Fault_Detector_Global_Dataset.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This program implements an SVM classification method on the ALFA's Global Locations dataset to develop a Fault Detector and design a sliding window to improve the detector's output stability."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>field_header_seq</th>\n",
       "      <th>field_header_stamp</th>\n",
       "      <th>field_status_status</th>\n",
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       "      <th>field_position_covariance8</th>\n",
       "      <th>field_position_covariance_type</th>\n",
       "      <th>fault resault</th>\n",
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       "<p>860 rows × 19 columns</p>\n",
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      ],
      "text/plain": [
       "                    time  field_header_seq   field_header_stamp  \\\n",
       "0    1536689966609720064              5447  1536689966608250112   \n",
       "1    1536689966835180032              5448  1536689966833600000   \n",
       "2    1536689967072649984              5449  1536689967072000000   \n",
       "3    1536689967344339968              5450  1536689967343810048   \n",
       "4    1536689967595360000              5451  1536689967593440000   \n",
       "..                   ...               ...                  ...   \n",
       "855  1536690511732730112              7727  1536690511732000000   \n",
       "856  1536690511955749888              7728  1536690511954990080   \n",
       "857  1536690512234070016              7729  1536690512232839936   \n",
       "858  1536690512496359936              7730  1536690512487460096   \n",
       "859  1536690512739109888              7731  1536690512737850112   \n",
       "\n",
       "     field_status_status  field_status_service  field_latitude  \\\n",
       "0                      0                     1       40.591424   \n",
       "1                      0                     1       40.591426   \n",
       "2                      0                     1       40.591429   \n",
       "3                      0                     1       40.591435   \n",
       "4                      0                     1       40.591444   \n",
       "..                   ...                   ...             ...   \n",
       "855                    0                     1       40.590797   \n",
       "856                    0                     1       40.590790   \n",
       "857                    0                     1       40.590782   \n",
       "858                    0                     1       40.590776   \n",
       "859                    0                     1       40.590771   \n",
       "\n",
       "     field_longitude  field_altitude  field_position_covariance0  \\\n",
       "0         -79.899018      374.872723                      0.6724   \n",
       "1         -79.899071      374.932668                      0.6724   \n",
       "2         -79.899128      374.992608                      0.6724   \n",
       "3         -79.899194      375.072539                      0.7225   \n",
       "4         -79.899249      375.162479                      0.6724   \n",
       "..               ...             ...                         ...   \n",
       "855       -79.896551      346.105511                      0.5476   \n",
       "856       -79.896587      345.165478                      0.5476   \n",
       "857       -79.896626      344.175441                      0.5476   \n",
       "858       -79.896662      343.295406                      0.5476   \n",
       "859       -79.896702      342.395368                      0.5476   \n",
       "\n",
       "     field_position_covariance1  field_position_covariance2  \\\n",
       "0                             0                           0   \n",
       "1                             0                           0   \n",
       "2                             0                           0   \n",
       "3                             0                           0   \n",
       "4                             0                           0   \n",
       "..                          ...                         ...   \n",
       "855                           0                           0   \n",
       "856                           0                           0   \n",
       "857                           0                           0   \n",
       "858                           0                           0   \n",
       "859                           0                           0   \n",
       "\n",
       "     field_position_covariance3  field_position_covariance4  \\\n",
       "0                             0                      0.6724   \n",
       "1                             0                      0.6724   \n",
       "2                             0                      0.6724   \n",
       "3                             0                      0.7225   \n",
       "4                             0                      0.6724   \n",
       "..                          ...                         ...   \n",
       "855                           0                      0.5476   \n",
       "856                           0                      0.5476   \n",
       "857                           0                      0.5476   \n",
       "858                           0                      0.5476   \n",
       "859                           0                      0.5476   \n",
       "\n",
       "     field_position_covariance5  field_position_covariance6  \\\n",
       "0                             0                           0   \n",
       "1                             0                           0   \n",
       "2                             0                           0   \n",
       "3                             0                           0   \n",
       "4                             0                           0   \n",
       "..                          ...                         ...   \n",
       "855                           0                           0   \n",
       "856                           0                           0   \n",
       "857                           0                           0   \n",
       "858                           0                           0   \n",
       "859                           0                           0   \n",
       "\n",
       "     field_position_covariance7  field_position_covariance8  \\\n",
       "0                             0                      2.6896   \n",
       "1                             0                      2.6896   \n",
       "2                             0                      2.6896   \n",
       "3                             0                      2.8900   \n",
       "4                             0                      2.6896   \n",
       "..                          ...                         ...   \n",
       "855                           0                      2.1904   \n",
       "856                           0                      2.1904   \n",
       "857                           0                      2.1904   \n",
       "858                           0                      2.1904   \n",
       "859                           0                      2.1904   \n",
       "\n",
       "     field_position_covariance_type  fault resault  \n",
       "0                                 1              0  \n",
       "1                                 1              0  \n",
       "2                                 1              0  \n",
       "3                                 1              0  \n",
       "4                                 1              0  \n",
       "..                              ...            ...  \n",
       "855                               1              1  \n",
       "856                               1              1  \n",
       "857                               1              1  \n",
       "858                               1              1  \n",
       "859                               1              1  \n",
       "\n",
       "[860 rows x 19 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd                        \n",
    "import numpy  as np\n",
    "from sklearn import preprocessing\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import svm\n",
    "from sklearn import metrics\n",
    "import matplotlib.pyplot as mplt\n",
    "\n",
    "\n",
    "Global = pd.read_excel('Global_dataset.xlsx')     # Importing dataset.\n",
    "Global\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    860.000000\n",
       "mean      40.592105\n",
       "std        0.000866\n",
       "min       40.590762\n",
       "25%       40.591344\n",
       "50%       40.592070\n",
       "75%       40.593106\n",
       "max       40.593277\n",
       "Name: field_latitude, dtype: float64"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Global.field_latitude.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    860.000000\n",
       "mean       0.569161\n",
       "std        0.047027\n",
       "min        0.547600\n",
       "25%        0.547600\n",
       "50%        0.547600\n",
       "75%        0.547600\n",
       "max        0.722500\n",
       "Name: field_position_covariance0, dtype: float64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Global.field_position_covariance0.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    860.000000\n",
       "mean     -79.898412\n",
       "std        0.001665\n",
       "min      -79.901041\n",
       "25%      -79.899698\n",
       "50%      -79.898645\n",
       "75%      -79.896698\n",
       "max      -79.895875\n",
       "Name: field_longitude, dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Global.field_longitude.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    860.000000\n",
       "mean       0.569161\n",
       "std        0.047027\n",
       "min        0.547600\n",
       "25%        0.547600\n",
       "50%        0.547600\n",
       "75%        0.547600\n",
       "max        0.722500\n",
       "Name: field_position_covariance4, dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Global.field_position_covariance4.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    860.000000\n",
       "mean     365.361109\n",
       "std        7.288007\n",
       "min      342.395368\n",
       "25%      361.606476\n",
       "50%      362.847070\n",
       "75%      367.072278\n",
       "max      399.422839\n",
       "Name: field_altitude, dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Global.field_altitude.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    860.000000\n",
       "mean       2.276643\n",
       "std        0.188106\n",
       "min        2.190400\n",
       "25%        2.190400\n",
       "50%        2.190400\n",
       "75%        2.190400\n",
       "max        2.890000\n",
       "Name: field_position_covariance8, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Global.field_position_covariance8.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Data Description.\n",
    "import seaborn as sns                      \n",
    "import matplotlib.pyplot as plt\n",
    "ax = sns.distplot(Global.field_latitude, rug=True)\n",
    "#ax = sns.distplot(Global.field_position_covariance0, rug=True)\n",
    "\n",
    "ax = sns.distplot(Global.field_longitude, rug=True)\n",
    "#ax = sns.distplot(Global.field_position_covariance4, rug=True)\n",
    "\n",
    "ax = sns.distplot(Global.field_altitude, rug=True, color=\"y\")\n",
    "#ax = sns.distplot(Global.field_position_covariance8, rug=True)\n",
    "\n",
    "ax.set( xlabel='Global Position',ylabel='Density')#xlabel='Compass Angel',\n",
    "plt.legend( loc='upper left', labels=['Latitude', 'Longitude','Altitude'])\n",
    "ax.figure.savefig(\"output2.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      0\n",
       "1      0\n",
       "2      0\n",
       "3      0\n",
       "4      0\n",
       "      ..\n",
       "855    1\n",
       "856    1\n",
       "857    1\n",
       "858    1\n",
       "859    1\n",
       "Name: fault resault, Length: 860, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols_to_use = ['time', 'field_header_seq', 'field_header_stamp', 'field_status_status', 'field_status_service','field_latitude','field_longitude','field_altitude','field_position_covariance0','field_position_covariance1','field_position_covariance2','field_position_covariance3','field_position_covariance4','field_position_covariance5','field_position_covariance6','field_position_covariance7','field_position_covariance8','field_position_covariance_type']\n",
    "X = Global[cols_to_use]\n",
    "y = Global['fault resault']\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>field_header_seq</th>\n",
       "      <th>field_header_stamp</th>\n",
       "      <th>field_status_status</th>\n",
       "      <th>field_status_service</th>\n",
       "      <th>field_latitude</th>\n",
       "      <th>field_longitude</th>\n",
       "      <th>field_altitude</th>\n",
       "      <th>field_position_covariance0</th>\n",
       "      <th>field_position_covariance1</th>\n",
       "      <th>field_position_covariance2</th>\n",
       "      <th>field_position_covariance3</th>\n",
       "      <th>field_position_covariance4</th>\n",
       "      <th>field_position_covariance5</th>\n",
       "      <th>field_position_covariance6</th>\n",
       "      <th>field_position_covariance7</th>\n",
       "      <th>field_position_covariance8</th>\n",
       "      <th>field_position_covariance_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>1536690285541410048</td>\n",
       "      <td>6795</td>\n",
       "      <td>1536690285539190016</td>\n",
       "      <td>0</td>\n",
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       "      <td>40.593275</td>\n",
       "      <td>-79.899542</td>\n",
       "      <td>362.671425</td>\n",
       "      <td>0.5476</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>2.1904</td>\n",
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       "    <tr>\n",
       "      <th>271</th>\n",
       "      <td>1536690301962279936</td>\n",
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       "      <td>40.592979</td>\n",
       "      <td>-79.896089</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.1904</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>258</th>\n",
       "      <td>1536690298768329984</td>\n",
       "      <td>6849</td>\n",
       "      <td>1536690298767439872</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>40.593123</td>\n",
       "      <td>-79.896732</td>\n",
       "      <td>361.324354</td>\n",
       "      <td>0.5476</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.5476</td>\n",
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       "      <td>0</td>\n",
       "      <td>2.1904</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>530</th>\n",
       "      <td>1536690365264110080</td>\n",
       "      <td>7121</td>\n",
       "      <td>1536690365263249920</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>40.593136</td>\n",
       "      <td>-79.897346</td>\n",
       "      <td>362.893721</td>\n",
       "      <td>0.5476</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.5476</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>1536689990115699968</td>\n",
       "      <td>5544</td>\n",
       "      <td>1536689990114739968</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>40.591721</td>\n",
       "      <td>-79.899373</td>\n",
       "      <td>376.192237</td>\n",
       "      <td>0.6724</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6724</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.6896</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>846</th>\n",
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       "      <td>7718</td>\n",
       "      <td>1536690509530510080</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>40.590900</td>\n",
       "      <td>-79.896263</td>\n",
       "      <td>354.575763</td>\n",
       "      <td>0.5476</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.5476</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.1904</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>334</th>\n",
       "      <td>1536690317423069952</td>\n",
       "      <td>6925</td>\n",
       "      <td>1536690317421989888</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>40.590802</td>\n",
       "      <td>-79.896965</td>\n",
       "      <td>367.115085</td>\n",
       "      <td>0.5476</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.5476</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.1904</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261</th>\n",
       "      <td>1536690299569530112</td>\n",
       "      <td>6852</td>\n",
       "      <td>1536690299568659968</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>40.593121</td>\n",
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       "      <td>0.5476</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0.5476</td>\n",
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       "      <td>0</td>\n",
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       "      <td>2.1904</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>824</th>\n",
       "      <td>1536690504104999936</td>\n",
       "      <td>7696</td>\n",
       "      <td>1536690504102990080</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>40.591531</td>\n",
       "      <td>-79.896002</td>\n",
       "      <td>361.015765</td>\n",
       "      <td>0.5476</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.5476</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.1904</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>258 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    time  field_header_seq   field_header_stamp  \\\n",
       "145  1536690271021659904              6736  1536690271017999872   \n",
       "204  1536690285541410048              6795  1536690285539190016   \n",
       "271  1536690301962279936              6862  1536690301959980032   \n",
       "258  1536690298768329984              6849  1536690298767439872   \n",
       "530  1536690365264110080              7121  1536690365263249920   \n",
       "..                   ...               ...                  ...   \n",
       "97   1536689990115699968              5544  1536689990114739968   \n",
       "846  1536690509531279872              7718  1536690509530510080   \n",
       "334  1536690317423069952              6925  1536690317421989888   \n",
       "261  1536690299569530112              6852  1536690299568659968   \n",
       "824  1536690504104999936              7696  1536690504102990080   \n",
       "\n",
       "     field_status_status  field_status_service  field_latitude  \\\n",
       "145                    0                     1       40.591620   \n",
       "204                    0                     1       40.593275   \n",
       "271                    0                     1       40.592979   \n",
       "258                    0                     1       40.593123   \n",
       "530                    0                     1       40.593136   \n",
       "..                   ...                   ...             ...   \n",
       "97                     0                     1       40.591721   \n",
       "846                    0                     1       40.590900   \n",
       "334                    0                     1       40.590802   \n",
       "261                    0                     1       40.593121   \n",
       "824                    0                     1       40.591531   \n",
       "\n",
       "     field_longitude  field_altitude  field_position_covariance0  \\\n",
       "145       -79.898373      385.273300                      0.5476   \n",
       "204       -79.899542      362.671425                      0.5476   \n",
       "271       -79.896089      359.895072                      0.5476   \n",
       "258       -79.896732      361.324354                      0.5476   \n",
       "530       -79.897346      362.893721                      0.5476   \n",
       "..               ...             ...                         ...   \n",
       "97        -79.899373      376.192237                      0.6724   \n",
       "846       -79.896263      354.575763                      0.5476   \n",
       "334       -79.896965      367.115085                      0.5476   \n",
       "261       -79.896564      361.434526                      0.5476   \n",
       "824       -79.896002      361.015765                      0.5476   \n",
       "\n",
       "     field_position_covariance1  field_position_covariance2  \\\n",
       "145                           0                           0   \n",
       "204                           0                           0   \n",
       "271                           0                           0   \n",
       "258                           0                           0   \n",
       "530                           0                           0   \n",
       "..                          ...                         ...   \n",
       "97                            0                           0   \n",
       "846                           0                           0   \n",
       "334                           0                           0   \n",
       "261                           0                           0   \n",
       "824                           0                           0   \n",
       "\n",
       "     field_position_covariance3  field_position_covariance4  \\\n",
       "145                           0                      0.5476   \n",
       "204                           0                      0.5476   \n",
       "271                           0                      0.5476   \n",
       "258                           0                      0.5476   \n",
       "530                           0                      0.5476   \n",
       "..                          ...                         ...   \n",
       "97                            0                      0.6724   \n",
       "846                           0                      0.5476   \n",
       "334                           0                      0.5476   \n",
       "261                           0                      0.5476   \n",
       "824                           0                      0.5476   \n",
       "\n",
       "     field_position_covariance5  field_position_covariance6  \\\n",
       "145                           0                           0   \n",
       "204                           0                           0   \n",
       "271                           0                           0   \n",
       "258                           0                           0   \n",
       "530                           0                           0   \n",
       "..                          ...                         ...   \n",
       "97                            0                           0   \n",
       "846                           0                           0   \n",
       "334                           0                           0   \n",
       "261                           0                           0   \n",
       "824                           0                           0   \n",
       "\n",
       "     field_position_covariance7  field_position_covariance8  \\\n",
       "145                           0                      2.1904   \n",
       "204                           0                      2.1904   \n",
       "271                           0                      2.1904   \n",
       "258                           0                      2.1904   \n",
       "530                           0                      2.1904   \n",
       "..                          ...                         ...   \n",
       "97                            0                      2.6896   \n",
       "846                           0                      2.1904   \n",
       "334                           0                      2.1904   \n",
       "261                           0                      2.1904   \n",
       "824                           0                      2.1904   \n",
       "\n",
       "     field_position_covariance_type  \n",
       "145                               1  \n",
       "204                               1  \n",
       "271                               1  \n",
       "258                               1  \n",
       "530                               1  \n",
       "..                              ...  \n",
       "97                                1  \n",
       "846                               1  \n",
       "334                               1  \n",
       "261                               1  \n",
       "824                               1  \n",
       "\n",
       "[258 rows x 18 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " # Separating Train and Test Data.\n",
    "X_train, X_valid, y_train, y_valid = train_test_split(X, y, test_size=0.3, random_state=0)\n",
    "X_valid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(258, 18)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_valid.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculating the standard deviation and mean of each feature.\n",
    "m1 = X_valid.time.mean()\n",
    "s1 = X_valid.time.std()\n",
    "\n",
    "m2 = X_valid.field_header_seq.mean()\n",
    "s2 = X_valid.field_header_seq.std()\n",
    "\n",
    "m3 = X_valid.field_header_stamp.mean()\n",
    "s3 = X_valid.field_header_stamp.std()\n",
    "\n",
    "m4 = X_valid.field_status_status.mean()\n",
    "s4 = X_valid.field_status_status.std()\n",
    "\n",
    "m5 = X_valid.field_status_service.mean()\n",
    "s5 = X_valid.field_status_service.std()\n",
    "\n",
    "m6 = X_valid.field_latitude.mean()\n",
    "s6 = X_valid.field_latitude.std()\n",
    "\n",
    "m7 = X_valid.field_longitude.mean()\n",
    "s7 = X_valid.field_longitude.std()\n",
    "\n",
    "m8 = X_valid.field_altitude.mean()\n",
    "s8 = X_valid.field_altitude.std()\n",
    "\n",
    "m9 = X_valid.field_position_covariance0.mean()\n",
    "s9 = X_valid.field_position_covariance0.std()\n",
    "\n",
    "m10 = X_valid.field_position_covariance1.mean()\n",
    "s10 = X_valid.field_position_covariance1.std()\n",
    "\n",
    "m11 = X_valid.field_position_covariance2.mean()\n",
    "s11 = X_valid.field_position_covariance2.std()\n",
    "\n",
    "m12 = X_valid.field_position_covariance3.mean()\n",
    "s12 = X_valid.field_position_covariance3.std()\n",
    "\n",
    "m13 = X_valid.field_position_covariance4.mean()\n",
    "s13 = X_valid.field_position_covariance4.std()\n",
    "\n",
    "m14 = X_valid.field_position_covariance5.mean()\n",
    "s14 = X_valid.field_position_covariance5.std()\n",
    "\n",
    "m15 = X_valid.field_position_covariance6.mean()\n",
    "s15 = X_valid.field_position_covariance6.std()\n",
    "\n",
    "m16 = X_valid.field_position_covariance7.mean()\n",
    "s16 = X_valid.field_position_covariance7.std()\n",
    "\n",
    "m17 = X_valid.field_position_covariance8.mean()\n",
    "s17 = X_valid.field_position_covariance8.std()\n",
    "\n",
    "m18 = X_valid.field_position_covariance_type.mean()\n",
    "s18 = X_valid.field_position_covariance_type.std()\n",
    "\n",
    "m18\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "145    6736\n",
       "204    6795\n",
       "271    6862\n",
       "258    6849\n",
       "530    7121\n",
       "       ... \n",
       "97     5544\n",
       "846    7718\n",
       "334    6925\n",
       "261    6852\n",
       "824    7696\n",
       "Name: field_header_seq, Length: 258, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_valid[cols_to_use[1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.53669021e+17, 7.62679820e+02, 1.53669021e+17, ...,\n",
       "        0.00000000e+00, 2.43942950e-01, 1.00000000e-01],\n",
       "       [1.53669045e+17, 6.62958999e+02, 1.53669003e+17, ...,\n",
       "        0.00000000e+00, 2.24138659e-01, 1.00000000e-01],\n",
       "       [1.53669033e+17, 7.42647137e+02, 1.53669042e+17, ...,\n",
       "        0.00000000e+00, 2.33309935e-01, 1.00000000e-01],\n",
       "       ...,\n",
       "       [1.53669022e+17, 5.73796007e+02, 1.53669034e+17, ...,\n",
       "        0.00000000e+00, 2.09638195e-01, 1.00000000e-01],\n",
       "       [1.53669029e+17, 8.47504943e+02, 1.53669034e+17, ...,\n",
       "        0.00000000e+00, 1.89472707e-01, 1.00000000e-01],\n",
       "       [1.53669021e+17, 7.70169032e+02, 1.53669023e+17, ...,\n",
       "        0.00000000e+00, 2.02476483e-01, 1.00000000e-01]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " # Preparing random noise based on standard deviation and mean of each feature.\n",
    "r1 =0.1* np.random.normal(m1,s1, X_valid[cols_to_use[0]].shape)\n",
    "r2 =0.1* np.random.normal(m2,s2, X_valid[cols_to_use[1]].shape)\n",
    "r3 =0.1* np.random.normal(m3,s3, X_valid[cols_to_use[2]].shape)\n",
    "r4 =0.1* np.random.normal(m4,s4, X_valid[cols_to_use[3]].shape)\n",
    "r5 = 0.1*np.random.normal(m5,s5, X_valid[cols_to_use[4]].shape)\n",
    "r6 =0.1* np.random.normal(m6,s6, X_valid[cols_to_use[5]].shape)\n",
    "r7 = 0.1*np.random.normal(m7,s7, X_valid[cols_to_use[6]].shape)\n",
    "r8 = 0.1*np.random.normal(m8,s8, X_valid[cols_to_use[7]].shape)\n",
    "r9 = 0.1*np.random.normal(m9,s9, X_valid[cols_to_use[8]].shape)\n",
    "#r10 = np.random.normal(m10,s10 X_valid[cols_to_use[9]].shape)\n",
    "r11 = 0.1*np.random.normal(m11,s11, X_valid[cols_to_use[10]].shape)\n",
    "r12 = 0.1*np.random.normal(m12,s12, X_valid[cols_to_use[11]].shape)\n",
    "r13 =0.1* np.random.normal(m13,s13, X_valid[cols_to_use[12]].shape)\n",
    "r14 =0.1* np.random.normal(m14,s14, X_valid[cols_to_use[13]].shape)\n",
    "r15 =0.1* np.random.normal(m15,s15, X_valid[cols_to_use[14]].shape)\n",
    "r16 =0.1* np.random.normal(m16,s16, X_valid[cols_to_use[15]].shape)\n",
    "r17 =0.1* np.random.normal(m17,s17, X_valid[cols_to_use[16]].shape)\n",
    "r18 =0.1* np.random.normal(m18,s18, X_valid[cols_to_use[17]].shape)\n",
    "\n",
    "\n",
    "f = [r1,r2,r3,r4,r5,r6,r7,r8,r9,r11,r11,r12,r13,r14,r15,r16,r17,r18]\n",
    "random_noise = np.transpose(f)\n",
    "random_noise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(258, 18)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_noise.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>field_header_seq</th>\n",
       "      <th>field_header_stamp</th>\n",
       "      <th>field_status_status</th>\n",
       "      <th>field_status_service</th>\n",
       "      <th>field_latitude</th>\n",
       "      <th>field_longitude</th>\n",
       "      <th>field_altitude</th>\n",
       "      <th>field_position_covariance0</th>\n",
       "      <th>field_position_covariance1</th>\n",
       "      <th>field_position_covariance2</th>\n",
       "      <th>field_position_covariance3</th>\n",
       "      <th>field_position_covariance4</th>\n",
       "      <th>field_position_covariance5</th>\n",
       "      <th>field_position_covariance6</th>\n",
       "      <th>field_position_covariance7</th>\n",
       "      <th>field_position_covariance8</th>\n",
       "      <th>field_position_covariance_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>1.690359e+18</td>\n",
       "      <td>7498.679820</td>\n",
       "      <td>1.690359e+18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>44.650849</td>\n",
       "      <td>-87.887974</td>\n",
       "      <td>421.373122</td>\n",
       "      <td>0.603336</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.613368</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.434343</td>\n",
       "      <td>1.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>1.690359e+18</td>\n",
       "      <td>7457.958999</td>\n",
       "      <td>1.690359e+18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>44.652654</td>\n",
       "      <td>-87.889554</td>\n",
       "      <td>399.191861</td>\n",
       "      <td>0.608968</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.602233</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.414539</td>\n",
       "      <td>1.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271</th>\n",
       "      <td>1.690359e+18</td>\n",
       "      <td>7604.647137</td>\n",
       "      <td>1.690359e+18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>44.652115</td>\n",
       "      <td>-87.885777</td>\n",
       "      <td>397.544277</td>\n",
       "      <td>0.604905</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.603188</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.423710</td>\n",
       "      <td>1.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>258</th>\n",
       "      <td>1.690359e+18</td>\n",
       "      <td>7482.274567</td>\n",
       "      <td>1.690359e+18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>44.652274</td>\n",
       "      <td>-87.886673</td>\n",
       "      <td>398.806385</td>\n",
       "      <td>0.602097</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.609666</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.414062</td>\n",
       "      <td>1.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>530</th>\n",
       "      <td>1.690359e+18</td>\n",
       "      <td>7677.734711</td>\n",
       "      <td>1.690359e+18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>44.652390</td>\n",
       "      <td>-87.887376</td>\n",
       "      <td>398.578389</td>\n",
       "      <td>0.608963</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.609719</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.418864</td>\n",
       "      <td>1.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>1.690359e+18</td>\n",
       "      <td>6095.521905</td>\n",
       "      <td>1.690359e+18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>44.650967</td>\n",
       "      <td>-87.889358</td>\n",
       "      <td>412.459854</td>\n",
       "      <td>0.723994</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.724633</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.920584</td>\n",
       "      <td>1.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>846</th>\n",
       "      <td>1.690360e+18</td>\n",
       "      <td>8395.915172</td>\n",
       "      <td>1.690360e+18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>44.650104</td>\n",
       "      <td>-87.886389</td>\n",
       "      <td>389.858405</td>\n",
       "      <td>0.602221</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.599385</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.433325</td>\n",
       "      <td>1.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>334</th>\n",
       "      <td>1.690359e+18</td>\n",
       "      <td>7498.796007</td>\n",
       "      <td>1.690359e+18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>44.649863</td>\n",
       "      <td>-87.886930</td>\n",
       "      <td>403.007864</td>\n",
       "      <td>0.600119</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.604518</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.400038</td>\n",
       "      <td>1.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261</th>\n",
       "      <td>1.690359e+18</td>\n",
       "      <td>7699.504943</td>\n",
       "      <td>1.690359e+18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>44.652465</td>\n",
       "      <td>-87.886400</td>\n",
       "      <td>398.467775</td>\n",
       "      <td>0.594654</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.607432</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.379873</td>\n",
       "      <td>1.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>824</th>\n",
       "      <td>1.690360e+18</td>\n",
       "      <td>8466.169032</td>\n",
       "      <td>1.690360e+18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>44.650766</td>\n",
       "      <td>-87.885553</td>\n",
       "      <td>398.497044</td>\n",
       "      <td>0.602986</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.605504</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.392877</td>\n",
       "      <td>1.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>258 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             time  field_header_seq  field_header_stamp  field_status_status  \\\n",
       "145  1.690359e+18       7498.679820        1.690359e+18                  0.0   \n",
       "204  1.690359e+18       7457.958999        1.690359e+18                  0.0   \n",
       "271  1.690359e+18       7604.647137        1.690359e+18                  0.0   \n",
       "258  1.690359e+18       7482.274567        1.690359e+18                  0.0   \n",
       "530  1.690359e+18       7677.734711        1.690359e+18                  0.0   \n",
       "..            ...               ...                 ...                  ...   \n",
       "97   1.690359e+18       6095.521905        1.690359e+18                  0.0   \n",
       "846  1.690360e+18       8395.915172        1.690360e+18                  0.0   \n",
       "334  1.690359e+18       7498.796007        1.690359e+18                  0.0   \n",
       "261  1.690359e+18       7699.504943        1.690359e+18                  0.0   \n",
       "824  1.690360e+18       8466.169032        1.690360e+18                  0.0   \n",
       "\n",
       "     field_status_service  field_latitude  field_longitude  field_altitude  \\\n",
       "145                   1.1       44.650849       -87.887974      421.373122   \n",
       "204                   1.1       44.652654       -87.889554      399.191861   \n",
       "271                   1.1       44.652115       -87.885777      397.544277   \n",
       "258                   1.1       44.652274       -87.886673      398.806385   \n",
       "530                   1.1       44.652390       -87.887376      398.578389   \n",
       "..                    ...             ...              ...             ...   \n",
       "97                    1.1       44.650967       -87.889358      412.459854   \n",
       "846                   1.1       44.650104       -87.886389      389.858405   \n",
       "334                   1.1       44.649863       -87.886930      403.007864   \n",
       "261                   1.1       44.652465       -87.886400      398.467775   \n",
       "824                   1.1       44.650766       -87.885553      398.497044   \n",
       "\n",
       "     field_position_covariance0  field_position_covariance1  \\\n",
       "145                    0.603336                         0.0   \n",
       "204                    0.608968                         0.0   \n",
       "271                    0.604905                         0.0   \n",
       "258                    0.602097                         0.0   \n",
       "530                    0.608963                         0.0   \n",
       "..                          ...                         ...   \n",
       "97                     0.723994                         0.0   \n",
       "846                    0.602221                         0.0   \n",
       "334                    0.600119                         0.0   \n",
       "261                    0.594654                         0.0   \n",
       "824                    0.602986                         0.0   \n",
       "\n",
       "     field_position_covariance2  field_position_covariance3  \\\n",
       "145                         0.0                         0.0   \n",
       "204                         0.0                         0.0   \n",
       "271                         0.0                         0.0   \n",
       "258                         0.0                         0.0   \n",
       "530                         0.0                         0.0   \n",
       "..                          ...                         ...   \n",
       "97                          0.0                         0.0   \n",
       "846                         0.0                         0.0   \n",
       "334                         0.0                         0.0   \n",
       "261                         0.0                         0.0   \n",
       "824                         0.0                         0.0   \n",
       "\n",
       "     field_position_covariance4  field_position_covariance5  \\\n",
       "145                    0.613368                         0.0   \n",
       "204                    0.602233                         0.0   \n",
       "271                    0.603188                         0.0   \n",
       "258                    0.609666                         0.0   \n",
       "530                    0.609719                         0.0   \n",
       "..                          ...                         ...   \n",
       "97                     0.724633                         0.0   \n",
       "846                    0.599385                         0.0   \n",
       "334                    0.604518                         0.0   \n",
       "261                    0.607432                         0.0   \n",
       "824                    0.605504                         0.0   \n",
       "\n",
       "     field_position_covariance6  field_position_covariance7  \\\n",
       "145                         0.0                         0.0   \n",
       "204                         0.0                         0.0   \n",
       "271                         0.0                         0.0   \n",
       "258                         0.0                         0.0   \n",
       "530                         0.0                         0.0   \n",
       "..                          ...                         ...   \n",
       "97                          0.0                         0.0   \n",
       "846                         0.0                         0.0   \n",
       "334                         0.0                         0.0   \n",
       "261                         0.0                         0.0   \n",
       "824                         0.0                         0.0   \n",
       "\n",
       "     field_position_covariance8  field_position_covariance_type  \n",
       "145                    2.434343                             1.1  \n",
       "204                    2.414539                             1.1  \n",
       "271                    2.423710                             1.1  \n",
       "258                    2.414062                             1.1  \n",
       "530                    2.418864                             1.1  \n",
       "..                          ...                             ...  \n",
       "97                     2.920584                             1.1  \n",
       "846                    2.433325                             1.1  \n",
       "334                    2.400038                             1.1  \n",
       "261                    2.379873                             1.1  \n",
       "824                    2.392877                             1.1  \n",
       "\n",
       "[258 rows x 18 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " # Adding random noise to data.\n",
    "X_test = X_valid + random_noise\n",
    "X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.53252474, 0.58546261, 0.53420976, ..., 0.        , 0.10933812,\n",
       "        0.        ],\n",
       "       [0.59681103, 0.56940396, 0.52837278, ..., 0.        , 0.07504938,\n",
       "        0.        ],\n",
       "       [0.60440169, 0.62725183, 0.62149718, ..., 0.        , 0.09092834,\n",
       "        0.        ],\n",
       "       ...,\n",
       "       [0.61054592, 0.58550843, 0.63452221, ..., 0.        , 0.04994357,\n",
       "        0.        ],\n",
       "       [0.59267792, 0.66465991, 0.60395293, ..., 0.        , 0.01502945,\n",
       "        0.        ],\n",
       "       [0.92203723, 0.96700119, 0.93293134, ..., 0.        , 0.03754392,\n",
       "        0.        ]])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Implementing min-max scaling for test dataset preprocessing.\n",
    "minmax_scalextest = preprocessing.MinMaxScaler().fit(X_test)\n",
    "X_test_minmax = minmax_scalextest.transform(X_test)\n",
    "X_test_minmax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Implementing min-max scaling for train dataset preprocessing.\n",
    "minmax_scalextrain = preprocessing.MinMaxScaler().fit(X_train)\n",
    "X_train_minmax = minmax_scalextrain.transform(X_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9031007751937985\n"
     ]
    }
   ],
   "source": [
    " # Implementing the SVM Classiffier.\n",
    "clf = svm.SVC(kernel='rbf')\n",
    "\n",
    "clf.fit(X_train_minmax, y_train)\n",
    "y_pred = clf.predict(X_test_minmax)\n",
    "print(\"Accuracy:\",metrics.accuracy_score(y_valid, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 46,  25],\n",
       "       [  0, 187]], dtype=int64)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Collecting the confusion matrix.\n",
    "from sklearn.metrics import confusion_matrix   \n",
    "cm = confusion_matrix(y_valid,y_pred)\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'              precision    recall  f1-score   support\\n\\n           0       1.00      0.65      0.79        71\\n           1       0.88      1.00      0.94       187\\n\\n    accuracy                           0.90       258\\n   macro avg       0.94      0.82      0.86       258\\nweighted avg       0.91      0.90      0.90       258\\n'"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Collecting the Classification Report.\n",
    "from sklearn.metrics import classification_report\n",
    "classification_report(y_valid,y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "'              precision    recall  f1-score   support\\n\\n\n",
    "           0       1.00      0.65      0.79        71\\n\n",
    "           1       0.88      1.00      0.94       187\\n\\n\n",
    "    accuracy                           0.90       258\\n   \n",
    "   macro avg       0.94      0.82      0.86       258\\n\n",
    "weighted avg       0.91      0.90      0.90       258\\n'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              Precision  Recall  F1-Score\n",
      "0                  1.00    0.65      0.79\n",
      "1                  0.88    1.00      0.94\n",
      "Macro-Avg          0.94    0.82      0.86\n",
      "Weighted-Avg       0.91    0.90      0.90\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[Text(33.0, 0.5, 'Metrics')]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the Classification Report.\n",
    "import pandas as pd\n",
    "datas = {'Precision':[1.00,0.88,0.94,0.91],'Recall':[0.65,1.00,0.82,0.90],\n",
    "         'F1-Score':[0.79,0.94,0.86,0.90]}\n",
    "Indexes=['0','1','Macro-Avg','Weighted-Avg']\n",
    "rt = pd.DataFrame(datas, index=Indexes)\n",
    "print(rt)\n",
    "\n",
    "p1=sns.heatmap(rt, annot=True,center=0.9)\n",
    "p1.set_yticklabels(p1.get_yticklabels(),rotation = 30)\n",
    "p1.set( ylabel='Metrics')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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3y7lsbLH1uG8Gbpb0xYiYVLYrmpnVoQrVuMcBEwv2z5P0NWAa8K2IeKeUTtN8jTpGUr+2HUn9Jf2wlIuZmdUrqbELmyZImlawTdi4P/UCTgLuTJquA4aTL6MsIF/JKEmaxH18RLzbtpN8QpxQ6gXNzOqR1CP1FhFNETG2YGtqp8vjgeciYiFARCyMiNaIyAHXA/uXGmuaWSWNknpHxOr8D6c+gKcDmlm30thYnlklBcZTUCaRNDgiFiS7pwAzS+04TeK+FZgi6ffkb8Q5E7il1AuamdUjlfEJOJL6AkcDZxc0/0TSKPJ5dO4Gx7okzVolP5E0AziK/MyS/xMRD5R6QTOz7i4iPgQGbNB2Wrn6T/URExH3A/cDSDpY0q8j4hvlCsLMrNa6252TJMP78cBXyK9VclclgzIzq7Zylkoqrdidk7uTn4M4HlhK/hZORcThVYrNzKxqGhuzM+ei2EfMbOBR4PMRMQdA0oVVicrMrMqyNOIuNo/7i8A/gYclXS/pSNbf9m5m1q105QacWuswcUfE3RHxFWBP4G/AhcB2kq6TdEyV4jMzq4qu3IBTa53eORkRH0TEHyPiRPILTk0HLql4ZGZmVaSGHqm3WutSBBGxDPhdspmZdRv1MJJOKzuRmplVkBo3q3UIqTlxm5nhEbeZWfbUQe06rexEamZWSXUwzS8tJ24zM6iL2SJpZSdSM7NKco3bzCxbwiNuM7OMaXCN28wsW5y4zcyyJZy4zcyyxYnbzCxjokfPWoeQmhO3mRkecZuZZU6usdNVrlOTNBdYAbQCLRExVtI25B8BOQyYC5waEe+U0n/5IjUzy7BoaEi9pXR4RIyKiLHJ/iXAlIgYAUxhE55r4BF3nXvp79O445dNRC7HwZ87hmP/9dRah2RVcvndLzD1lUVss3kvJp33WQBmL1jOj+6ZyeqWHD0axKUn7s0nd+zHTY+9xn0z5gPQmsvxxuL3efg7R7N13161/BEypQsJuVQnA4clr28m/2Sx75TSkRN3Hcu1tnLbz6/j/Gt+SP9BA7nq7AvZ5+ADGDxsaK1Dsyo4afSOjPv0ML5/1/R1bT9/cDZnHzaCz+y+LY++soifPziLG848kDM+M5wzPjMcgEdmL+TWJ99w0u6iXBcSt6QJwISCpqaIaCrYD+BBSQH8Ljm2XUQsAIiIBZK2LTVWJ+46NnfWKwwasgODdhgMwNgjDuWFx55y4v6YGDNsAPPe+fAjbQI+WN0CwPur1jJoy40X///vF+dz3Cd3qEaI3UquZ/ovJ5NE3FTklIMjYn6SnB+SNHtT4yvkGncde3fJUvpvO3Ddfv9BA3l3ydIaRmS1dvEJI7n2wVkce/UUfvbALM4/eo+PHF+5ppUn5izmqJHb1yhCA4iI+cmfi4C7gf2BhZIGAyR/Liq1/6onbklfL3JsgqRpkqbd+4fbqhlWXYqIjdpUgzisftz59FtcdNxIHrjoSC46fiRX/nnGR45PfXkho3bq7zJJCXKNSr0VI2lzSVu2vQaOAWYCk4HTk9NOB/6r1FhrMeK+sqMDEdEUEWMjYuyJp42rZkx1qf+ggbyzaMm6/XcWL2HrgQNqGJHV2j3TmzkyGU0fs9dgZs5b/pHj98+cz3H7uExSimhQ6q0T2wGPSXoBeBr4S0TcD1wFHC3pVeDoZL8kFalxS5rR0SHyP5SlsPOeu7OoeR5LFvyTfgMHMO2vUznzBxfXOiyroUFb9mba3GXst8sAnn59KUO36bvu2IpVa3l27jL+7xdH1TDC7EqRkNP1E/E68Kl22pcCR5bjGpX6cnI74Fhgw8nlAp6o0DW7ncYejYy74Fx+edEPyOVyHHTC0eywy861Dsuq5JI7n2faG0t598M1HHP1FM49fASXnbwPP7nvJVpzQa8ejfzg5H3Wnf/XWf/kwOED6dPLcw5KkeuZnUJkpf6F7wW2iIjpGx6Q9LcKXbNb2vuA/dj7gP1qHYbVwFVfHt1u+8RzD2m3/eTRO3Hy6J0qGVL3lqGpGhVJ3BFxVpFjX63ENc3MNkl2lirxPG4zM8AjbjOzzHHiNjPLFjlxm5llS0OPjW94q1dO3GZmZOpZwU7cZmYAlV/VtXycuM3MADW4VGJmlilZGnFnKFQzMwOPuM3MAOiRoWyYoVDNzConS6USJ24zM5y4zcwyp9GzSszMssUjbjOzjOmZoWyYoVDNzCqnTE8uqwonbjMzoDFDpZIMhWpmVjkNDem3YiTtJOlhSbMkvSTpP5L2KyTNkzQ92U4oNVaPuM3MKOuIuwX4VkQ8J2lL4FlJDyXHro2Iqzf1Ak7cZmaUL3FHxAJgQfJ6haRZwJDy9J7nUomZGflZJWk3SRMkTSvYJrTXp6RhwGjg70nTeZJmSLpRUv9SY3XiNjProohoioixBVvThudI2gKYBFwQEe8B1wHDgVHkR+TXlHp9l0rMzIDGMk4HlNSTfNL+Y0TcBRARCwuOXw/cW2r/TtxmZpSvxi1JwA3ArIj4WUH74KT+DXAKMLPUazhxm5lR1lklBwOnAS9Kmp60fRcYL2kUEMBc4OxSL+DEbWYG9CjTrZMR8RjQXmf3leUCOHGbmQHQy095NzPLlizd8u7EbWaGE7eZWeY0Zmh5QCduMzM84jYzy5xe5bwDp8KcuM3M8IjbzCxzXOM2M8uYLI24MxSqmZmBR9xmZgA0yKUSM7NM6dXDidvMLFOyVON24jYzw7NKzMwyx4nbzCxjXCoxM8uYBo+4zcyyxbNKzMwyxjVuM7OMyVKpJEPleDOzymlsSL91RtJxkl6WNEfSJeWO1SNuMzPKVyqR1Aj8GjgaaAaekTQ5Iv5RlgvgxG1mBpS1xr0/MCciXgeQdBtwMlC2xK2IKFdfViGSJkREU63jsPri34vakTQBmFDQ1NT2byHpS8BxEfFvyf5pwKcj4rxyXd817myY0Pkp9jHk34saiYimiBhbsBV+gLY3dC/rCNmJ28ysvJqBnQr2dwTml/MCTtxmZuX1DDBC0i6SegHjgMnlvIC/nMwG1zGtPf69qEMR0SLpPOABoBG4MSJeKuc1/OWkmVnGuFRiZpYxTtxmZhnjxF3nKn3rrGWPpBslLZI0s9axWG04cdexgltnjwdGAuMljaxtVFYHbgKOq3UQVjtO3PVt3a2zEbEGaLt11j7GImIqsKzWcVjtOHHXtyHA2wX7zUmbmX2MOXHXt4rfOmtm2ePEXd8qfuusmWWPE3d9q/its2aWPU7cdSwiWoC2W2dnAXeU+9ZZyx5JE4EngT0kNUs6q9YxWXX5lnczs4zxiNvMLGOcuM3MMsaJ28wsY5y4zcwyxonbzCxjnLhtI5JaJU2XNFPSnZL6bkJfh0m6N3l9UrEVDiX1k/TvJVzjCkkXddA+L/lZpku6qqt9J/0U/gyHSTqolH7MysWJ29qzMiJGRcTewBrgnMKDyuvy705ETI6IYsmzH9DlxN2Ja5OfZVRElGNZ3MMAJ26rKSdu68yjwG6ShkmaJek3wHPATpKOkfSkpOeSkfkWsG4N8dmSHgO+0NaRpDMk/Sp5vZ2kuyW9kGwHAVcBw5PR8U+T8y6W9IykGZKuLOjre8k65f8D7NGVH0jSZUmfMyU1SVLS/jdJY5PXAyXN3eB9w8h/iF2YxHiIpC8n/bwgaWpX4jArlRO3dUhSD/Jrgb+YNO0B3BIRo4EPgO8DR0XEvsA04JuSNgOuBz4PHAJs30H3vwAeiYhPAfsCLwGXAK8lo+OLJR0DjCC/vO0oYIykQyWNIX/7/2jyHwz7Ffkx2pLsdEnHJm2/ioj9kv9R9AFOTPP3ERFzgd+yfhT/KHAZcGzyc5yUph+zTeWnvFt7+kianrx+FLgB2AF4MyKeStoPIP9wh8eTAWsv8rdh7wm8ERGvAki6FZjQzjWOAL4GEBGtwHJJ/Tc455hkez7Z34J8It8SuDsiPkyuUWz9lmsj4uoN2g6X9G2gL7AN+Q+Ne4r0UczjwE2S7gDuKrEPsy5x4rb2rIyIUYUNSXL+oLAJeCgixm9w3ijKt/SsgB9HxO82uMYFpV4j+R/Bb4CxEfG2pCuAzZLDLaz/X+hm7bx9IxFxjqRPA58DpksaFRFLS4nNLC2XSqxUTwEHS9oNQFJfSbsDs4FdJA1PzhvfwfunAOcm722UtBWwgvxous0DwJkFtfMhkrYFpgKnSOojaUvyZZm02hLykqTfLxUcmwuMSV4Xthf6SIyShkfE3yPiMmAJH12G16winLitJBGxGDgDmChpBvlEvmdErCJfGvlL8uXkmx108R/kSxYvAs8CeyUj1ceTL/t+GhEPAv8JPJmc9ydgy4h4DrgdmA5MIl/OSRv3u+Rr8C8Cfya/dG6bq4FzJT0BDOygi3vIf2hMl3QI8FNJLyr/4N6pwAtpYzErlVcHNDPLGI+4zcwyxonbzCxjnLjNzDLGidvMLGOcuM3MMsaJ28wsY5y4zcwy5v8DebMpr9pg8yEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the Confusion Matrix.\n",
    "import seaborn as sns\n",
    "ax = sns.heatmap(cm, annot=True,fmt=\"d\", center=100)\n",
    "ax.set(xlabel='Predicted Faults', ylabel='Actual Faults')\n",
    "ax.figure.savefig(\"Confusion_Matrix_Global_Dataset.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA2oAAAHSCAYAAACdLTg6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAb2klEQVR4nO3df4xl53kX8O8Tu94qaWiJvK4q29lY4Ep1EUriUZwqCFLRghMkG6QCtkQpVVTzR8wPESE5gNQqCAmKoKiq+8OUKG1F4xpIqdUfcWkShIRI6oFu09iuxeLGzmIrXtdRQY2w4/XLHzOOJ+u5O/fOnHPvszufjzSac89955znvuc9792v7j1na4wRAAAA+njdpgsAAADgawlqAAAAzQhqAAAAzQhqAAAAzQhqAAAAzQhqAAAAzVy5qR3feuut4+Mf//imdg8AALBpteiJjX2i9txzz21q1wAAAK356iMAAEAzghoAAEAzghoAAEAzghoAAEAzghoAAEAzghoAAEAzghoAAEAzghoAAEAzghoAAEAzghoAAEAzghoAAEAzghoAAEAzghoAAEAzghoAAEAzghoAAEAzghoAAEAzghoAAEAzBwa1qvpwVT1bVZ9b8HxV1Y9W1Zmq+mxVvX36MgEAAI6PK5do85EkP5bkZxc8/54kN+7+3JLkJ3Z/X7Jefjl59tnkhReS178+OX9+2uUTJ5Jrrkle97qv3dei9ats/+WXX/19lFoWtVmmn6Z4HUfty6n7ac5aDtPf6+rjOfppU/26ruVFfbbqudbh/DrKPLDKNpPNHNc55+K5juem+2zuc3aKPjvKOTj3nL7Oefyw/dR5eap56LDvs0fpszne2+ecYw7Tx+uan6aaYy4JY4wDf5K8JcnnFjz3U0nu3PP48STfctA2b7755tHR+fNjnD49xqlTY9xyyxif/OS0y8nO79Onx/jKV17d16L1q2z/9tvH+I3fOHoti9qcP39wP03xOqasf4p+mrOWw/T3uvp4jn7aVL+ua3lRn616rh2lzdzHYZl5YJVt/u7vbua4zjkXz3U8N91nc5+zU/TZUc7Buef0OcbZYevvML9vah467PvsUfpsjvf2OeeYw/TxuuanqeaYva+vgcUZ7GJPfrXRxYPaLyf5U3sefyLJ1kHb7BrUnnnm1YP5sY9Nv/zKz6lTYzz11MHrV9n+VLUsavPMMwf30xSvY8r6p+inOWs5TH+vq4/n6KdN9eu6lqc6147SZu7jsMw8sMo2f+VXeo2tOc7lqY7npvts7nN2ij6bqq65x8Hc5/Jh+6nz8lTz0DLb2a//5q59jjF6lG2u2sfrmp+mmmP2vr4Gsuhnig/+ar8P6vZtWHVXVW1X1fa5c+cm2PX0XnghefLJneU3vWn65Vc8+WTy4osHr19l+1PVsqjNCy8c3E9TvI4p65+in+as5WLbWdTfU+1rE/20qX5d1/JU59pR2sx9HJaZB1bZ5hve0GtsLVo/9/y6zL423Wdzn7NT9NlUdR3ldaxznB22/g5jZVPz0DLb2a//5q59v30uu82p55jD9PEr5p6fVn1Ny7y+zqYIameTXL/n8XVJnt6v4RjjvjHG1hhj6+TJkxPsenonTiSnTu0sP//89MuvOHUqueqqg9evsv2palnU5sSJg/tpitcxZf1T9NOctVxsO4v6e6p9baKfNtWv61qe6lw7Spu5j8My88Aq2/zDP+w1thatn3t+XWZfm+6zuc/ZKfpsqrqO8jrWOc4OW3+HsbKpeWiZ7ezXf3PXvt8+l93m1HPMYfr4FXPPT6u+pmVeX2sX+7jtlZ9c/KuPfyHJr2Xnk7V3JvnNZbbZ9auPrlE72veVp3gdU9Y/RT/N/Z3pub/HftjlDtcwrHM8zdlnq55rrlHb3Nia41x2jdr6+sw1akfrp87LU81Dh32fdY1aj/lpqjnmUrlGrcZO2Fqoqj6a5N1Jrk7yxSQ/mOTrdkPeT1ZVZeeukLcm+XKS7x9jbB8UELe2tsb29oHNNsJdH492ByB3fXTXxw7HeFPL7vq4+jYTd31018fp+sxdH4/WT52X3fVxfXOMuz6uVS184qCgNpfOQQ0AAGANFga1XnkSAAAAQQ0AAKAbQQ0AAKAZQQ0AAKAZQQ0AAKAZQQ0AAKAZQQ0AAKAZQQ0AAKAZQQ0AAKAZQQ0AAKAZQQ0AAKAZQQ0AAKAZQQ0AAKAZQQ0AAKAZQQ0AAKAZQQ0AAKAZQQ0AAKAZQQ0AAKAZQQ0AAKAZQQ0AAKAZQQ0AAKAZQQ0AAKAZQQ0AAKAZQQ0AAKAZQQ0AAKAZQQ0AAKAZQQ0AAKAZQQ0AAKAZQQ0AAKAZQQ0AAKAZQQ0AAKAZQQ0AAKAZQQ0AAKAZQQ0AAKAZQQ0AAKAZQQ0AAKAZQQ0AAKAZQQ0AAKAZQQ0AAKAZQQ0AAKAZQQ0AAKAZQQ0AAKAZQQ0AAKAZQQ0AAKAZQQ0AAKAZQQ0AAKAZQQ0AAKAZQQ0AAKAZQQ0AAKAZQQ0AAKAZQQ0AAKAZQQ0AAKAZQQ0AAKAZQQ0AAKAZQQ0AAKAZQQ0AAKAZQQ0AAKAZQQ0AAKAZQQ0AAKAZQQ0AAKAZQQ0AAKAZQQ0AAKAZQQ0AAKAZQQ0AAKAZQQ0AAKAZQQ0AAKAZQQ0AAKAZQQ0AAKAZQQ0AAKCZpYJaVd1aVY9X1Zmqumef599cVZ+qqt+qqs9W1XunLxUAAOB4ODCoVdUVSe5N8p4kNyW5s6puuqDZP0rywBjjbUnuSPLjUxcKAABwXCzzido7kpwZYzwxxngxyf1Jbr+gzUjyR3aXvzHJ09OVCAAAcLxcuUSba5N8Yc/js0luuaDNDyX59ar6W0nekOS7JqkOAADgGFrmE7XaZ9244PGdST4yxrguyXuT/FxVvWbbVXVXVW1X1fa5c+dWrxYAAOAYWCaonU1y/Z7H1+W1X218X5IHkmSM8d+SfH2Sqy/c0BjjvjHG1hhj6+TJk4erGAAA4DK3TFB7OMmNVXVDVV2VnZuFPHhBm6eS/Nkkqapvy05Q85EZAADAIRwY1MYYLyW5O8lDSR7Lzt0dH6mqD1XVbbvNPpDkB6rqt5N8NMnfGGNc+PVIAAAAllCbylNbW1tje3t7I/sGAABoYL/7gSRZ8j+8BgAAYH0ENQAAgGYENQAAgGYENQAAgGYENQAAgGYENQAAgGYENQAAgGYENQAAgGYENQAAgGYENQAAgGYENQAAgGYENQAAgGYENQAAgGYENQAAgGYENQAAgGYENQAAgGYENQAAgGYENQAAgGYENQAAgGYENQAAgGYENQAAgGYENQAAgGYENQAAgGYENQAAgGYENQAAgGYENQAAgGYENQAAgGYENQAAgGYENQAAgGYENQAAgGYENQAAgGYENQAAgGYENQAAgGYENQAAgGYENQAAgGYENQAAgGYENQAAgGYENQAAgGYENQAAgGYENQAAgGYENQAAgGYENQAAgGYENQAAgGYENQAAgGYENQAAgGYENQAAgGYENQAAgGYENQAAgGYENQAAgGYENQAAgGYENQAAgGYENQAAgGYENQAAgGYENQAAgGYENQAAgGYENQAAgGYENQAAgGYENQAAgGYENQAAgGYENQAAgGYENQAAgGYENQAAgGYENQAAgGYENQAAgGYENQAAgGYENQAAgGYENQAAgGYENQAAgGYENQAAgGaWCmpVdWtVPV5VZ6rqngVt/kpVPVpVj1TVz09bJgAAwPFx5UENquqKJPcm+e4kZ5M8XFUPjjEe3dPmxiQfTPKuMcaXquqauQoGAAC43C3zido7kpwZYzwxxngxyf1Jbr+gzQ8kuXeM8aUkGWM8O22ZAAAAx8cyQe3aJF/Y8/js7rq9vjXJt1bVf62qT1fVrfttqKruqqrtqto+d+7c4SoGAAC4zC0T1GqfdeOCx1cmuTHJu5PcmeSnq+qbXvNHY9w3xtgaY2ydPHly1VoBAACOhWWC2tkk1+95fF2Sp/dp80tjjK+MMX4vyePZCW4AAACsaJmg9nCSG6vqhqq6KskdSR68oM1/TPKdSVJVV2fnq5BPTFkoAADAcXFgUBtjvJTk7iQPJXksyQNjjEeq6kNVddtus4eS/H5VPZrkU0n+/hjj9+cqGgAA4HJWY1x4udl6bG1tje3t7Y3sGwAAoIH97geSZMn/8BoAAID1EdQAAACaEdQAAACaEdQAAACaEdQAAACaEdQAAACaEdQAAACaEdQAAACaEdQAAACaEdQAAACaEdQAAACaEdQAAACaEdQAAACaEdQAAACaEdQAAACaEdQAAACaEdQAAACaEdQAAACaEdQAAACaEdQAAACaEdQAAACaEdQAAACaEdQAAACaEdQAAACaEdQAAACaEdQAAACaEdQAAACaEdQAAACaEdQAAACaEdQAAACaEdQAAACaEdQAAACaEdQAAACaEdQAAACaEdQAAACaEdQAAACaEdQAAACaEdQAAACaEdQAAACaEdQAAACaEdQAAACaEdQAAACaEdQAAACaEdQAAACaEdQAAACaEdQAAACaEdQAAACaEdQAAACaEdQAAACaEdQAAACaEdQAAACaEdQAAACaEdQAAACaEdQAAACaEdQAAACaEdQAAACaEdQAAACaEdQAAACaEdQAAACaEdQAAACaEdQAAACaEdQAAACaEdQAAACaEdQAAACaEdQAAACaEdQAAACaEdQAAACaEdQAAACaEdQAAACaWSqoVdWtVfV4VZ2pqnsu0u57qmpU1dZ0JQIAABwvBwa1qroiyb1J3pPkpiR3VtVN+7R7Y5K/neQzUxcJAABwnCzzido7kpwZYzwxxngxyf1Jbt+n3T9O8sNJ/t+E9QEAABw7ywS1a5N8Yc/js7vrvqqq3pbk+jHGL19sQ1V1V1VtV9X2uXPnVi4WAADgOFgmqNU+68ZXn6x6XZIfSfKBgzY0xrhvjLE1xtg6efLk8lUCAAAcI8sEtbNJrt/z+LokT+95/MYkfyLJf66qzyd5Z5IH3VAEAADgcJYJag8nubGqbqiqq5LckeTBV54cY/zBGOPqMcZbxhhvSfLpJLeNMbZnqRgAAOAyd2BQG2O8lOTuJA8leSzJA2OMR6rqQ1V129wFAgAAHDc1xji41Qy2trbG9rYP3QAAgGNrv/uBJFnyP7wGAABgfQQ1AACAZgQ1AACAZgQ1AACAZgQ1AACAZgQ1AACAZgQ1AACAZgQ1AACAZgQ1AACAZgQ1AACAZgQ1AACAZgQ1AACAZgQ1AACAZgQ1AACAZgQ1AACAZgQ1AACAZgQ1AACAZgQ1AACAZgQ1AACAZgQ1AACAZgQ1AACAZgQ1AACAZgQ1AACAZgQ1AACAZgQ1AACAZgQ1AACAZgQ1AACAZgQ1AACAZgQ1AACAZgQ1AACAZgQ1AACAZgQ1AACAZgQ1AACAZgQ1AACAZgQ1AACAZgQ1AACAZgQ1AACAZgQ1AACAZgQ1AACAZgQ1AACAZgQ1AACAZgQ1AACAZgQ1AACAZgQ1AACAZgQ1AACAZgQ1AACAZgQ1AACAZgQ1AACAZgQ1AACAZgQ1AACAZgQ1AACAZgQ1AACAZgQ1AACAZgQ1AACAZgQ1AACAZgQ1AACAZgQ1AACAZgQ1AACAZgQ1AACAZgQ1AACAZgQ1AACAZgQ1AACAZgQ1AACAZgQ1AACAZgQ1AACAZgQ1AACAZgQ1AACAZgQ1AACAZpYKalV1a1U9XlVnquqefZ7/e1X1aFV9tqo+UVWnpi8VAADgeDgwqFXVFUnuTfKeJDclubOqbrqg2W8l2Rpj/Mkk/z7JD09dKAAAwHGxzCdq70hyZozxxBjjxST3J7l9b4MxxqfGGF/effjpJNdNWyYAAMDxsUxQuzbJF/Y8Pru7bpH3Jfm1oxQFAABwnF25RJvaZ93Yt2HVX0uyleTPLHj+riR3Jcmb3/zmJUsEAAA4Xpb5RO1skuv3PL4uydMXNqqq70ryD5PcNsZ4Yb8NjTHuG2NsjTG2Tp48eZh6AQAALnvLBLWHk9xYVTdU1VVJ7kjy4N4GVfW2JD+VnZD27PRlAgAAHB8HBrUxxktJ7k7yUJLHkjwwxnikqj5UVbftNvvnSb4hyb+rqtNV9eCCzQEAAHCAGmPfy81mt7W1Nba3tzeybwAAgAb2ux9IkiX/w2sAAADWR1ADAABoRlADAABoRlADAABoRlADAABoRlADAABoRlADAABoRlADAABoRlADAABoRlADAABoRlADAABoRlADAABoRlADAABoRlADAABoRlADAABoRlADAABoRlADAABoRlADAABoRlADAABoRlADAABoRlADAABoRlADAABoRlADAABoRlADAABoRlADAABoRlADAABoRlADAABoRlADAABoRlADAABoRlADAABoRlADAABoRlADAABoRlADAABoRlADAABoRlADAABoRlADAABoRlADAABoRlADAABoRlADAABoRlADAABoRlADAABoRlADAABoRlADAABoRlADAABoRlADAABoRlADAABoRlADAABoRlADAABoRlADAABoRlADAABoRlADAABoRlADAABoRlADAABoRlADAABoRlADAABoRlADAABoRlADAABoRlADAABoRlADAABoRlADAABoRlADAABoRlADAABoRlADAABoRlADAABoRlADAABoRlADAABoRlADAABoRlADAABoRlADAABoZqmgVlW3VtXjVXWmqu7Z5/kTVfULu89/pqreMnWhAAAAx8WVBzWoqiuS3Jvku5OcTfJwVT04xnh0T7P3JfnSGOOPV9UdSf5Zkr86R8Fzefnl5NlnkxdeSE6cSK65JnndTJ83LtrXOms4qJZFbV7/+uT8+eXbb6ovN2GqWpbp706ve26Xy2td9VzrcH4t2u8y88Aq29vUcZ17Lp76eHbos2Ucpc45z4FV/27ucTD38bxUxssqppqHNvE+O8c+5zhfjtLH6xpzl+PYXmiMcdGfJN+R5KE9jz+Y5IMXtHkoyXfsLl+Z5LkkdbHt3nzzzaOL8+fHOH16jFOnxkh2fp8+vbN+Xfv6ylfWV8NBtezd5942t9wyxic/uXz7TfXlnH02dy3L9PcmxsqmdDrGR7Hqudbh/FpU/zLzwCrb29RxnXsunvp4duizZRylzjnPgVX/bo45fZ3z+KUyXlYx1Ty0iffZOfY5x/lylD5e15i7HMf2uFgOu9iTYyd4fU+Sn97z+HuT/NgFbT6X5Lo9j/9Xkqsvtt1OQe2ZZ1494K/8nDq1s35d+3rqqfXVcFAte/e5t83HPrZa+0315Zx9Nncty/T3JsbKpnQ6xkex6rnW4fxaVP8y88Aq29vUcZ17Lp76eHbos2Ucpc45z4FV/26OOX2d8/ilMl5WMdU8tIn32Tn2Ocf5cpQ+XteYuxzH9hiL89IyHxTWfh/EHaJNququqtququ1z584tsev1eOGF5Mknv3bdk0/urF/Xvl58cX01HFTL3n3ubfOmN63WflGbqaxzX+uqZZn+3sRY2ZROx/goVj3XjtJmDqvOA6ts77DbOKq55+Kpj2eHPlvGUeqc8xxY9e/mmNP3bmfuefxSGS+rmGoe2sT77Bz7nON8OUofr2vMXY5j+2KWCWpnk1y/5/F1SZ5e1KaqrkzyjUmev3BDY4z7xhhbY4ytkydPHq7iGZw4kZw69bXrTp3aWb+ufV111fpqOKiWvfvc2+b551drv6jNVNa5r3XVskx/b2KsbEqnY3wUq55rR2kzh1XngVW2d9htHNXcc/HUx7NDny3jKHXOeQ6s+ndzzOl7tzP3PH6pjJdVTDUPbeJ9do59znG+HKWP1zXmLsexfVEX+7htjJHsXHP2RJIbklyV5LeTfPsFbd6f5Cd3l+9I8sBB2+301ccO11W5Rm2e+tfFNWrz6HSMj8I1aou3t6nj6hq1ebhGbbntuEZtda5RW7zNqc4X16htzMK8VGO85huKr1FV703yr5JckeTDY4x/UlUfSrI9xniwqr4+yc8leVt2Pkm7Y4zxxMW2ubW1Nba3tw+TLWfR4U6F7vo4T/3r4q6P87hcXqu7Pi7enrs+uuvjKn/rro9H2++lzF0fF2/TXR+n388a7XcJ2c4TywS1OXQLagAAAGu2MKhd2vkTAADgMiSoAQAANCOoAQAANCOoAQAANCOoAQAANCOoAQAANCOoAQAANCOoAQAANCOoAQAANCOoAQAANCOoAQAANCOoAQAANCOoAQAANCOoAQAANCOoAQAANCOoAQAANCOoAQAANCOoAQAANFNjjM3suOpckic3svOLuzrJc5sugsuG8cRUjCWmZDwxFWOJqRzXsfTcGOPW/Z7YWFDrqqq2xxhbm66Dy4PxxFSMJaZkPDEVY4mpGEuv5auPAAAAzQhqAAAAzQhqr3XfpgvgsmI8MRVjiSkZT0zFWGIqxtIFXKMGAADQjE/UAAAAmhHU9qiqW6vq8ao6U1X3bLoeLi1V9fmq+p2qOl1V27vr3lRV/6mq/ufu7z+66Trpqao+XFXPVtXn9qzbd/zUjh/dnas+W1Vv31zldLNgLP1QVf3v3fnpdFW9d89zH9wdS49X1Z/fTNV0VFXXV9Wnquqxqnqkqv7O7npzEyu7yHgyPy0gqO2qqiuS3JvkPUluSnJnVd202aq4BH3nGOOte24ve0+ST4wxbkzyid3HsJ+PJLnw/1FZNH7ek+TG3Z+7kvzEmmrk0vCRvHYsJcmP7M5Pbx1j/GqS7L7P3ZHk23f/5sd33w8hSV5K8oExxrcleWeS9++OGXMTh7FoPCXmp30Jaq96R5IzY4wnxhgvJrk/ye0brolL3+1JfmZ3+WeS/MUN1kJjY4z/kuT5C1YvGj+3J/nZsePTSb6pqr5lPZXS3YKxtMjtSe4fY7wwxvi9JGey834IGWM8M8b4H7vL/zfJY0mujbmJQ7jIeFrk2M9Pgtqrrk3yhT2Pz+bigwcuNJL8elX996q6a3fdN48xnkl2Jqgk12ysOi5Fi8aP+YrDuHv362gf3vM1bGOJpVTVW5K8LclnYm7iiC4YT4n5aV+C2qtqn3Vuickq3jXGeHt2vvrx/qr605suiMuW+YpV/USSP5bkrUmeSfIvdtcbSxyoqr4hyX9I8nfHGP/nYk33WWc88TX2GU/mpwUEtVedTXL9nsfXJXl6Q7VwCRpjPL37+9kkv5idj+e/+MrXPnZ/P7u5CrkELRo/5itWMsb44hjj/Bjj5ST/Oq9+fchY4qKq6uuy84/qfzvG+NjuanMTh7LfeDI/LSaoverhJDdW1Q1VdVV2Ll58cMM1cYmoqjdU1RtfWU7y55J8Ljtj6Pt2m31fkl/aTIVcohaNnweT/PXdO6y9M8kfvPI1JNjPBdcJ/aXszE/Jzli6o6pOVNUN2bkJxG+uuz56qqpK8m+SPDbG+Jd7njI3sbJF48n8tNiVmy6gizHGS1V1d5KHklyR5MNjjEc2XBaXjm9O8os7c1CuTPLzY4yPV9XDSR6oqvcleSrJX95gjTRWVR9N8u4kV1fV2SQ/mOSfZv/x86tJ3pudC6u/nOT7114wbS0YS++uqrdm52tDn0/yN5NkjPFIVT2Q5NHs3JHt/WOM85uom5beleR7k/xOVZ3eXfcPYm7icBaNpzvNT/urMY7VVz0BAADa89VHAACAZgQ1AACAZgQ1AACAZgQ1AACAZgQ1AACAZgQ1AACAZgQ1AACAZgQ1AACAZv4/Bmcjz6TxupcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the Predicted Output.\n",
    "import numpy as np\n",
    "x = np.linspace(0,258,258)\n",
    "plt.figure(figsize = (15,8))\n",
    "sns.scatterplot(x, y_pred, color='b')\n",
    "sns.despine()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Developing Functions of the Window.\n",
    "def vectorized_stride_v1(array, clearing_time_index, max_time, sub_window_size,\n",
    "                         stride_size):       \n",
    "    start = clearing_time_index + 1 - sub_window_size + 1\n",
    "    \n",
    "    sub_windows = (\n",
    "        start + \n",
    "        np.expand_dims(np.arange(sub_window_size), 0) +\n",
    "        np.expand_dims(np.arange(max_time + 1), 0).T\n",
    "    )\n",
    "    \n",
    "    # Fancy indexing to select every V rows.\n",
    "    return array[sub_windows[::stride_size]]\n",
    "\n",
    "\n",
    "def vectorized_stride_v2(array, clearing_time_index, max_time, sub_window_size,\n",
    "                         stride_size):\n",
    "    start = clearing_time_index + 1 - sub_window_size + 1\n",
    "    \n",
    "    sub_windows = (\n",
    "        start + \n",
    "        np.expand_dims(np.arange(sub_window_size), 0) +\n",
    "        # Create a rightmost vector as [0, V, 2V, ...].\n",
    "        np.expand_dims(np.arange(max_time + 1, step=stride_size), 0).T\n",
    "    )\n",
    "    \n",
    "    return array[sub_windows]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 1, 0, 1, 1],\n",
       "       [1, 1, 1, 1, 1],\n",
       "       [1, 1, 1, 1, 1],\n",
       "       [1, 1, 0, 1, 0],\n",
       "       [1, 0, 1, 1, 1],\n",
       "       [1, 1, 1, 1, 1],\n",
       "       [1, 1, 1, 1, 1],\n",
       "       [1, 1, 0, 1, 1],\n",
       "       [1, 1, 0, 0, 1],\n",
       "       [0, 1, 1, 0, 1],\n",
       "       [0, 1, 1, 1, 1],\n",
       "       [1, 1, 1, 1, 1],\n",
       "       [1, 1, 1, 0, 1],\n",
       "       [0, 1, 1, 1, 1],\n",
       "       [1, 1, 1, 1, 1],\n",
       "       [1, 1, 1, 1, 1],\n",
       "       [1, 1, 1, 0, 1],\n",
       "       [0, 1, 1, 1, 1],\n",
       "       [1, 1, 1, 0, 0],\n",
       "       [0, 0, 1, 0, 1],\n",
       "       [0, 1, 1, 1, 1],\n",
       "       [1, 1, 1, 1, 1],\n",
       "       [1, 1, 1, 1, 1],\n",
       "       [1, 1, 1, 1, 1],\n",
       "       [1, 1, 1, 1, 1],\n",
       "       [1, 1, 1, 1, 1],\n",
       "       [1, 1, 0, 1, 1],\n",
       "       [1, 1, 1, 1, 1],\n",
       "       [1, 1, 1, 1, 1],\n",
       "       [1, 1, 1, 1, 1],\n",
       "       [1, 1, 1, 1, 1],\n",
       "       [1, 1, 1, 1, 0],\n",
       "       [1, 0, 1, 1, 0],\n",
       "       [1, 0, 1, 1, 0],\n",
       "       [1, 0, 1, 1, 1],\n",
       "       [1, 1, 1, 1, 1],\n",
       "       [1, 1, 1, 1, 1],\n",
       "       [1, 1, 0, 0, 1],\n",
       "       [0, 1, 1, 1, 1],\n",
       "       [1, 1, 1, 0, 1],\n",
       "       [0, 1, 1, 1, 1],\n",
       "       [1, 1, 1, 1, 1],\n",
       "       [1, 1, 1, 1, 1],\n",
       "       [1, 1, 0, 1, 0],\n",
       "       [1, 0, 1, 1, 1],\n",
       "       [1, 1, 1, 1, 0],\n",
       "       [1, 0, 1, 1, 0],\n",
       "       [1, 0, 1, 1, 1],\n",
       "       [1, 1, 0, 1, 1],\n",
       "       [1, 1, 1, 1, 1],\n",
       "       [1, 1, 1, 1, 1],\n",
       "       [1, 1, 1, 1, 1],\n",
       "       [1, 1, 1, 1, 0],\n",
       "       [1, 0, 1, 1, 1],\n",
       "       [1, 1, 1, 0, 1],\n",
       "       [0, 1, 1, 0, 1],\n",
       "       [0, 1, 1, 1, 1],\n",
       "       [1, 1, 0, 1, 1],\n",
       "       [1, 1, 1, 0, 1],\n",
       "       [0, 1, 1, 1, 1],\n",
       "       [1, 1, 1, 0, 1],\n",
       "       [0, 1, 0, 1, 1],\n",
       "       [1, 1, 1, 1, 1],\n",
       "       [1, 1, 1, 1, 0],\n",
       "       [1, 0, 1, 1, 1],\n",
       "       [1, 1, 1, 1, 1],\n",
       "       [1, 1, 1, 1, 1],\n",
       "       [1, 1, 1, 1, 1],\n",
       "       [1, 1, 1, 1, 1],\n",
       "       [1, 1, 1, 1, 0],\n",
       "       [1, 0, 0, 1, 1],\n",
       "       [1, 1, 1, 1, 0],\n",
       "       [1, 0, 0, 1, 0],\n",
       "       [1, 0, 1, 1, 1],\n",
       "       [1, 1, 1, 1, 1],\n",
       "       [1, 1, 0, 0, 1],\n",
       "       [0, 1, 0, 1, 1],\n",
       "       [1, 1, 1, 0, 1],\n",
       "       [0, 1, 1, 0, 1],\n",
       "       [0, 1, 1, 1, 0],\n",
       "       [1, 0, 0, 1, 1],\n",
       "       [1, 1, 1, 0, 1],\n",
       "       [0, 1, 1, 1, 1],\n",
       "       [1, 1, 1, 1, 1],\n",
       "       [1, 1, 1, 0, 1],\n",
       "       [0, 1, 1, 1, 1]], dtype=int64)"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Starting  to implement the Sliding Window over the Output.\n",
    "z1 = vectorized_stride_v2(y_pred, 1, 257, 5,3)\n",
    "z1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "85"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "az = z1.shape\n",
    "bz = az[0] -1\n",
    "bz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(86,)"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m = np.zeros(bz +1)\n",
    "m.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
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      " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
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      "[1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 1. 1. 1. 1.\n",
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      " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 1. 1. 1. 1.\n",
      " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
      " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
      " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 1. 1. 1. 1.\n",
      " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
      " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
      " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 1. 1. 1. 1.\n",
      " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
      " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
      " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 1. 1. 1. 1.\n",
      " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
      " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
      " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 1. 1. 1. 1.\n",
      " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
      " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
      " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 1. 1. 1. 1.\n",
      " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
      " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
      " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 1. 1. 1. 1.\n",
      " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 0. 0. 0. 0. 0. 0. 0.\n",
      " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
      " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 1. 1. 1. 1.\n",
      " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 0. 0. 0. 0. 0. 0.\n",
      " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
      " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 1. 1. 1. 1.\n",
      " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 0. 0. 0. 0. 0.\n",
      " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
      " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 1. 1. 1. 1.\n",
      " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 0. 0. 0. 0.\n",
      " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
      " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 1. 1. 1. 1.\n",
      " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 0. 0. 0.\n",
      " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
      " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 1. 1. 1. 1.\n",
      " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 0. 0.\n",
      " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
      " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 1. 1. 1. 1.\n",
      " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 0.\n",
      " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
      " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 1. 1. 1. 1.\n",
      " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0.\n",
      " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
      " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 1. 1. 1. 1.\n",
      " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n",
      " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
      " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 1. 1. 1. 1.\n",
      " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n",
      " 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
      " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 1. 1. 1. 1.\n",
      " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n",
      " 1. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
      " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 1. 1. 1. 1.\n",
      " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n",
      " 1. 1. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
      " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 1. 1. 1. 1.\n",
      " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n",
      " 1. 1. 1. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
      " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 1. 1. 1. 1.\n",
      " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n",
      " 1. 1. 1. 1. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
      " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 1. 1. 1. 1.\n",
      " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n",
      " 1. 1. 1. 1. 1. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
      " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 1. 1. 1. 1.\n",
      " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n",
      " 1. 1. 1. 1. 1. 1. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
      " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 1. 1. 1. 1.\n",
      " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n",
      " 1. 1. 1. 1. 1. 1. 1. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
      " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 1. 1. 1. 1.\n",
      " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n",
      " 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
      " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 1. 1. 1. 1.\n",
      " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n",
      " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
      " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 1. 1. 1. 1.\n",
      " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n",
      " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
      " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 1. 1. 1. 1.\n",
      " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n",
      " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
      " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 1. 1. 1. 1.\n",
      " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n",
      " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
      " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 1. 1. 1. 1.\n",
      " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n",
      " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
      " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 1. 1. 1. 1.\n",
      " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n",
      " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
      " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 1. 1. 1. 1.\n",
      " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n",
      " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 0. 0. 0. 0. 0. 0. 0.\n",
      " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 1. 1. 1. 1.\n",
      " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n",
      " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 0. 0. 0. 0. 0. 0.\n",
      " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 1. 1. 1. 1.\n",
      " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n",
      " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 0. 0. 0. 0. 0.\n",
      " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 1. 1. 1. 1.\n",
      " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n",
      " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 0. 0. 0. 0.\n",
      " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 1. 1. 1. 1.\n",
      " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n",
      " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 0. 0. 0.\n",
      " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 1. 1. 1. 1.\n",
      " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n",
      " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 0. 0.\n",
      " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 1. 1. 1. 1.\n",
      " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n",
      " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 0.\n",
      " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 1. 1. 1. 1.\n",
      " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n",
      " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0.\n",
      " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 1. 1. 1. 1.\n",
      " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n",
      " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n",
      " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 1. 1. 1. 1.\n",
      " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n",
      " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n",
      " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 1. 1. 1. 1.\n",
      " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n",
      " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n",
      " 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 1. 1. 1. 1.\n",
      " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n",
      " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n",
      " 0. 1. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 1. 1. 1. 1.\n",
      " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n",
      " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n",
      " 0. 1. 1. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 1. 1. 1. 1.\n",
      " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n",
      " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n",
      " 0. 1. 1. 1. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 1. 1. 1. 1.\n",
      " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n",
      " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n",
      " 0. 1. 1. 1. 1. 1. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 1. 1. 1. 1.\n",
      " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n",
      " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n",
      " 0. 1. 1. 1. 1. 1. 1. 0. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 1. 1. 1. 1.\n",
      " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n",
      " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n",
      " 0. 1. 1. 1. 1. 1. 1. 1. 0. 0. 0. 0. 0. 0.]\n",
      "[1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 1. 1. 1. 1.\n",
      " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n",
      " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n",
      " 0. 1. 1. 1. 1. 1. 1. 1. 1. 0. 0. 0. 0. 0.]\n",
      "[1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 1. 1. 1. 1.\n",
      " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n",
      " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n",
      " 0. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 0. 0. 0.]\n",
      "[1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 1. 1. 1. 1.\n",
      " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n",
      " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n",
      " 0. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 0. 0.]\n",
      "[1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 1. 1. 1. 1.\n",
      " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n",
      " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n",
      " 0. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 0.]\n",
      "[1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 1. 1. 1. 1.\n",
      " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n",
      " 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.\n",
      " 0. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0.]\n"
     ]
    }
   ],
   "source": [
    "# The Final Step of Sliding Window implementation.\n",
    "for i in range(0,bz,1):\n",
    "    er = z1[i]\n",
    "    ew = np.sum(er)\n",
    "    \n",
    "    if ew>2:\n",
    "        m[i]=1\n",
    "    else:\n",
    "        m[i]=0\n",
    "        \n",
    "    print(m)\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "86"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "83.0"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(258,)"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m2 = np.zeros(258)\n",
    "m2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Changing the Sliding Parameters for optimizing the results.\n",
    "for i in range(0,85,1):\n",
    "    r1 = i*3\n",
    "    r2 = r1+4\n",
    "    for k in range(r1,r2,1):\n",
    "        m2[k]=m[i]\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(258,)"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "250.0"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(m2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(0,258,258)\n",
    "plt.figure(figsize = (15,8))\n",
    "sns.scatterplot(x, m2,color='b')\n",
    "sns.despine()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.7248062015503876\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy:\",metrics.accuracy_score(y_valid, m2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 1, 0, 1, 1],\n",
       "       [1, 0, 1, 1, 1],\n",
       "       [0, 1, 1, 1, 1],\n",
       "       ...,\n",
       "       [1, 1, 0, 1, 1],\n",
       "       [1, 0, 1, 1, 1],\n",
       "       [0, 1, 1, 1, 1]], dtype=int64)"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Changing the Sliding Parameters for optimizing the results.\n",
    "z2 = vectorized_stride_v2(y_pred, 1, 255, 5,1)\n",
    "z2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "255"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "az4 = z2.shape\n",
    "bz4 = az4[0] -1\n",
    "bz4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(256,)"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m3 = np.zeros(bz4 +1)\n",
    "m3.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(0,bz4,1):\n",
    "    er = z2[i]\n",
    "    ew = np.sum(er)\n",
    "    \n",
    "    if ew>2:\n",
    "        m3[i]=1\n",
    "    else:\n",
    "        m3[i]=0\n",
    "        \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "       1., 1., 1., 1., 1., 1., 0., 1., 1., 0., 1., 1., 1., 1., 1., 1., 1.,\n",
       "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "       1., 1., 1., 1., 1., 0., 0., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 0., 0., 1., 1., 1.,\n",
       "       1., 1., 1., 1., 1., 0., 0., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "       0.])"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(258,)"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m4 = np.zeros(258)\n",
    "m4.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Another Changing the Sliding Parameters for optimizing the results.\n",
    "\n",
    "for i in range(0,85,1):\n",
    "    r1 = i*1\n",
    "    r2 = r1+4\n",
    "    for k in range(r1,r2,1):\n",
    "        m4[k]=m[i]\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "86.0"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(m4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(0,258,258)\n",
    "plt.figure(figsize = (15,8))\n",
    "sns.scatterplot(x, m4,color='b')\n",
    "sns.despine()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.44573643410852715\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy:\",metrics.accuracy_score(y_valid, m4))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}