Added functionality to execute a turn
This commit is contained in:
parent
eb7a4a7cbc
commit
ef9fdf39ca
GPG Key ID:
DD53EAC36AFB61B4
171
main.ipynb
171
main.ipynb
@ -58,35 +58,27 @@
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"def get_new_games(number_of_games:int):\n",
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" empty = np.zeros([number_of_games, 8,8], dtype=int)\n",
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" empty[:, 3:5, 3:5] = np.array([[-1,1], [1, -1]])\n",
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" return empty"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [
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{
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"data": {
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},
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"execution_count": 6,
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"get_new_games(10)"
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"def get_new_games(number_of_games:int):\n",
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" empty = np.zeros([number_of_games, 8,8], dtype=int)\n",
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" empty[:, 3:5, 3:5] = np.array([[-1,1], [1, -1]])\n",
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" return empty\n",
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"get_new_games(1)[0]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"execution_count": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -100,37 +92,43 @@
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},
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{
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"cell_type": "code",
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"execution_count": 16,
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"execution_count": 7,
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"metadata": {},
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"outputs": [],
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"source": [
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"def get_new_games(number_of_games:int):\n",
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" empty = np.zeros([number_of_games, 8,8], dtype=int)\n",
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" empty[:, 3:5, 3:5] = np.array([[-1,1], [1, -1]])\n",
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" return empty"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"8.58 ms ± 214 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
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"82.7 ms ± 2.17 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
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"17.2 ms ± 3.53 ms per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
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"169 ms ± 33.2 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
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]
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},
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{
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"data": {
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"text/plain": "array([[[False, False, False, False, False, False, False, False],\n [False, False, False, False, False, False, False, False],\n [False, False, False, True, False, False, False, False],\n [False, False, True, False, False, False, False, False],\n [False, False, False, False, False, True, False, False],\n [False, False, False, False, True, False, False, False],\n [False, False, False, False, False, False, False, False],\n [False, False, False, False, False, False, False, False]]])"
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},
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"execution_count": 16,
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"execution_count": 8,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"def get_new_games(number_of_games:int):\n",
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" empty = np.zeros([number_of_games, 8,8], dtype=int)\n",
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" empty[:, 3:5, 3:5] = np.array([[-1,1], [1, -1]])\n",
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" return empty\n",
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"\n",
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"def recursive_steps(_array, rec_direction, rec_position, step_one=True):\n",
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" rec_position = rec_position + rec_direction\n",
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" if np.any((rec_position >= 8) | ( rec_position < 0)):\n",
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" return False\n",
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" next_field = _array[rec_position[0], rec_position[1]]\n",
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" next_field = _array[tuple(rec_position.tolist())]\n",
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" if next_field == 0:\n",
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" return False\n",
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" if next_field == -1:\n",
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@ -152,17 +150,20 @@
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"%timeit get_possible_turns(get_new_games(10))\n",
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"%timeit get_possible_turns(get_new_games(100))\n",
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"get_possible_turns(get_new_games(3))[:1]"
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]
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],
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"metadata": {
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"collapsed": false
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}
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"execution_count": 9,
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"outputs": [
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{
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"data": {
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"text/plain": "(array([2, 2, 2]), array([2, 2, 2]))"
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},
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"execution_count": 10,
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"execution_count": 9,
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"metadata": {},
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"output_type": "execute_result"
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}
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@ -178,7 +179,117 @@
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 10,
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"outputs": [],
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"source": [
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"def move_possible(board:np.ndarray, move: np.ndarray) -> bool:\n",
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" if np.all(move == -1):\n",
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" return np.all(get_possible_turns(board))\n",
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" return any(recursive_steps(board[:, :], direction, move) for direction in DIRECTIONS)\n",
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"\n",
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"def moves_possible(boards:np.ndarray, moves: np.ndarray) -> np.ndarray:\n",
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" arr_moves_possible = np.zeros(boards.shape[0], dtype=bool)\n",
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" for game in range(boards.shape[0]):\n",
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" if np.all(moves[game] == -1):\n",
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" arr_moves_possible[game, :, :] = np.all(get_possible_turns(boards[game, : , :]))\n",
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" arr_moves_possible[game, :, :] = any(recursive_steps(boards[game, :, :], direction, moves[game]) for direction in DIRECTIONS)\n",
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" return arr_moves_possible"
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],
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"metadata": {
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"collapsed": false
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}
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"outputs": [
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{
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"data": {
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"text/plain": "array([[ 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0, 0, 0, -1, 0, 0, 0, 0],\n [ 0, 0, 0, -1, -1, 0, 0, 0],\n [ 0, 0, 0, -1, 1, 0, 0, 0],\n [ 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0, 0, 0, 0, 0, 0, 0, 0]])"
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},
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"execution_count": 11,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"class InvalidTurn(ValueError):\n",
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" pass\n",
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"\n",
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"\n",
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"def to_moves(boards: np.ndarray, moves: np.ndarray) -> np.ndarray:\n",
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"\n",
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" def _do_directional_move(board: np.ndarray, rec_move: np.ndarray, rev_direction, step_one=True) -> bool:\n",
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" rec_position = rec_move + rev_direction\n",
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" if np.any((rec_position >= 8) | (rec_position < 0)):\n",
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" return False\n",
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" next_field = board[tuple(rec_position.tolist())]\n",
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" if next_field == 0:\n",
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" return False\n",
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" if next_field == 1:\n",
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" return not step_one\n",
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" if next_field == -1:\n",
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" if _do_directional_move(board, rec_position, rev_direction, step_one=False):\n",
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" board[tuple(rec_position.tolist())] = 1\n",
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" return True\n",
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" return False\n",
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"\n",
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" def _do_move(_board: np.ndarray, move: np.ndarray) -> None:\n",
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" if _board[tuple(move.tolist())] != 0:\n",
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" raise InvalidTurn\n",
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" action = False\n",
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" for direction in DIRECTIONS:\n",
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" if _do_directional_move(_board, move, direction):\n",
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" action = True\n",
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" if not action:\n",
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" raise InvalidTurn()\n",
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" _board[tuple(move.tolist())] = 1\n",
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"\n",
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" for game in range(boards.shape[0]):\n",
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" _do_move(boards[game], moves[game])\n",
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"boards = get_new_games(10)\n",
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"to_moves(boards, np.array([[2,3]] * 10))\n",
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"boards = boards * -1\n",
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"boards[0]"
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],
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"metadata": {
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"collapsed": false
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}
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"outputs": [],
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"source": [
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"to_moves(get_new_games(10), np.array([[2,3]] * 10))"
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],
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"metadata": {
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"collapsed": false
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}
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"outputs": [
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{
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"data": {
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"text/plain": "array([[4, 3],\n [4, 3],\n [4, 3],\n [4, 3],\n [4, 3],\n [4, 3],\n [4, 3],\n [4, 3],\n [4, 3],\n [4, 3]])"
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},
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"execution_count": 13,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"np.array([[4,3]] * 10)"
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],
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"metadata": {
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"collapsed": false
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}
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"outputs": [],
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"source": [],
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"metadata": {
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