KNN.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# K-Nearest Neighbors\n",
    "\n",
    "The purpose of this notebook is to compare the use of grid search, random search, and Bayesian hyperparameter optimization to find optimal hyperparameter settings for a K-Nearest Neighbors model.\n",
    "\n",
    "The hyperparameters being evaluated are:\n",
    "\n",
    "* K = number of neighbors (n_neighbors)\n",
    "* L1 (p = 1) or L2 distance (p = 2)\n",
    "* (weights = 'uniform' or 'distance') = whether to weight points uniformly, or give more weight to those that are closer more than those far away"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2020-12-12T19:36:37.149685Z",
     "shell.execute_reply.started": "2020-12-12T19:36:15.021506Z"
    }
   },
   "outputs": [],
   "source": [
    "import os\n",
    "from datetime import datetime\n",
    "import pickle\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "import skopt\n",
    "from skopt import BayesSearchCV\n",
    "from skopt.utils import point_asdict\n",
    "from skopt.space.space import Integer, Real, Categorical\n",
    "from skopt.callbacks import DeltaYStopper, TimerCallback\n",
    "\n",
    "from data_utils import get_CIFAR10_data  # use code adapted from CS 682 to get the CIFAR10 data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2020-12-12T19:36:41.894605Z",
     "shell.execute_reply.started": "2020-12-12T19:36:37.152747Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train: (50000, 3, 32, 32) --> (50000, 3072)\n",
      "y_train: (50000,) --> (50000,)\n",
      "X_test: (1000, 3, 32, 32) --> (1000, 3072)\n",
      "y_test: (1000,) --> (1000,)\n"
     ]
    }
   ],
   "source": [
    "# Load the (preprocessed) CIFAR10 data\n",
    "\n",
    "# Note the data from get_CIFAR10_data are in shape (N, C, W, H). Then we reshape the data to have one row per image. \n",
    "# To visualize a given image (x_train[i], for example), run the following:\n",
    "#     plt.imshow(x_train[i].reshape(3, 32, 32).transpose(1, 2, 0).astype('uint8'))\n",
    "\n",
    "data = get_CIFAR10_data(subtract_mean=True)\n",
    "for k, v in data.items():\n",
    "    tmp = v.shape\n",
    "    if len(tmp) > 1:\n",
    "        data[k] = v.reshape((v.shape[0], -1))\n",
    "    print('{}: {} --> {}'.format(k, tmp, data[k].shape))\n",
    "\n",
    "x_train = data['X_train']\n",
    "y_train = data['y_train']\n",
    "x_test = data['X_test']\n",
    "y_test = data['y_test']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-12-12T19:36:41.897284Z",
     "iopub.status.busy": "2020-12-12T19:36:41.896987Z",
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     "shell.execute_reply": "2020-12-12T19:36:41.900125Z",
     "shell.execute_reply.started": "2020-12-12T19:36:41.897235Z"
    }
   },
   "outputs": [],
   "source": [
    "# define the splitting strategy, which will be used for all approaches\n",
    "cv_folds = StratifiedKFold(n_splits=5, shuffle=True, random_state=682)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-12-12T19:36:41.902096Z",
     "iopub.status.busy": "2020-12-12T19:36:41.901826Z",
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    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[7 2 8 0]\n",
      "[(20000, 3072), (20000,)]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(array([0, 2, 7, 8]), array([5000, 5000, 5000, 5000]))"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Subset the data to 4 classes for runtime purposes, and because the simpler models\n",
    "# don't tend to get good accuracy when there are many classes\n",
    "np.random.seed(175)\n",
    "classes = np.random.choice(range(10), size=4, replace=False)\n",
    "print(classes)\n",
    "\n",
    "x_train_4class = x_train[np.isin(y_train, classes)]\n",
    "y_train_4class = y_train[np.isin(y_train, classes)]\n",
    "\n",
    "# check on size of dataset\n",
    "print([x_train_4class.shape, y_train_4class.shape])\n",
    "\n",
    "# check on class balance\n",
    "np.unique(y_train_4class, return_counts=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Grid Search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-12-12T19:36:42.150925Z",
     "iopub.status.busy": "2020-12-12T19:36:42.150664Z",
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     "shell.execute_reply": "2020-12-12T19:36:42.154023Z",
     "shell.execute_reply.started": "2020-12-12T19:36:42.150896Z"
    }
   },
   "outputs": [],
   "source": [
    "gs_hyperparameters = {\n",
    "    'n_neighbors': range(2, 40, 6),\n",
    "    'weights': ['uniform', 'distance'],\n",
    "    'p': [1, 2]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-12-12T19:36:42.156239Z",
     "iopub.status.busy": "2020-12-12T19:36:42.155925Z",
     "iopub.status.idle": "2020-12-13T00:07:05.375054Z",
     "shell.execute_reply": "2020-12-13T00:07:05.374094Z",
     "shell.execute_reply.started": "2020-12-12T19:36:42.156203Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Beginning grid search at 14:36:42...\n",
      "Fitting 5 folds for each of 28 candidates, totalling 140 fits\n",
      "[CV] n_neighbors=2, p=1, weights=uniform .............................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] . n_neighbors=2, p=1, weights=uniform, score=0.572, total= 1.9min\n",
      "[CV] n_neighbors=2, p=1, weights=uniform .............................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:  1.9min remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] . n_neighbors=2, p=1, weights=uniform, score=0.589, total= 1.9min\n",
      "[CV] n_neighbors=2, p=1, weights=uniform .............................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:  3.9min remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] . n_neighbors=2, p=1, weights=uniform, score=0.564, total= 1.9min\n",
      "[CV] n_neighbors=2, p=1, weights=uniform .............................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:  5.8min remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] . n_neighbors=2, p=1, weights=uniform, score=0.568, total= 1.9min\n",
      "[CV] n_neighbors=2, p=1, weights=uniform .............................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:  7.7min remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] . n_neighbors=2, p=1, weights=uniform, score=0.560, total= 1.9min\n",
      "[CV] n_neighbors=2, p=1, weights=distance ............................\n",
      "[CV]  n_neighbors=2, p=1, weights=distance, score=0.618, total= 1.9min\n",
      "[CV] n_neighbors=2, p=1, weights=distance ............................\n",
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      "[CV] n_neighbors=2, p=1, weights=distance ............................\n",
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      "[CV] n_neighbors=2, p=1, weights=distance ............................\n",
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      "[CV] n_neighbors=2, p=1, weights=distance ............................\n",
      "[CV]  n_neighbors=2, p=1, weights=distance, score=0.603, total= 1.9min\n",
      "[CV] n_neighbors=2, p=2, weights=uniform .............................\n",
      "[CV] . n_neighbors=2, p=2, weights=uniform, score=0.559, total= 1.9min\n",
      "[CV] n_neighbors=2, p=2, weights=uniform .............................\n",
      "[CV] . n_neighbors=2, p=2, weights=uniform, score=0.552, total= 1.9min\n",
      "[CV] n_neighbors=2, p=2, weights=uniform .............................\n",
      "[CV] . n_neighbors=2, p=2, weights=uniform, score=0.559, total= 1.9min\n",
      "[CV] n_neighbors=2, p=2, weights=uniform .............................\n",
      "[CV] . n_neighbors=2, p=2, weights=uniform, score=0.543, total= 1.9min\n",
      "[CV] n_neighbors=2, p=2, weights=uniform .............................\n",
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      "[CV] n_neighbors=8, p=1, weights=uniform .............................\n",
      "[CV] . n_neighbors=8, p=1, weights=uniform, score=0.628, total= 1.9min\n",
      "[CV] n_neighbors=8, p=1, weights=uniform .............................\n",
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      "[CV] n_neighbors=8, p=2, weights=uniform .............................\n",
      "[CV] . n_neighbors=8, p=2, weights=uniform, score=0.599, total= 1.9min\n",
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      "[CV] n_neighbors=14, p=1, weights=uniform ............................\n",
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      "[CV] n_neighbors=14, p=1, weights=uniform ............................\n",
      "[CV]  n_neighbors=14, p=1, weights=uniform, score=0.617, total= 1.9min\n",
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      "[CV] n_neighbors=14, p=2, weights=uniform ............................\n",
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      "[CV] n_neighbors=14, p=2, weights=uniform ............................\n",
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      "[CV] n_neighbors=14, p=2, weights=distance ...........................\n",
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      "[CV] n_neighbors=14, p=2, weights=distance ...........................\n",
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      "[CV] n_neighbors=20, p=1, weights=uniform ............................\n",
      "[CV]  n_neighbors=20, p=1, weights=uniform, score=0.623, total= 1.9min\n",
      "[CV] n_neighbors=20, p=1, weights=uniform ............................\n",
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      "[CV] n_neighbors=20, p=1, weights=uniform ............................\n",
      "[CV]  n_neighbors=20, p=1, weights=uniform, score=0.608, total= 1.9min\n",
      "[CV] n_neighbors=20, p=1, weights=uniform ............................\n",
      "[CV]  n_neighbors=20, p=1, weights=uniform, score=0.610, total= 1.9min\n",
      "[CV] n_neighbors=20, p=1, weights=distance ...........................\n",
      "[CV]  n_neighbors=20, p=1, weights=distance, score=0.628, total= 1.9min\n",
      "[CV] n_neighbors=20, p=1, weights=distance ...........................\n",
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      "[CV] n_neighbors=20, p=1, weights=distance ...........................\n",
      "[CV]  n_neighbors=20, p=1, weights=distance, score=0.616, total= 1.9min\n",
      "[CV] n_neighbors=20, p=1, weights=distance ...........................\n",
      "[CV]  n_neighbors=20, p=1, weights=distance, score=0.617, total= 1.9min\n",
      "[CV] n_neighbors=20, p=1, weights=distance ...........................\n",
      "[CV]  n_neighbors=20, p=1, weights=distance, score=0.619, total= 1.9min\n",
      "[CV] n_neighbors=20, p=2, weights=uniform ............................\n",
      "[CV]  n_neighbors=20, p=2, weights=uniform, score=0.585, total= 1.9min\n",
      "[CV] n_neighbors=20, p=2, weights=uniform ............................\n",
      "[CV]  n_neighbors=20, p=2, weights=uniform, score=0.586, total= 1.9min\n",
      "[CV] n_neighbors=20, p=2, weights=uniform ............................\n",
      "[CV]  n_neighbors=20, p=2, weights=uniform, score=0.584, total= 1.9min\n",
      "[CV] n_neighbors=20, p=2, weights=uniform ............................\n",
      "[CV]  n_neighbors=20, p=2, weights=uniform, score=0.576, total= 1.9min\n",
      "[CV] n_neighbors=20, p=2, weights=uniform ............................\n",
      "[CV]  n_neighbors=20, p=2, weights=uniform, score=0.581, total= 1.9min\n",
      "[CV] n_neighbors=20, p=2, weights=distance ...........................\n",
      "[CV]  n_neighbors=20, p=2, weights=distance, score=0.591, total= 1.9min\n",
      "[CV] n_neighbors=20, p=2, weights=distance ...........................\n",
      "[CV]  n_neighbors=20, p=2, weights=distance, score=0.594, total= 1.9min\n",
      "[CV] n_neighbors=20, p=2, weights=distance ...........................\n",
      "[CV]  n_neighbors=20, p=2, weights=distance, score=0.588, total= 1.9min\n",
      "[CV] n_neighbors=20, p=2, weights=distance ...........................\n",
      "[CV]  n_neighbors=20, p=2, weights=distance, score=0.583, total= 1.9min\n",
      "[CV] n_neighbors=20, p=2, weights=distance ...........................\n",
      "[CV]  n_neighbors=20, p=2, weights=distance, score=0.590, total= 1.9min\n",
      "[CV] n_neighbors=26, p=1, weights=uniform ............................\n",
      "[CV]  n_neighbors=26, p=1, weights=uniform, score=0.617, total= 1.9min\n",
      "[CV] n_neighbors=26, p=1, weights=uniform ............................\n",
      "[CV]  n_neighbors=26, p=1, weights=uniform, score=0.618, total= 1.9min\n",
      "[CV] n_neighbors=26, p=1, weights=uniform ............................\n",
      "[CV]  n_neighbors=26, p=1, weights=uniform, score=0.604, total= 1.9min\n",
      "[CV] n_neighbors=26, p=1, weights=uniform ............................\n",
      "[CV]  n_neighbors=26, p=1, weights=uniform, score=0.602, total= 1.9min\n",
      "[CV] n_neighbors=26, p=1, weights=uniform ............................\n",
      "[CV]  n_neighbors=26, p=1, weights=uniform, score=0.604, total= 1.9min\n",
      "[CV] n_neighbors=26, p=1, weights=distance ...........................\n",
      "[CV]  n_neighbors=26, p=1, weights=distance, score=0.623, total= 1.9min\n",
      "[CV] n_neighbors=26, p=1, weights=distance ...........................\n",
      "[CV]  n_neighbors=26, p=1, weights=distance, score=0.624, total= 1.9min\n",
      "[CV] n_neighbors=26, p=1, weights=distance ...........................\n",
      "[CV]  n_neighbors=26, p=1, weights=distance, score=0.611, total= 1.9min\n",
      "[CV] n_neighbors=26, p=1, weights=distance ...........................\n",
      "[CV]  n_neighbors=26, p=1, weights=distance, score=0.607, total= 1.9min\n",
      "[CV] n_neighbors=26, p=1, weights=distance ...........................\n",
      "[CV]  n_neighbors=26, p=1, weights=distance, score=0.608, total= 1.9min\n",
      "[CV] n_neighbors=26, p=2, weights=uniform ............................\n",
      "[CV]  n_neighbors=26, p=2, weights=uniform, score=0.585, total= 1.9min\n",
      "[CV] n_neighbors=26, p=2, weights=uniform ............................\n",
      "[CV]  n_neighbors=26, p=2, weights=uniform, score=0.584, total= 1.9min\n",
      "[CV] n_neighbors=26, p=2, weights=uniform ............................\n",
      "[CV]  n_neighbors=26, p=2, weights=uniform, score=0.576, total= 1.9min\n",
      "[CV] n_neighbors=26, p=2, weights=uniform ............................\n",
      "[CV]  n_neighbors=26, p=2, weights=uniform, score=0.570, total= 1.9min\n",
      "[CV] n_neighbors=26, p=2, weights=uniform ............................\n",
      "[CV]  n_neighbors=26, p=2, weights=uniform, score=0.579, total= 1.9min\n",
      "[CV] n_neighbors=26, p=2, weights=distance ...........................\n",
      "[CV]  n_neighbors=26, p=2, weights=distance, score=0.588, total= 1.9min\n",
      "[CV] n_neighbors=26, p=2, weights=distance ...........................\n",
      "[CV]  n_neighbors=26, p=2, weights=distance, score=0.586, total= 1.9min\n",
      "[CV] n_neighbors=26, p=2, weights=distance ...........................\n",
      "[CV]  n_neighbors=26, p=2, weights=distance, score=0.580, total= 1.9min\n",
      "[CV] n_neighbors=26, p=2, weights=distance ...........................\n",
      "[CV]  n_neighbors=26, p=2, weights=distance, score=0.574, total= 1.9min\n",
      "[CV] n_neighbors=26, p=2, weights=distance ...........................\n",
      "[CV]  n_neighbors=26, p=2, weights=distance, score=0.587, total= 1.9min\n",
      "[CV] n_neighbors=32, p=1, weights=uniform ............................\n",
      "[CV]  n_neighbors=32, p=1, weights=uniform, score=0.609, total= 1.9min\n",
      "[CV] n_neighbors=32, p=1, weights=uniform ............................\n",
      "[CV]  n_neighbors=32, p=1, weights=uniform, score=0.614, total= 1.9min\n",
      "[CV] n_neighbors=32, p=1, weights=uniform ............................\n",
      "[CV]  n_neighbors=32, p=1, weights=uniform, score=0.600, total= 1.9min\n",
      "[CV] n_neighbors=32, p=1, weights=uniform ............................\n",
      "[CV]  n_neighbors=32, p=1, weights=uniform, score=0.604, total= 1.9min\n",
      "[CV] n_neighbors=32, p=1, weights=uniform ............................\n",
      "[CV]  n_neighbors=32, p=1, weights=uniform, score=0.599, total= 1.9min\n",
      "[CV] n_neighbors=32, p=1, weights=distance ...........................\n",
      "[CV]  n_neighbors=32, p=1, weights=distance, score=0.614, total= 1.9min\n",
      "[CV] n_neighbors=32, p=1, weights=distance ...........................\n",
      "[CV]  n_neighbors=32, p=1, weights=distance, score=0.619, total= 1.9min\n",
      "[CV] n_neighbors=32, p=1, weights=distance ...........................\n",
      "[CV]  n_neighbors=32, p=1, weights=distance, score=0.605, total= 1.9min\n",
      "[CV] n_neighbors=32, p=1, weights=distance ...........................\n",
      "[CV]  n_neighbors=32, p=1, weights=distance, score=0.609, total= 1.9min\n",
      "[CV] n_neighbors=32, p=1, weights=distance ...........................\n",
      "[CV]  n_neighbors=32, p=1, weights=distance, score=0.604, total= 1.9min\n",
      "[CV] n_neighbors=32, p=2, weights=uniform ............................\n",
      "[CV]  n_neighbors=32, p=2, weights=uniform, score=0.579, total= 1.9min\n",
      "[CV] n_neighbors=32, p=2, weights=uniform ............................\n",
      "[CV]  n_neighbors=32, p=2, weights=uniform, score=0.579, total= 1.9min\n",
      "[CV] n_neighbors=32, p=2, weights=uniform ............................\n",
      "[CV]  n_neighbors=32, p=2, weights=uniform, score=0.572, total= 1.9min\n",
      "[CV] n_neighbors=32, p=2, weights=uniform ............................\n",
      "[CV]  n_neighbors=32, p=2, weights=uniform, score=0.565, total= 1.9min\n",
      "[CV] n_neighbors=32, p=2, weights=uniform ............................\n",
      "[CV]  n_neighbors=32, p=2, weights=uniform, score=0.571, total= 1.9min\n",
      "[CV] n_neighbors=32, p=2, weights=distance ...........................\n",
      "[CV]  n_neighbors=32, p=2, weights=distance, score=0.581, total= 1.9min\n",
      "[CV] n_neighbors=32, p=2, weights=distance ...........................\n",
      "[CV]  n_neighbors=32, p=2, weights=distance, score=0.581, total= 1.9min\n",
      "[CV] n_neighbors=32, p=2, weights=distance ...........................\n",
      "[CV]  n_neighbors=32, p=2, weights=distance, score=0.575, total= 1.9min\n",
      "[CV] n_neighbors=32, p=2, weights=distance ...........................\n",
      "[CV]  n_neighbors=32, p=2, weights=distance, score=0.573, total= 1.9min\n",
      "[CV] n_neighbors=32, p=2, weights=distance ...........................\n",
      "[CV]  n_neighbors=32, p=2, weights=distance, score=0.574, total= 1.9min\n",
      "[CV] n_neighbors=38, p=1, weights=uniform ............................\n",
      "[CV]  n_neighbors=38, p=1, weights=uniform, score=0.606, total= 1.9min\n",
      "[CV] n_neighbors=38, p=1, weights=uniform ............................\n",
      "[CV]  n_neighbors=38, p=1, weights=uniform, score=0.610, total= 1.9min\n",
      "[CV] n_neighbors=38, p=1, weights=uniform ............................\n",
      "[CV]  n_neighbors=38, p=1, weights=uniform, score=0.599, total= 1.9min\n",
      "[CV] n_neighbors=38, p=1, weights=uniform ............................\n",
      "[CV]  n_neighbors=38, p=1, weights=uniform, score=0.596, total= 1.9min\n",
      "[CV] n_neighbors=38, p=1, weights=uniform ............................\n",
      "[CV]  n_neighbors=38, p=1, weights=uniform, score=0.596, total= 1.9min\n",
      "[CV] n_neighbors=38, p=1, weights=distance ...........................\n",
      "[CV]  n_neighbors=38, p=1, weights=distance, score=0.611, total= 1.9min\n",
      "[CV] n_neighbors=38, p=1, weights=distance ...........................\n",
      "[CV]  n_neighbors=38, p=1, weights=distance, score=0.613, total= 1.9min\n",
      "[CV] n_neighbors=38, p=1, weights=distance ...........................\n",
      "[CV]  n_neighbors=38, p=1, weights=distance, score=0.603, total= 1.9min\n",
      "[CV] n_neighbors=38, p=1, weights=distance ...........................\n",
      "[CV]  n_neighbors=38, p=1, weights=distance, score=0.602, total= 1.9min\n",
      "[CV] n_neighbors=38, p=1, weights=distance ...........................\n",
      "[CV]  n_neighbors=38, p=1, weights=distance, score=0.601, total= 1.9min\n",
      "[CV] n_neighbors=38, p=2, weights=uniform ............................\n",
      "[CV]  n_neighbors=38, p=2, weights=uniform, score=0.574, total= 2.0min\n",
      "[CV] n_neighbors=38, p=2, weights=uniform ............................\n",
      "[CV]  n_neighbors=38, p=2, weights=uniform, score=0.576, total= 1.9min\n",
      "[CV] n_neighbors=38, p=2, weights=uniform ............................\n",
      "[CV]  n_neighbors=38, p=2, weights=uniform, score=0.568, total= 2.0min\n",
      "[CV] n_neighbors=38, p=2, weights=uniform ............................\n",
      "[CV]  n_neighbors=38, p=2, weights=uniform, score=0.564, total= 1.9min\n",
      "[CV] n_neighbors=38, p=2, weights=uniform ............................\n",
      "[CV]  n_neighbors=38, p=2, weights=uniform, score=0.568, total= 1.9min\n",
      "[CV] n_neighbors=38, p=2, weights=distance ...........................\n",
      "[CV]  n_neighbors=38, p=2, weights=distance, score=0.577, total= 2.0min\n",
      "[CV] n_neighbors=38, p=2, weights=distance ...........................\n",
      "[CV]  n_neighbors=38, p=2, weights=distance, score=0.579, total= 2.0min\n",
      "[CV] n_neighbors=38, p=2, weights=distance ...........................\n",
      "[CV]  n_neighbors=38, p=2, weights=distance, score=0.571, total= 1.9min\n",
      "[CV] n_neighbors=38, p=2, weights=distance ...........................\n",
      "[CV]  n_neighbors=38, p=2, weights=distance, score=0.567, total= 1.9min\n",
      "[CV] n_neighbors=38, p=2, weights=distance ...........................\n",
      "[CV]  n_neighbors=38, p=2, weights=distance, score=0.571, total= 1.9min\n",
      "accuracy = 0.5705 (7.61 seconds): {'n_neighbors': 2, 'p': 1, 'weights': 'uniform'} \n",
      "accuracy = 0.6154 (7.62 seconds): {'n_neighbors': 2, 'p': 1, 'weights': 'distance'} \n",
      "accuracy = 0.5518 (7.58 seconds): {'n_neighbors': 2, 'p': 2, 'weights': 'uniform'} \n",
      "accuracy = 0.5937 (7.6 seconds): {'n_neighbors': 2, 'p': 2, 'weights': 'distance'} \n",
      "accuracy = 0.6206 (7.59 seconds): {'n_neighbors': 8, 'p': 1, 'weights': 'uniform'} \n",
      "accuracy = 0.6334 (7.6 seconds): {'n_neighbors': 8, 'p': 1, 'weights': 'distance'} \n",
      "accuracy = 0.5957 (7.58 seconds): {'n_neighbors': 8, 'p': 2, 'weights': 'uniform'} \n",
      "accuracy = 0.6052 (7.61 seconds): {'n_neighbors': 8, 'p': 2, 'weights': 'distance'} \n",
      "accuracy = 0.6199 (7.58 seconds): {'n_neighbors': 14, 'p': 1, 'weights': 'uniform'} \n",
      "accuracy = 0.6284 (7.54 seconds): {'n_neighbors': 14, 'p': 1, 'weights': 'distance'} \n",
      "accuracy = 0.5887 (7.59 seconds): {'n_neighbors': 14, 'p': 2, 'weights': 'uniform'} \n",
      "accuracy = 0.5983 (7.57 seconds): {'n_neighbors': 14, 'p': 2, 'weights': 'distance'} \n",
      "accuracy = 0.6144 (7.58 seconds): {'n_neighbors': 20, 'p': 1, 'weights': 'uniform'} \n",
      "accuracy = 0.6213 (7.58 seconds): {'n_neighbors': 20, 'p': 1, 'weights': 'distance'} \n",
      "accuracy = 0.5821 (7.56 seconds): {'n_neighbors': 20, 'p': 2, 'weights': 'uniform'} \n",
      "accuracy = 0.589 (7.6 seconds): {'n_neighbors': 20, 'p': 2, 'weights': 'distance'} \n",
      "accuracy = 0.609 (7.67 seconds): {'n_neighbors': 26, 'p': 1, 'weights': 'uniform'} \n",
      "accuracy = 0.6146 (7.56 seconds): {'n_neighbors': 26, 'p': 1, 'weights': 'distance'} \n",
      "accuracy = 0.5788 (7.55 seconds): {'n_neighbors': 26, 'p': 2, 'weights': 'uniform'} \n",
      "accuracy = 0.5828 (7.56 seconds): {'n_neighbors': 26, 'p': 2, 'weights': 'distance'} \n",
      "accuracy = 0.6052 (7.57 seconds): {'n_neighbors': 32, 'p': 1, 'weights': 'uniform'} \n",
      "accuracy = 0.6101 (7.54 seconds): {'n_neighbors': 32, 'p': 1, 'weights': 'distance'} \n",
      "accuracy = 0.573 (7.61 seconds): {'n_neighbors': 32, 'p': 2, 'weights': 'uniform'} \n",
      "accuracy = 0.5768 (7.56 seconds): {'n_neighbors': 32, 'p': 2, 'weights': 'distance'} \n",
      "accuracy = 0.6012 (7.55 seconds): {'n_neighbors': 38, 'p': 1, 'weights': 'uniform'} \n",
      "accuracy = 0.606 (7.59 seconds): {'n_neighbors': 38, 'p': 1, 'weights': 'distance'} \n",
      "accuracy = 0.5698 (7.6 seconds): {'n_neighbors': 38, 'p': 2, 'weights': 'uniform'} \n",
      "accuracy = 0.5731 (7.59 seconds): {'n_neighbors': 38, 'p': 2, 'weights': 'distance'} \n",
      "\n",
      "Best model:\n",
      "{'params': {'n_neighbors': 8, 'p': 1, 'weights': 'distance'}, 'mean_metric': 0.63345, 'mean_fit_time': 7.596310472488403, 'mean_score_time': 107.76474776268006}\n",
      "Finished grid search at 19:07:05\n",
      "\n",
      "\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done 140 out of 140 | elapsed: 270.4min finished\n"
     ]
    }
   ],
   "source": [
    "clf = KNeighborsClassifier(n_jobs=4)\n",
    "\n",
    "gs = GridSearchCV(clf,\n",
    "                  param_grid=gs_hyperparameters,\n",
    "                  scoring='accuracy',\n",
    "                  cv=cv_folds,\n",
    "                  refit=False,\n",
    "                  verbose=5, \n",
    "                  n_jobs=1)\n",
    "\n",
    "print('Beginning grid search at {}...'.format(datetime.now().strftime('%H:%M:%S')))\n",
    "gs.fit(x_train_4class, y_train_4class)\n",
    "\n",
    "gs_results = []\n",
    "for i in range(len(gs.cv_results_['params'])):\n",
    "    result = {'params' : gs.cv_results_['params'][i], \n",
    "              'mean_metric' : gs.cv_results_['mean_test_score'][i], \n",
    "              'mean_fit_time': gs.cv_results_['mean_fit_time'][i],\n",
    "              'mean_score_time': gs.cv_results_['mean_score_time'][i]}\n",
    "    gs_results += [result]\n",
    "    print('accuracy = {} ({} seconds): {} '.format(round(result['mean_metric'], 4),\n",
    "                                                   round(result['mean_fit_time'], 2),\n",
    "                                                   result['params']), flush=True)\n",
    "\n",
    "\n",
    "print('\\nBest model:')\n",
    "print(max(gs_results, key=lambda item: item['mean_metric']))\n",
    "\n",
    "with open('knn_gs_results_{}.pkl'.format(datetime.now().strftime('%Y%m%d_%H%M%S')), 'wb') as f:\n",
    "    pickle.dump({'gs_results': gs_results, 'gs': gs}, f)\n",
    "    \n",
    "print('Finished grid search at {}\\n\\n\\n'.format(datetime.now().strftime('%H:%M:%S')))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Grid Search Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-12-13T00:07:05.376767Z",
     "iopub.status.busy": "2020-12-13T00:07:05.376418Z",
     "iopub.status.idle": "2020-12-13T00:07:05.381420Z",
     "shell.execute_reply": "2020-12-13T00:07:05.380573Z",
     "shell.execute_reply.started": "2020-12-13T00:07:05.376722Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Best model:\n",
      "{'params': {'n_neighbors': 8, 'p': 1, 'weights': 'distance'}, 'mean_metric': 0.63345, 'mean_fit_time': 7.596310472488403, 'mean_score_time': 107.76474776268006}\n"
     ]
    }
   ],
   "source": [
    "print('\\nBest model:')\n",
    "print(max(gs_results, key=lambda item: item['mean_metric']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-12-13T00:07:05.384147Z",
     "iopub.status.busy": "2020-12-13T00:07:05.383881Z",
     "iopub.status.idle": "2020-12-13T00:07:06.319547Z",
     "shell.execute_reply": "2020-12-13T00:07:06.318757Z",
     "shell.execute_reply.started": "2020-12-13T00:07:05.384122Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f6b6ffd0450>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# convergence plot\n",
    "accs = np.array([x['mean_metric'] for x in gs_results])\n",
    "cumul_accs = np.maximum.accumulate(accs)\n",
    "errs = 1-accs\n",
    "cumul_errs = 1-cumul_accs\n",
    "\n",
    "plt.close('all')\n",
    "plt.figure()\n",
    "plt.plot(errs, linewidth=0.5, label=\"observed\", marker='.', markersize=3)\n",
    "plt.plot(cumul_errs, 'k', linewidth=2, label=\"best so far\")\n",
    "plt.xlabel(\"Iteration\")\n",
    "plt.ylabel(\"Misclassification Error\\n(1 - Accuracy)\")\n",
    "plt.title(\"KNN: Error over iterations\\nGrid Search\")\n",
    "plt.xlim((0,28))\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-12-13T00:07:06.321505Z",
     "iopub.status.busy": "2020-12-13T00:07:06.321239Z",
     "iopub.status.idle": "2020-12-13T00:07:07.972520Z",
     "shell.execute_reply": "2020-12-13T00:07:07.971560Z",
     "shell.execute_reply.started": "2020-12-13T00:07:06.321478Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x900 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "accs = np.array([x['mean_metric'] for x in gs_results])\n",
    "cumul_accs = np.maximum.accumulate(accs)\n",
    "errs = 1-accs\n",
    "cumul_errs = 1-cumul_accs\n",
    "\n",
    "params = gs_hyperparameters.keys()\n",
    "plt.figure(figsize=(12, 4*len(params)), dpi=75)\n",
    "\n",
    "for i, param in enumerate(params):\n",
    "    plt.subplot(len(params), 2, 2*i+1)\n",
    "    param_vals = [x['params'][param] for x in gs_results]\n",
    "    if type(param_vals[0]) == bool:\n",
    "        param_vals = [str(x) for x in param_vals]\n",
    "    best_val = gs.best_params_[param]\n",
    "\n",
    "    plt.scatter(np.arange(1,len(param_vals)+1), param_vals, s=15, c=accs, cmap=\"Oranges\", label=\"Accuracy\")\n",
    "    plt.plot(np.arange(1,len(param_vals)+1), param_vals, 'k', linewidth=0.5)\n",
    "    plt.xlim(1, len(param_vals))\n",
    "    plt.hlines(best_val, 0, len(param_vals), 'r', 'dashed')\n",
    "\n",
    "    plt.ylabel(param, fontsize=14)\n",
    "    plt.xlabel(\"iteration\", fontsize=14)\n",
    "    cb = plt.colorbar()\n",
    "    cb.set_label(\"accuracy\")\n",
    "    plt.tick_params(labelsize=14)\n",
    "\n",
    "    \n",
    "    plt.subplot(len(params), 2, 2*i+2)\n",
    "    plt.hist(param_vals, bins=30)\n",
    "    plt.axvline(x=best_val, color='red', linestyle='--')\n",
    "    plt.xlabel(param, fontsize=14)\n",
    "    plt.ylabel('# of evaluations', fontsize=14)\n",
    "    plt.tick_params(labelsize=14)\n",
    "    \n",
    "plt.suptitle(\"KNN - Grid Search\\nHyperparameter Evaluations\", size=16)\n",
    "plt.tight_layout(rect=[0, 0.0, 1, 0.98])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random Search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-12-13T00:07:07.974059Z",
     "iopub.status.busy": "2020-12-13T00:07:07.973734Z",
     "iopub.status.idle": "2020-12-13T00:07:07.978065Z",
     "shell.execute_reply": "2020-12-13T00:07:07.977344Z",
     "shell.execute_reply.started": "2020-12-13T00:07:07.974030Z"
    }
   },
   "outputs": [],
   "source": [
    "rs_hyperparameters = {\n",
    "    'n_neighbors': range(2, 40),\n",
    "    'weights': ['uniform', 'distance'],\n",
    "    'p': [1, 2]\n",
    "}\n",
    "\n",
    "N_EVALS = 28  # same number as were evaluated in grid search above"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-12-13T00:07:07.979314Z",
     "iopub.status.busy": "2020-12-13T00:07:07.979032Z",
     "iopub.status.idle": "2020-12-13T04:36:21.839042Z",
     "shell.execute_reply": "2020-12-13T04:36:21.838159Z",
     "shell.execute_reply.started": "2020-12-13T00:07:07.979287Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "--------------------------------------\n",
      "Beginning random search at 19:07:08...\n",
      "Fitting 5 folds for each of 28 candidates, totalling 140 fits\n",
      "[CV] weights=uniform, p=1, n_neighbors=28 ............................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  weights=uniform, p=1, n_neighbors=28, score=0.614, total= 1.9min\n",
      "[CV] weights=uniform, p=1, n_neighbors=28 ............................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:  1.9min remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  weights=uniform, p=1, n_neighbors=28, score=0.617, total= 1.9min\n",
      "[CV] weights=uniform, p=1, n_neighbors=28 ............................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:  3.9min remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  weights=uniform, p=1, n_neighbors=28, score=0.603, total= 1.9min\n",
      "[CV] weights=uniform, p=1, n_neighbors=28 ............................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:  5.8min remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  weights=uniform, p=1, n_neighbors=28, score=0.602, total= 1.9min\n",
      "[CV] weights=uniform, p=1, n_neighbors=28 ............................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:  7.7min remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  weights=uniform, p=1, n_neighbors=28, score=0.599, total= 1.9min\n",
      "[CV] weights=distance, p=1, n_neighbors=29 ...........................\n",
      "[CV]  weights=distance, p=1, n_neighbors=29, score=0.618, total= 1.9min\n",
      "[CV] weights=distance, p=1, n_neighbors=29 ...........................\n",
      "[CV]  weights=distance, p=1, n_neighbors=29, score=0.622, total= 1.9min\n",
      "[CV] weights=distance, p=1, n_neighbors=29 ...........................\n",
      "[CV]  weights=distance, p=1, n_neighbors=29, score=0.607, total= 1.9min\n",
      "[CV] weights=distance, p=1, n_neighbors=29 ...........................\n",
      "[CV]  weights=distance, p=1, n_neighbors=29, score=0.608, total= 1.9min\n",
      "[CV] weights=distance, p=1, n_neighbors=29 ...........................\n",
      "[CV]  weights=distance, p=1, n_neighbors=29, score=0.606, total= 1.9min\n",
      "[CV] weights=distance, p=2, n_neighbors=32 ...........................\n",
      "[CV]  weights=distance, p=2, n_neighbors=32, score=0.581, total= 1.9min\n",
      "[CV] weights=distance, p=2, n_neighbors=32 ...........................\n",
      "[CV]  weights=distance, p=2, n_neighbors=32, score=0.581, total= 1.9min\n",
      "[CV] weights=distance, p=2, n_neighbors=32 ...........................\n",
      "[CV]  weights=distance, p=2, n_neighbors=32, score=0.575, total= 1.9min\n",
      "[CV] weights=distance, p=2, n_neighbors=32 ...........................\n",
      "[CV]  weights=distance, p=2, n_neighbors=32, score=0.573, total= 1.9min\n",
      "[CV] weights=distance, p=2, n_neighbors=32 ...........................\n",
      "[CV]  weights=distance, p=2, n_neighbors=32, score=0.574, total= 1.9min\n",
      "[CV] weights=distance, p=2, n_neighbors=31 ...........................\n",
      "[CV]  weights=distance, p=2, n_neighbors=31, score=0.581, total= 1.9min\n",
      "[CV] weights=distance, p=2, n_neighbors=31 ...........................\n",
      "[CV]  weights=distance, p=2, n_neighbors=31, score=0.581, total= 1.9min\n",
      "[CV] weights=distance, p=2, n_neighbors=31 ...........................\n",
      "[CV]  weights=distance, p=2, n_neighbors=31, score=0.575, total= 1.9min\n",
      "[CV] weights=distance, p=2, n_neighbors=31 ...........................\n",
      "[CV]  weights=distance, p=2, n_neighbors=31, score=0.573, total= 1.9min\n",
      "[CV] weights=distance, p=2, n_neighbors=31 ...........................\n",
      "[CV]  weights=distance, p=2, n_neighbors=31, score=0.577, total= 1.9min\n",
      "[CV] weights=uniform, p=1, n_neighbors=29 ............................\n",
      "[CV]  weights=uniform, p=1, n_neighbors=29, score=0.612, total= 1.9min\n",
      "[CV] weights=uniform, p=1, n_neighbors=29 ............................\n",
      "[CV]  weights=uniform, p=1, n_neighbors=29, score=0.615, total= 1.9min\n",
      "[CV] weights=uniform, p=1, n_neighbors=29 ............................\n",
      "[CV]  weights=uniform, p=1, n_neighbors=29, score=0.600, total= 1.9min\n",
      "[CV] weights=uniform, p=1, n_neighbors=29 ............................\n",
      "[CV]  weights=uniform, p=1, n_neighbors=29, score=0.600, total= 1.9min\n",
      "[CV] weights=uniform, p=1, n_neighbors=29 ............................\n",
      "[CV]  weights=uniform, p=1, n_neighbors=29, score=0.600, total= 1.9min\n",
      "[CV] weights=uniform, p=1, n_neighbors=10 ............................\n",
      "[CV]  weights=uniform, p=1, n_neighbors=10, score=0.627, total= 1.9min\n",
      "[CV] weights=uniform, p=1, n_neighbors=10 ............................\n",
      "[CV]  weights=uniform, p=1, n_neighbors=10, score=0.630, total= 1.9min\n",
      "[CV] weights=uniform, p=1, n_neighbors=10 ............................\n",
      "[CV]  weights=uniform, p=1, n_neighbors=10, score=0.613, total= 1.9min\n",
      "[CV] weights=uniform, p=1, n_neighbors=10 ............................\n",
      "[CV]  weights=uniform, p=1, n_neighbors=10, score=0.619, total= 1.9min\n",
      "[CV] weights=uniform, p=1, n_neighbors=10 ............................\n",
      "[CV]  weights=uniform, p=1, n_neighbors=10, score=0.618, total= 1.9min\n",
      "[CV] weights=distance, p=2, n_neighbors=39 ...........................\n",
      "[CV]  weights=distance, p=2, n_neighbors=39, score=0.576, total= 1.9min\n",
      "[CV] weights=distance, p=2, n_neighbors=39 ...........................\n",
      "[CV]  weights=distance, p=2, n_neighbors=39, score=0.579, total= 1.9min\n",
      "[CV] weights=distance, p=2, n_neighbors=39 ...........................\n",
      "[CV]  weights=distance, p=2, n_neighbors=39, score=0.571, total= 1.9min\n",
      "[CV] weights=distance, p=2, n_neighbors=39 ...........................\n",
      "[CV]  weights=distance, p=2, n_neighbors=39, score=0.569, total= 1.9min\n",
      "[CV] weights=distance, p=2, n_neighbors=39 ...........................\n",
      "[CV]  weights=distance, p=2, n_neighbors=39, score=0.569, total= 1.9min\n",
      "[CV] weights=distance, p=1, n_neighbors=37 ...........................\n",
      "[CV]  weights=distance, p=1, n_neighbors=37, score=0.614, total= 1.9min\n",
      "[CV] weights=distance, p=1, n_neighbors=37 ...........................\n",
      "[CV]  weights=distance, p=1, n_neighbors=37, score=0.615, total= 1.9min\n",
      "[CV] weights=distance, p=1, n_neighbors=37 ...........................\n",
      "[CV]  weights=distance, p=1, n_neighbors=37, score=0.603, total= 1.9min\n",
      "[CV] weights=distance, p=1, n_neighbors=37 ...........................\n",
      "[CV]  weights=distance, p=1, n_neighbors=37, score=0.604, total= 1.9min\n",
      "[CV] weights=distance, p=1, n_neighbors=37 ...........................\n",
      "[CV]  weights=distance, p=1, n_neighbors=37, score=0.601, total= 1.9min\n",
      "[CV] weights=uniform, p=2, n_neighbors=7 .............................\n",
      "[CV] . weights=uniform, p=2, n_neighbors=7, score=0.606, total= 1.9min\n",
      "[CV] weights=uniform, p=2, n_neighbors=7 .............................\n",
      "[CV] . weights=uniform, p=2, n_neighbors=7, score=0.604, total= 1.9min\n",
      "[CV] weights=uniform, p=2, n_neighbors=7 .............................\n",
      "[CV] . weights=uniform, p=2, n_neighbors=7, score=0.594, total= 1.9min\n",
      "[CV] weights=uniform, p=2, n_neighbors=7 .............................\n",
      "[CV] . weights=uniform, p=2, n_neighbors=7, score=0.592, total= 1.9min\n",
      "[CV] weights=uniform, p=2, n_neighbors=7 .............................\n",
      "[CV] . weights=uniform, p=2, n_neighbors=7, score=0.595, total= 1.9min\n",
      "[CV] weights=distance, p=2, n_neighbors=12 ...........................\n",
      "[CV]  weights=distance, p=2, n_neighbors=12, score=0.604, total= 1.9min\n",
      "[CV] weights=distance, p=2, n_neighbors=12 ...........................\n",
      "[CV]  weights=distance, p=2, n_neighbors=12, score=0.602, total= 1.9min\n",
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      "[CV]  weights=distance, p=2, n_neighbors=12, score=0.600, total= 1.9min\n",
      "[CV] weights=distance, p=2, n_neighbors=12 ...........................\n",
      "[CV]  weights=distance, p=2, n_neighbors=12, score=0.592, total= 1.9min\n",
      "[CV] weights=distance, p=2, n_neighbors=12 ...........................\n",
      "[CV]  weights=distance, p=2, n_neighbors=12, score=0.600, total= 1.9min\n",
      "[CV] weights=distance, p=1, n_neighbors=3 ............................\n",
      "[CV]  weights=distance, p=1, n_neighbors=3, score=0.632, total= 1.9min\n",
      "[CV] weights=distance, p=1, n_neighbors=3 ............................\n",
      "[CV]  weights=distance, p=1, n_neighbors=3, score=0.642, total= 1.9min\n",
      "[CV] weights=distance, p=1, n_neighbors=3 ............................\n",
      "[CV]  weights=distance, p=1, n_neighbors=3, score=0.620, total= 1.9min\n",
      "[CV] weights=distance, p=1, n_neighbors=3 ............................\n",
      "[CV]  weights=distance, p=1, n_neighbors=3, score=0.623, total= 1.9min\n",
      "[CV] weights=distance, p=1, n_neighbors=3 ............................\n",
      "[CV]  weights=distance, p=1, n_neighbors=3, score=0.621, total= 1.9min\n",
      "[CV] weights=distance, p=1, n_neighbors=14 ...........................\n",
      "[CV]  weights=distance, p=1, n_neighbors=14, score=0.639, total= 1.9min\n",
      "[CV] weights=distance, p=1, n_neighbors=14 ...........................\n",
      "[CV]  weights=distance, p=1, n_neighbors=14, score=0.634, total= 1.9min\n",
      "[CV] weights=distance, p=1, n_neighbors=14 ...........................\n",
      "[CV]  weights=distance, p=1, n_neighbors=14, score=0.623, total= 1.9min\n",
      "[CV] weights=distance, p=1, n_neighbors=14 ...........................\n",
      "[CV]  weights=distance, p=1, n_neighbors=14, score=0.624, total= 1.9min\n",
      "[CV] weights=distance, p=1, n_neighbors=14 ...........................\n",
      "[CV]  weights=distance, p=1, n_neighbors=14, score=0.622, total= 1.9min\n",
      "[CV] weights=uniform, p=2, n_neighbors=11 ............................\n",
      "[CV]  weights=uniform, p=2, n_neighbors=11, score=0.596, total= 1.9min\n",
      "[CV] weights=uniform, p=2, n_neighbors=11 ............................\n",
      "[CV]  weights=uniform, p=2, n_neighbors=11, score=0.598, total= 1.9min\n",
      "[CV] weights=uniform, p=2, n_neighbors=11 ............................\n",
      "[CV]  weights=uniform, p=2, n_neighbors=11, score=0.592, total= 1.9min\n",
      "[CV] weights=uniform, p=2, n_neighbors=11 ............................\n",
      "[CV]  weights=uniform, p=2, n_neighbors=11, score=0.585, total= 1.9min\n",
      "[CV] weights=uniform, p=2, n_neighbors=11 ............................\n",
      "[CV]  weights=uniform, p=2, n_neighbors=11, score=0.592, total= 1.9min\n",
      "[CV] weights=distance, p=1, n_neighbors=24 ...........................\n",
      "[CV]  weights=distance, p=1, n_neighbors=24, score=0.627, total= 1.9min\n",
      "[CV] weights=distance, p=1, n_neighbors=24 ...........................\n",
      "[CV]  weights=distance, p=1, n_neighbors=24, score=0.624, total= 1.9min\n",
      "[CV] weights=distance, p=1, n_neighbors=24 ...........................\n",
      "[CV]  weights=distance, p=1, n_neighbors=24, score=0.614, total= 1.9min\n",
      "[CV] weights=distance, p=1, n_neighbors=24 ...........................\n",
      "[CV]  weights=distance, p=1, n_neighbors=24, score=0.613, total= 1.9min\n",
      "[CV] weights=distance, p=1, n_neighbors=24 ...........................\n",
      "[CV]  weights=distance, p=1, n_neighbors=24, score=0.610, total= 1.9min\n",
      "[CV] weights=uniform, p=1, n_neighbors=32 ............................\n",
      "[CV]  weights=uniform, p=1, n_neighbors=32, score=0.609, total= 1.9min\n",
      "[CV] weights=uniform, p=1, n_neighbors=32 ............................\n",
      "[CV]  weights=uniform, p=1, n_neighbors=32, score=0.614, total= 1.9min\n",
      "[CV] weights=uniform, p=1, n_neighbors=32 ............................\n",
      "[CV]  weights=uniform, p=1, n_neighbors=32, score=0.600, total= 1.9min\n",
      "[CV] weights=uniform, p=1, n_neighbors=32 ............................\n",
      "[CV]  weights=uniform, p=1, n_neighbors=32, score=0.604, total= 1.9min\n",
      "[CV] weights=uniform, p=1, n_neighbors=32 ............................\n",
      "[CV]  weights=uniform, p=1, n_neighbors=32, score=0.599, total= 1.9min\n",
      "[CV] weights=uniform, p=2, n_neighbors=15 ............................\n",
      "[CV]  weights=uniform, p=2, n_neighbors=15, score=0.593, total= 1.9min\n",
      "[CV] weights=uniform, p=2, n_neighbors=15 ............................\n",
      "[CV]  weights=uniform, p=2, n_neighbors=15, score=0.591, total= 1.9min\n",
      "[CV] weights=uniform, p=2, n_neighbors=15 ............................\n",
      "[CV]  weights=uniform, p=2, n_neighbors=15, score=0.586, total= 1.9min\n",
      "[CV] weights=uniform, p=2, n_neighbors=15 ............................\n",
      "[CV]  weights=uniform, p=2, n_neighbors=15, score=0.578, total= 1.9min\n",
      "[CV] weights=uniform, p=2, n_neighbors=15 ............................\n",
      "[CV]  weights=uniform, p=2, n_neighbors=15, score=0.589, total= 1.9min\n",
      "[CV] weights=distance, p=2, n_neighbors=8 ............................\n",
      "[CV]  weights=distance, p=2, n_neighbors=8, score=0.608, total= 1.9min\n",
      "[CV] weights=distance, p=2, n_neighbors=8 ............................\n",
      "[CV]  weights=distance, p=2, n_neighbors=8, score=0.612, total= 1.9min\n",
      "[CV] weights=distance, p=2, n_neighbors=8 ............................\n",
      "[CV]  weights=distance, p=2, n_neighbors=8, score=0.603, total= 1.9min\n",
      "[CV] weights=distance, p=2, n_neighbors=8 ............................\n",
      "[CV]  weights=distance, p=2, n_neighbors=8, score=0.598, total= 1.9min\n",
      "[CV] weights=distance, p=2, n_neighbors=8 ............................\n",
      "[CV]  weights=distance, p=2, n_neighbors=8, score=0.605, total= 1.9min\n",
      "[CV] weights=distance, p=1, n_neighbors=5 ............................\n",
      "[CV]  weights=distance, p=1, n_neighbors=5, score=0.645, total= 1.9min\n",
      "[CV] weights=distance, p=1, n_neighbors=5 ............................\n",
      "[CV]  weights=distance, p=1, n_neighbors=5, score=0.652, total= 1.8min\n",
      "[CV] weights=distance, p=1, n_neighbors=5 ............................\n",
      "[CV]  weights=distance, p=1, n_neighbors=5, score=0.628, total= 1.9min\n",
      "[CV] weights=distance, p=1, n_neighbors=5 ............................\n",
      "[CV]  weights=distance, p=1, n_neighbors=5, score=0.631, total= 1.9min\n",
      "[CV] weights=distance, p=1, n_neighbors=5 ............................\n",
      "[CV]  weights=distance, p=1, n_neighbors=5, score=0.625, total= 1.9min\n",
      "[CV] weights=distance, p=2, n_neighbors=26 ...........................\n",
      "[CV]  weights=distance, p=2, n_neighbors=26, score=0.588, total= 1.9min\n",
      "[CV] weights=distance, p=2, n_neighbors=26 ...........................\n",
      "[CV]  weights=distance, p=2, n_neighbors=26, score=0.586, total= 1.9min\n",
      "[CV] weights=distance, p=2, n_neighbors=26 ...........................\n",
      "[CV]  weights=distance, p=2, n_neighbors=26, score=0.580, total= 1.9min\n",
      "[CV] weights=distance, p=2, n_neighbors=26 ...........................\n",
      "[CV]  weights=distance, p=2, n_neighbors=26, score=0.574, total= 1.9min\n",
      "[CV] weights=distance, p=2, n_neighbors=26 ...........................\n",
      "[CV]  weights=distance, p=2, n_neighbors=26, score=0.587, total= 1.9min\n",
      "[CV] weights=uniform, p=1, n_neighbors=19 ............................\n",
      "[CV]  weights=uniform, p=1, n_neighbors=19, score=0.623, total= 1.9min\n",
      "[CV] weights=uniform, p=1, n_neighbors=19 ............................\n",
      "[CV]  weights=uniform, p=1, n_neighbors=19, score=0.622, total= 1.9min\n",
      "[CV] weights=uniform, p=1, n_neighbors=19 ............................\n",
      "[CV]  weights=uniform, p=1, n_neighbors=19, score=0.611, total= 1.9min\n",
      "[CV] weights=uniform, p=1, n_neighbors=19 ............................\n",
      "[CV]  weights=uniform, p=1, n_neighbors=19, score=0.609, total= 1.9min\n",
      "[CV] weights=uniform, p=1, n_neighbors=19 ............................\n",
      "[CV]  weights=uniform, p=1, n_neighbors=19, score=0.612, total= 1.9min\n",
      "[CV] weights=uniform, p=1, n_neighbors=4 .............................\n",
      "[CV] . weights=uniform, p=1, n_neighbors=4, score=0.625, total= 1.9min\n",
      "[CV] weights=uniform, p=1, n_neighbors=4 .............................\n",
      "[CV] . weights=uniform, p=1, n_neighbors=4, score=0.628, total= 1.9min\n",
      "[CV] weights=uniform, p=1, n_neighbors=4 .............................\n",
      "[CV] . weights=uniform, p=1, n_neighbors=4, score=0.611, total= 1.9min\n",
      "[CV] weights=uniform, p=1, n_neighbors=4 .............................\n",
      "[CV] . weights=uniform, p=1, n_neighbors=4, score=0.601, total= 1.9min\n",
      "[CV] weights=uniform, p=1, n_neighbors=4 .............................\n",
      "[CV] . weights=uniform, p=1, n_neighbors=4, score=0.609, total= 1.9min\n",
      "[CV] weights=uniform, p=1, n_neighbors=14 ............................\n",
      "[CV]  weights=uniform, p=1, n_neighbors=14, score=0.631, total= 1.9min\n",
      "[CV] weights=uniform, p=1, n_neighbors=14 ............................\n",
      "[CV]  weights=uniform, p=1, n_neighbors=14, score=0.626, total= 1.9min\n",
      "[CV] weights=uniform, p=1, n_neighbors=14 ............................\n",
      "[CV]  weights=uniform, p=1, n_neighbors=14, score=0.613, total= 1.9min\n",
      "[CV] weights=uniform, p=1, n_neighbors=14 ............................\n",
      "[CV]  weights=uniform, p=1, n_neighbors=14, score=0.612, total= 1.9min\n",
      "[CV] weights=uniform, p=1, n_neighbors=14 ............................\n",
      "[CV]  weights=uniform, p=1, n_neighbors=14, score=0.617, total= 1.9min\n",
      "[CV] weights=uniform, p=1, n_neighbors=12 ............................\n",
      "[CV]  weights=uniform, p=1, n_neighbors=12, score=0.628, total= 1.9min\n",
      "[CV] weights=uniform, p=1, n_neighbors=12 ............................\n",
      "[CV]  weights=uniform, p=1, n_neighbors=12, score=0.630, total= 1.9min\n",
      "[CV] weights=uniform, p=1, n_neighbors=12 ............................\n",
      "[CV]  weights=uniform, p=1, n_neighbors=12, score=0.618, total= 1.9min\n",
      "[CV] weights=uniform, p=1, n_neighbors=12 ............................\n",
      "[CV]  weights=uniform, p=1, n_neighbors=12, score=0.619, total= 1.9min\n",
      "[CV] weights=uniform, p=1, n_neighbors=12 ............................\n",
      "[CV]  weights=uniform, p=1, n_neighbors=12, score=0.622, total= 1.9min\n",
      "[CV] weights=distance, p=1, n_neighbors=10 ...........................\n",
      "[CV]  weights=distance, p=1, n_neighbors=10, score=0.641, total= 1.9min\n",
      "[CV] weights=distance, p=1, n_neighbors=10 ...........................\n",
      "[CV]  weights=distance, p=1, n_neighbors=10, score=0.642, total= 1.9min\n",
      "[CV] weights=distance, p=1, n_neighbors=10 ...........................\n",
      "[CV]  weights=distance, p=1, n_neighbors=10, score=0.620, total= 1.9min\n",
      "[CV] weights=distance, p=1, n_neighbors=10 ...........................\n",
      "[CV]  weights=distance, p=1, n_neighbors=10, score=0.628, total= 1.9min\n",
      "[CV] weights=distance, p=1, n_neighbors=10 ...........................\n",
      "[CV]  weights=distance, p=1, n_neighbors=10, score=0.625, total= 1.9min\n",
      "[CV] weights=distance, p=1, n_neighbors=33 ...........................\n",
      "[CV]  weights=distance, p=1, n_neighbors=33, score=0.614, total= 1.9min\n",
      "[CV] weights=distance, p=1, n_neighbors=33 ...........................\n",
      "[CV]  weights=distance, p=1, n_neighbors=33, score=0.615, total= 1.9min\n",
      "[CV] weights=distance, p=1, n_neighbors=33 ...........................\n",
      "[CV]  weights=distance, p=1, n_neighbors=33, score=0.605, total= 1.9min\n",
      "[CV] weights=distance, p=1, n_neighbors=33 ...........................\n",
      "[CV]  weights=distance, p=1, n_neighbors=33, score=0.608, total= 1.9min\n",
      "[CV] weights=distance, p=1, n_neighbors=33 ...........................\n",
      "[CV]  weights=distance, p=1, n_neighbors=33, score=0.603, total= 1.9min\n",
      "[CV] weights=distance, p=1, n_neighbors=26 ...........................\n",
      "[CV]  weights=distance, p=1, n_neighbors=26, score=0.623, total= 1.9min\n",
      "[CV] weights=distance, p=1, n_neighbors=26 ...........................\n",
      "[CV]  weights=distance, p=1, n_neighbors=26, score=0.624, total= 1.9min\n",
      "[CV] weights=distance, p=1, n_neighbors=26 ...........................\n",
      "[CV]  weights=distance, p=1, n_neighbors=26, score=0.611, total= 1.9min\n",
      "[CV] weights=distance, p=1, n_neighbors=26 ...........................\n",
      "[CV]  weights=distance, p=1, n_neighbors=26, score=0.607, total= 1.9min\n",
      "[CV] weights=distance, p=1, n_neighbors=26 ...........................\n",
      "[CV]  weights=distance, p=1, n_neighbors=26, score=0.608, total= 1.9min\n",
      "[CV] weights=uniform, p=2, n_neighbors=2 .............................\n",
      "[CV] . weights=uniform, p=2, n_neighbors=2, score=0.559, total= 1.9min\n",
      "[CV] weights=uniform, p=2, n_neighbors=2 .............................\n",
      "[CV] . weights=uniform, p=2, n_neighbors=2, score=0.552, total= 1.9min\n",
      "[CV] weights=uniform, p=2, n_neighbors=2 .............................\n",
      "[CV] . weights=uniform, p=2, n_neighbors=2, score=0.559, total= 1.9min\n",
      "[CV] weights=uniform, p=2, n_neighbors=2 .............................\n",
      "[CV] . weights=uniform, p=2, n_neighbors=2, score=0.543, total= 1.9min\n",
      "[CV] weights=uniform, p=2, n_neighbors=2 .............................\n",
      "[CV] . weights=uniform, p=2, n_neighbors=2, score=0.545, total= 1.9min\n",
      "[CV] weights=distance, p=2, n_neighbors=34 ...........................\n",
      "[CV]  weights=distance, p=2, n_neighbors=34, score=0.579, total= 2.0min\n",
      "[CV] weights=distance, p=2, n_neighbors=34 ...........................\n",
      "[CV]  weights=distance, p=2, n_neighbors=34, score=0.584, total= 1.9min\n",
      "[CV] weights=distance, p=2, n_neighbors=34 ...........................\n",
      "[CV]  weights=distance, p=2, n_neighbors=34, score=0.572, total= 1.9min\n",
      "[CV] weights=distance, p=2, n_neighbors=34 ...........................\n",
      "[CV]  weights=distance, p=2, n_neighbors=34, score=0.572, total= 1.9min\n",
      "[CV] weights=distance, p=2, n_neighbors=34 ...........................\n",
      "[CV]  weights=distance, p=2, n_neighbors=34, score=0.572, total= 2.0min\n",
      "accuracy = 0.607 (7.55 seconds): {'weights': 'uniform', 'p': 1, 'n_neighbors': 28} \n",
      "accuracy = 0.612 (7.58 seconds): {'weights': 'distance', 'p': 1, 'n_neighbors': 29} \n",
      "accuracy = 0.5768 (7.55 seconds): {'weights': 'distance', 'p': 2, 'n_neighbors': 32} \n",
      "accuracy = 0.5772 (7.59 seconds): {'weights': 'distance', 'p': 2, 'n_neighbors': 31} \n",
      "accuracy = 0.6053 (7.56 seconds): {'weights': 'uniform', 'p': 1, 'n_neighbors': 29} \n",
      "accuracy = 0.6212 (7.55 seconds): {'weights': 'uniform', 'p': 1, 'n_neighbors': 10} \n",
      "accuracy = 0.5728 (7.58 seconds): {'weights': 'distance', 'p': 2, 'n_neighbors': 39} \n",
      "accuracy = 0.6075 (7.52 seconds): {'weights': 'distance', 'p': 1, 'n_neighbors': 37} \n",
      "accuracy = 0.598 (7.58 seconds): {'weights': 'uniform', 'p': 2, 'n_neighbors': 7} \n",
      "accuracy = 0.5992 (7.59 seconds): {'weights': 'distance', 'p': 2, 'n_neighbors': 12} \n",
      "accuracy = 0.6276 (7.57 seconds): {'weights': 'distance', 'p': 1, 'n_neighbors': 3} \n",
      "accuracy = 0.6284 (7.6 seconds): {'weights': 'distance', 'p': 1, 'n_neighbors': 14} \n",
      "accuracy = 0.5924 (7.66 seconds): {'weights': 'uniform', 'p': 2, 'n_neighbors': 11} \n",
      "accuracy = 0.6174 (7.56 seconds): {'weights': 'distance', 'p': 1, 'n_neighbors': 24} \n",
      "accuracy = 0.6052 (7.55 seconds): {'weights': 'uniform', 'p': 1, 'n_neighbors': 32} \n",
      "accuracy = 0.587 (7.52 seconds): {'weights': 'uniform', 'p': 2, 'n_neighbors': 15} \n",
      "accuracy = 0.6052 (7.44 seconds): {'weights': 'distance', 'p': 2, 'n_neighbors': 8} \n",
      "accuracy = 0.6363 (7.47 seconds): {'weights': 'distance', 'p': 1, 'n_neighbors': 5} \n",
      "accuracy = 0.5828 (7.5 seconds): {'weights': 'distance', 'p': 2, 'n_neighbors': 26} \n",
      "accuracy = 0.6154 (7.53 seconds): {'weights': 'uniform', 'p': 1, 'n_neighbors': 19} \n",
      "accuracy = 0.6148 (7.56 seconds): {'weights': 'uniform', 'p': 1, 'n_neighbors': 4} \n",
      "accuracy = 0.6199 (7.59 seconds): {'weights': 'uniform', 'p': 1, 'n_neighbors': 14} \n",
      "accuracy = 0.6232 (7.6 seconds): {'weights': 'uniform', 'p': 1, 'n_neighbors': 12} \n",
      "accuracy = 0.6311 (7.56 seconds): {'weights': 'distance', 'p': 1, 'n_neighbors': 10} \n",
      "accuracy = 0.6089 (7.58 seconds): {'weights': 'distance', 'p': 1, 'n_neighbors': 33} \n",
      "accuracy = 0.6146 (7.55 seconds): {'weights': 'distance', 'p': 1, 'n_neighbors': 26} \n",
      "accuracy = 0.5518 (7.62 seconds): {'weights': 'uniform', 'p': 2, 'n_neighbors': 2} \n",
      "accuracy = 0.5758 (7.59 seconds): {'weights': 'distance', 'p': 2, 'n_neighbors': 34} \n",
      "\n",
      "Best model:\n",
      "{'params': {'weights': 'distance', 'p': 1, 'n_neighbors': 5}, 'mean_metric': 0.6363, 'mean_fit_time': 7.469804334640503, 'mean_score_time': 105.36066751480102}\n",
      "Finished with random search at 23:36:21\n",
      "\n",
      "\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done 140 out of 140 | elapsed: 269.2min finished\n"
     ]
    }
   ],
   "source": [
    "clf = KNeighborsClassifier(n_jobs=4)\n",
    "\n",
    "rs = RandomizedSearchCV(clf,\n",
    "                        param_distributions=rs_hyperparameters,\n",
    "                        n_iter=N_EVALS,\n",
    "                        scoring='accuracy',\n",
    "                        cv=cv_folds,\n",
    "                        refit=False,\n",
    "                        verbose=5, \n",
    "                        n_jobs=1,\n",
    "                        random_state=682)\n",
    "\n",
    "\n",
    "print('\\n--------------------------------------\\nBeginning random search at {}...'.format(datetime.now().strftime('%H:%M:%S')))\n",
    "rs.fit(x_train_4class, y_train_4class)\n",
    "\n",
    "rs_results = []\n",
    "for i in range(len(rs.cv_results_['params'])):\n",
    "    result = {'params' : rs.cv_results_['params'][i], \n",
    "              'mean_metric' : rs.cv_results_['mean_test_score'][i], \n",
    "              'mean_fit_time': rs.cv_results_['mean_fit_time'][i],\n",
    "              'mean_score_time': rs.cv_results_['mean_score_time'][i]}\n",
    "    rs_results += [result]\n",
    "    print('accuracy = {} ({} seconds): {} '.format(round(result['mean_metric'], 4),\n",
    "                                                   round(result['mean_fit_time'], 2),\n",
    "                                                   result['params']), flush=True)\n",
    "\n",
    "\n",
    "print('\\nBest model:')\n",
    "print(max(rs_results, key=lambda item: item['mean_metric']))\n",
    "\n",
    "with open('knn_rs_results_{}.pkl'.format(datetime.now().strftime('%Y%m%d_%H%M%S')), 'wb') as f:\n",
    "    pickle.dump({'rs_results': rs_results, 'rs': rs}, f)\n",
    "    \n",
    "print('Finished with random search at {}\\n\\n\\n'.format(datetime.now().strftime('%H:%M:%S')))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Search Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-12-13T04:36:21.840330Z",
     "iopub.status.busy": "2020-12-13T04:36:21.840056Z",
     "iopub.status.idle": "2020-12-13T04:36:21.844796Z",
     "shell.execute_reply": "2020-12-13T04:36:21.844000Z",
     "shell.execute_reply.started": "2020-12-13T04:36:21.840293Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Best model:\n",
      "{'params': {'weights': 'distance', 'p': 1, 'n_neighbors': 5}, 'mean_metric': 0.6363, 'mean_fit_time': 7.469804334640503, 'mean_score_time': 105.36066751480102}\n"
     ]
    }
   ],
   "source": [
    "print('\\nBest model:')\n",
    "print(max(rs_results, key=lambda item: item['mean_metric']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-12-13T04:36:21.845950Z",
     "iopub.status.busy": "2020-12-13T04:36:21.845697Z",
     "iopub.status.idle": "2020-12-13T04:36:21.874649Z",
     "shell.execute_reply": "2020-12-13T04:36:21.873819Z",
     "shell.execute_reply.started": "2020-12-13T04:36:21.845924Z"
    }
   },
   "outputs": [],
   "source": [
    "def moving_average(a, n=3) :\n",
    "    ret = np.cumsum(a, dtype=float)\n",
    "    ret[n:] = ret[n:] - ret[:-n]\n",
    "    return ret[n - 1:] / n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-12-13T04:36:21.875903Z",
     "iopub.status.busy": "2020-12-13T04:36:21.875648Z",
     "iopub.status.idle": "2020-12-13T04:36:22.094007Z",
     "shell.execute_reply": "2020-12-13T04:36:22.093176Z",
     "shell.execute_reply.started": "2020-12-13T04:36:21.875877Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f6b6e914750>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# convergence plot, RS\n",
    "accs = np.array([x['mean_metric'] for x in rs_results])\n",
    "cumul_accs = np.maximum.accumulate(accs)\n",
    "errs = 1-accs\n",
    "cumul_errs = 1-cumul_accs\n",
    "\n",
    "WINDOW_SIZE = 10\n",
    "move_avg_err = 1 - moving_average(accs, WINDOW_SIZE)\n",
    "move_avg_err = [*([None]*(WINDOW_SIZE - 1)), *list(move_avg_err)]\n",
    "\n",
    "plt.close('all')\n",
    "plt.figure()\n",
    "plt.plot(errs, linewidth=0.5, label=\"observed\", marker='.', markersize=3)\n",
    "plt.plot(move_avg_err, linewidth=0.5, label=\"10-iter moving average\", marker='.', markersize=3)\n",
    "plt.plot(cumul_errs, 'k', linewidth=2, label=\"best so far\")\n",
    "plt.xlabel(\"Iteration\")\n",
    "plt.ylabel(\"Misclassification Error\\n(1 - Accuracy)\")\n",
    "plt.title(\"KNN: Error over iterations\\nRandom Search\")\n",
    "plt.xlim((0,28))\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-12-13T04:36:22.095360Z",
     "iopub.status.busy": "2020-12-13T04:36:22.095041Z",
     "iopub.status.idle": "2020-12-13T04:36:23.674230Z",
     "shell.execute_reply": "2020-12-13T04:36:23.673424Z",
     "shell.execute_reply.started": "2020-12-13T04:36:22.095288Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x900 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "accs = np.array([x['mean_metric'] for x in rs_results])\n",
    "cumul_accs = np.maximum.accumulate(accs)\n",
    "errs = 1-accs\n",
    "cumul_errs = 1-cumul_accs\n",
    "\n",
    "\n",
    "params = rs_hyperparameters.keys()\n",
    "plt.figure(figsize=(12, 4*len(params)), dpi=75)\n",
    "\n",
    "for i, param in enumerate(params):\n",
    "    plt.subplot(len(params), 2, 2*i+1)\n",
    "    param_vals = [x['params'][param] for x in rs_results]\n",
    "    if type(param_vals[0]) == bool:\n",
    "        param_vals = [str(x) for x in param_vals]\n",
    "    best_val = rs.best_params_[param]\n",
    "\n",
    "    plt.scatter(np.arange(1,len(param_vals)+1), param_vals, s=15, c=accs, cmap=\"Oranges\", label=\"Accuracy\")\n",
    "    plt.plot(np.arange(1,len(param_vals)+1), param_vals, 'k', linewidth=0.5)\n",
    "    plt.xlim(1, len(param_vals))\n",
    "    plt.hlines(best_val, 0, len(param_vals), 'r', 'dashed')\n",
    "\n",
    "    plt.ylabel(param, fontsize=14)\n",
    "    plt.xlabel(\"iteration\", fontsize=14)\n",
    "    cb = plt.colorbar()\n",
    "    cb.set_label(\"accuracy\")\n",
    "    plt.tick_params(labelsize=14)\n",
    "\n",
    "    \n",
    "    plt.subplot(len(params), 2, 2*i+2)\n",
    "    plt.hist(param_vals, bins=30)\n",
    "    plt.axvline(x=best_val, color='red', linestyle='--')\n",
    "    plt.xlabel(param, fontsize=14)\n",
    "    plt.ylabel('# of evaluations', fontsize=14)\n",
    "    plt.tick_params(labelsize=14)\n",
    "    \n",
    "plt.suptitle(\"KNN - Random Search\\nHyperparameter Evaluations\", size=16)\n",
    "plt.tight_layout(rect=[0, 0.0, 1, 0.98])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bayesian Search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-12-13T04:36:23.675562Z",
     "iopub.status.busy": "2020-12-13T04:36:23.675288Z",
     "iopub.status.idle": "2020-12-13T04:36:23.682691Z",
     "shell.execute_reply": "2020-12-13T04:36:23.681859Z",
     "shell.execute_reply.started": "2020-12-13T04:36:23.675515Z"
    }
   },
   "outputs": [],
   "source": [
    "bs_hyperparameters = {\n",
    "    'n_neighbors': Integer(2, 40),\n",
    "    'weights': Categorical(['uniform', 'distance']),\n",
    "    'p': Integer(1, 2)\n",
    "}\n",
    "\n",
    "N_EVALS = 28  # same number as were evaluated in grid search above"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-12-13T04:36:23.683981Z",
     "iopub.status.busy": "2020-12-13T04:36:23.683715Z",
     "iopub.status.idle": "2020-12-13T04:36:23.692049Z",
     "shell.execute_reply": "2020-12-13T04:36:23.691326Z",
     "shell.execute_reply.started": "2020-12-13T04:36:23.683954Z"
    }
   },
   "outputs": [],
   "source": [
    "def cb_print(result):\n",
    "    ## to be called by BayesSearchCV fit() to print out each iteration\n",
    "    \n",
    "    # save progress at each iteration in case of error\n",
    "    skopt.dump(result, 'tmp_bayes_result_{}.pkl'.format(datetime.now().strftime(\"%Y%m%d\")))\n",
    "    \n",
    "    def stringify_params(params):\n",
    "        # to take the list of hyperparameter values and get a dictionary back out\n",
    "        params = point_asdict(bs_hyperparameters, params)\n",
    "        return ', '.join([f'{k}={v}' if type(v) != str else f'{k}=\"{v}\"' \\\n",
    "                          for k, v in params.items()])\n",
    "    \n",
    "    score = -(result.func_vals[-1])\n",
    "    params = stringify_params(result.x_iters[-1])\n",
    "\n",
    "    # argmin because the values are negative during search\n",
    "    best_score_pos = np.argmin(result.func_vals)\n",
    "    \n",
    "    num_tried = len(result.func_vals)\n",
    "    N = result.specs['args']['n_initial_points']\n",
    "\n",
    "    the_time = datetime.now().strftime(\"%H:%M:%S\")\n",
    "    if best_score_pos == (num_tried - 1):\n",
    "        print(f'***New best! [{the_time}] (Iter {num_tried}) Accuracy = {score} ({params})\\n', flush=True)\n",
    "    else:\n",
    "        print(f'[{the_time}] (Iter {num_tried}) Accuracy = {score} ({params})', flush=True)\n",
    "        best_params = stringify_params(result.x_iters[best_score_pos])\n",
    "        print('    Best accuracy = {} ({})\\n'.format(-result.func_vals[best_score_pos], best_params), flush=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-12-13T14:00:48.809781Z",
     "iopub.status.busy": "2020-12-13T14:00:48.809420Z",
     "iopub.status.idle": "2020-12-13T18:37:40.029382Z",
     "shell.execute_reply": "2020-12-13T18:37:40.028484Z",
     "shell.execute_reply.started": "2020-12-13T14:00:48.809750Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "--------------------------------------\n",
      "Beginning Bayes search at 09:00:48...\n",
      "***New best! [09:14:33] (Iter 1) Accuracy = 0.6012 (n_neighbors=11, p=2, weights=\"distance\")\n",
      "\n",
      "***New best! [09:25:15] (Iter 2) Accuracy = 0.61195 (n_neighbors=29, p=1, weights=\"distance\")\n",
      "\n",
      "[09:34:53] (Iter 3) Accuracy = 0.57785 (n_neighbors=30, p=2, weights=\"distance\")\n",
      "    Best accuracy = 0.61195 (n_neighbors=29, p=1, weights=\"distance\")\n",
      "\n",
      "[09:44:25] (Iter 4) Accuracy = 0.6075 (n_neighbors=37, p=1, weights=\"distance\")\n",
      "    Best accuracy = 0.61195 (n_neighbors=29, p=1, weights=\"distance\")\n",
      "\n",
      "***New best! [09:54:07] (Iter 5) Accuracy = 0.6221 (n_neighbors=18, p=1, weights=\"distance\")\n",
      "\n",
      "[10:03:56] (Iter 6) Accuracy = 0.57985 (n_neighbors=22, p=2, weights=\"uniform\")\n",
      "    Best accuracy = 0.6221 (n_neighbors=18, p=1, weights=\"distance\")\n",
      "\n",
      "[10:13:41] (Iter 7) Accuracy = 0.603 (n_neighbors=10, p=2, weights=\"distance\")\n",
      "    Best accuracy = 0.6221 (n_neighbors=18, p=1, weights=\"distance\")\n",
      "\n",
      "***New best! [10:23:24] (Iter 8) Accuracy = 0.62495 (n_neighbors=7, p=1, weights=\"uniform\")\n",
      "\n",
      "[10:33:15] (Iter 9) Accuracy = 0.57495 (n_neighbors=28, p=2, weights=\"uniform\")\n",
      "    Best accuracy = 0.62495 (n_neighbors=7, p=1, weights=\"uniform\")\n",
      "\n",
      "[10:43:04] (Iter 10) Accuracy = 0.57785 (n_neighbors=30, p=2, weights=\"distance\")\n",
      "    Best accuracy = 0.62495 (n_neighbors=7, p=1, weights=\"uniform\")\n",
      "\n",
      "[10:52:49] (Iter 11) Accuracy = 0.61535 (n_neighbors=2, p=1, weights=\"distance\")\n",
      "    Best accuracy = 0.62495 (n_neighbors=7, p=1, weights=\"uniform\")\n",
      "\n",
      "[11:02:26] (Iter 12) Accuracy = 0.6173 (n_neighbors=17, p=1, weights=\"uniform\")\n",
      "    Best accuracy = 0.62495 (n_neighbors=7, p=1, weights=\"uniform\")\n",
      "\n",
      "[11:12:10] (Iter 13) Accuracy = 0.5518 (n_neighbors=2, p=2, weights=\"uniform\")\n",
      "    Best accuracy = 0.62495 (n_neighbors=7, p=1, weights=\"uniform\")\n",
      "\n",
      "[11:21:54] (Iter 14) Accuracy = 0.59845 (n_neighbors=40, p=1, weights=\"uniform\")\n",
      "    Best accuracy = 0.62495 (n_neighbors=7, p=1, weights=\"uniform\")\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/skopt/optimizer/optimizer.py:449: UserWarning: The objective has been evaluated at this point before.\n",
      "  warnings.warn(\"The objective has been evaluated \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[11:31:35] (Iter 15) Accuracy = 0.62495 (n_neighbors=7, p=1, weights=\"uniform\")\n",
      "    Best accuracy = 0.62495 (n_neighbors=7, p=1, weights=\"uniform\")\n",
      "\n",
      "[11:41:19] (Iter 16) Accuracy = 0.5705 (n_neighbors=2, p=1, weights=\"uniform\")\n",
      "    Best accuracy = 0.62495 (n_neighbors=7, p=1, weights=\"uniform\")\n",
      "\n",
      "[11:51:00] (Iter 17) Accuracy = 0.6212 (n_neighbors=10, p=1, weights=\"uniform\")\n",
      "    Best accuracy = 0.62495 (n_neighbors=7, p=1, weights=\"uniform\")\n",
      "\n",
      "[12:00:44] (Iter 18) Accuracy = 0.6192 (n_neighbors=22, p=1, weights=\"distance\")\n",
      "    Best accuracy = 0.62495 (n_neighbors=7, p=1, weights=\"uniform\")\n",
      "\n",
      "***New best! [12:10:26] (Iter 19) Accuracy = 0.62875 (n_neighbors=13, p=1, weights=\"distance\")\n",
      "\n",
      "***New best! [12:20:06] (Iter 20) Accuracy = 0.63345 (n_neighbors=8, p=1, weights=\"distance\")\n",
      "\n",
      "[12:29:46] (Iter 21) Accuracy = 0.61185 (n_neighbors=24, p=1, weights=\"uniform\")\n",
      "    Best accuracy = 0.63345 (n_neighbors=8, p=1, weights=\"distance\")\n",
      "\n",
      "[12:39:19] (Iter 22) Accuracy = 0.5937 (n_neighbors=2, p=2, weights=\"distance\")\n",
      "    Best accuracy = 0.63345 (n_neighbors=8, p=1, weights=\"distance\")\n",
      "\n",
      "[12:49:05] (Iter 23) Accuracy = 0.5731 (n_neighbors=40, p=2, weights=\"distance\")\n",
      "    Best accuracy = 0.63345 (n_neighbors=8, p=1, weights=\"distance\")\n",
      "\n",
      "[12:58:45] (Iter 24) Accuracy = 0.6032 (n_neighbors=33, p=1, weights=\"uniform\")\n",
      "    Best accuracy = 0.63345 (n_neighbors=8, p=1, weights=\"distance\")\n",
      "\n",
      "***New best! [13:08:23] (Iter 25) Accuracy = 0.63735 (n_neighbors=6, p=1, weights=\"distance\")\n",
      "\n",
      "[13:18:13] (Iter 26) Accuracy = 0.5712 (n_neighbors=37, p=2, weights=\"uniform\")\n",
      "    Best accuracy = 0.63735 (n_neighbors=6, p=1, weights=\"distance\")\n",
      "\n",
      "[13:27:59] (Iter 27) Accuracy = 0.6089 (n_neighbors=33, p=1, weights=\"distance\")\n",
      "    Best accuracy = 0.63735 (n_neighbors=6, p=1, weights=\"distance\")\n",
      "\n",
      "[13:37:39] (Iter 28) Accuracy = 0.6151 (n_neighbors=25, p=1, weights=\"distance\")\n",
      "    Best accuracy = 0.63735 (n_neighbors=6, p=1, weights=\"distance\")\n",
      "\n",
      "Finished with Bayes search at 13:37:40\n",
      "\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>param_n_neighbors</th>\n",
       "      <th>param_p</th>\n",
       "      <th>param_weights</th>\n",
       "      <th>mean_test_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>distance</td>\n",
       "      <td>0.60120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>distance</td>\n",
       "      <td>0.61195</td>\n",
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       "      <th>2</th>\n",
       "      <td>30</td>\n",
       "      <td>2</td>\n",
       "      <td>distance</td>\n",
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       "      <th>4</th>\n",
       "      <td>18</td>\n",
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       "      <td>distance</td>\n",
       "      <td>0.62210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>22</td>\n",
       "      <td>2</td>\n",
       "      <td>uniform</td>\n",
       "      <td>0.57985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>distance</td>\n",
       "      <td>0.60300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>uniform</td>\n",
       "      <td>0.62495</td>\n",
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       "      <th>8</th>\n",
       "      <td>28</td>\n",
       "      <td>2</td>\n",
       "      <td>uniform</td>\n",
       "      <td>0.57495</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>30</td>\n",
       "      <td>2</td>\n",
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       "      <td>0.57785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>distance</td>\n",
       "      <td>0.61535</td>\n",
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       "      <th>11</th>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>uniform</td>\n",
       "      <td>0.61730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>uniform</td>\n",
       "      <td>0.55180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>uniform</td>\n",
       "      <td>0.59845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>uniform</td>\n",
       "      <td>0.62495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>uniform</td>\n",
       "      <td>0.57050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>uniform</td>\n",
       "      <td>0.62120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>distance</td>\n",
       "      <td>0.61920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>distance</td>\n",
       "      <td>0.62875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>distance</td>\n",
       "      <td>0.63345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>uniform</td>\n",
       "      <td>0.61185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>distance</td>\n",
       "      <td>0.59370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>40</td>\n",
       "      <td>2</td>\n",
       "      <td>distance</td>\n",
       "      <td>0.57310</td>\n",
       "    </tr>\n",
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       "      <th>23</th>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>uniform</td>\n",
       "      <td>0.60320</td>\n",
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       "      <th>24</th>\n",
       "      <td>6</td>\n",
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       "      <td>2</td>\n",
       "      <td>uniform</td>\n",
       "      <td>0.57120</td>\n",
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       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>distance</td>\n",
       "      <td>0.60890</td>\n",
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       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
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       "      <td>0.61510</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    param_n_neighbors  param_p param_weights  mean_test_score\n",
       "0                  11        2      distance          0.60120\n",
       "1                  29        1      distance          0.61195\n",
       "2                  30        2      distance          0.57785\n",
       "3                  37        1      distance          0.60750\n",
       "4                  18        1      distance          0.62210\n",
       "5                  22        2       uniform          0.57985\n",
       "6                  10        2      distance          0.60300\n",
       "7                   7        1       uniform          0.62495\n",
       "8                  28        2       uniform          0.57495\n",
       "9                  30        2      distance          0.57785\n",
       "10                  2        1      distance          0.61535\n",
       "11                 17        1       uniform          0.61730\n",
       "12                  2        2       uniform          0.55180\n",
       "13                 40        1       uniform          0.59845\n",
       "14                  7        1       uniform          0.62495\n",
       "15                  2        1       uniform          0.57050\n",
       "16                 10        1       uniform          0.62120\n",
       "17                 22        1      distance          0.61920\n",
       "18                 13        1      distance          0.62875\n",
       "19                  8        1      distance          0.63345\n",
       "20                 24        1       uniform          0.61185\n",
       "21                  2        2      distance          0.59370\n",
       "22                 40        2      distance          0.57310\n",
       "23                 33        1       uniform          0.60320\n",
       "24                  6        1      distance          0.63735\n",
       "25                 37        2       uniform          0.57120\n",
       "26                 33        1      distance          0.60890\n",
       "27                 25        1      distance          0.61510"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = KNeighborsClassifier(n_jobs=4)\n",
    "\n",
    "print('\\n--------------------------------------\\nBeginning Bayes search at {}...'.format(datetime.now().strftime('%H:%M:%S')))\n",
    "\n",
    "bayes = BayesSearchCV(\n",
    "    estimator = clf,\n",
    "    search_spaces = bs_hyperparameters,\n",
    "    scoring = 'accuracy',\n",
    "    cv = cv_folds,\n",
    "    n_jobs = 1,\n",
    "    n_iter = N_EVALS,\n",
    "    refit = False,\n",
    "    random_state = 682\n",
    ")\n",
    "\n",
    "early_stopper = DeltaYStopper(0.001)  # stop when the top 5 results are all within 0.1% accuracy of the best\n",
    "timercb = TimerCallback()\n",
    "bayes_result = bayes.fit(x_train_4class, y_train_4class, callback=[timercb, cb_print, early_stopper])\n",
    "\n",
    "# export both the results AND the fitted object\n",
    "the_time = datetime.now().strftime(\"%Y%m%d_%H%M%S\")\n",
    "skopt.dump(bayes_result, 'knn_bayes_result_{}.pkl'.format(the_time))\n",
    "\n",
    "with open(f'knn_bayes_timercb_{the_time}.pkl', 'wb') as f:\n",
    "    pickle.dump(timercb, f)\n",
    "\n",
    "print('Finished with Bayes search at {}\\n\\n\\n'.format(datetime.now().strftime('%H:%M:%S')))\n",
    "\n",
    "scores = bayes_result.cv_results_['mean_test_score']\n",
    "param_keys = [x for x in bayes_result.cv_results_.keys() if 'param_' in x]\n",
    "tmp = [bayes_result.cv_results_[x] for x in param_keys] + [scores]\n",
    "tmp = list(map(list, zip(*tmp)))\n",
    "tmp = [dict(zip(param_keys + ['mean_test_score'], x)) for x in tmp]\n",
    "pd.DataFrame(tmp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bayesian Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-12-14T00:06:32.937951Z",
     "iopub.status.busy": "2020-12-14T00:06:32.937415Z",
     "iopub.status.idle": "2020-12-14T00:06:32.946240Z",
     "shell.execute_reply": "2020-12-14T00:06:32.944778Z",
     "shell.execute_reply.started": "2020-12-14T00:06:32.937892Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OrderedDict([('n_neighbors', 6), ('p', 1), ('weights', 'distance')])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bayes_result.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-12-14T00:06:34.629904Z",
     "iopub.status.busy": "2020-12-14T00:06:34.629299Z",
     "iopub.status.idle": "2020-12-14T00:06:34.637618Z",
     "shell.execute_reply": "2020-12-14T00:06:34.636322Z",
     "shell.execute_reply.started": "2020-12-14T00:06:34.629832Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.63735"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bayes_result.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-12-14T00:06:35.258022Z",
     "iopub.status.busy": "2020-12-14T00:06:35.257504Z",
     "iopub.status.idle": "2020-12-14T00:06:35.264586Z",
     "shell.execute_reply": "2020-12-14T00:06:35.263404Z",
     "shell.execute_reply.started": "2020-12-14T00:06:35.257974Z"
    }
   },
   "outputs": [],
   "source": [
    "def moving_average(a, n=3) :\n",
    "    ret = np.cumsum(a, dtype=float)\n",
    "    ret[n:] = ret[n:] - ret[:-n]\n",
    "    return ret[n - 1:] / n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-12-14T00:06:36.955948Z",
     "iopub.status.busy": "2020-12-14T00:06:36.955455Z",
     "iopub.status.idle": "2020-12-14T00:06:37.289065Z",
     "shell.execute_reply": "2020-12-14T00:06:37.287814Z",
     "shell.execute_reply.started": "2020-12-14T00:06:36.955899Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f6b4a439410>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# convergence plot\n",
    "accs = np.array(bayes_result.cv_results_['mean_test_score'])\n",
    "cumul_accs = np.maximum.accumulate(accs)\n",
    "errs = 1-accs\n",
    "cumul_errs = 1-cumul_accs\n",
    "\n",
    "WINDOW_SIZE = 10\n",
    "move_avg_err = 1 - moving_average(accs, WINDOW_SIZE)\n",
    "move_avg_err = [*([None]*(WINDOW_SIZE - 1)), *list(move_avg_err)]\n",
    "\n",
    "plt.close('all')\n",
    "plt.figure()\n",
    "plt.plot(errs, linewidth=0.5, label=\"observed\", marker='.', markersize=3)\n",
    "plt.plot(move_avg_err, linewidth=0.5, label=\"10-iter moving average\", marker='.', markersize=3)\n",
    "plt.plot(cumul_errs, 'k', linewidth=2, label=\"best so far\")\n",
    "plt.xlabel(\"Iteration\")\n",
    "plt.ylabel(\"Misclassification Error\\n(1 - Accuracy)\")\n",
    "plt.title(\"KNN: Error over iterations\\nBayesian Hyperparameter Optimization\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-12-14T00:06:37.717912Z",
     "iopub.status.busy": "2020-12-14T00:06:37.717416Z",
     "iopub.status.idle": "2020-12-14T00:06:38.063727Z",
     "shell.execute_reply": "2020-12-14T00:06:38.062340Z",
     "shell.execute_reply.started": "2020-12-14T00:06:37.717868Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'KNN Runtime\\nModel vs. Bayesian Search Contribution')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#### runtime plot: model vs. bayesian\n",
    "\n",
    "plt.close('all')\n",
    "model_times = np.array(bayes_result.cv_results_['mean_fit_time'])*5 + \\\n",
    "              np.array(bayes_result.cv_results_['mean_score_time'])*5 \n",
    "total_times = np.array(timercb.iter_time)\n",
    "bayes_times = total_times - model_times\n",
    "\n",
    "cumul_totaltime = total_times.cumsum()\n",
    "cumul_modeltime = model_times.cumsum()\n",
    "cumul_bayestime = bayes_times.cumsum()\n",
    "\n",
    "bayes_frac = cumul_bayestime[-1]/cumul_totaltime[-1]\n",
    "model_frac = cumul_modeltime[-1]/cumul_totaltime[-1]\n",
    "\n",
    "plt.stackplot(range(0, len(model_times) + 1), [0, *cumul_modeltime], [0, *cumul_bayestime], \n",
    "              labels=['Model: Train + Score', 'Bayes Update + Proposal'])\n",
    "plt.legend(loc='upper left')\n",
    "plt.xlim((0, len(model_times)))\n",
    "plt.xlabel('Iteration')\n",
    "plt.ylabel('Cumulative Runtime (s)')\n",
    "plt.annotate('Cumulatively:\\nModel: {}%\\nBayes: {}%'.format(round(model_frac * 100, 1), round(bayes_frac * 100, 1)), \n",
    "             (0.02*len(model_times), 0.7*cumul_totaltime[-1]))\n",
    "plt.title('KNN Runtime\\nModel vs. Bayesian Search Contribution')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-12-14T00:06:42.941863Z",
     "iopub.status.busy": "2020-12-14T00:06:42.941363Z",
     "iopub.status.idle": "2020-12-14T00:06:45.747017Z",
     "shell.execute_reply": "2020-12-14T00:06:45.745727Z",
     "shell.execute_reply.started": "2020-12-14T00:06:42.941818Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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l4rgfuvumROwbDGwEft/HdS0APgGujdgf/vs6M2LfHZF/q9HvYy91hNy/gdOBzsjXDjwaq8yIa3VpxON73NczNsa/jRagPOrv+LGo40509+/tPt7TffylVP6N22abbbbl+mZdN40xpndnADNjbK9EHqSq3cCfgWNFZByAiEwCjgJuUtXoRUsfVNWNEec3AY8As0UkT0QG4LQOPgS0ikh+eMP5gDwA2DuqzAd6eR3/ior3NZwWxYPD+0RkmIj8VURW4nyw73CPAdgpRpnb1CcipSJytTjdPjvdcppxkr1YZTwa9Xih+/OxGPsju38ei5N4PRJ1bd7DaW07KEZd0Y7FSQLfiyrjKZxEIrqM51W1Po5ywz4l9t/O5RHH/AunhfHMiH1fB4qA28M73Nh+KSILRKQd57q2A9sT+7omxe0S+T8RqcVJgjuAO3ESyylJFnsI8JyqrozafwcwEGfylkj/i3r8vvsz/P4vAeqBq92utFOTjMsYY3Kadd00xpjeLVTVedE7RWQDThfISLfhdOv7HvBLnG5lbe7+aGt62FcIlLpbPk6Lzw97iG1I1OPPejiut/qqYHNXyKdxugheDnyA09oSAt4AimOcH6u+u3ES1MuBt4BGnKTp8R7KqIt63N7L/gERj4cDFRHHR4u+NrEMBybjJDPxlNHb9Y1lY6y/nUiqWici/wPOEJGLVbULJ+l7U1U/ijj0Wpy/p6txupvW43TPvYXY1zVh7hcUL+N0vf0xUI3TEjcLuDGFeqqIfe1WRzwfqTbqcXiW0mIAVd0gIgfidCm9EhgsIp8Bfweu0G27GBtjTL9kiZ4xxnjE/QD6D+AsEfk9ztiru3XLGLhII3rY147TAtaB06JyF86H7FiWRYfQS3g91bfE/X0aznisM1X1H+EDRGRyL2VGT+RRjtNK9mtVvSpifxHOmDIvfY6TEBzVw/NNcZbRxpaxZ7Gej9Tb9U3F7TjdEw8XkRU4rX7nRB1zGnCnqv4ycqc4E+U09FH+RpwWwmjRieyXcVpeT1DVcEsuIjK9j/L7UoszVjPaKPdn9HXuk6p+AJwsIgLsipMc/wrn/byql1ONMabfsETPGGO89SecySrux2lx+nMPx50gIj8Pd98UkUHAccDLbqtOq4i8gDNByfuq2lPLVbxOJaKrpYjsi9MV7hZ3VziJiV7j7ewE6lCcsX3RZZyF0/XPS4/ijNPLU9W5fRy7iditUY/itLzWqmp00pxJT+OM2fwmzsQ7G3HGtUVSoq6riBwDjGZLst6TamCYO/HJWvfcQpyJfaLrILIeN5H6Towye7qmsTwHHC8io1R1dcT+M4BWnBbjpLhdoucDPxFnkfg9ki3LGGNyjSV6xhjjIVX9RESexJlt8xVVnd/DoV3AMyJyLU73yF/gzDB4ScQxP8YZC/iyiPwF5wP7IJzuhsep6iEJhDZDRG4B/oPT5fQ3OMnFTe7zH+OMKbvK/XBfh5N4Hh5vBaraKCJzgJ+LyOduvAcC36bvVqdE3YuTvD4uIn8E3sRpBR2DM+7wYVV9yD023PpzEs4i5hvdFqHrcSZemSMi1+GMBQvhTEBzBPCHOJLI3hSLSPQ4SmDrtebUmUnyTpwZThtxxm9uiDrlUeBMEfnYjXNP4OfEtzD5fThdiu8Vkd/hdIH9Edsm38/gtCjfIyLXuMedgzM5TLQPcL6sOAd4G2cm0Z66qf4ap6X3BRG5DOdv61ScJRouiPFaeyUix+J8mfJfnPdTgBNwvlh5JpGyjDEml1miZ4wx3rsPJ9HrqTUP97kBOC2Aw4CPgGNU9dXwAaq6QET2AC7GWQ5hGE7CtJgt0+7H69s4syfei9ON7wXgx6pa59bVISLH4SwTcTPOZB/P4ixtsCKBek5xy7gG5x7zKk6yGD25Skrc5OiLOMnw6cD/c2OuwRnD9kHE4ZfgdB38O06ivBwYr6otInIAzuyb3wUm4HT9W4Hz2qtTDHMiznIV2xCRAt16Mfvb3dcwlIhJWCL8GCeR/X844zffwUlurugrCFVdJiJfwhnPdj/OeLlr3bouiTjuYxH5ilvmgzhdLu92j30iqtg/4ixFcSVQjpNsRS85Ei53kduCfCVbxvotBL6pqnf0FX8Mi3H+HVyA0/2zHWdc4Vbdjo0xpr8LT/dtjDHGIyLyAM6MmOOjJ4YQkfE4Y+t+rqq/z0AsZ+IkDjP7mhjEGGOMMbnDWvSMMcYD7oQje+DMUHg88FOb/c8YY4wxfrFEzxhjvDESeA1njNXNOIuaG2OMMcb4wrpuGmOMMcYYY0yOCfkdgDHGGGOMMcYYb1miZ4wxxhhjjDE5xhI9Y4wxxhhjjMkxlugZY4wxxhhjTI6xRM8YY4wxxhhjcowlesYYY4wxxhiTYyzRM8YYY4wxxpgcY4meMcYYY4wxxuQYS/SMMcYYY4wxJsdYomeMMcYYY4wxOcYSPWOMMcYYY4zJMZboGWOMMcYYY0yOsUTPGGOMMcYYY3KMJXrGGGOMMcYYk2Ms0TPGGGOMMcaYHGOJnjHGGGOMMcbkGEv0jDHGGGOMMSbHWKJnjDHGGGOMMTnGEj1jjDHGGGOMyTGW6BljjDHGGGNMjrFEzxhjjDHGGGNyTL7fAfRnZWVlOmbMGL/DMMb0EwsXLmxS1bJUyxk/ULSlK75jP2/nJVU9KNU6TfrkxL1o2TLn54QJ/sZhjDEp8upeDZbo+WrMmDEsWLDA7zCMMf2EiNR4UU5LF/xgQny3j0sXdQ7zok6TPjlxL3rwQefnCSf4G4cxxqTIq3s1WKJnjDEmQQKI+B2FMREswTPGmG1YomeMMSZhNsDbBEptrfOzqsrfOIwxJkAs0TPGGJMwa9EzgXLMMc7PN97wNw5jjAkQS/SMMcYkLGSJnjHGGBNo1vsmTiJyqIh0iUh11P7JIvKYiDSLSIOI/EtEhvoUpjHGpJ3g3Dzi2YwxxhjjD7sPx0FERgH/AJ6O2l8CPAcUAQcCxwDTgIdFrGOTSZ52tNH5/A103PcTOuf9G1X1O6R+Q7Wb7vUL6K5+ie61H6Ddca4j0J+I03Uzns1kXk9fTBpjjOlfrOtmH0QkD7gH+CNQAuwU8fQpwEhgD1WtdY8/HZgPHAw8n9loTS5QVTr/eQ66+kPobEeXvAoNn5F/2I/9Dq1f0FVvQv0y0C5oWo221SLjD/I7rMCxHC6Yor6Y3KmPw40xxuQwa9Hr25VAC/D7GM/tB8wNJ3kAqvo+UAMckJnwTM5p/hytmQ+d7Syp3QSdG+l+536/o+o/6peCdrGo+jMn2WusQbs7/Y4qcKxFL3iivpic63M4mTV7trMZY4zZzFr0eiEixwCnArurqsbojTkS+CzGqWuAUTHKOxc4N/x4xIgR3gVrckd+EbhdNa977XNuPG40FAzwOah+RPJAu/nJNf/kwet+zICiIhD7Tiya5XCBFPnF5CU9HeT1vWj8hY/FfWz1VcekVFePrrkmPeUaY0wWs08vPRCRMcDtwGmqur6nw3opYptBVap6o6pODW+DBw/2IlSTY6S4jNA+34D8IlY1d0NeIflHXeh3WP3HqBkgIZpbN/HKe0tgxHTEEr2tCM6sm/FsJjMivpj8hvYxqNfuRcYY0z9Yi17PZgBDgWcjWvJCgIhIJ/BdYDUwKca5I4jd0mdMXPIP/wkdE/dlzT9PIf+79xIaNtnvkPqNUOUktHgwM/Z6hpc/3cQRw3b2O6RAsiQuOCK+mPxaL19M5rbTT3d+3nWXv3EYY0yAWKLXs+eAXaL2fR/4EnAksArIA04RkUpVrQMQkWnAGODlDMZqclDrkB1p2KSW5PlAiisZOHgkmzptbF5PLM8LlD6/mFTV2/wKLiMWL/Y7AmOMCRxL9Hqgqk3Ah5H7RGQd0KGqH7qP7wYuAu4VkQuBAcBNwBvAC5mN2OSapqYmmpqa/A6j31JVRo8eTU1NDWPGjPE7nEBxum7akh8BEs8Xk8YYY/oZG3iSAlVtAQ4FOnBa8J4AFgBf7GuMhDF9aWxspLi42NbQ80FHRwf5+fkcddRRPPXUU36HE0gS52bST1WbVPXDyA3Y/MWkqtb7HaMxxpjMs0QvAap6qaqOj9q3RFWPUdUSVS1X1VP67RgJ46mmpiZGjhxJS0uL36H0O01NTZSVlTFlyhQWLVrkdziB5GWiJyIHi8gCEVkiIreJyDa9TUSkVETuFJFPRORjETnb3b+PiLznbh+KyPe9eH3GGGNMtrNEz5iAampqYuzYsWzYsMHvUPqdpqYmBg0ahIiQl5dHp43V24ZX6+i5a7/dCpyoqpOBUuD0GIdeC3yoqlNwFgJ/yN0/H5ihqtOBvYFfiMj4VF9ftov1xWROKyx0NmOMMZtZomdMQDU1NTFmzBgaGhr8DqXfCSd6AHvttRdz5/avtafj4WGL3kygRlU/ch/fCpywVV0ig4BjcZI91LHO/b1VVcOZeDHOJFnWa7S/mTPH2YwxxmxmiZ4xAdXY2MiYMWOsRc8HkYneIYccwnPPPedzRMEiAnlxbsBgt1tmeDs3qrgxwMqIxyvcfZEmAmuBP4vIOyLycGSrnYhMF5EP3HN/r6rLPH7JxhhjTNaxRM+YgLKum/5pbGykrKwMgLKyMpv9NIYEum7WRy7Orao3xigucsahWK1xBcB04GFV3QN4BNi8XICqvqequwATgK+KyA4evUyTLW680dmMMcZsZomeMQFliZ5/Ilv0AIYOHcr69TbHUiQPu26uBMZFPB4D1MQ4pk5Vn3Af3w3sEV2Qqq7BmQH5uPiqNjnjrrtssXRjjIliiZ4xAdXS0sKoUaNsjJ4PohO9I444gmeeecbHiIInJPFtcZgHjBaRqe7jbwEPRh6gqmuBD0VkhrvrcOAjABGZJCIF7u+DgCOIWgPVGGOM6Y8s0euHOha/Tuv/rmbjy3ehXTabYFB1d3dTUVFhLXo+iE70dtttN+bPn5+RurW7C637FF23AG2ry0idiYq3NS+ePE9Vu4DvAPeLyBKgFbhLRGaIyOMRh54D3CAi7wPnA2e5+w8C3hOR+cCrwD2q+mRqr9AYY4zJftusVWRy26Y3H6T51nOguxPyC2mf9zCDzvsPEs886CbjbHyYPyLH6AGICKpKd3c3oVD6vh9TVbT6Rdi4AbQbPl8I4/ZDSkekrc5keflfhqo+D0yN2j0P+ELEMQuAfWKceyvOTJ3GGGOMiWAtev1M639/A52bqGvtgPY2Oj6eQ/e6pX6HZWIQEUKhEN3d3X6H0u+0trYycODArfbtvvvuvPfee+mtuK0ONm5Auzv5vN5J9nTdR32f5wMvF0w3xhhjjPcs0etv1Jnc7nsvtdGtCojTcmCM2Up0K/fhhx/O008/nZG62za288Vzr0ZV+z7YJ14tmG6MJ266ydmMMcZsZoleP1N83AU0U8CHdV183llE/vZ7ERo+2e+wTAxB/pCf62Jd+yFDhlBbW5veiosHQ1EZDc1tlA4cwDOvf4AMje7RGAwJrKNnTPrtsYezGWOM2cwSvX5mwL4n8+6Mn/LlA2eyYf9zKDvvARufZ0ycysrK0jo5jkgImXAwGwrHcMZpX+eRt1dDEMfnJbAZkxHvvONsxhhjNrNErx96c2UTX//F1Xw+aiaSX+B3OKYHloD7p6drf9hhh/H888+nt+5QHhsop2r8rhxy+Bd48cUX01pfsjxcXsGY1H3/+85mjDFmM0v0+qGOjg622247Vq9e7XcoxmSVmTNnMnfu3LTXU19fT0VFBccffzwPPvhg3yf4wFr0jDHGmGCzRK+fWbJkCZMnT2bUqFGW6AWYjc/zV0/XPz8/n66urrS/Pw0NDVRUVBAKhdhvv/2YM2dOWutLWJytedaiZ4wxxvjHEr1+5sknn+TII4+kqKiITZs2+R2O6UHk9P62xEKw7LTTTnz88cdpraOhoYHBgwcDcOKJJ3L//fentb5ECc7NI57NGGOMMf6w+3A/s3z5csaPH+93GKYPTU1NDBo0CIBBgwbZoukZ1tv4yCOPPJInn3wyrfWHW/QA8vLymDVrFq+//npa60yULa9gjDHGBJslev3Ipk2bKCoq8jsME4fIRK+8vJyGhgZ/A+pHuru7e030Ro8enfZuzy0tLRQXF29+fPLJJ3Pvvfemtc5E2fIKJlBOP93ZjDHGbJbvdwAmc1555RUOOOAAv8MwcYhO9NI5pb/ZWktLCyUlJb0eU1xcvFX32nSITDbz8/OZPn06b731FjNnzkxbnfESICQ2jtQEyLnn+h2BMcYEjrXo9SMvvfQSs2fP3vy4sLDQxukFVGNjoyV6PolMsnty0EEHZXyClNNOO41//etfGa2zNzZGzxhjjAk2uw/3I21tbVt1Bxs1ahRr1qzxMSLTk6amJsrKygCoqKiwRC+DGhsbN1/7nuy33368/PLLaYshVtfRgoICpk2bxrvvvpu2ehNhY/RMoMye7WzGGGM2s0Svn6ipqWH06NFb7Rs1ahSrVq3yKSLTGxuj5594WvSKiopob29PWww9Ld9wxhlncOedd6at3njZrJsmcNrbnc0YY8xmdh/uJ5566imOOuqorfZlYlIJkxwbo+efeBI9gPHjx7Ns2bIMRLRFYWEhO+ywAx988EFG643FWvSMMcaYYLNEr59YtGgRO+yww1b7rEUvuGyMnn/iTfSOOuoonnrqqQxEtLUzzzyTO+64I+P1RrMWPWOMMSbY7D7cD3R2dpKXl7fNuJ9hw4axbt06n6IyvYlMNkpLS2lubvY5ov4jnjF6AJMmTeLTTz/1vH5V7XV5hwEDBjBhwgQWLlzoed3xEoG8UHybMcYYY/xht+F+4M0332SvvfbaZn8oFOpxLJDxV1dXFwUFBUDvi3cb78XbogfOBClej9Vrbm7us/5vf/vb3HrrrZ7WmygvW/RE5GARWSAiS0TkNhHZZukfESkVkTtF5BMR+VhEznb3nyoi80XkfRF5S0QOSv3Vmayz/fbOZowxZjNL9PqB5557jkMOOcTvMIzJCokkevvuuy+vvfaap/XX19dTUVHR6zHFxcWMHTuWxYsXe1p3IrwaoyciecCtwImqOhkoBWKtfH0t8KGqTgF2Ah5y9y8DDlbVXYEzgXtFxO5t/c1ddzmbMcaYzexm2A/E2xXNBIe1tPqnqamJ0tLSuI496KCDePHFFz2tv6Ghoc9ED+Css87i73//u6d1J8LDFr2ZQI2qfuQ+vhU4IfIAERkEHIuT7KGOde7vr6lqnXvoAmAATrJojDHG9GuW6OW49evXM2TIEL/DMCZrdHV1kZ+/Tc/BmArf/y/1T/yJ5p9NYOO/L0S7u1KuP95Eb2BRAWVLX+LNEytY/70xtL1xf8p1x0uAkMS3AYPdbpnh7dyo4sYAKyMer3D3RZoIrAX+LCLviMjDIjI+RminAB+paqMXr9NkkQsucDZjjDGbWaKX45555hmOOOKIHp8fNGgQjY32mShobFyef+K99l3L36X9oUsYXtTFmsaNdM67n45X/pFy/Q0NDQwePLjP41r+93tOGfgp//y4DW1rpOnv59C5+pOU64+XxLkB9ao6NWK7MUZxkU3Ysd6AAmA68LCq7gE8Aty2VTwiewBX4HTfNP3NnDnOZowxZjNL9HLc/Pnz2W233Xp83tbSMyY53as+glCIA8cW8fLKTdCxka5lb6Vcbjxj9AA6Fr9BqbRTWgDr27qR/AI6V36Ycv3xSqBFry8rgXERj8cANTGOqVPVJ9zHdwN7hJ8UkSnAf4CTVdW/gYvGGGNMgFiil8O6u7sBZ3bNnthaesFkY/T8E++1D42YAqqMKs1jbWs3FAwgb8wuKdcfb9fNgvHToWAAB47M5/W1XWhXB/mjdujzPC8IkCfxbXGYB4wWkanu428BD0YeoKprgQ9FZIa763DgIwARGQM8BnxPVeem/uqMMcaY3GCJXg6bP38+06dP7/UYa9HLDnl5eXR2dvodhomQN3EWhUf8hMEDC6jf2E3e1EMpOOg7KZcbb6JXcvz/o3DH/dmlMsSHDVD69d+SP3bnlOuPi0BINK6tL6raBXwHuF9ElgCtwF0iMkNEHo849BzgBhF5HzgfOMvdfwkwDPidiLznbuM9fLXGGGNMVopvxgGTlZ5++mm+/e1v93qMtegFU/Q4sfLychobG6msrPQpov4jkfGRhYedS8HBZxP65S8pPvMaT+pvb2+nqKioz+OksJiKnz1IeXsbRRdfwsBDz+rzHC95OYpUVZ8Hpkbtngd8IeKYBcA+Mc79Dk6iaIwxxpgIlujlsM8//7zPGTfLysrYsGFDhiIy8Whvb9+8WHpYeXk5DQ0NlugFkOTlI3ne/VeaaLddKSymqmoItbW1VFVVeRZHr3US9/g7YzLjscf8jsAYYwLHum7mqHjXzhMRm+ExYGIt2F1eXm4JuenRAQccwMsvv5zROhOYddOY9KuqcjZjjDGbWaKXo55//nkOPfRQv8MwSWhsbNwm0auoqLBELwOydRKcPffck3nz5mW0Tg9n3TQmdQ8+6GzGGGM2s0QvR82dO5dZs2b5HYZJQlNT0zatsdailxkbN25kwIABvsaQTAt7YWEhHR0daYimZ6E4N9M7ESkSkaKIx2NF5McicpifcWWda65xNmOMMZvZfTgHqSpdXV3k58c3bkhENi/FYPzXU9fNhoYGfwLqR2Jd+3gEoSUwk38jAuSF4ttMnx4GvgsgIqXAG8ClwJMi0vtsWsYYY0wv7DacgxYtWsSOO+4Y9/FDhw7l888/T2NEJhE2Rs8/ySZ6hYWFbNq0yZMYkk0a999/f1555RVPYoiHjdHzzB7AS+7vXwaagOE4yd+PfYrJGGNMDrBELwc9+eSTHHnkkXEfb0ssBIuN0fNPrG6z8aiqqqKuri7l+ru6ugiFkvtvedasWbz55pspxxCv8EROfW2mT2VAvfv7YcB/VbUdeBaY6FtUxhhjsp4lejlo9erVjB49Ou7jbdH0YImVbAwYMIC2tjafIuo/YiXZ8fAq0WtsbKS8vDypcwcMGOBZq2KfBCTOzfRpFTBdREI4id7z7v5yIENvqDHGmFxk6+jlmLa2toQnkxg1ahQfffRRmiIyiYrVfdBaRjKjqakpqUSrsrKS2tralOtvaGhg8ODBSZ9fWlqadPfThNnfpFduBf6Fk/C1Ay+4+/cCPvYrqKxz8cV+R2CMMYFjLXo9EJGvisibIlInIhtFZImIXCEihRHHTBaRx0SkWUQaRORfIjLUz7jnzJnDgQcemNA5o0aNsha9AMnYB3WzjWSvfVVVlSeJXn19PRUVFUmfv++++/Laa6+lHEc8rEXPG6p6JXAm8DfgAFUNT5/aDfw+3nLiuWfltGOOcTZjjDGbWaLXszrgGuAAYEfg5ziD438PICIlwHNAEXAgcAwwDXhYfGh+0fpldL99O3Pu+xP7TihJ6NyioiLa29vTFFmwaH013W/fTvdbf6d71Tt+hxNTMq2yxhupJHpedN1saGhIKdHbZ599eP3111OOoy+CjdHzkqo+qKrXqeqqiH23qepDCRTT6z0r5332mbMZY4zZzLpu9kBVn4/aVS0is3HGUACcAowE9lDVWgAROR2YDxzMlnEW6Y+15XP0o/+CdtG+sYWiz95AB1UgQ+OfebM/0NZadMFD0N3l7KiegxYODOR1sg/I/mhsbExqMhYvu26OGzcu6fMHDhyYsbGc9jfqHREpA2YBI9j6C1hV1bviKSOOe1ZuO/545+cbb/gbhzHGBIglenESkZ2Ao4Gn3V37AXPDSR6Aqr4vIjU436hmLNGjcRUK/Of5+UwaPQS6O9HaTwOZwPhqQw0gfLh0DR2dXew+ZXQgr5N9gPZPsi16Xq1h19DQwG677ZZSGcXFxbS2tjJw4MCU4+mRgOTZ36kXRORw4D6gIsbTCsSV6MUoN/qeZYwxpp+xRK8PItIMFACFwM3AT9ynRgKx+omsAUb1UNa5wLnhxyNGjEg5PlXlsZfe4on/PMgX99uJEw/eFSQPBiQ3c19OKxrEgmVr+N09LzBrx3HsvsO4QF6nICy+3V+1t7dTVFSU8HmhUMiT9y3VMXqwpfvmoYcemnI8vbHvIzxzLfA48IvIrpvJ6uWeFXmM5/civ727ogGA4y98rM9jq6+ysXzGmP7Bxuj1bTowA/gWcDzwK3d/bx9zYn7iU9UbVXVqeEtldj1V5amnnuKHP/whobJR/Ok3F3L4rB2RvHwoGYKMnZVQeQUFBZmbmt0nH6/dxN+eXshN53+VuuaNMLAq4etkTE+8aIltaGhIenmFsMxMyBLf+DxrnY7LJOBSL5I813Ri37M28/JeZIwxJrisRa8PqrrE/fUDEekGbhORa4DVODfoaCOI3dKXlI0fvkj70ncpGDuV4t2PRFV5/vnneeihhzjiiCO44YYbNn+Y0o37QVcnDKzEWZIpfqNGjWLNmjVst912XoUeKIsXL+amm27i2lvvJ7+rhU1Pr0N2Py3h6+SnwsJCNm3alFSLk8kOXV1d5Oen9t/yoEGDaG5u9iiiXlgO55UPgdHAkr4OjEdP9yxVbfGifGOMMdnDEr3ECU63mFeBU0SkUlXrAERkGjAGeNmLihofuZ7Ge3+NqiKhEPMnfYknaks4+OCD+dOf/kQotHWSIgMqkq5r1KhRrFq1KicTvU8//ZTrr7+e6667joKCAiioQAoHZlWSB844sA0bNjBs2DC/QzFp4lULWFFRERs3bkzr7K3WWueZHwDXichPgHdUtdPj8sP3rJz2yvjpfodgjDGBY4leD0TkYmAusBTnRjkLuBp4WFUbRORu4CLgXhG5EBgA3AS8wZYFb5OmqjTedxnasZH5dd08sLyLGUvv4Y8vNZJX4P2ySKNHj2bp0qWel+u36upq/vCHP3DddddRWLjlugV5HFxPH6At0TPxmjVrFm+++SazZ89OS/ler5EnIgcDN+KMK5sDfDc64RGRUpz/Y/fGWWPuOlW9WUTGA3cCewL3qOpZ3kWWEeFpIl+Hbf/9q2pePIX0dc/yKNbA+sPs0/0OwRhjAscSvZ4VAzcAY4FOoBq4DvgTgKq2iMihwB9xWvA6gceAH6tHWYS6ywA8tKKLS6bnkxcKEUrTl+ijRo3ilVdeSU/hPlmxYgVXX3011113XVZ1d+zpzyec6JngUtWUWrq8+gJi//3356abbkpbogfeteiJSB5wK3Ccqn4kIv8GTgdujzr0WuBDVT3DXat0qLu/EbgQ2BVnbFq2+RY9jOtOUK/3LGOMMf2PJXo9UNVfAr/s45glOAule05EKD3qHFqe/hsD8jrIKxpIyb4nIvnet+YBDBs2jHXr1qWlbD/U1NRw5ZVXct1118XsvhbUbmddXV3bdMkNq6iosEQvwAYOHEhrayslJSV+h5KRvxXx7lunmUCNqn7kPr4Vpzvj5kRPRAYBxwLjwFlcDljn/l4HvCYiU7wKKJNU9Q6PyunznpXL/vzw1QD84Eu/8DkSY4wJDkv0Aqzi9N8io3ZkQP1NDD7ze5Qccmba6gqFQnR3d6et/HRTVejaBHlFfPbZZ1x++eVcd911FBcX+x1aQpqbm3tcx628vJzly5dnOCITr6qqKmprawOR6IEzk257e/tWXZa95OGXJWOAlRGPV7j7Ik0E1gJ/FpFZ7vE/VtVqr4Lwm7u4+S44rXsfqKonY737i9EbcueLSmOM8UrOJnoishvQGf6WWESOAc4CPgJ+raodfsYXDxGhaYdDmXRMK6WHfcvvcAJLW+vQRY9BZxtr6tu45J55XHfDX/pcMDrVbnbp0NuC3dZ1M9gqKyupq6tj3LhxSZfh5d/jjBkzmDdvHvvuu69nZUZKINTBIrIg4vGNqnpj1DGRXRdjlVyAs2zAL1X1eyJyFnAbcEjcUQSUiFQCDwKzgQ04r79MRF4GTlDVWj/jM8YYk72ya9rBxNwMTAMQkTHAf3AmTPkm8Bsf40rIqlWrGDMm+sttE0mXPAWdbayra+SSv9zP77+9f5+tKhmbgj5Bluj5p7Ozk7y8uOa9iCncopesjo6OlJdWiHTAAQfw8stpahQStszI0tcG9ZFrtsVI8lbidsl0jQFqYhxTp6pPuI/vBvZIwyvzw7XAEGB3VR2sqhU4r60K+IOfgRljjMluuZzo7QC86/5+AjBPVY8GzgBO9C2qBNXU1Fii15dNTsJ21R1P8rsffZVBRQLdvc9QHm59CZrGxkZL9HzS1NREWVlZ0udXVVWl9DfV0NBARUVF0ud7HU9f4s/z+jQPGC0iU93H38Jp4dpMVdcCH4pIeLKVw3F6Z+SCY4Hvq+r88A5VfQ84133OGGOMSUouJ3qFQLv7+8HA4+7vn+Asap4VMpnoDRo0iKampozU5amBlYAwoKiAstJiKByE5PW+bFRQE73eko2ioiLa29tjPmdS11uSHY/KysqUWvS8TvQA8vLy6Oz0elk2EASR+La+qGoX8B3gfhFZArQCd4nIDBF5POLQc4AbROR94HycrviIyEARqcFpGfu6iNS4MyJnixLciWWirHWfM3FoKRxAS2H61o00xphslLNj9IBFwIkicg9wKFu6a44A6n2LKkHr169nyJAhGakrvGj6jjvumJH6vCKTj0SXPOMsgF5ciUw+vM9zgpzoWQuuP3rrNhuPVLtuNjQ0MHjw4KTPj2X33Xfn3XffZebMmZ6WC96OJ1TV54GpUbvnAV+IOGYBsE+Mc1vZdvKWbDIfp/Xuh1H7f+g+Z+Jw2slZMyLDGGMyJpdb9H4NXAEsB15T1Xnu/iPZ0qUz8FS1x+n2vTZ69GhWr16dkbq8JEWldG5/DIXjZhCa9lVkQHmf5wQ50Usl2TDJS/Xal5SU0NLSkvT59fX1nrfozZ49mzlz5nhaZpiEJK7N9Oki4GwReVtErnW3t3FaLC/2OTZjjDFZLGcTPVV9GGfh2BlEfCsMPAf83JegAi7copeN1q1bx/Dhw+M+PqiJXqrdB03yUh2jl2oLVzq6bg4fPjw962PGOT4vYJPaBpKqPoszo+iHODNvHuj+vruqPuNjaFnl22/9l2+/9V+/wzDGmEDJyURPRApFZBEwRFXfVdXNC8Sp6utuFyATJVtb9ADWrFnDiBHxD70MaqKXarJhkud3kp2ORA+cNTK7uro8L9erMXrG6Zaqqt9Q1Rmquqf7u92nEnDswpc5dqEtPWiMMZFyMtFT1XZgMOD9LAQZ1N3dndEPSmVlZVk7q2OiiV5JSUkgl1doaWkJzILb/Y3f3WbTMUYPYNddd+X999/3vFxr0TPGGGOCLScTPdfdwDf8DiIViXZHTFU2f/ueaKIX1Nfa3d2dsTGZZmt+J3rpalE88MADeemllzwv1zK95IlIl4gMc3/vdh/H3PyO1RhjTPbK5Vk3W4BzRWQ/YK77eDNVvcyXqBJga+jFb82aNRlNiv2kqoFNVLOZF91mVTXpc9OV5I8aNcrzLtnOeun2N5iCbwEbIn5P/g/HGGOM6UEuJ3qn4CyjsJ27RVLAEr0c0tra2i+6PBYXF9PW1sbAgQP9DiXntLa2pnxdRSSQrbLhuLwt09Pi+hVV/UfE73f4GIoxxpgclrOJnqpO8DuGVNXU1KRl/aveBPWDqnGUl5ezYcMGS/TSwIuW0vLychobG9MyqUoqdt55ZxYs8HBuD7GlE7wiIkuBmapaG7W/AnhHVSf6EliW+cXR0csQGmOMsU/zAZbouDMvDB06lM8//zyjdZr4hRM9E0xVVVVJz+aazq6Q6RinJ6FQXJvp03ggL8b+QpwlgkwcPhk6nk+Gjvc7DGOMCZScbdEDEJGDcRac3QWnu+aHwOWq+oKvgcWpq6uLvLxY9//0CY/nGTZsWEbrNfGpqKiwRC/AKisrqa2tZeLExBthUhnf15ftttuOFStWeFuo9d1MiYjMjni4j4jURzzOA44APH7TctceqxYC8M7onXyOxBhjgiNnEz0R+SpwH/A/4Dfu7oOAZ0Tk66r6H79iC7LRo0ezatUqpk+f7ncoaRce71ZcXOx3KHGzFr1gq6qqora2tu8Dc4Eleql6EecLSAUeivF8K3BOJgPKZhc/dwsAx5/xB58jMcaY4MjZRA+nJe/XUbNrXi8ivwIuAizRi2HUqFHejuXJgObmZkpLSxM+L7xo+ujRo9MQVXqUl5ezePFiv8MwPaiqqkrLmnWp2rT8I4bM/aeHJQoi1i0zRRNwJjBdCswC1kc81w6sVVVvZ9AxxhjTr+RyorcDcE+M/fcAv8xwLAlLZzeu3qRjKvZ0S3YsY9ASvXgmA7EWvWALd91M1MaNG9PWsqxdnaz+5WHsIeu8K9RZX8G78vohVV0OICJDoydiMcYYY7yQy4lePTAZiG7+mOI+F2h1dXUMGTIk4/UWFRWxadOmjNebilQTvaCIpxupjdELtmQTvYaGhrTN1NlZ9xldzQ2MK/b4yyNL9DwRmeSJyAicSVgin7dxesYYY5KSy31v7gduFpHjRKTM3b4I/MV9LtBsDb34JbtYetASvcbGRgYNGtTrMWVlZZboBdiAAQOS+qKkvr4+bYlefsUwJK/A81k9RSSuLYhE5B53si7fiUipiNwqIq3AKmBZ1GaMMcYkJZcTvV8ALwMP47Tg1QP/BV5xnws0S/Tilystek1NTZSVlfV6TH5+Pl1dXRmKqP/o7u72LClJppx0tuhJQREjL34QGVDiXZkIkpcX1xZQDwH/T0Q+EZELRSTxb4q881tgf+AbwCbgLOAyYA1wmo9xZZV7dzuCe3c7wu8wjDEmUHK266aqtgKnisjFwFScUSUfqmpWfENaU1PDtGnT/A4jK6xduzap5SCCmOj11aJntqZdnbQ8dxsdqxdTNHV/Bs76UlLltLS0UFLiXSKUqHQmegADdz+MiffVQkFh3wfHI8vH6Knqv4F/i8h44NvAPBF5C/iLqj6T4XCOA76jqs+IyG3AHFX9VERWAV8j9lhzE+W+3Y70OwRjjAmcnE30wlR1qYgzC4GqNvsdT7xWr17NyJEjfam7sLCQ9vZ2Cgs9+lCYZp2dnRQUFCR8XtC6QVqil7jaa7/OxvnPoe1tND97K51fu5iyL/4k4XL8vvYNDQ2MHZvetbElP/F/I70XmBMdQrbHmbirGXgf+KmInKmqp2YwhmHAIvf3ZqDc/f054NoMxmGMMSbH5MSduici8kMRWQ5sADaIyAoR+ZHfccWjo6PDt0Rr5MiRfPbZZ77UnUmhUMi32U1jiWeMntmia8M62t5+Am1vY22bQnsbTQ8nt4ZWPN1m0ymdY/TSxcsxeiJysIgsEJElInKbiGzzJaQ7lu1Ot7vlxyJydsRz17jnfuKuodpXfReJyGLgbOAWVd1JVS9V1aOBPeK/Cp6oAcJ9z6uB8NjBPXC6cpo4PHTn+Tx05/l+h2GMMYGSs4meiFwOXA38GzjB3e4DfisiV/gZW9Bl4xILyQpSoud3spF1QlvGf135fsc2+xLhd5Kd7q6baSES39ZnMZIH3AqcqKqTgVLg9BiHXovT/X4KsBPuIuMicgSwN7AjTpJ0nYjE82YeoKpfVdWno/YfE8e5XnoB+KL7+99x7lFvAXfh3LOMMcaYpORy182zge+r6h0R+x4WkY+Aa3AWTTcxjB49mqVLl/odRr/jd/fBbJM3qIqSA0/jsxfvY0lzOxQMoPzrv06qLC+vfV5eHp2dneTnx//fa3Nzs69jBBMXXxIXp5lAjap+5D6+FfgBcPvm2pzE7VhgHIA639CEFwY8AbhDVTuBVSLyCnAE8EAvdb4KtEaUXw7srqovqmpG//NT1bPFbfpU1dtEpBGYjfP6b85kLMYYY3JLzrboAcU4M2xGewUYmOFYEqKqvrY0ZVOLXqqzJQZp+ndL9BI3+Ht/oenYi2ktqmTYRY9QesiZSZXj5bUfPHgw9fWJLdUZ5KUIeiISimsDBrvdMsPbuVFFjQFWRjxe4e6LNBFYC/xZRN4RkYfdiVTiPT/ataraGPG4ER/Hw2nEf/iqer+q/khVb1JVm2LXGGNM0nK5Re9RnO4w0Tfv44AnMh9O/BobG33txjVs2DDWrVvX94EBUF9fT1VVld9heCLe7oMigqpmXWKQDiLCuvLJjN1hGoU77pd0OU1NTWy//faexFRVVUVdXR1Dhw6N+5wgdSGOi4Dkxf09Yb2qTu3jmMgLEOsPuwCYDvxSVb8nImcBtwGHxHl+tK2CV1WNNS4wE0RkXG/P24LpxhhjkpVTiZ6InBHx8A3gVyIyA3gd54PAfjhdei7zIby4+b2GXigUoru727f6E5HsGnpBFO/soSUlJTQ3N1vrn6u6uprddtuNDRs2JP0FiZctepWVldTW1npSVnCJl7NursTtkukagzNBSfQxdaoa/pLubuD3vZw/t486V4nIKap6N4CInAr41Y2hmq0T1WiBXYwwSD4Z0mu+bIwx/VJOJXrAHTH2nexuka4F/pj2aJLkd6KXTVJN9PLz8+no6EhqeQa/lJeXs2HDBkv0XGvXrmXXXXdl7dq1SSd6Xk7GEm7Ry3netSjPA0aLyFRVXQB8C3gw8gBVXSsiH4rIDFWdBxwOhMf0PQhcJCL/AIbjLD5+Nr07B/iXiFzvPl5E7AlgMuHgqMeFwJ7A94BfZD6c7PSLL/zY7xCMMSZwcirRU9WcGHNYU1PDQQcd5HcYWWHNmjVMnz496fMrKyupr69PasF1v4QTPfsywNHd3c3IkSNZu3YtO+ywQ1JlNDc3U1pa6kk8lZWVLF68OKFzsrEbrlcxq2qXiHwHuF9ECoE5wF1ub4zLVPUL7qHnALeKSAnQAJzlnv+MiByOk6wpcL6qNvVR53JgfxEpdR/7tsaqqr4UY/cz7tJAp2AzbxpjjElSTiV6uaKmpoZRo0b5HUZWSLVFr7Kykrq6uqxK9CoqKgK10HsQDB8+nE8//TTp87u6uhKaJbM3VVVVud91U/CyRQ9VfR6IHsc3D/hCxDELgH16OP8C4IJE6hSRKcA0YEA4aQ135QyIuTjLLZg4/OrZvwFw2WHf9TkSY4wJjpxO9ESkDJiFsxht9OD7O30JKg4bN26kuLjY1xgGDRoU97glVUVXzIXV70B+MbLDUUjZyAxECXV1dQwePDjp88OJXjYpLy+noaHB7zAA6O7soOa2i2h47RGKhm/H+PNuomjkhIzHMXz4cF577bWM1xtLorNu+j3LbtK8G6OXcSJyPnASzmyeL+J0BX0BZ+xfUHwZyPFvDLyz++pFfodgjDGBk7OJntuV5z6gIsbTCgQ20QuC8BIL8XSF01Vvw6fPQ3en8/jtO2DfHyBF6R9DpqqEQsl/4MzGiTPKy8upqYmeq8IfK/92IesevRnd1MbGVUtY8KMD2O1fSwgVDshI/S0tLZSUlARqptj8/Hy6uuKfFb+lpcWzbqOZk33LQUT5Bs44uHmq+lURmQD8zo9AROT56F3AaGASNkbPGGNMCrL3K9m+XQs8DoxV1VDUZrOY9WH06NGsWrUqvoPXLoDuTl75cDltmzoAgQ3BSET6kq0tekHpuln/ykPopjbeqVe0q5Ou1g1sXJm5b9aXL1/OdtttR0FBAZ2dnRmr10sNDQ2+LqeSDAEkLy+uLaA2qWoHoCKSr6rLcFr3/LA8aluKs9j7Yar6+95ONMYYY3qTsy16ON+GHq+qcWYrwRGEb8pHjRrFO++8E9/BA8rp6oaf/fUpbvjhMczcaTsozI4WimxM9II0Rq+gaiTt61Zw5/JuRhcLIwo6ya/I3HjH6upqxo8fn7H60qGhoSGl7se+8HiMng/q3a79zwH/EZHPgV4ncEkXVf2mH/UaY4zJfbncovchTveXrNLS0sLAgQP9DiOhFj2ZfCj3vbqE7xy3FwtWfA4jd4Py7JgRMtHxVEEwaNAgGhsb/Q4DgAnn/428kgq6Qnl82JLPmLN+Q2FVZsZnwpYWvWxWX1+fdS16gJPoxbMF0xdxErsLgIeA+cCXfI3IGGOM8Vgut+j9ALhORH4CvKOqWdGva9WqVYGYNr+srCzuViMtGMjrn5dy3a9/zWW/+zOhnY5Jc3SOjo6OlGdKzM/PD0SXv/b2dgoLC+M6NhQKBWbyjoHjd2bSzfM55LJfsaGwmJEn/jSj9a9atSrrZ6htaGhgyJAhfoeRsCD0PEiGiISAx1X1EKALH8ZrxxiX1yM3TtOH00663O8QjDEmcHIq0RORbpyJVjbvAl53n9vq2KCO0wvKYumJfIh74IEHOPHEr1E4ZAKdkrmFx9etW8fw4cMzVl86xTvDaRAtXrmamYcdw9y5czNet5fLIviloaGByZMn+x1GgiRrZ91U1W4R2SgiA1W11acwlvtUb85qKfK/J4wxxgRNdn9C2ta32DrRyzo1NTXMmjXL7zDi1t3dzUsvvcQNN9yQ8bpTXUMvSLI50VuwYAF77bUX77zzTkItk14rKSlJauHzdLSOFhYWsmnTJoqKivo8NivH6EGQu2XGYzUwV0QeATYvlq6qV2aichuX573jFjjrzj8y9UCfIzHGmODIqURPVe/wO4ZU1dTUcPzxx/sdRtwefvhhvvzlL29uASwuLqa1tTUj4wxzKdFrbGzM2kRvyZIlnHLKKUyfPp358+czc+ZMX+IYPnw4a9euTTjR27RpEwMGeLscRFVVFXV1dYwc2fd4xWwdo5etXTddK90NIHPdEEzafGve/wBL9IwxJlJOJXq5oLm5OTAf+EWE7u7uHtepU1WeffZZ/vznP2/et+OOO7Jo0SJ23333tMe3Zs0adtxxx7TXkwlNTU2UlZX5HUZSOjs7KSgoYO+99+ahhx7KWKK3cePGrRK0cKI3adKkhMpJR5IdXp8xnkQv+nVkBREI7tIJfVLVX/sdQyQRmQScCIwHtmoSV9Vv+RGTMcaY7JeziZ6ILCN2N04FNgKfAH9T1Sd6OP+bwBnANGCAe/y1qvqviGMmA38EDgQ6gceA81R1vYcvxTdDhw7l888/Z9iw2NPlP/bYYxx77LFbfbM/depUPvjgg4wlejZGLzjGjBkT/9qLHlixYgXjxo3b/Hj48OGsXr064XLSce2rqqqora31tMzAydIxegAi8qtY+1X1Mh9iOQx4BGf9vCk4M0ZPcJ+Oc42b+O5Zxhhj+pfsvVP37R6gElgL/Nfd1rj7ngbKgEdFpKcptQ8F/gd8AdgduBe4S0ROAhCREpw1mIpwEr1jcG6wD0uW92kKGzVqVI8fnFWVJ554gqOOOmqr/VOmTGHRoswsmN3S0pJwN71YQqEQXV1dHkSUvGxN9Nra2nxrjYpeWmHYsGGsXbs24XLSlejFuz5j1v53kd3LK3RFbPnAcWxJrjLtCuCPqrozzpeQXwHG4UwkdncC5fR6zzLGGNP/5GyLHlAO3KSqv4zcKSJXABWqeqiIXA38H/Bw9MmqelrUrt+JyMHA14D7gFOAkcAeqlrrln06znpMBwNxT58dVOG19KZPn77Nc0899RRHHXXUNh9Si4qKaG9vz1CE3ggvQF5ZWelbDIl2Hwwnp3k+d59btGjRVt1nhwwZwvr16xk6dGja666uruaQQ7bMPB/uupmodHSbraysZP78+Z6WGThZ3KKnqr+JfCwivwX+41M4OwGnu793AUWq2igiF+Hca26Jp5A47lnGGGP6mVxO9E4G9o6x/x/AXJx19u4CvpdAmRVsGcC/HzA3nOQBqOr7IlIDHECMRE9EzgXODT+Onkhk48aNcc3SlymjRo1iwYIF2+xXVR555JGtxub5wavZEisrK6mrq/M10Uu0VWnQoEE0NTX5PonHggUL2GWXXTY/3nvvvZk7dy7HHnts2uuuqalh7Nixmx8XFxezcePGhMtJR6KXSIteUNZETEiwW+uS0Y3TbdIPkd+MrcNpzVuIMxtoqotEVrDlnrVZX/eibPSHA6Lz3Mwaf+FjcR9bfVVm1prtj+J9H+w9SN/frN//FnL1dSUre7+S7VsesEOM/Tuy5XVvwrnB90lEvgHMAG52d40EPotx6Bp6uDmr6o2qOjW8RU+pvnr1akaPHh1POBnRU9fN559/nkMPPbTHLmfhqeWzRTjR81OiyUZ5eXncC9qn06JFi5gyZcvn4z322IN58+ZlpG6vlnJIx2QsZWVlgXh/0kpC8W0BJCLPiMjT7vYs8Cnwb5/C+QCnqyXAa8BFInIMcA1OwpeUGPeszfq6F2WjVybszisT0j823Bhjskkut+j9B7jF7f4yF2cSln2Ay9hyQ9+XOG6k7ji+m4GzVTU8OL63r7OT+oo+KIulh/XUDfOhhx7iT3/6U4/nTZkyhcWLFzNt2rR0hueZICR6iY51C3c39Vt7e/tWrdADBw6kra3Nx4gSl44xeqFQKDtb6hKR3S16V0T83glUq2rmZhLa2pVAeLDxJTiTej2C07r3lWQK7OGeldPGbHC6bdeU58YEXcYY44VcTvR+iNMl5s846ySJ+/gW4GfuMW/hJIE9EpGTgTuA70Wt07caiDWP+whit/T1qaamhl133TWZUzPmpZdeYvbs2T0uuQDOzJsLFixIa6LX3NxMSUmJJ2VVVlaycuU2vZsyLpFJOcrLy2loaEhfMCkIyvjBePm5tEVvy5cEXkBb6+K0Dliuqq3gTK4lIjuq6seZDkRVn434fTkwTUQqgXpN4tuCXu5ZOe2Gh68B4Pgz/uBzJMYYExxZfafujapuVNVzgSqcbjHTgSpV/YGqbnSPWdDbjV1EvoNzwzwzxg3zVWAv94YcPn4aMAZ4OZmYg9aiF8v999/PV77S+5fMO+ywAx9/nN7PS2vXrvVsXEkQWvQSFYSum5s2bYrZdXLq1KksXJh0j7O4dHR0UFDgzTrXfs542tjYmKXrJwqEQvFt8ZQmcrCILBCRJSJym4hs8yWkiFSLyEci8p67TXX3l4nIvSLygbt9OY4q/8nWY+Pa3X0ZJyLfdGdx3kxV65JM8nq7ZxljjOlncjbRC1PVFlV9391a4j1PRH4K/AX4MfCiiIxwt/B0gnfjtNzdKyJ7iMi+OB8U3gBeSCbW+vp6ysvLkzk1I1599VX23nvvPltqMtF9z8tEb/DgwZboJWHx4sVbjc8L23vvvXnjjTfSWndPX4oUFRUlPCHLpk2bfJsEqaGhwfcJdZIieDZGT0TygFuBE1V1Mk43xtN7OPxIVZ3ubuGZov4PWKWqu+BMhHW5iPSVueepamf4gap24PT88MOfgDUi8g8ROSjZQuK4ZxljjOlncirRc78JHhTxe49bHMX9CGdCl7/iJHTh7S1wEkicdYs6cFrwngAWAF9M5pvYiNeQ7KlpUVBQsHmc3n333cdJJ8W3JFO6xyetWbPGs0RvwIABWTV5DARjjN6CBQuYOnXqNvsnT57MkiVL0lp3dXU148eP32b/sGHDWLduXVrr9lLWJnrg5Tp6M4EaVf3IfXwrcEICkUwDngRQ1QaccddH9XYCsEFE9t/yUuQAoDGBOr00HGcW6DHAcyKyTEQuFZGJCZbT6z3LGGNM/5NrY/Qm4Nzowr8nTVXHx3HMEpyF0j0RtCQPYOTIkaxZs4bPPvuMPffck/z8+P5k8vPzPe1eF23NmjXMmjXLs/KybeKMIIzR+/jjj2Muo5CJv+Ply5ezzz77bLM/vJbeuHHj0h5DPFS11+vR0NBAds54KImM0RssIpHrtNyoqjdGPB7D1ksArHD3xfKIiIRwJiu51G2Vexv4mog8hzMb8v44PSt682PgfhGpd14M5SQ58Umq3HGC/wD+ISLbAWfgtGheLCIvq+pBcZYzPm1BGmOMyUo5leip6sGxfjfJGyybWPjcwzz65kdc+6cb4j5v8uTJfPrpp1stpu2ltWvXMmzYsLSUnQ1KSkpoaYm7J3JatLa2MnDgwJjPDRo0KK3jz1asWBGzdTnZRdPTIfwelZaW9nhMfX194Mfl9ij+hL5eVbdt+t1a5DctPRW8v6rWuL02/gmcD1wNXAX8AXgHJ2F8CWcmzZ4rU31PRHbEWYJHgI8ju3L6xZ2M5XIReR6nS+cBPoeUNZ6fNMPvEIwxJnByqutmT0RkuPstcGB1dHQEbpbCj6//EXW3/IK7rvg5A179Fx1rquM+N90TcnR0dHiyhlq2CmLrb6RZs2bx1lvp6zHW1tZGcXHxNvuDlOjFs2h6dnfd9GwdvZU4i4SHjQFqog9S1Rr3ZxPwd2Bv93Grqp7jjts7DhgILOo1dJFTgEGq+pGqfggMEpGvxxNsuojIdiJykYh8AswBWoHv+hlTNrlhv69zw36+voXGGBM4gU5+UiEi+SLyGxFpBFYB493914jIOb4GF8OaNWsYNSrmOuu+aP70A1Y/eiuVoU28+NkmDilvY9ENP4n7/J122okFCxb0fWBABD1xCprOzs5eu/HOnDmTuXN7XbkkLYKU6FVWVlJbW9vrMdmd6Hk2Rm8eMDo8iybwLeDBrauSEhEpc3/Px+lm+b77uFxEitzf98fptv9MH3VeoKr14Qfu7xfEE6zXRORMEXkBZ9H2bwP3Atur6gGqeqsfMRljjMkNOZvoARcCpwLnsvU02m/jjIEIlKAtrdDesB7Jy6eiAH62vVAgSnvdmrjPHzRoEM3NzWmMMHd0dXVl3Vpqn376KZMnT+7xeb8miyktLQ3M311Ot+iJQCgvvq0PqtoFfAdnzNwSnJasu0Rkhog87h42HJgjIu8D83EmwbrKfW4H4EMRWYizEPqJqtrdR7Wx/sH5NZThBmA5cLiqTlDVX6nqUp9iyVo3P3gFNz94hd9hGGNMoOTUGL0opwNnq+pTInJTxP4PgG3nhPdZTU1Nrx+cM61shz0JFRYR2tTKtPJuQkXFjDj8VL/DSqu+Js5Il+bm5l7HcQVRTzNuRkvHNe1tMfYgtcxWVlayePHiXo/p7OxM24RFaedhb3hVfR6I/oOaB3zBfX4pzlqosc59E9g+wSoXiMj/AX/EGR/4E5xZk/0wIpGlf0xsw5rr+z7IGGP6mexqRkjMOCDWqt2dwLaDe3wWtBa9/NJyZv7lNYbsdTSDdtiDSWddzrivnZdQGaFQiK6uLs9jS8cMmX62QDY1NWXdotkLFixgp5126vWYSZMmsXSp9w0Tq1evDlQ3555UVVX12XUz22Z73Yp3XTf9cC6wE7AUWIbTKuhLl35VbRGRoSLyUxH5i4gMARCR/UQkpdmjjTHG9G+53KK3DNgNp0tMpCOInQD6av369QwZMsTvMLYycMxkpl/9v6TPnzBhAtXV1UyaNMnDqJyZCisrKz0ts7Kykrq6OgYN6mud5W21fPQaK//wLbo21FK+/wmM+sENhArinyimqakpqXr91Nzc3GfM4YXTvX7/ly9fznbbbedJWZ2dnWmbBCmerpvZK6HlFQJHVWtF5E7gXWAATqve94ArMx2LiOwKvAjUAdsBvwM+x1kLcDw9Lx5vjDHG9Cp779R9uxH4k4h8wX28o4j8CPgNzpiIwAlStzMvTJ06NS0Tsni5WHpYONFLVGf9Opb9vyNpr/mErqZa6p/7J2vvuDihMhobG7Mu0YvHzjvvzEcffdT3gQlavnx5zMXSk5HO1tSBAwf6vgRGWmVxi56I/AGnBe98YDDOGMFEu3965ffAXW79GyP2P4WzJqAxxhiTlJxN9NwFeW8F/g2UAI/iDN7/nare7mds/UW6Zt4MUqLXVv0BhEJ0diuvfq5oextNbz+dUBnJtuiFF6XPtHgnj8nLy6O7u685MRJXXV3da4teItclna2p8Xxxk9Vf7ni3vIIfDgdOANar6s+AGYBfC3POBP6s2/bjXQl4+x9dDmsoLqWhOLvGOhtjTLoF9i7sBVW9HBgKzMJZc2moqtq0XBkyePBgGhoaPC83SIle4bDt0I521myCJ9cAeQUUjd0hoTKSTTbKyspobGxM+LxUVVdXM2FCfEOHioqKaGtr87T+viavGTp0KOvXr4+rLL+7zWbtGD0hq1v0gI1uYtUlIgNVtZat1/LLJAVi9fUeC2R+6tos9c0Tf803T/y132EYY0yg5HSiB6Cqbao6T1XfDOrMZtk4vb6fgpToFY2ezMjv/o5Vm/JY0y4UjZzIqO//MaEyku0+WF5e7ssSBvHOuAmw55578u6776Y5oq0lspZernabTT/vllfwyXIRGQz8B3hRRJ4GlvgUy3PA9yMeq4gMAC4GEuseYIwxxkTI2clYxOkTdQZOF50RRCW1qnqIH3HFsm7dOoYPH+53GGnT3d3taSJbW1ubtslYkjHkSz9Aa5TtnnmaKX//L5Lg5B7Jtir5tVbdggUL+O53vxvXsXvttRf/+te/2HfffdMc1RbDhg1j3bp1cR3r54ynfS06H3jB7ZbZJ1U90f31tyLyKlAGPOlTOBcAr4rITJyWvb/iLDUhwFk+xZR1vvfG/QD8de+v+hyJMcYER/beqft2NXALzkK7NTizb0ZugRG0pRW8NG7cOFauXOlpmarqeQtoSUlJSssrrFyzjinTdqO9szPhc5NtVSovL09L19i+1NfXM3jw4LiOTaR1LR7d3d19jmtLpE4/u25u2LAhOxdLDwtJfFvAqeocVX1UVRP/x+tN/eEZoh8FngG6gNuBPVTV2/88c9iRn7zOkZ+87ncYxhgTKFn8dXKfTgPOUNV7/A6kL7mc6E2dOpWFCxd6Nh1+uqQ6KUZnZyc77LADy5cvZ8qUKQmdm+yC6eXl5Xz22WcJn5fN4mn9DlKiJyI9tmjX19dnb6IXHqNnPKGq64HL/I7DGGNMbsnlFr2BwFy/g4hHrid66Zh5M4gmTJiQ1ALhybZQ+jFGT1UTTopHjRrFqlWrPKm/urq6z6UVBg8eTH19fVzlpXuMXnl5eY8T5jQ0NGRvohdeRy97Z900xhhjcl4u34UfAI72O4h4rFu3jmHD/JrZO70SmQEx202cODGpRC9ZfozRW7lyJePGJTY54d57783cud585xLPYumJJKLpbtGrrKyktrY25nPZneiR7bNuGmOMMTkvlxO9d4BfichNInKWiJwRufkdXKTu7m7yEpzAo7/q6OgI3AQWTU1NlJaWetpyFQ8/xuglMuNm2PTp03nvvfc8qd/LxdIBWltbKSkp8ay8aFVVVTmc6FmLnjHGGBNkwfrE7K0b3J/fi/GcAndmMJZeZe1aWglIpstfLOvXr2fo0KEeROSdZcuWMXHixLQtEN6TAQMGsHHjxozVB06id/rppyd0TlFREe3t7Z7UH29yFO+/Ka/+LntSVVXV42yuDQ0NcU9qEzwCoVy+faSXiOwKfKiqmfsPI8f99Nif+h2CMcYETs5+3aqqoV42az7LoFGjRnk2aUg61tBL1bJly+JeQNxL6UxQepJsop2Xl0dnEjOSRsu2L0V667qZE5OxWNfNZL0LDAEQkQUi4u16Mf3QssrRLKsc7XcYxhgTKDmb6MVLRF4REd/uDtn2wTUZXk7Iks5Er7i4mLa2toTPW7p0KRMnTkxDRLlj11135YMPPshYfaFQKKOtqz3prUVvw4YNvq3h5wnrupmKRiA8MHtHwL58TNFeKz5grxWZ+z/GGGOygd2FnfWLivyqvLa2liFDhvhVfUZ4meitXbs2bYlesoumR7ZyJTLjY7ZJ5UuJvffemzfeeCNj9fc2Ni6TemvRy+6xuTbrZopeAJ4TkRfcxw+JyPOxNj+DzCYXvngHF754h99hGGNMoNhd2Ge5vLRC2IgRI7Ki62ayiR5s6UaZ6Zk3M+mzzz5j1KhRSZ07btw4qqurU6o/kS9FvF6oPVm9jU/0o+utp6zrZiq+AfweWIIzZrwGWN7DZowxxiTFRtP7rD8kel5+oA3PcJkOqSR6YRMmTGDZsmXsueeecR2fTV13FyxYwE477ZTUuV78DcSztEJYONGbNm1ayvWmSza99zF52FonIgcDNwKFwBzgu6raGXVMNdACdLi7TlHVBeL8cV0HHI4zevAl4Aeq2uVZgB5T1SbgdwAichhwrqr63wRtjDEmp1iLns/6Q6IHzodarz7YpqslJJlEL/o1Jdqi19bWxsCBAxOq0y/JLK0QKdVEOpGlFYLSopfTPOq6KSJ5wK3Aiao6GSgFepra9UhVne5u4f7gBwMzgV2BXYBpwFGpvrxMUdUJluQZY4xJB0v0fLZ69WpGjhzpdxhpN2zYsMAvnJ5MIhLdlXTw4MEJrW3X2NiY1gW7vZTq3+pee+3Fm2++mfT51dXVCbfomTQRgVBefFvfZgI1qvqR+/hW4IQEolFgAE5rYCHOmOusevNF5GB3TN56EVnn/n6w33EZY4zJbpbo+ayzs5OCggK/w0i7qVOnsnDhQr/D6FUyiV6qSys0NTWllOgVFhayadOmpM9PVCqtqTNmzOCtt95K+vz169fHPUYvnslY0r2GXl+yf4xe3C16g90lBMLbuVEljQFWRjxe4e6L5RERmS8iV4hIPoCqvoAzucln7vayqs7z9LWmkYh8FXgW2AD8BrgSZ1bOZ0TkRD9jyyZ37fEF7trjC36HYYwxgZLziZ6IFIjIGBEZF7lFHPIakPic+iYhXs68mS5lZWVs2LAhoXNSXVoh1USvvLw84ZiTlWpiUlpaSktLS0ZiiGfx+paWFkpKSlKKJxXZPUYvoVk361V1asR2Y4wCdevCY9pfVXcH9sfponk+gIjMAMYBI91t5yxLkC4Gfq2qx6vq9e72ZeAy4CJ/Q8seD047lAenHep3GMYYEyg5m+iJyAR36uo2nJnLlrlbtfsTAFU9UlW9mRLS9Gjs2LGsXLmy7wN70draSnFxsUcRbSuZtdeWLVu2zbixUChEV1d880A0NTWltJZaRUVFRhK9ZBdKj6SqNK9exuJH76J5TWp/C17IVLfZWIvFb9y4kaIi31Z18UYoFN/Wt5U4iVrYGJxZKLeiqjXuzybg78De7lNnAs+oapuqtgEP4ozbyxY7APfE2H+P+5yJQ2FnB4WdHX0faIwx/Uguz7r5d6ASZxrrVWz9jbHJMBFJuQUjnWvoJau1tXWbVqHRo0ezevVqxo4d2+f5jY2NVFVVJV1/eXl5QmMCk5XqRCyqygsXnkT764/w4ILHGVUc4ui/Pc+QneKbnTQdUk2y41VZWUl9ff1WifKGDRuoqKhIe91pI3i5dMI8YLSITHUnWPkWTrK2pTqREiBPVRvdLptfAd53n14OHCYit7qRHY4z82a2qAcmA4uj9k9xnzNxuO/uCwE4/ow/+ByJMcYER8626AF7Ad9Q1X+p6ouq+lLk5ndw4CyYnNUf9jIsnWvoeWnChAlxz7yZLV03U030ahe9S80rTzC+qJ1PPm+hs62Ft67/Rdznb9iwgfLy8qTrjyXVax+vWIumNzQ0ZP+/fY9m3XSXQfgOcL+ILAFagbtEZIaIPO4eNhyYIyLvA/Nxlli4yn3uRqAT+BAn+dsA3OzlS02z+4GbReQ4ESlzty8Cf3GfM8YYY5KSyy1664DArqME0NHR0S+WVggbPHgwdXV1VFZWJnX+mjVr0n69vJggY+LEibz22msceOCBfR7rRaK3fHn611ReuXJlXC2UPelsbUZCIYYXwUvu5KsdLfEnqIksrRCptwlXMpXoVVVVbTPJT0NDA4MHD0573ekjnq6jp6rPA9HfJMwDvuA+vxSY3sO5rcCpngWTeb/A6X3yMFt6nghwr/ucMcYYk5RcbtG7DPi1iAR2IEx/S/RSnXkzaC167e3tFBYWbrN/u+22o7q6Oq4ysmWMXqozVA6ZOpMBFUPILyikNB9apIgdvxY9+WLPEllaIayioqLXbq2ZGqMXq0Wvvr4+B1r0JL7N9EpVW1X1VGAS8CXgy8AkVT3FTWKNMcaYpORyi96ZwO7AKhH5GGiPfFJVD/EjqEidnZ39LtF77rnn2G+//ZI6f+3atQwbNszjqJK3fPnymMlHUVFR3EseeNGil4kxeqnKH1DMsf94nff+fjlfWbKEj0JDmfLFM+M+f/ny5cycOTOhOocPH866det6bDlrampi++23T6jMZFRVVfHJJ59sta+hoYFRo0alve70EQjl8u0j81Q1PGGYMcYY44lcvlNXu1tgdXZ2ZvmHvcQk0tIVS3t7e9pnKszPz6ejoyOutQ2XLVuW0tIKQNx19SQTY/Tq6uo8aX0qrhzGPr+4gX2A8847L6FWwjVr1jB8+PCE6hs2bBhr165lhx1iT1zod9fNrG7REzztumlMqj4cMcnvEIwxJnByNtFT1W/6HUNfuru7GTBggN9hZEw8a5v5LTxDYjwth0uXLuXoo4/OQFQ9KywspKMjvVOKL1y4MKWJWGKZPXs2L7/8MrNnz47r+O7ubkLxTdW/2fDhw/n00097fD5TiV5FRQX19VtPnpj9Y/SwbpkmUC4+4vt+h2CMMYFjX8kaE6GysnKb1pee1NTU9Nj1tri4mNbW3Bhek+qMm7Ecd9xxPPLII56WGW348OGsXbu2x+ebmpooLS1NawzgtBJHr6uYqbrTyqNZN40xxhiTHnYXNhk1aNAgGhsb/Q6jR4kkel1dXeTl5cV8bsKECSl1Uw2SWIvCp6qgoICSkpJtWrp6ksxEMMOGDWPdunU9Pt/V1UV+vj+dGlKd3MZ/YomeCZTLn76Jy5++ye8wjDEmUOwubDJq6tSpfPzxxwmfl+pi6/FKJNHrzYQJE1i2LDfmVeju7u4xoU3Fqaeeyt13393ncS0tLQwcODDh8gsKCujs7Ozx+exOtALAZt30jIiMEbGsOBXT1nzKtDU9d9U2xpj+yG4sPgp1tNG9PjeSgXjttNNOLFiwIOHzMjWmyatEb+LEiXEvmh506UqItt9+exYvXtxnEt/T7KbZLCeSTGvR89L7wBgAEdkvyMsCGWOMyR52F/ZRfvcmNv35y3TXr/I7lIyZNGkSS5YsSfi8TK2hF2+i19esiSNGjOCzzz7zMDJ/NDY2pnUs2cyZM5k3b16vxyS7WDr03hKcqVbinCTu8grxbCYmEblARPYWkXyceUzDf5BPAKP9i8wYY0yusETPR/khgc4Ouj54wu9QMibWxBTxyFSiN3jw4LjGjS1btowJEyb0+HwoFMqJROLjjz/2fCKWSF/5yld44IEHej0mmcXSgy4X/jYISXyb6cnuwP3ABqAUOFtEdmbrpM8YY4xJmiV6PirOFwiFkIL+s8RCsjKV6OXn5/c6rits6dKlfa6hl8nueelKHNIx42akAQMGkJ+fT3Nzc4/HrF69Oun1JoPSRbKoqIiNGzf6HYa3rOtmSlT166o6BpgObAT2BF4BBgLXiMiZIjLOxxCNMcZkObsL+ygUCiGlQ8nb/Ut+h5JRAwcOpKWlJaFzklkwO536atEL6y0Ba29vT2mx9LDi4mLa2tpSLieWJUuWMGlSehciPumkk7jvvvt6fD6V2TFLSkp6TCIzmQRGdgnOidY8m3UzZSJyhIiUqOpioBM4GxgGtAGrgdOBhQmUN1tEHhaR5SKiInJpOuIOqpNOuYqTTrnK7zCMMSZQ7C7ci3hunCIyWUQeE5FmEWkQkX+JyNC4yh80lKLzHkEGpH/R5iCZMnYYC55/CG1tiPuc2tpaqqqq0hdUgmpra6msrOz1mCFDhlBbW9vj814t2F1eXs6GDRtSLieSqtKy9ENa19UQ0vQucr/LLrvwwQcf9BpLsnpbSy+TCVdVVdXmv4W2tjZKSkoyVndaCJbope7PQL2IzAOKgf3c/Qr8UVUPBRKZgaoUWABcAKzxMtBs0J5fQHt+6l+cGWNMLrG7cO96vXGKSAnwHFAEHAgcA0wDHpZ4mgsGDEKKsnzR5AS1v3EPE1+8nPl/+zktl+1F1/J34zovXVP8J0tE+mwRmjBhQq8zbzY1NVFWVpZyLBUVFTQ0NKRcTph2d7Pg0pN4+3t7se7Zu3n7rD3obPY2kYw2bdq0XpO9ZPWU6GW6Va2qqmpzi159fX2vE/lkDVteISWqOgVn0pXfAt3AVUAtMAD4hojspqrtCZT3uKr+P1W9D9iUjpiD7IQPn+OED5/zOwxjjAkUS/R6EceN8xRgJHCSqr6tqq/idLfZBzg4g6FmBd3YTPv9/49JZcqS9S2wqYWNd5/nd1hJiSdRmDhxYq9r6TU2NgayRa/29Uepm/sErW1tFHZ30rb6U1bcfbVn5cdy8sknx+y+uXHjRgYMSH4Ma0+J3qZNm1IqN1GVlZWbW/T6mrE1e0icm+mJqq5X1QdwxugdAOwFdODcQ54Rkc/9jC+bnP7O45z+zuN+h2GMMYFic1+nZj9grqpu7p+nqu+LSA3OTfv5yINF5Fzg3PDjTEwuEiTatgEQCvOEho3dtHcphU3Z9zmmu7ubUKjv70gmTJjA44/3/MEjqF032+ucxuulLTChBLSjnU3rVnpWfiylpaV0dnZuk9itXLmSceOSn49i+PDhvPPOO9vs9+rax6uqqor58+cDuZLoCYSC08KeAwRAVReKSBfwPVVdKiK7pqWyfn4vGn/hY3EdV33VMb7Wn6h0xJtNsaZLEK5BumIwuc9a9FIzEoi1WNoaYJtpAlX1RlWdGt4ysQB4kEj5CGTwaAjlc9ZuJfz5vY3k73yY32FtIxQK9boERLyzQJaVldHU1NTj80FN9CqmHwTdyvsNsGs5hAoHMGT28Z6V35MTTjiBBx98cKt9qS6tMGzYMNatW7fNfj8SvcgWvdz4tx+KczN9UdXBqrrCfbgcp1UPVX0/TfX163uRMcb0F3YXTk1v/ZJyYWo9T0koj+IfPEDeTgez0w47UDhhdz7Z6fQ+z+vq6sro+LyKiopeE6d4llYI662Lp5dj9LxM9AaOncIuv3uctrIRjJoyjck//hNDZ5/gWfk9mTlzJm+99dZW+1JZLB2cGUljLWvgVbfZeJWVlW1+j2yMXqyi5GARWSAiS0TkNncR8ehjqkXkIxF5z92muvu/F7HvPRFpF5GsncpYVaepanqb0I0xxvQLluilZjVOq160EcRu6ev3QuXDKT7rdkp+OYfzb3mCm/52S5/r1q1fv56hQ+OayNQTkVPhxxLv0gp98XKMnpeTsQAMmrYfo47+BjPveJ+Rx3zb07J7IiJMnjyZxYsXb963cuVKxowZ43ldXiXZ8YqcuCc3um7iWaInInnArcCJqjoZZxKsnr4BOlJVp7vbAgBV/Wt4H3Ac0AI85cVLNMYYY7KZJXqpeRXYS0Q2z7MvItOAMcDLvkWVJQoKCjjnnHO44YYbej0uU4ulh/WV6FVXV8fdytTbAuxedR8sKyujsbEx5XIivffee0yfPt3TMuNxyimncPfdd29+3NHRQWFhoef1ZLrrZqTcSPQED7tuzgRqVPUj9/GtQLJNyKcCD6pqjq1O3zcRKRWR6SIyHSgERriPp/ocWkZcddCZXHXQmX6HYYwxgWKJXi/iuHHejdNyd6+I7CEi+wL/BN4AXvAl6Cyz22670dbWxqJFi3o8JmiJXiIzQY4dO5aVK2P3wvIq2cjPz+91TGEy5syZw4EHHuhpmfEYPHgwzc3NdHR0pLUePxO9trY2iouLfanbU9513RwDRP4jWeHui+UREZkvIlfE6t4JnAbcldgLyRkzgHfdbSTOAuzvAv1iKsq543Zh7rhd/A7DGGMCxRK93vV641TVFuBQnIHzLwNP4Ky790XN9EJdWez888/nuuuuo7s79sLcQUv0EjFhwoQel1gI8gf+TF/zSMcddxyPPvpoWuvI9Bi9aPEssxloQiKJ3mB3/F14OzdGiRpVeiz7q+ruwP7ALsD5W4UksjtQBryU8uvLQqr6oqpKjG2837FlwoS6VUyoW+V3GMYYEyiW6PUinhunqi5R1WNUtURVy1X1FFVd72PYWaeoqIhvfetb/PWvf435fDYnehMnTux10fQgfuDv7u72Na4DDjiAOXPm0NHRQX5+6ivAFBUVbTMhS6bH6EXKje+ABCQvvg3qI2d4VNUbowpbCUSuoTEGqImuUVVr3J9NwN+BvaMOOQ34l33J1j9d++i1XPvotX6HYYwxgWKJngmEWbNmsX79+pitX5lufRk8eHCPiV5bW1tCC22PGzeOFStW9H1ggHzwwQdMmzbNt/pFhMqBhdz2q/NgbTUdG9tSKi/WEgt+dd3MqRxEQvFtfZsHjI7oEv8tYKt1NkSkRETK3N/zga8A70c8nwd8nf7bbdMYY4zZhiV6JjAuuOACfve738X8MJzJFqYBAwbEnJIfnOn+E5lxs6CgIO3jzbz20ksv+TI+L2ztR28jj93ItTf+lfUvPMA9p+1PZ/umpMsbPnw4a9eu3Wpfe3s7RUVFqYaakNLSUlpaWjJaZ3pJnFvvVLUL+A5wv4gsAVqBu0RkhoiEx5cNB+aIyPvAfJzu8ldFFHMosCY8E6cxxhhjLNEzAVJcXMzJJ5/M7bff7ncoPVq6dKknSysEWU1NDWPHjvWt/jf+9luKuzZRJEoZm2hY8Skr3ng+6fJiJXp+tKxVVlZSW1sbyO66SfFwHT1Vfd7t1jlZVb+lqp2qOk9Vv+A+v9RdQmFXVd1ZVb+rqm0R5z+tqnuk6ZUaY4wxWckSPRMos2fP5tNPP2XVqmAOql+2bFnci6VnI1X1PRHp7nKWozh+ZIghhYBs2ZeMWImeH6qqqqirq8uR7pviZddNY4wxxqSB3YVN4Fx44YVcddVVgfxAvHr1akaOHJnQOaWlpTQ3N6cpoi28uF4LFy5kp5128iCa5M34xk/JKxxAWYGQX1hE8eChbLfXIUmXFyvR8yOZrays5PPPP/c9kfaKiMS1GZMJT03Zh6em7ON3GMYYEyipT2lnjMcGDRrEcccdxz333MOxhx9CQSjzHxZ7+oDa3d1NKJTY9yPhJRZ22SV9azwVF+RTv/YzKkeMSqmcl156iaOOOsqjqJIzduZsTrrjORY9eT8DyiqYfvI5FAwsSbq8TCXafamqquKtt97ydVkHb9n3hCY4/rr3V/0OwRhjAscSPRNIhx96KGccsAsrrzqDdS3wXvkGdvvZDVnZQjBx4sS0JXra1cVbvz6Dlf+8lwce+y0zTzo7petUXV3N+PHjvQ0yCSN3mcXIXWZ5UpaIbNPa6UdrcVVVFUuXLg3E9U1ZeB09Y4wxxgSWfSVrAqn6kds4OrSSu1d0UVGgrHjiTla/9N+MxuBVcjBhwoRt1tLr6upKuGUwlupHb2fNy48wME9p6exO6TqpaiDG6OWqyspKPv30UyoqKvwOxQMJraNnTNrd/p9LuP0/l/gdhjHGBIoleiaQmpZ/THF3G8ePCjFuIHR3dtC8YlHG6h80aNA23f3q6uqoqqpKuKxY67g1NzdTWlqaUowATdUL6drUSlWhsH5Tatdp8eLFbL/99inHFETRyasfyezAgQNZtWpVjiR6eDrrpjGpqmhrpqLN/y7axhgTJJbomUCq2mVf8oqK2a1CGDFAkLw8KnfeK2P1V1ZWbrNo+tKlS5OacTNWUuHVgt1Vu+5HXlExO5XBBxsUCeVROTW5Lo9z5szxdf28XCciNDQ0WKJnjDHGmIywRM8E0qiDT2DKGReSX1JGwaDB7HLuNQzd8+CM1R8r0Vu2bJlna+h5leiNOuh4ppxxIUWDKigvKWbk6RczdEZyM1Tmcotefn7+5oXrOzs7ycvzp0thKBTKnUSPUJybMcYYY/xgd2ETSCLCjt/8P457po5jn1rPxK9+P6P1e9miFxY5xq+pqYmysrKkywrbfJ2eruWKZxfwamNh0rHl8vi8oUOHsn79esC59l50m01Ed3c3r9/xRzrWreS1G39Nc+26vk8KtDhb83L078kYY4zJBpboGRNDrERvw4YNlJeXJ1Xe8OHDtxqn19jY6Pk0+9tttx0rVqxIatKY6upqz1orgyhyLT2vkuxEzPnLlTx3/cWEWhtZ9uxD3Pr1A+hsb89oDJ6zBdNNgKwrHcy60sF+h2GMMYFiyyv4adky2Htv5/dnnoFBg+Dee+H667c99vLL4fDDoboaTj552+ePOQYuvtj5/fjj4bPPtn6+shIef9z5/eqr4aGHti3jzjthyhR46SX4xS+2ff7734czzoBNmyDWWK499oCbbtpy7DvvbHvMSy9BUZFTV/jYSFdf7ZT9ySdOXdGOP35LbF/4AkQlY4wcueW1XX45PPbYtmXcey+MH+9c8/A1i3TeeVTuuy91q1dveX8AVqyAF15w9oXfo29/Gz76aNsy3njD+XnLLXDLLUyor2fp3/7GcDe5a/rylxl9yCHw4Ydw1lnbnn/SSfCTnzi/H3YYRK8Dt912cN99zu8XXQTPPgvA/nV1vPLUUxxQVuZch5EjnWtw+eXb1nHBBXDCCVBby0uHHcaBJSXwj39seX72bLjmGuf300+HxYu3Pr+wEObMcX6/8Ua4665t67jpJufv4p13nL+JaKefDueeu6W+6ORn++23lHvBBVvqi/TYY1BVBQ8+uCXeSBdf7CR6CxfCOefQ1NrKoNZWuOMO5/nDDoMrrnB+P+kkWL586/NLSzdfX667bst1j3TLLTBtGrz++pb3LULHhqV0ahuDC+G7yzYiyz+le9YMGDDQOWDnneHWW53fzztvy99PJC/+j/CUtdaZ4Dj7hIv8DsEYYwLHvm41JobKykrq6uu32pdKt8aJRUUs3bRp8+Om1ta0LJx9XEUF/2toSPi8hW1t7FRc7Hk8QTFs2DDWhrtudnUxKMNj9AqLnQXfDxvi/JerqoRCWfw9mwiE8uLbjDHGGOML8WPhYOOYOnWqLliwwO8wTAyqykUXXcRvfvMbwJnA49JLL+WKcMtPgpqbm7n++uu56CLnW+cbb7yRL3/5y4wePdqzmMMuvfRSzjvvvIQm/bjgggu4JlZrWI6oq6vj1ltv5ec//zlPP/00BQUFHHxw5ib3qZk/l3+ceSQSCtHd2cGs087liJ9flbH6w0RkoapOTbWcqVMm6kcv/juuY0OjZ3pSp0mfVO9F4y+M0XOiB9VXed2y7PjDAacBcMN+X/eszERiTeQapEs6rm26Xle6/g7ijTcI720QYohXumL1+282qK/Lq3s1WNdNY2KKbr2rqalh7NixSZdXWlpKS0vL5sfpGKMXdsopp3D33Xfz/VhdJWNYuXIlY8aMSUssQTF48GDq3RbapqYmxo8fn9H6x+y2Fz98cgGrP5zHoGGjGL3LjIzWnx7WddMExyGfzgO8TfSMMSbbWaJnTBy8XFoBvFswPZYpU6Zw0003xT2LZn9YPy/yOni1tEWiyoaPomz4FzNeb9rYRCvGGGNMoNmd2pg4pLq0QjRnjFb6/vnNmDGDt99+O65jP/jgA3bZZZe0xRI0fiV6uUfi3IwxxhjjB0v0jInD8uXLGTduXEplFBQU0O7OKpnusbFf+cpXeOCBB+I6Nt1JZ9BYoucFW0fPGGOMCbr+8+nOmBR0dHRQWJjcYuRh4XXuMqG4uJhQKERz9LIMUT777DNGjBiRkZiCoqWlhZKSEr/DyAHWomeMMcYEmSV6xmTIxIkTWbZsGZDaUg3xOumkk/j3v3ufGXHOnDnMnj077bEEgYjQ1dW1+XeTIg9b9ETkYBFZICJLROQ2Edlm/LiIVIvIRyLynrtNjXhubxF5033+IxEZ5eErNVngh1+6gB9+6QK/wzDGmECxRM+YHhQXF9PW1uZZeRMmTGDp0qWeldeXXXfdlffff7/XY9577z2mT5+emYB8VlVVRV1dnd9h5AbBs0RPRPKAW4ETVXUyUAqc3sPhR6rqdHdb4J4/CLgT+Iaq7gzsC9T3cL7JUTXlw6kpH+53GMYYEyiW6BnTg8rKSurq6jzr6jdmzBhqamo8iCx+O++8Mx9++GGPz3d3d5OX4cXD/TJ8+HDWrl2b9vGR/YdnXTdnAjWq+pH7+FbghAQCORV4TFUXAqjqBlX17hsakxX2X/Yu+y971+8wjDEmUCzRM6YH4UTPq6UV8vPzN3cdzJSTTz6Z++67L+Zzn3/+OUOGDMloPH4KJ3rGI/G36A12u2WGt3OjShoDrIx4vMLdF8sjIjJfRK6I6N65A1AkIs+JyLvuc9Y3t585/+V/cv7L//Q7DGOMCRRbR8+YHoQTvcbGRk+XVsikQYMG0d7ezsaNGxkwYMBWz82ZM4cDDjjAp8gyb/jw4XzwwQc2Ps8zcV/HelWd2scxkc2sPRW8v6rWuF01/wmcD1wNFAAHAQcArcD/cLp+3hlvgMYYY0wushY9Y3oQTvSWLl3q6WLpme46eMIJJ/DQQw9ts//tt99mzz33zGgsfrIWPS95urzCSiBy7ZIxwDZ9nFW1xv3ZBPwd2Nt9agXwhKrWul02HwL2SOHFGWOMMTnBEj1jehBO9NauXcvw4d4M8i8rK2Pt2rUUFxd7Ul48Zs2axdy5c7fZ39nZSUFBQcbi8FtVVRW1tbU2Rs8zno3RmweMjphF81vAg1vVJFIiImXu7/nAV4DwTEMPAvuLyAARCQGHAB9hjDHG9HOW6BnTg3Cip6qedfebMGEC8+fPp6yszJPy4iEiTJo0iSVLlmzeV19fT0VFRcZiCIK8vLyMj5HMaRKKb+uDqnYB3wHuF5ElON0v7xKRGSLyuHvYcGCOiLwPzAc6gKvc85fgdNN8Byf5Wwfc5vGrNcYYY7KOjdEzpgdlZWVs2LDB0zInTpzICy+8kPFJUE499VRuuOEGLrnkEgBefvnlfjU+L0xVCYXs+y1PeDjWUVWfB6LH8c0DvuA+vxSY3sv5NwI3ehaQyTq3zfii3yEYY0zg2CceY3oQCoU8bwEKt+gNGjTI03L7UllZSVNTEx0dHQC89dZbzJw5M6MxBEFra6snS2UY8LDrpjEpe2TqgTwy9UC/wzDGmECxRM+YXqxbt86z8XngjBOrrq7OeKIHcOyxx/LYY48B0NHRQVFRUcZj8FtLS4sv194Yk14lm1op2dTqdxjGGBMolugZ04PuTW0s+/gjxg0d7FmZAmzatJGy0sy3Kh144IG8+MILbFi3kpKBmZsMJihUuykfEGJQXqdNyJIyQSS+zZhM+Od9F/PP+y72OwxjjAkUS/SMiaG9ZhHVZ06ga8k8Cv/+HRoevzn1QrUbNtYzdtQIBhUqtDenXmYi2moZXdTIvX+9mn23y0Ob12S2fh9pZzv6wE8ZvuolSt+5A33k/9Bum5glNdZ10xhjjAkyS/SMiWHt9d+hq7GW8rwuxhR2sv6v59FZn+IabO0toN1M2G4cgwaVQudG6OrwJuA46Kq3OPXYA/jNX+5j710nozVv9p+WrQ8fgTUfM6wkj0GFwMp3YNFzfkeVvQQv19EzxhhjTBrYrJvGxNC5fiVoN18dK5TkC5KXT1f9GvIHpzBeT7sBOPGELzJ82FBANu/LiM5NjBg6mBsu/g7FA4pAu5xNcv+/AW1aB12b2Gd8GSWFedDdBc3r/Q4ry1kSZ4wxxgSZtegZE8PAmUcjhQPYp0ogFCI0oISC0VNSKzSvEIB99prpLpiuEMpgkjWwCiTEcQfPBASKypBM1u8jGbcn5BUyZdhARlcUOeu7jdnd77Cym0fr6BljjDEmPfrHpzxjEjT07OsAofWtx8gfOpbh591KqCjFCUzyB4AqdG0EBApLIZTnRbhxkTF7oZ+9C231TpI3ao+M1e032W4WeshP4c27QATZ9yxkZPSybSZ+1i3TBMu7o3bwOwRjjAkcS/SMiSFUUMTwH3i8BrMIFA4EBnpXZiLV5xUiY/bype4gCE09EqYe6XcYOcQSPRMclx32Xb9DMMaYwLFEzxhjTOKsRc8YY4wJNEv0jDHGJMESPRMcVz/+RwB+8YUf+xyJMcYEhyV6xhhjEmd5ngmQKZ+v8DsEY4wJHJsSzRhjTBJswfSgEZEjRORtEdkoIqtE5AoRydyMT8YYYwLFWvSMMcYkSGzphIARkd2BR3FmkDoNmArcinOfv9DH0IwxxvjEEj1jjDGJEWwyluD5GfCBqv7EfbxQREYDV4nIFara7GNsxhhjfGBfyRpjjEmCdd0MmP2Ax6P2PQYUA3tmPhxjjDF+E1X1O4Z+S0QagRq/44jDYKDe7yA8Zq8pO9hr8tYYVS1LtRAReREYFufh61T1oFTrNL0TkU3AT1T1poh9JUAzcIqq3hOx/1zg3IjTJwDLMhVrP5GL/3f5za6p9+yapkeq19WTezVY101fefUmppuILFDVqX7H4SV7TdnBXlMwWeKWNTTqp/NA9UacsXwmTXLh33nQ2DX1nl3T9AjSdbWum8YYY0z2+wwYGbVvZMRzxhhj+hlL9Iwxxpjs9ypwdNS+LwBtwNuZD8cYY4zfLNEz8cjFLj72mrKDvSZj4vMHYFcR+YOI7CQiJwCXAX+yGTd9Yf/OvWfX1Ht2TdMjMNfVJmMxxhhjcoCIHAn8FtgZqAVuB36lql2+BmaMMcYXlugZY4wxxhhjTI6xrpvGGGOMMcYYk2Ms0TPGGGOMMcaYHGOJnolJRC4VEY2xjfc7tniIyGwReVhElrtxXxrjmMki8piINItIg4j8S0SG+hBuXPp6TSJyUA/v2Zn+RNw3EfmmiLwgIutFpElE3haRU6OOybb3qdfXlI3vkzFma7l4j/FbLt4P/CYiXxWRN0WkTkQ2isgSEblCRAojjrFrmgIROVREukSkOmp/IK6rJXqmNzU46zBFbit9jSh+pcAC4AJgTfSTIlICPAcUAQcCxwDTgIdFRDIYZyJ6fU0RZrH1e3Zf+kNL2qHA/3Cmgd8duBe4S0ROgqx9n3p9TRGy6X0yxmwtF+8xfsvF+4Hf6oBrgAOAHYGfA98Ffg92TVMlIqOAfwBPR+0PzHW1yVhMTO63k2eq6nifQ0mZ+y3LHap6acS+7+BMfztSVWvdfbsC84FDVfV5H0KNWw+v6SDgBWCCqlb7EZcXRORxoE1Vv5Lt71NY1Gs6iBx4n4wxjly8xwRFLt4P/CYi1wGHqeoudk2TJyJ5wPPAo0AJEZ+Zg3RdrUXP9GaEiKwUkRoReUJE9vU7IA/tB8wN/wMEUNX3cVoxD/AtKm+8KCLrROR1EflGFn4rVwF87v6eK+9TBVteU1i2v0/GmJ7lyv9dfqsg9+4HvhGRnYCjcb5sBLumqbgSaMFtHY0SmOuan8nKTFaZC5wBfAyU4zT1vywiR6nqM75G5o2RwGcx9q8BRmU4Fq98BpwDzHMfHw38DZgMXOxXUIkQkW8AM4AfuLuy/n2K8Zqy/n0yxvQp6//v8lsu3g/8IiLNQAFQCNwM/MR9yq5pEkTkGOBUYHdV1Rjf0wbmulqiZ2JS1Seidr0sItvh9O/OhUSvt9aTrOzPrKqLgEURu+a5A65/IiKXqWqHT6HFRUS+hHMDOltV3wnv7uWUwL9PsV5Ttr9Pxpi4ZPX/XX7LxfuBz6YDxTiJ81XAWuAS7JomTETGALcDX1PV9T0d1ksRGb2uluiZRLwJfNHvIDyyGpgUY/8IYn8Lk63m4vQdH4rzmgNJRE4G7gC+p6p3RDyVte9TL68plqx4n4wxccva/7v8lov3A7+p6hL31w9EpBu4TUSuwa5pMmbg3KufjWjJCwEiIp04PeACc11tjJ5JxO5kz6ybfXkV2EtEKsM7RGQaMAZ42beovLcH0Ma248MCwx20fAfOQOY7op7Oyvepj9cUS+DfJ2NMQrLy/y6/5eL9IKAEpyunXdPEPQfsgtNKGt7+ipPcTQceIkDX1WbdNDGJyB9wZhKqxhmjd7a7fUlVH/ExtLiISCnOmCeAx3GmbP4r0K6qC9ypbxfgdKG7EBgA3ITzYXtfDeA/jDhe03nAcpzXBc7Yr98Cf1bVn2c43LiIyE9xpn4+F3g44qkuVV2fpe9TX6/pPLLsfTLGbC0X7zF+y8X7gd9E5GKcHiNLcZK7WcAfgNdV9Xi7pt6Inqk+SNfVEj0Tk4jcgzMz0FBgA/AB8JtsmWo3Ygr7aMsj/iFOBv4IHAR0Ao8BP+6lz7Wv+npNIvJz4CxgLM7rWYLzweMWVe3OVJyJcKcl3y7GU9n8PlXTy2vKxvfJGLO1XLzH+C0X7wd+E5Erga+w5X5TDfwL+JOqtrnH2DVNUawlyYJyXS3RM8YYY4wxxpgcY2P0jDHGGGOMMSbHWKJnjDHGGGOMMTnGEj1jjDHGGGOMyTGW6BljjDHGGGNMjrFEzxhjjDHGGGNyjCV6xhhjjDHGGJNjLNEzJgUicoeIvOh3HLGIiIrImX7HYYwxxqRCRF4UkTsSPOdMEelzDTG7V5pclu93AMZkuR8DeRB7wcxMcBPNalU9M+qpkTiL3RtjjDHZ7ASgy+8gjMk2lugZkwJVTUsiJSJFqroplTJUdY1X8RhjjDF+UdU6v2NIlBf3cWNSZV03jUlBuOumiPwMuATYzu0Gom4LHyKSLyK/EpGlItImIgtE5BwRkYhyqkXkShH5o4isBz50939dRF4XkXp3e15E9ow471HgQOAbEfUe5D63VXcUERkqIneJSK0bxxsicnDU61ER+YGI3CYiG0TkMxH5nYjkpekSGmOMCaiIe9yZ7j2sWUSeE5GJcZ4f1z1FRM4VkYUislFEFovIxSJSEPH8Vl03RaRYRP7ullkvIjeJyG9FpDpGDHuJyFvufe9DETkiRqgVInKv+/rWi8gVIhKKKKNERG4QkTUisklE3heRE6Pq6ek+/iUReVdEWkWkQUTeFJHd47l+xqTKEj1jvHET8AegBqfL5Ejg9+5zNwNfBb4HTAUuBX4LfDeqjB8ArcABwPHuvmL32JnAbLf8p0Sk0n3+NOB14N8R9b7WQ4wPAXsAXwH2BN5xy9ox6rhLgHfdY34G/AQ4o88rYIwxJhftDnwZp/vkbGAocHsC5/d6TxGRi4FfAL8EdgJ+BHwHuKyXMn8PfMktZ2+cYQrf7+HY693ydwPmA/8WkYoYMc7DuUf+Ajgf+GHE83/DuS+fBewKPADcJyKHRpWz1X1cRIYD/wHuA6a5sV4HdPby2ozxjKj2OU7VGNMD9xvG8ap6UKwxeiIyAfgU2FVVP4zY/yvga6o6zX1cDaxQ1dl91JcHNABnq+rd7r4XiTFGzx2E/k1VvcNtuXse2F1V34s45l1gfvhc95y/qerZEcc8C6xT1VPivS7GGGOyn3uP+yIwRlVb3X2nAXcCxX11TezrniIiA4H1wEmq+mjEMWcANwJlqqqR9zkRKQVqgR+p6s0R57wBjAjfg90eLbcD+6rq6+6+McBK4EhVfToixn+r6kkRZf3ejWms23r5KXC8qv434pj/AhWqepD7uJqo+7iI7AG8DWynqit6u1bGpIO16BmTXjMAAd5wu4Q0i0gzzjeX20cdOzf6ZBHZVUTud7vMNOJ8a1kCjE8wjmnAhsgkzzUH2CVq39tRj1cBIxKszxhjTG74OJzkuVbh3NeGx3l+b/eUnYGBOK1jkffIm4FSYt97JgGFwBtR+1+Po/5V7s/ocqN7wrwCjBGRMjdGgBejjnmJbe+f0ffx+cDTwEci8l8R+bGIjO0hTmM8Z5OxGJNe4S9TZgONfRzbEvnA/abzGZwbx1nAGqAd5+ZWmGAc0stz0c367TGety+FjDGmf4p1T4D47wu93VPCP08GFsY4d30v5cbVJU1V2yN+V3GGx0fH3ltZidw/t7qPq2qXiByFM/ziMJxhHFeJyNdU9ZG+YjcmVfbhzRjvtOMutRAh/E3iOFVdEr31Ud5OwDDg/1T1eVVdgNOvvyqOeqN9CJSLyPSo/bPd54wxxphM+wjYCEyKdY9U1Vhj2T7Fue/tE7V/7xTi2C/G41Wq2siWe+RBUcccSBz3T3W8qapXquoBOC2BZ6UQqzFxsxY9Y7yzFBghInsDS4BWVV0iIrcBN7tdQF7D6Xq5J85Ygit7Ka8a5wb4IxG5Gqerye+Athj1zhaRSThdOzeoakfkAar6vIi8DvxLRL4PrMMZaD4NsLF3xhhjMk5Vm0XkSuByEenC6eaYj9Mlck9V/XkP59wCXCYinwEfA9/A+XK0PslQjhCRnwKP4CR55wL/59a3VETuBf4sIp3AJzgtkF8EYs3guZmI7AscCjyF0ytne5zJXP6RZJzGJMRa9IzxzoM4s2s9jtPd5AJ3/3dxZuT8JbAAeA7npvRpb4Wpai1OEjYb51vPvwFX4SRpka4BPscZC7Cebb+ZDDseeA9n9s33cJLNo1U1VncZY4wxJu1U9XLgPODbOPexV3Bm5qzu5bSf4yRl/wTeBAYDd+B8OZqMy4B9ce6Nvwf+6G5h3wEeBm7FacU7EThFVZ/to9wNOC2P/wMWA7cB9+DMvm1M2tmsm8YYY4wxJquJyPNAk6p+ye9YjAkK67ppjDHGGGOyhojsjDPByWs4k5OdDhwMHOdnXMYEjSV6xhhjjDEmISLyNE53x1jeV9WenvNCN3A2TvfKELAI+ErkWnzGGOu6aYwxxhhjEiQio4HiHp7epKorMxmPMWZblugZY4wxxhhjTI6xWTeNMcb8f/buO06Ouv7j+Ou911sgoSUQISBFAypEQBBEQLAAFrCLICJIVWw/xIKioiKKWAArRbCAItgognSkSKQnIDUNEpKQhFxv+/n9MbNks7d3t3s7s7O793k+HvO429nvzPc7s3O385lvc84551yN8UDPOeecc84552qMB3rOOeecc845V2M80HPOOeecc865GuOBnnPOOeecc87VGA/0nHPOOeecc67GeKDnnHPOOeecczXGAz3nnHPOOeecqzH1SRdgMpsyZYrNnDkz6WI45yaJxx57rNPMppS6n1mtsu7hwtKuHOA2M9u31DxdfEr6Lnr22eDn1ltHVyDnnJvEovquBg/0EjVz5kzmz5+fdDGcc5OEpCVR7Kd7GE7aurCvj2/8b2jTKPJ08Snpu+iqq4Kfhx0WXYGcc24Si+q7GjzQc845NwFKugCuMniA55xzFcsDPeecc0URII/0HMCLLwY/N9oo2XI455wbwQM955xzRavzQM8BHHxw8POee5Ith3POuRE80HPOOVc0j/Occ865ylYz0ytIWiDpjKzXdZIulLRSkkl6T3Klc8652iIVtjjnnHMuGbVUo7cb0Jv1+r3AEcD+wFPA6iQK5ZxztUbU0FNC55xzrkbVzHe1ma0ws66sVdsDS83sTjNbZmb9E9mvpKZoSli7zNKk/3cD6du+T/rOn2AvPh3Jfp+64idce/AWXPeuLVl4zW9Gz3+wh/Tj/yB9/yWkH/sr1t81ato4dM+7i8ePfhXz3rsJi8/9JOnBgVEKajDQBT0vQu8qGB4lHWAvLmDglx9k4Ht7M/iHT2F9a0dN+9QVP+HaQ7bgune9YuzzNNRPeuGdpP/3D9ILbsMGe0ZNu+L2q7jnA7O46z3TWXDJNzGzUdMmafD5J1l22l4s+fjmrPjee0l3rylr/kMPX0v3N3an6/TX0X/9OZGcJ+tZSfrJ60g//lfSz83F0ukIShqxAmvzvEbPOeecS07igZ6kWyVdkrPuKEkW/r5v2PTyrZJultQj6SlJH83Z5uWmm5JuBb4FbBlu2xeur5f0DUkLJQ1IekLSSXnK8xtJp0t6DujM2v+3Jf1Y0qpw+aYCX5b0fLjuPEm1VFM6Llt4Nzw3FwZ7oW8N9tAVWO+akvb5/O1/Y97Pv0L/6hfoW/k8D37/JFY+dGf+/J+8ATqXBoFT13LsiWtKyrsYQ6uX8+yX3sbAkicY7nyRNTf9jhcuOT1/4sFeGOoDDCwN/WshPXLWaUsPMXjJ0bDsf9C3Fnv6Loau+kreXS694+/BeVr1An0rlwbn6cE78qa1JfdAz0pID0LvKmzRv/Om63r6YR4/8wj6ly9mcM0KFv/hbJZdP3oAmRQbHmL5N97G4LMPYt2r6XvoX7z406PLlv/w8/Ppu+xT2JrnoetFBm/+GYP/uaKkfdpQP7bwjuCBwPAAvLQIW/5IRCWOlgpcXI3bZ59gcc45V3GqKSD5EXAaMB84HrhE0r1m9mSetIcBXwY+SNCkM/OY/RvASeHyH+BtwA8l9ZrZRVnbHwpcGb5fl7X+JOB7wBuAA4ALgNcTNA3dH5gNXAHcD2Tvr7a9+DSkh7j94QXsuv3mtLY1wtrnoWXDCe9y5QO3ke7vZXGPcdNyo7G+n+tO/xobvWbPEWnt+QcA2GiDNj57+IHQ9xI2PIjqGiacf6F6FzwCSjFsxrlPQFNdD/XP/4ZpC/L8aQ31BQHeywR1DZBaP631d5G+b/76aeuupm7elBG7XH7fv1j9ZPe6Fak+rvva19joNW8ckdZW/m9kmTa6GWn95z2dT9zPmqcGGRxM8+ZNxKun9LL6vhuZ8Y6j8p2CxAyvXkq6cyVYmluXDbPv9AH6H8sfvMYhveB+SKV4sXeYBS8N8/rpMPy/2+ENH5r4TvtfCvadTvOvux/hrXu9DrpeiKjE0fLaOgfA2WcnXQLnnHOjqKZA71tm9jcASV8CTgT2A0YEema2SlIXMGxmy8JtWoDPA6eZ2e/CpE9K2hb4GusHZiuBY80st7rlLjP7bta2nwVmAYdY0GbrcUm3AAeSJ9ALaw9frkGcPn16Mcdfudo2hjWLue2hBWy56QbMam2Flqkl7bJ95rakmlpYsLKb/TYR22zUwpxTTmDmW94/Im36od/BQDdfueDqYEVd44jgKS6Nm26FDQ3QPQTTGuHobRvZYO992eor3x6ZuL8ThnNaEDdvODLQG+xj8Oy7YCiTVrDZdjQeP3Kfz1z1Ch796XyG+4PuqXXNrcz59AnMPOADI9Kmn7wehrK6sabq0faHoJw79lVzb2TeV+5g2UtD3PUizN64mdYtXzXuuSi31JSNQXXAINcsTrPvjHrqN92qbPlro63AjEVrh/nvCwO8fmYHqU1fWdpOG1rB0nT19HHdHQ/w1r12hqaOSMobJeHTK7h1Zp1WeCuKBWcdHGNJnHPOZUu86WYR/pv5xcwGgRVAMZHStkATcGvO+tuArSRl3039N0+QB/BAzuulwIO2fsecpcBm+QpgZueb2ezMMnVqacFQpdAr94f2TejsHaSzdxBm7Y2mzChpn7PefQyb7ro/fVZHa2Mdm+97GFvs/75R8j8wCO5QELxs+9YRwUtcmrbYlhmf/D49Vk97Yz1NM7Zh8xN/nD9xY1sYmITqW/MGpGpopv5934f6pqDGr20qDe/N/9R81ruOYZNd90d19ahunPM08w2QagClQHVo5h55z9PU1x/AjEOOpbWxjl5LMeVVu/GKD39h/JNRZqnGFjb+3O+goZkX+oPAb6NTLi1b/nXb703Dnh+la0h0DorUljvTuP+JJe1Tje2w2Wvp7O6js6cPGtvQjF0iKnG0vOmmA+CII/jhP85JuhTOOefyqIQavTQj7wca86TLHbnCKC5QLeaeo3uU9YN5ypBvXTUF0CVTQzPsfiydG9xD1w7vJbVN6f01UvUN7HH2X7it/esc8tGPsPkrR69RUvumsPMRaNNH0S5HojLV5mVs/O6TmbHFbmx7791sf/ynUF1d/oRKBTV4lg5Hqhj9MkntsC8Np94O3augY9NRm6Gm6uvZ4+y/0P/iMkilaJ6W9xlDkH3LVNjuHUET0vpmlMpfTkls+6lzmfmR07jz29/hdef8qGyBc7FaXn8Qm/1yIWv2fCOb/+wBVB9/c90MSTQdegaDg9vTf+89tJz840jOU2qj7ejarI8u/RNt+/aKPfcVWixXbk8+ydar1iRdCuecc3lUQqC3HNgiZ92cGPJ5EugH9gUezFr/ZmChmXXGkOekIYmunn66ekcfSXIi++yzFJtsOX5zOKXqUH3T+jVmZdQzDFNnbj16kJchFVxGNbTAhrl/Gvl2KZo3LqwGVam6oGaxAM0bbYaa2yo20MjoHhimq7e/rEFetq4h6Bok0vPU1dNHZ3dvRZ/7yi2Zc84556AyAr0bgF9KOhy4h2AAlPdGnYmZ9Uo6FzhD0nLWDcZyAln95tzEdXV1sXbt6NMATMTg4CANDYXdwLe2ttLd3U17e3ukZSjE2rVr6eiovL5Uk8HatWvp6irvlBpx55/0MY1HQEqVOeWGc8455wKV0MTwUoIRNc8h6AO3B8HUCHE4HTiPYOTM+cBngP8zs1/HlN+k0tTURGdnchWjU6ZMSSz/zs5OpkwZOSqmi19nZydNTclNdxlH/kkfUyGi7KMnaT9J88Opcy7KN0WNpHZJl4bT4jwu6bhw/Z6SHgyXRyWV1lHSOeecqxGJ1+iZ2RDwhXDJ9pPw/VvJc79gZrPGeX0GcEaevL4aLqOVZ99R1s/Ks25EWjM7arR917qOjo7IA61imq5l8p8xo7SBYCais7OzJmv0KrnpYEZnZycbbrghQ0ND1NeX/19aHJ99NVxPUV0ZkuqAC4F3mtk8SX8EjgAuzkn6Q+BRMztSwYW5Sbj+IWBXMxuS1A7Mk3StmS2IqIhuLI2NDNQlfivhnHMuj0qo0XM1Io4atfUHNB1bHIFmoarhxnwiijn/Sens7GSLLbZIrKnj8PBw5AFmNdQQp1TYUoDdgCVmNi98fSHBXKgvC0dFPoQg2MMCy8Pfe8KHeAAtBHOfVv4Tilpx++188PDvJV0K55xzeXig5yLT0tJCT09PYvl3dHRE3kewUN5HLzlr165liy22SOyzj0OlX08qMMgrMNCbCSzOer0oXJdtG+AF4DxJ90v6q6RZ68qjnSU9Em77AzN7tpTjc84552qBB3ouMkk380uyRq+7u5u2tsJGs3TR6uzsZPPNN0+0f2jUurq6mDZtGv39/UkXZVRF9NGbGva/yyz5Br/KrjrO94+kAdgZ+KuZzQH+Dlz08sZmD5rZa4CtgfdJ2qGkg3OFO/98jrj/H0mXwjnnXB4e6LlIDA8Pk0olezklORiLmSV+/JNVLQZ6ZsYGG2xQ0cckFbYAq81sdtZyfs6uFgNbZr2eCSzJk2aVmV0Xvv49eabhMbNlwB3AOyM4xMhJ2iesjVwoySSdkfP+vuH63OWoAva9q6TbJfVKWiHpAkmtcR3Lyy67jMMevSX2bJxzzhXP70xdJLq6uhJvapZkjV4tq/R+epk+ekl/9lGfpyQfXBQiwlE35wJbSJodvj4auCo7gZm9ADwqaddw1YHAPABJr5TUEP7eAbwVeHSChxW3doIRn08Flo2RbndgRtZyxVg7lbQFcBNBgLw7cDhBsHvRWNs555yrbR7ouUhUQp+iJPvo1arm5mb6+vqSLsaYenp62GyzzRL97FtaWujt7Y10n5V+PUfVR8/MhoFjgSslPQX0AJeFNVTXZiU9AfippIeBzwPHhOv3BR6U9BDwb+APZnZ9ZAcaITO71sy+ZGZXAGO1y11hZsuylvEurhOBPuBIM3vEzG4gmB/2g5JeGVHxnXPOVRkfE9lFIo5RJwcHB4sazdBr9KKXOactLS1JF2VMSX/2mfxbW6NrKZf0MY2lmDnyCmFmNwOzc1bPBQ7KSjMf2DPPthcSjNRZS24Nm10+DfwcuNTGrjLeC7gha/RRgOuBNLB3uJ+Xhf0kX+4rOX369KjK7ZxzroJ4jZ6LRFxziRUzxHx9fT3Dw8ORlmGyq/TmgxlJB0Vx5J/0MY0nwlE33TpLCWou30cQ5F4L/BL45jjbzQi3fZmZDQCrgM1zE5vZ+dn9JqdOnRpF2Z1zzlUYr9FzkYhj3q9anZuumlR6sJGRdEAaR/5Tpkxh2bKxunElK+FBdmuSmf0P+F/WqrmSGoHPSvqmmQ2OsulYn0a8nWwvuICv/uSOWLNwzjk3MV6j5yIRRx+9Suj3N9lVej+xjLa2tkQmTO/v76exsTGW81Tp5z5V4OJKdi/QBmwyRprnCWr1XhYOUDONnJq+yM2Zw7zp28aahXPOuYnx72EXiUztmyTS6XSk+3TJqZYavaTmcMxco5Ox6WYR0yu40swBeoGVY6T5N3CgpLqsdW8n+I6/M8aywf33s+Oyp2LNwjnn3MR4oOcikbnhbW9vj6xmJY7moHEws8Qni49L0k0iK10cgV5mzI1KDvQKnVqhNv8qJk5Su6SdJe0MNALTw9ezw/c/I+lQSTuEy2eALwHnh33uMvt5XNLJWbu+AGgBfiNpJ0kHAOcBV5jZegOxRO7EEznzhp/FmoVzzrmJ8T56LhKZoCwTGEQRoHV2drLpppsWtU0SAVdPT0+koy1WkkoONipBJtCLMiDu6+ujpaWl4qe28IFWJmRXIHt28ePCZSEwC2gAzgJeAQwBTwGnAL/O2c8OwMaZF2b2nKS3AOcC9wHdwJ+AL8RxEM4556qDB3ouEt3d3bS1tUUaGEyk6WYSk3vXchNTD/TGlnmoEcd1X+m1xJVduspkZrcyxqkzs+8D3y9gPyP2YWZzgTeVUj7nnHO1xQM9F4lM88UoB5ColsFYqqWcE1HpA4Kk0+lEA6LMZz/prnufOsE555yreN5Hz0Uq6pqNauijVy3lnIimpiYGBgbGT5iQTE1yUjK1b42NjQwOjjby/cT2WckE1KmwxTnnnHPJ8EDPRSrKQK+vr4/m5uaitmlqaqK/vz+S/AtVDTfmpajkJoRJn/s48k/6mArl0ys4AI44gqt22i/pUjjnnMvDv4ddpKIclGIio1km0aesWm7MJyqJfo+FSro2NY78kz6mQvn0Cg6Ak07isjmHJF0K55xzeXig5yKVdJ+uJPKvij5VNSr33Jc7KI3js6+G60lASlbQ4pxzzrlkeKDnIpX0KI1eoze5ZJ/7trY2uru7y5p/f38/TU1Nke6zWq4nb7rpANhnH6743ReTLoVzzrk8/HvYRSrKCdMnIokJvqulqV0tyg6KknrIEHUfxmoJ9LzppgNgYIDG4aGkS+Gccy4PD/RcpOrq6kin04nl7zV6k0t2kJ1EkB+H7OsplUoxPDyccIlGEl6j55xzzlU6/x52JaukwTqS6KM3NDREfb1PSZmE7P5sSfcPjcrg4CCNjY1A8k2hx+LTKzjnnHOVzQM9V7Kenh5aW1uTLgZQ2TfGLnqV0HQzThV7TOGE6YUszjnnnEuGB3quZJXUdLFWmu9Vkvr6eoaGKrMPTldXF+3t7UDyQVF9fX1kk6ZnJH1MoxHeR8+FttuOZ6dtnnQpnHPO5eGBnitZHIORpNNpUqniL89KvTGuZpV8TtPpNHV1dUAyQX52s+U48q/kBxdR9tGTtJ+k+ZKeknSRpBFtoSW1S7pU0hOSHpd0XLj+cEkPSXpY0n2S9i396FzBLruMzx3y+aRL4ZxzLg8P9FzJ4pj3q6uri7a2tqK3a2pqoq+vL9KyTHbV0vct6XLGkX/SxzSWqGr0JNUBFwLvN7NtgXbgiDxJfwg8ambbA68Grg7XPwvsZ2avBY4CLpfk323OOecmPf8ydCWLo+nmRPcZ9VD3rrJr9LIlUc7s6y2O/Cv53EdYo7cbsMTM5oWvLwQOy04gqQM4hCDYwwLLw9/vMrNVYdL5QDNBsOjK4dRTOe2Wi5IuhXPOuTw80HMliyvQ87npKkMlNx/MVu6gaHh4eL3mxZMp0BNFDcYyNWyWmVlOytndTGBx1utF4bps2wAvAOdJul/SXyXNylO0jwDzzKwyq0Fr0e2384bF88ZP55xzrux8THhXss7OTrbZZpv11plZSbVrpQSP5a7Vq/VaxEoNNnLV19eXdc657IFgYPL10Sti6oTVZjZ7nDTZc7Tk23MDsDPwZTM7XtIxwEXA/i9vJM0BzgTeWnDJnHPOuRrmNXquZLlBWUtLC729vSXts5R+f+We16+S5hGMQ7UEeuWWW+scVx+9Sj33KnApwGJgy6zXM4EledKsMrPrwte/B+a8XBZpe+BPwIfM7MnijsQ555yrTR7ouZLlBmVR3JxW0pQNY+nv7395cutaVckDgiQpjut+cHCQ+vp1DS3a2tro6uoqaZ9xKLLp5njmAltIytT6HQ1clZ3AzF4AHpW0a7jqQGAegKSZwDXA8WZ2b+lH55xzztUGD/RcyXKDsqgCvWroo1ct5SxFJTcfTFJc1332PlOpVGXWGAtSsoKW8ZjZMHAscKWkp4Ae4DJJu0q6NivpCcBPJT0MfB44Jlz/dWBT4PuSHgyXWREerXPOOVeVvI+eK9nw8PB6tRBRBAbV0kevWmoeS1GpzQeTDoByP/uorvtqeXAQ5V+Zmd0M5PbjmwsclJVmPrBnnm2PJQgUXRKuuYaPf/OGpEvhnHMuD6/Rc5FLuulmOQOAyRDotba20tPTk3QxRujv76e5uTmx/HODspaWlpLPU7VcTxE33XTVbKONWNNSHQ8nnHNusvFAz0Uuij5dnZ2d641oWIy6ujqGhoZKyr9QcUwWX2kkJV57lk/S5z43/yhqkpM+pmJEOBiLq2ZXXcXb/ndX0qVwzjmXhwd6LnJR1OiZ2XpzlBWbf7kGsKimpna1Junar7jmj6yWQK9ehS1uHUn7hHMALpRkks7Ief9Nkv4saYmkXklPSjpDUtM4+z0q3F/usm+MhxM4+2yOv/fPsWfjnHOueN5Hz0UuikCvlNqRTP4bbrhhSWUoRGdnJzNmzIg9n6RV4lyBSQdFcQV6m2yySaT7jEsFXhKxyQRaZtYfvn4FcBjB5Oz/KmJX7cB8gukhfpTn/b2Ap4EfE0wpMQf4BbAZwWA048n9Z7SqiLI555yrMR7ouchFMShFKU0Fyzl4SNLBRrlUYtPNfLWpTU1N9Pf309Q0ZgVIJLq7u2lra4t0n9VSQ5zpozeJ/JVgCoefSmoH7gFagQ5Jx5nZhYXsxMyuBa4FkPS9PO+flbPqWUmvBL5IAYGemS0rpBzOOecmB2+66SKX9Lxr5cy/mvpU1Zp8576cn30pzYtHU03X0yTrozcHuC38/T1AJ0Et2yeBU2LOe0NgZSEJJT0jaamkWyUdEm+xnHPOVToP9FzkGhsbGRwcTCx/r9GbHPKd+0qdCqJQo11PlVijKqmgpUZMAVaHvx8A/MXMBoB/AdvElWk4ifyngfPGSfo/4GMEzUkPBe4H/i7pE6Ps9yRJ8zPL6tWr8yVzzjlX5bzppqs5U6ZMYdGiRWXJq6+vL9Eh/iezWgz0ent7aWlpWW9dZnqLqJuJlkSTq48e8Byws6TnCAK9o8P1GwD9cWQoaTvgn8AfzeynY6U1s7uBu7NW3SNpE4ImnyOalZrZ+cD5mdezZ8+e+JOE00/nJ5fcN+HNnXPOxcdr9FxJ+vv7aWxsTLoY6yn3zX4N1VpUlXz92aLoH1qouGrZcq+nch5TUaTCltpwIfA7YB4wANwSrn8D8HjUmUnaCbgduB44ZoK7uReYFVWZRnXwwdzyyt1iz8Y551zxPNBzJanEpotJ9xGsRZJIp9NJF2M9SffRyyeO85T0MY1mMsV5ZvYd4Cjgl8CbzCzTNj0N/CDKvCTtTtAf8E/AJ81sohfUHIKRO+O1dCmbdPngns45V4m86aYrSRyBXqk1JdXefK8StbW10d3dXVFB/eDg4Ija5I6ODp5//vmy5J+vJre9vZ2urq5IR86sxOtZUMRANMNxFqVszOyqPOsuKmYf4Yid24YvG4HpknYGBsxsvqR9CEb3/BPwHWCzrOtshZkNh/t5HDjPzM4LX38d+A/wJNAEfJAgMP1UMeWbkEMP5ZeL1nDokefEnpVzzrnieKDnShJHoNfX1zein1IxMkGJi06m+WAlBXr5JB0UZfKv9UAviPSSLkR5SZoC7A5MZ/2jNzO7rMDd7Mq6Zp8Ax4XLQoJmlkcTzLX38XDJtjWwIPx9B2DjrPc2AC4Iy9ZL0Jz0/WbmM5k759wk5oGeK0kc836VGlBEPeT9WCZL/7yKDDbySLo/Wxz5T5kyhZUrCxpdv6wmy7UPIOlA4AqCqQ5yGVBQoGdmtzLGrBNmdhRBTdx4+1HO688BnyukDM455yYPD/RcSeKo5YliLrFyDUdficPex6FaAr1ylXO0zz2O/nQdHR08++yzke4zCpMozgP4IcFE5180s+eSLoxzzrnymnXaNQWnXXDWwTGWpDge6LmSrF27lunTp0e6z2poIjjZVOqAILmampro6+uLPZ+enh5aW1tHrI8j0KzMc19Tc+QV4pXAoR7kOeecqyaTrJeFi9poQVl9ff2EJ02PozmoK03STSILVa7gY7TrPq5AryLPvQpcasOjwBZJF6IiHXAAd87aOelSOOecy8MDPVeS0YKyUgKDKGr0ynHDPzw8TF1dXez5VIKKDTYSMto1Wsp1n06n8163lXruJRW01IiTge9I2l2St4TJduaZnLPPEUmXwjnnXB4e6LmSjFWzMdHmZtXSR6+rq4v29vbY86kElRpsJGW0BxylXPejXU8NDQ0MDQ1NaJ9xkSCVUkFLYfvTfpLmS3pK0kX5gilJ7ZIulfSEpMclHReunyXpdkndkn4d8aFm3APsCdwN9Esazl5iytM555wriT+ZdCXp6+ujqalpxPpSAoNq6aMXRUBaLSqzn1hyRvvsJ8N1/7KIausk1QEXAu80s3mS/ggcAVyck/SHwKNmdqSCqsJNwvVrgdOA1xJMXxCHowlG13S5PvhBznt4KSe/+4tJl8Q551wOD/RcyaJublYtffSqpZxR6OjooKurK+livGxoaCjRZrOdnZ1svPHGI9ZnJkyf6D6rKdCLsFXmbsASM5sXvr6QoKnky4GepA7gEGBLCCauA5aHv68C7pK0fWQlymFml8S176q3cCFbvLQm6VI455zLwwM9F4tK6KNnZrH2Eaq2G/NS1NXVkU6nky7Gy5IOsjs7O9l6661HrC/lPCV9TMUq4m9rqqT5Wa/PN7Pzs17PBBZnvV4Ursu2DfACcJ6k3cP0p5jZgqIKXSJJ+wCvIajde8TM7ihn/s4551wxPNBzsejo6GDFihUT2nZwcJCGhoaS8m9tbaWnp4e2traS9jOWyRToQWXNGZj0uY8j/6SPqVhFPENZbWazx0mTfXHl23MDsDPwZTM7XtIxwEXA/gWXogSSpgFXAfsAL4VlnCLpDuAwM3uxHOVwzjnniuGDsbhYJN2nqxz5T6Y+epUm6XO/du3ayGvfkj6moogg0itkGd9iwiaZoZnAkjxpVpnZdeHr3wNzSj2MIvwQ2BjYxcymmtmGYf4bAeeUsRzOOedcwTzQc7FIepTGcuRfbTUwtWSsc19XVxf7KJVeoxdlnMdcYAtJmVq/owlqz15mZi8Aj0rKDLZyIDCP8jkEONHMHsoq04PASeF7k1d7O92NzUmXwjnnXB7edNPFIulArxwTfFdbn6paMlZQlBkQZcMNN4wt/+HhYerro/33WU2BnhCpVDTPCc1sWNKxwJWSGoHbgcvCoO6bZnZQmPQE4EJJbcAa4BgASa3AE0Ar0CTp7cDHzOymSAoYaCMc/CXHC+F7k9e//sVHT7sm6VI455zLwwM9F4tMH7mkeI1ebRttMBRYF+THGejF0V+x2h4cRDnOkZndDOT245sLHJSVZj7BXHa52/YwcvCWqD1EUHv3qZz1nwrfc8455yqOB3ouFnGOdlmIUgaDKVRXV1esg7240Y3Vny3p/qET1dnZmXfCdIDGxkb6+/vzzlmZmIT/xsvsq8C1kt4I3BauezOwE5O96ea55/KJ++Zz4W7vSbokzjnncngfPTdhw8PDkTXfilo5avTMrGKPv9aNVZtajs8+jgcZY11PSTeFHqHA/nm1Egua2b8IRv18lGDkzTeHv+9iZjcmWLTkXXEFhzzms0w451wl8ho9N2FdXV2j1kAkrRx99CajuOcmLFTSgd54oj5PmWPKN0l7UirhOiinsOnox5Iuh3POOVcor45wE1bJfYoq4Wa/1jQ3N9Pf3590MQDo6emhtbU173vlCPLH6qPX0tJCX19fpPlV4oOLyVSj55xzzlUjD/TchMUx79fg4GAkoxlWaz+tSlZp53S0GqWkyxlH/kkfU141HulJGpa0afh7Onydd0m6rM4551w+3nTTTVglzyVWX1/P8LDff0UpU0u66aabJl2UMSXdRy+T/2abbRZZfpVWQy0glareIK5ARwMvZf0e/VCrzjnnXIw80HMTFlegV6nNQSe7Smw+mE/SQVEc+Xd0dLBs2bJI91kSqeb76JnZb7J+vyTBolS2X/+aL5572/jpnHPOlZ033XQTFkdQFmXwGPeNaK3f6OZKOoAqVFtbG93d3bHtf2BggIaGhlHfjyMgrsggWwUuNUDSM5I2yrN+Q0nPJFGmirHTTjyxyaykS+Gccy4PD/TchI0XlEkinU4Xtc8o+/3FMal1Ofdfaaol0EulUrF+NuM94Iirj16lnXulUgUtNWIWUJdnfSPwivIWpcLcfTdznnss6VI455zLo6Smm5I2MLOXxk/patF4QVl7eztdXV1F1frF0Rw0DpMtyIMg2FiyZEnSxUjceNd9R0cHixYtKmqf411PFTsYS42TtE/Wyz0lrc56XQe8FSjuw641n/0spy9aw6FHnpN0SZxzzuUo+HGrpM9L+mDW68uAVZIWSdopltK5ijZeUDaRWohq6aPX09NDW1tb0sUoq4psPpiAOK77vr4+mpubR32/ubk58ikbSlbjo26GbgVuIRiI5erwdWa5CfgUcEahO5O0j6S/SlooySSN2FbStpKukdQlaY2k30napIB97yrpdkm9klZIukBS/jlInHPOTQrFtKs5AVgKIOlNwKHAR4B7gO9FXzRX6bq7u8cMdiYSGFRLH71qqXmMUiU2H0zCeJ/9RK/7sR5wVF5/UCGlClqq3NbAKwl6G+4evs4sWwBTzOyyIvbXDswHTgVGjK4jqY0ggGwC3gwcDOwE/FVjXASStgi3WxKW83DgncBFRZTNOedcjSmm6ebmwILw94OBP5vZFZIeBXzIrUnIzEiN0QdnIs3NqqWPngd6yUmn04kGPoXU6CV53ZeFqIXaunGZ2UIASZuY2YsR7O9a4Npwn/kekH4EmAHMyeQn6QjgIWA/4OZRdn0i0AccaWZDwCOSTiIIEL9iZk+XWnbnnHPVp5jHrd3A1PD3fVn3hdMLtERYJlcjJtp0sxpueKvuxjwCTU1N9Pf3J12McWuS47Z27doxa98aGxsZHBwsap/Vct1nm0yDsWQHeZKmS9oye4kwq72Ae7PzM7OHCWrq3jTOdjeEQV7G9UAa2Ds3saSTJM3PLKtXr85N4pxzrgYU8y18G3COpK8CuxB8iQBsDyyOumCu+k0k0Ovt7aWlJZrnBnEGJtXSl7AWJR0UxTV/ZLUFeihV2FKBJP1B0n5FpG+XdKGkHuA54NmcJSozCLtI5FhG0Kqm4O3MbABYlW87MzvfzGZnlqlTp+YmKdwxx3D569468e2dc87Fpphv4VOAHuD9wHFm9kK4/mDgX1EXzFW/ifRVUoQTMcfZ1LAqb8wjUAl9xZIOsuMK9KrpwYEQShW2VKirgS9JekLSaZI2Gyf9dwlqxj4G9APHAN8kCMA+GmG5xjphY7VFn+h2pTvmGK543dtizcI559zEFBzomdlzZvYuM3udmV2Stf5TZnZyLKVzVW0igVaU/eo80IteJUwrUci5lxRbWb1Gj3V99Kp01E0z+6OZvZVgeoQ2YK6kqyQdOMom7wRONrM/AcPA7Wb2DeDrwAciLNrzBLVzuaaTv6Zv1O0kNQDTxtnOOedcDStmeoVnJG2UZ/2Gkp6JtliuFiQ991ec+U/GPnqVopBz39raSnd3dyz59/b2jjkVwkRU5fUUYdNNSfuF/cWeknSRpBEDhYXNJy8Na+Eel3Rc1ntnh9s+Iel9RRzFdsAOQBfwMPA5Sb/Lk25T4H/h713ABuHvNwFvKSK/8fwbeIOkaZkV4fRFM4E7xtnuQEnZk7q/neA7/s4IyzfSHntw9aWfjzUL55xzE1NM081ZBBPE5moEXhFJaVxNyUyYnhSv0atNhZz7OD/7KJsXZ1Tj9ZQ5D+MtBeynDrgQeL+ZbUswBcEReZL+EHjUzLYHXk3Q/BJJbwX2AF5FMDLluZLGPJmSvirpSeA44Ndm9mozO8PM3gHMybPJEoJaNQhGn87075tD0JSzIGGwurOknQm+O6eHr2eHSX5PUAN3uaQ5kt4I/JZgGqNbsvbzuKTsljQXEAyK9htJO0k6ADgPuMJH3HTOuclr3OkVJO2T9XJPSdnDc9URNH1ZFHXBXPWrq6sjnU4nln+cE3xX4415rejs7GS77bYbM00m0JsxI18ruMpTyPWUSqUYHh6mri7f87YERBfs7gYsMbN54esLgZOBi9dlpQ7gEGBLAAva5S4P3z4MuCQccfI5SXcSfC/9eZx832RmI+ayI+h3nusW4F3Af4BfAT+X9CFgR4qbq25XsgI2gkDzOGAhMMvMuiW9BfgxQQ3eEHANcIqt3xZ5B2DjzAszey7c7lzgPoJRsv8EfKGIsjnnnKsxhcyjdytBZ24jfIKao4dgMnU3iVRCX63xdHR0sHRpPN1ThoaGaGhoiGXfbmyFDFwSZ5Afx7U/ODhIY2PjmGk6Ojro6upigw02GDNdeQilIgs4Z7L+yM2LwnXZtgFeAM6TtHuY/hQzWxCm/fM42+f6N8F3FwCSNgB2MbNbzWxEVwQzOy4zYbmZXSRpLbAPQTD6i3GPcN1+bmXsgVMws6fIH2xmpxmxDzOby9hTMDjnnJtkCgn0tib4YnoG2B1YkfXeAPCCmSVXbeMS0dPTQ2tra9LFGFPSfQRdPArpz1aLn33mmCoi0CtuwvSpkuZnvT7fzM7PSZMdPefbcQOwM/BlMzte0jEENWn7F7h9rh+a2S5Zr9cSNA3N12wzyCArwjezK4ErC8jHOeecS8y4gZ6ZLQSQtEn2JK5ucquGpotx9tOarOrq6hgaGqK+vpBnRPHo6uqivb19zDQdHR2sWLFizDQTVUi/s/r6egYHByOt9a2067mIqRNWm9nsMd5fTNgkMzSToE9cbppVZnZd+Pr3wA/G2P7eccq0Xv90M7N8A8BkjDcpuplN3u4LO+7IEz0+la5zzlWiYqZXeFFSStK2kvaWtE/2EmchXeWJI9BLp9ORDnIRZ/O9yaoSzmk6nR63n1rSQVEc+Sd9TOtTlKNuzgW2yBqQ5GjgquwE4bytj0raNVx1IJDp03cV8DFJdZI2J5jv7oZx8nxO0kdePhrpcIIpCkazgJGTpMcxYXr1ufBCvnjQKUmXwjnnXB4FP5aXNAe4gqCvRO7duJF/RE5Xo+KY4Lm7u3vcmppiNDU10d9f8IB4RamEicOTkAk2pk6dmnRRxpR0QJrJf9q0aeMnLnKfFSOivwEzG5Z0LHClpEbgduCyMKj7ppkdFCY9AbhQUhuwhmDScszsxnD+u/8RfBd93szGO1EnAL+T9KPw9f/IP9Jnxn45rxuB1wPHA18c/yidc8658ium/dXPCEYGOwp4jvX7RLhJJo4avajnEotz0uxqGIwmDpVVqzS6OMtZyGcfRx/Bjo4OlizJbdGYnCgfdpjZzUBu8865wEFZaeYDe46y/anAqUXktxDYW1J7+HrMeWDM7LY8q2+UtBD4CMFD0MnpM5/ha3c+yzcP+GTSJXHOOZejmEBvJ+D1ZvZ4XIVx1WPt2rVsvPHG4ycsQjX0+5vsqmWQk7jKOTw8TCo1fnPEuJpuVsy5L24wlookaXuC77XmTNBqZr8vcjf3Eky3MHndcw+7PL8m6VI455zLo5hA71nA78IdEARlW2+9dUFpzaygp/9xNAd10aq45oOjqK+vZ3h4OPL9dnV1FfQwYjL00YtweoWyk/R54IMEXRFuJejzdwvBIC/FeA/gg5Q555yrSMUEeqcCZ0n6SNgx3k1ihQZlLS0t9PX10dLSUtA+o67Ri6sv3WTuo7dgwYKki5GYQq/RKVOm8MwzI6ZjK0nFBdmFj7pZiT5G0Mdurpm9T9LWwPdHSyzp5txVwBbAK/E+es455yrUmIGepGdZvy/eZgSjlS0jmEPvZWa2TfTFc5Wq0BveTHOzQgK9tWvXsvnmm0dRvJfF0Zeuv79/0k6WXlm1SuVX7HVfiMHBwYKmq2hra6Ora8yuZGUTtNwseNDmStRvZoOSTFK9mT0raazvsIU5r9PAPcANZnZLfMV0zjnnJm68u4vf4IOuuDwKHTglExhsttlm46atlj561VLOOFRUP7EErF27tqCa7GIC4kKvp1QqVTmDAFV/H73VkqYANwF/krQSGPUDM7OPl61kzjnnXETGDPTM7IwylcNVmeHh4YJqIYq94a2GPnrVUs44JN18MOlAp5gavagDvYpT3YHeu4B+gi4JhwNTgP9LtETV6sYb+ejXrk+6FM455/Iopo+ec0UrJjColj56VXtjHoHW1lZ6enoSy7+/v5/m5ubE8u/s7GTmzJnjpivmPFXrg4Nq7aeqoM3ptWa2PzAMXDpKutx+eaMK9zU5dXTQ3dSadCmcc87lUcyE6beQvxmnAX3AE8DFZvZwRGVzNaCjo4PVq1cXlLazszPSCdMhaO42PDxMXV10IwRO5kAvzrkJC5H0uS80/2KCoKjnjywPQZX20TOztKQ+Sa1mNlY0ntsvz+Vz+eW8c/79/H32m5MuiXPOuRzF1OgtJBhKei3BRLYQjFrWAfwLOBg4SdJ+ZvbvKAvpqldHRweLFi0qKK2ZFTRHWTHa29vp7Oxkww03jGyf1XljXhuSPveF9tErRtLB60Qp4r/VMnseuFfS34GXR7gxs+9k/e798grxox9x9KI1Hug551wFKibQWwRcBXzSzIYAJNUBvwCWAB8CLga+C+wTcTldlUp6lMZM/lEGep2dnUyfPj2y/bnCJR0UxZF/Z2cnm2yySaT7jJ2A6g70FocLwOQcQtc551zNKybQOxbYLxPkAZjZsKQfALea2RmSzgUK7tfgal/Sg3fEkX+19qmqBcWc+6amJvr7+2lqaoos/+7ubtra2iLbH1Tr9aSqHozFzL5R7DaSXgm8H5gFNObs7+hoSuacc85Fp5hAr4NgHr3Hc9ZPBzIdq9YC0XWGclUv6eH446hRTLpWaTLr7Oxkgw02KCht5rOPMtBLp9ORNy9OujnqhFVpHz0ASV/Lt97MvjlK+gOAvwPPANsDjwJbh2/fH0cZnXPOuVIV8019PfArSQdI6pDULulAgqab14Vp5gBPR11IV70aGxsZHBxMLP84As2qvTGvAcWc+6QfMhSq2AcHSU8x8TKpsKUyDWct9cA7WRe45XMm8GMz25Fg8LH3AlsCdwO/j7eozjnn3MQUU6N3HMEw1DewbvRNAdcCx4evlwKfiapwzpWqo6ODxYsXj5+wCH19fYkO8T+ZFRMUxVGbG8eUAr29vbS0tBSUNjNtQ9TNRyekimv0zOzb2a8lfRf40xibvBo4Ivx9GGgys7WSvgpcAfw6loJWg299i3N+fW/SpXDOOZdHwYGemb0IHCxpO4IvPYDHzOzJrDQ+2uYkMDAwQENDdYxfEFcfwWqdQ6zaJR3oxaXQ6ylzTIkHepVdWzcRaYImmaMZyPp9OUFt3mMEI3ZuHmO5Kt+BB3LnTQPjp3POOVd2RU+YHgZ2T46b0NWsaho8oppu9quFpFj6qhWimEAvjiC/mGaTcZynzDFVxKivqertji3pRta1TEkBrwIuGWOTR4BdCL777gK+Kqke+CRBwDd5LVjAzJdeYMkGmyVdEueccznGDPTCDus/MLOe0TqvZ4zWid3Vnjj6qMXV76ha+mlVk7a2Nrq7uxPppzgwMFDw4CodHR08//zzMZdodO3t7XR1dUX6UKSirucqbrpJ0OcuYwhYYGbPjZH+O6wbdOzrwDUEg7MsJ+ivN3l96EP8dNEaDj3ynKRL4pxzLsd4NXofB84HesLfR2OAB3qTRByjTsbV7y0TlLjoZGpJK31AmqRrczP5Rx3oVUYNddU33VwOLDSzHgBJbZJeZWa5o0oDYGb/yvp9IbCTpGnAaov4KZWkW4F8s48bMN3MlufZ5hLgYyM2MKvqD8k551xpxgz0zGzrfL+7yS2uSaPjaA6aSqUqZ5TCGpH03IiFSnowljjy7+jo4MUXX4x0nxMWYaAnaT+Ch4qNwO3AJ7PnbA3TLAC6gcwwvh8xs/mSpgC/BHYM159uZn8ZJ8vfAm/Iej0Qrtt1lPJ9HPijmb381MjMVo1/ZBNyGDnz9BGMer0iX5CX5e5wW+eccw4obnoF54B4grI4a4iiHjhlsg/EUjm1SmOLOiAt9oFBHAFxxQTZImi6Wcgy3q6kOuBC4P1mti1BE8kjRkn+NjPbOVzmh+u+AjxnZq8B3gR8S9J4/0zqsgNJMxsExhph6ifAMkm/kbTvuAdVAjNbZWbLMgvBXLWvA342zqYD2duF2zrnnJvEigr0JB0r6RFJPZK2Cdd9SdL74ymeq0RxBGVxzk0XdY3eZK8hrJZAr6mpib6+vsj2V8w0CBBPf7qKOvfRzaO3G7DEzOaFry+kuJqpnQhqvDCzNQSDo7x9nG1ekrT3ukPRm4CxPqzNgJOBmcBNkp6VdEbmezBmJwDPA38bJ90cSUvDsv1Z0o7jpHfOOVfjCg70JB0PfA/4I8Hz3IxlwIkRl8tVsGKDsvr6+nEnTa+GPl8uUFEDgowh6prXtWvXFlWTHVfTzco49yqmRm+qpPlZy0k5O5sJZE92uShcl8/fJT0k6cxw1EuA/wIfkJSStAWwN/CKcQ7gFOASSfdJmgtcBOSW62Vm1mNmvzGztwDbhOk/AjwZ9qmLRdgs9SPAr3Kbsua4HvgocCBwDNAK3Cdpp1H2e1L2Z7J69eqJF/Lgg7n5lXlbvDrnnEtYMTV6JxP0m/gWwShlGf9lXd8INwkUG5QVcsNbLVM2DA8PJzKtQCWpmOaDZRbHdZ9OpxPv9zdhhdforTaz2VnL+Xn2ll1NPtoJ2dvMdiEI5F4DfD5cfxbBd9L9wM+B21j/O2pkZmYPEkypcFS4vNrMHi7gqDGzheH34MeBBwmai8blCKAF+NU4ZbrczP5mZo+a2U3Au4GlwKdHSX9+9mcyderUiZfw9NP56V4fnvj2zjnnYlPMHes2wH151ncDlX+H7iJTbFBWSGBQLX30urq6Jn3NY0UFG2VU7DVayHXf3d1Ne3v7mGmyNTQ0MDQ0ZgxTPqm6wpbxLSaYgDxjJrAkN5GZLQl/dhIEPnuEr3vM7ISw3947CWqz/jdWhpI+AnSY2TwzexTokDRutCJpK0lflfQEwaAxPQRz6cXleOBv40z9MIKZDQAPALPiKJRzzrnqUEygtxTYNs/6NwLPRFMcVw36+voKnssMCmtuVi199LyJqQd6hUr6uo+Vimq6OZ65wBaSZoevjwauWj87tYXNGAmbbL4XeDh8vYGkpvD3vYGtgRvHyfNUM3u5vWL4+6mjH66OknQL8DTwCeByYDsze5OZXVjIQRYr7De4E+MPwpJv23qCWs/F46Ut2aGH8ourzhw/nXPOubIrJtD7DXBO+GVsBE9A3wWcTdBfwU0iUTc3q5YAqmpvzCPU3t6eSKA3NDREXV1BNUSxKLaPXmbC9LFUy3WfV0SDsZjZMHAscKWkpwhqyS6TtKuka8NkmwG3S3oYeIhgioWzwvd2AB6V9BjBROjvN7P0ONnm++4ba7qhnwILgQPNbGsz+5qZxf2A8wTgKeBf2Ssl7S7pcUm7h6/bJZ0jaS9JsyS9AbgC2Ipgyop4LV3Kpl0l9PFzzjkXm/EmTM/2bYIvjkcI+lDcH66/GDgn4nK5GlIJgZ6ZRdKEs6pvzCNSX1/P8PBw2fNN+tx3dnay9daFTydaV1dHOj12vJH0MZWksNq6gpjZzcDsnNVzgYPC958Bdh5l2/8A2xWZ5XxJXwF+TPDg8rPA/DHST8+eQy9ukjYhqLX8Sp4J2VsJgtvW8PUwQT/5w4FpwEqCc/dGM7sf55xzk1Yxgd6rzewTkr5FMKlsCvivmT0dT9FcrZgyZQrPPDP2w+/BwUEaG3PnCI5GW1sbPT09tLW1lbyvahk0phYlfe7jCMqSPqaSVPd8kicRBHmZf0z/JKhBy8vMusPg6wiCoPJ0M1spaS/geTN7NsrCmdkKIG/7eDO7lawBa8ysl/Gnk3DOOTcJFRPoPSxpOXALcBNwcxmarrgakPSQ8JkaxagCvZkzRxv53cUp6dqvuOaP3HzzzSPdZ3ko0hq9cjOzFyVdSjBgSTNBrd7xwHfypZf0WuBWYBVBy5bvE9ScvZ1gwJPRJnh3zjnnElPsqJtfJfhC/BbwVDgx64XhCGbO5ZX04B1RBpreRy85Ezn3dXV1kY1SGcdnn3TwWpLoJkwvO0nnENTgfR6YStBHcKzmnz8ALgvT9GWt/yfBdA+T17RprGkpfORY55xz5VNwoGdmC8zs12b2ETObQTCi140ETzIvi6uArvpVQqAXVf5VfWNe5SZy7gsZEKVQQ0NDNDQ0RLKvjKq9nkSU0ysk4UDgMGCFmX2BoDvCpmOk3w04L09/ucXA9HiKWCWuvZaPv/8bSZfCOedcHsU03UTSlsBbwmV/oB24gaApp3N5tba20tPTk1j+UU7wXdV9qqpcsYOhwLrPfsMNNyw5/yjnY8yo3uupuptuAn1mZpKGJbWGTTm3HCO9Afk6Eb8CeCmeIjrnnHOlKfibWtKTBJPQHhn+fB8w1cwOMbNzYyqfqwFx3CAXI8oava6urkj6+rniTaT2K8rPPsr5GDM6OzuLmjAdoLGxkf7+/sjLUrQqbroJLJQ0FfgTcKukGwimMhjNTcCJWa9NUjNwOsHDzsnre9/j+HuuTLoUzjnn8iimRm9joB9YTdAhfVU4/5GbRIaHh0mlqutJfkdHx7ijfhbKzKru+GvFRPrIJT0Q0Hgmcj1lgtempryDMpZPFdfomdn7w1+/K+nfwBTg+jE2ORX4t6TdCGr2fk4wHYSAY+Isa8W7+mretmgNP9/jfUmXxDnnXI5ivqmnETTZvAd4JzBX0hJJl0o6MpbSuYrT1dVVdA1E0pLuI1ir4qjhGkvSNXoTFfV5qoRjAiClwpYKZ2a3m9k/zGzUUXvC6RNeB/yDoG/6MMEcsnPMbHF5Suqcc84Vp+AavbAT+n/D5WxJWwFfAY4imKj10jgK6CpLNQ4eEWUfPRdobm6mv7+f5ubmsuXZ09NDa2vr+AmzdHR0sHhxNPfhE2mC3NzcTF9fHy0tLZGUASok0BOV3CwzFuHcdt9MuhzOOedcoQoO9CRNAfZl3WAsrwbWAtfig7FMGnEMHjE4OEh9fVHjAhUlyhvjpPsbVorMOS1noAfFn/+kg/xM/lEGekkfU6DqB2Nxzjnnal4xd9cvAgPA3cDvCIK7uWaWjqNgrjLFUaMXdy1hfX19ZHOplbu5YqXKBHqbbLJJ0kUZU9KDsWT6CG666Vgj9xe/z+QDPSp56gTnnHPOUVyg9zbg32ZWAcO9uaT4pNEOKn+Qk4ykyxlHUNbR0cGyZcsi3eeEeI2eA7j0Uj73g1uTLoVzzrk8ipkw/WYP8lxcNXrVOZfY5FUZzQfH19bWRnd3d8n7GRgYoLEx3zRqY4sr0Ev+3IdNNwtZqpSk10pVfADlsv32PDtti6RL4ZxzLg//EnNFmWhQJol0On8r33LU6EXVt8776AUqI9gYXyqViqS57USv0TgC4ooIsjODsVTvPHqFeIBgWiEkzZc0LeHyVKbbbuMNix5JuhTOOefy8EDPFWWiN7zt7e10dXXlfS+O5qC5orjZ9/5561RLoBeViV73YzUdnej1VDHnvsZr9AgGG8t0rnwV4J0S8/niFznt1kuSLoVzzrk84hvq0NWkiQZlmZvTfLWB1TCoBwTD+0c5emI1S7rvW7mtXbt2QjXZYwVlfX19Exq1tLm5md7e3qK3i9akGHXzFuAmSfPD11dLGsiX0Mz2L1+xnHPOucJ4oOeK0t3dTVtbW9HbjXXDWy199KqlnOVQEc0Hy6iUGr2xrvuJ7LNimg9XSjni8zHgeGB7YB9gCZB0hO2cc84VzAM9VxQzI5Uq/kn+WIFBtfTR89FB1yl380EzSzTAiSvQq+oHB6novj4k7QecDzQCtwOfNLOhnDQLgG5gMFz1ETObr+DCOBc4kKD34G3AyWY2XEqZzKwT+H6Y9wHASWb2Yin7dM4558qp5tveuMowVlO/aumj54HeOk1NTfT3l28Q3onWJEdlop99U1MTAwN5W/uV5bqPjaIbdVNSHXAh8H4z2xZoB44YJfnbzGzncMk0qdwP2A14LfAaYCfg7aUeYjYz29qDPOecc9XGAz1XFmPVbPT29sbe962xsbHkwKSqb8yrXNLn3uePzCO6UTd3A5aY2bzw9YXAYUWUxIBmgtrARqAJeKGI7QsiaT9JN0taIWl5+Pt+UedTdU48kcvmHJR0KZxzzuXhgZ4ri/Ga+sXdLC+KpoZVf2NexZI+93E0s0z6mEpWeI3e1HB6gsxyUs6eZgKLs14vCtfl83dJD0k6U1I9gJndQjBwytJwucPM5kZ6qNL7gH8BLwHfBr5DMCrnjZLeH2VeVefII7lqp7ckXQrnnHN5eKDnyiLpwTuiyL/q+1RVsVLOvaSSm+7GEZRV9/VUVNPN1WY2O2s5P88Obf2d57W3me0C7E3QRPPzAJJ2BbYEZoTLjjEEX6cD3zCzQ83sR+HyHuCbwFcjzqu69PfTODQ4fjrnnHNl54GeK4ukh+P3Gr3qVsq5b21tpaenp6T8e3t7JzQVwliSbo5aslSqsGV8iwkCtYyZBCNcrsfMloQ/O4FfAXuEbx0F3GhmvWbWC1xF0G8vSjsAf8iz/g/he5PXm9/MFb8/LelSOOecy8MDPVcWY02YXg5RBJpVf2NexUo591E9ZIi6eXFVPzgQUfbRmwtsIWl2+PpogmBtXXZSm6Qp4e/1wHuBh8O3FwIHSEqFA7scCMwnWquBbfOs3z58zznnnKs4Hui5sqirqyOdTieWv9foVbdSzn25p4IoVCnHlEqlGB4uafaA0kU06mY4DcKxwJWSngJ6gMsk7Srp2jDZZsDtkh4GHiKYYuGs8L3zgSHgUYLg7yXgF1EeKnAl8AtJ75Q0JVzeBfwsfC8yks6QZHmWWeNsd3jYB7Jf0jOSPhNluZxzzlUfn0fPFSyKKQqSMmXKFJYtW1bSPoaGhmhoaIioRK4YpfRni6J/ZhyDBQ0ODtLY2DihbTs6Oujq6mKDDTaIuFSFEqTqItubmd0MzM5ZPRc4KHz/GWDnUbbtAQ6PrDD5fRGYBvyVdf0JBVwevhe1JQSjkWZbMVpiSe8ELgO+DPyFoB/j+ZL6zOznMZTPOedcFfBAzxWst7eX1tbWpIsxIVHU6iQ5YXclKuf5qMUavVJkjim5QI+CautqRSaYlPRVYEeCIO9RM3s2piyHzayYJ1OnAVebWaaW8/GwKeyXAA/0nHNukpo839SuZNXcdDGKm/1qrtGMQ11dHUNDQ2XJq7Ozk/b29gltm/RnX19fz+BgtKMSJh+8RjdhejUxs2fN7B9m9vcYgzyA6ZIWS1oi6TpJbxwtoaQGgtq/a3PeugbYUtJWebY5KXvKi9WrS+hmOGcOj05/5cS3d845F5va+hZ2sYpjMJJ0Ol2WmqGkR/2sRe3t7WULNtLpNHV1E2sqmPRnH0dQlvQxBYOxTL5Ar0zuBY4EDgY+DKwE7pB04CjpNwYaCOYQzJapEdw8dwMzOz97youpU6dOvLQXXMDpbz1x4ts755yLjTfddAWLo0avu7t7wjU1xWhubqavry/2fCaTTN+3km4SyyDpZruZ/KdNm1ZSGfLtM1HelDkWZnZdzqo7wlq5/wNuzLPJeB+EN0VwzrlJyh+3uoLFMcFzuZqDSiq55tD76K2vIoKNAkQxGEul5Z/0MQFeo1de/wFmjfLeCoJRR2fkrJ8e/syt6YvWiSfyrRsuiDUL55xzE+Pfwq5gcQRl5ZybrtQ+dt5Hb33VEuiVWs7h4eGSa/SibmaZ/LmfnH30ErQLwcTyI5jZIEEg+I6ctw4CFpnZwlhLdv/97LTs6VizcM45NzH+LewKFkVQlhssVfMAL5Nd4v3EClRfX1/SoDFdXV0lNS+uyT56EEyvUMhSIyTNlOKPXCWdI2k/SVtL2lnSz4D9gB+F7+8u6XFJu2dtdhZwqKRTJe0g6WjgJOC7cZfXOedc5fJAzxWs1KAsXz+5OJqDuvKoiOaDZVDqNRpXoJfoudekrNF7GJgJIGkvSU0x5bM5wZx4jwM3ANsDB5jZ38P3W4Edwp8AhO8dCXwceAQ4HTjN59BzzrnJzQdjcQUr9YY3Exi0tLSst88ZM3K7lsSjlOZ3AwMDE57culYlHmyUSakPOPIFxIODg9TXT/zfb3t7O93d3RPePhK1FcTlJelU4HaCydvFuoFNriOYwP2ZqPM0sw+P8/6t5BmAxcx+B/wu6vI455yrXrX/Te0iU+oNb77mZtXSR8+bmI7kgV5h8l33pe4zlUqRTqcnvH0kUipsqW67AFcCLwHtwHGSMhOme6dd55xzFc0DPVewoaGhkmoh8gUG1RJAlTMgrRbl6ieW9CA4pX721Xzdj2kSNN00sw+b2UyC2rs+4PXAnQTNJs+WdJSkLRMsYvJuu40PfuSspEvhnHMuj+r+FnZVpZpveKulnOXU1tZWluaD/f39NDXF1R1qfKU2WW5tbaWnp2fEPqv7epocffQkvVVSm5k9STCFwXHApkAv8DxwBPBYgkVMXlMTA/UNSZfCOedcHtX9LeyqSr6+SqWOaFiMVCrF8PDwhLb1QWNGKte8gkmf+1KDsnznKeljKpmYFIEecB6wWtJcoAXYK1xvwI/N7C3A1KQKVxEuvZTDHr0p6VI455zLo+q/hV31yNfUL51OU1dXniHYOzo66OrqmtC21V8DU72SPvfVPn9kbCZBoGdm2wNbEExTkCaYxuBFoBn4mKTXmdlAgkVM3gUXcMT91yZdCuecc3lU97ewqypJD95RSp+ymrgxr1JJn/s48k86eC3d5Gi6CWBmK8zszwR99N4EvAEYBPYEbpS0MsnyOeecc6Op/m9hVzUqIdCbaP7Vf2NevaI4942NjfT3909o2+7u7sibF9fE9SQVttQOAZjZY8AwcLyZbQrsn2ipnHPOuVH4PHqubJIO9EqZ4Lvq+1RVsc7OTjbYYIOS9pH57CcyqIuZkUpF+0ysNq6nyfWc0Myy++ItJKjVw8weTqZEzjnn3Ngm1ze1S1RTUxMDA8l1Z/EaveoUxbkv5bOPY3qHKJqDSkp26okIa/Qk7SdpvqSnJF0kacRDSEkLJM2T9GC4zA7XH5+17kFJA5LeHfHRrsfMdjKzxXHm4ZxzzpXKAz03aZTSR6+3t5fm5uaIS+QKEUVQVK45/wrV29tLS0tLSftoaWkZMW1DWUUU6EmqAy4E3m9m2xJMTH7EKMnfZmY7h8t8ADP7eWYd8E6gG/hnFIfoCvC973HWvkclXQrnnHN5eKDnCjIwMEBDQ3XPlVRq09FyTSfg1pd0jV5cSr2ekj0mEXx9FLKMazdgiZnNC19fCBw2wYIdDlxlZn0T3N4V681v5t4tX5N0KZxzzuXhgZ4rSC30KSqlj57LrxzNB6MI9JL+7CWRTqcj3WeixyQglSpsgalhs8zMclLO3mYC2c0gF4Xr8vm7pIcknZmveSfwUeCyUg/PFeGJJ9h61XNJl8I551weHui5gsTRR63c/YtKqQHx2rz82tra6O7ujjWPwcHBCQ2iki3pz769vX3CcziOJvlayoJr9Fab2eys5fw8O8v+ZzDaCd/bzHYB9gZeA3w++01JuwBTgNsmfEiueEceyQ//8cOkS+Gccy4PD/RcQeII9Pr6+sra762trW3CN9uJDnpRwcoRbERx7pMejCWO85R4083oBmNZDGyZ9XomsCQ3kZktCX92Ar8C9shJ8lHgd+Z/rM455xzggZ4rUC1MGp1KpTxgi1ilDXIymqTLGVegl+i5j27C9LnAFplRNIGjgavWy0pqkzQl/L0eeC/wcNb7dcCH8Wabzjnn3Ms80HMFiSMoq4V+f5Nd0n3fCjXRQCuqBwO1V6MHQQvLQpaxmdkwcCxwpaSngB7gMkm7Sro2TLYZcLukh4GHCOawOytrN28BlmVG4nTOOeecT5juCtTZ2cn06dMj32e556abaH8r76OXX/LBRmGam5vp6yt+IMbe3l5aW1tLzj+OgDjxIDvCvwkzuxmYnbN6LnBQ+P4zwM5jbH8DcENkBXLOOedqgNfouYJEFZTV19czODgIxNMcdDwTraHxJp/5VUugN9FAParrPo5mlsn30Yus6aarZoceyj+33zPpUjjnnMvDv4VdQaIKyrJvTpOo0ZuI4eFhUin/U8kn8X5iMYvjuk+n05HUECd97iUVtLga98Uv8vM93pd0KZxzzuXhd6+uIFHWbGQHetXQR6+rq6sqAtIkJN58MGZRXaPZ1313dzdtbW0l77OhoYGhoaGS9zMhAlRX2OKcc865RHig5woS1VQI2YFBtfTRq5aaxyRUS9PNiYrqs8+97qvhAce4optewVWzgw7i4j99PelSOOecy8MHY3EFi7q5WbX00fNAb3Tt7e2xBnpDQ0PU1yf3b6qzs5Np06aVvJ/s85TEdR894c8JHQCrVrFh78TmJ3XOORcv/6Z2ZVUJffSKDfZq48Y8HvX19QwPD8e2/87OTtrb22Pb/3ii+uzr6upIp9NADT048Bq9WEj6uKRbJK2Q1Cnpv5IOH2ebSyRZ7lKuMjvnnKtMHui5ssoO9AYHB2lsbCxr/q2trfT09BS1Tc3cmFehpM99HM0skz6myHigF5e3AH8jmFpiF+BygnkFPzjOdncDM3IW55xzk5g33XRllfTgHZn8ixkMo7Ozk5kzZ8ZYKjeapPuzxRGUdXZ2MmNGLdyD+3PCOJjZR3NWfV/SfsAHgCvG2HTAzJbFVzLnnHPVxr+pXVklPST8RAYPqZkamCoU5bmvq6sruplpHJ99bTQFLrA2z2v0orIhsHKcNHMkLZX0rKQ/S9qxDOWCGTNY3j61LFk555wrjgd6rqySHqVxIoFmbdyYV6coz/1EBo4ZHBykoaEhkvwzaubBgU+vUBaSPgbsCvxijGTXAx8FDgSOAVqB+yTtNMo+T5I0P7OsXr164gW8+mqOO+yrE9/eOedcbDzQc2U1kT5yUfIaveoS5blP+iFDRk1cT8Jr9MpA0rsJArzjzOz+0dKZ2eVm9jcze9TMbgLeDSwFPj1K+vPNbHZmmTrVa+Scc64WeaDnyiqKKRpKMZE+gt3d3YmO/DiZRdlHL+n+oRldXV2RXU+NjY0MDAxEsq/iCJQqbHETIulDBH3yjjezi4vZ1swGgAeAWTEUbX3f+haf+vcfYs/GOedc8fxb2I0rnU6TStXGpTKRWh0zq5njj0OcwXvSNXpxHFs6naauLpomjYnWUnqNXmwkHQtcAhxlZpdMYPt64DXA4mhLlsc117D/03Njz8Y551zx/O7VjSvKGoikTTTQc6Mzs9jOUdKBXtTHFfX+km2OqgIXVwxJnwN+BpwC3CpperhsEr6/u6THJe0evm6XdI6kvSTNkvQGgprArYDzkzoO55xzyfNAz42rlgYjSXrUz1rU1NREf39/LPvu7u6mtbU1kn0l/dk3NzfT19cX6T4TPSZvuhmXTwN1wM8J+tlllvvC91uBHcKfAMPAjsCfgSeAq4EG4I1j9etzzjlX+3wePTeuOOYyGxwcpL6+/JdfpQzIUUsyfd+am5tj2X9UzSeT7qMXR/7JHpPX1sXBzGaN8/6tZJ18M+sF3h5vqZxzzlUjf9zqxhXXpNFJ1BI2NDQUPZda0gPIVLo4g+coz33SffTiqH1L7sGFD8binHPOVTr/FnbjqqVAD4rvJ+V99MYWZ7AR5blPuo9eHOepVgZjkbRfOKfbU5IuCgcTyU2zQNI8SQ+Gy+ys9/aQ9J/w/XmSNo/wSN1YLr+cT7371KRL4ZxzLg8P9Ny44uijVxNziTkg+b5vhWpra6Orq6vg9AMDA5FOlh5XoFcN534skuqAC4H3m9m2QDtwxCjJ32ZmO4fL/HD7DuBS4GNmtiPwRqCEGcBdUWbNYskGmyVdCuecc3l4oOfGFXUfPUmsXbs28n5/LhlJ930rVCqVKqqGLurrvqb66EU7YfpuwBIzmxe+vhA4rIjSHA5cY2aPAZjZS2G/NVcON97I3s8+kHQpnHPO5eGBnhtX1LVv7e3tLF26NLEavWL6XZmZ99EbR7X00St2f1Ff97XVRw8inF5hJuvP97YoXJfP3yU9JOnMrOadOwBNkm6S9ED4nv/Rlsvpp/P5O36bdCmcc87l4YGeG1ccN7zPPfdcVfTR6+3tpaWlJcbSVL9q6aNX7P7iuO5XrFgR6eikzc3N9PYmVHlVeI3e1LD/XWY5Kc/esj+Y0YK0vc1sF2BvgsnAPx+ubwD2BT5A0GzzDYze9NM555ybNDzQc+Pq6uqira0tsv11dHTw/PPPV0UfvVqaQzAutdBPLJ+oP/s4rvtkK64KrtFbbWazs5bcSbwXA1tmvZ4JLMnNzcyWhD87gV8Be4RvLQKuM7MXwyabVwNzIjlE55xzrop5oOfGZWakUtFdKtUU6PmgMeOLq0Yv6dFO46jRq5brfnyRTq8wF9giaxTNo4Gr1stNapM0Jfy9Hngv8HD49lXA3pKaJaWA/YF5OOecc5OcB3qu7KZMmcJzzz1XFYOxxDFZfK1pamqiv78/8v12d3fT3t4e+X4LFfVn39TUxMqVK2vneopoMBYzGwaOBa6U9BTQA1wmaVdJ14bJNgNul/Qw8BAwCJwVbv8Uwaib9xMEf8uBiyI+Wuecc67qjJiryLm4dXR0sGzZssT6vjU2NtLf309TU9O4ab1Gb3xxNR9M+tzHkX9tNQWO7nM3s5uB2Tmr5wIHhe8/A+w8xvbnA7lNQl05fOYzXPT7+5MuhXPOuTw80HNll2nql1T/okz+hQR6a9euZaONNipDqVyupIMinz9yHD6wpQP40If4+4M1ck0751yNSbzppqQzJC0Ifz9KUrIdc1zsOjo6Eu1/VUyfspq6Ma8ycZ37Qq+9OPI3sxq6niKbXsFVs85O2vp7ki6Fc865PBIP9HJcAcwoNLGkSyTdGl9xXBymTJmSaD+lYiaZ9j56yYnj3Le1tdHTU9hNaRxTayR97TsXuQMP5LdXnJ50KZxzzuVRUYGemfWa2bKky1E2vWux3rGHpe+bfwed1/yUvodvGjOdDQ9gyx7Bnn8A6x87iBla9DC9N/6M/rl/xdLpUdOlh4ZY9q8/8NJjc+le+PiY+yyUmdG84G466GN44QOR7LOo/IcHaB96kbXP/nfc89T/9IO8cPd1MO+WMc/TZNf91IN0PX4fq267ctzr6ZnrL2f+5T9lzbOPjbnPNYuf4cG/X86KB+5kaCCagV6sZw3taxew9j9/xvq7xk7b9QLWsxK6lkaSN8Cyxx9ieO0qnvvPraQjup6Glz/L4LP303vn77HB0c+TmdH9n39EkmdASIUtzjnnnEtGWQM9SU2SfibpJUmrJV0ANGW9v17TTUlTJF0saZmkfkmLJf0wfO884GPAmyVZuBwVvvdpSQ9I6pS0QtLfJG2bU5YFks6U9ANJKyW9KOkiSW056T4g6T5JvWGZb5S0ec77D0jqC/d5bu4+RmNdK+n70cFYz0t53++89nxWfOddrPn911h59vt56Y9n5t/P8AD2wGXY0zdhz9yG/fdirHdN3rQDD17HS98+kJ4/f4OuC0+g65efyNuUzdJpHvjCO5h/9idZ/cDN3PuJXVnz8J2FHNaY+q/8MoO/+xSbDCyj97z3MTj3qvE3ikjmPLW/9DgvPXlPeJ5W503bde8/WPy5N7L87mvo+cXJLDv78MSH+69Ea+65hvkn70XnQ7fyzPc+ztNnfmTU6+mGTx/Mv8/8JHN/8iX+9tHdWHb/7Xn3ufx/D/Gbw3bh/qsv5eFLf8DlR76Z4cGBksppXSuxy46i/bl7eOmWX2G/PRrry/+QJb3ycezZm6HrBWzBbaSXPVhS3gBP3HYdv/7AXnQuepJ/ffvTXPnZ/OepGIOLHuHFr+7J0GN3sObiz7L6zLdiQ/nP04qffYplZ324pPzWI6KcXsE555xzMSj3YCxnAe8HjgLmEwypfRKwapT0ZwK7AocCzxFMpLtj+N6XgE2AVwCHhesyEVMK+CLwFDAN+BZwjaSdzGwwa/8nAz8G9gK2Bf5AMHnv1wEkfZxgYt4zwzILeBPheZN0BPAT4BTg38AWwHnAdGDcu6r+1f08du596Nc7oUsvh7Y2uP56+N3vMGBw8Xyw4Ml/17b1DKz4Npu1vBZ9+Svr7ceG+rFXbwqH7AaAzvs7dJ+LGrOGpt9gAzjvPNae93803L2G+qVDwforLqP+J3ehukY480zYaiuYO5ehs79L7/PP0mDGjCF4/YtDrPr8sWx442PQ3w9vfvPIA5ozBy64IPj9xBPh/vVHYrPhIYbeuBxSabboH6blt4vhD0fCzLPXJfre94J9P/EEHHnkyDwOPRS++MXg94MOglU5l86MGXD11cHv3/oWXHPNuvcGe1HfS3QcuT//WbicGUtXoo/nnCeAww/n+ft+xdDyXrqfhVkvDMHtV2B/egw1NMMee8CPfhSk/cQnYF6eKbvuuSf4+etfB0uuc8+FPfeERx+FY44Z+f4HPwif/Wzw+wEHQFdODdRWW8EVVwS/f/Wr8K9/jdzH1VcH5+Oaa4JzkevUU+Gww+DFF+Hgg0e+v88+cHb42RxxBDz55PrvNzayaNvV2GAfjavTDNzexfI7rmDt7x8g1dAYpPnyl+HVr2btDX+n8ZIb2SIruHnigHew7LhPB8cKcPTRMDjIS0sX8equtTwzDAe0wLyWx1l07y1s/beb4PY8weE118BGG8FVV60rb7bTT8c2WgErVjHln09xP9DbUA9nvQY1T4E3vAFOPvnlc2ILHgcz1q5ai/4zD1qbsbsfRkoFn1vmvGf79a9hp53g7rvXfW5ZlvQsYWignxYN84knOuHJPzF0++M0NDUHCXbcES68MPj9M59Zd/1ku/FG6OiAyy+HH/0ILXuKqd1r2LgnzRMLetFOvTTPvpDGjq3W/Y0Alh5mYNFj9E4bucvSeG2dc845V8nKFuiFtVwnAJ81s/BOnC9I2hfYeJTNZgH3mdnd4etFwF0AZtYpqRcYyG3uaWY/ynr5jKSPAS8Au2W2D91rZl8Pf/+fpD8CBxIGesA3gF+b2RlZ2zya9fs3gdPN7NLw9dOSTgZuk/QpM1uZcw5OIghsAZgheHIoDQMDpJ5+GlpaYNky6OvDgKHhddv29BqDnWnWLngW9fWtd5JseAB7qRuWBNmptx/60yidla6+Hp58kq6VXdT3GfUv79uo6+1H9WlYsAAGBuC55xjuH6DfgDR8tCFImR7KjpEnwsjcHH5h62Z4ouflQLY8giBj9syNeXpgiCefeh71D6x/ngCWLWP56h7SPfCe8NglgdfojZBpLvi+KfD0ckCisb+P1HD4uS5aBPX1dD73PGZarxarbmiY9uXL1wWQvb0wNETX0BADBq9JwaaCeRLDpTbfHB4ES/OW5nr+1TcU/N31DyD6YPXqdWXo6sL6g1qxD22Y9QDArKS4xoaCBytvmpaC54eDfZV6PZkBxoeb4N4h6OtO07RgIQ3tacj6H2HpYQaDw42WN8t0zjnnKprK1RxN0msJJrrd2cweylr/I+A9ZjYrbHp5sZkpfO/twJXAs8DNwPXAP82C6EDSJcAsM9s3J699gNMIav+mEdxWtQGHm9nvwzQLgN+b2ZeztvsGcISZbSNpU4Lg8L1mNqJ9oaRNCCbm7QOGs98CWoE3ZgWoeb164ya7/6RX0/TZa0ltuPmI91df8n90/+tCbKAXNbbQssdhbHTyyNohG+jB/nsRDPUDBqpDr/sw6pg+Im3vLRfSc8VXYLAP6puo3/I1TPnSDSi1fhOr9EA/9xw9h96lz2KD/aQam9nxq5ey2b7vHeuQxmRm9P3yCIafvhsG+6GhmYYDT6HpwE9NeJ9F5T/Yg829GIb6WHeePoQ6Ro7/s+baX7Dyl5/DBvpQQzON27yOV5xz54jzNNkt/8cvWXj+Z4Pz1NhM6ytfx+yfjDxPwwP9/PXwXelc8jTpwQHqGpt50zcvYesD3jdinwvvuZmrT3oXwwP9pOobaNt4M476y8M0tk18tEpbtQj7wydhaCAIUBqa0eEXoSmbjUibfn4uvPgk2DCoDjbYktSWe004b4C5f/w113/7swz191HX1Mz0HV7DJy6/k1QJ19PAvFtZ88P3B3/3dQ2kpmzKtO/+h1TLyPP0/NcPoffhW9n2L92PmVnufHVFm/2q7Wz+vTcXlFYbbhlJni4+s2fPtvnz509s4898hovufJZvHvDJgpIvOCtP6wHnnKtws067ZvxEoVL/z0mK7HuznE03M49/C44szex6SVsCbwP2A34LPCLpADMbyptJkP46ghE8vwusAOoIauIac5LnPuM2RvZbHK28mXSfA27M8/5zo2y3TusGNH36b3mDPIANP3Y2DVvuyMCT/6Fx651pO+ATedOpsRXmfAx77n5ID6LNdsob5AG07PcJ6jbYjIFHbiS18Va0HHhC3uAl1djE7j+/i4V//BEDq5ax6ZsPY6PdDhz3kMYiieZPXMTgHReTXv40ddvtRf0u7yppn0Xl39AKc44s6DxteNBx1G+4Gd1zr6dhs1ls+J5TPMjLY9NDPknD1M1Yc+91NE2fxWaHfTrveaprbOKQi+9k3u9/TO/KpWy1/6Fsscdb8+5zqz32532/vJ7Hr72c5g2mMueIU0oK8gA0bUv40M+wR/4BqRR67XvyBnkAmvF6aJ6KdS9HLdNgo+1Kyhtg1w8cQ/tGm/LErdex4cxZ7HHkp0oK8gAad9yXDb/4N/rv/iNq3ZDWt52UN8gDmPHVq1jzj/PhL58rKc915DV6LvCjH/HNIm6AnHPOlU85a/TaCPrifcbMfpa1/j/Apvlq9PLsYw/gbmCOmT0g6ZfAq83sTVlp3ktQCzjFzDrDdW8GbgU+bmaXhOsWAJdkN8uUdAZwlJnNCl8vAq41s+NHKc8i4Coz+0yRpwMo8Smqc84VKaqnhLNftb3N/88theW5wUyv0atwpX4XlfNJt3POJcFr9MZhZt2Sfg58Q9JS4DHgE8CrGGUwFknfBv5LUBtnwOFAD7AwTPIMcJikHQmaWXYCT4Rp/0/SxcAOwHcmWOyvA78Ky/uncL9vAq43s0XAV4CLJL0IXA0MhsdziJkdO8E8nXOu8nmNngP4xCf43n2L+eJBpyRdEueccznK3RbtNIKA6DfAf4ANgfPHSN9HMODJ/cBc4HXAO8wsExj+MtzPXQRNND9sZo8QDPryMYJg8kxgQp3AzOxi4KPAe4AHCEbWfC9BQIeZXQa8DzgoLMdcggFcxm+26ZxzVU0FLq6mzZvH9isXJV0K55xzeZR1egUz6wWOC5dsXwrfvwS4JCv9twimRhhtf6sIgqzc9b8AfpGzWjlpZuXZ7gzgjJx1lwOXj1GGvwJ/He1955yrSV6j55xzzlU0H13COedc8aTCFlc0SW+V9F9JfZKek3SmpLoCtjtc0nxJ/ZKekfSZMhTXOedchfJAzznnXJEKbbbpgV6xJO0C/AO4HdgF+DRwMvDtcbZ7J3AZcClBN4fvAN+TlHcwMeecc7WvrE03nXPO1QDhtXXx+QLwiJl9Nnz9mKQtgLMknWlmXaNsdxpwtZmdFb5+XNJsgq4RP4+3yM455ypR2aZXcCNJWgssSbocBZgKrE66EBHzY6oOfkzRmmlmU0rdiaRbgU0LTL7czPYtNc/JIpz65zIzOz1r3SuBp4B9zey2PNs0AN3ACWZ2Ydb6twD/AmaZ2cKs9ScBJ2XtYmvg2RKKXYt/p845N1Gl/k+M5LsavEYvUVF9iHGTNL/W5sHyY6oOfkyVyQO3WM0AluasWxb+3HyUbTYGGsbZ7uVAz8zOZ+wRr4tSC9e0c85FpZL+J3ofPeecc66yWc7PXOO1o/WmO845Nwl5oOecc85VjqUEtXrZZmS9l88KYCjPdtPH2c4551wN80DPFSKyJj4VxI+pOvgxucnm38A7ctYdBPQC/823gZkNAv8ZZbtF2f3zYuLXtHPOrVMx/xN9MBbnnHOuQkiaA9wD/BT4NfBq4ELgF2Z2Wphmd4JpFI40s/+E694J/IVglM2/AnsBFwCfMTMfddM55yYhD/Scc865CiLpbcB3gR2BF4GLga+Z2XD4/r7ALcB+ZnZr1naHA18FXgk8B/zYzH5UxqI755yrIB7oOeecc84551yN8T56zjnnXA2TdEY4Px+SjpLkT3idc65IkhZIOiPrdZ2kCyWtlGSS3pNc6fLzQM/lFd4YWJ5lVtJlK4SkfST9VdLCsNxn5EmzraRrJHVJWiPpd5I2SaC4BRnvmCTtO8pndlQyJR6fpI9LukXSCkmdkv4bNj/LTlNtn9OYx1SNn5OrKVcwcnTOUUm6RNKt8RXHOeeqxm7AD7Jevxc4AngPwf/V6xIo05h8wnQ3liUEF3W2FUkUZALagfnA74Ef5b4pqQ24CXgSeDPQTDBwwV8l7WWV2aZ5zGPKsjuwOOv1SzGWqVRvAf4GnAqsBg4FLpM0ZGZXVOnnNOYxZaWrps/J1Qgz6yUYwdM551wRzCz3Hnh7YKmZ3VnKfiU1mVl/KfsYjdfoubEMm9mynGU46UIVwsyuNbMvhTfW+f54PkLw9OWDZvZfM/s3wVOZPYH9yljUghVwTBkrcj6zir2pM7OPmtm5ZnafmT1lZt8Hrgc+ECapxs9pvGPKqJrPyVUPSU2SfibpJUmrJV0ANGW9v17TTUlTJF0saZmkfkmLJf0wfO884GPAm3NrniV9WtIDYa31Ckl/k7RtTlkWSDpT0g/Cpk0vSroofICTne4Dku6T1BuW+UZJm+e8/4CkvnCf5+buwznnRiPpVkmX5Kx7+X9hVkubt0q6WVKPpKckfTRnm5ebboYtHb4FbBlu2xeur5f0jbD11YCkJySdlKc8v5F0uqTngM6s/X9b0o8lrQqXbyrwZUnPh+vOk1RQZZ0Hem4s08Mv/SWSrpP0xqQLFKG9gHvN7MXMCjN7mKAW802JlSoat0paLuluSR+TpKQLVKQNgZXh77XyOW3IumPKqPbPyVWms4D3A0cBewA9wEljpD8T2JWg5nk74IPAY+F7XwL+CNxN8MBlBkHTTwjuH74IvI5g/r4G4BpJDTn7PxnoJvhbPhJ4H0FtNxA0dSZopXBNWI43AVcRtjiSdATwC+BcglFIjySoNf91ISfDOeeK8KNweS3BdDWXSNpulLSHAecQ3I/MALYK138DOAX4MsH/rJ8AP5R0dM72hwJbA29j/dZzJwHLgDcAXwFOB/4BbAbsDxwDHEfwv3Bc3nTTjeZegovocWAD4JPAHZLebmY3JlqyaMwAluZZvwzYPM/6arAUOAGYG75+B/BLYFuCfxQVT9LHCG72Tg5XVf3nlOeYqv5zcpUprOU6AfismV0drv6CgukYNh5ls1nAfWZ2d/h6EXAXgJl1SuoFBsxsWfZGOdM2PBNe5y8Q3LDclfXevWb29fD3/0n6I3AgkFn3DeDXZnZG1jaPZv3+TeB0M7s0fP20pJOB2yR9ysxyH6A459xEfcvM/gYg6UvAiQSth57MTWhmqyR1EbZ+C7dpAT4PnGZmvwuTPhm2dvgacFHWLlYCx+ZpKXeXmX03a9vPEvyfPiTsrvK4pFsI/o9exDg80HN5mVluh9I7JG0F/B9QC4HeWLUnldjva1xm9j/gf1mr5kpqBD4r6ZtmNphQ0Qoi6d0ET+6PM7P7M6vH2KTiP6d8x1Ttn5OraK8kaKZ5V876OwkGC8jnAuBKSbsBNxM0M/6nmaXHykjSPsBpBE+sp7Hub3VWTv7/zdn0OYKn0kjaFHgFcMMoeWwS7u/7ks7Kfiv8uR0ja8qdc26iXv5/ZWaDklYA04vYfluC/8G35qy/DThFUoeZdWbyGqU71AM5r5cCS3LGJFhK8L9zXN500xXjPwRfurXgefKPPDed/DVI1epeoA2o2FEqASR9iKBJ2PFmdnHWW1X7OY1xTPlUxefkKl4mACr4IYiZXQ9sCXwHaAF+C9w8Vv8PSVsSjC63DPgoQS3eG8K3G3OSD+Rmych7j9HKm0n3OWDnrOV1BEHeg6OV0TnnsqQZ+eA4938VFPb/aizFdMHoHmV97sNeG2VdQeXyQM8VYxfWHyWwmv0beIOkaZkVknYCZgJ3JFaq6M0hGGGvYp96SzoWuAQ4yswuyXm7Kj+ncY4pn4r/nFxVeIrgRmWvnPVj9q82s1Vm9gcz+yRwMMEIt68J3x4A6nI22Q1oBU4xszvM7HFGbxo6Vr7LCb5T3jbK+y+E7+8QDmyUu/gARs65QiwHtshZNyeGfJ4kGCxv35z1bwYWZtXmlY033XR5STqHoPPnAoI+escRtFN+d4LFKpikdoIqdAie2kyXtDNBX5PMFAVfBS6XdBrrhu2/B7il/CUe33jHJOkzwEKCKRgg6Pv1JeA8M8t9SlURJH0OOJug8/GtkjJNJIbDYYyr8XMa85iq8XNy1cHMuiX9HPiGpKUEg6p8AngVsCrfNpK+TdBc6VGCp8SHEwzgsjBM8gxwmKQdCfrgdQJPhGn/T9LFwA4ENYIT8XXgV2F5/xTu903A9Wa2iGAwgoskvQhcTfBk+1UE/VWOnWCezrnJ5QbglwrmtL2H4OHSe6POxMx6JZ0LnCFpOUFLuLcR9J0ea1Cs2Hig50azOXAZQVOyl4BHgAPM7OZES1W4XVk/EDguXBYCs8IborcAPyaoGRoiGPXtlAqdmw3GOSaCUe/OImi3PUTwdP8UKnt0uk8T1Bb8PFwyqvlzGvOYqM7PyVWPzAOR34SvrwDOBz48Svo+ggFPZgHDwEPAO8wsExj+EtiHoN/dFODjZnaJpBMIRpU7lSBI/BRBX8CimNnF4YAvXwz31w3cB/w9fP8ySWvD4/pSWManCUbEc865QlwKzCYYJbOV4P/HtwjuLaJ2OsH/qe8RjJS5EPg/M0vkO16Ve6/knHPOOeecc24ivI+ec84555xzztUYD/Scc84555xzrsZ4oOecc84555xzNcYDPeecc84555yrMR7oOeecc84551yN8UDPOeecc84552qMB3rOOeecc845V2M80HOuBJIukXRr0uXIR5JJOirpcjjnnHNRkbRA0hlFbnOGpAXxlMi5ylWfdAGcq3KnAHUQfJEAR5nZrHIWIAw0F5jZUTlvzQBeKmdZnHPOuZjtBvRGvVNJs4Bngf3M7Nao9+9cEjzQc64EZhZLICWpycz6S9mHmS2LqjzOOedcJTCzFUmXwblq4U03nStBpummpC8AXwe2CptMWqZpiaR6SV+T9IykXknzJZ0gSVn7WSDpO5J+LGkF8Gi4/sOS7pa0OlxulvT6rO3+AbwZ+FhWvvuG763XdFPSJpIuk/RiWI57JO2Xczwm6WRJF0l6SdJSSd+XVBfTKXTOOVejJL1F0pCkDcPXqfC77OmsNFuH3z2vLeL78oys1xtLulJSt6QXJH1L0m/ydauQdLCkeZJ6JP0n830qaWOC2jyAW8LyLAjfmynpz5JWhmV6RtL/RX+2nIueB3rOReMC4BxgCUGTyRnAD8L3fgG8DzgemA2cAXwX+GTOPk4GeoA3AYeG61rCtLsB+4T7/6ekaeH7HwXuBv6Yle9do5TxamAO8F7g9cD94b5elZPu68ADYZovAJ8Fjhz3DDjnnHPr+zcwCOwbvp5DcO85M2wqCfAWYAXwCIV/X2b7DbAz8E5gP2Am8J486TYl+E77BLAr0An8UVI98CKwe5juvQTfpbuFry8ApgJvBV4NHE3wXexcxfOmm85FwMx6JHUBw9lNJiVtDXwceK2ZPRqufjYMrj5F8KWW8aCZfSlnvxdlv5b0cWAN8Hbg92a2RtIA0DtWU82w5m4vYBczezBcfaKkPYHTgKOykl9lZj8Nf38qzPNA4OJxToNzzjn3MjPrk3QXQTD3l/DnLQSB01uAC7PWzaLw70sAJG0PHAS83cxuDtd9EjggT3GagcPN7Pkw3deBO4BtzOyJsDUNwKqc79NZwJVmdn/4ekFxZ8G55Hig51y8dgUE3JPV8gSCvz3lpL03d2NJrwW+RvAUdGOCJ6GtBF88xdgJeCkryMu4Hdg7Z91/c14/B7yiyPycc845gJsIWp8A7A/8g/UDvf0IvueK+b7MeHX4857MCjMblDQ3zCPbskyQF3ou/DkdeGKM8p8L/ELSwcCtwDVmdvsY6Z2rGB7oORevTPPofYC146Ttzn4hqRW4kSAAPAZYBgwQfKE1FlmO0b4kASzn9UCe972Zt3POuYm4Cfh22FRzb4LuAFMJWpXsBGwG3EzQXQAK+77Mlfs9lk++7zYY5/vNzC6WdD1BS5r9gesk/cXMDi+yjM6VnQd6zkVngHCqhSyZ2rEtzewvRe7v1QR9Cr5iZo/Ay8M/b1RAvrkeBTaQtHNOrd4+wENFlss555wr1FyCqX6+QtCyZH7YL66NYIqiRWb2VFYtXjHfl4+FP/cE/gnBAGgEQeMzRZYzEwiO+D41s6UE3RculnQN8AdJJ5vZ6iLzcK6sPNBzLjrPANMl7QE8BfSEX14XETT7mEIwUEobwZfQdDP7zhj7WwD0AZ+W9D2C5iXfZ+T8Qc8A+0h6JcGX6UtmNpidwMxulnQ38DtJJwLLCfo87AR8pJSDds4550ZjZsOSbiPoC35FuG5I0u3hut+G64r+vgz71l0LnBf2zVtGMODKFAqr5cv2AsGAaG+V9CgwYGarJZ0HXEPQvLMZOAx4nqC/vHMVzZtjORedq4A/AdcSjCB2arj+kwQjcn4ZmE/QjOVjwNN59vEyM3uRIAjbB5gH/BI4iyBIy3Y2sJKgZm4FwaAr+RwKPEgw+uaDBF+e7zCzx0ZJ75xzzkXhJoLKhZvyrLs5a91Evi+PAh4mCMZuI+h7dyPBg9KCmdkwcCLwgXAfD4RvCfgxQcuYO4AOgu/OYgNJ58pOfp0655xzzrlaEM77+jhwrZmdknR5nEuSN910zjnnnHNVSdLeBPPe3U9Q2/ZZYGt8SiDnPNBzzjnnnHNVq46gqee2wDBBE8v980wn5Nyk4003nXPOOeecc67G+GAszjnnnHPOOVdjPNBzzjnnnHPOuRrjgZ5zzjnnnHPO1RgP9JxzzjnnnHOuxnig55xzzjnnnHM1xgM955xzzjnnnKsxHug555xzzjnnXI3xQM8555xzzjnnaowHes4555xzzjlXY+qTLsBkNmXKFJs5c2bSxXDOTRKPPfZYp5lNKXU/s1pl3cOFpV05wG1mtm+pebr4lPRd9Oyzwc+tt46uQM45N4lF9V0NHuglaubMmcyfPz/pYjjnJglJS6LYT/cwnLR1YV8f3/jf0KZR5OniU9J30VVXBT8POyy6Ajnn3CQW1Xc1eKDnnHOuSAJSSroUriJ4gOeccxXLAz3nnHNF8w7eDoAXXwx+brRRsuVwzjk3ggd6zjnniiav0XMABx8c/LznnmTL4ZxzbgQP9JxzzhXN4zznnHOusnmg55xzrmheo+ecc85VNu9mEZL0cUm3SFohqVPSfyUdXsB2h0uaL6lf0jOSPlNwpumhUoo8gg10Y30vYWaR7regvM2wntXYQPf4afu6SK94Bhvqjy7/9FBw7MOD46dN8DxhBulhsHT5857kzAzrXI51rRg/bXoI61+LRfw3WigbGsAGurAKvU5E8OVRyOKcc865ZHiN3jpvAf4GnAqsBg4FLpM0ZGZX5NtA0juBy4AvA38B9gbOl9RnZj8fL0NbuZCByz9Hwwd+gFITvyUyM+zpm2DZw8GKjhmw0/tQXcOE91lU/oO92H9/A90vAobN2pvUK/fNm3bw4evov+zk4EVTGy0n/Ym6GTuUln/XcuyJayCdDqoZtnsbmrLFyHQjztN02On9ZTtPWBr6XgILJyCrb4HGtvLkPcnZ8BD2j6/Cov8Chm37ZvT2ryCN/LuznpXYMzetC8Zn7Ys6ZpStrOkXn4RlDwXXckMrzNoPNTSXLf+CyGv0nHPOuUrnD1xDZvZRMzvXzO4zs6fM7PvA9cAHxtjsNOBqMzvLzB43s18D5wNfKjBXhufdyPAj15VW+DUL4IVHgxtTS0PnMuy5uaXtswj21M1BkGdhTdXCu7C1z49MN9AbBHlD/cHSs5r+334qgvxvhOHBIP/0EPbUjflr60acpxewJfeVnH/BBrrXBXkAQ71BuV38Hv0HLHkwqEVPD8Mzd8ETt+RNagtvh3R4PdkwtvD2stX+2kAXvPAwYME1OtCNLb2/LHkXK6XCFlfj9tknWJxzzlUcr9Eb24bA4nxvSGoAdgNOyHnrGuCzkrYys4U525wEnJR5Pa05FQQmq0ucF7HvJQAWLF3FI88s4517zYaeVaXtsxg9QZB3yT8fYP7C5TQ2NsLfFqK2jddLZr1rGbzrRUgPU58SX9lzCunVz5We/2APAP/34ytpaggv6en/G1FLal0vwOqFdPb08pbXb8e79t4ReleXnn+hwiDvt3/4Ewe97QCmTZvmTTjLxF56Hob6uf3pNfx+7gts3NEEc3+CNr95ZNrljwKwprOb8758VBD0pYegHDW/gz2gFEODg/zqyps44UNvhYGu+POdAI/hHABnn510CZxzzo3CA71RSPoYsCtw8ihJNgYagKU565eFPzcH1gv0zOx8gho/ALbcoMFQitTWu5VW2ClbgBnLVnWyaNlqSNWjaVuXts9ibLQtvLSEBctWc+bH30JjUzPa80TUsuF6yWx4iJ5v3Y2tXc4Zd66GukbqXrlH6fm3bQo9y2lsqOPMEw+F5g1IvWZkRax1r8Ae+C1LV6zmqtsegVQ9TJ1Vev6FqmuE9BBPPv0Mq1avZtq0qUEZXOw0cxfs4b+yeHU/n9pnJjvOnIoOPRNt8ZoRadNPmuiBzwAAYNdJREFU/RN6VvKVn1wBCJo6yte8t2kDADq7e3nsmSWgFLRPL0/eRfAJ0122WaddU3DaBWcdHGNJnHPOZfOmm3lIejfwC+A4Mxut3dR4tznjtvUaJkXDe79D3VZzii3i+gVp2wS96hA6B+vp7E/DK/aATV5d0j6Lyn+rPeEVuzNodTRO2RTt/OERQR6A6uppOelPpF7xOmhooW7Ht9B8+I9Kz3+7t0LHFqB66JiOdjgof7q2TdCr30nH1E1ePk/adHbJ+ResvgXqW+js7KKzqweapkCqrnz5T2LaZk940wl0WjMd0zaFt3w+b5AHoFlvDvq5qg7aNkFbv6V85axvQlvtQ+dQPZ09A7DhLLTZTmXLvxgqcHE17ogj+OE/zkm6FM455/Lw6oQckj4EXAIcb2aXjJF0BTAE5I7SkHn8nlvTN4K1bUT9Lu+eQClH0sbb0bX53nQ+30xqywhqyYrJW0LbHYC2vJnUXqNVgAZSm2xN62f/TuOKL9Py8e9Ek39DC9rhIDT9TlKveufYaTfalo43n0z3jcvKfp6QoLGNtb2DrB1QUMPnyib1uvfQ9don2ODYY0lNnTpqOtU3o633R5v8i9Qr31rGEob5t25E17Rd6NTVpDZ/fdnzL5QPxuIAePJJtl61JulSOOecy8Nr9LJIOpYgyDtqnCAPMxsE/gO8I+etg4BFuf3z8kmno+2f1dnZSWdnZ6T7rEVK+A7VP6fkdHZ20tHRUVDauro6hoaSmV6hGq4Rr9FzzjnnKpsHeiFJnwN+BpwC3CpperhsEr6/u6THJe2etdlZwKGSTpW0g6SjCQZb+W4hecYR6PX29ka6z2phZokHcIXq7e2t+Jv4WjU8PEx9fWENGaZMmZLY51Tpf8tBHz0raHHOOedcMjzQW+fTQB3wc4Jml5klM/5+K7BD+BMAM/s7cCTwceAR4HTgtELm0IN4Ar329vZI91kturu7aWurjjnp2tvbPdCrAh0dHYkGepX+txzlhOmS9pM0X9JTki6SNCIal9Qu6VJJT4QP3Y4L1+8p6cFweVTSiVEcn3POOVftvI9eyMxmjfP+reRpiWRmvwN+F0+pitPd3U1ra+v4CStAfX09g4ODNDREM5rh2rVrC26Sl7SOjg7Wrl2bdDHcOJL8nKrheo6qAl1SHXAh8E4zmyfpj8ARwMU5SX8IPGpmRyqovt8kXP8QsKuZDUlqB+ZJutbMFkRTQjemxkYG6vxWwjnnKpHX6NWYamm+GHVtSTF9r5KWZE2RK1zSNXqVfj1H2EdvN2CJmc0LX18IHLZeXlIHcAhBsIcFloe/95hZpjNlC0HLjOr4R1gLbr+dDx7+vaRL4ZxzLg8P9Fwiou7/1NnZyZQpUyLbX5waGxsZHBxMuhhuHEn30evo6MCsMvu4ScE8eoUswNSwWWZmOSlndzOBxVmvF4Xrsm0DvACcJ+l+SX+VNGtdebSzpEfCbX9gZs9GfMjOOedc1fFAz0WmmNrEyVyj55KT5DVajJ6eHjbaaKOKH5ClwBq91WY2O2s5P8/usiPafB9SA7Az8FczmwP8Hbjo5Y3NHjSz1wBbA++TtENJB+cKd/75HHH/P5IuhXPOuTw80HORGBgYKHg0Q5i8gd7AwEBk/RJd8YqpIUu6ie0GG2xQ0U18pcKWAiwGtsx6PRNYkifNKjO7Lnz9e2BO7o7MbBlwBzD2hJouOpddxmGP3pJ0KZxzzuXhgV4NSqK5V7GBVtQDXUxk8IpqOE8uOUkPmpN0/uOJsI/eXGALSbPD10cDV2UnMLMXgEcl7RquOhCYByDplZIawt87gLcCj07wsJxzzrma4YFejWltbaWnp6fs+RbbRy7pPnptbW2JnScP9KpDkn30IPkaxbEIqFNhy3jMbBg4FrhS0lNAD3CZpF0lXZuV9ATgp5IeBj4PHBOu3xd4UNJDwL+BP5jZ9VEdq3POOVetfEzkGpO5OSz3nHITqdFLsulmtZwnF53+/n6ampoKTp/UQ5OMSg70ILrpFQDM7GZgds7qucBBWWnmA3vm2fZCgpE6nXPOOZfFa/QSJCnySdOTqoWohECvmAmmk7qJrqbRQWtNsdeopERHvUy6RnE8ETbddM4551wMPNBLkCS6u7sj2VfmhjSpfj3F9pGLupzDw8NFDwZTDefJRafazn3F99GLbjAWV80uuICvvvWEpEvhnHMuDw/0ElRXVxfZE/vu7m7a2tqqpqaqtbU10aHjkzxP1RRs1JJqO/eV3nQzVeDiatycOcybvm3SpXDOOZeHfw8nKJVKRfbEPnMTWy0BTDHzmcUh6Sau9fX1Pml6mVVbs9lKD/S8Rs8BcP/97LjsqaRL4ZxzLg8P9BKUSqUiu5HL3MQmHcAUI8n+T0nXfFZ6/6taVC01eul0GkkVHegV2j/P47xJ4MQTOfOGnyVdCuecc3l4oJegKAO9TP8j73tWmKTPU6X3v6pF1XKNZpphNzY2VnStb0qFLc4555xLhk+vkKCoa/SSbLrZ29tLS0tL2fOdqKTPUyXX1tSqaqnRq4pyFjhHnnPOOeeS4zV6CaqlPnqSiu53l2Q/vY6ODrq6uhLJu9Kb5dWqaumjVw2BnvDBWJxzzrlK59/DCYpy1M3MTWxDQwNDQ0OR7LMYE+lvl2Qfvfr6+kTOU4b30Su/iQRQccx1OZ5qCUh9MBbnnHOusnmgl6A4+uhNRkkGjBPlffTKr6+vj6ampqK2aWtri2yuy0JVy9+y1+g5AI44gqt22i/pUjjnnMvDv4cTFEcfvcmov7+/6Bv4pHnTzWQU21w4ic+pWv6WvUbPAXDSSVw255CkS+Gccy4PD/QSFlWzsK6uLtrb2yPZ10RMpL9dKpWK5PirpalbNg/0qkMSTWyrIdATkJIVtDjnnHMuGR7oJSyqAUmGh4epr6+uQVSjahZXDTfGubyPXvlN5G8tqRq9anhw4U03HQD77MMVv/ti0qVwzjmXh38Pu0hMpJ9cVP3UqqVPU7aWlhZ6enqSLsakMtFr1Gv0RhLB9AqFLK7GDQzQOJzcwFbOOedG54GeS0xUN9HVcGOcK8mpJVzhkhg0p7Oz8+Vm2A0NDQwMDJQ1/0J5Hz3nnHOusnmg50qWTqdJpYq/lKJqvlgtTd3S6bQHeFUmiSa26XSauro6oLL7cnrTTeecc66y+fewK9lEB4KZbDV6XV1dVVHOWlUtffSyVWxfTkGqwMU555xzyfBArwY1NjbS399ftvwmGmglHehVy3ly0aiWPnqVlP9ohDfddKHttuPZaZsnXQrnnHN5eKBXg8p9czjRwVCSHoylWs6TK93Q0NDLzSGL0d7ennigV+4+goWKsummpP0kzZf0lKSLJI0YQlhSu6RLJT0h6XFJx4XrD5f0kKSHJd0nad/Sj66ySNpH0l8lLZRkks7IeX/fcH3uclTshbvsMj53yOdjz8Y551zxPNCrABOpaRhLuQOYifaRS7qPXrmbxXmNXnIm2my2vr6e4eHhGEpUmEqt0YPoavQk1QEXAu83s22BduCIPEl/CDxqZtsDrwauDtc/C+xnZq8FjgIul1Rr323twHzgVGDZGOl2B2ZkLVfEXzTnnHOVqta+DKtOc3MzfX19ke4ziUAvyRq1gYEBmpqaEsu/UB7oJaeUc5/kADqVGugJqFdhSwF2A5aY2bzw9YXAYevlJ3UAhxAEe1hgefj7XWa2Kkw6H2gmCIxqhplda2ZfMrMrgLHam68ws2VZS2/shTv1VE675aLYs3HOOVc8D/QSFsWNXH9//3qBTrXUVEXVLG6iNaLlbhZXLaOD1qJSAr2oa9yLUbGDsRD20ytgAaaGzTIzy0k5u5oJLM56vShcl20b4AXgPEn3h80YZ+Up1keAeWZWme1d43erpOWS7pb0MZXjKcXtt/OGxfPGT+ecc67sPNBLWBSBXm7fr3IHMBPte1ZXV0c6nY6hRIVJuo+epESPfzKp1v6RFd1Hr/BRN1eb2eys5fw8u8uOpvMFJw3AzsBfzWwO8HdgvWokSXOAMwmab042S4ETgPcBBwHXAr8EvpkvsaSTsoPv1atXl6+kzjnnysYDvdB4nd1H2aZF0g8kLZDUF257rqTWQvONItjIra2olj56SUu66WZ7eztdXV1ly38yq5Zms7m1h5XadBOKqtEbz2Jgy6zXM4EledKsMrPrwte/B+a8XBZpe+BPwIfM7MnijqT6mdn/zOznZjY3XL4FnA18VlJDnvTnZwffU6dOLX+hnXPOxc4DvXUK7eye7RzgcIInqTsAxwMfAn5UaKZTpkwp+Yl9JQR61dj/KekmrpV8E19rquVhRG4z7JaWFnp74+9mVSwR6Tx6c4EtJM0OXx8NXJWdwMxeAB6VtGu46kBgHoCkmcA1wPFmdm/pR1cz7gXagE2SLohzzrlkjBjCerIys2sJmrsg6XsFbvYm4PdZT5kXSvoDwU1IQTo6OlizZk0xRR0h9ya23AHMRCdMh2T7P5W7WVzueark/le1plpq9HL/lpN8EDImQUrR/O2a2bCkY4ErJTUCtwOXhUHdN83soDDpCcCFktqANcAx4fqvA5sC3886X+8xswWRFLB6zQF6gZVJF8Q551wyPNArzR3AwZJ+bmZPStqOoH/EX/IlDgcheHkggunTp9PR0cHixYvzJS/Y2rVr17s5LHcAk06nSaWqr3K43DVq6XR6vbncKrn/Va2plj561VJOKLhZZkHM7GZgds7quQT/TzNp5gN75tn2WODYCItTcSS1A9uGLxuB6ZJ2BgbMbL6kzwALCVqlALwD+BJwnpkNxFq4a67h49+8IdYsnHPOTYwHeqU5hWC47yckDRGcz18QfMGOEA5C8PJABLNnz7ao+uhtscUWL7+OY8qGWtTU1ER//1gjlcfLm26WT6k1emZWltq1aql5FFBXoZWNNWpX4Jas18eFy0JgFsFgNWcBrwCGgKcIvp9+HXvJNtqINS2V3yzaOecmo+qrhqksxwPvBD5I0Ezmg+HrMwvdQRx99Mrd3KuU/JJsmpZ0szgP9Mqnu7ubtra2CW3b3NxctgcC1RLoQaR99Nw4zOxWM1OeZVb4/vfNbAczazWzKWY2x8x+aWbxD+t71VW87X93xZ6Nc8654nmgN0GSmoEfAF81sz+a2SNm9kfgNOCLhY68GVWNXjUMNJFPqX30hoaGqK+vzopp76NXXhNtXlzOgLya/pZTBS6TmaQmSU1Zr18h6RRJByRZrkidfTbH3/vnpEvhnHMuj8n+PVyKhnAZzlk/TBEjizc2NjI4OFhSQZLu11NqsFbK9p2dnRMeCCZpXqNXPqVcY+UO9KqlRk8qbJnk/gp8El7uZ3cPcAZwvaRPJFgu55xzk4AHeiFJ7ZJ2Dju4v9zZPTPkt6TdJT0uaXcAM+sEbgK+LekdkmZJOgj4DnC9mXUXmnepgdLAwMB6Q7JXk5aWlpL6E1bTjXEuH4ylOpTzc0r6oU2hIp5eoZbNAW4Lf38P0AlsRhD8nZJQmZxzzk0SHuitsyvwQLjMIOjo/gDhlAtAK8FcedlNMj8MXA/8HHgc+BnwD+CI8hS5MpTS163U2pJqauqWyydMrw7lbGJbTQ8uIpwwvZZNAVaHvx8A/CUcBfNfwDaJlco559ykUJ2dm2JgZrcyxn1JvvfNbCVwYql5Jz0oSJIygd6mm246oe2r6cY4V11dHel0/GMluNIfRpSrRi/f9ZxKpSpyCpPCz2lyc2VWgOeAnSU9RxDoHR2u3wBIbshf55xzk0Jl3Tm4qmNmJfd/KuUmulqauiU5Mbyrnj56+ZphV2TNb4H98ybxM6yMC4HfAfOAAdZNkfAGglYg1e/00/nJXh9KuhTOOefy8Bq9ClDNQUBvby+trQUNMJpXqc3iOjs72WqrrSa8fbn09vbS0tKSdDHcBCTdlzKTf6U1UZZ3wBuXmX1H0uPAVsAfzSwz8laaYNTm6nfwwdxyR9KFcM45l48HejUqlUoxPDxMXV1drPmU2nSyo6ODNWvWlJR/KTfA1XKeXHKSngajEkdnFZO7yXkxzOyqPOsuSqIssVi6lE26VrGifVrSJXHOOZfDA70KEMcNU0dHB11dXWywwQaR7ztbFIHe4sWLE82/XOep0mpkJgszS3TAoFIlHWiOxgO9wkiaAuwOTGf97hJmZpclU6oIHXoov1y0hkOPPCfpkjjnnMvhgV6NytycVkOgV2rTzVLzX7t2bcWfJzdxPT09JTUvbmpqor8/uXEzkg408xLew7sAkg4ErgA2zPO2AdUf6DnnnKtY/lVdAerq6kqeND1XufoVldp3aMqUKSWVs6uri7a2tglvX66b6GoZNKYWVfu5T7qP4GgkFbRMcj8kmKLnFWaWylnibS/unHNu0vNArwKUEmwMDQ3l7V9WrgAm6Ro9SHYev0J5jV5yqv3cV2SNHj7qZoFeCZxhZs8lXRDnnHOTjwd6FaCUPjhdXV20t7ePWF8tAUxjY2PktZnFKFf/p7HOUzWPuloNqr1/ZGUGeoXV5nmNHo8CWyRdCOecc5OTB3oVoJQbudFuYishgClUkoFOuZrFjfY5tbS00NfXF3v+k1m11+hV7GAsKRW0THInA9+RtLuk2uwTf8AB3Dlr56RL4ZxzLo/a/OKpMqUEeqP1PypnH71qvolOuo9e5nPyOfbis3btWjbZZJOkizGu0Zpht7e3V1yg580yC3ZP+PNuGNnMvCb66Z15Juecdk3SpXDOOZeHB3oVoNQavdECiGqp0UtS0ucpk/9mm20Wexkmq87OTrbZZpukizGu0a6RVCpVkc17o2yWKWk/4HygEbgd+KSZDeWkaQcuAPYgmHD8XDP7haRZwKXA64E/mNkxkRWsdEcTjK7pnHPOlZ0HehVgypQpPPPMMxPaNulAb2hoiMbGxpL2kWQ/nnI1ixscHMx7niqz/1VtqZY+elX30CSiv1tJdcCFwDvNbJ6kPwL/396dx8lRlfsf/3xnzayQyBIgbIIIYTEgIFyQRUUULigu1wVZRUXQK26IC8rFDbyi+FNQL4JccEFB0ItEBdkEBARZQ2RPIIGEhKyzr8/vj6oOnUnPTC9VXVU9z/v1qtdMV1fXOV291VPnPOccB/x8zKbfA+aZ2fEKvjRyzbRrgbOAPYC9I6lURMzs8qTrELv3vY8fPbKET7zjC0nXxDnn3Bge6KVApS1622677Qbr29vb6e7urrRqk4qipSHJ1oq2traqHKfxpDX/qpZkJYDKSkCaE+H1mX2AxWb2WHj7UoLctnWBnqQO4N+BbSCYaRxYFv6/Evi7pJ0iq1HEJB0E7E7Quveomd2RcJWi89xzbLVmddK1cM45V4AHeikQR45eWrt7pU3SowJ2dHSwatWqROtQ66LII62vr2d4eJiGhvi+MrOW71rCZ2e6pPl5ty8ys4vybs8CFuXdfj5cl+/VwEvAjyTtG27/KTNbWFKlq0zSDOBa4CBgDcFU852S7gDeZWYrkqyfc8652uajbqZAHDl6U0EtBLLedTN+IyMjFQdo1XidsvZZLmEevVVmNjtvuajA7vI/zIUiyEZgDvAHM9sLuB64LOKnFIfvAZsAe5rZdDPbGNgLeBVwQZIVc845V/s80EuB1tZWent7y3ps0ieHUbSIlbuPnp6egnMIZokHetlQjS62SX+WS6JIp1dYRNglMzQLWFxgm5Vm9qfw9q8IAqa0+3fgNDN7OLfCzB4CTg/vc84552LjgV4KVBIs9fT00NbWFmFtqq++vr6sSdMzdWI8Ds/Ri18UFyOq1aKXlRw9RTth+v3AVpJmh7dPJujuuI6ZvQTMk5QbbOUw4DHSr40wl3CMl8L7sq+9nZ6maUnXwjnnXAEe6NWAJPPMoug+We5JdNZymgrxFr34JfkeLUXWLlxEFeiZ2QjwEeAaSU8DvcCVkvaWNDdv048DP5T0CPBZ4JSwHq2SFhN0k/yApMWS3hzx0y3XwwStd2N9Mrwv+/76Vz70/m8mXQvnnHMF+GAsKVEL+WblyrVqzZgxo6THZe3EuJCmpiYGBweTroabRG5i+zhNduHCzBIfPChflFUxs1uA2WNW3w8ckbfNfGD/Ao/tZcPBW9LiK8BcSf8G3B6uOxjYDe+66ZxzLmbeoucqkmS3uCx1dXPZVo0utr29vbS2tha8r6Wlhb6+vljLL1kJo7FMVWb2V4JBZOYRjLx5cPj/nmZ2U4JVi873v8+H7/t90rVwzjlXgLfopUSartQXa2hoKJLh5isJ9DbbbLOKy4/b0NAQjY2NSVdjyspKjh6MX9dcoDleIFh1HsMVLWyJPCHpesTmN7/h359fzaX7vDPpmjjnnBvDW/RqmKRYu4RG1XWy3G5xUeboZeE4ufJkJUcvzeUXEuFgLM4555yLgQd6KRFHoNHS0lL2tA3FWLt2bSRdJ8vtFhdVAFXJ9BbFqIVBY6a6auTopbn8QiKcXqGmSBqRtFn4/2h4u+CSdF2dc87VNu+6WcNyrQBxTb8QZYtekoFeVo6TK93AwABNTU0V76etrY2enp4IalSetLXoiWx2N6+Sk4E1ef9P3ZG2nHPOJcoDvZSoq6tjdHSUurroGllzLWUzZ86MbJ/5kg70uru7Iym/GsfJB41JRlTHPumgprOzk5UrVyZah7E8zivMzP437//LE6yKc865Kc67bqZEOS0Gkw23HncrQNKB3vDwcGSDwcTZLc5b9JJTK8c+bS16FJmfl3SAnDRJz0p6VYH1G0t6Nok6Re5nP+MLb/9k0rVwzjlXgAd6KVHOiVxPT8+Eo/DFHcBElaMXd47cZOI+ifYcveTUyrFPY45e0H+ziGVq2w6oL7C+Cdi6ulWJyW678eSm2yVdC+eccwV4182UyJ3IbbnllkU/ZrLWio6ODlasWBFF9coqv1hJX/Xv6Ojg5Zdfjm3/kx2nhoYGn4IhJt6iFx9F2M281kg6KO/m/pJW5d2uB94KPF/dWsXk7rvZ64V/8cBWuyRdE+ecc2N4oJcS5Yw8WUygt3DhwgprVn75WdHZ2cmCBQti238xr1NXVxczZsyIrQ5TVVbyIycbdTeNgZ4n6U3oNoJBWAy4rsD9vcDHq1mh2Hz605z9/GqOOf6CpGvinHNuDL8kG5J0kKQ/SHpOkkk6p8jHbSvpl5KWS+qX9KSkd5ZafjkncpOdxJY7bUEp5WdhHrvJVCNHL8nXaSrLysWInp4e2tvbx72/qamJoaGhKtZoMkJ19UUtU9T2wA4EnVf3DW/nlq2ATjO7MrnqOeecmwq8Re8V7cB84FfAhcU8QNJWwD0EV2+PApYC2wLdpRbe0dHB0qVLS3rMZPlHcQcwvb29tLS0xLb/aom7tWSy45TK/KsakZUcvazUc51gfoWka5FaZvYcgKRNzSy+/vPOOefcBDzQC5nZXGAugKTzi3zYt4CFZvaBvHULyym/o6ODJ598sqTHdHV1sdlmm024zzgDmChH1UsyT68a3eKSHB11KstKi15W6rkeD/QmlR/kSZpJMAhL/v21kafnnHMulbzrZpkk1QHvBO6W9GtJyyQ9KukrkkoOoOPI0WtsbGR4eLjUqkw5DQ0NjIyMJFa+B3rxiTJHT1JsXYyzGOj59AqTk9Qu6VJJvcALwIIxi3POORcbD/TKtynQCZwGLAIOB84DPgN8s9ADJJ0uaX5uWbXqlYHY4sjRy5JST6CTzOmLmufoxWdgYIDm5uZI9lXOXJfFyuRnWXXFLSkUXpw7tApFfRs4EDgBGABOAc4l6Ob/oSqUH79TTuGq17016Vo455wrIJ2/wtmQG2XgETM708weNLNfEnTnPF0FLmWb2UVmNju3TJ8+fd197e3tdHeXltqXubyeCEV5Ap80b9GLj5lF1qoU5+uUtRY9IVRX3JJS1wFfDAfPOkvS5jGVcxTwCTO7GhgB/mZm/wV8DfiPmMqsrlNO4TevOzzpWjjnnCvAA73yLQeGCAZwyfcY0AZsUsrO6uvrS+4+2N3dPeFIfVlSV1fH6Oho0dtn7cR4Ij4YSzbE+Tpl7qJNbjCWYpYUMrPfmtlbCeazawPul3StpMMiLmoz4Inw/25go/D/m4E3R1yWc845tx4P9MpkZkPAvcBrx9z1WqAHiG8G7tDIyAgNDbUxnk6p3eIy2dVtHN6ilw1xdrHN5IWLCLtuSjo07NL+tKTLCuU5h/luV4StcI9L+ljefd8JH/ukpPeU8CxeQ/Cd3Q08AnxG0i9LePxkFgMzw/8XArnuonsRdOXMvv3247orPpt0LZxzzhXggV4oPImYI2kOwchoM8Pbs8P79w1PLvbNe9i3gTdI+qqkHSUdDXwZ+IGVkUQ2lQcuKDXYyeSJ8ThaW1vp6+tLuho1KcrPVNxdN7N24UJ1dUUtk+5HqgcuBd5rZjsSTHVzXIFNvwfMM7OdgF0IJyKX9FZgP2BngkDq+5Im/HIIB816CvgY8DMz28XMzjGztxMEYVG5FTg6/P8S4NuS7gOuBH4TYTnOOefcBmqjOSgaexP8KOd8LFyeA7YDWgmu/LbmNjCzuZI+AJwNfIlgUJbvA9+pTpWTFeWAKLlucVtuuWVR22euq9sE4hzNcaqL+j26bNmyyPaXr6ura9Ju2I2NjQwODtLU1DThdtURabfMfYDFZvZYePtS4BPAz9eVFgRu/w5sAxBeSMu9GO8CLjezYeAFSXcSdMn83STlvtHMCk1eemS5T2QsM/tYLl/bzC6TtBY4iOC5/TSqcpxzzrlCPNALmdltBJknJd1vZr8hoiuzWTrZHx0dpa6Iq/XFKrVbXFZa9EZHR6d0S20t6ejo4Omnn45l36Ojo9TX10+4Ta5F8VWvelUsdSiVih9Rc7qk/Fzmi8zsorzbswgukuU8H67L92rgJeBHYa+KRcCnzGxhuO3vJnn8WHcBvbkbkjYC9jSz28zs2cmfUvHye3eY2TXANVHu3znnnBuPB3quLFEPBFNO182tttoqsvLjUksD5kx1SU+DkapALzcYS3FWmdnsSbbJv8pVaMeNwBzgS2Z2qqRTgMuANxX5+LG+Z2Z75t1eS9A1NMpum0jaZqL7fcJ055xzcfJAL0XiaPlpamqKpbtX1C1qHR0dLF1aqBdVdcpvamqKZcqGLOZe1ZKs5OgVI+lAc6wIp05YRNglMzSLYBCTsdusNLM/hbd/BXx3gsffO0mZ6zVHmpkVGgAmAgtZPwgda+Jm3CzYdVee7F00+XbOOeeqzgdjqXFxnZzGEeiVMnR91Dl6WTlOrngjIyORdi9ub29PRYteOijKUTfvB7bKDXwFnAxcm7+Bmb0EzJO0d7jqMIKpbAi3PUFSvaQtCSYov3GSMl+Q9MF1z0Y6FnixmMqW6FCCVsfc8jaCAbsWAR+c4HHZcemlfOGITyVdC+eccwV4oJciZhZ5nl5cc3+tXbs20paqpHP04jqJrqVBY7KmmAFOStHQ0FDyXJdRSt18ixHNo2dmI8BHgGskPU2QO3elpL0lzc3b9OPADyU9AnwWOCV8/E0ELXhPALcDnzWzyT7MHwdOk7RM0jLg1HCJlJndPma5yczOA75ICYGepIMk/UHSc5JM0jkFttlR0g2SuiWtlvRLSZtG+HScc85ljHfdTJFp06bR399PS0tLZPvMSktVqfWMuptlVo6TK16tHfs4R/0smShq6oRimdktwNg8vvuBI/K2mQ/sP87jzwTOLKG854ADJbWHt7tLrXOF7iWYbqFY7cB8gi6rF469U1IbwSTsTwEHA9OAi4E/SDqgnOl+inbGGXz1zgWc+5aPxlaEc8658niglyK5Vq1iAr1iA5248nq6urqKngqhGO3t7XR3l3auFWX+VWdnZyytJVEfJ1e8WsuPTFuOXrGToaeVpJ2A3YBpue8SM/tVlYp/J7Ci2I3NbC4wF0DS+QU2+SCwBbCXma0ItzsOeJig++gtFdZ3fPfcw54vro5t984558rngV6K5FqVNttss0m3Lba1IistVfX19Yl3i0vyONXV1UU+ZcVUV4steukJ9JTpaUMkfRZ4H8G0DbcR5PzdStBiFmU5YwMsAVsBOwBfiLCoA4B7c0EegJk9Imkx8EbiDPScc86llgd6KVLKiVyxuV+ljmZZrKhz9JLW0dHBkiVLIt9vsa9TW1sb3d3dNXVMk5aV/Mhie9WlLkcvulE3k3AC8HrgfjN7j6Ttgf+OoZznxtweBe4BbjSzWyMsZwug0BfYUmCDLgWSTgdOz92eOXNmhFVxzrnas91ZNxS97cLzjoyxJqXxQC9FSjmRq7UWPYhneoliJX2ccuV7oBedrq4utt5666SrMaliu2G3tLTQ19dXhRpNLphGL9OtzwNmNhQObNJgZgskvTrqQszspKj3OY6Jvjw3uJIQTli/btL62bNnx5fD55xzLjEe6KVIZ2cnq1evLmrbpAO9WpsIPK4cvWKPU7q65dWGrATOxX6WU9VVsrQJ09NolaROggFMrpb0MpDlD+CLBN1Bx5pJ4ZY+55xzU4AHeinS0dHBokXFTTxb7ElsXAM4xJFPFufAcJOJK9AaHR2lvn7yOZFTN9BGDchKjl5WAtINZDvQOxoYIBip81igE/h8FDsukJc3LjN7UxRlAncBH5Q0w8xWhvXYjWDy+DsiKqOwm27iQ1/9c6xFOOecK48HeilSao5eMaM5Tps2LTXdvdKsubmZgYGBxMpPXf5VDYgrR8/MIm1dy0ou4fqE6ia/gJFGCvqczg2DrBHgioiLGJuXV7FwGogdw5tNwExJc4DBcNqJXwFfAa6SdBavTK9wD8EgM/Hp6KCnuTXWIpxzzpWn5gI9SYcCuxLkJfwrnJ8pE+LI0Yuru1cc+02ya1rS3eLiGgxmKuvp6aGtrS3SfeYuCEybNi2yfWal5XEDGW3RM7NRSf2SWs2sN4b9x5GXtzfrB2wfC5fngO3MrEfSm4EfELTgDQM3AJ+KdQ49gKuu4qj5D3D97INjLcY551zpaibQk7QNcC2wF7AmXL2RpAeBd4UT5KZaKS16mT05jMDw8HBR3SGzpKOjgyeffDLpatQUM4u8e3Gui23UgV4Wu24mfXGkQi8C90q6Hlg3gaeZfSu5Ko3PzG5j4gFXMLOngeoP9XbhhZz8/GoP9JxzLoVqJtADfgrUAbub2WMAknYF/hf4CfD2BOtWlObmZoaGhoraNqsnhxMxs6K6xXV3d9dckOs5etGLIxDJXYzZdNNNI9tnV1cXs2bNimx/VSEg23M+LgoXgMY4C5K0A/BeYDuCbpfrmNnJcZbtnHNuaqulQO8Q4JBckAdgZo9J+gQZmiy22F42xQ7JniUtLS309/fT0tIy4XbZzGmamI+6Gb04eqzF8Tpls3Veme26CWBm/1WNciS9BbgeeBbYCZgHbB/e/UA16uCcc27qqqVAbxlQKN+iF1hR5bq4MuROoicL9LJ5Yjyx9vZ2D/QyII5BczJ74SLD8+hJ+mqh9WZ2bsRFfQP4gZmdJakLeDfwMvAb4HcRl+Wcc86tJ7u/1Bs6Hzhf0sa5FeH/3wK+k1CdXAmKbS2pxW6r9fX1jI6OJl0NN4k4utiWcuFCUnreJ1JxSzqN5C0NwFG80tIWpV2AS/PKbDaztQQjZJ4VQ3nOOefcOrXUovdegoFYFkt6gmDUzZ0JRh9rlXRMbsMI5y6KXBYGOIhrELdSAr0stIAkOS+giy9H74UXXoh0n4ODg0V3w+7o6KC7uzv5Cx0SZHR6BQAz+2b+bUnfBq6OoajBvP+XAdsA/yIYAGby+XGy4Otf54Kf3Zt0LZxzzhVQS4HewnDJ92j1q1GZLAQHxeTRlaOzs7OobnFr165l2223jbz8qMV1nFxxspKjV075iQd6kOmumwWMEuTQRe1RYE/gKeDvwFckNQAfJQj4su+ww7jz5sHJt3POOVd1NRPoxTR3UU2oq6tjZGQksikJ4sop6ujoYPXq1ZNuF1eLXlaOk0tO0hPb58rfaqutEqvDOhnofTAeSTcR9PqAIIVhZ+DyGIr6FtAe/v81grntrido3Xt3DOVV38KFzFrzEos32jzpmjjnnBujZgI9N7729na6u7vZaKONItlfXIFWR0cHixYtmnS7uMrPynFyk0u6e3Fcki5/Pdlu0ftG3v/DwEIzi7ZPLmBmf837/zlgN0kzgFWxT2ReLe9/Pz98fjXHHH9B0jVxzjk3RqZ/qV1xoh5AIs5AL8nBWIrtOlqs1HSxm4J6e3tpa2uLfL/Nzc0MDAxEvt9ipWe+xSIHYimy1U/SoZLmS3pa0mVh98ax2yyU9Jikh8Jldri+U9JVkh4Nl3cWUeQy4D4zu93M7gJWS9q5hANQFEknSVrvjWhmK2smyHPOOZdqHuilTENDA8PDw5HuM+pWgKQDre7u7lhO4uM4Tt6il4y4jn3SgyWlq0UvmkBPUj3ByJTvNbMdCbo6HjfO5oeb2ZxwmR+u+zLwgpntDrwR+LqkyV78X7D+QCmD4bqo/T9gqaT/lXRIDPt3zjnnxuWBXsoUcyI3PDxcUh5Z1HlFceboFXsSG9eIilGeRHuOXnJq9dgnnSO4jgi6bhazTG4fYLGZPRbevhR4Vwm12Q34M4CZrSYY5ORtkzym3szWXVEzsyGgsYQyi7U58AlgFnCzpAWSzpH06hjKcs4559bjgV7KFHMi193dTXt7+4TbjN1nFlqqmpqaGBoainy/xUrDcfIeXdGo1dbUWmzRIwiC8pNznw/XFXK9pIclfSOve+c/gf+QVCdpK+BAYOtJylwj6cBXnoreCEQeQZtZr5n9r5m9GXg1cBnwQeApSbdFXZ5zzjmXzwO9lCkmB6fUrpNZydFLWtI5ei0tLfT390dW/lRWq/mRqcrRq6svboHpYf5dbjm9wA5t/Z0XdKCZ7UkQyO0OfDZcfx7BgCoPAD8Bbg9vT+RTwOWS7pN0P0EAVqhekTGz58zs68BJwEME3Uyz78gjuWWHvZOuhXPOuQJ81M2UKeaKfamBVhpaqoqVZItW0scpV77PvVe5rq4uNtlkk6SrManh4WEaGor/Gs6NDJsKxY+6ucrMZk9w/yKCicRzZgGLx25kZovDv12SLgE+HN7uBT6+rlrSH4AnJqqQmT0UDr7yWoLA8vH8rpxRk7QtQd7h8cAOBHPqfTSu8qrq7LP5Yc8NSdfCOedcAd6ilzLFBBul5h9lJUcvaUnn6KUm/6oGZOU92tXVVVI37Lq6OkZHR2OsUQmi67p5P7BVbhRN4GTg2vWLUpukzvD/BoI56B4Jb28kqTn8/0Bge+CmiauuDwIdZvaYmc0DOiR9oOjnXiRJJ0q6FXiGIDC9CniNmb3RzC6NujznnHMunwd6KVPMyX7SLXrDw8M0NTVFtr98SY5qGPVxGhoaKuk4pSr/KuOy0r04K/XcgBTZYCxmNgJ8BLhG0tNAL3ClpL0lzQ032xz4m6RHgIeBIYIumxC0ys2T9C+C+fHea2aTRcNnmtmqvDqsAs4s/gAU7YfAc8BhZra9mX3VzJ6NoZzkHHMMP732G5Nv55xzruq862ZI0kEEOR9zCLoR/ZeZnVPC408iyPO43cwOKbcexebobbPNNhNuky9V3b1SrK2tLdHj5IFedLKSo5fZQA+KniOvGGZ2CzC2e+f9wBHh/c8SfDcXeuw/gNeUWGShCDSO38OZZtYTw37TY8kSNutenXQtnHPOFeAteq9oB+YTXNVdWsoDJe0OfAv4W6WVKDZHr5ST2Lq6usyM5phkPevqkv04pGegjeyLM4CKcq7LrASkBUU3vUIS5kv6sqT2sFvoVwi+/yNlZj2SNpX0GUk/lrQJgKQDJG0fdXnOOedcvtT+Clebmc01sy+a2W+AgWIfF07MezXwn8CCSusRR45eLclKwFoOb9GLTqmDnJQiyhbyzLfoRZOjl4TTgV2AZwm+t19L3oAuUZG0B8HAMKcBpwC5qP5twLlRl+ecc87l80CvcpcAt5nZ1VHsrLW1lZ6eiXv6ZPrkcBKT5ej19PTQ1tYWW/lJj/rpg7GkX5SvU3Yv2pQ0vULqmNkK4ArgfOAHBJOsnxpDUd8FriToWpo/d8pfCKaJcM4552LjOXoVCOeDmg3sW8L26+ZqmjlzZqFtJt1P3MFOkurr6ydsjcl0V7dJeIteNkTZxTbTF23S2y1zUpIuIJjA/A3ArwhG8bwthqL2AU43Mxvz3b4I2PAHIItmzGD18thmpnDOOVeB7P5SJ0zSngRdb/7DzIqa5drMLjKz2bll+vTp4+27mPJLqW5mTBbsxH1iXEujfk5lcb6OUb5Omb1wIbLedfMw4F3AcjP7HLA3sFkM5RhQaOjdrYE1MZRXfXPnctJ7/yvpWjjnnCvAA73yHQxMJxjWe1jSMMFkuAeHt99U7o5rOQ9tMp2dnRN2i8tuV7fJNTc3Mzg4mHQ1akKcn6GoA71y3s/Jf0dEN71CQvotOIgjklrDrpzFD2VcvJsJ8vNyTNI04GzgxhjKc84559ZJ7a9wBlwJ7EEw5Hdu+T+CIcHnAPcmU61sS7pFL/kTaJd2Uebo9fT00NraWtJjWlpa6Ovri6T8imS7Re85SdMJBtK6TdKNwNMxlHMmcIykfxC07P0EeArYFfhyDOVV3/nnc+o91yRdC+eccwV4jl5IUjuwY3izCZgpaQ4waGbzJe1LkLx/vJn9I7wCvGLMPlYDG5vZvOrVvLqGhoZiG80Qigv0Ntssjh5W0Yr7OLnkRD0NRqndTHOfkVIDxMilt7VuUmb23vDfb0u6i2A0zD/HUM4CSa8jGNFzGcHF1Z8DPzKzZVGXl4jrruPw51fzk/3ek3RNnHPOjeFnoq/YG7g17/bHwuU5YDuglWAI7tjPruLKLzKzivcdd4taMYHeDjvsEFv5kiI7TpnMvaoRWcnRK0cu0Nx8880TqwMAdaltrSuJmVU8/+kk+1+OT6XgnHMuAR7ohczsNoIhBsq6P9zmxIjqEsVu1tPa2kpfX1/FrQBxBzCdnZ08++yz494fd45ea2srvb29FY9qmunRFGtAVnL0slg+EHTJTOnUCc4555wLZLfvjStJVHlFcQdaSefoRXUSXcuDxqTdwMAATU2FBjqMRltbW2QTppcjNfMtZjtHzznnnKt5HuilkCRGR0cj3WdUAUzSgVbS5RfLW/SSU8tTcEBKWvQg66NuOuecczXPf4VTqL29nZ6enoL3ldslLaoBJJIOtEZGRmId5GSy6R2K5Tl6yan1Yx/1YDBl80CvIEl7SFPoiV9xBZ/5988kXQvnnHMFTJ0fowyZKNjp6ekpK38sypaqOE+iczlySfEWveyr9WOfjha9zM+jF6cHgU0AJM2XNCPh+sRrp51YMGOrpGvhnHOugCn5K5x2E+XglHsSm5UcvVrpFlfucWpoaGBoaKji8qeyWs+PTEWOnvAcvfGtBXJzwOwM1PaoNbffzhuefzTpWjjnnCvAR91MoYmCjUoCvYULF1ZYs6nRWrJgwYKK91PJ69TV1cWMGbXdCBCnrLxHy+2GnY4WPaZqa10xbgVuljQ/vH2dpMFCG5rZm6pXrZh84Quc9fxqjjn+gqRr4pxzbgwP9FJoohyccrtOZiVHL2lJ5+jlXicP9MrX1dXFFltskXQ1JtXT00N7e3vJj2tqakpBq69PrzCBE4BTgZ2Ag4DFQF+iNXLOOTcleaCXQh0dHSxdurTgfZW2FFWqr6+PlpaWiveTVkkfp9S01mRYNS5GRNHFuJJ6Jt3FOaiEt+gVYmZdwH8DSHoLcLqZrUi2Vs4556Yi/6VOoYlycMrNP4oygEjFSWZMojpOZlbWcfJAr3LVCPSimJC9knrGOSF80SIcjEXSoeHAJU9LukzSBhchJS2U9Jikh8Jldrheki4M75sv6ceSUtHcaGbbe5DnnHMuKR7opVAcOXqNjY0p6O6Vfg0NDQwPDydWfioG2si4ag3GUmmwlelBYxTdqJthUHYp8F4z2xFoB44bZ/PDzWxOuORy4A4F9gH2AHYHdgPeVulTjEoYxN4iabmkZeH/hyZdL+ecc7XPA70UmixHL7Mnh25S3qJXuf7+fqZNmxZrGRPNdVmszH+Woxt1cx9gsZk9Ft6+FHhXCTUxYBrQFC7NwEslPD42kt4D/BVYA3wT+BbBqJw3SXpvknWLzGmnceVeRyRdC+eccwV4jl4Ktbe3093dXfC+Wp8MeiIDAwM0NzfHXk6SXVNTMxl2hkmK/TXMBeTlDKaS09XVxeabbx5hraqs+By96XkjUAJcZGYX5d2eBSzKu/18uK6Q68PJyK8HzjGzYTO7VdKtwJJwm0vN7P5iKxezs4H/MrNz89ZdKOmrwFeAq5OpVoSOP55r578q6Vo455wrwFv0Uqi+vp6RkZGC91V6cpkFkhgdHd1gfaa7uhXJW/QqV438tShep2y36JXUdXOVmc3OWy4qsENbf+cFHWhmewIHEnTR/CyApL2BbYAtwmXXFLWWvRb4dYH1vw7vy76BAZqGPS3AOefSyAO9jBkZGaGhobYbYtva2gp2i6vWiXGSA114jl42RPE6Zf7CRV1dccvkFhEEajmzCKYkWI+ZLQ7/dgGXAPuFd50I3GRmfWbWB1xLkLeXBquAHQus3ym8L/sOPpjf/OqspGvhnHOuAA/0XOqM11oyFbqttra20tvbm3Q13CSi6GKb6RY9EcyjV8wyufuBrXKjaAInEwRrrxQntUnqDP9vAN4NPBLe/RzwFkl14cAuhwH5XUWTdA3wU0lHSeoMl6OBH4f3Oeecc7HxQC+lankKg8lMFOhV48Q4yWM/lV/3qFTjGEbRdbO7u7vs93Pyo+hGN+qmmY0AHwGukfQ00AtcKWlvSXPDzTYH/ibpEeBhYAg4L7zvImAYmEcQ/K0Bfhrls63AF4A7gD8QtOCtAn4P3Bne55xzzsWmtvsAZlgq5skaY3R0tCon0Z2dnQW7xWWlq9vo6Ch1xXVZczGoVo7eM888U9E+RkZGqK8vb7q3XKA5Y8aMiupQkQi/C8zsFmD2mNX3A0eE9z8LzBnnsb3AsZFVJkK5ukn6CrArQVvoPDNbkGzNnHPOTQUe6LmidXd3V2UgmIla9LbccsvYy680UOju7qatrS2i2rg0SjqXMld+soGeX8woVhjYeXDnnHOuqvyXegppbGxkcHCw7MdXq+tk0l03m5qaGBgYKPvxmc69ckVJenTUpMuPsuumy7i99mLezB2SroVzzrkC/Fd4Cql0AIk0BHrVGIwliuNU64PGpNXIyEhVus12dHSMO9dlNSQ+36LwQM8FLr6Ys996WtK1cM45V4D/CqdY1LlGlbYCVDPQSjJHL4rj5C16yahkgJNSNDQ0MDw8HHs540m+RY8gR6+YxTnnnHOJ8EAvpaZNm0Z/f3+k+6w0ryjpQGtgYIDm5ubEyi9WVgaNqUVT5dgnnSMIgrqG4hZX2047ja/feHHStXDOOVeAB3opVSjYGBgYoKmpKdJ9lqJaLVXt7e3jdour1tD5lZxEe4tecqbKsfcWveyQNEuKvw+rpHMkWYFlu1gLfuABdlta2Qi0zjnn4uGBXkoVysGptOtkVnL06uvrGR0djb2c8SSdoycp0eefZVMlPzLxHD3wHL3iPQLMApB0gKQ4uyUsBrYYsyyKsTznnHMp5r/CKVXoin2lgVZWcvSSlnTLZ3t7Oz09PWU/firLSotepfm3ibfoyUfdnIikMyXtJ6mBYOia3Av+J2CrGIseMbOlY5aRGMtzzjmXYlPzVzgDCnUfrDT/KCs5eklLOkcv+fyr7MrKe7TSfNOWlhZ6e3sjrFEZPNCbyJ7ANcAaoB34mKTchOnRjrK1vpmSFklaLOlPkv4txrKcc86l3JT9FU67NLboVWvC9KRVGmhVepwSb63JsKy06FVaz2rkqk6qTsUtU5CZfcDMZgFzgH7g9cCdQCvwHUknStom4mLvBY4HjgQ+ALwM3CHpsLEbSjpd0vzcsmrVqoir4pxzLg18SLSU6uzsZPHixeutSzpHz8yqMkdZ0io9TqOjo9TX1ydW/lSWle7FWannhKZua92kJL0VuMvMnpI0DHwMWAKsAl4EjgMuAtqiKtPM/jRm1R2StgU+D9w0ZtuLwvIBmD17dvmtjLffzvu+PLZo55xzaeC/1CkVR4teHFM21KLm5mYGBgbKfnylrS3eole+arboVfI6R1HPRFv15NMrTOJHwCpJ9wMtwAHhegN+YGZvBqZXoR7/ALaLtYTmZgYbGmMtwjnnXHk80EupOHL0JEU+CXstqvQEOvMDbWRYd3c3bW2RNZJMqKmpqewLAlEEeol/ln16hXGZ2U4Eg658GxgFzgNWANOAEyS9zswGq1CVPYl71M0rruBd826OtQjnnHPl8UAvJOkgSX+Q9Fw499A5RTzm85LulrRK0mpJd0p6WxT1iaNFL8uGh4cr6g5ZqiRPon0wlvJVs3txJa9TVgaNmZiKXKYmM1tuZr8jyNF7I/AGYAjYH7hJ0stRlifpAkmHStpe0hxJPwYOBS6MspwNXHwxxz0wN9YinHPOlccDvVe0A/OBM4GlRT7mTcBlBD+mbyBIhv+jpAMmfFQRmpubGRxc/4LvVAv08oOt7u7uKfPcvUUvGyrJpcz+Z9mnVyiBAMzsX8AIcKqZbUbw+xGlLYErgceBG4GdgLeY2fURl+Occy4jpmwCxVhmNheYCyDp/CIf8/Yxqz4r6e3Au4C7oq1h5UOyZ8m0adMYGBhg2rRpQPVPjJPMf/LBWMpXzdetkoC8NgZjmbqtdaUws/xcvOcIWvUws0ciLucDUe7POedc9nmgFyFJ9UAHwbDWcZUR165TJdctLhfo1UZXt+K0t7d7oFemana5rTTQy/772VvrSmVmuyVdB+ecc1OH/1JH6ysEgd6Vhe70uYuKN/Ykutonxknm6NXX1zM6OppY+a44SefoSUr2fRLhYCxhbtl8SU9LukzSBhchJS2U9Jikh8Jldrj+1Lx1D0kalPSOiJ+tc845lzke6EVE0mnAWcB/mNniQtuY2UVmNju3TJ9ejdG1o1HtwCfpQK9ciY+E6Kqmkha9wcHBirtht7e3093dXdE+yidQfXHLZHsKekJcCrzXzHYkyJc+bpzNDzezOeEyH8DMfpJbBxwF9AB/ieJZuiKcfz7nHXJi0rVwzjlXgAd6EZD0OeC7wDvN7Mak6xOH/v5+Wlpaqlbe2Dy1rOQ0Vfs4ueQknUuZdPkRtujtAyw2s8fC25cS5DmX41jgWjPzCUOr5eCDuXeb3ZOuhXPOuQI80KuQpK8DXwOOMLPUX0Wuq6tjZGSk5MdVu0VtbLe4aufo1dfXZ+I4ueQkPTpqouWLUgK96fld1iWdPmZvs1h/rrfnw3WFXC/pYUnfKNS9E/gQ43SddzF58km2X/lC0rVwzjlXgAd6IUnt4dxDc4AmYGZ4O5cHsq+kxyXtm/eYHwCfI+hm9LikmeEyI4nnUIxyu3tVO9BKuutmVo6Te0US3YuTnO8w6fKDn49iFlbld1k3s4sK7Cz/xRuvGfBAM9sTOBDYHfhs/p2S9gQ6gdvLfkqudMcfz/f++L2ka+Gcc64AD/ResTfwYLhsAXws/D83E2wr8Nrwb85/AtOA64Alecu11aly6cptBUiiRS/JQK/ck2hv0UtOb28vra2tk28YkebmZgYGBqpW3ljJtigW2ZpXXNfNRcA2ebdnARvkOedyn82sC7gE2G/MJh8CfmmeKOucc84BPr3COmZ2G+NfSS54v5nFOtdBQ0MDw8PDNDRE9zKVm9dT7Ry5sfXs6emhra0tsfKLlZVcwlpU7WOf9FQnnZ2dvPjii8lVILrJ0O8HtpI0Oxxg5WTGXCyT1AbUm9nasMvmu4FH8u6vBz4AvCWqSjnnnHNZ5y16KZZ/xX54eJj6+slHsCtln6VIukXNzKirq97bNSvHyb1iqh37pHMEw0S9IpaJmdkI8BHgGklPA73AlZL2lpTrUbE58DdJjwAPE0w6fl7ebt4MLM2NxOmcc845b9FLtVywM336dLq7u2lvb49sn6Vau3Yt22yzzeQbRqSpqYmhoaGqlTdWuSfRa9euZeutt46kDmaWeKtRlky1/Mhkc/QEdZVfeMoxs1uA2WNW3w8cEd7/LDBngsffCNTkiMfOOedcubxFL8Xyg42oWiu8pao4SefoTZs2jf5+HyG+FFPxPZpki56kohZX4445hr/stH/StXDOOVeAB3oplp8nFlX+USW5Z1PpJDrpHL3E50jLoKzkR0bVDTvZCdOhhFE3XS37whf4yX7vSboWzjnnCvBf4RTzFr3kJH2ckm6tyaIk3qPltFh1d3dHUs/6+vqqTymxTmnz6DnnnHMuAR7opVh+98Go8o/KDSCGhoZoamqquPysKPc4DQ4ORnKcPNArXRKBXjmBVpT1THQmAQ/0HMARR/Dzq7+WdC2cc84V4IFeisXRotfe3u4BRBHa2toS7RaX/GTY2ZOVwViyUs+JCe+66QBYuZKN+5LsQuycc248/iucYnHk6NXV1SXbCpARSR8nz9ErXRItevX19QwPD5f0mKzkEk7KW/Scc865VPNAL8XiaNGD5Cd6LlVS0wyUU2ZU9fSum6UbGRmhsbGxqmV2dHSU3PJbM59lD/Scc865VPNAL8VaW1vp6ekBou3ulbUWvZ6eHlpbW6tebjnHKapj64Fe6ZJ4X5fzOtVGjp5A9cUtzjnnnEuET5ieYvlX63t6emhra0uwNtWX6xY31Ub8BM/Ry4pyXqfayNED5NcJHbDFFixb6S23zjmXRh7oZUjWulxWKtdaUjM5TSXwFr1sKCeXsnYuXEyt7yM3juuu42Nn3ZB0LZxzzhXggZ5LrdxJdFInxkkG1s3NzQwODiZWfhYl8XqV23UzygsXieSw5ubRc84551xqed8bl1q5bnFJdXXLWi7jVJeVHL3e3t7Ick5bWlro7++PZF+lUdB1s5jF1bavf51P3vXrpGvhnHOuAP8VdpMaGhqioaH6jb/5XTez0NUtqePkklNOjl6ULXCJ5nL6qJsO4IYbeNMz9yddC+eccwV4oDdFldL6kVSglYZALwvHySUn6VzKZMtXkYtzzjnnkuCB3hTU2tpKX19f0dsnNRhKfo5eEuW3tbXR29tb9PZTcdCYqS7pie0TLd+nV3DOOedSzQO9KajUVoAkW/SSzNHLynFyMDAwQFNTU9XLbWtrWzfXZRKSa9Erstumd910zjnnEuOBXspJYnR0NNJ9lprXk3SglXTX0WLVzPxoGZTkyKxJDtqT7HyL3nXTOeecSzMP9FKura2N7u7uSPeZlZaqXD1HRkYSGwymlJNob9FLzlQ99onm6EXYoifpUEnzJT0t6TJJG3zgJS2U9Jikh8Jldt59+0n6R3j/Y5K2jPCZuolcdRWffMeZSdfCOedcAR7opVxnZycvvfQSbW1tke6z1EAvidyz1tbWknLkopaV4+Sm7rFPOkcwCpLqgUuB95rZjkA7cNw4mx9uZnPCZX74+A7gCuAEM9sV+DdgVRWq7gC2247FG22edC2cc84V4IFeynV0dPDCCy9E2lqRlRa9JCcsh+SPU0NDA0NDQ5Htr5Z5i16V5SZMj6ZFbx9gsZk9Ft6+FHhXCbU5FrjBzP4FYGZrzKz40aZcZW66iQMXPJh0LZxzzhXggV7KdXR08OKLL0Ye6GUhRy9pSefoJT10f5Zk5T0adT5fRnL0pofdMnPL6WN2NAtYlHf7+XBdIddLeljSN/K6d74WaJZ0s6QHw/s8ObBazj6bz97xi6Rr4ZxzrgAP9FIurkAvCy16SUs6R88DveJl5T3a09MTaTfspqam5Fp9VVfcAqvMbHbeclGBveVHwOMFaQea2Z7AgcDuwGfD9Y3AIcB/EHTbfAPjd/10zjnnpgwP9FKus7OTF198MdL8o1Lzevr6+mhpaYms/Kwo5zi1trYmVv5UlpUcvazUc3KRTq+wCNgm7/YsYPHYjcxscfi3C7gE2C+863ngT2a2IuyyeR2wVwVPzjnnnKsJHuilXBpy9CQlni+XhHJa1KI8Tt6iV7wkW/RKec3jqGdyn83Iple4H9gqbxTNk4Fr1ytJapPUGf7fALwbeCS8+1rgQEnTJNUBbwIewznnnJviPNBLuTgCvcbGxpK6eyU5T1iSGhoaGB4eLnr7OPKvPNArTtJdN4t97eOoZ2Kfz4ha9MxsBPgIcI2kp4Fe4EpJe0uaG262OfA3SY8ADwNDwHnh458mGHXzAYLgbxlwWcTP1jnnnMuc6k9O5koSR46ey4aOjg4WLFiQdDUyoa+vj2nTpiVSdmtrKz09PbS3t0+6bVYGjSlOdC2JZnYLMHvM6vuBI8L7nwXmTPD4i4BCuX8ubmecwWW/eiDpWjjnnCvAW/RSrqOjgyVLltRIXk/pBgcHaW5uTroaifAcvdIk1YWxlNepdnL0iHTCdJdh738/188+OOlaOOecK8ADvZSrr6+nvr6+qNaCUpRyUpxkfl5jY2OiLSBJHifvulm8JN+jpbxOnqPnak5XF20DvUnXwjnnXAEe6IUkHSTpD5Kek2SSzinycceGc0MNSHpW0hlR123zzTenoSHaXral5PUkmaPX0dGRidyrUrcthgd6xUv6PZpkoJfMc9e6QZomW1yNO+wwfvGbs5OuhXPOuQI80HtFOzAfOBNYWswDJB0FXEkwEMDrgG8B50s6NYoK2fAga676LzYdXsmqK7/I6GBf5fs0o++mnzBwz9V0X/6fjHatGHfb/uUvMO+bJ7Lkpl/z4tzLq35C+fKDt7P8pl+y5PpLWDX/vqqWbWas+r8fsva2X/PSDz7KyJqXx9126OUXeP47J7Dq1l+z8s8/j+Q4WX8XTbddyNp7fsvwnZdioyPjbvvyg7fz988exd1nHjPhcTIbZXTZfEafu4PRlx7FRscfaKZnwWM89tX38sjnj2DFPXPH3c7MWHrdD3n8c4fx7Hc/wtAEx6ln2Qv87asn8ufT3sqT/zfx++mJv1zD1R99O3/8/LGsXvTMuNuN9q5l1WWfpueOq1h73XcmPE5xGFnyBE1/v5Tlv/gCw/NvHnc7M+OeK3/ErT/7Hn/7wVfoWbk8kvL77/s9/fdcw5qLT2L4pWfH3W60dy3Lf/yfkZT5Cm/Rc84559LMB2MJmdlcYC6ApPOLfNhZwHVmdl54+/FwiPAvAj+ptE4rLvoIffddz6aja+n+808Yev4xNvvy/1W0z77rv0Pfny5kdMUqBv7+a4ae/Dsbn3s3amhcb7vh3m7u+9h+DKxaRv/SQR7//icYHR5k1tEfraj8Yq2c/w/u+vSRDC/uZaBR3HH6mzj05/fRsd3O1Sn/qm+y6rfnMfxSD2tvvoK+eXew7Y8f2eA4jfb18NQn92N41UsMLR3ihR99Ahsa4FVHlR/rmxnDV34Ue+lJWL2E0dt/Al3LaXj7WRtsu2r+fdz16SMZHewHYNl9f+VN4xwne/Gf0PUi2Cj0rcQG1qJtDthgu/5li3jwtH9jpK8HMNY8fDu7fuv3zNj7sA22ffGX32LJr77N6EAfamik65E72P2yR6kbc5yG+nr44wn707dyGTYyzLKH72ZkoJ9d3rvhcXryxt/xpy+fxMhAP6qrY+Hfb+LkP86ndfomGxynZV8/gqHn5jHycjdrf3ceI6tfYvpJF0x4fKMyuvpF+i48mtbFq3i5b4T+n3+Uaaf8nIbXHrTBtnf89Dz+9pNvs3RxN0/9eTGXPnofp//xYeobGwvsuTj99/2etT/9KFqxhu57XmJw3s286vwHqet41XrbmRkvfOmtDCx8ZJw9lUF4/p1zzjmXch7olUlSI7AP8PExd90AfFrStmb23IQ7WbAA9gvn/L3pJujogKuuggsvxICOp+6jA2PnXqN5cQ8DQzdhTz6Bjj9hw30deSScHXafOeYYWLJk/ftnzIC5cxm48xdMu381dU8N8vNnVoBWUnfFtqi+EY49FjbdFJ55hpHfXc3g6uVgxjYGr1/dw8tD58HRH4WBATi4QPL9XnvBxRcH/592GjxQYCS222+H5ma44opXts13/vlw8MEs//UlvPGfPXQPw6tk7PtCDw1vORw+chp84QvBtkccAStXrv/4LbaA664L/v/61+GGGzYs46qrYLvtgmN+doEuR2ecwdobfw49vbQuhuteHAA9QcPcbYNAb9ttg2MMjFz2P4w88wKYsanBq1f1Unff5+ClMID52c+CZazvfx/23x/mzYNTTln/vpEhtMlL2H4zeGblID/63iMw+gjq/PEr20yfDiecwJI7rqfuoR46co296uaJvfensa0TTj4ZOjth/ny48UZsqA/Ia0V7w+7wxiNRbx9ccsm61cN93Qx3ddHfbKzYREAfW77j/bQ0jMkTbWjghY1XM7K2l/Y1RtvaAdCT/PXmbV8J9N7zHpg1i55/3o3dujivFa+bRSd+itarboIDDwxW/ehHMDxMz8plDA8EgWtfPTy5WRcvfulMNn1iITzzSuuejY4wsvJFeravo2cYpj3bTetffgg/vWv9ep59dvD5WLJk3eu2nre8Bb7xjeD/970PnhvzsW1vh7/+9ZXX7Te/CZ/CClpWLmLm0Ag/66hnwZoR6h/5JA2Pr96giCf6V7DA+ljaD6c80wcLnmBkn9dTP6012GDXXeHSS4P/zzgD7rlnw3qO+Y5oWPIk03vXsEWfcdnTQ9RtO0zjV8+ksX3L4POVd5yGX36BwWjTfPHWOueccy7dPNAr3yZAIzAmolrX7XNLYL0zRkmnA6fnbr9mgqv5Aqirg9ERzm2BLoCGJqgwV08tGwFwTpvoNkCiYZNNgkDv6KNhxx3hrrsYvu1meruXg0FHeD5X39JWUdmlaGzvBNVxeP1ocCwkVF9ftfLr2jZiBDh7GuFxMpo32SQIYHbZBY47DoDhW29k8PnHMYPO8DipvsKPlerIBWQ/OXorRn+1EEbrqdskr0Vr663huON4umkl3Ps3XjWc67IoWjbuZNpGm8C73w0zZ8KNN8IDD2ADayCvu6Ttsys67ni0atUrwTEw1NVAf/8q1rbCws1BdY3s3jWDjvoxo0U2NfGvndrpe3YVmxjM6AuOU2vuOEEQjL/udayZ3srQ76/JL56m1mY22nffdceS3/4WBgdZPdBN/0g/GKxphNumN3DEO45i6z/eDGvWvFL/0REGu19k5ax6prUJloyiuuq9R1RXjyF2bhDf2buDkU2m0ThjV5pffmKDbe8aHOWuvn723EiweATMqKu0rnUNgDhtmrHGoGeLBlrffRRNrdvA3Fe629roCANrX6C3E84ev2dt6bxFzznnnEs1TdXJsCciaSFwuZmdM8E2WwIvAEeG3T5z63cF5gH7m1mBy/KvmD17ts2fP3/c+3vuupqVF38ENTRhw4NMP+kC2t/y4dKezBhDT93D2u8dE5wkjgwx7dAP0/a+b26wnZnx2DdPZNnt164LsPa5+E7aX71bReUXa7BrNbedtC/9q14Cg/ZZO3DwJXdR39xSlfL75v+dF758ONTVw8gQGx15Gpt+5L832M7MWPTfJ7LmjmtQXQMIdvj+nbRsv3tF5Q/fcQmjf/sp1DXC6DAN77uQuh037GY52LWa207el/6VwXFqm/VqDrnk7wWP0+iaxbDkn0EgaaOw+R7UTd9+w+2Gh3j402+h+6kHgwsBrR28/n/up+lVMzfYtuuxv/PE5w+H+npseIjNj/4423z8uxtsZ2bccc7JLPzrNdTVN2DAkZfezozX7LHBtqsXPcsvP7A/I0OD2Ogo2+x7KO/84bWobsOU4rXX/TdrrvkWamjEhofY5PO/pWXOhl1M42AjQ/Rd/D5GF88LLkQ0t9HymT9Rt9HmG2y76MG7ueKkt6H6ekaHh9n7/R/lbV/c8DiVYmTZAlaecwg2MgSjozTt8kY2OuM3BY/TqqvPZ+Uvz2XHP/T+y8zGzldXstk7v8bm33tLUdtq420iKdPFZ7LfogmdcQaX3bmAc99SXLf+hecdWV45zjmXoO3OKtBDbByVfs9Jiux30wO9AooM9BqBXuBUM7s0b/2bgb8C203WdbOYH9ehF59k6Pl5NG61M41bR3OuNLJyMcPP/pO6GVvR+Oq9x93OzFj9yJ0MrV7ORrsfQPOMDU9g4zTc38vy+29G9Q1s+vo3Ud9U3fn0hl5eTP/j99K4ySym7fyGcbczM3rn3cnwmuW07noAjdOjOU6jS/4FqxajLXZB02eNu91wfy/L/3kLqquf9DjZQBcMrIWmDjRt/PncbHiYVQ/ewuhAHxvveSgNbeNvO7h8Md3/upemTWfRvsvEx+mlh+6kf9XLbLbH/rRusmHgmNO3egWL/3knze2dbL3PwQWDl3XlL3iI4ZcW0LT9HBo23zBwjZONDDPy1F0w1E/9jvujlvGP05qli3nh4X/QOXMrZr1u/ONUitHuFQw9cTdq6aBx5zdOeJwGnnmQaTvuFVGgt5PN/0eRgd5GW3ugl3IVBXpU9wTIOeeS4IFeDSkm0Au3uwtYYmbvyVt3AfAeM9t2snIq/XF1zrlSRPXjEQR6txZX5kazPNBLOQ/0nHNuYlkN9Hx6hZCkdklzJM0BmoCZ4e3Z4f37Snpc0r55DzsPOEbSmZJeK+lkghy8b1f9CTjnXDWprrjF1bYPf5jz5/4g6Vo455wrwAdjecXeQP4l6o+Fy3PAdkAr8NrwLwBmdr2k44GvAN8gyNk7y8wqnlrBOedSzcdicQCPPcZOL69OuhbOOecK8EAvZGa3McGpy3j3m9kvgV/GVjHnnEslj/TSRtJbCXqU7AqsAH4OfM3MRiZ8oHPOuZrkgZ5zzrnS+fQKqSJpT+CPwEXAh4DZwKUEv/NnJVg155xzCfFAzznnXImEt+ilzueAR83s0+Htf0naCjhP0jfMrDvBujnnnEuAZ8o755wrjQha9IpZXLUcAMwds+4GoAV4ffWr45xzLmk+vUKCJK0FFiddjyJMB1YlXYmI+XPKBn9O0ZplZuNP9lckSbcBmxW5+TIzO6TSMt3EJA0Anzazi/PWtQHdwAfN7Nd5608nGCE6Z3tgQQXF1+LntFR+DPwYgB8D8GMAlR+DSH6rwbtuJiqqFzFukubX2jxY/pyywZ9TOnnglhk25m9ww+wigly+SNTCe7pSfgz8GIAfA/BjAOk6Bt510znnnMu+JcAWY9ZtkXefc865KcYDPeeccy777gLePmbdEUAf8M/qV8c551zSPNBzxYisi0+K+HPKBn9OzhXnAmAPSRdI2kXSu4Bzgf9XhRE3/T3txwD8GIAfA/BjACk6Bj4Yi3POOVcDJB3OhhOmf9UnTHfOuanJAz3nnHPOOeecqzHeddM555xzzjnnaowHes4555xzzjlXYzzQcwVJOkeSFVi2S7puxZB0kKQ/SHourPc5BbbZUdINkrolrZb0S0mbJlDdokz2nCQdMs5rdmIyNZ6cpJMk3SppuaQuSf+UdOyYbbL2Ok34nLL4OrmpqZjv0XEed6yk+ZIGJD0r6Yx4axqfco6BpM9LulvSqvA7605Jb6tCdWNR7vsg7/EnhY+7LZ4axq+Cz8K24W/Wckn9kp6U9M54axuPMj8LLZK+K2lh+Pyfk/R9Sa1VqHKkijlfGedxiX4feqDnJrKYYB6m/GVRojUqXjswHzgTWDr2TkltwM1AM3AwcCSwG/AHSapiPUsx4XPKsy/rv2a/ib9qZXsz8H8Ew8DvCVwFXCnpfZDZ12nC55QnS6+Tm5qK/c5ZR9JRwJXAFcDrgG8B50s6Na5KxqzkYwC8CbgMOBR4A3Av8EdJB8RSw/iVcwwAkLQ7wXvgbzHUq5rK+SxsBdxDcK59FLAz8BGycx41VjnvgwuAY4GPA68FTgXeD1wYQ/3iVuxv+zpp+D70wVhcQeGVmhPNbLuEq1IxSQuBy83snLx1HyEY/nYLM1sRrtsDeBh4s5ndkkBVizbOczoEuBXY3swWJlGvKEiaC/SZ2buz/jrljHlOh1ADr5ObWgp954yz3V3AUjN7d9667wHvNrNtY61kzIo9BuM8dj7wJzP7bNT1qqZSjoGkDuA+4GyCi3TbmdkhcdavGkr4LPwvsJOZ7V+NelVTCcfgUeDG/Pd9+H1wmJntHmslqyD/t32c+xP/PvQWPTeRmZIWSVos6U+S/i3pCkXoAODeXPAAYGaPELRivjGxWkXjNknLwq5DJ6S45Ws8GwMvh//Xyuu0Ma88p5ysv07OrUdSI7APMHfMXTcA20jKdKBXLkn1QAcbfgfUukuA28zs6qQrUm2S6oB3AndL+nX4Xf+opK9Iaki4etV0B3CkpNcAhH+PIPhOqAUbM87nOi3fh1PpzeZKcy9wPPA4sBHwUeAOSW8zs5sSrVk0tgCWFFi/FNiyynWJyhKC7hH3h7ffDvwPsCPBFdXUk3QCsDfwiXBV5l+nAs8p86+Tc+PYBGhkw89srpvXlsBzVa1ROnyFINC7MumKVIuk04HZBF3Up6JNgU7gNOD/AYcTHI8fErwXvpBc1arqU8D3gCclDRPEHT8FvphorSJQ4Ld9rFR8H3qg5woysz+NWXVHePXh80AtBHoTtZ5ksj+zmT0BPJG36n5JTcCnJZ1rZkMJVa0okt5B8APwMTN7ILd6goek/nUq9Jyy/jo5N4HJWqVT/5mNmqTTgLOAd5jZ4qTrUw2S9gTOBQ4ws/6k65OQ+vDvI2Z2Zvj/g5K2AM6RdJZNjdypUwnyE98H/AvYBfg+sAL4coL1qsg45ysbbDbJbqry+nug50rxD+DopCsRkReBHQqsn0nhFqSsuhdoI7i6+GLCdRmXpPcDlwOnmtnleXdl9nWa4DkVkonXyblJLAeGCVri880M/6b6Mxs1SZ8jCHjeaWY3Jl2fKjoYmA7My+uRXgcobNV5a1byqyuwHBgiGLwk32ME3/WbhNvULEnTgO8CHzaz34arH5XUDPxc0jfNrDe5GpanhN/2VHwfeo6eK8WeZHe0qLHuAt4gaUZuhaTdgFkEfcprxV5AHynODQkHXLmcYPCfy8fcncnXaZLnVEjqXyfnJhO2Rv+DoDtyviOA581synTblPR14GvAEWb2l6TrU2VXAnsAc/KW/yPorj6H4MJWTQs/C/cSjDSZ77VAD1Pju74xXEbGrB8haO3KXF56Kb/tafk+9EDPFSTpAkmHStpe0hxJPyYYKvrChKtWFEntYb3nAE0EA8vMkTQ73ORXBFdTrpK0VzjQzC8IhkK+NZFKT2Ky5yTpDEnHSHptuJxB0A/+IjMbTK7m45P0GeDHBP34b5M0M1xy8+Rl8XWa8Dll8XVyU1MR3zn7SnpcUn4e1nnAMZLODN/fJwOnA9+u+hOIQDnHQNIPgM8BxwGP530HzChURtqVegzMbIWZzctfgNVAT3i7J6nnUq4yPwvfJrhQ+VUF88EeTdBd8QdZ7LZZxvugi2B6pG9Keruk7SQdQTDFwJ+z9j4o4rc9nd+HZuaLLxsswK8JRjYcAJYRfFjflHS9Sqj/IQT9n8cuC/O22ZFg9KMeYA1BULFp0nUv9zkR5E8+AfQCa4EHCAbRqUu67hM8p4U1+DpN+Jyy+Dr5MjWXIr5zcvcfMuZxxxLk4wwCC4Azkn4u1TwG42xvBCNQJv6cqvU+GLOPy7P6/Cs5BgS5afOAfuAp4EtAQ9LPp1rHgKCL6sUEg470h39/BMxI+vmU8fwn+21P5fehz6PnnHPOOeecczXGu24655xzzjnnXI3xQM8555xzzjnnaowHes4555xzzjlXYzzQc84555xzzrka44Gec84555xzztUYD/Scc84555xzrsZ4oOdcBSRdLum2pOtRiCSTdGLS9XDOOeecc9Xn8+g5VwFJGwH1ZrZS0jnAiWa2XZXrcBvBhJ0njlk/E1hjZn3VrI9zzjnnnEteQ9IVcC7LzGxNHPuV1GxmA5Xsw8yWRlUf55xzzjmXLd5107kK5LpuSvoc8DVg27DLpIUtfEhqkPRVSc9K6pM0X9LHJSlvPwslfUvSDyQtB+aF6z8g6W5Jq8LlFkmvz3vcH4GDgRPyyj0kvG+9rpuSNpV0paQVYT3ukXTomOdjkj4h6TJJayQtkfTfkupjOoTOOedcWfJ+g0+X9Lykfkl/lrRt0nVzLg28Rc+5aFwMzATeB+wTrusO//40XHcq8FT4//8Ao+F9OZ8ALgLeyCufzRbg28D88P/PA3+RtJOZrQQ+BMwFFgGfCh+zcpw6XgdMB94NLAvL+4ukPczs8bztvgacC3wLeAPwv2H5Py/uUDjnnHNVsxfQDxxN8Dt5MXCdpNeb5ye5Kc4DPeciYGa9krqBkfwuk5K2B04C9jCzeeHqBZJ2Bj7J+oHeQ2b2xTH7vSz/tqSTgNXA24BfmdlqSYNA30RdNcOWuwOAPc3soXD1aZL2B84CTszb/Foz+2H4/9NhmYfhgZ5zzrn0aQCONbMVAJKOAx4F3gTcnGTFnEuaB3rOxWtvQMA9eT01Ifjsacy29459sKQ9gK8SXLHchKC7dSuwXYn12I1gYJaHxqz/G3DgmHX/HHP7BWDrEstzzjnnquGJXJAHYGbzJK0BdsUDPTfFeaDnXLxyebAHAWsn2bYn/4akVuAmggDwFGApMAjcAzSVWI+xQWW+sV1bBgvc7/m8zjnn0mi87pnebdNNeR7oORedQWDsoCW51rFtzOz3Je5vF2Az4Mtm9iiApO2AVxVR7ljzgI0kzRnTqncQ8HCJ9XLOOefSYmdJM8K8dSTtCmwE/CvZajmXPL9K71x0ngVmStpP0iaSWs3saeAy4KeSjpe0o6TXSTpZ0pcm2d9CggTz/wwfdyDwa2DsvHjPAq+XtENYbuPYHZnZLcDdwC8lHSxpF0kXE3TpPL+iZ+2cc84lZxj4Rfjbuh9wBcEFTO+26aY8D/Sci861wNUEo2AuB84M138UuAD4EsHolTcDJwDPTLSzMOfggwStbo8RjNR5HsGImfm+A7xM8MO2nGDQlUKOAR4iGH3zIeD1wNvNzK96Ouecy6oHgL8ANwC3ASuAY3zETedA/jlwzjnnnHNZI+lyYDszOyThqjiXSt6i55xzzjnnnHM1xgM955xzzjnnnKsx3nXTOeecc84552qMt+g555xzzjnnXI3xQM8555xzzjnnaowHes4555xzzjlXYzzQc84555xzzrka44Gec84555xzztUYD/Scc84555xzrsb8fyOXa1tYVUCBAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 900x900 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "accs = np.array(bayes_result.cv_results_['mean_test_score'])\n",
    "cumul_accs = np.maximum.accumulate(accs)\n",
    "errs = 1-accs\n",
    "cumul_errs = 1-cumul_accs\n",
    "\n",
    "\n",
    "params = bayes_result.search_spaces.keys()\n",
    "plt.figure(figsize=(12, 4*len(params)), dpi=75)\n",
    "\n",
    "for i, param in enumerate(params):\n",
    "    plt.subplot(len(params), 2, 2*i+1)\n",
    "    param_vals = bayes_result.cv_results_[f'param_{param}']\n",
    "    if type(param_vals[0]) == bool:\n",
    "        param_vals = [str(x) for x in param_vals]\n",
    "    best_val = bayes_result.best_params_[param]\n",
    "\n",
    "    plt.scatter(np.arange(1,len(param_vals)+1), param_vals, s=15, c=accs, cmap=\"Oranges\", label=\"Accuracy\")\n",
    "    plt.plot(np.arange(1,len(param_vals)+1), param_vals, 'k', linewidth=0.5)\n",
    "    plt.xlim(1, len(param_vals))\n",
    "    plt.hlines(best_val, 0, len(param_vals), 'r', 'dashed')\n",
    "\n",
    "    plt.ylabel(param, fontsize=14)\n",
    "    plt.xlabel(\"iteration\", fontsize=14)\n",
    "    cb = plt.colorbar()\n",
    "    cb.set_label(\"accuracy\")\n",
    "    plt.tick_params(labelsize=14)\n",
    "\n",
    "    \n",
    "    plt.subplot(len(params), 2, 2*i+2)\n",
    "    plt.hist(param_vals, bins=30)\n",
    "    plt.axvline(x=best_val, color='red', linestyle='--')\n",
    "    plt.xlabel(param, fontsize=14)\n",
    "    plt.ylabel('# of evaluations', fontsize=14)\n",
    "    plt.tick_params(labelsize=14)\n",
    "    \n",
    "plt.suptitle(\"KNN - Bayesian Search\\nHyperparameter Evaluations\", size=16)\n",
    "plt.tight_layout(rect=[0, 0.0, 1, 0.98])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Combined Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-12-14T00:06:55.773160Z",
     "iopub.status.busy": "2020-12-14T00:06:55.772569Z",
     "iopub.status.idle": "2020-12-14T00:06:55.779964Z",
     "shell.execute_reply": "2020-12-14T00:06:55.778656Z",
     "shell.execute_reply.started": "2020-12-14T00:06:55.773113Z"
    }
   },
   "outputs": [],
   "source": [
    "def moving_average(a, n=3) :\n",
    "    ret = np.cumsum(a, dtype=float)\n",
    "    ret[n:] = ret[n:] - ret[:-n]\n",
    "    return ret[n - 1:] / n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-12-14T00:06:57.713214Z",
     "iopub.status.busy": "2020-12-14T00:06:57.712700Z",
     "iopub.status.idle": "2020-12-14T00:06:58.851007Z",
     "shell.execute_reply": "2020-12-14T00:06:58.849660Z",
     "shell.execute_reply.started": "2020-12-14T00:06:57.713170Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x600 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.close('all')\n",
    "plt.figure(figsize=(12, 4), dpi=150)\n",
    "\n",
    "\n",
    "####### convergence plot, GS\n",
    "plt.subplot(1, 3, 1)\n",
    "\n",
    "accs = np.array([x['mean_metric'] for x in gs_results])\n",
    "cumul_accs = np.maximum.accumulate(accs)\n",
    "errs = 1-accs\n",
    "cumul_errs = 1-cumul_accs\n",
    "plt.plot(errs, linewidth=0.5, label=\"observed\", marker='.', markersize=3)\n",
    "plt.plot(cumul_errs, 'k', linewidth=2, label=\"best so far\")\n",
    "plt.xlabel(\"Iteration\")\n",
    "plt.ylabel(\"Misclassification Error\\n(1 - Accuracy)\")\n",
    "plt.title(\"KNN: Error over iterations\\nGrid Search\")\n",
    "plt.legend()\n",
    "plt.ylim((.35, .45))\n",
    "\n",
    "\n",
    "####### convergence plot, RS\n",
    "plt.subplot(1, 3, 2)\n",
    "\n",
    "accs = np.array([x['mean_metric'] for x in rs_results])\n",
    "cumul_accs = np.maximum.accumulate(accs)\n",
    "errs = 1-accs\n",
    "cumul_errs = 1-cumul_accs\n",
    "\n",
    "WINDOW_SIZE = 10\n",
    "move_avg_err = 1 - moving_average(accs, WINDOW_SIZE)\n",
    "move_avg_err = [*([None]*(WINDOW_SIZE - 1)), *list(move_avg_err)]\n",
    "\n",
    "plt.plot(errs, linewidth=0.5, label=\"observed\", marker='.', markersize=3)\n",
    "plt.plot(move_avg_err, linewidth=0.5, label=\"10-iter moving average\", marker='.', markersize=3)\n",
    "plt.plot(cumul_errs, 'k', linewidth=2, label=\"best so far\")\n",
    "plt.xlabel(\"Iteration\")\n",
    "plt.ylabel(\"Misclassification Error\\n(1 - Accuracy)\")\n",
    "plt.title(\"KNN: Error over iterations\\nRandom Search\")\n",
    "plt.legend()\n",
    "plt.ylim((.35, .45))\n",
    "\n",
    "\n",
    "####### convergence plot, Bayesian\n",
    "plt.subplot(1, 3, 3)\n",
    "\n",
    "accs = np.array(bayes_result.cv_results_['mean_test_score'])\n",
    "cumul_accs = np.maximum.accumulate(accs)\n",
    "errs = 1-accs\n",
    "cumul_errs = 1-cumul_accs\n",
    "\n",
    "WINDOW_SIZE = 10\n",
    "move_avg_err = 1 - moving_average(accs, WINDOW_SIZE)\n",
    "move_avg_err = [*([None]*(WINDOW_SIZE - 1)), *list(move_avg_err)]\n",
    "\n",
    "plt.plot(errs, linewidth=0.5, label=\"observed\", marker='.', markersize=3)\n",
    "plt.plot(move_avg_err, linewidth=0.5, label=\"10-iter moving average\", marker='.', markersize=3)\n",
    "plt.plot(cumul_errs, 'k', linewidth=2, label=\"best so far\")\n",
    "plt.xlabel(\"Iteration\")\n",
    "plt.ylabel(\"Misclassification Error\\n(1 - Accuracy)\")\n",
    "plt.title(\"KNN: Error over iterations\\nBayesian Hyperparameter Optimization\")\n",
    "plt.legend()\n",
    "plt.ylim((.35, .45))\n",
    "\n",
    "\n",
    "plt.tight_layout(rect=[0, 0.0, .96, 0.98])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Get CSVs + best model summaries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {
     "iopub.status.busy": "2020-12-13T09:04:03.004648Z",
     "iopub.status.idle": "2020-12-13T09:04:03.005110Z",
     "shell.execute_reply": "2020-12-13T09:04:03.004860Z"
    }
   },
   "outputs": [],
   "source": [
    "############################ \n",
    "# Only uncomment the below if you are making these summaries in a different\n",
    "# session from when you ran the above.\n",
    "############################ \n",
    "\n",
    "#bayes_result = skopt.load('knn_bayes_result_20201107-020639.pkl')\n",
    "\n",
    "#with open('knn_gs_results_201107_072620.pkl', 'rb') as f:\n",
    "#    gs_results = pickle.load(f)\n",
    "#gs = gs_results['gs']\n",
    "#gs_results = gs_results['gs_results']\n",
    "\n",
    "#with open('knn_rs_results_201107_124711.pkl', 'rb') as f:\n",
    "#    rs_results = pickle.load(f)\n",
    "#rs = rs_results['rs']\n",
    "#rs_results = rs_results['rs_results']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-12-14T00:07:05.077225Z",
     "iopub.status.busy": "2020-12-14T00:07:05.076670Z",
     "iopub.status.idle": "2020-12-14T00:07:05.085707Z",
     "shell.execute_reply": "2020-12-14T00:07:05.084439Z",
     "shell.execute_reply.started": "2020-12-14T00:07:05.077176Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Grid search best model:\n",
      "    Score = 0.63345\n",
      "    n_neighbors = 8\n",
      "    p = 1\n",
      "    weights = distance\n"
     ]
    }
   ],
   "source": [
    "print('\\nGrid search best model:')\n",
    "best = max(gs_results, key=lambda item: item['mean_metric'])\n",
    "print('    Score = {}'.format(best['mean_metric']))\n",
    "\n",
    "for param in sorted(best['params'].keys()):\n",
    "    print('    {} = {}'.format(param, best['params'][param]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-12-14T00:07:05.609074Z",
     "iopub.status.busy": "2020-12-14T00:07:05.608555Z",
     "iopub.status.idle": "2020-12-14T00:07:05.618926Z",
     "shell.execute_reply": "2020-12-14T00:07:05.617563Z",
     "shell.execute_reply.started": "2020-12-14T00:07:05.609031Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Random search best model:\n",
      "    Score = 0.6363\n",
      "    n_neighbors = 5\n",
      "    p = 1\n",
      "    weights = distance\n"
     ]
    }
   ],
   "source": [
    "print('\\nRandom search best model:')\n",
    "best = max(rs_results, key=lambda item: item['mean_metric'])\n",
    "print('    Score = {}'.format(best['mean_metric']))\n",
    "\n",
    "for param in sorted(best['params'].keys()):\n",
    "    print('    {} = {}'.format(param, best['params'][param]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-12-14T00:07:06.070914Z",
     "iopub.status.busy": "2020-12-14T00:07:06.070359Z",
     "iopub.status.idle": "2020-12-14T00:07:06.079654Z",
     "shell.execute_reply": "2020-12-14T00:07:06.078436Z",
     "shell.execute_reply.started": "2020-12-14T00:07:06.070854Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Bayes search best model:\n",
      "    Score = 0.63735\n",
      "    n_neighbors = 6\n",
      "    p = 1\n",
      "    weights = distance\n"
     ]
    }
   ],
   "source": [
    "print('\\nBayes search best model:')\n",
    "print('    Score = {}'.format(bayes_result.best_score_))\n",
    "for param in sorted(bayes_result.best_params_.keys()):\n",
    "    print('    {} = {}'.format(param, bayes_result.best_params_[param]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-12-14T00:07:06.539056Z",
     "iopub.status.busy": "2020-12-14T00:07:06.538559Z",
     "iopub.status.idle": "2020-12-14T00:07:06.737968Z",
     "shell.execute_reply": "2020-12-14T00:07:06.736691Z",
     "shell.execute_reply.started": "2020-12-14T00:07:06.539012Z"
    }
   },
   "outputs": [],
   "source": [
    "df_results = pd.DataFrame()\n",
    "for res in gs_results:\n",
    "    row = {'search_type': 'grid', 'accuracy': res['mean_metric'], **res['params']}\n",
    "    df_results = df_results.append(row, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-12-14T00:07:08.305999Z",
     "iopub.status.busy": "2020-12-14T00:07:08.305491Z",
     "iopub.status.idle": "2020-12-14T00:07:08.494097Z",
     "shell.execute_reply": "2020-12-14T00:07:08.492833Z",
     "shell.execute_reply.started": "2020-12-14T00:07:08.305952Z"
    }
   },
   "outputs": [],
   "source": [
    "for res in rs_results:\n",
    "    row = {'search_type': 'random', 'accuracy': res['mean_metric'], **res['params']}\n",
    "    df_results = df_results.append(row, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-12-14T00:07:08.946004Z",
     "iopub.status.busy": "2020-12-14T00:07:08.945491Z",
     "iopub.status.idle": "2020-12-14T00:07:09.134183Z",
     "shell.execute_reply": "2020-12-14T00:07:09.132880Z",
     "shell.execute_reply.started": "2020-12-14T00:07:08.945958Z"
    }
   },
   "outputs": [],
   "source": [
    "for i, cv_score in enumerate(bayes_result.cv_results_['mean_test_score']):\n",
    "    row = {'search_type': 'bayes', 'accuracy': cv_score}\n",
    "    for param in bayes_result.search_spaces.keys():\n",
    "        row[param] = bayes_result.cv_results_['param_' + param][i]\n",
    "    df_results = df_results.append(row, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-12-14T00:07:17.198848Z",
     "iopub.status.busy": "2020-12-14T00:07:17.198321Z",
     "iopub.status.idle": "2020-12-14T00:07:17.209287Z",
     "shell.execute_reply": "2020-12-14T00:07:17.207989Z",
     "shell.execute_reply.started": "2020-12-14T00:07:17.198787Z"
    }
   },
   "outputs": [],
   "source": [
    "df_results.to_csv('knn_results_{}.csv'.format(datetime.now().strftime('%Y%m%d_%H%M%S')), index=False)"
   ]
  }
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