bs selection algorithm and save internet activity predictions

+{
+ "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