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."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## 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",
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    "\n",
    "## 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",
    "![Othello game board example](reversi_example.png)\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."
   ]
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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext blackcellmagic"
   ]
  },
  {
   "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 a 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 abc\n",
    "import itertools\n",
    "import os.path\n",
    "import warnings\n",
    "from abc import ABC\n",
    "from enum import Enum\n",
    "from typing import Final\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",
    "import torch.optim as optim\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] = True"
   ]
  },
  {
   "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",
    "![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,\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",
    "    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": [
    "## 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": 11,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[1, 1, 1],\n",
       "        [1, 0, 1],\n",
       "        [1, 1, 1]]])"
      ]
     },
     "execution_count": 11,
     "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": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8.75 ms ± 34.7 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
      "905 ms ± 28.2 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": 12,
     "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",
    "\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": 13,
   "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": 14,
   "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": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "197 µs ± 1.28 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n",
      "32.6 µs ± 267 ns per loop (mean ± std. dev. of 7 runs, 10,000 loops each)\n",
      "35.8 µs ± 141 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": 16,
   "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": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "97 ms ± 3.38 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": 18,
   "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",
    "                policies = policies * self.epsilon + np.random.rand(*boards.shape) * (\n",
    "                    1 - self.epsilon\n",
    "                )\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": 19,
   "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": 20,
   "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",
    "        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",
    "                    _recursive_steps(boards[game, :, :], direction, position)\n",
    "                    for direction in DIRECTIONS\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": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.02 s ± 11.2 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
      "1.04 s ± 5.07 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    },
    {
     "data": {
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4eXritYO65b0abwOj22MpBQAAAABANzeiulELyw5JI0fKt369NGiQ5DiS39/+A/z+2M8HDZJvwwapuFgLyw5peHWjt8HRrSW0lFq0aJFKS0uVk5OjnJwcXXXVVVq9enWqsgFA3OgnADajowDYin7KHI+VVcmXFZLv1Vel7GwpGIzvgcGglJ0t32uvyZ8V0oKyqtQGRUZJaCk1ePBg/exnP9PmzZu1adMmTZ48WTfddJP+53/+J1X5ACAu9BMAm9FRAGxFP2WGsYdPaHzVcQVnfFkaOjT+hdRJwaA0bJgCX5qh8VXHNfbIidQERcZJaCk1ffp0fe5zn9PIkSM1atQoPfjgg+rTp4/Wr1+fqnwAEBf6CYDN6CgAtqKfMsPMHdVqciTdfbcUjXbuSSIR6a671ORIM7dXJzMeMlinP30vEonod7/7nRoaGnTVVVclMxMAdAn9BMBmdBQAW9FP3ddVB48rOPbidj9lL25+vzRunIJjL9aV+7cnLRsyW8JLqW3btumqq67SiRMn1KdPH73wwgs6//zzO7x/OBxWOBxu+bq2trZzSQHgLOgnADZLpKPoJwBeop+6v5KjYWnK6CQ9WYlGby1PznMh4yX86XslJSUqLy/Xhg0b9O1vf1u333673n333Q7vP3/+fOXm5rbcioqKuhQYADpCPwGwWSIdRT8B8BL91L05rqusqGIXN0+GnBxlRWPPC3RVwkuprKwsFRcXa9y4cZo/f77Gjh2r//zP/+zw/vPmzVNNTU3Lbd++fV0KDAAdoZ8A2CyRjqKfAHiJfureXMdRo09SXV1ynrC2Vo2+2PMCXdXpa0qdFI1GW52+ebpQKKRQKNTVMQCQMPoJgM3O1FH0EwCT6KfuZ2ffkC7asSM5T7Zjh3b043cAyZHQUmrevHmaOnWqhgwZorq6Oj3zzDNau3at/vjHP6YqHwDEhX4CYDM6CoCt6KfMsK6wp0ZXlCu4ZYtUWhq7aHmiIhGpvFxNWyu0/sK8pGdEZkpoKXX48GF99atfVVVVlXJzc1VaWqo//vGPmjJlSqryAUBc6CcANqOjANiKfsoMT4/J053bqqVHH5WWLOnck/j90mOPKejGng9IhoSWUosXL05VDgDoEvoJgM3oKAC2op8yQ0V+D71V0FNXrFyhwAMPSIMGScFg/E/Q1CQdOKDmZ1dqQ0FPVeT3SF1YZJSEL3QOAAAAAADSy9zJBYo0hhW99trYRc+bmuJ7YFOTVFen6JQpijSGNXdyQWqDIqOwlAIAAAAAoJurzMvS7MkF0q5dil5xhXTgQOwHkUj7Dzj5/QMHYvffvVuzJxeoMi/Lm8DICCylAAAAAADIAM+PytGdUwrUVLlHzeePkWbNksrL279zebk0a5aazx+jpso9+uaUQj0/KsfDtMgECV1TCgAAAAAApK/nRuVq87k9taCsSuOXL1fTr5crOPZiqaREysmRamulnTvVVFGuoCutL+ypuyYN5wwppARLKQAAAAAAMkhlXpZu+OJQjT1yQjO3V+vK/ds1emu5sqJSo0/a0S+k9Rfm6ekxeVzUHCnFUgoAAAAAgAxUkd9DFfkDW752XFeu4xhMhEzDNaUAAAAAAAALKXjOcV3X9XJgbW2tcnNzY8N7e3+ilnusWXIlOZLTy8yJYmQgg00ZTM+XJLehWZJUU1OjnBxzF0803U+SJceD30kykKF1Bgs6in4igy3zyWBZBvpJkiXHggxksGS+NRni7Cejb987GdLMcMPzyUAG2zKYnm8Z46+FDcfDdAbT88lABksZfx1sOBZkMD+fDHZlsITx18GGY0EGMtgy35YMZ2F0KcWZUmQgg/kMpudLdhYl/9JnLoPp+WQgQ5sMlnUU/ZTZGUzPJ4NlGegnSZYcCzKQwZL51mSIs5/MLaV6+ZUzq9jzsbXLdsttaJbTK2BkPhnIYFsG0/MlqWbpLulYxMjsdhnqJ8mO42E6g+n5ZCDD6azqKPop4zOYnk8GuzLQTzE2HAsykMGW+bZkiLefuNA5AAAAAAAAPMdSCgCQdI63n6EBAHGjnwAAsIfRa0oBALqHsUdOaOb2al118LhKjoaVFZUafdLOviGtK+ypp8fkqSK/h+mYADIQ/QQAgL1YSqFTIkdOKHK0UWqKSkGf/H2z5PfwL3Slg8do5IDh6tOjt+pPNGjXR5Xaun+7Z/MBxAyvbtSCsiqNrzquJkcKjr1YmjJays5WVl2dLtqxQ6MrynXntmq9VdBTcycXqDIvK6WZbOgHGzIAmY5+sjcDAPvY0A02ZID3WEohbm5zVE27ahXeelTRj8Ntfu47J6RQaV8FR+bICST/naE9giHdPO5zunPCbRpbdH6bn1fse1ePv/mUnt/8sk40tc0HILluea9GC8sOyZ8VkmbNUvDuu6VLLmlzv+CWLdKjj+qKlSu0fkWlZk8u0POjcpKaxYZ+sCEDgBj6yb4MAOxjQzfYkAFmcU0pxCVS3ai6Z97X8bJD7S6kJCn6cVjHyw6p7pn3FaluTOr8884dpo3/8v+0YOaDunBQSbv3uXBQiRbMfFAb/+X/aUT+0KTOB9DaLe/V6PHXqhQcUazAu9ulJUuk0tL271xaKi1ZosC72xUcfp6eeO2gbnmvJmlZbOgHGzIAiKGf7MsAwD42dIMNGWAeSymcVaS6UfXP7ZVb3xzX/d36ZtU/tzdpi6nzzh2m1+9docK8AXIcR36fv937+X1+OY6jwrwBeuN7KyktIEVGVDdqYdkhaeRI+davlwYNkhxH8rf/v035/bGfDxok34YNUnGxFpYd0vAkdIQN/WBDBgAx9JN9GQDYx4ZusCED7NClpdTPfvYzOY6j73znO0mKA9u4zVE1rPpQaoxK8X5YjSupMfY4tznapfk9giG9MOdJZYd6K+CP792mAX9A2aHeenHuYvUIhro0H+mLfkqdx8qq5MsKyffqq1J2thQMxvfAYFDKzpbvtdfkzwppQVlVl3LY0A82ZED6oZ9Sh36yKwPSEx3VvdnQDTZkgD06vZR655139F//9V8q7eh0aHQLTbtq5dY1x7+QOsmV3LpmNe2q7dL8m8d9TkX9CuMuq5MC/oCG9B+kL146tUvzkZ7op9QZe/iExlcdV3DGl6WhQ+P/D76TgkFp2DAFvjRD46uOa+yRE53OYkM/2JAB6YV+Sh36yb4MSD90VPdnQzfYkAH26NRSqr6+XjNnztQTTzyhvn37JjsTLBLeetTo4++ccJuibufOtopEI7pzwm1dmo/0Qz+l1swd1WpyJN19txTt5JmQkYh0111qcqSZ26s7ncWGfrAhA9IH/ZRa9JN9GZBe6KjMYEM32JAB9ujUUmrOnDm64YYb9NnPfjbZeWCRyJETHV7UPF7Rj8OKdPJfGksHj9HYovM7fH/x2fh9fl085AKVDh7TqccjPdFPqXXVweOxj1W/5JKOr9FyNn6/NG6cgmMv1pVVxzv1FDb0gw0ZkF7op9Sin+zKgPRDR3V/NnSDDRlgl8TOl5O0YsUK/fWvf9U777wT1/3D4bDC4U8WG7W1XXs7F7wTOZqcC5VHqhvlz++R8ONGDhielPnFA4Zr6/7tSXku2I1+Sr2So2FpyugkPVmJRm8t79RDbegHGzIgfdBPqUc/2ZUB6SWRjqKf0pcN3WBDBtgloTOl9u3bp3vuuUdPP/20evSIb8kwf/585ebmttyKioo6FRQGNHXtIuUtGjv3PH169E7K+OwkPQ/sRj+lnuO6yooqdvHgZMjJUVY09ryJsqEfbMiA9EA/pR79ZF8GpI9EO4p+Sl82dIMNGWCXhJZSmzdv1uHDh3XppZcqEAgoEAjozTff1KOPPqpAIKBIJNLmMfPmzVNNTU3Lbd++fUkLjxQLdunDGT+R1bnnqT/RkJTxdUl6HtiNfko913HU6JNUV5ecJ6ytVaMv9ryJsqEfbMiA9EA/pR79ZF8GpI9EO4p+Sl82dIMNGWCXhN6+d80112jbtm2tvve1r31No0eP1n333Sd/O+/fD4VCCoX4yMZ05O+blZznyevc8+z6qDIp83cdej8pzwO70U/e2Nk3pIt27EjOk+3YoR39Ovf629APNmRAeqCfvEE/2ZUB6SPRjqKf0pcN3WBDBtgloaVUdna2Lrzwwlbf6927t/r379/m+0h//vwe8p0T6tLFzn3nhDp1PSlJ2rp/uyr2vasLB5V06kJ4kWhE2/bv0LYDSfoLKqxGP3ljXWFPja4oV3DLFqm0tHMXE45EpPJyNW2t0PoL8zqVw4Z+sCED0gP95A36ya4MSB90VOawoRtsyAC7JOn9WeiuQqVd+zjYrj7+8Tefks/p3K+p3+fX428+1aX5AFp7ekyegq6kRx+VfJ38fyF+v/TYYwq6sefrLBv6wYYMAGLoJ/syALCPDd1gQwbYo8tLqbVr1+qRRx5JQhTYKDgyR052QEr0kgqO5GQHFByZ06X5z29+Wfv+flDNkeaEHtccadaH/3tAv//r6i7NR3qjn5KvIr+H3iroqeaVK6QPPpCamhJ7gqYmae9eNT+7Um8V9FRFJ8+klOzoBxsyID3RT8lHP9mXAemLjuq+bOgGGzLAHpwphTNyAj71vnFI7GLl8S6mHElZscc5ga79ip1oCusLC7+hunBD3KXVHGlWXbhBn1/wdZ1o6vxbDwG0b+7kAkUaw4pee23sosLx/odfU5NUV6folCmKNIY1d3JBl3LY0A82ZADwCfrJrgwA7GNDN9iQAfZgKYWz8udlqc8tw+T0ie8SZE6fgPrcMqzTFzg/3Z7De3XNwzN0sPojSbH3Ebfn5PcPVn+kax6eofePfJCU+QBaq8zL0uzJBdKuXYpecYV04EDsB+18glir7x84ELv/7t2aPblAlUnoCBv6wYYMAGLoJ/syALCPDd1gQwbYgaUU4uLPy1L2P45Qz8kD5Tun/U/b8J0TUs/JA5X9jyOStpA66f0jH+hTP71Bs38zT9v2t39Ru237d2j2b+bpUz+9gbICUuz5UTm6c0qBmir3qPn8MdKsWVJ5eft3Li+XZs1S8/lj1FS5R9+cUqjnR3Xtrb2nsqEfbMgAIIZ+si8DAPvY0A02ZIB5CX36HjKbE/Apa0yessbkKXLkhCLVjVJjVMryyZ+X1elP2YvXiaawntnwop7Z8KJKB49R8YDhyu7RW3UnGrT7o0pt3b89pfMBtPbcqFxtPrenFpRVafzy5Wr69XIFx14slZRIOTlSba20c6eaKsoVdKX1hT1116ThSTkD4XQ29IMNGQDE0E/2ZQBgHxu6wYYMMIulFDrFn98j5UuoM9m6fzsFBVigMi9LN3xxqMYeOaGZ26t15f7tGr21XFlRqdEn7egX0voL8/T0mLwuXTQ4ETb0gw0ZgExHP9mbAYB9bOgGGzLAeyylAABdVpHfQxX5A1u+dlxXrpPox3YCQPLRTwAA2ItrSgEAko7/4ANgK/oJAAB7sJQCAAAAAACA5xzXdV0vB9bW1io3Nzc2vLf37x50jzVLriRHcnqZefciGchgUwbT8yXJbWiWJNXU1CgnJ3mfepQo0/0kWXI8+J0kAxlaZ7Cgo+gnMtgynwyWZaCfJFlyLMhABkvmW5Mhzn4yek2pkyHNDDc8nwxksC2D6fmWMf5a2HA8TGcwPZ8MZLCU8dfBhmNBBvPzyWBXBksYfx1sOBZkIIMt823JcBZGl1KcKUUGMpjPYHq+ZGdR8i995jKYnk8GMrTJYFlH0U+ZncH0fDJYloF+kmTJsSADGSyZb02GOPvJ3FKql185s4o9H1u7bLfchmY5vQJG5pOBDLZlMD1fkmqW7pKORYzMbpehfpLsOB6mM5ieTwYynM6qjqKfMj6D6flksCsD/RRjw7EgAxlsmW9Lhnj7iQudA13keHtZNgAAAAAAugWjb98D0tHYIyc0c3u1rjp4XCVHw8qKSo0+aWffkNYV9tTTY/JUkd/DdEwAAAAAAKzGUgqdUjp4jEYOGK4+PXqr/kSDdn1Uqa37t3fr+cOrG7WgrErjq46ryZGCYy+WpoyWsrOVVVeni3bs0OiKct25rVpvFfTU3MkFqszLSmkmAG2Z7idJihw5ocjRRqkpKgV98vfNkp9lNZDx6CcAtqKfYApLKcStRzCkm8d9TndOuE1ji85v8/OKfe/q8Tef0vObX9aJpnC3mn/LezVaWHZI/qyQNGuWgnffLV1ySZv7BbdskR59VFesXKH1Kyo1e3KBnh9l5uN5gUxiup8kyW2OqmlXrcJbjyr6cdsZvnNCCpX2VXBkjpwA754HMgX9BMBW9BNswFIKcTnv3GF6Yc6TKupXqKgbbfc+Fw4q0YKZD+q+qXP0+QVf1/tHPugW8295r0aPv1YljRwp36uvSkOHStH2M6i0VFqyRIEHHpBvyhQ98dpuOXL13KjcpGQB0JbpfpKkSHWjGlZ9KLeu408ZiX4c1vGyQzrxzsfqfeMQ+TmTEuj26CcAtqKfYAtWjTir884dptfvXaHCvAFyHEd+n7/d+/l9fjmOo8K8AXrjeys1In9o2s8fUd2ohWWHYgup9eulQYMkx5H87WeQ3x/7+aBB8m3YIBUXa2HZIQ2vbuxyFgBtme4nKfYXqvrn9sqtj+9jb936ZtU/t1cRegHo1ugnALain2ATllI4ox7BkF6Y86SyQ70V8Md3Yl3AH1B2qLdenLtYPYKhtJ7/WFmVfFmh2BlS2dlSMBjfA4NBKTtbvtdekz8rpAVlVV3KAaAt0/0gxU45b1j1odQYleL9IE5XUmPscW5zB2ddAkhr9BMAW9FPsE1CS6kf/ehHchyn1W306NGpygYL3DzucyrqVxh3YZ0U8Ac0pP8gffHSqWk7f+zhExpfdVzBGV+OvWUv3oXUScGgNGyYAl+aofFVxzX2yIlOZ8HZ0U+Zx3Q/SVLTrtrYKefx/oXqJFdy65rVtKu2yxmQHuiozEI/IZ3QT5mFfoJtEj5T6oILLlBVVVXL7S9/+UsqcsESd064rcP3GJ9NJBrRnRNuS9v5M3dUq8mRdPfdHV9D6qwhItJdd6nJkWZur+50FsSHfsospvtJksJbjxp9PNILHZU56CekG/opc9BPsE3CFzoPBAIaOHBgKrLAMqWDx7T7KQzx8vv8unjIBSodPKZTHydqev5VB48rOPbidj9lL/4QfmncOAXHXqwrPf5I1UxEP2UO0/0gxT62uL1PiUlE9OOwIkdO8HHHGYKOygz0E9IR/ZQZ6CfYKOEzpXbt2qXCwkKNGDFCM2fO1IcffnjG+4fDYdXW1ra6IT2MHDA8Kc9T3MnnMT2/5GhYStapyyUlGv331HyMKj5BP2UO0/0gSZGjybnQJhfszByJdBT9lL7oJ6Qj+ikz0E+wUUJLqSuuuELLli3TK6+8okWLFqmyslJXX3216urqOnzM/PnzlZub23IrKirqcmh4o0+P3kl5nuxOPo/J+Y7rKiuq2MXNkyEnR1nR2PMiNeinzGK6nyRJTUm6yGYjF+vMBIl2FP2UvugnpBv6KXPQT7BRQkupqVOn6tZbb1Vpaamuu+46vfzyy6qurtazzz7b4WPmzZunmpqaltu+ffu6HBreqD/RkJTnqevk85ic7zqOGn2SzrDQSEhtrRp9sedFatBPmcV0P0mSgkn6ANssPgg3EyTaUfRT+qKfkG7op8xBP8FGCV9T6lR5eXkaNWqUdu/e3eF9QqGQQqGuf2wkvLfro8rkPM+h99Ny/s6+IV20Y0dSMmjHDu3ox/8OvEQ/dW+m+0GS/H2zkpLBn5ec50F6OVtH0U/pi35CuqOfui/6CTbq0nqxvr5ee/bsUUFBQbLywCJb929Xxb53FYlGOvX4SDSi8g//R9sOdG6xY3r+usKeaqool7ZsiX2KXqdCRKTNm9W0tULrC3p27jnQKfRT92a6HyTJn99DvnO69pdy3zkhLtKZoeio7ot+Qrqjn7ov+gk2Smgpde+99+rNN9/U3r179fbbb+sLX/iC/H6/vvKVr6QqHwx7/M2n5HM6t7v0+/x6/M2n0nb+02PyFHQlPfqo5Ovk/tbvlx57TEE39nxIHfop85juJ0kKlfY1+nikDzoqs9BPSCf0U2ahn2CbhH4b9+/fr6985SsqKSnRl770JfXv31/r169Xfn5+qvLBsOc3v6x9fz+o5khzQo9rjjTrw/89oN//dXXazq/I76G3CnqqeeUK6YMPpKamxJ6gqUnau1fNz67UWwU9VcE2P6Xop8xjup8kKTgyR052QEr0cnGO5GQHFByZ0+UMSA90VGahn5BO6KfMQj/BNgldU2rFihWpygFLnWgK6wsLv6HX712h7FBvBfxn/5VpjjSrLtygzy/4uk40hdN6/tzJBVq/olK+a6+Vb/362KfxBYNnf2BTk1RXp+iUKYo0hjV3cnI+fhUdo58yj+l+kCQn4FPvG4eo/rm9sU+BiecDNh1JWbHHOQEu0pkp6KjMQj8hndBPmYV+gm04mjirPYf36pqHZ+hg9UeS1OF7kE9+/2D1R7rm4Rl6/8gHaT+/Mi9LsycXSLt2KXrFFdKBA/83rIP3YZ/8/oEDsfvv3q3ZkwtUyYX4gJQw3U9S7EKbfW4ZJqdPfP/O4/QJqM8tw7hAJ9DN0U8AbEU/wSZd+vQ9ZI73j3ygT/30Bn3x0qm6c8JtunjIBW3us23/Dj3+5lP6/V9XJ2WDbsv850flyJGrhWV75D9/jAJfmiHddZc0blzbO5eXS489puZnVyrSGNbsKYV6fhSnlwKpZLqfpNhfrLL/cYSadtUqvPWooh+3neE7J6RQad/YKev8Cx+QEegnALain2ALllKI24mmsJ7Z8KKe2fCiSgePUfGA4cru0Vt1Jxq0+6NKbd2/vdvOf25Urjaf21MLyqo0fvlyNf16uYJjL5ZKSqScHKm2Vtq5U00V5Qq60vrCnrpr0nDOkAI8YrqfpNip6Flj8pQ1Jk+RIycUqW6MnZKe5ZM/L4tPiQEyFP0EwFb0E2zAUgqdsnX/dk9Kyqb5lXlZuuGLQzX2yAnN3F6tK/dv1+it5cqKSo0+aUe/kNZfmKenx+RxUXPAINP9JMU+7pi/RAE4Hf0EwFb0E0xhKQUkqCK/hyryB7Z87biuXCfRj44AAAAAACCz8aZMoItYSAEAAAAAkDiWUgAAAAAAAPCc47qu6+XA2tpa5ebmxob39v7dg+6xZsmV5EhOLzPvXiQDGWzKYHq+JLkNzZKkmpoa5eSY+7RC0/0kWXI8+J0kAxlaZ7Cgo+gnMtgynwyWZaCfJFlyLMhABkvmW5Mhzn4yek2pkyHNDDc8nwxksC2D6fmWMf5a2HA8TGcwPZ8MZLCU8dfBhmNBBvPzyWBXBksYfx1sOBZkIIMt823JcBZGl1KcKUUGMpjPYHq+ZGdR8i995jKYnk8GMrTJYFlH0U+ZncH0fDJYloF+kmTJsSADGSyZb02GOPvJ3FKql185s4o9H1u7bLfchmY5vQJG5pOBDLZlMD1fkmqW7pKORYzMbpehfpLsOB6mM5ieTwYynM6qjqKfMj6D6flksCsD/RRjw7EgAxlsmW9Lhnj7iQudAwAAAAAAwHMspQAAAAAAAOA5llIAAAAAAADwHEspAAAAAAAAeI6lFAAAAAAAADzHUgoAAAAAAACeYykFAAAAAAAAzyW8lDpw4IBuu+029e/fXz179tRFF12kTZs2pSIbACSEfgJgMzoKgK3oJwCmBBK589GjRzV+/HhNmjRJq1evVn5+vnbt2qW+ffumKh8AxIV+AmAzOgqAregnACYltJT6+c9/rqKiIi1durTle8OHD096KABIFP0EwGZ0FABb0U8ATEro7XurVq3SZZddpltvvVXnnnuuLrnkEj3xxBOpygYAcaOfANiMjgJgK/oJgEkJLaXef/99LVq0SCNHjtQf//hHffvb39bdd9+t5cuXd/iYcDis2traVjcASDb6CYDNEu0o+gmAV+gnACYl9Pa9aDSqyy67TA899JAk6ZJLLtHf/vY3/epXv9Ltt9/e7mPmz5+vH//4x11PCgBnQD8BsFm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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": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\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 = 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 = 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": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(70, 8, 8)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.reshape(simulation_results[0], (-1, 8, 8)).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(70, 2)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "simulation_results[1].reshape(-1, 2).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9.83 s ± 240 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%timeit simulate_game(100, (RandomPolicy(1), RandomPolicy(1)))"
   ]
  },
  {
   "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 pupose we played a sample of 10k games and saved them for later analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(70, 10000, 8, 8)\n",
      "(70, 10000, 2)\n"
     ]
    }
   ],
   "source": [
    "if not os.path.exists(\"rnd_history.npy\") and not os.path.exists(\"rnd_action.npy\"):\n",
    "    rnds = RandomPolicy(1), RandomPolicy(1)\n",
    "    simulation_results = simulate_game(10_000, rnds, tqdm_on=True)\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",
    "print(_board_history.shape)\n",
    "print(_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": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(70, 10000, 8, 8)"
      ]
     },
     "execution_count": 27,
     "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 abolutely."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "490d626986f04f2ab5c7149b3199081d",
       "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_possibilitie_count = np.mean(count_poss_turns, axis=1)\n",
    "std_possibilitie_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_possibilitie_count = mean_possibilitie_count.copy()\n",
    "    _std_possibilitie_count = std_possibilitie_count.copy()\n",
    "    _mean_possibilitie_count[_mean_possibilitie_count <= 1] = 1\n",
    "    _std_possibilitie_count[_std_possibilitie_count <= 1] = 1\n",
    "    np.cumprod(_mean_possibilitie_count[::-1], axis=0)[::-1]\n",
    "    fig.suptitle(\n",
    "        f\"Action space size analysis\\nThe total size is estimated to be around {np.prod(_mean_possibilitie_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_possibilitie_count,\n",
    "        yerr=std_possibilitie_count,\n",
    "        label=\"Mean action space size with error bars\",\n",
    "    )\n",
    "    ax2.scatter(turn, mean_possibilitie_count[turn], marker=\"x\")\n",
    "    ax2.legend()\n",
    "\n",
    "    ax4.plot(\n",
    "        range(70),\n",
    "        np.cumprod((_mean_possibilitie_count)[::-1], axis=0)[::-1],\n",
    "        # yerr=np.cumprod(_std_possibilitie_count[::-1], axis=0)[::-1],\n",
    "    )\n",
    "    ax4.scatter(\n",
    "        turn,\n",
    "        np.cumprod(_mean_possibilitie_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 palyer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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 actionspace</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 actionspace\n",
       "white            5.687159e+18\n",
       "black            3.753117e+20"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "white = mean_possibilitie_count[::2]\n",
    "black = mean_possibilitie_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 actionspace\"],\n",
    ").T\n",
    "del white, black\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ac8ca8dc22b5490fba9241640ed87287",
       "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": [
    "@interact(turn=(0, 69))\n",
    "def turn_distribution_heatmap(turn):\n",
    "    turn_possibility_on_field = np.mean(_poss_turns[turn], axis=0)\n",
    "\n",
    "    uniform_data = np.random.rand(10, 12)\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": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(70, 10000)\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",
    "assert len(calculate_direct_score(_board_history).shape) == 2\n",
    "assert calculate_direct_score(_board_history).shape[0] == SIMULATE_TURNS\n",
    "print(calculate_direct_score(_board_history).shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "da049b98f9ad40c2a22e00968b63f7cc",
       "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, 5))\n",
    "    fig.suptitle(\n",
    "        f\"Action space size analysis\\nThe total size is estimated to be around {np.prod(np.extract(mean_possibilitie_count, mean_possibilitie_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 socre 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": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vvvkmVCoVxowZAwDS/7LffvttWd3y5ctbPM6G/x1e/b/BW/V02bFjx1rd3xtvvAEAje5Ei4iIQFlZGd577z18//33so+2AOBvf/sb7O3tsWjRokbHJIRo9HWxRVNfr5Zyc3PDyJEj8c477+DUqVON1p85c6bZ7a8eu52dnfTG2/B9NX78eHz//ffSXW9XapiPsWPHwmQyISMjQ1pXV1eH5cuXo3PnzhgxYsRNPY6xY8eirq4Oq1atktrq6+tb9H1bUVHRqM3f3x/A5Tm40a/T1f73f/8X586dk15v3LgRp06dkv4dNuzT2kfBDdfY7Ny5U2qrr6/H6tWrr7nflv4coLaNZ3qoXRgwYABGjhyJgIAAdOvWDXv37sXGjRsxY8YMqSYgIAAAMGvWLISGhsLe3h4TJkzAQw89hPvuuw8vvfQSSkpKMGjQIOzYsQOffvop5syZI/0gDQgIwPjx45Gamopff/1VumX9p59+AtCyMxNOTk4YPnw4UlJScPHiRXh4eGDHjh04evToTZiVxgYNGoRJkyZh9erVqKysxIgRI7Bnzx6sW7cO4eHhuO+++2T1Y8eORZcuXTBv3jzY29tj/PjxsvV9+/bFyy+/jPj4eJSUlCA8PBxdunTB0aNH8cknnyAmJgbz5s27rrE29fWyxcqVKxESEgI/Pz9MmzYNd9xxB8rLy5GXl4cTJ07g+++/b3LbqVOnoqKiAvfffz969eqFY8eOYfny5fD395fOkr3wwgvYuHEjHnvsMfz9739HQEAAKioqsGXLFqSlpWHQoEGIiYnBO++8g8mTJyM/Px9eXl7YuHEjvvnmG6Smpja6huTPPo6HHnoIf/3rXxEXF4eSkhIMGDAAmzZtshoarrZ48WLs3LkTYWFh6NOnD06fPo23334bvXr1kp471bdvX7i4uCAtLQ1dunRBp06dYDAYrF7P1hLdunVDSEgIpkyZgvLycqSmpqJfv36YNm2aVBMQEICMjAzExsZi6NCh6Ny5Mx566CH85S9/QWBgIOLj41FRUYFu3bohPT0ddXV119xvS38OUBt3628YI5JruM30u+++s7p+xIgR17xl/eWXXxbDhg0TLi4uwtHRUfj6+opXXnlF1NbWSjV1dXVi5syZokePHkKlUsluzT137px4/vnnhbu7u+jQoYPw8fERS5culW47blBdXS2mT58uunXrJjp37izCw8NFcXGxACC7hbzh1l9rt/qeOHFCPPLII8LFxUU4OzuLxx57TJSVlTV52/vVfUyaNEl06tSpRfNkzcWLF8WiRYuEt7e36NChg/D09BTx8fGy26Gv9OSTTwoAwmg0Ntnnv//9bxESEiI6deokOnXqJHx9fcX06dNFcXGxzeNr0NTXq+HW7qVLlzba5uo5FEKIn3/+WURFRQm9Xi86dOggPDw8xIMPPig2btzY7P43btwoRo8eLdzc3IRarRa9e/cWTz/9tDh16pSs7tdffxUzZswQHh4eQq1Wi169eolJkyaJs2fPSjXl5eViypQpwtXVVajVauHn59foFu/mjutGjqNhjBMnThROTk7C2dlZTJw4URQUFFzzlvXc3Fzx8MMPC3d3d6FWq4W7u7uIjIwUP/30k6z/Tz/9VAwYMEA4ODjI+mzua97ULesff/yxiI+PF25ubsLR0VGEhYVJj0hocP78efHEE08IFxcXAUB26/nPP/8sjEaj0Gg0QqfTifnz54ucnJxr3rIuRMt/DgAQ06dPb3RMV/9cotuPSghedUV0IwoLCzF48GB8+OGHePLJJ1t7OERE1ARe00NkA2u/jDE1NRV2dnbXfBIyERG1Ll7TQ2SDlJQU5Ofn47777oODg4N0K3NMTAw8PT1be3hERNQMfrxFZIOcnBwsWrQIP/74I86fP4/evXtj4sSJeOmll+DgwP9DEBHdzhh6iIiISBF4TQ8REREpAkMPERERKQIvQriCxWJBWVkZunTp8qc/Wp2IiIhuDiEEzp07B3d390a/3PdKDD1XKCsr4x04REREbdTx48fRq1evJtcz9Fyh4XHwx48fh5OTUyuPhoiIiFrCbDbD09Pzmr/WhaHnCg0faTk5OTH0EBERtTHXujSFFzITERGRIjD0EBERkSIw9BAREZEiMPQQERGRIjD0EBERkSIw9BAREZEiMPQQERGRIjD0EBERkSIw9BAREZEiMPQQERGRIjD0EBERkSIw9BAREZEiMPQQERGRIjD0EBERkSI4tPYAiIhuFa+4ba09BJuVLAlr7SEQtRs800NERESKwNBDREREisDQQ0RERIrA0ENERESKcF2hZ+XKlfDy8oJWq4XBYMCePXuarc/MzISvry+0Wi38/PyQlZUlrbt48SJefPFF+Pn5oVOnTnB3d0dUVBTKyspkfVRUVODJJ5+Ek5MTXFxcEB0djfPnz8tq9u/fj3vvvRdarRaenp5ISUm5nsMjIiKidsjm0JORkYHY2FgkJiZi3759GDRoEEJDQ3H69Gmr9bt27UJkZCSio6NRUFCA8PBwhIeHo6ioCADw+++/Y9++fVi4cCH27duHTZs2obi4GOPGjZP18+STT+LAgQPIycnB1q1bsXPnTsTExEjrzWYzRo8ejT59+iA/Px9Lly5FUlISVq9ebeshEhERUTukEkIIWzYwGAwYOnQoVqxYAQCwWCzw9PTEzJkzERcX16g+IiIC1dXV2Lp1q9QWGBgIf39/pKWlWd3Hd999h2HDhuHYsWPo3bs3Dh48iAEDBuC7777DkCFDAADZ2dkYO3YsTpw4AXd3d6xatQovvfQSTCYT1Go1ACAuLg6bN2/GoUOHWnRsZrMZzs7OqKqqgpOTky3TQkRtAG9ZJ2qfWvr+bdOZntraWuTn58NoNF7uwM4ORqMReXl5VrfJy8uT1QNAaGhok/UAUFVVBZVKBRcXF6kPFxcXKfAAgNFohJ2dHXbv3i3VDB8+XAo8DfspLi7Gb7/9ZnU/NTU1MJvNsoWIiIjaJ5tCz9mzZ1FfXw+dTidr1+l0MJlMVrcxmUw21V+4cAEvvvgiIiMjpbRmMpng5uYmq3NwcEC3bt2kfpraT8M6a5KTk+Hs7Cwtnp6eVuuIiIio7but7t66ePEiHn/8cQghsGrVqpu+v/j4eFRVVUnL8ePHb/o+iYiIqHXY9GsoXF1dYW9vj/Lycll7eXk59Hq91W30en2L6hsCz7Fjx/Dll1/KPpPT6/WNLpSuq6tDRUWF1E9T+2lYZ41Go4FGo2nqcImIiKgdselMj1qtRkBAAHJzc6U2i8WC3NxcBAUFWd0mKChIVg8AOTk5svqGwHP48GF88cUX6N69e6M+KisrkZ+fL7V9+eWXsFgsMBgMUs3OnTtx8eJF2X769++Prl272nKYRERE1A7Z/PFWbGws3n33Xaxbtw4HDx7Es88+i+rqakyZMgUAEBUVhfj4eKl+9uzZyM7OxrJly3Do0CEkJSVh7969mDFjBoBLgefRRx/F3r178dFHH6G+vh4mkwkmkwm1tbUAgLvuugsPPPAApk2bhj179uCbb77BjBkzMGHCBLi7uwMAnnjiCajVakRHR+PAgQPIyMjAv/71L8TGxt7wJBEREVHbZ/NvWY+IiMCZM2eQkJAAk8kEf39/ZGdnSxcNl5aWws7ucpYKDg7G+vXrsWDBAsyfPx8+Pj7YvHkzBg4cCAA4efIktmzZAgDw9/eX7eurr77CyJEjAQAfffQRZsyYgVGjRsHOzg7jx4/HW2+9JdU6Oztjx44dmD59OgICAuDq6oqEhATZs3yIiIhIuWx+Tk97xuf0ELVvfE4PUft0U57TQ0RERNRWMfQQERGRIjD0EBERkSIw9BAREZEiMPQQERGRIjD0EBERkSIw9BAREZEiMPQQERGRIjD0EBERkSIw9BAREZEiMPQQERGRIjD0EBERkSIw9BAREZEiMPQQERGRIjD0EBERkSIw9BAREZEiMPQQERGRIjD0EBERkSIw9BAREZEiMPQQERGRIjD0EBERkSIw9BAREZEiMPQQERGRIjD0EBERkSIw9BAREZEiMPQQERGRIjD0EBERkSIw9BAREZEiMPQQERGRIjD0EBERkSIw9BAREZEiMPQQERGRIlxX6Fm5ciW8vLyg1WphMBiwZ8+eZuszMzPh6+sLrVYLPz8/ZGVlydZv2rQJo0ePRvfu3aFSqVBYWChbX1JSApVKZXXJzMyU6qytT09Pv55DJCIionbG5tCTkZGB2NhYJCYmYt++fRg0aBBCQ0Nx+vRpq/W7du1CZGQkoqOjUVBQgPDwcISHh6OoqEiqqa6uRkhICF577TWrfXh6euLUqVOyZdGiRejcuTPGjBkjq12zZo2sLjw83NZDJCIionZIJYQQtmxgMBgwdOhQrFixAgBgsVjg6emJmTNnIi4urlF9REQEqqursXXrVqktMDAQ/v7+SEtLk9WWlJTA29sbBQUF8Pf3b3YcgwcPxj333IP333//8sGoVPjkk0+uO+iYzWY4OzujqqoKTk5O19UHEd2+vOK2tfYQbFayJKy1h0B022vp+7dNZ3pqa2uRn58Po9F4uQM7OxiNRuTl5VndJi8vT1YPAKGhoU3Wt0R+fj4KCwsRHR3daN306dPh6uqKYcOG4YMPPkBzma6mpgZms1m2EBERUfvkYEvx2bNnUV9fD51OJ2vX6XQ4dOiQ1W1MJpPVepPJZONQL3v//fdx1113ITg4WNa+ePFi3H///ejYsSN27NiB5557DufPn8esWbOs9pOcnIxFixZd9ziIlKwtnjUhImWzKfTcDv744w+sX78eCxcubLTuyrbBgwejuroaS5cubTL0xMfHIzY2VnptNpvh6en55w+aiIiIWp1NH2+5urrC3t4e5eXlsvby8nLo9Xqr2+j1epvqr2Xjxo34/fffERUVdc1ag8GAEydOoKamxup6jUYDJycn2UJERETtk02hR61WIyAgALm5uVKbxWJBbm4ugoKCrG4TFBQkqweAnJycJuuv5f3338e4cePQo0ePa9YWFhaia9eu0Gg017UvIiIiaj9s/ngrNjYWkyZNwpAhQzBs2DCkpqaiuroaU6ZMAQBERUXBw8MDycnJAIDZs2djxIgRWLZsGcLCwpCeno69e/di9erVUp8VFRUoLS1FWVkZAKC4uBjApbNEV54ROnLkCHbu3NnoOT8A8Nlnn6G8vByBgYHQarXIycnBq6++innz5tl6iERERNQO2Rx6IiIicObMGSQkJMBkMsHf3x/Z2dnSxcqlpaWws7t8Aik4OBjr16/HggULMH/+fPj4+GDz5s0YOHCgVLNlyxYpNAHAhAkTAACJiYlISkqS2j/44AP06tULo0ePbjSuDh06YOXKlXj++echhEC/fv3wxhtvYNq0abYeIhEREbVDNj+npz3jc3qIWo53b90afE4P0bXdlOf0EBEREbVVDD1ERESkCAw9REREpAgMPURERKQIDD1ERESkCAw9REREpAgMPURERKQIDD1ERESkCAw9REREpAg2/xoKIiK6ddrik6/5FGm6XfFMDxERESkCQw8REREpAkMPERERKQJDDxERESkCQw8REREpAkMPERERKQJDDxERESkCQw8REREpAkMPERERKQJDDxERESkCQw8REREpAkMPERERKQJDDxERESkCQw8REREpAkMPERERKQJDDxERESkCQw8REREpAkMPERERKQJDDxERESkCQw8REREpAkMPERERKQJDDxERESnCdYWelStXwsvLC1qtFgaDAXv27Gm2PjMzE76+vtBqtfDz80NWVpZs/aZNmzB69Gh0794dKpUKhYWFjfoYOXIkVCqVbHnmmWdkNaWlpQgLC0PHjh3h5uaGF154AXV1dddziERERNTO2Bx6MjIyEBsbi8TEROzbtw+DBg1CaGgoTp8+bbV+165diIyMRHR0NAoKChAeHo7w8HAUFRVJNdXV1QgJCcFrr73W7L6nTZuGU6dOSUtKSoq0rr6+HmFhYaitrcWuXbuwbt06rF27FgkJCbYeIhEREbVDKiGEsGUDg8GAoUOHYsWKFQAAi8UCT09PzJw5E3FxcY3qIyIiUF1dja1bt0ptgYGB8Pf3R1pamqy2pKQE3t7eKCgogL+/v2zdyJEj4e/vj9TUVKvj+vzzz/Hggw+irKwMOp0OAJCWloYXX3wRZ86cgVqtvuaxmc1mODs7o6qqCk5OTtesJ1Iyr7htrT0Euk2VLAlr7SGQwrT0/dumMz21tbXIz8+H0Wi83IGdHYxGI/Ly8qxuk5eXJ6sHgNDQ0Cbrm/PRRx/B1dUVAwcORHx8PH7//XfZfvz8/KTA07Afs9mMAwcOWO2vpqYGZrNZthAREVH75GBL8dmzZ1FfXy8LFgCg0+lw6NAhq9uYTCar9SaTyaaBPvHEE+jTpw/c3d2xf/9+vPjiiyguLsamTZua3U/DOmuSk5OxaNEim8ZBREREbZNNoac1xcTESH/38/NDz549MWrUKPz888/o27fvdfUZHx+P2NhY6bXZbIanp+cNj5WIiIhuPzZ9vOXq6gp7e3uUl5fL2svLy6HX661uo9frbapvKYPBAAA4cuRIs/tpWGeNRqOBk5OTbCEiIqL2yabQo1arERAQgNzcXKnNYrEgNzcXQUFBVrcJCgqS1QNATk5Ok/Ut1XBbe8+ePaX9/PDDD7K7yHJycuDk5IQBAwbc0L6IiIio7bP5463Y2FhMmjQJQ4YMwbBhw5Camorq6mpMmTIFABAVFQUPDw8kJycDAGbPno0RI0Zg2bJlCAsLQ3p6Ovbu3YvVq1dLfVZUVKC0tBRlZWUAgOLiYgCXztDo9Xr8/PPPWL9+PcaOHYvu3btj//79eP755zF8+HDcfffdAIDRo0djwIABmDhxIlJSUmAymbBgwQJMnz4dGo3mxmaJiIiI2jybQ09ERATOnDmDhIQEmEwm+Pv7Izs7W7pouLS0FHZ2l08gBQcHY/369ViwYAHmz58PHx8fbN68GQMHDpRqtmzZIoUmAJgwYQIAIDExEUlJSVCr1fjiiy+kgOXp6Ynx48djwYIF0jb29vbYunUrnn32WQQFBaFTp06YNGkSFi9ebPusEBERUbtj83N62jM+p4eo5ficHmoKn9NDt9pNeU4PERERUVvF0ENERESKwNBDREREisDQQ0RERIrA0ENERESKwNBDREREisDQQ0RERIrA0ENERESKwNBDREREisDQQ0RERIrA0ENERESKwNBDREREisDQQ0RERIrA0ENERESKwNBDREREisDQQ0RERIrA0ENERESKwNBDREREisDQQ0RERIrA0ENERESKwNBDREREisDQQ0RERIrA0ENERESKwNBDREREisDQQ0RERIrA0ENERESKwNBDREREisDQQ0RERIrA0ENERESKwNBDREREisDQQ0RERIrA0ENERESKcF2hZ+XKlfDy8oJWq4XBYMCePXuarc/MzISvry+0Wi38/PyQlZUlW79p0yaMHj0a3bt3h0qlQmFhoWx9RUUFZs6cif79+8PR0RG9e/fGrFmzUFVVJatTqVSNlvT09Os5RCIiImpnbA49GRkZiI2NRWJiIvbt24dBgwYhNDQUp0+ftlq/a9cuREZGIjo6GgUFBQgPD0d4eDiKioqkmurqaoSEhOC1116z2kdZWRnKysrw+uuvo6ioCGvXrkV2djaio6Mb1a5ZswanTp2SlvDwcFsPkYiIiNohlRBC2LKBwWDA0KFDsWLFCgCAxWKBp6cnZs6cibi4uEb1ERERqK6uxtatW6W2wMBA+Pv7Iy0tTVZbUlICb29vFBQUwN/fv9lxZGZm4qmnnkJ1dTUcHBwuHYxKhU8++eS6g47ZbIazszOqqqrg5OR0XX0QKYVX3LbWHgLdpkqWhLX2EEhhWvr+bdOZntraWuTn58NoNF7uwM4ORqMReXl5VrfJy8uT1QNAaGhok/Ut1XBgDYGnwfTp0+Hq6ophw4bhgw8+QHOZrqamBmazWbYQERFR++Rw7ZLLzp49i/r6euh0Olm7TqfDoUOHrG5jMpms1ptMJhuHKh/HP//5T8TExMjaFy9ejPvvvx8dO3bEjh078Nxzz+H8+fOYNWuW1X6Sk5OxaNGi6x4HERERtR02hZ7bgdlsRlhYGAYMGICkpCTZuoULF0p/Hzx4MKqrq7F06dImQ098fDxiY2NlfXt6et6UcRMREVHrsunjLVdXV9jb26O8vFzWXl5eDr1eb3UbvV5vU31zzp07hwceeABdunTBJ598gg4dOjRbbzAYcOLECdTU1Fhdr9Fo4OTkJFuIiIiofbIp9KjVagQEBCA3N1dqs1gsyM3NRVBQkNVtgoKCZPUAkJOT02R9U8xmM0aPHg21Wo0tW7ZAq9Vec5vCwkJ07doVGo3Gpn0RERFR+2Pzx1uxsbGYNGkShgwZgmHDhiE1NRXV1dWYMmUKACAqKgoeHh5ITk4GAMyePRsjRozAsmXLEBYWhvT0dOzduxerV6+W+qyoqEBpaSnKysoAAMXFxQAunSXS6/VS4Pn999/x4Ycfyi467tGjB+zt7fHZZ5+hvLwcgYGB0Gq1yMnJwauvvop58+bd2AwRERFRu2Bz6ImIiMCZM2eQkJAAk8kEf39/ZGdnSxcrl5aWws7u8gmk4OBgrF+/HgsWLMD8+fPh4+ODzZs3Y+DAgVLNli1bpNAEABMmTAAAJCYmIikpCfv27cPu3bsBAP369ZON5+jRo/Dy8kKHDh2wcuVKPP/88xBCoF+/fnjjjTcwbdo0Ww+RiIiI2iGbn9PTnvE5PUQtx+f0UFP4nB661W7Kc3qIiIiI2iqGHiIiIlIEhh4iIiJSBIYeIiIiUgSGHiIiIlIEhh4iIiJSBIYeIiIiUgSGHiIiIlIEhh4iIiJSBIYeIiIiUgSGHiIiIlIEhh4iIiJSBIYeIiIiUgSGHiIiIlIEhh4iIiJSBIYeIiIiUgSGHiIiIlIEhh4iIiJSBIYeIiIiUgSGHiIiIlIEhh4iIiJSBIYeIiIiUgSGHiIiIlIEhh4iIiJSBIYeIiIiUgSGHiIiIlIEhh4iIiJSBIYeIiIiUgSH1h4AEQFecdtaewhERO0ez/QQERGRIjD0EBERkSIw9BAREZEiXFfoWblyJby8vKDVamEwGLBnz55m6zMzM+Hr6wutVgs/Pz9kZWXJ1m/atAmjR49G9+7doVKpUFhY2KiPCxcuYPr06ejevTs6d+6M8ePHo7y8XFZTWlqKsLAwdOzYEW5ubnjhhRdQV1d3PYdIRERE7YzNoScjIwOxsbFITEzEvn37MGjQIISGhuL06dNW63ft2oXIyEhER0ejoKAA4eHhCA8PR1FRkVRTXV2NkJAQvPbaa03u9/nnn8dnn32GzMxM/Oc//0FZWRn+9re/Sevr6+sRFhaG2tpa7Nq1C+vWrcPatWuRkJBg6yESERFRO6QSQghbNjAYDBg6dChWrFgBALBYLPD09MTMmTMRFxfXqD4iIgLV1dXYunWr1BYYGAh/f3+kpaXJaktKSuDt7Y2CggL4+/tL7VVVVejRowfWr1+PRx99FABw6NAh3HXXXcjLy0NgYCA+//xzPPjggygrK4NOpwMApKWl4cUXX8SZM2egVquveWxmsxnOzs6oqqqCk5OTLdNCdEN49xa1JyVLwlp7CKQwLX3/tulMT21tLfLz82E0Gi93YGcHo9GIvLw8q9vk5eXJ6gEgNDS0yXpr8vPzcfHiRVk/vr6+6N27t9RPXl4e/Pz8pMDTsB+z2YwDBw5Y7bempgZms1m2EBERUftkU+g5e/Ys6uvrZcECAHQ6HUwmk9VtTCaTTfVN9aFWq+Hi4tJkP03tp2GdNcnJyXB2dpYWT0/PFo+JiIiI2hZF370VHx+PqqoqaTl+/HhrD4mIiIhuEpueyOzq6gp7e/tGd02Vl5dDr9db3Uav19tU31QftbW1qKyslJ3tubIfvV7f6C6yhv02tS+NRgONRtPicRAREVHbZdOZHrVajYCAAOTm5kptFosFubm5CAoKsrpNUFCQrB4AcnJymqy3JiAgAB06dJD1U1xcjNLSUqmfoKAg/PDDD7K7yHJycuDk5IQBAwa0eF9ERETUPtn8u7diY2MxadIkDBkyBMOGDUNqaiqqq6sxZcoUAEBUVBQ8PDyQnJwMAJg9ezZGjBiBZcuWISwsDOnp6di7dy9Wr14t9VlRUYHS0lKUlZUBuBRogEtnaPR6PZydnREdHY3Y2Fh069YNTk5OmDlzJoKCghAYGAgAGD16NAYMGICJEyciJSUFJpMJCxYswPTp03k2h4iIiGwPPREREThz5gwSEhJgMpng7++P7Oxs6aLh0tJS2NldPoEUHByM9evXY8GCBZg/fz58fHywefNmDBw4UKrZsmWLFJoAYMKECQCAxMREJCUlAQDefPNN2NnZYfz48aipqUFoaCjefvttaRt7e3ts3boVzz77LIKCgtCpUydMmjQJixcvtvUQiYiIqB2y+Tk97Rmf00Othc/pofaEz+mhW+2mPKeHiIiIqK1i6CEiIiJFYOghIiIiRWDoISIiIkVg6CEiIiJFYOghIiIiRWDoISIiIkVg6CEiIiJFYOghIiIiRWDoISIiIkVg6CEiIiJFYOghIiIiRWDoISIiIkVg6CEiIiJFYOghIiIiRWDoISIiIkVg6CEiIiJFYOghIiIiRWDoISIiIkVg6CEiIiJFYOghIiIiRXBo7QEQEVH74hW3rbWHYLOSJWGtPQS6BXimh4iIiBSBoYeIiIgUgaGHiIiIFIGhh4iIiBSBoYeIiIgUgaGHiIiIFIGhh4iIiBSBoYeIiIgUgaGHiIiIFIGhh4iIiBThukLPypUr4eXlBa1WC4PBgD179jRbn5mZCV9fX2i1Wvj5+SErK0u2XgiBhIQE9OzZE46OjjAajTh8+LC0/uuvv4ZKpbK6fPfddwCAkpISq+u//fbb6zlEIiIiamdsDj0ZGRmIjY1FYmIi9u3bh0GDBiE0NBSnT5+2Wr9r1y5ERkYiOjoaBQUFCA8PR3h4OIqKiqSalJQUvPXWW0hLS8Pu3bvRqVMnhIaG4sKFCwCA4OBgnDp1SrZMnToV3t7eGDJkiGx/X3zxhawuICDA1kMkIiKidkglhBC2bGAwGDB06FCsWLECAGCxWODp6YmZM2ciLi6uUX1ERASqq6uxdetWqS0wMBD+/v5IS0uDEALu7u6YO3cu5s2bBwCoqqqCTqfD2rVrMWHChEZ9Xrx4ER4eHpg5cyYWLlwI4NKZHm9vbxQUFMDf39+WQ5KYzWY4OzujqqoKTk5O19UH0fVoi7+gkag94S8cbdta+v5t05me2tpa5Ofnw2g0Xu7Azg5GoxF5eXlWt8nLy5PVA0BoaKhUf/ToUZhMJlmNs7MzDAZDk31u2bIFv/76K6ZMmdJo3bhx4+Dm5oaQkBBs2bKl2eOpqamB2WyWLURERNQ+2RR6zp49i/r6euh0Olm7TqeDyWSyuo3JZGq2vuFPW/p8//33ERoail69ekltnTt3xrJly5CZmYlt27YhJCQE4eHhzQaf5ORkODs7S4unp2eTtURERNS2ObT2AGx14sQJbN++HRs2bJC1u7q6IjY2Vno9dOhQlJWVYenSpRg3bpzVvuLj42XbmM1mBh8iIqJ2yqYzPa6urrC3t0d5ebmsvby8HHq93uo2er2+2fqGP1va55o1a9C9e/cmg8yVDAYDjhw50uR6jUYDJycn2UJERETtk02hR61WIyAgALm5uVKbxWJBbm4ugoKCrG4TFBQkqweAnJwcqd7b2xt6vV5WYzabsXv37kZ9CiGwZs0aREVFoUOHDtccb2FhIXr27Nni4yMiIqL2y+aPt2JjYzFp0iQMGTIEw4YNQ2pqKqqrq6WLiqOiouDh4YHk5GQAwOzZszFixAgsW7YMYWFhSE9Px969e7F69WoAgEqlwpw5c/Dyyy/Dx8cH3t7eWLhwIdzd3REeHi7b95dffomjR49i6tSpjca1bt06qNVqDB48GACwadMmfPDBB3jvvfdsPUQiIiJqh2wOPREREThz5gwSEhJgMpng7++P7Oxs6ULk0tJS2NldPoEUHByM9evXY8GCBZg/fz58fHywefNmDBw4UKr5xz/+gerqasTExKCyshIhISHIzs6GVquV7fv9999HcHAwfH19rY7tn//8J44dOwYHBwf4+voiIyMDjz76qK2HSERERO2Qzc/pac/4nB5qLXxOD1Hr4nN62rab8pweIiIioraKoYeIiIgUgaGHiIiIFIGhh4iIiBSBoYeIiIgUgaGHiIiIFIGhh4iIiBSBoYeIiIgUgaGHiIiIFIGhh4iIiBSBoYeIiIgUgaGHiIiIFIGhh4iIiBSBoYeIiIgUgaGHiIiIFIGhh4iIiBSBoYeIiIgUgaGHiIiIFIGhh4iIiBSBoYeIiIgUgaGHiIiIFIGhh4iIiBSBoYeIiIgUgaGHiIiIFIGhh4iIiBSBoYeIiIgUgaGHiIiIFIGhh4iIiBSBoYeIiIgUgaGHiIiIFIGhh4iIiBSBoYeIiIgU4bpCz8qVK+Hl5QWtVguDwYA9e/Y0W5+ZmQlfX19otVr4+fkhKytLtl4IgYSEBPTs2ROOjo4wGo04fPiwrMbLywsqlUq2LFmyRFazf/9+3HvvvdBqtfD09ERKSsr1HB4RERG1QzaHnoyMDMTGxiIxMRH79u3DoEGDEBoaitOnT1ut37VrFyIjIxEdHY2CggKEh4cjPDwcRUVFUk1KSgreeustpKWlYffu3ejUqRNCQ0Nx4cIFWV+LFy/GqVOnpGXmzJnSOrPZjNGjR6NPnz7Iz8/H0qVLkZSUhNWrV9t6iERERNQOqYQQwpYNDAYDhg4dihUrVgAALBYLPD09MXPmTMTFxTWqj4iIQHV1NbZu3Sq1BQYGwt/fH2lpaRBCwN3dHXPnzsW8efMAAFVVVdDpdFi7di0mTJgA4NKZnjlz5mDOnDlWx7Vq1Sq89NJLMJlMUKvVAIC4uDhs3rwZhw4dsrpNTU0NampqpNdmsxmenp6oqqqCk5OTLdNCdEO84ra19hCIFK1kSVhrD4FugNlshrOz8zXfv20601NbW4v8/HwYjcbLHdjZwWg0Ii8vz+o2eXl5snoACA0NleqPHj0Kk8kkq3F2dobBYGjU55IlS9C9e3cMHjwYS5cuRV1dnWw/w4cPlwJPw36Ki4vx22+/WR1bcnIynJ2dpcXT07OFM0FERERtjU2h5+zZs6ivr4dOp5O163Q6mEwmq9uYTKZm6xv+vFafs2bNQnp6Or766is8/fTTePXVV/GPf/zjmvu5ch9Xi4+PR1VVlbQcP368yWMnIiKits2htQfQUrGxsdLf7777bqjVajz99NNITk6GRqO5rj41Gs11b0tERERti01nelxdXWFvb4/y8nJZe3l5OfR6vdVt9Hp9s/UNf9rSJ3Dp2qK6ujqUlJQ0u58r90FERETKZVPoUavVCAgIQG5urtRmsViQm5uLoKAgq9sEBQXJ6gEgJydHqvf29oZer5fVmM1m7N69u8k+AaCwsBB2dnZwc3OT9rNz505cvHhRtp/+/fuja9euthwmERERtUM237IeGxuLd999F+vWrcPBgwfx7LPPorq6GlOmTAEAREVFIT4+XqqfPXs2srOzsWzZMhw6dAhJSUnYu3cvZsyYAQBQqVSYM2cOXn75ZWzZsgU//PADoqKi4O7ujvDwcACXLlJOTU3F999/j19++QUfffQRnn/+eTz11FNSoHniiSegVqsRHR2NAwcOICMjA//6179kH4sRERGRctl8TU9ERATOnDmDhIQEmEwm+Pv7Izs7W7pouLS0FHZ2l7NUcHAw1q9fjwULFmD+/Pnw8fHB5s2bMXDgQKnmH//4B6qrqxETE4PKykqEhIQgOzsbWq0WwKVrb9LT05GUlISamhp4e3vj+eeflwUaZ2dn7NixA9OnT0dAQABcXV2RkJCAmJiY654capt4+zcREVlj83N62rOW3udPtzeGHiKyFZ/T07bdlOf0EBEREbVVDD1ERESkCAw9REREpAgMPURERKQIDD1ERESkCAw9REREpAgMPURERKQIDD1ERESkCAw9REREpAgMPURERKQIDD1ERESkCAw9REREpAgMPURERKQIDD1ERESkCAw9REREpAgMPURERKQIDD1ERESkCAw9REREpAgMPURERKQIDD1ERESkCAw9REREpAgMPURERKQIDD1ERESkCAw9REREpAgMPURERKQIDD1ERESkCAw9REREpAgMPURERKQIDD1ERESkCAw9REREpAgMPURERKQIDD1ERESkCNcVelauXAkvLy9otVoYDAbs2bOn2frMzEz4+vpCq9XCz88PWVlZsvVCCCQkJKBnz55wdHSE0WjE4cOHpfUlJSWIjo6Gt7c3HB0d0bdvXyQmJqK2tlZWo1KpGi3ffvvt9RwiERERtTM2h56MjAzExsYiMTER+/btw6BBgxAaGorTp09brd+1axciIyMRHR2NgoIChIeHIzw8HEVFRVJNSkoK3nrrLaSlpWH37t3o1KkTQkNDceHCBQDAoUOHYLFY8M477+DAgQN48803kZaWhvnz5zfa3xdffIFTp05JS0BAgK2HSERERO2QSgghbNnAYDBg6NChWLFiBQDAYrHA09MTM2fORFxcXKP6iIgIVFdXY+vWrVJbYGAg/P39kZaWBiEE3N3dMXfuXMybNw8AUFVVBZ1Oh7Vr12LChAlWx7F06VKsWrUKv/zyC4BLZ3q8vb1RUFAAf3//Fh1LTU0NampqpNdmsxmenp6oqqqCk5NTi/qg249X3LbWHgIR0U1XsiSstYdw2zCbzXB2dr7m+7dNZ3pqa2uRn58Po9F4uQM7OxiNRuTl5VndJi8vT1YPAKGhoVL90aNHYTKZZDXOzs4wGAxN9glcCkbdunVr1D5u3Di4ubkhJCQEW7ZsafZ4kpOT4ezsLC2enp7N1hMREVHbZVPoOXv2LOrr66HT6WTtOp0OJpPJ6jYmk6nZ+oY/benzyJEjWL58OZ5++mmprXPnzli2bBkyMzOxbds2hISEIDw8vNngEx8fj6qqKmk5fvx4k7VERETUtjm09gBsdfLkSTzwwAN47LHHMG3aNKnd1dUVsbGx0uuhQ4eirKwMS5cuxbhx46z2pdFooNFobvqYiYiIqPXZdKbH1dUV9vb2KC8vl7WXl5dDr9db3Uav1zdb3/BnS/osKyvDfffdh+DgYKxevfqa4zUYDDhy5Mg164iIiKj9syn0qNVqBAQEIDc3V2qzWCzIzc1FUFCQ1W2CgoJk9QCQk5Mj1Xt7e0Ov18tqzGYzdu/eLevz5MmTGDlyJAICArBmzRrY2V176IWFhejZs6cth0hERETtlM0fb8XGxmLSpEkYMmQIhg0bhtTUVFRXV2PKlCkAgKioKHh4eCA5ORkAMHv2bIwYMQLLli1DWFgY0tPTsXfvXulMjUqlwpw5c/Dyyy/Dx8cH3t7eWLhwIdzd3REeHg7gcuDp06cPXn/9dZw5c0YaT8PZoHXr1kGtVmPw4MEAgE2bNuGDDz7Ae++9d/2zQ0RERO2GzaEnIiICZ86cQUJCAkwmE/z9/ZGdnS1diFxaWio7CxMcHIz169djwYIFmD9/Pnx8fLB582YMHDhQqvnHP/6B6upqxMTEoLKyEiEhIcjOzoZWqwVw6czQkSNHcOTIEfTq1Us2nivvuP/nP/+JY8eOwcHBAb6+vsjIyMCjjz5q6yESERFRO2Tzc3ras5be50+3Nz6nh4iUgM/pueymPKeHiIiIqK1i6CEiIiJFYOghIiIiRWDoISIiIkVg6CEiIiJFYOghIiIiRWDoISIiIkVg6CEiIiJFYOghIiIiRWDoISIiIkVg6CEiIiJFYOghIiIiRWDoISIiIkVg6CEiIiJFYOghIiIiRWDoISIiIkVg6CEiIiJFYOghIiIiRXBo7QHQ7c0rbltrD4GIiOhPwTM9REREpAgMPURERKQIDD1ERESkCAw9REREpAgMPURERKQIDD1ERESkCAw9REREpAgMPURERKQIDD1ERESkCAw9REREpAgMPURERKQI/N1bREREbVBb/N2IJUvCWnX/13WmZ+XKlfDy8oJWq4XBYMCePXuarc/MzISvry+0Wi38/PyQlZUlWy+EQEJCAnr27AlHR0cYjUYcPnxYVlNRUYEnn3wSTk5OcHFxQXR0NM6fPy+r2b9/P+69915otVp4enoiJSXleg6PiIiI2iGbQ09GRgZiY2ORmJiIffv2YdCgQQgNDcXp06et1u/atQuRkZGIjo5GQUEBwsPDER4ejqKiIqkmJSUFb731FtLS0rB792506tQJoaGhuHDhglTz5JNP4sCBA8jJycHWrVuxc+dOxMTESOvNZjNGjx6NPn36ID8/H0uXLkVSUhJWr15t6yESERFRO6QSQghbNjAYDBg6dChWrFgBALBYLPD09MTMmTMRFxfXqD4iIgLV1dXYunWr1BYYGAh/f3+kpaVBCAF3d3fMnTsX8+bNAwBUVVVBp9Nh7dq1mDBhAg4ePIgBAwbgu+++w5AhQwAA2dnZGDt2LE6cOAF3d3esWrUKL730EkwmE9RqNQAgLi4OmzdvxqFDh1p0bGazGc7OzqiqqoKTk5Mt09JutcXTp0REdHu6WR9vtfT926Zrempra5Gfn4/4+Hipzc7ODkajEXl5eVa3ycvLQ2xsrKwtNDQUmzdvBgAcPXoUJpMJRqNRWu/s7AyDwYC8vDxMmDABeXl5cHFxkQIPABiNRtjZ2WH37t145JFHkJeXh+HDh0uBp2E/r732Gn777Td07dq10dhqampQU1Mjva6qqgJwafJuhoGJ229Kv0RERG3BzXp/bej3WudxbAo9Z8+eRX19PXQ6naxdp9M1eTbFZDJZrTeZTNL6hrbmatzc3OQDd3BAt27dZDXe3t6N+mhYZy30JCcnY9GiRY3aPT09rR4LERERXT/n1Jvb/7lz5+Ds7NzkekXfvRUfHy87C2WxWFBRUYHu3btDpVK14sguM5vN8PT0xPHjx/mRWzM4Ty3DeWoZzlPLcJ5ahvPUMjcyT0IInDt3Du7u7s3W2RR6XF1dYW9vj/Lycll7eXk59Hq91W30en2z9Q1/lpeXo2fPnrIaf39/qebqC6Xr6upQUVEh68fafq7cx9U0Gg00Go2szcXFxWpta3NycuI/lhbgPLUM56llOE8tw3lqGc5Ty1zvPDV3hqeBTXdvqdVqBAQEIDc3V2qzWCzIzc1FUFCQ1W2CgoJk9QCQk5Mj1Xt7e0Ov18tqzGYzdu/eLdUEBQWhsrIS+fn5Us2XX34Ji8UCg8Eg1ezcuRMXL16U7ad///5WP9oiIiIihRE2Sk9PFxqNRqxdu1b8+OOPIiYmRri4uAiTySSEEGLixIkiLi5Oqv/mm2+Eg4ODeP3118XBgwdFYmKi6NChg/jhhx+kmiVLlggXFxfx6aefiv3794uHH35YeHt7iz/++EOqeeCBB8TgwYPF7t27xX//+1/h4+MjIiMjpfWVlZVCp9OJiRMniqKiIpGeni46duwo3nnnHVsP8bZSVVUlAIiqqqrWHsptjfPUMpynluE8tQznqWU4Ty1zK+bJ5tAjhBDLly8XvXv3Fmq1WgwbNkx8++230roRI0aISZMmyeo3bNgg7rzzTqFWq8Vf/vIXsW3bNtl6i8UiFi5cKHQ6ndBoNGLUqFGiuLhYVvPrr7+KyMhI0blzZ+Hk5CSmTJkizp07J6v5/vvvRUhIiNBoNMLDw0MsWbLkeg7vtnLhwgWRmJgoLly40NpDua1xnlqG89QynKeW4Ty1DOepZW7FPNn8nB4iIiKitoi/cJSIiIgUgaGHiIiIFIGhh4iIiBSBoYeIiIgUgaGHiIiIFIGh5za3bds2GAwGODo6omvXrggPD5etLy0tRVhYGDp27Ag3Nze88MILqKura53BtrKamhr4+/tDpVKhsLBQtm7//v249957odVq4enpiZSUlNYZZCspKSlBdHQ0vL294ejoiL59+yIxMRG1tbWyOqXPU4OVK1fCy8sLWq0WBoMBe/bsae0htZrk5GQMHToUXbp0gZubG8LDw1FcXCyruXDhAqZPn47u3bujc+fOGD9+fKMn5CvNkiVLoFKpMGfOHKmN83TJyZMn8dRTT6F79+5wdHSEn58f9u7dK60XQiAhIQE9e/aEo6MjjEYjDh8+/Ofs/KbdDE83bOPGjaJr165i1apVori4WBw4cEBkZGRI6+vq6sTAgQOF0WgUBQUFIisrS7i6uor4+PhWHHXrmTVrlhgzZowAIAoKCqT2qqoqodPpxJNPPimKiorExx9/LBwdHdv8gytt8fnnn4vJkyeL7du3i59//ll8+umnws3NTcydO1eq4Txdkp6eLtRqtfjggw/EgQMHxLRp04SLi4soLy9v7aG1itDQULFmzRpRVFQkCgsLxdixY0Xv3r3F+fPnpZpnnnlGeHp6itzcXLF3714RGBgogoODW3HUrWvPnj3Cy8tL3H333WL27NlSO+dJiIqKCtGnTx8xefJksXv3bvHLL7+I7du3iyNHjkg1S5YsEc7OzmLz5s3i+++/F+PGjWv0wOLrxdBzm7p48aLw8PAQ7733XpM1WVlZws7OTnoathBCrFq1Sjg5OYmamppbMczbRlZWlvD19RUHDhxoFHrefvtt0bVrV9mcvPjii6J///6tMNLbR0pKivD29pZec54uGTZsmJg+fbr0ur6+Xri7u4vk5ORWHNXt4/Tp0wKA+M9//iOEuPQ0/A4dOojMzEyp5uDBgwKAyMvLa61htppz584JHx8fkZOTI0aMGCGFHs7TJS+++KIICQlpcr3FYhF6vV4sXbpUaqusrBQajUZ8/PHHN7x/frx1m9q3bx9OnjwJOzs7DB48GD179sSYMWNQVFQk1eTl5cHPzw86nU5qCw0NhdlsxoEDB1pj2K2ivLwc06ZNw//93/+hY8eOjdbn5eVh+PDhUKvVUltoaCiKi4vx22+/3cqh3laqqqrQrVs36TXnCaitrUV+fj6MRqPUZmdnB6PRiLy8vFYc2e2jqqoKAKTvnfz8fFy8eFE2Z76+vujdu7ci52z69OkICwuTzQfAeWqwZcsWDBkyBI899hjc3NwwePBgvPvuu9L6o0ePwmQyyebJ2dkZBoPhT5knhp7b1C+//AIASEpKwoIFC7B161Z07doVI0eOREVFBQDAZDLJAg8A6bXJZLq1A24lQghMnjwZzzzzDIYMGWK1hvPU2JEjR7B8+XI8/fTTUhvnCTh79izq6+utzoNS5qA5FosFc+bMwV//+lcMHDgQwKXvDbVaDRcXF1mtEucsPT0d+/btQ3JycqN1nKdLfvnlF6xatQo+Pj7Yvn07nn32WcyaNQvr1q0DcPlnzc36N8jQc4vFxcVBpVI1uxw6dAgWiwUA8NJLL2H8+PEICAjAmjVroFKpkJmZ2cpHcfO1dJ6WL1+Oc+fOIT4+vrWH3CpaOk9XOnnyJB544AE89thjmDZtWiuNnNqi6dOno6ioCOnp6a09lNvO8ePHMXv2bHz00UfQarWtPZzblsViwT333INXX30VgwcPRkxMDKZNm4a0tLRbsn+HW7IXksydOxeTJ09utuaOO+7AqVOnAAADBgyQ2jUaDe644w6UlpYCAPR6faO7ShruBNDr9X/iqG+9ls7Tl19+iby8PGg0Gtm6IUOG4Mknn8S6deug1+sb3SGhtHlqUFZWhvvuuw/BwcFYvXq1rK49z1NLubq6wt7e3uo8KGUOmjJjxgxs3boVO3fuRK9evaR2vV6P2tpaVFZWys5iKG3O8vPzcfr0adxzzz1SW319PXbu3IkVK1Zg+/btnCcAPXv2lL2vAcBdd92Ff//73wAu/6wpLy9Hz549pZry8nL4+/vf+ABu+KoguimqqqqERqORXchcW1sr3NzcpLtpGi5kvvKuknfeeUc4OTkp5rf5Hjt2TPzwww/Ssn37dgFAbNy4URw/flwIcfkC3draWmm7+Ph4xV2ge+LECeHj4yMmTJgg6urqGq3nPF0ybNgwMWPGDOl1fX298PDwUOyFzBaLRUyfPl24u7uLn376qdH6hgt0N27cKLUdOnRIcRfoms1m2c+iH374QQwZMkQ89dRT4ocffuA8/X+RkZGNLmSeM2eOCAoKEkJcvpD59ddfl9Y3vB/+GRcyM/TcxmbPni08PDzE9u3bxaFDh0R0dLRwc3MTFRUVQojLt6yPHj1aFBYWiuzsbNGjRw/F3rIuhBBHjx5tdPdWZWWl0Ol0YuLEiaKoqEikp6eLjh07KupW7BMnToh+/fqJUaNGiRMnTohTp05JSwPO0yXp6elCo9GItWvXih9//FHExMQIFxcX2V2SSvLss88KZ2dn8fXXX8u+b37//Xep5plnnhG9e/cWX375pdi7d68ICgqS3sSU7Mq7t4TgPAlx6XZ+BwcH8corr4jDhw+Ljz76SHTs2FF8+OGHUs2SJUuEi4uL+PTTT8X+/fvFww8/zFvWlaC2tlbMnTtXuLm5iS5dugij0SiKiopkNSUlJWLMmDHC0dFRuLq6irlz54qLFy+20ohbn7XQI4QQ33//vQgJCREajUZ4eHiIJUuWtM4AW8maNWsEAKvLlZQ+Tw2WL18uevfuLdRqtRg2bJj49ttvW3tIraap75s1a9ZINX/88Yd47rnnRNeuXUXHjh3FI488IgvUSnV16OE8XfLZZ5+JgQMHCo1GI3x9fcXq1atl6y0Wi1i4cKHQ6XRCo9GIUaNGieLi4j9l3yohhLjxD8mIiIiIbm+8e4uIiIgUgaGHiIiIFIGhh4iIiBSBoYeIiIgUgaGHiIiIFIGhh4iIiBSBoYeIiIgUgaGHiIiIFIGhh4iIiBSBoYeIiIgUgaGHiIiIFOH/ARYDvll3jmw0AAAAAElFTkSuQmCC\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",
    "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": 34,
   "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 distribtuion\")\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": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ilJqaioEDB6JHjx5wdnZGXFwcdu/eLXVYREREZAEsPhE6cOAA7rvvPgQEBEAQBGzdurXFMUlJSQgJCYG9vT2GDx+O1NRU3b6cnBz06NFD97xHjx64du2aOUInIiIiC2fxXWOVlZWIjIzEvHnz8MADD7TYv3nzZiQmJmLt2rUYPnw4Vq9ejUmTJuH8+fPw8fGRIGIiIiLLJ4oi6tQaqDUi1BoRGg2gFpt+FkWIIiBCbDoWTT81vs7Y/JUOsJG1vziqqVh8IhQXF4e4uLg2969atQpPPPEE5s6dCwBYu3Yttm/fjo8++giLFy9GQEBAsxaga9euITY2ts3z1dbWora2VvdcpVIZ4V0QERGZlyiKOJ2jwp4z+bh6vRpl1XUoq65HaVU9SqvrUVZVjzq1RuowAQBH/joRns4KSa5t8YlQe+rq6nDkyBEsWbJEt00mk2HixIlISUkBAMTGxuLUqVO4du0alEolduzYgaVLl7Z5zpUrV+Lll182eexERETGpk1+tp/MxY6TubhSXNWp88gEQCY0ttA0/QcCBED3c/fRpROhoqIiqNVq+Pr6Ntvu6+uLc+fOAQBsbW3x9ttvY9y4cdBoNHjhhRfaHTG2ZMkSJCYm6p6rVCoEBgaa5g0QEREZQWF5Ldb9koEfTuYiq+SP5EdhK8O4/j4YHKiEu6Mcbg52UDrYQeloBzdHOVzsbWErEyATBNg0/VcmAILQnVKd9nXpREhfU6ZMwZQpU/Q6VqFQQKGQpnmOiIjIUA1qDR5bdwjn8soBAPZ2jcnPPYP8MT7MB04Kq/iq77QufXe8vLxgY2OD/Pz8Ztvz8/Ph5+cnUVRERETm81lqFs7llcPN0Q6v3h+Bcf2Z/BjC4ofPt0culyM6OhrJycm6bRqNBsnJyRgxYsQtnTspKQnh4eGIiYm51TCJiIhMoqSyDm/v/h0A8L939ce9gwOYBBnI4u9WRUUFLl68qHuekZGB9PR0eHh4ICgoCImJiYiPj8ewYcMQGxuL1atXo7KyUjeKrLMSEhKQkJAAlUoFpVJ5q2+DiIjI6N7efR5l1fUY4O+Kh2ODpA6nS7L4ROjw4cMYN26c7rm2kDk+Ph4bNmzArFmzUFhYiGXLliEvLw9RUVHYuXNniwJqIiKi7uTUtTJ8lpoFAFhxX7hk8/B0dYJoipmRuhFti1BZWRlcXV2lDoeIiAiiKOLB/6Qg7cp13BcZgHcfGiJ1SBZH3+/vLl0jREREZI2+O56DtCvX4WBngyVxYVKH06UxEWoDi6WJiMgSVdY2YOUPjXPlJYzrjQA3B4kj6trYNdYBdo0REZEl+ceuc0jadwmBHg7Y8+wdsLezkToki8SuMSIiom4ms7gSHxzIAAAsnRzOJMgImAgRERF1EX/bdhZ1ag3G9PXCneEcHW0MTISIiIi6gJNXy7D3bD5sZQKW3xduVeuBmRIToTawWJqIiCzJ6ZwyAMDIPl7o4+MicTTdBxOhNiQkJODMmTNIS0uTOhQiIiJkNq0qH+rpKHEk3QsTISIioi4gs7gSABDk6SRxJN0LEyEiIqIuILO4sUUohC1CRsVEiIiIyMKJoqhLhIKZCBkVEyEiIiILV1JZh4raBggC0NOdiZAxMRFqA0eNERGRpbjS1Brk72rPSRSNjIlQGzhqjIiILEVWibZQmq1BxsZEiIiIyMJdKdIWSnPEmLExESIiIrJwWU1zCLFFyPiYCBEREVm4K01zCLFFyPiYCBEREVm4rKZi6SAPtggZGxMhIiIiC1ZeU4/iyjoAnEPIFJgItYHD54mIyBJoJ1L0dJLDxd5O4mi6HyZCbeDweSIisgTaQmm2BpkGEyEiIiILpi2UDmahtEkwESIiIrJgWVxjzKSYCBEREVmwP1qEmAiZAhMhIiIiC/ZHixC7xkyBiRAREZGFqqlXI1dVAwAI5hxCJsFEiIiIyEJdvV4FUQRcFLbwcJJLHU63xESIiIjIQmkXWw3ydIQgCBJH0z0xEWoDJ1QkIiKpZZZw1XlTYyLUBk6oSEREUstqGjHGVedNh4kQERGRhbqiHTHGQmmTYSJERERkof5YXoNdY6bCRIiIiMgCNag1yOY6YybHRIiIiMgC5ZbVoEEjQm4rg5+rvdThdFtMhIiIiCyQdmmNIA9HyGQcOm8qTISIiIgsUGaxdug8u8VMiYkQERGRBcrUtQixUNqUmAgRERFZIF2LkBdbhEyJiRAREZEF0iZCQZxDyKSYCLWBS2wQEZFURFHUzSHE5TVMi4lQG7jEBhERSaWwvBbV9WrYyAT0cHeQOpxujYkQERGRhdEurdHDzQF2NvyqNiXeXSIiIgujHTHGGaVNj4kQERGRhdEWSjMRMj0mQkRERBYmU7vGGOcQMjkmQkRERBaGXWPmw0SIiIjIwvzRNcYWIVNjIkRERGRBSqvqUFZdD4CTKZoDEyEiIiILom0N8nVVwEFuI3E03R8TISIiIgtyRVcfxG4xc2AiREREZEGytPVB7BYzCyZCREREFuQK5xAyKyZCREREFiSrhF1j5sREiIiIyEKIoogLBRUAgF7eTITMgYkQERGRhchX1aK0qh42MgF9fJylDscqMBFqQ1JSEsLDwxETEyN1KEREZCXO5qkAAL28nKCw5dB5c2Ai1IaEhAScOXMGaWlpUodCRERW4nxeOQAgzN9V4kisBxMhIiIiC3Eut7FFKMzPReJIrAcTISIiIgtxTtsixETIbJgIERERWYB6tQaXChtHjPVnImQ2TISIiIgswOXCStSrRbgobNHDzUHqcKwGEyEiIiILcK5pxFiYvwsEQZA4GuvBRIiIiMgCnM1trA9it5h5MREiIiKyAOe1LUJ+HDpvTkyEiIiILABHjEmDiRAREZHEyqrqkVtWAwDox0TIrJgIERERSUxbKN3DzQGu9nYSR2NdmAgRERFJTNstNsCfrUHmxkSIiIhIYtpEiCPGzI+JEBERkcTOccSYZJgIERERSUijEfE7R4xJhokQERGRhK5er0ZlnRpyGxlCvZykDsfqMBEiIiKSkLZbrK+vM2xt+LVsbrzjREREEmKhtLSYCBEREUlI2yI0gIXSkmAiREREJCG2CEmLiRAREZFEaurVuFJUCQAI42SKkrCKRGjatGlwd3fHjBkzpA6FiIhI50J+BTQi4OEkh7ezQupwrJJVJEJPP/00Nm7cKHUYREREzZzVTaToAkEQJI7GOllFIjR27Fi4uLDJkYiILMu5XNYHSU3yROjAgQO47777EBAQAEEQsHXr1hbHJCUlISQkBPb29hg+fDhSU1PNHygREZGRnc/niDGpSZ4IVVZWIjIyEklJSa3u37x5MxITE7F8+XIcPXoUkZGRmDRpEgoKCnTHREVFISIiosUjJyfHXG+DiIjIYGwRkp6toS/Izs6GIAjo2bMnACA1NRWfffYZwsPDMX/+fIMDiIuLQ1xcXJv7V61ahSeeeAJz584FAKxduxbbt2/HRx99hMWLFwMA0tPTDb5uW2pra1FbW6t7rlKpjHZuIiIircLyWhRX1kEQgH6+TISkYnCL0MMPP4x9+/YBAPLy8nDnnXciNTUVL730El555RWjBldXV4cjR45g4sSJum0ymQwTJ05ESkqKUa+ltXLlSiiVSt0jMDDQJNchIiLrpp1IMdTTCQ5yG4mjsV4GJ0KnTp1CbGwsAOCLL75AREQEDh48iE2bNmHDhg1GDa6oqAhqtRq+vr7Ntvv6+iIvL0/v80ycOBEzZ87EDz/8gJ49e7abRC1ZsgRlZWW6R3Z2dqfjJyIiagu7xSyDwV1j9fX1UCga5zrYu3cvpkyZAgAICwtDbm6ucaMzkr179+p9rEKh0L0/IiIiU9HOKB3GQmlJGdwiNHDgQKxduxY///wz9uzZg7vvvhsAkJOTA09PT6MG5+XlBRsbG+Tn5zfbnp+fDz8/P6Nei4iIyJy0XWNsEZKWwYnQG2+8gf/85z8YO3YsHnroIURGRgIAvvvuO12XmbHI5XJER0cjOTlZt02j0SA5ORkjRoww6rVulpSUhPDwcMTExJj0OkREZH0a1BpcKKgAAAzg0hqSMrhrbOzYsSgqKoJKpYK7u7tu+/z58+Ho6GhwABUVFbh48aLueUZGBtLT0+Hh4YGgoCAkJiYiPj4ew4YNQ2xsLFavXo3KykrdKDJTSUhIQEJCAlQqFZRKpUmvRURE1uVKcSXqGjRwlNsg0N3w704yHoMTIQCwsbFplgQBQEhISKcCOHz4MMaNG6d7npiYCACIj4/Hhg0bMGvWLBQWFmLZsmXIy8tDVFQUdu7c2aKAmoiIqKs401Qo3dfXBTIZl9aQksGJUH5+Pp577jkkJyejoKAAoig2269Wqw0639ixY1uc42YLFy7EwoULDQ2ViIjIIh3PLgUADO7BHgepGZwIzZkzB1lZWVi6dCn8/f25SBwREZGB0psSoahAN0njoE4kQr/88gt+/vlnREVFmSAcy5GUlISkpCSDW7iIiIjaU6/W4NS1MgBAVJCbtMGQ4aPGAgMDO+zK6g4SEhJw5swZpKWlSR0KERF1I+dyy1HboIGrvS1CPZ2kDsfqGZwIrV69GosXL8aVK1dMEA4REVH3lp59HQAQGejGQmkLYHDX2KxZs1BVVYXevXvD0dERdnZ2zfaXlJQYLTgiIqLu5lhTfdAQ1gdZBIMTodWrV5sgDCIiIuugK5RmfZBFMCgRqq+vx/79+7F06VKEhoaaKiaLwGJpIiIytrKqelwurAQARPZ0kzYYAmBgjZCdnR2+/vprU8ViUVgsTURExnb8aikAIMjDEZ7OXODbEhhcLH3//fdj69atJgiFiIioe+P8QZbH4Bqhvn374pVXXsGvv/6K6OhoODk1H/r31FNPGS04IiKi7oSJkOUxOBFat24d3NzccOTIERw5cqTZPkEQmAgRERG1QhRFFkpbIIMToYyMDFPEQURE1K1ll1SjpLIOdjYCwv1dpQ6HmhhcI2QtkpKSEB4ejpiYGKlDISKibuBY00SK4f6usLezkTga0jK4RWjevHnt7v/oo486HYwlSUhIQEJCAlQqFZRKrg5MRES3hvVBlsngROj69evNntfX1+PUqVMoLS3F+PHjjRYYERFRd8L6IMtkcCL0zTfftNim0Wjw5JNPonfv3kYJioiIqDupa9DgdI4KABAV6C5xNHQjo9QIyWQyJCYm4p///KcxTkdERNStnM1Voa5BAzdHO4R4OkodDt3AaMXSly5dQkNDg7FOR0RE1G1ou8Uie7pBELjivCUxuGssMTGx2XNRFJGbm4vt27cjPj7eaIERERF1FyyUtlwGJ0LHjh1r9lwmk8Hb2xtvv/12hyPKuhIuukpERMbCQmnLJYiiKEodhCXTDp8vKyuDqysnwCIiIsOUVtUh6pU9AIBjS++Eu5Nc4oisg77f3wbXCI0fPx6lpaWtXpDD54mIiJrTtgaFeDoyCbJABidCP/30E+rq6lpsr6mpwc8//2yUoIiIiLoL1gdZNr1rhE6cOKH7+cyZM8jLy9M9V6vV2LlzJ3r06GHc6IiIiLo43YgxJkIWSe9EKCoqCoIgQBCEVrvAHBwc8O677xo1OCIioq5MFEUcZ4uQRdM7EcrIyIAoiujVqxdSU1Ph7e2t2yeXy+Hj4wMbGy4iR0REpJVZXIXrVfWQ28gQHsABN5ZI70QoODgYQONyGkRERNQxbbfYgABXKGzZWGCJjDazNBERETWnTYSGsFvMYjERakNSUhLCw8MRExMjdShERNRFHWN9kMVjItSGhIQEnDlzBmlpaVKHQkREXVBlbQNOXysDAAwN4orzloqJEBERkQmkXSlBg0ZEDzcHBHo4SB0OtYGJEBERkQmkXC4GAIzo7ckV5y2YXqPG3N3d9f5HLCkpuaWAiIiIuoOUS42J0MjenhJHQu3RKxFavXq17ufi4mK8+uqrmDRpEkaMGAEASElJwa5du7B06VKTBElERNSVqGrqcaqpPmgEEyGLZvDq89OnT8e4ceOwcOHCZtvfe+897N27F1u3bjVmfJLj6vNERGSovWfy8eeNhxHq5YR9z42VOhyrZLLV53ft2oW77767xfa7774be/fuNfR0RERE3c7Bpm6x23qxNcjSGZwIeXp64ttvv22x/dtvv4WnJ//BiYiItIXSrA+yfHovsaH18ssv489//jN++uknDB8+HABw6NAh7Ny5Ex988IHRAyQiIupKSirrcDZXBYAtQl2BwYnQnDlzMGDAALzzzjvYsmULAGDAgAH45ZdfdIkRERGRtTrU1BrUz9cZ3i4KiaOhjhicCAHA8OHDsWnTJmPHYlGSkpKQlJQEtVotdShERNSFaOuDRrA1qEvo1ISKly5dwl//+lc8/PDDKCgoAADs2LEDp0+fNmpwUuISG0RE1Bl/TKToJXEkpA+DE6H9+/dj0KBBOHToEL7++mtUVFQAAI4fP47ly5cbPUAiIqKuokBVg4sFFRAE4LZeHlKHQ3owOBFavHgxXn31VezZswdyuVy3ffz48fjtt9+MGhwREVFXom0NCvd3hZujvIOjyRIYnAidPHkS06ZNa7Hdx8cHRUVFRgmKiIioK0phfVCXY3Ai5Obmhtzc3Bbbjx07hh49ehglKCIioq7oxoVWqWswOBGaPXs2XnzxReTl5UEQBGg0Gvz666947rnn8Pjjj5siRiIiIot3rbQamcVVsJEJiA1lfVBXYXAi9Pe//x1hYWEIDAxERUUFwsPDcfvtt2PkyJH461//aooYiYiILJ62WyyihxIu9nYSR0P6MngeIblcjg8++ABLly7FqVOnUFFRgSFDhqBv376miI+IiKhL0CZCXFaja+nUhIoAEBQUhMDAQACAIAhGC4iIiKirEUURKZcaBwyxULpr6dSEiuvWrUNERATs7e1hb2+PiIgIfPjhh8aOjYiIqEvIKqlCTlkN7GwEDAtxlzocMoDBLULLli3DqlWrsGjRIowYMQIAkJKSgmeffRZZWVl45ZVXjB4kERGRJdMuqxEV6AZHeac7W0gCBv9rrVmzBh988AEeeugh3bYpU6Zg8ODBWLRoERMhIiKyOrr5g7isRpdjcNdYfX09hg0b1mJ7dHQ0GhoajBIUERFRVyGKIhda7cIMToQee+wxrFmzpsX2999/H4888ohRgiIiIuoqLhVWoKiiFgpbGYYEuUkdDhmoUx2Z69atw+7du3HbbbcBAA4dOoSsrCw8/vjjSExM1B23atUq40RJRERkoX6+0DhaLDrYHfZ2NhJHQ4YyOBE6deoUhg4dCgC4dOkSAMDLywteXl44deqU7riuPqQ+KSkJSUlJUKvVUodCREQW7IeTjctOTRjgK3Ek1BmCKIqi1EFYMpVKBaVSibKyMri6ukodDhERWZC8shrctjIZAJCyZDz8lQ4SR0Ra+n5/d2oeIa3s7GxkZ2ffyimIiIi6LG1r0LBgdyZBXZTBiVBDQwOWLl0KpVKJkJAQhISEQKlU4q9//Svq6+tNESMREZFF2t6UCE0e7C9xJNRZBtcILVq0CFu2bMGbb77ZbELFFStWoLi4uNURZURERN1NTmk1jmRehyAAcRFMhLoqgxOhzz77DJ9//jni4uJ02wYPHozAwEA89NBDTISIiMgqaLvFYoI94Ke0lzga6iyDu8YUCgVCQkJabA8NDYVcLjdGTERERBaP3WLdg8GJ0MKFC/G3v/0NtbW1um21tbV47bXXsHDhQqMGR0REZImuXq/CsazSpm4xP6nDoVtgcNfYsWPHkJycjJ49eyIyMhIAcPz4cdTV1WHChAl44IEHdMdu2bLFeJESERFZiB0n8wAAsSEe8HFlt1hXZnAi5ObmhunTpzfbFhgYaLSAiIiILN22pm6xeyMDJI6EbpXBidD69etNEQcREVGXkF1ShePZpZAJwN0D2S3W1d3ShIpERETWRjta7LZenvB2UUgcDd0qvVqEhg4diuTkZLi7u2PIkCHtriN29OhRowVHRERkaTharHvRKxGaOnUqFAqF7ueuvqAqERFRZ2QVV+HE1TJ2i3UjeiVCy5cv1/28YsWKNo/j+q1ERNSdbTuZAwAY2dsLns7sFusODK4R+sc//tHqdrVajYcffviWAyIiIrJU20+wW6y76VQitG7dumbb1Go1Zs+ejfT0dGPFRUREZFEyiipxOkcFG5mASewW6zYMHj6/fft23HXXXVAqlZgxYwYaGhrw4IMP4ty5c9i3b58pYiQiIpKcdrTYyN6e8HDiklLdhcGJUExMDL7++mvcf//9kMvlWLduHS5evIh9+/bB19fXFDESERFJTtstdi+7xbqVTs0jNH78eGzcuBHTp09HRkYG9u/fzySIiIi6rfKaepzJVQEAJgzg9113oleL0I3rh93I29sbbm5umD9/vm4b1xcjIqLu5nxeOQDAz9UeXhwt1q3olQgplcpWt0+aNMmowRAREVmis02J0AB/F4kjIWPTKxHqyuuLZWdn47HHHkNBQQFsbW2xdOlSzJw5U+qwiIioCznb1C0W5u8qcSRkbAYXS1dXV0MURTg6OgIAMjMz8c033yA8PBx33XWX0QO8Vba2tli9ejWioqKQl5eH6Oho3HPPPXBycpI6NCIi6iK0idAAJkLdjsHF0lOnTsXGjRsBAKWlpYiNjcXbb7+NqVOnYs2aNUYP8Fb5+/sjKioKAODn5wcvLy+UlJRIGxQREXUZGo2oqxEKZ9dYt2NwInT06FGMGTMGAPDVV1/Bz88PmZmZ2LhxI9555x2DAzhw4ADuu+8+BAQEQBAEbN26tcUxSUlJCAkJgb29PYYPH47U1FSDrwMAR44cgVqtRmBgYKdeT0RE1ierpApVdWrIbWUI8WRvQndjcCJUVVUFF5fGjHj37t144IEHIJPJcNtttyEzM9PgACorKxEZGYmkpKRW92/evBmJiYlYvnw5jh49isjISEyaNAkFBQW6Y6KiohAREdHikZOTozumpKQEjz/+ON5//32DYyQiIuul7Rbr7+sCW5tOzTpDFszgGqE+ffpg69atmDZtGnbt2oVnn30WAFBQUABXV8P7TuPi4hAXF9fm/lWrVuGJJ57A3LlzAQBr167F9u3b8dFHH2Hx4sUA0OHSHrW1tbj//vuxePFijBw5ssNja2trdc9VKpWe74SIiLojjhjr3gxObZctW4bnnnsOISEhGD58OEaMGAGgsXVoyJAhRg2urq4OR44cwcSJE3XbZDIZJk6ciJSUFL3OIYoi5syZg/Hjx+Oxxx7r8PiVK1dCqVTqHuxGIyKybroRY34slO6ODE6EZsyYgaysLBw+fBg7d+7UbZ8wYQL++c9/GjW4oqIiqNXqFrNW+/r6Ii8vT69z/Prrr9i8eTO2bt2KqKgoREVF4eTJk20ev2TJEpSVleke2dnZt/QeiIioa+OIse7N4K4xoHH0lZ9f85V3Y2NjjRKQsY0ePRoajUbv4xUKBRQKzhpKRESAqqYeV69XA2DXWHdl0VVfXl5esLGxQX5+frPt+fn5LRIxIiIiY9MOm/dX2sPNkSvOd0cWnQjJ5XJER0cjOTlZt02j0SA5OVlXm2QqSUlJCA8PR0xMjEmvQ0RElovdYt1fp7rGjKmiogIXL17UPc/IyEB6ejo8PDwQFBSExMRExMfHY9iwYYiNjcXq1atRWVmpG0VmKgkJCUhISIBKpWpzrTUiIure/kiE2C3WXenVIjR06FBcv34dAPDKK6+gqqrKaAEcPnwYQ4YM0Y04S0xMxJAhQ7Bs2TIAwKxZs/DWW29h2bJliIqKQnp6Onbu3NmigJqIiMjYzuY2do1xxFj3JYiiKHZ0kIODAy5cuICePXvCxsYGubm58PHxMUd8ktO2CJWVlXVqniQiIuqa1BoREct3obpejb2Jd6CPj7PUIZEB9P3+1qtrLCoqCnPnzsXo0aMhiiLeeustODu3/oHQtuR0dUlJSUhKSoJarZY6FCIikkBmcSWq69Wwt5Mh1ItLa3RXerUInT9/HsuXL8elS5dw9OhRhIeHw9a2ZQ4lCAKOHj1qkkClwhYhIiLr9MPJXCzYdBSRPZX4duFoqcMhAxm1Rah///74/PPPATTO7JycnGw1XWNERGSdOKO0dTB41JghkxMSERF1VRwxZh06NXz+0qVLWL16Nc6ePQsACA8Px9NPP43evXsbNTgiIiKpaEeMcQ6h7s3gCRV37dqF8PBwpKamYvDgwRg8eDAOHTqEgQMHYs+ePaaIkYiIyKzKqutxrbRxaQ12jXVvBrcILV68GM8++yxef/31FttffPFF3HnnnUYLTkocNUZEZL3ONXWL9XBzgNLRTuJoyJQMbhE6e/Ys/vSnP7XYPm/ePJw5c8YoQVmChIQEnDlzBmlpaVKHQkREZsb6IOthcCLk7e2N9PT0FtvT09M5koyIiLqFc3mcUdpaGNw19sQTT2D+/Pm4fPkyRo4cCQD49ddf8cYbbyAxMdHoARIREZkbF1u1HgYnQkuXLoWLiwvefvttLFmyBAAQEBCAFStW4KmnnjJ6gEREROak1og4n68dMcause7O4ERIEAQ8++yzePbZZ1Fe3vhBcXHhB4WIiLqHK8WVqKnXwN5OhmBPLq3R3RlcI3QjFxeXbpsEJSUlITw8HDExMVKHQkREZqTtFuvv5wobmSBxNGRqt5QIdWccNUZEZJ20iVA4u8WsAhMhIiKiG2hnlOaIMevARIiIiOgG5zhizKoYlAjV19djwoQJuHDhgqniISIikkxpVR1yymoAAGHsGrMKBiVCdnZ2OHHihKliISIikpS2W6yHmwNc7bm0hjUwuGvs0Ucfxbp160wRCxERkaR+PJcPgN1i1sTgeYQaGhrw0UcfYe/evYiOjoaTU/M5FlatWmW04KTERVeJiKzLhl8z8MHPGQCAyYP9JI6GzEUQRVE05AXjxo1r+2SCgB9//PGWg7IkKpUKSqUSZWVlcHXlXwhERN3RF2nZeOHrxtKPp8b3QeJd/SWOiG6Vvt/fBrcI7du375YCIyIisiTbTuRg8ZbGJOhPo0Px7J39JI6IzOmWhs9fvXoVV69eNVYsREREZpV8Nh/PfJ4OjQg8FBuIv04eAEHgbNLWxOBESKPR4JVXXoFSqURwcDCCg4Ph5uaGv/3tb9BoNKaIkYiIyOgOXizCk5uOokEjYmpUAF69fxCTICtkcNfYSy+9hHXr1uH111/HqFGjAAC//PILVqxYgZqaGrz22mtGD5KIiMiYjmRex583HkZdgwZ3hvvirZmRXFfMShlcLB0QEIC1a9diypQpzbZ/++23WLBgAa5du2bUAKXGYmkiou4lq7gK9733C8qq6zGmrxc+jB8Gha2N1GGRken7/W1w11hJSQnCwsJabA8LC0NJSYmhpyMiIjKb6jo1/ufTIyirrkdkoBv+81g0kyArZ3AiFBkZiffee6/F9vfeew+RkZFGCYqIiMjYRFHES1tP4myuCl7Ocqx9dCgc5QZXiFA3Y/An4M0338TkyZOxd+9ejBgxAgCQkpKC7Oxs/PDDD0YPUCqcUJGIqHv59LdMbDl6DTYyAe8+NBT+SgepQyILYHCNEADk5OQgKSkJ586dAwAMGDAACxYsQEBAgNEDlBprhIiIur4jmdcx+/0U1KtF/N89YZh/e2+pQyITM9mEillZWQgMDGx1dFhWVhaCgoIMPSUREZHJFJbXYsGmI6hXi5g8yB9PjOkldUhkQQyuEQoNDUVhYWGL7cXFxQgNDTVKUERERMbQoNZg4WdHka+qRR8fZ7wxYzDnCqJmDE6ERFFs9UNUUVEBe3t7owRFRERkDG/sPIdDGSVwVthi7aPRcFawOJqa0/sTkZiYCKBxYdWlS5fC0dFRt0+tVuPQoUOIiooyeoBERESdsedMvm41+bdmDkYfH2eJIyJLpHcidOzYMQCNLUInT56EXC7X7ZPL5YiMjMRzzz1n/AiJiIgMJIoi/pX8OwDgz6NDcXeEv8QRkaXSOxHSrjo/d+5c/Otf/+IIKiIislhHs0px6poKclsZFozrI3U4ZMEMrhFavXo1GhoaWmwvKSmBSqUySlBERES34uODVwAAUyMD4OEkb/9gsmoGJ0KzZ8/G559/3mL7F198gdmzZxslKCIios4qUNXgh5O5AID4kSHSBkMWz+BE6NChQxg3blyL7WPHjsWhQ4eMEhQREVFnfZaahQaNiOhgd0T0UEodDlk4gxOh2traVrvG6uvrUV1dbZSgLEFSUhLCw8MRExMjdShERKSnugYNNh3KAgA8PiJY4mioKzA4EYqNjcX777/fYvvatWsRHR1tlKAsQUJCAs6cOYO0tDSpQyEiIj3tPJ2HwvJaeLsoEMeRYqQHg2eWevXVVzFx4kQcP34cEyZMAAAkJycjLS0Nu3fvNnqARERE+trYVCT9cGwQ5LYG/61PVsjgT8moUaOQkpKCwMBAfPHFF/j+++/Rp08fnDhxAmPGjDFFjERERB06da0MhzOvw1Ym4JHhXPeS9NOpucajoqKwadMmY8dCRETUaRtTrgAA4gb5w8eVSz6Rfm5p0ZWamhrU1dU128aJFomIyNyuV9bh2/QcAMCckSySJv0Z3DVWVVWFhQsXwsfHB05OTnB3d2/2ICIiMrfNh7NR26DBwABXDA3idxHpz+BE6Pnnn8ePP/6INWvWQKFQ4MMPP8TLL7+MgIAAbNy40RQxEhERtUmtEfFJSiaAxgkUBUGQOCLqSgzuGvv++++xceNGjB07FnPnzsWYMWPQp08fBAcHY9OmTXjkkUdMEScREVGrks/m41ppNdwd7TAlMkDqcKiLMbhFqKSkBL169QLQWA9UUlICABg9ejQOHDhg3OiIiIg6sLGpNWhWTBDs7Wwkjoa6GoMToV69eiEjIwMAEBYWhi+++AJAY0uRm5ubUYMjIiJqz88XCvHLxSLIBHDIPHWKwYnQ3Llzcfz4cQDA4sWLkZSUBHt7ezz77LN4/vnnjR4gERFRa1Q19XjhqxMAgEdvC0agh6PEEVFXJIiiKOpz4OXLlxEaGtqiCC0zMxNHjhxBnz59MHjwYJMEKSWVSgWlUomysjJODUBEZEGe//I4vjxyFcGejtjx9Bg4ym9pRhjqZvT9/ta7Rahv374oLCzUPZ81axby8/MRHByMBx54oFsmQUREZJmSz+bjyyNXIQjAWzMjmQRRp+mdCN3ccPTDDz+gsrLS6AERERG1p7SqDku2nAQA/GlUKGJCPCSOiLoyrkhHRERdyorvTqOgvBa9vZ3w3KT+UodDXZzeiZAgCC3qg7rzpFVJSUkIDw9HTEyM1KEQEVGTnafysDU9B7KmLjEOl6dbpXextEwmQ1xcHBQKBYDG4fLjx4+Hk5NTs+O2bNli/CglxGJpIiLLUFxRi7v+eQDFlXVYMLY3Xrg7TOqQyILp+/2td3VZfHx8s+ePPvpo56MjIiIygCiKWPrtKRRX1iHMzwVPT+wrdUjUTeidCK1fv96UcRAREbXph5N5+OFkHmxlAt6aGQmFLbvEyDhYLE1ERBZv3S+XAQBPju2NiB5KiaOh7oSJEBERWbRrpdU4mlUKQQAeuy1Y6nCom2EiREREFu2HE7kAgNgQD/i42kscDXU3TISIiMiibTvZmAjdO9hf4kioO2IiREREFiu7pArHs0shE4BJEX5Sh0PdEBMhIiKyWD80tQYND/WEjwu7xcj4mAgREZHF2t6UCE1mtxiZCBMhIiKySFnFVThxtQwyAYhjtxiZCBMhIiKySNtO5gAARvb2gqezQuJoqLtiIkRERBZp+wl2i5HpMREiIiKLk1FUidM5KtjIBEwayG4xMh0mQkREZHG0o8VG9vaEh5Nc4mioO2MiREREFmfbCU6iSObBRIiIiCzKpcIKnM1VwVYm4K5wdouRaTERIiIii6JdW2xUHy+4s1uMTIyJEBERWRR2i5E5MREiIiKLcSG/HOfzy2Fnw24xMo9unwiVlpZi2LBhiIqKQkREBD744AOpQyIiojZol9QY09cbSkc7iaMha2ArdQCm5uLiggMHDsDR0RGVlZWIiIjAAw88AE9PT6lDIyIyO1EUceBCEaIC3aB0sLxEQzeJ4iB2i5F5dPsWIRsbGzg6OgIAamtrIYoiRFGUOCoiIml8m56D+I9SMXPtQZTX1EsdTjNHs67jQkEF5DYyTAz3lTocshKSJ0IHDhzAfffdh4CAAAiCgK1bt7Y4JikpCSEhIbC3t8fw4cORmppq0DVKS0sRGRmJnj174vnnn4eXl5eRoici6lo+T8sCAPyeX4FnN6dDrbGcPwzf3n0eADAlKsAiW6uoe5I8EaqsrERkZCSSkpJa3b9582YkJiZi+fLlOHr0KCIjIzFp0iQUFBTojtHW/9z8yMlpXLDPzc0Nx48fR0ZGBj777DPk5+eb5b0REVmSq9er8NvlEggCILeVYe/ZArzVlHxI7eClIvx6sRh2NgKentBX6nDIikheIxQXF4e4uLg2969atQpPPPEE5s6dCwBYu3Yttm/fjo8++giLFy8GAKSnp+t1LV9fX0RGRuLnn3/GjBkzWj2mtrYWtbW1uucqlUrPd0JEZNm2HrsGABjRyxOzYgLx9OfpWPPTJfTzdca0IT2Nei2NRsT7P19GRIASo/u23woviiLe2tWYkD0UG4RAD0ejxkLUHslbhNpTV1eHI0eOYOLEibptMpkMEydOREpKil7nyM/PR3l5OQCgrKwMBw4cQP/+/ds8fuXKlVAqlbpHYGDgrb0JIiILIIoithxtTIQeGNoTU6N64MmxvQEAL359Eseyrhv1ej9fLMLrO85h3oY0nM4pa/fYfecLcDSrFPZ2Miwc18eocRB1xKIToaKiIqjVavj6Ni+a8/X1RV5enl7nyMzMxJgxYxAZGYkxY8Zg0aJFGDRoUJvHL1myBGVlZbpHdnb2Lb0HIiJLcPxqGS4XVcLBzgZ3RzTOz/P8Xf0xcYAP6ho0mP/JEeSWVRvteocuFwMA6tQaLPzsGCpqG1o9TqMR8dau3wEA8SNC4ONqb7QYiPQhedeYqcXGxurddQYACoUCCoXCdAEREUlgy9GrAIC7I/zgrGj81S+TCVg9ewim//sgzueXY/7GI/jif0bAQW5zy9dLu1LSeA0ByCiqxEvfnMTqWVEQBKHZcTtO5eFMrgrOClv85Y7et3xdIkNZdCLk5eUFGxubFsXN+fn58PPjjKNdxcGLRXBS2CIy0E3qUIisUl2DBt8dbxw88sDQHs32OSts8WH8MEx57xecvFaGJzcdwcjeLedZkwmNMz0HeXZcv1NTr8bx7MbusDemD8biLSfxbXoORvX2woMxf5QbqDUiVu1prA360+hQritGkrDoREgulyM6OhrJycm4//77AQAajQbJyclYuHChtMGRXspr6vH4R6lwlNsgfdldkMmEjl9EREa173wBSqvq4euqwMjeLQuXAz0csebRaDz64SH8dL4QP50vbPU8207kYmvCqA6vdzy7FHVqDbycFZgR3RMF5bX4x67zWPbdKUQFuaGfrwsA4Jtj13CpsBJujnb485jQW3uTRJ0keSJUUVGBixcv6p5nZGQgPT0dHh4eCAoKQmJiIuLj4zFs2DDExsZi9erVqKys1I0iM5WkpCQkJSVBrVab9DrdXVWdGg0aEaqaBhRV1sLHhf3/ROam7Ra7P6oHbNr4Y+S2Xp74eF4svjl2DZqbJp0VxcakJT27FEUVtfBybr98QNstNjzUA4Ig4Mk7euO3y8X4+UIREjYdxXcLR8NGJmD13sbaoCfv6A0Xe84bRNKQPBE6fPgwxo0bp3uemJgIAIiPj8eGDRswa9YsFBYWYtmyZcjLy0NUVBR27tzZooDa2BISEpCQkACVSgWlUmnSa1mLvLIaJkJEZna9sg4/nmucd+2Boe0PkR/Vxwuj+rQ+1P18XjnO5Krw84XCDofap15pHIEWE+IOoLEW6Z+zohD3r59xoaACK747jYgerrh6vRreLgo8PiLEwHdFZDySjxobO3asbtmLGx8bNmzQHbNw4UJkZmaitrYWhw4dwvDhw6ULmDott6xG6hCIrM62k7moV4sYGOCK/n4unT7PHf29AQD72+g201JrRBzNbEqEQj10272cFfjX7CgIArD5cDZW7jgHAFg0vo9RirOJOkvyRIisRx4TISKz03aLddQa1JGx/RoToQMXiqBpZ1mOs7kqVNQ2wEVhizA/12b7Rvb2wlPjG2eNrqpTo4ebA2bHBN1SXES3iolQG5KSkhAeHo6YmBipQ+k22CJEZF6XCytwLKsUNjIBUyIDbulcQ4Pd4aywRUllHU61M0FiakZjfVB0iHur9UhPTeirG5X2/KT+kNvya4ikxU9gGxISEnDmzBmkpaVJHUq3YczJ2oioY980Lalxe18veLvc2vxodjYyjOrTmMC0NaoM+KNQOibEo9X9NjIB6+fG4IenxuD+IT1aPYbInJgIkdmwRYjIfDSa5ktqGMMd/XwAAPt/bz0REkVR1yI0PLT1RAgAFLY2CA9wbXM/kTkxESKzYY0QkfmkXinBtdJquChscWe4cUbZ3t6vcUTZsazrKKuqb7H/clEliivrILeVYVBPjralroGJEJlNXlkNRLHtIssb7T2Tj12n9VtPjoha0hZJTx7sD3s744zK6unuiD4+ztCIwC8Xi1rsT2tqDYoKdIPCliPBqGtgIkRmU6fWoKSyrsPjquvUWLDpKBZsOqrX8UTUXL1agx2nGv+QmGbkOpw7mkaP7f+9oMW+1Kb6oNg26oOILBEToTZw1Jhp6FMnlH29CnVqDdQaEaeutT06hYhad/jKdZTXNMDTSd5m0XJn/ZEIFbZo4dUVSrdTH0RkaZgItYGjxkxDn0Qoq7hK93N7w3SJqHU/nmtcqHpsfx+jr+8XG+oBezsZ8lW1OJ9frtueV1aD7JJqyAQgOtjdqNckMiUmQmRWeXoMoc8q+SMROn1NZcpwiLql5KYlNSYM8DH6ue3tbDCiV8th9NpusYEBSjgrJF+9iUhvTITIrPRqESphixBRZ10pqsTlwkrYygSM6dv6umG3Stc9dmMilFEMoO35g4gsFRMhMit9htBn35AIZRZXoay65TDd1pzJUeHQ5eJOx0ZkLGVV9fjycDYqahvMfm3tAquxoR4mW9H9jv6NLU2HM0t07zEt43rTddktRl0LEyEyK32LpW90Jqfj7rEGtQaPrjuER9cd4nxFJLmXt53G81+dwLz1aaipV5v12tpEaHyY8bvFtEI8HRHk4Yh6tYiUS8UorarT1QsNY4sQdTFMhNrAUWOm0dEyG6Io6rrGwppWyj6tR/fY6RwVSirrUK8WmxVwEplbSWUdth3PBdBYN7Pov8fQoNaY5drlNfU41NRFNWGAcSZRbI0gCM2G0R++0tga1MvbCV7Ot7aUB5G5MRFqA0eNmUZuB5MqFlbUoqZeA5kA3NU0G64+Q+i1w3aBxoUmiaTy5eFs1Kk16OHmALmtDHvO5OOlb07pPZnorfjlQhHq1SJ6eTkh1MvJpNfSJkI/nS/U/f/X3rIaRJaKiRCZVW2DBqWtTM2vpa0P8lc6YEhQY63BKT26xrTrGwFARlHlLUZJ1DkajYjPUrMAAIvG98G7Dw2BTAA2H87G27t/N/n1taPFxpmwW0xrRG9PyG1kuHq9GlvTG9c0Y6E0dUVMhMgsbGUCPJ3kANqvE9J2iwV5OGJgj8ZFGS8VVqCqru2iU41GvKlFiIkQSeOXi0XILK6Ci70tpkQFYNJAP7w2bRAA4L19F7Hh1wyTXVujEfHT+aZh82ZIhJwUtohpKozOV9UCYCJEXRMTITIbP6U9ACBP1XadUFZx474gD0f4uNjDx0UBUQTO5rbdKnSpsALXb2hlYtcYSWXToUwAwPShPeEob5xL56HYIPzvnf0AAC9vO4PvjueY5NonrpWhqKIOLgpbsxUsa7vHAMBfaY+e7g5muS6RMTERIrPxVzb+kswp1aNFyNMRABDRo3EF61PtTKyonchNW1ydU1aD6jrTjdSpqG1oNsSf9JdZXAm1xji1MnUNGqRcKsYvF4paPA5eLEJtg3lHa+WV1WDv2cYWmYeHBzXbt3B8H8SPCIYoAv/7RTp+vlDY2inapKqpR2F5bbvH/Hi2cTbpMf28ILc1z6/2O/r90fIUE+IBQTDuLNZE5sDpP8ls/LUtQu10jWmHzmv/sowIcMWP5wraLZjWrnh910A/5KlqUFpVj4yiSoQHuBor9Gae/PQIUi4VY2vCKF2iRh378OfLeHX7Wdzezxvr58TA5haWfqipV2PG2oPtJsj9fV3wTcJIXcuMqf03NQtqjYjYUA/083Vptk8QBCy7byCKKuuw/UQunvz0KL5fNFqvguac0mpMee8XVNaq8dWTIzAwoPXPXLJu2LzpRovdrJ+vM/xc7ZGnqkEsC6Wpi2KLUBs4fN74tF1j7dUIZd9QIwQAA7UtQu0UTKc1Dd2NDfHQfbGYqmA6p7QaP18oQoNGxDfHrpnkGt3R0azreH3HOQDAgd8Lseani7d0vr9tO4NT11RwktsgzM+lxcNFYYvz+eVY9u1pY4TfoQa1Bp+nNRZJP3JTa5CWjUzAqgcjERPijoraBiz87GiHrVYNag2e+u8xFFXUobpejYWfHWt1ksa8shqczlFBEICx/b1bOZNpCIKAlyYPQFyEH6ZEBZjtukTGxESoDRw+b3z+HdQI1dSrkadqTJK0idCgpkToQn55qxPTXb1ehWul1bCRCRgS5IZeXs4ATFcntPNUXrOfzTEkuqsrq6rHos+OoUEjoq9P47/Pqj2/NxvpZ4htJ3Kw6VBj0rHm0WjsfOb2Fo8P4odBJgBfHbmKr49cNdp7acveswXIV9XC00mOuyP82jxOYWuDdx4aAndHO5zOUWHlD+faPe8/9/6Ow5nX4aywhZ+rPTKKKvHSNydbfO72NRVJRwW6mX0en/siA7Dm0Wi4mmgWayJTYyJEZqOtEWqrRehaaTVEEXCS28CjaYSZv9IeHk5yNGhE/N7KRIna0WIRPZRwUtiil7dpW4RuTISulVbjpB5zHFkzURTxwtfHca20GkEejvh6wUg8MKQHNCLw1H+PoaSyzqDzZRZXYsnXJwEAC8b2xu39Wm/9uK2XJ56e0FigvPTbU7hYYNoCem2R9MxhgVDY2rR7rL/SAW8/GAkA2HDwSrPP1I1+vlCIf/90CQDw+vRBePfhIbCRCfg2PQdfHm6e3CU31SaN72/60WJE3Q0TITIbbYtQbmnrkypqC6UDPRx1RZeCIGBgU61Pa/Ugqdr1jUIah/H2auoau2SCRKigvAZpmY2J19AgNwDAjja+xKjRxpRM7DqdDzsbAe89PASu9nb42/0R6OXlhDxVDZ778jg0ehZP1zY0dg2V1zZgWLA7EptGYrVl4fg+GNHLE1V1aiz87KjJlrq4UlSJny8UQRDa7ha72fgwX8y/vRcA4IWvjuPqTcvKFJTX4NnN6RDFxsLrewcHICbEQ/eel313SveHQU29Gr9eLGo8rwlWmyfq7pgIkdloa4Sq69VQVbesc7i5PkhLN3KslaU2tC1C2vlLenn/0TVm7G6r3afzIYpAZE8l5o4KBcDusfaculaG17afBQAsiRuAwT3dADTOP/Pew0Mht5Xhx3MFWPeLfnPrvLHjPE5eK4Obox3eeWgIbG3a//VlIxPwr9lR8HSS41xeOf627cwtvZ+2/LdpAsU7+nkj8KbPbnuen9QfUYFuUNU0YNF/j6G+aRkOtUbEM5+no6iiDmF+Llh2b7juNU/e0Rtj+nqhpl6DhE1HUV2nRsrlYlTXq+Hnao9wf9MMECDqzpgIkdnY29nA3bGxjiC3lTqhrOI2EqGmUTKnb+qGKq6o1XV5aBOhYE9HCAJQXtOAYgO7XTqi7cK4O8If48J8ILeVIaOokmubtaK8ph4LPzuKOrUGEwf4Yu6okGb7wwNcdV/wb+w8h2NZ19s9354z+fioaTLCt2ZEIsBNv/lqfFzt8c9ZUQCATYeysO2EcefwqalX44vD2QCAR4YHG/RaOxsZ3n1oCFztbXEsqxRv7T4PAPj3vos4eKkYDnY2eO/hobC3+6OrTSYTsOrBKHi7KHChoAIrvjuNfdrRYgN8OHydqBOYCJFZ+bVTJ6QdOn/zX9URTTNMn80r1/3VDPwxWqyvjzPcm2qK7O1s0KPpS9KYM0xfr6xDyuXGxSzjIvzgrLDF7X0b61N2nGT32I1EUcT/fXMKV4qrEKC0x1szB7f6Bf3I8CBMHuSPBo2IhZ8dQ1kbS69cK63Gc18eBwD8eXQoJoYbNjz89n7eWDC2NwBgydcnkVlsvM/FjlO5uF5VjwClfadWew/0cMSbMxrrhf6z/zJW7T6Pf+5tXIrj1fsj0KepuPxG3i4K/GtWFISmpTu0iRjrg4g6h/MIkVkFKO1xNlfV6lxCWSV/zCp9oyAPR7jY26K8pgEXCyowoKn5X9stdvP8Jb28nXH1ejUyiiqMNrfJnrP5UGtEhPm5IKSpDikuwg97z+Zj56k8PNtBvYolOPB7IU7rsW7brcoprcb3x3NgIxPw7sND4OYob/U4QRCwcvognLxWhqySKjy56QjG9G1Z/LzjVC7KqusR2VOJF+4O61RMiXf2Q2pGCQ5nXsdfPj2KKZHGGeq9tWkKhdmxQZ2eF+nuCD/MGRmCDQev4J0fG6cVmD60J6ZH92zzNSP7eGHR+L54J/kCauo1UNjKMKqPV6euT2TtmAiRWenmEipt3jUmiqKuRujmFiFtwfRvl0tw6lpZx4mQlxMO/F5o1BYhbbdYXIS/btvEAb6wlQk4n1+Oy4UVuvokS3Tyahni16fCnOVMz0/qj+jg9hNRV3s7vPfwEExfcxAHLxXj4KXiVo9zsf+jrqgzbG1keOehIbjnnZ9xNlfV7pIthrKRCZgdE3hL51hyTxgOZ5bg1DUVens74W/3D+zwNU9P6IvUjGL8drkEI3t7wkHe/mg1ImodE6E2JCUlISkpCWq1eafp7+7825hU8XpVvW6iuNbWK4oIUOK3yyU4naPCTACVtQ261o2bF3rUDqG/ZKREqLymHr9caByVEzfojzlilI52GNnHCwd+L8SOU3lIGNfHKNczNlEU8fL3pyGKjfMy9fdz6fhFt2hQDyUeu02/mpnBPd3w8bxYbD12Da0NILOzEfBQbJBBhcitCXBzwKY/D8env2U162K9Vbf384aPq/0tnUNha4N18TH4b2oWZkT31Gs2bBuZgKSHh+KjXzPwwNC2W4+IqH1MhNqQkJCAhIQEqFQqKJVcRsFYtDVC2okTtbRD5/1c7ZsVh2r9seZYY8H00azrUGtE9HBzaFE4+8fs0saZO+bHcwWoU2vQy9tJNyGgVlyEHw78XoidFpwIfX8iF4czr8PBzgbvPx6tm8/Jkozs7YWRvU3ftTMwQImVDwwy+XU6w9fVHs9MNKyL1dNZgecnda67kIgasViazKqtFqGsNobOa2kLps/kqqDWiLpZiYe3UgOk7aLKKqlCgxH+8tcWQ8dF+LUo+r0r3BcyATh5rcwiF2KtrlPj9R8ah7A/Oba3RSZBRERSYiJEZtXWwqtt1QdphXo5w8HOBlV1amQUVeoSoZhWEiF/V3vY28lQrxZx9Xrry3noq6quAT/93jg8+cb6IC1PZ4WuRmnXacsbPfb+gcvIKatBDzcH3QR+RET0ByZCZFbaYumK2gaoav4YLt3WHEJaNjJBt5r80azrSM8uBdCyPghonGslxLOxe+zyLXaP7T9fiJp6DXq6O+hmuL6ZNkGytFmmc0qrsWZ/4yikJfeEtdrlSERk7ZgIkVk5ym2hdGicVPHGVqE/5hBqu+smoikR+Tw1C7UNGng6ydG7qTD6Zr11M0zfWsG0Nrm5Z5B/m5PVTRrYWEB9JPM68lWtr6MmhTd2nkNNvQaxIR6YPKhlaxYRETERIgm0VifUUY0QAAxsKpg+mlUKABgW4t5mcqItmL58C2uO1Tao8WPTrL3trSjup7TXrT1mKd1jRzJL8G16DgQBWHZfOGccJiJqAxMhMjs/XZ1QY/1OvVqDnNLWJ1O8kXapDa3YUM82j9UOob9c2PmusV8uFKGitgF+rvaIalonqy267jELmGVaoxHx8veN62o9GB2oG3FHREQtMREis/O/aZmNnNJqaERAYSuDt4uizdf19XWG/IaFNmNbqQ/S+mMIfedbhHbo1hbzg6yDWYO1LUaHMopRXFHb6Wsaw5Zj13DiahmcFbZ4blJ/SWMhIrJ0nEeIzE7XNVbamAjd2C3WXheOnY0MYf4uOHG1DE5yGwzwb3tiQO0Q+nxVLSpqG+CsMOyjXq/WYM+ZfADtd4tpBXo4IqKHK05dU+GbY9dwj0Q1OXUNGryx8xwAYNH4Pu0mlkRExESIJKBbZkPVMhHqyMAAJU5cLcPQYHfY2rTdoKl0sIOXsxxFFXW4UlRpcPfQwUvFKKuuh5ezvNWRaa2Ji/DHqWsqvLr9LF7dftag6xlbiKcj5ty04jsREbXERKgNXGLDdPxvqhHK6mAOoRvNignEwUtFmKvHl3yolxOKKupwqbDC4ETo89QsAI2jxfRdTHP60J7YnJbdYtZsc3OU2+DV+wdBYcvh8kREHWEi1AYusWE6N48au9q06rw+iVBUoBv2Pz9Or+v08nJG2pXrBg+hz1fVYHdTt9gjw/VbLwtobOk68IJ+sRERkWVgsTSZnXa9sfKaBlTUNhjUNWaIUO/OFUxvTsuGWiMiJsTdLAuUEhGRdJgIkdk5K2zhYt/YGJlXVm2yRKiXl+GzSzeoNfhvU7eYIa1BRETUNTERIklou8fO5ZWjrLpxqY32ZpXuDO1cQhmFlRBFUa/X7DtfiNyyGng4yRE3qOPRYkRE1LUxESJJaLvH0poWT/VyVsBRbtyStSAPJ9jIBFTWqVFQrt/cPp/+lgkAmBndk8XGRERWgIkQScLftbFF6FBTIhRk5NYgAJDbyhDo3njeS3rMMJ1VXIUDFwoBAA8PDzJ6PEREZHmYCJEk/N0aE6Hz+eUAjF8fpGXIDNOfpWZBFIHb+3kj2LP1xVyJiKh7YSJEktDWCGlLd/QZOt8ZvfRchb62QY0vDmcDAB5haxARkdVgIkSS0NYIaZkqEdK3RWjnqTyUVNbBz9UeE8J8TBILERFZHiZCJAlti5CWqbrG9F2FftNvjUPmZ8cGtrt0BxERdS/8jU+S8DNTItS7qWss+3o16ho0rR7ze345Uq+UwEYmYHYMu8WIiKwJEyGShKu9nW5FeLmNDL6u9h28onN8XBRwkttArRF1EzfebFPTkPmJA3xaJGhERNS9MREiyWiTjp7uDnovbGooQRB0S2201j1WWduALUevAQAevY0zSRMRWRsuukqS8Vfa42JBhckKpbVCvZxx6poKx6+WIjzAtdm+nafyUF7bgGBPR4zq7WXSOIiIyPIwESLJ+DV1h5mqPkhLu+ZY0r5LSNp3qdVjHhkeBJmJWqWIiMhysWusDUlJSQgPD0dMTIzUoXRb9wz2R093B8RFmHZNr0kD/eDrqoDCVtbqo7+vCx4cFmjSGIiIyDIJor6rUVoplUoFpVKJsrIyuLq6dvwCaiZfVYPhf0+GrUzAxb/fI3U4RERkJfT9/maLEBEREVktJkJERERktZgIERERkdViIkRERERWi4kQERERWS0mQkRERGS1mAgRERGR1WIiRERERFaLiRARERFZLSZCREREZLWYCBEREZHVYiJEREREVouJEBEREVktJkJERERktWylDsDSiaIIAFCpVBJH0jWVq2qgqa2CRibwHhIRkdlov3O03+NtYSLUgfLycgBAYGCgxJF0fcpVUkdARETWpry8HEqlss39gthRqmTlNBoNcnJy4OLiAkEQjHZelUqFwMBAZGdnw9XV1Wjn7Up4DxrxPvAeaPE+8B4AvAdat3ofRFFEeXk5AgICIJO1XQnEFqEOyGQy9OzZ02Tnd3V1teoPOsB7oMX7wHugxfvAewDwHmjdyn1oryVIi8XSREREZLWYCBEREZHVYiIkEYVCgeXLl0OhUEgdimR4DxrxPvAeaPE+8B4AvAda5roPLJYmIiIiq8UWISIiIrJaTISIiIjIajERIiIiIqvFRIiIiIisFhMhiSQlJSEkJAT29vYYPnw4UlNTpQ7JZA4cOID77rsPAQEBEAQBW7dubbZfFEUsW7YM/v7+cHBwwMSJE3HhwgVpgjWRlStXIiYmBi4uLvDx8cH999+P8+fPNzumpqYGCQkJ8PT0hLOzM6ZPn478/HyJIja+NWvWYPDgwbrJ0UaMGIEdO3bo9nf399+a119/HYIg4JlnntFts4b7sGLFCgiC0OwRFham228N9wAArl27hkcffRSenp5wcHDAoEGDcPjwYd1+a/jdGBIS0uKzIAgCEhISAJjns8BESAKbN29GYmIili9fjqNHjyIyMhKTJk1CQUGB1KGZRGVlJSIjI5GUlNTq/jfffBPvvPMO1q5di0OHDsHJyQmTJk1CTU2NmSM1nf379yMhIQG//fYb9uzZg/r6etx1112orKzUHfPss8/i+++/x5dffon9+/cjJycHDzzwgIRRG1fPnj3x+uuv48iRIzh8+DDGjx+PqVOn4vTp0wC6//u/WVpaGv7zn/9g8ODBzbZby30YOHAgcnNzdY9ffvlFt88a7sH169cxatQo2NnZYceOHThz5gzefvttuLu7646xht+NaWlpzT4He/bsAQDMnDkTgJk+CyKZXWxsrJiQkKB7rlarxYCAAHHlypUSRmUeAMRvvvlG91yj0Yh+fn7iP/7xD9220tJSUaFQiP/9738liNA8CgoKRADi/v37RVFsfM92dnbil19+qTvm7NmzIgAxJSVFqjBNzt3dXfzwww+t7v2Xl5eLffv2Fffs2SPecccd4tNPPy2KovV8DpYvXy5GRka2us9a7sGLL74ojh49us391vq78emnnxZ79+4tajQas30W2CJkZnV1dThy5AgmTpyo2yaTyTBx4kSkpKRIGJk0MjIykJeX1+x+KJVKDB8+vFvfj7KyMgCAh4cHAODIkSOor69vdh/CwsIQFBTULe+DWq3G559/jsrKSowYMcLq3n9CQgImT57c7P0C1vU5uHDhAgICAtCrVy888sgjyMrKAmA99+C7777DsGHDMHPmTPj4+GDIkCH44IMPdPut8XdjXV0dPv30U8ybNw+CIJjts8BEyMyKioqgVqvh6+vbbLuvry/y8vIkiko62vdsTfdDo9HgmWeewahRoxAREQGg8T7I5XK4ubk1O7a73YeTJ0/C2dkZCoUCf/nLX/DNN98gPDzcat4/AHz++ec4evQoVq5c2WKftdyH4cOHY8OGDdi5cyfWrFmDjIwMjBkzBuXl5VZzDy5fvow1a9agb9++2LVrF5588kk89dRT+PjjjwFY5+/GrVu3orS0FHPmzAFgvv8fuPo8kZklJCTg1KlTzWoirEX//v2Rnp6OsrIyfPXVV4iPj8f+/fulDstssrOz8fTTT2PPnj2wt7eXOhzJxMXF6X4ePHgwhg8fjuDgYHzxxRdwcHCQMDLz0Wg0GDZsGP7+978DAIYMGYJTp05h7dq1iI+Plzg6aaxbtw5xcXEICAgw63XZImRmXl5esLGxaVH1np+fDz8/P4miko72PVvL/Vi4cCG2bduGffv2oWfPnrrtfn5+qKurQ2lpabPju9t9kMvl6NOnD6Kjo7Fy5UpERkbiX//6l9W8/yNHjqCgoABDhw6Fra0tbG1tsX//frzzzjuwtbWFr6+vVdyHm7m5uaFfv364ePGi1XwW/P39ER4e3mzbgAEDdF2E1va7MTMzE3v37sWf//xn3TZzfRaYCJmZXC5HdHQ0kpOTdds0Gg2Sk5MxYsQICSOTRmhoKPz8/JrdD5VKhUOHDnWr+yGKIhYuXIhvvvkGP/74I0JDQ5vtj46Ohp2dXbP7cP78eWRlZXWr+3AzjUaD2tpaq3n/EyZMwMmTJ5Genq57DBs2DI888ojuZ2u4DzerqKjApUuX4O/vbzWfhVGjRrWYQuP3339HcHAwAOv53ai1fv16+Pj4YPLkybptZvssGK3smvT2+eefiwqFQtywYYN45swZcf78+aKbm5uYl5cndWgmUV5eLh47dkw8duyYCEBctWqVeOzYMTEzM1MURVF8/fXXRTc3N/Hbb78VT5w4IU6dOlUMDQ0Vq6urJY7ceJ588klRqVSKP/30k5ibm6t7VFVV6Y75y1/+IgYFBYk//vijePjwYXHEiBHiiBEjJIzauBYvXizu379fzMjIEE+cOCEuXrxYFARB3L17tyiK3f/9t+XGUWOiaB334X//93/Fn376SczIyBB//fVXceLEiaKXl5dYUFAgiqJ13IPU1FTR1tZWfO2118QLFy6ImzZtEh0dHcVPP/1Ud4w1/G4UxcaR00FBQeKLL77YYp85PgtMhCTy7rvvikFBQaJcLhdjY2PF3377TeqQTGbfvn0igBaP+Ph4URQbh4kuXbpU9PX1FRUKhThhwgTx/Pnz0gZtZK29fwDi+vXrdcdUV1eLCxYsEN3d3UVHR0dx2rRpYm5urnRBG9m8efPE4OBgUS6Xi97e3uKECRN0SZAodv/335abEyFruA+zZs0S/f39RblcLvbo0UOcNWuWePHiRd1+a7gHoiiK33//vRgRESEqFAoxLCxMfP/995vtt4bfjaIoirt27RIBtPrezPFZEERRFI3XvkRERETUdbBGiIiIiKwWEyEiIiKyWkyEiIiIyGoxESIiIiKrxUSIiIiIrBYTISIiIrJaTISIiIjIajERIiIiIqvFRIiIujxBENp9rFixQuoQichC2UodABHRrcrNzdX9vHnzZixbtqzZgpbOzs4Gna+urg5yudxo8RGR5WKLEBF1eX5+frqHUqmEIAi652vXrsXo0aObHb969WqEhITons+ZMwf3338/XnvtNQQEBKB///64cuUKBEHAli1bMG7cODg6OiIyMhIpKSlmfndEZEpMhIiIACQnJ+P8+fPYs2cPtm3bptv+0ksv4bnnnkN6ejr69euHhx56CA0NDRJGSkTGxK4xIiIATk5O+PDDD3VdYleuXAEAPPfcc5g8eTIA4OWXX8bAgQNx8eJFhIWFSRUqERkRW4SIiAAMGjSo1bqgwYMH63729/cHABQUFJgtLiIyLSZCRNStyWQyiKLYbFt9fX2L45ycnFp9vZ2dne5nQRAAABqNxogREpGUmAgRUbfm7e2NvLy8ZslQenq6dAERkUVhIkRE3drYsWNRWFiIN998E5cuXUJSUhJ27NghdVhEZCGYCBFRtzZgwAD8+9//RlJSEiIjI5GamornnntO6rCIyEII4s2d50RERERWgi1CREREZLWYCBEREZHVYiJEREREVouJEBEREVktJkJERERktZgIERERkdViIkRERERWi4kQERERWS0mQkRERGS1mAgRERGR1WIiRERERFaLiRARERFZrf8Hk9kM/2WSFeIAAAAASUVORK5CYII=\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": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(70, 10000)"
      ]
     },
     "execution_count": 36,
     "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": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.09677184,  0.0037773 ,  0.12190913,  0.03519891,  0.16118614,\n",
       "        0.00617017,  0.12490022, -0.03918723,  0.14632847, -0.01240192,\n",
       "        0.1016851 ,  0.00991888,  0.1295861 , -0.03332988,  0.07552515,\n",
       "       -0.10090606,  0.14730492, -0.08930635,  0.08367957, -0.09071304,\n",
       "        0.1600462 ,  0.08287025,  0.22077531, -0.07559336,  0.1789458 ,\n",
       "        0.02836975,  0.23077469,  0.01503086,  0.13597608, -0.18159241,\n",
       "       -0.03167801, -0.23491001,  0.05792499, -0.04478127,  0.06121092,\n",
       "       -0.04067385,  0.37884519,  0.04386898,  0.17202373, -0.05840784,\n",
       "        0.0441777 , -0.14009038,  0.02019953, -0.09193809,  0.15851489,\n",
       "        0.08095611,  0.45275764,  0.13625955,  0.36563693, -0.05076633,\n",
       "        0.28810459, -0.22580677, -0.16507096, -0.5579012 , -0.033314  ,\n",
       "       -0.15883   ,  0.23115   , -0.45325   , -0.37125   , -0.58125   ,\n",
       "       -0.21875   , -0.21875   , -0.21875   , -0.21875   , -0.21875   ,\n",
       "       -0.21875   , -0.21875   , -0.21875   , -0.21875   , -0.21875   ])"
      ]
     },
     "execution_count": 37,
     "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\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=0.8, who_won_fraction=0, final_score_fraction=1\n",
    ")[:, 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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": 38,
     "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": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  3.09670969,   0.12088712,   3.9011089 ,   1.12638612,\n",
       "         5.15798265,   0.19747831,   3.99684789,  -1.25394014,\n",
       "         4.68257483,  -0.39678147,   3.25402317,   0.31752896,\n",
       "         4.1469112 ,  -1.066361  ,   2.41704875,  -3.22868907,\n",
       "         4.71413867,  -2.85732667,   2.67834167,  -2.90207292,\n",
       "         5.12240885,   2.65301107,   7.06626383,  -2.41717021,\n",
       "         5.72853724,   0.91067155,   7.38833944,   0.4854243 ,\n",
       "         4.35678037,  -5.80402453,  -1.00503067,  -7.50628834,\n",
       "         1.86713958,  -1.41607552,   1.9799056 ,  -1.27511801,\n",
       "        12.15610249,   1.44512812,   5.55641015,  -1.80448732,\n",
       "         1.49439085,  -4.38201144,   0.77248571,  -2.78439287,\n",
       "         5.26950892,   2.83688614,  14.79610768,   4.7451346 ,\n",
       "        12.18141825,  -1.02322719,   9.97096602,  -6.28629248,\n",
       "        -4.1078656 , -16.384832  ,   0.76896   ,  -2.7888    ,\n",
       "        10.264     , -10.92      ,  -7.4       , -13.        ,\n",
       "         0.        ,   0.        ,   0.        ,   0.        ,\n",
       "         0.        ,   0.        ,   0.        ,   0.        ,\n",
       "         0.        ,   0.        ])"
      ]
     },
     "execution_count": 39,
     "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": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "BATCH_SIZE = 1000\n",
    "\n",
    "\n",
    "class DQLNet(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.fc1 = nn.Linear(BATCH_SIZE, 8 * 8 * 2)\n",
    "        self.fc2 = nn.Linear(BATCH_SIZE, 1)\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.dropout1(x)\n",
    "        x = self.fc2(x)\n",
    "        x = F.relu(x)\n",
    "        # x = self.dropout2(x)\n",
    "        x = torch.reshape(x, (BATCH_SIZE, 8, 8))\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "class SymmetryMode(Enum):\n",
    "    MULTIPLY = \"MULTIPLY\"\n",
    "    BREAK_SEQUENCE = \"BREAK_SEQUENCE\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(8, 8)\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "cannot unpack non-iterable int object",
     "output_type": "error",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mTypeError\u001B[0m                                 Traceback (most recent call last)",
      "Cell \u001B[1;32mIn[42], line 51\u001B[0m\n\u001B[0;32m     40\u001B[0m             \u001B[38;5;28;01mpass\u001B[39;00m\n\u001B[0;32m     43\u001B[0m ql_policy \u001B[38;5;241m=\u001B[39m QLPoicy(\n\u001B[0;32m     44\u001B[0m     \u001B[38;5;241m0.95\u001B[39m,\n\u001B[0;32m     45\u001B[0m     neural_netwerk\u001B[38;5;241m=\u001B[39mDQLNet(),\n\u001B[1;32m   (...)\u001B[0m\n\u001B[0;32m     49\u001B[0m     final_score_fraction\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m0\u001B[39m,\n\u001B[0;32m     50\u001B[0m )\n\u001B[1;32m---> 51\u001B[0m \u001B[43mql_policy\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mtrain_epoch\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;241;43m100\u001B[39;49m\u001B[43m)\u001B[49m\n",
      "Cell \u001B[1;32mIn[42], line 36\u001B[0m, in \u001B[0;36mQLPoicy.train_epoch\u001B[1;34m(self, generate_data_size)\u001B[0m\n\u001B[0;32m     34\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mtrain_epoch\u001B[39m(\u001B[38;5;28mself\u001B[39m, generate_data_size: \u001B[38;5;28mint\u001B[39m):\n\u001B[0;32m     35\u001B[0m     \u001B[38;5;66;03m# generate trainings data\u001B[39;00m\n\u001B[1;32m---> 36\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;241;43m*\u001B[39;49m\u001B[43m \u001B[49m\u001B[38;5;241;43m2\u001B[39;49m\u001B[43m)\u001B[49m\n",
      "Cell \u001B[1;32mIn[22], 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 current_boards \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[21], 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[18], line 56\u001B[0m, in \u001B[0;36mGamePolicy.get_policy\u001B[1;34m(self, boards)\u001B[0m\n\u001B[0;32m     54\u001B[0m     policies \u001B[38;5;241m=\u001B[39m np\u001B[38;5;241m.\u001B[39mrandom\u001B[38;5;241m.\u001B[39mrand(\u001B[38;5;241m*\u001B[39mboards\u001B[38;5;241m.\u001B[39mshape)\n\u001B[0;32m     55\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[1;32m---> 56\u001B[0m     policies \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_internal_policy\u001B[49m\u001B[43m(\u001B[49m\u001B[43mboards\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m     57\u001B[0m     \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mepsilon \u001B[38;5;241m<\u001B[39m \u001B[38;5;241m1\u001B[39m:\n\u001B[0;32m     58\u001B[0m         policies \u001B[38;5;241m=\u001B[39m policies \u001B[38;5;241m*\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mepsilon \u001B[38;5;241m+\u001B[39m np\u001B[38;5;241m.\u001B[39mrandom\u001B[38;5;241m.\u001B[39mrand(\u001B[38;5;241m*\u001B[39mboards\u001B[38;5;241m.\u001B[39mshape) \u001B[38;5;241m*\u001B[39m (\n\u001B[0;32m     59\u001B[0m             \u001B[38;5;241m1\u001B[39m \u001B[38;5;241m-\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mepsilon\n\u001B[0;32m     60\u001B[0m         )\n",
      "Cell \u001B[1;32mIn[42], line 31\u001B[0m, in \u001B[0;36mQLPoicy._internal_policy\u001B[1;34m(self, boards)\u001B[0m\n\u001B[0;32m     29\u001B[0m \u001B[38;5;28mprint\u001B[39m(turn_possible\u001B[38;5;241m.\u001B[39mshape)\n\u001B[0;32m     30\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m action_x, action_y \u001B[38;5;129;01min\u001B[39;00m itertools\u001B[38;5;241m.\u001B[39mproduct(\u001B[38;5;28mrange\u001B[39m(\u001B[38;5;241m8\u001B[39m), \u001B[38;5;28mrange\u001B[39m(\u001B[38;5;241m8\u001B[39m)):\n\u001B[1;32m---> 31\u001B[0m     boards, action \u001B[38;5;241m=\u001B[39m \u001B[38;5;241m0\u001B[39m\n\u001B[0;32m     32\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mneural_network\u001B[38;5;241m.\u001B[39mforword(boards)\n",
      "\u001B[1;31mTypeError\u001B[0m: cannot unpack non-iterable int object"
     ]
    }
   ],
   "source": [
    "class QLPoicy(GamePolicy):\n",
    "    def __init__(\n",
    "        self,\n",
    "        epsilon: float,\n",
    "        neural_netwerk: DQLNet,\n",
    "        symmetry_mode: SymmetryMode,\n",
    "        gamma: float = 0.8,\n",
    "        who_won_fraction: float = 0,\n",
    "        final_score_fraction: float = 0,\n",
    "    ):\n",
    "        super().__init__(epsilon)\n",
    "        assert 0 <= gamma <= 1\n",
    "        self.gamma = gamma\n",
    "        self.symmetry_mode = symmetry_mode\n",
    "        self.neural_network = neural_netwerk\n",
    "        self.who_won_fraction = who_won_fraction\n",
    "        self.final_score_fraction = final_score_fraction\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",
    "        q_learning_board = np.zeros((boards.shape[0], 2, 8, 8))\n",
    "        q_learning_board[:, 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",
    "        print(turn_possible.shape)\n",
    "        for action_x, action_y in itertools.product(range(8), range(8)):\n",
    "            boards, action = 0\n",
    "        return self.neural_network.forword(boards)\n",
    "\n",
    "    def train_epoch(self, generate_data_size: int):\n",
    "        # generate trainings data\n",
    "        train_boards, train_actions = simulate_game(generate_data_size, [self] * 2)\n",
    "\n",
    "    def evaluate_model(compare_models: list[GamePolicy]):\n",
    "        for i in range(compare_models):\n",
    "            pass\n",
    "\n",
    "\n",
    "ql_policy = QLPoicy(\n",
    "    0.95,\n",
    "    neural_netwerk=DQLNet(),\n",
    "    symmetry_mode=SymmetryMode.MULTIPLY,\n",
    "    gamma=0.8,\n",
    "    who_won_fraction=0,\n",
    "    final_score_fraction=0,\n",
    ")\n",
    "ql_policy.train_epoch(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "?simulate_game"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "?simulate_game"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ones = np.ones((1000, 8, 8), dtype=float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "DQLNet().forward(ones)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "t = torch.tensor([[[1, 2], [3, 4]], [[5, 6], [7, 8]]])\n",
    "torch.flatten(t)\n",
    "torch.tensor([1, 2, 3, 4, 5, 6, 7, 8])\n",
    "torch.flatten(t, start_dim=1)\n",
    "torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class DQLearningWinner(GamePolicy):\n",
    "\n",
    "    # network =\n",
    "\n",
    "    @property\n",
    "    def policy_name(self):\n",
    "        return \"DQL-Winner\"\n",
    "\n",
    "    def _internal_policy(boards) -> np.ndarray:\n",
    "        pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "DQLearningWinner(0.9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_simple_rewords()"
   ]
  },
  {
   "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": []
  }
 ],
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