- 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/Referee.java

@@ -30,19 +30,38 @@ public class Referee implements Runnable {
 	private final MainFrame mf;
 	private PlayerModel playerModel = null;
 
-	public Referee(MainFrame mnFrm) {
-		if (PlayerModel.TRY_LOAD && PlayerModel.exists()) {
-			playerModel = PlayerModel.load();
+	public Referee(MainFrame mnFrm, String player) {
+		if (PlayerModel.exists(player)) {
+			PlayerModel.getPlayerPath(player);
+			playerModel = PlayerModel.load(PlayerModel.getPlayerPath(player));
 		}
 
-		if (playerModel == null) {
-			playerModel = new PlayerModel();
+		if (getPlayerModel() == null) {
+			playerModel = new PlayerModel(player);
 		}
 
 		mf = mnFrm;
 		initGame();
 	}
 
+	public boolean[] getBoardState() {
+		boolean[] boardState = new boolean[getPlayerModel().getNumInputNodes()];
+
+		int i = 0;
+		for (int r = 0; r < Board.NUM_ROWS; r++) {
+			for (int c = 0; c < Board.NUM_COLS; c++) {
+				boardState[i] = (board.getTile(r, c) == TileColor.BLUE);
+				boardState[i + 1] = (board.getTile(r, c) == TileColor.GREEN);
+				boardState[i + 2] = (board.getTile(r, c) == TileColor.RED);
+				boardState[i + 3] = (board.getTile(r, c) == TileColor.YELLOW);
+
+				i += 4;
+			}
+		}
+
+		return boardState;
+	}
+
 	public Player getComputerPlayer() {
 		return computerPlayer;
 	}
@@ -83,13 +102,13 @@ public class Referee implements Runnable {
 			initGame();
 			mf.updateBoard();
 			play();
-			playerModel.logGame(getPlayerScore());
+			getPlayerModel().logGame(getPlayerScore());
 
-			if (!playerModel.save()) {
+			if (!getPlayerModel().save()) {
 				System.err.println("Saving PlayerModel failed.");
 			}
 
-			new HighScoreDialog(mf, playerModel);
+			new HighScoreDialog(mf, getPlayerModel());
 		}
 	}
 
@@ -97,6 +116,28 @@ public class Referee implements Runnable {
 		this.boardPanel = boardPanel;
 	}
 
+	private boolean[] getMoveArray(Move mv) {
+		boolean[] move = new boolean[getPlayerModel().getNumOutputNodes()];
+
+		move[0] = (mv.getColor() == TileColor.BLUE);
+		move[1] = (mv.getColor() == TileColor.GREEN);
+		move[2] = (mv.getColor() == TileColor.RED);
+		move[3] = (mv.getColor() == TileColor.YELLOW);
+
+		int tile = 0;
+		for (int r = 0; r < Board.NUM_ROWS; r++) {
+			for (int c = 0; c < Board.NUM_COLS; c++) {
+				move[tile] = (mv.getCell().r == r && mv.getCell().c == c);
+			}
+		}
+
+		return move;
+	}
+
+	private PlayerModel getPlayerModel() {
+		return playerModel;
+	}
+
 	private ScorePanel getScorePanel() {
 		return mf.getScorePanel();
 	}
@@ -126,6 +167,9 @@ public class Referee implements Runnable {
 					Move mv = humanPlayer.getMove(board);
 					if (board.getTile(mv.getCell().r, mv.getCell().c) == TileColor.NONE) {
 						playToken(humanPlayer.getMove(board));
+
+						getPlayerModel().train(getMoveArray(mv));
+
 					} else {
 						humanPlayer.denyMove();
 					}
@@ -133,6 +177,13 @@ public class Referee implements Runnable {
 			} else {
 				Move mv = computerPlayer.getMove(board);
 				playToken(mv);
+
+				// TODO
+				// This is the call that gets a prediction of a user's move.
+				// Some changes will probably be necessary to put it in the
+				// right place and also to get the node weights. But... all in
+				// due time.
+				getPlayerModel().getPrediction(getBoardState());
 			}
 
 			mf.updateMessage(getMessage());