Supervised Learning/ML_Logistic-Regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Logistic Regression will be used for Classification/prediction<br>\n",
    "We need to use Categorical Data<br>\n",
    "3 Types of Logistic Regression are there-Binomial,Multinomial and Ordinal<br>\n",
    "Binomial-True or False ,0 or 1<br>\n",
    "Multinomial- Multiple Categories-cat,dog,sheep<br>\n",
    "Ordinal-  Independent and Dependent Variable High,Low ,Medium"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Implementing Binomial Logistic Regression<br>\n",
    "To read CSV file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "# For importing algorithm\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "# For splitting between training and testing\n",
    "from sklearn.model_selection import train_test_split\n",
    "# Importing accuracy score and mean_squared_error\n",
    "from sklearn.metrics import mean_squared_error, accuracy_score,mean_absolute_error\n",
    "# For plotting\n",
    "import matplotlib.pyplot as plt\n",
    "# For dimension reduction\n",
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reading csv file<br>\n",
    "Data Gathering<br>\n",
    "When we have a lot of data(Bigger dataset) processing takes time<br>\n",
    "If we run the data in loop for 4 times it will take a lot of time to load<br>\n",
    "We can use nrows(Parameter for number of rows) to select required number of rows as the dataset is very big"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset is:\n",
      "          Time        V1        V2        V3        V4        V5        V6  \\\n",
      "0           0 -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388   \n",
      "1           0  1.191857  0.266151  0.166480  0.448154  0.060018 -0.082361   \n",
      "2           1 -1.358354 -1.340163  1.773209  0.379780 -0.503198  1.800499   \n",
      "3           1 -0.966272 -0.185226  1.792993 -0.863291 -0.010309  1.247203   \n",
      "4           2 -1.158233  0.877737  1.548718  0.403034 -0.407193  0.095921   \n",
      "...       ...       ...       ...       ...       ...       ...       ...   \n",
      "149995  92345  1.724254  0.054124  0.625215  4.308400 -0.532295  0.540379   \n",
      "149996  92346 -3.538487  2.921360 -1.298732 -1.035773 -0.030572 -0.441956   \n",
      "149997  92347 -0.019481  1.224302 -0.132075 -0.678606  1.768679 -0.283992   \n",
      "149998  92348 -0.511063  1.277683 -0.174698 -0.101018  0.970555 -1.040956   \n",
      "149999  92348  2.197093 -0.429996 -1.439194 -0.307975 -0.020266 -0.715370   \n",
      "\n",
      "              V7        V8        V9  ...       V21       V22       V23  \\\n",
      "0       0.239599  0.098698  0.363787  ... -0.018307  0.277838 -0.110474   \n",
      "1      -0.078803  0.085102 -0.255425  ... -0.225775 -0.638672  0.101288   \n",
      "2       0.791461  0.247676 -1.514654  ...  0.247998  0.771679  0.909412   \n",
      "3       0.237609  0.377436 -1.387024  ... -0.108300  0.005274 -0.190321   \n",
      "4       0.592941 -0.270533  0.817739  ... -0.009431  0.798278 -0.137458   \n",
      "...          ...       ...       ...  ...       ...       ...       ...   \n",
      "149995 -0.688445  0.156967  1.476695  ... -0.367098 -0.771443  0.364177   \n",
      "149996  0.631323  0.445681  2.495754  ... -0.703023 -0.851606 -0.214636   \n",
      "149997  1.579740 -0.978318  1.874612  ... -0.036681  1.134522 -0.311552   \n",
      "149998  1.563977 -1.449619  0.595721  ...  0.836970  0.470593 -0.314103   \n",
      "149999 -0.165705 -0.432532  0.585860  ... -0.683676 -1.145510  0.310466   \n",
      "\n",
      "             V24       V25       V26       V27       V28  Amount  Class  \n",
      "0       0.066928  0.128539 -0.189115  0.133558 -0.021053  149.62      0  \n",
      "1      -0.339846  0.167170  0.125895 -0.008983  0.014724    2.69      0  \n",
      "2      -0.689281 -0.327642 -0.139097 -0.055353 -0.059752  378.66      0  \n",
      "3      -1.175575  0.647376 -0.221929  0.062723  0.061458  123.50      0  \n",
      "4       0.141267 -0.206010  0.502292  0.219422  0.215153   69.99      0  \n",
      "...          ...       ...       ...       ...       ...     ...    ...  \n",
      "149995 -0.100579 -0.547578 -0.348022 -0.011480 -0.023193   62.91      0  \n",
      "149996 -1.054619  0.305106  0.132541 -1.261532 -1.029257   37.91      0  \n",
      "149997  0.051861 -0.616234 -0.329780  0.220292 -0.232384    3.98      0  \n",
      "149998  0.066781  0.363133 -0.073593  0.183600  0.121156   74.77      0  \n",
      "149999  0.518359 -0.180141  0.415691 -0.099064 -0.062928   16.94      0  \n",
      "\n",
      "[150000 rows x 31 columns]\n",
      "\n",
      "Columns of dataset are:\n",
      " Index(['Time', 'V1', 'V2', 'V3', 'V4', 'V5', 'V6', 'V7', 'V8', 'V9', 'V10',\n",
      "       'V11', 'V12', 'V13', 'V14', 'V15', 'V16', 'V17', 'V18', 'V19', 'V20',\n",
      "       'V21', 'V22', 'V23', 'V24', 'V25', 'V26', 'V27', 'V28', 'Amount',\n",
      "       'Class'],\n",
      "      dtype='object')\n",
      "\n",
      "Information of dataset is::\n",
      " <bound method DataFrame.info of          Time        V1        V2        V3        V4        V5        V6  \\\n",
      "0           0 -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388   \n",
      "1           0  1.191857  0.266151  0.166480  0.448154  0.060018 -0.082361   \n",
      "2           1 -1.358354 -1.340163  1.773209  0.379780 -0.503198  1.800499   \n",
      "3           1 -0.966272 -0.185226  1.792993 -0.863291 -0.010309  1.247203   \n",
      "4           2 -1.158233  0.877737  1.548718  0.403034 -0.407193  0.095921   \n",
      "...       ...       ...       ...       ...       ...       ...       ...   \n",
      "149995  92345  1.724254  0.054124  0.625215  4.308400 -0.532295  0.540379   \n",
      "149996  92346 -3.538487  2.921360 -1.298732 -1.035773 -0.030572 -0.441956   \n",
      "149997  92347 -0.019481  1.224302 -0.132075 -0.678606  1.768679 -0.283992   \n",
      "149998  92348 -0.511063  1.277683 -0.174698 -0.101018  0.970555 -1.040956   \n",
      "149999  92348  2.197093 -0.429996 -1.439194 -0.307975 -0.020266 -0.715370   \n",
      "\n",
      "              V7        V8        V9  ...       V21       V22       V23  \\\n",
      "0       0.239599  0.098698  0.363787  ... -0.018307  0.277838 -0.110474   \n",
      "1      -0.078803  0.085102 -0.255425  ... -0.225775 -0.638672  0.101288   \n",
      "2       0.791461  0.247676 -1.514654  ...  0.247998  0.771679  0.909412   \n",
      "3       0.237609  0.377436 -1.387024  ... -0.108300  0.005274 -0.190321   \n",
      "4       0.592941 -0.270533  0.817739  ... -0.009431  0.798278 -0.137458   \n",
      "...          ...       ...       ...  ...       ...       ...       ...   \n",
      "149995 -0.688445  0.156967  1.476695  ... -0.367098 -0.771443  0.364177   \n",
      "149996  0.631323  0.445681  2.495754  ... -0.703023 -0.851606 -0.214636   \n",
      "149997  1.579740 -0.978318  1.874612  ... -0.036681  1.134522 -0.311552   \n",
      "149998  1.563977 -1.449619  0.595721  ...  0.836970  0.470593 -0.314103   \n",
      "149999 -0.165705 -0.432532  0.585860  ... -0.683676 -1.145510  0.310466   \n",
      "\n",
      "             V24       V25       V26       V27       V28  Amount  Class  \n",
      "0       0.066928  0.128539 -0.189115  0.133558 -0.021053  149.62      0  \n",
      "1      -0.339846  0.167170  0.125895 -0.008983  0.014724    2.69      0  \n",
      "2      -0.689281 -0.327642 -0.139097 -0.055353 -0.059752  378.66      0  \n",
      "3      -1.175575  0.647376 -0.221929  0.062723  0.061458  123.50      0  \n",
      "4       0.141267 -0.206010  0.502292  0.219422  0.215153   69.99      0  \n",
      "...          ...       ...       ...       ...       ...     ...    ...  \n",
      "149995 -0.100579 -0.547578 -0.348022 -0.011480 -0.023193   62.91      0  \n",
      "149996 -1.054619  0.305106  0.132541 -1.261532 -1.029257   37.91      0  \n",
      "149997  0.051861 -0.616234 -0.329780  0.220292 -0.232384    3.98      0  \n",
      "149998  0.066781  0.363133 -0.073593  0.183600  0.121156   74.77      0  \n",
      "149999  0.518359 -0.180141  0.415691 -0.099064 -0.062928   16.94      0  \n",
      "\n",
      "[150000 rows x 31 columns]>\n"
     ]
    }
   ],
   "source": [
    "data=pd.read_csv(\"creditcard.csv\",nrows=150000)\n",
    "print(\"Dataset is:\\n\",data)\n",
    "print(\"\\nColumns of dataset are:\\n\",data.columns)\n",
    "print(\"\\nInformation of dataset is::\\n\",data.info)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Handling missing data<br>\n",
    "We have to drop the data<br>\n",
    "If we have smaller dataset ,we take mean of that column and update missing value with that<br>\n",
    "Using this we can avoid outliers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Separating as input and output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input is:\n",
      "          Time        V1        V2        V3        V4        V5        V6  \\\n",
      "0           0 -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388   \n",
      "1           0  1.191857  0.266151  0.166480  0.448154  0.060018 -0.082361   \n",
      "2           1 -1.358354 -1.340163  1.773209  0.379780 -0.503198  1.800499   \n",
      "3           1 -0.966272 -0.185226  1.792993 -0.863291 -0.010309  1.247203   \n",
      "4           2 -1.158233  0.877737  1.548718  0.403034 -0.407193  0.095921   \n",
      "...       ...       ...       ...       ...       ...       ...       ...   \n",
      "149995  92345  1.724254  0.054124  0.625215  4.308400 -0.532295  0.540379   \n",
      "149996  92346 -3.538487  2.921360 -1.298732 -1.035773 -0.030572 -0.441956   \n",
      "149997  92347 -0.019481  1.224302 -0.132075 -0.678606  1.768679 -0.283992   \n",
      "149998  92348 -0.511063  1.277683 -0.174698 -0.101018  0.970555 -1.040956   \n",
      "149999  92348  2.197093 -0.429996 -1.439194 -0.307975 -0.020266 -0.715370   \n",
      "\n",
      "              V7        V8        V9  ...       V20       V21       V22  \\\n",
      "0       0.239599  0.098698  0.363787  ...  0.251412 -0.018307  0.277838   \n",
      "1      -0.078803  0.085102 -0.255425  ... -0.069083 -0.225775 -0.638672   \n",
      "2       0.791461  0.247676 -1.514654  ...  0.524980  0.247998  0.771679   \n",
      "3       0.237609  0.377436 -1.387024  ... -0.208038 -0.108300  0.005274   \n",
      "4       0.592941 -0.270533  0.817739  ...  0.408542 -0.009431  0.798278   \n",
      "...          ...       ...       ...  ...       ...       ...       ...   \n",
      "149995 -0.688445  0.156967  1.476695  ... -0.280992 -0.367098 -0.771443   \n",
      "149996  0.631323  0.445681  2.495754  ...  0.510437 -0.703023 -0.851606   \n",
      "149997  1.579740 -0.978318  1.874612  ...  0.552350 -0.036681  1.134522   \n",
      "149998  1.563977 -1.449619  0.595721  ... -0.279702  0.836970  0.470593   \n",
      "149999 -0.165705 -0.432532  0.585860  ... -0.575688 -0.683676 -1.145510   \n",
      "\n",
      "             V23       V24       V25       V26       V27       V28  Amount  \n",
      "0      -0.110474  0.066928  0.128539 -0.189115  0.133558 -0.021053  149.62  \n",
      "1       0.101288 -0.339846  0.167170  0.125895 -0.008983  0.014724    2.69  \n",
      "2       0.909412 -0.689281 -0.327642 -0.139097 -0.055353 -0.059752  378.66  \n",
      "3      -0.190321 -1.175575  0.647376 -0.221929  0.062723  0.061458  123.50  \n",
      "4      -0.137458  0.141267 -0.206010  0.502292  0.219422  0.215153   69.99  \n",
      "...          ...       ...       ...       ...       ...       ...     ...  \n",
      "149995  0.364177 -0.100579 -0.547578 -0.348022 -0.011480 -0.023193   62.91  \n",
      "149996 -0.214636 -1.054619  0.305106  0.132541 -1.261532 -1.029257   37.91  \n",
      "149997 -0.311552  0.051861 -0.616234 -0.329780  0.220292 -0.232384    3.98  \n",
      "149998 -0.314103  0.066781  0.363133 -0.073593  0.183600  0.121156   74.77  \n",
      "149999  0.310466  0.518359 -0.180141  0.415691 -0.099064 -0.062928   16.94  \n",
      "\n",
      "[150000 rows x 30 columns] \n",
      "Output is:\n",
      " 0         0\n",
      "1         0\n",
      "2         0\n",
      "3         0\n",
      "4         0\n",
      "         ..\n",
      "149995    0\n",
      "149996    0\n",
      "149997    0\n",
      "149998    0\n",
      "149999    0\n",
      "Name: Class, Length: 150000, dtype: int64\n",
      "\n",
      "Shape of Input data is: (150000, 30) Shape of Output data is: (150000,)\n"
     ]
    }
   ],
   "source": [
    "X,Y=data.drop(['Class'],axis=1),data['Class']\n",
    "print(\"Input is:\\n\",X,\"\\nOutput is:\\n\",Y)\n",
    "print(\"\\nShape of Input data is:\",X.shape,\"Shape of Output data is:\",Y.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Splitting as training and testing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data is:\n",
      "          Time        V1        V2        V3        V4        V5        V6  \\\n",
      "71789   54443  0.872816 -0.529905 -0.401325  0.062774 -0.263370 -0.791265   \n",
      "105324  69468  0.885606 -0.587944  1.384818  1.756505 -1.072110  0.763968   \n",
      "53136   45829  1.207089 -0.438641  0.762859  0.409962 -0.934716 -0.023117   \n",
      "55587   47021 -0.354134  0.609726  2.340085  0.755874 -0.537448 -0.101369   \n",
      "106335  69927 -2.161465  0.731561  1.142183  2.830884 -0.334402  1.466455   \n",
      "...       ...       ...       ...       ...       ...       ...       ...   \n",
      "130599  79389  1.258454  0.401051  0.319707  0.686937 -0.317124 -1.059343   \n",
      "68891   53181  1.292122 -0.026513 -0.330078 -0.174160  0.222915 -0.175406   \n",
      "48904   43830 -0.430779  1.507154  1.421488  2.218152  0.658670 -0.609666   \n",
      "123983  77139 -1.418896  1.720858  0.936665  0.069946 -0.744892 -0.836783   \n",
      "112292  72598 -0.365316  0.750502  1.062237  0.405442 -0.030072 -0.565215   \n",
      "\n",
      "              V7        V8        V9  ...       V20       V21       V22  \\\n",
      "71789   0.568015 -0.231804 -0.366470  ...  0.373655 -0.223474 -1.216153   \n",
      "105324 -0.748743  0.323017  1.385533  ...  0.010253  0.135865  0.672814   \n",
      "53136  -0.673854  0.040645 -1.120002  ... -0.379310 -0.181616 -0.109741   \n",
      "55587   0.057073  0.179485  0.349269  ... -0.075744  0.011582  0.185808   \n",
      "106335 -0.936644  1.365194 -0.659597  ...  0.136432  0.074086  0.241076   \n",
      "...          ...       ...       ...  ...       ...       ...       ...   \n",
      "130599  0.126531 -0.237910 -0.058793  ... -0.043919 -0.281197 -0.779987   \n",
      "68891   0.163500 -0.147915  0.246576  ... -0.013292 -0.097956 -0.087283   \n",
      "48904   1.167394 -0.202969 -1.827419  ...  0.016741  0.074719  0.175318   \n",
      "123983 -0.020827  0.849998 -0.712369  ...  0.009345 -0.102633 -0.357489   \n",
      "112292  1.080217 -0.085157 -1.081214  ...  0.220956 -0.177817 -0.746056   \n",
      "\n",
      "             V23       V24       V25       V26       V27       V28  Amount  \n",
      "71789  -0.091129  0.044278  0.137474  0.684374 -0.153661  0.023543  223.00  \n",
      "105324 -0.229946  0.137640  0.586234 -0.104056  0.090596  0.046712  106.12  \n",
      "53136  -0.082860 -0.008779  0.409202 -0.303407  0.061646  0.032892   50.00  \n",
      "55587  -0.126017  0.385458 -0.020438 -0.448775  0.095420  0.043894    9.99  \n",
      "106335 -0.542951 -1.300496  0.271828  0.536123  0.435592 -0.186638  110.26  \n",
      "...          ...       ...       ...       ...       ...       ...     ...  \n",
      "130599  0.122921  0.361848  0.236821  0.092476 -0.018958  0.031965    0.89  \n",
      "68891  -0.183433 -0.635656  0.603957  1.199307 -0.078455 -0.015368   19.70  \n",
      "48904  -0.307486  0.387904  0.259672  0.147453  0.058193  0.100072    3.41  \n",
      "123983  0.172795  0.607207 -0.211307  0.074429  0.153073  0.048219    9.72  \n",
      "112292  0.225299  0.308560 -0.553317  0.191834  0.046843  0.130709   84.95  \n",
      "\n",
      "[105000 rows x 30 columns] \n",
      "Testing data is:\n",
      " 121389    0\n",
      "24499     0\n",
      "125074    0\n",
      "110863    0\n",
      "64047     0\n",
      "         ..\n",
      "84893     0\n",
      "104340    0\n",
      "39653     0\n",
      "107521    0\n",
      "3628      0\n",
      "Name: Class, Length: 45000, dtype: int64\n",
      "Shape of Training data is: (105000, 30) shape of Testing Data is: (45000,)\n"
     ]
    }
   ],
   "source": [
    "x_train,x_test,y_train,y_test=train_test_split(X,Y,train_size=0.7)\n",
    "print(\"Training data is:\\n\",x_train,\"\\nTesting data is:\\n\",y_test)\n",
    "print(\"Shape of Training data is:\",x_train.shape,\"shape of Testing Data is:\",y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Taking Algorithm<br>\n",
    "Since the dataset is very large it is showing maximum number of iterations reached<br>\n",
    "To avoid this we set max_iter parameter to our requirement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(max_iter=150000)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_reg=LogisticRegression(max_iter=150000)\n",
    "# Training Algorithm\n",
    "log_reg.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Evaluating model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted Values are:\n",
      " [0 0 0 ... 0 0 0]\n",
      "\n",
      "Length of Predicted values is: 45000\n",
      "\n",
      "Training Accuracy score is: 99.88\n",
      "\n",
      "Testing Accuracy score is: 99.88222222222221\n"
     ]
    }
   ],
   "source": [
    "pred=log_reg.predict(x_test)\n",
    "print(\"Predicted Values are:\\n\",pred)\n",
    "print(\"\\nLength of Predicted values is:\",len(pred))\n",
    "print(\"\\nTraining Accuracy score is:\",log_reg.score(x_train,y_train)*100)\n",
    "# Very good accuracy and also not overfitted\n",
    "print(\"\\nTesting Accuracy score is:\",accuracy_score(y_test,pred)*100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Seeing the count of 0 and 1<br>\n",
    "We do not need to focus on this"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Count of 0 or not fraud cases in dataset is: 149707\n",
      "Count of 1 or fraud cases in dataset is: 293\n"
     ]
    }
   ],
   "source": [
    "print(\"Count of 0 or not fraud cases in dataset is:\",list(Y).count(0))\n",
    "# This is our main focus\n",
    "print(\"Count of 1 or fraud cases in dataset is:\",list(Y).count(1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have many parameters to decide fraud data<br>\n",
    "More Information about dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Doing PCA for dimension reduction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Transformed Data is:\n",
      " [[-21953.73724372    -77.69441972]\n",
      " [ 20959.26515617    -73.95829183]\n",
      " [-23303.65149249    253.52247334]\n",
      " ...\n",
      " [ 14307.26326619    -80.45367468]\n",
      " [-16254.73773411    -80.30874721]\n",
      " [ 51118.38589513    388.22524809]]\n",
      "\n",
      "Shape of Transformed data is: (45000, 2)\n"
     ]
    }
   ],
   "source": [
    "pca=PCA(2)\n",
    "x=pca.fit(x_test).transform(x_test)\n",
    "print(\"Transformed Data is:\\n\",x)\n",
    "print(\"\\nShape of Transformed data is:\",x.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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SNJYEVxpwh82BJcHC6OcnKLfWW5gcm1lIKTcLIWYDOwA7sBOXiagA8IcQojcuhfKEe//9bo+pA+79+7k9oQBe5abr7GKUJ5RCETQ2/r0Vu82zRGnM1Vgun7lKyYrFgyCVIqfJ0TgLKeVHwEdpmi24Zhne9v8M8ChNI6XcBtQNuIAKhSLThISHeG13Op2Ywoy5LM3tiZSSjfO3seC7pSTEJtK+e2u6vNgRY0jOXX+VSFChyAGir8ZweOtxCpWIoHrjKjlSjCZYPPTafUx6d3qqsqVanZY6rWpRsFhEECW7fZg0cDoLv1+GOd71GRzffYrlv6zhmw2fojfoc2RMpSwUigAzbdgsZo38G4NJh8PupESF4oxY+gHFyxUNtmgBoevL93Bk23H+/X09OoMOp8NJqcolGfLbG8EW7bbg8tmrzJ+wBKv5ZnlVS4KVs4fOsfbPTXR8pm2OjKuUhUIRQP6bv5XZXy7AZrFhs7h+zJFHzvPRI6OYuHVkkKULDBqNhgFT+vLsh49zdPsJipcvSs1m1W6p2VNeZt+6g2j1WjCnrsVtjrew+Z/tSlkoFPmBOV//k2waSMLpcHLmQCTnj1+kTNVSQZIs8JSqVIJSlUpkvKMioEQUi0B4CT/T6jQUKV04x8ZVWWcVigASc817rWWtTkvs9fhclkZxK9Lo7rqYwoykncjpDDoe6NMpx8ZVykKhCCBtHmmO3uS5wCg0gir1KwRBoqwjpURaNuCM+Qxn7Hik/WzGBylyHK1Oy+hVwyhZqQSmMCOhESGEhocwcOprlK/pNRNSQFBmKIUigDz6xgMs/2UN1y7ewJpoRWgEBpOe1ye+mGNeKjmBlA7kjf5g/Q9kAqBDxk9GFvwCTcgDwRbvtqdCrbL8cuxbju8+hTneQo2mVTEYc/b7JW7ViMumTZvKbdu2BVsMxW1IfEwCiyavYOvinRQrX5RH+neheuMqwRYrU0jzEmT0YLeiSEkIosRGhCY0KHIpch4hxHYpZdO07WpmoVAEmLCIUJ54pxtPvNMt2KJkGZm4wIuiAIQWrJvB1CH3hVIEFbVmoVAoPBHpmDSEIffkUOQZlLJQKBQeiJAnQHhL66EBQ7Ncl0cRfJSyUCgUnhhaQcj/ACNgAhEGIgxR+DuEmlnclqg1C4VC4YEQAhExGBn6P7BuABEOxrsRmrBgi6YIEkpZKBQKnwhdRdBVDLYYijyAMkMpFAqFIkOUslAoFApFhihloVAoFIoMUcpCoVAoFBmilIVCoVAoMkQpC4VCoVBkiFIWCoVCocgQpSwUCoVCkSFKWSgUCoUiQ5SyUCgUCkWGKGWhUCgUigxRykKhUCgUGaKUhUKhUCgyRCkLhUKhUGSIUhYKhUKhyBClLBQKhUKRIUpZKBQKhSJDlLJQBByH3YE5wRJsMRQKRQBRykIRMMwJFsb2+Z5uET14qGBPetd5kz1rDwRbLIVCEQCUslAEjM+6j2XFjLVYzTacDidnDp5jSJfPOX0wMtiiKRSKbKKUhSIgXDx1mR0r9mA121K12yw2Zn+5IEhSKfICUkr2rjvIyl/XcfbwuWCLo8giumALoLg1uHjyMnqj3kNZOB1OTh84GySpFMEm6sJ1Btw9jKhz1wBwOJy0fLAp7814Ha1OG1zhFJkiR2cWQohCQojZQohDQoiDQoiWQogiQojlQoij7r+FU+z/nhDimBDisBCic4r2JkKIve5t44QQIiflVmSeCneU9VAUADq9jlrNqwdBIkVeYMSz47hw/CKJcWYS48xYE61sWriNeeMXBVs0RSbJaTPUN8ASKWUtoAFwEBgMrJRSVgdWuv9HCFEb6A7UAe4DJgohkh49vgP6ANXdr/tyWG5FJilSqjCdnm2LMdSQ3CYEGEL0PP5W1yBKpggWMddi2b/hEA67M1W7JcHKgu+XB0kqRVbJMWUhhIgA7gKmAEgprVLKG8BDwDT3btOAh93vHwJmSiktUsqTwDHgTiFEaSBCSrlRSimBX1Ico8hDvPF9H3oMfYKiZYpgKmCi2f2NGL/pC0pUKB5s0YJGQmwiMVGxwRYjKFjNNtcTgxcsyrU635GTaxZVgCvAz0KIBsB24A2gpJTyAoCU8oIQooR7/7LAphTHR7rbbO73ads9EEL0wTUDoUKFCoE7E4VfaLVanhr4ME8NfDjYogSd65ejGf3cBHau3ANCUKZqSd79uR+17rx9THJFSxemWNkiXDhxKVW7Tq+lzSPNgySVIqvkpBlKBzQGvpNSNgLicZucfODtEUSm0+7ZKOUkKWVTKWXT4sVv36dZRXCRUjKw03B2rNiD3ebAbrVz5uA5Bnb6mKvnooItXq4hhGDgtNcwhRnRG1zPpaZQI0VKF+bZoY8HWTpFZslJZREJREopN7v/n41LeVxym5Zw/72cYv/yKY4vB5x3t5fz0q5Q5EkObDzCpVNXcNgdqdrtNjv/TF4RJKmCQ93WtZhy4GueeLcb7Z9qxUujevDjvq8oWCwi2KIpMkmOmaGklBeFEGeFEDWllIeBjsAB96sXMML992/3IfOB34QQXwFlcC1kb5FSOoQQsUKIFsBmoCcwPqfkViiyy8WTl73Oh20WO2cP3X7POSXKF+P5T/4XbDEU2SSn4yz6A78KIQzACeB5XLOZP4QQvYEzwBMAUsr9Qog/cCkTO9BPSpn0aPYqMBUIARa7XwpFnqRa48oeHkAAxlAjddvUCoJECkX2yVFlIaXcBTT1sqmjj/0/Az7z0r4NqBtQ4RSKHKLiHeVo2rkB25fuxpJoBUCr01KgcBj39mrv9RirxcbOFXswJ1hp1LEuEUXCc1FihSJjVAS3QpGGk/vO8OPgGRzYeIRCJQrSfdDD3NurPZmJBf1w1tv8OWY+Cyctx5JgpWW3pjz/SXdCw0M89t3/32E+6PoFTqcTpGtt4+UxPenWV4UTKfIOwhW6cOvRtGlTuW3btmCLochnnD18jr7NBmOJN5P00zCGGuk++GGe/SDwHjxWs5UnS79EfHRCqnZjiIGvN3xKtYaVAz6mQpEeQojtUkoPi5BKJKhQpGDGJ7OxJlhI+QxlSbAwc8Q8EuPNAR9v29LdeHtgs1lsLP3p34CPp1BkFaUsFIoUHNp8FKfT8+at0QounboS8PHM8WavysLplMTHJHg5QqEIDkpZKBQpKFOtlNd2h81BkdKFAj5ew7vr4rA5PNpNBYy0eVRFOSvyDkpZKBQpeHrIYxhDDKnaDCEG7nqiZY54KBUpVZiew5/CGGpAaFwL6KYwI/Xvqk2Lrk0CPp5CkVXUArdCkYZ1czYz4fUpxETFIjQa7u3Zjle/fh6DUZ9jYx7acpTFU1aREJtIuyda0rJbU7RaVe9Bkfv4WuBWykKh8IKUkpioWELCQ3JUSSgUeQ1fykLFWeQgV89FsX/DYcKLhtOgfW31pJiPEEKo/EUKRQqUssgBpJT8NOQ3/vr6H/QGHRIIDTcxeuVHlK/pNbu6QpGvuXDiEvvWH6JQiQgad6qvSqbegihlkQNsWrided8uxmaxYbO4So2a4xJ5/4EvmHZ0fKYigRWKvIyUknF9J7Ns2mo0Wi1CAyEFQhizSj0Y3Woob6gcYP7EpZjjU1cCkxKuX7rB8d2ngiOUQpEDrJ71HytmrMVqtmGON5MYa+b6xRsMfWiU1/gRRf5FzSxygITYRK/tGq0Gc1zgo4AVtw5SSnau3MuyX9bgdDjp+Exb7ry/UZ6djS74ztuDkeRKZBRnDp2j4h3lfBypyG8oZZEDtH+yJcd3ncSSYE29QUKNplWDI5QiXzDhjZ9Y+vO/yTfgjfO3ctfjLRnwU988qTB8pUDRaAXWRKvXbYr8iTJD5QBdXupE+ZplMYUZAdeMwhhq4K1JL2MwGTI4WnG7cmr/WZZMWZXqSd0cb2Ht7I0c3nosiJL5pkP31hhCPL/TOr2OKvUrBkEiRU6hZhY5gDHEyDf/fcbqmRvYvGgHRUoW4oGX76FSnfIZH6y4bdm6eCcOh2fRJEuilc2LdlDrzupBkCp9uvW9j5W/ruP8sYuY4y3o9Dq0ei2Dpr2mPKJuMZSyyCEMRj339mrvs9iNQpGWkPAQtDotdqs9VbtOryM0PDRIUqWPKdTIt5u/YN3sTWxduotiZYvS5cWOlK5SMtiiKQKMXxHcQojWUsoNGbXlJVQEtyK/ERMVy9MVX/FY6zKEGJh2ZBzFyhYNkmSK24ns1rMY72ebQqHIIhFFwxn65wBCCpgIjQghNCIEY6iRwb/0V4pCEXTSNUMJIVoCrYDiQoi3U2yKAJRBUqEIMHfe34g/Lv7IzpV7kU5Jo451CSngWYpVochtMlqzMAAF3PulzM8cAwS+xqQiT2G12Ii9Fkeh4hFqsTIXMYUaafmghxVAoQgq6SoLKeUaYI0QYqqU8nQuyaQIMk6nk58/+J254xYjpURv0NHr46d4pH+XYIumUCiChL/eUFOFEB4r4VLKuwMsjyIPMP3j2cwdtxhLgsvf35poZcp7vxFeuACdnr0ryNLlHFJKDmw8wrUL16nVvDrFy6l1AoUiCX+VxYAU703AY4Ddx76KfIzD4WDO2IXJiiIJS4KFGZ/MvmWVxeWzVxnY6WOuXbiO0AhsFjtdXupIv29eyJOR0wpFbuOXspBSbk/TtEEIsSYH5FEEGUuCFYuPNA1R56/lsjS5x/DHxnDhxCWcKYLilv78L3c0r0HHZ9rmqiy7V+/nzy/nczXyGo3vqc8T7zxI4ZKFclUGhSItfikLIUSRFP9qgCaA98r2inxNSAEThUpEEHX+use2WzV9w6XTVzi170wqRQGuVBvzvl2cq8pi8ZSVTHjjp+RYi9MHI1n+yxp+2DWaIqUK55ocCkVa/I2z2A5sc//dCLwD9M4poRTBQwjBy1/2whiaOt+PMdTAS6N6BEmqnCUxzuzT2ys+OiHX5LBabHz/9rRUQXl2q52463HMGvV3rsmhUHjDXzNU5ZwWRJF36PBUa8IiQvll2B9cOHGJyvUr8MJnT1O7RY1gi5YjlK9VBr1JT2Ka9PF6o567Hm+Ra3KcORjptd1uc7B1yS5e/SrXRFEoPPDXDGUC+gJtAAmsB76TUqriDLcod97fiDvvbxRsMXIFrVbLwJ/78clTX2G32nHYnRhDDRQtXZjH334w1+QoVDwCu82730jR0oVyTQ6Fwhv+ekP9AsRyM8XH/4DpwBM5IZRCkds0f6AJE7eNYsF3S7l06gpNOjfg3l7tCQkz5ZoMxcoWpXbLmuzbcBC71ZHcbgo18vg73XJNDoXCG/4mEtwtpWyQUVteQiUSVORHYq7FMuzRMRzechSdQYfD7uS5j5/K1RmO4vbGVyJBf2cWO4UQLaSUm9ydNQfybMZZhSI7OBwODm46iiXBQu1WNVPNLmKuxTLj49ms+2sThhADXV++h0ffeCBg6VAiioTz1erhXDx1meuXorl28Tqzxyxgztf/UKtFdXoNf0qVKlUEBX9nFgeBmsAZd1MF4CDgBKSUsn6OSZhF1MxCkRWO7z7F+w98TkJsIkIIHHYnr098kXt7tsecYOGlem9z9dy15JoTxlADTTs3ZNhf7wZclqXT/mV8vx+TvaOERmAMNTJ+4+eqkJYix8juzOK+AMujUOQ57DY7g+79hOgrManax706meqNq3Bo81FuXI5OVZzIkmBl65JdnNp/NqA3cIfDwaQBv6Ryo5VOiSXews8f/M7wuQMDNpZC4Q/+KotPpZSpnOyFENPTtikUwURKyan9ZxFCULF2uUyn6dixYi82i82j3Wa1s/jHFcRei09VHzsJjUZwZNvxgCqL6xdveBRBgqT8VYcDNo5C4S/+Kos6Kf8RQuhwRXErFAFBSsnfE5bw+xdzib4STYXa5Xj1q+dodHc9v44/tOUowx8fQ9z1eADCixTgo9kDqNmsmt8yxN2Ix5tZ1ulwcuNKLOVrlkFv1HsoFKERFC8f2KSDBQoX8CoLQNEyRby2KxQ5SboR3EKI94QQsUB9IUSMECLW/f8lwK+QUiGEVgixUwix0P1/ESHEciHEUfffwin2fU8IcUwIcVgI0TlFexMhxF73tnFCZXa75fjt8zlMGfwr1y5cx2F3cnLPGT58cAT7NhzK8Nj46HgG3fMJVyOvYY63YI63cOVsFAPv+Zj4GP8jsOu3q53KZTUJU5iRVt2acv+LHT0WsjVaDYVLFKRB+zoex2UHU6iRTs/ehSEkbSS9kWfefyygYykU/pCuspBSfiGlDAdGSykjpJTh7ldRKeV7fo7xBq7F8CQGAyullNWBle7/EULUBrrjmsXcB0wUQiT9Mr8D+gDV3S+1hnILYbXYmDlyHua0mW4TrUwbOivD49f8sRGn0+nR7nQ4WfvnRr/lKFamCE8NehhTmDG5zRRmpGqDSrR5tDnFyhRh5LIPKFOtFAaTHp1BR53Wtfhy9XA0Gn8z5/hPv/G96dC9NXqTHlOYkdDwEHp/8TRtH8taVLk5wcL54xexmr0niswppJTsXr2fH9/7lVmj5nElMipXxw8WUReuM3fcIn7/Yi7Hd58KtjjZxl8z1GIhhEduainl2vQOEkKUAx4APgOSyrI+BLR3v58GrAYGudtnSiktwEkhxDHgTiHEKSBCSrnR3ecvwMPAYj9lV+RxblyORnq52QOc2n82w+Ov+bDvWxOtXLt4I1Oy9Br2JPXa1GLhD8tJiE2k/VOt6fhMG3R610+ldsuaTD08jqgL1zEY9UQUDc+gx6xjMOoZMKUvr459jhuXoylRoRh6gz7T/TgcDn4c/CsLJi5FaDUgJU8M6EaPoU/kePp1p9PJx098yfZluzHHW9AbdUwf/idDfnuTVg81y9Gxg8m6OZsZ2WMcUkocNge/fjab+56/m37j8m/Ke3+VRUq/QBNwJ66kghkVP/oaGEjqkqwlpZQXAKSUF4QQJdztZYFNKfaLdLfZ3O/TtnsghOiDawZChQoVMhBNkVcoVKKgzx9Q+VpeP+pU1LvrDoyhBo/FZ0OIgfp31c60PI071adxJ9/e4EIIiuXiukFYRChhEaFZPv63z/5iwXfLUqWe/2P0fAoWC+ehfvcHQkSfrPtrc7KiALBZXJ5kI3qM489LP2IMMaZ3eL4kPiaBkT3HpbrejgQrS6f+S+tH7vR7HS6v4dfcWUr5YIrXPUBdXOsWPhFCdAUue6mF4fMQb0On0+5NzklSyqZSyqbFixf3c1hFsDEY9Tz6VleMoalvHMZQA72GP5nh8fXvqk3tljVTZco1hhqo1/YO6rapFXB58xNSSmZ/5b2Y1cwR83J8/JW/rvXqQSY0gr3rMl6Pyo/sWL4HrdYzSNMcb2Hlr+uCIFFg8HdmkZZIXAojPVoD3YQQXXDNRiKEEDOAS0KI0u5ZRWngcoo+U/oelgPOu9vLeWlX5GNuXIlm77pDhEWE0KB9HXoNe5LQAib+GP03MVFxlKtRmle+eo4G7TJeOBZC8Nk/77Fo8kqW/LwKIQT3Pd+B+1/smG+n/P5y6fQVFk1ewaUzV2jcsT7tn2qFwXRTaTrsDhJjvef7jL4a47U9kKQX2a7VBX6dR5Fz+BvBPZ6bT/MaoBFwUkr5rF+DCNEeGCCl7CqEGA1ESSlHCCEGA0WklAOFEHWA33CZuMrgWvyuLqV0CCG2Av2BzcAiYLyUclF6Y6oIbv9IiE1k8z87sCRYaHJvg1ypOz1r9N/88tEsdAYdSJe5aOSyD5OLKzmdzhxZML7V2LFyLx89NBK73Y7d6sAUZqREhWKM2/h5KrNVrxr9OX/sosfxNZtV49vNX+SojJv/2c6n3cd6zC4KFArjj4uTs7QGk9eJj0ngqTIveayjmcKMfPz3oDxvhvIVwe3vL/IAcAQ4jGtdYaC/isILI4B7hBBHgXvc/yOl3A/84R5rCdBPSpnkx/gq8CNwDDiOWtwOCDtW7uWpMi8x9uXvmfDGTzxXoz+/j5ibo2PuXXeQ6cP/xGq2kRCTSEJsIjcuR/Pe/Z/icLg+7txQFAc2HmZcv8l8/cokdq/e7zOmIa/idDoZ2WMc5gRLsruvOd7CxZOX+WvswlT79v36eYwpXHCFcJnpXvmyZ47LeWeXxtzTsx3GEAN6ox5TASMhBUwMm/vuLakowLXGNOiX1zGEGDCEGNDqtBhDDHR+rgMNO2RkkMm7pDuzcAfffQ68gCsvlMBlKvoJeF9K6RnumkdQM4v0MSdYeLLUix4Ff4yhBr5c/TE1m1bNkXG/eHYc//6+jrRfu5BwE58ueC9LC9KZ5af3f2PON4uwJloBiTHUyD092/H6hJcCOo6Ukj1rDrB+7maM7riJQEV5nz4YyWt3Dva6HlC+Vll+OvB1qrZ96w8ybdgfRB4+T5X6Fek5/Kkc+4y9cfrAWbYv30OBQmG0fuTObC3Y5xeuXbzOmj82Yo630PyBxvmmLHFWc0ONxuXJVFlKGevuKAIY4369EWhBFbnD1iW78GbOt5ltLJv2b47dSFxR0p7tQggSYxNzZMyURB45z19jF2I133zOMcdbWDZtNZ2fvztg5y2lZGSvb9kwdzOWBAtCo2HuuEX0GfVsQDyQTKFGnE7vD3qmNCVxAeq2uYPRKz7K9rhZpWLt8lSsfXslPyxSqjCPvN4l2GIEjIzm+12Bl5IUBYCUMgaXWejWuQq3ITaLzevNxumUXmMWAsVdj7dIFfSWhN3qyBXPpS2Ldno1OVnNNjYtCNxMdOfKvWyYuxlzvAUpXQGC1kQrk96dzvXL0dnuv2TF4pSvWQaNJrXGN4UZ6dZXxawqAk9GykJKL78s91pC/jLyKlLRsEMdj4hpcD3ht320eY6Ne/fTbahSv2KywtBoBMZQA6+O7UVYwbAcGzcJQ4gBjdbza6/VaVO53maXdX9t8moi0uq0bFu6KyBjfPTXAIqVK0pIeAghBUwYTHraPt6Ce59rH5D+FYqUZGSGOiCE6Cml/CVloxDiWeDWdJK+TTi0+Rg6gw67JXXNZ41GYPWSeTVQ6A16vlw9nDV/bGTD3M2EFw3ngT6dqNEkd+znbR69k+/enurRrtEI2j/VOmDj6I16hEYg087ehMBgDMzCbunKJfnl+Lfs/nc/Ueevc0fLGpSrXjogfSsUaclIWfQD5gghXsAVsS2BZkAI8EgOy6bIQU7uPYPD5pk0z+Fwcnp/JG0fzbmxdXodHZ9pS8dn2ubcID4oVLwg7814nRE9xiXPMBw2J2/+0IdSlUpkcLT/3NOzHYsmr0gVxQsgnU7u7NIoYONotdp0o80VikCRrrKQUp4Dmgsh7saV4E8Ai6WUK3NDOEXOUa5GaUxhRo+ArZACJsre4k+nbR5pzqzzk9m6eCdOh5Om9zUkokhgczxVb1yFHh89wS/D/kCj1SCEwOmUDJ09gJACIQEdS6HIDfwKysuPKNfZ9LFabPSq3p9rF67jdLiS+Gm0GoqUKsS0Y98GzFSSFU7uPZ3sZtnm0eYUKJTzaxk5xdVzUWxdsguDyUCLB5vcFi6jivyNL9dZpSxuY66ei+LrVyYlu9E2ubcBb37/cq5EcXtDSsnXr05ixfS1SIcTrd6VKuLTBe8FvF7E7cSR7ceZ9+1ios5do/kDTbi/991qdqPwiVIWCp847K61i/Ty+OQGmxZu57P/3V6pIXKaFTPW8PUrk7CabUinxBhioGjZIkzcOiJXvM8U+Y/spvtQ3MJoddqgKwqAJT+v8upu6nQ6b9kMpTmJ1WxlXL8fsSRYk72yLIlWrkZG8feEJUGWTpHfUMoiSNhtdq5ERuV61bK8TNIMxxtJ6yq3M1JKTh84y8l9Z7xWBgRXNt8Jr//EM5VepXedt7Bb7R77WM021s/ZktPiKm4xspqiXJEN5nyzkGkf/eG6OUp4sG9nXhzxjNcc+HmV2OtxzB23iE0LtlOwRASPvdmVpvc2yFaf9/Rox65V+zxmF1JK6t91R7b6zu8c3XGC4Y+NSU4rXqBQGB/+8Ta1W9ZM3ic+JoG+TQdx/eIN7F7colMSUbRAjsqruPVQyiKXWTFjLT+9PzNVMZoF3y1Fp9fS+/NngiiZ/8TdiOeVxu9y/VI0NneOpb1rD9Jr+JM88U63LPfb5tHmrJ71H1uX7MScYEFv0KPRCIb8+maqGg23G4lxibzbcTjx0QnJbeZ4C4Pv+5QZJycmu/0u/flfYqJiM1QUpjDjLZWzSJE7KDNULvPrp395qVpm5e9vl6RrhslLzJ+4hBuXYpIVBbgqr00bOov4mIR0jkwfjUbDh3+8zeeL3ufpIY/y/Kf/Y+rR8bTo2iQQYucZpJQc2HiYdXM2cyUyKsP918/ZgsOLGc5pd7J65n/J/+9ctddrXi8hBHqTntCIEPRGPU+++xCV61dk2bTVrJ+7WZlCFX6hZha5TNT5a17brRYb5nhzvvBQ2bRwu9cbjE6v5diOk9lycxVCUK/tHdRre2uana6ei+LdTh8Tde4aQiOwWex0ebEj/ca9kKqq34WTl3DYnZStVoprF2+kUsxJWBKtRF24nvx/maql0Oq1HpH5xjAjr43vTaHiEdS6sxpzxy/iuRqvo9Vp0Gg0CI3giyUfcEfz6jl34op8j5pZ5DJVG1by2l6oWASh+SBg6/juUxzbedLrNrvdSaESEbksUf5i+ONfcv7YRRLjzCTEJGKz2Fg69V9WzFgLuOpU9K7zJi/VfZtXGr3Ls5X7El4kDL3R87nOVMBEg3Y3639069sZnT71flqdllKVSnBvz3Y079KYk3vP8NdXC7FZbJjjLSTEJhIfncAHXT/HbvNcDFcoklDKIpd5aVQPj+ymxlADL3/VK9fqRR/bdZLpn/zJrFF/c/HU5YwPcOOwOxjc+VNsFs+bikYjKFej9G1XsyAzXImM4sTuUx6eXeZ4C/PGL8ZqtvJOu484e+gclkQrlgQLl89c5fu3p1G7dS2MoTdTuxtDDdRsWpWGd9+svFa2Wmk+/nsQxcsVdVem01G3TS1GLf8w+bu16MeVXt2T7TYHe9YezKEzV9wKKDNULlO7RQ2+/Hc4P384k+O7TlG6cgl6DHuSZp0b5sr4378zlYXfL8dmtaPRavhl2Cz6T3iR+56/O8Nj96476K4u50lYoTDem/E6/87cgEYjaHpfQ5XaIg0JsYlodFrA06SUEJPAxgXbsVqsHsWhHA4nze5tQNtHmrN4ykqk08m9z3XggT6dPErQNu5Yj19Pf8el01cwhRkpVLxgqu1p18tS4uuzVShAKYugULNZNUYs+SDXxz2w8TALf7iZCTXpCXd8vx9p+WBTChZL34SUEJvoSiXphZKVitO32WC0Ok1y34Onv06bR7JeG8Nus3P5zFUKFo+4JRRPuRqlMYUYMKcpZas36mjzaHOizl9LrqedEmuilavnr/PKmJ50ffkeAM4cOsfkgTO4EhlF8y6NufvpNskeY0IIrxl0j+06yfljF73K5rA5qN8u/ZK2VouNJVNW8e/M9ZhCjXR95V5aPdQs12bEiuCilMVtxOpZ/3l9etTotGz+Zwf39mqf7vH12t7hNcjLGGrwmvL8i2fHMePkRAqXKOhxTEbMn7iEn97/HYfdgcPhpEP31rzxXZ+gJjjMLlqtlgE/9+OTJ7/CbrXhsDsxhhooXLIQT777EOePXfRamAkB0VeicdgdaHVa1s/dzIge47Bb7TjsTrYt3cXssQsZv/EznzmfTh+M5K27hnooKiEEBpOe/hNfJDTcd74oh93BgA7DOLHnVLLH1b4Nh7jvhbvp980LWb8oinyDWrO4jdBoNV5nBgIQmoyfDsMLF0hec0na3xRmpJAPZSAErP9rU6bl3DBvC5MGziA+OgFzvAWb2cbqWf8xrt/kTPeV12jepTHfbR9J11fupUXXJvT+4hkm7R5DeOEC1GxWjRpNqnimXpGwdvZGRvb6FrvNzpjeE7EkWHHYXTNDc7yFCycu8feEpT7H/e2zv7B6q4yoEXy5Zjide3VIV+71c7dwat+ZVK655ngLiyav4MLJS5m4Aor8ippZ3EZ0+F8bFv6wzMMX3+Fw+h3L8PBr93NH8+os/GE5sddiaftYSyKPnOe3z/7y2Ndpd5IQm8iC75ay7Jc1aHUa7u/dkU497ko3Wv23z+d42NatiVb+/W09/b5+Pt9nTC1fsyyvjeudqk1Kydg+P3Bk23Gv8TbWRBsb5m5my1OtcDq81BBPtLLmz//oPujhVH0e330Kh83BoS3HvNZcN4UZ/UrQuHXxDhLTzErA9QCyZ80BSlcumWEfivyNUha3ETWbVuXxdx7kz9ELkNKJRqNBAu/+3Jfwwv6nf6jZrBo1m1VL/v/I9uP8+eV8z4AwjWD93C2c3Hsm+eZ/fNcptizayYd/vO2z/6vnvMeiCI2G2Gtx+V5ZeGPb0l38O3O9R2W9lOgNOq5ERuF0eA/eTGlGOrbzJEMfHkns9XiEAJuPUrl2q51i5YpkKF/hkoXQ6bUe0eEarSbDtS7FrYEyQ91mPDe8Oz/sHsMLnz1Nn9E9mX78W9o/mb3a0zWaVOXe5zpgCjMihMsObgw10urBppzafzbVLMEcb2Hzoh0c2X7cZ3+1W9TwumiqN+ooWibjG1tuc2T7cf6ZtJzty3f7TPCXEcunr/Xq0poSu91JvTa1KFWphIfZ0BRm5KF+93H5zBXGvjKJfncO5srZKMxxZhJjzV4Xzo0hBto/1dqvKoH3v9jRa2ZivVFPk3tVWdfbATWzuA0pV700j7/9YED77D++N+2eaMm/v69HaASdnm3HpgXbPBZUAZx2B3vWHKBGk6pe+3ruk6fYtnw3lngLSfVWjKEG+ozukSdSqSdhtdgY2m0E+zYcBiQarYbCJQry5ZqPKRZgpaY36KjWsBJV6lfi478H8W7H4cRej0MgsFntPNCnE1UaVKJPgwEkxCUivZiqtHotpjAj5jgLOr2W+1/sSJ/RPfwav0zVUgye8Tqjn58AgHRKwosU4NMFg1WdkdsEpSyCjM1qY+qHM1n4w3IS48zc0bw6/b99kWqNKgdbtEwhhKBBuzo0aHcz1cfhrccwmPRY06Sq0Bl0PhfFwVUHPDTchCXRAg5AQOkqJen4dNtMy3X90g0W/biC0wfOcUeL6tzbq33A3HBnjpzH3vWHUnmYWROtjOr1LaOWD/XYX0rJmj/+Y9HkFdgsdjo+exedn2+P3qDnnh53sXH+Vq+zC61eS6uHm/HWDy8Drhv39BMT2Lv2INcv3aBO61oUL1eUT576isTYxOTaFWlx2Bzc26s9Tw7oRkTRcI/kjJdOX2HD3C1IKWn1cDOPdYg2jzTnzi6NObLtOMYQA9UaVVZus7cRqlJekPm0+1dsWrA9la06pICJH3aNoXSV/L1oeP1yND2r9vO4AYYVDOX3yB8ICTMBYE6wsGP5Huw2O4071Wfow6PYu+4ApPhqarSCpwY+zAufPe33+Md3n+LtdkOxW+1YzTaMoQbCIkKZsHUExcpmv3Ts/yq8zNVIz/UVnV7LX1d/9nBFHdN7Imv++C/5ehhDjdS6sxqjVgxFCMHXr/zAyl/XYbfY0Rlcz3EDfupLq4eaedzYrRYbp/adIbxIgeSb+uMlexN9JcanvHqTnrDwEGKvx6MzaHngpU68NKoHOr2O+ROX8P0709yL565Z0gufP8Pjb3XNziVS5ENUWdU8yOWzV3mu5useSeK0etcPuf+3L6Zqj74aw59fzmfLop0ULlmIx9/uSrP7GuWmyJlm95r9fPrUWCyJFqSE8MJhDJvzbrIJavvy3Qx/bIzLBi9dgXhWiy2VokjCGGpgYdyvfo/9atOBHNuRJo+VgLseb8mHs3wvsPvLE6V6c+Oy581ZZ9Ax6/ykVGsBpw+cpV+zwR4L2CEFTLz/+5s0f8DljXZs50m2LtlFSLiJdk+28hqjsnz6Gr59bQoIV5qOynXLM3zeIAZ2Gs6Zg+e8yqo36rDbHKlmHcZQA3c/3ZZn3n+M52r091i81uo0TDnwNWWrlfb/oihyDavZyr8zN7Bl8U6KlilM15fvpUKtstnu15eyUGaoIOFwOJg+/E/sXrxUHDYHa/7cyJHtJ6jTuiaPv9UVg8nAyw0HEBMVi81i5+TeM+z/7zDPffxUwNcfAkmDdnWYee4HTuw+jUaroUr9ismmi/joeIY9OjrDhd0kLAlW4qPjM8zMG3nkPJMGTvdUFAAS1v21CXOCBVOKXEspsdvszPhkNgu/X0ZinIUG7evw6thelK+Z+ofY5pHmLPlplcdNtkKtsh6LxrtXH0B60YCJcWa2Ld+TrCyqNaqcrgny4OajfPPqpFSeZ8d2nuT9Lp/zxDvdmPDGT6mupxCC8CJhFCpR0EORWBKsrJi+lmJlinitgeGwO5k/cRmvftXLpzyK4JAYb+aNVu9z4cQlzPEWtDoNiyavYNAvr9P20axnTUgP5Q0VJL55dTKrfl/vkQcoiZirMRzafJS/v13Mi/XeZtrQmcRExaVK4mdJsDB50AwunfY/GWAw0Gq1VG9chaoNKqWycf83f1umbN5avZZrF2+ku8/FU5fpd+dgNi3c7nMf6ZSsmL7W5/bPn/mG2V8uIPpqLFazlW1Ld9G/xRB2/ruPiW/9zKjnv2XDvC30GPYERcsWwRTmUjqGEAOhESG8O7WfR58Fi4X78CbSUbik/xHuc8ctwpqY+gHDYXdy7tgFajSryiOvd8GQVLvCpKd51ybMOPWdx7pREjqD1mcWYXBV6FPkPRZ8t4xzxy4mPxg47E4sCVa+7D0Rm9X7Z51d1MwiCFy/dIMV09f69H0HkpWI3erAYU9k5e/rve7vdDh5o/UHTD8xId95pZjjLZmqra3TaSlevli6+8waOQ9LgtXnIm8Su1btTc6zBOB0Opk1ch5/jP6buBupCzhJKUmIS2Rgp+HJ5rG1szdRp2VNJu3+krWzN3Fg42HK1SjNvb3aeyTvA2jxYBOvgYgarZZ7erRL1ZYYl8iR7SeIKFKASnUrIITAarGxYe4W9m84hDfTsVan5calaF747GmefPchzhw6R4nyRZPXZmo0rcql01c8rovTIanRrCobF3g32RYrW9hruyK4rPnTe+oe6ZQc33WKWncGvjaJmlkEgTOHzmEw+X9jl06Zrqkm7kY86+dsCYRouUrTzg283vi8YQw18tTgh32ajpLYv+FwhhUHNToNpSqnTrT34+Bf+fWzOR6KIgnpkKnWUSzxFnas3MMjRZ7jx8EzKFWpBI+91dWrogAwhhgZtWIoRcsUcXl7RYRQoHAYH81+h+Llbi62z5+4hCdKvsjQh0byeqv3eane2xzedozna77O2D7fc/nMVa/92yw2qjepArjqc9duUSPVIn6PoU9gDEmbGt/IEwO60fm5Dl5nPRqdhrCCYexctdfvz0mRO/jK4+V0OjG5HUcCjVIWQaBM1VKuRdw0aLQanzmaChYNR6v3HmNgSbByfJdvU0JOc3LvabYv301MVGymjitduSSPD+iWqk6DN0pWKk7fr5/j2Q8ez7DPsjVKk5Fly2l30r77zUDExHgz8ycsSTd9t1eka2YXfSWG3z6fw4TXf3I1S+n15lq9cRV+O/MdY/4dxheL3+fPiz+mclDYu+4gP7w7HUuilYSYRMzxFs4cPMe7HYdz9VyU13Qb4ArIe3rIo+lG4VeqU56v1nxMw7vrElLARKnKJXh1bC96fvQExcsVpeewJ1wPMEnXTrjeLv15FR89PIq+zQa5sg4r8gTd+t6XbP5MQghBsbJFqVi7XI6MqcxQQaB4uaI0v78xWxbvTFWeVG/U06hjPbYv253K5GQMNdJ98MOcPXyeBd8t8+jPVMBEuRplckX2lFy7eJ0h93/OuWMX0Gg12Cx2nhr0ML2GPel3H89/3J1mnRsyuPOnXm/WOoOOiVtHElE04yhjgO6DHmbrkp1ea1EnYTDpObH7NNUauhaSr0ZGec/2mgksCRaW/LSK88cvsWvVXoRGw12Pt6DfNy+kkl2j0fgMRpw9doGHaUFKSWKsdyUB0PDuujz2ZldCwk2823E4549fpHrjKvQc9iSV6pZny6KdbP5nOwWKFKDzcx0YveIjr/08PeQxGnWsz7Jp/7Jp4Q6uXbiOw+7EYXdis9g5vf8sU4b8Rv/xvb0er8hd2jxyJ/v/u4f5E5aiM7geIsMKhvLJgsE5FvuiXGeDhNVs5ft3prF06mrsFhvl7yjHGxNfolqjSnzyxFfsXrMfvdEV0Nb5+Q70/7Y3ToeTXtX7c/VcVHIyOaERRBQNZ/qJCclxC7nF663f58jWY8nZT8H1lDvol/6ZrmMxaeB05o1flGoBXwhBtcaVmbh1ZKb62rRwO9/0ncS1Cze8rokYQ430HfscXV7qBEDUhev0qNIv3TUkv3CnOklaF9DptZStXppJe770KFLkjV41+vusN+ENvVHHosTfWT93M58/8zU2881rpzPoqNa4Mif3nEmlhJt2bsDQ2QN8flfsNjtdCzzrkW4eXOatudem+i2fIue5fPYq+zccplCJCOq3q51ugk5/UXEWeRSn04ndavcIurpw8hKXT1+lQu1yqXztr56/xle9v2PHSpcduW6bWrzz46uUqVoqV+W+fOYKz9d6w6uXTb22d/DVmo8z1V9CbCJv3fUhF45fIjHOTEgBE3qjjrHrPiUxNpFDW45RvFxRGnasS8zVWAqXLIgxxLf5SkrJpn+289lTYz1iGwwmPdOOjqdg8Qi+fW0Ky2esRTqcnu6jAq/xHpkhJNzE0D8H0PTeBhnu27fJQI768EwSGpAp9J5Wr+Wux1rw3q9v8GTpF73Ge/iidqsafLP+M6/bbFYbXcOe9apkQ8ND+Dv6F492p9PJtqW72bZsF4VKFOSeHu1SrcMo8he5HmchhCgP/AKUApzAJCnlN0KIIsAsoBJwCnhSSnndfcx7QG9cSR5el1Iudbc3AaYCIcAi4A2Zg1rOZrWxe/UBHDY79dvVztEspxqNxkNRgMue7y3tc7EyRfh88fvYrDakU3o91hsXTl5i54q9hISH0OLBJtmehcRej3etoXhRFtGZXLsA141o4raRbFm0kyPbjlOyUglaPdSUEc+OZ8/aAzgdDqQ7aM9oMoAQPPL6/Tz/6f+8PrULIWjxQBM69WzHyhlrsSRYERqB3qDjuU+6U6xsUb59fQorf1vnERQJULleBU4fiPTtreWnIrFbHZw9dC5DZRF59AJlapT2qix0ei0RxSJIjEvEkmB1FUwqUZC+3zxPQkwCN9KJ2vbG0e0nObztODWbeprD9AY99drewZ61B1J5Tml1Wlo/cqfn+dnsDLn/Mw5uOYY5zozeqOO3z/7iwz/eoXmXxpmSS5G3yck1CzvwjpRyhxAiHNguhFgOPAeslFKOEEIMBgYDg4QQtYHuQB2gDLBCCFFDSukAvgP6AJtwKYv7gMU5IfSetQf46OFRydlDHXYnb//4Cnd3b5MTw2WZzLjJ/vT+b/w1diFCo0GjFfAyfLZwCPXa3pHl8SvcUdbrTVpv0NGqm8dDiV9otVpaPtiUlg825caVaD7r/rXLEyeNu2fSTGH22IUc23mKEhWKcmjLseRSnx2faYtGo0EIwRsTX6LTs3ex7q9N6I16Oj7Tlsp1K2Cz2lg8ZZXPutMRxcIpVDzCa1yHzqBDAHa7I0MXXZ1eS4U7fEfVmhMsDH9sNLvXHECv9/5zDC0YyuS9Y9j97wHWz91C1IVrVG1QiRuXYyhfs0ymZz9CIzi9/6xXZQHw9uRX6N9yCNZEK+Z4C6YCJiKKugpfpWXF9LUc3Hw02VsvyYz4xTPf8OelH/OdO7fCN7lmhhJC/A186361l1JeEEKUBlZLKWu6ZxVIKb9w778UGIZr9vGvlLKWu/1/7uNfTm+8rJihEuMSeapMHw+vE0OIgR/3fpUvczXt+ncfHzw4wmPxOKxQKH9ezN6PedXM9Xz1oivgSzolhhA9BYtF8P2O0X4vSHvj8pkrvNpkEDHXYjN9IzSFGbnriZa8+9PNwLjY63F8//Y01vy5Eel00rxrE3p+9CR9mw5MtUaSEp1eS7d+9/HPpBWprp1Or0VKMnTPBZdSKVejNN/vHM3VyGuYwoypaj9IKXmr3YfsX3843X4MIXpKVS5JoeIRHNl2AnO8GY1Wg1anpXTVkpw5EJmhLCkxhhoYteIjareo4XOfhNhEVs/cwJlDkVRtWJl2T7T0OosdcPcwdq/e79EeGhHCZwvfo24b7w8kTqeTI9uOu5JntqiRoUu0IvcIaroPIUQloBGwGSgppbwA4FYYSQ7vZXHNHJKIdLfZ3O/Ttnsbpw+uGQgVKlTItJwb53tXLk67g+XT19DzI/+9fPwhafbiz+JnVlny879evYykU7Lr3/0069zQ57EXT13m2/5T2L5sDzqDlg7/a8PLY3omZ229u3sbylYrzdxxi7h85gp33t+Yri/fQ4FC6afjyIgpQ34j7kZ8ltYLzPEWVs/cQPdBD1O+ZlmcTidvtf2Qc8cuJtcP/2/eFvauP4jOoPOpLOw2B5YEC4+/05XZXy5EoxE4HU6klD6joVOi1Wm5+39taP1wM3pU7kdMVCxOp5M6rWsx5Lc3KVyiIH9PWJyhogBXlbzIw+eJPHw+2SzmdDhxOpyZVhRanYaIohHs/ncfBpM+2SMsLaHhIckOAOmRlPAwLVJKtD5mSqcPnGVIl8+JvRaHRiOw2528+f1LdHq2ndf98wN2m50zB88RVjCUkhWLB1ucHCHHlYUQogDwF/CmlDImHbcubxtkOu2ejVJOAiaBa2aRWVnjYxK92qjtNofr5hUgLp+9ytevTGL7st0IAc0faMIb371EkVKBj5a1JvqOHUjP+yc+Op7+zd9z3+QkdpudFdPXcGL3acZv+jzZPa9m06oM/qV/QGXetnR3piK706LRati3/hDla5Zl+7LdXD5zNVlRgMu0eONidIZ1xxf/tIqvVn/M/wY/QtT56xQpXZgHCzyb4fghBUy88V0fat5ZjVcavZtKWe9de5BB93zM9ztHM/3j2X6fU3auRxIarQYpIfpKNNM++oNfP/uLjs+05c3vX86yu2WXFzuyf8Mhj6BRh83B2YPnqNqgYqoZicPu4N1OH3M9jXnv61cmUbVBJSrXq5glOYLJ2tkbGdvnBxwOBw6bgyr1K/LRnHcDXtMk2ORoUJ4QQo9LUfwqpZzjbr7kNj/h/puU2CgSKJ/i8HLAeXd7OS/tAadxp3peg6lMYUZaPOBfjeqMsJqtvN5yCNuX7sLpcPmxb/5nB2+0+sAv00Zmaf9ka/RGz2cCh81Bww51fR6XVLktZd1mm8XO6QNnObDxiNdjTh+MZNm01exYsQeHj9Kf/hAakT2HAo1WQ+GShVwyHYj0mSsno/UGp93JJ098yZHtJ/hhwC/8r1wfv8bX6l2LwfMnLEmlpMB1s7xw4hIHNh4h9lqcX/0FgmlHxmEw6XE6nFjNNhx2B5YEK6t+W8+2pbuy3G/bx1rQoXsbjCGGVLMMq9nG+P4/8mrTQcwcMYeXGw2gb7NBfD/gFyxeshHYLHYWTlqRZTmCxfHdpxjV61vibsSTGGvGarZxZPsJBnf+5JaLes8xZSFcjypTgINSyq9SbJoPJKWx7AX8naK9uxDCKISoDFQHtrhNVrFCiBbuPnumOCaglK1Wmodeuz9VZKQpzEjjTvVp1LFeQMZYO3sTCTGJqW7CDruD6KjY5OR3CbGJbFywja1LdnqN9JZScvVcFNFX0/eCkVKyauZ6r6mn35z0ss+UAeDKZGr2FtEsXTfglDgcDj7tPpa+TQcx/rUfGfbYaHpV78/lM1fSlc8XD792H8ZQ/7y80iKEq1xo084u76PyNcugN2Z9XSbqwnXeafcRG+dv85kKBFypMYyhRkpVLsGYVcMwhRo5f/yi1wcAjVbDtYs30i0AFUgMIQYW/LDca+ChOd7C8nSSKmaEEIK3J7/C+M1foE9jkjLHWzh76BxTh87ixO7THN1+goXfL8PsZbbrdDi5fulGluUIFvPGLfKYoTsdTi6dunLLJWHMSTNUa6AHsFcIscvdNgQYAfwhhOgNnAGeAJBS7hdC/AEcwOVJ1c/tCQXwKjddZxeTQ55QAH1G9aBp54Ys+WkVNouVu//XllYPNwtYVOTZQ+e8pm2wJlo5e/g8K39bx9iXvk9O7SGE4OO/B1H/rtoAHNh0hJE9x3M1MgopJTWaVmXIb29SwkuCvY0LtvHfvK2eTzhCJPfnC6FxpR7xePrWCCrUSh0tvuC7ZWxauD2VZ5Elwcqn3ccy7r/P0x0niRN7TnNo81GKlinMg307c2DTUdbP2exa1/HnAU2AwWSgRIVifDR7AH+Mmc/8CUtJjEtEStfN3GnPminHnyfEYmWK8MWSDyhfs0zyd6VGk6rsXLnXY43DZrVTs2lVHurXmakfzvLoKzQihEIlIrhw/HJAnk6tiVauXbjhc3tGMyy/8CGndEocKfpPO9NKwhRmpFW3ZtmXI5e5fPZqqge/JLRajYepLb+TY8pCSrke7+sNAB19HPMZ4BEtJKXcBvi2mQSYxh3r0ThAM4m0VK5XgZACJi8eV3oiihbgq5e+d910U6Th+aDrF8w6P4mEWDOD7/0k1bEHNx3l7XZDmXZ0vEf05oxPZnu92ThsDkY8+w2DZ7zhVcnMHb+IVb+t87iJCCGwma1803cy3Qc9wt1Pt0EIwYLvlnksojsdTo7tPMW1i9fTXYuxmq281vw9Tu47A7gyyxpCjdjMNjRaTbq2+gbt61CtkcvOXb5WWcILh1GuRhlG9BjHhrlbkl1shUag1WkROnA6JQK8/sCzQ3RULGWrl0IIQdSF63z61Fcc2nrM4+ZoDDVy9/9aU7JicaKvxCCEl/ushJe/fI7pH//Jse3ZfzoNKWCiZbembJi72ev2i6cuY0m0pApyPH0wkjV//IfT7qTNo80zLPOrM+o8gh/9xRhqoGLtcrR7siXgcvzYuXIvF09eplrjKj5dfPMCTTs3ZP+Gwx7nbrPaqdGsWpCkyhlUbqhMcGjLUf75YTmx1+No+1hL2j3ZEp0Pjw9ftH7kTqa891uy3RhcHiXFyxXl0ukr3tctBGxcsJ0LJzzNGk6Hk5ioWHat2keTe1IHfl04ccmnHPvWH+aVRu8yafeYVNlJrWYrP73/u/f0x1LisEtO7TvLyF7jGf3CBMrXKOMzgaBGIzL0HOrfcggn955J/t9uc2CP9m3uScnAqf04fSCSyYNmcPbweYqXK8JDr93P+jmbU40r3Qv0YQVDue+Fu6lctzzj+k3BYXd4TWuRFayJVnrXfhMhNFyJvIrNbEutkASUqVKSx99+kHZPteLs4XNcibzm9YHcarEx7JFRgXniBxwOJz8OnuHzZn5810m+f+cX3pj4EuCKX/n5g99x2BxIp5PZXy3godfu5/G3u2Iw6b0Wn9r974EsLcKbCpjoM6oHnZ9z1SKPunCdt+8ayrVLN1yKVkqqNKjEV2s/dgVj5jG6vNSJv79dwrWL15M960xhRh5+7X6vVQ7zMyrdh5/MHb+IKe/9mhxTYAozUrVhJcasGpZphXH9cjTfvzONDXO3IIRLgeiNBlbOWOPVldMYauCVL5/j0OYjrPtrJU+8epm7HryBOUHD/KnFWDO/NH2/fgFjqJEF3y3FkmilQ/fWLPxhGeeP+VYYWr2WB1++l37jXkhuO7nvDG+0fj/d5HVp0eg0CEiVIwqgZMXiTD8xwacJ7/SBs7xU722fBaDSo1i5IrzzY1+GPTIq1U1Qb9S56j/4UFJCQGhEKE8OfIgTu0+xYd5Wn6aRQKLVaXmgTycS4sysmfUfOr3WVeZUylwZPyOEEDzxbjfaPd6Ct+4a6nn9hGvWhxA07FCHgdP6p7oZ9q7zps+SrumRtlTu4M6fsHPV3uTcZ0lUrF3O7xxbuU3MtVj++mohG+ZtoUDhMB594wHaPtYiQ9P1ujmbmTxoOhdPXqZo6cL0HPYk9/f2anRJl7OHz7Fi+loS4sy0fqgZDdrXyZbZXOWGygYx12LpXu5lj7QQpjAjb3zXh07P3pXlvqWU9G8xhBN7Tvn0+TeY9Py4fyx71+7ijhoDKF7aisHk+twS4wXrFxVl89pH2bpkV7ILozHEQESxcK5duJGul5UxxMCnC9+jfrvarJ65gQXfL2P/hsOZtpVrtBoMJj3meAt6ox6tTsPni95PN0p86dR/GdN7YpbiKXQGHZXqlOPYzlOZPxjcaxx6hBDpZqgFvK/dZAFjqAGbxZbqRiiEQKvX5gmFoTPq0Om0OBxOrylQktDqtVSoVZYfdo1Jvik9VuIFYq56n2GGhof4TG9evlZZfjrwNeAKin206PNeS7wCNO/amL5jn8/1PGg5wX/zt/L501+n+u4ZQ4288mVPur58r9/9LJ6ykgmv/4Td5sDpcGAMNdLiwaYM+fWNLCsMX8oi76npPMi+dYc8PD3A5e2x7q9NXo7wn73rDnL6YKRPRWEKM/LoGw9QunJJ2j98jaIl7cmKAiAkTNL+oeuc2vNfKl93S6KV2Kg4SlYq7nvlyL3fkPs/4+32Qxn78g/sW++9EltGOB1O6revTZUGFenyUkd+OvhNhulESlQohs5HjQ5/xos8csH7RgFafQZfbekKdstIUUCAFoBxLfqnfWKWUlKweAR3tKhOo7vr5lh6aX+wW+yYEywZmuYcNgcXTl7m4OajyW0VavlIaSKg94hnuP/Fjmh0qT8TjVaDTq9lxid/En01Bofdme53b/PCHfRpMICtS3b6f1J5lJ+G/Obx3bMkWJg6dJbfv7+Ya7GM7/cjlkQrDrsrd5o53sLG+VvZumRXwGVWysIPQsJNXp9+hRDZjlY+sec0Th9P/mVrlOGT+YPp/cUzAOjkZkyh3tY0dFSr6+lGa06wcPHkpQyf3G1WO/vXH/ZajU9v0mMKM2YYwAawbfEuTu+PZPGPq/jv74wr99VtUyvLVb3K1ypLqUolvG4zmgxUbVApS/0Gg4ToBBreXZen338sU/W405I0S8kW0r8AQI0QXD590zX6uU+6ey06pdNrObr9OEt/XuXhjeZ0Ojm59wy/fzGXF+u8RWKc2ednmoQlwcLIXt9mK44nL+BrPTH2WlyqGjdXIqP4+pVJ9Kjaj9dbDWF9CieFf39bj83LjNSSYGX1HxsCLrNSFn5Q/67aGEI8F9f0Jj0PpKjjnBXKVi/t9QduCjNyb8927Fy5l/fu/5QpQ37l6qUSePNJkE4n1y55jyVI+ySbWe57vgNzr031OrNKIYFrLKfEYXdgNVv54d3pXD1/DYAbV6KZ/dUCPuw2gg+7jWDuuH+Ij0lg9PMTfSby84n7hhR9NYYqDSp6jcew2x0c3R68yoEp8ad8rjnBwu+fz2XwfZ9iMWfNowhcZqRBU1+jePmifin37GC32anWuEry/w3a1eHh/venCszTGXW0e7IVK2es8/49dDdZzTZirsUxdehMBv7yWoZjWxOtWVofyUv4yjNnDDEkR7xfPX+NVxoNYMlPq7h48jIHNx1lZI/xzBw5F4BN/+zw2b9Iz5yQRZSy8AOtTsuIpR9QqETBVD9+u9XOzBFzSYhNZP9/hxn13Ld89MgoVv66DrvNPxt04071KFq6cKoayEmunrNGzuPPLxewbeluZn+5gOca7eXYvhDsNtiwOILZ3xdn57pw4mND2LMxezMcbxhMesrVKINOr/NpR/Y1bdFoNGxeuJ1/Ji/nf+Vf5ocBv7Bp4XY2LdzO5EEzeL7WG6yfs8ljIdVbLeiUJP0IblyKZsPcLZSrUYbi5VPXTnC4F47zAiHpBD4mkWTmctgcxKcT+JcRdqudE/vOMOCnvhiyEYiYEcZQA60eaka56qVJiE3kn0nLmfDmz1RtWJnRKz/inh53UaBwGAJY9av3p9+0OB1OtizeyR3NaxBRzHd5WHB5d6UtKZrfeOytrl7brWYrVyKjAPhzzHwSYhNTrTmaEyzM+Hg2iXGJRLkfxryxbdluDmzynmkhqyjXWT+p2qAS7/zUl48fG5Pc5nQ42bZ0F/1bDuHSqStYE61IKdmxYg//TFrOqBVDM/SU0mq1fLX2E75++Qc2/7MDKSW1W9Xg0skryV8acLmUYoOBj1fEFOIkIU6DzSrQGwQRxQqj0cUGzA00CSEEHf7XhhN7TvsMuvJV18FhtxMdFcuMT2Zjt6aWy2axu2IMvDz9ZpTyJKUSsCRaOXv4HC+P6cX4fj9meD7BIDdTekinZNbIeZzYfcqvG3RWeWrQwzw95FEunLhE/5ZDSIxNTFb6Wp0mucRuZgkND0EIwZvfvcyIHuN8erSFFDDl++JK8dEJXgNFNVot62Zv4rG3urJr1T6P3w64HAyO7TzJtQvXffZ/7cJ1Bt37CZP3fJmhac9f1MwiE8weM98jtN9msXPmQCSWBEvyjcwcb+HojhOs+8t7EJTD7mDL4p0snfov549fpHCJggyfO5B/En5lQdx0DEZDKkWRkvgYLVGXdCTGa7HbNCTGC65djAu4okji4KYjLJ6ywveyhxR4W0G3Wx1cPH7Jp1xOhzMgMlsTbXlWUcDNzMK5hXRKdq/en2NmKK1ey9NDHkWr1TL25R+IuRqb6qaeVLM7sxhDjTzyehfAlW9q+LxB1GhSxetMM+56HF88Oy7rJ5EHsNu810JxOhzJs/gSFTwDZsE1g9y0YDvxPjzMUu7394Ql2RfWjZpZZIKr57zfwL1hjrewfu5mOnRvnar9zKFzDOgwDEuiJTnNdKee7Xjzuz5oda4nhgMbM0pbnfpGkJUfpz9YEq0Mf2wMBpM+Sx5Bh3ecCEi21GBiDDV6TfHuN9JVEConn/TT4o+HV1aJKFKA7mX7ULx8MY7tPJllc58QoDXoMJr0WM127v5fa7r17Qy44gamDJ7hcv7w8r1z2J2s/XMjCzos5cFXOmfrfLJL3I14zhyMpFi5ol6zIfiiUImCXn9TDruT5g+4Kgw++e5D7Fy1N9XnqTfoqNvmDjb8vQV7Br97u9XOmYOZS2GfHkpZZMDmf7Yzc+Q8rp67hk6vzTAFRRIajSC8cGrbq5SSoQ+N5MblG6msOqt+XUfD9nXp0L01R7YdD5irpl8k6R0fQzodTq9eUv5wYtepLB2Xl7Ckk+LdH3QGLdpcVhY5yY3L0UhJpmp+J6HVaShUoiC1W9Wkx4ePA3Dp9FWqNqyUbFa6dOYKL9V72yPA0xsT35rK3f9r4zWiPKeRUjL1w5nM/moBOoMOu9VOgw51+XDWW36VYfaV6Vdn0CUnCK3X9g7e+K4PE9/4GYfdNeNo1LEuQ359g75NB2c4hiHEQJ1WNTN1XumhlEU6zJ+4hEkDZyQ/WQqNQEqZSmEYQgxoNMLjhqo36enyUupozDMHI7l6zjPFgznewoLvltKhe2tKVirh4Y+eo2RTL+mNvgsI3RJk+/roSTfQJZ+RHb8Bp1MSGhHKh7PeTo4nSVu/Ymi3kX4pCnCt921fvoe7Hm+ZdaGyyPJf1jDn63+wmm3JZrhdq/bx5Uvf88Hvb6V77H/pxEHoDDqsiVYij5znm76T2bPmAFqdhuYPNKHP6B6UruzyourU4y5mjpjrc11HoxGYwoyZCvDLCKUsfGC12Phx8K+pTBDSKREaQZmqJXE6nBQtW4Tugx6hSKlCvHf/Z8n+0XarnT6jelCjSeoEaJZEq1dfdCA5HXiTe+sTXqSAO4Ar75twat1ZnX3/HUJm00X3VsVbhuH8iFanzXa9FemUXDl7ldMHIqlUp7zH9rgb8Zzaf9bv/hx2R6ZT7QSKP7+c75HC32ax8d+8rSTEJnqk/79xJZpFP65k3exNnNp3xqd3oTnOTOGShejfcgjxNxKQUuJ0ONn8z3aizl/nmw2fulKzDOjG5n+2c+agK4u1KcyI0+EkNCIEm9VOs84NeXHEs9kqb5wWpSx8cOH4Ra8PhNLp+vCmHf02VfvMyB/Ys/YAiXFm6t9V22ewntfAN6MueW3j0JZj1GpenT3mA8RGxSJxB0nlwXtxSLiJ/733CGN6f5euZ8ZtTR783DKLKcxIiQrFAhLboNFqiPeRKDIhNtFvMy+4TEGN76mfbZmyQvQV72Y4oRHERyekUhaRR87Tv+UQLAmWDGfhOr2WyYOmu3LQpZjG2Sx2Dm87xqyR83hiQDdMoUbGbfycbUt3c2jzUYqVK0r7p1qlW6Mmuyhl4QUpJVaLzecHW8yL255Wp6XR3d7TmkcevcDa2RtZNNl7JTAhBAkxiXQJ+V+qMYVWULpKSc4fvZiFs8h5dDotDe+uS5fedzPj07+CLY4iB9DqtNRsVo296w8GpkMJ1Rt7T3derGwRwosU8LsOxAufP40p9Ga8hcPhIPLweUxhphyvg92oYz1Wz9zgsQBfoGAo545dYGTP8UQeOU/lehWJiYpNniVkhN3m4MTeM16DVZ12J78M/5MN87bw5erhGEwG7ry/EXfe3yhg55UeKpFgGnas2MPoFyYSExXrcpOVqX37jaFGPpr9Ds3u8+8Dmj9xCd8P+CW5r1sBrV6LTqfls0VDaNCuDolxibxU7x0un72CzPuWM0Um8FZ7JS0anQbplOk6Zmg0AolrjS+8cBgFi0VQqlIJ6ratRYVa5ajbphah4SFsWridT576ymXSdXen0Qp0Bn3yDVRv1HFnl8Z8NHtA8trHtmW7GdlzvKsUsMNBxdrl+Oivd3NMaVw4cYlXmw7EEm/FbrMjhMAQoufxt7oye+zCLHukabQaajaryvHdp31mNzCGGuj9+dM88voDgOthdPaX8zm59ww1mlTl8XcezNZ5q6yzfhB55DyvNB6Y2lXSbYoyhRrRaDW8NKoHXftknOJDSsmOlXt4/4HPcdhuvTuoKcxIRLFwJmwZQcFiEXz+zDesnrXhllGICv8xhhpo+2gLVv+xwSOITKPTUKlOeU7sPu3zeL1Rj0Yj6D/xRTr36sCxXSf5a+xCzh29SMMOrjQiJ/eeYenU1Thsdjo+cxctHmySnK78/PGL9GkwINXvVqMRFK9QjF+OfZultOZOp5MF3y3lr7H/EB8dT6OO9en9xdPJC8zgytv0x+i/WT9nM9FRsditdrS67GUQNoYaGbXiQz54cIQroNPH76lms2p8u/kLDmw6wqB7PsZmseGwO9HptehNBr7Z8CmV61bIkgy+lIUyQ6Xg7wlLPD9o6foAB057jRZdm6A3ZJxG4eKpywzs9DEXT17KlvdIXsYcb8Ecb+GZiq+66oTfouepyBhLgpVVv633GoBoCjFy/lj6ZtSkQNfxfX+kZtNqVGtYmUHT+qfap0ipwh7FvZJY+MMyHGnS6zidkpioWPauPUiD9nVcciZauHzmKkXLFEm27Z8+cJYj205QomIx6rW9I1mxfNt/CsumrUlWQOtmb2T78t38uG8sRUu7Kj8WL1eUwiULEnc9Pjmle3YUhUarYfD0/tRuUZNvN33BqOcnsH/9Ia/7GkNc96FxfSenWge12xzYbYl899ZURi0fmmVZvKGURQrOHb3g1eNDq9Og0Wh8Korrl6P5e/widq89SNmqJdm5ah+Xz1zNaXHzBBlVwlPcHjh92B/tdoffySJtVjuLp6zk1a+e8z6G0+l1lnD5TJRX76LEWDNT3v+NUSuG8ufo+cwa9TcajcBhd9D5+Q5Enb/O9mW70WhdfRYtU5gx/w5HoxEs+enfVNkanE6JJd7CnK//4aWRz7rltTFzxDwPryhvaHVahEakq0yERnBiz2naPNKcMlVLMXbNx/Sq/hoXTlxOtZ8pzMgDL9+Lw+7wOWPb50PJZAeV7iMFDe+uh9FLdlm71U71JlU82q0WG399vZCnK7zCb1/MZd+6gyz7ZY1/ikK4lJBCcUvgY2aZGXdbp8PpNZfWujmb6VG1H511T/F4yd7M+eafVOuIjTvV95lY8ODGIzxcqBe/j5iLJcFCYpwZq9nGoh9Xsumf7VgSrSTGmUmMM3PhxCVG9BjHyX1nvWYLtlnt7N9w8yYcfSUmU55bGZn8HTZHKicYIQQf/z2YgsUjCA0PwRhqxBBioEP3NnTo3jq54Jg3QiMC7xWlZhYp6PJiR+Z8vRB7itrMxlAjHZ9u4xHKb0m08FbboZzYcypVEJHf0dfSswypQpEv8JE80hsOm8Pv/Y0hBlp2a5aqbfOiHYzsOS55wTj6Sgw/f/A7Nqudto82Z8IbP7F9+R6PhHxpZUibh8xbXjKH3cm+9YcoUCjUIwccuMxE5WuWSf6/YPEIfAZOpcHpcIIfevPaxRtcu3idmKg4lk37l8RYMwN+ehWH3Un0lVjq33UHl89c5e32H3Hh+CUKlijI1cirqVLAG0MMyalTAola4E7D9Us3mPHJbDbO30ZoRAgP9+9Cl5c6otFoOH3gLJMGTmfvuoPYbY5sezhpdRqlMBT5DiFSR3K7UthnP4q/QOEw/rz4Y6pAO1+1vUPDQ9DqtcTdiA9oehyNVsPni4Ywa9Q89q0/lMqV3Rhq5NvNXyQHFJ4/fpGX6r0dUFOsRiOocEc5zh2/iN1iT56NhIaHMPSvAcRei2XMCxPT9bYq4V7Yzyjdvy+UN1Q2uXDiEi83epfEuMSALuYGqr6zQhEMwgqFYk20unJfZfNrbAjR89Hsd5PjBs4cjqT3HT5SZwgwGPU5smZmDDXSslsTkLBh3hakhGJlivDWpJdp3OlmEODwx8e4tvv5+xUChMb/oENvGEL0WBPTP2dDiIEJW0Z4jZL3B1WD2w9sVhurfl/PmBcmMHXoTC6eurmwNHPkPKyJloB7/ShFocjPxEcnBERRgCvd/L+zXOVAY6/HZZgsLzuKQqPVEFEs3GulRUuChf/mbSXqwnWkdFV/NIWbMBVIXQJ456q9mfr9arRaSlTIXtxHRooiiQP/ZZS5OvMoZeHGnGChf4shjO3zPUunrubXz/6iZ7XXmPL+b0gpObjpSMBNRjpDNuslKxTBRhKwByghBAajni+e/YZHiz6PJZ1sxwWLR/hVsja9sYqXK0q3vp29mmusZht71x7EbnXVnTi19wyD7vkkVe3szGa7LV2lBJdOX854x2xiTbRy6oD/Obb8RSkLN/MnLOH0wcibPsvS9dQ/84u5PFb8efRGfXK0aKDwVgVLobhd0Zv0HNx8lFW/rc9w3/ue64A+G8rCYXdwfPcpoq/GoNX7dxu0WW3M+eYfAOJjErjn2bswhvpX3lWr13Lu6IVcsyT8M2kFJ/f6DoTMCmrNws0rjd/l+C1Qf0GhyK9UbVSZ4ztP+rXvvOtTuXr+Ov2aDc5ecapMElYwlNJVS3Fq3xl30kNHnn3ou6/33bwz+dVMH6fWLDJAb1JexApFMPFXUUQUCyfmWhwV7yjHSyOf8fvpPhDERydwbMcJ7FY71kRrnlUUAKtnbghof0pZuClVMTBFzRUKRc4SczWWnlVfo2/TQbR5tDlNO3tPA5InCGLdK3O8hStnA5dJQikLXLbILYt3BlsMhUKRCY7uOEHPqq/R5cWO2VrszlGCbOU/6udszR+UsgDOH7voNQmaQqHI21jNNiYP/tXvSOrbCgFlq5cOWHdKWQARxSLytO1RoVD45uyhc9zb8y70RrXumBJjiJEKtcoGrD+lLIDCJQrSqGPdYIuhUCiygMPmYPfagxmWLL3dsCRY2LsuQBUOUcoimTotawRbBIVCkUXOpsgfpdM70RuchIQ50BudBH3hIIhsmLc1YH2peRuuQvHTP1E1pBWK/I/EbhOAwGa92Xa7EnnEMwljVsk3MwshxH1CiMNCiGNCiPSTxmSSg5uOeE1ZrFAo8huCoPqr5jEunAxcepF8oSyEEFpgAnA/UBv4nxCidqD6v3ElJlBdKRSKPEdSQY0AJrLKJ1w6fSVgfeULZQHcCRyTUp6QUlqBmcBDgep827JdgepKocgBJOWrmXnl40i6PX8FIW6vG172SXm9AjPrMIXaEdqk9ZC8q4Ss6dS9yCz5Zc2iLJAyjWIk0DxQna/8bV2gulIocoQaDeN55MUoEuIE29eEc+6EKeODFG4Ca5YqWMRK9LWkIMCkvvOmsggk+WVm4e3T9vh0hBB9hBDbhBDbrlzxf/ol7bf+B63Izwh2rA0H4OC2MKIu5tFo5dsEp0y6HaW8Ld366yT5RVlEAinLPpUDzqfdSUo5SUrZVErZtHjx7BUZUSjyFO7nmcO7QjEn5pef7a1J7A0dt4NySEt++dZtBaoLISoLIQxAd2B+oDqvWLdcoLpSKHIAScM2cQAULWXDaFKpaYKJ3nB7Xv98oSyklHbgNWApcBD4Q0q5P1D9j9/0RaC6UigCjEQI6PuJy1++1X3RaHWudkVwqFongfxx/QMrY75QFgBSykVSyhpSyqpSys8C2XdIqInppyYEsks30sfL17b8TLDPx9e1zuvXNf3vh0Yr+WDySYwhEqtFcOaICYPR4eXY7IyXnWuU3nXP6c8kpz9v7/0f3lWACtUT0xk7t84/rZxpxxYstc8K2CiqUp7CL6SUyS+NRoPD4UAIkfySUmKzuYrJm81mrl+/gTXRgtFoIiTMhM1q58qlywiNhkSrlZgzMZjNFhITErFEWyheuTiWOCsXIq9QoICe6GtWnHYr5rgELp2OonCpQhQICyHuegLXomOxx5oJKRxC1Plorl+OwmlzIoQO7A5KVSqK1ApiLiUSEm7EHG8h9kY0UmoIDw9Do9Nw7fx1rDH5xZxgoWQ5sJg13LiqQR9iR6MFpMASb8hCfxJIyqMUKPu7Ha0eHHZA5rCTpQZwgiHcQEi4iehL18HhdG/wUddeA+FFwnA6JEaTHo1Oi0avxRRi4NKpK1jib7qYanTgTE4z5SQk3IbRpOXGVQ3Im8/XxUpbKFDQQewNHVEX034ON91qjSESg8FJQrwOh92HfNkm9Xe5auOKfL/tqyz15KtSXn5xnVUEmSSlkIROp/PYbjS6KpYZjUYKFizo0UfZ8mVu/tMiZ+RUKBQ5Q74xQykUCoUieChloVAoFIoMUcpCoVAoFBmilIVCoVAoMkQpC4VCoVBkyC3rOiuEuAKczmC3YsDVXBAnr6Ougwt1HdQ1SOJ2vg4VpZQe+ZJuWWXhD0KIbd78iW831HVwoa6DugZJqOvgiTJDKRQKhSJDlLJQKBQKRYbc7spiUrAFyCOo6+BCXQd1DZJQ1yENt/WahUKhUCj843afWSgUCoXCD5SyUCgUCkWG3JLKQggxQAghhRDFUrS9J4Q4JoQ4LITonKK9iRBir3vbOOFOrSqEMAohZrnbNwshKqU4ppcQ4qj71StXT84PhBCjhRCHhBB7hBBzhRCFUmy7ba6Dvwgh7nNfj2NCiMHBlie7CCHKCyH+FUIcFELsF0K84W4vIoRY7v68lgshCqc4JmDfi7yGEEIrhNgphFjo/v+2vA7ZJmWdglvhhatW91JcAXnF3G21gd2AEagMHAe07m1bgJa4kvovBu53t/cFvne/7w7Mcr8vApxw/y3sfl842Oed5hrcC+jc70cCI2/H6+DntdK6r0MVwOC+PrWDLVc2z6k00Nj9Phw44v7sRwGD3e2Dc+J7kRdfwNvAb8BC9/+35XXI7utWnFmMBQaSuhzVQ8BMKaVFSnkSOAbcKYQoDURIKTdK16f9C/BwimOmud/PBjq6nyY6A8ullNeklNeB5cB9OX1SmUFKuUy6StECbAKSiozfVtfBT+4EjkkpT0gprcBMXOecb5FSXpBS7nC/j8VVirgsqT/LaaT+jAP1vchTCCHKAQ8AP6Zovu2uQyC4pZSFEKIbcE5KuTvNprLA2RT/R7rbyrrfp21PdYz7xhsNFE2nr7zKC7iehOD2vg6+uFXOwytus0gjYDNQUkp5AVwKBSjh3i2Q34u8xte4Hh5TlpK7Ha9Dtsl3lfKEECuAUl42vQ8MwWWC8TjMS5tMpz2rx+Qa6V0HKeXf7n3ex1U/89ekw7zsn6+vQwC4Vc7DAyFEAeAv4E0pZUw6D7yB/F7kGYQQXYHLUsrtQoj2/hzipS3fX4dAke+UhZSyk7d2IUQ9XHbG3e4fRTlghxDiTlxPAuVT7F4OOO9uL+elnRTHRAohdEBB4Jq7vX2aY1Zn55yygq/rkIR7wbkr0NE9dYZb8DoEAF/XJF8jhNDjUhS/SinnuJsvCSFKSykvuE0rl93tgfxe5CVaA92EEF0AExAhhJjB7XcdAkOwF01y6gWc4uYCdx1SL1yd4ObC1VZcFaGTFq66uNv7kXrh6g/3+yLASVyLuoXd74sE+3zTnPt9wAGgeJr22+o6+HmtdO7rUJmbC9x1gi1XNs9J4LKrf52mfTSpF3ZHBfp7kVdfuB5skha4b9vrkK1rGGwBcvDLkaws3P+/j8u74TBuTwZ3e1Ngn3vbt9yMajcBf+Ja5NoCVElxzAvu9mPA88E+Vy/nfgyXHXWX+/X97XgdMnG9uuDyGDqOy4wXdJmyeT5tcJlC9qT4DnTBZUtfCRx1/y2S4piAfS/y4iuNsrhtr0N2Xirdh0KhUCgy5JbyhlIoFApFzqCUhUKhUCgyRCkLhUKhUGSIUhYKhUKhyBClLBQKhUKRIUpZKG4bhBBxOdBnJSHE0wHqa6oQ4vFA9KVQBBqlLBSK7FEJCIiyUCjyMkpZKG47hBDthRCrhRCz3XU/fk1Rn+CUEGKkEGKL+1XN3Z7qqT/FLGUE0FYIsUsI8ZaXsQa66yDsFkKMcLc1FEJsEjfrjRT2ctwp4a7HIoRoKoRY7X4/TAgxTQixzL3Po0KIUe4xlrjTfCQdP1wIscO9rZa7vZ1b1l3uGg/hAby0ilsYpSwUtyuNgDdx1TCogiuPUBIxUso7cUXqfp1BP4OBdVLKhlLKsSk3CCHux5XKurmUsgGuOgrgSsUxSEpZH9gLfJRJ2aviSrv9EDAD+FdKWQ9IdLcncVVK2Rj4DhjgbhsA9JNSNgTauo9RKDJEKQvF7coWKWWklNKJKx1GpRTbfk/xt2U2xugE/CylTACQUl4TQhQECkkp17j3mQbclcl+F0spbbgUjRZY4m7fS+rzSEoguD1F+wbgKyHE62457CgUfqCUheJ2xZLivYPUGZill/d23L8Xt8nK4McYgqynq04eD1f+oZRYANyKziZv5uxxkvo8ks4x+fyklCOAF4EQYFOSeUqhyAilLBQKT55K8Xej+/0poIn7/UOA3v0+FlfpUm8sA14QQoSCq/azlDIauC6EaOvepwewxsuxKcd7LPOn4B0hRFUp5V4p5UhgG6CUhcIvlLJQKDwxCiE2A28ASYvWk4F2QogtQHMg3t2+B7C7F7BTLXBLKZcA84FtQohd3Fw36AWMFkLsARoCH3uRYTjwjRBiHa6ZQaB4UwixTwixG9d6xeKMDlAoAJV1VqFIiRDiFNBUSnk12LIoFHkJNbNQKBQKRYaomYVCoVAoMkTNLBQKhUKRIUpZKBQKhSJDlLJQKBQKRYYoZaFQKBSKDFHKQqFQKBQZ8n8Vv0/SZ32F4wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x[:,0],x[:,1],c=pred)\n",
    "plt.legend(['Not Fraud'])\n",
    "plt.xlabel(\"Input columns\")\n",
    "plt.ylabel(\"Output\")\n",
    "plt.title(\"Logistic Regression plot\")\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}