Fault_Detector_GPS_Dataset.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This program implements an SVM classification method on the ALFA's GPS dataset to develop a Fault Detector and design a sliding window to improve the detector's output stability."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<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_twist_linear_x</th>\n",
       "      <th>field_twist_linear_y</th>\n",
       "      <th>field_twist_linear_z</th>\n",
       "      <th>field_twist_angular_x</th>\n",
       "      <th>field_twist_angular_y</th>\n",
       "      <th>field_twist_angular_z</th>\n",
       "      <th>FAULT STATUS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1536690000000000000</td>\n",
       "      <td>6821</td>\n",
       "      <td>1536690000000000000</td>\n",
       "      <td>-20.219015</td>\n",
       "      <td>1.119799</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1536690000000000000</td>\n",
       "      <td>6822</td>\n",
       "      <td>1536690000000000000</td>\n",
       "      <td>-20.385284</td>\n",
       "      <td>1.004140</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1536690000000000000</td>\n",
       "      <td>6823</td>\n",
       "      <td>1536690000000000000</td>\n",
       "      <td>-20.385522</td>\n",
       "      <td>1.186131</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1536690000000000000</td>\n",
       "      <td>6824</td>\n",
       "      <td>1536690000000000000</td>\n",
       "      <td>-20.265690</td>\n",
       "      <td>1.484663</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1536690000000000000</td>\n",
       "      <td>6825</td>\n",
       "      <td>1536690000000000000</td>\n",
       "      <td>-20.020459</td>\n",
       "      <td>2.097170</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1045</th>\n",
       "      <td>1536690511872410112</td>\n",
       "      <td>9548</td>\n",
       "      <td>1536690511870749952</td>\n",
       "      <td>-12.130383</td>\n",
       "      <td>-3.794458</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1046</th>\n",
       "      <td>1536690512046810112</td>\n",
       "      <td>9549</td>\n",
       "      <td>1536690512046210048</td>\n",
       "      <td>-12.307983</td>\n",
       "      <td>-3.478168</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1047</th>\n",
       "      <td>1536690512255950080</td>\n",
       "      <td>9550</td>\n",
       "      <td>1536690512255460096</td>\n",
       "      <td>-12.468168</td>\n",
       "      <td>-3.150301</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1048</th>\n",
       "      <td>1536690512449060096</td>\n",
       "      <td>9551</td>\n",
       "      <td>1536690512447849984</td>\n",
       "      <td>-12.616632</td>\n",
       "      <td>-2.829401</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1049</th>\n",
       "      <td>1536690512680590080</td>\n",
       "      <td>9552</td>\n",
       "      <td>1536690512680130048</td>\n",
       "      <td>-12.631824</td>\n",
       "      <td>-2.464531</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1050 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     time  field_header_seq   field_header_stamp  \\\n",
       "0     1536690000000000000              6821  1536690000000000000   \n",
       "1     1536690000000000000              6822  1536690000000000000   \n",
       "2     1536690000000000000              6823  1536690000000000000   \n",
       "3     1536690000000000000              6824  1536690000000000000   \n",
       "4     1536690000000000000              6825  1536690000000000000   \n",
       "...                   ...               ...                  ...   \n",
       "1045  1536690511872410112              9548  1536690511870749952   \n",
       "1046  1536690512046810112              9549  1536690512046210048   \n",
       "1047  1536690512255950080              9550  1536690512255460096   \n",
       "1048  1536690512449060096              9551  1536690512447849984   \n",
       "1049  1536690512680590080              9552  1536690512680130048   \n",
       "\n",
       "      field_twist_linear_x  field_twist_linear_y  field_twist_linear_z  \\\n",
       "0               -20.219015              1.119799                     0   \n",
       "1               -20.385284              1.004140                     0   \n",
       "2               -20.385522              1.186131                     0   \n",
       "3               -20.265690              1.484663                     0   \n",
       "4               -20.020459              2.097170                     0   \n",
       "...                    ...                   ...                   ...   \n",
       "1045            -12.130383             -3.794458                     0   \n",
       "1046            -12.307983             -3.478168                     0   \n",
       "1047            -12.468168             -3.150301                     0   \n",
       "1048            -12.616632             -2.829401                     0   \n",
       "1049            -12.631824             -2.464531                     0   \n",
       "\n",
       "      field_twist_angular_x  field_twist_angular_y  field_twist_angular_z  \\\n",
       "0                         0                      0                      0   \n",
       "1                         0                      0                      0   \n",
       "2                         0                      0                      0   \n",
       "3                         0                      0                      0   \n",
       "4                         0                      0                      0   \n",
       "...                     ...                    ...                    ...   \n",
       "1045                      0                      0                      0   \n",
       "1046                      0                      0                      0   \n",
       "1047                      0                      0                      0   \n",
       "1048                      0                      0                      0   \n",
       "1049                      0                      0                      0   \n",
       "\n",
       "      FAULT STATUS  \n",
       "0                0  \n",
       "1                0  \n",
       "2                0  \n",
       "3                0  \n",
       "4                0  \n",
       "...            ...  \n",
       "1045             1  \n",
       "1046             1  \n",
       "1047             1  \n",
       "1048             1  \n",
       "1049             1  \n",
       "\n",
       "[1050 rows x 10 columns]"
      ]
     },
     "execution_count": 20,
     "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",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "GPS = pd.read_excel('Raq_GPS_dataset.xlsx')    # Importing dataset.\n",
    "GPS\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1050.000000\n",
       "mean        1.559078\n",
       "std        14.009710\n",
       "min       -23.856790\n",
       "25%       -12.601650\n",
       "50%         0.716963\n",
       "75%        16.969094\n",
       "max        21.179422\n",
       "Name: field_twist_linear_x, dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GPS.field_twist_linear_x.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1050.000000\n",
       "mean       -0.841898\n",
       "std        12.248355\n",
       "min       -23.209600\n",
       "25%       -11.804684\n",
       "50%         0.031604\n",
       "75%         9.143594\n",
       "max        21.805206\n",
       "Name: field_twist_linear_y, dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GPS.field_twist_linear_y.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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(GPS.field_twist_linear_x, rug=True)\n",
    "ax = sns.distplot(GPS.field_twist_linear_y, rug=True)\n",
    "ax.set( xlabel='Twisting around axes',ylabel='Density')#xlabel='Compass Angel',\n",
    "plt.legend( loc='upper left', labels=['X-axes', 'Y-axes'])\n",
    "ax.figure.savefig(\"output2.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       0\n",
       "1       0\n",
       "2       0\n",
       "3       0\n",
       "4       0\n",
       "       ..\n",
       "1045    1\n",
       "1046    1\n",
       "1047    1\n",
       "1048    1\n",
       "1049    1\n",
       "Name: FAULT STATUS, Length: 1050, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols_to_use = ['time', 'field_header_seq', 'field_header_stamp', 'field_twist_linear_x', 'field_twist_linear_y','field_twist_linear_z','field_twist_angular_x','field_twist_angular_y','field_twist_angular_z']\n",
    "X = GPS[cols_to_use]\n",
    "y = GPS['FAULT STATUS']\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>field_header_seq</th>\n",
       "      <th>field_header_stamp</th>\n",
       "      <th>field_twist_linear_x</th>\n",
       "      <th>field_twist_linear_y</th>\n",
       "      <th>field_twist_linear_z</th>\n",
       "      <th>field_twist_angular_x</th>\n",
       "      <th>field_twist_angular_y</th>\n",
       "      <th>field_twist_angular_z</th>\n",
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       "      <th>50</th>\n",
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       "      <td>6871</td>\n",
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       "      <td>0</td>\n",
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       "</table>\n",
       "<p>315 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     time  field_header_seq   field_header_stamp  \\\n",
       "743   1536690451474550016              9246  1536690451474149888   \n",
       "656   1536690367278889984              8825  1536690367276570112   \n",
       "704   1536690376850050048              8873  1536690376849390080   \n",
       "738   1536690450461469952              9241  1536690450461050112   \n",
       "496   1536690335256600064              8665  1536690335255480064   \n",
       "...                   ...               ...                  ...   \n",
       "50    1536690000000000000              6871  1536690000000000000   \n",
       "162   1536690000000000000              6983  1536690000000000000   \n",
       "1008  1536690504471689984              9511  1536690504470810112   \n",
       "320   1536690300054240000              8489  1536690300052859904   \n",
       "424   1536690320877459968              8593  1536690320876450048   \n",
       "\n",
       "      field_twist_linear_x  field_twist_linear_y  field_twist_linear_z  \\\n",
       "743             -18.945602              1.142169                     0   \n",
       "656              16.713234              0.475601                     0   \n",
       "704              -1.404819            -16.590629                     0   \n",
       "738             -19.856400              0.735042                     0   \n",
       "496             -14.000699             12.456646                     0   \n",
       "...                    ...                   ...                   ...   \n",
       "50               15.653802            -16.728018                     0   \n",
       "162             -20.265983            -11.210510                     0   \n",
       "1008              0.520524            -16.471778                     0   \n",
       "320              17.484349             -2.261512                     0   \n",
       "424             -18.669727              0.101014                     0   \n",
       "\n",
       "      field_twist_angular_x  field_twist_angular_y  field_twist_angular_z  \n",
       "743                       0                      0                      0  \n",
       "656                       0                      0                      0  \n",
       "704                       0                      0                      0  \n",
       "738                       0                      0                      0  \n",
       "496                       0                      0                      0  \n",
       "...                     ...                    ...                    ...  \n",
       "50                        0                      0                      0  \n",
       "162                       0                      0                      0  \n",
       "1008                      0                      0                      0  \n",
       "320                       0                      0                      0  \n",
       "424                       0                      0                      0  \n",
       "\n",
       "[315 rows x 9 columns]"
      ]
     },
     "execution_count": 25,
     "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": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.9939484044000282"
      ]
     },
     "execution_count": 27,
     "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_twist_linear_x.mean()\n",
    "s4 = X_valid.field_twist_linear_x.std()\n",
    "\n",
    "m5 = X_valid.field_twist_linear_y.mean()\n",
    "s5 = X_valid.field_twist_linear_y.std()\n",
    "\n",
    "m6 = X_valid.field_twist_linear_z.mean()\n",
    "s6 = X_valid.field_twist_linear_z.std()\n",
    "\n",
    "m7 = X_valid.field_twist_angular_x.mean()\n",
    "s7 = X_valid.field_twist_angular_x.std()\n",
    "\n",
    "m8 = X_valid.field_twist_angular_y.mean()\n",
    "s8 = X_valid.field_twist_angular_y.std()\n",
    "\n",
    "m9 = X_valid.field_twist_angular_z.mean()\n",
    "s9 = X_valid.field_twist_angular_z.std()\n",
    "\n",
    "m4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.53669061e+18, 8.74907728e+03, 1.53669039e+18, ...,\n",
       "        0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "       [1.53669033e+18, 8.95751633e+03, 1.53669040e+18, ...,\n",
       "        0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "       [1.53669050e+18, 9.12305457e+03, 1.53669040e+18, ...,\n",
       "        0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "       ...,\n",
       "       [1.53669040e+18, 8.64254631e+03, 1.53669060e+18, ...,\n",
       "        0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "       [1.53669037e+18, 7.81897602e+03, 1.53669055e+18, ...,\n",
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       "       [1.53669011e+18, 8.89289037e+03, 1.53669037e+18, ...,\n",
       "        0.00000000e+00, 0.00000000e+00, 0.00000000e+00]])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " # Preparing random noise based on standard deviation and mean of each feature.\n",
    "r1 =1* np.random.normal(m1,s1, X_valid[cols_to_use[0]].shape)\n",
    "r2 =1* np.random.normal(m2,s2, X_valid[cols_to_use[1]].shape)\n",
    "r3 =1* np.random.normal(m3,s3, X_valid[cols_to_use[2]].shape)\n",
    "r4 =1* np.random.normal(m4,s4, X_valid[cols_to_use[3]].shape)\n",
    "r5 = 1*np.random.normal(m5,s5, X_valid[cols_to_use[4]].shape)\n",
    "r6 =1* np.random.normal(m6,s6, X_valid[cols_to_use[5]].shape)\n",
    "r7 = 1*np.random.normal(m7,s7, X_valid[cols_to_use[6]].shape)\n",
    "r8 = 1*np.random.normal(m8,s8, X_valid[cols_to_use[7]].shape)\n",
    "r9 = 1*np.random.normal(m9,s9, X_valid[cols_to_use[8]].shape)\n",
    "\n",
    "f = [r1,r2,r3,r4,r5,r6,r7,r8,r9]\n",
    "random_noise = np.transpose(f)\n",
    "random_noise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>field_header_seq</th>\n",
       "      <th>field_header_stamp</th>\n",
       "      <th>field_twist_linear_x</th>\n",
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       "      <th>field_twist_angular_x</th>\n",
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       "      <th>field_twist_angular_z</th>\n",
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       "      <th>743</th>\n",
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       "      <th>738</th>\n",
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       "      <td>17364.557494</td>\n",
       "      <td>3.073381e+18</td>\n",
       "      <td>-11.888873</td>\n",
       "      <td>20.502296</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>3.073381e+18</td>\n",
       "      <td>-6.732412</td>\n",
       "      <td>18.844146</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>3.073380e+18</td>\n",
       "      <td>16003.002380</td>\n",
       "      <td>3.073380e+18</td>\n",
       "      <td>30.217413</td>\n",
       "      <td>-23.691927</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>3.073380e+18</td>\n",
       "      <td>16039.831194</td>\n",
       "      <td>3.073380e+18</td>\n",
       "      <td>-11.742925</td>\n",
       "      <td>-17.105225</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>1008</th>\n",
       "      <td>3.073381e+18</td>\n",
       "      <td>18153.546310</td>\n",
       "      <td>3.073381e+18</td>\n",
       "      <td>-4.637573</td>\n",
       "      <td>-20.558284</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>320</th>\n",
       "      <td>3.073381e+18</td>\n",
       "      <td>16307.976021</td>\n",
       "      <td>3.073381e+18</td>\n",
       "      <td>35.224678</td>\n",
       "      <td>10.666661</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>424</th>\n",
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       "      <td>17485.890366</td>\n",
       "      <td>3.073381e+18</td>\n",
       "      <td>-30.385442</td>\n",
       "      <td>2.128463</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>315 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              time  field_header_seq  field_header_stamp  \\\n",
       "743   3.073381e+18      17995.077275        3.073381e+18   \n",
       "656   3.073381e+18      17782.516327        3.073381e+18   \n",
       "704   3.073381e+18      17996.054569        3.073381e+18   \n",
       "738   3.073381e+18      17364.557494        3.073381e+18   \n",
       "496   3.073381e+18      17337.651606        3.073381e+18   \n",
       "...            ...               ...                 ...   \n",
       "50    3.073380e+18      16003.002380        3.073380e+18   \n",
       "162   3.073380e+18      16039.831194        3.073380e+18   \n",
       "1008  3.073381e+18      18153.546310        3.073381e+18   \n",
       "320   3.073381e+18      16307.976021        3.073381e+18   \n",
       "424   3.073380e+18      17485.890366        3.073381e+18   \n",
       "\n",
       "      field_twist_linear_x  field_twist_linear_y  field_twist_linear_z  \\\n",
       "743               4.376277            -12.634373                   0.0   \n",
       "656               5.307851              0.531428                   0.0   \n",
       "704              -5.367626             -1.923762                   0.0   \n",
       "738             -11.888873             20.502296                   0.0   \n",
       "496              -6.732412             18.844146                   0.0   \n",
       "...                    ...                   ...                   ...   \n",
       "50               30.217413            -23.691927                   0.0   \n",
       "162             -11.742925            -17.105225                   0.0   \n",
       "1008             -4.637573            -20.558284                   0.0   \n",
       "320              35.224678             10.666661                   0.0   \n",
       "424             -30.385442              2.128463                   0.0   \n",
       "\n",
       "      field_twist_angular_x  field_twist_angular_y  field_twist_angular_z  \n",
       "743                     0.0                    0.0                    0.0  \n",
       "656                     0.0                    0.0                    0.0  \n",
       "704                     0.0                    0.0                    0.0  \n",
       "738                     0.0                    0.0                    0.0  \n",
       "496                     0.0                    0.0                    0.0  \n",
       "...                     ...                    ...                    ...  \n",
       "50                      0.0                    0.0                    0.0  \n",
       "162                     0.0                    0.0                    0.0  \n",
       "1008                    0.0                    0.0                    0.0  \n",
       "320                     0.0                    0.0                    0.0  \n",
       "424                     0.0                    0.0                    0.0  \n",
       "\n",
       "[315 rows x 9 columns]"
      ]
     },
     "execution_count": 30,
     "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": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.95961773, 0.67571026, 0.59637824, ..., 0.        , 0.        ,\n",
       "        0.        ],\n",
       "       [0.67956563, 0.63987728, 0.5299614 , ..., 0.        , 0.        ,\n",
       "        0.        ],\n",
       "       [0.81765503, 0.67587501, 0.54266091, ..., 0.        , 0.        ,\n",
       "        0.        ],\n",
       "       ...,\n",
       "       [0.83854804, 0.70242457, 0.82419343, ..., 0.        , 0.        ,\n",
       "        0.        ],\n",
       "       [0.65339662, 0.39130305, 0.60193967, ..., 0.        , 0.        ,\n",
       "        0.        ],\n",
       "       [0.4730024 , 0.58987284, 0.4635843 , ..., 0.        , 0.        ,\n",
       "        0.        ]])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Implementing min-max scaling for test and train datasets preprocessing.\n",
    "minmax_scalextest = preprocessing.MinMaxScaler().fit(X_test)\n",
    "X_test_minmax = minmax_scalextest.transform(X_test)\n",
    "\n",
    "minmax_scalextrain = preprocessing.MinMaxScaler().fit(X_train)\n",
    "X_train_minmax = minmax_scalextrain.transform(X_train)\n",
    "\n",
    "X_test_minmax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9333333333333333\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": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x22dfa0e9e08>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "x=np.linspace(0,1,315)\n",
    "mplt.scatter(x,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 21,  21],\n",
       "       [  0, 273]], dtype=int64)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Collecting the confusion matrix.\n",
    "cm = confusion_matrix(y_valid,y_pred)\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'              precision    recall  f1-score   support\\n\\n           0       1.00      0.50      0.67        42\\n           1       0.93      1.00      0.96       273\\n\\n    accuracy                           0.93       315\\n   macro avg       0.96      0.75      0.81       315\\nweighted avg       0.94      0.93      0.92       315\\n'"
      ]
     },
     "execution_count": 37,
     "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      0.50      0.67        42\\n\n",
    "           1       0.93      1.00      0.96       273\\n\\n\n",
    "    accuracy                           0.93       315\\n   \n",
    "   macro avg       0.96      0.75      0.81       315\\n\n",
    "weighted avg       0.94      0.93      0.92       315\\n'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              Precision  Recall  F1-Score\n",
      "0                  1.00    0.50      0.67\n",
      "1                  0.93    1.00      0.96\n",
      "Macro-Avg          0.96    0.75      0.81\n",
      "Weighted-Avg       0.94    0.93      0.92\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[Text(33.0, 0.5, 'Metrics')]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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dp5MWizH67RnMXpzDLecNYvrcbMZNWcDhP+nI8AsPx8z4ZEYmVz/8HgAFBcaNj3/I+Dt/iQRfzlvBU29+HfErqlyVOTAh/NL9CHAsQd/5VEljzezbuGQ3A/82s39K6gmMB7qUmWeSj0qOnKRY2F/0CpBFUNt8zsw+L++6RGqmq6kK5n8RdRFSQ8vOUZegxtvyzvV7HEmO/+dnFf7MefPSg8r9fZIOBv5sZj8P928EMLMRcWkeAxaa2Z1h+nvM7JCy8vRmuj0UBqJ6QCvgHGDezgKRc85Vt10ZwBA/2CrchpXIrj0Q35afGR6L92fgV5IyCWpFV5ZXPm+mqxyXAdOBY81sa9SFcc65kkobNVuW+MFWZWZXymUl9s8GnjGze8Ka0XOSepc10MuDUeW4d2cj6ZxzLkqVPGI7E+gYt98BWFYizUXAYAAzmyypDtACWFlq+Sq1eCnKA5FzLtFV8gwMU4HukvaSlAGcBYwtkWYJcEz4u/cD6gCrysrQa0bOOZcCKvMxIzPLk3QF8DaQBjxlZrMkDQemmdlY4FrgcUnXEDThnV/ePJ4ejJxzLgVU9kOvZjaeYGBC/LFb437+FhhU0fw8GDnnXApI9Ln2KtRnJOl3khop8KSk6YUzDjjnnEt8NWWi1AvNbD1wHNASuAD4e5WVyjnnXKWKSRXeolDRZrrC0p0APG1mXyvRlw10zjlXJNE/sCsajL6Q9A6wF3CjpIaAD2d2zrkkkej1h4oGo4uAPgTzDG2W1Jygqc4551wSSPDxCxXuMxoCLDCzteF+PrB31RTJOedcZUv0xfUqGoxuM7N1hTthULqtaorknHOuslXyDAyVrqLNdKUFLX9GyTnnkkSiN9NVNKBMk3QvwWJKRjAVuC8W45xzSSLRBzBUtJnuSmAb8BLwMvADcHlVFco551zl0i5sUahQzcjMNgE7rHHunHMuOaQleDtducFI0v1mdrWkN9hx4STM7JQqK5lzzrlKk+jNdDurGT0X/v/uqi6Ic865qpPgsaj8YGRmX0hKAy4xs19VU5mcc85VsqjmnKuonfYZmVm+pJaSMsxsW3UUyjnnXOVK8FhU4aHdi4BJksYCmwoPmtm9VVGoVFCwbnXURajxLrzVB3xWh7lZ63aeyEUu2fuMCi0LtxjQMDxW5vKxzjnnEktaDQlG35rZy/EHJJ1RBeVxzjlXBRJ8ZHeFH3q9sYLHnHPOJaCYKr5FYWfPGR1PsKBee0kPxp1qBORVZcGcc85VnmTvM1oGTANOofhcdBuAa6qqUM455ypXojfT7ew5o6+BryU9H6btZGZzqqVkzjnnKk2CV4wq3Gc0GPgKeAtAUp9wmLdzzrkkkC5VeItCRYPRn4EBwFoAM/sK6FI1RXLOOVfZpIpvUajo0O48M1uX6B1gzjnnSpf00wGFZko6B0iT1B24Cvi06orlnHOuMiV4LNqlxfV6AVuBF4D1wNVVVSjnnHOVK6mfMypkZpuBm8LNOedckkn2xfXKHTHni+s551xySPBYtNOa0cHAUoKmuc+Ibnl055xze0AJ/vG9s2DUBjgWOBs4BxgHvGBms6q6YM455ypPoteMyh3AYGb5ZvaWmf0GGAjMBz6UdGW1lM4551ylSPoBDJJqAycS1I66AA8Cr1ZtsZxzzlWmRH9OdGcDGEYDvYE3gb+Y2cxqKZVzzrlKlVbRB3kisrOa0a8JlhnfB7gqLrIKMDNrVIVlc845V0kqewYGSYOBB4A04Akz+3uJ8/cBR4W79YBWZtakrPx2Nmt3gsdS55xzFVGZfUGS0oBHCAa4ZQJTJY01s28L05jZNXHprwT6llu+yiuec865RFXJE6UOAOab2UIz2wa8CAwpJ/3ZBI8IlcmDkXPOpYAYqvAmaZikaXHbsBLZtSd4BrVQZnhsB5I6A3sBH5RXvopOlOpquGP7deHuS48hLSaeeesb7n7p82LnO7VqxMhrB9OicT3WbNjChXeOI2v1RgA6tmzIo78fTIeWDTEzht78H5asWB/Fy0hoPVvV5/QDWhOTmLR4Le/OzSl2/rT9W7FPi/oA1EoXDTPSuX7cXAAeGtqDZeu2ApC7ZTuPTcms3sIniX6dGnPZYV2ISbz57Upemr6s2PmWDTL4w8+60qB2OjHBk5OX8vnitTSsk86tg7uzb6sGvPPdKh7+aFE0L6AK7UqXkZmNAkaVl11pl5WR9izgFTPLL+93ejByxGLi/iuO5cQb/k3W6g188tCv+d/kBXy35McPyxHDjmTMe7MY8+4sjujTieEXHs5Fd40H4Ik/nMCdL0zhg+mLqV+nFgVW1nsydQk48ydteGjSEtZu2c4fjtqLGcs3kL1hW1Ga/8xYWfTzEXs3pWOTOkX72/ONERO+r84iJ52Y4Moj9uKPr89m9cZtPHxmbyZ/v4Yla7YUpTm3f3smzs/lfzNX0KlpXf52cg9+/eyXbM8r4JnPMtmrWV26NK8X4auoOumV+wBRJtAxbr8DsKyMtGcBl+8sQ2+m2wMKRV2OPdV/37YsWLaGRdnr2J5XwMsTv+OkQ7oVS9OjU3M+/HIxABO/WsJJB3crOp6eFuOD6cG5TT9sZ8vWvOp9AUmgS7O6rNq0jZzN28k3+CJzPQe0bVhm+n4dGjFtqdcud8W+rRuwbN0PZK/fSl6B8eG8HA7Zu2mxNGZQPyMNgPq108jZFHwZ+CGvgFnLN7Atv+Z+karkPqOpQHdJe0nKIAg4O8xlKmlfoCkweWcZejDaTZLSLCSpraTGUZdpd7Vr0YDMVRuK9rNWbaB98wbF0sxYuJKhh+4DwJBB3WlUvzbNGtahe4emrN24lRdvHcLkR8/jjkuOIJbo845EoEmddNZs+TFIr92ynSZ1Sm+YaFY3neb1M5izalPRsfSY+MORXbjuiC4c0LZBqdeluhb1M1gVV9NcvXEbLepnFEvz3OeZHLNvC54/vy9/O6kHj9TA5riyxKQKbztjZnnAFcDbwGzg32Y2S9JwSfETaJ8NvGi28+YSb6bbTWaWL6k5cAEwDPgLMKa8a8JOwGEA6fv9gvQOA6u8nBVRauNvibfOjaM+5L4rfsavjuvNpBmZZK3aQF5+AelpMQbt34GBl45m6cr1/OumU/j1cb0Z/daMail7Mivrr/PADo35Mmt9sfO3vD2fdT/k0bxeLX53aCeWrd/K6k3bq6OYSaMinRhH7dOcd2av4pWvlrNfmwb88diuXPL8N2X+W9Qkld2GY2bjgfEljt1aYv/PFc3Pa0a7SdJRwEfAFoIq69WS2pV3jZmNMrN+ZtYvUQIRQNbqjXRo+WOTUfuWDVmWu7FYmuW5mzhr+OscfNmz3Pb0xwCs37yNrFUb+Hr+ChZlryO/wBj76Tz6dGtdreVPBmt/yKNp3R+/+zWpW4t1P5TenHlgh0ZMyyzeRFeYNmfzduat3kzHxnVKuzSlrdq0jZYNf6wJtWiQUdQMV2jwfq2YOD/oC52dvZGMtBiN66bGd/LYLmxRlc/tAkm9JDUF2gLvmtkjZnYuQefd6ZKS7p5Om7Ocbu2b0rlNY2qlxzjjiB6Mmzy/WJrmjeoWfbO6/qyDGP12UPOZNjebJg3q0KJxXQCO7NOJ7xYXHyXmYPGaLbRqkEHzerVIUxBwZizfsEO6Vg0yqFcrxve5P3a6160VK+p8rp+Rxt7N67F8w9ZqK3uymLNiI+0b16FNw9qkx8SR3Zsz+fs1xdKs3LiNvh2CFvVOTeuQkR5j7ZbU6OOszGa6qpAaXwl2k6SYmRXE7bcnmCj2YqAFkCupkZmtB54iGAr5DvBdFOXdXfkFxjUPv8cbd5xOWizG6LdnMHtxDrecN4jpc7MZN2UBh/+kI8MvPBwz45MZmVz98HsAFBQYNz7+IePv/CUSfDlvBU+9+XXEryjxFBj8++tsLh/UkRhi8uK1LN+wjRP3a8GSNT8wIzuoifbr0IgvsorXito0rM3ZfdpgBE1R78xdXWwUngsUGDz80SJGDOlBTOLtb1eyOHcLvxnQgbkrNzF50Roe+2Qxvz96b37Rpw0Y/OO9BUXXP3deX+plpFErJg7Zuyk3vP5dsZF4yS6qIFNRqkC/UkoJBybkx+3XAVoDWWaWJ+kN4BVgBnAbMMLMpkhqCXwIPGVm90hSeZ12dY/7h9/4KnbhpSdFXYSUMDdrXdRFqPHevWLgHkeSMV9kVvgz59wDO1R75Eq6JqWqJOlwYL6kQ8O5lwCuA24FLgr3XwY6mdl0gr6iyyWNIZjqYgxwhaS6FRk94pxz1aWSh3ZXOm+mK64JwQy0FwMnATcA9wE9geclfQc0Agp7+x/gx9VwXyZY76kzUO6Txs45V90S/ZFID0YED6+GNZlVwBrgRuA9SbOBD81sqqRbgVOA+sAJku4ws7XABkmrgYeBbsCfw4kDnXMuYSR6M1iil69aFDapmdlkIIeghjQYOAC4Mzz3AvAM0Iyg9tQsLovNwAdmdpCZvVl9JXfOuYrx0XQJpryBBZKaANMIFoI6DDgViEm6F3jVzD6RdDEQC2tFAJjZVuDJqi+9c87tnkRvpkuZmlE4jVwsPhAVHit8NigMMC2BScBQ4GSgK7AdOFpSbWCTma2NG+DgnHMJL9Efek2ZmlEYhExSG4LBCW8Cqwr7dyS1BjIInhXqamYnFF4r6W/hs0Tx+fkgBedc0vCaUYRKzoYQzg33AXA0QV/QTeHxvxI8rNoTqAssldQ6rsa0vrT8nHMuWWgXtijU2JpR2DdUEP48CFgM7Aeca2ZfSuoNPCvpPWAm8KSZfS9pL2ARkBs/+wJAyX3nnEsWaV4zqj7x6wuFSzt0l3QlcCnBBL4nEwzNxsxmAv8FTjSzF8NAVMvMvjezm8zMp0R2ztUYif7Qa40JRiXWF6olqRYwARgEXGFmWcDjwC1xlxnhPHLh4IbtcfnVmHvjnHPahf+iUGM+cAsHFEj6I3BvGFiGA/sCW8PgchfQWtJfJf0JOA3IDq/3JjnnXI3lNaMqUnK5b0lNwznifgo8AsH6QUABcKaZFYQj6s4EviVYv/10M3urekvunHPVL4YqvEUhKQcwlJxZO9SAYK31k8NVWDPCYdu3An+V9KqZbTCzucBc4PkwrxhBF5NPbOqcq7ESfPxC8tWMwlFy+ZLqS7otnGG7BcEEpmuBNuFAhG0AZjYuPH5dKXnF4mpMzjlXYyX6dEBJEYwkXSZpBBSNkjsH+BSoAxxF8MxQ4YzagwjmjkPSRZL6AL8I0xTj/ULOuVQRU8W3KCRLM931QC1JH4UTkW4DjieY0PRhoBfwM+AvwFXAGZLaEkx6+qaZrYEdV251zrlUEdUouYpKyGAUrppaYGY54RxwTxGsFXSJpIlm9oqkE4G/AtcSrMR6O3AQwVpEJwDrzez9+Hw9EDnnUpX3Ge0iSXsDK/hx6YZ8ghrQtwRLfV8dJt0XeMzMPiBYzK4HcKuZbTWz/xYGIp/Q1DnnEv85o4SrGZnZQkmfAD+XdCHwFfBvgnnk7gOuldQKqA30Cgcv9AIuAN4vJT+f0NQ5l/Ki6guqqISrGYXOIZi2Zx7wd6AD8AmwkKB2dBlBzekdYH/gfjP7T7i0Q6K+Jueci0yij6ZLuJoRgJllSnoV6A6MAO4BaoX//w/wAPC2mb0EvAQ/PgTr/ULOObejBK8YJWYwCv0O+J6gVvRLgtFy9YDZwO/N7LPChEk5Sq5e46hLUOM98/yUqIuQEtJrJfLHSA1xxcA9ziKqGk9FJey7yMw2SboFeMTMLpG0uPBBVuCzEmmTKxA551w1S+xQlLh9RoUeJ3hmqGtcIHLOOberEnx1vYStGUFQ45G0j5mtDKcB8ml7nHNuN3gz3R4ys5Xh/z0QOefcbkrsUJQEwcg551wlSPBo5MHIOedSgM9N55xzLnIJ3mWU8KPpnHPOVYLKHkwnabCkOZLmS7qhjDRnSvpW0ixJz5eXn9eMnHMuBagSq0bhBNSPAMcCmcBUSWPN7Nu4NN2BG4FBZrYmnFO0TF4zcs65FCBVfKuAAcB8M1sYPgP6IjCkRJpLCCYtWAM/jowuiwcj55xLAbvSTCdpmKRpcduwEtm1B5bG7WeGx+LtA+wjaZKkKZIGl1c+b6ZzzrlUsAutdGY2Chi1i7mVfBY0nWCy6yMJ5uln74kAABPQSURBVBj9WFJvM1tbWoZeM3LOuRRQyYvrZQId4/Y7AMtKSfO6mW03s++BOQTBqVQejJxzLgVUcp/RVKC7pL0kZQBnAWNLpHkNOCr43WpB0Gy3sKwMvZnOOedSQGU+Z2RmeZKuAN4G0oCnzGyWpOHANDMbG547TtK3QD5wvZnllJWnByPnnEsBlT0Dg5mNB8aXOHZr3M8G/D7cdsqDkXPOpYBEn4HBg5FzzqWABI9FHoyccy4lJHg08mDknHMpwBfXc845F7nEDkUejJxzLjUkeDTyYOQAOLZvB+6++BDSYuKZd7/j7le/Lna+U8sGjLzyCFo0qsOajVu58L4JZOVsKjrfsG4tvnr4TMZOWcQ1j0+q7uInhWP7tOeuCw4iLSZGvz+Xe16bUex8xxb1GXnZobRoVIfcjVu56MGPWJa7mY4t6vPC9UeTFhPpaTFGvjmbJ9+dE9GrSGzHHNCOO8/rR1pMPDthPve9MavY+Y4t6vPIsINpHr6Phz06iWW5m9m/c1PuvfAgGtatRX6Bcc9rM3h1yuKIXkXV8MX1XMKLxcT9/3coJ942jqycTXzyj1P53+eL+S7zxymkRpw/kDET5jJmwjyO2L8dw389gIvun1B0/rZz+vHxrOVRFD8pxGLi3osGcvLtb5OVu5mPR5zMuGlL+C5zXVGaO87rz/MTFzBm4nyO6N2W4eceyMUPfUz22i0cfdM4tuUVUL9OOlPvGcq4aUvIXrMlwleUeGIS91wwgKEj3iMrZzMT/no846dnMifrx3v813MP5IWPF/LCxws5vGcbbvtlX/7vn5PYvDWP//vnJBZmb6BNk7pM/NsJvP/NMtZt3h7hK6pcCd5l5NMB7alwXQ9UmYuFVLP+3VuyYPk6Fq3YwPa8Al7+ZAEnHdSlWJoeHZvw4TfB1FMTZyzjpAGdi8717dqCVk3q8d5XmdVZ7KTSr1sLFmZvYNHKjWzPK+CVSQs5qV+nYml6dGjChBnhPZ65nBPD89vzCtiWVwBA7fQ0YrGkfatVqQO7NWfhivAe5xfw6uTFnHhgx2Jp9m3fmImzsgH46NtsTjiwAwALsjewMHsDANlrt7Bq/Q80b1Snel9AFavsxfUqmwej3VQYhMwsPzzUMDyedJ8U7ZrVJ3P1j01uWTmbaN+sfrE0MxblMvTgvQAYMrALjepl0KxhbST4+wUD+dPoKdVa5mTTrlk9MuOaNbNyN9O2efF7PHNxLkMHdgHglAGdg3vcoDYA7ZvX57O7hzBn5Jnc+9oMrxWVol3TesWajrNyN9G2Wd1iaWYuXsMpA4Igf3L/jjSql0HTBhnF0vy0a3My0tP4fsWGqi90NZJU4S0KHox2U2EQkrS/pMeAp8LjJadRT3ilvfesxGzwNz49hcN6tWXyvb/gsF5tyVq9kbz8Av7v+F68/cXSYsHM7ai09vqSb5Ubn53KoT3b8Oldp3BYrzZk5WwiryCoEWXlbOKg615n/ytf4dwju9Gqcc361l4ZSn0fl/hrvHnMFxzaozUf33Eig/ZrTVbOJvLzf0zUukldRl06iMse+3SHa5NdJU+UWum8z2gXSEozs3xJMaAeMIagVrsE2F/S0Wb2gaSYmRWUcv0wYBhA+k/OJb3L4dVZ/DJl5WyiQ4sfv6W3b16fZbmbi6VZvmYzZ935LgD166Qz9OC9WL95Owft24pBPdsy7Pie1K9Ti4z0GBt/2M4tz31era8h0WXlbqJDXE2ofbN6ZJe4x9lrtnDO3R8AwT0eclBn1pfos8hes4XZS9dyyH6tea2GdbDvqazczbQvdo/r71CDzF67hV/dPxGA+rXTOaV/J9ZvCe5xw7q1ePn6o/jry18xbf7q6it4NUn0JhuvGe2CMBBlEPy7tgQ2mNkpwB+Ah4Fbw3Q7BKLw+Cgz62dm/RIlEAFMm7eKbm0b07lVQ2qlxzjj0K6M+7z4B13zsEkO4PrT+jL6/WA01wX3TWCfS56nx7AXuPGZKTw/YZ4HolJ8MX81Xds2onOrBtRKj3H6oL0ZN21psTTx9/i6Uw/g2QnzgKCJr05GGgBN6mcwcN9WzFu2vlrLnwymL8iha5uGdG7ZgFppMX5xcGfGf1H8HjeLu8e/H9Kbf01cAECttBhjrjmCFz5eyGufLanuolePBO808prRTkhSiaa3J4D5wFtAv/DYNoJa0nmSLjCzp8uqHSWi/ALjmscn8cZtx5OWFmP0e3OYvXQNt5x9INPnr2bc1MUc3jsYQWdmfPJtNlc/9knUxU4q+QXGtU9O4fWbjguHHc9jduZabv5lX6YvWM34aUs5rFcb/nJOP8yMSbNXcM0Tk4FgYMOI8/pjFjShPPDGTGYtWRPxK0o8+QXGdc98zqs3HENaTPzrw/l8l7WOP53+E75cmMOb0zM5bL/W3HZWX8zg0+9WcO3TwRenUwd25pAerWnaoDbnHN4VgMse+5QZi2vOfU70od1Kwi6OalMYiCR1NLOl4bEzgJ5m9hdJE4ExZjZKUm1gNMGKhz83s3I7UeoOHeU3vorFatWKuggpIb2Wf6etauue//UeR5IluVsr/JnTqVntao9c3kwXR1JDSfdLOgeCwQiSugJfSDq4MBnQNvz5r8CtYfqngblAFnBYNRfdOefKFVPFtyj4V5riugJXAl9K2gRMNrMFkm4DzpY0AHgUuEtSGzN7V9KlQF/gXeBV4J/AzIjK75xzZUjsZjoPRnHM7CtJY4AfgP4E67qfbWb/lLQv8HyY9AOgB5BtZm9Iegs4BHgHmA7klNLX5JxzkUn0JyC9mS6OpIZAJvA/M7sZaCjpdkndzGwOwbDsAcD5BOu+F2pBMJjhejO71My2eCByziWSBB9Ml3rBSFJjSceEPxe772a2gSCwFM4h0hA4ExgZDmL4ArgGON7M3o+7brmZ3WNmH1XLi3DOuV2U6A+9plwwAg4A/iOpeWHtRVJa+CArBEO075O0GJgK9AS+BR6V1MTMVprZ2+F1CV7xdc65gE8HlCAKg42ZfQxMBH4XHk8zs3wzK5DUHMglGIww1MyuC6f9uRq4yMzWxufpTXHOuWThzXQJIgw2+0saCWwCrpfUN5xVYR9JnxAEqFygO8EUP0hKDy63lXG1J+ecSyreTBeRwlm1S/grsBj4DfAywRDtGDAEeM7MbjWzLOAzoBeAmeUV1oCSZUYF55wrSbvwXxRq7NDuuFm12wGrCV7rJuA/ZradYOqeJcDJZvaPEpef501wzrkaJcF7uGtyzehYSZ8BtxFM0/MD0BroEpdsLMHAhGILmoQzLyT4P51zzlVcovcZJX3NSFItoBuwzMzWhc1zTYCLgN8Ci4Acgul6niHoK8oLj68hGCnXhrCPqJDXjJxzNUkswb9fJ30wIgg8hwO1JLUnGI69CfgOGAycBvzJzN6B4Dkj4FcEMybcYWa3RFJq55yrRgkei5IzGIW1nwILrJLUA7gYmGhmN0ram2DqnueBw8xsi6SmQG8ze1hSfSAWPuRaNLw7qtfjnHOpLqmCkaQDgdVmtjjcL1za4XWgGTA5DCwLJY0DGgAtwhrT34FPJU0BtoRDvQufMfJA5Jyr0RK9ZpRsAxguAR6UVEvSv4C3JA0H5gAPEcyuMCRMexHBKLoRwD+Ax83sT2a2vXCItgch51yq8KHdeyh8DsjCAQW/A6YBDwBTgD8DFwK3mdlvJf0c2F/SNwSDEm4naI5bG5+fPy/knEs1XjPaTQqZWUE41LqnmW0leHD1ZGCSmc0HngQ6SToa+BdQG/iYYJDCtsJAVPgQrAci51wq8hkYdlM4OMEk/VTSA8BoSaeY2UvAQoLRcBAMyX4RuJlg+YebgSPM7Ldm9kNcft4k55xLWYneTJdQwUjSFZKuj9s/D3iKYGLTNQSrrTYCbgSuktQsnE3hXYIlv/cLa1Jzw4pVQr0+55yLiteMKiAuaDxlZv8IF7mDYH64MWb2KkHf0FbgNDP7lOB5onugaD2h35pZ0XLfYcXKm+Scc47Kn4FB0mBJcyTNl3RDKefPl7RK0lfhdnF5+UUajCTF4gcUmNlmSX8gmKgUgtpQQbiOUCbBg6xXS+oA/BHYHs7AUJhfgnfROedcRCoxGoV98I8AxxOs+Xa2pJ6lJH3JzPqE2xPl5RlJMJLUQlLjsEmtQFJXSYcCmNldBMt9Hw58DuwFDAwvnQxsAI42sywzGxY20xFe61P4OOdcKWJShbcKGADMN7OFZraNoN9+yE6uKVe1D+0Oo+f1wChJnwN3AUcDX0g6EfgLQa3nn2bWS9J+wDWShhEsA/4pwRIQz4b5JeXsCVteG5Z0tThJw8xsVNTlqMn8HlePVLzPddIrPjIh/LwdFndoVIn71R5YGrefCRxUSlanhRWLucA14SQFpaq2mlE4ZQ/AAqATUIegemdm1pfgwdXTgQPN7Hlgi6TfmdkjBAMWngeOA74GPosbqp10gSiJDdt5EreH/B5XD7/P5TCzUWbWL24rGbhLC2wlW6beALqY2QHAewSrJ5SpyoNR2C/0APCipAHhs0LvESztkAH0kPQOcBjwCzObFF56OXCfpDpmNh2YRTBq7jfA0x6EnHMuMplAx7j9DsCy+ARmlhN+3gM8DhxYXoZVFowkNSzsFwKmE8yIcHU4NPtZgql6jieIsOPM7BQzmyFpkKRBZvYZMCDuWaG1wP1mdpyZzauqcjvnnNupqUB3SXuF68GdRbA+XBFJbeN2TwFml5dhlfQZSWoB3AnMI5ig9D/A/gQB6VxgHcGL2UCwntABko4k6Ds6E7gBwMymhfnJzJYD/6uK8roKS6k29oj4Pa4efp/3gJnlSboCeBtII3gsZ1Y4V+g0MxtL8CzoKUAekAucX16eqqoBaJKOAa4DXjWzx8OCLw0L/jOgK8GCd28BZwN9CWpqfzSznCoplHPOuYRUZaPpzOz98Lmfp8JRcx2BxWb2elitOwfoZGYvAv8M+4Z+gOQdIeecc273VFnNqOgXSLcRjJxrArQ3s1PC448QzC93BrCg8Bkhn1XbOedST3UM7b4T+J5gobs+ko4Kj99kZn3NbH78w6oeiCqHpPxwCo6Zkl6WVK8S8uwn6cFyzreT9Mqe/p6aoMT9f0NSk0rO/3xJD4c//1nSdZWZfyKJu5eFWxdJzSVNkLSx8D6UcW09SWMkzQj/LT6R1KA6y+8qpsqDUdj09hwwgWD4X73weLGlHVyl2xJOwdEb2Ab8Nv7k7kwka2bTzOyqcs4vM7PTd6+4NU78/c8leFTB7Z7Ce1m4LQJ+AG4h6Jcuz++AFWa2f/hvcRGwfSfXlEtSwq8Dl4yq5aFXM9tiZk8B+5vZuBLnvG+o6n0MdAu/Uc6W9CjBcPuOko6TNFnS9LAG1QBAUn9Jn0r6WtLn4VD9IyX9Lzx/RNw31S/D810kzQzP15H0dPiN9MvCGnH4jf5VSW9JmifprojuSXWaTPDEOgCSrpc0VdI3kv4Sd/y88NjXkp4Lj50s6bPwHr4nqXUE5U84ZrbJzD4hCErlaQtkxV03p/DZlzLud2dJ74fH35fUKTz+jKR7JU0A7pRUX9JT4b/jl5L2aCocV83TAYVD/xT+7PPIVYPwW9zxBKMWAfYFLjCzy8Ih+DcDPzOzTZL+CPxe0t+Bl4BfmtlUBc+GbSmR9XXA5WY2KQxgJT8ULgcws/0VzL7xjqR9wnN9CEZPbgXmSHqovGlCkllY8z+GYBFIJB0HdCeY20vAWAXTpeQANwGDzGy1pGZhFp8AA8O1vS4G/gBcW80vI2p1JX0V/vy9mZ26C9c+RfDeOx14HxhtZvMk9aL0+/0w8KyZjZZ0IfAgMDQ8tw/B30q+pDuAD8zswrAJ9nNJ75nZpj18rSmr2qubHoSqTfwf8McEH4btCEY0TgmPDySYkmlS+B0hg+Bb/L7AcjObCmBm6wFUfALFScC9ksYQDN/PLHH+UOCh8PrvJC0m+GMGeN/M1oV5fgt0pvg8VzVB4f3vAnxBMHsIBFNaHQd8Ge43IAhOPwFeMbPVAGaWG57vALyk4AHCDIL+11Szxcz67M6FZvaVpL0J7vnPgKmSDiZ4prG0+30w8Ivw5+cI5s4s9HJcS85xwClxfXV1CKY5K/fBTlc2b/usuXb4Aw6DRfw3NwHvmtnZJdIdwI7zTBVjZn+XNA44AZgi6WcUrx2VNynj1rif86mZ78MtZtZHUmOCh7UvJ/iWLWCEmT0Wn1jSVZR+zx8C7jWzsQoeDP9zlZY6yUk6lWCqMYCLw37OjcCrwKuSCgjes9vZyXs8FJ+m5N/OaWY2pxKK7UiQxfVcZKYAgyR1g6KRR/sQrBvVTlL/8HjDkp22krqa2QwzuxOYBvQokfdHBLNtEObZiWAy3JQS1gCvAq5TsPbW28CFcX1z7SW1ImhCOlNS8/B4YbNRY37s8/hNtRY+CZnZf+MGOkxTML1YUwAFzzf2BBZT9v3+lGBqGwjev5+U8aveBq4s7HaQ1LdqXlHqqInfSF0FmdkqSecDL0iqHR6+2YJl238JPCSpLkF/0c9KXH51OCghn2BKpzcJOosLPQqMlDSDYDqQ881sq1Jw/UMz+1LS18BZZvacgmVRJof3YiPwq7A/9W/AREn5BM145xPUhF6WlEXw5WGvKF5DIpK0CGgEZEgaChxnZt+WSNaV4KF6EXz5Hgf8J+yDK+1+X0XwoP71wCrggjJ+/e3A/cA3Yd6LgJMq8eWlnCp/6NU555zbGW+mc845FzkPRs455yLnwcg551zkPBg555yLnAcj55xzkfNg5JxzLnIejJxzzkXu/wEDArytIWm2fgAAAABJRU5ErkJggg==\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':[1.00,0.93,0.96,0.94],'Recall':[0.50,1.00,0.75,0.93],\n",
    "         'F1-Score':[0.67,0.96,0.81,0.92]}\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,cmap='Blues')\n",
    "p1.set_yticklabels(p1.get_yticklabels(),rotation = 30)\n",
    "p1.set( ylabel='Metrics')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "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 Confusion Matrix.\n",
    "import seaborn as sns\n",
    "ax = sns.heatmap(cm, annot=True, fmt=\"d\",cmap=\"BuPu\")\n",
    "ax.set(xlabel='Predicted Faults', ylabel='Actual Faults')\n",
    "ax.figure.savefig(\"Confusion_Matrix_GPS_Dataset.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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": [
    "# Plotting the Predicted Output.\n",
    "import numpy as np\n",
    "x = np.linspace(0,315,315)\n",
    "plt.figure(figsize = (15,8))\n",
    "sns.scatterplot(x, y_pred, color='m')\n",
    "sns.despine()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "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": 61,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[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, 0, 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, 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, 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, 1, 1, 0],\n",
       "       [1, 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, 0, 0],\n",
       "       [0, 0, 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, 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, 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, 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, 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, 1, 1],\n",
       "       [1, 1, 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, 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, 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, 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, 0, 1],\n",
       "       [0, 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, 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, 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, 1, 0],\n",
       "       [1, 0, 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, 0, 1],\n",
       "       [0, 1, 1, 1, 1]], dtype=int64)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Starting  to implement the Sliding Window over the Output.\n",
    "z1 = vectorized_stride_v2(y_pred, 1, 314, 5,3)\n",
    "z1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "104"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "az = z1.shape\n",
    "bz = az[0] -1\n",
    "bz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(105,)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m = np.zeros(bz +1)\n",
    "m.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "[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",
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      "[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",
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      "[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",
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      "[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",
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      "[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",
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      "[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",
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      "[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",
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      "[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",
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      "[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",
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      "[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",
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      "[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",
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      "[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",
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      "[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",
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      "[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",
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      "[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",
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      "[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",
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      "[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",
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      "[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",
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      "[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",
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      "[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",
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      "[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",
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      "[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",
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      "[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",
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      "[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",
      " 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",
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      "[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",
      " 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",
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      "[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",
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      "[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",
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      "[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",
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      "[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",
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      "[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",
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      "[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",
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      "[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",
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      " 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",
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      "[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",
      " 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",
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      "[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",
      " 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",
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      "[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",
      " 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",
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      "[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",
      " 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",
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      "[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",
      " 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",
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      "[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",
      " 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",
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      "[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",
      " 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",
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      "[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",
      " 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",
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      "[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",
      " 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",
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      "[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",
      " 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",
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      "[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",
      " 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.]\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",
      " 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",
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      "[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",
      " 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.]\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",
      " 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.]\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",
      " 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.]\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",
      " 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.]\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",
      " 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.]\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",
      " 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.]\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",
      " 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.]\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",
      " 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",
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      "[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",
      " 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",
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      "[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",
      " 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",
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      "[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",
      " 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",
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      "[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",
      " 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",
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      "[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",
      " 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",
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      "[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",
      " 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",
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      "[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",
      " 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",
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      "[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",
      " 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",
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      "[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",
      " 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",
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      "[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",
      " 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",
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      "[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",
      " 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",
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      "[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",
      " 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",
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      "[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",
      " 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",
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      "[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",
      " 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.]\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",
      " 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.]\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",
      " 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.]\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",
      " 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.]\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",
      " 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.]\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",
      " 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",
      " 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. 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",
      " 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",
      " 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. 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",
      " 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",
      " 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. 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",
      " 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",
      " 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. 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",
      " 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",
      " 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. 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",
      " 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",
      " 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. 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",
      " 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",
      " 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. 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",
      " 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",
      " 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": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(315,)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m2 = np.zeros(315)\n",
    "m2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Changing the Sliding Parameters for optimizing the results.\n",
    "for i in range(0,104,1):\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": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "313.0"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(m2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "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(0,315,315)\n",
    "plt.figure(figsize = (15,8))\n",
    "sns.scatterplot(x, m2,color='m')\n",
    "sns.despine()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.8603174603174604\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy:\",metrics.accuracy_score(y_valid, m2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 1, 1, 1, 1],\n",
       "       [1, 1, 1, 1, 1],\n",
       "       [1, 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": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Changing the Sliding Parameters for optimizing the results.\n",
    "z2 = vectorized_stride_v2(y_pred, 1, 312, 5,1)\n",
    "z2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "312"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "az4 = z2.shape\n",
    "bz4 = az4[0] -1\n",
    "bz4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(313,)"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m3 = np.zeros(bz4 +1)\n",
    "m3.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[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. 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. 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. 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. 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. 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. 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.]\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. 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. 1. 1. 1. 1. 1.\n",
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      " 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",
      " 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",
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      " 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",
      " 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.]\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",
      " 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",
      " 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",
      " 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",
      " 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",
      " 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",
      " 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.]\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",
      " 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",
      " 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",
      " 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",
      " 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",
      " 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",
      " 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.]\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",
      " 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",
      " 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",
      " 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",
      " 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",
      " 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",
      " 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.]\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",
      " 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",
      " 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",
      " 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",
      " 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",
      " 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",
      " 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.]\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",
      " 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",
      " 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",
      " 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",
      " 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",
      " 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",
      " 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.]\n"
     ]
    }
   ],
   "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",
    "    print(m3)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "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., 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., 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., 0., 0., 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., 0.])"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(315,)"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m4 = np.zeros(315)\n",
    "m4.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Another Changing the Sliding Parameters for optimizing the results.\n",
    "\n",
    "for i in range(0,104,1):\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": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "107.0"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(m4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "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(0,315,315)\n",
    "plt.figure(figsize = (15,8))\n",
    "sns.scatterplot(x, m4,color='m')\n",
    "sns.despine()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.37142857142857144\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy:\",metrics.accuracy_score(y_valid, m4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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
}