master-thesis/EvolutionaryAlgorithm/ea_tools.ipynb
2019-11-08 10:47:58 +01:00

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{
"nbformat": 4,
"nbformat_minor": 2,
"metadata": {
"language_info": {
"name": "python",
"codemirror_mode": {
"name": "ipython",
"version": 3
}
},
"orig_nbformat": 2,
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"npconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": 3
},
"cells": [
{
"cell_type": "markdown",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Statistical Tools"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np"
]
},
{
"cell_type": "markdown",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"* Helper function to calculate the wheel of fortune"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"def wheel_of_fortune(rank_i,n):\n",
" return rank_i / (0.5 * n * (n + 1))"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"def wheel_of_fortune_selection(items: list, item_scores:list):\n",
" ordering = np.argsort(item_scores)\n",
" ordering = ordering + 1\n",
"\n",
" wheel_weights = wheel_of_fortune(ordering, len(ordering))\n",
"\n",
" return np.random.choice(items, p=wheel_weights)\n"
]
}
]
}