDataPair that) {
+		this.gameState = new GameState(that.gameState);
+		mlDataPairDelegate = new BasicMLDataPair(
+				that.mlDataPairDelegate.getInput(),
+				that.mlDataPairDelegate.getIdeal());
+	}
+
+	@Override
+	public MLDataPair clone() {
+		return new GameStateMLDataPair(this);
+	}
+
+	@Override
+	public Centroid<MLDataPair> createCentroid() {
+		return mlDataPairDelegate.createCentroid();
+	}
+
+	/**
+	 * Creates a vector of normalized scores from GameState.
+	 * 
+	 * @return
+	 */
+	private double[] createInput() {
+
+		GameResult result = gameState.getResult();
+
+		double maxScore = gameState.getGameConfig().getSize()
+				* gameState.getGameConfig().getSize();
+
+		double whiteScore = Math.min(1.0, result.getWhiteScore() / maxScore);
+		double blackScore = Math.min(1.0, result.getBlackScore() / maxScore);
+
+		return new double[] { blackScore, whiteScore };
+	}
+
+	/**
+	 * Creates a vector of values indicating strength of black/white win output
+	 * from network.
+	 * 
+	 * @return
+	 */
+	private double[] createIdeal() {
+		GameResult result = gameState.getResult();
+
+		double blackWinner = result.isWinner(Player.BLACK) ? 1.0 : 0.0;
+		double whiteWinner = result.isWinner(Player.WHITE) ? 1.0 : 0.0;
+
+		return new double[] { blackWinner, whiteWinner };
+	}
+
+	@Override
+	public MLData getIdeal() {
+		return mlDataPairDelegate.getIdeal();
+	}
+
+	@Override
+	public double[] getIdealArray() {
+		return mlDataPairDelegate.getIdealArray();
+	}
+
+	@Override
+	public MLData getInput() {
+		return mlDataPairDelegate.getInput();
+	}
+
+	@Override
+	public double[] getInputArray() {
+		return mlDataPairDelegate.getInputArray();
+	}
+
+	@Override
+	public double getSignificance() {
+		return mlDataPairDelegate.getSignificance();
+	}
+
+	@Override
+	public boolean isSupervised() {
+		return mlDataPairDelegate.isSupervised();
+	}
+
+	@Override
+	public void setIdealArray(double[] arg0) {
+		mlDataPairDelegate.setIdealArray(arg0);
+	}
+
+	@Override
+	public void setInputArray(double[] arg0) {
+		mlDataPairDelegate.setInputArray(arg0);
+	}
+
+	@Override
+	public void setSignificance(double arg0) {
+		mlDataPairDelegate.setSignificance(arg0);
+	}
+
+}

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

@@ -0,0 +1,172 @@
+package net.woodyfolsom.msproj.ann;
+/*
+ * Class copied verbatim from Encog framework due to dependency on Propagation
+ * implementation.
+ * 
+ * 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");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ *   
+ * For more information on Heaton Research copyrights, licenses 
+ * and trademarks visit:
+ * http://www.heatonresearch.com/copyright
+ */
+
+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;
+import org.encog.util.EngineArray;
+import org.encog.util.concurrency.EngineTask;
+
+public class GradientWorker implements EngineTask {
+
+	private final FlatNetwork network;
+	private final ErrorCalculation errorCalculation = new ErrorCalculation();
+	private final double[] actual;
+	private final double[] layerDelta;
+	private final int[] layerCounts;
+	private final int[] layerFeedCounts;
+	private final int[] layerIndex;
+	private final int[] weightIndex;
+	private final double[] layerOutput;
+	private final double[] layerSums;
+	private final double[] gradients;
+	private final double[] weights;
+	private final MLDataPair pair;
+	private final Set<List<MLDataPair>> training;
+	private final int low;
+	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 int theHigh, final double[] flatSpot, 
+			ErrorFunction ef) {
+		this.network = theNetwork;
+		this.training = theTraining;
+		this.low = theLow;
+		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.weights = network.getWeights();
+		this.layerIndex = network.getLayerIndex();
+		this.layerCounts = network.getLayerCounts();
+		this.weightIndex = network.getWeightIndex();
+		this.layerOutput = network.getLayerOutput();
+		this.layerSums = network.getLayerSums();
+		this.layerFeedCounts = network.getLayerFeedCounts();
+
+		this.pair = BasicMLDataPair.createPair(network.getInputCount(), network
+				.getOutputCount());
+	}
+
+	public FlatNetwork getNetwork() {
+		return this.network;
+	}
+
+	public double[] getWeights() {
+		return this.weights;
+	}
+
+	private void process(final double[] input, final double[] ideal, double s) {
+		this.network.compute(input, this.actual);
+
+		this.errorCalculation.updateError(this.actual, ideal, s);
+		this.errorFunction.calculateError(ideal, actual, this.layerDelta);
+
+		for (int i = 0; i < this.actual.length; i++) {
+
+			this.layerDelta[i] = ((this.network.getActivationFunctions()[0]
+					.derivativeFunction(this.layerSums[i],this.layerOutput[i]) + this.flatSpot[0]))
+					* (this.layerDelta[i] * s);
+		}
+
+		for (int i = this.network.getBeginTraining(); i < this.network
+				.getEndTraining(); i++) {
+			processLevel(i);
+		}
+	}
+
+	private void processLevel(final int currentLevel) {
+		final int fromLayerIndex = this.layerIndex[currentLevel + 1];
+		final int toLayerIndex = this.layerIndex[currentLevel];
+		final int fromLayerSize = this.layerCounts[currentLevel + 1];
+		final int toLayerSize = this.layerFeedCounts[currentLevel];
+
+		final int index = this.weightIndex[currentLevel];
+		final ActivationFunction activation = this.network
+				.getActivationFunctions()[currentLevel];
+		final double currentFlatSpot = this.flatSpot[currentLevel + 1];
+
+		// handle weights
+		int yi = fromLayerIndex;
+		for (int y = 0; y < fromLayerSize; y++) {
+			final double output = this.layerOutput[yi];
+			double sum = 0;
+			int xi = toLayerIndex;
+			int wi = index + y;
+			for (int x = 0; x < toLayerSize; x++) {
+				this.gradients[wi] += output * this.layerDelta[xi];
+				sum += this.weights[wi] * this.layerDelta[xi];
+				wi += fromLayerSize;
+				xi++;
+			}
+
+			this.layerDelta[yi] = sum
+					* (activation.derivativeFunction(this.layerSums[yi],this.layerOutput[yi])+currentFlatSpot);
+			yi++;
+		}
+	}
+
+	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);
+				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);
+		} catch (final Throwable ex) {
+			this.owner.report(null, 0, ex);
+		}
+	}
+
+}

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

@@ -0,0 +1,31 @@
+package net.woodyfolsom.msproj.ann;
+
+import java.io.IOException;
+import java.util.List;
+import java.util.Set;
+
+import org.encog.ml.data.MLData;
+import org.encog.ml.data.MLDataPair;
+import org.encog.ml.data.MLDataSet;
+import org.encog.neural.networks.BasicNetwork;
+
+public interface NeuralNetFilter {
+	BasicNetwork getNeuralNetwork();
+
+	public int getActualTrainingEpochs();
+	public int getInputSize();
+	public int getMaxTrainingEpochs();
+	public int getOutputSize();
+	
+	public double computeValue(MLData input);
+	public double[] computeVector(MLData input);
+	
+	public void learn(MLDataSet trainingSet);
+	public void learn(Set<List<MLDataPair>> trainingSet);
+	
+	public void load(String fileName) throws IOException;
+	public void reset();
+	public void reset(int seed);
+	public void save(String fileName) throws IOException;
+	public void setMaxTrainingEpochs(int max);
+}

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

@@ -1,15 +0,0 @@
-package net.woodyfolsom.msproj.ann;
-
-import org.neuroph.core.NeuralNetwork;
-import org.neuroph.core.learning.SupervisedTrainingElement;
-import org.neuroph.core.learning.TrainingSet;
-
-public interface NeuralNetLearner {
-	void learn(TrainingSet<SupervisedTrainingElement> trainingSet);
-
-	void reset();
-
-	NeuralNetwork getNeuralNetwork();
-
-	void setNeuralNetwork(NeuralNetwork neuralNetwork);
-}

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

@@ -1,98 +0,0 @@
-package net.woodyfolsom.msproj.ann;
-
-import java.util.Arrays;
-
-import net.woodyfolsom.msproj.GameRecord;
-import net.woodyfolsom.msproj.GameResult;
-import net.woodyfolsom.msproj.GameState;
-import net.woodyfolsom.msproj.Player;
-
-import org.neuroph.core.learning.SupervisedTrainingElement;
-import org.neuroph.core.learning.TrainingSet;
-
-public class PassData {
-	public enum DATA_TYPE { TRAINING, TEST, VALIDATION };
-
-	public String[] inputs = { "BlackScore", "WhiteScore" };
-	public String[] outputs  = { "BlackWins", "WhiteWins" };
-
-	private TrainingSet<SupervisedTrainingElement> testSet;
-	private TrainingSet<SupervisedTrainingElement> trainingSet;
-	private TrainingSet<SupervisedTrainingElement> valSet;
-	
-	public PassData() {
-		testSet = new TrainingSet<SupervisedTrainingElement>(inputs.length, outputs.length);
-		trainingSet = new TrainingSet<SupervisedTrainingElement>(inputs.length, outputs.length);
-		valSet = new TrainingSet<SupervisedTrainingElement>(inputs.length, outputs.length);		
-	}
-	
-	public void addData(DATA_TYPE dataType, GameRecord gameRecord) {
-		GameState finalState = gameRecord.getGameState(gameRecord.getNumTurns());
-		GameResult result = finalState.getResult();
-		double maxScore = finalState.getGameConfig().getSize() * finalState.getGameConfig().getSize();
-		
-		double whiteScore = Math.min(1.0, result.getWhiteScore() / maxScore);
-		double blackScore = Math.min(1.0, result.getBlackScore() / maxScore);
-		
-		double blackWinner = result.isWinner(Player.BLACK) ? 1.0 : 0.0;
-		double whiteWinner = result.isWinner(Player.WHITE) ? 1.0 : 0.0;
-
-		addData(dataType, blackScore, whiteScore, blackWinner, whiteWinner);
-	}
-	
-	public void addData(DATA_TYPE dataType, double...data ) {
-		double[] desiredInput = Arrays.copyOfRange(data,0,inputs.length);
-		double[] desiredOutput = Arrays.copyOfRange(data, inputs.length, data.length);
-		
-		switch (dataType) {
-		case TEST :
-			testSet.addElement(new SupervisedTrainingElement(desiredInput, desiredOutput));
-			break;
-		case TRAINING :
-			trainingSet.addElement(new SupervisedTrainingElement(desiredInput, desiredOutput));
-			System.out.println("Added training input data: " + getInput(desiredInput) + ", output data: " + getOutput(desiredOutput));
-			break;
-		case VALIDATION :
-			valSet.addElement(new SupervisedTrainingElement(desiredInput, desiredOutput));
-			break;
-		default :
-			throw new UnsupportedOperationException("invalid dataType " + dataType);
-		}
-	}
-	
-	public String getInput(double... inputValues) {
-		StringBuilder sbuilder = new StringBuilder();
-		boolean first = true;
-		for (int i = 0; i < outputs.length; i++) {
-			if (first) {
-				first = false;
-			} else {
-				sbuilder.append(",");
-			}
-			sbuilder.append(inputs[i]);
-			sbuilder.append(": ");
-			sbuilder.append(inputValues[i]);
-		}
-		return sbuilder.toString();
-	}
-	
-	public String getOutput(double... outputValues) {
-		StringBuilder sbuilder = new StringBuilder();
-		boolean first = true;
-		for (int i = 0; i < outputs.length; i++) {
-			if (first) {
-				first = false;
-			} else {
-				sbuilder.append(",");
-			}
-			sbuilder.append(outputs[i]);
-			sbuilder.append(": ");
-			sbuilder.append(outputValues[i]);
-		}
-		return sbuilder.toString();
-	}
-	
-	public TrainingSet<SupervisedTrainingElement> getTrainingSet() {
-		return trainingSet;
-	}
-}

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

@@ -1,127 +0,0 @@
-package net.woodyfolsom.msproj.ann;
-
-import java.io.File;
-import java.io.FileInputStream;
-import java.io.FilenameFilter;
-import java.io.IOException;
-import java.util.ArrayList;
-import java.util.Arrays;
-import java.util.List;
-
-import net.woodyfolsom.msproj.Action;
-import net.woodyfolsom.msproj.GameRecord;
-import net.woodyfolsom.msproj.Referee;
-import net.woodyfolsom.msproj.ann.PassData.DATA_TYPE;
-
-import org.antlr.runtime.RecognitionException;
-import org.neuroph.core.NeuralNetwork;
-import org.neuroph.core.learning.SupervisedTrainingElement;
-import org.neuroph.core.learning.TrainingSet;
-import org.neuroph.nnet.MultiLayerPerceptron;
-import org.neuroph.util.TransferFunctionType;
-
-public class PassLearner implements NeuralNetLearner {
-	private NeuralNetwork neuralNetwork;
-
-	public PassLearner() {
-		reset();
-	}
-	
-	private File[] getDataFiles(String dirName) {
-		File file = new File(dirName);
-		return file.listFiles(new FilenameFilter() {
-			@Override
-			public boolean accept(File dir, String name) {
-				return name.toLowerCase().endsWith(".sgf");
-			}
-		});
-	}
-
-	public static void main(String[] args) {
-		new PassLearner().learnANN();
-	}
-
-	private void learnANN() {
-		List<GameRecord> parsedRecords = new ArrayList<GameRecord>();
-
-		for (File sgfFile : getDataFiles("data/games/random_vs_random")) {
-			System.out.println("Parsing " + sgfFile.getPath() + "...");
-			try {
-				GameRecord gameRecord = parseSGF(sgfFile);
-				while (!gameRecord.isFinished()) {
-					System.out.println("Game is not finished, passing as player to move");
-					gameRecord.play(gameRecord.getPlayerToMove(), Action.PASS);
-				}
-				parsedRecords.add(gameRecord);
-			} catch (RecognitionException re) {
-				re.printStackTrace();
-			} catch (IOException ioe) {
-				ioe.printStackTrace();
-			}
-		}
-
-		PassData passData = new PassData();
-		
-		for (GameRecord gameRecord : parsedRecords) {
-			System.out.println(gameRecord.getResult().getFullText());
-			passData.addData(DATA_TYPE.TRAINING, gameRecord);
-		}
-		
-		System.out.println("PassData: ");
-		System.out.println(passData);
-		
-		learn(passData.getTrainingSet());
-		
-		getNeuralNetwork().setInput(0.75,0.25);	
-		System.out.println("Output of ann(0.75,0.25): " + passData.getOutput(getNeuralNetwork().getOutput()));
-		
-		getNeuralNetwork().setInput(0.25,0.50);	
-		System.out.println("Output of ann(0.50,0.99): " + passData.getOutput(getNeuralNetwork().getOutput()));
-		
-		getNeuralNetwork().save("data/networks/Pass2.nn");
-		
-		testNetwork(getNeuralNetwork(), passData.getTrainingSet());
-	}
-
-	public GameRecord parseSGF(File sgfFile) throws IOException,
-			RecognitionException {
-		FileInputStream sgfInputStream;
-
-		sgfInputStream = new FileInputStream(sgfFile);
-		return Referee.replay(sgfInputStream);
-	}
-
-	@Override
-	public NeuralNetwork getNeuralNetwork() {
-		return neuralNetwork;
-	}
-
-	@Override
-	public void learn(TrainingSet<SupervisedTrainingElement> trainingSet) {
-		this.neuralNetwork.learn(trainingSet);
-	}
-
-	@Override
-	public void reset() {
-		this.neuralNetwork = new MultiLayerPerceptron(
-				TransferFunctionType.TANH, 2, 3, 2);
-	}
-
-	@Override
-	public void setNeuralNetwork(NeuralNetwork neuralNetwork) {
-		this.neuralNetwork = neuralNetwork;
-	}
-
-private void testNetwork(NeuralNetwork nnet, TrainingSet<SupervisedTrainingElement> trainingSet) {
-	for (SupervisedTrainingElement trainingElement : trainingSet.elements()) {
-
-		nnet.setInput(trainingElement.getInput());
-		nnet.calculate();
-		double[] networkOutput = nnet.getOutput();
-		System.out.print("Input: "
-				+ Arrays.toString(trainingElement.getInput()));
-		System.out.println(" Output: " + Arrays.toString(networkOutput));
-
-	}
-}
-}

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

@@ -0,0 +1,484 @@
+package net.woodyfolsom.msproj.ann;
+
+import java.util.ArrayList;
+import java.util.HashSet;
+import java.util.List;
+import java.util.Set;
+
+import org.encog.EncogError;
+import org.encog.engine.network.activation.ActivationFunction;
+import org.encog.engine.network.activation.ActivationSigmoid;
+import org.encog.mathutil.IntRange;
+import org.encog.ml.MLMethod;
+import org.encog.ml.TrainingImplementationType;
+import org.encog.ml.data.MLDataPair;
+import org.encog.ml.data.MLDataSet;
+import org.encog.ml.train.MLTrain;
+import org.encog.ml.train.strategy.Strategy;
+import org.encog.ml.train.strategy.end.EndTrainingStrategy;
+import org.encog.neural.error.ErrorFunction;
+import org.encog.neural.error.LinearErrorFunction;
+import org.encog.neural.flat.FlatNetwork;
+import org.encog.neural.networks.ContainsFlat;
+import org.encog.neural.networks.training.LearningRate;
+import org.encog.neural.networks.training.Momentum;
+import org.encog.neural.networks.training.Train;
+import org.encog.neural.networks.training.TrainingError;
+import org.encog.neural.networks.training.propagation.TrainingContinuation;
+import org.encog.neural.networks.training.propagation.back.Backpropagation;
+import org.encog.neural.networks.training.strategy.SmartLearningRate;
+import org.encog.neural.networks.training.strategy.SmartMomentum;
+import org.encog.util.EncogValidate;
+import org.encog.util.EngineArray;
+import org.encog.util.concurrency.DetermineWorkload;
+import org.encog.util.concurrency.EngineConcurrency;
+import org.encog.util.concurrency.MultiThreadable;
+import org.encog.util.concurrency.TaskGroup;
+import org.encog.util.logging.EncogLogging;
+
+/**
+ * This class started as a verbatim copy of BackPropagation from the open-source
+ * Encog framework. It was merged with its super-classes to access protected
+ * fields without resorting to reflection.
+ */
+public class TemporalDifferenceLearning implements MLTrain, Momentum,
+		LearningRate, Train, MultiThreadable {
+	// New fields for TD(lambda)
+	private final double lambda;
+	// end new fields
+
+	// BackProp
+	public static final String LAST_DELTA = "LAST_DELTA";
+	private double learningRate;
+	private double momentum;
+	private double[] lastDelta;
+	// End BackProp
+
+	// Propagation
+	private FlatNetwork currentFlatNetwork;
+	private int numThreads;
+	protected double[] gradients;
+	private double[] lastGradient;
+	protected ContainsFlat network;
+	// private MLDataSet indexable;
+	private Set<List<MLDataPair>> indexable;
+	private GradientWorker[] workers;
+	private double totalError;
+	protected double lastError;
+	private Throwable reportedException;
+	private double[] flatSpot;
+	private boolean shouldFixFlatSpot;
+	private ErrorFunction ef = new LinearErrorFunction();
+	// End Propagation
+
+	// BasicTraining
+	private final List<Strategy> strategies = new ArrayList<Strategy>();
+	private Set<List<MLDataPair>> training;
+	private double error;
+	private int iteration;
+	private TrainingImplementationType implementationType;
+
+	// End BasicTraining
+
+	public TemporalDifferenceLearning(final ContainsFlat network,
+			final Set<List<MLDataPair>> training, double lambda) {
+		this(network, training, 0, 0, lambda);
+		addStrategy(new SmartLearningRate());
+		addStrategy(new SmartMomentum());
+	}
+
+	public TemporalDifferenceLearning(final ContainsFlat network,
+			Set<List<MLDataPair>> training, final double theLearnRate,
+			final double theMomentum, double lambda) {
+		initPropagation(network, training);
+		// TODO consider how to re-implement validation
+		// ValidateNetwork.validateMethodToData(network, training);
+		this.momentum = theMomentum;
+		this.learningRate = theLearnRate;
+		this.lastDelta = new double[network.getFlat().getWeights().length];
+		this.lambda = lambda;
+	}
+
+	private void initPropagation(final ContainsFlat network,
+			final Set<List<MLDataPair>> training) {
+		initBasicTraining(TrainingImplementationType.Iterative);
+		this.network = network;
+		this.currentFlatNetwork = network.getFlat();
+		setTraining(training);
+
+		this.gradients = new double[this.currentFlatNetwork.getWeights().length];
+		this.lastGradient = new double[this.currentFlatNetwork.getWeights().length];
+
+		this.indexable = training;
+		this.numThreads = 0;
+		this.reportedException = null;
+		this.shouldFixFlatSpot = true;
+	}
+
+	private void initBasicTraining(TrainingImplementationType implementationType) {
+		this.implementationType = implementationType;
+	}
+
+	// Methods from BackPropagation
+	@Override
+	public boolean canContinue() {
+		return false;
+	}
+
+	public double[] getLastDelta() {
+		return this.lastDelta;
+	}
+
+	@Override
+	public double getLearningRate() {
+		return this.learningRate;
+	}
+
+	@Override
+	public double getMomentum() {
+		return this.momentum;
+	}
+
+	public boolean isValidResume(final TrainingContinuation state) {
+		if (!state.getContents().containsKey(Backpropagation.LAST_DELTA)) {
+			return false;
+		}
+
+		if (!state.getTrainingType().equals(getClass().getSimpleName())) {
+			return false;
+		}
+
+		final double[] d = (double[]) state.get(Backpropagation.LAST_DELTA);
+		return d.length == ((ContainsFlat) getMethod()).getFlat().getWeights().length;
+	}
+
+	@Override
+	public TrainingContinuation pause() {
+		final TrainingContinuation result = new TrainingContinuation();
+		result.setTrainingType(this.getClass().getSimpleName());
+		result.set(Backpropagation.LAST_DELTA, this.lastDelta);
+		return result;
+	}
+
+	@Override
+	public void resume(final TrainingContinuation state) {
+		if (!isValidResume(state)) {
+			throw new TrainingError("Invalid training resume data length");
+		}
+
+		this.lastDelta = ((double[]) state.get(Backpropagation.LAST_DELTA));
+	}
+
+	@Override
+	public void setLearningRate(final double rate) {
+		this.learningRate = rate;
+	}
+
+	@Override
+	public void setMomentum(final double m) {
+		this.momentum = m;
+	}
+
+	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
+				+ " with lambda: " + lambda);
+
+		return delta;
+	}
+
+	public void initOthers() {
+	}
+
+	// End methods from BackPropagation
+
+	// Methods from Propagation
+	public void finishTraining() {
+		basicFinishTraining();
+	}
+
+	public FlatNetwork getCurrentFlatNetwork() {
+		return this.currentFlatNetwork;
+	}
+
+	public MLMethod getMethod() {
+		return this.network;
+	}
+
+	public void iteration() {
+		iteration(1);
+	}
+
+	public void rollIteration() {
+		this.iteration++;
+	}
+
+	public void iteration(final int count) {
+
+		try {
+			for (int i = 0; i < count; i++) {
+
+				preIteration();
+
+				rollIteration();
+
+				calculateGradients();
+
+				if (this.currentFlatNetwork.isLimited()) {
+					learnLimited();
+				} else {
+					learn();
+				}
+
+				this.lastError = this.getError();
+
+				for (final GradientWorker worker : this.workers) {
+					EngineArray.arrayCopy(this.currentFlatNetwork.getWeights(),
+							0, worker.getWeights(), 0,
+							this.currentFlatNetwork.getWeights().length);
+				}
+
+				if (this.currentFlatNetwork.getHasContext()) {
+					copyContexts();
+				}
+
+				if (this.reportedException != null) {
+					throw (new EncogError(this.reportedException));
+				}
+
+				postIteration();
+
+				EncogLogging.log(EncogLogging.LEVEL_INFO,
+						"Training iteration done, error: " + getError());
+
+			}
+		} catch (final ArrayIndexOutOfBoundsException ex) {
+			EncogValidate.validateNetworkForTraining(this.network,
+					getTraining());
+			throw new EncogError(ex);
+		}
+	}
+
+	public void setThreadCount(final int numThreads) {
+		this.numThreads = numThreads;
+	}
+
+	@Override
+	public int getThreadCount() {
+		return this.numThreads;
+	}
+
+	public void fixFlatSpot(boolean b) {
+		this.shouldFixFlatSpot = b;
+	}
+
+	public void setErrorFunction(ErrorFunction ef) {
+		this.ef = ef;
+	}
+
+	public void calculateGradients() {
+		if (this.workers == null) {
+			init();
+		}
+
+		if (this.currentFlatNetwork.getHasContext()) {
+			this.workers[0].getNetwork().clearContext();
+		}
+
+		this.totalError = 0;
+
+		if (this.workers.length > 1) {
+
+			final TaskGroup group = EngineConcurrency.getInstance()
+					.createTaskGroup();
+
+			for (final GradientWorker worker : this.workers) {
+				EngineConcurrency.getInstance().processTask(worker, group);
+			}
+
+			group.waitForComplete();
+		} else {
+			this.workers[0].run();
+		}
+
+		this.setError(this.totalError / this.workers.length);
+
+	}
+
+	/**
+	 * Copy the contexts to keep them consistent with multithreaded training.
+	 */
+	private void copyContexts() {
+
+		// copy the contexts(layer outputO from each group to the next group
+		for (int i = 0; i < (this.workers.length - 1); i++) {
+			final double[] src = this.workers[i].getNetwork().getLayerOutput();
+			final double[] dst = this.workers[i + 1].getNetwork()
+					.getLayerOutput();
+			EngineArray.arrayCopy(src, dst);
+		}
+
+		// copy the contexts from the final group to the real network
+		EngineArray.arrayCopy(this.workers[this.workers.length - 1]
+				.getNetwork().getLayerOutput(), this.currentFlatNetwork
+				.getLayerOutput());
+	}
+
+	private void init() {
+		// fix flat spot, if needed
+		this.flatSpot = new double[this.currentFlatNetwork
+				.getActivationFunctions().length];
+
+		if (this.shouldFixFlatSpot) {
+			for (int i = 0; i < this.currentFlatNetwork
+					.getActivationFunctions().length; i++) {
+				final ActivationFunction af = this.currentFlatNetwork
+						.getActivationFunctions()[i];
+
+				if (af instanceof ActivationSigmoid) {
+					this.flatSpot[i] = 0.1;
+				} else {
+					this.flatSpot[i] = 0.0;
+				}
+			}
+		} else {
+			EngineArray.fill(this.flatSpot, 0.0);
+		}
+
+		// setup workers
+		final DetermineWorkload determine = new DetermineWorkload(
+				this.numThreads, (int) this.indexable.size());
+		// this.numThreads, (int) this.indexable.getRecordCount());
+
+		this.workers = new GradientWorker[determine.getThreadCount()];
+
+		int index = 0;
+
+		// handle CPU
+		for (final IntRange r : determine.calculateWorkers()) {
+			this.workers[index++] = new GradientWorker(
+					this.currentFlatNetwork.clone(), this, new HashSet(
+							this.indexable), r.getLow(), r.getHigh(),
+					this.flatSpot, this.ef);
+		}
+
+		initOthers();
+	}
+
+	public void report(final double[] gradients, final double error,
+			final Throwable ex) {
+		synchronized (this) {
+			if (ex == null) {
+
+				for (int i = 0; i < gradients.length; i++) {
+					this.gradients[i] += gradients[i];
+				}
+				this.totalError += error;
+			} else {
+				this.reportedException = ex;
+			}
+		}
+	}
+
+	protected void learn() {
+		final double[] weights = this.currentFlatNetwork.getWeights();
+		for (int i = 0; i < this.gradients.length; i++) {
+			weights[i] += updateWeight(this.gradients, this.lastGradient, i);
+			this.gradients[i] = 0;
+		}
+	}
+
+	protected void learnLimited() {
+		final double limit = this.currentFlatNetwork.getConnectionLimit();
+		final double[] weights = this.currentFlatNetwork.getWeights();
+		for (int i = 0; i < this.gradients.length; i++) {
+			if (Math.abs(weights[i]) < limit) {
+				weights[i] = 0;
+			} else {
+				weights[i] += updateWeight(this.gradients, this.lastGradient, i);
+			}
+			this.gradients[i] = 0;
+		}
+	}
+
+	public double[] getLastGradient() {
+		return lastGradient;
+	}
+
+	// End methods from Propagation
+
+	// Methods from BasicTraining/
+	public void addStrategy(final Strategy strategy) {
+		strategy.init(this);
+		this.strategies.add(strategy);
+	}
+
+	public void basicFinishTraining() {
+	}
+
+	public double getError() {
+		return this.error;
+	}
+
+	public int getIteration() {
+		return this.iteration;
+	}
+
+	public List<Strategy> getStrategies() {
+		return this.strategies;
+	}
+
+	public MLDataSet getTraining() {
+		throw new UnsupportedOperationException(
+				"This learning method operates on Set<List<MLData>>, not MLDataSet");
+	}
+
+	public boolean isTrainingDone() {
+		for (Strategy strategy : this.strategies) {
+			if (strategy instanceof EndTrainingStrategy) {
+				EndTrainingStrategy end = (EndTrainingStrategy) strategy;
+				if (end.shouldStop()) {
+					return true;
+				}
+			}
+		}
+
+		return false;
+	}
+
+	public void postIteration() {
+		for (final Strategy strategy : this.strategies) {
+			strategy.postIteration();
+		}
+	}
+
+	public void preIteration() {
+
+		this.iteration++;
+
+		for (final Strategy strategy : this.strategies) {
+			strategy.preIteration();
+		}
+	}
+
+	public void setError(final double error) {
+		this.error = error;
+	}
+
+	public void setIteration(final int iteration) {
+		this.iteration = iteration;
+	}
+
+	public void setTraining(final Set<List<MLDataPair>> training) {
+		this.training = training;
+	}
+
+	public TrainingImplementationType getImplementationType() {
+		return this.implementationType;
+	}
+	// End Methods from BasicTraining
+}

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

@@ -0,0 +1,112 @@
+package net.woodyfolsom.msproj.ann;
+
+import java.util.List;
+import java.util.Set;
+
+import net.woodyfolsom.msproj.GameState;
+import net.woodyfolsom.msproj.Player;
+
+import org.encog.engine.network.activation.ActivationSigmoid;
+import org.encog.ml.data.MLData;
+import org.encog.ml.data.MLDataPair;
+import org.encog.ml.data.MLDataSet;
+import org.encog.ml.train.MLTrain;
+import org.encog.neural.networks.BasicNetwork;
+import org.encog.neural.networks.layers.BasicLayer;
+
+public class WinFilter extends AbstractNeuralNetFilter implements
+		NeuralNetFilter {
+
+	public WinFilter() {
+		// create a neural network, without using a factory
+		BasicNetwork network = new BasicNetwork();
+		network.addLayer(new BasicLayer(null, false, 2));
+		network.addLayer(new BasicLayer(new ActivationSigmoid(), true, 4));
+		network.addLayer(new BasicLayer(new ActivationSigmoid(), true, 2));
+		network.getStructure().finalizeStructure();
+		network.reset();
+
+		this.neuralNetwork = network;
+	}
+
+	@Override
+	public double computeValue(MLData input) {
+		if (input instanceof GameStateMLData) {
+			double[] idealVector = computeVector(input);
+			GameState gameState = ((GameStateMLData) input).getGameState();
+			Player playerToMove = gameState.getPlayerToMove();
+			if (playerToMove == Player.BLACK) {
+				return idealVector[0];
+			} else if (playerToMove == Player.WHITE) {
+				return idealVector[1];
+			} else {
+				throw new RuntimeException("Invalid GameState.playerToMove: "
+						+ playerToMove);
+			}
+		} else {
+			throw new UnsupportedOperationException(
+					"This NeuralNetFilter only accepts GameStates as input.");
+		}
+	}
+
+	@Override
+	public double[] computeVector(MLData input) {
+		if (input instanceof GameStateMLData) {
+			return neuralNetwork.compute(input).getData();
+		} else {
+			throw new UnsupportedOperationException(
+					"This NeuralNetFilter only accepts GameStates as input.");
+		}
+	}
+
+	@Override
+	public void learn(MLDataSet trainingData) {
+		throw new UnsupportedOperationException("This filter learns a Set<List<MLData>>, not an MLDataSet");
+	}
+	
+	@Override
+	public void learn(Set<List<MLDataPair>> trainingSet) {
+
+		// train the neural network
+		final MLTrain train = new TemporalDifferenceLearning(neuralNetwork,
+				trainingSet, 0.7, 0.8, 0.25);
+
+		actualTrainingEpochs = 0;
+
+		do {
+			train.iteration();
+			System.out.println("Epoch #" + actualTrainingEpochs + " Error:"
+					+ train.getError());
+			actualTrainingEpochs++;
+		} while (train.getError() > 0.01
+				&& actualTrainingEpochs <= maxTrainingEpochs);
+	}
+
+	@Override
+	public void reset() {
+		neuralNetwork.reset();
+	}
+
+	@Override
+	public void reset(int seed) {
+		neuralNetwork.reset(seed);
+	}
+
+	@Override
+	public BasicNetwork getNeuralNetwork() {
+		// TODO Auto-generated method stub
+		return null;
+	}
+
+	@Override
+	public int getInputSize() {
+		// TODO Auto-generated method stub
+		return 0;
+	}
+
+	@Override
+	public int getOutputSize() {
+		// TODO Auto-generated method stub
+		return 0;
+	}
+}

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

@@ -0,0 +1,83 @@
+package net.woodyfolsom.msproj.ann;
+
+import java.util.List;
+import java.util.Set;
+
+import org.encog.engine.network.activation.ActivationSigmoid;
+import org.encog.ml.data.MLData;
+import org.encog.ml.data.MLDataPair;
+import org.encog.ml.data.MLDataSet;
+import org.encog.ml.data.basic.BasicMLDataSet;
+import org.encog.ml.train.MLTrain;
+import org.encog.neural.networks.BasicNetwork;
+import org.encog.neural.networks.layers.BasicLayer;
+import org.encog.neural.networks.training.propagation.back.Backpropagation;
+
+/**
+ * Based on sample code from http://neuroph.sourceforge.net
+ * 
+ * @author Woody
+ * 
+ */
+public class XORFilter extends AbstractNeuralNetFilter implements
+		NeuralNetFilter {
+
+	public XORFilter() {
+		// create a neural network, without using a factory
+		BasicNetwork network = new BasicNetwork();
+		network.addLayer(new BasicLayer(null, false, 2));
+		network.addLayer(new BasicLayer(new ActivationSigmoid(), true, 3));
+		network.addLayer(new BasicLayer(new ActivationSigmoid(), true, 1));
+		network.getStructure().finalizeStructure();
+		network.reset();
+
+		this.neuralNetwork = network;
+	}
+
+	@Override
+	public void learn(MLDataSet trainingSet) {
+
+		// train the neural network
+		final MLTrain train = new Backpropagation(neuralNetwork,
+				trainingSet, 0.7, 0.8);
+
+		actualTrainingEpochs = 0;
+
+		do {
+			train.iteration();
+			System.out.println("Epoch #" + actualTrainingEpochs + " Error:"
+					+ train.getError());
+			actualTrainingEpochs++;
+		} while (train.getError() > 0.01
+				&& actualTrainingEpochs <= maxTrainingEpochs);
+	}
+
+	@Override
+	public double[] computeVector(MLData mlData) {
+		MLDataSet dataset = new BasicMLDataSet(new double[][] { mlData.getData() },
+				new double[][] { new double[getOutputSize()] });
+		MLData output = neuralNetwork.compute(dataset.get(0).getInput());
+		return output.getData();
+	}
+
+	@Override
+	public int getInputSize() {
+		return 2;
+	}
+
+	@Override
+	public int getOutputSize() {
+		// TODO Auto-generated method stub
+		return 1;
+	}
+
+	@Override
+	public double computeValue(MLData input) {
+		return computeVector(input)[0];
+	}
+
+	@Override
+	public void learn(Set<List<MLDataPair>> trainingSet) {
+		throw new UnsupportedOperationException("This Filter learns an MLDataSet, not a Set<List<MLData>>.");
+	}
+}

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

@@ -1,42 +0,0 @@
-package net.woodyfolsom.msproj.ann;
-
-import org.neuroph.core.NeuralNetwork;
-import org.neuroph.core.learning.SupervisedTrainingElement;
-import org.neuroph.core.learning.TrainingSet;
-import org.neuroph.nnet.MultiLayerPerceptron;
-import org.neuroph.util.TransferFunctionType;
-
-/**
- * Based on sample code from http://neuroph.sourceforge.net
- * 
- * @author Woody
- * 
- */
-public class XORLearner implements NeuralNetLearner {
-	private NeuralNetwork neuralNetwork;
-
-	public XORLearner() {
-		reset();
-	}
-
-	@Override
-	public NeuralNetwork getNeuralNetwork() {
-		return neuralNetwork;
-	}
-
-	@Override
-	public void learn(TrainingSet<SupervisedTrainingElement> trainingSet) {
-		this.neuralNetwork.learn(trainingSet);
-	}
-
-	@Override
-	public void reset() {
-		this.neuralNetwork = new MultiLayerPerceptron(
-				TransferFunctionType.TANH, 2, 3, 1);
-	}
-
-	@Override
-	public void setNeuralNetwork(NeuralNetwork neuralNetwork) {
-		this.neuralNetwork = neuralNetwork;
-	}
-}

src/net/woodyfolsom/msproj/gui/Goban.java

@@ -5,6 +5,7 @@ import java.awt.event.ActionEvent;
 import java.awt.event.ActionListener;
 import java.awt.event.WindowAdapter;
 import java.awt.event.WindowEvent;
+import java.util.concurrent.atomic.AtomicInteger;
 
 import javax.swing.JButton;
 import javax.swing.JFrame;
@@ -14,22 +15,31 @@ import net.woodyfolsom.msproj.Action;
 import net.woodyfolsom.msproj.GameConfig;
 import net.woodyfolsom.msproj.GameState;
 import net.woodyfolsom.msproj.Player;
+import net.woodyfolsom.msproj.StandAloneGame;
 import net.woodyfolsom.msproj.sfx.SfxPlayer;
 
 public class Goban extends JFrame {
 	
 	private static final long serialVersionUID = 1L;
 
+	private static AtomicInteger openWindows = new AtomicInteger(0);
+	
 	private GridPanel gridPanel;
 	private SfxPlayer sfxPlayer;
 	
-	public Goban(GameConfig gameConfig, Player guiPlayer) {
+	public Goban(GameConfig gameConfig, Player guiPlayer, String gameName) {
+		super(gameName);
+		
 		setLayout(new BorderLayout());
 		
 		addWindowListener(new WindowAdapter() {
 			@Override
 	        public void windowClosing(WindowEvent e) {
 				sfxPlayer.cleanup();
+				int windowsLeftOpen = openWindows.addAndGet(-1);
+				if (windowsLeftOpen < 1) {
+					System.exit(StandAloneGame.EXIT_USER_QUIT);
+				}
 	        }
 		});
 		
@@ -42,9 +52,6 @@ public class Goban extends JFrame {
 		this.gridPanel = new GridPanel(gameConfig, guiPlayer, sfxPlayer);
 		add(gridPanel,BorderLayout.CENTER);
 		
-		setDefaultCloseOperation(JFrame.EXIT_ON_CLOSE);
-		setVisible(true);
-		
 		JButton passBtn = new JButton("Pass");
 		JButton resignBtn = new JButton("Resign");
 		
@@ -67,6 +74,9 @@ public class Goban extends JFrame {
 		bottomPanel.add(resignBtn);
 		
 		add(bottomPanel, BorderLayout.SOUTH);
+
+		setVisible(true);
+		openWindows.addAndGet(1);
 		pack();
 	}
 

src/net/woodyfolsom/msproj/gui/GridPanel.java

@@ -229,7 +229,9 @@ public class GridPanel extends JPanel implements MouseListener,
 
 			@Override
 			public void run() {
-				sfxPlayer.play();
+				if (sfxPlayer != null) {
+					sfxPlayer.play();
+				}
 			}}).start();
 	}
 

src/net/woodyfolsom/msproj/policy/MonteCarlo.java

@@ -13,7 +13,7 @@ import net.woodyfolsom.msproj.tree.GameTreeNode;
 import net.woodyfolsom.msproj.tree.MonteCarloProperties;
 
 public abstract class MonteCarlo implements Policy {
-	protected static final int ROLLOUT_DEPTH_LIMIT = 150;
+	protected static final int ROLLOUT_DEPTH_LIMIT = 250;
 
 	protected int numStateEvaluations = 0;
 	protected Policy movePolicy;

src/net/woodyfolsom/msproj/policy/RootParallelization.java

@@ -101,8 +101,8 @@ public class RootParallelization implements Policy {
 		System.out.println("It won "
 				+ bestWins + " out of " + bestSims 
 				+ " rollouts among " + totalRollouts 
-				+ " total rollouts (" + totalReward.keySet()
-				+ " possible actions) from the current state.");
+				+ " total rollouts (" + totalReward.size()
+				+ " possible moves evaluated) from the current state.");
 		
 		return bestAction;
 	}

test/net/woodyfolsom/msproj/ann/NeuralNetLearnerTest.java

@@ -1,86 +0,0 @@
-package net.woodyfolsom.msproj.ann;
-
-import java.io.File;
-import java.util.Arrays;
-
-import org.junit.AfterClass;
-import org.junit.BeforeClass;
-import org.junit.Test;
-import org.neuroph.core.NeuralNetwork;
-import org.neuroph.core.learning.SupervisedTrainingElement;
-import org.neuroph.core.learning.TrainingSet;
-
-public class NeuralNetLearnerTest {
-	private static final String FILENAME = "myMlPerceptron.nnet";
-
-	@AfterClass
-	public static void deleteNewNet() {
-		File file = new File(FILENAME);
-		if (file.exists()) {
-			file.delete();
-		}
-	}
-
-	@BeforeClass
-	public static void deleteSavedNet() {
-		File file = new File(FILENAME);
-		if (file.exists()) {
-			file.delete();
-		}
-	}
-
-	@Test
-	public void testLearnSaveLoad() {
-		NeuralNetLearner nnLearner = new XORLearner();
-
-		// create training set (logical XOR function)
-		TrainingSet<SupervisedTrainingElement> trainingSet = new TrainingSet<SupervisedTrainingElement>(
-				2, 1);
-		for (int x = 0; x < 1000; x++) {
-		trainingSet.addElement(new SupervisedTrainingElement(new double[] { 0,
-				0 }, new double[] { 0 }));
-		trainingSet.addElement(new SupervisedTrainingElement(new double[] { 0,
-				1 }, new double[] { 1 }));
-		trainingSet.addElement(new SupervisedTrainingElement(new double[] { 1,
-				0 }, new double[] { 1 }));
-		trainingSet.addElement(new SupervisedTrainingElement(new double[] { 1,
-				1 }, new double[] { 0 }));
-		}
-		
-		nnLearner.learn(trainingSet);
-		NeuralNetwork nnet = nnLearner.getNeuralNetwork();
-
-		TrainingSet<SupervisedTrainingElement> valSet = new TrainingSet<SupervisedTrainingElement>(
-				2, 1);
-		valSet.addElement(new SupervisedTrainingElement(new double[] { 0,
-				0 }, new double[] { 0 }));
-		valSet.addElement(new SupervisedTrainingElement(new double[] { 0,
-				1 }, new double[] { 1 }));
-		valSet.addElement(new SupervisedTrainingElement(new double[] { 1,
-				0 }, new double[] { 1 }));
-		valSet.addElement(new SupervisedTrainingElement(new double[] { 1,
-				1 }, new double[] { 0 }));
-		
-		System.out.println("Output from eval set (learned network):");
-		testNetwork(nnet, valSet);
-
-		nnet.save(FILENAME);
-		nnet = NeuralNetwork.load(FILENAME);
-		
-		System.out.println("Output from eval set (learned network):");
-		testNetwork(nnet, valSet);
-	}
-	
-	private void testNetwork(NeuralNetwork nnet, TrainingSet<SupervisedTrainingElement> trainingSet) {
-		for (SupervisedTrainingElement trainingElement : trainingSet.elements()) {
-
-			nnet.setInput(trainingElement.getInput());
-			nnet.calculate();
-			double[] networkOutput = nnet.getOutput();
-			System.out.print("Input: "
-					+ Arrays.toString(trainingElement.getInput()));
-			System.out.println(" Output: " + Arrays.toString(networkOutput));
-
-		}
-	}
-}

test/net/woodyfolsom/msproj/ann/PassNetworkTest.java

@@ -1,51 +0,0 @@
-package net.woodyfolsom.msproj.ann;
-
-import static org.junit.Assert.assertTrue;
-
-import org.junit.Test;
-import org.neuroph.core.NeuralNetwork;
-
-public class PassNetworkTest {
-
-	@Test
-	public void testSavedNetwork1() {
-		NeuralNetwork passFilter = NeuralNetwork.load("data/networks/Pass1.nn");
-		passFilter.setInput(0.75,0.25);
-		passFilter.calculate();
-		
-		PassData passData = new PassData();
-		double[] output = passFilter.getOutput();
-		System.out.println("Output: " + passData.getOutput(output));
-		
-		assertTrue(output[0] > 0.50);
-		assertTrue(output[1] < 0.50);
-		
-		passFilter.setInput(0.25,0.50);
-		passFilter.calculate();
-		output = passFilter.getOutput();
-		System.out.println("Output: " + passData.getOutput(output));
-		assertTrue(output[0] < 0.50);
-		assertTrue(output[1] > 0.50);
-	}
-	
-	@Test
-	public void testSavedNetwork2() {
-		NeuralNetwork passFilter = NeuralNetwork.load("data/networks/Pass2.nn");
-		passFilter.setInput(0.75,0.25);
-		passFilter.calculate();
-		
-		PassData passData = new PassData();
-		double[] output = passFilter.getOutput();
-		System.out.println("Output: " + passData.getOutput(output));
-		
-		assertTrue(output[0] > 0.50);
-		assertTrue(output[1] < 0.50);
-		
-		passFilter.setInput(0.45,0.55);
-		passFilter.calculate();
-		output = passFilter.getOutput();
-		System.out.println("Output: " + passData.getOutput(output));
-		assertTrue(output[0] < 0.50);
-		assertTrue(output[1] > 0.50);
-	}
-}

test/net/woodyfolsom/msproj/ann/WinFilterTest.java

@@ -0,0 +1,66 @@
+package net.woodyfolsom.msproj.ann;
+
+import java.io.File;
+import java.io.FileFilter;
+import java.io.FileInputStream;
+import java.io.IOException;
+import java.util.ArrayList;
+import java.util.HashSet;
+import java.util.List;
+import java.util.Set;
+
+import net.woodyfolsom.msproj.GameRecord;
+import net.woodyfolsom.msproj.Referee;
+
+import org.antlr.runtime.RecognitionException;
+import org.encog.ml.data.MLData;
+import org.encog.ml.data.MLDataPair;
+import org.junit.Test;
+
+public class WinFilterTest {
+
+	@Test
+	public void testLearnSaveLoad() throws IOException, RecognitionException {
+		File[] sgfFiles = new File("data/games/random_vs_random")
+				.listFiles(new FileFilter() {
+					@Override
+					public boolean accept(File pathname) {
+						return pathname.getName().endsWith(".sgf");
+					}
+				});
+
+		Set<List<MLDataPair>> trainingData = new HashSet<List<MLDataPair>>();
+
+		for (File file : sgfFiles) {
+			FileInputStream fis = new FileInputStream(file);
+			GameRecord gameRecord = Referee.replay(fis);
+
+			List<MLDataPair> gameData = new ArrayList<MLDataPair>();
+			for (int i = 0; i <= gameRecord.getNumTurns(); i++) {
+				gameData.add(new GameStateMLDataPair(gameRecord.getGameState(i)));
+			}
+
+			trainingData.add(gameData);
+
+			fis.close();
+		}
+
+		WinFilter winFilter = new WinFilter();
+
+		winFilter.learn(trainingData);
+
+		for (List<MLDataPair> trainingSequence : trainingData) {
+			//for (MLDataPair mlDataPair : trainingSequence) {
+			for (int stateIndex = 0; stateIndex < trainingSequence.size(); stateIndex++) {
+				if (stateIndex > 0 && stateIndex < trainingSequence.size()-1) {
+					continue;
+				}
+				MLData input = trainingSequence.get(stateIndex).getInput();
+				
+				System.out.println("Turn " + stateIndex + ": " + input + " => "
+						+ winFilter.computeValue(input));
+			}
+			//}
+		}
+	}
+}

test/net/woodyfolsom/msproj/ann/XORFilterTest.java

@@ -0,0 +1,79 @@
+package net.woodyfolsom.msproj.ann;
+
+import java.io.File;
+import java.io.IOException;
+
+import org.encog.ml.data.MLDataSet;
+import org.encog.ml.data.basic.BasicMLDataSet;
+import org.junit.AfterClass;
+import org.junit.BeforeClass;
+import org.junit.Test;
+
+public class XORFilterTest {
+	private static final String FILENAME = "xorPerceptron.net";
+
+	@AfterClass
+	public static void deleteNewNet() {
+		File file = new File(FILENAME);
+		if (file.exists()) {
+			file.delete();
+		}
+	}
+
+	@BeforeClass
+	public static void deleteSavedNet() {
+		File file = new File(FILENAME);
+		if (file.exists()) {
+			file.delete();
+		}
+	}
+
+	@Test
+	public void testLearnSaveLoad() throws IOException {
+		NeuralNetFilter nnLearner = new XORFilter();
+		System.out.println("Learned network after " + nnLearner.getActualTrainingEpochs() + " training epochs.");
+		
+		// create training set (logical XOR function)
+		int size = 1;
+		double[][] trainingInput = new double[4 * size][];
+		double[][] trainingOutput = new double[4 * size][];
+		for (int i = 0; i < size; i++) {
+			trainingInput[i * 4 + 0] = new double[] { 0, 0 };
+			trainingInput[i * 4 + 1] = new double[] { 0, 1 };
+			trainingInput[i * 4 + 2] = new double[] { 1, 0 };
+			trainingInput[i * 4 + 3] = new double[] { 1, 1 };
+			trainingOutput[i * 4 + 0] = new double[] { 0 };
+			trainingOutput[i * 4 + 1] = new double[] { 1 };
+			trainingOutput[i * 4 + 2] = new double[] { 1 };
+			trainingOutput[i * 4 + 3] = new double[] { 0 };
+		}
+
+		// create training data
+		MLDataSet trainingSet = new BasicMLDataSet(trainingInput, trainingOutput);
+		
+		nnLearner.learn(trainingSet);
+
+		double[][] validationSet = new double[4][2];
+
+		validationSet[0] = new double[] { 0, 0 };
+		validationSet[1] = new double[] { 0, 1 };
+		validationSet[2] = new double[] { 1, 0 };
+		validationSet[3] = new double[] { 1, 1 };
+
+		System.out.println("Output from eval set (learned network, pre-serialization):");
+		testNetwork(nnLearner, validationSet);
+
+		nnLearner.save(FILENAME);
+		nnLearner.load(FILENAME);
+
+		System.out.println("Output from eval set (learned network, post-serialization):");
+		testNetwork(nnLearner, validationSet);
+	}
+
+	private void testNetwork(NeuralNetFilter nnLearner, double[][] validationSet) {
+		for (int valIndex = 0; valIndex < validationSet.length; valIndex++) {
+			DoublePair dp = new DoublePair(validationSet[valIndex][0],validationSet[valIndex][1]);
+			System.out.println(dp + " => " + nnLearner.computeValue(dp));
+		}
+	}
+}