{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The game rules\n",
    "\n",
    "Othello is played on a board with 8 x 8 fields for two player.\n",
    "The board geometry is equal to a chess game.\n",
    "The game is played with game stones that are black on one siede and white on the other.\n",
    "\n",
    "![Othello game board example](reversi_example.png)\n",
    "\n",
    "The player take turns.\n",
    "A player places a stone with his or her color up on the game board.\n",
    "The player can only place stones when he surrounds a number of stones with the opponents color with the new stone and already placed stones of his color.\n",
    "Those surrounded stones can either be horizontally, vertically and/or diagonally be placed.\n",
    "All stones thus surrounded will be flipped to be of the players color.\n",
    "Turns are only possible if the player is also changing the color of the opponents stones. If a player can't act he is skipped.\n",
    "The game ends if both players can't act. The player with the most stones wins.\n",
    "If the score is counted in detail unclaimed fields go to the player with more stones of his or her color on the board.\n",
    "The game begins with four stones places in the center of the game. Each player gets two. They are placed diagonally to each other.\n",
    "\n",
    "\n",
    "<img alt=\"Startaufstellung.png\" src=\"Startaufstellung.png\"/>\n",
    "\n",
    "## Some common Othello strategies\n",
    "\n",
    "As can be easily understood the placement of stones and on the bord is always a careful balance of attack and defence.\n",
    "If the player occupies huge homogenous stretches on the board it can be attacked easier.\n",
    "The boards corners provide safety from wich occupied territory is impossible to loos but since it is only possible to reach the corners if the enemy is forced to allow this or calculates the cost of giving a stable base to the enemy it is difficult to obtain.\n",
    "There are some text on otello computer strategies which implement greedy algorithms for reversi based on a modified score to each field.\n",
    "Those different values are score modifiers for a traditional greedy algorithm.\n",
    "If a players stone has captured such a filed the score reached is multiplied by the modifier.\n",
    "The total score is the score reached by the player subtracted with the score of the enemy.\n",
    "The scores change in the course of the game and converges against one. This gives some indications of what to expect from an Othello AI.\n",
    "\n",
    "<img alt=\"ComputerPossitionScore\" src=\"computer-score.png\"/>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initial design decisions\n",
    "\n",
    "At the beginning of this project I made some design decisions.\n",
    "The first onw was that I do not want to use a gym library because it limits the data formats accessible.\n",
    "I choose to implement the hole game as entry in a stack in numpy arrays to be able to accommodate interfacing with a neural network easier and to use scipy pattern recognition tools to implement some game mechanics for a fast simulation cycle.\n",
    "I chose to ignore player colors as far as I could instead a player perspective was used. Which allowed to change the perspective with a flipping of the sign. (multiplying with -1).\n",
    "The array format should also allow for data multiplication or the breaking of strikt sequences by flipping the game along one the for axis, (horizontal, vertical, transpose along both diagonals).\n",
    "\n",
    "I wanted to implement different agents as classes that act on those game stacks.\n",
    "\n",
    "Since computation time is critical all computational have results are saved.\n",
    "The analysis of those is then repeated in real time. If a recalculation of such a section is required the save file can be deleted and the code should be executed again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext blackcellmagic\n",
    "%load_ext line_profiler\n",
    "%load_ext memory_profiler"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports and dependencies\n",
    "\n",
    "The following direct dependencies where used for this project:\n",
    "```toml\n",
    "jupyter = \"^1.0.0\"\n",
    "matplotlib = \"^3.6.3\"\n",
    "numpy = \"^1.24.1\"\n",
    "pytest = \"^7.2.1\"\n",
    "python = \"3.10.*\"\n",
    "scipy = \"^1.10.0\"\n",
    "tqdm = \"^4.64.1\"\n",
    "jupyterlab = \"^3.6.1\"\n",
    "torchvision = \"^0.14.1\"\n",
    "torchaudio = \"^0.13.1\"\n",
    "```\n",
    "* `Jupyter` and `jupyterlab` on pycharm was used as 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",
    "from IPython.display import clear_output\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",
    "\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": [
      "9.8 ms ± 376 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
      "954 ms ± 33 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": [
      "211 µs ± 3.23 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n",
      "38.1 µs ± 421 ns per loop (mean ± std. dev. of 7 runs, 10,000 loops each)\n",
      "40 µs ± 568 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": [
      "98.8 ms ± 1.08 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",
    "        poss_turns = boards == 0  # checks where fields are empty.\n",
    "        poss_turns &= binary_dilation(boards == -1, SURROUNDING)\n",
    "        for game, idx, idy in itertools.product(\n",
    "            range(boards.shape[0]), range(BOARD_SIZE), range(BOARD_SIZE)\n",
    "        ):\n",
    "\n",
    "            if poss_turns[game, idx, idy]:\n",
    "                position = idx, idy\n",
    "                policies[game, idx, idy] += np.sum(\n",
    "                    np.array(\n",
    "                        list(\n",
    "                            _recursive_steps(boards[game, :, :], direction, position)\n",
    "                            for direction in DIRECTIONS\n",
    "                        )\n",
    "                    )\n",
    "                )\n",
    "        return policies"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Putting the game simulation together\n",
    "Now it's time to bring all together for a proper simulation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Playing a single turn\n",
    "\n",
    "The next function needed is used to request a policy, verify that the turn is legit and place a stone and turn enemy stones if possible."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.01 s ± 7.84 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
      "1 s ± 64.6 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "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": [
      "peak memory: 341.53 MiB, increment: 0.57 MiB\n",
      "9.39 s ± 314 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%memit simulate_game(100, (RandomPolicy(1), RandomPolicy(1)))\n",
    "%timeit simulate_game(100, (RandomPolicy(1), RandomPolicy(1)))"
   ]
  },
  {
   "cell_type": "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": [
    {
     "data": {
      "text/plain": [
       "((70, 10000, 8, 8), (70, 10000, 2))"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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",
    "_board_history.shape, _action_history.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For those 10k games the possible actions where evaluated and saved for each and every turn in the game."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 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": "c56322a13a314b7388ddf5273af31017",
       "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": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(70, 10000, 8, 8)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "_poss_turns.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7c4dc898a32a4d2bac0bdca6826a1425",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=34, description='turn', max=69), Output()), _dom_classes=('widget-intera…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mean_poss_turn = np.mean(_poss_turns, axis=1)\n",
    "del _poss_turns\n",
    "\n",
    "\n",
    "@interact(turn=(0, 69))\n",
    "def turn_distribution_heatmap(turn):\n",
    "    turn_possibility_on_field = mean_poss_turn[turn]\n",
    "\n",
    "    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": 32,
   "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": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "527a6a38d5c646b4a066451f73b733e0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=29, description='turn', max=59), Output()), _dom_classes=('widget-intera…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "score_history = calculate_direct_score(_board_history) * 64\n",
    "score_history[1::2] = score_history[1::2] * -1\n",
    "\n",
    "\n",
    "@interact(turn=(0, 59))\n",
    "def hist_direct_score(turn):\n",
    "    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 6))\n",
    "    fig.suptitle(\n",
    "        f\"Action space size analysis / total size estimat {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": 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_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": 35,
   "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": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkIAAAHHCAYAAABTMjf2AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy88F64QAAAACXBIWXMAAA9hAAAPYQGoP6dpAABegklEQVR4nO3deXxTVdoH8N9N26R7um/QjbWUQgulRTZlU6woiIDgWmBG3pGCS18XeB0WHR3UUYZROzAqgiiOuCAqyFoRVCotS9lBltIWure06b4k9/2jTaR0S0qSmza/7+eTj829N/c+ucTm6TnPOUcQRVEEERERkRWSSR0AERERkVSYCBEREZHVYiJEREREVouJEBEREVktJkJERERktZgIERERkdViIkRERERWi4kQERERWS0mQkRERGS1mAgRdVGCIGDhwoVSh2FU+fn5mDFjBjw9PSEIAlavXi11SJL56aefIAgCvvrqq3aP27BhAwRBwJUrV8wTWCfNmTMHISEhUodB1AITISILc/LkScyYMQPBwcGwt7dHjx49cOedd+Ldd9+VOjSTe/bZZ7Fr1y4sWbIEn3zyCe6+++5Wj6uqqsKKFSvw008/mTdAIup2bKUOgIj+cPDgQYwbNw5BQUF44okn4Ofnh+zsbPz222/417/+hUWLFkkdokn9+OOPmDp1Kp577rl2j6uqqsLLL78MABg7dqwZIrNcjz32GGbPng2FQiF1KERdEhMhIgvy2muvQalUIi0tDW5ubs32FRQUmD2eyspKODk5me16BQUFLd63OTU0NECj0UAul0sWg6FsbGxgY2MjdRhEXRa7xogsyKVLlzBw4MBWkwEfH59WX7N161ZERERAoVBg4MCB2LlzZ7P9mZmZWLBgAfr37w8HBwd4enpi5syZLWpKtLUm+/fvx4IFC+Dj44OePXvq9u/YsQNjxoyBk5MTXFxcMHnyZJw+fVqv93X58mXMnDkTHh4ecHR0xG233Ybt27e3uLYoikhKSoIgCBAEodVzXblyBd7e3gCAl19+WXfsihUrADS2ELXWSnRzjcqVK1cgCALeeustrF69Gr1794ZCocCZM2ewYsUKCIKAixcvYs6cOXBzc4NSqcTcuXNRVVXV7Lx79uzB6NGj4ebmBmdnZ/Tv3x//93//1+E96czramtrce+990KpVOLgwYPN7t2N/54hISG49957sXv3bkRFRcHe3h7h4eHYsmVLs/NpX3vgwAH8z//8Dzw9PeHq6orHH38c169fb3F9fT8D2s+kvb09IiIi8M0333R4P4ikwhYhIgsSHByMlJQUnDp1ChERER0e/8svv2DLli1YsGABXFxc8M4772D69OnIysqCp6cnACAtLQ0HDx7E7Nmz0bNnT1y5cgVr1qzB2LFjcebMGTg6OjY754IFC+Dt7Y1ly5ahsrISAPDJJ58gPj4ekyZNwhtvvIGqqiqsWbMGo0ePxrFjx9otgs3Pz8fIkSNRVVWFp556Cp6envj4448xZcoUfPXVV5g2bRpuv/12fPLJJ3jsscdw55134vHHH2/zfN7e3lizZg2efPJJTJs2DQ888AAAYPDgwR3er9asX78eNTU1mD9/PhQKBTw8PHT7HnzwQYSGhmLlypU4evQoPvzwQ/j4+OCNN94AAJw+fRr33nsvBg8ejFdeeQUKhQIXL17Er7/+2u41O/O66upqTJ06FYcPH8bevXsRExPT7jUuXLiAWbNm4S9/+Qvi4+Oxfv16zJw5Ezt37sSdd97Z7NiFCxfCzc0NK1aswPnz57FmzRpkZmbqCrYB/T8Du3fvxvTp0xEeHo6VK1eiuLgYc+fObZZUE1kUkYgsxu7du0UbGxvRxsZGHDFihPjCCy+Iu3btEuvq6locC0CUy+XixYsXdduOHz8uAhDfffdd3baqqqoWr01JSREBiBs3btRtW79+vQhAHD16tNjQ0KDbXl5eLrq5uYlPPPFEs3Pk5eWJSqWyxfabPfPMMyIA8eeff252ztDQUDEkJERUq9XN3lNCQkK75xNFUSwsLBQBiMuXL2+x74477hDvuOOOFtvj4+PF4OBg3fOMjAwRgOjq6ioWFBQ0O3b58uUiAHHevHnNtk+bNk309PTUPf/nP/8pAhALCws7jPlG+rxu3759IgDxyy+/FMvLy8U77rhD9PLyEo8dO9bsOO2/W0ZGhm5bcHCwCED8+uuvddvKyspEf39/cciQIS1eGx0d3ewz9uabb4oAxG+//VYURcM+A1FRUaK/v79YWlqq27Z7924RQLP7T2Qp2DVGZEHuvPNOpKSkYMqUKTh+/DjefPNNTJo0CT169MB3333X4viJEyeid+/euueDBw+Gq6srLl++rNvm4OCg+7m+vh7FxcXo06cP3NzccPTo0RbnfOKJJ5rVnOzZswelpaV46KGHUFRUpHvY2Nhg+PDh2LdvX7vv6YcffkBsbCxGjx6t2+bs7Iz58+fjypUrOHPmjH43x0SmT5+u62q72V/+8pdmz8eMGYPi4mKoVCoA0HVhfvvtt9BoNHpf05DXlZWV4a677sK5c+fw008/ISoqSq9rBAQEYNq0abrn2i6vY8eOIS8vr9mx8+fPh52dne75k08+CVtbW/zwww8A9P8M5ObmIj09HfHx8VAqlbrz3XnnnQgPD9crbiJzYyJEZGFiYmKwZcsWXL9+HampqViyZAnKy8sxY8aMFklDUFBQi9e7u7s3q++orq7GsmXLEBgYCIVCAS8vL3h7e6O0tBRlZWUtXh8aGtrs+YULFwAA48ePh7e3d7PH7t27OyzizszMRP/+/VtsHzBggG6/lG5+vze6+f66u7sDgO7+zpo1C6NGjcKf//xn+Pr6Yvbs2fjiiy86TG4Med0zzzyDtLQ07N27FwMHDtT7ffXp06dFnVW/fv0AoEV9WN++fZs9d3Z2hr+/v+44fT8D2n/Lm88HoNXPAJElYI0QkYWSy+WIiYlBTEwM+vXrh7lz5+LLL7/E8uXLdce0NVpIFEXdz4sWLcL69evxzDPPYMSIEVAqlRAEAbNnz271i/fGFiQAumM++eQT+Pn5tTje1tayfo1oi65vplarWz3+5vd7o47ur4ODAw4cOIB9+/Zh+/bt2LlzJzZv3ozx48dj9+7dbb7ekNdNnToVn3/+OV5//XVs3LgRMpn5/37tap8BIkPw00vUBQwbNgxAY9eDob766ivEx8fj7bff1m2rqalBaWmpXq/Xdr35+Phg4sSJBl8/ODgY58+fb7H93Llzuv2GamtEGdDYanNj16CWqVqeZDIZJkyYgAkTJmDVqlX4+9//jpdeegn79u1r937p+7r7778fd911F+bMmQMXFxesWbNGr7guXrwIURSb3avff/8dAFoUt1+4cAHjxo3TPa+oqEBubi7uueceAPp/BrT/ltoWpBu19hkgsgTsGiOyIPv27Wu1NUNbq9GZ7gUbG5sW53z33XfbbCG52aRJk+Dq6oq///3vqK+vb7G/sLCw3dffc889SE1NRUpKim5bZWUl3n//fYSEhHSqdkQ70q21ZK537944d+5cs7iOHz/e4UiuzigpKWmxTVvDU1tba7TXPf7443jnnXewdu1avPjii3rFlpOT02zYukqlwsaNGxEVFdWiVef9999v9m+7Zs0aNDQ0IC4uDoD+nwF/f39ERUXh448/btbtumfPHslrwYjawhYhIguyaNEiVFVVYdq0aQgLC0NdXR0OHjyIzZs3IyQkBHPnzjX4nPfeey8++eQTKJVKhIeHIyUlBXv37tUNr++Iq6sr1qxZg8ceewxDhw7F7Nmz4e3tjaysLGzfvh2jRo3Ce++91+brFy9ejP/+97+Ii4vDU089BQ8PD3z88cfIyMjA119/3amuHgcHB4SHh2Pz5s3o168fPDw8EBERgYiICMybNw+rVq3CpEmT8Kc//QkFBQVYu3YtBg4cqCtyNpZXXnkFBw4cwOTJkxEcHIyCggL8+9//Rs+ePZsVhxvjdQsXLoRKpcJLL70EpVLZ4ZxD/fr1w5/+9CekpaXB19cXH330EfLz87F+/foWx9bV1WHChAl48MEHcf78efz73//G6NGjMWXKFACGfQZWrlyJyZMnY/To0Zg3bx5KSkrw7rvvYuDAgaioqND31hKZj5RD1oiouR07dojz5s0Tw8LCRGdnZ1Eul4t9+vQRFy1aJObn5zc7Fm0MNQ8ODhbj4+N1z69fvy7OnTtX9PLyEp2dncVJkyaJ586da3Gcdih1Wlpaq7Ht27dPnDRpkqhUKkV7e3uxd+/e4pw5c8TDhw93+L4uXbokzpgxQ3RzcxPt7e3F2NhYcdu2bS2Oa+s9tebgwYNidHS0KJfLWwyl//TTT8VevXqJcrlcjIqKEnft2tXm8Pl//OMfLc6tHT5/8/D2m4eqJycni1OnThUDAgJEuVwuBgQEiA899JD4+++/txu7Pq+7cfj8jV544QURgPjee++1GpMoNn4GJk+eLO7atUscPHiwqFAoxLCwsBbn0r52//794vz580V3d3fR2dlZfOSRR8Ti4uIWcev7Gfj666/FAQMGiAqFQgwPDxe3bNnS4v4TWQpBFFtphycioi4rJCQEERER2LZtW7vHbdiwAXPnzkVaWpquDo3I2rBGiIiIiKwWEyEiIiKyWkyEiIiIyGqxRoiIiIisFluEiIiIyGoxESIiIiKrxQkVO6DRaJCTkwMXF5d2p/UnIiIiyyGKIsrLyxEQENDuxK1MhDqQk5ODwMBAqcMgIiKiTsjOzkbPnj3b3M9EqAMuLi4AGm+kq6urxNEQERGRPlQqFQIDA3Xf421hItQBbXeYq6srEyEiIqIupqOyFhZLExERkdViIkRERERWi4kQERERWS0mQkRERGS1mAgRERGR1WIiRERERFaLiRARERFZLSZCREREZLWsIhHatm0b+vfvj759++LDDz+UOhwiIiKyEN1+ZumGhgYkJiZi3759UCqViI6OxrRp0+Dp6Sl1aERERCSxbt8ilJqaioEDB6JHjx5wdnZGXFwcdu/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": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(70, 10000)"
      ]
     },
     "execution_count": 37,
     "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": 38,
   "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": 38,
     "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": 39,
   "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": 39,
     "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": 40,
   "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": 40,
     "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": 41,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def weights_init_normal(m):\n",
    "    \"\"\"Takes in a module and initializes all linear layers with weight\n",
    "       values taken from a normal distribution.\n",
    "    Source: https://stackoverflow.com/a/55546528/11003343\n",
    "    \"\"\"\n",
    "\n",
    "    classname = m.__class__.__name__\n",
    "    # for every Linear layer in a model\n",
    "    if classname.find(\"Linear\") != -1:\n",
    "        y = m.in_features\n",
    "        # m.weight.data shoud be taken from a normal distribution\n",
    "        m.weight.data.normal_(0.0, 1 / np.sqrt(y))\n",
    "        # m.bias.data should be 0\n",
    "        m.bias.data.fill_(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[0.],\n",
       "        [0.],\n",
       "        [0.],\n",
       "        [0.],\n",
       "        [0.]], grad_fn=<TanhBackward0>)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BATCH_SIZE = 1000\n",
    "\n",
    "\n",
    "class DQLNet(nn.Module):\n",
    "    def __init__(self, load_from: str | None = None):\n",
    "        super().__init__()\n",
    "        self.fc1 = nn.Linear(8 * 8 * 2, 128 * 2)\n",
    "        # self.nb1 = nn.BatchNorm1d([128 * 2])\n",
    "        self.fc2 = nn.Linear(128 * 2, 128 * 3)\n",
    "        # self.nb2 = nn.BatchNorm1d([128 * 3])\n",
    "        self.fc3 = nn.Linear(128 * 3, 128 * 2)\n",
    "        self.fc4 = nn.Linear(128 * 2, 1)\n",
    "        if not load_from:\n",
    "            self.apply(weights_init_normal)\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.nb1(x)\n",
    "        # x = self.dropout1(x)\n",
    "        x = self.fc2(x)\n",
    "        x = F.relu(x)\n",
    "        # x = self.nb2(x)\n",
    "        x = self.fc3(x)\n",
    "        x = F.relu(x)\n",
    "        x = self.fc4(x)\n",
    "        x = torch.tanh(x)\n",
    "        return x\n",
    "\n",
    "\n",
    "DQLNet().forward(np.zeros((5, 2, 8, 8)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "class SymmetryMode(Enum):\n",
    "    MULTIPLY = \"MULTIPLY\"\n",
    "    BREAK_SEQUENCE = \"BREAK_SEQUENCE\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((70, 100, 8, 8), (70, 100, 2))"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "_board_history, _action_history = simulate_game(100, (RandomPolicy(1), RandomPolicy(1)))\n",
    "_board_history.shape, _action_history.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "443 ms ± 5.94 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
      "peak memory: 383.86 MiB, increment: 6.84 MiB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(70, 100, 2, 8, 8)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def action_to_q_learning_format(\n",
    "    board_history: np.ndarray, action_history: np.ndarray\n",
    ") -> np.ndarray:\n",
    "    q_learning_format = np.zeros(\n",
    "        (SIMULATE_TURNS, board_history.shape[1], 2, 8, 8), dtype=float\n",
    "    )\n",
    "    q_learning_format[:, :, 1, :, :] = -1\n",
    "    q_learning_format[:, :, 1, action_history[:, :, 0], action_history[:, :, 0]] = 1\n",
    "    return q_learning_format\n",
    "\n",
    "\n",
    "%timeit action_to_q_learning_format(_board_history, _action_history)\n",
    "%memit action_to_q_learning_format(_board_history, _action_history)\n",
    "action_to_q_learning_format(_board_history, _action_history).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "478 ms ± 5.93 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
      "peak memory: 379.64 MiB, increment: 6.84 MiB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(2, 2, 2, 70, 100, 2, 8, 8)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def build_symetry_action(\n",
    "    board_history: np.ndarray, action_history: np.ndarray\n",
    ") -> np.ndarray:\n",
    "    board_history = board_history.copy()\n",
    "    board_history[::2] *= -1\n",
    "    q_learning_format = np.zeros(\n",
    "        (2, 2, 2, SIMULATE_TURNS, board_history.shape[1], 2, 8, 8)\n",
    "    )\n",
    "    q_learning_format[0, 0, 0, :, :, :, :, :] = action_to_q_learning_format(\n",
    "        board_history, action_history\n",
    "    )\n",
    "    q_learning_format[1, 0, 0, :, :, :, :, :] = np.transpose(\n",
    "        q_learning_format[0, 0, 0, :, :, :, :, :], [0, 1, 2, 4, 3]\n",
    "    )\n",
    "    q_learning_format[:, 1, 0, :, :, :, :, :] = q_learning_format[\n",
    "        :, 0, 0, :, :, :, ::-1, :\n",
    "    ]\n",
    "    q_learning_format[:, :, 1, :, :, :, :, :] = q_learning_format[\n",
    "        :, :, 0, :, :, :, :, ::-1\n",
    "    ]\n",
    "    return q_learning_format\n",
    "\n",
    "\n",
    "%timeit build_symetry_action(_board_history, _action_history)\n",
    "%memit build_symetry_action(_board_history, _action_history)\n",
    "build_symetry_action(_board_history, _action_history).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def live_history(training_history: pd.DataFrame, trainable, max_epochs: int):\n",
    "    clear_output(wait=True)\n",
    "    # plt.ylim(0, 100)\n",
    "    _ = training_history.plot(\n",
    "        secondary_y=[c for c in training_history.columns if c[1] == \"final_score\"]\n",
    "    )\n",
    "    plt.xlim(0, max_epochs)\n",
    "\n",
    "    plt.title(\"title\")\n",
    "    plt.xlabel(\"axis x\")\n",
    "    plt.ylabel(\"axis y\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "class QLPolicy(GamePolicy):\n",
    "    def __init__(\n",
    "        self,\n",
    "        epsilon: float,\n",
    "        neural_network: DQLNet,\n",
    "        symmetry_mode: SymmetryMode,\n",
    "        gamma: float = 0.8,\n",
    "        who_won_fraction: float = 0,\n",
    "        final_score_fraction: float = 0,\n",
    "        optimizer: torch.optim.Optimizer | None = None,\n",
    "        loss: nn.modules.loss._Loss | None = None,\n",
    "    ):\n",
    "        super().__init__(epsilon)\n",
    "        assert 0 <= gamma <= 1\n",
    "        self.gamma: str = gamma\n",
    "        del gamma\n",
    "        self.symmetry_mode: SymmetryMode = symmetry_mode\n",
    "        del symmetry_mode\n",
    "        self.neural_network: DQLNet = neural_network\n",
    "        del neural_network\n",
    "        self.who_won_fraction: final = who_won_fraction\n",
    "        del who_won_fraction\n",
    "        self.final_score_fraction: final = final_score_fraction\n",
    "        del final_score_fraction\n",
    "\n",
    "        if optimizer is None:\n",
    "            self.optimizer = torch.optim.Adam(self.neural_network.parameters(), lr=1e-2)\n",
    "        else:\n",
    "            self.optimizer = optimizer\n",
    "        if loss is None:\n",
    "            self.loss = nn.MSELoss()\n",
    "        else:\n",
    "            self.loss = loss\n",
    "\n",
    "    @property\n",
    "    def policy_name(self) -> str:\n",
    "        # name=\"QL-M-G08-WW00-FSF00\n",
    "        symmetry_name = {SymmetryMode.MULTIPLY: \"M\", SymmetryMode.BREAK_SEQUENCE: \"B\"}\n",
    "        g = f\"{self.gamma:.1f}\".replace(\".\", \"\")\n",
    "        ww = f\"{self.who_won_fraction:.1f}\".replace(\".\", \"\")\n",
    "        fsf = f\"{self.final_score_fraction:.1f}\".replace(\".\", \"\")\n",
    "        return f\"QL-{symmetry_name[self.symmetry_mode]}-G{g}-WW{ww}-FSF{fsf}\"\n",
    "\n",
    "    def _internal_policy(self, boards: np.ndarray) -> np.ndarray:\n",
    "        results = np.ones_like(boards, dtype=float)\n",
    "        results *= -1\n",
    "        results = torch.from_numpy(results).float()\n",
    "        q_learning_boards = np.zeros((boards.shape[0], 2, 8, 8))\n",
    "        q_learning_boards[:, 0, :, :] = boards\n",
    "        poss_turns = boards == 0  # checks where fields are empty.\n",
    "        poss_turns &= binary_dilation(boards == -1, SURROUNDING)\n",
    "        turn_possible = np.any(poss_turns, axis=0)\n",
    "        for action_x, action_y in itertools.product(range(8), range(8)):\n",
    "            if not turn_possible[action_x, action_y]:\n",
    "                continue\n",
    "            _q_learning_board = q_learning_boards[\n",
    "                poss_turns[:, action_x, action_y]\n",
    "            ].copy()\n",
    "            _q_learning_board[:, 1, action_x, action_y] = 1\n",
    "            ql_result = self.neural_network.forward(_q_learning_board)\n",
    "            results[\n",
    "                poss_turns[:, action_x, action_y], action_x, action_y\n",
    "            ] = ql_result.reshape(-1)\n",
    "        return results.cpu().detach().numpy()\n",
    "\n",
    "    def generate_trainings_data(self, generate_data_size: int) -> np.ndarray:\n",
    "        train_boards, train_actions = simulate_game(generate_data_size, [self] * 2)\n",
    "        action_possible = ~np.all(train_actions[:, :] == -1, axis=(2))\n",
    "        q_leaning_formated_action = build_symetry_action(train_boards, train_actions)\n",
    "        q_rewords = calculate_q_reword(\n",
    "            board_history=train_boards,\n",
    "            who_won_fraction=self.who_won_fraction,\n",
    "            final_score_fraction=self.final_score_fraction,\n",
    "        )\n",
    "        if self.symmetry_mode == SymmetryMode.MULTIPLY:\n",
    "            q_rewords = np.array([q_rewords] * 8)\n",
    "            action_possible = np.array([action_possible] * 8).reshape(-1)\n",
    "\n",
    "        elif self.symmetry_mode == SymmetryMode.BREAK_SEQUENCE:\n",
    "            axis1 = np.random.randint(0, high=2, size=SIMULATE_TURNS, dtype=int)\n",
    "            axis2 = np.random.randint(0, high=2, size=SIMULATE_TURNS, dtype=int)\n",
    "            axis3 = np.random.randint(0, high=2, size=SIMULATE_TURNS, dtype=int)\n",
    "            q_leaning_formated_action = q_leaning_formated_action[\n",
    "                axis1, axis2, axis3, range(SIMULATE_TURNS)\n",
    "            ]\n",
    "            action_possible = action_possible.reshape(-1)\n",
    "\n",
    "        return (\n",
    "            torch.from_numpy(\n",
    "                q_leaning_formated_action.reshape(-1, 2, BOARD_SIZE, BOARD_SIZE)[\n",
    "                    action_possible\n",
    "                ]\n",
    "            ).float(),\n",
    "            torch.from_numpy(q_rewords.reshape(-1, 1)[action_possible]).float(),\n",
    "        )\n",
    "\n",
    "    def train_batch(self, nr_of_games: int):\n",
    "        x_train, y_train = self.generate_trainings_data(nr_of_games)\n",
    "        y_pred = self.neural_network.forward(x_train)\n",
    "        loss_score = self.loss(y_pred, y_train)\n",
    "        self.optimizer.zero_grad()\n",
    "\n",
    "        loss_score.backward()\n",
    "        # Update the parameters\n",
    "        self.optimizer.step()\n",
    "        # generate trainings data\n",
    "\n",
    "    def evaluate_model(self, compare_models: list[GamePolicy], nr_of_games: int):\n",
    "        result_dict = {}\n",
    "        for model in compare_models:\n",
    "            boards_white, _ = simulate_game(nr_of_games, (self, model))\n",
    "            boards_black, _ = simulate_game(nr_of_games, (model, self))\n",
    "            win_eval_white = evaluate_who_won(boards_white[-1])\n",
    "            win_eval_black = evaluate_who_won(boards_black[-1])\n",
    "            result_dict[(model.policy_name, \"final_score\")] = np.mean(\n",
    "                final_boards_evaluation(boards_white[-1])\n",
    "                + final_boards_evaluation(boards_black[-1]) * -1\n",
    "            )\n",
    "            result_dict[(model.policy_name, \"white_win\")] = (\n",
    "                np.sum(win_eval_white == 1) / nr_of_games\n",
    "            )\n",
    "            result_dict[(model.policy_name, \"white_lose\")] = (\n",
    "                np.sum(win_eval_white == -1) / nr_of_games\n",
    "            )\n",
    "            result_dict[(model.policy_name, \"black_win\")] = (\n",
    "                np.sum(win_eval_black == 1) / nr_of_games\n",
    "            )\n",
    "            result_dict[(model.policy_name, \"black_lose\")] = (\n",
    "                np.sum(win_eval_black == -1) / nr_of_games\n",
    "            )\n",
    "        return result_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16.9 s ± 342 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
      "peak memory: 646.36 MiB, increment: 260.09 MiB\n",
      "25.8 s ± 264 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n",
      "peak memory: 390.86 MiB, increment: 3.25 MiB\n"
     ]
    }
   ],
   "source": [
    "ql_policy = QLPolicy(\n",
    "    0.95,\n",
    "    neural_network=DQLNet(),\n",
    "    symmetry_mode=SymmetryMode.MULTIPLY,\n",
    "    gamma=0.8,\n",
    "    who_won_fraction=0,\n",
    "    final_score_fraction=0,\n",
    ")\n",
    "_batch_size = 100\n",
    "%timeit ql_policy.train_batch(_batch_size)\n",
    "%memit ql_policy.train_batch(_batch_size)\n",
    "%timeit ql_policy.evaluate_model([RandomPolicy(0)], _batch_size)\n",
    "%memit ql_policy.evaluate_model([RandomPolicy(0)], _batch_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# %time ql_policy.train_batch(1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def train(\n",
    "    trainable: QLPolicy,\n",
    "    epochs: int,\n",
    "    batches: int,\n",
    "    batch_size: int,\n",
    "    eval_batch_size: int,\n",
    "    compare_with: list[GamePolicy],\n",
    ") -> pd.DataFrame:\n",
    "    assert epochs > 0\n",
    "    training_results = []\n",
    "    for epoch in tqdm(range(epochs)):\n",
    "        for batch in tqdm(range(batches)):\n",
    "            trainable.train_batch(batch_size)\n",
    "        training_results.append(trainable.evaluate_model(compare_with, eval_batch_size))\n",
    "        pandas_result = pd.DataFrame(training_results)\n",
    "        pandas_result.columns = pd.MultiIndex.from_tuples(pandas_result.columns)\n",
    "\n",
    "        live_history(pandas_result, trainable, epochs)\n",
    "    return pandas_result\n",
    "\n",
    "\n",
    "ql_winner_onyl = QLPolicy(\n",
    "    0.9,\n",
    "    neural_network=DQLNet(),\n",
    "    symmetry_mode=SymmetryMode.MULTIPLY,\n",
    "    gamma=0.9,\n",
    "    who_won_fraction=1,\n",
    "    final_score_fraction=0,\n",
    ")\n",
    "df1 = train(ql_winner_onyl, 6, 7, 8, 9, [RandomPolicy(0), GreedyPolicy(0)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df2 = train(ql_winner_onyl, 5, 5, 1000, 50, [RandomPolicy(0), GreedyPolicy(0)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df3 = train(ql_winner_onyl, 5, 5, 1000, 50, [RandomPolicy(0), GreedyPolicy(0)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train a model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sources\n",
    "\n",
    "* Game rules and example board images [https://en.wikipedia.org/wiki/Reversi](https://en.wikipedia.org/wiki/Reversi)\n",
    "* Game rules and example game images [https://de.wikipedia.org/wiki/Othello_(Spiel)](https://de.wikipedia.org/wiki/Othello_(Spiel))\n",
    "* Game strategy examples [https://de.wikipedia.org/wiki/Computer-Othello](https://de.wikipedia.org/wiki/Computer-Othello)\n",
    "* Image for 8 directions [https://www.researchgate.net/journal/EURASIP-Journal-on-Image-and-Video-Processing-1687-5281](https://www.researchgate.net/journal/EURASIP-Journal-on-Image-and-Video-Processing-1687-5281)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 score_history:  5.3 MiB\n",
      "              count_poss_turns:  2.7 MiB\n",
      "                      cum_prod:  2.7 MiB\n",
      "                _board_history: 437.7 KiB\n",
      "                       _boards: 250.1 KiB\n",
      "                   _final_eval: 78.2 KiB\n",
      "                mean_poss_turn: 35.1 KiB\n",
      "               _action_history: 13.8 KiB\n",
      "                          _i48:  5.5 KiB\n",
      "                          _i55:  5.5 KiB\n",
      "                          _i58:  5.5 KiB\n",
      "                          _i61:  5.5 KiB\n",
      "                 _unique_bords:  4.0 KiB\n",
      "                          _i17:  3.4 KiB\n",
      "                          _i12:  3.0 KiB\n",
      "                          _i18:  2.8 KiB\n",
      "                          tqdm:  2.0 KiB\n",
      "                          _i28:  1.9 KiB\n",
      "                           _i8:  1.8 KiB\n",
      "                          _i14:  1.8 KiB\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "\n",
    "\n",
    "def sizeof_fmt(num, suffix=\"B\"):\n",
    "    \"\"\"by Fred Cirera,  https://stackoverflow.com/a/1094933/1870254, modified\"\"\"\n",
    "    for unit in [\"\", \"Ki\", \"Mi\", \"Gi\", \"Ti\", \"Pi\", \"Ei\", \"Zi\"]:\n",
    "        if abs(num) < 1024.0:\n",
    "            return \"%3.1f %s%s\" % (num, unit, suffix)\n",
    "        num /= 1024.0\n",
    "    return \"%.1f %s%s\" % (num, \"Yi\", suffix)\n",
    "\n",
    "\n",
    "for name, size in sorted(\n",
    "    ((name, sys.getsizeof(value)) for name, value in list(locals().items())),\n",
    "    key=lambda x: -x[1],\n",
    ")[:20]:\n",
    "    print(\"{:>30}: {:>8}\".format(name, sizeof_fmt(size)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.8"
  },
  "toc-autonumbering": true,
  "toc-showcode": false
 },
 "nbformat": 4,
 "nbformat_minor": 4
}