Elisa_Classify.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Backend ok\n"
     ]
    }
   ],
   "source": [
    "!set \"KERAS_BACKEND=tensorflow\"\n",
    "\n",
    "%load_ext autoreload\n",
    "\n",
    "%autoreload 2\n",
    "\n",
    "\n",
    "import os\n",
    "os.environ[\"KERAS_BACKEND\"] = \"tensorflow\"\n",
    "import keras; import keras.backend\n",
    "if keras.backend.backend() != 'tensorflow':\n",
    "    raise BaseException(\"This script uses other backend\")\n",
    "else:\n",
    "    keras.backend.set_image_dim_ordering('tf')\n",
    "    print(\"Backend ok\")\n",
    "\n",
    "import cv2\n",
    "import numpy as np\n",
    "from sklearn.metrics import classification_report\n",
    "from keras.utils import to_categorical\n",
    "from keras.preprocessing.image import ImageDataGenerator, img_to_array\n",
    "import os\n",
    "from sklearn.preprocessing import normalize\n",
    "\n",
    "\n",
    "import models_elisa\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def clean_data_labels_pre(orig):\n",
    "    index1 = orig[\"totIndPositiveG\"]==1\n",
    "    index2 = orig[\"totIndNegativeG\"]==1\n",
    "    index3 = orig[\"totIndNeutralG\"]==1\n",
    "    \n",
    "    idx = index1 & index2 & index3\n",
    "    orig.loc[idx,\"totIndPositiveG\"] = 0\n",
    "\n",
    "    orig.loc[idx,\"totIndNegativeG\"] = 0\n",
    "\n",
    "    orig.loc[idx,\"totIndNeutralG\"] = 1\n",
    "\n",
    "    index1 = orig[\"totIndPositiveG\"]==1\n",
    "    index2 = orig[\"totIndNegativeG\"]==0\n",
    "    index3 = orig[\"totIndNeutralG\"]==1\n",
    "    \n",
    "    idx = index1 & index2 & index3\n",
    "    orig.loc[idx,\"totIndPositiveG\"] = 1\n",
    "\n",
    "    orig.loc[idx,\"totIndNegativeG\"] = 0\n",
    "\n",
    "    orig.loc[idx,\"totIndNeutralG\"] = 0\n",
    "\n",
    "    \n",
    "    index1 = orig[\"totIndPositiveG\"]==0\n",
    "    index2 = orig[\"totIndNegativeG\"]==1\n",
    "    index3 = orig[\"totIndNeutralG\"]==1\n",
    "    \n",
    "    idx = index1 & index2 & index3\n",
    "    orig.loc[idx,\"totIndPositiveG\"] = 0\n",
    "\n",
    "    orig.loc[idx,\"totIndNegativeG\"] = 1\n",
    "\n",
    "    orig.loc[idx,\"totIndNeutralG\"] = 0\n",
    "    \n",
    "\n",
    "    index1 = orig[\"totIndPositiveG\"]==1\n",
    "    index2 = orig[\"totIndNegativeG\"]==1\n",
    "    index3 = orig[\"totIndNeutralG\"]==0\n",
    "\n",
    "    idx = index1 & index2 & index3\n",
    "    orig.loc[idx,\"totIndPositiveG\"] = 0\n",
    "    orig.loc[idx,\"totIndNegativeG\"] = 0\n",
    "    orig.loc[idx,\"totIndNeutralG\"] = 1\n",
    "    \n",
    "    \n",
    "    index1 = orig[\"totIndPositiveG\"]==0\n",
    "    index2 = orig[\"totIndNegativeG\"]==0\n",
    "    index3 = orig[\"totIndNeutralG\"]==0\n",
    "\n",
    "    idx = index1 & index2 & index3\n",
    "    orig.loc[idx,\"totIndPositiveG\"] = 0\n",
    "    orig.loc[idx,\"totIndNegativeG\"] = 0\n",
    "    orig.loc[idx,\"totIndNeutralG\"] = 1\n",
    "    \n",
    "    return orig\n",
    "\n",
    "def clean_data_labels_post(orig):\n",
    "    index1 = orig[0]==1\n",
    "    index2 = orig[1]==1\n",
    "    index3 = orig[2]==1\n",
    "\n",
    "    idx = index1 & index2 & index3\n",
    "    orig.loc[idx,0] = 0\n",
    "\n",
    "    orig.loc[idx,1] = 0\n",
    "\n",
    "    orig.loc[idx,2] = 1\n",
    "\n",
    "\n",
    "    index1 = orig[0]==1\n",
    "    index2 = orig[1]==0\n",
    "    index3 = orig[2]==1\n",
    "\n",
    "    idx = index1 & index2 & index3\n",
    "    orig.loc[idx,0] = 1\n",
    "\n",
    "    orig.loc[idx,1] = 0\n",
    "\n",
    "    orig.loc[idx,2] = 0\n",
    "\n",
    "    index1 = orig[0]==0\n",
    "    index2 = orig[1]==1\n",
    "    index3 = orig[2]==1\n",
    "\n",
    "    idx = index1 & index2 & index3\n",
    "    orig.loc[idx,0] = 0\n",
    "\n",
    "    orig.loc[idx,1] = 1\n",
    "\n",
    "    orig.loc[idx,2] = 0\n",
    "\n",
    "    \n",
    "    index1 = orig[0]==1\n",
    "    index2 = orig[1]==1\n",
    "    index3 = orig[2]==0\n",
    "\n",
    "    idx = index1 & index2 & index3\n",
    "    orig.loc[idx,0] = 0\n",
    "    orig.loc[idx,1] = 0\n",
    "    orig.loc[idx,2] = 1\n",
    "    \n",
    "    \n",
    "    index1 = orig[0]==0\n",
    "    index2 = orig[1]==0\n",
    "    index3 = orig[2]==0\n",
    "\n",
    "    idx = index1 & index2 & index3\n",
    "    orig.loc[idx,0] = 0\n",
    "    orig.loc[idx,1] = 0\n",
    "    orig.loc[idx,2] = 1\n",
    "    \n",
    "    return orig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "      <th>optimistic</th>\n",
       "      <th>annoyed</th>\n",
       "      <th>apathetic</th>\n",
       "      <th>frustrated</th>\n",
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      "text/plain": [
       "   Unnamed: 0                         filename            timestamp  amused  \\\n",
       "0           0  20151021q3iberdrola_f000149.mp3  2018-07-25 03:00:45       0   \n",
       "1           1  20151021q3iberdrola_f000150.mp3  2018-07-25 03:01:14       0   \n",
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       "4           4  20151021q3iberdrola_f000153.mp3  2018-07-25 03:02:16       0   \n",
       "\n",
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       "0     1        0        1           1        1          1           0   \n",
       "1     1        0        0           0        1          0           1   \n",
       "2     0        0        0           0        0          0           0   \n",
       "3     0        0        0           0        0          1           0   \n",
       "4     1        1        0           1        0          0           0   \n",
       "\n",
       "   pessimistic  stressed  neutral  \n",
       "0            0         0        0  \n",
       "1            0         1        0  \n",
       "2            0         1        0  \n",
       "3            0         1        1  \n",
       "4            0         1        0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "feature_start = 3\n",
    "feature_end= 14\n",
    "\n",
    "#was union_voting.csv before.\n",
    "#labelling = pd.read_csv(\"totGroupByNewLabels.csv\")\n",
    "labelling = pd.read_csv(\"union_voting_elisa.csv\")\n",
    "\n",
    "\n",
    "#print(labelling.head())\n",
    "\n",
    "data = []\n",
    "labels = []\n",
    "\n",
    "files = labelling[\"filename\"]\n",
    "\n",
    "labels_ind = labelling.iloc[:,feature_start:feature_end]\n",
    "\n",
    "lab=labels_ind #clean_data_labels_pre(labels_ind)\n",
    "\n",
    "#lab.head()\n",
    "\n",
    "labelling.iloc[:,feature_start:feature_end] = lab\n",
    "lab.head()\n",
    "\n",
    "labelling.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>20151021q3iberdrola_f000158.mp3</td>\n",
       "      <td>2018-07-25 03:03:50</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>20151021q3iberdrola_f000159.mp3</td>\n",
       "      <td>2018-07-25 03:04:11</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>20151021q3iberdrola_f000160.mp3</td>\n",
       "      <td>2018-07-25 03:04:31</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>12</th>\n",
       "      <td>12</td>\n",
       "      <td>20151021q3iberdrola_f000161.mp3</td>\n",
       "      <td>2018-07-25 03:04:42</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>20151021q3iberdrola_f000162.mp3</td>\n",
       "      <td>2018-07-25 03:05:02</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>20151021q3iberdrola_f000163.mp3</td>\n",
       "      <td>2018-07-25 03:05:12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>15</th>\n",
       "      <td>15</td>\n",
       "      <td>20151021q3iberdrola_f000164.mp3</td>\n",
       "      <td>2018-07-25 03:05:27</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>16</th>\n",
       "      <td>16</td>\n",
       "      <td>20151021q3iberdrola_f000165.mp3</td>\n",
       "      <td>2018-07-25 03:08:44</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>20151021q3iberdrola_f000166.mp3</td>\n",
       "      <td>2018-07-25 03:08:56</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>18</th>\n",
       "      <td>18</td>\n",
       "      <td>20151021q3iberdrola_f000167.mp3</td>\n",
       "      <td>2018-07-25 03:10:02</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>20151021q3iberdrola_f000168.mp3</td>\n",
       "      <td>2018-08-02 07:58:57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Unnamed: 0                         filename            timestamp  amused  \\\n",
       "0            0  20151021q3iberdrola_f000149.mp3  2018-07-25 03:00:45       0   \n",
       "1            1  20151021q3iberdrola_f000150.mp3  2018-07-25 03:01:14       0   \n",
       "2            2  20151021q3iberdrola_f000151.mp3  2018-07-25 03:01:37       0   \n",
       "3            3  20151021q3iberdrola_f000152.mp3  2018-07-25 03:01:51       0   \n",
       "4            4  20151021q3iberdrola_f000153.mp3  2018-07-25 03:02:16       0   \n",
       "5            5  20151021q3iberdrola_f000154.mp3  2018-07-25 03:02:37       0   \n",
       "6            6  20151021q3iberdrola_f000155.mp3  2018-07-25 03:02:48       0   \n",
       "7            7  20151021q3iberdrola_f000156.mp3  2018-07-25 03:03:04       0   \n",
       "8            8  20151021q3iberdrola_f000157.mp3  2018-07-25 03:03:38       0   \n",
       "9            9  20151021q3iberdrola_f000158.mp3  2018-07-25 03:03:50       1   \n",
       "10          10  20151021q3iberdrola_f000159.mp3  2018-07-25 03:04:11       0   \n",
       "11          11  20151021q3iberdrola_f000160.mp3  2018-07-25 03:04:31       0   \n",
       "12          12  20151021q3iberdrola_f000161.mp3  2018-07-25 03:04:42       0   \n",
       "13          13  20151021q3iberdrola_f000162.mp3  2018-07-25 03:05:02       0   \n",
       "14          14  20151021q3iberdrola_f000163.mp3  2018-07-25 03:05:12       0   \n",
       "15          15  20151021q3iberdrola_f000164.mp3  2018-07-25 03:05:27       0   \n",
       "16          16  20151021q3iberdrola_f000165.mp3  2018-07-25 03:08:44       0   \n",
       "17          17  20151021q3iberdrola_f000166.mp3  2018-07-25 03:08:56       0   \n",
       "18          18  20151021q3iberdrola_f000167.mp3  2018-07-25 03:10:02       1   \n",
       "19          19  20151021q3iberdrola_f000168.mp3  2018-08-02 07:58:57       0   \n",
       "\n",
       "    calm  content  excited  optimistic  annoyed  apathetic  frustrated  \\\n",
       "0      1        0        1           1        1          1           0   \n",
       "1      1        0        0           0        1          0           1   \n",
       "2      0        0        0           0        0          0           0   \n",
       "3      0        0        0           0        0          1           0   \n",
       "4      1        1        0           1        0          0           0   \n",
       "5      0        1        1           1        0          0           0   \n",
       "6      1        1        1           0        0          1           0   \n",
       "7      0        0        1           1        0          0           0   \n",
       "8      1        1        1           0        0          0           0   \n",
       "9      0        1        1           0        0          0           0   \n",
       "10     1        1        0           1        0          0           0   \n",
       "11     1        1        0           0        0          0           0   \n",
       "12     0        1        0           0        1          1           0   \n",
       "13     1        1        0           0        0          0           0   \n",
       "14     0        0        0           0        0          0           0   \n",
       "15     0        0        0           0        1          0           0   \n",
       "16     1        0        0           0        0          1           0   \n",
       "17     1        1        0           0        0          0           0   \n",
       "18     1        1        0           0        0          0           0   \n",
       "19     0        0        1           1        0          0           0   \n",
       "\n",
       "    pessimistic  stressed  neutral  \n",
       "0             0         0        0  \n",
       "1             0         1        0  \n",
       "2             0         1        0  \n",
       "3             0         1        1  \n",
       "4             0         1        0  \n",
       "5             0         1        1  \n",
       "6             0         1        0  \n",
       "7             1         1        0  \n",
       "8             0         1        1  \n",
       "9             0         1        1  \n",
       "10            0         1        0  \n",
       "11            0         0        1  \n",
       "12            0         0        1  \n",
       "13            0         1        0  \n",
       "14            0         0        1  \n",
       "15            0         0        1  \n",
       "16            1         0        1  \n",
       "17            0         0        1  \n",
       "18            0         1        1  \n",
       "19            0         1        1  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#normalizing labels instead\n",
    "#from sklearn.preprocessing import RobustScaler\n",
    "\n",
    "#lab = MinMaxScaler().fit_transform(lab)\n",
    "\n",
    "labelling.iloc[:,feature_start:feature_end] = lab\n",
    "\n",
    "labelling.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for i in range(0,len(files)):\n",
    "    try:\n",
    "        file_name=\"./Spectrograms_10K/\"+files.iloc[i]+\".png\"\n",
    "        #print(file_name)\n",
    "        image = cv2.imread(file_name)\n",
    "        image= cv2.resize(image,(300,150))\n",
    "        image = image/255 #between 0 and 1\n",
    "        data.append(image)\n",
    "        #multi-class\n",
    "        #labels.append(labelling.iloc[i,-1])\n",
    "        #multi-label\n",
    "        labels.append(labelling.iloc[i,feature_start:feature_end].get_values())\n",
    "    except:\n",
    "        del files.iloc[i]\n",
    "        continue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       " array([0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1], dtype=object),\n",
       " array([0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1], dtype=object),\n",
       " array([0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1], dtype=object),\n",
       " array([0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1], dtype=object),\n",
       " array([0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1], dtype=object),\n",
       " array([0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0], dtype=object),\n",
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       " array([0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1], dtype=object),\n",
       " array([0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1], dtype=object),\n",
       " array([0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1], dtype=object),\n",
       " array([0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1], dtype=object),\n",
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       " array([0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1], dtype=object),\n",
       " array([0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1], dtype=object),\n",
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       " array([0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0], dtype=object),\n",
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       " array([0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1], dtype=object),\n",
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       " array([0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1], dtype=object),\n",
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       " array([0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1], dtype=object),\n",
       " array([0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1], dtype=object),\n",
       " array([0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1], dtype=object),\n",
       " array([0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1], dtype=object),\n",
       " array([0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1], dtype=object),\n",
       " array([0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1], dtype=object),\n",
       " array([0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1], dtype=object),\n",
       " array([0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1], dtype=object),\n",
       " array([0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1], dtype=object),\n",
       " array([0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1], dtype=object),\n",
       " array([0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1], dtype=object),\n",
       " array([0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1], dtype=object),\n",
       " array([0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1], dtype=object),\n",
       " array([0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1], dtype=object),\n",
       " array([0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1], dtype=object),\n",
       " array([1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1], dtype=object),\n",
       " array([0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1], dtype=object),\n",
       " array([0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1], dtype=object),\n",
       " array([0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1], dtype=object),\n",
       " array([0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1], dtype=object),\n",
       " array([0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0], dtype=object),\n",
       " array([0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0], dtype=object),\n",
       " array([0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0], dtype=object),\n",
       " ...]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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W76wGNxhxMOI4+W0k8sQUMks73j2Vm+h/H8VzWQqbvQ2ormu+bzv7gz/M3k6j\noI3tUlpb90sFNhO+ZLx1bWLJGPFU/+vZi1jC8KOBquVWRxxPGkgp99XJMRu/x85MasdxMRsdbXVj\nnasvvvtSEyfJcUf0/wz4HSnlZeAZ4Drw88AfSCkvAH/QeP6nEum6aJrStUgE8PSnP8jTn/4g85dn\ngJahdywbpzp45OhWK5S2drq0OaSE4vrgcLImY7OjCAHm9hau4/ouE1VVcIpFnGIR7RTEpHa3ul0z\njuPiOG5AlGwQnTpBVt3m/vWlwGLu+HR3nVcjEQtEdXSi1Ct0BndUyzVfL6gzTFJVVd/N0ItKoUK1\nUNp3DTeSTuPaDpuL6xQ2dil0FDqPxiJd5QdFvYKoV3ClJ553UvV99cZ3PkjbR4lESGa6cwue/jMf\nCSz4u8PjAcO9u7yFZTpomooajaJGo1QqLZfk1urOoSNb6lWTaNzoiripVUwy2WTX9k6at0NhN2jo\nzbrtuxZr+QKV3WKgnu2jxnFqxqaBj9MoLCKlNKWUeeAzwJcbu30Z+OxxG/mo4rqSRCrGTg9fdb1Y\npF4stnzAjRsunk3zzjdfG3xiRWXxxiJ7HTcngG7oBwoD3FzeQlEUrGKJ+69c810OjuPimCaOaXLj\nO9f3Pc9hGSTP8ODGwabIihCB5BUpJfVqPRBN0+sSKJo6uCydUHC0oDF3HSfgW+5oSFdhmXYmLs4T\nTcTI9Cm+0cQqFll7uIFh6ESTUaIdiWSxZIz5Z4LRI06ljFMpI2tVyqXaiZW4sxuzpb4CX4333lzu\nHlBodhXd0PwIIaEofuFvgNn3P4HrughFoOkamq6x+faN1nltl9mrh8vsdl0vsqlSqgXWsVxXsrdT\nwuzjpuv6TB0RNVJKf9vu2ja2ZXetCTxKHGdEfxbYBP6VEOJ7Qoh/KYRIAONSylWAxv+PthrQMVAU\nhWQ22dPw3vyTt7n5J293bd96sMbIZH9v1/hMjtW3b1Ot1Hsas+JOgUxu/0zO5ih68fp9Zi/P+/5I\ngNe+8jVe+8rXTqTk3UnSjIEfvTAfqKk7MjXMWMcIfqeHa2GQZs30mTHqlRritf8UWCeolKr+KFPv\nSMV3TCtQ+q6TnaU1kpkEu/to5VjVGrblIFSV228+4PabQXGwWrlGdS/ozqvcu+v97eyd2GgevFKS\nAM4AIbGtO4uMTXfPvl79/VfJ5tJkc2n0iEqk0vrciiJQ4nHGJrPeulOtiqxV2XyrZeiHJ7LUCqUT\nkxpIpKKkR4/ufmy6+ZbvbeyrAvte5ziGXgOeA/65lPJZoMwh3DRCiC8KIV4WQry8udm/AvujjBbR\niKRTyLa5e7ahwZLMxElm4l0Rf5wbAAAgAElEQVRaHNVyjfoAaWAtolHdKxIxtJ4LV4u3VtjbHuzj\n997Hczkouka5aqG0xRGbpo1p2kydPd16pIfXMPGSx4zp4ELg1spOl5Rtr06qVhmkCGphxAz0kTG0\nRny60khUaiYrdY76+kk4Nw1ErVRj+d462X3S8FdvLeFKyeqDDWbOjjNzNpgfYcQNEmPBjuzaN9/g\n2jffoLJ5chK+8WSUi5/+ODC4Ktb2ep6tHrUKPvrXPsfGyg4bKzs4totitkbzQlGwS2XWl3dASmyh\nYQuNcx951t9H0xQyCzPUqiczci4Vqqze6y/6tp+YXHtmdyHfPXt+lDiOoV8ClqSULzWe/yqe4V8X\nQkwCNP7vGfYhpfySlPJ5KeXzo6PHS7V/NziKjqOmqWQmxwI+2uYiob9o1eFK8JJsBvh99yoNWYXe\n/kLXlSTTB9dmSWUS5Jc3Ap1Rw56ys3r8KI6TpBmB5Gxvdo1iO2dN7YukTcM7yKVV2CliVqpsvHPP\nvxISb7GvGc3T+Z79IoSaLqTCTgFUlczk4Pt7Z2O3IdUgqVZMqpWgu6FSqrJ07V5gWywVI5aKMXTx\n5ITn9IiGmh3uEo3rZHcj37OwvaJrDI2kGRpJ47qSzdffbukGSYmaTnvJgq6LSA8h0kMkxlsTftty\nsCq1E8tAlVJiDajrsB/tciGWab+rwnEnzZENvZRyDVgUQlxqbPokcA34CvCFxrYvAL9xrBaGhISE\nhByL4yZM/XXgl4UQEeAu8F/gdR7/VgjxM8BD4HPHfI/3BEdZ63Jsm2JbYeN4MspeIx3erHujNqdj\nRF+rmmhtwk6tEZH3v2XZoCieGJOhY9tO1zQ7nU1Q3CsdaIGusFvEte1AWGMztbxTAO2wKKoYGC45\nKNRxEDsPV7pkDBRV4NitbRFDx4ja/n6uaQ9cc9A0Fdc0WX7tWlsIrKTWFlXTeZ2jcSOw2NiJbTno\njrNv6ryiKBhRHaGoVBvRLpqm+v5/s2axfi8YXtlUeYwvnBl47sNgmTY4Noqq7NvmWMLouj/s/A4T\nl73F1PxWAXtvF28uLL0Q3mwOIbwZj4g0XJZtCV+VUpXy2mbP2bOqKYcOIRXCuw/6zZDbz6dHtIH3\nux5RGR4folL6/uj/H5djhVdKKV9ruF+ellJ+Vkq5K6XcllJ+Ukp5ofH/e2v+/y5jl0rkxjO8/9PP\nkxlO+VEf5WLNT4rqpJRvRekM5ZIBGdtSoUo0ZhBNGNiW0yVZLIQgPZbdd/rd9E86lo2maQElyVrV\npFY1j63tMTk3eB1eGyCg1YuJ2RwTszlGL53tcsN0Pt/ZLFCvWUgpmT3vqVEOUsscnc4xNDHMkz/0\ngcD2UqFCqSEY1jS8zbcaZOTB6wgm5scZmu9OLmrHcVyy41lcx6ZSrlMp18mOpv01jGg8QioTjC8f\n/9hHGf/YR3HzJ+ejr5RqiIhBamhw3P9QLtVzAbi8skpEF0R0gWXajHzqR3xXpRAChEo667kVVemi\nShfdbRnhaqmGkYjy5Mef7jr3UfIEMsNJ5p9YONC+gxa0I4bG0HCqK7z2USLMjD1FRieHqZbKXP7k\nhzArdfLbLQPeTPLoRFEFqqaSaJRqK+xWKOwGDYpQFVRV5alPfYCzzwbD7oTw6onaA34YC5em/R9O\nYbccSDU/SfaTKW624YM//QN88Kd/gBd/6mMD92+GaAtV7UqI6gzbzAwnyAwnsS2HB+8ssx+u7SKj\ncYwLV3nuxz4MeIlYk3NjfofVjMkeNFNq1/svF6tU8wVKS4Pf37Yc1h5u8uSnPohQBEIR2JbtG5aI\nEelak3GzY7jZMdR4grFGlNbw2NELpDSRpQKjM7mBiWD57SJCdN+/lXzJF+sTAoTdGsSomoooF/yB\njmMkcIwE5fXNwD4gKa53jw1jXZLc3SQzQUkQoQiKPfI2msxdmGKhkcfSV+gOmJwfY3Mtz0aPkNJH\nhdDQnwJNNUbXdTGrJjvv3OXNb76JaHMAZYZTZIZbxRSa4Y2uI5k6M+67GRRVBBb9jKjO8vX7RJJx\n3vzaq9x69XbgvcdncmysDJ5EHdclc1B6haTNXmjpmM9c8h7nHy6Tf7jMje9c67tgKoSgVrep1W2k\n7fSUrW2nXKj6brLAwqyqYMRaRmP+4jSpTJxIPIqiqrhCxRQaF589j5SS1cVNVhc3G+3a3xeWyLRG\nw5qugaISSfSXym22TygKW/cWufCBK1z4wBXmnjrnx+/Xa3XSuVaY4NBwCmX1AcrqA5xIjEq5TjRu\nBCKnjkr+2nXym3tI3btGzRE4eDkaTdnnWCLaZRy3ljYp7RYp7RaREsqZKT8DW9VVZDyJaTretSzt\neX+RqD9Diiej7K5ue7KvHVQHJKZlhpNkhpNdM6xOSYROkqNZ7h8gd2Pxluc2M04hgfDdIjT0p0BT\nR1uLaMRScWzLcxm0Z1HubhUCWaL1tpCy7bUd3xhbphMI46vXLAo7JaJDGS69cJlLH3zCr0IF3vR3\nv4SpeI+sxnYGabYfFCEEIz1irSefaNVt3bjvFfRoViV65vM/1VMXHLzO8+KHn+Hih59BjbZ05vvR\nPqNpT1iKJaIsPDHf2lERFPcqjF1aoLa9g7DqVJdXiDXcF1JKpJRoukK2EZPdb21BCEFusiUdYJk2\nQxM57HrvpJ3meVLZFEJRyF08z8iHP8LIhz+Cpuv+KF5VVbKzrVDXiTPjOMUCTrFAceIyM5dmGRrL\n+un8x0ExDHbWd3nu8z+OFtECGaMTc2NMNGY37bPTJtGEwc7KJjsNuej09l1iDeE9x7JxExnOPDHv\n5SPsrMPOOtf+49f9GVJ2bIhI1GBreXC4dXsnryiedlEv/aJINDKwWMi1b3XnsfSimU29tX702sHf\nb0JDfwo00+Y3V3a489YDaodMttivaMjkwjh7iyssvn2P17763YDwWX6ntG/5us2Hg4XOorFIoHbn\nUTBies/PXV7f8tcPmp1ZamKc1MQ4TjzjK1l2IqXEmFvAmFtAui5W7eAa7KNnWrOIerWO3jbSa2bj\nCkVl4ukrsLeF6zis3HjoF9GQrsQyHX8838+HbUR11tuubTQWIZ7Lsvjm7Z77Zxq1aGPJGI5pcveP\nX2L9936X9d/7XbYfrvodcjydIDrWWu8wEjGavbuQLmpEZ+65Kz2lNg7Lg9duEjF06iNzXHj/pcBr\n1VKZaskz/L1q4Uaihl/+D8BdXWSrEaJrJBNIQI/HiKeiqCPjqCPjgXUgPRYlEjUYnRkcjtp+bxqx\nCNnRjN8Jt5MeyZAZP5j2Uy8OUrT9USGUKT5FHNthaDjJ4u3VrteaU+JOjY1+5MbSbG94M4CRM9NY\nmkHUULHLZV75ne8cql3VAYlD3uvHNxi1itkohRhEi0b8H3c6m6RW3eGlr3zDe7H5fw9UXaN0zRuB\nJRYWBvpUO3n9D1/1HwsEWqzb/+zaNlQqaOk09968R3Y8mJ2pagq5+WnWHmxi9RmhG7EIezsldEPD\nqtsMjaTR0ykmz8+w2UP6t9Jc5LUszLqNlJLdDW+/na2C7ykyKzXsQmsdRU/EKa97HUpm/SZ5VfDw\nlbeoH7E0YzvSlZSLNa79k3/GWMdietwvt5enUuq+h9YergfcYmq85bKSjoOQLlsPVsmMDFHMerVi\nJ85Ns3bHW8OoFCpoKn3VPpvUGvdnPGFw8cUrLL19r+d+QlVZfufoUtsnJRT3XiA09KeIXwpOiK5R\n9kENfHP0mxhKsb1R4OpHnsSYmODmv/sqthSegToA7W2wrNO/gRVFcP/tB13bb750DaEIpCvJN5QN\nm6PD889f5p3vXO8ZAWGbFt/7A89gn33ycJEm7ddeMzScerAjE0KwdXeR6FCa6MwlhBCUOyQHJudG\nqTfUEfsJmRXzZYTwRNYA5j74LNW1Na691FszqKmdkm8Y9+JepRUZ1Ha7VCt17n635WZ44w9e4QN/\n6ccBMJceUN0rsXkCEsUAqmEAJWKZJEsdipmRRgcZS0S9wUJHX+vYkuFGFnBht8zdP/62/5rrOGz/\nzn9gd7tItVhhas2b5ShGq2NwTJPlh/tnyUeTMSqFCpVynbe+8SYjk71H7Va1zuyTZ9lae2Xfcz7u\nPD5zk5CQkJCQnoSG/hSxTYdysXqgYhr9cFyJ40rWF72RTnkzD5ZJejRLPBk9sF+2fVSbSJx+Krfr\nyp5x+MlM0g+xa+p9p4cSpIcS3Hv9Fk4fpcjmjERK6SssHpR2gS7btAO68hFD9/z/MYNILIZW2ARk\n12K0lJLc5f71R6EZotc67sHLbxKbmuLc+3ofF0sYZEdSTCxMIoRg9tJsz0IzqqYE6u+6rgu2BbZF\ndXOrcezMPlfhgDSar0gHsxq8t9YerLH2YI16zewZgBSNRdjZ2GOnEa67fr+VXCQUgW5oCCSKqqK6\nDqrrINtmV1pEO5DMQK1NIsG2HLbXdtle2+3azzatA9chftwJDf0pUquaWJZzoGIafWn88u3GwqWq\nq5h7e+Q3d32lwcMySJnwJJm9PNe1rVcJuvGz04yfnWZyYaJvBGN7nHz8EFo+QOCctuUgtJbHsrng\nFh1KUcvvIaolhBC++8U/zrRxi/uXOWzvUDO5NNWNLZJDvdtr1m2GJ4aplipIKakWy34cfTuu4wau\nm+NIFE1D0TSMoQzp6YkT8ycbDYlhpUeIY6VYpVKs9o1kcaUkOzbki7id/9AzrTZbNslLV8jk0oxO\nZn2tm3ZLbFs2EWN/b3KnLnyzaHknZq1OvXh0MbKjZm6/Fwl99KeI60rOXJnh3vWjlyJr3mzN0bFr\nOzx49QaZXJqVu4dLx24m/PRaSDsNJp+4yOKNYK3UXmFw1769f8lCt61zapY9PApSBkfrzfjsvaV1\nbMsmi8R1JdF4BF1XKDbq0FqmTb10uI51e2mDnaVNNvuIwzm2Q2m36KmNikbVpca6TnuNVLNuB4yr\nEdWob3nJO6vvPGD+Q89x5uw5Fm/+34dqXy+a6yWrPXzl+y2A16pmwDjmzs+jf+s1LNPGthzU8SkE\ngrXFLRbWGklkbd+Fqqp+wMEgOkXf+qEoCvffvn+gfXtxmAX/9zrhiP4USWQSRJODk2X2w3Vl4Iar\nFCtsrOxQP4KxNmuW7y55N9i9dWfg69ML4wNf70fniPegpIe872LzVvcisapriIjBgz/xFnzNuuUb\nefAWxXuFFHbSnsFZr9T9Itj9cF1Jve65QvJrO9Sqli/T23T5GVE9kBcwlEsTGRsnMjZOpVDGKZep\nP+gdeXJYxs7N+lnZg+jlYnEdl/WlTdaXvE5CSQ35I+1INIJUdZJDCYxoBFURqEowmGCnsSh9GPng\nQeS39wbOdNJ9ZlqPI6GhP0X0iEa5MFgPZRBCwNmnz3L26VbVnbOf/gFmL0xSO0QceZN0LkU6lzqC\nDvzRuH+jO7StvbRbftsbvU0sjDOxMM7Cc/3rckbjBqmhOKmhuF/28KCMTXlRGbWqRSxhMPm+JwKv\nG7EIZt0mkkoxfuUc47MjRONGYORf3KtgjOwvp92ewRlNRMlOjw683qZpk2gkFdWqJi9+7lO8+LlP\nAS39HlfKwDmMRAy3XMItl5h97knMYgmE2Ddb+CAUN7bRGq6tfudTFMHUuamu7TNPnufKR57hykc8\nl832W61IIVXXcFWdUr7E3OUZiCchnqS00/oupZTMXZja1w110GS+3ESOdLa/bo96SK2lR5nQ0J8i\nxd0i7pGU7JuLj+AiAucwt7awLSdQEaoTVVN7/hj0iIYeGVwg+yTp5TdtF1uLNUa7xZ0CxZ0CW7cf\ndu3fJJmOkRzJkhzJMjRyOE0XI5MiEvXS99VIBCUR/PEP5dJkp0cwy2WiIzlq5ZpvbDK5lPeXTWLX\nBy98d45Eq+Uq0UyqZzJPk8JuyT9O0zXWbt5n7eZ9ACKNGUQynQho3cSSMZrKP9FsBqtWp7i9h7FP\ntvBB0GNRbNshnU0OzCpVe/jw53/wY+0lY6nvtbJnpZQI6ZCZHKVaqODubHl/VuseMWsWiqZ2+eBb\n8fuNNnb48ZuZ1V1ISWlA7dtB38vjxp+eLu37gGO7zD59geV3Fg9d01MIbx324Vt3A9vteh1VVQa6\nYIZG0uQ39/zKSK1z7l+A47QpF1vuENH4cY688CIAqoDy7/9xz8xeRQjmnvKKbBRX1tha7Y6y6MfE\nuRmKm7ukR7Oe/o0eNCRaRENLJEiOKKDqGPFow30ifWPgOg713cEp8LFElHKh6rc/Mz6CEouTHk6x\n2Ud/SLoSs2ZhRCPsbO4Fkp7UxiheKCIwXHAlKI3s3sLte9TKVYpbeweaqTXlj5v3Vyd6Ig5IcuND\nFHZ7r0lEohH2trqvhRweQ2a8dSNFEYxcPMuD19/BqltIVyKKe+QuX6CKhsh6GdDjCxPcv+a50vSI\njqKq6BEtUJ81k0tSr9b9dars+BDVNrng5gJurc13L4Q3Yh+UZd4roe9x5VgjeiHEfy+EeFsI8ZYQ\n4v8VQkSFEGeEEC8JIW4JIX6loVX/p4b2H1vE0FA0nfaQu+boramH0wtFVRCNEZOqKoFRcGLhDKqu\nkUj39/1Pnp3q6RsenhpheGqEkancvjLGp0UkGvFHX6nhhgTAj36O2I9+jvinfrKv/90ybcxiCbNY\nQh5SvKtWKKGoCpFEDNeyAiF94CX3RCen0bPDmJsbJLMpYo3ru726zfbqNtVSravj7CQ3mQu0PzM7\nhT424SdE9UIIgaapDI16Ane58Sy5Rlau3uiQ9raL/v0AkJmZwKrWsap1kJLyTqH/qLaDSFTz37cX\nlulgWw57A1QfRyazrLeVbmze83p+Hd73UXjfR73OMxrxF5el4yBXH0A0zvTHP4qcOoOcOsPEE60K\nWWMzOfIbu4GFaPBq5ravCUSMoB5SJKL5s592pJT911UExIdSvV97DDnyr10IMQ38HPC8lPJJQAU+\nD/xD4J9IKS8Au8DPnERDHxVsy0E3NHRDQ4torL91039Nj6hMznp+3vmr88xfne95DulKvxC1ZTlY\nbaGFd/7DV3FMq++0+umPP0328gWe/cwPdr02fPEcwxfPYZvWsbXm96Ofe8ismf5UXG38YPf+6d9n\n75/+fW7+3b/Z93NVKnWK69sU17fJrxyuxnD20nn0aIT88gbJ4TSbr70ReP3MUwuIeAK3VEBJZdAi\nOrZlkxlOghQgBbmpHMIZnIU8efVcoP2lpWXM1eWBMtCRqI4e1dlY8kb822vbbK9tg8APy0ykY0Ta\n3Bl3X3qD8tIS5aUlHl6/z/rSFtVS1XeFDaJZhKNXRMnk/BjZFz/IzIXpgQve009dZGy2tV7RDH3d\nfuVl4uUt4uUtzw0TjWE21pKEqlDb3sYem4OIgXjnNcQ7r2GX26KLaiaXPvECs+cmAh3R8GQu6Lpq\nG+Q0C4Z0ugmlhO3lbe877EEyk0Ae0K0aHeAmfVQ47rBOA2JCCA2IA6vAD+HVjwX4MvDZY75HSEhI\nSMgxOE7N2GXg/8IrF7gK7AGvAHkpZbN7XQKmex0vhPiiEOJlIcTLm5uHG6G910mNj5IaH8W2bDaW\nNjhzZcYLz9M1X961uLVLcau3n1lKSa1HvDlAZnSIjeWdvj5fLRpB1mtdC44AVrWKVa0yfu6EsigH\n0M81EE/FKe15SSxq3BvZX/z8n+Pi5/8cz/+9n+9yP5x/agGARCrKnbcfcOftB4fyzwO4tToKkN/M\nc/bHPk0iG1yES5y/hJMcplazqRVKaE99gPknzzNxZgrHdXFcl+HzZyg3ImpGp3prq0TGJgIzmbnP\nfpbiw8E5FPWqiaapvuxys/IYEkbONipTSUi0hQLmJnMY6TRGOo2uq7iuxLZsalXrWOsvydEsa8NX\nwZUsPHeZzLD3np2zs5f+/dfZXmm5bpp6/2998y02f/s32fzt3wS8Iu5N96R0JbHRUUwjRT06hJ4b\nQc+NsHq7dX10Q8csl1m8s0bEUNF07y+1sMCl//KL/n5GvLU4+9Qn3sfFF65w8YUrgTZOnxknM5ph\ne733bKqUL+M0Qjv3C04YVILyUeE4rpss8BngDDAFJIAf7bFrT8emlPJLjTKEz4+O7h+29ighLQtp\nWcxemObM0+dxLK9uqKIofuLP5soumyuHM1gAqQnvWp1/37mery+/fY+dd+5RffgAVVMCkq7V3SLV\n3SLp8xd48sNX/e2n4a/v5xp631/+acbOen1/fNRbkCvn5ijn5lhKX0TrKO4Qa2TBTl5a8CUQrAFl\n39q5+FzD/6tpVIrVRiy6RG2TKZ6cG6W+skgtPkxsNAfFXYwLV3BqVZZuL/ta/+/88Xf9QibRhkLj\nlQ9cDLyfUFWvsEnDaFff+p5ffnAQiUyyK1V/fDqH0ZAmjkQjJIcz6BEVIaC0W+Sdb7/BO99+w08w\nKhWqaEZkX4nqQTx48w7jq9/j/o1FXvv977K343XIvdw87SGQ7W6VkU//KCOf9szA2rVbfoKcomnY\nlQr6y1/FWLuDvbWBvbVBvdTKXDUScSJpb92mXrP9ugiFpRXqyRGmz00C8PrXvgfA2Sfmsas1NF1H\n61hg31nPs3y3WzW2iaoK4sNDqJrKxWfPMznX3wZpuuoXUHlUOU7UzaeAe1LKTQAhxK8DHwaGhBBa\nY1Q/A6wcv5mPFs2RelSH1OgwD255lyA3OUxxr0KlVGP2vHfT9pIwHsTa9XuYdQujTyLW+vI2qWwS\nZ83TQLHbwtdS5z3DZ8USvPUtLxt1v6LIRyWdTfaM2qjcu8uFH/wQG3d/lcJdL8ln+8v/AoCIInA6\ndGz2GmXl4tn0oSu0u5aNYWhUV1aYf/YS9brFzV/7TfbaCraPnplCzWSR0mX7zWsI6TB7/Vu89vJN\nb3TceM+RyWEKjQiOciNkr3P2sfi1r3P5xSu89c23AIhlM9R39+/MlYjWpdi5vrzN+fkLJFIxIskE\nqdGcJ32gqqw86D0D3l3b5twz57jz+uBEtX6YdRuhKgyNpNluFNnIZJPsNb7H5qKrbTlMnxlj6a5X\nOKY9r8GJt2ZL0UyGdC7N9touTt1ETyax9vYQ5YI/mt5ea81M66UKajLF0x9/mtvfu02lcZ3jiSjx\n699iuaGm2ezMoukEO1tFxme7jbCiCur13oMNIbwIH6TEsR1uvHKrK6SzHcdxMes1FEU8stmyxxnK\nPQQ+KISIC2+++EngGvA14M839vkC8BvHa+KjRzQeJRqPsrmaR1EVnvvU+9F0jYmPfgiz5k3/R+an\nGJnvTjrZD1X3vrLNe/37T8u00SKaZ+jbRl5L3/w2S9/8dkDH5LTKCmp9CilbxRK3fucPvfC7M55r\n4t71Re5dX+TO2w+7DN7DRkf4yn/4lr9tdPxg8c+337xPvW4TGx/ztN8Vlcs/8jHGZ1s661v3VjE0\nwYPIGbaXN6lWbRzLZuHqHK7jcu6pec49Nc/M80+10vUbhubtb98IvF9qZIjUJ3/Mfy7mL7C3sb+h\nL2zs+kJvTbSIilvIo+oq5Z08WjrNyHjWvz7JdJRkOhoQzIunYsQnjpZt7LfZrDF79Qwf/9m/zPln\nzxNPxZia90a7iqL4906tavXMjt3+tV9m+9d+GYDKbt5vn1AESm4M88U/g1UsYtdq2LVaI/DAG29K\n6bL20ndZfPsez37mB1qf6+xZnHjaD1BouvNufvcGs889ycqN+6zcuB9oh23a/v6dSAm1moldrfk1\nY0cmsj33hdbs5UN/8Uf6X7j3OMfx0b+Et+j6KvBm41xfAv4W8DeEELeBHPCLJ9DORwrV0FENnff/\n2IfZuLuMbbs88b//Au7YjP+D3nq4ytbD4Gj+IAkv8aE0qqZQr/ZP3rFth73NfJf7RFW8P3n/Bmev\nekY21qd033GxexTBmJwfo/pnf47kaI6h0Qyb9z29kyc/dIUnP3SFD/7Pf5uzz1/tOg7g+R/9oP94\ns4/ftZP3/dx/zdM/9lE2VvMkZ6apbGyiDOUCVYdKhRLVc88RVUwyU2M4lo06OsH0T/wUAEu3V1i6\nvUJ1c8uvCGUkYo3P4xnAZs1SI5vl+pf+lX9u8/5tIgeo1LWzsUen78Z1JMrWKlc/8QEA1t+4Rrat\n8pJluViWGxDMq5ZrlBaPXmgDwMxOUlZi1FZX2VzcwKqbRJqddls2lOO4fgGQ9pDizNkFMmcXvM+1\ntMlu47uSUmI+vEflK/8PdqlErVT1VSibCVAPb63iOC65v/RX+e6vf80/5/Xf/iNsJcKZH/aM/1Jj\nhnzlw0+xe+ceE1fOMXHlXKMt3rnqdRuzbvV1ZSXTcRRVYWs9z8f/x/8K4wARS4oRJZZ6NGUTjuWc\nlVL+fSnlZSnlk1LKvyKlrEsp70opX5BSnpdSfk5KefxyRY8YVs3EqpnUCkWSw2m0J55j97f/HWo8\n6RuJaCpBtOOmsW1nXz2V5dvLjE7nmDg/23efrZVt6uW6H+rX9KEa8ShGPIqWzrC26IliOc7+hbaP\nQm5usmtbNBEl/trvMzw9ih4zcBqhd0u3Vli6tcKtf/dbnvxuGzONVPvqXpHx2RHG+5Qa7EUiaZA9\nu4BbrWJXKkjHBdtEa1vMq1VMeHCTbH0DQxOoEY3a+jpurFEztlFKUL/4JNVGslexUTBleGoEI6oT\nSxjEEgbGxAQj0zlicc8wLr70Gjs95HPbUTWFSFRHdiiKuo4LIxOeTLXtEE3FKTZmB5qm9qyTalsO\njnWw9YtexBIGtmpgGUmK514kncsw9qkf8Rf+nfbzS+nfV+3KorGpKWJT3ncWier+mpRjOSAEbq2K\nFo9ilyvYZc81kxnJkEjFPCPtStSFS5z5zE8SMby6uUO5FMU3v4cseS6i7Jg3o7MqVRRdZ/vBKtsP\nVhttac1QBy1X6LpKaTvP7A99gvu//0c9VVU7qa5vYBxWOfU9QiiBcAo0F/BqewVK+RJr0x+ievMa\nSn7D/0HHUomu0YHjuPsujBpRnXgiOlCTfWRmDLPt9eZg0aqbWHXTm7o26rlapn2sBbx+pKe6F7fq\nlRpGaZPF6/eRjku94jIs6aEAACAASURBVBnO/NYe+a09tt94k63F9cAxqUYc9O7KJrVynVqf6k69\n2PnOt7n1jVfYvfcQaVvsbuyCZXoGv4Fp2hTeehMUFV1XUQ2DnXduI/JeR2jbjregOnXWj87wjZft\noDXkJoQQ4EpGn36C7LTnPpGmSSk/WOtIurKv77e2m6e0vdswsDa1Rsy5K9vqrLaNpnVdY3v98Av8\nTWzbQS9tkzx7juWhp5j4wLPoV55mqCE77DiuP0uM5bKMXDzTdY7y8grlZW/EnRhK+Z/LlZJafo/0\nE0965RwjqvenqcSSMaJxg2g8gms7JNfeIfH0s7iui+u6pEazxIez1Da8tYnxi17+yeqdFVJXrlLY\nylNoZOo2tZRSQ4PFBBNDKeIjWbTnP05yOO3PtOeuLjB3daHnMbsPVw6Uq/BeJDT0B+QwQWuqpqFq\nGoqiEknEsbQomRc/TP6N16k2pqu1QolaocTEwoR/3PB4FmOfqf7o3AT5rQIb9/tLFMcThi+uFU/F\nPA12AVtLm2wtbYIeYeYJTyhNSrr8wyeBXa0FlBwB8lsFtLEJ1u+uePosnaMoKSnvBfXDm9N713bY\n2ymyt1PkoGy9fZPVN25Q2NzFrtUo71W8xdUON4lVqbKj5ijsFrFrJvGpaer3bnmfw/IyRaWi+jfB\nSMPH79RNzIYEr90YsZrFEvH5eVLDKVK59L5FZwYt7q1893vUdvIIRbC9vOXXY21317T30RJJuSOr\n9DBYdRutsIUcXyBh7sL8ZfSdVcbmuv3+ejpNosd6wMrr11l53SudWC60vkvpSkobOxiJGHatjqqq\nqKrXSRV2ilQrdaR02dstITSV8uuv+td1c3GD2MIZ9CGvw2kWhi/sehnPejyKHg8a4E59nE4qxSrx\noQxqcZuRZ56i3pBPGJufYGx+oucx5XwJu/buSHyfNKGhDwkJCXnMCQ39ATnMmLe4U6S4U8SVkng8\nwtRv/SNGnn8/iaG0v4C1vbHL9sYua20j87mnL2DvI01QLRTJbxYG+hRvvX7XLzKtKgIjFkHKVrvE\n5DwzV70R/fBomtGp3CE+3cHYe7gSkOwFr+BJxKlx5vIMetTwXShXX7jE1Rcu8fG/9mcZ7oh+mHnx\nWaC/ZO4gLv2Fz/LB/+XvkLuwgGjo49RWlnjrj18N7BfPJDh3+3fY29pj8+Ea+id+ArcUnDnENu9R\naYxQ555/GvBG05Zpc+b9Vznz/qswPMabv/mHKFad9GiWBzcWfd2afggBAtFzyjg5P4Zr21RKNUp7\n5Z7FQNrj9O1j+OebWONnKLoJzlz7NTSrgrjxCjvL3YVell99i+qD+13bt1e2/WSq4k7B13yXUmLE\no/ynL/0Kdr3Gje/c4MZ3bmDbDuV8qVG9qk5+q8De2EXu/vq/98+5+WCN+//x99Aa61dvf9OTsNAj\nGrd+/beo7pWoNoq5N39f60uDC8jvrO9SXF7ltf/jH7L59W9QbRz38I1bPHzjVs9j1ha3KO8efEb5\nXiI09KdAM7Fn8+E6yYlRYqPDfPvv/gPcYitaJJ4bJp4bZnKhFer32le/ExCv6sWDm8u4h/CpF/cq\nvmsmnowST0YRy3f41q98FfCmv0ctSTiI4ZneYX7f+Je/jm3aqLrmqw4uvbPI0juLbL15jSs/Hcy5\nE3HPUKz2iR0fRP6V7+KqGroq2Lq7iGHouJbVVdox/cRTOLkp3zVS/Le/ROz8pcA+9soiew1xsu1r\nnn5RIhcM85S6gVmz/n/23jtOjru+/3/OzPZebm+v9zuVU++2ZEmWsSzLnWKbZsdg+GIcSCgJ8AUS\n+IWEEkILCQFCMcZUY4NtYhv3Jku2itV1vffbve19Zn5/zN7ere7UbDnYfPV6PO5xd5+dnZ2ZnfmU\n9/v1fr1of+ZlVEUhncycMWauqlpF6HwzCUVWCm5KJw+arxd0kXHKY21kVl6GONZPZGQCW158blrm\nGiCXU+g/yT0MYMX2dazYvg4Aq8NSVOGdy2qVuyank0wqU9DBme5kVVWlflE1FYMvcfGH3oVer0Ov\n17Fw3UISwXAh9r9ojebBm83k8DdUEgsniIXP3fchGgix+op1HN/bjpL3Kp59XA733MTr/4s8+gs4\nA4ITEY48/hLSopXoDDpGe2bolMlogmQ0Qc37P1RoE0QB9QyVlN48h/x0SVu7y1KYIUo6ERXtIZ1W\nOBS8s2KQglB40M4nzKXzs2McXicD3aMkInGM+WR0JJQgEkpwbM8Jol3FssyTe/ejN+helQjbsV1H\nOfDFLxEeGteSpQIcf/7QnO1S8ZRG/YsmEUSBkY4+xva9AmiG1yazgbbnZlYB0w5KqqywYFUTPfuP\n0bP/GOJIL60bl2D3OIiMBWhc1lAkt3sqTJ1CKbJjXxsTg5PnfN6vBUfuvhfSSUYNFSg6A46qMkyN\n2qA3WzyssqWahW+5aM77x7sGGe/SZA3knEJ5U14BRYB0KsuaqzcydqwTlbljm96gw+ZzkxnoY2po\nrEDnzCGxaMcWKrdeAoA0qwrWs24dzauaaV51euP2+TDQOYqxvonWixYXOnBfdSm+fA4mMlWcL3J5\nbQUSw5sNFzr61xnRcIK0LOCrLWNsVkefDkdIhyNEEjMdmCSJGM7Aa5/W1z5dx2dz2gqFLUajHr1B\nKjyk2UwO1WQuqnJ8PRDsmt9EpG6F9kCGJyOM943Neb1r9ytF/wuShOUsrO3mg6ST0FvMxKaiGCxG\nMpncvBpCI8+/QGD/AUBFJ4lULW5g4HAnoLk+pZIZmtcvKejI1C7VONvZZIaxvlHikaSmQ5/NEJkI\nUdZSi06vw1Nbgd6gOyuJCUEQ5mzXuLL5daG+ng6yrDBRux5PsJ1M1ULQG8mu3DpnO0Gnw1xTV9Qm\nigK9x/oK+vKhQJRk3nxEEAQG2gcx+f2ahLEkojvp3BRZAUFC7y3h0O8eLdyv3XuPEZsIoHq0VWJ2\nFqMs3tGBwWLCYDn3e0TOyUzseQmddYahY7BZMdismB22OQbp58uA/c+BCx39/wKSzz5Kw0Urcc2K\n18bDceLhOKGf/1ehLZeVC1ztU+7rLGbfI33jBTGpRDxd4FtbHRZNxz6bOaV86/lC1yud87br9eKM\nRV5+FjWtzW8wGUjGijtis9etmWfDDI3xLOGr8LDium1YHWZUQcRo1BVZGU4jl81R1tpMLqdJQvuW\nLqZ20UnCbzp9YXA0l2irlVgoQigQw+t34fW7UDJZJgYnkGUFQRQJdA3gLXOf1UAl6URsJ3kMmF1O\nHJ7/Xc30umWNRGQL2Sd+T0YyEh0ZRwjOHZDjE0GUqeJw2nyMsUz+3lMVFZfXRjo4RSKSmCO/Ddog\nc/yFQ3NYUaqqcvDhF9DL2r4mZpnD9+8/Rnwq+qpj5x0HOjn4+N7C/+GJKcITU7Retg6ztfh8oq8i\nPPRGwYWO/n8BR184wgs/e7BIw9tbU463ppya9SuKtk2dRUd+Ol2OaWRnVaZOq+8FRqcIjE4x9ugj\nBMbCr2tnv+Si+Stc9z3y0hzefmVdKZV1pRiNOi762O1Frx167KXC39O5j7NFJBhBzaRQFIVMNE4q\nmaV18/I52/mbqtH5/IVYwq4f3Yu9orjgK9TdV5Af2P8HrWqzapXmPTvaP8Fo/wSpqSlSiTTdLx9l\naiJM97E+RvsniJ6BSw/aIB8+SRvoxNMvzymker1hcNhZPP40usvfjmX/4yQmAvR//z/mbKeqKgfv\nf7yoLRlPs3zLcpZvmbnG0ysSg1HP5GiYvb9/ilwmS8Pi6kJ19jSMZiNltSW8fNd9NLbWFNqrF1Tj\nr/KSOqR1yI3rl84+EnqP9tJ7tPdVne/Gd1+Z/2ztmRpsG2CwbYC99z+pqYj+heBCR3+eYXOYi1yh\naprKaVmuLfXjoZlZx7RbkrJs4znt3+W1o6rqaaVVTxUqcPmcuHxOzE5tlli/asG8250XnMOdlUqk\nSCVSxCIJcr2dlNfOxPcz80gpnC3i0RR77n2CeDRZGPgE3dxZp9ntRM3NaLcoikrniweLtgkMTRRm\n9NPFONloFG+pozA7lXQSTcsbOF+oW9H8v253lxibAFHHkKkZVZFx+j00rmuds53Zaad+eXFcXBAE\nBo71MHCsp9A2lRc8my4yW3nFBiKRJONDQcaHiqW2PT4H5csWsWB9Kz3HZ+SLR7qGcfqcmBu050iY\nFd33+OeXjD5bxIaGMRh1pJPa/VHZWF5QyfxLwoWO/jwjEUsVVRBOTYZxVJTgLXUWFSYlInEtIZkI\nFIUjzqSNnU6myWZyp83+nyp+b7FbsNgtRCaCrH3H5a/StvzsEDuF3+jJ9EmAwHiYwHgYVYXRo+2E\nJk5tYzcbJ5txnwxVRdOuEUWM+RhuNlGcYNPpJRTRQGKgv0BVdLptREPF2wXGw5riITPepOa6Bhov\n24TeIKE3SKiKQmAkSGWLFvZp3bjkrM7jVDA57MRfB0bU6SDpJI65L8ZiVkCnJxFNIq3ZDBSHzgJD\n45Rs3lz0XrfXTjScKApxTK/ACsyvulqcbhvxaKKgAjqNwFiI5FQE27IVhYEBNJe1nmMDTL5yGACD\nZ6Zz7zkxv7bPmfIi0+yh43uO45wVHtMZDeiMBta8Y/tp3/9mw4WO/gIu4AIu4C8cFzr684yT59nx\naIoTu4/RsPMtRRry08v9Iz//XVHc+Uwc+VQqWygdP1dMjU0xNTbFSM8YQut6kqGzmzm/GsSm5t+3\nr3Iu7VLOKQVGQyqWIDWLkniyjMJsnCkxW9lYTvPKJtx+d4EnreRk9IaZ6yfnFCSTEd3iVbhLNOpq\n8zWXU9da7Odb1VyBLm9YUhDOcnqZau/CaNRr7Ca7nUgwitnlRK+XyOZFu8SzTCCfnHsRbI7XRYfo\ndLD4fVj1aXR/+D6p4BRjAxPkbNoMenaOJJPOorcW88wj4fiMHEQeHp92TUVJRBBAiUWpWNpcoK3O\nhqIojHUOoDrnFvBl01n6jmjU2/FZPszZU4T25DPw3adXb5lkpuD6BpDLZMllsoiL12B3nl4v582E\nM3b0giD8WBCEcUEQjsxq8wiC8JggCB353+58uyAIwncEQegUBOGQIAirXs+DfyPCaCxWn1RkhdhU\nlGRXB+6KkoL2idPrwOl1EBo8yXjkDM+1qqhz3Ijmg805V+sjGU+RjKfIZnOYx7tw1rx+loLTxUcn\nY6CtH7e3mEni9TsL9QFKTikKcVmdp2adnIkaKgDl2y9HkHRMjuQNTCoryWZm3ucrdyMmYxgkmA4W\nGPViQX1zOlxRu/kiptl25bUazU8eH6ZjXxuxaIpYNEVkYAg5J9P50lEknUT7K1rHdLYFbhabCVEU\nCuG7yMDQObGMbPbT67ucCQaTHn3rKqRn7qX3mV0MHe8hnUhhGzgyd2MV0gd2FzXlsvKchPmiyzWu\nfTaTw2a30PfCXqyNzVQ1VVDVVDHn/RODE+giAdw+R6G9tNJLRV1pgajQvq+98JrdfQpCwRmu+ezn\nKJvJFf5OR+Oko3GSRge2U+37TYizmdH/FNhxUtungSdUVW0Gnsj/D5qVYHP+54PA987PYb55IMvz\n32BHnjuEbHHicGsdlygJiJKApBPn5UoLp4nVn409XSx8appmTUslSDqMDedeZHK2ONVMNBFLMxUo\npsIt3nkpi3deypZP3E71iuIEcTJWHCs/Fwx2jTDy8MNMzFLEFP3Fg9vk6BTpaBQhGkIyaoPTiYce\nJxGc0VFXVZVcOFSoqA3m5YKlyrqifQUGZ2h/03z9acnis0EoEKW81lfQuY8OjeKtPHubzVg0Wag2\nfjWoWlBN2mjHmg7jLXWRiGlJ7CO/fXDOtjaHmY49xQOA1WZizc4NrNk54x2QjWrftaZSaUSSRDL9\nPQiihCAWr0x1eonSCjf9jzzGom3rC+0lDdWUL6jjkr9+L0DBCAWYkxAu7OsM+RvQJkOb3rWD5mV1\nhXHBZDVhspowp8MFaug0pj1034w449VQVfVZ4GQn6uuAu/J/3wVcP6v9Z6qG3Wi2gn95KezTIJvJ\nFWZlDcsaKKsuoX7VQgAmXjlMKF8FGZmMEJmMsHL7+oJu/OwwhaqopzQF8ZZ5EAThlIlbo8kwL6fZ\nYjNjsZlJJrLI4RAJ96k17U+G4SxMUYqOsW5eT/h5sfc3D7P3Nw9z5Bf3YfCV4p2VsD2TGuOZZrwd\nh3rxz6K1Rva/XGTwogI6k4lEeQuBgTHcpS4CY2GOvnisaD/hvkF8+Zm8Oa+MmDHaqWiYub0TsRSr\nb9iGLs+UsVhNBQmDs4GqwsTIFBN583NbbQ06SShMBFzeM3PqX0tRTy4rozuym+RFN+B7119R0VBB\n49J6ajZqkgYWuxlLftWgNxnmfMclVSVMDk8xmfdCFgSB0KA2yCqKgreqFM8lW5CaFsM8tbG5rIy7\n0s/kSLAg0QHQ9uIhckYr2bystdM/E9qZHBwv1GHADJlB0p96wBMAvUEiFk7Re7iL0qWLaFqp2Ww6\nyktxlJei9hzHUV48yFacxgPijY5XG6P3q6o6ApD/PS3YUgnMToMP5tv+n4KiqCiKWii7r1rShKfM\njSrLs7bRtLZjkzNaKJmTyuVVVAwGHYaTzEiUM3Cr06kMrhLHnHZBFDTJ2+EAh8Vl6FLRc9NfPgeY\nHbZ5WTFVrXNNzZPxNMl4msBIkIG9hxFFAWN+kNOdwg7ubCDpJAxGXdE+5GQST9WMvpAkiejLKoiY\nfNpnCgLlzdW4fcU6NqJOQsp3TNM5krjJTcXGmZmnw21DbzHjKtM6osolTWdkUU1DCxFp98D0feBZ\nuQKzzYxOJ2Iw6s4qXn+6leCZMC2/EbWWIVTUYfG4SKeyWFqX5fctFrSYRFHEv6R49ZWOJRk61sXQ\nMc2z1lPmLujEq4C1xIOhrhlF1Sqjw/NJP4iiVtQ3C3JOITk2hjymOZLpZ9lUjnYPI+fkQo3DNBvt\nZFOW2ZB0ErmcgqqqDB7uYODA8cI9MtY9xFj3EPrqevS24tDNeP/cwrE3C853Mna+u2zeu1MQhA8K\ngrBXEIS9ExPnLlj1RoPDY0cQtBnFdFw3FYkRCycY6x6iekkj9vLSQudntpkx28wEh2bO/WRapJxT\nCvo0RVC1kMLpKJa+WZ3ZNNLJNOlkGp1e4hlhK6SS6HRnt9Q/Vz77eM/wvG5Z9RevwjyP1+g0+g93\nERwLFehx1vwMUtJJODz2OZWip+v8ShsqKVuzgnhce+hFUcDsdlLWPBO+0eklRF85CgKSJJGIxKlf\nuwR/XbEoWzwYIZI3FY/kqzATko1o44zei6KojB06jqNUW5HYfa7CDPhMkHTSnDCNokA2p6AqWsca\nDp6ZankuMf2TYXY5wGxFL8jkOo+RTWcJB8IokgFREsmms4XkZyaVIThSvNAXJbGo07XazVh9+USu\nomryB9EggqowPjTJ+NBcHZ/JgbF5w08mk57wmKZIGRyeed+rERk7+Rr1H+tlrFvLlQVGggRGgqhG\nC4KheEUdOINb2BsZr7ajH5sOyeR/TwcnB4HZ65sqYF4Xa1VVf6Cq6hpVVdf4fGcfh3yjYsVNV1Fa\n46esthSDUVNmdJR6iYUTtD9/gGQghG/1ysLSunldK83rWqnLh3WmMfsmz+a1WU7WZ5mdqJoPFquR\n4c7BOe3TjIgF6xZxw/J+HhR2Fvw6zze6X+ko8M1nI9LeRkVjcRLO43PgmXVOeoMOIf/8Tndukk5k\n4eZVLNx89vl9p9NMxY6dOCv9mMwGrHYz5oZmpnpmrk0qkWHCWEZCshKaDJNOZjj22K45y3ZXhW/G\nESzfUdhCw5xQmigtd2k/9RX0HO6me38beoOOQ4/soqTi7KwPc9kcmZPyEfu/9xMCgxNks/Nr9MyH\n2Yyic0X/oQ6iizcjjA3T+cMfEugdIhGJo8sm0BsNRcqO0VCc9id2Fb3faDYUVrMA/e1DlC9bBGgr\nDTkW4eAPf4pkNBQsGk9GYDiAq8zLmp0b0Ok0B6rVV23EXl1F+SatuHCwc6ZLaVxSO2cfZ0I2m0NV\nVKqby2ldrz1/08JyvnI3vnI3cjSCmpt/cvNarvGfC6+2o38AuDX/963AH2a135Jn32wAwtMhnr8E\nzK54nYNsBkWWGe4ZK3gJtu09AUBNczntBzpJdHYUZqiBvmECfcN0bflI4Wb1V3rI5QcCp9eO6RSJ\nvK6j8wuGTUOWFYLzFB1Nrw7CY5NUH3uE4Uk9BstrY2qcKw4+c4iug8UKlYvuvINFd97Bhhu2UFLh\noaTcg8VRfFyZVJb+vUfp33v0rD/rxMttHP23f6Oszo/BZCAaTqBYbPS2DRVtp2bS+DMjBR2c0ESY\nlx54oWgbk9ulyQkDkfzgYyTN5tgjhIIxQsEYgr54kHa4bfQeP/V3dTKdMjJLKsFdYmf59vVYKysK\n98zZCJy9lhm9Chhj47hi3ZrM9mgIURTJHj2AnD3zis5V6WfLx25jy8duK7QN7d4HaM9O21N7aVzW\niBA6vVb8oeeOEOwbRm/UozfqiY9PMLVoCyO2uVXH9lIPkk48Y/HcfBjoGMG7ehVbPvLewnUzOWyY\nHDbEwQ7kRHFB1+J1Wqhq2tvgzYSzoVf+EngRWCAIwqAgCO8HvgJcLghCB3B5/n+A/wG6gU7gh8CH\nX5ej/jNhdsXryQgePV6QlJ02bp5OskbzVaLBvqGC8l7FonoqFtUjCOBprMNg1OGp9BXeEw5EyWVy\n83ySBsdpPDHTqey8D/z0LMrqshPt6qS+PFvkn3o+cark7XwDpZSMIiWjZONxFmxeQ92Oy5gYDmI7\nicc8OjjJ6DnK9kZDcRKTQSL570AYHZgTN5fiYSYNpacNA40f7yxI9BZw4Fk6fnMfLq8dl9eOTl98\nzmfSLTpdOEyRFcRlF2G2mQsx+6r6+TX+Z+O16LPIsoI1NomtooyNt1yNv8pL49J6At395LIyRpO+\nMDgJAkVJbQA5k2W0Zj2jNTN5i54TM5LFwckIztVrSI2eeu43TUjoPtpfoAO37+tAJ6eRRe3zGhfP\n6OCcePEoBoMeg6H4WE41SZrGtNzFwV89CCZLYaCwtCzE0rKQ7JKNyMliIsBkXp76tchy/LlwxnW7\nqqrvPMVLl82zrQrc+VoP6gIu4AIu4ALOH958a5A3KILzJJZAW0q3XnUp/sYqBjuGC/HLQM8ggZ5B\nGp75D17+wzNk0jmOv9RW9N5cTqF+cTX1J6n8IYC3uowSvwv3POwaoKD+N5uxM037zKUzWOoa2Gbc\nTSQQnvf9rxUL1i+at93mnksRPH7XLzh+1y/Y/9heXvzNY3RXX8qqz33mVbkGWe3mQqijtFKjoR55\n7nDh9b4X9xW5elXV+/E5RbyZCY3SajVSt3RuiKDzSB/eJu2aLlilUfHMFRVULm5Ab9CjN+iJB0JU\n1PoKIaA1t7z1tMd68gJi9iosEUsRHx4hFU+SzVfi9ndqM+FThSmmbfteLdwlDlQ5x4+GLuVIy7uo\nXNqCo6YS57s+OH2ETPMtzFYTq951TdH7JYMe72Q73smZgqbpGbqqqtS2VPCo7a1kvTXMB1EUyOUU\napqLcziqCsn7fkpFZgBBEKjauqnwWiqZIZlIz5Hvni8/NBuJmLbyCQejDD/6p0I4Rp4cQ54cY0JX\nPpd1Mxycl7b8ZsCFjv48weqyzhuuEESBXDRM5f/5KMuv2oTNqT2MQ10jDHWN8NIDzxVvf1LIJRKI\nEAkUx9ttdhPZRJLARPiU7kRivoPPzAo3TIeeJocCdC+5HlPXgXM/0bOEdArVxWXXzvDMpzExGmJi\nNITdaUFRVByJYdqsK4u2OVsDDqvNhDW/LC9bUI+zzItjlhxzLJLEXjLD0w+Ohxj3tRLXO6hvrSUZ\nT1P29nfOyx8z2rX9WPPWetgc6C1mHD4XDp8Li9dFOBilvKlacwuznJ2WvE4vYTDqixLS2ayMEJqg\nfPVcWeVTceXLaucyrc4F0XAc2VXKyKiMzxhGb7UQ7B0ipdfOO5POFsIW6WQGxVQ8sKQTKQIPP0Dg\n4QcKbXWLtfyTyWoilUhT4wwj+OdnXCuKCqqKp7o4ROUpdVK2bhXZzuNaAVvm/MoHdxzqQc5pz8nI\nkQ5GjnRQGzxAKjR3EjRfAvnNgAsd/XlCxRVX4Guspm5hVcFbs7xWi7m//Ifn0GfioDMUCqZqNqyk\nZsNKVr7t8qL9nJzRD4yFCYzN3HBGk45YJEVoMnzamy4+FZ/ToU4jEkrgIE6u6vWrjH3lyfkHESWV\nZOGm4s6rpqmcmqbyQjIy/svvYZfSVK9qLRQJKWeZSxgfmWJ8uuCouYWWj3yUhh2XFeoKaldohVHT\nkPQS+4PVJEUTiqr17vpMjGWbiwcaj8/BwB5NujiQ51OnvDX8vOofcNZW4aytwtyymHg0pXXyikr/\nQ388q2POZWUUWaGiqbhq12Y3oT+FlMR8aD/Yc+aNToNUIkNU52DdEoX0f32RbGgKOZnEO7AfURKL\n5A1kWSF+vDgx3rGvncGuEQa7tJWHzWHG6Jj+/mTKm6tp2X83gu3U0gLZTA52vIeLbp5Rj2zYtIZQ\ny0YMtVoNRs+TGtunoqaU5rUL593PmbAwn1i15k1hSsq1wX/VO3aw6h07CL28h/G+0aL3NCyu/l/X\nHjpfuNDRnwMsViOWU4hs7f3xr1EVhaHu0YIFWi5v+FFa6eGlz/9/BDu6CsnI0tYWSltb6N7wQVrW\nzhSeTJuEAFQ3lWOyGIoSS9M6LaczpCivLiH+ri8gy/K8wkwOj43SyeMMliw7h7M/P+h/6RXadmm+\nrdOhpMHrv8Tg9V/C8InvserGK5EMesoO3Q+KTCQvF+z22otYT16/aw5PX5RELDYjFpv2HU3s3sP4\nL36M3WnBYjfjKXUS2HIbvrqZatZYOIkqKxwYLsNTr4XI9nz9PxjpKWbmNP3132Cr00IOk6Mafzwg\nltDinWT4cDvDh9vJ9HUjigJjXVrNYH978T5OBZPZgCzLHN51tHBNll22mu+kP0DK4pkzYFfUzT9z\n91aXzRlEzwVuRMaOdQAAIABJREFUr4PU739G4y8/QGljFSdePEZv2xB7vn8PkiQW6fBIOpFMvDi0\n5i11UX7HJyi/4xPYnFZ0eh0Hn9S8djOpLI7WJWSjMcx9R4r2Nb2ILazanvwdg4dnHMpiiRzH45X8\nWrkBgMFebaCNhGOUNFRT2VRBZV43ZzqsVVY1VxhtNroPdqM36Fh27TZWfflfCkVYB6uu5WDVtYQ2\nvgtfTfHKYuAdX6d225Y5BYxvBlzo6M8B8xYv5REPhMhEokXMDFs+Zlp6/Y1U1PtBlgudk6jkEJUc\n9d2PkI3PZPdnW92lkhkW3LCTBTfsLIQjFEUzHWlYtwRXqYvmNQun2ZwFBCbCHAjXYbKYimKXBpMB\ng8lAPJ5BHR+mfOrEa78op8B0iGo2rDYTJcuWFK6RqqooqsqwUM2wUE2P2oCltg5nhZ/MQC+ZULgw\nkxckAVlRkPMDnLfcO0fzRxQFrHYLVrs2uAmiQGQsCJIOOStjcViI5wwYF844FHl8TkRZZqFnAiXv\n27pg82qMeu17ni5+S9q8GOtbAMjmB2PP6BGaD93NWP84Y/3jBHv6WbxtHSar9l3VL60vOr5TWSFa\n7OZCqGuah56OxBkNiOhFkOXi83R458/LuGor0JtfPV1W0kvoBAXHpTt4sf791K5oYcFFS2i+6e0a\n68Zqwpg/N19DFcnN7yp6vygJPBZYzWOB1Rg9boxmA7NtV3OBSQwuJ0o0jNlhwZyvgJ2eJKuKolVF\nL1zCWOdMgf3k0eMsSe1nracPSScVLBdLa/1MdfdjsRixTFdS5wsAZ4c0PSflsTTJBM3SMtTVi3Gw\nDVeNNlCUjb1M2djLHEk3IekkdLMos37dBGa/v5ADejPhQkd/DiipLKFkHpldALPFSGwqiigKRdob\nAE9kLuHI8o9i27AFs0PrANNTIe3nwG7SiZmO3uKaFU8Ox3E4rTic1hnVS4+NqpYqDGYTdRevxrd6\nJS07ttKyY2vhfQ6XDQQRo9NepPBothoxW43IisrPBjdBd7Gey/lE3fZtVLcWd3SuUhfPu96GO18B\nqublTtYre1iv7GGD+hK50UFMdQ0YFi8nNDZTeRmPpIrkUSZHgnNCV4qsUNpcS2mzFhfOXPJWXNfe\nhCwrhCYjWJw27MkJJn0zNocOr4Mq0zg+fRjJbKZiUT0H6t5L2fq1AJSUuSgpcyEAUl7fcjq8Zpjs\nQzfWW6DTmh12XEuWFPj2ns3bKKuZmX2fPCBPw2QxojPo85/lpqTMjaoqXLYsjGKxF8kaeP0uUrG5\n+j8V9WVI2TSDR+f36j0bpBJpLCUe7hvbxE9+KzC89EZcy1dwrGwnkl7CV+XDV6V9d0a3hx6ppej9\nnroqdBYjOotRM7iZimFxztzPSirJaP0lYDQjCsIc+WZVBUeJk/3mzYXqWgBRVbCrEUraH0cQoGHT\nagBcK9dgctoJjAYJ5FdZ06GV9Cw5kUQ8RcOKJhpWNGF1WFAVJd+JS2RicTr/+BiioqDTiXiy43iy\n45jNKpNDk0WJb+ehR9BnYiTibz6LwQsd/TnA4rBicczPbFh62RoAFq1dgNlqwmw1kYyncLgs9Ebc\n/OM/y3zypasxWLXZQNLfTNLfjHPVasYHZzq03CyObjaV5fh9f+T4fX8kmlejbFy/FINRx76Hnic1\n0MfgU8/gX7oQ/9KFON3asTn+7qtcVd+OaLEWMRjSiQzpRIbGZQ187e/H2d948/m9QLPgbKyjYuOG\norbKbVv5wlfiTLzv+0XttaH91Ib24973W8Jt7UQMbo5UX4+qyGy4Ucth6Ox2Nn3wRjZ98EYAEpG5\njBxFUfGsXIlnpRZf/2H7Jj7xxGU8Iu3A5jCjZHOE3HUohhkJhvJLt7BI7KZ05BBCNk35wnr+5cd6\n2ldo9YDVSxupXtrIEGWMP/00AKs/8B4A0lNT2FeuKezLtXgxokHPaJ/Gt07Wr8BVqXX0lfWlRVWj\n09A6fwHB7kL/qX9n8Q07WHzDDlJXvo81yl5MsUnKav2FgT4UiKI36imtcFNaMZNUtjkttO85ytTw\nudUZzEYskkDvcvNSt42E3soLUwsQrQ5eHi1HbzJSd8VbqLviLdq5NtYRlIuTzTavkxuD/8mNwf/E\nqKaRczJLd14CaFWzgk6HwWElEwyQjCVJ5gcso0mPxWZkzZUbMFrNfPVHKss+/w+FXNfIO7/Db1NX\nYitxk8vK2PPMrf76rSQvuwW7z4Xd5wLm57inkhkio5NERieJRxIoikouk2PhxUvI3f6PLNi4HJ1e\nwlXiYGDZdQwsu44F+n6MBl1BM0cQBfa33kFubAiL7cKM/i8WApCIp0nE5y+CMTcv1Dr4Eg+Ldm5l\n0c6t2DwOcjmZDy07wIZbljLaH0cvasUcqaxEKivRX7WZldvXsnB1ExffdHlhtrbo4iWseMtqFlyx\nmQVXzFi2CZJIKplj6aZWOg/10N8+zOjjTzD6+BMFq7ufv1hFfDKJkE1TunYV5XVlAKSSaVLJNK7q\nMrZ8Zj3/cbfAxTddPvdkzgNG//ggmc7i0JBsddK0voKuMQsVs/Rm1FwONZfD6vOSCMcw9h2lVunG\nXeZFtdjR6SWWX7mRqQXajyiJpFMZahdWs/G9VxV9RvjAXsIHNBPppgYDk/1hdh3VVBcFSWJPtxdf\nYiZ2rsoyiQ6N1ionU/TtOcji9eW80q91JhafD4vPR2fAjWrRZqcRvzaTnXjL7WSqZ2ikai6DPBXE\nW+qiZcsa7IeeoG23lrBsvOLSouN0eexUNFejN+qxexy4vVZ0gsyvre/j19b30Sc30C/UoY70o8oy\nLq+dBaubkHMyfW2D2Jw2bLNmy+2vdOP02vGeJMZ2rohlRb50e5h7PnScixZn0IXHuGr5KAgiiRNH\nSZzQzscgyGyu1Kp+p/0FRL2e3FSA3FQAg9mETieRGNYSs6JOh97pRPnldxjad7ho1bvk8otY9Z63\nMtrWSzYeJx5M8sVn1rBg23oWbFvPvi4jxztkBmo1WuXBB54EwCom+eqjjTje/n4cb38/AHWLaxAE\ngfVf/GxRGHRyNMTkaKjwfyqZIRWK8ul/t/KZwN9gra4mnciwq9vPrm4/tuHDlBYxngTuuTdFZHgc\nZ8lru8Z/Dlzo6C/gAi7gAv7CcaGjP0uoaCXecuYU1mVjwwiiSHw8QDQYIRqM4Fq0gKaVLfD0/Xyn\n9jtc9VYfRpOBVCKDrcKLrcLL4UgVptp6/Fu30rbmfdRepIl2RUNxDq//W37vuJXfO25lzXVbAFAy\nWUobKnHUVBeohyM9I4z0jCDnZARBwCApPDaxiNRUmIC7Cf1SLbxQUe+not6PqNfz5bd3k4jnSAVO\ntho4P1Bkmch48b5VQeI7/2eU91U8iV6nsTiMZgPB5dsJLt+OZDbjLi/hlT8+h3jkBQylfgZeeBlf\nhZfM5Bjezhfxdr4IqorbZ6fy5vcQHigupz/07GEOPasVSO1o6uWbX3Wgt5sJjodw11bS059DOLpn\n5nuzushJFkL+hUhGI5FAhK+v/j0ba4ax2k2kgkFSwSCH2gWsC1sBsMQ0Db/fH6zmG3tWUt9aS31r\nLYLByPDzL6KiYvJ6IZ3EXWKjsr6U0LHi1Y3T78JWUUYmlcXqcaLLphj++9sLFEarTeSR/kbigSmm\nxsO4vE5Kr9iJyWJgzVu3gShoP7Ow9EO3z+Ggnyv0mTiVhx7EFBphxcB95CJhmkeeJ5dM0fbiYdpe\n1K5tKpXDkdTYL9OG75LLQygQJRSIMtg2QC4nYynTjieXzqCU19NxsJuJ4WAR60asqCMtmRnsHkVn\nNPLODzVgsevIxpNk40nW1seorDZiljUZi1RSewbLO58jMJHiifBynghrs++SlStoWt1Cl20JtYuq\nWbS2hU3v2Vk4v/L6MtbcfBUbbrkOg1WTPnj6Z0eIljQjKzI6MujIYCrzo8Rj1M4KfebSMo6rb8Zb\n++az2Hjz8YT+jHDn460D7XOVIae6esllc0SDYaz5JJNYWUbv0R4sH/wSxj98g3fe8AwBRXtNNmhx\nvm3JB4mOBxl68VEcV0kFPY/BYz2UDh/iaw8tAWDHNm25GA+G8LUuYrhmM2rbBC5/Dl1AU/NLTEUR\nBIF3rujiP55rYoPTjsNpItq4FHiIssVaRadktmCOjPKRd3po//H5T8iKooBj0WLkqSDQV2jP6K3k\n2o8TPPocFrsFt8+Jp7mRT92thXF87g9w+UUZFlc/hCMbIDo1ha++kkBnL6GRAJERLf68dMtyMm95\nN+Hhbo4+vf+Ux9GXKWPl/v9k88q/pimykGQwREbMYF69Eh7VOvt9xvXUNEcYSFexRhApWbueoOqh\n8cQDZHdcQi6i1TDksirDvlUYjH8gY9Wkdwe7Y9RVKnhqtc5AUVXSq6+hMtKGLptiTPKz6JrL6Xr0\naTr3nygwrrKZHKM9Y/hyCnq9Dr3bQ3lFDZGpJ3j70L9p+zIvJeBrxeGrw7jnEANdI1QkE6z+xEeI\nxhX6732ycJ4bP/dxwnGBr/fs4IP1J2D/TGXquUKnlwhl9CgxiVQgy4GWjyIJMpXWn1OZ194//tAT\n9IgNeKYkjXZp0nIeSW8NE5doYcZ6//1khgcQSrROUWcyIOSy1K9fSt9Lh4s0o5RsmvRQP4Ig0Lzz\nMo5ZZT6S/Rec+QKy9aP3crG/krCyiMYldZhWrKPn9w8Q9zXyf7e3ERe0CU8MEFMxdHYH7n2/p2Tj\nJsafe55ETmTVtdpEyey0o9jdpNNZAiMB/v3d7TzYZCPmqCCXldni1cJ4faZmcu09tGxeTV+H9nx9\n/JYccbmM8rr6uVZMb3BcmNGfA0S9DvEUzjX25mYEnR6qGsFsA7MNUacjFU/z27Zmcq2XQjqJ1asl\njURVQVQVUrueYHzfASaGgyjPPIhe1ZI/NreN0on9rF2hY+0KHfEujU2RiqVIhsI8O7UQ4aoP4rrp\nQ1RsupiKTRcjmY0oioIlE2FTSxgQMIs5XJLGRRcNRkSDEVSV6FSajfLTp1bjfA1QVRhq2EGocXNR\ne07SM+loISFZyYv3k8tkMAhZDEKWtkMhdrW56Ki5im77WjKJJObla1BECcOilZiaFmJqWshY1yBp\n0ULwyUdPexzBuJ6oaKXFMYYqSBgcdprrYMwyY36ik8Cgk6kzjqGzO4jXrOHR4ArG0nYM1bUY3U6M\nbid1VQLtQhNGs4GYqtH7Llkc56qa48THJomPTaJ4K3iQa4k1X0JMtLPH9zbSZc0oikoilsZsNWC2\nanxtRVGQMxkknchkRw/p2qWYVm8ji0gWESUaZZv6OLKoR1FU5JxMLpUiE0/S5d+EqiiFWopEJMFo\n2UqaTcMokdemmR4fHqGjagdPpDcSWv12Xjqu57EXBMobK1FXbEVdsVW7tiGFo+EKJJ2EL89yIpvi\n5fgCXo4vQGpZhsXlIJbPZwgICKkY1s07sXhcRTLFibrVyCW1SDqRVFkTWRm6ntxFJpEik0gRSqik\ne9qRFZAFkcMVV+PxOWk3LaP0+B+pO/Yr6o79SjuEWJzM1BThvXsQzBYmB8boe+o5jP4yjP4yMuks\nkePH0OtEJgcncE12cHX6F5Qkhsikc5THuyiPd1EZ7yASjJCYmCysPBqMIzjVMEWc0TcJ3nxH/GfE\n6PFuRo93z/vaVO1F2BcuJnTZh7CvWod91ToUkxWj2cCRgxF+qbyTTvtqsrLGFVZkBUVWyKYzpLMq\nVq8LqaalwNFtvGQNckkNt7Ye5NbWg0zkC3jKli5g5Eg7o6M5XhippjdRQmL1FSRWX0HzBo0fflhc\nwjb1KTCZSZrcmFPaw5+YmCQxMQmqwrOWK0j09CC8Dh290WzgN20L+M3UtqJ2fTrGHwZaia2/malo\njsBIkHQwyE1vyXLTW7Js32qgypWgZ8LCPz+2EGtdPaPOxbiXLqV/4bX0Nuykt2EnE8NB/CP7GJnn\nuzBZTJgs2gzzRJ+e9pa3Y0mHsThtGH0+VtVF2R2sK2zvNwTxRvrwR7qRFUjrXDz0hMzT3pvIWD2o\n2SxqNktNaZbuCZumwy5rtNGd3v2UvvIA2VRa+zFYOHQwwe7wAjqz5dz1yyTtNKEoCtYSN3aXDbvL\nhk4v4SnzYLSYSScz9B3tIaC6uc90G2NVGxmr2khf+Qbifb0opZUk4yn0Rj2yyU5yzzPEM3oESSx8\nd8GnHsczcZBrsveSGBufc03OBc6GOp7rK+PeJ/TsGiwnPJXlyO4x3C2N9Jma6TNp1dRuh0Rn2AkC\nKGltcmKYHKQ0N0RpbgglHiOrCLSJzRrjRpIQclm6HCtYePlGdDqx4OvaodbTX7EFvUFHyOhnYFQi\nXdJMeCpBeCpB29JbGUuZUWWZcCjJzx93kEqkOTzkoi/pom//Cfr2a6GxrCxALod3+w2IuQzxaIrA\nWJjx3XsY372HgRf3ceLZAyihIKqsEFLt9BzqxDCu3UtZi5OsxYn5xB6cHgfBwXHNfUoQCAoeTNEx\nsuOj81y5NzbORqb4x4IgjAuCcGRW278KgnBCEIRDgiDcLwiCa9ZrnxEEoVMQhDZBEK54vQ78z4HZ\nD9fJ+OXgWkLLbuB/DpUy7FrGsGsZcn45+l93jPL2DZM81VVOuC/P+Og5Bj3HUG/7LAtvv434397N\n19KfRMwbVB9fdQd/t2cnZeokZeok9bdqGt+5aJRsIsknKu7j1qmvw5feQVnHM5R1PAN5s+W97RZS\nJXUkhobZF6ojGM4gSSLjQwHGhwIkR0a4xr6Ljo0ffV2KP1KJNHeOfoy6Wn3BPAJAd/xlLl6a4fO/\nKie5XRM5HesZofa+T1J73ye5Yv8naPzJDdyku5ctmwzkVlxBOArhyiVY1QSf/FeJT/6rxJK3XMS+\n79+t7fMkq0EBGQGNg72yMcXT7X6eCraSiCbJBINIZjOCfqbS2GiUiFu80HkQMRFhkfwKn/mogeN9\nOtJmT2E7uzHD6lZtBjo4pV2ztN2HucxPLieTy8kYOg7w4XcpNPuiOMt8fO1zRrqSflRg5Z23ExwP\nExwPa96oq1cjLV6FihbKKYl20dErc9tXPdz2VQ8HJyvp2/xh9BODmK1mSsrd/DD8Vvp2fpZVhmP4\na8vw12psqpprriLym58Q6e5mcuD0dndnMs0ODE3wIcOP+e/F38ZjSfM3bwvxrg81kJJ1rMzsZ2VG\nC5WVDL3AhxfvpaTMjZC3VpTDIbZ0fJstHd8m/OTDHHr8JVRZZeG6RSjZLKPP76L6d5/FVOImmcgU\n/HQNJpFj8SqS8TTlJ/5ElV/lG5n/y/Ftn+P4ts/x1EE7T9feSdTgIToyxg9W3cPkWIilNXG+1PVu\ndl9zD7uvuQer3czkqhsp3345dz60nkDpQppXNrLkokV0He6l63AvtuZm1n34VpRICBX47sg1HLn5\n12DTurBXjGt5xbgWoboBg1lP5eIGrVpdVXmyp5r+x59CUOR5r90bGWcTo/8p8F3gZ7PaHgM+o6pq\nThCErwKfAT4lCMJi4GagFagAHhcEoUVV1TfflZkH1Su1Qpt4OE5kqtgNaGtrCP94hJsvHqcionXm\nqioTj6VwDR8i/PDjvPfWjzH6cAaDUY8lpG1jTgxA7wkWLa9l2ZJmYmGtc1nFQTZv3sr3YzcBUGmN\n4uRbKJkMRpMB1eogF9eOYfAlTVfG21gHwJUrJtgVWUmJKOLXBZkUfciygj7PxdZZrKj7nmHJBiv7\nes5uBmi1mYjHzq5QRBDAu2ghN0v38eyswhe914v/Z3fyg4//PUIuRxitKrT+Kk3XpO13f2Tt1ZuQ\n+7vY0XKA+IkjtC6NMJkIY3nmCb74pS9o++FKlMdfZOWnPk6odDE9n7i98BnJxEyyfOvYr1kxcpRd\nzV8mE4/jbKhDBTZV9jGdGjWSRuxvJ7fkYgzt+4mOBVmh/pyyS67GMdyB4Nfi72NRM6tLBugEKtwa\nxfaF5DJcCxZQuU9LUAqiyNrRh+j9n0cpu/E9CM/+J+olH2ZyLEzihScLNRgOlw1VlChZ0EgXYHda\n0Pce5JuuZ7n/b/8VgHJnnOOTPpZOTRKLxFEUGZMBHtxfwrZVNlx5Gq7BpOe7o9dQdtNORBE2pj9K\nZG+xCups1C2qIRlPMdQ9/6w0Nz5KIpNGTcR4i/8Y0oO/4Nqrb6f3oQMsyMxoLqkeP1ImQSQYRTTo\nEUUBOZvFVqENPtHxAGaLkRXKK8RyOUSDHlupFwQIHTyMNW8sE48kCUYl6lxRdAYdcjLOTeY/4v7w\n5Qh5m7HPrNnFqK2WinAnUYcVQc3hLnFQt/vbXHbll0jmb0u9UY+kEwgYKli2xkOfLFCeSFNxxXa2\nrNPyC2mnn5yoxzA5gt6o592bRhmMuUlPaIN/i6Jdu8GKdYz0/hdN77wJHngeVIVNDWOU5dai8/vh\nxJltHd9IOOOMXlXVZzkp9aCq6p9UVZ2u9d+NZhkIcB3wK1VV06qq9qAZkKw7j8f7Z4Wppg5TTR2N\n65bMeW1R4iDOrheoSPcz/qu7GP/VXeSiMQwGiWRXB5lkGlf/AUqaakglMwgmC4LJwsBPfoiSiJP6\n6Td4a9cX0CnaXRt9/EF2dHyZixoDXNQYoNSpdWBGfymSXoeQy5AKR7HazUgiSCLoXNqspPq5H9Fq\n7cfkclC+9x68tRrzITg4TnBwHNGgJxOLozzzwJzzOBXi8RRWuxmbQ/s5HWwOC7lomOTRQ0XtiruM\nhotXkvnlf+Lu1pKhC2+8nvv17+B+/TvwN1WS2HQ9Ym0zJSeewhQaRpocRGrfj8FmhS+/F778XvRy\nkiWf+jT3Ri6H7KnNWTCaCY9O0uoeJZtVUDx+UjkdJZMzHaEjPEyut5396jIor0XWGTn26PP4jz9O\nzmgDgxEMRiYjOkoPPQzAWlVzTXphv8jHbmtDbzahN5s0MbNkgngkidR/AmNlFdXyAC0rGrAtWU7T\nxlU0bVxFJp3B6HBgTGohNY/fg7GsgvHuYa5t/yLXtn+RVn0P1+seIZtOo6oQj6W4pr6Nhmo42GvG\nX+3DX+1Dycn4HBlePigjqgojHTPSAUaTDqOpeC7XfawfRZZPaQpfuWUT2XgMd101ru69WCsq8I6f\nwO81kcmqZLJa52uobWLUtZB4NMnu3z6BJInk4nGEqgaEqgbKVy/HU+pEkHPorWbUnIxl8VJUWWHo\nRE/hmgEsN3awQGzH63eic7gQ42FMX7mR9eI+1ov7OPrNb1F1/E9IoTEkgxHVV0koECUbT/A+4S7e\n3biPdzfuw2AxUTPyHHqdwIeFfyeHhN5s4IXv/ISkv4GkvwFdNokxOEgqkUZRVCpG92P6xi1INotW\nKfvQPWQeuoeKqRN4/S6YmvZ0FjD86HMYDRJq+vSGMm9EnA/WzfuAX+f/rkTr+KcxmG/7i0B2Snsw\nzc658rOGXJyDT+1hRWUF3kpNUEkw6Mikc/RtvYMavgcjvcTzIl1Cvpbf7Xeir67D2NNP29MvsfRy\nrZrUu3Il++/6LeWjvdr/jTXsBgRvGZPDz9BjuZLule+gzZZl5yZtCWzJactqc2kJlvu/Q8Rowlnu\nRclp4/S0u9XhR3ex8p3XEWs7e8aNwaAjk86i04lzNGZORsOKZu4u/xzvLXkM/jjjKxqz+XFaLISf\neIGSoMagGfav5/vf1FYVvyz5DMaf6iivaeWOrQMI93wNOVlCKhKjZM0a1ud1XOSXn+Gf4p9h7QpI\n//CLhf1b7BZWvONKAHb9+HekfLWYrGZK9t/LYCxK2lZCJCFprtt5yDojkl7P88ft1Nf6iZujNN/y\nHqRsAjUSQPZqrBFjTKW3bjtwH71WLcEYj+UQ7GZKLr5Y25fVwS9iV7L5zkvIvvwb9i5+PxcF/4Rt\nxRJSpXWY4tqMuKSiBHR6Enn1R4vXSaaimdLGEyQntJl24qffYjQUxfr3/0bF3qNMDE/iivRz9b7v\n037VF4k9mWcD5RRuzP6C5VfspP657zNc4iSa1/FPp+YOgoqsMNI3gSAIlJQ5mZhVRASQrl5M4J3X\nEZREvLowPQk/tdZJqtcF6K7QmCvSIy8QT+uIUcryKzaQmpjkxL5OlJzCZw9qVMZlzTKXerT7q23X\nEbKZHKIAR7d9lhXrXuDgN79T+MyMzUNp21MMhuJEWi5GlSSM3E3i91p4rqa5nFxwAikWoWn7Zh7W\nXUWd724cGzaizyTwTWif0z0SRA4HsR5+hvhUlDXpPYgrV3BsTxuGoy8CMHb4BPbyUpR0ClARBztJ\nBaeQBjvJZWVKN2rfZa59PxaXna7Hn9UOUhCoWtKIMjGKoUwAzl5V9I2A15SJEwThs0AOuGe6aZ7N\n5tX1FAThg4Ig7BUEYe/ExMR8m1zABVzABVzAecCrntELgnArcDVwmToj0jwIzLZDqgKGT34vgKqq\nPwB+ALBmzZo3hciz4M2HQJ57as5rv0nu4JK3RflB7iZu2qaV4Otf0pb6P37EjSB+jjVL4dLoPzJy\nooepJq2c25XN8ivhJja9+yJWvnWEvZ//RwB2lb+V5Z9eQOfXvwyANE3rFAREncS+YwK//tY+4l1R\njl6t0QU//zGtHL934U7KhodQesZILNmM8YDGubZ7tJVIMpXjR+otvGfdI/CnvWd17tPyydnTG/cA\nIF52A/d8voMXl6/lllnteiXDN/uvoewd11LpeBJf+SsYdCqL12txXVTY/YcORrosfLrNzneXNzDW\ntA2naKKtfCsnLNp2G5/8G0x+HdeF/puDwzNG05JeR+/iadej33FX/0XYt17ENu8R2HsYVdJTYw/x\n4PgVuPmedl46E2NHO9h+5xTDmQYkS4KvdG+hoQrew13k9Noq4nr/Pu4bWk4NMBJz4nFZuPmKDF/c\nFkXNakJ3CcnMrsdTZBaWs6XiYr76zSgfeO9lXG27nz8EN3BdWDMcd1SU0mdqwLdxMcKje3A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rQMG35bHEsAoofA543xoyUbeKz5agD+R9EGBkbhZ62X89XlBXRYapl75RHGvGZ+97a6xzz9fv7Z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\nJsnRhl6j0WiSHG3oNRqNJsn5/0OcBVEqbVKKAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2303297c438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7091\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "plt.imshow(data[1300])\n",
    "plt.show()\n",
    "\n",
    "labelling.head()\n",
    "\n",
    "print(len(files))\n",
    "\n",
    "#labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "    \n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "\n",
    "#train, test, train_l, test_l= train_test_split(data,pd.DataFrame(labels),test_size=0.33, random_state=42)\n",
    "\n",
    "\n",
    "train_validate, test, train_l_validate, test_l= train_test_split(data,pd.DataFrame(labels),test_size=0.33, random_state=42)\n",
    "\n",
    "train, validate, train_l, validate_l= train_test_split(train_validate,train_l_validate,test_size=0.1, random_state=42)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "len(train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4275, 300, 150, 3)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "X_train = np.array(train).reshape(-1,300,150,3)\n",
    "X_validate = np.array(validate).reshape(-1,300,150,3) # I need validate to find the threeshold.\n",
    "X_test = np.array(test).reshape(-1,300,150,3)\n",
    "\n",
    "print(np.shape(X_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4275, 300, 150, 3)\n"
     ]
    }
   ],
   "source": [
    "train = None\n",
    "test= None\n",
    "\n",
    "print(np.shape(X_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_6 (Conv2D)            (None, 300, 150, 16)      1216      \n",
      "_________________________________________________________________\n",
      "activation_8 (Activation)    (None, 300, 150, 16)      0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_7 (Batch (None, 300, 150, 16)      64        \n",
      "_________________________________________________________________\n",
      "max_pooling2d_4 (MaxPooling2 (None, 150, 75, 16)       0         \n",
      "_________________________________________________________________\n",
      "dropout_5 (Dropout)          (None, 150, 75, 16)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_7 (Conv2D)            (None, 150, 75, 32)       12832     \n",
      "_________________________________________________________________\n",
      "activation_9 (Activation)    (None, 150, 75, 32)       0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_8 (Batch (None, 150, 75, 32)       128       \n",
      "_________________________________________________________________\n",
      "conv2d_8 (Conv2D)            (None, 150, 75, 32)       25632     \n",
      "_________________________________________________________________\n",
      "activation_10 (Activation)   (None, 150, 75, 32)       0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_9 (Batch (None, 150, 75, 32)       128       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_5 (MaxPooling2 (None, 75, 37, 32)        0         \n",
      "_________________________________________________________________\n",
      "dropout_6 (Dropout)          (None, 75, 37, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_9 (Conv2D)            (None, 75, 37, 32)        25632     \n",
      "_________________________________________________________________\n",
      "activation_11 (Activation)   (None, 75, 37, 32)        0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_10 (Batc (None, 75, 37, 32)        128       \n",
      "_________________________________________________________________\n",
      "conv2d_10 (Conv2D)           (None, 75, 37, 32)        25632     \n",
      "_________________________________________________________________\n",
      "activation_12 (Activation)   (None, 75, 37, 32)        0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_11 (Batc (None, 75, 37, 32)        128       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_6 (MaxPooling2 (None, 37, 18, 32)        0         \n",
      "_________________________________________________________________\n",
      "dropout_7 (Dropout)          (None, 37, 18, 32)        0         \n",
      "_________________________________________________________________\n",
      "flatten_2 (Flatten)          (None, 21312)             0         \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 32)                682016    \n",
      "_________________________________________________________________\n",
      "activation_13 (Activation)   (None, 32)                0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_12 (Batc (None, 32)                128       \n",
      "_________________________________________________________________\n",
      "dropout_8 (Dropout)          (None, 32)                0         \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 11)                363       \n",
      "_________________________________________________________________\n",
      "activation_14 (Activation)   (None, 11)                0         \n",
      "=================================================================\n",
      "Total params: 774,027\n",
      "Trainable params: 773,675\n",
      "Non-trainable params: 352\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "import models_elisa\n",
    "\n",
    "model=models_elisa.small_vgg(150,300,3,11,params=[16,32,32,32], stride=5) #this creates a feature extractor for VGG...\n",
    "\n",
    "#model.compile(loss='categorical_crossentropy', optimizer=Adam(lr=0.0001), metrics=['accuracy'])\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 1.,  1.,  1.],\n",
       "        [ 1.,  1.,  1.],\n",
       "        [ 1.,  1.,  1.],\n",
       "        ..., \n",
       "        [ 1.,  1.,  1.],\n",
       "        [ 1.,  1.,  1.],\n",
       "        [ 1.,  1.,  1.]],\n",
       "\n",
       "       [[ 1.,  1.,  1.],\n",
       "        [ 1.,  1.,  1.],\n",
       "        [ 1.,  1.,  1.],\n",
       "        ..., \n",
       "        [ 1.,  1.,  1.],\n",
       "        [ 1.,  1.,  1.],\n",
       "        [ 1.,  1.,  1.]],\n",
       "\n",
       "       [[ 1.,  1.,  1.],\n",
       "        [ 1.,  1.,  1.],\n",
       "        [ 1.,  1.,  1.],\n",
       "        ..., \n",
       "        [ 1.,  1.,  1.],\n",
       "        [ 1.,  1.,  1.],\n",
       "        [ 1.,  1.,  1.]],\n",
       "\n",
       "       ..., \n",
       "       [[ 1.,  1.,  1.],\n",
       "        [ 1.,  1.,  1.],\n",
       "        [ 1.,  1.,  1.],\n",
       "        ..., \n",
       "        [ 1.,  1.,  1.],\n",
       "        [ 1.,  1.,  1.],\n",
       "        [ 1.,  1.,  1.]],\n",
       "\n",
       "       [[ 1.,  1.,  1.],\n",
       "        [ 1.,  1.,  1.],\n",
       "        [ 1.,  1.,  1.],\n",
       "        ..., \n",
       "        [ 1.,  1.,  1.],\n",
       "        [ 1.,  1.,  1.],\n",
       "        [ 1.,  1.,  1.]],\n",
       "\n",
       "       [[ 1.,  1.,  1.],\n",
       "        [ 1.,  1.,  1.],\n",
       "        [ 1.,  1.,  1.],\n",
       "        ..., \n",
       "        [ 1.,  1.,  1.],\n",
       "        [ 1.,  1.,  1.],\n",
       "        [ 1.,  1.,  1.]]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 3847 samples, validate on 428 samples\n",
      "Epoch 1/50\n",
      "3847/3847 [==============================] - 66s 17ms/step - loss: 0.4892 - acc: 0.7862 - val_loss: 0.4711 - val_acc: 0.7991\n",
      "Epoch 2/50\n",
      "3847/3847 [==============================] - 58s 15ms/step - loss: 0.4859 - acc: 0.7873 - val_loss: 0.4635 - val_acc: 0.7995\n",
      "Epoch 3/50\n",
      "3847/3847 [==============================] - 59s 15ms/step - loss: 0.4835 - acc: 0.7873 - val_loss: 0.4711 - val_acc: 0.7995\n",
      "Epoch 4/50\n",
      "3847/3847 [==============================] - 60s 16ms/step - loss: 0.4812 - acc: 0.7867 - val_loss: 0.4568 - val_acc: 0.7999\n",
      "Epoch 5/50\n",
      "3847/3847 [==============================] - 61s 16ms/step - loss: 0.4760 - acc: 0.7897 - val_loss: 0.4828 - val_acc: 0.7993\n",
      "Epoch 6/50\n",
      "3847/3847 [==============================] - 61s 16ms/step - loss: 0.4765 - acc: 0.7886 - val_loss: 0.4818 - val_acc: 0.7991\n",
      "Epoch 7/50\n",
      "2720/3847 [====================>.........] - ETA: 16s - loss: 0.4697 - acc: 0.7926"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-58-ed0882769661>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     20\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     21\u001b[0m \u001b[1;31m#before identifying thresshold on validation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 22\u001b[1;33m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrain_l\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbatch_size\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m32\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m50\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mearly_stop\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcheck_point\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalidation_split\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0.1\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# validation_data=[X_validate]) #,validation_data=(X_test1,Y_test))\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     23\u001b[0m \u001b[1;31m#model.fit(train_data, train_labels, batch_size = 32, epochs=5, callbacks= [early_stop, check_point], validation_split=0.1) # validation_data=[X_validate]) #,validation_data=(X_test1,Y_test))\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     24\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda3\\lib\\site-packages\\keras\\engine\\training.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, **kwargs)\u001b[0m\n\u001b[0;32m   1040\u001b[0m                                         \u001b[0minitial_epoch\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0minitial_epoch\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1041\u001b[0m                                         \u001b[0msteps_per_epoch\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msteps_per_epoch\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1042\u001b[1;33m                                         validation_steps=validation_steps)\n\u001b[0m\u001b[0;32m   1043\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1044\u001b[0m     def evaluate(self, x=None, y=None,\n",
      "\u001b[1;32mD:\\Anaconda3\\lib\\site-packages\\keras\\engine\\training_arrays.py\u001b[0m in \u001b[0;36mfit_loop\u001b[1;34m(model, f, ins, out_labels, batch_size, epochs, verbose, callbacks, val_f, val_ins, shuffle, callback_metrics, initial_epoch, steps_per_epoch, validation_steps)\u001b[0m\n\u001b[0;32m    197\u001b[0m                     \u001b[0mins_batch\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mins_batch\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtoarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    198\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 199\u001b[1;33m                 \u001b[0mouts\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mins_batch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    200\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mouts\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    201\u001b[0m                     \u001b[0mouts\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mouts\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda3\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, inputs)\u001b[0m\n\u001b[0;32m   2659\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_legacy_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2660\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2661\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2662\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2663\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mpy_any\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mis_tensor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[1;32min\u001b[0m \u001b[0minputs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda3\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py\u001b[0m in \u001b[0;36m_call\u001b[1;34m(self, inputs)\u001b[0m\n\u001b[0;32m   2629\u001b[0m                                 \u001b[0msymbol_vals\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2630\u001b[0m                                 session)\n\u001b[1;32m-> 2631\u001b[1;33m         \u001b[0mfetched\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_callable_fn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0marray_vals\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2632\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mfetched\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2633\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args)\u001b[0m\n\u001b[0;32m   1449\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_created_with_new_api\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1450\u001b[0m           return tf_session.TF_SessionRunCallable(\n\u001b[1;32m-> 1451\u001b[1;33m               self._session._session, self._handle, args, status, None)\n\u001b[0m\u001b[0;32m   1452\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1453\u001b[0m           return tf_session.TF_DeprecatedSessionRunCallable(\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "from keras.callbacks import EarlyStopping,ModelCheckpoint\n",
    "from keras.callbacks import LambdaCallback\n",
    "from keras import optimizers\n",
    "\n",
    "#Binary crossentropy: this is a multi-label classification problem\n",
    "\n",
    "opt = optimizers.Adam(lr=0.0001)\n",
    "\n",
    "model.compile(loss='binary_crossentropy', optimizer=opt, metrics=['accuracy'])\n",
    "#model.summary()\n",
    "\n",
    "early_stop = EarlyStopping(monitor='val_loss', patience=10)\n",
    "check_point = ModelCheckpoint('elisa_small_11_bce_final.npy', save_best_only=True)\n",
    "\n",
    "\n",
    "train_data= None\n",
    "train_labels=None\n",
    "#train_data = np.append(X_train,X_validate, axis = 0)\n",
    "#train_labels = np.append(train_l,validate_l, axis=0)\n",
    "\n",
    "#before identifying thresshold on validation\n",
    "model.fit(X_train, np.array(train_l), batch_size = 32, epochs=50, callbacks= [early_stop, check_point], validation_split=0.1) # validation_data=[X_validate]) #,validation_data=(X_test1,Y_test))\n",
    "#model.fit(train_data, train_labels, batch_size = 32, epochs=5, callbacks= [early_stop, check_point], validation_split=0.1) # validation_data=[X_validate]) #,validation_data=(X_test1,Y_test))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2328"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(test_l.iloc[:,2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model.load_weights(\"elisa_small_11_bce_final.npy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "predicted_labels=model.predict(X_test)\n",
    "pr = [i.tolist() for i in predicted_labels]\n",
    "pr_max = [np.argmax(i) for i in predicted_labels]\n",
    "pr_round = [np.round(i) for i in predicted_labels]\n",
    "\n",
    "\n",
    "\n",
    "my_test_l=np.array(test_l).tolist()\n",
    "\n",
    "my_test_max = [np.argmax(i) for i in my_test_l]\n",
    "\n",
    "\n",
    "original = pd.DataFrame(my_test_l)\n",
    "\n",
    "\n",
    "predicted = pd.DataFrame(pr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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9Pc0j3Q9Y9SBTeBC6TiFf4hyNHTuo7YU17llZxIa9Fm8TMne/hpzAZ//Xv1KY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wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2140416c240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([ 0.72838801,  0.72523516,  0.6964196 ,  0.68077689,  0.81079865,\n",
       "        0.86178052,  0.76870143,  0.87034059,  0.75648677,  0.90389448,\n",
       "        0.8581295 ,  0.76396418,  0.83574766,  0.86732358,  0.71261066,\n",
       "        0.5151692 ,  0.88990355,  0.84924209,  0.7975598 ,  0.84889853,\n",
       "        0.69176579,  0.7907151 ,  0.84870553,  0.84077322,  0.81665623,\n",
       "        0.81552964,  0.91946226,  0.87447578,  0.80709499,  0.74493766,\n",
       "        0.68553668,  0.93218821,  0.78551549,  0.69067669,  0.667642  ,\n",
       "        0.88241929,  0.93049657,  0.79831868,  0.89392751,  0.83192134,\n",
       "        0.68391949,  0.51223987,  0.58102143,  0.87329191,  0.73401338,\n",
       "        0.86640513,  0.67525518,  0.76898372,  0.79595715,  0.73104006,\n",
       "        0.67207831,  0.72072625,  0.85879403,  0.80900776,  0.54541659,\n",
       "        0.78843874,  0.67229986,  0.93553317,  0.86197269,  0.83180434,\n",
       "        0.77295566,  0.65508884,  0.86392444,  0.83553445,  0.84583414,\n",
       "        0.48005226,  0.60659105,  0.79126132,  0.91951144,  0.76533431,\n",
       "        0.74260718,  0.80545753,  0.61064702,  0.73797435,  0.58042425,\n",
       "        0.8263225 ,  0.90068245,  0.82322681,  0.90212095,  0.72153687,\n",
       "        0.87389344,  0.63471693,  0.81475848,  0.81984884,  0.801929  ,\n",
       "        0.8030206 ,  0.82606792,  0.87368399,  0.64761192,  0.85668308,\n",
       "        0.82045352,  0.84490085,  0.79769009,  0.84575683,  0.8628093 ,\n",
       "        0.68263662,  0.91396517,  0.88138044,  0.79217803,  0.91759235], dtype=float32)"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "limit = 100\n",
    "\n",
    "plt.subplot(3,1,1)\n",
    "plt.plot(list(range(0,limit)),predicted_labels[0:limit,0], 'b')\n",
    "plt.plot(list(range(0,limit)),np.array(test_l)[0:limit,0], 'r')\n",
    "\n",
    "\n",
    "plt.subplot(3,1,2)\n",
    "plt.plot(list(range(0,limit)),predicted_labels[0:limit,1], 'b')\n",
    "plt.plot(list(range(0,limit)),np.array(test_l)[0:limit,1], 'r')\n",
    "\n",
    "\n",
    "plt.subplot(3,1,3)\n",
    "\n",
    "plt.plot(list(range(0,limit)),predicted_labels[0:limit,2], 'b')\n",
    "plt.plot(list(range(0,limit)),np.array(test_l)[0:limit,2], 'r')\n",
    "\n",
    "\n",
    "plt.show()\n",
    "\n",
    "np.array(test_l)[0:limit,0]\n",
    "predicted_labels[0:limit,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "predicted_labels_v=model.predict(X_validate)\n",
    "pr_v = [i.tolist() for i in predicted_labels_v]\n",
    "\n",
    "my_validate_l=np.array(validate_l).tolist()\n",
    "\n",
    "original_v = pd.DataFrame(my_validate_l)\n",
    "\n",
    "\n",
    "predicted_v = pd.DataFrame(pr_v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def Find_Optimal_Cutoff(target, predicted):\n",
    "    \"\"\" Find the optimal probability cutoff point for a classification model related to event rate\n",
    "    Parameters\n",
    "    ----------\n",
    "    target : Matrix with dependent or target data, where rows are observations\n",
    "\n",
    "    predicted : Matrix with predicted data, where rows are observations\n",
    "\n",
    "    Returns\n",
    "    -------     \n",
    "    list type, with optimal cutoff value\n",
    "\n",
    "    \"\"\"\n",
    "    fpr, tpr, threshold = roc_curve(target, predicted)\n",
    "    i = np.arange(len(tpr)) \n",
    "    roc = pd.DataFrame({'tf' : pd.Series(tpr-(1-fpr), index=i), 'threshold' : pd.Series(threshold, index=i)})\n",
    "    roc_t = roc.ix[(roc.tf-0).abs().argsort()[:1]]\n",
    "\n",
    "    return list(roc_t['threshold']) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:17: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n"
     ]
    },
    {
     "data": {
      "image/png": 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jfCguLoF33/2DXbuOM3bsHQA0b16elSt7+uWdiEthicIYY3xk+fK9PPHELNas+ReAnj0b\nULNmCYAskyTAHo81xpgr7vjxaHr3nk2jRh+yZs2/lCtXkO+/75KUJLIau6MwxpgraOrUDfTrN48D\nB04TGJiLgQMb88ILTcmXL4+/Q7tkliiMMeYK+uGHvzlw4DRNmpRh4sS21K6dsQ34+YIlCmOMuQwx\nMXHs3XuSihULAzByZAtuvrksDz9cL0vVQ6TF6iiMMeYS/fTTTurUCadt2885ezYegGLF8tK9e/1s\nkyTAEoUxxly0AwdO0a3bdG677VO2bj0CQETECT9H5TtW9GSMMV5KSFA++GAVzz33I8ePRxMcHMiw\nYTczaFAT8uQJ8Hd4PmOJwhhjvHTPPV8yc+ZfALRqVYkJE9pQqVIRP0fle1b0ZIwxXurQoRpXXZWf\nL7/syNy5XXNEkgC7ozDGmFTNnPkXEREn6N37egAeeqguHTpUJzQ0yM+RZSxLFMYYk8zu3ZE8/fRc\nZsz4i6CgAFq3rkzFioURkRyXJMAShTHGJImNjWfcuD946aXFnD4dS2hoHl577VbKlSvo79D8yhKF\nMcYAy5ZF8MQTs1i37gAA991XgzFjWlGqVAE/R+Z/liiMMdnPt21h55yLWuSFSd1Yt60SFYocY/w9\nc2hTfRtM9VF8WYwlCmNM9uNFklCFkzFBFAiOAWD8PXP4dFVdht72C3nzxPo6wiuvQhufrdoShTEm\n+xqoKY7+66/D9O49BxFYsKAbIkJV4PWMjS7LsERhjMkxoqPjeOONXxgx4jfOno2naNEQdu06ToUK\nhf0dWqZmicIYkyMsWPA3vXvPYfv2owA8+mg9Ro5sQdGief0cWebn0zezRaS1iPwlIttF5LkUppcV\nkUUislpE1omI7wrZjDE5kqry6KMzaNlyCtu3H6VGjeIsWfIIH33UzpKEl3x2RyEiAcAEoAUQAawQ\nkZmqusljtmHAV6o6UURqAHOA8r6KyRiT84gI5csXIiQkkBdfbMaAAY2zdQN+vuDLoqeGwHZV3QEg\nIlOBdoBnolAg8SHlgsA+H8ZjjMkh1uy9iv0n8nOHOzx4cBO6datjdRGXyJdFT6WAPR7DEe44T8OB\nB0UkAuduom9KKxKRniKyUkRWHjp0yBexGmOygZMnYxgwYD4N3unJw1Pv4ejRKACCggItSVwGXyaK\nlLp3Sv6sWhdgsqqWBtoA/xORC2JS1fdVNUxVw4oXL+6DUI0xWZmqMn36ZmrUeI8xY5YB8MB168md\n2xrIvhJ8WfQUAZTxGC7NhUVLPYDWAKq6VESCgWLAQR/GZYzJRv755zh9+sxl1qytAISFXcOkm17i\nutL7IQc24OcLvky3K4AqIlJBRPIAnYGZyebZDdwGICLVgWDAypaMMV5RVe699ytmzdpKgQJBjB9/\nB8uW9XCShLlifHZHoapxItIHmA8EAB+r6kYReQVYqaozgYHAByLSH6dY6hFVTflVSmOMcSUkKLly\nCSLCqFEtCQ9fyZgxrbj66lB/h5YtSVb7XQ4LC9OVK1f6OwxjjB8cOXKG555bCMAHH9yd+oxvu1Wk\nqTThkROJyCpVDbuUZa2mxxiT6akq//d/a6hWbQIffriaTz9dR0TECX+HlWNYEx7GmExt8+ZDPPnk\nbH7++R8Amjcvz8SJbSld2vqJyCiWKIwxmZKq8uKLi3jzzd+IjU2gWLG8vP12S7p1q4NISk/fG1+x\nRGGMyZREhL17TxIbm8Djj1/HiBG3U6RIiL/DypEsURhjMo19+05y+PAZ6tQpCcDIkS3o0aM+TZqU\n9XNkOZtVZhtj/C4+PoHx45dTvfoEOneextmz8QAUK5bXkkQmYHcUxhi/+vPP/TzxxCxWrnQabmja\ntBwnTsRQrJg1AZ5ZeJUo3Dery6rqdh/HY4zJIU6ciOGFF35i/PgVJCQopUsXYNy41rRvX80qqzOZ\ndBOFiLQFRgN5gAoiUg94SVXv8XVwxpjsSVVp2vQT1q49QECAMGBAI4YPb06otc2UKXlTR/EKcANw\nHEBV1wCVfRmUMSZ7ExH6929Ew4alWLmyJ2+/3cqSRCbmTdFTrKoeT3YraO/FG2O8dvZsPKNHLyUg\nQBg0qAkADz1UlwcfrENAgD1Tk9l5kyg2i8j9QC4RqQA8AyzzbVjGmOzil1/+oVev2WzadIigoAAe\neqguJUvmR0QICLC6iKzAm1TeB2gAJADfAtE4ycIYY1J1+PAZHn10Bk2bTmbTpkNUqVKEWbMeoGTJ\n/P4OzVwkb+4oWqnqYGBw4ggR6YCTNIwx5jyqyuTJaxg0aAFHjkSRJ08AQ4bcxHPP3URwsD2RnxV5\nc0cxLIVxQ690IMaY7GPKlPUcORLFrbdWYN26Xgwf3tySRBaW6l9ORFrhdFNaSkRGe0wqgFMMZYwx\nAJw5E0tkZDRXXx2KiPDee21YsWIfXbvWtncisoG0UvxBYANOncRGj/Enged8GZQxJuuYO3cbTz01\nh4oVC7NgQTdEhKpVi1G1ajF/h2aukFQThaquBlaLyGeqGp2BMRljsoC9e0/Qr998pk3bBEBoaBBH\njkRZ0xvZkDeFhqVE5HWgBhCcOFJVr/VZVMaYTCs+PoEJE1YwbNhPnDx5lnz5cvPKK7fw9NM3EBho\n70RkR94kisnAa8Ao4A6gO1ZHYUyOlJCgNGs2md9+2wNA+/bVGDu2NWXLFvRzZMaXvEn/eVV1PoCq\n/q2qw4BbfBuWMSYzypVLaNmyEmXKFGDGjM5Mn97JkkQO4M0dRYw4jy38LSK9gL1ACd+GZYzJDFSV\nr77aSGBgLu69twYAgwc3YcCAxuTPn8fP0ZmM4k2i6A/kB54GXgcKAo/6MihjjP/9/fdReveeww8/\n/E3x4nm59dYKFC4cQlBQIEHWfl+Okm6iUNU/3I8ngW4AIlLal0EZY/wnJiaOt976nddf/4Xo6DgK\nFw7m9ddvpWDB4PQXNtlSmolCRK4HSgG/quphEamJ05THrYAlC2OymcWLd/Hkk7PZsuUwAN261WHU\nqJaUKJHPz5EZf0q1MltE3gA+A7oC80RkKLAIWAvYo7HGZDPx8Qn07u0kiapVi/LTTw/x6af3WJIw\nad5RtAPqqmqUiBQB9rnDf2VMaMYYX0tIUKKj48ibNzcBAbmYOLEtS5b8w7PPNiEoyNpmMo60zoRo\nVY0CUNWjIrLFkoQx2cf69Qfo1Ws21aoV5aOP2gHQrFl5mjUr79/ATKaTVqKoKCKJTYkLUN5jGFXt\n4NPIjDE+cfr0WV555WdGj15GXFwCO3ce49ixKAoXDvF3aCaTSitR3JtseLwvAzHG+N733/9Fnz5z\n2b07EhHo3TuM11+/jUKF7Ikmk7q0GgX8MSMDMcb4TlxcAp06TePbbzcDUK/eVUyadCcNG5byc2Qm\nK7DaKmNygMDAXBQsGET+/Hl49dVb6NOnoTXgZ7zm0zNFRFqLyF8isl1EUuzDQkTuF5FNIrJRRD73\nZTzG5CR//BHBH39EJA2/9VYLNm9+in79GlmSMBfF6zsKEQlS1ZiLmD8AmAC0ACKAFSIyU1U3ecxT\nBRgCNFHVYyJibUgZc5mOH49myJCFTJq0imrVirFmTS/y5AmgaFHrJ8JcmnQvK0SkoYisB7a5w3VF\n5F0v1t0Q2K6qO1T1LDAV590MT48DE1T1GICqHryo6I0xSVSVzz9fT7Vq4wkPX0VAQC7uvrsq8fHW\nK4C5PN7cUYwD7gS+A1DVtSLiTTPjpYA9HsMRwA3J5rkWQER+AwKA4ao6z4t1G2M8bNt2hN6957Bw\n4Q4AmjQpQ3j4ndSqZTfp5vJ5kyhyqeo/yTpIj/diuZR6VNcUtl8FaI7TdtQvIlJLVY+ftyKRnkBP\ngLJly3qxaWNyjtjYeG699VMiIk5QpEgII0feTvfu9cmVK6WvoDEXz5tEsUdEGgLq1jv0BbZ6sVwE\nUMZjuDROMyDJ51mmqrHAThH5CydxrPCcSVXfB94HCAsLS55sjMmRVBURIXfuAF5//VYWLdrFyJG3\nU7y4tc1krixvHn14EhgAlAUOAI3ccelZAVQRkQoikgfoDMxMNs93uL3liUgxnKKoHd6FbkzOdODA\nKbp1m85rry1JGvfQQ3X55JN2liSMT3hzRxGnqp0vdsWqGicifYD5OPUPH6vqRhF5BVipqjPdaS1F\nZBNOcdYgVT1ysdsyJidISFA++GAVzz33I8ePR1OoUDD9+jUiNNR6ETK+5U2iWOEWCX0JfKuqJ71d\nuarOAeYkG/eix2fFuVsZ4O06jcmJ1q79l169ZrNsmfNeROvWlZkwoY0lCZMhvOnhrpKI3IhTdPSy\niKwBpqrqVJ9HZ0wOFxsbz5AhP/LOO8uIj1euvjo/Y8e2pmPHGiR7wMQYn/Hq9UxV/V1VnwauA07g\ndGhkjPGxwMBcrF79LwkJSt++Ddm8+Snuu6+mJQmTodK9oxCR/DgvynUGqgMzgBt9HJcxOdbu3ZHE\nxydQoUJhRITw8LZERsYQFnaNv0MzOZQ3dRQbgO+Bkar6i4/jMSbHio2NZ+zYP3jppcU0blyaBQu6\nISJUqVLU36GZHM6bRFFRVa0NAGN8aOnSPfTqNZt16w4AUKRICGfOxJIvXx4/R2ZMGolCRN5W1YHA\nNyJywUtu1sOdMZfv2LEonntuIe+//ycAFSoUYsKENtxxRxU/R2bMOWndUXzp/m892xnjAzExcdSr\nN4nduyPJnTsXgwbdyNChTcmbN7e/QzPmPGn1cLfc/VhdVc9LFu6LdNYDnjGXISgokB496vPjjzuZ\nOLEtNWoU93dIxqTIm8djH01hXI8rHYgx2V10dBwvvbSIzz9fnzTu+edvZvHihy1JmEwtrTqKTjiP\nxFYQkW89JoUCx1NeyhiTkgUL/qZ37zls336UEiXycc891QgJyW09zZksIa06iuXAEZxWXyd4jD8J\nrPZlUMZkF//+e4oBA+bzxRcbAKhZszjh4XcSEmL1ECbrSKuOYiewE1iYceEYkz3ExycwadIqnn/+\nRyIjYwgJCeSll5rRv39j8uQJ8Hd4xlyUtIqeflbVZiJyjPM7HBKc9vyK+Dw6Y7Ko+Hjl3XeXExkZ\nQ5s2VRg//g4qVCjs77CMuSRpFT0ldndaLCMCMSarO3kyhvh4pVChYPLkCeCDD+7iwIFTdOhQ3dpm\nMllaWkVPiW9jlwH2qepZEbkJqANMwWkc0JgcT1WZPn0LTz89l1atKvHRR+0AuOmmZN32ftsWds5J\nYQ3GZG7ePHLxHU43qJWAT3EaBvzcp1EZk0Xs2nWcu++eyr33fsXevSfZsOEQ0dFxKc9sSSJjVWjj\n7wiyDW/aekpQ1VgR6QC8o6rjRMSeejI5WmxsPKNHL+Xll38mKiqOAgWC+O9/b6VXrzACAtK5/hpo\n3b6brMWrrlBF5D6gG9DeHWfP9pkc68yZWBo1+pD16w8C0LlzLUaPbsnVV4f6OTJjfMObRPEo0Bun\nmfEdIlIB+MK3YRmTeeXNm5uwsGs4cyaW995rS8uWlfwdkjE+5U1XqBtE5GmgsohUA7ar6uu+D82Y\nzEFV+fTTtVSqVCSpgnrMmFbkyRNgL86ZHMGbHu5uBv4H7MV5h+IqEemmqr/5Ojhj/G3z5kM8+eRs\nfv75H6pXL8aaNb3IkyeAggWD/R2aMRnGm6KnMUAbVd0EICLVcRJHmC8DM8afoqJief31Xxg58jdi\nYxMoXjwvQ4bcRO7c1jaTyXm8SRR5EpMEgKpuFhHrdstkW/Pmbeepp+awY8cxAB5//DpGjLidIkVC\n/ByZMf7hTaL4U0Qm4dxFAHTFGgU02dSpU2fp1m06hw+foVatEoSHt6VJk7LpL2hMNuZNougFPA08\ni1NHsQR415dBGZOR4uMTSEhQcucOIH/+PIwd25qIiBP079+I3LmtAT9j0kwUIlIbqARMV9WRGROS\nMRln1ap9PPHELNq1q8oLLzQD4IEHavs5KmMyl1Rr5kTkeZzmO7oCC0QkpZ7ujMmSTpyI4Zln5tKw\n4YesWrWf//1vHbGx8f4Oy5hMKa07iq5AHVU9LSLFgTnAxxkTljG+oapMm7aJZ56Zx/79pwgIEAYM\naMTLL99ixUzGpCKtRBGjqqcBVPWQiNhzgSZLO3kyhk6dpjF37nYAbrihFOHhd1Kv3lV+jsyYzC2t\nRFHRo69sASp59p2tqh18GpkxV1j+/HmIiYmnYMEgRoy4nZ49G5Arl/UTYUx60koU9yYbHu/LQIzx\nhSVL/uHqq/NTpUpRRISPP74cBX7vAAAehElEQVSb4OBASpbM7+/QjMky0uq46MeMDMSYK+nw4TM8\n++wCPvlkDbfdVoEFC7ohIpQrV8jfoRmT5XjzHoUxWUZCgjJ58hoGDVrA0aNR5MkTwM03lyU+XgkM\ntGImYy6FTyuoRaS1iPwlIttF5Lk05usoIioi1n6UuWQbNx6kefPJ9Ogxk6NHo7jttgqsX/8kL73U\nnMBAexbDmEvl9R2FiASpasxFzB8ATABaABHAChGZ6dlulDtfKM6b3394u25jkouMjKZRo484deos\nJUrkY/ToljzwQG1E7C7CmMuV7mWWiDQUkfXANne4roh404RHQ5y+K3ao6llgKtAuhfleBUYC0d6H\nbYxD1elWtGDBYAYPbkKvXg3YsuUpunatY0nCmCvEm/vxccCdwBEAVV0L3OLFcqWAPR7DEe64JCJS\nHyijqrPSWpGI9BSRlSKy8tChQ15s2mR3e/eeoGPHr5gyZV3SuKFDb2bixDspXNhaeTXmSvImUeRS\n1X+SjfOmrYOULueSepV3X+AbAwxMb0Wq+r6qhqlqWPHixb3YtMmu4uISGDt2GdWqTeCbbzbz0kuL\niY9PALA7CGN8xJs6ij0i0hBQt96hL7DVi+UigDIew6WBfR7DoUAtYLH7Bb8KmCkid6vqSm+CNznL\nihV76dVrNn/+uR+A9u2rMW5cawICrKLaGF/yJlE8iVP8VBY4ACx0x6VnBVBFRCrgdKPaGXggcaKq\nRgLFEodFZDHwH0sSJrnTp88yePBC3ntvBapQtmxB3n33Du6+u6q/QzMmR0g3UajqQZwf+YuiqnEi\n0geYDwQAH6vqRhF5BVipqjMvOlqTIwUG5mLhwh3kyiUMGNCYl15qRr581smiMRkl3UQhIh/gUbeQ\nSFV7presqs7BaXXWc9yLqczbPL31mZzj77+PUqhQMEWL5iUoKJD//e8egoMDqV27pL9DMybH8aZw\ndyHwo/vvN6AE4PX7FMZcjJiYOF57bQm1ak1k8OCFSeOvv76UJQlj/MSboqcvPYdF5H/AAp9FZHKs\nxYt38eSTs9my5TDgPOEUH59gldXG+NmltPVUASh3pQMxOdfBg6cZNGgBn366FoCqVYsycWJbbrml\ngp8jM8aAd3UUxzhXR5ELOAqk2m6TMRfj8OEzVK8+gaNHowgKCmDo0Jt59tkmBAVZe5XGZBZpfhvF\necGhLs7jrQAJmthmgjFXQLFieWnXrioRESd47722VK5cxN8hGWOSSTNRqKqKyHRVbZBRAZns7fTp\ns7zyys+0bXstTZs6JZjvvdeWoKAAe7PamEzKm1rC5SJync8jMdne99//RY0a7zFy5O/07j2bhATn\n5jQ4ONCShDGZWKp3FCISqKpxwE3A4yLyN3Aapw0nVVVLHsYre/ZE8swz85g+fQsA9etfxaRJd/q/\nv+pv28LOOenPZ0wOl1bR03LgOqB9BsVispm4uATGjfuDF19cxOnTseTPn4fXXruFp55qmDk6EvJH\nkqjQJuO3acxlSitRCICq/p1BsZhs5sSJGN5441dOn47l3nur8847rSlduoC/w7rQQHs+w5i0pJUo\niovIgNQmqupoH8Rjsrjjx6MJCQkkKCiQIkVCmDTpToKCAmjb9lp/h2aMuURp3f8HAPlxmgNP6Z8x\nSVSVzz9fT9Wq4xk58rek8R06VLckYUwWl9YdxX5VfSXDIjFZ1tatR+jdezY//rgTgCVLdqOq9iST\nMdlEunUUxqQmOjqON9/8lf/+91fOno2nSJEQ3nqrBY88Us+ShDHZSFqJ4rYMi8JkOf/+e4qmTT9h\n27ajADzySD3eeqsFxYrl9XNkxpgrLdVEoapHMzIQk7WULJmPMmUKEhiYi4kT29KsWXl/h2SM8RFr\nec14JSFB+eCDVdxySwWuvbYoIsLnn3egcOEQ8uQJ8Hd4xhgfygRvPZnMbu3af2nS5GN69ZpN796z\nSWwXsmTJ/JYkjMkB7I7CpOrUqbMMH76Yd95ZRny8cs01ofTqFebvsIwxGcwShUnRd99toW/fuURE\nnCBXLqFv34a89tqtFCgQ5O/QjDEZzBKFucDevSfo3HkaMTHxNGhwNeHhdxIWdo2/wzLG+IklCgNA\nbGw8gYG5EBFKlSrA66/fSp48AfTufb31WW1MDme/AIbff99DgwbvM2XKuqRxAwfeSN++N1iSMMZY\nosjJjh6N4oknvqdJk49Zv/4g7723Euvp1hiTnBU95UCqypQp6xg48AcOHTpD7ty5ePbZJgwderM1\nvWGMuYAlihzmwIFTdOnyDYsW7QKgWbNyTJzYlurVi/s3MGNMpmWJIocpVCiY/ftPUaxYXkaNasFD\nD9W1uwhjTJosUeQACxb8zXXXXU3RonkJCgrk66/v4+qr81O0qDXgZ4xJn1VmZ2P795+kS5dvaNly\nCoMHL0waX6tWCUsSxhiv2R1FNhQfn8CkSasYMuRHTpyIISQkkKpVi1pnQsaYS2KJIpv588/99Oo1\nixUr9gHQtm0Vxo9vQ/nyhfwcmTEmq7JEkY3s2nWchg0/ID5eKVUqlHHj7uCee6rZXYQx5rL4NFGI\nSGtgLBAAfKiqI5JNHwA8BsQBh4BHVfUfX8aUnZUvX4ju3esRGhrEyy83JzTUGvAzxlw+n1Vmi0gA\nMAG4A6gBdBGRGslmWw2EqWodYBow0lfxZEe7dh3nrru+4OefdyWNe//9uxg9upUlCWPMFePLO4qG\nwHZV3QEgIlOBdsCmxBlUdZHH/MuAB30YT7YRGxvP6NFLefnln4mKiuPw4TMsXdoDwIqZjDFXnC8T\nRSlgj8dwBHBDGvP3AOamNEFEegI9AcqWLXul4suSfv11N716zWLjxkMAdO5ci9GjW/o5KmNMdubL\nRJHSpW2KLc6JyINAGNAspemq+j7wPkBYWFiObLXu2LEoBg1awEcfrQagUqXCvPdeW1q2rOTnyIwx\n2Z0vE0UEUMZjuDSwL/lMInI7MBRopqoxPownS0tIUGbM+IvcuXPx3HM3MWTITYSE5PZ3WMaYHMCX\niWIFUEVEKgB7gc7AA54ziEh9YBLQWlUP+jCWLGnLlsNUqFCIoKBAihbNy2efdaBs2YJUq1bM36EZ\nY3IQnz31pKpxQB9gPrAZ+EpVN4rIKyJytzvbW0B+4GsRWSMiM30VT1Zy5kwsQ4f+SJ06Exk58rek\n8S1bVrIkYYzJcD59j0JV5wBzko170ePz7b7cflY0b952eveezc6dxwE4fPiMnyMyxuR09mZ2JrFv\n30n69ZvH1187Tw/Xrl2C8PA7ufHGMuksmYl92xZ2zkl/PmNMpmaJIhPYuvUIYWHvc/LkWfLmzc3w\n4c3o168RuXMH+Du0y5MVkkSFNv6OwJhMzxJFJlClShGuv74U+fLl5t1376BcuWzWgN/AHPlEszHZ\nhiUKPzhxIoYXX1xE797Xc+21RRERZs7sTL58efwdmjHGXMASRQZSVaZN28Qzz8xj//5TbNlymHnz\nnFZLLEkYYzIrSxQZZMeOY/TpM4e5c7cD0KhRad580x76MsZkfpYofOzs2XhGjfqdV19dQnR0HIUK\nBTNixG08/ngDcuWyBvyMMZmfJQof27Mnklde+ZmYmHi6dq3N22+3pGTJ/P4OyxhjvGaJwgeOHYui\nUKFgRIRKlYowdmxrKlcuwm23VfR3aMYYc9F81oRHTpSQoHz88WoqV36XKVPWJY1/4okwSxLGmCzL\nEsUVsnHjQZo3n0yPHjM5ejQqqdLaGGOyOit6ukxnzsTy6qs/M2rUUuLiEihRIh9jxrSiS5da/g7N\nGGOuCEsUl2Hr1iO0ajWFXbuOIwK9ejXgv/+9jcKFQ/wdmjHGXDGWKC5DuXIFCQ4OpG7dkoSH30mj\nRqX9HZLJRGJjY4mIiCA6OtrfoZgcJDg4mNKlS5M795Xr2MwSxUWIi0sgPHwlXbrUomjRvAQFBTJv\nXldKlSpAYKBV95jzRUREEBoaSvny5RGxd2aM76kqR44cISIiggoVKlyx9dqvm5eWL99Lw4Yf0Lfv\nXAYPXpg0vly5QpYkTIqio6MpWrSoJQmTYUSEokWLXvG7WLujSEdkZDRDh/7Ee++tQBXKli1Iu3ZV\n/R2WySIsSZiM5otzzhJFKlSVL7/cSP/+8/n331MEBuZiwIBGvPhiM2vAzxiTo1iZSSrWrj1Aly7f\n8O+/p7jxxjL8+WdP3nyzhSUJk6UEBARQr149atWqxV133cXx48eTpm3cuJFbb72Va6+9lipVqvDq\nq6+ieq7vkLlz5xIWFkb16tWpVq0a//nPf/yxC2lavXo1jz32mL/DSNMbb7xB5cqVqVq1KvPnz09x\nnh49elC3bl3q1KlDx44dOXXqVNK0r776iho1alCzZk0eeOABAA4dOkTr1q0zJH7AuXLOSv8aNGig\nvhIXF3/ecP/+8/SDD1ZpfHyCz7aZrY3C+ZdDbdq0yd8haL58+ZI+P/TQQ/raa6+pquqZM2e0YsWK\nOn/+fFVVPX36tLZu3VrHjx+vqqrr16/XihUr6ubNm1VVNTY2VidMmHBFY4uNjb3sdXTs2FHXrFmT\nodu8GBs3btQ6depodHS07tixQytWrKhxcXEXzBcZGZn0uX///vrGG2+oqurWrVu1Xr16evToUVVV\nPXDgQNJ8jzzyiP76668pbjelcw9YqZf4u2tFT65Fi3bSu/ccJk26k6ZNywEwenQrP0dlso23fVRX\ncRG9BzZu3Jh165ymZT7//HOaNGlCy5YtAcibNy/jx4+nefPmPPXUU4wcOZKhQ4dSrVo1AAIDA+nd\nu/cF6zx16hR9+/Zl5cqViAgvvfQS9957L/nz50+6Kp42bRqzZs1i8uTJPPLIIxQpUoTVq1dTr149\npk+fzpo1ayhUyOnVsXLlyvz222/kypWLXr16sXv3bgDeeecdmjRpct62T548ybp166hbty4Ay5cv\np1+/fkRFRRESEsInn3xC1apVmTx5MrNnzyY6OprTp0/z008/8dZbb/HVV18RExPDPffcw8svvwxA\n+/bt2bNnD9HR0TzzzDP07NnT6+ObkhkzZtC5c2eCgoKoUKEClStXZvny5TRu3Pi8+QoUKAA4F+5R\nUVFJ9QwffPABTz31FIULFwagRIkSScu0b9+ezz777ILj4gs5PlEcPHiaQYMW8OmnawEYPXppUqIw\nJruIj4/nxx9/pEePHoBT7NSgQYPz5qlUqRKnTp3ixIkTbNiwgYEDB6a73ldffZWCBQuyfv16AI4d\nO5buMlu3bmXhwoUEBASQkJDA9OnT6d69O3/88Qfly5enZMmSPPDAA/Tv35+bbrqJ3bt306pVKzZv\n3nzeelauXEmtWudaQKhWrRpLliwhMDCQhQsX8vzzz/PNN98AsHTpUtatW0eRIkX44Ycf2LZtG8uX\nL0dVufvuu1myZAlNmzbl448/pkiRIkRFRXH99ddz7733UrRo0fO2279/fxYtWnTBfnXu3Jnnnnvu\nvHF79+6lUaNGScOlS5dm7969KR6X7t27M2fOHGrUqMHbb7+ddKwAmjRpQnx8PMOHD08qcgoLC2PY\nsGHpHu8rIccmioQE5aOP/mTw4IUcOxZNUFAAw4Y1ZdCgG/0dmsmO/NRveFRUFPXq1WPXrl00aNCA\nFi1aAM6Va2pPx1zMUzMLFy5k6tSpScOJV75pue+++wgICACgU6dOvPLKK3Tv3p2pU6fSqVOnpPVu\n2rQpaZkTJ05w8uRJQkNDk8bt37+f4sWLJw1HRkby8MMPs23bNkSE2NjYpGktWrSgSJEiAPzwww/8\n8MMP1K9fH3DuirZt20bTpk0ZN24c06dPB2DPnj1s27btgkQxZswY7w4OnFfnkyi14/vJJ58QHx9P\n3759+fLLL+nevTtxcXFs27aNxYsXExERwc0338yGDRsoVKgQJUqUYN++fV7HcjlyZGX2zp3HuPnm\nT+jZcxbHjkXTsmUlNmzozbBhTQkKyrG502RDISEhrFmzhn/++YezZ88yYcIEAGrWrMnKlSvPm3fH\njh3kz5+f0NBQatasyapVq9Jdf2oJx3Nc8mf68+XLl/S5cePGbN++nUOHDvHdd9/RoUMHABISEli6\ndClr1qxhzZo17N2797wkkbhvnut+4YUXuOWWW9iwYQPff//9edM8t6mqDBkyJGnd27dvp0ePHixe\nvJiFCxeydOlS1q5dS/369VN8H6F///7Uq1fvgn8jRoy4YN7SpUuzZ8+epOGIiAiuueaaCw+kKyAg\ngE6dOiXdCZUuXZp27dqRO3duKlSoQNWqVdm2bVvScQ0JyZjmgnJkoihQIIitW49w1VX5mTr1XubN\n60rlykX8HZYxPlOwYEHGjRvHqFGjiI2NpWvXrvz6668sXOi8PBoVFcXTTz/Ns88+C8CgQYP473//\nm1T0kZCQwOjRoy9Yb8uWLRk/fnzScGLRU8mSJdm8eXNS0VJqRIR77rmHAQMGUL169aSr9+TrXbNm\nzQXLVq9ene3bz7XSHBkZSalSpQCYPHlyqtts1aoVH3/8cVIdyt69ezl48CCRkZEULlyYvHnzsmXL\nFpYtW5bi8mPGjElKMp7/khc7Adx9991MnTqVmJgYdu7cybZt22jYsOF586hq0n6oKt9//31S3VD7\n9u2TirkOHz7M1q1bqVjR6bJg69at5xW9+VKOSRTz528nJiYOgKJF8zJzZme2bHmKTp1q2UtRJkeo\nX78+devWZerUqYSEhDBjxgxee+01qlatSu3atbn++uvp06cPAHXq1OGdd96hS5cuVK9enVq1arF/\n//4L1jls2DCOHTtGrVq1qFu3btKP2ogRI7jzzju59dZbufrqq9OMq1OnTkyZMiWp2Alg3LhxrFy5\nkjp16lCjRg3Cw8MvWK5atWpERkZy8uRJAJ599lmGDBmSVJ6fmpYtW/LAAw/QuHFjateuTceOHTl5\n8iStW7cmLi6OOnXq8MILL5xXt3Cpatasyf3330+NGjVo3bo1EyZMSCp2a9OmDfv27UNVefjhh6ld\nuza1a9dm//79vPjii4CT1IoWLUqNGjW45ZZbeOutt5KS6aJFi2jbtu1lx+gNSakMLTMLCwvT5LfM\nadmzJ5Knn57Hd99t4dVXb2HYsKY+jM6cJ/FJHz+Vz/vb5s2bqV69ur/DyNbGjBlDaGhopn+Xwhea\nNm3KjBkzUqwXSuncE5FVqhp2KdvKtncUcXEJjB69lOrVJ/Ddd1vInz8PRYpY89/GZCdPPvkkQUFB\n/g4jwx06dIgBAwZ49fDAlZAta26XLYugV69ZrF17AIB7763O2LGtKVWqgJ8jM8ZcScHBwXTr1s3f\nYWS44sWL0759+wzbXrZLFH/8EcGNN36EKpQvX4jx4++gbdtr/R2WyaHSegzVGF/wRXVCtksUDRuW\nolWrytSvfxXDhjUlb94r13mHMRcjODiYI0eOWFPjJsOo2x9FcHDwFV1vlk8U27YdoX//+Ywe3Ypr\nr3W+kLNnP0CuXPbFNP5VunRpIiIiOHTokL9DMTlIYg93V1KWTRQxMXGMGPErb7zxKzEx8QQHBzJt\n2v0AliRMppD4kpQxWZ1Pn3oSkdYi8peIbBeRC95GEZEgEfnSnf6HiJT3Zr0//riDOnXCGT78Z2Ji\n4unevR7h4Xde6fCNMcbgwzsKEQkAJgAtgAhghYjMVNVNHrP1AI6pamUR6Qy8CXS6cG3n7Nx5nNtv\n/x8A1asXIzz8TmvEzxhjfMiXRU8Nge2qugNARKYC7QDPRNEOGO5+ngaMFxHRNKrtjx09Q3BgLC+2\n+JmBzZaSZ0UfWOGbHTDGGOPDN7NFpCPQWlUfc4e7ATeoah+PeTa480S4w3+78xxOtq6eQGLD8LWA\nDT4JOuspBhxOd66cwY7FOXYszrFjcU5VVQ1Nf7YL+fKOIqUa5eRZyZt5UNX3gfcBRGTlpb6Gnt3Y\nsTjHjsU5dizOsWNxjoh43/ZRMr6szI4AyngMlwaSN56eNI+IBAIFgaM+jMkYY8xF8mWiWAFUEZEK\nIpIH6AzMTDbPTOBh93NH4Ke06ieMMcZkPJ8VPalqnIj0AeYDAcDHqrpRRF7B6eR7JvAR8D8R2Y5z\nJ9HZi1W/76uYsyA7FufYsTjHjsU5dizOueRjkeWaGTfGGJOxsm0z48YYY64MSxTGGGPSlGkTha+a\n/8iKvDgWA0Rkk4isE5EfRSTbvqqe3rHwmK+jiKiIZNtHI705FiJyv3tubBSRzzM6xozixXekrIgs\nEpHV7vekjT/i9DUR+VhEDrrvqKU0XURknHuc1onIdV6tWFUz3T+cyu+/gYpAHmAtUCPZPL2BcPdz\nZ+BLf8ftx2NxC5DX/fxkTj4W7nyhwBJgGRDm77j9eF5UAVYDhd3hEv6O24/H4n3gSfdzDWCXv+P2\n0bFoClwHbEhlehtgLs47bI2AP7xZb2a9o0hq/kNVzwKJzX94agf8n/t5GnCbZM9G/9M9Fqq6SFXP\nuIPLcN5ZyY68OS8AXgVGAtEZGVwG8+ZYPA5MUNVjAKp6MINjzCjeHAsFEru4LMiF73RlC6q6hLTf\nRWsHfKqOZUAhEbk6vfVm1kRRCtjjMRzhjktxHlWNAyKBohkSXcby5lh46oFzxZAdpXssRKQ+UEZV\nZ2VkYH7gzXlxLXCtiPwmIstEpHWGRZexvDkWw4EHRSQCmAP0zZjQMp2L/T0BMm9/FFes+Y9swOv9\nFJEHgTCgmU8j8p80j4WI5ALGAI9kVEB+5M15EYhT/NQc5y7zFxGpparHfRxbRvPmWHQBJqvq2yLS\nGOf9rVqqmuD78DKVS/rdzKx3FNb8xzneHAtE5HZgKHC3qsZkUGwZLb1jEYrTaORiEdmFUwY7M5tW\naHv7HZmhqrGquhP4CydxZDfeHIsewFcAqroUCMZpMDCn8er3JLnMmiis+Y9z0j0WbnHLJJwkkV3L\noSGdY6GqkapaTFXLq2p5nPqau1X1khtDy8S8+Y58h/OgAyJSDKcoakeGRpkxvDkWu4HbAESkOk6i\nyIl91M4EHnKffmoERKrq/vQWypRFT+q75j+yHC+PxVtAfuBrtz5/t6re7begfcTLY5EjeHks5gMt\nRWQTEA8MUtUj/ovaN7w8FgOBD0SkP05RyyPZ8cJSRL7AKWos5tbHvATkBlDVcJz6mTbAduAM0N2r\n9WbDY2WMMeYKyqxFT8YYYzIJSxTGGGPSZInCGGNMmixRGGOMSZMlCmOMMWmyRGEyHRGJF5E1Hv/K\npzFv+dRayrzIbS52Wx9d6zZ5UfUS1tFLRB5yPz8iItd4TPtQRGpc4ThXiEg9L5bpJyJ5L3fbJuey\nRGEyoyhVrefxb1cGbberqtbFaWzyrYtdWFXDVfVTd/AR4BqPaY+p6qYrEuW5ON/Duzj7AZYozCWz\nRGGyBPfO4RcR+dP9d2MK89QUkeXuXcg6Eanijn/QY/wkEQlIZ3NLgMrusre5fRisd9v6D3LHj5Bz\nfYCMcscNF5H/iEhHnDa3PnO3GeLeCYSJyJMiMtIj5kdE5N1LjHMpHg26ichEEVkpTt8TL7vjnsZJ\nWItEZJE7rqWILHWP49cikj+d7ZgczhKFyYxCPIqdprvjDgItVPU6oBMwLoXlegFjVbUezg91hNtc\nQyegiTs+HuiazvbvAtaLSDAwGeikqrVxWjJ4UkSKAPcANVW1DvCa58KqOg1YiXPlX09VozwmTwM6\neAx3Ar68xDhb4zTTkWioqoYBdYBmIlJHVcfhtOVzi6re4jblMQy43T2WK4EB6WzH5HCZsgkPk+NF\nuT+WnnID490y+XicdouSWwoMFZHSwLequk1EbgMaACvc5k1CcJJOSj4TkShgF04z1FWBnaq61Z3+\nf8BTwHicvi4+FJHZgNdNmqvqIRHZ4bazs83dxm/uei8mznw4zVV49lB2v4j0xPleX43TQc+6ZMs2\ncsf/5m4nD85xMyZVlihMVtEfOADUxbkTvqBTIlX9XET+ANoC80XkMZxmlf9PVYd4sY2ung0IikiK\n/Zu4bQs1xGlkrjPQB7j1IvblS+B+YAswXVVVnF9tr+PE6cVtBDAB6CAiFYD/ANer6jERmYzT8F1y\nAixQ1S4XEa/J4azoyWQVBYH9bv8B3XCups8jIhWBHW5xy0ycIpgfgY4iUsKdp4h436f4FqC8iFR2\nh7sBP7tl+gVVdQ5ORXFKTx6dxGn2PCXfAu1x+kj40h13UXGqaixOEVIjt9iqAHAaiBSRksAdqcSy\nDGiSuE8ikldEUro7MyaJJQqTVbwHPCwiy3CKnU6nME8nYIOIrAGq4XT5uAnnB/UHEVkHLMAplkmX\nqkbjtK75tYisBxKAcJwf3Vnu+n7GudtJbjIQnliZnWy9x4BNQDlVXe6Ou+g43bqPt4H/qOpanP6x\nNwIf4xRnJXofmCsii1T1EM4TWV+421mGc6yMSZW1HmuMMSZNdkdhjDEmTZYojDHGpMkShTHGmDRZ\nojDGGJMmSxTGGGPSZInCGGNMmixRGGOMSdP/AzX4UpvOm+3KAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a91dff4748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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D7dsP0rbtaUyY0IUzzwzuAH6BYInCGBNZvDVIh3BspvT0TLZs2U/duhUAGDHici68sBY3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\noxCRaOB34HIgBVgCXKeqqzy2GQA0V9V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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1aa86ff2710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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JpKdnULt2RUSEhISuJCWlEBd3bqBDMyHKlzaKX4D/AWNV9Rs/x2OCWU53OVnD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\n1l8K3OrHeIJGamo648cv4Yk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444tISUnn9ttbkJDQrbDDN8YYgx+foxCRcOA3oCOQCCwH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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a9218aba20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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GFFvbtyeRnp5BXFx5RIQpU7qRlJRCfPz5wQ7NFFO+3FH8AvwPGK2q3/g5HmMK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\nXdoXwJKEMabA+TNRVAV2eAw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TLH5NFCLSBRgPhANvquqobNOjgHeBFsA+oKeqbstrvV9s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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1aa851cf048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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hUKycOXE5WGpqOv37L+Xbb/cB0LjxbSxY0JPmzSuYHJlwBXK1Sri3DcPh+E8Z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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1aa85211d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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cv0dnTCGXkJDEyJHLef311SQlKZUqlWDy5I7ccceVpLnBxBi/8enxTFX9n6o+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\nq+pmj9XWA1GqekpE+gPjgZ7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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1aa85257a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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xTtXRUyKSAExT1WlBj86YcNV9ZkB2k5qaztCh3/LSSwtJT1eqVSvD+PFduemm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\nKIwxxvhkicIYY4xP/w8WsramxxVytwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1aa85299e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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PaAJ8Blzi47iMKbJ27YonJSWVOnXKISLExPQgPj6R6OjzAx2aKaK8uaP4Ffgf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\niEgIsBnoAsQBq4H+qrrBY57BQEtVHSQ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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1aa852e3048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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HcrovY4qD1FTl3XdX8vTT33L4cALlypXg0UdbEhlpowgZ//IlUaxwq4Q+Bb5U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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1aa853265f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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L449/yZEjiZQrF8lDD7UhOtpGETKBleNzFCLyG/AL8AHwsaoeC0Zg2bHnKEyh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\nPZbPkRlj/CkyMpK+ffvmdxhBFx4eTr9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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1aa8536dac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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aqmp9AJiiowCVHtItWbKTvn1nsXbtXgCioyM4eTKZ0qVLBTkyY7wkChF5U1Wf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\njDFeWaIwxhjj1f8Dr4Dz8ikAAAADSURBVF9VQsE0roIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1aa853a16d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.052649993449449539, 0.26828080415725708, 0.29003441333770752, 0.14435943961143494, 0.28669834136962891, 0.10782142728567123, 0.14602121710777283, 0.079208120703697205, 0.12269972264766693, 0.099530883133411407, 0.91244196891784668]\n"
     ]
    }
   ],
   "source": [
    "# Validation, to select cutoff\n",
    "\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Compute ROC curve and ROC area for each class\n",
    "fpr = dict()\n",
    "tpr = dict()\n",
    "roc_auc = dict()\n",
    "\n",
    "cut_off_list=[]\n",
    "\n",
    "names = [\"amused\" ,\"calm\" ,\"content\" ,\"excited\" ,\"optimistic\" ,\"annoyed\" ,\"apathetic\" ,\"frustrated\",\"pessimistic\",\"stressed\",\"neutral\"]\n",
    "#names = [\"positive\", \"negative\", \"neutral\"]\n",
    "\n",
    "\n",
    "for i in range(0,len(names)):\n",
    "    fpr[i], tpr[i], _ = roc_curve(original_v[:][i],predicted_v[:][i])\n",
    "    roc_auc[i] = auc(fpr[i], tpr[i])\n",
    "    cut_off_list.append(Find_Optimal_Cutoff(original_v[:][i],predicted_v[:][i])[0])\n",
    "    \n",
    "cl = 0\n",
    "\n",
    "\n",
    "for cl in range(0,len(names)):\n",
    "    plt.figure()\n",
    "    lw = 2\n",
    "    plt.plot(fpr[cl], tpr[cl], color='darkorange',\n",
    "         lw=lw, label='ROC curve (area = %0.2f)' % roc_auc[cl])\n",
    "    plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')\n",
    "    plt.xlim([0.0, 1.0])\n",
    "    plt.ylim([0.0, 1.05])\n",
    "    plt.xlabel('False Positive Rate')\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    plt.title('Receiver operating characteristic ' + names[cl])# ROC curves\n",
    "    plt.legend(loc=\"lower right\")\n",
    "\n",
    "\n",
    "plt.show()\n",
    "\n",
    "print(cut_off_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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kLTBXVefm5YDGBFwYlyJSUtIYMeIzXnxxBWlpykUXlWDy5E7cemt9stxgYozf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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1aa854fcd30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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4BIAWLSrSokVF1wamcp2MEkVlEbnxcLgAFe2mMcZ0dmpkSrlCfGzqJNFjO5Ss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\nMsMDGeWU7HcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1aa85566a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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1PzZvfpGlS5+VJCEyvaLoCtTVWkcrpXyBK+bpkzkTmhA2Evw3LG+WssxBq5lM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\ny1OsIgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1aa855a9d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ky+571aX7Y0ru119P8txzyzh48BIAffvWYdKkdhQvXsDJkSlnSu/J7HeB7sAe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\nTAU6AZcBjDF7gNaZ2C4QOG0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T66dhul/ifJUeUKhsklX27DlP794/cO7cDZo2vZs//xzI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\nv3WymX2T+fDySoOOm/l6RuHlP2rFyQWSzgfGApe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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1aa855f72b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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wN45Yn+/ubdyx1HYp5C3q2Ngy4ddf/2XgwFWcPRuOUtC/fwiTJzcjIEDuaBJp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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1aa85633f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Vu0CXReCZdXNRdPRlxo79nSlTNpOYmMzRoxe5eDGW0qV9XRy8KqiySxQPplue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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1aa8567aa90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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zoSlVgDj27FqoLHRf6bVQHNlshoSEZEJC/PH19WHGjM6sXv0vzz/fnMBA7cBP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\ncOHChSvrtm/fTtu2bbn55pupXr06r7/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MRb1b0CgLI0kuPgBGAUiqJHQUXXGO2uXABfHb\nTyOArWb2Ua6NSnLoyfJX/qPsJMzFrcDewEPxev4HZnZm0YLOk4S56BIS5mIFcKqk9cAO4Fdmtrl4\nUedHwlz8EvijpBmEoZYpnfEfS0kPEoYa+8TrMb8GugOYWS3h+sxoYBOwDbgo0X47Ya6cc851oFId\nenLOOVcivKNwzjmXlXcUzjnnsvKOwjnnXFbeUTjnnMvKOwpXciTtkLQm7WdAlrYDdlUps43HfCZW\nH10bS14c3o59TJV0QXw8RdJBac8tlDSkg+N8WdLwBNtcKanntz2267q8o3ClaLuZDU/7eb9Axz3P\nzI4iFJu8ta0bm1mtmd0XF6cAB6U9d4mZre+QKFNxzidZnFcC3lG4dvOOwpWFeObwvKR/xp/jMrQZ\nKumleBayTtJhcf35aevvkrR7jsM9BwyK246Kcxi8Fmv994jrb1ZqDpDfxXXXSbpK0jhCza374zH3\njGcC1ZKmSbolLeYpkn7fzjhXklbQTdIfJK1WmHvi+rhuOqHDqpNUF9edKmllzONDkvbOcRzXxXlH\n4UrRnmnDTo/GdZ8Ap5jZMcAE4I4M200Fbjez4YQP6oZYrmECcHxcvwM4L8fxfwq8JqkCWARMMLNh\nhEoG0yT1Bs4GhppZFfDb9I3NbAmwmvCf/3Az25729BLgnLTlCcDidsZZQyjT0eIaM6sGqoATJFWZ\n2R2EWj4nmtmJsZTHbODkmMvVwMwcx3FdXEmW8HBd3vb4YZmuO3BnHJPfQahb1NpK4BpJ/YFHzGyj\npFHAD4CXY3mTPQmdTib3S9oOvE8oQ3048J6ZvR2fvxf4OXAnYa6LhZIeBxKXNDezRknvxjo7G+Mx\nXoz7bUucexHKVaTPUDZe0mWE9/WBhAl61rXadkRc/2I8zh6EvDm3S95RuHIxA/gYOIpwJvyNSYnM\n7AFJq4AxwApJlxDKKt9rZrMSHOO89AKCkjLObxJrCx1LKDI3EbgCOKkNf8tiYDywAXjUzEzhUztx\nnIRZ3G4G5gHnSDoUuAr4oZltkbSIUPiuNQFPmdmkNsTrujgfenLlYh/gozh/wGTCf9NfI2kg8G4c\nbllOGIJ5Ghgn6YDYpreSzym+ARggaVBcngw8G8f09zGzJwgXijN98+g/hLLnmTwCjCXMkbA4rmtT\nnGb2JWEIaUQctvoO8DmwVdJ3gdN3EUs9cHzL3ySpp6RMZ2fO7eQdhSsX84ELJdUThp0+z9BmAvC6\npDXAEYQpH9cTPlD/Lmkd8BRhWCYnM/uCUF3zIUmvAV8BtYQP3cfi/p4lnO20tgiobbmY3Wq/W4D1\nwCFm9lJc1+Y447WP24CrzGwtYX7sN4B7CMNZLRYAT0qqM7NGwjeyHozHqSfkyrld8uqxzjnnsvIz\nCuecc1l5R+Gccy4r7yicc85l5R2Fc865rLyjcM45l5V3FM4557LyjsI551xW/weThCAaLFvT3gAA\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1aa856c52b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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NaEoVY1GXIDY89bLdM+Hc9oy3v3vyTTU9JSYaoqPj8fPzxN3djblzu7Nly9+8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\n448/TnBwMM2aNaNZs2bs2bMHgHfeeSd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CXNwP/F7SXMJQS201/rCU9AxhqLFXPB/zY6Az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\nzuXkHYVzzrmc/gPj//xs4oU0JgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1aa8570a710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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QSqkM6NatSMLCoujVqzpTprSiUKEc1oIFVeIKtfsPFLm/7jkcoYlCKaUygEuXbnL48BUa\nNbL6sxsxoiHNmpWkSZMScOsiXDoBIWfhxilrhQpdoVKPdIlNE4VSSrlQTIxh4cLdvPnmWjw83Dh0\naBD+/r54e3tYSeLID/C/p+9esd3X6RajJgqllHKRf/65SL9+y/nzT6sj7ZYtS3PrViT+/r5xheyT\nRIGqEB4MzaaCW/p9fWuiUEqpdHbzZgTjx//O1KlbiIqKoXDhHHzySRu6dq2CiK1j7Yt74cfWcSs9\nuwsK13JJvJoolFIqnT311BJWrTqGCAwYEMCECS3Im9cnrsCVg/BVzfgruShJgCYKpZRKdyNGNOTC\nhVDmzGlP3brF4i8MvwGLKsdN1x8LtYemb4AJaKJQSiknioqK4dNPt3Lq1HWmT28LQLNmJdmxoy9u\nbnbjt0WEwIbX4Z8FcfNazoPqfdM54rtpolBKKSfZti2IV15Zzp495wHo27c2VaoUAohLEjdOw/pB\ncGJ5/JVrD80QSQI0USilVJq7fj2Mt99ez9y5OzAGSpTIw8yZ7WKTBPv/D878aiWJwN/jr/xAHXh8\nCeQunv6BJ0EThVJKpaHFi/9hyJBVXLhwEw8PN4YNq8+YMU3IkcMLYqJgdgHrFteEqjwHjSY6vTuO\ne6GJQiml0tCaNce5cOEmDRs+xJw57alWrXDcwmme8Qu3+hzcvaBkW/ArkL6BpoImCqWUug/h4VEE\nBYVQunQ+ACZPbknjxsV57rma8S9Wx0THvfavCL33gXuCxJFBaTfjSil1j3799STVq8+lfftviIiw\nEkGBAn688EKt+EkiKgw+Lx03/ezOTJMkQBOFUkql2oULofTqtZQWLb7kyJErAAQG3ki8cPBJmO4L\nIWes6QrdwNMvnSJNG9r0pJRSDoqJMcyfv5O33lrP9eth+Ph4MHp0Y4YPb4iXVyIjzMVExT+TqPIC\ntFmYfgGnEU0USinloCef/I5lyw4D0Lp1GWbNakeZkjkhMgTC7ApePWjdArtvXty8eu9Aw3fTN+A0\noolCKaUc1LlzRbZtC2L69DY83dIH2TIEfvoq5RVLtoYGY50foJNoolBKqSQsW3aYwMAbDBjwCAC9\ne1ai88OB5LqyABZOi1/YO0/86fBgeGQEVHke8ldMn4CdRBOFUkolcOZMMK++upKffz6Mt7c7bapf\nofSZd5FzW8iVsHCN/lBnBOQu4YpQ04UmCqWUsomMjGbGjK2MHfsbN29GkiuXJ+83X0aJze+Am4lf\nuPEkeLAhFGvkmmDTkSYKpZQCtmwJ5JVXlrNv3wUAnm7hzrQGkyiaJySuUNOPoeoL4JPPRVG6hiYK\npZQCxozZwL59Fyjlf42ZT66gXaWjcQtbzIaa/V0XnItpolBKZUvGGEJCIsid2xtiopnZ5yRfmo2M\navEHfl6RIO7W09PPH4A8pVwdrktpolBKZTuHD19mwIAViMDa+YWQ7R9SIeg3JrS1FWj7JVTu5dIY\nMxJNFEqpbCMsLIqJE/9g0qQ/iYiIJn9+X059MYJSec7HFXrxCOQr57ogMyBNFEqpbGHt2uMMGLCC\nY8euAvBik1NMbvEd+XPcBnGDBuOhYnfIWzqFmrIfpyYKEWkDTAfcgc+NMZMSLC8O/B+Q11bmLWPM\nCmfGpJTKXowx9OmzjC++2ANA5cIXmdtlOY1Ln4krVLoD1BvloggzPqclChFxB2YBLYFAYLuILDPG\nHLArNhr43hgzR0QqAyuAks6KSSmV/YgIJUvkwddHeKf5WoY22YyXRzTUHgY5H7TGpfbK6eowMzRn\nnlHUAY4ZY04AiMhioBNgnygMkNv2Og/wrxPjUUplE3v2nOfcuRDaNi8EB75ihO8wer3uR6n8160C\n9cdCg3EujTEzcWaiKAqctZsOBOomKDMOWCMig4EcwGOJVSQifYG+AMWLZ5wBx5VSGUtISDhjx/7G\n9Olbye/vzaFX38Xf7zbe7lAqf4RVqN3XUKmnawPNZJyZKCSReQmegac7sMgY87GI1Ae+EpGqxpiY\neCsZ8xnwGUBAQEDCOpRS2ZyJieGn2V/w6ntBBF40uLlBj0q/4elmG360cG0o9xQ8/GqmGzQoI3Bm\noggEHrKbLsbdTUt9gDYAxpjNIuIDFAAuOjEupVQWcvr0dQb1+Yrl6627mQKKBTHvqeU8XOycVeDR\nT+Dh11wYYebnzESxHSgnIqWAIKAb0CNBmTNAC2CRiFQCfIBLToxJKZWFmOgourSfy8794eT2CeOD\ntuvpN7Ax7u7dAWONA1GytavDzPScliiMMVEiMghYjXXr60JjzH4RGQ/sMMYsA4YB80XkdaxmqeeN\nMdq0pJRKVsyta7htGIgc+pYpjUsy1zOAaR1XUaTTB/DwYFeHl+VIZvteDggIMDt27HB1GEqp9BQe\nDEd+5Mrlm7w14yac+4v5T/8vbrm4QeuFUOU518WYwYnITmNMwL2sq09mK6UyNmMwn+blyx01eGN5\nKy7fzIGXew3GtvydYpWqQIdvs/SgQRmBJgqlVIZ2cNPv9J/zPL+fKAlAsxoRzHn1BsWafAVlO7o2\nuGxCE4VSKkMyxvDOOxv4cNJGIqNKUiDHTT6e1Z1evWsiktjd98pZ3FwdgFJKJUauHSVo3Rwio4SX\n6+7k8IRf6P1cLU0SLqBnFEqpDOPff0O4fOEG1XNug+VdmdzWkz61t9Kw1Fl4/qSrw8u2NFEopVwu\nOjqGOXN2MOrtdRT1/Zc9Q+fi5RFNgRyRFHi0CTxx3BptTrmEJgqllEvt2nWOV15Zzo4dVscNTYpd\n5Ua4NwU8bkGtV6Hu25okXMyhRCEiXkBxY8wxJ8ejlMombtwIZ8yYX5k5czsxMYZixXIzo8V8nqh6\nEKn9GjSbaj0foVwuxUQhIu2BqYAXUEpEagJjjTFPOjs4pVTWZCJv06TuDPYeCsPdHYY+l5dxtT8l\nV8Rxq4AmiQzFkTOK8Vjdg28AMMbsEZGyTo1KKZU1GQOn1yI/tub1GjWYffMR5j21nJpFz4OtF3D8\nK5F459PKVRxJFJHGmOsJbknLXP1+KKVcKiIimqmTN+C+ZSzDH/0LgN4Be3m2/incy7azChkD/hWg\nzkjQW2AzFEcSxUEReQZws/UE+xqwxblhKaWyij/+OE2/fr9w4MAlvD2a0ztgL4Vz3UQ6LMa9YldX\nh6cc4Egj4CCgNhAD/BcIw0oWSimVpMuXb/Hiiz/TpMkiDhy4RLkCV1j+4jcUrtsZhsaAJolMw5Ez\nitbGmBHAiDszRKQzVtJQSql4jDEsmvcHw0du5Mr1aLw8ohn56B+81XwTPp5R0Pa4q0NUqeRIohjN\n3UlhVCLzlFIKbl7g648XcOV6aZqXPcHszr9QodAVa9mAy66NTd2TJBOFiLTGGqa0qIhMtVuUG6sZ\nSimlALh1K5Lg4DCKFPJG5hVhduf8bD9blJ5tPZEcda1R5ir3Bp+8rg5V3YPkziguAv9gXZPYbzc/\nBHjLmUEppTKPlSuPMnDgCkqXzsfaIasRoEKhK1Ro+Qw8NtvV4ak0kGSiMMbsBnaLyH+MMWHpGJNS\nKhMICrrBkCGr+eGHAwDk8gzlyq6fKJADyFNKk0QW4sg1iqIiMgGoDPjcmWmMKe+0qJRSGVZ0dAyz\nZm1n9OhfCQmJIEcOT8b3uMCrZWfi4W5rle6x1bVBqjTlSKJYBLwPTAHaAi+g1yiUypZiYgxNmy7i\nzz/PAvBEp3JM77iS4te+jivUey/4FXRRhMoZHEkUfsaY1SIyxRhzHBgtIn84OzClVMbj5ia0alWG\nM2eCmflpWzqeqQPXQuIKDLwKPvlcF6ByCkcSRbhY/XccF5F+QBBQyLlhKaUyAmMM33+/Hw8PN7pU\n2Ap75zCitB9D33Mj57GhcQXzloGuf2iSyKIcSRSvAzmBV4EJQB7gRWcGpZRyveN/rWdAv6Ws+bsg\nBXPeovmbM8jnF4Y34G1f0N0Lnj9g/a+ypBQThTHmzlWpEKAXgIgUc2ZQSinXCQ+P4qPx/2PC5J2E\nRRUkn+9tJrRZRx6fcKvA40vA13YNwjMHFK6tnfhlcckmChF5BCgKbDLGXBaRKlhdeTQHNFkolcX8\ntv4Y/Z/7nENBvoAnvWrvZcrERhSqMAXcPKFIXR1tLhtK7snsiUAXYC/WBeylWJ0Bfgj0S5/wlFLp\nIiqc6PO7GdDjaw5dLEiFgpeZ02U5j/YfCdX7ujo65WLJnVF0AmoYY26LiD/wr236cPqEppRytphz\nOwhbNwK/i7/iDszpUoKNJ0rwZqs9eL+4G/KWdnWIKgNILlGEGWNuAxhjrorIIU0SSmUBwSfh9hX+\n/n4a/ab5ULFQThY8Yy1qWguatvCDztf1uoOKlVyiKC0id3qIFaCk3TTGmM5OjUwplfYC/+Dmly0Y\nv7YpUzfWJyrGnZNX83Gt5ofkq9XJGmFOqQSSSxRdEkzPdGYgSiknu7SP/73zEoOWDuTM9byIGAa0\nuciEuYPJW6Kiq6NTGVhynQKuT89AlFJOcvsKUWsG0XVMDP/9uwcANctEMe+bftSpU9TFwanMwJEH\n7pRSmdX147CgLB5AHp9O5PQO572+Hgya8g4eXnqbq3KMI2Nm3zMRaSMih0XkmIgkOoaFiDwjIgdE\nZL+IfOPMeJTKTrZuDWTrwomx0x8NiOLg5nYMmTFek4RKFYfPKETE2xgTnory7sAsoCUQCGwXkWXG\nmAN2ZcoBI4GGxphrIqJ9SCl1n65fD2PkyHXMm7eTisWLsqe/O17F65G/+3JXh6YyqRTPKESkjoj8\nDRy1TdcQkU8dqLsOcMwYc8IYEwEsxno2w97LwCxjzDUAY8zFVEWvlIpljOGbb/6mYsWZzJ27E3d3\nNzpW/JtoI1C6vavDU5mYI2cUM4AOwE8Axpi9IvKoA+sVBc7aTQcCdROUKQ8gIn8C7sA4Y8wqB+pW\nStk5evQKAwasYN26EwA0rBzG3P5BVA1fahVw905mbaWS50iicDPGnJb4D99EO7BeYk/rmES2Xw5o\nhtV31B8iUtUYcz1eRSJ9gb4AxYsXd2DTSmUfkZHRNG/+JYGBN/D392Vyz+O88NB83MLtPm5VX3Bd\ngCrTcyRRnBWROoCxXXcYDBxxYL1A4CG76WJY3YAkLLPFGBMJnBSRw1iJY7t9IWPMZ8BnAAEBAQmT\njVLZkjEGEcHT050JE5qzYd1hJrdfR8HA+VaBCl2hzONQpL6OE6HuiyOJoj9W81Nx4AKwzjYvJduB\nciJSCmuwo25AjwRlfgK6A4tEpABWU9QJx0JXKnu6cCGUN95YS/ny/owZ0xSA3r1r0DuyCwQejyvY\neiF4+rkoSpWVOJIooowx3VJbsTEmSkQGAauxrj8sNMbsF5HxwA5jzDLbslYicgCrOWu4MeZKarel\nVHYQE7iZ+ZO/4a0v/Lke6kbenDEMqfoVufwMnPkVbpyyCuZ8EJ5ap0lCpRkxJvmWHBE5DhwGvgP+\na4wJSXYFJwsICDA7duxwZQhKpa+IUPZuO0K/Z2ez5bTVmtumwlFmdV5B6fzX7i4/KBi8c6dzkCqj\nE5GdxpiAe1nXkRHuyohIA6ymo3dFZA+w2Biz+F42qJRyQOQtOPIDkbsXMPJzHz75ox7RMQ9RJHcI\n098qwFPtm3HXzYcevlC2kzXqnFJpyKEH7owxfwF/icg44BPgP1jPRSil0tqNs7CoMkSG4mFgd1Bv\nYowwuOk+3nujFHk6JNrJgVJOk2KiEJGcWA/KdQMqAT8DDZwcl1LZQ+RtuPIP/LModtaZE+eJPu9B\nqfwgbm7MnVqf4HxNCGg0zmVhquzNkTOKf4D/AZONMX84OR6lsr6wa3BpL2weD2c3xM6OjHZj+h/1\nGLu6GfVL5mTtlDCk43eUc2GoSoFjiaK0MSbG6ZEolVWFB4MxcPRHWPNSokU2nypGvxXPs++E9ZH0\nL1mJW3V7oFcbVEaQZKIQkY+NMcOAH0XkrlujdIQ7pZIREQomBja8Cvv/L8li16pP4K3F5fhsodVX\nZqlSeZk1qx1t2+p5hMo4kjuj+M72v45sp5QjrhyEfxbCjimJL/fKDdFh0PG/ULo94eFR1Cw/kzNn\nDuDp6cbw4Q0YNaoJfn7aBbjKWJIb4W6b7WUlY0y8ZGF7kE5HwFPZ18Fv4cLOuOmrB+DkyrvLeeWC\nvGWh26a7HoDz9vagT59arF9/kjlz2lO5ckEnB63UvXHkgbtdxpiHE8zbbYyp5dTIkqAP3CmnMgZ2\nz4SQs0mX2fFR8nXUHATFmkAfZ2oDAAAgAElEQVT5p8CuM82wsCgmTvyDChUK0KNHNQCiomJwdxcS\ndLqpVJpzygN3ItIV65bYUiLyX7tFuYDria+lVCYWHQlzC1t3JTmqiV3ScPeyOuLLUfiuYmvXHmfA\ngBUcO3aVQoVy8OSTFfH19cTDw6mDTCqVJpK7RrENuILV6+ssu/khwG5nBqVUuooKh41vwu4Z8ec3\n/jDpdXIVg4rd450xJOb8+VCGDl3Nt9/+A0CVKgWZO7cDvr56HUJlHsldozgJnMTqLVaprGvv7PhJ\nomAN6L4ZPH3vucro6BjmzdvJ22+vJzg4HF9fD8aObcrrr9fHy8s9DYJWKv0k1/T0uzGmqYhcI/6A\nQwIYY4y/06NTKj38NjTu9ctnIPdDSZd1UHS04dNPtxEcHE67duWYObMtpUrpmBAqc0qu6elOj2MF\n0iMQpVzi1qW4148vua8kERISTnS0IW9eH7y83Jk//3EuXAilc+dKerFaZWpJXkmzexr7IcDdGBMN\n1AdeAX1gVGURu6bHvS7W5J6qMMbw3/8epFKlWQwbtjp2fqNGxenSpbImCZXpOXLLxU9Yw6CWAb7E\n6hjwG6dGpVR6uHkBtk6wXhdtDH6FUl3FqVPX6dhxMV26fE9QUAj//HOJsLCoNA5UKddyJFHE2Ma0\n7gx8YowZDBR1blhKOVF4MOyaAXMfiJtXf2yqqoiMjObDDzdRufIsli8/Qu7c3syc2Za//noRHx+H\neu9XKtNwaChUEXka6AU8YZun9/apzOn2VZidP/688k9BiRYOV3HrViT16n3O339fBKBbt6pMndqK\nIkVypWWkSmUYjiSKF4EBWN2MnxCRUsC3zg1LKSf4423YNjFu+oE6UOEZqNE/VdX4+XkSEPAgt25F\nMnt2e1q1KpPGgSqVsaTYhQeAiHgAZW2Tx4wxLmuE1S48VKrFRMFXteDyP3Hz6o2Ghu85tLoxhi+/\n3EuZMv40alQcgODgMLy83PXBOZVpOHXMbBFpDHwFBGE9Q/GAiPQyxvx5LxtUKt3tmRM/SQy8Cj6O\nPdNw8OAl+vf/hd9/P02lSgXYs6cfXl7u5Mnj46Rglcp4HGl6mga0M8YcABCRSliJ454yk1Lp4spB\na9xpr9wQcSNu/tBokJTv4bh9O5IJE/5g8uQ/iYyMoWBBP0aObISnp/bNpLIfRxKF150kAWCMOSgi\nXk6MSanUCz4F6wfCqVVWcgi39VtpnyR673MoSaxadYyBA1dw4oTVOeDLLz/MpEmP4e9/7116KJWZ\nOZIodonIPKyzCICeaKeAKiO59Dd8WT1uOtyuc+MWs6BST/DMAW4pH+6hoRH06rWUy5dvUbVqIebO\nbU/DhsWdELRSmYcjiaIf8CrwJtY1io3Ap84MSqkUGQMnllvjRqwfGDe/ah/rQrV3HvDwBY+UryVE\nR8cQE2Pw9HQnZ04vpk9vQ2DgDV5/vR6entqBn1LJJgoRqQaUAZYaYyanT0hKJWCMdddS6L9x3Xrf\nunh3uSaT4ZHhqap6585/eeWV5XTqVIExY5oCxA4qpJSyJNd77NtAH2AX8IiIjDfGLEy3yJQCuHUZ\n5qQwRGiN/pC3DAQMc7jaGzfCGTPmV2bO3E5MjOHGjXDeequRnkEolYjkzih6AtWNMTdFpCCwAtBE\nodLPrUswx67/Jd+C8NzfcdPiDn6p69zYGMMPPxzgtddWce5cKO7uwtCh9Xj33Uc1SSiVhOQSRbgx\n5iaAMeaSiAO3iyiVFkKC4IuKEBkaN6/GAHhsVtLrOFJtSDhdu/7AypXHAKhbtyhz53agZs0HUlhT\nqewtuURR2m6sbAHK2I+dbYzp7NTIVPYUEwWfFYs/r9470PDd+646Z04vwsOjyZPHm0mTHqNv39q4\nuWkX4EqlJLlE0SXB9ExnBqIUANdPxL1uNAFqDQave+9sb+PG0xQpkpNy5fIjIixc2BEfHw8KF86Z\nBsEqlT0kN2b2+vQMRClunIVd06zXOR+Eum/fc1WXL9/izTfX8sUXe2jRohRr1/ZCRChRIm8aBatU\n9qEd56uM4d8t8G39uOnQf++pmpgYw6JFexg+fC1Xr97Gy8udxo2LEx1t8PDQZial7oVTL1CLSBsR\nOSwix0TkrWTKPSUiRkS0/6jsJjoS9n0eP0mUfQKeTX0Pwfv3X6RZs0X06bOMq1dv06JFKf7+uz9j\nxzbDw0PvxVDqXjl8RiEi3saY8FSUdwdmAS2BQGC7iCyz7zfKVi4X1pPfWx2tW2UBIYHw/aNw/Vj8\n+Z1XQKm2qa4uODiMevUWEBoaQaFCOZg6tRU9elTT8aqVSgOOdDNeB1gA5AGKi0gN4CXbkKjJqYM1\ndsUJWz2LgU7AgQTl3gMmA2+kMnaV2Rz7Gf4aCzGRcCXhYQB0/R2KNUlVlcYYRIQ8eXwYMaIhQUE3\n+OCDFuTLpx34KZVWHDmjmAF0AH4CMMbsFZFHHVivKHDWbjoQqGtfQERqAQ8ZY5aLSJKJQkT6An0B\nihfXDtoypdBz8PMTd88PeANqvQq5H0pVdUFBN3jttVV06lSBXr1qADBqVGM9g1DKCRxJFG7GmNMJ\nPoDRDqyX2Cc2djg92wN804DnU6rIGPMZ8BlYI9w5sG2VkUSEwrwH46ZbzLbOHHIXT/Wtr1FRMcya\ntY3RozcQGhrBrl3n6NGjGu7ubpoklHISRxLFWVvzk7FddxgMHHFgvUDA/mdiMcD+VpZcQFXgN9sH\n/AFgmYh0NMboWKdZyX/trjlUfRGqPAeefqmuZvv2IPr1+4Vdu84B8MQTFZkxow3u7nqhWilnciRR\n9MdqfioOXADW2ealZDtQTkRKYQ2j2g3ocWehMSYYiO2oR0R+A97QJJFFRN6EH1pBVBhc3GXN88wB\nrRekuqqbNyMYMWIds2dvxxgoXjwPn37alo4dK6Rx0EqpxKSYKIwxF7G+5FPFGBMlIoOA1YA7sNAY\ns19ExgM7jDHLUh2tyhxOr4cfHrt7/otH76k6Dw831q07gZubMHRofcaObUqOHDrIolLpRYxJvslf\nROZjd23hDmNMX2cFlZyAgACzY4eedGRI4cGwfTJs/SBuXrGm0OxjyFMGfBx/Kvr48avkzetD/vxW\nE9X27UH4+HhQrVrhtI5aqWxBRHYaY+7pWTVHmp7W2b32AZ4k/t1MSlm+rAE3TsdNd/wRyqWu78jw\n8Cg++ugvJkz4g549q/H55x0BeOSRomkZqVIqFRxpevrOflpEvgLWOi0ilfkYAydXxiWJnEWtB+cK\nVk9+vQR+++0U/fv/wqFDlwHrDqfo6Bi9WK2Ui91LX0+lgBJpHYjKxE6ugKUd4qb7no0bstQBFy/e\nZPjwtXz55V4AKlTIz5w57Xn00VJpHalS6h448mT2NeKuUbgBV4Ek+21S2YwxsG5A3PTjS1KVJC5f\nvkWlSrO4evU23t7ujBrVmDffbIi3t/ZXqVRGkeynUawHHGpg3d4KEGNSuvqtso9Lf8OXds1LVZ6D\n8k+lqooCBfzo1KkCgYE3mD27PWXL+qdxkEqp+5VsojDGGBFZaoypnV4BqUziwk74OsENFC3mpLja\nzZsRjB//O+3bl6dJE6sFc/bs9nh7u+uT1UplUI5cJdwmIg87PRKVeVzaFz9JNJ0CQ6PBM/mO+P73\nv8NUrjybyZP/YsCAX4iJsU5OfXw8NEkolYEleUYhIh7GmCigEfCyiBwHbmL14WSMMZo8spuw63Bm\nHfzv6bh5DtwCe/ZsMK+9toqlSw8BUKvWA8yb10HHq1Yqk0iu6Wkb8DCQSJefKts4sQL2zAQTA6dW\nx1/W9ONkk0RUVAwzZmzlnXc2cPNmJDlzevH++48ycGAdHUhIqUwkuUQhAMaY4+kUi8poIm/B0vZ3\nzy9QFWoOgurJP5x/40Y4Eydu4ubNSLp0qcQnn7ShWLHcTgpWKeUsySWKgiIyNKmFxpipTohHZRTG\nxB+etMUsyFMaCtaAnEWSXO369TB8fT3w9vbA39+XefM64O3tTvv25dMhaKWUMySXKNyBnCQ+roTK\n6q4fsy5aAxSpDzUHJFvcGMO33/7D66+vZtCgRxgzpikAnTtXcnakSiknSy5RnDPGjE+3SJTrnd8B\nm8eBeMDxn+PmP70uyVUAjhy5woABv7B+/UkANm48EztEqVIq80vxGoXKRr6tDzFR8ecVa5rkIENh\nYVF8+OEmPvhgExER0fj7+/LRRy15/vmamiSUykKSSxQt0i0K5RrREbD6RWtwIYhLElX7QOkO1jCl\nDzVNdNXz50Np0uQLjh69CsDzz9fko49aUqBA6keuU0plbEkmCmPM1fQMRLnAgnIQcubu+S1mgYd3\nsqsWLpyDhx7Kg4eHG3PmtKdp05LOiVEp5XLa81p2ZUz8JPH4Euv/QrUSTRIxMYb583fy6KOlKF8+\nPyLCN990Jl8+X7y83NMpaKWUK2iiyI7+HAtb7O5TeDXUGs86CXv3nqdfv1/YsiWQFi1KsXZtL0SE\nwoVzpkOwSilX00SR3SRMEgVrJJkkQkMjGDfuNz75ZAvR0YYHH8xFv373NJKiUioT00SR1UWFwc1z\ncPkfWPMS3LoYt+ypdVC8eaKr/fTTIQYPXklg4A3c3ITBg+vw/vvNyZ07+WsXSqmsRxNFVnXzAvz+\nBhz8OvHlLxwC/wqJLgoKukG3bj8QHh5N7dpFmDu3AwEBDzoxWKVURqaJIisKD4a5D8Sfl7ukdTbR\n+AOo9vJdz0ZERkbj4eGGiFC0aG4mTGiOl5c7AwY8omNWK5XNaaLIas5vh28bxk0XqQvt/gN5yyS5\nyl9/naVfv+UMH96AXr1qADBsWANnR6qUyiQ0UWQFESEQEQp/z4cz6yEmEjx8oUg96LIK3L0SXe3q\n1duMHLmOzz7bBcDs2Tt49tnq+lS1UioeTRSZ3YGvYWWvu+fX6AfNEu/g1xjD11/vY9iwNVy6dAtP\nTzfefLMho0Y11iShlLqLJorM5tZl2DYRom7Dga8gMjRumV9h60yiwTgo0ynR1S9cCKV79x/ZsOEU\nAE2blmDOnPZUqlTQ+bErpTIlTRSZyeHvYXnXxJc9vT7JW13t5c3rw7lzoRQo4MeUKS3p3buGnkUo\npZKliSKjMwbCr8Pty/GTxEOPQvmnwDsflO+S5HUIgLVrj/Pww0XIn98Pb28Plix5miJFcpI/v3bg\np5RKmSaKjCwmGuYUgrAE/TN2/R2KNUlx9XPnQhg6dA2LF/9Dnz61+PzzjgBUrVrIGdEqpbIoTRQZ\nTVQ47PwYbp6H3Z/GXyZu0OSjFJNEdHQM8+btZOTI9dy4EY6vrwcVKuTXwYSUUvdEE0VGEBUO4ddg\nz5z4/TDdUagW9NrlUFW7dp2jX7/lbN/+LwDt25dj5sx2lCyZNy0jVkplI5ooXCkmGv4cDdsmJb68\n6cdQvAUUquFQdadOXadOnflERxuKFs3FjBltefLJinoWoZS6L05NFCLSBpgOuAOfG2MmJVg+FHgJ\niAIuAS8aY047M6YM4+Z5WFQVwq7En1+0MVTqaf3zSl033iVL5uWFF2qSK5c3777bjFy5tAM/pdT9\nc1qiEBF3YBbQEggEtovIMmPMAbtiu4EAY8wtEekPTAaSuP8zCzDGGm501fNw6Jv4ywZcBt/8qaru\n1KnrDB68kjfeqB87wtxnnz2uZxBKqTTlzDOKOsAxY8wJABFZDHQCYhOFMWaDXfktwLNOjMe1jIFv\nG8C5LfHnN5oIdd9KVVWRkdFMnbqZd9/9ndu3o7h8+RabN/cB0CShlEpzzkwURYGzdtOBQN1kyvcB\nVia2QET6An0BihcvnlbxpZ/Qf+H75nDtsDXt5gEmBl69leLY1Alt2nSGfv2Ws3//JQC6davK1Kmt\n0jpipZSK5cxEkdhPW5NoQZFngQCgaWLLjTGfAZ8BBAQEJFpHhhUdAfOKxk175YbBwamu5tq12wwf\nvpYFC3YDUKZMPmbPbk+rVkn3CquUUmnBmYkiEHjIbroY8G/CQiLyGDAKaGqMCXdiPOkv8jbMtrvu\nUL1vkh31pSQmxvDzz4fx9HTjrbcaMXJkI3x9PdMoUKWUSpozE8V2oJyIlAKCgG5AD/sCIlILmAe0\nMcZcvLuKTGrrB7BpVPx5NfrDY7NTVc2hQ5cpVSov3t4e5M/vx3/+05nixfNQsWKBNAxWKaWS57Sh\ny4wxUcAgYDVwEPjeGLNfRMaLSEdbsY+AnMASEdkjIsucFU+6OPw9bB5/d5J4eEiqksStW5GMGrWe\n6tXnMHnyn7HzW7Uqo0lCKZXunPochTFmBbAiwbx37F4/5sztp6vfhsLOafHnvXwachYFN3eHq1m1\n6hgDBvzCyZPXAbh8+VZaRqmUUqmmT2anhaiw+Emizkgo3QFyO36H1r//hjBkyCqWLLHuHq5WrRBz\n53agQYOHUlhTKaWcSxPF/VpYMe62V4CuG6FY41RVceTIFQICPiMkJAI/P0/GjWvKkCH18PR0/ExE\nKaWcRRPF/dj6QfwkUaQePNgg1dWUK+fPI48UJUcOTz79tC0lSmgHfkqpjEMTxb36613YPC5u+rXb\n4OHj0Ko3boTzzjsbGDDgEcqXz4+IsGxZN3LkSHrwIaWUchVNFPfi2rH4SeLl0w4lCWMMP/xwgNde\nW8W5c6EcOnSZVausXks0SSilMipNFKlhYmBhBbh+LG5e/4vgVzDFVU+cuMagQStYudJat169Ynz4\nYda56UsplXVponBURCh81zR+kmi9MMUkERERzZQpf/HeexsJC4sib14fJk1qwcsv18bNTTvwU0pl\nfJooHPXbULhoN8rca2EOdeh39mww48f/Tnh4ND17VuPjj1tRuHDqxplQSilX0kThiP+2g5N2Hdv2\nO5dskrh27TZ58/ogIpQp48/06W0oW9afFi1Kp0OwSimVtpzWhUeWEREaP0n0PQs5Hki0aEyMYeHC\n3ZQt+ylff70vdv4rrwRoklBKZVqaKFISeTPu9ZBwyFUs0WL791+kWbNF9OmzjKtXb8detFZKqcxO\nm55ScmdMa79C4H73Lay3bkXy3nu/M2XKZqKiYihUKAfTprWme/eq6RyoUko5hyaK5BgDi6pYr2/d\n3Qv6kSNXaN36a06duo4I9OtXmw8+aEG+fL7pHKhSSjmPJorkrOwV97raS3ctLlEiDz4+HtSoUZi5\ncztQr17izVIqe4qMjCQwMJCwsDBXh6KyER8fH4oVK4anZ9oNbKaJIjGBm2D9ALj8d9y8VvOJioph\n7twddO9elfz5/fD29mDVqp4ULZobDw+93KPiCwwMJFeuXJQsWRIRfWZGOZ8xhitXrhAYGEipUqXS\nrF79dksoJgq+axw/SQy8xrZtQdSpM5/Bg1cyYsS62EUlSuTVJKESFRYWRv78+TVJqHQjIuTPnz/N\nz2L1G87e8f/BNLvTtUYTCO59iUFv/EW9ep+ze/d5ihfPQ6dOFVwXo8pUNEmo9OaMY06bngCiwmHD\na7BvXuws41eE70525PUn/o/z50Px8HBj6NB6vPNOU+3ATymVregZxbbJMN0nXpLgmQ3srb+L7t1/\n5Pz5UBo0eIhdu/ry4YctNUmoTMXd3Z2aNWtStWpVHn/8ca5fvx67bP/+/TRv3pzy5ctTrlw53nvv\nPYwxsctXrlxJQEAAlSpVomLFirzxxhuu2IVk7d69m5deuvtGk4xk4sSJlC1blgoVKrB69epEyzz/\n/POUKlWKmjVrUrNmTfbs2QPAf/7zH6pXr0716tVp0KABe/fuBSAiIoImTZoQFRWVPjthjMlU/2rX\nrm3uW1SEMSdXG7NuoDFTiP0XNdXXmFuXY4u9/voqM3/+ThMdHXP/21TZzoEDB1wdgsmRI0fs6969\ne5v333/fGGPMrVu3TOnSpc3q1auNMcbcvHnTtGnTxsycOdMYY8zff/9tSpcubQ4ePGiMMSYyMtLM\nmjUrTWOLjIy87zqeeuops2fPnnTdZmrs37/fVK9e3YSFhZkTJ06Y0qVLm6ioqLvKPffcc2bJkiV3\nzf/zzz/N1atXjTHGrFixwtSpUyd22bhx48zXX3+d6HYTO/aAHeYev3ezX9PTrumwYchdszeU+4MB\nI/Yyr3YoTZrkB2Dq1NbpHZ3Kqj520rWKYSblMjb169dn3z6ra5lvvvmGhg0b0qpVKwD8/PyYOXMm\nzZo1Y+DAgUyePJlRo0ZRsWJFADw8PBgwYMBddYaGhjJ48GB27NiBiDB27Fi6dOlCzpw5CQ0NBeCH\nH35g+fLlLFq0iOeffx5/f392795NzZo1Wbp0KXv27CFvXmtUx7Jly/Lnn3/i5uZGv379OHPmDACf\nfPIJDRs2jLftkJAQ9u3bR40aNQDYtm0bQ4YM4fbt2/j6+vLFF19QoUIFFi1axC+//EJYWBg3b97k\n119/5aOPPuL7778nPDycJ598knfffReAJ554grNnzxIWFsZrr71G3759HX5/E/Pzzz/TrVs3vL29\nKVWqFGXLlmXbtm3Ur1/fofUbNIgbMbNevXoEBgbGTj/xxBOMHDmSnj173leMjsheieLs7wmShHDR\nvxPD13bhyzfWAzB16maaNCnhmviUcpLo6GjWr19Pnz59AKvZqXbt2vHKlClThtDQUG7cuME///zD\nsGHDUqz3vffeI0+ePPz9t3WX4LVr11Jc58iRI6xbtw53d3diYmJYunQpL7zwAlu3bqVkyZIULlyY\nHj168Prrr9OoUSPOnDlD69atOXjwYLx6duzYQdWqcT0gVKxYkY0bN+Lh4cG6det4++23+fHHHwHY\nvHkz+/btw9/fnzVr1nD06FG2bduGMYaOHTuyceNGmjRpwsKFC/H39+f27ds88sgjdOnShfz588fb\n7uuvv86GDRvu2q9u3brx1ltvxZsXFBREvXr1YqeLFStGUFBQou/LqFGjGD9+PC1atGDSpEl4e8fv\neHTBggW0bds2drpq1aps3749ubc6zWSfRBF2Db5vFjsZ02sfC36OYMSwdVy7dhxvb3dGj27C8OGp\nH/NaqRSl4pd/Wrp9+zY1a9bk1KlT1K5dm5YtWwJWk3NSd8ek5q6ZdevWsXjx4tjpfPnypbjO008/\njbu7OwBdu3Zl/PjxvPDCCyxevJiuXbvG1nvgwIHYdW7cuEFISAi5cuWKnXfu3DkKFowbDyY4OJjn\nnnuOo0ePIiJERkbGLmvZsiX+/v4ArFmzhjVr1lCrVi3AOis6evQoTZo0YcaMGSxduhSAs2fPcvTo\n0bsSxbRp0xx7cyDeNZ87Ent/J06cyAMPPEBERAR9+/blww8/5J133oldvmHDBhYsWMCmTZti57m7\nu+Pl5XXX++IM2SNRGANfPRw7efLh73j2ye389ddZAFq1KsOsWe0oW9bfVREq5RS+vr7s2bOH4OBg\nOnTowKxZs3j11VepUqUKGzdujFf2xIkT5MyZk1y5clGlShV27twZ26yTlKQSjv28hPf058iRI/Z1\n/fr1OXbsGJcuXeKnn35i9OjRAMTExLB582Z8fZPuDsfX1zde3WPGjOHRRx9l6dKlnDp1imbNmiW6\nTWMMI0eO5JVXXolX32+//ca6devYvHkzfn5+NGvWLNHnEVJzRlGsWDHOnj0bOx0YGMiDDz5417pF\nihQBwNvbmxdeeIEpU6bELtu3bx8vvfQSK1euvCtphYeH4+OT8jDM9yt73PX0bUO4ccp6XaQuuat3\n4MiRKzzwQE4WL+7CqlU9NUmoLC1PnjzMmDGDKVOmEBkZSc+ePdm0aRPr1lkPj96+fZtXX32VN998\nE4Dhw4fzwQcfcOTIEcD64p46depd9bZq1YqZM2fGTt9peipcuDAHDx6MbVpKiojw5JNPMnToUCpV\nqhT7RZiw3jt3AdmrVKkSx47F9dIcHBxM0aJFAVi0aFGS22zdujULFy6MvYYSFBTExYsXCQ4OJl++\nfPj5+XHo0CG2bNmS6PrTpk1jz549d/1LmCQAOnbsyOLFiwkPD+fkyZMcPXqUOnXq3FXu3LlzgJXE\nfvrpp9gmtTNnztC5c2e++uorypcvH2+dK1euULBgwTTtqiMpWTtRGGNdlzi3mdWHyxAe5Q7d/iR/\nfj+WLevGoUMD6dq1qj4UpbKFWrVqUaNGDRYvXoyvry8///wz77//PhUqVKBatWo88sgjDBo0CIDq\n1avzySef0L17dypVqkTVqlVjv8zsjR49mmvXrlG1alVq1KgR+0t70qRJdOjQgebNm8f+Wk5K165d\n+frrr2ObnQBmzJjBjh07qF69OpUrV2bu3Ll3rVexYkWCg4MJCQkB4M0332TkyJE0bNiQ6OjoJLfX\nqlUrevToQf369alWrRpPPfUUISEhtGnThqioKKpXr86YMWPiXVu4V1WqVOGZZ56hcuXKtGnThlmz\nZsU2u7Vr145///0XgJ49e1KtWjWqVavG5cuXY8+sxo8fz5UrVxgwYAA1a9YkICAgtu4NGzbQrl27\n+47REZJYG1pGFhAQYHbs2JF0gbDrcHotbH0fLu3j7PXcvPpTW376pxLvjW/G6DFN0y1Wlb0dPHiQ\nSpUquTqMLG3atGnkypUrwz9L4QydO3dm4sSJVKhwd08RiR17IrLTGBNwV2EHZK1rFDfOwvziAERF\nuzFjU33eWf0oNyO8yOkH/vn9XBygUiot9e/fnyVLlrg6jHQXERHBE088kWiScIaskSj+GAlBf0LQ\nHwBsOV2Mfj8/zd4zeQDo0qUS06e3oWjR3K6MUimVxnx8fOjVq1fKBbMYLy8vevfunW7by/yJYvdM\n2DYpdnLr6aI0mNkHY4SSJfMyc2Zb2rcvn0wFSjlPcrehKuUMzrickLkTRUQI/Do4bvrpX6nj4Uvr\no8eoVasIo0c3wc/P+XcEKJUYHx8frly5ol2Nq3RjbONRpPUts5k3UVw/AQvKcPSSP68va8PUMWUo\nX/xRBPjll7q4uekHU7lWsWLFCAwM5NKlS64ORWUjd0a4S0uZM1EE/UX4102Y9GtTJv7amPAoD3yW\nlOOH7tZiTRIqI/D09H2Sg4sAAAidSURBVEzTUcaUchWnPkchIm1E5PD/t3f3MXJVZRzHvz8phfJi\nBRoMCFqQFym11FqxSiLWAkGMvKWhrW2hDUgoVgNYTUxNrGIMARsjAi4rNgUDWCGgG4QUUgslTbe0\nkb6xQSilwQ3EgtTG0IJt+fnHOeuMy3bm7rYzOzv7fJJJ5t45c+8zT2bmzD137nMkbZb0gatRJB0i\naUl+fLWkkdW3apbdPIMxC+ew4MmJvLdnCLNnj6Wl9dID/wJCCCHU7joKSQcBLwHnA53AGmCa7Y6y\nNtcDY2xfJ2kqcJntKT1uMDvm8KP89s5U2O+MTw6lZdE3oohfCCFUsT/XUdTyiOJsYLPtLbb/A/we\nuKRbm0uAe/P9h4FJqnLWb/vOYRw6ZDc/m/E66zq+H51ECCHUWC2PKCYDF9q+Ji/PBD5ve25Zm025\nTWdefiW3eavbtq4FugrDjwY21STogWcE8FbVVoND5KIkclESuSg53XafyszW8mR2T0cG3XulIm2w\n3Qq0Akha29fDp2YTuSiJXJRELkoiFyWSKtQ+qqyWQ0+dwIllyycAr++rjaQhwHDg7RrGFEIIoZdq\n2VGsAU6VdJKkocBUoK1bmzbgqnx/MvAXD7QqhSGE0ORqNvRke4+kucBS4CBgke0XJP2ENMl3G/Bb\n4HeSNpOOJKYW2HRrrWIegCIXJZGLkshFSeSipM+5GHBlxkMIIdRXc09cFEIIYb9FRxFCCKGihu0o\nalP+Y2AqkIubJHVI2iBpmaSmvQqxWi7K2k2WZElN+9fIIrmQdEV+b7wg6YF6x1gvBT4jH5e0XNLz\n+XNSnzlE60zSIknb8jVqPT0uSbfnPG2QNK7Qhm033I108vsV4GRgKLAeGNWtzfVAS74/FVjS33H3\nYy4mAofl+3MGcy5yuyOBFUA7ML6/4+7H98WpwPPAUXn52P6Oux9z0QrMyfdHAVv7O+4a5eJLwDhg\n0z4evwh4gnQN2wRgdZHtNuoRRU3KfwxQVXNhe7ntnXmxnXTNSjMq8r4AuBm4FXi3nsHVWZFcfBO4\n0/Z2ANvb6hxjvRTJhYGuKS6H88FrupqC7RVUvhbtEuA+J+3ARyQdV227jdpRfAz4e9lyZ17XYxvb\ne4AdwDF1ia6+iuSi3NWkXwzNqGouJH0GONH2Y/UMrB8UeV+cBpwmaaWkdkkX1i26+iqSiwXADEmd\nwOPAtxmcevt9AjTufBQHrPxHEyj8OiXNAMYD59Y0ov5TMReSPgT8AphVr4D6UZH3xRDS8NOXSUeZ\nz0oabftfNY6t3orkYhqw2PZCSV8gXb812vb7tQ+vofTpe7NRjyii/EdJkVwg6TxgPnCx7ffqFFu9\nVcvFkaSikU9L2koag21r0hPaRT8jf7K92/arwN9IHUezKZKLq4E/ANheBRxKKhg42BT6PumuUTuK\nKP9RUjUXebjlblIn0azj0FAlF7Z32B5he6TtkaTzNRfb7nMxtAZW5DPyR9IfHZA0gjQUtaWuUdZH\nkVy8BkwCkHQGqaMYjHPUtgFX5n8/TQB22H6j2pMacujJtSv/MeAUzMVtwBHAQ/l8/mu2L+63oGuk\nYC4GhYK5WApcIKkD2At8z/Y/+y/q2iiYi+8Cv5F0I2moZVYz/rCU9CBpqHFEPh/zI+BgANstpPMz\nFwGbgZ3A7ELbbcJchRBCOIAadegphBBCg4iOIoQQQkXRUYQQQqgoOooQQggVRUcRQgihougoQsOR\ntFfSurLbyAptR+6rUmYv9/l0rj66Ppe8OL0P27hO0pX5/ixJx5c9do+kUQc4zjWSxhZ4zg2SDtvf\nfYfBKzqK0Ih22R5bdttap/1Ot30Wqdjkbb19su0W2/flxVnA8WWPXWO744BEWYrzLorFeQMQHUXo\ns+gowoCQjxyelfTXfPtiD23OlPRcPgrZIOnUvH5G2fq7JR1UZXcrgFPycyflOQw25lr/h+T1t6g0\nB8jP87oFkuZJmkyquXV/3uewfCQwXtIcSbeWxTxL0q/6GOcqygq6Sfq1pLVKc0/8OK/7DqnDWi5p\neV53gaRVOY8PSTqiyn7CIBcdRWhEw8qGnR7N67YB59seB0wBbu/hedcBv7Q9lvRF3ZnLNUwBzsnr\n9wLTq+z/68BGSYcCi4Eptj9NqmQwR9LRwGXAmbbHAD8tf7Lth4G1pF/+Y23vKnv4YeDysuUpwJI+\nxnkhqUxHl/m2xwNjgHMljbF9O6mWz0TbE3Mpjx8C5+VcrgVuqrKfMMg1ZAmPMOjtyl+W5Q4G7shj\n8ntJdYu6WwXMl3QC8IjtlyVNAj4LrMnlTYaROp2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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1aa85748630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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c2pC87ol1VqLIxF9/hQBwzz3WI08XL0YTE5NAuXKFXBGpyuVEZJsxJvhm9nX6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\ndcWLF6d06dJs2LDB5ecA+bnpKSIEPkk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WAwPja8fGOQw2xFr/PePyO5SdA+S3cdkcSTdL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\noJEfquq/AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1aa8578c940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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zXkR2aZJQysVsyXB+N6Q4DH7Qbz+UrOr2qrduPcWAAfOpXTuEzz+/G4C2bSvT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\nlwTlCTOKAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1aa857d0e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.052649993449449539, 0.26828080415725708, 0.29003441333770752, 0.14435943961143494, 0.28669834136962891, 0.10782142728567123, 0.14602121710777283, 0.079208120703697205, 0.12269972264766693, 0.099530883133411407, 0.91244196891784668]\n"
     ]
    }
   ],
   "source": [
    "# Testing to see actual Roc Curves\n",
    "\n",
    "\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Compute ROC curve and ROC area for each class\n",
    "fpr = dict()\n",
    "tpr = dict()\n",
    "roc_auc = dict()\n",
    "\n",
    "\n",
    "names = [\"amused\" ,\"calm\" ,\"content\" ,\"excited\" ,\"optimistic\" ,\"annoyed\" ,\"apathetic\" ,\"frustrated\",\"pessimistic\",\"stressed\",\"neutral\"]\n",
    "#names = [\"positive\", \"negative\", \"neutral\"]\n",
    "\n",
    "\n",
    "for i in range(0,len(names)):\n",
    "    fpr[i], tpr[i], _ = roc_curve(original[:][i],predicted[:][i])\n",
    "    roc_auc[i] = auc(fpr[i], tpr[i])\n",
    "    \n",
    "cl = 0\n",
    "\n",
    "\n",
    "for cl in range(0,len(names)):\n",
    "    plt.figure()\n",
    "    lw = 2\n",
    "    plt.plot(fpr[cl], tpr[cl], color='darkorange',\n",
    "         lw=lw, label='ROC curve (area = %0.2f)' % roc_auc[cl])\n",
    "    plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')\n",
    "    plt.xlim([0.0, 1.0])\n",
    "    plt.ylim([0.0, 1.05])\n",
    "    plt.xlabel('False Positive Rate')\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    plt.title('Receiver operating characteristic ' + names[cl])# ROC curves\n",
    "    plt.legend(loc=\"lower right\")\n",
    "\n",
    "\n",
    "plt.show()\n",
    "\n",
    "print(cut_off_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cut_off_list = [0.10932411253452301, 0.22017614543437958, 0.28619441390037537, 0.21891899406909943, 0.30378702282905579, 0.11781904846429825, 0.15280061960220337, 0.14604103565216064, 0.12796874344348907, 0.10556335747241974, 0.83351844549179077]\n",
    "#[0.67946451902389526, 0.57581603527069092, 0.84728133678436279] # identified before.\n",
    "\n",
    "cut_off_prediction = []\n",
    "\n",
    "for i in predicted_labels:\n",
    "    list_labels=i.tolist()\n",
    "    my_prediction = []\n",
    "    for j in range(0,len(list_labels)):\n",
    "        if list_labels[j]> cut_off_list[j]:\n",
    "            my_prediction.append(1)\n",
    "        else:\n",
    "            my_prediction.append(0)\n",
    "    cut_off_prediction.append(my_prediction)\n",
    "    \n",
    "#pr = [i.tolist() for i in predicted_labels]\n",
    "\n",
    "predicted = pd.DataFrame(cut_off_prediction)\n",
    "#original"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "amused\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.96      0.99      0.98      2259\n",
      "          1       0.00      0.00      0.00        82\n",
      "\n",
      "avg / total       0.93      0.96      0.94      2341\n",
      "\n",
      "[[2245   14]\n",
      " [  82    0]]\n",
      "calm\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.72      0.31      0.44      1381\n",
      "          1       0.45      0.82      0.59       960\n",
      "\n",
      "avg / total       0.61      0.52      0.50      2341\n",
      "\n",
      "[[432 949]\n",
      " [170 790]]\n",
      "content\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.61      0.59      0.60      1184\n",
      "          1       0.59      0.61      0.60      1157\n",
      "\n",
      "avg / total       0.60      0.60      0.60      2341\n",
      "\n",
      "[[699 485]\n",
      " [453 704]]\n",
      "excited\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.86      0.93      0.90      2010\n",
      "          1       0.20      0.10      0.13       331\n",
      "\n",
      "avg / total       0.77      0.82      0.79      2341\n",
      "\n",
      "[[1876  134]\n",
      " [ 298   33]]\n",
      "optimistic\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.71      0.68      0.70      1534\n",
      "          1       0.44      0.48      0.46       807\n",
      "\n",
      "avg / total       0.62      0.61      0.61      2341\n",
      "\n",
      "[[1041  493]\n",
      " [ 420  387]]\n",
      "annoyed\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.81      0.77      0.79      1880\n",
      "          1       0.22      0.26      0.24       461\n",
      "\n",
      "avg / total       0.69      0.67      0.68      2341\n",
      "\n",
      "[[1439  441]\n",
      " [ 340  121]]\n",
      "apathetic\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.80      0.65      0.72      1786\n",
      "          1       0.30      0.48      0.37       555\n",
      "\n",
      "avg / total       0.68      0.61      0.63      2341\n",
      "\n",
      "[[1156  630]\n",
      " [ 286  269]]\n",
      "frustrated\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.95      1.00      0.97      2217\n",
      "          1       0.00      0.00      0.00       124\n",
      "\n",
      "avg / total       0.90      0.95      0.92      2341\n",
      "\n",
      "[[2217    0]\n",
      " [ 124    0]]\n",
      "pessimistic\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.84      0.63      0.72      1930\n",
      "          1       0.20      0.43      0.27       411\n",
      "\n",
      "avg / total       0.73      0.60      0.64      2341\n",
      "\n",
      "[[1219  711]\n",
      " [ 233  178]]\n",
      "stressed\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.80      0.65      0.72      1842\n",
      "          1       0.23      0.39      0.29       499\n",
      "\n",
      "avg / total       0.68      0.60      0.63      2341\n",
      "\n",
      "[[1201  641]\n",
      " [ 304  195]]\n",
      "neutral\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.06      0.00      0.01       233\n",
      "          1       0.90      0.99      0.94      2108\n",
      "\n",
      "avg / total       0.82      0.89      0.85      2341\n",
      "\n",
      "[[   1  232]\n",
      " [  17 2091]]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1113: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'\\nprint(\"Presence of positive speech.\")\\nprint(classification_report(original.iloc[:,0],predicted.iloc[:,0])) # 16%\\nprint(confusion_matrix(original.iloc[:,0],predicted.iloc[:,0]))\\n\\nprint(\"Presence of negative speech.\")\\n\\nprint(classification_report(original.iloc[:,1],predicted.iloc[:,1])) # 16%\\nprint(confusion_matrix(original.iloc[:,1],predicted.iloc[:,1]))\\n\\n\\nprint(\"Neutral Non Neutral.\")\\n\\nprint(classification_report(original.iloc[:,2],predicted.iloc[:,2])) # 16%\\nprint(confusion_matrix(original.iloc[:,2],predicted.iloc[:,2]))\\n'"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# One vs All predictions\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "\n",
    "for i in range(0,len(names)):\n",
    "    print(\"\" + names[i])\n",
    "    print(classification_report(original.iloc[:,i],predicted.iloc[:,i])) # 16%\n",
    "    print(confusion_matrix(original.iloc[:,i],predicted.iloc[:,i]))\n",
    "\n",
    "\n",
    "\n",
    "'''\n",
    "print(\"Presence of positive speech.\")\n",
    "print(classification_report(original.iloc[:,0],predicted.iloc[:,0])) # 16%\n",
    "print(confusion_matrix(original.iloc[:,0],predicted.iloc[:,0]))\n",
    "\n",
    "print(\"Presence of negative speech.\")\n",
    "\n",
    "print(classification_report(original.iloc[:,1],predicted.iloc[:,1])) # 16%\n",
    "print(confusion_matrix(original.iloc[:,1],predicted.iloc[:,1]))\n",
    "\n",
    "\n",
    "print(\"Neutral Non Neutral.\")\n",
    "\n",
    "print(classification_report(original.iloc[:,2],predicted.iloc[:,2])) # 16%\n",
    "print(confusion_matrix(original.iloc[:,2],predicted.iloc[:,2]))\n",
    "'''\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HAMMING LOSS\n",
      "0.28958098714613023\n",
      "Jaccard SCORE\n",
      "0.412160554098\n",
      "f1 SCORE\n",
      "0.5579471006\n",
      "Precision\n",
      "0.552387054779\n",
      "Recall\n",
      "0.661617949187\n",
      "Accuracy\n",
      "0.0311832550192\n",
      "Zero One Loss\n",
      "0.968816744981\n",
      "Classification Report\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "     amused       0.00      0.00      0.00        82\n",
      "       calm       0.45      0.82      0.59       960\n",
      "    content       0.59      0.61      0.60      1157\n",
      "    excited       0.20      0.10      0.13       331\n",
      " optimistic       0.44      0.48      0.46       807\n",
      "    annoyed       0.22      0.26      0.24       461\n",
      "  apathetic       0.30      0.48      0.37       555\n",
      " frustrated       0.00      0.00      0.00       124\n",
      "pessimistic       0.20      0.43      0.27       411\n",
      "   stressed       0.23      0.39      0.29       499\n",
      "    neutral       0.90      0.99      0.94      2108\n",
      "\n",
      "avg / total       0.52      0.64      0.56      7495\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1113: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    }
   ],
   "source": [
    "# Check hamming loss\n",
    "from sklearn.metrics import hamming_loss\n",
    "from sklearn.metrics import zero_one_loss\n",
    "\n",
    "from sklearn.metrics import jaccard_similarity_score\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import precision_score\n",
    "from sklearn.metrics import recall_score\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "\n",
    "print(\"HAMMING LOSS\")\n",
    "print(hamming_loss(original,predicted)) # 16%\n",
    "\n",
    "\n",
    "print(\"Jaccard SCORE\")\n",
    "#how many times to the EXACTLY agree.\n",
    "\n",
    "print(jaccard_similarity_score(original,predicted)) # 16%\n",
    "\n",
    "\n",
    "print(\"f1 SCORE\")\n",
    "#how many times to the EXACTLY agree.\n",
    "\n",
    "print(f1_score(original,predicted, average='samples')) # 16%\n",
    "\n",
    "\n",
    "print(\"Precision\")\n",
    "\n",
    "print(precision_score(original,predicted, average='samples')) # 16%\n",
    "\n",
    "print(\"Recall\")\n",
    "\n",
    "print(recall_score(original,predicted, average='samples')) # 16%\n",
    "\n",
    "\n",
    "print(\"Accuracy\")\n",
    "\n",
    "print(accuracy_score(original,predicted)) # 16%\n",
    "\n",
    "\n",
    "print(\"Zero One Loss\")\n",
    "\n",
    "print(zero_one_loss(original,predicted)) # 16%\n",
    "\n",
    "\n",
    "\n",
    "print(\"Classification Report\")\n",
    "\n",
    "#[\"amused\" ,\"calm\" ,\"content\" ,\"excited\" ,\"optimistic\" ,\"annoyed\" ,\"apathetic\" ,\"frustrated\",\"pessimistic\",\"stressed\",\"neutral\"]\n",
    "\n",
    "print(classification_report(original,predicted, target_names=names)) # 16%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7495\n",
      "7295\n",
      "2341\n"
     ]
    }
   ],
   "source": [
    "print(original.sum().sum())\n",
    "print(predicted.sum().sum())\n",
    "print(len(original))\n",
    "\n",
    "#predicted.head(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HAMMING LOSS\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "Can't handle mix of multilabel-indicator and continuous-multioutput",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-183-bbeaff618f00>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     21\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     22\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"HAMMING LOSS\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 23\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhamming_loss\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mor1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mprd1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# 16%\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     24\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     25\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py\u001b[0m in \u001b[0;36mhamming_loss\u001b[1;34m(y_true, y_pred, labels, sample_weight, classes)\u001b[0m\n\u001b[0;32m   1518\u001b[0m         \u001b[0mlabels\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mclasses\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1519\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1520\u001b[1;33m     \u001b[0my_type\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_true\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_pred\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_check_targets\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1521\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1522\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mlabels\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py\u001b[0m in \u001b[0;36m_check_targets\u001b[1;34m(y_true, y_pred)\u001b[0m\n\u001b[0;32m     80\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_type\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     81\u001b[0m         raise ValueError(\"Can't handle mix of {0} and {1}\"\n\u001b[1;32m---> 82\u001b[1;33m                          \"\".format(type_true, type_pred))\n\u001b[0m\u001b[0;32m     83\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     84\u001b[0m     \u001b[1;31m# We can't have more than one value on y_type => The set is no more needed\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: Can't handle mix of multilabel-indicator and continuous-multioutput"
     ]
    }
   ],
   "source": [
    "#predicted\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "#and original[1]==1 and original[2]==1\n",
    "\n",
    "or1= clean_data_labels_post(original.copy())\n",
    "prd1 = clean_data_labels_post(predicted.copy())\n",
    "\n",
    "\n",
    "# Check hamming loss\n",
    "from sklearn.metrics import hamming_loss\n",
    "from sklearn.metrics import jaccard_similarity_score\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import precision_score\n",
    "from sklearn.metrics import recall_score\n",
    "\n",
    "\n",
    "print(\"HAMMING LOSS\")\n",
    "print(hamming_loss(or1,prd1)) # 16%\n",
    "\n",
    "\n",
    "print(\"Jaccard SCORE\")\n",
    "#how many times to the EXACTLY agree.\n",
    "\n",
    "print(jaccard_similarity_score(or1,prd1)) # 16%\n",
    "\n",
    "\n",
    "print(\"f1 SCORE\")\n",
    "#how many times to the EXACTLY agree.\n",
    "\n",
    "print(f1_score(or1,prd1, average='samples')) # 16%\n",
    "\n",
    "\n",
    "print(\"Precision\")\n",
    "\n",
    "print(precision_score(or1,prd1, average='samples')) # 16%\n",
    "\n",
    "print(\"Recall\")\n",
    "\n",
    "print(recall_score(or1,prd1, average='samples')) # 16%\n",
    "\n",
    "\n",
    "print(\"Classification Report\")\n",
    "\n",
    "#[\"amused\" ,\"calm\" ,\"content\" ,\"excited\" ,\"optimistic\" ,\"annoyed\" ,\"apathetic\" ,\"frustrated\",\"pessimistic\",\"stressed\",\"neutral\"]\n",
    "\n",
    "print(classification_report(or1,prd1, target_names=names)) # 16%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#print(or1.sum().sum())\n",
    "#print(prd1.sum().sum())\n",
    "\n",
    "\n",
    "SeriesFileName = pd.DataFrame(files.iloc[test_l.index[0::]] )\n",
    "\n",
    "original.index = test_l.index\n",
    "\n",
    "SeriesFileName[\"Predicted_Positive\"] = original[0]\n",
    "SeriesFileName[\"Predicted_Negative\"] = original[1]\n",
    "SeriesFileName[\"Predicted_Neutral\"] = original[2]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "SeriesFileName.to_csv(\"original_3_classes_indlabel.csv\")"
   ]
  }
 ],
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