diff --git a/IntroToNeuralNetworks.ipynb b/IntroToNeuralNetworks.ipynb new file mode 100644 index 000000000..7b861aa55 --- /dev/null +++ b/IntroToNeuralNetworks.ipynb @@ -0,0 +1,666 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 132, + "metadata": {}, + "outputs": [], + "source": [ + "# used for manipulating directory paths\n", + "import os\n", + "\n", + "# Scientific and vector computation for python\n", + "import numpy as np\n", + "\n", + "# Plotting library\n", + "from matplotlib import pyplot\n", + "\n", + "# Optimization module in scipy\n", + "from scipy import optimize\n", + "\n", + "# will be used to load MATLAB mat datafile format\n", + "from scipy.io import loadmat\n", + "\n", + "# library written for this exercise providing additional functions for assignment submission, and others\n", + "import utils\n", + "\n", + "# define the submission/grader object for this exercise\n", + "grader = utils.Grader()\n", + "\n", + "# tells matplotlib to embed plots within the notebook\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 133, + "metadata": {}, + "outputs": [], + "source": [ + "# 20x20 Input Images of Digits\n", + "input_layer_size = 400\n", + "\n", + "# 10 labels, from 1 to 10 (note that we have mapped \"0\" to label 10)\n", + "num_labels = 10\n", + "\n", + "# training data stored in arrays X, y\n", + "data = loadmat('ex3data1.mat')\n", + "X, y = data['X'], data['y'].ravel()\n", + "\n", + "# set the zero digit to 0, rather than its mapped 10 in this dataset\n", + "# This is an artifact due to the fact that this dataset was used in \n", + "# MATLAB where there is no index 0\n", + "y[y == 10] = 0\n", + "\n", + "m = y.size" + ] + }, + { + "cell_type": "code", + "execution_count": 134, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Randomly select 100 data points to display\n", + "rand_indices = np.random.choice(m, 100, replace=False)\n", + "sel = X[rand_indices, :]\n", + "\n", + "utils.displayData(sel)" + ] + }, + { + "cell_type": "code", + "execution_count": 135, + "metadata": {}, + "outputs": [], + "source": [ + "# test values for the parameters theta\n", + "theta_t = np.array([-2, -1, 1, 2], dtype=float)\n", + "\n", + "# test values for the inputs\n", + "X_t = np.concatenate([np.ones((5, 1)), np.arange(1, 16).reshape(5, 3, order='F')/10.0], axis=1)\n", + "\n", + "# test values for the labels\n", + "y_t = np.array([1, 0, 1, 0, 1])\n", + "\n", + "# test value for the regularization parameter\n", + "lambda_t = 3" + ] + }, + { + "cell_type": "code", + "execution_count": 136, + "metadata": {}, + "outputs": [], + "source": [ + "def lrCostFunction(theta, X, y, lambda_):\n", + " \"\"\"\n", + " Computes the cost of using theta as the parameter for regularized\n", + " logistic regression and the gradient of the cost w.r.t. to the parameters.\n", + " \n", + " Parameters\n", + " ----------\n", + " theta : array_like\n", + " Logistic regression parameters. A vector with shape (n, ). n is \n", + " the number of features including any intercept. \n", + " \n", + " X : array_like\n", + " The data set with shape (m x n). m is the number of examples, and\n", + " n is the number of features (including intercept).\n", + " \n", + " y : array_like\n", + " The data labels. A vector with shape (m, ).\n", + " \n", + " lambda_ : float\n", + " The regularization parameter. \n", + " \n", + " Returns\n", + " -------\n", + " J : float\n", + " The computed value for the regularized cost function. \n", + " \n", + " grad : array_like\n", + " A vector of shape (n, ) which is the gradient of the cost\n", + " function with respect to theta, at the current values of theta.\n", + " \n", + " Instructions\n", + " ------------\n", + " Compute the cost of a particular choice of theta. You should set J to the cost.\n", + " Compute the partial derivatives and set grad to the partial\n", + " derivatives of the cost w.r.t. each parameter in theta\n", + " \n", + " Hint 1\n", + " ------\n", + " The computation of the cost function and gradients can be efficiently\n", + " vectorized. For example, consider the computation\n", + " \n", + " sigmoid(X * theta)\n", + " \n", + " Each row of the resulting matrix will contain the value of the prediction\n", + " for that example. You can make use of this to vectorize the cost function\n", + " and gradient computations. \n", + " \n", + " Hint 2\n", + " ------\n", + " When computing the gradient of the regularized cost function, there are\n", + " many possible vectorized solutions, but one solution looks like:\n", + " \n", + " grad = (unregularized gradient for logistic regression)\n", + " temp = theta \n", + " temp[0] = 0 # because we don't add anything for j = 0\n", + " grad = grad + YOUR_CODE_HERE (using the temp variable)\n", + " \n", + " Hint 3\n", + " ------\n", + " We have provided the implementatation of the sigmoid function within \n", + " the file `utils.py`. At the start of the notebook, we imported this file\n", + " as a module. Thus to access the sigmoid function within that file, you can\n", + " do the following: `utils.sigmoid(z)`.\n", + " \n", + " \"\"\"\n", + " #Initialize some useful values\n", + " m = y.size\n", + " \n", + " # convert labels to ints if their type is bool\n", + " if y.dtype == bool:\n", + " y = y.astype(int)\n", + " \n", + " # You need to return the following variables correctly\n", + " J = 0\n", + " grad = np.zeros(theta.shape)\n", + " \n", + " # ====================== YOUR CODE HERE ======================\n", + " \n", + " z = np.array(theta.dot(X.transpose())) \n", + " H = np.array(utils.sigmoid(z))\n", + " \n", + " J += ((-1 / m) * ((np.log(H)).dot(y.transpose()) + (np.log(1 - H)).dot((1 - y).transpose())))\n", + " J += (lambda_ / (2 * m)) * (theta[1:]).dot(theta[1:].transpose())\n", + " \n", + " grad = (1 / m) * (H - y).dot(X) \n", + " grad[1:] += ((lambda_ / m) * theta[1:])\n", + " \n", + " # =============================================================\n", + " return J, grad" + ] + }, + { + "cell_type": "code", + "execution_count": 137, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cost : 2.534819\n", + "Expected cost: 2.534819\n", + "-----------------------\n", + "Gradients:\n", + " [0.146561, -0.548558, 0.724722, 1.398003]\n", + "Expected gradients:\n", + " [0.146561, -0.548558, 0.724722, 1.398003]\n" + ] + } + ], + "source": [ + "J, grad = lrCostFunction(theta_t, X_t, y_t, lambda_t)\n", + "\n", + "print('Cost : {:.6f}'.format(J))\n", + "print('Expected cost: 2.534819')\n", + "print('-----------------------')\n", + "print('Gradients:')\n", + "print(' [{:.6f}, {:.6f}, {:.6f}, {:.6f}]'.format(*grad))\n", + "print('Expected gradients:')\n", + "print(' [0.146561, -0.548558, 0.724722, 1.398003]');" + ] + }, + { + "cell_type": "code", + "execution_count": 138, + "metadata": {}, + "outputs": [], + "source": [ + "def oneVsAll(X, y, num_labels, lambda_):\n", + " \"\"\"\n", + " Trains num_labels logistic regression classifiers and returns\n", + " each of these classifiers in a matrix all_theta, where the i-th\n", + " row of all_theta corresponds to the classifier for label i.\n", + " \n", + " Parameters\n", + " ----------\n", + " X : array_like\n", + " The input dataset of shape (m x n). m is the number of \n", + " data points, and n is the number of features. Note that we \n", + " do not assume that the intercept term (or bias) is in X, however\n", + " we provide the code below to add the bias term to X. \n", + " \n", + " y : array_like\n", + " The data labels. A vector of shape (m, ).\n", + " \n", + " num_labels : int\n", + " Number of possible labels.\n", + " \n", + " lambda_ : float\n", + " The logistic regularization parameter.\n", + " \n", + " Returns\n", + " -------\n", + " all_theta : array_like\n", + " The trained parameters for logistic regression for each class.\n", + " This is a matrix of shape (K x n+1) where K is number of classes\n", + " (ie. `numlabels`) and n is number of features without the bias.\n", + " \n", + " Instructions\n", + " ------------\n", + " You should complete the following code to train `num_labels`\n", + " logistic regression classifiers with regularization parameter `lambda_`. \n", + " \n", + " Hint\n", + " ----\n", + " You can use y == c to obtain a vector of 1's and 0's that tell you\n", + " whether the ground truth is true/false for this class.\n", + " \n", + " Note\n", + " ----\n", + " For this assignment, we recommend using `scipy.optimize.minimize(method='CG')`\n", + " to optimize the cost function. It is okay to use a for-loop \n", + " (`for c in range(num_labels):`) to loop over the different classes.\n", + " \n", + " Example Code\n", + " ------------\n", + " \n", + " # Set Initial theta\n", + " initial_theta = np.zeros(n + 1)\n", + " \n", + " # Set options for minimize\n", + " options = {'maxiter': 50}\n", + " \n", + " # Run minimize to obtain the optimal theta. This function will \n", + " # return a class object where theta is in `res.x` and cost in `res.fun`\n", + " res = optimize.minimize(lrCostFunction, \n", + " initial_theta, \n", + " (X, (y == c), lambda_), \n", + " jac=True, \n", + " method='TNC',\n", + " options=options) \n", + " \"\"\"\n", + " # Some useful variables\n", + " m, n = X.shape\n", + " \n", + " # You need to return the following variables correctly \n", + " all_theta = np.zeros((num_labels, n + 1))\n", + "\n", + " # Add ones to the X data matrix\n", + " X = np.concatenate([np.ones((m, 1)), X], axis=1)\n", + "\n", + " # ====================== YOUR CODE HERE ======================\n", + " \n", + " for i in range(num_labels):\n", + " initial_theta = np.zeros(n + 1)\n", + " options = {'maxiter': 50}\n", + " res = optimize.minimize(lrCostFunction, \n", + " initial_theta, \n", + " (X, (y == i), lambda_), \n", + " jac=True, \n", + " method='TNC',\n", + " options=options) \n", + " all_theta[i,:] = res.x\n", + "\n", + " # ============================================================\n", + " return all_theta" + ] + }, + { + "cell_type": "code", + "execution_count": 139, + "metadata": {}, + "outputs": [], + "source": [ + "lambda_ = 0.1\n", + "all_theta = oneVsAll(X, y, num_labels, lambda_)" + ] + }, + { + "cell_type": "code", + "execution_count": 140, + "metadata": {}, + "outputs": [], + "source": [ + "def predictOneVsAll(all_theta, X):\n", + " \"\"\"\n", + " Return a vector of predictions for each example in the matrix X. \n", + " Note that X contains the examples in rows. all_theta is a matrix where\n", + " the i-th row is a trained logistic regression theta vector for the \n", + " i-th class. You should set p to a vector of values from 0..K-1 \n", + " (e.g., p = [0, 2, 0, 1] predicts classes 0, 2, 0, 1 for 4 examples) .\n", + " \n", + " Parameters\n", + " ----------\n", + " all_theta : array_like\n", + " The trained parameters for logistic regression for each class.\n", + " This is a matrix of shape (K x n+1) where K is number of classes\n", + " and n is number of features without the bias.\n", + " \n", + " X : array_like\n", + " Data points to predict their labels. This is a matrix of shape \n", + " (m x n) where m is number of data points to predict, and n is number \n", + " of features without the bias term. Note we add the bias term for X in \n", + " this function. \n", + " \n", + " Returns\n", + " -------\n", + " p : array_like\n", + " The predictions for each data point in X. This is a vector of shape (m, ).\n", + " \n", + " Instructions\n", + " ------------\n", + " Complete the following code to make predictions using your learned logistic\n", + " regression parameters (one-vs-all). You should set p to a vector of predictions\n", + " (from 0 to num_labels-1).\n", + " \n", + " Hint\n", + " ----\n", + " This code can be done all vectorized using the numpy argmax function.\n", + " In particular, the argmax function returns the index of the max element,\n", + " for more information see '?np.argmax' or search online. If your examples\n", + " are in rows, then, you can use np.argmax(A, axis=1) to obtain the index \n", + " of the max for each row.\n", + " \"\"\"\n", + " m = X.shape[0];\n", + " num_labels = all_theta.shape[0]\n", + "\n", + " # You need to return the following variables correctly \n", + " p = np.zeros(m)\n", + "\n", + " # Add ones to the X data matrix\n", + " X = np.concatenate([np.ones((m, 1)), X], axis=1)\n", + "\n", + " # ====================== YOUR CODE HERE ======================\n", + " \n", + " for i in range(m):\n", + " p[i] = np.argmax(all_theta.dot(X.transpose())[:, i])\n", + "\n", + " # ============================================================\n", + " return p" + ] + }, + { + "cell_type": "code", + "execution_count": 141, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training Set Accuracy: 95.20%\n" + ] + } + ], + "source": [ + "pred = predictOneVsAll(all_theta, X)\n", + "print('Training Set Accuracy: {:.2f}%'.format(np.mean(pred == y) * 100))" + ] + }, + { + "cell_type": "code", + "execution_count": 142, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# training data stored in arrays X, y\n", + "data = loadmat('ex3data1.mat')\n", + "X, y = data['X'], data['y'].ravel()\n", + "\n", + "# set the zero digit to 0, rather than its mapped 10 in this dataset\n", + "# This is an artifact due to the fact that this dataset was used in \n", + "# MATLAB where there is no index 0\n", + "y[y == 10] = 0\n", + "\n", + "# get number of examples in dataset\n", + "m = y.size\n", + "\n", + "# randomly permute examples, to be used for visualizing one \n", + "# picture at a time\n", + "indices = np.random.permutation(m)\n", + "\n", + "# Randomly select 100 data points to display\n", + "rand_indices = np.random.choice(m, 100, replace=False)\n", + "sel = X[rand_indices, :]\n", + "\n", + "utils.displayData(sel)" + ] + }, + { + "cell_type": "code", + "execution_count": 143, + "metadata": {}, + "outputs": [], + "source": [ + "# Setup the parameters you will use for this exercise\n", + "input_layer_size = 400 # 20x20 Input Images of Digits\n", + "hidden_layer_size = 25 # 25 hidden units\n", + "num_labels = 10 # 10 labels, from 0 to 9\n", + "\n", + "# Load the .mat file, which returns a dictionary \n", + "weights = loadmat('ex3weights.mat')\n", + "\n", + "# get the model weights from the dictionary\n", + "# Theta1 has size 25 x 401\n", + "# Theta2 has size 10 x 26\n", + "Theta1, Theta2 = weights['Theta1'], weights['Theta2']\n", + "\n", + "# swap first and last columns of Theta2, due to legacy from MATLAB indexing, \n", + "# since the weight file ex3weights.mat was saved based on MATLAB indexing\n", + "Theta2 = np.roll(Theta2, 1, axis=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 144, + "metadata": {}, + "outputs": [], + "source": [ + "def predict(Theta1, Theta2, X):\n", + " \"\"\"\n", + " Predict the label of an input given a trained neural network.\n", + " \n", + " Parameters\n", + " ----------\n", + " Theta1 : array_like\n", + " Weights for the first layer in the neural network.\n", + " It has shape (2nd hidden layer size x input size)\n", + " \n", + " Theta2: array_like\n", + " Weights for the second layer in the neural network. \n", + " It has shape (output layer size x 2nd hidden layer size)\n", + " \n", + " X : array_like\n", + " The image inputs having shape (number of examples x image dimensions).\n", + " \n", + " Return \n", + " ------\n", + " p : array_like\n", + " Predictions vector containing the predicted label for each example.\n", + " It has a length equal to the number of examples.\n", + " \n", + " Instructions\n", + " ------------\n", + " Complete the following code to make predictions using your learned neural\n", + " network. You should set p to a vector containing labels \n", + " between 0 to (num_labels-1).\n", + " \n", + " Hint\n", + " ----\n", + " This code can be done all vectorized using the numpy argmax function.\n", + " In particular, the argmax function returns the index of the max element,\n", + " for more information see '?np.argmax' or search online. If your examples\n", + " are in rows, then, you can use np.argmax(A, axis=1) to obtain the index\n", + " of the max for each row.\n", + " \n", + " Note\n", + " ----\n", + " Remember, we have supplied the `sigmoid` function in the `utils.py` file. \n", + " You can use this function by calling `utils.sigmoid(z)`, where you can \n", + " replace `z` by the required input variable to sigmoid.\n", + " \"\"\"\n", + " # Make sure the input has two dimensions\n", + " if X.ndim == 1:\n", + " X = X[None] # promote to 2-dimensions\n", + " \n", + " # useful variables\n", + " m = X.shape[0]\n", + " num_labels = Theta2.shape[0]\n", + "\n", + " # You need to return the following variables correctly \n", + " p = np.zeros(X.shape[0])\n", + "\n", + " # ====================== YOUR CODE HERE ======================\n", + " \n", + " X = np.concatenate([np.ones((m, 1)), X], axis=1)\n", + " \n", + " A2 = utils.sigmoid((Theta1.dot(X.transpose())).transpose())\n", + " A2 = np.concatenate([np.ones((A2.shape[0], 1)), A2], axis=1)\n", + " \n", + " H = utils.sigmoid(Theta2.dot(A2.transpose()))\n", + " \n", + " for i in range(m):\n", + " p[i] = np.argmax(H[:, i])\n", + " \n", + " # =============================================================\n", + " return p" + ] + }, + { + "cell_type": "code", + "execution_count": 145, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training Set Accuracy: 97.5%\n" + ] + } + ], + "source": [ + "pred = predict(Theta1, Theta2, X)\n", + "print('Training Set Accuracy: {:.1f}%'.format(np.mean(pred == y) * 100))" + ] + }, + { + "cell_type": "code", + "execution_count": 160, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Neural Network Prediction: 8.0\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "if indices.size > 0:\n", + " i, indices = indices[0], indices[1:]\n", + " utils.displayData(X[i, :], figsize=(4, 4))\n", + " pred = predict(Theta1, Theta2, X[i, :])\n", + " print('Neural Network Prediction: {}'.format(*pred))\n", + "else:\n", + " print('No more images to display!')" + ] + }, + { + "cell_type": "code", + "execution_count": 151, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Submitting Solutions | Programming Exercise multi-class-classification-and-neural-networks\n", + "\n", + "Use token from last successful submission (rohitramesh4547@gmail.com)? (Y/n): Y\n", + " Part Name | Score | Feedback\n", + " --------- | ----- | --------\n", + " Regularized Logistic Regression | 30 / 30 | Nice work!\n", + " One-vs-All Classifier Training | 20 / 20 | Nice work!\n", + " One-vs-All Classifier Prediction | 20 / 20 | Nice work!\n", + " Neural Network Prediction Function | 30 / 30 | Nice work!\n", + " --------------------------------\n", + " | 100 / 100 | \n", + "\n" + ] + } + ], + "source": [ + "grader[1] = lrCostFunction\n", + "grader.grade()" + ] + } + ], + "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" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/LogisticRegression.ipynb b/LogisticRegression.ipynb new file mode 100644 index 000000000..2d8c1ee4c --- /dev/null +++ b/LogisticRegression.ipynb @@ -0,0 +1,484 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# used for manipulating directory paths\n", + "import os\n", + "\n", + "# used for mathematical operations of elements\n", + "import math\n", + "\n", + "# Scientific and vector computation for python\n", + "import numpy as np\n", + "\n", + "# Plotting library\n", + "from matplotlib import pyplot\n", + "\n", + "# Optimization module in scipy\n", + "from scipy import optimize\n", + "\n", + "# library written for this exercise providing additional functions for assignment submission, and others\n", + "import utils\n", + "\n", + "# define the submission/grader object for this exercise\n", + "grader = utils.Grader()\n", + "\n", + "# tells matplotlib to embed plots within the notebook\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Load data\n", + "# The first two columns contains the exam scores and the third column\n", + "# contains the label.\n", + "data = np.loadtxt('ex2data1.txt', delimiter=',')\n", + "X, y = data[:, 0:2], data[:, 2]" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "def plotData(X, y):\n", + " \"\"\"\n", + " Plots the data points X and y into a new figure. Plots the data \n", + " points with * for the positive examples and o for the negative examples.\n", + " \n", + " Parameters\n", + " ----------\n", + " X : array_like\n", + " An Mx2 matrix representing the dataset. \n", + " \n", + " y : array_like\n", + " Label values for the dataset. A vector of size (M, ).\n", + " \n", + " Instructions\n", + " ------------\n", + " Plot the positive and negative examples on a 2D plot, using the\n", + " option 'k*' for the positive examples and 'ko' for the negative examples. \n", + " \"\"\"\n", + " # Create New Figure\n", + " fig = pyplot.figure()\n", + "\n", + " # ====================== YOUR CODE HERE ======================\n", + " # Find Indices of Positive and Negative Examples\n", + " pos = y == 1\n", + " neg = y == 0\n", + " \n", + " pyplot.plot(X[neg,0],X[neg,1],'ko', mfc='y', ms=8, mec='k', mew=1)\n", + "\n", + " pyplot.plot(X[pos,0],X[pos,1],'k*', lw=2, ms=10)\n", + "\n", + " \n", + " # ============================================================" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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PXrqFxQaTsI/t+DGm8GecAeXngZddGRYA3wMqROTvQD9gkdeydYYguG4GDOjHm2/G3/fmm85+IzMEoZduYbHBJOxjO75EH6nqLFX9lKqepapXqeoBVf2Hqn5aVYeq6hWqesAP2ZIlCK6b66//FjU1UdomulWFhx+Oct11N2RchlwlKL30bAiLDXvPui1hH9uxGc0pEgTXTUXFLTQ0DGHOnChbtkBTE2zZAnPmRGloGEJFxS0ZlyFXCVIvPUhhsakQ9p51EKzGdGJKIUUy6bpJdgC7oKCA9ev/xJgx3+W224oYPz7CbbcVMWbMd1m//k8UFBSkLENXCWLvL90yBaWXHqSw2FQIe886KFZjujClkCKZct10dgC7oKCAysoqtm3bSVPTYbZt20llZZWvCgGC2fvLhExB6KUHpZJasko323rWQbIa04KqhraNGjVK/WLv3r06atSZOnZsVBcsQFetQhcsQMeOjeqoUWfq3r17UzpvVVWljh0b1bVr0XXrjra1a51zV1VVpvmTZIbS0lIFtKyszG9RmsmETKWlpRqJRLSkpERXrlypJSUlGolEAvW5vWLJkiUKaHV1dbvvW7t2rebn5yvOXKS4LT8/X9etW+eN4GlixYoVGo1GW32OaDSqK1as8Fu0YwDqNMFz1SyFFGhsbGT+/Lm8++67rFu3n+nThUsvhaqq/l123QRhADsVgtj780KmoPTSg0CybqCs61m7BMFqTAuJtEUYmh+WQksLYeFCdPVqdOHCrlsIMSIR0dWrW1sJsbZqFZqXF0nTJ0kvQez9BVGmbKK8vLzVvezRo0erZayVl5fHPT5MPetkCJPVSFcsBRE5TUTWiMgr7vpwEQnHiEkGyPT8hLDOPQhi7y+IMmUTyQyw5uXlMX369LjHZ03P2iVrrMZE2iLWgCeBTwN/bbHtlY6O86L5YSkMHNhfFy6M35NfsAAtLi7q0vnDPqYQxN5fEGXKFtqzxmIWQ6IxhjD1rLMNujimkK+qf2mzrSnuO3OATM9PCPvcgyD2/oIoU7bQXljuaaedBiQeY8iannWWkYxS2C0iQ3CzlorIl3BSXuckmXbvBHnuQTIEMWY+GZmCOK8iLMSUbkv279/Pa6+9BiQe2I8lE4xEnMdQXl6eZ8kEjcQkoxRuBP4X+JSI/BOYDnwzo1IFmGTmJ3Q1e2pQ5x4kQxB7f8nI1HYOgymJ5Ikp3aFDh9KzZ8/m7U1NjkMhrJO4cpZEfiXH7UQE+LL7+jigd3vv97r5HX0Ub37Cjh07MhqdZGSGtnMYko25N1QnTpyoP/nJT/Tw4cPtjjFYpFf6qK+v10mTJml9fX1Kx9POmEIyA80bOnqPX82vyWt79+7VqqpKLS4u0ry8iBYXF2lVVWXz9jAPFOcKyYZTnnDCCX6LGjpsYD/zdLXT0p5SSMZ9tEpEbhaRYhHpG2tpMVNCSnvunbBOPss1kgmnBNi7d28oUy/4iQ3sZ55M5otKRin8O864wgacWsobgbq0S5IlBCF7qtExHc1hiBHzi4P5w5MliMEGYcfLjAEdKgVVHRynfaLLV85S0h2d5HfJz2wmUThlPGyiW/IEMdgg7HiZiTWZGc3dReQ/RORRt31bRLp3+cpZSjqzpwah5Ge2E8/V0ZYwFawJAhZqmn68nJ2fjPvoPmAUcK/bRrnbjDikc/JZEEp+ZjttXR3FxcXN+8wf3hoL0/UXr+p3JKMUzlXVqaq61m1fB85Ny9WzkHROPrNB68zT1tUxaNCg5u0t/eELFizIqgdiKg/4INbISJWwKjhPBvEThSXFGvA8MKTF+ieA5zs6zovmZz0FLwhrxtSO6GqMdSZpGXOvqtrU1KTz5s3Tc845J6vmLaQS0hjEGhmpEtZ5KOnKF0UXQ1JvAdaJyHoReRJYC8xIVQmJyCdF5IUW7UMRme6Guq4SkS3u8oRUr5EthDVjakcEuceZyB8eGyTNlgiaZEIag1gjI12EtQSoJ4P4ibRFywb0BIYDI4CeyRyT5HnzgHeBU4E7ge+7278P3NHR8dluKWTrRLgw9Di7WisgaKTyeZKpR9GrV69QzFIOy/fZnhWdTgubLs5ovhHo02L9BOBbHR2XTAMuBp52X78OnOS+Pgl4vaPjs10pZKrkp9eE5Q/Zkmwr0JPq5+nouB/84Af+fKBOEpbvsz23VjpdXl1VCi/E2fbXjo5LpgEPAN92X9e32fdBgmOm4UyeqzvllFO6fHOCTnspNcJCWP6Qbcm2PD6pfp54aSsikUjgrb22hOH7bM+KTqeF3VWl8BIgLdbzgFc7Oi6J8/YAdgMf104ohZYt2y2FbCIMf8h4ZFsen1Q+T3V1tebl5cX97oJs7cUjaN9nslZ0ui3s9pRCMgPNfwD+r4iUi8hYoAb4fRLHdcSlOFFM77nr74nISQDucmcarmEEBK9irNNNtuXxSeXzLFq0iCNHjiBtY6MJX1rsoH2fyebgSrQvE/c8GaXwPWANcAPO+MIa4LtpuPYUHAUTYzkw1X09FViWhmsYASJof8hkyLY8Pql8nsLCQubNm8eqVatCX+86aN9nRzOVe/bs2apGRUsyds8TmRBtG467pwT4WLLHtHOufGAPUNhiWz8chbPFXfbt6DzmPgoXYazJm2jewsSJE32WLDW6+nmC5n7pLEH9Ptu7r5m456QypgD8HDjTfV0IbAJeBv4JTEl0nJfNlEK4COof0k+CPJEvHtXV1VpQUKCRSER79eqlkUhECwoKQjcJLGi0d18zcc9TVQqvtng9Hah1X59ImqKPutpMKXSOWCTTwIH9NRIRHTiwf+gimbKNsM2sDaO1Fwbau6+ZuOftKYX2xhRajnRcBNS67qZ3O+2jMnzHMq4Gk7DNrM3GtNhByIPU3n31/J4n0hbAOmACzjhCPXCiu70b8Fqi47xsZikkT7bOjg4bYZzIl+1kwloLuluQFC2FbwDfBh4EputRC6EceDy9qsnINJZxNRh4WSzFSI5MWGtBzu/VEQmVgqr+TVXHq+pIVV3cYvsfVDXlhHjGsXhRXS1byoQGwdTvCl4WSzHi40Wiv7C5BVuSzDwFI4N45evPloyrYe6BxQjrRL5sIRPWWjZllDWl4DNeVVdLZ5lQPwlzD6wlYZzIly1kwlrLKrdgosGGMLRsGGgeOLC/LlwYv5DOggVocXFRWq4T1oyr2Towa6Gd/pPuSWFhyu9FqrmPRORTbs6jgjbbx6dXNeUuXvn601km1EuyqgfWgmwM7Qwb6bbWssYtmEhbAP+BU+OgFtgKTGyxz8pxpgmvLIUwE6YemBEeMmGthWXGNylaCtcDo1R1ElAK/JeIfMfdd2y6RCMlssXXnwypRlllTQ/MCBSZsNaClnAvFUTbPo1iO0Q2qeoZLdYLgEdxciCNVdWR3oiYmNGjR2tdXZ3fYnSJWPRRYeEbTJ68n8GDnWighx+O0tAwJNCunc7Q8nNOmXL0c9bUJPc5f/GLX3DDDTewb98+evbsyYEDB8jPz+e+++7ja1/7moefxDASM2nSJC688EKmT59OJBLh8OHD3H333Tz11FPU1tb6LV4zIrJRVUfH3ZnIhADWAiPbbOsGLAEOJzrOy5YN7iPV7Kiu1hFdnVFtA7OGkT5ox33UnqUwEGjSOLmORORzqvp0WlRWF8gGSyFXKC4uYtas3Qwdeuy+LVvgttuK2LYtcV2lsPTADCMMtGcpJFQKYcCUQnjIy4uwcqWSl3fsvqYmGD8+QlPTYe8FM4wcpD2lYJPXDE/IlhnV6SDsqToMf/Dqd2NKwfCEXIqy6ohsSNVheI9Xv5uklYKIHC8ifWMtk0IZ2UdFxS00NAxhzpwoL78MixfDFVdAeTn85S+HOHjwYM7UdMiWVB2Gt3j1u+lQKYjIN0TkPeAlYKPbzJGPN9lNs4XYjOrzzpvOrbfm8be/wY9/DKtWwV13HebZZ+/O2mI/2ZQszfAOv343yVgKN+PUah6kqoPd9om0ShFCrJKZQ2cUY0FBAT179uDcc7tz++1kJAFgEP312Zqqw8gsfv1uklEKbwD70nlREekjIo+KyGsisllEznfdUqtEZIu7PCGd10w3XmU3DTKpKMZMF/sJor/eaigYqeDX7yYZpfCfwDMi8r8ick+sdfG6PwV+r6qfAkYAm4HvA2tUdRiwxl0PLFbJLDXFmOkEgEH111uqDiMV/PjdJKMU/hdndvOfODqmsDHVC4rI8cCFwCIAVT2oqvXAROAh920PAZNSvYYXZEsls66QimJMd2hqmPz1VkPBSAWvfzfJKIUmVa1Q1QdV9aFY68I1PwHsAh4Ukb+KyP0ichzwcVXdAeAuPxbvYBGZJiJ1IlK3a9euLojRNSzuPjXFmO7Q1DD567MhWZrhPV7/bpJRCuvcB/FJaQpJ7QacA9ynqiXAv+iEq0hVF6jqaFUdXVRU1AUxuobF3aemGFuGpm7Z4sxm3rIF5sxxEuNVVNzSKRnC5K+3GgpGKnj9u+kwzYWIxPvba6oRSCJyIvAnVR3krv9/OEphKFCqqjtE5CRgvap+sr1z+ZnmIleym7bH7NmzePLJO5k5s7ULSdV5yI8Z810qK6uOOa6xsZH58+dy//33sX37HgYM6Md1191ARcUtKd+zxx57jCuuuIL9+/c3b4tGozzyyCNMmDAhpXMaRrbSpTQXLcJQB6cjJNVNsPe2iMQe+OU46biXA1PdbVOBZalewwvCWsksnaTa6y8oKKCysopt23bS1HSYbdt2UllZ1aV7Zv763COI4cdZQaL0qS0bcBbwZeDqWEvmuHbONxJnAtxLOJXdTgD64UQdbXGXfTs6T7akzg4zQUn7bam1c48lS5YoELiqZmGAVFJnxxCRWTiV184AngAuBf6oql9Kr3rqPJYl1YhhqbWzk4aGBq655hoWL15MYWFhq31lZWWsX7+esrIy1q5d65OE4SSlIjt6tFf/Mo6b6UV3/ePAio6O86KZpZAasd79wIH9NRIRHTiwf9YV9TGyg5bWQHl5eav63D169Gi1jLXy8nK/xQ48pFijOcZHqnoEaHLnGOzECSs1Qoil5zDCRMvJiGEKPw4zySiFOhHpAyzEmbT2PPCXjEplZAxLz2EEmfYmI44dO5Z9+xJn3AlS+HGY6VTlNREZBByvqi9lSqDOYGMKnaerZTENI5OsW7eOCRMmtPvw79mzJ0eOHOHQoUPN2yz8uHN0KSRVRK6NvVbVrcCr7uCzEUIsPYcRZJKZjDhjxgx69uxp4ccZIhn3UbmIPOHOaD4LJwdS7wzLZWQIS89hBJ2OksA988wzli4kgyQzee2rOAnqXsYJSZ2uqjdnWjAjM1h6DiMMtDcZ0dKFZJZk5ikM46hSOB1n9nGFqqa1xkIq2JhC57H0HEYYKCsrY8OGDYwYMYI77riD733ve7z44ouMGTPG5iSkgS6NKQArgP9S1W8AY3BmHD+XRvkMD7H0HEYYMGvAP5KxFI5X1Q/bbBumqlsyKlkSmKVgGIbReVKyFETkuwCq+qGIXNFm99fTKJ9hGHGwhG+GH7TnPprc4vV/ttk3PgOyGIbRgiDWmzayn/aUgiR4HW/dMNJOY2Mjs2fPori4iLy8CMXFRcyePStnUnEEtd60kd20pxQ0wet460aOkekHdlByNHmpmMJUb9rIXhIONIvIYZxSmQL0AmIhqAJEVbW7JxK2gw00+0PLsNYpU46GtdbUpC+sNdWqbunEi8/ZkmRSPFh+HyMdpDTQrKp5qnq8qvZW1W7u69i67wrB8A8vkuotXHgvU6a0VggAIjB58n7uv/++Ll+jI7xOHhimetNG9pLMPAXDaIUXD+wg5GjyQzF1lOLBFIKRaUwpGJ3Giwd2EHI0+aWYrN604SemFIxO48UD2+8cTY2NjfTp08sXxbRo0SJL+Gb4hikFo9O098CuqYkybNjpXY7Wqai4hYaGIcyZE2XLFmhqcuo9zJnjDPJWVNySxk/UmtgA8wknHKC6Gs8Vk6V4MPykU0V20nZRka3AXuAw0KSqo0WkL7AUGARsBb6sqh+0d57ORh81NjYyf/5cFi68l+3b9zBgQD+uv/5bVFTcYjl/OkGipHo1NVH++ldl+HC46qoDXY7WiX1f999/X/P3dd11N2T8+4pFPs2Ysa9GCJUAABwOSURBVJ8ZM+DjH4crr6T581RXd2Pfvk9arigjtLQXfeSnUhitqrtbbLsTeF9V/1tEvg+coKrfa+88nVEKXocXZjvxHtjDhp1OU9Of+eEPD/gWRpoOWlan++gjeOQR+N3vYOdO6NcPDh3K5+2337PfixFawqIUXgdKVXWHiJwErFfVT7Z3ns4ohSDEvWc7HZX6nD27P2+/vct7wTpJXl6ElSuVvLxj9zU1wfjxEZqaDnsvWA7R0NDANddcw+LFiyksLPRbnKyjq6mzM4ECK0Vko4hMc7d9XFV3ALjLj8U7UESmiUidiNTt2pX8AyYIce/ZTkfROv/85+5QpKgIQuRTrmN5n/zDL6XwOVU9B7gUuFFELkz2QFVdoKqjVXV0UVFR0hcMQtx7ttPRw7R3b9I+4SsT+B35ZFjeJz/xRSmo6nZ3uRP4LfBp4D3XbYS73JnOa1rvL/Ncf/23ePDB+NE6v/wllJURCovMz8inoJLpNN6W9yk4eK4UROQ4Eekdew1cDLwCLAemum+bCixL53Wt95d5Kipu4fnnoaqKVg/Tqip47z249tpwWGRWne5YMu3OufXWW1ul9zh48GCrJThpPmbOnJmR6xstUFVPG/AJ4EW3vQrc6m7vB6zBKfe5Bujb0blGjRqlybJ3714dNepMHTs2qgsWoKtWoQsWoGPHRnXUqDN17969SZ8r0fmrqip14MD+GomIDhzYX6uqKrt83rAxYEA/nTgRPfFENBJxll//OvrEE879Li4u8ltEIwVKS0sV0LKysoxdY+3atZqfn684Y46tWn5+vq5bty5j1841gDpN9IxOtCMMrTNKQfXog7u4uEjz8iJaXFyUlgd3S4WzcCG6ejW6cGH6FE6YqKqq1LFjo7p2LbpundOeeMJRDL17oyLkrMJMhN8divr6ep00aZLW19c3bysvL2/1UO7Ro0erZayVl5enVZYVK1ZoNBptdY1oNKorVqxI63VyHVMKGSbeg3DdOnTtWkcxVFVV+i2iZ7S1yJYvRz/xCfSCC8h5hRmPIHQolixZooBWV1c3b2uv157J3nt1dbUWFBRoJBLRXr16aSQS0YKCglayGV2nPaVgaS7SgIW7HqWtP/7f/g1OPBFmz8aT9NNhw+v03PGIF+njVxrvbMv7FMY626YU0oCFu7amoKCAysoqNm36BwUFvfjb32DcOJg8GZYscWYJ56LCjIcfHYpkI31uv/12z9N4+5X3KVMP7zDOtzClkAYs3PVYYmlFTj/9I378Y1i5EubMgX/8AyoqHMWQiwqzLX50KDoT6eN1Gu/a2loqKiqIRJxHU15eHjNmzKC2tjYj14uRqYd3GOdbmFJIAxbueiwxt8jtt7d2G82a5SSYe+SR3FWYLfGjQ9EZ11BX3TlhcZ+k6+GdDfMtTCmkAZvsdCztuUWuvNJJMJerCrMlfnUokq3w1lV3TlDdJ5l6eGfFfItEI9BhaEGJPlLNXLhrWIlERFevbh2NFWurVjmhqRZ9lPn5M+3hRaSPF/MbUiGT0VVhmG+BhaQeJR0x4X7HlYeBgQP768KF8ZXCggVov375dr9c/OpQlJaWaiQS0ZKSEl25cqWWlJRoJBLp0gM8U/Mb4s2l6CqZfHgHfb6FKQWXdMSEByGuPAzY3I3gM3HiRP3JT36ihw8fVlXVpqYmnTdvnk6cODHlc2aqBx5vLkU6SPTwPu+887qkgII+38KUgktnH1TxLIKxYy/UCy/saQ+7DvDTLWL4SyZ64JlyQ8V7ePfs2bPLCigTVlg6aU8p5NRAc2diwmMhlU8+eSezZu1m5Upl1qzd7N+/gXfeOcD+/R2fI5expHLt09jYyOzZs7pcyzqIJDuI3R5eRfHEi646cOAA0LVIpFDX2U6kLcLQOmspdDT4mZcXaX5ve1bFmDFOLp+OzmEEHz/Gh3LBBZmM+6S9e+9Vmo2JEyfq0KFDW503Eol4kufJTzBLwaEzMeHJhFR2dA4j2CSyBp988k5KS8/LWK89CKktMk1H8xs6uvfnnnuuJ2k2amtrWbBgQavrHDlyBAhZGGkaySml0JmY8I5mmu5sUwIo3jmMYOPXwzkXcmV15D5J5t6nww2VDH7leQoqOaUUOjPJrCOroqAAm6gWcvx6OAchV1amxzQ6SleR7L33Ks2GVwooDOSUUujM4Gd7VkVNTZTRoy+0AdSQ49fD2e9cWX65zVqS7L33Mmuq13megkpOKQU4msFz27adNDUdZtu2nVRWVh3zMG/PqvjwwyEsW/Z4h+cwgo3XD+dY73zfvn1Mm9Y6ayx454IMwphGsvfeyyiebEvbnSo5pxSSxUIqsx8v8w617J3fccc+Vq1yssa+8YaTNfbll71zQQZhTOP667/Fr36V2BKP3Xsvs6aGOow0jYi2/VZCxOjRo7Wurs5vMYyQEntQFxa+weTJ+xk82OmlPvyw83BOp/KfPXsWTz55JzNntn4Yq8Ktt8LmzfncdNPNVFTckvEOR15ehJUrlby8Y/c1NcH48RGamg5nVIZ3332X008fxJlnHmDqVJrv/UMPwauv9mTz5q2ceOKJGZUhlxGRjao6Ot4+sxSMnMVLa7C93vnXvw69ex/nmQvS7zENgAUL7mP4cDjtNKishEsucZannQbDhzv7DX/wzVIQkTygDvinqk4QkcHAw0Bf4HngKlU92N45zFIwwkIQeucx2rNa5syJMmbMd6msrMqoDMXFRcyatZuhQ4/dt2UL3HZbEdu27Tx2p5EWgmopfAfY3GL9DuAuVR0GfABc64tURigIW5qIIPTOYwSh/kcQwnKN+HTz46IiMhD4PHA7UCEiAowFvuq+5SHgh0CnbchDhw7xzjvvsL9tciLDV6LRKAMHDqR79+5dPlfLsYBZs2JjAbupqbmT5ct/HchAAGdQO37v3OtJjzG32fz5c7nttvvYvn0PAwb047rrbvBkTANiSjK+pRCGzACNjY3Mnz+XhQvvbb5/11//Lc/uXybxxX0kIo8CPwZ6AzcD1wB/UtWh7v5i4HeqelacY6cB0wBOOeWUUW+99Var/W+++Sa9e/emX79+SFsHruELqsqePXvYu3cvgxN1DztBENwfncXLQe0wEMbvMEbL73LKlKPfZU1NeL7LQLmPRGQCsFNVN7bcHOetcbWVqi5Q1dGqOrqoqOiY/fv37zeFEDBEhH79+qXNegtCSGVnsRDn1gTBhZUqqczzCJO703NLQUR+DFwFNAFR4Hjgt8AlwImq2iQi5wM/VNVL2jtXvIHmzZs3c/rpp2dEdqNrpOu7CdKgrZE6MRfM/ff748JKlc4OkgfRsgiUpaCq/6mqA1V1EDAZWKuqVwLrgC+5b5sKLMu0LGHS3sZRgjRoa6ROstkFgkZnB8mDMIO8MwRpnsL3cAad/w70AxZl8mKZzP8iIsyYMaN5fd68efzwhz9s95ja2lo2bdrU7ntGjBjBlClTEu7funUrZ511zDBMu1RWVrJ69WoA7r77bvbt29e870c/+lGnzgWwePFivv3tb3f6uM7g5Uxkw2hLZzslYXN3+qoUVHW9qk5wX/9DVT+tqkNV9QpVPZDJa2dSe/fs2ZPf/OY37N69O+ljOlIKmzdv5siRI2zYsIF//etfKcvWltmzZzdXr0qHUvCCMPujjfDT2U5J2MJvg2QpeEomtXe3bt2YNm0ad9111zH73nrrLcrLyxk+fDjl5eVs27aNZ555huXLl3PLLbcwcuRI3njjjWOO+9WvfsVVV13FxRdfzPLly5u3b9y4kREjRnD++efzP//zP83bFy9ezKRJk7jssssYPHgwP/vZz5g/fz4lJSWcd955vP/++wBcc801PProo9xzzz1s376dsrIyysrK+P73v89HH33EyJEjufLKKwH4xS9+wac//WlGjhzJN77xDQ4fdvz2Dz74IKeddhpjxozh6aefTvm+JYsN2hp+0tlOSejcnYlKsoWhxSvHuWnTpg4K0Tl0pjRnZznuuOO0oaFBTz31VK2vr9e5c+fqrFmzVFV1woQJunjxYlVVXbRokU6cOFFVVadOnaqPPPJIwnMOGzZMt27dqn/4wx/0sssua95+9tln6/r161VV9eabb9YzzzxTVVUffPBBHTJkiH744Ye6c+dOPf744/W+++5TVdXp06frXXfddcx1Tz31VN21a1erzxFj06ZNOmHCBD148KCqqt5www360EMP6fbt27W4uFh37typBw4c0M9+9rN64403xv0MyX43hhF0YqVEi4uLNC8vosXFRQnLuLZX2nfs2KhWVVV6Lj9WjvNYMq29jz/+eK6++mruueeeVtufffZZvvpVZ47eVVddxR//+McOz/Xcc89RVFTEqaeeSnl5Oc8//zwffPABDQ0N1NfXM2bMmObztaSsrIzevXtTVFREYWEhl112GQBnn302W7du7dTnWbNmDRs3buTcc89l5MiRrFmzhn/84x/8+c9/prS0lKKiInr06MFXvvKVTp3XMMJIZwbJw+buzFml4MVg5fTp01m0aFG7YwDJzKeoqanhtddeY9CgQQwZMoQPP/yQX//616hqu8f37Nmz+XUkEmlej0QiNDU1deKTOBbl1KlTeeGFF3jhhRd4/fXXmwfPvZgTYpFiRlgJm7szZ5WCF9q7b9++fPnLX2bRoqOBVJ/97Gd5+OGHAfjlL3/JBRdcAEDv3r3Zu3fvMec4cuQIjzzyCC+99BJbt25l69atLFu2jJqaGvr06UNhYWGztfHLX/6yS/K2laF79+4cOnQIgPLych599FF2usWp33//fd566y0+85nPsH79evbs2cOhQ4d45JFHuiRDPIJQKcwwukKYwm9zVil4pb1nzJjRKgrpnnvu4cEHH2T48OFUV1fz05/+FIDJkyczd+5cSkpKWg00b9iwgZNPPpmTTz65eduFF17Ipk2b2LFjBw8++CA33ngj559/Pr169eqSrNOmTePSSy+lrKyseX348OFceeWVnHHGGcyZM4eLL76Y4cOHc9FFF7Fjxw5OOukkfvjDH3L++eczbtw4zjnnnC7JEI+wxXkbRpjJuiI7NqM5uKT63ViaZcNIL4Ga0WwYnSVscd6GEWZMKRiBJ3Rx3oYRYkwpGIHH0loYhneYUjACT9jivA0jzJhSABoaGrj88stpaGjwWxQjDmGL8zaMMGNKAVi+fDm1tbWsWLHCb1GMBIQpztswwowpBeCBBx5otUwHv/3tbxERXnvttbj7Y4nokmX79u186UtOuYkXXniBJ554onnf+vXreeaZZzot46BBgzqVydUwjOwnJ5XCuHHjEJHmFnugPv300622x1JKp0JNTQ0XXHBB8+zlrjJgwIBmJZIupWAYhtGWnFQKt956K/n5+c3rBw8ebLUEyM/PZ+bMmSmdv7GxkaeffppFixY1KwVV5dvf/jZnnHEGn//855vTRYDTY//BD37A+eefz+jRo3n++ee55JJLGDJkCD//+c+BowV0Dh48SGVlJUuXLmXkyJHccccd/PznP+euu+5i5MiRPPXUU+zatYsvfvGLnHvuuZx77rnN6az37NnDxRdfTElJCd/4xjcI88RFwzAyQze/BfCDsrIyHnvsMSZMmNCqqEyM/Px8Hn/8cUpLS1M6f21tLePHj+e0006jb9++PP/882zdupXXX3+dl19+mffee48zzjiDf//3f28+pri4mGeffZabbrqJa665hqeffpr9+/dz5pln8s1vfrP5fT169GD27NnU1dXxs5/9DICPPvqIgoICbr75ZgC++tWvctNNN3HBBRewbds2LrnkEjZv3kxVVRUXXHABlZWVPP744yxYsCClz2cYRvaSk0oBHMWwdOlSrrjiCvbv39+8PRqNsnTp0pQVAjiuo+nTpwNOTqOamhoOHTrElClTyMvLY8CAAYwdO7bVMV/4whcAJ611Y2MjvXv3pnfv3kSjUerr6zt1/dWrV7eq4vbhhx+yd+9eNmzYwG9+8xsAPv/5z3PCCSek/BmN8NPY2Mj8+XNZuPBetm/fw4AB/bj++m9RUXGLDeDnMDmrFADq6+vp1q1bc1rpAwcO0K1bt04/hFuyZ88e1q5dyyuvvIKIcPjwYUSEyy+/PKk01y1TXMfWO5vm+siRIzz77LNxE+R5kebaCD6xzLOFhW8wa9Z+Bg+GN9/cTU3NnSxf/msL9c1hcnJMIcaiRYvYt28fI0aMYNmyZYwYMYJ9+/Z1KQrp0Ucf5eqrr+att95i69atvP322wwePJi+ffvy8MMPc/jwYXbs2MG6detSvkbbFNdt1y+++OJm1xI4A9PgZFeNpdf+3e9+xwcffJCyDEa4scyzRiI8VwoiEhWRv4jIiyLyqohUudsHi8ifRWSLiCwVkR6ZlqWwsJC5c+dSV1fHRRddxHPPPcedd97J8ccfn/I5a2pquPzyy1tt++IXv8i7777LsGHDOPvss7nhhhuaq6WlQllZGZs2bWLkyJEsXbqUyy67jN/+9rfNA8333HMPdXV1DB8+nDPOOKN5sHrWrFls2LCBc845h5UrV3LKKaekLIMRbjJZo9wIN56nzhbHf3GcqjaKSHfgj8B3gArgN6r6sIj8HHhRVdv9ZVrq7HBh301wyMuLsHKlkpd37L6mJhg/PkJT02HvBTM8IVCps9260bFSWd3dpsBYIDab6yFgkteyGUauYJlnjUT4MqYgInki8gKwE1gFvAHUq2psRPUd4OQEx04TkToRqdu1a5c3AhtGlmGZZ41E+KIUVPWwqo4EBgKfBuL5FOL6tVR1gaqOVtXRRUVFic6fNlmN9GDfSbCwzLNGInyNPlLVemA9cB7QR0RiIbIDge2pnDMajbJnzx57CAUIVWXPnj1Eo1G/RTFcLPOskQg/BpqLgEOqWi8ivYCVwB3AVODXLQaaX1LVe9s7V7yB5kOHDvHOO++0mpBm+E80GmXgwIF0797db1EMI+dpb6DZj8lrJwEPiUgejqXyf1X1MRHZBDwsInOAvwKLUjl59+7dGZyooK9hGIbRLp4rBVV9CSiJs/0fOOMLhmEYhk/k9IxmwzAMozWmFAzDMIxmPB9oTicisgt4K8XD+wNhKjsWJnnDJCuYvJkkTLJCuOTtiqynqmrcmP5QK4WuICJ1iUbfg0iY5A2TrGDyZpIwyQrhkjdTspr7yDAMw2jGlIJhGIbRTC4rhbDVogyTvGGSFUzeTBImWSFc8mZE1pwdUzAMwzCOJZctBcMwDKMNphQMwzCMZnJCKQSpBGiyuDUn/ioij7nrQZZ1q4i8LCIviEidu62viKxy5V0lIif4LSeAiPQRkUdF5DUR2Swi5wdY1k+69zTWPhSR6UGVF0BEbnL/Y6+ISI373wvkb1dEvuPK+aqITHe3BebeisgDIrJTRF5psS2ufOJwj4j8XUReEpFzUr1uTigF4AAwVlVHACOB8SJyHk521rtUdRjwAXCtjzK25TvA5hbrQZYVoExVR7aIm/4+sMaVd427HgR+CvxeVT8FjMC5x4GUVVVfd+/pSGAUsA/4LQGVV0ROBv4DGK2qZwF5wGQC+NsVkbOA63HyrY0AJojIMIJ1bxcD49tsSyTfpcAwt00DUi+yrao51YB84HngMzizAbu5288H/uC3fK4sA90vfCzwGCBBldWVZyvQv82214GT3NcnAa8HQM7jgTdxAyyCLGsc2S8Gng6yvDjVEt8G+uIk23wMuCSIv13gCuD+Fuv/BXw3aPcWGAS80mI9rnzA/wJT4r2vsy1XLIUulQD1gbtxfqBH3PV+BFdWcKrkrRSRjSIyzd32cVXdAeAuP+abdEf5BLALeNB1zd0vIscRTFnbMhmocV8HUl5V/ScwD9gG7AAagI0E87f7CnChiPQTkXzg/wDFBPTetiCRfDGFHCPl+5wzSkG7UALUS0RkArBTVTe23Bznrb7L2oLPqeo5OCbsjSJyod8CJaAbcA5wn6qWAP8iIK6X9nB98F8AHvFblvZw/dsTgcHAAOA4nN9EW3z/7arqZhy31irg98CLQFO7BwWbtD0jckYpxNAMlABNM58DviAiW4GHcVxIdxNMWQFQ1e3ucieOz/vTwHsichKAu9zpn4TNvAO8o6p/dtcfxVESQZS1JZcCz6vqe+56UOUdB7ypqrtU9RDwG+CzBPS3q6qLVPUcVb0QeB/YQnDvbYxE8r2DY+nESPk+54RSEJEiEenjvu6F8+PdDKwDvuS+bSqwzB8Jj6Kq/6mqA1V1EI7LYK2qXkkAZQUQkeNEpHfsNY7v+xVgOY6cEBB5VfVd4G0R+aS7qRzYRABlbcMUjrqOILjybgPOE5F8ERGO3t+g/nY/5i5PAf4N5x4H9d7GSCTfcuBqNwrpPKAh5mbqNH4P+Hg0WDMcp8TnSzgPrEp3+yeAvwB/xzHNe/otaxu5S4HHgiyrK9eLbnsVuNXd3g9nsHyLu+zrt6yuXCOBOve3UAucEFRZXXnzgT1AYYttQZa3CnjN/Z9VAz0D/Nt9CkdpvQiUB+3e4iipHcAhHEvg2kTy4biP/gdnrPRlnAiwlK5raS4MwzCMZnLCfWQYhmEkhykFwzAMoxlTCoZhGEYzphQMwzCMZkwpGIZhGM2YUjCyDhE53Ca7qGezluNltozznk+KyHpXts0iEqZqX0aWYyGpRtYhIo2qWuDTtS8EGoEl6mQKjfeePwD3quoyd/1sVX25i9fNU9XDXTmHYYBZCkaOICKFIvJ6bDazm+v/evf1fSJSJy1qbbjbt4rIj0TkWXf/OSLyBxF5Q0S+Ge86qroBJ2VCe5yEMxkpdszL7vXyRGSeOLUpXhKR/9/dXu4m8HvZtUR6tpCvUkT+CFwhIkNE5PduYsKnRORTqd8xI1fp1vFbDCN09HIz4sb4saouFZFvA4tF5KfACaq60N1/q6q+LyJ5wBoRGa6qL7n73lbV80XkLpz89p8Dojizt3+eonx3AWtF5BlgJfCgOjm5puEkkytR1Sa3oErUvW65qv5NRJYAN+DkwwLYr6oXAIjIGuCbqrpFRD4D3IuTO8swksaUgpGNfKRORtxWqOoqEbkCJx3AiBa7vuym/O6G04s/AycNBjg5ZcBJHVCgqnuBvSKyX0T6uA/zTqGqD7oupPE4WUW/ISIjcHJy/VzdNNOuohqBk2Tub+7hDwE3clQpLAUQkQKc5HOPOGmHACfFhGF0ClMKRs4gIhGclOkf4RSCeUdEBgM3A+eq6gcishjHEohxwF0eafE6tp7y/0edzLIPAA+4g9Jn4eSvaTvIFy8lckv+5S4jOHULjlGGhtEZbEzByCVuwsmOOwXnYdwdpxrbv4AGEfk48fP/pxURGe9eGxE5ESfJ2T9xXEnfjKWZFpG+OMnlBonIUPfwq4An255TVT8E3nQtoVjN3hFt32cYHWFKwchGerUJSf1vETkNuA6YoapPARuAmar6Ik4G3Vdxeu5Pd+XCIlIDPAt8UkTeEZF49YgvBl4RkReBPwC3qJPW+36c9NMvufu+qqr7ga/juIVexrFQEo1lXAlc6x77Ko5ryjA6hYWkGoZhGM2YpWAYhmE0Y0rBMAzDaMaUgmEYhtGMKQXDMAyjGVMKhmEYRjOmFAzDMIxmTCkYhmEYzfw/N4/xvaIQSC8AAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plotData(X,y)\n", + "pyplot.xlabel('Exam 1 Score')\n", + "pyplot.ylabel('Exam 2 Score')\n", + "pyplot.legend(['Not Admitted','Admitted'])\n", + "pass" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "def sigmoid(z):\n", + " \"\"\"\n", + " Compute sigmoid function given the input z.\n", + " \n", + " Parameters\n", + " ----------\n", + " z : array_like\n", + " The input to the sigmoid function. This can be a 1-D vector \n", + " or a 2-D matrix. \n", + " \n", + " Returns\n", + " -------\n", + " g : array_like\n", + " The computed sigmoid function. g has the same shape as z, since\n", + " the sigmoid is computed element-wise on z.\n", + " \n", + " Instructions\n", + " ------------\n", + " Compute the sigmoid of each value of z (z can be a matrix, vector or scalar).\n", + " \"\"\"\n", + " # convert input to a numpy array\n", + " z = np.array(z)\n", + " \n", + " # You need to return the following variables correctly \n", + " g = np.zeros(z.shape)\n", + "\n", + " # ====================== YOUR CODE HERE ======================\n", + " g = 1/(1+np.exp((-z)))\n", + " \n", + " # =============================================================\n", + " return g" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "g( [0, 100] ) = [0.5 1. ]\n" + ] + } + ], + "source": [ + "# Test the implementation of sigmoid function here\n", + "z = [0,100]\n", + "g = sigmoid(z)\n", + "\n", + "print('g(', z, ') = ', g)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# Setup the data matrix appropriately, and add ones for the intercept term\n", + "m, n = X.shape\n", + "\n", + "# Add intercept term to X\n", + "X = np.concatenate([np.ones((m, 1)), X], axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "def costFunction(theta, X, y):\n", + " \"\"\"\n", + " Compute cost and gradient for logistic regression. \n", + " \n", + " Parameters\n", + " ----------\n", + " theta : array_like\n", + " The parameters for logistic regression. This a vector\n", + " of shape (n+1, ).\n", + " \n", + " X : array_like\n", + " The input dataset of shape (m x n+1) where m is the total number\n", + " of data points and n is the number of features. We assume the \n", + " intercept has already been added to the input.\n", + " \n", + " y : arra_like\n", + " Labels for the input. This is a vector of shape (m, ).\n", + " \n", + " Returns\n", + " -------\n", + " J : float\n", + " The computed value for the cost function. \n", + " \n", + " grad : array_like\n", + " A vector of shape (n+1, ) which is the gradient of the cost\n", + " function with respect to theta, at the current values of theta.\n", + " \n", + " Instructions\n", + " ------------\n", + " Compute the cost of a particular choice of theta. You should set J to \n", + " the cost. Compute the partial derivatives and set grad to the partial\n", + " derivatives of the cost w.r.t. each parameter in theta.\n", + " \"\"\"\n", + " # Initialize some useful values\n", + " m = y.size # number of training examples\n", + "\n", + " # You need to return the following variables correctly \n", + " J = 0\n", + " grad = np.zeros(theta.shape)\n", + "\n", + " # ====================== YOUR CODE HERE ======================\n", + " z=theta.dot(X.transpose())\n", + " h=sigmoid(z)\n", + " \n", + " for i in range(m):\n", + " J=J+((-1*(y[i]*math.log(h[i])+(1-y[i])*math.log(1-h[i])))/m)\n", + " \n", + " for i in range(theta.shape[0]):\n", + " grad[i]=((h-y).dot(X[:,i]))/m\n", + "\n", + " # =============================================================\n", + " return J, grad" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cost at initial theta (zeros): 0.693\n", + "Expected cost (approx): 0.693\n", + "\n", + "Gradient at initial theta (zeros):\n", + "\t[-0.1000, -12.0092, -11.2628]\n", + "Expected gradients (approx):\n", + "\t[-0.1000, -12.0092, -11.2628]\n", + "\n", + "Cost at test theta: 0.218\n", + "Expected cost (approx): 0.218\n", + "\n", + "Gradient at test theta:\n", + "\t[0.043, 2.566, 2.647]\n", + "Expected gradients (approx):\n", + "\t[0.043, 2.566, 2.647]\n" + ] + } + ], + "source": [ + "# Initialize fitting parameters\n", + "initial_theta = np.zeros(n+1)\n", + "\n", + "cost, grad = costFunction(initial_theta, X, y)\n", + "\n", + "print('Cost at initial theta (zeros): {:.3f}'.format(cost))\n", + "print('Expected cost (approx): 0.693\\n')\n", + "\n", + "print('Gradient at initial theta (zeros):')\n", + "print('\\t[{:.4f}, {:.4f}, {:.4f}]'.format(*grad))\n", + "print('Expected gradients (approx):\\n\\t[-0.1000, -12.0092, -11.2628]\\n')\n", + "\n", + "# Compute and display cost and gradient with non-zero theta\n", + "test_theta = np.array([-24, 0.2, 0.2])\n", + "cost, grad = costFunction(test_theta, X, y)\n", + "\n", + "print('Cost at test theta: {:.3f}'.format(cost))\n", + "print('Expected cost (approx): 0.218\\n')\n", + "\n", + "print('Gradient at test theta:')\n", + "print('\\t[{:.3f}, {:.3f}, {:.3f}]'.format(*grad))\n", + "print('Expected gradients (approx):\\n\\t[0.043, 2.566, 2.647]')" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cost at theta found by optimize.minimize: 0.203\n", + "Expected cost (approx): 0.203\n", + "\n", + "theta:\n", + "\t[-25.161, 0.206, 0.201]\n", + "Expected theta (approx):\n", + "\t[-25.161, 0.206, 0.201]\n" + ] + } + ], + "source": [ + "# set options for optimize.minimize\n", + "options= {'maxiter': 400}\n", + "\n", + "# see documention for scipy's optimize.minimize for description about\n", + "# the different parameters\n", + "# The function returns an object `OptimizeResult`\n", + "# We use truncated Newton algorithm for optimization which is \n", + "# equivalent to MATLAB's fminunc\n", + "# See https://stackoverflow.com/questions/18801002/fminunc-alternate-in-numpy\n", + "res = optimize.minimize(costFunction,\n", + " initial_theta,\n", + " (X, y),\n", + " jac=True,\n", + " method='TNC',\n", + " options=options)\n", + "\n", + "# the fun property of `OptimizeResult` object returns\n", + "# the value of costFunction at optimized theta\n", + "cost = res.fun\n", + "\n", + "# the optimized theta is in the x property\n", + "theta = res.x\n", + "\n", + "# Print theta to screen\n", + "print('Cost at theta found by optimize.minimize: {:.3f}'.format(cost))\n", + "print('Expected cost (approx): 0.203\\n');\n", + "\n", + "print('theta:')\n", + "print('\\t[{:.3f}, {:.3f}, {:.3f}]'.format(*theta))\n", + "print('Expected theta (approx):\\n\\t[-25.161, 0.206, 0.201]')" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot Boundary\n", + "utils.plotDecisionBoundary(plotData, theta, X, y)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "def predict(theta, X):\n", + " \"\"\"\n", + " Predict whether the label is 0 or 1 using learned logistic regression.\n", + " Computes the predictions for X using a threshold at 0.5 \n", + " (i.e., if sigmoid(theta.T*x) >= 0.5, predict 1)\n", + " \n", + " Parameters\n", + " ----------\n", + " theta : array_like\n", + " Parameters for logistic regression. A vector of shape (n+1, ).\n", + " \n", + " X : array_like\n", + " The data to use for computing predictions. The rows is the number \n", + " of points to compute predictions, and columns is the number of\n", + " features.\n", + "\n", + " Returns\n", + " -------\n", + " p : array_like\n", + " Predictions and 0 or 1 for each row in X. \n", + " \n", + " Instructions\n", + " ------------\n", + " Complete the following code to make predictions using your learned \n", + " logistic regression parameters.You should set p to a vector of 0's and 1's \n", + " \"\"\"\n", + " m = X.shape[0] # Number of training examples\n", + "\n", + " # You need to return the following variables correctly\n", + " p = np.zeros(m)\n", + "\n", + " # ====================== YOUR CODE HERE ======================\n", + " for i in range(m):\n", + " if sigmoid(theta.dot(X.transpose()))[i]>=0.5 :\n", + " p[i]=1\n", + " else :\n", + " p[i]=0\n", + "\n", + " \n", + " # ============================================================\n", + " return p" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "For a student with scores 45 and 85,we predict an admission probability of 0.776\n", + "Expected value: 0.775 +/- 0.002\n", + "\n", + "Train Accuracy: 89.00 %\n", + "Expected accuracy (approx): 89.00 %\n" + ] + } + ], + "source": [ + "# Predict probability for a student with score 45 on exam 1 \n", + "# and score 85 on exam 2 \n", + "prob = sigmoid(np.dot([1, 45, 85], theta))\n", + "print('For a student with scores 45 and 85,'\n", + " 'we predict an admission probability of {:.3f}'.format(prob))\n", + "print('Expected value: 0.775 +/- 0.002\\n')\n", + "\n", + "# Compute accuracy on our training set\n", + "p = predict(theta, X)\n", + "print('Train Accuracy: {:.2f} %'.format(np.mean(p == y) * 100))\n", + "print('Expected accuracy (approx): 89.00 %')\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "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" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/SVM.ipynb b/SVM.ipynb new file mode 100644 index 000000000..62d22eeed --- /dev/null +++ b/SVM.ipynb @@ -0,0 +1,576 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "# used for manipulating directory paths\n", + "import os\n", + "\n", + "# Scientific and vector computation for python\n", + "import numpy as np\n", + "\n", + "# Import regular expressions to process emails\n", + "import re\n", + "\n", + "# Plotting library\n", + "from matplotlib import pyplot\n", + "\n", + "# Optimization module in scipy\n", + "from scipy import optimize\n", + "\n", + "# will be used to load MATLAB mat datafile format\n", + "from scipy.io import loadmat\n", + "\n", + "# library written for this exercise providing additional functions for assignment submission, and others\n", + "import utils\n", + "\n", + "# define the submission/grader object for this exercise\n", + "grader = utils.Grader()\n", + "\n", + "# tells matplotlib to embed plots within the notebook\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Load from ex6data1\n", + "# You will have X, y as keys in the dict data\n", + "data = loadmat('ex6data1.mat')\n", + "X, y = data['X'], data['y'][:, 0]\n", + "\n", + "# Plot training data\n", + "utils.plotData(X, y)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# You should try to change the C value below and see how the decision\n", + "# boundary varies (e.g., try C = 1000)\n", + "C = 1\n", + "\n", + "model = utils.svmTrain(X, y, C, utils.linearKernel, 1e-3, 20)\n", + "utils.visualizeBoundaryLinear(X, y, model)" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "def gaussianKernel(x1, x2, sigma):\n", + " \"\"\"\n", + " Computes the radial basis function\n", + " Returns a radial basis function kernel between x1 and x2.\n", + " \n", + " Parameters\n", + " ----------\n", + " x1 : numpy ndarray\n", + " A vector of size (n, ), representing the first datapoint.\n", + " \n", + " x2 : numpy ndarray\n", + " A vector of size (n, ), representing the second datapoint.\n", + " \n", + " sigma : float\n", + " The bandwidth parameter for the Gaussian kernel.\n", + "\n", + " Returns\n", + " -------\n", + " sim : float\n", + " The computed RBF between the two provided data points.\n", + " \n", + " Instructions\n", + " ------------\n", + " Fill in this function to return the similarity between `x1` and `x2`\n", + " computed using a Gaussian kernel with bandwidth `sigma`.\n", + " \"\"\"\n", + " sim = 0\n", + " # ====================== YOUR CODE HERE ======================\n", + " \n", + " sim = np.exp((-1) * (((x1 - x2).transpose().dot(x1 - x2)) / (2 * sigma * sigma))) \n", + "\n", + " # =============================================================\n", + " return sim" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Gaussian Kernel between x1 = [1, 2, 1], x2 = [0, 4, -1], sigma = 2.00:\n", + "\t0.324652\n", + "(for sigma = 2, this value should be about 0.324652)\n", + "\n" + ] + } + ], + "source": [ + "x1 = np.array([1, 2, 1])\n", + "x2 = np.array([0, 4, -1])\n", + "sigma = 2\n", + "\n", + "sim = gaussianKernel(x1, x2, sigma)\n", + "\n", + "print('Gaussian Kernel between x1 = [1, 2, 1], x2 = [0, 4, -1], sigma = %0.2f:'\n", + " '\\n\\t%f\\n(for sigma = 2, this value should be about 0.324652)\\n' % (sigma, sim))" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Load from ex6data2\n", + "# You will have X, y as keys in the dict data\n", + "data = loadmat('ex6data2.mat')\n", + "X, y = data['X'], data['y'][:, 0]\n", + "\n", + "# Plot training data\n", + "utils.plotData(X, y)" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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iTXluOgXhvrj2GsGNaxHk5Zei6jEC9eXjvPfhXB4+uMuxI1uRmMiQd3xRFN76PmSH+oo7BUGQYOf5KeEHF2NdbVdyJmi96JnycPc8li6cLQrr6qTo6sFm341kZqSJRlH92FfXeGWd9G6AOhc+I3x+9zxs5UpKcu7iMEaHlafsnE3+RX/Qasg9uwd7r9kou3txNOAg+fm5HD2yh25uw0l5GF/nLqIg3JfeA14X36Xv1s2YtemDzLE5uWf8sBn8EbmhmxFMFKg6vkBupB8O3nNRNOvCow0Tef75ocRdicHSRE5OlfkiLpK1wGnmPXSpHaruDrr3HsvVmCN19jc/bDNKM4saF5Dqc/tZ+y6fpbaeRPUa+jO0OscUp5Kfm4XG1JyMAwtp+O56g+szj6xg1rQ5DB/pVSOPiuJUrsRdxNJzNtVJr1kNHvqyQVDMoq9PolE5kXN6F7ZDPqzxIy59eF00ikLlBxq0BrNuHjSavBFBIqWiIJPQ4F8oKS3FrG1fos6HGGKkLdyIv3SEOdP2MHBAb1avWUNE+F4mvfsBv+3fx+rVP3Hu3Gn27NmBh+d/OHJ4B2++/hYBR8HGez7ZIb5GME+FOhfN/QQDvLfBxLViv4vvxpF9cj3yxh1FoS/vMoy0wHW1at4Wrh48OLvbwChq0WUoWUHrMW/jjvp65GONN3A9361ch1ThJO6wquLzlsOmkn7IB/sxnz0OnOo2gqyQn6CiDGW7fmQc+Y5GH24hNfowhw/tQNmuH3GXAjlyJJTvln9DRA2Ls34X0bBpV/H9690W826m4vjy1zqYDAH7we+haNrFIOLT3NWT0FA/ZA3bUfowEYexn4vnqi6SVd+1oquHCKfJuwzl7rnHuwOA2VPf1CX7qqW/48a8wsnQUNq1bMSlo8sNBD7oFI7a0grAs68112vo9Rq6wW89fq7XlO1q0JQteo5iw6Z15Bdp+W3vVrr3Hiue0wfBOFTRoquSeQ8Pkn8OZMPaRZi16St6NTRt6Uba2YMo2/Wj4PJxzDu+QEnyVTKOrEDVcxSWbt4GRlGVqxdZQevx9HqNS5cjyUrSQSx5wRtBkGDR5z8UXA3lvkQQMVKl+8sUXA0lDUT8X6Zqy2tvthDD+2d9OgUQGD7yP+zetQlF694sW7EY05Y6XNjeazbpB30M8Hebge+Sc/ZXyjLv1Yr3eni+RsTpcFEzzL94yEjzzvBfjqX7GGQOzckJ+xll58EUxQViYuuMxLIhWUHraN6yG+Ul6WQJWrSWTmQFrsd71BtIFU4GO6y1a3wMtFDHdwyx8szAdQgCon97yvYZpGz5hAp1jsGxkcP6I0il2HjPM3qXtXkR9R7wFsnXg4j97Wud0XpUzQZSlasX+bGBlDxKwrGax5G912xDOC1oPa+98SH+/nsfR5EGbaD/oPEG329dyb7kXUew+1c/ZA3aEnk20mAB0ZNZ95rTCuj51/S7rnP/RBnwtOfqonoN/RlZnVf4zERwbC3CHlU/Mn0kpJ3nLDITwtm0djHSRu0JPraeN8Y+T0ZGGldiAhHk5kjMbcV7co6toAITHF75Gk1BNqV5GeLWX+/VEBcdYiBIMvxXor13mQH9BhEW6kdhQjiWbl5IinJwc+3JuaD1yJ07kph4ia1bdrF/nx87d27D1s6BXFM7cs/+ilmrXpRl3CUtL0tcoMxa9aLk0XVOhR1l+rRpRl45KdunoyktZteOdTiM1cEjKdtuUXQzitTtM7Do4YlQmEGnTq7EhWxCYaVLBy1RZ6NFi+1L71GdzHt4kph4gc+/WskJ/5+JPLq8RoHVqIETjyJ2oEWoXNgC8BjuQVaumrORwcgbu1Benm/wvItWrqOHq7vhu3Rpjr3DDwQc2kJkDVpoxpEVCBIJyqrul16zSdv/rcGOQbdorsOximZflWrzIkpKjOVo3EUqTM2NtOzM46ux6OGBpasngkSq2yFU7ib012T4L0fVazSOry6k4FIA2eE/A5CZdpuSojxM23Uj97QfChsnbFWP0xM/KdmXytWLwmsRlD66brSAPL7Gk5ybZ2sM1BLHt4bfdZ37p8mAeg39Ka57Vng86boJ78xg6aKZmLUy/BDT/ZejLS9F2aY3WcfXYOHqSU7oFipSbmDWsicffPgOJUUFCFJTzJp3J3XnbKxfmEjOyQ1IpCbIW3cjbddcMDXDrAocYDFkCqcO+Rh8hCpXL7JPrkepMOdU+AnsRy+gNO022Sc34WDvTHTMRQMvhR/XrmXg4FEMGWWPXPuIXTvWY9X/dQquhoIgIKn0b7fqo9PQ0UJHl+7Ext8x8sqx95pD+sElOIx9vMNQuXmTc2o7JRnJlIVsxs7WlpadhjPh3RmEBftz7IgvCAJ2oxbUugA+TIrkx1XLuHfnMlqlDZYY4v/KTgN5FB+m8wmvxL7LMpM5fvwoWkEiPm/6zjl8+eUCbtxIpM+LE5AqnET7QFUf6rp8wVU9R5EbsZOipLOkpN8VPYqcJ1X3b1+HiV1TY/dONy9RIFf3IqqK34tG651zsOg6jKygDVj1/Q9FSWcoSjqDRdehVBTlU/LgGo+2TcPS1ZusoHWoXD3JO/cb6msRqFw9QFOB7ZCPiIg+hI33PIOc6FXbrinZV/UFRNVjJNnBm6t5Rv2Ahau3gWdUVZvEn/m9/V+ve1baehLVa+h/II+a0r3WFGJeG4+2LXfx30/eJWXbDFSunmSd3IggCDhWJrxK2TGbrBM/IpEpcKgUPinbZ1BRmI/juMr/v8wkK/BHZKYKbEfpg2c+RZv7CJuC5DrhgLzQnzCRSFFXaDBr7Y6iWRfMmndD2boXOU/wtlnh44upcwcDDb007RZWfQw19IT4S3R1aS565SRvn4Gdp06wVYVMiu/GkXVyIwBOL39l4EffvbMH3TtP4+yZEAptWhgIiZxjKygrKcGscgE07zKEWyfWIpHJMWvUgcyAVTi97oNlT28se3qj1VRw/1qEgXHV3mOWzqvmxUmPjatNOnE28gjK9v0rbQHjuXzpgoEP9aAXXyLi5LZaYS9LN280t87zyvAh7NqxhbR93+Jczcsm3X8FipY90RRkiQI5O+QnbAZNpiD2BOrECFTdRxh5EVXH7636vU720eVkh29F7twedWIEDSas1C1kkX44jl5ARsAqTCzsyQ7eiPVz47HsOQpzl0Gk7p5PVtBGbAd/gEWngVh0GmjwXqp7S+nfZcruuZh2GkrWyQ1Y9TFcQLKDNyPIzUXITH1qK6+8PJGoqBBS9p5F1mkw6vCtLF6yUrRJPM23V9e5Z1GO/Nkaen3o/x9EMdHnmT9vBoXWLfj6m/loNJoaQ8zroszMdEqLSzBt1I7cM35ILWwwqwysESRS7D1mYGLVQPRAECRSVK5eCFLZ42s8P0Xp0FRMlCVIpCi7jcTcwpLtv+yjY5NG5PgvNW776HeUl5ageul9rAe8hTrpDI9+eo+Hvh8B4PDWKlFI6T/qzz9fKN7/5utvUfogQQzDF6QyTB0fR6Dqj/Xv9zwAjk7ObNy4nV7tmpO+f6FRfzICViFRWOA49jODUPvQYH/xmskfzKFhRQY5e+ZTcCWY7IOL0JaV6kLUh09BW1FB1okfEaQmOIyej93wT9BqKnj443jyog6g1VQgSKSYteuLOukMKdtnUpb1oNIDY7X4vHlRB8iP8cdh3BfYDvuE7FLwWfw58+fN0KVDGP4JD3KL2bBhNWbVjMf3fxxPbpW2ZJ2GsGuHL0UlxdgN/cjouS17jab4TgzWQz5E2a6/zoto9HwsOg3Cqu/rVGQmU352J4uXrKRt+67ifUsWr8S68KE4FrlHlzFrxhwaOdhR8jARm4Hv6lIY9PTG+f3NKJp1QeXqibakgMZTd2PZU6dtawqzMUWDW/ceFEUfMupfTWkd9O9y/ChPys/74da9B+W/n8bptSUo2/bTJfvq8wra4jx6tGoC0XtZ4vM91rb2FBcV4TGgD+Xn/Fi8ZKUBjFVP/3eqh1z+AB41BQR9+P5bXE9KEN3l0nfO4bPP5nLjRmKNVXmq8tALkrKsByLeq/ficK4Wfp4dshmphW21yL7VBtfog1b2HzxCTMw57GrwL7Zw8yb/4mFyz/6GRp2Dsm0f1ElnMWvdW9Rq9b7Tmf7LMFcoyMgpE90nA/Z9BzIzJOa2CBIpdiOmGoXmW7p5Exi0lZdGjGff/sN8ecGfQnU+tjUY/ixdvSi4epKs4M04eM+hIj+TgnBfnJu4MGigO+99MIf0fAUfT/2GX/02cvbkBlQqKyoadaqiac8g69ASrAa9Z+Blkhu6mdwzfhRej0TRpBPqhDAcxnxOYXwIafsX4jzJ0LsoJ3IXynb96wzaUnQZTlH2NsqzH5KyfQaqHp4ijFGUdBb1tXAdThy4Dg3U6mVj6eaFOiGMdL95NJm628A7JfvYd5jKzflkxkLRGFs1lL73gLcoL7xBaLAfvQe8Tr4a0tPTa8WtLV09USdG8GD9RJxeXYTM1pnsgB94/rnBREQE1pJC4LFBtqo7KgygfefnadYqnugYnXFev4CYOrbQBX+16UNM7EV8Vmznxo14MSVFRGQkg72nizDWPw0GqYdc+HdBLjUFBN3c962B37ay23DOBq1H2b6/Udh2dR7wGIc07zaM8ozkmgN+jq/GZtAkzDu+UBnZt5hGVdz2QKdRvfrae1SUZLFh4xoDjLoqWbp5o752itL0ZJxe0UEcZVkPMHVqSVFuqkF+lYryMsob9jCoOKRWF2DWpi+pO2fj9MZSTO2aGPRXn/vEwtyciuJUg/wmNRvJPCi4GowglZEZsAqJOov+ffsREhaCsl0/tviuZPGyn9GWZhBzMRJl237YFD9AVpFuEFzl9I4hjJMX+hOCVoP9qPmUpd8lJ3Inyvb9ASi6ebFGP3yL7iMpvHSMnPwULIZMqdGHOuvkRhzGLEDRpBM5ETvIOrlBhDG0mgoyjnxPdpDOrqFsXc0YeWQFqp6jsXTzEotLZAUaLirFd+OoKCvFtI27wbjrXUL1c6p7Zw+mT5tGbPydGudUxtGVOiy+MtWCqvsIsoM3i4u2vGVPQkKO1ppvR2+QfXTrFP5RpzBp2ZPYqAPMmTaeX/dsFzNUVt3NVQ3+Stk+g42rPyP53r06U1I87bdXWwbL6tBnXTz+TZCL9KuvvnqqC/9oWrbi+69adehDanoOEecSkMtlRr/rOve01/0VPAa9+CKJUUFkx55E5twRma0zKlcPTKycgMcfrfVz47F+fjxp0YHcuXEH5yatjXikXjimE2LH12A9YAL5F/0pfZSE/cjpIj+RtFoKE0IxsXIi79xe7EdMM7pGU6ElPmw/Z8+ewqxtP1RuXgiCQPHdOFJ2zUaLgLxhGwSJFMFERunD69i+9D6CIEGQmJB/YT+WPUeRF7UPU4fmpB/ywXH0fFSunjyKOkbchXP4H/bDfsxnqFw9USdGUnA5QNzC6yl1zwIEqYxWLVqxd+8ONGZWmLXogcrV83F//Oai1WqQN2yr64/MlOKbF5GYmlFeXsqtpHgcx32JytWT/IRTnA3xJyDgEFZec1C5epAZG4x7d3cUQgV3zvpj0X2EQR8y9y5AYWKCtIUbKjcvFM4dkFrYkXd+P+prp3Acs6BGIaZo2pmi389hJ9OQnXAa827DDc4/+mUGaLWUPkjArEUPzDs8pyuwHLUfRdMulKXfpejCr/QbOJ7OHduTcO446utnEKQyMo6soH2bDjy4FII66SyCiYyswHVY938TuXMHQAf5ZAauxXbIB1j1+Q+Poo5x+uQxAgJ+w3LQe1j1eaXOOZUec4Ky7Edkh/6Equco8qP2o74eSXlOCrmRu7AZ9B5lKTfRlhahvhmFomkXg3mSuXcBGo0G08p5UoGEpLC9qPq9RtHty6hLSrkSc4HD/r+hbNff4N70Awt1aXgrFytF0y48uHAC6xHTdFCaIKFCKyExZA+du7/4f/v2IsLZsHYxZU4diTyxjwppI5JvX2OZz2dUNHQh8sR+WrRxIy0j95mQI39EW6npOQT473r01VdfbapJrv5tAn3jxk1fTZ48mYf3rrNz2/eM9hpB29bNkctldHVpTgNHaxo4Whv8r+23/n9FcSorV3zJoBdfpK97Fxo4WtfJ/2nbelI/+rp3YZT3WAqc6gYAACAASURBVJKT4og/uQdVDw+DZ03Z+SkW3YZh3fdVBEGCBgnZV04wfepUIx7FuWlE+/+MwsoeWaOOFP1+rlZBY9qwDQWXjpIfc6RWFzdNWRE5cUHYDvmQ4tuXKLgcIAoSwUROyb0rFN04DxKpLjTc81NMrJ0qdwhrsOw5mpyI7UjQor5xAUXLSiEskWLapBP3LxzDatgU8eMUpDKKk+OMBDpaLaUZd8h4eAd5697YDP6QwisnKbwSBIKUjGM/oC0rpiI/g8K4IAQTGdkhvth7zMS804vkRR9G2a6vzmZQKRhy4sOxqSyEoR/Xm6f28ujRfayGTalxAVTk3yfvXhJFV0+Se+EQpTfOYK40pwwppg3akhWwCsFETvqBRaDVikKsLCeFzKQL2FRbWIvvxlGYEI6ytTulmcmok85iat+U9EM+yBt3ovDqSfJjT/DpjDlYW1vx2x5fZC17QtoNCm+cx9tzFBejo5C16EnJg6sU3byIVb83sHIfQ/HdONJ2zkJ9OwZlu74U37mMRZfBIDXlftQxlO36icc0grTWObXTdzXq+wko2/XXKQej5pF34TClD6+hbNePkruxmHcZQn7UfhzHfkbehQMUXjmJIDWlIGgt06fOIPH0UXKvhFKBQHbgekxsG1N08wKKJp3QlBSSmpGKuctACq+GUpR0tnKOfUcf9+e5cyUSddIZFE0716jsFAStZdL7nzJwgLvR99rA0Zq7txPYunk5niOG4eDgQFeX5jy8d50VS7/AxnsuKlcPcq+EICtN4/ixXysXeE9yr4TgbKtg0MAB/yc58r/IgD/yuied+9l33bMn0Jct//6rCkHJMp/PKFE15MCuTTRo1J5L8feRy2VERITz9VezKShRYmlp/cRVa9/+w/zsu4JShw5Eh/nTrmNvTp8+ZbRanz5/7U9ZnU+fjmD/vl+wGVGDJi1IKL4dg3nnQZQkXyEvcA1u/V7G1s7JgEdaRh6Nm7VHad0BuSaHW2cOYdbG3VCL3TELLTzWYk1MKb4Ti+2QD8VrMnbPQ1MpjDIOLUXRtDNWfV/FovNLlOelkxfph/Vzb2Lv9SmlGfcoTb1J6YME7D1momimWxRS9yzAesAEcs/9iqSsmO49R1JelEnegxuok86iaNIJma0zyq7DDD7OzIBVOHjPNhoD04ZtKIgLokuHjlTkpJCdeBabYVMQpDLyovYhMTXDrKUrDmM/p+TBNfIvHESQK7Hs4YHM1hmphS350UcounEeeWMXI8GQF3WA7PAtVJSXYzNK52JXfDeO9IOLUTTtgtTMElmDNqRHn0ArlVGWn4lZix5ICnMoKlJj6tSKgsvHUDTtQt6FgzrDcEIYBbGBFN+JpTAhFNuhH6Fs01t81tRfv6AgLgjHMQtQuXpSmBCOicqO/OjDOIzSCZrCK8HIbJ25ej6M06cCsfGeq8Ouk87RvWMnTkeGYeU1B0s3T8pvX8RMCqV56WgFKTkB36OpqMBh7Oe6XcmlY+SEb6f4dgyOY3U7ooK4QHJO7aDs9gV69v+P0Zw6fTqCqPOnRB6FcUGUZz2kPPuB2O+CuEAKrwRhamGNxNKBst/P0LObK8nnjzHp/U9p1qorFZJGNHM051bEPiRoKS3Irrzfg8L4UGQOLVBfO4XDqLlIlFbkhP+MoCnnYcpD5K3cKX8QT+HNi1i6Gio7mXsXMG7sBHJLrLh7O8H4ez19ykgLV8hNWbJoPuUNXUTlwqRRR+6c9TdQLiq0EmIDdtCx64B6Df3Ppq+++vKrixcjMXMfhzrxNIqWPYk7fYg33ngLTUkaK5Z+QXlDF/LvRfPB5Ek0dLKpddV6eO86P/uuwNqr8iO6Fk5JdjIH9+80Wq1dXd3+8NW5ojiVFUu/wMprTq2adOGVYN2W9swOpk+ZSa/K4Ima2kq+c43jx37FcuAkim9fovDqSRAkZB5fg7J5N4rigylMPI1gYkpBmC9Ojo5kXg5GKzEh78RqZkz/VNSoTNv0oTD2BOW3LyBr3AnzDs9h1edl5M7tKUm+St7Z3Th4z8F20GRMrKsI4Uo4R+kyEDtNHh989CkfffA+mWmPSLx8nrLb0VhU7kT0grPgajDWL07EvG0fcauOVousQRsRQslNiMDP7yBXLpzlwcUAbIZPw9LNC7MWPSiMO0le1H7K0m6hbNMHbVYyBUnnMLVvRubx1Sha9qT0URJFv5/T+UlXUvHdODJP/IiybV/KC7KxHvguJclXST+4GHljF/LO78ei2zAkUhMEmakOXhn3hc7v+mooJnaNKU29iWMlbFR0MwqZdSNsh3xESepNiq6fFrVh886DyL9wkMwTP2LWvDsVle2V3kug6GYU2pJC7Ct3DSXJVym8FkZFXgZF6oLKxdmrcnfjwv0Lx7Ac+snjHYYgRZJ6jYHPvUTy2YOYW6jQNu4mCi1F0y4U37oo8tfDYsW3onFwcGDye1Pp1qmFwZzyWbKAsgYdRR7yJi4UXDqK3fApBjwkqYm8/eYEYgN2sGjRCt4c/w6du7/IwAHuNHC0RqEwZZTXMF57/S2OHj2ExrmLCKPIG3cUeZo161oJZdlSdPsiDmM/x9SxGQXXInDwmmUMCWoqyEk6QxeX9mzZtNzgey3OusvB/TsNtPBmjuaM8hpGn959iQ71Jz36hAhzWnQfYaT5L1y0nG5dXeo19D+bPvviq68s+r4qurWpXD3JvHySq9HnOXigUhD38CAjJoiMRykoVQ1qXbWWLJpPRaNO4qSVOXckKfyAwceiX61lFm3+8NV557bvH2sLVTVpQRA1aSQS8qL2Yd7dk8TTR0VNozZc0MZ7Lso2vTHvNBBtaTF5Ufuw95yJys2b0ptRtHayIy02lEnvf4rnqAkk30om+8oJJr5nqFHdPneMXv3GYaOoIPn8URH/fZyEaV6di5DMvinqjIfcuXEHtVrNnt1bULTuTXlRHhbdhlGSfEWEFzTqHIpunEdiZkV+0Bq6dHuJknsXyY4NRiNIyA5cj1yhpFBdTlDgIayGTaE8J5X0g4tRtuuHqVNLncZbqX0WXovAxNJe1HhNHZpRGB+GfRXBoH8OvbZZdC2MvEg/ChPCcNBrzleDyTv3GxIzFVmButwuVn1e0QnJZl0ovBpiJOByIndi6tSKvDO7RUFfcOkYhfHhFF4LE7H8wvhQcs/sRn3tFIqmXdFWlGLV/3XduBxcjKJZNzRlasy7DUMdG0j57YuiAKq+u8kLXMPb786kSOPAhAkTad+hG9Gh/uQnhGCq35X0GGm0I7L3nkvOrVju3b6Lc5M2BnOqdZtOxjy6DTe07xz7ATOFgt9v/E7P516jXXsXcde7fu0irO2aEnP1vjhH27bvQlTgPnKuBKNo2qVmnkdWYOrcEblzBzIOL68DNmxLWnQg0WdOYtLC1UDjrukb1mPtRSXQ0/0FYqPOkHUl2Miukb57Hi+Pe4vGLTo/M7a4f7WGvnj9tq/K0u4gb9LJQAPJuhJssG3SSqQknz3I1E8+qXXV6tO7L6cD9pF7JQSTRh2Q2TpjXm2C6Vfr5s2b/+Gr82ivEUSH+pN5KYgKrUDGke+w6DasUrsOBolExIOV7fsbaBrV+RloVIJASfJV1Gd2YNHDE7N2/REkUjQSKQVJZ1i4dAsDB/SmoZMtzk1aM33qVDp2aGekUWVmpOo0/qGfiGOSfnDx47GvXIRS9yxAq60wWITyo/Zj3nMU6dGHuRh1CptROoOoOiGckvsJ5J7dI8IL6sTTCBVlaO9G4+PzA9169OX9Se8i05Rw+ejPVGg0CE26cSnsIHaj5yEgGGDNRTcvomjW9fF8qCJs0ULG4WVGgsHgOSRS5E27UHLrIrZVtVipKZrkaNQ3LmCqskWj0VAYH4q8cccahVHmke9o1MCJ1JhAzFr1MjDoFV4JxN7zU5F3eW4qJffjKxchHcQijsvoeZULSghlv5/lvY/mYyEt4UbEQZRdhxl8D5l7FzBj6ky8vLzF+dCqRVPaubgTdHArBTcvouox0uCeFL952LzwNuZt+4LUlJSLh5k1c6bBnGrVoimC1IzzoYcouhOLqpqhOHXPAsxdXiQ/OR6cu5B/L4YPJk/i0f0kfvZdQUWjTiRfCWfsmFdE7T836xHHjh3AxNaZwqshqKoJ09Q9CxBM5GiK8lAnhBnBhtXnmUaQoL0fR0MLEzIvBdX5Deux9gaO1jy6/7tO+avRXgLZSZG8+847de7u6zX0P4gWfrfmK3uv2RTGnaTg0lFxpa/+ErMDVjFx8izKNPJaV62iEqiQNkJWmmagheqp6mr9Z6zODg4O9HR/AaFUTeyxLcgbd8RuxFQsOg2iNPUWeed+xbzjAGR2zmQcWoppmz5cCzuA/5GD5OSqWb9uKQ5OLYmJuUTMxTNo87NQJ57SGU8DVvHyuLd5GBtKekwgSE3ID93M2+/OIOFGFndvJxjZGqr2sarGX1UQKpp2oTDupAjnZBz5DmVrdwouHaXkZhRaiQlZQRuw6PQSRVG/IUjlSJv3MNhm50f7Yz9iqijcEKSob8dgJlcwaNjrRJxLwEwhJys7h6jz4dh4Vwq4xEjKsx6Sd+GAuBjkXzqGacO2lGfeJ7/KfFB1Gy5q8fKmnbB0H4sgCORFHSB17xeYtehBeeY9Cq8GiwK6+tY769gPmCtVTJu1mEd3r5ORk4OpY0sKLh01EkYpfvOQmZnzfL/BaAUl6b9HoY4PIe/SMcw7PI91v9cxsXISMfTiu7GPPTuqwQ9VNX7to2vIzJw5fcrfYGHVk0ajIfH0USM7z/4DR7h94xL2njONhRZQmBCK1NJBZ8wVBLq6vmQ0R1f4zKWsogJ7D2P7TmnWfQrjgnAcp8PYM2KCiLtwlp07fXUQZg8PIw+ar7+cgcamKaWpN7EbXrPxuTT9DpoSNTL7JpTeT6Do5gURNrQeMIGc8G0U/34OpCYUhPnSq/8rTHj7fR7diq/VQ0mPtevn9TKfz2rNH2PasA1p0YGkP3xU5+6+XkP/g2jhd2u+kjdqj/pmFGg1FF0LR9XDw8CQlXd0GS+Pe5vxb75W66r18N51Vq74klbNGxEWchTLoZ+IAkBvDKu6WisUpn/K6tzQyZY+vXvj3NSFe4lROm0dKaWXDjNr1nyuRASQEXMcRdPOFMaeAE05ZdaNuRwZgEmz7iSeP87p8BNoG3fBSijmZU8P4k/uZuLkWYx/8zWaNmnCicO7Kbp9mQZOjixY8CXJd67hu3GZka2hJgxVZt+MjINLEEzk5B5ejCC3QNG2L4UJ4ahvXkBuIkFWlMXiRSto1sCB2IAdDB08jAdXIvnmm2V0c32BqxGHSAnbgalTS+SN2qHqPkIca8FETnbwJqTachYt/o7uXTsZ9KHUsYOB5p0f7Y+9h6EWnXf+N1R9XkZ9NZji2zGP58MhH8xa96Lo5gXUiZGU56aSd/43lG37UpR0BoWFFRokFMYFGmDrAJl756MtLULS3JXr549y5+4trNzHUBAXVKMw0qKlJPUWqfeSmD1vCQNf6E/g0f3Im3ah8GqwaNhOP+SDomkXSjPuQkU56mvhtWr82QHfM+nd9zl88Oc6BVB1O4/eNlS1nmf1ewqvBJN/6RgmaHj/4/kMHNDb6PsIDDyCfS08ck79oosadasD26/mlbXHbxsF6ffq9r6KC6KiMBtNYTYOo+cjNbMUYUNlmz5oBQnFv59FkXWLb75ZhktnVzQl6fjt2lqrh1L29Ui6uLTnZ9/lXI27QHlD3c6sJPkKqb9+QWHUPgRBEG02GkHyxN39v01Df6rQf0EQhgmCcF0QhBuCIBhFXgiC0EwQhGBBEOIEQQgTBKHxk3hqSopIP+SDqWMLynPTsH7pffHjlTm0IDNgFYquIwk/ddwoZD4m+jyLvp7CiYDDutB6M2d27diAymMWaDHgodVqsHD1IKWgjF/3PrlC+/8v6cOg3/QeSfl5P5b4fI+jYwPycrNxHPt5pRBpgEmjDpSm3MBh7OfYDp9Cmrock7b9sR32CTllAoJEwv4DgbRp14WY6PN8tmAW9mM+p/EnO8gqqsDT4wU2rlukCz0f9gnpeYWMGT1ErOaupyWLV6LI+J30A4uQOTQn++QGpnz4MWY3Q8g45IOiaWckmnIWLf6eb318cXXrzX9encC3Pr7M+PRz9h8IpIerOznZGeTm5mDWuhcZ/ivQajUG7ys7eBNmpjLe//gzo/DtJYtXYp1/l5Rt08XQeufJGwyCT7JDfTG1ckAd5ouJTK5zs6wSlGI3fAoyuyaUZyaTH+OP47gvsRsxFRPrBpiX51OaehPbGkLpFd1GIlg3xKzDczx4+BCr/m/UmNFST5aunkhMZDzXf0Bl6obp2I35DLsRU0ELmcdWP+7TiKnI7JqgdHkBE+tGpB/0MeKXcex7FKZy/I8eNgrySd88mYKLhwzSAuzZo5ujMdHnmT1nqtE999dOIPfCQfEei25DESRSnBo1pnXbzkbtf//DMkwrS83peaRsnCi26+A9h5JHSQbvxm78D0ZpHia8M/XxmJopjfLjp2ycSF7UfrFflj29xTTBZs27PU45UMnXqucopNYNade2PT1c3cU0GVWjb6uSeQ8P7mfksHHdYrKUjVEozGhQnk7a1k9IP7AIecN2qMzMsH0URc7eBRRcDUYdvpXPPvvWiNe/mZ4YKSoIghRYCwwG7gMXBEE4rNVqq8ajrgC2a7XabYIgDASWAOPr4lue8xCnVxdVRiTep/BqmFheTN7YhdRdOte7jCKNQQ1NfYi84NAKn6Xf4DD2c+SNXSjNuKvzgb154XFU2o5PybtwCKteo2vM5PZnhv227/w8Q0bZI5E7sujrKQaRpPbec0g/tNRAqKhcvck9o6v3KO88lJ07t9G+8/NEnEvg1IkNYja7kuSrFOdmYtaqJ6TfFo+V5mUgbe0u5jnX55ROSowlJzsTx8pxSsu8R0hImBgSLm/sQkb2fSIizxvUcqz6XNUr8aTsnE3msdUG7ytlRzKysjxSsqU1pnQoLatAU1psUA9UT5nHV2Mz8F3KMu7pfOorI2wf/Ty1WtKsmaTtX4hdlUIcKlcv0oJqL1hh6eZNUdJZsgLW6FLjXg2tMbWsQe1NN2/Cwv0oCw7GpGUvtBoNKdtnYD3gLXLCt+miSbWQsn0G5u2fI+f0LiRSGQ5jFhi1r3L1xDQplPFvTWPd2mVk+c1F3nkouSE/8epr7xF+6riYU74gzJfJH8wVq0jJnDtTfCOKjF2zMeuiKysnc2xF3rlfHye9CvHF3utTcs74GVQR0o991Rzt8s5DKQj3pYvrcFLuPM5lLynKoWWLNiTXUJwi5/gqXq6sdPXrvkNV6rJuIX3Hpyi7jSAneBOvvf4+JwIP8CghDEs3b7IC1yF37mgcEdtrNJaujyNizwVtMMq+qb8+98RqlN09sXD1oORePMU56ThUJqpL3z2PDo3tuX3rdzEldPrOOfR3ccNFkHDi+C+8+/7sGtMJ/C/feV3n/ujrnnSuLnqa0P9ewA2tVnsLQBCE3YA3ULWVjsD0yt+hwMEnMZUoLB5/qCNnGOX9sOg6lNwzflj1e03M6lc173JW8GbMquaV9vyUtP0LDYVk95E6GEBpaZTJrabw4KTEWBZ++QEmJqZMfO9xNrmarq0pLLe2bIsSQYtF7h1yqoSkVxVqeg3VYdRcsRTX0qWr6OrSnKTEWKQSsFM/IHXrJ5TmZ2HV95XKFLUSnWC9cU6c6Dl75xvklK6ehc/WY2aN9R5rquWo/63j4V7n+9KPdW0h3JMmThYX4Oqk6uFJ3oWDlGc/wrxK8QmVmzfZJzeSkpGMvcfMylSzj0Pi9alm5Y1d6qywY9FtGDnh2ynPegiCQMnD61XSxm5AamFDfuxxCuJDsKws4LBixY8sWvQV2TcvoL5+BmW7vuSe2U2Dt77XuUQe8sGsVU9yInchCIIu7L+WBSXn1nmyM27z5Tc/kBQfyZ49O/jwvwsYN8aLiRPf5rdfd7Jz5zZ8fHT506vmBcr0m0tZxl2ygtZj/dx4XYTujk8pS79DdthWLN3H6opydHiOu1eO09VlicE87OrSD3uHx+0uXboKidyRTu2biO1OevcDtm79qcZKV8ruIysrXY1j09ollEtNOXZkF6++NhG/HT9ScuYXLFVWDB/2EieO76U8J5uciB1Y9XuNvPP7xcyh+oIkBbEnKEo6i0XXoWQFrsPK0hI7S8GgJq7MZTDqU1uZPn02+/bv5eHuSIrzswzSP1sOm8q1o8sNqkEpuw3n9Km97D8QyMDBT1+X9I8Ix/8r26qLBK1WW/cFgjAOGKbVaidV/h8PuGu12v9WuWYXcF6r1a4SBGEMsA+w12q1mdV4vQe8ByA1kbkqGrTGavg0ZLbOBm0W340jbf+3qHqMRH35OB98PJ+27bvy2bz30TRsj+3wKZRnPyLDfzloEfNKV+eR4++DW8/niIk5z7uTZ9C2fVciziXgZF0mVsqxKXrEzNlLuZF0hXWrv0ErSFC27Y1p+h2+/GYVN5KuGF0bGZXIc707ArrV87neHcWdQ9XrDhw8otO0WvTCJP0mbVq2JP72HSMt6MHGSVj1ew2LToNI3TSJzp0HMH78BFEzVrTujbX6IamP7iN1akVp6k0xRa1WU4HtoMdpXguuBFN+3o8ho2byXO+OpKU+YO0aH8pMFWLa3JrG6b0P55GSbWL0XABpqQ9YsnAmUusG2HvOqpFH+iEfLLoOoSwhjKXfbatxbGrDj7WaCh5tm04zGwuKy8rJLNGg7DqcgnBfOncfwuWoo6CyN0qadf/HCXRp70J6dqbBPS+/MomjAQcpkymQdxlKdtAGZOY2OE1aT9bJTRRcOYlZi+6Upd/Bduh/KUu/S170YcxaulF8KxoTUznDXhzCg5Q8os8dEgtupO6ah2CqoOT+46Rr99a8gbK1O3YjpiIIEiPNUpBIKbgaTPm5x++k+vhW/b/o6ykU2rTAdvgUBEFCWdYDsg/7YDlwssE7Ljq9DYlUTmFhjs6W8PtZ+r/4Jl1cmtc5X6u3rd8N1PVuUn+ZSWl6MoLMFLPW7hT9fg4BLWZt+qD+XZfATZl9k9ycLGQtelJ8OxqrAe+QH7YZmYmcohK1mCBMq6kgP/oIOad3QEU55h0GYFP0EPcBb9HfvQNhwf6cOH6ASe/NpE27Lmg0FWzZsplb189gplRRIMixGTG9xjmYeXAxH/53AW3adal1fOv6/b+e+yvbApgyeWS0Vqt1M3pZPJ2GLtRwrPoqMAv4URCEt4FTwAOg3OgmrXYTsAmgSdPm2uych+QeWYr9BMMteF7garp17sKVy8d49fUPeHmsLuOcl9cr7N2zmcwdM7EaOYsG478jM2BNjYmr9HUXhw33NqhRWL1STs7e+ezY4kPMpWi0ggTHsbrt2yPfj/hsztuUlZZi7T0PeWMXsnfPq7GiSmJcGBs2/ojNkA8x7zCgCs8Y8Vjaz1O4fPk8tqOMMx2qenhSEHsCc5cXMe/hScqdSMqLUvDduEyEOTL95lBeVkT5g2uiMEndNQ/zji8Y4Z1LfL5HInfU9dGl7io6ddVyFH+7NEejWc6alfNJ37+QRtUEa8aRFSjb9qHs6kmDHN16HjUmiTqyAlWvMWIBBMue3twMXM+JwDOsW7+eiPC9LF26irDw00RrNTgO+dB43HqN5trF/Rw+HGxwT/cevXDr/QJJ8ZHs2LEVqUTAeth/ESRSSh8mYt7hOcw7vkBO2FYqCrIoiT2CtZsXUvumlD5MRNHhOSLCj2EiUxhkWbQbMZW0aknXrNzHkXt2D2UZyah6eFAQ7suMGXPZt3/vE/N816SR6WuDVt3NOb5tmLe+IGwzkyZ9yGbfDSJslpnzgOK8W/hu3Gswt59UAWjR1ydrSOD1na78YCU0YuHqSVbgehxGz0erqUB9PVIs/FGadR8EyMhIewzh7ZpN8emfeW/yR2z6aZ1Btkf9u9ZqKyiMD8Vm2Mfk/vqZQZ776tr18/17k5ORxKKFy1mzZhUJNcxjfRK6cWNqrrdb/f+/VUN/GoF+H2hS5X9j4GHVC7Ra7UNgDIAgCBbAWK1Wm0sd9OD+PZApcPSYZHRO3nUEl0/7oWzbh6PHDuLm/iI3kq5U1pp0R3s/jsyDS7Ae9J6I41YnfZrP6jUKq1fKkXUaSnTgOpRt+1KcHIdp446U3IunQp2HtlVPNJU4tSBIMK3EtqtWed/y00qKiwowa9eXgsvHkZrbUpidTvTDm2KdTqm5bWX5t5q35SpXD9TXI8m/6I/K1YP0axF8+eU8g35aDZ9O6UEfbF+abARL6VOsZh/7nldenohE7miAu9VVRae2Wo7VcbvIs5coLSvHdrixYLXsOZr8s3t4/6O5pObIjDD0Ce/M4MdVX4kwR3aoLxKFBfkXDlEYH6qDOUJ8kVrYMmP6x3RwHcOXC0cRErifQwd3PDHVbNV7AGLj7xAZdR0naytKSoqxHf14W27vNZv0A4tRJ0aibNeXrKD1dOvak/vXAklPT0XZrh95Z/fy4cfzibp4lejzhynNvI/9yOk1pi/OPeOHVf83AIHs4E00b9mVhk278tGUTpXa5mMs92kxU31t0Ks1YNrZAT/wyn8m89v+fQZ2Gavh04k75INVFaOizKX2CkD61LeODdsTHxdKaeZ9VN10OL2JIJB7ehfqxEhU3YeTFbgeqcoBeZNOpGybLtZkrQq/VRXaZl1HUBz5C5t9N4h5+Y3fnTdFSecoiDmGzMWw+lJN9htF697MmPlfsrMya0z/rOjqwdGAg7j1fhGJRPKX4tr/NAz9AtBGEIQW6DTvV4HXq14gCII9kKXVajXAPGDLk5hqAadaUqdaunlTcPkExfeuolWqCDjky6lTYaj66goQI1UgtXISvQ3qSvMZenwn5y5cEHHtKdO+YOtPS3mw5k1U7mPJO79P1HgfbZvOg7VvgUaDw+h5ohacf9EfU8cWZJ3cgJfHKJ7roLSxlgAAIABJREFU3ZGK4lQ2rV1EmUYr3p+yfQZpv32DIDUx0KIz/FeI+B/UbIiz6DaU3NM64azsPpyKc7twqMjgUZUKQ40mGmpqetxdT0pXT6KiQnj33XcARFtBXVvqumo5VrUN1MnDzYuKW+fQlufzXO/uxjxcmuOzKA+JtYWuYMOouUgt7Ej77RvkjdqTG+mHg/dcyvPSSTi5gfc+/oyuLs2ZOXWvqCHrn7mmVLMJQY/vqUo17Qxyjq2gvKQEx3Gfi+XmEq5eRJDKxHeWmfMAbXk+ybcuYNamD0CNedIzjqxA5eqJVa8xAEjNLMk87yf2oyZt82k0st8TYzgad7FGTFvesie/7t3Mwm+Xs+rHVeTsmY+s0xDyLx7C3nuuCEXUuFurpKppdxPjI1C07o1EpiAraD1yZxfK0m5g7zGL8ox75Eb6oerhQf7lAFJ3zcV6wFvkRu42yL9fPU2y+tRWVJYq1HbdDN9dwCosXb0M5nzOqV8w1ZYbVEDSj0VM9Hlxl6rVVJC+/7TB7qgqWbh6kPV7JEnxp/nPqxPqHN+nfQ9/Bo+/XUPXarXlgiD8FzgBSIEtWq02XhCEb4CLWq32MPACsEQQBC06yOXjJ/GVKMyNaiZaunmjqrRoa9Q5mLV2pyzzHqei41D0GmtQyqw4OQ7zaq5TmcdXY9F9hJjrmQYdOHzkIObt+oneHxGR0WRkpKNo5UbumT2PKwIJEhy855B+YBG2gz8w0oIBzNs/T1h4KDlFlpw75YdGaY3SuYNBwd/0GrTorKD1cD9e9DTIPrkBqbk1+bHHKaxSSd3UypGCq8HkBv/EBx/Pp3XbTny/4hvSDvv8P/beOyCqa93//kxj6L0qdkUsgKBii5rEJIq9xTQ1idEk5hwsMdHYUu2axKgxscZeo4IFFMSO2FBpFlQUlSYdhjIw5f1jM8MMM6A55703v3vvWf8wzMwus9faaz/reb7FaNkNtcgQw8Ft32UYmXsu8cuqVXrEypKlC00mtbo53uchgP7dfaTeScDOzoGSggwkTl5IbF1Ql+YjtrDEvstQXGogigXRaxk2Ypx+u0kfz2T9uuVGhhHNWwaQceVPvWFEQdRahg0fazbCGf/h52zZvJLcnTP1+XVLS2ukTQON7OYKw5dg36+2zyz9Q9i5cys9XhnPtdi95OcL6YS6zT54FOWpsWi1GpSPk1Gc3UT3vu/qVyh1r+PzIjKRqJKinPlsWPcAhyGmq5LK9ETK7pzHqmUXFi7+nn6D/knK1f3cjVqLdduXjIxICiNXml2t1UUsPds5Ex5fp0ypNApCVHlPjMwp3IbPpizlDIWnNuH14ap6U5264zZv5Wt07Yti1iG2sKY8NZby1Fg9QkcikTCqBkVTd2W3ZOlC/SrVcGWguxbC/T5I/3CX+xujw+pe3xfth7/62f9LEfpzi6L/VU0klmjljdoKnokGFXB1RSkaRYEetZGzazZSp0ZGELmsrdORuTalOjcdkVgsmBvX7KP40p+IpRZYNg/UQ+B0+cQeHdpw5uypWqf5HTNRlxcjtXPWRxuGzRAHrcx5QGnsbpYvX83SZYsoc2qBXbfRFESuArS4DDRf3M0NX4Jtu97YF6UxZOBg9u3fxdy533Pg4EEuxZ3m5b6vcC0+nq+/XsC5Myc5FnmEt9/9hEkTP+J6/GW++mqa2ci45Go4iuSTiCoVWHceon+IKZJj4No+vlmwnoAOzXn6JJ1Zs7+gRC1G1uF14xyvQlVvjtcwn/7v7ENdmcOc2Z8jadEFx7IMrC0kPMjMRltdhVWrYAG++u4SMtZ+wFvDR/DJZ58bHTs+IZU/d63h0uWLTP50Cm39+tCuTSO+mf8Fly5f5NNP/kkbn3YsWbqQn36sRRftPxDO4bBtLPhhOQfDDnP+bATz5y/Azc2DWbNnUKDU4Djwc7N9VnJ0GYuX/Mz9h1lsXLcUhyHmRde0GjU5u2YLErQJR5n6WSheTQOMoinD31Lfa4C0h3to3mgZ738EBTa9cA6Zpi+ylkavRto0CMWd80ZGES1dHYxcsXTnYt91GKVXw3HNucq6ddtJuv1Yf6xx40ZRYO1tVHQtObYcm74TjIquxRd30/iTjfqCvdTOTX8v6LgedVfHusCsibsLoaEz+OWX5bi6epGQcAWxVotzTY69NP4opdeP4DJgCqrch7hkX60x52hpdG2ePknn2+/mkFFSiW3PdymO3QMisPXvT+HpTdj6v0HpjQhkrk2xCwyhOGY9y5evJqhzN5Pr+6L98K989t95LIC+PTvUWxT9++Rzly7/tkpZQVXmbT17zKbjqxSd24a1T4+GqdRSCxQJJ5BUKejo40v6pSNIbZ2Qe7en+t5FvD1bknvnvJF+tk7sx0i7WyxF+SQZC49WlMYfMatz4dh3PNZteiD38kF5/woyJLg1DqLkcTwlqZdx7B9Kdf7TemnkTi+/j323UeTdiMbDxYMPJ33J/QcPCD+0Q0AJPHtCn/6foqpSsG/vH8hbdedBQhxiiRXLls5vkFmoSDiBX+s2qLLvkncjGq1YguLMJt6fIMgCWMrB0X4No4ddx0KbR0rMNb6dV8VLPc8xsH8uUnUeKSev8e38KgIDInBz3AOa9WjV64TX2lNoRV1xcPREK/PGxVpK2vkDdOn1JsHd+9AlWJA7uHNqLx99/AVunq2MaMo6SWOd0Fru9RPkPn0AWm2NzslgvQqlZeP2ZCVfNKG+x165y6BBQxkwaAyuHs04f+kW1taWdAzogY1LR+xsLBqUXL0YdYimPq/y/vsfIZLa6GUi5Ko8szIROoq5dws/fv7xG0RNAv5l0TVzFO6S4jx2bf+GnX8sY/1vawk7sJbcnN/p4HsWe3s1XTuruX42k/ybF1FrLSiNXsHUycVciHyMvGV3I12Zp1cicB1aqyuDSDgX+y5DjajvT3M1+nPQiXWV3T5Tr0hY/vHVeqkBrVZN8dmtKG6fw33knAYn89zwJVi1CKTg8V2iThyh0rYxT1NvYNWmB2qNCse+7yMSS9CqqlA+ScbW/3UsWwTxLD6K5Phr7Nq50UgITCfAdfzAZioz7+A5/kcjobrii3txfPl9ZI6NKIrdhUQEA4a8/99Ox/8P9R+Y//XX37qPmifIttYMJpFIjKaqkvLb5yi7c65BJTcrmYwlS1fywYcfM3bsBApyC3gcF8YPPyyjZ58Q3nv7LeJPH21Q7Cf/+GqsWnej/PbZGgPmOjRwjYrSq4f10qtaiZTHcWFM+jiUTyZ9xOPURG5H70aZ+9A8jVyroTzlTI0pgbBt757BzxHmF+R/U+LPIGpq7OhjVo729nl27QojPyubx3FhfP/9Ml7r1w8nh9v4Np+OteVtpFI1HTpoGDNajZeXBlAjFte892bteyKRGlAbvM4FzR6aN/WikdfL9OjRm3fefR9nFyEK18kdCDKr3U1oyr/9utBIBVPepCMV6Ym4DppmMgm5DJ7xlyWO0x/eYtO6ZS8suWoiT2xGU0VHMf/oww9x92xFeuLZf1l0zdPdkZLiPOIvhrF53ULi445xJfYwQZ0ymDYNJk+GPn0gLU3LypXQogW0bw8DB6iRqotIPBFP505VRERAuUKFqOghZTePIXFrhdzLB7vOQ+rcFz9i69sLyxZBRtT38eMn6K+ZTvBLWfjYrEiYYRADgu5++d1YJDaOOPYeS17YYuRNOgiBUs24zNo+g7JbZ/ROVuWpcajLi9FUFOtVMMtvnUFbVYky4zb5J9bUCJ2dxNb/dZQFmeTcOgtNAkyEwLKe3uNkzAmcB05H5uiFvLEv9l2G6n93+a0zWPv2Rn3vIh9P/kov3PXfScf/H0f9/69oEkdPs5GnY+/3ENu6oK2uJu/wMpPP84+vQmzlQMtWvgQGBQv7kkh49fXhzJv3Az+vXMaznAy8mzRj3bpt+Hg6mHeVP/ojFl5tKUs5VW+hxb7LMMQWlmRunIwiOYaC6N9p28aHhd9N4WTUMU6fiqa6oqR+GnmXoWhVSvKO/kTJqQ1IJGKWL6/NC4rEEmzfmEJy2kM95VnHFBUhwkudR8Hur1AkxVBydBmjh7+LS/ZVPbVZcWYT8+b9gIg7fPT+Jf7cY0enjsvQKAfQsvFsQPEv9EzdpuHhw2/RVg1Fo3n0l7ac9OksvNR5FO2do6eVN5qw2pjyH7Me5/7/NKG+19dS7yQwbtwonj5JZ//eTSbXMvZmstG1lHV4g9MxR/TbX4+/zIbflzRIMdfJRBjKOBTFrEPu3R7HPuPxeGexIHcQ/Tu2vr2RN+mgP3/DY12KO8/Kpf/AQvon8+aV4WAPK1bAJ59A48YgkQh/J02ChQth8WLIznYiMHA/Pm2aoVVV0bgxrF4NUVGwaRMMDymhJHw+FQ+uGZ133pHlDOxvjafy8XOp7/dTkzl37gw2fSeYfGYbNIiimA0UG9D47boMRV2QQd7OL7H27U1Fahy5O79EkRTDs4MLEFlYYq0j/YglQnDk6KW/L0RiCbYBAyiO20fRhZ24j/4al5ApaFVKAXRQkxp1HhDK07wi5n/1EU+fpHM9/jJzZn+OTfcxFJ7aRHVBhvAA2TKV6oIMQbdHC/nhi7G2scHB0aXhAfl/oP1tEfrCn3/71q7TAL2cpqHdl7r4GZXpN+v10azKfUTOk/sEBvfXL0PMORbt3b2V06eP4zrEVFgfEYJCX+N2ggNPM3+qnz0ie/dXVBVkUHR+B1bNOyGxc6HiwTWqMu9g2/E1Hlw7hcrRm7PHD4CNE5bNA81Ig2oMfDotUFwLQ6vRQJMgZJVFSMryyb8R1aDC5Ecff0n/gW/x6P4jCpNO8OGkGRQrHRg18k1EVeWknT9A15dG8MYrF7CULSUv9xnbt+exZEke69eXEB4OeXnChGFv/6/1UUYGbN8OS5bA+vUKwg/uIjfnJPmlrogl1s9dHl5PfsqoUW+SlZZC+qUj2HQyTmnl7vkKextryjLumqSLzC5Z63hI+nUZRl5qrNG1NKcx3rptT/7YvIqi4nJ+/mkBsiZ+2HcbqVdtfLb/G7TKCuRNO+o9M3Xa+VaWclw9mqGReKDIvkVhwkk0IimV18N4+51PyE69ZpTu6tJzNM6uHiSn3GbZwlAWL1IyYICGgwehXTsYMMDclQZ3dygtFVFQ8DYBAb0ZN+53FixQMWCA0H9isfC3SxcIDIQjv8chb9MHiZWdMJxFUPkknjUrs5Bp/Yk/Fs2ESaZpsPrUN3VN7uVD5b04yu9eFFQ3a9QQ23V8meKcB2iy7zLxk5koiivJT4hAKpWikVqiqVSgSDqpd7Kyq6N4WRi5Eo2qCpt2LxnZCJYlRuE69Au9IUjJzeNImwURe+IgZ86cQmnvRfmdC8i9O1Jy+QClNyMFWeWa6F5VmIky8y6ipoFcP32Uth26m3iI/l9KufxtRVGxzFLr/MZn+mJmydUwEImxah6oF1+qK97kOnQmqpJngh6IuwcSmYzFi34i9sJpI2JPwZ7Z+DZyIv56fL3Rt1ajJnvzP6kueYa1Ty+U2fdQlzxDamWHqkKBtU9PlNn3UBXnIra0QSyVoVIU4NRnHIrk00isHVCV5CG2kINYKmB4Y9bj9OpEypKi0WrU2AUOpCDqN8RoENm54TZyHgUHvkWpKEIsswJ1NZ7jfzIqzGWsfZ8xo8bzyccCPr++4ohGfRW18jMkknIuXxaiu0GDYOBA8PSE7GyIiIBjx2D9+rkMH/5RzRGUgNzgShj+X/v62LHDTJjwOQMHaszuc/a8OfTo9V6D52hYFDUXESuuheOSdZV2Hbpw4Vwk8+b9YFLQ0r3WRWv2Q2bqi9y+jb1ITLgK7q1QlxWZaMTkbpzE8JCBHDi0H5F7a6oyBA/NqrSryF28EXm1o/T6EWzavoQq/ToSB08sAwbUW+A1pMt/991iAoOCUavVrFq9mvNnI5g37wf9NqtX/oCFdB+TJgnCciNHCpF2Y+MarFHLyIDp0+15553R5OdvZ+LE6nq/+9s6CdFpA7B/ReAFaDVqivZO573BDxnzphZoCbINiMWeRtezblFUV3SVBwzUT7SK5BgKYzZib2uDRCLhrTHvsmnzOmQtg2mkyTMqtKrVar6a9TkpDx+CSwuqcx+aIF9yN05i6mehSOSuAr2/TKUnTemaTmXVrQZ9U7RvDgHNvYiNi9XrNWVtnY68cTucX/uY7G2fI5JZUpVzH/fR39RouXzJh6NH8Nbb4//PFkVfBIf+X9Jsra0pilmPxMYRtKAqycOqZWdKrx81wh6XXDlE0YWdWLftRW74ElQlecg925CZcQubti8x/fN/kJuTibWvQOKxaf8y9gOmcn37DKx9jWGNuUdWYN9NYCcqn6SgUhToB0PW1unYWDtQqSyrxaVv+xzL5gEonyQhbxYAmXex6zIMsZUDBToyUlYqKPIEKjZaFDcisA0aSGH0Ogqi1iJ1boKq4CnWjXzJPbAAVWkhMpemqAqemEDNAGy7DCPs0C6Ce7xqQpCIjDrHoX2ruHH1JCUlahwcoFs3uHgRFi2CDh1qr69uKd+zJ0ye/BOdO4+gVasWQDlgbdAThv8Lrx88eMjEibNZsEBT7z7nzl3Er2uv4N38Bx48eqr/juH5Po9WbhM0mMw9saiSbiGVWZJfrNILKemIL4HdRwHGEDaRSIztG1NIDF+CVbfRevXEuk3uP5A9+3dj3/NtimP3ILF1xq7bKIryn+AorubJtXCkDu7YdR9NaXEmdmIJ5Zd2N0gEMhRd08HsZHY+elLT+Uu3kEoKOBm1lzVraoOl4mLhodhQ8/CAgoJSdu3ax8qV9U/mAEMHqwmbcAxVbhrVuY9QllViYSVj+w4RrVtpiYtLIyamH8XFYG1jSdfur/LSyyNNxLoMRcLyUmOxCgihMGYDrsNmUXp+J37Nvdm0eZ0eGZa5Z7YRLDb1TgLXr1/Crudb9faDZcAgduzaQbe+7/OPqd+za+sqE9JU4elNWLXuZtS/8UeW4WZAWLLvUitg5zp0Zg1rtzbws+406D+wxb8rQvdt11H78eTpfDVzChoLa1wHfy7oS+c/IffAD4gsrIyidd2kK7FxFGRn9fCt6Vg0qn1qW7YIwqnv+1QXZJAf8Yse5lQQvRbboCFUZdwCrRZ1WSFy7w56DY7qggxyD/yAc//PjOBbBdG/GcHCpE6NBDEsnfLgts8J6RXM51/M43F6Gv/85yRKS0vQarU49n5XP8h15y+SSKjOe6z/TYZQM6hZOWz7nIlvjTaKNC7FnWfhd6EMHlxtFDEvXAh+fkKBrb62di1ERsqYMOEDpkz5hFat2hl8WglYGr2eOnUa+flbGowQN2yAqiqYMsUetWgFYkkvwDiaGDNmqJEuiS4atOo0GJugWp2TgqjfsPF9CcfSh4glUvr0HcSBfZuRtOiCVf49rKwsmRr6Bb+v/5Ws0io90coQVlrvKmznTFSFWWjVKqx9eqAqysJ5QCiFh36gqqwEq9bdURVlYuP3Buorezh8JEa//V+NtLRaLRkZv9DIbRP9+mmIihLy5PDXIvT8/FKiorT6bc21ixfh++9h+HAYMqR2PGzYAJcvw4gRwoqtdmUl4dgxGXO+XonctpFerOvdcf9k9MihqNVqRo54nRJFOa7DZmHVvFO9kEbiBVisbvWl44g01A9F++bg16I591ITKS4uNnnIVxdk8OzP75DIrXEZYl6b6dmB75HYu+E+cp7ZzwvCF7F8+WoCg4L/z0bof1sO/fsFi76NiDyC0/A5OL/2iT7fJrF2wC5wINpqJSWXDxi5wVg280eRFIPrQAPoocSCsqQo7LsOF0wSYnfj0ONNJFb2ej/OwtObsbG2gaoKHPuHIpLIUD69hUZZbmxMYOAir0MNyBu3x6HnGGMIpYFLj0hiQVrsEdoH9KWySoTUuhVpqZewaBVMxcMbWDY1sNhr5o8i+RRuQ74wQnkUntmMSCrX1xBEUgsjt3JFaTrLF4ayaJHaJKe6aRNMn95wnrxRIzh+XIO3dxKzZm3F3789bdo0RZDbKUeQ66l9PX78R4SGVja4T09P+Pln2L9fyfrfjxB2YB25uRnklYgQS6zIyS1CobSm5HG8kHeu476kyzsXRK3FsfdYLJt3IvtqBCrP9tw4HYbTsFnI3JqRFx+J2rMjCbHRhE7/gcSrcXoPSXM2etk7v6yBFQrXUlWYhTLjtt6RpywxmsrHSShzH+tt4xSJUZQnn0QstcTDqzWKcs1fzoXa2hRgK5+Ak/0ZQEt4uIBg0V3DvDxIS4POneu/pvv2yejW7V3u30/lpZeq6r3+GRkwZw4sWyak2HTjobQUNm+GpUshJMR4nHTurMXPT8V335zE0b0t/gHdefnVIdy6n49cLuNZXgkd/LqQmHADZebt5/qe3rqfr/fSNRrnBrUko7oYYlLP7KWsrAyRjRN2nUIE85ma/RacWIPL0FmUXT9Kxf0rJnZ7+fvm4tfen5zH96hOT8CmDsQ4b89sAoLeoFNQz//AFv+ONm/e3G8tWvcwGgSFf85DpVIhb+SLpXd7EItRJERRcTe2FsIYaAo9dB0yA1VRDnlHf8Sx93vIG/sCAgxS3tgXsVSOh1iBn09L7p0Lw3ngNOy7Dsc2oD/Kp7cpvXLQxOkmb89sUCnRiqUNutEUHf8FW1sbRgwbgk/r5jx+dJurcadQFmQiklmgrVQY+VfaB9V9aKzArvNgKh9eR3EjQiiintnEggXLCQzoiIfLOS7EfEu7dlqzBbX164XoXNwAXsnaWpj4f/lFQ8eOKkJDIxg9+m2cnd0QJnNLhOyb8Hr27O9eaJ9//CFEhAL8TkvagzscOXCBHt170SUoAHt7Rz6ZJHiKpkTv1rsvDRs2ivysbJKO/4FF4/ZY+/TUGwnbdR5C+YMrqEsLKbm0X/9ecdIpKM8l4eYlvaONSCqnNP4IFfcvg0hC/rGfaOrlQU7yRSrTroFYQnHsbqx9e+nzw/ImHSi9EYnb0NqHqkgspSLtGvKWXXmcdPYv+VB6uDnQxPNPmjdaiFRSUjt+6kzgjRvDypXCasrd3fR6pqTAhg2WbNy4ifLyUhITkwkK0ph+EaFQ3batMGnXff/5hVcVlQoxQ4eauoA9D9JY+Oc8pk8RfE/TH97i7u0b2GrKqVRpqCp+RlnSSUQSC/KP/YjM0obKnAfCe1ILCqJ+A6kcEWDVPJCy5JNI7N3I3v4FZbfOYNmkI4r4I6grFWYhxGi1VGWkUFFRjmPIVFNXMqA49SxXL8cwZOAA3Nzc9L8r88lddm79mRFDB+LTujlyuQx1ZQ4/rfiGfq+8Qs9u/v9rYIt/24T+228bv7XWKvXRW0nUakaPfJ+7F45SnBQDUhnF57aDWkUzD3eybkRjF2Q86RpiZrN3fIFtpwE49nzb5FgWXm3IvhZJatJVNFI5Vi2CkFjZo3ycTFHsLrN+jWqNmqqibLw+WIny6R2zxKFnu2ahrqqAJkHEnjiASiPjt9U/oBVLsW7TDVVBBkhkWLg2M0882vEltp0G4NT3A2w6virgm29G0OPld+nUqQXWsk9xdTzGkiUwdar5KLxuJGiuZWXBqVPw1lu6mxqSkysYMOAVzEXoa9b82mCEaLjPSZMMo0Dw81PzzdfRuDf253rSEz1KpG40+DRXw/Bhw0m9eYGcaxG1RsJiiXBz1yGTqbViEo7v0Nux6ajfli27Up3/mMrHSUikMgrzc7Hy6Ul1/hOUT5JxenkClQ9vorh5HMumAgLD3mQltgLH3uNw7DPuL/tQlpXF0MRjlcn1adWqOT/9VEnHjirc3YXr06IFfPedcP09PYWHYlaWEJlv2CBnx44tBAcH4OPjzaxZO/Xb1m0LF5pflTU0TnTN0xN+XZPGmDczQRzMxav3qKrW6KO/g4eO1u97qtZy58IxVBoZ69YuRu3lh7xaQSOvtpTk3KNKUUjlowScnd14pW9/MtJu4+nahJzrx5A4e6OtVOgf0IqEKEquhiESiXAfOReZWzMUNyONRL4Mm8yzDTlXI5G4tUCRGC0g1JoI5uI2fv3QVFfqV3OxJw7qCV7nz59lyaI5qL06cPboXlq1DebgoSNs3rCMKjdfrp0Kw7djT57lFf8nQv932oaNG7/dtHkPMo2ShMgdLFy4gkGDh+DbsQdNXK25fvQPLC0sGDj4LW7euIzjwGlmn9plt05j49cPRGIqH93Qez4W/jkPraaWhFNVkIUy6x5WLTrro4PcsEX1+iLKvXwoSzpJdW46Famxgt1Z3QGu1aAqzcdt1HyKk09zI/Y41WqtftCWpZxG5uhBZfpN88QjkYjKh9ex8euHSCzBum0vNOnxdGlfTP9X1iCTCnLyDUXhL7KU37sXmjWDYAG2j4eHhhUrHvDll19hLkLPyMggMTGp3gjR3D51zd0dFAp4lm1N7z79Gow6enbzZ/iwUVy/dpWsezeouH+l3pVQ/rEfkXu3x6HHGMHTM2wRbiNmY995CBV3Y7HwaE3Vs4e4j5pf895FbP1ewy4wBImdGyXx4VSkxWNfZyWWvfNLfSCg88/8Kz6UTvZpoDkJGMI8xezYUYRMJiM6GsrKxHh6amjTBnx9ITJSxJYtsHWriDNnbKmubklhYTF//LGHNWvWUl5exbhxHzJnzmlKS4X+Mpz84+M1fPaZ6Xh40dXaxo3w/vg7SEX78Wk9AC8PP30ku2XTigbZyQU3o7l0KhyRrTNOr3+K4v5VvBwtycnJxHn4HJxf/5SyO+d5qUsnFi76iRat/Xnv7bcI378Veavg2vRpk45UGpDM8sIEj1a7LkONWbnUsnJFMgtKroajVVXX3GODUSScQJlxi5JLfxqt5pq529C0sRPLFs/FabhAeMq7cYLq4qecPxOOc817xUknaepqzWv9Xv5PhP7vtGUrfv62TfueuHo0Q2bbhubNm5OTW8SFy3fo0qUrr70xEu8mLdm147d6MbM6k1xtVQX2XYaguBFB5ZNbKC7t1edqCxNiUOZB8GThAAAgAElEQVRn6IurupxpafyRWokBHeNtxxcAtQNIakHJpX31Rg1yLx+BAVetxKbrCCrS4nEeZGB8LLUQSBP1PjTaoLgRQcXD61i37aXPNT44f5gxo2vl5BuKwl9kKf/77zBjhhAZbt8uFOcKC5UsXryI779fxs8/ryQz8wk+Pt44Ozvh49OKWbO21hshGu7T3Dl5emr59deHNG3Twyj6Mxd1XLhwnsiIP3EZOgt1ab7ZlUz+vrmEDBhBcXY6eTeiUNw+K+D/9fIQHSiNP4zroOmghbzwJVj79KT44m7EUjkFpzeDRoXbUHN8BLH+oap8nKTPEVdr5M+NtHLzsrCzWoBUWszlyzB7tpDymDpVy+TJ0LevivJyEZGREBVlyaZNauLj7Rg+fCxbt66if//+HDx4iB498pgyRcnkyfDSS1UkJiaxdu1Jli9fQmmpKytWpLFhQzXnztnRrdsYUlPv07t3tcm1/6urNY1GiVZ9mHsPfcnIFrN44ZxaZm89k6pGLKH8wTWsWgRRlhyDQ/9Q0uOOGklqqLVioxqQm5sbAQHduHBsB4pb57Fs6meCVRdJ5ZReC6Mq7QpakYS8oyv0rFzFjUhEUhkFUWuRWDti1aqL0Wqu9GoYrgb3nVor5s6pvcRePIfWO6B2nDT141bMPqN7FIkFV8I34R/U7z8R+r/T1q1b/+2kSZMafBrpHOsbKrYgFlNy+YBQFJVaUHo1jFf7DeL69VgWLViGi60Vccd2YuXTyyg6qLgXh6aijPLbZ2us3FaiqapErcgX3hOLKTy5HrTg3P8fxkU3tAZaHhKKL+3Dqc84bDoNNC2q1kSV9Z2/SGpB2c1INOmXUSOl7OxavpuvxMur9loplZ25ezfPbMSsW8rPny8QU7y8apfye/cKE+/s2VBSIvz19YVp0+Czz6BfP7CwgLt3qxGLE1mwYBf+/p0JDu6Ov397QkMjTCLEHTuEvLlu8jLXhJx9NYsWfoCXR5t6ow51ZY5eBkGEiOK4vWZXMhqNmor062zYsJ2C7BwyH93FQVxJYeJpffHOLnCgkFcNX4LcuwOKhOO8PeZdHl6KoFxRXK+mumFQUHpuK59P+4KhQ4c9N9Ly872Hu+OnSKUFZGQI12PhQkyK1j16aAkI0BIRoUKlAisrOX5+fnh6evPOO+P5/vsKBgzQGG0TFCTUOubMOcfGjb+zePFSvv56DiNHjiQ6OpqbN5PYv19rQh57kdXanj3GKyuRCFxd+uPp0Zke3XtyIfIAxUmnaqQODCbVm7WTqmPvsTj2GY/iRgTa6kpch89G6uAhjO/9X6O6H8fCRT/SKaCD/roVF2YTHR2B2KkRZcmnTB7aufu/Yca0L2nfuiWx+9di0bg9LgOnYuvXD1VJLiUXd+P40ns49h5HWeJJFIknaklMdepSiuhfmfjJl4KfbsZ9Si4fwMKjJfJGbbELGqTPv4ukcsHc3FLO9GnT/1dE6H8b9f9F2uJFP+FYlknR3jkokmLIPfgDUomE0oTjZG2broe8WVha6l9LXZpwKuYoBdbe/LBgPm+OGcs7731Kxb04srfNqHWd/2QTXh+sxNqnF0Xnt6PVavEY8y1e79e8d257DfVZRPaOmcLxw5dg1bIrirh9enp10cl1yGQyk3PPP/YjMmt7VCW5ZG+dXu/5l53dxIJvJbw3KBWurWXR90oCA4V9WFq2pVu3BObO3UJkpIyUFPPXydYWpFI5lpZvMXmyjP79ITRUgBX++it4ewvEo4UL4eOPTWnnixbBjRtqQkMrGDt2LA8epBES8hpXrlzG1fVDpk+3Z8AAEdOn2xMZKePrrwX8e30tJ6cmSqx+E60mvt7v/bxS0OTWajQNwg/tOg8lW1HNwQO7efX14YQfOcW2bQdo36QRJceWA8bKmC4hU5C7eFNQkE9BYaGJ7OrTNeMovnpIT2237dRfIBh1HsaBg/vQaOpPNQG4O+0C1TSgCoCwMAEiaIjZN2wdOggwwpEjYeXKUvLztzBs2Gj8/ZUNbhMSUs3q1YIGe2TkCTp37kJ5+Z9s2KAhOlpYaVlYwD/+IUAVhw8XSF/1jZOUFDh0SAgADJtWHY1Wq8W7STO+mLWEscMGUXxqHXLvDoLUwbuLsWweSOHJdTj2Hot91xGCJEDnoZTeiECr1eivv9yrLU4urgR0qkXVGVL4q7Lv4/TqR9Rttl2H89PKpfTo0ZvZ81fSxEpD0d45qIqycer7Pk2m7cO+6whkzo1x6PUuVTkPKDy8xGQ/pdGrmTZ9Jm3a+vPhB5NAXY1Vq67kHVlhdJ4ytxYUnFyPSKPi+x+Wm79gBu16/GUWfjeFp0/Sjd4bN24Uz3Iynrv9f1f7W1Murdr1aHB5oVPGa+ZuQ9r5AwS/NAZPFxvS7yUh92xN+e0LWFjb42prSc6Nk9h0fIWqjNu4jZyLXdBgnsVHkXDlItFR4bgMn426rIDSq2H64qoOBVNy5SBOr36EdZsete/FH8HplQnY+vWnLOUUlU+ScAgehTL5BL5+fSnMvEN1+k1U1VU4D/nSJKoUIUKTlYoFKjr7BfD4cgQffzoLD1c3nqbdopFbE8rvneC7+SV07arE0RGUFWq2bBFyoUeOyBGJBtK+fXtatWqJv78/oaHHKC0VmeRU162zoG/fXsTEnKakRIlMJkRg77wjTNovhn4Q8Mq+vrqCaS8KC4u5fPkySUnJVFRUY2Ulo2XL5hQWFtKjR/38BcP8enauLWmPvc32c79XXuHOlWihKNo62Dh/uvNLtNSmvwzp+OkPbzFv7lQepd3DMUSobeTsmy+wc4OGILVxRN7Ej9SzB7HrPY7KhzcpSz4JIrFR1GlOYOtFiqI2lmuwta5FtLxoMXLtWuEBGhSkISBAw44d2gZTJEKt4x4jRw6mf/9BLFyoNIIp1haihWJrly4CgemPP6CsDLOrtfHjYcsW6N3b8Lj3QB2JVtyDS/GZ+LT1I7BzL27EnhRUGb07YtOuNw7d30TeWFiW6RBmUlsXqrLuUxy3F7fhX9XkqqPIy8rB2t6L85dusXPrz1TaCRT++h7aci8fyu5e5OSxP7F378ToUWO4e+M8T65EmtY9tk0DwHmQKRpGV7jNyqlg/97fcRlRg5y6dwnl0zsG5zmYinuXEFcreX3g2AaLoufPnzVR9Dx46IiJ1MjzCqv/HSmXv40pam0lNwLL1/da+H8w06dNIyHlESuWzMC2XW9jE91jy3Ef/TWFpzZh1brWlst+wFRSjgqOJ2ih4sE1s0w2u+ARek9PHWPTLmiQ4ERfkod1626oijKpTDjKqJFvcuDgfmQtuqK8F4drPa5Ltp2HUHXvIuNGDDGhIk+b+gHKsmFYyIRJwZC6v3q1jgyiJDJyB8HBe9ixYychIYO5cuU8q1evZ+rUXRQWKpDJQKOpRiSq5v7908yerSUgQJiYDx8WRKA++wxOnoQ1axruj4EDhah+9epqpk/fT//+bzB27IeEhFSzcmV1zTkpiIy8z4EDaho3hjFjTPeTkiJEib/+Kvzv5e5Eo0b19/OrfbuzaNECzpyJoGD3LOR+A1Cc3UTfnr05d2EfVfcE9qKOjv/gUTYbfluMtEVXxAj2gJXpiWgqSrFq1ZWcXbPweHcJFi5NcHtfQJ/YtOtLwcn1FESv1UedaLU17/2OXYdX9QJbcv/+nD+7j+nTppk9XwBFsZXRb35RFmixgSljhw5Cf4eFCRF2fdsUFJSxatU6BgxoOJp/4w2YO1d4kC5cCHFxQn8WF4NUCkOHCn3SuDEUF4sJC9PUOe5DqB7K8AHLEEuaP9eLVmewIm/kS97hZUYTtW3QUE5GbeXz6dMBGP7Gaj6c8DZWrbqacZ8artfyt+s8hMKT61Ep7pOanKfXejdslemJaLXaBp2Lih5c5Pqlg0YWfTqrPMPztAscRMHJddxNOsfb736g34dhn6src0w8iCPDN+ktHeXeHSio8RrWOSXV3UfDc9u/9ll97f+pCP38+bP8/OM3uHm01JM7Dhw8zG+/LtTrJOuiurwbUVh4dzAiP1g29af02hEhv6YXawrR51YbjA5q8qg6DLvcywdFwgmsWnbBpf9nlCXFoJHISLwaW/vUT72E2NoBy8bt9PK2Go1Gnx/XiIyLQ/qVR3kMLg4nABrMv+pyqaGhhxk9egitWnmg1co4ePAQI0aImDFDw6efCrnw0lIBvdCqlRCNd+0KAQGwYIEQrZlDRRg2Hfrh009h/XolERHHjPK7WVnC5BMVpaGsDBIS4Nw5Ed7e4OamiwJF+py9Lr+enedTb4SugzA+K7Zg4kefIK6u1Outjxw9ltf7jyD9QTqFiSf4YOLnFBYp+P3XRXqEQvmtMyif3hZs7XTSrXcvoLgRiX3X4frfJhKJKY1Zx8D+IynOEQqriGVUXg8jMHgwqry7FCbEoBGJ9QJhKq1lvVGSTHzIKEL/q8VIXfP0FCZZw/fqbnPunC1JSclMmdIwjLRRI0GRsbxcqJEEBwv7fe014YH+44+15+fpqeX3320YN64RarWh9a+WiopMbt/v+lwIo1ajoizxJPbdR5kIceVHrEQmkdAx8FWjoujpsM2Up8YhksjIP74a+67DKbl8gPJ7lxFJJBSe2oRtwABuXzjMlStxZidtAXvuZ1RXy90toLUMSUzKhzepyk+n7O7F2iKsGflsu8ABxEf9WW9RVEee0hVhdb4KVoGDKLlyCMtmAWDlSELkDqRyR777diYKpTX29o7/t4uiugKZyNtfT+7IeprKlk0rUDfqqNdJ7tU9gOHDRpF0NY6nVyOMyA8SK3sUcbtRFedRmZ6oN6yol1EIRkYFhWc2g0RqBJMqSxRYqHLv9pTdPm9UTRdJZBSf3YrExhlF9K9Mn/o5dy4cozjpNGpERgQhw8KGo91d0JwBXjQdIiI5WUObNr6EhAw3W0gzXHrrltPu7lBZKSMtTcLLL2teaMLp0weiomQMHqxhwAAhl2yM4KgtqJaWCmmErVvh3Dl7fH1bEhqaZ1QstbUNxMtz0HOLRZ06tqRH9+4meuuNm7Rm+tSpdGjva1wkN0K3TDPqk8rHiUYTOgBaLeXp8frCqqCdv5xOQT35ZOJHevjsggXLea1fvwbPVyrahlRSqt91Xh48fCghKOjF0lC6pocQvm9+mx074PZtDcXFlS/0QN60CZRK4/3Vf1wVCxasJC8vzGg/Mpk9Xl6TnwthFAKe42hV1fogCCB791dI1FUsXvqL0bhv1bIZXt6+JF89ReGtWJz6fYx90CDsuw4XWOFXBGBD1c1jWNvYIWkeZAKG0Go12HcfTVlSDIrEE4jEUvIjVmIpFeEhKeNZfBQakZjys38w8ZMveZR2h5LcDAGuGmQKV7Vu042y5FMNFkVHDB1I/OkjRr4KEqfGFJxcj9y7I6XXj1CVeoEPP5jI5o2rUHl1oPRJPJ9OmtggOe1/JWxRF6EbOdt0rs1979y5CcehX2EXNJica8dJf5BO4yat2bNrC6dOH8PupbFYuBtXeIouHQTAdXCt9K4g03kSRYIwCPIiViITaVA+vU3Z3Vh99V4qkaLMSaPiXhyIxYJSotwaqxZBZlmqJVGradrcn7LU83w4aQbNWgWgFtfm/Lv0HE0Hv85GT1ll5X7srJYiEgnn+yL5VyGXmkp5eTGNGyfqJ9q6TZcLT0kxxpwfOyZGIpG8EK784UMZd+7A9OnVlJbCb78JOdelS01XEDop17g4Ky5fPkufPsVkZFw1kNyF8LAEcnNO09i7E8l3s/Qwxn8lOjHntmMSHUb+gtuwmSYRpcxTcPDJy8pGZufD+PETEEltOH/plp78JMBnW9R7HlaW1cjFX2Bjdddo340bw6pVFnTsqP5LMM+sLCF6ftuUC1fDHIVvv9Vw4QK88srzVwAxMWBpWRvxN3Tcc+fsCA0dwrNnh/TvZ2TA9m0FLF6wjvBDYUgkKsRSGWLHJlQ/e0j+vrloNcYILZ1Dkq5ptRpsK3Lp8+poExnbGykZjB37PvnPMsm8dQmbTiH6mpV9l6EUHfuR0aPG4+wVQMnjeApuRqMRScg7+iP2XYdTcf8SFfevYBPwOmXJp6h8nATqaj7+x9xaqema1VxOkQyfVt4kJ101K5+tRUvpjUhQq5g0eTYqraXZPndzc6Nrt5fJSkvhUdwRpM7e+tW+XefBlCVGYSe34tLl83pnrrzr0eRlZZvUYQyzDYpyDQcOHuaXn+ezb+9OyqvtsLd35Pz5sw1G+f8jIvTffl2I0r4R5XcvYtW8E/LW3Xl6NQLLTgP1yxqRtROFSSdo5O7Ajh0bsW7bi8pHNwVSTs3sWJmeSPntcya4b52ui6aqgqJzWxBr1Cz/cS2vhbxDUWYq988d4rPJU3gt5B0y0u9QUphP2YNrSNHi27I56bFhlKWcNqIZ5+3+ElsbGyZMnMa8ufNo364tnu6OWFpaMHzoACHSdPXQP1ltrEvp0GoeDrZH9ZM5vDgZZMOGau7ff/BCGiuGy3hra9i8WcPTp5Z07FhtdsI5c0YopKWlQXy8BtCQlSVEj1qtMJnUpZnrmuEKwta2Gx9/fBhfX+EhVevIk8fKn/YR8kYAXTr3/svRiY6m/faYN+na4w2UhY9JPXvQxD4ue9csHF/+ABufHnopCUNymTkHnxc9D1ena/g0m4Hc4onJNWjd+m1efXU+U6YcNYF5GkJH68I89+yRcPu2mPJyab3bdO0KhYVw757wAK2v7d0rELqCg4UHTEPH3btXSnV1SxYt2s26dVWEh0NSkjAGdDj6zz6Dfq9qUeXe5+afkZTdusCM6TO5fS6cnGvHEUkt9MVkqWPtZCn38qE05SzeLlb0e7Uv6Q9v8cfG5Xo6vkaZy+5dfzToFvXOux/w6ccTefb4HgnHdyC2dUH56AZuI+YhksgovXIQh+BRVKYnMGjIW0z6aILJai794S12bluD8/A59a4wKu7G0r1zMFNCQ0363FAuoKzkGbt3/YFV4CAKon9H3rRGT79G070w5QxOA2tX7lqxxIScplvx6LINQQEd2LRuEZVqqKyqoqI0k25d/Fm6aA4KZRWlBY/47NNPTKL8/xERevqTXJ7ejsWyiR+Km8ex7z4KsaPxskaRchpHB3fOnY3GffTXAhMzKYaqnAeUxqxDo9FQfPmASX5Nh/uWN/LB0rs9EltnVI8TuBYfT3m1HSNGvcOAQWMoKCxm/boVTJg4Awc7Ox6l3WXgoDeJu3gajVqNvImfnmasfJxESfJpRE0CjET1zUeXUlRVW2jR6GtEojyT63D4sIzevZ+fDjlzxoaSkjJOnhQit/oMLOou43V52O3bNxMaeoziYi2enlr95PHTT8LNP3QofP55bTolMxPu3xci/ueJf3l4aFi69A7h4Uf54Ydqk0henw769hxujYMoLVe/cIRuaFwSe+IA2bmVnD8Trke3GDatVkNJ7C5U5SUUnt5IyBsjyL8bR96NaKoKMimJ3c3I0R+QllFpNmrSeVnWjZJy85/QpsmUGlu+2iaRONCpUxhNmoylTRsfunbtyq5d19i4MZ8tW4TisLMzzJtnOqmmpMDGjZYcOrSP5ORqFi++w5YtWk6dElZKM2bUbtO4MfzyC/j7108eW7sWcnPh1i04erTh4/7yi4bAwEJmzKhk8mRBF2b3bmFVVbfvunaBwE4aTsXAS6++xZVLZ6lERmXaNVwHzwAgd89stFoNFjpEUk3tSCp3NDIkyXpWwdZNK7DqNloI1AwCpMr0RIouH6Cssor0h08oLy9n394/sGzXh6qnKVj79KQsJQbn/v/Awq0FBTHrsW7TjTuXo+nY6RVy80uMxo3gB+tvYuFYd4Xx+HIEfoGvGI299Ie39KiWU+E7OXrkINbd3qQ4bj9WrYOpTE+g/N5lLGtqeHVx8IWRvzBh0hd6cppuf7psQ2bsISIP7wWJDKtWXVEVZlBaWkLU8SNoxBKsWnWlJPM+BQWleqTQ/5gIPfPJXXZuX6MvNCoSjqPMTDWCFykSo7Bwa0G1sgwnQ5aXSEzJpX042NpgUfwUtUiCsigHRVK0IBIU8QuOL79P2a3TKBKjEUkk5EetRavVgHcAefcucDnuJK2aebF2zTJE3v48ST7HggVLsbR2Yc+OtagR1xTcBENj5dNbFMft1fsnliTH0NTNhn6v9jWJ6mxt8unQchYlRaeN0xDhUFBgQ0jIIVQqq+fS7PfskZCSomLYMC3TpxtGvui9KL29he/WLb4JCn7vERo6ldGjR5OSUsmyZffYsKGKI0eEZfaPP2ICh+vaVZhAwsJerKC6ZUs1w4aJnpsOepZtw9AhQ18oMjbM49p1Hkz+1SM8vB2n13Op2+RePihTYym/fwUb35dQ5qWxYf127ibeIO3KCazb9qLk6S1GjRyDRvmMRT98wf27V9HURE3+Hdry+5rvuBgbg7pRR30u1MNNC5ptdY4mpXfvLKysWgIiIiPP8s4779GjRz4zZgjUfFdX4fppNELRsi7UtHfvl1m+/EcuX05ALNayZYsAawwOrmX1LlkikIGkUjh+XCCIGe5rzx5hRaZUwrx5X3HqlIBXX7v2MCCqE/lL+eUXDR9+CBMn1tZgDh4UkDIN1XEUZSJys62ZNm02Ny+foxILxI5eKKJ/pXevV7h77hDl968ikkgoiP6djh06cCIyzMjbNevBdTQuzfUORKXXj1Bx7SBV+RkUxe7CsklHQeQr/zHp6amUiqxRpt+sYXgPMbr/dBDJ8ruxFGXe5b133zEaR24eLY38YEuiVvP51BmkX4s2yrV/9PEXej9cT3dTn9r8m1FoEFP56GbtMe+cR11WRMWDq9gFGq8SC/+cx5ujP2Dc2Frxsz82LqfKvZ2+9lN06QBatUovVVBx7xLqimJEYnFtcT81jke3rjLj8xn/syJ0I7pxzRKmrjCTSGKBIuE4Xp9sQOYo4MN0TEyxCERNO6MtLeSV3q+QlnKVqtJClI8Tkdi54Nz/M6R27sLgeXANEQgRfo0LfWlZOedPn6hBTgj5+1s34wk7uB21WIJ1jWiU8kkK5fcuoirO0Wu3C8srqR7JYozeyKFN0w+4drXAqKBoaAw8d+4Bxo17l7VrTzVIs1+1SsvMmVpGj64fg+zjI9yYP/8sTAbh4ZCaCmfPyti06SecnZ1wdrZjwIBezJw5k2++mUVeXh4tWiQ1OAnv3/9i+dvDh2HGjIZXGp6esHpVGu0D33ihCL3u2CiOP4q8WYAxXn33VzWWf0J0qJXIUGbexeOdRTyLjyLxahzXrsbiOmoedp2H6B3mD+zfjKKsAtcRAmch+2okcTFhlFcqcRkxxygX6uDsiKujcfFQLJbTvPl0QMWDB7cICRlhUqxu0wb69hW0y9esgW3bRJw7Z4enZ28ePnyEn98Dpk6t4rPPhMKyLvVWtwg9ebLw+vTp2uLn5s1CJH7/vlAEf+cdWLUqntGjhxAc3InRoweQnKxlxYr7etmA6urmBAYWMXGicX+/SB3Hy1PL6lVpdO/7Jl27vWxkjRgRcQhJs05YNvOn9MpBnF6eQNbteOz7hxrR8cvuXUZV8kzgidTosDR2tOfZ/ev698oSo/F0bcrY9ydz9vg+rNv2MpB4aE/JlYO4Dp5uNDfcP3+IwK7GY0pnf6iza+zS602Cu/ehS/DLJrl2w7FnFNmLJcib+lN+50IdMIQF5XcvmFWF1Ki1pMZF6nHphnwLHTKv4l4cls0MJAmadECZnmAsyy2Won6aTMfAV/5ShP63Glys37yPp0/SmTb9HxSrxbgOnWlWuD43fAkSGyds/d/QG0Fk/z4BTaUC55Hz9FjQXn4+RJ88jtuoWkMKsbU9lY9u4j76awpiNmLh3sLY1CJ8Kc79JhoJ+BdG/4Z1215UP7xGtQbE1vZoyoqwqsGj6xyG6orqg6HZQS5PH77MP/4hQBLNYYhTUuDrr61ZvnwxX345m5CQakJCqvHwENiWkZEywsO1dOmi5Ztv1KY7qGnr1gkT6rBhxqYGhw/DiRNydu/eRkjIYB48SGPVqp/ZtWs/BQUKZDItmzY1bLqwdKlwozdkoLFxo4xdu6qJjqZBUwaVCvoPEHH6fPILifs/fZLOrNlfUKIWY/tGKICRaUnhqQ1ItBo8GnlTpJIi9+9PQfQ63EbMqdegofDMFkqvH0Vi54K8cTtcDPgMuWFLcH5tkomZw4GDW9FWvWb0W8RiK/r0EVJoU6eGPtcybuNGGa6u4wkNnUZwcDe+/77caEzoDDAAs2Pm118FVuikSQ33g6vrh6xc+TPmjEvc3DxZubLUpL/79cPIjMNcM+w7MO6z6FOx9VrLQQ2A4OgyLK1tqfbwNeKQmDPQUF3ezeEjMVyOu8DceV8gsvfAdWh9phff8dnkqYx5+/3/X0wnXuS3PDvwfQ1jtg6SCsHMo2DPbMbX4Z/orPpuPXqE/aAvyY9YiVatwnXIF2aPkR+2iBUrzJt1NGRw8bdT/72bNEMkEqGpqiA3fKnJ5zoCg323kZRer3VUtwoajMbCBgvv9ojEEuwHTCX2ZjJuo742cBrvLygw1ljRuQ2bRXVeOtlbp9e60H+4ysi7tCD6Nxx6j8U5ZApyV29aN2uCuuQZbiNm4xISCloovSacR/GJX3jr7Y/1k7lh02ouvRAlPCSkmsTERPbv/5Pbt9vw0UcCSeSjj+D27TaIxVImTqx/MgcYPBhkMlNa/+TJsHChkrFjP2Dz5q0EB3cjP387K1cKjjjV1c8nxLzzjhDtN0Qnj4yU4uRkQ3Z2w/vKyQEHB5uGv2TQdFT0Hh1aU3JsOTLnxni8u7iGtr0OsVjMFzPnsW3bnwS0akHRyXW4u7pSeWmPvn9dxq006l/FjWPY+PbCbeR8VAWZ5OycVTsWJqw2/u7ZTcyfv+C557lr1z5CQhq2jAsJqWbXrn2sWrWKkJBqkzHRr5/g11rfmImJEdJizz/Gnno/LyhQmO1vBwf+rb5z92jMuttiONsAACAASURBVHXb6NGhNUVHlpl8rqPj/2PK13ip8yjaO6fe/ik/9wfjP5wKQLceLxEReQ4PKzG5B38w2W/e0RX06zeEMW+b4j5T7yQwbtyov0zVf95vyT+6AguPltjVIHsq0xPJWT8RxbVwvZSE3L8/e/fuMNou4eY1EhKuYNN3Qo18wXtU5aWTG2YqX5AX8TP29vZG8gkv2v72lItQFM0j82ECroOmmyUwlMYfpfzuRdwGf6GvqMu9fKi4fQbFlUNYtuxs1l0l7+iPvPb6MHLu36Ls9hnkrbtj320UysxU8/rkO79E5tpEEOOqIRHk3DyNs0GKBZFYD9XSajBaXuXkFnHp2nVaN1mKXLrthSGJS5bcYf/+ffTokafPv/brB0VFRSQlVREYCE2a1L8P3TLcEH+sk3LdtAkKC1VERR1j8eJqo5TAixBiFAqIjBSW+3V1vHfvFrFxoxU7dqzD2tq2QVMGEPK9to6v4+Te5oWLonXJLcrHyRTH7sK6TXfU5SWk3buLBkvCD+3Aqk0PbLRKmnt5kh53BNs6rjb5++YS0n8ET+/cpOJRPI79Q6nOf2p2LOTtnY1/4Ot0CupJQWG2ScpFJJLRrNkUQMVXXwmT/tKltTWSugXrWqTSXbNIJZ1q5oMHAjGo7ueGaKhamV7j47VpA3v2VPHeeyP47rvvGD/+I2bP/o41a34lI+MxqampZlUaX0TUa/duMXaOr+Hk3sZs8f/JozscPLBdbz5i2HR0fEuHtoweNYastBQeXz5mglLK3zeX0aPGU6x00O/74sULnDkdgYuZ9IZIJCYz6aJRekNX1DYsxjZE1Tc39hr6LSKRiKr7lyhPjQOxlJKo1TRv7k92YjRVD66gEUsojtnA628M5+efF6NQWpOdma4viuq0/HMPLUQskeI6yFQWXAuUpN38l4qif1uErqP+qytzSIyPNDKDNWz2XYYhksiw8GhJ8YlVKK6FU/HoBtnbPsfKtzfi6nK9QJNhK41ezVdffc28ed/wzfcr6eXnQ8mx5SifpFDx4IpZgSCH4JGIirMo3DNbH0G4f2gctRWe3oRLyBRAoBlXyyxJTblAQIfm+LfTMGHMQuxtrwIvTgkvKqrg++8rmDix2ijCnjixmh9/FG7cjAb0f4ToSXidkSGgGyZOFG7+1auF5fyIEaZRny4qbKhFRAjFsl9/FcS+QkNrVxBHjkgoKqpk/PjPKCkp4+hRSYORfEQETJr0TwI6NKd39/YEdGhu8trwf3VljpHJtLEAVygSa3tynmWz/rfFuIyYg3NIKIVVGm5ej8Ohn2luwiZoCHfu3mD+tz/Ry8+HwrAf6h0Ltp2HkZ2RhF+7pvi2NhVfE5olkZFnkctBLheudVSUqWiWro+cnW0pKCgzOyYaNxby5rqHZt2mi6IvXxb2a2FherzQULCykhEc3If8/D2sXKkgKkrLypUK8vO3U1VVybFjprf8i4h6RUZqmDhxhNk+83Cs1tPj66PjF6tEqBT30VblkZR4DbtXTK+5TdAQrlw5Ra9gX33/b1onSHfUt1/D+6939/ZG2ziHhFKsEtVQ9YVx5BwSanabFUtm4GIveoHfMgRLV29auztD/D4mfvQpjx7EY9GyK1aqUri2j8FD3uJE5AHKnFqQcuMoh8O26mUIAHKP/QSAWz2/y77zECQOnkQf/9Ps/dFQ+9sjdHMazAK8qJZCL5JZoLh5nE5Bb1CSeo6868exbOpH2c0TaLVqHEJMn3K6qKBFmy4cCjvGhXNHsOw0kIKT6+uVALDwaoPy/iWcpGryk86aRHjZu2cj0aqRODUyS+/XqPZjb1OrLmgYAdcXVVlYQGysEMmba+7uwoPh9m1TMwld270bmjcXMOMzZwo3/vLltciVpUvNrxTq01LXnevChRAfD0+fCiiKnj3h/+PuveOjqL7//+fuZtN7pQQEIr23UAUBISSU0BHeqCggKgKCShNRASEIagTpQQUCoRMCIfReQ+9ForRAEkJ63zK/P252sz3R9/vz1cfv/JPJ7OzdO3Nn7px7zuu8XtnZClJSJAYOVDB5skbP4X3v3n3u35dx8KCCggK5EboiKkpALQE2xWxk+9ZfuHv3Pk6ufmYQRqtJUZmM1K2zjJNJgQ0pSLqAd1hZpahWpqAw+Q7eb4w1u0664qJbly9x7cpp1KoS/PpPt6qQk375IIWZ+2je5Gej2gEApdKPkpLuhIb2ZvJkNSkp4qUXFYUeeti3LyxaJJKWCQlK2rQZwoMHD6yqQQUGCjbEzp3Nxyo9XdAtbNhgmSaiZUuBSkpI0PDppyoGDDCn5HV0lIiOlmje3Hi8dRTMX30lMO+GKBpDPLunjy9/Pqli5qFXBCKoQc6tA+s5fHgfHn2nGjlJOik5x5ot9Enr3379md1xO7B/tY1ZhbdMJrNKr2GtVN+Qr11L2XdMYbGJ504hq95YSBbaOJe0a0d5+92JRK2OLEVh9SH3zinq1qjF8eP7BbqnNLHevGFT8p4nkX7lAJJMQeHvZ3Cu0962FoPCDs2TG/83SVGZTNYT+AlQAFGSJEWYfF4dWAt4lh4zTZIkm76fYVJUl/hSNuxO3vE1TJ48jfXRv5FWoMGtVV8yj6zB3sWTJnXqcP36BTz6TMUhsCEpaydhX7U+Pj3MM3Y6pfHXWzRm/4G9ePadRuaRNSj9a+oTYToZM9fmYXqCoLybh8k8sBw/CzwS2Yk7cHxwFDulIzkaBcpG3ck7toaIiEhatGyDpF6FpPlJf/zSpeDgIKNxY0lPvhUWZqjELib2+vXFBGzNkpOFVxYba/7ZrVvw2WcCK75smXiwK1UyTp7ZSnrpiMHCwkT/Hj0SL4CwMBGbN0ywxsUByFm0SGs1yTtzpiPh4f2Ij99NRkY+zs7iRdO3r3HCNj5ewd69QoXeyb1auUlRZcPu5B2LwsXNk3yZg3Vl+B1z8RvwBTJkvDywFDttCc4t+uHasjfFT26RHv8DmsIc7Nz9jJKi+nuhRW/cSyeDvJuH4eIytm0qNvodB4eaNGsWz7RpP3L9+i9cuaKxOLbx8aKSVi4XSJchQwYhlytQqXZYTaAuXSogimNN3kfJyWJf374iV2LNVqwAjcYy4dfSpZCWJl4Muv7qEvB794rx9fGBjAwlBQUiNNOtm/Dgq1YFmWISMrvRRmMExolEZcPuFJz4lU8mTWH7ji08yynBoUkIBcd/xdHZBVVAfX1SNCdxJ1mnNuBctwPqzOcE/CeCrOPryL28B+c67Sl6dA071Ci9quLQOISMg8ux96+FLOc59t5VUDbqYfT8Xbv1EB93GV9/M4PnuSW495xo8R7J2h3BggU/IUkS06Z9YkS8VcPbg1u3r6LwqY5bs1AyDq2gS+euPH2WbHQuo8ZOIS52HRnOgeUmebm0ha3bEli8ZAknj++lZ0goMZs3YucViFuLXmQcXEYl/0qkpr8Q+1r2IuPAcvr27sfkz7/8S0nRcid0mUymAO4D3YGnwAVgmCRJtw2OWQVckSRpuUwmawDslSSphqX2dFa9Rm3p85li8jtx5ibq/AccPbybZm36MbB/H+Z+PZ4srRJtYTY+PSegcPMhY1cEHt2MUQjZZ2KoOjaKokfXyd6/GOfmfXBt2Vv/QGYcWI5TzRb49puGOvM5adu+QWbvhHvLvmQeXYNX11Fkn9uG3M4et1Z99QT+7q37m/VZl8GuVbkWtWpUFv0NDmfgAJEg8fPaTCWfMrxycjJ8+KEC0DB/vnWky/TposTeGtpErRZhjmHDzB/C+HhBwpWaKibzhASxBDdsS4egsNZ+crLw/A4fFoIH339vva9TpwpUjbW2oqLs8PV9h3nzPmDnztblonymz3CgY8gnhPbopN9/8txtXmvbADC+N95+dyLPX8p5dPcA1x88oNJ7S43aS1k5CmelgkKUlOSk4/RqGxzTf0eDHUVarX5f0ePr+A2dTWbCz0iSVjy4h1fh1XU0+TcOChre5mHkHzfmpweoVWsu1aq9jUzmgrd3NTSaAubNs35+M2ZAUZFYIT17ZseePeKt+u23ltkTjx0TL9RFi8zbDA8XL21bqKTkZBF62bHD/DNDJE1srBjv7GwRztFN3ACTJrlx4kRfUlI2GH3/efpI0rMGA8ZjdPLcbToE12VrzErOnjnKf976kNZtu6LValj8YwSP/rzKsP98QHqOHTcv7yFLBfIqDfQqYg6BDUlZNxmZ0pGS1Af4D/oKSashfdd3KJQOuDo6k5WVhkujrhQn38VOJiOosj9Pnvxp9Pzp+qTVavhx0Wxe5Gbi985PRueQumo0jRt35q233ubbbyaQ71XTaELO2BWBe5dRqF48IvfybpzrdkT24BTfzFvFL79E8ehBIm+NnEBqlpK6NT347ZdIXhZr8QqbZBWt8uHHX1C7bhN9/779ZgJ5njVQ+tcg73I8Hh2Gk39hp8XfnROxxuhaA0wY0+u/mtDbAV9LkhRS+v90AEmS5hscsxL4Q5KkBaXHfy9JUntb7eo8dLAMI3r65FG5b9oXO+bg6BWAU8v+es/e0Ct4ue9n5PZOKNDi6PcKssr1ybkQi9zeCa26GEfPSri0GUj+sV/o0SOU+L1xKKvUw3/w13qvLffgEpya9calRdlLQn1OwKpM+y6plyFpjCeZb78NxNv7qU3Y37JlwqubPdvy58nJ8PHHgjnP2kM4apRIgL79trk3XlHI2507QdSvn2QTfrdqFahU1ilfk5Nh0iR30tKe8957HSkuvmLzd1evtuP5ixC+/qYMUWALVrZ1+y7WrPwO9z5TzFZQeRd34fTHcVKeJ+PT/wu911W3SiWuXj2Pd78ZAs66fjIUF+DSdgjZR1bh5OSCp6cHWRoH7Bt3J/fwapxc7Ph6Zo7RZF6lyofUqbMIHQzQ3t6FwYNtX9dVq2DbNjEmICb5adMcsLOT0auXxgimGhcnVmxyuT0KhZa+fSEsTK3//K23qBg0NETcJ6ZWUXhiz54ybt58m5SUtUafyRTjkdl9AJiPkaYolRnTJ6Oo0YoqUjorV67n6pULTJv2CcpawVTRpvPRhDk0afAK27ZuYNWqn3Gs3RbvUAMI8fY5eId8BBJCTCaoNQW/nwPArXkoedcO4BQUTPHze7grNOyMPWjxXrl86bze87Z0jzg/PM26tZt5lvzECBZrDW45P+JHM/igJTiiKcXwi6gxDOg7jDFjRhl9pyJzm24V8VdhixXhQ68KGBJYPAVM9Wq+Bg7IZLLxgAvwBhZMJpO9D7wP4OXtx7VbDwHxZtWZ4fa4ibPFm3b3d2Zv2syESN4cPpaCgnyOHo6hbefhVK7elI8mNGLxjxH8se9nZHb2OAW1puiPi3jISnh6cRdye0dRvvvHRTyd3Sg8F8PoD6ZSp15TShTVuXl5Dxkx03BoHELe8TUMHjKa4yf28eLuSZybhZJzZDVKpSMHj5zGP6AqJ8/d5v7da2zfspQF3yaboVEuX05lsbkovJGFh4sJOTnZsvcVHy8m83HjLE+karVIWFaqVJY8M2ynXz/xvfbtrXuSCQl2wGMmT7YNv+vVS3iAtjm8c4Eidu/+nUjje5zkZGPv0M1NTUlJAoeO9sfPX+juWbsf7t+9xoql86wmyVxa9Ob5jcPI3Pz1PNjKRiFcOrwKrzfeN+Lrzj/xK5rEzXTs9g4D+/dBq9UID+z8ZsZ+PJOO7W4R4LPB5Bc0iMm8ABChlPKghL16GXvLDRtCeLgWO7veeHp6M2nSFjIy8vD2dmH48KHcuvUhQUEBJCWlsmTJciZN2kxGRj7e3q64uBSRkqKy6aEbJshNzdK9Yen73t6uen4kQ5M0S5Fk9ZAr2pL08Kl+//Ydcfqko0NgQ55tms7UKZO4di3RaN8vv6xm9Oix1GvciWkzg1i6JIKMmGn6Sa3KmBVGiW+HwIaUpP2JpCoi91I8/oO+1IdaGzRswrVbD83ulft3r7F6RQTObQYLrhmD2paiR9fJuXGYPOCnxYvp2r0fbTu/w+N7B7lpZY4ZMvg95A7+Fn8LxD15+fI5oblgYo5NexOfEEurtl2Qy+UVntuy9y+mSctQi79bnlUE5WI+sgJZY2jDgN8kSQoEwoD1Mp1ShOGXJGmVJEmtJElq5e/vbzFjbrgtlaST8uy+xYy4a8twLl44xsQJE4jbfZiB/fvQtGENmjcOIjP9ITKFEv8BXwiNSs/KpLxIRa50wK//DHxCJ6DwqETWy6fE7T7M4IHhaIpSuXJuO/O/XcDb/fugPh/D6FEfcOLEXubNXUBIh9dQnd2ATJKQV2/GlphlNK5fncpeeUStnEOuZ13mRjhgqF6mULiTlaWuENJFpbIeI4+PL/PELVlqqvDAFy0SE/tbb4nl9dKlZS+J6dOFAMLq1WKfWi3+RkUpmTXLmejoDWRlFf5loQZLffH2dgMczRAdlhAaS5dCnz5aFn/3MYU5T2zeD3Gx63Cs3dZIJOFF1BgjDLB763DUWc95uXEqeTePkHkkCqdX25J3dZ9egizv+BoiIn40u29Gjx6rvx8q+XtaODsFoljHGXDUv0TLu14qk3dkaKiKvXsPERm5mLS0FNTqPM6ePYYkyWnbtgsKRXXatu2CJMk5e/YYanUeaWkpvPfeSHbvtvw7Otu9W3jilqxbN1FdassSEpQMHz6MwMBxKBRuJp9qQT0OT5cv6B9WXz8ud28cKhOTKK0Juf3wod5DlskVODQO4dGDRP13unftoEefZe0uqz/JPGogUiNX4Bc+FZnSEf9BX+rbcmsVzp3bVy3eK3Gx65D5v0rWqRjUeRmkbZ9D2urRZBxYTtqOuWjyM1GXFHDiWLwop/fWWEXduLYMJzHxCI3rV7f4W38HhVORuc2lRR89wuqvolwq4qE/BQz9zkDgmckxo4CeAJIknZXJZI6AL5BmrdGCwmKbHrruTWsLCvVs02n9m9awDZVGwrlOO72X5tNrEulx3+HTdZSBWkkYecd/49qth/rfUtZszYyZU/l0ygKaBWcRtWYFyprBzJg5lZp1OlJcXIRX6VJe54Vcv3YKj76iMjVl80O2bf+TIYMl/P0HU7fuPLy9m5OSkleuV+TuLrg6+vUTk8D16wIx8ccfZXBBwwSVocXHK5DLNXh6ivi2YWJu3DgxmbdpIybPX34RqwG1Woa3tyvDhw8gMXE8QUE18fZ2qVBfrXmAIDz94cMHA0VG7SUnl+maGq4SdAVQnToVMX3GRD6ZupS7f2Sa3Q8Ab787We/VOTQOIfvIat4c9j4HDuzk+c0juJcm0B08/SHnBRkHluE/qKxqOPfibgqv7Lbq/Rhu+3tlEeBjenbGHroopsqv0NgamljF5JW2BQkJhxgxYiShoWoDdahcEhJ+Izg4mujo36hTJ4isrExiYwVqxtpKa9cuEQJautQ8PFe5snihd+xoe6WWmPg+Li41ad/+EnfvTuPFC+OAfEbGPopK/uT3xwJ+17ztQP3KVudtG4YfdC/Rtp2H6595gJ2x8XrlH5359p3Cy70/kbL+M3x7f6ov/jNsK/PQCj78+EuL49epcxgb1pWuzmu2oDDpApWqBPLn9QPIlQ76ffWadmTr9l02V3y25piT525zYv8KFDVbGTkY2fsX49S8dxl1QJMQNmxYS73GnSo8t7m06M2LOyct/m55VpEYuh0iKdoNSEYkRYdLknTL4JgEYLMkSb/JZLL6wGGgqmSj8fJi6G+9NdAog6y7WC4t+hjFs7m4hR07D1jIui/laVYhXr0+tRijytw1j7HjvqDWKwHMmD4Z9z5T9DHXdg1rc+z4EaPst5TzAimwSQUy2su4ff03/P37AkVMnDiNlJTVNmPoq1cLWODOneDj487LlzkolSLMotHAuXOClMmpVPnso4/Klvo6lMvUqfD66+Zt37olPPNZs0Q7Bw86snHjRkJDQwBISrrD4sUr2bhxEy9f5uLsLKhyLb04QMT7X76EL7+0/FuzZjmRmJhIUFAto5L4isTxV6+2Q6UZzGtvjLAaQ79yI4n7t06zeXM0w9/6mEED+jJixABSCzFKoKdtn4NPyEdmCXSPln30MVS5XG41Xm8pH2IaQ5848RPS039hzBjbtAxqtXGISuQZ3EhLSyEp6Q+LVACG19Qw5l6liopVq8pQSaYJ8uBgOHlSjJ8hSmnPHrECHDToTfbu3UVoqNqMZiIhQUl09K+EhurEIMR5pqbu4M6dt0x65ovc4bj+ujWqV40F82dx8vINsxDCi6gxTPxoPJWrN9VfX1txbkmr4WXCTxQ//52qo5cbfZa8cjR2CgXv/WeEvrReU5TKj5Hf8fa7k1m1bA4v0l/iP1BQgqRumIom6xlarRa//iKHkhI9Bbu8VHz9K/3tOcYUUePQOESP7oneGE22Wm6GgvtfzG3wX5b+S5KkBj4G9gN3gC2SJN2SyWSzZTKZjtn+U2CMTCa7BsQAI21N5hWx+fN+oLImnYyYaeTdOEzOnu8Y1G84PikXyNryBXk3D1Nw/FdmzjQvCfYPqMoHY8dT8sJ6aa23tzdBtRvpled1SzxloxAOHjmEa5fR+iUeleqTn5eDb9Ezm2XL+SeWMXNaMc7ONfS/NWHCBOLibBdtxMeL+LaPjztnz57G09OJ996DU6cEDerPP4vwxMqVAra2ZIlApURFKZk2zY62bRUWJ3Mo05ucMQNUqp5cvHhKP5knJOwnOPg1Xr78jcjIXA4eFL+hVBoXxRj2df9+e86dU7B8uXHoZvVqu9LQzW8EBdUqPfePSEhQcutWxUrXw8LUHDpoO6YglysY+ubb7Nh5gNp1xbWPmP8j/k4KnD18Ubj5oPSuStUxKywWhLm06E1mCWzdEm31NySpBEmTYLuziLHdt8+hnIIc83CZCGsMISnpD4YMGUJxcQETJhiHyXTm6QmSVMzcuUWMHq0iLEwco1KJVVtIiFhxZWaKl3Ziogi9jR1rTAMxdqx46cfGbkWjgS1bVIwaJb4/caIrvr7vkph4ntBQ8/SXs3NNs32mdu3qRU6cOGYxhODUrDfbd2xBaxCP/DHyO6Nim6JH13m2fCTZiTsoenydwqSLFuHIbi36oEbOpk3rAeHtzpg+mQznQNb+GomdnX3Z6lyuwKfXJORufvoiHplcgVvzMBQKO+bP+wHP/GdkxEwl78ZhsnZHVHiOAUFNsXLlOt5o1wEubWF+xI/0DA3n0ykRjAjvBRe3MOaDabRoaZxytDS3dWzTkbxzW8jcPJ28m4fJO7aGoUOGl0tVYGr/GDmXIWzRFAKl2zaGPX1IsaxSGTzq9GFc3dyZMHmuPjkZ4Kli6+Y1VK/VmquJcXrqWzM8+YVYcs9tpUGjTjRtVJvNMatRevjj1DJcxFyDgvUkXLkXdpF1Khrnuh3xKnhG5YAq3Ej6g4B3lxi1+SLqLd4alMnLl3D0qDOZmYWlia4h3Lp1j3PnThMebhl2OH063LghyJskSeLhw7UcP66xCff77DMYOrQ/cXH7WbKkwCYkMTpalO6rVDJ9n8LD+zJ48DBmzy60CblbskRMCgkJAnKn0Uh07apBpdJw7pxY0gusuYLFi3/gvfcGIGLMAAUkJJxhxIiRZGYWVhChIWPgu99ZvB9s3Ssnztzk/NEonj5/RuW3fzBKhKXtmIN9QG0C3pxjhFQyhYSdPHebkNfV1Kg8B4WiwKx/jo6ziYl5yMaNW0oTlS7UqVObixev0L8/9OlTNrZ79ojJXBfuMryus2Y5sXDhbD7/fBbduxfSp485ft0wTKbj6bFmq1eLsBxYP1ZXbxAaauy5JyTYlXrmv5VO5gVG4wfO5OZe5dIl48C8WuPFnT/FS9EwKWophKCH+1Z5ldGjBcA+LTWZpUsiUNk74tA4hKzDq5BJGiRnT7T52fqYuaW2UtZOonWD+jRv2UEfMnEIbMiLDVOpHeDNzdtXsfOqgq+1WoXtc/D18+eLr35mx844Th+NRpLZ4ePtTeeeH9GxTX2OHd7N/n07GTVmMgBbN6+heduBhIV01p/z3RuHGPPBVO79mV3uPXr/7jXWrV3GhE9m4R9Q1Whu69K1F8eOJuAQ1AZl6i0kCV4JasW9WydQ1gxGmf4HX33zE/JSDmtbsMWKxND/T0xX+q8zS9uXL50n+clNnOq0J/H8YT6aMAep5AUXzh1DksnRVGnElphlrFy5nvt3r7FmpfC2L5+PQyuB/0DrpbUFd05y78ZRHtw+icOrbdA+umQWc325dzEF907psbLp6yaSWgp/MzVFYCvWrTtI//7w008FpQ9MHgkJ67l0SYEk2ZGSotYrsevimkuXQlaW8NoSEz+hbdv2tGqlKZfUq29f8PT0JjvbeiJT9xD36iUgjZUqSfo+hYevo1UryeZv9OwpKAQ8Pd3p1SsE2E1EhGX89K1bGj7/fDqdO3cgKKhMVSE0tDeJiYk0bdq4QggLD08XfRJIZ6aJIEufbY7+mafJj3Gu25GXCT8RMDyC4sc3ebErAuc67Sm4f4aXMdNwbNpTvwzWfVf3N8B7Lf7eW8z6JZM58vz5eMaNiyA0VGUQ584jLu4K16+Lalrd2Lq6ipdTly6i+lOtNg5rLFy4iM8//5TZswvx9DTHhLdsKcS9V6wQ+5csMeuSkYWFCVirSiVWWKZmK38xerSadu3UjBghPPSgoCqUsTRSuu1g1qadIpMmDSohkzny7TeHzLxtU7ivQ5MQHp2LoWnDUrRzwxr4+kVy/9ZpoqN/RS6T8B4gGFEdAxsatWVa/OfWOpy7F7fw5EmSPkkuk8nx6jWJP+IX4t5+KFknN/AiNoIq7xlfvPS9P6Jw9aLIzoWEXWs4f/IIvgO+1IdW1XkPaN64N80bf0LX7v3K4Jg1W3Hryh6mfvIWV69c4NyJGJS1gtkSs4yPJsyxeY/qkqeKGq3089XVKxdIfnITZZV6HDm8B7+BX1oN+WZsms79W6cY+ubbtm8E/gWl/9Y0JE+ePG6k8pF26QDnj+1jx86NrYRr+wAAIABJREFUaCUZ/qX81k9PbiNm3SrOnzmKV7/puLXoTU5iLM612xrzZkd/ZiQKLVPYkf/7ebxL+bAzzm7FuU47I+7lvCvxeh7k4sc3ybm6D18L4gqqzOfkn1jOou80ZiXZLVpoadxYzbFjdjx/bkevXnKmTBEiA9WqiYd89WoHoqN/Izi4KdOnz+XFi/JJvapUgUWLfsfJyd5iKXlysvDyLJWJt2ihpWlTLdHRkk1yripV4PhxN9LSHnL06CGqVbPNnZ6RoWLlymO88UbnUqRLASDD29uN69eXk5RUYpsAapMCN883SM91+Ut6o4t/nMuZM0dsCiEU3j+DsuAl0rPbFrVeU19kUdnnW5R2xpAUb+8eeHmtZsCACRbFuVu3FoVdmzaJ0NjHH4sCsNdfF0RbkZFC2u3ECTfatBlCVNQK4uJ2UbXqFby8tBa58p8/h7t3xUviwYOKSRRGRYkJ3ZIYScWEyOHmzUJ69uyAALap9eMnScU8fbrC/Iua9Uiy2uSW1OX5nbNkXjuEFjk5B5YwaMA7PLt2lPQrB5HkCvKOraFV+0F4+wbox/LU+bu0bNmKs2eOoK0qqAOcXmlK7qXd5F8/IERq9i3Bs/Pb5F7aQ/6NQ8js7Mk7toZ33ptMm3bdzDRm5Z5VhBawDeKrokdXsQ9qy8O714woATSSnLtHNusVjIy0jkvL+K9fOGukdZx26QAPHzykarVXbSpuWdNLLrh3BsfqTfTzTnlUBf96PnQwTkZt3b6LuNh1qEpKyPWshXfoBIof3yR9byTawhxREvz4BlU+WE3Jk9u8iJ2PU1BrSl78SeWRPyGTyck+t428xG3Ye1fFqWmYKBkOqIUkSaDV4tYijIwDy/F87T/6itDsc9vIOb8NpW91fELNwf7Jq8fiUKWenku9zAsJRZvznB5BB/hgrPXkWFSUEqVyIB4e7mzcuImMjFy8vd0YPvxNxo9/X+/V+vlVIiMjt9wCkMePRYLR3l5Jfr4KT09jFExFkpDlFQnpikzU6jz8/AKIjLSNgElOhg8+AHt7Z6KjowkN7YzO29u2rR6jRz+xGUb64gtHlq/eSXq29i/xWr/+eiuc67Q34rm3lBTNOLiMY8cvW02ElhS0xU6Ra9Sv1q0vMmPGSl6+/M1mwZUu7GF6LZOTRYw6PT0VQ17y6dNzmT3bdhXtZ5+JMaxIdeikSe5IktbiGJVXKWzYRlran5jyqINAtty8+TZabb75l2Vd0cq/Z/u2TWzYsJZvvplP8xbBXLxwlq+/noFCLuOrr+ejcAzQJzDnz/uBlzmSRYqH3GNRdOzQiRMnj2Hv4YdTq/764r/jJ47x9dfz9HHpKzeSSNj1C6ev3sTnrUiSV72PJj8L/4EzrYdsoqegzk6h2viyOgNdEdHoD6YyqLTydMiQvmaVpLb423VmeE+V14YqI5m0HXOR29lb1YMwLDIC20nRf8xDnz133tfbdmw10nKM2fAL69f9jMqrFo6aQpzU+aSdjyX3yj4kjQr/QV8Keabbx8g+tZGCOyfx6z+9dN9xpJJCJHUJ2YdXMvaj6fh7evDHye00btETbX4qJVqwr96YvCvx+PWdgkuD14HSpNmR1fiGT6Uk5XfyriTg1rK3UX+zT2/CVZNH3p2TpV7IYsaNkXhy5RLp9+7x+We21XoCArQsWfKIw4f3MGBAOJJUyI0bdzh+/BwbNmwkOfkxderUpKCggBs3rthUCTp/XsS3+/RBT7drKkm3dq1lGlZDMxWUNjWdOvyAAb34/vufK6Rn+uuv8N13KsaPj2PQoJ54e3sBanJyoqhWLYtvvjGn4d24Uc6KlfYMHzkDF4/KFr1wU81HneZnyrNH3L1zjbxnv1N4/yyO1RujyX1J8ePruDbujsLJvZRKeREdOnTDt1Idq+17um0289CrVn2HUaOm/mVxbsNrsmpVCcHBTalduxIgY/r0ubi4QIMGtr3mnBzIyID8fLESsGbR0VC1amc6dmzF9et3zCiMKypEvmpVCV999TGmHjqocXLyJzBwPEVFj8nPN4XR/UnS4/p4+rRA6VqbGjVqcPLkcRYu+BJ59eZ42Glo3ynciML2cOw6Du6Pw69SLeRKNzSKKjiTzYMT22jepg9Dh42me88BKLRq7h7ZzOixn9O6XY/S9mvq7wdTeuW8qwllBG6lq/MXm6ah1WqNVufFT24aCVRYou7NK3Ym5/ElMq8d0q8ATCm6cw4soVWHwXj7BFj00MtrQ5X2kOJbh/By8SDn7kmLlMJNmr1BsxbtK0TO9Y/F0NNfpODSsIs+BnV47zri9sTiXLcj6szn5CmU1PXz5NnTP1C4+eJQtb4+Vubbd4qZB+baNITsMzHYK2S8Oex9hgwSgzXpk0/0sKopn33I5asJVHrre5Ok2VyUzm4UPbxK8dPb+A+aZdZfj3aD8Em5yBtde7Bp82oi5ubSrBmE9oTu3StWYJKRkUdCwnFGjBhhFotNSFhPcPAmFi5chEy2lvh4jcXkli4easofUrWq8Mbbt7dNw2raJ1tFQgkJStq370CrVh3p31/E7a1h3KEMoy6EO9QsWfIbkZE6DLFMn+SLjS2NN+fI8PBwoVnLzqyM+piqgdX1v20thm6YK7l0ZjPH9mVhV6MV8tyzqLLTSNn0BahLcAoK1sfSX+xeSJvWbfh23kKb7avM86CAg1XK24pcSx0OXcSoTxAUVB9vb1cOH85l6VLz4w0tPFzQAezbZ0+nTiVWPfl9+0AuP8GOHZsYPHg77doZC2hUtEJUqZRISko1yoEYxtMVCkcaNFhLdvZ5ioufGH2/di0fZPIaQFnM2BD2KyhsRVxY0mp4sfM0LnXaG+XA4reexqlOe1KeXqdx/erI5XJ9LNtajs00GevX/wte7o0kZf3nuDUXq/Mur3fjftI5Xtw7hXPzXmQcWoV//y+M+q+j7jWNh0+ZOIIF82dxOn6hWWl/7sElTJ48zQiOadpHwzZOWqgKzT24hEEDh7B9x1YjPL5hv1IentZfj/LsH/PQ5y5e9bX/kNmlsfH9XL5yzigGahfYkCe/38Kv7+e4NQsl//oh8q7vx7FaI5TeVXFvaay0/XLfEnz7fIrc1d9MdOLkuducPRHP0aP7hHr4zUO4NO5G8eMbImlWuw2qtD8oSr5LwOCvrFDr1uHF5QNU8ZPzfcRV/QNeUaGI58/h2DEXduzYbjEW26KFlkaNVMyYcYQxY0aydu0ltm4VsVFDjzg2FurVE2gFS+bvLyBsDx9WTAv00CF4803zz27dghUrlDx69Ii5c4vMRKQN9Uxfe03s27hR0PgGB4sVyaJFv/P55+MBNcnJv6BWZ+DuLj4fOhTeeduDoSPWkllYDUcnN5tx8tQXWXrhAq/waSh9q5N+KUEvLl7w4DyavHRkSKWrNiHqLZUU4lC1Ho8vH6Vh085m6vAnz93G0VFCIX2Di9N9s+sQGDiKlStjrFLeGl5LQ3FunW3eLK5J7dpqzpx5Qb9+YSQnJ3Py5JUKiW+vWyejR4+urFr1B/n5xisbHbXtjBng6SnjyRMlU6Z8zvjx8eTmoqcwfvxYsGi2siGAs3kzlJTISE8vpmfPbph66IbbyclRqNVZxg1ojyLJX+PsxeesWBqhp7AtfnKL3HtneJz8DM+wSYKnZec8FK7eeHX/gJe3z3It8Qy742KMKGfTn6fg7FbJ5v1giXo7a/cCnBt2Q+lbndwLO3Bt8gbPb52nfpM3eHj7FEVP7+DXdyqOrxg/4zpqZcN4uKHYhW4FYHTKpRTdGkUVHB3srd6/tgQzitOTuXI0zqrwuY7G2fB6/CsFLuRKJ32Z8MvCfPwHfWUkHVdw96QeSywkm4ZTkvYnL3bOM2tLJ1PnWL2JWblt04Y1UOXcZVfsBvwHfSXEKSR4uXexgVjCBOTuAdj7vWKzrNyhSQiHDhxn6VIRl+zWTfwNCFCwZYvtxU5CgpJXXnmF0FC1TWRJkyYlrFjxCwMGKFi2zFjA4KOPhGfcq5ftaxseLlgTyxOviI9XoFYriIpSWqADcKJLl9fp1Utjs7+9eomXjK5KUYe5FiuSfISH50jNml9jfrtlg2oA/XqcpEmDwHKpIAxL/7OO/YZzvY56vLFv709RelU1whu7Ng0h9/Ju3Fv1BVdftm1cbNZmyOsqGgWNwNvDnM3Kx6c3Tk4NGD58GAkJ1kQuhO3da15yb0jb0Lu3REzMThISjjNhwiTs7S3LviUno7+/QkLAzk7i6NFjTJhQVjEcEiL+lpSIY9u00cnP7dCjinx932HSJHd69pSRmOhSroxgfDyMGiWxceMO/ZjpKA5Mt2vV+hpzRpCs0rE8w/eLIqmsSSd97QRexM7HvlIdJEc3HKo14uX+nwFwqFKPjH1LcAsZL4itDMbNuVkoJ4/vLfd++OH7JXjmi9oQHY580scT8Eu7hPrBWTw6DEN18xBffz2PezeP4FSnA1U//AXHV5ron+/cC8bPtyE9wV8R7rAk0FKRNoqS7+BUp73ZvGPYL+fmxtfDlv1jIRd1xlN9gU7VMWUZdJ2up2nYI33PImQyGd7dzYULXJuHkX1mC871OyFXKI3KbQF2x23CuW6HMiqAsIm82D7HSOjCvVU4GQeXk/breNSqYqSCHIb95wOOn9hH2vUDpfsycbLX6LlIysIPGnbsEB70kCHm56orqZakhzbJr5KT4fJlDRER1sMp48dXLJRSWFhWsGQJGte8OVy6ZEdc3Fri4g4akES5Mnz4EBITR9K2bSiRkbbJusLCRNHKwYOi2lW3rBecLi7oytv9/Xvi4XGBGzdGkZd31bgRbTSpKUWkZrwLCIzvt9+UYXxBhFoKCgqQZ90jI2Yanp3fIfv0JlI3TtMnsQ0hakWPrpNxeBV+/aaX8n/05dzBFUbl4vbKp9Spbg5BlcmcaNjwF3x9OwIlTJgwluDgaLNQhs50L7PZs8sgioZ48qpVxX6VCkaM+A+JiScJDe1KXNwRowpiQ5ip4f0VF6di2TLRlm1SNEEnEBRUhfHjRyJJkh4zr9WKimFLPOi6fjZpYkxJoKM4MN329++Nu/sFrl8fSUHBTYNjJNCuxc6uhB49B7Fi6Tz8SkXcdTBgbWGuURVn0R+XzWgCso+s5sOPvyiXvA+gbed3SumVjQn6fvklikfnYhg1dgoKxwAjegJD4r34hFgyfj+NQ5MQ8o6toW2nYfrfXbd2mcXSfkOKbmWjHuzft56u3ftZ7GN5bfiFTyVtx1xS1k7CrWVfi/3KPryaD8bNMKJNsGb/GMrFw8NL0rr6msWlnvz8Fq6Nu+HVeaR+X9Gj66Tt/Bb/AZaXJZJWQ8q6yWgKsvHs9BYZB5YxbepX9AwLB+D77xexZ89m7DyrmKmHFz26zssDS1HnvKBD+24kXjiJIqA26pTf+fXXTaSlpTBt2ifYBTZCenKeRYssY7d1qIQePewYMkRtsaS6d+83OXBAMkKvGLIPZmVRbul97962+ch1bY4fLyoDZ88W3rqpwERcHOzf70BMzDqzUm/dtkLha9ZfU9PRtS5cKLDTOmZBncp9WQy9rP3ff59KcvLPxg3JuyNXRupLwg0pV6WSF3ossGf+MxrUrc2Za7fw/s/3vExYQklaElVMCr2eLRsJqgJknlVwa9mHjAPL+fCD8QwdNlKPQpC055BUxpWNCoUX7dvfR6FwNroeCQl7GDHiXUJDVWYl89u3q2jUSMAUTamNdeOkG5OwMCW+vu/SpElDxo2bqOc914mY2EYBCY/c0tgb0gkkJOxnxIj/6Mv7K1WCgQNFJenZs5YpmHV8O7o2LN0Pxvh08f/9+5N59swE/C4P483ht8jzetUIeZQe951+JQ1ldAw+PSeQeVQwI2bt+JoBfYfRskUjMzQMVAz1ZO24RvWqsW3rBiMkjiGVxMyZc1A4BlgV7rBE0a0Tuxg8MNxinyoi/pF/7Bdatu7I3TtX+fLLuWb9GjZinFH7/y197v+J5ebl4t9ritl+9zYDKLx/FknSoiNszDy6Rl/OC6XLkt0LcQ/uX1ZsUErMlH0qBoWrD6ujltGjZx+2bl7Hnt2bcHilGdrkG7zYPocqpSsCHVWnU61WOKPiwsVTuLYbSvbZrTgFBTPu41GUFBXh3W8GJQ9O0r2F7UKcAQPsuHevNpMmJZfCEl0ZPnxYKflVFby9XUlJydU/kNY8MksJR5299ppg1PvgA+vXNj5ePKiBgUL9xlICVWCeixkxYqSee8XUTPtryXSJ0EuXykIOYkWiJDHRMoGNs3OQxf2XL51nxvTJ+mRaypYZbFz7EzeuX9Rz7aRvnMLhw/vxHThLrw/r1888meTWuj8+KYko5TLuHFjOsDdHMHTYSOsnUmr29r6lk7mxhYa+QWLieZYsWcKkSTGlqxkBOx0yJAONZhcLF1pfzehCMqGhKiZM2AhoePfdMq85I4Nyi8l04S1LXvqWLXZUq1YVHx9/MjPzcXMTKyYQ8Nfu3cUYWaNgFn20Y/hwC0tMG2ZpLK9cOkpWpgJtidxoBVV5ZFlCUIcs82g/tJT7XCSxXZr2Yv/+7Wzbuga7mq35ZvYMPppgufT+r5pCIWgj6jXupJ9wdVQSuqIdQy/YP6AqK1euY9vWDWzeHM37H06nZ2hfuvfoXao+tIV5839A4Rhg4dfM29iwYa2eW92wjfkRPxq9SEz7VRHPXGf/2ISu8KxkpYqzL4X3zpJxcCVFf17CtXkvfPp8RkbCElKiP8etWRgZh1bi9cb75F8/QOH9s7g260nm4dX49ZuB4ytNBOb48Co+HPsO9+7fwrluR0pSHqBWqfEP/wgom8x1vMspayehdbAj++zWsn3rJmNfpxWO1ZuQvWcOfa3ofuosLEzNwYNPSEt7VLrHuIx6+PDBJCQIsipb1Xu68Iolj2zYMFHabYtxLy5OePGxscKjtzVJhIaqWLIkksjI7zBdYhv215rt3SvCN/HxwgOMirLTr0iCggIoW77rrgeAeXvZOflELPi2jFdHJse1xwSu74rAw0DMwqlpGMUn1+tFEKzpw7q27E3yxhMUpz/GpW4HTp45Q3DHcCNeahenVGqZvawkrIUcgoKqEBk5n8jILykbV0hKuk1wcLzNkEx8vBjLgADIy8tj8GA7hgyBDh3EOB09Kqp5bVlYmDkXfXKyWB3dvasmPPwukydbdgwqwom/Y4eapUvrWT1/YxP/u7rWx9Qif9aiDGqHV8h4MvYttVKxGYlr055Gz1vqxukUp/1BwYtUfcW2IY86mIdcrIVj/pfH1Wvcia9K2RJ1k6vSrQ5fze33l9ro0c9Xz/L5d9qoiP1jE7pcKagDix5dJ333QtyCB+BeWi3l2iyEjAPL8e7xIXlX91Nw5ziuLXqRuX85RafWUqVyZbJvHMClSQ+yT0aTdTJaP5mLGPwKlAE1uXf/tl7OquDeGfwNdEKNeJdlcnzDp5rF1d1a9iX7TAwAxXkV4wovSwTqrGx7woRJBAdvol07FceO/T2PTKEQfB0zZojQjCF/iE7xRqMRk8fVq2XizNYsNFTNpElbDUIjlvtrK3asUonQS0SEO8OHDyYx8ZNSj990ma5r3zzB6OH6mO8XreCb2TN5bkDDasiZU/ToOhmHVuI34IvS8WttoUS8l/4+cmoWRvGJ9XiHTiBrywyj8ummDWugVR8TjLhGJsPa+FnbDgpqQHT0BgYMGED//pZZEHWx9ORkEQILDVUDYt+4cSJUVVFoZHKy2D5wQFSnyuXmsoGmjsGsWfDqq/Dpp9C/v3jRm/bx3Xfh889n0blzT4MVm7XzF/97enalefODXL8+FI0mA4Bvvylm9rzTJK+/Q1FWDn4DvsDU3Fr1JefMZpxqBxvlttLjvjN6Th0ah/DovAFlABWjgvi/OO7f8lu27B9NiubdOEzmweUoA2pR+PtZCn8/i2vTEDIOLMPxlWa4NHgdlwavk7plFpkHV+BQrSHu2jw+nbaQBXOnkHZwhcCtZz3Dobp4sF/uW4xH+6EU3D2pvzGe/zYR57rGmWQpP5Pip4XGSTWT5Gzm0TX65bzSXkZKilQBtZeyRKAlDy86+ldGjBhJcXGhRd4NQ7Pkke3dK4pR+vUTk/0HH4jCExcX4bWvXi0e8Lg4uHxZQNZs9TkgAF6+zMHPL0BPODV8+BAmTHi3dKL6tZSrW0VoqBqNBmJiBEVrQQE4OSkYPrwPM2fOJCioJmWrkjLecMNrAODg4GehJ0+pGjCK5ct/Yvz4n3hkAbObvmcRyOVkn9qAc73XyD67BVXaQ9xa9iXj4ApcGnQi+/RG8m8fE9zopas2mVyBsmEPfaL8zIVrBPovxMv9mFkvHBwqU76Han5eoaGdGTJkEBcvbmffPsmMr0c3BgkJSkpKVGaTd0Wx4i4uSiZNcuTly1zs7cWEXamSbcegeXMxoVevLkIvajUWOYWqVoWcHBVz5szGw8PNiIRs+PD+TJgwsXSMja+Bh0c9OnS4w4MHM3j2bA2BgTB2VDHTv8zAb4BlGLB7KTy54N4ZtBlP8QqbbDEsY8qj/k946P+m3yrP/rEJ3cfLE+2lLUyf/jVro9eRrZahcfEjozTeefHyJVK2fAGV6lHy7K6eICt1/ads/HUhaWmPyvaVihe4tw7HtUVvCu+fodI7P+pj8G6lCBZt5lOcm4aRdyxKuLFO7qgynvEidj5V3jNO0hlCIQHsK9dj9+47NmPXghZ1KLa8Oh2srHbtxn+5WMVw6V61qpjUDx4U8XfTB1rHCzJjhu3S8dRUkYiNjMwzKXKKITp6g76/S5ZE8tFHGykuLqRfP0MhDQ0JCfEEBx8wK/c3P3/xv49PP2rXXsDvv08HDKsaU7l+6U3+/MMFjz7mXp1nm4HkntuClJdB7pV4/PrPQPXiEdmnY3Bp1JXCB+eRtBIOVeqRfTrGaNVWcOJX5kf8SON6xTSoOdcim6K3d1caNNhgo/+2z2vWrK8IDo5nzpxCfaJTX0RVStql1Wpxd3ciJaXQaEy6dRMva1tUDfHxCt57TySa+/cfgL39fv34WzOBnBLC019+Kbx13arAklWpombJkq0MHKg0KXzbRHDw9tIx1nHpP2Px4sVs3Bijn/hff13cl4uXOeDwagczdIch57d7cD9UZ6Jp17Q+J3cvwO8dY61Ga4U7/0av+b85TpK0uDmfp0a1C/r9Xu7pBFbON9tG5oQt+8cmdEcnV3T0uWXQo9207j6Stp36ENwxnGOHdxMXK6hr9cuyPp9zdfscPW4dyqpE3VuH496yD4X3zugneIDs0xtxqtUa+/T7FJxaj7OzG6rKDXCq34n0XQvwfsO8JNOtRR/yru3HpWEXZDI5bm9MZFf0h7z2mnWUi1B7GUl5Hl5QUBV8fFwrrA6UnGy+dAcxWVgL2yQnCwV5jUZI0plyvehszx7h8ev2CQY+Fe3aqfQQu6CgmowfP5L16zdaTLAaH3+AoKAGFs6/7H+ZDKpWHY6fXzjXrw8jL+8KAFeuwIxZDnj0sYxmcmnRm4I7J/B1gJeFapxqNMOpRjP9OD97cB6Fi7sZj7ahNmRW5nw83Yz7JZM50ajRWnx82gH2/B0PHShdga1kxIixNGlSwuXLGnr3Nk54x8dLxMYWsWqVKMrSWUVi3Dt3aoiP705S0h0SEvazZo0QoLblGBjmUbKzbR+bnCxoAgTypizPYemeuH//NiNGjDVRWcrTx+3HjClmV/xpUjc/RNkwnPwTa+jUZQB3bp8m434ZHK93n6Ek7LVcJenYtDfRG6Np07kMZvVv9Jr/m+PslY+oWWUWNaqkG4X/Av3R/2+4XZ79i+hze+vL9HX7mzf+hJPH95H16JpRaMQSbt25YRc9EN+1WQjZp2P0D7pbiz4U3DuNxtmbkK4dGDywP1OmjOdZ7Gn8B1rmXXZr2Zv8W0dIj52Pb/g07H0CUTYO57PPYhkwwBzLu2+fIKQSk1n5Xt3w4UPKTTjGxYkSfp38nGmC1Bq1qiF6xpYknU6AwVIJeln5/ioiI39k8eJfyy2KMi/3Nz1/4//t7b1p3vwgJ0/6AhD5swN2NW17dc7Nw0g+sBw/kzqFokfX0crkyBT2eoSULmzmVP91EhOPMGrUu6Lo0QSpW7v2Anx8emE95l/edtn/oaG92Lp1O+HhfS3WE7z/vpYOHQTE9dixMpUpQ91XHWe5aYy7bVsFcXEHkaTDNkXBDc3wHinv2NhYcV+XN8Zz5y4iLm6XGZe+adx+yZJizp77ky3blzF/TjHNmmeila9j+7atbN4cTe8+Q9mfsN1m4U5W0pnSwp0ybqV/m4eukGfTsG7Zc+xg70zdIJXNbdBS2fc4vp7liLz+RfvHJvTyNEV11vq1Ydy4tJvUtGe82BVhhjd+sXshklpFwa2jqNL+xK15KJlH1uAXXvbGd2vZW0zorv7s37eTrt37odZIOJtUaKXvWYRb635GUMisw6vI2DQdhyYhlNw+wlsj3yQrfZNRDLJHDzcSE48SFFSTpKTbLF78q0n8sSwmrbMJE94lODjGYsIxOVlof54+LTxsrWXGWoseV0XQMzNmCHTFqVNlS3BLFhqqYtKkGCIj57Nx45Zyi4zE8ZtLETNgzZM13JbJSvR7dMk0nVdXcOIXOnXpb+TVZRxYhudrI4wmAD38NKg1hX9cJOdCHA4BtfRwuKInN0mWSaxeNYYx754z67cQuLce89dtJyX9yeLFy8oZ2wJ27dpGeLjM5sTYr59Ap/z+e5lzEBgo8iBxcWISz8szjnGDhkmTNiNJgmohJUV8FheHVYlDw3ukvLBORbjXQ0NVfPjhdvr2tQ3h7dVL9GvcOIkhg0vxk9Iu5JrDDB78A0PfjKN///7lanKaFu78mzx0GSqq+P1M/ZqHkAwejTrV0f9vbRvA15IO+X9p/yIP3fI2QPfX6jB1ygS8u5nfie7B/ck+HYPjq21xqFSL7NMxeLQdTHr897i1CtdPzq7NQsg8sJyPJszX9DgjAAAgAElEQVSiacMajHp3DBHfzSElOhm3ZqFkHl2D5+vvknNuG/m3j+Peqi/5x3/huwU/kZT0Oxs2rCUiIpLmzRVIqk1GMUhHR1+CguobFXSYE2+JmHSdOrX1ccfMzEI++wz69ZPRu7ekRy4sW2YoSmEdm27J47IVhgGxPyREPGxK29XsBtWHjhUmqLKF8jH/3xG53JFq1Sby5MlPBAbC8iXFbN8hvLp5c9Q0a/UKEpPZtnUDq1YtwSGwIW6txMqr6NF1svYuoqS4GP8BX+j1IrNPCSUdQ7ESyT2A/fvPMuZd4944OdXG17e/Qb8s91cIOb9rhVRNl28QseWNG7eW+/Lr3VuQaunK+bOzhWZsYKAIe1h6yarV4vpKkngh7N0r/o4aJfIllsbc1bXsHikvrJOVVTGkTV6eyiqXkM7CwuCTT5z4+GMtklRs8EkeqN/H2zOcxT8t4JvZC0jdPENfdDOpVJMzK+kcykbd9YU7/7YYurPjdYICR4jz+Z+YEk/PTgYEXFrKqDLKthUKT2Cr1Vb+9R769h1xnDm2AZ8BlvmN3VuFk3/7OIX3T6N58Qce7YeReWQ1nl1GkX38N4p1DGsHlhPe7z+kZinZun0Xq1dE4NuvNKl2JkYPV3Rt1JWci3FkHlpJu3ZdUDpXpl7jyvTo54vCMYAHf94kKND8fJKS7jBixH8sLkN18cdBgwahVmtRqYSH1b8/1K0LW7dK7NwpPPGKQNB0oZdu3cRS3JCVsSJeVni4mAwiImxXHwrUjitQhLe3S4Vi/tZRPqb/G+YUPsPfvz/Xrg0C0hky2MCrU3+DjE0MHvITHTq0ZdKnn+vLt7OPrMbFxRW76s3L4Ke9J1tk4sw4uIy5CwwnFqhZ82uqV38Pmcw2Kicp6U9GjBhpc2zL8g0BZGTkVWhizMkxLvTRVZOCGBPTis527cT11WgkOnTIZ/ZscU/4+oqqYEsShxpN2T2iC+tMny7yJuHhZcfGx6PnlymflbFiE39mZhEdO97lzp1JpKfvNfo8PX0XVQN2sXwxbN8hZ8v235k3pxXNg7uQp/biSdJljh4WpfupWcr/xygXLX5em2hQazPa4rLgdaMgCW2xDJAszgF/17y8utKw4RLs7Awvqg4tZroN/8oJvaIe+ldf7ME+qI1xaCT+B9xa9dV73wKiFoU6N53ME+uMdESLTq1FfS6GH35YplfeXhTxKcpawTi+0gSnGs2w969J7oElqJr2xrVlbzyC+6NwdufuxS1m/ZK06UbLJp0tXryy3Bhznz5acnIEFljndf/0k8CWDxggWBK9vW1712FhsHOneBmUlNixZ4+aDh3KvlNe4gvKuF7Kqz4UqJ1hgGOFYv7lo3xM/y/bdnNrTYcOf5CUNIunTyNNvnMPVGG81m4eMRs3sH3bDjZvjubDj7+gbeumfP3NdJ5Gf4ZXr08t5lgyDy7jwzHFNG8u9jk51adp0zgcHatgHjc3729FxrYs3zD/L1XYGpoO1TRunOXq4Vmz4I032vPkyRMuXLijj7m3bCnCZykp5nDE2bPh66/R3yOBgSBJ8PKl+bFdupg7CKaWkKAsnfhVFXi5u6FQ+NKo0VYyM09y48ZgtFpjARGFAoYM1pa+wE8j14QwIGwRcsUnTPrkE8C8jP//0kO3Vz6m7itjAHPmNBGas0yVYmfnrUfVVdQUChdeffVHfH1D+Gv5G+v2r1MsMh28Af17klVQiMLNV4RGSr3vrGO/Yefhj1vL3mQeFuIU2adicK7bAffW4XoFkvkRPyJ38Ddq38ddxtffzOB5bgkOjUP0/ArRG6PJVstRNuqu153UKaOU8X9cRFK9Y3Qujo416dMnnchI2w+xzgPT8Z0kJ4vY5/z5OuqAiinLjBolluouLkratWtHYmIivXtrCA1V8fHHotikov0w7ZPOhJixc6nOZC2Sku4QHNyJ2bMLrKIwxPEnDPi0LfN/2N6GgoJbXL06iJKSxxZ67wy4AqBSq1Ha2aHRSEQsyOHcLT983jKWSnsR9RYTxmQSEgKgoE6dxVSuPBSZHv5VPl+Jn18li2NryMOTnS0817Fj3yMnJx+VasdfVjjSja3pCk1nt27BzJmOSJIMSSpk3rwy4rU9e9CHYgyLzXbvFmNrby888owM4TRYiqNXhE9m2jQ7evQIwcnpgM3zE1w+7xIZOV9/TbXabO7dm0pq6nqr3yszL3QFaLpxNt229dnfO06LJKWXTtwVM7ncncaNo/HyMqTarNh9/nc/k8lc/n1cLhW1D8d/yaYNS0lKuk/mkSh8+03H6ZWmOFSpR+qmGWSdWK/HG2tyM/TwxdyDS/hk0hSatwg240IIrPYKK1euM+JSaN4imIDARnpCnDEfTNNP5hWxii6zDXHlsbHi4fur3rVKJfDnKSkqEhLOIpMpyMvrwqRJ5ygoyLGZJANjqlfT6sMyQjE7Fi5cVBrv30RGRh7u7o5Mm6aga1eZFQKyaIPCk79vzs5BtGt3m0ePvufhw69MPi1AFwpRlt6916/DqbMOePT5yKwtp2b92RG3gQEDmtGs2VaUSh+M6QjKN0tja42HJyFhPXv2KAA57dpZnxh19QSGFhcn1KZsJxs1bNyo4ttvy3hg+vUTjJdHjgj+/NhY8bKwtxdc7BERYmzLoxjQhWRmzBB5FtOQTHw8tGolcfjw4XLPT3D5jDfaL5c7UL/+CqpWfZ+bN4dQUvLcckcAyNRvKQ1mKaXJjGXts797XMUncxmVKr1FnTqRyOX/jFNsyf71E7p/QFVWrVpP1OolbNkSQ8HpaOzcfLH3rUa1j8ve9KaVnU7NerN9xxZ6hPTRH3P50nkiFnzLD98vIbDaK3Tt3o/Or7Xjhx8XMH/eD3+bEAf+GpGVzkzj3RWtFvTwEEtVwxjurFknSUxMBKBZs+Z06mQ5RGA6mZRVHzoZ6ZwuXNiQzz//zCQJWEhCgh27dsGxY07k5xeVHm9a7v/fm0wmo0aNz6hUqT/Xrg2lsPCOxePKsOuWKxJdWoSTuvUsJ072p3Vrn7/VF9OxtYUk0o3HtGkOzJzpSI8eRfTtWzYx7tkjoKKG9QRQRqMwz5zuX/+bsbFw8KAKpVJUfRqpP5UmVV95BX780fI9NG4cbN9u22lo0wZmzhRFSAcOCNisLiQjCtQ03LqlYdo0e2bOdKRXL40Z+2TZy93y/eDu3oL27R9g6HlmZh7ixo0RZiGZf86U1Ku3mEqV3jbYZ8vz/nfYPxZyqV6jtqQrLDp57javtW1gtm36/4kzN3l87yA3//jTrCw8eeVoPDoMw7WRcD0lrYaMTdN5o10HlG51CPBUsXpFBMqarfEqfM6nUxawM3YP507EoKwZjFfhM9p0fodO7RrZ7Iez402CAqca/bajY01iYrrw8qXtGLPpMrtbN4zEoCsi7GxNjHjVKgX+/iOJjPyOX36JZty4iYSHGy+/DYuTdEiZqCg7fH3fMSDnciYp6U+Cg18zSwLqTIRXnPRFR9YTOKbJnL93nCQ5kZz8Gw8eTMW0wuKdUQ5kuHTAO/QTA/HuhTg1649LC5Fjybt5GPvr23j2+KrF9svrx8SJn/Py5TpGjxb8KxUZp6goJXZ2/di4cSuurmWVomq1GPehQ4293x07xGf795uLgxuuBsLCxLEODuax7oqETCZPFpBYW07DggViErdVFR0VZYedXX88Pd3ZuHFzKYzTleHDBzB+/HgTioCKjbNWq+DevU9JTY2x/sP/D8zLqzMNGvyKUqnkf3H//vXjbH8mk/n8+0Iu1pKi9+9eY1HEp8yf9wOB1V4B0CuFd+ocpqdSNTXXFr3IPLwadX4WHq376RVITh7fwrARNW1qHOr2VaSAwVpS1BaRlSGuXKUSnnm3bsaQMqhYtaClpTqIpfjEiZuJjFzMe++NBmD8+MnExmpQqSzzipQtjT/BELb315KAP+q/V2Z/PSlq6ziZzJHAwA+pVOktCgtvIao5AUqI3ZbKu6O/5dHW6Sjq96Do1C/88N3HrIiK59H2C8jrv0HRqd/YuvPvl/SLsY2hXTtxTSqK1x4/fg9yuXFexJQOwMMD2rYVLwgwX6FZWg0MGCDuE8NkONhGsezdK+LpMplt3DoInp7yeIYEqdt+0tKelzoD1mLBUNFxlssdqV8/ilq1vqSkJMPgmBIMx7xs29Znf+84pdIVR8faf+Nc/tfHlfeZZftXeej3715jxdJ5OL7aFq/CZ0ZetMzvVYqTbxsxsRmapNXwfO0k1DkvUHoE4NaqD3nH1jDmg2msW7sMbeV6eIdO0JPtZ+3+DrcuxmT7BafWM2rMZLZuXkPztgMJC+msb788D71t24ulWOWRemEBU1y5qcjyrl1Czf0rgzCxqTdm6MXt3WuZIx3KhCY0mpelewpISkplxIgxXLlyxWKybNcumD79U2bNmqH/Djjj5/cKkZG2IYplYggP+ac9F41GQ+RPP/N95K9sXPczr7/eEY0ml8if1vN95Co2rF1Ely5v/Ff9mD17HvPnf094OGzdaryysmRqNfToIZAjvr62J9DVqwV3+c6dwnM39LytrQas3Sd794oJu3lzuHmzLBTTogVcvAhdu4oJ25TCQWe3bomXzcGDts/v8WPRJ3d3V5sFdP+cJ/v/19+y7aH/a1AumqJUZkz//7g77/goqvX/v7elkZBOr0ZQpBcDiIiiAoGQEJqCgEoVNaGI0hQuvSgaiFRBiqFLCQRCEaSI0hEQFK5RWhACCeltk53fHyezu7M7u9lw7/1e7++8XrzI7s7OzM6cOec5z/MpY81GBhlbJtG2YT2OHBVRdPqhlbhVqmt2QCm4eYm0pEV4t+hqhi/m/HKIjB82oNNoMEhFzJz5GS1atla4hnh3ilY4FkEpQy1xHl269mH/3m/R1W2Ff95d1q3dbAb6l4VyadNGWHElJ/9KXNwKNmzYRFpaFm5umF1pbJvscjR+vIUCDtb5UoFVdnMTD+IbbzheKqekwNChkJ+fK/8qkpPvEhramujoPK5cscc1N2wIcXGeVgYXIiLR6bxdcirq0kXDe+8NtRJn8qZ//z7ExIwxI2MWLVpuLqqKh74fMTExhIRU499b/f/P7SM5+Q9CQ0OJjs7nyhUxGa5aVTaS6J13hInz0KHO+4AsbzttmkinWW/rDPkk95PvvhP3VU2vJyUF3n9f4NFlNNW6dUIx0zYlJ0/yOp1zV6xTp8SE0KWLMkiRi+nWJKu/27383z+Wc5TL3yZCP7Z/Gbn+dR1G0cb0FFK3z0SjN1CxZSTpB5fh/+oIsk5tE++1iiT9wFKCe35MSXYaBT+swbtigNmXsl3oU2xYu4hLvycrNLYB7iweRO0qVbh15xaBUZNwr9GQB+vH0/n59grKcVkRumiW2XTUqNGkpW2yy6vb2s4ZDBASomHoUIkmTSzFpYQEiVatTJw4YaJPH+c52xUrhFBTUVFaKUV9IatXbyQsrNhpdKiWQ3c1Qh8yBHr3NphtzsRDLQwuRo0axRdfxNKlSyHh4ZL588REDfv2ubNhwwrCwrrZXTP71//9SOtxcuhLl4qC4kcfiSLjzz+rE3/kmsalS8JRqGpVUeiVI+9Bg1xbDXTuLPqT2medOkGfPmKVIOfZR4/G4ST/2WdioFbDo7uSp3etvmL7+n85av4fi9A1Gk0XYCGgA1ZKkjTX5vMvgJdKX3oBlSRJcqpUULNWHUnr5mn2DAysqOGjj6L5KzWVoO4f4lm3hWL7gpuXuP/tP/Cs2wJj6g3QajEE16bg5iUqPPMiBX+cA60Wj9rNKPztCFqtDkNIaztfyopW7jdyyzyzk+yTWwiM+AjP2s0AkYLh3Ba27zgAuB6hW8+mwcGVFQNjSop40M+dEw+MdXSTmKhh506JoiIIDPShf/9+rFu3nri4XN4tReM5WyZPmgR6vTfx8esYMGAAYWFFJCYWO2SByk2kTiqSmvqX+dzFRLTGaYF3yRJBTvnkE/vPjhwRhTXnUak7586ddWCE8feKtGxx6K4Mah98IFIpNWtaovXQUDFY2/p5ZmSIKF2SxHUFy4Sfm1t2EdMRl0D+bMgQ0Xd++kkgbLp1c54C+vxzAYGcN8/+9y1eLAIQZ+QjCwb9C/5u9/J//1j/YoSu0Wh0wHXgVeAOcAboJ0nSVQfbRwPNJUka7Gy/Wp1OqvBMRzO6pIp/CV8tm4u+bisKks9SPTpewby6s3gQer8qVO4/1xzB21K8cy4fIv3gUrRaLUGlbuPpmyZSqYIfKbd/cajqJplKuL9hooKUlLZzNiPfn0y9p8T2x09epbKfkW1bljB/9h1qlFJ/L1yAhYu9OZh0mHr1QrCeTa1Nlk+dEibKkqT+oIB1dCMkaOXvL1sGDx8qIzfbKK95cy116vRi165EMzrFFkWj1uTUSXHxQ/O5l4VyOXIE5s8XUao1rE1e6s+bJ6QNnA0aS5ZAhQp9WL16Gf+3UVL596Fmlu0ohy2vUIqLJZYsKVD1j7XePiFBpDreflukOWzv17+CfJI/27JFGKB066bU03fU5AlIpxPRvXVxdcQI10zKR440cOHCT6U2hOW/D8nJ950KoakLpVmbcPz3+81/5ljOI3RXuKqhwO+SJP0hSVIRsAmIdLJ9P6BM3JHOryoBYdFkFmu49dtBMwolMGwUOv+qZJ/drdjer3UvpEcpPNo0EWN6ipnibab437zEo8Mr0Xr5ElSq+6LR6oSF1Z8/Y3giVGkyvXIYWad3KCR3s8+LY2YfjOP1fsPp3TOCpg3r0LRhHSr7GVm1fD7Zfk8xY447JpMFA53mFcrrA6IxmdwQF94D8CjVP7EgFUJDxcPhigSt+L439+6JgfLCBbFMlsWcOncW/xcVifcvXXLHZNISFmZB2ci4dmdNpmiLcxbnHhLSgPj4NUye7MF772no0cMyYA8aJH5LZKQYGA4cEDleNzcxoJw6JYpuERHOjxsZCdu27VQc1/LPy8Hf6p8lJ99l1KipBAfXQacLIji4DqNGTSU5+W4596++nXwfrFvr1uL3W9+PIUMgKOhNTp8+zVtvDSQpyeB0+xEjRKolOhrWrxfFS9vj9OghBuErV9Svo5zT79FD/bPERKEPNGuWmBSys10jr+Xlibz+rl0iBy/3t7w8176fm2skNPQFkpJ+tLueZV37pKQfCQ19gbS0b4iNzeHAAYnY2BzS0r4hNLQT06d/4eDzTaXHPIqyb0wgOLjBv9g3yt9vHn+7sj5z3FwZ0KsDt61e3yl9z65pNJraQF3gsIPPh2s0mrMajeas1uApBtJOMVz6PdmcCtFodfg062oeXOVWoUU4Ot8qeBRlkbrN3gX8YdJC/F8eSo13VikG+Zyjq2jRJgK/3Lukb5xAzuVDZOyey3PPtiPzxEburR9Pzi+HeHR4FYFdYgDwaBrOnqSdXLj8Bxev3GDrtgSWLZ5Nxe4fEdAlhns51Zk3X2MmtAR0GcUfD3L5IjYWMZsWAAX07x9FUpLBrIB44YKIzpy1sDAjy5atQqfzpqCggNmzBXVt4kSIjRUDZ1ycwCvHxYkoasECPfHxy9m9ezdpacX07CkG4KIi8SCnpDg+XlKSnv79+2ARpyoo/VeIRgONG1sG7sWLhUCUuzs0ayaiNJngNGyYONacOSJN4Kpqn/1xbV87/ywpKZHQ0FDS0tYRG5td+nBnk5a2jtDQUJKSEsuxf/XthFm2PcLX2g+0b1/QarVkZqbRpk1bvvxyJd9+a2TGDOEYtHixMi3y4osCQvjJJ6JPvPiijooV/di92/4YsmbLV1+Je1lcbEnfffABFBTA7t0axWdLlsCHHwoCUlQU5Z7kfX1FarCoSNRmDh0S5+7n5/r3p0/PY8CAt0hO/hVRpL/KqFHRBAdXRqfzJji4NqNGRZd+nle6za+lQmh5DB1qVPSxoUONREfnM2fOTIefi2O+QXLyVau+scZq4H/cvlH+fvP425X1mePmyoCuRoZ1lKd5HfhWkiRVfw1JklZIktRKkqRWxY/umCPtym/HKQbh9INL8Xq6PQ9WDiP7TII5ivZq3pWHaakEdn7Xbt8+LbqRc3E/kmQRD5ctrAYOGMi6tZsYFNWd4lMbGTrkHU788B1BUZOo8HR7YVcWOQGP2qXqfC3DMRo8uH7lB5o2rMOunevwqNdGKPppdXh3Hs+Pl6uZ2YkarQ5dg1dZEPsV1rNpTMwokpIMHDggHtryUPtFqsVIo0aSOXVhG+G99x4kJupISNgFuJOXl0dAgBjoDxwQS+PGjUXq49Qp+2PJOPToaBmHbolqBgwYwcyZBbzzjqR4aGTtmTlz7CcKWezL3d21h15grx8/chGwzLeZPj2foUOLbR7uYqZPz2fAgLdJTr5vt4+yIzfLsWJixpCQ4DxK3rULNBoTBQUJ5sFj1SpxL0V9w3Jf4uIElFGjgTt3xPd/+MGN+PgNHDzoZXccObq/d0+sAjp1EimR7GwxyM+dCxcvSgwZYomkc3IEMur6dYFkkZusie6s7d0rYI979oCPj/Jeuvp9ucAq8xWSko7SqtWrpKauVkTVqamradXqeXMkXxYH4soVsRopa5U7Y8Yiq75hO/A77xv/v0fod4CaVq9rAHcdbPs6LqRbADwMOh6oRNoPdn+KodIT5F7YQ8zI96hw8wQZWyaT88shVXMDuVVsFQkSilSNvlYLPv98Lvf/uoNOJ2j9A9+KYcXKpWiqPYNH7SZUfDaSwC4xZB+MU0we7k06s3mz0NWeM/tz/HLvkrF5knkSChy4XDEJFZxYw4Z1SjZGSEhd4uPjzcvc8kRH9+5ZoIs5OQKrvmKFeDjj4qBfPwM6nSdbt26ldu2aDBjwFgsWiEhZbQCWI0XrCG7KFC/i49cAApETHFwbnc6b5s1b0KlTQZkGBjt32n8mr0D27HH+O4VcaxmC7GW0RYuWKFJMaucZFmYkLm6p4v2kpO9o1epZUlNXKiI3Mbg8S1LSfsX2ISFPoNXqmTTJPkr+6iuYMEFc7/nzYcQIywQIIqWyYIFIrzi6L5MnexAfv4aOHTsQHx/PlCmerFxpUBxn7149p07pMJnE/d+9WyBoatYUGPOlS8VxfHzE5x99JCCxRqMyiHAlhZOQAKdPi5VBtWpisirP9/fssaSAwsKMxMdvoF+/fsycWaC4PtWri+s1c2YB/foNIjn5DzZs2ERYmONi/KFDQkveWQsLM7J9+87H6hv/682VAf0MUE+j0dTVaDRuiEF7l+1GGo3mKYRE2k+uHDgvL48AlUi7YuueaAC3wBr8dv0mrV94k46t25Lx3XI7c4M7Xw4k8/R21Tx4wc1LZF/5HiM6FsyfxIXLf7B1WwJLFk2nRKPHePsSqfEfmlMwPSP6IV09RPqmieT8cojMQ1/Rb8B7XLxyg7QsiTYd3uTp6lXI3D3X7pyzD37GkkUzefHFUGyXSmFhHfD3r2B2l3ElumnUSETfcnrl4EGL2cXw4fDuu54EBQ3i9OkDhIV1YNGiL8rsvOHhIicq52737TNw+vQxoNBuWarVGgkPd14s79pVHSZXubJYRezaVXZE27NnD/6VpeiGDVucPvwgHtoNGzabv5Oc/Cv9+g0sY3Dpp0gDQAE5OYV8+aVlhdSpk7iOCQmCFBQWZh81umI20r27hqio7oSFPQcUEBbWgdOnDxAUNIgxY3zo0kVDdLQnCQkSVauW0Lt3WeJdyonWNkVincJZsQK7NM24cSKdtmyZYDLfvIlideIsBfTVV+J9a50aoYueQ+fOhU7Pu1OnQmbOnF6myJ2rq9zCQqO5b6SkiBWOnIrs2VO8btFC2TfK0/eU6aMggoMr26WP/pYpF0mSioH3gf3Ar8AWSZKuaDSa6RqNxrr01Q/YJLkIbNf7V1OPtFuKXWp8q3H86F5eeK4RY0aPZt26bwnSFJCxeZJ5EPat4EH22d3c3ygG4UeHvkJnKiL9wFKBWddq8azbgqISI3t3rmTF4lmg0+NZtwWSVk9IsD+c28K8eQsZNmwIU6fFMiiqO5zdwjvvTaJPr0hzUbRKQAmXL53F+yV7uIFnsx4sXLzWrigq/z1gQH+SkgwuRTeJiSLXLhexbKO6BQtAr9cQHT3a7F+6bp3Amztr3buLZb+PD7z8sp7Bg98G3BkwYITdstTVwpm1cqTc5FSKJOmZOFH9oZ84UQyMkqQtTXE83lK0/C5KHsyc+SmdOxepyjMsXixy2llZhTRt2kaRggkIqIBWKyba8eMFiicyUqS1KlRQLwIfOlR2zSQ8XGLbtp2KpX9IyDPExi4iNfUe166dQaPRMG9eCWlpYsB21mwnWtkExbrJKRyjEd591zI55eSI3zNypAgsxo0TBVGDAcXqpGVLERycO4cizVNUJPZrzWSWDTFcLZKrFaCtm6urXHllcuqUMjiyLuJPnw5pacKRqzx9LynpKKGhnRwUbZ0Vgv8eKRckSdorSVJ9SZJCJEmaVfreFEmSdllt8w9Jkia4sj8ArUHoURfcvETK8qFkWaU7vJt1Jv/3k/Qf+D7HT15VRMkdW7el+NRG2nToz7sx06gSFISUk86jI2vQYaJPr7fI/UX06OCoSQSGxWDwr8ahI4cpRmN+T1uxMr/9ehG9wYOHGUYuXrnBidPX0Bp80Rs8+P1mBhev3LArijpS9Pv9XhoffDSOlL/uYzuzxsSMIClJT0aG4+hmyRLx0DRuLDp/2UtFUYBNTv6VzMx8lx1y+veHH34wEB093GFkX57UkG1LTNQQHh5Gp04dKSoSjMQhQ8RAM3KkmKwkSSBzSkq2lxan9mCJfH4tTf+oFc4s1zQpKRGDQXIRxSO7KBXw7bfb7QYX24f+4EFYvryY1NTVNGrUGK22AgUFhcyereH8eYu2ijzhZmWpT4CuRpM5OUZatmxXWqRTRmSLFi003yNX92c90cpWdbZBhFzQnT0bPD119OnTh8OH4a23lCuPr74S277wgrJ+M3Om2E+nTpaC6Xvv2cMZkw+oGlgAACAASURBVJL0lJS4fh1q1arOli2OcbYvvyyCHrUmT8ojRojXPXuKlNbo0fbBkVzEd3PDqm+VHTVbO5M5Ksr27Pkab775ZhnR+n+vKPofacWP7pBz+RCp26bj264feddPmCPt9ANL6djhJRJ2rOWpur6KKPn8ueMsWfI1jRvUYt3qz5kzaz5DB72Jj4c7w4e/z7Fje/H1C8SrfltzETOw2xh0FSuZnYw0Wh0+zbtikiRy/euyZeMSGjeoZYYm5vrX5cqFRBo3qGVXFAVKYY8DyTlrSfcYGkfwTfwabt9ege3MKsMAp0zx4vJlA1OmiIdFhoMNGQI3bohB/epV15AwGzZsBbxYtGg5Xl6uDcBeXrB5syfx8esJCWnAhg1bVSP78hS+rJsosrrx3XffU6HC96xaZUkXRUWJqCkgQCznu3aVH4B8BgwYQXLy3dLI5wXS0jY5iHwEHE0Ubd+mXbuyz1PpouRBbq5RMbhYi1/ZPvQjRkh89pmIxqdNEwVqGZViPQk6mgBdnRi9vMBkKqRv30F2K5YNG3aYUwePM9FWry7SdB98IPqXdRCxfLmIwuPi4li9eg1FRcqVx8GDwiylfn0hL/Dii2LglgfwTz6BY8ecrziTkgxoNK6dt8EADRr8zoEDJWzZIt63TZfs3y80b44cUX5fnpQNBsu5L1kiVqaxseqgAGHWrSEuzv6ZdRQ1uyJcFxUFZ89ue2zY5r8Sof/XqP/ePn5SXmEhfu0HUPHZHkimErLPJZJ9fjcedZuTd/Uonk+2xS3tT6ZOW8jv1y+bhbu8HiWT9jAVz3pt7US8DHVD0d67RkUfbx4WFBMQPk5VuyV12zT82g/Ep2V30jdN5Jma1Th//qQq9T/1fgqL4+ZidPMwe1n2f6MfP57YRGp+MIZGkeQeXcLs6cLmLDT0DF5eT6BGloiLW1pKhsgp1T7py6NHaZSU7GHoUGM5yUC3CA5uQKtWOQ5daOS2ZAn88UcDNm9eYRZPUiPMgGtMyPHjxZLVWqpgzx4dxcUm5s61T2nI31PzMP3iCx2//lqN27dvO2WXyqSrRYtWk5b2DWFhRhdp6AfMv9ndPVDBvCwvcScyUtYFt3zuaB/l2feLL4pr2rt3H9as+Rw1ctrjkoyWLBGDcOPGokibmSkmEUnSsWjRXAYPHkxy8p80atTK4fXfsgVWrxaDVbduFqLRihVisIyKEnUai5icjv373YiPX85rrw0mPLzYKbt0xQpRV0lMtGgcNW2q5ddfTYSHi0lUZlXv2aNlxw4TbdroGDq0hJISsXJwxqR25J2rFJmDssg+wcENXJLFiI4WqwPHMghlH+s/Rv3/TzStTi9Vem2GQ+bmvfUf4fXU8xT//hPtGtfn2LEjZuGue+vG4FatAQGvDLcT8ZLZoS+1bMTePQlo/apRbbCNdsuXg9D5VaHKGxbWadaeT6nQYbCCdWpN/b9wOdnsZtR/4Pv07hlBcXExWza25NvtOiaPt3hWtmr1E97eTRDLI3lGtf5b+dra3u2TT4T2x08/2etsyExMC13/T3S6INaskYiOLpuKfuXKZbMoVlLSfqKietqJTMk6M/v2iYg6IsLeuSYxURTbUlPBaNQQGOhN//79ePQok5IS59ZrS5aI/Xt7i99Ut66IAkEcR14uqzVBKR/E+vVbzFR8RwzMxERxHDc3T3JyCkonz9dZtWo1ERGWwcVV2z8ZQ6424TqaAF2ZGK0HmhUrIDHRQFbWPeS+YS0fUd79ye99/LEHkZE92Lt3b6mJibhfQrdc9IdRo0aTmrqSESMcjweffSb6pMkk+oanpyV99s9/Wvqrjw8UF+vYtWs3HTu25s0332Xbts1OGdLjx0P79uJ/gM8/1/HddyY+/VRyaofn5uZOTk4uUVHO9dsdsWktwVFO6TvOn1lHQZDtPmVtHccyCGUfy9Fnf0txLp1XRalG9HqzcmLm/kV4Ne+Od8tws3Ji5omNVOozTVXqNvPHjVQfsdKhFG7a/i/RGtwVhtFyyzyzk8wTGzD4VSMo4kPVCF6N+q9mwhFS/TW8PHMU32/V6ije3o0ozwyclPQjAwa8RYUK+aSnW4ScrOV2ZSGny5fFwBYb+w9zxHDnjmM5VZFz9CQz8w7ySiE0tD3Nmoncuxzx2Q6OJpPIgR87JhiCbm7iPR8fLwYO7Ed09EhF5OGqqFd0tBhEN28WS2h3d4HJto181b47Zow3aWm5iofK1ttTNpJ48UVRM7BWA9y500RxcYnZt9PVFZH8gDqaABxNLCtXwsmTYulvq09uazYi664UFNw29w1bgTdnEgJ791qKnNZWgvHxy62E0DD3AWvqvMEguaQg+f77gmh0756IqJOSxCRiK+ksT76xsf8gOfk+zZo9h0ZTRPfu9v1z926hBrliheX4rplsGAgK6s/69dtc7ne2ejf/yQhd9uxVl5ku+1j/UxG6h6eX5F45BEPDV8k5uoqxYyewbfsWUjIL8WjahfSDywh4dSTejToqvldw8xIPts/AzbcSAT0mqadTtk4FnZ5KvT5xvAL4ZhxFqX9gCKhOtSFLFJ8/WDmMnhH9GDZsiPk9R0bWhbntMOgzFN9Xi9BdkZI9fPgoERHdmTevxGFUMnGiyL9XqFCBgQNfIyMj1xwV2w5scmRfVKSnbt3B5ihh1KgJpKWtUaQs/PzKjv7GjYO+fV9jypQPrYygMf9OV2V35cFx8WKx3+bNYdMm11NN/v7eDg25XYlix40Tx4mMdF3bRH5AnaU9zp8Xg9KNG0pPz5494dNPReSqtuKy/n2dOoHJlIba6k3+PWrm1LVr16ZRo2c4fvyEwyhcbklJiQwY8DZhYUazUmanTo+n6ugonWG9ipRXhP369aN69SLu3pXIyhL5+mrVhLb6xx8rJ4WoKNfMzseM8SEtLcflfufr6+zZgLKiZvnZcdWZTLkC+M9H6P+1omilStUViJWqtZrybsx0QqrXo/jkRp6q14D8cwl238vcv4jX+4+gsl8wGbvnq37u7umDV/3nFEXMO0sHk3lmp7mI6dMyHI3OQMCr9iGALfX/4pUbZrSN7d9GoxpcsAhbREZoaPtSrHe2VbFvjQLlkZDwLT16aJ0WXLp0EZowcXG5pKWtY9euBBISNFy5oqSiy4WrF1+0oFrkc9qwYSNhYUYFpnjWLPtin+2xRbFnC6Gh7a0QGZbfKWvXOGvWRbtDh+DuXXFc13VnKpRS8dVJSa5gv6OidEiSlp9/1lBcrCTOqDXrAnCPHpY8r3U7dUrUFFq0QFEMbtlSrDx0OhHZO0OE3L8P3t4GrFENISGViY9frSAbVa4szqNLFzFJxMTAs8/e5ciRI6xd+xXFxQ9JTb1KbOyc0sHcGj+tTq1/XGSTI5KZgItmm48dFtaBc+e+49VXB5OfL7bRaOCZZ0SR3DbCl5FDjjDk8nWQgyNXzt3TUwldNBjgwIFimjRpiG1fdoQ8kRFrZUGPZWKV6LPeDvf3/w3KxcvLnTGjR7Nr9yF6RXWnacM6NG8cwtChI/jHP2Zz8+Yf+Lw0xO57Hs26cvhQAn+lXFP9vEKL7vj5+1O15CEP1wviUOr2mfg+35/86z9yf8MEK9bpG6oRvHfLcIr0bmbqf0nBfY7tX0ZgRQ1NG9ahfZtnzMgbg0Fv1+nq1Alj1KgJJCffNyMyHMOcLCgPgTpxTpSJjMQ8eA8dWszMmQXodBomT3a3YxeuXGkws0FFRC0q5tb4bRmTfOtW2Rjnbt3gr78kK9q0UuCof/9+qgOt9fUZONCCV87IsGDeXUHWyIiVmJgxJCUZVB8qV7Df3bqVoNVqCQ19DXf3CmXS+q2Zj9WrQ9OmgokpQ09v3RIFuVmzBKJEDR6n08GGDc7PKyEBevXqhS3CISwsnNOnT2Mw9FLgvo1GcR2ViKG3VbD9lr8doTQeF9kE6iQzgd7Rq+LrR4wYRr9+BocTG4jBd/9+xxjy994Tr+WViKMJXm6JiYL8ZX1vhg8XdYEPP5zoQKzLHnkiEGvrmTzZ3Q41tGKFqAEUFwtJB5D7bD+H+/uv4ND/L9v13y461C0HQfFPK9Tg9tQLDjDh4eTgzisvd+KtXj0wnd5IYGAwxVe+I6BzNF5PPU/6QXvW6f0VQ8g6Y1Ff9GgaxubN8Zw/d4pJE8eS61eXadMnYTKZFMc7fdpo1+kWLswrjb7bM3bshy5QkIuJi4srkyUHFpyxLRGmuLiEX3+tR0yMN126aBgzxoegoLc5ffoUYWGvKPZhS96oXh3y813HOFuw8Mpic0xMDElJeo4csQzgHTuKvLAozskYb3G93N0xQy5do6RLRER0JyTkCYcU+YwMV9UEi9m1axfx8V+zY8d2JkzQs2xZ2czHK1cELV52F4qOFo5EXbo4XxVERgqNcWe/78ABdz7+eLLq5yEhT1Cxog+9exscRvmO7ot1c0StLy+l37qpkcx27waj0Ui9eq3w9w9i1KjRJCf/Ccj9RH1Clo8lSVqWLFGHk8qT5JIl0KxZU5f2l5Skfu6uXDPbVr9+PUD4AVgrnxqNom/Pny/qHEeOCBx+dHS0y/v+V9vfxrFILjJ+PHGEwv+z4OYlMpJi8WwRrrCayzwhiqIFNy+Rtm8R3s2VVnTFJzcyY+4qAI79+Au3rh3klz/+JPjNhRjTU0jbuxA04N1EwBBffLELhw/vRR9UC5/mYaQfWMpT9Z/hzxu/K9Azr7Rth8GnPu3bPMOD1L9YOG8Ys2c7rsSPGyc6X4sW9p/LTc4FSpLkUsHl3XfFclUujNlbgK0ppZKrFVXyGDVqKmlp3yjygK6iPeSim2yPJxAuFh1q2XuzRw8BY1Mr6srL6ytXhCJgjx4iWnJU8Nu9W/wLDdVx6ZJb6e97heTkq8TFrVHAQPPzC1i2zOgyrOzjjz2IiAhn06ZvefllUcCU89IhIWIykqGZe/YIDLSbm4dC69zVazdkCLi7u9G1q5BWsCByZBentaWTr3pBrHxer1dV9yGjNGStIOt6yzPPwOXL4vpbQxPL8rO1LThaw1qbNlU6Wcn3Ts1/17qQ27JlC/z9T5Spqb9nj5ZLl05z/Xqy6v527RKIrUmT1M9dec1uOLz21q9tHazU2rJlQjhv69bV/2Znrr9pUdTWU1QuMlr7fxoavkrW4eWY0CDp3DD4V8W7aWfS9n2JRu+Gb5s+ZJ3ejn/HIWSe/BaN3o2KrSLIObKKuXNjadFS3MGt2xJYtXy+IuqXce+Zp7ZSLTgQjVZPulcNDJXqkHN+D77t+lN4YZcqlHHqzBU0bViHuNgZGHRbGT5cVVwSEJ0uO9sCx1Jr1v6cZRVcVqwQS/P5851hr704ffqYVeFSWWCRvUatC22uYJynTYMzZ+wROOIhNPDpp5/x4YcfODTGUCugzZ8PR49afo9twc/HR0Q+M2aISdHy+06p+pK64rZkXbRasgSuXNHw22+SoiioVmDu2FEM6O+/P1gxIb78MqxZIwYPR1BT+R5fu3aJuLi4Uh/WHAICfOjf/3Wio4c7vF/y3+XxehWGJfb7CA6uwttvZ7NihX1AIBtMN2ggBnajUYOXl56aNYuZNElyOJGsWCGuZ1SU6Jv79qkjX5T37gmF/64o5FquRZs2LzksfMtNNuIYMmQ4sbFfqO4vIyOLVauEiFnZ18y2cKleqLR1sHJ0bqNGefPw4c0y9wf2oAlfX0/q1q3NjRu3yMjIM8NuY2JiePLJxn+/Ad1RhH785FXahT7FkUO7ObjvWwoKi/Cs14bC5DMU6wxoTMVgKsHzydbk/36KoB4T0KDl/rYZaDDh5uZBmxdep1fPCK7/dpH1a78kOyeLQBX4IoiBPX3TRNo0asS582cxunlQscsoVfRMxu65DB85kXuP9LRv8wwfj+vNksX5LkVm06aVHSH89NP3Tp2C5MinY0cYO9bxMWU4V2zs5wAkJ19l0aLVCneX555ry7Fjx+nWrZiwsOIyyRmu2MpNmKAjIgKGDXM8wdnigVNSBN3c3V1g3q0jQ0eDgzUkzjaKKcttyXZSkaNLcC3KFvdpb6nRQn6pwJYwkbAlwFivSmrUEJDL1NSbivO1NEcR2b83Qn/zzRFs2/ZtmZhwmeAkQ1xtr6c84R04IAIWg8GSEvnHPxxfR8u9m+/0N+t0tVxGrgQGKqNr2VN3w4YdZGXluGThV54I3VUsukz+K2t/tquVW7eEJHLXrspVrrx6SU/P/6ckSfXVjvvfK4p6upsLi9ZFxvZtnqF54xA6tG+LySQR1HMyAWExaH2DoSgfyVho0WgJrIUx9SYPdn+KVqvF66nnKTGZiOoRTknBfVYtn09mfj7uT7a2cyvKtkK8uDfpzIXzJ+jXfzBSRgqZierombFjJ9C7Z4T5fHOyC1zWN1fTD5ebXDiRCy62ueHz50Wq44MPBB782DFLpV+tCWmA7TgTEvLxOYIkQW5uR8aMqcjgwRrAszSXrLHLJX/2mdIowbY1bAiSVELXro4Hc7AvoFWuDCaTht69X2f3bj2DByvFopYutZ8IraUP7AtH7jz/fAfGjbOnujtSA8zMLE9Rth8hIc+U3icvvvhCj8kkJkJHud45c2DLFr1CguBxNLRdKf5ZinDq+9BqdWWigLp2BeE86aV6PX/8UfRHvV68d/Cg8D3t3l2QzeSCoFoLCzPy9dfrSt2lapXq0E+w0SX3KlOkCyyom/T0HPP3bOUjwsPLlnJ2XLhUv0eunptwAnO+P1vQBIjAafZse8llufCt0RDi6Lj/tQE9L7/QKSRw7rxZ6Oq2suixdP8Inbc/lXpPMeuxeDftLAqZxUUE95xMYFgMbgHV+OKzaUyYMJqK3T+icr/ZFPxxlgelUrlpO2fz3LPtyDixgXvfjCPnl0PkHFlF+xe6sOzLmeQVGvF+0R4949k8nPgN8Vy4/If5HL19PFzudGFh6vrhomCjN8MKbeVTO3cW+b8mTSxwODk9Ilu+2TYZzlWWkNCsWQUcO3acn346THHxQzIzr/Pzzyfx9Oxtp6Kn15eNgilPYRXE4LBgARgMEt98sxnQIEmwdq1AJXz0kXpkZQuJk6FdskONj88hZs0SJChrvZzbt9XVAH19XSsKWu5TXul9OsadO08SHl6WmBocOiQRHf0W5YepWf52BTJnfY4WqKJF6nXDhs0uKR/u2bOHpKQ9dtdz5EiRG1cbcEaOFO87C15kezqlu9QaGyhsHv3797Fzb7Jte/dCmzYWWGBy8tXS/m6BZPbsqS5Opn7NXIMSOnKwsm5btuipWbOa2WNAyOuOthMCsxXIcwV26+enajoE/E0j9KYN6/D5gjiqljwkfeMEKw9RpanEo+9XodG7KYS4/LqO5UH2I7MhtFtgTXw7vIUm+z6c20J4xOvCrajHJLwavMCj71bQuFFj9u7eVEpG+lg1NePTsjuZxRquX/nBfI6dOkeyZ4+TdRcWqFd4uEgfqMEKP/10LosWLTd7YrZpE4Ykadm0aTN+fp4sWCByhY6iP9uHR8a+uiIkJDvKWMOyVq9ew44dm/Hz86JrVyH764qkbsWKrmOZZTElPz8xUckOTVFRYhBWm6iUv08Z/Sjdi4y0aCH2s2OHWBEsWCB0TGybfH+c6XyL+2QRNbMWXbt1K6XMATI8HNzc3Mxyx86iP0lyJyfnBiaT1m47a5E3dYiq/TnartCKi12bdNPSshXSyvL17NwZevUqnya7dZPvvzp81wK5jIkZw549zievPXvAYNCbo+tFi1bb9ffy3VfXInRnsFkQujcHDhTToMHvTjxPxf6socopKQKNUxbs1tmA/rfKoV//7SLr1i4hZvQUKlWujslUwpzpH5KWn0v1YcsU37/z5SDcazXGt93rpCfFIUklBHUbWyaN3xZFI0sHFBUV4F69AYFW6Bo1OYLikxvp1OMDM8rl89kjmTdPHZZona+tXNmS77MwRV+jSZMmfPjhBHP+zDpflpAg0aqVialTTfY7L21qGhXlpUQ7yh9ai4llZmaXmYucN08M6mUhE3JyxLK9PJoktvuoVm2IIoduS5FXa7bXSu04tsVQNzcYMWII0dFvmQW+1FAj/458al7efS5e7EVh4U3AC2/fJVStEkr1qpUV21nuy+bSmogQebM9R7X8t6uInJEjDURESHZIDle/P3iw0Im3ZcQ60lQB+9rI11/H8957o8yIKVvZhP79hXqoLH7lrMYg39fvvhP3NSjIR+WaievmCvIkKWkPAwaMsEPVbNmi58CBYhdE5o4TElLZXCs4e1YEZ1lZYhXurD8NHw7Xr0uqg/rfBuVSUnCfSRPHoqvTimrSQ5Yv/4atm9exdNkiKvWeaq/HcnoHWae+Re9fDe+mnUg/sBSDbyWqDVUO/PeWDsLD4MaSJV9To2ZtDh4+weqv5nEv9QGB3cfhWVfgCWUooySZ8GkWRtb3XzFu3CS2bd/CvZxiDI1eJe/oambP+RydR2UzKmfjpm9Zs2KqnQqdLUxPLr4JswVwhDaxbleuiHSLM40TNciYjHKpX//Zcgw2ziv8LVu2pH7935yKZy1YAN9/j9OC2wcfiOtRrZpzIa7ly8W52T74MhT0l18uK1Au1iJWjpq1jszu3RoSE4UcrqNitWNhJSVqxBXEw5gxPty9e0l1HyBx8+an3L79hd139YaOhD77NVqtu8r3itDrg6y2Vp6jGk3dFTTTypUGEhJQhX+WR/tm3Trlc+Dt7XyitpULAPj667XExESj0ZSQlyei+zZtRGQufFhXExYmPOnKhwJSE+Oyfe38s+Tku3aIpZo1a9CgwXWnkEZLv5pDcHAdJk7MZvp0EeB88knZE+bgwfDHH+oDuvNE0H+wyTl0gG3bd3Hy2EYz3vvupomMHPEm165fUR3MASq2iiDv6hGkogIyjq5Dq3cjoNO7im0Kbl6iqLAQfc1mjJ84jnHj53H8xDkePnyAR0grHu7+jBrR8Wg0WmFW3X8OdxcPIv+HtTz30oBSOYJGHDm0m/37vmHIiI/QeVTm+Mmr5mOk5XrRrGUHfv75KPv2KSFr1h13716RB69brwELFy6jZfM6LlnHyctXtYgGlEQjCw59NSEhlUsp0c4HOXtqsvkOKbb7889b/P47PP+848H62DFRLJs82bFIWEmJSKesWuX4nEBEZCNGiOjOeh8JCfD888+zaNEXCtROWpprpKyMDIiO9qSwsBBJkvD2Vt9Wzq2ePm2dW7W/NiKf6hyTvHcvdOiQzQ8/1HV+giqt2HiYH3+s4/BzjaY63r7L0BtCCArAKpoXEg+xscoVS48eoi8995zj+5iUpCcvT73gL0sElNWn5LTKsGHiWOPHC+7Exx87/q6yNiLa4MGv0aFDYwXf4OxZsSL5/PORhIRURqbDu97fK5i/Y9vPrV+rocP69+9LTMy7hIRUJiSkGrGxc4iN/QRrJNLYsc7dw8LCjIwZs5HY2E/o378Pq1atpls3ySwWt2uX81WumlOY3P4WOfTfLn+H4YlQcx68YpdRXE++jtfTzyvQKbdtPER9WkVgyrqPZCywU1UsuHmJBwlzqdRzMoFdR5FeWEJSwirzxBEYNgq9f1WFqbRGq8O3bV9q1KxFVGQ3Sgru8/n8j+jTK4p5C9bSp1ekQgZA/rtv30HcvevBjBnqWh1XrghizE9n3Hjo+SQffjiQ+6lZLlH9u3WzoELUdC0WLBDR0siRIqrKyChg0KChjBo1la5du5UDFaHMGSYn32fUqAnmvH5GRh4TJjj2khw/XuDEu3YV52jtbiMXVl98Uc977w3HaNS4NPjm5yv3ce8eaDRunDlz1g6146rJR0CANxoNzJ9vYupUx+5RH3/sYSeZoJZbLSuf6oxh+e9okpRCdkZ3Hj2cw4O0YsU5qln0uZpTdoTkeByJABk507Kl49UQiPuj10s26Je7Cks+oVNzj9jYRYpagUAB9XWxv5eNNnpcmznXbRFzkGsFycmSOW/eowdlSlE4G9D/FikXmUyUkpaBscREcNRkCu9e49Hhlej9q+LTrCvpB5fQrFlrLv9yHm3Fyvi0iiDv6Gp8fHzIC6pPQNgoRe67uMSEe+0m5py4I81zWYZXbpKphIwtk2hctw7nz55AV7cV1UwPeTdmBlLRA3NayC/3Fg9T76Ov+yzVpId0aP8i36xZSPfukkImNSEBEvdAsUlPQNR03Gs0JGPzGN4IT2H5siKXsbYyesCWDLJihaCi9+ypo2vXEkUOXhhOSMydq27Qa8nnnSYk5AnkJWVS0n4GDHhDkdfv1UsM1KCu6Ni2rcDaOzMZkI/Vps1zLqUp3n8ftm51zUBj8WKBhXZmorBypYFff61Hgwb/NKch1AhEVatqePbZ11izZjHqy2/lUlxWL+zSpZCuXUucSuTatgsXIPZLd2ZNK6RGDUDTnJ17OrNj21fMmpYm3nO56dFqxXlptXoiI/NYtKjIxZxyRQXByRFB63E02eXvjRjh2D4OBLsyK0uk5awJa9ZpFVd9BR6PdCdeu5IKVXtuoDwpOEtqyTZVZMtrsO1P3t5w587fLIduWxSt5FtodiQqup9MSe4jPJ5oRd61E2j1bnjotRQVl+Ae0hrD/SsUFhUzZNhY/PyDWPN1LGmFJryahpFzdBVNWoZx+8+zPEh7iM6visNi6YOEufi26Uv+z4mK4uejI2vIPp9Ipd5TkEwlPNw1nyqVapP28BZ+ERPEewnz8KjbiqDwMaSuicGY9QC3Wk0ovnUOrdZEUaEJg0FC61edooxMgqIm4Fm7GSAzTpdAsYaFCwvKvPnvvCOi8MczUBC51/BwEy1aGDlxQgxeWVkilxoW1pHPPptv1jV3RCRxjUmq4+xZDZGRGlU6t6zL7Qp1evlyDXv26MnLK7ZydsqkpGSn6vdcuRZTpngiSRri4vL+JXKObbHMaExh796BxMdftJvoIiOhWnU9W7eWsGqNOzOnFdGqlRbJJLFwUQmJSR64V3+KGh7XmfRxDOcv+JqfPOll1gAAIABJREFUg4CCP1n4WRoVK1r09iWThEYrnmUNxWgc4h1czZfrCQp6s5ToY/ltzghap06J69ylS9ka73KTpYHj4so3ESiLiGW7/si+AkIauNiqD1qkB+rXr+Y0laImjeH8ulnOwZW+bVv8tS3mLl4sJLLd3dWZx9Onw7Vr6gP63yLlUtnPyMpl8wjq+TEVGr1EcWYqwT0mENR1FG6V6uD55LPkFxYSGDWJwK4xmCoE0rlLFH16RfJqx3asW7uJzu3aw7ktzJu3kIEDB7Fhww6GDR6O5tFtHiXMtTv+w8QFeNVrS8GZbxnzfgxB98+Qvkl4muZc2EOFp9shmUw83PUpniGh3E+9hW/3j0BCvPdkawr+PEvBrcsUZacT1PNjgntMRBsYglHjAzp3/HvOptKgJRiCBAEKxESSfWQxjZ4upKDAyMCBSklQ27Znj46SEoFltn0QXMGshoebiIqKICfnZSZPFnjyxYst8q4VKhxX+HU6gjq6gtO+dMmdhIRvCQp6m1GjvM347y1bjEiSln37jpCcfNelNMWBA55cuHBescTeu3e/qgcqWFIJkyYJMpK66uR6lw215SWxM7KPJLlz9+4mTpxojq/vRTvp4veiXyOjYCfbdoxh+Up33EKeY+oMX9CfY+qMTiQmueP11HMUpd7gbnZtNm+4xqrl8wmMmkRAWDQZRg/WbuiD3uOi+d/VP3dZ/b0ZNG0d/g7XsPUGoqNH2/02a4ikLUHr0iXhVHTvnkiFdeokVlOyiqbaauT+ffDz81SFXC5bZk/4kpsatNYZIUtWpgwKepMxYyqWCtVVJCjoTU6fPg0YykyluJIKDQsrLiW3lT8FZ7nm4nu2hLEePUQ9ytbDVU7jyvLDau1vEaFPGPc2mpqNCew6intrx2CoVFeRKnm4az7+HZWORHk/fMO8BWvN+1NzFLr+20W+WjbXjEm3bllndpB1YhMj359E/aebYTKV8PXXK7n5+2m6R/Zn546N5OZmUKnXJ7jXaMj9DRPR+1cjP/k0wT0mlFrhjaU4Jx3PJ1oqzvfBzjkEvDJcNbVzb8nrGKQcIiIc08StxaumTPHEZIIvv7SXGHAGIZOX1LKIlpsbtGsnKuS221t7b7ZpE+YQLSKLZ3XtqkT0KIWXnrOLkixpIMt2UKgK+1IKjL2iKEylpeXg6wuvvGJvDiG3M2cEUgAsJhMNG9bn00/n07Fj+38LfR7yKCjI5OLFPuTn/2r3fWOxP3+mzKDQWJeVXy3l8oWDVOo91dxnKkr5pD1KU7yn8fRBV5Bt57wl9/Prv11k6+ZVNG/Ti66dOwACTPDb5e8YPaYLzzZbhU5rr5XtSPDMdtUk6PJLbKLWKCIjexEZ2Ru9vpisLHVzDlfTXUFB/YmOjlb46ur1Eh07whtvOEdyld/1x347R6tPucnPwaNH+WVCB5WaOcpjOYI02gqUOVsNObtvGzYYTSaTpHp2f4sces+oLmTk5aP3rYTvc6+TeWITaCAwTF1TJStxPkPfGU/vnhZGh62jkAyDdCTDK2u4DIrqzmuvD7LbR9++3cnxq0tg11FOJ5b075bhFlzX6fk+SJhLcI8J6HyCSY8fyWfzLRGwdQ43I0MMQK1bg5+fBZYVHv66aq7dEYTMtjM4mzTktnKlAb2+Bxs2bKVCBRw+vCkpgqyTmAjFxZpSQaU+REePNgsuuZrHBHdmzpzFtm3byMkxYjCAm5uBnj0jmTJlKtev/9Mul+/sd5w6JXL4YWEiD2krHhYfH8++fUllindZw8ps8+aSJHHr1mf8+ec/VL6p4X56b6pU+aSUOg8vd2yN+5OtFf3owfaZBHQaadePao3Zat6TdT9/onZlUbup2wr/vLusW7uZny+cYcKE0RieCKWa6SHLlq3h2u8XaFBfJN1v395NjcpLAXsnKz8/PQMGvElMzGhCQqqRlHSUAQMGKByMrK/bCy+0p0KFww6vmZwfdwZXtc9di2taPhx/eVx/7LdzxW3IGWTTulny4H+pHksN0mj9nNieo62LVOXKYiW0apWG5GSp1LtXiJctXLjiF0mSGqud198iQt+7/yiXz+3mfupfaLz8qPrWQtKS4jA++JOqby1UfO/+iqH07tGfQm1Vc0QO9hH6sf3LyPWvq5Dhzdy/CM/m4fi07K4qs2u9j2+++YZzp3ahD6xFULcx6lZ326bh134gPi27OzzflOVD8W3XD+9GL5P1/RI6PbGfd0YIvRNHA++uXbB3r56FCxcweHBPhz6GahH64xat9u4V+4qMVA6Gjrwv1XwYk5P/JDKyN3/8cYOSEqFh4+UlzH8HDLAcT47WOnfu6jCST0wUT/isWY4LurYiW67k0Ldu3UifPv3KjNJk4od1xKck/ihbYVEV/rw7g8M/ZCj65dZvd3Dqh63gE0xQd3X/WrkfVXzWAoWR+/nte7kKSO+D9eNpXKcGFy+edijrDKIvd3iuFrWqzMHH62f7H4oX3r6LKSyoSkR4VBliZu5oNBpmzixwIszmhl6vNYu9OVq92UbNrnp0/jsidFePJUhVPFYOvbzn5IjIJ8tBq5Gf/ifkc415fzFhwmgCekwCCXNUaxtd55xNIPDeGd6NmUHzxk+Y37eN0AMravjHtElmGd68Y6sZPeYj4jfEk1msxdDoVTuZXXkf58+dYsKE0fiGf0TulSOqA/WdxYPQB9SkSr9Ziijc3pB6B9k/bqZi29fJP7OelcvyyzEAiahm0aLlqpGFWtHLlUKYmuJhWefywQeiSNOpk7oPY1LSUfr27YckFZpVE60nKGsz4ZQUiImpAEgOI/kZMyAoyDke1/p3uL7sf5vOnV+2i4YsaQgRyYeFdcYSlZfwxx9TzcQfJTJFy18P3+T+X+35YuF8Br09llc7tjMf8+KVGzSoV40334jkQYFEtaFLFed0+8uBuFV5ksq9pyrel/t5ZmYWuf5PKJjNmYnz8X5RuVI0nd7ItDmrVH1v//hzM3WqzUfNvmzJkuq4ud0vs4iXk/MSx4//YL5mJSXCQPz4cUoJP55ERESg1ZrYs2e/lRyudVT6+FGzOsELyhOhl2c14Ovr+VgoF1fPqaQkj6ysE0iSXMYsAtys9mH92vK3VuuBv38nhwP634pYVNZgDsKR6O6mE3z99VcMHSqohraSAcdPXqWyn5HMzCxCm7TixA/fMPSd8VSt1YTWL+gozv2d7w9tpM0L/dB5VDafh0wYmjtvFoYnQgHMOXPb5vNsFFk/bSb/xs88TJhrh4OXW8WWERT+9gMF53ZSmGspyLlS0JSdVGJiogkNjadtWyUJSY0kcuiQiLSdta5dRTFLHtB37izbT7RnT5ESMhggMbGYxYsbIg8QyclX6devHxpNIXPnKvcjiza98IKF9Vq5MuTk5NKnj8HhMS9ccP47UlIgPV0wU7dvF+f10kvifUcRmDWh4/TpY8TFLWXMGGv6fE9On44uRVMIMlFOzi/8/HMfiotTzec1aYo7+rrtmDbrLKPHLWTrtpOcPDYaQ91QFn85l6DghWi14kE9fvIqhw9s56/U+1SyGbQBKob2JOvUt5hMxWi1lsdR7ufVA+tgyL7Lww0f4RcmVopBgyzBRcHNS6R/t5ygoGCO/fSL+X1r8tvxkz680HY9NSp9jp/PCcXxv/supcz+Iq7bj+Zr9u67GygszKdHD8HoFRN3PklJ20lM1FvliMEShdqSs8RnMTEjVPu23JwTvLB57ZgYB3nlItqtXbvSqQFHfPzyUqZy+c/pwYM9XL06DEkqdHwij9n+FigXa2LRo+9X4fnkswpCUcryoWSe3qGQu735+2mz3+eq5fMxVXmaLRuX0LhBLSr7GVm1fD55AU9w69Z15ny6mt49I2jasA4vPNfI4mVa+p6aMJjHw3/yYMcshxNLxVYR6H0r83C3QLwoDanfVhCgvJp3xc2gp0IFNzNZwxXvS1kG15GsLkDz5jrGjYMVK3SkpIgcqSMUh0xMev99MTjL6JoDB1zzEz15Ut2HcdGi1VSvXkT37mWrDu7cKR4MjQanSAJnv0MW9goIkIW9xP8BAY4VKEGJXgkJaVBKVrlJcXFOKZLmczORyGTSce3aFM6e7WA3mPt2n0pAl1Gk5tdk9/YEc0okICwao8HD7EXbtGEdjFm/kbBzvVPGs94niPsbJ/Ng5TByziYo+vm9O1cZM2oshff/5MFOFbTW3lj8Xx5GocGb4pzfHQreNXnmaQKCVoB2oMvXWXndcgkJaUB09Gj0eo2qYJy9yFbZYleO+rYzT1xnKBdnxyqP/LBAyxwnKOhtK7SMbOt4utSFyDk5CTwoKZEwGnMxGnMpKHjEhQs9uHJl0H9kMIe/SYTevE0vfjmfSPrGCXg93Z6sk1t4kH4Hr6ZdST+4lArPdCDrx40UXPuBCs27knNkFW1e6MfWbQlmFIssGTD+ozGcP3+SwKhJ5veso3ll5HJVcU7Wr4uKSqjw1HOKgVrk4Lvh0zLCzFRNP7AUr0d/kL5xAu6NO5NxaDluBgNu17/nwfUf8WoWZj5fbfFt9uzZz/DhJeV4kEQhSJbVjYtbw5gxWxQCX5980p1du3YwZsx29PpsVWq2db7+yy+VOfLCQmF07CxysZa9taweYomNnc+GDVswGiUmTXL+e7p3FysDd3cDRUVGp7/fEcU8JUX8Dtv0kDXN3JFeiIDOeeFKVHf5ch8ePTqs+H7sl+7o6wr2skajxbtTDMd3z1egqNwbd2b9+rU83fgFAHbv2oTXU+0U/ehh4mf4PBtFxVbW/WgJfQe8x4EDO7j3y2F8Sp232rzQj6lTJ2DS6Ah6xT6PVrFVJLmXD1KhcSf274un46siD++on/v7+FPDSuvLVSq/TJd3Ra7Cum+UFTUDDvq2yB+fPq2k9/8rEXp5VwMhIZVLqf1zrPZpnQ93fKzi4hx+/fUd0tKS7A/0L7SyMuQuRegajaaLRqO5ptFoftdoNPb5B7FNX41Gc1Wj0VzRaDRl+JsrI/SunTuwbu0mBkV1R/plH599Gken0OZkHl5B8+ZtcL/3C5/OX0TL+k+SeWgFH4ydSK+eEezauc5OMuDqjRtmdyKNVod7Y0s0L0crJQX3+WzuBzxV19f8vjWlv2nDOsSMmULVkjQyNk8SKISE2XgZNPjdPmnBq3+/Ej/fAD7/bBGDorrDuS28G/0J+w78yKZNCQIbf3YLc+fG0qtnBMOGRbN3r8Coyg+Ss2bRWRGzvZICnUNq6k1iYxfRseOrxMZ+TmrqPd55Z5hdFGI9CKqZMCxYIBxSHGlYy+fi62t5bTGZEHRnV+R15UkhKcmAv38Fp7//5ZfVjQlcSVU5km/dvRsKCgoc0rat/87N/c3u+7Nm9KRqSZpC0jn4zYUKSeeco6uYNm2OuY+99vowSm6e4966seRcPkTqtumEdexI3ulvuffNBzw6sob0A0tp0bwNLVs0IisjHZOxiPSDSxk7ZgL+3sXk5mY7lHU2BNfC+PA2Wd8tY+jwD1T7snW0XrPGy0hWiDfXjT0EXd41jLZRFaNtG8lay0vUr/8C69dvoV+/17l27YxDev+/EqH/X60GHjz4nhMn6pc5mJeUeAJPAk9SUFjL/Lfta+u/NdqWTvdZ5oCuEfirxUAY8AzQT6PRPGOzTT1gItBOkqSGwOiy9mvbdDodr70+iBlzV6HRaDhwcB8e9dryIC2Vb7ftQ6vVcvbMcTzqteXb7ZsxmUzMmf05VUsekrF5kvkBCxwYq3jA8o6tZtDbo8zHuf7bRSZNHEu6Vw3Wro7FZDKxeeMaPvjgPbI9KzNt+iRMJhMZjx5SUJBPeIe2GE+uB8lEUeWG6A16Xm7zHMaf1iOZTBirPMOMmZ/Qp+8Atu84QL2nmph/T8dXe7B9xwFz0bV6jVpMmhLL5MkeVKniqpNK33JdRzUHdNfy9Y41rMFen8MipCSiNx8f1yYogwHi4+MZMOA1p8vfHj1EMdWWoOFKqsrWFQnEfvbtg3HjShgw4C2Sk/9w+P2ioocYjY/s3q9ZO4Lly9fxTM1qZO35FBB97K81o4Ra554F9OozhOvXrvLBB+9y++5dDh/eTULCIVrVq82jg0vx8/Wl3xuDmfvZGuoF+5B9fjcVnmrH7ZRbTJwwBs/WvTHlZ+JV7zkWL11Iws71dppGKcuHknUmgfwbF0pJbqFIOh0h9Rpx/twpJk0cS65fXXNftm4a7ZP8dmMVYoAoD/lIVKfT05UiaGr6Qtu3Q1palrNbRFLSfkJD25OWtsaB2cV+p99/3BYW1lkllVKxNJVyyir373r7/vvjNGjcmqtXz3HhQheuXBnE+fOFvDnE3bF7k/Ztrv65Ga17Alr3BP55e6n5b9vXis/c1jk9lzJRLhqNpi3wD0mSOpe+ngggSdIcq23mA9clSVqpvhf75shT1FZ50RFM64lqTzJ06AhMphI2rF3Epd+Tqfy2srpjC3G8/ttFli2ebU7HPFg/ntr+Xly7fgWvp56n+NFf6DUSzZ58glMnj+LxZBu8HiWT8egh/pETzccO9PDi3t3rdrCxjq/2UCU42f79IPUv4tct4a+b553qJk+erGP3np20b/dc6buuwaNsPQrff1+kWcqCa733nmNXJTXdcBlKNmrUaL77Lp5mzSSH6JqUFJg5E27f1pOfX4KfnydFRYV88EEJL76ofswxY4SmRWSk8BuVNeVdlW/dv1+dku4IciZJudy9u4V//nM8oLTSM0lart1YzdUrKeY+JBfwPUNCKc64i8eTrck7+S3FJUV41W9Hwa1LSMVFdA/vS81aT5gp/f75d6lb/3lOHdtk7kP31o3FVJSPqSBHQVzTB9akJDPVLOv86PBX+L88jKxT2ynOTKVS70/M21ap6Mf9u/90CmcEuS82IKDiXqpXWuKExGIPObQmZjmC3e7ZI7gK27dvtoEqli0rIN97x3T/fw9EsPzbqX/2/ffH6R71FtpaLalacoKlcQVcvGgpnFcrOcHihSVotRpMkkRxcTVu3ptMkbGGw/HB9rXtZzHDuj0+bFGj0fQGukiSNLT09UCgtSRJ71ttsxO4DrQDdIgJYJ+z/drCFi2EnggFftyRqFbxqY3s2n3IHJGoEYgeHVhK4fXjjBv/KZ1eeZ6+fbqSaQigJC+T4MjxFN69RvqBJVTqPcXMBgUNxtRkguX3vhmLpNFTZeCnaDRacn45zKNDK/B/ZTjeDTuK4xxZQ97FJNau3UpalqQKHbP+W36dn3Wb2dNHE9bVSHg3R6JOFWjadBv+/u0pj3aztQP6w4dZLjHfOnWC/v0Niqq+IxKPNZQsOflXWrZ8HpOpgDlz7CcoWfsjLEwMzJb8vY7t20to00bH0KElCkGzvXuFFG/jxhZ4XG6ugGSuWlX25DRkiMDBOyJH2ZJCTKYiLlx4lezssyp7rMT1m/8gL9vD3Nes0VgWJnEV8q79aNWfJlCcnY7emI1W50bF7h8JcbYtk5CyHiDVaKLo5w+3z8TfhnCU+eNGAjq9z4MdM9F6eBMUPhaPWk24uzoGt0pK4tuDbTMI6Pyu4vuc28LUmSvs+l7ThnWQJAmpqJH5msjko6wsdcLYokXLWb16DWFhRiIiXIfd2gphORL+sm6uaNHbv1b/Ozn5j1K55a1Wtad+xMTEKDT1ne/P/rPvvz9F9x798e72kVl0r02DPzl2wg3f7lNLJ9XxDIqK5LXXB6mOAc7GB0efdXiu4b80oPcBOtsM6KGSJEVbbZMIGIG+QA3gONBIkqQMm30NB4YD+AcEt5w2bw2gnIH27j/KL+cTyTBCxS4OmJfbptOybQ9aP9uEr5bNxat1H3KvHCEo4iPz9jI23POJVkh3rvD2kGiWfTkTkwRe9Z/DmHYbyVSCW+UnFLT91G0zCLR5KDIOrcAtqCZezbry6PBKc0RWuf9css8kkPFDPBWeeh7//L9o3eFNXmjbyO53OZqBH6T+xZbNX3Pv5s9kZ+fj6yvZDUAAesMr1K4zl5rVrTW1XYsmXKG7nz8PU6fqAQ25uYK1aTBYSEFgz2jt1683H388gZCQyiQl/UjfvgORpCIzDl1mu02Z4pxFOGGCHjc3dzIz89DrJacSBRMm6IiM1DjFTa9YIQZzRxryarTtrKwLnD9vv9y+n/Y6qY/e4PjJ3xRkNSFRUYfAMOdM4owf4nHz8Kbiy8MU7+ccXUNw5arO+3nphPHo8CoMwXXMg3fBzUs82D0frZsXugp+DhnKGbvnMnzkRO490juI/iSerhOFQW87sLpRoeJ8qlXtRPWqlRUrvhYtjEyfLqRwq1Qp2ygjKKg/sbGfl74jrnX55BduYN2Xy9Iot30eLOduS15TSh9YmmvPlCTlUq9BW+67PUVA2Gir4HM6FTq8Zxd8zpi7qlxR+H8yQncl5bIMOClJ0prS14eACZIknXG0X0cR+sUrN2j0dE3mzZnCiZ9/IXBgrOJ7d74ciN6/KpXcAamEB1IFiu79jmdIKEWpyeiL89HXbknub8etlq5jKElPQdIZCC5Nt9xbNxaPui0ovH2lTJmBWbMXsGL5Yq5dv6qI5vX+1ci79oP5PVl29/btZObM/twcrZ8/d4q582bx+YI4atSsrfqb5b8lKY+M9Bh8vX9SuWruNGmyg4CADvIZ4ko0MWpUtFP1uFOnBJGne3cN4eGSghC0Z49Q1fvuO4uGiz2lXsibJif/wYwZ09m+fSdFRUaKisDTU0NEBIwY4bifydGYJEllRm5ffKHn8GGYO1fdK9VVlyfbCD0z8zQXLrxks2UwWvcjgJKs9ld2EfonQsk+uQWP4Fr4hqkziR/snENw1EQ7nX6Z0h8V2c1hP7dmGBvTU0j9dhoaN08qtozg0eGv8HtpCI++X41GI6HzDqTa4C8V37+/Yihj3o+hS1ik0+jv2u/7qFdzGmCf89bpQ6lc6Uvatn1RQbI5dQqmTnVtpSQGZVFckaP8Zcu+orhYffUkNzW6v5qsszOZ3fLL4IIrz1R+/k1+/ln09+mz3bmfVx3vTuMdTqrz5i2keYvQ/5MI3RWUyxmgnkajqavRaNyA14FdNtvsBF4C0Gg0QUB9wHHVCQts8eKVGxw/eVXx9/adiRw5epgKHQbbfc+ndU+x3M0rwd83kKKUXwnuMYHAsGj+H3dvHh7T+cd/v2bJNlllRZSgdoIkgmqrqkVIYq222uqqutmr1ra0paraWmqn9n0XBLElpAiCbAhBENn3yWSZ7fnjZE5mMjOR9vv8rl+f574ul5NZzpxz7vvc53N/Pu9FgoR2z7dFdSsah5bdRPSLZ/g0pK4+IvlHIpXhHBiO6vZ5fEb9jNytMXmHF5r9VvGJpQx/4yNizv/NndQUFG17ifv0GDgBde4DvEd8J+6Thu34O/Y0BQ5NmD7za2IuJrFn3yGmT59ImZsf02d+zfXE+xbP2bCdkJLDtoPDSHuyAJ3OttYRVXLnzhgsu5Ibb5v+PX78h1bd4jMyhGXzL78Ik25tF/dffhF0WyZOFDDo5rhjFSNHjsLd3YvWrf05ejSSDz98j+Tkq+h0j1EoHAkNrTtoEFARO9i+fcczERQjR2qQSgUjhtqqisZGG3VNNJGRckaNekO8TkVF57h5c7DZ58orJSZ9lF+i58sJP9Ci8fPok47z2ZezeM5VQc6+H82+m3f0dyRyW3PmcPWYyiqU1z3OA8JQ3jyBXq/Dxt2XRh/9iaYgg8Iz6/AcMgNn/9dp0Ocj9Bo17q+Z02MduoaydftWrifeNxlftcdbVLSOpHubyS8KMduHVhPHwoWTzGCK3bsLK6B/AruNjDwiFkH/+kuogyxbJqz0LHEHajtppaXd4t133xFNqy2NQ6HYfUv8zj+BWNbnntLrldy//z2XL7ensvI+TZrAymWV9Gj7gJIjP5jtv/jEUvwDQ5DaeZtc9z37DjFyZDjHTkSL/bBv/2FGjgwn6kwse/YdYvaMsUSdiRW/Z/hO1JlYs98xbs/Eoev1eo1EIvkKOIGQH/9Lr9cnSySSH4Crer3+cPV7/SQSSQpCNWmqXq/Pr2u/BtiioRm2U2/fZP3qhRYVEkFgXpbfjsW2mT/3H13Ha/i34uecgwaTfm0302cuYs3K+WRtmiSmYRp/vELcR0V6AoVn1+M1ZDqVj5OtskEdA8I4cWIvuTnZeAz4EuX142Rvny5G88ZyACVxB0QNdUO0/uh2FEcTropFqqLdM0lNviCKgVk6f0Nr1dIPva4ZevXbJq/rdGpMowZr2zV/t2zZnq1bt5kIMBny1fPnSwgJ0dc56IcMEaIZS8VLASaopaREVW1MoCQycgvBwTvZunXDP3Jw0evrN0mUlVVw504C7dp1IirKVC/6hx+Ef8nJdVms2RAXNxGtVs+tW+PIy7ME77EnM3eCSb9oK7L5Y7GB3v8zu3Zs5PbtROSu3iLKytCcg8IpSz5L+cMbFJ3bII5Dx4Awzp09RJlShaq8zOo4dw4MpSz5DLkH5guyzHJbXHu9RcmlvRRf2Ia2NJ+ic3+JAYX598MoSrtIavIFXurxstn4Mv7bv4MfsAhdVRHoTVeGBw9eZckS84ds/fHrTqSlPeXddz80K4LWxR0wddLCqqyzoXXoAK+9Vs7IkaPZvXsXLVs2Zvv2PWYWfLVbSIiGSZP2sHjxUqNXze+psrJbXL8eikZjCuVKSICYWDtcw8zze44BYWQ9jKVTu6Yic9hAhJQ1DyL5+hGmTXyPG9evcClmBzYtgtm47hfy8vKQ+3Vj944VrF69RZwTZc2D2L1jhdnvGLd6EYv0ev0x4Fit174z2tYDk6v/1asZE4uMiQ+bN61A1jywTkKPU9cQimNNnYYMGOBPP59Banops+b8yfZNS0mKWIjX+6Y6LPnHl9Lg1Y+pykqj6MI2qzeFY0AomYmnsfVqhszRHZ26AomtPTn7fsR3zCrxweAZ/g1FsdtxNETw1aSThEMLcDUq1tp06CeSTupDcLK3zaVVU0tXzxJvgye6AAAgAElEQVTJom7ChUDeiGHZsmVMmrRfJG+Ul1cwc2bdg95ACLKWkza8bxwx9eyp5t13P8DNTUFWVlm9bny9Xl9vanbLlo1xdXWymIs1WKzVJRnbsmVj7t2bY3EyL1b24En210RffMBLFQ+BGvSVgd5fWPARK1csRmpjh13jduRHLsFn1AIkEuHGdQkajOpOLLkH5qFo3Ut8X+bZlIzzT5HY2OPQ3JQRnX9kEU7dhuASNNiEuJa1bRrOXUMoubwfz/DplFzZT8Gp1Sha9zT5fm7Er7h2HybeJzYdhfHWb4inyflZG3tNG0pxreWxWlhYafEha8Cv15VDFyblYf/YO9cS3d+SP2rt1r07RESk0KFDJ9Rq6h0gCPBby/eUTqfm3r3ZPH1qDuCLj5cw8zsFbuGzrM4fubfOs2TpUl59fYhFBN+0byaJCD69TkvGgVhRstsaURLwMfux6vZ/jSlqLUIPDx/Jjm2ryMrPwLlLCAWnVtGhbTtuxe6g7HYszl1DKDi5EseOfU32V3xiKZMnT2dASDh79h1i/twvKS4uxi3cPPJ26jqQ4kt70ZTk4liLxZcb8Ssu3YfhUn1TuHQbTP7JleTun4eiTU9UqRfxGjarpujaMpj8yCU4dx1EVcopCnZMF4tcxjBKAyb+5wV/iOdaV4TeuYMfel05eotj+J9F6IZtge7+O4sX1zzlZTKnehOC/sn7BmOCW7daERl5t868+JEjEsrLKygrUzN2rICGsaZ3bhy5CR6S5rWB7t2FaO/gQYGerlKBh4dgsSZotQjIhqqqPPMfkA6lgcdPNPAAnd4BbUU2CxbMpaAgD/chs7Br0oGcDV+x8s956KUyvA01ma3f8HjJ27i+8GbNhNx1IEUxW/AIGUfW1qnkH1tMedpVvId/i9SxATnbp5O3bSoOnQeijF7P1xO/Yf2GNWSlRIuTuWuvUUikMopjd+A1eDpV2WlUPknGvX/NitHJvz+FZ9dj79eF4tidaNMuYdOxH6roDcz/+Xdk9j51RuiGbZ1aAaawdRo0sCMrq9KsL+pnNm1DXNw4evTo88zJeOBAQZLCzs7w0N1mIrX7rJWeAUIZFlaDpBo+vL6rCGcs3UfFxddISHgTrdack1BW3pYlK7TYtGiGXqcjc+MEnIMGU35pB/adQ5F7NaXo3EYU7V7ifPQxJk2cyLy5Rt7JEikNBk0i5eiv4iotc+MEFK1fEN93GTCBlKO/ikRJEJjIyse3/nsTurUI/fDh3di36omNt58A2Xr9c+5fO4THkJmoc9Mpjt2Bc0AoqjsXTPZnyBmWlOlZuexH9BKpCbvOOJp2CRpMWUo0do3bUH4vjsxNk3AJDKcgagVyeydUKTGUp17EqXN/CqJWI5XJxWKquiCDsqRzJkYX2dtnIFW4InNthE1FKUUWVgWFkYsZ+cZHJvk0Q7MeoT81i9DV6lyuXt+NjW13M5f3Wle4Htv1d0o3ZonW9/2QEDWRkek8fCivk24dEaHnhx/UdO5cg2P+8ktzqGTtyG38+A8JDt5hcd++vkKK6MwZB27cOGkiPwoqysufUFBQi30EZOZ4kFf0EKiJyuV+QehKyrBt0p7Kx8lUKYtwaNWTqtwH4s3n3HUgyui/0CedIvtWDE6BYRRErcZr6EyhjhM6hdz9P5loA7n2/gBVzEY0l3fQo/coGvt1Zdb3f/LXX+tIPrUauybtcQkegkQixda7OXkH56PTqkVdGMd2vSm9doTiv3eIYzFz0yQUGhXF57fw8dhvkNn71ClxYRqhq8wi9MGDuxIZec3soWlsNj1ggDCRmq+ENtCypY8ZGclSMwQFnp6jiYszyMXWRM11jVNrchCvv16fVYRxPUX4La22gjt3JpKTs8fs8zqdDY9zJnL0lDejP3Rl1fJ55O6/gKJNLwpPreLtt8dy6NBOCsuKULR+geILO3Bz9yTqTKwocZKzYRxavR6vobNMiuHOQYMpPLWKvO0ZohCb8fuGLAToH1g7H9mcOXOsn+3/wbZ69Zo5Y8aMoaG3G3Z2NnTu4EdDbze8fFrwKCEGZfZjGoRPw6FZZxy7hGDj1hA737bYevlRFLMJz7ApyF1rHlS2jVqRfyWCK7EnkTg2wKF5gKB7LpFQEneA/BN/Yt/Un7LEUzj5v462OIfy1IsoWvdEU5SFKi0OhZ0tvV96mYysbOzb9qYkbh9SOwX2fl1FDXX75zqivH4Uj5DxQjFUIgWJlNIr+7FrHkTJgxu4D5pkcmwA6KEwNZaAzh34/bfv6dunDy9096ehtxvpD1LYsO5XwgYOoPXzfuL18HD3QV25x8yJpqryEBXlKeh4hYbejRCeyxKEyEJu4W9r23IyMh6RkJBEQECt0Myobd8Ofn4QHGz5/V27oFkz8/cVCli/XsPevbsYNy6C0lLw8dGhUEBmJmzdKqj1zZ4N3boJJCIXFwES16kTzJkjuCwplbB7tw1r19qzdetGgoN7AXLc3Rvg7x9ocd81n99GcHA38Zz1einp6X+QnPwOOl2ZaRfppbi4fUNDn9Y8fXyHjesX4RY+HefAMMqSz1Ecux3VrfN4DZ2Bc2AYqpRo9FXl6DVV5B35jUEDw/nw02k856ng6uH1aLVqdAWPsG3SARt3X5wDQsVxUZGeQNmpFSz4ZTGTp8wkLzeLDet+JXxQCO06BPLOW28Se/IAT89uAT2ootej02lxaP0CzkHhSCQSKh8lUXh2Pc5B4Ti2fQmJVEbFo0SKntxhzNipjHxjKA293YiNjuDPpXN59eUXCOjayeR+M9m2OQ16UyxDYOB7/PjjFTp2VOPtbdq/TZoIkfDGjTJOnXJk3To1MTHOdO/+HuvWrSM4uDsg4c8/V/Dii1W4uFgdYmRmwvnzLpw+fRp39wbUHssZGdkkJCRaHKdbtkC7dsKDxbj5+sLixcJYqn3sIAQIa9fasW7detzdvQA5BQXRXLv2KkqluYZ8aVkADs77aODWHTs7G+zl5UQeO4TH0Fk4B4ahuX8VTycZ9++n4jF0FjZezVDdPo+sWQCPEmN4e9QHdA/y5+SRfdg19af06mFKb57Avpk/MgcXig/PZ1j4EFITrlB+/xqOXUwp0YV7ZzNp/BQuxJx+OGfOnDWWruP/NbVFa83bx5fVqzfTs8PzIr3auBny3+gxU6erqqzAtmV3vIZ9i6bgKdnbplF4biNFF7biPeI7PELGo9dUkr19BqXxEeJrUnsn5NoqXu8/jJiYczi98jEu3QbjO3Yd3sO/Q1PwlKwtX4uFr0YfmOp3FJxahW2j1mJR1FI+zSkwlCd5RUybNsFEdiD+2mXWrlpAgaKJGVVbInHkTvp6kPQ3v1D6sxTl9UWtLvifrvf48V880wPx4EHLy+qMDAHuuG+fQPceNkxAxfzyi7Ddvz/I5XqOH49kz569eHqOFunWn39uQ3KyhJUrLXtQdugg3KCffEKd1GyByn3ZZN/C50dXf9702t24MZCHD+dbOFM/7qSvQyIV0m9/LF5oohPkNXgaMoWrCEOUSGU4de5PaXwE+ceX4tiuNxdiY5BKZbw96gOOn4jl1Ok4OjZtYnEcF59YysRJ39A1INjiGMjJySI/Pw9Fy2CUcfuYN/83flmwGPWDq2RtEnRhcg8twK33+5SnXiR7xwxyDy2kPO0yijYvsG7tIjQaDbt2bOTQwa3YtujGrG+/QaOxjt+31Pz8PNm6datV/ZNlyxTs2bOdvLzsan2hhyxe/IcRDBBGjXqrniqHb1l935KkhaFZk4MwXkWsWWPNZ3ajeKyPHy8jIWG42YMeHEC+ioeZPyKROIuv/rF4IfLmNWg6l5CJxN5IEtK81d7D3iO+E3gLSjXbNy1h5oxJeBh5J9t6Nyc/cgl6vQ550wB27dqGqlKNy6vmywqHLqHs27+7zuv4n3Asqg2i93FTW/UCLY7bjzIhCr2ygLdHjSU65jiFVWDn35+SM2twa+CNUmKH24AJVDyIpyh2G4rWPU0IIJYYdWUxG9BoNCb5KkPT67TkRy6hKvOumUFB5qqP6NimHTdvXsGhdU/cQ2rIH4WRi3EKHIxTYCiVj5MFOd5hs/6x88xLPdqjsE+iZZNpZtexS5cjuLn15H+hOltzSj96VFiyvvaaoIMycGCNy/vJkwLWe9CgmpzliRPmr1kjcdSfXOJETo6xQ9D/RuE+d86L2onirLz3yC16k/OXbonXPSc7g+XLFqC2tX8m8acyO42S2B188dUssotsxX2cObmfQ4e24d7vC5FVbEj92TcPwDU3mfDB77Bu9S8mY8DDXsHTxynoFQ3wGjab0hPLeK1nL5o815x1qxdRWV4KMjne1QgvvU5LzoH5VKbfeKZ3aetG3rQLHGaRtNK04XxcnUxhcc8/v4AmTcaQlpbCsmUbjdx0BKXPceM+t0joMe6H+np5Wqb61+zTQBJ67bVyUQoiOxvee486mdDmtomWjz0+fiAlJab4SVVFS46ffo/dO7eY+bkm34hEJrehRGdDg4GmXITMjRPMiGf5B+fj9tpYKyzjxqhS/0YilVr1VjDYZiofpzzR6/XPWTrX/4xjkaEws2ffIdavXlinF2jW5skEt2/LvHkL0Wq17N2zjW3bNjFnznw6dwli+rTJgurie4uFC3lsyTPJQ84uzqg82phY1pVGLcOhSygyz6bkHf7VojZ66ZVDeGZf4dvZP/HDj7PJLK3CrlN/VDEbGP7GR8TFnSFLqUFVnIddkw4mVG1Lkga1qdrG16a8tC92tqawqY4d9+HpOYD/xclFUL67z08//cCOHXssUuYzMuCvvyA2VsAg29piokNTXws4A4lDJnP6h16S/+68jLe12jLOnzdfe0tsLyKRuJgROK4nphF56C/Oxyfg9f5Sk+8YE3/0Oi15O77hg2FDaNtJgAju2rGRlauWoWjTC01RJj6jFlD5KKlG+6XwKbYykJcXom7YwUQCIH/vXKqUBUhsHZA5ueMcEIbm0lZUSiUanRapjZ3ZTf94yVs41MO7tPDUKhYv32txfOnUE0EXZXKezz//G02afGZ2fQ0koe3bdxrJ3b7F+PFjzXxDLZGCzF2iTElB1voyLe0WI0eOJjU1pdopSXCtWr26PiQnSx6gBlcqPXFxQZSXm6ps3kj4nJmzdlj3c9Xm4erqwa30dJN8t7ogg+wtU7D18KXBwMnPnOxzD/2CXlOJnW87kblumIPsO4fiFBgq2mbmH12s1uvNSCrA/8Uc+sJFf8xp2a4n2blFnL+Ugp2dDdm5Rfzx2/dInvMX898V6Qnk756FXqfHtlErJFIZErktjy4do1PXPuTkleDp0wwbp1b4+TXnwoXzRBzegeuA8chdfZA5uODY8VUqn9yi9OohnANMnRzyd89ixPDR9O0/nGtnIyi7dQ4dUgojl/DGiA+4f+kIefHH8R5mGZpk26gVOddOUqlUMfLtz3h47yGFiSf4cMwUiitdGT7sDSRVKtJuJ+KkL6c0ORob3/bYuPui6DzAJKdacnIZH3w8hZR7+eL1ML42MkkkTgpTRl9Ozm6eZhcgkbbHxdkB0FT/UyHky+varvnb3d2Zs2dP0bz5LZYs0fHmm0JO3JD3dHGB3r2hosIGubwVL79cwoABNZGutTymoXl7Q2kpJCVVMGBAH/78c3m98qoxMU5MnTruX5+XYTsv7wTXrvWp/rum6fQyklMHkZVbanKts3OL2H/gCBdiIsSxZNz0Og2lVw7j1GUAUpkcvVTOzcit2Di1Iv1BCiuXC8tt58AwyhJPo7pzgZLLe/EeNgvnwFCU14+hV7iBMh83uY7sSwcovX4MuasPytsXkMhkOLTsRlX2PcpT/0aj0aDVa5HIbCyORVufFpRc3ofq1gXsm3aymLPPO7KIIUPf40muzuL4src5g73tY5P9uru/iotLZ5PrGRl5nJCQwfj6xjNuXDmffw4vvlhFQkIi06Ztwt+/Pa1aNTXph1at/BgxYgBJSXoWLbrH2rVqzp1zRK32o7CwmA0bdvLnn0vIyHhE69ZNqvPo5n3p7m7Ha6/146+/trBwoYZx46CwEO7fF2ov1tru3XK6dx/FgAF9zMaGSvWQa9d6U1GRZvKd69dhxuzbuIZPxzkglLz4KBKuXGTz5jW4D5mJc0AoWVcjeXw3EZsWgeQf+R1bnxbYNGiEzMEFpDIq716iKv06jl1CqEhPIHv3t+i1aqqy7qG6cwH7pv5CX3UNwaF5AGUJp1AmnEQilVF0fAlvDP+A1IuRlN2KRieRojy3Hq26Mm3OnDl/WjjN/4ZjkbFe8/iJ34mSuMrE0xRFLGDSV+PxzL4i6pCrojfw8ZjJZm5DBtB+7bSJgTzk/vpYs+NwDAgjLu4MfV/pyfc/LBZ1zT//ahZjxnyMg729mdFF9ppPzJxlzkcfo2unlnzyyVgOR5xmxLBwXurRnq6dWjJp4kR+XbyVXbsO0atTa4s51dKoZUyePJ0WzXzMtKwN2tq3740DWpl9t6p8Fffuvk5FRQ7/RifasL19+4F6aV0nJ98x+1z9HJg0ok62ADmsG2RlrMP9b89Lp5Nw8+aHJCW9g15f21PTiQdPfsa/QyuzcaityBYxw5adhgYjtbXn6brPUSadpiBqFa+8/Aov9WjP4YObRclbA6tYW5xjwip2DgynMj2BuXMXMGnCZLSl+dj6PE/h6TXo9Xq8hs7EI2Q8Nm6NkNrao5fKkTt5mGHPDVK69s064/v5BjSl+ZaZq0cWMSgkjIkTJ5q5GRm2XV0UZt8DG5PraUwSMmZsAlRWqtFoyhk06E08PJoyYcJ00tKyxT4x1vOPiNiHRqOhouIOOp0K0KNWKzl1agOBga8RGRlttZ9rdM0V/PGHnJwcoYbzbAngiWb7S09fS1xckAXjbymLFntj06KH2I8NBk0i4d49PIbOEvvRofNAkNtSlnQGh+eDyTvyGzqdEDTYePuh1mpw7vOJYAa+/ye0ZUXYNW6H1MEVijJN+srG3RefUT+jL86k/MImfv11GWPGfMz3c6vnpWpvBaDU8pn+ByP0+KQnDB8uRLX3z+8jqNcbBPd4maDgV4ToN+EEH3wymewiG5No6vylFLZt+gNNow4m0X3Otq8pTT6Ht5W8lE1DIcLOfZpJRh4EBgbxyqthYpT8fOuO3I47Rd71k+glMkpOLsO/y2tUPr5K4c3T4lPz/Y8mo9Hbm5xL7e3HD2+zf98WXPqPM4v4dFo9N6J2c/ToQbSNOhB7Yj/NWwWx/0AEG9cvosqrHVfORtHW/w80WnecFVeQSIz3oEStzsPLqz//NpKdMWMen38uoE2sNYUCNm4UlBCNP7dmDfX67tq1VXz33WQyM5+waNEJunZ9FgLhN6vRWn3OKzf3kGjubNwKil8j7ckCzsQWWOyvn+fNRNu4o8lYytoxHb1eh12j1uJKsTztKlVPb+PU8TXSr8dg49SKvn36cDsuisKbp8TVmHOgabScf3wZts7u5GVlsXXrOhEpoboTi8zRDbeX3hWChec6UPHwBh4DJ+DcdSBliVEor0cikdmQf3wZbr1HU5ZylrKk01SkJ6DOe4hX+FTzFQVw+8IROnTuTWzc7X8doc+d+wO+vtdNVmeXLwvFx3btBImIL76Al15SW4jYhX2kpd2lb98QoIrevYXvfP65sAIsLYU7d7Ts2XOAN98cXI0RN+/nVq388PLyZuXKkwQE6OjXD379FUpKhNqNKdrJjq1bVxMcHGiyD7U6n4SEYWZjo7zSj3uPF1Ok8qXk0TWTfnTqOtBs1aPXqPF5Y47Qf7fPo7weibxBI6EoOmwW6CFn/09IpNLqFVoYpfFH0FaqzPpKQM3JkRVl8krf4eTkFXPh8m1xXpLKnYiM2J5pDeXyn4Mt2tnZ0KVjC+xsJFy/cY3w8GG80N2fRj7ulJWVkf7wDh+Mfh8vLy/xO4bvDQ0fyLWzEeReO4EOKcqo5Tg5OiJ5rosI9RJSOIKWtU1DIYWjk0h5dPEgo0d/ZAbnatm8KUMGDyc/M4tHFw/y44+/0iXgBcZ+8jE2ukpuRm7lp59+5bW+fS2ei2E7/UEK61cvxDV8msUHi05dTl78cfHGLk48Q0VBOhdiIqqhc6GU3YqmiYcDvXp9ggQ56E0LOApFa7y9R/JPYIvGf//555I60yAZGUKu8tEj2LRJkLnNyxNyl2fOCEbQz06huDBs2EhGjhzFu+9qWLIE0e3IcBPu2AFLl8LixUvp27eP1eOtz3mVliaQlxdheiCS9ji6/IWPt4fV/urZ4wUuRO6jOPEMWr2E/KO/Y+vgREX2A8oSTyGR21JwcgX2rp449xhJ5ZX9jB79IcePbOHdt99i9OiPeJSaQGr0ARy7mOqkZO+ahVvv0TgHD+PR5SO4hUwQIbASmS1lKecoT71YkzrpEiKmD7XlpZTdPk9V5h28wqeiaNUTx46voroXR8WDa1a9S+0atUZ56zxFT+8waFB4vWGL7u4huLj0EK/n6NEfMW5cudjPGRnCZD5vnpBuc3GpgZ8GBOjo2FHDuHHHGDHiLdzd3QF7Jk2aRkpKAgsWmH8nMBD8/SEqSkteXglDhgy32M9paY8YOfIt5s2rZOBAaNVKUAZNSRFIZevWCUXQzp1HsmXLlurJ3HQfWm0Zjx+b8kUAbBUX8PbyxcXFjbFjPuZRagL3zh9E0dk0n5i1cyZIpGIKzLj/Ku5exqFlN5yDwsk7+DN6rRqH54NxDgyj8nEyZUlnrLpQ2TZqJd7rfV/tbdJHDb3d2Lh+xX9vQrcWoZ+/lEL6gxQWLphNlVc7rp2LoE37Hly4EMOq5fNR+7Qn9sQ+tLLG2NvZmkToXl5euLm5c+3vU+gzEvj406m82DuEv49up+DiLgAKTi5n5Bsf8TDuGLnXTyKR21IYtYoPP5nCrTQhWjt/Ppo/fvseL58WKFU6cvJKiL+ZSkHeI9p17El80hMc7O1McvfGx2Ep+nlWbSDv0C/YN+0kPHikMuSN25MafQDXATV4dx1SbkZupX3n3mg0t3B2NNXudnRsjbd3KP82Qs/ISCch4ZZFrK8hCuvUCaZOFSKql18WcpeLFwuF0Nzc+uUxL1++iK/vdd57T8dLLwnRuOEmPHNGwLz7+cmpqvJmwIBeVo+3PuelVCaQl2dqDaWqbMite0F19ld5JWhljWnm7cj98/sIfnEkndq14sn9FFwVruTePImdb3vsK4uoeHCD/gOGsnv3ZnF1pdPbsX/fVqv597KEU7j0GI5jF9OILz9yiXAWynwq0hNw7lqTx6pIT6DgxHJ83piD+2tjxe9JJFIKo1aiMMKoV6QnkLVtKnowWVHcizmAo0fHfx2hz5jxk8lKrP61k3KxL99//yPCwnSEmOuBid9RKuHQoTuMHDmYuXPnMnr0x8yYMZc//1xORsYjoqJO0qJFislKwcVFqPu8+Sa8/75Q73F378aoUcMsjhWttoTHj5eb9o1eTmJqSL1W1ehBpyrE9cVR4jXPO7KITl1ep2P7dqTFn6P87iUc2vSi4uENNEVZlN+7jOruReybda61+puBXq8V+0onqbnXa9d26orQ/3PUf2NxLgOMK/LQemJizol6BkW7Z1Y7nIea7FNbkc1faxYh9wuisT6PYUNC2bNrMxUVpSja9KLk8l5sXb3Jz3lASVEBdq1fpPjCDuwb+KDXlPBSj5drxHP8gkRxHGPxnN07VvDF+B+fSds33o6/dhkbmQTniqcU7JqJTYfXKTm7FmdnZ1wf/03u3VgU7V6i9OJuCnY8xWXARNGv0tAMLLFfflkiyAJo3dGbQYpl1E8WwLJEwPjxEwgO3mfGvIyPF/DmtTXNjcWVZs6EmzfNqeAG44STJ6G0VEODBjtRq6uYM0cj7uPLL811YjIyDKJJC60eb/3Oyxz/rLC3t9p/5n0ZyqSJE0U0SO+XejJ9+kRTyeTWjTgRuU8cs3mbJ7B6xTw8hlqOwFyCBqNKiab0agQu3WpUHvOO/YFEW4UeKVIbO9xrmUIXnl1vZjhtQGJ5hE0l/+jvZG2ajHNgGAVRKxgUEsaps/tR3YoWXju5ks8/G0db//b1pv7X5NCF6+nu7kRWVqmIKDl9WlBNtNQMfR8VpaakZD3btu1i1Ki3qazUMGiQ5e8Y2qBBsG+fmuDglwkJUbN4sUEuVxB/27tXzfr1de8jJERdS3ir9lixM/uORCKpt1Cgc2AoqjuxYj8a6hT9wj6kcwc/Zs6czdJlyzgffYzfflvOjGkTqMp9iFThKvBkjGQb9FotyhvHUd2JFVjH59azYMFiizIhdbX/HPVfEOcKMtEzOB9hCmO06dCPE8e3iA7nYG5dZxC2uXrlgojDzd4+A7vnOnA+Pt4k9aFMPM22bZvoElxkcR/GWPGnO2fw119r+eSTsaTevsnmTSsYP/E7vH18xeOYN/cUYz6bxp0HxaTevsnaVQuwad4NB9VT+gT34NTJLWg1Gqq8O6BWZdCicUvSE48z9ouZXL18joRDC8zs9GpLcXq4FtDYq/ZV1fJs4S7rEgEtW/qwdesGE0x6erqwnA4Pf7Yx8717gnytAa+eni48BAYOFPDpwg1ZxuHDgiJibWq/casRTbJ8vGlpD1i6dAnbtx+wYHQgYJk1GiWZmRvN9l1UUsbj7IeAdRq8tfcW/DLPRI/DkghbpUqJ/fM9TCbeosjFOASEmghvFcfuMJnQnQPDKL24C51Wh4+Fmo9n+DfkH1ssTtrFZ9by1tufClyMVHDtPZqiU6spPLUS/4B+9Av7kFdD3uHXBd+SHbWKwUPeoa3/K/+I+i/41hikZGHUqDdM9HOKiy0LYBnb0y1fjtFkvPGZ0rsZGUKRE6CwUMWxY1BZWQOh/eQTNdu3/1PhLdN7QK/Xk5FhHuTqdHoSq+cl47kIqjkEx/5AERgm9qNTl/5iPzp3G8rJ03ux86hZpto4t+b7n4aQevsmVVWVIJXiGTIeuyYdTGQbKrPSKL64G11ZAQrxb24AACAASURBVBUXttDj5beR2fuYyYQ8q/2nUS7WnNVVMRtEh3PDv9uJp0yYfS4DJpDy8CFeRsgCp879Ud0+j/MrH1N4Zj3qggxxf+++M5rYs9uQ+XY024fhCS2RyrDr1J/0e3FiJK9r2JbdO1bQqV1TERlR1qA5u3eswNu1UnzCu4eMp0QrpaIsD51Wg9eI73APGUeJVkqLZo04HHGaVi0ak5hwFde+5iwxx4AwsjISUasyWbRgioXoHGoi9H+HcgEFISGhxMXF4en5PuPHO/HDDyCXQ2io+a8Zt0GDIDERRox4A0fHt8Tvzp8PY8ea6qiHhwupme+/rzEWXr5cuJENrUY0yfx4IyOjCQ5+mfz8nSxerKw2GFaSn7+F4OCXiYyMFp3Xi4vN9aOV5a9YHHuGbQOqyE4vXGsPFwk+bmrmzfmCSlUpFfcukb91CuqCDLSl+egkUmROHoAgo6wtL6Eq+wHZ26ejTDxN/oGf0FUoKUs+S/YOAalVcGoNHgPGmxyXS9Bg5K4NsfV8zmQSyVr9ESVx+5G7NRQUHe0dKYhaydvvjGXMmI/ZtHEnfYP8KTmznhnT5/LbbyvIykjBw0VCYOfWzJj9C7/9tpz4+Fg8XCT/E8pl/PhJJoxNg4yucTPWVhkzxlxDvy5D8cuXhdWanZ3Ae4iKsqyb7uZWP1PyGuGtmnGkUmVx8WIg6ekLLIyNzlYRd/kH51GlLKD44m4yN09GmXSawjPrce46iMyNE3BoGQROnty5ftgMfbdq6Q8glYo1DoPwn+/Yddg39cclKBwbV29a+LXkWGQ0w4eFW+yjZ0Xq/5+j/pdGLWPipG9o1cY0ehnz2TSzB4HHe4tNKfpRK3HuGkruoQXYeAmU25KTSxk6dDh//bUG++e7o8u8ReHOGVb3oYrZwKt9Q0VvSfeQcWQp1SyY/y0zZ0wWJu8Bwmub/lpcs9qQynDqN57YG0niakMilWHToR9nT0fU6Y0KghRnfoWW6d+Mp0DRhB9+ikRnXX7lf2otW7Zg8eKFvPvu24wYYYNSWb9oSK2WsHHjKjZsWC9+15K/6JdfCvtbv9660YExFTwt7T4TJkzEy6shUqkHw4YN44cfVFaNDkaNepOTJ0ej11fWOkpnkG+isMT6et/QDwUOvmzftop8B1+mTRvH6hXzUbq1pFhZim3zbsgqS8jfN4ec/T+hURaQe3gher2O4r934NTuJRq+/zuK1r0ourAVuZMb7sNm0+j9xSha96Lw3EYkcoEXUlu+wikoHG1BBnnbpqJMPE3BoflMHjcRr+x48rZ/Q1HMFqqe3saxTS/OnYtEp9Nx88ZVTkYdx6HVC2zeuoEZ0ydR5tZclBFIvX1TOCcL8hL/tLVs2cJECqB7d4FNbNwOHhQe8NZWdP36CQXL2s34QVDbTGXMGOH1n38WPte3r8BkrqtZkhMoLr5MXFx3qqrMYYrIppKeOVd8xTAXvTt4EFzbjVZdhURuh+L57uirKiiM3oxeo6bo753YePlRcHwZToFhpKSY6sD8sXghWokERZsXTR7UT5aPpvjKAbHvnYPCSXuQ9j/1z3+yKPoseN/tC0fNiqIGuGPm/WQeXozAyaiYBJC19WvsmwdQlnQaryHVqJHE0+hkNiRciRWIAoFhVN67RAO5lvzEaLN9GEhIx08cqilwGhUwXfqPEwuYWr2Uygc3cLfVi7AnbWk+Zffjcer0OjIHl+ql+B8gs+XSpQvofDuZF0rQY1dNqNJL5ZQ/uYXPW/PIjT+DXFtgctP8r0XR2tujR3/GuHEV/wDB4szUqZ8CEkaP/phx4ypMvlMXIsIgyDV3rjDZb9tmx7p1y7l8+W8TEgtAx47UWVArLdWRnGwqFqZUdSA1/U+y85ytFkL37T/MxvWLcA2fJkDQ7sUhc3Cm4EEKnsNm4xwYRnlaHPbNOqPKy6CqMBupXC4SgJDIsfFsSuWd86jvX8EpeBgNXn4Pp4Aw5K4+SCRS9JoqypLP4Ny5P2UXNjFi2Ps8vXmWvOtR6KUylOfW07P3KDq2asb98/vo9uJIAbbb/RUKnj4gLe4E3iO+xzkwTCS6bNu2vvqYQ8m+EolN80Dc+39BzrWT3Iz7m4jDO2gwWCDH5Fw7ycN7D/F97vl/TSwyJglFRqZy86baBH66YAFMmGB9vBhEs/z9TSGr9S2wJidD8+YC0urZsNflJtDH7OydFBVFm3xWq7PnzsO/yMptx/lLt0zGhgH80LhxU65d/Ruv4cI4KEuJRlP0FJlcjlf12FDePIky4SSdAl7H97lW4j769ulDcuJVSjJSKb/ztwg5bdDnQ1Qp5yhLOg1SKQUnVyBXuFGQX2SV/PWsouh/LuXi46ausxDhFBhKsUZSXRQ1JRbpq/Kspiycuw+j4uEN7I2s6TwGTkCv1YiuR0JKZQAZT9LFfVSkJ5C5cQLqggyRhBQW9gbq+1co3Dm9zrTQmM+msnnTTnp1ak3+3rkmKwO9XkfRsUVIJFKkTTrh5OhII22uuLzL2f8Tri+Oojz1b2HpnnSawtNr8Rw0GYlUhm2nQezeV5v9+7+nXIy3DRrUBjOD2i0jQ0iVDBsm6GkUF5fi4tKc99//wqJ+9bMitw4dhOhtwQJ4+eWXSU/P4d13x5qQWM6e5ZkFtYEDhWKdSf+7fG6RQGS8XTtt5zloMurcdFNCUJeBKK8frc6hSkUCkNy1IcV/76D8/jXkLp74OsmsrjD79H4Vyb0L/PLLEjFl0r/XSyJxZMTwwUyaOJHDEafp1K4pixZMwctNzuPHaTgaEZYaDJpklhJ0DhpMxYNrYsrw+p07ItHOOGX4b1MutUlC+fk5HDiwi+++U4jiXdby6obm6yvUWqZMEWorBtGsqKhnk9MGDhRWdWvWwIcfCsJba9eaCm+tXg1ffw2//vpbtQyB8fGblw1l8lA6tA2oc2wcPrgZh2pSlyCHPBk7jyZ4Dptdc+27DsTeQUH201smxMDXX+3Fnj1HsZXJqMp7RMGpVXgNmY5Tx764vjAKTXEOBSdX4hwQhsuLozgffczqcfxnUy7WPEUtFSKy13xC6ZWaZalNx36cOH7AxCdx3/7DTJ8+Efvg4WJ+3NAq0hMoSzqLzNmTioc3yNo0SZyIG3+4tFZaZgWuL72LfVN/UYDJMAkrug7kUU4hO7evRu4XhLbwKQWHTPNwFekJ5B74idf7DRV9I09FRVKlLBC9T9FD/rGlVFVW0mDITNxDxpFXKaF9+yBe7d6TsphNeHh4UZV4Evf+41C0eVEwOBgyE/tm/tWIlzXMnl47pWAoitbljWi8XfN3Wtqt6rSGDzKZE15ezVAo5GRlCcWoo0dNmXiG1ImtrZAyiYoScp6hoRr27duFVKo3y3HWh006eDA4OICj4xkGDw7H37/S5AHwrMkCLBtu3H1g3cPWsN21x3Dcyp6Sv32aVWXNwrPrce4aSkHUStyqx4lEKsO+aSfQabH1bkFFWQmp9+5a9Aq16zyQmAvRaLSQV6TmZvJD9h88wsnjB5BIZeQXa0y8J1ctny961L73/iQkGbdMxq95WnEFchu7mvH9yUqT95XR6+nSfYjZ+e/Zd4j33r/Ckyc1x3r9OvR57Q/u3k2mLs/akJAXiIuLqVa8dEIuf3Z+u1kzYeyUlgrplX79akhBz+rb8nLhoT5ypBBQVFUJjln9+wv/azTQr5+MhIRrFo7XvPiUX1hq8XoY+3qO/nBy9bWfLF7bhh8tN0vpqqsqTfyDDfvbf/AIGq0Wx7YvIndriN1zHQWYY8RC7Jt1QebkTtndS5SeXcfb735pdYwagCTW2n8uQv+31P/biaeQeD9P8cU92Hg1J/fQAnLWfkLhyZXkHlqArXcL9Fo1ugolrX1cyTvwk9kx5R1ZhK1PS5yDwqtd2+cbTcJ6Co4vp6okD89hs3Hs+ArllZUmqwHRxahVT05GHcTLpZw1y+ehk0hEuJmJwfSwWSaR04Xzx5k0cSIL/9jC7t0R9OrUmuKji2qKJ82EwVMatYzJE1+lS5faZ/DvInRrRcamTTUcPmwqQ7p2rQBjtFb0+vRTAdliayvoqBu3+k7GJSVCPnzBAg3x8TqTYqmlIlztZmq4IeNJzle0fn5AnYXQzh38GNi/N5s37eRF/zZ1SjeXXDuM1MEFh5bdAKEQWnL1EDJnD+yadkRfXlqH12c4Emdv1A4u7N6xArUqkzXL51FeoaLc43l271hBr+C2JjIW7iHjKNZIuHopksqKEqQKV3IP/WLx+OycPXC31ZO1cxaZGyegTDojrjCLji3CycmZ5xoqxGKvsWRGkXMAP/5sh04nTOYzv7Mj27YNb707Dp3OeMxYpuMLtP50PvtszDPlco8dgy5d4MIFYXI3pGeGDzcvkNfuW6m0JjAwwF737xcChv37hb9HjtSKMhPPitA9GjhblBAxBjx4uMqprChBV6WyLK0Q8Ss23s2RujWiwYCvKNZIqv1chf2tXfkznsNm4zFwAlIbB/KPLRUVFz1CxiFTuIK6ggH9B/HG8MH/OkL/z+XQ/y31P/1xLk9uxeJVLX5UdvMEvm4u5NyLF19TJkThpnAmKzuDBgMtmFBIoOreZSrvXaY0JVowtqgm+ggwo8N4DpokyF8emC8o4XUJEfPhOft/Quboivvrn6F6cJ24c0fRIsG93+dUPLiO8nqkGfsPTIW51Do7kzqCJWKKTqvn3qXzhA0qMaH//5scelpaCiEhQ/nhh3IGDNCZ5LW7dKnJdXbtikgCWrtWyGFbi7a9vaGsDCIjTXOchw7VLxd/5oxADjHkTFNSavLheXnPFmIyGG506NSWe4+XcOKc1Ewm4t/IM6DXU5ZyFkWHPlQ9TaXiUQJOnV4je9e3SGRyHPy6oow/ikOrHrgYEXyyd81Cr6shjUhsbKlIu0apRsLxA9vRSeUiJTwz7hiP7j/iROS+GhkLozqNQ9dBqG5fwHOg+bhAr6ci+z7FuZlIJCBv4IvyhjDmSuMjqFIWo3dvxvULkWgbCgSozOxyNv1VXTcICCPv+t+kpxbx12Y7XMO+xykgjKyrx7HVlfJCzyDqU3tp3bot06ZtomNHjdX89sqV8PixEAgY6P9ffCFsG8hqzZsLJhrGbedOge9QW3oiI0PIwS9YIKRjTp8GpbKSUaOGmeTQi4v/pqgoxmSf+UXPce9hGwt1lFCxDrFt23oce4yk4uENPEPN5w69RIIm/wlI5aCuRN64nSjWJkqSiHNJe0qvReA5sIYhjERG+cPrZGY8MSMT/ZMc+n+W+t+zRw/eHvU+7h6CH2IjH3d8n3ueSRMm0KF9WzM67Lo1v0GTmkKlfTN/ClJicAuZWEPLldtSmHxWFNep3WwbtUbz4Ar+fo3Iz8mkgayK4qRo5I3bVSuiCay+7N3foldX4ODXlbKkU8hcvMjd/yNIJNWvncZj0CRUd2KROjjhGjwcl25DKEuOpizhJM6BphjAvJ0zmDzxa8LDB9dLJsC2USsKE6KRa2oXRdv/Y+r/3Lnz8fW9YcK4MzQXF2jRAr79Voiu27QRbri9e2Hy5Lon5oYNhQn9+HGBsefjo6OiQrgZr1+vufGM5QNcXMzdjxo2FCK2N98U/q6PC82qVTBlihSvRufw9mpoNlZqSzIY3KIK858+87qXJZ7GxrMpOmU+WmUh6pwHaAoz8K4ulqnuXkKd95jy1FiQSMk/vgzHDn0oiduPKvVvNEXZFMfuoEGfj3AODKc8PQGXoDBK4g5g36wzUkd3ChNPsHTpalHGwqAjImvgS8GpNRZlnA3Hp0yIQldegtTBBU1RpnhcJVcOItFp0ZTm4T38W1Fe4mnaVWR+gSIlvfROHBnZ9rgMEK5B5aMkipPOci3uGlO/nkJ9JBjc3Rvh79/ZopPUzp1Cjvv556GgAKv0f0OB/KWXasZZcrIgCWFrC3361LxurCUzYUINi1kqhRkztuLvH0irVq0Ae4qLL1FUdM7kuikUXWnUUHB3Wrl8Xo2Gj1SGjW97nlw5hn2XgRRf3GP12ts1ak1Z4mnkrj6obsegS4/np3m/4ufnx9DwgVyK2k/WlWPYGQK6rgPRFGWTe3A+ErkdhafXINdr+eCDT9i4YZnoaPb08R22bfqDoeEDaf28n4H6r7Smtvifi9BrP43qiqaMP6esVJgJ6dSWp80/8ht2Tdrj2nOkVWleLVKyr59hwLCpvPXmKDLvJ/Po8lFRj0PIx58WIyrlzZMorx4yqXaXJUShKclDnfMA+2ZdKEs6ha6iDNXtaDwtCCfp9AJyp3mrIHLyiq3KBOh0OqPjlJN8Op6RI7Tifv5NhD569BgzNIpxa9IE2rYVJslDh2DTJgmVleYRUu0m2M+BRAKdOw/nzz8fEx9fRXq6gFIxvvEMEZlUKiyZp0ypuVkVCkES4P33hb9dXITIbe5ccw2YXbuE45wxA9q0sSMxtX+dY+r8+WgTOYm4SxeQNO0kpEWMo2sjSjZSKaVx+3EJHkpF+k30VeV4DjQKGqRyKh8n4dTpNUri9uEZNgXV5T2E9BvMvaQrVDy9haL1C5SnXcGlx3BsPZ6j4NQa7Jp0pDQ+gorbMQS/9CZ+LVrTrfsr3Iz7m4LE0zh2CSH34HzsnutoNi70RuNCU5xNVdZdHFoEoVUW4vbqx1Q9ThHUAFt2Q69V49b7fWEMGSGx8uIOUHLzFPZNO6NVa3F9cRSVjxLJPTgf+2Zd8HZQ8+Xn7yGRaOs1vgQkzFCSkspZtOg+a9eqiYgQJvIpUwSBt/DwutFKJSXCJO7rW9O3334rkIwePxZsC+tCTgUFQceOasaNO8yIEQOQSm+TmjoZvb7K5LfyC/24l966znmkIGqV2bXP3jUL9DVzB1IppVcOgl6LwsGRTl17E5/0BC8vL7r37Evi1Uvk3jyBU9dBYnrWrklHSq8eRAoMCn+L3bs3i5InUpmChb98i7ZRB84c2saePTsoLikn4fqlBnPmfP+Dpev2nzO4qG0yYO09S5/r2PY5fvn5O87HJ5qZNOeuG8PoN0dx5HgkJVopNh1eRxm9nsmTp7Nv/26ylBpsOr5u4pSurcg2w4ZnrB2LXeO2JkYCBYd+xrXvp+JnCs9tFO3o7Jp0IHPDeDTFWVaFk/Q6LUW7Z/Lu4FDefGs0UWdi2bV9BdllGmw6vI4qZgMTJ33D1u1bKdZIq49zLfN+UNK1a81+vLyG0qHD1uq/6mcEUV+jif79hejou+8UgISlS8ueaSjw1VfC96KiFPz66yKmTp1Sp2vN118LRS3jVI7BOOPgQfP9Gww3NBoJLi56E0MOcEBqJ2jdWBorxn1roPD7ubuSnHIDmcdzOHcZSEHUShzb9absdgw2DXxxDgqrRiOEorx5Aq8hM8S6BlBTd6nF8lRePYRD2hlyc3JxGzxddA+y8WpuYjaetXkyL/q3ZcjIL0TJiOnTJ4ooFmOzFif//hSeWsU3U2ezdfsmclRa7P26Vlsr1jgUaUrzQafFa+gMkS2taNMLW+/mlBxZyKeff8VzPluZPisTj2E1jGp5g8Ymx1aydxazv3qXKZPHWRhH9TMa8fLyEV2q+vcX+u9ZY+jDD4WIHEClEh7eIEMikbFgQRXnzgnv12UEvW6dHInEl48/ro09FyCLcrvtSKRtxLFhaR4xXHu9phLnwHAKolbi3u9zlDdPAAiG8idX4tAymPIH11C06o6vvoAvxv9I104tTPqytmNR1tapyJU56PR6XMOmYdekAzkbx6NV5uM+ZGZ1X05CV1WBrqwQqZM76vwnErOT4f9HEfqz88467sQeQ6lU0arpc6RfPEyX4FC8vHyIv3aRgI6duX/hEB9/+jVeDVua5NJMUAIJUWiV+ahSosV8eG1JzcKza1G07SVEelIZpdeP1uTjrUixao2Et4zrCLfP7OKTsVNp1rIzWmmNWNT4Cb3o1TPV5Bz/TYReX6OJM2eEyba0FFSqRhQWFhEUZP07htTJF18IUdLXXx8nNFRnMbUDQkSmUkF+vil+fOdOGbduSVGp5CZL98hIGy5csGPXri189503AwdeMTHk0GplJNURoZtILVfnqJ9cOU6DkAnIFG6UXN6HjWczKh/G8/aoz7iTcJnS1IvoJTKqnt5GrnCmQd9PxUncvqk/uQd/psErH6Bo1dPk3GwatiLj7BbsWgaLeVT7pv5mZuMSmS13zu1D0aAdjx7cYuGC2SbwXYNZi76qgpLL+5A5OKOprOJuajK2zQNRJkahaFMz7uyb+lOeFofnoEkmhuYlcftQp11mxPD3GDxoBd/MKEbt1h5V6kUc/LqgaNUD5fWjOAcMElNBOjtX4g5vYsqkT/7R+LIm/rZhw7NXeUlJEBMjRPJTpgif79MH5HIZ9+5JiYqScfeu9pnpPx8fHUuWFItpO0MrVvZgy4FPsLVt8sx5xHDtyx/EUxp/BNcXR+HSdVBNf8Ttw6F1T8pTL+I1bCYugeFiPaSsrMykL01WWtXG86Wpl2lgtNIrjjuAnVENz76pP6q7l/EcNInKx0l8+/X4uRZO9b+ZQ7eW76zrc/XJO+fFn0Ct05P56C6KNr1QZSRy8cIptI07oit6zIxvl9D3lZ7muTSJhJK4A+Sf+BPv4d/i9sqHFMfuQHXvMi4BpvnwrB0zcAkeSlXGHcqSTmHXpD2O7V6mLPEUZUmnhLxq5BKk6grcdcUUJ0WjRYIqegM//rSQrp07mtQROnXtw6u9u9PQ2w17e1uGhA/g7VHv07J5NujOm/z2v8mhZ2Q8IiEhyaLCoqEZ57WzsnQcO1ZEWhp07vysPLZwo3l7w/bteiZN0j0z726cL09OhnXr7Dlw4CCPHzuwaNFd1q6tIibGhe7d32PJkonAJIqLz5rtq6TsBZr7vWt1TBlLLRtrXdu4NcTOty22Xn5U3DjCokV/Ehoahq2DG9fiYpDY2INOg0f4VLTFOeTs/wm9Vk35g3gcO/dHlXIOmYsXRXu/Ra/TixLN6sJMVLfPo0r9u8alplZhPP/4MmycGtDS151zZyOo8m5nssQv3DsbnU4QhXLpNgSJrQNp5/dh93wP3Pt/iWObXpRejaAs4WTNbwQMMvsNz7ApyF28KbwTS7Mmj4g4AuqSAuybdkJ54zguPYZj497EJBVUeSeGg3u34OfX8h+NL+O/W7duybRpm+nYUU10tGkevHbLyBAE3xYuFFZstWV5O3XScu6cDaWlmnql/4zTdgDXb47i+x8e0Kf3a7zQ3Z+G3m48fXyHDavnc/nSGYs8GIlEiqJNL8rTrmDr1Rw737ZIJFLsfNviEhSOQ4tAytPikDm4Yd+kHXqpnMLEE6Q/vGPSlxK5Hcqrh1ClXsL+uY4W3aUqkk/haacj59oJo7y7MF6UN49bndD/c9T/f9v27Fpvhl9/8ud7ptTaboPRlubiNfxb3AeMo0gtQd76RZGqf+70YXF/xlICysTTFMdux7GtAD2syrgNOi0e/T43Ow7noHDK78Xh8sJbaHIekntwvuhEomjdi+K/d2Bja8tb73zB5s17eXfwIDSXdjD/598JCLSiVPV/sI0f/4VVN3UQJtWjR4VURkaGUMj8/Xchl2mJ1LF2rfD6jBmmy+ny8vrjx2u7sr/6am8WL/6DnJxMNJo8cnIy+f77cAoK3kajqY1hlIN8Lo+zzQ21jVtOThaVFeW08HQTIYrGJLLSqGW8MfITugYEE3/tMmtWzEcvlePQPACJjR3KxLOiaYFD8wC0pXmg16PXaik8OJ8JX4zDI6sGalt+KwZs7NFXlVuFHDZ49WOcuo/g7OkIfp7/O420eRTsEPRgSo4sZMIX43BMj6Vo9yyUSadRnlvPN1Nni+O0MuM2lGSjyX9E7oH5Vn/Dvqk/jgGhZCkrmfODHK3eDu8R3+ExcALodWaQOiRSZBJ45ZWX6+7AZ7SWLZtXywYoaNRIwuHD1j978KAwkdctCKfFyUn+D2GsQlF+1uwjFCiasGnDYnQ6nSj5UFxejrx5N5N5pLY8g3PXgUK9Q6TvH7T4nvLcOkZ/OMGsL1XRf/H1lOnIldnk7jeHT5dGLePNt8ayefM+gtv4WfyMtfb/m5SLcTFDh5T8I79h49kUTf4TI2rtStxeegfH9q+IqJeyxJO4dBuCVi/l9plddOraxyJ8slPgADR5aeRePkRpQhReQ2dYqXa3QnnzJGXxh5FIpXgMmizSvg1Pcj1SUi9G0q5jT7wa+tVbU914W6O++f+KHrq7ux3+/oGMG3eUggINjRtbLjK2ayfAwtq2FQpZTZrUwBgXLoTNm4W0TLNmQmTezsgnOCNDQLvs3StM+LWRLYaWmSlofJw750T37u+wbt0KgoPbYWkJn5W1leLiv03OX6N15vbDDWTlNjejcBuPFUM6rcq7HRm3r+I6YBza4hyxSFWWdAqH9n25ezGSNu178MN3k1FVVIiF8PK7l6jKvI1EIsFrqCAZoUq9SMX9K7i+MBL901sMeeMzE6jtiJEfce92ElWqIjxDJ1v2KL12hMo7Fwh+cSR+LdrQrfsrJh61tdNuQS+MILjHy3Tr/oqYnrOxtaFKo7P+G0Y+qFpkKO9ew8Ggo15HKqj8/lVmz/yq3kVRa/DGVq1aMWLEUB48KGT//hQz6r4BfhgRIYi9WRsrIKRSjh6VIZPJ6r3CNODrXcNmm/iEivIJAaEUx2ymMvUieqmckpPLauQZ4k+il8kpiFqFU8fXKIn+C6oqqHx6h/K0KyCVUnhmPZ6hUyiIWIiDgwJFgzY0ea4Z3bq/QtK1q2THH+XNUZ8ilTty+VI0Hhb6SafVk3oxErmNIxERu/8f9t48Pqbz/f9/zmSTPSILQtFYWmrXoKpalCQSoXQRSpVWUSG2iCofaomlFYnaaREJUbGLLfYQsS+xxx6yTvZ1tt8fJ3Myk5ksVD/fd9+f3/V4eJicc8+Zc+5zn/tc93W9rtdLD2JdmYf+X5cU/Wv7FrZs2ciPYycQ+ddWkvPkSOo2p+DO64Zt7AAAIABJREFUaWq5+4lJLE2W2bGfkKTI2beIkT8EMPCzvpUev6/3J6jrtRGTosIyNgSrtp4ipaZ2UrSiJKhsayBD+3vz5VdDX+ua1cotqBW6XtjrJEU1fycmPuSLLz7n3r07opq6bpJRKPEPDdVPZFW0HcpoVN3dwdtbQ6MqFJbs369LobtunQkODsMJDl5Q5fk+evSLPluedBhSk6mV9pt2ckqtUpK+exFm9VtS/Pwmjv3Ktlm27o36xS2G9vdm166/yLZpqJMIT/1rNkgkOA2YiYm9i0CkFbMGiVrFtICZ9PbQHUflE5zlTa1SkrxpIu4fvE9Pz69f+XkA2L5jN2tXBVX6Gy83TURVkI3dR1+Tf3IF/uOK2RRuRlqRCw59A3TU6UGrWM7WntlTfvhbSdHy+6Kj9+HrOwx392K8vNQ6dMteXpWPFRBWhe7uEmxtzZkzp6DCZPtPPwmhPBcXGDbCDJllF+w9Joj3Mmf/Yiy7fSv2We71IxSd2YiJaQ1mz15A23ZuKJVKkdv8ww+6cuLUCYYMHsqGDWuw7jMZedoTci/vxbptH3KvRqMsyMKiSSdqFiTjP34iCxfOJisrC1PXjtjlPiI9PR1bb8PhYbVKqZMULd/m5cYJFL+8bzAp+h/Hh14dTurK2r3T8iN69XPAuX5zxo5vTvjGEK4/uIjLqHU67bWXnylrRjLw829JzjQ2eE6av+/duUZxcRGqZwk8DxmMTccB5MRHUbP7CDJPbibnXCQ2nQaSd2W/GJ6B0kRpdDBW7X2wau8lCktv2bKRd1p+9FrX/Kb50F1d6xIZuRY3t14EBRlGomRlGQ6baLheyiMNtNnzKhLG0DxsWVkQHW1MfPz31TpfQyXcaRk5JGc8BiruNw2fuVqlIn3PYswbu1H44DwScxsUOelkHluHeWM38q5GI61hxaZN65kweT6/hy4gdfMk7L0mC5S5RXmYu7qREb0M2y6+ZB75HZBi3qwLm8M341TvPaRSqUEedRDGRPahECzaeotjwrpDX06eisDMvr3Bc6/sukDg7y7/G7IDv2HZvq/obNh06Ev+yRVwcQXz5xTTti2EbQVVSRFpuxdSd3iIzjE1zwnAr8G/M2mi5ia/Ot9++X0eHh9w8eIZQkNX4ucXQX5+Ab/+WvVY0TgOAj2uJT/++COTJwfh4yM4Dc7Owr69ewUPf/BgC1xchN+eN7uYOfNjeRnxUhSSqfV1sPh7RU+uk3/qD74fHUhyprGoPwBl3OYAWUW2rFu/Snx5mjdsg6lTI1JLwyNOA2YIiKHNk5gyaQxqqbG47fnyIVg0dqt0flDIizFz7ajTJuNgCNbtvKESJ7xaE7pEInEHliHUlq9Tq9VB5fZ/AywGNAW7y9Vqte4MWs4qUiwq/7myfVW1u3zpvEDW5T1V7/et23mTd+0Qli0+EUm3KlMi0qiXWHX+iuxz2zFv7EbO+b9w6DcNCVJQlmDu6kbOuUimTpnBjp3beRkxDbOWvSk49QdffP4t8fHHSI48h8l7n1apSFLVNf8TikWurs0JC9vCkCFDSgUu5OLDER1tgqmpnORkfU+8Xz8BWlherag6ZFyenjB/voTkZHPCwsJKyZTKe3n656tS6TsojrVscK7dUPzbUL/99mso4yeMJm1nLE4DfhZgpRv9UcuLkB1eidNAYVtJ6iMkUmPszKHHx52RZYxg9fJfSImchbqkQIScpYQHkrZzHhKkOipG9xLO8OVXQ8Xf/u3XUAICJ5NVqlilA5ktHRPakNnXeR78JswU4K7bAjFp0YuCU+uYPN6brZGHyHh4ApP3fMg/WTaRAzg49OGXWZ6MGDWZWp/9rNenVm09yY77C1N5Ltt3hVdyX6oeX4b2aSgD1Go1GRmbadFCrncOUCaismtXmbpVdLQJffp4sGxZCAEBgjc+bpyQh9GsMAMCIDi4gE8+EcZtvXqwMrSYxb/WJnb/Yp3JHAQhmYkTp+Hu0VdvBQRl/T1vtkA1IotZh6NPAIUP4sk6swVJDStQKZFa2gs0H95TSNsVhH3P70RP28ZtAHnxfyGrZH6QFOdSM+8pKZsmYtXOm8zj6wXaiQu7UMheGOwjqMaELpFIjIDfgU+B58AFiUSyR61Wl5fR2KZWq3+s6nga+6c8dM1njVJQdeSjrNt78Tz8FAETv2ZK4CKcnF3EY2zftp7vfghg08YVqB3fFqvFNDjfrJObUGQla217wp37jxk7fg4bNqzjyfkIRv4QQHKmMWP85nAiZi/HYyIqVST5f+Ghaz57eHQjPv4UoaGh+PtHIZPlYW9vha/vZwwalE909G5RrQbKZMaUSiF23revQLDl7CwQdv3+O5Vanz6wb58xV66cKlUa0leX0Ta1OpfExNk8f67rSQKkpBeTKnsMVD5WSkqUWDT9QFQdcvQJIG33QpwG/lz20HXwITs2giwTe2bMmMa52BgwMkFqZESNxoKHn7xpInbdhpF5bD32n44CNSRvmojFO10Nrr46dRuGIv+BcP+7+VLnrdaM8XuPEzF7OXRwMyNGTcWohvNrPQ8Adx9lMyXgcy6dm8NfOx8w/5di2rQJp8cnsCNKQuSOsslcKrWmZcswrl4tYuz4H3D47GeDz4lGLm/oF3355JOO/N3xVdG+8PBIgoMNT+Ya8/QUJuyxY4XJOzraGC8vwfH4+GP4+GN9KUOA+/d1XwTXr8OJUxex6xuo19a8rRdh4WE412tJbPwdnX066k5vd+BS3B7Mm3QmNWouypw0zOq9S8nLB5i7vk9KeADOvkGY1qpP3W/L1MeKnlyn8GIUo0YHEn1gP09iVjNo8GiKJbUZ4zeH7RGriTu6mnYdvRk8eDD7doVx7OhqTG0dQQ2SvHTUimJZRX1UZQxdIpF0Bv5HrVb3Lv07EECtVi/QavMN0OFVJvR/Ioau+Xv7jt2sXrkAM9eO2Hv4ibHu9H1LsHb7DJtS/GfezRiyz0Tg8sO6UnradTR2dWX16s3s2LlX0BZt1IG6qnQ+/3I0ixdOwaTR+zqx1LSoudj3Gq0jZ8elSKJ2Hn6t66ruNb/pGHp12iUmvsDNraMYr9SWGfP0BJUKIiLg9GmBywWESb2qoiV3dwkKRV6V55SXd5urVz1QKNL0jlNQ1BhLm41IJHZA5f27dt16IrasQmpXB8e+Uw3GjVN3zMG+1xhQq8mKWYVKLcFpwAyMrGqRtnM+iuxULJp9gCLrJc6+QRQ/vSnEml3fp+j+ORYvDqV9h06vNX5fp51aXUCWbDy2VrqJYkPm7OxLs2bLkUrNeLelG0nSutR0Hyc+JwUxyzFr3QeLtl7ic2J6/S9ePNWe4P5+DF3771cpcPvqK2MOHjQlLCyMoUOHisVKFVlSkvAiiIrSTopWXeT3TsuPDPa9dj5E49g1q21Hwu1bOJau+pI3T0aZn0m9MX/qHD9lzQj8fxyPk1Ntpk2bgMnbbtRVpTPG7xfUJWlMD5yIUaMO1Cx4waaN25BKpVy5kci9hFi2bQvj55/nMuHH4ZfUarXBKpDqhFxcAG3W++eAIXzdAIlE8hFwD/BXq9XPyjeQSCTfA98D1LR3/Ec89GOHo9i9ewtmdd6l5OEFZBHTkNZtTu7lfRjbu5BzdhsFCcex7tAX2eGVmNo6lnGN+wTw4mwEAVP9uXw5ThSlfrE1kL379zMlYBG/h84heaM/DqUTQd2RK8VzKHpynexjaxn94096nvereFr/Lz30ytq5utYVNUc//FDOiRMK5s/XDalMnSr8S0gQPHZDIRpt08RBy8674vO4ccNHbzJXq6U8T/Vj7xEXunbKArKAivtNMz5qfvoD+deOkLrjF1y+W6VzzLRS5rzsuO1IC7OwtLSmxMYFWcw6ARGVlyGGZlLCA8k4EKJTVZkme86pM3EYm9d+7RXmq7SztjjPW7UXYmtVnk5Z14yMatG69XZsbJoAaqCIPVEr+XLwRB7/9RPSd3pSFPsHK0Lmsez3P3m04xzSdz+l6MyfbN+5iurco9f10O3tLUlOrnxiTkkBExPYvVtNSMgCPDy6GeTdL2/adMrBy80wbtSl0lyGyXu92LJlI736OegcRzsfooE2SiRSHPpOJXHvIlFXAcC6nRdZpzfrnYtF276s37CWrMx0avoEivPL0iWzSX5xT3xJpG0JYFlICN0/7Uds/F26dvqIWS2rho1WZ0I3lE0t79bvBSLUanWxRCL5AdgIdNf7klq9BlgDgof+pmPod66fYPeuLVg064Ii8yWm9nWpKSnk+eW9WDT7EEXmS9TWDjRxtiX5YiRjxs1gy6ZQMmPW4eAzDfOGbTC2ceLW/sWiKAAgiAKcj6BXzwX0+OQg0wImCm3Kxd9yj4Ty1aDvGfhZXy5fOs+pQ6vo1yuUevUbAKAsSmFp8CIWzP9NpMR83Wv+J2Lo1WknaI6e5osvhuLhcavS+HijRrBnj8DZUpEJMmFfVuuclMp8ve9LTTfS4K12dO1UcbxT8/nypfPs2b0Fi6YfkH06AlVhFk4DZ+kd06bjZxTePQdSYz7s0pVOnboStHA25k0+IDNmTZlIikRKLc/xpO9ZpEPaZNHGk9MnI/GfMMHgeRj6/DrtpNI8WrjOAfV5vWuQSCywttZ4oCpq1vyUBg2mIpUao+0ZN2nSggvnjhG8bAW/Bq/gr10b+fjjngwe/DXBy5bxa/Batu/cUhpuef1xU9U+X98vdMSnDdmBA0LS8+OPlUyZEki3bt2r/SLQ4NA1SdGUbcmYtOj9SrkMEPp+xPCRBC36heRNT3HwniwK3Gis6Ml1Mo+txbH/dL1zsWrvRcqtY2BbVxxDNu7jSdurK+pj0cajwjFUmVVnQn8O1Nf6ux6gE5VXq9UZWn+uBfQrJ8rZm46h74jaw+mjG8XEVEp4IEpzO54/uiJyW6SEB2Lq0IDnSXf4JWg9p+Nu8eOEX/hzQzBZZ8MxtnE0mPXOO7meTt18uZbwmHt3rnH5chzWH3zJyz/Hi546gNFb7QgPW8GeXWEUFRdi+rYb06ZPpq+PL3+sC0ahKMb07Y5Mmz6Zjt2GVXgt/6keusZcXZ15+vQpEyeW/31d++47AZnw0UeGE6OaOGh8/DdU5f2lpkahUGTpHePug3yK5VXnITQ5FccBM1GrlBTcja2QW8emfV8K757D1KEBJ04cI/bsGRwHCOMqeaPAopmc+RKHPv6iCIbGip5cR3Z0FR980L3SVdqr3nOJpIh6zr/R/O3zqEod8eaNFPquFeDsPJhmzWYjldbU6kMLdLHhiPuMjCyYNPG7UgRLAVCEkRFMmvgNkyaO0ToGOt+r+nP19/n5DcfNLYLOneUVjpX9+8tQLh4eckJDg/H17U909NYqXwQ9egifNUnRVeubceRw9XMZIMwxs37aR15uFg79ppOfcIy0qF+oO1J3hZe+bwk1GrUTRXIyDoZg1c5LDPVate9L1tE1pG2ZSk1Pf4MvhIpW+lVZdSb0C0ATiUTSCAHF8hXgq91AIpHUUavVL0v/7AvcruqgbxrlMm/2USw08lxanpM2FtyqdW9kR1Yyf+lKHXRJ926dBDKevYv0SL1yj4QyceI06rzVWiS+t/7gSwHpUgpb08RQ8xKOo1aryc3PEyFKGREBrAr9BZXESNyWFTm9VEKvjDbg3+Kha6w6S91WrQRmvIAA8PaW4OWl1kHNREeblCJbmlf4WyUl6dy48Rm5uRf0jl9cUodmTToikQjsTZX14ZKgSSKkL3mjvzhWQBcSZl267LZqI5AtSc2tsSvlQgGw7uBD5rF1SE3NSd48idpf/yq+0IueXCd151zM327PndtXDSKYXnWcp6W+5NThIGKOnhbRGxos9vnzuoiOAQPs8PTch41NW6qDFHr1ff+ch66NsOrUqQCVCuLiBMZFc3Mhfq490Xt4yPH33865c8dwc9tRrReBxoyM4Mexgxk3bq5OHqJtywl0/7Sfwftw+dJ54k5FYNyoA6rCG6hRU/gg3qAXbv1+f7Jjw8m9fpTMY2up2X0k+TeOUHDnNNZtPSk4+QeLFi1j27atVa70y59HVVZl6b9arVYAPwKHECbqSLVanSCRSOZIJBLNL/pJJJIEiURyDfADvqnWr79BKyvVD6xQPkx2ZCU9enjTtp2bznevXb3IqVMnsP5khN5xzdt4sSMqEpVKxdLgRTpIl1oe41DLi0nZPFksAze2ccCi2QeiOpGthz9Su7o4DSjTHjRp0YvjMXv/V/rlnzJ7e6tqlVzb2cGcObB/vzH+/tb07g2jR5uwezdkZRUydOgwxo+fQmLiQ73vFxe/5Ny5NgYnc6SDufd0lTiZV2Wa8uusbdOx6zYMheyloNV6I4bUHXOw7TKIgnuxpEQIpfoaVsV6o//Qk3CrYWJMcdItzBt3FPVhNQU4Fk06U/jwEtOm6YdyXtXizh4iZNF3FBedpmNHoVIyK0vQVX38GGbMEPQ1Q0PBzEzK2LHF/PXX9VIpwQalUoK1GT9+CseOnSzdXhsjI4fS7RNITHz0t8/zTZmHR28WL15CTIxwrcuXC9e3erVQvHb3rqCIdf68EBeXyXJ16AQ0mqYaGgoNfcSMGXaVhmSqMg0tgF3fadTyGI/U2IT0qHl6rJoas+nQF2O7OuSdXIdDLQcUt2KwbPUpyoynyM9tYf6C30h8cJeLF2Mxa1c2aWuoJ0ybduXkqYOoVBVXv1Zk1cKhq9XqA8CBcttman0OBPQxQJXYmw653H2Uzdjxc1gV8j88MJDoyjgYgkXjjly6eokrNx6KRR9VwRst23nxYmssGzasZejwiSycNwlzVzedhEha1FyMrGpi5vIuNh0HIosOIWVLALU8hcKF8rAl7RDO617z/0bIJTHxESEhKwgPj0Qmy8fe3hJf3y/w8xuOr+/n1Yp59ugheOoFBQq2bfudIUNG4eGhwMNDXloJmEt09Cbc3CIIC/sTD4+e4nnk519FrS7UO+69Jysoljco7Q9jvb4p//fpuFt07dScsePnCIVmJ/7AeVgwsqNrRG1Qq/d6YNn8Y3Iv7SM7NgKLFp9QnHQLtVolsBQCWQeX0aJ5G65cPY9d1yHk3TwOEoleUlSe9piNG//AzMrltUIu0YdPsX3zJC7E32H4cEHKr08fYYLTrp6cM6esenLkSBU2NoWMHTuGAQOMCQ5WiP27bt2frFmzgc8+MyI4WKnV73/i5ham1e+Vjwdde7MhF814mzRposHiou+/hy5dhOudO1fgw7e3t6JiqK0lvr5fEh8/muRkT+Tlhum9xBcUlZhX69kLWjhP5ImSSKRgZCxKSoLwTKcfWIp1e++yAq73fVDERdCt95hSqOo2Puw+lAGf9eXY4Sh2RW1CWsOKnAu7sWz+McVPbwpOoYkZ+Q8vUSQ1FpOibzrk8o/YP1FYdOf6Ce7eu4XTwJmUN+t23hTcjQVjM52iD+2lOAg3J/dIKDVae+lUdT6Ji+DT7gto+nY4fuNH6SFdNHzJGQeCqeUxnpzzO0jfs0gnvgqQfWiZGML5T06KRkcfZciQ4Xh4yAkO1ky+eURHb8bNLYLFi39lypSt1VrqCkkpS4YMGaXHh+7iAiNHKujcWcGQIcOJjz+Pq2vd0vMw4H1LGvFO026V9k1F+7QLzYqfJVBw57ROSE7zINq874NapSQlPJDci3uxed8HAIu2fbgauxXTuu+KITd5+hOKk27rJEWt2/fl1tFV1Q65qJRncXE6iL3dGc7HpXD28Hnq1lXTs6cwmVen0haEtkuWQIsWugPjyhVV6XalzjFGjpTTubNcq9/fLt37z4dcEhMfEhKylPDw7eIE/NZb9enSpajSRLu3N1y9CuvXS/D1HYRGK1QoUPqN4OAVpa3LQk7Jyfq4jqaudZFIGwJVj5vffg3lf2ZP52XENGzcx+PY/ycyDiwjJXyawEt/bC1qpYL8hOMU3InFup0n+Sc3sGDBUhIfJ7Pn1GlWrNhARo4aZVEKu6M2ITUxw7xROwofXiQlchbFz27qbDNu0IbTJw+8clL0v4aca0fUHqIi11TIn6KRD5PYOpNwLkbU7evxySfciT8iknplRS+lhqkZqtRHFNw9jUoiJSdmNUhNcK7bBCNTW4zM38ZcJdNRMtLwJWcdX0/Rg3OUpD2hloc+L7taBXfO7EdpVJcaZqavfc1vipzL0L7ExPt4ePgY1Blt107Fe+8pmD79GIsXBzF58lFyclTUqVMxqVdkpAkKRUM6d06vlA89Nxdu3izE3b0LIKGwMJGUlEiddiVyaxLuf1jtcXP69EmW/jqL/PwSQpbNx7ZvgCAwsGs+5k066tDTJm+Zghq1jjpRTvwObDr0LR1DTSm8f47i5PulMm5e5CccL/XuhZeMpt7Bp/8QLKzrVHovzc2LMTeegKnRWszNHpH0/C6Bgc+ZN08oW69bV6Aodnc3/Axp+iwhAe7cEfq6fNvNmw1v1z5GRoac8eNXMmfOfJYuXc6LF89o2rQR9vZmrzRuqtsuOvogHh59cHG5wrhxRYweDR9+WEJ6ejoxMYLsYXktUY3Vri1cU3o6xMVdZvny30lKelrp+T57thyVShchdf/xhyQlS6v17BUWI6pHpV7YQ0HiBWp5+iMxMiEnfgcqRQlGFjY49vsJiZEx2Sf/xK1jN8ws7HUUsZRGdVm7YjZypUqX6C31EVIjYx2it+KnNxjx/VQU6hp651iZpuh/jYeunRSF0gdr72JsO36GVSnhvybRNe+3FXpJ0b+2b2HDut9RKlUo67Sg+OEFBnr0ZvfO9SiVKszqt2TxgimsW7eF2vZK9l+/iE05SoHiZwmo1Gooyq8wvmbV3ousxLP/0UnRkJDVeHgoKvWUPDwUXL+ewO7de+jb15uDB5Xk5pYl6DRoBAHJYgI8Y+JEff4VbdMkuoKDF1GRQrupqXG1x40miW3UsAPbt63HuNH7pRwuAiFXwd2zyNOfYd3Wk8zj67Fo2pns2K0U3o/DqnVvMmPWgkRK9oVdWggFbxSnNusk2rPPRohefPq+JQz6cgijxvhXeo52VjHUrz0dKIsFaFMlZGcLScHlBpUjy0xTPQlCLL28xcQY3q5tPj5CAZinJ+zbV8D16xtxc9tKWNgfeHho8/3/fQ89MfEFQ4YMN7hS++EHgcGzPGeLtjk7Q1GRwO5Zu7Zaa9VY2fn+PQ8dhDj6i2e3UCLFvN57yA6FCpWgTo1I3TkPM5fmZBwMofbghRhZ2HDj7GYuX4wVa1myIqfz9M4RCgrydaqUa/XxJ33PIpFXCsC6rSf5p/7g8wE+Bs+pMvuvIedq22kANy/vE/kRZEdW0qOnN3fvnSPtbiwWbTyQHV5Jy7Y9DZbcv3iZTrFcXibftXkSO3dso1heorNt9A/DKCwqwuEzXaFpTVLM2LoWZi7v6iEotGFLJu/14tDBzXT/tN9rX/M/GUMPD4+osgxbmHwjCA5ewPbtfzBkyCgGDVLo8L+sW2dcimT5Ay+vr6pVACKT5QIFJCdv584d/cLj3Dx4/PIxUDWMNe5UhFiokRERgPrJJdLunRU5XFLDpqDMTEJ2dDUSEzMKb5/G3t6B3DwZmcc2oFIUY2Ndk9y47RTejcWqjTuZx9bj6DMN0KhTrRcYO0vNxu0zTp87i1vXhzrkXJpzNDaS0aDObOrXfqB3bdqTr62tMKm/StGMobbVPUZOjnYYR8mECQUMGfIN8fGnSykZ3kwMPSRkKR4ecuzshEk7JkYXqdOvnz5ni7ZpMOWayV43dFTR+epjO18lhn7vzjVW/z4XJVIcSydo7YIyp89+EitG0/f9RsnDi1jb2KKq10acuK16+XF9dxA1e40m70o0yZsmiRj28rDX7GNr6dJ9SIU5tsrsv8ZDB5g6fohIn7t06cpytJeR/PbbCoPER0LBSTgWzcpgjw7eU0iNmouTz2it6i9vZEdXY1Krvl7MvUSuxLyxm5gUTQ6binUbDzJj1mBnALY0YtTUvxdDV9igVlLO3oyHXt3qO5ksD6HYqA/x8fGlSamI0pioNb6+nxMfPwFX17dLUTG51aoavXSpH7m5lwy0sOdlesX9pv15VuBe5BiJJEm2Hv7IomZjZGKJkVUtJFIjLNv2oejMnyCXY9qwLZZZj5g6eTqz5wQilxdi9c5H1FZlYG1pweVrV8k6tRlHn2kiBbM2Y6fGrNt7k5V4To+cS61WY2+7GxfHAEA/7OToOJDs7L/Efu/RA6Kjq1dpqymaMdTW1vbVjqEhwUpIEFZhoaFrCA5eWtry73vo4eHbGT5cztixwu+EhuomeseOFRKg69YZntC1MeXaplk1Gj7fv+ehLwmahNLIDPO32+vAotN2ztfLnWQeXcWYcT/T2a2NTtzdxN4F5+HC29rIwp7UHbNJ2xWkA5gASD+wFMda9vT36VPp/FCR/dfE0E/H3cK8hhkOzg1KBSMakpKWRWp6Ds/TVAwd+i0PHiSy9NdZODq/TV6BitNxt3jy6BYL5k3HpE4zip/dpOBurKgValNOFirjYCjWbdwpengJxaOLqJCSGb2Mzwd+Q/KLR+QkPaDo2U2sWvemICEGxdMb1LS3pzj9GeatPqUw4Ri8uM23308mJcvkta85N/cUdlbzkEh0PQ8rqxY4Onrwd2Oc1dUZPXXKmilTfiQx8RYhISsJD9+qhYb5HD+/4aWJNgVJSUlcv36jUiGCyEgTmjRxoUULXfmkK1dgSmAtbB3nc+Fank5sXPteiv10+iRnY49Sw7UD2ac2UaOxG6o8Gbk3j1GjQRvybx7FyMaR7IPByBUqavUX4pnpF/Zx+EAURg3ao1YrcRzwMy9io3j+5AEWzboAamw/9EUiKZ0g1Gpyrx4gN247IBHV37X1YU/H3cLKUoaV2bfY25ygvLdoYlKP9u2P4OLyOcuXrxX73cVFEPuQSqF9eyo0jXjD229DYqJ+2/R0ePiw8mOEh0PDhmVarhopQD8/FUuW3GPKlB95UzH0adOCuHlTSPS6u+u8U1snAAAgAElEQVRKy7VvDy1bwuLFgteuLRkH+tKG5c3Z2fD5/t0YeuMm73Hn1mVykh5QcO+clmycrrxf1sFljBn3MylZJjg6Oopxd9mNGDHXVvTkOmk75yE1NsGhzwT9HBuQ+eAyz5PScanf2OD88H8ihl7VPu14amTEClav3ixS4tZo0gnF40uYWteiJDuF1Ki5uGhxtIDgjVm16oU84Sijf/wJtSKHbdvCGP3jTwz8rC/ffvsNkds28+cfa1Ccj+C331YiNXPSEd1YsuR3UWauPOlSda5FIinivcZLQX2U8iaRmFOvnj9vwkP39R1EdPSflUIShZL9QURHn2TIkMF4eCgMomHCwrbg4dEbPz//0kpAw7F5Tax906a6QKK4XSBTssS4UVsiI1Yxxu8Xg/dS00+XL51n/epFYuwyNWwyqVumokaiQ3mbuW8xNrY1KXYSwmPFT2+iyM/C8bMZYhvZwd+R58p0Ko21US9CUjSG995pQV7KBb3S8VbN38Kx5hZq14rA0LK/QYOZNGw4pRQWWaTT7y4ugihyaKgA16sMSTRzpqAWtXevftuKqI21j7F7t+ARa0wTxtFehWnGhq69+viysjLB09MwMgrKKJV37hSw5Jrw3e7dAia9vLShtlV8vn8zht6iId277WTGTwHExR1HtnMuziOE+UETdqMwi8mTp+vQ7l6+dF7kZ9FYxiEhKVIhhr29NwW3T3M/4RTKov4sDV7E0OETdWL5IKmg9/6LYuivEk99sTVQj4BLtnUa5GRQrCgxrBXazpvssxEM+uo7UrNNRbKcHVF7iNzal+9+CKB564/x/soJZzs5C4Lm0rbTAKCbKLqhid2/7rXYWQaAWj/26uDQh3ff/Q0jIwfeRAzdz28Ubm5hFUIST5yA3btVGBltZtWqNQZxw2VxzcFs3x7B7t17UKnUjBsnoGG6doVBg4Sqvejosli7i8t6ZKXkoGXMeD9VSGL0Ymsgy0JCMLFuCuhjhu29JuvxUVu17k3m8fVk52RjobiDLGIaxYV5mNZpIvJb1/IcT9quIBy1ciVWrXsjO7oKUGFdmmi37uDD7dObWbB4g07puLl1McX5n1C7VjpQRjOsiRkLq5jn+PndFWO+5fvd01Poh4AA4bOGllhbvKG4WMBlC5WiMHky9O8vhDOcS52/1q0Fr7Z/f0EJSHMMjRJQcbHuJKkJwQghMCv0KY11x42wQvtDp17By8sdlQoOHDiotWrrj1Kppk8f/TGlbUIMXcLo0cbk5ckxLUWvzp8P7dpV/L2Kz/fvxdBBiKOfP38K8yYfUJL2CLVapcWw6UZx0m02b9ks0u5WVN8iMTLGomnncuIWS7FoV4Zht27nSc6x9QIbYyM3fl8ehIPjMh7cu8HaVUFIa1iVf2OK9n/CQ583+6iINdeQ4egTcLkjO7KiQn4P6/Ze5N8+yaGD25kxe6X4Bo47FYHJ2256Xr9Row4kXNlHwISvkZZKkr/KisLQvsLcPMpbs2arqFPna/RLveF1PXRX13cJC/tTxKHrJjqNiItT8tlnIJMVYm9fuYBFq1Yl+PgMxMdHwvLlcjFeunevECs1MzNn+HBfMdZ+/foG8fvazHgVkRiZtezN6ZORzJrbTwcz/DxsMjX7TDJY2JUZsxa1WoVZk05IU+/S6b0mnDx3juLnt7Fo2pn0vUuoPfRXve/Jjqxk0FdDOH32LFmJ50VvfMSoqbRt6VpaOu5Ny6aRoAoTv6tNM1wWM84X0RmLFy/h2rWLhIdvJzOzkMmToV8/gSqhVy+h/fr1EvbsUVNSIlTf9ugheNXlvdXcXLh8WdBw1SQbO3USqI0VCn0BiJkzhReCtmni1AcOGJdivSv20KOjDxlcoe3Z8xf79wurjN69Nau2rSiVCp4+rTym7+wMJSVQUKDh7ili/PhpXL78J+3aVb1qfNMe+uVL51m7cgEOpclPQwybKeHTSMvM4l7CGbp2+shgfUv2oRAs3/mIgifXSd48Beu2nsiOrGTatJls3hJG+r1YzNt4knNsLahV2PWdUepsBhK9ez2nTp3Aru80Mo+tr7APqiz9/0+1e3eu8fXXA3j+7Im47fKl83z99QBSU5J02pbRAkwXaQFqfR1cjhZgBWYuLXRuQNLqkeRc0FL7btcHWWYmJ2L26JQD27uPIzlPTtD8n1m7Kggb76nYu48jswS2R4bxT5q5+dtVN3oN8/DoSXz8eRwchuPvb4O7uwQ/PysuXpSwZAl8952S8+fLvEhDlpQEly8rCQpSiGEEI6MyiNqvv4KxsYRx40ZrFbWU2bzZxdRRxJK1bap43xyH6dI5FJz6g59/LpuR6tVvwA+jfqQw+SFpO+fpHTN936+oFMU4DZhBLc/xFEjNiDl6EHlRAU4Df6aW53hQq8i9uLfc95Yw6KshjBrtz6SpQQzx6QMXI5m/4DeavtNabOdab7LOZK4tw/fdd+j0wciRcubMKWDs2DE8fryJ4OBcjhwR2l6/DiNGQO/eEoKCbPj00++wsLBk82aB13vsWMOT4pdfQmqq0CYmRvh/4kRhMh87Vnf72LFw6ZJuklETxmnRAqKiFLRq1abC+3vs2Em++OJzFIpCIiLkjBsnxLhBYNhcuBDWrClLyo4cKefXXyEoSOiXikzwtK11tvn5+REdbUJCguHvaIjexmkwnK9phuaVBQtmIZcYl6kQeY6nJCVRTIhKpEZYtXZHXlLEtm3CvddQTcgiBHqJnH2LGNjPF6ece5gZG2Fcsw6yIysZM9oPdw8fJgcE8c2AfnAxkpo1a2LWpIw+xMZ9PLFXb2LjPVUY+xL9F5TG/pVJ0dOnT+oA9hs16UDUzr2Ckrvju1w6sZdmzTuRmp5NSloWl28+p2WLZlw8tZ+Ch5ewaqs7CyVvmYKxXW3kGc8ouBuLxMiEjIOhWLb4hNyLuyi4fx6JkTGZx9Zj1dqde3EHuXAxHkWdFkJhitQI47rNuXdyJ7buZWrpqnLJseokPivbZ1njL2qY6ZbC1679JTVq1OFNFn5oPtvb2+Pu3pMpUyYwc+aPvHiRxltvXReLg9asER5caQVuQXWKWsqKiXoAClJS/qKwUIih29iAp7uS29cdeXT2lJhY0lja1kA+HziMeo1a6oyNhfMDwcgEhz7+BpJOauRpT7DrPgKp1Jgab7Wi8PE1HPpM0FG51y4oAkAiIfnmWRo16UBs/F3at+/Ax929kRpb6dyjug6/6ySrN2+G5s2luLsbFpJxchLEQCQSFT16CH1Zp47gzbdtC+fOmXP+/El8fQcyffq8SvsbhHDWunW6CcWXL4VJesgQ3bYJCbByJUyYAHl5sHWrMCG3ayeEh4YOhaVLjzFwoHe5oh0F0dH7GTDgK7y8lPj7C+Pgo4+EBGxwsECd3LZtWeGTJuHq5CSsEG7fLttW3rZtM6ZTJ1/c3T9Beyy2atWKceP2k5srwdlZJRayRUaasHq1KV27fsCsWXMIDJytU3CUl7elWknR8vOK0qguTx/dJvbMUcwatSfr1EbMG7thWqse1m09dQETB4IxUiv4ZuRkbj2QiUnRxw8ek3njEMO/m0R2sS0DPvscqbyQpzfO0rHrl3T9uDcpaVmcOX9HHFPvNG/LpeN7yb99AhOX5pjYu2DR2l38vZz4ncwMmDjbUN/960IumoSYNmA/evd64k4dE+Oqsq2BOrCxe3euseb3BchVapx6fq93Ljbv96Pw4k4hKZqbjuzoaqzbepJ3ZT8mVjWR56QiOyJskyccZcSoqQZhSeUpMPNOrmfhwmUGS8Bf5ZrLQi6GiKhMeZNJq8rahYdv18GnVwWJq05Ri4BnjyI4eIUQlyxO0dl//TpcuvQMG29D3NI+xMcfY8SI4YDQT3Nn/YBCBU4DKiJO8iH/1kmSVo2k9lfzDIdkymHLi55cJ+/mMUqMpeKS2hC3vVqdhbpEF8Uj9EHlJEteXmXSatpWHopXXeinBoKosb17hW3aScYDB4zZuVOJSqVm+HBBOEKlEpgNHRzKCntycjS/vwDNeBA88+FIpUoiI+HQoTIMeXlKAm3ZOI15ewt/G4IlJiQIK4Pff29P+bGo4eIPDV2Dv/9WZLJc7O2t6NKlC2r1CaytTxtIzG8lMNCUDuX0fcqHXMon0zWFQPuvXxSFKF5umkhKRCD1f9RddafvW4K5qQlz5/9Gu/YddQAPI0eOonULQdxNs71tywn4T5ig0+7enWssCZrEgvm/0bpFFxwcgwnbsJDL5Rg9ARTZyRUOqCol6P4pe6thE/WUGcIEqCFPKv/Z0L5Th1aRX7ORKC0nlyWRtXcR1p+U4YHzbsSgOB/BL0HruXfnGitC5kBpaW1FslNpW6ZgZ2JKfm4arVp34NKl83w70p+k5485dnQPDk6uZKQ95Otv/EjJMqFrp+aoVEqWLplDWm6mHu1uypqRtGzZja+/Hlqt66rOvrecv8bWWldOsE2bfdjZdaaM81pj2n9X9LnqdtoEXRkZedjaQs+ewsO7axeYmgoPsSHr0UNAJlRHgi47+wxXrw5ELn8p7quOXJhsayA9O3chM1fKnRtHKSkposTpHVEmsOjJddKjl2HTvq9IjZt3M4acmDUY2zrh9I3uGydp9UhsuwxCVZBDVmwENVw7UPz4qpD4enkXC3UJbTv2E3InjdyoWfiCjt2G0bfXS1ycQpFKdIsDqtsHvXsLk395S0oCf39rUlMfM378FDIyNjFypELcp51otbUV4u0NGgiJVBAmyMmTwcPDgzNnzugQV23aFEFoaEGV8m3C798CLIiOPsoXXwzGy0uBl5cuhnz//jKysLVrhTj4qFH616ZQQK9eQnjIUJLW1xe2bTMvVyRkePwmJt7Cza2XXuWpxhISDFee3n8aQlGJq/h8zZvtpzevyHYHYdvjO515Jev0Zj1Zuez4KLh5iMFfj+avyD9o22kAnr0FKogdUXu4c+Mo3/0QwN1H2Qaf83t3rrHq9/nUaNyJmoUvmDR1ISFL5/Eo8YogzKMlc5h5fD2KPBnKPJnBuMu/zkPv1yv0lTzjJUGTwNRcLArQtEk/EIxNBx/xIbdo24eCuAj27jsG6MIKy79NK4MlacyynTfJj2Np+e5bbzAp+r/roQuQxCHlCLp0C0DWrDEMiUtKgho1BOSFNiVAv376iAo7OxMuXOiid2VCUvRD/cRSO28s25URpx07spnikhJM3nbDoTAJY2U6z0rV0mVHV2NkaUfBvVgK7sUKpfpHVyOVSLDprv8msmrrSfaZCBT5MiybfUjB/XMC0qV+S5I3+dO04Vs6iKmsyEBsjX6jvnOK3rGMjZ2oWTOP5OTKJ01DXrXGtKF42tDPvDxDiVZBIergQeEePX0qIGECA6cxc+bPWkcVEuihoRteoYDMQizbDwpSVEkWpvHM+/XTv7aUFCE0ZChJq78yMFTUVPY5JOSPKmkqDFWelvfQyxNwaRcCQeUqRDYdfEi+dZI1v8/HrOkHIhji6pULnD2xBVNXN7ZsXMb4yUHivLE0eBEfdfNk7qwQsrOzxZVBRkQAU/2+QK5SacFlpyE7spqCO6cFQjhZxQmIf10MvSLAvsYyIn+iVZuetGn3ASlpWTRu8h5XLsZRmPqIwvvnQWJExsFQan48jPxbx8m7fgSJkRE5x9byfpfPsXdw1ovXz/6fqeQVW2BjYycSgS1b+jP79++kpo9hr9+kdhPSLx8h/WUyFta1/3Ux9MTEW3h49DdI0KVdAPL998L/ubnCpGJhIUwos2YJ3tfEiYbjqxrypchIY+rXl+vFUwsKm+JUN4CLxw6Tf/sEKqTkHA6lVZueFD+7SOa1GJE4TaFUUtMnEOt2XmRci8HWzISUp/cpeXkXh75TKHl5H/OmnTF1aEDW6c0o5cU4VEDiZlanKXk3j2HRuCO1eo+l8H4cUgtbatRrTo23WvEs/pBOnkSplnLnxGm+GKjrmder50fr1lu5desWERF32LBBePnt3i0U+7i4lBXHaIqDDMWUtQu47O2tadWqGWPGHODECSULFugX57z/vkBX/MsvoFS6snnzH3zzjY/Be/5qBWTfM3v2AlxcrlRJsJaQAN26CbF8MzP9a9uyRcgT+PsLXvqwYcL/bm5lfVJRkVD58Tt06HeMG1dU6TVoCqW+/LJsW/kYujYBl6F5JTl8KnYfD8eyaWeKnlwnZdtPoFaLxWQSY1MKn9/G+at5pF8+wvUL59i8eS32/QTCreQL+3n26An5+QUsCppBieM7XD6+iwK5CrNGHbDuIMBgTeq1IO9BPI59p4hjDIkROXHbcPpsBtbtvciJ+4tZP8/474ihv7Jn3KIhDo6/c2DXek6cPUv22QgxO23Z/GNerhhKcexmFi0K0aMFEAtYtCCIV69c0FEuMa0nLKEM0e5atK1cF/A/2UOvrufz6JEAfVuzBnbsEDDNZmYYxKaX9+KysgSoWUiIPmmXpe0Oun8spWatJqLi+cKFy/SKtexrOZJv30QH2vhwxy84fzFbnLCVuRlkn43AZdQ68m7GYO7UqFK1Ipv3fciOjShFNnmRGbMGC1c3g6vBglNrmf+Lrjizre2HNG68gOjoQ+zZc5BevaBvX/0S98BAsLLSV9TRtvJQPA+PPvTv34+iokhatDAcLm3RAgYONMHBoQfdu39KRZDWVykgAwu9HIoh0/bMra31r01TyFRSIpCPGVq1QVVFTWWfX1UkWmOGYIuVzSvWHXzIOr4B1JB1YgM1u48g79oh8hOOCYLzR1bj2H86EqkRNfv4c3PvQuz7lTl7Vu36cufEOu4nnBFXdyWy5wJJ3I0jpG6ehL2XwO3i8t1q8Xc1cFm7rl+Lx5KaV/z2+tcVFlVHkCLt9mmRHB5g5679xJ06Ts3yIH+pEdYdv8DySSwSU0d2RO1h3mwh3nU69hJxpyKw6Pg5eTePkwoETPXn2rV48YakbJ7IixXDkZiYISnO5cuvRrE/ehepNw6jkBejLshh9LgZBnUBX6ew6C3nEsz05vQSqir8eB0BgvDwyGo9vD/8IMSIPT3h558FOJyZWeXYdE9PmD9fQnJyDTZuXI2V1VC9dtdvPQakeornmv7QFGs1a2TLnxuCdfQZ62qJm2geCKv3eqBWKXH0CSA1ai7JG/2xbt8X2ZGVmFrbk3/rhBiS0RBwacJ3TRs34/HuIJ0lOEDOoWD8xhbSphyyTyKRkph4myFDBjN3blGFL7aAAJDLBXiioZBMmebq92gXjO3bt5/g4MpzX9rkaRXd86oKyHR/vwCZLK/ak+fevcLLfdw4YVtSkjCRHzgAfn4abLoQIhoxQkjKuruXTe4VFwnpXsurikRrrHxhUVXzik0HHwpunybr5B86DmH6riAyj6yiVi1HCrR0iR2HhYjfFcI16zCqYYmNh18Z90sbT7LPRuA0KIjULVP1uF00koa2Xb7C5n1hLsuJ34kyN63Ca/3XeejVEaTQ9ow1xT8V3SgNne3xg1uIO3VALBLKyspB4tRYR8gg4dEjneNYtRMmBIuGbalZ8JwRI4ZjW9OeNSuDMHvbDcusRwzo7/3mYuh5/3seenU8H6VSmJC0vfHjx8uQLYaSdj16QOfOsG+fMVeuxNOwYR1On9Y/dqvmDUWloKr6pnu3TkwLmGhQnzF972I6d/qYdFkSyZE/YfLep0gLs3Dv3YfYs5GMGfczD+5c4MyVG5g27UJ2bIRIwJWyZiQD+n/Ozl07sC1HlQxg0c6LnXu30KuXUgtKaErDhj8za1bVFMReXkIYKjwcZDIB/aFJEO7ZI0yAZmZqQkJW4+fnJ2L1X5U8TTD9e15ZAZmg+WpMWNgWXF3fBYqqjbKxtoZ9+4zw8HDnzz9P8dtveRgbq+nSRZCT02ZK1ITjpk8XXgCalcuNGxUVCel+9vX9otrKWdpW3kOvqBDIoq0XVqXQZOv2XmTHRugIojj0m4ZsayADffrw6OF9Yg2Nwehl1OwxEqv3yk5C42jY9xqDKj8TtUSCfc/vdPZrJA1z4ndi/X4/Sp7dIuvMFqRmVhVe67/OQx86fKLokVm09iDv5Ho+/2Ik+6N3Ibsfi1mr3mTHrOWHsdO5lvCYoIXzKrhR3uILgNrvsmffLpwGzhTLyW2MLShJuo1jKdVqSnggps26gBpe/jke6w4+yI6swK7r15g4NuDFnkWMHzeKW7eviwmOtC0BFcpIvaqHbm0ZS8M6yQZ6Us6b9NA1qBYTE6FKsaJkJkBEhLBde9LS0LUaro4sk07Lz5fj6loXpVJfYg7KPPTq9M2xw1FcuHgG+15jxH0a+KFlyx7cvn+Zr776hs1/hlByNozO3Xzp6dmXt95uxYZ1wchL8rHvJ5T4a3haAMzb9GHr9ggcKkBHWbbzIXnbCf7a8YgvPldjb9+D5s1DMTZ2Jjy8f5UrHG9vwYOdMAGWLDHi4EETcnKKsLQUqBHWrgWptKicVNwH1fZKq1O27+HxQal820r8/SNLUTBW+Pp+QXz8N6UC3sIxqiM7uHcvKBRGbN8eLsoJjh8/i4yMir+nebmVlAhFVYGBIJVKuXRJszIxPH4TEx+RlSVjxw45nTtXzndTPqSl8dA1q/IBA79hz54IcV7JPLKSFm26cycukrxbJ7Fu70XmsfXYdvqclDUjMW/rJdagmLXqzeZN61AolYbDNe36kHftEJYtPhGdlPS9izG2dSYrNgJ1cb4Oa6NmMtdUoSZvmkhK2FQUWck4DZxZaaXo/zPY4jvvvqdesyESwCCCRGOG9pVR4h7g55/n0radG1duJIqx1kFDxork8M+fPSEgcDI5SikmLT4l7+R6Jk6cxo6oSF7klGDWqjeZR1Zh0axLlVDIzJN/gkqJuasbhffPYezwFnYfDiZ9zyLMXd+n6OEFavWdinmDNuJ3uBRJ1M7D1bouQ/tu3r5Bc9fFoNankzUxqUPHjpcxNrZBP06q/XdFn3X/jo7ep+OtVQRJ05iXl67HBYKY74wZwqRdXjpNYxoo3c2bN0o9dAe9NhLTG0gk0ir77c71E6xcFYpFsy468C4Nx4Yi8wUSlQKl7DlmTT/ALvcRcoWaEcO/49df52Pc6H1K0h5R55tl4sOmMbVKSfLmyVg0/wjb9/uLq0HzNh5YtvMRIZBcXMGVC7OpX3+c2J9GRlYcPqyuFlzRzs6CxYsXMGVKIHPmFFTYZzNnWhAff4qQkNVkZFQe+163zgQHh+GlKJHqjo3K9hWRmPgCN7eOlZ7jtGnG7N69h+7du4nfc3RsSHBw5Z59UpLwcouKghUrQC53Z+fOHRWek0A5ICCw6taVs2aNEMrTcNiUrTJMSnHoWTq/JzH5iytXcgS+lLfdqKtKZ8WKP5k16yfOxcZgWq85jtIi5s5ZwKRJY5FlZmLVxgN5wlEm+E8lLDyMbIUUk/c+JSdmNVKpEXY+gRXCa1PCA7Fo1kV0GLLjo8iO3SpqEdcqnXuKnlwnNeoXLJp+IEJv5bIkUnf8Qq3eY6jxVitebpxA8cv7BmGL/8rSfyMjI7p/2o+onYdp205In0ulRnz51VCidh7WKcWuV78BkwNKS7UvRfL96EDcPXxYtWoTPTt3gYuRTAuYKZbpVlRiLju6GpRyHPtNo5bHOIztXVCmPiQtam7pNj+Ma9ZDnvpE/E7eyfU6Zemvamp1Ds0afGtwMq9TZySdO2sm879viYkPGTLkG+bMKdAr0//uO2FyXrCgrGQ7IQEKCvTFEzQcIxrlHW1LShI8pZ9/FpbXbdq0Y8yYwZWWgVdm9+5cY9XqUJwGzqSWhx+oIXX7/5C6Y454nwCkNeuiMjXHonk3kl68IM+uIQsX/YJtX4GES2pSg9yLewXPaN135F3Uonto70VO7FaxfHv8mHHUTPoL2ZYfSd86mdyjS5GlFtO48TRsbOwYNmwUiYkPS8MTlZ9/SgpYWpoQH3+ea9eu4eFROQuhh4ec0NCV/2tl8OXN1fVtwsLCmDnTgrVrjUlKEl5KSUlCxem0acYsW/ar1mQu2KvE3kEgIYuNPVthW2GsDhHHqqenMK7kcuGl0Lu3Ji4/gPj483TubKZ3jCuXb+pRdyxe+D9cvHAax4EzcRo4i8wSOH/+LJHbD/DDqB+RPDjDgqCluHv46NA/WNnUxMS1o04UIGXNCLLjo8RxZNWmN7mXy+gkbDr4YOJQH4umXVDIXpASLlAEpO6Yg3HNuhQ+vEjyxonIZUkoczOQmphhZFULAFWx4VUt/AsLi161XXWPUVmRUNLqkQCY1X9PfJPKZUmkRc3FvtdoHS9eg6Z4E4VFNUwTafKWX7mek9Khw3GsrN7j7xQMlW9XvmjFkK1ZIyBT7O0FdkSVCpYvL9TxvJKShIdp/Xpdz107BOPpWXFBCkBhkTMPnq8HJJX224zAUajqNMPew7AnA8I9EXh6mov8G2b1WpC8ZSqW73yEzfs+4upLqpTz1aDvOXnqIJklYNaqN3kn1tPh/a5cvnyekd9PokmzVjx/MJTloRl4eemiV/bvF2LfEokpPXt+jKXl8Wp40UMJDl6Eo2MDgoMrD6MIRT5WpKY+KRXx/gYPD0UFse/VeHhoaA2rOzYq21f2ecOGMPz8JiKRKCkoKCMAMzEx5swZk9LQUE/xe46O71br2jQeuqbYTKFIN3geVYVwQFDLcnAYRnDwImJj30UuT9XZP2hoffLsmlW7QBEqfmYPHDrJzcv7yJILZHHZx9ZiYW5BvkKNsa2jKGcoNTLGpuPnYqg372YM2bER2Pf6kfR9i0Eiwa7bcPKvHUKplKMuzEWtUoCiRFhtZr3A2TeIp4v7oVYp/zsKi16XD72qY1QKWWrnTe7VaIrux5Gc/gwHr4kCmkKLM718yfibKCxSqwpRlxuzJiYOWFlp1zH//ZJ+qFEKSSubzA0lNDt2FHi3R48eRnz8BEJCQvRgby4uQixU2yPTJqiqCsroUs+TxKSRtG7RSGynXWbvbCdnSdAkhvgOQ6JWUHAnlqIX9/ZanDoAAA9YSURBVJBIJDj2/wkXPYTLCkzrvIMyN11XXaYUYWDq1Iic42uxqlGDYSOmitz2GlhkUFCwTjn382eJzFiZwaJF+tfSp48wqcbGlrB792FMTQUc97ff6ucfNPzv8fETeDWVqHwqK4P39R1EfPw4XF3rVnnPX31fDRITHzJlSiALFyoNrCYU9OqlYMiQ4cTHnxeTuNWBR2onLsvIuQyfR3Xgkx4eilJ92hAMsS0uWTSV2b+sfyXqDqj4mdWopWk0Ejq935pZ/xPIk5fJZJ74EyNUfPnFt5w4sY9kDdTx8Eqs23mRvndRGQzy+iEsW3+K7ODvIJEgNTHTk72rzP51SdFXbVedY1QFWbJu70XezRiaNmnO48f3SIuaqzOZg74cWXn45Oucbw3TFzR5S+90qJrzvLJ9httpL4srS2hKJNC7dzdcXetWCHuzs9PleNEWPzZkGijjxrAP8B4wltNxt1CrhWWyhsvepJEb/hPHkpGeikmdZgQtnIN5k85QdAVFTjqWTTuTEb0MZ98gMRaecTAEy3c/pujpNVxGlSk4aL98Mw8E8+VX3+P2QQ9Ox90Sx2R5DvvTcbcwN7tN9K5peHoK56z90svKEmgQ3n5b4O1u3boMljdqlEAj26tXeS/6j9KJt+gVEp2WaO6/q6szwcELSqGJmnup7VFXfs9fbZ/wWaMJWnVoKLhU7Fuf5728lU9cRkcb4+v7ORWN8+qGcAR92iIM8aFfvnaXnJwcbIwsyNq7CMdhy8Rx4dB3KtmHQmjV3gOpmVOV89SOqD3MKy3vnzV3DafjbnEu/irpqako87PByAQ7e0eeJueR/DIJk/otkR1eiVmD1hTcPaMDg8y9tI+sk5uQGBljpKVPrJG9S9+zCOOadSq87v/fQ68EsqRdYm7zvg93Dq/E1MwMewMCGFbt+pAVsxZVQS7WHbzfSGGRIQ9dsDdLugU1REgaVO1NDxkyivj4+FLY2xaRD1uz9O/YUZBPGzVK+G51SLr69IFxfteZMbOheP3akFMh2++PSd1mlCQ/ENFHLzf6Y9H0Xex7fq+nKGTVrg/ZZ8Jx/Ownnd/SfvkaIvjSNoF0S01dh+XUsosWYZlVoXg0ISQNLG/yZAHeWauWDb6+XxEf/30pHFCw6sDvhCKfL3lT9/x1jlE971iu5R2jM07c3eV4eir0+Fs0SkTlVy6GzqP6+rTWqNVmqFS653vlCqxcvgmjhu9TcP8cDv2ni8gSc1c3MqKXYdnWi+QnZ3VW2GC4yNGQJsKa3+cJZIClyLmsyOlcOLMds8adKEl9hMU7H+okQpNWjxSL2/ITjmHi2BCbTp+L+sQOffxFQemXGydUeN3/pz30qiBLKXeOknyzrBIMwNY7oALpqL4U3zlD0aVdyB+c1YNP/qd76AIkbRPFxYoqvWltD8zDoxvx8YcJDf1ThL3Z2lpQUlLMhx8KOOxqq9dn5ev0lTbkVCKR4tA3gLTdC3VCJzYdfMg+G1HKSd2b7LMR4oRu074vBbdPIU99IiKPQAihaWBkVu29eLE1Vkf1SGOa8zA2kvFuo2hAuBaV6hVCSC5CmwEDTHBw8CU4+Detfi8Sv+vnN7yUp6WqIp9vMHz/y//9z3jor+4dC9/THifjxoWTlVUowjNDQoQE/Lp1ZfF/zcrF0HlUBz4ZHW3MF1+4ExfXCqUyU9xeRvhWVq2Zf/NEObGKQFRqNemFKp0CxcqKHMsroWFhh4WWd23Vy4/0Hb+gSHkIUinytMekhE/DqpWgnlWz+whyLuwiP+EYFu92JedsJOqsJCxb9Sbz8ApkuxfgPHx55R3PvxS2+CrtKtp3+dL5KiFLapWC1CIJqsJskBrpwYsyDoZg1bYPNqU8DHk3Y1DERTB48DA9+OTrnK9adRu1fKBOv5mYONGly6PSv14PfmaoXWLiQ9zc3FAoClmxonJFGSE5Z0Nq6kudY2h/1lay2bdPrsd2Z+iY4/ys2LXvvFa8ugxyatVrnA6FKOjidVFD2q75YGSCbceBOsyK2bEROiEXtUpJctgULN7piq1b/1LoYSSz5q4xfB/UL1GXCEm+zz4TPG97+4pZJqGMbVBDCFXGWqiBvujfB20yNP1EpwlhYWF4eHTT62vtY7wqVPVVj+HoWLtaEMSy8WH4eImJDwkNDSY8fLtO/H/cOEPx/1eHT86YYcLy5XK98xw2wgyZ5Yc6ifT0PYt0wqUacINtl0FwUYAdg/4zuyRoEjKLejqJ1Zz9i7Hs9i1GVrXIOLAMtVqJQ5+J4thVq5RkRIdSnPwA61afknt5L7VKq0dT146kc/v2XLt+lS99R4Myl7DN6yksKtKhEagMtvh/0kM39GadHjiZy5fjcCxdIqVtCaBL6zZw9yZZxvZYdP6K7NitOm9Vq1a9yImNoOR+LOatPcg7sZ5OHw3inZZCqbp2TPY/3UN3da1LWNhq+vQZ+ooemOHja3tkKtUm/r/2zj226euK459j502dBJQ0U0khW9JqpQhRYLQVUtMs0wRlYSBtCKqs7UBspSta6VSNdSrr4w8Y08Br1JXy6FoNlUIfGwiSkhaRhTIY45WosLFlPDMKCY+mSQdJnJz98XOMHTv2L2liJ879SJb8uP7l5Pu7Pr97z++ec7dvb2dxcKTKx86dMHFyYdBs5r7CRzl38kM+8cY5/fEPndS/+kO0w8Oo4kW01OzypfFfrVxL8tgJ1L/yCK5vzPFdfF2TZnLto3U409JpqdrIoseX9XgeEhMa+Xqe9V5xMVRUWOvvw9G9DvjNzM2eR9eWZtWUlZWxdOn7AaVuDx5c7FdKlh6PEfl5Xz/zn8nZCQ11xcBDHy8//zbc7ue9cfauz0LF/4PtsPrqH7yrfNqZMcMTcPErL3eybNmNIGd+o3UMCxY9we/c67jy1s/JmPGUL4zha3O2lqsfrcU1aWbADBvCJzl2lZ3wzxLNmPYwje+9wLVtK7nVWzai9fxx32wgOJGthH+f2sdzL65l38F/kpPpoLWtLcCZR2JYxtD9Y+Y97TGaNnEGRw9v5Z13K/j1iuXsrX6TnB+spvnwDl+Br+bKMubN/xGZGcls2bKJlSvdQQW+voy90Yyhg1X4aeTIEVy8+IWt+GSk4+fnj8PtfpklS55i6tR7eeCBnkdU5eUOXl3/JKNzrSuYf+H/nbWHSA+Reu8fOnFNLqHl2C7+t38zIybOpFOhad9mXJNm0nxkB0UPFlP98VY8dX8lecJ0vvjL65Q8VEJV9eaglSz+WCP0ZLTNej17tlWErLcFoW5mboY/D/n5d+F2r8bt/r33vd7sFRvus/45hlXC9+0IoaHuMfD+t9da5XOQsjI3S5cGjvJ3755GU9Oj3b4/glRXOUWFQuaocVRsez1kmn7TrpcpKvwmNbX7efwnz/pm2F30puxE0wercSYmk15sTeX8Z5ThypCE25e0+cMyOtp6vpgOycSiL4v/fn897THalRRUc+wQ1dVVuIoW+m6Ojv7xBlLGTCB14nfY+3El359byvt/qmTS5Hsj/OXekoKnw8XNZVdCSkpeP/+NQEpLH6aiIjFsG2sENs/2MW8mpKSyYUNiQELK+vVWvPkXzxb5nHkXRw7/zbdHa0+rj1CsG6FTvosjKZn7J08m69JhPHX7yZg2n5YjO5g9p5RfvbCKysp9PDJnFp4Dm1mxYg1PP/McL63caOO8JdDuyQSssJHLha2kIf+CUJZmc+3INagJdy43bEhk+fJUNm3aFHKP2IGwxe1eRUPDp3g8LTQ0nMHtXkNBQQEJCf7VuBwgdyPevTjr/vUJ1dVVjChcEHTMtHtKqL/wX95974OABMWeqDl2iJqag0HHunG2lvbW1oBB4rU9G0ktCHTQF9cuCEhkSxz/7bD7kv70iSV0tFzpPoXxEbMYuog0AmcjNhxY8nAmjkzKHhtwYWtrON1JZ8c5oA2kIGHUbQ5HUmrwt1Vpv3K+Uz1tFwD/HQ6ygMvBXxgSJIswLjcXR2qIf/n6daivp1OVE0BrcIuwfEWEJGCUKk4ROoCrqlwKfSy525FyS0pCRg6IlSHn+fwSzrRMnGkZIELH9c/paLlKUnae9bypsR20FsgByQE9DTT30s6wiDAmM5Os7OwQC5y9XL4MqpCdbUuzodhfkkXIwfa57BMDoYurj7/pXh2r/fJ5JDGJhHS/vvvZp504nA5xOHGkptPRfNkKqou0ijMpxTEiw9HR1NgJWkdgn+3el8eqanYog2Lm0OMZETmkqlMitxxeGF1CY3QJjdGl9wzLkIvBYDDEI8ahGwwGQ5xgHPrAsC7WBgxSjC6hMbqExujSS0wM3WAwGOIEM0I3GAyGOME4dIPBYIgTjEPvIyIyXUROikidiAQVUReRp0XkhIjUishuERkbCzujTSRd/Np9T0RURIbNsjQ72ojIXG+/OS4ib0Xbxlhg47c0RkT2iMhR7+/poVjYOSRQVfPo5QNwAv8BvgYkATXAuG5tioA07/PFwJZY2z0YdPG2cwHVwAFgSqztHizaAHcAR4GR3te3xtruQaLLOmCx9/k44Eys7R6sDzNC7xtTgTpVPaWqbcDbQEDRB1Xdo6pdKboHgNwo2xgLIuri5SVgFf71Y+MfO9osAl5R1WsAqtpA/GNHFwW6Ns/NAC5E0b4hhXHofWM0cN7vdb33vZ5YCFQMqEWDg4i6iMg9wO2quiOahg0C7PSZO4E7RWSfiBwQkelRsy522NHleaBUROqBcqB/d7+OI2JWbXGIE6qGR8j1nyJSCkwBCkN9HmeE1UWsveHWAI9Fy6BBhJ0+k4AVdnkQa0a3V0TGq+pnA2xbLLGjy3zgDVX9rYjcD/zRq0vnwJs3tDAj9L5RD9zu9zqXENNAEfkW8Etglqr2V8GiwUwkXVzAeKBKRM4A9wHbh8mNUTt9ph7YpqrtqnoaOInl4OMZO7osBLYCqOp+rFq6WVGxbohhHHrf+Dtwh4h8VUSSgHnAdv8G3tDCa1jOfDjEQiGCLqrapKpZqpqnqnlY9xZmqeqh2JgbVSL2GeDPWDfTEZEsrBDMqahaGX3s6HIOKAYQkbuwHHpjVK0cIhiH3gdU1QM8CewC/gFsVdXjIvKiiMzyNvsNcAvwjogcE5HunTTusKnLsMSmNruAKyJyAtgDPKOqV2JjcXSwqcvPgEUiUgNsBh5T75IXQyAm9d9gMBjiBDNCNxgMhjjBOHSDwWCIE4xDNxgMhjjBOHSDwWCIE4xDNxgMhjjBOHSDwWCIE4xDNxgMhjjh/9nU7REHBzDwAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# SVM Parameters\n", + "C = 1\n", + "sigma = 0.1\n", + "\n", + "model= utils.svmTrain(X, y, C, gaussianKernel, args=(sigma,))\n", + "utils.visualizeBoundary(X, y, model)" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Load from ex6data3\n", + "# You will have X, y, Xval, yval as keys in the dict data\n", + "data = loadmat('ex6data3.mat')\n", + "X, y, Xval, yval = data['X'], data['y'][:, 0], data['Xval'], data['yval'][:, 0]\n", + "\n", + "# Plot training data\n", + "utils.plotData(X, y)" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [], + "source": [ + "def dataset3Params(X, y, Xval, yval):\n", + " \"\"\"\n", + " Returns your choice of C and sigma for Part 3 of the exercise \n", + " where you select the optimal (C, sigma) learning parameters to use for SVM\n", + " with RBF kernel.\n", + " \n", + " Parameters\n", + " ----------\n", + " X : array_like\n", + " (m x n) matrix of training data where m is number of training examples, and \n", + " n is the number of features.\n", + " \n", + " y : array_like\n", + " (m, ) vector of labels for ther training data.\n", + " \n", + " Xval : array_like\n", + " (mv x n) matrix of validation data where mv is the number of validation examples\n", + " and n is the number of features\n", + " \n", + " yval : array_like\n", + " (mv, ) vector of labels for the validation data.\n", + " \n", + " Returns\n", + " -------\n", + " C, sigma : float, float\n", + " The best performing values for the regularization parameter C and \n", + " RBF parameter sigma.\n", + " \n", + " Instructions\n", + " ------------\n", + " Fill in this function to return the optimal C and sigma learning \n", + " parameters found using the cross validation set.\n", + " You can use `svmPredict` to predict the labels on the cross\n", + " validation set. For example, \n", + " \n", + " predictions = svmPredict(model, Xval)\n", + "\n", + " will return the predictions on the cross validation set.\n", + " \n", + " Note\n", + " ----\n", + " You can compute the prediction error using \n", + " \n", + " np.mean(predictions != yval)\n", + " \"\"\"\n", + " # You need to return the following variables correctly.\n", + " C = 1\n", + " sigma = 0.3\n", + "\n", + " # ====================== YOUR CODE HERE ======================\n", + " \n", + " C_list = [0.01, 0.03, 0.1, 0.3, 1, 3, 10, 30]\n", + " sigma_list = [0.01, 0.03, 0.1, 0.3, 1, 3, 10, 30]\n", + " least_error = Xval.shape[0]\n", + " \n", + " for C_ in C_list:\n", + " for sigma_ in sigma_list:\n", + " model = utils.svmTrain(X, y, C_, gaussianKernel, args=(sigma_,))\n", + " predictions = utils.svmPredict(model, Xval)\n", + " if np.mean(predictions != yval) <= least_error :\n", + " least_error = np.mean(predictions != yval)\n", + " C = C_\n", + " sigma = sigma_\n", + " \n", + " # ============================================================\n", + " return C, sigma\n" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3 0.1\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Try different SVM Parameters here\n", + "C, sigma = dataset3Params(X, y, Xval, yval)\n", + "\n", + "# Train the SVM\n", + "# model = utils.svmTrain(X, y, C, lambda x1, x2: gaussianKernel(x1, x2, sigma))\n", + "model = utils.svmTrain(X, y, C, gaussianKernel, args=(sigma,))\n", + "utils.visualizeBoundary(X, y, model)\n", + "print(C, sigma)" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [], + "source": [ + "def processEmail(email_contents, verbose=True):\n", + " \"\"\"\n", + " Preprocesses the body of an email and returns a list of indices \n", + " of the words contained in the email. \n", + " \n", + " Parameters\n", + " ----------\n", + " email_contents : str\n", + " A string containing one email. \n", + " \n", + " verbose : bool\n", + " If True, print the resulting email after processing.\n", + " \n", + " Returns\n", + " -------\n", + " word_indices : list\n", + " A list of integers containing the index of each word in the \n", + " email which is also present in the vocabulary.\n", + " \n", + " Instructions\n", + " ------------\n", + " Fill in this function to add the index of word to word_indices \n", + " if it is in the vocabulary. At this point of the code, you have \n", + " a stemmed word from the email in the variable word.\n", + " You should look up word in the vocabulary list (vocabList). \n", + " If a match exists, you should add the index of the word to the word_indices\n", + " list. Concretely, if word = 'action', then you should\n", + " look up the vocabulary list to find where in vocabList\n", + " 'action' appears. For example, if vocabList[18] =\n", + " 'action', then, you should add 18 to the word_indices \n", + " vector (e.g., word_indices.append(18)).\n", + " \n", + " Notes\n", + " -----\n", + " - vocabList[idx] returns a the word with index idx in the vocabulary list.\n", + " \n", + " - vocabList.index(word) return index of word `word` in the vocabulary list.\n", + " (A ValueError exception is raised if the word does not exist.)\n", + " \"\"\"\n", + " # Load Vocabulary\n", + " vocabList = utils.getVocabList()\n", + "\n", + " # Init return value\n", + " word_indices = []\n", + "\n", + " # ========================== Preprocess Email ===========================\n", + " # Find the Headers ( \\n\\n and remove )\n", + " # Uncomment the following lines if you are working with raw emails with the\n", + " # full headers\n", + " # hdrstart = email_contents.find(chr(10) + chr(10))\n", + " # email_contents = email_contents[hdrstart:]\n", + "\n", + " # Lower case\n", + " email_contents = email_contents.lower()\n", + " \n", + " # Strip all HTML\n", + " # Looks for any expression that starts with < and ends with > and replace\n", + " # and does not have any < or > in the tag it with a space\n", + " email_contents =re.compile('<[^<>]+>').sub(' ', email_contents)\n", + "\n", + " # Handle Numbers\n", + " # Look for one or more characters between 0-9\n", + " email_contents = re.compile('[0-9]+').sub(' number ', email_contents)\n", + "\n", + " # Handle URLS\n", + " # Look for strings starting with http:// or https://\n", + " email_contents = re.compile('(http|https)://[^\\s]*').sub(' httpaddr ', email_contents)\n", + "\n", + " # Handle Email Addresses\n", + " # Look for strings with @ in the middle\n", + " email_contents = re.compile('[^\\s]+@[^\\s]+').sub(' emailaddr ', email_contents)\n", + " \n", + " # Handle $ sign\n", + " email_contents = re.compile('[$]+').sub(' dollar ', email_contents)\n", + " \n", + " # get rid of any punctuation\n", + " email_contents = re.split('[ @$/#.-:&*+=\\[\\]?!(){},''\">_<;%\\n\\r]', email_contents)\n", + "\n", + " # remove any empty word string\n", + " email_contents = [word for word in email_contents if len(word) > 0]\n", + " \n", + " # Stem the email contents word by word\n", + " stemmer = utils.PorterStemmer()\n", + " processed_email = []\n", + " for word in email_contents:\n", + " # Remove any remaining non alphanumeric characters in word\n", + " word = re.compile('[^a-zA-Z0-9]').sub('', word).strip()\n", + " word = stemmer.stem(word)\n", + " processed_email.append(word)\n", + "\n", + " if len(word) < 1:\n", + " continue\n", + "\n", + " # Look up the word in the dictionary and add to word_indices if found\n", + " # ====================== YOUR CODE HERE ======================\n", + " \n", + " for i in range(np.array(vocabList).size):\n", + " if word == vocabList[i] :\n", + " word_indices.append(i + 1)\n", + " \n", + " # =============================================================\n", + "\n", + " if verbose:\n", + " print('----------------')\n", + " print('Processed email:')\n", + " print('----------------')\n", + " print(' '.join(processed_email))\n", + " return word_indices" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "----------------\n", + "Processed email:\n", + "----------------\n", + "anyon know how much it cost to host a web portal well it depend on how mani visitor your expect thi can be anywher from less than number buck a month to a coupl of dollar number you should checkout httpaddr or perhap amazon ec number if your run someth big to unsubscrib yourself from thi mail list send an email to emailaddr\n", + "-------------\n", + "Word Indices:\n", + "-------------\n", + "[86, 916, 794, 1077, 883, 370, 1699, 790, 1822, 1831, 883, 431, 1171, 794, 1002, 1895, 592, 1676, 238, 162, 89, 688, 945, 1663, 1120, 1062, 1699, 375, 1162, 477, 1120, 1893, 1510, 799, 1182, 1237, 512, 1120, 810, 1895, 1440, 1547, 181, 1699, 1758, 1896, 688, 1676, 992, 961, 1477, 71, 530, 1699, 531]\n" + ] + } + ], + "source": [ + "# To use an SVM to classify emails into Spam v.s. Non-Spam, you first need\n", + "# to convert each email into a vector of features. In this part, you will\n", + "# implement the preprocessing steps for each email. You should\n", + "# complete the code in processEmail.m to produce a word indices vector\n", + "# for a given email.\n", + "\n", + "# Extract Features\n", + "with open('emailSample1.txt') as fid:\n", + " file_contents = fid.read()\n", + "\n", + "word_indices = processEmail(file_contents)\n", + "\n", + "#Print Stats\n", + "print('-------------')\n", + "print('Word Indices:')\n", + "print('-------------')\n", + "print(word_indices)" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [], + "source": [ + "def emailFeatures(word_indices):\n", + " n = 1899\n", + " x = np.zeros((n, 1))\n", + " \n", + " for i in range(1, word_indices.size):\n", + " x[word_indices[i]] = 1 \n", + " \n", + " return x" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Length of feature vector: 1899\n", + "\n", + "Number of non-zero entries: 44\n", + "\n" + ] + } + ], + "source": [ + "features = emailFeatures(np.array(word_indices))\n", + "print('Length of feature vector: %d\\n' % features.size)\n", + "print('Number of non-zero entries: %d\\n' % sum(features > 0))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "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" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/edit_assignment.py b/edit_assignment.py new file mode 100644 index 000000000..0b2b5e2d6 --- /dev/null +++ b/edit_assignment.py @@ -0,0 +1,33 @@ +import pandas as pd + +from matplotlib import pyplot as plt + +df=pd.read_csv('data.txt',delimiter='\t') + +plt.rcParams['figure.figsize']=(20,10) + +fig,ax=plt.subplots(nrows=5,ncols=9) + +r=c=0 + +list=[[0,0],[0,1],[0,2],[0,3],[0,4],[0,5],[0,6],[0,7],[0,8],[1,0],[1,1],[1,2],[1,3],[1,4],[1,5],[1,6],[1,7],[1,8],[2,0],[2,1],[2,2],[2,3],[2,4],[2,5],[2,6],[2,7],[2,8],[3,0],[3,1],[3,2],[3,3],[3,4],[3,5],[3,6],[3,7],[3,8],[4,0],[4,1],[4,2],[4,3],[4,4],[4,5],[4,6],[4,7],[4,8]] + +flag=-1 + +for i in range(1,10): + for j in range(i+1,11): + flag=flag+1 + r=list[flag][0] + c=list[flag][1] + for k in range(999): + if df['Label'][k]==1: + ax[r][c].scatter(df[str(i)][k],df[str(j)][k],color='r') + elif df['Label'][k]==2: + ax[r][c].scatter(df[str(i)][k],df[str(j)][k],color='b') + + ax[r][c].set_xlabel('feature '+str(i)) + ax[r][c].set_ylabel('feature '+str(j)) + ax[0][4].set_title('My Plot') + +# print(plt.rcParams['figure.figsize']) +plt.show() \ No newline at end of file diff --git a/gradientdescentMulti.ipynb b/gradientdescentMulti.ipynb new file mode 100644 index 000000000..755bcf1fc --- /dev/null +++ b/gradientdescentMulti.ipynb @@ -0,0 +1,456 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 177, + "metadata": {}, + "outputs": [], + "source": [ + "# used for manipulating directory paths\n", + "import os\n", + "\n", + "# Scientific and vector computation for python\n", + "import numpy as np\n", + "\n", + "# Plotting library\n", + "from matplotlib import pyplot\n", + "from mpl_toolkits.mplot3d import Axes3D # needed to plot 3-D surfaces\n", + "\n", + "# library written for this exercise providing additional functions for assignment submission, and others\n", + "import utils \n", + "\n", + "# define the submission/grader object for this exercise\n", + "grader = utils.Grader()\n", + "\n", + "# tells matplotlib to embed plots within the notebook\n", + "%matplotlib inline\n" + ] + }, + { + "cell_type": "code", + "execution_count": 178, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " X[:,0] X[:, 1] y\n", + "--------------------------\n", + " 2104 3 399900\n", + " 1600 3 329900\n", + " 2400 3 369000\n", + " 1416 2 232000\n", + " 3000 4 539900\n", + " 1985 4 299900\n", + " 1534 3 314900\n", + " 1427 3 198999\n", + " 1380 3 212000\n", + " 1494 3 242500\n" + ] + } + ], + "source": [ + "# Load data\n", + "data = np.loadtxt('ex1data2.txt', delimiter=',')\n", + "X = data[:, :2]\n", + "y = data[:, 2]\n", + "m = y.size\n", + "\n", + "# print out some data points\n", + "print('{:>8s}{:>8s}{:>10s}'.format('X[:,0]', 'X[:, 1]', 'y'))\n", + "print('-'*26)\n", + "for i in range(10):\n", + " print('{:8.0f}{:8.0f}{:10.0f}'.format(X[i, 0], X[i, 1], y[i]))" + ] + }, + { + "cell_type": "code", + "execution_count": 179, + "metadata": {}, + "outputs": [], + "source": [ + "def featureNormalize(X):\n", + " \"\"\"\n", + " Normalizes the features in X. returns a normalized version of X where\n", + " the mean value of each feature is 0 and the standard deviation\n", + " is 1. This is often a good preprocessing step to do when working with\n", + " learning algorithms.\n", + " \n", + " Parameters\n", + " ----------\n", + " X : array_like\n", + " The dataset of shape (m x n).\n", + " \n", + " Returns\n", + " -------\n", + " X_norm : array_like\n", + " The normalized dataset of shape (m x n).\n", + " \n", + " Instructions\n", + " ------------\n", + " First, for each feature dimension, compute the mean of the feature\n", + " and subtract it from the dataset, storing the mean value in mu. \n", + " Next, compute the standard deviation of each feature and divide\n", + " each feature by it's standard deviation, storing the standard deviation \n", + " in sigma. \n", + " \n", + " Note that X is a matrix where each column is a feature and each row is\n", + " an example. You needto perform the normalization separately for each feature. \n", + " \n", + " Hint\n", + " ----\n", + " You might find the 'np.mean' and 'np.std' functions useful.\n", + " \"\"\"\n", + " # You need to set these values correctly\n", + " X_norm = X.copy()\n", + " mu = np.zeros(X.shape[1])\n", + " sigma = np.zeros(X.shape[1])\n", + "\n", + " # =========================== YOUR CODE HERE =====================\n", + " for i in range(X.shape[1]):\n", + " sigma[i]=np.std(X[:,i])\n", + " mu[i]=np.sum(X[:,i])/X.shape[0]\n", + " \n", + " for j in range(X.shape[0]):\n", + " for k in range(X.shape[1]):\n", + " X_norm[j][k]=(X[j][k]-mu[k])/sigma[k]\n", + "\n", + " # ================================================================\n", + " return X_norm, mu, sigma" + ] + }, + { + "cell_type": "code", + "execution_count": 180, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Computed mean: [2000.68085106 3.17021277]\n", + "Computed standard deviation: [7.86202619e+02 7.52842809e-01]\n" + ] + } + ], + "source": [ + "# call featureNormalize on the loaded data\n", + "X_norm, mu, sigma = featureNormalize(X)\n", + "\n", + "print('Computed mean:', mu)\n", + "print('Computed standard deviation:', sigma)" + ] + }, + { + "cell_type": "code", + "execution_count": 181, + "metadata": {}, + "outputs": [], + "source": [ + "# Add intercept term to X\n", + "X = np.concatenate([np.ones((m, 1)), X_norm], axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 182, + "metadata": {}, + "outputs": [], + "source": [ + "def computeCostMulti(X, y, theta):\n", + " \"\"\"\n", + " Compute cost for linear regression with multiple variables.\n", + " Computes the cost of using theta as the parameter for linear regression to fit the data points in X and y.\n", + " \n", + " Parameters\n", + " ----------\n", + " X : array_like\n", + " The dataset of shape (m x n+1).\n", + " \n", + " y : array_like\n", + " A vector of shape (m, ) for the values at a given data point.\n", + " \n", + " theta : array_like\n", + " The linear regression parameters. A vector of shape (n+1, )\n", + " \n", + " Returns\n", + " -------\n", + " J : float\n", + " The value of the cost function. \n", + " \n", + " Instructions\n", + " ------------\n", + " Compute the cost of a particular choice of theta. You should set J to the cost.\n", + " \"\"\"\n", + " # Initialize some useful values\n", + " m = y.shape[0] # number of training examples\n", + " \n", + " # You need to return the following variable correctly\n", + " J = 0\n", + " \n", + " # ======================= YOUR CODE HERE ===========================\n", + " H=theta.dot(X.transpose())\n", + " sum=0\n", + " for element in range(len(H.transpose()-y)):\n", + " sum=sum+((H.transpose()-y)[element]*(H.transpose()-y)[element])\n", + " \n", + " J=sum/(2*m) \n", + " \n", + " \n", + " # ==================================================================\n", + " return J" + ] + }, + { + "cell_type": "code", + "execution_count": 183, + "metadata": {}, + "outputs": [], + "source": [ + "def gradientDescentMulti(X, y, theta, alpha, num_iters):\n", + " \"\"\"\n", + " Performs gradient descent to learn theta.\n", + " Updates theta by taking num_iters gradient steps with learning rate alpha.\n", + " \n", + " Parameters\n", + " ----------\n", + " X : array_like\n", + " The dataset of shape (m x n+1).\n", + " \n", + " y : array_like\n", + " A vector of shape (m, ) for the values at a given data point.\n", + " \n", + " theta : array_like\n", + " The linear regression parameters. A vector of shape (n+1, )\n", + " \n", + " alpha : float\n", + " The learning rate for gradient descent. \n", + " \n", + " num_iters : int\n", + " The number of iterations to run gradient descent. \n", + " \n", + " Returns\n", + " -------\n", + " theta : array_like\n", + " The learned linear regression parameters. A vector of shape (n+1, ).\n", + " \n", + " J_history : list\n", + " A python list for the values of the cost function after each iteration.\n", + " \n", + " Instructions\n", + " ------------\n", + " Peform a single gradient step on the parameter vector theta.\n", + "\n", + " While debugging, it can be useful to print out the values of \n", + " the cost function (computeCost) and gradient here.\n", + " \"\"\"\n", + " # Initialize some useful values\n", + " m = y.shape[0] # number of training examples\n", + " \n", + " # make a copy of theta, which will be updated by gradient descent\n", + " theta = theta.copy()\n", + " \n", + " J_history = []\n", + " \n", + " for i in range(num_iters):\n", + " # ======================= YOUR CODE HERE ==========================\n", + " H=theta.dot(X.transpose())\n", + " \n", + " for j in range(theta.size):\n", + " theta[j]-=(alpha/m)*((H.transpose()-y).transpose()).dot(X[:,j])\n", + " \n", + " # =================================================================\n", + " \n", + " # save the cost J in every iteration\n", + " J_history.append(computeCostMulti(X, y, theta))\n", + " \n", + " return theta, J_history" + ] + }, + { + "cell_type": "code", + "execution_count": 184, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[340412.56301439 109370.05670466 -6500.61509507]\n" + ] + }, + { + "data": { + "image/png": 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PvrKdjs7uCldjZjb6ChvwzRMbWDRzEh2d3azY4MslzSx/ChvwAIsXpMM0L3mYxszyp9ABf1o6TPOEx+HNLIeKHfALpgPw+EtbK1yJmdnoK3TAHz97Ck31NazZvJu29n2VLsfMbFQVOuDra2s4feEMAB5avbnC1ZiZja5CBzzAmYuOAOBBB7yZ5UzhA/7Nx6YB/7wD3szypfAB//p505jcWMcLm3axYfveSpdjZjZqCh/wdbU1nHFMMg7/4OpNFa7GzGz0FD7gAc5Kx+F/s8rDNGaWHw544Jzjko8KvP/3G+n2B4CYWU444IHjjpzM/OkT2LSzgyfW+q5WM8sHBzwgibefeCQA9zz7aoWrMTMbHQ741HknzALgnmc3VrgSM7PR4YBPvWnRDCY11LJiQzsvb9ld6XLMzEbMAZ9qrKvl3OOTXvwdT62vcDVmZiPngC/zn06dC8BPn1hX4UrMzEbOAV/m3ONbmNJUx9PrdvDcxvZKl2NmNiKZBbykb0jaKOmprNoYbU31tVx48hzAvXgzG/+y7MF/C3hnhvvPxMWLk2Ga2x5/xTc9mdm4llnAR8QDwJas9p+VNy06gnnNE1i7dQ8PrGqrdDlmZofNY/B91NaIK85cAMD3HnqxwtWYmR2+ige8pKslLZW0tK2tOnrM7209iobaGu5ZsdHXxJvZuFXxgI+IJRHRGhGtLS0tlS4HgJmTG7nw9bOJgO88uKbS5ZiZHZaKB3y1uursRQDc9PBLbNnVUeFqzMyGL8vLJG8GHgSOl7RW0lVZtZWF18+fxrnHt7C7o4tv/OaFSpdjZjZsWV5Fc3lEzImI+oiYHxFfz6qtrFxz3msB+Pb/W8NW9+LNbJzxEM0g3nj0dP7gtTNp39fJ5+9ZVelyzMyGxQF/CNf/8YnUCL770IuevsDMxhUH/CGcMHsql5+xgK7u4JM/e5oI391qZuODA34IPvGO45g+sZ7fPreZmx95udLlmJkNiQN+CI6Y3MinLj4ZgH/8j2d885OZjQsO+CG66JQ5XPj62ezq6OLD31vG3v1dlS7JzGxQDvghksQ/v/sUFsyYyNPrdvA/b3vS4/FmVtUc8MMwbWI9X33/G5lQX8utj73C/7n795UuycxsQA74YTpxzlRuvGwxtTXiC796jq/c/3ylSzIz65cD/jBc8LrZ3HDJKQB85o4VfPbOFR6uMbOq44A/TO95w3xuuOQUamvEl+97nmtufpyd+zorXZaZWQ8H/Ahc2noUX7+ylYkNtdy+fD0XfeHXLF+7rdJlmZkBDvgRO/f4WfzsL8/mhNlTWLN5N3/ypd/yyZ8+xfY9+ytdmpkVnAN+FLxm1mR+8tG38OdnH4Mkvv3gi7z1hnv54j2r2LHXQW9mlaFqOjnY2toaS5curXQZI/LMuh38/c+f5pEXks8bn9JYx8WnzeWy0xdw8rxpFa7OzPJG0rKIaO13nQN+9EUED67ezBfuWcVDq7f0LF/UMol3nHQk5590JKfMb6a+1n9AmdnIOOAraOWGdm5+5CV+8sQrbNvdO1wzob6WU4+axukLZ/C6udM47sjJHH3EJGprVMFqzWy8ccBXgf1d3Ty6Zgt3P/Mq969sY/WmXQdt01hXw6KWycyfPoF5zROY29zE3OYJzJzcyIxJDTRPrGf6xAb3/M2shwO+Cm3auY9lL27lsRe3smJDO79/tZ312/cO6bVTGuuYNrGeSQ11TGysZWJDLRPq65jYkD5uqKWpvpb6GlFXW0N9bQ31taK+toa6WlFfU0N9nairSdbV1ogaQY2E0u/JVzIHT42gpqb8+WDbl1fa+6S0vHy1yjZWn+2SZQe//oC9l2+bPtFA69M1By7rt9RcUU7/Yf39fxjvmifUU3cYnbfBAr5uxFXZYZk5uZELXjebC143u2fZjr37Wd22i/Xb9vBK+rVu2x427+xg6+4Otu3ez9bdHbTv66TdN1WZ5covP/FWXjNr8qju0wFfRaY21bP4qGYWH9U84Dbd3UH73k627elg174u9uzvZHdHF7s7utiTft/d0cm+zm72d3XT2RXs7+pmf1fQ2d3d87i0rqOrm+7uoDuCALojOUncHUF3N8nySL4nX6X19HkedHX3/jV4wN+FcfCy8r8co2dZ+UvK1sfB6w/YfbriwP0fvK8Dl/W/bb7k8x+W159XFuffHPDjTE2NmDaxnmkT6ytdiplVOZ+tMzPLqUwDXtI7Ja2U9Jyka7Nsy8zMDpRZwEuqBb4E/BFwEnC5pJOyas/MzA6UZQ/+DOC5iFgdER3AvwMXZ9iemZmVyTLg5wEvlz1fmy47gKSrJS2VtLStrS3DcszMiiXLgO/vmp+DLnCKiCUR0RoRrS0tLRmWY2ZWLFkG/FrgqLLn84F1GbZnZmZlsgz4R4HXSjpGUgNwGfCzDNszM7Mymc5FI+lC4EagFvhGRPzjIbZvA148zOZmApsO87VjodrrA9c4Gqq9Pqj+Gqu9PqiuGo+OiH7Ht6tqsrGRkLR0oAl3qkG11weucTRUe31Q/TVWe30wPmoE38lqZpZbDngzs5zKU8AvqXQBh1Dt9YFrHA3VXh9Uf43VXh+MjxrzMwZvZmYHylMP3szMyjjgzcxyatwHfLVMSSzpKEn3SnpW0tOSPpYunyHpbkmr0u/T0+WS9IW07uWS3jBGddZKelzS7enzYyQ9nNb3g/SmNCQ1ps+fS9cvHKP6miXdImlFeizPqqZjKOmv0p/vU5JultRU6WMo6RuSNkp6qmzZsI+ZpCvT7VdJunIMarwh/Tkvl3SbpOayddelNa6UdEHZ8sze7/3VWLburyWFpJnp84ocx2GLiHH7RXID1fPAIqAB+B1wUoVqmQO8IX08Bfg9yTTJ/wJcmy6/Fvhs+vhC4A6SOXvOBB4eozo/AXwfuD19/kPgsvTxV4C/SB9/BPhK+vgy4AdjVN+3gT9PHzcAzdVyDEkmy3sBmFB27D5Y6WMInAO8AXiqbNmwjhkwA1idfp+ePp6ecY3nA3Xp48+W1XhS+l5uBI5J3+O1Wb/f+6sxXX4UcBfJTZgzK3kch/1vqlTDo/QDOQu4q+z5dcB1la4rreWnwDuAlcCcdNkcYGX6+KvA5WXb92yXYU3zgXuA84Db0/+cm8reZD3HM/0PfVb6uC7dThnXNzUNUPVZXhXHkN4ZUmekx+R24IJqOIbAwj7hOaxjBlwOfLVs+QHbZVFjn3XvBm5KHx/wPi4dx7F4v/dXI3ALcCqwht6Ar9hxHM7XeB+iGdKUxGMt/VP8NOBh4MiIWA+Qfp+VblaJ2m8E/gboTp8fAWyLiM5+auipL12/Pd0+S4uANuCb6TDS1yRNokqOYUS8Avxv4CVgPckxWUZ1HcOS4R6zSr+XPkTSI2aQWsa8RknvAl6JiN/1WVU1NQ5mvAf8kKYkHkuSJgM/Bj4eETsG27SfZZnVLukiYGNELBtiDZU4tnUkfyJ/OSJOA3aRDC8MZKyP4XSSD605BpgLTCL5xLKBaqi6/58MXFPFapV0PdAJ3FRaNEAtY/3znghcD/xdf6sHqKWqfubjPeCrakpiSfUk4X5TRNyaLn5V0px0/RxgY7p8rGt/C/AuSWtIPl3rPJIefbOkun5q6KkvXT8N2JJhfaU210bEw+nzW0gCv1qO4duBFyKiLSL2A7cCb6a6jmHJcI9ZRd5L6UnIi4ArIh3TqKIajyX5Zf679H0zH3hM0uwqqnFQ4z3gq2ZKYkkCvg48GxGfK1v1M6B0Jv1KkrH50vIPpGfjzwS2l/6kzkJEXBcR8yNiIclx+lVEXAHcC1wyQH2lui9Jt8+0JxIRG4CXJR2fLvpD4Bmq5BiSDM2cKWli+vMu1Vc1x7DMcI/ZXcD5kqanf6mcny7LjKR3An8LvCsidvep/bL0KqRjgNcCjzDG7/eIeDIiZkXEwvR9s5bkQooNVNFxHFSlBv9H8aTIhSRXrDwPXF/BOs4m+VNsOfBE+nUhyZjrPcCq9PuMdHuRfCj588CTQOsY1nouvVfRLCJ58zwH/AhoTJc3pc+fS9cvGqPaFgNL0+P4E5IrEarmGAL/AKwAngK+S3KlR0WPIXAzyTmB/SQhdNXhHDOScfDn0q8/G4ManyMZry69X75Stv31aY0rgT8qW57Z+72/GvusX0PvSdaKHMfhfnmqAjOznBrvQzRmZjYAB7yZWU454M3McsoBb2aWUw54M7OccsBbZtLZ9/617PlfS/r7Udr3tyRdcugtR9zOpUpmtby3z/K5km5JHy+WdOEottks6SP9tWU2HA54y9I+4D2lKVarhaTaYWx+FfCRiHhb+cKIWBcRpV8wi0muzx5ODXWDrG4mmYmyv7bMhswBb1nqJPnsyr/qu6JvD1zSzvT7uZLul/RDSb+X9BlJV0h6RNKTko4t283bJf063e6i9PW1SuYZfzSdp/u/lu33XknfJ7kxpW89l6f7f0rSZ9Nlf0dyA9tXJN3QZ/uF6bYNwKeA90l6QtL7JE1SMrf4o+mkaRenr/mgpB9J+jnwC0mTJd0j6bG07YvT3X8GODbd3w2lttJ9NEn6Zrr945LeVrbvWyXdqWQe8n8pOx7fSmt9UtJBPwvLr8F6EWaj4UvA8lLgDNGpwIkk87asBr4WEWco+RCVa4CPp9stBN5KMmfIvZJeA3yA5Lbx0yU1Ar+V9It0+zOAkyPihfLGJM0lmY/8jcBWkvD9k4j4lKTzgL+OiKX9FRoRHekvgtaI+Mt0f/9EMi3Bh5R8iMUjkn6ZvuQs4JSI2JL24t8dETvSv3IekvQzkgnWTo6Ixen+FpY1+dG03ddLOiGt9bh03WKSWUz3ASslfZFkFsl5EXFyuq9mrDDcg7dMRTKj5neA/zaMlz0aEesjYh/JreClgH6SJNRLfhgR3RGxiuQXwQkkc398QNITJNM1H0EylwnAI33DPXU6cF8kk4iVZjU8Zxj19nU+cG1aw30kUxYsSNfdHRGlCccE/JOk5cAvSaaVPfIQ+z6bZIoEImIFyYdQlAL+noj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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "theta=np.zeros(X.shape[1])\n", + "alpha=0.01\n", + "num_iters=1500\n", + "theta,J_history=gradientDescentMulti(X, y, theta, alpha, num_iters)\n", + "print(theta)\n", + "pyplot.plot(np.arange(len(J_history)), J_history, lw=2) \n", + "pyplot.xlabel('Number of iterations')\n", + "pyplot.ylabel('Cost J')\n", + "pyplot.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 185, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Predicted price of a 1650 sq-ft, 3 br house (using gradient descent): $293098\n" + ] + } + ], + "source": [ + "# Estimate the price of a 1650 sq-ft, 3 br house\n", + "# ======================= YOUR CODE HERE ===========================\n", + "# Recall that the first column of X is all-ones. \n", + "# Thus, it does not need to be normalized.\n", + "sqft=1650\n", + "numhouse=3\n", + "\n", + "price = 0 # You should change this\n", + "\n", + "norm_sqft=(sqft-mu[0])/sigma[0]\n", + "norm_numhouse=(numhouse-mu[1])/sigma[1]\n", + "\n", + "price = theta[0] + (theta[1]*norm_sqft) + (theta[2]*norm_numhouse)\n", + "\n", + "# ===================================================================\n", + "\n", + "print('Predicted price of a 1650 sq-ft, 3 br house (using gradient descent): ${:.0f}'.format(price))" + ] + }, + { + "cell_type": "code", + "execution_count": 186, + "metadata": {}, + "outputs": [], + "source": [ + "# Load data\n", + "data = np.loadtxt('ex1data2.txt', delimiter=',')\n", + "X = data[:, :2]\n", + "y = data[:, 2]\n", + "m = y.size\n", + "X = np.concatenate([np.ones((m, 1)), X], axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 187, + "metadata": {}, + "outputs": [], + "source": [ + "def normalEqn(X, y):\n", + " \"\"\"\n", + " Computes the closed-form solution to linear regression using the normal equations.\n", + " \n", + " Parameters\n", + " ----------\n", + " X : array_like\n", + " The dataset of shape (m x n+1).\n", + " \n", + " y : array_like\n", + " The value at each data point. A vector of shape (m, ).\n", + " \n", + " Returns\n", + " -------\n", + " theta : array_like\n", + " Estimated linear regression parameters. A vector of shape (n+1, ).\n", + " \n", + " Instructions\n", + " ------------\n", + " Complete the code to compute the closed form solution to linear\n", + " regression and put the result in theta.\n", + " \n", + " Hint\n", + " ----\n", + " Look up the function `np.linalg.pinv` for computing matrix inverse.\n", + " \"\"\"\n", + " theta = np.zeros(X.shape[1])\n", + " \n", + " # ===================== YOUR CODE HERE ============================\n", + " \n", + " theta=((np.linalg.inv(((X.transpose()).dot(X)))).dot(X.transpose())).dot(y)\n", + "\n", + " \n", + " # =================================================================\n", + " return theta" + ] + }, + { + "cell_type": "code", + "execution_count": 188, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Theta computed from the normal equations: [89597.9095428 139.21067402 -8738.01911233]\n", + "Predicted price of a 1650 sq-ft, 3 br house (using normal equations): $293081\n" + ] + } + ], + "source": [ + "# Calculate the parameters from the normal equation\n", + "theta = normalEqn(X, y);\n", + "\n", + "# Display normal equation's result\n", + "print('Theta computed from the normal equations: {:s}'.format(str(theta)));\n", + "\n", + "# Estimate the price of a 1650 sq-ft, 3 br house\n", + "# ====================== YOUR CODE HERE ======================\n", + "\n", + "price = 0 # You should change this\n", + "\n", + "sqft=1650\n", + "numhouse=3\n", + "\n", + "price = theta[0] + (theta[1]*sqft) + (theta[2]*numhouse)\n", + "\n", + "# ============================================================\n", + "\n", + "print('Predicted price of a 1650 sq-ft, 3 br house (using normal equations): ${:.0f}'.format(price))" + ] + } + ], + "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" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/gradientdescentSingle.ipynb b/gradientdescentSingle.ipynb new file mode 100644 index 000000000..6530ab445 --- /dev/null +++ b/gradientdescentSingle.ipynb @@ -0,0 +1,388 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# used for manipulating directory paths\n", + "import os\n", + "\n", + "# Scientific and vector computation for python\n", + "import numpy as np\n", + "\n", + "# Plotting library\n", + "from matplotlib import pyplot\n", + "from mpl_toolkits.mplot3d import Axes3D # needed to plot 3-D surfaces\n", + "\n", + "# library written for this exercise providing additional functions for assignment submission, and others\n", + "import utils \n", + "\n", + "# define the submission/grader object for this exercise\n", + "grader = utils.Grader()\n", + "\n", + "# tells matplotlib to embed plots within the notebook\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Read comma separated data\n", + "data = np.loadtxt('ex1data1.txt', delimiter=',')\n", + "X, y = data[:, 0], data[:, 1]\n", + "m = y.size # number of training examples" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Add a column of ones to X. The numpy function stack joins arrays along a given axis. \n", + "# The first axis (axis=0) refers to rows (training examples) \n", + "# and second axis (axis=1) refers to columns (features).\n", + "X = np.stack([np.ones(m), X], axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "def computeCost(X, y, theta):\n", + " \"\"\"\n", + " Compute cost for linear regression. Computes the cost of using theta as the\n", + " parameter for linear regression to fit the data points in X and y.\n", + " \n", + " Parameters\n", + " ----------\n", + " X : array_like\n", + " The input dataset of shape (m x n+1), where m is the number of examples,\n", + " and n is the number of features. We assume a vector of one's already \n", + " appended to the features so we have n+1 columns.\n", + " \n", + " y : array_like\n", + " The values of the function at each data point. This is a vector of\n", + " shape (m, ).\n", + " \n", + " theta : array_like\n", + " The parameters for the regression function. This is a vector of \n", + " shape (n+1, ).\n", + " \n", + " Returns\n", + " -------\n", + " J : float\n", + " The value of the regression cost function.\n", + " \n", + " Instructions\n", + " ------------\n", + " Compute the cost of a particular choice of theta. \n", + " You should set J to the cost.\n", + " \"\"\"\n", + " \n", + " # initialize some useful values\n", + " m = y.size # number of training examples\n", + "\n", + " # You need to return the following variables correctly\n", + " J = 0\n", + "\n", + " # ====================== YOUR CODE HERE =====================\n", + " H=theta.dot(X.transpose())\n", + " sum=0\n", + " for element in range(len(H.transpose()-y)):\n", + " sum=sum+((H.transpose()-y)[element]*(H.transpose()-y)[element])\n", + " \n", + " J=sum/(2*m) \n", + "\n", + " # ===========================================================\n", + " return J\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# J = computeCost(X, y, theta=np.array([0.0, 0.0]))\n", + "# print('With theta = [0, 0] \\nCost computed = %.2f' % J)\n", + "# print('Expected cost value (approximately) 32.07\\n')\n", + "\n", + "# # further testing of the cost function\n", + "# J = computeCost(X, y, theta=np.array([-1, 2]))\n", + "# print('With theta = [-1, 2]\\nCost computed = %.2f' % J)\n", + "# print('Expected cost value (approximately) 54.24')" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "def gradientDescent(X, y, theta, alpha, num_iters):\n", + " \"\"\"\n", + " Performs gradient descent to learn `theta`. Updates theta by taking `num_iters`\n", + " gradient steps with learning rate `alpha`.\n", + " \n", + " Parameters\n", + " ----------\n", + " X : array_like\n", + " The input dataset of shape (m x n+1).\n", + " \n", + " y : arra_like\n", + " Value at given features. A vector of shape (m, ).\n", + " \n", + " theta : array_like\n", + " Initial values for the linear regression parameters. \n", + " A vector of shape (n+1, ).\n", + " \n", + " alpha : float\n", + " The learning rate.\n", + " \n", + " num_iters : int\n", + " The number of iterations for gradient descent. \n", + " \n", + " Returns\n", + " -------\n", + " theta : array_like\n", + " The learned linear regression parameters. A vector of shape (n+1, ).\n", + " \n", + " J_history : list\n", + " A python list for the values of the cost function after each iteration.\n", + " \n", + " Instructions\n", + " ------------\n", + " Peform a single gradient step on the parameter vector theta.\n", + "\n", + " While debugging, it can be useful to print out the values of \n", + " the cost function (computeCost) and gradient here.\n", + " \"\"\"\n", + " # Initialize some useful values\n", + " m = y.shape[0] # number of training examples\n", + " \n", + " # make a copy of theta, to avoid changing the original array, since numpy arrays\n", + " # are passed by reference to functions\n", + " theta = theta.copy()\n", + " \n", + " J_history = [] # Use a python list to save cost in every iteration\n", + " \n", + " for i in range(num_iters):\n", + " # ==================== YOUR CODE HERE =================================\n", + " H=theta.dot(X.transpose())\n", + " temp1=(alpha/m)*((H.transpose()-y).transpose()).dot(X[:,0])\n", + " temp2=(alpha/m)*((H.transpose()-y).transpose()).dot(X[:,1])\n", + " theta[0]-=temp1\n", + " theta[1]-=temp2\n", + " # =====================================================================\n", + " \n", + " # save the cost J in every iteration\n", + " J_history.append(computeCost(X, y, theta))\n", + " \n", + " return theta, J_history" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Theta found by gradient descent: -3.6303, 1.1664\n", + "Expected theta values (approximately): [-3.6303, 1.1664]\n" + ] + } + ], + "source": [ + "# initialize fitting parameters\n", + "theta = np.zeros(2)\n", + "\n", + "# some gradient descent settings\n", + "iterations = 1500\n", + "alpha = 0.01\n", + "\n", + "theta, J_history = gradientDescent(X ,y, theta, alpha, iterations)\n", + "print('Theta found by gradient descent: {:.4f}, {:.4f}'.format(*theta))\n", + "print('Expected theta values (approximately): [-3.6303, 1.1664]')" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "def plotData(x, y):\n", + " \"\"\"\n", + " Plots the data points x and y into a new figure. Plots the data \n", + " points and gives the figure axes labels of population and profit.\n", + " \n", + " Parameters\n", + " ----------\n", + " x : array_like\n", + " Data point values for x-axis.\n", + "\n", + " y : array_like\n", + " Data point values for y-axis. Note x and y should have the same size.\n", + " \n", + " Instructions\n", + " ------------\n", + " Plot the training data into a figure using the \"figure\" and \"plot\"\n", + " functions. Set the axes labels using the \"xlabel\" and \"ylabel\" functions.\n", + " Assume the population and revenue data have been passed in as the x\n", + " and y arguments of this function. \n", + " \n", + " Hint\n", + " ----\n", + " You can use the 'ro' option with plot to have the markers\n", + " appear as red circles. Furthermore, you can make the markers larger by\n", + " using plot(..., 'ro', ms=10), where `ms` refers to marker size. You \n", + " can also set the marker edge color using the `mec` property.\n", + " \"\"\"\n", + " fig = pyplot.figure() # open a new figure\n", + " \n", + " # ====================== YOUR CODE HERE ======================= \n", + " pyplot.plot(x, y, 'ro',ms=10, mec='k')\n", + " pyplot.ylabel('Profit in $10,000')\n", + " pyplot.xlabel('Population of City in 10,000s')\n", + "\n", + " # =============================================================" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# plot the linear fit\n", + "plotData(X[:, 1], y)\n", + "pyplot.plot(X[:, 1], np.dot(X, theta), '-')\n", + "pyplot.legend(['Training data', 'Linear regression']);" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "For population = 35,000, we predict a profit of 4519.77\n", + "\n", + "For population = 70,000, we predict a profit of 45342.45\n", + "\n" + ] + } + ], + "source": [ + "# Predict values for population sizes of 35,000 and 70,000\n", + "predict1 = np.dot([1, 3.5], theta)\n", + "print('For population = 35,000, we predict a profit of {:.2f}\\n'.format(predict1*10000))\n", + "\n", + "predict2 = np.dot([1, 7], theta)\n", + "print('For population = 70,000, we predict a profit of {:.2f}\\n'.format(predict2*10000))" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# grid over which we will calculate J\n", + "theta0_vals = np.linspace(-10, 10, 100)\n", + "theta1_vals = np.linspace(-1, 4, 100)\n", + "\n", + "# initialize J_vals to a matrix of 0's\n", + "J_vals = np.zeros((theta0_vals.shape[0], theta1_vals.shape[0]))\n", + "\n", + "# Fill out J_vals\n", + "for i, theta0 in enumerate(theta0_vals):\n", + " for j, theta1 in enumerate(theta1_vals):\n", + " J_vals[i][j] = computeCost(X, y, np.array([theta0, theta1]))\n", + " \n", + "# Because of the way meshgrids work in the surf command, we need to\n", + "# transpose J_vals before calling surf, or else the axes will be flipped\n", + "J_vals = J_vals.T\n", + "\n", + "# surface plot\n", + "fig = pyplot.figure(figsize=(12, 5))\n", + "ax = fig.add_subplot(121, projection='3d')\n", + "ax.plot_surface(theta0_vals, theta1_vals, J_vals, cmap='viridis')\n", + "pyplot.xlabel('theta0')\n", + "pyplot.ylabel('theta1')\n", + "pyplot.title('Surface')\n", + "\n", + "# contour plot\n", + "# Plot J_vals as 15 contours spaced logarithmically between 0.01 and 100\n", + "ax = pyplot.subplot(122)\n", + "pyplot.contour(theta0_vals, theta1_vals, J_vals, linewidths=2, cmap='viridis', levels=np.logspace(-2, 3, 20))\n", + "pyplot.xlabel('theta0')\n", + "pyplot.ylabel('theta1')\n", + "pyplot.plot(theta[0], theta[1], 'ro', ms=10, lw=2)\n", + "pyplot.title('Contour, showing minimum')\n", + "pass" + ] + } + ], + "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" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/introduction_to_matrix.ipynb b/introduction_to_matrix.ipynb new file mode 100644 index 000000000..f92f097c1 --- /dev/null +++ b/introduction_to_matrix.ipynb @@ -0,0 +1,102 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# used for manipulating directory paths\n", + "import os\n", + "\n", + "# Scientific and vector computation for python\n", + "import numpy as np\n", + "\n", + "# Plotting library\n", + "from matplotlib import pyplot\n", + "from mpl_toolkits.mplot3d import Axes3D # needed to plot 3-D surfaces\n", + "\n", + "# library written for this exercise providing additional functions for assignment submission, and others\n", + "import utils \n", + "\n", + "# define the submission/grader object for this exercise\n", + "grader = utils.Grader()\n", + "\n", + "# tells matplotlib to embed plots within the notebook\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "def warmUpExercise():\n", + " \"\"\"\n", + " Example function in Python which computes the identity matrix.\n", + " \n", + " Returns\n", + " -------\n", + " A : array_like\n", + " The 5x5 identity matrix.\n", + " \n", + " Instructions\n", + " ------------\n", + " Return the 5x5 identity matrix.\n", + " \"\"\" \n", + " # ======== YOUR CODE HERE ======\n", + " A = [] # modify this line\n", + " A=np.eye(5)\n", + " \n", + " # ==============================\n", + " return A" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[1., 0., 0., 0., 0.],\n", + " [0., 1., 0., 0., 0.],\n", + " [0., 0., 1., 0., 0.],\n", + " [0., 0., 0., 1., 0.],\n", + " [0., 0., 0., 0., 1.]])" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "warmUpExercise()" + ] + } + ], + "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" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/plotData.ipynb b/plotData.ipynb new file mode 100644 index 000000000..c4f11e1cb --- /dev/null +++ b/plotData.ipynb @@ -0,0 +1,136 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# used for manipulating directory paths\n", + "import os\n", + "\n", + "# Scientific and vector computation for python\n", + "import numpy as np\n", + "\n", + "# Plotting library\n", + "from matplotlib import pyplot\n", + "from mpl_toolkits.mplot3d import Axes3D # needed to plot 3-D surfaces\n", + "\n", + "# library written for this exercise providing additional functions for assignment submission, and others\n", + "import utils \n", + "\n", + "# define the submission/grader object for this exercise\n", + "grader = utils.Grader()\n", + "\n", + "# tells matplotlib to embed plots within the notebook\n", + "%matplotlib inline\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Read comma separated data\n", + "data = np.loadtxt('ex1data1.txt', delimiter=',')\n", + "X, y = data[:, 0], data[:, 1]\n", + "\n", + "m = y.size # number of training examples" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "def plotData(x, y):\n", + " \"\"\"\n", + " Plots the data points x and y into a new figure. Plots the data \n", + " points and gives the figure axes labels of population and profit.\n", + " \n", + " Parameters\n", + " ----------\n", + " x : array_like\n", + " Data point values for x-axis.\n", + "\n", + " y : array_like\n", + " Data point values for y-axis. Note x and y should have the same size.\n", + " \n", + " Instructions\n", + " ------------\n", + " Plot the training data into a figure using the \"figure\" and \"plot\"\n", + " functions. Set the axes labels using the \"xlabel\" and \"ylabel\" functions.\n", + " Assume the population and revenue data have been passed in as the x\n", + " and y arguments of this function. \n", + " \n", + " Hint\n", + " ----\n", + " You can use the 'ro' option with plot to have the markers\n", + " appear as red circles. Furthermore, you can make the markers larger by\n", + " using plot(..., 'ro', ms=10), where `ms` refers to marker size. You \n", + " can also set the marker edge color using the `mec` property.\n", + " \"\"\"\n", + " fig = pyplot.figure() # open a new figure\n", + " \n", + " # ====================== YOUR CODE HERE ======================= \n", + " pyplot.plot(x, y, 'ro',ms=10, mec='k')\n", + " pyplot.ylabel('Profit in $10,000')\n", + " pyplot.xlabel('Population of City in 10,000s')\n", + "\n", + " # =============================================================" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plotData(X, y)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "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" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/regularisedLogisticRegression.ipynb b/regularisedLogisticRegression.ipynb new file mode 100644 index 000000000..29006e75c --- /dev/null +++ b/regularisedLogisticRegression.ipynb @@ -0,0 +1,440 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# used for manipulating directory paths\n", + "import os\n", + "\n", + "# used for mathematical operations of elements\n", + "import math\n", + "\n", + "# Scientific and vector computation for python\n", + "import numpy as np\n", + "\n", + "# Plotting library\n", + "from matplotlib import pyplot\n", + "\n", + "# Optimization module in scipy\n", + "from scipy import optimize\n", + "\n", + "# library written for this exercise providing additional functions for assignment submission, and others\n", + "import utils\n", + "\n", + "# define the submission/grader object for this exercise\n", + "grader = utils.Grader()\n", + "\n", + "# tells matplotlib to embed plots within the notebook\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Load Data\n", + "# The first two columns contains the X values and the third column\n", + "# contains the label (y).\n", + "data = np.loadtxt('ex2data2.txt', delimiter=',')\n", + "X = data[:, :2]\n", + "y = data[:, 2]" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "def sigmoid(z):\n", + " \"\"\"\n", + " Compute sigmoid function given the input z.\n", + " \n", + " Parameters\n", + " ----------\n", + " z : array_like\n", + " The input to the sigmoid function. This can be a 1-D vector \n", + " or a 2-D matrix. \n", + " \n", + " Returns\n", + " -------\n", + " g : array_like\n", + " The computed sigmoid function. g has the same shape as z, since\n", + " the sigmoid is computed element-wise on z.\n", + " \n", + " Instructions\n", + " ------------\n", + " Compute the sigmoid of each value of z (z can be a matrix, vector or scalar).\n", + " \"\"\"\n", + " # convert input to a numpy array\n", + " z = np.array(z)\n", + " \n", + " # You need to return the following variables correctly \n", + " g = np.zeros(z.shape)\n", + "\n", + " # ====================== YOUR CODE HERE ======================\n", + " g = 1/(1+np.exp((-z)))\n", + " \n", + " # =============================================================\n", + " return g" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "def plotData(X, y):\n", + " \"\"\"\n", + " Plots the data points X and y into a new figure. Plots the data \n", + " points with * for the positive examples and o for the negative examples.\n", + " \n", + " Parameters\n", + " ----------\n", + " X : array_like\n", + " An Mx2 matrix representing the dataset. \n", + " \n", + " y : array_like\n", + " Label values for the dataset. A vector of size (M, ).\n", + " \n", + " Instructions\n", + " ------------\n", + " Plot the positive and negative examples on a 2D plot, using the\n", + " option 'k*' for the positive examples and 'ko' for the negative examples. \n", + " \"\"\"\n", + " # Create New Figure\n", + " fig = pyplot.figure()\n", + "\n", + " # ====================== YOUR CODE HERE ======================\n", + " # Find Indices of Positive and Negative Examples\n", + " pos = y == 1\n", + " neg = y == 0\n", + " \n", + " pyplot.plot(X[neg,0],X[neg,1],'ko',mfc='y', ms=8, mec='k', mew=1)\n", + "\n", + " pyplot.plot(X[pos,0],X[pos,1],'k*',lw=2, ms=10)\n", + "\n", + " \n", + " # ============================================================" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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6IM36qIYMvNyYvd6sg7xp9iVrnJSHarAKL4zhO7+GwvyQY968OdrcPEhTKdHm5kFFT65MAvkUiKUyCZGgHX9BmvVRDRn0Nfmwf//+1NbW9lqXb05J0Bl3g86l5Sd+TuqMYvguDhU2LZTYcmGFStAzh0uNcPESgRPlnINCN+bPf/7z1NbWHq4aJyJ555SEkXE3yFxafuOXwosqFb6XmfFJ9TkmBVMgIRL0U3yp8wK8OJOjnnOQ78b88MMPs2/fPo477jgAjjvuuIJzSoK2EoKY4xIkfii8sFLhF0vQFoKFEpsCCZWgn+JLNeu9ROBEPWSQfWOur69n7969vPTSSxw6dKjXU++hQ4fYsGFD3mGTIK2EJMyIz8QvhRfH4bugLQQrsoU50cMkLo6/oJ3JQTB58mS98847taenR1VV161bp8ccc0xJTuAgM+7GnWynb11drV5wwfm6Z88eVVXt7u7WO+64QydPnlz0sZcvX64NDQ2aSqX02GOP1VQqpQ0NDYHPLclH0JGDUUUmRgEWhRW9AlH1JyV2uRPgkhZymo9So56ylVE5N80kEXQYedwUc9CRg9UUSmwKJCYKxA/8iLNPUshpIfyqA1INBH3DC0sxew2dDdpCiMuIQhjkUyDmA0kgfswajuOYdSkkKerJC0HmpCrk9L3wwi6+9rVbyjpvkKnw0xTjGI+rz7GSMAWSAIKKs6+Em2/Sop76IsikkIWcvjt2QFfXgdgko8xHMY7xMCIH/SiylWRMgSSAoOLsK+Hmm7Sop77wMydVNoXCyNNGQty/+2JCZ81CCIFc41qV2pLsAwnCZ1ENzuRCAQdBZuP1SpgRcZk+kLPP7v0bEiGw8/qJZeGNBswHkmyC8FmEMWYdNYWGhOJQQ8SLdXnsscfS1dVVtl8kc0hn3Djo3//ItrSfIOjZ4+ViWXjjhSmQEPArnUIl+CzCptCQUJDDRV7xMov7mmuu4Ze//GXZii5zSGfVqiYOHpSjhoIyzxvF7PG+SGIZ34oml1lSqS2KISw/Y+/jFmcfR7wOCRXaFsWwTaFwZC/ZbUsdjktaGHQ1hc7GCWwIKxr8TKdQaQ7jIPAyJFRoW1TDNpnWZXpIsauri3/6p3/yFHVX6nBc0qxac4zHjFxapVJbFBZItaQ7iINDOk2hgIP+/ftr//79YzeBMtO6vO2221REisoUUGoNDrNqDS9gFkg0VEvCtTg4pNMUCjhYvXo1q1evjt0Eykzr8rrrrmPt2rUcc8wxOfvW19dzxhln0NLSUvbcILNq8xNkKvhKIa8CEZGzROQXIrJVRL4tIo0Z236Vb79iEJGJIvKyiGwWkRtybL9CRHaKyLNum5mxbbqIbHLbdD/kCYIgokbi+MOOg0M6k0JDM3EctsmOiJswYQI//vGP8yq6O+64w5e5QXGMxAtyNr5XrFiUNwpZIP8F3AK8D9gG/EJE0s/SdXn38oiI1ADfAj4CDAemicjwHF3vU9WRblvq7jsAmAu8HzgHmCsiJ5UrUxD4HTUSlx+2n7Pjg7hhFJokmZQJlIUUXVxrcPhBHKxZKxbljUIK5HhVfUhVd6nqLcC/AmtF5H0447Dlcg6wWVW3qOoBYCUw2eO+FwDrVPUNVX0TWAdM9EEm3/E7nUK5P2y/btZ+zo4P4oZRaGgmKcM2fSm6Sslnlk0crFkrFuWRXI4Rx2fC88AJWetGApuAXfn289qAi4GlGe8vB76Z1ecK4DVXltXAYHf9vwGzM/p9Gfi3POe5EmgH2k899VRfHUte8SOFe5pynfJ+ZPJN49fs+FIdwJWOl0wBcavBUQpxrE9jM957Q7Hp3N0b+rk51g8Bvp9vP68NuCSHAvnPrD4Dgf7u8lVAm7t8XQ4Fcm1f50xyKpM05f6w/b5ZlzKPII43jKRSCVFUcahPkx1FWC3Rk17Jp0DyDmGp6nJVPcpZrqq/V9VP5duvCLYDgzPeNwM7ss61W1X3u2+XAKO97lupFOuUDyqTb5pSHNJBJYesRpIyHFeIOPhzsodRbca7R3JplTAa0A/YAgwFaoHngLOy+pycsXwR8KS7PADYCpzktq3AgL7OWQkWSLFFgYJ+uiv1CbhSCloZ/hHlrPhsy9xmvPeGOFYkBD4KvAK8CtzkrpsPXOgufw34natcNgBnZOz7aWCz2z7l5XyVoEBK+WEHebMuJ6Nv0tJoGMESpj/H6zDqaacN9cV3mXRKViDAB7ysS0KrBAWiWppTPo4360pwABv+EaY/Jw5+lySRT4F4mYn+7RzrvuVhPyMgSqmCFsfJc0mZj5FNHCa6VSJh+nPi4HepCHJpFUfhcA7wReCPwBcy2mzg+Xz7xblVigVSCnGM1klqQSs/Q6GNaImjZR5HKMECOQ4YhOPsbspoB3BCcI0EEcdonTim0fBCHCa6Gf4QR8s8UeTSKpkNOC1jWYDj+tonrq2aLRCjdGzeSuUSR8s8jlCGD+QrInKCiNTjRERtFZFZ/qsyw/BG2D4Im7dSucTRMk8UubRKZgOecV8/ASzCmbNhPhAjMqLwQdi8FSMI0hGVzc2DNJUSbW4eFMtQYcqwQGpFpB9OosP71Ul8eMhvRWYYXonCB1GpiQuN6IhLZu1y8KJAluKkcz8JeFRETgXi/8kqiGoPGw06HYtXzOFq+EklpIzvU4Go6tdV9RRV/bBrymwHxgcvmpEmDvURoiQuPoikzlsx4kklpIzvU4GISJOIfEdEHnJXnYHjDzFCIklho0FYS3GZ9GUOV8NPKqHctZchrGXAoxzJfrsJuDYogeJEVKVj4zJkUwpBWUtx8EEkdd6KEU+CKHcdNl4UyFtU9Ye4jnNVPQj0BCpVDIjSwRWXIZtSCNJaMh+EUUlUQsp4Lwrkb24NcmcmoVPStjNQqWJAKQ4uvyyWuAzZeCFMa8l8EEYl4Xe560jIFdub2YAxwC+BDpyhrM3AyL72i2MrZh5IsRXJMtOsL1mCrl+PLllSXv2AJOTpCTOraVJzZxlGPvwsdx0kFDsPREQ+4CqYdqAFGIuTXHG4qj4bhDKLE8U6uIIIyUvCkE2Y1pL5IKqXuIaylzvqUEpm7ThRaAjrcBp3VT2gqs+p6rPqTCSseIp1cAURkpeUIZs4OLiNyiaOoeyVMBGwXLz4QKqSYh1cQYTkJSlsNAnWkpFc4hjKXgkTActFNPsOmd4g0gE8lm9HVb2w7JOLTATuAmqApap6S9b2WcBMoBvYCXxaVf/gbusBXnC7bvMiz5gxY7S9vd2TbOmni8bGV5k6tYuhQx3LY+VKx8G1ceOTvczMwYObmDt3F8OGHX2sTZtgwYImtm173dO5k0hLSwuPPfYYI0aM4NZbb+VLX/oSzz33HGPHjqWtrS1q8YyEMWHCBB555JHD72trazlw4MDh1zStra2sX78+ChGr6j8vIk+r6pjs9YUskJ3AnQVauQLV4FQ2/AgwHJgmIsOzuj0DjFHV9wKrgdsytv1dVUe6rWxllk1DQwMbNz7J2LHXs2BBExMnpliwoImxY68/SnlAZYTklUOSrCUj/iQhlL0SJgKWSyEL5DeqenZgJxY5F/iKql7gvr8RQFW/lqf/KOCbqvqP7vu9qlqUp6kYC6RYirVYksKePXu44oorWLZsGY2NjVGLY1QRGzZsYNKkSezbt++obXEIZTcLpLAF8vvgxAHg7TjlctNsd9flYwbwcMb7OhFpF5EnRWRKEAIWQ7EWS9iUGsUSR+elUR3EPTij2kcdoIACUdWPBXxuybEupzkkIpfhzEfJ9Eqd6mrETwCLROSdefa90lU07Tt37ixX5oLEOSSvVEUQR+elUT3EOTijIiYClkmUUVjbOZJfC6AZ2JHdSUQmADcBF6rq/vR6Vd3hvm4BNgKjcp1EVRer6hhVHdPU1OSf9AnDqyJIch4uo/KIcyh73EcdQiHX7MIwGtAP2AIMxaly+BxwVlafUcCrwOlZ608C+rvLg3ASPA7v65zVVJGw1DreYc4sN4y+sOwD8YA8M9G93uw/BizEib66yMs+Ho/7UeAVV0nc5K6bj2NtAKwH/gI867Y17voP4oTwPue+zvByvmpSIOUogriXb+3o6NApU6ZoR0dHpHIYRrVQsgLBmZG+FviU234GfKuv/eLYqkmBqJanCOKchyuKmuhGvLGHimDJp0C8+EDGAheo6vdV9fuu1TDOw35GxJQTxRJn56U59o1sLFowGrwokJeBUzPeDwaeD0Ycw29KVQRxcl6aY9/oC3uoiAYvCmQg8JKIbBSRjcCLQJOIrBGRNYFKZ5RNqYogTjPLkzAr2QgXe6iIB3lnoh/uIDK20HZVfdRXiQIkyJnocWXKlCmcd955XHPNNaRSKXp6eli0aBGPP/54otKgx31WshEuhX4Paex34R/5ZqL3qUAqiWpUIJXEQw89xCWXXEJXV9fhdXV1daxatYpJkyZFKJkRBfZQER5FpzIRkV+4r50i8teM1ikifw1SWMPIRZwd+0b4xD3VSTVQKJXJ/3Bfj1fVEzLa8apqKVaN0ImTY9+IB/ZQkZ9yqyV6wVMqExGpEZFTROTUdPNNAsPwSJwc+0Y8sIeK3IRVLbFPBSIi/4IzG3wd8FO3PeTL2Q2jCKwmupFNpT5UlGs9hFUt0UsU1mbg/aqa+Ooo5kQ3DCPuZNYWmjbtSG2hFSu81xbyu1ZJKfVA0vwRKK6IhGEYhlESflgPYVVLLBSFNcutSb4F2CgiN6bXuesNwzAMn1my5NtMm9aFZFVMEoGpU7tYuvTuPo9xyikD2bo197atW53tflDIAjnebdtw/B+1GeuO9+XsRmIptcKhYRiF8cN6CKtaYqEw3nmFmi9nr0LCCK0Lg3KT15kCMozc+GE9hFUt0UsU1joROTHj/Uki8nNfzl5lhBVaFwblJq+z7KmGkRs/rIewqiV6caI3qerhWTmq+ibwFl/OXmX4HVoX5lO838nrLHtqdWCWZvH4ZT00NDQwZ848tm17ne7uHrZte505c+b5WmrXiwLpyZw4KCLvwCkwZBSJH86xTMJ8ii83I65lT61OzNL0RubQdmPjCfz5z38GzmHevEGxrrXuRYHcBPxCRJaLyHLgMeDGYMWqTPwOrQvzKb6lpYWHHnqolxLJpK/kdZaSvToxS7Nvcg1tf+Uru4Ff87a3vZWOjj2BWA9+0KcCUdWfAWcD97lttKr64gMRkYki8rKIbBaRG3Js7y8i97nb/1tEhmRsu9Fd/7KIXOCHPEFTrnMs6qf4cpLXlauAwIZDkkDUv9EkEtas8SDwlAsL+CBOGdtxwAf8OLGI1ADfAj4CDAemicjwrG4zgDdVdRjwdeBWd9/hwFTgLGAi8G33eLGmXOdYHJ7iy0leV272VBsOiT9x+I0mDb+HtsPESxTWLcAXcSoRvgh8UUS+5sO5zwE2q+oWVT0ArAQmZ/WZDNzjLq8GWkVE3PUrVXW/qm4FNrvHizXlOsf8eIovl3KT15WjgGw4JP5E8RtNemh8WLPGA0FVCzac+uepjPc1wPN97efhuBcDSzPeXw58M6vPb4HmjPevAoOAbwKXZaz/LnBxnvNcCbQD7aeeeqpGTWdnp86bN0cHD27SmpqUDh7cpPPmzdHOzk7Px3jwwQe1rq5OcYIZFNC6ujp98MEHA5TcYfLkyXrnnXdqT0+Pqqp2d3frHXfcoZMnT/a0/7hx4zSVSumoUaN07dq1OmrUKE2lUtrS0nJU39bW1l6fsba2ttdrurW2tvr6GY3yCes32tnZqaNHn6Xjx9fpkiXo+vXokiXo+PF1Onr0WUX9r6KiuXmQLlmCbthwdFu8GB08uClqERVo1xz3V69DWCdmLDcWoZ8KITnWZUd35evjZV9npepiVR2jqmOampqKFNF//Aiti7IGQrkZcYvJnmrDIcklrN9okv0HacKaNR4IubSK9n6Cnwb8AViGM5y0FZja134ejnsu8POM9zcCN2b1+TlwrrvcD9iFozx69c3sV6iNHj3af9UcAcU8xceVtCXW3DxIUynR5uZBOS2xtrY2ra+v7/Ukm2719fW6YcOGaD6AUZCwfqNJeHrvi0wravFidN06R/Y4WVGUYoG4/oZf4DjOf+y2c1V1pQ+66yngdBEZKiK1OE7xNVl91gDT3eWLgTb3wye7TlsAABsySURBVKwBprpRWkOB04Ff+yBTbMkc5924cSPHH1/HhRdO4txzz01cDYRiZuRb2dJkEladjkT7D1zCmjUeBF7qgTytqqMDObnIR4FFOH6V76nqV0VkPo62WyMidcByYBTwBo7ls8Xd9ybg00A3cI2qPtzX+ZJaD8SP+gBxYv78uTz66G3Mnt078kTVCSgYO/Z65sw5km7tBz/4AVdffTX79u2jf//+7N+/n/r6eu6++24uu+yyCD6BERf8rnth5KaceiBPisj7ApAJVf1/qvouVX2nqn7VXTdHVde4y12qeomqDlPVc9LKw932VXe/f/CiPKLCjwiRShjnzaTYsEUrW2rkI67+g6RHhnnFiwXyIvAuHD/I33B8EKqq7w1ePH8J2wLxy3KotKesmpoUa9cqNTlm7nR3w8SJKbq7ew6vmzJlCueddx7XXHMNqVSKnp4eFi1axOOPP27lbKuczP/Y1KlH/mMrV0ZnnVfaiAHkt0C8KJB35Fqvqn/wSbbQCFuBFDtUk49ib7hxp9IUohEte/fuZeHC21m69G527NjNKacMZObMq5k167pIbtR+/e/jRDlDWCcDb6jqH1yl8QbwNr8FrET8mmEaVnWxsIjrsIORTMLIOlsMSZ5ZXixeFMjdQObA3d/cdUYf+BUhUmk33LCK3RhGFFRCZJhXvCgQ0YxxLlU9hDMnw+gDvyyHoG+4YTv8khy2aMSfqJNuVtqIQSG8KJAtIvIFETnGbV8EtvS5l+Gb5RDkDTeqKolxG3YwKoeok25W2ohBIbw40d8CfAMYjzMD+BGceReJ83JGGYUVlwiRbCrR4Rcn9uzZwxVXXMGyZctobPQrC5BRiJaWFjZu3EhLSwttbW2hnz8J//tiyedELysdSdJaFKlM/EieGCSVkAoiztx7770K6PLly6MWpWKJY9LNuP/vi4U8qUzyWiAicr2q3iYi/0mORIWq+gW/tFtYJHUmepBUWohwufhtMUT9NBwngrLGNmzYwKRJk9i3b1/ePl7TyJvFmJtSwnhfcl/bgadzNKMCiMLhF7WTsxDljp9bRb78BOWb8LMGSdT+k6SRV4Go6oPu6z25WngiGkEShcMvzn/ScotWWQr6/ARZEMyvpJtWtKw48ioQEVlTqIUppBEcUczJKOZPGrS14rfFEIeqkXEhbGuslBokZjGWSS7HiOsX2Qn8BrgOOA8Ym9ny7RfnFud6IF7rYwR57qAcfuU4OYN2QheqN5JupdQdibJqZFwI6trmo5QaJGHLGOX/vBzI40QvpEBqgIk4RaSeAW4GzsrXPwktrgqkEspyFqKcP+m4ceMUCLRYVhBFq5YvX64NDQ2aSqX02GOP1VQqpQ0NDbGIxuro6NApU6ZoR0dH4OcKsyBYqeWWw5Ixyf/zohVIr07QH7jCtUr+xcs+cWxxVSDz5s3R8ePrtK2tdxhtW5vz45o3b07UIpaN1z9pVCGZflsMca4aGXZocRKssTBkTPL/PJ8C6asiYX8R+RjwA+DzOBMKf1xoH6N4qiH5mlcnZ1ROaL9reIdVka8UwnYUh1UfHUr3mYUhYyX+zws50e8BngDOBuap6vtUdYGq/ik06aqEakm+5uVPGpUT2u+iVffffz+zZs0ilXL+YjU1NVx77bWR1C+J2lEcZkGwUiP8wpCxEv/nhSyQy3EKSX0ReEJE/uq2ThH5azknFZEBIrJORDa5ryfl6DNSRH4lIr8TkedF5OMZ25aJyFYRedZtI8uRx2+KTU6YtORrpSZf9PonjaIOepwthnKJOrQ4zGtbqnUVhoxJ+597Ite4VtANuA24wV2+Abg1R593Aae7y6cArwEnuu+XARcXe94wfCClOMqSMjba0dGhkyZN0pEjzyjJEViMk9MvJ3SYDuM4E6YzO0zimMYkH0n5n+eCcpzofjfgZeBkd/lk4GUP+zyXoVBiq0BK+ZFkKp3Fi9F165w8VHGLzkg7X4cPPybwP4FfTmjLRXWEJDiziyXsMNxySMr/PBf5FIiXdO5B8FZVfQ3AfX1Loc4icg5QC7yasfqr7tDW10Wkf4F9rxSRdhFp37lzpx+yF6QUR1lS6mOkhwV6eg4G7gj0a0jBZhYfIUxndlgkaeJmUv7nRZFLq/jRgPXAb3O0yUBHVt83CxznZByL5QNZ6wQnvPgeYI4XmcKwQFIp0fXrc2e3XbcOralJBS6DX+QbHujXr/cT3tlnx+fzJWlII2ziHFpcLpVoXcUJwrZAVHWCqr47R3sA+IuInAzgvuasLSIiJwA/BWar6pMZx37N/Vz7ge8D5wT1OYolCY4yr07wfM7X7u4jffr3h8svd5bj8PmidhjHmUoOFKhE6yoJRDWEtQaY7i5PBx7I7iAitcBPgHtVdVXWtrTyEWAKjmUTC+JejayYCoR9DQ/07w+33AIjR8bn8yVpSCNs4hRa7DdhhgobR4hKgdwCnC8im4Dz3feIyBgRWer2uRQnB9cVOcJ1/4+IvAC8AAzCSbMSC6JITlgMCxfeTmPjq8ye3cWwYVBTA8OGwezZXTQ2vsrChbf36p8vpDaVgs98Bt797nh9PogmDNiIlkq2rmJNrnGtSm1hpTKJczWyUioQ5gqpra2t1QEDjo/d50sT51xUhpE0iFkUVkXT0NDAnDnz2Lbtdbq7e9i27XXmzJkXiyiLUmbD5hoe6O7uZsSIMbH7fGlsSMMwgscUSJVRipM/icMDSZTZMJJG3prolYjVRIf58+fy6KO3MXt277kqqo4fY+zY65kzZ150AhqGETtKqYluVCBxd/KXS6l5ugzDD6rt92cWSBWyd+9eFi68naVL72bHjt2ccspAZs68mlmzrouVH6NY0iHKjY2vMm1aF0OHOsNyK1Y4yjGxs32NRFDJvz+zQIzDxNnJXw59hSj/x38sCKS+etB1241kUGyIfCVgCsSoGPrKQ/ad79xdUq2Ivii1BoXfmCKLlkosGNUXpkAMTyRhbLevEOU33ugE/E+sGJeEjXFRZNVKJRaM6gtTIAkjiht5MelPoiQ7RPnaa6GlxWnnn39kfbmV+KKu8JePuCiyNEl46PCTJOTB8xtTIAkiqht5UsZ2s/OQXXaZk68rm3ITK8YlYWNcFRkk56HDT+KeBy8ITIEkiKhu5EkZ280OUX7Pe+Dzn3fyduWi1MSKcUnYGBdFloukPHT4SaWHyOfCwngTxODBTcydu4thw47etmkTLFjQxLZtOTPjl0VNTYq1a5WamqO3dXfDxIkpurt7fD9vKfQOUd7FiSceS1dXN3/724Fe/erq6li1ahWTJk0q+VwPPfQQl1xyCV1dXb4etxg2bNjApEmT2Ldv31Hbosw8HNVvNWoqNUTewngrgKicdEka202HKL/44hZGjhzOiBGHmDr1AMce61hMaWukpqam7FoRcahBEdfMw9XoUIbKDZHPhymQBBHVjTyJY7uZQyhPPw1dXfDOdzr1SxoahH37/la2szkuCRvjoMiySdJDh1E6pkASRFQ38iSO7Wb6bY47Dq66Cr7zHXjf++COO5QTTjiu7MSKcUnYGBdFlkkSHzqM4jEfSILITJUwdeqRVAkrVwafKiFpY7tJ8tuUy5QpUzjvvPO45pprSKVS9PT0sGjRIh5//PHIqg1G+Vs1/CefD8QUSMJI2o08KqrViRsn7LdaOcRKgYjIAOA+YAjwe+BSVX0zR78enLK1ANtU9UJ3/VBgJTAA+A1wuaoeyN4/m0pQIIY3kpi2fs+ePVxxxRUsW7aMxsbGqMUxjMPELQrrBuARVT0deMR9n4u/q+pIt12Ysf5W4Ovu/m8CM4IV10gaSfTbVHsqEsvllTyiUiCTgXvc5XuAKV53FBEBxgOrS9nfqA4aGhrYuPFJxo69ngULmpg4McWCBU2MHXt9bMff45aKxC+8KoYgFWiQyqnaUrb0Ileh9KAb0JH1/s08/bqBduBJYIq7bhCwOaPPYOC3Xs47evToUmvKG4bvtLa2KnC41dbW9npNt9bW1qhFLYt7771XAV2+fHnBfuPGjVNAW1paIpOhWDo7O3X06LN0/Pg6XbIEXb8eXbIEHT++TkePPks7Ozt9PV9UAO2a454amAUiIutF5Lc52uQiDnOqOuNunwAWicg7AcnRL68jR0SuFJF2EWnfuXNnkZ/CMIIjzqlI/CSfZRVmLq+grLtqTNnSi1xaJegGvAyc7C6fDLzsYZ9lwMU4CmQX0M9dfy7wcy/nNQvEiBttbW1aX1/fy+JIt/r6et2wYUPUIvaio6NDp0yZoh0dHXn7eLWszj777LyfvdxrEJZ119w8SJcsQTdsOLotXowOHtxU1vHjAmFbIH2wBpjuLk8HHsjuICIniUh/d3kQ8I/Ai+6H2YCjTPLubxhJIK6pSPLhxU/h1bK68847A0tKGYR1l8vX8dpru3jb23L3r+SULWmiUiC3AOeLyCbgfPc9IjJGRJa6fc4E2kXkORyFcYuqvuhu+xIwS0Q2AwOB74YqvWH4SBxTkeTDy1BQMdmKg1KgfmdMzpee/v3vhy9+Ef7+96P3qYaULZEoEFXdraqtqnq6+/qGu75dVWe6y0+o6ntUdYT7+t2M/beo6jmqOkxVL1HV/VF8DqPyiCKiJjMVycqVK3nLW97C3r17ufzyy0OP6Mn+/HV1tSX5KYpRDEEpUD+VUz5fx803w8knw6pVvftXS8oWy4VlGC5RFUFK59TauHEj8+f/O2ee+SYXXwwf/CB5zx9EWGquz/+///fBXvVUihkK6ksxpJXVjBmfYu/evdTUCJdc8jHe/e53+5bLyy/lVKgmzvTp8KMfkZj5Rn5iCsQwXKKKqLn//vuZNWsWixbdSWPjq3z5y/v5/Ofhq1/Nf/4g5kzk+vyTJsHtt5dWlKtQksdMZXXmmd1cdRV885s9bN/+I/r162bBggW+JKX0K9FkX+np9+6VxMw38hNTIIbhEnXlxWLOH0RYar7zn302fO5zR/fvayioULbiTGW1aBF8/OPwrnc5yvLEE7fQ3b3fl0SQfmVM7is9/dvfPqhqaoBkYskUDcMl6gy+hc4/axY888yR97W1tRw4cODwa5rW1lbWr1/v+/l/9jO49VZIpVL079+f/fv3U19fz913381ll11W9LmSluwyibnV/CRuubAMI3ZEXQSp0PlbWuh14wpi0mGh8//kJ86rXzVHklaxMIm51cLAFIgRS6KIhoq6CFKh8z/7bB3Tp18eyJwJL+d/440UF1xwvm/Fs6JW1sWSxNxqYWBDWEbsyCxGNG3akWJEK1YEW4wo6iJIXs6/ceNGLrnkErq6ug7vV1dXx6pVq5g0aVLg5/fr81f7kFDSsCEsIzFEFQ0V9VOml/MHOekwzM9vQ0KVgVkgRuxImoM1TFpaWnjssccYMWIEt956K1/60pd47rnnGDt2LG1tbVGLVxRWsTA5xKoiYVSYAkkGUUdDxZk41j83Kh9TIJgCSQpmgRhGvDAfiJEYoo6GMgzDG6ZAjNhhDlbDSAamQIzYEXU0lGEY3jAfiGEYhlEQ84EYhmEYvmIKxDCMRBBFehujMKZADCME7OZXHlEV+zIKE4kCEZEBIrJORDa5ryfl6NMiIs9mtC4RmeJuWyYiWzO2jQz/UxiGN+zmVz5RpbcxChOVBXID8Iiqng484r7vhapuUNWRqjoSGA/sA9ZmdLkuvV1Vnw1FasMogTjc/JJuAUVd7MvITVQKZDJwj7t8DzClj/4XAw+r6r5ApTKMAIj65lcJFlDS6odUC1EpkLeq6msA7utb+ug/FViRte6rIvK8iHxdRPrn21FErhSRdhFp37lzZ3lSG0YJRH3zi4MFVC5Jqx9SLQSmQERkvYj8NkebXORxTgbeA/w8Y/WNwBnA+4ABwJfy7a+qi1V1jKqOaWpqKuGTGEZ5RH3zi9oC8gNLbxNPAlMgqjpBVd+doz0A/MVVDGkFUSgz3qXAT1T1YMaxX1OH/cD3gXOC+hyGUS5R3/yitoD8wNLbxJOohrDWANPd5enAAwX6TiNr+CpD+QiO/+S3AchoGL4Q9c0vagvIDyy9TTyJJJWJiAwE/i9wKrANuERV3xCRMcBVqjrT7TcE+CUwWFUPZezfBjQBAjzr7tOnJ9BSmRhREWXxJCsfa5SL1QPBFIhRnURd691IPpYLyzCqFBv+MYLCLBDDMAyjIGaBGIZhGL5iCsQwDMMoCVMghmEYRkmYAjEMwzBKoqqc6CKyE/hDiKccBOwK8XzFYvKVh8lXHiZfeYQp3ztU9ahcUFWlQMJGRNpzRS7EBZOvPEy+8jD5yiMO8tkQlmEYhlESpkAMwzCMkjAFEiyLoxagD0y+8jD5ysPkK4/I5TMfiGEYhlESZoEYhmEYJWEKxDAMwygJUyBlIiIDRGSdiGxyX0/K0adFRJ7NaF0iMsXdtkxEtmZsGxm2fG6/ngwZ1mSsHyoi/+3uf5+I1IYtn4iMFJFficjvROR5Efl4xrZArp+ITBSRl0Vks4jckGN7f/d6bHavz5CMbTe6618WkQv8kKdI2WaJyIvutXpERN6RsS3n9xyBjFeIyM4MWWZmbJvu/h42icj07H1Dku/rGbK9IiIdGdsCvYYi8j0ReV1EchbKE4dvuLI/LyJnZ2wL/Nr1QlWtldGA24Ab3OUbgFv76D8AeAOod98vAy6OWj5gb571/xeY6i7/F3B12PIB7wJOd5dPAV4DTgzq+gE1wKvAaUAt8BwwPKvP54D/cpenAve5y8Pd/v2Boe5xakKWrSXj93V1WrZC33ME1+8K4Js59h0AbHFfT3KXTwpbvqz+/wJ8L6xrCJwHnA38Ns/2jwIP4xTU+wDw32Fdu+xmFkj5TAbucZfvwSmxW4iLgYdVdV+gUh2hWPkOIyICjAdWl7K/R/qUT1VfUdVN7vIO4HWcipRBcQ6wWVW3qOoBYKUrZyaZcq8GWt3rNRlYqar7VXUrsNk9XmiyqeqGjN/Xk0Czj+f3RcYCXACsU9U3VPVNYB0wMWL5jiqrHSSq+hjOQ2Y+JgP3qsOTwInilPkO49r1whRI+bxVVV8DcF/f0kf/qRz9Y/yqa4p+XUT6RyRfnYi0i8iT6eE1YCDQoard7vvtwNsjkg8AETkH56nx1YzVfl+/twN/zHif63Mf7uNenz0418vLvkHLlskMnKfVNLm+Z7/xKuP/cr+31SIyuMh9w5APd/hvKNCWsTqMa1iIfPKHce160S/Ig1cKIrIeeFuOTTcVeZyTgfcAP89YfSPwZ5yb4mLgS8D8COQ7VVV3iMhpQJuIvAD8NUe/ouO+fb5+y4HpqnrIXV329ct1qhzrsj93vj5e9i0Hz8cXkcuAMcDYjNVHfc+q+mqu/QOW8UFgharuF5GrcKy58R73DUO+NFOB1arak7EujGtYiKh+e0dhCsQDqjoh3zYR+YuInKyqr7k3uNcLHOpS4CeqejDj2K+5i/tF5PvAv0Uhnzs0hKpuEZGNwCjgRzjmcT/3KbsZ2BGFfCJyAvBTYLZrtqePXfb1y8F2YHDG+1yfO91nu4j0Axpxhh287Bu0bIjIBBwFPVZV96fX5/me/b759Smjqu7OeLsEuDVj33FZ+24MW74MpgKfz1wR0jUsRD75w7h2vbAhrPJZA6SjHaYDDxToe9RYqnvTTPsbpgA5Iy+ClE9ETkoP/YjIIOAfgRfV8cxtwPHb5N0/BPlqgZ/gjPuuytoWxPV7CjhdnAi0WpybSHa0TabcFwNt7vVaA0wVJ0prKHA68GsfZPIsm4iMAr4DXKiqr2esz/k9+yhbMTKenPH2QuAld/nnwIddWU8CPkxviz0U+VwZ/wHHGf2rjHVhXcNCrAE+6UZjfQDY4z5IhXHtehOkh74aGs649yPAJvd1gLt+DLA0o98Q4E9AKmv/NuAFnBvfD4CGsOUDPujK8Jz7OiNj/9NwboCbgVVA/wjkuww4CDyb0UYGef1wIl1ewXmyvMldNx/npgxQ516Pze71OS1j35vc/V4GPhLAb64v2dYDf8m4Vmv6+p4jkPFrwO9cWTYAZ2Ts+2n3um4GPhWFfO77rwC3ZO0X+DXEech8zf3Nb8fxY10FXOVuF+BbruwvAGPCvHaZzVKZGIZhGCVhQ1iGYRhGSZgCMQzDMErCFIhhGIZREqZADMMwjJIwBWIYhmGUhCkQoyoQERWR5Rnv+4mTDfYh9/2FkiMrq4/n/4qI5JzkKCJPFHGcn7hZYDeLyB45khX2g0XKM96dQ5Br21niZD/eLyLXFHNco7qwmehGtfA34N0icqyq/h04H2deDgCquoYck8ly4U5aFD2STqUsVNXzzV9VL3JlGAf8m6pOKvG044FdOMkWs9mFk4H24hzbDOMwZoEY1cTDwP90l3tlBRCnPsU33eW3uk/6z7ntgyIyREReEpFvA78BBovINBF5QUR+KyK3Zhxrooj8xt33kYzzDxeRjSKyRUS+kNF/r/s6TkQec8/9ooj8l4h4/o+KyPtE5FEReVpEHhaRt7rr/9U93nMi8gMReScwE7gul/Wiqn9R1XagO8dpDOMwZoEY1cRKYI47bPVe4HvAh3L0+wbwqKpeJCI1QANOSot/wJnd+zkROQUnf9No4E1grTiZWX+Jk9vpPFXdKiIDMo57Bk6tjuOBl0Xkbs3Ii+ZyDk5NkT8APwM+xpF0+nlx02vchTOTepeI/DOwALgSuB54h6oeEJETVbVDRJYCu1R1UV/HNox8mAIxqgZVfV6cyoHTgP9XoOt44JPuPj3AHje30B/0SCLH9wEbVXUngIj8H5xCQD3AY+rUAkFVM+s6/FSdxIb7ReR14K04qSoy+bWqbnGPuQL4H3hQIMCZwFnAemeEjZqMY/8O+IGIPADc7+FYhuEJUyBGtbEGuAMna+nAIvf9W8ZyrtTZ6fX58gPtz1juIff/L3tfr7mGBHheVXNZVBfgpHSfDMwWkXd7PKZhFMR8IEa18T1gvqq+UKDPIzilYBGRGnFSyWfz38BYERnkDnNNAx7Fydw61s3ES9YQlhfOcbPEpoCPA7/wuN+LwNvFKbiFiNS60VQ1QLOqtgHX4VRyrAc6cYbSDKNkTIEYVYWqblfVu/ro9kWgRZyiWk/jDA1lH+c1nGJWG3Ays/5GVR9wh7SuBH4sIs8B9xUp4q+AW3CyC2/FSWPfJ+7Q2MXAQve8zwDvx7Fyfigiz+M4/29V1U6ctPmXisgz2U50EWkWke3AF4CviMh2Eakv8nMYVYBl4zWMmOBDaK5hhIpZIIZhGEZJmAViGIZhlIRZIIZhGEZJmAIxDMMwSsIUiGEYhlESpkAMwzCMkjAFYhiGYZTE/wc8v4mmwk08UgAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plotData(X, y)\n", + "# Labels and Legend\n", + "pyplot.xlabel('Microchip Test 1')\n", + "pyplot.ylabel('Microchip Test 2')\n", + "\n", + "# Specified in plot order\n", + "pyplot.legend(['y = 1', 'y = 0'], loc='upper right')\n", + "pass" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# Note that mapFeature also adds a column of ones for us, so the intercept\n", + "# term is handled\n", + "X = utils.mapFeature(X[:, 0], X[:, 1])" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "def costFunctionReg(theta, X, y, lambda_):\n", + " \"\"\"\n", + " Compute cost and gradient for logistic regression with regularization.\n", + " \n", + " Parameters\n", + " ----------\n", + " theta : array_like\n", + " Logistic regression parameters. A vector with shape (n, ). n is \n", + " the number of features including any intercept. If we have mapped\n", + " our initial features into polynomial features, then n is the total \n", + " number of polynomial features. \n", + " \n", + " X : array_like\n", + " The data set with shape (m x n). m is the number of examples, and\n", + " n is the number of features (after feature mapping).\n", + " \n", + " y : array_like\n", + " The data labels. A vector with shape (m, ).\n", + " \n", + " lambda_ : float\n", + " The regularization parameter. \n", + " \n", + " Returns\n", + " -------\n", + " J : float\n", + " The computed value for the regularized cost function. \n", + " \n", + " grad : array_like\n", + " A vector of shape (n, ) which is the gradient of the cost\n", + " function with respect to theta, at the current values of theta.\n", + " \n", + " Instructions\n", + " ------------\n", + " Compute the cost `J` of a particular choice of theta.\n", + " Compute the partial derivatives and set `grad` to the partial\n", + " derivatives of the cost w.r.t. each parameter in theta.\n", + " \"\"\"\n", + " # Initialize some useful values\n", + " m = y.size # number of training examples\n", + "\n", + " # You need to return the following variables correctly \n", + " J = 0\n", + " grad = np.zeros(theta.shape)\n", + "\n", + " # ===================== YOUR CODE HERE ======================\n", + " z=theta.dot(X.transpose())\n", + " h=sigmoid(z)\n", + " \n", + " for i in range(m):\n", + " J=J+((-1*(y[i]*math.log(h[i])+(1-y[i])*math.log(1-h[i])))/m)\n", + " \n", + " for i in range(1,theta.shape[0]):\n", + " J=J+(lambda_*theta[i]*theta[i])/(2*m)\n", + " \n", + " \n", + " for i in range(theta.shape[0]):\n", + " grad[i]=(((h-y).dot(X[:,i]))/m) \n", + " \n", + " for i in range(1,theta.shape[0]):\n", + " grad[i]=grad[i]+(lambda_*theta[i])/m\n", + " \n", + " # =============================================================\n", + " return J, grad" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cost at initial theta (zeros): 0.693\n", + "Expected cost (approx) : 0.693\n", + "\n", + "Gradient at initial theta (zeros) - first five values only:\n", + "\t[0.0085, 0.0188, 0.0001, 0.0503, 0.0115]\n", + "Expected gradients (approx) - first five values only:\n", + "\t[0.0085, 0.0188, 0.0001, 0.0503, 0.0115]\n", + "\n", + "------------------------------\n", + "\n", + "Cost at test theta : 3.16\n", + "Expected cost (approx): 3.16\n", + "\n", + "Gradient at initial theta (zeros) - first five values only:\n", + "\t[0.3460, 0.1614, 0.1948, 0.2269, 0.0922]\n", + "Expected gradients (approx) - first five values only:\n", + "\t[0.3460, 0.1614, 0.1948, 0.2269, 0.0922]\n" + ] + } + ], + "source": [ + "# Initialize fitting parameters\n", + "initial_theta = np.zeros(X.shape[1])\n", + "\n", + "# Set regularization parameter lambda to 1\n", + "# DO NOT use `lambda` as a variable name in python\n", + "# because it is a python keyword\n", + "lambda_ = 1\n", + "\n", + "# Compute and display initial cost and gradient for regularized logistic\n", + "# regression\n", + "cost, grad = costFunctionReg(initial_theta, X, y, lambda_)\n", + "\n", + "print('Cost at initial theta (zeros): {:.3f}'.format(cost))\n", + "print('Expected cost (approx) : 0.693\\n')\n", + "\n", + "print('Gradient at initial theta (zeros) - first five values only:')\n", + "print('\\t[{:.4f}, {:.4f}, {:.4f}, {:.4f}, {:.4f}]'.format(*grad[:5]))\n", + "print('Expected gradients (approx) - first five values only:')\n", + "print('\\t[0.0085, 0.0188, 0.0001, 0.0503, 0.0115]\\n')\n", + "\n", + "\n", + "# Compute and display cost and gradient\n", + "# with all-ones theta and lambda = 10\n", + "test_theta = np.ones(X.shape[1])\n", + "cost, grad = costFunctionReg(test_theta, X, y, 10)\n", + "\n", + "print('------------------------------\\n')\n", + "print('Cost at test theta : {:.2f}'.format(cost))\n", + "print('Expected cost (approx): 3.16\\n')\n", + "\n", + "print('Gradient at initial theta (zeros) - first five values only:')\n", + "print('\\t[{:.4f}, {:.4f}, {:.4f}, {:.4f}, {:.4f}]'.format(*grad[:5]))\n", + "print('Expected gradients (approx) - first five values only:')\n", + "print('\\t[0.3460, 0.1614, 0.1948, 0.2269, 0.0922]')" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "def predict(theta, X):\n", + " \"\"\"\n", + " Predict whether the label is 0 or 1 using learned logistic regression.\n", + " Computes the predictions for X using a threshold at 0.5 \n", + " (i.e., if sigmoid(theta.T*x) >= 0.5, predict 1)\n", + " \n", + " Parameters\n", + " ----------\n", + " theta : array_like\n", + " Parameters for logistic regression. A vector of shape (n+1, ).\n", + " \n", + " X : array_like\n", + " The data to use for computing predictions. The rows is the number \n", + " of points to compute predictions, and columns is the number of\n", + " features.\n", + "\n", + " Returns\n", + " -------\n", + " p : array_like\n", + " Predictions and 0 or 1 for each row in X. \n", + " \n", + " Instructions\n", + " ------------\n", + " Complete the following code to make predictions using your learned \n", + " logistic regression parameters.You should set p to a vector of 0's and 1's \n", + " \"\"\"\n", + " m = X.shape[0] # Number of training examples\n", + "\n", + " # You need to return the following variables correctly\n", + " p = np.zeros(m)\n", + "\n", + " # ====================== YOUR CODE HERE ======================\n", + " for i in range(m):\n", + " if sigmoid(theta.dot(X.transpose()))[i]>=0.5 :\n", + " p[i]=1\n", + " else :\n", + " p[i]=0\n", + "\n", + " \n", + " # ============================================================\n", + " return p" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train Accuracy: 83.1 %\n", + "Expected accuracy (with lambda = 1): 83.1 % (approx)\n", + "\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Initialize fitting parameters\n", + "initial_theta = np.zeros(X.shape[1])\n", + "\n", + "# Set regularization parameter lambda to 1 (you should vary this)\n", + "lambda_ = 1\n", + "\n", + "# set options for optimize.minimize\n", + "options= {'maxiter': 100}\n", + "\n", + "res = optimize.minimize(costFunctionReg,\n", + " initial_theta,\n", + " (X, y, lambda_),\n", + " jac=True,\n", + " method='TNC',\n", + " options=options)\n", + "\n", + "# the fun property of OptimizeResult object returns\n", + "# the value of costFunction at optimized theta\n", + "cost = res.fun\n", + "\n", + "# the optimized theta is in the x property of the result\n", + "theta = res.x\n", + "\n", + "utils.plotDecisionBoundary(plotData, theta, X, y)\n", + "pyplot.xlabel('Microchip Test 1')\n", + "pyplot.ylabel('Microchip Test 2')\n", + "pyplot.legend(['y = 1', 'y = 0'])\n", + "pyplot.grid(False)\n", + "pyplot.title('lambda = %0.2f' % lambda_)\n", + "\n", + "# Compute accuracy on our training set\n", + "p = predict(theta, X)\n", + "\n", + "print('Train Accuracy: %.1f %%' % (np.mean(p == y) * 100))\n", + "print('Expected accuracy (with lambda = 1): 83.1 % (approx)\\n')" + ] + } + ], + "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" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +}