class LearnerTestCase(ut.TestCase): def setUp(self): self.board = Board() self.learner = Learner() def tearDown(self): self.board = None self.learner = None def testMinimax(self): # self.board= Board(new_grid = RED_EASY_LOOKAHEAD) # print(self.learner.getNextMove(self.board)) # self.board= Board(new_grid = START) self.board = Board(new_grid = RED_EASY_LOOKAHEAD_2) best = self.learner.getNextMove(self.board) def testNearestNeighbor(self): weights = [0] * len(self.board.getMoveList(AI_COLOR)) weights[0] = 1 self.learner = Learner(data_points = [(self.board.getArray().tolist(), weights)]) # self.board.getMoveList(AI_COLOR)[0][1].printMove() # self.learner.getNextMove(self.board).printBoard() self.assertEqual(self.learner.getNextMove(self.board), self.board.getMoveList(AI_COLOR)[0][1], \ 'predicted best move does not match') def testUpdateWeights(self): pass
def playLoser(winner, game_count):
loser = Learner()
wins = 0
losses = 0
ties = 0
for game in tqdm(range(game_count)):
game_board = Board()
winner_moves = []
loser_moves = []
game_history = []
turn_count = 0
tie_flag = False
while game_board.checkGameStatus(AI_COLOR) == CONTINUE:
if turn_count > 100:
tie_flag = True
break
loser_move = loser.getNextMove(game_board.getInverse())
loser_moves.append(loser_move.getInverse())
assert(loser_move is not None and game_board is not None)
game_history.append((game_board, loser_move.getInverse()))
game_board = Board(game_board.applyMove(loser_move.getInverse()))
if game_board.checkGameStatus(AI_COLOR) == LOSE:
break
# assert (temp != game_board)
winner_move = winner.getNextMove(game_board)
assert(winner_move is not None and game_board is not None)
game_history.append((game_board, winner_move))
winner_moves.append(winner_move)
game_board = Board(game_board.applyMove(winner_move))
turn_count += 1
if tie_flag or game_board.checkGameStatus(AI_COLOR) == TIE:
ties += 1
elif game_board.checkGameStatus(AI_COLOR) == WIN:
wins += 1
elif game_board.checkGameStatus(AI_COLOR) == LOSE:
losses += 1
return { WIN : wins, LOSE : losses, TIE : ties}
def trainWithLoser(winner, game_count): loser = Learner() # game_board = Board() for game in tqdm(range(game_count)): game_board = Board() winner_moves = [] loser_moves = [] game_history = [] turn_count = 0 tie_flag = False while game_board.checkGameStatus(AI_COLOR) == CONTINUE: if turn_count > 100: tie_flag = True break loser_move = loser.getNextMove(game_board.getInverse()) loser_moves.append(loser_move.getInverse()) assert(loser_move is not None and game_board is not None) game_history.append((game_board, loser_move.getInverse())) # loser_move.printMove() # loser_move.getInverse().printMove() # game_board.printBoard() # temp = game_board # game_board.printBoard() game_board = Board(game_board.applyMove(loser_move.getInverse())) # game_board.printBoard() # assert(temp != game_board) # loser_move.getInverse().printMove() # print turn_count # game_board.printBoard() if game_board.checkGameStatus(AI_COLOR) == LOSE: break # assert (temp != game_board) winner_move = winner.getNextMove(game_board) assert(winner_move is not None and game_board is not None) game_history.append((game_board, winner_move)) # winner_move.printMove() winner_moves.append(winner_move) # temp = game_board game_board = Board(game_board.applyMove(winner_move)) # game_history.append(game_board) # game_board.printBoard() turn_count += 1 # print game if not tie_flag and game_board.checkGameStatus(AI_COLOR) != TIE: winner.updateWeights(game_history, status = game_board.checkGameStatus(AI_COLOR)) # winner.updateWeights(game_board, loser_moves, winner_moves, game_history = game_history) else: winner.updateWeights(game_history, status = TIE) # game_board.printBoard() # winner.updateWeights(game_board, loser_moves, winner_moves, status = TIE, game_history = game_history) loser_moves = None winner_moves = None return winner
