771 lines
25 KiB
Plaintext
771 lines
25 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"%load_ext blackcellmagic"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"from typing import Final\n",
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"from scipy.ndimage import binary_dilation\n",
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"from tqdm.auto import tqdm\n",
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"import matplotlib.pyplot as plt"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"ENEMY: Final[int] = -1\n",
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"PLAYER: Final[int] = 1\n",
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"BOARD_SIZE: Final[int] = 8"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([[-1, -1],\n",
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" [-1, 0],\n",
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" [-1, 1],\n",
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" [ 0, -1],\n",
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" [ 0, 1],\n",
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" [ 1, -1],\n",
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" [ 1, 0],\n",
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" [ 1, 1]])"
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"DIRECTIONS: Final[np.ndarray] = np.array(\n",
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" [[i, j] for i in range(-1, 2) for j in range(-1, 2) if j != 0 or i != 0], dtype=int\n",
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")\n",
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"DIRECTIONS"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([[ 0, 0, 0, 0, 0, 0, 0, 0],\n",
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" [ 0, 0, 0, 0, 0, 0, 0, 0],\n",
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" [ 0, 0, 0, 0, 0, 0, 0, 0],\n",
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" [ 0, 0, 0, -1, 1, 0, 0, 0],\n",
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" [ 0, 0, 0, 1, -1, 0, 0, 0],\n",
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" [ 0, 0, 0, 0, 0, 0, 0, 0],\n",
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" [ 0, 0, 0, 0, 0, 0, 0, 0],\n",
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" [ 0, 0, 0, 0, 0, 0, 0, 0]])"
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]
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"def get_new_games(number_of_games: int):\n",
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" empty = np.zeros([number_of_games, BOARD_SIZE, BOARD_SIZE], dtype=int)\n",
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" empty[:, 3:5, 3:5] = np.array([[-1, 1], [1, -1]])\n",
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" return empty\n",
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"\n",
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"\n",
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"get_new_games(1)[0]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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"test_number_of_games = 3\n",
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"assert get_new_games(test_number_of_games).shape == (\n",
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" test_number_of_games,\n",
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" BOARD_SIZE,\n",
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" BOARD_SIZE,\n",
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")\n",
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"np.testing.assert_equal(\n",
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" get_new_games(test_number_of_games).sum(axis=1),\n",
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" np.zeros(\n",
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" [\n",
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" test_number_of_games,\n",
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" 8,\n",
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" ]\n",
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" ),\n",
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")\n",
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"np.testing.assert_equal(\n",
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" get_new_games(test_number_of_games).sum(axis=2),\n",
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" np.zeros(\n",
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" [\n",
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" test_number_of_games,\n",
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" 8,\n",
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" ]\n",
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" ),\n",
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")\n",
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"assert np.all(get_new_games(test_number_of_games)[:, 3:4, 3:4] != 0)\n",
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"del test_number_of_games"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [],
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"source": [
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"def plot_othello_board(board, ax=None):\n",
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" size = 3\n",
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" plot_all = False\n",
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" if ax is None:\n",
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" plot_all = True\n",
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" fig, ax = plt.subplots(figsize=(size, size))\n",
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"\n",
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" ax.set_facecolor(\"green\")\n",
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" for i in range(BOARD_SIZE):\n",
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" for j in range(BOARD_SIZE):\n",
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" if board[i, j] == -1:\n",
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" color = \"white\"\n",
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" elif board[i, j] == 1:\n",
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" color = \"black\"\n",
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" else:\n",
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" continue\n",
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" ax.scatter(j, i, s=300 if plot_all else 150, c=color)\n",
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" for i in range(-1, 8):\n",
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" ax.axhline(i + 0.5, color=\"black\", lw=2)\n",
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" ax.axvline(i + 0.5, color=\"black\", lw=2)\n",
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" ax.set_xlim(-0.5, 7.5)\n",
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" ax.set_ylim(7.5, -0.5)\n",
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" ax.set_xticks(np.arange(8))\n",
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" ax.set_xticklabels(list(\"ABCDEFGH\"))\n",
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" ax.set_yticks(np.arange(8))\n",
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" ax.set_yticklabels(list(\"12345678\"))\n",
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" if plot_all:\n",
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" plt.tight_layout()\n",
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" plt.show()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [],
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"source": [
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"def plot_othello_boards(boards: np.ndarray) -> None:\n",
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" assert boards.shape[0] < 70\n",
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" plots_per_row = 4\n",
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" rows = int(np.ceil(boards.shape[0] / plots_per_row))\n",
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" fig, axs = plt.subplots(rows, plots_per_row, figsize=(12, 3 * rows))\n",
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" for game_index, ax in enumerate(axs.flatten()):\n",
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" if game_index >= boards.shape[0]:\n",
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" fig.delaxes(ax)\n",
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" else:\n",
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" plot_othello_board(boards[game_index], ax)\n",
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" plt.tight_layout()\n",
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" plt.show()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"8.49 ms ± 143 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
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"80.9 ms ± 537 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"array([[[False, False, False, False, False, False, False, False],\n",
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" [False, False, False, False, False, False, False, False],\n",
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" [False, False, False, True, False, False, False, False],\n",
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" [False, False, True, False, False, False, False, False],\n",
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" [False, False, False, False, False, True, False, False],\n",
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" [False, False, False, False, True, False, False, False],\n",
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" [False, False, False, False, False, False, False, False],\n",
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" [False, False, False, False, False, False, False, False]]])"
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]
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},
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"execution_count": 9,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"def recursive_steps(_array, rec_direction, rec_position, step_one=True):\n",
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" rec_position = rec_position + rec_direction\n",
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" if np.any((rec_position >= BOARD_SIZE) | (rec_position < 0)):\n",
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" return False\n",
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" next_field = _array[tuple(rec_position.tolist())]\n",
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" if next_field == 0:\n",
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" return False\n",
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" if next_field == -1:\n",
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" return recursive_steps(_array, rec_direction, rec_position, step_one=False)\n",
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" if next_field == 1:\n",
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" return not step_one\n",
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"\n",
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"\n",
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"def get_possible_turns(boards: np.ndarray) -> np.ndarray:\n",
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" _poss_turns = (boards == 0) & binary_dilation(\n",
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" boards == -1, np.array([[[1, 1, 1], [1, 0, 1], [1, 1, 1]]])\n",
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" )\n",
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" for game in range(boards.shape[0]):\n",
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" for idx in range(BOARD_SIZE):\n",
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" for idy in range(BOARD_SIZE):\n",
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"\n",
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" position = idx, idy\n",
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" if _poss_turns[game, idx, idy]:\n",
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" _poss_turns[game, idx, idy] = any(\n",
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" recursive_steps(boards[game, :, :], direction, position)\n",
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" for direction in DIRECTIONS\n",
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" )\n",
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" return _poss_turns\n",
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"\n",
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"\n",
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"%timeit get_possible_turns(get_new_games(10))\n",
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"%timeit get_possible_turns(get_new_games(100))\n",
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"get_possible_turns(get_new_games(3))[:1]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"(array([2, 2, 2]), array([2, 2, 2]))"
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]
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},
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"execution_count": 10,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"def evaluate_boards(array: np.ndarray):\n",
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" return np.sum(array == 1, axis=(1, 2)), np.sum(array == -1, axis=(1, 2))\n",
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"\n",
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"\n",
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"evaluate_boards(get_new_games(3))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"metadata": {},
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"outputs": [],
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"source": [
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"def move_possible(board: np.ndarray, move: np.ndarray) -> bool:\n",
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" if np.all(move == -1):\n",
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" return np.all(get_possible_turns(board))\n",
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" return any(\n",
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" recursive_steps(board[:, :], direction, move) for direction in DIRECTIONS\n",
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" )\n",
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"\n",
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"\n",
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"def moves_possible(boards: np.ndarray, moves: np.ndarray) -> np.ndarray:\n",
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" arr_moves_possible = np.zeros(boards.shape[0], dtype=bool)\n",
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" for game in range(boards.shape[0]):\n",
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" if np.all(moves[game] == -1):\n",
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" arr_moves_possible[game, :, :] = np.all(\n",
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" get_possible_turns(boards[game, :, :])\n",
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" )\n",
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" arr_moves_possible[game, :, :] = any(\n",
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" recursive_steps(boards[game, :, :], direction, moves[game])\n",
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" for direction in DIRECTIONS\n",
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" )\n",
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" return arr_moves_possible"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([[ 0, 0, 0, 0, 0, 0, 0, 0],\n",
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" [ 0, 0, 0, 0, 0, 0, 0, 0],\n",
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" [ 0, 0, 0, -1, 0, 0, 0, 0],\n",
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" [ 0, 0, 0, -1, -1, 0, 0, 0],\n",
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" [ 0, 0, 0, -1, 1, 0, 0, 0],\n",
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" [ 0, 0, 0, 0, 0, 0, 0, 0],\n",
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" [ 0, 0, 0, 0, 0, 0, 0, 0],\n",
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" [ 0, 0, 0, 0, 0, 0, 0, 0]])"
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]
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},
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"execution_count": 12,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"class InvalidTurn(ValueError):\n",
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" pass\n",
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"\n",
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"\n",
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"def to_moves(boards: np.ndarray, moves: np.ndarray) -> np.ndarray:\n",
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" def _do_directional_move(\n",
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" board: np.ndarray, rec_move: np.ndarray, rev_direction, step_one=True\n",
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" ) -> bool:\n",
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" rec_position = rec_move + rev_direction\n",
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" if np.any((rec_position >= 8) | (rec_position < 0)):\n",
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" return False\n",
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" next_field = board[tuple(rec_position.tolist())]\n",
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" if next_field == 0:\n",
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" return False\n",
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" if next_field == 1:\n",
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" return not step_one\n",
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" if next_field == -1:\n",
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" if _do_directional_move(board, rec_position, rev_direction, step_one=False):\n",
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" board[tuple(rec_position.tolist())] = 1\n",
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" return True\n",
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" return False\n",
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"\n",
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" def _do_move(_board: np.ndarray, move: np.ndarray) -> None:\n",
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" if _board[tuple(move.tolist())] != 0:\n",
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" raise InvalidTurn\n",
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" action = False\n",
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" for direction in DIRECTIONS:\n",
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" if _do_directional_move(_board, move, direction):\n",
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" action = True\n",
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" if not action:\n",
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" raise InvalidTurn()\n",
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" _board[tuple(move.tolist())] = 1\n",
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"\n",
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" for game in range(boards.shape[0]):\n",
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" _do_move(boards[game], moves[game])\n",
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"\n",
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"\n",
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"boards = get_new_games(10)\n",
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"to_moves(boards, np.array([[2, 3]] * 10))\n",
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"boards = boards * -1\n",
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"boards[0]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"metadata": {},
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"outputs": [],
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"source": [
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"to_moves(get_new_games(10), np.array([[2, 3]] * 10))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
|
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"array([[4, 3],\n",
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" [4, 3],\n",
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" [4, 3],\n",
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" [4, 3],\n",
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" [4, 3],\n",
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" [4, 3],\n",
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" [4, 3],\n",
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" [4, 3],\n",
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" [4, 3],\n",
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" [4, 3]])"
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]
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},
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"execution_count": 14,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"np.array([[4, 3]] * 10)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
|
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"def create_test_game():\n",
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" test_array = []\n",
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" test_array.append(\n",
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" np.array(\n",
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" [\n",
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" [0, 0, 0, 0, 0, 0, 0, 0],\n",
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" [0, 0, 0, 0, 0, 0, 0, 0],\n",
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" [0, 0, 0, 0, 0, 0, 0, 0],\n",
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" [0, 0, 0, 1, 2, 0, 0, 0],\n",
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" [0, 0, 0, 2, 1, 0, 0, 0],\n",
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" [0, 0, 0, 0, 0, 0, 0, 0],\n",
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" [0, 0, 0, 0, 0, 0, 0, 0],\n",
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" [0, 0, 0, 0, 0, 0, 0, 0],\n",
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" ]\n",
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" )\n",
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" )\n",
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" test_array.append(\n",
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" np.array(\n",
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" [\n",
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" [0, 0, 0, 0, 0, 0, 0, 0],\n",
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" [0, 0, 0, 0, 0, 0, 0, 0],\n",
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" [0, 0, 0, 2, 0, 0, 0, 0],\n",
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" [0, 0, 0, 2, 2, 0, 0, 0],\n",
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" [0, 0, 0, 2, 1, 0, 0, 0],\n",
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" [0, 0, 0, 0, 0, 0, 0, 0],\n",
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" [0, 0, 0, 0, 0, 0, 0, 0],\n",
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" [0, 0, 0, 0, 0, 0, 0, 0],\n",
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" ]\n",
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" )\n",
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" )\n",
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" test_array.append(\n",
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" np.array(\n",
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" [\n",
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" [0, 0, 0, 0, 0, 0, 0, 0],\n",
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" [0, 0, 0, 0, 0, 0, 0, 0],\n",
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" [0, 0, 0, 2, 0, 0, 0, 0],\n",
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" [0, 0, 0, 2, 2, 0, 0, 0],\n",
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" [0, 0, 1, 1, 1, 0, 0, 0],\n",
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" [0, 0, 0, 0, 0, 0, 0, 0],\n",
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" [0, 0, 0, 0, 0, 0, 0, 0],\n",
|
|
" [0, 0, 0, 0, 0, 0, 0, 0],\n",
|
|
" ]\n",
|
|
" )\n",
|
|
" )\n",
|
|
" test_array.append(\n",
|
|
" np.array(\n",
|
|
" [\n",
|
|
" [0, 0, 0, 0, 0, 0, 0, 0],\n",
|
|
" [0, 0, 0, 0, 0, 0, 0, 0],\n",
|
|
" [0, 0, 0, 2, 0, 0, 0, 0],\n",
|
|
" [0, 0, 0, 2, 2, 0, 0, 0],\n",
|
|
" [0, 0, 2, 1, 1, 0, 0, 0],\n",
|
|
" [0, 2, 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",
|
|
" ]\n",
|
|
" )\n",
|
|
" )\n",
|
|
" test_array.append(\n",
|
|
" np.array(\n",
|
|
" [\n",
|
|
" [0, 0, 0, 0, 0, 0, 0, 0],\n",
|
|
" [0, 0, 0, 1, 0, 0, 0, 0],\n",
|
|
" [0, 0, 0, 1, 0, 0, 0, 0],\n",
|
|
" [0, 0, 0, 1, 2, 0, 0, 0],\n",
|
|
" [0, 0, 2, 1, 1, 0, 0, 0],\n",
|
|
" [0, 2, 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",
|
|
" ]\n",
|
|
" )\n",
|
|
" )\n",
|
|
" test_array.append(\n",
|
|
" np.array(\n",
|
|
" [\n",
|
|
" [0, 0, 0, 0, 0, 0, 0, 0],\n",
|
|
" [0, 0, 2, 1, 0, 0, 0, 0],\n",
|
|
" [0, 0, 0, 2, 0, 0, 0, 0],\n",
|
|
" [0, 0, 0, 1, 2, 0, 0, 0],\n",
|
|
" [0, 0, 2, 1, 1, 0, 0, 0],\n",
|
|
" [0, 2, 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",
|
|
" ]\n",
|
|
" )\n",
|
|
" )\n",
|
|
" test_array.append(\n",
|
|
" np.array(\n",
|
|
" [\n",
|
|
" [0, 0, 0, 0, 0, 0, 0, 0],\n",
|
|
" [0, 0, 2, 1, 0, 0, 0, 0],\n",
|
|
" [0, 0, 0, 2, 0, 0, 0, 0],\n",
|
|
" [0, 0, 0, 1, 2, 0, 0, 0],\n",
|
|
" [0, 0, 2, 2, 2, 2, 0, 0],\n",
|
|
" [0, 2, 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",
|
|
" ]\n",
|
|
" )\n",
|
|
" )\n",
|
|
" test_array.append(\n",
|
|
" np.array(\n",
|
|
" [\n",
|
|
" [0, 0, 0, 0, 0, 0, 0, 0],\n",
|
|
" [0, 0, 2, 1, 0, 0, 0, 0],\n",
|
|
" [0, 0, 0, 2, 0, 0, 0, 0],\n",
|
|
" [0, 0, 0, 1, 1, 1, 0, 0],\n",
|
|
" [0, 0, 2, 2, 2, 2, 0, 0],\n",
|
|
" [0, 2, 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",
|
|
" ]\n",
|
|
" )\n",
|
|
" )\n",
|
|
" test_array.append(\n",
|
|
" np.array(\n",
|
|
" [\n",
|
|
" [0, 0, 0, 0, 0, 0, 0, 0],\n",
|
|
" [0, 0, 2, 1, 0, 0, 0, 0],\n",
|
|
" [0, 0, 0, 2, 0, 2, 0, 0],\n",
|
|
" [0, 0, 0, 1, 2, 2, 0, 0],\n",
|
|
" [0, 0, 2, 2, 2, 2, 0, 0],\n",
|
|
" [0, 2, 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",
|
|
" ]\n",
|
|
" )\n",
|
|
" )\n",
|
|
" test_array.append(\n",
|
|
" np.array(\n",
|
|
" [\n",
|
|
" [0, 0, 0, 0, 0, 0, 0, 0],\n",
|
|
" [0, 0, 2, 1, 0, 0, 0, 0],\n",
|
|
" [0, 0, 0, 2, 0, 2, 0, 0],\n",
|
|
" [0, 0, 0, 1, 2, 2, 0, 0],\n",
|
|
" [0, 0, 2, 2, 1, 2, 0, 0],\n",
|
|
" [0, 2, 0, 0, 0, 1, 0, 0],\n",
|
|
" [0, 0, 0, 0, 0, 0, 0, 0],\n",
|
|
" [0, 0, 0, 0, 0, 0, 0, 0],\n",
|
|
" ]\n",
|
|
" )\n",
|
|
" )\n",
|
|
" test_array.append(\n",
|
|
" np.array(\n",
|
|
" [\n",
|
|
" [0, 0, 0, 0, 0, 0, 0, 0],\n",
|
|
" [0, 0, 2, 1, 0, 0, 0, 0],\n",
|
|
" [0, 0, 0, 2, 0, 2, 0, 0],\n",
|
|
" [0, 0, 0, 1, 2, 2, 0, 0],\n",
|
|
" [0, 0, 2, 2, 1, 2, 0, 0],\n",
|
|
" [0, 2, 0, 0, 0, 2, 0, 0],\n",
|
|
" [0, 0, 0, 0, 0, 2, 0, 0],\n",
|
|
" [0, 0, 0, 0, 0, 0, 0, 0],\n",
|
|
" ]\n",
|
|
" )\n",
|
|
" )\n",
|
|
" test_array.append(\n",
|
|
" np.array(\n",
|
|
" [\n",
|
|
" [0, 0, 0, 0, 0, 0, 0, 0],\n",
|
|
" [0, 0, 2, 1, 0, 0, 0, 0],\n",
|
|
" [0, 0, 0, 2, 0, 2, 0, 0],\n",
|
|
" [0, 0, 0, 1, 2, 2, 0, 0],\n",
|
|
" [0, 1, 1, 1, 1, 2, 0, 0],\n",
|
|
" [0, 2, 0, 0, 0, 2, 0, 0],\n",
|
|
" [0, 0, 0, 0, 0, 2, 0, 0],\n",
|
|
" [0, 0, 0, 0, 0, 0, 0, 0],\n",
|
|
" ]\n",
|
|
" )\n",
|
|
" )\n",
|
|
" test_array.append(\n",
|
|
" np.array(\n",
|
|
" [\n",
|
|
" [0, 0, 0, 0, 0, 0, 0, 0],\n",
|
|
" [0, 0, 2, 1, 0, 0, 0, 0],\n",
|
|
" [0, 0, 0, 2, 0, 2, 0, 0],\n",
|
|
" [0, 0, 0, 1, 2, 2, 0, 0],\n",
|
|
" [2, 2, 2, 2, 2, 2, 0, 0],\n",
|
|
" [0, 2, 0, 0, 0, 2, 0, 0],\n",
|
|
" [0, 0, 0, 0, 0, 2, 0, 0],\n",
|
|
" [0, 0, 0, 0, 0, 0, 0, 0],\n",
|
|
" ]\n",
|
|
" )\n",
|
|
" )\n",
|
|
" test_array.append(\n",
|
|
" np.array(\n",
|
|
" [\n",
|
|
" [0, 0, 0, 0, 0, 0, 0, 0],\n",
|
|
" [0, 0, 2, 1, 0, 0, 0, 0],\n",
|
|
" [0, 0, 0, 2, 0, 2, 0, 0],\n",
|
|
" [0, 0, 0, 1, 1, 1, 1, 0],\n",
|
|
" [2, 2, 2, 2, 2, 2, 0, 0],\n",
|
|
" [0, 2, 0, 0, 0, 2, 0, 0],\n",
|
|
" [0, 0, 0, 0, 0, 2, 0, 0],\n",
|
|
" [0, 0, 0, 0, 0, 0, 0, 0],\n",
|
|
" ]\n",
|
|
" )\n",
|
|
" )\n",
|
|
" test_array.append(\n",
|
|
" np.array(\n",
|
|
" [\n",
|
|
" [0, 0, 0, 0, 0, 0, 0, 0],\n",
|
|
" [0, 0, 2, 1, 0, 0, 0, 0],\n",
|
|
" [0, 0, 0, 2, 0, 2, 0, 0],\n",
|
|
" [0, 0, 0, 1, 1, 1, 1, 0],\n",
|
|
" [2, 2, 2, 1, 2, 2, 0, 0],\n",
|
|
" [0, 2, 0, 1, 0, 2, 0, 0],\n",
|
|
" [0, 0, 0, 0, 0, 2, 0, 0],\n",
|
|
" [0, 0, 0, 0, 0, 0, 0, 0],\n",
|
|
" ]\n",
|
|
" )\n",
|
|
" )\n",
|
|
" test_array.append(\n",
|
|
" np.array(\n",
|
|
" [\n",
|
|
" [0, 0, 0, 0, 0, 0, 0, 0],\n",
|
|
" [0, 0, 2, 1, 0, 0, 0, 0],\n",
|
|
" [0, 0, 0, 2, 2, 2, 0, 0],\n",
|
|
" [0, 0, 0, 2, 2, 1, 1, 0],\n",
|
|
" [2, 2, 2, 1, 2, 2, 0, 0],\n",
|
|
" [0, 2, 0, 1, 0, 2, 0, 0],\n",
|
|
" [0, 0, 0, 0, 0, 2, 0, 0],\n",
|
|
" [0, 0, 0, 0, 0, 0, 0, 0],\n",
|
|
" ]\n",
|
|
" )\n",
|
|
" )\n",
|
|
" test_array.append(\n",
|
|
" np.array(\n",
|
|
" [\n",
|
|
" [0, 0, 0, 0, 0, 0, 0, 0],\n",
|
|
" [0, 0, 2, 1, 0, 1, 0, 0],\n",
|
|
" [0, 0, 0, 2, 2, 1, 0, 0],\n",
|
|
" [0, 0, 0, 2, 2, 1, 1, 0],\n",
|
|
" [2, 2, 2, 1, 2, 2, 0, 0],\n",
|
|
" [0, 2, 0, 1, 0, 2, 0, 0],\n",
|
|
" [0, 0, 0, 0, 0, 2, 0, 0],\n",
|
|
" [0, 0, 0, 0, 0, 0, 0, 0],\n",
|
|
" ]\n",
|
|
" )\n",
|
|
" )\n",
|
|
" test_array.append(\n",
|
|
" np.array(\n",
|
|
" [\n",
|
|
" [0, 0, 0, 0, 0, 0, 0, 0],\n",
|
|
" [0, 0, 2, 1, 0, 1, 0, 0],\n",
|
|
" [0, 0, 0, 2, 2, 2, 2, 0],\n",
|
|
" [0, 0, 0, 2, 2, 2, 1, 0],\n",
|
|
" [2, 2, 2, 1, 2, 2, 0, 0],\n",
|
|
" [0, 2, 0, 1, 0, 2, 0, 0],\n",
|
|
" [0, 0, 0, 0, 0, 2, 0, 0],\n",
|
|
" [0, 0, 0, 0, 0, 0, 0, 0],\n",
|
|
" ]\n",
|
|
" )\n",
|
|
" )\n",
|
|
" test_array.append(\n",
|
|
" np.array(\n",
|
|
" [\n",
|
|
" [0, 0, 0, 0, 0, 0, 0, 0],\n",
|
|
" [0, 0, 2, 1, 0, 1, 0, 0],\n",
|
|
" [0, 0, 0, 2, 1, 2, 2, 0],\n",
|
|
" [0, 0, 0, 2, 2, 1, 1, 0],\n",
|
|
" [2, 2, 2, 1, 1, 1, 1, 0],\n",
|
|
" [0, 2, 0, 1, 0, 2, 0, 0],\n",
|
|
" [0, 0, 0, 0, 0, 2, 0, 0],\n",
|
|
" [0, 0, 0, 0, 0, 0, 0, 0],\n",
|
|
" ]\n",
|
|
" )\n",
|
|
" )\n",
|
|
" test_array.append(\n",
|
|
" np.array(\n",
|
|
" [\n",
|
|
" [0, 0, 0, 0, 0, 0, 0, 0],\n",
|
|
" [0, 0, 2, 1, 0, 1, 0, 0],\n",
|
|
" [0, 0, 0, 2, 1, 2, 2, 0],\n",
|
|
" [0, 0, 0, 2, 2, 1, 2, 0],\n",
|
|
" [2, 2, 2, 2, 2, 2, 2, 2],\n",
|
|
" [0, 2, 0, 1, 0, 2, 0, 0],\n",
|
|
" [0, 0, 0, 0, 0, 2, 0, 0],\n",
|
|
" [0, 0, 0, 0, 0, 0, 0, 0],\n",
|
|
" ]\n",
|
|
" )\n",
|
|
" )\n",
|
|
" test_array.append(\n",
|
|
" np.array(\n",
|
|
" [\n",
|
|
" [0, 0, 0, 0, 0, 0, 0, 0],\n",
|
|
" [0, 0, 2, 1, 0, 1, 0, 0],\n",
|
|
" [0, 0, 0, 2, 1, 2, 2, 0],\n",
|
|
" [0, 0, 0, 2, 1, 1, 2, 0],\n",
|
|
" [2, 2, 2, 2, 1, 2, 2, 2],\n",
|
|
" [0, 2, 0, 1, 1, 2, 0, 0],\n",
|
|
" [0, 0, 0, 0, 0, 2, 0, 0],\n",
|
|
" [0, 0, 0, 0, 0, 0, 0, 0],\n",
|
|
" ]\n",
|
|
" )\n",
|
|
" )\n",
|
|
" test_array = np.array(test_array)\n",
|
|
" test_array[test_array == 2] = -1\n",
|
|
" assert np.all(np.diff(np.count_nonzero(create_test_game(), axis=(1, 2))) == 1)\n",
|
|
" return test_array\n",
|
|
"\n",
|
|
"\n",
|
|
"plot_othello_boards(create_test_game()[-3:])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"np.diff(create_test_game(), axis=0).shape"
|
|
]
|
|
},
|
|
{
|
|
"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"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 1
|
|
}
|