reversi/main.ipynb

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    "# Deep Otello AI\n",
    "\n",
    "The game reversi is a very good game to apply deep learning methods to.\n",
    "\n",
    "Othello also known as reversi is a board game first published in 1883 by eiter Lewis Waterman or John W. Mollet in England (each one was denouncing the other as fraud).\n",
    "It is a strickt turn based zero-sum game with a clear Markov chain and now hidden states like in card games with an unknown distribution of cards or unknown player allegiance.\n",
    "There is like for the game go only one set of stones with two colors which is much easier to abstract than chess with its 6 unique pieces.\n",
    "The game has a symmetrical game board wich allows to play with rotating the state around an axis to allow for a breaking of sequences or interesting ANN architectures, quadruple the data generation by simulation or interesting test cases where a symetry in turns should be observable if the AI reaches an \"objective\" policy."
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Content\n",
    "\n",
    "* [The game rules](#the-game-rules) A short overview over the rules of the game.\n",
    "* [Some common Otello strategies](#some-common-otello-strategies) introduces some easy approaches to a classic Otello AI and defines some behavioral expectations.\n",
    "* [Initial design decisions](#initial-design-decisions) an explanation about some initial design decision and assumptions\n",
    "* [Imports and dependencies](#imports-and-dependencies) explains what libraries where used"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The game rules\n",
    "\n",
    "Othello is played on a board with 8 x 8 fields for two player.\n",
    "The board geometry is equal to a chess game.\n",
    "The game is played with game stones that are black on one siede and white on the other.\n",
    "\n",
    "![Othello game board example](reversi_example.png)\n",
    "\n",
    "The player take turns.\n",
    "A player places a stone with his or her color up on the game board.\n",
    "The player can only place stones when he surrounds a number of stones with the opponents color with the new stone and already placed stones of his color.\n",
    "Those surrounded stones can either be horizontally, vertically and/or diagonally be placed.\n",
    "All stones thus surrounded will be flipped to be of the players color.\n",
    "Turns are only possible if the player is also changing the color of the opponents stones. If a player can't act he is skipped.\n",
    "The game ends if both players can't act. The player with the most stones wins.\n",
    "If the score is counted in detail unclaimed fields go to the player with more stones of his or her color on the board.\n",
    "The game begins with four stones places in the center of the game. Each player gets two. They are placed diagonally to each other.\n",
    "\n",
    "\n",
    "<img alt=\"Startaufstellung.png\" src=\"Startaufstellung.png\"/>\n",
    "\n",
    "## Some common Othello strategies\n",
    "\n",
    "As can be easily understood the placement of stones and on the bord is always a careful balance of attack and defence.\n",
    "If the player occupies huge homogenous stretches on the board it can be attacked easier.\n",
    "The boards corners provide safety from wich occupied territory is impossible to loos but since it is only possible to reach the corners if the enemy is forced to allow this or calculates the cost of giving a stable base to the enemy it is difficult to obtain.\n",
    "There are some text on otello computer strategies which implement greedy algorithms for reversi based on a modified score to each field.\n",
    "Those different values are score modifiers for a traditional greedy algorithm.\n",
    "If a players stone has captured such a filed the score reached is multiplied by the modifier.\n",
    "The total score is the score reached by the player subtracted with the score of the enemy.\n",
    "The scores change in the course of the game and converges against one. This gives some indications of what to expect from an Othello AI.\n",
    "\n",
    "<img alt=\"ComputerPossitionScore\" src=\"computer-score.png\"/>\n"
   ]
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   "cell_type": "markdown",
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   "source": [
    "## Initial design decisions\n",
    "\n",
    "At the beginning of this project I made some design decisions.\n",
    "The first onw was that I do not want to use a gym library because it limits the data formats accessible.\n",
    "I choose to implement the hole game as entry in a stack in numpy arrays to be able to accommodate interfacing with a neural network easier and to use scipy pattern recognition tools to implement some game mechanics for a fast simulation cycle.\n",
    "I chose to ignore player colors as far as I could instead a player perspective was used. Which allowed to change the perspective with a flipping of the sign. (multiplying with -1).\n",
    "The array format should also allow for data multiplication or the breaking of strikt sequences by flipping the game along one the for axis, (horizontal, vertical, transpose along both diagonals).\n",
    "\n",
    "I wanted to implement different agents as classes that act on those game stacks.\n",
    "\n",
    "Since computation time is critical all computational have results are saved.\n",
    "The analysis of those is then repeated in real time. If a recalculation of such a section is required the save file can be deleted and the code should be executed again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext blackcellmagic\n",
    "%load_ext line_profiler\n",
    "%load_ext memory_profiler"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports and dependencies\n",
    "\n",
    "The following direct dependencies where used for this project:\n",
    "```toml\n",
    "jupyter = \"^1.0.0\"\n",
    "matplotlib = \"^3.6.3\"\n",
    "numpy = \"^1.24.1\"\n",
    "pytest = \"^7.2.1\"\n",
    "python = \"3.10.*\"\n",
    "scipy = \"^1.10.0\"\n",
    "tqdm = \"^4.64.1\"\n",
    "jupyterlab = \"^3.6.1\"\n",
    "torchvision = \"^0.14.1\"\n",
    "torchaudio = \"^0.13.1\"\n",
    "```\n",
    "* `Jupyter` and `jupyterlab` on pycharm was used as an IDE / Ipython was used to implement this code.\n",
    "* `matplotlib` was used for visualisation and statistics.\n",
    "* `numpy` was used for array support and mathematical functions\n",
    "* `tqdm` was used for progress bars\n",
    "* `scipy` contains fast pattern recognition tools for images. It was used to make an initial estimation about where possible turns should be.\n",
    "* `torch` supplied the ANN functionalities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "import abc\n",
    "import itertools\n",
    "import os.path\n",
    "from abc import ABC\n",
    "from enum import Enum\n",
    "from typing import Final\n",
    "from IPython.display import clear_output\n",
    "from pathlib import Path\n",
    "import glob\n",
    "import copy\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "from ipywidgets import interact\n",
    "from scipy.ndimage import binary_dilation\n",
    "from tqdm.notebook import tqdm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Constants\n",
    "\n",
    "Some general constants needed to be defined. Such as board game size and Player and Enemy representations. Also, directional offsets and the initial placement of blocks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "BOARD_SIZE: Final[int] = 8  # defines the board side length as 8\n",
    "PLAYER: Final[int] = 1  # defines the number symbolising the player as 1\n",
    "ENEMY: Final[int] = -1  # defines the number symbolising the enemy as -1\n",
    "EXAMPLE_STACK_SIZE: Final[int] = 1000  # defines the game stack size for examples\n",
    "IMPOSSIBLE: Final[np.ndarray] = np.array([-1, -1], dtype=int)\n",
    "IMPOSSIBLE.setflags(write=False)\n",
    "SIMULATE_TURNS: Final[int] = 70\n",
    "VERIFY_POLICY: Final[bool] = False\n",
    "TRAINING_RESULT_PATH: Final[Path] = Path(\"training_data\")\n",
    "if not os.path.exists(TRAINING_RESULT_PATH):\n",
    "    os.mkdir(TRAINING_RESULT_PATH)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The directions array contains all the numerical offsets needed to move along one of the 8 directions in a 2 dimensional grid. This will allow an iteration over the game board.\n",
    "\n",
    "![8-directions.png](8-directions.png \"Offset in 8 directions\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1, -1],\n",
       "       [-1,  0],\n",
       "       [-1,  1],\n",
       "       [ 0, -1],\n",
       "       [ 0,  1],\n",
       "       [ 1, -1],\n",
       "       [ 1,  0],\n",
       "       [ 1,  1]])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DIRECTIONS: Final[np.ndarray] = np.array(\n",
    "    [[i, j] for i in range(-1, 2) for j in range(-1, 2) if j != 0 or i != 0],\n",
    "    dtype=int,\n",
    ")\n",
    "DIRECTIONS.setflags(write=False)\n",
    "DIRECTIONS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another constant needed is the initial start square at the center of the board."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1,  1],\n",
       "       [ 1, -1]])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "START_SQUARE: Final[np.ndarray] = np.array(\n",
    "    [[ENEMY, PLAYER], [PLAYER, ENEMY]], dtype=int\n",
    ")\n",
    "START_SQUARE.setflags(write=False)\n",
    "START_SQUARE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating new boards\n",
    "\n",
    "The first function implemented and tested is a function to generate the starting environment as a stack of games.\n",
    "As described above I simply placed a 2 by 2 square in the center of an empty stack of boards."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "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,  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,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_new_games(number_of_games: int) -> np.ndarray:\n",
    "    \"\"\"Generates a stack of initialised game boards.\n",
    "\n",
    "    Args:\n",
    "        number_of_games: The size of the board stack.\n",
    "\n",
    "    Returns: The generates stack of games as a stack n x 8 x 8.\n",
    "\n",
    "    \"\"\"\n",
    "    empty = np.zeros([number_of_games, BOARD_SIZE, BOARD_SIZE], dtype=int)\n",
    "    empty[:, 3:5, 3:5] = START_SQUARE\n",
    "    return empty\n",
    "\n",
    "\n",
    "get_new_games(1)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_number_of_games = 3\n",
    "assert get_new_games(test_number_of_games).shape == (\n",
    "    test_number_of_games,\n",
    "    BOARD_SIZE,\n",
    "    BOARD_SIZE,\n",
    ")\n",
    "np.testing.assert_equal(\n",
    "    get_new_games(test_number_of_games).sum(axis=1),\n",
    "    np.zeros(\n",
    "        [\n",
    "            test_number_of_games,\n",
    "            8,\n",
    "        ]\n",
    "    ),\n",
    ")\n",
    "np.testing.assert_equal(\n",
    "    get_new_games(test_number_of_games).sum(axis=2),\n",
    "    np.zeros(\n",
    "        [\n",
    "            test_number_of_games,\n",
    "            8,\n",
    "        ]\n",
    "    ),\n",
    ")\n",
    "assert np.all(get_new_games(test_number_of_games)[:, 3:4, 3:4] != 0)\n",
    "del test_number_of_games"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualisation tools\n",
    "\n",
    "In this section a visualisation help was implemented for debugging of the game and a proper display of the results.\n",
    "For this visualisation ChatGPT was used as a prompted code generator that was later reviewed and refactored by hand to integrate seamlessly into the project as a whole.\n",
    "White stones represent the player, black stones the enemy. A single plot can be used as a subplot when the `ax` argument is used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 300x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_othello_board(\n",
    "    board: np.ndarray | torch.Tensor,\n",
    "    action: np.ndarray | None = None,\n",
    "    ax=None,\n",
    ") -> None:\n",
    "    \"\"\"Plots a single otello board.\n",
    "\n",
    "    If a matplot axis object is given the board will be plotted into that axis. If not an axis object will be generated.\n",
    "    The image generated will be shown directly.\n",
    "\n",
    "    Args:\n",
    "        board: The bord that should be plotted. Only a single games is allowed. A numpy array of the form 8x8 is expected.\n",
    "        ax: If needed a matplotlib axis object can be defined that is used to place the board as a sublot into a bigger context.\n",
    "    \"\"\"\n",
    "    if isinstance(board, torch.Tensor):\n",
    "        board = board.cpu().detach().numpy()\n",
    "    assert board.shape == (8, 8)\n",
    "    plot_all = False\n",
    "    if ax is None:\n",
    "        fig_size = 3\n",
    "        plot_all = True\n",
    "        fig, ax = plt.subplots(figsize=(fig_size, fig_size))\n",
    "\n",
    "    ax.set_facecolor(\"#0f6b28\")\n",
    "    if action is not None:\n",
    "        ax.scatter(action[0], action[1], s=350 if plot_all else 200, c=\"red\")\n",
    "    for x_pos, y_pos in itertools.product(range(BOARD_SIZE), range(BOARD_SIZE)):\n",
    "        if board[x_pos, y_pos] == PLAYER:\n",
    "            color = \"white\"\n",
    "        elif board[x_pos, y_pos] == ENEMY:\n",
    "            color = \"black\"\n",
    "        else:\n",
    "            continue\n",
    "        ax.scatter(x_pos, y_pos, s=280 if plot_all else 140, c=color)\n",
    "    for x_pos in range(-1, 8):\n",
    "        ax.axhline(x_pos + 0.5, color=\"black\", lw=2)\n",
    "        ax.axvline(x_pos + 0.5, color=\"black\", lw=2)\n",
    "    ax.set_xlim(-0.5, 7.5)\n",
    "    ax.set_ylim(7.5, -0.5)\n",
    "    ax.set_xticks(np.arange(8))\n",
    "    ax.set_xticklabels(list(\"ABCDEFGH\"))\n",
    "    ax.set_yticks(np.arange(8))\n",
    "    ax.set_yticklabels(list(\"12345678\"))\n",
    "    ax.set_xlabel(\n",
    "        f\"W{np.sum(board == ENEMY)} / {np.sum(board == 0)} / B{np.sum(board == PLAYER)}\"\n",
    "    )\n",
    "    if plot_all:\n",
    "        plt.tight_layout()\n",
    "        plt.show()\n",
    "\n",
    "\n",
    "plot_othello_board(get_new_games(1)[0], action=np.array([3, 3]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_othello_boards(boards: np.ndarray, actions: np.ndarray | None = None) -> None:\n",
    "    \"\"\"Plots multiple boards into subplots.\n",
    "\n",
    "    The plots are shown directly.\n",
    "\n",
    "    Args:\n",
    "        boards: Plots the boards given into subplots. The maximum number of boards accepted is 70.\n",
    "    \"\"\"\n",
    "    assert len(boards.shape) == 3\n",
    "    assert boards.shape[1:] == (BOARD_SIZE, BOARD_SIZE)\n",
    "    assert boards.shape[0] < 70\n",
    "\n",
    "    if actions is not None:\n",
    "        assert len(actions.shape) == 2\n",
    "        assert actions.shape[1] == 2\n",
    "        assert boards.shape[0] == actions.shape[0]\n",
    "\n",
    "    plots_per_row = 4\n",
    "    rows = int(np.ceil(boards.shape[0] / plots_per_row))\n",
    "    fig, axs = plt.subplots(rows, plots_per_row, figsize=(12, 3 * rows))\n",
    "    for game_index, ax in enumerate(axs.flatten()):\n",
    "        if game_index >= boards.shape[0]:\n",
    "            fig.delaxes(ax)\n",
    "        else:\n",
    "            action = actions[game_index] if actions is not None else None\n",
    "            plot_othello_board(boards[game_index], action=action, ax=ax)\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def drop_duplicate_boards(\n",
    "    boards: np.ndarray, actions: np.ndarray | None\n",
    ") -> tuple[np.ndarray, np.ndarray | None]:\n",
    "    \"\"\"Drop boards that follow each other and are duplicates will be dropped.\n",
    "\n",
    "    Args:\n",
    "        boards: A set of boards to be reduced.\n",
    "\n",
    "    Returns:\n",
    "        A sequence of boards where boards that where equal are dropped.\n",
    "    \"\"\"\n",
    "    non_duplicates = ~np.all(boards == np.roll(boards, axis=0, shift=1), axis=(1, 2))\n",
    "    return (\n",
    "        boards[non_duplicates],\n",
    "        np.roll(actions, axis=0, shift=1)[non_duplicates]\n",
    "        if actions is not None\n",
    "        else None,\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hash Otello Boards\n",
    "\n",
    "\n",
    "### TODO ADD a text here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def hash_board(board: np.ndarray) -> int:\n",
    "    assert board.shape == (8,8) or board.shape == (64,)\n",
    "    return hash(tuple(board.reshape(-1)))\n",
    "\n",
    "def count_unique_baords(boards: np.ndarray) -> int:\n",
    "    return np.unique(np.apply_along_axis(hash_board, axis=1, arr=boards.reshape(-1, 64))).size\n",
    "    \n",
    "a = count_unique_baords(np.random.randint(-1, 2, size=(10000, 8, 8)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Find possible actions to take\n",
    "\n",
    "The frist step in the implementation of an AI like this is to get an overview over the possible actions that can be taken in a situation.\n",
    "Here was the design choice taken to first find fields that are empty and have at least one neighbouring enemy stone.\n",
    "This was implemented with element wise check for a stone and a binary dilation marking all fields neighboring an enemy stone.\n",
    "For that the `SURROUNDING` mask was used. Both aries are then element wise combined using and.\n",
    "The resulting array contains all filed where a turn could potentially be made. Those are then check in detail.\n",
    "The previous element wise operations on the numpy array increase the spead for this operation dramatically.\n",
    "\n",
    "The check for a possible turn is done in detail by following each direction step by step as long as there are enemy stones in that direction.\n",
    "If the board end is reached or en empty filed before reaching a field occupied by the player that direction does not surround enemy stones.\n",
    "If one direction surrounds enemy stone a turn is possible.\n",
    "This detailed step is implemented as a recursion and need to go at leas one step to return True."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[1, 1, 1],\n",
       "        [1, 0, 1],\n",
       "        [1, 1, 1]]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SURROUNDING: Final = np.array(\n",
    "    [[[1, 1, 1], [1, 0, 1], [1, 1, 1]]]\n",
    ")  # defines the binary dilation mask to check if a field is next to an enemy stones\n",
    "SURROUNDING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10 ms ± 617 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
      "948 ms ± 47.7 ms per loop (mean ± std. dev. of 7 runs, 1 loop 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": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def _recursive_steps(\n",
    "    board: np.ndarray,\n",
    "    rec_direction: np.ndarray,\n",
    "    rec_position: np.ndarray,\n",
    "    step_one: int = 0,\n",
    ") -> int:\n",
    "    \"\"\"Check if a player can place a stone on the board specified in the direction specified and direction specified.\n",
    "\n",
    "    Args:\n",
    "        board: The board that should be checked for a playable action.\n",
    "        rec_direction: The direction that should be checked.\n",
    "        rec_position: The position that should be checked.\n",
    "        step_one: Defines if the call of this function is the firs or not. Should be kept to the default value for proper functionality.\n",
    "\n",
    "    Returns:\n",
    "        True if a turn is possible for possition and direction on the board defined.\n",
    "    \"\"\"\n",
    "    rec_position = rec_position + rec_direction\n",
    "    if np.any((rec_position >= BOARD_SIZE) | (rec_position < 0)):\n",
    "        return 0\n",
    "    next_field = board[tuple(rec_position.tolist())]\n",
    "    if next_field == 0:\n",
    "        return 0\n",
    "    if next_field == -1:\n",
    "        return _recursive_steps(\n",
    "            board, rec_direction, rec_position, step_one=step_one + 1\n",
    "        )\n",
    "    if next_field == 1:\n",
    "        return step_one\n",
    "\n",
    "\n",
    "def get_possible_turns(boards: np.ndarray, tqdm_on: bool = False) -> np.ndarray:\n",
    "    \"\"\"Analyses a stack of boards.\n",
    "\n",
    "    Args:\n",
    "        boards: A stack of boards to check.\n",
    "        tqdm_on: Uses tqdm to track the progress.\n",
    "\n",
    "    Returns:\n",
    "        A stack of game boards containing boolean values showing where turns are possible for the player.\n",
    "    \"\"\"\n",
    "    assert len(boards.shape) == 3, \"The number fo input dimensions does not fit.\"\n",
    "    assert boards.shape[1:] == (\n",
    "        BOARD_SIZE,\n",
    "        BOARD_SIZE,\n",
    "    ), \"The input dimensions do not fit.\"\n",
    "\n",
    "    poss_turns = boards == 0  # checks where fields are empty.\n",
    "    poss_turns &= binary_dilation(\n",
    "        boards == -1, SURROUNDING\n",
    "    )  # checks where fields are next to an enemy filed an empty\n",
    "    iterate_over = itertools.product(\n",
    "        range(boards.shape[0]), range(BOARD_SIZE), range(BOARD_SIZE)\n",
    "    )\n",
    "    if tqdm_on:\n",
    "        iterate_over = tqdm(iterate_over, total=np.prod(boards.shape))\n",
    "    for game, idx, idy in iterate_over:\n",
    "        if poss_turns[game, idx, idy]:\n",
    "            position = idx, idy\n",
    "            poss_turns[game, idx, idy] = any(\n",
    "                _recursive_steps(boards[game, :, :], direction, position) > 0\n",
    "                for direction in DIRECTIONS\n",
    "            )\n",
    "    return poss_turns\n",
    "\n",
    "\n",
    "# some simple testing to ensure the function works after simple changes\n",
    "# this testing is complete, its more of a smoke-test\n",
    "test_array = get_new_games(3)\n",
    "expected_result = np.zeros_like(test_array, dtype=bool)\n",
    "expected_result[:, 4, 5] = expected_result[:, 2, 3] = True\n",
    "expected_result[:, 5, 4] = expected_result[:, 3, 2] = True\n",
    "np.testing.assert_equal(get_possible_turns(test_array), expected_result)\n",
    "\n",
    "\n",
    "%timeit get_possible_turns(get_new_games(10))  # checks turn possibility evaluation time for 10 initial games\n",
    "%timeit get_possible_turns(get_new_games(EXAMPLE_STACK_SIZE))  # check turn possibility evaluation time for EXAMPLE_STACK_SIZE initial games\n",
    "\n",
    "# shows a singe game\n",
    "get_possible_turns(get_new_games(3))[:1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Besides the ability to generate an array of possible turns there needs to be a functions that check if a given turn is possible.\n",
    "On is needed for the action space validation. The other is for validating a players turn."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def move_possible(board: np.ndarray, move: np.ndarray) -> bool:\n",
    "    \"\"\"Checks if a turn is possible.\n",
    "\n",
    "    Checks if a turn is possible. If no turn is possible to input array [-1, -1] is expected.\n",
    "\n",
    "    Args:\n",
    "        board: A board where it should be checkt if a turn is possible.\n",
    "        move: The move that should be taken. Expected is the index of the filed where a stone should be placed [x, y]. If no placement is possible [-1, -1] is expected as an input.\n",
    "\n",
    "    Returns:\n",
    "        True if the move is possible\n",
    "    \"\"\"\n",
    "    if np.all(move == -1):\n",
    "        return not np.any(get_possible_turns(np.reshape(board, (1, 8, 8))))\n",
    "    return any(\n",
    "        _recursive_steps(board[:, :], direction, move) > 0 for direction in DIRECTIONS\n",
    "    )\n",
    "\n",
    "\n",
    "# Some testing for this function and the underlying recursive functions that are called.\n",
    "assert move_possible(get_new_games(1)[0], np.array([2, 3])) is True\n",
    "assert move_possible(get_new_games(1)[0], np.array([3, 2])) is True\n",
    "assert move_possible(get_new_games(1)[0], np.array([2, 2])) is False\n",
    "assert move_possible(np.zeros((8, 8)), np.array([3, 2])) is False\n",
    "assert move_possible(np.ones((8, 8)) * 1, np.array([-1, -1])) is True\n",
    "assert move_possible(np.ones((8, 8)) * -1, np.array([-1, -1])) is True\n",
    "assert move_possible(np.ones((8, 8)) * 0, np.array([-1, -1])) is True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def moves_possible(boards: np.ndarray, moves: np.ndarray) -> np.ndarray:\n",
    "    \"\"\"Checks if a stack of moves can be executed on a stack of boards.\n",
    "\n",
    "    Args:\n",
    "        boards: A board where the next stone should be placed.\n",
    "        moves: A stack stones to be placed. Each move is formatted as an array in the form of [x, y] if no turn is possible the value [-1, -1] is expected.\n",
    "\n",
    "    Returns:\n",
    "        An array marking for each and every game and move in the stack if the move can be executed.\n",
    "    \"\"\"\n",
    "    arr_moves_possible = np.zeros(boards.shape[0], dtype=bool)\n",
    "    for game in range(boards.shape[0]):\n",
    "        if np.all(\n",
    "            moves[game] == -1\n",
    "        ):  # can be all or any. All should be faster since most times neither value will be -1.\n",
    "            arr_moves_possible[game] = not np.any(\n",
    "                get_possible_turns(np.reshape(boards[game], (1, 8, 8)))\n",
    "            )\n",
    "        else:\n",
    "            arr_moves_possible[game] = any(\n",
    "                _recursive_steps(boards[game, :, :], direction, moves[game]) > 0\n",
    "                for direction in DIRECTIONS\n",
    "            )\n",
    "    return arr_moves_possible\n",
    "\n",
    "\n",
    "np.testing.assert_array_equal(\n",
    "    moves_possible(np.ones((3, 8, 8)) * 1, np.array([[-1, -1]] * 3)),\n",
    "    np.array([True] * 3),\n",
    ")\n",
    "\n",
    "np.testing.assert_array_equal(\n",
    "    moves_possible(get_new_games(3), np.array([[2, 3], [3, 2], [3, 2]])),\n",
    "    np.array([True] * 3),\n",
    ")\n",
    "np.testing.assert_array_equal(\n",
    "    moves_possible(get_new_games(3), np.array([[2, 2], [1, 1], [0, 0]])),\n",
    "    np.array([False] * 3),\n",
    ")\n",
    "np.testing.assert_array_equal(\n",
    "    moves_possible(np.ones((3, 8, 8)) * -1, np.array([[-1, -1]] * 3)),\n",
    "    np.array([True] * 3),\n",
    ")\n",
    "np.testing.assert_array_equal(\n",
    "    moves_possible(np.zeros((3, 8, 8)), np.array([[-1, -1]] * 3)),\n",
    "    np.array([True] * 3),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reword functions\n",
    "\n",
    "For any kind of reinforcement learning is a reword function needed.\n",
    "For otello this would be the final score, the information who won or changes to the score.\n",
    "A combination of those three would also be possible.\n",
    "It is probably not be possible to weight the current score to high in a reword function since that would be to close to a classic greedy algorithm.\n",
    "But some direct influence would increase the learning speed.\n",
    "In the next section are all three reword functions implemented to be combined and weight later on as needed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "207 µs ± 1.35 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n",
      "35.5 µs ± 316 ns per loop (mean ± std. dev. of 7 runs, 10,000 loops each)\n",
      "37.6 µs ± 270 ns per loop (mean ± std. dev. of 7 runs, 10,000 loops each)\n"
     ]
    }
   ],
   "source": [
    "def final_boards_evaluation(boards: np.ndarray) -> np.ndarray:\n",
    "    \"\"\"Evaluates the board at the end of the game.\n",
    "\n",
    "    All unused fields are added to the score of the player that has more stones with his color up.\n",
    "    This score only applies to the end of the game.\n",
    "    Normally the score is represented by the number of stones each player has.\n",
    "    In this case the score was combined by building the difference.\n",
    "\n",
    "    Args:\n",
    "        boards: A stack of game bords ot the end of the game.\n",
    "\n",
    "    Returns:\n",
    "        the combined score for both player.\n",
    "    \"\"\"\n",
    "    score1, score2 = np.sum(boards == 1, axis=(1, 2)), np.sum(boards == -1, axis=(1, 2))\n",
    "    player_1_won = score1 > score2\n",
    "    player_2_won = score1 < score2\n",
    "    score1_final = 64 - score2[player_1_won]\n",
    "    score2_final = 64 - score1[player_2_won]\n",
    "    score1[player_1_won] = score1_final\n",
    "    score2[player_2_won] = score2_final\n",
    "    return score1 - score2\n",
    "\n",
    "\n",
    "def evaluate_boards(boards: np.ndarray) -> np.ndarray:\n",
    "    \"\"\"Counts the stones each player has on the board.\n",
    "\n",
    "    Args:\n",
    "        boards: A stack of boards for evaluation.\n",
    "\n",
    "    Returns:\n",
    "        the combined score for both player.\n",
    "    \"\"\"\n",
    "    return np.sum(boards, axis=(1, 2))\n",
    "\n",
    "\n",
    "def evaluate_who_won(boards: np.ndarray) -> np.ndarray:\n",
    "    \"\"\"Checks who won or is winning a game.\n",
    "\n",
    "    Args:\n",
    "        boards: A stack of boards for evaluation.\n",
    "\n",
    "    Returns:\n",
    "        The information who won for both player. 1 meaning the player won, -1 means the opponent lost. 0 represents a patt.\n",
    "    \"\"\"\n",
    "    return np.sign(np.sum(boards, axis=(1, 2)))\n",
    "\n",
    "\n",
    "_boards = get_new_games(EXAMPLE_STACK_SIZE)\n",
    "%timeit final_boards_evaluation(_boards)\n",
    "%timeit evaluate_boards(_boards)\n",
    "%timeit evaluate_who_won(_boards)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Execute a chosen action\n",
    "\n",
    "After an evaluation what turns are possible there needs to be a function that executes a turn.\n",
    "This next sections does that."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "class InvalidTurn(ValueError):\n",
    "    \"\"\"\n",
    "    This error is thrown if a given turn is not valid.\n",
    "    \"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "101 ms ± 4.05 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 300x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def do_moves(boards: np.ndarray, moves: np.ndarray) -> np.ndarray:\n",
    "    \"\"\"Executes a single move on a stack o Othello boards.\n",
    "\n",
    "    Args:\n",
    "        boards: A stack of Othello boards where the next stone should be placed.\n",
    "        moves: A stack of stone placement orders for the game. Formatted as coordinates in an array [x, y] of the place where the stone should be placed. Should contain [-1,-1] if no new placement is possible.\n",
    "\n",
    "    Returns:\n",
    "        The new state of the board.\n",
    "    \"\"\"\n",
    "\n",
    "    def _do_directional_move(\n",
    "        board: np.ndarray, rec_move: np.ndarray, rev_direction, step_one=True\n",
    "    ) -> bool:\n",
    "        \"\"\"Changes the color of enemy stones in one direction.\n",
    "\n",
    "        This function works recursive. The argument step_one should always be used in its default value.\n",
    "\n",
    "        Args:\n",
    "            board: A bord on which a stone was placed.\n",
    "            rec_move: The position on the board in x and y where this function is called from. Will be moved by recursive called.\n",
    "            rev_direction: The position where the stone was placed. Inside this recursion it will also be the last step that was checked.\n",
    "            step_one: Set to true if this is the first step in the recursion. False later on.\n",
    "\n",
    "        Returns:\n",
    "            True if a stone could be flipped.\n",
    "            All changes are made on the view of the numpy array and therefore not included in the return value.\n",
    "        \"\"\"\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",
    "        \"\"\"Executes a turn on a board.\n",
    "\n",
    "        Args:\n",
    "            _board: The game board on wich to place a stone.\n",
    "            move: The coordinates of a stone that should be placed. Should be formatted as an array of the form [x, y]. The value [-1, -1] is expected if no turn is possible.\n",
    "\n",
    "        Returns:\n",
    "            All changes are made on the view of the numpy array.\n",
    "        \"\"\"\n",
    "        if np.all(move == -1):\n",
    "            if not move_possible(_board, move):\n",
    "                raise InvalidTurn(\"An action should be taken. A turn is possible.\")\n",
    "            return\n",
    "\n",
    "        # noinspection PyTypeChecker \n",
    "        if _board[tuple(move.tolist())] != 0:\n",
    "            raise InvalidTurn(\"This turn is not possible.\")\n",
    "\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(\"This turn is not possible.\")\n",
    "\n",
    "        # noinspection PyTypeChecker\n",
    "        _board[tuple(move.tolist())] = 1\n",
    "\n",
    "    boards = boards.copy()\n",
    "    for game in range(boards.shape[0]):\n",
    "        _do_move(boards[game], moves[game])\n",
    "    return boards\n",
    "\n",
    "\n",
    "%timeit do_moves(get_new_games(EXAMPLE_STACK_SIZE), np.array([[2, 3]] * EXAMPLE_STACK_SIZE))[0]\n",
    "\n",
    "plot_othello_board(\n",
    "    do_moves(\n",
    "        get_new_games(EXAMPLE_STACK_SIZE), np.array([[2, 3]] * EXAMPLE_STACK_SIZE)\n",
    "    )[0],\n",
    "    action=np.array([2, 3]),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## An abstract reversi game policy\n",
    "\n",
    "For an easy use of policies an abstract class containing the policy generation / requests an action in an inherited instance of this class.\n",
    "This class filters the policy to only propose valid actions. Inherited instance do not need to care about this. This super class also manges exploration and exploitation with the epsilon value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "class GamePolicy(ABC):\n",
    "    \"\"\"\n",
    "    A game policy. Proposes where to place a stone next.\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, epsilon: float):\n",
    "        \"\"\"\n",
    "\n",
    "        Args:\n",
    "            epsilon: the epsilon / greedy value. Should be between zero and one. Set the mixture of policy and exploration. One means only the policy is used. Zero means only random policies are used. All mixtures inbetween between are possible.\n",
    "        \"\"\"\n",
    "        if 0 > epsilon > 1:\n",
    "            raise ValueError(\"Epsilon should be between zero and one.\")\n",
    "        self._epsilon: float = epsilon\n",
    "\n",
    "    @property\n",
    "    def epsilon(self):\n",
    "        return self._epsilon\n",
    "\n",
    "    @property\n",
    "    @abc.abstractmethod\n",
    "    def policy_name(self) -> str:\n",
    "        \"\"\"The name of this policy\"\"\"\n",
    "        raise NotImplementedError()\n",
    "\n",
    "    @abc.abstractmethod\n",
    "    def _internal_policy(self, boards: np.ndarray) -> np.ndarray:\n",
    "        \"\"\"The internal policy is an unfiltered policy. It should only be called from inside this function\n",
    "\n",
    "        Args:\n",
    "            boards: A board where a policy should be calculated for.\n",
    "\n",
    "        Returns:\n",
    "            The policy for this board. Should have the same size as the boards array.\n",
    "        \"\"\"\n",
    "        raise NotImplementedError()\n",
    "\n",
    "    def get_policy(self, boards: np.ndarray) -> np.ndarray:\n",
    "        \"\"\"Calculates the policy that should be followed.\n",
    "\n",
    "        Calculates the policy that should be followed.\n",
    "        This function does include the usage of epsilon to configure greediness and exploration.\n",
    "\n",
    "        Args:\n",
    "            boards: A set of boards that show the environment where the policy should be calculated for.\n",
    "\n",
    "        Returns:\n",
    "            A vector of indices. Should be formatted as an array of the form [x, y]. The value [-1, -1] is expected if no turn is possible.\n",
    "        \"\"\"\n",
    "        assert len(boards.shape) == 3\n",
    "        assert boards.shape[1:] == (BOARD_SIZE, BOARD_SIZE)\n",
    "\n",
    "        if self.epsilon <= 0:\n",
    "            policies = np.random.rand(*boards.shape)\n",
    "        else:\n",
    "            policies = self._internal_policy(boards)\n",
    "            if self.epsilon < 1:\n",
    "                random_choices = self.epsilon <= np.random.rand((boards.shape[0]))\n",
    "                policies[random_choices] = np.random.rand(np.sum(random_choices), 8 ,8)\n",
    "\n",
    "        # todo talk to team about backpropagation of score and epsilon for greedy factor\n",
    "\n",
    "        # todo possibly change this function to only validate the purpose turn and not all turns\n",
    "        possible_turns = get_possible_turns(boards)\n",
    "        policies[possible_turns == False] = -1.0\n",
    "        max_indices = [\n",
    "            np.unravel_index(policy.argmax(), policy.shape) for policy in policies\n",
    "        ]\n",
    "        policy_vector = np.array(max_indices, dtype=int)\n",
    "        no_turn_possible = np.all(policy_vector == 0, 1) & (policies[:, 0, 0] == -1.0)\n",
    "\n",
    "        policy_vector[no_turn_possible, :] = IMPOSSIBLE\n",
    "        return policy_vector"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A first policy\n",
    "\n",
    "To quantify the quality of a game AI there needs to be some benchmarks.\n",
    "The easiest benchmark is to play against a random player.\n",
    "The easiest player to use as a benchmark is the random player.\n",
    "For this and testing purpose the random policy was implemented."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "class RandomPolicy(GamePolicy):\n",
    "    \"\"\"\n",
    "    A policy playing a random turn by setting epsilon to 0.\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, epsilon: float = 0):\n",
    "        _ = epsilon\n",
    "        super().__init__(epsilon=0)\n",
    "\n",
    "    @property\n",
    "    def policy_name(self) -> str:\n",
    "        return \"random\"\n",
    "\n",
    "    def _internal_policy(self, boards: np.ndarray) -> np.ndarray:\n",
    "        pass\n",
    "\n",
    "\n",
    "rnd_policy = RandomPolicy(1)\n",
    "assert rnd_policy.policy_name == \"random\"\n",
    "assert rnd_policy.epsilon == 0\n",
    "\n",
    "rnd_policy_result = rnd_policy.get_policy(get_new_games(10))\n",
    "assert np.any((5 >= rnd_policy_result) & (rnd_policy_result >= 3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "class GreedyPolicy(GamePolicy):\n",
    "    \"\"\"\n",
    "    A policy playing always one of the strongest turns.\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, epsilon: float = 1):\n",
    "        _ = epsilon\n",
    "        super().__init__(1)\n",
    "\n",
    "    @property\n",
    "    def policy_name(self) -> str:\n",
    "        return \"greedy_policy\"\n",
    "\n",
    "    def _internal_policy(self, boards: np.ndarray) -> np.ndarray:\n",
    "        policies = np.random.rand(*boards.shape)\n",
    "        poss_turns = boards == 0  # checks where fields are empty.\n",
    "        poss_turns &= binary_dilation(boards == -1, SURROUNDING)\n",
    "        for game, idx, idy in itertools.product(\n",
    "            range(boards.shape[0]), range(BOARD_SIZE), range(BOARD_SIZE)\n",
    "        ):\n",
    "\n",
    "            if poss_turns[game, idx, idy]:\n",
    "                position = idx, idy\n",
    "                policies[game, idx, idy] += np.sum(\n",
    "                    np.array(\n",
    "                        list(\n",
    "                            _recursive_steps(boards[game, :, :], direction, position)\n",
    "                            for direction in DIRECTIONS\n",
    "                        )\n",
    "                    )\n",
    "                )\n",
    "        return policies"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Putting the game simulation together\n",
    "Now it's time to bring all together for a proper simulation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Playing a single turn\n",
    "\n",
    "The next function needed is used to request a policy, verify that the turn is legit and place a stone and turn enemy stones if possible."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.06 s ± 7.6 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
      "1.08 s ± 4.54 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    },
    {
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      "text/plain": [
       "<Figure size 1200x600 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def single_turn(\n",
    "    current_boards: np, policy: GamePolicy\n",
    ") -> tuple[np.ndarray, np.ndarray]:\n",
    "    \"\"\"Execute a single turn on a board.\n",
    "\n",
    "    Places a new stone on the board. Turns captured enemy stones.\n",
    "\n",
    "    Args:\n",
    "        current_boards: The current board before the game.\n",
    "        policy: The game policy to be used.\n",
    "\n",
    "    Returns:\n",
    "        The new game board and the policy vector containing the index of the action used.\n",
    "    \"\"\"\n",
    "    policy_results = policy.get_policy(current_boards)\n",
    "\n",
    "    # if the constant VERIFY_POLICY is set to true the policy is verified. Should be good though.\n",
    "    # todo deactivate the policy verification after some testing.\n",
    "    if VERIFY_POLICY:\n",
    "        assert np.all(moves_possible(current_boards, policy_results)), (\n",
    "            current_boards[(moves_possible(current_boards, policy_results) == False)],\n",
    "            policy_results[(moves_possible(current_boards, policy_results) == False)],\n",
    "            np.where(moves_possible(current_boards, policy_results) == False),\n",
    "        )\n",
    "    return do_moves(current_boards, policy_results), policy_results\n",
    "\n",
    "\n",
    "%timeit single_turn(get_new_games(EXAMPLE_STACK_SIZE), RandomPolicy(1))\n",
    "VERIFY_POLICY = False  # type: ignore\n",
    "%timeit single_turn(get_new_games(EXAMPLE_STACK_SIZE), RandomPolicy(1))\n",
    "VERIFY_POLICY = True  # type: ignore\n",
    "_turn_result = single_turn(get_new_games(EXAMPLE_STACK_SIZE), RandomPolicy(1))\n",
    "plot_othello_boards(_turn_result[0][:8], _turn_result[1][:8])\n",
    "del _turn_result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Simulate a stack of games\n",
    "This function will simulate a stack of games and return an array of policies and histories."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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dCu+sPej7Gb2CCo7qrsySPPkCyTn5zIUMANzjQje4kAGAe1zoBhcyAHCT7X6wPR9wBYtS7QjtqVP1nI0yla2/nWF4Z632zd+mmvd2KufcAfIXZHW6DADc40I3uJABgHtc6AYXMgBwk+1+sD0fcAlLrm0I7alT1XMbZKpaL4v9maoGVT23QaE9dZ0qAwD3uNANLmQA4B4XusGFDADcZLsfbM8HXMOiVCtMQ1jVczZKdWHJRHsnSXWR+5mGcKfIAMA9LnSDCxkAuMeFbnAhAwA32e4H2/MBF8W0KHXfffdp1KhRysvLU15enk488UTNnTs3Wdmsqi+tiJxOGW1ZNDKSqWxQfWlFp8gApAr6KQr0E2BNunSUC93gQgYglaRLP0n2+8H2fMBFMS1K9evXT3fccYeWLl2q999/X+PHj9d5552nf//738nKZ03tit1W7+9KBiBV0E/e3d+VDEAqSZeOcqEbXMgApJJ06SfJfj/Yvj/gopgWpc455xydeeaZKikp0bBhw3TbbbepW7duWrx4cbLyWRHaUdPiOyDEIryzVqEdNSmdAUgl9FP06CfAe+nQUS50gwsZgFSTDv0k2e8H2/MBV8X97nuhUEjPPvusqqurdeKJJ7Z6u9raWtXWfvXgq6hw/5TD0O7EXcTOX5idshmAVEU/RbEd+gmwJpqOop/oJ8CGztpPkv1+sD0fcFXMFzpfuXKlunXrpmAwqB/84AeaPXu2DjvssFZvP336dOXn5zd99O/fv0OBPVGfoAvI1XVgOy5kAFIM/RQD+gnwXCwdRT+lcAYgBXX6fpLs94Pt+YCjYl6UGj58uJYtW6YlS5bo2muv1RVXXKGPPvqo1dtPmzZN5eXlTR+bNm3qUGBPZCboTQmzOrAdFzIAKYZ+igH9BHgulo6in1I4A5CCOn0/Sfb7wfZ8wFExv3wvKytLQ4cOlSQde+yxeu+99/SnP/1J999/f4u3DwaDCgaDHUvpMX/3rMRspyD+7biQAUg19FMM26GfAM/F0lH0U+pmAFJRZ+8nyX4/2J4PuKrDy6zhcLjZa4o7A39htjJ6daxoM3oFO/RaXxcyAKmOfmoZ/QS4obN1lAvd4EIGoDPobP0k2e8H2/MBV8V0ptS0adM0adIkDRgwQJWVlXriiSe0cOFCvfLKK8nKZ01wVHftm7+tQ/fvDBmAVEE/xXb/zpABSCXp0lEudIMLGYBUki79JNnvB9vzARfFtCi1fft2XX755SorK1N+fr5GjRqlV155RRMmTEhWPmsyS/JU895OmaoGycRwR5/k6xZQZklep8gApAr6KQr0E2BNunSUC93gQgYglaRLP0n2+8H2fMBFMS1KzZw5M1k5nOMLZCjn3AGqem5D5B0OoikNn6SsyP18gY5fgM6FDECqoJ/au5PoJ8CidOkoF7rBhQxAKkmXfpLs94Pt+YCL+K1ug78gS90uHiRft+jW7nzdAup28aCEXnzOhQwA3ONCN7iQAYB7XOgGFzIAcJPtfrA9H3BNzO++l278BVnK/fYQ1ZdWqHbFboV3HnzBv4xeQQVHdVdmSV5SVq9dyADAPS50gwsZALjHhW5wIQMAN9nuB9vzAZewKBUFXyBDWSMLlDWyQKEdNQrtqYucbpmVIX9BlifvgOBCBgDucaEbXMgAwD0udIMLGQC4yXY/2J4PuIJFqRj5C7OtF4QLGQC4x4VucCEDAPe40A0uZADgJtv9YHs+YBPnAQIAAAAAAMBzLEoBAAAAAADAcyxKAQAAAAAAwHM+Y4zxcmBFRYXy8/Mjw3O8v6SV2dsgGUk+ydfVziW1yEAGlzLYni9JprpBklReXq68vDwrGST7/SQ5cjz4nSQDGZpncKCj6CcyuDKfDI5loJ8kOXIsyEAGR+Y7kyHKfrJ6ofPGkHaGW55PBjK4lsH2fMdY3xcuHA/bGWzPJwMZHGV9P7hwLMhgfz4Z3MrgCOv7wYVjQQYyuDLflQztsLooxZlSZCCD/Qy250tuFiX/0mcvg+35ZCDDQRkc6yj6Kb0z2J5PBscy0E+SHDkWZCCDI/OdyRBlP9lblOrqV96VQz0fWzFrnUx1g3xdA1bmk4EMrmWwPV+Syh8plfaGrMxukaV+ktw4HrYz2J5PBjIcyKmOop/SPoPt+WRwKwP9FOHCsSADGVyZ70qGaPuJC50DAAAAAADAcyxKAQAAAAAAwHMsSgEAAAAAAKAZnzFJn2H1QucAAAAAAACwb/SOGk1evUcnbt2n4btrlRWW6jKktd2DWlTcRY+PLNDywuyEzkypRalR/UaqpM9gdcvOUVVNtUo/X68Vm1enXYbQjhqFdtdJ9WEpM0P+7lnyJ/gXoz2294Pt+WTAgVw4Fi5koJ/cyGB7visZ8BXbx8P2fIl+IoN7GRDhwrGwncGFfiJDemcYvKdOM+aXaWzZPtX7pFVGel5SpaTcsDTii1p9d1etrlm5R28XddH144u0viArIbOdX5TKzgzqomPP1DXjpmh0/8MO+v7yTR/pgTce0/NLX1ZNfW2nzWAawqovrVDtit0K7zx4RkavoIKjuiuzJE++foHLmgABAABJREFUQHJelWl7P9ieTwYcyIVj4UIG+smNDLbnu5IBX7F9PGzPl+gnMriXAREuHAvbGVzoJzKQQZIu/rhc987fprqQ0SOS7jbSshZud5SRbpR0cdk+LX5qva4bX6Tnh+V1eL7PGA9eJLifiooK5efnS139yv9uSZu3PbT3IM2e+pD69yhW2ITlz/AfdJtQOKQMX4Y27dqq82dcpU93fNb2/Ma3RsyJ7q0RXcgQ2lOn6jkbZSob2r2tLzegnHMHyN/OqmWq7YdkzCdD7POTpfHtQsvLy5WX1/Fii5ftfpLs/z7EmoF+ciODC78LLmRIFhc6KpZ+kvidlJLTT7FmsH0cyOBWhmSgn77MkGK/Dy70ExmSlyGV/t6/+ONyPfBqmT6WNFHSZ4pceDzcwm0bvz5Q0iuSSiRdM6FIzw3Lb3Hb0faTsxc6P7T3IL32k6dUXNBHPp+vxaKQJH+GXz6fT8UFffT6T5/WkMKBnSpDaE+dqp7bIFPV/i+oJJmqBlU9t0GhPXUJy2B7P9ieTwYcyIVj4UIG+smNDLbnu5IBX7F9PGzPl+gnMriXAREuHAvbGVzoJzKQQZKG7KnTvfO36WNJJ0na/OXXW1qQ2v/rmyWdKKlU0r3zt2lwB7N0aFHqjjvukM/n049+9KMOhThQdmZQs6c+pNxgjgL+6F5hGPAHlBvM0QvXz1R2ZrBTZDANYVXP2SjVhaVoz2czkuoi9zMNrf06Rc/2frA9nwypi35Kbgb6yY0Mtue7kiHVJKufJPvHw/Z8iX4ig3sZUg3PoZKXwYV+IgMZGt0zv0y1IaOJkiokhaK8X+jL239TUl3IaMb8sg7liHtR6r333tP999+vUaNGdShASy469kz171EcdVE0CvgDGtCzry48ZlKnyFBfWhE5hS/WF1gayVQ2qL60osMZbO8H2/PJkJrop+RnoJ/cyGB7visZUkky+0myfzxsz5foJzK4lyGV8BwquRlc6CcykEGSRm+v0diyfXpekZfsRbsg1Sj05f2elzS2bJ9G76iJO0tci1JVVVWaPHmyHnzwQXXv3j3u4a25ZtwUhU18q36hcEjXjJvSKTLUrtht9f6S/f1gez4ZUg/95E0G+smNDLbnu5IhVSS7nyT7x8P2fIl+IoN7GVIFz6GSn8GFfiIDGSRp8po9qvdJdyv+M5UyFLl/vU+avHpP3Fnimj916lSdddZZOv300+Me3JpR/UZqdP/DWn1tb3v8GX4dNeBwjeo3MqUzhHbUtHjV/ViEd9Yq1IEVS9v7wfZ8MqQm+in5GegnNzLYnu9KhlSSzH6S7B8P2/Ml+okM7mVIJTyHSm4GF/qJDGRodOLWfVr15bvsxfsiwLCkDyWtMtLXyvbFuZU4FqWeeuopffDBB5o+fXpUt6+trVVFRUWzj7aU9Bkca6QWDe3AdlzIENqdmIundeQCaLb3g+35ZEg99JM3GegnNzLYnu9KhlSR7H6S7B8P2/Ml+okM7mVIFbF0VCr2kwsZXOgnMpCh0fDdtVqTkATSWkkjdsW/wBbTotSmTZv0wx/+UI8//riys7Ojus/06dOVn5/f9NG/f/82b98tOyeWSK3K7cB2XMig+o5fOE1S5KJpcbK9H2zPJ0NqoZ+8y0A/uZHB9nxXMqQCL/pJsn88bM+XRD+RwbkMqSDWjkrFfnIigwP9RAYySJLPGGWFpcrEJFCFpKxwZLvxiGlRaunSpdq+fbuOOeYYBQIBBQIBvfHGG7r77rsVCAQUCh18eaxp06apvLy86WPTpk1tzqiqqY7tJ2hFZQe240IGZXbojRG/khX/dmzvB9vzyZBa6CfvMtBPbmSwPd+VDKnAi36S7B8P2/Ml0U9kcC5DKoi1o1Kxn5zI4EA/kYEMkmR8PtVlSLmJSaA8SXUZke3GI6a3HTjttNO0cuXKZl/77ne/qxEjRujnP/+5/P6DX58bDAYVDEb/1pmln6+PJVLr29n2afz3dSCDv3tWQjL4C+Lfju39YHs+GVIL/eRdBvrJjQy257uSIRV40U+S/eNhe75EP5HBvQypINaOSsV+ciGDC/1EBjI0Wts9qBFfdOyaVo1GSFrTI7ZO2F9My2q5ubk64ogjmn3k5OSoZ8+eOuKII+IOsb8Vm1dr+aaPFArH+qaEEaFwSMs2/lsrt8T/CkkXMvgLs5XRK/4DK0kZvYLyF0b3MoGW2N4PtueTIbXQT95loJ/cyGB7visZUoEX/STZPx6250v0Exncy5AKeA7lTQYX+okMZGi0qLiLjvBJR6lj7753tKTDfdLioi5xbiX++Un1wBuPKcMXXzR/hl8PvPFYp8gQHNWxt2Lt6P0l+/vB9nwy4EAuHAsXMtBPbmSwPd+VDPiK7eNhe75EP5HBvQyIcOFY2M7gQj+RgQyS9PjIAmUa6UZ17N33bpSUaSLbi1eHF6UWLlyou+66q6Obaeb5pS9r066tagg1xHS/hlCDNn6xRX/7YG6nyJBZkidfbkCK9aWZPsmXG1BmSV6HM9jeD7bnkyG10U/Jy0A/uZHB9nxXMqSiZPSTZP942J4v0U9kcC9DKuI5VHIyuNBPZCCDJC0vzNbbRV10saSBkg6+kEDb/F/e7yJJbxd10fIOnDXm5JlSNfW1uuDe76uytjrqwmgINaiytlrnz7hKNfUdf22kCxl8gQzlnDsgcvGyaH9RfZKyIvfzBTp+eG3vB9vzyYADuXAsXMhAP7mRwfZ8VzLgK7aPh+35Ev1EBvcyIMKFY2E7gwv9RAYyNLp+fJGy/D7NU+Ri5dEuTPm/vP0rkrL8Pl0/vqhDOZxclJKkT7Zv0Gl3Xqqtez6XpFZf+9v49a17Ptdpd16qT3d81qky+Auy1O3iQfJ1i+6a9L5uAXW7eFCHLrp2INv7wfZ8MuBALhwLFzLQT25ksD3flQz4iu3jYXu+RD+Rwb0MiHDhWNjO4EI/kYEMkrS+IEvXjS/SMEmLJPX78uutLRI1fr3fl7cvkXTd+CKt72CWmN59z2uf7vhMJ/z3WbrwmEm6ZtwUHTXg8INus3LzGj3wxmP62wdzk/IvGS5k8BdkKffbQ1RfWqHaFbsV3nnwjIxeQQVHdY+cApiAVdsD2d4PtueTAQdy4Vi4kIF+ciOD7fmuZMBXbB8P2/Ml+okM7mVAhAvHwnYGF/qJDGSQpOeH5ckno3vnb9PKkNHzku6W9GELtx2tyDWkLlLkDKmrxxfp+WEdfxmj04tSUuQUyyeWvKAnlrygUf1GamifwcrNzlFlTbXWfb5eKzavTosMvkCGskYWKGtkgUI7ahTaUyfVhaWsDPkLsjp05f9o2d4PtueTAQdy4Vi4kIF+ciOD7fmuZMBXbB8P2/Ml+okM7mVAhAvHwnYGF/qJDGSQpOeG5Wtp7y6aMb9MV5bt02SftMpIayVVKPJSveGSjvBFLmr+VnEX3XBqx8+QauT8otT+Vmxebf1/Fi5k8Bdme/LAaIvt/WB7PhlwIBeOhQsZ6Cc3Mtie70oGfMX28bA9X6KfyOBeBkS4cCxsZ3Chn8iQ3hnWF2TprAsHavSOGk1evUdfK9unC3fVKiss1WVIa3oE9UhRFz0+sqBDFzVvSUotSgEAAAAAACDxlhdma3nhIU2f+4yR8cX69oCxcfZC5wAAAAAAALAj2QtSEotSAAAAAAAAsMBnjDFeDqyoqFB+fn5keI73rx40exskI8kn+braefUiGcjgUgbb8yXJVDdIksrLy5WX1/F3cIiX7X6SHDke/E6SgQzNMzjQUfQTGVyZTwbHMtBPkhw5FmQggyPznckQZT9ZvaZUY0g7wy3PJwMZXMtge75jrO8LF46H7Qy255OBDI6yvh9cOBZksD+fDG5lcIT1/eDCsSADGVyZ70qGdlhdlOJMKTKQwX4G2/MlN4uSf+mzl8H2fDKQ4aAMjnUU/ZTeGWzPJ4NjGegnSY4cCzKQwZH5zmSIsp/sLUp19SvvyqGej62YtU6mukG+rgEr88lABtcy2J4vSeWPlEp7Q1Zmt8hSP0luHA/bGWzPJwMZDuRUR9FPaZ/B9nwyuJWBfopw4ViQgQyuzHclQ7T9xIXOAQAAAAAA4DkWpQAAAAAAAOA5FqUAAAAAAADgOasXOk9Fo/qNVEmfweqWnaOqmmqVfr5eKzav9jRDaEeNQrvrpPqwlJkhf/cs+Quz0yqDC8cBcI0Ljwvb3eBKBheOBeASFx4TdIM7GQDX2H5c0E/uZID3WJSKQnZmUBcde6auGTdFo/sfdtD3l2/6SA+88ZieX/qyauprk5LBNIRVX1qh2hW7Fd558IyMXkEFR3VXZkmefIHknABnO4MLxwFwjQuPC9vd4EoGF44F4BIXHhN0gzsZANfYflzQT+5kgF0sSrXj0N6DNHvqQ+rfo1hhE27xNkf0Ha4Zk2/TzydN1fkzrtKnOz5LaIbQnjpVz9koU9n6WyqGd9Zq3/xtqnlvp3LOHSB/QVanyuDCcQBc48LjwnY3uJLBhWMBuMSFxwTd4E4GwDW2Hxf0kzsZYB/XlGrDob0H6bWfPKXigj7y+XzyZ/hbvJ0/wy+fz6figj56/adPa0jhwIRlCO2pU9VzG2SqWi+s/ZmqBlU9t0GhPXWdJoMLxwFwjQuPC9vd4EoGF44F4BIXHhN0gzsZANfYflzQT+5kgBtiWpT69a9/LZ/P1+xjxIgRycpmVXZmULOnPqTcYI4C/uhOKAv4A8oN5uiF62cqOzPY4QymIazqORulurBkor2TpLrI/UxDy6vNqZTBheOA1EA/tY1+Sk4GF44FUkO6dJQLjwm6wZ0MSA3p0k+S/ccF/eROBrgj5jOlDj/8cJWVlTV9vPXWW8nIZd1Fx56p/j2Ko36QNAr4AxrQs68uPGZShzPUl1ZETumMtrAaGclUNqi+tCLlM7hwHJA66Ke20U+Jz+DCsUDqSIeOcuExQTe4kwGpIx36SbL/uKCf3MkAd8S8KBUIBHTIIYc0ffTq1SsZuay7ZtyUVl/X2p5QOKRrxk3pcIbaFbut3t+FDC4cB6QO+ql99FNiM7hwLJA60qGjXHhM0A3uZEDqSId+kuw/LugndzLAHTEvSpWWlqq4uFhDhgzR5MmTtXHjxmTksmpUv5Ea3f+wVl/X2h5/hl9HDThco/qNjDtDaEdNi+/CEIvwzlqFdtSkbAYXjgNSC/3UPvopcRlcOBZILZ29o1x4TNAN7mRAauns/STZf1zQT+5kgFtiWpQaM2aMZs2apXnz5um+++7T+vXr9Y1vfEOVlZWt3qe2tlYVFRXNPlxX0mdwQrYztAPbCe1OzIXsOnJBPNsZXDgOSB30U2zop45ncOFYIHXE2lH0U3zoBncyIHWkQz9J9h8X9JM7GeCWmF7EOWnSV6/dHDVqlMaMGaOBAwfqmWee0VVXXdXifaZPn67f/OY3HUvpsW7ZOQnZTm5HtlPf8YvYSYpcRC9FMzhxHJAy6KfY0E8dz+DEsUDKiLWj6Kc40Q3OZEDqSId+khx4XNBPzmSAW2J++d7+CgoKNGzYMK1bt67V20ybNk3l5eVNH5s2berISE9U1VQnZDuVHdlOZocOzVeyOrAdyxmcOA5IWfRT2+injmdw4lggZbXXUfRTnOgGZzIgdXXGfpIceFzQT85kgFs69MioqqrSJ598oqKiolZvEwwGlZeX1+zDdaWfr0/MdrZ9Gvd9/d2zEpLBXxD/dmxncOE4IHXRT+1sh37qcAYXjgVSV3sdRT/Fh25wJwNSV2fsJ8n+44J+cicD3BLTotRPfvITvfHGG9qwYYPeeecdXXDBBfL7/brsssuSlc+KFZtXa/mmjxQKh+K6fygc0rKN/9bKLWvizuAvzFZGr2Dc95ekjF5B+QuzUzaDC8cBqYN+ig79lLgMLhwLpI506CgXHhN0gzsZkDrSoZ8k+48L+smdDHBLTItSmzdv1mWXXabhw4frkksuUc+ePbV48WIVFhYmK581D7zxmDJ88Z1I5s/w64E3HutwhuCo7lbv70IGF44DUgP9FB36KbEZXDgWSA3p0lEuPCboBncyIDWkSz9J9h8X9JM7GeCOmH4TnnrqKW3dulW1tbXavHmznnrqKR166KHJymbV80tf1qZdW9UQaojpfg2hBm38Yov+9sHcDmfILMmTLzcg+WK8o0/y5QaUWdLxU2ltZ3DhOCA10E/to58Sn8GFY4HUkC4d5cJjgm5wJwNSQ7r0k2T/cUE/uZMB7kjQ1dY6n5r6Wl1w7/dVWVsd9YOlIdSgytpqnT/jKtXU13Y4gy+QoZxzB0QuZhdtcfkkZUXu5wt0/PDazuDCcQBc48LjwnY3uJLBhWMBuMSFxwTd4E4GwDW2Hxf0kzsZ4A4WpdrwyfYNOu3OS7V1z+eS1OrrXhu/vnXP5zrtzkv16Y7PEpbBX5ClbhcPkq9bIKrb+7oF1O3iQR26AJ5rGVw4DoBrXHhc2O4GVzK4cCwAl7jwmKAb3MkAuMb244J+cicD3BDdIyGNfbrjM53w32fpwmMm6ZpxU3TUgMMPus3KzWv0wBuP6W8fzE3Kqq2/IEu53x6i+tIK1a7YrfDOg2dk9AoqOKp75JTQBKygu5bBheMAuMaFx4XtbnAlgwvHAnCJC48JusGdDIBrbD8u6Cd3MsA+FqWiUFNfqyeWvKAnlrygUf1GamifwcrNzlFlTbXWfb5eKzavTnoGXyBDWSMLlDWyQKEdNQrtqZPqwlJWhvwFWR16F4ZUyeDCcQBc48LjwnY3uJLBhWMBuMSFxwTd4E4GwDW2Hxf0kzsZYBeLUjFasXm19QeGvzDbk5JyOYMLxwFwjQuPC9vd4EoGF44F4BIXHhN0gzsZANfYflzQT+5kgPe4phQAAAAAAAA8x6IUAAAAAAAAPMeiFAAAAAAAADznM8YYLwdWVFQoPz8/MjzH+0tamb0NkpHkk3xd7VxSiwxkcCmD7fmSZKobJEnl5eXKy8uzkkGy30+SI8eD30kykKF5Bgc6in4igyvzyeBYBvpJkiPHggxkcGS+Mxmi7CerFzpvDGlnuOX5ZCCDaxlsz3eM9X3hwvGwncH2fDKQwVHW94MLx4IM9ueTwa0MjrC+H1w4FmQggyvzXcnQDquLUpwpRQYy2M9ge77kZlHyL332MtieTwYyHJTBsY6in9I7g+35ZHAsA/0kyZFjQQYyODLfmQxR9pO9RamufuVdOdTzsRWz1slUN8jXNWBlPhnI4FoG2/MlqfyRUmlvyMrsFlnqJ8mN42E7g+35ZCDDgZzqKPop7TPYnk8GtzLQTxEuHAsykMGV+a5kiLafuNA5AAAAAAAAPMeiFFKaz9vr9AMAAAAAgASxek0pIFajd9Ro8uo9OnHrPg3fXaussFSXIa3tHtSi4i56fGSBlhdm244JAAAAAADawaJUjEb1G6mSPoPVLTtHVTXVKv18vVZsXm07ludCO2oU2l0n1YelzAz5u2fJn8TFoMF76jRjfpnGlu1TvU/KHH2UNGGElJurrMpKHblmjUYsX6ZrVu7R20VddP34Iq0vyEpaHsBFXj8uXeXCfuD/FUBzLjwuXeBCN7iQAXANHeVGN7iQAd5jUSoK2ZlBXXTsmbpm3BSN7n/YQd9fvukjPfDGY3p+6cuqqa+1kNAbpiGs+tIK1a7YrfDOg3/OjF5BBUd1V2ZJnnyBxL0y9OKPy3Xv/G3yZwWlK69U5o03SkcffdDtMj/8ULr7bo15+iktfmq9rhtfpOeH5SUsB+AiW49L17iwH/h/BdCcC49LF7jQDS5kAFxDR7nRDS5kgF0sSrXj0N6DNHvqQ+rfo1hhE27xNkf0Ha4Zk2/TzydN1fkzrtKnOz7zOGXyhfbUqXrORpnK1t/WMbyzVvvmb1PNezuVc+4A+RNwptLFH5frgVfLpJISZfzzn9LAgVK45eOgUaOkhx9W4Fe/UsaECXrw1XXyyei5YfkdzgG4yNbj0jUu7Af+XwE058Lj0gUudIMLGQDX0FFudIMLGWBf51zyTZBDew/Saz95SsUFfeTz+eTP8Ld4O3+GXz6fT8UFffT6T5/WkMKBHidNrtCeOlU9t0GmqvXS3p+palDVcxsU2lPXoblD9tTp3vnbIgtSixdLfftKPp/kb/k4yO+PfL9vX2UsWSINHap752/T4A7mAFxk63HpGhf2A/+vAJpz4XHpAhe6wYUMgGvoKDe6wYUMcEPMi1JbtmzRlClT1LNnT3Xp0kVHHnmk3n///WRksyo7M6jZUx9SbjBHAX90J5QF/AHlBnP0wvUzlZ0ZTHJCb5iGsKrnbJTqwlK0b3RnJNVF7mcaWjmrKQr3zC9TRlYwcoZUbq6UmRndHTMzpdxcZbz6qvxZQc2YXxZ3BqSWdOknm49Ll7iwH/h/BWKRDh3lwuPSBS50gwsZkDrSoZ8kOkpyoxtcyAB3xLQotXv3bo0dO1aZmZmaO3euPvroI/3hD39Q9+7dk5XPmouOPVP9exRH/SBpFPAHNKBnX114zKQkJfNWfWlF5LTWaEu7kZFMZYPqSyvimjt6e43Glu1T5qX/EXnJXrQLUo0yM6VBgxS45FKNLdun0Ttq4sqB1JFO/WTrcekaF/YD/69AtNKlo1x4XLrAhW5wIQNSQ7r0k0RHSW50gwsZ4I6YFqV+97vfqX///nrkkUd0wgknaPDgwTrjjDN06KGHJiufNdeMm9Lq61rbEwqHdM24KQlOZEftit1W7j95zR7V+yTdeGPr15BqTygk3XCD6n3S5NV74tsGUkY69ZOtx6VrXNgP/L8C0UqXjnLhcekCF7rBhQxIDenSTxIdJbnRDS5kgDtiWpSaM2eOjjvuOH3rW99S7969dfTRR+vBBx9MVjZrRvUbqdH9D2v1da3t8Wf4ddSAwzWq38gEJ/NWaEdNi+9EEYvwzlqF4jhL6cSt+5Q5+qjIu+y1dg2p9vj90rHHKnP0Ufpa2b74toGUkS79ZPNx6RIX9gP/r0As0qGjXHhcusCFbnAhA1JHOvSTREdJbnSDCxnglpgWpT799FPdd999Kikp0SuvvKJrr71WN954ox599NFW71NbW6uKiopmH64r6TM4IdsZmqDt2BLanZiL+cVzUcDhu2ulESMSMl/Dh2vELt4+tLNLl36y+bh0iQv7gf9XIBaxdhT9lLpc6AYXMiB1pEM/SXSU5EY3uJABbonpRZzhcFjHHXecbr/9dknS0UcfrVWrVukvf/mLrrjiihbvM336dP3mN7/peFIPdcvOSch2chO0HWvqE3Qhv7rYtuMzRllhRS5ungh5ecoKR7ZrfL7EbBPOSZd+svW4dI4D+4H/VyAWsXYU/ZS6XOgGFzIgdaRFP0l0lNzoBhcywC0xnSlVVFSkww47rNnXRo4cqY0bN7Z6n2nTpqm8vLzpY9OmTfEl9VBVTXVCtlOZoO1YkxnzmzO2LCu27RifT3UZkiorEzO/okJ1GWJBqpNLl36y9bh0jgP7gf9XIBaxdhT9lLpc6AYXMiB1pEU/SXSU3OgGFzLALTGdKTV27FitXbu22dc+/vhjDRw4sNX7BINBBYOp9ZaNpZ+vT8x2tn2akO3Y4u+elZjtFMS+nbXdgzpyzZqEzNeaNVrTI7V+BxG7dOknm49Ll7iwH/h/BWIRa0fRT6nLhW5wIQNSRzr0k0RHSW50gwsZ4JaYlnlvuukmLV68WLfffrvWrVunJ554Qg888ICmTp2arHxWrNi8Wss3faRQOBTX/UPhkJZt/LdWbknQoool/sJsZfTq2P9wMnoF5S/Mjvl+i4q7qH75MunDDyPvohePUEhaulT1K5ZrcVGX+LaBlJEu/WTzcekSF/YD/69ALNKho1x4XLrAhW5wIQNSRzr0k0RHSW50gwsZ4JaYFqWOP/54zZ49W08++aSOOOII/fa3v9Vdd92lyZMnJyufNQ+88ZgyfPGdmunP8OuBNx5LcCI7gqO6W7n/4yMLlGkk3X23lBHnKbJ+v3TPPco0ke2hc0unfrL1uHSNC/uB/1cgWunSUS48Ll3gQje4kAGpIV36SaKjJDe6wYUMcEfMvwlnn322Vq5cqZqaGq1evVpXX311MnJZ9/zSl7Vp11Y1hBpiul9DqEEbv9iiv30wN0nJvJVZkidfbkCK9XJMPsmXG1BmSV5cc5cXZuvtoi5qePop6bPPpPr62DZQXy9t2KCGZ57W20VdtDyF/0UD0UuXfrL1uHSNC/uB/1cgFunQUS48Ll3gQje4kAGpIx36SaKjJDe6wYUMcEfqXqUtyWrqa3XBvd9XZW111A+WhlCDKmurdf6Mq1RTX5vkhN7wBTKUc+6AyAX9oi1vn6SsyP18gfh/xa4fX6RQXa3CZ5wRueh5tAtT9fVSZaXCEyYoVFer68cXxZ0BcJHNx6VLXNgP/L8CaM6Fx6ULXOgGFzIArqGj3OgGFzLAHan/qEqiT7Zv0Gl3Xqqtez6XpFZf99r49a17Ptdpd16qT3d85llGL/gLstTt4kHydYvuuvi+bgF1u3hQhy8CuL4gS9eNL5JKSxUeM0basiXyjdauMdX49S1bIrdft07XjS/S+hS+GCHQGluPS9e4sB/4fwXQnAuPSxe40A0uZABcQ0e50Q0uZIAbYnr3vXT06Y7PdMJ/n6ULj5mka8ZN0VEDDj/oNis3r9EDbzymv30w9/9n787jo6rv/Y+/T2aSCYQsLAES9p24BJVWRdqyuIG4XZdaCxZcawW17b3Wcn/2emtraa+3V6tQWhXRFhesVMv1ulQBl1pZRAKorMpqAqKQFbLNnN8fMZGQhMwkM+f7nczr+Xjk8TCTM+fzZs7MO+M3M2c67KqtLytF6d8drJptparacEihz5v+O5N6BBTI71r3stgo/RVhyfAMOXI1b/nH8p2QJ/+3r5JuvVUaPbrpxgUF0kMPqfbZxQpWV+mWc3O1ZHj8v8QWaImpx6VtbLgd+F0BNGbD49IGNnSDDRkA29BRdnSDDRlgHotSYaisqdJTq17QU6teUH7fPA3tNUjpqWkqq6zQ9v07tGHvJtMRPeH4k5SSl6WUvCwFD1QqWFwtVYeklCT5slJi9kkUzw3P1NqenTR3eZHGPvGEav70hJJHnSKNGCFlZEilpdKWLapZX6BkV1qZ20m3ThjEK6SQEEw9Lm1jw+3A7wqgMRselzawoRtsyADYho6yoxtsyACzWJSK0Ia9m3hgqO4jVb0s6h1ZKZpy2QCNOlCpqZuKdebeTRq5oUApIak6SdrcLaCVJ2XpybwsTmqOhOX149JWNtwO/K4AGrPhcWkDG7rBhgyAbegoO7rBhgzwHotSiCvrs1O1Prt3w/eO68p1Iv34DAAAAAAAYFrHe3MsEgoLUgAAAAAAxCcWpQAAAAAAAOA5x3Vd18uBpaWlyszMrBue5v27B93DtZIryZGczmbevUgGMtiUwfR8SXIraiVJJSUlysgw94mJpvtJsuR4cJ8kAxkaZ7Cgo+gnMtgynwyWZaCfJFlyLMhABkvmW5MhzH4yek6p+pBmhhueTwYy2JbB9HzLGL8tbDgepjOYnk8GMljK+O1gw7Egg/n5ZLArgyWM3w42HAsykMGW+bZkaIXRRSleKUUGMpjPYHq+ZGdR8pc+cxlMzycDGZpksKyj6KfEzmB6Phksy0A/SbLkWJCBDJbMtyZDmP1kblGqs08ZM4Z6Prb08e1yK2rldPYbmU8GMtiWwfR8SSpZuE06HDQyu1mG+kmy43iYzmB6PhnIcCyrOop+SvgMpueTwa4M9FMdG44FGchgy3xbMoTbT5zoHAAAAAAAAJ5jUQoAosjx9rMjAABxiN8VAADUMXpOKQCId6MOVGrqpmKNKTyiEYeqlBKSqpOkLV0Deje3k57My9L67FTTMQEABvG7AgCA5rEoBQBtMKi4WnOXF2ls0RHVONIHrrREUpmk9JA08osqXXuwSjdtLNY7OZ00a2KOdmSlmI4NIEEFD1QqeKhaqglJyUnydU2Rj0WQmDv2d0XyqFOkc0dK6elKKSvTyZs3a+T6An5XIKHRT3bI75unYb0GqUtqmsorK7Rt/w5t2LvJdCwkABalACBCV2wt0bzl+1QddLVQ0oOuVNDMdqe40m2Srig6opXP7NAtE3O0ZHiGt2EBJCy3NqSabaWq2nBIoc+rmvw8qUdAgfyuSh6WIcfPGR2irf53hS8lIM2YoeTbbpNOPbXJdsnr1kkPPqgzFj/D7wokDPrJDqnJAV0++gLdNG6aRvU7ocnP1+/5SA+/uUhL1r6kypqmxwmIBh7hABCBK7aW6OHXirQj6OpkSddJ2tDCthu+/PnJknYGXT3yWqGu2FriVVQACSxYXK2ypz7RkeX7mv0fPkkKfV6lI8v3qeypTxQsrvY4YcdW/7siefBQ+T/aJD32mJSf3/zG+fnSY4/J/9EmJQ8awu8KdHj0kx2G9Byo1Xf9n+ZOvVcn9RnR7DYn9RmhuVPv1eq7/k+Dswd4nBCJgkUpAAjT4OJqzVu+T1slnSVp75eXh1rYvv7yvZLGSNomad7yfRrEkysAMRQsrlb5czvllteGtb1bXqvy53byP35RUv+7QsOGKWnlSqlPH8lxJJ+v+Sv4fHU/79NHSatWSUOH8rsCHRb9ZIchPQfq9X97RrlZveQ4jnxJzfeTL8knx3GUm9VLy+5YzMIUYiKiRamBAwfKcZwmXzNnzoxVPgAIW6w76qHlRaoKupokqVRSMMzrBb/c/nxJ1UFXc5cXRSUPgPjh1XMotzakiqW7peqQFO4HvLmSquuu59a2tMyOcD20vEhJKQEl/f3vUnq6lJwc3hWTk6X0dCW99pp8KQF+V8Az9FNiSU0O6PmZjyo9kCa/L7yz+fh9fqUH0vTCrAVKTQ7EOCESTUSLUmvWrFFRUVHD12uvvSZJuvLKK2MSDgAiEcuOGvVZpcYWHdESSbsU/oJUveCX11siaWzREY06UNnuTADih1fPoWq2lcotqw3/f/jquZJbVquabaVRzZNo6n9XJF/1HWnAgPAXpOolJ0sDB8r/7av4XQHP0E+J5fLRF6hft9ywF6Tq+X1+9e/eR5edNjlGyZCoIlqUys7OVu/evRu+XnzxRQ0ZMkTjxo2LVT4ACFssO2rq5mLVONKDavv7npNUd/0aR5q6qbjdmQDED6+eQ1VtOGT0+omu/neFbrtNCrXxVR3BoHTrrfyugGfop8Ry07hpCrlt66dgKKibxk2LciIkujafU6q6ulqLFi3SddddJ8dxopkJANot2h01pvCIPvjyU/ba+uLxkKR1kj5wpTOLjrQ7E4D4FKvnUMEDlS2eNDhcoc+rFOTVOW02pvCIkkedUvcpey2dQ6o1Pp80erSSR53C7wp4jn7q2PL75mlUvxNaPIdUa3xJPp3S/0Tl982LcjIksshes3eUF154QcXFxZoxY8Zxt6uqqlJV1VcFVFrKyy4BxF44HRVJP404VKUlUcq2RdJlB/lYXSBRRbuf6gUPRedEwMHiavmyU6Oyr0Qz4lCVdO7IKO1shEZuKIjOvoAw0U8d27Beg6Kyn6G9BmnD3k1R2RfQ5ldKLViwQJMnT1Zubu5xt5szZ44yMzMbvvr169fWkQAQtnA6Ktx+clxXKSGpLErZSiWlhOr2CyDxRLOfGqmJ0kmAqzmZcFvU/65Qenp0dpiRwe8KeI5+6ti6pKZFZT/pUdoPILVxUWrXrl16/fXXdcMNN7S67ezZs1VSUtLwtWfPnraMBICwhdtR4faT6ziqTpKi9L8ZypBUnVS3XwCJJdr91Ehym//W2FhKlPaTYOp/V6gsSn/CKC3ldwU8RT91fOWVFVHZT1mU9gNIbXz73sKFC9WzZ09NmTKl1W0DgYACAT42EoB3wu2oSPppS9eARn4RnbfcjZS0uRu9CCSiWPRTPV/XlPZE+2o/WdHZTyLa0jWgkzdvjs7ONm/mdwU8RT91fNv274jOfvZ9EpX9AFIbXikVCoW0cOFCTZ8+XX5/m09JBQAxEauOeje3k05ypFPUvk/fO1XSiY60MqdT1LIBiA+xfg7ly05VUo/2LWIk9QhwvpZ2eDe3k2rWF0jr1tV9il5bBIPS2rWq2bCe3xXwDP2UGDbs3aT1ez5SMNS2fgqGgirY/aE2fhqlxXdAbfh/q9dff127d+/WddddF4s8ANAuseqoJ/OylOxKt6l9n753m6Rkt25/ABKLF8+hAvldjV4/0dX/rtCDD0pJbfwThs8nPfQQvyvgKfopcTz85iIlOW3rJ1+STw+/uSjKiZDoIr43nnfeeXJdV8OHD49FHgBol1h11PrsVL2T00lXSBogKdIP0vV9eb3LJb2T00nr+UsfkHC8eA6VPCxDTrpfivQ0RI7kpPuVPCwjJrkSRf3vitrFz0i7dkk1NZHtoKZG2rlTtc8u5ncFPEU/JY4la1/SnoOFqg3WRnS92mCtdn/xqf76/ssxSoZExZniACBMsybmKMXn6BXVnaw83IUp35fbvyopxedo1sScWEUEkOAcf5LSLu5fdzLgcP/Hz5GUUnc9x89Tw/aaNTFHweoqhc47r+6k5+EuTNXUSGVlCp17roLVVfyuQIdDP9mhsqZK/zLvBpVVVYS9MFUbrFVZVYUunXu9Kmuic45VoB6PbAAI046sFN0yMUfDJb0rqe+Xl7dUpPWX9/1y+2GSbpmYox2cpBNADPmyUtTlioFyuoR3Xhini19drhjICYSjpP53hbZtU+iMM6RPP637QUvnmKq//NNP67bfvp3fFeiw6Cc7fPzZTp1931UqLN4vSS2eY6r+8sLi/Tr7vqv0yYFdnmVE4mBRCgAisGR4hm46N0cDfY42SlooaVQL24768ucbJQ30Obrx3FwtGc5LzwHEni8rRenfHaxOE3u3eHLhpB4BdZrYW+nfHcz/8EVZ/e+Kmh0fq/aEPGnGDKmgoPmNCwqkGTNUe0KeanZ8zO8KdHj0kx0+ObBLp/9yim7582xt3Nv8ics37t2sW/48W6f/cgoLUogZPj4PACL03PBMre3ZSXOXF2lG0RFNdaQPXGmLpFLVvVVvhKSTnLqTmv8jt5NuncBfvQF4y/EnKSUvSyl5WQoeqFSwuFqqDkkpSfJlpfApVjF29O+KsU88oZo/PaHkUadII0ZIGRlSaam0ZYtq1hco2ZVW5nbSrRMG8bsCCYF+skNlTZWeWvWCnlr1gvL75mlor0FKT01TWWWFtu/foQ17N5mOiATAohQAtMGOrBRNuWyARh2o1NRNxTqz6IguO1illJBUnSRt7hbQwpxOejIvixPVAjDOl53K/+QZ0OR3xd5NGrmhoNHvipUnZfG7AgmNfrLDhr2bWISCESxKAUA7rM9O1frs3g3fO64r14n0Y2UAAB0ZvysAAGge55QCgCjifzIAAK3hdwUAAHVYlAIAAAAAAIDnHNd1XS8HlpaWKjMzs254mvfvHnQP10quJEdyOpt59yIZyGBTBtPzJcmtqJUklZSUKCPD3CcOme4nyZLjwX2SDGRonMGCjqKfyGDLfDJYloF+kmTJsSADGSyZb02GMPvJ6Dml6kOaGW54PhnIYFsG0/MtY/y2sOF4mM5gej4ZyGAp47eDDceCDObnk8GuDJYwfjvYcCzIQAZb5tuSoRVGF6V4pRQZyGA+g+n5kp1FyV/6zGUwPZ8MZGiSwbKOop8SO4Pp+WSwLAP9JMmSY0EGMlgy35oMYfaTuUWpzj5lzBjq+djSx7fLraiV09lvZD4ZyGBbBtPzJalk4TbpcNDI7GYZ6ifJjuNhOoPp+WQgw7Gs6ij6KeEzmJ5PBrsy0E91bDgWZCCDLfNtyRBuP3GicwAAAAAAAHiORSkAAAAAAAB4jkUpAAAAAAAAeI5FKQAAAAAAAHiORSkAAAAAAAB4jkUpAAAAAAAAeI5FKQAAAAAAAHguokWpYDCon/3sZxo0aJA6deqkIUOG6Be/+IVc141VPgAIC/0EwGZ0FABb0U8ATPJHsvFvfvMbzZ8/X0888YROPPFEvffee7r22muVmZmp2267LVYZAaBV9BMAm9FRAGxFPwEwKaJFqX/+85+65JJLNGXKFEnSwIED9fTTT2v16tUxCQcA4aKfANiMjgJgK/oJgEkRvX3vrLPO0rJly7R161ZJ0vr16/WPf/xDkydPjkk4AAgX/QTAZnQUAFvRTwBMiuiVUj/96U9VWlqqkSNHyufzKRgM6t5779XUqVNbvE5VVZWqqqoavi8tLW17WgBoAf0EwGaRdhT9BMAr9BMAkyJ6pdSzzz6rJ598Uk899ZTef/99PfHEE/rv//5vPfHEEy1eZ86cOcrMzGz46tevX7tDA8Cx6CcANou0o+gnAF6hnwCYFNGi1B133KGf/vSn+s53vqOTTz5Z11xzjX70ox9pzpw5LV5n9uzZKikpafjas2dPu0MDwLHoJwA2i7Sj6CcAXqGfAJgU0dv3Dh8+rKSkxutYPp9PoVCoxesEAgEFAoG2pQOAMNFPAGwWaUfRTwC8Qj8BMCmiRamLLrpI9957r/r3768TTzxR69at0//8z//ouuuui1U+AAgL/QTAZnQUAFvRTwBMimhR6qGHHtLPfvYz3XLLLfrss8+Um5ur73//+/qP//iPWOUDgLDQTwBsRkcBsBX9BMCkiBal0tPT9cADD+iBBx6IURwAaBv6CYDN6CgAtqKfAJgU0YnOAQAAAAAAgGhgUQoAAAAAAACeY1EKAAAAAAAAnmNRCgAAAAAAAJ5jUQoAAAAAAACeY1EKAAAAAAAAnmNRCgAAAAAAAJ5jUQoAAAAAAACec1zXdb0cWFJSoqysrLpvOvu8HF3ncPCr/zYxnwxksC2D6flHZSguLlZmZqaZDLKgnySrjoexDKbnk4EMLWQw2VH0ExmsmU8GKzPQT/YcCzKQwfh8yzK01k9+r/LUKysr++qbo28oE0zPJwMZbMtgeH5ZWZnRRSmr+okMdswnAxmOYrKj6CcyWDmfDNZkoJ+OQgYy2JTB9HwLMrTWT56/UioUCqmwsFDp6elyHCfi65eWlqpfv37as2ePMjIyYpCQDPGSwfR8MkQvg+u6KisrU25urpKSzL2rmH4iQ0fKYHp+R8pgQ0e1t58k88fD9HwykMG2DPTTV0wfCxsymJ5PBjJEO0O4/eT5K6WSkpLUt2/fdu8nIyPD2MEhg10ZTM8nQ3QymHyFVD36iQwdMYPp+R0lg+mOilY/SeaPh+n5ZCCDbRnop6+YPhY2ZDA9nwxkiGaGcPqJE50DAAAAAADAcyxKAQAAAAAAwHNxtygVCAR09913KxAIkCHBM5ieTwa7MtjAhtuBDGSwZT4Z7GP6tjA9nwxksC2D6fk2seG2MJ3B9HwykMFUBs9PdA4AAAAAAADE3SulAAAAAAAAEP9YlAIAAAAAAIDnWJQCAAAAAACA51iUAgAAAAAAgOfialHq3Xfflc/n05QpUzyfPWPGDDmO0/DVvXt3TZo0SRs2bPA8y759+3Trrbdq8ODBCgQC6tevny666CItW7Ys5rOPvh2Sk5PVq1cvnXvuuXrssccUCoViPv/YDEd/TZo0yZP5reXYvn27J/P37dun22+/XUOHDlVqaqp69eqlsWPHav78+Tp8+HDM58+YMUOXXnppk8vfeOMNOY6j4uLimGewDR1FPx2bw1RHme4nyWxH0U9N0U/007E56CeeQ9mCfqKfjs1BPyVWP8XVotSCBQt066236q233lJhYaHn8ydNmqSioiIVFRVp2bJl8vv9uvDCCz3NsHPnTo0ePVrLly/Xfffdp40bN+qVV17RhAkTNHPmTE8y1N8OO3fu1Msvv6wJEybo9ttv14UXXqja2lpPMxz99fTTT3syu7UcgwYNivncTz75RKeeeqr+/ve/61e/+pXWrVund999Vz/5yU/04osv6vXXX495BjSV6B1FPzXNYbKjTPWTREfZiH6in47NQT/RT7agn+inY3PQT4nVT37TAcJVXl6uxYsX67333tO+ffv0+OOP69///d89zRAIBNS7d29JUu/evfXTn/5U3/zmN3XgwAFlZ2d7kuGWW26R4zhavXq10tLSGi4/8cQTdd1113mS4ejboU+fPjrttNN05pln6uyzz9bjjz+uG264wdMMJpnKccstt8jv9+u9995rdD8YPHiwLrnkErmu63mmREdH0U8t5TDFZAY6yi70E/3UUg5T6CfUo5/op5ZymEI/eS9uXin17LPPauTIkRoxYoSmTZumxx57zOhBKS8v16JFizR06FB1797dk5kHDx7UK6+8opkzZza6k9bLysryJEdzJk6cqFGjRumvf/2rsQyJ4osvvtDf//73Fu8HkuQ4jsepkOgdRT+hHh1lH/qJfkId+sk+9BP9hDqJ3E9xsyi1YMECTZs2TVLdS+pKSkr05ptveprhxRdfVJcuXdSlSxelp6dr6dKlWrx4sZKSvLkZt2/fLtd1NXLkSE/mRWrkyJHauXOnJ7OOPhb1X7/61a88mX28HFdeeWXMZ9bfD0aMGNHo8h49ejTkuPPOO2OeQ2r+OEyePNmT2bZJ9I6inxqzoaNM9JNkT0fRT1+hn+ino9FP5vtJoqPq0U/009Hop8Tsp7h4+96WLVu0evVqPf/885Ikv9+vq666SgsWLND48eM9yzFhwgTNnz9fknTo0CH9/ve/1+TJk7V69WoNGDAg5vNtf7me67qerd4efSzqdevWzZPZx8vR0qq2F1avXq1QKKSpU6eqqqrKk5nNHYdVq1Y1PLlIFHQU/XQsGzrKpn6SvO8o+qkO/UQ/HYt+aornUGbQT/TTseinphKhn+JiUWrBggWqra1Vbm5uw2Wu6yoQCGju3LnKzMz0JEdaWpqGDh3a8P2jjz6qzMxMPfLII/rlL38Z8/nDhg2T4zjavHlzzGe1xaZNmzw7Cdyxx8IUEzmGDh0qx3G0ZcuWRpcPHjxYktSpUyfPsjT379+7d69n821BR9FPx7Kho0xlsKWj6Kc69BP9dCz6yXw/SXSURD9J9NOx6KfE7Cfr375XW1urP/3pT/rtb3+rgoKChq/169crNzfXyCeu1XMcR0lJSTpy5Ign87p166bzzz9f8+bNU0VFRZOfm/z42OXLl2vjxo26/PLLjWVIFN27d9e5556ruXPnNns/gLfoqDr0E+rRUfagn+rQT6hHP9mDfqpDP6FeIveT9a+UevHFF3Xo0CFdf/31TVbLL7/8ci1YsEA333yzJ1mqqqq0b98+SXUv7Zw7d67Ky8t10UUXeTJfkubNm6exY8fq9NNP1z333KP8/HzV1tbqtdde0/z587Vp06aYZ6i/HYLBoPbv369XXnlFc+bM0YUXXqjvfe97MZ9/dIaj+f1+9ejRw5P5pv3+97/X2LFj9bWvfU3/+Z//qfz8fCUlJWnNmjXavHmzRo8ebTpiwqCjvkI/Nc1xNDqKjvIa/fQV+qlpjqPRT/ST1+inr9BPTXMcjX5KgH5yLXfhhRe6F1xwQbM/W7VqlSvJXb9+fcxzTJ8+3ZXU8JWenu5+/etfd5977rmYzz5WYWGhO3PmTHfAgAFuSkqK26dPH/fiiy92V6xYEfPZR98Ofr/fzc7Ods855xz3sccec4PBYMznH5vh6K8RI0Z4Mv/oHJdccomnM49WWFjozpo1yx00aJCbnJzsdunSxT399NPd++67z62oqIj5/Jb+/StWrHAluYcOHYp5BhvQUY0lej8dm8NUR5nuJ9c121H0Ux36qTH6iX6qx3Mo8+inxugn+qleIvaT47qWn10NAAAAAAAAHY7155QCAAAAAABAx8OiFAAAAAAAADzHohQAAAAAAAA8x6IUAAAAAAAAPMeiFAAAAAAAADzHohQAAAAAAAA8x6IUAAAAAAAAPMeiFAAAAAAAADzHohQAAAAAAAA8x6JUB/CHP/xB6enpqq2tbbisvLxcycnJGj9+fKNt33jjDTmOo48//rjhsnfffVcTJ05UWlqaMjIy9K1vfUtHjhxpde61116ru+66q9Xtfv3rX8txHP3whz9sdPm+fft0zTXXqHfv3kpLS9Npp52mJUuWtLq/1mbPmDFDjuM0fHXv3l2TJk3Shg0bGm1377336qyzzlLnzp2VlZXV7L52796tKVOmqHPnzurZs6fuuOOORrczgMi0p69i1RlHa66vdu7c2ahTjv76y1/+0uo+J0yYoEcffbTZn40fP77R/nr16qUrr7xSu3btarTdsmXLdNZZZyk9PV29e/fWnXfeSRcB7RSvz59iNTtaz5/Wr1+vq6++Wv369VOnTp2Ul5en3/3ud61mA9BYezrq4Ycf1vjx45WRkSHHcVRcXNxk/+H8v1BLjvfc5mg333yzHMfRAw880OjygQMHNnlO9etf/zqs2YMGDdLrr7/e7M+O3q/P51Nubq6uv/56HTp0qNF2r776qs4880ylp6crOztbl19+uXbu3BnWfEQfi1IdwIQJE1ReXq733nuv4bK3335bvXv31qpVq1RZWdlw+YoVK9S/f38NGTJEUt2TmkmTJum8887T6tWrtWbNGs2aNUtJSce/awSDQb344ou6+OKLj7vdmjVr9Mc//lH5+flNfva9731PW7Zs0dKlS7Vx40Zddtll+va3v61169a1e/akSZNUVFSkoqIiLVu2TH6/XxdeeGGjbaqrq3XllVfqBz/4QYtzpkyZourqav3zn//UE088occff1z/8R//cdx8AFrWnr6KZWdILfdVv379Gvqk/uvnP/+5unTposmTJx93nwcPHtQ777yjiy66qMVtbrzxRhUVFamwsFB/+9vftGfPHk2bNq3h5+vXr9cFF1ygSZMmad26dVq8eLGWLl2qn/70p8edDeD44vX5UyxnR+P509q1a9WzZ08tWrRIH374of7f//t/mj17tubOnXvcfAAaa09HHT58WJMmTdK///u/t7j/1h7LLQnnuY0kPf/881q5cqVyc3Ob/fk999zT6LnVrbfe2ursDRs26NChQxo3blyL29Tvd/fu3XryySf11ltv6bbbbmv4+Y4dO3TJJZdo4sSJKigo0KuvvqrPP/9cl112WavzESMuOoScnBx3zpw5Dd//5Cc/cWfOnOnm5eW5K1asaLj8W9/6ljt9+vSG78844wz3rrvuinjeW2+95ebk5LihUKjFbcrKytxhw4a5r732mjtu3Dj39ttvb/TztLQ0909/+lOjy7p16+Y+8sgj7Zo9ffp095JLLml02dtvv+1Kcj/77LMm2y9cuNDNzMxscvlLL73kJiUlufv27Wu4bP78+W5GRoZbVVV13IwAWtbWvopVZ7hu6311rFNOOcW97rrrjruN67run/70J/eMM85o8efNzfrzn//sdu7cueH72bNnu1/72tcabbN06VI3NTXVLS0tbTUDgJbF4/OnWM2O1vOn5txyyy3uhAkTIo0MJLy2dlS9FStWuJLcQ4cOtTgjksey67b+3MZ1XXfv3r1unz593A8++MAdMGCAe//99zf6eXOXheOee+5xr7rqqhZ/3tx+f/GLX7gnnHBCw/d/+ctfXL/f7waDwYbLli5d6jqO41ZXV0ecCe3HK6U6iAkTJmjFihUN369YsULjx4/XuHHjGi4/cuSIVq1apQkTJkiSPvvsM61atUo9e/bUWWedpV69emncuHH6xz/+0eq8pUuX6qKLLpLjOC1uM3PmTE2ZMkXnnHNOsz8/66yztHjxYh08eFChUEjPPPOMKisrm7wctS2zj1ZeXq5FixZp6NCh6t69e1jXker+EnnyySerV69eDZedf/75Ki0t1Ycffhj2fgA01pa+kmLbGa311dHWrl2rgoICXX/99a1uu3TpUl1yySWtblfv4MGDevbZZ3XGGWc0XFZVVaXU1NRG23Xq1EmVlZVau3Zt2PsG0FS8PX+K9eyjtfX5U3NKSkrUrVu3du0DSERtfc4US609twmFQrrmmmt0xx136MQTT2xxu1//+tfq3r27Tj31VN13331hnZYg0udVn376qf73f/+30fOq0aNHKykpSQsXLlQwGFRJSYn+/Oc/65xzzlFycnLY+0YUmV4VQ3Q88sgjblpamltTU+OWlpa6fr/f/eyzz9ynnnrK/da3vuW6rusuW7bMleTu2rXLdV3Xfffdd11Jbrdu3dzHHnvMff/9990f/vCHbkpKirt169bjzhs2bJj74osvtvjzp59+2j3ppJPcI0eOuK7b/KsBDh065J533nmuJNfv97sZGRnuq6++2uq/tbXZ06dPd30+n5uWluampaW5ktycnBx37dq1zW7f0l8HbrzxRve8885rdFlFRYUryX3ppZdazQmgeW3pK9eNXWeE01dH+8EPfuDm5eW1OreystLt0qWL+8EHH7S4zbhx49zk5GQ3LS3N7dy5syvJHT58uLtjx46GbV599VU3KSnJfeqpp9za2lp379697je/+U1XkvvUU0+1mgNAy+Lt+VMsZ0fr+dOx3nnnHdfv94fV1wAaa+tzpnrRfqVUOM9tfvWrX7nnnntuw6sym3v10m9/+1t3xYoV7vr169358+e7WVlZ7o9+9KPjzt67d6+bkpJy3H/LgAED3JSUFDctLc1NTU11JblnnHFGk+u88cYbbs+ePV2fz+dKcseMGXPc/SK2eKVUBzF+/HhVVFRozZo1evvttzV8+HBlZ2dr3LhxDe85fuONNzR48GD1799fUt0qtiR9//vf17XXXqtTTz1V999/v0aMGKHHHnusxVmbNm1SYWGhzj777GZ/vmfPHt1+++168sknm/x1/2g/+9nPVFxcrNdff13vvfeefvzjH+vb3/62Nm7c2ObZ9SZMmKCCggIVFBRo9erVOv/88zV58uQmJw8G4L229JUUm84It6/qHTlyRE899VRYr5Javny5evbsedy/EkrS1KlTVVBQoPXr1+sf//iHhg4dqvPOO09lZWWSpPPOO0/33Xefbr75ZgUCAQ0fPlwXXHCBJLV6DhkAxxdvz59iNbtetJ8/ffDBB7rkkkt0991367zzzmvTPoBE1tbnTLHS2nObtWvX6ne/+50ef/zx474q88c//rHGjx+v/Px83Xzzzfrtb3+rhx56SFVVVS1eZ+nSpfrGN77R6knZ77jjDhUUFGjDhg1atmyZJGnKlCkKBoOS6j4458Ybb9T06dO1Zs0avfnmm0pJSdEVV1wh13VbuQUQC37TARAdQ4cOVd++fbVixYpGJ3/Lzc1Vv3799M9//lMrVqzQxIkTG66Tk5MjSTrhhBMa7SsvL0+7d+9ucdbSpUt17rnntviEae3atfrss8902mmnNVwWDAb11ltvae7cuaqqqtLOnTs1d+5cffDBBw2lNmrUKL399tuaN2+e/vCHP7Rpdr20tDQNHTq04ftHH31UmZmZeuSRR/TLX/7yuNet17t3b61evbrRZfv372/4GYC2aUtfffzxxzHpjHD6yufzNfzsueee0+HDh/W9732v1X/n0qVLWz2ZsSRlZmY29NXQoUO1YMEC5eTkaPHixbrhhhsk1T15+9GPfqSioiJ17dpVO3fu1OzZszV48OBW9w+gZfH2/ClWs+tF4/lTvY8++khnn322brrpprA+bRBAU23pqFhq7bnN22+/rc8++6zRAlkwGNS//uu/6oEHHmjxE+7OOOMM1dbWaufOnRoxYkSbZtfr0aNHQ48NGzZMDzzwgMaMGaMVK1bonHPO0bx585SZman/+q//arjOokWL1K9fP61atUpnnnlmqzMQXfyJtQOZMGGC3njjDb3xxhuNzrHyrW99Sy+//LJWr17d6L3GAwcOVG5urrZs2dJoP1u3btWAAQNanPO3v/3tuO/lPfvss7Vx48aGv7QVFBToa1/7WsOrAXw+nw4fPiyp6V/5fT5fw18B2zK7JY7jKCkpKayPS643ZswYbdy4UZ999lnDZa+99poyMjKaPBkEEJlI+ypWnRFOXx1twYIFuvjii5WdnX3cf5/ruvrf//3fNvVV/cxj+8pxHOXm5qpTp056+umn1a9fv0b/8wqgbeLp+VOsZrekLc+fJOnDDz/UhAkTNH36dN17770RzwXwlUg7KlbCeW5zzTXXaMOGDY16LDc3V3fccYdeffXVFq9XUFCgpKQk9ezZs9mfl5eXa8WKFVF5XnX48OFmn09KOu5zSsSQ4bcPIooee+wxt1OnTq7f72/0iXFPPPGEm56e7kpyCwsLG13n/vvvdzMyMty//OUv7rZt29y77rrLTU1Ndbdv397sjP3797vJycnugQMHIsp27DkRqqur3aFDh7rf/OY33VWrVrnbt293//u//9t1HMf9v//7v3bNnj59ujtp0iS3qKjILSoqcj/66CP3lltucR3HafQpFbt27XLXrVvn/vznP3e7dOnirlu3zl23bp1bVlbmuq7r1tbWuieddJJ73nnnuQUFBe4rr7ziZmdnu7Nnz47o3w6gqUj7KpadcayWzim1bds213Ec9+WXX251H2vWrHG7du3q1tTUtDrrxhtvbOirgoIC9/LLL3dTU1PdzZs3N2z3X//1X+6GDRvcDz74wL3nnnvc5ORk9/nnn4/o3wWgefH0/CmWs6P1/Gnjxo1udna2O23atIZ9FRUVNfsJfgBa15aOKioqctetW+c+8sgjriT3rbfectetW+d+8cUXDdu09lg+VrjPbY517Dml/vnPf7r333+/W1BQ4H788cfuokWL3OzsbPd73/tei/v4y1/+4p588slhzbrnnnvcoqIit7Cw0F21apU7btw4Nzs72/38889d1607B5fjOO7Pf/5zd+vWre7atWvd888/3x0wYIB7+PDhiP5tiA4WpTqQHTt2uJLckSNHNrp8586driR3xIgRzV5vzpw5bt++fd3OnTu7Y8aMcd9+++0WZzz66KPu2LFjI87W3JOqrVu3updddpnbs2dPt3Pnzm5+fn6Tj3tvy+zp06e7khq+0tPT3a9//evuc889d9zt6r+OfuK1c+dOd/LkyW6nTp3cHj16uP/6r/8acREDaKotfRWrzjhWS4tSs2fPdvv169foI4Rbctddd7lTp04Na9bR/dO1a1d33Lhx7vLlyxttN2HCBDczM9NNTU11zzjjDD5sAYiieHv+FKvZ0Xr+dPfddzf78wEDBkTyTwfwpbZ0VEuPw4ULFzZsE87/Cx0t3Oc2xzp2UWrt2rXuGWec0fC8Ji8vz/3Vr37lVlZWtriPadOmuf/v//2/sGYd/W/Jzs52L7jgAnfdunWNtnv66afdU0891U1LS3Ozs7Pdiy++2N20aVPE/zZEh+O6nM0L4bv44ov1jW98Qz/5yU8SajaA+GOyM/Lz83XXXXfp29/+tuezAdiH508A4p2p5za1tbXq1auXXn75ZZ1++umezoY3OKcUIvKNb3xDV199dcLNBhB/THVGdXW1Lr/8ck2ePNnz2QDsxPMnAPHM5HObgwcP6kc/+pG+/vWvez4b3uCVUgAAAAAAAPAcr5QCAAAAAACA51iUAgAAAAAAgOdYlAIAAAAAAIDnWJQCAAAAAACA51iUAgAAAAAAgOdYlAIAAAAAAIDnWJQCAAAAAACA51iUAgAAAAAAgOdYlAIAAAAAAIDnWJQCAAAAAACA51iUAgAAAAAAgOdYlAIAAAAAAIDnWJQCAAAAAACA5/xeDwyFQiosLFR6erocx/F6PAALua6rsrIy5ebmKinJ3Fo5/QSgOTZ0FP0EoDn0EwBbhdtPni9KFRYWql+/fl6PBRAH9uzZo759+xqbTz8BOB6THUU/ATge+gmArVrrJ88XpdLT07/6prPP6/HS4aDZ+WQgg20ZTM8/KkOjfjDAeD9JVh0PYxlMzycDGVrIYLKj6CcyWDOfDFZmoJ/sORZkIIPx+ZZlaK2fPF+UanhJZ2efMq8d5vV4lT6+XW5FrZw0vzJmDPV8PhnIYFsG0/MlqWThNulw0PhLvk33k2TH8TCdwfR8MpDhWDZ0FP1EBlvmk8GuDPRTHRuOBRnIYMt8WzKE20+c6BwAAAAAAACeY1EKAAAAAAAAnmNRCgAAAAAAAJ5jUQoAAAAAAACeY1EKAAAAAAAAnmNRCgAAAAAAAJ5jUQoAAAAAAACeY1EKAAAAAAAAnot4Ueqtt97SRRddpNzcXDmOoxdeeCEGsQAgcvQTAFvRTwBsRT8BMCniRamKigqNGjVK8+bNi0UeAGgz+gmAregnALainwCY5I/0CpMnT9bkyZNjkQUA2oV+AmAr+gmAregnACZFvCgVqaqqKlVVVTV8X1paGuuRABAW+gmAregnALainwBEU8xPdD5nzhxlZmY2fPXr1y/WIwEgLPQTAFvRTwBsRT8BiKaYL0rNnj1bJSUlDV979uyJ9UgACAv9BMBW9BMAW9FPAKIp5m/fCwQCCgQCsR4DABGjnwDYin4CYCv6CUA0xfyVUgAAAAAAAMCxIn6lVHl5ubZv397w/Y4dO1RQUKBu3bqpf//+UQ0HAJGgnwDYin4CYCv6CYBJES9Kvffee5owYULD9z/+8Y8lSdOnT9fjjz8etWAAECn6CYCt6CcAtqKfAJgU8aLU+PHj5bpuLLIAQLvQTwBsRT8BsBX9BMAkzikFAAAAAAAAz7EoBQAAAAAAAM+xKAUAAAAAAADPsSgFAAAAAAAAz7EoBQAAAAAAAM+xKAUAAAAAAADPsSgFAAAAAAAAzzmu67peDiwtLVVmZmbd8DS/l6MlSe7hWsmV5EhOZ+/nk4EMtmUwPV+S3IpaSVJJSYkyMjKMZJDM95NkyfHgPkkGMjTOYEFH0U9ksGU+GSzLQD9JsuRYkIEMlsy3JkOY/WQm3ZfqQ5oZbng+GchgWwbT8y1j/Law4XiYzmB6PhnIYCnjt4MNx4IM5ueTwa4MljB+O9hwLMhABlvm25KhFUYXpXilFBnIYD6D6fmSnUXJX/rMZTA9nwxkaJLBso6inxI7g+n5ZLAsA/0kyZJjQQYyWDLfmgxh9pO5RanOPmXMGOr52NLHt8utqJXT2W9kPhnIYFsG0/MlqWThNulw0MjsZhnqJ8mO42E6g+n5ZCDDsazqKPop4TOYnk8GuzLQT3VsOBZkIIMt823JEG4/caJzAAAAAAAAeI5FKbSL4+158gEAAAAAQAdh9JxSiD+jDlRq6qZijSk8ohGHqpQSkqqTpC1dA3o3t5OezMvS+uxU0zEBAAAAAIDlWJRCWAYVV2vu8iKNLTqiGkdKHnWKdO5IKT1dKWVlOnnzZo1cX6CbNhbrnZxOmjUxRzuyUkzHBpCA8vvmaVivQeqSmqbyygpt279DG/ZuMh0LAOgnANain2AKi1Jo1RVbSzRv+T75UgLSjBlKvu026dRTm2yXvG6d9OCDOmPxM1r5zA7dMjFHS4ZnGEgMINGkJgd0+egLdNO4aRrV74QmP1+/5yM9/OYiLVn7kiprqgwkBJCo6CcAtqKfYAPOKYXjumJriR5+rUjJg4fK/9Em6bHHpPz85jfOz5cee0z+jzYpedAQPfJaoa7YWuJtYAAJZ0jPgVp91/9p7tR7dVKfEc1uc1KfEZo79V6tvuv/NDh7gMcJASQq+gmAregn2IJFKbRocHG15i3fJw0bpqSVK6U+fSTHkXy+5q/g89X9vE8fJa1aJQ0dqnnL92lQcbW3wQEkjCE9B+r1f3tGuVm95DiOfEnN95MvySfHcZSb1UvL7ljMEysAMUc/AbAV/QSbsCiFFj20vEhJKQEl/f3vUnq6lJwc3hWTk6X0dCW99pp8KQHNXV4U26AAElJqckDPz3xU6YE0+X3hvRvd7/MrPZCmF2YtUGpyIMYJASQq+gmAregn2CaiRak5c+bo61//utLT09WzZ09deuml2rJlS6yywaBRn1VqbNERJV/1HWnAgPAXpOolJ0sDB8r/7as0tuiIRh2ojE1Q4Ev0U+K5fPQF6tctN+wnVPX8Pr/6d++jy06bHKNkQFN0VGKhnxBP6KfEQj/BNhEtSr355puaOXOmVq5cqddee001NTU677zzVFFREat8MGTq5mLVOJJuu00Khdq2k2BQuvVW1TjS1E3F0YwHNEE/JZ6bxk1TyG1bPwVDQd00blqUEwEto6MSC/2EeEI/JRb6CbaJaHn0lVdeafT9448/rp49e2rt2rX61re+FdVgMGtM4REljzql2U/ZC5vPJ40ereRRp+hMPk4UMUY/JZb8vnnNfkpMuHxJPp3S/0Tl983j447hCToqcdBPiDf0U+Kgn2Cjdp1TqqSk7pPVunXr1uI2VVVVKi0tbfQF+404VCWNHBmlnY3QyIN8hCi8RT91bMN6DYrKfoZGaT9ApFrrKPopftFPiHf0U8dFP8FGbV6UCoVC+uEPf6ixY8fqpJNOanG7OXPmKDMzs+GrX79+bR0Jjziuq5SQ6k5uHg0ZGUoJ1e0X8AL91PF1SU2Lyn7So7QfIBLhdBT9FL/oJ8Qz+qljo59gozYvSs2cOVMffPCBnnnmmeNuN3v2bJWUlDR87dmzp60j4RHXcVSdJKmsLDo7LC1VdVLdfgEv0E8dX3lldM5zURal/QCRCKej6Kf4RT8hntFPHRv9BBtFdsr9L82aNUsvvvii3nrrLfXt2/e42wYCAQUCfGxkvNnSNaCTN2+Ozs42b9bmbtwH4A36KTFs278jOvvZ90lU9gOEK9yOop/iF/2EeEU/dXz0E2wU0SulXNfVrFmz9Pzzz2v58uUaNIj3knZU7+Z2Us36AmndurpP0WuLYFBau1Y1G9ZrZU6nqOYDjkU/JZYNezdp/Z6PFAy1rZ+CoaAKdn+ojZ9GafEdaAUdlTjoJ8Qb+ilx0E+wUUSLUjNnztSiRYv01FNPKT09Xfv27dO+fft05MiRWOWDIU/mZSnZlfTgg1JSG9/l6fNJDz2kZLduf0As0U+J5+E3FynJaVs/+ZJ8evjNRVFOBLSMjkos9BPiCf2UWOgn2Caie+P8+fNVUlKi8ePHKycnp+Fr8eLFscoHQ9Znp+qdnE6qXfyMtGuXVFMT2Q5qaqSdO1X77GK9k9NJ67NTYxMU+BL9lHiWrH1Jew4WqjZYG9H1aoO12v3Fp/rr+y/HKBnQFB2VWOgnxBP6KbHQT7BNROeUcvn0tIQya2KOVj6zQ0nnnaeklSvrPo0vObn1K9bUSGVlCp17roLVVZo1kZcAI/bop8RTWVOlf5l3g17/t2eUHkiT39f6r7TaYK3Kqip06dzrVVlT5UFKoA4dlVjoJ8QT+imx0E+wTZs/fQ8d346sFN0yMUfatk2hM86QPv207gctnWOq/vJPP63bfvt23TIxRzuyUrwJDCDhfPzZTp1931UqLN4vSS2eI6H+8sLi/Tr7vqv0yYFdnmUEkJjoJwC2op9gExalcFxLhmfopnNzVLPjY9WekCfNmCEVFDS/cUGBNGOGak/IU82Oj3XjublaMjzDw7QAEtEnB3bp9F9O0S1/nq2Ne5s/8ebGvZt1y59n6/RfTuEJFQDP0E8AbEU/wRYRvX0Piem54Zla27OT5i4v0tgnnlDNn55Q8qhTpBEjpIwMqbRU2rJFNesLlOxKK3M76dYJg3iFFADPVNZU6alVL+ipVS8ov2+ehvYapPTUNJVVVmj7/h3asHeT6YgAEhT9BMBW9BNswKIUwrIjK0VTLhugUQcqNXVTsc7cu0kjNxQoJSRVJ0mbuwW08qQsPZmXxUnNARi1Ye8mnkQBsBL9BMBW9BNMYVEKEVmfnar12b0bvndcV67jGEwEAAAAAADiEeeUQruwIAUAAAAAANqCRSkAAAAAAAB4znFd1/VyYGlpqTIzM+uGp3n/7kH3cK3kSnIkp7OZdy+SgQw2ZTA9X5LcilpJUklJiTIyzH1io+l+kiw5HtwnyUCGxhks6Cj6iQy2zCeDZRnoJ0mWHAsykMGS+dZkCLOfjJ5Tqj6kmeGG55OBDLZlMD3fMsZvCxuOh+kMpueTgQyWMn472HAsyGB+PhnsymAJ47eDDceCDGSwZb4tGVphdFGKV0qRgQzmM5ieL9lZlPylz1wG0/PJQIYmGSzrKPopsTOYnk8GyzLQT5IsORZkIIMl863JEGY/mVuU6uxTxoyhno8tfXy73IpaOZ39RuaTgQy2ZTA9X5JKFm6TDgeNzG6WoX6S7DgepjOYnk8GMhzLqo6inxI+g+n5ZLArA/1Ux4ZjQQYy2DLflgzh9hMnOgcAAAAAAIDnWJQCAAAAAAByvP0cNMDsOaUAAAAAAIAZow5UauqmYo0pPKIRh6qUEpKqk6QtXQN6N7eTnszL0vrsVNMx0YGxKBWh4IFKBQ9VSzUhKTlJvq4p8nn8IM3vm6dhvQapS2qayisrtG3/Dm3Yu8nTDADsQz8BsBX9BMBmpjvKxPxBxdWau7xIY4uOqMaRPnClJZLKJKWHpJFfVOnag1W6aWOx3snppFkTc7QjKyWmmZCYWJQKg1sbUs22UlVtOKTQ51VNfp7UI6BAflclD8uQ44/NOyJTkwO6fPQFumncNI3qd0KTn6/f85EefnORlqx9SZU1TTMC6JjoJwC2op8A2Mx0R5mcf8XWEs1bvk/VQVcLJT3oSgXNbHeKK90m6YqiI1r5zA7dMjFHS4ZnRDULwKJUK4LF1apYultuWcsfZxj6vEpHlu9T5ZrPlXZxf/mivII8pOdAPT/zUfXrlquQG2p2m5P6jNDcqffqzskzdenc6/XJgV1RzQDAPvQTAFvRTwBsZrqjTM6/YmuJHn6tSFslTZK0Sy2faHqDpOsk/VzSq0FXj7xWKEeunhueGZUsgMSJzo8rWFyt8ud2yi1vuSyO5pbXqvy5nQoWV0ctw5CeA/X6vz2j3KxechxHviRfs9v5knxyHEe5Wb207I7FGpw9IGoZANiHfgJgK/oJgM1Md5TJ+YOLqzVv+T5tlXSWpL1fXt78sv1Xl++VNEbSNknzlu/ToCj2NcCiVAvc2pAqlu6WqkNSuB9A4EqqrrueW9vSQzt8qckBPT/zUaUH0uT3hfeiNr/Pr/RAml6YtUCpyYF2ZwBgH/oJgK3oJwA2M91Rpuc/tLxIVUFXkySVSgqGeb3gl9ufL6k66Gru8qJ25QCOFtGi1Pz585Wfn6+MjAxlZGRozJgxevnll2OVzaiabaV1L6eM9BMxXcktq1XNttJ2Z7h89AXq1y037CdU9fw+v/p376PLTpvc7gxAvKCfwkA/AcYkSkfRT0D8SZR+ksx3lMn5oz6r1NiiI1qiurfshbsgVS/45fWWSBpbdESjDlS2OQtwtIgWpfr27atf//rXWrt2rd577z1NnDhRl1xyiT788MNY5TOmasMho9eXpJvGTWvxHAitCYaCumnctHZnAOIF/eTd9SX6CYhUonQU/QTEn0TpJ8l8R5m8/tTNxapxpAfV9rdLJanu+jWONHVTcZuzAEeL6P540UUX6YILLtCwYcM0fPhw3XvvverSpYtWrlwZq3xGBA9UNvsJCJEIfV6lYDtWj/P75mlUvxNaPAdCa3xJPp3S/0Tl981rcwYgntBP4aOfAO8lQkfRT0B8SoR+ksx3lOn5YwqP6IMvP2WvrW8CDElaJ+kDVzqz6Egb9wI01uZzSgWDQT3zzDOqqKjQmDFjWtyuqqpKpaWljb5sFzwUvZPYtdWwXoOikmFolPYDxBP6KYz90E+AMeF0FP3UNvQT0D4dtZ8k8x1lev6IQ1XaHJUE0hZJIw+2b4ENqBfxotTGjRvVpUsXBQIB3XzzzXr++ed1wgkntLj9nDlzlJmZ2fDVr1+/dgX2RE37T7Ipqe4Edm3UJTUtKhHSo7QfIB7QTxGgnwDPRdJR9FPb0E9A23T4fpLMd5TB+Y7rKiUklUUngUolpYTq9gu0V8SLUiNGjFBBQYFWrVqlH/zgB5o+fbo++uijFrefPXu2SkpKGr727NnTrsCeSI7ShxKmtH0/5ZUVUYlQFqX9APGAfooA/QR4LpKOop/ahn4C2qbD95NkvqMMzncdR9VJUnp0EihDUnVS3X6B9orsY0kkpaSkaOjQoZKk0aNHa82aNfrd736nP/7xj81uHwgEFAjE10fr+rqmRGc/WW3fz7b9O6KSYdu+T6KyHyAe0E8R7Id+AjwXSUfRT21DPwFt09H7STLfUabnb+ka0MgvovOWu5GSNneLv/sA7NTu5dpQKKSqqo71flJfdqqSerTvQZbUIyBfdmqbr79h7yat3/ORgqFIP6yzTjAUVMHuD7Xx02i9cxiIP/RT8+gnwA4draPoJ6Dj6Gj9JJnvKNPz383tpJMc6RS179P3TpV0oiOtzOnUxr0AjUV0f5w9e7beeust7dy5Uxs3btTs2bP1xhtvaOrUqbHKZ0wgv6vR60vSw28uUpLTtsrwJfn08JuL2p0BiBf0k3fXl+gnIFKJ0lH0ExB/EqWfJPMdZfL6T+ZlKdmVblP7Pn3vNknJbt3+gGiI6Df2Z599pu9973saMWKEzj77bK1Zs0avvvqqzj333FjlMyZ5WIacdL8U6dtkHclJ9yt5WEa7MyxZ+5L2HCxUbbA2ouvVBmu1+4tP9df3X253BiBe0E9hoJ8AYxKlo+gnIP4kSj9J5jvK5Pz12al6J6eTrpA0QJIvwuv7vrze5ZLeyemk9e14VStwtIjOKbVgwYJY5bCO409S2sX9Vf7czrpPOAjngwUcSSl113P87T+RXWVNlf5l3g16/d+eUXogTX5f64erNlirsqoKXTr3elXWdKyX3ALHQz+1diXRT4BBidJR9BMQfxKlnyTzHWV6/qyJOVr5zA69EnR1luo+RS+cNzv7VHdy81clpfgczZqY064cwNGi9BEAHZMvK0Vdrhgop0t4a3dOF7+6XDGwXSfoPNbHn+3U2fddpcLi/ZLU4jkS6i8vLN6vs++7Sp8c2BW1DADsQz8BsBX9BMBmpjvK5PwdWSm6ZWKOhkt6V1LfLy9vaVGg/vK+X24/TNItE3O0I4p9DUT86XuJxpeVovTvDlbNtlJVbTik0OdN/3qW1COgQH7XupdjRuEvfMf65MAunf7LKbrstMm6adw0ndL/xCbbbNy7WQ+/uUh/ff9l/sIHJAj6CYCt6CcANjPdUSbnLxmeIUeu5i3fp41BV0skPShpXTPbjlLdOaQuV90rpG6cmKMlw9v/NmvgaCxKhcHxJyklL0speVkKHqhUsLi67uWWKUnyZaW061NiwlVZU6WnVr2gp1a9oPy+eRraa5DSU9NUVlmh7ft3aMPeTTHPAMA+9BMAW9FPAGxmuqNMzn9ueKbW9uykucuLNKPoiKY60geutEV1b+nLkDRC0klO3UnN/5HbSbdO4BVSiA0WpSLky0715EnU8WzYu4knUQCaoJ8A2Ip+AmAz0x1lYv6OrBRNuWyARh2o1NRNxTqz6IguO1illJBUnSRt7hbQwpxOejIvi5OaI6ZYlAIAAAAAIAGtz07V+uzeDd87rivXifTjAYG240TnAAAAAACABSl4jkUpAAAAAAAAeI5FKQAAAAAAAHjOcV3X9XJgaWmpMjMz64aneX9KK/dwreRKciSns5lTapGBDDZlMD1fktyKWklSSUmJMjLMfcys6X6SLDke3CfJQIbGGSzoKPqJDLbMJ4NlGegnSZYcCzKQwZL51mQIs5+Mnui8PqSZ4Ybnk4EMtmUwPd8yxm8LG46H6Qym55OBDJYyfjvYcCzIYH4+GezKYAnjt4MNx4IMZLBlvi0ZWmF0UYpXSpGBDOYzmJ4v2VmU/KXPXAbT88lAhiYZLOso+imxM5ieTwbLMtBPkiw5FmQggyXzrckQZj+ZW5Tq7FPGjKGejy19fLvcilo5nf1G5pOBDLZlMD1fkkoWbpMOB43MbpahfpLsOB6mM5ieTwYyHMuqjqKfEj6D6flksCsD/VTHhmNBBjLYMt+WDOH2Eyc6BwAAAAAAgOdYlAIAAAAAAIDnWJQCAAAAAACA54ye6DxS+X3zNKzXIHVJTVN5ZYW27d+hDXs3JVyG4IFKBQ9VSzUhKTlJvq4p8mWnJlQG0/PJYFcGG9jQDTZksOH+QAbz88lgH9P9YHq+ZMf9gduBDLbMt4kNjwvTGWy4P5CBDKYyWL8olZoc0OWjL9BN46ZpVL8Tmvx8/Z6P9PCbi7Rk7UuqrKnqsBnc2pBqtpWqasMhhT5vOiOpR0CB/K5KHpYhxx+bF8CZzmB6PhnsymADG7rBhgw23B/IYH4+Gexjuh9Mz5fsuD9wO5DBlvk2seFxYTqDDfcHMpDBhgxWL0oN6TlQz898VP265Srkhprd5qQ+IzR36r26c/JMXTr3en1yYFeHyxAsrlbF0t1yy1r+SMXQ51U6snyfKtd8rrSL+8uXldKhMpieTwa7MtjAhm6wIYMN9wcymJ9PBvuY7gfT8yU77g/cDmSwZb5NbHhcmM5gw/2BDGSwJYO1S/BDeg7U6//2jHKzeslxHPmSfM1u50vyyXEc5Wb10rI7Fmtw9oAOlSFYXK3y53bKLW/5znE0t7xW5c/tVLC4usNkMD2fDHZlsIEN3WBDBhvuD2QwP58M9jHdD6bnS3bcH7gdyGDLfJvY8LgwncGG+wMZyGBThnYtSv3617+W4zj64Q9/GJUw9VKTA3p+5qNKD6TJ7wvvxVx+n1/pgTS9MGuBUpMDHSKDWxtSxdLdUnVIcsO9kqTquuu5tc2v+sdTBtPzyWBXhkjQT7HNYMP9gQzm55OhbWLVT5L5fjA9X7Lj/sDtQAZb5rcFz6Fil8GG+wMZyGBbhjYvSq1Zs0Z//OMflZ+f3+4Qx7p89AXq1y037KKo5/f51b97H1122uQOkaFmW2ndy+fCvXPUcyW3rFY120rjPoPp+WSwK0O46KfYZ7Dh/kAG8/PJELlY9pNkvh9Mz5fsuD9wO5DBlvmR4jlUbDPYcH8gAxlsy9CmRany8nJNnTpVjzzyiLp27druEMe6ady0Ft/b25pgKKibxk3rEBmqNhwyen0bMpi+PhnsyhAO+smbDDbcH8hg/vpkiEys+0ky3w+m50t23B+4Hchgy/UjwXOo2Gew4f5ABjLYlqFNi1IzZ87UlClTdM4557Q7wLHy++ZpVL8TWnxvb2t8ST6d0v9E5ffNi+sMwQOVzZ7xPhKhz6sUPFAZtxlMzyeDXRnCRT/FPoMN9wcymJ9PhsjFsp8k8/1ger5kx/2B24EMtsyPFM+hYpvBhvsDGchgWwapDYtSzzzzjN5//33NmTMnrO2rqqpUWlra6Ot4hvUaFGmkZg1tx35syBA8FL0TK8ZrBtPzyWBXhnDQT95ksOH+QAbz88kQmVj3k2S+H0zPl+y4P3A7kMGW+ZGIpKPisZ9syGDD/YEMZLAtgxThotSePXt0++2368knn1RqampY15kzZ44yMzMbvvr163fc7bukpkUSqUXp7diPDRlUE6WTGla3Yz+mM5ieTwa7MrSCfvIugxX3BzKYn0+GsHnRT5L5fjA9X5IV9wduBzJYMz9MkXZUPPaTFRlsuD+QgQy2ZVCEi1Jr167VZ599ptNOO01+v19+v19vvvmmHnzwQfn9fgWDwSbXmT17tkpKShq+9uzZc9wZ5ZUVkf0LWlDWjv3YkEHJ7fpgxK+ktGM/pjOYnk8GuzK0gn7yLoMV9wcymJ9PhrB50U+S+X4wPV+SFfcHbgcyWDM/TJF2VDz2kxUZbLg/kIEMtmWQFNHHDpx99tnauHFjo8uuvfZajRw5Unfeead8vqbvzw0EAgoEwv/ozG37d0QSqeX97Puk7de1IIOva0pUMviy2r4f0xlMzyeDXRlaQz95l8GG+wMZzM8nQ/i86CfJfD+Yni/ZcX/gdiCDLfPDFWlHxWM/2ZDBhvsDGchgWwYpwldKpaen66STTmr0lZaWpu7du+ukk05qV5B6G/Zu0vo9HykYavpXw3AEQ0EV7P5QGz/dHNcZfNmpSuoRWdkfK6lHQL7s8N4mYGMG0/PJYFeG1tBP3mWw4f5ABvPzyRA+L/pJMt8PpudLdtwfuB3IYMv8cPEcypsMNtwfyEAG2zJIbfz0vVh7+M1FSnLaFs2X5NPDby7qEBkC+e37KNb2Xt+GDKavTwa7MtjAhm6wIYMN9wcymL8+Gexjuh9Mz5fsuD9wO5DBluvbxIbHhekMNtwfyEAG2zK0e1HqjTfe0AMPPNDuIEdbsvYl7TlYqNpgbUTXqw3WavcXn+qv77/cITIkD8uQk+6XnAiv6EhOul/JwzLiPoPp+WSwK0Ok6KfYZbDh/kAG8/PJ0Hax6CfJfD+Yni/ZcX/gdiCDLfPbiudQsclgw/2BDGSwLYOVr5SqrKnSv8y7QWVVFWEXRm2wVmVVFbp07vWqrKnqEBkcf5LSLu5fd+KwcO8kjqSUuus5/vYfXtMZTM8ng10ZbGBDN9iQwYb7AxnMzyeDfUz3g+n5kh33B24HMtgy3yY2PC5MZ7Dh/kAGMtiWwdqW+/iznTr7vqtUWLxfklp872/95YXF+3X2fVfpkwO7OlQGX1aKulwxUE6X8M5J73Txq8sVA6N6QkTTGUzPJ4NdGWxgQzfYkMGG+wMZzM8ng31M94Pp+ZId9wduBzLYMt8mNjwuTGew4f5ABjLYlCGiT9/z2icHdun0X07RZadN1k3jpumU/ic22Wbj3s16+M1F+uv7L0dl9dzGDL6sFKV/d7BqtpWqasMhhT5vOiOpR0CB/K51L7+LwV9UTGcwPZ8MdmWwgQ3dYEMGG+4PZDA/nwz2Md0PpudLdtwfuB3IYMt8m9jwuDCdwYb7AxnIYEsGqxelpLqXWD616gU9teoF5ffN09Beg5Semqayygpt379DG/ZuSogMjj9JKXlZSsnLUvBApYLF1VJ1SEpJki8rJeafymFDBtPzyWBXBhvY0A02ZLDh/kAG8/PJYB/T/WB6vmTH/YHbgQy2zLeJDY8L0xlsuD+QgQw2ZLB+UepoG/Zu8qSgbM/gy041/kvLdAbT88lgVwYb2NANNmSw4f5ABvPzyWAf0/1ger5kx/2B24EMtsy3iQ2PC9MZbLg/kIEMpjJ03NeFAgAAAAAAwFosSgEAAAAAAMBzLEoBAAAAAADAc47ruq6XA0tLS5WZmVk3PM37U1q5h2slV5IjOZ3NnFKLDGSwKYPp+ZLkVtRKkkpKSpSRkWEkg2S+nyRLjgf3STKQoXEGCzqKfiKDLfPJYFkG+kmSJceCDGSwZL41GcLsJ6MnOq8PaWa44flkIINtGUzPt4zx28KG42E6g+n5ZCCDpYzfDjYcCzKYn08GuzJYwvjtYMOxIAMZbJlvS4ZWGF2U4pVSZCCD+Qym50t2FiV/6TOXwfR8MpChSQbLOop+SuwMpueTwbIM9JMkS44FGchgyXxrMoTZT+YWpTr7lDFjqOdjSx/fLreiVk5nv5H5ZCCDbRlMz5ekkoXbpMNBI7ObZaifJDuOh+kMpueTgQzHsqqj6KeEz2B6PhnsykA/1bHhWJCBDLbMtyVDuP3Eic4BAAAAAADgORalAADooBxvP8sEAAAAiIjRc0oBAIDoGXWgUlM3FWtM4RGNOFSllJBUnSRt6RrQu7md9GReltZnp5qOCQAAAEhiUSpi+X3zNKzXIHVJTVN5ZYW27d+hDXs3eZoheKBSwUPVUk1ISk6Sr2uKfB7/T4bp28H0fMBGNjwubOinRMwwqLhac5cXaWzREdU40geutERSmaT0kDTyiypde7BKN20s1js5nTRrYo52ZKXELA9wLPrJngwcC6Ap048LGx4TNmQwfRwkO26HRMOiVBhSkwO6fPQFumncNI3qd0KTn6/f85EefnORlqx9SZU1VTHJ4NaGVLOtVFUbDin0edMZST0CCuR3VfKwDDn+2Lwr0/TtYHo+YCMbHhc29FMiZ7hia4nmLd+n6qCrhZIedKWCZrY7xZVuk3RF0RGtfGaHbpmYoyXDM6KWAzgW/WRPBo4F0JTpx4UNjwkbMpg+DpIdt0MiY1GqFUN6DtTzMx9Vv265CrmhZrc5qc8IzZ16r+6cPFOXzr1enxzYFdUMweJqVSzdLbes5Y9UDH1epSPL96lyzedKu7i/fFH+C7jp28H0fMBGNjwubOinRM5wxdYSPfxakbZKmiRpl1o+WeQGSddJ+rmkV4OuHnmtUI5cPTc8s905gGPRT/Zk4FgATZl+XNjwmLAhg+njINlxOyQ6lvmOY0jPgXr9355RblYvOY4jX5Kv2e18ST45jqPcrF5adsdiDc4eELUMweJqlT+3U255yw+So7nltSp/bqeCxdVRy2D6djA9H7CRDY8LG/opkTMMLq7WvOX7tFXSWZL2fnl580/pvrp8r6QxkrZJmrd8nwZF8bYAJPrJpgwcC6Ap048LGx4TNmQwfRwkO24HRLgo9Z//+Z9yHKfR18iRI2OVzajU5ICen/mo0gNp8vvCe0GZ3+dXeiBNL8xaoNTkQLszuLUhVSzdLVWHpHA/QMmVVF13Pbe2pf81CZ/p28H0fMQP+un4OmI/JXqGh5YXqSroapKkUknBMK8X/HL78yVVB13NXV7U5gwIX6J0FP1kTwaOBcKVKP0kmX9c2PCYsCGD6eMg2XE7oE7Er5Q68cQTVVRU1PD1j3/8Ixa5jLt89AXq1y037AdJPb/Pr/7d++iy0ya3O0PNttK6lxFG+oneruSW1apmW2m7M5i+HUzPR3yhn46vo/VTImcY9VmlxhYd0RLVvWUv3AWpesEvr7dE0tiiIxp1oLJNORCZROgo+smeDBwLRCIR+kky/7iw4TFhQwbTx0Gy43ZAnYgXpfx+v3r37t3w1aNHj1jkMu6mcdNafF9ra4KhoG4aN63dGao2HDJ6fcn87WB6PuIL/dS6jtRPiZxh6uZi1TjSg2r7+/CTVHf9Gkeauqm4jXtBJBKho+gnezJwLBCJROgnyfzjwobHhA0ZTB8HyY7bAXUifi67bds25ebmavDgwZo6dap2794di1xG5ffN06h+J7T4vtbW+JJ8OqX/icrvm9fmDMEDlc2e+T8Soc+rFGzHX79N3w6m5yP+0E+t6yj9lOgZxhQe0QdffspeW188HpK0TtIHrnRm0ZE27gWR6OgdRT/Zk4FjgUh19H6SzD8ubHhM2JDB9HGQ7Lgd8JWIFqXOOOMMPf7443rllVc0f/587dixQ9/85jdVVlbW4nWqqqpUWlra6Mt2w3oNisp+hrZjP8FD0Tl5WntOwmb6djA9H/GFfopMvPdTomcYcahKm6MyXdoiaeTB2HzEMr4SaUfRT22T6N1Qj2OBSCRCP0nmHxc2PCZsyGD6OEh23A74SkRv4pw8+av3bubn5+uMM87QgAED9Oyzz+r6669v9jpz5szRz3/+8/al9FiX1LSo7Ce9PfupidKJ06rbvh/Tt4Pp+Ygv9FNk4r2fEjmD47pKCUktL7dGplRSSqhuv67jRGmvOFakHUU/tVECd8PROBaIRCL0k2TB48KGx4QFGYwfB8mK2wFfaeupKCRJWVlZGj58uLZv397iNrNnz1ZJSUnD1549e9oz0hPllRVR2U9Ze/aT3K5D85WUtu/H9O1gej7iG/10fPHeT4mcwXUcVSdJ6dGZrgxJ1UliQcpjrXUU/dRGCdwNR+NYoD06Yj9JFjwubHhMWJDB+HGQrLgd8JV23Yrl5eX6+OOPlZOT0+I2gUBAGRkZjb5st23/jujsZ98nbb6ur2tKVDL4stq+H9O3g+n5iG/0Uyv7ifN+SvQMW7oGFK0P6x4paXO39n+0MiLTWkfRT22T6N1Qj2OB9uiI/SSZf1zY8JiwIYPp4yDZcTvgKxEtSv3bv/2b3nzzTe3cuVP//Oc/9S//8i/y+Xy6+uqrY5XPiA17N2n9no8UDEX6Idt1gqGgCnZ/qI2ftv2MH77sVCX1aN//JCT1CMiXndrm65u+HUzPR3yhn8LTUfop0TO8m9tJJznSKWrfp++dKulER1qZ06mNe0G4EqGj6Cd7MnAsEIlE6CfJ/OPChseEDRlMHwfJjtsBX4nouezevXt19dVXa8SIEfr2t7+t7t27a+XKlcrOzo5VPmMefnORkpy2PdX3Jfn08JuL2p0hkN/V6PUl87eD6fmIH/RTeDpSPyVyhifzspTsSrepfZ++d5ukZLduf4itROko+smeDBwLhCtR+kky/7iw4TFhQwbTx0Gy43ZAnYjuCc8884wKCwtVVVWlvXv36plnntGQIUNilc2oJWtf0p6DhaoN1kZ0vdpgrXZ/8an++v7L7c6QPCxDTrpfivQ0H47kpPuVPKz9L6U1fTuYno/4QT+1rqP1UyJnWJ+dqndyOukKSQMkRfqhyr4vr3e5pHdyOmk9f+mLuUTpKPrJngwcC4QrUfpJMv+4sOExYUMG08dBsuN2QB3OzNWCypoq/cu8G1RWVRH2g6U2WKuyqgpdOvd6Vda0/+O1HX+S0i7uX3cCtXAfLI6klLrrOf72H17Tt4Pp+YCNbHhc2NBPiZ5h1sQcpfgcvaK6k5WHuzDl+3L7VyWl+BzNmtjyedeASNFP9mTgWABNmX5c2PCYsCGD6eMg2XE7oA635HF8/NlOnX3fVSos3i9JLb7vtf7ywuL9Ovu+q/TJgV1Ry+DLSlGXKwbK6eIPa3uni19drhgY1ZOumb4dTM8HbGTD48KGfkrkDDuyUnTLxBwNl/SupL5fXt7SL/b6y/t+uf0wSbdMzNEOTtKJKKOf7MnAsQCaMv24sOExYUMG08dBsuN2gBTerZ/APjmwS6f/coouO22ybho3Taf0P7HJNhv3btbDby7SX99/OSavzPFlpSj9u4NVs61UVRsOKfR50xlJPQIK5HetexliDFZtTd8OpucDNrLhcWFDPyVyhiXDM+TI1bzl+7Qx6GqJpAclrWtm21GqO4fU5ap7hdSNE3O0ZDgvPUds0E/2ZOBYAE2ZflzY8JiwIYPp4yDZcTskOhalwlBZU6WnVr2gp1a9oPy+eRraa5DSU9NUVlmh7ft3aMPeTTHP4PiTlJKXpZS8LAUPVCpYXC1Vh6SUJPmyUjw587/p28H0fMBGNjwubOinRM7w3PBMre3ZSXOXF2lG0RFNdaQPXGmLpFLVvVVvhKSTnLqTmv8jt5NuncArpBB79JM9GTgWQFOmHxc2PCZsyGD6OEh23A6JjEWpCG3Yu8n44ocvO9X4A8P07WB6PmAjGx4XNvRTImbYkZWiKZcN0KgDlZq6qVhnFh3RZQerlBKSqpOkzd0CWpjTSU/mZXFScxhBP9mTgWMBNGX6cWHDY8KGDKaPg2TH7ZBoWJQCAKCDWJ+dqvXZvRu+d1xXrhPpx8oAAAAA3uANkQAAdFAsSAEAAMBmLEoBAAAAAADAc47ruq6XA0tLS5WZmVk3PM37dw+6h2slV5IjOZ3NvHuRDGSwKYPp+ZLkVtRKkkpKSpSRYe7TwEz3k2TJ8eA+SQYyNM5gQUfRT2SwZT4ZLMtAP0my5FiQgQyWzLcmQ5j9ZPScUvUhzQw3PJ8MZLAtg+n5ljF+W9hwPExnMD2fDGSwlPHbwYZjQQbz88lgVwZLGL8dbDgWZCCDLfNtydAKo4tSvFKKDGQwn8H0fMnOouQvfeYymJ5PBjI0yWBZR9FPiZ3B9HwyWJaBfpJkybEgAxksmW9NhjD7ydyiVGefMmYM9Xxs6ePb5VbUyunsNzKfDGSwLYPp+ZJUsnCbdDhoZHazDPWTZMfxMJ3B9HwykOFYVnUU/ZTwGUzPJ4NdGeinOjYcCzKQwZb5tmQIt5840TkAAAAAAAA8x6IUAAAAAM853n7eEgDAQkbPKQUAAAAgMYw6UKmpm4o1pvCIRhyqUkpIqk6StnQN6N3cTnoyL0vrs1NNxwQAeIhFqQgFD1QqeKhaqglJyUnydU2RLwF/eeb3zdOwXoPUJTVN5ZUV2rZ/hzbs3ZQw8wEb0U91bOgHGzIANqGf6tjQDSYyDCqu1tzlRRpbdEQ1jpQ86hTp3JFSerpSysp08ubNGrm+QDdtLNY7OZ00a2KOdmSlxDQTcDQ6KnH7ycYMiYZFqTC4tSHVbCtV1YZDCn1e1eTnST0CCuR3VfKwDDn+jvuOyNTkgC4ffYFuGjdNo/qd0OTn6/d8pIffXKQla19SZU3T2yne5wM2op/q2NAPNmQAbEI/1bGhG0xmuGJrieYt3ydfSkCaMUPJt90mnXpqk+2S162THnxQZyx+Riuf2aFbJuZoyfCMqGYBjkZH0U82ZUhkLEq1IlhcrYqlu+WWtfxxhqHPq3Rk+T5VrvlcaRf3l68D/mVnSM+Ben7mo+rXLVchN9TsNif1GaG5U+/VnZNn6tK51+uTA7s6zHzARvRTHRv6wYYMgE3opzo2dIPJDFdsLdHDrxVJw4Yp6e9/lwYMkELNZ1B+vvTYY/LffbeSzj1Xj7y2XY5cPTc8MypZgKPRUfSTTRkSXcdc8o2SYHG1yp/bKbe85bI6mlteq/LndipYXB3jZN4a0nOgXv+3Z5Sb1UuO48iX5Gt2O1+ST47jKDerl5bdsViDswd0iPmAjeinOjb0gw0ZAJvQT3Vs6AaTGQYXV2ve8n11C1IrV0p9+kiOI/mazyCfr+7nffooadUqaehQzVu+T4M62P0C5tFR9JNNGdCGRalPP/1U06ZNU/fu3dWpUyedfPLJeu+992KRzSi3NqSKpbul6pAU7geDuJKq667n1rbwV6A4k5oc0PMzH1V6IE1+X3gvrPP7/EoPpOmFWQuUmhyI6/mIL/TT8a4k+knR7wcbMiB+JEJH0U91bOgG0xkeWl6kpJRA3Suk0tOl5OTwrpicLKWnK+m11+RLCWju8qJ25UB4EqGfJDpKMt8NZMCxIlqUOnTokMaOHavk5GS9/PLL+uijj/Tb3/5WXbt2jVU+Y2q2lda9nDPST6p1JbesVjXbSmOSy2uXj75A/brlhv1Aref3+dW/ex9ddtrkuJ6P+EE/hYF+khTdfrAhA+JDonQU/VTHhm4wmWHUZ5UaW3REyVd9p+4te+EuSNVLTpYGDpT/21dpbNERjTpQ2eYsaF2i9JNER0n0k00ZUCeiRanf/OY36tevnxYuXKjTTz9dgwYN0nnnnachQ4bEKp8xVRsOGb2+LW4aN63F99a2JhgK6qZx0+J6PuIH/eTd9W1hQz/YkAHxIVE6in6qY0M3mMwwdXOxahxJt93W8jmkWg0RlG69VTWONHVTcZuzoHWJ0k8SHSXRTzZlQJ2IFqWWLl2qr33ta7ryyivVs2dPnXrqqXrkkUdilc2Y4IHKZj+BIRKhz6sUjPO/6uT3zdOofie0+N7a1viSfDql/4nK75sXl/MRX+in8NFP0ekHGzIgfiRCR9FPdWzoBtMZxhQeUfKoU+o+Za+lc0i1GsInjR6t5FGn6MyiI23bB8KSCP0k0VGS+W4gA5oT0aLUJ598ovnz52vYsGF69dVX9YMf/EC33XabnnjiiRavU1VVpdLS0kZftgseis5J7OL9ZHjDeg2Kyn6GtnE/pucjvtBPEe6HfpLUvn6wIQPiR6QdRT/FLxu6wXSGEYeqpJEjo5JBI0Zo5EE+gj2WEqGfJDpKMt8NZEBzInoDZSgU0te+9jX96le/kiSdeuqp+uCDD/SHP/xB06dPb/Y6c+bM0c9//vP2J/VSTZROYFcd3yfC65KaFpX9pLdxP6bnI77QTxGinyS1rx9syID4EWlH0U/xy4ZuMJnBcV2lhFR3cvNoyMhQSqhuv67jRGefaCQh+kmio0Q/2ZQBX4nolVI5OTk64YQTGl2Wl5en3bt3t3id2bNnq6SkpOFrz549bUvqpeSIP5SweSlR2o8h5ZUVUdlPWRv3Y3o+4gv9FCH6SVL7+sGGDIgfkXYU/RS/bOgGkxlcx1F1kqSysqhkUGmpqpPEglQMJUQ/SXSU6CebMuArEb1SauzYsdqyZUujy7Zu3aoBAwa0eJ1AIKBAIL4+LtHXNSU6+8mKzn5M2bZ/R3T2s++TuJyP+EI/Rbgf+qluP+3oBxsyIH5E2lH0U/yyoRtMZ9jSNaCTN2+OSgZt3qzN3eLrsRBvEqGfJDpKMt8NZEBzIlrm/dGPfqSVK1fqV7/6lbZv366nnnpKDz/8sGbOnBmrfEb4slOV1KN9RZvUIyBfdmqUEpmxYe8mrd/zkYKhYJuuHwwFVbD7Q238tG1PSkzPR3yhn8JHP0WnH2zIgPiRCB1FP9WxoRtMZ3g3t5Nq1hdI69bVfYpem0IEpbVrVbNhvVbmdGrbPhCWROgniY6SzHcDGdCciBalvv71r+v555/X008/rZNOOkm/+MUv9MADD2jq1KmxymdMIL+r0evb4uE3FynJadtLVH1JPj385qK4no/4QT95d31b2NAPNmRAfEiUjqKf6tjQDSYzPJmXpWRX0oMPSkltfKuTzyc99JCS3br9IXYSpZ8kOkqin2zKgDoRH4ULL7xQGzduVGVlpTZt2qQbb7wxFrmMSx6WISfdL0X69nVHctL9Sh6WEZNcXluy9iXtOVio2mBtRNerDdZq9xef6q/vvxzX8xFf6KdW0E+SotsPNmRA/EiEjqKf6tjQDSYzrM9O1Ts5nVS7+Blp1y6ppiayHdTUSDt3qvbZxXonp5PWx/ErU+JFIvSTREdJ9JNNGVAnfs/SFmOOP0lpF/evO5FduKXlSEqpu57j7xg3bWVNlf5l3g0qq6oI+wFbG6xVWVWFLp17vSpr2vcRvqbnAzain+rY0A82ZABsQj/VsaEbTGeYNTFHweoqhc47r+6k5+EuTNXUSGVlCp17roLVVZo1MaddOYCj0VHmu4EMOFb8P6piyJeVoi5XDJTTJbzzwTtd/OpyxcC4Pvldcz7+bKfOvu8qFRbvl6QW33tbf3lh8X6dfd9V+uTArg4xH7AR/VTHhn6wIQNgE/qpjg3dYDLDjqwU3TIxR9q2TaEzzpA+/fTLYS2cw6X+8k8/rdt++3bdMjFHOzrY/QLm0VH0k00ZEOGn7yUiX1aK0r87WDXbSlW14ZBCnzddEU3qEVAgv2vdy0E7wOp5cz45sEun/3KKLjttsm4aN02n9D+xyTYb927Ww28u0l/ffznqK8em5wM2op/q2NAPNmQAbEI/1bGhG0xmWDI8Q45czVv+sXwn5Mn/7aukW2+VRo9uunFBgfTQQ6p9drGC1VW65dxcLRke/2+Vgp3oKPrJpgyJjkWpMDj+JKXkZSklL0vBA5UKFldL1SEpJUm+rJS4/gSGSFTWVOmpVS/oqVUvKL9vnob2GqT01DSVVVZo+/4d2rB3U4eeD9iIfqpjQz/YkAGwCf1Ux4ZuMJnhueGZWtuzk+YuL9LYJ55QzZ+eUPKoU6QRI6SMDKm0VNqyRTXrC5TsSitzO+nWCYN4hRRijo6in2zKkMhYlIqQLzs1IQqqNRv2bjL64DQ9H7AR/VTHhn6wIQNgE/qpjg3dYCLDjqwUTblsgEYdqNTUTcU6c+8mjdxQoJSQVJ0kbe4W0MqTsvRkXhYnNYcRdFTi9pONGRINi1IAAAAAYm59dqrWZ/du+N5xXblOpB+DBgDoSDrem2MBAAAAWI8FKQAAi1IAAAAAAADwnOO6ruvlwNLSUmVmZtYNT/P+3YPu4VrJleRITmcz714kAxlsymB6viS5FbWSpJKSEmVkmPukHdP9JFlyPLhPkoEMjTNY0FH0ExlsmU8GyzLQT5IsORZkIIMl863JEGY/GT2nVH1IM8MNzycDGWzLYHq+ZYzfFjYcD9MZTM8nAxksZfx2sOFYkMH8fDLYlcESxm8HG44FGchgy3xbMrTC6KIUr5QiAxnMZzA9X7KzKPlLn7kMpueTgQxNMljWUfRTYmcwPZ8MlmWgnyRZcizIQAZL5luTIcx+Mrco1dmnjBlDPR9b+vh2uRW1cjr7jcwnAxlsy2B6viSVLNwmHQ4amd0sQ/0k2XE8TGcwPZ8MZDiWVR1FPyV8BtPzyWBXBvqpjg3HggxksGW+LRnC7SdOdA4AAAAAAADPsSgFAAAAAAAAz7EoBQAAAAAAAM8ZPdF5pIIHKhU8VC3VhKTkJPm6psiXnWo6VkLK75unYb0GqUtqmsorK7Rt/w5t2LvJdCzAGPrJHvQT0BQdZQf6CWiKfrID/QRTrF+UcmtDqtlWqqoNhxT6vKrJz5N6BBTI76rkYRly/LzwK5ZSkwO6fPQFumncNI3qd0KTn6/f85EefnORlqx9SZU1TY8V0NHQT/agn4Cm6Cg70E9AU/STHegn2MDqRalgcbUqlu6WW9byRwmGPq/SkeX7VLnmc6Vd3F++rBQPEyaOIT0H6vmZj6pft1yF3FCz25zUZ4TmTr1Xd06eqUvnXq9PDuzyOCXgHfrJHvQT0BQdZQf6CWiKfrID/QRbWLvsHCyuVvlzO+WWt1xWR3PLa1X+3E4Fi6tjnCzxDOk5UK//2zPKzeolx3HkS/I1u50vySfHcZSb1UvL7liswdkDPE4KeIN+sgf9BDRFR9mBfgKaop/sQD/BJhEtSg0cOFCO4zT5mjlzZlRDubUhVSzdLVWHJDfcK0mqrrueW9v8Si8il5oc0PMzH1V6IE1+X3gvrPP7/EoPpOmFWQuUmhyIcULgK150FP1kD/oJ8YTnUImFfkI8oZ8SC/0E20S0KLVmzRoVFRU1fL322muSpCuvvDKqoWq2lda9nDPcsqrnSm5ZrWq2lUY1TyK7fPQF6tctN+zCquf3+dW/ex9ddtrkGCUDmvKio+gne9BPiCc8h0os9BPiCf2UWOgn2CaiRans7Gz17t274evFF1/UkCFDNG7cuKiGqtpwyOj18ZWbxk1r8T3GrQmGgrpp3LQoJwJa5kVH0U/2oJ8QT3gOlVjoJ8QT+imx0E+wTZvPKVVdXa1Fixbpuuuuk+M4UQsUPFDZ7CcwRCL0eZWCByqjlChx5ffN06h+J7T4HuPW+JJ8OqX/icrvmxflZEDrYtFR9JM96CfEM55DdWz0E+IZ/dSx0U+wUZsXpV544QUVFxdrxowZx92uqqpKpaWljb6OJ3goOiex42R47Tes16Co7GdolPYDRCKcjqKf4hf9hHgWi36S6Chb0E+IZ/RTx0Y/wUZtXpRasGCBJk+erNzc3ONuN2fOHGVmZjZ89evX7/g7ronSCeyqORFee3VJTYvKftKjtB8gEuF0FP0Uv+gnxLOY9JNER1mCfkI8o586NvoJNmrTotSuXbv0+uuv64Ybbmh129mzZ6ukpKTha8+ePce/QnKb18kaS4nSfhJYeWVFVPZTFqX9AOEKt6Pop/hFPyFexayfJDrKEvQT4hX91PHRT7BRZKfc/9LChQvVs2dPTZkypdVtA4GAAoHwPzbS1zWlLZGa7icrOvtJZNv274jOfvZ9EpX9AOEKt6Pop/hFPyFexaqfJDrKFvQT4hX91PHRT7BRxEvNoVBICxcu1PTp0+X3t2lN67h82alK6hFZyR0rqUdAvuzUKCVKXBv2btL6PR8pGAq26frBUFAFuz/Uxk83RzkZ0LJYdhT9ZA/6CfGI51CJgX5CPKKfEgP9BBtFvCj1+uuva/fu3bruuutikUeSFMjvavT6+MrDby5SktO2l8n6knx6+M1FUU4EHF+sO4p+sgf9hHjDc6jEQT8h3tBPiYN+gm0ivjeed955cl1Xw4cPj0UeSVLysAw56X4p0k8hdSQn3a/kYRkxyZWIlqx9SXsOFqo2WBv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wjIB+h7KyMi1cuFAZGRnq1q1b7fdHjx7VPffco//93/9V586d663397//Xf3799eyZcvUr18/9e3bV3fffbdOnDgRUF0AAAAAANA4BqUixJgxY/TBBx/I6/WqtLRUH3/8sXJycnT55ZfX3gW1bt06eTyeoAalDMPQypUr9fHHHys+Pl4xMTH63e9+pxUrVqhLly6NrufxeLRixYpGH7GrefTv7LuoUlNTm30s8K233mry0T1JWrZsmeLi4hQXF6f4+HgtXbpUixYtksNRfcqbpqmpU6fq3nvv1QUXXNDgNvbs2aP9+/frb3/7m1566SUtWLBAmzZt0s0339xkbQAAAAAA0DwGpSLE6NGjVV5ero8++kj//Oc/NXDgQKWkpCgnJ6d2Xqk1a9aof//+6t27d8DbNU1Tubm56t69u/75z39qw4YNuuGGGzRp0iQdPny40fVWrVql7t27a+jQoaH49Wpt375dhYWFGjt2bJPLjRkzRlu2bNGWLVu0YcMGXXPNNRo/frz2798vSfrjH/+o0tJSzZgxo9Ft+P1+eTwevfTSS7rssss0evRozZs3T6tXr9bOnTtD+nsBAAAAANDRMCgVITIyMtSrVy+tXr1aq1evVk5OjiQpLS1N6enp+ve//63Vq1friiuuCGq7q1at0rJly/Tqq69q1KhRGjFihJ5//nl16tRJL774YqPrLV26tMm7mWrmezp69Gid748ePdrkPFVLly7VVVddpZiYmCZzx8bGKiMjQxkZGbrwwgs1d+5clZeX64UXXqj9vdatWye32y2Xy6WMjAxJ0gUXXKA77rhDktSzZ0+5XC4NHDiwdrtZWVmSpAMHDjRZHwAAAAAANI1BqQgyZswYrVmzRmvWrKmd2FySLr/8ci1fvlwbNmwI6tE9qfrtfJJqH3ur4XA45Pf7G1zHNE39/e9/b3Q+Kal6wvAePXrovffeq/2upKRE69ev1yWXXNLoem+99VaT222MYRhyOBw6ffq0JOkPf/iDtm7dWns31dtvvy1JWrRokX75y19KkkaNGiWv16vdu3fXbufzzz+XJPXp0yfoDAAAAAAA4Cu8fS+CjBkzRrm5uaqqqqq9U0qScnJydP/996uysrLeoNSRI0d05MgR7dq1S5K0bds2xcfHq3fv3uratasuueQSdenSRXfccYd++tOfqlOnTnrhhRe0d+9eTZgwocEcmzZt0qlTp3TppZc2mtUwDH3ve9/Tk08+qczMTPXr108/+clPlJaWphtuuKHBdb744gtt3LhRS5cubXZfeDye2rmpTp48qVmzZqmsrEyTJk2SpHqPMMbFxUmSBgwYoF69ekmSrrzySo0YMUJ33XWXnn32Wfn9fuXm5uqqq66qc/cUAAAAAAAIHndKRZAxY8bo9OnTysjIqDOBeE5OjkpLSzVo0CD17Nmzzjp/+tOfNHz4cN1zzz2Squ+qGj58eO3AT3JyslasWKGysjJdccUVuuCCC/Svf/1Lb731loYNG9ZgjrfeekvXXnutXK6mxzwfeeQRPfDAA5o+fbouvPBClZWVacWKFY0+mvf3v/9dF110kZKTk5vdFytWrFDPnj3Vs2dPXXzxxfroo4/0t7/9rc4dZM1xOBz6+9//ruTkZF1++eWaMGGCsrKy9Oqrrwa8DQAAAAAA0DDDNE3T6hCILNnZ2Xrsscf0jW98I6Tbve6663TppZfqkUceCel2AQAAAABA+HGnFEKqsrJSkydP1vjx40O+7UsvvVTf/OY3Q75dAAAAAAAQftwpBQAAAAAAgLDjTikAAAAAAACEHYNSAAAAAAAACDsGpQAAAAAAABB2DEoBAAAAAAAg7BiUAgAAAAAAQNgxKAUAAAAAAICwY1AKAAAAAAAAYcegFAAAAAAAAMKOQSkAAAAAAACE3f8HgTdp579nJgwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1200x4800 with 61 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def simulate_game(\n",
    "    nr_of_games: int,\n",
    "    policies: tuple[GamePolicy, GamePolicy],\n",
    "    tqdm_on: bool = False,\n",
    ") -> tuple[np.ndarray, np.ndarray]:\n",
    "    \"\"\"Simulates a stack of games.\n",
    "\n",
    "    Args:\n",
    "        nr_of_games: The number of games that should be simulated.\n",
    "        policies: The policies that should be used to simulate the game.\n",
    "        tqdm_on: Switches tqdm on.\n",
    "\n",
    "    Returns:\n",
    "        A stack of board histories and actions.\n",
    "    \"\"\"\n",
    "    board_history_stack = np.zeros((SIMULATE_TURNS, nr_of_games, 8, 8), dtype=np.int8)\n",
    "    action_history_stack = np.zeros((SIMULATE_TURNS, nr_of_games, 2), dtype=np.int8)\n",
    "    current_boards = get_new_games(nr_of_games)\n",
    "    for turn_index in tqdm(range(SIMULATE_TURNS)) if tqdm_on else range(SIMULATE_TURNS):\n",
    "        policy_index = turn_index % 2\n",
    "        policy = policies[policy_index]\n",
    "        board_history_stack[turn_index, :, :, :] = current_boards\n",
    "        if policy_index == 0:\n",
    "            current_boards *= -1\n",
    "        current_boards, action_taken = single_turn(current_boards, policy)\n",
    "        action_history_stack[turn_index, :] = action_taken\n",
    "\n",
    "        if policy_index == 0:\n",
    "            current_boards *= -1\n",
    "\n",
    "    return board_history_stack, action_history_stack\n",
    "\n",
    "\n",
    "simulation_results = simulate_game(1, (RandomPolicy(1), RandomPolicy(1)))\n",
    "_unique_bords, _unique_actions = drop_duplicate_boards(\n",
    "    simulation_results[0].reshape(-1, 8, 8), simulation_results[1].reshape(-1, 2)\n",
    ")\n",
    "plot_othello_boards(_unique_bords, actions=_unique_actions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(70, 8, 8)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.reshape(simulation_results[0], (-1, 8, 8)).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(70, 2)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "simulation_results[1].reshape(-1, 2).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "peak memory: 341.61 MiB, increment: 0.52 MiB\n",
      "10.1 s ± 214 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%memit simulate_game(100, (RandomPolicy(1), RandomPolicy(1)))\n",
    "%timeit simulate_game(100, (RandomPolicy(1), RandomPolicy(1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "board, action = simulate_game(1, (GreedyPolicy(0), GreedyPolicy(0)))\n",
    "\n",
    "plot_othello_boards(\n",
    "    *drop_duplicate_boards(board.reshape(-1, 8, 8), action.reshape(-1, 2))\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Statistical examination of the natural action space and result\n",
    "As for many project some evaluation of the project is in order.\n",
    "\n",
    "1. What is the expected distribution of scores\n",
    "2. What is the expected distribution of possible actions\n",
    "\n",
    "    a. over time\n",
    "    \n",
    "    b. ober space\n",
    "\n",
    "The easiest and robustest way to analyse this is when analyzing randomly played games."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this purpose we played a sample of 10k games and saved them for later analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((70, 10000, 8, 8), (70, 10000, 2))"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "if not os.path.exists(\"rnd_history.npy\") and not os.path.exists(\"rnd_action.npy\"):\n",
    "    simulation_results = simulate_game(\n",
    "        10_000, (RandomPolicy(1), RandomPolicy(1)), tqdm_on=True\n",
    "    )\n",
    "    _board_history, _action_history = simulation_results\n",
    "    np.save(\"rnd_history.npy\", np.astpye.astype(np.int8))\n",
    "    np.save(\"rnd_action.npy\", _action_history.astype(np.int8))\n",
    "else:\n",
    "    _board_history = np.load(\"rnd_history.npy\")\n",
    "    _action_history = np.load(\"rnd_action.npy\")\n",
    "_board_history.shape, _action_history.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For those 10k games the possible actions where evaluated and saved for each and every turn in the game."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(70, 10000, 8, 8)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "if not os.path.exists(\"turn_possible.npy\"):\n",
    "    __board_history = _board_history.copy()\n",
    "    __board_history[1::2] = __board_history[1::2] * -1\n",
    "\n",
    "    _poss_turns = get_possible_turns(\n",
    "        __board_history.reshape((-1, 8, 8)), tqdm_on=True\n",
    "    ).reshape((SIMULATE_TURNS, -1, 8, 8))\n",
    "    np.save(\"turn_possible.npy\", _poss_turns)\n",
    "    del __board_history\n",
    "_poss_turns = np.load(\"turn_possible.npy\")\n",
    "_poss_turns.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Those possible turms then where counted for all games in the history stack."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The action space size can be drawn into a histogram by turn and a curve over the mean action space size.\n",
    "This can be used to analyse in which area of the game that cant be solved absolutely."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f2e1405d5a9b43958ae45080bdae83f2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=34, description='turn', max=69), Output()), _dom_classes=('widget-intera…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "count_poss_turns = np.sum(_poss_turns, axis=(2, 3))\n",
    "mean_possibility_count = np.mean(count_poss_turns, axis=1)\n",
    "std_possibility_count = np.std(count_poss_turns, axis=1)\n",
    "cum_prod = count_poss_turns\n",
    "\n",
    "\n",
    "@interact(turn=(0, 69))\n",
    "def poss_turn_count(turn):\n",
    "    fig, axes = plt.subplots(2, 2, figsize=(15, 8))\n",
    "    ax1, ax2, ax3, ax4 = axes.flatten()\n",
    "    _mean_possibility_count = mean_possibility_count.copy()\n",
    "    _std_possibility_count = std_possibility_count.copy()\n",
    "    _mean_possibility_count[_mean_possibility_count <= 1] = 1\n",
    "    _std_possibility_count[_std_possibility_count <= 1] = 1\n",
    "    # np.cumprod(_mean_possibility_count[::-1], axis=0)[::-1]\n",
    "    # todo what happens here=\n",
    "    fig.suptitle(\n",
    "        f\"Action space size analysis\\nThe total size is estimated to be around {np.prod(_mean_possibility_count):.4g}\"\n",
    "    )\n",
    "    ax1.hist(count_poss_turns[turn], density=True)\n",
    "    ax1.set_title(f\"Histogram of the action space size for turn {turn}\")\n",
    "    ax1.set_xlabel(\"Action space size\")\n",
    "    ax1.set_ylabel(\"Action space size probability\")\n",
    "    ax2.set_title(f\"Mean size of the action space per turn\")\n",
    "    ax2.set_xlabel(\"Turn\")\n",
    "    ax2.set_ylabel(\"Average possible moves\")\n",
    "\n",
    "    ax2.errorbar(\n",
    "        range(70),\n",
    "        mean_possibility_count,\n",
    "        yerr=std_possibility_count,\n",
    "        label=\"Mean action space size with error bars\",\n",
    "    )\n",
    "    ax2.scatter(turn, mean_possibility_count[turn], marker=\"x\")\n",
    "    ax2.legend()\n",
    "\n",
    "    ax4.plot(\n",
    "        range(70),\n",
    "        np.cumprod(_mean_possibility_count[::-1], axis=0)[::-1],\n",
    "        # yerr=np.cumprod(_std_possibility_count[::-1], axis=0)[::-1],\n",
    "    )\n",
    "    ax4.scatter(\n",
    "        turn,\n",
    "        np.cumprod(_mean_possibility_count[::-1], axis=0)[::-1][turn],\n",
    "        marker=\"x\",\n",
    "    )\n",
    "    ax4.set_yscale(\"log\", base=10)\n",
    "    ax4.set_xlabel(\"Turn\")\n",
    "    ax4.set_ylabel(\"Mean remaining total action space size\")\n",
    "    fig.delaxes(ax3)\n",
    "    fig.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is interesting to see that the action space for the first player (white) is much smaller than for the second player."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total mean action-space</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>white</th>\n",
       "      <td>5.687159e+18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>black</th>\n",
       "      <td>3.753117e+20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Total mean action-space\n",
       "white             5.687159e+18\n",
       "black             3.753117e+20"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "white = mean_possibility_count[::2]\n",
    "black = mean_possibility_count[1::2]\n",
    "df = pd.DataFrame(\n",
    "    [\n",
    "        {\n",
    "            \"white\": np.prod(np.extract(white, white)),\n",
    "            \"black\": np.prod(np.extract(black, black)),\n",
    "        }\n",
    "    ],\n",
    "    index=[\"Total mean action-space\"],\n",
    ").T\n",
    "del white, black\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Hash branching"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(70, 10000, 8, 8)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "_board_history.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def calculate_board_branching(board_history) -> pd.Series:\n",
    "    assert len(board_history.shape) == 4\n",
    "    assert board_history.shape[-2:] == (8,8)\n",
    "    assert board_history.shape[0] == SIMULATE_TURNS\n",
    "    return pd.Series([count_unique_baords(board_history[turn]) for turn in range(SIMULATE_TURNS)])\n",
    "_ = calculate_board_branching(_board_history).plot(title=f\"Exploration history over {_board_history.shape[0]} games\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(70, 10000, 8, 8)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "_poss_turns.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f25a578dba884528952f28b36436c523",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=34, description='turn', max=69), Output()), _dom_classes=('widget-intera…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mean_poss_turn = np.mean(_poss_turns, axis=1)\n",
    "del _poss_turns\n",
    "\n",
    "\n",
    "@interact(turn=(0, 69))\n",
    "def turn_distribution_heatmap(turn):\n",
    "    turn_possibility_on_field = mean_poss_turn[turn]\n",
    "\n",
    "    sns.heatmap(\n",
    "        turn_possibility_on_field,\n",
    "        linewidth=0.5,\n",
    "        square=True,\n",
    "        annot=True,\n",
    "        xticklabels=\"ABCDEFGH\",\n",
    "        yticklabels=list(range(1, 9)),\n",
    "    )\n",
    "    plt.title(f\"Headmap of where stones can be placed on turn {turn}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.578125\n"
     ]
    }
   ],
   "source": [
    "def calculate_direct_score(board_history: np.ndarray) -> np.ndarray:\n",
    "    boards_evaluated = np.reshape(\n",
    "        evaluate_boards(np.reshape(board_history, (-1, 8, 8))), (SIMULATE_TURNS, -1)\n",
    "    )\n",
    "    direct_score = boards_evaluated - np.roll(boards_evaluated, shift=-1, axis=0)\n",
    "    direct_score[-1] = 0\n",
    "    return direct_score / 64\n",
    "\n",
    "\n",
    "print(np.max(np.abs(calculate_direct_score(_board_history))))\n",
    "assert len(calculate_direct_score(_board_history).shape) == 2\n",
    "assert calculate_direct_score(_board_history).shape[0] == SIMULATE_TURNS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6197b034e03a46c6883916a45b09ce07",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=29, description='turn', max=59), Output()), _dom_classes=('widget-intera…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "score_history = calculate_direct_score(_board_history) * 64\n",
    "score_history[1::2] = score_history[1::2] * -1\n",
    "\n",
    "\n",
    "@interact(turn=(0, 59))\n",
    "def hist_direct_score(turn):\n",
    "    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 6))\n",
    "    fig.suptitle(\n",
    "        f\"Action space size analysis / total size estimate {np.prod(np.extract(mean_possibility_count, mean_possibility_count)):.4g}\"\n",
    "    )\n",
    "\n",
    "    ax1.set_title(\n",
    "        f\"Histogram of scores on turn {turn} by {'white' if turn % 2 == 0 else 'black'}\"\n",
    "    )\n",
    "\n",
    "    ax1.hist(score_history[turn], density=True)\n",
    "    ax1.set_xlabel(\"Points made\")\n",
    "    ax1.set_ylabel(\"Score probability\")\n",
    "    ax2.set_title(f\"Points scored at turn\")\n",
    "    ax2.set_xlabel(\"Turn\")\n",
    "    ax2.set_ylabel(\"Average points scored\")\n",
    "\n",
    "    ax2.errorbar(\n",
    "        range(60),\n",
    "        np.mean(score_history, axis=1)[:60],\n",
    "        yerr=np.std(score_history, axis=1)[:60],\n",
    "        label=\"Mean score at turn\",\n",
    "    )\n",
    "    ax2.scatter(turn, np.mean(score_history, axis=1)[turn], marker=\"x\", color=\"red\")\n",
    "    ax2.legend()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def calculate_final_evaluation_for_history(board_history: np.ndarray) -> np.ndarray:\n",
    "    final_evaluation = final_boards_evaluation(board_history[-1])\n",
    "    return final_evaluation / 64\n",
    "\n",
    "\n",
    "print(np.max(np.abs(calculate_final_evaluation_for_history(_board_history))))\n",
    "assert len(calculate_final_evaluation_for_history(_board_history).shape) == 1\n",
    "_final_eval = calculate_final_evaluation_for_history(_board_history)\n",
    "plt.title(\"Histogram over the score distribution\")\n",
    "plt.hist((_final_eval * 64), density=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def calculate_who_won(board_history: np.ndarray) -> np.ndarray:\n",
    "    who_won = evaluate_who_won(board_history[-1])\n",
    "    return who_won\n",
    "\n",
    "\n",
    "plt.title(\"Win distribution\")\n",
    "plt.bar(\n",
    "    [\"black\", \"draw\", \"white\"],\n",
    "    pd.Series(calculate_who_won(_board_history)).value_counts().sort_index() / 10000,\n",
    ")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def history_changed(board_history: np.ndarray) -> np.ndarray:\n",
    "    return ~np.all(\n",
    "        np.roll(board_history, shift=1, axis=0) == board_history, axis=(2, 3)\n",
    "    )\n",
    "\n",
    "\n",
    "plt.title(\"Share of turns skipped\")\n",
    "plt.plot(1 - np.mean(history_changed(_board_history), axis=1))\n",
    "plt.xlabel(\"Turn\")\n",
    "plt.ylabel(\"Factor of skipped turns\")\n",
    "plt.yscale(\"log\", base=10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(70, 10000)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_gamma_table(board_history, gamma_value: float):\n",
    "    unchanged = history_changed(board_history)\n",
    "    gamma_values = np.ones_like(unchanged, dtype=float)\n",
    "    gamma_values[unchanged] = gamma_value\n",
    "    return gamma_values\n",
    "\n",
    "\n",
    "get_gamma_table(_board_history, 0.8).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 2.26795474e-10, -4.53590947e-11, -2.26795474e-11, ...,\n",
       "         0.00000000e+00,  6.80386421e-11, -4.53590947e-11],\n",
       "       [ 3.23993534e-10, -6.47987067e-11, -3.23993534e-11, ...,\n",
       "         0.00000000e+00,  9.71980601e-11, -6.47987067e-11],\n",
       "       [ 4.62847905e-10, -9.25695810e-11, -4.62847905e-11, ...,\n",
       "         0.00000000e+00,  1.38854372e-10, -9.25695810e-11],\n",
       "       ...,\n",
       "       [ 4.46428571e-01, -8.92857143e-02, -4.46428571e-02, ...,\n",
       "         0.00000000e+00,  1.33928571e-01, -8.92857143e-02],\n",
       "       [ 4.46428571e-01, -8.92857143e-02, -4.46428571e-02, ...,\n",
       "         0.00000000e+00,  1.33928571e-01, -8.92857143e-02],\n",
       "       [ 4.46428571e-01, -8.92857143e-02, -4.46428571e-02, ...,\n",
       "         0.00000000e+00,  1.33928571e-01, -8.92857143e-02]])"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def calculate_q_reword(\n",
    "    board_history: np.ndarray,\n",
    "    who_won_fraction: float = 0.2,\n",
    "    final_score_fraction=0.2,\n",
    "    gamma=0.8,\n",
    ") -> np.ndarray:\n",
    "    assert who_won_fraction + final_score_fraction <= 1\n",
    "    assert final_score_fraction >= 0\n",
    "    assert who_won_fraction >= 0\n",
    "\n",
    "    gama_table = get_gamma_table(board_history, gamma)\n",
    "    combined_score = np.zeros_like(gama_table)\n",
    "    combined_score += calculate_direct_score(board_history) * (\n",
    "        1 - (who_won_fraction + final_score_fraction)\n",
    "    )\n",
    "    combined_score[-1] += (\n",
    "        calculate_final_evaluation_for_history(board_history) * final_score_fraction / 0.7\n",
    "    )\n",
    "    combined_score[-1] += calculate_who_won(board_history) * who_won_fraction\n",
    "    for turn in range(SIMULATE_TURNS - 1, 0, -1):\n",
    "        values = gama_table[turn] * combined_score[turn]\n",
    "        combined_score[turn - 1] += values\n",
    "\n",
    "    return combined_score\n",
    "\n",
    "\n",
    "calculate_q_reword(\n",
    "    _board_history, gamma=0.7, who_won_fraction=0, final_score_fraction=1\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.53249554e-06, 1.91561943e-06, 2.39452428e-06, 2.99315535e-06,\n",
       "       3.74144419e-06, 4.67680524e-06, 5.84600655e-06, 7.30750819e-06,\n",
       "       9.13438523e-06, 1.14179815e-05, 1.42724769e-05, 1.78405962e-05,\n",
       "       2.23007452e-05, 2.78759315e-05, 3.48449144e-05, 4.35561430e-05,\n",
       "       5.44451787e-05, 6.80564734e-05, 8.50705917e-05, 1.06338240e-04,\n",
       "       1.32922800e-04, 1.66153499e-04, 2.07691874e-04, 2.59614843e-04,\n",
       "       3.24518554e-04, 4.05648192e-04, 5.07060240e-04, 6.33825300e-04,\n",
       "       7.92281625e-04, 9.90352031e-04, 1.23794004e-03, 1.54742505e-03,\n",
       "       1.93428131e-03, 2.41785164e-03, 3.02231455e-03, 3.77789319e-03,\n",
       "       4.72236648e-03, 5.90295810e-03, 7.37869763e-03, 9.22337204e-03,\n",
       "       1.15292150e-02, 1.44115188e-02, 1.80143985e-02, 2.25179981e-02,\n",
       "       2.81474977e-02, 3.51843721e-02, 4.39804651e-02, 5.49755814e-02,\n",
       "       6.87194767e-02, 8.58993459e-02, 1.07374182e-01, 1.34217728e-01,\n",
       "       1.67772160e-01, 2.09715200e-01, 2.62144000e-01, 3.27680000e-01,\n",
       "       4.09600000e-01, 5.12000000e-01, 6.40000000e-01, 8.00000000e-01,\n",
       "       1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00,\n",
       "       1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00,\n",
       "       1.00000000e+00, 1.00000000e+00])"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calculate_q_reword(\n",
    "    _board_history, gamma=0.8, who_won_fraction=1, final_score_fraction=0\n",
    ")[:, 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.80163817e+00, -1.49795229e+00,  1.87755963e+00, -1.40305046e+00,\n",
       "        1.99618693e+00, -1.25476634e+00,  4.68154208e+00, -3.98072406e-01,\n",
       "        3.25240949e+00, -2.18448813e+00,  3.51938983e+00, -4.35076271e+00,\n",
       "        8.11546612e-01, -2.73556673e+00,  2.83054158e+00, -2.11823023e-01,\n",
       "        3.48522122e+00, -1.89347347e+00,  1.38315816e+00, -2.02105230e+00,\n",
       "        3.72368462e+00,  9.04605778e-01,  4.88075722e+00, -2.64905347e+00,\n",
       "        4.38683161e-01, -3.20164605e+00, -2.52057562e-01, -9.06507195e+00,\n",
       "       -8.13399398e-02, -3.85167492e+00,  6.43540634e+00,  1.79425793e+00,\n",
       "        1.09928224e+01, -5.00897198e+00, -1.12149805e-02, -6.26401873e+00,\n",
       "        3.41997659e+00, -9.47502926e+00, -8.09378657e+00, -1.38672332e+01,\n",
       "       -6.08404152e+00, -1.13550519e+01, -2.94381488e+00, -7.42976860e+00,\n",
       "        1.96278926e+00, -1.12965134e+01,  2.12935821e+00, -6.08830224e+00,\n",
       "       -1.36037779e+00, -1.04504722e+01,  3.18690970e+00, -7.26636288e+00,\n",
       "        4.66704640e+00, -1.04161920e+01, -6.77024000e+00, -1.22128000e+01,\n",
       "        9.84000000e-01, -2.52000000e+00,  6.00000000e-01, -3.00000000e+00,\n",
       "        0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
       "        0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
       "        0.00000000e+00,  0.00000000e+00])"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calculate_q_reword(\n",
    "    _board_history, gamma=0.8, who_won_fraction=0, final_score_fraction=0\n",
    ")[:, 0] * 64"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def weights_init_normal(m):\n",
    "    \"\"\"Takes in a module and initializes all linear layers with weight\n",
    "       values taken from a normal distribution.\n",
    "    Source: https://stackoverflow.com/a/55546528/11003343\n",
    "    \"\"\"\n",
    "\n",
    "    classname = m.__class__.__name__\n",
    "    # for every Linear layer in a model\n",
    "    if classname.find(\"Linear\") != -1:\n",
    "        y = m.in_features\n",
    "        # m.weight.data should be taken from a normal distribution\n",
    "        m.weight.data.normal_(0.0, 1 / np.sqrt(y))\n",
    "        # m.bias.data should be 0\n",
    "        m.bias.data.fill_(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[0.0016],\n",
       "        [0.0016],\n",
       "        [0.0016],\n",
       "        [0.0016],\n",
       "        [0.0016]], grad_fn=<TanhBackward0>)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BATCH_SIZE = 1000\n",
    "\n",
    "\n",
    "class DQLNet(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.fc1 = nn.Linear(8 * 8 * 2, 128 * 2)\n",
    "        self.fc2 = nn.Linear(128 * 2, 128 * 3)\n",
    "        self.fc3 = nn.Linear(128 * 3, 128 * 2)\n",
    "        self.fc4 = nn.Linear(128 * 2, 1)\n",
    "        \n",
    "\n",
    "    def forward(self, x):\n",
    "        if isinstance(x, np.ndarray):\n",
    "            x = torch.from_numpy(x).float()\n",
    "        x = torch.flatten(x, 1)\n",
    "        x = self.fc1(x)\n",
    "        x = F.relu(x)\n",
    "        x = self.fc2(x)\n",
    "        x = F.relu(x)\n",
    "        x = self.fc3(x)\n",
    "        x = F.relu(x)\n",
    "        x = self.fc4(x)\n",
    "        x = torch.tanh(x)\n",
    "        return x\n",
    "\n",
    "\n",
    "class DQL_Simple(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.fc1 = nn.Linear(8 * 8 * 2, 64 * 3)\n",
    "        self.fc2 = nn.Linear(64 * 3, 128 * 2)\n",
    "        self.fc3 = nn.Linear(128 * 2, 1)\n",
    "        \n",
    "\n",
    "    def forward(self, x):\n",
    "        if isinstance(x, np.ndarray):\n",
    "            x = torch.from_numpy(x).float()\n",
    "        x = torch.flatten(x, 1)\n",
    "        x = self.fc1(x)\n",
    "        x = F.relu(x)\n",
    "        x = self.fc2(x)\n",
    "        x = F.relu(x)\n",
    "        x = self.fc3(x)\n",
    "        x = torch.tanh(x)\n",
    "        return x\n",
    "\n",
    "\n",
    "DQLNet().forward(np.zeros((5, 2, 8, 8)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "class SymmetryMode(Enum):\n",
    "    MULTIPLY = \"MULTIPLY\"\n",
    "    BREAK_SEQUENCE = \"BREAK_SEQUENCE\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((70, 100, 8, 8), (70, 100, 2))"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "_board_history, _action_history = simulate_game(100, (RandomPolicy(1), RandomPolicy(1)))\n",
    "_board_history.shape, _action_history.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(70, 100, 8, 8)\n",
      "(70, 100, 2)\n",
      "(70, 100, 2, 8, 8)\n"
     ]
    },
    {
     "data": {
      "image/png": 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jVbJ7a2weZ9eHCZvB9nxXMiA89JN3XDgu/L3SYpLBnxv949h+HmzPR/joJ++4cFzQT+5kQHgi7Sj6KTouHBMu9BMZ3MngiogWpbKysnTssce2+l5GRob69OnT5vvonvU7Nuqd0vd07MCRUV0ML9QQ0oYd72vDx+8nbAbb813JgPDQT95x4bjw56UrpW+wWxeITOkblD8vvLdStcf282B7PsJHP3nHheOCfnInA8JDR3nDhWPChX4igzsZXJH453odxu5f+YhSfNHtIn+KX/evfCThM9ie70oGwDUuHBfBou59XHV3t5fsPw+25wMucuG4oJ/cyQC4xIVjwoV+IoM7GVzQ7UWpFStW6O67745BFBzq2bUvqvSzMtWH6iParj5Ur+2ffqzn3lqc8Blsz3clA6JDP8WPC8dF6ohs+bICki/CDX2SLyug1BHZ3c5g+3mwPR/Ro5/ix4Xjgn5yJwOiQ0fFhwvHhAv9RAZ3MriAM6UcVl1Xo4vu/a4O1FSFXVz1oXodqKnShffMUHVd9KcCupLB9nxXMgCuceG48AVSlDFpcOMFHsP9P3OfpLTG7XyB7v9foO3nwfZ8wEUuHBf0kzsZAJe4cEy40E9kcCeDCw6P3+IwtuWTbTrrrktUtn+3pMb3Eren6ftl+3frrLsu0Yd7PjpsMtie70oGwDUuHBf+3DRlfnOofJnhXSLRlxlQ5jeHxvSikLafB9vzARe5cFzQT+5kAFziwjHhQj+RwZ0MtkV0oXPY8eGej3TKf56vb5xYrGvGTdUJg49pc58NO97X/Ssf0XNvLY7LvyrZzmB7visZANe4cFz4c9OUddlw1ZVUqGb9vnYvGJnSN6hgUa/G06Tj8K9Ktp8H2/MBF7lwXNBP7mQAXOLCMeFCP5HBnQw2sSiVIKrravTYquf12KrnVTRotAr7D1NWeoYOVFdp8+6tWr9j42GfwfZ8VzIArnHhuPAFUpQ2Oldpo3MV2lOt0P5aqbZBSkuRPzfNk08msf082J4PuMiF44J+cicD4BIXjgkX+okM7mSwhUWpBLR+x0br/8dtO4Pt+a5kAFzjwnHhz0u3/n/ctp8H2/MBF7lwXNBP7mQAXOLCMeFCP5HBnQxeOrzO+wIAAAAAAEBCYFEKAAAAAAAAnmNRCgAAAAAAAJ7zGWOMlwMrKiqUk5PTODzD+0tamYP1kpHkk3w97VxSiwxkcCmD7fmSZKrqJUnl5eXKzs62kkGy30+SI/uDv0kykKF1Bgc6in4igyvzyeBYBvpJkiP7ggxkcGS+MxnC7CerFzpvCmlnuOX5ZCCDaxlsz3eM9efChf1hO4Pt+WQgg6OsPw8u7Asy2J9PBrcyOML68+DCviADGVyZ70qGLlhdlOJMKTKQwX4G2/MlN4uSf+mzl8H2fDKQoU0GxzqKfkruDLbnk8GxDPSTJEf2BRnI4Mh8ZzKE2U/2FqV6+pU9vdDzsRXzN8tU1cvXM2BlPhnI4FoG2/MlqXxeiXQwZGV2uyz1k+TG/rCdwfZ8MpDhUE51FP2U9BlszyeDWxnop0Yu7AsykMGV+a5kCLefuNA5AAAAAAAAPMeiFAAAAAAAADxn9ZpSSFyhPdUK7auV6hqk1BT5e6XJn5eeNPMBtM+FY5MMANpTNGi0RvQfpsz0DFVWV6lk91at37Ex6TLQT4B7XOgGFzLQT8mJRSmEzdQ3qK6kQjXr96lhb02bn6f0DSpY1EupI7LlC8T+JDzb8wG0z4VjkwwA2pOeGtSUk87TNeOm6viCo9v8/J3S93T/ykf07NoXVV3X9rg9XDLQT4B7XOgGFzLQT2BRCmEJ7a9V1cLtMgc6voJ+w94afb5sl6pX71XGpMHy56YdNvMBtM+FY5MMANpzZL+hWjDzQRX0zleDaWj3PscOHKl7Lr9DtxTP1IX3zNCHez467DLQT4B7XOgGFzLQT5C4phTCENpfq8pntslUhveRjqayXpXPbFNof+1hMR9A+1w4NskAoD1H9huqpTc/ofzc/vL5fPKn+Nu9nz/FL5/Pp/zc/nr5R09qeN6QwyoD/QS4x4VucCED/YQmLEqhU6a+QVULt0u1DZIJdyNJtY3bmfr2V90TZT6A9rlwbJIBQHvSU4NaMPNBZQUzFPCH96aAgD+grGCGnr9+rtJTg4dFBvoJcI8L3eBCBvoJLUW0KPXzn/9cPp+v1W3UqFHxygYH1JVUNJ5OGW5ZNDGSOVCvupKKhJ6PxEE/ecuFY5MMSCR0lHemnHSeCnrnh/0fW00C/oAG9xmob5xYfFhkoJ8QLvrJOy50gwsZ6Ce0FPGZUsccc4x27tzZfHv11VfjkQuOqFm/L6m3R2Khn7zjwrFJBiQaOsob14yb2uH1UboSagjpmnFTD4sM9BMiQT95w4VucCED/YSWIr7QeSAQ0BFHHBGPLHBMaE91u5+AEImGvTUK7amO6qM8bc9H4qGfvOHCsUkGJCI6Kv6KBo1u9xOkwuVP8euEwceoaNDoqD8K3YUM9BMiRT/Fnwvd4EIG+gmHivhMqZKSEuXn52v48OG6/PLLtX379k7vX1NTo4qKilY3JIbQvthdqDwR5yPx0E/ecOHYJAMSUSQdRT9FZ0T/YTF5nMJuPI4LGegnRIp+ij8XusGFDPQTDhXRotSYMWM0f/58LVmyRHPmzNHWrVv1ta99TQcOHOhwm9mzZysnJ6f5VlBQ0O3Q8EhdjC4gVxvl49iej4RCP3nIhWOTDEgwkXYU/RSdzPSMmDxOVjcex4UM9BMiQT95w4VucCED/YRDRbQoVVxcrIsvvlhFRUX6+te/rhdffFH79+/XU0891eE2s2bNUnl5efOttLS026HhkdQYfThjWpSPY3s+Egr95CEXjk0yIMFE2lH0U3Qqq6ti8jgHuvE4LmSgnxAJ+skbLnSDCxnoJxwq4mtKtZSbm6ujjjpKmzdv7vA+wWBQwWD3PzYS3vP3SovN4+RG9zi25yOx0U/x48KxSQYkuq46in6KTsnurbF5nF0fJnQG+gndQT/Fhwvd4EIG+gmH6tbyYmVlpbZs2aIBAwbEKg8c4s9LV0rf7v0fTkrfYNQXoLM9H4mNfoofF45NMiDR0VHxsX7HRr1T+p5CDaGotg81hLRu+7+04eP3EzoD/YTuoJ/iw4VucCED/YRDRbQodfPNN2vlypXatm2bXnvtNV100UXy+/269NJL45UPlgWLeiX19kgc9JO3XDg2yYBEQkd55/6VjyjFF92/u/pT/Lp/5SOHRQb6CeGin7zjQje4kIF+QksR/TXu2LFDl156qUaOHKlvfetb6tOnj9544w3l5eXFKx8sSx2RLV9WQPJFuKFP8mUFlDoiO6HnI3HQT95y4dgkAxIJHeWdZ9e+qNLPylQfqo9ou/pQvbZ/+rGee2vxYZGBfkK46CfvuNANLmSgn9BSRNeUeuKJJ+KVA47yBVKUMWmwKp/Z1vgJByacjSSlNW7nC3TvAnS25yNx0E/ecuHYJAMSCR3lneq6Gl1073e19OYnlBXMUMDf9cvd+lC9DtRU6cJ7Zqi6ruawyEA/IVz0k3dc6AYXMtBPaIm9iS75c9OU+c2h8mWGt4bpywwo85tDY3bxOdvzAbTPhWOTDADas+WTbTrrrktUtn+3JHV4/ZSm75ft362z7rpEH+756LDKQD8B7nGhG1zIQD+hSbc+fQ/Jw5+bpqzLhquupEI16/epYW/bFfKUvkEFi3o1no4Z49Vr2/MBtM+FY5MMANrz4Z6PdMp/nq9vnFisa8ZN1QmDj2lznw073tf9Kx/Rc28tjsm//ruYgX4C3ONCN7iQgX6CxKIUIuALpChtdK7SRucqtKdaof21jadbpqXIn5sW909AsD0fQPtcODbJAKA91XU1emzV83ps1fMqGjRahf2HKSs9Qweqq7R591at37ExKTLQT4B7XOgGFzLQT2BRClHx56VbLQjb8wG0z4VjkwwA2rN+x0ZP/gPL9Qz0E+AeF7rBhQz0U3Li/DcAAAAAAAB4jkUpAAAAAAAAeI5FKQAAAAAAAHjOZ4wxXg6sqKhQTk5O4/AM7y9pZQ7WS0aST/L1tHNJLTKQwaUMtudLkqmqlySVl5crOzvbSgbJfj9JjuwP/ibJQIbWGRzoKPqJDK7MJ4NjGegnSY7sCzKQwZH5zmQIs5+sXui8KaSd4Zbnk4EMrmWwPd8x1p8LF/aH7Qy255OBDI6y/jy4sC/IYH8+GdzK4Ajrz4ML+4IMZHBlvisZumB1UYozpchABvsZbM+X3CxK/qXPXgbb88lAhjYZHOso+im5M9ieTwbHMtBPkhzZF2QggyPznckQZj/ZW5Tq6Vf29ELPx1bM3yxTVS9fz4CV+WQgg2sZbM+XpPJ5JdLBkJXZ7bLUT5Ib+8N2BtvzyUCGQznVUfRT0mewPZ8MbmWgnxq5sC/IQAZX5ruSIdx+4kLnAAAAAAAA8ByLUgAAAAAAAPAci1IAAAAAAADwnNULnSNxhfZUK7SvVqprkFJT5O+VJn9euu1YAEA/AWhX0aDRGtF/mDLTM1RZXaWS3Vu1fsdGTzPQTwDaQz8hmbEohbCZ+gbVlVSoZv0+NeytafPzlL5BBYt6KXVEtnwBTsID4B36CUB70lODmnLSebpm3FQdX3B0m5+/U/qe7l/5iJ5d+6Kq69p2RyzQTwDaQz8BjViUQlhC+2tVtXC7zIGOP9axYW+NPl+2S9Wr9ypj0mD5c9M8TAggWdFPANpzZL+hWjDzQRX0zleDaWj3PscOHKl7Lr9DtxTP1IX3zNCHez6KaQb6CUB76CfgCyx3okuh/bWqfGabTGXHhdWSqaxX5TPbFNpfG+dkAJId/QSgPUf2G6qlNz+h/Nz+8vl88qf4272fP8Uvn8+n/Nz+evlHT2p43pCYZaCfALSHfgJai3hR6uOPP9bUqVPVp08f9ejRQ8cdd5zWrFkTj2xwgKlvUNXC7VJtg2TC3UhSbeN2pr79lX8gHuin5EI/IdHQUd5ITw1qwcwHlRXMUMAf3ps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\n",
      "text/plain": [
       "<Figure size 1200x600 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x600 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def action_to_q_learning_format(\n",
    "    board_history: np.ndarray, action_history: np.ndarray\n",
    ") -> np.ndarray:\n",
    "    q_learning_format = np.zeros(\n",
    "        (SIMULATE_TURNS, board_history.shape[1], 2, 8, 8), dtype=float\n",
    "    )\n",
    "    q_learning_format[:, :, 0, :, :] = board_history\n",
    "    q_learning_format[:, :, 1, :, :] = -1\n",
    "\n",
    "    game_index = list(range(board_history.shape[1]))\n",
    "    for turn_index in range(SIMULATE_TURNS):\n",
    "        q_learning_format[\n",
    "            turn_index,\n",
    "            game_index,\n",
    "            1,\n",
    "            action_history[turn_index, game_index, 0],\n",
    "            action_history[turn_index, game_index, 1],\n",
    "        ] = 1\n",
    "    return q_learning_format\n",
    "\n",
    "\n",
    "# %timeit action_to_q_learning_format(_board_history, _action_history)\n",
    "# %memit action_to_q_learning_format(_board_history, _action_history)\n",
    "print(_board_history.shape)\n",
    "print(_action_history.shape)\n",
    "print(action_to_q_learning_format(_board_history, _action_history).shape)\n",
    "plot_othello_boards(\n",
    "    action_to_q_learning_format(_board_history, _action_history)[:8, 0, 0]\n",
    ")\n",
    "plot_othello_boards(\n",
    "    action_to_q_learning_format(_board_history, _action_history)[:8, 0, 1]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "39.5 ms ± 611 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n",
      "peak memory: 393.63 MiB, increment: 0.00 MiB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(2, 2, 2, 70, 100, 2, 8, 8)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def build_symetry_action(\n",
    "    board_history: np.ndarray, action_history: np.ndarray\n",
    ") -> np.ndarray:\n",
    "    board_history = board_history.copy()\n",
    "    board_history[::2] *= -1\n",
    "    q_learning_format = np.zeros(\n",
    "        (2, 2, 2, SIMULATE_TURNS, board_history.shape[1], 2, 8, 8)\n",
    "    )\n",
    "    q_learning_format[0, 0, 0, :, :, :, :, :] = action_to_q_learning_format(\n",
    "        board_history, action_history\n",
    "    )\n",
    "    q_learning_format[1, 0, 0, :, :, :, :, :] = np.transpose(\n",
    "        q_learning_format[0, 0, 0, :, :, :, :, :], [0, 1, 2, 4, 3]\n",
    "    )\n",
    "    q_learning_format[:, 1, 0, :, :, :, :, :] = q_learning_format[\n",
    "        :, 0, 0, :, :, :, ::-1, :\n",
    "    ]\n",
    "    q_learning_format[:, :, 1, :, :, :, :, :] = q_learning_format[\n",
    "        :, :, 0, :, :, :, :, ::-1\n",
    "    ]\n",
    "    return q_learning_format\n",
    "\n",
    "\n",
    "%timeit build_symetry_action(_board_history, _action_history)\n",
    "%memit build_symetry_action(_board_history, _action_history)\n",
    "build_symetry_action(_board_history, _action_history).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def live_history(training_history: pd.DataFrame, max_epochs: int | None):\n",
    "    clear_output(wait=True)\n",
    "    # plt.ylim(0, 100)\n",
    "    _ = training_history[\n",
    "        [c for c in training_history.columns if c[1] == \"final_score\"]\n",
    "    ].plot()\n",
    "    plt.xlim(0, max_epochs)\n",
    "\n",
    "    plt.title(\"Training history\")\n",
    "    plt.xlabel(\"epochs\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "class QLPolicy(GamePolicy):\n",
    "    def __init__(\n",
    "        self,\n",
    "        epsilon: float,\n",
    "        neural_network: DQLNet,\n",
    "        symmetry_mode: SymmetryMode,\n",
    "        gamma: float = 0.8,\n",
    "        who_won_fraction: float = 0,\n",
    "        final_score_fraction: float = 0,\n",
    "        optimizer: torch.optim.Optimizer | None = None,\n",
    "        loss: nn.modules.loss._Loss | None = None,\n",
    "    ):\n",
    "        super().__init__(epsilon)\n",
    "        assert 0 <= gamma <= 1\n",
    "        self.gamma: float = gamma\n",
    "        del gamma\n",
    "        self.symmetry_mode: SymmetryMode = symmetry_mode\n",
    "        del symmetry_mode\n",
    "        self.neural_network: DQLNet = neural_network\n",
    "        del neural_network\n",
    "        self.who_won_fraction: float = who_won_fraction\n",
    "        del who_won_fraction\n",
    "        self.final_score_fraction: float = final_score_fraction\n",
    "        del final_score_fraction\n",
    "\n",
    "        if optimizer is None:\n",
    "            self.optimizer = torch.optim.Adam(self.neural_network.parameters(), lr=5e-3)\n",
    "        else:\n",
    "            self.optimizer = optimizer\n",
    "        if loss is None:\n",
    "            self.loss = nn.MSELoss()\n",
    "        else:\n",
    "            self.loss = loss\n",
    "        self.training_results: list[dict[tuple[str, str], float]] = []\n",
    "\n",
    "    @property\n",
    "    def policy_name(self) -> str:\n",
    "        symmetry_name = {SymmetryMode.MULTIPLY: \"M\", SymmetryMode.BREAK_SEQUENCE: \"B\"}\n",
    "        g = f\"{self.gamma:.1f}\".replace(\".\", \"\")\n",
    "        ww = f\"{self.who_won_fraction:.1f}\".replace(\".\", \"\")\n",
    "        fsf = f\"{self.final_score_fraction:.1f}\".replace(\".\", \"\")\n",
    "        return f\"QL-{symmetry_name[self.symmetry_mode]}-G{g}-WW{ww}-FSF{fsf}-{ql_policy.neural_network.__class__.__name__}-{self.loss.__class__.__name__}\"\n",
    "\n",
    "    def _internal_policy(self, boards: np.ndarray) -> np.ndarray:\n",
    "        results = np.zeros_like(boards, dtype=float)\n",
    "        results = torch.from_numpy(results).float()\n",
    "        q_learning_boards = np.zeros((boards.shape[0], 2, 8, 8))\n",
    "        q_learning_boards[:, 0, :, :] = boards\n",
    "        poss_turns = boards == 0  # checks where fields are empty.\n",
    "        poss_turns &= binary_dilation(boards == -1, SURROUNDING)\n",
    "        turn_possible = np.any(poss_turns, axis=0)\n",
    "        for action_x, action_y in itertools.product(range(8), range(8)):\n",
    "            if not turn_possible[action_x, action_y]:\n",
    "                continue\n",
    "            _q_learning_board = q_learning_boards[\n",
    "                poss_turns[range(boards.shape[0]), action_x, action_y]\n",
    "            ].copy()\n",
    "            _q_learning_board[\n",
    "                range(_q_learning_board.shape[0]), 1, action_x, action_y\n",
    "            ] = 1\n",
    "\n",
    "            ql_result = self.neural_network.forward(_q_learning_board)\n",
    "            results[poss_turns[:, action_x, action_y], action_x, action_y] = (\n",
    "                ql_result.reshape(-1) + 0.1\n",
    "            )\n",
    "        return results.cpu().detach().numpy()\n",
    "\n",
    "    def generate_trainings_data(\n",
    "        self, generate_data_size: int\n",
    "    ) -> tuple[torch.Tensor, torch.Tensor]:\n",
    "        train_boards, train_actions = simulate_game(generate_data_size, (self, self))\n",
    "        action_possible = ~np.all(train_actions[:, :] == -1, axis=2)\n",
    "        q_leaning_formatted_action = build_symetry_action(train_boards, train_actions)\n",
    "        q_rewords = calculate_q_reword(\n",
    "            board_history=train_boards,\n",
    "            who_won_fraction=self.who_won_fraction,\n",
    "            final_score_fraction=self.final_score_fraction,\n",
    "        )\n",
    "        q_rewords[::2, :] *= -1\n",
    "        if self.symmetry_mode == SymmetryMode.MULTIPLY:\n",
    "            new_q_rewords = np.zeros((2, 2, 2) + q_rewords.shape)\n",
    "            for i, k, j in itertools.product((0, 1), (0, 1), (0, 1)):\n",
    "                new_q_rewords[i, k, j] = q_rewords\n",
    "            q_rewords = new_q_rewords\n",
    "            action_possible = np.array([action_possible] * 8).reshape(-1)\n",
    "\n",
    "        elif self.symmetry_mode == SymmetryMode.BREAK_SEQUENCE:\n",
    "            axis1 = np.random.randint(0, high=2, size=SIMULATE_TURNS, dtype=int)\n",
    "            axis2 = np.random.randint(0, high=2, size=SIMULATE_TURNS, dtype=int)\n",
    "            axis3 = np.random.randint(0, high=2, size=SIMULATE_TURNS, dtype=int)\n",
    "            q_leaning_formatted_action = q_leaning_formatted_action[\n",
    "                axis1, axis2, axis3, range(SIMULATE_TURNS)\n",
    "            ]\n",
    "            action_possible = action_possible.reshape(-1)\n",
    "\n",
    "        return (\n",
    "            torch.from_numpy(\n",
    "                q_leaning_formatted_action.reshape(-1, 2, BOARD_SIZE, BOARD_SIZE)[\n",
    "                    action_possible\n",
    "                ]\n",
    "            ).float(),\n",
    "            torch.from_numpy(q_rewords.reshape(-1, 1)[action_possible]).float(),\n",
    "        )\n",
    "\n",
    "    def train_batch(self, nr_of_games: int):\n",
    "        x_train, y_train = self.generate_trainings_data(nr_of_games)\n",
    "        y_pred = self.neural_network.forward(x_train)\n",
    "        loss_score = self.loss(y_pred, y_train)\n",
    "        self.optimizer.zero_grad()\n",
    "\n",
    "        loss_score.backward()\n",
    "        # Update the parameters\n",
    "        self.optimizer.step()\n",
    "        # generate trainings data\n",
    "\n",
    "    def evaluate_model(self, compare_models: list[GamePolicy], nr_of_games: int):\n",
    "        result_dict: dict[tuple[str, str], float] = {}\n",
    "        eval_copy = copy.copy(self)\n",
    "        eval_copy._epsilon = 1\n",
    "        for model in compare_models:\n",
    "            boards_white, _ = simulate_game(nr_of_games, (eval_copy, model))\n",
    "            boards_black, _ = simulate_game(nr_of_games, (model, eval_copy))\n",
    "            win_eval_white = evaluate_who_won(boards_white[-1])\n",
    "            win_eval_black = evaluate_who_won(boards_black[-1])\n",
    "            result_dict[(model.policy_name, \"final_score\")] = float(\n",
    "                np.mean(\n",
    "                    final_boards_evaluation(boards_white[-1])\n",
    "                    + final_boards_evaluation(boards_black[-1]) * -1\n",
    "                )\n",
    "            )\n",
    "            result_dict[(model.policy_name, \"white_win\")] = (\n",
    "                np.sum(win_eval_white == 1) / nr_of_games\n",
    "            )\n",
    "            result_dict[(model.policy_name, \"white_lose\")] = (\n",
    "                np.sum(win_eval_white == -1) / nr_of_games\n",
    "            )\n",
    "            result_dict[(model.policy_name, \"black_win\")] = (\n",
    "                np.sum(win_eval_black == 1) / nr_of_games\n",
    "            )\n",
    "            result_dict[(model.policy_name, \"black_lose\")] = (\n",
    "                np.sum(win_eval_black == -1) / nr_of_games\n",
    "            )\n",
    "            result_dict[(\"base\", \"base\")] = nr_of_games\n",
    "        return result_dict\n",
    "\n",
    "    def save(self):\n",
    "        filename: str = f\"{self.policy_name}-{len(self.training_results)}\"\n",
    "        with open(TRAINING_RESULT_PATH / Path(f\"{filename}.pickle\"), \"wb\") as f:\n",
    "            pickle.dump(self.training_results, f)\n",
    "        torch.save(\n",
    "            self.neural_network.state_dict(),\n",
    "            TRAINING_RESULT_PATH / Path(f\"{filename}.torch\"),\n",
    "        )\n",
    "\n",
    "    def load(self):\n",
    "        pickle_files = glob.glob(f\"{TRAINING_RESULT_PATH}/{self.policy_name}-*.pickle\")\n",
    "        torch_files = glob.glob(f\"{TRAINING_RESULT_PATH}/{self.policy_name}-*.torch\")\n",
    "\n",
    "        assert len(pickle_files) == len(torch_files)\n",
    "        if not pickle_files:\n",
    "            return\n",
    "\n",
    "        pickle_dict = {\n",
    "            int(file.split(\"-\")[-1].split(\".\")[0]): file for file in pickle_files\n",
    "        }\n",
    "        torch_dict = {\n",
    "            int(file.split(\"-\")[-1].split(\".\")[0]): file for file in torch_files\n",
    "        }\n",
    "        pickle_file = pickle_dict[max(pickle_dict.keys())]\n",
    "        torch_file = torch_dict[max(torch_dict.keys())]\n",
    "\n",
    "        with open(pickle_file, \"rb\") as f:\n",
    "            self.training_results = pickle.load(f)\n",
    "\n",
    "        self.neural_network.load_state_dict(torch.load(Path(torch_file)))\n",
    "\n",
    "    def train(\n",
    "        self,\n",
    "        epochs: int,\n",
    "        batches: int,\n",
    "        batch_size: int,\n",
    "        eval_batch_size: int,\n",
    "        compare_with: list[GamePolicy],\n",
    "        save_every_epoch: bool = True,\n",
    "        live_plot: bool = True,\n",
    "    ) -> pd.DataFrame:\n",
    "        max_epochs = epochs + len(self.training_results)\n",
    "        assert epochs > 0\n",
    "        for _ in tqdm(range(epochs)):\n",
    "            for _ in tqdm(range(batches)):\n",
    "                self.train_batch(batch_size)\n",
    "            self.training_results.append(\n",
    "                self.evaluate_model(compare_with, eval_batch_size)\n",
    "            )\n",
    "            if save_every_epoch:\n",
    "                self.save()\n",
    "            if live_plot:\n",
    "                self.plot_history(max_epochs)\n",
    "        return self.history\n",
    "    \n",
    "    def plot_history(self, max_epochs: int | None):\n",
    "        return live_history(self.history, max_epochs)\n",
    "\n",
    "    @property\n",
    "    def history(self) -> pd.DataFrame:\n",
    "        pandas_result = pd.DataFrame(self.training_results)\n",
    "        pandas_result.columns = pd.MultiIndex.from_tuples(pandas_result.columns)\n",
    "        return pandas_result\n",
    "\n",
    "\n",
    "ql_policy1 = QLPolicy(\n",
    "    0.95,\n",
    "    neural_network=DQLNet(),\n",
    "    symmetry_mode=SymmetryMode.MULTIPLY,\n",
    "    gamma=0.8,\n",
    "    who_won_fraction=1,\n",
    "    final_score_fraction=0,\n",
    ")\n",
    "t1, t2 = ql_policy1._internal_policy(get_new_games(2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Symmetry debug"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(70, 10, 8, 8)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_boards, train_actions = simulate_game(10, (RandomPolicy(0), RandomPolicy(0)))\n",
    "action_possible = ~np.all(train_actions[:, :] == -1, axis=2)\n",
    "q_leaning_formatted_action = action_to_q_learning_format(train_boards, train_actions)\n",
    "train_boards.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x600 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x600 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_othello_boards(train_boards[:8, 0])\n",
    "plot_othello_boards(q_leaning_formatted_action[1:9, 0, 1])"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "ql_policy = QLPolicy(\n",
    "    0.95,\n",
    "    neural_network=DQLNet(),\n",
    "    symmetry_mode=SymmetryMode.MULTIPLY,\n",
    "    gamma=0.8,\n",
    "    who_won_fraction=0,\n",
    "    final_score_fraction=0,\n",
    ")\n",
    "_batch_size = 100\n",
    "%timeit ql_policy.train_batch(_batch_size)\n",
    "%memit ql_policy.train_batch(_batch_size)\n",
    "%timeit ql_policy.evaluate_model([RandomPolicy(0)], _batch_size)\n",
    "%memit ql_policy.evaluate_model([RandomPolicy(0)], _batch_size)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "ql_policy = QLPolicy(\n",
    "    0.95,\n",
    "    neural_network=DQLNet(),\n",
    "    symmetry_mode=SymmetryMode.MULTIPLY,\n",
    "    gamma=0.8,\n",
    "    who_won_fraction=1,\n",
    "    final_score_fraction=0,\n",
    ")\n",
    "ql_policy.policy_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'QL-M-G08-WW00-FSF10-DQL_Simple-MSELoss'"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ql_policy = QLPolicy(\n",
    "    0.95,\n",
    "    neural_network=DQL_Simple(),\n",
    "    symmetry_mode=SymmetryMode.MULTIPLY,\n",
    "    gamma=0.8,\n",
    "    who_won_fraction=0,\n",
    "    final_score_fraction=1,\n",
    ")\n",
    "ql_policy.policy_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# pd.Series.plot?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "probes: int = 1000\n",
    "_ = (\n",
    "    calculate_board_branching(simulate_game(probes, (ql_policy, ql_policy))[0]) / probes\n",
    ").plot(\n",
    "    ylim=(0, 1),\n",
    "    title=f\"Branching rate for a QL policy with epsilon={ql_policy.epsilon}\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "ql_policy.load()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ql_policy.plot_history(None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "616f06c422444f12bdc42677b1ad8ef4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/10 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[86], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mql_policy\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrain\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m200\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m10\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1000\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m100\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[43mRandomPolicy\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mGreedyPolicy\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n",
      "Cell \u001b[1;32mIn[55], line 191\u001b[0m, in \u001b[0;36mQLPolicy.train\u001b[1;34m(self, epochs, batches, batch_size, eval_batch_size, compare_with, save_every_epoch, live_plot)\u001b[0m\n\u001b[0;32m    189\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m _ \u001b[38;5;129;01min\u001b[39;00m tqdm(\u001b[38;5;28mrange\u001b[39m(epochs)):\n\u001b[0;32m    190\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m _ \u001b[38;5;129;01min\u001b[39;00m tqdm(\u001b[38;5;28mrange\u001b[39m(batches)):\n\u001b[1;32m--> 191\u001b[0m         \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrain_batch\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    192\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtraining_results\u001b[38;5;241m.\u001b[39mappend(\n\u001b[0;32m    193\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mevaluate_model(compare_with, eval_batch_size)\n\u001b[0;32m    194\u001b[0m     )\n\u001b[0;32m    195\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m save_every_epoch:\n",
      "Cell \u001b[1;32mIn[55], line 106\u001b[0m, in \u001b[0;36mQLPolicy.train_batch\u001b[1;34m(self, nr_of_games)\u001b[0m\n\u001b[0;32m    105\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mtrain_batch\u001b[39m(\u001b[38;5;28mself\u001b[39m, nr_of_games: \u001b[38;5;28mint\u001b[39m):\n\u001b[1;32m--> 106\u001b[0m     x_train, y_train \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgenerate_trainings_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnr_of_games\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    107\u001b[0m     y_pred \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mneural_network\u001b[38;5;241m.\u001b[39mforward(x_train)\n\u001b[0;32m    108\u001b[0m     loss_score \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mloss(y_pred, y_train)\n",
      "Cell \u001b[1;32mIn[55], line 71\u001b[0m, in \u001b[0;36mQLPolicy.generate_trainings_data\u001b[1;34m(self, generate_data_size)\u001b[0m\n\u001b[0;32m     68\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mgenerate_trainings_data\u001b[39m(\n\u001b[0;32m     69\u001b[0m     \u001b[38;5;28mself\u001b[39m, generate_data_size: \u001b[38;5;28mint\u001b[39m\n\u001b[0;32m     70\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mtuple\u001b[39m[torch\u001b[38;5;241m.\u001b[39mTensor, torch\u001b[38;5;241m.\u001b[39mTensor]:\n\u001b[1;32m---> 71\u001b[0m     train_boards, train_actions \u001b[38;5;241m=\u001b[39m \u001b[43msimulate_game\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgenerate_data_size\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     72\u001b[0m     action_possible \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m~\u001b[39mnp\u001b[38;5;241m.\u001b[39mall(train_actions[:, :] \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2\u001b[39m)\n\u001b[0;32m     73\u001b[0m     q_leaning_formatted_action \u001b[38;5;241m=\u001b[39m build_symetry_action(train_boards, train_actions)\n",
      "Cell \u001b[1;32mIn[23], line 25\u001b[0m, in \u001b[0;36msimulate_game\u001b[1;34m(nr_of_games, policies, tqdm_on)\u001b[0m\n\u001b[0;32m     23\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m policy_index \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m     24\u001b[0m     current_boards \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m\n\u001b[1;32m---> 25\u001b[0m current_boards, action_taken \u001b[38;5;241m=\u001b[39m \u001b[43msingle_turn\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcurrent_boards\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpolicy\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     26\u001b[0m action_history_stack[turn_index, :] \u001b[38;5;241m=\u001b[39m action_taken\n\u001b[0;32m     28\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m policy_index \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n",
      "Cell \u001b[1;32mIn[22], line 15\u001b[0m, in \u001b[0;36msingle_turn\u001b[1;34m(current_boards, policy)\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21msingle_turn\u001b[39m(\n\u001b[0;32m      2\u001b[0m     current_boards: np, policy: GamePolicy\n\u001b[0;32m      3\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mtuple\u001b[39m[np\u001b[38;5;241m.\u001b[39mndarray, np\u001b[38;5;241m.\u001b[39mndarray]:\n\u001b[0;32m      4\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Execute a single turn on a board.\u001b[39;00m\n\u001b[0;32m      5\u001b[0m \n\u001b[0;32m      6\u001b[0m \u001b[38;5;124;03m    Places a new stone on the board. Turns captured enemy stones.\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m     13\u001b[0m \u001b[38;5;124;03m        The new game board and the policy vector containing the index of the action used.\u001b[39;00m\n\u001b[0;32m     14\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m---> 15\u001b[0m     policy_results \u001b[38;5;241m=\u001b[39m \u001b[43mpolicy\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_policy\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcurrent_boards\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     17\u001b[0m     \u001b[38;5;66;03m# if the constant VERIFY_POLICY is set to true the policy is verified. Should be good though.\u001b[39;00m\n\u001b[0;32m     18\u001b[0m     \u001b[38;5;66;03m# todo deactivate the policy verification after some testing.\u001b[39;00m\n\u001b[0;32m     19\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m VERIFY_POLICY:\n",
      "Cell \u001b[1;32mIn[19], line 64\u001b[0m, in \u001b[0;36mGamePolicy.get_policy\u001b[1;34m(self, boards)\u001b[0m\n\u001b[0;32m     59\u001b[0m         policies[random_choices] \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mrandom\u001b[38;5;241m.\u001b[39mrand(np\u001b[38;5;241m.\u001b[39msum(random_choices), \u001b[38;5;241m8\u001b[39m ,\u001b[38;5;241m8\u001b[39m)\n\u001b[0;32m     61\u001b[0m \u001b[38;5;66;03m# todo talk to team about backpropagation of score and epsilon for greedy factor\u001b[39;00m\n\u001b[0;32m     62\u001b[0m \n\u001b[0;32m     63\u001b[0m \u001b[38;5;66;03m# todo possibly change this function to only validate the purpose turn and not all turns\u001b[39;00m\n\u001b[1;32m---> 64\u001b[0m possible_turns \u001b[38;5;241m=\u001b[39m \u001b[43mget_possible_turns\u001b[49m\u001b[43m(\u001b[49m\u001b[43mboards\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     65\u001b[0m policies[possible_turns \u001b[38;5;241m==\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1.0\u001b[39m\n\u001b[0;32m     66\u001b[0m max_indices \u001b[38;5;241m=\u001b[39m [\n\u001b[0;32m     67\u001b[0m     np\u001b[38;5;241m.\u001b[39munravel_index(policy\u001b[38;5;241m.\u001b[39margmax(), policy\u001b[38;5;241m.\u001b[39mshape) \u001b[38;5;28;01mfor\u001b[39;00m policy \u001b[38;5;129;01min\u001b[39;00m policies\n\u001b[0;32m     68\u001b[0m ]\n",
      "Cell \u001b[1;32mIn[13], line 60\u001b[0m, in \u001b[0;36mget_possible_turns\u001b[1;34m(boards, tqdm_on)\u001b[0m\n\u001b[0;32m     58\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m poss_turns[game, idx, idy]:\n\u001b[0;32m     59\u001b[0m         position \u001b[38;5;241m=\u001b[39m idx, idy\n\u001b[1;32m---> 60\u001b[0m         poss_turns[game, idx, idy] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43many\u001b[39;49m\u001b[43m(\u001b[49m\n\u001b[0;32m     61\u001b[0m \u001b[43m            \u001b[49m\u001b[43m_recursive_steps\u001b[49m\u001b[43m(\u001b[49m\u001b[43mboards\u001b[49m\u001b[43m[\u001b[49m\u001b[43mgame\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m:\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m:\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdirection\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mposition\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m>\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\n\u001b[0;32m     62\u001b[0m \u001b[43m            \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mdirection\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mDIRECTIONS\u001b[49m\n\u001b[0;32m     63\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     64\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m poss_turns\n",
      "Cell \u001b[1;32mIn[13], line 61\u001b[0m, in \u001b[0;36m<genexpr>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m     58\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m poss_turns[game, idx, idy]:\n\u001b[0;32m     59\u001b[0m         position \u001b[38;5;241m=\u001b[39m idx, idy\n\u001b[0;32m     60\u001b[0m         poss_turns[game, idx, idy] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28many\u001b[39m(\n\u001b[1;32m---> 61\u001b[0m             \u001b[43m_recursive_steps\u001b[49m\u001b[43m(\u001b[49m\u001b[43mboards\u001b[49m\u001b[43m[\u001b[49m\u001b[43mgame\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m:\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m:\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdirection\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mposition\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m\n\u001b[0;32m     62\u001b[0m             \u001b[38;5;28;01mfor\u001b[39;00m direction \u001b[38;5;129;01min\u001b[39;00m DIRECTIONS\n\u001b[0;32m     63\u001b[0m         )\n\u001b[0;32m     64\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m poss_turns\n",
      "Cell \u001b[1;32mIn[13], line 19\u001b[0m, in \u001b[0;36m_recursive_steps\u001b[1;34m(board, rec_direction, rec_position, step_one)\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Check if a player can place a stone on the board specified in the direction specified and direction specified.\u001b[39;00m\n\u001b[0;32m      8\u001b[0m \n\u001b[0;32m      9\u001b[0m \u001b[38;5;124;03mArgs:\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m     16\u001b[0m \u001b[38;5;124;03m    True if a turn is possible for possition and direction on the board defined.\u001b[39;00m\n\u001b[0;32m     17\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m     18\u001b[0m rec_position \u001b[38;5;241m=\u001b[39m rec_position \u001b[38;5;241m+\u001b[39m rec_direction\n\u001b[1;32m---> 19\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43many\u001b[49m\u001b[43m(\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrec_position\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m>\u001b[39;49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mBOARD_SIZE\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m|\u001b[39;49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43mrec_position\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m<\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m:\n\u001b[0;32m     20\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;241m0\u001b[39m\n\u001b[0;32m     21\u001b[0m next_field \u001b[38;5;241m=\u001b[39m board[\u001b[38;5;28mtuple\u001b[39m(rec_position\u001b[38;5;241m.\u001b[39mtolist())]\n",
      "File \u001b[1;32m<__array_function__ internals>:180\u001b[0m, in \u001b[0;36many\u001b[1;34m(*args, **kwargs)\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "ql_policy.train(200, 10, 1000, 100, [RandomPolicy(0), GreedyPolicy(0)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "raise NotImplementedError"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "boards_and_actions, score = ql_policy.generate_trainings_data(1)\n",
    "print(boards_and_actions.shape)\n",
    "print(score.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "boards_and_actions.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "plot_othello_boards(boards_and_actions[:8, 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "score[:8, 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "plot_othello_boards(boards1[:60,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train a model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sources\n",
    "\n",
    "* Game rules and example board images [https://en.wikipedia.org/wiki/Reversi](https://en.wikipedia.org/wiki/Reversi)\n",
    "* Game rules and example game images [https://de.wikipedia.org/wiki/Othello_(Spiel)](https://de.wikipedia.org/wiki/Othello_(Spiel))\n",
    "* Game strategy examples [https://de.wikipedia.org/wiki/Computer-Othello](https://de.wikipedia.org/wiki/Computer-Othello)\n",
    "* Image for 8 directions [https://www.researchgate.net/journal/EURASIP-Journal-on-Image-and-Video-Processing-1687-5281](https://www.researchgate.net/journal/EURASIP-Journal-on-Image-and-Video-Processing-1687-5281)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "\n",
    "\n",
    "def sizeof_fmt(num, suffix=\"B\"):\n",
    "    \"\"\"by Fred Cirera,  https://stackoverflow.com/a/1094933/1870254, modified\"\"\"\n",
    "    for unit in [\"\", \"Ki\", \"Mi\", \"Gi\", \"Ti\", \"Pi\", \"Ei\", \"Zi\"]:\n",
    "        if abs(num) < 1024.0:\n",
    "            return \"%3.1f %s%s\" % (num, unit, suffix)\n",
    "        num /= 1024.0\n",
    "    return \"%.1f %s%s\" % (num, \"Yi\", suffix)\n",
    "\n",
    "\n",
    "for name, size in sorted(\n",
    "    ((name, sys.getsizeof(value)) for name, value in list(locals().items())),\n",
    "    key=lambda x: -x[1],\n",
    ")[:20]:\n",
    "    print(\"{:>30}: {:>8}\".format(name, sizeof_fmt(size)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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