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

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- 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.
This commit is contained in:
Marshall
2012-04-29 03:22:19 -04:00
parent dc11e2c48b
commit 15ed56134e
6 changed files with 274 additions and 65 deletions
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67
src/model/Referee.java
View File

@@ -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());

75
src/model/playerModel/PlayerModel.java
View File

@@ -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;
}
}