2020-1-capstone-design2 / 2016104140

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bs selection algorithm and save internet activity predictions

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{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Unnamed: 0 gridID Internet Activity Prediction\n",
"0 0 1 9952.919155\n",
"1 0 2 10007.809479\n",
"2 0 3 12864.899626\n"
]
}
],
"source": [
"import pandas as pd\n",
"df = pd.read_csv('predictions.csv')\n",
"#sort internet activity prediction\n",
"df.sort_values(by=['Internet Activity Prediction'],inplace=True)\n",
"print(df)\n"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"g1 cache remainder size 137\n",
"g2 cache remainder size 582\n",
"g3 cache remainder size 867\n",
"contents size 821\n",
"[[3, 867]]\n"
]
}
],
"source": [
"from random import seed\n",
"from random import randint\n",
"seed(1)\n",
"g1_cache=randint(0,1000)\n",
"g2_cache=randint(0,1000)\n",
"g3_cache=randint(0,1000)\n",
"\n",
"contents_cache=randint(0,1000)\n",
"\n",
"print(\"g1 cache remainder size\" , g1_cache)\n",
"print(\"g2 cache remainder size\" ,g2_cache)\n",
"print(\"g3 cache remainder size\" ,g3_cache)\n",
"print(\"contents size\" ,contents_cache)\n",
"\n",
"l_bs_cache=[]\n",
"if(contents_cache<g1_cache):\n",
" arr=[1,g1_cache]\n",
" l_bs_cache.append(arr)\n",
"if(contents_cache<g2_cache):\n",
" arr=[2,g2_cache]\n",
" l_bs_cache.append(arr)\n",
"if(contents_cache<g3_cache):\n",
" arr=[3,g3_cache]\n",
" l_bs_cache.append(arr)\n",
"\n",
"print(l_bs_cache)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1, 9.18594536053351, 45.47304470082458]\n",
"[2, 9.193139700323409, 45.46987577194055]\n",
"[3, 9.195377006955539, 45.46770952221271]\n"
]
}
],
"source": [
"import random\n",
"import sys\n",
"import math\n",
"#milan lat,lon\n",
"latitude = 45.4654219\n",
"longitude = 9.1859243\n",
"bs_locations=[]\n",
"def generate_random_data(lat, lon):\n",
" for i in range(1,4):\n",
" dec_lat = random.random()/100\n",
" dec_lon = random.random()/100\n",
" arr=[i, lon+dec_lon, lat+dec_lat]\n",
" print(arr)\n",
" bs_locations.append(arr)\n",
"\n",
"generate_random_data(latitude, longitude)\n"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(9.194398637369373, 45.466765542441124)\n",
"[[3, 0.1499061393976395, 867]]\n",
" gridID distance internet traffic prediction\n",
"0 3 0.149906 867\n",
"0.1499061393976395\n",
"bs selection result: 3\n"
]
}
],
"source": [
"import geopy.distance\n",
"seed(1)\n",
"dec_lat = random.random()/100\n",
"dec_lon = random.random()/100\n",
"request_location=(longitude+dec_lon, latitude+dec_lat)\n",
"print(request_location)\n",
"\n",
"bs_distances=[]\n",
"for bs in l_bs_cache:\n",
" grid_num=bs[0]\n",
" for l in bs_locations:\n",
" if(l[0]==grid_num):\n",
" coords_1=(l[1],l[2])\n",
" dist=geopy.distance.geodesic(coords_1, request_location).km\n",
" arr=[grid_num,dist,bs[1]]\n",
" bs_distances.append(arr)\n",
"#[gridID, distance, traffic]\n",
"print(bs_distances)\n",
"\n",
"#sort bs_distances\n",
"col=['gridID','distance','internet traffic prediction']\n",
"df=pd.DataFrame(bs_distances, columns=col)\n",
"df.sort_values(by=['distance'], inplace=True)\n",
"print(df)\n",
"\n",
"min_value=df['distance'][0]\n",
"grid_num=df['gridID'][0]\n",
"print(min_value)\n",
"count_row=df.shape[0]\n",
"for x in range(count_row):\n",
" if(df.loc[x]['distance']<min_value):\n",
" min_value=df.loc[x]['distance']\n",
" grid_num=df.loc[x]['gridID']\n",
" \n",
"print(\"bs selection result: \",grid_num)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"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": "3.7.7"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
import pandas as pd
from tqdm import tqdm
data=open("sms-call-internet-mi-2013-11-01.txt").read()
lines_of_data=data.splitlines()
tmp = []
for i in tqdm(range(len(lines_of_data))):
tmp.append(lines_of_data[i].split())
data_df = pd.DataFrame(tmp)
data_df.to_csv('dataset.csv')
data_df.shape
data_df.ndim
,gridID,Internet Activity Prediction
0,1,9952.919154515539
0,2,10007.809478855686
0,3,12864.899626276776
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