QuAM_report.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The QuAM"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We return to our question: Can we predict the pricing of a property, given different information about it?\n",
    "In other words, given feature values x<sub>1</sub>, x<sub>2</sub>, ..., x<sub>n</sub>, can we find some relation \n",
    "y = w<sub>0</sub> + w<sub>1</sub>\\*x<sub>1</sub> + ... + w<sub>n</sub>\\*x<sub>n</sub> ?\n",
    "\n",
    "We carry out different classification tests on our data. We will run through a couple different classifiers and see which is best suited for the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Size</th>\n",
       "      <th>Bedrooms</th>\n",
       "      <th>Bathrooms</th>\n",
       "      <th>Nr of amenities</th>\n",
       "      <th>Balcony</th>\n",
       "      <th>Built in Wardrobes</th>\n",
       "      <th>Maids Room</th>\n",
       "      <th>Private Garden</th>\n",
       "      <th>Type_Apartment</th>\n",
       "      <th>Type_Compound</th>\n",
       "      <th>...</th>\n",
       "      <th>Location_Salwa Road</th>\n",
       "      <th>Location_The Pearl</th>\n",
       "      <th>Location_Umm Al Seneem</th>\n",
       "      <th>Location_Umm Ghuwailina</th>\n",
       "      <th>Location_Umm Salal Ali</th>\n",
       "      <th>Location_Umm Salal Mohammad</th>\n",
       "      <th>Location_Wadi Al Sail</th>\n",
       "      <th>Location_West Bay</th>\n",
       "      <th>Location_West Bay Lagoon</th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>0</td>\n",
       "      <td>6700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>61.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
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       "      <td>6500.0</td>\n",
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       "      <td>60.0</td>\n",
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       "      <td>7500.0</td>\n",
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       "      <td>8</td>\n",
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       "      <td>18000.0</td>\n",
       "    </tr>\n",
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       "    </tr>\n",
       "  </tbody>\n",
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       "<p>6580 rows × 89 columns</p>\n",
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      ],
      "text/plain": [
       "       Size  Bedrooms  Bathrooms  Nr of amenities  Balcony  \\\n",
       "0      60.0         1          1                8        0   \n",
       "1      62.0         1          1                8        0   \n",
       "2      60.0         1          1                9        1   \n",
       "3      61.0         1          1               10        1   \n",
       "4      60.0         1          1               10        0   \n",
       "...     ...       ...        ...              ...      ...   \n",
       "6575  700.0         9          7                1        0   \n",
       "6576  680.0         8          6                2        1   \n",
       "6577  400.0         8          7                5        1   \n",
       "6578  375.0         8          8                5        1   \n",
       "6579  350.0         9          8                5        1   \n",
       "\n",
       "      Built in Wardrobes  Maids Room  Private Garden  Type_Apartment  \\\n",
       "0                      0           0               0               1   \n",
       "1                      0           0               0               1   \n",
       "2                      0           0               0               1   \n",
       "3                      1           0               0               1   \n",
       "4                      1           0               0               1   \n",
       "...                  ...         ...             ...             ...   \n",
       "6575                   0           1               0               0   \n",
       "6576                   0           0               0               0   \n",
       "6577                   1           1               0               0   \n",
       "6578                   0           1               1               0   \n",
       "6579                   0           1               1               0   \n",
       "\n",
       "      Type_Compound  ...  Location_Salwa Road  Location_The Pearl  \\\n",
       "0                 0  ...                    0                   1   \n",
       "1                 0  ...                    0                   0   \n",
       "2                 0  ...                    0                   1   \n",
       "3                 0  ...                    0                   1   \n",
       "4                 0  ...                    0                   0   \n",
       "...             ...  ...                  ...                 ...   \n",
       "6575              0  ...                    0                   0   \n",
       "6576              0  ...                    0                   0   \n",
       "6577              0  ...                    0                   0   \n",
       "6578              0  ...                    0                   0   \n",
       "6579              0  ...                    0                   0   \n",
       "\n",
       "      Location_Umm Al Seneem  Location_Umm Ghuwailina  Location_Umm Salal Ali  \\\n",
       "0                          0                        0                       0   \n",
       "1                          0                        0                       0   \n",
       "2                          0                        0                       0   \n",
       "3                          0                        0                       0   \n",
       "4                          0                        0                       0   \n",
       "...                      ...                      ...                     ...   \n",
       "6575                       0                        0                       0   \n",
       "6576                       0                        0                       0   \n",
       "6577                       0                        0                       0   \n",
       "6578                       0                        0                       0   \n",
       "6579                       0                        0                       0   \n",
       "\n",
       "      Location_Umm Salal Mohammad  Location_Wadi Al Sail  Location_West Bay  \\\n",
       "0                               0                      0                  0   \n",
       "1                               0                      0                  1   \n",
       "2                               0                      0                  0   \n",
       "3                               0                      0                  0   \n",
       "4                               0                      0                  1   \n",
       "...                           ...                    ...                ...   \n",
       "6575                            0                      0                  0   \n",
       "6576                            0                      0                  0   \n",
       "6577                            0                      0                  0   \n",
       "6578                            0                      0                  0   \n",
       "6579                            0                      0                  0   \n",
       "\n",
       "      Location_West Bay Lagoon    Value  \n",
       "0                            0   6500.0  \n",
       "1                            0   7000.0  \n",
       "2                            0   6700.0  \n",
       "3                            0   6500.0  \n",
       "4                            0   7500.0  \n",
       "...                        ...      ...  \n",
       "6575                         0  35000.0  \n",
       "6576                         0  30000.0  \n",
       "6577                         0  15000.0  \n",
       "6578                         0  18000.0  \n",
       "6579                         0  22000.0  \n",
       "\n",
       "[6580 rows x 89 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reading in the csv file\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "data = pd.read_csv(\"data_clean.csv\")\n",
    "\n",
    "features = data.iloc[:, 0:-1]\n",
    "targets = data.iloc[:, -1]\n",
    "\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Process Iterations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### First Iteration: Linear Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Solution justification\n",
    "\n",
    "We assume that some linear relationship between the features exists, and begin with a basic learning algorithm, namely Linear Regression. Based on its accuracy in prediction and finding a linear relationship between the variables, we determine whether this is a good solution and continue accordingly.  \n",
    "\n",
    "(All **Solution Details** to be found as comments within the code)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting data into training and testing\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "features = features\n",
    "targets = targets\n",
    "\n",
    "# We split the data into 25% for testing and 75% for training,\n",
    "# for a good ratio between learning and testing\n",
    "test_size = 0.25\n",
    "\n",
    "# Splitting the data into training and testing\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(features.values, \n",
    "                                                    targets.values, \n",
    "                                                    test_size=test_size,\n",
    "                                                    random_state=99)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Root Mean squared error = 4750.12\n",
      "R2 score on train = 0.5837615311767284\n",
      "R2 score on test = 0.5453961995206947\n"
     ]
    }
   ],
   "source": [
    "# Training the Linear model\n",
    "\n",
    "from sklearn.linear_model import LinearRegression\n",
    "import sklearn.metrics as sm\n",
    "import numpy as np\n",
    "\n",
    "# Fitting and predicting on the model\n",
    "regressor = LinearRegression()  \n",
    "regressor.fit(X_train, Y_train) \n",
    "Y_pred = regressor.predict(X_test)\n",
    "\n",
    "# Measuring the accuracy of the model\n",
    "linear_train_acc = regressor.score(X_train, Y_train)\n",
    "linear_test_acc = regressor.score(X_test, Y_test)\n",
    "\n",
    "print(\"Root Mean squared error =\", round(np.sqrt(sm.mean_squared_error(Y_test, Y_pred)), 2)) \n",
    "print(\"R2 score on train =\", linear_train_acc)\n",
    "print(\"R2 score on test =\", linear_test_acc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Lessons Learned  \n",
    "\n",
    "Results obtained:\n",
    "- R2 score for train: ~0.6\n",
    "- R2 score for test: ~0.55\n",
    "- RMSE: ~4754\n",
    "\n",
    "These results tell us that around 60% of the variation in the labels can be predicted by the model. We can see this in the plot used above.  \n",
    "This is indeed the majority of the labels, but perhaps - using a more complex model - we can do better than 60%."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Second Iteration: Polynomial Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Solution Justification\n",
    "\n",
    "We see that the coefficient of determination returned could be improved upon. To do this, we try a polynomial regression model. Polynomial Regression is a form of linear regression in which the relationship between the independent variable x and dependent variable y is modeled as an nth degree polynomial. We think this might be a better approach than linear regression because our feature's values vary a lot (sqm (size) vs nr of bedrooms vs bool for maids room).\n",
    "\n",
    "(All **Solution Details** to be found as comments within the code)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Poly with degree 2\n",
      "Root Mean squared error = 58718000689.77\n",
      "R2 score on train = 0.697\n",
      "R2 score on test = -69465066127035.328\n"
     ]
    }
   ],
   "source": [
    "# Training the Polynomial model\n",
    "\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "\n",
    "# We use degree=2 because any higher degree was not able to run on our machines (huge nr of features)\n",
    "d = 2\n",
    "\n",
    "# Fitting the polynomial features\n",
    "poly = PolynomialFeatures(degree=d)\n",
    "X_poly = poly.fit_transform(features) \n",
    "\n",
    "# Splitting the new polynomial features into training and testing\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X_poly, \n",
    "                                                targets.values, \n",
    "                                                test_size=test_size,\n",
    "                                                random_state=99)\n",
    "\n",
    "# Fitting and Predicting on the new data\n",
    "poly.fit(X_train, Y_train) \n",
    "lin2 = LinearRegression() \n",
    "lin2.fit(X_train, Y_train)\n",
    "Y_pred = lin2.predict(X_test)\n",
    "\n",
    "# Measuring the accuracy of the model\n",
    "poly_train_acc = lin2.score(X_train, Y_train)\n",
    "poly_test_acc = lin2.score(X_test, Y_test)\n",
    "\n",
    "print(\"\\nPoly with degree\", d)\n",
    "print(\"Root Mean squared error =\", round(np.sqrt(sm.mean_squared_error(Y_test, Y_pred)), 2)) \n",
    "print(\"R2 score on train = {:.3f}\".format(poly_train_acc))\n",
    "print(\"R2 score on test = {:.3f}\".format(poly_test_acc))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Lessons Learned  \n",
    "\n",
    "Best result obtained:\n",
    "- R2 score for train: ~0.7\n",
    "- (R2 score for test: ~-69465066127035)\n",
    "- RMSE: ~58718000690\n",
    "\n",
    "These results tell us that our model can predict the training data with an ccuracy of 70%, but we see that the testing data does not do well at all.\n",
    "This was a better approach than a simple linear regression model for the training data, however we are still not at a great accuracy. We think that a more complex model, that could filter through features e.g. Decision Tree model more useful."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Third Iteration: Decision Tree Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Solution Justification\n",
    "\n",
    "To improve our accuracy we will try an approach with Decision Trees. Decision Trees (DTs) are a non-parametric supervised learning method used for classification and regression. The goal is to create a model that predicts the value of a target variable by learning simple decision rules inferred from the data features. This seems like a good solution for us because we have many features and we think that some of the features values really determine the target's range, therefore a tree with decision splits dependent on the features would be smart. \n",
    "<linebreak/>\n",
    "\n",
    "\n",
    "#### Solution Details\n",
    "\n",
    "We chose to not limit the max_depth _at first_."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We split the data into 25% for testing and 75% for training,\n",
    "# for a good ratio between learning and testing\n",
    "test_size = 0.25\n",
    "\n",
    "# Splitting the data into training and testing\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(features.values, \n",
    "                                                    targets.values, \n",
    "                                                    test_size=test_size,\n",
    "                                                    random_state=99)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "DT with unbounded max depth\n",
      "Root Mean squared error = 5897.4\n",
      "R2 score on train = 0.9969819269210263\n",
      "R2 score on train = 0.2992796363876601\n"
     ]
    }
   ],
   "source": [
    "# Training the DT model\n",
    "\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "# Fitting the DT model\n",
    "tree = DecisionTreeRegressor()\n",
    "tree.fit(X_train, Y_train)\n",
    "\n",
    "Y_pred = tree.predict(X_test)\n",
    "print(\"\\nDT with unbounded max depth\")\n",
    "print(\"Root Mean squared error =\", round(np.sqrt(sm.mean_squared_error(Y_test, Y_pred)), 2)) \n",
    "print(\"R2 score on train =\", tree.score(X_train, Y_train)) \n",
    "print(\"R2 score on train =\", tree.score(X_test, Y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Are we overfitting?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of leaves / labels in the DT: 3229\n"
     ]
    }
   ],
   "source": [
    "n_labels = tree.get_n_leaves()\n",
    "print('Number of leaves / labels in the DT: {}'.format(n_labels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tree_training_accuracies = []\n",
    "tree_test_accuracies = []\n",
    "\n",
    "max_depth = list(range(1,16))\n",
    "max_depth.append(None)\n",
    "for d in max_depth:\n",
    "    tree = DecisionTreeRegressor(max_depth=d)\n",
    "    tree.fit(X_train, Y_train)\n",
    "    Y_pred = tree.predict(X_test)\n",
    "    acc_train = tree.score(X_train, Y_train)\n",
    "    acc_test = tree.score(X_test, Y_test)\n",
    "    tree_training_accuracies.append(acc_train)\n",
    "    tree_test_accuracies.append(acc_test)\n",
    "\n",
    "fig = plt.figure('ref', figsize=(20, 15))\n",
    "data_plt = fig.add_subplot(1,1,1)\n",
    "\n",
    "data_plt.plot(max_depth, tree_training_accuracies)\n",
    "data_plt.scatter(max_depth, tree_training_accuracies, c='red', s=100)\n",
    "\n",
    "data_plt.set_xlabel('Max Depth')\n",
    "data_plt.set_ylabel('R2 Score')\n",
    "data_plt.set_title('Accuracy Scores')\n",
    "\n",
    "data_plt.plot(max_depth, tree_test_accuracies)\n",
    "data_plt.scatter(max_depth, tree_test_accuracies, c='yellow', s=100)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see from this graph above, the high/unbounded max depth values give the training data a perfect accuracy, however our testing data does not do very well then (which means we did overfit when training the model with anunbounded depth). So, we see that the best accuracy score for testing is at max depths of 5 or 6 - where the R2 score is  ~0.5 - so we should choose those as max depths for a general model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best R2 score on train = 0.7407029086033885\n",
      "Best R2 score on test = 0.4570987734425682\n"
     ]
    }
   ],
   "source": [
    "tree_train_acc = tree_training_accuracies[6]\n",
    "tree_test_acc = tree_test_accuracies[6]\n",
    "\n",
    "print(\"Best R2 score on train =\", tree_train_acc) \n",
    "print(\"Best R2 score on test =\", tree_test_acc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Lessons Learned  \n",
    "\n",
    "Best result obtained:\n",
    "- R2 score for train: ~0.75\n",
    "- R2 score for test: ~0.45\n",
    "\n",
    "These results tell us that even with the optimal depth for a generalized DT regression model, this one is doing worse at predicting the targets than a simple Linear Regression model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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0mgXFpcpMidd1E0cEHQUAAPQzFKYBAEC/Z2aaV5SvLbsP6HfrdgQdBwB6xdtb9mpFWY3uKsxVfEx00HEAAEA/Q2EaAABA0sXjhmpsZpIWFpfJORd0HADocQuLS5U6IFY3XZgddBQAANAPUZgGAACQFBVlundWntZX1usvH+0KOg4A9KjSXfX6w/uVuv2i0UqKjwk6DgAA6IcoTAMAAPiuOm+4RgwaoIXFZUFHAYAe9fCSciXERumO6TlBRwEAAP0UhWkAANAlM7vezN43s3Yzmxx0np4UGx2le2bmas2mPVpdsTvoOADQI7btPaD/fWubbpySrSGJcUHHAQAA/RSFaQAAcDzvSfq0pKVBB+kNn508SmmJcVpYXBp0FADoEYtLyiVJd8/MDTgJAADozyhMAwCALjnnPnTOrQ86R28ZEBetLxTm6K/rq/TB9rqg4wBAt9q9r1nPrt6iayaM0IhBA4KOAwAA+jEK0wAAAB3cMtU7GNjCJcw1DSCyPL68Qo2tbZpXRLc0AAAIFoVpAAAgM3vNzN7r5O+ak1jHPWa2xszWVFVV9WTcHpc6IFafm5qt36/brk01+4KOAwDdoqGpVU+s3KTLxmcqf2hy0HEAAEA/R2EaAADIOXeJc+7sTv5+cxLrWOScm+ycm5yRkdGTcXvFndNzFBMdpUeWlgcdBQC6xTOvb1btgRbNK8oPOgoAAACFaQAAgM4MTUnQnEkj9cKardpV1xh0HAA4LU2tbVq8rFzT8tI0YdSgoOMAAAD0bGHazC43s/VmVmpmX+/k+mwz+6uZvWVm68zsip7MAwAATp6ZXWdmWyVdJOn3ZvaHoDP1lrkzc9Xa3q6fLa8IOgoAnJZfv7lNlXVNmleUF3QUnAa2sQEAkaTHCtNmFi3pp5JmSxov6SYzG99hsX+R9Cvn3PmSbpS0oKfyAACAU+Oc+7VzbqRzLt45l+mc+2TQmXrL6LREferc4Xp6lbf7OwCEo7Z2p0eWluucEakqzE8POg5OEdvYAIBI05Md0xdIKnXOlTvnmiU9K6njAZScpBT/dKqk7T2YBwAA4KTNm5WnhqZWPbVqU9BRAOCU/N97O1VRvU/zi/JkZkHHwaljGxsAEFF6siLxiQcAACAASURBVDA9QtKWkPNb/ctCfUfSLf7uwa9I+lIP5gEAADhp44enqOiMDD22rEIHmtuCjgMAJ8U5pwXFpcpNT9RlZ2UFHQenh21sAEBE6cnCdGc/xbsO52+S9LhzbqSkKyQ9aWZHZTKze8xsjZmtqaqq6oGoAAAAxza/KF81+5r1/Notx18YAPqQkg3Ven97ne6dlafoKLqlwxzb2ACAiNKThemtkkaFnB+po3cjulPSryTJObdSUoKkoyY9c84tcs5Nds5NzsjI6KG4AAAAnZsyZrAmjR6sR5aUq6WtPeg4AHDCFhSXKislQdee37GxFmGIbWwAQETpycL0G5IKzCzHzOLkHXjh5Q7LbJZ0sSSZ2ZnyBk1+rgUAAH2KmWl+UZ627T2g361juk4A4eHNzXu0qny37pqRo7iYntz0Qy9hGxsAEFF67NuJc65V0hcl/UHSh/KODPy+mX3PzK72F7tf0t1m9o6kZyTd4ZzruCsSAABA4D4xbqjOyEzWwuIytbfzdQVA37ewuEyDBsbqpguyg46CbsA2NgAg0sT05Mqdc6/IO+BC6GXfCjn9gaTpPZkBAACgO5iZ5hXl6e+fe1t//miXLh2fGXQkADimjyvr9acPKvV3FxcoMb5HN/vQi9jGBgBEEvbnAgAAOEFXnjtMIwcP0ILiUtGABqAve3hJmQbERuuOaWOCjgIAANApCtMAAAAnKCY6SnNn5uqtzXv1esXuoOMAQKe27tmvl9/erpsuyNbgxLig4wAAAHSKwjQAAMBJuH7yKKUnxWlhcVnQUQCgU4tLKmQm3T0zJ+goAAAAx0RhGgAA4CQkxEbrC4U5WvJxld7bVht0HAA4Qk1Dk559Y7OunTBCw1IHBB0HAADgmChMAwAAnKRbpo5WcnyMFi6haxpA3/Lz5RvV1Nque4vygo4CAADQJQrTAAAAJyklIVa3XDRar767QxXV+4KOAwCSpPrGFv1i5UZdflaW8jKSgo4DAADQJQrTAAAAp+Dz08coJjpKi5bSNQ2gb/jl65tV19iqeXRLAwCAMEBhGgAA4BQMTU7QZyeP1Itrt6myrjHoOAD6ucaWNi1eVqHC/HSdO3JQ0HEAAACOi8I0AADAKZo7M09tzulnyyqCjgKgn3vpzW2qqm+iWxoAAIQNCtMAAACnaNSQgbry3GF6etUm1e5vCToOgH6qta1djywt03kjUzUtLy3oOAAAACeEwjQAAMBpmFeUp33NbfrFyo1BRwHQT7363k5tqtmveUV5MrOg4wAAAJwQCtMAAACnYVxWij4xbqh+vmKjDjS3BR0HQD/jnNOC4jLlZSTqsvFZQccBAAA4YRSmAQAATtP8ojzt3tes597YHHQUAP3Mko+r9OGOOt07K09RUXRLAwCA8EFhGgAA4DRNHjNEU8YM1qMlFWppaw86DoB+ZEFxmYalJuiaCSOCjgIAAHBSKEwDAAB0g/lF+dq294Befnt70FEA9BNrN+3W6orduntGruJi2LQDAADhhW8vAAAA3aDojAyNy0rWwiVlam93QccB0A8sLC7T4IGxuvGCUUFHAQAAOGkUpgEA4aGsTJo/X0pJkaKivH/nz/cuB/oAM9O8ojyV7mrQnz6sDDoOgAi3fme9Xvtwl+6YlqOBcTFBxwEAADhpFKYBAH3fq69K554rLV4s1ddLznn/Ll7sXf7qq0EnBCRJnzpnmLKHDNSC4jI5R9c0gJ7z8JIyDYyL1u3TRgcdBQAA4JRQmAYA9G1lZdKcOdL+/VJLy5HXtbR4l8+ZQ+c0+oSY6CjdMzNX72zZq5XlNUHHARChtuzer5ff2a6bL8jWoIFxQccBAAA4JRSmAQB92wMPHFGQPhATr9r4xCOXaWmRHnywl4MBnZszaaQykuO1sJgfSwD0jEdLyhVl0l0zcoOOAgAAcMqYjAwA0Ce0tztV72vSrrom7axt1M66RlXWNWrntkTtvO6bqkxK087kNNUlJOnWN3+nf/3Tw4dv3NIiPfmk9NBDwT0AwJcQG607C3P0w1c/0rtba3XOyNSgIwGIIFX1TXrujS369PkjlZWaEHQcAACAU0ZhGgDQ4/Y3t6rSLzhX1nlF59DTlbWN2lXfpNb2I+fkjTIpY+Q5yqqv0Zg92zV187vKbKjR+dvXH30nDQ299GiA4/vchdn66V9LtXBJqRZ8blLQcQBEkJ8vr1BzW7vmzqJbGgAAhDcK0wCAU9bW7lSzr0mVtU1esdkvMh/qdvZP1ze2HnXbpPgYZabEKys1QVPz0pSZkqCslATv31TvdHpSnGIGD/IOdHg8SUk98AiBU5OcEKvbLhqtBcVlKq9qUG4Gr08Ap6+usUVPrtyk2Wdn8bkCAADCHoVpAECn9je3HjmlRm3TEcXmyrpGVR2jy3locoIyUxOUk56oi0KKzlmphwvPSfEnOATdcou0ePHRBz4MFRsr3XrraTxaoPt9fnqOFpdU6JEl5fr3OecGHQdABHh61WbVN7VqflF+0FEAAABOG4VpAOhn2tqdahqajppO41Dh2e96rm86uss5OT5GmakJykyJ10V5aUcWm/3T6Unxio6y7gt8//3SE08cvzB9333dd59AN0hPitcNU0bpmdWbdd+lY5kLFsBpaWxp08+WVWhGQbrOHsHc9QAAIPxRmAaACLKvqfWI6TSOPN2kytpGVTU0qa1Dl3N0lGlocryGpiQoLyNR0/PSlOlPp5GVknDodOKJdjl3p7w86YUXpDlzvOJ0aIE6Ntb7e+EFbzmgj7l7Rq6efn2zFpeU61+uHB90HABh7IW1W1Xd0KR5RROCjgIAANAtKEwDQBhoa3eqbmjqMLXG4dOVftG5qy7nrJQE5eenKys1/qi5nNO6u8u5u82eLa1bJz34oPTkk96BDpOSvOk77ruPojT6rFFDBurq84brl6s364ufyNeggXFBRwIQhlrb2rVoabkmjBqki3LTgo4DAADQLShMA0DAGppaD0+p0aHwXFl//C7nzJQE5WckqTA/3S82xx9xIMFAupx7Ql6e9NBD3h8QRu6dladfv7VNT6zYpL+7pCDoOADC0O/f3aHNu/frG586U2Z9+IdkAACAkxAh1QoA6Hta29pV3dB81FzOlbWNqqw/eFmTGjrrck6IOTRnc8HQ9COm0/BOxystsY93OQOQJJ2RlaxLzhyqx1dU6O6ZORoYx9cvACfOOaeFxWUqGJqkS8/MDDoOAABAt2HLCABOQX1ji9/V3HTU1Bq7/AJ0VX2TOjQ5K+Zgl3NqgsZmJmtGQcah6TQyDx1IMJ7CFRBh5hXl6zMLV+jZ1Vv0hcKcoOMACCPF66v00c56PXD9eYriB2kAABBBqHwAQIjWtnZV+XM5Hy42Nx0qNh/seN7X3HbUbVMSYvzCsld0Pnj6YNE5MzVe6YnxbFQC/dCk0YN1Qc4QPVpSrlumjlZcTFTQkQCEiQXFpRoxaICunjA86CgAAADdisI0gH7BOaf6plZV+l3NBwvPlXVHdjxXN3Te5ZyZ4nUyj8tK1qyxGYem2RiafPgAggPiooN5cADCwvyiPN3x8zf0m7e36frJo4KOAyAMvLFxt97YuEffuWq8YqP5QQsAAEQWCtMAwl5LW7uq6psOdTPv9AvOlR3mdt7fSZdz6oDYQ/M3j8tKVlZKgob6Xc4HO57TEuPocgZw2maNzdD4YSl6eEmZPjNxJJ8rAI5rYXGZhiTG6YYp2UFHAQAA6HYUpgH0Wc451TW2djp/887apkMF5+qGJrkOXc6x0Xaom/nMYSmadUbGEcXmg9Nr0OUMoLeYmeYV5elLz7ylP36wU5efPSzoSAD6sA931OkvH+3S/ZeO5fsKAACISBSmAQSipa1du+qbQqbUaDyq43lnbaMOtBzd5TxoYOyhwvKZw5IPdTyHHkBwyEC6nAH0PVecM0z/9cf1Wlhcpk+elSUzPqcAdO7hJWVKjIvWbReNCToKAABAj6AwDUSisjLpgQekp56SGhqkpCTplluk+++X8vJ69K6dc6o70Hr4QIFHFJsPdzvX7Du6yzkuOkpDU+KVlZKg8cNS9DdnDFVWavyhDueD3c4JsXQNAQhP0VGmuTPz9M+/flcrymo0PT896EgA+qDNNfv123e2664ZuUodGBt0HAAAgB5BYRqINK++Ks2ZI7W0eH+SVF8vLV4sPfGE9MIL0uzZp7Tq5tZ27arvfP7m0NONLe1H3XbwwFj/AIIJOmtY6qEO59DC82C6nAH0A5+ZNEI/fu1jLSwuozANoFOLSsoUExWlOwtzgo4CAADQYyhMA5GkrMwrSu/ff/R1BwvVc+ZI69Yd0TntnFPtgRZv+oyQDuedHbqdqxuaj1ptaJfzWSNSdfGZmUdMreEdTDCeLmcA8MXHROvOwhz926sfad3WvTp35KCgIwHoQ3bVN+pXa7bqM5NGKDMlIeg4AAAAPYbCNBBJHnjgcJe0rzkqRruSBqsyOU07k9K0M3WoKn/6inZOKTw81cZxupyzUhN0zojUQ6dD53IePDCWOVIB4CR9bupo/fSvpVrw1zI9fOukoOMA6EMeW7ZRrW3tmjuzZ6dfAwAACBqFaSCSPPWUGp1p9ZjztWzMBJWMOV8fDR0jZ1FHLBbX2qzMLXuUleIVnC89M/PQ/M0HC88ZyXQ5A0BPSYqP0e3Txuihv5aqdFeD8ocmBR0JQB9Q19iip1dt0uxzhmlMemLQcQAAAHoUhWkgzLW3O32wo07LSqu17IqvafXIs9QcE6fYthZN3vqBvrTiOQ2vq1JmQ42y6muU2bBbg5saZG1tQUcHECbM7D8lXSWpWVKZpM875/YGmyr83TFtjB4tKdcjS8r0n9efF3QcAH3Akys3qb6pVfNm0S0NAAAiH4VpIAztqD2gkg3VWrahWstLq1Wzz5v7+YzkNN361isq3PiWLtzynga2NHW+gpSUXkwLIAL8SdI/OedazezfJf2TpH8MOFPYS0uK141TsvX065t036VjNXzQgKAjAQhQY0ubfr68QrPGZujsEalBxwEAAOhxFKaBMLCvqVWrymu8YnRptUp3NUiS0pPiNXNshgrz01VYkK7Mr39FKnniqHmmjxAbK916ay8lBxAJnHN/DDm7StKcoLJEmrtm5OipVZu0uKRC37pqfNBxAATo+TVbVN3QrHlFdEsDAID+gcI00Ae1tTu9u61WJR9XqaS0Wm9t3qOWNqf4mChdmJumGyaPUmFBusZlJR954MH775eeOIHC9H339fyDABCpviDpuaBDRIqRgwfq6gnD9czqzfrSJ/I1ODEu6EgAAtDa1q5HlpZrYvYgXZgzJOg4AAAAvYLCNNBHbNm9X0s3VGnZhmqtKKtR7QGvuHzW8BTdWZirGQXpmjR6cNcHJMzLk154QZozxytOhxaoY2O9vxde8JYDgBBm9pqkrE6u+oZz7jf+Mt+Q1Crp6WOs4x5J90hSdnZ2DyWNPPNm5emlN7fp8RUbdd+lY4OOAyAAv1u3Q1v3HNC3rzrryKYDAACACEZhGghI7YEWrSyrUcmGKi0rrdammv2SpGGpCbpsfKZmjM3Q9Lw0pSXFn9yKZ8+W1q2THnxQevJJqaFBSkrypu+47z6K0gA65Zy7pKvrzex2SVdKutg5546xjkWSFknS5MmTO10GRyvITNal4zP1+IqNumdmrhLj+XoG9Cft7U4Li8s0NjNJF48bGnQcAACAXsOWD9BLWtra9faWvSrZUK2SDVV6Z8tetTspMS5aU3PT9PlpY1RYkKG8jMTT75TJy5Meesj7A4DTZGaXyzvY4Szn3P6g80SieUV5+tMHlXpm9WbdNSM36DgAetFf1+/S+sp6/eiz5ykqim5pAADQf1CYBnqIc07l1fu0zC9EryrfrYamVkWZdO7IQfrbv8nXjIIMTRg1SHExUUHHBYCuPCQpXtKf/B/OVjnn7g02UmSZmD1YU3OHaHFJhW69aLTiY7qYtglAxHDOaUFxmUYMGqCrzhsedBwAAIBeRWEa6Ea79zVreWn1oWL09tpGSVL2EO/gVjML0nVRbrpSB8YGnBQATpxzLj/oDP3B/KJ83fbYav3mre367JRRQccB0Ave2LhHazft0feuOUux0TQqAACA/oXCNHAamlrbtHbjHpWUeoXo97fXyTkpJSFG0/LSNf9v0jWjIF2j0xKDjgoA6ONmFKTr7BEpenhJmT4zaaSi2aUfiHgLikuVlhin6yfxYxQAAOh/KEwDJ8E5p/WV9X5HdLVer6hRY0u7YqJME7MH675LxqqwIF3njkhVDF0vAICTYGaaNytff/vLN/WH93fqinOGBR0JQA96f3utitdX6aufPEMD4pi+BwAA9D8UpoHj2FXXqGX+9BzLSqu1q75JkpSXkagbp2SrMD9dU/PSlBTP2wkAcHouPztLOemJWlhcptlnZ53+wXAB9FkPLylXUnyMbpk6OugoAAAAgaCSBnRwoLlNr1fUHCpEf7SzXpI0eGCsCgsyNCM/XYUF6Ro+aEDASQEAkSY6yjR3Zq6+/tK7WlZarRkFGUFHAtADNlbv0+/XbdfdM3OVOoBjjwAAgP6JwjT6vfZ2p/e316mktErLNlRrzcY9am5rV1x0lKbkDNY/Xj5OMwrSNX5YiqKY7xMA0MOumzhCD772sRYWl1GYBiLUopJyxURH6c7pOUFHAQAACAyFafRL2/Ye0LINVSrZUK0VZTXava9ZkjQuK1m3TxutwoIMXTBmCPP9AQB6XXxMtO4qzNUPXvlQb2/ZqwmjBgUdCUA32lXXqBfWbNWcySM1NCUh6DgAAACBoTCNfqGhqVWrympUsqFKJaXVKq/aJ0nKSI5X0RkZmlGQrun56RqazMYBACB4N12YrYf+WqqFxaV65NbJQccB0I1+trxCre3tmjszN+goAAAAgaIwjYjU2tauddtqVfJxtZaVVumtzXvV2u6UEBulC3PSdPMF2ZpRkKGxmUkcWAoA0Ockxcfo9mlj9JM/b1DprnrlD00OOhKAblB7oEVPr9qsT507XKPTEoOOAwAAECgK04gYm2r2aemGai3bUKUVZTWqb2yVmXT28FTdPTNXMwrSNWn0YMXHMD0HAKDvu2PaGD26tFwLi8v1wGfPCzoOgG7w5MqNamhq1bxZeUFHAQAACByFaYSt2v0tWlFW7RWjS6u0ZfcBSdKIQQN0xdnDNGNsuqblpWtIYlzASQEAOHlDEuN04wWj9OTKTfrKZWM1YtCAoCMBOA0Hmtv08+UbVXRGhsYPTwk6DgAAQOAoTCNsNLe2663Ne7Ss1CtGv7t1r9qdt7vz1Nw03VXodUXnpCcyPQcAICLcNSNXT67cpEeXlus7V58VdBwAp+FXa7aoZl+z5hflBx0FAACgT6AwjT7LOaeyqgaVbKjWsg3VWlleo/3NbYoyacKoQfriJwo0syBd540apNjoqKDjAgDQ7UYMGqBrzx+hZ9/YrC9fXMBeQECYamlr16Kl5Zo8erAuyBkSdBwAAIA+gcI0+pSahiYtK/UK0ctKq7WjtlGSNCZtoD49cYQK8zN0UV6aUgfEBpwUAIDece+sXL345lY9vrxCX7nsjKDjADgFv31nu7btPaDvXcOeDwAAAAdRmEagGlvatGbjHpWUVmnZhmq9v71OkpQ6IFbT89P0pfwMzShI16ghAwNOCgBAMPKHJuuy8Zl6fMVG3TMrT0nxfH0Dwkl7u9PC4jKdkZmsT4wbGnQcAACAPoMtG/Qq55w+3FGvZaVVKtlQrdUVu9XU2q7YaNPE7MH6h8vGqrAgQ+eMSFV0FPNEAwAgSfOK8vWH9yv1zOubdffM3KDjADgJf/5olzbsatCPb5jAcVAAAABCUJhGj6usa/Tnia7SstIaVTc0SZLyhybp5guzNaMgXRfmpCmRDjAAADo1YdQgTctL0+Jl5bpt2mjFx0QHHQnACXDOaUFxqUYNGaArzx0WdBwAAIA+hUogut3+5la9Xr7bK0aXVunjygZJUlpinAoL0lWYn67CgnQNSx0QcFIAAMLH/KJ83fKz1/XrN7fpxguyg44D4AS8XrFbb23eq3+99mzFcLBuAACAI1CYxmlra3d6f3utSjZUq2RDldZu2qOWNqe4mChdMGaIPjNxpAoL0nVmVoqimJ4DAIBTMj0/TeeMSNUjS8t1/eRRTHkFhIEFxWVKT4rT9ZNGBh0FAACgz6EwjVOydc9+LdtQrZIN1VpeVq29+1skSWcOS9EXpueosCBdU8YMUUIsuxoDANAdzEzzi/I07+k39ep7O3TlucODjgSgC+9tq9XSj6v0tcvP4DsxAABAJyhM44TUN7ZoZVmNPz1HtSqq90mSMlPidfG4TM0cm65peenKSI4POCkAAJHrk2dlKTcjUQuLy/Spc4ZxIDWgD1u4pEzJ8TG6ZerooKMAAAD0SRSm0anWtna9s3Wvln7sFaLf3rJXbe1OA2KjNTV3iG6ZOlozC9KVPzSJjWIAAHpJVJTp3pl5+tqL67R0Q7Vmjc0IOhKATlRU79Or7+7Q3Fl5SkmIDToOAABAn0RhGpK8I4ZvrNmvZRuqtHRDtVaV1ai+qVVm0rkjUnXvrFzNKMjQxOzBiovhwC0AAATl2vNH6Ed/+lgLi0spTAN91KKlZYqJjtLnp48JOgoAAECfRWG6H9u7v1nLS2u0rLRKSz+u1ra9ByRJIwcP0JXnDVNhfoam5aVpcGJcwEkBAMBBcTFRumtGjr7/+w/15uY9mpg9OOhIAEJU1jXqxbXb9NkpIzU0OSHoOAAAAH0Whel+pLm1XWs37dGy0iot21Ctddtq5ZyUHB+ji/LSDnVFj04byPQcAAD0YTddkK2H/lqqhcVlevS2yUHHARDiZ8sq1Oac5s7MCzoKAABAn0ZhOoI557RhV4N3wMINVVpVvlsHWtoUHWU6f9Qg/d3FBZpRkK7zRg5STDTTcwAAEC4S42N0x7Qx+vFrG/RxZb3GZiYHHQmApNr9LXp61SZdee4wjRoyMOg4AAAAfRqF6QhTVd+k5aXVXjG6tEqVdU2SpNz0RF0/eaQK89M1NS+Ng7AAABDmbr9ojBYtLdfDxWX60Q0Tgo4DQNITKzdqX3Ob5hXRLQ0AAHA8FKbDXGNLm1ZX7NYyvxj94Y46SdKggbGanp+uGfnpKixI18jBdGwAABBJBifG6aYLsvX4io36ymVjGeuBgO1vbtXPl1foE+OGalxWStBxAAAA+jwK02Gmvd3pw511/vQc1Vq9cbeaW9sVG22aNHqwvvrJMzSjIF1nDU9VdBTzRAMAEMnumpGjX6zcqEeXluu715wddBygX3vujS3as79F8+mWBgAAOCEUpsPAjtoDhwrRy0urVbOvWZI0NjNJt04drcKCdF2YM0QD4/jvBACgPxmWOkDXnT9Cz76xRV+6uEDpSfFBRwL6pZa2dj26tFwXjBmiyWOGBB0HAAAgLPRoJdPMLpf035KiJS12zv2wk2U+K+k7kpykd5xzN/dkpnCwr6lVr1fUaOnH1VpWWq3SXQ2SpPSkeM0oSNeMggwVFqQrMyUh4KQAACBoc2fl6fm1W/X48o36h0+eEXQcoF/6zdvbtb22UT+47pygoyDCsY0NAIgkPVaYNrNoST+VdKmkrZLeMLOXnXMfhCxTIOmfJE13zu0xs6E9lacva2t3endbrZZtqNLSDdV6a/MetbQ5xcdE6YKcIbph8igVFqRrXFayzJieAwAAHJaXkaTLz8rSEys3au6sXCVzgGOgV7W3Oz28pEzjspJVdEZG0HEQwdjGBgBEmp7smL5AUqlzrlySzOxZSddI+iBkmbsl/dQ5t0eSnHO7ejBPn7Jl936VbKhWyYYqrSirUe2BFknSWcNT9IXCHM0syNCk0YP1/9m78/gq6zvv/+/POdlXyM6eBVwQccMFZAmtY6u1aqvtqNWq1aIwnZm740x/c9+d6WPuztbb3k5/nbFEKFqdamtb21prtYutQRARcQFxhZOEPZAEyELI/r3/SMBAFgLJyfeck9fz8bgeOct1rrzzaOV7rs/1ub7fpPig56QAACDSLS0t0fNbqvWjV3fonkXMbwuMpj+8t0/b9jfpuzedTxMJwo1zbABATAlnYXqSpJ29nu+SdOkJ+5whSWb2srpvRfon59xvw5jJm/oj7XolVKc1W2u0dlutttc1S5ImZCbpypn5mj8jR5dPz2FuSAAAcMpmTx6n+dNztGptpW6fV8iFbWCUOOe0vDykqVkp+tS5E3zHQezjHBsAEFOGXJg2s0TnXOspHLu/dgHXz++fIalU0mRJa8xslnPu0Am/e4mkJZI0derUU4jgT3tnl97aeehYV/SmnYfU5aTUhKAuK87WHfMKtWBGrkpyU+msAAAAw7astES3rHpVv3hjt265NDq+LwHR7pWKOm3aeUj/cv0sxQUDvuMg9o3pc2wAQOw5aWHazC6R9LCkTElTzew8SXc75/7yJB/dJWlKr+eTJe3pZ5/1zrl2SZVm9oG6B9HXeu/knFspaaUkzZkz58SBNyI451RRe1hrt9ZqzdZara+oU1NrhwLW3cX0F4una/70HF0wdbwS4vjSCgAARtbckmydNzlTK14K6fNzJlMkA0ZBWXlIuemJuvGiyb6jYGwYU+fYAIDYN5SO6f+UdI2kpyXJObfJzBYP4XOvSZphZkWSdku6SdKJqwE/LelmSY+aWY66bzuqGGL2kRMKSQ88ID3+uNTUJKWlSbfeKt13n1Qy8DyNBw636eVttVq7tVZrt9Vq96EjkqQpWcm69vyJWjA9R/NKcpSZwiJEAAAgvMxMS0un697HX9fzW6r16fMm+o4ExLS3d9VrzdZa/f1VZzF9DkZL9JxjAwAwBEMpTAecc9tPmG6i82Qfcs51mNlXJP1O3XNbPeKce8fMvilpo3PumZ73rjSzd3uO+XfOubpT/iuG4/nnpRtvlNrbuzdJamyUVq2SHntMeuop6aqrJEmtHZ16veqgSPC1TgAAIABJREFU1vQUo7fsqZdzUnpSnC4vydHS0hItmJGjadmpo/onAABwlJnlSvoXSZOcc9eY2UxJlzjnHvWbDKPhypn5KslN1fLykK6ZPYHpwoAwKlu9TelJcfoCU+fgFJ3uWB0159gAAAzRUArTO3um83BmFpT0l5I+HMrBnXPPSXruhNe+0euxk/Q3PdvoC4W6i9LNzX3fa2+Xa2/XB/d8VWu/m6M1B51eraxTS3uX4gKmC6eO11evOEPzZ+Ro9qRMbpcFAESKRyU9Ien/63m+VdJPel5HjAsETPcuKtHfPbVZ5R/WaPGZeb4jATEpVNOk57dUa1lpidKTuDsSp+xRneZYHfHn2AAAnIKhFKaXqns6j6mS9kl6oee16PfAAx91SffYnzpOawsv6N6mnaf96dnSq/tVnJuqmy6eqvnTc3RZSbbSEoe8biQAAKMpzzn3IzP7O0lyzrWb2UnvdELsuO78SfrOHz5UWXmIwjQQJitXVyghGNCdlxf5joLoxFgNAIBOUpju6ZC+yTl30yjlGV2PP36sML01e4r+8tqv6f287i+X45vrdfn2TVpY+abm127VxD2VPpMCADBUh80sS5KTJDO7WFKj30gYTQlxAd29oFjffPZdvb79gC6aluU7EhBT9tYf0S/e3KWbL5mqnLRE33EQnRirAQDQSQrTzrlOM7tB3R3Tsaep6djDgsZa5TQf0tfKH9XCqjc1c1+FAupZnDjANB0AgKjxt5J+LanYzFZLmiTpRr+RMNpuumSK/utPW1VWHtKq2ylMAyPp4TWV6nLSlxcU+46C6MVYDQCAhjaVxxoz+66kJyUdPvqic25z2FKNlrS07oUOJaW3HdHjP/nHgfcDACDCmVlA3YshLZZ0tiST9K5zrs1rMIy6lIQ43Xl5kf7jDx/qg+pGnVmQ7jsSEBMOHm7Tjzbs0LXnTdSUrBTfcRCFGKsBAPjIUFqBF0m6UNL9kr7Xsz0YzlCj5tZbpfiTLFYSHy/ddtvo5AEAYBicc12Svuuca3PObXLOvcWJ7tj1xbnTlJoQVFn5Nt9RgJjx2CtVam7r1L2LSnxHQZRirAYA4CMnLUw75xb0sy0cjXBhd999QytMf/Wro5MHAIDh+4OZXec7BPwbl5KgWy6dql9v3qudB5p9xwGiXnNbhx5dV6Urzs7jLgQMF2M1AAAaQmHazNLN7H4zW9+z/R8zi41vYiUl0lNPSSkpfQvU8fHdrz/1VPd+AABEh69I+qWZHTGzA2Z20MwO+A4FP+6aX6yASStfqvAdBYh6P96wU4ea27W0dLrvKIh+jNUAAGhoU3k8Iqld0hd7tjZJPwhnqFF11VXS5s3SkiVSRkb3QocZGd3PN2/ufh8AgOiRIyleUpqk3J7nuV4TwZuCzCTdcOFk/XTjTtU0tvqOA0Stto4urVpToUuKsnTRtPG+4yD6MVYDAKChLX44wzn3uV7P/9HM3gpXIC9KSqQHH+zeAACIYs65TjO7WtLRabfKnXO/Hc4xzeyfJV0nqUvSfkl3OOf2DC8pRsuShcX6ycad+sHLlfraJ8/yHQeISk+/tVt761v0758913cUxIBwjNUAAESjoXRMt5jZ3KNPzOwySS3hiwQAAE6Xmf2rpK9JqujZvmZm/zLMw37bOTfbOXe+pGclfWOYx8MoKs5N09WzJuiHr2xXQ0u77zhA1OnscnpodUgzJ2Ro0Rk0tWL4wjRWAwAQdYZSmF4maZWZbTOzbZK+L2lpeGMBAIDT9GlJH3fOrXTOrZR0paRrh3NA51xDr6epktxwjofRt7S0RI2tHXpi/Q7fUYCo84d3q1VRc1hLS0tkZr7jIDaM+FgNAEA0OulUHs65NySdY2ZZksw5Vxf+WAAAYBgyJB3seTwiCxb3dHd9UVK9pMUjcUyMnlmTMrVgRo4eXlupOy8vVFJ80HckICo457S8PKRp2Sm6+twJvuMgtoz4WA0AQLQ5ace0mf2zmY1zzh1wztWZ2Xgz+9+jEQ4AAJyy+yW9YWarzOxhSRsl/Z+TfcjMXjCzLf1s10mSc+7rzrkpkp6Q9JUBjrHEzDaa2caampoR/JMwEpaVTldtU6ueen2X7yhA1FgXqtPmXfW6Z2GJggG6pTFiTmusBgAg1gxlKo9rnHOHjj5xzh1U961HAAAgwjjnHpc0X9JzPdtC59wTQ/jcFc65Wf1svzph1x9JumGAY6x0zs1xzs3JzWUe1khzWXGWzp8yTiteCqmjs8t3HCAqLC/fprz0RN1w0STfURBDTnesBgAg1gylMB00s4SjT8wsSVLCIPsDAABPzOxaSU3OuV84534u6bCZXTPMY87o9fRaSe8P53jww8y0rLREOw8c0W/e3us7DhDxNu08pJe31enuBUVKjGP6G4yccIzVESUUkpYtkzIypECg++eyZd2vAwDQy1AK009K+oOZ3W5mX5T0O3XfxgsAACLPN51z9Uef9Nz19M/DPOa3eqb12KzuBZr+epjHgydXnJ2vGXlpKisPyTnWsAQGU1YeUkZSnG65dJrvKIg94RirI8Pzz0uzZ0urVkmNjZJz3T9Xrep+/fnnfScEAESQkxamnXP/Junbki6QdKGk+51z/x7uYAAA4LT0N7afdLHjwTjnbuiZ1mO2c+7Tzrndwzke/AkETPcuKtH71Y168YP9vuMAEWvb/ib97t1q3T6vUGmJw/onFOjPiI/VESEUkm68UWpultrbj3+vvb379RtvpHMaAHDMUDqm5Zx7VtI3Jf1REivmAAAQud4ws/vNbJqZTTWzb0t603coRI5rz5+oSeOSVVZOYQAYyIrVISXGBXTHvELfURCbYnOsfuCB4wrSB5PStTs9V+2BXlPhtLdL3/mOh3AAgEg04FVZM3ta0j8457aYWYGkNyRtklRoZsudc/81WiEBAMCQfUXSP0n6lSST9HtJy3wGQmSJDwb05QVF+qdfv6vXqg7o4sIs35GAiLLn0BH98s3duvWyacpOS/QdB7EpNsfqxx8/rjD983M/rn/52N0y16Xsw/UqaKpTQWOd8rc3quCPW5WfmaSCjCQVZCYpPyNJGUlxMjOPfwAAYLQNdrvQDOfclp7Hd0r6o3PuNjPLkLRWEoVpAAAijHOuSdLfSpKZpTvnGj1HQgT684un6j//tE1l5SFdfAeFaaC3VWsqJUl3LyjynASxKmbH6qam456WVmxUatsRVadla196tqrTsrUrM0+vp52tg3/4sM/Hk+ODPUXqxJ6CdbIKMhKPFa4LMpOUm5aouOCQbvwGAESBwQrTvSeF+rikhyXJOddgZl1hTQUAAE6JmX1d0s+dc++bWYKkZyVdYmatkm52zv3Jb0JEkuSEoO6cV6gH/vCh3tvboLMnZPiOBESEA4fb9OMNO3Tt+RM1eXyK7ziIMTE/VqeldS902GN63S5Nr+tnJtCMDLXUHtD+hlZVN7SouqFF++pbjnv8WtVB7W/cq/bO4xfqDZiUk9arWN2r47r7caLyM5KUnhQf7r8WADACBitM7zazpeqeU/oiSZ+TJDNLkpQwCtkAAMDQ3SLp33oef1FSkqQcSWdKekTSpZ5yIUJ9cW6hHlodUll5SP958wW+4wAR4dF1VTrS3qmli0p8R0Fsiu2x+tZbpVWr+i582Ft8vHTbbUqKD2pqdoqmZg98Aairy+lAc5uq61u0r08Bu1U76pq1ofKA6o/0/X2pCUHlZyZpwoAF7CTlpCUqGGDqEADwabDC9F2S/kXSNZJucc4d7Hl9nqTHwh0MAACckjbn3NG2ok9K+pFzrkPSO2ZG2xD6yEyJ162XTdP311Tob688c9DiADAWHG7t0GPrqvRnM/M1Iz/ddxzEptgeq++7T3rssZMXpr/61SEdLhAw5aQlKictUbMmZQ6435G2zo8K1w0tqq5v0d5exez1oTrtb2xVR9fx3dfBgCk3LbFnruvu6UOOzXvdU7wuyExSSsJgZRMAwHAM+C+sc65a0t39vP4nSdF9ixEAALGn1czOlrRf0sckfa3Xe1Qc0a8vzS/SD16u0oqXQvrXz5zrOw7g1Y837FD9kXYtLaVbGmET22N1SYn01FPSjTd2F6d7F6jj47u3p57q3m8EJScEVZiTqsKc1AH36epyqj3cqn31facP2dfQooqaw1oXqlNjS0efz6YnxfXpuO5dwM7PTFROaqICdF8DwCnj0h8AALHhPknPqPuW4O865yokycyulrTZZzBErvyMJN1w0WT97PVd+usrZigvPcl3JMCL1o5OfX9NhS4rztKFU8f7joPYFftj9VVXSZs3S9/5jvTDH3YviJiWJt12W3en9AgXpYcqEDDlpScpLz1J52rg7uvDrR3Huq77zn/dqq37alXT1KrOE7qv4wKmvPTEYwXr/IzuaUROnD4kKT4Y7j8VAKIKhWkAAGKAc+5lSTP6ef05Sc+NfiJEi3sWFusnr+3QI2ur9PdXneU7DuDF02/u1r6GVn37xvN8R0EMGzNjdUmJ9OCD3VuUSU2MU3Fumopz0wbcp7PLqbap9Vjxunche19Diz7c16g1W2vV1Nq3+zozOb5Xx/Xx04fk9xSvs1IS6L4GMGZQmAYAABjDCnNSdfW5E/TE+u1atrhEGUnRP80pcCo6u5xWrK7QrEkZWjAjx3ccABEuGDDl9xSSB7uU1djS3lO0bu23gP3+3gbVNLXKHd98rfhgd3f30TmuC/qZPiQvI5HuawAx4aSFaTP7j35erpe00Tn3m5GPBAAAgNG0tLREz27eqx++sl1/sXi67zjAqPrdO9WqqD2s791yoczoUgQwMtKT4pWeFK/peQMvptrR2aWanu7rjwrXrccev7enQX96b7+OtHf2+ez4lPhjXda9O657TyUyLiWef9cARLShdEynS5op6ame55+VtEXSMjP7mHPuvnCFAwAAQPidMzFTi87I1Q9ertRd84vowsKY4ZzT8vJtKspJ1SdnFfiOA2CMiQsGNCEzWRMykwfcxzmnhpbj577uvXBjdUOLtuxuUN3hvt3XCXGBEzquE/sUsPMzkpQQFwjzXwoA/RtKYbpEUqlzrl2SzOxBSb+V9AlJm9S9gAMAAIgAZnZtPy/XS9rinKsb7TyIHstKS/TnK9frZxt36ra5hb7jAKNi7bZabdndoG999lwFmdMVo4SxGqfCzJSZHK/M5HidkT9w93V7Z5f2Nx7ffX20cL23vkWbdx3S7+tb1NrR1eez2akJxwrWHy3WmKiCzORjhe2M5Di6rwGMuKEUpidJSpbU3vM8WdIk51yHmbWGLRkAADgdSyXNlbS65/lCSeslzTCzbzjnfuQtGSLaJUVZunDqOK14qUI3XzJVcUG6pxD7lr8YUn5Goj5z4STfUTC2MFZjxMUHA5o0LlmTxg3efV1/pF3VvQvXJ8yBvWnnIdUdbuvz2aT4QL9ThhwrZmcmKS89UfF8fwBwCoZSmP4PSW+Z2R8lmaRSSd82s1RJ5eGLBgAATkO7pLOdc3slycwmSPovSZepe9zmZBf9MjMtK52uu/97o57dvFfXX0ChDrHtzR0H9UpFnb5+9dlKjGP6Gowqxmp4YWYal5KgcSkJOqsgY8D9Wjs6tb9nruu99X0Xbnxjx0Htq29VW+fx3ddmUnZqYne3dUZyz88TitmZSUpPpPsaQLeTFqadcyvM7DeSLlV3Yfp/O+d29rz9N+EMBwAATlnR0RNdSXLO7TWzM51ztWbW4TMYIt/HzsrTmfnpKisP6drzJirA1AaIYWXlIWUmx+vmS6f6joKxh7EaES0xLqgpWSmakpUy4D7OOR1sbj9uypDej3cdbNbr2w/oYHN7n8+mJAT7dlxnJB7XfZ2blsjdW8AYMJSOaUnqkLSzZ/8pZjbFObcufLEAAMBpetnMfiXppz3Pb5S0rudOpwZ/sRANAgHTvaXF+upPNulP7+/XFTPzfUcCwmLrvkb9/t19+quPz1Ba4lBPiYARw1iNqGdmykpNUFZqgmZOHLj7uqW9s0/HdXV967EC9obKA9rf2KL2zuNXbgyYlJue2E8B+/jpQ/g3HIhuJ/0v2Mz+TdKtkt6TdPQ+DSfp6jDmAgAAp2eZpM9Lulzddzr9RNJPnXNd6p7DEhjUp2dP1AO//1DLy7fp42fncastYtJDqyuUHB/UHfMKfUfB2MRYjTEjKT6oadmpmpadOuA+XV1OB5rbjuu43tdTyK5uaNX2umatr6hTQ0vfGwrSEuOU37vbulfhekLP9CHZaYkscAtEqKFcWrpB0hnOuZZwhwEAAMPTc1L7ZM8GnLK4YEBLFhbrG796RxsqD+jS4mzfkYARtfvQEf3qrd26be40ZaUm+I6DMYixGjheIGDKSUtUTlqiZk3KHHC/I22dfaYM6f14fahO+xtb1dF1fPd1MGDKS0/sp+P6+NdSEui+BkbbUP6rq5TExD4AAEQBM7tO0rckTVR3F5ZJcs65ge+xBE7w+TlT9N0XtqpsdYjCNGLO91+qkCTdvaDYcxKMVYzVwOlJTgiqKCdVRTmDd1/XHm7VvvrWno7rXt3X9S3aVtOkl7fVqrG1b/d1elLcsSJ1wQDTh2SnJrAGBzCChlKYbpT0ppm9IKn16IvOORY+BAAg8jwg6TPOubd9B0H0SooP6kvzi/Tt332gd/bU65yJA3cvAdGkrqlVT762Q9dfMEmTxiX7joOxi7EaCJNAwJSXnqS89CSdq4G/vxxu7ThhypDjpw/Zuq9W+xtbdELzteKD3ccfaPqQo4+T4oNh/kuB2DCUwvRvezYAABD59nGii5Fw62XTVFYeUll5SA/ecqHvOMCIeHRdlVo7unTvIrql4RVjNeBZamKcSnLTVJKbNuA+nV1OtU2txy3cuLf+owL2+9WNWv1BjQ63dfb5bGZyvCb0KlbnH+vC/mj6kKzUBNbywJh30sK0c+7h0QgCAABGxGtm9oSkp3X8nU7P+IuEaJSZHK9bL5umlS+FVFV7WIWD3DYLRIOm1g49tq5KV87M1/S8dN9xMLYxVgNRIBgw5Wd0F5fPG2S/xpb27nmue6YP2dczbcjRx+/tbVBNU6vcCd3XCcGA8jISjy9c9zw+unBjXkaiEuPovkbsGrAwbWY/ds7dbGZvSnInvu+co3UGAIDIky2pS9K1vV5zkjjZxSn70vxCPfJypVa8VKF//+y5vuMAw/KjV7eroaVDS0un+44CMFYDMSQ9KV7pSfGDXvRs7+xSTWPrgNOHvLunQX96b7+OtPftvs5KTejpsj5++pDexexxKfF0XyMqDdYx/Xc9P28cjSAAAGD4nHO3+c6A2JGXnqTPXTRZP9u4S1+9YobyMpJ8RwJOS2tHp1atqdS8kmydP2Wc7zgY4xirgbEnPhjQxHHJmjjI+gbOOTW0dBzfcd2r87q6oUVv765XbVNbn88mxgWOK1hPOG7O6+7pQ/LSk5QQFwjnnwmcsgEL0865XT0/Q6MXBwAAnA4zu88594CZfUf93+nEosU4LUsWFuvHG3bo4bWV+p9Xn+07DnBafvHGbu1vbNV/fP5831EwhjFWAxiMmSkzOV6ZyfE6I3/g7uu2ji7tb2wZcPqQzbsO6ffvtKi1o6vPZ3PSEvrtuO79OCM5ju5rjJqTzjFtZtdJ+pakiZKsZ3POuYwwZwMAAEN39ELyFq8pEHOmZafqmtkT9fj67VpWOl2ZKfG+IwGnpLPLacXqkM6dlKnLp2f7joOxjbEawLAlxAU0eXyKJo9PGXAf55zqj7R3TxlSf3wRu7r+iPbUt+jNnYd04HDf7uvk+GDPlCH9z39dkJGk3PRExQfpvsbwnbQwLekBSZ9h1WAAACKXc+7pnocHnXO/6P2emX3WQyTEkKWlJXpm0x79cH2VvvKxGb7jAKfk+S17VVXXrLIvXEgHGLxirAYwWsxM41ISNC4lQWcVDNxX2trRqf0NrScUsD+aPuT1HQe1r75VbZ3Hd1+bSTlpPYXrnulCPnr80VQi6Uk0NGBwQylM76MoDQBA1PgHSb844bWv9/MaMGRnT8jQ4jNz9cjLVbprfrGSE1gdHtHBOaflL4ZUnJuqT5xT4DsOcBRjNYCIkBgX1JSsFE3JGrz7+mBz+0eF66PF654C9q6Dzdq4/YAONbf3+WxqQrDfjuujBeyj3dfBABeOx6qhFKZfM7MnJD0tqfXoi845VgwGACBCmNknJH1S0iQz+49eb2VI6jvBHHCKli2ers899Ip+unGnbp9X6DsOMCQvba3Vu3sbdP8NsxXgpBeeMVYDiEZmpqzUBGWlJmjmxIG7r1vaO/t0XFfXtx4rZr9aeUD7G1vU3nn8FPsBk3LTj++4LuinmJ2aOJQSJqLNUP5XzVb3IHltr9ecJArTAABEjv3qnrOyRdI7vV5vlPT3XhIhplxcmKU508Zr5UsVuuXSqcwriKiw/MVtKshI0vUXTPIdBZAYqwHEsKT4oKZlp2paduqA+3R1OR1objvWfb33hOlDquoOa31FnRpaOvp8Nj0x7oSO68Q+3dfZaXRfR5uTFqadc7eNRhAAAHD6nHNvSnrTzJ5wzrVIkpllSprknKv1mw6xYtniEn3p0Y369aY9+uyFk33HAQb1+vaDerXygP7hU2crIY4LKfCPsRrAWBcImHLSEpWTlqhZkzIH3O9IW+fx816f8PiVUK32Nbaqs+v47uu4gHV3X/czZUjvx0xLFzkGLEyb2X3OuQdOuMXoGOfc34QvFgAAOE2/MbPPSApK2iTpgJn9wTn3d55zIQYsPjNPZxWkq6w8pOvPn8TUCIhoZeUhjUuJ182XTPUdBTgRYzUADCI5IaiinFQV5Qzcfd3Z5VTX1NpPAbt7+pCt+5u0dmutGlv7dl9nJMWpoGeBxoKegnXvxwWZScpKSeC77igYrGM61PPznUH2AQAAkSXLOddgZndJekzSN9R90svJLobNzLS0tER//eRbeuG9fbqSxeQQoT6obtQL7+3T/7hiBnNSIhIxVgPAMAUDpryMJOVlJGn2IDfyHW7t6J7zume6kOMe17fow32Nqmls1QnN14oPmvLST+y4TuxTzE6Kp/t6OAb8luace7rn58OjFwcAAAxTnJnlSvqcpG8455wZV/oxcj517gT9399/oOXlIf3ZzHzx/y9EohWrQ0pJCOr2uYW+owD9YawGgFGSmhinktw0leSmDbhPR2eXapva+nRfHy1gv1fdoPIP9utwW2efz45Lif+ocN2zWOOEE6YPGZ8Sz3fmAZy0fcDMciTdJ+kcSUlHX3fOXRnGXAAA4PT8q6TVktY65zaYWbGkSs+ZEEPiggEtWViif3x6i9ZXHNDckmzfkYDj7DzQrF9t2qM75hVqfGqC7zhAfxirASCCxAUDx6bw0JSB92tsae9ZrLF7CpHuBRyPHJs+5N29DaptapU7ofs6IS6g/IzE4wrYx6YP6Sli52UkKjFulLuvQyHpgQekxx+XmpqktDTp1lul++6TSkpGJcJQ7mt7XNIvJX1G0l9Iul1SdThDAQCA0+Oce1LSk72eV0i6zl8ixKLPXTRZ331hq8pWhyhMI+KsWlOhgEl3LyjyHQXoF2M1AESn9KR4pSfFa3pe+oD7tHd2qaaxdcDpQ97Z06A/vrdfR9r7dl9npSYoP6O74/qjAnbicQXszOQR6r5+/nnpxhul9vbuTZIaG6VVq6THHpOeekq66qrh/56TGEphOtc5t8LM/sI590cz+5OkF8MdDAAAnDozmy7pe5IKnHPnmdlsSZ9yzv2752iIIUnxQX1pfqHu/+0H2rK7ftBV1YHRVNvUqidf26nPXDBJEzKTfccB+sVYDQCxKz4Y0MRxyZo4buDvIc45NbR09HRf92wnFLA37zqk2qa2Pp9NjAsMsnBjdxE7Lz1JCXGBgUOGQt1F6ebmvu8dLVTfeKO0eXPYO6eHUpjuKZur2sw+IWmPBm1sBwAAHq2S9L/UfcIrSW9L+rEkTnYxom69bJrKXgypbHVI37vlQt9xAEnSoy9Xqa2zS/csGp3bT4HTxFgNAGOYmSkzOV6ZyfE6I3/g7uu2ji7tb2zpM33I0UL2pl2H9Lt3WtTa0XXC8aXs1EQVZCb2mf+6ICNJBf+5QvmBeGVIGrD3ur1d+s53pAcfHLG/uz9DKUz/m5llSvpbdQ+cGWK1YAAAIlWqc27d0du7ehZUaj/JZ4BTlpEUr9vmTtNDq0OqrD2sopxU35EwxjW2tOuxV6r0yXMKBl3gCIgAjNUAgJNKiAto8vgUTR6fMuA+zjnVH2nX3vrjO66PFrB3H2rRGzsO6cDhXt3XaYukv1ik5LYWFTTVKb+xTveteVwX7373o33a26Uf/tBvYdrMgpIKnXPPSNosaUFY0wAAgOGqM7MiSU6SzOx6sTYEwuTOy4v08NpKrVgd0rdumO07Dsa4J17docaWDi0tpVsaEY+xGgAwIsxM41ISNC4lQWdPyBhwv9aOTu1v6O66rr76eu1Ly1Z1Wraq07O1Ly1bAdfV90NNTWFM3m3QwrRzrtPMPivpP8OeBAAAjISvSHpY0llmtl3SXkk3+Y2EWJWbnqjPz5miJ1/bof9xxRndK5kDHrS0d+rhtZWaPz1HsyeP8x0HOBnGagDAqEqMC2pKVoqmZKVIu97qXujwZNLCfwfaIDNhH7PWzL5rZnPNbPbRLezJAADAkPVcSJZzbptz7mOSJkg6zzl3mXOuyms4xLQlC4vV5aSH11b4joIx7Odv7FJNY6uW0S2NCMZYDQCICLfeKsXHD75PfLx0221hjzKUwvQiSRdKul/dc0x/T1J4JxgBAACn6h96P3HO1TvnDvkKg7FjSlaKPj17gn706g4dau67cjgQbh2dXVqxukLnTc7U3JJs33GAwTBWAwD8u+++oRWmv/rVsEcZsDDd62rugn62hWFPBgAAgKhwb2mJDrd16r9f2e47Csag57ZUa8eBZi0tna6ji8kBAABgACUl0lNPSSkpfQs93fZPAAAgAElEQVTU8fHdrz/1VPd+YTZYx/Q/DPIeAACILGeZ2eZ+trfNbLPvcIhtZxVk6ONn5ekHL1equa3DdxyMIc45lZWHVJKbqitn5vuOA5wMYzUAIDJcdZW0ebO0ZImUkSEFAt0/lyzpfv2qq0YlxqCLHwIAgKhRKenTvkNg7Fq2uEQ3lL2in7y2U3deXuQ7DsaI8g9r9N7eBn37xtkKBOiWRsRjrAYARI6SEunBB7s3TwYrTJ81wFVbk+SccyyACABA5GhzzoV1HgUz+1tJ35aU65yrDefvQvS5aFqWLinM0vdfqtAXLp2mhLihLGUCDE/ZiyFNzEzSdedP8h0FGIqwj9UAAESTwQrTXM0FACB6vBzOg5vZFEl/JmlHOH8PotvSxSW68wev6ZlNe3TjRZN9x0GM21h1QBuqDugb18zkQgiiRVjHagAAos1g3+DanHPbB9pGLSEAADgp59xXwvwrviPpa5JcmH8PoljpGbk6e0KGHlodUlcX/1dBeJWVhzQ+JV43XTLFdxRgSEZhrAYAIKoMVpjmai4AAJCZXStpt3Nuk+8siGxmpqWlJdq2v0m/f3ef7ziIYe9XN+iP7+/XHfOKlJLAsjkAAADRaMDCNFdzAQAYO8zsBTPb0s92naSvS/rGEI6xxMw2mtnGmpqa8IdGRLp6VoGmZaeobHVIztE1jfB4qDyk1ISgbp83zXcUAAAAnCYmYwMAIEaYWYaZlfTz+kkXLHbOXeGcm3XiJqlCUpGkTWZWJWmypDfMrKCfY6x0zs1xzs3Jzc0d/h+EqBQXDGjJwmJt2nlIr4TqfMdBDNp5oFm/3rxXt1w6VeNSEnzHAU7JcMZqAABiDYVpAABigJl9XtL7kn5uZu+Y2cW93n70dI/rnHvbOZfnnCt0zhVK2iXpQudc9bACI6bdcOFk5aYnqmx1yHcUxKCVL1UoYNJd84t9RwFOSbjGagAAotWghWmu5gIAEDX+l6SLnHPnS7pT0g/N7LM975m/WBiLkuKDumt+kdZsrdXbu+p9x0EMqWls1U837tQNF05WQWaS7zjAqWKsBgCglwEL01zNBQAgqgSdc3slyTm3QdJiSV83s7+SNGIT/fZ0TteO1PEQu75w6VRlJMWpbPU231EQQ37wcqXaOru0ZCHd0ohKozJWAwAQLQbrmOZqLgAA0aOx911OPSe+pZKuk3SOr1AYu9KT4vXFuYV6fku1QjVNvuMgBjS0tOuHr2zX1bMmqDg3zXcc4HQwVgMA0MtghWmu5gIAED2W6oRx3TnXKOmTkr7kJRHGvDsuL1RCMKAVzDWNEfD4+u1qbO3Q0tI+Mw0C0YKxGgCAXgYrTHM1FwCAKOGc2+Sc29rPW12jHgbokZOWqJsunqJfvrlbe+uP+I6DKNbS3qlH1lZpwYwczZqU6TsOcFoYqwEAON5ghWmu5gIAECV6Fiz+n2b2oJldad3+UlKFpM/7zoex6+4Fxepy0qo1lb6jIIr97PVdqm1q1bLS6b6jAKeNsRoAgOMNWJjmai4AAFHlh5LOlPS2pLsl/V7SjZKuc85d5zMYxrYpWSm67ryJ+vGGHTp4uM13HEShjs4urXwppPOnjNNlxVm+4wDDwVgNAEAvAxamR+Jqrpl90sw+MLNtZvb3g+x3o5k5M5tz6n8CAACQVOycu8M5t0LSzZLmSLrGOfeW51yA7i0tUXNbpx57pcp3FESh37y9VzsPHNGy0hKZsQY7otqwx2rOsQEAsWSwqTyGdTXXzIKSvifpKkkzJd1sZjP72S9d0l9JevWU0wMAgKPajz5wznVKquyZggvw7oz8dF1xdr4eXVelw60dvuMgijjnVFYe0oy8NF1xdr7vOMBwDWus5hwbABBrBitMD/dq7iWStjnnKpxzbZKeVPfCiSf6Z0n3S2o5hdwAAOB455lZQ8/WKGn20cdm1uA7HLBscYkONbfrydd2+o6CKPLiB/v1fnWj7l1UokCAbmlEveGO1ZxjAwBiymCF6eF2Xk2S1PvMY1fPa8eY2QWSpjjnnj2F4wIAgBM454LOuYyeLd05F9frcYbvfMCFU8fr0qIsrVpTobYOlizB0Cx/MaRJ45J17fkTfUcBhm0ExmrOsQEAMWWwwvRwr+b219Lgjr1pFpD0HUn3nfRAZkvMbKOZbaypqRnCrwYAAECkWbZ4uvbWt+jpt3b7joIo8FrVAW3cflBfXlCk+OBgpy3AmME5NgAgpgz4DW8ErubukjSl1/PJkvb0ep4uaZakcjOrknSZpGf6W5zBObfSOTfHOTcnNzd3KH8XAAAAIszCGTk6Z2KGHlodUmeXO/kHMKYtf3GbslIT9OcXT/UdBYgUnGMDAGJKOFsPXpM0w8yKzCxB0k2Snjn6pnOu3jmX45wrdM4VSlov6Vrn3MYwZgIAAIAnZqalpSWqqDmsP7xb7TsOIth7exv04gc1unNeoZITgr7jAJGCc2wAQEwJW2HaOdch6SuSfifpPUk/dc69Y2bfNLNrw/V7AQAAELmumjVBhdkpWl4eknN0TaN/ZeUhpSYE9cW5hb6jABGDc2wAQKyJC+fBnXPPSXruhNe+McC+peHMAgAAAP+CAdM9i0r0P3/xtl7eVqf5M3J8R0KE2V53WM9u3qMvLyhWZkq87zhAROEcGwAQS1hFBAAAAKPqsxdOUl56ospWb/MdBRFo5UsVigsE9KX5Rb6jAAAAIIwoTAMAAGBUJcYFdfeCIr28rU6bdh7yHQcRZH9ji372+i7dcNFk5Wck+Y4DAACAMKIwDQAAgFF3y6XTlJEUp7LykO8oiCCPrK1SR2eX7llY7DsKAAAAwozCNAAAAEZdWmKcbp9XqN+9W61t+5t8x0EEqD/SrsfXb9fV505QYU6q7zgAAAAIMwrTAAAA8OKOeYVKjAvoodV0TUN6fP12NbV26N5FJb6jAAAAYBRQmAYAAIAX2WmJuuniqXr6zd3ac+iI7zjwqKW9U4+srdSiM3I1a1Km7zgAAAAYBRSmAQAA4M2Xe+YS/v6aCs9J4NNPN+5U3eE2LSulWxoAAGCsoDANAAAAbyaNS9Z150/Skxt26sDhNt9x4EF7Z5dWrK7QhVPH6ZKiLN9xAAAAMEooTAMAAMCrexcV60h7px5dV+U7Cjx4dvMe7T50RMtKp8vMfMcBAADAKKEwDQAAAK9m5Kfrypn5emxdlZpaO3zHwSjq6nIqKw/pjPw0feysPN9xAAAAMIooTAMAAMC7paUlqj/Sric37PAdBaPoT+/v14f7mrS0tESBAN3SAAAAYwmFaQAAAHh3wdTxmlucre+vqVBrR6fvOBgFzjktL9+mSeOSdc3sib7jAAAAYJRRmAYAAEBEWLa4RPsaWvX0m7t9R8Eo2FB5QG/sOKR7FhUrPshpCQAAwFjDN0AAAABEhPnTczRrUoYeWl2hzi7nOw7CbHl5SNmpCfr8nCm+owAAAMADCtMAAACICGamZaXTVVl7WL97p9p3HITRO3vqtfrDGn1pfpGS4oO+4wAAAMADCtMAAACIGJ84p0DFOalaXr5NztE1HavKykNKS4zTrZdN8x0FAAAAnlCYBgAAQMQIBkz3LCrWlt0NWrO11ncchEFV7WE99/ZefeGyqcpMjvcdBwAAAJ5QmAYAAEBEuf6CSSrISFJZech3FITBipcqFBcM6K75Rb6jAAAAwCMK0wAAAIgoiXFB3b2gSK9U1OnNHQd9x8EI2tfQop+/vkufu2iy8tKTfMcBAACARxSmAQAAEHFuvqR7mge6pmPLI2sr1dHVpSULi31HAQAAgGcUpgEAABBxUhPjdPu8Qv3+3X3auq/RdxyMgPrmdj2+fruumT1R07JTfccBAACAZxSmAQAAEJHunFeo5PigylbTNR0L/vuVKh1u69S9i0p8RwEAAEAEoDANAACAiDQ+NUE3XzJVz7y1R7sONvuOg2E40tapH6yr0uIzczVzYobvOAAAAIgAFKYBAAAQse5eUCQzadWaSt9RMAw/eW2HDhxu09LS6b6jAAAAIEJQmAYAAEDEmjguWdefP0lPvrZDdU2tvuPgNLR3dun7ayo1Z9p4XVKU5TsOAAAAIgSFaQAAAES0exaVqLWjS4+uq/IdBafhmbf2aPehI1q2mLmlAQAA8BEK0wAAAIho0/PS9ImZBXpsXZUaW9p9x8Ep6OpyKlsd0lkF6Vp8Zp7vOAAAAIggFKYBAAAQ8ZaWlqihpUM/3rDDdxScghfe26dt+5u0tLREZuY7DgAAACIIhWkAAABEvPOmjNPl07O1ak2lWjs6fcfBEDjntLw8pClZyfrUuRN8xwEAAECEoTANAACAqLCsdLr2N7bqF2/s9h0FQ7C+4oDe2nlISxaWKC7IaQcAAACOxzdEAAAARIV5JdmaPTlTK1aH1NnlfMfBSSwv36actER97qLJvqMAAAAgAlGYBgAAQFQwMy0rLVFVXbOe37LXdxwMYsvueq3ZWqsvzS9UUnzQdxwAAABEIArTAABgUGb2T2a228ze6tmu9p0JY9eVMwtUnJuq5S+G5Bxd05GqrDyk9MQ43XrZNN9RAAAAEKEoTAMAgKH4jnPu/J7tOd9hMHYFAqZ7F5Xo3b0NWv1hje846EdFTZOe27JXt86dpoykeN9xAAAAEKEoTAMAACCqXH/+JE3ITFJZech3FPRj5UsVig8G9KXLi3xHAQAAQASjMA0AAIbiK2a22cweMbPxvsNgbEuIC+juBcV6tfKAXt9+0Hcc9FJd36Kfv7FLn58zWbnpib7jAAAAIIJRmAYAADKzF8xsSz/bdZLKJJVIOl/SXkkPDHCMJWa20cw21tQwxQLC6+ZLpmhcSjxd0xHm4bUV6nLSPQtLfEcBAABAhIvzHQAAAPjnnLtiKPuZ2fclPTvAMVZKWilJc+bMYVU6hFVKQpzumFeo//+FrfqgulFnFqT7jjTmHWpu0xOv7tCnZ0/QlKwU33EAAAAQ4eiYBgAAgzKzCb2efkbSFl9ZgN7umFeolISgHlpN13QkeGzddjW3dereUrqlAQAAcHIUpgEAwMncb2Zvm9lmSYslfdV3IECSxqUk6JZLpuqZTXu080Cz7zhjWnNbhx5dV6mPn5WnswoyfMcBAABAFKAwDQAABuWcu805d65zbrZz7lrn3F7fmYCj7lpQpIBJ319T4TvKmPbkhp062NyupXRLAwAAYIgoTAMAACBqTchM1mcvmKyfvLZTtU2tvuOMSW0dXVq1pkKXFGZpTmGW7zgAAACIEhSmAQAAENWWLCpWW2eXfvBype8oY9Kv3tqtPfUtWrqYbmkAAAAMHYVpAAAARLWS3DRdNatA//3KdjW2tPuOM6Z0dTk9tDqksydkqPSMXN9xAAAAEEUoTAMAACDqLV00XY0tHXri1R2+o4wpv393n0I1h7W0tERm5jsOAAAAogiFaQAAAES9cydnasGMHD28tlIt7Z2+44wJzjmVlW/T1KwUXT2rwHccAAAARBkK0wAAAIgJS0tLVNPYqp+/sct3lDHhlVCdNu2q1z2LihUX5LQCAAAAp4ZvkAAAAIgJc4uzdd6UcVqxukIdnV2+48S85eUh5aYn6oYLJ/uOAgAAgChEYRoAAAAxwcy0rLREOw4067kt1b7jxLTNuw5p7bZa3TW/SEnxQd9xAAAAEIUoTAMAACBm/NnZ+Zqel6ay8pCcc77jxKyy8pDSk+L0hUun+o4CAACAKEVhGgAAADEjEDDdu6hE7+1tUPkHNb7jxKRt+5v023eq9cW505SeFO87DgAAAKIUhWkAAADElOvOn6iJmUkqKw/5jhKTVr4UUkIwoDsvL/IdBQAAAFGMwjQAAABiSnwwoC8vLNaGqgPaWHXAd5yYsrf+iH755m79+cVTlJOW6DsOAAAAohiFaQAAAMScmy6eqqzUBLqmR9iqNZXqctKXFxT7jgIAAIAoR2EaAAAAMSc5Iag75hXqj+/v1/vVDb7jxISDh9v04w07dN15EzUlK8V3HAAAAEQ5CtMAAACISbfPLVRqQpCu6RHy6LoqNbd16t7SEt9RAAAAEAMoTAMAACAmZabE6wuXTdOvN+3Rjrpm33Gi2uHWDj32SpWuODtfZ+Sn+44DAACAGEBhGgAAADHrrvlFigsEtHINXdPD8eMNO3SouV1L6ZYGAADACKEwDQAAgJiVn5GkGy6apJ9u3KWaxlbfcaJSW0eXVq2p1KVFWbpo2njfcQAAABAjKEwDAAAgpi1ZWKKOzi498nKl7yhR6ek3d6u6oYVuaQAAAIwoCtMAAACIaUU5qbrq3Al6/JXtamhp9x0nqnR2OT30UkjnTMzQojNyfccBAABADKEwDQAAgJi3dFGJGls79Pj67b6jRJXfv1OtiprDWlpaIjPzHQcAAAAxhMI0AAAAYt6sSZlaeEauHllbqZb2Tt9xooJzTsvLQyrMTtFVsyb4jgMAAIAYQ2EaAAAAY8Ky0hLVNrXpZ6/v8h0lKry8rU5v767XPYtKFAzQLQ0AAICRRWEaAAAAY8KlRVm6YOo4rXwppI7OLt9xIt7y8m3KS0/UZy+c5DsKAAAAYhCFaQAAAIwJZqZlpdO188AR/ebtvb7jRLS3dh7SulCd7l5QpMS4oO84AAAAiEEUpgEAADBmfPysPM3IS1NZeUjOOd9xIlZZ+TZlJMXplkun+Y4CAACAGEVhGgAAAGNGIGBaWlqi96sb9af39/uOE5G27W/U797Zp9vnFSotMc53HAAAAMQoCtMAAAAYUz593kRNGpessvKQ7ygR6aHVFUqKD+iOeYW+owAAACCGUZgGAADAmBIfDGjJwmJt3H5QGyoP+I4TUXYfOqKn39ytmy6equy0RN9xAAAAEMMoTAMAAGDM+fycKcpOTVBZ+TbfUSLKqjUVkqS7FxR5TgIAAIBYR2EaAAAAY05yQlB3Xl6oFz+o0bt7GnzHiQgHDrfpyQ07de35EzV5fIrvOAAAAIhxFKYBAAAwJt02t3txv4dWM9e0JD26rkpH2ju1dFGJ7ygAAAAYAyhMAwAAYEzKTI7XFy6dqmc379H2usO+43jV1Nqhx9ZV6cqZ+ZqRn+47DgAAAMYACtMAAAAYs+6aX6S4YEArXqrwHcWrH7+6Q/VH2rW0lG5pAAAAjA4K0wAAABiz8jKSdONFk/XUxl3a39DiO44XrR2dWrW2QnOLs3XB1PG+4wAAAGCMoDANAACAMe2ehcXq6OrSwy9X+o7ixS/f2K19Da10SwMAAGBUUZgGAADAmDYtO1Wfmj1RT6zvns5iLOnsclrxUoVmTcrQghk5vuMAAABgDAlrYdrMPmlmH5jZNjP7+37e/xsze9fMNpvZH81sWjjzAAAAAP25d1Gxmlo79Pj67b6jjKrfbqlWZe1hLSudLjPzHQfASXCODQCIJWErTJtZUNL3JF0laaakm81s5gm7vSlpjnNutqSnJN0frjwAAADAQM6ZmKnSM3P1yNpKHWnr9B1nVDjntLx8m4pzUvWJcwp8xwFwEpxjAwBiTTg7pi+RtM05V+Gca5P0pKTreu/gnHvROdfc83S9pMlhzAMAAAAMaFnpdNUdbtPPXt/pO8qoWLO1Vu/sadA9i4oVDNAtDUQBzrEBADElnIXpSZJ6f6vf1fPaQO6S9HwY8wAAAAADurhwvC6aNl4rVleovbPLd5ywW16+TfkZibr+gsG+ogOIIJxjAwBiSjgL0/21Xbh+dzS7VdIcSd8e4P0lZrbRzDbW1NSMYEQAAACgm5lpWWmJdh86omc37/EdJ6ze2HFQ6ysO6MsLipUYF/QdB8DQcI4NAIgp4SxM75I0pdfzyZL6fMM3syskfV3Stc651v4O5Jxb6Zyb45ybk5ubG5awAAAAwOIz83RmfrrKykPq6uq33hMTyspDykyO102XTPUdBcDQcY4NAIgp4SxMvyZphpkVmVmCpJskPdN7BzO7QNIKdQ+Y+8OYBQAAADipQMC0tLREH+5r0h/fj82vpx/ua9Qf3t2n2+cVKi0xznccAEPHOTYAIKaErTDtnOuQ9BVJv5P0nqSfOufeMbNvmtm1Pbt9W1KapJ+Z2Vtm9swAhwMAAABGxTWzJ2jy+GQtL98m52Kva/qh1SElxwd1x7xC31EAnALOsQEAsSasLRLOueckPXfCa9/o9fiKcP5+AAAA4FTFBQO6Z2Gx/vFX7+jVygO6rDjbd6QRs+tgs555a49umztNWakJvuMAOEWcYwMAYkk4p/IAAAAAotLn5kxRTlqCyspDvqOMqFVrKiVJX15Q7DkJAAAAxjoK0wAA4KTM7C/N7AMze8fM7vedBwi3pPig7ry8SKs/rNGW3fW+44yIuqZWPfnaDl1/wSRNHJfsOw4AAADGOArTAABgUGa2WNJ1kmY7586R9H89RwJGxW1zpyk9MU4PrY6NrulH11WptaNL9y4q8R0FAAAAoDANAABOaqmkbznnWiXJObffcx5gVGQkxesLl03Tc2/vVVXtYd9xhqWxpV2PravSJ2YWaHpemu84AAAAAIVpAABwUmdIWmBmr5rZajO72HcgYLR8aX6h4oIBrXgpurumf/TqDjW0dGhpKd3SAAAAiAwUpgEAgMzsBTPb0s92naQ4SeMlXSbp7yT91Mysn2MsMbONZraxpqZmlP8CIDzy0pP0+TmT9fPXd2tfQ4vvOKelpb1Tq9ZW6vLp2TpvyjjfcQAAAABJFKYBAIAk59wVzrlZ/Wy/krRL0i9ctw2SuiTl9HOMlc65Oc65Obm5uaP9JwBhs2RBiTq6uvTw2krfUU7LL97Yrf/X3r1H3VWXdwL/PrkRwv1+h5AXVMAiCCIOl6RqHbFrtCpV7OBd0dCuGVuna7WdWXX0n2lXx3FNpyWANxAdtKWORYWRcTSIgnKRu8g0b7iFi4RbCIRLLr/54z1AwJi8JOfskxw+n7XOyjn77HfvL3v9eJ/9e84++126/KnMn3vQsKMAAMCzNKYBgA35VpLXJ0lVvSzJjCQPDDURdGj/XWbl37xq73ztp3dk2YqVw47zoqxe03LWj8Zz+L475LiDdhl2HAAAeJbGNACwIV9KMqeqbkry9STvb621IWeCTn187lgef3p1vnLF7cOO8qJcdOO9uePBFZk/dyzruAMPAAAMjcY0ALBerbWnW2un9m7t8erW2g+GnQm6dshe2+f1r9g9X7789jzx9Ophx5mU1lrOWDieObttk3992J7DjgMAAM+jMQ0AAJNw+ryxPPT40/nGVXcOO8qkXPr/luaWex/Nx+eOZcoUV0sDALB50ZgGAIBJOHr2znnN7J3y+ctuy8rVa4YdZ4POWDievXaYmd87Yp9hRwEAgF+jMQ0AAJN0+ryDcvcjT+TC6+4ZdpT1uuaOh3LlbQ/lIyfMyYxpTvkBANj8OEsFAIBJmvfy3fKKPbfLgkvHs2bN5vs3QBcsHM+Os6bnlNfsN+woAACwThrTAAAwSVWV+fPGsuj+x/L9W3417DjrdOt9y/P9W+7PB/7V7Gyz1bRhxwEAgHXSmAYAgBfhd39rr+y/86ycsXA8rW1+V02feel4Zs2Ymve/bvawowAAwG+kMQ0AAC/CtKlTctqJc3LdXY/kisUPDjvO89z10IpceP09ec8x+2enbWYMOw4AAPxGGtMAAPAinXzUvtl1262yYOH4sKM8z+cvW5wplXzkhAOHHQUAANZLYxoAAF6kmdOn5sPHH5jL/uWB3Lhk2bDjJEmWLn8q37jqrrz9yH2y1w5bDzsOAACsl8Y0AABshFOP3T/bzZyWMy/dPK6aPufy2/L06jX52NyxYUcBAIAN0pgGAICNsN3M6XnvsQfkopvuzeKljw01y/InV+YrV9yRNx+2Z8Z223aoWQAAYDI0pgEAYCN96PgDM2PqlJx16eKh5vjqT+/M8idX5fR5Bw01BwAATJbGNAAAbKRdt90q737NfvnmtUty37Inh5LhyZWr88Uf35YTDt41v7XvDkPJAAAAL5bGNAAAbIKPnjAna1ryhcuGc9X0BdcsyQOPPZX57i0NAMAWRGMaAAA2wX47z8pbX7V3/ueVd+aRFU93uu9Vq9fk7B8tzqv22zGvG9ul030DAMCm0JgGAIBN9PG5Y1nx9Oqce/kdne73uzfemzsfWpH5c8dSVZ3uGwAANoXGNAAAbKKX77ld3njI7jnn8tuy4ulVneyztZYFC8dz0O7b5k2H7tHJPgEAoF80pgEAoA/mzxvLwytW5utX3tXJ/hbeujS/vG95Pj53LFOmuFoaAIAti8Y0AAD0wVEH7JxjDtw5n79scZ5etWbg+ztj4aLsvcPMvPVVew98XwAA0G8a0wAA0CenzxvLvcuezD9fd/dA93PV7Q/lqtsfzkdPnJMZ05zSAwCw5XEWCwAAfTL3Zbvl0L22z5mXjmfNmjaw/SxYOJ6dZk3Pu1+z38D2AQAAg6QxDQAAfVJVmT9vLONLH88lv/jVQPZxy72P5ge/vD8fPO7AzJoxbSD7AACAQdOYBgCAPjrplXvmgF1mZcHCRWmt/1dNn3npeLaZMTXve90Bfd82AAB0RWMaAAD6aNrUKfnYiWO5fsmyXD7+YF+3feeDK/Lt6+/JH7x2/+w4a0Zftw0AAF3SmAYAgD5751H7ZPfttsqCheN93e7Zl41n2pQp+cgJc/q6XQAA6JrGNAAA9NlW06bmw8cfmB8veiA3LHmkL9u8f/mT+Yerl+Qdr94ne2w/sy/bBACAYdGYBgCAAfiD1+6f7WdO69tV01/+ye1ZuXpNPjZ3rC/bAwCAYdKYBgCAAdhu5vS873Wz879vvi+L7n9sk7b16JMr89Ur7shbXrlXDtx1mz4lBACA4dGYBgCAAfngcbOz1bQpOevSTbtq+rwr7sjyp1Zl/jxXSwMAMBo0pgEAYEB22XarvPvo/fKt6+7OPY88sVHbeHLl6nz5J7flxJftllfus0OfEwIAwHBoTAMAwAB99MQ5aS35wmW3bdTP/+PVd5mQwssAAA6ESURBVOWBx57OfPeWBgBghGhMAwDAAO2706y89Yi9c/6Vd+bhx59+UT+7avWanPWjxTly/x1z7JydB5QQAAC6pzENAAAD9vG5Y3li5eqcc/ntL+rnvnPDvVny8BOZP3csVTWYcAAAMAQa0wAAMGAv22O7/M6he+TcK27P40+tmtTPtNayYOF4Dt5927zxkD0GGxAAADqmMQ0AAB2YP28sj6xYmfOvvHNS6//gl/fn1l8tz8fnjmXKFFdLAwAwWjSmAQCgA6/ef6ccO2fnfOGy2/LUqtXrXbe1ljMWjmefHbfOW4/Yu6OEAADQHY1pAADoyOnzDsp9jz6Zf772nvWud9XtD+eaOx7OaSfOyfSpTtkBABg9znIBAKAjJxy8aw7be/uceel4Vq9pv3G9MxYuyi7bzMi7jt6vw3QAANAdjWkAAOhIVeX0eQdl8QOP55Kb71vnOr+459EsvHVpPnjc7Gw9Y2rHCQEAoBsa0wAA0KE3v3LPHLjrNjlj4Xha+/WrphdcOp5tZkzNe4+d3X04AADoiMY0AAB0aOqUysdOnJMb716WHy964Hnv3f7A4/nuDffk1GMPyA6zpg8pIQAADJ7GNAAAdOztr94ne2y/VRYsHH/e8rMvW5xpU6bkw8cfOKRkAADQDY1pAADo2FbTpuYjx8/J5eMP5ro/+vNk++1z/3a75ILLx/POJ27L7kvvHnZEAAAYKI1pAAAYgvcs+2V2ePKxLLh3WrJ8eb541FuzqqbkY1/9q+Tww5OLLx52RAAAGBiNaQAA6Nr4eLY95ffz/mu+ne8dfGyu2fsV+dqRb8lbbv1JZi+9K1mxIjn55GR8fMPbAgCALZDGNAAAdO2zn01WrswHrvl2Zq58Mh86+VN5bKtZmf/Tf3xunZUrk899bngZAQBggDSmAYD1qqpvVNV1vcftVXXdsDPBFu+rX01WrszOTzyaU66/JMu23i5zF1+dw+6/7bl1Vq5MzjtveBkBAGCApg07AACweWutvfuZ51X12STLhhgHRsNjjz379GNX/lOu3vfQ/MllX1vvegAAMEo0pgGASamqSvKuJK8fdhbY4m27bbJ8eZJkr+UP5jvnfuI3rwcAACPIrTwAgMk6IcmvWmv/MuwgsMU79dRk+vT1rzN9evLe93aTBwAAOqYxDQCkqr5fVTet4/G2tVZ7T5Lz17ON06rq6qq6eunSpYMPDVuyT35yco3pP/7jbvIAAEDH3MoDAEhr7Y3re7+qpiV5R5Kj1rONs5OcnSRHH31062tAGDVjY8kFFyQnnzzxRw5XrnzuvenTJx4XXDCxHgAAjCBXTAMAk/HGJL9srS0ZdhAYGSedlNxwQ3Laacn22ydTpkz8e9ppE8tPOmnYCQEAYGBcMQ0ATMYpWc9tPICNNDaW/N3fTTwAAOAlRGMaANig1toHhp0BAACA0eFWHgAAAAAAdEpjGgAAAACATmlMAwAAAADQKY1pAAAAAAA6pTENAAAAAECnNKYBAAAAAOiUxjQAAAAAAJ3SmAYAAAAAoFMa0wAAAAAAdEpjGgAAAACATmlMAwAAAADQKY1pAAAAAAA6pTENAAAAAECnBtqYrqo3V9WtVbWoqv5sHe9vVVXf6L3/s6qaPcg8AAAAsKUyxwZglAysMV1VU5P8fZKTkhya5D1VdegLVvtwkodbawcl+VySvx5UHgAAANhSmWMDMGoGecX0MUkWtdYWt9aeTvL1JG97wTpvS3Ju7/kFSd5QVTXATAAAALAlMscGYKQMsjG9T5K71nq9pLdsneu01lYlWZZklwFmAgAAgC2ROTYAI2XaALe9rk9l20ask6o6LclpvZePVdWtm5htfXZN8sAAtw9dM6YZRYMc1wcMaLsvKddcc80DVXXHAHfhd1t3HOvuONbdcJy7M+hj/VKr2VviHNv/b4wi45pRNJQ59iAb00uS7LfW632T3PMb1llSVdOS7JDkoRduqLV2dpKzB5Tzearq6tba0V3sC7pgTDOKjOvNX2ttt0Fu3xjojmPdHce6G45zdxzrvtvi5tjGAKPIuGYUDWtcD/JWHlclObiqDqyqGUlOSXLhC9a5MMn7e89PTvKD1tqvfZoLAAAAL3Hm2ACMlIFdMd1aW1VVf5Tke0mmJvlSa+3mqvpMkqtbaxcm+WKS86pqUSY+xT1lUHkAAABgS2WODcCoGeStPNJauyjJRS9Y9pdrPX8yye8PMsNG6OSWIdAhY5pRZFxjDHTHse6OY90Nx7k7jnWfbYFzbGOAUWRcM4qGMq7Lt3oAAAAAAOjSIO8xDQAAAAAAv0ZjuqeqvlRV91fVTcPOAv1QVftV1Q+r6paqurmq/v2wM8GmqKqZVXVlVV3fG9OfHnYmBmtDtbkm/G1VLaqqG6rq1V1nHBWTONbzqmpZVV3Xe/zlutZj/SZTm43r/pjksTau+2Ay9bmqtqqqb/TG9c+qanb3SemS+TWjxvyaUbQ5zLE1pp9zTpI3DzsE9NGqJJ9srR2S5Ngkf1hVhw45E2yKp5K8vrX2qiRHJHlzVR075EwM1jlZf20+KcnBvcdpSRZ0kGlUnZMNnwdd1lo7ovf4TAeZRtFkarNx3R+TPQ8yrjfdZOrzh5M83Fo7KMnnkvx1xxnp3jkxv2a0mF8zioY+x9aY7mmt/SgTf7UYRkJr7d7W2s97z5cnuSXJPsNNBRuvTXis93J67+EPJYywSdTmtyX5Sm9s/DTJjlW1VzfpRovzoG5MsjYb133gPKg7k6zPb0tybu/5BUneUFXVUUSGQF1h1KgrjKLNYY6tMQ0vAb2vSx6Z5GfDTQKbpqqmVtV1Se5P8n9aa8b0S9s+Se5a6/WSmCAM0ut6X/O7uKoOG3aYLd16arNx3WcbOA8yrvtgEvX52XHdWluVZFmSXbpNCdAf5teMkmHPsTWmYcRV1bZJ/inJJ1prjw47D2yK1trq1toRSfZNckxVvXLYmRiqdV1t5yr6wfh5kgN6X/P7H0m+NeQ8W7QN1Gbjuo82cKyN6z6ZRH02roGRYH7NqBn2HFtjGkZYVU3PRNH8Wmvtm8POA/3SWnskycK4d+FL3ZIk+631et8k9wwpy0hrrT36zNf8WmsXJZleVbsOOdYWaRK12bjukw0da+O6/9ZTn58d11U1LckOcZsHYAtjfs0oG9YcW2MaRlTvvn1fTHJLa+2/DTsPbKqq2q2qduw93zrJG5P8cripGLILk7yvJhybZFlr7d5hhxpFVbXnM/eDrapjMnEO+eBwU215Jlmbjes+mMyxNq77Y5L1+cIk7+89PznJD1prrpgGthjm14yizWGOPa3LnW3Oqur8JPOS7FpVS5J8qrX2xeGmgk1yXJL3Jrmxd7+gJPmL3hVBsCXaK8m5VTU1E82Df2itfWfImRigddXmTPxBjrTWzkxyUZK3JFmUZEWSDw4n6ZZvEsf65CTzq2pVkieSnKKptFHWWZuT7J8Y1302mWNtXPfHOutzVX0mydWttQsz0cw5r6oWZeJK6VOGF5cumF8zgsyvGUVDn2OXcy8AAAAAALrkVh4AAAAAAHRKYxoAAAAAgE5pTAMAAAAA0CmNaQAAAAAAOqUxDQAAAABApzSmoc+qqlXVeWu9nlZVS6vqOy9yO7dX1a4bs84zy6tqx6o6/cXsdxK5PlFVs9Z6fVFV7djPfQDAoKnXALD5U69htGlMQ/89nuSVVbV17/XvJLl7SFl2TPKiCmdNWN/vhk8kebZwttbe0lp7ZCPzAcCwqNcAsPlTr2GEaUzDYFyc5Hd7z9+T5Pxn3qiqnavqW1V1Q1X9tKoO7y3fpaouqaprq+qsJLXWz5xaVVdW1XVVdVZVTZ1kjr9KMtb7ub/pbetPq+qq3v4/3Vs2u6puqaozkvw8yX5VtaCqrq6qm9da798l2TvJD6vqh71lz36qXFV/UlU39R6feMG2P9/b1iVrnVQAwDCp1+o1AJs/9Vq9ZkRpTMNgfD3JKVU1M8nhSX621nufTnJta+3wJH+R5Cu95Z9K8uPW2pFJLkyyf5JU1SFJ3p3kuNbaEUlWJ/m3k8zxZ0nGW2tHtNb+tKrelOTgJMckOSLJUVV1Ym/dlyf5SmvtyNbaHUn+Y2vt6F7+uVV1eGvtb5Pck+S3W2u/vfaOquqoJB9M8tokxyb5aFUd2Xv74CR/31o7LMkjSd45yfwAMEjqtXoNwOZPvVavGVHThh0ARlFr7Yaqmp2JT3MvesHbx6dXOFprP+h9krtDkhOTvKO3/LtV9XBv/TckOSrJVVWVJFsnuX8jo72p97i293rbTBS1O5Pc0Vr76VrrvquqTsvE74m9khya5Ib1bPv4JP+rtfZ4klTVN5OckImTgNtaa9f11rsmyeyNzA8AfaNeq9cAbP7Ua/Wa0aUxDYNzYZL/mmRekl3WWl7rWLe94N+1VZJzW2t/3odMleS/tNbOet7CiSL/+FqvD0zyH5K8prX2cFWdk2TmJLb9mzy11vPVmSj+ALA5UK+fo14DsLlSr5+jXjMy3MoDBudLST7TWrvxBct/lN5XhapqXpIHWmuPvmD5SUl26q3/f5OcXFW7997buaoOmGSG5Um2W+v195J8qKq27W1rn2e2+wLbZ6KQLquqPZKctJ5trv3f9XtVNauqtkny9iSXTTInAAyLeq1eA7D5U6/Va0aQK6ZhQFprS5L893W89Z+TfLmqbkiyIsn7e8s/neT8qvp5kksz8fWftNZ+UVX/KcklNfHXfFcm+cMkd0wiw4NV9ZOquinJxb37YB2S5Ire15YeS3JqJj5lXfvnrq+qa5PcnGRxkp+s9fbZSS6uqnvXvg9Wa+3nvU9+r+wt+kJr7drep8UAsFlSr9VrADZ/6rV6zWiq1tb1zQYAAAAAABgMt/IAAAAAAKBTGtMAAAAAAHRKYxoAAAAAgE5pTAMAAAAA0CmNaQAAAAAAOqUxDQAAAABApzSmAQAAAADolMY0AAAAAACd+v8YTZJqqshLCAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1800x576 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "training_accuracies = [linear_train_acc, poly_train_acc, tree_train_acc]\n",
    "test_accuracies = [linear_test_acc, poly_test_acc, tree_test_acc]\n",
    "\n",
    "fig, data_plt = plt.subplots(nrows=1, ncols=3, figsize=(25,8))\n",
    "\n",
    "data_plt[0].axis(ymin=0,ymax=1)\n",
    "data_plt[0].set_xticks([1, 2, 3])\n",
    "data_plt[2].axis(ymin=0,ymax=1)\n",
    "data_plt[2].set_xticks([1, 2, 3])\n",
    "\n",
    "data_plt[0].plot([1,2,3], training_accuracies)\n",
    "data_plt[0].scatter([1,2,3], training_accuracies, c='red', s=100)\n",
    "\n",
    "data_plt[0].set_xlabel('Model Iteration')\n",
    "data_plt[0].set_ylabel('R2 Training Score')\n",
    "data_plt[0].set_title('Training Scores')\n",
    "\n",
    "data_plt[1].plot([1,2,3], test_accuracies)\n",
    "data_plt[1].scatter([1,2,3], test_accuracies, c='red', s=100)\n",
    "\n",
    "data_plt[1].set_title('Testing Scores')\n",
    "data_plt[1].set_xlabel('Model Iteration')\n",
    "data_plt[1].set_ylabel('R2 Testing Score')\n",
    "\n",
    "# We remove polynomial testing scores, as it is an outlier\n",
    "test_acc_clean = [linear_test_acc, tree_test_acc]\n",
    "data_plt[2].plot([1,3], test_acc_clean)\n",
    "data_plt[2].scatter([1,3], test_acc_clean, c='red', s=100)\n",
    "\n",
    "data_plt[2].set_title('Testing Scores - without Polynomial Regression')\n",
    "data_plt[2].set_xlabel('Model Iteration')\n",
    "data_plt[2].set_ylabel('R2 Testing Score')\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By looking at the graphs above, we decide to choose the Linear Regression model for our QuAM software because it is the best generalized predictor since it has the best R2 score for the testing data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Future improvements"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### What have we learnt about the real-estate market in Qatar?\n",
    "\n",
    "When looking at the factors that determine rental price of properties in the Qatari market, we have found that some factors are much more important than others. This is mostly true of Amenities offered, which seem only to grow in number when other, more important, factors increase - namely Bedrooms, Bathrooms, Size, availability of Maids' rooms, and Location, in that order. \n",
    "\n",
    "Given that the relationship found, however, is only around 55% accurate, we also conclude that prices do have a random element to them as well, such that a definite relationship in all the variables may not completely exist. This \"human\" element may explain the variance in the true labels.\n",
    "\n",
    "#### What could we do in the future to get even better accuracies? Why?\n",
    "\n",
    "Revealuating the importance of the features was something we would have liked to do, given more time.  \n",
    "Also, cleaning of the features was sometimes done in ways that were not very structured or systematic, and we would like to improve upon that.   \n",
    "We would also like to improve upon our models and their accuracies, by trying different paramaters and having cleaner, more optimized, data."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
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 },
 "nbformat": 4,
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}