Added functionality to execute a turn

main.ipynb

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