HAR.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Proband 1\n",
      "Acc Sitting Records:  (32833, 4)\n",
      "Acc Running Records:  (30429, 4)\n",
      "Acc Laying Records:  (31229, 4)\n",
      "Acc Walking Records:  (31793, 4)\n",
      "Acc Standing Records:  (31924, 4)\n",
      "\n",
      "Gyr Sitting Records:  (33267, 4)\n",
      "Gyr Running Records:  (30958, 4)\n",
      "Gyr Laying Records:  (31822, 4)\n",
      "Gyr Walking Records:  (31912, 4)\n",
      "Gyr Standing Records:  (32546, 4)\n",
      "\n",
      "\n",
      "Proband 3\n",
      "Acc Sitting Records:  (31352, 4)\n",
      "Acc Running Records:  (37708, 4)\n",
      "Acc Laying Records:  (31379, 4)\n",
      "Acc Walking Records:  (33910, 4)\n",
      "Acc Standing Records:  (31054, 4)\n",
      "\n",
      "Gyr Sitting Records:  (31893, 4)\n",
      "Gyr Running Records:  (38359, 4)\n",
      "Gyr Laying Records:  (31967, 4)\n",
      "Gyr Walking Records:  (34558, 4)\n",
      "Gyr Standing Records:  (31560, 4)\n",
      "\n",
      "\n",
      "Proband 4\n",
      "Acc Sitting Records:  (31626, 4)\n",
      "Acc Running Records:  (51914, 4)\n",
      "Acc Laying Records:  (33607, 4)\n",
      "Acc Walking Records:  (30842, 4)\n",
      "Acc Standing Records:  (30057, 4)\n",
      "\n",
      "Gyr Sitting Records:  (32254, 4)\n",
      "Gyr Running Records:  (52686, 4)\n",
      "Gyr Laying Records:  (34196, 4)\n",
      "Gyr Walking Records:  (31398, 4)\n",
      "Gyr Standing Records:  (30657, 4)\n",
      "\n",
      "\n",
      "Proband 5\n",
      "Acc Sitting Records:  (32570, 4)\n",
      "Acc Running Records:  (55569, 4)\n",
      "Acc Laying Records:  (33008, 4)\n",
      "Acc Walking Records:  (34625, 4)\n",
      "Acc Standing Records:  (29858, 4)\n",
      "\n",
      "Gyr Sitting Records:  (33167, 4)\n",
      "Gyr Running Records:  (55889, 4)\n",
      "Gyr Laying Records:  (33393, 4)\n",
      "Gyr Walking Records:  (35108, 4)\n",
      "Gyr Standing Records:  (30260, 4)\n",
      "\n",
      "\n",
      "Proband 7\n",
      "Acc Sitting Records:  (32346, 4)\n",
      "Acc Running Records:  (36552, 4)\n",
      "Acc Laying Records:  (32443, 4)\n",
      "Acc Walking Records:  (30718, 4)\n",
      "Acc Standing Records:  (32940, 4)\n",
      "\n",
      "Gyr Sitting Records:  (32731, 4)\n",
      "Gyr Running Records:  (37169, 4)\n",
      "Gyr Laying Records:  (33019, 4)\n",
      "Gyr Walking Records:  (31261, 4)\n",
      "Gyr Standing Records:  (33537, 4)\n",
      "\n",
      "\n",
      "Proband 9\n",
      "Acc Sitting Records:  (32271, 4)\n",
      "Acc Running Records:  (40692, 4)\n",
      "Acc Laying Records:  (31278, 4)\n",
      "Acc Walking Records:  (31204, 4)\n",
      "Acc Standing Records:  (31974, 4)\n",
      "\n",
      "Gyr Sitting Records:  (32942, 4)\n",
      "Gyr Running Records:  (41460, 4)\n",
      "Gyr Laying Records:  (31790, 4)\n",
      "Gyr Walking Records:  (31803, 4)\n",
      "Gyr Standing Records:  (32622, 4)\n",
      "\n",
      "\n",
      "Proband 10\n",
      "Acc Sitting Records:  (31933, 4)\n",
      "Acc Running Records:  (32024, 4)\n",
      "Acc Laying Records:  (32033, 4)\n",
      "Acc Walking Records:  (31577, 4)\n",
      "Acc Standing Records:  (32868, 4)\n",
      "\n",
      "Gyr Sitting Records:  (32456, 4)\n",
      "Gyr Running Records:  (32485, 4)\n",
      "Gyr Laying Records:  (32328, 4)\n",
      "Gyr Walking Records:  (32057, 4)\n",
      "Gyr Standing Records:  (33435, 4)\n",
      "\n",
      "\n",
      "Proband 11\n",
      "Acc Sitting Records:  (31146, 4)\n",
      "Acc Running Records:  (30896, 4)\n",
      "Acc Laying Records:  (32579, 4)\n",
      "Acc Walking Records:  (33095, 4)\n",
      "Acc Standing Records:  (31433, 4)\n",
      "\n",
      "Gyr Sitting Records:  (31642, 4)\n",
      "Gyr Running Records:  (31453, 4)\n",
      "Gyr Laying Records:  (33184, 4)\n",
      "Gyr Walking Records:  (33695, 4)\n",
      "Gyr Standing Records:  (31834, 4)\n",
      "\n",
      "\n",
      "Proband 12\n",
      "Acc Sitting Records:  (31149, 4)\n",
      "Acc Running Records:  (30788, 4)\n",
      "Acc Laying Records:  (31109, 4)\n",
      "Acc Walking Records:  (31557, 4)\n",
      "Acc Standing Records:  (30682, 4)\n",
      "\n",
      "Gyr Sitting Records:  (31795, 4)\n",
      "Gyr Running Records:  (31345, 4)\n",
      "Gyr Laying Records:  (31666, 4)\n",
      "Gyr Walking Records:  (32132, 4)\n",
      "Gyr Standing Records:  (31295, 4)\n",
      "\n",
      "\n",
      "Proband 13\n",
      "Acc Sitting Records:  (32128, 4)\n",
      "Acc Running Records:  (30797, 4)\n",
      "Acc Laying Records:  (32226, 4)\n",
      "Acc Walking Records:  (32594, 4)\n",
      "Acc Standing Records:  (33861, 4)\n",
      "\n",
      "Gyr Sitting Records:  (32787, 4)\n",
      "Gyr Running Records:  (31307, 4)\n",
      "Gyr Laying Records:  (32689, 4)\n",
      "Gyr Walking Records:  (33115, 4)\n",
      "Gyr Standing Records:  (34447, 4)\n",
      "\n",
      "\n",
      "Proband 14\n",
      "Acc Sitting Records:  (31378, 4)\n",
      "Acc Running Records:  (30852, 4)\n",
      "Acc Laying Records:  (31358, 4)\n",
      "Acc Walking Records:  (33374, 4)\n",
      "Acc Standing Records:  (31032, 4)\n",
      "\n",
      "Gyr Sitting Records:  (32028, 4)\n",
      "Gyr Running Records:  (31419, 4)\n",
      "Gyr Laying Records:  (31969, 4)\n",
      "Gyr Walking Records:  (34079, 4)\n",
      "Gyr Standing Records:  (31623, 4)\n",
      "\n",
      "\n",
      "Proband 15\n",
      "Acc Sitting Records:  (32073, 4)\n",
      "Acc Running Records:  (33587, 4)\n",
      "Acc Laying Records:  (32882, 4)\n",
      "Acc Walking Records:  (33016, 4)\n",
      "Acc Standing Records:  (31277, 4)\n",
      "\n",
      "Gyr Sitting Records:  (32724, 4)\n",
      "Gyr Running Records:  (34208, 4)\n",
      "Gyr Laying Records:  (33506, 4)\n",
      "Gyr Walking Records:  (33605, 4)\n",
      "Gyr Standing Records:  (31878, 4)\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%matplotlib notebook\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "from __future__ import print_function\n",
    "\n",
    "from sklearn_xarray import wrap, Target\n",
    "from sklearn_xarray.preprocessing import Splitter, Sanitizer, Featurizer\n",
    "from sklearn_xarray.model_selection import CrossValidatorWrapper\n",
    "from sklearn_xarray.datasets import load_wisdm_dataarray\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler, LabelEncoder\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.model_selection import GroupShuffleSplit, GridSearchCV\n",
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "from sklearn.preprocessing import normalize\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "\n",
    "from yellowbrick.classifier import ClassificationReport\n",
    "\n",
    "\n",
    "sit_dataset_acc = []\n",
    "run_dataset_acc = []\n",
    "lay_dataset_acc = []\n",
    "walk_dataset_acc = []\n",
    "stand_dataset_acc = []\n",
    "\n",
    "sit_dataset_gyr = []\n",
    "run_dataset_gyr = []\n",
    "lay_dataset_gyr = []\n",
    "walk_dataset_gyr = []\n",
    "stand_dataset_gyr = []\n",
    "\n",
    "for x in range(1, 16):\n",
    "    if(x in [2,6,8]):\n",
    "        continue\n",
    "        \n",
    "    sit_path_acc = \"/Users/omar/Downloads/realworld2016-dataset/proband\"+str(x)+\"/data/acc_sitting_csv/acc_sitting_forearm.csv\"\n",
    "    sit_dataset_acc.append(np.genfromtxt(sit_path_acc,delimiter=',',skip_header=1,usecols = (1, 2, 3, 4)))\n",
    "\n",
    "    run_path_acc = \"/Users/omar/Downloads/realworld2016-dataset/proband\"+str(x)+\"/data/acc_running_csv/acc_running_forearm.csv\"\n",
    "    run_dataset_acc.append(np.genfromtxt(run_path_acc,delimiter=',',skip_header=1,usecols = (1, 2, 3, 4)))\n",
    "\n",
    "    lay_path_acc = \"/Users/omar/Downloads/realworld2016-dataset/proband\"+str(x)+\"/data/acc_lying_csv/acc_lying_forearm.csv\"\n",
    "    lay_dataset_acc.append(np.genfromtxt(lay_path_acc,delimiter=',',skip_header=1,usecols = (1, 2, 3, 4)))\n",
    "\n",
    "    walk_path_acc = \"/Users/omar/Downloads/realworld2016-dataset/proband\"+str(x)+\"/data/acc_walking_csv/acc_walking_forearm.csv\"\n",
    "    walk_dataset_acc.append(np.genfromtxt(walk_path_acc,delimiter=',',skip_header=1,usecols = (1, 2, 3, 4)))\n",
    "\n",
    "    stand_path_acc = \"/Users/omar/Downloads/realworld2016-dataset/proband\"+str(x)+\"/data/acc_standing_csv/acc_standing_forearm.csv\"\n",
    "    stand_dataset_acc.append(np.genfromtxt(stand_path_acc,delimiter=',',skip_header=1,usecols = (1, 2, 3, 4)))\n",
    "\n",
    "\n",
    "    sit_path_gyr = \"/Users/omar/Downloads/realworld2016-dataset/proband\"+str(x)+\"/data/gyr_sitting_csv/Gyroscope_sitting_forearm.csv\"\n",
    "    sit_dataset_gyr.append(np.genfromtxt(sit_path_gyr,delimiter=',',skip_header=1,usecols = (1, 2, 3, 4)))\n",
    "\n",
    "    run_path_gyr = \"/Users/omar/Downloads/realworld2016-dataset/proband\"+str(x)+\"/data/gyr_running_csv/Gyroscope_running_forearm.csv\"\n",
    "    run_dataset_gyr.append(np.genfromtxt(run_path_gyr,delimiter=',',skip_header=1,usecols = (1, 2, 3, 4)))\n",
    "\n",
    "    lay_path_gyr = \"/Users/omar/Downloads/realworld2016-dataset/proband\"+str(x)+\"/data/gyr_lying_csv/Gyroscope_lying_forearm.csv\"\n",
    "    lay_dataset_gyr.append(np.genfromtxt(lay_path_gyr,delimiter=',',skip_header=1,usecols = (1, 2, 3, 4)))\n",
    "\n",
    "    walk_path_gyr = \"/Users/omar/Downloads/realworld2016-dataset/proband\"+str(x)+\"/data/gyr_walking_csv/Gyroscope_walking_forearm.csv\"\n",
    "    walk_dataset_gyr.append(np.genfromtxt(walk_path_gyr,delimiter=',',skip_header=1,usecols = (1, 2, 3, 4)))\n",
    "\n",
    "    stand_path_gyr = \"/Users/omar/Downloads/realworld2016-dataset/proband\"+str(x)+\"/data/gyr_standing_csv/Gyroscope_standing_forearm.csv\"\n",
    "    stand_dataset_gyr.append(np.genfromtxt(stand_path_gyr,delimiter=',',skip_header=1,usecols = (1, 2, 3, 4)))\n",
    "\n",
    "    \n",
    "\n",
    "    # Check data size\n",
    "    print(\"Proband \"+str(x))\n",
    "    print(\"Acc Sitting Records: \", sit_dataset_acc[-1].shape)\n",
    "    print(\"Acc Running Records: \", run_dataset_acc[-1].shape)\n",
    "    print(\"Acc Laying Records: \", lay_dataset_acc[-1].shape)\n",
    "    print(\"Acc Walking Records: \", walk_dataset_acc[-1].shape)\n",
    "    print(\"Acc Standing Records: \", stand_dataset_acc[-1].shape)\n",
    "    print(\"\")\n",
    "    print(\"Gyr Sitting Records: \", sit_dataset_gyr[-1].shape)\n",
    "    print(\"Gyr Running Records: \", run_dataset_gyr[-1].shape)\n",
    "    print(\"Gyr Laying Records: \", lay_dataset_gyr[-1].shape)\n",
    "    print(\"Gyr Walking Records: \", walk_dataset_gyr[-1].shape)\n",
    "    print(\"Gyr Standing Records: \", stand_dataset_gyr[-1].shape)\n",
    "    print(\"\")\n",
    "    print(\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Proband0\n",
      "Acc Sitting Total Time: 10.97  minutes\n",
      "Acc Running Total Time: 10.21  minutes\n",
      "Acc Laying Total Time: 10.48  minutes\n",
      "Acc Walking Total Time: 10.66  minutes\n",
      "Acc Standing Total Time: 10.72  minutes\n",
      "\n",
      "Gyr Sitting Total Time: 10.97  minutes\n",
      "Gyr Running Total Time: 10.21  minutes\n",
      "Gyr Laying Total Time: 10.48  minutes\n",
      "Gyr Walking Total Time: 10.66  minutes\n",
      "Gyr Standing Total Time: 10.72  minutes\n",
      "\n",
      "\n",
      "Proband1\n",
      "Acc Sitting Total Time: 10.52  minutes\n",
      "Acc Running Total Time: 12.66  minutes\n",
      "Acc Laying Total Time: 10.53  minutes\n",
      "Acc Walking Total Time: 11.38  minutes\n",
      "Acc Standing Total Time: 10.42  minutes\n",
      "\n",
      "Gyr Sitting Total Time: 10.52  minutes\n",
      "Gyr Running Total Time: 12.66  minutes\n",
      "Gyr Laying Total Time: 10.53  minutes\n",
      "Gyr Walking Total Time: 11.38  minutes\n",
      "Gyr Standing Total Time: 10.41  minutes\n",
      "\n",
      "\n",
      "Proband2\n",
      "Acc Sitting Total Time: 10.62  minutes\n",
      "Acc Running Total Time: 17.41  minutes\n",
      "Acc Laying Total Time: 11.27  minutes\n",
      "Acc Walking Total Time: 10.35  minutes\n",
      "Acc Standing Total Time: 10.09  minutes\n",
      "\n",
      "Gyr Sitting Total Time: 10.62  minutes\n",
      "Gyr Running Total Time: 17.41  minutes\n",
      "Gyr Laying Total Time: 11.27  minutes\n",
      "Gyr Walking Total Time: 10.35  minutes\n",
      "Gyr Standing Total Time: 10.09  minutes\n",
      "\n",
      "\n",
      "Proband3\n",
      "Acc Sitting Total Time: 10.93  minutes\n",
      "Acc Running Total Time: 18.57  minutes\n",
      "Acc Laying Total Time: 11.05  minutes\n",
      "Acc Walking Total Time: 11.62  minutes\n",
      "Acc Standing Total Time: 10.02  minutes\n",
      "\n",
      "Gyr Sitting Total Time: 10.93  minutes\n",
      "Gyr Running Total Time: 18.57  minutes\n",
      "Gyr Laying Total Time: 11.05  minutes\n",
      "Gyr Walking Total Time: 11.62  minutes\n",
      "Gyr Standing Total Time: 10.02  minutes\n",
      "\n",
      "\n",
      "Proband4\n",
      "Acc Sitting Total Time: 10.82  minutes\n",
      "Acc Running Total Time: 12.27  minutes\n",
      "Acc Laying Total Time: 10.88  minutes\n",
      "Acc Walking Total Time: 10.31  minutes\n",
      "Acc Standing Total Time: 11.05  minutes\n",
      "\n",
      "Gyr Sitting Total Time: 10.82  minutes\n",
      "Gyr Running Total Time: 12.27  minutes\n",
      "Gyr Laying Total Time: 10.88  minutes\n",
      "Gyr Walking Total Time: 10.31  minutes\n",
      "Gyr Standing Total Time: 11.05  minutes\n",
      "\n",
      "\n",
      "Proband5\n",
      "Acc Sitting Total Time: 10.83  minutes\n",
      "Acc Running Total Time: 13.66  minutes\n",
      "Acc Laying Total Time: 10.5  minutes\n",
      "Acc Walking Total Time: 10.47  minutes\n",
      "Acc Standing Total Time: 10.73  minutes\n",
      "\n",
      "Gyr Sitting Total Time: 10.83  minutes\n",
      "Gyr Running Total Time: 13.66  minutes\n",
      "Gyr Laying Total Time: 10.5  minutes\n",
      "Gyr Walking Total Time: 10.47  minutes\n",
      "Gyr Standing Total Time: 10.73  minutes\n",
      "\n",
      "\n",
      "Proband6\n",
      "Acc Sitting Total Time: 10.72  minutes\n",
      "Acc Running Total Time: 10.75  minutes\n",
      "Acc Laying Total Time: 10.7  minutes\n",
      "Acc Walking Total Time: 10.6  minutes\n",
      "Acc Standing Total Time: 11.03  minutes\n",
      "\n",
      "Gyr Sitting Total Time: 10.72  minutes\n",
      "Gyr Running Total Time: 10.75  minutes\n",
      "Gyr Laying Total Time: 10.7  minutes\n",
      "Gyr Walking Total Time: 10.6  minutes\n",
      "Gyr Standing Total Time: 11.03  minutes\n",
      "\n",
      "\n",
      "Proband7\n",
      "Acc Sitting Total Time: 10.46  minutes\n",
      "Acc Running Total Time: 10.37  minutes\n",
      "Acc Laying Total Time: 10.94  minutes\n",
      "Acc Walking Total Time: 11.11  minutes\n",
      "Acc Standing Total Time: 10.5  minutes\n",
      "\n",
      "Gyr Sitting Total Time: 10.44  minutes\n",
      "Gyr Running Total Time: 10.37  minutes\n",
      "Gyr Laying Total Time: 10.94  minutes\n",
      "Gyr Walking Total Time: 11.11  minutes\n",
      "Gyr Standing Total Time: 10.5  minutes\n",
      "\n",
      "\n",
      "Proband8\n",
      "Acc Sitting Total Time: 10.46  minutes\n",
      "Acc Running Total Time: 10.34  minutes\n",
      "Acc Laying Total Time: 10.44  minutes\n",
      "Acc Walking Total Time: 10.59  minutes\n",
      "Acc Standing Total Time: 10.3  minutes\n",
      "\n",
      "Gyr Sitting Total Time: 10.46  minutes\n",
      "Gyr Running Total Time: 10.33  minutes\n",
      "Gyr Laying Total Time: 10.44  minutes\n",
      "Gyr Walking Total Time: 10.59  minutes\n",
      "Gyr Standing Total Time: 10.3  minutes\n",
      "\n",
      "\n",
      "Proband9\n",
      "Acc Sitting Total Time: 10.78  minutes\n",
      "Acc Running Total Time: 10.34  minutes\n",
      "Acc Laying Total Time: 10.81  minutes\n",
      "Acc Walking Total Time: 10.94  minutes\n",
      "Acc Standing Total Time: 11.37  minutes\n",
      "\n",
      "Gyr Sitting Total Time: 10.78  minutes\n",
      "Gyr Running Total Time: 10.34  minutes\n",
      "Gyr Laying Total Time: 10.81  minutes\n",
      "Gyr Walking Total Time: 10.94  minutes\n",
      "Gyr Standing Total Time: 11.37  minutes\n",
      "\n",
      "\n",
      "Proband10\n",
      "Acc Sitting Total Time: 10.53  minutes\n",
      "Acc Running Total Time: 10.36  minutes\n",
      "Acc Laying Total Time: 10.53  minutes\n",
      "Acc Walking Total Time: 11.2  minutes\n",
      "Acc Standing Total Time: 10.42  minutes\n",
      "\n",
      "Gyr Sitting Total Time: 10.53  minutes\n",
      "Gyr Running Total Time: 10.36  minutes\n",
      "Gyr Laying Total Time: 10.53  minutes\n",
      "Gyr Walking Total Time: 11.2  minutes\n",
      "Gyr Standing Total Time: 10.42  minutes\n",
      "\n",
      "\n",
      "Proband11\n",
      "Acc Sitting Total Time: 10.77  minutes\n",
      "Acc Running Total Time: 11.27  minutes\n",
      "Acc Laying Total Time: 11.04  minutes\n",
      "Acc Walking Total Time: 11.08  minutes\n",
      "Acc Standing Total Time: 10.5  minutes\n",
      "\n",
      "Gyr Sitting Total Time: 10.77  minutes\n",
      "Gyr Running Total Time: 11.27  minutes\n",
      "Gyr Laying Total Time: 11.03  minutes\n",
      "Gyr Walking Total Time: 11.08  minutes\n",
      "Gyr Standing Total Time: 10.5  minutes\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for x in range(len(sit_dataset_acc)):\n",
    "    # Normalize Time Column\n",
    "    print(\"Proband\" + str(x))\n",
    "\n",
    "    time = sit_dataset_acc[x][:,0]\n",
    "    time = time-time[0]\n",
    "    time = time.astype(int)\n",
    "    print(\"Acc Sitting Total Time:\" , round(time[-1]/60000,2) , \" minutes\")\n",
    "\n",
    "    time = run_dataset_acc[x][:,0]\n",
    "    time = time-time[0]\n",
    "    time = time.astype(int)\n",
    "    print(\"Acc Running Total Time:\" , round(time[-1]/60000,2) , \" minutes\")\n",
    "\n",
    "    time = lay_dataset_acc[x][:,0]\n",
    "    time = time-time[0]\n",
    "    time = time.astype(int)\n",
    "    print(\"Acc Laying Total Time:\" , round(time[-1]/60000,2) , \" minutes\")\n",
    "\n",
    "    time = walk_dataset_acc[x][:,0]\n",
    "    time = time-time[0]\n",
    "    time = time.astype(int)\n",
    "    print(\"Acc Walking Total Time:\" , round(time[-1]/60000,2) , \" minutes\")\n",
    "\n",
    "    time = stand_dataset_acc[x][:,0]\n",
    "    time = time-time[0]\n",
    "    time = time.astype(int)\n",
    "    print(\"Acc Standing Total Time:\" , round(time[-1]/60000,2) , \" minutes\")\n",
    "\n",
    "    print(\"\")\n",
    "\n",
    "    time = sit_dataset_gyr[x][:,0]\n",
    "    time = time-time[0]\n",
    "    time = time.astype(int)\n",
    "    print(\"Gyr Sitting Total Time:\" , round(time[-1]/60000,2) , \" minutes\")\n",
    "\n",
    "    time = run_dataset_gyr[x][:,0]\n",
    "    time = time-time[0]\n",
    "    time = time.astype(int)\n",
    "    print(\"Gyr Running Total Time:\" , round(time[-1]/60000,2) , \" minutes\")\n",
    "\n",
    "    time = lay_dataset_gyr[x][:,0]\n",
    "    time = time-time[0]\n",
    "    time = time.astype(int)\n",
    "    print(\"Gyr Laying Total Time:\" , round(time[-1]/60000,2) , \" minutes\")\n",
    "\n",
    "    time = walk_dataset_gyr[x][:,0]\n",
    "    time = time-time[0]\n",
    "    time = time.astype(int)\n",
    "    print(\"Gyr Walking Total Time:\" , round(time[-1]/60000,2) , \" minutes\")\n",
    "\n",
    "    time = stand_dataset_gyr[x][:,0]\n",
    "    time = time-time[0]\n",
    "    time = time.astype(int)\n",
    "    print(\"Gyr Standing Total Time:\" , round(time[-1]/60000,2) , \" minutes\")\n",
    "\n",
    "    print(\"\")\n",
    "    print(\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1977009, 4)\n",
      "(2010370, 4)\n",
      "1977009\n",
      "2010370\n",
      "-33361\n",
      "(1977009, 4)\n"
     ]
    }
   ],
   "source": [
    "# Combine Data\n",
    "\n",
    "# dataset = np.concatenate([cd_dataset_1,cd_dataset_2,cd_dataset_3,cd_dataset_4,cd_dataset_5, sit_dataset_1,sit_dataset_2,sit_dataset_3,sit_dataset_4,sit_dataset_5, run_dataset_1,run_dataset_2,run_dataset_3,run_dataset_4,run_dataset_5, lay_dataset_1, lay_dataset_2, lay_dataset_3, lay_dataset_4, lay_dataset_5, walk_dataset_1,walk_dataset_2, walk_dataset_3,walk_dataset_4,walk_dataset_5, stand_dataset_1,stand_dataset_2,stand_dataset_3,stand_dataset_4,stand_dataset_5])\n",
    "\n",
    "\n",
    "dataset_acc = np.concatenate([np.concatenate(sit_dataset_acc), np.concatenate(run_dataset_acc), np.concatenate(lay_dataset_acc), np.concatenate(walk_dataset_acc), np.concatenate(stand_dataset_acc)])\n",
    "dataset_gyr = np.concatenate([np.concatenate(sit_dataset_gyr), np.concatenate(run_dataset_gyr), np.concatenate(lay_dataset_gyr), np.concatenate(walk_dataset_gyr), np.concatenate(stand_dataset_gyr)])\n",
    "\n",
    "print(dataset_acc.shape)\n",
    "print(dataset_gyr.shape)\n",
    "\n",
    "print(len(dataset_acc))\n",
    "print(len(dataset_gyr))\n",
    "diff = 1977009 - 2010370\n",
    "print(diff)\n",
    "dataset_gyr = dataset_gyr[:diff]\n",
    "print(dataset_gyr.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1977009, 8)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Combine Acc and Gyr\n",
    "\n",
    "dataset = np.c_[dataset_acc, dataset_gyr]\n",
    "\n",
    "dataset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12\n",
      "Proband0\n",
      "30000\n",
      "30000\n",
      "30000\n",
      "30000\n",
      "30000\n",
      "Proband1\n",
      "30000\n",
      "30000\n",
      "30000\n",
      "30000\n",
      "30000\n",
      "Proband2\n",
      "30000\n",
      "30000\n",
      "30000\n",
      "30000\n",
      "30000\n",
      "Proband3\n",
      "30000\n",
      "30000\n",
      "30000\n",
      "30000\n",
      "30000\n",
      "Proband4\n",
      "30000\n",
      "30000\n",
      "30000\n",
      "30000\n",
      "30000\n",
      "Proband5\n",
      "30000\n",
      "30000\n",
      "30000\n",
      "30000\n",
      "30000\n",
      "Proband6\n",
      "30000\n",
      "30000\n",
      "30000\n",
      "30000\n",
      "30000\n",
      "Proband7\n",
      "30000\n",
      "30000\n",
      "30000\n",
      "30000\n",
      "30000\n",
      "Proband8\n",
      "30000\n",
      "30000\n",
      "30000\n",
      "30000\n",
      "30000\n",
      "Proband9\n",
      "30000\n",
      "30000\n",
      "30000\n",
      "30000\n",
      "30000\n",
      "Proband10\n",
      "30000\n",
      "30000\n",
      "30000\n",
      "30000\n",
      "30000\n",
      "Proband11\n",
      "30000\n",
      "30000\n",
      "30000\n",
      "30000\n",
      "30000\n"
     ]
    }
   ],
   "source": [
    "# plt.plot(X)\n",
    "# plt.plot(dataset[:,1])\n",
    "# plt.plot(dataset[:,2])\n",
    "# plt.plot(dataset[0:100,1])\n",
    "\n",
    "readings_per_second = 50\n",
    "\n",
    "head = 0;\n",
    "\n",
    "print(len(sit_dataset_acc))\n",
    "\n",
    "# cd_start = []\n",
    "# cd_end = []\n",
    "sit_start = []\n",
    "sit_end = []\n",
    "run_start = []\n",
    "run_end = []\n",
    "lay_start = []\n",
    "lay_end = []\n",
    "walk_start = []\n",
    "walk_end = []\n",
    "stand_start = []\n",
    "stand_end = []\n",
    "\n",
    "# for x in range(len(sit_dataset_acc)):\n",
    "#     cd_start.append(head + (readings_per_second * 10))\n",
    "#     cd_end.append(head + (readings_per_second * 310))\n",
    "\n",
    "#     head += cd_dataset[x].shape[0]\n",
    "\n",
    "for x in range(len(sit_dataset_acc)):\n",
    "    sit_start.append(head + (readings_per_second * 0))\n",
    "    sit_end.append(head + (readings_per_second * 600))\n",
    "\n",
    "    head += sit_dataset_acc[x].shape[0]\n",
    "\n",
    "\n",
    "for x in range(len(sit_dataset_acc)):\n",
    "    run_start.append(head + (readings_per_second * 0))\n",
    "    run_end.append(head + (readings_per_second * 600))\n",
    "\n",
    "    head += run_dataset_acc[x].shape[0]\n",
    "\n",
    "for x in range(len(sit_dataset_acc)):\n",
    "    lay_start.append(head + (readings_per_second * 0))\n",
    "    lay_end.append(head + (readings_per_second * 600))\n",
    "\n",
    "    head += lay_dataset_acc[x].shape[0]\n",
    "\n",
    "for x in range(len(sit_dataset_acc)):\n",
    "    walk_start.append(head + (readings_per_second * 0))\n",
    "    walk_end.append(head + (readings_per_second * 600))\n",
    "\n",
    "    head += walk_dataset_acc[x].shape[0]\n",
    "\n",
    "for x in range(len(sit_dataset_acc)):\n",
    "    stand_start.append(head + (readings_per_second * 0))\n",
    "    stand_end.append(head + (readings_per_second * 600))\n",
    "\n",
    "    head += stand_dataset_acc[x].shape[0]\n",
    "\n",
    "    \n",
    "    \n",
    "for x in range(len(sit_dataset_acc)):\n",
    "    print(\"Proband\"+str(x))\n",
    "#     print(cd_end[x]-cd_start[x])\n",
    "    print(sit_end[x]-sit_start[x])\n",
    "    print(run_end[x]-run_start[x])\n",
    "    print(lay_end[x]-lay_start[x])\n",
    "    print(walk_end[x]-walk_start[x])\n",
    "    print(stand_end[x]-stand_start[x])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "combined\n",
      "(360000,)\n"
     ]
    }
   ],
   "source": [
    "\n",
    "#Normalize Data, Get only the data between start and end\n",
    "# time = dataset[cd_start:cd_end,0]-dataset[0,0]\n",
    "X_acc = dataset[:,1]\n",
    "Y_acc = dataset[:,2]\n",
    "Z_acc = dataset[:,3]\n",
    "\n",
    "X_gyr = dataset[:,1]\n",
    "Y_gyr = dataset[:,2]\n",
    "Z_gyr = dataset[:,3]\n",
    "\n",
    "X_acc = normalize(X_acc[:,np.newaxis],norm='max', axis=0).ravel()\n",
    "Y_acc = normalize(Y_acc[:,np.newaxis],norm='max', axis=0).ravel()\n",
    "Z_acc = normalize(Z_acc[:,np.newaxis],norm='max', axis=0).ravel()\n",
    "\n",
    "\n",
    "X_gyr = normalize(X_gyr[:,np.newaxis],norm='max', axis=0).ravel()\n",
    "Y_gyr = normalize(Y_gyr[:,np.newaxis],norm='max', axis=0).ravel()\n",
    "Z_gyr = normalize(Z_gyr[:,np.newaxis],norm='max', axis=0).ravel()\n",
    "\n",
    "\n",
    "sit_X_acc = []\n",
    "sit_Y_acc = []\n",
    "sit_Z_acc = []\n",
    "\n",
    "run_X_acc = []\n",
    "run_Y_acc = []\n",
    "run_Z_acc = []\n",
    "\n",
    "lay_X_acc = []\n",
    "lay_Y_acc = []\n",
    "lay_Z_acc = []\n",
    "\n",
    "walk_X_acc = []\n",
    "walk_Y_acc = []\n",
    "walk_Z_acc = []\n",
    "\n",
    "stand_X_acc = []\n",
    "stand_Y_acc = []\n",
    "stand_Z_acc = []\n",
    "\n",
    "\n",
    "sit_X_gyr = []\n",
    "sit_Y_gyr = []\n",
    "sit_Z_gyr = []\n",
    "\n",
    "run_X_gyr = []\n",
    "run_Y_gyr = []\n",
    "run_Z_gyr = []\n",
    "\n",
    "lay_X_gyr = []\n",
    "lay_Y_gyr = []\n",
    "lay_Z_gyr = []\n",
    "\n",
    "walk_X_gyr = []\n",
    "walk_Y_gyr = []\n",
    "walk_Z_gyr = []\n",
    "\n",
    "stand_X_gyr = []\n",
    "stand_Y_gyr = []\n",
    "stand_Z_gyr = []\n",
    "\n",
    "\n",
    "for i in range(len(sit_dataset_acc)):\n",
    "\n",
    "    sit_X_acc = np.append(sit_X_acc,X_acc[sit_start[i]:sit_end[i]])\n",
    "    sit_Y_acc = np.append(sit_Y_acc,Y_acc[sit_start[i]:sit_end[i]])\n",
    "    sit_Z_acc = np.append(sit_Z_acc,Z_acc[sit_start[i]:sit_end[i]])\n",
    "\n",
    "    run_X_acc = np.append(run_X_acc,X_acc[run_start[i]:run_end[i]])\n",
    "    run_Y_acc = np.append(run_Y_acc,Y_acc[run_start[i]:run_end[i]])\n",
    "    run_Z_acc = np.append(run_Z_acc,Z_acc[run_start[i]:run_end[i]])\n",
    "\n",
    "    lay_X_acc = np.append(lay_X_acc,X_acc[lay_start[i]:lay_end[i]])\n",
    "    lay_Y_acc = np.append(lay_Y_acc,Y_acc[lay_start[i]:lay_end[i]])\n",
    "    lay_Z_acc = np.append(lay_Z_acc,Z_acc[lay_start[i]:lay_end[i]])\n",
    "\n",
    "    walk_X_acc = np.append(walk_X_acc,X_acc[walk_start[i]:walk_end[i]])\n",
    "    walk_Y_acc = np.append(walk_Y_acc,Y_acc[walk_start[i]:walk_end[i]])\n",
    "    walk_Z_acc = np.append(walk_Z_acc,Z_acc[walk_start[i]:walk_end[i]])\n",
    "\n",
    "    stand_X_acc = np.append(stand_X_acc,X_acc[stand_start[i]:stand_end[i]])\n",
    "    stand_Y_acc = np.append(stand_Y_acc,Y_acc[stand_start[i]:stand_end[i]])\n",
    "    stand_Z_acc = np.append(stand_Z_acc,Z_acc[stand_start[i]:stand_end[i]])\n",
    "\n",
    "\n",
    "    sit_X_gyr = np.append(sit_X_gyr,X_gyr[sit_start[i]:sit_end[i]])\n",
    "    sit_Y_gyr = np.append(sit_Y_gyr,Y_gyr[sit_start[i]:sit_end[i]])\n",
    "    sit_Z_gyr = np.append(sit_Z_gyr,Z_gyr[sit_start[i]:sit_end[i]])\n",
    "\n",
    "    run_X_gyr = np.append(run_X_gyr,X_gyr[run_start[i]:run_end[i]])\n",
    "    run_Y_gyr = np.append(run_Y_gyr,Y_gyr[run_start[i]:run_end[i]])\n",
    "    run_Z_gyr = np.append(run_Z_gyr,Z_gyr[run_start[i]:run_end[i]])\n",
    "\n",
    "    lay_X_gyr = np.append(lay_X_gyr,X_gyr[lay_start[i]:lay_end[i]])\n",
    "    lay_Y_gyr = np.append(lay_Y_gyr,Y_gyr[lay_start[i]:lay_end[i]])\n",
    "    lay_Z_gyr = np.append(lay_Z_gyr,Z_gyr[lay_start[i]:lay_end[i]])\n",
    "\n",
    "    walk_X_gyr = np.append(walk_X_gyr,X_gyr[walk_start[i]:walk_end[i]])\n",
    "    walk_Y_gyr = np.append(walk_Y_gyr,Y_gyr[walk_start[i]:walk_end[i]])\n",
    "    walk_Z_gyr = np.append(walk_Z_gyr,Z_gyr[walk_start[i]:walk_end[i]])\n",
    "\n",
    "    stand_X_gyr = np.append(stand_X_gyr,X_gyr[stand_start[i]:stand_end[i]])\n",
    "    stand_Y_gyr = np.append(stand_Y_gyr,Y_gyr[stand_start[i]:stand_end[i]])\n",
    "    stand_Z_gyr = np.append(stand_Z_gyr,Z_gyr[stand_start[i]:stand_end[i]])\n",
    "\n",
    "print(\"combined\")\n",
    "print(sit_X_acc.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# # plt.plot(X)\n",
    "# # plt.plot(Y)\n",
    "# # plt.plot(Z)\n",
    "# plt.rcParams['figure.figsize'] = (10, 15)\n",
    "\n",
    "# plt.subplot(7, 3, 1)\n",
    "# plt.plot(time/1000, cd_X, 'b')\n",
    "# plt.title('X_attr')\n",
    "# plt.ylabel('Climbing Down')\n",
    "# plt.ylim([-0.015, 0.005])\n",
    "\n",
    "# plt.subplot(7, 3, 4)\n",
    "# plt.plot(time/1000, sit_X, 'b')\n",
    "# plt.ylabel('Sitting Acceleration')\n",
    "# plt.ylim([-0.015, 0.005])\n",
    "\n",
    "\n",
    "# plt.subplot(7, 3, 10)\n",
    "# plt.plot(time/1000, run_X, 'b')\n",
    "# plt.ylabel('Running Acceleration')\n",
    "# plt.ylim([-0.015, 0.005])\n",
    "\n",
    "# plt.subplot(7, 3, 13)\n",
    "# plt.plot(time/1000, lay_X, 'b')\n",
    "# plt.ylabel('Laying Acceleration')\n",
    "# plt.ylim([-0.015, 0.005])\n",
    "\n",
    "\n",
    "# plt.subplot(7, 3, 16)\n",
    "# plt.plot(time/1000, walk_X, 'b')\n",
    "# plt.ylabel('Walking Acceleration')\n",
    "# plt.ylim([-0.015, 0.005])\n",
    "\n",
    "# plt.subplot(7, 3, 19)\n",
    "# plt.plot(time/1000, stand_X, 'b')\n",
    "# plt.ylabel('Laying Acceleration')\n",
    "# plt.xlabel('time (s)')\n",
    "# plt.ylim([-0.015, 0.005])\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# plt.subplot(7, 3, 2)\n",
    "# plt.plot(time/1000, cd_Y, 'g')\n",
    "# plt.title('Y_attr')\n",
    "# plt.ylim([-0.007, 0.003])\n",
    "\n",
    "# plt.subplot(7, 3, 5)\n",
    "# plt.plot(time/1000, sit_Y, 'g')\n",
    "# plt.ylim([-0.007, 0.003])\n",
    "\n",
    "\n",
    "# plt.subplot(7, 3, 11)\n",
    "# plt.plot(time/1000, run_Y, 'g')\n",
    "# plt.ylim([-0.007, 0.003])\n",
    "\n",
    "# plt.subplot(7, 3, 14)\n",
    "# plt.plot(time/1000, lay_Y, 'g')\n",
    "# plt.ylim([-0.007, 0.003])\n",
    "\n",
    "\n",
    "# plt.subplot(7, 3, 17)\n",
    "# plt.plot(time/1000, walk_Y, 'g')\n",
    "# plt.ylim([-0.007, 0.003])\n",
    "\n",
    "\n",
    "# plt.subplot(7, 3, 20)\n",
    "# plt.plot(time/1000, stand_Y, 'g')\n",
    "# plt.xlabel('time (s)')\n",
    "# plt.ylim([-0.007, 0.003])\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# plt.subplot(7, 3, 3)\n",
    "# plt.plot(time/1000, cd_Z, 'r')\n",
    "# plt.title('Z_attr')\n",
    "# plt.ylim([-0.01, 0.01])\n",
    "\n",
    "# plt.subplot(7, 3, 6)\n",
    "# plt.plot(time/1000, sit_Z, 'r')\n",
    "# plt.ylim([-0.01, 0.01])\n",
    "\n",
    "\n",
    "# plt.subplot(7, 3, 12)\n",
    "# plt.plot(time/1000, run_Z, 'r')\n",
    "# plt.ylim([-0.007, 0.003])\n",
    "\n",
    "# plt.subplot(7, 3, 15)\n",
    "# plt.plot(time/1000, lay_Z, 'r')\n",
    "# plt.ylim([-0.007, 0.003])\n",
    "\n",
    "\n",
    "# plt.subplot(7, 3, 18)\n",
    "# plt.plot(time/1000, walk_Z, 'r')\n",
    "# plt.ylim([-0.007, 0.003])\n",
    "\n",
    "# plt.subplot(7, 3, 21)\n",
    "# plt.plot(time/1000, stand_Z, 'r')\n",
    "# plt.xlabel('time (s)')\n",
    "# plt.ylim([-0.007, 0.003])\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "def rolling_window(a, window):\n",
    "    shape = a.shape[:-1] + (a.shape[-1] - window + 1, window)\n",
    "    strides = a.strides + (a.strides[-1],)\n",
    "    return np.lib.stride_tricks.as_strided(a, shape=shape, strides=strides)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "# cd_X_rolling = np.std(rolling_window(cd_X, readings_per_second), 1)\n",
    "# cd_X_rolling\n",
    "# cd_Y_rolling = np.std(rolling_window(cd_Y, readings_per_second), 1)\n",
    "# cd_Y_rolling\n",
    "# cd_Z_rolling = np.std(rolling_window(cd_Z, readings_per_second), 1)\n",
    "# cd_Z_rolling\n",
    "# cd_X_Y_Z = np.vstack((cd_X_rolling, cd_Y_rolling, cd_Z_rolling)).T\n",
    "# cd_X_Y_Z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.00100838, 0.00124832, 0.00193375],\n",
       "       [0.00102433, 0.00124382, 0.00197818],\n",
       "       [0.00101821, 0.00120642, 0.00199244],\n",
       "       ...,\n",
       "       [0.0010903 , 0.00224553, 0.00114732],\n",
       "       [0.00110921, 0.00223613, 0.00114781],\n",
       "       [0.00110571, 0.00219145, 0.0011585 ]])"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sit_X_acc_rolling = np.std(rolling_window(sit_X_acc, readings_per_second), 1)\n",
    "sit_Y_acc_rolling = np.std(rolling_window(sit_Y_acc, readings_per_second), 1)\n",
    "sit_Z_acc_rolling = np.std(rolling_window(sit_Z_acc, readings_per_second), 1)\n",
    "sit_X_Y_Z_acc = np.vstack((sit_X_acc_rolling, sit_Y_acc_rolling, sit_Z_acc_rolling)).T\n",
    "\n",
    "sit_X_gyr_rolling = np.std(rolling_window(sit_X_gyr, readings_per_second), 1)\n",
    "sit_Y_gyr_rolling = np.std(rolling_window(sit_Y_gyr, readings_per_second), 1)\n",
    "sit_Z_gyr_rolling = np.std(rolling_window(sit_Z_gyr, readings_per_second), 1)\n",
    "sit_X_Y_Z_gyr = np.vstack((sit_X_gyr_rolling, sit_Y_gyr_rolling, sit_Z_gyr_rolling)).T\n",
    "sit_X_Y_Z_gyr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "# jump_X_rolling = np.std(rolling_window(jump_X, readings_per_second), 1)\n",
    "# jump_X_rolling\n",
    "# jump_Y_rolling = np.std(rolling_window(jump_Y, readings_per_second), 1)\n",
    "# jump_Y_rolling\n",
    "# jump_Z_rolling = np.std(rolling_window(jump_Z, readings_per_second), 1)\n",
    "# jump_Z_rolling\n",
    "# jump_X_Y_Z = np.vstack((jump_X_rolling, jump_Y_rolling, jump_Z_rolling)).T\n",
    "# jump_X_Y_Z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.00146764, 0.00341628, 0.00235216],\n",
       "       [0.00146401, 0.00350197, 0.00232939],\n",
       "       [0.00146278, 0.00355245, 0.00233608],\n",
       "       ...,\n",
       "       [0.01189202, 0.00615153, 0.00675908],\n",
       "       [0.01189278, 0.00597584, 0.00697835],\n",
       "       [0.01192144, 0.00585154, 0.00734958]])"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "run_X_acc_rolling = np.std(rolling_window(run_X_acc, readings_per_second), 1)\n",
    "run_Y_acc_rolling = np.std(rolling_window(run_Y_acc, readings_per_second), 1)\n",
    "run_Z_acc_rolling = np.std(rolling_window(run_Z_acc, readings_per_second), 1)\n",
    "run_X_Y_Z_acc = np.vstack((run_X_acc_rolling, run_Y_acc_rolling, run_Z_acc_rolling)).T\n",
    "\n",
    "run_X_gyr_rolling = np.std(rolling_window(run_X_gyr, readings_per_second), 1)\n",
    "run_Y_gyr_rolling = np.std(rolling_window(run_Y_gyr, readings_per_second), 1)\n",
    "run_Z_gyr_rolling = np.std(rolling_window(run_Z_gyr, readings_per_second), 1)\n",
    "run_X_Y_Z_gyr = np.vstack((run_X_gyr_rolling, run_Y_gyr_rolling, run_Z_gyr_rolling)).T\n",
    "run_X_Y_Z_gyr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.0032616 , 0.01712011, 0.01813946],\n",
       "       [0.00368641, 0.02186054, 0.01857997],\n",
       "       [0.00588405, 0.02333983, 0.01989158],\n",
       "       ...,\n",
       "       [0.00441921, 0.00678803, 0.00861072],\n",
       "       [0.0043999 , 0.00678802, 0.0087187 ],\n",
       "       [0.00442811, 0.00686226, 0.00910594]])"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lay_X_acc_rolling = np.std(rolling_window(lay_X_acc, readings_per_second), 1)\n",
    "lay_Y_acc_rolling = np.std(rolling_window(lay_Y_acc, readings_per_second), 1)\n",
    "lay_Z_acc_rolling = np.std(rolling_window(lay_Z_acc, readings_per_second), 1)\n",
    "lay_X_Y_Z_acc = np.vstack((lay_X_acc_rolling, lay_Y_acc_rolling, lay_Z_acc_rolling)).T\n",
    "\n",
    "lay_X_gyr_rolling = np.std(rolling_window(lay_X_gyr, readings_per_second), 1)\n",
    "lay_Y_gyr_rolling = np.std(rolling_window(lay_Y_gyr, readings_per_second), 1)\n",
    "lay_Z_gyr_rolling = np.std(rolling_window(lay_Z_gyr, readings_per_second), 1)\n",
    "lay_X_Y_Z_gyr = np.vstack((lay_X_gyr_rolling, lay_Y_gyr_rolling, lay_Z_gyr_rolling)).T\n",
    "lay_X_Y_Z_gyr\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.00120453, 0.00109145, 0.001939  ],\n",
       "       [0.00118244, 0.00109774, 0.00189853],\n",
       "       [0.0011876 , 0.00102111, 0.00188918],\n",
       "       ...,\n",
       "       [0.10188681, 0.03985165, 0.04161925],\n",
       "       [0.10587824, 0.040313  , 0.03965026],\n",
       "       [0.10841033, 0.04024665, 0.03881043]])"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "walk_X_acc_rolling = np.std(rolling_window(walk_X_acc, readings_per_second), 1)\n",
    "walk_Y_acc_rolling = np.std(rolling_window(walk_Y_acc, readings_per_second), 1)\n",
    "walk_Z_acc_rolling = np.std(rolling_window(walk_Z_acc, readings_per_second), 1)\n",
    "walk_X_Y_Z_acc = np.vstack((walk_X_acc_rolling, walk_Y_acc_rolling, walk_Z_acc_rolling)).T\n",
    "\n",
    "walk_X_gyr_rolling = np.std(rolling_window(walk_X_gyr, readings_per_second), 1)\n",
    "walk_Y_gyr_rolling = np.std(rolling_window(walk_Y_gyr, readings_per_second), 1)\n",
    "walk_Z_gyr_rolling = np.std(rolling_window(walk_Z_gyr, readings_per_second), 1)\n",
    "walk_X_Y_Z_gyr = np.vstack((walk_X_gyr_rolling, walk_Y_gyr_rolling, walk_Z_gyr_rolling)).T\n",
    "walk_X_Y_Z_gyr\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.00125141, 0.00252818, 0.00178652],\n",
       "       [0.00126136, 0.00253312, 0.00178394],\n",
       "       [0.00129241, 0.00253789, 0.00172743],\n",
       "       ...,\n",
       "       [0.00831602, 0.01333192, 0.01314223],\n",
       "       [0.008373  , 0.01347998, 0.01311109],\n",
       "       [0.0083085 , 0.01363407, 0.01311992]])"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stand_X_acc_rolling = np.std(rolling_window(stand_X_acc, readings_per_second), 1)\n",
    "stand_Y_acc_rolling = np.std(rolling_window(stand_Y_acc, readings_per_second), 1)\n",
    "stand_Z_acc_rolling = np.std(rolling_window(stand_Z_acc, readings_per_second), 1)\n",
    "stand_X_Y_Z_acc = np.vstack((stand_X_acc_rolling, stand_Y_acc_rolling, stand_Z_acc_rolling)).T\n",
    "\n",
    "stand_X_gyr_rolling = np.std(rolling_window(stand_X_gyr, readings_per_second), 1)\n",
    "stand_Y_gyr_rolling = np.std(rolling_window(stand_Y_gyr, readings_per_second), 1)\n",
    "stand_Z_gyr_rolling = np.std(rolling_window(stand_Z_gyr, readings_per_second), 1)\n",
    "stand_X_Y_Z_gyr = np.vstack((stand_X_gyr_rolling, stand_Y_gyr_rolling, stand_Z_gyr_rolling)).T\n",
    "stand_X_Y_Z_gyr\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "# cu_X_rolling = np.std(rolling_window(cu_X, readings_per_second), 1)\n",
    "# cu_X_rolling\n",
    "# cu_Y_rolling = np.std(rolling_window(cu_Y, readings_per_second), 1)\n",
    "# cu_Y_rolling\n",
    "# cu_Z_rolling = np.std(rolling_window(cu_Z, readings_per_second), 1)\n",
    "# cu_Z_rolling\n",
    "# cu_X_Y_Z = np.vstack((cu_X_rolling, cu_Y_rolling, cu_Z_rolling)).T\n",
    "# cu_X_Y_Z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(359951, 3)\n",
      "(359951, 3)\n",
      "(359951, 3)\n",
      "(359951, 3)\n",
      "(359951, 3)\n",
      "(359951, 3)\n",
      "(359951, 3)\n",
      "(359951, 3)\n",
      "(359951, 3)\n",
      "(359951, 3)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(359951, 6)"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(sit_X_Y_Z_acc.shape)\n",
    "print(run_X_Y_Z_acc.shape)\n",
    "print(lay_X_Y_Z_acc.shape)\n",
    "print(walk_X_Y_Z_acc.shape)\n",
    "print(stand_X_Y_Z_acc.shape)\n",
    "\n",
    "print(sit_X_Y_Z_gyr.shape)\n",
    "print(run_X_Y_Z_gyr.shape)\n",
    "print(lay_X_Y_Z_gyr.shape)\n",
    "print(walk_X_Y_Z_gyr.shape)\n",
    "print(stand_X_Y_Z_gyr.shape)\n",
    "\n",
    "sit_X_Y_Z = np.c_[sit_X_Y_Z_acc, sit_X_Y_Z_gyr]\n",
    "run_X_Y_Z = np.c_[run_X_Y_Z_acc, run_X_Y_Z_gyr]\n",
    "lay_X_Y_Z = np.c_[lay_X_Y_Z_acc, lay_X_Y_Z_gyr]\n",
    "walk_X_Y_Z = np.c_[walk_X_Y_Z_acc, walk_X_Y_Z_gyr]\n",
    "stand_X_Y_Z = np.c_[stand_X_Y_Z_acc, stand_X_Y_Z_gyr]\n",
    "stand_X_Y_Z.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1799700, 6)\n",
      "(1799700,)\n"
     ]
    }
   ],
   "source": [
    "sit_splits = np.split(sit_X_Y_Z[:-11],len(sit_dataset_acc))\n",
    "run_splits = np.split(run_X_Y_Z[:-11],len(sit_dataset_acc))\n",
    "lay_splits = np.split(lay_X_Y_Z[:-11],len(sit_dataset_acc))\n",
    "walk_splits = np.split(walk_X_Y_Z[:-11],len(sit_dataset_acc))\n",
    "stand_splits = np.split(stand_X_Y_Z[:-11],len(sit_dataset_acc))\n",
    "\n",
    "\n",
    "\n",
    "values = [[0,0,0,0,0,0]]\n",
    "labels = []\n",
    "\n",
    "for i in range(len(sit_dataset_acc)):\n",
    "    i_values = np.concatenate(([sit_splits[i],run_splits[i],lay_splits[i],walk_splits[i],stand_splits[i]]))\n",
    "    values = np.append(values,i_values, axis=0)\n",
    "    i_labels = np.concatenate((np.repeat(\"Sit\", sit_splits[i].shape[0]),np.repeat(\"Run\", run_splits[i].shape[0]),np.repeat(\"Lay\", lay_splits[i].shape[0]),np.repeat(\"Walk\", walk_splits[i].shape[0]),np.repeat(\"Stand\", stand_splits[i].shape[0])))\n",
    "    labels = np.append(labels,i_labels)\n",
    "    \n",
    "values = np.delete(values,0,0)\n",
    "    \n",
    "print(values.shape)\n",
    "print(labels.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.00356446 0.009999   0.00501552 0.00356446 0.009999   0.00501552]\n",
      " [0.00977315 0.01837564 0.03393313 0.00977315 0.01837564 0.03393313]\n",
      " [0.13446014 0.04366378 0.05113296 0.13446014 0.04366378 0.05113296]\n",
      " ...\n",
      " [0.01613304 0.05710143 0.03515275 0.01613304 0.05710143 0.03515275]\n",
      " [0.29915194 0.40883856 0.10333463 0.29915194 0.40883856 0.10333463]\n",
      " [0.15091293 0.07822987 0.07880021 0.15091293 0.07822987 0.07880021]]\n",
      "[[0.15605455 0.09123832 0.10283747 0.15605455 0.09123832 0.10283747]\n",
      " [0.02604107 0.03906237 0.03206502 0.02604107 0.03906237 0.03206502]\n",
      " [0.10061252 0.03724614 0.04714586 0.10061252 0.03724614 0.04714586]\n",
      " ...\n",
      " [0.07199841 0.03712014 0.05975839 0.07199841 0.03712014 0.05975839]\n",
      " [0.00212363 0.00365336 0.00376107 0.00212363 0.00365336 0.00376107]\n",
      " [0.28820494 0.39063401 0.12280914 0.28820494 0.39063401 0.12280914]]\n",
      "['Lay' 'Stand' 'Walk' ... 'Lay' 'Run' 'Walk']\n",
      "['Walk' 'Stand' 'Walk' ... 'Walk' 'Run' 'Run']\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(values, labels, test_size=0.20)\n",
    "print(X_train)\n",
    "print(X_test)\n",
    "print(y_train)\n",
    "print(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "# scaler = StandardScaler()\n",
    "# scaler.fit(X_train)\n",
    "\n",
    "# X_train = scaler.transform(X_train)\n",
    "# X_test = scaler.transform(X_test)\n",
    "# print(X_train)\n",
    "# print(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=8)"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier = KNeighborsClassifier(n_neighbors=8)\n",
    "classifier.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[56309   428  7834  6901   369]\n",
      " [  924 64005  2560  3589   782]\n",
      " [15240  1505 41672 12626  1264]\n",
      " [12113  2046 14468 42006  1305]\n",
      " [  362   521   944   935 69232]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         Lay       0.66      0.78      0.72     71841\n",
      "         Run       0.93      0.89      0.91     71860\n",
      "         Sit       0.62      0.58      0.60     72307\n",
      "       Stand       0.64      0.58      0.61     71938\n",
      "        Walk       0.95      0.96      0.96     71994\n",
      "\n",
      "    accuracy                           0.76    359940\n",
      "   macro avg       0.76      0.76      0.76    359940\n",
      "weighted avg       0.76      0.76      0.76    359940\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test, y_pred))\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "/* global mpl */\n",
       "window.mpl = {};\n",
       "\n",
       "mpl.get_websocket_type = function () {\n",
       "    if (typeof WebSocket !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof MozWebSocket !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert(\n",
       "            'Your browser does not have WebSocket support. ' +\n",
       "                'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "                'Firefox 4 and 5 are also supported but you ' +\n",
       "                'have to enable WebSockets in about:config.'\n",
       "        );\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = this.ws.binaryType !== undefined;\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById('mpl-warnings');\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent =\n",
       "                'This browser does not support binary websocket messages. ' +\n",
       "                'Performance may be slow.';\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = document.createElement('div');\n",
       "    this.root.setAttribute('style', 'display: inline-block');\n",
       "    this._root_extra_style(this.root);\n",
       "\n",
       "    parent_element.appendChild(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen = function () {\n",
       "        fig.send_message('supports_binary', { value: fig.supports_binary });\n",
       "        fig.send_message('send_image_mode', {});\n",
       "        if (fig.ratio !== 1) {\n",
       "            fig.send_message('set_dpi_ratio', { dpi_ratio: fig.ratio });\n",
       "        }\n",
       "        fig.send_message('refresh', {});\n",
       "    };\n",
       "\n",
       "    this.imageObj.onload = function () {\n",
       "        if (fig.image_mode === 'full') {\n",
       "            // Full images could contain transparency (where diff images\n",
       "            // almost always do), so we need to clear the canvas so that\n",
       "            // there is no ghosting.\n",
       "            fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "        }\n",
       "        fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "    };\n",
       "\n",
       "    this.imageObj.onunload = function () {\n",
       "        fig.ws.close();\n",
       "    };\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_header = function () {\n",
       "    var titlebar = document.createElement('div');\n",
       "    titlebar.classList =\n",
       "        'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n",
       "    var titletext = document.createElement('div');\n",
       "    titletext.classList = 'ui-dialog-title';\n",
       "    titletext.setAttribute(\n",
       "        'style',\n",
       "        'width: 100%; text-align: center; padding: 3px;'\n",
       "    );\n",
       "    titlebar.appendChild(titletext);\n",
       "    this.root.appendChild(titlebar);\n",
       "    this.header = titletext;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = (this.canvas_div = document.createElement('div'));\n",
       "    canvas_div.setAttribute(\n",
       "        'style',\n",
       "        'border: 1px solid #ddd;' +\n",
       "            'box-sizing: content-box;' +\n",
       "            'clear: both;' +\n",
       "            'min-height: 1px;' +\n",
       "            'min-width: 1px;' +\n",
       "            'outline: 0;' +\n",
       "            'overflow: hidden;' +\n",
       "            'position: relative;' +\n",
       "            'resize: both;'\n",
       "    );\n",
       "\n",
       "    function on_keyboard_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.key_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    canvas_div.addEventListener(\n",
       "        'keydown',\n",
       "        on_keyboard_event_closure('key_press')\n",
       "    );\n",
       "    canvas_div.addEventListener(\n",
       "        'keyup',\n",
       "        on_keyboard_event_closure('key_release')\n",
       "    );\n",
       "\n",
       "    this._canvas_extra_style(canvas_div);\n",
       "    this.root.appendChild(canvas_div);\n",
       "\n",
       "    var canvas = (this.canvas = document.createElement('canvas'));\n",
       "    canvas.classList.add('mpl-canvas');\n",
       "    canvas.setAttribute('style', 'box-sizing: content-box;');\n",
       "\n",
       "    this.context = canvas.getContext('2d');\n",
       "\n",
       "    var backingStore =\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        this.context.webkitBackingStorePixelRatio ||\n",
       "        this.context.mozBackingStorePixelRatio ||\n",
       "        this.context.msBackingStorePixelRatio ||\n",
       "        this.context.oBackingStorePixelRatio ||\n",
       "        this.context.backingStorePixelRatio ||\n",
       "        1;\n",
       "\n",
       "    this.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "    if (this.ratio !== 1) {\n",
       "        fig.send_message('set_dpi_ratio', { dpi_ratio: this.ratio });\n",
       "    }\n",
       "\n",
       "    var rubberband_canvas = (this.rubberband_canvas = document.createElement(\n",
       "        'canvas'\n",
       "    ));\n",
       "    rubberband_canvas.setAttribute(\n",
       "        'style',\n",
       "        'box-sizing: content-box; position: absolute; left: 0; top: 0; z-index: 1;'\n",
       "    );\n",
       "\n",
       "    var resizeObserver = new ResizeObserver(function (entries) {\n",
       "        var nentries = entries.length;\n",
       "        for (var i = 0; i < nentries; i++) {\n",
       "            var entry = entries[i];\n",
       "            var width, height;\n",
       "            if (entry.contentBoxSize) {\n",
       "                if (entry.contentBoxSize instanceof Array) {\n",
       "                    // Chrome 84 implements new version of spec.\n",
       "                    width = entry.contentBoxSize[0].inlineSize;\n",
       "                    height = entry.contentBoxSize[0].blockSize;\n",
       "                } else {\n",
       "                    // Firefox implements old version of spec.\n",
       "                    width = entry.contentBoxSize.inlineSize;\n",
       "                    height = entry.contentBoxSize.blockSize;\n",
       "                }\n",
       "            } else {\n",
       "                // Chrome <84 implements even older version of spec.\n",
       "                width = entry.contentRect.width;\n",
       "                height = entry.contentRect.height;\n",
       "            }\n",
       "\n",
       "            // Keep the size of the canvas and rubber band canvas in sync with\n",
       "            // the canvas container.\n",
       "            if (entry.devicePixelContentBoxSize) {\n",
       "                // Chrome 84 implements new version of spec.\n",
       "                canvas.setAttribute(\n",
       "                    'width',\n",
       "                    entry.devicePixelContentBoxSize[0].inlineSize\n",
       "                );\n",
       "                canvas.setAttribute(\n",
       "                    'height',\n",
       "                    entry.devicePixelContentBoxSize[0].blockSize\n",
       "                );\n",
       "            } else {\n",
       "                canvas.setAttribute('width', width * fig.ratio);\n",
       "                canvas.setAttribute('height', height * fig.ratio);\n",
       "            }\n",
       "            canvas.setAttribute(\n",
       "                'style',\n",
       "                'width: ' + width + 'px; height: ' + height + 'px;'\n",
       "            );\n",
       "\n",
       "            rubberband_canvas.setAttribute('width', width);\n",
       "            rubberband_canvas.setAttribute('height', height);\n",
       "\n",
       "            // And update the size in Python. We ignore the initial 0/0 size\n",
       "            // that occurs as the element is placed into the DOM, which should\n",
       "            // otherwise not happen due to the minimum size styling.\n",
       "            if (width != 0 && height != 0) {\n",
       "                fig.request_resize(width, height);\n",
       "            }\n",
       "        }\n",
       "    });\n",
       "    resizeObserver.observe(canvas_div);\n",
       "\n",
       "    function on_mouse_event_closure(name) {\n",
       "        return function (event) {\n",
       "            return fig.mouse_event(event, name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousedown',\n",
       "        on_mouse_event_closure('button_press')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseup',\n",
       "        on_mouse_event_closure('button_release')\n",
       "    );\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mousemove',\n",
       "        on_mouse_event_closure('motion_notify')\n",
       "    );\n",
       "\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseenter',\n",
       "        on_mouse_event_closure('figure_enter')\n",
       "    );\n",
       "    rubberband_canvas.addEventListener(\n",
       "        'mouseleave',\n",
       "        on_mouse_event_closure('figure_leave')\n",
       "    );\n",
       "\n",
       "    canvas_div.addEventListener('wheel', function (event) {\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        on_mouse_event_closure('scroll')(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.appendChild(canvas);\n",
       "    canvas_div.appendChild(rubberband_canvas);\n",
       "\n",
       "    this.rubberband_context = rubberband_canvas.getContext('2d');\n",
       "    this.rubberband_context.strokeStyle = '#000000';\n",
       "\n",
       "    this._resize_canvas = function (width, height, forward) {\n",
       "        if (forward) {\n",
       "            canvas_div.style.width = width + 'px';\n",
       "            canvas_div.style.height = height + 'px';\n",
       "        }\n",
       "    };\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    this.rubberband_canvas.addEventListener('contextmenu', function (_e) {\n",
       "        event.preventDefault();\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus() {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'mpl-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'mpl-button-group';\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'mpl-button-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        var button = (fig.buttons[name] = document.createElement('button'));\n",
       "        button.classList = 'mpl-widget';\n",
       "        button.setAttribute('role', 'button');\n",
       "        button.setAttribute('aria-disabled', 'false');\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "\n",
       "        var icon_img = document.createElement('img');\n",
       "        icon_img.src = '_images/' + image + '.png';\n",
       "        icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
       "        icon_img.alt = tooltip;\n",
       "        button.appendChild(icon_img);\n",
       "\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    var fmt_picker = document.createElement('select');\n",
       "    fmt_picker.classList = 'mpl-widget';\n",
       "    toolbar.appendChild(fmt_picker);\n",
       "    this.format_dropdown = fmt_picker;\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = document.createElement('option');\n",
       "        option.selected = fmt === mpl.default_extension;\n",
       "        option.innerHTML = fmt;\n",
       "        fmt_picker.appendChild(option);\n",
       "    }\n",
       "\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', { width: x_pixels, height: y_pixels });\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_message = function (type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function () {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function (fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1], msg['forward']);\n",
       "        fig.send_message('refresh', {});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
       "    var x0 = msg['x0'] / fig.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
       "    var x1 = msg['x1'] / fig.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0,\n",
       "        0,\n",
       "        fig.canvas.width / fig.ratio,\n",
       "        fig.canvas.height / fig.ratio\n",
       "    );\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function (fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch (cursor) {\n",
       "        case 0:\n",
       "            cursor = 'pointer';\n",
       "            break;\n",
       "        case 1:\n",
       "            cursor = 'default';\n",
       "            break;\n",
       "        case 2:\n",
       "            cursor = 'crosshair';\n",
       "            break;\n",
       "        case 3:\n",
       "            cursor = 'move';\n",
       "            break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_message = function (fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function (fig, _msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n",
       "    for (var key in msg) {\n",
       "        if (!(key in fig.buttons)) {\n",
       "            continue;\n",
       "        }\n",
       "        fig.buttons[key].disabled = !msg[key];\n",
       "        fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
       "    if (msg['mode'] === 'PAN') {\n",
       "        fig.buttons['Pan'].classList.add('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    } else if (msg['mode'] === 'ZOOM') {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.add('active');\n",
       "    } else {\n",
       "        fig.buttons['Pan'].classList.remove('active');\n",
       "        fig.buttons['Zoom'].classList.remove('active');\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message('ack', {});\n",
       "};\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function (fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = 'image/png';\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src\n",
       "                );\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data\n",
       "            );\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        } else if (\n",
       "            typeof evt.data === 'string' &&\n",
       "            evt.data.slice(0, 21) === 'data:image/png;base64'\n",
       "        ) {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig['handle_' + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\n",
       "                \"No handler for the '\" + msg_type + \"' message type: \",\n",
       "                msg\n",
       "            );\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\n",
       "                    \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
       "                    e,\n",
       "                    e.stack,\n",
       "                    msg\n",
       "                );\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "};\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function (e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e) {\n",
       "        e = window.event;\n",
       "    }\n",
       "    if (e.target) {\n",
       "        targ = e.target;\n",
       "    } else if (e.srcElement) {\n",
       "        targ = e.srcElement;\n",
       "    }\n",
       "    if (targ.nodeType === 3) {\n",
       "        // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "    }\n",
       "\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    var boundingRect = targ.getBoundingClientRect();\n",
       "    var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
       "    var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n",
       "\n",
       "    return { x: x, y: y };\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys(original) {\n",
       "    return Object.keys(original).reduce(function (obj, key) {\n",
       "        if (typeof original[key] !== 'object') {\n",
       "            obj[key] = original[key];\n",
       "        }\n",
       "        return obj;\n",
       "    }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function (event, name) {\n",
       "    var canvas_pos = mpl.findpos(event);\n",
       "\n",
       "    if (name === 'button_press') {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * this.ratio;\n",
       "    var y = canvas_pos.y * this.ratio;\n",
       "\n",
       "    this.send_message(name, {\n",
       "        x: x,\n",
       "        y: y,\n",
       "        button: event.button,\n",
       "        step: event.step,\n",
       "        guiEvent: simpleKeys(event),\n",
       "    });\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (_event, _name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.key_event = function (event, name) {\n",
       "    // Prevent repeat events\n",
       "    if (name === 'key_press') {\n",
       "        if (event.which === this._key) {\n",
       "            return;\n",
       "        } else {\n",
       "            this._key = event.which;\n",
       "        }\n",
       "    }\n",
       "    if (name === 'key_release') {\n",
       "        this._key = null;\n",
       "    }\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which !== 17) {\n",
       "        value += 'ctrl+';\n",
       "    }\n",
       "    if (event.altKey && event.which !== 18) {\n",
       "        value += 'alt+';\n",
       "    }\n",
       "    if (event.shiftKey && event.which !== 16) {\n",
       "        value += 'shift+';\n",
       "    }\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n",
       "    return false;\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function (name) {\n",
       "    if (name === 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message('toolbar_button', { name: name });\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";/* global mpl */\n",
       "\n",
       "var comm_websocket_adapter = function (comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function () {\n",
       "        comm.close();\n",
       "    };\n",
       "    ws.send = function (m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function (msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data']);\n",
       "    });\n",
       "    return ws;\n",
       "};\n",
       "\n",
       "mpl.mpl_figure_comm = function (comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = document.getElementById(id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm);\n",
       "\n",
       "    function ondownload(figure, _format) {\n",
       "        window.open(figure.canvas.toDataURL());\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element;\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error('Failed to find cell for figure', id, fig);\n",
       "        return;\n",
       "    }\n",
       "    fig.cell_info[0].output_area.element.one(\n",
       "        'cleared',\n",
       "        { fig: fig },\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function (fig, msg) {\n",
       "    var width = fig.canvas.width / fig.ratio;\n",
       "    fig.cell_info[0].output_area.element.off(\n",
       "        'cleared',\n",
       "        fig._remove_fig_handler\n",
       "    );\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable();\n",
       "    fig.parent_element.innerHTML =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "    fig.close_ws(fig, msg);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.close_ws = function (fig, msg) {\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width / this.ratio;\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] =\n",
       "        '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function () {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message('ack', {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () {\n",
       "        fig.push_to_output();\n",
       "    }, 1000);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function () {\n",
       "    var fig = this;\n",
       "\n",
       "    var toolbar = document.createElement('div');\n",
       "    toolbar.classList = 'btn-toolbar';\n",
       "    this.root.appendChild(toolbar);\n",
       "\n",
       "    function on_click_closure(name) {\n",
       "        return function (_event) {\n",
       "            return fig.toolbar_button_onclick(name);\n",
       "        };\n",
       "    }\n",
       "\n",
       "    function on_mouseover_closure(tooltip) {\n",
       "        return function (event) {\n",
       "            if (!event.currentTarget.disabled) {\n",
       "                return fig.toolbar_button_onmouseover(tooltip);\n",
       "            }\n",
       "        };\n",
       "    }\n",
       "\n",
       "    fig.buttons = {};\n",
       "    var buttonGroup = document.createElement('div');\n",
       "    buttonGroup.classList = 'btn-group';\n",
       "    var button;\n",
       "    for (var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            /* Instead of a spacer, we start a new button group. */\n",
       "            if (buttonGroup.hasChildNodes()) {\n",
       "                toolbar.appendChild(buttonGroup);\n",
       "            }\n",
       "            buttonGroup = document.createElement('div');\n",
       "            buttonGroup.classList = 'btn-group';\n",
       "            continue;\n",
       "        }\n",
       "\n",
       "        button = fig.buttons[name] = document.createElement('button');\n",
       "        button.classList = 'btn btn-default';\n",
       "        button.href = '#';\n",
       "        button.title = name;\n",
       "        button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
       "        button.addEventListener('click', on_click_closure(method_name));\n",
       "        button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
       "        buttonGroup.appendChild(button);\n",
       "    }\n",
       "\n",
       "    if (buttonGroup.hasChildNodes()) {\n",
       "        toolbar.appendChild(buttonGroup);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = document.createElement('span');\n",
       "    status_bar.classList = 'mpl-message pull-right';\n",
       "    toolbar.appendChild(status_bar);\n",
       "    this.message = status_bar;\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = document.createElement('div');\n",
       "    buttongrp.classList = 'btn-group inline pull-right';\n",
       "    button = document.createElement('button');\n",
       "    button.classList = 'btn btn-mini btn-primary';\n",
       "    button.href = '#';\n",
       "    button.title = 'Stop Interaction';\n",
       "    button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
       "    button.addEventListener('click', function (_evt) {\n",
       "        fig.handle_close(fig, {});\n",
       "    });\n",
       "    button.addEventListener(\n",
       "        'mouseover',\n",
       "        on_mouseover_closure('Stop Interaction')\n",
       "    );\n",
       "    buttongrp.appendChild(button);\n",
       "    var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
       "    titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._remove_fig_handler = function (event) {\n",
       "    var fig = event.data.fig;\n",
       "    fig.close_ws(fig, {});\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function (el) {\n",
       "    el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function (el) {\n",
       "    // this is important to make the div 'focusable\n",
       "    el.setAttribute('tabindex', 0);\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    } else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function (event, _name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager) {\n",
       "        manager = IPython.keyboard_manager;\n",
       "    }\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which === 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "};\n",
       "\n",
       "mpl.find_output_cell = function (html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i = 0; i < ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code') {\n",
       "            for (var j = 0; j < cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] === html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "};\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel !== null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target(\n",
       "        'matplotlib',\n",
       "        mpl.mpl_figure_comm\n",
       "    );\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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\" width=\"800\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'KNeighborsClassifier Classification Report'}>"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visualizer = ClassificationReport(classifier)\n",
    "\n",
    "visualizer.fit(X_train, y_train)        # Fit the visualizer and the model\n",
    "visualizer.score(X_test, y_test)        # Evaluate the model on the test data\n",
    "visualizer.show()                       # Finalize and show the figure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.56849617 0.60629543 0.50326109 0.61191064 0.59439813 0.56336112\n",
      " 0.58730243 0.54578193 0.66960987 0.67163721 0.65895298 0.59130377]\n",
      "cv_scores mean:0.5976925641880626\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "#create a new KNN model\n",
    "knn_cv = KNeighborsClassifier(n_neighbors=8)\n",
    "#train model with cv of 5 \n",
    "cv_scores = cross_val_score(knn_cv, values, labels, cv=len(sit_dataset_acc))\n",
    "#print each cv score (accuracy) and average them\n",
    "print(cv_scores)\n",
    "print('cv_scores mean:{}'.format(np.mean(cv_scores)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-60-a70cd2af971c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0mknn_gscv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mGridSearchCV\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mknn2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparam_grid\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcv\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m13\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;31m#fit model to data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m \u001b[0mknn_gscv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mknn_gscv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbest_params_\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36minner_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     61\u001b[0m             \u001b[0mextra_args\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mall_args\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     62\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mextra_args\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 63\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     64\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     65\u001b[0m             \u001b[0;31m# extra_args > 0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_search.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, groups, **fit_params)\u001b[0m\n\u001b[1;32m    839\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mresults\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    840\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 841\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_run_search\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mevaluate_candidates\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    842\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    843\u001b[0m             \u001b[0;31m# multimetric is determined here because in the case of a callable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_search.py\u001b[0m in \u001b[0;36m_run_search\u001b[0;34m(self, evaluate_candidates)\u001b[0m\n\u001b[1;32m   1286\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_run_search\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mevaluate_candidates\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1287\u001b[0m         \u001b[0;34m\"\"\"Search all candidates in param_grid\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1288\u001b[0;31m         \u001b[0mevaluate_candidates\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mParameterGrid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparam_grid\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1289\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1290\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_search.py\u001b[0m in \u001b[0;36mevaluate_candidates\u001b[0;34m(candidate_params, cv, more_results)\u001b[0m\n\u001b[1;32m    793\u001b[0m                               n_splits, n_candidates, n_candidates * n_splits))\n\u001b[1;32m    794\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 795\u001b[0;31m                 out = parallel(delayed(_fit_and_score)(clone(base_estimator),\n\u001b[0m\u001b[1;32m    796\u001b[0m                                                        \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    797\u001b[0m                                                        \u001b[0mtrain\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtest\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m   1049\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_original_iterator\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1050\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1051\u001b[0;31m             \u001b[0;32mwhile\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdispatch_one_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1052\u001b[0m                 \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1053\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36mdispatch_one_batch\u001b[0;34m(self, iterator)\u001b[0m\n\u001b[1;32m    864\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    865\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 866\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dispatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtasks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    867\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    868\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m_dispatch\u001b[0;34m(self, batch)\u001b[0m\n\u001b[1;32m    782\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_lock\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    783\u001b[0m             \u001b[0mjob_idx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jobs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 784\u001b[0;31m             \u001b[0mjob\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_async\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    785\u001b[0m             \u001b[0;31m# A job can complete so quickly than its callback is\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    786\u001b[0m             \u001b[0;31m# called before we get here, causing self._jobs to\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/joblib/_parallel_backends.py\u001b[0m in \u001b[0;36mapply_async\u001b[0;34m(self, func, callback)\u001b[0m\n\u001b[1;32m    206\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mapply_async\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    207\u001b[0m         \u001b[0;34m\"\"\"Schedule a func to be run\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 208\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mImmediateResult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    209\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    210\u001b[0m             \u001b[0mcallback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/joblib/_parallel_backends.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, batch)\u001b[0m\n\u001b[1;32m    570\u001b[0m         \u001b[0;31m# Don't delay the application, to avoid keeping the input\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    571\u001b[0m         \u001b[0;31m# arguments in memory\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 572\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    573\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    574\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    260\u001b[0m         \u001b[0;31m# change the default number of processes to -1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    261\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mparallel_backend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_n_jobs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 262\u001b[0;31m             return [func(*args, **kwargs)\n\u001b[0m\u001b[1;32m    263\u001b[0m                     for func, args, kwargs in self.items]\n\u001b[1;32m    264\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    260\u001b[0m         \u001b[0;31m# change the default number of processes to -1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    261\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mparallel_backend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_n_jobs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 262\u001b[0;31m             return [func(*args, **kwargs)\n\u001b[0m\u001b[1;32m    263\u001b[0m                     for func, args, kwargs in self.items]\n\u001b[1;32m    264\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/sklearn/utils/fixes.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    220\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    221\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mconfig_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 222\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfunction\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\u001b[0m in \u001b[0;36m_fit_and_score\u001b[0;34m(estimator, X, y, scorer, train, test, verbose, parameters, fit_params, return_train_score, return_parameters, return_n_test_samples, return_times, return_estimator, split_progress, candidate_progress, error_score)\u001b[0m\n\u001b[1;32m    618\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    619\u001b[0m         \u001b[0mfit_time\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mstart_time\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 620\u001b[0;31m         \u001b[0mtest_scores\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscorer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merror_score\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    621\u001b[0m         \u001b[0mscore_time\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mstart_time\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mfit_time\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    622\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mreturn_train_score\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\u001b[0m in \u001b[0;36m_score\u001b[0;34m(estimator, X_test, y_test, scorer, error_score)\u001b[0m\n\u001b[1;32m    672\u001b[0m             \u001b[0mscores\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mscorer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    673\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 674\u001b[0;31m             \u001b[0mscores\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mscorer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    675\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    676\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0merror_score\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'raise'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/sklearn/metrics/_scorer.py\u001b[0m in \u001b[0;36m_passthrough_scorer\u001b[0;34m(estimator, *args, **kwargs)\u001b[0m\n\u001b[1;32m    395\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_passthrough_scorer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    396\u001b[0m     \u001b[0;34m\"\"\"Function that wraps estimator.score\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 397\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mestimator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    398\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    399\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py\u001b[0m in \u001b[0;36mscore\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m    498\u001b[0m         \"\"\"\n\u001b[1;32m    499\u001b[0m         \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mmetrics\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0maccuracy_score\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 500\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0maccuracy_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    501\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    502\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_more_tags\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/sklearn/neighbors/_classification.py\u001b[0m in \u001b[0;36mpredict\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m    209\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclasses_k\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclasses_\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    210\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mweights\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 211\u001b[0;31m                 \u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstats\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_y\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mneigh_ind\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    212\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    213\u001b[0m                 \u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mweighted_mode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_y\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mneigh_ind\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mweights\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/scipy/stats/stats.py\u001b[0m in \u001b[0;36mmode\u001b[0;34m(a, axis, nan_policy)\u001b[0m\n\u001b[1;32m    570\u001b[0m     \u001b[0mcounts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma_view\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mint_\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    571\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mind\u001b[0m \u001b[0;32min\u001b[0m \u001b[0minds\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 572\u001b[0;31m         \u001b[0mmodes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mind\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcounts\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mind\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_mode1D\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma_view\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mind\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    573\u001b[0m     \u001b[0mnewshape\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    574\u001b[0m     \u001b[0mnewshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/scipy/stats/stats.py\u001b[0m in \u001b[0;36m_mode1D\u001b[0;34m(a)\u001b[0m\n\u001b[1;32m    557\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    558\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_mode1D\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 559\u001b[0;31m         \u001b[0mvals\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcnts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreturn_counts\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    560\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mvals\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcnts\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcnts\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    561\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<__array_function__ internals>\u001b[0m in \u001b[0;36munique\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/numpy/lib/arraysetops.py\u001b[0m in \u001b[0;36munique\u001b[0;34m(ar, return_index, return_inverse, return_counts, axis)\u001b[0m\n\u001b[1;32m    259\u001b[0m     \u001b[0mar\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masanyarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mar\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    260\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0maxis\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 261\u001b[0;31m         \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_unique1d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mar\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreturn_index\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreturn_inverse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreturn_counts\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    262\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0m_unpack_tuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mret\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    263\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/numpy/lib/arraysetops.py\u001b[0m in \u001b[0;36m_unique1d\u001b[0;34m(ar, return_index, return_inverse, return_counts)\u001b[0m\n\u001b[1;32m    324\u001b[0m     \u001b[0mmask\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mempty\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maux\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbool_\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    325\u001b[0m     \u001b[0mmask\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 326\u001b[0;31m     \u001b[0mmask\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maux\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0maux\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    327\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    328\u001b[0m     \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0maux\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# Search for the best KNN number\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "#create new a knn model\n",
    "knn2 = KNeighborsClassifier()\n",
    "#create a dictionary of all values we want to test for n_neighbors\n",
    "param_grid = {'n_neighbors': np.arange(1, 10)}\n",
    "#use gridsearch to test all values for n_neighbors\n",
    "knn_gscv = GridSearchCV(knn2, param_grid, cv=len(sit_dataset_acc))\n",
    "#fit model to data\n",
    "knn_gscv.fit(values, labels)\n",
    "\n",
    "print(knn_gscv.best_params_)\n",
    "print(knn_gscv.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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