- Implemented multiple users, including a selection dialog and automatic preference saving and loading.

- Integrated the ANN with the game. The network now predicts a user move, completely ignores it, and trains itself on the players actual move. This integration also included implementing two new functions. The first translates a board state to a boolean array to correspond with input nodes. The second translates a move to a boolean array to correspond with output nodes.

src/model/playerModel/PlayerModel.java

@@ -17,30 +17,27 @@ import model.playerModel.node.SigmoidNode;
 
 public class PlayerModel implements Serializable {
 
-	public static final String PLAYER_MODEL_PATH = "playerModel.dat"; // Path to
-																		// the
-																		// stored
-																		// player
-																		// model.
+	public static final String DATA_FOLDER = "data/";
+	public static final String PLAYER_MODEL_PREFIX = "playerModel";
+	public static final String PLAYER_MODEL_SUFFIX = ".dat";
 	public static final Random rand = new Random(); // Randomizer object.
-	public static final boolean TRY_LOAD = true; // Set to false if any existing
-													// player model should be
-													// discarded and the next
-													// game should begin a new
-													// sequence.
-
 	private static final long serialVersionUID = 1L;
 
-	public static boolean exists() {
-		return (new File(PLAYER_MODEL_PATH)).exists();
+	public static boolean exists(String playerName) {
+		return (new File(getPlayerPath(playerName))).exists();
 	}
 
-	public static PlayerModel load() {
+	public static String getPlayerPath(String playerName) {
+		return DATA_FOLDER + PLAYER_MODEL_PREFIX + "_" + playerName
+				+ PLAYER_MODEL_SUFFIX;
+	}
+
+	public static PlayerModel load(String path) {
 		FileInputStream fin = null;
 		ObjectInputStream oin = null;
 
 		try {
-			fin = new FileInputStream(PLAYER_MODEL_PATH);
+			fin = new FileInputStream(path);
 			oin = new ObjectInputStream(fin);
 			PlayerModel pm = (PlayerModel) oin.readObject();
 			oin.close();
@@ -54,29 +51,31 @@ public class PlayerModel implements Serializable {
 	private final SigmoidNode[] hiddenLayer;
 	private int highScoresAchieved = 0;
 
-	// One node for each tile-color combination, plus one for each upcoming
-	// tile-color combination.
+	// One node for each tile-color combination.
 	private final InputNode[] inputNode = new InputNode[(Board.NUM_COLS
 			* Board.NUM_ROWS * (Board.TileColor.values().length - 1))];
 
+	private final String name;
+
 	private int nextHighInGames = 0;
 
-	// One node for each tile plus four for the colors to be selected.
-	// outputNode[0] is blue.
-	// outputNode[1] is green.
-	// outputNode[2] is red.
-	// outputNode[3] is yellow.
-	// outputNode[4] through outputNode[n] represent grid spaces. A true means
-	// that the player is predicted to place on that tile.
-	// They should be read from the top-left to bottom-right, across rows.
-	// Ideally, the network should return only one true between 0 and 3 and only
-	// one true between 4 and n, representing one color and the tile in which it
-	// should be placed.
+	/*
+	 * One node for each tile plus four for the colors to be selected.
+	 * outputNode[0] is blue. outputNode[1] is green. outputNode[2] is red.
+	 * outputNode[3] is yellow. outputNode[4] through outputNode[n] represent
+	 * grid spaces. A true means that the player is predicted to place on that
+	 * tile. They should be read from the top-left to bottom-right, across rows.
+	 * Ideally, the network should return only one true between 0 and 3 and only
+	 * one true between 4 and n, representing one color and the tile in which it
+	 * should be placed. See Referee.getMoveArray().
+	 */
 	private final SigmoidNode[] outputNode = new SigmoidNode[(Board.NUM_COLS * Board.NUM_ROWS) + 4];
 
 	private final ArrayList<GameLog> scores = new ArrayList<GameLog>();
 
-	public PlayerModel() {
+	public PlayerModel(String nme) {
+		name = nme;
+
 		hiddenLayer = new SigmoidNode[inputNode.length
 				+ ((inputNode.length * 2) / 3)];
 
@@ -121,6 +120,18 @@ public class PlayerModel implements Serializable {
 		return highScores;
 	}
 
+	public String getName() {
+		return name;
+	}
+
+	public int getNumInputNodes() {
+		return inputNode.length;
+	}
+
+	public int getNumOutputNodes() {
+		return outputNode.length;
+	}
+
 	public boolean[] getPrediction(boolean[] input) {
 		if (input.length == inputNode.length) {
 			boolean[] prediction = new boolean[outputNode.length];
@@ -187,13 +198,17 @@ public class PlayerModel implements Serializable {
 	public boolean save() {
 		FileOutputStream fout = null;
 		ObjectOutputStream oout = null;
+		String path = getPlayerPath(getName());
 		try {
-			fout = new FileOutputStream(PLAYER_MODEL_PATH);
+			(new File(DATA_FOLDER)).mkdirs();
+
+			fout = new FileOutputStream(path);
 			oout = new ObjectOutputStream(fout);
 			oout.writeObject(this);
 			oout.close();
 			return true;
 		} catch (IOException ex) {
+			ex.printStackTrace();
 			return false;
 		}
 	}