HAR_CS205.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.model_selection import cross_val_score\n",
    "import matplotlib.pyplot as plt\n",
    "from xgboost.sklearn import XGBClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import KFold\n",
    "import math\n",
    "import cmath"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Tprocess_avg_acceleration(df):\n",
    "    i = 0\n",
    "    avg_x_full = []\n",
    "    avg_y_full = []\n",
    "    avg_z_full = []\n",
    "    label = []\n",
    "    while i < len(df[0]):\n",
    "        max_window = df[0][i] + 2000\n",
    "        min_window = df[0][i]\n",
    "        if i != 0:                                                                 #handling overlap\n",
    "            min_window -= 1000\n",
    "            max_window -= 1000\n",
    "            i -= int(count/2)\n",
    "        avg_x = [df[1][i]]\n",
    "        avg_y = [df[2][i]]\n",
    "        avg_z = [df[3][i]]\n",
    "        count = 0\n",
    "        while min_window < max_window:\n",
    "            i += 1\n",
    "            count += 1\n",
    "            try:\n",
    "                avg_x.append(df[1][i])\n",
    "                avg_y.append(df[2][i])\n",
    "                avg_z.append(df[3][i])\n",
    "                min_window = df[0][i]\n",
    "            except KeyError:\n",
    "                break\n",
    "        avg_x_full.append(sum(avg_x)/len(avg_x))\n",
    "        avg_y_full.append(sum(avg_y)/len(avg_y))\n",
    "        avg_z_full.append(sum(avg_z)/len(avg_z))\n",
    "        label.append(df[5][1])\n",
    "        \n",
    "    return pd.DataFrame({'Tavg_acc_x' : avg_x_full, 'Tavg_acc_y' : avg_y_full, 'Tavg_acc_z' : avg_z_full})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Fprocess_avg_acceleration(df):\n",
    "    i = 0\n",
    "    avg_x_full = []\n",
    "    avg_y_full = []\n",
    "    avg_z_full = []\n",
    "    label = []\n",
    "    while i < len(df[0]):\n",
    "        max_window = df[0][i] + 2000\n",
    "        min_window = df[0][i]\n",
    "        if i != 0:                                                                 #handling overlap\n",
    "            min_window -= 1000\n",
    "            max_window -= 1000\n",
    "            i -= int(count/2)\n",
    "        avg_x = [df[1][i]]\n",
    "        avg_y = [df[2][i]]\n",
    "        avg_z = [df[3][i]]\n",
    "        count = 0\n",
    "        while min_window < max_window:\n",
    "            i += 1\n",
    "            count += 1\n",
    "            try:\n",
    "                avg_x.append(df[1][i])\n",
    "                avg_y.append(df[2][i])\n",
    "                avg_z.append(df[3][i])\n",
    "                min_window = df[0][i]\n",
    "            except KeyError:\n",
    "                break\n",
    "        avg_x_full.append(sum(avg_x)/len(avg_x))\n",
    "        avg_y_full.append(sum(avg_y)/len(avg_y))\n",
    "        avg_z_full.append(sum(avg_z)/len(avg_z))\n",
    "        label.append(df[5][1])\n",
    "        \n",
    "    return pd.DataFrame({'Favg_acc_x' : avg_x_full, 'Favg_acc_y' : avg_y_full, 'Favg_acc_z' : avg_z_full})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Tprocess_avg_gyroscope(df):\n",
    "    i = 0\n",
    "    avg_x_full = []\n",
    "    avg_y_full = []\n",
    "    avg_z_full = []\n",
    "    label = []\n",
    "    while i < len(df[0]):\n",
    "        max_window = df[0][i] + 2000\n",
    "        min_window = df[0][i]\n",
    "        if i != 0:                                                                 #handling overlap\n",
    "            min_window -= 1000\n",
    "            max_window -= 1000\n",
    "            i -= int(count/2)\n",
    "        avg_x = [df[1][i]]\n",
    "        avg_y = [df[2][i]]\n",
    "        avg_z = [df[3][i]]\n",
    "        count = 0\n",
    "        while min_window < max_window:\n",
    "            i += 1\n",
    "            count += 1\n",
    "            try:\n",
    "                avg_x.append(df[1][i])\n",
    "                avg_y.append(df[2][i])\n",
    "                avg_z.append(df[3][i])\n",
    "                min_window = df[0][i]\n",
    "            except KeyError:\n",
    "                break\n",
    "        avg_x_full.append(sum(avg_x)/len(avg_x))\n",
    "        avg_y_full.append(sum(avg_y)/len(avg_y))\n",
    "        avg_z_full.append(sum(avg_z)/len(avg_z))\n",
    "        label.append(df[5][1])\n",
    "        \n",
    "    return pd.DataFrame({'Tavg_gy_x' : avg_x_full, 'Tavg_gy_y' : avg_y_full, 'Tavg_gy_z' : avg_z_full})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Fprocess_avg_gyroscope(df):\n",
    "    i = 0\n",
    "    avg_x_full = []\n",
    "    avg_y_full = []\n",
    "    avg_z_full = []\n",
    "    label = []\n",
    "    while i < len(df[0]):\n",
    "        max_window = df[0][i] + 2000\n",
    "        min_window = df[0][i]\n",
    "        if i != 0:                                                                 #handling overlap\n",
    "            min_window -= 1000\n",
    "            max_window -= 1000\n",
    "            i -= int(count/2)\n",
    "        avg_x = [df[1][i]]\n",
    "        avg_y = [df[2][i]]\n",
    "        avg_z = [df[3][i]]\n",
    "        count = 0\n",
    "        while min_window < max_window:\n",
    "            i += 1\n",
    "            count += 1\n",
    "            try:\n",
    "                avg_x.append(df[1][i])\n",
    "                avg_y.append(df[2][i])\n",
    "                avg_z.append(df[3][i])\n",
    "                min_window = df[0][i]\n",
    "            except KeyError:\n",
    "                break\n",
    "        avg_x_full.append(sum(avg_x)/len(avg_x))\n",
    "        avg_y_full.append(sum(avg_y)/len(avg_y))\n",
    "        avg_z_full.append(sum(avg_z)/len(avg_z))\n",
    "        label.append(df[5][1])\n",
    "        \n",
    "    return pd.DataFrame({'Favg_gy_x' : avg_x_full, 'Favg_gy_y' : avg_y_full, 'Favg_gy_z' : avg_z_full, 'label' : label})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Tprocess_avg_jerk(df):\n",
    "    i = 0\n",
    "    avg_x_full = []\n",
    "    avg_y_full = []\n",
    "    avg_z_full = []\n",
    "    label = []\n",
    "    while i < len(df[0]):\n",
    "        max_window = df[0][i] + 2000\n",
    "        min_window = df[0][i]\n",
    "        if i != 0:                                                                 #handling overlap\n",
    "            min_window -= 1000\n",
    "            max_window -= 1000\n",
    "            i -= int(count/2)\n",
    "        avg_x = [df[1][i]]\n",
    "        avg_y = [df[2][i]]\n",
    "        avg_z = [df[3][i]]\n",
    "        count = 0\n",
    "        while min_window < max_window:\n",
    "            i += 1\n",
    "            count += 1\n",
    "            try:\n",
    "                avg_x.append(df[1][i])\n",
    "                avg_y.append(df[2][i])\n",
    "                avg_z.append(df[3][i])\n",
    "                min_window = df[0][i]\n",
    "            except KeyError:\n",
    "                break\n",
    "        avg_x_full.append(sum(avg_x)/len(avg_x))\n",
    "        avg_y_full.append(sum(avg_y)/len(avg_y))\n",
    "        avg_z_full.append(sum(avg_z)/len(avg_z))\n",
    "        label.append(df[5][1])\n",
    "        \n",
    "    return pd.DataFrame({'Tavg_jerk_x' : avg_x_full, 'Tavg_jerk_y' : avg_y_full, 'Tavg_jerk_z' : avg_z_full})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Fprocess_avg_jerk(df):\n",
    "    i = 0\n",
    "    avg_x_full = []\n",
    "    avg_y_full = []\n",
    "    avg_z_full = []\n",
    "    label = []\n",
    "    while i < len(df[0]):\n",
    "        max_window = df[0][i] + 2000\n",
    "        min_window = df[0][i]\n",
    "        if i != 0:                                                                 #handling overlap\n",
    "            min_window -= 1000\n",
    "            max_window -= 1000\n",
    "            i -= int(count/2)\n",
    "        avg_x = [df[1][i]]\n",
    "        avg_y = [df[2][i]]\n",
    "        avg_z = [df[3][i]]\n",
    "        count = 0\n",
    "        while min_window < max_window:\n",
    "            i += 1\n",
    "            count += 1\n",
    "            try:\n",
    "                avg_x.append(df[1][i])\n",
    "                avg_y.append(df[2][i])\n",
    "                avg_z.append(df[3][i])\n",
    "                min_window = df[0][i]\n",
    "            except KeyError:\n",
    "                break\n",
    "        avg_x_full.append(sum(avg_x)/len(avg_x))\n",
    "        avg_y_full.append(sum(avg_y)/len(avg_y))\n",
    "        avg_z_full.append(sum(avg_z)/len(avg_z))\n",
    "        label.append(df[5][1])\n",
    "        \n",
    "    return pd.DataFrame({'Favg_jerk_x' : avg_x_full, 'Favg_jerk_y' : avg_y_full, 'Favg_jerk_z' : avg_z_full})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "def jerk(acc, ts):\n",
    "    jk = [0]* len(acc)\n",
    "    for i in range(1,len(acc)):\n",
    "        jk[i-1] = 1000*(acc[i] - acc[i-1])/(ts[i] - ts[i-1])\n",
    "    return jk\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def magnitude(num):\n",
    "    return math.sqrt(num.real**2 + num.imag**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sessions_new/14442D57F7E1B88_Thu_Feb_08_00-16_2018_PST\\data\n",
      "processing : Sessions_new\\14442D57F7E1B88_Thu_Feb_08_00-16_2018_PST\\data\\1_android.sensor.accelerometer.data.csv.gz\n",
      "0                        (3469.7441179800007, 0.0)\n",
      "1           (442.743656477568, -0.940840487178257)\n",
      "2         (136.1703439359992, 0.25074906452449264)\n",
      "3         (42.11268018096864, -1.6525309711322451)\n",
      "4         (100.1775617871649, -1.5209094132062093)\n",
      "5          (83.39291990957639, 2.2135677325958194)\n",
      "6         (88.60996467727317, -0.8391914409924339)\n",
      "7          (49.12959325314137, 0.1867838279286504)\n",
      "8          (51.71510053578363, -1.930241272109904)\n",
      "9         (23.675800910167688, -2.464212532233471)\n",
      "10          (43.46794641382337, 2.852921806253504)\n",
      "11           (64.18771739065, -0.9765655312212781)\n",
      "12           (50.8106206335912, 1.249808779447564)\n",
      "13        (42.65986949862889, -1.7054793646226447)\n",
      "14        (17.01573878097434, -1.4803795830057462)\n",
      "15        (31.145659286104397, 3.0150211385425023)\n",
      "16        (19.51181442500573, -0.7703244191322139)\n",
      "17         (36.11811176264199, 0.5884534185399645)\n",
      "18       (24.464864647901372, -1.3794065257071726)\n",
      "19         (32.351080137646164, 2.617556701290555)\n",
      "20         (42.485270555679534, -1.45392928113234)\n",
      "21        (13.921380212476864, 0.9939038297780688)\n",
      "22        (4.108620684191083, -0.7208332014496719)\n",
      "23      (35.05346055936208, -0.033409857652163956)\n",
      "24         (34.89052246254384, 2.4644396701454525)\n",
      "25          (56.5035670562125, -1.517072217907792)\n",
      "26         (47.95355551834348, 1.0597638949739125)\n",
      "27       (28.927534713206356, -3.0993957369993472)\n",
      "28       (19.101477334902704, -0.5894035790614828)\n",
      "29          (38.84985067737251, 2.510801542460272)\n",
      "                           ...                    \n",
      "2764      (51.366405527371136, 1.9805863651940123)\n",
      "2765      (38.84985067737257, -2.5108015424602725)\n",
      "2766      (19.101477334902633, 0.5894035790614771)\n",
      "2767       (28.927534713206317, 3.099395736999346)\n",
      "2768       (47.95355551834354, -1.059763894973916)\n",
      "2769       (56.50356705621242, 1.5170722179077893)\n",
      "2770      (34.89052246254394, -2.4644396701454565)\n",
      "2771     (35.053460559361795, 0.03340985765215475)\n",
      "2772       (4.108620684191083, 0.7208332014496719)\n",
      "2773     (13.921380212476514, -0.9939038297780698)\n",
      "2774       (42.48527055567951, 1.4539292811323412)\n",
      "2775     (32.351080137646186, -2.6175567012905603)\n",
      "2776      (24.464864647901475, 1.3794065257071693)\n",
      "2777     (36.118111762642066, -0.5884534185399644)\n",
      "2778        (19.51181442500572, 0.770324419132214)\n",
      "2779     (31.145659286104213, -3.0150211385425076)\n",
      "2780      (17.015738780974367, 1.4803795830057465)\n",
      "2781         (42.6598694986289, 1.705479364622638)\n",
      "2782      (50.810620633591185, -1.249808779447565)\n",
      "2783        (64.18771739065001, 0.976565531221273)\n",
      "2784      (43.46794641382338, -2.8529218062535047)\n",
      "2785        (23.6758009101675, 2.4642125322334656)\n",
      "2786        (51.7151005357837, 1.9302412721099032)\n",
      "2787    (49.129593253141756, -0.18678382792865686)\n",
      "2788       (88.60996467727315, 0.8391914409924323)\n",
      "2789         (83.3929199095765, -2.21356773259582)\n",
      "2790       (100.1775617871649, 1.5209094132062075)\n",
      "2791      (42.112680180968354, 1.6525309711322467)\n",
      "2792      (136.1703439359991, -0.2507490645244941)\n",
      "2793        (442.7436564775682, 0.940840487178255)\n",
      "Name: 1, dtype: object\n",
      "2794\n",
      "2794\n"
     ]
    },
    {
     "data": {
      "image/png": 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ShrZmd4WcQh6ceUfD/BOAqwBSSudFxFbAN8lHjdwNvDOltLqu/ERgLXAdMBK4GTi12cpL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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fd8cd7ca20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sampling freq = 233.70429252782196\n"
     ]
    },
    {
     "ename": "AttributeError",
     "evalue": "'tuple' object has no attribute 'real'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-194-761112939a99>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     49\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m     50\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m---> 51\u001b[0;31m     \u001b[0mfreq_df_ac\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mmagnitude\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mj\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mj\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mfreq_df_ac\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     52\u001b[0m     \u001b[0mfreq_df_ac\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mmagnitude\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mj\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mj\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mfreq_df_ac\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m     53\u001b[0m     \u001b[0mfreq_df_ac\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mmagnitude\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mj\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mj\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mfreq_df_ac\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-194-761112939a99>\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m     49\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m     50\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m---> 51\u001b[0;31m     \u001b[0mfreq_df_ac\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mmagnitude\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mj\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mj\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mfreq_df_ac\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     52\u001b[0m     \u001b[0mfreq_df_ac\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mmagnitude\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mj\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mj\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mfreq_df_ac\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m     53\u001b[0m     \u001b[0mfreq_df_ac\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mmagnitude\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mj\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mj\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mfreq_df_ac\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-19-0e4031d163c6>\u001b[0m in \u001b[0;36mmagnitude\u001b[0;34m(num)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mmagnitude\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnum\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m     \u001b[1;32mreturn\u001b[0m \u001b[0mmath\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msqrt\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnum\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreal\u001b[0m\u001b[1;33m**\u001b[0m\u001b[1;36m2\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mnum\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mimag\u001b[0m\u001b[1;33m**\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m: 'tuple' object has no attribute 'real'"
     ]
    }
   ],
   "source": [
    "last_dirs = [\"1_android.sensor.accelerometer.data.csv.gz\",\"4_android.sensor.gyroscope.data.csv.gz\"]\n",
    "path = \"Sessions_new/\"\n",
    "dirs = []\n",
    "dir_list = [x[0] for x in os.walk(path)]\n",
    "for each_dir in dir_list[2:]:\n",
    "\n",
    "    if \"data\" in each_dir:\n",
    "    #each_file = os.listdir(each_dir)\n",
    "        dirs.append(each_dir)\n",
    "        \n",
    "\n",
    "\n",
    "dirs.sort(key=lambda x:x[-22:])\n",
    "gy_shapes = []\n",
    "print(dirs[0])\n",
    "#avg_acc_gy = pd.DataFrame(columns = [\"avg_acc_x\",\"avg_acc_y\",\"avg_acc_x\", \"avg_gyroscope_x\",  \"avg_gyroscope_y\",  \"avg_gyroscope_z\",  \"label\"])\n",
    "for i in dirs[0:1]:\n",
    "    #print(i)\n",
    "    full_path_acc = os.path.normpath(i + \"/\" + \"1_android.sensor.accelerometer.data.csv.gz\")\n",
    "    full_path_gy = os.path.normpath(i + \"/\" + \"4_android.sensor.gyroscope.data.csv.gz\")\n",
    "    print(\"processing : \" + full_path_acc)\n",
    "    df_ac = pd.read_csv(full_path_acc, compression='gzip', sep=',', header=None)\n",
    "    freq_df_ac = df_ac.copy()\n",
    "    \n",
    "    df_jerk = df_ac.copy()\n",
    "    \n",
    "    df_jerk[1] = jerk(df_jerk[1],df_jerk[0])\n",
    "    df_jerk[2] = jerk(df_jerk[2],df_jerk[0])\n",
    "    df_jerk[3] = jerk(df_jerk[3],df_jerk[0])\n",
    "    freq_df_jerk = df_jerk.copy()\n",
    "    \n",
    "    freq_df_ac[1] = np.fft.fft(freq_df_ac[1])\n",
    "    freq_df_ac[2] = np.fft.fft(freq_df_ac[2])\n",
    "    freq_df_ac[3] = np.fft.fft(freq_df_ac[3])\n",
    "    \n",
    "    freq_df_ac[1] = [cmath.polar(val) for val in freq_df_ac[1]]\n",
    "    print(freq_df_ac[1])\n",
    "    plting = [val[0] for val in freq_df_ac[1]]\n",
    "    plting2 = plting[1398:]\n",
    "    plting2.extend(plting[:1398])\n",
    "    print(len(plting2))\n",
    "    print(len(freq_df_ac))\n",
    "    plt.plot(range(len(freq_df_ac)),plting2)\n",
    "    plt.ylim([0,500])\n",
    "    plt.show()\n",
    "    \n",
    "    fs = np.mean([freq_df_ac[0][val] - freq_df_ac[0][val-1] for val in range(1,len(freq_df_ac[0]))])\n",
    "    print(\"Sampling freq = \" + str(1000/fs))\n",
    "                 \n",
    "    \n",
    "    freq_df_ac[1] = [magnitude(j) for j in freq_df_ac[1]]\n",
    "    freq_df_ac[2] = [magnitude(j) for j in freq_df_ac[2]]\n",
    "    freq_df_ac[3] = [magnitude(j) for j in freq_df_ac[3]]\n",
    "    df_gy = pd.read_csv(full_path_gy, compression='gzip', sep=',', header=None)\n",
    "    freq_df_gy = df_gy.copy()\n",
    "    \n",
    "    \n",
    "    freq_df_gy[1] = np.fft.fft(freq_df_gy[1])\n",
    "    freq_df_gy[2] = np.fft.fft(freq_df_gy[2])\n",
    "    freq_df_gy[3] = np.fft.fft(freq_df_gy[3])\n",
    "    \n",
    "    freq_df_jerk[1] = np.fft.fft(freq_df_jerk[1])\n",
    "    freq_df_jerk[2] = np.fft.fft(freq_df_jerk[2])\n",
    "    freq_df_jerk[3] = np.fft.fft(freq_df_jerk[3]) \n",
    "    \n",
    "    freq_df_gy[1] = [magnitude(j) for j in freq_df_gy[1]]\n",
    "    freq_df_gy[2] = [magnitude(j) for j in freq_df_gy[2]]\n",
    "    freq_df_gy[3] = [magnitude(j) for j in freq_df_gy[3]]\n",
    "    \n",
    "    freq_df_jerk[1] = [magnitude(j) for j in freq_df_jerk[1]]\n",
    "    freq_df_jerk[2] = [magnitude(j) for j in freq_df_jerk[2]]\n",
    "    freq_df_jerk[3] = [magnitude(j) for j in freq_df_jerk[3]]\n",
    "    \n",
    "    new_df_ac = Tprocess_avg_acceleration(df_ac)\n",
    "    new_freq_df_ac = Fprocess_avg_acceleration(freq_df_ac)\n",
    "    new_df_gy = Tprocess_avg_gyroscope(df_gy)\n",
    "    new_freq_df_gy = Fprocess_avg_gyroscope(freq_df_gy)\n",
    "    new_df_jerk = Tprocess_avg_jerk(df_jerk)\n",
    "    new_freq_df_jerk = Fprocess_avg_jerk(freq_df_jerk)\n",
    "    \n",
    "    \n",
    "    if new_df_gy.shape[0] < new_df_ac.shape[0]:\n",
    "        new_df_ac = new_df_ac[:new_df_gy.shape[0]]\n",
    "        new_freq_df_ac = new_freq_df_ac[:new_df_gy.shape[0]]\n",
    "    \n",
    "    new_df = pd.concat([new_df_ac,new_df_gy,new_df_jerk,new_freq_df_ac,new_freq_df_jerk, new_freq_df_gy], axis = 1)\n",
    "    #print(new_df.head())   \n",
    "    #print(new_df.shape)\n",
    "    #print(avg_acc_gy.shape)\n",
    "    #gy_shapes.append(new_df.shape)\n",
    "    avg_acc_gy = new_df\n",
    "    #print(avg_acc_gy.head())\n",
    "\"\"\"\n",
    "for i in dirs[1:]:\n",
    "    print(i)\n",
    "    full_path_acc = os.path.normpath(i + \"/\" + \"1_android.sensor.accelerometer.data.csv.gz\")\n",
    "    full_path_gy = os.path.normpath(i + \"/\" + \"4_android.sensor.gyroscope.data.csv.gz\")\n",
    "    print(\"processing : \" + full_path_acc)\n",
    "    df_ac = pd.read_csv(full_path_acc, compression='gzip', sep=',', header=None)\n",
    "    freq_df_ac = df_ac.copy()\n",
    "    \n",
    "    df_jerk = df_ac.copy()\n",
    "    \n",
    "    df_jerk[1] = jerk(df_jerk[1],df_jerk[0])\n",
    "    df_jerk[2] = jerk(df_jerk[2],df_jerk[0])\n",
    "    df_jerk[3] = jerk(df_jerk[3],df_jerk[0])\n",
    "    freq_df_jerk = df_jerk.copy()\n",
    "    \n",
    "    freq_df_ac[1] = np.fft.fft(freq_df_ac[1])\n",
    "    freq_df_ac[2] = np.fft.fft(freq_df_ac[2])\n",
    "    freq_df_ac[3] = np.fft.fft(freq_df_ac[3])\n",
    "    freq_df_ac[1] = [magnitude(j) for j in freq_df_ac[1]]\n",
    "    freq_df_ac[2] = [magnitude(j) for j in freq_df_ac[2]]\n",
    "    freq_df_ac[3] = [magnitude(j) for j in freq_df_ac[3]]\n",
    "    df_gy = pd.read_csv(full_path_gy, compression='gzip', sep=',', header=None)\n",
    "    freq_df_gy = df_gy.copy()\n",
    "    \n",
    "    \n",
    "    freq_df_gy[1] = np.fft.fft(freq_df_gy[1])\n",
    "    freq_df_gy[2] = np.fft.fft(freq_df_gy[2])\n",
    "    freq_df_gy[3] = np.fft.fft(freq_df_gy[3])\n",
    "    \n",
    "    freq_df_jerk[1] = np.fft.fft(freq_df_jerk[1])\n",
    "    freq_df_jerk[2] = np.fft.fft(freq_df_jerk[2])\n",
    "    freq_df_jerk[3] = np.fft.fft(freq_df_jerk[3]) \n",
    "    \n",
    "    freq_df_gy[1] = [magnitude(j) for j in freq_df_gy[1]]\n",
    "    freq_df_gy[2] = [magnitude(j) for j in freq_df_gy[2]]\n",
    "    freq_df_gy[3] = [magnitude(j) for j in freq_df_gy[3]]\n",
    "    \n",
    "    freq_df_jerk[1] = [magnitude(j) for j in freq_df_jerk[1]]\n",
    "    freq_df_jerk[2] = [magnitude(j) for j in freq_df_jerk[2]]\n",
    "    freq_df_jerk[3] = [magnitude(j) for j in freq_df_jerk[3]]\n",
    "    \n",
    "    new_df_ac = Tprocess_avg_acceleration(df_ac)\n",
    "    new_freq_df_ac = Fprocess_avg_acceleration(freq_df_ac)\n",
    "    new_df_gy = Tprocess_avg_gyroscope(df_gy)\n",
    "    new_freq_df_gy = Fprocess_avg_gyroscope(freq_df_gy)\n",
    "    new_df_jerk = Tprocess_avg_jerk(df_jerk)\n",
    "    new_freq_df_jerk = Fprocess_avg_jerk(freq_df_jerk)\n",
    "    \n",
    "    \n",
    "    if new_df_gy.shape[0] < new_df_ac.shape[0]:\n",
    "        new_df_ac = new_df_ac[:new_df_gy.shape[0]]\n",
    "        new_freq_df_ac = new_freq_df_ac[:new_df_gy.shape[0]]\n",
    "    \n",
    "    new_df = pd.concat([new_df_ac,new_df_gy,new_df_jerk,new_freq_df_ac,new_freq_df_jerk, new_freq_df_gy], axis = 1)\n",
    "    avg_acc_gy = pd.concat([avg_acc_gy,new_df])\"\"\"\n",
    "\n",
    "\n",
    "print(avg_acc_gy.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(18934, 19)\n"
     ]
    }
   ],
   "source": [
    "print(avg_acc_gy.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Tavg_acc_x</th>\n",
       "      <th>Tavg_acc_y</th>\n",
       "      <th>Tavg_acc_z</th>\n",
       "      <th>Tavg_gy_x</th>\n",
       "      <th>Tavg_gy_y</th>\n",
       "      <th>Tavg_gy_z</th>\n",
       "      <th>Tavg_jerk_x</th>\n",
       "      <th>Tavg_jerk_y</th>\n",
       "      <th>Tavg_jerk_z</th>\n",
       "      <th>Favg_acc_x</th>\n",
       "      <th>Favg_acc_y</th>\n",
       "      <th>Favg_acc_z</th>\n",
       "      <th>Favg_jerk_x</th>\n",
       "      <th>Favg_jerk_y</th>\n",
       "      <th>Favg_jerk_z</th>\n",
       "      <th>Favg_gy_x</th>\n",
       "      <th>Favg_gy_y</th>\n",
       "      <th>Favg_gy_z</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.601597</td>\n",
       "      <td>2.097127</td>\n",
       "      <td>8.921956</td>\n",
       "      <td>-0.009461</td>\n",
       "      <td>0.011914</td>\n",
       "      <td>0.011216</td>\n",
       "      <td>0.276648</td>\n",
       "      <td>-0.073260</td>\n",
       "      <td>-0.292825</td>\n",
       "      <td>28.155261</td>\n",
       "      <td>28.101900</td>\n",
       "      <td>66.620204</td>\n",
       "      <td>1564.027583</td>\n",
       "      <td>1348.566402</td>\n",
       "      <td>1263.088604</td>\n",
       "      <td>5.088078</td>\n",
       "      <td>5.285396</td>\n",
       "      <td>7.228957</td>\n",
       "      <td>sitting</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.456576</td>\n",
       "      <td>2.018378</td>\n",
       "      <td>8.966372</td>\n",
       "      <td>-0.019683</td>\n",
       "      <td>0.012028</td>\n",
       "      <td>0.038526</td>\n",
       "      <td>-0.384939</td>\n",
       "      <td>0.167644</td>\n",
       "      <td>-0.120612</td>\n",
       "      <td>5.038758</td>\n",
       "      <td>5.225270</td>\n",
       "      <td>4.560480</td>\n",
       "      <td>1126.284859</td>\n",
       "      <td>1257.257559</td>\n",
       "      <td>1174.247429</td>\n",
       "      <td>0.609021</td>\n",
       "      <td>0.634836</td>\n",
       "      <td>0.594670</td>\n",
       "      <td>sitting</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.374311</td>\n",
       "      <td>1.927387</td>\n",
       "      <td>9.007423</td>\n",
       "      <td>0.034423</td>\n",
       "      <td>0.031449</td>\n",
       "      <td>0.151335</td>\n",
       "      <td>-0.641634</td>\n",
       "      <td>0.350010</td>\n",
       "      <td>0.135549</td>\n",
       "      <td>2.651597</td>\n",
       "      <td>2.791230</td>\n",
       "      <td>2.585310</td>\n",
       "      <td>992.964690</td>\n",
       "      <td>1040.364587</td>\n",
       "      <td>1019.798586</td>\n",
       "      <td>0.191345</td>\n",
       "      <td>0.188884</td>\n",
       "      <td>0.229233</td>\n",
       "      <td>sitting</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.390089</td>\n",
       "      <td>2.098303</td>\n",
       "      <td>8.970777</td>\n",
       "      <td>0.040809</td>\n",
       "      <td>0.037362</td>\n",
       "      <td>0.101426</td>\n",
       "      <td>-1.520973</td>\n",
       "      <td>1.216378</td>\n",
       "      <td>-1.163732</td>\n",
       "      <td>2.196161</td>\n",
       "      <td>1.977951</td>\n",
       "      <td>1.782841</td>\n",
       "      <td>1012.320770</td>\n",
       "      <td>921.089916</td>\n",
       "      <td>899.108232</td>\n",
       "      <td>0.196680</td>\n",
       "      <td>0.153983</td>\n",
       "      <td>0.191350</td>\n",
       "      <td>sitting</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.245988</td>\n",
       "      <td>2.237359</td>\n",
       "      <td>8.940339</td>\n",
       "      <td>-0.003299</td>\n",
       "      <td>-0.008490</td>\n",
       "      <td>-0.040654</td>\n",
       "      <td>-1.545470</td>\n",
       "      <td>1.431555</td>\n",
       "      <td>-1.227546</td>\n",
       "      <td>2.042629</td>\n",
       "      <td>1.643899</td>\n",
       "      <td>1.549376</td>\n",
       "      <td>1056.792749</td>\n",
       "      <td>828.356304</td>\n",
       "      <td>843.664854</td>\n",
       "      <td>0.193396</td>\n",
       "      <td>0.153468</td>\n",
       "      <td>0.188185</td>\n",
       "      <td>sitting</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Tavg_acc_x  Tavg_acc_y  Tavg_acc_z  Tavg_gy_x  Tavg_gy_y  Tavg_gy_z  \\\n",
       "0    1.601597    2.097127    8.921956  -0.009461   0.011914   0.011216   \n",
       "1    1.456576    2.018378    8.966372  -0.019683   0.012028   0.038526   \n",
       "2    1.374311    1.927387    9.007423   0.034423   0.031449   0.151335   \n",
       "3    1.390089    2.098303    8.970777   0.040809   0.037362   0.101426   \n",
       "4    1.245988    2.237359    8.940339  -0.003299  -0.008490  -0.040654   \n",
       "\n",
       "   Tavg_jerk_x  Tavg_jerk_y  Tavg_jerk_z  Favg_acc_x  Favg_acc_y  Favg_acc_z  \\\n",
       "0     0.276648    -0.073260    -0.292825   28.155261   28.101900   66.620204   \n",
       "1    -0.384939     0.167644    -0.120612    5.038758    5.225270    4.560480   \n",
       "2    -0.641634     0.350010     0.135549    2.651597    2.791230    2.585310   \n",
       "3    -1.520973     1.216378    -1.163732    2.196161    1.977951    1.782841   \n",
       "4    -1.545470     1.431555    -1.227546    2.042629    1.643899    1.549376   \n",
       "\n",
       "   Favg_jerk_x  Favg_jerk_y  Favg_jerk_z  Favg_gy_x  Favg_gy_y  Favg_gy_z  \\\n",
       "0  1564.027583  1348.566402  1263.088604   5.088078   5.285396   7.228957   \n",
       "1  1126.284859  1257.257559  1174.247429   0.609021   0.634836   0.594670   \n",
       "2   992.964690  1040.364587  1019.798586   0.191345   0.188884   0.229233   \n",
       "3  1012.320770   921.089916   899.108232   0.196680   0.153983   0.191350   \n",
       "4  1056.792749   828.356304   843.664854   0.193396   0.153468   0.188185   \n",
       "\n",
       "     label  \n",
       "0  sitting  \n",
       "1  sitting  \n",
       "2  sitting  \n",
       "3  sitting  \n",
       "4  sitting  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avg_acc_gy.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "avg_acc_gy = avg_acc_gy.iloc[np.random.permutation(len(avg_acc_gy))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Tavg_acc_x</th>\n",
       "      <th>Tavg_acc_y</th>\n",
       "      <th>Tavg_acc_z</th>\n",
       "      <th>Tavg_gy_x</th>\n",
       "      <th>Tavg_gy_y</th>\n",
       "      <th>Tavg_gy_z</th>\n",
       "      <th>Tavg_jerk_x</th>\n",
       "      <th>Tavg_jerk_y</th>\n",
       "      <th>Tavg_jerk_z</th>\n",
       "      <th>Favg_acc_x</th>\n",
       "      <th>Favg_acc_y</th>\n",
       "      <th>Favg_acc_z</th>\n",
       "      <th>Favg_jerk_x</th>\n",
       "      <th>Favg_jerk_y</th>\n",
       "      <th>Favg_jerk_z</th>\n",
       "      <th>Favg_gy_x</th>\n",
       "      <th>Favg_gy_y</th>\n",
       "      <th>Favg_gy_z</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1198</th>\n",
       "      <td>1.549310</td>\n",
       "      <td>9.421983</td>\n",
       "      <td>-0.930482</td>\n",
       "      <td>0.002193</td>\n",
       "      <td>0.000020</td>\n",
       "      <td>-0.000701</td>\n",
       "      <td>-0.148506</td>\n",
       "      <td>0.029144</td>\n",
       "      <td>-0.206756</td>\n",
       "      <td>25.942297</td>\n",
       "      <td>741.094385</td>\n",
       "      <td>48.752463</td>\n",
       "      <td>11668.411712</td>\n",
       "      <td>336286.005714</td>\n",
       "      <td>22034.028071</td>\n",
       "      <td>7.242062</td>\n",
       "      <td>3.507116</td>\n",
       "      <td>8.490373</td>\n",
       "      <td>laying_down</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>291</th>\n",
       "      <td>-8.747109</td>\n",
       "      <td>-3.198181</td>\n",
       "      <td>-1.379811</td>\n",
       "      <td>0.025205</td>\n",
       "      <td>-0.040930</td>\n",
       "      <td>-0.030634</td>\n",
       "      <td>-0.755569</td>\n",
       "      <td>1.170297</td>\n",
       "      <td>2.338379</td>\n",
       "      <td>22.235094</td>\n",
       "      <td>450.253583</td>\n",
       "      <td>70.609596</td>\n",
       "      <td>11330.525559</td>\n",
       "      <td>214512.260038</td>\n",
       "      <td>34564.151571</td>\n",
       "      <td>36.554868</td>\n",
       "      <td>21.536116</td>\n",
       "      <td>23.491984</td>\n",
       "      <td>standing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1179</th>\n",
       "      <td>-3.034819</td>\n",
       "      <td>8.239324</td>\n",
       "      <td>2.889107</td>\n",
       "      <td>-0.000601</td>\n",
       "      <td>0.000294</td>\n",
       "      <td>-0.000060</td>\n",
       "      <td>-0.051816</td>\n",
       "      <td>-0.112157</td>\n",
       "      <td>0.056818</td>\n",
       "      <td>13.363799</td>\n",
       "      <td>14.214326</td>\n",
       "      <td>22.576472</td>\n",
       "      <td>4311.392875</td>\n",
       "      <td>4261.969264</td>\n",
       "      <td>8159.925900</td>\n",
       "      <td>5.021690</td>\n",
       "      <td>3.889362</td>\n",
       "      <td>4.617213</td>\n",
       "      <td>laying_down</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>-9.505232</td>\n",
       "      <td>-1.405273</td>\n",
       "      <td>0.878063</td>\n",
       "      <td>-0.043697</td>\n",
       "      <td>-0.021024</td>\n",
       "      <td>0.103777</td>\n",
       "      <td>0.082174</td>\n",
       "      <td>0.336129</td>\n",
       "      <td>-0.128706</td>\n",
       "      <td>69.517155</td>\n",
       "      <td>105.898323</td>\n",
       "      <td>90.665312</td>\n",
       "      <td>20291.094974</td>\n",
       "      <td>30772.098150</td>\n",
       "      <td>26620.922965</td>\n",
       "      <td>13.070017</td>\n",
       "      <td>7.165808</td>\n",
       "      <td>8.778704</td>\n",
       "      <td>walking</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>-2.032823</td>\n",
       "      <td>1.820032</td>\n",
       "      <td>8.769014</td>\n",
       "      <td>0.014413</td>\n",
       "      <td>0.012483</td>\n",
       "      <td>-0.008864</td>\n",
       "      <td>-0.301900</td>\n",
       "      <td>0.010047</td>\n",
       "      <td>-0.749822</td>\n",
       "      <td>13.128181</td>\n",
       "      <td>9.527429</td>\n",
       "      <td>12.103152</td>\n",
       "      <td>4664.751896</td>\n",
       "      <td>3715.629426</td>\n",
       "      <td>4405.290122</td>\n",
       "      <td>2.990728</td>\n",
       "      <td>1.026880</td>\n",
       "      <td>1.620773</td>\n",
       "      <td>walking</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Tavg_acc_x  Tavg_acc_y  Tavg_acc_z  Tavg_gy_x  Tavg_gy_y  Tavg_gy_z  \\\n",
       "1198    1.549310    9.421983   -0.930482   0.002193   0.000020  -0.000701   \n",
       "291    -8.747109   -3.198181   -1.379811   0.025205  -0.040930  -0.030634   \n",
       "1179   -3.034819    8.239324    2.889107  -0.000601   0.000294  -0.000060   \n",
       "120    -9.505232   -1.405273    0.878063  -0.043697  -0.021024   0.103777   \n",
       "44     -2.032823    1.820032    8.769014   0.014413   0.012483  -0.008864   \n",
       "\n",
       "      Tavg_jerk_x  Tavg_jerk_y  Tavg_jerk_z  Favg_acc_x  Favg_acc_y  \\\n",
       "1198    -0.148506     0.029144    -0.206756   25.942297  741.094385   \n",
       "291     -0.755569     1.170297     2.338379   22.235094  450.253583   \n",
       "1179    -0.051816    -0.112157     0.056818   13.363799   14.214326   \n",
       "120      0.082174     0.336129    -0.128706   69.517155  105.898323   \n",
       "44      -0.301900     0.010047    -0.749822   13.128181    9.527429   \n",
       "\n",
       "      Favg_acc_z   Favg_jerk_x    Favg_jerk_y   Favg_jerk_z  Favg_gy_x  \\\n",
       "1198   48.752463  11668.411712  336286.005714  22034.028071   7.242062   \n",
       "291    70.609596  11330.525559  214512.260038  34564.151571  36.554868   \n",
       "1179   22.576472   4311.392875    4261.969264   8159.925900   5.021690   \n",
       "120    90.665312  20291.094974   30772.098150  26620.922965  13.070017   \n",
       "44     12.103152   4664.751896    3715.629426   4405.290122   2.990728   \n",
       "\n",
       "      Favg_gy_y  Favg_gy_z        label  \n",
       "1198   3.507116   8.490373  laying_down  \n",
       "291   21.536116  23.491984     standing  \n",
       "1179   3.889362   4.617213  laying_down  \n",
       "120    7.165808   8.778704      walking  \n",
       "44     1.026880   1.620773      walking  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avg_acc_gy.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 7.38228043e-01  1.62159826e+00 -4.05381739e-01  6.31827040e-02\n",
      "   6.29967513e-02 -3.24685231e-03 -0.00000000e+00  0.00000000e+00\n",
      "  -0.00000000e+00 -2.07675676e-01  6.86312916e-01 -2.16353330e-01\n",
      "  -2.98985708e-01  3.08774472e+00 -3.52381680e-02 -2.62766706e-01\n",
      "  -2.48193907e-01 -1.69404705e-01]\n",
      " [-1.10679450e+00 -4.11940152e-01 -5.08602740e-01  1.76786310e-01\n",
      "  -1.91949527e-01 -1.60487970e-01 -0.00000000e+00  0.00000000e+00\n",
      "  -0.00000000e+00 -2.13510493e-01  2.16544156e-01 -1.78343557e-01\n",
      "  -3.23147804e-01  1.67377404e+00  6.13046135e-01 -1.65511694e-01\n",
      "  -1.64503382e-01 -1.26068623e-01]\n",
      " [-8.32053060e-02  1.43103164e+00  4.72063471e-01  4.93920073e-02\n",
      "   6.47006158e-02  1.19396007e-04  0.00000000e+00 -0.00000000e+00\n",
      "  -0.00000000e+00 -2.27473144e-01 -4.87750496e-01 -2.61873638e-01\n",
      "  -8.25083023e-01 -7.67538306e-01 -7.53057322e-01 -2.70133529e-01\n",
      "  -2.46419521e-01 -1.80593342e-01]\n",
      " [-1.24264302e+00 -1.23041596e-01  1.00816317e-02 -1.63357725e-01\n",
      "  -6.80213471e-02  5.45588167e-01  0.00000000e+00  0.00000000e+00\n",
      "  -0.00000000e+00 -1.39092615e-01 -3.39661654e-01 -1.43466470e-01\n",
      "   3.17618737e-01 -4.59717068e-01  2.02078883e-01 -2.43430520e-01\n",
      "  -2.31210274e-01 -1.68571784e-01]\n",
      " [ 9.63432225e-02  3.96665065e-01  1.82280967e+00  1.23510323e-01\n",
      "   1.40587442e-01 -4.61309219e-02 -0.00000000e+00  0.00000000e+00\n",
      "  -0.00000000e+00 -2.27843986e-01 -4.95320816e-01 -2.80086845e-01\n",
      "  -7.99814468e-01 -7.73882108e-01 -9.47314900e-01 -2.76871926e-01\n",
      "  -2.59707148e-01 -1.89249341e-01]]\n"
     ]
    }
   ],
   "source": [
    "#print(data[:5])\n",
    "targets = avg_acc_gy[[\"label\"]].as_matrix()\n",
    "data = avg_acc_gy.copy()\n",
    "data = data.drop([\"label\"],axis = 1).as_matrix()\n",
    "data = np.nan_to_num(data)\n",
    "for i in range(len(data)):\n",
    "    for j in range(len(data[0])):\n",
    "        x = float(data[i][j])\n",
    "        if math.isnan(x):\n",
    "            print(\"Nan found\")\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "data = scaler.fit_transform(data)\n",
    "print(data[:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "cache = {'laying_down' : 0, 'sitting' : 1, 'walking' : 2, 'standing' : 3, '0.0' : 4}\n",
    "#targets.reshape(len(targets))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'str'>\n",
      "[['laying_down']\n",
      " ['standing']\n",
      " ['laying_down']\n",
      " ['walking']\n",
      " ['walking']]\n",
      "[array('laying_down', dtype='<U11'), array('standing', dtype='<U8'), array('laying_down', dtype='<U11'), array('walking', dtype='<U7'), array('walking', dtype='<U7')]\n",
      "<class 'str'>\n",
      "['laying_down', 'standing', 'laying_down', 'walking', 'walking']\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "le = LabelEncoder()\n",
    "#bin_targets = le.fit(targets).transform(targets)\n",
    "targets_1 = [np.nan_to_num(targets[i][0]) for i in range(len(targets))]\n",
    "#targets = [str(target) for target in targets]\n",
    "print(type(str(targets[1])))\n",
    "#targets = [str(target) for target in targets]\n",
    "print(targets[:5])\n",
    "print(targets_1[:5])\n",
    "print(type(str(targets_1[1])))\n",
    "\n",
    "targets_2 = [str(targets_1[i]) for i in range(len(targets_1))]\n",
    "\n",
    "print(targets_2[:5])\n",
    "bin_targets = []\n",
    "for i,t in enumerate(targets_2):\n",
    "    #print(i)\n",
    "    #print(t)\n",
    "    bin_targets.append(cache[t])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "bin_targets[:5]\n",
    "for i in bin_targets:\n",
    "    if math.isnan(float(i)):\n",
    "        print(\"Nan found\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = DecisionTreeClassifier(random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
       "            max_features=None, max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, presort=False, random_state=0,\n",
       "            splitter='best')"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(data,bin_targets)\n",
    "#cross_val_score(clf, data, bin_targets, cv=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1.15648939e+00  5.11083103e-01 -1.00345343e+01  1.07843789e+00\n",
      "  -3.71504200e-01  6.11642989e-01]\n",
      " [ 1.66466443e+00  9.29820698e+00  1.02058889e+00 -4.61913359e-04\n",
      "  -4.91397154e-04 -6.87956011e-04]\n",
      " [ 3.68973606e+00 -1.18689781e+00  8.93274398e+00  1.13193334e-02\n",
      "  -2.54432786e-02  9.71054876e-02]\n",
      " [-3.05512066e+00  8.24012528e+00  2.87908769e+00 -1.35845382e-03\n",
      "   2.27524280e-04 -1.67297275e-04]\n",
      " [ 1.08849704e+00  9.48876357e+00 -6.52135015e-01  2.24273660e-02\n",
      "  -1.86730990e-04 -1.62259338e-02]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([0.9088017 , 0.89496021, 0.89496021, 0.90450928, 0.91564987,\n",
       "       0.9066313 , 0.90552017, 0.89968153, 0.90015932, 0.91343601])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_acc = avg_acc_gy[[\"avg_acc_x\",\"avg_acc_y\",\"avg_acc_z\"]].as_matrix()\n",
    "print(data[:5])\n",
    "targets = avg_acc_gy[[\"label\"]].as_matrix()\n",
    "cross_val_score(clf, data_acc, bin_targets, cv=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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MdtScCGwcDxit+4ACnNBveyVJ0uTqaSYDIMm7gFXAgcBm4COllCeSnEQTBDZ0vWQDTfgA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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x251b83c3cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(bin_targets)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.74496288, 0.72519894, 0.72201592, 0.73050398, 0.73474801,\n",
       "       0.74058355, 0.72558386, 0.73036093, 0.73021774, 0.71853425])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "cross_val_score(clf, data_acc, bin_targets, cv=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1.20301083e+00  9.35390331e+00 -2.13122411e+00 -1.30294485e-03\n",
      "  -9.58047676e-05 -8.33501487e-04]\n",
      " [-7.76177700e+00 -5.42556269e+00 -6.69809507e-01  7.78985905e-02\n",
      "   1.19696681e-01  9.50048624e-03]\n",
      " [ 7.50540219e+00 -3.20755435e+00  4.61325302e+00 -1.66707500e-03\n",
      "  -1.59013207e-01  5.79530118e-02]\n",
      " [ 1.19015239e+00  9.37106758e+00 -2.03177351e+00 -7.14802205e-04\n",
      "   1.47058825e-12 -2.83962510e-04]\n",
      " [-3.84957035e+00 -5.09591333e+00  6.89115424e+00  3.10164919e-04\n",
      "   1.48339743e-04 -6.74271139e-06]]\n"
     ]
    }
   ],
   "source": [
    "print(data[0:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 3, 3, 0, 1]\n"
     ]
    }
   ],
   "source": [
    "print(bin_targets[0:5])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Testing SOBO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['newData1/', 'newData1/14442D53F81BFEA_Mon_Mar_05_21-54_2018_PST', 'newData1/14442D53F81BFEA_Mon_Mar_05_21-54_2018_PST\\\\attr', 'newData1/14442D53F81BFEA_Mon_Mar_05_21-54_2018_PST\\\\data', 'newData1/14442D53F81BFEA_Mon_Mar_05_22-01_2018_PST', 'newData1/14442D53F81BFEA_Mon_Mar_05_22-01_2018_PST\\\\attr', 'newData1/14442D53F81BFEA_Mon_Mar_05_22-01_2018_PST\\\\data', 'newData1/14442D53F81BFEA_Mon_Mar_05_22-06_2018_PST', 'newData1/14442D53F81BFEA_Mon_Mar_05_22-06_2018_PST\\\\attr', 'newData1/14442D53F81BFEA_Mon_Mar_05_22-06_2018_PST\\\\data', 'newData1/14442D53F81BFEA_Mon_Mar_05_22-14_2018_PST', 'newData1/14442D53F81BFEA_Mon_Mar_05_22-14_2018_PST\\\\attr', 'newData1/14442D53F81BFEA_Mon_Mar_05_22-14_2018_PST\\\\data']\n",
      "newData1/14442D53F81BFEA_Mon_Mar_05_21-54_2018_PST\\data\n",
      "processing : newData1\\14442D53F81BFEA_Mon_Mar_05_21-54_2018_PST\\data\\1_android.sensor.accelerometer.data.csv.gz\n",
      "newData1/14442D53F81BFEA_Mon_Mar_05_22-01_2018_PST\\data\n",
      "processing : newData1\\14442D53F81BFEA_Mon_Mar_05_22-01_2018_PST\\data\\1_android.sensor.accelerometer.data.csv.gz\n",
      "newData1/14442D53F81BFEA_Mon_Mar_05_22-06_2018_PST\\data\n",
      "processing : newData1\\14442D53F81BFEA_Mon_Mar_05_22-06_2018_PST\\data\\1_android.sensor.accelerometer.data.csv.gz\n",
      "newData1/14442D53F81BFEA_Mon_Mar_05_22-14_2018_PST\\data\n",
      "processing : newData1\\14442D53F81BFEA_Mon_Mar_05_22-14_2018_PST\\data\\1_android.sensor.accelerometer.data.csv.gz\n",
      "(19219, 19)\n"
     ]
    }
   ],
   "source": [
    "last_dirs = [\"1_android.sensor.accelerometer.data.csv.gz\",\"4_android.sensor.gyroscope.data.csv.gz\"]\n",
    "path = \"newData1/\"\n",
    "dirs = []\n",
    "dir_list = [x[0] for x in os.walk(path)]\n",
    "print(dir_list)\n",
    "for each_dir in dir_list[2:]:\n",
    "\n",
    "    if \"data\" in each_dir:\n",
    "    #each_file = os.listdir(each_dir)\n",
    "        dirs.append(each_dir)\n",
    "        \n",
    "\n",
    "\n",
    "dirs.sort(key=lambda x:x[-22:])\n",
    "gy_shapes = []\n",
    "print(dirs[0])\n",
    "#avg_acc_gy = pd.DataFrame(columns = [\"avg_acc_x\",\"avg_acc_y\",\"avg_acc_x\", \"avg_gyroscope_x\",  \"avg_gyroscope_y\",  \"avg_gyroscope_z\",  \"label\"])\n",
    "for i in dirs[0:1]:\n",
    "    #print(i)\n",
    "    full_path_acc = os.path.normpath(i + \"/\" + \"1_android.sensor.accelerometer.data.csv.gz\")\n",
    "    full_path_gy = os.path.normpath(i + \"/\" + \"4_android.sensor.gyroscope.data.csv.gz\")\n",
    "    print(\"processing : \" + full_path_acc)\n",
    "    df_ac = pd.read_csv(full_path_acc, sep=',', header=None)\n",
    "    freq_df_ac = df_ac.copy()\n",
    "    \n",
    "    df_jerk = df_ac.copy()\n",
    "    \n",
    "    df_jerk[1] = jerk(df_jerk[1],df_jerk[0])\n",
    "    df_jerk[2] = jerk(df_jerk[2],df_jerk[0])\n",
    "    df_jerk[3] = jerk(df_jerk[3],df_jerk[0])\n",
    "    freq_df_jerk = df_jerk.copy()\n",
    "    \n",
    "    freq_df_ac[1] = np.fft.fft(freq_df_ac[1])\n",
    "    freq_df_ac[2] = np.fft.fft(freq_df_ac[2])\n",
    "    freq_df_ac[3] = np.fft.fft(freq_df_ac[3])\n",
    "    freq_df_ac[1] = [magnitude(j) for j in freq_df_ac[1]]\n",
    "    freq_df_ac[2] = [magnitude(j) for j in freq_df_ac[2]]\n",
    "    freq_df_ac[3] = [magnitude(j) for j in freq_df_ac[3]]\n",
    "    df_gy = pd.read_csv(full_path_gy, sep=',', header=None)\n",
    "    freq_df_gy = df_gy.copy()\n",
    "    \n",
    "    \n",
    "    freq_df_gy[1] = np.fft.fft(freq_df_gy[1])\n",
    "    freq_df_gy[2] = np.fft.fft(freq_df_gy[2])\n",
    "    freq_df_gy[3] = np.fft.fft(freq_df_gy[3])\n",
    "    \n",
    "    freq_df_jerk[1] = np.fft.fft(freq_df_jerk[1])\n",
    "    freq_df_jerk[2] = np.fft.fft(freq_df_jerk[2])\n",
    "    freq_df_jerk[3] = np.fft.fft(freq_df_jerk[3]) \n",
    "    \n",
    "    freq_df_gy[1] = [magnitude(j) for j in freq_df_gy[1]]\n",
    "    freq_df_gy[2] = [magnitude(j) for j in freq_df_gy[2]]\n",
    "    freq_df_gy[3] = [magnitude(j) for j in freq_df_gy[3]]\n",
    "    \n",
    "    freq_df_jerk[1] = [magnitude(j) for j in freq_df_jerk[1]]\n",
    "    freq_df_jerk[2] = [magnitude(j) for j in freq_df_jerk[2]]\n",
    "    freq_df_jerk[3] = [magnitude(j) for j in freq_df_jerk[3]]\n",
    "    \n",
    "    new_df_ac = Tprocess_avg_acceleration(df_ac)\n",
    "    new_freq_df_ac = Fprocess_avg_acceleration(freq_df_ac)\n",
    "    new_df_gy = Tprocess_avg_gyroscope(df_gy)\n",
    "    new_freq_df_gy = Fprocess_avg_gyroscope(freq_df_gy)\n",
    "    new_df_jerk = Tprocess_avg_jerk(df_jerk)\n",
    "    new_freq_df_jerk = Fprocess_avg_jerk(freq_df_jerk)\n",
    "    \n",
    "    \n",
    "    if new_df_gy.shape[0] < new_df_ac.shape[0]:\n",
    "        new_df_ac = new_df_ac[:new_df_gy.shape[0]]\n",
    "        new_freq_df_ac = new_freq_df_ac[:new_df_gy.shape[0]]\n",
    "    \n",
    "    new_df = pd.concat([new_df_ac,new_df_gy,new_df_jerk,new_freq_df_ac,new_freq_df_jerk, new_freq_df_gy], axis = 1)\n",
    "    #print(new_df.head())   \n",
    "    #print(new_df.shape)\n",
    "    #print(avg_acc_gy.shape)\n",
    "    #gy_shapes.append(new_df.shape)\n",
    "    avg_acc_gy_test1 = new_df\n",
    "    #print(avg_acc_gy.head())\n",
    "\n",
    "for i in dirs[1:]:\n",
    "    print(i)\n",
    "    full_path_acc = os.path.normpath(i + \"/\" + \"1_android.sensor.accelerometer.data.csv.gz\")\n",
    "    full_path_gy = os.path.normpath(i + \"/\" + \"4_android.sensor.gyroscope.data.csv.gz\")\n",
    "    print(\"processing : \" + full_path_acc)\n",
    "    df_ac = pd.read_csv(full_path_acc, compression='gzip', sep=',', header=None)\n",
    "    freq_df_ac = df_ac.copy()\n",
    "    \n",
    "    df_jerk = df_ac.copy()\n",
    "    \n",
    "    df_jerk[1] = jerk(df_jerk[1],df_jerk[0])\n",
    "    df_jerk[2] = jerk(df_jerk[2],df_jerk[0])\n",
    "    df_jerk[3] = jerk(df_jerk[3],df_jerk[0])\n",
    "    freq_df_jerk = df_jerk.copy()\n",
    "    \n",
    "    freq_df_ac[1] = np.fft.fft(freq_df_ac[1])\n",
    "    freq_df_ac[2] = np.fft.fft(freq_df_ac[2])\n",
    "    freq_df_ac[3] = np.fft.fft(freq_df_ac[3])\n",
    "    freq_df_ac[1] = [magnitude(j) for j in freq_df_ac[1]]\n",
    "    freq_df_ac[2] = [magnitude(j) for j in freq_df_ac[2]]\n",
    "    freq_df_ac[3] = [magnitude(j) for j in freq_df_ac[3]]\n",
    "    df_gy = pd.read_csv(full_path_gy, compression='gzip', sep=',', header=None)\n",
    "    freq_df_gy = df_gy.copy()\n",
    "    \n",
    "    \n",
    "    freq_df_gy[1] = np.fft.fft(freq_df_gy[1])\n",
    "    freq_df_gy[2] = np.fft.fft(freq_df_gy[2])\n",
    "    freq_df_gy[3] = np.fft.fft(freq_df_gy[3])\n",
    "    \n",
    "    freq_df_jerk[1] = np.fft.fft(freq_df_jerk[1])\n",
    "    freq_df_jerk[2] = np.fft.fft(freq_df_jerk[2])\n",
    "    freq_df_jerk[3] = np.fft.fft(freq_df_jerk[3]) \n",
    "    \n",
    "    freq_df_gy[1] = [magnitude(j) for j in freq_df_gy[1]]\n",
    "    freq_df_gy[2] = [magnitude(j) for j in freq_df_gy[2]]\n",
    "    freq_df_gy[3] = [magnitude(j) for j in freq_df_gy[3]]\n",
    "    \n",
    "    freq_df_jerk[1] = [magnitude(j) for j in freq_df_jerk[1]]\n",
    "    freq_df_jerk[2] = [magnitude(j) for j in freq_df_jerk[2]]\n",
    "    freq_df_jerk[3] = [magnitude(j) for j in freq_df_jerk[3]]\n",
    "    \n",
    "    new_df_ac = Tprocess_avg_acceleration(df_ac)\n",
    "    new_freq_df_ac = Fprocess_avg_acceleration(freq_df_ac)\n",
    "    new_df_gy = Tprocess_avg_gyroscope(df_gy)\n",
    "    new_freq_df_gy = Fprocess_avg_gyroscope(freq_df_gy)\n",
    "    new_df_jerk = Tprocess_avg_jerk(df_jerk)\n",
    "    new_freq_df_jerk = Fprocess_avg_jerk(freq_df_jerk)\n",
    "    \n",
    "    \n",
    "    if new_df_gy.shape[0] < new_df_ac.shape[0]:\n",
    "        new_df_ac = new_df_ac[:new_df_gy.shape[0]]\n",
    "        new_freq_df_ac = new_freq_df_ac[:new_df_gy.shape[0]]\n",
    "    \n",
    "    new_df = pd.concat([new_df_ac,new_df_gy,new_df_jerk,new_freq_df_ac,new_freq_df_jerk, new_freq_df_gy], axis = 1)\n",
    "    avg_acc_gy_test1 = pd.concat([avg_acc_gy,new_df])\n",
    "\n",
    "\n",
    "print(avg_acc_gy_test1.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "avg_acc_gy_test = avg_acc_gy_test1.iloc[np.random.permutation(len(avg_acc_gy_test1))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 6.39902197e-01 -1.01434470e+00  1.35883344e+00  4.30889377e-02\n",
      "   1.42873571e-02 -3.12209388e-02  0.00000000e+00 -0.00000000e+00\n",
      "  -0.00000000e+00  2.18469573e-01  3.02406389e-02  6.89179476e-02\n",
      "   5.40549629e-01 -5.15601358e-01 -2.80457671e-01  8.14971987e-03\n",
      "   1.02759248e-01  2.27692369e-01]\n",
      " [ 6.24431164e-01  1.64546493e+00 -2.24181344e-01  6.30666294e-02\n",
      "   6.05821950e-02 -6.26245631e-03  0.00000000e+00  0.00000000e+00\n",
      "  -0.00000000e+00 -2.05506078e-01  2.90352145e-01 -2.30401991e-01\n",
      "  -1.75776673e-01  2.00586645e+00 -1.35633375e-01 -2.51809917e-01\n",
      "  -2.46784831e-01 -1.74794063e-01]\n",
      " [-9.88965314e-01 -7.13519040e-01 -3.12719703e-01 -1.01927775e-01\n",
      "  -9.97247904e-04 -3.93422623e-01  0.00000000e+00 -0.00000000e+00\n",
      "  -0.00000000e+00 -2.29142756e-01 -4.88709812e-01 -2.27498239e-01\n",
      "  -1.13142961e+00 -8.27805250e-01 -1.16634034e+00 -2.70475000e-01\n",
      "  -2.55498939e-01 -1.87814807e-01]\n",
      " [-1.54931753e+00 -3.10296090e-01 -4.32412104e-01 -1.27933226e+00\n",
      "   9.45612603e-01  6.69882348e-02 -0.00000000e+00  0.00000000e+00\n",
      "  -0.00000000e+00 -1.64548172e-01 -4.33279809e-01 -2.14962729e-01\n",
      "  -5.70415719e-01 -7.27987057e-01 -5.97962779e-01 -2.16211327e-01\n",
      "  -2.00830240e-01 -1.45759677e-01]\n",
      " [-1.32040662e-01 -1.51770525e-01 -1.73904708e+00  7.85783777e+00\n",
      "  -5.61743104e-01  1.33608441e+00 -0.00000000e+00 -0.00000000e+00\n",
      "  -0.00000000e+00 -1.47591444e-01 -1.60443214e-01 -5.83847122e-02\n",
      "   3.43517189e-01  6.94036183e-02  1.21078490e+00 -1.68171733e-01\n",
      "  -1.10303380e-01 -1.20843530e-01]]\n"
     ]
    }
   ],
   "source": [
    "targets_test = avg_acc_gy_test[[\"label\"]].as_matrix()\n",
    "data_test = avg_acc_gy_test.copy()\n",
    "data_test = data_test.drop([\"label\"],axis = 1).as_matrix()\n",
    "data_test = np.nan_to_num(data_test)\n",
    "for i in range(len(data_test)):\n",
    "    for j in range(len(data_test[0])):\n",
    "        x = float(data_test[i][j])\n",
    "        if math.isnan(x):\n",
    "            print(\"Nan found\")\n",
    "#from sklearn.preprocessing import StandardScaler\n",
    "#scaler = StandardScaler()\n",
    "data_test = scaler.fit_transform(data_test)\n",
    "print(data_test[:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[['sitting']\n",
      " ['laying_down']\n",
      " ['standing']\n",
      " ['walking']\n",
      " ['standing']]\n",
      "[array('sitting', dtype='<U7'), array('laying_down', dtype='<U11'), array('standing', dtype='<U8'), array('walking', dtype='<U7'), array('standing', dtype='<U8')]\n",
      "<class 'str'>\n",
      "['sitting', 'laying_down', 'standing', 'walking', 'standing']\n"
     ]
    }
   ],
   "source": [
    "targets_1_test = [np.nan_to_num(targets_test[i][0]) for i in range(len(targets_test))]\n",
    "#targets = [str(target) for target in targets]\n",
    "#print(type(str(targets[1])))\n",
    "#targets = [str(target) for target in targets]\n",
    "print(targets_test[:5])\n",
    "print(targets_1_test[:5])\n",
    "print(type(str(targets_1_test[1])))\n",
    "\n",
    "targets_2_test = [str(targets_1_test[i]) for i in range(len(targets_1_test))]\n",
    "\n",
    "print(targets_2_test[:5])\n",
    "bin_targets_test = []\n",
    "for i,t in enumerate(targets_2_test):\n",
    "    #print(i)\n",
    "    #print(t)\n",
    "    bin_targets_test.append(cache[t])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "preds_test = clf.predict(data_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9181539101930382\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "print(accuracy_score(bin_targets_test,preds_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Testing Vidushi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Vidushi_data/14442D53F81BFEA_Mon_Mar_05_21-54_2018_PST\\attr\n",
      "processing : Vidushi_data\\14442D53F81BFEA_Mon_Mar_05_21-54_2018_PST\\attr\\1_android.sensor.accelerometer.data.csv.gz\n"
     ]
    },
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: 'Vidushi_data\\\\14442D53F81BFEA_Mon_Mar_05_21-54_2018_PST\\\\attr\\\\1_android.sensor.accelerometer.data.csv.gz'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-141-3adac41661f4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     20\u001b[0m     \u001b[0mfull_path_gy\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnormpath\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mi\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;34m\"/\"\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;34m\"4_android.sensor.gyroscope.data.csv.gz\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m     21\u001b[0m     \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"processing : \"\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mfull_path_acc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m---> 22\u001b[0;31m     \u001b[0mdf_ac\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfull_path_acc\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mcompression\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'gzip'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msep\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m','\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mheader\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     23\u001b[0m     \u001b[0mfreq_df_ac\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf_ac\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m     24\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32md:\\anaconda\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36mparser_f\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, escapechar, comment, encoding, dialect, tupleize_cols, error_bad_lines, warn_bad_lines, skip_footer, doublequote, delim_whitespace, as_recarray, compact_ints, use_unsigned, low_memory, buffer_lines, memory_map, float_precision)\u001b[0m\n\u001b[1;32m    560\u001b[0m                     skip_blank_lines=skip_blank_lines)\n\u001b[1;32m    561\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 562\u001b[0;31m         \u001b[1;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    563\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    564\u001b[0m     \u001b[0mparser_f\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32md:\\anaconda\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m    313\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    314\u001b[0m     \u001b[1;31m# Create the parser.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 315\u001b[0;31m     \u001b[0mparser\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    316\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    317\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mnrows\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mand\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mchunksize\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32md:\\anaconda\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m    643\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'has_index_names'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'has_index_names'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    644\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 645\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    646\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    647\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32md:\\anaconda\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[0;34m(self, engine)\u001b[0m\n\u001b[1;32m    797\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_make_engine\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mengine\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'c'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    798\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'c'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 799\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mCParserWrapper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    800\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    801\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'python'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32md:\\anaconda\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, src, **kwds)\u001b[0m\n\u001b[1;32m   1211\u001b[0m         \u001b[0mkwds\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'allow_leading_cols'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex_col\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1212\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1213\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_reader\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_parser\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTextReader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1214\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1215\u001b[0m         \u001b[1;31m# XXX\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas\\parser.pyx\u001b[0m in \u001b[0;36mpandas.parser.TextReader.__cinit__ (pandas\\parser.c:3427)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas\\parser.pyx\u001b[0m in \u001b[0;36mpandas.parser.TextReader._setup_parser_source (pandas\\parser.c:5916)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32md:\\anaconda\\lib\\gzip.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, filename, mode, compresslevel, fileobj, mtime)\u001b[0m\n\u001b[1;32m    161\u001b[0m             \u001b[0mmode\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[1;34m'b'\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    162\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mfileobj\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 163\u001b[0;31m             \u001b[0mfileobj\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmyfileobj\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbuiltins\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmode\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;34m'rb'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    164\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mfilename\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    165\u001b[0m             \u001b[0mfilename\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfileobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'name'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m''\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'Vidushi_data\\\\14442D53F81BFEA_Mon_Mar_05_21-54_2018_PST\\\\attr\\\\1_android.sensor.accelerometer.data.csv.gz'"
     ]
    }
   ],
   "source": [
    "last_dirs = [\"1_android.sensor.accelerometer.data.csv.gz\",\"4_android.sensor.gyroscope.data.csv.gz\"]\n",
    "path = \"Vidushi_data/\"\n",
    "dirs = []\n",
    "dir_list = [x[0] for x in os.walk(path)]\n",
    "for each_dir in dir_list[2:]:\n",
    "\n",
    "    if \"data\" in each_dir:\n",
    "    #each_file = os.listdir(each_dir)\n",
    "        dirs.append(each_dir)\n",
    "        \n",
    "\n",
    "\n",
    "dirs.sort(key=lambda x:x[-22:])\n",
    "gy_shapes = []\n",
    "print(dirs[0])\n",
    "#avg_acc_gy = pd.DataFrame(columns = [\"avg_acc_x\",\"avg_acc_y\",\"avg_acc_x\", \"avg_gyroscope_x\",  \"avg_gyroscope_y\",  \"avg_gyroscope_z\",  \"label\"])\n",
    "for i in dirs[0:1]:\n",
    "    #print(i)\n",
    "    full_path_acc = os.path.normpath(i + \"/\" + \"1_android.sensor.accelerometer.data.csv.gz\")\n",
    "    full_path_gy = os.path.normpath(i + \"/\" + \"4_android.sensor.gyroscope.data.csv.gz\")\n",
    "    print(\"processing : \" + full_path_acc)\n",
    "    df_ac = pd.read_csv(full_path_acc,compression='gzip', sep=',', header=None)\n",
    "    freq_df_ac = df_ac.copy()\n",
    "    \n",
    "    df_jerk = df_ac.copy()\n",
    "    \n",
    "    df_jerk[1] = jerk(df_jerk[1],df_jerk[0])\n",
    "    df_jerk[2] = jerk(df_jerk[2],df_jerk[0])\n",
    "    df_jerk[3] = jerk(df_jerk[3],df_jerk[0])\n",
    "    freq_df_jerk = df_jerk.copy()\n",
    "    \n",
    "    freq_df_ac[1] = np.fft.fft(freq_df_ac[1])\n",
    "    freq_df_ac[2] = np.fft.fft(freq_df_ac[2])\n",
    "    freq_df_ac[3] = np.fft.fft(freq_df_ac[3])\n",
    "    freq_df_ac[1] = [magnitude(j) for j in freq_df_ac[1]]\n",
    "    freq_df_ac[2] = [magnitude(j) for j in freq_df_ac[2]]\n",
    "    freq_df_ac[3] = [magnitude(j) for j in freq_df_ac[3]]\n",
    "    df_gy = pd.read_csv(full_path_gy,compression='gzip', sep=',', header=None)\n",
    "    freq_df_gy = df_gy.copy()\n",
    "    \n",
    "    \n",
    "    freq_df_gy[1] = np.fft.fft(freq_df_gy[1])\n",
    "    freq_df_gy[2] = np.fft.fft(freq_df_gy[2])\n",
    "    freq_df_gy[3] = np.fft.fft(freq_df_gy[3])\n",
    "    \n",
    "    freq_df_jerk[1] = np.fft.fft(freq_df_jerk[1])\n",
    "    freq_df_jerk[2] = np.fft.fft(freq_df_jerk[2])\n",
    "    freq_df_jerk[3] = np.fft.fft(freq_df_jerk[3]) \n",
    "    \n",
    "    freq_df_gy[1] = [magnitude(j) for j in freq_df_gy[1]]\n",
    "    freq_df_gy[2] = [magnitude(j) for j in freq_df_gy[2]]\n",
    "    freq_df_gy[3] = [magnitude(j) for j in freq_df_gy[3]]\n",
    "    \n",
    "    freq_df_jerk[1] = [magnitude(j) for j in freq_df_jerk[1]]\n",
    "    freq_df_jerk[2] = [magnitude(j) for j in freq_df_jerk[2]]\n",
    "    freq_df_jerk[3] = [magnitude(j) for j in freq_df_jerk[3]]\n",
    "    \n",
    "    new_df_ac = Tprocess_avg_acceleration(df_ac)\n",
    "    new_freq_df_ac = Fprocess_avg_acceleration(freq_df_ac)\n",
    "    new_df_gy = Tprocess_avg_gyroscope(df_gy)\n",
    "    new_freq_df_gy = Fprocess_avg_gyroscope(freq_df_gy)\n",
    "    new_df_jerk = Tprocess_avg_jerk(df_jerk)\n",
    "    new_freq_df_jerk = Fprocess_avg_jerk(freq_df_jerk)\n",
    "    \n",
    "    \n",
    "    if new_df_gy.shape[0] < new_df_ac.shape[0]:\n",
    "        new_df_ac = new_df_ac[:new_df_gy.shape[0]]\n",
    "        new_freq_df_ac = new_freq_df_ac[:new_df_gy.shape[0]]\n",
    "    \n",
    "    new_df = pd.concat([new_df_ac,new_df_gy,new_df_jerk,new_freq_df_ac,new_freq_df_jerk, new_freq_df_gy], axis = 1)\n",
    "    #print(new_df.head())   \n",
    "    #print(new_df.shape)\n",
    "    #print(avg_acc_gy.shape)\n",
    "    #gy_shapes.append(new_df.shape)\n",
    "    avg_acc_gy_test = new_df\n",
    "    #print(avg_acc_gy.head())\n",
    "\n",
    "for i in dirs[1:]:\n",
    "    print(i)\n",
    "    full_path_acc = os.path.normpath(i + \"/\" + \"1_android.sensor.accelerometer.data.csv.gz\")\n",
    "    full_path_gy = os.path.normpath(i + \"/\" + \"4_android.sensor.gyroscope.data.csv.gz\")\n",
    "    print(\"processing : \" + full_path_acc)\n",
    "    df_ac = pd.read_csv(full_path_acc, compression='gzip', sep=',', header=None)\n",
    "    freq_df_ac = df_ac.copy()\n",
    "    \n",
    "    df_jerk = df_ac.copy()\n",
    "    \n",
    "    df_jerk[1] = jerk(df_jerk[1],df_jerk[0])\n",
    "    df_jerk[2] = jerk(df_jerk[2],df_jerk[0])\n",
    "    df_jerk[3] = jerk(df_jerk[3],df_jerk[0])\n",
    "    freq_df_jerk = df_jerk.copy()\n",
    "    \n",
    "    freq_df_ac[1] = np.fft.fft(freq_df_ac[1])\n",
    "    freq_df_ac[2] = np.fft.fft(freq_df_ac[2])\n",
    "    freq_df_ac[3] = np.fft.fft(freq_df_ac[3])\n",
    "    freq_df_ac[1] = [magnitude(j) for j in freq_df_ac[1]]\n",
    "    freq_df_ac[2] = [magnitude(j) for j in freq_df_ac[2]]\n",
    "    freq_df_ac[3] = [magnitude(j) for j in freq_df_ac[3]]\n",
    "    df_gy = pd.read_csv(full_path_gy, compression='gzip', sep=',', header=None)\n",
    "    freq_df_gy = df_gy.copy()\n",
    "    \n",
    "    \n",
    "    freq_df_gy[1] = np.fft.fft(freq_df_gy[1])\n",
    "    freq_df_gy[2] = np.fft.fft(freq_df_gy[2])\n",
    "    freq_df_gy[3] = np.fft.fft(freq_df_gy[3])\n",
    "    \n",
    "    freq_df_jerk[1] = np.fft.fft(freq_df_jerk[1])\n",
    "    freq_df_jerk[2] = np.fft.fft(freq_df_jerk[2])\n",
    "    freq_df_jerk[3] = np.fft.fft(freq_df_jerk[3]) \n",
    "    \n",
    "    freq_df_gy[1] = [magnitude(j) for j in freq_df_gy[1]]\n",
    "    freq_df_gy[2] = [magnitude(j) for j in freq_df_gy[2]]\n",
    "    freq_df_gy[3] = [magnitude(j) for j in freq_df_gy[3]]\n",
    "    \n",
    "    freq_df_jerk[1] = [magnitude(j) for j in freq_df_jerk[1]]\n",
    "    freq_df_jerk[2] = [magnitude(j) for j in freq_df_jerk[2]]\n",
    "    freq_df_jerk[3] = [magnitude(j) for j in freq_df_jerk[3]]\n",
    "    \n",
    "    new_df_ac = Tprocess_avg_acceleration(df_ac)\n",
    "    new_freq_df_ac = Fprocess_avg_acceleration(freq_df_ac)\n",
    "    new_df_gy = Tprocess_avg_gyroscope(df_gy)\n",
    "    new_freq_df_gy = Fprocess_avg_gyroscope(freq_df_gy)\n",
    "    new_df_jerk = Tprocess_avg_jerk(df_jerk)\n",
    "    new_freq_df_jerk = Fprocess_avg_jerk(freq_df_jerk)\n",
    "    \n",
    "    \n",
    "    if new_df_gy.shape[0] < new_df_ac.shape[0]:\n",
    "        new_df_ac = new_df_ac[:new_df_gy.shape[0]]\n",
    "        new_freq_df_ac = new_freq_df_ac[:new_df_gy.shape[0]]\n",
    "    \n",
    "    new_df = pd.concat([new_df_ac,new_df_gy,new_df_jerk,new_freq_df_ac,new_freq_df_jerk, new_freq_df_gy], axis = 1)\n",
    "    avg_acc_gy_test = pd.concat([avg_acc_gy,new_df])\n",
    "\n",
    "\n",
    "print(avg_acc_gy_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18934, 18)"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(19219, 18)"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "newData/14442D4AF81DBE4_Thu_Mar_01_20-47_2018_PST\\data\n",
      "processing : newData\\14442D4AF81DBE4_Thu_Mar_01_20-47_2018_PST\\data\\1_android.sensor.accelerometer.data.csv.gz\n",
      "(253, 19)\n",
      "processing : newData\\14442D4AF81DBE4_Fri_Mar_02_01-53_2018_PST\\data\\1_android.sensor.accelerometer.data.csv.gz\n",
      "(522, 19)\n",
      "processing : newData\\14442D4AF81DBE4_Fri_Mar_02_02-08_2018_PST\\data\\1_android.sensor.accelerometer.data.csv.gz\n",
      "(821, 19)\n",
      "processing : newData\\14442D4AF81DBE4_Wed_Mar_07_22-13_2018_PST\\data\\1_android.sensor.accelerometer.data.csv.gz\n",
      "(844, 19)\n",
      "processing : newData\\14442D4AF81DBE4_Wed_Mar_07_22-33_2018_PST\\data\\1_android.sensor.accelerometer.data.csv.gz\n",
      "(1086, 19)\n",
      "processing : newData\\14442D4AF81DBE4_Wed_Mar_07_22-37_2018_PST\\data\\1_android.sensor.accelerometer.data.csv.gz\n",
      "(1329, 19)\n",
      "processing : newData\\14442D4AF81DBE4_Wed_Mar_07_22-42_2018_PST\\data\\1_android.sensor.accelerometer.data.csv.gz\n",
      "(1571, 19)\n",
      "processing : newData\\14442D4AF81DBE4_Wed_Mar_07_22-47_2018_PST\\data\\1_android.sensor.accelerometer.data.csv.gz\n",
      "(1815, 19)\n",
      "processing : newData\\14442D4AF81DBE4_Wed_Mar_07_22-53_2018_PST\\data\\1_android.sensor.accelerometer.data.csv.gz\n",
      "(2058, 19)\n",
      "processing : newData\\14442D4AF81DBE4_Wed_Mar_07_22-57_2018_PST\\data\\1_android.sensor.accelerometer.data.csv.gz\n",
      "(2301, 19)\n",
      "processing : newData\\14442D4AF81DBE4_Wed_Mar_07_23-02_2018_PST\\data\\1_android.sensor.accelerometer.data.csv.gz\n",
      "(2542, 19)\n",
      "processing : newData\\14442D4AF81DBE4_Wed_Mar_07_23-09_2018_PST\\data\\1_android.sensor.accelerometer.data.csv.gz\n",
      "(2785, 19)\n",
      "processing : newData\\14442D4AF81DBE4_Wed_Mar_07_23-14_2018_PST\\data\\1_android.sensor.accelerometer.data.csv.gz\n",
      "(3026, 19)\n",
      "processing : newData\\14442D4AF81DBE4_Wed_Mar_07_23-26_2018_PST\\data\\1_android.sensor.accelerometer.data.csv.gz\n",
      "(3270, 19)\n",
      "processing : newData\\14442D4AF81DBE4_Wed_Mar_07_23-46_2018_PST\\data\\1_android.sensor.accelerometer.data.csv.gz\n",
      "(3511, 19)\n",
      "processing : newData\\14442D4AF81DBE4_Wed_Mar_07_23-53_2018_PST\\data\\1_android.sensor.accelerometer.data.csv.gz\n",
      "(3755, 19)\n",
      "processing : newData\\14442D4AF81DBE4_Thu_Mar_08_00-09_2018_PST\\data\\1_android.sensor.accelerometer.data.csv.gz\n",
      "(3997, 19)\n"
     ]
    }
   ],
   "source": [
    "t_last_dirs = [\"1_android.sensor.accelerometer.data.csv.gz\",\"4_android.sensor.gyroscope.data.csv.gz\"]\n",
    "t_path = \"newData/\"\n",
    "t_dirs = []\n",
    "t_dir_list = [x[0] for x in os.walk(t_path)]\n",
    "for t_each_dir in t_dir_list[2:]:\n",
    "\n",
    "    if \"data\" in t_each_dir:\n",
    "    #each_file = os.listdir(each_dir)\n",
    "        t_dirs.append(t_each_dir)\n",
    "        \n",
    "\n",
    "\n",
    "t_dirs.sort(key=lambda x:x[-22:])\n",
    "t_gy_shapes = []\n",
    "print(t_dirs[0])\n",
    "#avg_acc_gy = pd.DataFrame(columns = [\"avg_acc_x\",\"avg_acc_y\",\"avg_acc_x\", \"avg_gyroscope_x\",  \"avg_gyroscope_y\",  \"avg_gyroscope_z\",  \"label\"])\n",
    "for i in t_dirs[0:1]:\n",
    "    #print(i)\n",
    "    t_full_path_acc = os.path.normpath(i + \"/\" + \"1_android.sensor.accelerometer.data.csv.gz\")\n",
    "    t_full_path_gy = os.path.normpath(i + \"/\" + \"4_android.sensor.gyroscope.data.csv.gz\")\n",
    "    print(\"processing : \" + t_full_path_acc)\n",
    "    t_df_ac = pd.read_csv(t_full_path_acc,compression='gzip', sep=',', header=None)\n",
    "    t_freq_df_ac = t_df_ac.copy()\n",
    "    \n",
    "    t_df_jerk = t_df_ac.copy()\n",
    "    \n",
    "    t_df_jerk[1] = jerk(t_df_jerk[1],t_df_jerk[0])\n",
    "    t_df_jerk[2] = jerk(t_df_jerk[2],t_df_jerk[0])\n",
    "    t_df_jerk[3] = jerk(t_df_jerk[3],t_df_jerk[0])\n",
    "    t_freq_df_jerk = t_df_jerk.copy()\n",
    "    \n",
    "    t_freq_df_ac[1] = np.fft.fft(t_freq_df_ac[1])\n",
    "    t_freq_df_ac[2] = np.fft.fft(t_freq_df_ac[2])\n",
    "    t_freq_df_ac[3] = np.fft.fft(t_freq_df_ac[3])\n",
    "    t_freq_df_ac[1] = [magnitude(j) for j in t_freq_df_ac[1]]\n",
    "    t_freq_df_ac[2] = [magnitude(j) for j in t_freq_df_ac[2]]\n",
    "    t_freq_df_ac[3] = [magnitude(j) for j in t_freq_df_ac[3]]\n",
    "    t_df_gy = pd.read_csv(t_full_path_gy,compression='gzip', sep=',', header=None)\n",
    "    t_freq_df_gy = t_df_gy.copy()\n",
    "    \n",
    "    \n",
    "    t_freq_df_gy[1] = np.fft.fft(t_freq_df_gy[1])\n",
    "    t_freq_df_gy[2] = np.fft.fft(t_freq_df_gy[2])\n",
    "    t_freq_df_gy[3] = np.fft.fft(t_freq_df_gy[3])\n",
    "    \n",
    "    t_freq_df_jerk[1] = np.fft.fft(t_freq_df_jerk[1])\n",
    "    t_freq_df_jerk[2] = np.fft.fft(t_freq_df_jerk[2])\n",
    "    t_freq_df_jerk[3] = np.fft.fft(t_freq_df_jerk[3]) \n",
    "    \n",
    "    t_freq_df_gy[1] = [magnitude(j) for j in t_freq_df_gy[1]]\n",
    "    t_freq_df_gy[2] = [magnitude(j) for j in t_freq_df_gy[2]]\n",
    "    t_freq_df_gy[3] = [magnitude(j) for j in t_freq_df_gy[3]]\n",
    "    \n",
    "    t_freq_df_jerk[1] = [magnitude(j) for j in t_freq_df_jerk[1]]\n",
    "    t_freq_df_jerk[2] = [magnitude(j) for j in t_freq_df_jerk[2]]\n",
    "    t_freq_df_jerk[3] = [magnitude(j) for j in t_freq_df_jerk[3]]\n",
    "    \n",
    "    t_new_df_ac = Tprocess_avg_acceleration(t_df_ac)\n",
    "    t_new_freq_df_ac = Fprocess_avg_acceleration(t_freq_df_ac)\n",
    "    t_new_df_gy = Tprocess_avg_gyroscope(t_df_gy)\n",
    "    t_new_freq_df_gy = Fprocess_avg_gyroscope(t_freq_df_gy)\n",
    "    t_new_df_jerk = Tprocess_avg_jerk(t_df_jerk)\n",
    "    t_new_freq_df_jerk = Fprocess_avg_jerk(t_freq_df_jerk)\n",
    "    \n",
    "    \n",
    "    if t_new_df_gy.shape[0] < t_new_df_ac.shape[0]:\n",
    "        t_new_df_ac = t_new_df_ac[:t_new_df_gy.shape[0]]\n",
    "        t_new_freq_df_ac = t_new_freq_df_ac[:t_new_df_gy.shape[0]]\n",
    "    \n",
    "    t_new_df = pd.concat([t_new_df_ac,t_new_df_gy,t_new_df_jerk,t_new_freq_df_ac,t_new_freq_df_jerk, t_new_freq_df_gy], axis = 1)\n",
    "    #print(new_df.head())   \n",
    "    #print(new_df.shape)\n",
    "    #print(avg_acc_gy.shape)\n",
    "    #gy_shapes.append(new_df.shape)\n",
    "    t_avg_acc_gy_test = t_new_df\n",
    "    print(t_avg_acc_gy_test.shape)\n",
    "    \n",
    "for i in t_dirs[1:]:\n",
    "    #print(i)\n",
    "    t_full_path_acc = os.path.normpath(i + \"/\" + \"1_android.sensor.accelerometer.data.csv.gz\")\n",
    "    t_full_path_gy = os.path.normpath(i + \"/\" + \"4_android.sensor.gyroscope.data.csv.gz\")\n",
    "    print(\"processing : \" + t_full_path_acc)\n",
    "    t_df_ac = pd.read_csv(t_full_path_acc,compression='gzip', sep=',', header=None)\n",
    "    t_freq_df_ac = t_df_ac.copy()\n",
    "    \n",
    "    t_df_jerk = t_df_ac.copy()\n",
    "    \n",
    "    t_df_jerk[1] = jerk(t_df_jerk[1],t_df_jerk[0])\n",
    "    t_df_jerk[2] = jerk(t_df_jerk[2],t_df_jerk[0])\n",
    "    t_df_jerk[3] = jerk(t_df_jerk[3],t_df_jerk[0])\n",
    "    t_freq_df_jerk = t_df_jerk.copy()\n",
    "    \n",
    "    t_freq_df_ac[1] = np.fft.fft(t_freq_df_ac[1])\n",
    "    t_freq_df_ac[2] = np.fft.fft(t_freq_df_ac[2])\n",
    "    t_freq_df_ac[3] = np.fft.fft(t_freq_df_ac[3])\n",
    "    t_freq_df_ac[1] = [magnitude(j) for j in t_freq_df_ac[1]]\n",
    "    t_freq_df_ac[2] = [magnitude(j) for j in t_freq_df_ac[2]]\n",
    "    t_freq_df_ac[3] = [magnitude(j) for j in t_freq_df_ac[3]]\n",
    "    t_df_gy = pd.read_csv(t_full_path_gy,compression='gzip', sep=',', header=None)\n",
    "    t_freq_df_gy = t_df_gy.copy()\n",
    "    \n",
    "    \n",
    "    t_freq_df_gy[1] = np.fft.fft(t_freq_df_gy[1])\n",
    "    t_freq_df_gy[2] = np.fft.fft(t_freq_df_gy[2])\n",
    "    t_freq_df_gy[3] = np.fft.fft(t_freq_df_gy[3])\n",
    "    \n",
    "    t_freq_df_jerk[1] = np.fft.fft(t_freq_df_jerk[1])\n",
    "    t_freq_df_jerk[2] = np.fft.fft(t_freq_df_jerk[2])\n",
    "    t_freq_df_jerk[3] = np.fft.fft(t_freq_df_jerk[3]) \n",
    "    \n",
    "    t_freq_df_gy[1] = [magnitude(j) for j in t_freq_df_gy[1]]\n",
    "    t_freq_df_gy[2] = [magnitude(j) for j in t_freq_df_gy[2]]\n",
    "    t_freq_df_gy[3] = [magnitude(j) for j in t_freq_df_gy[3]]\n",
    "    \n",
    "    t_freq_df_jerk[1] = [magnitude(j) for j in t_freq_df_jerk[1]]\n",
    "    t_freq_df_jerk[2] = [magnitude(j) for j in t_freq_df_jerk[2]]\n",
    "    t_freq_df_jerk[3] = [magnitude(j) for j in t_freq_df_jerk[3]]\n",
    "    \n",
    "    t_new_df_ac = Tprocess_avg_acceleration(t_df_ac)\n",
    "    t_new_freq_df_ac = Fprocess_avg_acceleration(t_freq_df_ac)\n",
    "    t_new_df_gy = Tprocess_avg_gyroscope(t_df_gy)\n",
    "    t_new_freq_df_gy = Fprocess_avg_gyroscope(t_freq_df_gy)\n",
    "    t_new_df_jerk = Tprocess_avg_jerk(t_df_jerk)\n",
    "    t_new_freq_df_jerk = Fprocess_avg_jerk(t_freq_df_jerk)\n",
    "    \n",
    "    \n",
    "    if t_new_df_gy.shape[0] < t_new_df_ac.shape[0]:\n",
    "        t_new_df_ac = t_new_df_ac[:t_new_df_gy.shape[0]]\n",
    "        t_new_freq_df_ac = t_new_freq_df_ac[:t_new_df_gy.shape[0]]\n",
    "    \n",
    "    t_new_df = pd.concat([t_new_df_ac,t_new_df_gy,t_new_df_jerk,t_new_freq_df_ac,t_new_freq_df_jerk, t_new_freq_df_gy], axis = 1)\n",
    "    #print(new_df.head())   \n",
    "    #print(new_df.shape)\n",
    "    #print(avg_acc_gy.shape)\n",
    "    #gy_shapes.append(new_df.shape)\n",
    "    t_avg_acc_gy_test = pd.concat([t_avg_acc_gy_test,t_new_df])\n",
    "    print(t_avg_acc_gy_test.shape)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [],
   "source": [
    "t_avg_acc_gy_test = t_avg_acc_gy_test.iloc[np.random.permutation(len(t_avg_acc_gy_test))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-1.87415318 -0.5496779  -0.92768597 -1.37378546 -1.00182148  0.63416717\n",
      "  -1.98615411 -0.80116277 -0.08630885 -0.09281354 -0.19652787 -0.11631538\n",
      "   0.29633611  0.26564256  0.11150125 -0.20377268 -0.12211002 -0.16028753]\n",
      " [-1.91768002 -0.5643732  -0.93221202 -1.51065941 -1.40079548 -0.09579852\n",
      "  -0.5264734  -0.15964332  0.6923455   0.20603537 -0.07958932  0.04748403\n",
      "   1.10530695 -0.5460476   0.13367479  0.69244869  0.60741668  0.54018674]\n",
      " [ 0.65328273  1.26216214  0.11008179 -0.00560755  0.11858145 -0.04271649\n",
      "   0.03085743 -0.03825478 -0.03498616 -0.20220079  0.33380412 -0.20943892\n",
      "  -0.247633    0.92458044 -0.55221221 -0.30957229 -0.22759184 -0.25257254]\n",
      " [ 0.51071808 -1.66664581  0.48332334  0.00852326  0.09813708 -0.00651626\n",
      "  -0.02296132  0.01666764  0.02902413 -0.0967397  -0.86983774 -0.12413047\n",
      "  -0.13251707 -1.31297281 -0.50171007 -0.0894309  -0.09317003 -0.09431609]\n",
      " [-1.12010414  0.99031958 -0.09561228 -0.03662003  0.10571773 -0.006248\n",
      "   0.199762   -0.08719667  0.01186255 -0.18418632 -0.25475905 -0.18802562\n",
      "  -0.06481861 -0.27601161 -0.31592273 -0.35463239 -0.23621128 -0.26521682]]\n",
      "(3997, 18)\n"
     ]
    }
   ],
   "source": [
    "targets_test = t_avg_acc_gy_test[[\"label\"]].as_matrix()\n",
    "data_test = t_avg_acc_gy_test.copy()\n",
    "data_test = data_test.drop([\"label\"],axis = 1).as_matrix()\n",
    "data_test = np.nan_to_num(data_test)\n",
    "for i in range(len(data_test)):\n",
    "    for j in range(len(data_test[0])):\n",
    "        x = float(data_test[i][j])\n",
    "        if math.isnan(x):\n",
    "            print(\"Nan found\")\n",
    "#from sklearn.preprocessing import StandardScaler\n",
    "#scaler = StandardScaler()\n",
    "data_test = scaler.fit_transform(data_test)\n",
    "print(data_test[:5])\n",
    "print(data_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[['walking']\n",
      " ['walking']\n",
      " ['laying_down']\n",
      " ['standing']\n",
      " ['laying_down']]\n",
      "[array('walking', dtype='<U7'), array('walking', dtype='<U7'), array('laying_down', dtype='<U11'), array('standing', dtype='<U8'), array('laying_down', dtype='<U11')]\n",
      "<class 'str'>\n",
      "['walking', 'walking', 'laying_down', 'standing', 'laying_down']\n"
     ]
    }
   ],
   "source": [
    "targets_1_test = [np.nan_to_num(targets_test[i][0]) for i in range(len(targets_test))]\n",
    "#targets = [str(target) for target in targets]\n",
    "#print(type(str(targets[1])))\n",
    "#targets = [str(target) for target in targets]\n",
    "print(targets_test[:5])\n",
    "print(targets_1_test[:5])\n",
    "print(type(str(targets_1_test[1])))\n",
    "\n",
    "targets_2_test = [str(targets_1_test[i]) for i in range(len(targets_1_test))]\n",
    "\n",
    "print(targets_2_test[:5])\n",
    "bin_targets_test = []\n",
    "for i,t in enumerate(targets_2_test):\n",
    "    #print(i)\n",
    "    #print(t)\n",
    "    bin_targets_test.append(cache[t])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3997"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(bin_targets_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [],
   "source": [
    "preds_test = clf.predict(data_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.2146609957468101\n"
     ]
    }
   ],
   "source": [
    "print(accuracy_score(bin_targets_test,preds_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.9800995  0.99253731 0.98258706 0.98503741 0.9775     0.98994975\n",
      " 0.98241206 0.98241206 0.9798995  0.98241206]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([0.98191214, 0.09591195, 0.34894091, 0.04173623, 0.10357143])"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf1 = DecisionTreeClassifier(random_state=0)\n",
    "print(cross_val_score(clf1, data_test, bin_targets_test, cv=10))\n",
    "from sklearn.metrics import precision_score\n",
    "precision_score(bin_targets_test, preds_test, average=None)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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