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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"There are 24 buildings in the testing set.\n"
]
},
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"data": {
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"<div>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>file</th>\n",
" <th>building</th>\n",
" <th>site</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>../input/indoor-location-navigation/test//00ff...</td>\n",
" <td>5da1389e4db8ce0c98bd0547</td>\n",
" <td>SiteName:和达城商场</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>../input/indoor-location-navigation/test//01c4...</td>\n",
" <td>5da138b74db8ce0c98bd4774</td>\n",
" <td>SiteName:万象城</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>../input/indoor-location-navigation/test//030b...</td>\n",
" <td>5da138764db8ce0c98bcaa46</td>\n",
" <td>SiteName:银泰百货</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>../input/indoor-location-navigation/test//0389...</td>\n",
" <td>5dbc1d84c1eb61796cf7c010</td>\n",
" <td>SiteName:杭州大悦城</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>../input/indoor-location-navigation/test//0402...</td>\n",
" <td>5da1383b4db8ce0c98bc11ab</td>\n",
" <td>SiteName:永旺梦乐城</td>\n",
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"text/plain": [
" file \\\n",
"0 ../input/indoor-location-navigation/test//00ff... \n",
"1 ../input/indoor-location-navigation/test//01c4... \n",
"2 ../input/indoor-location-navigation/test//030b... \n",
"3 ../input/indoor-location-navigation/test//0389... \n",
"4 ../input/indoor-location-navigation/test//0402... \n",
"\n",
" building site \n",
"0 5da1389e4db8ce0c98bd0547 SiteName:和达城商场 \n",
"1 5da138b74db8ce0c98bd4774 SiteName:万象城 \n",
"2 5da138764db8ce0c98bcaa46 SiteName:银泰百货 \n",
"3 5dbc1d84c1eb61796cf7c010 SiteName:杭州大悦城 \n",
"4 5da1383b4db8ce0c98bc11ab SiteName:永旺梦乐城 "
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import numpy as np # linear algebra\n",
"import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
"\n",
"# Input data files are available in the read-only \"../input/\" directory\n",
"# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
"\n",
"import os\n",
"# Prepare paths:\n",
"import glob\n",
"from pathlib import Path\n",
"inpath = '../input/indoor-location-navigation/'\n",
"metapath = inpath + 'metadata/'\n",
"trainpath = inpath + 'train/'\n",
"testpath = inpath + 'test/'\n",
"\n",
"# Extract testing files, buildings and sites:\n",
"os.system(f'grep SiteID {testpath}/* > test_buildings.txt' )\n",
"test_buildings = pd.read_csv('test_buildings.txt',sep='\\t',header=None,names=['file','building','site'])\n",
"test_buildings['file'] = test_buildings['file'].apply(lambda x: x[:-2])\n",
"test_buildings['building'] = test_buildings['building'].apply(lambda x: x[7:])\n",
"\n",
"# How many buildings in the testing set?\n",
"buildings = np.unique(test_buildings['building'])\n",
"print('There are',len(buildings),'buildings in the testing set.')\n",
"\n",
"test_buildings.head()\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"# Compile C++ pre-processing code:\n",
"er=os.system(\"g++ ../input/indoor-cpp/1_preprocess.cpp -std=c++11 -o preprocess\")\n",
"if(er): print(\"Error\")\n",
"\n",
"# Reformat the testing set:\n",
"os.system('mkdir test')\n",
"for i,(path_filename,building) in enumerate(zip(test_buildings['file'],test_buildings['building'])):\n",
" er=os.system(f'./preprocess {path_filename} test {building} {0}') #since we do not know the floor, I put 0.\n",
" if(er): print(\"Error:\",path_filename)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"# Acceleration, magnetic and orientation testing data:\n",
"os.system('mkdir indoor_testing_accel')\n",
"os.system(\"g++ ../input/indoor-cpp/2_preprocess_accel.cpp -std=c++11 -o preprocess_accel\")\n",
"for building in buildings:\n",
" os.system(f'./preprocess_accel {building}')"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"# Wifi testing data:\n",
"os.system('mkdir test_wifi')\n",
"os.system(\"g++ /kaggle/input/indoor-cpp/2_preprocess_wifi.cpp -std=c++11 -o preprocess_wifi\")\n",
"for building in buildings:\n",
" os.system(f'./preprocess_wifi {building}')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
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