Local init
This commit is contained in:
commit
f2801d76fe
GPG Key ID:
DD53EAC36AFB61B4
210
main.ipynb
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210
main.ipynb
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{
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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"
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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"
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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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"outputs": [
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{
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"data": {
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"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]])"
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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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"metadata": {
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"collapsed": false
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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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"source": [
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"def get_new_games(number_of_games:int):\n",
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" empty = np.zeros([number_of_games, 8,8], 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"
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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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{
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"data": {
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"text/plain": "array([[[ 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0, 0, 0, -1, 1, 0, 0, 0],\n [ 0, 0, 0, 1, -1, 0, 0, 0],\n [ 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0, 0, 0, 0, 0, 0, 0, 0],\n [ 0, 0, 0, 0, 0, 0, 0, 0]],\n\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],\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]],\n\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],\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]],\n\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],\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]],\n\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],\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]],\n\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],\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]],\n\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],\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]],\n\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],\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]],\n\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],\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]],\n\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],\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]]])"
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},
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"execution_count": 6,
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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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"get_new_games(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": 7,
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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 == (test_number_of_games, 8, 8 )\n",
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"np.testing.assert_equal( get_new_games(test_number_of_games).sum(axis=1), np.zeros([test_number_of_games, 8, ]))\n",
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"np.testing.assert_equal( get_new_games(test_number_of_games).sum(axis=2), np.zeros([test_number_of_games, 8, ]))\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": 16,
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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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"8.58 ms ± 214 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n",
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"82.7 ms ± 2.17 ms 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": "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]]])"
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},
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"execution_count": 16,
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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, 8,8], 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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"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 >= 8) | ( rec_position < 0)):\n",
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" return False\n",
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" next_field = _array[rec_position[0], rec_position[1]]\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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"def get_possible_turns(boards: np.ndarray) -> np.ndarray:\n",
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" _poss_turns = (boards == 0) & binary_dilation(boards == -1, np.array([[[1,1,1],[1,0,1],[1,1,1]]]))\n",
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" for game in range(boards.shape[0]):\n",
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" for idx in range(8):\n",
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" for idy in range(8):\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(recursive_steps(boards[game, :, :], direction, position) for direction in DIRECTIONS)\n",
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" return _poss_turns\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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"outputs": [
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{
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"data": {
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"text/plain": "(array([2, 2, 2]), array([2, 2, 2]))"
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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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"evaluate_boards(get_new_games(3))"
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],
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"metadata": {
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"collapsed": false
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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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"outputs": [],
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"source": [],
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"metadata": {
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"collapsed": false
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}
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.8"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 1
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}
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2103
poetry.lock
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2103
poetry.lock
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Load Diff
23
pyproject.toml
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pyproject.toml
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[tool.poetry]
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name = "reversi"
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version = "0.1.0"
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description = ""
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authors = ["Philipp Horstenkamp <philipp@horstenkamp.de>"]
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readme = "README.md"
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[tool.poetry.dependencies]
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python = "3.10.*"
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jupyter = "^1.0.0"
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numpy = "^1.24.1"
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pytest = "^7.2.1"
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ipytest = "^0.13.0"
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scipy = "^1.10.0"
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tqdm = "^4.64.1"
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[tool.poetry.group.build.dependencies]
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blackcellmagic = "^0.0.3"
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[build-system]
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requires = ["poetry-core"]
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build-backend = "poetry.core.masonry.api"
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