uci.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# IOT internship for HAR - Deepak Thandra"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Renaming file names to access in sequence\n",
    "\n",
    "import glob, os\n",
    "os.chdir(\"C://Users/babi/1Acode/parag/chest\")\n",
    "i=0\n",
    "for file in glob.glob(\"*.csv\"):\n",
    "    \n",
    "    pid=file[0:-4]\n",
    "    if len(pid)==1 :\n",
    "        os.rename(file,\"0\"+file)\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reading data from files and appending in a dataframe\n",
    "\n",
    "for file in glob.glob(\"*.csv\"):\n",
    "\n",
    "    pid=file[0:-4]\n",
    "    dft=pd.read_csv(file,header=None,names=[\"sid\",\"acc_x\",\"acc_y\",\"acc_z\",\"label\"])\n",
    "    dft[\"pid\"]=pid\n",
    "    dft.count()\n",
    "\n",
    "    if i==0 :\n",
    "        df=dft\n",
    "    else :\n",
    "        df=df.append(dft,ignore_index=True)\n",
    "    i=i+1\n",
    "    \n",
    "    del dft\n",
    "    \n",
    "df[\"sid\"]=df[\"sid\"].astype(int)\n",
    "df[\"pid\"]=df[\"pid\"].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sid      1926896\n",
       "acc_x    1926896\n",
       "acc_y    1926896\n",
       "acc_z    1926896\n",
       "label    1926896\n",
       "pid      1926896\n",
       "dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sid      int32\n",
       "acc_x    int64\n",
       "acc_y    int64\n",
       "acc_z    int64\n",
       "label    int64\n",
       "pid      int32\n",
       "dtype: object"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>sid</th>\n",
       "      <th>acc_x</th>\n",
       "      <th>acc_y</th>\n",
       "      <th>acc_z</th>\n",
       "      <th>label</th>\n",
       "      <th>pid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1502</td>\n",
       "      <td>2215</td>\n",
       "      <td>2153</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1667</td>\n",
       "      <td>2072</td>\n",
       "      <td>2047</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1611</td>\n",
       "      <td>1957</td>\n",
       "      <td>1906</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1601</td>\n",
       "      <td>1939</td>\n",
       "      <td>1831</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1643</td>\n",
       "      <td>1965</td>\n",
       "      <td>1879</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sid  acc_x  acc_y  acc_z  label  pid\n",
       "0    0   1502   2215   2153      1    1\n",
       "1    1   1667   2072   2047      1    1\n",
       "2    2   1611   1957   1906      1    1\n",
       "3    3   1601   1939   1831      1    1\n",
       "4    4   1643   1965   1879      1    1"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>sid</th>\n",
       "      <th>acc_x</th>\n",
       "      <th>acc_y</th>\n",
       "      <th>acc_z</th>\n",
       "      <th>label</th>\n",
       "      <th>pid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1926891</th>\n",
       "      <td>103500</td>\n",
       "      <td>2043</td>\n",
       "      <td>2539</td>\n",
       "      <td>1990</td>\n",
       "      <td>7</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1926892</th>\n",
       "      <td>103500</td>\n",
       "      <td>2036</td>\n",
       "      <td>2508</td>\n",
       "      <td>1976</td>\n",
       "      <td>7</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1926893</th>\n",
       "      <td>103500</td>\n",
       "      <td>2025</td>\n",
       "      <td>2520</td>\n",
       "      <td>1991</td>\n",
       "      <td>7</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1926894</th>\n",
       "      <td>103500</td>\n",
       "      <td>2009</td>\n",
       "      <td>2546</td>\n",
       "      <td>2007</td>\n",
       "      <td>7</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1926895</th>\n",
       "      <td>103500</td>\n",
       "      <td>1999</td>\n",
       "      <td>2532</td>\n",
       "      <td>1996</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            sid  acc_x  acc_y  acc_z  label  pid\n",
       "1926891  103500   2043   2539   1990      7   15\n",
       "1926892  103500   2036   2508   1976      7   15\n",
       "1926893  103500   2025   2520   1991      7   15\n",
       "1926894  103500   2009   2546   2007      7   15\n",
       "1926895  103500   1999   2532   1996      0   15"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    608667\n",
       "7    593563\n",
       "4    357064\n",
       "3    216737\n",
       "5     51498\n",
       "2     47878\n",
       "6     47770\n",
       "0      3719\n",
       "Name: label, dtype: int64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.label.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1926896"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df[df.label!=0]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1923177"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Series([], Name: label, dtype: int64)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We have label 0 for all the persons data as the last record \n",
    "# Forpersons 2, 6, 8,9 there are labels in the midde rows also \n",
    "# Thought these are for idle position  rows with label 0\n",
    "\n",
    "a=df.loc[df.label==0]\n",
    "a[[\"label\",\"pid\"]].groupby([\"pid\",'label'])['label'].count()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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"
    },
    {
     "data": {
      "image/png": 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JWJyuTd8bEZ+LiBWS5gOPkp22mhoRr6U85wCLgAHArIhYkTZxHjBP0iXAg8DMFJ8JXCepjewIY1If7K+ZmZXQY9GIiNOrhGdWiXX2vxS4tEp8IbCwSnw12d1VlfFXgNN6Gp+Zlbf3nQ/1qv8zH3lvnUZi2zt/ItzMzArzV6ObWV2NmnZr4b5PTT+5jiOxvuAjDTMzK8xFw8zMCnPRMDOzwlw0zMysMBcNMzMrzEXDzMwKc9EwM7PCXDTMzKwwFw0zMyvMRcPMzApz0TAzs8JcNMzMrDB/YaGZ9Uv+IsTGcNEwM8u7aHAv+2+qzzi2Uz2enpI0S9J6SY/kYrtLWixpVfo5NMUl6UpJbZKWSTo8t05L6r8qPV+8M36EpOVpnSuVHgXY1TbMzKxxihxpzAa+A8zNxaYBt0fEdEnT0vJ5wIlkzwUfAxwFXAMcJWl3ssfENgMB3C9pQURsTH2mAPeSPdlvHHBbN9swM+uXDptzWOG+y1uW13EktevxSCMi7iZ7RnfeeGBOmp8DTMjF50bmXmCIpH2AE4DFEbEhFYrFwLjUtltE3BMRQVaYJvSwDTMza5Ba757aKyLWAaSfe6b4cGBtrl97inUXb68S724bZmbWIH19y62qxKKGeO82Kk2R1CqptaOjo7erm5lZQbUWjWfTqSXSz/Up3g6MzPUbATzdQ3xElXh329hKRMyIiOaIaG5qaqpxl8zMrCe1Fo0FQOcdUC3Azbn4mekuqqOBTenU0iLgeElD011QxwOLUtuLko5Od02dWZGr2jbMzKxBerx7StINwDHAHpLaye6Cmg7MlzQZWAOclrovBE4C2oCXgLMAImKDpK8DS1O/iyOi8+L62WR3aO1MdtfUbSne1TbMzKxBeiwaEXF6F01jq/QNYGoXeWYBs6rEW4FDq8R/W20bZmbWOP7uKTMzK8xFw8zMCnPRMDOzwlw0zMysMBcNMzMrzEXDzMwKc9EwM7PCXDTMzKwwFw0zMyvMRcPMzApz0TAzs8JcNMzMrDAXDTMzK8xFw8zMCnPRMDOzwlw0zMyssFJFQ9IXJa2Q9IikGyS9TdJoSUskrZJ0o6RBqe9OabkttY/K5Tk/xR+XdEIuPi7F2iRNKzNWMzMrr+aiIWk4cC7QHBGHAgOAScBlwBURMQbYCExOq0wGNkbEAcAVqR+SDknrvRsYB1wtaYCkAcBVwInAIcDpqa+ZmTVI2dNTA4GdJQ0EdgHWAccCN6X2OcCEND8+LZPax0pSis+LiFcj4kmy54sfmaa2iFgdEZuBeamvmZk1SM1FIyL+B/gXYA1ZsdgE3A88HxFbUrd2YHiaHw6sTetuSf2H5eMV63QVNzOzBilzemoo2Tv/0cC+wNvJTiVVis5VumjrbbzaWKZIapXU2tHR0dPQzcysRmVOTx0HPBkRHRHxB+DHwF8CQ9LpKoARwNNpvh0YCZDaBwMb8vGKdbqKbyUiZkREc0Q0NzU1ldglMzPrTpmisQY4WtIu6drEWOBR4E5gYurTAtyc5hekZVL7HRERKT4p3V01GhgD3AcsBcaku7EGkV0sX1BivGZmVtLAnrtUFxFLJN0EPABsAR4EZgC3AvMkXZJiM9MqM4HrJLWRHWFMSnlWSJpPVnC2AFMj4jUASecAi8juzJoVEStqHa+ZmZVXc9EAiIgLgQsrwqvJ7nyq7PsKcFoXeS4FLq0SXwgsLDNGMzPrO/5EuJmZFeaiYWZmhblomJlZYS4aZmZWmIuGmZkV5qJhZmaFuWiYmVlhLhpmZlaYi4aZmRXmomFmZoWV+hoRMzNrvJUHHVy478GPrSy1LR9pmJlZYS4aZmZWmIuGmZkV5qJhZmaFuWiYmVlhLhpmZlZYqaIhaYikmyQ9JmmlpL+QtLukxZJWpZ9DU19JulJSm6Rlkg7P5WlJ/VdJasnFj5C0PK1zZXoWuZmZNUjZI41vAf8ZEQcB7wFWAtOA2yNiDHB7WgY4ERiTpinANQCSdid7ZOxRZI+JvbCz0KQ+U3LrjSs5XjMzK6HmoiFpN+CvgJkAEbE5Ip4HxgNzUrc5wIQ0Px6YG5l7gSGS9gFOABZHxIaI2AgsBsaltt0i4p6ICGBuLpeZmTVAmSONPwM6gO9JelDSdyW9HdgrItYBpJ97pv7DgbW59dtTrLt4e5X4ViRNkdQqqbWjo6PELpmZWXfKFI2BwOHANRHxPuD3/OlUVDXVrkdEDfGtgxEzIqI5Ipqbmpq6H7WZmdWsTNFoB9ojYklavomsiDybTi2Rfq7P9R+ZW38E8HQP8RFV4mZm1iA1F42IeAZYK+ldKTQWeBRYAHTeAdUC3JzmFwBnpruojgY2pdNXi4DjJQ1NF8CPBxalthclHZ3umjozl8vMzBqg7Lfc/i3wfUmDgNXAWWSFaL6kycAa4LTUdyFwEtAGvJT6EhEbJH0dWJr6XRwRG9L82cBsYGfgtjSZmVmDlCoaEfEQ0FylaWyVvgFM7SLPLGBWlXgrcGiZMZqZWd/xJ8LNzKwwFw0zMyvMRcPMzApz0TAzs8JcNMzMrDAXDTMzK8xFw8zMCnPRMDOzwlw0zMysMBcNMzMrzEXDzMwKc9EwM7PCXDTMzKwwFw0zMyvMRcPMzApz0TAzs8JKFw1JAyQ9KOmWtDxa0hJJqyTdmJ7qh6Sd0nJbah+Vy3F+ij8u6YRcfFyKtUmaVnasZmZWTl8caXwBWJlbvgy4IiLGABuBySk+GdgYEQcAV6R+SDoEmAS8GxgHXJ0K0QDgKuBE4BDg9NTXzMwapFTRkDQCOBn4bloWcCxwU+oyB5iQ5senZVL72NR/PDAvIl6NiCfJniF+ZJraImJ1RGwG5qW+ZmbWIGWPNP4V+BrweloeBjwfEVvScjswPM0PB9YCpPZNqf8f4xXrdBXfiqQpkloltXZ0dJTcJTMz60rNRUPSx4D1EXF/Plyla/TQ1tv41sGIGRHRHBHNTU1N3YzazMzKGFhi3Q8Ap0o6CXgbsBvZkccQSQPT0cQI4OnUvx0YCbRLGggMBjbk4p3y63QVNzOzBqj5SCMizo+IERExiuxC9h0R8UngTmBi6tYC3JzmF6RlUvsdEREpPindXTUaGAPcBywFxqS7sQalbSyodbxmZlZemSONrpwHzJN0CfAgMDPFZwLXSWojO8KYBBARKyTNBx4FtgBTI+I1AEnnAIuAAcCsiFhRh/GamVlBfVI0IuIu4K40v5rszqfKPq8Ap3Wx/qXApVXiC4GFfTFGMzMrz58INzOzwlw0zMysMBcNMzMrzEXDzMwKc9EwM7PCXDTMzKwwFw0zMyvMRcPMzApz0TAzs8JcNMzMrDAXDTMzK8xFw8zMCnPRMDOzwlw0zMysMBcNMzMrzEXDzMwKq7loSBop6U5JKyWtkPSFFN9d0mJJq9LPoSkuSVdKapO0TNLhuVwtqf8qSS25+BGSlqd1rpSkMjtrZmbllDnS2AJ8OSIOBo4Gpko6BJgG3B4RY4Db0zLAiWTP/x4DTAGugazIABcCR5E98e/CzkKT+kzJrTeuxHjNzKykmotGRKyLiAfS/IvASmA4MB6Yk7rNASak+fHA3MjcCwyRtA9wArA4IjZExEZgMTAute0WEfdERABzc7nMzKwB+uSahqRRwPuAJcBeEbEOssIC7Jm6DQfW5lZrT7Hu4u1V4mZm1iCli4akXYEfAf8nIl7ormuVWNQQrzaGKZJaJbV2dHT0NGQzM6tRqaIh6a1kBeP7EfHjFH42nVoi/Vyf4u3AyNzqI4Cne4iPqBLfSkTMiIjmiGhuamoqs0tmZtaNMndPCZgJrIyIy3NNC4DOO6BagJtz8TPTXVRHA5vS6atFwPGShqYL4McDi1Lbi5KOTts6M5fLzMwaYGCJdT8AfBpYLumhFPu/wHRgvqTJwBrgtNS2EDgJaANeAs4CiIgNkr4OLE39Lo6IDWn+bGA2sDNwW5rMzKxBai4aEfErql93ABhbpX8AU7vINQuYVSXeChxa6xjNzKxv+RPhZmZWmIuGmZkV5qJhZmaFuWiYmVlhLhpmZlaYi4aZmRXmomFmZoW5aJiZWWEuGmZmVpiLhpmZFeaiYWZmhblomJlZYS4aZmZWmIuGmZkV5qJhZmaFuWiYmVlhLhpmZlbYdl80JI2T9LikNknTGj0eM7Md2XZdNCQNAK4CTgQOAU6XdEhjR2VmtuParosGcCTQFhGrI2IzMA8Y3+AxmZntsBQRjR5DlyRNBMZFxGfT8qeBoyLinIp+U4ApafFdwOMFN7EH8FwfDXdb5K1n7v6Wt565+1veeubub3nrmbu/5e1t7ndGRFNPnQaWG0/dqUpsqyoXETOAGb1OLrVGRHMtA2tE3nrm7m9565m7v+WtZ+7+lreeuftb3nrl3t5PT7UDI3PLI4CnGzQWM7Md3vZeNJYCYySNljQImAQsaPCYzMx2WNv16amI2CLpHGARMACYFREr+nATvT6l1eC89czd3/LWM3d/y1vP3P0tbz1z97e8dcm9XV8INzOz7cv2fnrKzMy2Iy4aZmZWmIuGmZkVtkMVDUn7S/qKpG9J+qakz0kaXDLnuZJG9txz+yXpg5K+JOn4knmOkrRbmt9Z0j9I+qmky8q8zpIGSTpT0nFp+QxJ35E0VdJby4w55TtI0lhJu1bEx5XNvS1ImtvoMVhxko6U9P40f0j6v3dSo8dV1A5zIVzSucApwC+Ak4CHgI3Ax4HPR8RdNebdBPweeAK4AfhhRHT0xZh72O5ZEfG9Gte9LyKOTPP/C5gK/AQ4HvhpREyvMe8K4D3prrcZwEvATcDYFP/rGvN+n+xOv12A54FdgR+nvIqIllryptznku3/SuC9wBci4ubU9kBEHF5r7nqQVHnLuYCPAHcARMSpfby9PSNifV/m3JFJupDsu/QGAouBo4C7gOOARRFxaeNGV1BE7BATsBwYkOZ3Ae5K8/sBD5bI+yDZEdvxwEygA/hPoAV4Rx33Z02ZMefmlwJNaf7twPISeVfm5h+oaHuoRN5l6edA4Nnc71GdbSX/Xeya5kcBrWSF4w2vUw15BwPTgceA36ZpZYoNKZH3AeB64Bjgw+nnujT/4ZKvxe4V0zDgKWAosHuJvLsB/whcB5xR0XZ1mTH3sN3bSqw7ruJ3ORNYBvwA2Kvkv7cB6W/QC8BuKb5zH/xb3hu4huxLXocBF6XtzQf26avXdYc6PcWfPpeyE/AOgIhYA5Q5xRER8XpE/CwiJgP7AlcD44DVZQYraVkX03JgrxKp3yJpqKRhZO/UO9KO/B7YUiLvI5LOSvMPS2pO+3Eg8IeS4x1E9jvbhew/MWS/x7KnpwZExO8AIuIpsj/CJ0q6nOpfY1PUfLIj2WMiYlhEDCM7ItgI/LBE3mbgfuACYFNkR8gvR8QvIuIXJfJC9h1F9+emVmA4WaFqLZH3e2Sv5Y+ASZJ+JGmn1HZ0ibxIOryL6QiyI8dafSM3/02ywnwK2ZusfyuRd0tEvBYRLwFPRMQLABHxMvB6ibwAs4FHgbXAncDLwMnAL4FrS+b+k3pV+e1tAr5A9k5hBtm7v7NSvAm4u0TeLt+NAjuXHPOzZP/w31kxjQKeLpH3KbKC9mT6uXeK70q5I4LB6R/uE8ASskKxmuyU4HtK5P1iyvMb4FzgduDfyd5FXVjyNb4DeG9FbCAwF3itRN7Ha2nrRf4RZMXnO5Q46qzI+RWyo+TDcrEn+yDvQxXLFwC/Jns3/EDJ3K+l3+GdVaaXS+R9IDdfOf4y/0eWALuk+bfk4oP74LXIn0FYU9FW85i32k5fJeoPE/BuYCJwUB/mPLCO450JfLCLth/UYXu7AKP7IM87gPcAR1DiUL4i577Avml+SPo9HtkHeUd0Fs0qbR8okfdnwNfy+092dHge8PM+/J2dDHyjD/N1FqPL0+9xdR/kXJn/A5liLcAK4Dclcz8CjOmibW2JvO3Al4Avk71hUa6t5tNIwE5dxPfIF+sacz+cm7+koq3m086V0w5zIdxsW5I0FJhG9vyXPVP4WbLvTpseERsbNbYiJJ1CdkQwKiL2Lpnrn4CfRcTPK+LjgG9HxJgSuSeS/UHc6nEIkiZExH/UmPfCitDVEdEhaW/gnyLizFry1pOki8nG9ruK+AFk/+Ym9sl2XDTQzyWmAAAAwUlEQVTMtq0yd75tS5J2BvaPiEfqNeZ6vhb9ccz10pdjdtEw28YkrYmI/Ro9jt6o15jr+Vr0xzHXS1+Oebv+lluz/krSsq6aKHfnW93Ua8z1fC3645jrZVuN2UXDrD72Ak4gu8U2T8B/bfvhFFKvMdfzteiPY66XbTJmFw2z+riF7EODD1U2SLpr2w+nkHqNuZ6vRX8cc71skzH7moaZmRW2o30i3MzMSnDRMDOzwlw0zMysMBcNMzMrzEXDzMwK+/8xW357FT0UPwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Understanding distribution of data for various activities and persons\n",
    "# We see class imbalance for some activities\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "\n",
    "df['label'].value_counts().plot(kind='bar',title='Training Examples by Activity Type')\n",
    "plt.show()\n",
    "df['pid'].value_counts().plot(kind='bar',title='Training Examples by User')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "std_scale=StandardScaler().fit(df.iloc[: ,1:4])\n",
    "df.iloc[: ,1:4]=std_scale.transform(df.iloc[:,1:4])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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>acc_x</th>\n",
       "      <th>acc_y</th>\n",
       "      <th>acc_z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-2.878360</td>\n",
       "      <td>-3.096472</td>\n",
       "      <td>0.809722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-3.381321</td>\n",
       "      <td>-4.244054</td>\n",
       "      <td>-0.682480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-3.471135</td>\n",
       "      <td>-4.423675</td>\n",
       "      <td>-1.476205</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      acc_x     acc_y     acc_z\n",
       "1 -2.878360 -3.096472  0.809722\n",
       "2 -3.381321 -4.244054 -0.682480\n",
       "3 -3.471135 -4.423675 -1.476205"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[1:4 ,1:4].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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>sid</th>\n",
       "      <th>acc_x</th>\n",
       "      <th>acc_y</th>\n",
       "      <th>acc_z</th>\n",
       "      <th>label</th>\n",
       "      <th>pid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>-4.360297</td>\n",
       "      <td>-1.669479</td>\n",
       "      <td>1.931520</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>-2.878360</td>\n",
       "      <td>-3.096472</td>\n",
       "      <td>0.809722</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>-3.381321</td>\n",
       "      <td>-4.244054</td>\n",
       "      <td>-0.682480</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>-3.471135</td>\n",
       "      <td>-4.423675</td>\n",
       "      <td>-1.476205</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>-3.093915</td>\n",
       "      <td>-4.164222</td>\n",
       "      <td>-0.968221</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sid     acc_x     acc_y     acc_z  label  pid\n",
       "0    0 -4.360297 -1.669479  1.931520      1    1\n",
       "1    1 -2.878360 -3.096472  0.809722      1    1\n",
       "2    2 -3.381321 -4.244054 -0.682480      1    1\n",
       "3    3 -3.471135 -4.423675 -1.476205      1    1\n",
       "4    4 -3.093915 -4.164222 -0.968221      1    1"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sliding Window Approach"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\nfor i in range(0,len(X_train)-128,128) :\\n    \\n    if(i==0):\\n        trainx=X_train[i:i+128]\\n    else :\\n        trainx=np.append(trainx,X_train[i:i+128],axis=0)\\n        \\n'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Data windows are created with timesteps of 128\n",
    "# Overlapping is not considered because it may lead to overfitting .\n",
    "\n",
    "'''\n",
    "for i in range(0,len(X_train)-128,128) :\n",
    "    \n",
    "    if(i==0):\n",
    "        trainx=X_train[i:i+128]\n",
    "    else :\n",
    "        trainx=np.append(trainx,X_train[i:i+128],axis=0)\n",
    "        \n",
    "'''    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "from statistics import mode\n",
    "\n",
    "N_TIME_STEPS = 127\n",
    "N_FEATURES = 3\n",
    "step = 52\n",
    "segments = []\n",
    "labels = []\n",
    "for i in range(0, len(df) - N_TIME_STEPS, step):\n",
    "    xs = df['acc_x'].values[i: i + N_TIME_STEPS]\n",
    "    ys = df['acc_y'].values[i: i + N_TIME_STEPS]\n",
    "    zs = df['acc_z'].values[i: i + N_TIME_STEPS]\n",
    "    label = mode(df['label'][i: i + N_TIME_STEPS])\n",
    "    segments.append([xs, ys, zs])\n",
    "    labels.append(label)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4, 5, 6, 7])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(np.array(labels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(36982, 3, 127)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(segments).shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "reshaped_segments = np.asarray(segments, dtype= np.float32).reshape(-1, N_TIME_STEPS, N_FEATURES)\n",
    "labels = np.asarray(pd.get_dummies(labels), dtype = np.float32)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(36982, 127, 3)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reshaped_segments.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 0., 0., 0., 0., 0., 0.],\n",
       "       [1., 0., 0., 0., 0., 0., 0.],\n",
       "       [1., 0., 0., 0., 0., 0., 0.],\n",
       "       [1., 0., 0., 0., 0., 0., 0.],\n",
       "       [1., 0., 0., 0., 0., 0., 0.]], dtype=float32)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels[0:5]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(36982, 7)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "RANDOM_SEED=42\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "        reshaped_segments, labels, test_size=0.2, random_state=RANDOM_SEED)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\babi\\Anaconda3\\lib\\site-packages\\h5py\\__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n",
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import keras\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout, Flatten, Reshape\n",
    "from keras.layers import Conv2D, MaxPooling2D,LSTM\n",
    "from keras.utils import np_utils\n",
    "from keras.optimizers import Adam\n",
    "from keras.regularizers import l2\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 23668 samples, validate on 5917 samples\n",
      "Epoch 1/10\n",
      "23668/23668 [==============================] - 203s 9ms/step - loss: 1.4190 - acc: 0.4485 - val_loss: 1.1862 - val_acc: 0.5673\n",
      "Epoch 2/10\n",
      "23668/23668 [==============================] - 197s 8ms/step - loss: 1.1395 - acc: 0.6089 - val_loss: 0.9669 - val_acc: 0.6868\n",
      "Epoch 3/10\n",
      "23668/23668 [==============================] - 194s 8ms/step - loss: 0.9107 - acc: 0.7056 - val_loss: 0.8598 - val_acc: 0.7235\n",
      "Epoch 4/10\n",
      "23668/23668 [==============================] - 193s 8ms/step - loss: 0.8544 - acc: 0.7206 - val_loss: 0.8244 - val_acc: 0.7313\n",
      "Epoch 5/10\n",
      "23668/23668 [==============================] - 191s 8ms/step - loss: 0.7710 - acc: 0.7456 - val_loss: 0.7552 - val_acc: 0.7455\n",
      "Epoch 6/10\n",
      "23668/23668 [==============================] - 192s 8ms/step - loss: 0.7457 - acc: 0.7502 - val_loss: 0.7795 - val_acc: 0.7399\n",
      "Epoch 7/10\n",
      "23668/23668 [==============================] - 193s 8ms/step - loss: 0.7040 - acc: 0.7650 - val_loss: 0.7219 - val_acc: 0.7627\n",
      "Epoch 8/10\n",
      "23668/23668 [==============================] - 189s 8ms/step - loss: 0.6717 - acc: 0.7696 - val_loss: 0.7294 - val_acc: 0.7576\n",
      "Epoch 9/10\n",
      "23668/23668 [==============================] - 190s 8ms/step - loss: 0.6448 - acc: 0.7803 - val_loss: 0.6753 - val_acc: 0.7718\n",
      "Epoch 10/10\n",
      "23668/23668 [==============================] - 191s 8ms/step - loss: 0.6367 - acc: 0.7805 - val_loss: 0.6794 - val_acc: 0.7688\n",
      "7397/7397 [==============================] - 18s 2ms/step\n"
     ]
    }
   ],
   "source": [
    "n_timesteps,n_features=127,3\n",
    "epochs,batch_size=10,64\n",
    "n_outputs=7\n",
    "\n",
    "model = Sequential()\n",
    "model.add(LSTM(100, return_sequences=True,input_shape=(n_timesteps,n_features)))\n",
    "#model.add(LSTM(100, return_sequences=True))\n",
    "model.add(LSTM(100))\n",
    "#model.add(Dropout(0.25))\n",
    "model.add(Dense(100, activation='relu'))\n",
    "model.add(Dense(n_outputs, activation='softmax'))\n",
    "\n",
    "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'] )\n",
    "# fit network\n",
    "model.fit(X_train, y_train, epochs=epochs, batch_size=batch_size ,validation_split=0.2)\n",
    "# evaluate model\n",
    "_, accuracy = model.evaluate(X_test, y_test, batch_size=batch_size)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7677436799991448"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 61654 samples, validate on 15414 samples\n",
      "Epoch 1/10\n",
      "61654/61654 [==============================] - 888s 14ms/step - loss: 1.3253 - acc: 0.5007 - val_loss: 1.0762 - val_acc: 0.6238\n",
      "Epoch 2/10\n",
      "61654/61654 [==============================] - 912s 15ms/step - loss: 1.0649 - acc: 0.6379 - val_loss: 0.9739 - val_acc: 0.6752\n",
      "Epoch 3/10\n",
      "61654/61654 [==============================] - 910s 15ms/step - loss: 0.9812 - acc: 0.6732 - val_loss: 0.9664 - val_acc: 0.6787\n",
      "Epoch 4/10\n",
      "61654/61654 [==============================] - 884s 14ms/step - loss: 0.8831 - acc: 0.7097 - val_loss: 0.7837 - val_acc: 0.7404\n",
      "Epoch 5/10\n",
      "61654/61654 [==============================] - 882s 14ms/step - loss: 0.7545 - acc: 0.7459 - val_loss: 0.7362 - val_acc: 0.7474\n",
      "Epoch 6/10\n",
      "61654/61654 [==============================] - 885s 14ms/step - loss: 0.7585 - acc: 0.7443 - val_loss: 0.7376 - val_acc: 0.7471\n",
      "Epoch 7/10\n",
      "61654/61654 [==============================] - 886s 14ms/step - loss: 0.6751 - acc: 0.7731 - val_loss: 0.6358 - val_acc: 0.7887\n",
      "Epoch 8/10\n",
      "61654/61654 [==============================] - 884s 14ms/step - loss: 0.6313 - acc: 0.7856 - val_loss: 0.5946 - val_acc: 0.7983\n",
      "Epoch 9/10\n",
      "61654/61654 [==============================] - 886s 14ms/step - loss: 0.5939 - acc: 0.7988 - val_loss: 0.5737 - val_acc: 0.8043\n",
      "Epoch 10/10\n",
      "61654/61654 [==============================] - 889s 14ms/step - loss: 0.5539 - acc: 0.8107 - val_loss: 0.5382 - val_acc: 0.8152\n",
      "19267/19267 [==============================] - 66s 3ms/step\n"
     ]
    }
   ],
   "source": [
    "n_timesteps,n_features=199,3\n",
    "epochs,batch_size=10,64\n",
    "n_outputs=8\n",
    "\n",
    "model = Sequential()\n",
    "model.add(LSTM(100, return_sequences=True,input_shape=(n_timesteps,n_features)))\n",
    "#model.add(LSTM(100, return_sequences=True))\n",
    "model.add(LSTM(100))\n",
    "#model.add(Dropout(0.25))\n",
    "model.add(Dense(100, activation='relu'))\n",
    "model.add(Dense(n_outputs, activation='softmax'))\n",
    "\n",
    "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'] )\n",
    "# fit network\n",
    "model.fit(X_train, y_train, epochs=epochs, batch_size=batch_size ,validation_split=0.2)\n",
    "# evaluate model\n",
    "_, accuracy = model.evaluate(X_test, y_test, batch_size=batch_size)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8113354440265534"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Building the model using LSTM\n",
    "\n",
    "n_timesteps,n_features=125,3\n",
    "epochs,batch_size=10,64\n",
    "n_outputs=8\n",
    "\n",
    "model = Sequential()\n",
    "model.add(LSTM(256, input_shape=(n_timesteps,n_features)))\n",
    "#model.add(Dropout(0.5))\n",
    "model.add(Dense(512, activation='relu'))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(512, activation='relu'))\n",
    "model.add(Dense(n_outputs, activation='softmax'))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 9865 samples, validate on 2467 samples\n",
      "Epoch 1/10\n",
      "9865/9865 [==============================] - 130s 13ms/step - loss: 1.5010 - acc: 0.4229 - val_loss: 1.4387 - val_acc: 0.4248\n",
      "Epoch 2/10\n",
      "9865/9865 [==============================] - 131s 13ms/step - loss: 1.3743 - acc: 0.4953 - val_loss: 1.2532 - val_acc: 0.5565\n",
      "Epoch 3/10\n",
      "9865/9865 [==============================] - 125s 13ms/step - loss: 1.1484 - acc: 0.6121 - val_loss: 1.0729 - val_acc: 0.6267\n",
      "Epoch 4/10\n",
      "9865/9865 [==============================] - 123s 12ms/step - loss: 1.1196 - acc: 0.6303 - val_loss: 1.0696 - val_acc: 0.6267\n",
      "Epoch 5/10\n",
      "9865/9865 [==============================] - 123s 12ms/step - loss: 1.0794 - acc: 0.6417 - val_loss: 1.0700 - val_acc: 0.6246\n",
      "Epoch 6/10\n",
      "9865/9865 [==============================] - 122s 12ms/step - loss: 1.0594 - acc: 0.6447 - val_loss: 1.0447 - val_acc: 0.6405\n",
      "Epoch 7/10\n",
      "9865/9865 [==============================] - 122s 12ms/step - loss: 1.0543 - acc: 0.6570 - val_loss: 1.0155 - val_acc: 0.6542\n",
      "Epoch 8/10\n",
      "9865/9865 [==============================] - 123s 12ms/step - loss: 1.0307 - acc: 0.6670 - val_loss: 0.9893 - val_acc: 0.6802\n",
      "Epoch 9/10\n",
      "9865/9865 [==============================] - 122s 12ms/step - loss: 0.9871 - acc: 0.6879 - val_loss: 0.9693 - val_acc: 0.6826\n",
      "Epoch 10/10\n",
      "9865/9865 [==============================] - 127s 13ms/step - loss: 0.9803 - acc: 0.6833 - val_loss: 0.9815 - val_acc: 0.6717\n",
      "3083/3083 [==============================] - 10s 3ms/step\n"
     ]
    }
   ],
   "source": [
    "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "# fit network\n",
    "model.fit(X_train, y_train, epochs=epochs, batch_size=batch_size ,validation_split=0.2)\n",
    "# evaluate model\n",
    "_, accuracy = model.evaluate(X_test, y_test, batch_size=batch_size)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6772624068046753\n"
     ]
    }
   ],
   "source": [
    "print(accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2244,)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_classes_lstm = model.predict_classes(testx)\n",
    "test_classes_lstm.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Have implemented sliding window with LSTM architecture.\n",
    "Other alterative approches are :\n",
    "\n",
    "1. Hand crafting features and applying models. Deep learning models perform better than this.\n",
    "2. CNN \n",
    "3. Combination of CNN- LSTM\n",
    "4. ConvLSTM\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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