def optimum_move(board: Board, opponent_move, player_move, cur_move):
available_spots = board.empty_fields()
if board.winning(opponent_move):
return Move(None, -10) # when computer loses -> move.score = -10
elif board.winning(player_move):
return Move(None, 10) # when computer wins -> move.score = 10
elif len(available_spots) == 0:
return Move(None, 0) # when there is no more fields -> tie
moves = []
for i in range(0, len(available_spots)):
index = available_spots[i]
board = board.put(available_spots[i][0], available_spots[i][1],
cur_move) # simulation
if cur_move == player_move:
result = optimum_move(board, opponent_move, player_move,
opponent_move)
else:
result = optimum_move(board, opponent_move, player_move,
player_move)
board = board.put(available_spots[i][0], available_spots[i][1],
0) # back to previous state
moves.append(Move(
index, result.score)) # creating a list of potential best moves
# choosing best move for current player
if cur_move == player_move:
best_score = -inf
for i in range(0, len(moves)):
if moves[i].score > best_score:
best_score = moves[i].score
best_move = i
else:
best_score = inf
for i in range(0, len(moves)):
if moves[i].score < best_score:
best_score = moves[i].score
best_move = i
return moves[best_move]
