Philipp/reversi
Philipp/reversi
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Philipp Horstenkamp 2023-02-18 23:40:00 +01:00
parent fc65735bca
commit 7cc8b6c025
Signed by: Philipp
GPG Key ID: DD53EAC36AFB61B4
3 changed files with 203 additions and 380 deletions
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README.md
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A Deep Learning implementation of the game Reversi aka. Otello. A Deep Learning implementation of the game Reversi aka. Otello.
This is a Jupyter implementation only because it was requested in such a format for a class in my master’s degree. Enjoy the read or ignore it. This is a Jupyter implementation only because it was requested in such a format for a class in my master’s degree. Enjoy the read or ignore it.
## Comments from Gawron
- Use Zobrist hashing for symetry

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main.ipynb
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test_main.ipynb
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 2,
"outputs": [
{
"data": {
"text/plain": "array([[0., 0., 0., 0., 0.],\n [0., 0., 0., 0., 0.],\n [0., 0., 0., 0., 0.],\n [0., 0., 0., 0., 0.],\n [0., 0., 0., 0., 0.]])"
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a = np.zeros((5,5))\n",
"a"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 3,
"outputs": [
{
"data": {
"text/plain": "array([[ 0., 0., 0., 0., 0.],\n [ 0., 0., 0., 0., 0.],\n [ 0., 0., 10., 0., 0.],\n [ 0., 0., 0., 0., 0.],\n [ 0., 0., 0., 0., 0.]])"
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a[2,2] = 10\n",
"a"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 4,
"outputs": [],
"source": [
"index_array = np.array([2,2], dtype=int)"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 5,
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"234 ns ± 7.47 ns per loop (mean ± std. dev. of 7 runs, 1,000,000 loops each)\n",
"311 ns ± 2.15 ns per loop (mean ± std. dev. of 7 runs, 1,000,000 loops each)\n"
]
}
],
"source": [
"%timeit a[tuple(index_array.tolist())]\n",
"%timeit a[index_array[0], index_array[1]]"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 6,
"outputs": [],
"source": [
"def array_change(array):\n",
" array[1] = 1"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 12,
"outputs": [
{
"data": {
"text/plain": "array([[ 0., 0., 0., 0., 0.],\n [ 1., 1., 1., 1., 1.],\n [ 0., 1., 10., 0., 0.],\n [ 1., 1., 1., 1., 1.],\n [ 0., 0., 0., 0., 0.]])"
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"array_change(a[2:])\n",
"a"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 10,
"outputs": [
{
"data": {
"text/plain": "array([[ 0., 0., 0., 0., 0.],\n [ 1., 1., 1., 1., 1.],\n [ 0., 1., 10., 0., 0.],\n [ 0., 0., 0., 0., 0.],\n [ 0., 0., 0., 0., 0.]])"
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": null,
"outputs": [],
"source": [],
"metadata": {
"collapsed": false
}
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 0
}