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szdr/sampling.ipynb

Last active Jul 22, 2016
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ランダムサンプリングの計測
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Sampling test\n",
"* list\n",
"* set"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import random\n",
"import time\n",
"import statistics\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"sns.set_context(\"poster\")"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def sample_test(items, loop):\n",
" start = time.time()\n",
" for _ in range(loop):\n",
" random.sample(items, 1)\n",
" end = time.time()\n",
" return end - start"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"loop = 1000\n",
"times = 3\n",
"ns = [10, 100, 1000, 10000, 100000]"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"time_from_list = [statistics.median([sample_test(list(range(n)), loop) for t in range(times)]) for n in ns]"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"time_from_set = [statistics.median([sample_test(set(range(n)), loop) for t in range(times)]) for n in ns]"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x7f18607a3780>"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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e4IBnZmY9ofJlCZJmkBaT70Ba9zYnIhYOUH4qKTPDu0gZDG4GZua1c7Uyh5My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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f18606a7fd0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(6, 3))\n",
"plt.bar(ns, time_from_list, width=ns)\n",
"plt.xscale(\"log\")\n",
"plt.title(\"from list\")\n",
"plt.xlabel(\"list size\")\n",
"plt.ylabel(\"time[s]\")"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x7f186066af60>"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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/An8sJaDofsJlvd2rPL+IiCRWaU9oNd0v1TCWcH+oGksIl/XGA49mtu8MrCc8M1TumK1y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\nSUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f1860693cf8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(6, 3))\n",
"plt.bar(ns, time_from_set, width=ns)\n",
"plt.xscale(\"log\")\n",
"plt.title(\"from set\")\n",
"plt.xlabel(\"set size\")\n",
"plt.ylabel(\"time[s]\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.5.1"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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