3_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predicting Airbnb Listing Price | Analysis\n",
    "\n",
    "This notebook explores feature creation and machine learning modeling to predict the price variable from other features in the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import shutil\n",
    "import warnings\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import multiprocessing as mp\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import xgboost as xgb\n",
    "\n",
    "from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor\n",
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "SEED = 1\n",
    "pd.set_option('display.max_columns', None)\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "sns.set_style(\"white\")\n",
    "cores = mp.cpu_count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Engineering\n",
    "\n",
    "Most of the data being used for modeling is numeric, but the only string feature, the review text, can be turned into an additional numeric feature using natural language processing techniques. I chose to use VADER sentiment analysis to engineer this feature. According to it's [Github page](https://github.com/cjhutto/vaderSentiment), VADER (Valence Aware Dictionary and sEntiment Reasoner) \"is a lexicon and rule-based sentiment analysis tool that is specifically attuned to sentiments expressed in social media, and works well on texts from other domains\". After running a string through the Sentiment Intensity Analyzer from the vaderSentiment package, the analyzer returns numeric scores for the positivity, negativity, and neutrality of a sentence.\n",
    "\n",
    "Before using VADER, I preprocessed the reviews text to remove some extraneous characters such as \"\\\\\", \"\\[\", and \"\\]\". For VADER, stopwords and punctuation should be kept in since the classifier incorporates those elements into the analysis.\n",
    "\n",
    "To use the Sentiment Intensity Analyzer, I wrote my own function called `get_review_sentiment()` that wraps the analyzer in a try-except loop and only returns the positive aspect of the score. Once all sentiment scores were retrieved from review data, I added them to the dataframe as a new column called `pos_score`. Therefore, the review text gets turned into a numeric feature about the positivity of the reviews that a certain listing receives.\n",
    "\n",
    "For the initial exploration of the creation of `pos_score` and ease of use, I only used the first 1000 rows of the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "directory = '/Users/limesncoconuts2/datasets/airbnb/'\n",
    "df = pd.read_csv(directory + 'df_clean.csv', nrows=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.reviews = df.reviews.str.replace(\"[\",\"\").str.replace(\"]\",\"\") \\\n",
    "                    .str.replace(\"\\\\\",\"\").str.replace(\"\\\"\",\"\") \\\n",
    "                    .str.replace(\"\\'\",\"\").str.replace(\"rn\",\"\").str.strip()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_review_sentiment(review, analyzer):\n",
    "    \"\"\"Returns the positive sentiment score of a given string\n",
    "    using a pre-defined VADER sentiment analysis object.\n",
    "    If string is null, return 0.\n",
    "    \"\"\"\n",
    "    try:\n",
    "        score = analyzer.polarity_scores(review)\n",
    "        score = score['pos']\n",
    "    \n",
    "    except: # if nan\n",
    "        score = 0\n",
    "    \n",
    "    return score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "analyzer = SentimentIntensityAnalyzer()\n",
    "\n",
    "pool = mp.Pool(cores)\n",
    "results = [pool.apply(get_review_sentiment, args=(row, analyzer)) for row in df.reviews]\n",
    "pool.close()    \n",
    "\n",
    "df['pos_score'] = results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_sorted = df[['reviews', 'pos_score']].dropna().sort_values(by=['pos_score'], ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Examples of most and least positive reviews using vaderSentiment:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Most positive reviews:\n",
      "-----------------------\n",
      "\n",
      "Great\n",
      "\n",
      "Perfect 👌🏼\n",
      "\n",
      "Marcy was great very sweet!\n",
      "\n",
      "Amazing spectacular experience , highly recommend\n",
      "\n",
      "Awesome host. Great communication and accommodation skills. Made sure I was taking care of.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "most_pos = list(df_sorted.head(5).reviews.values)\n",
    "print(\"Most positive reviews:\")\n",
    "print(\"-----------------------\")\n",
    "print(\"\")\n",
    "for i in most_pos:\n",
    "    print(i)\n",
    "    print(\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Least positive reviews:\n",
      "-----------------------\n",
      "\n",
      "helenaさん。素敵なお家をありがとうございました！nn家族7人が過ごしても広々と使うことができました。キッチンも使いやすく、リビングでのお食事はとても楽しく有意義なものでした。nnまた、洗濯機や乾燥機も使いやすく大変助かりました。nnどんなことにも、親切に対応して下さったhelenaさんに感謝しています。nnまた戻って来たくなる家です。nありがとうございました‼️\n",
      "\n",
      "\n",
      "\n",
      "Denis非常热情，房间干净舒服，位置非常好，走两条街就是星光大道，门口各种餐厅，有一个挺大的超市，可以自己做早餐，很方便, 房子非常好。尽管二楼房间似乎比较闷热，但中央空调足够将它变得更加凉爽。房间的厨房足够房客烹饪简单的菜肴。房子的地理位置绝佳，公共交通方便，步行也很安全。我们与房东只有一面之缘，但很明显，他非常欢迎我们的到来。下次如果我们还去洛杉矶，这间房子仍是我们的首选。, 房东非常好，我们在那里住的也不错，地理位置很好，旅游的话住这里会节省时间。去环球影城、和好莱坞标志都很近。\n",
      "\n",
      "这是我们第一次使用Airbnb，也是我第一次在国外过生日！非常完美的体验！Lisa和Jeff非常热情，和我一起庆祝生日，还帮我洗衣服，真是非常感谢他们！房子离威尼斯海滩不远，周围很安静，停车非常方便，并且免费。房间装饰得很温馨，很有家的感觉！总之这次体验非常完美，非常感谢Lisa和Jeff！\n",
      "\n",
      "房子地理位置很好，旁边就是环球影城，还有很多shopping malls. 因为人多，租了三个房间，所以可以正好一次性拥有整个公寓。最好和房东确认过，租一个房间是否会和别人share房间. 房东很细致，房间里东西也一应俱全。价格也适当。如果家庭出游，真的很方便，推荐！\n",
      "\n"
     ]
    }
   ],
   "source": [
    "least_pos = df_sorted.tail(5).reviews.values\n",
    "print(\"Least positive reviews:\")\n",
    "print(\"-----------------------\")\n",
    "print(\"\")\n",
    "for i in least_pos:\n",
    "    print(i)\n",
    "    print(\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Reduction\n",
    "\n",
    "#### Is VADER data important? \n",
    "The negative aspect to using the vaderSentiment package is the very slow speed. For example, 1000 loops of `get_review_sentiment()` takes over 20 minutes, so all 1.1 million data points would take over 10 days to process. Using a random forest regressor and the small dataset for the initial VADER analysis on 1000 rows of data, I analyzed the feature importances to see is `pos_score` would show up."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=5,\n",
       "           max_features='auto', max_leaf_nodes=None,\n",
       "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "           min_samples_leaf=1, min_samples_split=2,\n",
       "           min_weight_fraction_leaf=0.0, n_estimators=500, n_jobs=-1,\n",
       "           oob_score=False, random_state=1, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# random forests for feature selection\n",
    "X_sent = df.drop(columns=['price_USD', 'reviews'])\n",
    "y_sent = df.price_USD\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_sent, y_sent,\n",
    "                                                    test_size=0.2,\n",
    "                                                    random_state=SEED)\n",
    "\n",
    "rf = RandomForestRegressor(n_estimators=500,\n",
    "                           max_depth=5,\n",
    "                           n_jobs=-1,\n",
    "                           random_state=SEED)\n",
    "rf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "importances_rf = pd.Series(rf.feature_importances_, index = X_sent.columns)\n",
    "sorted_importances_rf = importances_rf.sort_values()\n",
    "sorted_importances_rf = sorted_importances_rf[sorted_importances_rf > 0]\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(15,8))\n",
    "\n",
    "sorted_importances_rf[:25].plot(kind='barh', color='lightgreen')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'pos_score' in list(sorted_importances_rf[:25].index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Although `pos_score` did not show up in the top 25 features, I ended up deciding to still include the feature in my final modeling given the very small sample size of 1000 data points for this feature analysis. Since the dataset is already large and vaderSentiment analyses are very slow given my hardware constraints, I decided to only work with 50,000 rows of the dataset for the final modeling process.\n",
    "\n",
    "#### Which Features to Keep?\n",
    "Even though there will only be one-twentieth of the data in the final modeling process, I still used larger subsets of the data to narrow down the dataset to its most important features. With all features present and with all 1.1 million rows, the dataset was larger than my computer's RAM would allow. Using 500,000 rows of the original clean dataset, I used another random forest regressor to narrow down the 50 most important features that I would then keep for the final modeling process. I kept a list of these column names in a variable called `important_cols`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(directory + 'df_clean.csv', nrows=500000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=5,\n",
       "           max_features='auto', max_leaf_nodes=None,\n",
       "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "           min_samples_leaf=1, min_samples_split=2,\n",
       "           min_weight_fraction_leaf=0.0, n_estimators=500, n_jobs=-1,\n",
       "           oob_score=False, random_state=1, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# random forests for feature selection\n",
    "X_feat = df.drop(columns=['price_USD', 'reviews'])\n",
    "y_feat = df.price_USD\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_feat, y_feat, \n",
    "                                                    test_size=0.2,random_state=SEED)\n",
    "\n",
    "rf = RandomForestRegressor(n_estimators=500,\n",
    "                           max_depth=5,\n",
    "                           n_jobs=-1,\n",
    "                           random_state=SEED)\n",
    "rf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "75.36293542584507"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = rf.predict(X_test)\n",
    "\n",
    "rmse_test = mean_squared_error(y_test, y_pred)**(1/2)\n",
    "rmse_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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i2Lt3LzNnzsTS0pKmTZtWHO8oIiIiIiIiv54eVZDfLDs7m23btrF+/XrWr19PdnY2//jHPwCwsbFhyZIlTJgwgeXLl1NWVsakSZNYvHgxq1atwtbW9oa6OnXqhJ+fH++++26Vk/4FCxbQq1cvVq1aRbdu3QAwm83ExsYyb948Vq9ejYeHB5s3b76zHRcREREREXkAKHEgv9nRo0d57LHHsLa2xmAw0KZNG06cOAFQ8fiBp6cn165do6CgAHt7e9zd3QFo06ZNtdsxm80AnD59Gn9/fwACAgIAKCgoID8/n8jISMLDw/niiy84c+bMbeujiIiIiIjIg0qJA/nN/Pz8OHjwICaTCbPZzL59+2jRogUABoPhhnvd3Ny4cuUKBQUFAPz73/++qT6DwYDZbMbW1paLFy9SVlbG5cuXyc3NBcDb25uvv/4agEOHDgHg4uKCp6cn8+fPZ9WqVYwcOZJ27drdsT6LiIiIiIg8KLTHgfxmDz30EAEBAYSEhFBeXs6TTz5Jt27dOHbs2E33WlhYEBsby/Dhw3FwcKC8vJyHHnrohnueeOIJxo4dS1paGk8//TSvvPIKTZs2rbjv1Vdf5a233uLDDz+kSZMmFfVOmDCBESNGYDabqVevHsnJyb8Y+728AaHJbMLKoI+oiIiIiIjULoP5P+u/Re6ShQsXMmTIkIpNDv/4xz/y0ksv3fU4goODMRqNd71dERERERGRe0F150T6OlPuunr16tG3b1/s7Oxo3LgxQUFBtR2SiIiIiIiIVEGJA7nrwsLCCAsLq+0wREREREREpBq0OaKIiIiIiIiIVEmJAxERERERERGpkhIH97Djx4+zb9++u9pmbm4uffv2vWP19+3bt+JYxfuByWyq7RBERERERETuKO1xcA/75JNPcHd3JzAwsLZDuW/NLpz9m8rfy8c5ioiIiIiI3A5KHFST0Whk06ZNlJeX8+abb3L+/HlWrFiBjY0NzZs3JyEhAYBx48aRm5tLWVkZQ4YMISgoiPDwcFq2bMmJEyeoW7cubdq04fPPP+fy5cukpaXh5OR0U3t5eXls3rwZa2trWrVqRUJCAu+99x4AkZGRDB06lLfffps2bdpw4sQJnJycmDFjBtbW1kycOJHs7GzKy8uJjIykbdu2lfapoKCAyMhIzGYzpaWlxMfH4+DgQEFBAa+99hrnz5+nZcuWTJo0iczMTKZOnUpZWRmFhYXExcUREBBA586d8fLywtvbmyFDhhAbG0tpaSm2trYkJibSsGFDZs6cyWeffYanpyeFhYU3xREeHo6rqyuXLl1i0aJFjB8//qYxPHLkCImJiVhaWlbUXV5ezujRo2nYsCG5ubn07NmTEydOcOTIETp16kRUVNRtfAeIiIiIiIg8mJQ4qAFHR0dSU1MpLCzknXfeYfPmzdjb25OUlER6ejoArq6upKSkUFxcTHBwMO3atQPA39+fmJgYhg0bhp2dHcuWLSM6Opp9+/bRrVu3m9ry8PDg5Zdfxt3dHX9/f+zs7Dh58iTu7u7k5ubi7+9PSUkJvXv3JjAwkOTkZNLT07G1tcXFxYWkpCQKCwsJCwtj69atlfbn4MGDODs7k5yczMmTJ7l69SoODg4UFxczZcoUHBwc6N69OxcvXuTkyZNER0fTsmVLtmzZgtFoJCAggHPnzmE0GnFxcSEyMpLw8HCeeeYZvvrqK1JSUhg8eDD79u3jvffe4+rVq/To0aPSWHr16kX37t1ZvXp1pWMYExPD5MmT8fPzY8eOHUydOpWxY8eSk5NDWloaJSUldO3ald27d1OnTh06d+6sxIGIiIiIiMhtoMRBDbRo0QKAnJwcfHx8sLe3ByAwMJDPP/8cCwsLOnToAIC9vT3e3t7k5OQA0KpVK+Dn5IOPj0/Fz6WlpdVqu0+fPhiNRho1asQLL7wAgJWVVcVjDAEBAezevRsLCwsOHDjAwYMHATCZTBQUFODq6npTnR07duT06dO89tprWFlZ8eqrrwLQtGnTilUQbm5u/PTTTzRo0ID58+djZ2fHlStXKvru4uKCi4sLAJmZmSxcuJAlS5ZgNpuxsrLi9OnTPProo1hYWGBvb88f/vCHW45tVlZWpWOYn5+Pn59fxXhPnz69IlYHBwdsbGxwd3fH2dkZAIPBUK1xFRERERERkVtT4qAGLCx+3kuySZMmZGVlcfXqVerWrcvevXtp0aIFVlZW7N+/n+7du1NcXExmZiZNmjT51e0ZDAbKy8sBeO6550hLS8PZ2ZnZs39+Lt9kMnHs2DF8fX05cOBARULC09OTkSNHUlJSQmpqasVk+v/KyMigQYMGpKWl8fXXXzNjxgymTJlS6aR78uTJpKSk4O3tzZw5czhz5swNYwLg5eXF0KFDCQgIICsri3379uHj48OaNWsoLy+npKSEkydPVtlXAG9v70rHsEGDBhV93bdvH82bN7+h3K/1W/coMJlNWBn0MRIRERERkfuXZjy/gqurK2+88QYDBw7EwsKCZs2aMWbMGAwGA7GxsYSEhFBaWsqoUaNwc3P71e08+uijJCcn4+3tTbt27QgMDKSgoOCGRMDixYs5e/YsjRo1YvTo0QDExMQQFhZGcXExoaGhN0zu/5uvry9RUVGsW7cOk8nE66+/XmUsL7zwAhERETg6Ola5V0F0dDRxcXGUlpZSUlLChAkT8PPzo2PHjrzyyis0aNDgF8ejb9++lY7hpEmTSExMxGw2Y2lpSVJSUnWG8I5T0kBERERERO53BrPZbK7tIKR64uPj6dGjB+3btwegS5cubNu2DVtb21qO7PcpODgYo9FY22GIiIiIiIjUiurOifR1aS07e/Ys0dHRN10PDAzkzTffrPh96NChuLi4VCQNamLevHlkZGTcdD0pKYmmTZvWuD4RERERERF5cGjFgTywtOJAREREREQeZNWdE1X+8LuIiIiIiIiICEoc3POOHz/Ovn37ajWG3bt3k56eXqMyRUVFbNmy5Q5FVHtMZlNthyAiIiIiInJXaY+De9wnn3yCu7s7gYGBtRZDx44da1zm+PHjfPrpp/Tu3fsORHT7zC6cXaP7f+vxjSIiIiIiIr83ShzUgNFoZNOmTZSXl/Pmm29y/vx5VqxYgY2NDc2bNychIQGAcePGkZubS1lZGUOGDCEoKIjw8HBatmzJiRMnqFu3Lm3atOHzzz/n8uXLpKWl4eTkdFN7eXl5bN68GWtra1q1akVCQgLvvfceAJGRkQwdOpS3336bNm3acOLECZycnJgxYwbW1tZMnDiR7OxsysvLiYyMpG3btpX2KSMjgwULFmBhYcH58+fp168fAwYMIDw8HFdXVy5dukTPnj3Jzs6mQYMGXL58mVGjRnHt2jVeeOEFPvjgA+bOncu3335LUVERvr6+TJkyhQULFnDs2DHS09Pp2LEjsbGxlJaWYmtrS2JiIg0bNqy1cRUREREREZHq06MKNeTo6Mi6devw9fVl7ty5rFixgnXr1uHg4EB6ejrp6em4urqyfv16li1bxqxZsygoKADA39+fFStWcO3aNezs7Fi2bBk+Pj5VPorg4eHByy+/zODBg/H398fOzo6TJ09SVFREbm4u/v7+lJSU0Lt3b9atW4eXlxfp6els3LgRFxcX1qxZw/z58ysm3lXJy8sjNTWVDRs2sHz5ci5evAhAr169WL58OZaWlgC8+OKLbNu2DbPZzM6dO+ncuTPXrl3D0dGRZcuWsWnTJr755hvy8vIYOXIk7dq1o1+/frz77ruEh4ezatUqhg0bRkpKSq2Oq4iIiIiIiFSfVhzUUIsWLQDIycnBx8cHe3t74OfjEz///HMsLCzo0KEDAPb29nh7e5OTkwNAq1atgJ8nyT4+PhU/l5aWVqvtPn36YDQaadSoES+88AIAVlZWFY8xBAQEsHv3biwsLDhw4AAHDx4EwGQyUVBQgKura6X1PvHEE9jY2ADw8MMP8/3339/Q1/9wcnLCz8+PAwcOsHnzZqKjo7G1taWgoICoqCjq1q3L1atXuX79+g3lMjMzWbhwIUuWLMFsNmNldfPbrjbHVURERERERKqmxEENWVj8vEijSZMmZGVlcfXqVerWrcvevXtp0aIFVlZW7N+/n+7du1NcXExmZiZNmjT51e0ZDAbKy8sBeO6550hLS8PZ2ZnZs39+Nt9kMnHs2DF8fX05cOBAxcTZ09OTkSNHUlJSQmpqKs7OzlW2cfToUcrKyrh27RonT57koYceqmj7/+rbty8rVqygpKQEb29vdu7cyblz5ypWAGzfvh2z2YyFhUVF3F5eXgwdOpSAgACysrIqXQlwt8dVREREREREqkeJg1/J1dWVN954g4EDB2JhYUGzZs0YM2YMBoOB2NhYQkJCKC0tZdSoUbi5uf3qdh599FGSk5Px9vamXbt2BAYGUlBQcEMiYPHixZw9e5ZGjRoxevRoAGJiYggLC6O4uJjQ0NCKiXllTCYTw4cPp6ioiFdffbXKlQkATz31FLGxsbz66qvAz48JzJ8/nwEDBmAwGGjatCn5+fk0a9aMzMxMli9fTnR0NHFxcZSWllJSUsKECROqrP9ujet/1HSzQ5PZhJVBHxsREREREXlwGMxms7m2g5Dqi4+Pp0ePHrRv3x6ALl26sG3bNmxtbX9VfRkZGaxfv56ZM2fezjB/F4KDgzEajbUdhoiIiIiISK2o7pxIX53eA86ePUt0dPRN1wMDA3nzzTcrfh86dCguLi4VSYOamDdvHhkZGTddf+mll2pcl4iIiIiIiDw4tOJAHlhacSAiIiIiIg+y6s6JdByjiIiIiIiIiFRJiQORKpjMptoOQUREREREpNZpjwO5J6xbt44LFy7wxhtvVPp6UVERn332Gb17976t7c4unF3lazU9cUFEREREROR+pBUH8rtw/PhxPv3009oOQ0RERERE5IGjFQf3meLiYiZMmMCPP/5Ifn4+oaGhtGrViqSkJMrLy/Hw8CAlJYXjx4/fdO3UqVMkJiZiaWmJra0tiYmJlJeXM3r0aBo2bEhubi49e/bkxIkTHDlyhE6dOhEVFUV4eDgtW7bkxIkT1K1blzZt2vD5559z+fJl0tLSqFu3LuPGjSM3N5eysjKGDBlCUFAQ+/fvJykpCUdHRywtLXn88ccBmD59Ot9++y1FRUX4+voyZcoUFixYwLFjx0hPT6djx47ExsZSWlpaEaerqysREREUFxfz008/MXr0aP74xz/W8l9DRERERETk90+Jg/tMdnY2PXv2pEePHuTl5REeHk6dOnWYMWMG3t7ebNy4kaysLN55552brsXGxjJ58mT8/PzYsWMHU6dOZezYseTk5JCWlkZJSQldu3Zl9+7d1KlTh86dOxMVFQWAv78/MTExDBs2DDs7O5YtW0Z0dDT79u3jhx9+wNXVlZSUFIqLiwkODqZdu3bEx8czZ84cWrRowcSJE4GfEx+Ojo4sW7aM8vJyevbsSV5eHiNHjmT9+vX069ePyMhIwsPDeeaZZ/jqq69ISUlh5MiRFBUVsWTJEi5evMjp06dr8a8gIiIiIiJy/1Di4D7j7u7OihUr+OSTT7C3t8dkMnHhwgW8vb0B6NOnD0Cl1/Lz8/Hz8wMgMDCQ6dOnA9C0aVMcHBywsbHB3d0dZ2dnAAwGQ0W7rVq1AsDR0REfH5+Kn0tLS8nKyqJDhw4A2Nvb4+3tTU5ODhcuXKBFixYABAQE8P3332Nra0tBQQFRUVHUrVuXq1evcv369Rv6mJmZycKFC1myZAlmsxkrKysefvhh+vXrR1RUFCaTifDw8Ns8siIiIiIiIg8mJQ7uM2lpaTz++OOEhoayZ88edu3aRYMGDTh9+jTNmzdn0aJFtGjRosprx44dw9fXl3379tG8eXPgxgTBr+Ht7c3+/fvp3r07xcXFZGZm0qRJEzw8PMjKysLb25tDhw7h5OTE7t27OXfuHLNmzaKgoIDt27djNpuxsLCgvLwcAC8vL4YOHUpAQABZWVns27eP48ePc+XKFRYtWkR+fj79+/enc+fOvxjbrTZANJlNWBn0ERERERERkQebZkX3mc6dOzNp0iQ+/PBDHBwcsLS0JC4ujvHjx2NhYUH9+vUZPHgwHh4eN11r3LgxiYmJmM1mLC2UGrcWAAAgAElEQVQtSUpKui0x9e3bl9jYWEJCQigtLWXUqFG4ubmRkJDA2LFjsbe3p169ejg5OeHv78/8+fMZMGAABoOBpk2bkp+fT7NmzcjMzGT58uVER0cTFxdHaWkpJSUlTJgwgebNm/PXv/6Vbdu2UV5ezptvvvmb41bSQEREREREBAxms9lc20GI1Ibg4GCMRmNthyEiIiIiIlIrqjsn0nGMIiIiIiIiIlIlJQ5EREREREREpEpKHIiIiIiIiIhIlZQ4kDvKaDSSkpJS6WtFRUVs2bIFgEWLFnHw4EFKS0vZuHFjtetPSUm57fsUmMym21qfiIiIiIjI75m2jZdac/z4cT799FN69+7NiBEjAMjNzWXjxo306dPnrsQwu3D2TddudUSjiIiIiIjIg0aJA7krpk+fzrfffktRURG+vr5MmTKFBQsWcOzYMdLT0/n6668JCgrik08+4eTJk8ybNw+z2Yy7uzshISFkZWURFxfHqlWr+Pjjj0lNTcXV1ZXr16/j5eVV0cb+/fspLy9n8ODBPP/887XcaxERERERkd8/Paogd9z169dxdHRk2bJlbNq0iW+++Ya8vDxGjhxJu3bt6NevX8W9I0eOxMfHh1GjRlVZ19SpU1m2bBlLly7Fzs4OgF27dpGbm8u6detYuXIlCxYs4PLly3elfyIiIiIiIvczrTiQO85gMFBQUEBUVBR169bl6tWrXL9+/VfVVVBQgJOTEy4uLgA88cQTAGRmZnL48GHCw8MBMJlMnDlzBkdHx9vTCRERERERkQeUEgdyx2VkZPDQQw8xa9YsCgoK2L59O2azGQsLC8rLy2+497+v2dracv78eQAOHz4MgJubG5cvX6agoABXV1cOHTqEp6cnXl5etG3blsTERMrLy5k/fz5Nmza9ux0VERERERG5DylxIHdc69atOXz4MAMGDMBgMNC0aVPy8/Np1qwZmZmZLF++vOJeNzc3rl+/zrRp0+jfvz+RkZHs27ePVq1aAWBlZcU777zDsGHDcHJywsrq57dwly5d2Lt3L6GhoVy9epVu3bphb2//i7FVthGiyWzCyqCPhoiIiIiICIDBbDabazsIkdoQHBx8249yFBERERER+b2o7pxImyOKiIiIiIiISJWUOBARERERERGRKilxICIiIiIiIiJVUuJA7ordu3eTnp5+2+sdPXo0GRkZv6kOk9l0m6IRERERERG5/2jreLkrOnbsWNshVGp24exKT1YQERERERGRnylxIHfE2rVr2bZtGwDZ2dnk5eUxfPhw+vfvT0REBPXr1ycvL4+OHTsyevRoTp8+TUxMDNevX8fOzo6ZM2dy9epVxo8fT1lZGQaDgZiYGHx9fVmzZg0bN26kfv36XLx4EYDr168zceJEsrOzKS8vJzIykrZt29bmEIiIiIiIiNwXlDiQOyI0NJTQ0FAOHTrE5MmTGTp0KBcuXADgzJkzLF26FAcHB0JDQzl8+DDz5s1jxIgRdOzYkZ07d3LkyBE2bNjAwIED6datG0ePHmX8+PEsWrSIlStXsmXLFgwGA8HBwQBs3LgRFxcXkpKSKCwsJCwsjK1bt9bmEIiIiIiIiNwXlDiQOyYrK4uJEyeSmprKF198UZE48PX1xdnZGQB/f3++++47vvvuO5544gkAunbtCsCUKVMIDAwEwM/Pjx9++IHvv/8eHx8fbGxsKsoDZGZmcuDAAQ4ePAiAyWSioKAAV1fXu9dhERERERGR+5A2R5Q74syZM0RFRTFt2jQ8PDxueC0rK4uffvqJsrIyDh48iI+PD97e3hw6dAiADz74gFWrVuHt7c3+/fsBOHr0KO7u7jRv3pyTJ09SUlJCWVkZR48eBcDLy4uePXuyatUqFi9ezHPPPVeRnBAREREREZFfTysO5I6Ij4+npKSE+Ph4zGYzOTk59OrVCwBra2siIiK4cOECzz33HL6+vowdO5Z33nmH1NRU7OzsmDZtGp07dyY2Npa0tDRMJhOTJ0/G1dW1Yq8EV1dX6tSpA0D//v2JiYkhLCyM4uJiQkNDsbD45bxYhEsEJrMJK4M+CiIiIiIiIpUxmM1mc20HIQ+O3NxcoqKi2LBhQ22HQnBwMEajsbbDEBERERERqRXVnRPpUQURERERERERqZISB3JXNWnS5J5YbSAiIiIiIiLVo8SBiIiIiIiIiFRJiQMRERERERERqZISB79T+/bt49ixY7UdRqUyMjIYPXp0jcuFh4eTlZVV8XtpaSldunQBoKCggDfeeIOhQ4fSv39/JkyYQElJCQCPPvoo4eHhhIeH06dPH2bPnk1N9vw0mU01jlVERERERORBocTB79SmTZvIz8+v7TDumiVLltChQwfS0tJYv349devWZf369QA4OTmxatUqVq1axYYNG7h48SKrV6+uVr2zC2frKEYREREREZFb0IzpHnP9+nUmTpxIdnY25eXl/M///A/Tp09n5syZWFpaMnr0aGJjY/nss884fPgwPj4+DBgwAC8vL7y9vXnllVeYOnUqZWVlFBYWEhcXR0BAQKVtGY1GduzYwZUrVygsLOT111/n2WefZe/evRXtNW3alISEBADGjRtHbm4uZWVlDBkyhKCgIMLDw2nRogXfffcdZrOZmTNn3tDGtm3bWL58ORYWFjz55JOMGTPmV42Lu7s7H3/8MQ899BABAQFER0djMBhuus9gMDBkyBDGjx9PeHj4r2pLRERERERE/pcSB/eYjRs34uLiQlJSEoWFhYSFhTF16lRiY2Mxm80kJyfj5+fHn/70J4KCgmjUqBHnzp3DaDTi4uLChx9+SHR0NC1btmTLli0YjcYqEwcAP/30E8uWLaOgoIA+ffrQpUsXYmNjWbt2LW5ubsyaNYvNmzdz7do1XF1dSUlJobi4mODgYNq1awdAQEAACQkJrFmzhoULF9K9e3cAioqKmDt3Lps2baJOnTq89dZbfPHFFzz99NM1HpfBgwfj6OjI0qVLiYiI4Mknn2TixIk0bNjwpnvd3d0pLCyscRsiIiIiIiJyMyUO7jGZmZkcOHCAgwcPAmAymWjSpAkODg5YW1vj5+d3UxkXFxdcXFwAaNCgAfPnz8fOzo4rV65gb29/y/YCAwOxsLDA3d0dR0dH8vPzyc/PJzIyEoCSkhI6dOjA5cuX6dChAwD29vZ4e3uTk5MDcEMC4dNPP62o+/vvv6egoIARI0YAcOXKFb7//vsqEwe2trZcv3694vcrV65gZ2cHwJ49e3jppZd45ZVXuHbtGosXLyYpKYm5c+feVM+ZM2fw9PS8Zb9FRERERESkepQ4uMd4eXnh6enJyJEjKSkpITU1lT179lCvXj3Ky8v56KOPeO655zAYDBUbAFpY/O9WFZMnTyYlJQVvb2/mzJnDmTNnbtne4cOHAbhw4QLFxcV4enri6enJ/PnzcXBwYOfOndStW5esrCz2799P9+7dKS4uJjMzkyZNmgDw7bff4unpyb/+9S98fHwq6m7SpAkNGzYkLS0Na2trjEZjpYmP/2jVqhUff/wxvr6+AOzevZvWrVsDsHLlSvLz83nppZewsbHh4Ycf5tSpUzfVUV5eTlpaGj179qzOcIuIiIiIiMgvUOLgHtO/f39iYmIICwujuLiYbt26MXfuXNasWYPZbCY0NJTWrVvz2GOPkZKSUjF5/48XXniBiIgIHB0d8fT0/MUl+xcuXGDQoEH8+OOPTJw4EUtLSyZMmMCIESMwm83Uq1eP5ORknnzySWJjYwkJCaG0tJRRo0bh5uYGwObNm1m+fDl16tQhOTmZzMxMAFxdXRk8eDDh4eGUlZXRuHFjnn/++SpjGT58OO+88w4vv/wytra2ODs7k5iYCEB8fDzx8fEsX74cOzs7XFxciIuLA+DSpUuEh4djMBgwmUx06NCBV155pVrjHeESgcls0gaJIiIiIiIiVTCYa3JundxXjEYjp06d+tUbFsLPRyjGxcXh7e19GyO7O4KDgzEajbUdhoiIiIiISK2o7pxIX7M+AOLi4sjKyrrp+q2+/b9TDh48yLRp0yqNJTQ09K7HIyIiIiIiIremFQfywNKKAxEREREReZBVd05k8Yt3iIiIiIiIiMgDS4mDe0xRURFbtmypcbnt27eTl5d3ByK6M0pLS9m4ceMdqbsmY2Eym+5IDCIiIiIiIvcLJQ7uMcePH+fTTz+tcbmVK1dSXFx8ByK6M86fP3/HEgc1GQudpiAiIiIiInJrmjX9AqPRyI4dO7hy5QqFhYW8/vrrzJ07l+bNm2NtbU18fDxvvfUWxcXFlJWVERERQfv27QkKCqJNmzacOHECJycnZsyYgbW1NRMnTiQ7O5vy8nIiIyNp27YtvXr1qqivqKiIY8eOkZ6ezpIlS9i4cSPOzs6sXbuWK1euMHz48Jti/Oc//8nRo0eJjo6mT58+nD59mujoaMrKynjppZeYNWsWY8eOpX79+uTl5dGxY0dGjx7NuXPniI2NpbS0FFtbWxITE2nYsGGl4/DDDz8QFxdHaWkp58+fJzIykm7dulXaz/LyciZMmMCPP/5Ifn4+oaGhhIaGEh4ejqurK5cuXaJJkyacPHmSefPmYTabyc7OprCwkKKiIgYMGMAnn3zCd999x7vvvsvjjz/OqlWr+Pvf/47BYCAoKIiBAwfy9ttvY2Njw5kzZ8jPz2fq1KmcP3++YizWrl2LjY3NnX6LiIiIiIiI3NeUOKiGn376iWXLllFQUECfPn0oKyvjtdde45FHHuHdd9+lQ4cODBo0iLy8PEJCQti5cyclJSX07t2bwMBAkpOTSU9Px9bWFhcXF5KSkigsLCQsLIytW7dy9erVivoyMjJYv349/fr1Iy8vj61btzJgwAA++OAD5s2bV2l8nTp1ws/Pj7i4ODw8PAgODmbMmDF89tlntG3bFltbW86cOcPSpUtxcHAgNDSUw4cPs3jxYsLDw3nmmWf46quvSElJYfr06ZW2cerUKYYMGULbtm3517/+xdy5c+nWrVul/Xzqqafo2bMnPXr0IC8vj/Dw8IoTE3r16kX37t3Jzc0lMzOTUaNGMXfuXOzs7Fi6dCmLFi1i165dLFiwgE2bNrF161bs7e358MMPWbt2LQBDhgzhj3/8IwCNGjUiISGBDRs2kJ6eTkJCQsVYKGkgIiIiIiLy2ylxUA2BgYFYWFjg7u6Oo6MjWVlZtGjRAoCsrCx69+4NgIeHB/b29ly8eBErKysCAwMBCAgIYPfu3VhYWHDgwAEOHjwIgMlkoqCgAKCivv/25z//maioKAIDA3F3d8fd3f0XY7W3tycwMJDPP/8co9HIa6+9BoCvry/Ozs4A+Pv7891335GZmcnChQtZsmQJZrMZK6uq3w7169cnNTWV9957D4PBgMn0894AlfUzKCiIFStW8Mknn2Bvb19xb1X9BHjkkUcAcHBwwMfHBwAnJydKS0vJzMzk7NmzDB48GIBLly6RnZ0NgJ+fHwCenp7861//+sXxERERERERkZpR4qAaDh8+DMCFCxcoLi7Gzc0NC4uft4fw9vZm//79PPLII+Tl5XH58mWcnZ0xmUwcO3YMX19fDhw4UDEZ9vT0ZOTIkZSUlJCamloxmf9PfRYWFpSXlwPQuHFjHBwcWLBgAa+88sotYzQYDPznZM2+ffuyePFiCgsL8fX1JTc3l6ysLH766SdsbGw4ePAgf/7zn/Hy8mLo0KEEBASQlZXFvn37qqx/9uzZ9OnTh2eeeYZNmzaxefNmgEr7mZaWxuOPP05oaCh79uxh165dN8T5f/v539cr4+XlhY+PD0uWLMFgMLB8+XJatmzJxx9/XGm5/x4LERERERER+W20OWI1XLhwgUGDBjFixAgmTpyIpaVlxWt/+ctf2LNnDwMGDOC1114jISGh4pv7xYsXExISQn5+Pv3796d///6cOnWKsLAw+vfvT+PGjSsSBv/RrFkzMjMzWb58OfBzEmD//v386U9/umWMTzzxBGPHjqWoqIjHHnuM7OzsipUQANbW1kRERNCnTx+6du2Kr68v0dHR/PWvfyUsLIzo6GhatmxZZf3PPfccycnJDBgwgC+//JLCwsKK1/5vPzt37szatWsJCwtjxYoVWFpacu3atRvqc3Nz4/r160ybNu3Wg8/PqyXat29PSEgIwcHBnD59Gg8Pj2qNxS/RqQoiIiIiIiK3ZjDrq9lbMhqNnDp1ijFjxtSoXJcuXdi2bRu2tra/qf1t27aRmZlJREREtcuUl5cTEhLC0qVLsbe3Jzc3l6ioKDZs2PCbYqnM7epnbQgODsZoNNZ2GCIiIiIiIrWiunMiPapwD5sxYwYZGRksWLAAgJ07d1asRPhvAwcOpHv37gDk5OQwatQogoODsbe3r3Gbo0aN4tKlSzdcs7e3JzU1teYdEBERERERkd89rTiQB5ZWHIiIiIiIyIOsunMi7XEgIiIiIiIiIlVS4kBEREREREREqqTEgTzQdKqCiIiIiIjIrSlxcJ8JDw8nKyvrjrczbdo0evfuTUZGxm2td/LkyZw9e/a21nkrVgbtDyoiIiIiInIrmjXJr/LRRx/xt7/97Ved3HArEyZMuK31iYiIiIiIyG+jxME9wGg0smvXLkpKSvj+++8ZPnw4mzdvJi4uDm9vb9atW8eFCxd4+eWXGT16NA0bNiQ3N5eePXty4sQJjhw5QqdOnYiKigJgzpw5FBYWYmNjQ3JyMq6urkyfPp39+/dTXl7O4MGDef755wkPD8fV1ZVLly6xdOlSLC0tb4rtyJEjJCYmYmlpia2tLYmJiRiNRvLz8/nLX/7C0qVLsbOzu6nc22+/TVFREUVFRSxcuJAlS5bc0H7btm0ZMGAAH374IQaDgYSEBNq3b8/KlSuJi4ujQYMGTJgwgcLCQgBiYmLYs2cPJpOJYcOG8c4772BjY0NMTAypqak0adKEy5cv8/7772NhYUHr1q2JiYm5s384ERERERGRB4ASB/eI4uJili5dyunTpxk5ciT169ev9L6cnBzS0tIoKSmha9eu7N69mzp16tC5c+eKxEGPHj3o2bMna9asYeHChXTo0IHc3FzWrVtHaWkpffv25emnnwagV69edO/evcq4YmJimDx5Mn5+fuzYsYOpU6cyZ84cjEYjaWlp2NraVlm2Xbt2DB48mF27dlXafsuWLdm/fz+PPfYYGRkZjB8/npUrVwKwYMEC2rVrR2hoKKdPn2bcuHFMnz6d8ePHM2zYML777jtKSkoA+Oyzz1i0aBGDBg1i4sSJ+Pv7s3btWkwmE1ZWeouLiIiIiIj8FppV3SN8fX0BaNiwIdeuXbvhNbPZXPFz06ZNcXBwwMbGBnd3d5ydnQEwGAwV97Rp0waAgIAAdu3ahbu7O4cPHyY8PBwAk8nEmTNnAGjRosUt48rPz8fPzw+AwMBApk+fXu0+/afuzMzMStvv27cvmzdv5vz583Tp0uWGSX5mZiZ79uxh27ZtAFy6dIlGjRpRUlLCwYMH8fb25ty5cxw8eBAHBwfs7e2ZMmUKaWlpJCcn8/jjj98wblUxmU3a50BEREREROQWtDniPeK/J/4ANjY2nD9/Hvj5cYGq7qvMoUOHANi/fz8PP/wwXl5etG3bllWrVrFixQqef/55mjZtWq36GjRowLFjxwDYt28fzZs3r3Gfqmq/ffv2HD16lE2bNtGnT58bynp5eTF48GBWrVrFrFmzeOGFFwB45plnmDZtGn/84x95+umnmTRpEt26dQNgw4YNxMfHs3r1ao4ePcrXX3/9izEqaSAiIiIiInJrmjXdowYOHEh8fDyNGjWiQYMGNSq7Y8cOVqxYQb169Xj33XdxdHRk7969hIaGcvXqVbp161btTQ0nTZpEYmIiZrMZS0tLkpKSatyXLl26VNn+s88+y5dffkmzZs1uKDNy5EgmTJjAhg0bKC4uZtSoUcDPj2HMmzeP1NRU8vPzmTp1KgsWLACgZcuWhIaGUq9ePTw8PHjsscdqHKuIiIiIiIjcyGCuznpukftQcHAwRqOxtsMQERERERGpFdWdE2nFgXD27Fmio6Nvuh4YGMibb75ZZblr164xbNiwm663aNGChISE2xqjiIiIiIiI1A4lDoRGjRqxatWqGpezsbH5VeVERERERETk90ObI96nioqK2LJlS43Lbd++nby8vDsQ0c/mzp3LunXr7lj9NWUym2o7BBERERERkXuaEgf3qePHj/Ppp5/WuNzKlSspLi6+AxHdm3SqgoiIiIiIyK1p1nSXGI1GduzYwZUrVygsLOT1119n7ty5NG/eHGtra+Lj43nrrbcoLi6mrKyMiIgI2rdvT1BQEG3atOHEiRM4OTkxY8YMrK2tmThxItnZ2ZSXlxMZGUnbtm3p1atXRX1FRUUcO3aM9PR0lixZwsaNG3F2dmbt2rVcuXKF4cOH3xTjP//5T44ePUp0dDR9+vTh9OnTREdHU1ZWxksvvcSsWbMYO3Ys9evXJy8vj44dOzJ69GjOnTtHbGwspaWl2NrakpiYSMOGDasci507d/LRRx9RVFREREQEXbp04YMPPmDFihXY2NjQvHlzEhIS2LJlC6dOnWLMmDGUlpby/PPP8+mnn7JmzRref/99LCwsaN26NTExMTWOQURERERERKpHiYO76KeffmLZsmUUFBTQp08fysrKeO2113jkkUd499136dChA4MGDSIvL4+QkBB27txJSUkJvXv3JjAwkOTkZNLT07G1tcXFxYWkpCQKCwsJCwtj69atXL16taK+jIwM1q9fT79+/cjLy2Pr1q0MGDCADz74gHnz5lUaX6dOnfDz8yMuLg4PDw+Cg4MZM2YMn332GW3btsXW1pYzZ86wdOlSHBwcCA0N5fDhwyxevJjw8HCeeeYZvvrqK1JSUpg+fXqV4+Dh4cHkyZPJyMhgyZIlPPHEE8ydO5fNmzdjb29PUlIS/5+9ew2oskr////em5MpIAqKB3QUsFALy2TMNB2tHA/pFAkKihqlmVGKY2nlAcQwFUWD8SyBpIjm1q+OZXloMm0iNYskFcQjaRxEMkRQYP8f+Hf/cjiIZVr6eT2Ce99rrWutvZ+s6173WsnJydSuXbvS8iaTialTp+Lt7c2qVasoLS1l5syZNxSDiIiIiIiI1IwSB7eQj48PRqMRFxcXHB0dyczMpGXLlgBkZmbSr18/4MrE2t7enrNnz2JtbY2Pjw8A7du3Z+fOnRiNRvbt20dqaioApaWl5OfnA1jq+6Vnn32WcePG4ePjg4uLCy4uLteN1d7eHh8fH3bt2oXJZGL06NEAeHl54eTkBIC3tzfHjh0jPT2dxYsXs2zZMsxmM9bW1f+s2rZtC4CLiwvFxcWcOnUKT09P7O3tLeO0a9cu2rVrZynzy1NDZ8yYQVxcHLNmzeLBBx/EbDbfcAwiIiIiIiJSM5pd3UJpaWkA5OXlUVhYiLOzM0bjlW0mPDw82Lt3L23atCE7O5vz58/j5OREaWkphw4dwsvLi3379uHp6QlAo0aNGDVqFMXFxSxcuNAymb9an9FopLy8HICmTZvi4ODAokWLGDBgQLUxGgwGyyTd39+fpUuXcu7cOby8vMjKyiIzM5OLFy9ia2tLamoqzz77LO7u7gQHB9O+fXsyMzPZs2fPddv4JTc3NzIzMykqKqJ27dp89dVXtGzZEjs7O3Jzc68ZO4A1a9YQHh6OnZ0dzz//PPv377/hGERERERERKRmlDi4hfLy8hg2bBg///wzU6dOJSwszPLZiy++yJtvvsnHH39McXEx06ZNszw1X7p0KadPn6ZJkyaEhoYCMGnSJIYMGUJhYSGBgYGWhMFVzZs3Jz09nfj4eIYPH46/vz/Tp09n9uzZ1cb40EMP8frrrxMXF0e7du04ceIEgwcPtnxuY2PDmDFjyMvLo1evXnh5eTFhwgTCwsIoKSmhuLiYt95664bGpX79+rzyyisMHToUo9FI8+bNLfsaJCUlERAQQNu2balTpw4A9913H4GBgdSpUwdXV1fatWv3q2MoNZdqg0QREREREZFqGMy/XAMuvxuTyWTZ6O9G9OjRg48++gg7O7vf1P5HH31Eeno6Y8aMqXGZ8vJyAgICWL58Ofb29mRlZTFu3DjWrFnzm2L5o/D19cVkMt3uMERERERERG6Lms6J9Kj1LjB37lxSUlJYtGgRcOVUg/j4+Ar3DR06lCeffBKAU6dOERISgq+vr2XvgRsREhLCTz/9dM01e3t7Fi5ceOMdEBERERERkdtGKw7krqUVByIiIiIicjer6ZzIeN07REREREREROSupcSBiIiIiIiIiFRJiYM7WEhISJWf5ebmXnOqw+128OBBYmNjq/zcZDIRFRVV4fqePXs4dOjQr2631Fz6q8uKiIiIiIjcDZQ4uINVNxFv0KDBHypx0Lp162oTHVVZt24dOTk5v7pdHcUoIiIiIiJSPc2a/sRMJhOffvopxcXF5ObmMnToULZv305GRgavv/46U6dOZffu3QQFBeHl5UVGRgaFhYXMnz8fs9lsOVqxX79+dOjQgcOHD+Pu7o6zszN79+7F1taWJUuWsGjRIlxcXAgICCAzM5OwsDASExOvW87GxqbSuPv168df//pXDh8+jMFgYMGCBXz//fesXr2a6Oho1q5dy8qVK6lbty42Njb06dMHgG+//Zbg4GDy8/MJCAigbdu2fP7556SlpeHp6UlMTAwnTpyguLiYoUOH8vTTT9/Kr0NEREREROSOpBUHf3IXLlxg6dKljBgxgqSkJGJjY5k2bVqFnTG9vb2Jj4+nc+fObN68uUIdTz31FKtWrWLv3r20b9+elStXcvnyZY4cOVJt27+2XN++fXn//fdp2LAhO3futHyWn5/PsmXLSEpKIi4ujosXL1o+s7a2Zvny5cTGxpKQkMD999/PY489xmuvvYajoyN79uwhNjaWZcuWYWVldaNDKSIiIkUgV2kAACAASURBVCIiIpXQioM/udatWwPg4OCAh4cHBoOBunXrUlJScs19bdq0AaBRo0bk5eVVqKdt27YAODo64uHhYfn7f+u5WeWuxtO4ceNr7j158iQeHh7cc889ADz00EPXlDEYDDRo0IDi4uJr6rO3t+fNN99k8uTJFBYW0r9//2rbFxERERERkZpR4uBPzmAw/O712NnZkZubC0BaWtpNab+qcs2bN+fo0aMUFxdja2tLamoq7u7uVZYxGAyYzWZycnJIS0vjX//6FyUlJXTr1o1//OMfWFtX/xMvNZdqnwMREREREZFqaMYk19W7d2/Gjh3Lnj17LCsMfi/169dnxIgRBAYG4uTkRElJCdbW1pSWVn76Qbt27YiKimLevHnk5uYyaNAgjEYjwcHB100agDZHFBERERERuR6D2Ww23+4gRK4qLS1l6dKlvPTSS5jNZgYPHkxoaCg+Pj43vS1fX98Ke0GIiIiIiIjcLWo6J9LjVvldpKamMnv27ArXe/fuTWBgYJXlrK2tuXjxIs888ww2NjZ4e3vToUOH3zNUERERERERqYZWHMhdSysORERERETkblbTOZGOYxQRERERERGRKilxUEMmk4moqKhfXT45OZnLly9X+fnp06fZsWPHb6pj4sSJ7Ny5s8rPg4KCyMzMxGQysX379uu2c/DgQWJjY6uN6Y/qemMlIiIiIiIiNaPEwS2yePFiysvLq/z8yy+/5Ouvv/5NddSUr68vjz/++HXbad26NSEhIb+5vdvhZo2ViIiIiIjI3U6bI96Ab7/9luDgYPLz8wkICMDNzY158+ZhZ2eHk5MTkZGRlJaWMnbsWMxmMyUlJYSHh3PgwAFyc3MJDQ1lwYIFFeotKytjyZIlFBcX89BDD9G4cWMiIiKwsrLCzs6OiIgIdu/ebakjJiaGKVOm8OOPP5KTk0OPHj0IDQ2tcT9iYmJwcXHh73//e7WxDhs2jNWrVxMdHU3Pnj1p3749x44dw9nZmZiYGC5fvszrr79OTk4OjRs3Zs+ePezatYuVK1eyYcMGjEYjDzzwAJMmTaoylrVr15KUlER5eTk9evTg1VdfZePGjSQkJGBra0uLFi2YNm0amzZt4ujRo4wfP56SkhJ69+7Njh07CAoKwsvLi4yMDAoLC5k/fz5ffPFFteMtIiIiIiIiNafEwQ2wtrZm+fLl/PDDD4wYMYKSkhKSkpJwdXUlISGBhQsX0rFjR5ycnJg1axZHjhyhqKgIPz8/Fi5cSHR0dKX1WllZMXLkSI4ePcrjjz+Or68vb7/9Nq1bt2bbtm288847vPvuu5Y6zpw5w4MPPoifnx8lJSV07dr1hhIHV6WmplYb6zfffGO599SpUyQkJNC4cWMGDRrEd999x7fffoubmxvvvvsumZmZPPXUU8CV1zqmTp2Kt7c3q1atorS0FGvrij+1s2fPsnTpUjZu3IidnR1z5szhhx9+ICYmhvXr12Nvb09kZCTJycnUrl27yn54e3vz1ltvER0dzebNmxk5cmS14y0iIiIiIiI1p1cVbkCbNm0wGAw0aNCAM2fOYG9vj6urKwA+Pj5kZGTQtWtX2rdvz+jRo3n33XcxGm98iHNycmjduvU19f6Sk5MT3333Hf/85z+JjIzk0qVLv6o/NxJrvXr1aNy4MQCNGzempKSEzMxM2rdvD4CHhwf169cHYMaMGaxatYohQ4Zw+vRpqjq449SpU7Rq1YpatWphMBgYP348Z8+exdPTE3t7+yr7/7/1tWnTBoBGjRpRUlLyK0ZCREREREREqqLEwQ0wGAyWv+vVq0dhYSE5OTkAfPXVV7Ro0YKUlBQaNmxIXFwcL730EnPnzrWUre6de6PRaPm8YcOGHDp0CIA9e/bQokWLa+owmUw4ODgwZ84cgoODKS4urnJyXp0bifWXfb/q3nvvZf/+/QCcPHmSc+fOAbBmzRrCw8N5//33OXjwoOWe/9W8eXOOHj1qSXy8+uqrODs7k5mZSVFREXBlXFu2bImdnR25ubkApKWlXbdv1xtvERERERERqRm9qvArGQwGpk+fziuvvILBYKBu3brMmDEDg8HAuHHjSEpKorS0lJdffhmADh06MHLkSFasWFHlJHzhwoW0bduW6dOnExERgdlsxsrKisjIyGvqmDJlCv/85z/55ptvsLW15S9/+YslgXEjvLy8qo316v9VGTBgABMnTmTw4ME0adIEOzs7AO677z4CAwOpU6cOrq6utGvXrtLy9evXZ8SIEQwZMgSDwUD37t1p2rQpr7zyCkOHDsVoNNK8eXPLvgZJSUkEBATQtm1b6tSpU21s1xtvERERERERqRmD+dc8qhYBvv76a4qKiujSpQvHjx/nhRdeYNu2bbc7rBrz9fXFZDLd7jBERERERERui5rOibTi4Ba6dOkSzz//fIXrLVu2ZNq0aTeljdOnTzNhwoQK1318fHj11VdvShtXNWvWjHHjxhEbG0tpaSlTpkyp9L7t27cTHx9f4frQoUN58sknb2pMIiIiIiIicnNpxYHctbTiQERERERE7mY1nRNpc0QRERERERERqZISByIiIiIiIiJSJSUO5E9v586dJCcnX3PN39+frKys2xSRiIiIiIjInUObI8qfXteuXW93CCIiIiIiIncsJQ7kT89kMnH06FGsrKz4/PPPadSoEefOnbvdYYmIiIiIiNwRlDiQO8KBAwe4dOkSH3zwAUVFRfTs2fN2hyQiIiIiInJH0B4Hckc4c+YM999/P0ajEXt7e+69997bHZKIiIiIiMgdQSsO5I7g5uZGamoq5eXlFBcXc+TIkdsdkoiIiIiIyB1BiQO5I7Ru3ZratWszYMAAGjZsiLOz8+0OSURERERE5I6gxIH86fn6+lr+Hj169G2MRERERERE5M6jPQ5EREREREREpEpKHIiIiIiIiIhIlZQ4EBEREREREZEqKXHwB2Uymdi+ffvtDuO2KigoYNOmTQAsWbKE1NTU2xyRiIiIiIjI3UebI/5B/XLDv7vV4cOH2bFjB/369WPkyJG3OxwREREREZG7khIHt4DJZGLdunWUl5cTFBREQkICRqORhx9+mPHjx+Pr68u7776Lm5sbW7ZsYe/evdStWxcXFxcCAgKYM2cOe/fupby8nOHDh9OyZUuio6NZvHgxmzdvZtGiRWzatIl9+/axYcMGIiIiKo0jOjqalJQUSktL6dmzJyNHjuTbb78lMjKS8vJyXF1diYqK4ujRo0RERGBlZYWdnR0RERGUl5fz0ksv4eTkRNeuXenatSvTp08HwMnJicjISC5fvszYsWMxm82UlJQQHh5O69atK40lJiaG/fv3U1RUxNtvv82GDRs4cOAABQUFeHl5MWPGDBYtWsShQ4dITk5m//799OnTh7y8PD777DOKi4s5efIkI0aMwNfXl9TUVMLDw6lTpw7Ozs7Y2dnxzjvv/G7fqYiIiIiIyN1CryrcIo6OjixcuJDY2Fji4+NJSkoiOzub3bt3M2DAADZs2ABcSTL4+/tbyn322WdkZWWRlJTEihUrWLRoEU2aNOH06dNcunSJnTt3YjQaycvLY/v27Tz55JNVxrBp0yaioqJYtWoVjo6OAEyZMoXIyEjWrl1Lt27dyMzMZNKkSUyZMoX333+fgIAAywQ8NzeX5cuXM2LECCZPnszUqVNJTEyka9euLFu2jNTUVJycnFi6dClTpkyhqKio2jFxd3dn9erVuLq64ujoyHvvvce6dev45ptvyM7OZtSoUTzyyCMMHDjwmnKFhYUsXryYhQsXsmTJEgCmTp3KO++8w4oVK2jevPmNf0EiIiIiIiJSKa04uEVatmzJyZMnyc/Ptyy7v3DhAidPnqRfv34EBgbi5+dHYWEh9957Lx9//DEA6enppKWlERQUBEBpaSk//PADXbp04csvv+TMmTP069ePL774gn379hEaGlplDLNnz2bOnDnk5eXx2GOPAZCXl4eHhwcAfn5+AOTk5FhWCvj4+DBnzhwA3NzcsLW1BSAzM5Pw8HAALl++TIsWLejatSvHjx9n9OjRWFtb89JLL113TADs7OzIz89n3Lhx1K5dm6KiIi5fvlxlOS8vLwAaN27MpUuXLDG3atUKgIcffpgPP/yw2rZFRERERESkZpQ4uEWMRiNubm40btyYuLg4bGxsMJlMtG7dGgcHB+6//35mzJhRYW8Dd3d3OnbsaHldYMGCBTRr1ownnniCefPm4eXlRZcuXZgyZQp/+ctfsLGxqbT9S5cusWXLFubOnQtAnz596Nu3Lw0bNuT48eO0aNGCJUuW0LJlSxo2bMihQ4fw8vJiz549tGjRwtKHq1q2bMnMmTNp0qQJ+/btIzc3l5SUFBo2bEhcXBz79+9n7ty5JCYmVjsmADt37uTMmTPMmzeP/Px8tm7ditlsxmg0Ul5eXqGcwWCocK1Ro0YcOXIET09Pvv322+q/DBEREREREakxJQ5uofr16zN8+HCCgoIoKyujadOm9O7dG7jytP+FF14gMjLymjI9evTgq6++IjAwkKKiIp544gns7e156KGHOHbsGC+88AJeXl6cPn2aESNGVNm2ra0tdevWxd/fn1q1atG5c2eaNGlCeHg4b775JkajkQYNGjB8+HCaNm1KREQEZrMZKyurCjEBhIWFMWHCBEpLSzEYDLz99ts4OTkxbtw4kpKSKC0t5eWXX67RuHh7e7NgwQIGDx6MwWCgWbNm5OTk0Lx5c9LT04mPj79uHVOnTuXNN9+kdu3a2NjY4OrqWqO2RUREREREpHoGs9lsvt1BiPxWK1eupHfv3tSvX5/o6GhsbGwICQmptoyvry8mk+kWRSgiIiIiIvLHUtM5kVYc3GFSU1OZPXt2heu9e/cmMDDwlscTEhLCTz/9dM01e3t7Fi5ceFPbcXZ2Jjg4mNq1a+Pg4KATFURERERERG4SrTiQu5ZWHIiIiIiIyN2spnMiHccoIiIiIiIiIlW6oxIHEydOZOfOnZhMJqKioqq8r6CggE2bNgGwZMkSUlNTb1WIFldjzM3NJSws7Ja3/3srKSlh7dq1Fa5fr7979uzh0KFD1da9ePFiBg4ciK+vr6WNEydOEBAQQGBgIFOnTq30NAYRERERERG5cXdU4qCmDh8+zI4dOwAYOXIk3t7ety2WBg0a3JGJg9zc3EoTB9fr77p168jJyany85SUFPbv309SUhKJiYn8+OOPAMyYMYOxY8eyatUqzGYz27dv/819EBERERERkVu0OWJxcTFvvPEGp0+f5vLly0ycOJGVK1fy888/k5OTQ2BgIIGBgQQFBeHl5UVGRgaFhYXMnz+fpk2bsmDBArZt20ZZWRkBAQEMGjSIxMRE/v3vf2MwGOjTpw9Dhw6ttO05c+Zw4MABCgoK8PLyYsaMGSxatIhDhw6RnJzM/v376dOnD506deKNN94gKyuLsrIynnvuOfr06VNlTJWJiYnh6NGjnD17lvPnzzNp0iQ6dOjAxo0bSUhIwNbWlhYtWjBt2jRLmaysLMaNG8eaNWv49NNPiY2NxWw207ZtW4KDg3nttdf44IMPABg7dizBwcGVJjry8/MZO3YsZrOZkpISwsPDcXBwsNQN4O/vz9y5c1m/fn2lcX700UfEx8djNBp5+OGHGT9+PDExMezfv5+ioiLefvttPDw8KrS9b98+Zs6cibW1Nffccw/z589n0aJFHDlyxNKfX9bxxhtvVNrfgQMH8vnnn5OWloanpydNmjSp0NauXbu49957efnllyksLOT1118HIC0tjb/+9a8AdO3ald27d/Pkk09W97MUERERERGRGrglKw5Wr15N06ZNSU5OZu7cuaSlpdG3b1/i4uJYvnw58fHxlnu9vb2Jj4+nc+fObN68me+//56dO3eydu1a1q5dy/Hjx8nIyODDDz9k1apVrFy5km3btnH06NEK7RYWFuLo6Mh7773HunXr+Oabb8jOzmbUqFE88sgjDBw40HJvcnIy9evXZ/Xq1bz33nvMmzeP/Pz8SmOqTq1atVixYgWzZ89m2rRpnDt3jpiYGBISEkhKSsLBwYHk5OQK5UpLS4mIiGDJkiWYTCaaN2+OnZ0dtWrV4siRIxQUFJCVlVXl6ojU1FScnJxYunQpU6ZMoaio6IbiLCgoICYmhvj4eJKSksjOzmb37t0AuLu7s3r16kqTBgDbtm2jd+/evP/++wQEBHD+/HlGjRqFp6en5UjEq3XY2dlV2d/69evz2GOP8dprr1WaNAA4d+4cBw4cYP78+YSHhzN+/HjMZjNmsxmDwQBAnTp1+Pnnn6vtv4iIiIiIiNTMLVlxcPToUbp27QpAixYt6NOnD3PmzOGTTz7B3t6e0tJSy71t2rQBoFGjRuTl5XHs2DG8vb2xsrLCysqKiRMn8uGHH3L69GmGDx8OwE8//cSJEycqtGtnZ0d+fj7jxo2jdu3aFBUVcfny5UpjzMzM5NFHHwWuHBfo4eHBqVOnKo2pOo888ggArVq1Ii8vj1OnTuHp6Ym9vT0APj4+7Nq1i3bt2l1T7ty5czg6OuLs7AzAiBEjAPDz88NkMtGkSRP69+9fZbtdu3bl+PHjjB49Gmtra1566aUK9/zyAI3/jfPkyZPk5+czcuRIAC5cuMDJkycBaNmyZbV9HjVqFIsWLWLYsGG4urri7e3NpUuXrrnnf+uoqr/X4+TkhLu7O7a2tri7u1u+Y6Px/+XALly4gKOjY43qExERERERkerdkhUHHh4efPfddwCcOnWKiIgIHnzwQaKioujVqxfVnQjp7u7O999/T3l5OZcvX+a5557D3d0dT09PVqxYQWJiIr6+vtx3330Vyu7cuZMzZ84wd+5cxo0bR3FxMWazGaPRWGHzPA8PD/bu3QtcWamQnp6Om5vbDfc1LS0NgPT0dFxdXXFzcyMzM9OyAuCrr76qdCLu7OzM+fPnKSgoAGD69OmkpqbSq1cvdu/ezdatW6tNHKSkpNCwYUPi4uJ46aWXmDt3LnZ2dpw9e5aysjLOnz9PVlZWtXE2btyYuLg4EhMTGTJkCA8++CDANZPyymzcuJFnnnmGxMREWrVqxZo1ayqM8f/WUVV/DQZDtb+Hhx9+mM8//xyz2Ux2djYXL17EycmJNm3akJKSAlz53jt06FBtzCIiIiIiIlIzt2TFwaBBg3jzzTcZMmQIZWVlPP7446xatYoPP/wQBwcHrKysKjyhvqp169Y89thjBAQEUF5eTkBAAF5eXnTq1ImAgAAuXbqEt7c3rq6uFcp6e3uzYMECBg8ejMFgoFmzZuTk5NC8eXPS09OveUXC39+fyZMnExAQQElJCSEhIZan4Tfi4MGDDBs2jIsXLxIREUH9+vV55ZVXGDp0KEajkebNmzN+/PgKrzwYjUamTp3Kiy++iNFopE2bNjzwwAMYDAZ8fHzIz8/Hycmpyna9vLwYN24cSUlJlJaW8vLLL9OgQQM6d+7MgAEDaNasGX/5y1+qjXP48OEEBQVRVlZG06ZN6d27d4367O3tzaRJk7jnnnswGo1MmzYNZ2dnLl++zOzZs6lVq1aFMlX19/vvvycqKgo3N7dKX43o3r07e/bsYcCAAZjNZqZMmYKVlRUTJkxg8uTJzJ07F3d3d/7+97/XKHYRERERERGpnsFc3eNduSExMTG4uLgQEBBwU+sNDw+nZ8+edOrU6abU93vF+Wfj6+uLyWS63WGIiIiIiIjcFjWdE92SFQd3mpCQEH766adrrtnb21v2QriZgoODqVevniVpEBsba1mS/0uRkZE0a9bsprf/S1X1e+HChTe9rbCwMDIzMytcX7p0aaUrGEREREREROT3oRUHctfSigMREREREbmb1XROdEs2RxQRERERERGRPyclDkRERERERESkSkoc/EoTJ05k586dmEwmoqKiqryvoKCATZs2AbBkyRJSU1NvVYgWV2PMzc0lLCzslrdflc6dO//mOpKTk7l8+fJNiEZEREREREQqo8TB7+zw4cPs2LEDgJEjR+Lt7X3bYmnQoMEfKnFwMyxevJjy8vLbHYaIiIiIiMgd6447VaG4uJg33niD06dPc/nyZSZOnMjKlSv5+eefycnJITAwkMDAQIKCgvDy8iIjI4PCwkLmz59P06ZNWbBgAdu2baOsrIyAgAAGDRpEYmIi//73vzEYDPTp04ehQ4dW2vacOXM4cOAABQUFeHl5MWPGDBYtWsShQ4dITk5m//799OnTh06dOvHGG2+QlZVFWVkZzz33HH369KkypsrExMRw9OhRzp49y/nz55k0aRIdOnRg48aNJCQkYGtrS4sWLZg2bZqlTFZWFuPGjWPNmjV8+umnxMbGYjabadu2LcHBwbz22mt88MEHAIwdO5bg4OBKEx0pKSksWbIEGxsbfvzxRwYNGsSXX37JoUOHGDp0KIGBgezevZt58+ZhZ2eHk5MTkZGR1KlTh8mTJ3PkyBGaNWvGpUuXADhz5gyTJ0+mpKQEOzs7IiIiKCsr46WXXsLJyYmuXbvSrl07S7wXLlxgzpw57N27l9zcXEJDQ1mwYIHlWnl5OcOHD6d3796/9eckIiIiIiJy17vjEgerV6+madOmREdHc/z4cT799FP69u1Lz549yc7OJigoiMDAQAC8vb156623iI6OZvPmzXTp0oWdO3eydu1aysrKmDt3LhkZGXz44YesWrUKgOeee44uXbpUaLewsBBHR0fee+89ysvL6du3L9nZ2YwaNYrVq1czcOBA9u/fD1xZXl+/fn2ioqIoLCzE19eXRx55pNKYRo4cWWVfa9WqxYoVK8jIyOCf//wnCQkJxMTEsH79euzt7YmMjCQ5OZnatWtfU660tJSIiAjWrl2Ls7MzS5cuxc7Ojlq1anHkyBFcXFzIysqqdnXEjz/+yIYNG0hLS2PMmDFs3bqV7OxsQkJCCAgIYPLkySQlJeHq6kpCQgILFy6kXbt2lJSUsGbNGk6fPs3HH38MwMyZMwkKCqJbt27897//JSoqitDQUHJzc1m3bh22trasXLmS2bNn4+rqyqJFi9iyZQsvvfQSCxcuJDo6ms8++4ysrCySkpIoKSnB39+fzp074+joeAO/HhEREREREflfd1zi4OjRo3Tt2hWAFi1a0KdPH+bMmcMnn3yCvb09paWllnvbtGkDQKNGjcjLy+PYsWN4e3tjZWWFlZUVEydO5MMPP+T06dMMHz4cgJ9++okTJ05UaNfOzo78/HzGjRtH7dq1KSoqqvLd+8zMTB599FEA7O3t8fDw4NSpU5XGVJ2ryYZWrVqRl5fHqVOn8PT0xN7eHgAfHx927dpFu3btril37tw5HB0dcXZ2BmDEiBEA+Pn5YTKZaNKkCf3796+27VatWmFjY4ODgwPNmzfH1taWunXrUlJSwrlz57C3t8fV1dUSx9y5c6lbt64lGdGkSRMaN24MQHp6OosXL2bZsmWYzWasra/8LN3c3LC1tQXA1dWVt99+m9q1a5OdnU379u2viSc9PZ20tDSCgoKAK8mRH374QYkDERERERGR3+iO2+PAw8OD7777DoBTp04RERHBgw8+SFRUFL169cJsNldZ1t3dne+//57y8nIuX77Mc889h7u7O56enqxYsYLExER8fX257777KpTduXMnZ86cYe7cuYwbN47i4mLMZjNGo7HCO/geHh7s3bsXuLJSIT09HTc3txvua1paGnBl0uzq6oqbmxuZmZkUFRUB8NVXX9GyZcsK5ZydnTl//jwFBQUATJ8+ndTUVHr16sXu3bvZunXrdRMHBoOhys/q1atHYWEhOTk5ljhatGiBp6cn33zzDQDZ2dlkZ2cDV8Z9/PjxJCYmEh4eTq9evQAwGv/fz3Py5MlERkbyzjvv0LBhQ8v3aDAYKC8vx93dnY4dO5KYmEhCQgK9e/emWbNm1x9EERERERERqdYdt+Jg0KBBvPnmmwwZMoSysjIef/xxVq1axYcffoiDgwNWVlaWd+v/V+vWrXnssccICAigvLycgIAAvLy86NSpEwEBAVy6dAlvb2/Lk/Rf8vb2ZsGCBQwePBiDwUCzZs3IycmhefPmpKenEx8fb7nX39+fyZMnExAQQElJCSEhIZan/zfi4MGDDBs2jIsXLxIREUH9+vV55ZVXGDp0KEajkebNmzN+/Hg2b958TTmj0cjUqVN58cUXMRqNtGnThgceeACDwYCPjw/5+fk4OTndcDxXGQwGpk+fziuvvILBYKBu3brMmDGDevXqsXv3bvz8/GjSpAn16tUDYMKECYSFhVFSUkJxcTFvvfVWhTr79+/P4MGDueeee3BxcbEkJTp06MDIkSNZsWIFX331FYGBgRQVFfHEE09YVl6IiIiIiIjIr2cwV/cIXv6wYmJicHFxISAg4KbWGx4eTs+ePenUqdNNrfePyNfXF5PJdLvDEBERERERuS1qOie641Yc3GlCQkL46aefrrlmb29v2QvhZgoODqZevXqWpEFsbCwpKSkV7ouMjNRrACIiIiIiIncJrTiQu5ZWHIiIiIiIyN2spnOiO25zRBERERERERG5eZQ4+JMymUxs3779dodx2yUnJ3P58mVSUlIIDQ293eGIiIiIiIjccZQ4+JPy9fXl8ccfv91h3HaLFy+ucNyliIiIiIiI3DzaHPEPwGQysW7dOsrLywkKCiIhIQGj0cjDDz/M+PHj8fX15d1338XNzY0tW7awd+9e6tatazlVYc6cOezdu5fy8nKGDx9Oy5YtiY6OZvHixWzevJlFixaxadMm9u3bx4YNG3j66aeZOXMm1tbW3HPPPcyfP7/KowuDgoJo2bIlx44dw2w2Ex0dTYMGDSq02bt3b4KCgqhfvz4//fQTy5cvx8rKqtL67rvvPjIyMqhduzYdOnRg165dnD9/nri4OGrXrs0bb7xBVlYWZWVlPPfcc/Tp04egoCC8vLzIyMigPoB3KQAAIABJREFUsLCQ+fPn88UXX5Cbm0toaCjDhg3jxIkTvPDCC+Tn59O9e3deeeWV3/urExERERERueNpxcEfhKOjIwsXLiQ2Npb4+HiSkpLIzs5m9+7dDBgwgA0bNgBXkgz+/v6Wcp999hlZWVkkJSWxYsUKFi1aRJMmTTh9+jSXLl1i586dGI1G8vLy2L59O08++STbtm2jd+/evP/++wQEBHD+/PlqY2vfvj2JiYn07t2bxYsXV9rm1Tqeeuop4uPjK00aXOXt7U1CQgKXLl2iVq1avPfee3h6erJnzx6Sk5OpX78+q1ev5r333mPevHnk5+dbysXHx9O5c2c2b96Mn58fDRo0IDo6GoCSkhIWLFjAypUref/993/T9yEiIiIiIiJXaMXBH0TLli05efIk+fn5jBw5EoALFy5w8uRJ+vXrR2BgIH5+fhQWFnLvvffy8ccfA5Cenk5aWhpBQUEAlJaW8sMPP9ClSxe+/PJLzpw5Q79+/fjiiy/Yt28foaGhtGvXjkWLFjFs2DBcXV3x9vauNrZHHnkEuJJA2LFjB66urpW2ebUf19O2bVvgSrLE09PT8ndJSQmZmZk8+uijwJVjJz08PDh16hSA5QjKRo0akZeXV6HeVq1aYWtrC4C1tX7aIiIiIiIiN4NWHPxBGI1G3NzcaNy4MXFxcSQmJjJkyBAefPBBHBwcuP/++5kxYwa+vr7XlHN3d6djx44kJiaSkJBA7969adasGU888QRLly7lvvvuo0uXLrz//vs0b94cGxsbNm7cyDPPPENiYiKtWrVizZo11cZ24MABAL7++ms8PT2rbBPAYDD8pnHw8PBg7969ABQWFpKeno6bm1uV9xsMBsseB7+1bREREREREalIiYM/kPr16zN8+HCCgoLw8/Nj586dtGjRAsDyf58+fa4p06NHD2rXrk1gYKAlqWBvb89DDz3EsWPH6NKlC15eXpw+fZqePXsCV5b8T5o0iWHDhvHll1/yj3/8o9q41q9fz5AhQ/jPf/7DqFGjqmzzZvD396egoICAgACGDh1KSEgIzs7OVd7foUMHRo4cidlsvinti4iIiIiIyLUMZs24pBpBQUGEhYXh4eFxu0O56Xx9fTGZTLc7DBERERERkduipnMivQgunD59mgkTJlS47uPjc9Pre/XVV39VnSIiIiIiInJ7KHEgNGnShMTExD9sfSIiIiIiInL7aI8DEREREREREamSEgciIiIiIiIiUiUlDsQiNzeXsLAw4MppDSUlJVXe27lz5wrXTCYT27dvJyUlhdDQUABCQkIAOHz4MHv27LnhmKKjo/Hz88Pf35+UlBQAioqKeP311wkMDMTPz4/U1FQA4uPj6du3L0FBQQQFBXH06NEbbk9ERERERESupT0OxKJBgwaWxMGvcfVoxqsTfIDY2FgAPvnkE1xcXG5ow8Xvv/+eb775hjVr1vDDDz8wevRoNm7cyPLly2nVqhWzZs3i0KFDHDp0CG9vbw4cOMDMmTO5//77f3UfRERERERE5FpacXCHKSwsZMyYMQQHB/PUU0/xr3/9i969e3P11M1p06axdetWvvrqK4YOHUpQUBC+vr4cO3aMrKws/P39r6kvPT2d4OBghg0bRv/+/fn6668BuHTpEqGhoQwaNIipU6diNpuJiYkhKSnpmvKdO3cmOzub9evXEx8fT2pqKgMGDLB8PnbsWMuKgf/Vpk0bli9fjsFg4PTp0zg6OgKwa9cubGxseP7551mwYAGPPfYYAGlpaSxZsoSAgAAWL158cwZURERERETkLqfEwR3mxIkT9O3bl7i4OJYvX87//d//cd9997F3714uXbpESkoK3bt3JyMjg9mzZ5OYmEjPnj3ZsmVLpfUdOXKECRMmkJCQwIgRIyxnfBYXFzN+/HhWr15NQUEBO3bsqDImV1dXnnnmGYYPH463tze1atXiyJEjFBQUkJWVhbe3d5Vlra2tiY6O5sUXX7SsaDh37hznz59n+fLl9OjRg5kzZwLQt29fwsLCSEhIYN++fXz66ae/dhhFRERERETk/6dXFe4wLi4uJCQk8Mknn2Bvb09paSn+/v6sX7+e3NxcevTogbW1Na6urrz99tvUrl2b7Oxs2rdvX2l9DRs2ZMGCBdSqVYsLFy5gb28PXDlysWnTpgA89NBDHDt2rMYx+vn5YTKZaNKkCf3797/u/aGhoYwYMYKBAwfSoUMHnJyc6NGjBwDdu3dnyZIlmM1mhg0bhoODAwDdunXj+++/p3v37jWOS0RERERERCrSioM7TFxcHA8++CBRUVH06tULs9lMp06dOHjwIOvWrcPPzw+AyZMnExkZyTvvvEPDhg0trzL8r7fffptXX32VmTNncu+991ru+/HHH8nJyQHg66+/plWrVtXGZTAYKC8vB6BXr17s3r2brVu3Vps4+O9//0t4eDgAdnZ2WFtbYzAYePjhh/nss88A2LNnD56enhQWFvLUU09x4cIFzGYzKSkp2utARERERETkJtCKgztM9+7dmT59Oh9++CEODg5YWVlx+fJl/v73v/PFF1/QvHlzAPr378/gwYO55557cHFxsSQB/lf//v0ZM2YMjo6ONGrUiHPnzgHg5OTE9OnTyc7O5qGHHqJbt25V7lUAcP/99zNr1iw8PDx45JFH8PHxIT8/HycnpyrL/PWvf2XLli0MGjSI8vJyBg8eTLNmzXjxxReZNGkSAwcOxNrampkzZ+Lg4EBoaChDhw7F1taWTp060a1bt98wkiIiIiIiIgJgMFf1qFnkdxQeHk7Pnj3p1KnTbYvB19fXsmeDiIiIiIjI3aamcyKtOJBbLjg4mHr16lmSBrGxsdcc4XhVZGQkzZo1u9XhiYiIiIiIyC8ocSC3XFxc3DX/h4SEEBIScpuiERERERERkepoc0QRERERERERqZISB3ewgoICNm3adMPltm7dSnZ29u8Q0c1x+PBh9uzZA0CPHj0oKSm5zRGJiIiIiIjcuZQ4uIMdPnyYHTt23HC5FStWUFhY+DtEdHN88sknHDly5HaHISIiIiIiclfQHge3kMlkYtu2bVy4cIFz587x8ssvExMTQ4sWLbCxsSE8PJzXXnuNwsJCysrKGDNmDJ06daJPnz506NCBjIwM6taty9y5c7GxsWHq1KmcOHGC8vJyxo4dS8eOHXnqqacs9RUUFHDo0CGSk5NZtmwZa9euxcnJiVWrVnHhwgVGjBhRIcb//Oc/HDx4kAkTJuDn58fx48eZMGECZWVlPP3008ybN4/XX3+dBg0akJ2dTdeuXQkNDeXMmTNMnjyZkpIS7OzsiIiIoHHjxpWOQ35+PhMmTODnn3/GbDYzc+ZMatWqRVhYGCUlJeTm5jJ27FieeOIJoqOjSUlJobS0lJ49e/KPf/yD9evXY2NjQ9u2bS113kj7IiIiIiIiUnNKHNxiFy9e5L333iM/Px8/Pz/KysoYPXo0bdq0YebMmTz66KMMGzaM7OxsAgIC2L59O8XFxfTr1w8fHx9mzZpFcnIydnZ21KtXj8jISM6dO8eQIUPYvHkzRUVFlvpSUlJYvXo1AwcOJDs7m82bNzN48GA2btxIbGxspfH97W9/o3Xr1oSFheHq6oqvry/jx4/n888/p2PHjtjZ2fHDDz+wfPlyHBwcCAwMJC0tjaVLlxIUFES3bt3473//S1RUFHPmzKm0jQULFtCjRw8CAgL4+uuvSU1NxcXFheeee46OHTvy9ddfExMTwxNPPMGmTZtYsWIFDRs2xGQy4erqyjPPPIOLiwve3t6WOmfOnFnj9kVERERERKTmlDi4xXx8fDAajbi4uODo6EhmZiYtW7YEIDMzk379+gHg6uqKvb09Z8+exdraGh8fHwDat2/Pzp07MRqN7Nu3j9TUVABKS0vJz88HsNT3S88++yzjxo3Dx8cHFxcXXFxcrhurvb09Pj4+7Nq1C5PJxOjRowHw8vLCyckJAG9vb44dO0Z6ejqLFy9m2bJlmM1mrK2r/mkdO3aMAQMGWPrTvn17MjIyWLhwIR988AEGg4HS0lIAZs+ezZw5c8jLy+Oxxx6rss4baV9ERERERERqTrOrWywtLQ2AvLw8CgsLcXZ2xmi8stWEh4cHe/fupU2bNmRnZ3P+/HmcnJwoLS3l0KFDeHl5sW/fPjw9PQFo1KgRo0aNori4mIULF1om81frMxqNlJeXA9C0aVMcHBxYtGiRZdJeFYPBgNlsBsDf35+lS5dy7tw5vLy8yMrKIjMzk4sXL2Jra0tqairPPvss7u7uBAcH0759ezIzMy2bF1bGw8OD7777Di8vL/bs2cN//vMfTpw4gZ+fH926dWPdunWsX7+eS5cusWXLFubOnQtAnz596Nu3LwaDwdKvq26kfREREREREak5JQ5usby8PIYNG8bPP//M1KlTCQsLs3z24osv8uabb/Lxxx9TXFzMtGnTLE/Oly5dyunTp2nSpAmhoaEATJo0iSFDhlBYWEhgYKAlYXBV8+bNSU9PJz4+nuHDh+Pv78/06dOZPXt2tTE+9NBDvP7668TFxdGuXTtOnDjB4MGDLZ/b2NgwZswY8vLy6NWrF15eXkyYMMGyR0FxcTFvvfVWlfWPGjWKN998k40bNwIQGRnJt99+y6xZs1iyZAmNGjXi3Llz2NraUrduXfz9/alVqxadO3emSZMm3H///cyaNQsPDw9LnTfSvoiIiIiIiNScwXz10bL87kwmE0ePHmX8+PE3VK5Hjx589NFH2NnZ/ab2P/roI9LT0xkzZkyNy5SXlxMQEMDy5cuxt7cnKyuLcePGsWbNmt8Uyx+Br68vJpPpdochIiIiIiJyW9R0TqQVB3eJuXPnkpKSwqJFiwDYvn078fHxFe4bOnQoTz75JACnTp0iJCQEX19f7O3tb7jNkJAQfvrpp2uu2dvbs3DhwhvvgIiIiIiIiNwWWnEgdy2tOBARERERkbtZTedExuveISIiIiIiIiJ3LSUORERERERERKRKShwIAAcPHiQ2Nvam12symYiKiiI3N/eaEyRERERERETkz0GbIwoArVu3pnXr1r9b/Q0aNFDiQERERERE5E9IiYM/MZPJxLp16ygvLycoKIiEhASMRiMPP/ww48ePJycnh3/+858AtGrVioyMDBITE6853jEqKgp3d3eaNm3K6tWriY6Opnv37ri7u+Ph4cFzzz3H5MmTKSkpwc7OjoiICOrXr8+YMWMoLCzk4sWLhIaG0qVLl2pj/eUxjp9++imxsbGYzWbatm1LeHg4e/fuJTo6GisrK5o1a8a0adPYtGkTn332GcXFxZw8eZIRI0bg6+vLypUr2bBhA0ajkQceeIBJkyZx5syZCnE2btz4VnwNIiIiIiIidzQlDv7kHB0dmTFjBoGBgaxbt4577rmH1157jd27d7N161b69euHv78/mzZtIiMjo0Z1njlzBpPJRL169Rg7dixBQUF069aN//73v0RFRTFq1CgKCgpYtmwZZ8+e5fjx4zWOt7S0lIiICNauXYuzszNLly61TPpXrVqFs7Mz8+bNY/369VhbW1NYWMjy5cs5fvw4o0aNsuz6OXXqVLy9vVm1ahWlpaXMnDmzQpxz5sz5laMqIiIiIiIiVylx8CfXsmVLTp48SX5+PiNHjgTgwoULnDx5kqysLAYNGgRAx44dWbNmTYXylZ3GWa9ePerVqwdAeno6ixcvZtmyZZjNZqytrWnVqhUDBw5k3LhxlJaWEhQUVON4z507h6OjI87OzgCMGDGCs2fPkpOTw9ixYwEoLi7m0Ucf5S9/+QteXl4ANG7cmEuXLgEwY8YM4uLimDVrFg8++CBms7nSOEVEREREROS30+zqT85oNOLm5kbjxo2Ji4vDxsYGk8lE69atycrKYt++fXh5eXHgwAFLGVtbW3JycnBzc+PQoUN4eHhUqPMqd3d3goODad++PZmZmezZs4fDhw9z4cIFlixZQk5ODoMGDaJ79+41itfZ2Znz589TUFCAk5MT06dPp3///jRq1IgFCxbg4ODA9u3bqV27NmfOnMFgMFSoY82aNYSHh2NnZ8fzzz/P/v37K41TREREREREfjslDu4A9evXZ/jw4QQFBVFWVkbTpk3p3bs3I0eOZOLEiXz88cfUrVvXcv8LL7zAyJEjadq0KY6OjtXWPWHCBMLCwigpKaG4uJi33nqLFi1a8K9//YuPPvqI8vJyXn311RrHajQamTp1Ki+++CJGo5E2bdrwwAMP8NZbbzFy5EjMZjN16tRh1qxZnDlzptI67rvvPgIDA6lTpw6urq60a9eu0jhFRERERETktzOYK1urLneczMxMwsLCSExMvN2h/GFc3S9BRERERETkblTTOZFWHMhNERYWRmZmZoXrS5cupVatWrchIhEREREREbkZlDi4S3h4ePyuqw3CwsJ+t7pFRERERETk9jFe/xYRERERERERuVspcXCXKygoYNOmTTdcbuvWrWRnZ/8OEVXNZDKxfft2UlJSCA0NBaBz5863NAYREREREZG7jRIHd7nDhw+zY8eOGy63YsUKCgsLf4eIqubr68vjjz9+S9sUERERERG522mPgz8Yk8nEtm3buHDhAufOnePll18mJiaGFi1aYGNjQ3h4OK+99hqFhYWUlZUxZswYOnXqRJ8+fejQoQMZGRnUrVuXuXPnYmNjw9SpUzlx4gTl5eWMHTuWjh078tRTT1nqKygo4NChQyQnJ7Ns2TLWrl2Lk5MTq1at4sKFC4wYMaJCjP/5z384ePAgEyZMwM/Pj+PHjzNhwgTKysp4+umnmTdvHq+//joNGjQgOzubrl27EhoaypkzZ5g8eTIlJSXY2dkRERFB48aNKx0HX19f3n33Xdzc3NiyZQt79+6lbt26uLi44O7uXuH+r776itjYWMxmMxcuXGDOnDm0bNnypn8/IiIiIiIidxutOPgDunjxIu+99x5xcXG88847nD9/ntGjRxMdHc3ChQt59NFHWblyJfPnz+ett97CbDZTXFxMv379SEpKwt3dneTkZNauXUu9evVYuXIlCxYsYNq0aQAUFRVZ6hs1ahSPPPIIAwcOpF+/fmzevBmAjRs38swzz1Qa39/+9jdat27NzJkz6du3L9u3b6esrIzPP/+cjh07Ymdnxw8//MA777zDBx98wJdffklaWhozZ84kKCiIxMREnn/+eaKioqocgwEDBrBhwwbgSjLF39+/2jHLyMhg9uzZJCYm0rNnT7Zs2fJrhl5ERERERET+h1Yc/AH5+PhgNBpxcXHB0dGRzMxMy9PzzMxM+vXrB4Crqyv29vacPXsWa2trfHx8AGjfvj07d+7EaDSyb98+UlNTASgt/f/Yu9e4qqq17+O/hSCpC0VBxeNOsAQ1NIut6c481UctvXfkARCUtPAQihKJZ4HshAfc6lY8pqIiWLifTn6ytG6znaZmYW6VBDFBAxVQERewWPN50SNPbkHRMkr/31c65xzXuMZYvJnXHHNMK3l5eQAVPo1/7rnnCA8Px8fHB1dXV1xdXW+aq9lsxsfHh927d5OSksK4ceMA8PT0xNnZGQBvb29OnDhBWloay5cvZ9WqVRiGgb195X9+AwYMICAggMGDB1NYWMiDDz7Ixx9/XOn1jRs35rXXXqN27drk5OTQqVOnm+YuIiIiIiIiN6fCwR/Q4cOHATh37hyFhYW4uLhgZ/fz4hAPDw/2799P27ZtycnJ4eLFizg7O2O1Wjl69Cienp4cOHCA1q1bA+Dm5saYMWOwWCwsW7as/Gb+ajw7OztsNhsAzZo1w8nJifj4eAYNGnTDHE0mE4ZhADBkyBBWrlxJfn4+np6eZGVlkZ6ezpUrV6hZsyapqak899xzuLu7M3LkSDp16kR6ejr79u2rNL6TkxPt27fnjTfewNfX96ZzNnPmTD755BPMZjORkZHluYmIiIiIiMivo1cV/oDOnTvHiBEjCAkJYfbs2dSoUaP83OjRo9mzZw/Dhg1j3LhxxMTElD+5X7lyJf7+/uTm5uLn54efnx8ZGRkEBgbi5+dHs2bNygsGV7Vs2ZK0tDTWrl0L/FwE2L9/P48//vgNc3z44YeZPHkyBQUFdOjQgZMnT5avhABwcHAgLCyMwYMH07t3bzw9PYmMjOSf//wngYGBREZG0qZNmxv2MXjwYHbt2kX//v1vOmcDBw5k2LBh+Pn5cfnyZXJzc2/aRkRERERERG7OZOjR7B9KSkoKGRkZRERE3FK7Xr16sW3bNhwdHX9V/9u2bSMtLY2wsLAqt7HZbPj7+7N69WrMZjNZWVmEh4eTnJz8q3K503x9fUlJSanuNERERERERKpFVe+J9KqClFuwYAF79+4lPj4egB07dpSvRPil4cOH8+STTwJw6tQpQkND8fX1xWw233KfoaGhXLhw4ZpjZrOZZcuW3foARERERERE5DenFQdyz9KKAxERERERuZdV9Z5IexyIiIiIiIiISKVUOBARERERERGRSqlwINcoKCjg/fffv+V2kyZNoqSk5JbadOvWrcrXDhkyhKysrCrnsnfv3lvKRURERERERCqmwoFc49ixY+zcufOW28XFxVGzZs07kJGIiIiIiIhUJ31V4U/MYrEwefJkcnNzadKkCfv27aNVq1ZERUXh4eFBYmIi586dY/z48SQkJPDBBx9gMpno378/w4cPZ/v27axcuRJ7e3saNWpEXFwc8fHxHD16lKSkJOrXr3/deTu7imtNVz8HOXv2bGrWrEl2dja5ubm8+eabtGvXji1btpCYmIjNZqNXr15MmDChvG1QUFCFOcfFxfHFF1/g5uZGfn4+AJcuXWL69Onl/58xYwZt2rRh48aNbNmyhYYNG3L+/Pk7P/kiIiIiIiL3CK04+BNLSkqiefPmbN68mdDQ0EpvmI8fP85HH33Epk2b2LhxI59++ikZGRl88MEHjBo1isTERHr27ElhYSFjxoyhS5cuDB06tMLzVdG0aVNWr15NUFAQSUlJnD9/npUrV7Jp0ya2bt1KSUkJly9fvmGMQ4cOsW/fPt555x1iY2PLr4+Pj6dLly4kJCTw6quvEhUVxblz51i/fj3JycksXbqU0tLSW5tIERERERERqZRWHPyJpaen0717dwA8PDxo0KDBNeevfmkzLS2N06dPExwcDMCFCxc4efIkU6dOZfny5WzYsAF3d3f69OlzTfubna+Ml5cXAG5ubnzzzTecOnWKBx54gPvuuw+AiIiISttezTkzM5P27dtjZ2eH2WzmwQcfLB/Lnj172LZtW/lYfvzxR1q3bl3+qoS3t3eV8hQREREREZGb04qDP7EHH3yQgwcPAvDjjz+Sn59PzZo1OXv2LAD/+c9/AHB3d6d169asX7+ehIQEfH19adOmDUlJSYwfP54NGzYA8Mknn2BnZ4fNZgOo8HxVmEyma/7fsmVLMjIyyjdPnDBhAjk5OeXnK8q5devWpKamYrPZKCoq4vjx4+VjCQ4OJiEhgYULFzJw4EDuv/9+jh8/jsVioaysjCNHjtziTIqIiIiIiEhltOLgT2zQoEFMmTKFYcOG0bRpUxwdHRk+fDjR0dE0bdqURo0aAeDp6cljjz2Gv78/JSUleHt707hxY7y9vRk9ejR16tShdu3a9OjRg5KSEtLS0li7dm2F529HgwYNePHFFwkMDMRkMtGzZ08aN25cfr6inL28vOjevTuDBg2iUaNGuLi4ADBmzBimT59OcnIyhYWFhIaGlsf38/OjQYMG1KpV69dNrIiIiIiIiJQzGVfXhsufzjfffENRURF/+9vfyMzM5IUXXuDTTz+t7rT+NHx9fUlJSanuNERERERERKpFVe+JtOLgT6xFixaEh4ezZMkSrFYrs2bNuqP9paamMnfu3OuO9+vXj4CAgDvat4iIiIiIiFQPFQ7+xBo2bEhCQsLv1p+3t/fv2p+IiIiIiIhUP22OKCIiIiIiIiKVUuFA/jBSUlKYN2/eLbUpLi6mV69edygjERERERERUeFARERERERERCqlPQ7kD+Xbb79lxIgRFBYWMn78eGrXrk1cXBw1atSgRYsWxMTEUFJSQkREBBcvXqRly5blbTdu3Mi//vUv7OzseOihh5gxY0Y1jkREREREROTuoMKB/KHUqlWLFStWkJeXx+DBg3FwcGDTpk24uLiwcOFCtm7dyqVLl3jwwQeZNGkS3333HXv37gV+ftVh9uzZeHt7s2nTJqxWK/b2+hMXERERERH5NXRXJX8ojzzyCCaTCRcXF+677z6ysrKYOHEiABaLha5du5KXl8cTTzwBQIcOHcqLA2+88QZr1qwhNjaWjh07YhhGtY1DRERERETkbqHCgfyhHDp0CICzZ89SXFxMs2bNWLp0KU5OTuzYsYPatWtz7Ngxvv32W/r06cN//vMfrFYrAMnJyURHR+Po6MioUaM4ePAgf/3rX6tzOCIiIiIiIn96KhzIH4rFYmH48OEUFRUxZ84cysrKCAkJwTAM6tSpQ2xsLJ06dWLy5Mn4+/vj7u6Og4MDAG3atCEgIIA6derQuHFjOnToUM2jERERERER+fMzGVrPLfcoX19fUlJSqjsNERERERGRalHVeyJ9jlFEREREREREKqXCgYiIiIiIiIhUSoUDEREREREREamUCgdVcOTIEZYsWVLdaVTo9OnT7Ny5847EPnXqFH379iUyMpLXXnuN06dPs3jxYhITE3+T+CtWrCA1NbXCc8XFxWzZsuU36UdERERERERun76qUAVeXl54eXlVdxoV2rNnDxkZGfTq1es3j33gwAF69OjBlClTfvPYACEhIZWeO3v2LFu2bGHw4MF3pG8RERERERGpmru2cJCSksK7776LzWYjKCiIdevWYWdnxyOPPEJERAS5ubm8/PLLADzwwAP88MMPJCQk0KtXL7Zt24ajoyPz5s3D3d2dZs2asXnzZuLi4ujbeWR+AAAgAElEQVTZsyfu7u54eHjw/PPPM3PmTIqLi3F0dOTVV1+lSZMmFeaTl5dHREQEJSUltGrVij179vDJJ59U2J+vry/z589n//792Gw2goOD6devHxs3buRf//oXdnZ2PPTQQ0ydOpUVK1ZgsVh4+OGH6d2793X9ZmVlERYWRsOGDcnJyaF79+5MmjSJKVOmUFBQQEFBAcuXL2fZsmUcOHAAgGeeeYYnn3yS+Ph4LBYLLVu2ZNu2bURFRV0Tu6IcK3KjHPr3789f//pXpk6dyunTpyktLWXmzJm8++67HD9+nCVLlmAYBq6urvj7+5Oenk5UVBQJCQk888wz3H///Tg4OBATE8P06dPJz88HYMaMGbRp0+Z2/3xERERERETk/7lrCwcAdevW5Y033iAgIIB3332XWrVq8corr/Dll1/yySefMGDAAIYMGcL777/PDz/8UKWYZ86cISUlhfr16zNx4kSCgoJ44okn+Oqrr5g3bx7z58+vsF18fDy9e/dm2LBhfPnll3z55ZeV9vG///u/ZGVlkZiYSHFxMUOGDKFbt26kpKQwe/ZsvL292bRpE4ZhEBISQkZGRoVFg6uys7NZvXo1Tk5OBAQEcPjwYQC6dOlCcHAwn332GVlZWSQnJ2O1WgkICKBLly7lsQMCAti2bVuVcqxbt+4t5QCwefNmmjVrRlxcHJmZmXz++eeMGTOGtLQ0QkNDWbx4cYUxi4qKGDduHG3btmXu3Ll06dKFgIAAMjMzmTp16m/2SoWIiIiIiMi97K4uHLRq1Yoff/yRvLy88mXxly9f5scffyQrKws/Pz8AOnfuTHJy8nXtDcO47lj9+vWpX78+AGlpaSxfvpxVq1ZhGAb29pVPZ3p6Os8++ywAjz76aIXXXO0vLS2Nw4cPExQUBIDVaiU7O5s33niDNWvWEBsbS8eOHSvMryKenp44OzsD4O3tzYkTJ4Cf5+dqbo8++igmkwkHBwc6dOhAenr6DWNWlmNlhYPKcgDIyMige/fuANx///0EBweTlZVVpbFdHUNaWhp79uwpL3BcuHChSu1FRERERETkxu7qzRHt7Oxo3rw5TZo0Yc2aNSQkJBAYGEjHjh1p06ZN+dL877//vrxNzZo1yc3NxTAMjh49WmHMq9zd3YmIiCAhIYHo6Gj69u1baS4PPvggBw8eBODbb7+9YX/u7u507tyZhIQE1q1bR79+/WjRogXJyclER0ezYcMGjhw5wsGDB7Gzs8Nms91wHtLT07ly5QplZWWkpqbSunVrAEwmEwAeHh7lc1FaWsrBgwf5y1/+csOYleV4qzlc7f/QoUPAzxsyvvzyy9eMy9HRkbNnzwJcs1IB/v/v4e7uTnBwMAkJCSxcuJCBAwfeMH8RERERERGpmrt6xQFAgwYNCA4OJigoiLKyMpo1a0a/fv0ICQlhypQpfPzxx9SrV6/8+hdeeIGQkBCaNWtW6dPzqyIjI4mKiqK4uBiLxcL06dMrvfbFF19k8uTJbNu2jUaNGpWvTqiov169evH1118TEBBAUVERffr0wWw206ZNGwICAqhTpw6NGzemQ4cOmM1mli1bRrt27Xj66acr7NvBwYGwsDDOnTtH37598fT0vOZ8z549+frrrxk6dCilpaX07duXdu3acezYsUrHU1mOlblRDn5+fkybNo3AwEDKysqYNm0aLi4ulJaWMnfuXPz8/Jg4cSL79u2jXbt2FcYfM2YM06dPJzk5mcLCQkJDQyvNRURERERERKrOZFR1vftd7Jcb7t0p//u//0v9+vXx9vbm3//+N/Hx8axfv/6O9XdVVlYW4eHhFb6K8Xv5I+RQEV9fX1JSUqo7DRERERERkWpR1Xuiu37Fwe8tNDT0uvfrzWYzERERTJs2jRo1amCz2W64OuF2JCUl8cEHH1x3PDw8/Dft54+eg4iIiIiIiPy2tOJA7llacSAiIiIiIveyqt4T3dWbI4qIiIiIiIjIr6PCgYiIiIiIiIhUSoWDP4CCggLef//9W273ySefkJOTcwcy+tnt5nUjWVlZDBky5DeNKSIiIiIiIneOCgd/AMeOHWPnzp233G79+vUUFhbegYx+drt5iYiIiIiIyN1DX1WoQEpKCp9++imXL18mPz+fl156icWLF3P//ffj4OBAdHQ0r7zyCoWFhZSVlREWFsZjjz1G//79efTRR/nhhx+oV68eCxYswMHBgdmzZ3Py5ElsNhsTJ06kc+fOPPPMM+XxCgoKOHr0KElJSaxatYotW7bg7OzMpk2buHz5Mi+++OJ1OX7++eccOXKEyMhIBg8eTGZmJpGRkZSVlfH3v/+dhQsXMnnyZBo2bEhOTg7du3dn0qRJnDlzhpkzZ1JcXIyjoyOvvvoqTZo0qXAe4uPjy/Pq1q0b06ZNo6ysDJPJxIwZM9i7dy9Wq5VRo0Yxa9YsatasyYwZM1i2bBnNmzcnOTkZT09PfvjhBwoLC/nHP/4BQF5eHuPGjePs2bO0adOGOXPmkJWVdV18T09PunXrxpdffgnApEmT8PPzo1GjRkydOhV7e3tsNhvz58+nSZMmzJ8/n/3792Oz2QgODqZfv3537o9ERERERETkHqHCQSWuXLnC22+/TV5eHoMHD6asrIxx48bRtm1b3nrrLbp27cqIESPIycnB39+fHTt2YLFYGDBgAD4+PsTGxpKUlISjoyP169fn9ddfJz8/n8DAQD788EOKiorK4+3du5fNmzczdOhQcnJy+PDDDxk2bBjvvfceS5YsqTC/Hj164OXlRVRUFI0bN8bX15eIiAi++OILOnfujKOjI9nZ2axevRonJycCAgI4fPgwK1euJCgoiCeeeIKvvvqKefPmMX/+/Ar7GDNmTHleEyZMYPjw4fTp04cjR44wbdo0lixZwrRp0xg1ahQnTpzAYrEA8MUXX7BixQqSk5Px9vZm+vTpxMXF8eGHH9K/f38KCwt54403cHJy4sknn+T8+fPExsZeF7+y3T3//e9/4+3tzSuvvML+/fu5dOkSaWlpZGVlkZiYSHFxMUOGDKFbt27UrVv3t/mDEBERERERuUepcFAJHx8f7OzscHV1pW7duqSnp9OqVSsA0tPTGTBgAACNGzfGbDZz/vx57O3t8fHxAaBTp07s2rULOzs7Dhw4QGpqKgBWq5W8vDyA8ni/9NxzzxEeHo6Pjw+urq64urreNFez2YyPjw+7d+8mJSWFcePGAeDp6YmzszMA3t7enDhxgrS0NJYvX86qVaswDAN7+6r9CaSnp5ePzcvLi59++ommTZtisVhITU3Fw8ODM2fOkJqaipOTE2azGYC2bdsC4Obmxrlz5wBo0aIF9erVA8DFxYUrV65UGP+/Xf1y6KBBg1i5ciUvvPACTk5OTJo0ibS0NA4fPkxQUBDw8zxnZ2ercCAiIiIiIvIraY+DShw+fBiAc+fOUVhYiIuLC3Z2P0+Xh4cH+/fvByAnJ4eLFy/i7OyM1Wrl6NGjABw4cIDWrVvj7u7O008/TUJCAitXrqRv377lN/NX49nZ2WGz2QBo1qwZTk5OxMfHM2jQoBvmaDKZym+mhwwZwpYtWzh//jyenp7Azzf7V65coaysjNTU1PJ8IiIiSEhIIDo6mr59+1Ya/5d5/XLMR44cKS9oPPHEE8ydO5e//e1vdOvWjTlz5tCnT5+b5v3fKotvtVq5fPkyJSUlHD9+HIAdO3bwyCOPsG7dOvr27cuqVatwd3enc+fOJCQksG7dOvr160eLFi1umIeIiIiIiIjcnAoHlTh37hwjRowgJCSE2bNnU6NGjfJzo0ePZs+ePQwbNoxx48YRExNT/uR+5cqV+Pv7k5ubi5+fH35+fmRkZBAYGIifnx/NmjUrLxhc1bJlS9LS0li7di3wcxFg//79PP744zfM8eGHH2by5MkUFBTQoUMHTp48Wb4SAsDBwYGwsDAGDx5M79698fT0JDIykn/+858EBgYSGRlJmzZtKo3/y7wmT57Mhg0bGDZsGFFRUbz22msAPPXUU3zzzTd06dKFv/3tb3z//ff07t37luYaqDT+8OHDy1+VaNq0KQDt27dn0aJFDB8+nM2bNxMYGEivXr2oXbs2AQEB+Pr6ApSvehAREREREZHbZzKuPrKWcikpKWRkZBAREXFL7Xr16sW2bdtwdHT8Vf1v27aNtLQ0wsLCqtzGZrPh7+/P6tWrMZvNZGVlER4eTnJy8q/K5W7m6+tb6T4KIiIiIiIid7uq3hNpj4M/mAULFrB3717i4+OBn5flX12J8EvDhw/nySefBODUqVOEhobi6+t7W0/ZQ0NDuXDhwjXHzGYzy5Ytu/UBiIiIiIiIyF1FKw7knqUVByIiIiIici+r6j2R9jgQERERERERkUpVW+EgJSWFefPm3Xb7pKQkSktLf8OMbt8nn3xCTk5OdadRoWPHjrFv377qTuOWbNiwobpTEBERERERkf/nT7viYPny5eWfCqxu69evp7CwsLrTqND27dvLP2P4Z6G9FURERERERP44qnVzxO+++46RI0eSl5eHv78/zZs3Z+HChTg6OuLs7Mzrr7+O1Wpl4sSJGIZBcXEx0dHRfP/995w9e5ZJkyaxdOnSCmOnpaXx5ptvUlZWRn5+PlFRUXTq1IktW7aQmJiIzWajV69eTJgwocJj27ZtY+3atdjZ2fHII48QERHB4sWLycjI4Pz581y8eJEZM2ZQWFjIkSNHiIyMZNOmTSxevJjvv/+egoICPD09eeONN1i8eDFZWVmcP3+e06dPM3XqVB5//HE+++wzlixZgmEYtGvXjujoaPbv309cXBw1atSgRYsWxMTE4ODgUOEYg4KCaNCgARcuXGDx4sXMmDGDS5cukZubS0BAAL1792br1q04ODjQrl07LBbLLcVu1aoVJ06cwDAM4uLiaNiwIfPnz2f//v3YbDaCg4Pp16/fNXksXbqU6dOnc/r0aUpLS5k5cybt27dn9uzZnDx5EpvNxsSJE+ncuTMDBgzgr3/9K8eOHcNkMrF06VI2bNjAhQsXiIqKIiIigunTp18zpoCAAFJTU4mOjqZOnTq4uLjg6OjIm2++SUJCAh988AEmk4n+/fszfPjw3+xvVURERERE5J5lVJN3333XCA4ONmw2m3Hq1Cmjb9++Rs+ePY2ffvrJMAzDWLt2rfHmm28an332mTF+/HjjypUrxqFDh4z9+/cbhmEYPXv2NCwWS6XxP/zwQ+Po0aOGYRjGe++9Z0yfPt04d+6c8eSTTxpXrlwxbDabMXfuXCM7O7vCY/369TOKiooMwzCMiIgIY/fu3caiRYuMKVOmGIZhGGlpacaAAQMMwzCMwMBA4/jx48alS5eMFStWGIZhGGVlZUbfvn2Nn376yVi0aJExY8YMwzAMY/fu3cbIkSON0tJSo2fPnsa5c+cMwzCMFStWGFlZWcZTTz1VfiwuLs5ISkqqdIyBgYHG9u3bDcMwjO+//974+OOPDcMwjJ9++sl48sknDcMwjEWLFhmbNm0ybDbbLcfeunWrYRiGsWHDBuPVV181Pv/8c2PixImGYRiGxWIxBg4caFy4cOGaPN5++21j7ty5hmEYxokTJ4y3337b2LhxoxEbG2sYhmHk5eUZ/fv3L/8NDxw4YBiGYYSHhxsffPCBYRiG0bVr1xuO6e9//7uRlpZmGIZhLFiwwIiMjDR++OEHw8/Pz7BarYbVajWCgoKM9PT0SsdnGIbx7LPP3vC8iIiIiIjI3ayq90TVuuKgbdu2mEwmGjZsyJkzZ2jZsiWNGzcGwMfHhwULFvDKK6+QmZnJuHHjsLe3Z+zYsVWK3ahRI5YuXcp9993H5cuXMZvNnDp1igceeID77rsPgIiICL799tvrjqWmppKXl0dISAgAly9f5scffwSgS5cuADzwwAOcO3fumj4dHR3Jy8sjPDyc2rVrU1RUVL4Pg5eXFwBubm6UlJSQn59P3bp1cXFxAeDFF1/k/Pnz5ObmMnHiRAAsFgtdu3a94ThbtWoFgKurK+vWrWP79u2YzWasVus11+Xl5d1y7Ktj7dSpEzt37qRx48YcPnyYoKAgAKxWK9nZ2dfkkZGRQffu3QG4//77CQ4OJioqigMHDpCamlreLi8vD/j5bwCgSZMmFBcXX9N/ZWPKzc3lgQceAOCRRx7ho48+Ii0tjdOnTxMcHAzAhQsXOHnyJO7u7jcco4iIiIiIiNxYte5xYDKZyv9dv359CgsLyc3NBeDrr7/m/vvvZ+/evTRq1Ig1a9YwduxYFixYUN72RnscvPbaa0yYMIG33nqLBx98EMMwaNmyJRkZGZSUlAAwYcIEGjZseN0xFxcXmjRpwpo1a0hISCAwMJCOHTsCcPjwYeDnVyGuFjlMJhOGYbBr1y7OnDnDggULCA8Px2KxYPy/r13+cqwALi4uXLx4kYKCAgDmzJlDdnY2bm5uLF26lISEBMaMGVN+836zOVyzZg0dO3Zk3rx59O3b95p+bTYb9evXv+XY33//PQDffPMNrVu3xt3dnc6dO5OQkMC6devo168fLVq0uCYPDw8PDh06BMCpU6d4+eWXcXd35+mnnyYhIYGVK1fSt29fnJ2dK5wXoDz3ysbk5uZWvm/Dd999B4C7uzutW7dm/fr1JCQk4OvrS5s2bW44PhEREREREbm5al1x8Esmk4k5c+Ywfvx4TCYT9erV44033sBkMhEeHk5iYiJWq5WXXnoJgEcffZSQkBDWr19f4c3nwIEDCQsLo27duri5uZGfn0+DBg148cUXCQwMxGQy0bNnT5o1a1bhseDgYIKCgigrK6NZs2b069cPgCNHjjBixAiuXLnCq6++CsDDDz/M5MmTWbZsGUuXLmXYsGGYTCZatGhRXgj5b3Z2dsyePZvRo0djZ2dH27Zteeihh5g+fTohISEYhkGdOnWIjY2t0vz17NmTOXPm8NFHH+Hk5ESNGjUoKSmhffv2xMbG4uHhccuxt27dytq1a6lVqxaxsbE4Ozvz9ddfExAQQFFREX369MFsNl/Txs/Pj2nTphEYGEhZWRnTpk2jTZs2zJgxg8DAQAoLCwkICMDOrvKalYeHBxEREQwaNKjCMc2ePZtp06ZRu3ZtHBwcaNy4MZ6enjz22GP4+/tTUlKCt7d3eWFHREREREREbp/JuPoYV25q8eLFuLq64u/vX92p3HFBQUFERUXh4eFR3alcZ+PGjfTr148GDRoQFxeHg4MDoaGhtxzH19eXlJSUO5ChiIiIiIjIH19V74n+MCsObkdJSQmjRo267nirVq2IiYmphox+e6dPnyYyMvK64z4+PkyYMOGOxf4jc3FxYeTIkdSuXRsnJyfefPPN6k5JRERERETkrqUVB3LP0ooDERERERG5l1X1nqhaN0cUERERERERkT82FQ5EREREREREpFIqHIiIiIiIiIhIpVQ4kCpLSUlh3rx5d7yf4uJitmzZcsf7ERERERERkZtT4UD+cM6ePavCgYiIiIiIyB/En/pzjPL7++677xg5ciR5eXn4+/tTr149Nm7ciNVqxWQysWTJEgAmTpyIYRgUFxcTHR2Nl5dXhfEOHDjAW2+9hb29PbVq1eIf//gH8fHxHD9+nCVLljB8+HBeeeUVCgsLKSsrIywsjPr16/Paa6+RkJAAwOjRowkLC6OwsJC4uDhq1KhBixYtiImJwcHB4XebGxERERERkbuRCgdyS+zt7Vm9ejXZ2dmEhIQwcOBAVqxYQa1atZg1axa7d++mbt26ODs7Exsby/HjxykqKqo03qeffkq/fv0YMWIEO3fu5OLFi4wZM4a0tDRCQ0N566236Nq1KyNGjCAnJwd/f3927NhBSUkJ2dnZODg4kJ+fj5eXF3379mXTpk24uLiwcOFCtm7dypAhQ37H2REREREREbn7qHAgt6Rt27aYTCYaNmyIxWLBxcWFyMhI6tSpQ0ZGBh07dqR79+5kZmYybtw47O3tGTt2bKXxxowZQ3x8PCNGjKBx48Z4e3tTUlJSfj49PZ0BAwYA0LhxY8xmM+fPn2fQoEH861//ombNmvj6+pKXl0dubi4TJ04EwGKx0LVr1zs7GSIiIiIiIvcAFQ7klphMpvJ/X7p0iUWLFvH5558D8Pzzz2MYBnv37qVRo0asWbOGgwcPsmDBgvLXCv7be++9x7PPPktkZCTLly8nOTkZX19fbDYbAB4eHuzfv5+2bduSk5PDxYsXcXZ2pn///gQHB2NnZ8fq1aupXbs2bm5uLF26FCcnJ3bs2EHt2rXv+HyIiIiIiIjc7VQ4kNtmNpvx9vZm6NCh2NvbU7duXXJzc+nVqxfh4eEkJiZitVp56aWXKo3h7e3NjBkzqFWrFnZ2dsTExODi4kJpaSlz585l9OjRTJs2jY8//hiLxUJMTAz29vbY29vj6emJ1WrFbDYDMH36dEJCQjAMgzp16hAbG/t7TYWIiIiIiMhdy2QYhlHdSYhUB19fX1JSUqo7DRERERERkWpR1XsirTiQ30VoaCgXLly45pjZbGbZsmXVlJGIiIiIiIhUhQoH8ru4+plGERERERER+XOxq+4EpHrs2rWLpKSk6k6jXEpKCjt27Kj0/JQpU9i1a9d1x5OSkigtLb2TqYmIiIiIiNzTtOLgHtW9e/fqTuEavr6+t9Vu+fLl/P3vf/+NsxEREREREZGrVDj4k0lJSeGzzz7DYrFw9uxZhg8fzo4dO/jhhx+YPHkyP/30E9u3b+fKlSvUr1+fJUuWsGXLFg4cOMCCBQuIjIzE29ubWrVqkZGRgZ+fH5MmTaJJkyZkZWXx9NNP88MPP/Cf//yHHj16EB4eTlBQEFFRUXh4eJCYmMi5c+d49tlnb9quIllZWbz88su4ublx6tQpHnroIaKjo1m8eDGurq74+fkRHR3N999/j6urK9nZ2eX7ICQlJbFq1SoKCwuJiori2LFjnD17lkmTJjFnzhwmTpyIYRgUFxcTHR2Nl5fX7/nTiIiIiIiI3JVUOPgTunz5MmvWrOHDDz9k7dq1JCcns3fvXtauXUv79u1Zu3YtdnZ2jBo1ikOHDjFs2DC+/PJLpkyZQmlpKcOGDbtm58xTp06xZs0aLBYLvXv3ZteuXdSqVYuePXtWWgD4Ne0yMzNZvXo1tWrVok+fPpw9e7b83I4dOygoKOCdd94hLy+Pp556qvxcu3btGDduHCkpKaSkpBAVFcWyZcuIi4vjq6++wtnZmdjYWI4fP05RUdGvnGUREREREREBFQ7+lK4+SXdycsLDwwOTyUS9evUoLS3FwcGB8PBwateuzU8//YTVagUgJCSEoUOHVvipjRYtWuDk5ETNmjVxdXXF2dkZAJPJdN21v/x65620+6WWLVtiNpsBaNiwIcXFxeXnMjIy6NixIwANGjTA3d29/Fy7du0AcHV1xWKxXBOze/fuZGZmMm7cOOzt7Rk7duwNcxAREREREZGq0eaIf0KV3ZiXlpby6aefsnDhQmbOnInNZsMwDEpKSnj99deJiYkhOjqakpKSKsW7qmbNmuWrAv7zn/9Uud2t5g/wwAMP8O233wJw4cIFMjMzb9jOZDJhs9nYu3cvjRo1Ys2aNYwdO5YFCxbcVm4iIiIiIiJyLa04uIvY29tTq1Yt/Pz8gJ+f5ufm5jJv3jx69OjB0KFDyc3NZf78+bRp06bKcYcPH050dDRNmzalUaNGdyp9AHr06MGuXbvw8/PD1dWV++67DwcHh0qvf/TRRwkJCWHRokWEh4eTmJiI1WrlpZdeuqN5ioiIiIiI3CtMxi/XnotUs/T0dI4ePcrTTz9Nfn4+zzzzDJ999hk1a9b8zfvy9fWt8NUNERERERGRe0FV74m04kDuiKSkJD744IPrjoeHh/Pwww9X2q5JkybMmzePdevWUVZWRkRExB0pGoiIiIiIiEjVqHAgd8TQoUMZOnToLberXbt2+ecXRUREREREpPppc0QRERERERERqZQKByIiIiIiIiJSKRUO7iEFBQW8//77t9zuk08+IScn5w5kdGP79u3j6NGjAHTr1u13719ERERERERUOLinHDt2jJ07d95yu/Xr11NYWHgHMrqxd999l9zc3N+9XxEREREREfn/tDliNUpJSeHTTz/l8uXL5Ofn89JLL7F48WLuv/9+HBwciI6O5pVXXqGwsJCysjLCwsJ47LHH6N+/P48++ig//PAD9erVY8GCBTg4ODB79mxOnjyJzWZj4sSJdO7cmWeeeaY8XkFBAUePHiUpKYlVq1axZcsWnJ2d2bRpE5cvX+bFF1+8LsfPP/+cI0eOEBkZyeDBg8nMzCQyMpKysjL+/ve/s3DhQiZPnkzDhg3Jycmhe/fuTJo0iTNnzjBz5kyKi4txdHTk1VdfpUmTJhXOw8WLF68bp5OTE1988QWHDx+mdevWlJSU8PLLL3P69GmcnZ1ZtGgRFouF6dOnk5+fD8CMGTNo06YNPXv2xN3dHQ8PD6ZNm3ZHf0MREREREZG7nQoH1ezKlSu8/fbb5OXlMXjwYMrKyhg3bhxt27blrbfeomvXrowYMYKcnBz8/f3ZsWMHFouFAQMG4OPjQ2xsLElJSTg6OlK/fn1ef/118vPzCQwM5MMPP6SoqKg83t69e9m8eTNDhw4lJyeHDz/8kGHDhvHee++xZMmSCvPr0aMHXl5eREVF0bhxY3x9fYmIiOCLL76gc+fOODo6kp2dzerVq3FyciIgIIDDhw+zcuVKgoKCeOKJJ/jqq6+YN28e8+fPr7CPZcuWVTjOxx9/nP79+9O0aVOKioqYNGkSzZs3JygoiCNHjvDxxx/TpUsXAgICyMzMZOrUqSQmJnLmzBlSUlKoX7/+nfzpRERERERE7gkqHFQzHx8f7OzscHV1pW7duqSnp9OqVSsA0tPTGTBgAACNGzfGbDZz/vx57O3t8fHxAaBTp07s2rULOzs7Dhw4QGpqKgBWq5W8vKeNMrEAACAASURBVDyA8ni/9NxzzxEeHo6Pjw+urq64urreNFez2YyPjw+7d+8mJSWFcePGAeDp6YmzszMA3t7enDhxgrS0NJYvX86qVaswDAN7+8r/1Cob5y/Vq1eP5s2bA+Dq6sqVK1dIS0tjz549bNu2DYALFy4AUL9+fRUNREREREREfiMqHFSzw4cPA3Du3DkKCwtxcXHBzu7nrSc8PDzYv38/bdu2JScnh4sXL+Ls7IzVauXo0aN4enpy4MABWrduDYCbmxtjxozBYrGwbNmy8pv5q/Hs7Oyw2WwANGvWDCcnJ+Lj4xk0aNANczSZTBiGAcCQIUNYuXIl+fn5eHp6kpWVRXp6OleuXKFmzZqkpqby3HPP4e7uzsiRI+nUqRPp6ens27ev0viVjfOX/ZpMpuvaubu7M3DgQAYMGMD58+fZsmXLNeMVERERERGRX093WNXs3LlzjBgxgpCQEGbPnk2NGjXKz40ePZo9e/YwbNgwxo0bR0xMTPmT+5UrV+Lv709ubi5+fn74+fmRkZFBYGAgfn5+NGvW7Lob6JYtW5KWlsbatWuBn4sA+/fv5/HHH79hjg8//DCTJ0+moKCADh06cPLkyfIVAgAODg6EhYUxePBgevfujaenJ5GRkfzzn/8kMDCQyMhI2rRpU2n8ysbZoUMH5s2bR3p6eoXtxowZw7Zt2wgKCuKFF17ggQceuOE4RERERERE5NaZjKuPdOV3l5KSQkZGBhEREbfUrlevXmzbtg1HR8df1f+2bdtIS0sjLCysym1sNhv+/v6sXr0as9lMVlYW4eHhJCcn/6pcqoOvry8pKSnVnYaIiIiIiEi1qOo9kV5VuEctWLCAvXv3Eh8fD8COHTvKVyL80vDhw3nyyScBOHXqFKGhofj6+mI2m2+5z9DQ0PJ9CK4ym80sW7bs1gcgIiIiIiIivwutOJB7llYciIiIiIjIvayq90Ta40BEREREREREKqXCwS1KSUlh3rx5v2nMCxcu8Oyzz/L888//pnErU1BQwPvvv/+r42zYsOE3yOZ6e/fuZdKkSdccO3LkCEuWLLkj/YmIiIiIiEjlVDj4A0hLS6N58+a8/fbbv0t/x44dY+fOnb86zu+5N4GXlxehoaG/W38iIiIiIiLyM22OeBu+++47Ro4cSV5eHv7+/tSrV4+NGzditVoxmUzlT8YnTpyIYRgUFxcTHR2Nl5fXdbFKSkqYM2cOubm5LFq0iNOnT1NQUEBBQQHLly9n1apV7N+/H5vNRnBwMP369ePYsWPMmTMHAGdnZ15//XWcnJwqzHX79u2sXLkSe3t7GjVqRFxcHPHx8Rw9epSkpCQOHjx4W/1t2LCBCxcuEBUVRVRUVIV9BwUF0apVK06cOIFhGMTFxdGgQQNmzZrFTz/9RG5uLr169WLSpElMmTKlPI9Ro0YBcOXKFcaPH8/AgQNp3LgxmzdvJi4ujqeeeopOnTpx4sQJXFxcWLx4MaWlpUyePJnc3FyaNGnCvn372L1796/9qUVERERERO55WnFwG+zt7Vm9ejVLlixh3bp1ZGZmsmLFChITE2ndujW7d+8mNTUVZ2dnVq5cyaxZsygqKqowVs2aNZk2bRpdunRhwoQJAHTp0oXNmzfz7bffkpWVRWJiIuvXryc+Pp6LFy8yc+ZMZs+eTUJCAt27d2fVqlWV5vrBBx8watQoEhMT6dmzJ4WFhYwZM4YuXbowdOjQ2+5v7Nix1KtXr9KiwVWdOnUiISGBfv36sXz5cs6cOUPHjh1ZvXo177zzDps3by6/9moedevWpaioiDFjxuDv78/AgQOviXnq1CnCwsJISkoiLy+PQ4cOkZSURPPmzdm8eTOhoaGcP3++Kj+liIiIiIiI3IRWHNyGtm3bYjKZaNiwIRaLBRcXFyIjI6lTpw4ZGRl07NiR7t27k5mZybhx47C3t2fs2LFVjt+qVSvg51cYDh8+TFBQEABWq5Xs7GzS09OJjo4GoLS0lPvvv7/SWFOnTmX58uVs2LABd3d3+vTpc0f7+29dunQBfi4g7Ny5E2dnZw4dOsSePXswm82UlJRclwfA119/TZs2ba45f1X9+vVp0qQJAE2aNKG4uJj09HS6d+8OgIeHBw0aNKhyjiIiIiIiIlI5FQ5ug8lkKv/3pUuXWLRoEZ9//jkAzz//PIZhsHfvXho1asSaNWs4ePAgCxYsICEh4Zbiu7u707lzZ1599VVsNhtLly6lRYsWtGrVirfeeoumTZty4MABzp49W2mspKQkxo8fj4uLC7NmzeKTTz6hefPm2Gy2X91fVb7k+f333+Pm5sY333xD69atSUlJwcnJiZiYGE6ePElycnJ5nF/Oa48ePZg+fTrDhg2jU6dOFc7PLz344IMcPHiQPn368OOPP5Kfn3/T3EREREREROTmVDj4lcxmM97e3gwdOhR7e3vq1q1b/u5+eHg4iYmJWK1WXnrppVuO3atXL77++msCAgIoKiqiT58+mM1moqKiiIyMLN9T4bXXXqs0hre3N6NHj6ZOnTrUrl2bHj16UFJSQlpaGmvXrv1V/Xl4eBAREXHDr0xs3bqVtWvXUqtWLWJjYzl37hwvv/wy3377LTVr1uQvf/kLubm5FbZ1dXVl/PjxTJs2jRdffPGGczVo0CCmTJnCsGHDaNq0KY6Ojje8XkRERERERKrGZFTlsbHIbQgKCiIqKgoPD4873tc333xDUVERf/vb38jMzOSFF17g008/vWEbX19fUlJS7nhuIiIiIiIif0RVvSfSioPfUWhoKBcuXLjmmNls/tWfNSwpKSn/EsEvtWrVipiYmF8V+2ZOnz5NZGTkdcd9fHzuaL//rUWLFoSHh7NkyRKsViuzZs36XfsXERERERG5W2nFgdyztOJARERERETuZVW9J9LnGEVERERERESkUiociIiIiIiIiEil7orCQUpKyg139r8dixcvJjEx8TeN+d+OHTvGvn377mgfldm3bx9Hjx793fvt1q0b8PPGienp6XesnyFDhpCVlXXH4ouIiIiIiNwr7orCwZ/V9u3bOX78eLX0/e6771b6GUQRERERERGRq+6aryp89913jBw5kry8PPz9/alXrx4bN27EarViMplYsmQJABMnTsQwDIqLi4mOjsbLy+umsd98800OHDgAwDPPPMOIESOYMmUKNWvWJDs7m9zcXN58803atWvHli1b2LhxI/Xq1cPBwYH+/fvj6+t7XcycnBy2bt2Kg4MD7dq1w2KxEBcXR40aNWjRogUxMTG8//77vPvuu9hsNiZMmMCsWbN4+OGHyczM5LHHHuPSpUukpqbSqlUr5s6dW2n+U6dO5eTJk1gsFoYPH07r1q354osvOHz4MK1bt2bnzp1s376dK1euUL9+fZYsWcLUqVMZMGAAPXr0ID09nbfeeoupU6cydepU7O3tsdlszJ8/nyZNmlTYZ1paGm+++SZlZWXk5+cTFRVFp06drrvu4sWLvPLKKxQWFlJWVkZYWBiXL1/m3//+N7NmzWLFihV88803xMfH895773H69Gn+53/+h5kzZ1JcXIyjoyOvvvoqTZo0IS4uji+++AI3Nzfy8/Nv+ruKiIiIiIjIzd01hQN7e3tWr15NdnY2ISEhDBw4kBUrVlCrVi1mzZrF7t27qVu3Ls7OzsTGxnL8+HGKiopuGvezzz4jKyuL5ORkrFYrAQEBdOnSBYCmTZsSExNDcnIySUlJTJw4kVWrVvGvf/2LmjVrMnz48ErjNm7cmGeffRZXV1ceeugh+vbty6ZNm3BxcWHhwoVs3boVe3t76tatW/65xuzsbNatW0fDhg3561//ypYtW5g5cya9e/fm4sWL1K1b97p+CgsL2bdvH8nJyQB8+eWXtG/fnscff5z+/fvj5uZGQUEBa9euxc7OjlGjRnHo0CEGDx5MYmIiPXr04J133mHQoEH8+9//xtvbm1deeYX9+/dz6dKlSgsHx48fJzIykjZt2vD++++TkpJSYeFg2bJldO3alREjRpCTk4O/vz8fffQR//jHP4CfX6k4f/48VquVnTt3Mn78eN566y2CgoJ44okn+Oqrr5g3bx7BwcHs27ePd955h6KiIp566qmb/rYiIiIiIiJyc3dN4aBt27aYTCYaNmyIxWLBxcWFyMhI6tSpQ0ZGBh07dqR79+5kZmYybtw47O3tGTt27E3jpqen8+ijj2IymXBwcKBDhw7l7+ZfXa3g5ubGN998w48//oiHhwe1atUC4OGHH65S7nl5eeTm5jJx4kQALBYLXbt25S9/+QutWrUqv87Z2ZmmTZsCULt2bVq3bg2Ak5MTxcXFFcY2m81MmzaNmTNnUlhYyMCBA685b2dnh4ODA+Hh4dSuXZuffvoJq9VK586dmTNnDnl5eXz55ZeEh4djs9lYuXIlL7zwAk5OTkyaNKnSMTVq1IilS5dy3333cfnyZcxmc4XXpaenM2DAAODnYorZbKawsJBWrVqRmpqKvb09HTp0YN++fZw5cwYPDw/S0tJYvnw5q1atwjAM7O3tyczMpH379tjZ2WE2m3nwwQerNPciIiIiIiJyY3dN4cBkMpX/+9KlSyxatIjPP/8cgOeffx7DMNi7dy+NGjVizZo1HDx4kAULFpCQkHDDuB4eHqSkpBAcHExpaSkHDx7k2Wefva5PgJYtW5KRkYHFYqFmzZqkpqbi7u5+w5xtNhv169fHzc2NpUuX4uTkxI4dO6hduzZnzpzBzs7umutvVW5uLocPH+af//wnxcXFPPHEE/zP//wPJpMJwzA4evQon376KVu2bOHKlSv4+vpiGAYmk4mBAwcyZ84cunXrhoODAx999BGPPPIIoaGhfPDBB6xatYo33nijwn5fe+015s2bh4eHB4sWLSI7O7vC6zw8PNi/fz9t27YlJyeHixcv4uzsTJ8+fZg7dy69e/emRYsWxMXF0bVrVwDc3d0ZOXIknTp1Ij09nX379tG6dWs2btyIzWbDYrFU294RIiIiIiIid5u7pnDwS2azGW9vb4YOHVq+3D83N5devXoRHh5OYmIiVquVl1566aaxevbsyddff83QoUMpLS2lb9++tGvXrsJrGzRowIsvvkhAQADOzs4UFxdjb1/5FLdv357Y2Fg8PDyYPn06ISEhGIZBnTp1iI2N5cyZM7c9B1c1bNiQs2fP4ufnh52dHSNHjix/ij9v3jwWLFhArVq18PPzK7/+6qaJvr6+9OjRg//zf/5Peb6RkZEsW7YMm83G1KlTK+134MCBhIWFUbdu3RvuOTB69GimTZvGxx9/jMViISYmBnt7e3r27Mm0adOYPXs2bm5uhIWFERUVBUBkZCRRUVEUFxdjsViYPn06Xl5edO/enUGDBtGoUSNcXFx+9dyJiIiIiIgImAzDMKo7ibuF1Wpl5cqVjB07FsMwGDZsGJMmTcLHx6e6U7stOTk5TJ48mXXr1lV3KneEr68vKSkp1Z2GiIiIiIhItajqPdFdueLgVoSGhnLhwoVrjpnN5vINCW+Fvb09V65c4dlnn8XBwQFvb2+aNm1KUFDQddf6+PgwYcKE2877v+3YsYO1a9ded3z48OE8+eSTtxxv+/btLF68uPwpf0VKSkoYNWrUdcdbtWpFTEzMLfcpIiIiIiIifzxacSD3LK04EBERERGRe1lV74nsbnqFiIiIiIiIiNyz7urCwZEjR1iyZEl1p1Gh06dPs3PnzltuN2XKFHbt2nUHMvptTZo0iZKSEk6dOkXfvn2JjIzktdde4/Tp07cUZ9++fRw9ehT4+bUSERERERER+X3d1XsceHl54eXlVd1pVGjPnj1kZGTQq1ev6k7ljoiLiwPgwIED9OjRgylTptxWnHfffZf/2969x/V8/4//v73qVVilQiREORS+SpNhTm+0vZXZ3nzeDjVZ2HLeYixnRSJSLFORUDlPXJyyjRnG2ylhGLUkS7ZKGFq9enk9f3/4eV1mHW2mZvfrX3keHo/743Gv9Ly/Hs/n08PDA0dHx2pbBBJCCCGEEEKIl1m1KxwkJiayfft2dDod3t7erF+/HgMDAzp06MCUKVPIycnh448/BqBly5akpaURHx9P7969SUpKokaNGoSGhmJvb0+jRo3YvHkz4eHh9OrVC3t7e5o3b86IESOYPXs2RUVF1KhRg/nz59OwYcNS48nPz2fKlCloNBrs7Ow4ceIEX331Van9DRw4kKVLl3LmzBl0Oh0+Pj64u7uzYcMGdu7ciYGBAe3atWP69OmsWrWKwsJCXFxc6NOnT4l+s7Ky+Oijj7CysuLnn3+mR48eTJo0CYAtW7YQExPDgwcPCAgIwMnJidjYWPbu3YtarcbV1ZWpU6cSERFBVlYWt2/fJjs7m+nTp9O9e3dOnTpFeHg4hoaGNGnShHnz5mFkZFTq2N9991327duHSqVi3rx5dOnSBVtbW4KCggCwsLAgODiYy5cvExoaipGREYMHD+bTTz8lISGBqKgoCgsLsbW1JSkpiYCAACwtLfH39+f+/fsoikJISAg1a9bUv2IxNzcXPz8/rK2tOXr0KJcuXaJFixYMGjSIY8eOcfnyZebPn4+hoaE+fzqdjo8//hhra2t+/PFH2rVrR2Bg4PP6thRCCCGEEEKIf6xqVzgAqF27NgsXLsTLy4vt27dTq1Ytpk6dyrFjx/jqq6/o378/gwcPZvfu3aSlpVWqzVu3bpGYmIilpSV+fn54e3vTs2dP/ve//xEaGsrSpUtLPS8qKoo+ffrw7rvvcuzYMY4dO1ZmH4cPHyYrK4tNmzZRVFTE4MGD6dq1K4mJicydOxcnJyc2btyIoij4+vpy7dq1UosGT9y8eZM1a9ZgZmaGl5cXly5dAqBt27aMGzeOxMREEhMTqVGjBklJSWzevBm1Ws3EiRM5dOgQAMbGxsTExHDs2DFiY2Pp1q0bs2fPZuPGjdStW5dly5axY8cOBg8eXKL/OnXq4ODgwJkzZ3B2dubkyZPMmDEDLy8vgoODadGiBdu2bSMmJobXX3+doqIitm3bBsCnn35K3bp19eP08vIiKSkJgJUrV9K7d288PT05e/YsFy5coF69eowYMYJOnTpx9uxZIiIiWLt2Ld27d8fDwwMbGxt9XLNmzWLBggW0bt2aAwcOsGjRIj755BOuX7/OmjVrqFWrFm5ubuTm5mJlZVXBd4YQQgghhBBCiPJUy8KBnZ0dN27cID8/H19fXwAePnzIjRs3yMrKYujQoQB06tSJrVu3lji/tBdFWFpaYmlpCUBqairR0dHExMSgKApqddnTkJ6ezoABAwBwdXUt9Zgn/aWmpnLp0iX96xe1Wi03b95k4cKFxMbGsnjxYtq3b19qfKVxdHTEwsICACcnJzIyMoDHhQOAevXqUVhYyLVr13B2dtavGnB1ddUXVJ7cqmFtbY1GoyE/P5+cnBz8/PwAKCws5PXXXy8zhsGDB7Njxw5yc3Pp3bs3arWa9PR0/af5xcXFNGvWDHict8rIyMjgv//9LwCvvvoqr776KmlpaURGRvL555+jUqnQarVlnp+Tk6MfV8eOHfVFH1tbW0xNTQGwsrKiqKioUvEIIYQQQgghhChbtSwcGBgY0LhxYxo2bEhsbCxGRkYkJibSunVrsrKySE5OxtHRkYsXL+rPMTY2Jicnh8aNG3PlyhWaN29eos0n7O3tGTlyJK+++irp6emcPn26zFhatWpFSkoKrVu35ty5c+X2Z29vT6dOnfRL51euXEmTJk1YtmwZgYGB1KhRg1GjRpGSkoKBgQE6na7ceUhPT+fXX3/F2NiYCxcu8H//9398++23qFSqp46zt7dn7dq1aLVaDA0NOX36NP/5z3+4cuVKiWMtLS2xtrZm5cqVmJmZcfDgQV555ZUyY+jSpQtLlizh559/Zu7cucDjAkFISAg2NjYkJyeTm5tbYo7L07x5c7777jscHR05ffo033zzDZmZmQwaNIiePXuyfft2duzYAYBKpSpRaKlfvz5XrlzRn/+kcPH7sQohhBBCCCGE+POqZeEAHi+T9/Hxwdvbm0ePHtGoUSPc3d3x9fVl2rRpfPHFF5ibm+uPf//99/H19aVRo0bUrl273Lb9/f3199MXFhYyc+bMMo/94IMP+OSTT0hKSqJ+/fr61Qml9de7d29OnTqFl5cXBQUFuLm5YWpqioODA15eXpiYmNCgQQOcnZ0xNTUlMjKStm3b0q9fv1L7NjIy4qOPPiIvL4++ffvi6OhY6nEODg64u7vj6emJTqejQ4cOuLm56d9G8FsGBgbMnDkTX19fFEXBxMSExYsXlzl+lUrFv//9b44fP46trS0AAQEB+Pv7o9VqUalULFiwgJycnDLb+L0xY8YwY8YMdu3aBUBwcDDnz59n8eLFrFq1Cmtra+7cuQOAs7MzoaGhNG7cWH9+UFAQ8+fPR1EUDA0NCQ4OrnTfQgghhBBCCCGejUqp7Lr5aig9PZ2AgADi4+P/sj4OHz6MpaUlTk5OHD9+nKioKOLi4v6y/p7Iyspi8uTJpd6KIZ6PgQMHkpiYWNVhCCGEEEIIIUSVqOw1UbVdcfCiTZgwgXv37j21zdTUlClTpjBjxgwMDQ3R6XTlrk74I7Zs2cKePXtKbJ88efJz7ac82dnZ+Pv7l9jesWNHPvzwwxcWhxBCCCGEEEKI6udvveJAiD9DVhwIIYQQQggh/skqe01UuafZCSGEEEIIIYQQ4h+p2hcOEhMTCQ0N/cPnb9myheLi4ucY0d9LdnY2X3/9dVWH8Uz+6TkTQgghhBBCiOqk2hcO/qzo6OgKX3v4Mjtx4gRnz56t6jCeyT89Z0IIIYQQQghRnfwtHo54/vx5Ro4cSX5+Pp6enjRu3Jhly5ZRo0YNLCwsCA4ORqvV4ufnh6IoFBUVERgYyMWLF8nNzWXSpEmsXLmy1LanTZvG3bt3uXv3LtHR0cTExHDmzBl0Oh0+Pj64u7uzYcMGdu7ciYGBAe3atWPWrFlMmzYNRVG4desWBQUFhISE0Lx5c2JjY9m7dy9qtRpXV1emTp1KREQEWVlZ3L59m+zsbKZPn0737t0JDw/n5MmTaLVa3nzzTXx9fbl69SpBQUEA+rGZmZlVGHtkZCShoaH89NNP5OTk0Lt3bz788ENWrVpFYWEhLi4uNG7c+JnaLm188fHx7NmzB5VKhYeHB8OHDy8xh8uXL+fChQsUFxczceJE3NzcWLp0aYl59fb2xtHRkbS0NB48eMDy5cs5fvy4PmcRERHMmTPnqTFNmjSJzMxMpk2bhlqtplGjRty8eZP4+HiSkpJYt24dBgYGdOjQgSlTpjyH7z4hhBBCCCGE+IdTqrnt27crPj4+ik6nU3788Uelb9++Sq9evZSffvpJURRFWbdunbJo0SLl0KFDysSJE5Vff/1V+e6775QzZ84oiqIovXr1UgoLC8ts39/fX1m7dq2iKIryzTffKH5+foqiKEphYaHy9ttvK/fu3VMGDhyonD9/XlEURdmwYYNSXFys+Pv7KxEREfrzRo8erVy5ckX573//q2g0GkWn0ynjx49Xvv76a+XTTz9VZs2apSiKonz77bfKyJEj9bH9+OOPSlFRkbJp0yZFURRl0KBBSlpamqIoirJ161YlLCysUrH/+OOPytatW/Wxv/baa/r5W7JkyR9q+/fjS0tLU4YOHapotVpFq9Uq3t7eSnp6+lNxfPnll/o5vHv3rhIeHl7mvA4bNkzZtWuXoiiKEhYWpkRHR+vnpbCwsMwxjRs3Tvnmm28URVGULVu2KMOGDVPu3LmjuLu7KwUFBYqiKMqUKVOUb7/9tszxKYqiDBgwoNz9QgghhBBCCPEyq+w10d9ixUGbNm1QqVRYWVlx69YtbG1tadCgAfD4lYFhYWFMnTqV69evM27cONRqNWPHjq10+3Z2dgCkpqZy6dIlvL29AdBqtdy8eZOFCxcSGxvL4sWLad++Pcr//yKKzp07A+Di4kJwcDDXrl3D2dkZIyMjAFxdXUlLSwOgdevWAFhbW6PRaABYsmQJS5cuJS8vj+7duwOQnp5OYGAgAMXFxTRr1qxSsVtYWPDdd99x4sQJTE1N9X381rO2/fvxpaamkp2djY+PDwD37t0jMzPzqTgyMjJo3749AObm5vj5+bF69epS5xUe5/bJvOTl5T3Vf1ljSk9Px8XFBYAOHTqwe/dubty4QX5+Pr6+vgA8fPiQGzdu0LVr13LHKIQQQgghhBCifH+LwoFKpdJ/bWlpyYMHD8jJyaF+/fqcOnWKZs2acfLkSerXr09sbCwpKSmEhYURHx+PSqWq8H75J+3b29vTqVMn5s+fj06nY+XKlTRp0oRly5YRGBhIjRo1GDVqFCkpKQBcunQJV1dXzp49S8uWLbG3t2ft2rVotVoMDQ05ffo0//nPf7hy5cpTYwDQaDTs37+fsLAwADw8POjXrx92dnaEhIRgY2NDcnIyubm5lYo9MTERMzMz5s2bR2ZmJlu3bkVRFAwMDPTjf9a2SxtfixYtiImJQaVSsW7dOhwcHPjiiy+emsP9+/cDcP/+ffz8/PDy8ip1Xssbk06nK3NMrVq1IiUlhZ49e3L+/HkAGjduTMOGDYmNjcXIyIjExER9sUYIIYQQQgghxB/3tygc/JZKpSIoKIiJEyeiUqkwNzdn4cKFqFQqJk+ezKZNm9BqtYwfPx54/Km/r68vcXFxJS7ef693796cOnUKLy8vCgoKcHNzw9TUFAcHB7y8vDAxMaFBgwY4OzuTmJjIkSNHOHjwIDqdjoULF9KkSRPc3d3x9PREp9PRoUMH3NzcuHLlSom+jI2NMTc3Z/DgwdSsWZOuXbtiY2NDQEAA/v7+aLVaVCoVCxYsqNS8dOnShY8//phz585hbGxM06ZNycnJoVWrVkRGRtK2bdtnbru08XXp0gVPT080Gg1OTk76lR9P9OnTh//97394enryTsg3ogAAF/NJREFU6NEjxo8fT48ePUqd17I8ydmcOXNKHdOUKVOYMWMGsbGxmJmZoVarqVOnDj4+Pnh7e/Po0SMaNWqEu7t7peZOCCGEEEIIIUTZVMqTdffimUybNg0PDw969OhR1aH8Jarz+Hbt2oWzszNNmzZl27ZtnD17loULFz5zOwMHDiQxMfEviFAIIYQQQgghqr/KXhP97VYc/BEajYZRo0aV2G5nZ8e8efOqIKLK+ytjL6/t6qxhw4ZMmjSJWrVqYWBgQHBwcFWHJIQQQgghhBAvLVlxIP6xZMWBEEIIIYQQ4p+sstdEBi8gFiGEEEIIIYQQQvxNSeFACCGEEEIIIYQQZSq3cJCYmEhoaOif6mDLli0UFxf/qTaqq+zsbL7++msAFixYQHZ29gvt/0mfd+/eZffu3cDjhxoeOXLkqeNyc3MJCAh4bv0uWbKE/v37c/LkyT/Vjre3N+np6c8pKiGEEEIIIYQQf4W/fMVBdHQ0Op3ur+6mSpw4cYKzZ88CMHPmTGxsbF5o/0/6vHr1qr6AURorK6vnWjjYv38/mzZtolOnTs+tTSGEEEIIIYQQ1VOFb1U4f/48I0eOJD8/H09PT4YMGcKxY8dYtmwZNWrUwMLCguDgYLRaLX5+fiiKQlFREYGBgVy8eJHc3FwmTZrEypUrS21/2rRp3L17l7t37xIdHU1MTAxnzpxBp9Ph4+ODu7s7GzZsYOfOnRgYGNCuXTtmzZrFtGnTUBSFW7duUVBQQEhICM2bNyc2Npa9e/eiVqtxdXVl6tSpREREkJWVxe3bt8nOzmb69Ol0796d8PBwTp48iVar5c0338TX15erV68SFBQEoB+bmZlZibgfPXrEqlWrKCwsxMXFhXXr1hEQEMC+ffvIzMzkzp073L17l3fffZcvv/ySjIwMQkJCaN++PfHx8ezZsweVSoWHhwfDhw8vdW7Wr1+PVqtl1KhRzJkzB2NjY2bNmkVkZCSNGzdm69atBAQEEBUVxZUrV9iyZQvweJVHTEwMDx48ICAggDp16jB58mS2bt1K//79ee2117h69SoqlYqVK1c+Nb6ffvqJgIAAioqKyM3Nxc/PDzc3N/3+FStWkJOTw+jRo1mzZg2fffbZU/mys7MjPDyc6Oho9u7dS1RUFLt37yY5OZmdO3cyf/78p8b42WefkZeXx6+//kpYWBhNmjRh0aJFJCcnA/DWW2/x3nvvPfV6yCNHjrBv3z4WLVpEr169sLe3p3nz5ri6urJ69WrUajX169cnPDwcAwO5G0cIIYQQQggh/owKr6rUajVr1qxhxYoVrF+/HkVRmD17NitWrCAhIYGOHTsSGRnJhQsXsLCwYPXq1cyZM4eCggIGDRqElZUV4eHh5fbRuXNnNm/ezLlz58jKymLTpk3ExcURFRXFL7/8QmJiIrNnz2bLli3Y29uj1WoBaNKkCXFxcUycOJElS5Zw9epVkpKS2Lx5M5s3byYzM5NDhw4BYGxsTExMDDNnzmTdunUA7N69m9DQUDZu3Ejt2rUBmD17NnPnziU+Pp4ePXoQExNTasyGhob4+vry1ltv0adPn6f21axZkzVr1vDvf/+bw4cPExUVha+vL3v37uWHH35g3759bNy4kQ0bNnDgwAGuXbtWah9vvPEGR48eBSAjI4Pz588DcPToUXr16qU/bsyYMXTu3JkhQ4YA0LZtW+Li4hg2bFiJJ2Q+fPiQfv36kZCQQP369Uvc1nDt2jVGjBjB2rVrmTdvHhs2bHhq/4QJE7CysiI2NpaTJ0+WyJeNjQ3Z2dloNBqOHDmCgYEBeXl5HDx4kDfeeKPEGHv27ElcXBw9evRg//79HDp0iKysLLZu3crGjRvZs2cPV69eLXV+AG7dukVoaCgzZsxgz549jBo1ik2bNtGrVy8ePHhQ5nlCCCGEEEIIISqnwhUHbdq0QaVSYWVlRWFhIXfu3MHU1JQGDRoA0LFjR8LCwpg6dSrXr19n3LhxqNVqxo4dW+kg7OzsAEhNTeXSpUt4e3sDoNVquXnzJgsXLiQ2NpbFixfTvn17nrxBsnPnzgC4uLgQHBzMtWvXcHZ2xsjICABXV1fS0tIAaN26NQDW1tZoNBrg8b36S5cuJS8vj+7duwOQnp5OYGAgAMXFxTRr1qzS4/jtnAGYmZnRokULAMzNzSkqKiI1NZXs7Gx8fHwAuHfvHpmZmdjb25dox8bGhsLCQi5cuEDz5s25desWFy5cwMzMDFNT0zL7b9u2LQD16tWjsLCwzPgaNmxIUVHRU/usrKyIjIzk888/R6VS6Ys0pSkrX926dePEiRPcunWL/v37c/z4cZKTk5k0aVKJNv7f//t/+ljz8vJIT0/H1dUVlUqFkZERzs7OJZ6D8Ns3iFpaWmJpaQnA9OnTiY6OJiEhAXt7+6dWSgghhBBCCCGE+GMqXHGgUqme+relpSUPHjwgJycHgFOnTtGsWTNOnjxJ/fr1iY2NZezYsYSFhenPr+gZB0/6sLe3p1OnTsTHx7N+/Xrc3d1p0qQJW7duJTAwkISEBL7//ntSUlIAuHTpEgBnz56lZcuW2Nvbc+HCBbRaLYqicPr0aX1R4vfj0Gg07N+/n7CwMOLi4tixYwc3b97Ezs6OkJAQ4uPjmTp1Kv/617/KnjwDg1LH9vu+fsve3p4WLVoQFxdHfHw8AwcOxMHBoczje/bsyZIlS+jWrRtdu3YlKCioxAXx7+Mor/+K9i9fvpx33nmHJUuW0KlTp6cu0ksbS2n5cnNzY/Xq1Tg4ONCtWzcSEhKwtbXVF3TK07x5c/1tCsXFxaSkpNC0aVOMjY3Jzc0F4PLly0+N/YktW7YwceJEEhISAPjqq68q7E8IIYQQQgghRPkqXHHweyqViqCgICZOnIhKpcLc3JyFCxeiUqmYPHkymzZtQqvVMn78eODxp/6+vr7ExcVVeEHbu3dvTp06hZeXFwUFBbi5uWFqaoqDgwNeXl6YmJjQoEEDnJ2dSUxM5MiRIxw8eBCdTsfChQtp0qQJ7u7ueHp6otPp6NChA25ubly5cqVEX8bGxpibmzN48GBq1qxJ165dsbGxISAgAH9/f7RaLSqVigULFpQZb6tWrYiMjNR/wl8Zjo6OdOnSBU9PTzQaDU5OTvrVG6V58803WbFiBZGRkeTk5LBo0SKioqKeOsbW1pbU1FT9LRh/Rt++fVm8eDGrVq3C2tqaO3fulHlsWflycXEhIyOD999/H0dHR7Kzs/nggw8q1X+vXr04deoUQ4YMobi4mL59+9K2bVsGDRrEjBkz2L17d5mrQJycnBg9ejQmJia88sor5RZ9hBBCCCGEEEJUjkop7yPlauy3D8sT4o8YOHBgiWdACCGEEEIIIcQ/RWWviZ55xcEfodFoGDVqVIntdnZ2zJs370WE8Ie9iNgPHjxY6mqB4cOHl/pAQSGEEEIIIYQQ4kX52644EOLPkhUHQgghhBBCiH+yyl4TyUvuhRBCCCGEEEIIUSYpHAghhBBCCCGEEKJML0XhIDExkdDQ0OfaZkREBJs2bXqubVbWpUuXWL58ebnHJCYmcvDgwUq191fMz28tWLCA7OzsMvefPn261DdbCCGEEEIIIYSo/l7IwxHFszl06BC9evUq95iBAwe+oGgqNnPmzHL3b9++HQ8PDxwdHV9QREIIIYQQQgghnpeXpnBw/vx5Ro4cSX5+Pp6enpibm7Nhwwa0Wi0qlYoVK1YA4Ofnh6IoFBUVERgYSOvWrcts88CBAyQlJVFYWMisWbNwcnKia9euHDt2DIBJkyYxdOhQ0tPTSUpKAiAzM5OuXbvSrl27EtvmzZvH3LlzyczMRKfT4efnR6dOnXjrrbdo1qwZRkZGhIeHc/HiRdzc3Bg9ejTR0dHs3buXqKgodu/eTXJyMjt37qR+/frUq1cPe3t7QkNDMTIyYvDgwdjY2BAeHo6hoSFNmjQp8eaH+Ph49uzZg0qlwsPDg+HDh/Pll1+yevVq1Go19evXJzw8nJSUFEJCQlCr1dSqVYvly5djampa6jx5e3sTEBDAvn37yMrK4vbt22RnZzN9+nQsLS05evQoly5dokWLFpw/f55169ZhYGBAhw4dmDJlChEREaSkpFBQUMCCBQv44osvOHDgAI8ePcLT05OhQ4cSGxvL3r17UavVuLq6MnXqVCIiIsjMzOTOnTvcvXuXd999ly+//JKMjAxCQkJo3779n/6+EkIIIYQQQoh/upemcKBWq1mzZg03b97E19eXt99+m1WrVlGrVi3mzJnDt99+S+3atbGwsGDx4sX88MMPFBQUlNtmo0aNmDdvHmlpaXzyySfs2LGj1OO8vLzw8vLiu+++Y8GCBUybNg1zc/MS27Zt24alpSXBwcHcuXOHYcOGsXfvXgoKChg3bhxt2rQhLy+PunXr4ujoSHZ2NhqNhiNHjmBgYEBeXh4HDx7kjTfe4Pz58/r+i4qK2LZtG4qi0LdvXzZu3EjdunVZtmwZO3bsQK1+nOYffviBffv2sXHjRgBGjBhBt27d2LNnD6NGjaJv377s3LmTBw8ecODAAdzd3Xnvvff4+uuv+eWXX8osHPyWsbExMTExHDt2jNjYWNasWUP37t3x8PDglVdeISIigu3bt1OrVi2mTp2qL8LY29sza9YsLl++zJEjR9i2bRuPHj0iLCyMq1evkpSUxObNm1Gr1UycOJFDhw4BULNmTdasWcOqVas4fPgwUVFRbN++nb1790rhQAghhBBCCCGeg5emcNCmTRtUKhVWVlYUFhZSt25d/P39MTEx4dq1a7Rv354ePXpw/fp1xo0bh1qtZuzYseW22bFjRwBatmxJbm5uif2/fZNleno6c+fOJTIyEnNz81K3paamkpyczIULFwDQarXk5+cDYGdnB8A333xDz549AejWrRsnTpzg1q1b9O/fn+PHj5OcnMykSZOeKhw8OTc/P5+cnBz8/PwAKCws5PXXX6dp06YApKamkp2djY+PDwD37t0jMzOT6dOnEx0dTUJCAvb29ri5uTFmzBiioqJ47733aNCgAU5OTpXKw5MVHNbW1mg0mqf23bhxg/z8fHx9fQF4+PAhN27ceGoMGRkZODk5YWhoiKGhIdOmTSMpKQlnZ2eMjIwAcHV1JS0tDXicdwAzMzNatGgBgLm5OUVFRZWKVwghhBBCCCFE+V6KhyMCqFQq/df379/n008/JTw8nKCgIGrUqIGiKJw8eZL69esTGxvL2LFjCQsLK7fNJxf4V69excbGBnh8sf/w4UM0Gg0//PADADdv3mTy5MksWbKEBg0alLnN3t6efv36ER8fz+rVq+nbty8WFhYAGBg8TsXx48fp2rUrAG5ubqxevRoHBwe6detGQkICtra2+gvoJ56ca2lpibW1NStXriQ+Pp4xY8bQuXNn/XH29va0aNGCuLg44uPjGThwIA4ODmzZsoWJEyeSkJAAwFdffcWuXbsYMGAA8fHxtGzZkq1btz5zHn67TVEUGjduTMOGDYmNjSU+Pp5hw4bpVwU8GYO9vT2XL19Gp9NRXFzMiBEjsLOz48KFC2i1WhRF4fTp0/pCQ2n9CSGEEEIIIYR4fl6aFQe/ZWpqipOTE0OGDEGtVlO7dm1ycnLo3bs3kydPZtOmTWi1WsaPH19uO1lZWQwfPhyNRqN/VsDw4cMZMmQIjRs31hcTAgMDKSwsJDAwEEVRaNiwIXfv3i2xLSgoiFmzZjFs2DAePHiAl5eX/oIZQKPRUFxcjImJCQAuLi5kZGTw/vvv629d+OCDD8qM18DAgJkzZ+Lr64uiKJiYmLB48WJu3boFgKOjI126dMHT0xONRoOTk5N+NcHo0aMxMTHhlVde4V//+hc3btxg1qxZ1KpVCwMDgxLPSngWzs7OhIaGsmzZMnx8fPD29ubRo0c0atQId3f3p45t3bo13bt3x9PTE51Oh6enJ46Ojri7u+u3dejQATc3N3lTgxBCCCGEEEK8ACrlt+vthfgHGThwIImJiVUdhhBCCCGEEEJUicpeE72UKw6exYQJE7h3795T20xNTYmMjKyiiKqn7Oxs/P39S2zv2LEjH374YRVE9OfdvHmzWr3WUgghhBBCCCFepJs3b1bqOFlxIIQQQgghhBBCiDK9NA9HFEIIIYQQQgghxPMnhQMhhBBCCCGEEEKUSQoHQgghhBBCCCGEKJMUDoQQQgghhBBCCFEmKRwIIYQQQgghhBCiTFI4EEIIIYQQQgghRJnUVR2AEH81nU5HQEAAV69exdjYmKCgIJo2barfv3XrVjZv3oxarWbs2LH06tWrCqMV5akolwD5+fl4enqya9cuatSoUUWRiopUlMt169axd+9eAHr27MmECROqKlRRCRXlc8OGDSQmJqJSqRg5ciQeHh5VGK0oT2V+z+p0Onx9fenTpw+enp5VFKmojIryGRQUxNmzZzExMQFg5cqVmJmZVVW4ohwV5fLw4cN89tlnKIpC27ZtmTt3LiqVqgojFuUpL5/ff/89wcHB+mPPnTvHZ599Ro8ePaoq3McUIV5yX3zxheLv768oiqKkpKQoY8aM0e/LyclR3nrrLaWoqEj55Zdf9F+L6qm8XCqKohw5ckR55513FBcXF6WwsLAqQhSVVF4ub9y4oQwYMEDRarWKTqdThgwZonz//fdVFaqohPLyefv2baVfv36KRqNR7t+/r/To0UPR6XRVFaqoQEW/ZxVFUZYuXaoMGjRI2bhx44sOTzyjivI5dOhQ5fbt21URmnhG5eXy/v37Sr9+/fS5XLVqleS1mqvM71pFUZR9+/YpkydPfpGhlUluVRAvveTkZLp37w5A+/btuXjxon7fhQsXcHFxwdjYGDMzM2xtbbly5UpVhSoqUF4uAQwMDFi7di0WFhZVEZ54BuXl0trampiYGAwNDVGpVGi1Wlk9Us2Vl886deqwc+dOjIyMyMvLo0aNGvIpWDVW0e/Z/fv3o1Kp9MeI6q28fOp0OjIzM5kzZw5Dhw7l888/r6owRSWUl8uUlBRatWpFSEgIXl5e1KtXjzp16lRVqKISKvpdC1BQUEBERAQzZ8580eGVSgoH4qX34MEDTE1N9f82NDREq9Xq9/12SZ6JiQkPHjx44TGKyikvlwBdu3bF0tKyKkITz6i8XBoZGVGnTh0URSEkJIQ2bdpgZ2dXVaGKSqjoZ1OtVpOQkMCQIUN4++23qyJEUUnl5TI1NZU9e/bw0UcfVVV44hmVl8+CggKGDRvGkiVLiImJYePGjfLhSTVWXi7v3LnDyZMnmTJlCqtXr2b9+vVkZGRUVaiiEir6fxPg888/p2/fvtWmCCSFA/HSMzU15eHDh/p/63Q61Gp1qfsePnwo9/ZVY+XlUvy9VJTLoqIipkyZwsOHD5k7d25VhCieQWV+NocNG8bRo0c5ffo0J06ceNEhikoqL5c7d+7k559/5r333mPHjh2sW7eOI0eOVFWoohLKy2etWrUYPnw4tWrVwtTUlM6dO0vhoBorL5cWFha0a9cOKysrTExMcHV15fvvv6+qUEUlVOb/zd27dzNo0KAXHVqZpHAgXnqvvvqq/g+bc+fO0apVK/0+JycnkpOTKSoq4v79+6Snpz+1X1Qv5eVS/L2Ul0tFURg3bhwODg7MmzcPQ0PDqgpTVFJ5+bx27RoTJkxAURSMjIwwNjbGwED+/KiuysvlJ598wrZt24iPj2fAgAH4+PhU/cO6RLnKy+f169fx9PTk0aNHFBcXc/bsWdq2bVtVoYoKlJfLtm3bkpqaSn5+PlqtlvPnz9OiRYuqClVUQkV/096/fx+NRkPDhg2rIrxSyUd14qX3xhtvcOzYMYYOHYqiKAQHB7N27VpsbW3p06cP3t7eeHl5oSgKkyZNknupq7GKcin+PsrLpU6n49SpU2g0Go4ePQrA5MmTcXFxqeKoRVkq+tl0dHRkyJAh+nvjX3vttaoOWZRBfs++XCrK5zvvvMPgwYMxMjLinXfeoWXLllUdsihDRbn8+OOPef/99wHo27evfLhSzVWUz4yMDBo1alTVYT5FpSiKUtVBCCGEEEIIIYQQonqStYJCCCGEEEIIIYQokxQOhBBCCCGEEEIIUSYpHAghhBBCCCGEEKJMUjgQQgghhBBCCCFEmaRwIIQQQgghhBBCiDJJ4UAIIYQQQgghhBBlksKBEEIIIYQQQgghyvT/AVlrWBXNhFYpAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "importances_rf = pd.Series(rf.feature_importances_, index = X_feat.columns)\n",
    "sorted_importances_rf = importances_rf.sort_values()\n",
    "sorted_importances_rf = sorted_importances_rf[sorted_importances_rf > 0]\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(15,8))\n",
    "\n",
    "sorted_importances_rf[:50].plot(kind='barh', color='lightgreen')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "important_cols = list(sorted_importances_rf[:50].index) + ['price_USD', 'reviews']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Modeling\n",
    "Like previously mentioned, I only used one-twentieth of the dataset and the columns that I previously identified as the most important for creating my model. While not ideal, it allows me to experiment with different methods within the time constraints of this project and the hardware constraints of my computer.\n",
    "\n",
    "I ran the VADER feature engineering process to create the `pos_score` column for this particular dataset, and then saved the dataframe as a csv file on my computer called `df_ml.csv`. Then I dropped the `reviews` and `price_USD` columns to create my inputs to the model, and identified `price_USD` as the target of my model. After splitting the data into training and testing sets, I trained three different tree-based machine learning models, random forest, stochastic gradient boosting, and XGBoost, and used the root mean squared error to determine which model performed the best. \n",
    "\n",
    "Tree-based ensemble models are ideal for this type of analysis, which uses a complex dataset with dozens of features and complex interactions between all of them. Trees allow for the flexibility to fit a model to the specific non-linear trends in the dataset without setting the degree of nonlinearity ahead of time. Ensemble models allow for the combination of several weak tree models to create a model that reduces the variance of the model without compromising too much on bias. Random Forest models are the most simplistic tree-based ensemble model that are built from many aggregated decision trees. The stochastic gradient boosting takes this one step further by improving and building upon iterative models based on their errors, while adding variation from choosing a random subsample of the data at each iteration. The use of subsets of data also makes it harder for the model to overfit to the training data. XGBoost takes gradient boosting even further by implementing second-order gradients of the loss function, which provides more information about minimizing the loss function of the model. It also uses advanced regularization which helps the model to generalize better in many cases. For this dataset, stochastic gradient boosting worked best, with the lowest RMSE score of 43.9."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "destination = '/Users/limesncoconuts2/datasets/airbnb/'\n",
    "filename = 'df_ml.csv'\n",
    "\n",
    "if(not os.path.isfile(destination + filename)): \n",
    "    df = pd.read_csv(directory + 'df_clean.csv',\n",
    "                 nrows=50000,\n",
    "                 usecols=important_cols)\n",
    "    \n",
    "    df.reviews = df.reviews.str.replace(\"[\",\"\").str.replace(\"]\",\"\") \\\n",
    "                    .str.replace(\"\\\\\",\"\").str.replace(\"\\\"\",\"\") \\\n",
    "                    .str.replace(\"\\'\",\"\").str.replace(\"rn\",\"\").str.strip()\n",
    "    \n",
    "    analyzer = SentimentIntensityAnalyzer()\n",
    "    pool = mp.Pool(cores)\n",
    "    results = [pool.apply(get_review_sentiment, args=(row, analyzer)) for row in df.reviews]\n",
    "    pool.close()\n",
    "    df['pos_score'] = results\n",
    "\n",
    "    # save df with pos_score column\n",
    "    current_dir = os.getcwd() + '/' + filename\n",
    "    df.to_csv(filename, index=False)\n",
    "    shutil.move(os.path.join(current_dir), os.path.join(destination, filename))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(directory + 'df_ml.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop(columns=['reviews', 'price_USD'])\n",
    "y = df['price_USD']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=SEED)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf = RandomForestRegressor(n_estimators=800,\n",
    "                          max_depth=5,\n",
    "                          n_jobs=-1,\n",
    "                          random_state=SEED)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf.fit(X_train, y_train)\n",
    "y_pred = rf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest Root Mean Squared Error: 71.9\n"
     ]
    }
   ],
   "source": [
    "rmse_test = mean_squared_error(y_test, y_pred)**(1/2)\n",
    "print('Random Forest Root Mean Squared Error:', round(rmse_test, 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Stochastic Gradient Boosting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "gb = GradientBoostingRegressor(n_estimators=800,\n",
    "                                subsample=0.8,\n",
    "                                max_depth=5,\n",
    "                                random_state=SEED)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "gb.fit(X_train, y_train)\n",
    "y_pred = gb.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stochastic Gradient Boosting Root Mean Squared Error: 43.9\n"
     ]
    }
   ],
   "source": [
    "rmse_test = mean_squared_error(y_test, y_pred)**(1/2)\n",
    "print('Stochastic Gradient Boosting Root Mean Squared Error:', round(rmse_test, 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "xg_reg = xgb.XGBRegressor(n_estimators=800,\n",
    "                          max_depth=5,\n",
    "                          objective='reg:linear',\n",
    "                          n_jobs=cores,\n",
    "                          random_state=SEED)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "xg_reg.fit(X_train, y_train)\n",
    "y_pred = xg_reg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "XGBoost Root Mean Squared Error: 44.7\n"
     ]
    }
   ],
   "source": [
    "rmse_test = mean_squared_error(y_test, y_pred)**(1/2)\n",
    "print('XGBoost Root Mean Squared Error:', round(rmse_test, 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Tuning\n",
    "\n",
    "I used grid search cross validation to optimize the best performing models, which were the stochastic gradient boosting and xgboost models. The grid search validation chooses the best model based on the negative mean squared error metric, which is a measure of how much the predicted value differs from the actual value."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Stochastic Gradient Boosting Model Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "gb_params = {\n",
    "    \"n_estimators\": [500],\n",
    "    \"subsample\": [0.7, 1],\n",
    "    \"max_depth\": [5, 7, 9],\n",
    "    \"learning_rate\": [0.01, 0.1],\n",
    "    \"random_state\": [SEED]\n",
    "}\n",
    "\n",
    "gb = GradientBoostingRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "gb_grid = GridSearchCV(estimator=gb,\n",
    "                   param_grid=gb_params,\n",
    "                   scoring = 'neg_mean_squared_error',\n",
    "                   cv=5,\n",
    "                   n_jobs=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise-deprecating',\n",
       "       estimator=GradientBoostingRegressor(alpha=0.9, criterion='friedman_mse', init=None,\n",
       "             learning_rate=0.1, loss='ls', max_depth=3, max_features=None,\n",
       "             max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "             min_impurity_split=None, min_samples_leaf=1,\n",
       "             min_sampl...=None, subsample=1.0, tol=0.0001,\n",
       "             validation_fraction=0.1, verbose=0, warm_start=False),\n",
       "       fit_params=None, iid='warn', n_jobs=-1,\n",
       "       param_grid={'n_estimators': [500], 'subsample': [0.7, 1], 'max_depth': [5, 7, 9], 'learning_rate': [0.01, 0.1], 'random_state': [1]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring='neg_mean_squared_error', verbose=0)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gb_grid.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best hyperparameters:\n",
      " {'learning_rate': 0.1, 'max_depth': 9, 'n_estimators': 500, 'random_state': 1, 'subsample': 0.7} \n",
      "\n",
      "RMSE: 46.8\n"
     ]
    }
   ],
   "source": [
    "gb_best_hyperparams = gb_grid.best_params_\n",
    "gb_best_model = gb_grid.best_estimator_\n",
    "gb_best_score = gb_grid.best_score_\n",
    "print('Best hyperparameters:\\n', gb_best_hyperparams, '\\n')\n",
    "print('RMSE:', round(np.sqrt(np.abs(gb_best_score)), 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### XGBoost Model Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "xg_params = {\n",
    "    \"n_estimators\": [500],\n",
    "    \"objective\": ['reg:linear'],\n",
    "    \"subsample\": [0.5, 0.8],\n",
    "    \"max_depth\": [5, 7],\n",
    "    \"learning_rate\": [0.01],\n",
    "    \"colsample_bytree\": [0.9, 1],\n",
    "    \"eval_metrics\": ['rmse'],\n",
    "    \"n_jobs\": [cores],\n",
    "    \"random_state\": [SEED]\n",
    "}\n",
    "\n",
    "xg = xgb.XGBRegressor(verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "xg_grid = GridSearchCV(estimator=xg,\n",
    "                   param_grid=xg_params,\n",
    "                   scoring = 'neg_mean_squared_error',\n",
    "                   cv=5,\n",
    "                   n_jobs=-1,\n",
    "                   verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "xg_grid.fit(X_train, y_train, \n",
    "            eval_set=[(X_train, y_train), (X_test, y_test)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best hyperparameters:\n",
      " {'colsample_bytree': 0.9, 'eval_metrics': 'rmse', 'learning_rate': 0.01, 'max_depth': 7, 'n_estimators': 500, 'n_jobs': 4, 'objective': 'reg:linear', 'random_state': 1, 'subsample': 0.8} \n",
      "\n",
      "RMSE: 56.4\n"
     ]
    }
   ],
   "source": [
    "xg_best_hyperparams = xg_grid.best_params_\n",
    "xg_best_model = xg_grid.best_estimator_\n",
    "xg_best_score = xg_grid.best_score_\n",
    "print('Best hyperparameters:\\n', xg_best_hyperparams, '\\n')\n",
    "print('RMSE:', round(np.sqrt(np.abs(xg_best_score)), 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "test = xg_best_model.evals_result()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### RMSE Boxplots for Training and Validation Datasets\n",
    "Boxplots showed that the stochastic gradient boosting model resulted in lower error during the cross-validation process than the XGBoost model, although the difference was not very far off, and the test set hasn't yet been evaluated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "xg_results = pd.DataFrame.from_dict(xg_grid.cv_results_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "gb_results = pd.DataFrame.from_dict(gb_grid.cv_results_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "gb_train = np.sqrt(np.abs(gb_results.mean_train_score))\n",
    "gb_test = np.sqrt(np.abs(gb_results.mean_test_score))\n",
    "xg_train = np.sqrt(np.abs(xg_results.mean_train_score))\n",
    "xg_test = np.sqrt(np.abs(xg_results.mean_test_score))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15, 8))\n",
    "plt_data = [gb_train, gb_test, xg_train, xg_test]\n",
    "labels = ['GB Train', 'GB Validation', 'XGB Train', 'XGB Validation']\n",
    "plt.boxplot(plt_data, vert=0)\n",
    "plt.yticks(np.arange(len(labels))+1, labels)\n",
    "plt.title(\"Cross Validation Root Mean Squared Error for \\n Gradient Boosting and XGBoost Models\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Running Models on the Test Dataset:\n",
    "\n",
    "The initial modeling process used cross-validation to evaluate the ability of the models to generalize to data they haven't seen before, but it uses data that the models actually have seen in the process. A better test, however, is to use the test dataset, which is data that the model has not seen at any stage in the analysis.\n",
    "\n",
    "The plots for deviance for the stochastic gradient boosting and xgboost models show the differences in error between the entire training set and the held-out test set, while the plots for feature importance show which features of the dataset were most important to the model.\n",
    "\n",
    "As expected, the results for the training set for both models has less error overall than the results for the test set, with stochastic gradient boostings having less overall error. Additionally, there is significant overlap in the two model's most important features, which is evidence that the inputs to the models are likely to be reproducible between different types of models with varying parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "################ STOCHASTIC GRADIENT BOOSTING ######################\n",
    "# deviances\n",
    "test_score = np.zeros((500,), dtype=np.float64)\n",
    "for i, y_pred in enumerate(gb_best_model.staged_predict(X_test)):\n",
    "    test_score[i] = gb_best_model.loss_(y_test, y_pred)\n",
    "\n",
    "plt.figure(figsize=(12, 12))\n",
    "plt.subplot(2, 2, 1)\n",
    "plt.title('Stochastic Gradient Boosting Model Deviance (Least Squares)')\n",
    "plt.plot(np.arange(500) + 1, gb_best_model.train_score_, 'b-', label='Training Set Deviance')\n",
    "plt.plot(np.arange(500) + 1, test_score, 'r-', label='Test Set Deviance')\n",
    "plt.legend(loc='upper right')\n",
    "plt.xlabel('Boosting Iterations')\n",
    "plt.ylabel('Deviance')\n",
    "\n",
    "# feature importances\n",
    "importances = pd.Series(gb_best_model.feature_importances_, index = X.columns)\n",
    "sorted_importances = importances.sort_values()\n",
    "sorted_importances = sorted_importances[sorted_importances > 0]\n",
    "\n",
    "plt.subplot(2, 2, 2)\n",
    "sorted_importances[:25].plot(kind='barh', color='lightgreen')\n",
    "plt.xlabel('Relative Importance')\n",
    "plt.title('Stochastic Gradient Boosting Model Variable Importance')\n",
    "\n",
    "################ XGBOOST ######################\n",
    "# deviances\n",
    "train_score = np.zeros((500,), dtype=np.float64)\n",
    "test_score = np.zeros((500,), dtype=np.float64)\n",
    "\n",
    "xg_evals = xg_best_model.evals_result()\n",
    "scores = []\n",
    "\n",
    "for i in xg_evals.keys():\n",
    "    scores.append(xg_evals[i]['rmse'])\n",
    "train_scores = scores[0]\n",
    "test_scores = scores[1]\n",
    "test_score = xg_evals[i]['rmse']\n",
    "    \n",
    "plt.subplot(2, 2, 3)\n",
    "plt.title('XGBoost Model Deviance (RMSE)')\n",
    "plt.plot(np.arange(500) + 1, train_scores, 'b-', label='Training Set Deviance')\n",
    "plt.plot(np.arange(500) + 1, test_scores, 'r-', label='Test Set Deviance')\n",
    "plt.legend(loc='upper right')\n",
    "plt.xlabel('Boosting Iterations')\n",
    "plt.ylabel('Deviance')\n",
    "\n",
    "# feature importances\n",
    "importances_xg = pd.Series(xg_best_model.feature_importances_, index = X.columns)\n",
    "sorted_importances_xg = importances_xg.sort_values()\n",
    "sorted_importances_xg = sorted_importances_xg[sorted_importances_xg > 0]\n",
    "\n",
    "plt.subplot(2, 2, 4)\n",
    "sorted_importances_xg[:25].plot(kind='barh', color='lightgreen')\n",
    "plt.xlabel('Relative Importance')\n",
    "plt.title('XGBoost Model Variable Importance')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Predicted Price vs. Actual Price\n",
    "\n",
    "Comparing the predicted prices to their actual values communicates the strength of our model in a more understandable way. The scatter plots show this relationship for every point in the test set. For both models, there is a visible positive trend, which is evidence that the models are often predicting prices close to their actual values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "gb_predictions = gb_best_model.predict(X_test)\n",
    "xg_predictions = xg_best_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 8))\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.scatter(y_test, gb_predictions, color='blue')\n",
    "plt.xlabel(\"Actual Price\")\n",
    "plt.ylabel(\"GB Predicted Price\")\n",
    "plt.title(\"Actual Price vs. Predicted Price\")\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.scatter(y_test, xg_predictions, color='red')\n",
    "plt.xlabel(\"Actual Price\")\n",
    "plt.ylabel(\"XGB Predicted Price\")\n",
    "plt.title(\"Actual Price vs. Predicted Price\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Price Percent Change\n",
    "\n",
    "To bring this analysis further, I calculated the percent change between the actual prices and their predicted values. For the stochastic gradient boosting model, more than 60% of price predictions are within 10% of their actual prices, while that number drops to about 30% for the xgboost model. In this sense, we see that the stochastic gradient boosting is much better for getting predictions that closer to their actuals than the xgboost model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stochastic Gradient Boosting Model:\n",
      "61.4% price predictions are within 10% of actual prices. \n",
      "\n",
      "XGBoost Model:\n",
      "32.5% price predictions are within 10% of actual prices.\n"
     ]
    }
   ],
   "source": [
    "gb_pct_chng = ((gb_predictions - y_test) / y_test)*100\n",
    "gb_pct_chng = np.abs(round(gb_pct_chng, 2))\n",
    "gb_frac = len(gb_pct_chng[gb_pct_chng < 10]) / len(gb_pct_chng)\n",
    "print('Stochastic Gradient Boosting Model:')\n",
    "print('{0:.1f}% price predictions are within 10% of actual prices. \\n'.format(gb_frac*100))\n",
    "\n",
    "xg_pct_chng = ((xg_predictions - y_test) / y_test)*100\n",
    "xg_pct_chng = np.abs(round(xg_pct_chng, 2))\n",
    "xg_frac = len(xg_pct_chng[xg_pct_chng < 10]) / len(xg_pct_chng)\n",
    "print('XGBoost Model:')      \n",
    "print('{0:.1f}% price predictions are within 10% of actual prices.'.format(xg_frac*100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusions & Next Steps\n",
    "\n",
    "The stochastic gradient boosting model for price prediction offers the opportunity for a company like Airbnb to increase their number of listings and be more transparent with its users. There are several uses for a model such as this, including:\n",
    "1. For those looking to add listings, Airbnb can provide an estimate for how much a host can charge for their listing based on the most important features in the predictive model. For example, the potential host can input how many people they can accommodate, the amenities available, and the room type to get a range of appropriate price points, which could be calculated using the prediction and a confidence interval. This can help with encouraging people to follow-through with posting listings, or for forecasting the financial success of a listing.\n",
    "\n",
    "\n",
    "2. The model can be implemented in marketing tactics to approach potential hosts with targeted advertising along the lines of \"Have an extra private bedroom in Manhattan? You can make X dollars per month by putting your room on Airbnb!\". In that way, Airbnb will amass more users and listings.\n",
    "\n",
    "\n",
    "3. For existing listings, Airbnb can use the model to suggest improvements for hosts to easily increase the value of their listing(s). For example, one insight might be \"Hosts that respond to messages within one hour increase the value of their listing by 10%\" or \"Add a buzzer or wireless intercom to your listing and charge \\$10 more per night\". Hosts will likely appreciate suggestions for how to easy improve their listings and make more money. And as a result, Airbnb will increase its revenue too.\n",
    "\n",
    "It's important to note that this model was only trained on a small sample of fifty thousand data points, but there are millions of other rows of data on which to train the model. In fact, adding more training data is probably one of the strongest ways to increase the accuracy of the model. A model with stronger predictive power will only increase the benefits that this model can bring.\n",
    "\n",
    "#### Future Improvements\n",
    "\n",
    "In order for the model to work best for the client, the most obvious next step would be to make it production ready. If given more time, I would implement a pipeline in which the data could be read directly into the program, automatically cleaned and processed for feature creation, and run through the machine learning model. This would allow for the client to use my model without having to go through all of the laborious steps themselves.\n",
    "\n",
    "Looking more inside the model, for the VADER sentiment analysis I would do more preprocessing of the review data to remove non-English reviews to have a cleaner analysis that doesn't numerically de-value non-English entries. I would also like to include more string features into the dataset using VADER, like the listing description or rules data. Additionally, it would also be interesting to see what other NLP methods could be applied to the text data using a package like word2vec or doc2vec.\n",
    "\n",
    "To make this analysis stronger, I would train these models on the entire Los Angeles dataset, and then to other cities represented in the entire Inside Airbnb dataset. The infrastructure for scaling this model has already been incorporated through the use of functions, but further generalization using object-oriented programming principles could make for an even stronger model. I would also spend more time tuning the model using Randomized Search or the more thorough Grid Search method."
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