Data-Preprocessing.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "| Device      | Name                      | Model         |\n",
    "| :---        |           :----:          |          ---: |\n",
    "| D1          | Xiaomi Redmi Note 10 Pro  |  M2101K6G     |\n",
    "| D2          | Xiaomi Redmi 9            | M2004J19C     |\n",
    "| D3          | Nokia 5.1 Plus            | -     |\n",
    "| D4          |  Nokia 2.3                | -     |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn import preprocessing\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.utils import shuffle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1152x1152 with 0 Axes>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1152x1152 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "horizontal_length = 16\n",
    "vertical_length = 16\n",
    "plt.figure(figsize=(horizontal_length, vertical_length))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# open3feet"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### D1 to D2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "path_d1tod2 = './dataset/open3feet/hands/D1_to_D2.csv'\n",
    "df_d1tod2 = pd.read_csv(path_d1tod2)\n",
    "open3feet_d1tod2 = df_d1tod2['rssi']\n",
    "sns.distplot(open3feet_d1tod2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1451,)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "open3feet_d1tod2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-85.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "open3feet_d1tod2.median()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### D2 to D1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "path_d2tod1 = './dataset/open3feet/hands/D2_to_D1.csv'\n",
    "df_d2tod1 = pd.read_csv(path_d2tod1)\n",
    "open3feet_d2tod1 = df_d2tod1['rssi']\n",
    "sns.distplot(open3feet_d2tod1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1889,)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "open3feet_d2tod1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-74.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "open3feet_d2tod1.median()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### D3 to D4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "path_d3tod4 = './dataset/open3feet/floor/D3_to_D4.csv'\n",
    "df_d3tod4 = pd.read_csv(path_d3tod4)\n",
    "open3feet_d3tod4 = df_d3tod4['rssi']\n",
    "sns.distplot(open3feet_d3tod4)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1685,)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "open3feet_d3tod4.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-69.0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "open3feet_d3tod4.median()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### D4 to D3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "path_d4tod3 = './dataset/open3feet/floor/D4_to_D3.csv'\n",
    "df_d4tod3 = pd.read_csv(path_d4tod3)\n",
    "open3feet_d4tod3 = df_d4tod3['rssi']\n",
    "sns.distplot(open3feet_d4tod3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2207,)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "open3feet_d4tod3.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-69.0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "open3feet_d4tod3.median()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# open9feet"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### D1 to D2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "path_d1tod2 = './dataset/open9feet/hands/D1_to_D2.csv'\n",
    "df_d1tod2 = pd.read_csv(path_d1tod2)\n",
    "open9feet_d1tod2 = df_d1tod2['rssi']\n",
    "sns.distplot(open9feet_d1tod2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1110,)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "open9feet_d1tod2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-91.0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "open9feet_d1tod2.median()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### D2 to D1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "path_d2tod1 = './dataset/open9feet/hands/D2_to_D1.csv'\n",
    "df_d2tod1 = pd.read_csv(path_d2tod1)\n",
    "open9feet_d2tod1 = df_d2tod1['rssi']\n",
    "sns.distplot(open9feet_d2tod1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1873,)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "open9feet_d2tod1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-82.0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "open9feet_d2tod1.median()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### D3 to D4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "path_d3tod4 = './dataset/open9feet/floor/D3_to_D4.csv'\n",
    "df_d3tod4 = pd.read_csv(path_d3tod4)\n",
    "open9feet_d3tod4 = df_d3tod4['rssi']\n",
    "sns.distplot(open9feet_d3tod4)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(954,)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "open9feet_d3tod4.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-96.0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "open9feet_d3tod4.median()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### D4 to D3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "path_d4tod3 = './dataset/open9feet/floor/D4_to_D3.csv'\n",
    "df_d4tod3 = pd.read_csv(path_d4tod3)\n",
    "open9feet_d4tod3 = df_d4tod3['rssi']\n",
    "sns.distplot(open9feet_d4tod3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1365,)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "open9feet_d4tod3.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-96.0"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "open9feet_d4tod3.median()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# wall3feet"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### D1 to D2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ZKYl8/rwi/rx6N9v21Xodx3dU9OKpbfuOsPNgPZeMzNc58z3cP110FmmJQX6pUX2nU9GLZ5xzvFxSSU5qIpMG9/I6jnisd3oSX5gxhOfWVrBxz2Gv4/iKil48s6XyCGWHjnLJyHwSdDqlAF+8YCiZKQn85lWN6juTfrvEE8dH873SEpmg0bxEZKcmcuO0wby4YS+7DtR7Hcc3NAWCeGLz3lp2Vx/l0+cWEgzE1s1ETjZFg3S9W84v4oG3Snnonff5/tVjvI7jC1GN6M1stpltNrNtZnZ3G+vNzO6JrF9rZhNOWB80s1Vm9mxnBZf4dXw03zs9iXMHaTQvH1aQlcJV4/rzx+Vl1NQ3eR3HFzosejMLAvcClwPFwPVmVnzCZpcDwyMf84D7Tlj/FaDkjNOKL5RUHGZPTQMzR+bH3GheYsMXZwylvrGFR5fu9DqKL0Qzop8CbHPOlTrnGoFFwJwTtpkDLHRh7wE5ZtYPwMwGAFcCD3RibolToZDjlU376JOepPuGSruK+2cxY1guf3h3B00tIa/jxL1oir4QKGv1dXlkWbTb/Ar4JnDSfy0zm2dmy81seVVVVRSxJB69uGEvFTUNzByl0byc3C3nF1F5+BivlOzzOkrci+bN2LZ+G0+8/1eb25jZJ4B9zrkVZnbxyb6Jc24BsABg0qRJur9YnDiVNy5DzvGbV7eSm5Gs0Xyc8PKN6YtH5tE3K4XHl+5i9ljNgXQmohnRlwMDW309ANgT5TbTgavNbAfhQz4zzeyR004rcW397hoqDx/j0tH5BEyjeTm5hGCAaycP5M2tVZQd1KmWZyKaol8GDDezIWaWBFwHPHPCNs8AN0fOvpkG1DjnKpxz33LODXDOFUX2e9U5d2Nn/gASH0IufGw+PzOZswuzvY4jceJzk8PjxyeXl3WwpZxMh4dunHPNZnYX8CIQBB5yzm0wszsi6+cDzwNXANuAeuDWross8WhdeQ1Vtce4fsogjebPUE86z78wJ5WLR+TxxPIyvnzpcF1BfZqiumDKOfc84TJvvWx+q88dcGcHf8frwOunnFDiXkvI8cqmSvpmpTCmf5bXcSTOXD9lEPP+ewWvba7isuICr+PEJV0ZK11ubXk1+480MlejeTmJ9l6ptIQc6ckJ/PrlLVTVHvvQOt0wPjp6HSRdqiXkeHXTPvplp1Cs0bychmDAOGdANpv21nK0scXrOHFJRS9danVZNQfqGrl0VIFG83Lazh2YQ3PIsWFPjddR4pKKXrpMeDRfSf+cFEb3y/Q6jsSxwpxUcjOSWFVW7XWUuKSily6zcuchDtU3MWtUAabRvJwBM2P8wBze319HdX2j13HijopeukRTS4hXNlUyqHcaI/tqNC9nbvzA8Eyna8p1+OZUqeilS7xXeoDDDc18bIxG89I5eqcnMah3Gmt0+OaUqeil0zU0tfD65ipGFGQwNDfD6zjiI2cXZrP3cMNHTrOUk1PRS6d7c2sVR5ta+FixJqKSzjU2Mn3Gep19c0pU9NKpDtU38vbW/ZwzIJv+OalexxGfyU5NZFDvNNbvVtGfChW9dKoX1u8FYPYYjeala4wtzKaipoH9R3T4Jloqeuk0O/bXsW53DRcMzyMnLcnrOOJTYyNXWGtUHz0VvXSKkHM8t66CrJQELhqR53Uc8bGctCQG9kpV0Z8CFb10iqXvH2R39VFmj+1LUoKeVtK1xhZms6emgZ0H6ryOEhf0GylnrOZoEy9u2Muw/AzGDdAtAqXrjekfPvvmpY2VHieJDyp6OWN/XbOHkHN8cnyhLo6SbtE7PYmCrGQVfZRU9HJG1u+uYWPFYS4dVUDvdL0BK91ndL8slu88xKE6zX3TERW9nLbKww38z+rdFOakMn1YrtdxpIcp7pdFS8jx2uZ9XkeJeSp6OS2hkOP/PLmGppYQn500gGBAh2yke/XPSSU/U4dvoqGil9Py+3d38NbW/Vxxdj/yM1O8jiM9UMCMWcUFvLGlioYm3XnqZFT0cspWl1Xzk79tYtbofKYU9fY6jvRglxUXUN/YwuLSA15HiWkqejklB44c438/soL8rGR+9tlxOstGPHXe0D6kJQV5WYdvTkpFL1FrCTm+smg1++samX/jRE1zIJ5LSQxy0Yg8Xi6pJBRyXseJWSp6idoPn9vI29v288M5Yz+YLlbEa7NGF1B5+BjrNCVCu1T0EpU/vLuDh9/ZwW0zhnDt5IFexxH5wMxR+QQDxsslOnzTHhW9dOjVTZX82183MGt0Ad++YrTXcUQ+pFd6EpMG99JplieR4HUA6XyPLdl1yvvMnTqozeUb9tRw12OrKO6fxT3Xj/f0fPnT+bmkZ7isuIAfPldC2cF6BvZO8zpOzNGIXtpVUXOUL/x+GTmpiTz4+cmkJWlcILHpsuICQJOctUdFL206cqyZ236/nLpjLTx4y2QKsnRRlMSuwX3SGVGQoaJvh4pePqK5JcSXH1/F5spafjv3XEb3y/I6kkiHZo0uYOmOg9TUN3kdJeao6OUjfvhcCa9u2sf3rx7DxSPzvY4jEpXLigtoCTle36JJzk6kopcP+cO7O/j9uzu4/YIh3DRtsNdxRKI2bkAOuRma5KwtKnr5wLvb9/Pvz25k1uh87r5cp1FKfAkEjFmj83ljcxWNzSGv48QUFb0AUHawnjsfXcmQ3HR++TlvT6MUOV2zRhdQe6yZpe8f9DpKTFHRC8eaW7h94XJaQo77b55EZkqi15FETsv0YbmkJAZ4aeNer6PEFJ0Y3cM553hqRTmb99by+fOLWLz9AIu3a8pXiU+pSUFmDMvj5ZJ9fP9qp9lVIzSi7+Fe31LF+j2HmT22LyMKMr2OI3LGLivOZ3f1UUoqar2OEjNU9D3YlspaXt5YyfiBOczQPV/FJ2aOKsAMTXLWSlRFb2azzWyzmW0zs7vbWG9mdk9k/VozmxBZPtDMXjOzEjPbYGZf6ewfQE5PbUMTT64oJz8rmU+dW6iXuOIbeZnJnDswR0XfSodFb2ZB4F7gcqAYuN7Mik/Y7HJgeORjHnBfZHkz8A3n3GhgGnBnG/tKNws5x5MrymlsbuG6yYNIDOqFnfjLrOIC1pbXsLemwesoMSGa3/ApwDbnXKlzrhFYBMw5YZs5wEIX9h6QY2b9nHMVzrmVAM65WqAEKOzE/HIa3t66n237jnDl2f01h4340mWjw5OcvbJJo3qIrugLgbJWX5fz0bLucBszKwLOBZacakjpPGUH6/n7xr2M6Z/F5KJeXscR6RLD8jMY3CdNV8lGRFP0bR28PfHmjCfdxswygKeArzrnDrf5TczmmdlyM1teVVUVRSw5VQ1NLTyxvIyslEQ+fe4AHZcX3zIzZo0u4N1tB6g71ux1HM9FU/TlQOt7xw0A9kS7jZklEi75R51zT7f3TZxzC5xzk5xzk/Ly8qLJLqfoL6t3c6iukc9NHkhqUtDrOCJdatboAhpbQry1VQPHaC6YWgYMN7MhwG7gOmDuCds8A9xlZouAqUCNc67CwkPGB4ES59wvOjG3nKK15dWsKa/h0tH5DO6T7nUckU5xsruOtYQcqYlBfvdGKQfrPjx1cXt3VPOrDoveOddsZncBLwJB4CHn3AYzuyOyfj7wPHAFsA2oB26N7D4duAlYZ2arI8u+7Zx7vnN/DDmZ2oYm/rJ6DwN6pXLxCE07LD1DMGCM6pvJpr21tIRcj56/KaopECLF/PwJy+a3+twBd7ax39u0ffxeuolzjv9ZtZumlhDXTBjQo5/s0vOM6Z/NqrJqSvcfYXh+z73yWydQ+9yqsmpK9tZyWXEB+TqVUnqY4QUZJCUEWL+7zXNAegwVvY/VHG3i2bV7GNw7jema4kB6oMRggJEFmWzcU0PInXiyYM+hovcp5xxPryynJeS4ZuIAAjqVUnqosYXZ1DW2sGN/nddRPKOi96nlOw6xdd8RZo/pS5+MZK/jiHhmZEEmiUFj/Z6ee/hGRe9Dh+oaeW59BUNz05k6tI/XcUQ8lZQQYERBJht68OEbFb3PhEKOp1aVA/CZCTpkIwIwtn82tQ3N7DxQ73UUT6jofeaRJTsprarjyrH96JWe5HUckZgwql/48M2asmqvo3hCRe8jO/bX8ePnNzGiIINJmrBM5APJCUGK+2WxbncNzaGQ13G6nYreJ1pCjq//cTWJQeNTmrBM5CPGD8zhaFMLWyuPeB2l2+nm4D4x/43trNxVza+vG0/dsRav44jEnGH5maQlBVndAw/faETvAxv21PCrl7dw5dn9uHpcf6/jiMSkYMA4uzCbkorD1DY0dbyDj6jo41xDUwtff2INOWlJ/PCTY3XIRuQkxg/MoTnkeHFDz7ohiYo+zv3ypS1srqzlp9eco7NsRDowqHcavdOTeHJ5Wccb+4iKPo4tKT3AgrdKmTt1EJeM1PTDIh0xMyYP7sWS9w+yvarnvCmroo9Thxua+MaTaxjUO43vXDHa6zgicWPC4F4kBIxFS9u/aYnfqOjjkHOOu59aS0VNA7+4djzpyTp5SiRamSmJXFZcwFMrd3OsuWecoaaij0MLF+/k+XV7+ebHRzJxsC6MEjlV108ZxMG6Rv7eQ96UVdHHmbXl1fzouRJmjsrn9guGeh1HJC7NGJbLgF6pJ73nrJ+o6ONIzdEm7nxsJbkZSfz8s+MI6LaAIqclEDDmTh3E4tIDlFT4f/piFX2c+OC4fHUDv5k7QadSipyhG6YMJi0pyP1vlnodpcup6OPEH97dwd/W7+Wbs3VcXqQzZKclct3kQTyzZg97qo96HadLqejjwHulB/jhcyVcOiqfL87QcXmRzvKFGUU44KG33/c6SpdS0ce4XQfq+V+PrGBwnzR+ed14HZcX6UQDeqVx1Tn9eHzpLmrq/Tv/jYo+htU2NPHFhcsIOXjg85PJSkn0OpKI78y78CzqGlt44G3/HqtX0ceoY80t/NN/r6C0qo7/umECQ3LTvY4k4kvF/bP4xDn9eOCt99l3uMHrOF1CRR+DQiHHN/64hne3H+Cn15zD9GG5XkcS8bX/+/GRNIdC/PLlrV5H6RK6dj7GOOf4/l838OzaCu6+fBQNTaEec1GHiFcG90nnhqmDWbh4B7fNKGJYfqbXkTqVRvQxxDnHvz+7kYWLd3L7BUP4pwt1ho1Id/nSzGGkJyXwg2dLcM55HadTqehjhHOOHz1XwsPv7ODW6UV8+4rRuomISDfqk5HM1y4bwRtbqnhq5W6v43QqFX0MaG4J8c9PreWBt9/nlvOL+O4nilXyIh645fwiJhf14t/+uoG9Nf55Y1ZF77GjjS3c8cgK/ri8nC9fOpzvXaWSF/FKIGD89JpxNLWE+NbTa31zCEdF76HyQ/VcM/9dXtm0jx98cixfv2yESl7EY0Ny0/nmx0fx2uYq7ntju9dxOoXOuvHIu9v2c9fjq2hqDvHg5ycxc1SB15FEJOLW6UWsKqvmpy9sZmhuBrPH9vU60hlR0XezhqYWfvHSFu5/q5Sz8jJYcNNEhuZleB1LRFoxM/7zmnMoO1jP155YTf+caZwzIMfrWKdNh2660YqdB5nz23dY8GYpc6cM4pm7pqvkRWJUSmKQBTdPpHd6Ejfcv4QlpQe8jnTaVPTdoPJwA1//42o+c99iao428fAtk/nRp84mLUkvqERiWX5mCk/ecR75Wcnc/NBS/r5hr9eRTovF4rvKkyZNcsuXL/c6xhmrPNzAfa9v5/Glu2huccwYnsvFI/NITgh6HU2kR5s7ddApbX+wrpFbf7+MNWXV3Dq9iH+ePYqUxNj6PTazFc65SW2t05CykznnWPL+QR5dsosX1lcQcvDpcwsZ1DuNPhnJXscTkdPQOz2JRbdP4z9e2MTD7+zgzS1V/Msnirl4RF5cnCmnou8Ex5pbWLWrmpc2VvK3dRXsqWkgKyWBm6YVccv5RQzqk6b5akTiXGpSkO9fPYZZowu4++m13PrwMsYPzOGOi87iklGx/Uo9qqI3s9nAr4Eg8IBz7icnrLfI+iuAeuAW59zKaPaNNy0hx+5DR9m09zDrdtewuqyaZTsO0tAUIikY4MIRufyfj4/kirP7xdxLOxE5czOG5/LqNy7mqZXl/PbVbdzxyAqyUxOZPaYv04fnMm1Ib/KzUryO+XRwdN8AAAcNSURBVCEdFr2ZBYF7gcuAcmCZmT3jnNvYarPLgeGRj6nAfcDUKPftEs45Qg5CzhFyDhf53H2wLLxNQ1OI+sZm6htbONrUEv4z8vWh+ib21Taw7/Ax9tU2sLemgbJDR2lsDgEQDBjD8zO4bvIgzjurD+ed1Uc3BxHpAZISAlw/ZRDXTBzA29v285dVu3luXQVPLC8DID8zmREFmRTlppGbkUyfjGTyMpLok5FMRnICSQkBkoIBkhMC4c8TAiQGAyQErEsOBUUzop8CbHPOlQKY2SJgDtC6rOcAC134nd33zCzHzPoBRVHs22nO/t6L1DU2E+rE95eTggHyMpMpyEpmeH4ml44uYGhuOsMLMinul0VqkkbtIj1VYjDAJSPzuWRkPs0tITbsOczS9w+yaW8t2/bV8uzaCqpP4RaFuRnJLP+XWZ2eM5qiLwTKWn1dTnjU3tE2hVHuC4CZzQPmRb48Ymabo8jWllxg/2nu26ZuvhVBp+fvRvGcHeI7v7Kfghs696/rtPw7AfvX0959cHsroin6tl5HnDhmbm+baPYNL3RuAbAgijwnZWbL2zvFKB7Ec/54zg7xnV/ZvRMP+aMp+nJgYKuvBwB7otwmKYp9RUSkC0VzZewyYLiZDTGzJOA64JkTtnkGuNnCpgE1zrmKKPcVEZEu1OGI3jnXbGZ3AS8SPkXyIefcBjO7I7J+PvA84VMrtxE+vfLWk+3bJT/JP5zx4R+PxXP+eM4O8Z1f2b0T8/ljcgoEERHpPJrUTETE51T0IiI+F9dFb2afNbMNZhYys0knrPuWmW0zs81m9vFWy1+PLFsd+cjv/uSnnX2ima2LrLvHYmQ2JTMbZ2aLI9n+amZZkeVFZna01WM93+usJ2ove2Rdm/8OscLMxpvZe5HHdrmZTYksj/nHHdrPH1kX64/9E60e3x1mtjqyPDYfe+dc3H4Ao4GRwOvApFbLi4E1QDIwBNgOBCPrPrRtnGVfCpxH+PqEvwGXe/1zRHItAy6KfP4F4AeRz4uA9V7nO83s7f47xMoH8PfjzwHCJ0O8Hi+Pewf5Y/6xP+Hn+Dnw3Vh+7ON6RO+cK3HOtXUF7RxgkXPumHPufcJnA01pYzvPnGr2yJQSWc65xS78jFoIfLIbI5/MSODNyOcvAZ/xMMupai97zD+HCF98ePwVSDbxd41Ke/nj4bEHPpjQ8Vrgca+znExcF/1JtDclw3EPR15W/WusHP5o5WTTSZS3sTwWrAeujnz+WT58kdwQM1tlZm+Y2QXdH61D7WXv6DkUC74K/KeZlQE/A77Val2sP+7Qfv54eOyPuwCodM61nikl5h77mJ+P3sxeBtq6Bft3nHN/aW+3NpYdP4/0BufcbjPLBJ4CbiI8Ou50nZw96ukkusLJfhbChzzuMbPvEr4grjGyrgIY5Jw7YGYTgf8xszHOucPdEjriNLN7+ngf10H2S4GvOeeeMrNrgQeBWcTI4w6nnT/mH/tWv7/X8+HRfMw89q3FfNE7505nKrd2p21wzu2O/FlrZo8RfknYJUXfydnLI5+fuLxbRPGzfAzAzEYAV0b2OQYci3y+wsy2AyOAbr1P5OlkJ7qpP7rcybKb2ULgK5EvnwQeiOwTE4975Pufcn7i4LEHMLME4NPAxFb7xMxj35pfD908A1xnZslmNoTwPPlLzSzBzHIBzCwR+AThl+6xpM3sLjylRK2ZTYscbroZaO9VQbc6fuaSmQWAfwHmR77Os/A9CTCzoYR/llKvcralvey08+/gTcp27QEuinw+k8hEq/HwuEe0mZ/4eOwh/Opjk3Pug0OqMfvYe/1u8Jl8AJ8i/L//MaASeLHVuu8Qfrd+M/94Zz8dWAGsBTYQufNVPGSPLJ9E+D+m7cBviVzZ7PUH4VHZlsjHT47nIvzG5gbCZ1CsBK7yOmu02U/27xArH8CMyPN5DbAEmBgvj/vJ8sfDYx/J+HvgjhOWxeRjrykQRER8zq+HbkREJEJFLyLicyp6ERGfU9GLiPicil5ExOdU9CKdwMyuNrO7vc4h0hadXinSjsiFaeacC3mdReRMaEQv0kpkPvESM/svwhe8vGJm6yPz1X8tss2XzWyjma01s0WRZbeY2W+9zC7Snpif60bEAyMJ3+D+QeAnzrmxAGaWE1l/NzDEOXes1TKRmKURvchH7XTOvUd4jpKhZvYbM5sNHJ+BcC3wqJndCDR7FVIkWip6kY+qA3DOHQLGEb4L2J38Y3bFK4F7Cc9auCIyi6FIzNITVKQdkZlOG114vvTtwO8js1wOdM69ZmZvA3OBDE+DinRARS/SvkLCdyM7/sr3W0AQeMTMsgnfIOOXzrnq2LtRmcg/6PRKERGf0zF6ERGfU9GLiPicil5ExOdU9CIiPqeiFxHxORW9iIjPqehFRHzu/wME61XLHXKWyQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "path_d1tod2_w = './dataset/wall3feet/hands/D1_to_D2.csv'\n",
    "df_d1tod2_w = pd.read_csv(path_d1tod2_w)\n",
    "wall3feet_d1tod2 = df_d1tod2_w['rssi']\n",
    "sns.distplot(wall3feet_d1tod2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1359,)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wall3feet_d1tod2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-87.0"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wall3feet_d1tod2.median()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### D2 to D1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "path_d2tod1_w = './dataset/wall3feet/hands/D2_to_D1.csv'\n",
    "df_d2tod1_w = pd.read_csv(path_d2tod1_w)\n",
    "wall3feet_d2tod1 = df_d2tod1_w['rssi']\n",
    "sns.distplot(wall3feet_d2tod1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1850,)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wall3feet_d2tod1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-77.0"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wall3feet_d2tod1.median()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### D3 to D4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "path_d3tod4 = './dataset/wall3feet/floor/D3_to_D4.csv'\n",
    "df_d3tod4= pd.read_csv(path_d3tod4)\n",
    "wall3feet_d3tod4 = df_d3tod4['rssi']\n",
    "sns.distplot(wall3feet_d3tod4)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9167,)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wall3feet_d3tod4.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-86.0"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wall3feet_d3tod4.median()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### D4 to D3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "path_d4tod3 = './dataset/wall3feet/floor/D4_to_D3.csv'\n",
    "df_d4tod3 = pd.read_csv(path_d4tod3)\n",
    "wall3feet_d4tod3 = df_d4tod3['rssi']\n",
    "sns.distplot(wall3feet_d4tod3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5205,)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wall3feet_d4tod3.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-86.0"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wall3feet_d4tod3.median()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# wall9feet"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### D1 to D2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "path_d1tod2 = './dataset/wall9feet/hands/D1_to_D2.csv'\n",
    "df_d1tod2 = pd.read_csv(path_d1tod2)\n",
    "wall9feet_d1tod2 = df_d1tod2['rssi']\n",
    "sns.distplot(wall9feet_d1tod2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(198,)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wall9feet_d1tod2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-98.0"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wall9feet_d1tod2.median()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### D2 to D1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "path_d2tod1 = './dataset/wall9feet/hands/D2_to_D1.csv'\n",
    "df_d2tod1 = pd.read_csv(path_d2tod1)\n",
    "wall9feet_d2tod1 = df_d2tod1['rssi']\n",
    "sns.distplot(wall9feet_d2tod1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1558,)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wall9feet_d2tod1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-92.0"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wall9feet_d2tod1.median()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### D3 to D4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "path_d3tod4 = './dataset/wall9feet/floor/D3_to_D4.csv'\n",
    "df_d3tod4 = pd.read_csv(path_d3tod4)\n",
    "wall9feet_d3tod4 = df_d3tod4['rssi']\n",
    "sns.distplot(wall9feet_d3tod4)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5161,)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wall9feet_d3tod4.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-89.0"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wall9feet_d3tod4.median()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### D4 to D3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "path_d4tod3 = './dataset/wall9feet/floor/D4_to_D3.csv'\n",
    "df_d4tod3 = pd.read_csv(path_d4tod3)\n",
    "wall9feet_d4tod3 = df_d4tod3['rssi']\n",
    "sns.distplot(wall9feet_d4tod3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2514,)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wall9feet_d4tod3.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-90.0"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wall9feet_d4tod3.median()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prepare Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### open3feet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_open3feet = pd.DataFrame()\n",
    "dataset_open3feet['rssi'] = open3feet_d3tod4\n",
    "dataset_open3feet['target'] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "temp = pd.DataFrame()\n",
    "temp['rssi'] = open3feet_d4tod3\n",
    "temp['target'] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_open3feet = dataset_open3feet.append(temp)\n",
    "dataset_open3feet.reset_index(drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rssi</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-66</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-66</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-64</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-69</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-67</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3887</th>\n",
       "      <td>-68</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3888</th>\n",
       "      <td>-72</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3889</th>\n",
       "      <td>-72</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3890</th>\n",
       "      <td>-70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3891</th>\n",
       "      <td>-70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3892 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      rssi  target\n",
       "0      -66       1\n",
       "1      -66       1\n",
       "2      -64       1\n",
       "3      -69       1\n",
       "4      -67       1\n",
       "...    ...     ...\n",
       "3887   -68       1\n",
       "3888   -72       1\n",
       "3889   -72       1\n",
       "3890   -70       1\n",
       "3891   -70       1\n",
       "\n",
       "[3892 rows x 2 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_open3feet"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### open9feet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_open9feet = pd.DataFrame()\n",
    "dataset_open9feet['rssi'] = open9feet_d3tod4\n",
    "dataset_open9feet['target'] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "temp = pd.DataFrame()\n",
    "temp['rssi'] = open9feet_d4tod3\n",
    "temp['target'] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_open9feet = dataset_open9feet.append(temp)\n",
    "dataset_open9feet.reset_index(drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rssi</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-94</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-98</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-97</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-96</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-96</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2314</th>\n",
       "      <td>-98</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2315</th>\n",
       "      <td>-96</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2316</th>\n",
       "      <td>-98</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2317</th>\n",
       "      <td>-97</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2318</th>\n",
       "      <td>-98</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2319 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      rssi  target\n",
       "0      -94       0\n",
       "1      -98       0\n",
       "2      -97       0\n",
       "3      -96       0\n",
       "4      -96       0\n",
       "...    ...     ...\n",
       "2314   -98       0\n",
       "2315   -96       0\n",
       "2316   -98       0\n",
       "2317   -97       0\n",
       "2318   -98       0\n",
       "\n",
       "[2319 rows x 2 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_open9feet"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### wall3feet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_wall3feet = pd.DataFrame()\n",
    "dataset_wall3feet['rssi'] = wall3feet_d3tod4\n",
    "dataset_wall3feet['target'] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "temp = pd.DataFrame()\n",
    "temp['rssi'] = wall3feet_d4tod3\n",
    "temp['target'] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_wall3feet = dataset_wall3feet.append(temp)\n",
    "dataset_wall3feet.reset_index(drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
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       "      <th>14369</th>\n",
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       "      <th>14371</th>\n",
       "      <td>-80</td>\n",
       "      <td>0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>14372 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       rssi  target\n",
       "0       -85       0\n",
       "1       -78       0\n",
       "2       -78       0\n",
       "3       -78       0\n",
       "4       -78       0\n",
       "...     ...     ...\n",
       "14367   -86       0\n",
       "14368   -86       0\n",
       "14369   -80       0\n",
       "14370   -80       0\n",
       "14371   -80       0\n",
       "\n",
       "[14372 rows x 2 columns]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_wall3feet"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### wall9feet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_wall9feet = pd.DataFrame()\n",
    "dataset_wall9feet['rssi'] = wall9feet_d3tod4\n",
    "dataset_wall9feet['target'] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "temp = pd.DataFrame()\n",
    "temp['rssi'] = wall9feet_d4tod3\n",
    "temp['target'] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_wall9feet = dataset_wall9feet.append(temp)\n",
    "dataset_wall9feet.reset_index(drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <th></th>\n",
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       "      <th>target</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>4</th>\n",
       "      <td>-96</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>7670</th>\n",
       "      <td>-98</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>7671</th>\n",
       "      <td>-91</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7672</th>\n",
       "      <td>-91</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>7673</th>\n",
       "      <td>-90</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>7674</th>\n",
       "      <td>-90</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7675 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      rssi  target\n",
       "0      -89       0\n",
       "1      -89       0\n",
       "2      -98       0\n",
       "3      -98       0\n",
       "4      -96       0\n",
       "...    ...     ...\n",
       "7670   -98       0\n",
       "7671   -91       0\n",
       "7672   -91       0\n",
       "7673   -90       0\n",
       "7674   -90       0\n",
       "\n",
       "[7675 rows x 2 columns]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_wall9feet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Concat All Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = dataset_open3feet.append(dataset_open9feet)\n",
    "dataset = dataset.append(dataset_wall3feet)\n",
    "dataset = dataset.append(dataset_wall9feet)\n",
    "dataset.reset_index(drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>-98</td>\n",
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       "      <th>28254</th>\n",
       "      <td>-91</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>28255</th>\n",
       "      <td>-91</td>\n",
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       "      <th>28256</th>\n",
       "      <td>-90</td>\n",
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       "      <td>-90</td>\n",
       "      <td>0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>28258 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       rssi  target\n",
       "0       -66       1\n",
       "1       -66       1\n",
       "2       -64       1\n",
       "3       -69       1\n",
       "4       -67       1\n",
       "...     ...     ...\n",
       "28253   -98       0\n",
       "28254   -91       0\n",
       "28255   -91       0\n",
       "28256   -90       0\n",
       "28257   -90       0\n",
       "\n",
       "[28258 rows x 2 columns]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = shuffle(dataset)\n",
    "dataset.reset_index(drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>28257</th>\n",
       "      <td>-78</td>\n",
       "      <td>0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>28258 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       rssi  target\n",
       "0       -86       0\n",
       "1       -78       0\n",
       "2       -70       1\n",
       "3       -86       0\n",
       "4       -90       0\n",
       "...     ...     ...\n",
       "28253   -99       0\n",
       "28254   -97       0\n",
       "28255   -86       0\n",
       "28256   -69       1\n",
       "28257   -78       0\n",
       "\n",
       "[28258 rows x 2 columns]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset.to_csv(\"rssi_dataset_3.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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