Morten Hjorth-Jensen
2017 lines
499 KiB
Plaintext
2017 lines
499 KiB
Plaintext
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"<!-- HTML file automatically generated from DocOnce source (https://github.com/doconce/doconce/)\n",
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"doconce format html week45.do.txt --no_mako -->\n",
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"<!-- dom:TITLE: Week 45, Recurrent Neural Networks -->"
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]
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},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Week 45, Recurrent Neural Networks\n",
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"**Morten Hjorth-Jensen**, Department of Physics, University of Oslo and Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University\n",
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"\n",
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"Date: **November 6-10**"
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]
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},
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{
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"cell_type": "markdown",
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"id": "6ecf1c2b",
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"metadata": {},
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"source": [
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"## Plan for week 45\n",
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"\n",
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"**Material for the active learning sessions on Tuesday and Wednesday.**\n",
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"\n",
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" * Discussion of project 2\n",
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"\n",
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" * [Video of lab session from week 43](https://youtu.be/Ia6wwDLxqtM)\n",
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"\n",
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" * [Video of lab session from week 44](https://youtu.be/EajWMW__k0I)\n",
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"\n",
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" * [See also whiteboard notes from lab session week 44](https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2023/Exercisesweek44.pdf)\n",
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"\n",
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" \n",
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"\n",
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"**Material for the lecture on Thursday November 9, 2023.**\n",
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"\n",
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" * Short repetition on Convolutional Neural Networks\n",
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"\n",
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" * Recurrent Neural Networks (RNNs)\n",
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"\n",
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" * Readings and Videos:\n",
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"\n",
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" * These lecture notes\n",
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"\n",
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" * For a more in depth discussion on neural networks we recommend Goodfellow et al chapter 10. See also chapter 11 and 12 on practicalities and applications \n",
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"\n",
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" * Reading suggestions for implementation of RNNs: [Aurelien Geron's chapter 14](https://github.com/CompPhysics/MachineLearning/blob/master/doc/Textbooks/TensorflowML.pdf).\n",
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"\n",
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" * [Video on Recurrent Neural Networks from MIT](https://www.youtube.com/watch?v=SEnXr6v2ifU&ab_channel=AlexanderAmini)\n",
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"\n",
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" * [Video on Deep Learning](https://www.youtube.com/playlist?list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "d80a4db8",
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"metadata": {},
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"source": [
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"## Material for the lab sessions, additional ways to present classification results and other practicalities"
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]
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},
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{
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"cell_type": "markdown",
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"id": "1ded5c92",
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"metadata": {},
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"source": [
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"## Searching for Optimal Regularization Parameters $\\lambda$\n",
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"\n",
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"In project 1, when using Ridge and Lasso regression, we end up\n",
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"searching for the optimal parameter $\\lambda$ which minimizes our\n",
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"selected scores (MSE or $R2$ values for example). The brute force\n",
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"approach, as discussed in the code here for Ridge regression, consists\n",
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"in evaluating the MSE as function of different $\\lambda$ values.\n",
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"Based on these calculations, one tries then to determine the value of the hyperparameter $\\lambda$\n",
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"which results in optimal scores (for example the smallest MSE or an $R2=1$)."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "6dea0513",
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"metadata": {},
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"outputs": [],
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"source": [
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"%matplotlib inline\n",
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"\n",
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"import numpy as np\n",
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"import pandas as pd\n",
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"import matplotlib.pyplot as plt\n",
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"from sklearn.model_selection import train_test_split\n",
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"from sklearn import linear_model\n",
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"\n",
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"def MSE(y_data,y_model):\n",
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" n = np.size(y_model)\n",
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" return np.sum((y_data-y_model)**2)/n\n",
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"# A seed just to ensure that the random numbers are the same for every run.\n",
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"# Useful for eventual debugging.\n",
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"np.random.seed(2021)\n",
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"\n",
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"n = 100\n",
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"x = np.random.rand(n)\n",
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"y = np.exp(-x**2) + 1.5 * np.exp(-(x-2)**2)+ np.random.randn(n)\n",
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"\n",
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"Maxpolydegree = 5\n",
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"X = np.zeros((n,Maxpolydegree-1))\n",
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"\n",
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"for degree in range(1,Maxpolydegree): #No intercept column\n",
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" X[:,degree-1] = x**(degree)\n",
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"\n",
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"# We split the data in test and training data\n",
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"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n",
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"\n",
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"# Decide which values of lambda to use\n",
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"nlambdas = 500\n",
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"MSERidgePredict = np.zeros(nlambdas)\n",
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"lambdas = np.logspace(-4, 2, nlambdas)\n",
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"for i in range(nlambdas):\n",
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" lmb = lambdas[i]\n",
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" RegRidge = linear_model.Ridge(lmb)\n",
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" RegRidge.fit(X_train,y_train)\n",
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" ypredictRidge = RegRidge.predict(X_test)\n",
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" MSERidgePredict[i] = MSE(y_test,ypredictRidge)\n",
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"\n",
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"# Now plot the results\n",
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"plt.figure()\n",
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"plt.plot(np.log10(lambdas), MSERidgePredict, 'g--', label = 'MSE SL Ridge Test')\n",
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"plt.xlabel('log10(lambda)')\n",
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"plt.ylabel('MSE')\n",
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"plt.legend()\n",
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"plt.show()"
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]
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},
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{
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"cell_type": "markdown",
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"source": [
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"Here we have performed a rather data greedy calculation as function of the regularization parameter $\\lambda$. There is no resampling here. The latter can easily be added by employing the function **RidgeCV** instead of just calling the **Ridge** function. For **RidgeCV** we need to pass the array of $\\lambda$ values.\n",
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"By inspecting the figure we can in turn determine which is the optimal regularization parameter.\n",
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"This becomes however less functional in the long run."
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]
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},
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{
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"cell_type": "markdown",
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"id": "683b1da2",
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"metadata": {},
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"source": [
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"## Grid Search\n",
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"\n",
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"An alternative is to use the so-called grid search functionality\n",
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"included with the library **Scikit-Learn**, as demonstrated for the same\n",
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"example here."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "38be79e0",
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"from sklearn.model_selection import train_test_split\n",
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"from sklearn.linear_model import Ridge\n",
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"from sklearn.model_selection import GridSearchCV\n",
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"\n",
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"def R2(y_data, y_model):\n",
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" return 1 - np.sum((y_data - y_model) ** 2) / np.sum((y_data - np.mean(y_data)) ** 2)\n",
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"\n",
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"def MSE(y_data,y_model):\n",
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" n = np.size(y_model)\n",
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" return np.sum((y_data-y_model)**2)/n\n",
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"\n",
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"# A seed just to ensure that the random numbers are the same for every run.\n",
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"# Useful for eventual debugging.\n",
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"np.random.seed(2021)\n",
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"\n",
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"n = 100\n",
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"x = np.random.rand(n)\n",
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"y = np.exp(-x**2) + 1.5 * np.exp(-(x-2)**2)+ np.random.randn(n)\n",
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"\n",
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"Maxpolydegree = 5\n",
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"X = np.zeros((n,Maxpolydegree-1))\n",
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"\n",
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"for degree in range(1,Maxpolydegree): #No intercept column\n",
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" X[:,degree-1] = x**(degree)\n",
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"\n",
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"# We split the data in test and training data\n",
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"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n",
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"\n",
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"# Decide which values of lambda to use\n",
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"nlambdas = 10\n",
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"lambdas = np.logspace(-4, 2, nlambdas)\n",
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"# create and fit a ridge regression model, testing each alpha\n",
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"model = Ridge()\n",
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"gridsearch = GridSearchCV(estimator=model, param_grid=dict(alpha=lambdas))\n",
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"gridsearch.fit(X_train, y_train)\n",
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"print(gridsearch)\n",
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"ypredictRidge = gridsearch.predict(X_test)\n",
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"# summarize the results of the grid search\n",
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"print(f\"Best estimated lambda-value: {gridsearch.best_estimator_.alpha}\")\n",
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"print(f\"MSE score: {MSE(y_test,ypredictRidge)}\")\n",
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"print(f\"R2 score: {R2(y_test,ypredictRidge)}\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "081fbe5c",
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"metadata": {},
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"source": [
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"By default the grid search function includes cross validation with\n",
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"five folds. The [Scikit-Learn\n",
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"documentation](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html#sklearn.model_selection.GridSearchCV)\n",
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"contains more information on how to set the different parameters.\n",
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"\n",
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"If we take out the random noise, running the above codes results in $\\lambda=0$ yielding the best fit."
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]
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},
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{
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"cell_type": "markdown",
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"id": "a9798c07",
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"metadata": {},
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"source": [
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"## Randomized Grid Search\n",
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"\n",
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"An alternative to the above manual grid set up, is to use a random\n",
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"search where the parameters are tuned from a random distribution\n",
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"(uniform below) for a fixed number of iterations. A model is\n",
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"constructed and evaluated for each combination of chosen parameters.\n",
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"We repeat the previous example but now with a random search. Note\n",
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"that values of $\\lambda$ are now limited to be within $x\\in\n",
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"[0,1]$. This domain may not be the most relevant one for the specific\n",
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"case under study."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "0a7e4e2e",
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"from sklearn.model_selection import train_test_split\n",
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"from sklearn.linear_model import Ridge\n",
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"from sklearn.model_selection import GridSearchCV\n",
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"from scipy.stats import uniform as randuniform\n",
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"from sklearn.model_selection import RandomizedSearchCV\n",
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"\n",
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"\n",
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"def R2(y_data, y_model):\n",
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" return 1 - np.sum((y_data - y_model) ** 2) / np.sum((y_data - np.mean(y_data)) ** 2)\n",
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"\n",
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"def MSE(y_data,y_model):\n",
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" n = np.size(y_model)\n",
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" return np.sum((y_data-y_model)**2)/n\n",
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"\n",
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"# A seed just to ensure that the random numbers are the same for every run.\n",
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"# Useful for eventual debugging.\n",
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"np.random.seed(2021)\n",
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"\n",
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"n = 100\n",
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"x = np.random.rand(n)\n",
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"y = np.exp(-x**2) + 1.5 * np.exp(-(x-2)**2)+ np.random.randn(n)\n",
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"\n",
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"Maxpolydegree = 5\n",
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"X = np.zeros((n,Maxpolydegree-1))\n",
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"\n",
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"for degree in range(1,Maxpolydegree): #No intercept column\n",
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" X[:,degree-1] = x**(degree)\n",
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"\n",
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"# We split the data in test and training data\n",
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"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n",
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"\n",
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"param_grid = {'alpha': randuniform()}\n",
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"# create and fit a ridge regression model, testing each alpha\n",
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"model = Ridge()\n",
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"gridsearch = RandomizedSearchCV(estimator=model, param_distributions=param_grid, n_iter=100)\n",
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"gridsearch.fit(X_train, y_train)\n",
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"print(gridsearch)\n",
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"ypredictRidge = gridsearch.predict(X_test)\n",
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"# summarize the results of the grid search\n",
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"print(f\"Best estimated lambda-value: {gridsearch.best_estimator_.alpha}\")\n",
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"print(f\"MSE score: {MSE(y_test,ypredictRidge)}\")\n",
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"print(f\"R2 score: {R2(y_test,ypredictRidge)}\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "dcd07cb8",
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"metadata": {},
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"source": [
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"## Wisconsin Cancer Data\n",
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"\n",
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"We show here how we can use a simple regression case on the breast\n",
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"cancer data using Logistic regression as our algorithm for\n",
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"classification."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "65061d95",
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"metadata": {},
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"outputs": [],
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"source": [
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"import matplotlib.pyplot as plt\n",
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"import numpy as np\n",
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"from sklearn.model_selection import train_test_split \n",
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"from sklearn.datasets import load_breast_cancer\n",
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"from sklearn.linear_model import LogisticRegression\n",
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"\n",
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"# Load the data\n",
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"cancer = load_breast_cancer()\n",
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"\n",
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"X_train, X_test, y_train, y_test = train_test_split(cancer.data,cancer.target,random_state=0)\n",
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"print(X_train.shape)\n",
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"print(X_test.shape)\n",
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"# Logistic Regression\n",
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"logreg = LogisticRegression(solver='lbfgs')\n",
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"logreg.fit(X_train, y_train)\n",
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"print(\"Test set accuracy with Logistic Regression: {:.2f}\".format(logreg.score(X_test,y_test)))"
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]
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},
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{
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"cell_type": "markdown",
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"id": "50492453",
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"metadata": {},
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"source": [
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"## Using the correlation matrix\n",
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"\n",
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"In addition to the above scores, we could also study the covariance (and the correlation matrix).\n",
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"We use **Pandas** to compute the correlation matrix."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "08a2ab17",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"image/png": 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r2LbRgbQdJ8HWB/4HoXdbb701Tz31FPPnzyciOO644/jMZz6z2D6zZ89myJAhby0PGjTorWEV1Sp9/KJFi/oXuFkTVOohrnX/WnuUzQaSaodVfLmn7RHx0/qEY52g1rumzfLuv/9+3njjDVZeeWV23HFHvvGNb7DvvvsyfPhw5s6dW/XQh3e9613MmjWL2bNn09XVxYUXXtjgyM0Gnlrbe3cEWDuo5dsqtgAuS8u7A7cC0xsRlA0s9epNrbWccvvXsm9P2voNoICv7usecwwQEZx99tkMGjSIHXbYgfvuu4+tt94agOHDh3PeeecxaNCgXstcdtll+fWvf81OO+3EsGHD2GKLLRpZBTMz6xDVJsdjgE0j4nkASScCV0TEfj0+qp14mERFlW4k2Xrcyk2OxDrVG2+8UXHbkUceyZFHHrnE+rvvvvut+aOPPvqt+bPOOuut+W233Zb777+fiOCII45g882zb6U88cQTK5Zllldr+9foG+/qpV2GW9TSiWFWL9Umx6sCr+WWX0vrbACr+CawVnPjqKeihoR4LHJjnH766Zx99tm89tprbLLJJkuMXTbrq3ZJguulXm2j2zprB9Umx+cAt0r6S1r+CHB2QyJqNPcQuyfYBoyjjjqKo446qugwzO1ux6q1B7ryDYU/rlNEZv1X7bdVnCzpKuD9adVBEXF748KyIrRLT0i9fia1kdrtpsSIQFLRYXSMiCg6BDMz66NafshjOeC5iDhT0mhJa0fEQ40KzMyaY+jQoSxYsICVV17ZCXIdRAQLFixg6NChRYdiZlD5k4sCbj629lDtV7l9i+wbK94FnAksDZwH/FfjQusnf4zXNj3BlbRb72sz1XPc3pgxY3j00UeZP39+f8OyZOjQof5BkRbQ7m2gmRWj2p7j/wY2AW4DiIjHJC3fsKisrbXLXdDtoBn/ICy99NJv/Ypcb8f1TTMDTK2dDO6Js6TWHyupxc1nHN37Tjk130/jnuYBr9rk+LWICEkBIGlYA2MqbwBdrO7tMLO21OBP7HwzceN1YudGxeumwm//1rq/dZ5qk+OLJP0vMELSocDBwOmNC6sGbTJ8otyLbaA16PXqSWjnRtrMzMxaW6/JsbI7dC4E3g08Rzbu+JsRMakhEbVJslsP7iHum3p9dVAnJtmN/iW/eg3zqMcvEbZa7G2tQrtbrx/AGGgdAZ2okcMkoJh7TGodnlHr66TSe0yt7V+l/SvFv/Uh7fu1eK0ypE/VfOWQpLsiYqOqC5XmAw/3J7A2MAp4quggWpTPTXk+L+UNpPMyNiJGN6LgOra7nfJ8dEo9oHPq4nq0loFSj5rb3WqHVdwmaYuI+E81Ozeq8W8lkqZExOZFx9GKfG7K83kpz+elPurV7nbK89Ep9YDOqYvr0Vpcj8qqTY63AvaTNBt4ERAQEbFxPYMxMzMzMytSj8mxpLUi4hFgxybFY2ZmZmZWmN56ji8FNo2IhyVdEhEfbUJM7aKxdye0N5+b8nxeyvN5aS2d8nx0Sj2gc+rierQW16OCHm/Ik3R7RGxSOm9mZmZm1omW6mV7VJg3MzMzM+s4vfUcv8HbN+AtC7zUvYnshrwVGh6hmZmZmVmT9NhzHBGDImKFiFg+Igan+e7lAZMYS/q9pCcl3Z1bt5KkSZKmp78ji4yxKBXOzYmS5kqalqZdioyxCJLWlHS9pHsl3SPpyLR+QF83PZyXAX/NFKFT2rZOaYc6pd3opNe5pKGSbpV0R6rLt9P6tSXdImmGpAslLVN0rD3poR5nSXoo95yMLzjUqkgaJOl2SZen5bo+H70Nq7DMWcBOJeuOBf4eEesCf0/LA9FZLHluAE6JiPFpurLJMbWCRcBXImJ9YAJwhKT18XVT6byAr5kinEVntG1n0RntUKe0G530On8V+FBEvBcYD+wkaQLwA7K6vBN4BjikuBCrUqkeAF/NPSfTigqwRkcC9+WW6/p8ODmuQkTcCDxdsnpP4Ow0fzbwkWbG1CoqnJsBLyLmRcRtaf55shfxGgzw66aH82IF6JS2rVPaoU5pNzrpdR6ZF9Li0mkK4EPAxWl9OzwnlerRdiSNAXYFfpeWRZ2fDyfHfbdqRMxL848DqxYZTAv6vKQ708edLf0RYKNJ6gI2AW7B181bSs4L+JppFZ10jbbtNdUp7UYnvM7TR/jTgCeBScBMYGFELEq7PEobJP+l9YiI7ufk5PScnCJpSHERVu1U4GvAm2l5Zer8fDg5roPI7mpsy//AGuQ3wDpkH93MA35SaDQFkjQcuAT4UkQ8l982kK+bMufF10wLavNrtG2vqU5pNzrldR4Rb0TEeGAMsCXw7mIj6pvSekjaEDiOrD5bACsBxxQXYe8k7QY8GRFTG3kcJ8d994Sk1QDS3ycLjqdlRMQT6UX4JnA6WWMy4EhamuyN4Q8R8ee0esBfN+XOi6+ZltIR12i7XlOd0m504us8IhYC1wNbAyMkdf+Q2hhgblFx1SpXj53SEJiIiFeBM2n95+S/gD0kzQYuIBtO8TPq/Hw4Oe67y4AD0vwBwF8LjKWldDfiyX8Dd1fat1OlMVBnAPdFxE9zmwb0dVPpvPiaaSkdcY224zXVKe1GJ73OJY2WNCLNLwtsTzaG+npgr7RbOzwn5epxf+6fLpGN023p5yQijouIMRHRBewDXBcR+1Ln56PH7zm2jKTzgYnAKOAJ4FtkP619EbAW8DDw8Yho+xtCalXh3Ewk+9gsgNnAZ3Lj5QYESdsA/wTu4u1xUV8nG3c3YK+bHs7LJxjg10wROqVt65R2qFPajU56nUvamOwGr0FkHYoXRcRJksaR9VyuBNwO7Jd6X1tSD/W4DhhN9vsV04DDczfutTRJE4GjI2K3ej8fTo7NzMzMzBIPqzAzMzMzS5wcm5mZmZklTo7NzMzMzBInx2ZmZmZmiZNjMzMzM7PEybFVRdIbkqblpq4+lPERSes3ILxCSDpJ0nZp/kuSlutDGW3xlTlmZmYDhb/Kzaoi6YWIGN7PMs4CLo+Ii2t4zODc76W3rPRrPZtHxFM1Pq7f59XMzMzqxz3H1meSNpP0D0lTJV2d+6WdQyX9R9Idki6RtJyk9wF7AD9KPc/rSLpB0ubpMaNSgomkAyVdlr6c/O+Shkn6vaRbJd0uac8ysUxMsfxV0ixJ35e0b3rMXZLWSfvtLumWVM61klZN60dLmiTpHkm/k/RwiqlL0n2STk/brkm/LoSksyTtJemLwOrA9ZKuT9teyMW2V/rHAElrS7o5xfTdkjp8NZ23OyV9u65PlpmZmVXFybFVa9nckIq/SFoa+AWwV0RsBvweODnt++eI2CIi3kv2M5uHRMS/yX4C9asRMT4iZvZyvE1T2R8Ejif7icgtgW3JEuxhZR7zXuBw4D3Ap4D10mN+B3wh7fMvYEJEbEL2azpfS+u/lY6xAXAx2S9RdVsX+FXathD4aP6gEfFz4DFg24jYtpd6/Qz4TURsBLz1y1CSdkjH2ZLsF6Q2k/SBXsoyMzOzOhtcdADWNl6OiPHdC5I2BDYEJmU/yc4g3k72Nky9oiOA4cDVfTjepNzPo+4A7CHp6LQ8lCx5va/kMf/p/ilSSTOBa9L6u8iSaoAxwIWpl3sZ4KG0fhvgvwEi4v8kPZMr96GImJbmpwJdfahPt//i7eT6XOAHaX6HNN2eloeTJcs39uNYZmZmViMnx9ZXAu6JiK3LbDsL+EhE3CHpQGBihTIW8fanF0NLtr1YcqyPRsQDvcSU/x31N3PLb/L2tf4L4KcRcVn6XfYTeymztNw3gGWreEx+MH9p3coN9BfwvYj43yrKNjMzswbxsArrqweA0ZK2BpC0tKQN0rblgXlp6MW+ucc8n7Z1mw1slub36uFYVwNfUOqilrRJP+JeEZib5g/Irb8J+HgqfwdgZI3lltbtCUnvkbQUqUc6d5x90nz+3FwNHCxpeIphDUmr1BiDmZmZ9ZOTY+uTiHiNLKH9gaQ7gGnA+9LmbwC3kCWC9+cedgHw1XQz3DrAj4HPSrodGNXD4b4DLA3cKemetNxXJwJ/kjQVyH+zxLeBHSTdDXwMeJws4a3Wb4H/674hDzgWuBz4N7mxxcCRwBGS7gLW6F4ZEdcAfwRuTtsuZvFk28zMzJrAX+VmBkgaArwREYtSb/hv8mOszczMbGDwmGOzzFrARWkYxGvAoQXHY2ZmZgVwz7GZmZmZWeIxx2ZmZmZmiZNjMzMzM7PEybGZmZmZWeLk2MzMzMwscXJsZmZmZpY4OTYzMzMzS5wcm5mZmZklTo7NzMzMzBInx2ZmZmZmiZNjs0TSVZIOKDoOMzMzK45/PtqswSRNBM6LiDEFh2JmZma9cM+xDXjKtOxrQdLgomMwM2s2t31WlJZNCKx1SJot6auS7pT0oqQzJK2ahiE8L+laSSNz+0+Q9G9JCyXdkXpOu7cdJOm+9LhZkj6T2zZR0qOSviLpSUnzJB3UQ1w3SPqepFslPSfpr5JWqjKOGySdLOkm4CVgXFr36bT9QEk3STolPX6WpPel9XNSfAfkyhsi6ceSHpH0hKTTJC0raRhwFbC6pBfStLqkpSQdK2mmpAWSLuqOXVKXpJB0iKRHgOv68fSZ2QDUwu12NWUdI+lx4Mye2sr0mD9JelzSs5JulLRBHU+jDVBOjq1aHwW2B9YDdidL+L4OjCa7jr4IIGkN4Argu8BKwNHAJZJGp3KeBHYDVgAOAk6RtGnuOO8AVgTWAA4BfpVvwMvYHzgYWA1YBPy8yjgAPgUcBiwPPFym7K2AO4GVgT8CFwBbAO8E9gN+KWl42vf76dyMT9vXAL4ZES8COwOPRcTwND0GfAH4CPBBYHXgGeBXJcf/IPAeYMce6m9mVkkrttvVlLUSMJasfe6trbwKWBdYBbgN+EM1J8asRxHhyVOPEzAb2De3fAnwm9zyF4BL0/wxwLklj78aOKBC2ZcCR6b5icDLwODc9ieBCRUeewPw/dzy+sBrwKDe4kiPPalMeZ9O8wcC03PbNgICWDW3bgFZMizgRWCd3LatgYdy9Xq05Fj3AR/OLa8GvA4MBrrSscYV/dx78uSpPadWbberKOs1YGhue8W2skxZI1LbuWLR599Te08ez2PVeiI3/3KZ5e4e1LHAxyTtntu+NHA9gKSdgW+R9WQsBSwH3JXbd0FELMotv5Qru5w5ufmH07FG9RZHmceWU1pHIqJcvUeT1WOqpO5tIkvSKxkL/EXSm7l1bwCr1hCfmVlPWq7drqKs+RHxSm65YluZhl6cDHyMrB3u3mcU8Gy545tVw8mx1dscsh6IQ0s3SBpC1nuxP/DXiHhd0qVkiWRfrZmbX4usR+GpnuLIqddXtTxF9kazQUTMrfI4c4CDI+Km0g2Suuocn5lZT5rSbldZVmm711Nb+SlgT2A7sp7yFcmGXfTnPcXMY46t7s4Ddpe0o6RBkoammyzGAMsAQ4D5wKLUg7BDP4+3n6T1JS0HnARcHBFv9BJHXUXEm8DpZGPnVoFsDJ+k7rHCTwArS1ox97DTgJMljU37j5a0Z71jMzOrQrPa7b6U1VNbuTzwKtkQt+WA/9fHuMwW4+TY6ioi5pD9J/91sgZwDvBVYKmIeJ7sBpCLyP67/yRwWT8PeS5wFvA4MDSV32Mc/TxeJccAM4DJkp4DrgXelWK5HzgfmJXuBF8d+BlZ3a+R9DwwmewGQDOzpmpWu93HsnpqK88hG043F7g3bTPrN/8IiLUtSTeQ/bjG74qOxczMzDqDe47NzMzMzBInx2ZmZmZmiYdVmJmZmZkl7jk2MzMzM0ucHJuZmZmZJQ35EZBRo0ZFV1dXI4o2M2tbU6dOfSoiRjeibLe7ZmZL6ku725DkuKuriylTpjSi6M5y/ffKr9/2uObGYWZNIenhRpXdlu2u20Aza7C+tLseVmFmZmZmljSk59jMzKzP3KNsZgVyctyKan1j8BuJmZmZWV04OTYzs/bgjgAzawKPOTYzMzMzS5wcm5mZmZklTo7NzMzMzBKPOR6IPG7PzMzMrCwnx+2kUlJrZmZmZnXhYRVmZmZmZol7jpvFvb5mZmZmLc89x2ZmZmZmiZNjMzMzM7PEwyo6mYdymJmZmdXEybGZmbU3fz2lmdWRh1WYmZmZmSXuOTYzs8byEC8zayNOjs3MrD5aLQn2cAsz6wMPqzAzMzMzS5wcm5mZmZklHlbRrdaP3/xxnZmZmVnHcXJsvavTPwKnTHqw7Pqjtl+v1ojMzMzMGsLJcW9a7QYTMzMzM2sYJ8f1NpCSaQ8tcW+4DUzt3s657TKzHjg5NjMzg/JJsxNmswHHybG1rE7sle3EOpmZmXUSJ8fWq5tnLSi7futxKzc5kp458TQzM7P+cnJsfVYxad62yYHUkRNss85Rj3/s3SaYDTxOju0tN59xdNEhNESlN7dOO6aZNU+9kmYn32atx8mxFc6JpJm1grI9zWvVVka7JM2Nbned3Fs769zk2F/VU1GljxrrpdGNrnuCzZqk3b+yrYJGt4FFcBtlVj+dmxxbR74BdCp/tGrWO7dpToLNmsHJcQfwG0bnqvWNsNU+ujUzM2s37ZMc12uYRId+TNgOJjzy25r2n7zWYQ2KxMwW43ax4QZaj2+9/rEvV06jx127c8DaJznuQO7xtXprl8a+HeJshxg7VSu1jf6n3mzgab3kuNYejDbp8Wilxr7Ran0zqbWcSm8+te5vlTV6OEctGtkD1dP+Vj9u/ypze5ap5XVe1Gu51uO2yzeXtNJxW6WdVkTUv1BpPvBwhc2jgKfqftD6coz14Rjrpx3idIy9GxsRoxtRcC/tbicq+rkskus+MLnufVNzu9uQ5LjHA0pTImLzph60Ro6xPhxj/bRDnI7RmmkgP5euu+s+0DS77ks160BmZmZmZq3OybGZmZmZWVJEclyfu7UayzHWh2Osn3aI0zFaMw3k59J1H5hc9yZp+phjMzMzM7NW5WEVZmZmZmaJk2MzMzMzs6ThybGkQZJul3R5Wl5b0i2SZki6UNIyjY6hihhHSLpY0v2S7pO0taSVJE2SND39HVlwjEdJukfS3ZLOlzS06HMp6feSnpR0d25d2fOmzM9TrHdK2rTAGH+Unus7Jf1F0ojctuNSjA9I2rGoGHPbviIpJI1Kyy1zHtP6L6RzeY+kH+bWt8R5lDRe0mRJ0yRNkbRlWl/IebTqSZot6a7u5y6ta6n2pZ7q1Z5KOiDtP13SAUXUpVYV6n6ipLnp+Z8maZfctrLti6Sd0roZko5tdj1qJWlNSddLuje1oUem9R3/vPdQ99Z43iOioRPwZeCPwOVp+SJgnzR/GvDZRsdQRYxnA59O88sAI4AfAsemdccCPygwvjWAh4Blc+fwwKLPJfABYFPg7ty6sucN2AW4ChAwAbilwBh3AAan+R/kYlwfuAMYAqwNzAQGFRFjWr8mcDXZDzuMasHzuC1wLTAkLa/SaucRuAbYOXfubijyPHqq6fmc3X3d59a1VPtS5/r2uz0FVgJmpb8j0/zIouvWx7qfCBxdZt+y7UuaZgLjyN7H7wDWL7puvdR7NWDTNL888GCqX8c/7z3UvSWe94b2HEsaA+wK/C4tC/gQcHHa5WzgI42MoTeSViR7YZ4BEBGvRcRCYE+y+KAF4iT7qe9lJQ0GlgPmUfC5jIgbgadLVlc6b3sC50RmMjBC0mpFxBgR10TEorQ4GRiTi/GCiHg1Ih4CZgBbFhFjcgrwNSB/12zLnEfgs8D3I+LVtM+TuRhb5TwGsEKaXxF4LBdj08+j9VtLtS/1VKf2dEdgUkQ8HRHPAJOAnRoefD/10AaWU6l92RKYERGzIuI14IK0b8uKiHkRcVuafx64j6wzrOOf9x7qXklTn/dGD6s4lezN/c20vDKwMJeYPErPJ6MZ1gbmA2cqG/7xO0nDgFUjYl7a53Fg1aICjIi5wI+BR8iS4meBqbTeuYTK520NYE5uv1aJ92Cy/8ShhWKUtCcwNyLuKNnUMjEC6wHvVza05x+StkjrWynGLwE/kjSH7DV0XFrfSjFaeQFcI2mqpMPSunZrX/qr1vp22nn4fBo+8Hu9PbSxI+suqQvYBLiFAfa8l9QdWuB5b1hyLGk34MmImNqoY9TJYLKPc34TEZsAL5J9jPGWyPr0C/vOu3Rx7EmWyK8ODKPF/yuE4s9bbyQdDywC/lB0LHmSlgO+Dnyz6Fh6MZjsY7wJwFeBi9KnQ63ks8BREbEmcBTpEyJrC9tExKbAzsARkj6Q39jq7Uu9DbT6Ar8B1gHGk3UK/aTQaBpI0nDgEuBLEfFcflunP+9l6t4Sz3sje47/C9hD0myybu4PAT8j+xhgcNpnDDC3gTFU41Hg0Yjo/o/lYrJk+Ynuj+XS3ycrPL4ZtgMeioj5EfE68Gey89tq5xIqn7e5ZGNouxUar6QDgd2AfVPjA60T4zpk/wjdkV4/Y4DbJL2D1okRstfOn9NHfLeSfUI0itaK8QCy1wvAn3h7eEcrxWhlpE/Muofr/IXsuWuL9qWOaq1vx5yHiHgiIt6IiDeB0+n9tduWdZe0NFly+IeI6G6rBsTzXq7urfK8Nyw5jojjImJMRHQB+wDXRcS+wPXAXmm3A4C/NiqGakTE48AcSe9Kqz4M3AtcRhYfFB/nI8AESculnrnuGFvqXCaVzttlwP7pbtsJwLO5j42aStJOZMN99oiIl3KbLgP2kTRE0trAusCtzY4vIu6KiFUioiu9fh4lu3HhcVroPAKXkt2Uh6T1yG6GeIoWOY/JY8AH0/yHgOlpvpXOo5WQNEzS8t3zZDfR3k0btC91Vmt9rwZ2kDQyfeK4Q1rXdkrGjP832fMPlduX/wDrKvsWp2XI8o7LmhlzrdL7+RnAfRHx09ymjn/eK9W9ZZ73aM5diRN5+9sqxqUKzSDryRnSjBh6iW88MAW4k+wNfyTZ+Oi/k72ZXgusVHCM3wbuTxfKuWR3bBZ6LoHzyT72eJ0sgTuk0nkju7v2V2R3ld4FbF5gjDPIxihNS9Npuf2PTzE+QPqWgyJiLNk+m7e/raKVzuMywHnpmrwN+FCrnUdgG7Lx+XeQjWfbrMjz6Knq53Jces7uAO4Bjk/rW6p9qXOd69Kekt1HMSNNBxVdr37U/dxUtzvJkp3VcvuXbV/Ivs3hwbTt+KLrVUW9tyEbMnFn7v1ol4HwvPdQ95Z43v3z0WZmZmZmiX8hz8zMzMwscXJsZmZmZpY4OTYzMzMzS5wcm5mZmZklTo7NzMzMzBInx2ZmZmZmiZNjMzMzM7PEybGZmZmZWeLk2MzMzMwscXJsZmZmZpY4OTYzMzMzS5wcm5mZmZklTo7NzMzMzBInx2ZmZmZmiZNjMzMzM7PEybGZmZmZWeLk2MzMzMwscXJsZmZmZpY4OTYzMzMzS5wcm5mZmZklTo7NzMzMzBInx2ZmZmZmiZNjMzMzM7PEybGZmZmZWeLk2MzMzMwscXJsZmZmZpY4OTarkaQDJf2r6DjMzMys/pwcm/VAUpekkDS46FjMzGxJks6S9N2i47DO4eTYzMzMzCxxcmwASJot6auS7pT0oqQzJK0q6SpJz0u6VtLI3P4TJP1b0kJJd0iamNt2kKT70uNmSfpMbttESY9K+oqkJyXNk3RQD3EdmMp4XtJDkvbNrb9J0ikphlmS3pfWz0llH5ArZ0VJ50iaL+lhSSdIWiptWyotP5wed46kFdNDb0x/F0p6QdLWuTJ/LOmZFNfOufU3SPpOiu95SddIGlXluatU33dK+oekZyU9JenCap9bM2sdLdzWriTpTEmPpXbt0ty2QyXNkPS0pMskrZ7bFpI+J2l6iuM7ktZJMT8n6SJJy5TE9PXUjs3ubuPS9l0l3Z4eN0fSiSUxbpM7F3NSe3kYsC/wtdRG/y13no9O5/lZSRdKGporazdJ01JZ/5a0cW7bMZLmpvo8IOnDaf2Wkqak+J6Q9NPqnnVrOxHhyRPAbGAysCqwBvAkcBuwCTAUuA74Vtp3DWABsAvZP1jbp+XRafuuwDqAgA8CLwGbpm0TgUXAScDSqYyXgJFlYhoGPAe8Ky2vBmyQ5g9M5RwEDAK+CzwC/AoYAuwAPA8MT/ufA/wVWB7oAh4EDknbDgZmAOOA4cCfgXPTti4ggMG5uA4EXgcOTcf+LPAYoLT9BmAmsB6wbFr+fm/nrpf6ng8cnx4zFNim6GvGkydPtU+t2Nam/a8ALgRGpv0/mNZ/CHgK2DS1rb8Absw9LlLbugKwAfAq8PfUnq4I3AscUBLTT1NZHwRezLV5E4GNUl03Bp4APpK2jSVr0z+R4lsZGJ+2nQV8t8x5vhVYHVgJuA84PG3bJJ33rcja8APS/kOAdwFzgNXTvl3AOmn+ZuBTaX44MKHo68lTg16nRQfgqTWm1DDsm1u+BPhNbvkLwKVp/hhS8pjbfnV3A1im7EuBI9P8ROBlFk82nyzXyJAliwuBjwLLlmw7EJieW94oNdKr5tYtAManxu81YP3cts8AN6T5vwOfy217F1nyO5jKyfGM3PJyaZ93pOUbgBNy2z8H/F9v566X+p4D/BYYU/S14smTp75PLdrWrga8SflOijOAH+aWh6f2sSstB/Bfue1TgWNyyz8BTs3FtAgYltt+EfCNCvU5FTglzR8H/KXCfmdRPjneL7f8Q+C0NP8b4Dsl+z9Alqy/M52n7YClS/a5Efg2MKro68hTYycPq7C8J3LzL5dZHp7mxwIfSx9HLZS0ENiGrIFF0s6SJqeP4BaS9ViMypW1ICIW5ZZfypX9loh4EdgbOByYJ+kKSe/uIV4iolzMo8h6Gh7ObXuYrFcGsp6F0m2DyXp2Knk8F+dLaXZ4ue0sXr+K566X+n6NrHfoVkn3SDq4h9jMrLW1VFsLrAk8HRHPlNm2WPsYES+QdTyskdun2voAPJPaum4Pp2MgaStJ1ysb/vYsWVvYXZ81yT6Rq0VP7fBXSs7rmmS9xTOALwEnAk9KuiA3jOQQsk8E75f0H0m71RiPtQknx9YXc8h6M0bkpmER8X1JQ8h6Qn5M1os7AriSLLGrWURcHRHbk70Z3A+c3odiniLr6RibW7cWMDfNP1Zm2yKyBj76cLyeVDx3ULm+EfF4RBwaEauT9Xr/WtI76xybmbWWZrW1c4CVJI0os22x9lHSMLIhDXPL7FuNkamMbmulYwD8EbgMWDMiVgRO4+36zCEbQlJOre30HODkkvO6XEScDxARf4yIbcjqHcAP0vrpEfEJYJW07uKSuliHcHJsfXEesLukHSUNkjQ03WgxBliGbNzWfGCRshvVdujLQZTdpLJnanxeBV4g++ivJhHxBtlHdydLWl7SWODLqR6Qjec9StLakoYD/w+4MPW4zE/HHNeXOpRR8dz1VF9JH0vnF+AZsga75nNhZm2lKW1tRMwDriL7p3ukpKUlfSBtPh84SNL4lJD/P+CWiJjdj3p9W9Iykt4P7Ab8Ka1fnqwH+xVJWwKfzD3mD8B2kj4uabCklSWNT9ueoLY2+nTg8NRTLUnDlN0MuLykd0n6UKrrK2Q9393t8H6SRkfEm2RD4MDtcEdycmw1i4g5wJ7A18ka5jnAV4GlIuJ54ItkyegzZI3bZX081FJkSexjwNNk48E+28eyvkB248cs4F9kPRS/T9t+D5xLNp7sIbIG8Qvw1pCJk4Gb0sdvE/p4fFJ5Fc8dPdd3C+AWSS+Qnc8jI2JWf2Ixs9bWxLYW4FNkn7DdTzbm9ksphmuBb5D1Us8j673dpx/HeZws3sfIEt7DI+L+tO1zwEmSnge+SVY3UhyPkA0b+QpZ+zgNeG/afAawfmqjL+0tgIiYQnZD9S9TLDPI7iWB7B+O75N94vg4WS/xcWnbTsA9qR3+GbBPRLxcU+2tLXTfXW9mZmbWMMq+hu68iBjTy65mhXLPsZmZmZlZ4uTYzMzMzCzxsAozMzMzs8Q9x2ZmZmZmyeBGFDpq1Kjo6upqRNFmZm1r6tSpT0XE6EaU7XbXzGxJfWl3G5Icd3V1MWXKlEYUbWbWtiQ93PtefeN218xsSX1pdxuSHFuLu/575ddve1z59WZmrcxtmpnVkZPjduI3ADMzM7OGcnJsZmbF8D/8ZtaC/G0VZmZmZmaJe447WaVeGTMzMzMry8mxmZm1Fg+3MLMCeViFmZmZmVni5NjMzMzMLPGwCjMz60wenmFmfeDk2IrnNzAzMzNrEU6OzcysPfgbeMysCTzm2MzMzMwscXJsZmZmZpZ4WIX1rtYxwRX2v3nWgrLrtx63cl+iMjMzM6s7J8f2tlrH83n8n5mZmXUYJ8dmZtZY/kfazNqIk+NOUNAbT8sNk/BXwplZNRrZVrgdMmt7To7NzMygfGLrpNZswHFybGZmtfEwCTPrYE6Ore3cfMbRZdf7Wy/MzMysv5wcW68qjS1udPlbb1tMOWZmRTtl0oNl1x+1/XpNjsRs4HFybC2rUg+xmZmZWaM4OW4W3+hRGPfAmFmrqtQ+mVlxnBy3It/sUlcTHvlthS0/rroMJ9hmVg0P7zJrf06ObcAql/DWmuw6aTbrcP7eYrMBZ6miAzAzMzMzaxXuOS5Siw2faPS3UpiZtZuK7eKs2m4YbvTY4np9iuVPw8ycHPedP2rrSL45xsxaQT3ulQAnu2Z94eS4g1W8McQ/llFR5Tek8iavdViDIjEzM7MiODnuAK02HKLV4jEzyyui46DSP97+B9us9Tg5HoCcvLYgD9Mx61UnfhpW66dV9VLrELJah2d4OIe1MyfHZmbWUur1D7w7AhqvXvdp1JJMO/G2RnNybNZEFRt1vxKtFbXYN+pY7cloqw3n8E3P1g78lmxmZkZ79zTXa3hGJybT7T4kpNZz4B70/nNy3K1eYz7d0zKgVHojOWVS+TeSSvvfXKH8yYv83aXWBA1utxqddLZzUmv1U6mdq7WdrrV8t6Odp/WS40bfmFTrm0Cd3jTceLeeRt4I0+ibbG4+o/wPENStd6fCdX/Koo/Wpfi2eDPxTZLWghrdQ9zociq1UfXosS7q5sZK2r3Ht4h/BlrlHxBFRP0LleYDD9e94L4ZBTxVdBB11ol1AternXRinaDx9RobEaMbUXCLtbu9adfrpx3jbseYoT3jbseYoT3jriXmmtvdhiTHrUTSlIjYvOg46qkT6wSuVzvpxDpB59ar1bTreW7HuNsxZmjPuNsxZmjPuBsd81KNKtjMzMzMrN04OTYzMzMzSwZCctxaI/TroxPrBK5XO+nEOkHn1qvVtOt5bse42zFmaM+42zFmaM+4Gxpzx485NjMzMzOr1kDoOTYzMzMzq4qTYzMzMzOzpG2TY0k7SXpA0gxJx5bZPkTShWn7LZK6cts2lnSzpHsk3SVpaFOD70Ff6yVpaUlnp/rcJ6mlfqmginp9QNJtkhZJ2qtk2wGSpqfpgOZF3bO+1knS+Nz1d6ekvZsbec/681yl7StIelTSL5sTce/6ef2tJema9Lq6N9+W2JL60YZ1SXpZ0rQ0ndZCMbdk+9TPuN/InevLWijmL6fX2Z2S/i5pbG5bK5/rnuIu5FxXGffhKW+YJulfktbPbTsuPe4BSTu2esx1bUMiou0mYBAwExgHLAPcAaxfss/ngNPS/D7AhWl+MHAn8N60vDIwqOg61aFenwQuSPPLAbOBrqLrVEO9uoCNgXOAvXLrVwJmpb8j0/zINq/TesC6aX51YB4woug69bdeue0/A/4I/LLo+tSjTsANwPZpfjiwXNF1atWpn21YF3B3i8bccu1THa7rF1r0XG/b/RoDPpu7Plr9XJeNu6hzXUPcK+Tm9wD+L82vn/YfAqydyml4rtTPmOvWhrRrz/GWwIyImBURrwEXAHuW7LMncHaavxj4sCQBOwB3RsQdABGxICLeaFLcvelPvQIYJmkwsCzwGvBcc8LuVa/1iojZEXEn8GbJY3cEJkXE0xHxDDAJ2KkZQfeiz3WKiAcjYnqafwx4EmjIr6b1QX+eKyRtBqwKXNOMYKvU5zqlHonBETEp7fdCRLzUpLjbUX/asKK0a/vUr9dqQaqJ+frca2wyMCbNt/q5rhR3kaqJO58nDCPLJUj7XRARr0bEQ8CMVF4rx1w37ZocrwHMyS0/mtaV3SciFgHPkvUSrweEpKvTx01fa0K81epPvS4GXiTrhXwE+HFEPN3ogKtUTb0a8dhGqktckrYk++94Zp3i6q8+10vSUsBPgKMbEFd/9Oe5Wg9YKOnPkm6X9CNJg+oeYefoTxsGsHY6z/+Q9P5GB1saT9Iu7VN/jz1U0hRJkyV9pK6RVVZrzIcAV/XxsfXUn7ihmHMNVcYt6QhJM4EfAl+s5bEN0J+YoU5tyOC+PrCNDQa2AbYAXgL+LmlqRPy92LD6bUvgDbKP6UcC/5R0bUTMKjYsq0TSasC5wAER0So9O/3xOeDKiHi02I7AuhoMvB/YhOyfzguBA4EzCoypU80D1oqIBekTiEslbVDSS2T1MzYi5koaB1wn6a6IaJV/0pG0H7A58MGiY6lFhbhb+lxHxK+AX0n6JHAC0DL39lRSIea6tSHt2nM8F1gztzwmrSu7TxpqsCKwgOy/kBsj4qn0EciVwKYNj7g6/anXJ8nG3bweEU8CN5G9QFtBNfVqxGMbqV9xSVoBuAI4PiIm1zm2/uhPvbYGPi9pNvBjYH9J369veH3Snzo9CkxLH/EtAi6lddqLVtTnNix9fLsAICKmkn2asl7DI27f9qlfx46IuenvLLJx9ZvUM7gKqopZ0nbA8cAeEfFqLY9tkP7EXdS5htrP2QXAR/r42Hrpc8x1bUPqMXC52RNZb84sskHi3QO2NyjZ5wgWv+njojQ/EriN7Ka1wcC1wK5F16kO9ToGODPNDwPuBTYuuk7V1iu371ksecPLQ+l5G5nmV2rzOi0D/B34UtH1qGe9SrYdSOvckNef52pQ2n90Wj4TOKLoOrXq1M82bDTphh+ym3HmNuO13q7tUz/jHgkMSfOjgOmU3PRU4PWxCVlSs27J+pY+1z3EXci5riHudXPzuwNT0vwGLH5D3iyac0Nef2KuWxvS8CengSdwF+DBdDEen9adRPYfG8BQ4E9kg8hvBcblHrsfcA9wN/DDoutSj3qR3UX/p1Sve4GvFl2XGuu1BVkv3YtkPeH35B57cKrvDOCgouvS3zql6+91YFpuGl90ferxXOXKOJAWSY7rcP1tT/YNN3eRJRnLFF2fVp760YZ9NLVf08g6MHZvoZhbsn3qRxv0vnQ935H+HtJCMV8LPJFrGy9rk3NdNu4iz3WVcf8s97q7nlwiStYLPhN4ANi51WOuZxvin482MzMzM0vadcyxmZmZmVndOTk2MzMzM0ucHJuZmZmZJU6OzczMzMwSJ8dmZmZmZomTYzMzMzOzxMmxmZmZmVni5NjMzMzMLHFybGZmZmaWODk2MzMzM0ucHJuZmZmZJU6OzczMzMwSJ8dmZmZmZomTYzMzMzOzxMmxmZmZmVni5NjMzMzMLHFybGZmZmaWODk2MzMzM0ucHJuZmZmZJU6OzczMzMwSJ8dmZmZmZomTYzMzMzOzxMmxmZmZmVni5NjMzMzMLHFybGZmZmaWODk2MzMzM0ucHJs1gaT3S3qg6DjMzOxtktaS9IKkQUXHYq1DEVF0DGYDjqTZwKcj4tqiYzEzs95J6gIeApaOiEUFh2MN5J5jMzMzM7PEybFVRdJsSV+VdKekFyWdIWlVSVdJel7StZJG5vafIOnfkhZKukPSxNy2gyTdlx43S9JnctsmSnpU0lckPSlpnqSDeohrJUlnSnpM0jOSLs1tO1TSDElPS7pM0uq5bSHpcEnTU4y/kqSSx3bHeK+kTdP6YyXNzK3/77R+SCpnw1wZoyW9LGmV7nql9ecCawF/Sx/nfU3SFZK+UFK3O7vLN7P24jazf21mWt5N0rS0378lbdxDvULSF9P5eUrSjyQtlbYtJekESQ+nc3SOpBXTtq702MFp+QZJ35F0U4r5Gkmj0mFuTH8XprZ7a0nvlPQPSc+m415YKUZrIxHhyVOvEzAbmAysCqwBPAncBmwCDAWuA76V9l0DWADsQvYP2PZpeXTaviuwDiDgg8BLwKZp20RgEXASsHQq4yVgZIW4rgAuBEam/T+Y1n8IeArYFBgC/AK4Mfe4AC4HRpAlqvOBndK2jwFzgS1SjO8Exua2rZ7qtTfwIrBa2vZ74OTcMY4A/i9Xr0dLzud2ueWPA7fklt+bztkyRT/3njx5qn1ym9nvNnOTdM62AgYBB6RzOqRCvQK4Hlgpxfcg2dA1gIOBGcA4YDjwZ+DctK0rPXZwWr4BmAmsByyblr9fbt+07nzg+FS/ocA2RV97nurw+i06AE/tMaVGad/c8iXAb3LLXwAuTfPHdDc8ue1XAwdUKPtS4Mg0PxF4uaTxeRKYUOZxqwFvlnsTAM4AfphbHg68DnSl5cg3YsBFwLG5WI+s8rxMA/ZM89sBM3PbbgL2z9Wrp+R4KPAMsG5a/jHw66Kfd0+ePPVtcptZ8bxU22b+BvhOyWMfICXzZcoNUrKelj8H/D3N/x34XG7bu1LdBlM+OT6hpJzuhH2xfdO6c4DfAmOKvuY81W/ysAqrxRO5+ZfLLA9P82OBj6WPwhZKWghsQ9YwI2lnSZPTR3cLyXo6RuXKWhCL3+zwUq7svDWBpyPimTLbVgce7l6IiBfIemLWyO3zeIVjrEnWc7AESfvnPuZbCGyYi/16YDlJWym7cWM88Jdy5ZSKiFfIenP2Sx8FfgI4t5rHmlnLcpvZ9zZzLPCVknOyZoqzkjm5+Ydz+y5WtzQ/mKxXv5xK9Szna2S95bdKukfSwT3sa21icNEBWEeaQ9YLcmjpBklDyHpQ9gf+GhGvpzFvKt23yuOsJGlERCws2fYYWePafdxhwMpkH/1VU+46ZWIfC5wOfBi4OSLekDStO/a0fBFZYvsEcHlEPF/hGOW+JuZssoT4X8BLEXFzFbGaWftzm7lkmzmHbMjFydVXjzWBe9L8WqlOS9QtbVuUjjmmhvKXaLcj4nHgUABJ2wDXSroxImbUUK61GPccWyOcB+wuaUdJgyQNTTeNjAGWIRvPNh9YJGlnYIe+HCQi5gFXAb+WNFLS0pI+kDafDxwkaXx6c/l/ZGN6Z1dR9O+AoyVtpsw7UyM/jKxxnA/ZTTJkvSB5fyQbV7dvmq/kCbLxb/n63Ez2kedPcK+x2UDiNnPJNvN04PDUqyxJwyTtKmn5HuL4aqrXmsCRZJ/GddftKElrSxqe6nZh1P51bPPJ2ui32m5JH0vPE2RD4yLtY23MybHVXUTMAfYEvk7WmMwBvgoslXoFvkg2Xu0Z4JPAZf043KfIxo7dTzbO7ksphmuBb5D1uMwj69XYp8r4/wScTNZQP082vm+liLiXLHG9mSy53YhsjFz+sbeQ3XCyOtmbUCXfA05IHxcenVt/Tir3vGpiNbP25zZzyTYzIqaQ9cj+kqzeM4ADewnlr8BUsnHNV5CNo4bsxr9zyb5t4iHgFbIx3zWJiJdSPW9KbfcEspsQb5H0AtnzcmREzKq1bGst/hEQsxYiaX/gsIjYpuhYzMzahaQgu6HZwxms39xzbNYiJC1Hdmf0b4uOxczMbKBycmzWAiTtSPZx6hP0PFbZzMzMGsjDKszMzMzMEvccm5mZmZklDfme41GjRkVXV1cjijYza1tTp059KiJGN6Jst7tmZkvqS7vbkOS4q6uLKVOmNKJoM7O2Jenh3vfqG7e7ZmZL6ku72/6/kHf992rbf9vjGhOHmZnVplL77XbazArU/slxrdwYm5mZmVkFviHPzMzMzCxxcmxmZmZmlgy8YRVmZtZctd4bYmZWIPccm5mZmZklrddz7BvmzMzMzKwg7jk2MzMzM0tar+fYzMwGNn9/vZkVyD3HZmZmZmaJk2MzMzMzs8TJsZmZmZlZ0j5jjhv9PZn+lgwzMzOzAa99kmMzM2tt/rEPM+sAHlZhZmZmZpY4OTYzMzMzS5wcm5mZmZklHnPcG9+oZ2ZmZjZguOfYzMzMzCxxz7GZmdXG30phZh3MyXFfebiFmZmZWcdxcmxmNtD5n/0B55RJD5Zdf9T26zU5ErPW4+TY6s6NrlmHaJfhE07uzayOfEOemZmZmVni5NjMzMzMLPGwig7Q6GEMHiZhZm3Jwy3MrA+cHDdLuUbaDbSZ2YDWap0PrRaPWRGcHNdbu9zAYmY2UNWhR9lJpFnncnJcpAoN9CmLPlp2vRtdM7MGqsMnfEUlzQPtuGaN5OS4BU145Ldl158y6bCayhlojdZAq6+ZFadSOz15rdra6XZRqX0160ROjgt086wFhRzXjZyZWXuqtf12e29WOyfH1mf1anRrLcc9wWZmZtYoTo6bpJG9xP54r2/715Jke8iG2QBVp5usB1oPbq1tZiPbWLffVquOTY4rJaNbj1u5oeUUkQS3i1Z7cygXT62NZbs3uu0evw0stbavtbb39ThmrZ0V9brHpF008n2gHd5jeuJ2t3V0bHJslQ20nuZatFrjambN00qdG26nM63UJrszYeAYcMlxUTfBtYN69XgMtMa7Htql0W2lj0obrZ1jbxdFtced+D7g9rh27fIab7U46/Gpa6tTRNS/UGk+8HAfHz4KeKqO4XQKn5cl+ZyU5/OypFY5J2MjYnQjCu7AdtcxVccxVccxVafVYqpHPDW3uw1JjvtD0pSI2LzoOFqNz8uSfE7K83lZks9Jz1rx/Dim6jim6jim6rRaTEXFs1SzD2hmZmZm1qqcHJuZmZmZJa2YHLf395U1js/LknxOyvN5WZLPSc9a8fw4puo4puo4puq0WkyFxNNyY47NzMzMzIrSij3HZmZmZmaFcHJsZmZmZpYUlhxL2knSA5JmSDq2zPYhki5M22+R1FVAmE1VxTn5gKTbJC2StFcRMRahivPyZUn3SrpT0t8ljS0izmar4rwcLukuSdMk/UvS+kXE2Uy9nZPcfh+VFJJa5iuL6qk/7auk49L6ByTtWG2ZjYhH0vaSpqbreKqkD+Uec0Mqc1qaVmlSTF2SXs4d97TcYzZLsc6Q9HNJalJM++bimSbpTUnjm3SeKr4vSTpA0vQ0HZBb3+jzVDYmSeMl3SzpnvR+sXdu21mSHsqdp/HNiClteyN33Mty69dOz/OM9Lwv04yYJG1bcj29IukjaVujz1PF9/NGXU9lRUTTJ2AQMBMYBywD3AGsX7LP54DT0vw+wIVFxNpi56QL2Bg4B9ir6Jhb6LxsCyyX5j/b6ddKDedlhdz8HsD/FR130eck7bc8cCMwGdi86LgLujbKtq/A+mn/IcDaqZxB1Z7bBsSzCbB6mt8QmJt7zA19ff76GVMXcHeFcm8FJgACrgJ2bkZMJftsBMxs4nnqosz7ErASMCv9HZnmRzbpPFWKaT1g3TS/OjAPGJGWz6KP76v9iSlte6FCuRcB+6T504DPNiumkufxad5+j230eSr7ft6o66nSVFTP8ZbAjIiYFRGvARcAe5bssydwdpq/GPhwXf4baF29npOImB0RdwJvFhFgQao5L9dHxEtpcTIwpskxFqGa8/JcbnEY0Ol331bTrgB8B/gB8Eozg2ui/rSvewIXRMSrEfEQMCOVV+25rWs8EXF7RDyW1t8DLCtpSJXHbUhMlQqUtBrZP6STI3vHPgf4SAExfSI9th768760IzApIp6OiGeAScBOzThPlWKKiAcjYnqafwx4EqjHL1bW/f07Pa8fInueIXveP1JATHsBV+XeY/ujP+/njbqeyioqOV4DmJNbfjStK7tPRCwCngVWbkp0xajmnAxEtZ6XQ8j+c+x0VZ0XSUdImgn8EPhik2IrSq/nRNKmwJoRcUUzA2uy/rSvlR7bn/apXu39R4HbIuLV3Loz00e736ix86S/Ma0t6XZJ/5D0/tz+j/ZSZiNj6rY3cH7Jukaep1of24zz1CtJW5L1Xs7MrT45fZx/So3/hPU3pqGSpkia3D18gex5XZie576UWa+cYh+WvJ6adZ7y7+eNup7K8g151jEk7QdsDvyo6FhaRUT8KiLWAY4BTig6niJJWgr4KfCVomOx2kjagKy3/zO51ftGxEbA+9P0qSaFMw9YKyI2Ab4M/FHSCk06do8kbQW8FBF351YXdZ5aVuptPBc4KCK6e02PA94NbEH20f0xTQxpbGQ/kfxJ4FRJ6zTx2BWl87QRcHVudVPOU9Hv50Ulx3OBNXPLY9K6svtIGgysCCxoSnTFqOacDERVnRdJ2wHHA3uU9Cx1qlqvlwuow0dNLa63c7I82bjVGyTNJhujdpk676a8/rSvlR7bn/apX+29pDHAX4D9I+KtXr6ImJv+Pg/8kewj22r1OaY05GRBOvZUsp7H9dL++SFdtbbh9XhfXKKXrwnnqdbHNuM8VZT+kbkCOD4iJnevj4h5kXkVOJPmnaf8czSLbIz4JmTP64j0PNdcZn9jSj4O/CUiXs/F2vDzVOH9vFHXU3n9GbDc1wkYTDaYem3eHpS9Qck+R7D4jQcXFRFrK52T3L5nMXBuyKvmWtmE7A1q3aLjbbHzsm5ufndgStFxF31OSva/gc68Ia/P7SuwAYvfkDeL7Caams5tHeMZkfb/nzJljkrzS5ONyzy8SedoNDAozY8jeyNeKS2X3hi0SzNiSstLpVjGNfM85fY9iyVvyHuI7OapkWm+Keeph5iWAf4OfKnMvqulvwJOBb7fpJhGAkPS/ChgOukmNeBPLH5D3ueaEVNu/WRg22aeJyq8nzfqeqoYa38L6POBYRfgwXQSjk/rTiL7TwFgaLowZqSKjysq1hY6J1uQjad5key/ynuKjrlFzsu1wBPAtDRdVnTMLXJefkZ2E9M04PpKDWMnTb2dk5J9b6ADk+Mqr42K7StZj81M4AFyd32XK7PR8ZANBXox99qeBqxCdoPpVODOdI3/jJSwNiGmj+ZeV7cBu+fK3By4O5X5S9Kv0DbpeZsITC4prxnnqeL7EnBwinUG2RCGZp2nsjEB+wGvl1xP49O264C7UlznAcObFNP70nHvSH8PyZU5Lj3PM9LzPqSJz10X2T9bS5WU2ejzVPH9vFHXU7nJPx9tZmZmZpb4hjwzMzMzs8TJsZmZmZlZ4uTYzMzMzCxxcmxmZmZmljg5NjMzMzNLnBybmZmZmSVOjs3MzMzMEifHZmZmZmaJk2MzMzMzs8TJsZmZmZlZ4uTYzMzMzCxxcmxmZmZmljg5NjMzMzNLnBybmZmZmSVOjs3MzMzMEifHZmZmZmaJk2MzMzMzs8TJsZmZmZlZ4uTYzMzMzCxxcmxmZmZmljg5NjMzMzNLnBybmZmZmSVOjs3MzMzMEifHZmZmZmaJk2MzMzMzs8TJsZmZmZlZ4uTYzMysg0haVdKNkp6X9JOCY5ko6dEa9r9B0qfT/L6SrmlcdLWTdI+kiUXHYY01uOgAzAYCSQcCn46IbYqOxcw63mHAU8AKERH1LFjSicA7I2K/epZbTkT8AfhDo49Ti4jYoOgYrPHcc2zWIiQNKjoGM+sIY4F7KyXGktwxZtYDJ8fWI0mzJX1V0p2SXpR0RvrI7qr0kd21kkbm9p8g6d+SFkq6I//xk6SDJN2XHjdL0mdy2yZKelTSVyQ9KWmepIN6iOvAVMbzkh5KH78tI+lpSRvl9ltF0kuSRueO8bXcMT4iaRdJD6bHfj332BMl/UnSeek4d0laT9Jx6fFzJO2Q23/FdH7mSZor6buSBkl6D3AasLWkFyQtTPufJek3kq6U9CLwZUlP5JNkSf8j6Y6+P4Nm1iit2D5KOgs4APhaam+2S23Zxaktew44UNKWkm5OscyT9EtJy+TK2UDSpNQuPiHp65J2Ar4O7J3KvqO32Ks4h9tLul/Ss5J+CSi37UBJ/8oth6TPSZqejvUdSeukc/qcpItK6rCbpGmpjv+WtHHJc3d0eu6elXShpKFp2yhJl6fHPS3pn5KWyj1uuzQ/RNKpkh5L06mShtT6nFkLighPnipOwGxgMrAqsAbwJHAbsAkwFLgO+Fbadw1gAbAL2T9e26fl0Wn7rsA6ZI3fB4GXgE3TtonAIuAkYOlUxkvAyDIxDQOeA96VllcDNkjzvwZ+kNv3SOBvJcf4ZjrGocB84I/A8sAGwMvA2mn/E4FXgB3JhiCdAzwEHJ97/EO5Y/0F+N8U3yrArcBn0rYDgX+V1OMs4Fngv9L5GgrcC+xcUuZXir4OPHnytOTUiu1j2v8s4Lu55ROB14GPpGMvC2wGTEhtWxdwH/CltP/ywDzgK6keywNb5co6r+R4vcX+aIU4RwHPA3uleh2V6vnptH2xdhMI4K/ACmTt9avA34FxwIqp/Twg7btJej62AgaR/cMwGxiSe+5uBVYHVkr1Pzxt+x5Zh8bSaXo/oNzjtkvzJ6XnfxVgNPBv4Dt9ec48tdbknmOrxi8i4omImAv8E7glIm6PiFfIkrdN0n77AVdGxJUR8WZETAKmkDUKRMQVETEzMv8AriFrdLq9DpwUEa9HxJXAC8C7KsT0JrChpGUjYl5E3JPWnw18QlJ378OngHNLjnFyRLwOXEDWOP8sIp5PZdwLvDe3/z8j4uqIWAT8iawB/H7u8V2SRkhaNdXzSxHxYkQ8CZwC7NPLuf1rRNyUztcrKf79ACStRJaY/7GXMsysOK3YPpZzc0Rcmo79ckRMjYjJEbEoImaT/WP/wbTvbsDjEfGTiHgltY+3VCq4itgr2QW4JyIuTm3qqcDjvTzmhxHxXGqv7wauiYhZEfEscBVvn+/DgP+NiFsi4o2IOJssmZ6QK+vnEfFYRDwN/A0Yn9a/TtbpMjad739GRLkhKvuSPSdPRsR84Ntk7znkyunPc2YFcXJs1XgiN/9ymeXhaX4s8LH0UdTCNHxgG7JGBkk7S5qcPqZaSNYwjsqVtSAlod1eypX9loh4EdgbOByYJ+kKSe9O225Jj5uY1r0TuKzkGG/kYi9Xv/wxS7c9Vebxw1Pdl07xdNf9f8l6FHoyp2T5PGB3ScOAj5Ml5/N6KcPMitNS7WMPFmtrlA0Ru1zS42moxf/LHW9NYGa1BVcReyWr5+NKCWhpm1iqlvP9lZLzvWY6Zrd8Ip4/nz8CZgDXpGEix/YQ/8O55YdLyu/vc2YFcXJs9TQHODciRuSmYRHx/TQO6xLgx8CqETECuJLc+LJapN7c7cneWO4HTs9t7u59/RRwcerBabQ5ZL0So3J1XyHevrO50h3ji61PvU83A//Dkr3eZta+mtY+VlDaBv2GrO1cNyJWIBtL3H28OWRDFXotp5+xzyNLWLvLUn65n+aQfUqYP9/LRcT5vT0w9ZR/JSLGAXuQ3Q/y4TK7PkaWhHdbK62zNufk2Oqpu9dzR2U3og1NNyWMAZYBhpCN8V0kaWdgh54Kq0TZDS97pt7VV8k+qnqzJI7/JkuQz+lHfaqWenevAX4iaQVJS6UbRbo/pnwCGJO/WaQH5wBfAzYC/tyYiM2syZrSPtZgebJ7N15In7J9NrftcmA1SV9KN50tL2mrtO0JsuFk3flDf2K/AthA2Y3Hg4EvAu/oX7XecjpwuKStlBkmaVdJy/f2wHQj3ztTsv4s8AaLv8d0Ox84QdkN36PI7mc5r07xW4GcHFvdRMQcYE+yHoj5ZP+5fxVYKiKeJ2v4LgKeAT7J4sMdarEU8GWy/9CfJhsn91bDnuK4jayH4599PEZf7E/2RnEvWR0vJn1kSnZjzj3A45Ke6qWcv5D1RvwlIl5qUKxm1kRNbB+rdXQ6zvNkieSFuVifJ7thcHeyoQfTgW3T5j+lvwsk3daf2CPiKeBjwPfJbk5cF7ipX7V6u+wpZDdN/zLFNYPsBr9qrAtcS9bxcjPw64i4vsx+3yUbN34ncBfZ+853+xW4tYTuuy/NOoqk3wOPRcQJRcfSF5Jmkn3TxbVFx2JmZjaQ+IvAreNI6iIbs7tJL7u2JEkfJev1vq7oWMzMzAYaJ8fWUSR9h+y7Mr8XEQ8VHU+tJN0ArA98KiLKjXEzMzOzBvKwCjMzMzOzxDfkmZmZmZklDRlWMWrUqOjq6mpE0WZmbWvq1KlPRcToRpTtdtfMbEl9aXcbkhx3dXUxZcqURhRtZta2JD3c+15943bXzGxJfWl3fUNeJ7v+e+XXb3tcc+MwMyuC20Az6wOPOTYzMzMzS5wcm5mZmZklHlZhb/NHkGZmZjbAuefYzMzMzCxxz7GZmRmU//TMn5yZDTjuOTYzMzMzS5wcm5mZmZklHlZhZmbtwTcNm1kTODkeiCq9wZiZmZkNcE6OO0FRya57cczMzKzDODm25nEybWZmZi3OybHVn4dtmJmZWZtycmytyz3NZmZm1mROjs3MzOrF/9SbtT0nx61ogDWup0x6sOz6o3x1mlkj1DL0y8PEzAYcpx/tpMUa6ZtnLSi7futxK9dUzoRHflt+Q43lmJmZmfWXk2MzM7NGG2CfCJq1MyfHRWqxnuCK2iXOGpUbznHU9usVEImZLaZD2xwzaw9Ojs3MzCg/VKzWYWI1c4+yWctxcmwtq+KY5m2bHIiZmZkNGE6O7S31usHOzKypPAzDzOrIybGZmbWUVvpHvVIslbgzwaz9OTm2XtX65lDr/jWrtZfIY/fMWtLNZxxddAity2ORzQrj5LivnKB1pIo/SOJvsTAzMxsQnBybmVlbqHW4RSsNz2g49zSb1Y2T42apww0jfmPIdGq9zDpVpU9kJjQ5jgHJSbNZzZwcd4CGj/E1M+sgbjPNrCdOjq3jVbzpZ63Dqi7DY5HNrBFq/jYM/LV1Zo3m5NgGrAmP/HaJdZNrSJibwUm5meV5WJlZ4zk5NjOzuqj0z9xA0i7Ja83/eNc4dtn/2Fs7c3JsHaOVxhH6jcHaSd0SJT5adm25T2kGmka3T7Um5RWfk+trS+LrMWytZr7J0BrMyfEA1EpJpJl1DifBrcftvVntnBy3IDdmbaTGHrRae+gqJxs/7iWwKrkHxszMbDGdmxzX602/Dt9PDE5420XNPV/1GkdYp+uskopJeYUWoNYk3sNIOlOt101R3L42XqPPsdsQayUt1sRRe1Jba1JR4/7tcnOFFaPiG8ZadSqngspJyyUVHlG+J7vScSfQvj3W9XqT9Zt1ZU5GO1e9nttKHQ2nTKptLHLFMc1lbL1tTUX7kzOrqPWSYzMzawkeQ2zNUo9rreIvMdZY9uRFtZWz9SEVOg0a/Ql2rZ2G9Ur6y5Vfpw7MUxaV78hpdqeEIqL+hUrzgYfLbBoFPFX3A/ZfK8bVijFBa8bVijFBa8bVijFBa8bViJjGRsToOpcJ9NjuVqMVz3+zuO4Dz0CtNwzMutfc7jYkOa54MGlKRGzetANWqRXjasWYoDXjasWYoDXjasWYoDXjasWYGmUg1bWU6z7w6j5Q6w0Du+61WKroAMzMzMzMWoWTYzMzMzOzpNnJcave3dGKcbViTNCacbViTNCacbViTNCacbViTI0ykOpaynUfeAZqvWFg171qTR1zbGZmZmbWyjyswszMzMws6XNyLGknSQ9ImiHp2DLbPyDpNkmLJO1Vsu0ASdPTdEBu/WaS7kpl/lySmhWXpPGSbpZ0j6Q7Je2d23aWpIckTUvT+GbElLa9kTvuZbn1a0u6JZV5oaRlaompP3FJ2jYX0zRJr0j6SNrWr3NVZVxflnRvep7+LmlsbltDrq2+xlTwddXTeSryuqp0roq+rg5P18g0Sf+StH5u23HpcQ9I2rHaMltBFfUekp7rGem570rruyS9nDvnpzU9+H7qa93Tto1zr927JA1tavD90I/nfN+S1+CbfXmtFakfdV9a0tnpub5PUlv9Ekg/6r2MpDNTve+QNLHJobemiKh5AgYBM4FxwDLAHcD6Jft0ARsD5wB75davBMxKf0em+ZFp263ABEDAVcDOTYxrPWDdNL86MA8YkZbPyu/brJjSthcqlHsRsE+aPw34bDPjKnk+nwaW6++5qiGubXPH+yxwYSOvrX7GVOR1VTamFriuKsZV8HW1Qm5+D+D/0vz6af8hwNqpnEHVlFn0VGW9Pweclub3yV27XcDdRdehoLoPBu4E3puWVwYGFV2nRte7ZJ+NgJlF16eJz/kngQvS/HLAbKCr6Do1od5HAGem+VWAqcBSRdep6KmvPcdbAjMiYlZEvAZcAOyZ3yEiZkfEncCbJY/dEZgUEU9HxDPAJGAnSauRvTlNjuxZOgf4SLPiiogHI2J6mn8MeBKox5f19+dclSVJwIeAi9Oqs2niuSqxF3BVRLxU4/H7E9f1ueNNBsak+UZdW32OqeDrqtJ5KquJ11U1cRVxXT2XWxwGdN+QsSfZm+arEfEQMCOV12uZLaCaGPcke64he+4/nK6Fdtefuu8A3BkRdwBExIKIeKNJcfdXvZ7zT6THtpP+1D2AYZIGA8sCrwHP0R76U+/1gesAIuJJYCEw4L8Hua/J8RrAnNzyo2ldfx67RprvS5n1iOstkrYk++9rZm71yelj4FMkDWliTEMlTZE0ufsjZrJejIURsaiPZdYjrm77AOeXrOvruepLXIeQ9QT39Nj+Xlv9iektBV9XpTG1ynVV9lxR0HUl6QhJM4EfAl/s5bH1eg01UjUxvrVPeu6fJbsWANaWdLukf0h6f6ODrbP+1H09ICRdrWxo2deaEG+99Pc577Y3S74GW11/6n4x8CLZp3uPAD+OiKcbHXCd9KfedwB7SBosaW1gM2DNhkfc4nxDXonUy3gucFBEdPeYHge8G9iC7OPeY5oY0tjIfs3mk8CpktZp4rF7lM7VRsDVudVNO1eS9iP7D/dHjTpGrSrFVOR1VSGmwq+rXs5VIddVRPwqItZJ5Z/QiGO0kXnAWhGxCfBl4I+SVig4pmYZDGwD7Jv+/rekDxcbUvNI2gp4KSLuLjqWJtoSeINs+NvawFckjSs2pKb4PVkyPQU4Ffg32XkY0PqaHM9l8f8sxqR1/XnsXBb/eLWWMusRF6nhvwI4PiImd6+PiHmReRU4k+xF1JSYImJu+jsLuAHYBFgAjEgf/9RcZj3iSj4O/CUiXs/F259zVXVckrYDjgf2SMfq6bH9vbb6E1Oh11WlmIq+rirFlRR2XeVcwNtDSnq6rvr7Gmq0amJ8a5/03K8ILEjDSBYARMRUsk881mt4xPXT57qTJQs3RsRTaWjPlcCmDY+4PvpT727lPrlpB/2p+yfJ7jN4PQ0vuIn2GV7Qn9f5oog4KiLGR8SewAjgwcaH3OKib4O/B5Pd7LQ2bw/+3qDCvmex5A15D5HdMDUyza+UtpXeNLVLE+NaBvg78KUy+66W/orsP6vvNymmkcCQND8KmE4aZA/8icVvnPpcs85Vbv1kYNt6natq4yJL5GaSbnRr9LXVz5gKu656iKnQ66pSXC1wXa2bm98dmJLmN2DxG/Jmkd0AU/VrqKipynofweI36lyU5keTbkIju9FnbvfrqR2mftZ9JHAb2Y1Zg4FrgV2LrlOj652Wl0rP9bii69Lk5/wY3r4xbRhwL7Bx0XVqQr2XA4al+e3J/iksvE5FT/15MnYh++9iJlmPGMBJZD1BkH38+SjZGJ4FwD25xx5MdlPLDLKPmbvXbw7cncr8JelHSpoRF7Af8DowLTeNT9uuA+5KsZ0HDG9STO9Lx70j/T0kV+Y4soRvBllCM6TJz2EXWQO6VEmZ/TpXVcZ1LfBE7nm6rNHXVl9jKvi6qhRT0ddVT89fkdfVz4B7UkzXk3tzIevlngk8QO6bTsqV2WpTFfUemp7rGem5H5fWfzR3Pm4Ddi+6Ls2qe9q2X6r/3cAPi65LE+s9EZhcdB2aXXdgeFp/D1li/NWi69Kkeneldu0+srZ5bNF1aYXJv5BnZmZmZpb4hjwzMzMzs8TJsZmZmZlZ4uTYzMzMzCxxcmxmZmZmljg5NjMzMzNLnBybmZmZmSVOjs3MzMzMEifHZmZmZmaJk2MzMzMzs8TJsZmZmZlZ4uTYzMzMzCxxcmxmZmZmljg5NjMzMzNLnBybmZmZmSVOjs3MzMzMEifHZmZmZmaJk2MzMzMzs8TJsZmZmZlZ4uTYzMzMzCxxcmxmZmZmljg5NjMzMzNLnBybmZmZmSVOjs3MzMzMEifHZmZmZmaJk2MzMzMzs8TJsZmZmZlZ4uTYWpKkLkkhaXBavkrSAUXHZWZmZp3NybG1hYjYOSLOLjoOM7NWImm2pO1arSyzdubk2Bquu/e3nZWrQ6316oTzYGZWSSPbOLfB1kxOjq0hUg/EMZLuBF6UNFjSsZJmSnpe0r2S/ju3/yBJP5b0lKRZwK4l5d0g6dNp/kRJ5+W2lQ7BOFDSrHSchyTtWyHGpXIxLZB0kaSVSso8RNIjwHWp3JsknSJpAXCipBUlnSNpvqSHJZ0gaalcHIvtX89zbGYDm6RzgbWAv0l6QdLX0voJkv4taaGkOyRNTOvfl9rYNdPyeyU9I+nd5cqSNFHSoyXHfKt3ObXFF0s6T9JzwIGpTTxD0jxJcyV9V9KgCvG7DbaW5OTYGukTZEnuiIhYBMwE3g+sCHwbOE/SamnfQ4HdgE2AzYG9+nJAScOAnwM7R8TywPuAaRV2/wLwEeCDwOrAM8CvSvb5IPAeYMe0vBUwC1gVOBn4RarPuLTv/sBBuceX7m9mVhcR8SngEWD3iBgeET+UtAZwBfBdYCXgaOASSaMj4t/A/wJnS1oWOA/4RkTcX66sKsPYE7gYGAH8ATgLWAS8k6w93wH4dIXHug22luTk2Brp5xExJyJeBoiIP0XEYxHxZkRcCEwHtkz7fhw4Ne3/NPC9fhz3TWBDSctGxLyIuKfCfocDx0fEoxHxKlmvwl4lH72dGBEvdtcBeCwifpGS/deAfYDjIuL5iJgN/AT4VO7xb+2fK8PMrFH2A66MiCtTWzsJmALskrafSJZM3grMZclktFY3R8SlEfEmsEI6zpdSu/kkcApZO1mO22BrSU6OrZHm5Bck7S9pWvqobyGwITAqbV69ZP+H+3LAiHgR2Jus0Z0n6QpJ766w+1jgL7l47gPeIOthKFuHkuVRwNIlsT4MrNHD483MGmks8LHudi21bdsAqwFExOtkvbsbAj+JiOjn8fJt3FiyNnFe7tj/C6zSQ6xug63lODm2Rnqr0ZU0Fjgd+DywckSMAO4GlHaZB6yZe+xaPZT7IrBcbvkdix004uqI2J7szeD+dNxy5pANvxiRm4ZGxNxydSiz/BTwOlkDn4+7p8ebmdVTaRszBzi3pF0bFhHfB0jDLr4FnAn8RNKQHsparK1NY4dH93D8OcCrwKjcsVeIiA0qxO422FqSk2NrlmFkjdR8AEkHkfVcdLsI+KKkMZJGAsf2UNY04AOS1pK0InBc9wZJq0raM409fhV4gWyYRTmnASenxB1JoyXtWW2FIuKNFPfJkpZP5XyZbByfmVkzPEE23rbbecDuknZUdqPz0HRj3RhJIus1PgM4hKxT4js9lPUgMFTSrpKWBk4A8sn0YiJiHnANWdK9Qrrhbh1JH6zwELfB1pKcHFtTRMS9ZGPBbiZrgDcCbsrtcjpwNXAHcBvw5x7KmgRcCNwJTAUuz21eiqxxfAx4muwGjc9WKOpnwGXANZKeByaT3bxRiy+Q9a7MAv4F/BH4fY1lmJn11feAE9LQhKMjYg7ZTXJfJ+uMmAN8laxt/CLZEIdvpOEUBwEHSXp/hbKeBT4H/I6sN/ZFYLFvryhjf2AZ4F6yG+wuJg3pKMNtsLUk9X+4kZmZmZlZZ3DPsZmZmZlZ4uTYzMzMzCxxcmxmZmZmljg5NjMzMzNLBve+S+1GjRoVXV1djSjazKxtTZ069amIKP2e2Lpwu2tmtqS+tLsNSY67urqYMmVKI4o2M2tbkvr0y4/VcLtrZrakvrS7DUmOm+r675Vfv+1x5debmVl7cntvZk3Q/smxmZkNCDfPWlB2/dbbNjkQM+to7ZMcV+oxMDMzMzOrE39bhZmZmZlZ4uTYzMzMzCxpn2EVZmbWnmq9kc7D6MysQO45NjMzMzNL3HNsZmb14R5fM+sA7jk2MzMzM0ucHJuZmZmZJR5WYWZmxWj0MAz/op6Z9YF7js3MzMzMEvccm5nZwOIeZTPrgZNjMzMrxM2zFtSnoEYOz3AibTbgdG5y7AbNzMxq4a+iMzM85tjMzMzM7C2d23NciXuUzcwGhErDNrYet3KTIzGzdjLwkmMzM6uOOxPMbABycmxmZgOKe5TNrCdOjs3MrDa+ca3x3GtvVhjfkGdmZmZmljg5NjMzMzNLWm9YhT+uMzPrKHX7sY8CVByfvG2TAzGzpmm95NjMzNpSOyfBUKf4PVbYrO05OTYzM2s0J81mbcPJcXLKpAfLrj9q+/WaHImZmbU8DwE061hOjpMJj/y2wpYfNzUOM7Omc6JXM39XslnncnJsZmY1abWxxa0Wj5m1N3+Vm5mZmZlZ0rE9x0V95OWxy2ZmVq2K7xkd++5s1vr88uuFk10zM2sZ/tYLs4Zr++S4qLFmN59xdPkNax1WdnW5JNsJtpmZFcJJtllFbZ8c16odbtxwb7WZWXvykD6z9tc2yXE7JLW1qtSY1aucSo2iG1Ezs+aq+B62Vm3715xkF/U1fe6ZtjbWNslxUSp//3Hz1SuZbjQn32ZmZtaunBx3sFqT6Vr3d7Jr1tk68RO7VtPoDph69UBXus+m4T96Uq4H2r3P1mBOjuuslXqaJ1e4ObBeGj0spJJak3L3ZLe3op6/gXTdOAluf7W+91RMmmnwMIwKwy1quQa33rb8+pZrKwZfUv4BtSb3A2mISovUVRFR/0Kl+cDDaXEU8FTdD1I816t9dGKdwPVqN6OAYRExuhGFl7S7napTr41a+TxkfB58Drr1dB7G1truNiQ5XuwA0pSI2LyhBymA69U+OrFO4Hq1m06tVzP5HGZ8HjI+Dz4H3ep9Hvzz0WZmZmZmiZNjMzMzM7OkGclx69yhVl+uV/voxDqB69VuOrVezeRzmPF5yPg8+Bx0q+t5aPiYYzMzMzOzduFhFWZmZmZmiZNjMzMzM7OkbsmxpJ0kPSBphqRjy2wfIunCtP0WSV31OnYjVVGvAyXNlzQtTZ8uIs5aSPq9pCcl3V1huyT9PNX5TkmbNjvGvqiiXhMlPZt7rr7Z7BhrJWlNSddLulfSPZKOLLNP2z1fVdarHZ+voZJulXRHqte3y+zTlm1hkXp7bQ8E1bxmBoJqXmMDhaRBkm6XdHnRsRRF0mxJd6X3iCl1Kzgi+j0Bg4CZwDhgGeAOYP2SfT4HnJbm9wEurMexGzlVWa8DgV8WHWuN9foAsClwd4XtuwBXAQImALcUHXOd6jURuLzoOGus02rApml+eeDBMtdg2z1fVdarHZ8vAcPT/NLALcCEkn3ari0seurttT0QpmpeMwNhquY1NlAm4MvAH9utnazzOZgNjKp3ufXqOd4SmBERsyLiNeACYM+SffYEzk7zFwMflqQ6Hb9RqqlX24mIG4Gne9hlT+CcyEwGRkharTnR9V0V9Wo7ETEvIm5L888D9wFrlOzWds9XlfVqO+k5eCEtLp2m0rue27EtLFQnvrZr1amvmVpV+RrreJLGALsCvys6lk5Ur+R4DWBObvlRlnzRvrVPRCwCngVWrtPxG6WaegF8NH2cfbGkNZsTWkNVW+92tHX6OO4qSRsUHUwt0sfvm5D1lOS19fPVQ72gDZ+v9FHnNOBJYFJEVHy+2qgttBbSy2um41XxGhsITgW+BrxZcBxFC+AaSVMlHVavQn1DXv/9DeiKiI2BSbzdI2St5zay31h/L/AL4NJiw6mepOHAJcCXIuK5ouOpl17q1ZbPV0S8ERHjgTHAlpI2LDgk6yCd2hbUYqC/xiTtBjwZEVOLjqUFbBMRmwI7A0dI+kA9Cq1XcjwXyPeYjknryu4jaTCwIrCgTsdvlF7rFRELIuLVtPg7YLMmxdZI1TyfbScinuv+OC4irgSWljSq4LB6JWlpsjfDP0TEn8vs0pbPV2/1atfnq1tELASuB3Yq2dSObaG1gCraggGlh9dYp/svYA9Js8mGe35I0nnFhlSMiJib/j4J/IVsOGy/1Ss5/g+wrqS1JS1DdpPJZSX7XAYckOb3Aq6LNJq6hfVar5KxnXuQjQNrd5cB+6dvQZgAPBsR84oOqr8kvaN7bKekLcmu/5ZOSlK8ZwD3RcRPK+zWds9XNfVq0+drtKQRaX5ZYHvg/pLd2rEttIJV2RZ0vCpfYx0tIo6LiDER0UWWl1wXEfsVHFbTSRomafnueWAHoC7faDO4HoVExCJJnweuJvuGh99HxD2STgKmRMRlZC/qcyXNILuxYp96HLuRqqzXFyXtASwiq9eBhQVcJUnnk30TwChJjwLfIrupgYg4DbiS7BsQZgAvAQcVE2ltqqjXXsBnJS0CXgb2aYOk5L+ATwF3pTF2AF8H1oK2fr6qqVc7Pl+rAWdLGkSWzF8UEZe3e1tYtHKv7Yg4o9iomq7sayZ9qjKQlH2NFRyTFWNV4C+pD2Uw8MeI+L96FOyfjzYzMzMzS3xDnpmZmZlZ4uTYzMzMzCxxcmxmZmZmljg5NjMzMzNLnBybmZmZmSVOjs3MzMzMEifHZmZmZmaJk2MzMzMzs8TJsZmZmZlZ4uTYzMzMzCxxcmxmZmZmljg5NjMzMzNLnBybmZmZmSVOjs3MzMzMEifHZmZmZmaJk2MzMzMzs8TJsZmZmZlZ4uTYzMzMzCxxcmxmZmZmljg5NjMzMzNLnBybmZmZmSVOjs3MzMzMEifHZmZmZmaJk2MzMzMzs8TJsZmZmZlZ4uTYCiPpHkkTi47DzMzMrJsiougYzOpK0oHApyNim6JjMTMzs/binmNrOkmDi46hJ/2Nr9zjay2z1c+RmXWmItue0mMrU3WeUuv+ZpX4IrKqSZot6ThJ90p6RtKZkobmtu8maZqkhZL+LWnjksceI+lO4EVJg9O67dL2EyX9SdJ5kp6XdJek9dLxnpQ0R9IOufJWlHSGpHmS5kr6rqRBkt4DnAZsLekFSQvT/kMk/VjSI5KekHSapGXTtomSHk3xPQ6cWaH+B0u6L9X9akljc9tC0hGSpgPTy5WZYjhV0mNpOlXSkFpiMDOrlaRjJc1Mbeu9kv47t+1ASTdJOkXSAuDEXtrLkZIulzQ/tYWXSxrTw7FXl3RJ2v8hSV/MbTtR0sWp3X8OOFDSDZJOlnQT8BIwTtL7JP1H0rPp7/tyZSyxf/3PoA00To6tVvsCOwLrAOsBJwBI2gT4PfAZYGXgf4HLupO/5BPArsCIiFhUpuzdgXOBkcDtwNVk1+gawEmpzG5nAYuAdwKbADuQDaW4DzgcuDkihkfEiLT/91O849Nj1gC+mSvvHcBKwFjgsNLAJO0JfB34H2A08E/g/JLdPgJsBaxfoczjgQkphvcCW5LOXzUxmJn10Uzg/cCKwLeB8yStltu+FTALWBU4mZ7by6XI/nkfC6wFvAz8stxBUy/u34A7UhkfBr4kacfcbnsCFwMjgD+kdZ8iawOXB54HrgB+Tvbe8lPgCkkr58rI7/9wNSfErEcR4clTVRMwGzg8t7wLMDPN/wb4Tsn+DwAfzD324DLlbZfmTwQm5bbtDrwADErLywNB1oCuCrwKLJvb/xPA9Wn+QOBfuW0CXgTWya3bGngozU8EXgOG9lD3q4BDcstLkfVSjE3LAXwot32JMsneoHbJLe8IzK42Bk+ePHmqxwRMA/ZM8wcCj+S29dhelilrPPBMhW1b5ctO644DzkzzJwI3lmy/ATgpt/wp4NaSfW4GDiy3vydP9Zg8rtFqNSc3/zCwepofCxwg6Qu57cvktpc+tpwncvMvA09FxBu5ZYDhqcylgXmSuvdfqofyRwPLAVNz+wsYlNtnfkS80kNsY4GfSfpJbp3IekO6eypKj19a5uos3quRP3/VxGBmVjNJ+wNfBrrSquHAqNwu+barx/ZS0nLAKcBOZJ/yASwvaVCuve42Fli9e3hbMojsk7dyxy63rrTdJC2v0UsZZn3m5NhqtWZufi3gsTQ/Bzg5Ik7u4bH1+mqUOWQ9x6Oi/PCM0uM8RZZcbxARc/sYW3f9/tDDPqVllC4/RvZmcU9azp+/amIwM6tJujfidLIhDTdHxBuSppElvN3ybU9v7eVXgHcBW0XE45LGkw2DU5l955D1OK/bQ4jl2r38uu52M28t4P96KcOszzzm2Gp1hKQxklYiG0N7YVp/OnC4pK2UGSZpV0nL1zuAiJgHXAP8RNIKkpaStI6kD6ZdngDGSFom7f9miu8USasASFqjZNxbb04DjpO0QXr8ipI+VmPo5wMnSBotaRTZGL7zaizDzKwWw8iSx/kAkg4CNqy0cxXt5fJkyfPC9D7wrR6OfSvwfLrReFllN01vKGmLGuK/ElhP0ieV3ci9N9l9HZfXUIZZTZwcW63+SJaYziIbQ/tdgIiYAhxKdmPGM8AMsrFsjbI/2bCNe9PxLga6bzC5jqx39nFJT6V1x6SYJqe7oq8l6/2oSkT8BfgBcEF6/N3AzjXG/F1gCnAncBdwW1pnZtYQEXEv8BOycbpPABsBN/XysJ7ay1OBZcl6mCezeA9u6bHfAHYjG5f8UHrM78huDKw2/gWpjK8AC4CvAbtFxFM9PtCsH/wjIFY1SbPJvhHi2qJjMTMzM2sE9xybmZmZmSVOjs3MzMzMEg+rMDMzMzNL3HNsZmZmZpY4OTYzMzMzSxryIyCjRo2Krq6uRhRtZta2pk6d+lREjG5E2W53zcyW1Jd2tyHJcVdXF1OmTGlE0WZmbUtS6c/g1o3bXTOzJfWl3W2fn4++/nvl1297XHPjMDMbKNzumtkA5DHHZmZmZmaJk2MzMzMzs8TJsZmZmZlZ4uTYzMzMzCxxcmxmZmZmljg5NjMzMzNLnBybmZmZmSXt8z3Hlfh7OM3M+qdSO2pmNgC559jMzMzMLHFybGZmZmaWtP+wiko83MLMzMzMauSeYzMzMzOzxMmxmZmZmVni5NjMzMzMLHFybGZmZmaWODk2MzMzM0ucHJuZmZmZJU6OzczMzMySzv2e40r8/cdmZmZmVoF7js3MzMzMEifHZmZmZmZJ6w2rqDTswczMzMyswdxzbGZmZmaWODk2MzMzM0tab1hFQU6Z9GDZ9Udtv16TIzEzMzOzojg5NjOz2vgrMc2sg3lYhZmZmZlZ0rE9xzfPWlDbA9ZqTBxmZmZm1j46Njmu1YRHflt2/SmTDqupHI9RNrOByvdumFkncHJsZmZl+RM4MxuInBzXWaWek3Lcm2JmA0Et7SK4bTSzYrV9clxzz0Yb8EeTZmZmZsVom+S4qCS40ljkSiavVf0Y5Vp7U+rFybeZNUKl9rKWdhFqb6PcpplZPbVNcmyN5zcYM2tltXYoeDiHmfWFk+M6q7WnuZxKvSxF9TRX0uh4/EZl1hyN/mSuXj3KjdbINs3tmVn7UETUv1BpPvBwbtUo4Km6H6j9+byU5/NSns9Lee10XsZGxOhGFFym3e1JO52zvujk+nVy3aCz69fJdYPWrV/N7W5DkuMlDiJNiYjNG36gNuPzUp7PS3k+L+X5vNSu089ZJ9evk+sGnV2/Tq4bdFb9/PPRZmZmZmaJk2MzMzMzs6RZyXH/71LrTD4v5fm8lOfzUp7PS+06/Zx1cv06uW7Q2fXr5LpBB9WvKWOOzczMzMzagYdVmJmZmZklTo7NzMzMzJKGJ8eSdpL0gKQZko5t9PHahaTZku6SNE3SlKLjKYqk30t6UtLduXUrSZokaXr6O7LIGItQ4bycKGluumamSdqlyBiLIGlNSddLulfSPZKOTOsH/DVTrU5ok2tpN5T5earvnZI2LS7y3tV6jbdT/SQNlXSrpDtS3b6d1q8t6ZZUhwslLZPWD0nLM9L2rkIrUAVJgyTdLunytNxJdVsib+mE67KchibHkgYBvwJ2BtYHPiFp/UYes81sGxHjO+V7AfvoLGCnknXHAn+PiHWBv6flgeYsljwvAKeka2Z8RFzZ5JhawSLgKxGxPjABOCK1Kb5mqtBBbfJZVN9u7Aysm6bDgN80Kca+qvUab6f6vQp8KCLeC4wHdpI0AfgBWdv2TuAZ4JC0/yHAM2n9KWm/VnckcF9uuZPqBkvmLZ1wXS6h0T3HWwIzImJWRLwGXADs2eBjWhuJiBuBp0tW7wmcnebPBj7SzJhaQYXzMuBFxLyIuC3NP0/2JrQGvmaq1RFtco3txp7AOZGZDIyQtFpTAu2DPlzjbVO/FOMLaXHpNAXwIeDitL60bt11vhj4sCQ1J9raSRoD7Ar8Li2LDqlbD9r+uiyn0cnxGsCc3PKjaZ1lDcI1kqZKOqzoYFrMqhExL80/DqxaZDAt5vPpI6rfD/ShA+ljyE2AW/A1U61ObpMrXQNtW+cqr/G2ql8adjANeBKYBMwEFkbEorRLPv636pa2Pwus3NSAa3Mq8DXgzbS8Mp1TNyift3TEdVnKN+QVZ5uI2JTso4cjJH2g6IBaUWTfNejvG8z8BliH7OPIecBPCo2mQJKGA5cAX4qI5/LbfM1YJ1wDnXqNR8QbETEeGEP2Sca7i42oPiTtBjwZEVOLjqWBesxb2vm6LNXo5HgusGZueUxaN+BFxNz090ngL2SNhGWe6P74Jf19suB4WkJEPJHeWN4ETmeAXjOSliZLGv4QEX9Oq33NVKeT2+RK10Db1bnGa7zt6gcQEQuB64GtyT5yH5w25eN/q25p+4rAguZGWrX/AvaQNJtsuNKHgJ/RGXUDKuYtHXVddmt0cvwfYN10t+YywD7AZQ0+ZsuTNEzS8t3zwA7A3T0/akC5DDggzR8A/LXAWFpGyXit/2YAXjNpTN4ZwH0R8dPcJl8z1enkNrnSNXAZsH+6e34C8GzuY+CW04drvG3qJ2m0pBFpfllge7Ix1dcDe6XdSuvWXee9gOuiRX+5LCKOi4gxEdFF9rq6LiL2pQPqBj3mLW1/XZYVEQ2dgF2AB8nGFR3f6OO1wwSMA+5I0z0D+bwA55MNEXidbEzSIWTjrv4OTAeuBVYqOs4WOS/nAncBd5I1PKsVHWcB52Ubso/t7gSmpWkXXzM1ncO2b5NraTcAkX1Dx8z0+tm86Ph7qVtN13g71Q/YGLg91e1u4Jtp/TjgVmAG8CdgSFo/NC3PSNvHFV2HKus5Ebi8k+pWKW/phOuy3OSfjzYzMzMzS3xDnpmZmZlZ4uTYzMzMzCxxcmxmZmZmljg5NjMzMzNLnBybmZmZmSVOjs3MzMzMEifHZmZmZmaJk2MzMzMzs8TJsZmZmZlZ4uTYzMzMzCxxcmxmZmZmljg5NjMzMzNLnBybmZmZmSVOjs3MzMzMEifHZmZmZmaJk2MzMzMzs8TJsZmZmZlZ4uTYzMzMzCxxcmxmZmZmljg5NjMzMzNLnBybmZmZmSVOjs3MzMzMEifHZmZmZmaJk2MzMzMzs8TJsZmZmZlZ4uTYzMzMzCxxcmxtQdKJks4rOg4zMzPrbE6OreVImijp0aLjMDOzJUm6QdKni47DrFGcHJs1kaTB1ayrtQwzM+ud22CrhpNjW4KkYyTNlfS8pAckfTitP1HSnySdl7bdJWk9ScdJelLSHEk75MpZXdJlkp6WNEPSobltQySdKumxNJ2a1g0DrgJWl/RCmlZPD1tG0jnp2PdI2jxX3mxJR0u6U9Kzki6UNDS3fTdJ0yQtlPRvSRtXUd8tJU2R9JykJyT9tIdz1lP5s9Mx7gRelPROSSHpEEmPANdJWkrSCZIeTufyHEkrpsd3le7f1+fWzFqDpDUl/VnSfEkLJP0yra+mLTgotbfPSDpc0hap7VvYXU7a/0BJN0n6ZWoX7+9u39L2gyTdl9q+WZI+UxLjnqlde07STEk7SToZeD/wy9Q+d8cdKZbpKY5fSVKurIPTsZ6RdLWksWm9JJ2S6vqcsveVDdO2XSTdm+KbK+noHs5n2fJzsR0haTowXenTydQuPw6cqQrvSenxS+zf5yfe2kNEePL01gS8C5gDrJ6Wu4B10vyJwCvAjsBg4BzgIeB4YGngUOChXFk3Ar8GhgLjgfnAh9K2k4DJwCrAaODfwHfStonAoyVxdR97F2AQ8D1gcm77bOBWYHVgJeA+4PC0bRPgSWCr9NgD0v5DeqnvzcCn0vxwYEKFc1ax/Fxs04A1gWXTMSKdv2Fp3cHADGBcOtafgXNzMS22f9HXiSdPnvo+pXbiDuCU9JoeCmyTtlXTFpyWHrNDahcvTW3pGqkt+mDa/0BgEXBUaqP3Bp4FVkrbdwXWAQR8EHgJ2DRt2zLtuz1ZR9oawLvTthuAT5fUKYDLgRHAWmTt/U5p256pTu8he+84Afh32rYjMDU9Tmmf1dK2ecD70/zI7tjKnM+K5edim0T23rAs2XvMIuAHZO8Dy9L7e9Ji+xd9DXlq8Gu06AA8tdYEvDM1rtsBS5dsOxGYlFveHXgBGJSWl0+N0AiyRPANYPnc/t8DzkrzM4Fdctt2BGan+YmUT46vzS2vD7ycW54N7Jdb/iFwWpr/TXcjl9v+QHoz6Km+NwLfBkb1cs4qlp+L7eDctq50nsbl1v0d+Fxu+V3A66mhX2J/T548te8EbE2WPA4us62atmCN3PYFwN655UuAL6X5A4HHAOW230r6p7/MsS8Fjkzz/wucUmG/GyifHG+TW74IODbNXwUcktu2FFkiPhb4EPAgMAFYqqTMR4DPACv0cj4rlp+L7UO57ROB14ChuXW9vScttr+nzp48rMIWExEzgC+RJaNPSrpAbw9rAHgiN/8y8FREvJFbhqy3Y3Xg6Yh4Prf/w2S9D6TtD5dsyx+nnMdz8y8BQ7X42K/S7cPT/FjgK+mjvoWSFpIl76v3Ut9DgPWA+yX9R9JuFeKqWH5unzllHpdfV+58DAZW7aUMM2s/awIPR8SiMtuqaQtK2+HS5eG55bkR/5+9O4+XoyrzP/75QiDsW4IMW3KJgAoOIiKLw6qigCjOgIIsAuKCOjouiCLO/BiXcRmU0cEZRgdlcdjEUVFhBGXHBEzYw5qEhISELRB2kMDz+6POhUqn+97ue7u6qvp+369Xv251LaefU9V96ulTp/pmGV6uvI0AJO0jaZqyoW9LyK7MTczFOLvtGmWGaoO/n2sfHyXrJd44Ii4DTgF+SNYG/0jSWmm7A1JM8yRdKWnnFq/bsvzcOo3t58MR8Vzu+XDnpMb1rY85ObblRMTZEbELWYMTZJeSOrUQWE/Smrl5k4D7c8snNyxbOBjCCF5vKPOBb0TEOrnHahFxDrSub0TcExEfILvM9m3gAmVjojsqf4g65ec12x9LWfak1+39YmblmA9MUvMbu9ppCzqxcX7sbypvYRpP+wvgJGCDiFgHuIgsqRyM8dUtyuy0LZoPfKyhjVw1Iv4EEBE/iIg3kV0R3BL4Qpr/54jYn6wN/hVZb3TH5beIufH5UOekZutbH3NybMuQ9BpJb00N53NkvRAvdVpORMwnG7P1TUmrKLtB7Whg8LeKzwG+Iml9SROBf8otexCYMHgTShf8GDhG0o7p5o/VJb1L0ppD1VfSYZLWj4iXgCWprGb7omX5HcR4DvBZSZtJWgP4F+C8Fj1LZlZv15ONp/1Wai9WkfQ3aVm324JXAZ+WtJKk95GNy70IWJls/OzDwFJJ+5CNYR50GnCUpLcpu0lwY0mvTcseJBsT3a5TgeMlbQ0gae0UC8puJtxR0krA02Tt8EuSVpZ0qKS1I+IF4Alan4talt+Boc5JNsY4ObZG44FvAY+QXSJ7FXD8CMv6ANkYuYXAL4H/FxF/SMu+DkwHbgFuBW5I84iIO8kaqjnpMtlwwy2GFBHTyW4WPAV4jOzGjSPT4qHquzcwU9JTwPeBgyPiWRoMU367fgKcRTbO+V6yE8SnOizDzGogDUV7N9k9D/cBC8huloPutwXXAVuQtXHfAA6MiMVpyNunyXpjHwMOAS7MxXg9cBTZTYOPA1fySs/q94ED0y9D/KCN+v6S7OrbuZKeAG4D9kmL1yLrYHiMbCjDYuBf07LDgblpm2OAQ0dQfrtanpNs7NGyQ5HMzMysH0g6kuzGuV3KjsWsTtxzbGZmZmaWODk2MzMzM0s8rMLMzMzMLHHPsZmZmZlZ0uw3Fkdt4sSJMTAwUETRZma1NWPGjEciYv0iyna7a2a2vJG0u4UkxwMDA0yfPr2Ios3MakvSvOHXGhm3u2ZmyxtJu1tIctxXLv9m8/l7jvSnf83M+pTbSzPrAx5zbGZmZmaWODk2MzMzM0ucHJuZmZmZJR5zPFIeW2dm1h63l2ZWI+45NjMzMzNL3HPcK816TtxrYmZmZlYpTo7NzKwcHm5hZhU09pLjohvjVuWbmfULt3Nm1sfGXnLcoalzFjedv/OUCT2OxMzMzMyK5hvyzMzMzMwSJ8dmZmZmZomHVQzyGDozMzOzMc/J8Qh5LLKZmZlZ//GwCjMzMzOzxMmxmZmZmVniYRVJq2ES3SrHwy3MzMzMqs89x2ZmZmZmiZNjMzMzM7PEwyrMzKweWv3k5p7H9zYOM+tr7jk2MzMzM0vGXM9xt268K5R7R8xsLPM/ZTKzEvVvclyDxvXkS+9uOv+z/XtUzMzMzCrNaViJdrrvR80X+GffzKwKatDJYGbWbU6O+4GHYZiZmZl1hZPjHunKWOdOe3GcNJtZDXX6z5RaDVHr1Gf32rIr5ZhZvTk5NjMz61DLe0acYJvVnpNjMzPrik6vkLXqCe64/EkdFdO6/NOOXW7ezkef1J3Czaw26p8cj6EbRjq91GhmVmVl/bRmy5uhzczoh+TYzMzGNCe7ZtZNfZsc1+KffbTQaezuUTazURlDV+DMzIbTt8mx9YB/DcPM+lyrG+9a9VaffOlHm85veaNei3b05KUHdFaOmXWNk+M+1rIHes7yN53AED3NTnbNbIzykA2zsaf2yXGdh0/URtV+X9k91malcrvbWutkulq/euGfojNrrTbJcctLWz2OwwrgZNfM+lyzn4kbyk50NmyjlcKT3W613z4PWIVULzlu8QHZ6T73VBSt8N6gTodzFN1j3Wz9TtYdav1OdSP2IdZ3L1HnvM+silr1TE+b1Dxp7tqY6XG/aCO64V+3jM9PlWKxelBEdL9Q6WFgXtcL7p2JwCNlB9El/VKXfqkH9E9d+qUe0Lu6TI6I9YsouIN2t47HrW4x1y1ecMy9ULd4oT9i7rjdLSQ5rjtJ0yNi+7Lj6IZ+qUu/1AP6py79Ug/or7oMp451rVvMdYsXHHMv1C1eGLsxr9CtYMzMzMzM6s7JsZmZmZlZ4uS4uX76Yct+qUu/1AP6py79Ug/or7oMp451rVvMdYsXHHMv1C1eGKMxe8yxmZmZmVninmMzMzMzs8TJsZmZmZlZMiaSY0l7S7pL0ixJX2qyfLyk89Ly6yQN5JYdn+bfJemduflzJd0q6SZJ06tcD0kTJF0u6SlJpzRs86ZUj1mSfiBJNa7LFanMm9LjVRWux16SZqR9P0PSW3Pb1O2YDFWXOh2THXJx3izpb9stsyqKaOuqGO9Q77mqxpxbPim1X539y7ySYpa0jaSpkmam/b1KVeOVtJKkM1Kcd0jq2b/XayPm3STdIGmppAMblh0h6Z70OKLK8UraNvd+uEXSQb2IdzQx55avJWmBGnKHpiKirx/AisBsYAqwMnAzsFXDOp8ATk3TBwPnpemt0vrjgc1SOSumZXOBiTWpx+rALsAxwCkN21xP9l+4BVwM7FPjulwBbF+TY/JGYKM0/Xrg/hofk6HqUqdjshowLk1vCDxE9l9Ehy2zCo9R1r1lW1fReFu+56oac275BcDPgWOrHnN6/98CvCE9n1Dx98UhwLlpejWy8/RARfbxALANcCZwYG7+esCc9HfdNL1uhePdEtgiTW8ELALWqfI+zi3/PnA2DblDs8dY6DneAZgVEXMi4i/AucD+DevsD5yRpi8A3iZJaf65EfF8RNwLzErllWHE9YiIpyPiGuC5/MqSNgTWiohpkb1zzgTeW2Qlkq7XpSSjqceNEbEwzZ8JrJp6Q+p4TJrWpQcxNzOaejwTEUvT/FWAwbuV2ymzCurW1tXxPTeafYyk9wL3pph7ZTQxvwO4JSJuBoiIxRHxYoXjDWB1SeOAVYG/AE8UHG9bMUfE3Ii4BXipYdt3ApdGxKMR8RhwKbB3VeONiLsj4p40vZCsE6GQ//rZrZghuyILbABc0s6LjYXkeGNgfu75gjSv6Trp5Pg42TfkobYN4JJ0Sa/5P6PvrtHUY6gyFwxTZhGKqMugnyq7LP6PgyekAnWrHgcAN0TE89T/mOTrMqg2x0TSjpJmArcCx6Tl7ZRZBUW1dUUp8j1XlBHHLGkN4IvAP/cgzqbxJJ3s5y2BkPT7dLn6uIrHewHwNFlv5n3ASRHxaNEBM7rPT1U/e8OStANZL+7sLsU1lBHHLGkF4LtA20OZxnUUmuXtEhH3KxtDeamkOyPiqrKDGuMOTcdkTeAXwOFkPa+VJWlr4NtkPTS11qIutTomEXEdsLWk1wFnSLq47JistZp9fk4ETo6Ip4r/jtg148iGsb0ZeAb4o6QZEfHHcsNqaQfgRbLL/esCV0v6Q0TMKTes/pOucp4FHBERy/XUVswngIsiYkG7n72x0HN8P7Bp7vkmaV7TddLlmLWBxUNtGxGDfx8CfknxlyBHU4+hytxkmDKLUERd8sfkSbJxRZU+JpI2IXvvfDAiZufWr90xaVGX2h2TQRFxB/AUaTxrG2VWQSFtXYEKec8VbDQx7wh8R9Jc4DPAlyX9fcHxLhNP0knMC4CrIuKRiHgGuAjYrsLxHgL8X0S8kM7N1wLbFxzvMvEknXx+qvrZa0nSWsDvgBMiYlqXY2tlNDHvDPx9+uydBHxQ0reG3GK4Qcl1f5B9851DdpPJ4CDurRvW+STLDu4/P01vzbI3qcwhGxS+OrBmWmd14E/A3lWtR275kQx/Q96+VT4mreqSypyYplciu7x2TFXrAayT1v+7JuXW6pi0qksNj8lmvHJD3mRgITCxnTKr8Bhl3Zu2dRWOt+Xnp6oxN6xzIr27IW80+3ld4AbSzarAH4B3VTjeLwI/TdOrA7cD21RhH+fWPZ3lb8i7N+3rddP0ehWOd2Xgj8BnevH+7UbMDcuOpI0b8npWsTIfwL7A3WTjYk5I874KvCdNr0J29/AsssRkSm7bE9J2d5F+NYDsbsmb02PmYJkVr8dc4FGy3rAFpLs8yb5V35bKPIX0XxPrVpfUEM4gu7N6JtldqYWe3EdTD+ArZGPjbso9XlXHY9KqLjU8JoenOG8iSwjeO1SZVXyM8nO1XFtX1XiH+vxUNeaGMk6kR8lxF94Xh6XPxW3Ad6ocL7BGmj+TLDH+QoX28ZvJzldPk/Vyz8xt+6FUl1nAUVWON70fXmj47G1b5ZgbyjiSNpJj//toMzMzM7NkLIw5NjMzMzNri5NjMzMzM7PEybGZmZmZWeLk2MzMzMwscXJsZmZmZpY4OTYzMzMzS5wcm5mZmZklTo7NzMzMzBInx2ZmZmZmiZNjMzMzM7PEybGZmZmZWeLk2MzMzMwscXJsZmZmZpY4OTYzMzMzS5wcm5mZmZklTo7NzMzMzBInx2ZmZmZmiZNjMzMzM7PEybGZmZmZWeLk2MzMzMwscXJsZmZmZpY4OTYzMzMzS5wcm5mZmZklTo7NzMzMzBInx2ZmZmZmiZNjMzMzM7PEybH1JUmHSrqk7DjMzGxZknaVdFfZcZi1oogoOwazwkkKYIuImFV2LGZm1h5JewA/i4hNSg7FxhD3HJt1kaRx7czrtAwzMyuO227Lc3JsHZG0qaT/lfSwpMWSTknzV5D0FUnzJD0k6UxJa6dlA5JC0hGS7pP0iKQTcmWuKOnLkmZLelLSDEmbpmXflzRf0hNp/q5p/kaSnpW0Xq6cN6ayV5J0pKRr0vyr0io3S3pK0kGSbpP07ty2K6Vt39ii3vtJuknSEkl/krRNbtlcSV+UdAvwtKTNU32PlnQfcFmb++fl9btxrMysGurSbqbnH5J0h6THJP1e0uQWdRqM76OSFkpaJOnY3PLxkv4tLVuYpsenZXtIWpBbd66kYyXdIulxSedJWkXS6sDFwEap7X4q1WEHSdNT/R6U9L0h9r3bbutcRPjhR1sPYEXgZuBkYHVgFWCXtOxDwCxgCrAG8L/AWWnZABDAj4FVgTcAzwOvS8u/ANwKvAZQWj4hLTsMmACMAz4PPACskpZdBnwkF9+/Aqem6SOBa3LLAtg89/w44Lzc8/2BW1vU+43AQ8COaR8cAcwFxqflc4GbgE1T/Qbre2baT6u2uX9eXr/sY+2HH35051GzdnP/FM/r0rZfAf7Uol6D8Z2T6vXXwMPA29PyrwLTgFcB6wN/Ar6Wlu0BLMiVNRe4HtgIWA+4Azim2bpp3lTg8DS9BrBTixjddvsxss9t2QH4UZ8HsHNq/MY1WfZH4BO5568BXkgN7GADsklu+fXAwWn6LmD/NmN4DHhDmv4wcFmaFjAf2C09P5Khk+ONgCeBtdLzC4DjWrzmfw426rl5dwG7p+m5wIdyywbrO6XD/TNlqLr74Ycf9XvUrN28GDg6t90KwDPA5CZlDsb32ty87wCnpenZwL65Ze8E5qbpPVg+OT6soZxTm62b5l0F/DMwcZh6u+32Y0QPD6uwTmwKzIuIpU2WbQTMyz2fR9Z4bJCb90Bu+hmyb+GD5c5u9oLpUtsd6VLbEmBtYGJa/AtgZ0kbArsBLwFXt1ORiFgIXAscIGkdYB/gf1qsPhn4fLostyTFsSlZnQfNb7Jdfl47+6dZGWZWb3VqNycD38+1c4+SJdAbD1G/fLs1j1faxWZ1y7eZjVrVs5mjgS2BOyX9WdJ+LdZz220j4uTYOjEfmKTmNx0sJGuIBk0ClgIPtlnuqxtnpnFyxwHvB9aNiHWAx8kaayLiMeAS4CDgEODcSF/t23QG2eXH9wFTI+L+IeL7RkSsk3usFhHn5NZp9rr5ee3sH/90jFn/qVO7OR/4WENbt2pE/GmIODZtiH/hEHVbSOeWaxcj4p6I+ADZkI1vAxek8cmN3HbbiDg5tk5cDywCviVp9XTDxN+kZecAn5W0maQ1gH8hG9PbrLek0X8DX5O0hTLbSJoArEnWCD0MjJP0T8BaDdueDXwQODBNt/Ig2ZixvF8B2wH/QDZmrJUfA8dI2jHFt7qkd0las426DRrN/jGz+qpTu3kqcLykrQEkrS3pfcPE8Y+SVkvbHAWcl6vbVyStL2ki8E/Az9qoV6MHgQmDN8GluA6TtH5EvAQsSbNfarKt224bESfH1raIeBF4N7A5cB+wgKz3AeAnwFlkY8HuBZ4DPtVm0d8DzifrzXgCOI3sRojfA/8H3E12Kes5lr98dSGwBfBARNw8xGucCJyRLq29P9XnWbJLjJuR3WTRVERMBz4CnEI2dm8W2ZjmToxm/5hZTdWp3YyIX5L1xJ4r6QngNrIhZ0O5kqxN/CNwUkQM/vOlrwPTgVvIbhy8Ic3rSETcSZagzknt90bA3sBMSU8B3ycbh/1sk23ddtuI+J+A2JiWelW2jIjDyo7FzKwuJA2QJYsruRfV+o1/sNrGLGW/9Xk0cHjZsZiZmVk1eFiFjUmSPkJ2qfHiiLhquPXNzMxsbPCwCjMzMzOzxD3HZmZmZmZJIWOOJ06cGAMDA0UUbWZWWzNmzHgkItYvomy3u2ZmyxtJu1tIcjwwMMD06dOLKNrMrLYkzRt+rZFxu2tmtryRtLv1/7WKy7/ZfP6ex/c2DjMzK4fPA2bWRfVJjls1fmZmZmZmXVKf5NjMzMYG9wSbWYn8axVmZmZmZol7js3MrB48vM7MesDJsZmZlcPJrplVkIdVmJmZmZklTo7NzMzMzBIPqzAzs/7kX70wsxFwz7GZmZmZWeKeYzMzG1vco2xmQ3DPsZmZmZlZUr2eY/+0j5mZmZmVpHrJsZmZWUWcfOndTed/dq8texyJmfWKh1WYmZmZmSXuOTYzM2thp/t+1GLJST2Nw8x6xz3HZmZmZmaJk2MzMzMzs8TDKszMzDrkG/XM+lffJsdTTzu26fydj/Y4MTMza8I/JWpmeFiFmZmZmdnL+rbnuBVfCjMzs2amzllcdghmVgFjLjk2M7MeK2m4Qqtkd+cpEwp7zVYdMK24Y8asepwcJ+5RNjMbpZqM2a1DD7HPSWblqX1yXIdGzszMzMzqofbJsZmZjVKrHt89j+9tHCNUpU6SVv9Rb9qkj/Y4EjMbKf9ahZmZmZlZ4p5jMzOzknR6A5+ZFW/MJcetLnm1cvKlzS+F+aYIMzMbLQ/DMKueMZccm5mZjVanHS1mVh9OjkfIP7NjZmZm1n+cHA/Dl7zMzGy0utXT7I4Zs+I5OTYzs84U/M8+yvjPdnVX1n/mc7Ju/ag2yXGVfsfSzMx6z+eB7pl62rFN5+989Ek9jsSsemqTHFdNq0tkrX7dohl/szYzs2Y6HYZR9FC/bv3knHuarQ6cHHdZJw3a1NOaz2916fDkpQc0ne9GxczqyD3B9VH4/Tc1/y+N1l+cHFdQqxPGTnTWW92qMetW8t3yslyrcYFu5OrDJyrvA+ja2GInwcUr+oa/nTotqMV7Z6f7WrwXWp2XmsTTcYdQi1hanvPG/aJ5OV367Jc1PrwWKtLuKiK6X6j0MDBvhJtPBB7pYjjdUsW4qhgTVDOuKsYE1YyrijFBNePqNKbJEbF+EYH0abvbLf1eP+j/Orp+9VdWHTtudwtJjkdD0vSI2L7sOBpVMa4qxgTVjKuKMUE146piTFDNuKoY00j0Sz1a6ff6Qf/X0fWrvzrVcYWyAzAzMzMzqwonx2ZmZmZmSRWT46r+w/oqxlXFmKCacVUxJqhmXFWMCaoZVxVjGol+qUcr/V4/6P86un71V5s6Vm7MsZmZmZlZWarYc2xmZmZmVoqeJseS9pZ0l6RZkr7UZPl4Seel5ddJGsgtOz7Nv0vSO8uOSdKApGcl3ZQep3Yrpjbj2k3SDZKWSjqwYdkRku5JjyMqEtOLuX11YbdiajOuz0m6XdItkv4oaXJuWVn7aqiYytxXx0i6Nb32NZK2yi0r6zPYNKayP4O59Q6QFJK2z80rZF+NRBXb3W4aRRs+QdLlkp6SdErPA2/TKOq3l6QZ6bMzQ9Jbex58m0ZRxx1yn/+bJf1tz4Nvw2g+g2n5pPQ+bf7PBUo2iuNXaBs+KhHRkwewIjAbmAKsDNwMbNWwzieAU9P0wcB5aXqrtP54YLNUzoolxzQA3FbivhoAtgHOBA7MzV8PmJP+rpum1y0zprTsqRL31Z7Aamn647ljWOa+ahpTBfbVWrnp9wD/l6bL/Ay2iqnUz2Bab03gKmAasH2R+6rA/dvTdrdC9Vsd2AU4Bjil7LoUUL83Ahul6dcD95ddnwLquBowLk1vCDw0+Lwqj9HUL7f8AuDnwLFl16fLx2+Agtrw0T562XO8AzArIuZExF+Ac4H9G9bZHzgjTV8AvE2S0vxzI+L5iLgXmJXKKzOmIg0bV0TMjYhbgJcatn0ncGlEPBoRjwGXAnuXHFOR2onr8oh4Jj2dBmySpsvcV61iKlI7cT2Re7o6MHhTQmmfwSFiKlI7bQPA14BvA8/l5hW1r0aiiu1uN424fhHxdERcw7LHrmpGU78bI2Jhmj8TWFXS+J5E3ZnR1PGZiFia5q9Cb9qGTo0qz5D0XuBesmNYRVXNo0all8nxxsD83PMFaV7TddIb/nFgQpvb9jomgM0k3SjpSkm7diGeTuIqYtsiy11F0nRJ09KHvVs6jeto4OIRbtuLmKDkfSXpk5JmA98BPt3Jtj2OCUr8DEraDtg0In7X6bY9VMV2t5tG24ZXXbfqdwBwQ0Q8X1CcozGqOkraUdJM4FbgmFyyXBUjrp+kNYAvAv/cgzhHqqp51KiMKzuAGlsETIqIxZLeBPxK0tYNvVz2iskRcb+kKcBlkm6NiNm9DEDSYcD2wO69fN2htIip1H0VET8EfijpEOArQNfGYo9Ui5hK+wxKWgH4HnBk0a9lNhqStia7uvGOsmMpQkRcB2wt6XXAGZIujogqXw3oxInAyRHxVMU7WkeqsnlUL3uO7wc2zT3fJM1ruo6kccDawOI2t+1pTOlS42KAiJhBNuZmyy7E1G5cRWxbWLkRcX/6Owe4gmw8XDe0FZektwMnAO/J9Z6Uuq9axFT6vso5F3jvCLctPKaSP4Nrko3jvELSXGAn4EJlN+UVta9GoortbjeNpn51MKr6SdoE+CXwwV53RnSgK8cwIu4AniL7XFbJaOq3I/Cd1MZ8BviypL8vON5OVTWPGp0iBjI3e5D1Us8hu7FjcND21g3rfJJlB22fn6a3ZtkbQ+bQnZuBRhPT+oMxkA1Evx9Yr1f7Krfu6Sx/Q969ZDeYrZumRx3XKGNaFxifpicC99Dk5qYCj+EbyT50WzTML21fDRFT2ftqi9z0u4HpabrMz2CrmCrxGUzrX8ErN+QVsq8K3L89bXerUr/c8iOp7g15ozl+66T1/67sehRYx8145Ya8ycBCYGLZdepW/RrWOZFq3pBXyTxq1PXq8U7cF7ibLCk4Ic37KlnPGWQD6n9OduPH9cCU3LYnpO3uAvYpOyayMVwzgZuAG4B393hfvZlsbM/TZN8wZ+a2/VCKdxZwVNkxAW8hGw92c/p7dI/31R+AB9Oxugm4sAL7qmlMFdhX38+9ry8n18iV+BlsGlPZn8GGda8gJcdF7quC9m/P290K1W8u8ChZj+MCuvRFtAr1Ixt+9HSujbkJeFXZ9elyHQ9vaAPeW3Zduv0ezZVxIhVMjkd5/Aptw0fz8H/IMzMzMzNL/B/yzMzMzMwSJ8dmZmZmZomTYzMzMzOzxMmxmZmZmVni5NjMzMzMLHFybGZmZmaWODk2MzMzM0ucHJuZmZmZJU6OzczMzMwSJ8dmZmZmZomTYzMzMzOzxMmxmZmZmVni5NjMzMzMLHFybGZmZmaWODk2MzMzM0ucHJuZmZmZJU6OzczMzMwSJ8dmZmZmZomTYzMzMzOzxMmxmZmZmVni5NjMzMzMLHFybGZmZmaWODk2MzMzM0ucHJuZmZmZJU6OzczMzMwSJ8dmZmZmZomTYzMzs1GS9BpJN0l6UtKnS47lSEnXdLD+XElvT9NflvTfxUXXOUlPSZpSdhw2dowrOwCzIkg6Edg8Ig4rOxYzGxOOAy6PiG27XbCk04EFEfGVbpfdKCL+pejX6FRErFF2DDa2uOfYxiRlCnv/S1rui6ekFTsso6P1zaxUk4GZrRb681xvjW16p+eQos851l0+UNYWSV+UdH+6ZHiXpLdJ+itJz0iakFtvO0kPS1opXdq7VtLJkpZImiPpLWn+fEkPSToit+3pkv5D0sXpMtq16TX+TdJjku6U9Mbc+htJ+kV6vXsHL2VK2hv4MnBQKufmNP8KSd+QdC3wDPB5STMa6vk5Sb9usQ/WlnSapEVpX3x98ITXUNfFwImpPv8p6SJJTwN7SnpdimOJpJmS3tNQ/2XWH/WBM7PCSbqM7PN6Smpztmzx+X+XpBslPZHawBMbytlF0p9S+zA/tSsfBQ4Fjktl/yat+yVJs1ObfLukv+0g3sMlzZO0WNIJDctOlPSzND0gKSQdleJ5TNIxkt4s6ZYU5ykN239I0h1p3d9LmpxbFmn7e9K2P5SktGxzSVdKelzSI5LOa9hu8zS9tqQzU7s/T9JXlJLOtL+ukXRSev17Je0zxH5oeg7J7YcLJP1M0hPAkU3OIVOUndP+nOL+s6S35MpYbv12j5GVLCL88GPIB/AaYD6wUXo+ALw6TV8EfDy37snAv6fpI4GlwFHAisDXgfuAHwLjgXcATwJrpPVPBx4B3gSsAlwG3At8MLf95WndFYAZwD8BK5M1OnOAd6blJwI/a6jHFen1tyYbUjQeeBR4XW6dG4EDWuyHXwL/BawOvAq4HvhYQ10/lcpeNdXnceBvUrxrArPIEveVgbem+r8mV//8+quUfez98MOP9h6pfflw7vlyn2dgD+Cv0/NtgAeB96b1J6f24APASsAEYNtcWV9veL33ARulsg4CngY2TMuOBK5pEedWwFPAbqkN/F5qu96elr/cdpK19QGcmuJ/B/Ac8KvUBm4MPATsntbfP7Vxr0vt4FeAP+VeO4DfAusAk4CHgb3TsnOAE3L7apeG7TZP02cCv07t6QBwN3B0rt4vAB8hO2d8HFgIqMl+aOcc8gLw3rTuqix/DtkAeAw4PD3/QHo+IfeeyK+/UtnvUz/ae7jn2NrxIlkjupWklSJibkTMTsvOAA6Dly8bfgA4K7ftvRHx04h4ETgP2BT4akQ8HxGXAH8BNs+t/8uImBERz5Elo89FxJm57Qd7jt8MrB8RX42Iv0TEHODHwMHD1OX0iJgZEUsj4vlU5mD8W5M1tr9t3EjSBsC+wGci4umIeIjsi0D+9RZGxL+nsp9N834dEddGxEvAtsAawLdSzJel1/pAroyX10/7wMzqa5nPc0RcERG3pue3kCWEu6d1DwH+EBHnRMQLEbE4Im5qVXBE/DwiFqayzgPuAXZoI6YDgd9GxFWpDfxH4KVhtvlaiv8SsiT8nIh4KCLuB67mlXb5GOCbEXFHRCwF/gXYNt97TNb+LYmI+4DLydpFyBLRyWSdMM9FxHI3FKZzzMHA8RHxZETMBb5LlpwOmhcRP07njDOADcmS2EbtnEOmRsSv0j4ebNNfPoeQfVm4JyLOSu3+OcCdwLtzZeTPOS803btWOU6ObVgRMQv4DNk36YcknStpo7T412RJ82bAXsDjEXF9bvMHc9PPpvIa560xxPqt1p0MbJQuzS2RtISsR7ZZI5g3v+H5GcAh6dLe4cD56YTRaDJZb86i3Ov9F1nvSauyG+dtBMxPifKgeWS9L0OVYWb1tMznWdKOki5Pl/EfJ0smJ6bFmwKzGwtoRdIHlf06xmB79PpcWUPZKB9XRDwNLB5mm07a5e/nYnoUEMu2cQ/kpp/JbXtcWvd6ZUPOPtQkjolk7fC83LzGNvTl8iPimTTZ7Ia+ds4h7bTp8xqWu03vA06OrS0RcXZE7ELWoATw7TT/OeB8st7Xw1m217hI88l6pdfJPdaMiH0HQ26x3TLzI2IaWe/1rmQ9N63inw88D0zMvd5aEbF1q7KbzFsIbKplb8qYBNw/TBlmVk+Nn+ezgQuBTSNibbLhCkrL5gOvbqec1BP7Y+DvyS7hrwPclitrKIvIEvHBslYjG8LRDfPJhprl2+VVI+JPw20YEQ9ExEciYiPgY8B/DI4zznmEV3qYBzW2oZ3EOtQ5BNpr0yc3LHeb3gecHNuwlP1+51sljScbb/Ysy16GO5NsrNd76F1yfD3wpLIbBVeVtKKk10t6c1r+IDCg9u4OPhM4BXih2aU8gIhYBFwCfFfSWpJWkPRqSbs3W7+F68h6So5TdsPiHmSX387toAwzq681gUcj4jlJO5B9IR/0P8DbJb1f0jhJEyRtm5Y9yLI3c61OlnQ9DCDpKLKe43ZcAOyn7Oa/lYGv0r1c4FTg+DREbfDmufe1s6Gk90naJD19jKx+ywz3SEMlzge+IWnN9CXhc8DPRhDrcOeQdlwEbCnpkHTMDiIb073c0DyrFyfH1o7xwLfIvrU/QDaU4PjBhRFxLVkjdkNENF5iKkRqJPcjG692b4rtv4G10yo/T38XS7phmOLOIjuxDNfAfpDsxo3byRrvC8jGs7Ub81/IkuF9Urz/AXwwIu5stwwzq7VPAF+V9CTZjWDnDy5IY3D3BT5PNhzhJuANafFpZMPXlkj6VUTcTjbWdipZ4vzXwLXtBBARM4FPkvViLyJryxaMumZZ2b8ku6p4bvqFh9vI2rt2vBm4TtJTZL3r/5DGATf6FNm45znANWT1+MkIYh3uHNJOGYtTGZ8nG5pyHLBfRDzSaTxWLYpwj7+NnrKfMjo7Iir1n5XaIWlVsjuut4uIe8qOx8zMzMrj/5Bno5YuQ21H9jM+dfRx4M9OjM3MzMzJsY2KpDPIfgfyHyLiyZLD6ZikuWQ3sby33EjMzMysCjyswszMzMws8Q15ZmZmZmZJIcMqJk6cGAMDA0UUbWZWWzNmzHgkItYvomy3u2ZmyxtJu1tIcjwwMMD06dOLKNrMrLYkFfZTh253zcyWN5J21zfkjdTl32w+f8/jm883M+sXbv/MrI95zLGZmZmZWeLk2MzMzMwscXJsZmZmZpZ4zPEgj6EzM1tWq3bRzKyPuefYzMzMzCxxcmxmZmZmljg5NjMzMzNLnBybmZmZmSVOjs3MzMzMEv9aRbf5Vy/MzMzMamvMJccnX3p30/mfHXN7wszMzMwaOSU0M7Pu8JUzM+sD/Zsct2ikd7pvcfP1p0woMBgzMzMzq4P+TY67xf8hyszMzGzMcHLcK82SbF9qNLOxwMMtzKxGnBwnU+c0H26xs4dbmJmZmY0Z/p1jMzMzM7PEybGZmZmZWeLk2MzMzMws8ZjjYdRiLLJvdjGzCmvZju7Z40DMzNrg5LjLOkqmndSamZmZVYqT4xFqlQSbmY1VbhfNrB84Oa4T/0MSM+sjJ196d9P5nx33i+Yb+KqamfVA3ybH7sEwMzMzs071bXJcNc2S9Urd1Gdm1qDoToad7vtR8wVuG82sRE6OzczGuooN2fKvW5hZmeqfHFesUe+KfqyTmZmZWQ3UPjn22GIzMzMz65baJ8dWIv9Os5n1UMtft9hryx5HYmb9zMlxiTrt9W51A5/H55mZmZl1h5PjftatscvuCTazCpt62rFN5+989Ek9jsTM+kFtkuNWjZ+ZmVkzHoZhZiNRm+TYitdyeAYV+/UMj3U2G5Na/i5yp9yGmNkQnBz3sdJ+yaPFiefkpQc0nV9WL06zXiX3KJmNYc3aLifMZmOOk+MaqfvP1rXs9bm8xX/DqvFJyZdzrU7q3ra00qrNmdpBGS2vnLVqn9wrbVZ7lUuOWyUVO/U4Ditfy5tsWv1r2QJPSq3el90qp1XS3OnrdlrOZ8f9onlBPpH7C44BQ3xxmNPhfTAdrj9t0kebzm+V8Hf8a0Z1vlmxLl9AOr0pvmrxl6Eix1YR0f1CpYeBeV0v+BUTgUcKLL9b6hBnHWIEx9ltjrN7OolxckSsX0QQQ7S7ddiH7eqnukB/1cd1qa5+qs9I6tJxu1tIclw0SdMjYvuy4xhOHeKsQ4zgOLvNcXZP1WOsenyd6Ke6QH/Vx3Wprn6qT6/qskLRL2BmZmZmVhdOjs3MzMzMkromx136scvC1SHOOsQIjrPbHGf3VD3GqsfXiX6qC/RXfVyX6uqn+vSkLrUcc2xmZmZmVoS69hybmZmZmXWdk2MzMzMzs6RSybGkvSXdJWmWpC81WT5e0nlp+XWSBtL8CZIul/SUpFMqHOdekmZIujX9fWtF49xB0k3pcbOkv61inLnlk9Kx7/BX+XsTp6QBSc/m9umpVYsxLdtG0lRJM9N7dJWqxSnp0Nx+vEnSS5K2rWCcK0k6I+3HOyR17RfsR3mMj0/z75L0znbLLEpBdZmb9vtNkqb3qCqFnL8kvSnVZZakH0hSjetyRSpz8LP7ql7UJb1218/ZNTw2Q9WllGMzirq0zFG61pZFRCUewIrAbGAKsDJwM7BVwzqfAE5N0wcD56Xp1YFdgGOAUyoc5xuBjdL064H7KxrnasC4NL0h8NDg8yrFmVt+AfBz4NiK7s8B4LYi35ddiHEccAvwhvR8ArBi1eJsWOevgdkV3Z+HAOem6dWAucBAyTFtldYfD2yWylmxnTIruH+b1iUtmwtMLDr+Ltal5fkLuJ7sH8QKuBjYp8Z1uQLYvpfHpQv1aXnOruGxGaouPT82o6xL0xylnTLbfVSp53gHYFZEzImIvwDnAvs3rLM/cEaavgB4myRFxNMRcQ3wXMXjvDEiFqb5M4FVJY2vYJzPRMTSNH8VoMi7NkccJ4Ck9wL3ku3PIo0qzh4ZTYzvAG6JiJsBImJxRLxYwTjzPpC2Lcpo4gxgdUnjgFWBvwBPlBzT/mQJ+/MRcS8wK5XXTplFKKIuZen6+UvShsBaETEtsizgTOC9RVYiqcu5uF1dP2fX9Nj0Mv9oRxE5StfasiolxxsD83PPF6R5TddJO+Zxsh6uXupWnAcAN0TE81WMU9KOkmYCtwLH5N6IlYlT0hrAF4F/Lii2rsSZlm0m6UZJV0ratYIxbgmEpN9LukHScQXFONo48w4CzikoxmViSDqJ8wLgaWARcB9wUkQ8WnJMrbZtp8wiFFEXyE6Ul6RLxx8tIO5mijh/bZzKGarMIhR5Lv5puhT+jz3sOCjinF33Y9Ms/+j1sSkiR+laWzZuJBvZ6EjaGvg2WW9dJUXEdcDWkl4HnCHp4oioUm8AwInAyRHxVG87aDu2CJgUEYslvQn4laStI6IbPYndMo7scuibgWeAP0qaERF/LDes5iTtCDwTEbeVHUsLOwAvAhsB6wJXS/pDRMwpN6wxYZeIuD+Nm7xU0p0RcVXZQRmHpuOyJvAL4HCyHtfKq8M5u10t6lK7Y9MsR+lm+VXqOb4f2DT3fJM0r+k66XLl2sDinkTXJIakozglbQL8EvhgRMyuapyDIuIO4CmyMUpVi3NH4DuS5gKfAb4s6e+rFme69LsYICJmkI2J2rJKMZJ9w74qIh6JiGeAi4DtCohxtHEOOphie42XiSHpJM5DgP+LiBci4iHgWmD7kmNqtW07ZRahiLoQEYN/HyJra3sx3KKI89f9qZyhyixCIefi3HF5Ejib3g2DKeKcXctj0yr/KOnYFJGjdK8ta2dgci8eZD1Xc8hurhgcSL11wzqfZNnB2ec3LD+S4m/IG3GcwDpp/b+r8v5M2wwOdp8MLKSgG1y6cdzT/BMp9oa80ezP9XnlZqEpZB/W9SoW47rADaQbHYA/AO+q2r5Mz1dI+3BKUce7C/vzi8BP0/TqwO3ANiXHtDXL3sQ2h+wGlmHLrOD+bVWX1YE1c/v9T8DeVa5LbvmRDH9D3r51rEsqc2KaXols2NExRdelC++zdWhxzq7bsWlVl7KOzSjr0jRHaafMtuPrxZuzg521L3A3Wc/aCWneV4H3pOlVyH6VYFZ6Y07JbTsXeJTsG8QCCrzbeqRxAl8hG4d4U+7xqgrGeTjZgP2byBKm91b1uOfKOJECk+NR7s8DGvbnu6sWY1p2WIrzNuA7VdyXadkewLQi4+vCMV8jzZ9Jlhh/oeyY0rIT0nZ3kbu7vlmZVd6/repC9uXz5vSYWaO6zKXJ+YvsasNtqcxTSP/Vtm51IfuiMoPsF3FmAt+noF/D6WZ9GOKcXbdj06ouZR6bUdSlZY7SrMyRPPzvo83MzMzMkiqNOTYzMzMzK5WTYzMzMzOzxMmxmZmZmVni5NjMzMzMLHFybGZmZmaWODk2MzMzM0ucHJuZmZmZJU6OzczMzMwSJ8dmZmZmZomTYzMzMzOzxMmxmZmZmVni5NjMzMzMLHFybGZmZmaWODk2MzMzM0ucHJuZmZmZJU6OzczMzMwSJ8dmZmZmZomTYzMzMzOzxMmxmZmZmVni5NjMzMzMLHFybGZmZmaWODk2MzMzM0ucHJuZmZmZJU6OzczMzMwSJ8dmZmZmZomTY7M2SApJm6fpUyX9Y9kxmZmZWfc5ObZak3SkpGt6+ZoRcUxEfK2Xr2lmVqZ22lpJV0j6cJder2tlmXXKybFVmjJde592uzwzs37Qb22jpHFlx2D11TcfBCufpKMk/Sb3/B5JP889ny9p2zT9Fkl/lvR4+vuW3HpXSPqGpGuBZ4ApqddijqQnJd0r6VBJrwNOBXaW9JSkJS3ialbeUZLuSOXNkfSxhm2+IGmRpIWSPtSw7HRJX0/Ty/WmNAzB2FfS7el17pd0bOd71szsFVVsayV9A9gVOCWtc0qa/1pJl0p6VNJdkt6f5r86zdsuPd9I0sOS9mhWlqSB1LaOy73my73LKe5rJZ0saTFwoqTxkk6SdJ+kB9OQuFW7dBisn0WEH3505QFMAZaQfenaCJgHLMgteywtWy9NHw6MAz6Qnk9I614B3AdsnZavDTwBvCYt3xDYOk0fCVwzTFyN5a0EvAt4NSBgd7ITw3Zp/b2BB4HXA6sDZwMBbJ6Wnw58vdXrN6y7CNg1Ta87+Bp++OGHHyN9VLyt/XDu+erAfOCoVP4bgUeArdLyjwC3A6sBvwdOGqKsgdS2jmu2TopvKfCp9FqrAicDF6b9sCbwG+CbZR8/P6r/cM+xdU1EzAGeBLYFdiNr7BZKei1ZAnp1RLxElpjeExFnRcTSiDgHuBN4d6640yNiZkQsJWvwXgJeL2nViFgUETM7DO/l8iLihYj4XUTMjsyVwCVkPRUA7wd+GhG3RcTTwIkj2B2DXgC2krRWRDwWETeMoiwzs6q3tXn7AXMj4qfp9W8EfgG8L9Xjx8As4DqyRPyEUbwWwMKI+PdUl+eAjwKfjYhHI+JJ4F+Ag0f5GjYGODm2brsS2IOswb6S7Jv97ulxZVpnsKcjbx6wce75/MGJlKAeBBwDLJL0u3QS6MT8/BNJ+0iali7rLQH2BSbm4suv3xhrJw5IZc+TdKWknUdRlpnZoKq2tXmTgR0lLRl8AIcCf5Vb58dkV+n+PSKeH8VrwbLt9vpkPdIzcq/9f2m+2ZCcHFu3DTbYu6bpK1m+wV5I1mjmTQLuzz2P/MKI+H1E7EXWu3AnWYO63HpDeHk9SePJei9OAjaIiHWAi8iGWEA2FGLThthaeZqsAR4sO9/oExF/joj9gVcBvwLObzNeM7OhVLGtbVxnPnBlRKyTe6wRER8HkLQG8G/AaWRjhNcboqyn09/VcvP+qmGd/DaPAM+SDQsZfO21I2KNNuphY5yTY+u2K4E9gVUjYgFwNdkY3gnAjWmdi4AtJR0iaZykg4CtgN82K1DSBpL2l7Q68DzwFNmlP8jGBm8iaeUOYlwZGA88DCyVtA/wjtzy84EjJW0laTXg/w1R1s3A1pK2lbQKuSEYklZON7OsHREvkI3le6lFOWZmnahiW/sg2ZjnQb9Nr3+4pJXS483pBj+A7wPTI+LDwO/IbvprWlZEPEyW1B8macV0o/SrWwWShpX8GDhZ0qtS/TaW9M4h4jcDnBxbl0XE3WQN6tXp+RPAHODaiHgxzVtMNhbt88Bi4Dhgv4h4pEWxKwCfI+sFeZSsZ+TjadllwEzgAUmttm+M8Ung02RJ8GPAIWQ3bQwuv5isN+MysvFwlw1T368CfwDuARp/B/RwYK6kJ8guVR7aToxmZkOpaFv7feBASY9J+kFqa99BNs53IfAA8G1gvKT9yZL5wfI/B2wn6dBmZaV5HwG+kOqyNfCnYXbTF8na8GmpDf4D8JphtjFDEe1elTYzMzMz62/uOTYzMzMzS5wcm5mZmZklTo7NzMzMzBInx2ZmZmZmiZNjMzMzM7NkXBGFTpw4MQYGBooo2systmbMmPFIRBTyH7rc7pqZLW8k7W4hyfHAwADTp08vouj6uvyby8/b8/jex2FmpZE0mn9FPiS3uxXRrK0Ht/dmJRlJu+thFWZmZmZmiZNjMzMzM7PEybGZmZmZWeLk2MzMzMwsKeSGPDMzs8pqddOcmRlOjs3MzKrHv3phVhoPqzAzMzMzS5wcm5mZmZklHlZRJl82MzMzM6sUJ8dmZtaffOOdmY2Ak2MzM7O68BVHs8J5zLGZmZmZWeKe45Eq8tu7ewbMzJbXj21jSUM/Tr707qbzP7vXlj2OxKx6nBybmZnZiDjJtn7k5LjbiuwF6MdeEzMzM7MKcXJsZmZWMVPnLG46f+cpE5pv0GHnyU73/ajFK580TGRm/c/JsZmZlcM/tdY1LZPpPTssqOArlB6GYXXg5HiQhyyYmZmZjXlOjs3MrN7q0APdpRhb9RBXTaseYg/nsDpwctwP3OttZmZNtExSexyHWZ34n4CYmZmZmSXuOTYzs2LVYdiDVVKrnu9mfFOfdYuTYzMzsz7VeoxvZzpJUs3qzsMqzMzMzMwS9xz3M9+oZ2ZmZtYRJ8fD8Vi5wvlH4c1srKrLT7MVzcM2rEo8rMLMzMzMLBl7PcfuCe54uIV7dr0PzGx06tJD3DLOSb2Nw6xMYy85tpaqdlnLCamZVVVdkt2itfo1jGmTPtrjSFrzucQ61b/J8RjqIe5aI91hz0CnDU63ku+yXtfMzMz6X/2T4zGUBNvQ6pAEuwfDzMaCbv2+cifK6oCx/lP/5NisRG5Ezaqr1VW1nadMGHUZ1hvNkuxOh2y0StRPvrR5Oa0T+5M6el2fH+rLyXGNFN1Id2vsWB16cIvW6T5wI2pmZlYNTo7N+oiTbBuRTv9hUMH/YKjTjoBOeoJtZIoeJtFJ+WUM2QBGMIzzgGJf1//QqzD1SY49ttisspyU96lO292S2ulOk2kPlTDoPMnu+H3T4U3uU087tqP1d96z+fyib1pvVk63zgFVOZdULzmueRLcj41upw1It8aDVemngMpS9A0mrXSrES2ykS66saxKI21mZr2liOh+odLDwLyuFzw6E4FHyg6iy1yn6uu3+oDrNBqTI2L9IgquaLs7qMrvmSrHBo5vNKocG1Q7virHBp3F13G7W0hyXEWSpkfE9mXH0U2uU/X1W33AdbLOVXn/Vjk2cHyjUeXYoNrxVTk2KD6+FYoq2MzMzMysbpwcm5mZmZklYyk5Lum3XwrlOlVfv9UHXCfrXJX3b5VjA8c3GlWODaodX5Vjg4LjGzNjjs3MzMzMhjOWeo7NzMzMzIbk5NjMzMzMLOnL5FjSTyQ9JOm23Lz1JF0q6Z70d90yY+xUizqdKOl+STelx75lxtgJSZtKulzS7ZJmSvqHNL+2x2mIOtX5OK0i6XpJN6c6/XOav5mk6yTNknSepJXLjrVdQ9TpdEn35o7TtiWHWktVbquq3O5Uvf2oeltQh8+1pBUl3Sjpt+l5JfZdi9iqtN/mSro1xTE9zSv0M9uXyTFwOrB3w7wvAX+MiC2AP6bndXI6y9cJ4OSI2DY9LupxTKOxFPh8RGwF7AR8UtJW1Ps4taoT1Pc4PQ+8NSLeAGwL7C1pJ+DbZHXaHHgMOLq8EDvWqk4AX8gdp5vKCrDmTqe6bVWV252qtx9Vbwvq8Ln+B+CO3POq7DtYPjaozn4D2DPFMfjbxoV+ZvsyOY6Iq4BHG2bvD5yRps8A3tvLmEarRZ1qKyIWRcQNafpJsg/lxtT4OA1Rp9qKzFPp6UrpEcBbgQvS/Lodp1Z1si6ocltV5Xan6u1H1duCqn+uJW0CvAv47/RcVGTfNcZWE4V+ZvsyOW5hg4hYlKYfADYoM5gu+ntJt6RLmbUZgpAnaQB4I3AdfXKcGuoENT5O6XLbTcBDwKXAbGBJRCxNqyygQifxdjTWKSIGj9M30nE6WdL48iLsS5X6DFS53alq+1H1tqDin+t/A44DXkrPJ1CdffdvLBvboCrsN8i+5FwiaYakj6Z5hX5mx1Jy/LLIfr+uMt8oR+E/gVeTXUJaBHy31GhGQNIawC+Az0TEE/lldT1OTepU6+MUES9GxLbAJsAOwGvLjWj0Gusk6fXA8WR1ezOwHvDF8iLsO5X6DFS53aly+1H1tqCqn2tJ+wEPRcSMXr/2cIaIrfT9lrNLRGwH7EM23Gi3/MIiPrNjKTl+UNKGAOnvQyXHM2oR8WBqDF4CfkzWWNWGpJXITgL/ExH/m2bX+jg1q1Pdj9OgiFgCXA7sDKwjaVxatAlwf1lxjUauTnuny9oREc8DP6Wmx6mKqvQZqHK7U5f2o+ptQQU/138DvEfSXOBcsuEU36ca+2652CT9rCL7DYCIuD/9fQj4ZYql0M/sWEqOLwSOSNNHAL8uMZauGHxjJH8L3NZq3apJ461OA+6IiO/lFtX2OLWqU82P0/qS1knTqwJ7kY2FvBw4MK1Wt+PUrE535hpakY1fq81xqrqqfAaq3O5Uvf2oeltQ5c91RBwfEZtExABwMHBZRBxKBfZdi9gOq8J+S6+/uqQ1B6eBd6RYCv3M9uV/yJN0DrAHMBF4EPh/wK+A84FJwDzg/RFRyZtGmmlRpz3ILrUFMBf4WG4MTqVJ2gW4GriVV8Y5fZlsjF0tj9MQdfoA9T1O25Dd7LAi2Zfp8yPiq5KmkPUyrAfcCByWehgqb4g6XQasDwi4CTgmd4OPtanKbVWV252qtx9Vbwvq8rmWtAdwbETsV5V91yK2Suy3tI9+mZ6OA86OiG9ImkCBn9m+TI7NzMzMzEZiLA2rMDMzMzMbkpNjMzMzM7PEybGZmZmZWeLk2MzMzMwscXJsZmZmZpY4OTYzMzMzS5wcm5mZmZklTo7NzMzMzBInx2ZmZmZmiZNjMzMzM7PEybGZmZmZWeLk2MzMzMwscXJsZmZmZpY4OTYzMzMzS5wcm5mZmZklTo7NzMzMzBInx2ZmZmZmiZNjMzMzM7PEybGZmZmZWeLk2MzMzMwscXJsZmZmZpY4OTYzMzMzS5wcm5mZmZklTo7NzMzMzBInx2ZmZmZmiZNjMzMzM7PEybEZIOliSUeUHYeZmZmVy8mxVY6kIyVd08vXjIh9IuKMbpcraQ9JC7pdrplZ1ZTRdpsVwcmx9ZwylXjvVSmWZiSNKzsGMzPofXvp9s/KUtmkwKpB0lGSfpN7fo+kn+eez5e0bZp+i6Q/S3o8/X1Lbr0rJH1D0rXAM8CU1MswR9KTku6VdKik1wGnAjtLekrSkhZxXSHpm5Kul/SEpF9LWi+3fCdJf5K0RNLNkvYYJpYrJH04LT9S0rWSTk7bz0l1OzLV96H8EAxJ4yWdJOk+SQ9KOlXSqpJWBy4GNkp1eUrSRpJWkPQlSbMlLZZ0/mDskgYkhaSjJd0HXDbig2dmY1aF2+6jJN2Rtp0j6WO5ZXtIWiDpi5IeAH46VHuZtvm5pAdS7FdJ2rprO9HGLCfHNpwrgV1TA7URsDKwM4CkKcAawC2psfod8ANgAvA94HeSJuTKOhz4KLAm8HBad5+IWBN4C3BTRNwBHANMjYg1ImKdIWL7IPAhYENgaSoPSRunWL4OrAccC/xC0votYpnXpOwdgVtSXc4GzgXeDGwOHAacImmNtO63gC2BbdPyjYF/ioingX2Ahakua0TEQuBTwHuB3YGNgMeAHza8/u7A64B3DlF/M7NWqtp2PwTsB6wFHAWcLGm73PK/Imu3J6fXHK69vBjYAngVcAPwP+3vIrPmnBzbkCJiDvAkWeK3G/B7YKGk15I1VldHxEvAu4B7IuKsiFgaEecAdwLvzhV3ekTMjIilZMnsS8DrJa0aEYsiYmaH4Z0VEbelJPQfgfdLWpEseb0oIi6KiJci4lJgOrBvs1gi4oUmZd8bET+NiBeB84BNga9GxPMRcQnwF2BzSSJrwD8bEY9GxJPAvwAHDxH3McAJEbEgIp4HTgQO1LKXEE+MiKcj4tkO94mZWWXb7oj4XUTMjsyVwCXArrlVXgL+X2prn2WY9jIifhIRT+aWvUHS2p3tLbNlOTm2dlwJ7EHWwF4JXEHWuO6enkP2jb6xB3YeWS/qoPmDEymhPYis4Vsk6Xep0e7E/Nz0PGAlYCJZj8P70pCIJeny3i5kPczNtm3mwdz0synmxnlrAOsDqwEzcq/1f2l+K5OBX+bWvwN4Edigg/jMzIZTubZb0j6Spkl6NLV/+5K124Mejojncs9btpeSVpT0rTTk4glgbtomX55Zx5wcWzsGG9hd0/SVLN/ALiRrxPImAffnnkd+YUT8PiL2Ikta7wR+3Gy9IWza8FovAI+QNeRnRcQ6ucfqEfGtVrGMwiNkifLWuddaOyIGh1w0e535ZJck8/GtEhEt95WZ2QhUqu2WNB74BXASsEEaenERoFavxdDt5SHA/sDbgbWBgcGXGioOs+E4ObZ2XAnsCawaEQuAq4G9ycan3ZjWuQjYUtIhksZJOgjYCvhtswIlbSBp/3TT2vPAU2SX0yDrtd1E0srDxHWYpK0krQZ8FbggDYP4GfBuSe9MPQurpBs9NhnpDmglXZb8Mdm4uVelum0saXCs8IPAhIbLfKcC35A0Oa2/vqT9ux2bmY15VWu7VwbGk41bXippH+Adw9RhqPZyzRTDYrIreP8yTFlmbXFybMOKiLvJGsCr0/MngDnAtSkZJSIWk91k8Xmyhuo4YL+IeKRFsSsAnyPrtXiUrCfj42nZZcBM4AFJrbYHOAs4HXgAWAX4dIplPllvwpfJGuH5wBco7v3+RWAWMC1d2vsD8JoUy53AOcCcdFlwI+D7wIXAJZKeBKaR3QBoZtY1VWu70z0ZnwbOJ7ux7hCytnAoQ7WXZ5INAbkfuD0tMxs1RfjqrdWPpCuAn0XEf5cdi5mZmfUP9xybmZmZmSVOjs3MzMzMEg+rMDMzMzNL3HNsZmZmZpaMG36Vzk2cODEGBgaKKNrMrLZmzJjxSEQM9Q9iRsztrpnZ8kbS7haSHA8MDDB9+vQiijYzqy1Jjf+JrGvc7pqZLW8k7W4hyfGYdvk3m8/f8/jexmFmVhVuF82sRpwc90qzk4NPDGZmZmaV4hvyzMzMzMwSJ8dmZmZmZomTYzMzMzOzxMmxmZmZmVni5NjMzMzMLHFybGZmZmaWODk2MzMzM0v8O8dmZtYdrf7Zh5lZjbjn2MzMzMwscc9xmfwvVc3MzMwqxclxFTlpNjMzMyuFk+OR8tg6M7PRcUeAmVWQxxybmZmZmSXuOa4T97KYmZmZFcrJ8SAnnmZm1eD22MxK5OTYzMw643suzKyPecyxmZmZmVni5NjMzMzMLPGwiuH48qGZmZnZmDH2kmMnu2ZmZmbWgodVmJmZmZklY6/n2Ap38qV3N53/2b227HEkZmZmZp1xcmxmZs1VbRiaf//YzHrAyXE/8AnDzMzMrCucHNuItRo+UTUe5mFmZmbtcnLcz/q0R7nIZNeJtJmZ2djWv8lx1cbKVcjU045tOn/no0/qcSRDq0vPtJn1xtQ5i5vO33nPHgeCv0ib9bP+TY7HkFYnjE45GTWzfuIE1sxGwsmxla6spNxfBsz6Q5Gf5aq1T07szYrn5Nh6puiTTBknSJ+ozKwd3WpD3BaZFa8+ybHHEFsf8InNbHidDhXb6b4fNZ0/bdJHOyrHV5PMDOqUHFvXxhZb9xSd7DqZtp4YY50PVRqG0a1Yik7s3ebYWOLkuEQt77yeMqHHkWS61fti5Z0gu6XTpNxJvA2lrLbObZqZjUT1kuOa92B0o3e3aj3EnZ5gfEIqXreS17J0En9ZibcT/v7lNqpz3WpDimyj6nLFzm1L9Skiul+o9DAwbwSbTgQe6XI4o+WY2uOY2uOY2tOvMU2OiPW7EUyjXLtbtX1XtXigejFVLR6oXkxViweqF1PV4oFqxNRxu1tIcjxSkqZHxPZlx5HnmNrjmNrjmNrjmEauanFWLR6oXkxViweqF1PV4oHqxVS1eKCaMbVjhbIDMDMzMzOrCifHZmZmZmZJ1ZLj5ndJlMsxtccxtccxtccxjVzV4qxaPFC9mKoWD1QvpqrFA9WLqWrxQDVjGlalxhybmZmZmZWpaj3HZmZmZmalcXJsZmZmZpaUmhxLmivpVkk3SZqe5q0n6VJJ96S/6/YwntekWAYfT0j6jKQTJd2fm79vwXH8RNJDkm7LzWu6X5T5gaRZkm6RtF0PY/pXSXem1/2lpHXS/AFJz+b216k9jKnlsZJ0fNpPd0l6Zw9jOi8Xz1xJN6X5vdpPm0q6XNLtkmZK+oc0v7T31BAxlfaeGiKmUt9T7ZK0d4pjlqQv9fi1227Li3h/davNlHREWv8eSUcUEFPH76VuHddutgPd2E/d/Lx1cR+tIul6STenmP45zd9M0nWp/PMkrZzmj0/PZ6XlA8PF2qV4Tpd0b24fbZvm9+S9ncpbUdKNkn6bnpeyjwoTEaU9gLnAxIZ53wG+lKa/BHy7pNhWBB4AJgMnAsf28LV3A7YDbhtuvwD7AhcDAnYCruthTO8AxqXpb+diGsiv1+P91PRYAVsBNwPjgc2A2cCKvYipYfl3gX/q8X7aENguTa8J3J32R2nvqSFiKu09NURMpb6n2ox9xfT6U4CVU1xb9fD159JmW17E+6tFW9DR6wPrAXPS33XT9Lpdjqmj91I3j2u32oFu7adufd66vI8ErJGmVwKuS3U/Hzg4zT8V+Hia/gRwapo+GDhvqFi7GM/pwIFN1u/JezuV+TngbOC36Xkp+6ioRxWHVewPnJGmzwDeW1IcbwNmR8RI/tPfqETEVcCjDbNb7Zf9gTMjMw1YR9KGvYgpIi6JiKXp6TRgk26/bqcxDWF/4NyIeD4i7gVmATv0MiZJAt4PnNPt1x0mpkURcUOafhK4A9iYEt9TrWIq8z01xH5qpSfvqTbtAMyKiDkR8Rfg3BRfmXr2/upSm/lO4NKIeDQiHgMuBfbuckyttHovde24drEd6Mp+6uLnrZv7KCLiqfR0pfQI4K3ABWl+4z4a3HcXAG9L7XxX2oYh4mmlJ+9tSZsA7wL+Oz0XJe2jopSdHAdwiaQZkgb/qf0GEbEoTT8AbFBOaBzMsknM36fLFD9RD4d65LTaLxsD83PrLWDoBqYoHyL7xjpos3TJ5UpJu/Y4lmbHqgr7aVfgwYi4Jzevp/spXdJ6I1kPRCXeUw0x5ZX2nmoSU1XfU4PKjqWTtrxXsXb6+r2Kq5P3UiExjbId6HpMo/y8dTWeNFzgJuAhsiRyNrAk96U9X/7Lr52WPw5M6GZMjfFExOA++kbaRydLGt8YT8PrdvuY/RtwHPBSej6BEvdREcpOjneJiO2AfYBPStotvzAigqG/JRUijZV5D/DzNOs/gVcD2wKLyC6Nl6as/dKKpBOApcD/pFmLgEkR8UbSpRdJa/UonEodqwYfYNkvXD3dT5LWAH4BfCYinsgvK/Gz1jSmMt9TTWKq8nuqKirZllfl9XNKfy9VrR2o2uctIl6MiG3JrlrtALy2l68/XDySXg8cn+J6M9lQiS/2Kh5J+wEPRcSMXr1mGUpNjiPi/vT3IeCXZG/EBwcvsaW/D5UQ2j7ADRHxYIrvwfQGfQn4MeV0/bfaL/cDm+bW2yTN6wlJRwL7AYemhpV0mWRxmp5B9s17y17EM8SxKns/jQP+DjgvF2vP9pOklchOQP8TEf+bZpf6nmoRU6nvqWYxVfU91aDUWDpsy3sVa6evX3hcI3gvdTWmLrUDXYupS5+3Qo5bRCwBLgd2JhueMK5J+S+/dlq+NrC4iJhy8eydhqRERDwP/JTe7qO/Ad4jaS7ZEJa3At+nAvuom0pLjiWtLmnNwWmyG3FuAy4EBu+kPAL4dQnhLdPD1zAe7m/J4uy1VvvlQuCDyuwEPJ67RFYoSXuTXVp5T0Q8k5u/vqQV0/QUYAuyGwB6EVOrY3UhcHC6c3azFNP1vYgpeTtwZ0QsGJzRq/2UxnedBtwREd/LLSrtPdUqpjLfU0PEVNX3VN6fgS2U3TG+MtmwsAt78cIjaMt71WZ1+vq/B94had10Kf8daV7XjOC91LXj2sV2oCv7qYuft27uo/X1yi/krArsRTYW+nLgwLRa4z4a3HcHApelL/RdaRtaxHNn7suMyMb25vdRoe/tiDg+IjaJiAGyfX1ZRBxKSfuoMFHSnYBkd5benB4zgRPS/AnAH4F7gD8A6/U4rtXJvtWsnZt3FnArcAvZAd2w4BjOIbuc9ALZOJyjW+0XsrtSf0jWk3YrsH0PY5pFNmbopvQYvCP1gHRMbwJuAN7dw5haHivghLSf7gL26VVMaf7pwDEN6/ZqP+1Cdqn0ltyx2rfM99QQMZX2nhoiplLfUx3Evy/ZHf+zSe1pj163o7a8iPdXi7ag49cnG+c+Kz2OKiCmjt9L3Tqu3WwHurGfuvl56+I+2ga4Mb32bbzyy0JTyBK3WWTDLcen+auk57PS8inDxdqleC5L++g24Ge88osWPXlv58rcg1d+raKUfVTUw/8+2szMzMwsKfuGPDMzMzOzynBybGZmZmaWODk2MzMzM0ucHJuZmZmZJU6OzczMzMwSJ8dmZmZmZomTYzMzMzOzxMmxmZmZmVni5NjMzMzMLHFybGZmZmaWODk2MzMzM0ucHJuZmZmZJU6OzczMzMwSJ8dmZmZmZomTYzMzMzOzxMmxmZmZmVni5NjMzMzMLHFybGZmZmaWODk2MzMzM0ucHJuZmZmZJU6OzczMzMwSJ8dmZmZmZomTYzMzMzOzxMmxmZmZmVni5NjMzMzMLHFybGZmZmaWODk265CkIyVdU3YcZmZm1n1Ojq00dUgyJQ1ICknjyo7FzKxTdWhnR0PS6ZK+XnYc1l+cHFthlPF7zMysIG5nzbrPHygDQNJRkn6Te36PpJ/nns+XtG2afoukP0t6PP19S269KyR9Q9K1wDPAlNRzMUfSk5LulXSopNcBpwI7S3pK0pIWcS23bW7+tZJOlrQkrfOWNH++pIckHZErZ21JZ0p6WNI8SV8ZPKFIWiE9n5e2O1PS2mnTq9LfJSnOnXNlniTpsRTXPg374GspviclXSJpYm75TpL+lOK+WdIebdR3c0lXpn3+iKTz2juyZlYVFW5n15P0U0kLU5v2q9yyj0iaJelRSRdK2ii3LCR9ItXjydTuvTq1b09IOl/SymndPSQtkPTl1IbNHWzf0vJ3SboxbTdf0okNMe6Sazfnp/p+FDgUOC7V7zdp3bmSjpV0S9p/50laJVfWfpJuSmX9SdI2uWVflHR/qs9dkt6W5u8gaXqK70FJ32vnmFtNRYQffgBMAZaQfWHaCJgHLMgteywtWy9NHw6MAz6Qnk9I614B3AdsnZavDTwBvCYt3xDYOk0fCVwzREyrD7PtUuAoYEXg6+l1fwiMB94BPAmskdY/E/g1sCYwANwNHJ2WfQiYleq5BvC/wFlp2QAQwLhcXEcCLwAfSa/9cWAhoNw+mA1sCayann8rLdsYWAzsm/bnXun5+sPU9xzghLTNKsAuZb9n/PDDj84eVWxn0zq/A84D1gVWAnZP898KPAJsl9rVfweuym0XqV1dK8XyPPDHVJe1gduBI9K6e5C12d9LZe0OPJ2LeQ/gr1P9twEeBN6blk0ma88/kOKbAGyblp0OfL2hPnOB69M+Xg+4AzgmLXsj8BCwI1n7fURafzzwGmA+sFFadwB4dZqeChyeptcAdir7/eRHcQ/3HBsAETGHrPHZFtgN+D2wUNJryRqxqyPiJeBdwD0RcVZELI2Ic4A7gXfnijs9ImZGxFKyxvAl4PWSVo2IRRExs4PQhtr23oj4aUS8SNawbwp8NSKej4hLgL8Am0taETgYOD4inoyIucB3yU48kPU8fC8i5kTEU8DxwMEaepzxvIj4cXrtM8hORhvklv80Iu6OiGeB88n2K8BhwEURcVFEvBQRlwLTyZLloer7AtkJYqOIeC4i+nYMoVm/qmI7K2lDYB+y5PGxiHghIq5Miw8FfhIRN0TE82Rt486SBnJFfCcinkivdxtwSWpLHwcuJktG8/4xtdFXkiXl70/75oqIuDW1i7eQdQjsnrY5BPhDRJyT4lscETcNU7UfRMTCiHgU+A2vtMEfBf4rIq6LiBcj4gyypH4n4EWyJHkrSStFxNyImJ22e4HsfDIxIp6KiGnDvL7VmJNjy7uS7Nv7bmn6CrLGaff0HF7p7cibR9YjOmj+4EREPA0cBBwDLJL0u3QiGFYb2z6Ym342bdM4bw1gIllvQz7ufMyNdZpH1huTT3YbPZCL85k0uUaz5WSXPQeXTQbely7nLUmXOXcBNhymvscBAq6XNFPSh4aIzcyqq1LtLFmnwqMR8ViTZcvEkToPFjfE0djmNmuDBz2WYh00L70GknaUdLmyoW+Pp7oMDkfblOxqXCeGaoM/39AGb0rW8TAL+AxwIvCQpHNzw0iOJrsaeGca5rJfh/FYjTg5trzBRnvXNH0lyzfaC8kal7xJwP2555FfGBG/j4i9yHpX7wR+3Gy9ZobYthOP8ErPa7OYG+s0iawn5sF2YuzQfLIhG+vkHqtHxLegdX0j4oGI+EhEbAR8DPgPSZt3OTYzK17V2tn5wHqS1mmybJk4JK1ONqTh/ibrtmPdVMagSek1AM4GLgQ2jYi1ycZKKxfjq1uU2WkbPR/4RkMbvFrqnScizo6IXcjqHcC30/x7IuIDwKvSvAsa6mJ9xMmx5V0J7AmsGhELgKuBvckawxvTOhcBW0o6RNI4SQcBWwG/bVagpA0k7Z8akeeBp8gu/0GWfG4yeMNGh9u2LQ19OB/4hqQ1JU0GPgf8LK1yDvBZSZtJWgP4F+C8dLny4fSaUzp93RZ+Brxb0jslrShplXSjyiZD1VfS+yRtksp4jKzR7nhfmFnpKtXORsQisuEP/yFpXUkrSdotLT4HOErStpLGk7WN16WhaSP1z5JWlrQrsB8weEPimmQ92M9J2oFsKMWg/wHeLun9aX9MULpxMdWvk/b5x8AxqadaklZXdjPgmpJeI+mtqa7PkfV8D7bBh0laPw17WZLKchvcp5wc28si4m6yRvXq9PwJYA5wbUowiYjFZA3a58kurx0H7BcRj7QodgWyRHQh8ChZ78jH07LLgJnAA5KabT/Utp36FNnNH3OAa8h6KX6Slv0EOIvslynuJWsUPwUvD5n4BnBtugS30whfn1TefGB/4Mtkifd84AtkdR2qvm8GrpP0FFnvyj+k8YtmViMVbGchu//iBbIe54fIhhYQEX8A/hH4BbCIrPf24BFUe9ADZF/uF5IlvMdExJ1p2SeAr0p6Evgnsg4NUhz3kd2X8flUv5uAN6TFp5GNEV6i3K9stBIR08lupj4lxTKL7KZFyMYbf4vsauMDZL3Ex6dlewMzUxv8feDgdE+J9aHBu+vNzMzMCqHsJyt/FhGbDLOqWencc2xmZmZmljg5NjMzMzNLPKzCzMzMzCxxz7GZmZmZWTLUfwAbsYkTJ8bAwEARRZuZ1daMGTMeiYj1iyjb7a6Z2fJG0u4WkhwPDAwwffr0Ioo2M6stSY3/9axr3O6amS1vJO1uIcmxjXGXf7P5/D2Pbz7fzKwdblvMrAecHJuZWbW0SoLNzHrAybG9wr0yZmZmNsb51yrMzMzMzBL3HJuZWXf46pOZ9QEnx2ZmVg6PLTazCvKwCjMzMzOzxMmxmZmZmVni5NjMzMzMLPGYYzMzK5bHFptZjTg5NjOzzlQt2fWvZJhZF3lYhZmZmZlZ4uTYzMzMzCzxsIo6qdqlw04vrXYp/qmnHdt0/s5Hn9RZPGbW36rWZppZLTg5tvL5BGZmZmYV4WEVZmZmZmaJe477Wbd6ZKt2Z7qZ2Wj4apWZDcHJsVVWq7HFZmZmZkVxctwPunVj3Bhz8qV3Lzfvs3ttWUIkZmZmVhVOjkeqG5flfGnPzKrMX6TNbAxycmxmZrU2dc7ipvN3njKhx5GYWT9wctwrnfTAuLemNM2GWoCHW5iZmY0VTo5tWHXvlWmV8JqZ1Y2/wJsVz8mx9Y+WPe4H9DQMM6upZm2I7wExG3OcHFvpWvVMd62cSe2XsdN9P2qxxP+a2sxGrtMeX1/xMiuPk2MbsboPtyiSL32a9YeiP8tOgs2qx/8+2szMzMwscc+xdZ17lM2syrrRRrX6D547H13OECxfrTLrHifHY9BYS15bjyPuPz5BmnXXWGo/zCzj5Ljb/BvFNoROxxc6qTUbuU5v9u3GzcEeQ2xWf06O7WXd+tWIssovkk94Zr1T57ailVY90NMmfbTHkWR8lcmsNSfHw6lBT3A/nkjqrmonwlZ8gjTrrk6HYRTdVviLvVnnnBybmY11NegEMDPrlbGXHNf4JOAeYusV9yib1Uu3eqDr0NPc6T9OcbtlnRp7yXGFONmtj6LvWG9V/tTTmq9f1s9FmY2E27rO+VcyqqdqyXfV4ukn1UuOW/Xsdvr/7TvsIe705826tb6NLd064bX6jdWdWqx/8qXNe4867W3q2m+7duFz3q0Tg39BpDW3W/XXrTanW2OgO42nyPs0utVL3q1yqpZkt1JknFVJ+BUR3S9UehiY1/WC2zcReKTE1x9OleNzbCNT5dig2vGNpdgmR8T6XSzvZcO0u1Xex93kevaPsVBHcD17oeN2t5DkuGySpkfE9mXH0UqV43NsI1Pl2KDa8Tm24vVLPYbjevaPsVBHcD2raoWyAzAzMzMzqwonx2ZmZmZmSb8mx1W/zbfK8Tm2kalybFDt+Bxb8fqlHsNxPfvHWKgjuJ6V1Jdjjs3MzMzMRqJfe47NzMzMzDrm5NjMzMzMLKldcixpb0l3SZol6UtNlo+XdF5afp2kgTR/JUlnSLpV0h2SOvyvIl2JbTdJN0haKunAhmVHSLonPY6oSmyStpU0VdJMSbdIOqjbsY0mvtzytSQtkHRKlWKTNEnSJek9d/vg+7EisX0nHdc7JP1Aknoc2+fSPrlF0h8lTc4tK/vz0DS2Xn0eRmqk7WPdjOa9VRfD1TG33gGSQlJtfiYrr516Snp/Op4zJZ3d6xi7oY337CRJl0u6Mb1v9y0jztGQ9BNJD0m6rcVypXPNrFTH7XodY9siojYPYEVgNjAFWBm4GdiqYZ1PAKem6YOB89L0IcC5aXo1YC4w0OPYBoBtgDOBA3Pz1wPmpL/rpul1KxLblsAWaXojYBGwTgnHtWl8ueXfB84GTqlSbMAVwF5peg1gtSrEBrwFuDaVsSIwFdijx7HtObg/gI/nPqtV+Dy0iq3wz0PB9WraPtbpMZrjV5dHO3VM660JXAVMA7YvO+6CjuUWwI2DbQDwqrLjLqiePwI+nqa3AuaWHfcI6rkbsB1wW4vl+wIXAyL7h67XlR1zq0fdeo53AGZFxJyI+AtwLrB/wzr7A2ek6QuAt6UesQBWlzQOWBX4C/BEL2OLiLkRcQvwUsO27wQujYhHI+Ix4FJg7yrEFhF3R8Q9aXoh8BDQ7f/wNZp9h6Q3ARsAl3Q5rlHFJmkrYFxEXJrWeyoinqlCbGSfh1XIGurxwErAgz2O7fLc/pgGbJKmq/B5aBpbjz4PIzWa9rFORvPeqot2jiXA14BvA8/1MrguaqeeHwF+mNoCIuKhHsfYDe3UM4C10vTawMIextcVEXEV8OgQq+wPnBmZacA6kjbsTXSdqVtyvDEwP/d8QZrXdJ2IWAo8DkwgOxE8TdbTcx9wUkQMdRCLiK2IbXtWvqQdyJKp2V2Ka9CI45O0AvBd4NguxzRoNPtuS2CJpP9Nl8r+VdKKVYgtIqYCl5N9HhYBv4+IO0qM7WiyHoWRbNvL2F5W4OdhpEbTPtZJV45fxQ1bx3RJetOI+F0vA+uydo7llsCWkq6VNE1SN78o90o79TwROEzSAuAi4FO9Ca2nim7bu2Zc2QH00A7Ai2SXQtcFrpb0h4iYU25Y9ZC+3Z0FHBERy/XelugTwEURsaCCHWDjgF2BN5J9ITsPOBI4rcSYAJC0OfA6XulRu1TSrhFxdQmxHAZsD+ze69ceTqvYKvx5sJwqv7dGI3UKfI+sPel348iGVuxB1l5dJemvI2JJmUEV4APA6RHxXUk7A2dJer3bl3LUref4fmDT3PNN0rym66QhFGsDi8nGHP9fRLyQLstcS9Zo9jK2IrYtvHxJawG/A05Il0K6bTTx7Qz8vaS5wEnAByV9qyKxLQBuSpfSlgK/IhuPVYXY/haYloZ6PEXWs7Zzr2OT9HbgBOA9EfF8J9uWFFsvPg8jNZr2sU5GdfxqYrg6rgm8HrgitX07ARfW8Ka8do7lAuDCdO6+F7ibLFmuk3bqeTRwPrx8ZW8VYGJPouudotv27il70HMnD7JvkHOAzXhlUPvWDet8kmVvODk/TX8R+GmaXh24Hdiml7Hl1j2d5W/Iu5esR3vdNL1eRWJbGfgj8Jkyj2ur+BqWHUn3b8gbzb5bMa2/fnr+U+CTFYntIOAPqYyV0jF+dy9jI+tRn026wS03v/TPwxCxFf55KLheTdvHOj1Gc/zq8ujks53Wv4J63pDXzrHcGzgjTU8kuyw/oezYC6jnxcCRafp1ZGOOVXbsI6jrAK1vyHsXy96Qd33Z8basR9kBjGDH70v2zXE2Wc8NwFfJegcg+7b1c2AWcD0wJc1fI82fSZYYf6GE2N5M9i34abLempm5bT+UYp4FHFWV2IDDgBeAm3KPbasSX0MZR9Ll5LgLx3Uv4BbgVrIEdeUqxEaWuP8XcEf6PHyvhP32B7KbAAffVxdW6PPQNLZefR4KrFfT9rFuj9G8t+ryGK6ODeteQQ2T4zaPpciGkNye2tGDy465oHpuRXZF++b0nn1H2TGPoI7nkN3D8gLZeedo4BjgmNyx/GHaB7dW+T3rfx9tZmZmZpbUbcyxmZmZmVlhnBybmZmZmSVOjs3MzMzMEifHZmZmZmaJk2MzMzMzs8TJsZmZmZlZ4uTYzMzMzCxxcmxmZmZmljg5NjMzMzNLnBybmZmZmSVOjs3MzMzMEifHZmZmZmaJk2MzMzMzs8TJsZmZmZlZ4uTYzMzMzCxxcmxmZmZmljg5NjMzMzNLnBybmZmZmSVOjs3MzMzMEifHZmZmZmaJk2MzMzMzs8TJsZmZmZlZ4uTYzMzMzCxxcmxmZmZmljg5NjMzMzNLnBybmZmZmSVOjs0KJmlXSXeVHYeZmS1L0iRJT0lasexYrDqcHFslSTpS0jVlx9ENEXF1RLxm8LmkuZLeXmZMZtZ/+qnd7JWIuC8i1oiIF4dbV9KApJA0rhexWXmcHFsplPH7z8ysTW43zXrDHzIblqSjJP0m9/weST/PPZ8vads0/RZJf5b0ePr7ltx6V0j6hqRrgWeAKamnY46kJyXdK+lQSa8DTgV2Tpe7lrSIaz1JP5W0UNJjkn6VW/YRSbMkPSrpQkkb5ZaFpGNSPZZI+qEkNWx7R4rpdknbpflfkjQ7N/9v0/zxqZzX58pYX9Kzkl4laQ9JC9L8s4BJwG9S3Y6T9DtJn2qo2y2D5ZtZ/bjdHF27mZ7vJ+mmtN6fJG0zxP4OSZ9O++URSf+q9EVC0gqSviJpnqSHJJ0pae20bJne4LS/vybp2hTzJZImppe5Kv1dkvbxzpI2l3RlOnaPSDqvVYxWIxHhhx9DPoApwBKyL1MbAfOABbllj6Vl66Xpw4FxwAfS8wlp3SuA+4Ct0/K1gSeA16TlGwJbp+kjgWuGiet3wHnAusBKwO5p/luBR4DtgPHAvwNX5bYL4LfAOmSJ6sPA3mnZ+4D7gTcDAjYHJueWbZTqehDwNLBhWvYT4Bu51/gk8H9peo/B/ZWezwXennv+fuC63PM3AIuBlcs+9n744cfIHm43R91uvhF4CNgRWBE4IrWd41vUK4DL0/6cBNwNfDgt+xAwK+33NYD/Bc5KywbStuNy+3s2sCWwanr+rWbrpnnnACek+q0C7FL2e8+P0T/cc2zDiog5wJPAtsBuwO+BhZJeC+wOXB0RLwHvAu6JiLMiYmlEnAPcCbw7V9zpETEzIpYCS4GXgNdLWjUiFkXEzHZikrQhsA9wTEQ8FhEvRMSVafGhwE8i4oaIeB44nqw3ZSBXxLciYklE3EfWoG6b5n8Y+E5E/DkysyJiXtoPP4+IhRHxUkScB9wD7JC2Oxs4OFf+IWleOy4EtpS0RXp+OHBeRPylze3NrGLcbo663fwo8F8RcV1EvBgRZwDPAzsNUcVvR8SjKb5/I/uiMVi370XEnIh4KtXtYLUeO/zTiLg7Ip4Fzs/Vs5kXgMnARhHxXER4zHcfcHJs7bqSrAd0tzR9BVkDv3t6Dq/0juTNAzbOPZ8/OBERT5P1JBwDLErDC17bZjybAo9GxGNNli0TR2oMFzfE8UBu+hmy3oTBcmc3e0FJH8xd4lsCvB4YvNx2ObCapB3TyWRb4JftVCQiniPryTksXQb8AHBWO9uaWaW53Rx5uzkZ+PzgdmnbTVOcrczPTc/Lrdu4j+eR9cJv0KKcVvVs5jiy3vLrJc2U9KEh1rWacHJs7Rps5HdN01eyfCO/kKxBy5tEdrltUOQXRsTvI2IvskuDdwI/brZeE/OB9SSt02TZMnFIWh2Y0BDHUOW+unGmpMkptr8nu9y5DnAbWaNIZHc6n0+W2H4A+G1EPNniNZrV7Qyy3o23Ac9ExNQ2YjWzanO7OfJ2cz7ZkIt1co/VUs96K5vmpielOi1Xt7RsKfBgG3XLW27/RsQDEfGRiNgI+BjwH5I277Bcqxgnx9auK4E9gVUjYgFwNbA3WeN5Y1rnIrLhAYdIGifpIGArsnFqy5G0gaT9UyP8PPAU2eVCyBqtTSSt3GzbiFgEXEzWEK0raSVJu6XF5wBHSdpW0njgX8jG9M5to57/DRwr6U3KbJ4a+NXJGsaHU+xHkfWA5J1N1qNzKEMPqXiQbOxbvj5TU92/i3uNzfqF282Rt5s/Bo5JvcqStLqkd0lac4g4vpDqtSnwD2RX5Abr9llJm0laI9XtvDRMpRMPk+3rl9tvSe+TtEl6+liq70tNtrUacXJsbYmIu8ka4avT8yeAOcC16ds/EbEY2A/4PNnluOOA/SLikRbFrgB8juxb/aNkvSkfT8suA2YCD0hqtf3hZOO97iS7ceMzKY4/AP8I/AJYRNajcXDzIpar58+Bb5A10k8CvwLWi4jbyRLXqWQnoL8Grm3Y9jqym002IjsBtfJN4CvpUuGxuflnpnJ/1k6sZlZtbjdH3m5GxHTgI8ApZEnnLLIbDofya2AGcBPZjYenpfk/Iet0uAq4F3gO+FST7Yer5zOpntem9nsnspsQr5P0FNn9I/+QxptbjSliuKswZtYLkj4IfDQidik7FjOzOpEUwBYRMavsWKz+3HNsVgGSVgM+Afyo7FjMzMzGMifHZiWT9E6ysWwP0v7Pv5mZmVkBPKzCzMzMzCxxz7GZmZmZWdLqv8OMysSJE2NgYKCIos3MamvGjBmPRMT6RZTtdtfMbHkjaXcLSY4HBgaYPn16EUWbmdWWpMb/hNY1bnfNzJY3kna3kOR4TLj8m83n73l8b+MwM7Pq8LnBrPY85tjMzMzMLHFybGZmZmaWODk2MzMzM0uqN+a4rPFaHidmZmZmNuZVLznuFie7ZmZmZtYhD6swMzMzM0v6t+e4lVY9ymZmZmY25rnn2MzMzMwscXJsZmZmZpY4OTYzMzMzS8bemONOeYyymVl/6aRd9y8cmY057jk2MzMzM0vq03Ps3y02MzMzs4LVJzluxcMezMysmSLPDz73mPWt+ifH1rGTL7276fzP7rVljyMxMzMzqxaPOTYzMzMzS9xzbGZmVhe+/8ascE6Ozcys3jy22My6yMmxmZlZ0TpNst0TbFYaJ8dmZmbWE2XcEO6b0K1TTo57pVmvgXsGzGws8DhZM6sRJ8dmZmZV47HOZqVxcmxmNtZVrWe3avGY4eEZY4mT4zrxCcPMzMysUE6Ou62TS2EFJ7utvuV2ur6/FZtZJXioQd/q5Pzjc5UVzclxjUyds7jp/GlLO0uCzczMitRp50yVXrNbHUutOImvPifHJeo02d2pyGDMzMw61bI3/4CehtEP3CNeHU6Oe6RVItzMTvf9qKOyW60/bdJHOyqnlW59K67DB78OMZr1DQ+T6B7/kxGzrnFybKXrNCF1AmtmVpCK3fhdxvAMMyfH1nVljfvqtBwn02Zd5p7g/uVj27d8jlyek+M+VvRwCzMzM7N+4+S4yzoZW2z14W/WNiZV7BK7tdbq3LPzlAmFlt/SpK68bKH6dciGz1ej5+TY+ka/NnRmZiPVMmnes7P1rXvKOlcV+br9lpA7OR6Dih5u4eEc3dNvDY71CY8/rb2ppx1baPmd/uqSzw+dc5JdHCfHNqx+TXa78QGvygd5OHWJ06zuOu15bTXswT24VraxfDW2cslxp+OmurW+de/3lbv1up0m352W00n8nZYx9bTOyvnsuF+0eOVif0i/Dv/ZqbSf+vN4264pejxst3RyfihtLG+f6qT9Hmu90nVOUuvaMaOI6H6h0sPAvBFuPhF4pIvh9Fqd43fs5alz/HWOHXob/+SIWL+Igsd4u5vnulST61JNY6EuHbe7hSTHoyFpekRsX3YcI1Xn+B17eeocf51jh/rH3w39tA9cl2pyXarJdWluhW4UYmZmZmbWD5wcm5mZmZklVUyOu3OXV3nqHL9jL0+d469z7FD/+Luhn/aB61JNrks1uS5NVG7MsZmZmZlZWarYc2xmZmZmVorSkmNJe0u6S9IsSV9qsny8pPPS8uskDZQQZlNtxP45SbdLukXSHyVNLiPOVoaLP7feAZJCUmXuZG0ndknvT/t/pqSzex3jUNp470ySdLmkG9P7Z98y4mwk6SeSHpJ0W4vlkvSDVK9bJG3X6xiH0kb8h6a4b5X0J0lv6HWMRRlNWyvp+DT/Lknv7GngTYy0LpIGJD0r6ab0OLXnwTdooy67SbpB0lJJBzYsO0LSPelxRO+ibm6UdXkxd1wu7F3UzY3m/F7D4zJUXep2XI5J7fdNkq6RtFVuWeftWET0/AGsCMwGpgArAzcDWzWs8wng1DR9MHBeGbGOMPY9gdXS9MerEnu78af11gSuAqYB25cddwf7fgvgRmDd9PxVZcfdYfw/Aj6eprcC5pYdd4plN2A74LYWy/cFLgYE7ARcV3bMHcb/ltx7Zp+qxT+Keo+4rU3vv5uB8cBmqZwVa1qXgVbHvsJ1GQC2Ac4EDszNXw+Yk/6um6bXrWNd0rKnyj4eHdal6fm9pselZa5Sw+OyVm76PcD/pekRtWNl9RzvAMyKiDkR8RfgXGD/hnX2B85I0xcAb5OkHsbYyrCxR8TlEfFMejoN2KTHMQ6lnX0P8DXg28BzvQxuGO3E/hHghxHxGEBEPNTjGIfSTvwBrJWm1wYW9jC+liLiKuDRIVbZHzgzMtOAdSRt2Jvohjdc/BHxp8H3DNX7zI7GaNra/YFzI+L5iLgXmJXKK0udzxuN2jmPzI2IW4CXGrZ9J3BpRDya3rOXAnv3IugWRlOXqhnN+b2Ox6XKuUpeO3V5Ivd0dbJzKYywHSsrOd4YmJ97viDNa7pORCwFHgeq8L9G24k972iyHrWqGDb+dEl804j4XS8Da0M7+35LYEtJ10qaJqnMxqlRO/GfCBwmaQFwEfCp3oQ2ap1+Lqqsap/Z0RhNW1u1Yzra88ZmabjSlZJ2LTrYYYxm39bxuAxlFUnTU3v93q5G1rnRnN/rflwa273aHRdJn5Q0G/gO8OlOtm00bsSh2rAkHQZsD+xediztkrQC8D3gyJJDGalxZEMr9iD7FnyVpL+OiCVlBtWBDwCnR8R3Je0MnCXp9RFR9R6XviBpT7KTxC5lx2JdtQiYFBGLJb0J+JWkrRt6m6wckyPifklTgMsk3RoRs8sOajh1PL+30qIutTsuEfFD4IeSDgG+Aox43HdZPcf3A5vmnm+S5jVdR9I4skvMi3sS3dDaiR1JbwdOAN4TEc/3KLZ2DBf/msDrgSskzSUbP3qhqnFTXjv7fgFwYUS8kC6h3E2WLFdBO/EfDZwPEBFTgVXI/l981bX1uagySdsA/w3sHxFVaGu6YTRtbdWO6Yjrki6pLgaIiBlk4w63LDzi1kazb+t4XFqKiPvT3znAFcAbuxlch0Zzfq/lcWmVq9TxuOScC7x3hNtmejWgumHg9Diyweqb8crg6q0b1vkky95YcX4ZsY4w9jeSNb5blB3vSOJvWP8KqnNDXjv7fm/gjDQ9kexyyoSyY+8g/ouBI9P068jGHKvs2FM8A7S+oe1dLHtD3vVlx9th/JPIxqK9pew4u1znEbe1wNYseyPLHMq9IW80dVl/MHaym3ruB9arcl1y657O8jfk3Ut209e6abqudVkXGJ+mJwL30OQG8SrVhRbn9zoelyHqUsfjskVu+t3A9DQ9onaslIqmgPcl69WbDZyQ5n2V7NsLZD1mPyc7YV0PTCkr1hHE/gfgQeCm9Liw7Jg7ib9h3SuoSHLc5r4X2bCQ24FbgYPLjrnD+LcCrk0f5puAd5Qdc4rrHLJL0y+Q9c4fDRwDHJPb7z9M9bq1Su+ZNuP/b+Cx3Gd2etkx9/A917KtJetRmg3cBexT17oABwAz07G9AXh3Dery5vRefZqsJ39mbtsPpTrOAo6qa13IfiXm1tTe3QocXYO6tDy/1/C4NK1LTY/L93Of8cvJJc8jacf8H/LMzMzMzBL/hzwzMzMzs8TJsZmZmZlZ4uTYzMzMzCxxcmxmZmZmljg5NjMzMzNLnBybmZmZmSVOjs3MzMzMEifHZmZmZmaJk2MzMzMzs8TJsZmZmZlZ4uTYzMzMzCxxcmxmZmZmljg5NjMzMzNLnBybmZmZmSVOjs3MzMzMEifHZmZmZmaJk2MzMzMzs8TJsZmZmZlZ4uTYzMzMzCxxcmxmZmZmljg5NjMzMzNLnBybmZmZmSVOjs3MzMzMEifHZmZmZmaJk2MzMzMzs8TJsZmZmZlZ4uTYzMysj0jaQNJVkp6U9N2SY9lD0oIO1r9C0ofT9KGSLikuus5Jmilpj7LjsGI5ObZKkXSkpGvKjqOb+rFOZlaONtuTjwKPAGtFxOe7/PonSvpZN8tsJSL+JyLe0YvXaldEbB0RV5QdhxXLybH1lDJ+3zWQtGLZMZhZ+brURk4Gbo+IaPEa40ZZvllfc5JiLUk6StJvcs/vkfTz3PP5krZN02+R9GdJj6e/b8mtd4Wkb0i6FngGmJJ6P+aky373pstnrwNOBXaW9JSkJS3iarbtypIelfTXufVeJekZSesPXtqTdJykhyQtkvReSftKujtt++XctidK+rmkn6XXuVXSlpKOT9vPl/SO3PprSzotlXu/pK9LWrFVnSSdLuk/JV0k6Wngc5IezCfJkv5O0s0jPX5mVqwqtpGSTgeOAI5L67w9tWcXpPbsCeBISTtImippSWq3TpG0cq6crSVdmtrGByV9WdLewJeBg1LZN+f2wx0p1jmSPtbBPtxL0p1pv5wCKLdsmV5ySSHpE2k/Pynpa5JeLelPkp6QdH5DHfaTdFOq458kbZNbNlfSsZJuSa99nqRV0rKJkn6btntU0tVKX1jSdm9P0+Ml/Zukhenxb5LGp2WD55zP65VzzlHt7hcrWUT44UfTBzAFWEL2JWojYB6wILfssbRsvTR9ODAO+EB6PiGtewVwH7B1Wr428ATwmrR8Q2DrNH0kcM0QMa0+xLb/AXw7t+4/AL9J03sAS4F/AlYCPgI8DJwNrJliexbYLK1/IvAc8M4U85nAvcAJue3vzb3WL4H/SvG9Crge+FirOgGnA48Df5P24SrA7cA+DWV+vuz3gR9++NH8UcU2Mq1zOvD13PMTgReA96Z4VgXeBOyUXm8AuAP4TFp/TWAR8PnUNq0J7Jgr62cNr/cu4NVkie3uZAn+dmnZHoP7pEmcE4EngQNTu/rZ1E5/uFldgQB+DayV9tXzwB/Tvl47taFHpHXfCDwE7AisSPaFYS4wPi2fS9ZOb5SOzx3AMWnZN8m+hKyUHrsCym339jT9VWAaWZu/PvAn4Gu5ei9N66wE7Jv2y7plv2/9GP7hnmNrKSLmkDVc2wK7Ab8HFkp6LVkDeHVEvETWMN4TEWdFxNKIOAe4E3h3rrjTI2JmRCwlazBeAl4vadWIWBQRMzsIrdW2ZwAfkDTY83A4cFZuuxeAb0TEC8C5ZA3z9yPiyVTG7cAbcutfHRG/TzH/nKzx+1Zu+wFJ60jagKzh+0xEPB0RDwEnAwcPU49fR8S1EfFSRDyX4j8MQNJ6ZIn52R3sFzProQq3kc1MjYhfpfbm2YiYERHTUjxzyb7c757W3Q94ICK+GxHPpTbyuiH2w+8iYnZkrgQuIUsoh7MvMDMiLkjt6r8BDwyzzXci4om0P24DLomIORHxOHAxWVIM2bjr/4qI6yLixYg4gyyZ3ilX1g8iYmFEPAr8huw4Qnau2BCYHBEvRMTVEdFsiMqhwFcj4qGIeBj4Z7LzDrlyvprKuAh4CnjN8LvFyubk2IZzJdk34N3S9BVkDeju6Tm80mOSNw/YOPd8/uBERDwNHAQcAyyS9Lt0MhnWUNumxvsZYI80b3PgwtzmiyPixTT9bPr7YG75s8AaueeNyx5psv0aZOP7VkrxLEmXOv+LrDdhKPMbnv8MeLek1YH3k51YFw1ThpmVq1Jt5BCWaW+UDRP7raQH0lCLfyHrMADYFJjdbsGS9pE0LQ1BWEKW9E4cZjPI9ku+3tEYZxON7XKrNnwy8PnBNjnFtWl6zUH5RPyZ3Lb/CswCLknDRL40RPz54zqvofzF6ctOs9ewCnNybMMZbPh3TdNXsnzDv5CsIcqbBNyfe77Mt+7UI7sX2bfzO4EfN1uvmSG2hVd6Xw8HLkg9skWbT9YjMTEi1kmPtSJi68GQW2zXuE/uB6YCf8fyvd5mVk2VayNbaNzuP1O5W0TEWmRjiQevus0nG6owbDlpjO0vgJOADSJiHeCiXFlDWUSWsA6WpfzzUZpPdqVwndxjtdRrP6TUU/75iJgCvIfsnpC3NVm18bhOSvOs5pwc23CuBPYEVo2IBcDVwN7ABODGtM5FwJaSDpE0TtJBwFbAb5sVqOw3OPdPPaTPk11qeiktfhDYJH9TRQfbQtb7+rdkCfKZI610J1Lv7iXAdyWtJWmFdJPI4CXKIevU4EzgOOCvgf8tJmIz66JKtZEdWJNsXPNTqVf647llvwU2lPSZdNPZmpJ2zL3+gF75RY2VgfFk93AslbQP0O7Pr/0O2FrZzcfjgE8DfzW6ar3sx8AxknZUZnVJ75K05nAbphv5Nk/J+uPAiyx7nhl0DvAVZTd9TyS7p6UnP3NnxXJybEOKiLvJGuar0/MngDnAtYNDDCJiMdkYtc8Di8mSu/0i4pEWxa4AfI7sG/ajZD0sgw3zZcBM4AFJzbYfalsiYj5wA1nvxtUjqvTIfJDsJHE72Y02F5D1+MDwdcr7JVlPxC8j4pmCYjWzLqlgG9muY4FDyMZM/xg4L1enJ4G9yMZEPwDcQ/YFALL7LwAWS7ohrftp4Hyytu8Qlh3O1lKq//uAb5Htly2Aa0dRp3zZ08lunD4lxTWL7Aa/dmwB/IHsuE4F/iMiLm+y3teB6cAtwK1k556vjypwq4TBuy/N+oaknwALI+IrZccyEpJmk/3SxR/KjsXMzGys8Q+BW1+RNEA2ZveNw6xaSZIOIOv1vqzsWMzMzMYiJ8fWNyR9jex3Mr8ZEfeWHU+nJF1BNg7x8PTzT2ZmZtZjHlZhZmZmZpb4hjwzMzMzs6SQYRUTJ06MgYGBIoo2M6utGTNmPBIR6xdRtttdM7PljaTdLSQ5HhgYYPr06UUUbWZWW5Ia/0ta17jdNTNb3kjaXd+QV0WXf7P5/D2P720cZmZV4rbRzHrAybGZmRXLSa2Z1YiT427zScDMzMystpwcl6lVIm1mNha4M8HMKsg/5WZmZmZmljg5NjMzMzNLnBybmZmZmSUec1wnHp9nZmZmVignx73im+/MzIrhjgMz6yIPqzAzMzMzS9xzbGZm1eIrbWZWIifHI+XG28zMzKzveFiFmZmZmVninuN+4JtRzMzMzLrCyfFwPHzCzKye3HFgZiPg5LifdZrY+4RhZqPhzgQz6wNOjvvY1DmLm87fecqEHkdiZmZmVg++Ic/MzMzMLHHPsb3s5Evvbjr/s3tt2eNIzMzMzMrh5LhGPEzCzMzMrFgeVmFmZmZmlrjn2F62030/arHkpJ7GYWZjW6dXyXxVzcy6ycmxmZmNLZ38/rF/K9lszPGwCjMzMzOzxD3HFdTqEmFp5bvnxMzMzMYIJ8eD/J+dCuefijPrE24vzayPOTnukWa9td26WaTonmYzszrq+Ea9ApN+dw6Y1YeTYzMza65iPcTd6gjoRjk779mFQHDSbFZFTo5L5B5fM7N6apXUmln9OTkeIf+uZms+aZhZnttLM6sTJ8c2rFYntmlLq5UE+/KkmZmZjVb/Jsf++TEzs57qdKhYnYeWtfqPotMmfbQr65tZefo3OW6l4BtM6tzYd6pbjb17fM2sX7RqF82sPuqfHFfsbmrr/OTQrWS6U83KcUJu9oqx9GW/LK3ay6mnNV9/56NPajq/ZSfDuF80X3/pAcMHly/HbaONIZVLjqvWi+iTg41EtxJ4n5CsF1q9X3fqcRw2vE7blpbnsEldCKYH3IlhZahccty617H5t+VO+a7p+ijjVy+q9uXMrBc8FMCGM/W0Y5vO7/TKX6u21L9yZFVSueS4JY8VHnO6Naa5VaPerFesVdl1abi7ldx3Wt9OT3h1+LJR59itf3WrXezWF6KO42l5Lm9/mEenQ0jqciO+25zq7ANFRPcLlR4G5nW94O6aCDxSdhA9MFbqCWOnrmOlntB/dZ0cEesXUXCP2t0qHw/HNjKObWQc28iUEVvH7W4hyXEdSJoeEduXHUfRxko9YezUdazUE8ZWXeugysfDsY2MYxsZxzYyVY4tb4WyAzAzMzMzqwonx2ZmZmZmyVhOjsfK7dljpZ4wduo6VuoJY6uudVDl4+HYRsaxjYxjG5kqx/ayMTvm2MzMzMys0VjuOTYzMzMzW4aTYzMzMzOzpK+TY0l7S7pL0ixJX2qy/HOSbpd0i6Q/SppcRpzd0EZdj5F0q6SbJF0jaasy4hyt4eqZW+8ASSGp8j8Z00obx/RISQ+nY3qTpA+XEedotXNMJb0/0+4KZQAABnFJREFUfVZnSjq71zH2uzbea+MlnZeWXydpILdsG0lT07G5VdIqVYhN0kqSzkgx3SGp6/8Joo3YdpN0g6Slkg5sWHaEpHvS44iqxCZp29zxvEXSQVWJLbd8LUkLJJ1SpdgkTZJ0SXq/3Z7/nFQgtu+kY3qHpB9IUo9ja5lvFf1ZGJGI6MsHsCIwG5gCrAzcDGzVsM6ewGpp+uPAeWXHXWBd18pNvwf4v7LjLqKeab01gauAacD2Zcdd4DE9Ejil7Fh7UM8tgBuBddPzV5Uddz892jwGnwBOTdMHD7aVZP9l9RbgDen5BGDFisR2CHBuml4NmAsM9Di2AWAb4EzgwNz89YA56e+6aXrdisS2JbBFmt4IWASsU4XYcsu/D5zd7fZvtLEBVwB7pek1SPlF2bEBbwGuTWWsCEwF9uhxbE3zraI/CyN99HPP8Q7ArIiYExF/Ac4F9s+vEBGXR8Qz6ek0YJMex9gt7dT1idzT1YE63ok5bD2TrwHfBp7rZXBd1m5d666den4E+GFEPAYQEQ/1OMZ+184x2B84I01fALwt9Ty9A7glIm4GiIjFEfFiRWILYHVJ44BVgb8AT9A97bS7cyPiFuClhm3fCVwaEY+m9/WlwN5ViC0i7o6Ie9L0QuAhoJv/1XE0+w1JbwI2AC7pYkyjjk3Z1dhxEXFpWu+pXH5Ramxkn4VVyBLX8cBKwIM9jq1VvlX0Z2FE+jk53hiYn3u+IM1r5Wjg4kIjKk5bdZX0SUmzge8An+5RbN00bD0lbQdsGhG/62VgBWj3/XtAukx1gaRNexNaV7VTzy2BLSVdK2mapNIbzj7TzjF4eZ2IWAo8TtZLvCUQkn6fLuceV6HYLgCeJuv5vA84KSIe7XFsRWzbs/Il7UCWUM3uUlwwitgkrQB8Fzi2i/HkjWa/bQkskfS/km6U9K+SVqxCbBExFbic7LOwCPh9RNxRYmz5fKvoz8KI9HNy3DZJhwHbA/9adixFiogfRsSrgS8CXyk7nm5LDef3gM+XHUuP/IbsMvE2ZN+2zxhm/boaRza0Yg/+f3v3E2JVGYdx/PvAOEiROuBGjBgloVChPygJysRoLQoGB1qkhljLcKetZuemkAhaii2iNoJBcUFhIGoIgkFdTJhD+K+QaVcLwRYxyc/F+850M29zm3v+3cvzgcMwwzmX5533njm/e87vnIFDwFlJG+oMZMuGgL3Akfx1UtL+eiMt2w3cJ7UGbAFOSNpab6T+IWkT8DnwdkT86wxuTd4FLkbEQt1BHmEI2Ecq3HeRWgyO1RloiaSngWdJZ2s3A+OS9tWUpS/qrUEujn8F2s+kPZl/9g+SDgBTwERE/FlRtqJ1NdY254CDZQYqyUrjfALYAcxI+gV4CWipP2/KW3FO8yXspffsJ8CLFWUrUjfv3QWgFRGLEfEzcJ1ULFsxupmD5XVym8J64HfS3HwXEb/lS6YXgRcaku0w6d6KxdyK8z3poFxltjK2Lf31Ja0DLgBTETFbYK5es+0Bjue/7x8CRyV90JBsC8Bcbi34C/iK6veFTiaB2dzqcY901nZP1dk61Ftl7wurMsjF8WVgm6QtkoZJN2q02leQ9DxwhjRR/dzH2M1Y24uJ14EbFeYryn+OMyLuRsTGiBiNiFFSX9NERFypJ25PupnTTW3fTgBFXiaryorjJB1kXgaQtJF0+fJ2hRkHXTdz0AKW7iJ/A/gm0t0008BOSY/lwnQMmG9ItjvAOICkx0kfln+qOFsn08CrkkYkjZB6t6ebkC2v/yXwWUR8UWCmnrNFxJGIeCr/fT+ZM3Z8alGV2fK2GyQt9WePU/2+0MkdYEzSkKQ1pP20yONFL/VW2fvC6lR9B2CVC/Aa6SzTLdInYIBTpMkB+JrUlD6Xl1bdmUsc68fAtTzOb4HtdWcuY5wPrTtDnz6toss5fT/P6Q95Tp+pO3NJ4xSpXWYeuAq8WXfmQVu6mIO1wHngJnAJ2Nq27Vv5ffgjcLop2UhPCzifs80D79WQbRfpjOIfpLPZ19q2fSdnvklqXWhEtjyfi/x9XJwDnmtCtode4xglPK2nxzl9hfT0lqvAp8BwE7KRniZxhlQQzwMf1fB761hvlb0vrGbxv482MzMzM8sGua3CzMzMzOx/cXFsZmZmZpa5ODYzMzMzy1wcm5mZmZllLo7NzMzMzDIXx2ZmZmZmmYtjMzMzM7PsAV/aLr3146rjAAAAAElFTkSuQmCC\n",
|
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"text/plain": [
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|
"<Figure size 720x1440 with 30 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
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"data": {
|
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"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 1080x576 with 2 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"import matplotlib.pyplot as plt\n",
|
|
"import numpy as np\n",
|
|
"from sklearn.model_selection import train_test_split \n",
|
|
"from sklearn.datasets import load_breast_cancer\n",
|
|
"from sklearn.linear_model import LogisticRegression\n",
|
|
"cancer = load_breast_cancer()\n",
|
|
"import pandas as pd\n",
|
|
"# Making a data frame\n",
|
|
"cancerpd = pd.DataFrame(cancer.data, columns=cancer.feature_names)\n",
|
|
"\n",
|
|
"fig, axes = plt.subplots(15,2,figsize=(10,20))\n",
|
|
"malignant = cancer.data[cancer.target == 0]\n",
|
|
"benign = cancer.data[cancer.target == 1]\n",
|
|
"ax = axes.ravel()\n",
|
|
"\n",
|
|
"for i in range(30):\n",
|
|
" _, bins = np.histogram(cancer.data[:,i], bins =50)\n",
|
|
" ax[i].hist(malignant[:,i], bins = bins, alpha = 0.5)\n",
|
|
" ax[i].hist(benign[:,i], bins = bins, alpha = 0.5)\n",
|
|
" ax[i].set_title(cancer.feature_names[i])\n",
|
|
" ax[i].set_yticks(())\n",
|
|
"ax[0].set_xlabel(\"Feature magnitude\")\n",
|
|
"ax[0].set_ylabel(\"Frequency\")\n",
|
|
"ax[0].legend([\"Malignant\", \"Benign\"], loc =\"best\")\n",
|
|
"fig.tight_layout()\n",
|
|
"plt.show()\n",
|
|
"\n",
|
|
"import seaborn as sns\n",
|
|
"correlation_matrix = cancerpd.corr().round(1)\n",
|
|
"# use the heatmap function from seaborn to plot the correlation matrix\n",
|
|
"# annot = True to print the values inside the square\n",
|
|
"plt.figure(figsize=(15,8))\n",
|
|
"sns.heatmap(data=correlation_matrix, annot=True)\n",
|
|
"plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "5408b7dd",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Discussing the correlation data\n",
|
|
"\n",
|
|
"In the above example we note two things. In the first plot we display\n",
|
|
"the overlap of benign and malignant tumors as functions of the various\n",
|
|
"features in the Wisconsing breast cancer data set. We see that for\n",
|
|
"some of the features we can distinguish clearly the benign and\n",
|
|
"malignant cases while for other features we cannot. This can point to\n",
|
|
"us which features may be of greater interest when we wish to classify\n",
|
|
"a benign or not benign tumour.\n",
|
|
"\n",
|
|
"In the second figure we have computed the so-called correlation\n",
|
|
"matrix, which in our case with thirty features becomes a $30\\times 30$\n",
|
|
"matrix.\n",
|
|
"\n",
|
|
"We constructed this matrix using **pandas** via the statements"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 6,
|
|
"id": "76c3d259",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"cancerpd = pd.DataFrame(cancer.data, columns=cancer.feature_names)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "83903231",
|
|
"metadata": {},
|
|
"source": [
|
|
"and then"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 7,
|
|
"id": "58ccd6c3",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"correlation_matrix = cancerpd.corr().round(1)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "adda54cb",
|
|
"metadata": {},
|
|
"source": [
|
|
"Diagonalizing this matrix we can in turn say something about which\n",
|
|
"features are of relevance and which are not. This leads us to\n",
|
|
"the classical Principal Component Analysis (PCA) theorem with\n",
|
|
"applications. This will be discussed later this semester ([week 43](https://compphysics.github.io/MachineLearning/doc/pub/week43/html/week43-bs.html))."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "8799c034",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Other ways of presenting a classification problem\n",
|
|
"\n",
|
|
"For a binary classifcation matrix, the so-called **confusion matrix**, is often used. It can also be extended to more catgeories/classes as well.\n",
|
|
"The following quantities are then used\n",
|
|
"1. positive condition number $P$, which represents the number of real positive cases in the data (output one/true etc)\n",
|
|
"\n",
|
|
"2. The condition negative number $N$ which is the number of negative cases (ouput zero/false etc)\n",
|
|
"\n",
|
|
"3. The true positive number $TP$ which represents whether a positive test result has been correctly classified (the application of our trained model on a test data set)\n",
|
|
"\n",
|
|
"4. The true negative $TN$ number which represents whether a negative test has been correctly classified\n",
|
|
"\n",
|
|
"5. The false positive $FP$ number, a so-called type I error which tells us about the fraction of positive test result which are wrongly classified\n",
|
|
"\n",
|
|
"6. A false negative $FN$ number, a so-called type II error which, should be pretty obvious, indicates if a negative test has been wrongly classified.\n",
|
|
"\n",
|
|
"It is is easy to think in terms of illness. You could think of the above as\n",
|
|
"1. True positive: Sick people correctly identified as sick\n",
|
|
"\n",
|
|
"2. False positive: Healthy people incorrectly identified as sick\n",
|
|
"\n",
|
|
"3. True negative: Healthy people correctly identified as healthy\n",
|
|
"\n",
|
|
"4. False negative: Sick people incorrectly identified as healthy"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "dfbe0571",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Combinations of classification results\n",
|
|
"\n",
|
|
"It is common in the literature to define various combinations the above numbers. The most commonly used are\n",
|
|
"\n",
|
|
"**Sensitivity, recall, hit rate, or true positive rate $TPR$. It is the probability of a positive test result, conditioned on the individual truly being positive.**"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "65f7c63d",
|
|
"metadata": {},
|
|
"source": [
|
|
"$$\n",
|
|
"{\\displaystyle \\mathrm {TPR} ={\\frac {\\mathrm {TP} }{\\mathrm {P} }}={\\frac {\\mathrm {TP} }{\\mathrm {TP} +\\mathrm {FN} }}=1-\\mathrm {FNR} }\n",
|
|
"$$"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "2e674d10",
|
|
"metadata": {},
|
|
"source": [
|
|
"The $TPR$ defines how many correct positive results occur among all positive samples available during the test\n",
|
|
"\n",
|
|
"**Miss rate or false negative rate $FNR$.**"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "65577837",
|
|
"metadata": {},
|
|
"source": [
|
|
"$$\n",
|
|
"{\\displaystyle \\mathrm {FNR} ={\\frac {\\mathrm {FN} }{\\mathrm {P} }}={\\frac {\\mathrm {FN} }{\\mathrm {FN} +\\mathrm {TP} }} }\n",
|
|
"$$"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "e5e8802d",
|
|
"metadata": {},
|
|
"source": [
|
|
"**Specificity, selectivity or true negative rate $TNR$. It is the probability of a negative test result, conditioned on the individual truly being negative.**"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "07c61591",
|
|
"metadata": {},
|
|
"source": [
|
|
"$$\n",
|
|
"{\\displaystyle \\mathrm {TNR} ={\\frac {\\mathrm {TN} }{\\mathrm {N} }}={\\frac {\\mathrm {TN} }{\\mathrm {TN} +\\mathrm {FP} }}=1-\\mathrm {FPR} }\n",
|
|
"$$"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "b3f61bb3",
|
|
"metadata": {},
|
|
"source": [
|
|
"with the fall-out false positive rate"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "6544105e",
|
|
"metadata": {},
|
|
"source": [
|
|
"$$\n",
|
|
"{\\displaystyle \\mathrm {FPR} ={\\frac {\\mathrm {FP} }{\\mathrm {N} }}={\\frac {\\mathrm {FP} }{\\mathrm {FP} +\\mathrm {TN} }}=1-\\mathrm {TNR} }\n",
|
|
"$$"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "0b571e85",
|
|
"metadata": {},
|
|
"source": [
|
|
"The $FPR$ defines how many incorrect positive results occur among\n",
|
|
"all negative samples available during the test."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "17d6872a",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Positive and negative prediction values\n",
|
|
"\n",
|
|
"The positive and negative predictive values \n",
|
|
"are the proportions of positive and negative results in statistics and\n",
|
|
"diagnostic tests that are true positive and true negative results,\n",
|
|
"respectively.[1] The PPV and NPV describe the performance of a\n",
|
|
"diagnostic test or other statistical measure. A high result can be\n",
|
|
"interpreted as indicating the accuracy of such a statistic.\n",
|
|
"\n",
|
|
"**Precision or positive predictive value $PPV$.**"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "122f7e6c",
|
|
"metadata": {},
|
|
"source": [
|
|
"$$\n",
|
|
"{\\displaystyle \\mathrm {PPV} ={\\frac {\\mathrm {TP} }{\\mathrm {TP} +\\mathrm {FP} }}=1-\\mathrm {FDR} }\n",
|
|
"$$"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "f44a5e78",
|
|
"metadata": {},
|
|
"source": [
|
|
"**Negative predictive value $NPV$.**"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "86fa9670",
|
|
"metadata": {},
|
|
"source": [
|
|
"$$\n",
|
|
"{\\displaystyle \\mathrm {NPV} ={\\frac {\\mathrm {TN} }{\\mathrm {TN} +\\mathrm {FN} }}=1-\\mathrm {FOR} }\n",
|
|
"$$"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "a30e358d",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Other quantities\n",
|
|
"\n",
|
|
"**False discovery rate $FDR$.**"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "3f0de6de",
|
|
"metadata": {},
|
|
"source": [
|
|
"$$\n",
|
|
"{\\displaystyle \\mathrm {FDR} ={\\frac {\\mathrm {FP} }{\\mathrm {FP} +\\mathrm {TP} }}=1-\\mathrm {PPV} }\n",
|
|
"$$"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "0b4cc93f",
|
|
"metadata": {},
|
|
"source": [
|
|
"**False omission rate $FOR$.**"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "4866ec0a",
|
|
"metadata": {},
|
|
"source": [
|
|
"$$\n",
|
|
"{\\displaystyle \\mathrm {FOR} ={\\frac {\\mathrm {FN} }{\\mathrm {FN} +\\mathrm {TN} }}=1-\\mathrm {NPV} }\n",
|
|
"$$"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "9b1c8c45",
|
|
"metadata": {},
|
|
"source": [
|
|
"## $F_1$ score\n",
|
|
"\n",
|
|
"In statistical analysis of binary classification, the F-score or\n",
|
|
"F-measure is a measure of a test's accuracy. It is calculated from the\n",
|
|
"precision and recall of the test, where the precision is the number of\n",
|
|
"true positive results divided by the number of all positive results,\n",
|
|
"including those not identified correctly, and the recall is the number\n",
|
|
"of true positive results divided by the number of all samples that\n",
|
|
"should have been identified as positive. Precision is also known as\n",
|
|
"positive predictive value, and recall is also known as sensitivity in\n",
|
|
"diagnostic binary classification.\n",
|
|
"\n",
|
|
"The F1 score is the harmonic mean of the precision and recall. It thus\n",
|
|
"symmetrically represents both precision and recall in one metric. The\n",
|
|
"highest possible value of an F-score is 1.0, indicating perfect\n",
|
|
"precision and recall, and the lowest possible value is 0, if either\n",
|
|
"precision or recall are zero.\n",
|
|
"\n",
|
|
"It is defined as"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "0e651a1f",
|
|
"metadata": {},
|
|
"source": [
|
|
"$$\n",
|
|
"{\\displaystyle \\mathrm {F} _{1}=2\\times {\\frac {\\mathrm {PPV} \\times \\mathrm {TPR} }{\\mathrm {PPV} +\\mathrm {TPR} }}={\\frac {2\\mathrm {TP} }{2\\mathrm {TP} +\\mathrm {FP} +\\mathrm {FN} }}}\n",
|
|
"$$"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "d83efe41",
|
|
"metadata": {},
|
|
"source": [
|
|
"## ROC curve\n",
|
|
"\n",
|
|
"A receiver operating characteristic curve, or ROC curve, is a\n",
|
|
"graphical plot that illustrates the performance of a binary classifier\n",
|
|
"model at varying threshold values.\n",
|
|
"\n",
|
|
"The ROC curve is the plot of the true positive rate (TPR) against the false positive rate (FPR) at each threshold setting.\n",
|
|
"\n",
|
|
"To draw a ROC curve, only the true positive rate (TPR) and false\n",
|
|
"positive rate (FPR) are needed (as functions of some classifier\n",
|
|
"parameter). The TPR defines how many correct positive results occur\n",
|
|
"among all positive samples available during the test. FPR, on the\n",
|
|
"other hand, defines how many incorrect positive results occur among\n",
|
|
"all negative samples available during the test.\n",
|
|
"\n",
|
|
"See <https://en.wikipedia.org/wiki/Receiver_operating_characteristic> for more discussions."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "d088215b",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Cumulative gain curve\n",
|
|
"\n",
|
|
"The cumulative gain curve is a performance evaluation used typically for binary classification problems.\n",
|
|
"It plots the $TPR$ True Positive Rate or Sensitivity (which represents the \n",
|
|
"fraction of examples correctly classified\n",
|
|
"against Predictive Positive Rate, which represents \n",
|
|
"the fraction of positively predicted examples.\n",
|
|
"\n",
|
|
"The examples below show the confusion matrix (or error matrix), the ROC curve and the cumulative gain for the Wisconsin cancer data."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "5c0bcd1f",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Other measures in classification studies: Cancer Data again"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 1,
|
|
"id": "2a6f80df",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"(426, 30)\n",
|
|
"(143, 30)\n",
|
|
"[1. 0.86666667 1. 0.92857143 1. 0.85714286\n",
|
|
" 1. 0.92857143 0.92857143 1. ]\n",
|
|
"Test set accuracy with Logistic Regression: 0.94\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
|
|
"STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
|
|
"\n",
|
|
"Increase the number of iterations (max_iter) or scale the data as shown in:\n",
|
|
" https://scikit-learn.org/stable/modules/preprocessing.html\n",
|
|
"Please also refer to the documentation for alternative solver options:\n",
|
|
" https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
|
|
" n_iter_i = _check_optimize_result(\n",
|
|
"/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
|
|
"STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
|
|
"\n",
|
|
"Increase the number of iterations (max_iter) or scale the data as shown in:\n",
|
|
" https://scikit-learn.org/stable/modules/preprocessing.html\n",
|
|
"Please also refer to the documentation for alternative solver options:\n",
|
|
" https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
|
|
" n_iter_i = _check_optimize_result(\n",
|
|
"/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
|
|
"STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
|
|
"\n",
|
|
"Increase the number of iterations (max_iter) or scale the data as shown in:\n",
|
|
" https://scikit-learn.org/stable/modules/preprocessing.html\n",
|
|
"Please also refer to the documentation for alternative solver options:\n",
|
|
" https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
|
|
" n_iter_i = _check_optimize_result(\n",
|
|
"/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
|
|
"STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
|
|
"\n",
|
|
"Increase the number of iterations (max_iter) or scale the data as shown in:\n",
|
|
" https://scikit-learn.org/stable/modules/preprocessing.html\n",
|
|
"Please also refer to the documentation for alternative solver options:\n",
|
|
" https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
|
|
" n_iter_i = _check_optimize_result(\n",
|
|
"/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
|
|
"STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
|
|
"\n",
|
|
"Increase the number of iterations (max_iter) or scale the data as shown in:\n",
|
|
" https://scikit-learn.org/stable/modules/preprocessing.html\n",
|
|
"Please also refer to the documentation for alternative solver options:\n",
|
|
" https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
|
|
" n_iter_i = _check_optimize_result(\n",
|
|
"/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
|
|
"STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
|
|
"\n",
|
|
"Increase the number of iterations (max_iter) or scale the data as shown in:\n",
|
|
" https://scikit-learn.org/stable/modules/preprocessing.html\n",
|
|
"Please also refer to the documentation for alternative solver options:\n",
|
|
" https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
|
|
" n_iter_i = _check_optimize_result(\n",
|
|
"/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
|
|
"STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
|
|
"\n",
|
|
"Increase the number of iterations (max_iter) or scale the data as shown in:\n",
|
|
" https://scikit-learn.org/stable/modules/preprocessing.html\n",
|
|
"Please also refer to the documentation for alternative solver options:\n",
|
|
" https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
|
|
" n_iter_i = _check_optimize_result(\n",
|
|
"/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
|
|
"STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
|
|
"\n",
|
|
"Increase the number of iterations (max_iter) or scale the data as shown in:\n",
|
|
" https://scikit-learn.org/stable/modules/preprocessing.html\n",
|
|
"Please also refer to the documentation for alternative solver options:\n",
|
|
" https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
|
|
" n_iter_i = _check_optimize_result(\n",
|
|
"/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
|
|
"STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
|
|
"\n",
|
|
"Increase the number of iterations (max_iter) or scale the data as shown in:\n",
|
|
" https://scikit-learn.org/stable/modules/preprocessing.html\n",
|
|
"Please also refer to the documentation for alternative solver options:\n",
|
|
" https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
|
|
" n_iter_i = _check_optimize_result(\n",
|
|
"/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
|
|
"STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
|
|
"\n",
|
|
"Increase the number of iterations (max_iter) or scale the data as shown in:\n",
|
|
" https://scikit-learn.org/stable/modules/preprocessing.html\n",
|
|
"Please also refer to the documentation for alternative solver options:\n",
|
|
" https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
|
|
" n_iter_i = _check_optimize_result(\n",
|
|
"/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
|
|
"STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
|
|
"\n",
|
|
"Increase the number of iterations (max_iter) or scale the data as shown in:\n",
|
|
" https://scikit-learn.org/stable/modules/preprocessing.html\n",
|
|
"Please also refer to the documentation for alternative solver options:\n",
|
|
" https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
|
|
" n_iter_i = _check_optimize_result(\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": 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45s+fr3fffVcLFy5Uo0aN1KhRIz322GMaM2aM2rRpU2Q+AuCwspu3B2f686z4S/Xu3duQZDMr3jAM448//jDGjBljREVFGb6+vkbVqlWNRx55xDh16pRNv+JmVxuG7cxke2KZOHGiIck4duyYte3jjz82mjVrZgQEBBjVq1c3Ro0aZXz22Wc2M7QNw75Z8ZIuu/x5lvKxY8eMhIQEIzo62vD19TVCQkKMmJgYY/z48cbZs2et/Q4ePGj84x//MCpUqGBUrFjR+Mc//mFs3LjRrlnxhfbs2WMMHTrUqFOnjuHv728EBgYaDRs2NIYPH15kZvr8+fONpk2bGn5+fkZwcLDRvXt36yz9P38fypcvX2Q/hd/bS78/xf3cfv31V6Nz585GUFCQUblyZePxxx83Pv30U5vv+X/+8x/jgQceMGrXrm0EBgYawcHBRsuWLY3k5OQi+7j0yoT9+/cbvXv3NkJDQw1fX1+jXr16xksvvWTk5+db+xTOHn/ppZeKxCfJmDhxYpH2P7vce+9Sxc1st/c9d/LkSePee+81KlWqZFgsFuv390qxXzorfseOHUZgYGCR79H58+eNmJgYo2bNmkV+3wBHWQzDMK7i5wgAAOBCnGMHAMCDkNgBAPAgJHYAADwIiR0AAA9CYgcAwIOQ2AEA8CBufYOagoICHT58WBUrVnTJLU8BAK5lGIbOnDmjatWqycvLdbXm+fPnlZub6/B2/Pz8ijyr4e/GrRP74cOHFRkZWdZhAAAcdODAAV177bUu2fb58+cVWDFUyjvn8LaqVKmitLS0v3Vyd+vEXrFiRUmSX8N+snj7lXE0gGukr3+5rEMAXOZMVpbqREda/567Qm5urpR3Tv4N+0mO5Ir8XGXuWqTc3FwSu6sUDr9bvP1I7PBYQUFBZR0C4HJX5XSqT4BDucKwuMe0NLdO7AAA2M0iyZEPEG4ylYvEDgAwB4vXxcWR9d2Ae0QJAADsQsUOADAHi8XBoXj3GIsnsQMAzIGheAAA4G6o2AEA5sBQPAAAnsTBoXg3GeR2jygBAIBdqNgBAObAUDwAAB6EWfEAAMDdULEDAMyBoXgAADyISYbiSewAAHMwScXuHh8/AACAXajYAQDmwFA8AAAexGJxMLEzFA8AAK4yKnYAgDl4WS4ujqzvBkjsAABzMMk5dveIEgAA2IWKHQBgDia5jp3EDgAwB4biAQCAu6FiBwCYA0PxAAB4EJMMxZPYAQDmYJKK3T0+fgAAALtQsQMAzIGheAAAPAhD8QAAwN1QsQMATMLBoXg3qYVJ7AAAc2AoHgAAuBsqdgCAOVgsDs6Kd4+KncQOADAHk1zu5h5RAgAAu1CxAwDMwSST50jsAABzMMlQPIkdAGAOJqnY3ePjBwAAsAsVOwDAHBiKBwDAgzAUDwAA3A0VOwDAFCwWiywmqNhJ7AAAUzBLYmcoHgAAD0LFDgAwB8v/FkfWdwMkdgCAKTAUDwAA3A4VOwDAFMxSsZPYAQCmQGIHAMCDmCWxc44dAAAPQsUOADAHLncDAMBzMBQPAAAclpSUpOjoaAUEBCgmJkYbNmy4Yv/FixerWbNmKleunKpWraoBAwboxIkTdu+PxA4AMIWLT221OLCUfJ/Lli3TsGHDNH78eKWmpqpt27bq0qWL0tPTi+3/zTffqG/fvoqPj9fOnTv13nvvaevWrRo0aJDd+ySxAwBMwSJHkrpFllKcZJ8+fbri4+M1aNAgNWjQQDNnzlRkZKRmz55dbP/NmzerZs2aSkhIUHR0tG666SY9/PDD2rZtm937JLEDAFACWVlZNktOTk6x/XJzc7V9+3Z17tzZpr1z587auHFjseu0bt1aBw8e1OrVq2UYho4cOaL3339fXbt2tTs+EjsAwBQcG4b//4l3kZGRCg4Oti6JiYnF7u/48ePKz89XRESETXtERIQyMzOLXad169ZavHixevXqJT8/P1WpUkWVKlXSa6+9ZvdxktgBAOZgccIi6cCBAzp9+rR1GTt27JV3e8nJecMwLjs7f9euXUpISNCECRO0fft2ff7550pLS9OQIUPsPkwudwMAoASCgoIUFBT0l/3CwsLk7e1dpDo/evRokSq+UGJiotq0aaNRo0ZJkpo2bary5curbdu2mjJliqpWrfqX+6ViBwCYg6PD8CWcFu/n56eYmBilpKTYtKekpKh169bFrnPu3Dl5edmmZm9vb0kXK317ULEDAEzB0RvUlGbd4cOHq0+fPoqNjVVcXJzmzp2r9PR069D62LFjdejQIb399tuSpG7dumnw4MGaPXu2brvtNmVkZGjYsGFq2bKlqlWrZtc+SewAAFMoi8Teq1cvnThxQpMnT1ZGRoYaN26s1atXKyoqSpKUkZFhc017//79debMGb3++usaMWKEKlWqpA4dOmjatGn2x2nYW9v/DWVlZSk4OFj+TQbL4u1X1uEALnFq6+tlHQLgMllZWYoIDdbp06ftOm9d2n0EBwcr9MGF8vIrV+rtFOSe04nFA1waqzNQsQMAzIGHwAAA4DnKYii+LDArHgAAD0LFDgAwBbNU7CR2AIApmCWxMxQPAIAHoWIHAJiCWSp2EjsAwBxMcrkbQ/EAAHgQKnYAgCkwFA8AgAchsQMA4EHMktg5xw4AgAehYgcAmINJZsWT2AEApsBQPAAAcDskdpN76L622v3Jszq1eYa+XTxabZrXvmL/h3verNQPntbJTdP148pn1PvOlkX6BFcI1Iynemrvl1N1avMMpX7wtG67qaGrDgG4ojmzk1T/umhVqhCg1i1j9M03G67Yf8PXX6l1yxhVqhCgBnVrad6cNy/bd/myfynQ16L7/tHDyVHDFQordkcWd1DmiT0pKUnR0dEKCAhQTEyMNmy48i8dnOfezi300qh/aNpbX6jVAy9oY+oerXp9qCKrXFNs/8H33aTJj3fT1Dmr1eLeqZry5mrNfKqn7ri5sbWPr4+3Pn3zMUVVC9GDo95Ss7sn69Hnlujw0dNX67AAq/eWL9OoEcM05qnx2rw1Va1vaqsed3ZRenp6sf33paWpR7c71Pqmttq8NVWjx4zTiCcTtHLFB0X67t+/X2PHjFSbm9q6+jDgJBY5mNjd5CR7mSb2ZcuWadiwYRo/frxSU1PVtm1bdely+V86OFfCPzsoedUmJa/cpF/SjmjUyx/oYOYpDb6v+D9Uvbu21FsffKv3v/xe+w6d0HtfbNeiVZs0on8na59+PeJ0TVA59Rw+V5t+3Kv0jFPa+MNe/fTroat1WIDVrJnT1X9AvAbED1L9Bg308vSZujYyUvPmzC62/7y5byqyRg29PH2m6jdooAHxg9Sv/0DNnP6yTb/8/HwN6PugnpkwSdHRta7GoQB2K9PEPn36dMXHx2vQoEFq0KCBZs6cqcjISM2eXfwvHZzH18dbzRtEas2m3TbtazbvVqtm0cWu4+fro/O5F2za/si5oNjGUfLxufhW6npLE23ZkaaZT/XSvn8/r23vjdOogZ3l5eUen3ThOXJzc5X6/XZ17NTZpr3jrZ21edPGYtfZsnmTOt5q2//Wzrfp++3bdOHC/7/3n58yWWGVK6v/wHjnBw6XYSjexXJzc7V9+3Z17mz7S9S5c2dt3Fj8Lx2cJ+yaCvLx8dbRk2ds2o+cOKOI0KBi1/n3pt3q36O1mjeIlCS1aFhDfbu3kp+vj8IqVZAkRVcP1d23Npe3t0V3Pz5b0+Z/oSf6dNSYQbe59oCASxw/flz5+fkKD4+waY+IiNCRI5nFrnPkSKYiImz7h4dHKC8vT8ePH5ckbfz2WyUvfEtJb85zTeBwHYsTFjdQZpe7Ff7SXfpLFBERoczM4n/pcnJylJOTY/06KyvLpTGagWHYfm2xWGRc2vg/ifM+V0RokL5aNFIWi3T05Bm9+9EWjRjQSfn5BZIkLy8vHTt5Ro8+t1QFBYZSdx9Q1crBGta3oxLnfu7qwwGKuLTKMgzjipVXcf0L28+cOaOB/f+ppDfnKSwszPnBAk5Q5texl+SXLjExUZMmTboaYXm846fOKi8vXxGhFW3aw0MqFKniC53PuaAhkxbrsalLFRESpIzjpxX/jzbKOvuHjv+eLUnKPH5aF/LyVVDw/x8O/pOWqaqVg+Xr460LefmuOyjgT8LCwuTt7V2kOj969GiRKr5QRESVIoXFsWNH5ePjo9DQUO3auVP79+3TP3p0s75eUHDxQ22FAB/t2PmLatW+8pUlKDtcx+5ihb90l/4SHT16tEgVX2js2LE6ffq0dTlw4MDVCNUjXcjLV+ruA+rQqr5Ne4dW9bX5x7QrrpuXV6BDR39XQYGh+26L0Wcbdlqrmk0/7FXtyMo2vwDX1QhXxrHTJHVcVX5+fmreIkZr/51i0752TYpaxbUudp0bW8Vp7Rrb/mtSvlSLmFj5+vqqXv362pb6k7Zs+8G6dO12l25p115btv2gayMjXXY8cJxZzrGXWcXu5+enmJgYpaSk6O6777a2p6SkqHv37sWu4+/vL39//6sVoseb9e5avTWlr77fla4tO9IUf08bRVYJ0fz3L15yOPnxu1QtPFiDnnlHklSnRrhiG0dp68/7dE3Fckro00ENa1ezvi5J897boEfuv0WvjL5XSUu/Up0alTUqvrOSln5VJscIc0sYNlzx/fuoRUysbmwVp7fmz9WB9HQNemiIJOmZ8WN1+NAhvZX8tiRp8END9GbS6xo9crgGxg/Wls2blLzwLS16d6kkKSAgQI0aN7bZR6XgSpJUpB1/PxbLxcWR9d1BmQ7FDx8+XH369FFsbKzi4uI0d+5cpaena8iQIWUZlmm8/+X3Cgkur3EPdVGVsCDt/C1DPR5PUnrGKUlSlbAgRVYJsfb39rboiT4dVDcqQhfy8vX1tl/Vvv8rSs84ae1z8Mjv6jb0Db044h5tXT5Wh4/+rjeWrNcrySlF9g+42n09e+nkiRN6fupkZWZkqFGjxlr18WpFRUVJkjIzMnTgwP9fXlszOlqrPl6t0SOe1JzZb6hqtWp6ZcYs3X3PP8rqEIASsxiXmyl1lSQlJenFF19URkaGGjdurBkzZujmm2+2a92srCwFBwfLv8lgWbz9XBwpUDZObX29rEMAXCYrK0sRocE6ffq0goKKvyLHGfsIDg5Wrcffl5d/+VJvpyAnW3tfu9elsTpDmU+eGzp0qIYOHVrWYQAAPJ2DQ/Hucrlbmd9SFgAAOE+ZV+wAAFwNZrncjcQOADAFs8yKZygeAAAPQsUOADAFLy+LQw+kMtzkYVYkdgCAKTAUDwAA3A4VOwDAFJgVDwCABzHLUDyJHQBgCmap2DnHDgCAB6FiBwCYglkqdhI7AMAUzHKOnaF4AAA8CBU7AMAULHJwKN5NnttKYgcAmAJD8QAAwO1QsQMATIFZ8QAAeBCG4gEAgNuhYgcAmAJD8QAAeBCzDMWT2AEApmCWip1z7AAAeBAqdgCAOTg4FO8mN54jsQMAzIGheAAA4Hao2AEApsCseAAAPAhD8QAAwO1QsQMATIGheAAAPAhD8QAAwO1QsQMATMEsFTuJHQBgCpxjBwDAg5ilYuccOwAAHoSKHQBgCgzFAwDgQRiKBwAAboeKHQBgChY5OBTvtEhci4odAGAKXhaLw0tpJCUlKTo6WgEBAYqJidGGDRuu2D8nJ0fjx49XVFSU/P39Vbt2bS1YsMDu/VGxAwDgIsuWLdOwYcOUlJSkNm3aaM6cOerSpYt27dqlGjVqFLtOz549deTIEb311luqU6eOjh49qry8PLv3SWIHAJhCWcyKnz59uuLj4zVo0CBJ0syZM/XFF19o9uzZSkxMLNL/888/11dffaW9e/cqJCREklSzZs0S7ZOheACAKRTOindkKYnc3Fxt375dnTt3tmnv3LmzNm7cWOw6H330kWJjY/Xiiy+qevXqqlu3rkaOHKk//vjD7v1SsQMATMHLcnFxZH1JysrKsmn39/eXv79/kf7Hjx9Xfn6+IiIibNojIiKUmZlZ7D727t2rb775RgEBAVq5cqWOHz+uoUOH6uTJk3afZ6diBwCgBCIjIxUcHGxdihtS/7NLK33DMC5b/RcUFMhisWjx4sVq2bKl7rjjDk2fPl3Jycl2V+1U7AAAc7A4eJOZ/6164MABBQUFWZuLq9YlKSwsTN7e3kWq86NHjxap4gtVrVpV1atXV3BwsLWtQYMGMgxDBw8e1HXXXfeXYVKxAwBMoXDynCOLJAUFBdksl0vsfn5+iomJUUpKik17SkqKWrduXew6bdq00eHDh3X27Flr26+//iovLy9de+21dh0niR0AABcZPny45s+frwULFmj37t168sknlZ6eriFDhkiSxo4dq759+1r79+7dW6GhoRowYIB27dqlr7/+WqNGjdLAgQMVGBho1z4ZigcAmILlf/8cWb+kevXqpRMnTmjy5MnKyMhQ48aNtXr1akVFRUmSMjIylJ6ebu1foUIFpaSk6PHHH1dsbKxCQ0PVs2dPTZkyxe59ktgBAKbgrFnxJTV06FANHTq02NeSk5OLtNWvX7/I8H1JMBQPAIAHoWIHAJiCWR7baldinzVrlt0bTEhIKHUwAAC4SlncUrYs2JXYZ8yYYdfGLBYLiR0AgDJkV2JPS0tzdRwAALiUI49eLVzfHZR68lxubq5++eWXEj1KDgCAsuKsG9T83ZU4sZ87d07x8fEqV66cGjVqZL3+LiEhQS+88ILTAwQAwBmu9tPdykqJE/vYsWP1448/av369QoICLC233rrrVq2bJlTgwMAACVT4svdVq1apWXLlqlVq1Y2n14aNmyoPXv2ODU4AACchVnxl3Hs2DGFh4cXac/OznabYQoAgPkwee4ybrjhBn366afWrwuT+bx58xQXF+e8yAAAQImVuGJPTEzU7bffrl27dikvL0+vvvqqdu7cqU2bNumrr75yRYwAADjMIjnwCBjH1r2aSlyxt27dWt9++63OnTun2rVr68svv1RERIQ2bdqkmJgYV8QIAIDDzDIrvlT3im/SpIkWLVrk7FgAAICDSpXY8/PztXLlSu3evVsWi0UNGjRQ9+7d5ePDM2UAAH9PZfXY1qutxJn4559/Vvfu3ZWZmal69epJkn799VdVrlxZH330kZo0aeL0IAEAcJRZnu5W4nPsgwYNUqNGjXTw4EF9//33+v7773XgwAE1bdpUDz30kCtiBAAAdipxxf7jjz9q27Ztuuaaa6xt11xzjaZOnaobbrjBqcEBAOBMblJ0O6TEFXu9evV05MiRIu1Hjx5VnTp1nBIUAADOxqz4P8nKyrL+//nnn1dCQoKeffZZtWrVSpK0efNmTZ48WdOmTXNNlAAAOIjJc39SqVIlm08qhmGoZ8+e1jbDMCRJ3bp1U35+vgvCBAAA9rArsa9bt87VcQAA4FJmmRVvV2K/5ZZbXB0HAAAuZZZbypb6jjLnzp1Tenq6cnNzbdqbNm3qcFAAAKB0SvXY1gEDBuizzz4r9nXOsQMA/o54bOtlDBs2TKdOndLmzZsVGBiozz//XIsWLdJ1112njz76yBUxAgDgMIvF8cUdlLhiX7t2rT788EPdcMMN8vLyUlRUlDp16qSgoCAlJiaqa9eurogTAADYocQVe3Z2tsLDwyVJISEhOnbsmKSLT3z7/vvvnRsdAABOYpYb1JTqznO//PKLJOn666/XnDlzdOjQIb355puqWrWq0wMEAMAZGIq/jGHDhikjI0OSNHHiRN12221avHix/Pz8lJyc7Oz4AABACZQ4sT/44IPW/zdv3lz79u3Tf/7zH9WoUUNhYWFODQ4AAGcxy6z4Ul/HXqhcuXJq0aKFM2IBAMBlHB1Od5O8bl9iHz58uN0bnD59eqmDAQDAVbil7J+kpqbatTF3OWgAADyVRzwE5r8p0xQUFFTWYQAucU3bp8o6BMBljLycq7YvL5XiUrBL1ncHDp9jBwDAHZhlKN5dPoAAAAA7ULEDAEzBYpG8mBUPAIBn8HIwsTuy7tXEUDwAAB6kVIn9nXfeUZs2bVStWjXt379fkjRz5kx9+OGHTg0OAABn4SEwlzF79mwNHz5cd9xxh37//Xfl5+dLkipVqqSZM2c6Oz4AAJyicCjekcUdlDixv/baa5o3b57Gjx8vb29va3tsbKx++uknpwYHAABKpsST59LS0tS8efMi7f7+/srOznZKUAAAOJtZ7hVf4oo9OjpaP/zwQ5H2zz77TA0bNnRGTAAAOF3h090cWdxBiSv2UaNG6dFHH9X58+dlGIa+++47LV26VImJiZo/f74rYgQAwGHcUvYyBgwYoLy8PI0ePVrnzp1T7969Vb16db366qu6//77XREjAACwU6luUDN48GANHjxYx48fV0FBgcLDw50dFwAATmWWc+wO3XkuLCzMWXEAAOBSXnLsPLmX3COzlzixR0dHX/Ei/b179zoUEAAAKL0SJ/Zhw4bZfH3hwgWlpqbq888/16hRo5wVFwAATsVQ/GU88cQTxba/8cYb2rZtm8MBAQDgCjwEpoS6dOmiDz74wFmbAwAApeC0x7a+//77CgkJcdbmAABwqovPYy992e2xQ/HNmze3mTxnGIYyMzN17NgxJSUlOTU4AACchXPsl9GjRw+br728vFS5cmW1a9dO9evXd1ZcAACgFEqU2PPy8lSzZk3ddtttqlKliqtiAgDA6Zg8VwwfHx898sgjysnJcVU8AAC4hMUJ/9xBiWfF33jjjUpNTXVFLAAAuExhxe7I4g5KfI596NChGjFihA4ePKiYmBiVL1/e5vWmTZs6LTgAAFAydif2gQMHaubMmerVq5ckKSEhwfqaxWKRYRiyWCzKz893fpQAADjILOfY7U7sixYt0gsvvKC0tDRXxgMAgEtYLJYrPuvEnvXdgd2J3TAMSVJUVJTLggEAAI4p0Tl2d/m0AgDApRiKL0bdunX/MrmfPHnSoYAAAHAF7jxXjEmTJik4ONhVsQAAAAeVKLHff//9Cg8Pd1UsAAC4jJfF4tBDYBxZ92qyO7Fzfh0A4M7Mco7d7jvPFc6KBwAAf192V+wFBQWujAMAANdycPKcm9wqvuT3igcAwB15yeLwUhpJSUmKjo5WQECAYmJitGHDBrvW+/bbb+Xj46Prr7++RPsjsQMATKHwcjdHlpJatmyZhg0bpvHjxys1NVVt27ZVly5dlJ6efsX1Tp8+rb59+6pjx44l3ieJHQAAF5k+fbri4+M1aNAgNWjQQDNnzlRkZKRmz559xfUefvhh9e7dW3FxcSXeJ4kdAGAKznpsa1ZWls2Sk5NT7P5yc3O1fft2de7c2aa9c+fO2rhx42XjXLhwofbs2aOJEyeW7jhLtRYAAG6m8Dp2RxZJioyMVHBwsHVJTEwsdn/Hjx9Xfn6+IiIibNojIiKUmZlZ7Dr//e9/9dRTT2nx4sXy8Snxk9UlleJ57AAAmNmBAwcUFBRk/drf3/+K/S+9D0zhY84vlZ+fr969e2vSpEmqW7duqeMjsQMATMFZ94oPCgqySeyXExYWJm9v7yLV+dGjR4tU8ZJ05swZbdu2TampqXrsscckXbzU3DAM+fj46Msvv1SHDh3+cr8kdgCAKXjJwVvKlvByNz8/P8XExCglJUV33323tT0lJUXdu3cv0j8oKEg//fSTTVtSUpLWrl2r999/X9HR0Xbtl8QOAICLDB8+XH369FFsbKzi4uI0d+5cpaena8iQIZKksWPH6tChQ3r77bfl5eWlxo0b26wfHh6ugICAIu1XQmIHAJhCWTy2tVevXjpx4oQmT56sjIwMNW7cWKtXr1ZUVJQkKSMj4y+vaS9xnIYb3wQ+KytLwcHBSs88adf5DsAdVekwvqxDAFzGyMtRzraZOn36tMv+jhfmiqS1PyuwQsVSb+ePs2c0tENjl8bqDFzuBgCAB2EoHgBgChaLxaFHkLvL48tJ7AAAU7DIsQe0uUdaJ7EDAEziz3ePK+367oBz7AAAeBAqdgCAabhHze0YEjsAwBTK4jr2ssBQPAAAHoSKHQBgClzuBgCAB/GSY8PU7jLE7S5xAgAAO1CxAwBMgaF4AAA8iFnuPMdQPAAAHoSKHQBgCgzFAwDgQcwyK57EDgAwBbNU7O7yAQQAANiBih0AYApmmRVPYgcAmAIPgQEAAG6Hih0AYApessjLgQF1R9a9mkjsAABTYCgeAAC4HSp2AIApWP73z5H13QGJHQBgCgzFAwAAt0PFDgAwBYuDs+IZigcA4G/ELEPxJHYAgCmYJbFzjh0AAA9CxQ4AMAUudwMAwIN4WS4ujqzvDhiKBwDAg1CxAwBMgaF4AAA8CLPiAQCA26FiBwCYgkWODae7ScFOYgcAmAOz4gEAgNshsZvc/Dmz1bRBHUVcU163tG6pjd9uuGL/bzZ8pVtat1TENeXVrOF1WjBvjs3rXW/roErlfIosPe/u5srDAC7roXtaafcHo3Vq/XP6duFjatOs5hX7P/yPVkpdOlwn1z+nH/81Qr27tLB5vfstjfTNgseU8eVEHV87WZsXJeiB25u78AjgLBYn/HMHZZrYv/76a3Xr1k3VqlWTxWLRqlWryjIc01nx/nKNHT1cI0eP1debtimuzU26r8edOnAgvdj++/alqefd3RTX5iZ9vWmbRox6SmNGDtOHq1ZY+7y79H39svegddm07Ud5e3ur+z33Xq3DAqzu7dhULw27U9OS16lVv1na+OM+rZo+QJERwcX2H3z3jZr8yO2aOv/fatF7hqbMT9HMEd11x00NrH1OZv2hFxetU7vBSbqhz0y98+l2zR1/r2698bqrdVgopcJZ8Y4s7qBME3t2draaNWum119/vSzDMK03Zs1Qn34D1XdAvOrVb6AXXpqu6tdGasG8N4vtv3D+HF0bWUMvvDRd9eo3UN8B8fpn3wF6feYr1j7XhIQookoV67Ju7b9Vrlw59SCxowwkPHCTkj/epuSPt+qX/cc0auYnOnj0tAbf06rY/r27tNBbq7bo/TU7tO/wSb337x1a9MlWjfjnLdY+G1L36qOvduqX/ceUduik3lj+rX7ak6nWfzESgLJnccLiDso0sXfp0kVTpkzRPffcU5ZhmFJubq5+SP1e7Tt2smlv37GTtmzeVOw6323ZXKR/h1s7K/X77bpw4UKx67y7aKHuubeXypcv75zAATv5+nireb3qWvPdf23a12z5r1o1iSp2HT9fb53PzbNp+yPngmIbXisf7+L/XLaLra26NSrrm9Q05wQOOMitZsXn5OQoJyfH+nVWVlYZRuPeThw/rvz8fIVHhNu0h4eH6+iRI8Wuc/TIEYWHX9I/Ilx5eXk6cfy4qlStavPa9q3fadfOn/Va0lznBg/YIaxSOfn4eOvoyTM27UdOnVFESN1i1/n3lv+qf7cb9PFXu5T6yyG1qF9dfe+MlZ+vj8IqlVfmiYvbCirvrz0fjZO/n4/y8wv0xMsfau3W31x+THCMlyzycmA83ctNana3SuyJiYmaNGlSWYfhUSyXvMkNwyjS9lf9i2uXpHcWLVTDRo0Vc0NLJ0QKlM7/3qJWFllkyCi2b+LCNYoIraiv5g+VRdLRU2f17qfbNaJPO+UXFFj7nTmXqxv7zVKFQD+1j62jaQldlXbopDak7nXhkcBRjg6nu0dad7NZ8WPHjtXp06ety4EDB8o6JLcVGhYmb29vHcm0rc6PHTumypdU5YXCIyJ05JJq/tjRY/Lx8VFIaKhN+7lz57Ti/WXq03+gcwMH7HT893PKy8tXRGhFm/bwayro6Mmzxa5zPidPQ6a+r5B2z6j+PdN0XY8XtD/jlLKyz+v47+es/QzD0N6DJ7Tjvxl6dekGrVz3s0b1befKwwHs5laJ3d/fX0FBQTYLSsfPz0/XN2+h9Wv/bdO+fu2/dWOruGLXaXljqyL9161JUfMWMfL19bVpX/nBe8rJyVGv+x90buCAnS7k5Sv1l0PqcEMdm/YOLeto80/7r7huXn6BDh3LUkGBofs6NdNn3/7HOjpVHItF8vdzqwFQczLJ7DneiSb2aMKTeji+n65vEaOWN7ZS8oJ5OnggXQMGPSxJmjRhnA4fPqw585MlSQMGPax5byZp3JgR6jdgkL7bslnvLFqg+YsWF9n2u4sWqGu37kUqeeBqmrX0G701sae+/88hbflpv+J73KjIiEqav3KLJGnyI7epWuVgDZq8XJJUJzJMsQ2v1dadB3RNUKAS7m+rhrUiNGjye9ZtjuzbTt/vPqi9h07Kz9dbt8fV04NdWijhxVVlcYgoAZ7udhWcPXtWv/32/xNO0tLS9MMPPygkJEQ1atQow8jM4Z57e+rkiRN6MXGKjmRmqEHDxlq+8mPVqHFxxnBmZqYO/uma9po1o7V85ccaN3qk5s+ZrSpVq2nayzPVvYftVQ2//fdXbdr4rVZ+/NlVPR7gUu+v2aGQ4HIaN7CjqoRW1M69meoxIlnpmb9LkqqEBikyopK1v7eXRU/0vll1a4TpQl6Bvt6+R+0fmq30zFPWPuUD/PTqqB6qHh6sP3Iu6Nf9xzTw2WV6f82Oq3x0QPEsxpXGl1xs/fr1at++fZH2fv36KTk5+S/Xz8rKUnBwsNIzTzIsD49VpcP4sg4BcBkjL0c522bq9OnTLvs7Xpgr1vyQrgoVS7+Ps2ey1PH6Gi6N1RnKtGJv167dFc9bAQDgLMyKBwAAbofJcwAAczBJyU5iBwCYArPiAQDwII4+oY2nuwEAgKuOih0AYAomOcVOYgcAmIRJMjtD8QAAeBAqdgCAKTArHgAAD8KseAAA4Hao2AEApmCSuXMkdgCASZgkszMUDwCAB6FiBwCYArPiAQDwIGaZFU9iBwCYgklOsXOOHQAAV0pKSlJ0dLQCAgIUExOjDRs2XLbvihUr1KlTJ1WuXFlBQUGKi4vTF198UaL9kdgBAOZgccJSQsuWLdOwYcM0fvx4paamqm3bturSpYvS09OL7f/111+rU6dOWr16tbZv36727durW7duSk1Ntf8wDcMwSh7q30NWVpaCg4OVnnlSQUFBZR0O4BJVOowv6xAAlzHycpSzbaZOnz7tsr/jhbli8+7DqlCx9Ps4eyZLrRpUK1GsN954o1q0aKHZs2db2xo0aKAePXooMTHRrm00atRIvXr10oQJE+zqT8UOAEAJZGVl2Sw5OTnF9svNzdX27dvVuXNnm/bOnTtr48aNdu2roKBAZ86cUUhIiN3xkdgBAKZQOCvekUWSIiMjFRwcbF0uV3kfP35c+fn5ioiIsGmPiIhQZmamXTG/8sorys7OVs+ePe0+TmbFAwBMwVmz4g8cOGAzFO/v73/l9S65Ts4wjCJtxVm6dKmeffZZffjhhwoPD7c7ThI7AAAlEBQUZNc59rCwMHl7exepzo8ePVqkir/UsmXLFB8fr/fee0+33nprieJjKB4AYA5XeVa8n5+fYmJilJKSYtOekpKi1q1bX3a9pUuXqn///lqyZIm6du1asp2Kih0AYBJlcUvZ4cOHq0+fPoqNjVVcXJzmzp2r9PR0DRkyRJI0duxYHTp0SG+//baki0m9b9++evXVV9WqVStrtR8YGKjg4GC79kliBwDARXr16qUTJ05o8uTJysjIUOPGjbV69WpFRUVJkjIyMmyuaZ8zZ47y8vL06KOP6tFHH7W29+vXT8nJyXbtk8QOADCFsrpX/NChQzV06NBiX7s0Wa9fv750O/kTEjsAwBTMcq94EjsAwBxMktmZFQ8AgAehYgcAmEJZzIovCyR2AIA5ODh5zk3yOkPxAAB4Eip2AIApmGTuHIkdAGASJsnsDMUDAOBBqNgBAKbArHgAADxIWd1S9mpjKB4AAA9CxQ4AMAWTzJ0jsQMATMIkmZ3EDgAwBbNMnuMcOwAAHoSKHQBgChY5OCveaZG4FokdAGAKJjnFzlA8AACehIodAGAKZrlBDYkdAGAS5hiMZygeAAAPQsUOADAFhuIBAPAg5hiIZygeAACPQsUOADAFhuIBAPAgZrlXPIkdAGAOJjnJzjl2AAA8CBU7AMAUTFKwk9gBAOZglslzDMUDAOBBqNgBAKbArHgAADyJSU6yMxQPAIAHoWIHAJiCSQp2EjsAwByYFQ8AANwOFTsAwCQcmxXvLoPxJHYAgCkwFA8AANwOiR0AAA/CUDwAwBTMMhRPYgcAmIJZbinLUDwAAB6Eih0AYAoMxQMA4EHMcktZhuIBAPAgVOwAAHMwSclOYgcAmAKz4gEAgNuhYgcAmAKz4gEA8CAmOcVOYgcAmIRJMjvn2AEA8CBU7AAAUzDLrHgSOwDAFJg85wYMw5AknTmTVcaRAK5j5OWUdQiAyxj5F9/fhX/PXSkry7Fc4ej6V4tbJ/YzZ85IkhpdV7NsAwEAOOTMmTMKDg52ybb9/PxUpUoVXRcd6fC2qlSpIj8/PydE5ToW42p8THKRgoICHT58WBUrVpTFXcZI3FxWVpYiIyN14MABBQUFlXU4gFPx/r76DMPQmTNnVK1aNXl5uW4+9/nz55Wbm+vwdvz8/BQQEOCEiFzHrSt2Ly8vXXvttWUdhikFBQXxhw8ei/f31eWqSv3PAgIC/vYJ2Vm43A0AAA9CYgcAwIOQ2FEi/v7+mjhxovz9/cs6FMDpeH/DE7j15DkAAGCLih0AAA9CYgcAwIOQ2AEA8CAkdgAAPAiJHXZLSkpSdHS0AgICFBMTow0bNpR1SIBTfP311+rWrZuqVasmi8WiVatWlXVIQKmR2GGXZcuWadiwYRo/frxSU1PVtm1bdenSRenp6WUdGuCw7OxsNWvWTK+//npZhwI4jMvdYJcbb7xRLVq00OzZs61tDRo0UI8ePZSYmFiGkQHOZbFYtHLlSvXo0aOsQwFKhYodfyk3N1fbt29X586dbdo7d+6sjRs3llFUAIDikNjxl44fP678/HxFRETYtEdERCgzM7OMogIAFIfEDrtd+mhcwzB4XC4A/M2Q2PGXwsLC5O3tXaQ6P3r0aJEqHgBQtkjs+Et+fn6KiYlRSkqKTXtKSopat25dRlEBAIrjU9YBwD0MHz5cffr0UWxsrOLi4jR37lylp6dryJAhZR0a4LCzZ8/qt99+s36dlpamH374QSEhIapRo0YZRgaUHJe7wW5JSUl68cUXlZGRocaNG2vGjBm6+eabyzoswGHr169X+/bti7T369dPycnJVz8gwAEkdgAAPAjn2AEA8CAkdgAAPAiJHQAAD0JiBwDAg5DYAQDwICR2AAA8CIkdAAAPQmIHHPTss8/q+uuvt37dv3//MnmW9759+2SxWPTDDz9ctk/NmjU1c+ZMu7eZnJysSpUqORybxWLRqlWrHN4OgL9GYodH6t+/vywWiywWi3x9fVWrVi2NHDlS2dnZLt/3q6++avfdyuxJxgBQEtwrHh7r9ttv18KFC3XhwgVt2LBBgwYNUnZ2tmbPnl2k74ULF+Tr6+uU/QYHBztlOwBQGlTs8Fj+/v6qUqWKIiMj1bt3bz344IPW4eDC4fMFCxaoVq1a8vf3l2EYOn36tB566CGFh4crKChIHTp00I8//miz3RdeeEERERGqWLGi4uPjdf78eZvXLx2KLygo0LRp01SnTh35+/urRo0amjp1qiQpOjpaktS8eXNZLBa1a9fOut7ChQvVoEEDBQQEqH79+kpKSrLZz3fffafmzZsrICBAsbGxSk1NLfH3aPr06WrSpInKly+vyMhIDR06VGfPni3Sb9WqVapbt64CAgLUqVMnHThwwOb1jz/+WDExMQoICFCtWrU0adIk5eXllTgeAI4jscM0AgMDdeHCBevXv/32m5YvX64PPvjAOhTetWtXZWZmavXq1dq+fbtatGihjh076uTJk5Kk5cuXa+LEiZo6daq2bdumqlWrFkm4lxo7dqymTZumZ555Rrt27dKSJUusz7H/7rvvJEn//ve/lZGRoRUrVkiS5s2bp/Hjx2vq1KnavXu3nn/+eT3zzDNatGiRJCk7O1t33nmn6tWrp+3bt+vZZ5/VyJEjS/w98fLy0qxZs/Tzzz9r0aJFWrt2rUaPHm3T59y5c5o6daoWLVqkb7/9VllZWbr//vutr3/xxRf65z//qYSEBO3atUtz5sxRcnKy9cMLgKvMADxQv379jO7du1u/3rJlixEaGmr07NnTMAzDmDhxouHr62scPXrU2mfNmjVGUFCQcf78eZtt1a5d25gzZ45hGIYRFxdnDBkyxOb1G2+80WjWrFmx+87KyjL8/f2NefPmFRtnWlqaIclITU21aY+MjDSWLFli0/bcc88ZcXFxhmEYxpw5c4yQkBAjOzvb+vrs2bOL3dafRUVFGTNmzLjs68uXLzdCQ0OtXy9cuNCQZGzevNnatnv3bkOSsWXLFsMwDKNt27bG888/b7Odd955x6hatar1a0nGypUrL7tfAM7DOXZ4rE8++UQVKlRQXl6eLly4oO7du+u1116zvh4VFaXKlStbv96+fbvOnj2r0NBQm+388ccf2rNnjyRp9+7dRZ5BHxcXp3Xr1hUbw+7du5WTk6OOHTvaHfexY8d04MABxcfHa/Dgwdb2vLw86/n73bt3q1mzZipXrpxNHCW1bt06Pf/889q1a5eysrKUl5en8+fPKzs7W+XLl5ck+fj4KDY21rpO/fr1ValSJe3evVstW7bU9u3btXXrVpsKPT8/X+fPn9e5c+dsYgTgeiR2eKz27dtr9uzZ8vX1VbVq1YpMjitMXIUKCgpUtWpVrV+/vsi2SnvJV2BgYInXKSgokHRxOP7GG2+0ec3b21uSZDjhacv79+/XHXfcoSFDhui5555TSEiIvvnmG8XHx9ucspAuXq52qcK2goICTZo0Sffcc0+RPgEBAQ7HCaBkSOzwWOXLl1edOnXs7t+iRQtlZmbKx8dHNWvWLLZPgwYNtHnzZvXt29fatnnz5stu87rrrlNgYKDWrFmjQYMGFXndz89P0sUKt1BERISqV6+uvXv36sEHHyx2uw0bNtQ777yjP/74w/rh4UpxFGfbtm3Ky8vTK6+8Ii+vi9Ntli9fXqRfXl6etm3bppYtW0qSfvnlF/3++++qX7++pIvft19++aVE32sArkNiB/7n1ltvVVxcnHr06KFp06apXr16Onz4sFavXq0ePXooNjZWTzzxhPr166fY2FjddNNNWrx4sXbu3KlatWoVu82AgACNGTNGo0ePlp+fn9q0aaNjx45p586dio+PV3h4uAIDA/X555/r2muvVUBAgIKDg/Xss88qISFBQUFB6tKli3JycrRt2zadOnVKw4cPV+/evTV+/HjFx8fr6aef1r59+/Tyyy+X6Hhr166tvLw8vfbaa+rWrZu+/fZbvfnmm0X6+fr66vHHH9esWbPk6+urxx57TK1atbIm+gkTJujOO+9UZGSk7rvvPnl5eWnHjh366aefNGXKlJL/IAA4hFnxwP9YLBatXr1aN998swYOHKi6devq/vvv1759+6yz2Hv16qUJEyZozJgxiomJ0f79+/XII49ccbvPPPOMRowYoQkTJqhBgwbq1auXjh49Kuni+etZs2Zpzpw5qlatmrp37y5JGjRokObPn6/k5GQ1adJEt9xyi5KTk62Xx1WoUEEff/yxdu3apebNm2v8+PGaNm1aiY73+uuv1/Tp0zVt2jQ1btxYixcvVmJiYpF+5cqV05gxY9S7d2/FxcUpMDBQ//rXv6yv33bbbfrkk0+UkpKiG264Qa1atdL06dMVFRVVongAOIfFcMbJOgAA8LdAxQ4AgAchsQMA4EFI7AAAeBASOwAAHoTEDgCAByGxAwDgQUjsAAB4EBI7AAAehMQOAIAHIbEDAOBBSOwAAHgQEjsAAB7k/wBOlcO9LR7lIQAAAABJRU5ErkJggg==\n",
|
|
"text/plain": [
|
|
"<Figure size 640x480 with 2 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
|
"data": {
|
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"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 640x480 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
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"data": {
|
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"image/png": 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cvHlTFYuKisLOzg5PT88Mz3F0dEy3cSSAh4fHE88ReUev1+Pi4oKnp6cUQhrL7Wsx/rcr6BxcePRjraCTHYt7vIy9razikVZKSgp79+2lXt162Nlp92PaJvkBPoe+oEDkAXSKARQDjvevAv/9ctI7eRLR+AcM9gU0yzM3GQwGTvx1gmpVq2Fra6t1Olbj8LG/GDn2M0YN7U+b15sAEBsbB9+1zfFhLRZVCNWpU0e1wzLAli1bqFmzpvwSFcKMnbt1n40nI1WxnvX8qVlaurIZ0ev1XCsAVUu4a/ezzZACK3rChad0f2wdsQ9ZSdmStfMurzym1+s5e+M+5QIayO+ZPGA0Gvnqq68YN24cBoOB8ZO/442OXSlfvjzR0dG58p6a/in24MEDjh8/zvHjx4HU6fHHjx8nIiICSL2t1a1bN9Px/fv358qVK4wYMYLTp0+zaNEiFi5cyPvvv69F+kKITJq+/bzqllhBRzt61/PXLiHxbJs/fHoRBPDmTMjHRZDIW1FRUbRs2ZKPPvoIg8EAQGBgIK6urrn6vpp2hA4fPkzjxo1Njx+N5enevTtLliwhMjLSVBQB+Pv7s2nTJoYPH87MmTPx9fVl+vTptG/fPs9zFyI/MirwyYZT/PLXTeKTU3L0dR/Xs15p3F3kr2uz9ec8ODT/6cc0GQ9VO+ZNPiLf27lzJyEhIURGpnaOdTod48aN45NPPsn1W5KaFkKNGjXiacsYLVmyJF2sYcOGHD16NBezEsJ67YzU8dOVa7n6HgUc7ehVX7pBmrpzDnZ9Dfcj0z+nKBCxTx2zdYTW34Dz/9/KLFIRvMrnfp4i3zMYDHz++edMnDgRo9EIpM4GDw0NpUmTJnmSg0WNERJC5J6HySlsu577d8t71itNIReHXH8f8QQxl2BRC3iYhfEWb82ClzrkXk7CKt26dYuQkBC2b//vFmzTpk1Zvnx5uqVycpNM1xBCALDi4DUepOTuIqO1SnswoFHZXH0P8RQJ92BFcNaKoIYfShEkcoWiKPz9998A2NjY8Nlnn7F58+Y8LYJAOkJCCCAh2cCCPy6rYg3Ke/FRq8o59h4FHO0oUdhZVnTXiiEFVveAO/9m/pxqnaHRh7mWkrBuxYoVIzQ0lJ49e7J8+XIaNmyoSR5SCAkhCP3zCtHxyarYyOYVqezjplFGIlvO/Q6n1kNKUvrn4q7Dlb3qWNEXoGavjF/Lwx/KNgEpXEUOuX79Os7OzqrFjJs2bcq5c+dwcnLSLC8phISwIikGY7pYYoqRObsuqmKNKhYhwK9QHmUlcsSJMFjXL/PHuxaBkHAoVDL3chLi/23evJmuXbtSt25d1q9fr+oMa1kEgRRCQliF05FxDAs7zr+37mfq+PeayIwgi3JlH/w0OPPH2zpCp5VSBIlcp9fr+eSTT/jyyy8B2LBhA3PnzqV///4aZ/YfKYSEyOdSDEYGLD/C5eiHmTr+1fKeVC9ZOJezEjkm5iKEdQGjPnPH62zg7dng93Lu5iWs3tWrV+nUqRP79v23HEObNm3o2NG81p+SQkiIfG798RuZLoIABjeWWV1mLeo0nNoA+vjUx2c2QkKM+piKraFIhfTn2jpAuWZSBIlc9/PPP9OjRw9iYlK/Nu3s7Pjqq68YPny42U2YkEJIiHwsxWBkxvZzmTrW1kZHM18D1WVskPm6sg+WvQ0piU8+pnwLCF4GNrJBqMh7ycnJjBkzhqlTp5pipUqVIjw8nNq1zXM7FimEhMjHfsqgG/Rtx2oElCyU7tjCTjbs2b41jzITWfboFtjTiqCiL0KHhVIECU3ExcXRrFkzDh48aIq99dZbLFq0iMKFzfd2uxRCQuRTKQYjM3acV8WqlnCnXY3iGbam9fpMjjERee/RQohpb4E9zrUohISBY8E8S0uIxxUsWJDSpUtz8OBBHBwc+Oabbxg8eLDZ3QpLSwohIfKZRL0BRYFf/rrBpTvxqueGNS1v9j+URBoG/f8vhHhWHfcJgCKVUv/f1Qtq94dCfnmdnRAmOp2O+fPnExsby+eff05gYKDWKWWKFEJC5BPnox4wMPQIZ289yPD5qiXcaVyxaB5nJZ6LosCvH8DFHep40Rehxy/S/RGaunDhAlevXqVRo0ammJubG5s3b9YuqWyQvcaEyAeMRoXBK44+sQgCGPqadIMsjc3h+XB4kToot8CEGVi9ejU1atSgffv2XL16Vet0nosUQkLkA1tO3eTMzScvlliluBtNKks3yJJ4xx7HZuvH6qCdE3SWhRCFdhITExk4cCBBQUHExcURExPDRx99pHVaz0VujQlh4YxGhWm/P3mKvK+7E98FBUg3yNxFX4CoU6Ao6BLiqHl5FjolzZYob82GEjW1yU9YvbNnzxIUFMSJEydMsZCQEGbNmqVhVs9PCiEhLNyWU7fSdYO+ePslGpT3wtZGh4+7kxRB5u7QQtj0Pvx/4ZPhD+bGY6FKuzxNS4hHVqxYQb9+/XjwIPX2u5OTEzNmzKBXr14W//NFCiEhLJiiKEzfpu4GlStagOCX/bC1sewfTlbj7G+qIihDL3WEV0flXU5C/L+HDx8ydOhQFi5caIpVrlyZVatWUaVKFQ0zyzlSCAlhwbacusWpyDhVbGiT8lIEWYqbf8OaXk8vgvxqQ9sZYOF/dQvLoygKr7/+Onv27DHFunfvzsyZM3F1ddUws5wlg6WFsFAZdYPKFnGl9Us+GmUksuT+LVjZCZLTzPQrVArFsxz3nXwxVu0MncPA3kmbHIVV0+l0jBgxAgAXFxeWLFnCkiVL8lURBNIREsJi/X46in9uSDfIIukTICwEYtNMO34pCNrNIyUlhe2bNtGqVSts7O21yVEIUrfI+Oabb2jVqhWVK1fWOp1cIR0hISyQoihM+1290nCZIq60qeqrUUYi0xQF1g+E64fVcb/a0PYHuQUmNPP333/z8ccfoyiKKj5y5Mh8WwSBdISEsEjbMuoGvSbdIIuwczL8s1YdK1QKOq2QW2BCE4qisHDhQoYMGUJiYiL+/v707t1b67TyjHSEhLAwiqLwfZqxQWW8XHmjmnSDzN5fq2DXV+qYoxuEhKfuFyZEHrt//z7vvPMOffv2JTExEYCFCxdiND5lAH8+I4WQEBZm+5koTl6PVcWGNCkn3SBzF/En/DRIHdPZQMfFUDT/3nYQ5uv48eMEBgayYsUKU2zAgAFs374dGxvrKQ/k1pgQFiBRbyAuUQ+Qrhvk7+XKGzI2yLzdvZI6ONqQrI63/BrKNdUmJ2G1FEVhzpw5DB8+nKSkJCB1s9T58+cTFBSkcXZ5TwohIczcd1vPMnvXBZJTMm5VD25cDjtb6/nrzeIkxsGKYHh4Rx2v1Q9q9dUmJ2G1YmNj6du3L6tXrzbFAgMDCQ8Pp2zZshpmph356SmEGdt34Q7fbzv3xCKotKcLbwZIN8hsGVJgTU+4fVodL9cUWnyhTU7Cqn3wwQeqImjo0KHs3bvXaosgkEJICLP2tM1UAQa/Vl66Qebst4/g/O/qWJHK0GER2EpDXuS9SZMmUbx4cQoVKsS6dev4/vvvcXR01DotTcl3ohBmav+FaA5eisnwOQdbG4Jf9qNd9eJ5nJXItIPz4eBcdczFC0LCwMldm5yE1VEURbUpqpeXF+vXr8fLy4vSpUtrl5gZkUJICDOVdsFEH3cnNgyuj52NDmcHW5zsbTXKTDzT+d/h1w/UMVvH1LWCCpfWJCVhff7880+GDx/OunXr8Pb2NsVr1qypYVbmR3rqQpihAxej+TNNN2hg43IUKehIYVcHKYLMWdRpWN0TFIM6/uZMKFlbm5yEVVEUhW+//Zb69euzf/9+unbtalXrAmWVdISEMEPfpxkb5OPuRFDNEhplIzIt/g6sCIIk9arfNPwAqnbUJidhVaKjo+nRowe//PKLKRYfH09sbCyFCxfWMDPzJR0hIczMnxej2X8xWhUb2KgsjnbSBTJr+sTUtYLuRajjL7aDRmO0yUlYlb1791K9enVVEfThhx+yc+dOKYKeQjpCQpiZtAsmFnNzIuhlP42yEZmiKPDzULj6pzpePBDemiUbqYpcZTQa+frrr/n4448xGFJvyXp5ebFs2TJef/11jbMzf1IICWFGDl6KYd8FdTdogHSDzN+eb+CvcHXM3Q86rQR7Z21yElbh9u3bdOvWjc2bN5tir776KitWrKB4cZlVmhlya0wIM/L9NvVMMW83R4KlG2Te/l4L2yepYw4FoHMYFPTO+Bwhcsi2bdtMRZBOp2PcuHFs27ZNiqAskI6QEGbi8OUY9p5P0w1qWFZmiJmza0dg/QB1TGeTumBisSra5CSsSqdOndiyZQubNm1i+fLlNG0qe9dllRRCQpiJtGODihZ0pFOtkhplI57p3lVY2QlSEtXx5p9DhRba5CTyvfv371OwYEFVbMaMGcTFxVGsWDGNsrJscmtMCI0oisK1uw+5dCeeLf/cZM859aacAxpJN8hsJd1PLYLio9TxwJ7wyoCMzxHiOW3bto0KFSqwatUqVdzFxUWKoOcgHSEhNHAzNpEuCw5w4XZ8hs8XKehIZ+kGmY+7V2DfdLh/8/8fX4Zbf6uPKdMIWk2RGWIixxkMBj799FM+++wzFEWhT58+BAYGWvVGqTlJCiEhNDDup7+fWASBjA0yK/euwoKm6bs/j/OqAB1/BFv7vMtLWIUbN24QEhLCrl27TLE6deqkuz0msk9ujQmRx/6+HsvWU7ee+HyRgo6E1JZukFl40i2wxzl7QEg4OBfKs7SEdfjtt98ICAgwFUG2trZMnjyZX3/9laJFi2qcXf4hHSEh8tj0NIOiH1fGy5UpHatJN8gcGA3wvz7pb4E9zs4ZgpeDR5m8y0vkeykpKYwbN44vv/zSFCtRogQrV66kfv36GmaWP0khJEQe+udGLFvSdINGNqtA/0ap9/rtbaVJaza2jIOzm9Uxz/JQNTj1/+2doMLr4FU+73MT+db169cJDg5m7969pljr1q1ZsmQJXl5eGmaWf0khJEQeStsNKuRiT496paUAMjeHF8GBmeqYswd0WSXdH5GrdDodZ8+mLqxqZ2fHl19+yfDhw7GxkZ8RuUUKISFykaIoXIl+SFKKkRv3EvjtH3U3qE99fwo6yQBbs3JhB2x8Xx2zsYdOoVIEiVzn6+vLsmXL6N+/PytXruSVV17ROqV8TwohIXLJnQdJhMw/wNlbDzJ83t3Znu51S+dtUuLpbp+FVd1BMajjbX+AUnW1yUnka1euXMHd3Z1ChQqZYi1atODMmTM4Ojpql5gVkV6bELnk842nn1gEgXSDzE58NKwIgqRYdbzBSAjorE1OIl9bv349AQEB9O7dG0VRVM9JEZR3pBASIhdcvP2An45ff+Lz7s72dK9XOu8SEk+XkgTh78DdS+p45bbQ+GNtchL5VlJSEu+99x5vv/029+7dY+3atSxatEjrtKyW3BoTIhfM2H4eo5Lxc2WLuDLprZdwk26QeVAU+HkYROxTx30C4O25IINURQ66ePEiQUFBHDlyxBTr0KEDHTp00DAr6yaFkBA57NKdeNan6QYNaFSWD16vpFFG4qn2ToMTK9Qxt+LQOQwcXDRJSeRPa9asoXfv3sTFxQGpt7++++47+vfvj062ZtGMFEJC5LAftp9TdYNcHGzp20BmG5mlUxvg9wnqmL1rahHk5qNJSiL/SUxMZOTIkcyaNcsUK1++PKtWrSIgIEC7xAQghZAQOerynXh+On5DFetWpzQerg4aZSSe6MYxWPtumqAO2s8Hn6qapCTyn7t37/Laa69x/PhxU6xz587MnTtX9gszE3LzW4gcNGPHeQyPtYOc7W3p28Bfw4xEhuJuwMrOkJKgjjf7FCq11iYnkS8VKlSIcuXKAeDk5MS8efMIDQ2VIsiMSEdIiBxyJTqedcfUY4O61S2FZwGZBmtWkuNhRTDcj1THq3eFukO0yUnkWzqdjgULFvDw4UMmT55M1arSbTQ3UggJkUNmbE/fDXpXxgaZF6Mx9XbYzb/U8dINoPVUkAGr4jmdPn2aW7du0ahRI1PM3d2djRs3apeUeCq5NSZEDoiIfsjaNN2grnWkG2R2tk2AM7+oYx5lIWgp2Mk4LvF8li5dSs2aNenYsSPXrz95HTFhXqQQEiIHzNhxTtUNcrK34d1XpRtkVo4ug73fq2NOhSBkFbh4aJKSyB/i4+Pp2bMn3bt35+HDh9y5c4fx48drnZbIJLk1JsRzuhrzkLVH03SDXimFl3SDzMelPfDLMHXMxg6Cl4FXOU1SEvnD33//TVBQEKdPnzbF+vTpw/fff/+Us4Q5kY6QEM9p5o7zpKTrBpXVMCOhEn0BVnUFY4o63noq+L+qTU7C4imKwsKFC6lVq5apCCpQoAChoaHMnz8fFxdZjNNSSEdIiOdwNeYha45cU8XeqV2KIgWlG2QWEu6mbqSacFcdrzsEArtrk5OwePfv32fAgAGEhoaaYtWqVWPVqlVUqFBBw8xEdkghJMRzmLVT3Q1ytLPh3YYyNsgsGPSwqhtEn1fHK7aGphO1yUlYPEVRaNq0KQcPHjTFBgwYwNSpU3FyctIwM5Fdmt8amzVrFv7+/jg5OREYGMiePXueenxoaCjVqlXDxcUFHx8fevbsSXR0dB5lK8R/rt19yOrD6m5Ql9qlKFpQfhhqTlFg4wi4tFsdL/YStJsHNrba5CUsnk6nY9SoUQAULFiQ8PBwZs2aJUWQBdO0EAoPD2fYsGGMHTuWY8eO0aBBA1q2bElERESGx//xxx9069aN3r17888//7B69WoOHTpEnz598jhzIWDmjgvpukH9pRtkHvbPhKNL1bECxaBzODgW0CYnkW906NCBb7/9lmPHjhEUFKR1OuI5aVoITZ06ld69e9OnTx8qV67MtGnT8PPzY/bs2Rkef+DAAUqXLs3QoUPx9/enfv369OvXj8OHD+dx5sLaXb+XwJojV1WxkNolKeomfxVq7t9fYcvH6pidM3ReCe7FtclJWKyjR4+qxgI9MmLECMqWlUkR+YFmY4SSk5M5cuQIH374oSrevHlz9u3bl+E5devWZezYsWzatImWLVsSFRXFmjVraN36yXsDJSUlkZSUZHocFxcHgF6vR6/X58BHIp7Ho2tgaddixraz6A3/dYMc7GzoXbekxX0cj7PUa6Fy8yR2a3qjQ1GFU96chVL0JbCQjy1fXAsLpygKs2bN4oMPPiA5OZmmTZvSs2dPrdOyarn1/aBZIXTnzh0MBgPe3t6quLe3Nzdv3szwnLp16xIaGkpwcDCJiYmkpKTQtm1bfvjhhye+z+TJk5k4Mf3AyB07dsj0RjOydetWrVPItJgkWHXMFvhvO4ZXvFI48sd27ZLKQZZ0LR7nqL9Hw38nYK+PV8VP+XTk3EVbuLhJo8yyz1KvhaV78OABM2bM4MCBA6bYjBkzKFq0KDrZhkUzDx8+zJXX1XzWWNovKkVRnviFdurUKYYOHconn3xCixYtiIyMZNSoUfTv35+FCxdmeM6YMWMYMWKE6XFcXBx+fn40btwYT0/PnPtARLbo9Xq2bt1Ks2bNsLe31zqdTBn/8ykMyn+DpB3sbPii66t4W/htMUu8Fib6h9guexMbfYwqbHwpmPJvzKC8hf3ysuhrYeEOHjzIsGHDuHz5sinWtm1bfvzxR1xdXbVLTOTaxCjNCiEvLy9sbW3TdX+ioqLSdYkemTx5MvXq1TON2K9atSqurq40aNCASZMm4ePjk+4cR0dHHB3Tr+lib28vP2DMiKVcjxv3Elhz5IYq1vllP0p4FtQoo5xnKdfCxGiEdUMg8pg6XrIONm/+gI0F7yFmcdfCgimKwnfffccHH3xASkrq4puFCxdm4cKF2NjY4OrqKtdCY7n1+ddssLSDgwOBgYHpWr9bt26lbt26GZ7z8OFDbGzUKdvapk6DVRQlo1OEyFGzd14g2WA0PXawtWFAI9miQVM7v4BTP6ljhUtDcCjYycKW4tmio6Np27YtI0eONBVBdevW5fjx47Rp00bj7ERu03TW2IgRI1iwYAGLFi3i9OnTDB8+nIiICPr37w+k3tbq1q2b6fg33niDtWvXMnv2bC5evMjevXsZOnQotWrVwtfXV6sPQ1iJyNgEwg+pZ4p1quVHMXfLviVm0U6Ewe4p6pije+pGqq5y61tkzvvvv88vv/xievzBBx+wc+dOSpYsqWFWIq9oOkYoODiY6OhoPv30UyIjI6lSpQqbNm2iVKlSAERGRqrWFOrRowf3799nxowZjBw5kkKFCvHaa6/x1VdfafUhCCsyJ8NukEyf1cyV/bBhiDqms4WgJVCkoiYpCcv01Vdf8dtvv6HX61m2bBmvv/661imJPKT5YOmBAwcycODADJ9bsmRJutiQIUMYMmRI+oOFyEU3YxNZeVDdDQp+2Q8fd2eNMrJyMZcgvAsYktXxVlOg7Gva5CQsRtpJOUWLFuWnn37C19eX4sVlrSlro/kWG0JYgjm71N0ge1uddIO0khgLK4LhYZoZJLUHwMu9tclJWIzdu3fzyiuvcPv2bVX85ZdfliLISkkhJMQz3IpLZMVB9bYvQTX98C0k3aA8Z0iB1T3gzr/qePkW0OJzTVISlsFgMDBp0iQaN27MwYMH6d69O0aj8dkninxP81tjQpi72TsvkJyi7gYNbCwzxfKcosCvo+FCmoUri74IHRbKRqriiW7dusU777zD77//boolJSXx4MED3NzcNMxMmAPpCAnxFFFxiaxM0w3qWNOP4tINynsH58HhNAunuhaBkDBwzD/rOImctX37dqpVq2YqgmxsbJg4cSJbtmyRIkgA0hES4qnm7LpIUtpukIwNynvntsJm9b6E2DpCp5VQSKY4i/QMBgOffvopn332mWmdOR8fH1asWEGjRo20TU6YFSmEhHiCqLhEQv+8oop1CPSjRGHZoy5P3ToFq3uCkmY8x1uzwO9lbXISZu3GjRt06dKFnTt3mmLNmzdn2bJlFC1aVLvEhFmSW2NCPMHc3epukJ2NdIPy3IOo1BliyffV8UYfwUsdtMlJmL1t27aZiiBbW1u++OILfv31VymCRIakIyREBqLuZ9QNKoGfh3SD8ow+EcJCIFY9RosqHaDhaG1yEhbhnXfeYevWrWzfvp2wsDDq16+vdUrCjEkhJEQG5u26SKJe3Q0aJDPF8o6iwE+D4NohdbxELXhzJljYbvIid8XGxuLu7m56rNPpmDVrFomJiXh5eWmYmbAEcmtMiDRu309ieZpuUPsa0g3KU7u+hr/XqGPuJaFTKNjL3m7iP5s2baJs2bKsXbtWFS9QoIAUQSJTpBASIo35e9TdIFvpBuWtk2tSd5R/nENBCAmHAjLGQ6TS6/WMHj2a1q1bEx0dTa9evbh06ZLWaQkLJLfGhHjMnQdJLN1/WRVrX6M4JT2lG5Qnrh6C9Wn2HtTZQMfF4P2CNjkJs3PlyhU6derEgQMHTLGGDRuqbo8JkVnSERLiMfN3p+8GDW5cXsOMrMi9CAjrDIYkdfz1L6F8M21yEmbnp59+IiAgwFQE2dvbM23aNNavX4+Hh4fG2QlLJB0hIf5f9IMklu5Xjw16u7p0g/JEYlzqNPl49UaYvNwHar2rTU7CrCQnJzN69Gi+//57U8zf35/w8HBeflnWkxLZJ4WQEP9v3p6LJOgNpsep3SAZG5TrjAb4X2+IOqWOl30NXv9KZogJLl++TMeOHTl8+LAp1r59exYsWEChQoW0S0zkC3JrTAggJj6ZZWm6QW8FFKe0l6tGGVmR38bCuS3qWJFK0HEJ2MrfagLs7OxMA6EdHByYOXMmq1evliJI5AgphIQgdabYw+T/ukE2Ohj8mnSDct2hBfDnbHXMxRM6h4GTDHwVqUqUKMGPP/5IhQoVOHDgAAMHDkQnnUKRQ6QQElYvJj6ZH/ddVsXeql4cf+kG5a4L22FTmhWibR0gOBQ8/LXJSZiF8+fPExsbq4q1bt2av//+m+rVq2uUlcivpBASVm9BBt2gIa/JTLFcdftfWNUDFIM63vYHKFVHk5SEeQgLC6NGjRr07dvXtGv8I/b29hplJfIzKYSEVbubQTfozQDpBuWq+GhYEQRJ6r/4afA+VOukTU5CcwkJCfTr14/OnTtz//59Vq9ezbJly7ROS1gBGYkorNqCPy4SL2OD8k5KEoR3gbuX1fEX3oLGY7XISJiBM2fOEBQUxMmTJ02xrl270q5dOw2zEtZCOkLCat17mMyP+9QzxdpW86VskQIaZZTPKQr8/B5E7FfHfWvAW7PBRn4cWaNly5ZRs2ZNUxHk7OzMokWL+PHHHylQQL4XRe6TjpCwWgv/uMSDpBTTY50OBsvYoNzzx1Q4sVIdcysOnVeCgyxaaW3i4+MZPHgwS5YsMcVeeOEFVq9ezQsvyHYqIu9IISSs0r2HySzee1kVa1vNl3JF5S/QXHHqJ9j2qTpm75q6kWrBYtrkJDRz584dGjZsyKlT/y2i2atXL3744QdcXKQoFnlLetHCKi3KoBs0RMYG5Y7rR2FtvzRBHXRYCMVe0iQloS1PT08qVKgAgKurK8uWLWPhwoVSBAlNSEdIWJ3Yh/p03aA2VX0pV7SgNgnlZ7HXYGUnSElQx5tPgoottclJaE6n07Fo0SIMBgNTpkyhYsWKWqckrJgUQsLqLNx7iftpukFDpRuU85IepBZBD26p4zW6Q51B2uQkNPHXX39x9+5dGjZsaIoVLlyYDRs2aJiVEKnk1piwKrEJehbvvaSKtX7Jh/Le0g3KUUYDrO0LN0+q4/6vQutvZSNVK6EoCnPnzqVWrVp07NiRGzduaJ2SEOlIISSsyuK9l7ifmKYb1ERmiuW438fDv5vUMc9yELQUbGV1YGsQFxdH586d6d+/P0lJSdy+fZvPP/9c67SESEdujQmrEZugZ+Ef6m5Qq5d8qCDdoJx15EfY94M65lwYQlal/lfke0ePHiUoKIgLFy6YYoMHD2bKlCkaZiVExqQjJKzGkr2XVd0ggKGyblDOurQbNo5Qx2zsIXg5eJbVJieRZxRFYcaMGdSpU8dUBLm7u7NmzRp++OEHnJycNM5QiPSkIySsQlyinoV/XFTFWr1UjIrFpBuUY+6ch/CuYFQXm7wxDUrX1yQlkXfu3btH7969Wbt2rSn28ssvEx4ejr+/v4aZCfF0UggJq7Bk72Xi0naDZGxQznkYk7qRauI9dbzee1D9HU1SEnnHaDTSqFEjTpw4YYoNHz6cL7/8EgcHBw0zE+LZ5NaYyPfuJ6YfG9SySjEqFXPTKKN8JiUZVnWDmAvqeKU20GSCJimJvGVjY8OYMWOA1GnxP/30E1OnTpUiSFgE6QiJfO/HfZeJTdCrYtINyiGKAhuHw+U96nixqtBunmykakWCg4O5ceMG7du3p2TJklqnI0SmyU8pka/dT9Qzf4+6G/T6i8Wo7CPdoByx7wc4tlwdK+iTuoeYg6s2OYlct3//fsaOHZsuPnz4cCmChMWRjpDI15buvyLdoNxyZiNs/UQds3NO3U3ezVebnESuMhqNfPPNN3z00UcYDAYqV67MO+/IGDBh2aQjJPKtB0kpzN+jninW/AVvXvCVbtBzizwB/+sDKOp4u3ngW12TlETuunPnDm3atOGDDz7AYDAAsGLFChRFecaZQpg3KYREvvXjvsvceyjdoBwXFwkrOoH+oTredAK80FaTlETu2rNnDwEBAfz6669A6qapY8eOZcOGDehkuxRh4eTWmMiX4pNSWJCmG9TsBW+qFHfXKKN8Ivlh6kaq99PsGRXQBeoN0yQlkXuMRiOTJ0/mk08+wWg0AlCkSBFCQ0Np1qyZxtkJkTOkEBL50tL9V7ibphv0nnSDno/RCOv6QeRxdbxUPWgzTTZSzWdu3bpF165d2bp1qynWuHFjQkND8fHx0TAzIXKW3BoT+U58UgrzdqvXtGlaWbpBz23HJDi9QR0r7A9By8BO1ovJb0aMGGEqgnQ6HRMmTGDr1q1SBIl8RzpCIt9ZdkC6QTnu+ArY86065uSeupGqq6c2OYlcNXXqVLZv3w6kDopu3LixxhkJkTukEBL5ysPkFObtVo8NalKpKC+VkG5Qtl3ZBxuGqmM6WwhaCkUqaJOTyHGKoqgGPnt7e/Pzzz/j5+eHt7e3hpkJkbvk1pjIV5btv0JMfLIq9l5T6QZlW8xFCOsCRnWHjdbfQplGmqQkct7WrVt5+eWXiY6OVsVr1qwpRZDI96QQEvlGRt2g1yoVpWqJQtokZOkS7sGKYEiIUcdfGQQ1e2qSkshZKSkpfPzxx7Ro0YIjR47Qo0cPWRdIWB25NSbyjdADEUSn7QbJ2KDsMehhdQ+4c1Ydr/A6NP9Mk5REzrp27RohISHs2fPfPnEGg4GHDx/i6irbowjrIR0hkS8kJBuYm2amWOOKRajmV0ibhCyZosCvo+HiDnXcuwq0XwA2ttrkJXLMpk2bCAgIMBVBtra2fP311/zyyy9SBAmrIx0hkS+E/nmFOw/Sjg2SgbzZYXNoHhxepA66FoXOYeBYUJukRI7Q6/WMHTuWKVOmmGIlS5YkLCyMOnXqaJiZENqRQkhYvIRkA3N2qccGNaxQhADpBmWZd+xxbI5PUwftnFI3Ui3kp0lOImdERETQqVMn9u/fb4q1bduWxYsX4+HhoWFmQmhLbo0Ji5faDUpSxWSmWDZEnaLm5VnoFKM6/tZsKFFTm5xEjtm2bZupCLK3t+e7775j/fr1UgQJqycdIWHREvUG5qaZKfZqhSLUKFlYo4ws1P1b2IWHoDMmquONx0KVdtrkJHJUjx49+P3339m3bx+rVq3i5Zdf1jolIcyCFELCoq34M4Lb99N0g2SmWNboEyAsBF3cNXX8pSB4dZQ2OYnndvfuXQoX/u8PAp1Ox5w5czAYDBQqVEi7xIQwM3JrTFisRL2B2bvUM8UalPcisJR0gzJNUWD9QLh+WB33qw1tf5CNVC3U2rVrKVOmDBs2qPeGK1iwoBRBQqQhhZCwWCsPpu8GDZOxQVmz80v4Z60qpLiXhOBQsHfSKCmRXYmJiQwZMoT27dtz7949evTowZUrV7ROSwizJrfGhEVK1BuYk6YbVL+cF4GlZOBnpv21GnZ9qQrpbZwheAX2BYpolJTIrvPnzxMUFMSxY8dMsebNm6tujwkh0pOOkLBI4YeucitOZoplW8Sf8NMgVUjR2XDIfxAUqaRRUiK7wsPDqVGjhqkIcnR0ZO7cuaxcuRI3NzeNsxPCvElHSFicRL2BWTvPq2L1ynnycmnpBmXK3SsQFgIGdSFpbD6Z21E+GiUlsiMhIYHhw4czd+5cU6xixYqsWrWKqlWrapiZEJZDOkLC4qw6nEE3qImsIp0piXGpG6k+vKOO13oXY83e2uQksuX8+fO88sorqiLonXfe4fDhw1IECZEFUggJi5KUYmDWDvXYoLplPanlL92gZzKkwJpecPu0Ol62CbSYrE1OItscHBy4evUqAM7OzixcuJClS5dSoEABjTMTwrJIISQsyqpDV7kZp170T9YNyqTfPoLzW9WxIpWg42KwlbvklqZkyZL8+OOPvPDCCxw6dIhevXqhk+UOhMgyKYSExUhKMTBrp7ob9EoZD2qX8dQoIwtycD4cnKuOuXhBSDg4uWuTk8iS06dPc//+fVXsjTfe4MSJE7z44osaZSWE5ZNCSFiM1YevERmbthskY4Oe6fzv8OsH6pitA3RaAYVLa5KSyDxFUVi8eDGBgYH069cPRVFUz9vZSTdPiOeheSE0a9Ys/P39cXJyIjAwkD179jz1+KSkJMaOHUupUqVwdHSkbNmyLFq0KI+yFVpJHRuknilW29+DOmWlG/RUUWdgdU9QDOr4mzOhZG1tchKZ9uDBA7p3706vXr1ISEhg5cqVhIeHa52WEPmKpn9KhIeHM2zYMGbNmkW9evWYO3cuLVu25NSpU5QsWTLDc4KCgrh16xYLFy6kXLlyREVFkZKSkseZi7y25sg1bqTtBsm6QU8XfwdWBEFSnDr+6mioGqRNTiLTLl++zOjRozl79qwp1q9fP958800NsxIi/9G0EJo6dSq9e/emT58+AEybNo3ffvuN2bNnM3ly+lksmzdvZteuXVy8eBEPj9RZQqVLl87LlIUGklOM6WaK1fL3oI6MDXqylCQI6wL30myv8GI7aPyRNjmJTFEUhQULFjB69GiSk5OB1D3C5s2bR6dOnTTOToj8R7NCKDk5mSNHjvDhhx+q4s2bN2ffvn0ZnrNhwwZq1qzJ119/zbJly3B1daVt27Z89tlnODs7Z3hOUlISSUn/rTkTF5f617Fer0ev1+fQRyOy69E1eNq1CD90jev3ElSxwY38pRP4JIqC7YZB2Fw9oAobfWtgaP09POHzlplrIXJXXFwcAwcOZNWqVaZYQEAAoaGhlC9fXq6NBuT7wnzk1jXQrBC6c+cOBoMBb29vVdzb25ubN29meM7Fixf5448/cHJyYt26ddy5c4eBAwcSExPzxHFCkydPZuLEieniO3bswMXF5fk/EJEjtm7dmmE8xQjfHbcF/psWXKagQszpP9l0Jo+SszAVbm6gcuQaVeyhvQe7PXqQtHXHM89/0rUQuevevXuMGTOGyMhIU6xVq1b06NGDc+fOce7cOQ2zE/J9ob2HDx/myutqPt0g7boXiqI8cS0Mo9GITqcjNDQUd/fUKb9Tp06lQ4cOzJw5M8Ou0JgxYxgxYoTpcVxcHH5+fjRu3BhPT7m1ojW9Xs/WrVtp1qwZ9vb26Z5fdfgaMUmnVLHx7WtSVwZJZ0h3+ifsjqmLIMXBFftu62ji/fQp1s+6FiJ3KYrC6tWr2bhxI+7u7vTr14/x48fLtdCYfF+Yj+jo6Fx5Xc0KIS8vL2xtbdN1f6KiotJ1iR7x8fGhePHipiIIoHLlyiiKwrVr1yhfPv3gWUdHRxwdHdPF7e3t5YvajGR0PfQGI7N3X1LFapYqzKsVvWXhuIxcOwIb1BuporNB134R9iUCMv0y8r2hnaVLl9KnTx8mT57MmTNn5FqYEbkW2sutz79m0+cdHBwIDAxM127cunUrdevWzfCcevXqcePGDR48eGCKnT17FhsbG0qUKJGr+Yq8t/boNa7dVY8Neq9peSmCMnLvKqzsBCnqmXU0/xwqvq5NTuKpDh06xO7du1UxDw8P1q5dS5kyZTTKSgjro+k6QiNGjGDBggUsWrSI06dPM3z4cCIiIujfvz+QelurW7dupuNDQkLw9PSkZ8+enDp1it27dzNq1Ch69er1xMHSwjLpDUZmpFk3KLBUYeqX89IoIzOWdD+1CIqPUscDe8IrA7TJSTyRoihMmzaNevXqERQU9MQxkUKIvKFpIRQcHMy0adP49NNPCQgIYPfu3WzatIlSpUoBEBkZSUREhOn4AgUKsHXrVu7du0fNmjXp0qULb7zxBtOnT9fqQxC5ZN3R61yNSdMNaiLdoHSMBvhfX7j1tzru3xBaTQH5fJmVmJgY3nrrLYYPH45er+fWrVt8/fXXWqclhFXTfLD0wIEDGThwYIbPLVmyJF2sUqVKMno/n8uoG1S9ZCEalJduUDpbP4Gzv6pjnuUh6EewlfEM5mT//v106tRJ9cfdqFGj+PzzzzXMSgih+RYbQqS1/th1ImLU0ySHNa0g3aC0jiyB/TPUMWcP6LIKnAtrkpJIz2g0MmXKFF599VVTEeTp6ckvv/zC119/LQNwhdCY5h0hIR6XkkE3KMCvEK9KN0jt4k7YOFIds7GH4OXgIQNtzcWdO3fo3r07mzZtMsXq16/PypUrZYKHEGZCOkLCrKw/foMr0epukMwUS+POOVjVDYxpVoh+43soXU+bnEQ6KSkpNGjQwFQE6XQ6PvroI3bs2CFFkBBmRAohYTZSDEZmbFevnlvNrxCNKhTRKCMz9DAmdSPVxFh1vP5wqN5Fm5xEhuzs7Pj4448BKFKkCJs3b+bzzz/Hzk4a8UKYE/mOFGbjp+M3uJymGzRMZor9JyUZwrtCzEV1vPIb8Non2uQknqpLly7cvn2b4OBgfHx8tE5HCJEB6QgJs5DR2KCqJdxpVFG6QQAoCvwyHK78oY77BMDbc8FGvpW1tmPHDsaOHZsuPmzYMCmChDBj0hESZmHjyZtcuhOvism6QY/Z+z0cX66OFfSFzmHg4KpNTgIAg8HAZ599xqeffoqiKLz00kt06tRJ67SEEJkkf0YKzRkVmLlTfbvnpeLuvFapqEYZmZnTP8PvE9QxexfovBLcpNOgpcjISJo1a8bEiRNRFAWA1atXa5yVECIrpBASmjt6R8eltDPFpBuU6sZxWPsuoDwW1EG7+eAboE1OAkjdFzEgIIAdO3YAYGNjw6RJk6QQEsLCyK0xoSmDUeG3a+p6vEpxN5pUlm4QcTdS9xDTq4tEmk6Aym00SUmkToufMGECX3zxhakL5Ovry8qVK3n11Vc1zk4IkVVSCAlNbTx5k6hEdefnvSayijTJ8alF0P1Idbz6O1DvPW1yEly7do2QkBD27Nljir3++ussXbqUIkVkYL8QlkhujQnNGIxKurFBL/q60dTau0FGY+rtsMgT6nip+tD6O9lIVUPDhw83FUG2trZ89dVXbNy4UYogISyYdISEZjaejORimpliQ2VsEGz/FM78oo55lIHgZWDnoE1OAoDp06eze/duHB0dCQsLo27dulqnJIR4TlIICU0YjArTt6lXkX7Bx43mL3hrlJGZOLYc/vhOHXNyh5BV4OKhTU5WzGg0YvPYGk0+Pj5s3LiRMmXK4OEh10OI/EBujQlNbDoZyfmoB6qY1XeDLv8BPw9Tx2zsIGgZeJXXJCVrtmHDBmrWrElMTIwqXrNmTSmChMhHpBASec6YQTeokncB6+4GRV+A8HfAqFfHW0+FMg21yclKJScnM2LECN58802OHTtGr169TLPDhBD5j9waE3lu09+RnEvTDRrcuCw2NlbaDUq4CyuCU//7uDqDIbC7NjlZqUuXLhEcHMyhQ4dMMRsbGxITE3F2dtYwMyFEbslWIRQfH8+XX37Jtm3biIqKwmg0qp6/ePHiE84U1i6jbpCPi0Iza50pZtDDqu4Qrf6cUKElNPtUm5ys1Nq1a+nVqxexsbEAODg48O233zJo0CDrvmUrRD6XrUKoT58+7Nq1i65du+Lj4yM/JESmbf7nJmdvqbtBr5cwWmc3SFFg0/twaZc67v0StF8ANrba5GVlkpKSeP/995kxY4YpVrZsWcLDwwkMDNQwMyFEXshWIfTrr7+yceNG6tWrl9P5iHzMaFT4/nd156NC0QJU9binTUJaOzALjixRxwp4Q0gYOBbQJCVrc/78eYKDgzl69KgpFhQUxPz583Fzc9MwMyFEXsnWYOnChQvLrAmRZb/9c5N/b91XxQY3LoM1NoP491f4baw6ZueUupGqewltcrJC27dvNxVBjo6OzJkzh7CwMCmChLAi2SqEPvvsMz755BMePnz47IOF4P+7QWnGBlXwLkALa5wpdvMkrOmNeiNV4O05UFxuxeSlvn37EhQURIUKFfjzzz/p16+f3OoXwspk69bYt99+y4ULF/D29qZ06dLY29urnn+8zSwEwJZTNzlzU90NGtqkvPWNDbp/E1Z0Ar16RW1e+xhefFubnKxIdHQ0np6epsc6nY758+ej0+koWLCghpkJIbSSrULorbfeyuE0RH6W2g06r4qVL1qAVlV8MBhSNMpKA/oEWNkZ4q6p41U7QYP3tcnJiixfvpwBAwawcuVK2rRpY4rLbTAhrFu2CqHx48fndB4iH9t6+hanI+NUsSH/3w0yGDRKKq8ZjbCuP9xI0y31ewXaTpeNVHPRw4cPGTJkCIsWLQKge/fuHD9+HD8/P40zE0KYA1lQUeQqRUk/U6xc0QK0fslHo4w0snMynFqvjhUqBZ1Cwc5Rk5SswalTp+jYsSOnTp0yxdq2bSuTPYQQJpkuhDw8PDh79ixeXl4ULlz4qQMK0+7NI6zX1lO3OJW2G/RaOWytaWzQX6tg99fqmKNb6kaqrl7a5GQFlixZwsCBA0lISADAxcWF2bNn061bN40zE0KYk0wXQt99951pMOG0adNyKx+RjyhK+pliZYu40qaqr0YZaSDiAPw0SB3T2ULHJVC0kiYp5XcPHjxg0KBBLF261BSrUqUKq1evplIl+ZwLIdQyXQh17949w/8X4kl+Px3FPzfU3aChTcpbTzco5hKEhYAhWR1v9TWUa6JNTvnc6dOnadeuHWfOnDHF+vbty/fffy97hQkhMvTcY4QSEhLQ69U7ZsssDJHaDTqripWxpm5QYiys7AQPo9Xx2v3h5T7a5GQFnJ2diYyMBKBAgQLMmzePzp07a5yVEMKcZWtBxfj4eAYPHkzRokUpUKAAhQsXVv0TYvuZKP6+bqVjgwwpsLon3D6jjpdrBs0/1yYnK1G6dGkWL15M9erVOXr0qBRBQohnylYhNHr0aLZv386sWbNwdHRkwYIFTJw4EV9fX9V9eWGdFEVhWpqZYmW8XHnDWrpBmz+EC9vUsSKVocMisJWJmjnpxIkT3L+vXqjz7bff5uDBg5QvX16jrIQQliRbhdDPP//MrFmz6NChA3Z2djRo0ICPP/6YL774gtDQ0JzOUViYHf9GcfJ6rCo2+LVy2Nlm68vNsvw5Dw7NV8dci0BIODjJLeOcoigKs2bNolatWgwYMABFUW9XYmcnBacQInOy9ZspJiYGf39/IHU80KPp8vXr12f37t05l52wOBmtG1Ta04W21aygG3Tud9j8gTpm6widVkDhUtrklA/FxsYSFBTEoEGDSE5OJjQ0lLVr12qdlhDCQmWrECpTpgyXL18G4IUXXmDVqlVAaqeoUKFCOZWbsEA7/73NiWvqbtCQ18rn/27QrVOwugcoRnX8rVngV0uTlPKjQ4cOUb16ddasWWOKvffee6otM4QQIiuy9dupZ8+enDhxAoAxY8aYxgoNHz6cUaNG5WiCwnIoisK0bem7QW8G5PNu0IPbsDIYktVjVWj4IbzUQZuc8hlFUfj++++pV68ely5dAqBQoUKsW7eOadOm4egoq3MLIbInWzfShw8fbvr/xo0bc+bMGQ4fPkzZsmWpVq1ajiUnLMuus7c5cfWeKjaocT4fG6RPTF0r6F6EOl6lPTT6UJuc8pm7d+/Sq1cv1q9fb4rVrl2b8PBwSpWSW45CiOeTpUIoISGBbdu2mdrQY8aMISkpyfT8gQMHqFixIk5OTjmbpTB7Gc0UK+XpwtvVi2uUUR5QFNgwGK4dVMdLvAxvzpKNVHPAtWvXqF+/PleuXDHF3n//fb744gvs7e01zEwIkV9kqRBaunQpv/zyi6kQmjFjBi+++KJpxdYzZ87g4+Oj6hgJ67D73B2OW1s3aPcUOLlaHXP3Sx0cbS9/DOQEX19fXnjhBa5cuYKHhwdLly6ldevWWqclhMhHsvRbKjQ0lF69eqliK1asYMeOHezYsYMpU6aYBk4L65E6U0y9irSfh3P+7gb9/T/YkWZxRIcCqdPkCxTVJqd8yMbGhqVLl9KxY0eOHz8uRZAQIsdlqRA6e/YsFSpUMD12cnLCxua/l6hVqxanTp3KueyERdhz7g5HI+6pYkMal8c+v3aDrh2G9QPVMZ1N6oKJ3i9qk1M+8ccff6RbgsPLy4tVq1bh5+enUVZCiPwsS7+pYmNjVQuV3b59m9KlS5seG41G1Zghkf9ltMO8n4czb9fIp92gexGpe4ilJKrjLb6ACi20ySkfMBqNTJ48mUaNGhEcHExUVJTWKQkhrESWCqESJUrw999/P/H5v/76ixIlSjx3UsJy7D0fzZErd1WxQY3K5c9uUNJ9WNEJ4m+r4zV7p26mKrIlKiqKli1b8tFHH2EwGLh58ybfffed1mkJIaxEln5btWrVik8++YTExMR0zyUkJDBx4kS5h29FUmeKqccGlSjsTLsa+bAYNhpgTW+I+kcdL9MYWn4lM8SyaefOnQQEBLBlyxYAdDodn3zyCZ999pnGmQkhrEWWZo199NFHrFq1iooVKzJ48GAqVKiATqfjzJkzzJgxg5SUFD766KPcylWYmX0XojmcthvUuBwOdvmwG7RlHJz7TR3zqgAdl4CtTOPOKoPBwKRJk/j0008xGlNX4/b29iY0NJQmTZponJ0QwppkqRDy9vZm3759DBgwgA8//NC00aFOp6NZs2bMmjULb2/vXElUmJeM9hQrXsiZ9vmxG3R4ERyYqY45e6TOEHMupElKluzmzZt06dKF7du3m2JNmjRh+fLlFCtWTMPMhBDWKMsrS/v7+7N582ZiYmI4f/48AOXKlcPDwyPHkxPma/+FaA5ejlHF8mU36MIO2Pi+OmbrkLpWkEcZbXKyYHq9nnr16nHx4kUgdXr8xIkTGTNmDLa2thpnJ4SwRtnaYgPAw8ODWrVkM0lrlXZPseKFnOkQmM+6QbfPwqruoBjU8TemQ6k62uRk4ezt7ZkwYQLdunXD19eXFStW0LBhQ63TEkJYsWwXQsJ67b8QzcFL6m7QgEZl81c3KD4aVgRBUqw63mAkBHTWJqd8omvXrty9e5fOnTtTpEgRrdMRQli5fPSbS+SVtDPFfN2d6FgzH3WDUpIg/B24e0kdr9wWGn+sTU4WavPmzYwdOzZdfOjQoVIECSHMgnSERJYcuBjNn2m7QY3L4WiXT8Z3KAr8PAwi9qnjvtXh7blgI387ZIZer2fcuHF89dVXAAQEBNCxY0eNsxJCiPTkp7rIkrQzxXzcnQjKT92gP76DEyvUMbfi0DkMHFy0ycnCRERE0KhRI1MRBPDTTz9pmJEQQjyZFEIi0/68GM3+i9Gq2MBGZfNPN+jUBtg2UR2zd00tggrKtO7M+Pnnn6levTr79qV21Ozs7Pj2229ZtmyZxpkJIUTG5NaYyLS0e4oVc3Mi6OV8shHmjWOw9t00QR20nw8+VTVJyZIkJyczZswYpk6daoqVKlWK8PBwateurWFmQgjxdFIIiUw5eCmGfRfU3aAB+aUbFHs9dQ+xlAR1vNmnUEm2jHmWS5cu0alTJw4ePGiKvfXWWyxatIjChQtrmJkQQjyb3BoTmfL9NvVMMW83R4LzQzco6QGsDIYHN9XxGt2g7hBtcrIww4cPNxVBDg4OTJ8+nbVr10oRJISwCNIREs90+HIMe8+n6QY1LIuTvYV3g4zG1NthN0+q46UbQKtvZSPVTJo5cyZ79+7Fzc2NVatWERgYqHVKQgiRaVIIiWdKOzaoaEFHOtUqqVE2OWjbBPh3ozrmURaCloKdgyYpWQKj0YjNY8sIFC9enF9//ZXy5cvj7u6uYWZCCJF1cmtMPNWRKzHsOXdHFRvQKB90g44ug73fq2NOhSBkFbjIvnlPsmrVKmrUqMG9e/dU8Zo1a0oRJISwSFIIiaealmbdoCIFHels6d2gS3vgl2HqmI0dBC8Dr3KapGTuEhIS6N+/P8HBwZw4cYLevXujKIrWaQkhxHOTW2PiiY5cuZuuG9Tf0scGRV9I3T7DmKKOt/kO/F/VJicz9++//xIUFMRff/1lijk7O5OcnIyjo6OGmQkhxPOTjpB4orRjg4oUdKRLbQvuBj2MSd1INfGeOl53aOosMZFOaGgogYGBpiLI2dmZhQsXsmzZMimChBD5gnSERIaORdxl99nbqli/V8tYbjfIoIdV3SD6vDpesTU0naBJSubs4cOHDB06lIULF5pilStXZtWqVVSpUkXDzIQQImdp3hGaNWsW/v7+ODk5ERgYyJ49ezJ13t69e7GzsyMgICB3E7RSabtBXgUc6VK7lEbZPCdFgY0j4HKar61iL0G7eWBjocVdLjl16hS1atVSFUHdu3fn0KFDUgQJIfIdTQuh8PBwhg0bxtixYzl27BgNGjSgZcuWREREPPW82NhYunXrRpMmTfIoU+ty/Oo9dv6r7gb1b1gGZwcLLRj2z4CjS9WxAsWgczg4FtAmJzO2Z88e/vnnHwBcXFxYsmQJS5YswdXVVePMhBAi52laCE2dOpXevXvTp08fKleuzLRp0/Dz82P27NlPPa9fv36EhIRQp06dPMrUunz/u3oVaa8CDpbbDTqzCbaMU8fsnKHzSnAvrk1OZu7dd9+lQ4cOVKlShcOHD9O9e3etUxJCiFyj2Rih5ORkjhw5wocffqiKN2/e3LRzdUYWL17MhQsXWL58OZMmTXrm+yQlJZGUlGR6HBcXB4Ber0ev12cz+/zrr2ux7EjTDepdrzR2OiN6vTHH3+/RNciVa3HzJHb/64MO9TTvlDdnoRR9CeT6AxAVFUXRokVN1yAlJYU5c+ZgZ2eHi4uLfJ9oIFe/L0SWyLUwH7l1DTQrhO7cuYPBYMDb21sV9/b25ubNmxmec+7cOT788EP27NmDnV3mUp88eTITJ05MF9+xYwcuLi5ZTzyfm3fGhscbhQXsFDzvnmLTplO5+r5bt27N0ddz1N+j4b8TsNfHq+KnfDpy7qItXNyUo+9niRRFYevWrSxcuJDRo0ebtsbI6Wshsk+uhfmQa6G9hw8f5srraj5rTJdmPydFUdLFAAwGAyEhIUycOJEKFSpk+vXHjBnDiBEjTI/j4uLw8/OjcePGeHp6Zj/xfOjk9Vj+2f+nKjawSQXeru+fa++p1+vZunUrzZo1w97ePode9CG2y9pio49RhY1VO1G+zQ+Ulz3EiIuLY+DAgaxatQqA2bNns3//fv7555+cvRYiW3Ll+0Jki1wL8xEdHf3sg7JBs0LIy8sLW1vbdN2fqKiodF0igPv373P48GGOHTvG4MGDgdQ9jxRFwc7Oji1btvDaa6+lO8/R0THD9U7s7e3lizqNWbsuqR57uDrQo14Z7O1z/8skx66H0QjrhkDkcXW8ZF1s2k7HRvYQ49ixYwQFBXH+/H9LCQQFBeHt7c0///wj3xtmRK6F+ZBrob3c+vxrNljawcGBwMDAdO3GrVu3Urdu3XTHu7m5cfLkSY4fP276179/fypWrMjx48epXbt2XqWeL/19PZbfT0epYu++WgYXB82bhlmz43M49ZM6Vrg0BC8HO+teAFBRFGbNmkWdOnVMRZCbmxurV69m5syZODk5aZyhEELkPU1/y40YMYKuXbtSs2ZN6tSpw7x584iIiKB///5A6m2t69evs3TpUmxsbNKtYVK0aFGcnJxkbZMckHZPMQ9XB7q+YmEzxU6EwZ5v1DFH99SNVF2t+zZobGwsffr0Yc2aNaZYYGAg4eHhlC1bVsPMhBBCW5oWQsHBwURHR/Ppp58SGRlJlSpV2LRpE6VKpf4CjoyMfOaaQuL5pXaDbqlifRr44+poQd2gK/thwxB1TGcLQUugSEVNUjIXf/31F2+//TYXL140xd577z2++uor2SZDCGH1NP9NN3DgQAYOHJjhc0uWLHnquRMmTGDChAk5n5SVmZ5mFenCLvZ0q1Nam2SyI+YihIWAIVkdbzUFyqYfN2ZtChQowJ07qZvnFipUiMWLF/PWW29pm5QQQpgJzbfYENr650YsW06l7QaVoYCldIMS7sGKTpCgniHGKwPh5d6apGRuypQpw4IFC6hduzbHjh2TIkgIIR4jhZCVS9sNKuRiT/e6pbVJJqsMKbC6B9z5Vx0v3wKaP3uxzfzq8OHDxMer10/q2LEje/fupXTp0tokJYQQZkoKISt26kYcv/2j7gb1tZRukKLAr6Ph4g51vOiL0GGhVW6kajQa+eabb6hTpw6DBg1K97ytrfV9ToQQ4lmkELJiabtB7s72dKtjITPF/pwLhxeqY65FISQMHAtqk5OG7ty5Q9u2bRk1ahQpKSn8+OOP/Pzzz1qnJYQQZs8C/vQXueF0ZByb/1EvZtmnvj8FnSxgwbCzW+C3MeqYrWPqRqqFSmqTk4b++OMPOnfuzLVr10yxMWPG0LJlSw2zEkIIyyAdISv1w/b03aDu9Uprk0xW3PoH1vQCJc0GsG/PhhI1tclJI0ajkcmTJ9OoUSNTEVSkSBE2b97MF198ken9+IQQwprJT0ordOZmHJtOqrtBvev742bu3aAHUakzxJLvq+ONPoIq7bXJSSNRUVF07dqVLVu2mGINGzZkxYoV+Pr6apiZEEJYFimErNAP286rHrs52dHD3LtB+oTUtYJi0yywWaUDNBytTU4auXz5MnXr1iUyMhJI3bh43LhxjBs3TrpAQgiRRfJT08r8e/M+m/6OVMV61y9j3t0gRYGfBsG1Q+p4iVrw5kywst3kS5YsSdWqVYmMjMTb25vQ0FCaNGmidVpCCGGRZIyQlZm+/RyK8t/jgpbQDdr1Ffz9P3WsUEnotALsrW+jUBsbG5YuXUpISAjHjx+XIkgIIZ6DdISsyLlb99l0Ut0N6lXPH3dnM+4GnVwDOyerYw4FoXM4FCiiTU557Pfff8fJyYn69eubYkWLFiU0NFTDrIQQIn+QjpAVmb79fLpuUK/6/tol9CxXD8H6NPvQ6Wyg42LwfkGbnPJQSkoK48aNo3nz5gQHB3P79m2tUxJCiHxHCiErce7WfX7564Yq1tOcu0F3r0BYZzAkqeOvfwnlm2mTUx66fv06TZo0YdKkSSiKwo0bN5g1a5bWaQkhRL4jt8asxA9pu0GOdvSuZ6bdoMQ4WNkJ4tN0QF7uC7X7aZNTHtq8eTNdu3Y17Rhva2vL559/zqhRozTOTAgh8h8phKzA+agH/JyuG1Qadxcz7AYZUlIXTIw6pY6XfS21G5SP6fV6xo0bx1dffWWKlShRgrCwMOrVq6dhZkIIkX9JIWQFfkgzU6yAoxmPDdryMZzfqo4VqQQdl4Bt/v1yvXr1Kp06dWLfvn2mWJs2bViyZAmenp4aZiaEEPlb/v3NIgC4cPsBP59Qd4N61C1NIRcHjTJ6ikML4M/Z6piLJ3QOAyd3bXLKA0lJSdSrV4+rV68CYGdnx5dffsmIESPQWdkaSUIIkddksHQ+N2P7eYxpukG9zbAbpLu4AzalWSHa1iF1rSAP88s3Jzk6OjJx4kQASpUqxZ49exg5cqQUQUIIkQekI5SPXbz9gJ+OX1fFutctRWFX8+oGFUy4ju3aL0AxqJ9oOwNKvqJNUnmsR48exMfH06VLFwoXLqx1OkIIYTWkI5SPpe0GuTrY0qd+Ge0Sykj8HWpfnIouKc1Gqq+OgmrB2uSUy9atW8fYsWNVMZ1Ox+DBg6UIEkKIPCYdoXzq0p141qfrBpU2r25QShK2/+uBa3KaafIvvJW6o3w+k5SUxKhRo/jhhx8ACAwMpF27dhpnJYQQ1k06QvnUD9vPqbpBLg629GlgRt0gRYENQ7G5ekAd960Bb80Gm/z1pXnhwgXq1atnKoIAfv31Vw0zEkIIAVII5UuX78Tz03H1TLFudUrjYU7doD3fwl9h6phbCei8EhxctMkpl6xevZoaNWpw5MgRIHVw9OzZs5k3b57GmQkhhJBbY/nQjB3nMTzWDnJxsKVvAzOaefXPetj+mSqk2LuiCwmDgsW0ySkXJCYmMmLECGbP/m9JgPLly7Nq1SoCAgK0S0wIIYSJFEL5zJXoeNYdU48N6lqnFJ4FHDXKKI3rR2Bdf1VIQYfhrbnYFXtJo6Ry3tmzZwkKCuLEiROmWEhICHPmzKFgwYIaZiaEEOJxcmssn5mxXd0Ncra35V1zGRsUew1WdoaUBFX4n+KdUCq8rlFSuWPYsGGmIsjJyYkFCxawfPlyKYKEEMLMSEcoH4mIfsjaNN2gbubSDUp6ACs6wYNbqrAxoCsXaEpFjdLKLfPmzSMgIICiRYuyatUqqlSponVKQgghMiAdoXxkxo5z6bpBfV81g26Q0QBr+8Ktk+q4/6sYXv8a8sEKygaDejHIEiVKsGXLFg4dOiRFkBBCmDEphPKJqzEPWXtU3Q1655WSeJlDN+j38fDvJnXMsxwELQVbe21yykE//vgjNWrUIDY2VhWvUaMGrq6uGmUlhBAiM6QQyidm7jhPymPdICd7G959tayGGf2/Iz/Cvh/UMefCELIq9b8WLD4+nh49etCjRw/++usv+vbti6Iozz5RCCGE2ZAxQvnA1ZiHrDlyTRV7p3YpihTUuBt0cRdsHKGO2dhD8HLwNIMi7TmcPHmSoKAgzpw5Y4q5u7uTkpKCvb3ld7mEEMJaSEcoH5i1U90NcrSz4d2GGo8NunMeVnUFY4o6/sY0KF1fk5RygqIoLFiwgFq1apmKoAIFChAaGsr8+fOlCBJCCAsjHSELd+3uQ1YfTtMNeqUURQs6aZQR8DAGVnSERPWYGeoNg+rvaJJSTrh//z79+/dnxYoVpli1atVYtWoVFSpU0DAzIYQQ2SUdIQs3c8eFdN2gflp2g1KSYVU3iLmojldqA03Ga5NTDjh+/DiBgYGqImjAgAEcOHBAiiAhhLBg0hGyYNfvJbDmyFVVLKR2Se26QYoCG4fD5T3qeLGq0G6eRW+kunv3bs6dOweAm5sb8+fPJygoSOOshBBCPC8phCzYrB3n0RvU3aABDTUchLxvOhxbro4V9IGQcHCw7GnkQ4YMYceOHVy9epXw8HDKlrXswd5CCCFSSSFkoa7fS2DVYXU3qHOtkhR106gbdPoX2Jrm1pe9C3QOAzdfbXJ6Djdv3qRYsf82gNXpdPz44484Ojri6GgGazMJIYTIEZZ7r8LKzd6p7gY52NkwoJFGXYrIE6krR5NmDZ1288A3QIuMsk1RFKZPn07p0qXZsmWL6jk3NzcpgoQQIp+RQsgC3biXwKpD6pliIbVK4q1FNyguMnUPMf1DdbzpBKj8Rt7n8xzu3r1L+/btee+990hKSuKdd94hMjJS67SEEELkIrk1ZoFm77xAssFoeuxga0N/LcYGJcfDyk5w/4Y6HtAldaq8Bfnzzz8JDg7mypUrpli3bt3w9PTUMCshhBC5TTpCFiYyNoHwQ+qxQZ1q+VHMPY+7QUYjrOsHkcfV8VL1oM00i9lIVVEUvv32W+rXr28qgjw8PNiwYQPffPMNDg4OGmcohBAiN0lHyMLMyaAbpMnYoO2fwemf1bHC/qnbZ9hZRvEQHR1Njx49+OWXX0yxunXrEhYWhp+fn4aZCSGEyCvSEbIgN2MTWXlQ3Q0KftkPH3fnvE3k+Ar4Y6o65uSeupGqi0fe5pJNhw8fJiAgQFUEffjhh+zcuVOKICGEsCLSEbIgc3apu0H2trq87wZd3gsbhqpjOlsIWgpFLGeF5UKFChEbm7oFiJeXF8uWLeP111/XOCshhBB5TTpCFuJWXCIrDkaoYsEv++FbKA+7QdEXILwLGPXqeOtvoUyjvMsjB5QrV4758+fz6quvcvz4cSmChBDCSkkhZCFm77xAckrablC5vEsg4S6sCE797+PqDIaaPfMuj2zat28fDx+qp/gHBwezY8cOihcvrlFWQgghtCaFkAWIiktkZZpuUMeafhTPq26QQQ+re0D0OXW8wuvQ7NO8ySGbDAYDkyZNokGDBgwdOjTd8zYWvP+ZEEKI5ye/BSzAnF0XSUrTDRqYV2ODFAU2jYKLO9Vx7yrQfgHY2OZNHtlw8+ZNWrRowbhx4zAajSxcuJDffvtN67SEEEKYESmEzFxUXCKhf15RxToE+lGisEveJHBgNhxZrI65Fk3dQ8yxYN7kkA3btm0jICCAbdu2Aamdn08//ZSmTZtqnJkQQghzIrPGzNzc3epukJ1NHnaD/t0Mv32kjtk5pRZBhcxzirnBYODTTz/ls88+Q1FS9z7z8fFh5cqVNGzYUOPshBBCmBsphMxY1P303aCONUvg55EH3aCbf8P/epNuI9W3ZkOJwNx//2y4ceMGISEh7Nq1yxRr0aIFS5cupWjRohpmJoQQwlxJIWTG5u26SKI+bTcoD2aK3b+VuodY8gN1vPHHUKVd7r9/Npw/f566dety+/ZtAGxtbZk0aRKjR4+WAdFCCCGeSH5DmKnb95NYnqYb1L5GHnSD9AkQFgKx6hWseSkIXn0/d9/7Ofj7+1OtWjUASpQowc6dO/nwww+lCBJCCPFU8lvCTM3fk74bNKhxLneDjEZYPxCuH1bH/WpD2x/MeiNVW1tbli9fTvfu3Tl+/Dj169fXOiUhhBAWQG6NmaE7D5JYuv+yKtauRnFKeuZyN2jXl/DPWnWsUEkIDgX7PN7d/hk2btxI4cKFqVu3rinm7e3NkiVLtEtKCCGExZGOkBmav1vdDbK10TG4cfncfdO/VsGur9QxR7fUjVQLFMnd984CvV7P+++/T5s2bQgODiY6OlrrlIQQQlgwKYTMTPSDJJbuV48Nalc9l7tBEX/CT4PUMZ0NdFwMRSvn3vtm0eXLl2nQoAHffvstANeuXWPBggUaZyWEEMKSSSFkZubtuUiC3mB6bGujY/BruTg26O7l1MHRhmR1vOXXUM58Fh9cv3491atX588//wTA3t6e77//ntGjR2ucmRBCCEsmY4TMSEx8MsvSdIPeCihOKU/X3HnDxNjUjVQf3lHHa70LtfrmzntmUVJSEh988AHff/+9KVamTBnCw8OpWbOmhpkJIYTID6QQMiPz91zkYbK6GzQkt7pBhhRY0wtun1HHyzWFFpNz5z2z6OLFiwQFBXHkyBFTrEOHDixYsAB3d3cNMxNCCJFfSCFkJmLik/lx32VV7M0AX0p75VI36LeP4Pzv6liRytBhEdhq/2WRkJBAvXr1uHnzJgCOjo5899139O/fH50ZT+MXQghhWWSMkJlYkKYbZKODIa/l0kyxg/Ph4Fx1zMULQsLAyTw6Lc7Oznz22WcAlC9fngMHDjBgwAApgoQQQuQo7f/0F9zNoBv0VkBx/HOjG3T+d/j1A3XM1gE6rYDCpXP+/Z5D79690ev1vPPOOxQsaL473QshhLBcmneEZs2ahb+/P05OTgQGBrJnz54nHrt27VqaNWtGkSJFcHNzo06dOvz22295mG3uWPDHReLTdINyZaZY1GlY3RMUgzr+5kwoWTvn3y8LwsLCGDdunCqm0+kYMGCAFEFCCCFyjaaFUHh4OMOGDWPs2LEcO3aMBg0a0LJlSyIiIjI8fvfu3TRr1oxNmzZx5MgRGjduzBtvvMGxY8fyOPOcc+9hMj/uU88Ua1vNlzJFCuTsG8XfgRVBkBSnjjf8AKoG5ex7ZUFCQgIzZ86kW7duTJo0iQ0bNmiWixBCCOujaSE0depUevfuTZ8+fahcuTLTpk3Dz8+P2bNnZ3j8tGnTGD16NC+//DLly5fniy++oHz58vz88895nHnOWfjHJR4kpZgep3aDcnhskD4xda2ge2kKzBfbQaMxOfteWXD69Gnq1q3L1q1bTbFt27Zplo8QQgjro9kYoeTkZI4cOcKHH36oijdv3px9+/Zl6jWMRiP379/Hw8PjicckJSWRlJRkehwXl9oR0ev16PX6bGSec+491LNo7yVVrPVLxShV2DHnclMUbDcMxubqn6qw0bcGhtbfQ0rKE07MXcuWLWPIkCE8fPgQABcXF6ZPn063bt00vy7W6tHnXT7/2pNrkfOMRiN6vR5FUbJ0XkpKCnZ2djx48AA7OxlWm5t0Oh329vbY2GTco8mt7wfNruqdO3cwGAx4e3ur4t7e3qYp08/y7bffEh8fT1DQk2/tTJ48mYkTJ6aL79ixAxeXXN7E9Bk2RdgQn/TfBdehUEV3jU2bruXYe1S4+ROVI/+nij2092S3Rw+Stu7IsffJrMTERObNm8f27dtNsZIlSzJq1Ci8vLzYtGlTnuck1B7v0AltybXIGba2tnh5eWFvb5+t84sVK8bFixdzOCuREb1ez+3btzEajemee/SHc07TvLxNOx1aUZRMTZFeuXIlEyZM4KeffqJo0aJPPG7MmDGMGDHC9DguLg4/Pz8aN26Mp6dn9hN/TrEJesZ+uwf4ryPT+iUfenWommPvoTu1Hrtj6iJIcXDFvttamni/mGPvk1l///03ISEhnDnz3yKOPXr0oGXLlrRp0ybbP6REztDr9WzdupVmzZrJtdCYXIucoygK169fJyUlBR8fnyd2G552fnx8PK6urrJ8Ry4zGo1ERkbi7e1N8eLF032+c2uTbc0KIS8vL2xtbdN1f6KiotJ1idIKDw+nd+/erF69mqZNn74flqOjI46Ojuni9vb2mv6AWbpTPTZIp4NhzSrkXE7XjsDPg9UxnQ26DouxLxGQM++RRaNGjTIVQQUKFGDu3Ll07NiRTZs2aX49xH/kWpgPuRbPT6/Xk5iYiK+vLwUKZH0SyqNbas7OzlkuokTWFS1alBs3bphukz0ut74XNLuqDg4OBAYGpmv9bt26lbp16z7xvJUrV9KjRw9WrFhB69atczvNXBGboGdxurFBPpQrmkPTxO9dhZWdICVRHW/+OVRokTPvkQ2LFi2icOHCVKtWjSNHjhASEqJZLkII62AwpC4X4uDgoHEmIjMeXadH1y0vaHprbMSIEXTt2pWaNWtSp04d5s2bR0REBP379wdSb2tdv36dpUuXAqlFULdu3fj+++955ZVXTN0kZ2dni9p7avHeS9xPVHeDhjbJoZliSfdTi6D4KHU8sCe8MiBn3iOTHg0yfKRkyZJs27aNypUr4+TklKe5CCGsm9zWsgxaXCdN+3zBwcFMmzaNTz/9lICAAHbv3s2mTZsoVaoUAJGRkao1hebOnUtKSgqDBg3Cx8fH9O+9997T6kPIstgEPYv+UHeDWr3kQwXvHOgGGQ3wvz5w6291vEwjaDUlteLKA4qiMGfOHGrUqMH9+/dVz1WvXl2KICGEEGZD88HSAwcOZODAgRk+t2TJEtXjnTt35n5CuWzJ3svEJaqnrA/NqXWDtn4CZzerY14VoOOPYJs34wxiY2N59913WbVqFQD9+vUjNDRU/hoTQohcoNPpWLduHW+99ZbWqVgsGfmVh+IS9Sz8Qz0Fs/VLPlQslgPdoMOLYf8MdczZA0LCwbnQ879+Jhw5coTAwEBTEQSpg+Lz8l6vEELkFzdv3mTIkCGUKVMGR0dH/Pz8eOONN8xm4VlFUZgwYQK+vr44OzvTqFEj/vnnH63TyjLNO0LW5McMukFDmuTAnmIXd8Km99UxG3sIXg4eZZ7/9Z9BURRmzJjB+++/T3JyMgDu7u4sWrSIdu3a5fr7CyFEZhiNCncfJmfxHCP3H+rR2yTlyKyxwi4O2Ng8u0N++fJl6tWrR6FChfj666+pWrUqer2e3377jUGDBqmWIdHK119/zdSpU1myZAkVKlRg0qRJNGvWjH///dei9oiUQiiP3E/UsyDN2KCWVYpRqZjb873w7bMQ3g2MaVaIbjsdStd7vtfOhLt379K7d2/WrVtnitWqVYuwsDD8/f1z/f2FECKz7j5MJnDS75rmcOTjpngWSL+kS1oDBw5Ep9Nx8OBBXF1dTfEXX3yRXr16PfG8Dz74gHXr1nHt2jWKFStGly5d+OSTT0xTz0+cOMGwYcM4fPgwOp2O8uXLM3fuXGrWrMmVK1cYPHgwf/zxB8nJyZQuXZopU6bQqlWrdO+jKArTpk1j7Nixpj94f/zxR7y9vVmxYgX9+vXL6qdGM1II5ZEf910mNkG9PPhzzxR7GPP/G6nGquP1R0BA7k9N//PPP+nUqROXL182xUaOHMkXX3whU1WFECKbYmJi2Lx5M59//rmqCHqkUKFCTzy3YMGCLFmyBF9fX06ePEnfvn0pWLAgo0ePBqBLly5Ur16d2bNnY2try/Hjx01F0qBBg0hOTmb37t24urpy6tSpJ669dOnSJW7evEnz5s1NMUdHRxo2bMi+ffukEBJqGXWDXn+xGJV9nqMblJIM4e/AXfXrUvkNeG1c9l83C/bu3Wsqgjw8PFiyZAlvvPFGnry3EELkV+fPn0dRFCpVqpTlcz/++GPT/5cuXZqRI0cSHh5uKoQiIiIYNWqU6bXLl//vD/KIiAjat2/PSy+9BECZMk8eWvFo+ZqMtsm6cuVKlvPWkhRCeWDp/ivce5iD3SBFgV+Gw5W96rhPALw9F/Jo9dPhw4ezY8cOYmJiWLlyJSVLlsyT9xVCiPzs0caw2Zltu2bNGqZNm8b58+d58OABKSkpuLn990f3iBEj6NOnD8uWLaNp06Z07NiRsmXLAjB06FAGDBjAli1baNq0Ke3bt6dq1adv+5TdbbLMiRRCuexBUgrz96hnirV40ZsXfJ+jG7R3Ghxfro4V9IXOYeCQvo2aU65fv07x4sVNj3U6HaGhoTg7O8s2AEIIs1fYxYEjHz99W6a0jEYj9x88oGCBAjk2WPpZypcvj06n4/Tp01maFn/gwAE6derExIkTadGiBe7u7oSFhfHtt9+ajpkwYQIhISFs3LiRX3/9lfHjxxMWFsbbb79Nnz59aNGiBRs3bmTLli1MnjyZb7/9liFDhqR7r2LFigGpnSEfHx9TPDPbZJkbmT6fy5buv5yz3aBTG+D3CeqYvQt0XgluPhme8ryMRiNfffUVZcqUSTdt083NTYogIYRFsLHR4VnAMcv/PFzss3VeRv8yM2PMw8ODFi1aMHPmTOLj49M9f+/evQzP27t3L6VKlWLs2LHUrFmT8uXLZ3ibqkKFCgwfPpwtW7bQrl07Fi9ebHrOz8+P/v37s3btWkaOHMn8+fMzfC9/f3+KFSum2iYrOTmZXbt2PXWbLHMkhVAuik9KYf5udTeo2QvevOibze1AbhyDte+mCeqg3XzwDcjeaz7D7du3adOmDR9++CHJycl06dKFqKioZ58ohBAi22bNmoXBYKBWrVr873//49y5c5w+fZrp06dTp06dDM8pV64cERERhIWFceHCBaZPn66a0ZuQkMDgwYPZuXMnV65cYe/evRw6dIjKlSsDMGzYMH777TcuXbrE0aNH2b59u+m5tHQ6HcOGDeOLL75g3bp1/P333/To0QMXFxeL20dSbo3loqX7r3A3TTfovex2g+JuwMrOkJKgjjebCJXbZDPDp9u9ezedO3fmxo0bQOoXft++ffHw8MiV9xNCCJHK39+fo0eP8vnnnzNy5EgiIyMpUqQIgYGBzJ49O8Nz3nzzTYYPH87gwYNJSkqidevWjBs3jgkTJgBga2tLdHQ03bp149atW3h5edGuXTsmTpwIpG50OmjQIK5du4abmxuvv/4633333RNzHD16NAkJCQwcOJC7d+9Su3ZttmzZYlFrCAHolEejsqxEXFwc7u7u3LlzB09Pz1x7n/ikFBp8vYOY+P8W72pa2ZsF3Wtm/cWS42HR63DzL3W8+jvQdkaO7yFmMBiYPHky48ePx2g0AlC0aFFCQ0Np2jRr99efRa/Xs2nTJlq1aiW32DQm18J8yLXIOYmJiVy6dAl/f/9s7XNoNBqJi4vDzc0tR8YIiad72vWKjo7Gy8uL2NhY1QDw5yUdoVyy7MAVVREE2ewGGY2pt8PSFkGl6kPr73K8CLp16xbvvPMOv//+36Jjr732GsuXL1cNiBNCCCHyAylvc8HD5PRjg5pWLspLJbIxNmjbRDjzizrmURaCl4Fdzi5auG/fPqpVq2YqgmxsbJg4cSJbtmyRIkgIIUS+JB2hXLD8wBWi03WDKmT9hY4tT50q/zinQhCyClxyfpyOp6cnDx48AMDHx4cVK1bQqFGjHH8fIYQQwlxIRyiHPUxOYe4udTfotUrZ6AZd/gN+HqaO2dhB0FLwyoGNWjNQsWJF5s6dS4sWLTh+/LgUQUIIIfI9KYRyWOiBiAy6QVkcGxR9IXX7DKN6xhmtp0KZhs+Z4X927dpFQoJ6FlqXLl349ddfKVq0aI69jxBCCGGupBDKQQnJBubuvqCKNa5YhGp+hbLwIndTN1JNuKuO1xkMgd2fP0kgJSWFjz76iEaNGjFs2LB0z1va8uhCCCFEdkkhlINC/7zCnQdpukFNszA2yKCHVd0g+rw6XrEVNPs0BzKEa9eu0bhxYyZPngzAvHnz2L59e468thBCCGFppBDKIQnJBuakGRvUqGIRAjLbDVIU2PQ+XNqtjnu/lLpytI3tc+e4ceNGAgIC+OOPPwCws7NjypQpMhZICCGE1ZJCKIesOBjBnQdJqliWxgbtnwlHlqhjBbwhJAwcCzxXbnq9nlGjRtGmTRuio6MBKFmyJLt37+b999+XRcKEEMJC6XQ61q9fr3UaFk1+A+aARL2BObvUY4NerVCE6iULZ+4F/v0Vtnysjtk5pW6k6l7iuXK7cuUKr776Kt98840p9uabb3Ls2LEn7lcjhBBCezdv3mTIkCGUKVMGR0dH/Pz8eOONN9Jtfq2VtWvX0qJFC7y8vNDpdBw/flzrlLJF1hHKASv+jOD2/Wx2g26ehDW9gTQ7nbw9F4oHPldep06dol69eqadiu3t7ZkyZQpDhw6VAdFCCGHGLl++TL169ShUqBBff/01VatWRa/X89tvvzFo0CDOnDmjdYrEx8dTr149OnbsSN++fbVOJ9ukEHpOGXWDGpT3IrBUJrpB92/Cik6gj1fHXxsHL7713LlVrFiR6tWrs2PHDvz9/QkPD+fll19+7tcVQgiLZDRCQkyWz9E9vA+2yZATwwicPTL1OgMHDkSn03Hw4EFcXV1N8RdffJFevXo98bwPPviAdevWce3aNYoVK0aXLl345JNPTHvWnThxgmHDhnH48GF0Oh3ly5dn7ty51KxZkytXrjB48GD++OMPkpOTKV26NFOmTKFVq1YZvlfXrl2B1KLNkkkh9JxWHowgKk03aFjTTHSD9Ampu8nHXVPHq3aCBiNzJDdbW1tCQ0MZP348X3/9NYUKFcqR1xVCCIuUEANTymbpFBsgG5sjPdmoC+Dq9dRDYmJi2Lx5M59//rmqCHrkaT/LCxYsyJIlS/D19eXkyZP07duXggULMnr0aCB1rbjq1asze/ZsbG1tOX78uKlIGjRoEMnJyezevRtXV1dOnTpFgQLPN0bVEkgh9Bye3A16xvYXRiOs6w83jqrjfq9A2+nZ3kj1f//7H8WLF+eVV14xxXx8fJg3b162Xk8IIUTeO3/+PIqiUKlSpSyf+/HH/403LV26NCNHjiQ8PNxUCEVERDBq1CjTa5cv/98f7hEREbRv356XXnoJgDJlyjzPh2ExZLD0cwg/dJVbcdkYG7TzCzi1Xh0rXBo6hYKdY5bzSExMZPDgwXTo0IHg4GBiYrLY+hVCCGE2FCV1zGh2xnKuWbOG+vXrU6xYMQoUKMC4ceOIiIgwPT9ixAj69OlD06ZN+fLLL7lw4b8/5ocOHcqkSZOoV68e48eP56+//nr+D8YCSCGUTYl6A7N2qhc+rFfOk5qln9ENOhEOu6eoY47uqRupPqNdmpFz585Rt25dZs6cCaRW9EuXLs3y6wghhDAP5cuXR6fTcfr06Sydd+DAATp16kTLli355ZdfOHbsGGPHjiU5+b+FfidMmMA///xD69at2b59Oy+88ALr1q0DoE+fPly8eJGuXbty8uRJatasyQ8//JCjH5s5kltj2bTqcEbdoGesIh1xADYMVsd0ttBxMRSpmOUcwsLC6Nu3r2nHeCcnJ6ZPn06fPn2y/FpCCJHvOXukjtHJAqPRyP379ylYsGDOrLnm/Iw/lgEPDw9atGjBzJkzGTp0aLpxQvfu3ctwnNDevXspVaoUY8eONcWuXLmS7rgKFSpQoUIFhg8fTufOnVm8eDFvv/02AH5+fvTv35/+/fszZswY5s+fz5AhQ7L4QVoWKYSyISnFwKwd6m+mumU9qeX/lC/wmEsQFgIG9RYctPoayjXJ0vsnJCQwbNgw1difihUrsmrVKqpWrZql1xJCCKthY5P1zrvRiGJwAFe3nJk1lkmzZs2ibt261KpVi08//ZSqVauSkpLC1q1bmT17dobdonLlyhEREUFYWBgvv/wyGzduNHV7IPV3x6hRo+jQoQP+/v5cu3aNQ4cO0b59ewCGDRtGy5YtqVChAnfv3mX79u1Urlz5iTnGxMQQERHBjRs3APj3338BKFasGMWKFcvJT0eukltj2bDq0FVuxiWqYk8dG5QYCyuC4WG0Ol67P7ycte7NmTNnqF27tqoI6tq1K4cPH5YiSAgh8gl/f3+OHj1K48aNGTlyJFWqVKFZs2Zs27aN2bNnZ3jOm2++yfDhwxk8eDABAQHs27ePcePGmZ63tbUlOjqabt26UaFCBYKCgmjZsiUTJ04EwGAwMGjQICpXrszrr79OxYoVmTVr1hNz3LBhA9WrV6d169YAdOrUierVqzNnzpwc/EzkPp3yaFSWlYiLi8Pd3Z07d+7g6emZ5fOTUgw0mrKTyNj/CqE6ZTxZ+e4rGZ9gSIEVHeFCmo1NyzWDzmFgm/mm3IMHDyhdurRpmwxnZ2dmzpxJjx49LHaBRL1ez6ZNm2jVqpVpCqfQhlwL8yHXIuckJiZy6dIl/P39cXJyyvL5RqORuLg43NzcZDuiPPC06xUdHY2XlxexsbG4ubnl2HvKVc2i1YevqYoggPeetm7Q5g/TF0FFX4AOi7JUBAEUKFCASZMmAamLah0+fJiePXtabBEkhBBCaE3GCGVB6tgg9Uyx2v4evFLmCZ2lP+fBofnqmGuR1E6QU/aq2X79+mFjY8M777yDi4tLtl5DCCGEEKmkI5QFa45c40aabtCwpk+YKXZuK2z+QB2zdYROK6BwqWe+l6IoLFq0SHV/F1LXlXj33XelCBJCCCFygHSEMik5xZhuplgtfw/qlM2gG3TrFKzuCYpRHX9rFvjVeuZ7PXjwgAEDBrB8+XIAXnnlFdNgNCGEEELkHOkIZdKaI9e4fi9BFRuW0UyxB7dTZ4gl31fHG42Blzo8833++usvAgMDTUUQwJ49e7KVsxBCCCGeTgqhTEhOMTIzzdigWqUz6AbpE1PXCoqNUMerdICGaW6TpaEoCnPnzqVWrVqcPXsWSN08b+XKlXz55ZfP/TEIIYQQIj25NZYJa4+m7wa917S8eraWosBPg+DaQfXJJV6GN2c+dSPVuLg43n33XcLDw02xGjVqEB4eTrly5XLkYxBCCCFEetIRega9wciMNN2gmqUKUzdtN2jX1/D3GnXM3S91cLT9k9euOHr0qKnoeWTw4MHs27dPiiAhhBAil0lH6BnWHr3GtbtpxgY1raDuBp1ck7qj/OMcCkJIOBQo+sTXVhSF4cOHm3b/dXd3Z+HChablzoUQQgiRu6Qj9BQZdYMCSxWmXrnHukFXD8H6geoTdTapCyZ6v/jU19fpdPz4448UKlSIl19+mWPHjkkRJIQQwiyULl2aadOmmR7rdDrWr1+vWT65RQqhp1h39DpXY9KMDWry2NigexEQ1hkM6l3oaTEZKjTP8DX1er3qcenSpdmxYwd//PEH/v7+OZa7EEIIy/Vo66RH/zw9PXn99df566+/NMspMjKSli1bavb+uUUKoSfIqBtUo2QhGpT//52LE+NgRSeIv60+sWZvqN0v3espisK0adMIDAzkwYMHqucCAgJwcHDI0fyFEEJYttdff53IyEgiIyPZtm0bdnZ2tGnTRrN8ihUrhqOjo2bvn1ukEHqC9ceuExHzUBV779HYIKMB/tcbov5Rn1SmMbT8Kt0MsZiYGN566y2GDx/OyZMnGTBgAFa2160QQogscnR0pFixYhQrVoyAgAA++OADrl69yu3bqX+Af/DBB1SoUAEXFxfKlCnDuHHjVHcdTpw4QePGjSlYsCBubm4EBgZy+PBh0/P79u3j1VdfxdnZGT8/P4YOHUp8fPwT83n81tjly5fR6XSsXbuWxo0b4+LiQrVq1di/f7/qnKy+hxakEMpASgbdoAC/Qrz6qBu05WM4t0V9kldF6LgEbNU7Re/fv5+AgAA2bNhgivn6+kohJIQQItMePHhAaGgo5cqVw9MzdZxqwYIFWbJkCadOneL7779n/vz5fPfdd6ZzunTpQokSJTh06BBHjhzhww8/xN4+9XfUyZMnadGiBe3ateOvv/4iPDycP/74g8GDB2cpr7Fjx/L+++9z/PhxKlSoQOfOnUlJScnR98htMmssA+uP3+BKtLobNOzRukGHFsKBWeoTnD1SZ4g5FzKFjEYj33zzDR999BEGgwEAT09Pli5dSqtWrXL7QxBCCPEUU6dOZerUqc88rnr16ixbtkwVa9u2LUePHn3muSNGjGDEiBHZzvGXX36hQIECAMTHx+Pj48Mvv/yCjU1qD+Pjjz82HVu6dGlGjhxJeHg4o0ePBiAiIoJRo0ZRqVIlAMqX/283hClTphASEsKwYcNMz02fPp2GDRsye/ZsnJyevOzL495//33TFlATJ07kxRdf5Pz581SqVCnH3iO3SSGURorByIzt51Sxan6FaFihCFzYDptGqU+wdUhdK8jjv4HOt2/fpnv37vz666+mWP369Vm5ciUlSpTI1fyFEEI8W1xcHNevX3/mcX5+fulit2/fztS5cXFx2crtkcaNGzN79mwgdYjFrFmzaNmyJQcPHqRUqVKsWbOGadOmcf78eR48eEBKSgpubm6m80eMGEGfPn1YtmwZTZs2pWPHjpQtWxaAI0eOcP78eUJDQ03HK4qC0Wjk0qVLVK5cOVM5Vq1a1fT/Pj4+AERFRVGpUqUce4/cJoVQGj8dv8HltN2gJuXR3TkLq3qAYlCf8MZ0KFXH9HDPnj106tSJGzduAKn3VD/66CMmTJiAnZ18uoUQwhy4ublRvHjxZx7n5eWVLlakSJFMnft4UZIdrq6uqoV1AwMDcXd3Z/78+bRp04ZOnToxceJEWrRogbu7O2FhYXz77bem4ydMmEBISAgbN27k119/Zfz48YSFhfH2229jNBrp168fQ4cOTfe+JUuWzHSOj261AaYZ1Uaj0fTfnHiP3Ca/mR+T0digaiXcaeRnAwuCIClWfUKDkRDQWRU6cOCAqQgqWrQoy5cvp1mzZrmatxBCiKzJ7G0ro9GYrrPz+JjPvKTT6bCxsSEhIYG9e/dSqlQpxo4da3r+ypUr6c6pUKECFSpUYPjw4XTu3JnFixfz9ttvU6NGDf75559c3cEgL94jJ8hg6cf8/NcNLt1Rj2Yf1rgUuvCucPey+uAX3oTGH5PWyJEjadmyJY0bN+b48eNSBAkhhMiWpKQkbt68yc2bNzl9+jRDhgzhwYMHvPHGG5QrV46IiAjCwsK4cOEC06dPZ926daZzExISGDx4MDt37uTKlSvs3buXQ4cOmW5HffDBB+zfv59BgwZx/Phxzp07x4YNGxgyZEiO5Z8X75ETpCP0/wxGhR+2qbtBVYu70ejs5xCxT32wb3V4aw7Y2HD16lXVPWQbGxvCwsJwdXXF1tY2L1IXQgiRD23evNk07qZgwYJUqlSJ1atX06hRIwCGDx/O4MGDSUpKonXr1owbN44JEyYAYGtrS3R0NN26dePWrVt4eXnRrl07Jk6cCKSO7dm1axdjx46lQYMGKIpC2bJlCQ4OzrH88+I9coJOsbJ53HFxcbi7u3Pnzh3TFERIXTdoWPhx1bG/v3yYcifTzCpwKw59t2NwKcKkSZP4/PPP2bJli+kLU2SNXq9n06ZNtGrVSnWvWeQ9uRbmQ65FzklMTOTSpUv4+/tna5bSo1tjbm5uptlaIvc87XpFR0fj5eVFbGzsc4+/epx0hEjtBk1PM1Osn9ff6Ysge1foHEbkA4UubzZjx44dAISEhHDy5ElVYSWEEEII8yeFEPDLXze4ePu/sUEv6S4yOiHt+hI6aL+ArX/f4p13mhEVFQWk3gobPHgwhQsXzsOMhRBCCJETrL4QMhgVpm/7rxtUjGiWOE3F1pCoOi7ltQlMWL6PL774wrQqdPHixVm5ciUNGjTIy5SFEEIIkUOsvhDaeDKSC//fDXIhkYUO3+CpxKiOuVayHSEfr2XPnj2mWMuWLVm6dGmGa0wIIYQQwjJYdSH0eDfIBiPT7Gfyoo16HYadiS/QYfTPREdHA6kj8SdPnszIkSNl4JwQQghh4az6N/mmk5Gcj3oAwGi7MJrbHlEf4FkO76CpJCQkAKkrYe7Zs4dRo0ZJESSEEBbEyiZIWywtrpPV/jY3PtYNCrLdQX+7X9QHOBWCkFVUrlGH2bNn07ZtW44dO0adOnXSv5gQQgiz9Gg9t+TkZI0zEZnx6Drl5Tp8Vntr7PfTUZyLesArNqf43G6RKb71QgoNSjviFLwcPFM3p+vWrRtdu3Y17aMihBDCMtjZ2eHi4sLt27ext7fPcjffaDSSnJxMYmKi3AnIZUajkdu3b+Pi4pKne3NabSE0749L+OsimWP/HfY6A8kGhQ+2JjHtz2QGdqzDTH/1TDApgoQQwvLodDp8fHy4dOlShntxPYuiKCQkJODs7Cy/B/KAjY0NJUuWzNPPtdUWQrdvR7Gh4BQK6eK5dNdI8JqHHLqRumPurNXb6PzHH9SvX1/jLIUQQjwvBwcHypcvn63bY3q9nt27d/Pqq6/KKt95wMHBIc87b5oXQrNmzWLKlClERkby4osvMm3atKeuy7Nr1y5GjBjBP//8g6+vL6NHj6Z///5Zft/v7GdRxuYm/zulp/eGBGKTUuMODg5MnTqVevXqZfdDEkIIYWZsbGyytcWGra0tKSkpODk5SSGUT2l6wzM8PJxhw4YxduxYjh07RoMGDWjZsiUREREZHn/p0iVatWpFgwYNOHbsGB999BFDhw7lf//7X5bfu5pyhsGbEuiw+r8iqGyZMqadcqUFKoQQQuR/mhZCU6dOpXfv3vTp04fKlSszbdo0/Pz8mD17dobHz5kzh5IlSzJt2jQqV65Mnz596NWrF998802W37vZ0nhmHtKbHge3a8vRY8eoUaNGtj8eIYQQQlgWzQqh5ORkjhw5QvPmzVXx5s2bs2/fvgzP2b9/f7rjW7RoweHDh9Hr9Rme8yR/RaWuVeBoB3O/HMvKNetzdDdbIYQQQpg/zcYI3blzB4PBgLe3tyru7e3NzZs3Mzzn5s2bGR6fkpLCnTt38PHxSXdOUlISSUlJpsexsbGm/y9bWMfC7z6jSpv+xMTEpDtX5D69Xs/Dhw+Jjo6W++8ak2thPuRamA+5Fubj0e/pnF50UfPB0mnH4iiK8tTxORkdn1H8kcmTJzNx4sQMn7twV6FRj4+Bj7OQsRBCCCG0Eh0djbu7e469nmaFkJeXF7a2tum6P1FRUem6Po8UK1Ysw+Pt7Ozw9PTM8JwxY8YwYsQI0+N79+5RqlQpIiIicvQTKbInLi4OPz8/rl69KrcmNSbXwnzItTAfci3MR2xsLCVLlsTDwyNHX1ezQsjBwYHAwEC2bt3K22+/bYpv3bqVN998M8Nz6tSpw88//6yKbdmyhZo1az6xZeno6Iijo2O6uLu7u3xRmxE3Nze5HmZCroX5kGthPuRamI+cXmdI01ljI0aMYMGCBSxatIjTp08zfPhwIiIiTOsCjRkzhm7dupmO79+/P1euXGHEiBGcPn2aRYsWsXDhQt5//32tPgQhhBBCWDBNxwgFBwcTHR3Np59+SmRkJFWqVGHTpk2UKlUKgMjISNWaQv7+/mzatInhw4czc+ZMfH19mT59Ou3bt9fqQxBCCCGEBdN8sPTAgQMZOHBghs8tWbIkXaxhw4YcPXo02+/n6OjI+PHjM7xdJvKeXA/zIdfCfMi1MB9yLcxHbl0LnZLT89CEEEIIISyEpmOEhBBCCCG0JIWQEEIIIayWFEJCCCGEsFpSCAkhhBDCauXLQmjWrFn4+/vj5OREYGAge/bseerxu3btIjAwECcnJ8qUKcOcOXPyKNP8LyvXYu3atTRr1owiRYrg5uZGnTp1+O233/Iw2/wvq98bj+zduxc7OzsCAgJyN0ErktVrkZSUxNixYylVqhSOjo6ULVuWRYsW5VG2+VtWr0VoaCjVqlXDxcUFHx8fevbsSXR0dB5lm3/t3r2bN954A19fX3Q6HevXr3/mOTny+1vJZ8LCwhR7e3tl/vz5yqlTp5T33ntPcXV1Va5cuZLh8RcvXlRcXFyU9957Tzl16pQyf/58xd7eXlmzZk0eZ57/ZPVavPfee8pXX32lHDx4UDl79qwyZswYxd7eXjl69GgeZ54/ZfV6PHLv3j2lTJkySvPmzZVq1arlTbL5XHauRdu2bZXatWsrW7duVS5duqT8+eefyt69e/Mw6/wpq9diz549io2NjfL9998rFy9eVPbs2aO8+OKLyltvvZXHmec/mzZtUsaOHav873//UwBl3bp1Tz0+p35/57tCqFatWkr//v1VsUqVKikffvhhhsePHj1aqVSpkirWr18/5ZVXXsm1HK1FVq9FRl544QVl4sSJOZ2aVcru9QgODlY+/vhjZfz48VII5ZCsXotff/1VcXd3V6Kjo/MiPauS1WsxZcoUpUyZMqrY9OnTlRIlSuRajtYoM4VQTv3+zle3xpKTkzly5AjNmzdXxZs3b86+ffsyPGf//v3pjm/RogWHDx9Gr9fnWq75XXauRVpGo5H79+/n+AZ71ii712Px4sVcuHCB8ePH53aKViM712LDhg3UrFmTr7/+muLFi1OhQgXef/99EhIS8iLlfCs716Ju3bpcu3aNTZs2oSgKt27dYs2aNbRu3TovUhaPyanf35qvLJ2T7ty5g8FgSLd7vbe3d7pd6x+5efNmhsenpKRw584dfHx8ci3f/Cw71yKtb7/9lvj4eIKCgnIjRauSnetx7tw5PvzwQ/bs2YOdXb76UaGp7FyLixcv8scff+Dk5MS6deu4c+cOAwcOJCYmRsYJPYfsXIu6desSGhpKcHAwiYmJpKSk0LZtW3744Ye8SFk8Jqd+f+erjtAjOp1O9VhRlHSxZx2fUVxkXVavxSMrV65kwoQJhIeHU7Ro0dxKz+pk9noYDAZCQkKYOHEiFSpUyKv0rEpWvjeMRiM6nY7Q0FBq1apFq1atmDp1KkuWLJGuUA7IyrU4deoUQ4cO5ZNPPuHIkSNs3ryZS5cumTYLF3krJ35/56s/87y8vLC1tU1XyUdFRaWrGh8pVqxYhsfb2dnh6emZa7nmd9m5Fo+Eh4fTu3dvVq9eTdOmTXMzTauR1etx//59Dh8+zLFjxxg8eDCQ+stYURTs7OzYsmULr732Wp7knt9k53vDx8eH4sWL4+7ubopVrlwZRVG4du0a5cuXz9Wc86vsXIvJkydTr149Ro0aBUDVqlVxdXWlQYMGTJo0Se4i5KGc+v2drzpC/9fe3QdFVb1xAP/u4rLsiwRhCQKyAbpBQfEiDjkFJsZmTss4yI6uAgkjTukAYhgz4R8wRZiAA0M0NgyQSbw45hhRuhYvC45QsE4Kq7wIkkQ55SgIgbyc3x8ONzcJwZ9K7D6fmZ3hnHvPuc/ZM8t95t5zd83NzeHj4wONRmNQr9Fo8NJLL03Zxt/f/579T506BV9fXwgEgkcWq7F7kLkA7lwJioyMRHFxMd1zf4hmOx+WlpY4f/48zp07x7127NgBuVyOc+fOYeXKlY8rdKPzIJ+NVatW4ddff8WtW7e4ura2NvD5fDg4ODzSeI3Zg8zF0NAQ+HzDU6eZmRmAv69GkMfjoZ2/Z7W0eh6YfBQyPz+ftba2sri4OCaRSFh3dzdjjLH33nuPbd26ldt/8vG7+Ph41trayvLz8+nx+YdktnNRXFzMFixYwHJzc1lfXx/3unHjxlwNwajMdj7+iZ4ae3hmOxcDAwPMwcGBhYaGspaWFlZTU8OWLVvGoqOj52oIRmO2c1FQUMAWLFjAPvnkE9bZ2cnq6uqYr68v8/Pzm6shGI2BgQGm0+mYTqdjAFhmZibT6XTcVxk8qvO30SVCjDGWm5vLnJycmLm5OfP29mY1NTXctoiICBYQEGCwf3V1NfPy8mLm5uZMJpOxvLy8xxyx8ZrNXAQEBDAA97wiIiIef+BGarafjbtRIvRwzXYu9Ho9CwoKYiKRiDk4OLDdu3ezoaGhxxy1cZrtXGRnZzN3d3cmEomYnZ0dU6vV7OrVq485auNTVVU17TngUZ2/eYzRtTxCCCGEmCajWiNECCGEEDIblAgRQgghxGRRIkQIIYQQk0WJECGEEEJMFiVChBBCCDFZlAgRQgghxGRRIkQIIYQQk0WJECGETOHQoUNwdHQEn8/HwYMH5zqcWeHxeDh+/Phch0HIvECJECHzRGRkJHg8Hng8HgQCAZydnbFnzx4MDg7OdWj3JZPJ5lUy0d/fj507d2Lv3r3o7e3F9u3b5zokQsgjYlS/Pk+IsVMoFCgoKMDo6Ci0Wi2io6MxODiIvLy8WffFGMP4+DgWLKB/A//U09OD0dFRvPHGG/Rr4oQYOboiRMg8IhQKYWtrC0dHR2zevBlqtZq7BcIYw/79++Hs7AyRSIQXXngBR48e5dpWV1eDx+Ph5MmT8PX1hVAohFarxcTEBNLT0+Hq6gqhUIilS5figw8+4Nr19vZCpVLB2toaNjY2UCqV6O7u5rZHRkYiJCQEBw4cgJ2dHWxsbPDOO+9gdHQUABAYGIgrV64gPj6eu6IFAH/++Sc2bdoEBwcHiMVieHh44MsvvzQY78DAANRqNSQSCezs7JCVlYXAwEDExcVx+9y+fRuJiYmwt7eHRCLBypUrUV1dPe372NPTA6VSCalUCktLS4SFheH3338HABQWFsLDwwMA4OzsDB6PZzDeu4+7c+dO2NnZwcLCAjKZDGlpadz2zMxMeHh4QCKRwNHREW+//bbBr8cXFhbCysoKFRUVkMvlEIvFCA0NxeDgIIqKiiCTyWBtbY1du3ZhfHycayeTyZCamorNmzdDKpViyZIlyMnJmXa895tDQkwZJUKEzGMikYhLON5//30UFBQgLy8PLS0tiI+Px5YtW1BTU2PQJjExEWlpadDr9fD09ERSUhLS09ORnJyM1tZWFBcXY/HixQCAoaEhrF69GlKpFLW1tairq4NUKoVCocDt27e5PquqqtDZ2YmqqioUFRWhsLAQhYWFAIBjx47BwcEBKSkp6OvrQ19fHwBgeHgYPj4+qKiowIULF7B9+3Zs3boVDQ0NXL+7d+9GfX09Tpw4AY1GA61Wi+bmZoPxvPXWW6ivr0dJSQl+/vlnbNy4EQqFAu3t7VO+Z4wxhISE4Pr166ipqYFGo0FnZydUKhUAQKVS4fTp0wCAxsZG9PX1wdHR8Z5+srOzceLECZSVleHSpUv44osvIJPJuO18Ph/Z2dm4cOECioqK8MMPPyAxMdGgj6GhIWRnZ6OkpATfffcdqqursWHDBlRWVqKyshKHDx/GoUOHDBJaAPj444/h6emJ5uZmJCUlIT4+HhqNZsrxznQOCTFZ/99vxRJCHpeIiAimVCq5ckNDA7OxsWFhYWHs1q1bzMLCgp05c8agTVRUFNu0aRNj7O9fdj5+/Di3vb+/nwmFQvbZZ59Necz8/Hwml8vZxMQEVzcyMsJEIhE7efIkF5eTkxMbGxvj9tm4cSNTqVRc2cnJiWVlZd13jOvWrWMJCQlcbAKBgJWXl3Pbb9y4wcRiMYuNjWWMMdbR0cF4PB7r7e016GfNmjUsKSlpymOcOnWKmZmZsZ6eHq6upaWFAWCNjY2MMcZ0Oh0DwLq6uv411l27drFXX33V4L2ZTllZGbOxseHKBQUFDADr6Ojg6mJiYphYLGYDAwNcXXBwMIuJieHKTk5OTKFQGPStUqnY66+/zpUBsK+++ooxNrM5JMSU0eIAQuaRiooKSKVSjI2NYXR0FEqlEjk5OWhtbcXw8DDWrl1rsP/t27fh5eVlUOfr68v9rdfrMTIygjVr1kx5vKamJnR0dGDhwoUG9cPDw+js7OTKzz33HMzMzLiynZ0dzp8/P+1YxsfH8dFHH6G0tBS9vb0YGRnByMgIJBIJAODy5csYHR2Fn58f1+aJJ56AXC7nys3NzWCMYfny5QZ9j4yMwMbGZsrj6vV6ODo6GlzlcXd3h5WVFfR6PVasWDFt3JMiIyOxdu1ayOVyKBQKrF+/Hq+99hq3vaqqCh9++CFaW1vR39+PsbExDA8PY3BwkBujWCyGi4sL12bx4sWQyWSQSqUGddeuXTM4tr+//z3lf1uMPtM5JMRUUSJEyDyyevVq5OXlQSAQYMmSJRAIBACArq4uAMA333wDe3t7gzZCodCgPHkSBu7cWpvOxMQEfHx8cOTIkXu2PfXUU9zfk3FM4vF4mJiYmLbvjIwMZGVl4eDBg9xamri4OO52DWOM6+tuk/WT8ZmZmaGpqckgEQNgkEz8s/0/+5yu/t94e3ujq6sL3377LU6fPo2wsDAEBQXh6NGjuHLlCtatW4cdO3YgNTUVTz75JOrq6hAVFcXdygSmft8e5L2c3G8qM51DQkwVJUKEzCMSiQSurq731Lu7u0MoFKKnpwcBAQEz7m/ZsmUQiUT4/vvvER0dfc92b29vlJaW4umnn4alpeUDx21ubm6w4BcAtFotlEoltmzZAuDOCbu9vR1ubm4AABcXFwgEAjQ2NnJXb/r7+9He3s6N0cvLC+Pj47h27RpefvnlGcXi7u6Onp4e/PLLL1y/ra2tuHnzJnfsmbK0tIRKpYJKpUJoaCgUCgWuX7+On376CWNjY8jIyACff2cpZllZ2az6ns7Zs2fvKT/77LNT7vuw5pAQY0WLpQkxAgsXLsSePXsQHx+PoqIidHZ2QqfTITc3F0VFRf/azsLCAnv37kViYiI+//xzdHZ24uzZs8jPzwcAqNVqLFq0CEqlElqtFl1dXaipqUFsbCyuXr064/hkMhlqa2vR29uLP/74AwDg6uoKjUaDM2fOQK/XIyYmBr/99pvBmCIiIvDuu++iqqoKLS0t2LZtG/h8Pnf1Y/ny5VCr1QgPD8exY8fQ1dWFH3/8Eenp6aisrJwylqCgIHh6ekKtVqO5uRmNjY0IDw9HQECAwW3D+8nKykJJSQkuXryItrY2lJeXw9bWFlZWVnBxccHY2BhycnJw+fJlHD58GJ9++umM+76f+vp67N+/H21tbcjNzUV5eTliY2On3PdhzSEhxooSIUKMRGpqKvbt24e0tDS4ubkhODgYX3/9NZ555plp2yUnJyMhIQH79u2Dm5sbVCoVtyZFLBajtrYWS5cuxYYNG+Dm5oZt27bhr7/+mtXVhZSUFHR3d8PFxYW7HZOcnAxvb28EBwcjMDAQtra2CAkJMWiXmZkJf39/rF+/HkFBQVi1ahXc3NxgYWHB7VNQUIDw8HAkJCRALpfjzTffRENDw5RPegF/f+uytbU1XnnlFQQFBcHZ2RmlpaUzHg9w59Zbeno6fH19sWLFCnR3d6OyshJ8Ph8vvvgiMjMzkZ6ejueffx5HjhwxeLT+/5WQkICmpiZ4eXkhNTUVGRkZCA4OnnLfhzWHhBgrHrv7hjshhPyHDQ4Owt7eHhkZGYiKiprrcOaETCZDXFycwXcpEUIeHK0RIoT8Z+l0Oly8eBF+fn64efMmUlJSAABKpXKOIyOEGAtKhAgh/2kHDhzApUuXYG5uDh8fH2i1WixatGiuwyKEGAm6NUYIIYQQk0WLpQkhhBBisigRIoQQQojJokSIEEIIISaLEiFCCCGEmCxKhAghhBBisigRIoQQQojJokSIEEIIISaLEiFCCCGEmCxKhAghhBBisv4HRVbbzHFKSYoAAAAASUVORK5CYII=\n",
|
|
"text/plain": [
|
|
"<Figure size 640x480 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"import matplotlib.pyplot as plt\n",
|
|
"import numpy as np\n",
|
|
"from sklearn.model_selection import train_test_split \n",
|
|
"from sklearn.datasets import load_breast_cancer\n",
|
|
"from sklearn.linear_model import LogisticRegression\n",
|
|
"\n",
|
|
"# Load the data\n",
|
|
"cancer = load_breast_cancer()\n",
|
|
"\n",
|
|
"X_train, X_test, y_train, y_test = train_test_split(cancer.data,cancer.target,random_state=0)\n",
|
|
"print(X_train.shape)\n",
|
|
"print(X_test.shape)\n",
|
|
"# Logistic Regression\n",
|
|
"logreg = LogisticRegression(solver='lbfgs')\n",
|
|
"logreg.fit(X_train, y_train)\n",
|
|
"\n",
|
|
"from sklearn.preprocessing import LabelEncoder\n",
|
|
"from sklearn.model_selection import cross_validate\n",
|
|
"#Cross validation\n",
|
|
"accuracy = cross_validate(logreg,X_test,y_test,cv=10)['test_score']\n",
|
|
"print(accuracy)\n",
|
|
"print(\"Test set accuracy with Logistic Regression: {:.2f}\".format(logreg.score(X_test,y_test)))\n",
|
|
"\n",
|
|
"import scikitplot as skplt\n",
|
|
"y_pred = logreg.predict(X_test)\n",
|
|
"skplt.metrics.plot_confusion_matrix(y_test, y_pred, normalize=True)\n",
|
|
"plt.show()\n",
|
|
"y_probas = logreg.predict_proba(X_test)\n",
|
|
"skplt.metrics.plot_roc(y_test, y_probas)\n",
|
|
"plt.show()\n",
|
|
"skplt.metrics.plot_cumulative_gain(y_test, y_probas)\n",
|
|
"plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "58e5b521",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Material for Lecture Thursday November 9"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "4ebe5a91",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Recurrent neural networks (RNNs): Overarching view\n",
|
|
"\n",
|
|
"Till now our focus has been, including convolutional neural networks\n",
|
|
"as well, on feedforward neural networks. The output or the activations\n",
|
|
"flow only in one direction, from the input layer to the output layer.\n",
|
|
"\n",
|
|
"A recurrent neural network (RNN) looks very much like a feedforward\n",
|
|
"neural network, except that it also has connections pointing\n",
|
|
"backward. \n",
|
|
"\n",
|
|
"RNNs are used to analyze time series data such as stock prices, and\n",
|
|
"tell you when to buy or sell. In autonomous driving systems, they can\n",
|
|
"anticipate car trajectories and help avoid accidents. More generally,\n",
|
|
"they can work on sequences of arbitrary lengths, rather than on\n",
|
|
"fixed-sized inputs like all the nets we have discussed so far. For\n",
|
|
"example, they can take sentences, documents, or audio samples as\n",
|
|
"input, making them extremely useful for natural language processing\n",
|
|
"systems such as automatic translation and speech-to-text."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "182f425e",
|
|
"metadata": {},
|
|
"source": [
|
|
"## A simple example"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 9,
|
|
"id": "8b3ca785",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Start importing packages\n",
|
|
"import pandas as pd\n",
|
|
"import numpy as np\n",
|
|
"import matplotlib.pyplot as plt\n",
|
|
"import tensorflow as tf\n",
|
|
"from tensorflow.keras import datasets, layers, models\n",
|
|
"from tensorflow.keras.layers import Input\n",
|
|
"from tensorflow.keras.models import Model, Sequential \n",
|
|
"from tensorflow.keras.layers import Dense, SimpleRNN, LSTM, GRU\n",
|
|
"from tensorflow.keras import optimizers \n",
|
|
"from tensorflow.keras import regularizers \n",
|
|
"from tensorflow.keras.utils import to_categorical \n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"# convert into dataset matrix\n",
|
|
"def convertToMatrix(data, step):\n",
|
|
" X, Y =[], []\n",
|
|
" for i in range(len(data)-step):\n",
|
|
" d=i+step \n",
|
|
" X.append(data[i:d,])\n",
|
|
" Y.append(data[d,])\n",
|
|
" return np.array(X), np.array(Y)\n",
|
|
"\n",
|
|
"step = 4\n",
|
|
"N = 1000 \n",
|
|
"Tp = 800 \n",
|
|
"\n",
|
|
"t=np.arange(0,N)\n",
|
|
"x=np.sin(0.02*t)+2*np.random.rand(N)\n",
|
|
"df = pd.DataFrame(x)\n",
|
|
"df.head()\n",
|
|
"\n",
|
|
"values=df.values\n",
|
|
"train,test = values[0:Tp,:], values[Tp:N,:]\n",
|
|
"\n",
|
|
"# add step elements into train and test\n",
|
|
"test = np.append(test,np.repeat(test[-1,],step))\n",
|
|
"train = np.append(train,np.repeat(train[-1,],step))\n",
|
|
" \n",
|
|
"trainX,trainY =convertToMatrix(train,step)\n",
|
|
"testX,testY =convertToMatrix(test,step)\n",
|
|
"trainX = np.reshape(trainX, (trainX.shape[0], 1, trainX.shape[1]))\n",
|
|
"testX = np.reshape(testX, (testX.shape[0], 1, testX.shape[1]))\n",
|
|
"\n",
|
|
"model = Sequential()\n",
|
|
"model.add(SimpleRNN(units=32, input_shape=(1,step), activation=\"relu\"))\n",
|
|
"model.add(Dense(8, activation=\"relu\")) \n",
|
|
"model.add(Dense(1))\n",
|
|
"model.compile(loss='mean_squared_error', optimizer='rmsprop')\n",
|
|
"model.summary()\n",
|
|
"\n",
|
|
"model.fit(trainX,trainY, epochs=100, batch_size=16, verbose=2)\n",
|
|
"trainPredict = model.predict(trainX)\n",
|
|
"testPredict= model.predict(testX)\n",
|
|
"predicted=np.concatenate((trainPredict,testPredict),axis=0)\n",
|
|
"\n",
|
|
"trainScore = model.evaluate(trainX, trainY, verbose=0)\n",
|
|
"print(trainScore)\n",
|
|
"plt.plot(df)\n",
|
|
"plt.plot(predicted)\n",
|
|
"plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "b0c12b4c",
|
|
"metadata": {},
|
|
"source": [
|
|
"### RNNs\n",
|
|
"\n",
|
|
"RNNs are very powerful, because they\n",
|
|
"combine two properties:\n",
|
|
"1. Distributed hidden state that allows them to store a lot of information about the past efficiently.\n",
|
|
"\n",
|
|
"2. Non-linear dynamics that allows them to update their hidden state in complicated ways.\n",
|
|
"\n",
|
|
"With enough neurons and time, RNNs\n",
|
|
"can compute anything that can be\n",
|
|
"computed by your computer!"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "d81020f6",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Basic layout\n",
|
|
"\n",
|
|
"<!-- dom:FIGURE: [figslides/RNN1.png, width=700 frac=0.9] -->\n",
|
|
"<!-- begin figure -->\n",
|
|
"\n",
|
|
"<img src=\"figslides/RNN1.png\" width=\"700\"><p style=\"font-size: 0.9em\"><i>Figure 1: </i></p>\n",
|
|
"<!-- end figure -->"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "2d7453c9",
|
|
"metadata": {},
|
|
"source": [
|
|
"### We need to specify the initial activity state of all the hidden and output units\n",
|
|
"\n",
|
|
"1. We could just fix these initial states to have some default value like 0.5.\n",
|
|
"\n",
|
|
"2. But it is better to treat the initial states as learned parameters.\n",
|
|
"\n",
|
|
"3. We learn them in the same way as we learn the weights.\n",
|
|
"\n",
|
|
"* Start off with an initial random guess for the initial states.\n",
|
|
"\n",
|
|
"a. At the end of each training sequence, backpropagate through time all the way to the initial states to get the gradient of the error function with respect to each initial state.\n",
|
|
"\n",
|
|
"b. Adjust the initial states by following the negative gradient."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "fc4a082e",
|
|
"metadata": {},
|
|
"source": [
|
|
"### We can specify inputs in several ways\n",
|
|
"\n",
|
|
"1. Specify the initial states of all the units.\n",
|
|
"\n",
|
|
"2. Specify the initial states of a subset of the units.\n",
|
|
"\n",
|
|
"3. Specify the states of the same subset of the units at every time step.\n",
|
|
"\n",
|
|
"This is the natural way to model most sequential data."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "c1106ad9",
|
|
"metadata": {},
|
|
"source": [
|
|
"### We can specify targets in several ways\n",
|
|
"\n",
|
|
"1. Specify desired final activities of all the units\n",
|
|
"\n",
|
|
"2. Specify desired activities of all units for the last few steps\n",
|
|
"\n",
|
|
"* Good for learning attractors\n",
|
|
"\n",
|
|
"* It is easy to add in extra error derivatives as we backpropagate.\n",
|
|
"\n",
|
|
" * Specify the desired activity of a subset of the units.\n",
|
|
"\n",
|
|
"* The other units are input or hidden units. \n",
|
|
"\n",
|
|
"<!-- dom:FIGURE: [figslides/RNN2.png, width=700 frac=0.9] -->\n",
|
|
"<!-- begin figure -->\n",
|
|
"\n",
|
|
"<img src=\"figslides/RNN2.png\" width=\"700\"><p style=\"font-size: 0.9em\"><i>Figure 1: </i></p>\n",
|
|
"<!-- end figure -->\n",
|
|
"\n",
|
|
"<!-- dom:FIGURE: [figslides/RNN3.png, width=700 frac=0.9] -->\n",
|
|
"<!-- begin figure -->\n",
|
|
"\n",
|
|
"<img src=\"figslides/RNN3.png\" width=\"700\"><p style=\"font-size: 0.9em\"><i>Figure 1: </i></p>\n",
|
|
"<!-- end figure -->\n",
|
|
"\n",
|
|
"<!-- dom:FIGURE: [figslides/RNN4.png, width=700 frac=0.9] -->\n",
|
|
"<!-- begin figure -->\n",
|
|
"\n",
|
|
"<img src=\"figslides/RNN4.png\" width=\"700\"><p style=\"font-size: 0.9em\"><i>Figure 1: </i></p>\n",
|
|
"<!-- end figure -->\n",
|
|
"\n",
|
|
"<!-- dom:FIGURE: [figslides/RNN5.png, width=700 frac=0.9] -->\n",
|
|
"<!-- begin figure -->\n",
|
|
"\n",
|
|
"<img src=\"figslides/RNN5.png\" width=\"700\"><p style=\"font-size: 0.9em\"><i>Figure 1: </i></p>\n",
|
|
"<!-- end figure -->"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "b139ef7b",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Backpropagation through time\n",
|
|
"\n",
|
|
"We can think of the recurrent net as a layered, feed-forward\n",
|
|
"net with shared weights and then train the feed-forward net\n",
|
|
"with weight constraints.\n",
|
|
"\n",
|
|
"We can also think of this training algorithm in the time domain:\n",
|
|
"1. The forward pass builds up a stack of the activities of all the units at each time step.\n",
|
|
"\n",
|
|
"2. The backward pass peels activities off the stack to compute the error derivatives at each time step.\n",
|
|
"\n",
|
|
"3. After the backward pass we add together the derivatives at all the different times for each weight."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "70c95078",
|
|
"metadata": {},
|
|
"source": [
|
|
"### The backward pass is linear\n",
|
|
"\n",
|
|
"1. There is a big difference between the forward and backward passes.\n",
|
|
"\n",
|
|
"2. In the forward pass we use squashing functions (like the logistic) to prevent the activity vectors from exploding.\n",
|
|
"\n",
|
|
"3. The backward pass, is completely linear. If you double the error derivatives at the final layer, all the error derivatives will double.\n",
|
|
"\n",
|
|
"The forward pass determines the slope of the linear function used for\n",
|
|
"backpropagating through each neuron\n",
|
|
"\n",
|
|
"<!-- dom:FIGURE: [figslides/RNN6.png, width=700 frac=0.9] -->\n",
|
|
"<!-- begin figure -->\n",
|
|
"\n",
|
|
"<img src=\"figslides/RNN6.png\" width=\"700\"><p style=\"font-size: 0.9em\"><i>Figure 1: </i></p>\n",
|
|
"<!-- end figure -->\n",
|
|
"\n",
|
|
"<!-- dom:FIGURE: [figslides/RNN7.png, width=700 frac=0.9] -->\n",
|
|
"<!-- begin figure -->\n",
|
|
"\n",
|
|
"<img src=\"figslides/RNN7.png\" width=\"700\"><p style=\"font-size: 0.9em\"><i>Figure 1: </i></p>\n",
|
|
"<!-- end figure -->\n",
|
|
"\n",
|
|
"<!-- dom:FIGURE: [figslides/RNN8.png, width=700 frac=0.9] -->\n",
|
|
"<!-- begin figure -->\n",
|
|
"\n",
|
|
"<img src=\"figslides/RNN8.png\" width=\"700\"><p style=\"font-size: 0.9em\"><i>Figure 1: </i></p>\n",
|
|
"<!-- end figure -->\n",
|
|
"\n",
|
|
"<!-- dom:FIGURE: [figslides/RNN9.png, width=700 frac=0.9] -->\n",
|
|
"<!-- begin figure -->\n",
|
|
"\n",
|
|
"<img src=\"figslides/RNN9.png\" width=\"700\"><p style=\"font-size: 0.9em\"><i>Figure 1: </i></p>\n",
|
|
"<!-- end figure -->\n",
|
|
"\n",
|
|
"<!-- dom:FIGURE: [figslides/RNN10.png, width=700 frac=0.9] -->\n",
|
|
"<!-- begin figure -->\n",
|
|
"\n",
|
|
"<img src=\"figslides/RNN10.png\" width=\"700\"><p style=\"font-size: 0.9em\"><i>Figure 1: </i></p>\n",
|
|
"<!-- end figure -->\n",
|
|
"\n",
|
|
"<!-- dom:FIGURE: [figslides/RNN11.png, width=700 frac=0.9] -->\n",
|
|
"<!-- begin figure -->\n",
|
|
"\n",
|
|
"<img src=\"figslides/RNN11.png\" width=\"700\"><p style=\"font-size: 0.9em\"><i>Figure 1: </i></p>\n",
|
|
"<!-- end figure -->\n",
|
|
"\n",
|
|
"<!-- dom:FIGURE: [figslides/RNN12.png, width=700 frac=0.9] -->\n",
|
|
"<!-- begin figure -->\n",
|
|
"\n",
|
|
"<img src=\"figslides/RNN12.png\" width=\"700\"><p style=\"font-size: 0.9em\"><i>Figure 1: </i></p>\n",
|
|
"<!-- end figure -->"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "2ca24031",
|
|
"metadata": {},
|
|
"source": [
|
|
"## The problem of exploding or vanishing gradients\n",
|
|
"* What happens to the magnitude of the gradients as we backpropagate through many layers?\n",
|
|
"\n",
|
|
"a. If the weights are small, the gradients shrink exponentially.\n",
|
|
"\n",
|
|
"b. If the weights are big the gradients grow exponentially.\n",
|
|
"\n",
|
|
"* Typical feed-forward neural nets can cope with these exponential effects because they only have a few hidden layers.\n",
|
|
"\n",
|
|
"* In an RNN trained on long sequences (e.g. 100 time steps) the gradients can easily explode or vanish.\n",
|
|
"\n",
|
|
"a. We can avoid this by initializing the weights very carefully.\n",
|
|
"\n",
|
|
"* Even with good initial weights, its very hard to detect that the current target output depends on an input from many time-steps ago.\n",
|
|
"\n",
|
|
"RNNs have difficulty dealing with long-range dependencies."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "93648979",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Four effective ways to learn an RNN\n",
|
|
"1. Long Short Term Memory Make the RNN out of little modules that are designed to remember values for a long time.\n",
|
|
"\n",
|
|
"2. Hessian Free Optimization: Deal with the vanishing gradients problem by using a fancy optimizer that can detect directions with a tiny gradient but even smaller curvature.\n",
|
|
"\n",
|
|
"3. Echo State Networks: Initialize the input a hidden and hidden-hidden and output-hidden connections very carefully so that the hidden state has a huge reservoir of weakly coupled oscillators which can be selectively driven by the input.\n",
|
|
"\n",
|
|
" * ESNs only need to learn the hidden-output connections.\n",
|
|
"\n",
|
|
"4. Good initialization with momentum Initialize like in Echo State Networks, but then learn all of the connections using momentum"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "b8806dcd",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Long Short Term Memory (LSTM)\n",
|
|
"\n",
|
|
"LSTM uses a memory cell for \n",
|
|
" modeling long-range dependencies and avoid vanishing gradient\n",
|
|
" problems.\n",
|
|
"\n",
|
|
"1. Introduced by Hochreiter and Schmidhuber (1997) who solved the problem of getting an RNN to remember things for a long time (like hundreds of time steps).\n",
|
|
"\n",
|
|
"2. They designed a memory cell using logistic and linear units with multiplicative interactions.\n",
|
|
"\n",
|
|
"3. Information gets into the cell whenever its “write” gate is on.\n",
|
|
"\n",
|
|
"4. The information stays in the cell so long as its **keep** gate is on.\n",
|
|
"\n",
|
|
"5. Information can be read from the cell by turning on its **read** gate."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "b0edad40",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Implementing a memory cell in a neural network\n",
|
|
"\n",
|
|
"To preserve information for a long time in\n",
|
|
"the activities of an RNN, we use a circuit\n",
|
|
"that implements an analog memory cell.\n",
|
|
"\n",
|
|
"1. A linear unit that has a self-link with a weight of 1 will maintain its state.\n",
|
|
"\n",
|
|
"2. Information is stored in the cell by activating its write gate.\n",
|
|
"\n",
|
|
"3. Information is retrieved by activating the read gate.\n",
|
|
"\n",
|
|
"4. We can backpropagate through this circuit because logistics are have nice derivatives. \n",
|
|
"\n",
|
|
"<!-- dom:FIGURE: [figslides/RNN13.png, width=700 frac=0.9] -->\n",
|
|
"<!-- begin figure -->\n",
|
|
"\n",
|
|
"<img src=\"figslides/RNN13.png\" width=\"700\"><p style=\"font-size: 0.9em\"><i>Figure 1: </i></p>\n",
|
|
"<!-- end figure -->\n",
|
|
"\n",
|
|
"<!-- dom:FIGURE: [figslides/RNN14.png, width=700 frac=0.9] -->\n",
|
|
"<!-- begin figure -->\n",
|
|
"\n",
|
|
"<img src=\"figslides/RNN14.png\" width=\"700\"><p style=\"font-size: 0.9em\"><i>Figure 1: </i></p>\n",
|
|
"<!-- end figure -->\n",
|
|
"\n",
|
|
"<!-- dom:FIGURE: [figslides/RNN15.png, width=700 frac=0.9] -->\n",
|
|
"<!-- begin figure -->\n",
|
|
"\n",
|
|
"<img src=\"figslides/RNN15.png\" width=\"700\"><p style=\"font-size: 0.9em\"><i>Figure 1: </i></p>\n",
|
|
"<!-- end figure -->\n",
|
|
"\n",
|
|
"<!-- dom:FIGURE: [figslides/RNN16.png, width=700 frac=0.9] -->\n",
|
|
"<!-- begin figure -->\n",
|
|
"\n",
|
|
"<img src=\"figslides/RNN16.png\" width=\"700\"><p style=\"font-size: 0.9em\"><i>Figure 1: </i></p>\n",
|
|
"<!-- end figure -->\n",
|
|
"\n",
|
|
"<!-- dom:FIGURE: [figslides/RNN17.png, width=700 frac=0.9] -->\n",
|
|
"<!-- begin figure -->\n",
|
|
"\n",
|
|
"<img src=\"figslides/RNN17.png\" width=\"700\"><p style=\"font-size: 0.9em\"><i>Figure 1: </i></p>\n",
|
|
"<!-- end figure -->\n",
|
|
"\n",
|
|
"<!-- dom:FIGURE: [figslides/RNN18.png, width=700 frac=0.9] -->\n",
|
|
"<!-- begin figure -->\n",
|
|
"\n",
|
|
"<img src=\"figslides/RNN18.png\" width=\"700\"><p style=\"font-size: 0.9em\"><i>Figure 1: </i></p>\n",
|
|
"<!-- end figure -->\n",
|
|
"\n",
|
|
"<!-- dom:FIGURE: [figslides/RNN19.png, width=700 frac=0.9] -->\n",
|
|
"<!-- begin figure -->\n",
|
|
"\n",
|
|
"<img src=\"figslides/RNN19.png\" width=\"700\"><p style=\"font-size: 0.9em\"><i>Figure 1: </i></p>\n",
|
|
"<!-- end figure -->\n",
|
|
"\n",
|
|
"<!-- dom:FIGURE: [figslides/RNN20.png, width=700 frac=0.9] -->\n",
|
|
"<!-- begin figure -->\n",
|
|
"\n",
|
|
"<img src=\"figslides/RNN20.png\" width=\"700\"><p style=\"font-size: 0.9em\"><i>Figure 1: </i></p>\n",
|
|
"<!-- end figure -->\n",
|
|
"\n",
|
|
"<!-- dom:FIGURE: [figslides/RNN21.png, width=700 frac=0.9] -->\n",
|
|
"<!-- begin figure -->\n",
|
|
"\n",
|
|
"<img src=\"figslides/RNN21.png\" width=\"700\"><p style=\"font-size: 0.9em\"><i>Figure 1: </i></p>\n",
|
|
"<!-- end figure -->\n",
|
|
"\n",
|
|
"<!-- dom:FIGURE: [figslides/RNN22.png, width=700 frac=0.9] -->\n",
|
|
"<!-- begin figure -->\n",
|
|
"\n",
|
|
"<img src=\"figslides/RNN22.png\" width=\"700\"><p style=\"font-size: 0.9em\"><i>Figure 1: </i></p>\n",
|
|
"<!-- end figure -->"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "58fd18f9",
|
|
"metadata": {},
|
|
"source": [
|
|
"## An extrapolation example\n",
|
|
"\n",
|
|
"The following code provides an example of how recurrent neural\n",
|
|
"networks can be used to extrapolate to unknown values of physics data\n",
|
|
"sets. Specifically, the data sets used in this program come from\n",
|
|
"a quantum mechanical many-body calculation of energies as functions of the number of particles."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 10,
|
|
"id": "a604caa5",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"\n",
|
|
"# For matrices and calculations\n",
|
|
"import numpy as np\n",
|
|
"# For machine learning (backend for keras)\n",
|
|
"import tensorflow as tf\n",
|
|
"# User-friendly machine learning library\n",
|
|
"# Front end for TensorFlow\n",
|
|
"import tensorflow.keras\n",
|
|
"# Different methods from Keras needed to create an RNN\n",
|
|
"# This is not necessary but it shortened function calls \n",
|
|
"# that need to be used in the code.\n",
|
|
"from tensorflow.keras import datasets, layers, models\n",
|
|
"from tensorflow.keras.layers import Input\n",
|
|
"from tensorflow.keras import regularizers\n",
|
|
"from tensorflow.keras.models import Model, Sequential\n",
|
|
"from tensorflow.keras.layers import Dense, SimpleRNN, LSTM, GRU\n",
|
|
"# For timing the code\n",
|
|
"from timeit import default_timer as timer\n",
|
|
"# For plotting\n",
|
|
"import matplotlib.pyplot as plt\n",
|
|
"\n",
|
|
"\n",
|
|
"# The data set\n",
|
|
"datatype='VaryDimension'\n",
|
|
"X_tot = np.arange(2, 42, 2)\n",
|
|
"y_tot = np.array([-0.03077640549, -0.08336233266, -0.1446729567, -0.2116753732, -0.2830637392, -0.3581341341, -0.436462435, -0.5177783846,\n",
|
|
"\t-0.6019067271, -0.6887363571, -0.7782028952, -0.8702784034, -0.9649652536, -1.062292565, -1.16231451, \n",
|
|
"\t-1.265109911, -1.370782966, -1.479465113, -1.591317992, -1.70653767])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "545336c9",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Formatting the Data\n",
|
|
"\n",
|
|
"The way the recurrent neural networks are trained in this program\n",
|
|
"differs from how machine learning algorithms are usually trained.\n",
|
|
"Typically a machine learning algorithm is trained by learning the\n",
|
|
"relationship between the x data and the y data. In this program, the\n",
|
|
"recurrent neural network will be trained to recognize the relationship\n",
|
|
"in a sequence of y values. This is type of data formatting is\n",
|
|
"typically used time series forcasting, but it can also be used in any\n",
|
|
"extrapolation (time series forecasting is just a specific type of\n",
|
|
"extrapolation along the time axis). This method of data formatting\n",
|
|
"does not use the x data and assumes that the y data are evenly spaced.\n",
|
|
"\n",
|
|
"For a standard machine learning algorithm, the training data has the\n",
|
|
"form of (x,y) so the machine learning algorithm learns to assiciate a\n",
|
|
"y value with a given x value. This is useful when the test data has x\n",
|
|
"values within the same range as the training data. However, for this\n",
|
|
"application, the x values of the test data are outside of the x values\n",
|
|
"of the training data and the traditional method of training a machine\n",
|
|
"learning algorithm does not work as well. For this reason, the\n",
|
|
"recurrent neural network is trained on sequences of y values of the\n",
|
|
"form ((y1, y2), y3), so that the network is concerned with learning\n",
|
|
"the pattern of the y data and not the relation between the x and y\n",
|
|
"data. As long as the pattern of y data outside of the training region\n",
|
|
"stays relatively stable compared to what was inside the training\n",
|
|
"region, this method of training can produce accurate extrapolations to\n",
|
|
"y values far removed from the training data set.\n",
|
|
"\n",
|
|
"<!-- -->\n",
|
|
"<!-- The idea behind formatting the data in this way comes from [this resource](https://machinelearningmastery.com/time-series-prediction-lstm-recurrent-neural-networks-python-keras/) and [this one](https://fairyonice.github.io/Understand-Keras%27s-RNN-behind-the-scenes-with-a-sin-wave-example.html). -->\n",
|
|
"<!-- -->\n",
|
|
"<!-- The following method takes in a y data set and formats it so the \"x data\" are of the form (y1, y2) and the \"y data\" are of the form y3, with extra brackets added in to make the resulting arrays compatable with both Keras and Tensorflow. -->\n",
|
|
"<!-- -->\n",
|
|
"<!-- Note: Using a sequence length of two is not required for time series forecasting so any lenght of sequence could be used (for example instead of ((y1, y2) y3) you could change the length of sequence to be 4 and the resulting data points would have the form ((y1, y2, y3, y4), y5)). While the following method can be used to create a data set of any sequence length, the remainder of the code expects the length of sequence to be 2. This is because the data sets are very small and the higher the lenght of the sequence the less resulting data points. -->"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 11,
|
|
"id": "3ef3ed13",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# FORMAT_DATA\n",
|
|
"def format_data(data, length_of_sequence = 2): \n",
|
|
" \"\"\"\n",
|
|
" Inputs:\n",
|
|
" data(a numpy array): the data that will be the inputs to the recurrent neural\n",
|
|
" network\n",
|
|
" length_of_sequence (an int): the number of elements in one iteration of the\n",
|
|
" sequence patter. For a function approximator use length_of_sequence = 2.\n",
|
|
" Returns:\n",
|
|
" rnn_input (a 3D numpy array): the input data for the recurrent neural network. Its\n",
|
|
" dimensions are length of data - length of sequence, length of sequence, \n",
|
|
" dimnsion of data\n",
|
|
" rnn_output (a numpy array): the training data for the neural network\n",
|
|
" Formats data to be used in a recurrent neural network.\n",
|
|
" \"\"\"\n",
|
|
"\n",
|
|
" X, Y = [], []\n",
|
|
" for i in range(len(data)-length_of_sequence):\n",
|
|
" # Get the next length_of_sequence elements\n",
|
|
" a = data[i:i+length_of_sequence]\n",
|
|
" # Get the element that immediately follows that\n",
|
|
" b = data[i+length_of_sequence]\n",
|
|
" # Reshape so that each data point is contained in its own array\n",
|
|
" a = np.reshape (a, (len(a), 1))\n",
|
|
" X.append(a)\n",
|
|
" Y.append(b)\n",
|
|
" rnn_input = np.array(X)\n",
|
|
" rnn_output = np.array(Y)\n",
|
|
"\n",
|
|
" return rnn_input, rnn_output\n",
|
|
"\n",
|
|
"\n",
|
|
"# ## Defining the Recurrent Neural Network Using Keras\n",
|
|
"# \n",
|
|
"# The following method defines a simple recurrent neural network in keras consisting of one input layer, one hidden layer, and one output layer.\n",
|
|
"\n",
|
|
"def rnn(length_of_sequences, batch_size = None, stateful = False):\n",
|
|
" \"\"\"\n",
|
|
" Inputs:\n",
|
|
" length_of_sequences (an int): the number of y values in \"x data\". This is determined\n",
|
|
" when the data is formatted\n",
|
|
" batch_size (an int): Default value is None. See Keras documentation of SimpleRNN.\n",
|
|
" stateful (a boolean): Default value is False. See Keras documentation of SimpleRNN.\n",
|
|
" Returns:\n",
|
|
" model (a Keras model): The recurrent neural network that is built and compiled by this\n",
|
|
" method\n",
|
|
" Builds and compiles a recurrent neural network with one hidden layer and returns the model.\n",
|
|
" \"\"\"\n",
|
|
" # Number of neurons in the input and output layers\n",
|
|
" in_out_neurons = 1\n",
|
|
" # Number of neurons in the hidden layer\n",
|
|
" hidden_neurons = 200\n",
|
|
" # Define the input layer\n",
|
|
" inp = Input(batch_shape=(batch_size, \n",
|
|
" length_of_sequences, \n",
|
|
" in_out_neurons)) \n",
|
|
" # Define the hidden layer as a simple RNN layer with a set number of neurons and add it to \n",
|
|
" # the network immediately after the input layer\n",
|
|
" rnn = SimpleRNN(hidden_neurons, \n",
|
|
" return_sequences=False,\n",
|
|
" stateful = stateful,\n",
|
|
" name=\"RNN\")(inp)\n",
|
|
" # Define the output layer as a dense neural network layer (standard neural network layer)\n",
|
|
" #and add it to the network immediately after the hidden layer.\n",
|
|
" dens = Dense(in_out_neurons,name=\"dense\")(rnn)\n",
|
|
" # Create the machine learning model starting with the input layer and ending with the \n",
|
|
" # output layer\n",
|
|
" model = Model(inputs=[inp],outputs=[dens])\n",
|
|
" # Compile the machine learning model using the mean squared error function as the loss \n",
|
|
" # function and an Adams optimizer.\n",
|
|
" model.compile(loss=\"mean_squared_error\", optimizer=\"adam\") \n",
|
|
" return model"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "8a8b29b8",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Predicting New Points With A Trained Recurrent Neural Network"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 12,
|
|
"id": "d840beea",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"def test_rnn (x1, y_test, plot_min, plot_max):\n",
|
|
" \"\"\"\n",
|
|
" Inputs:\n",
|
|
" x1 (a list or numpy array): The complete x component of the data set\n",
|
|
" y_test (a list or numpy array): The complete y component of the data set\n",
|
|
" plot_min (an int or float): the smallest x value used in the training data\n",
|
|
" plot_max (an int or float): the largest x valye used in the training data\n",
|
|
" Returns:\n",
|
|
" None.\n",
|
|
" Uses a trained recurrent neural network model to predict future points in the \n",
|
|
" series. Computes the MSE of the predicted data set from the true data set, saves\n",
|
|
" the predicted data set to a csv file, and plots the predicted and true data sets w\n",
|
|
" while also displaying the data range used for training.\n",
|
|
" \"\"\"\n",
|
|
" # Add the training data as the first dim points in the predicted data array as these\n",
|
|
" # are known values.\n",
|
|
" y_pred = y_test[:dim].tolist()\n",
|
|
" # Generate the first input to the trained recurrent neural network using the last two \n",
|
|
" # points of the training data. Based on how the network was trained this means that it\n",
|
|
" # will predict the first point in the data set after the training data. All of the \n",
|
|
" # brackets are necessary for Tensorflow.\n",
|
|
" next_input = np.array([[[y_test[dim-2]], [y_test[dim-1]]]])\n",
|
|
" # Save the very last point in the training data set. This will be used later.\n",
|
|
" last = [y_test[dim-1]]\n",
|
|
"\n",
|
|
" # Iterate until the complete data set is created.\n",
|
|
" for i in range (dim, len(y_test)):\n",
|
|
" # Predict the next point in the data set using the previous two points.\n",
|
|
" next = model.predict(next_input)\n",
|
|
" # Append just the number of the predicted data set\n",
|
|
" y_pred.append(next[0][0])\n",
|
|
" # Create the input that will be used to predict the next data point in the data set.\n",
|
|
" next_input = np.array([[last, next[0]]], dtype=np.float64)\n",
|
|
" last = next\n",
|
|
"\n",
|
|
" # Print the mean squared error between the known data set and the predicted data set.\n",
|
|
" print('MSE: ', np.square(np.subtract(y_test, y_pred)).mean())\n",
|
|
" # Save the predicted data set as a csv file for later use\n",
|
|
" name = datatype + 'Predicted'+str(dim)+'.csv'\n",
|
|
" np.savetxt(name, y_pred, delimiter=',')\n",
|
|
" # Plot the known data set and the predicted data set. The red box represents the region that was used\n",
|
|
" # for the training data.\n",
|
|
" fig, ax = plt.subplots()\n",
|
|
" ax.plot(x1, y_test, label=\"true\", linewidth=3)\n",
|
|
" ax.plot(x1, y_pred, 'g-.',label=\"predicted\", linewidth=4)\n",
|
|
" ax.legend()\n",
|
|
" # Created a red region to represent the points used in the training data.\n",
|
|
" ax.axvspan(plot_min, plot_max, alpha=0.25, color='red')\n",
|
|
" plt.show()\n",
|
|
"\n",
|
|
"# Check to make sure the data set is complete\n",
|
|
"assert len(X_tot) == len(y_tot)\n",
|
|
"\n",
|
|
"# This is the number of points that will be used in as the training data\n",
|
|
"dim=12\n",
|
|
"\n",
|
|
"# Separate the training data from the whole data set\n",
|
|
"X_train = X_tot[:dim]\n",
|
|
"y_train = y_tot[:dim]\n",
|
|
"\n",
|
|
"\n",
|
|
"# Generate the training data for the RNN, using a sequence of 2\n",
|
|
"rnn_input, rnn_training = format_data(y_train, 2)\n",
|
|
"\n",
|
|
"\n",
|
|
"# Create a recurrent neural network in Keras and produce a summary of the \n",
|
|
"# machine learning model\n",
|
|
"model = rnn(length_of_sequences = rnn_input.shape[1])\n",
|
|
"model.summary()\n",
|
|
"\n",
|
|
"# Start the timer. Want to time training+testing\n",
|
|
"start = timer()\n",
|
|
"# Fit the model using the training data genenerated above using 150 training iterations and a 5%\n",
|
|
"# validation split. Setting verbose to True prints information about each training iteration.\n",
|
|
"hist = model.fit(rnn_input, rnn_training, batch_size=None, epochs=150, \n",
|
|
" verbose=True,validation_split=0.05)\n",
|
|
"\n",
|
|
"for label in [\"loss\",\"val_loss\"]:\n",
|
|
" plt.plot(hist.history[label],label=label)\n",
|
|
"\n",
|
|
"plt.ylabel(\"loss\")\n",
|
|
"plt.xlabel(\"epoch\")\n",
|
|
"plt.title(\"The final validation loss: {}\".format(hist.history[\"val_loss\"][-1]))\n",
|
|
"plt.legend()\n",
|
|
"plt.show()\n",
|
|
"\n",
|
|
"# Use the trained neural network to predict more points of the data set\n",
|
|
"test_rnn(X_tot, y_tot, X_tot[0], X_tot[dim-1])\n",
|
|
"# Stop the timer and calculate the total time needed.\n",
|
|
"end = timer()\n",
|
|
"print('Time: ', end-start)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "2e5b339f",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Other Things to Try\n",
|
|
"\n",
|
|
"Changing the size of the recurrent neural network and its parameters\n",
|
|
"can drastically change the results you get from the model. The below\n",
|
|
"code takes the simple recurrent neural network from above and adds a\n",
|
|
"second hidden layer, changes the number of neurons in the hidden\n",
|
|
"layer, and explicitly declares the activation function of the hidden\n",
|
|
"layers to be a sigmoid function. The loss function and optimizer can\n",
|
|
"also be changed but are kept the same as the above network. These\n",
|
|
"parameters can be tuned to provide the optimal result from the\n",
|
|
"network. For some ideas on how to improve the performance of a\n",
|
|
"[recurrent neural network](https://danijar.com/tips-for-training-recurrent-neural-networks)."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 13,
|
|
"id": "370e799d",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"def rnn_2layers(length_of_sequences, batch_size = None, stateful = False):\n",
|
|
" \"\"\"\n",
|
|
" Inputs:\n",
|
|
" length_of_sequences (an int): the number of y values in \"x data\". This is determined\n",
|
|
" when the data is formatted\n",
|
|
" batch_size (an int): Default value is None. See Keras documentation of SimpleRNN.\n",
|
|
" stateful (a boolean): Default value is False. See Keras documentation of SimpleRNN.\n",
|
|
" Returns:\n",
|
|
" model (a Keras model): The recurrent neural network that is built and compiled by this\n",
|
|
" method\n",
|
|
" Builds and compiles a recurrent neural network with two hidden layers and returns the model.\n",
|
|
" \"\"\"\n",
|
|
" # Number of neurons in the input and output layers\n",
|
|
" in_out_neurons = 1\n",
|
|
" # Number of neurons in the hidden layer, increased from the first network\n",
|
|
" hidden_neurons = 500\n",
|
|
" # Define the input layer\n",
|
|
" inp = Input(batch_shape=(batch_size, \n",
|
|
" length_of_sequences, \n",
|
|
" in_out_neurons)) \n",
|
|
" # Create two hidden layers instead of one hidden layer. Explicitly set the activation\n",
|
|
" # function to be the sigmoid function (the default value is hyperbolic tangent)\n",
|
|
" rnn1 = SimpleRNN(hidden_neurons, \n",
|
|
" return_sequences=True, # This needs to be True if another hidden layer is to follow\n",
|
|
" stateful = stateful, activation = 'sigmoid',\n",
|
|
" name=\"RNN1\")(inp)\n",
|
|
" rnn2 = SimpleRNN(hidden_neurons, \n",
|
|
" return_sequences=False, activation = 'sigmoid',\n",
|
|
" stateful = stateful,\n",
|
|
" name=\"RNN2\")(rnn1)\n",
|
|
" # Define the output layer as a dense neural network layer (standard neural network layer)\n",
|
|
" #and add it to the network immediately after the hidden layer.\n",
|
|
" dens = Dense(in_out_neurons,name=\"dense\")(rnn2)\n",
|
|
" # Create the machine learning model starting with the input layer and ending with the \n",
|
|
" # output layer\n",
|
|
" model = Model(inputs=[inp],outputs=[dens])\n",
|
|
" # Compile the machine learning model using the mean squared error function as the loss \n",
|
|
" # function and an Adams optimizer.\n",
|
|
" model.compile(loss=\"mean_squared_error\", optimizer=\"adam\") \n",
|
|
" return model\n",
|
|
"\n",
|
|
"# Check to make sure the data set is complete\n",
|
|
"assert len(X_tot) == len(y_tot)\n",
|
|
"\n",
|
|
"# This is the number of points that will be used in as the training data\n",
|
|
"dim=12\n",
|
|
"\n",
|
|
"# Separate the training data from the whole data set\n",
|
|
"X_train = X_tot[:dim]\n",
|
|
"y_train = y_tot[:dim]\n",
|
|
"\n",
|
|
"\n",
|
|
"# Generate the training data for the RNN, using a sequence of 2\n",
|
|
"rnn_input, rnn_training = format_data(y_train, 2)\n",
|
|
"\n",
|
|
"\n",
|
|
"# Create a recurrent neural network in Keras and produce a summary of the \n",
|
|
"# machine learning model\n",
|
|
"model = rnn_2layers(length_of_sequences = 2)\n",
|
|
"model.summary()\n",
|
|
"\n",
|
|
"# Start the timer. Want to time training+testing\n",
|
|
"start = timer()\n",
|
|
"# Fit the model using the training data genenerated above using 150 training iterations and a 5%\n",
|
|
"# validation split. Setting verbose to True prints information about each training iteration.\n",
|
|
"hist = model.fit(rnn_input, rnn_training, batch_size=None, epochs=150, \n",
|
|
" verbose=True,validation_split=0.05)\n",
|
|
"\n",
|
|
"\n",
|
|
"# This section plots the training loss and the validation loss as a function of training iteration.\n",
|
|
"# This is not required for analyzing the couple cluster data but can help determine if the network is\n",
|
|
"# being overtrained.\n",
|
|
"for label in [\"loss\",\"val_loss\"]:\n",
|
|
" plt.plot(hist.history[label],label=label)\n",
|
|
"\n",
|
|
"plt.ylabel(\"loss\")\n",
|
|
"plt.xlabel(\"epoch\")\n",
|
|
"plt.title(\"The final validation loss: {}\".format(hist.history[\"val_loss\"][-1]))\n",
|
|
"plt.legend()\n",
|
|
"plt.show()\n",
|
|
"\n",
|
|
"# Use the trained neural network to predict more points of the data set\n",
|
|
"test_rnn(X_tot, y_tot, X_tot[0], X_tot[dim-1])\n",
|
|
"# Stop the timer and calculate the total time needed.\n",
|
|
"end = timer()\n",
|
|
"print('Time: ', end-start)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "d8f62fc4",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Other Types of Recurrent Neural Networks\n",
|
|
"\n",
|
|
"Besides a simple recurrent neural network layer, there are two other\n",
|
|
"commonly used types of recurrent neural network layers: Long Short\n",
|
|
"Term Memory (LSTM) and Gated Recurrent Unit (GRU). For a short\n",
|
|
"introduction to these layers see <https://medium.com/mindboard/lstm-vs-gru-experimental-comparison-955820c21e8b>\n",
|
|
"and <https://medium.com/mindboard/lstm-vs-gru-experimental-comparison-955820c21e8b>.\n",
|
|
"\n",
|
|
"The first network created below is similar to the previous network,\n",
|
|
"but it replaces the SimpleRNN layers with LSTM layers. The second\n",
|
|
"network below has two hidden layers made up of GRUs, which are\n",
|
|
"preceeded by two dense (feeddorward) neural network layers. These\n",
|
|
"dense layers \"preprocess\" the data before it reaches the recurrent\n",
|
|
"layers. This architecture has been shown to improve the performance\n",
|
|
"of recurrent neural networks (see the link above and also\n",
|
|
"<https://arxiv.org/pdf/1807.02857.pdf>."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 14,
|
|
"id": "4ea36ea9",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"def lstm_2layers(length_of_sequences, batch_size = None, stateful = False):\n",
|
|
" \"\"\"\n",
|
|
" Inputs:\n",
|
|
" length_of_sequences (an int): the number of y values in \"x data\". This is determined\n",
|
|
" when the data is formatted\n",
|
|
" batch_size (an int): Default value is None. See Keras documentation of SimpleRNN.\n",
|
|
" stateful (a boolean): Default value is False. See Keras documentation of SimpleRNN.\n",
|
|
" Returns:\n",
|
|
" model (a Keras model): The recurrent neural network that is built and compiled by this\n",
|
|
" method\n",
|
|
" Builds and compiles a recurrent neural network with two LSTM hidden layers and returns the model.\n",
|
|
" \"\"\"\n",
|
|
" # Number of neurons on the input/output layer and the number of neurons in the hidden layer\n",
|
|
" in_out_neurons = 1\n",
|
|
" hidden_neurons = 250\n",
|
|
" # Input Layer\n",
|
|
" inp = Input(batch_shape=(batch_size, \n",
|
|
" length_of_sequences, \n",
|
|
" in_out_neurons)) \n",
|
|
" # Hidden layers (in this case they are LSTM layers instead if SimpleRNN layers)\n",
|
|
" rnn= LSTM(hidden_neurons, \n",
|
|
" return_sequences=True,\n",
|
|
" stateful = stateful,\n",
|
|
" name=\"RNN\", use_bias=True, activation='tanh')(inp)\n",
|
|
" rnn1 = LSTM(hidden_neurons, \n",
|
|
" return_sequences=False,\n",
|
|
" stateful = stateful,\n",
|
|
" name=\"RNN1\", use_bias=True, activation='tanh')(rnn)\n",
|
|
" # Output layer\n",
|
|
" dens = Dense(in_out_neurons,name=\"dense\")(rnn1)\n",
|
|
" # Define the midel\n",
|
|
" model = Model(inputs=[inp],outputs=[dens])\n",
|
|
" # Compile the model\n",
|
|
" model.compile(loss='mean_squared_error', optimizer='adam') \n",
|
|
" # Return the model\n",
|
|
" return model\n",
|
|
"\n",
|
|
"def dnn2_gru2(length_of_sequences, batch_size = None, stateful = False):\n",
|
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" \"\"\"\n",
|
|
" Inputs:\n",
|
|
" length_of_sequences (an int): the number of y values in \"x data\". This is determined\n",
|
|
" when the data is formatted\n",
|
|
" batch_size (an int): Default value is None. See Keras documentation of SimpleRNN.\n",
|
|
" stateful (a boolean): Default value is False. See Keras documentation of SimpleRNN.\n",
|
|
" Returns:\n",
|
|
" model (a Keras model): The recurrent neural network that is built and compiled by this\n",
|
|
" method\n",
|
|
" Builds and compiles a recurrent neural network with four hidden layers (two dense followed by\n",
|
|
" two GRU layers) and returns the model.\n",
|
|
" \"\"\" \n",
|
|
" # Number of neurons on the input/output layers and hidden layers\n",
|
|
" in_out_neurons = 1\n",
|
|
" hidden_neurons = 250\n",
|
|
" # Input layer\n",
|
|
" inp = Input(batch_shape=(batch_size, \n",
|
|
" length_of_sequences, \n",
|
|
" in_out_neurons)) \n",
|
|
" # Hidden Dense (feedforward) layers\n",
|
|
" dnn = Dense(hidden_neurons/2, activation='relu', name='dnn')(inp)\n",
|
|
" dnn1 = Dense(hidden_neurons/2, activation='relu', name='dnn1')(dnn)\n",
|
|
" # Hidden GRU layers\n",
|
|
" rnn1 = GRU(hidden_neurons, \n",
|
|
" return_sequences=True,\n",
|
|
" stateful = stateful,\n",
|
|
" name=\"RNN1\", use_bias=True)(dnn1)\n",
|
|
" rnn = GRU(hidden_neurons, \n",
|
|
" return_sequences=False,\n",
|
|
" stateful = stateful,\n",
|
|
" name=\"RNN\", use_bias=True)(rnn1)\n",
|
|
" # Output layer\n",
|
|
" dens = Dense(in_out_neurons,name=\"dense\")(rnn)\n",
|
|
" # Define the model\n",
|
|
" model = Model(inputs=[inp],outputs=[dens])\n",
|
|
" # Compile the mdoel\n",
|
|
" model.compile(loss='mean_squared_error', optimizer='adam') \n",
|
|
" # Return the model\n",
|
|
" return model\n",
|
|
"\n",
|
|
"# Check to make sure the data set is complete\n",
|
|
"assert len(X_tot) == len(y_tot)\n",
|
|
"\n",
|
|
"# This is the number of points that will be used in as the training data\n",
|
|
"dim=12\n",
|
|
"\n",
|
|
"# Separate the training data from the whole data set\n",
|
|
"X_train = X_tot[:dim]\n",
|
|
"y_train = y_tot[:dim]\n",
|
|
"\n",
|
|
"\n",
|
|
"# Generate the training data for the RNN, using a sequence of 2\n",
|
|
"rnn_input, rnn_training = format_data(y_train, 2)\n",
|
|
"\n",
|
|
"\n",
|
|
"# Create a recurrent neural network in Keras and produce a summary of the \n",
|
|
"# machine learning model\n",
|
|
"# Change the method name to reflect which network you want to use\n",
|
|
"model = dnn2_gru2(length_of_sequences = 2)\n",
|
|
"model.summary()\n",
|
|
"\n",
|
|
"# Start the timer. Want to time training+testing\n",
|
|
"start = timer()\n",
|
|
"# Fit the model using the training data genenerated above using 150 training iterations and a 5%\n",
|
|
"# validation split. Setting verbose to True prints information about each training iteration.\n",
|
|
"hist = model.fit(rnn_input, rnn_training, batch_size=None, epochs=150, \n",
|
|
" verbose=True,validation_split=0.05)\n",
|
|
"\n",
|
|
"\n",
|
|
"# This section plots the training loss and the validation loss as a function of training iteration.\n",
|
|
"# This is not required for analyzing the couple cluster data but can help determine if the network is\n",
|
|
"# being overtrained.\n",
|
|
"for label in [\"loss\",\"val_loss\"]:\n",
|
|
" plt.plot(hist.history[label],label=label)\n",
|
|
"\n",
|
|
"plt.ylabel(\"loss\")\n",
|
|
"plt.xlabel(\"epoch\")\n",
|
|
"plt.title(\"The final validation loss: {}\".format(hist.history[\"val_loss\"][-1]))\n",
|
|
"plt.legend()\n",
|
|
"plt.show()\n",
|
|
"\n",
|
|
"# Use the trained neural network to predict more points of the data set\n",
|
|
"test_rnn(X_tot, y_tot, X_tot[0], X_tot[dim-1])\n",
|
|
"# Stop the timer and calculate the total time needed.\n",
|
|
"end = timer()\n",
|
|
"print('Time: ', end-start)\n",
|
|
"\n",
|
|
"\n",
|
|
"# ### Training Recurrent Neural Networks in the Standard Way (i.e. learning the relationship between the X and Y data)\n",
|
|
"# \n",
|
|
"# Finally, comparing the performace of a recurrent neural network using the standard data formatting to the performance of the network with time sequence data formatting shows the benefit of this type of data formatting with extrapolation.\n",
|
|
"\n",
|
|
"# Check to make sure the data set is complete\n",
|
|
"assert len(X_tot) == len(y_tot)\n",
|
|
"\n",
|
|
"# This is the number of points that will be used in as the training data\n",
|
|
"dim=12\n",
|
|
"\n",
|
|
"# Separate the training data from the whole data set\n",
|
|
"X_train = X_tot[:dim]\n",
|
|
"y_train = y_tot[:dim]\n",
|
|
"\n",
|
|
"# Reshape the data for Keras specifications\n",
|
|
"X_train = X_train.reshape((dim, 1))\n",
|
|
"y_train = y_train.reshape((dim, 1))\n",
|
|
"\n",
|
|
"\n",
|
|
"# Create a recurrent neural network in Keras and produce a summary of the \n",
|
|
"# machine learning model\n",
|
|
"# Set the sequence length to 1 for regular data formatting \n",
|
|
"model = rnn(length_of_sequences = 1)\n",
|
|
"model.summary()\n",
|
|
"\n",
|
|
"# Start the timer. Want to time training+testing\n",
|
|
"start = timer()\n",
|
|
"# Fit the model using the training data genenerated above using 150 training iterations and a 5%\n",
|
|
"# validation split. Setting verbose to True prints information about each training iteration.\n",
|
|
"hist = model.fit(X_train, y_train, batch_size=None, epochs=150, \n",
|
|
" verbose=True,validation_split=0.05)\n",
|
|
"\n",
|
|
"\n",
|
|
"# This section plots the training loss and the validation loss as a function of training iteration.\n",
|
|
"# This is not required for analyzing the couple cluster data but can help determine if the network is\n",
|
|
"# being overtrained.\n",
|
|
"for label in [\"loss\",\"val_loss\"]:\n",
|
|
" plt.plot(hist.history[label],label=label)\n",
|
|
"\n",
|
|
"plt.ylabel(\"loss\")\n",
|
|
"plt.xlabel(\"epoch\")\n",
|
|
"plt.title(\"The final validation loss: {}\".format(hist.history[\"val_loss\"][-1]))\n",
|
|
"plt.legend()\n",
|
|
"plt.show()\n",
|
|
"\n",
|
|
"# Use the trained neural network to predict the remaining data points\n",
|
|
"X_pred = X_tot[dim:]\n",
|
|
"X_pred = X_pred.reshape((len(X_pred), 1))\n",
|
|
"y_model = model.predict(X_pred)\n",
|
|
"y_pred = np.concatenate((y_tot[:dim], y_model.flatten()))\n",
|
|
"\n",
|
|
"# Plot the known data set and the predicted data set. The red box represents the region that was used\n",
|
|
"# for the training data.\n",
|
|
"fig, ax = plt.subplots()\n",
|
|
"ax.plot(X_tot, y_tot, label=\"true\", linewidth=3)\n",
|
|
"ax.plot(X_tot, y_pred, 'g-.',label=\"predicted\", linewidth=4)\n",
|
|
"ax.legend()\n",
|
|
"# Created a red region to represent the points used in the training data.\n",
|
|
"ax.axvspan(X_tot[0], X_tot[dim], alpha=0.25, color='red')\n",
|
|
"plt.show()\n",
|
|
"\n",
|
|
"# Stop the timer and calculate the total time needed.\n",
|
|
"end = timer()\n",
|
|
"print('Time: ', end-start)"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
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|
|
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|
|
"language": "python",
|
|
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|
|
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|
|
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|
|
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|
|
"name": "ipython",
|
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|
|
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|
|
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|
|
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|
|
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|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.9.18"
|
|
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|
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|
|
"nbformat": 4,
|
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"nbformat_minor": 5
|
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|