{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "

Machine Learning Using Python (MEAFA Workshop)

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Lesson 4: Logistic Regression and Optimal Decisions

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\n", "\n", "The [Lending Club](https://www.lendingclub.com) is a [peer-to-peer lending](https://en.wikipedia.org/wiki/Peer-to-peer_lending) company that provides a platform to match individuals seeking personal loans to investors willing purchase claims on personal loan repayments. \n", "\n", "In this lesson we will apply statistical learning methods to predict the risk of loans in the platform, and to decide whether it is worth it to invest in each of the them based on these predictions. At the end of the tutorial, we use these tools to build and assess an investment strategy \n", "\n", "#Lending-Club-Data\">Lending Club Data
\n", "#Decision-Problem\">Decision Problem
\n", "Exploratory Data Analysis
\n", "Feature Engineering and Data Preparation
\n", "Logistic Regression
\n", "Model Evaluation
\n", "Investment strategy
\n", "\n", "This notebook relies on the following libraries and settings." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Packages\n", "import numpy as np\n", "from scipy import stats\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "import warnings\n", "warnings.filterwarnings('ignore') " ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Plot settings\n", "sns.set_context('notebook') \n", "sns.set_style('ticks') \n", "colours = ['#1F77B4', '#FF7F0E', '#2CA02C', '#DB2728', '#9467BD', '#8C564B', '#E377C2','#7F7F7F', '#BCBD22', '#17BECF']\n", "crayon = ['#4E79A7','#F28E2C','#E15759','#76B7B2','#59A14F', '#EDC949','#AF7AA1','#FF9DA7','#9C755F','#BAB0AB']\n", "sns.set_palette(colours)\n", "%matplotlib inline\n", "plt.rcParams['figure.figsize'] = (9, 6)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Model selection and evaluation tools\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.metrics import accuracy_score, confusion_matrix, roc_auc_score\n", "from sklearn.metrics import precision_score, average_precision_score, log_loss\n", "\n", "# Data processing\n", "from sklearn.preprocessing import StandardScaler" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Lending Club Data\n", "\n", "**Business objective:** to decide whether to invest in specific loans. \n", "\n", "### Data\n", "\n", "The Lending Club makes its data openly available on its [loan statistics](https://www.lendingclub.com/info/download-data.action) page. Our analysis is based on the 2007-2011 dataset. The LoanStats-clean.csv provides a processed version of the data to save time. )" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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funded_amntint_rateinstallmentannual_incdtirevol_balrevol_utiltotal_accmonths_since_earliest_cr_lineemp_length...term_60home_ownership_ownhome_ownership_rentverification_status_source verifiedverification_status_verifiedmonths_to_last_pymnttotal_pymnttotal_rec_prncptotal_rec_intfully_paid
05000.010.65162.8724000.027.6513648.083.79.032310...00101375863.1551875000.00863.161
12500.015.2759.8330000.01.001687.09.44.01520...10110161014.530000456.46435.170
22400.015.9684.3312252.08.722956.098.510.012110...00100303005.6668442400.00605.671
310000.013.49339.3149200.020.005598.021.037.019010...001103712231.89000010000.002214.921
43000.012.6967.7980000.017.9427783.053.938.01911...10110614066.9081613000.001066.911
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5 rows × 26 columns

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" ], "text/plain": [ " funded_amnt int_rate installment annual_inc dti revol_bal \\\n", "0 5000.0 10.65 162.87 24000.0 27.65 13648.0 \n", "1 2500.0 15.27 59.83 30000.0 1.00 1687.0 \n", "2 2400.0 15.96 84.33 12252.0 8.72 2956.0 \n", "3 10000.0 13.49 339.31 49200.0 20.00 5598.0 \n", "4 3000.0 12.69 67.79 80000.0 17.94 27783.0 \n", "\n", " revol_util total_acc months_since_earliest_cr_line emp_length \\\n", "0 83.7 9.0 323 10 \n", "1 9.4 4.0 152 0 \n", "2 98.5 10.0 121 10 \n", "3 21.0 37.0 190 10 \n", "4 53.9 38.0 191 1 \n", "\n", " ... term_60 home_ownership_own home_ownership_rent \\\n", "0 ... 0 0 1 \n", "1 ... 1 0 1 \n", "2 ... 0 0 1 \n", "3 ... 0 0 1 \n", "4 ... 1 0 1 \n", "\n", " verification_status_source verified verification_status_verified \\\n", "0 0 1 \n", "1 1 0 \n", "2 0 0 \n", "3 1 0 \n", "4 1 0 \n", "\n", " months_to_last_pymnt total_pymnt total_rec_prncp total_rec_int \\\n", "0 37 5863.155187 5000.00 863.16 \n", "1 16 1014.530000 456.46 435.17 \n", "2 30 3005.666844 2400.00 605.67 \n", "3 37 12231.890000 10000.00 2214.92 \n", "4 61 4066.908161 3000.00 1066.91 \n", "\n", " fully_paid \n", "0 1 \n", "1 0 \n", "2 1 \n", "3 1 \n", "4 1 \n", "\n", "[5 rows x 26 columns]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = pd.read_csv('Datasets/LoanStats-clean.csv')\n", "data.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since the dataset contains many variable, a separate file maps each to column to its type. " ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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nametype
0funded_amntcontinuous
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2installmentcontinuous
3annual_inccontinuous
4dticontinuous
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" ], "text/plain": [ " name type\n", "0 funded_amnt continuous\n", "1 int_rate continuous\n", "2 installment continuous\n", "3 annual_inc continuous\n", "4 dti continuous" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "variables = pd.read_csv('Datasets/LoanStats-clean-variables.csv')\n", "variables.head()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "continuous 9\n", "discrete 6\n", "outcome 5\n", "dummy 5\n", "response 1\n", "categorical 1\n", "Name: type, dtype: int64" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "variables['type'].value_counts()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "response = variables.loc[variables['type'] == 'response', 'name'].values[0]\n", "outcomes = list(variables.loc[variables['type'] == 'outcome', 'name'])\n", "continuous = list(variables.loc[variables['type'] == 'continuous', 'name'])\n", "discrete = list(variables.loc[variables['type'] == 'discrete', 'name'])\n", "dummies = list(variables.loc[variables['type'] == 'dummy', 'name'])\n", "categorical = list(variables.loc[variables['type'] == 'dummy', 'name'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Investment Returns\n", "\n", "The main outcome of interest is the return on a loan. If the loan is paid back in full, the return is the interest rate for the loan. If there is a default, we need to compute the realised return based on " ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "returns = np.array(data['int_rate'].copy())\n", "\n", "for i in range(len(data)):\n", " \n", " if data['fully_paid'].iloc[i]==0: # if default for observation i\n", " \n", " # number of payments\n", " periods = data['months_to_last_pymnt'].iloc[i] \n", " \n", " # we assume equal payments until default \n", " cash_flow = data['total_pymnt'].iloc[i]/periods\n", " \n", " # interest rate formula based on the loan amont and cash flows\n", " ret = np.rate(periods, -cash_flow, data['funded_amnt'].iloc[i], fv=0)\n", " returns[i] = 100*((1+ret)**12-1)\n", "\n", "data['return'] = returns" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Data Splitting\n", "\n", "We split the data into training and test samples as usual. Since the number of defaults is relatively low, we use stratification to ensure that the training and test sets have the proportion of defaults. " ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "index_train, index_test = train_test_split(np.array(data.index), stratify=data[response], train_size=0.5, random_state=10)\n", "\n", "train = data.loc[index_train,:].copy()\n", "test = data.loc[index_test,:].copy()\n", "\n", "# The response is the fully paid variable (1 if fully paid, 0 if defaulty)\n", "\n", "y_train = train[response]\n", "y_test = test[response]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Decision problem\n", "\n", "We adopt the point of view of a personal investor that wants to allocate funds between loans posted on the Lending Club and an alternavive investment. \n", "\n", "The detailed information in the dataset allows us to build a realistic loss matrix for the lending decision problem and to evaulate the investment performance of applying different prediction strategies. Because the interest rates depend on the loan, we have unique loss matrices for each borrower. \n", "\n", "First, we define each classification outcome and the associated gain or loss for the investor. \n", "\n", "**True positive:** we invest in a loan that is fully repayed. Return from interest payments. \n", "\n", "**False positive:** we invest in a loan but incur a loss from default. We will use the training data to compute the expected (negative) return in case of default. \n", "\n", "**True negative:** we do not invest in a loan that ends in a a default. Return from an alternative investment.\n", "\n", "**False negative:** we do not invest in a loan that is fully repaid. Return from an alternative investment.\n", "\n", "The investor should purchase the loan if the expected return (which depends on the interest rate and the default risk) is higher than the opportunity cost (the return from the alternative investment). " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We use training data to compute useful statistics and build a loss matrix. For example, the average return across all loans is 3.3%." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "count 19479.00\n", "mean 3.32\n", "std 25.83\n", "min -100.00\n", "25% 7.49\n", "50% 10.99\n", "75% 13.72\n", "max 154.83\n", "Name: return, dtype: float64" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train['return'].describe().round(2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The proportion of fully paid loans in the training data is 86%. " ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.86\n", "0.14\n" ] } ], "source": [ "print(round(train['fully_paid'].mean(),3))\n", "print(round(1-train['fully_paid'].mean(),3))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The fully paid loans yield an interest rate of of 11.7% on average, while the defaults lead to a return of -48% on average. " ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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countmeanstdmin25%50%75%max
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02734.0-48.1239.93-100.00-88.20-48.42-12.01154.83
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" ], "text/plain": [ " count mean std min 25% 50% 75% max\n", "fully_paid \n", "0 2734.0 -48.12 39.93 -100.00 -88.20 -48.42 -12.01 154.83\n", "1 16745.0 11.72 3.68 5.42 8.59 11.49 14.22 24.59" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train.groupby('fully_paid').describe()['return'].round(2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The return in case of a default is very weakly correlated with the predictors, so that we take the sample average of -48% to be the loss from a false positive. " ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.024\n" ] } ], "source": [ "import statsmodels.api as sm\n", "y = train.loc[train['fully_paid']==0,'return']\n", "X = train.loc[train['fully_paid']==0, :][continuous + discrete + dummies]\n", "ols = sm.OLS(y, sm.add_constant(X)).fit()\n", "print(round(ols.rsquared,3))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To build the loss matrices, we assume that the opportunity cost is 3.33%, which is average return from classifying all observations as positive. " ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true }, "outputs": [], "source": [ "loss_fn = -train['return'].mean()\n", "loss_tn = -train['return'].mean()\n", "\n", "loss_fp = -train.loc[train['fully_paid']==0,'return'].mean()\n", "\n", "# We know the interest rate (loss from )\n", "loss_tp = -test['int_rate']\n", "\n", "tau = (loss_fp - loss_tn) / (loss_fp + loss_fn - loss_tp - loss_tn)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will typically require a predicted default probabilty of less than 15% to invest in a loan. " ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "count 19480.000\n", "mean 0.858\n", "std 0.053\n", "min 0.709\n", "25% 0.820\n", "50% 0.858\n", "75% 0.895\n", "max 0.961\n", "Name: int_rate, dtype: float64" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tau.describe().round(3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exploratory Data Analysis\n", "\n", "The distribution plots suggest that most predictors are positively skewed, with income and the total credit revolving balance being particularly so. " ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "image/png": 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Iu/Amjmx6j+by7bQfrqT2S9POWKbdCodPKa5RVFSE0+mMalwisUYJloiIiMgw11M+veWA\nH4c7Cd/I8YPyHofLQ8FnvsLh9W/RsGMVP/7xj7n++uu55pprTkmcuprquOvpGrxZlYDOzRLpoQRL\nREQGVSgUory8/ISfaZRbpH8CtWUAJI0YNWjvcLjcjLzwJlwpGeQ4Snnvvfd477336OrqorreTWrR\nDDImLwDAkxFJ/ETkU0qwRETktE6XGEHfk6PjlzeBRrlFBqKtpgzD4cCTMbBDgE+3vO/kf04pMPn3\nu79OfX09GzZsYO3atQT2bKWzqY7G3Z+QNn4OqWn9r14oEq+UYA3AyZ2PUHfFnZM7HhqpFZFYdHJi\nBP1PjnqWN4lI/4W7OumoP0hSVj4Ol3tAzzp5eR9AS8VOkgumnHCdYRgsWLCABQsWcM0117DhNx/Q\n2VDN4Q3LqCt5G0dSMsm50Su2IRIPlGANwMmdj5aKnbhSM0/oRGikVkRimRIjkeEjUFeBFQ6TPHJc\nVJ538vK+zqO157zH4U4ic+qFJGUXUPbK/0f91uWMmHohTo83KjGJxAMlWOdgWdZZPz++89F5tBZX\nWrY6IyIy7EVr+Z+IDJ22mjIAvHnjCHcGbI3FlzuGtPGzaS7fxpHN75O34Hpb4xEZTpRgnYZlWTSX\nbuLQqr9iOFykT5hN5rSL7Q4rarThXESiufxPRIZGe205hgG+vLG0HthtdzikjZtFoK6KRv/HZEya\nZ3c4IsOGEqyTWJbFoRXP0nLAT7izA6fPTVPpZloqd5Izf4nd4UWFNpyLCGj5n0gsscIhOuoO4Mkc\nhdPjszscAAyHk8wZl1C/6T2O+teRMeUCu0MSGRaUYJ2keV8JLQf8+EaOI6VgKkm5Ywm11lOz+iVq\nVr/E/v3z4yIRUcdKREQkdnQ1HSEcCuLNKbA7lBP4Ro7HnZJB8/7NpI2fbXc4IsOCw+4AhpODBw/S\nsGMVTm8Koy/9Ii5fKoZhkD5+NqMu+yJWKMgf/vAHAgF71z2LiERDOBSk7dA+msu2EQ4F7Q5HRM6i\no6EaAG/28KrYZxgOMiYvIBzsoqVyl93hiAwLCTWDda69R8899xxWOBQ5XM+XesJ1aWNnkDF5PnV1\n+3jyySe5++67hyxuEZFoW7t2LZVvPo7hjPxvwJ2cTtrE84H42W8qEk86jvYkWMNrBgsgfeJcjmz5\ngJayLecsDiaSCBIqwTrb3iO/34/f78eXN47UMdNOe3/m1EXkH2ln6dKlXHTRRaSnpw9l+CIiUbFl\nyxaeeuopDKeTEdMuAiwadxdTV7KMDz+cEhfLoEXiTWdDDQ6n64TCNMOFKzmdlMKpNO0roaysjIkT\nJ9odkoitEm6JYM/eI2/W6BMaqZdeegmAjMkLzniv4XRxxx13YBgGv/vd7+jq6hr0eEVEoqm2tpaH\nH34YwzDIW3gTufOvI3f+Esbe8C0cHh/PPvss69at6/XzQqEQpaWlJ/zVc+i6iERHR0cHnc11JGXl\nYziGZ8Xf9AmR/VcbNmywORIR+yVcgnU6VVVVfPzxx4wbN46knLOvbS4qKmLJkiVUVVXx3nvvDVGE\nIiLR8cILL9DS0sKtt96K97j2zpOew8gLb8btdvOrX/2Kw4cP9+p5PSsDbn5sNTc/tpolD7182vO1\nRKT/KisrwbKGXYGL4yWPnozhdLNhwwYtE5SEpwQLeO2117Asi2uvvRbDMM55/e23305GRgZvvPEG\nwfbmIYhQRGTg6uvr+eCDD8jPz+fSSy895fOkrNHceuuttLe38+ijj/a6k3SmlQEiEh379+8HIGkY\n7r/q4XC5SR41gbq6Ovbt22d3OCK2SvgEq7Ozkw8//JDs7Gzmzp3bq3tSU1P56le/SmdnJw1bPxzk\nCEVEBi4UCvHEE0/Q1NTEhRdeyIEDB0573cUXX8zcuXMpKSlhzZo1QxyliJxOWVkZMDwLXBwvuWAK\nAKtXr7Y5EhF7JXyCtWnTJlpbW/nMZz6Dw9H7fx2LFy9m/PjxtB7cTduhvYMYoYjIwO3atYt//69n\nKTkU4Feb4Y7H3iF8mr1ShmHwne98B6/Xy1//+ldCHW02RCsix6usrMTh8uBOy7Y7lLPyjRxPUlIS\nq1at0jJBSWgJn2CtWrUKgKuuuqpP9xmGwW233QYY1Ba/iaVN3SIyjG3cuBEMg6zzriA5dwye9DN3\n1HJzc7n99ttpa2ujYftHQxiliJysq6uLmpoa3Ok5vdrGYCeH08V5551HdXW19mJKQkvoBCvY1sSu\nXbuYNm0aBQV9n3YvKioibfwsOhvraNilpTQiMnyVlJQAkDbuvF5df8MNN1BQUEBLxXbaa9VRErFL\nZWUl4XAYT3qO3aH0yuzZkWqCn3zyic2RiNgnoROs1gO7sCyLxYsX9/sZmdMuwpnko37LckKB1ihG\nJyISHYFAgO3bt+NOy+51J83pdHL77bcDULvuNc3Si9ikZybIbUOCZYXDVFRUHDuCoaKi4pz3zJw5\nE4fDoQRLElpCHTR8stYDu3DmOrn44ov7/Qynx0fO3Guo+fhVGvd8Ql7u2ChGKCIycOvXrycYDJKc\nP6VP902aNInUopm0HdzLUf9aRky/ZJAiFJEz6Umw7JjB6mqq466na/BmVQLQUrHzWCGLM0lNTWX6\n9Ols376dxsZGMjIyhiJUkWElYWewOo7W0tlUx8yZM0lJSRnQs9InzcObU0BbdSmBw5VRilBEJDp6\nqgGm5E/u870jZlyKM8nHkc0f0NXa2Kt7wuEwVVVVrFu3rtfnaYnI6X06g2VPgQtPxnHHMPQyhgsu\nuADLsli/fv0gRycyPCXsDFZL2VYg0ggMlGEY5C24gZaKndRvXc4I8wIM5/A8aV1EEktnZyfFxcXk\n5ORQ248RcKfHR875n6Vm7StUr3qRnHnXnvFay7JoqdzJ/fe/TFdXFwAOh4Np06YRbC/q9+8gksjK\nysrIyMjgsMdndyi9dsEFF/Dkk0/yySef9LmImEg8SMgZLMuyaC7fiuFwHduMOVDenEJSCky6Wuo5\n6v84Ks8UERmoXbt2EQgEmDNnTr8rkKVPPJ+0ohm015ZTv3XFaa/pammg6r0/UbdhKW1tbVx55ZXc\ndtttjB07luLiYmrW/J1QZ2AAv4lI4mltbaWurq5fhbjsVFBQQH5+PiUlJccGW0QSSUImWB0N1XQ2\nHSF51ASSkpKi9tyMyfNwur0c2fw+nU11UXuuiEh/bd0ama2fOnVqv59hGAYjF32OpBEjad6/maef\nfpq2tsj5WB0dHTTt20j5G4/RVr0f36gJ/OxnP+Pee+/lS1/6Er/97W+58sor6Wquo3rlX7DCKpYh\n0ls9RSUKCwttjqR3ji+KMX78eOrr61m6dCkhFcmRBJOQSwRbyrcBkFxoRvW5DreXEbM+Q8PW5VSv\n+htjPvsvUX2+iEhfbd68GcMwmDx5MqzaeNZrezpHPY7/s8PtIf+K26hc9kdWrVrF3r17GTVqFHv3\n7qV+WwWu5HRGXXQL7sxR5OR8uhTRMAy++MUv8r/fKqH10D4adq4hefSk6P+iInGoZ/9Vfn4+1Noc\nTC8cXxQjcDhMdVk9d/3H06yZOZMJEybYHZ7IkEnMBKtyJw6nC19e9PcEpBRMIdRaT1PpZo5sfp/U\nXp45IyISbYFAgD179jBp0iSSk5PPef25Koa5U0cw+oovc8PYBkpKSti/fz+WZZExZSG5867F5Uul\nva7qlFLOVVVV5My7juoPn6Nh20qSsmNruZPIUAuFQpSXl7N+/Xra2tpwOBxA2O6weqWnKEZSRh5H\nNr1LZ+NhLMuyOyyRIRXXCVZPA9WjoqKCruZ6OhsPkzpmKg6XZ1Dem7vgetprK6jf/hGWYQD9LwMv\nItJfO3fuJBgMMmvWrF7f09M5Aug8euqQueFwctNNN/G9730PgH379nHL42tw+VKBU5M0+DRRy5p1\nJYfXL6Vx18dA/88fFIl35eXlLHnoZeq3FxM4XM++lzaTOna63WH1ieF0kpw/mcY9xVRVVTFx4kS7\nQxIZMnGdYPU0UJ7MPCDyP/mutkiZ4dSxMwbtvU6Pj4KrvkLlsj9wZNO7rF8/T1PjIjLktmzZAtCn\nBKuvTlc44/gkDT5N1DKnLKTRv47m/Zupq6tTuyhyFp7MPKzOAEkZeXhHjLQ7nH5JKTRp3FPM5s2b\nueyyy+wOR2TIxH2RC0/miec3tB/ai+FwkFIQ3f1Xp7w3PYf8K2/HMJz8/ve/59FHH6W2NgYWUItI\n1IRCIUpLS4/9dfLSucG2ZcsWnE4n06cPj5Fvw+kka9aVWFaY999/3+5wRIa1cLCTrrYm3DYcMBwt\nKflTwDDYvHmz3aGIDKm4nsE6WbC9hY6jNaQVzcSZ5KOr9eigvs+XO5bRV3yZMV2befvtt3n77bfJ\nysoiKSkJy7IIh8OEw2GajmbhScvC4Y5eRUMRsd/xs+jB9maq3nuK5AKTwitvH/Sz8jo6Oti7dy9T\npkzB6/UO6rv6Iq3oPJzeFFatWkVbW1uv9oaJJKJg98HenhhOsJxJPpJG5FNWVkZzczNpaWl2hyQy\nJOJ+But4gcOR/VhDuY7Zk57DD3/4Q775zW+yaNEiHA4HHR0dBINBLMuiqqqK+i0fUPbqIyrtLhKH\nPJl5uJLTqduwlFCghZbyrVR98DShzvZBfW9ZWRnhcJhp06YN6nv6ynA6SRs/h0AgoFkskbPoaqkH\nwJ2ebXMkA+PLK6KtrY2lS5cem81X2XaJdwk1g9VeW46BQWph/8+D6Q+3282SJUtYsmTJKZ9t2rSJ\nf3jgcZrLtnLgnSfJXXD9kMY2FE4uNgJQVFSEc5BH8EWGA8uyOLTiOTob60gdOwMrbNFWvZ8jG98j\nffL8QXtvaWkpMLDzrwZL2rjzcO3Zw+uvv84NN9zQ7wOQReJZV0sDEBmoDXUM7oDMYHL50tiy5Sjf\n/a/XyDm/g86jtbz1o1u0B1PiWsIkWMFAKx0N1XjzinAlp9sdzjHp6emMmHEp3pxCDq9fSs2avxMI\nXG13WFF1crERNa6SSDobqmk/XEFKwRRSi87DlZZFzcq/0Fy2hdQJcwbtvfv27QPANAd3v2l/OJOS\nWbhwIRs3bqS4uJgLLrjglGs0MCOJrifBcqdnEzp8wOZo+s+dnoMrJYPOo7UkjRhldzgiQyJhlgi2\nHtgFWCSPGp5lQkdMu4isGZcSbG/ihRdesDucqDuh2Eh3oiWSCFoqtgOQOfVCDMPAMBykjZ9FqDNA\ne3XpoLzTsixKS0vJyckhO3t4Li9avDhSpv3VV1897ec9AzM3P7aamx9bzZKHXj4l4RKJZ8GWoxgO\nJ+6UEXaHMiCGYeDNHUuwvZnOozV2hyMyJBImwWqp2AGAb/QkmyM5s+zZV+FJz2XVqlUUFxfbHY6I\nDFBHRwetB3bhTk4/YXAnfcJcAFordw7Ke4NtTTQ3Nw/L5YE9CgsLmTVrFlu2bKGsrOy012hgRhKV\nZVl0tdTjTsvCcMR+V82XNw6AtkN77Q1EZIjE/re2F8JdHbQd2oc7LRt3Sobd4ZyR4XSSM+9anE4n\njz32GO3tsbvmWkSgpKSEcLCTtIlzT+gkJY0YSVLWKNpr9tPc3Bz193Y2HAKG5/LA4914440AvPba\nazZHIjK8tLS0EO7qwJM2PGeg+8qbVwRAa9UemyMRGRoJkWC1Ve/DCofw5Y61O5Rz8mTkct1113Hk\nyBGeffZZu8MRkQFYs2YNAOkT557yWfqEuVhWmA0bNkT9vR31kQRrOM9gASxYsIDRo0ezYsUKGhsb\n7Q5HZNioqYkspfNkxG6J9uM5k5Ijg0q15YSDnXaHIzLoEiLBaj2wGwBvTqHNkfTOddddx+jRo3nt\ntdeOVQITkdjS1tbG7t278WSOPO0odErBFAD8fn/U3x2oP4jT6Rz2hWQcDgf/8A//QFdXF2+99Zbd\n4YgMG9XV1QAxfcjwyVJGT8YKhwjUxW7BDpHeivsEy7IsWqt240xKjpmGyuPxcPfdd2NZFo888gjB\nYNDukESkj7Zs2UI4HMY3cvxpP3enZeP0puD3+7EsK2rvDQe76Go8zNixY/F4PFF77mC5+uqrSUlJ\n4c0336SzUyPbIvDpDJY7LcvmSKInuWAyAIFaFauR+Bf3CVZXUx3B9mZSCiZjGLHz686ZM4fFixdT\nWlrKiy+WzAKQAAAgAElEQVS+aHc4ItJHJSUlQOSQzdMxDANvdiHNzc1UVVVF7b0d9QexwiEyMzOP\nHepZWlpKRUVF1N4xUFY4TEVFBaWlpRw8eJB58+Zx6NAhHTws0q2urg4gbvZgAfhyxuJwuWmvKbM7\nFJFBF1fnYJ18bkpFRQXtNfsBSMmfEtVR4qFw5513smnTJl544QUuuOACJk4cniXmReRElmWxYcMG\nfD4fSSNGn/E6b04hNB5m27ZtFBZGZwlzoK6ScLCTF7a38M5jq4/9vKViJ8ndyxLt1tVUx11P1+DN\nqgQg2J5C5d4annnmGa655hqddSUJr66uDsPhxOlLszuUqDGcTpJHTaRp/yYOHz487JcwiwxE7Ezp\n9MLJ56bc8dg7tB7ah2FAcv7wLc9+JikpKdxzzz2EQiH+4z/+g9bWVrtDEpFeOHjwILW1tUyfPv2s\nJZaTuveFbt26NWrvbj8cSVpSxkw/VuLcmzUaT/rwGgn3ZHxagj21YAppE8+npqaGFStW2B2aiO0O\nHz6MKzkdwzDsDiWqevpi27ZtszkSkcEVVwkWnHhuisuXSmfDIZKyC3EmJdsdWr/MmTOHL3zhCxw6\ndIhf//rXMTcLJ5KIepYHzpw586zXuVOzSEtLY9u2bVH7bgfqKnF6kmNu5Dtz6oW4XC6ee+45urq6\n7A5HxDZtbW20trbiSh6+x8r0V0p+ZB/W9u3bbY5EZHDFXYJ1vI6GaizLImUYHy7cG7fddhuzZs1i\n3bp1vP3223aHIyLn0FN6ffr06We9zjAMpkyZQn19/bGqYQPR1dpIsK0ZT0ZuzI18u3xpXHHFFdTW\n1rJs2TK7wxGxTU+BC1dKps2RRJ87LQt3ygh27dqlAl4S1+I7weo+C8Y3OrbX+TocDu6//36ysrL4\n+9//TqB7CZCIDD+dnZ1s3bqVsWPHkpV17gpgU6ZE9kXt2LFjwO/uaRs8GbkDfpYdlixZgs/n49ln\nn6WhocHucERs0TPY4kpOtzmSweEbOY6Ojg527txpdygigybOE6wqHE433pwxdodyTCgU6ldlr4yM\nDB544AEMw+Bw8ZsE21sGOVIR6Y8dO3bQ2dnJvHnzenV9z0bvXbt2DfjdgbruBCszb8DPskNaWhp3\n3HEHra2t/PnPf9aSaElIn85gxd8SQQBvd2XVnqXUIvEobhOsYFsTXa1HScrKx+EcPsUST1eIIxwK\n9ereadOm8Y//+I+EOts4XPzGIEcqIv3Rszzw/PPP79X1hYWFeDyeqBw4HKg7gGEYuNNi48y/0/ns\nZz/LrFmz2LJlCy0V2qchiefYIcNxuAcLItVTnU6nEiyJa3GbYLVVR8qze3OHz+xVj+MLcfS1stfV\nV19NUlY+zeXbaakc+JIiEYmuDRs24PF4zrn/qofL5WLy5MmUlZXR3t7e7/da4RCB+io8I0bicLn7\n/Ry7GYbBPffcg8/no37T+7R1H7UhkijifQbL4fIwZcoUSktLtRRY4lYcJ1j7APDmjLU5kugyDIPs\nuVdjOJzUrnuDcFen3SGJSLe6ujoqKyuZNWsWHo+n1/eZpollWezZs6ff7+5srMUKhfDFQZuXl5fH\n3XffDcChFc/R0TDwAiAiw9nx2wd2794NgMOdZHNUg2fGjBmAlglK/Bo+a+eirL16Pw63F3eMbvY+\nG09aNlkzLuXI1hU07SsBrrQ7JBHh085Cb5cH9jBNE4jsw5o1a1a/3t1RH0lChuOsfX9MnTqV7LlX\nU791BQfeeYKs2VdRUXHq71ZUVKSDiSXm9WwfcGfkUvHJbjAMxpzXu+0DsWj27Nm89dZbrF27lquu\nuuqs14ZCIcrLy0/5ub77MpzFZYLV1doY2X+VOTLmShX31ogZl3B0TzFNe9fT3NxsdzgiAqxfvx6g\n1wUuekydOhVgQPuwOuoPApH9DYG6qn4/ZzhJHTsdT0YONWtepnrlC9xeW07WzMuOfd55tJa3fnTL\nsUIhIrHMk5mHy5uC4XThTjt3BdJYNmrUKIqKiigpKaG9vR2fz3fGa3uSz+OL9+i7L8NdXC4RbK8t\nA8CTOdLeQAaRw51E1szLCAc7Wbp0qd3hiMS1k6t/9vwVOq5ATWdnJxs3bqSgoID8/Pw+PT8rK4vc\n3Fz8fn+/K+d11B/CmeTDnda3fZ3DXfr42RR85p8xnC4a9xTTuHsdnvScyB7WGK2WKHImXS2RPUmu\nGDsovD8uvvhiurq6KC4uPue1x+9d13dfYkFcJliB2kjp86QR8ZtgAWRMuQCXL43ly5dTX19vdzgi\ncevk6p83P7aaJQ+9fMKylW3bthEIBFiwYEG/3mGaJo2Njf06cLixsZFgexPenDFxOWufPHoieQtv\nxJORS+OeDRx454ljHVGReJJoCRbA6tWrbY5EJPriMsFqry3D4XTF3UjuyRxOFxlTFhIMBnn55Zft\nDkckrp1rBLVnFPaCCy7o1/N7qg7258Dh0tJSIH72X52OKzmdkRd/gfSJcwkcqaLirf+ivfbUfRki\nsayrOTJY6kyABGvMmDEUFhayfv16AoGA3eGIRFXcJVihznY6jtbizR2D4Yj/zY+pY6eTmZnJ0qVL\naWxstDucYeN0S7pCvTxvTKSvLMvik08+ISUlhWnTpvXrGT1VtbZv7/vZT/v2Raqm+obRoeqDweFy\nM3LRLYxceCPhrk5q1vydpUuX6kBiiRtdrUeBxJjBMgyDiy++mM7OTs1iSdyJuwSr40hko7cvt8jm\nSIaG4XRx7bXX0tHRwauvvmp3OMPGyUu6Tl7OJRJNFRUV1NbWMm/ePFyu/tUOGjduHMnJyf1KsHbv\n3o1hOPDGeYIFkU5ZxpQFFF7zDVy+VP7+97/z+OOPawBF4kJXcz2GAU5vit2hDBorHKaiooLS0lJM\n0yQQCPC3v/1NAyUSV+IvwequpOUbmRgJFsCll15KZmYmr7/+OkePHrU7nGHjhAOdtSFWBtHatWsB\n+r3/CsDhcDBt2jQOHjzYp8M3W1tbKSsrwzNiFA5378/einW+3DGMuuxWxowZw7Jly3j44Yfp7NS5\ngBLbgi0NuJIz43oFTldTHXc9/Qk3P7aab/xlF5ubk9m9ezeffPKJ3aGJRE3cJViBIwcxDBJiJLeH\nx+Ph1ltvJRAI8Pzzz9sdjkhCsSyLDz/8EI/H0+/9Vz36sw9r27ZtWJaFNzf2DxjuK5cvle9///vM\nnj2bdevW8eCDD+rYColZ4VCQYHsT7tRMu0MZdJ6MTwdAs2ZFzvJ88cUXsSzrlCX+FRUVNkcr0ndx\ndQ5WKBSi82gN3pyCuD4B/XSuueYaXnvtNZYtW8aNN95IQUGB3SGJxKyTD7Y82//g9+/fz4EDB7jo\nootOqQDY147BzJkzgcg+rJ4KW+eyefNmAHxDlGD1LO/pYXfnx+fz8ZOf/IRf//rXrFy5kn/913/l\n5z//ORkZGbbGJdJXobYmLIu4PwPrZJ60bObMmYPf7+e5555j0aJFJ5x71VKxE+/I8ViWFZdVUiU+\nxVWCdfDgQaxwEG924iUXLpeLr3zlKzz88MM8/vjj/OxnP8PpdJ7SUWxsbOTNN9+kfut+MqdeiC9v\nnH1BiwxTJx9s2VKxk+SCKae9dsWKFQBMmTLllMMwz3bf6UyePBm3292nfVibN2/G7XaTNGJUr+8Z\niMjynhq8WZVA33/HMxlI4uZyubjvvvtIS0vjzTff5MEHH1SSJTEn2BYpVOVOTawEC+C2227jd7/7\nHX/5y1/Yv38/Xa0Bgu1NtFbuorVqNxYWySPHkzN3Mcn5A29vRAZbXCVY+/fvB8CbXWhzJPZYtGgR\nCxYsoLi4mCeeeIK77rrrWEfRlZJBw/aPaC7fSrCtGac3hZaKHXizC8iZf53doYsMOz17+AA6j9ae\n9hrLsli5ciUpKSnMnDkTz5q2Y/ec7b4zcbvdTJkyhR07dtDc3Exa2tkriTU0NFBRUcHkyZM56By6\n5rxneQ/0/Xc8k4EmboZhcOedd1JXV8fy5cv57ne/y3333YfP56OoqAinM373tEh86GptAsCVmgkJ\nVu8hMzOThx56iAceeIDly5dzuKweh9sbKfiR5MOVMoLOo9VUffBnss67nNSxM+wOWeSs4jLBSsoZ\nnARruC2NOZlhGNx3333cf//9vP7667S0tDB+/HhaD+2l/eAegoFWvNkFeMbnkZQzhkDtflqrdnNk\n8/tY1hV2hy8Sc/x+P0eOHOHqq6/G7XZH5Znz5s1j+/btrF+/niuvvPKs127ZsgWAadOm8eG+qLze\nVgNN3CoqKnim1EuzlU/xyk28ueOHjJh2MUt//E9MmDAh2uGKRFWwLVKkyp2adew8rESSm5vLI488\nwtKlS9n37Fq8WaNJKZhC68G9uNKycbpcHFzxHA3bPsTpTQV6t4xaxA5xVeRi//79GA4XSRmDUzHu\n+Mo3Nz+2mjsee4fwMCsNnJyczI9//GNGjx7N8uXLefTRR2n0f0w42EnO3MUUXf9tUsdOxzdyHPmX\nfxlvTgGtlTtZtWqV3aGLxJye5YGLFy+O2jMvvPBCAD7++ONzXrtmzRrg071bAkkjRlJw5e2kT5hL\nZ+Nhmko3Ul5erjPxZNgLtkVmsNypI2yOxD6pqanMmzeP9IlzSZ8494Ry9UkjRjH60i+A4aBuwzIV\ntJFhLW4SrEAgwMGDB/Fk5mEM4lKQ4yvfeNKzB+09A5GXl8fvf/97HnroIT772c+SO/96Jnz+AbJm\nXn7CvxvD6WT0pV/E4U7ixRdf1EnqIn0QbG9m48aNTJgwod+HC59OYWEh+fn5lJSUnLXseFtbG8XF\nxYwZM0ZFbU5iOJyMuuSf8OYU0ly2hS//7AmdiSfDXrC1EYfLHddnYA2UN6eQ7DmLCXW08uabb9od\njsgZxU2CtW/fPizLGrKN3sOdYRjMmjWLz3/+86QUmmc8H8edOoL0iefT3t7Ou+++O8RRisSu5v1b\nCIfD3HDDDVGtbGUYBgsXLiQQCByrEHg6H3/8MV1dXVx22WWqrHUaDpeb/Cu+jNObQmv5dkLtzToT\nT4a1YFsj7tRMfZ/PYcTUi3D50li5ciWNjY12hyNyWnGTYO3evRtACVY/pI2fjdvt5tVXXyUcDtsd\njsiwFw4FaSnbSnJyMpdddlnUn79o0SIA1q1bd8ZrVq5cCTAo748XLl8a2bMXg8NB9eq/0dWqzpgM\nT62trYS7OhKygmBfGU4n6ZPn09XVxauvvmp3OCKnFXcJlkcJVp85k5JZtGgRNTU1rF271u5wRIa9\nlorthDrbuOSSS0hKiv6Ze6ZpkpGRwdq1a2lrazth/1BpaSmbN29m48aNTJo0ifz8/Ki/P5540nMY\nMeMyQh3tVK/6K5YGkWQYOnz4MACuBN5/1RepRecdO5ahtbXV7nBEThE3VQR3795NSkoKrmSde9If\nV199NevXr+eNN97gwgsvPGWPgsoci3yq0b8OMLj88ssH5fkOh4Nrr72WF154gWeeeYZff9JywtK2\nug3LmOZsGbT3x5vUcbMIB5ppLt9O455iKiqKTvhc7ZvYra6uDkjsAhd9YRgO5s6dy8qVK3nuuee4\n6qqr9D2WYSUuEqzGxkZqa2uZMGECZVq73C+jRo1i5syZbNu2jY0bN3L7f314rEPXebSWt350i8oc\niwCBI1W0H67EN3IceXmDt5/npptu4rXXXmPZsmW4xv7DsfLlXa2NtFbtJuO8MVx77bWD9v54YhgG\neQtvpL22nIatH/L1x9NJGxMpTKL2TYaDYwlWmpYI9kZXUx3/XQt15Y1seuwFctY2svTBz+l7LMNG\nXCwR7FkeOH78eJsjiW09o+HFxcXHDlnVpnCREx31R/ZFpU+YM6jvSUtL48Ybb6SpqYnm0o3Hfl63\n8R2scJBbbrkFr9c7qDHEE2dSMnkLb8SyQjTvLSGpu41T+ybDQc8SQXeKZrB6y5dbRPqkuYQCrVjh\noN3hiJwgLhKsPXv2ADBu3Dh7A4lxF198MS6Xq1fn74gkolBHGy1lW/GkZeHNGzfo77v55pvx+Xw0\n7FhF7SdvcOijv9K8fwuezJFcdNFFg/7+eJM6ZhrJoybScbSahp1r7A5H5BgtEeyfjMkLAGgp32pz\nJCIniosESzNY0ZGWlsa8efM4cOAAnU11docjMuw0799MOBQkffL8ISmlnJqayn333Yc7dQRH/eto\nLtuKNzufnHnXqpRzP2VOvRBnUjJHNr9PZ+Nhu8MRASIzWE5P8hmPVJHT840cjycti9YDu1XsQoaV\nmE+wLMti9+7djBw5krS0NLvDiXlXXHEFAK0H/PYGIjLMWJZF455iDIeT9InnD9l7x44dy+grbidn\nzmJGX/pFxlz3P/CkDc9DzmOBw+0la9ZVWKEQ1Wv+jmWpqqDYKxwOU19fjyslMYt0WeEwFRUVx6qk\nVlRU9PpewzBInzwfKxw867EWIkMt5otc1NTU0NzczJw5g7sfIlEsWLCApKQkWit2YlmWRslFunU2\nVNNx9DBpRTNweVMItjUN2bsdLjdZ56liYLQkj55IV2M1zWXbaN6/FbjU7pAkgR05coRQKIQrOTEL\nXHQ11XHX0zV4syoBaKnYSXLBlF7fnz7xfA5/8iYrV67kG9/4hvotMizE/AxWz/LAKVN6/2WUM0tK\nSuL8888n2N5E4HDvR5FE4l3PGv+MyfOBgY26SnQM5L9B7vwlOD1eju5cRWOjDiAW+9TU1ADgSsm0\nORL7eDKOK6yV3rcZepc3heTRE6mqqjrWJxSxW8zPYPV8mSZPntzne3v+59xDHaSIhQsXwvNv0rx/\nC768onPfIBLnwqEgbQf34MnIwTdqIjDwUVcZuIH8N3D50sies5jq1X/jhRdeYO7cuYMZqsgZVVdX\nAyTsEsFoSB13HhxczrJlyzBN0+5wRGI/wdqzZw+GYTBx4kQOHjzYp3uHooM02Encyc+HgR+aOXXq\nVJyeZJrLt5E7f8lAQxSJeYHDlYRDXaSNm3XC8pOeUVeInKckQ28g/w0yplxAw47VFBcXs3HjRiVZ\nYotjCVZyus2RxC5v7lhyOnNYuXIld955JykpKXaHJAkuphOsUCjEvn37GDt2bL/PgxnsDlJfk7i+\nJkwnPz8ah2Y6nU5SCk1aKnfSVr0Xp+/ERj8UClFeXt7rGEViXXtNKQBp486zORKJJsMwyJr1GQL+\nl/nFL37BT3/6UzyeSBU3tWkyVHqWCLoTeIngQBmGwWWXXcayZcv48MMPWbJEg8Nir5hOsCorK+no\n6OjX8sCh1Jckrj8J0/HPj5aUMVNpqdxJ0/4tjJh+yQmflZeXs+Shl48d0BmNpE5kuAp3dRCoO4A7\nLRtP5ki7w5EoMwyDvUY+W9fvZs33fsOI6RerTZMhVV1djcPhwOlLtTuUmHbRRRfx7rvvsnTpUq67\n7joVuxBbxXSRi54Dhod7gtVXJ2z27E5ihjyGzFGRsyUqdxIOdp7mc/tjFBkKrQf8WOEgyQVT9D/s\nOJU95xqSMvJoKd+G4XCqTZMhVVNTQ3Z2NoYR010y22VkZLBw4ULKysqO9Q9F7BKz3+ZQKMTatWtp\na2sjKSlJVbyizDAM0sbNIhzsou1Qqd3hiNimubt6YHL+0BSwCIVCx6riqV0bGg6Xm9wLbsAKh6hd\n9xqWZdkdkiSIjo4OGhoayMnJsTuUmNazvWLmzJm0tbXx5z//mVAoZHdYksBidolgeXk5v/nr+4QC\nrXz3jUoMx0FV8YqytAmzObJ1Ba0HdtodiogtIgMM+3AlZ/S5dHB/nbwEV+3a0EgdM43UMdNoqdxJ\nS8V24JJz3iMyUD0FLnJzc2HojtaLOz3bK5JGjKKqNsQn//0Kn/vc53RGqtgmZmewurq6CAVa8Y0c\nhy+nsF9nJ8jZedJz8Gbn015bRnNzs93hiAy59pr9hINd+HLHDul7T1iCq3ZtyOQuuB6Hy03DtpVq\n82RIHDp0CIC8PC1LHShPRh6+7Hxy5l6N4XTy7rvv2h2SJLCYTbAOHDiAZYXwZhfYHUpcSxs/GyyL\nNWvW2B2KyJBrrYqcs5eUU2hzJDIU3CkZZM9ZTLgrwN/+9je7w5EE0HO8jBKs6EmfeD7OpGRWrFhB\na2ur3eFIgorZBKusrAwAb44SrMGUPmEuhtPN+++/TzAYtDsckSFjWRatVX4c7iSSVD0wYWSaC/Fk\n5LFmzRq2bNlidzgS53oSrJEj1cZEi8PlJn3SPAKBAG+88Ybd4UiCivkEKylbI8uDyZnkI61oJg0N\nDZrFkoTS1VJPV8tRUkZPwnDoPKREYTicZM9ZjMPh4De/+Q1tbW12hyRx7ODBgxiGoSIXUZY2bjYp\nKSm89NJLNDQ02B2OJKCYTrAMpxtPuhqlwZY2cS6GYfDKK68MqLpWONhFMKDpeokN7dX7AUgpVIGJ\nRJM0YhRLlizh8OHD/PGPf7Q7HIljhw4dIicn59gB1xIdDreHm266ifb2dp566im7w5EEFJMJViAQ\n4NChQyRljsRwxOSvEFPcKZnMnTuXPXv2sH79+j7ff/ToUeq3LGf/S7+k9MVfcHD5n1m1atUgRCoS\nPe21ZcDQlWeX4eX6669n4sSJvPfee2qvZFB0dHRQV1fH6NGj7Q4lLl1++eVMmDCBDz74gJ07VQ1Z\nhlZMZid79+7Fsiw82hcxZG666SacTie///3v6ew89eDhM9m7dy8///nPaSrdiOF0kTxqPF1NdTz9\n9NO8/vrrgxhxYjj5zKTS0lKd/REFHR0ddNRVkZQ1Cpcv1e5wxAYul4v77rsPr9fLI488QlVVld0h\nSZzpqSCYn59vcyTxyeFw8M1vfhOAX/3qV6oMKkMqJhOsnhO6k0YowRoq+fn53HjjjdTU1LB06dJe\n3VNcXMwDDzxAU1MTI2Zcxvhb7qPw6jvIv+prpKen88c//lEjwwPUc2bSzY+t5ubHVrPkoZcpLy+3\nO6yYt2fPHiwrRPKoiXaHIjYqLCzkO9/5Du3t7TzwwAPs2LFDgxkSNT0FLpRgRV/PwcMej4fPfOYz\n7N+/n4cffljfWRkyMZ1geTJH2RxJYrn11lvJzs5m6dKltB+uOOu1q1at4qGHHsKyLO6++24yJs/H\ncEYKBbhTM7nnnnvwer08+uijNDY2Dji2k2dyKirOHl88OeHMpEyV+o2GnuUkKfmTbI5E7HbZZZex\naNEiXv5wA1fe8a/c9L9XajBDokIzWIMncvDwJ9z82GqerMxm81E369at45e//CWBQMDu8CQBuOwO\noD/27t1LSkoKrpQMu0NJKD6fj3vvvZf77ruP2o9fxZddgPek84Esy+Kll17iv//7v/F6vfzbv/0b\nPp8PVq8+4bqxY8fyz//8z/zhD3/gmWee4dvf/vaAYuuZyelJMFoqdpJcoL0z0j/bt2/HcLjw5hbZ\nHYoMA1/4whf47ZsldNQfpNG/jpEXfc7ukCQOHD+D1dXVZXM08ceTERl8BBh18eeZ3LmeNWvWcOjQ\nIe666y58Ph+GYZxwT1FREc7uweBQKHTKIMrxn4ucTcwlWM3NzRw6dIhx48ZRdtIXQwbfrFmzuPPO\nO/n4h7/gwLtPMmL6JXjzigiFQmzatImXXnqJTZs2kZ2dzY9+9CMmT55MaWnpaZ+1ZMkSli1bxjvv\nvMN1113HxIkDW47VM5MD0Hm0dkDPksTV0NBAVVUVSdn5OFxuu8MRG/QsL+pRVVVF7oIl1K1fSlPp\nJjAMMsyFNkYosS4UCrFjxw7a29tpbW2lurra7pDimsOdxL3fvpf33nuPpUuXcs8997CxJkhywWTc\nadk43EkEWxv52T+ez7Rp08jOzqayspL/8cyGYwO3nUdreetHtzBhwgSbfxuJBTGXYO3evRuA8ePH\n86H2HJ/i5I7BYCyVmz9/PrkLrqfR/zFHtiwn3BXgf+z8K8nJyQDMmzePe++9l/T09LM+x+l0ctdd\nd/Hggw/y5JNP8tBDD0U9VpG+2rRpEwC+PM1eJarI8qIavFmVwKcz4gVXfYWq95+mad9GuloaCAYX\n2RypxKry8nKee68YZ1IKX/hjsVZdDAGXy8Xdd9/N5ZdfzhNPPEHxqytoKd9+7PNgWyPf2vguDk8S\nDpcHhyeZnHnXHRu4FemLmEuwduzYAcCkSZOgauB7d4a7viZMZ+oYRFtKwRRGTF3E0V1raTmwi0mT\nspg1axZXXnkl06ZNO2Xa/Uxmz57N+eefT0lJCdu3b2fGjBlRj1WkL44lWFoemNCOX17UMyPuTEqm\nYPHXOPjBM7RW7uSnP/0pP/jBD0hNjVSa1PIh6a1AIIAVDuHNHYM3a7RWXQyhGTNm8O1vf5t3gjMw\nsOhsrscKdtByYDcOdxKGw0HHkSraavZzaOVzdBypJG/BDXaHLTEmJhMswzAiy8k+LLE7nEHXn4Tp\ndB2DweBwe8g673KSC6bwg29d3O9p81tvvZWSkhKef/55fv7zn0cltlBnO63+dTTuWkPq2Om4UjKj\n8lyJb5ZlsWnTJtLS0nBn5NodjgxDTo+PgsVfo/z1R3l15UaWbf0ueYtuxgp2afmQ9FpP2f8kFSYa\nEqcbrHa4PXizRuMbOQ4AhycZV1o2ybmFWJbF4fVLaS7bSuOe9QRbGxkx83KbopdYFFMJVldXF7t3\n72b8+PGRwgkJYqgSJrtMnTo1qrNYTftKqP7oBXA4cXp8NJdtxeHx0vTVOVGKWOJVRUUF9fX1zJw5\nk9JO7fGU03O4PGTN+gytB3bRUrGd2o9fIXvO1XaHJTGkJ8HSeZ5Do6+D1YZh4M0uILlwGo07V9Fa\ntZtQRxuWdcUQRSyxLmbKtIdCIVasWMHRo0fJzc1NqDLcdusZ+RnMEui33norAM8///yAntNcsZ2a\ntS9jON1kzbyCsUu+SfrEuQRbj/L4448TDAajEa7EqZ7lgdOnT7c5EhnuDMMgc/oljFx0M+GuDmpW\n/433338fy7LsDk1iwKczWEqwhkrPYLU3azSe9Oxe3eNwucm/4sskj5pAW/W+Xp8DKhIzCVZ5eTlf\n+2/iYYsAACAASURBVMWzFJfV86etAe547B3COjBuSBx/nsTNj60elH/3PbNYmzdvZvv27ee+4TQC\nR6qo/uhFDKebnLnXkDZhDt7sfEYuuoWUApN9+/bxhz/8IapxS3zZuHEjoARLei9j0jwKF38dh9vL\nX/7yF37961/T2dlpd1gyzEUSLANPhpYIDneGw8moS7+Ay5fGK6+8wrZt2+wOSWJAzCRYAKFACw63\nl/SJc3s9+iDR0Z+Rn74ayCxWW1sbdeuXghUi/4ov4zlu/4xhGGTPvYaCggKWLl3K3r17oxazxI+u\nri62bt3KmDFjGDFihN3hSAzxjRzH6CtvY/z48Sxfvpz777+f2tr4W84t0WFZFgcOHMCVkoHD7bE7\nHOkFlzeFnAXXA/Db3/5WgyhyTjGTYFmWReBIFe6UTNw6YDguHT+L1ZcRIsuyeOaZZwi2N5E18wqS\nR0865RqHy82XvvQlAJ566ikt45FT7Ny5k87OTubOnWt3KOc0FMt2pW9cvjTuu+8+rr76avbt28d3\nv/tdtmzZYndYMgw1NDTQ2tqKJz3H7lCkD5IyRzF37lz27dvHI488QmlpKSGtpJIziJkE68CBA4S7\nAvhGqnRyvAmFQsc6ipdddhltbW388pe/7PUI0QcffMD69etJysona9YVZ7zu+ASuZ6+NSI+SkkhV\n0lhIsIZi2a70ncfj4Tvf+Q7f+ta3aG9v58EHH+SVV17RgI6coGdAxK0EK6Z0NdXxcnUmG2uC/Pvv\nn2fxD/4P5eXlfXrG8f2dnr+UpMWnmKki2LMv53SzExLbysvLWfLQy8dOS68L57NuxXou/tOf+P/Z\nu/P4qOqz//+vM/tk30nCEhbhIJsoiLvVonWpWhdQa9Xau731bm+11a9371btT2m12uVurVqXrm6t\norZqVVArCgiKbMrOCARCErKQhJA9k1l+f0wmArIkYTJnkryfj0ceNpPJOVconJzrXNfn+tx0002H\n/d7y8nKeeOIJvF4vueMuwLAdfg+aG264gU8++YRnnnmGqVOndnu/ru4IBoNfuNhqX5z+Y/ny5bhc\nLqZMmUJZWZnV4RzRQJ8u2t/sOwZ63Lhx3HjjjTz55JM88sgjLFu2jG9+85u43W5dE4QdO3YAaKlD\nP+TJHkrBl77eueH4KoLBb/Xo+w+83/HXV2t7hwGq3yRY0ZaxpMKxFkcifcGV8fnNYsHps2ip2Mpr\nr73GZZddRl7ewRcB+/1+fv3rX9PW1sa1117Lz1d0HPE8o0aN4rTTTmPJkiWsXr2aadOmxexn0IWz\n/6qsrKS0tJQZM2bgcmlNhPTcwcZAG0lTaShbzoqX3uaP85aTbp7Mu7/4jq4Jg1w0wVIFq39KLjyG\ntNFTqfct491332Xs2J7dl+57vyMDV79oEWxra2Pr1q240vNweJKtDkf6mN2TTOakM2lvb2fOnDk0\nNzd/4T2BQIAHH3yQrVu3cu655zJjxoxuH3/27NkAzJ07N+atO9ELpyeroCvRksS3YsUKAE488USL\nI5H+7MBhQN68kYy85PtkHzeToL+V2k/e0Sh3oaSkBIfDgTNZw3T6q9zpF2B3JfHaa69RWlra4+8P\ntDXTuGM99b6Peeqpp3j22WdZuHAh27ZtU8vgANEvKljr168nGAzizRtjdSgSJykjJvLl49NYtmwZ\n999/Pz/84Q/JyMgAoLGxkUcffZQVK1Ywbdo0vve97/Vokf/o0aM58cQTWbFiBRs2bGDSpEl99WNI\nP7F8+XJACZbEnmG3kzvtfJLyR1H+3nP89a9/ZePGjVx//fWkp6erZXCQ8fv97Nixg2HDhlFh6xfP\nuOUg7O4ksqbOpKN6KQ888AC/+c1v8Hg8R/y+Xbt2sXvFm7TVlBEOBQm07OU3a8DmcgORkfD/881L\nueaaa1Tp7uf6RYIVXXzuydOAi8HCMAyuuuoqDMPgo48+4sYbb2TmzJkALFq0iMbGRo499lh+9KMf\n4XD0/K/xVVddxYoVK5g7d64SrEGutbWV9evXM3r0aLKztSZC+kbyUJPc6Reyed37rP/XQv46fxlp\no6fy/iN3MGaMHh4OFj6fj0AgwDHHHMOKGqujkaORXDiWL09ys2zZMh577DFuu+22Q67rbm9v54UX\nXuC5556jubwGT85w0kZPpaOpHkdqFg6nm9bdJTRsX8v777/Pxx9/zMSJE7niiivIysr6wnH1YCbx\nJXyCFQ6HWb16NW63G0/2UKvDGZD2XZwNJMTI53AoRFlZGbNnz2bIkCG89tprvP766xiGgdfr5T/+\n4z+4+OKLe5VcAZimyXHHHcenn37KZ599xrhx42L8E0h/sWrVKgKBQI/aTEV6w+5OouCs6+jYs4ua\n1W9T74vcmN11111kZWVZHZ7EQXRg17hx46CmxeJo5GjNmjWL2tpa3n//fQBuvfXW/e5LwuEwK1eu\n5IknnqC6uprMzEzyTj6dzGNPxTAMGorX4EjNJil3GOnjTiRt7ElcYbbi8/lYsWIFixcv5rOWZHJn\nXIQzNXKN0Pru/iHhE6wdO3ZQXl7OxIkTKQspW+8LB1ucnTS09wnHgdP0epOwfR5TAeCiLfMUHrvu\nREaNGkV+fj7JyUe/Fu/KK69kzZo1vPjii9x9991HfTzpnxYuXAjAGWecEfdzH/hwAxLjAYf0HcMw\nyDBPIrlwLGULnuGjjz7i2muv5bzzzuMrX/mKJg0OcNEE65hjjoEPtU9af+d0OpkzZw5z5szh/fff\np7S0lIsuuojCwkIqKip44403WLNmDTabja985Sscd9xx3PF21SErXYGmOh5aEcSTNR3/yBFUffQq\ngbbdVC55iZwTvkLG+FPi/BNKbyV8grVo0SIAZsyYwdvL2iyOZuCK5cjnA6fp9TZh2zcmiKydiuUT\nm8mTJzN+/Hg+/vhjduzYwciRI2N2bOkfGhsbWblyJaNGjWLEiBFxP/+BDzfg6B9wSP/gTM0ia/JZ\nlJRtYtP2zSx5+Cnu/cPLpIyYyPuP3BG5AZcBJRgMsnnzZoYPH05qaqrV4UiMpKamct999/Hwww+z\nZMkSHnrooa6vtba2srbeRfYJX+HZyhweX77wiNf3fYflhMNhOpr2sPezj9m9cj6tldvJmHBaX/9I\nEgMJnWCFw2EWLVpEUlISkydPhmUrrA5JDuJgLYb7jiGNxR49B3vSf7RPeaPrvObMmcNTTz3FPffc\nE9N9sSTxLVmyhGAwyNlnn21ZDAc+SNCeVoOHYRhkTjiD/FMuY8+GD6jf9CF7P/uYu+++m29+85vM\nnDlT2wYMIMXFxbS1tTFx4kSrQ5EYOPC+ZNasWVx77bUsXryY9vZ2MjMzycnJ4b/+sa3X90OGYZBU\nOJaMcdOpXPIyTWWbaa0uYdeV49UimOASOsHasGEDNTU1nHPOOfolk8Bi3WLYnXPEqgd52rRpTJ06\nlVWrVvHxxx9z8sknxyJc6ScWLlyIYRiceeaZVocig5jd5SHn+HNJH3ciVcv+RV1dJY899hh/+9vf\nuPDCC5k4ceIXKh5qI+x/Nm7cCMCECRMsjkRi4cD7kva6Sp785on7reeNVcu3w5vK0JnfpPbTd6n5\n9N888MAD3HPPPVo7nMASOsGKtgeeddZZ1gYiRxTLFsPunCNWDMPgpptu4pZbbuGPf/wjxx9/PG63\nO6bniIdQoIO22jJad5dSXT2WUaNGqRp3BLt27WLjxo1MmTJF0wMlITiTM8g5/lx+ce0k1q1bx7x5\n8/jzn//M8m27SR19HGljTsCVltN1I7dvW6sSrsS3bt06ACZNmkRjY6PF0UgsHHjvc+PTy/us5duw\n2cg54SsYDifBvau47777+MY3vsGVV16p3/cJKGETrIaGBhYuXEh2djaTJ0/u2vlcJNaGDRvGpZde\nyssvv8zjjz/O97///X5zsWpsbKTmk3/TWllMOBQk1NHGXXctZcyYMXz7299mxowZ/eZnibd//vOf\nAFxwwQUWRyKyv/T0dK6//npmz57Nc889xyeP/J2WXVtp2bWVpIIxOFOz+c+nQnizY1vRl77T1NTE\nqlWrGD58OLm5uUqwBqh4tHwnFY7j+q9N5JVXXuEPf/gDK1eu5N57743J8C+JnYRNsF577TXa2tq4\n7rrrsGkzPuljX//611mzZg0LFixgxIgRXH755V94z4HTEcHap8ZLlizhwQcfpKmkHE/WUJKHjSMU\nCDB9ghefz8d9993HKaecwu23396tDRAHk7q6OhYsWEBhYSGnnnqq1eGIHJTX62XmzJkM3eQi2NJA\n/eaPaKnYRmDbalzpeeQcfy5po4+3Okzphg8++IBAINC1n6NIb3U01HDvv4M4h5zD7pI3+Hjum5SX\nl3PPPfdgmqbV4UmnhEywmpubeeONN0hPT+e8886zOhyJsUTcd8vlcnH33Xdz++2389RTT9HW1sbV\nV1+9X3J/4HTEfZ8aNzY2snbtWup9H2NzOAkHA4QCHXz4oUFmZiaZmZkxizUUCvHss8/y8ssvEwqF\nyJp8NrnTzsOw2Wmrq+Cmm07D5XLx+9//no8++ogf//jH/OQnP9E+O/t49dVXCQQCXHHFFXqAIwnl\nYNdHw7CRMvxYUoYfS3tdBZVL/0FrTSnVy9+g9tN38RYcQ22tFr0nsgULFmAYhqUDdWTgcKXnkZQ7\njKSLbqZiyUtUVW3nf/7nf7j00ku5+uqrSUpKsjrEQS8hE6xXX32VlpYWbrjhhn65HkYOLx5DMXoj\nKyuLe++9l5/97Gc8//zzfPrpp1xyySXMmDGja8iKKyMPV3ouHQ21tO+t5qmnnmL37t2UlZXR0tJC\n/Y46bM5ItSjU0cZf/7qFuXPncuyxx3LRRRdx6qmn9npzZIi0BP76179m9erVFBQUcP311/O9V7Zj\n2Pavog0bNoyf/exnPP7447zzzjvccccd3HPPPRQVFfX+D2iAqK6uZv78+WRlZelmRxLOka6P7qwC\nMieeQZbLi7+2jPrPltOwdSV33rmDc845h/PPP58pU6aoNTiBlJeX4/P5OOGEE/SgS2LKsNvJnHgG\n35h8Km+//TZ/+9vfeP311/nOd77Deeedd1T3G3J0Eu5PfsuWLbz00ktkZmZy4YUXWh2O9JF4DMXo\njZEjR/K73/2ORx99lKVLl7Jp0yYMwyA7O5v29nZK15RCKEA4HEmglu7JIisri6lTp5KVlUXJygaS\n8kdj2Oy0Vu1g1imZlJWVsXbtWjZt2kRubi6zZs3q1WRMn8/Hr3/9ayorK5k+fTp33HEHVVVVwPaD\nvt/hcHDzzTeTn5/PM888ww9/+EN+/OMfM3Xq1Bj8SfVP4XCY3/72t7S1tfHd734Xp9NpdUgiX9Cd\n66PdnUT2cV8mc9KZ1K1bSIF3F0uWLGHJkiXk5eUxc+ZMZs6cyZAhQ+IZuhzE/PnzAdQeKH2io6GG\nBz8I4io4n4amlez5ZDFV99/P008/zZe//GVOPfVUJk6cqCE4cZZQCVZrayu/+tWvCIVC3H777Xi9\nXqtDkgR1tG2Gh1tPlZKSwo9+9CNKS0tZsGABmzdvprq6mpaWFgybHXd2Ia60HAynl//vBxdzxhln\nYLPZKC4u5q9lS7tujMKhIOeddxqjR49m165dvP7667zzzjs8/vjjzJ07l8svv5xzzz33iKX8lpYW\n/vGPf/Dyyy8TDoe58sor+cY3vtGt1jbDMJg9ezZDhgzht7/9Lffeey///d//zbnnntujP6+B4rXX\nXmP9+vWcfPLJql7JgGCzO0gedizfuuQ8/H4/S5cuZcWKFfzpT3/iz3/+M8OHD2fq1KlMnTqV008/\nXU+042zbtm28/vrr5ObmahsQ6TNdLYO5w3Fn5rNt+6ds+KSYt1ZtIRx8jNuuv5TrrruOY445RtXt\nOEmYK21jYyM///nPqaioYNasWYP6KbscWU/bDA+WkP3Xs6u61lMdauzxDTfc0PV5cXExl/7+8wSq\nra6C4cOHdyvRKSws5KabbuKqq67i1Vdf5c033+RPf/oTzzzzDKeddhrTp09nwoQJZGVlYbPZ8Pv9\nbNu2jRUrVvDWW2/R2NhIbm4ut912W2TT7R4688wzyc7O5v777+fhhx9m06ZNfPvb3x5UU4feeust\n/vKXv5Cens7NN9+sXzIyYHQ01HDTM1Wd16axNHjbCITq6WjYzcol6/nnB2sIdfyBr0wfz6mnnsrk\nyZOZPHkyBQUF+nfQhwKBAA899BChUIhbb71V+3lKXNjdSeSeeDHutCwatq2mdu37vPvuuyxdupTc\n3FymTZvG1772NcaOHat//33I8gQrHA6zbt06Hn30USoqKjj99NP5xje+YXVY0g/0pM3wUAnZofav\nOFjCdWCVrDdVtIyMDG644QauuOIK3nzzza5k68033wQgKSkJt9tNe3t71/ekpqZy3XXXccEFF7B7\n926Ki4t7dM6oiRMn8qtf/Ypf/OIX/Pvf/2b16tXMnj2bc845Z0CvdWxsbGTu3Lm89tprpKenc889\n95Cenm51WCIxdeD10DFiAkm5wwj622jZtZV63zIaG2uZP39+V8taXl4exxxzDKNHj2b06NEUFhZS\nUFBASkqKlT/KgNDY2Mjvfvc7duzYwfnnn6+HxhJ3dncSmRNOx+ZOoWz3Tjr2VtOyupg3PlzLW2+9\nRVFREVOmTGH8+PEUFRWRl5dHWloadrudYDBIfX09dXV11NXVUVVVhc/no7a2ltraWhoaGmhpaQHA\n6XRit9txOBykpqaSkpJCSkoKqampjBo1ioyMDNLT00lLS+v6SElJGfDJXVwTrHA4TEdHB3v37qWy\nspJNmzaxfPlyfD4fAFdeeSXXXnvtgP9DF2scKSHr6YaBR6qiHZiABYNBgK4+6Pz8fD5Jmg5FYdp2\nl9JSuY3LjynE4/GQlpZGfn4+BQUFHHvssbjdblatWrVf1e1g5zySoUOH8pvf/IaXX36Zl156iSee\neIJnn32WE044gSlTpux3ke2v65Pa29uprq6mpKSENWvWsHjxYlpaWhgyZAhz5sxh6NChVocoEjd2\nl4fUkZMIh4LsCAawu9y07S6juXQTJ3kb+fDDD/nwww+ByO9ogOTkZHJyckhJSSEtLa3r5ig1NZUx\nY8aQkpJCUlISHo8Hr9eL1+vF4XBgt9sH9e/v1tZWKioqWLlyJfPnz6empoYpU6bwrW99y+rQZBAz\nDIO0MSeQlDuMUKCDPRuXMjazpmvpwuuvvw5EtoWI/vsNh8O0trZ2HaO1tZW1ZfXYnG4Mw04o2IHd\nk4LDk0w41Ia/oRbDbsfAIByO3OuE/O1MGZ7Rtdwnen0xDAPDMHC73bhcLjweDy6Xq+vD7Xbjdrtx\nOp24XC68Xm/X6y6XixEjRnRdg5KTk0lOTiYpKYmkpKSEmgrcmwTLDlBZWdmtN7/yyiu8++67BAKB\nrhvMA02dOpWvfvWrjBo1ivLy8oO+p6qqivbKbYRb6gHw1+wk1FKP0f75Zn0Hvmb154kQQ3+IKVFj\ndCSlEW7ZZx2gvxl/1bZDv+eArzfv2soN/7ccd0pkRHtr9U5sSan7fe7JK8KdkYdr2Fg8aVmce+6k\nrgSgvLycO556D2fy6v3eH963zeTAmBpqqao65oitKKeffjrHHXccCxYsYOnSpbz33nu89957+73n\n3HPP5aqrrjrscfY1c+bMkUCZz+cLdPubuqfb15zi4mJ++ctfEgh8HkJqaiqXXXYZZ599NuFwmLKy\nsiMeJxbXm+68p78ddyD+TP3tuEdzbkdSGk5vEs78ImyhDtaHAjizUuhorifQUk9rdVnk5ijUSGhd\nMYG2ZgybHZsj8rAlFAgwYXj2F/bV23e9tGEY2O32/T5sNhvhcLjrBmvf/x39PCMjgzvvvLPbe/Yl\nwvUG4IknnmDTpk34/X46Ojo+P4jdzkUXXcRFF13UVQXY15GuMVb8ntQxB8cxw22NvFVux5U8mQB7\n6Wiqx7+nkvOnZONwOAiFQrS0tPBxyV4cKZnYXV4C/gZyphyPN3c4dncSLRXbcCSl4e584Nu8ayuO\npDRc6bmEg4FI5bzMx46OVhy4CXX4aavbBTYbNpudUKCNjqYKDMOGYbcTDgYJtDbud70J+tv2+xwO\nfg068Ppjs9m6rjvR/0Y/oux2O1dffXWPKss9veYY+17kusM0zdOBD3r0TSIyWIzy+Xw7YnlAXXNE\n5BB0vRGReOr2Nac3FawVwBlABXDwkpSIDFZHLg/1nK45InIwut6ISDx1+5rT4wqWiIiIiIiIHFzi\nrAYTERERERHp55RgiYiIiIiIxIgSLBERERERkRhRgiUiIiIiIhIjSrBERERERERiRAmWiIiIiIhI\njCjBEhERERERiRElWCIiIiIiIjGiBEtERERERCRGlGCJiIiIiIjEiBIsERERERGRGFGCJSIiIiIi\nEiNKsERERERERGJECZaIiIiIiEiMKMESERERERGJESVYEnOmaYZN08w5wnv+ZJrmOfGKSUT6L9M0\nZ5mmudA0zRNN03yi87Xppmm+bHVsIjIwmaa5wzTN6Ud4z1Omad7Rw+PeYJrmG0cXnSQ6h9UByODk\n8/m+Y3UMItLvTASGAfh8vpXALGvDERER+SIlWAOMaZo24LfAyUAqYADfAf4TaAAmA8OBzcDVPp+v\nyTTNNuBB4FygEPidz+d7yDTNG4BZPp/vos5jd31umuY44PdASuf3fApc5fP52roZ50LgUWAlsACY\nB5wEZAF3+Xy+uaZpOoBfAhcBAeBD4Hs+n8/f6z8gEekXTNP8KfANoBbYQuS69VMg3TTNvwJPA4/6\nfL5J1kUpIlYyTfMs4HdAM5AM/H/AnYALaAHuAD4GSoDLOh/MYJrmC8Ai4E/Ab4CZQLDzvbf5fL7G\nHoRxummas4A04B3gDp/PFzBN8z+AmzpjyQIe9Pl8jx/VDyz9hloEB56TiCQ8p/h8vglEbkJ+1Pm1\nacD5wLGd75nd+bobqPH5fKcReSL8oGmaniOc5z+Bp30+3ynAMcAo4Ku9jHk08LbP55sB/C+RpArg\ne50xHwdMIpIwXtXLc4hIP2Ga5teAK4CpwKlAOlBK5ObpA5/P9y0LwxORxDIJ+DqR+5f7gAt9Pt/x\nwI3APwEv8BfgBgDTNDOJPFD+O3A3kfuh4zo/bMCvenj+YUQStKmdx/hP0zRTiNwnRWO5is/vbWQQ\nUAVrgPH5fB+Zpnk3cJNpmmOAs4BGIk+B3/L5fO0ApmmuI/JEJeq1zv+uJpJwJR/hVP8LnGua5g+B\ncUQuUCm9DLuDSAUrev5oXOcAz/p8vtbOz5VciQwO5wD/jD5FNk3zL8Ct1oYkIgmq1OfzlZim+T2g\nAFhgmmb0ayEiD4H/AqwwTfN2IsnY6z6fb69pmhcQ6ZrpADBN8xHg1R6e/1mfz9fc+f3PAV/1+XyP\nm6Z5EfBV0zTHEkm+enuPJP2QKlgDjGmaXwXe7Pz0NeAJIm2CAK37vDW8z+tdX/P5fOHOz42DvMe1\nz/9+nsjToRIiLYmrD3hvT/h9Pl/oIHEFOj8HwDTNIaZpFvTyHCLSfxx47QlYFYiIJLymzv/agQU+\nn29q9IPIcon1Pp+vhMh9ykXAt4A/dn7PgffBNsDZw/MH9/nfBtBhmuYwIksnioAlRCplMogowRp4\nziXyZOZxYAVwKZGLTm/sBiaZpunpXA918T5fOw/4qc/nm0vkZuikozjPobwLXGOaprtzbdnjRJ48\nicjA9hYw2zTNjM5/+9d1vh6g5zc/IjI4vAd8xTTN8QCmaV4IrAWiSx7+SKT7Jsnn8y3tfO1t4L9M\n03R2Xmv+G/h3D897ded9iodIG+J8YDqRe6j7fD7f20QSO0zTjPV9kiQoJVgDzxPAl0zTXAt8BGwj\nsj6qN/9fv0NkEehm4ANg3T5fuxN4xTTNlZ3nXESkDB9LTwKrOj/WARXAwzE+h4gkGJ/PN49IS89K\nIovO93Z+6SNgvGmar1gVm4gkJp/Pt4FIZ80LpmmuAX4GXBJt3wP+BYwE/rzPt90HVBKpNm0i8gDn\n+z089XYiVapPgMVE1r6/A5QBPtM0PwFGEEm4Yn2fJAnKCIfDR36XiIiIiIiIHJGGXEifME3zbCJr\nsw7mfZ/Pd1s84xERERHpCTMyLWPuIb7s8/l8Gr4lB6UKloiIiIiISIxoDZaIiIiIiEiM9LhFsHOa\n3DCgzOfzaXSuiPQpXXNEJF50vRGRWOjNGqxhwPYFCxbEOhYR6d96uw/akeiaIyIH0vVGROKpR9cc\ntQiKiIiIiIjEiBIsERERERGRGFGCJSIiIiIiEiNKsERERERERGJECZaIiIiIiEiMKMESERERERGJ\nkd6Mae9zwWCQkpIS6urq8Pv95OfnA1BUVITdbrc4OhER6UvR3wEH0u8A6S/0d1hkcEvIBKukpITz\n57xIzap5hENBhl/wX/jrq5l312WMHj3a6vBERKQPlZSUcOH9r+DKyOt6Tb8DpD/R32GRwS0hEyyA\n1t0lhAIdADhTMi2ORkRE4smVkYcnq8DqMER6TX+HRQavhFyDVVNTQ8OWlV2fB1oaLIxGRERERESk\nexIywXrrrbcIhwK4M3IBCLYqwRIRERERkcSXkAlWVVUVAOnjTgJUwRIRERERkf4hIROspqYmbA5X\n19qrQEujxRGJiIiIiIgcWUIOuWhubsbm8uJISgNUwRIRGaii46zr6+tZtWoVa9eupby8nPJte0ka\nMoqkwjGkjpxidZgiIiLdlpAJVlNTEzaXRwmWiMgA5/P5mPnd+2jZtYVwKABAqKMduzeVxp0baNy5\ngZpPF5A2eiqh0CkWRysiInJkCZdgtbe309HRgd2Vhs3lxWZ3ENCQCxGRAWfr1q3MmTOH5rLPcKXn\nkjXxTFJGTKClcjv2lCycHi+N29eyZ+MSatcs4JFHOrjvvvtITU21OnQREZFDSrgEq7Exst7K5vJi\nGAaOpDRVsEREBoBoOyDAhx9+yDPPPENTUxPpY09kyMmXYnO6ut5rGAautByyj/sy6eNOpPy9dnYI\nqgAAIABJREFUZ1m/fj0/+MEP+OlPf8rQoUOt+jFEREQOK+ESrIaGSDJld3ki/01Ko6N6B+FQ0Mqw\nRETkKJWUlHDh/a/QVlNK3fpF2JwekoeNJ3P8qfslVwdyeFPJO/lSLhpey3vvvcf//u//ct999zFy\n5Mj4BS8iItJNCTdFMJpg2VxeABxJaYTDEGxrtjIsERHpoWAwSHFxcddHSUkJLRVbqfd9jCstl6KL\nbyG1aFL3DhYOc/zxx3PppZdSUVHBLbfcwuLFiwkG9fBNREQSS8JVsPZtEYTIk0uAQFuTZTGJiEjP\nRStWrow8wuEwlYtfoK1uF56sQoadcwPO1Cza6yq6dayOhhpufLoKT1YBTVknUrP6bRb/x+28/dyj\nnHzyyX38k4iIiHRfwlWwoglWtEUwOkkw2KoES0Skv3Fl5OHOyGOvbxmt1TtwpmQx/Lzv4EzN6vmx\n0vPwZBWQM/Uc8k+9nHA4yG9/+1vq6ur6IHIREZHeSbgE6/MWwQMSLFWwRET6nVAwQMXiuTRs+wRX\nWi5DTpvddV0/GhnjTyFj/CnU1NTwk5/8pOvhnIiIiNUSNsGy77MGCyDQql+eIiL9SXt7O9XLXqWp\ndBNJ+aPImXYBdrc3ZsdPGzuDqVOnsnnzZm677TY2bdpEcXGx1mWJiIilEi7B6lqD5Y4mWOmAWgRF\nRPqTuro6/u///o+23TtJGTaewrOvw+ZwxvQcgcZa/lWbx8bgEF58fxVnfeduLvjZP7pGwYuIiFgh\n4YZcdLUIOjtbBD0pGIamCIqIJIp997PaV1FREXa7nfXr1/OLX/yCXbt2kTJ8AgVfuhrDZu+TWNwZ\nQ8j48jcpf/9ZWiq20bRzPeHw5X1yLhERke5IuASrsbERp9PZ9aTTsNuxe1LVIigikiD2nQ4Y5a+v\n5p//7wKWLFnCG2+8gc1m48orr+RRn7fPkqsow26n4MyrKXvnzzTuWMu8efO45ZZb+vScIiIih5Jw\nLYINDQ2kpKTs95ojOZ1gaxPhcNiiqEREZF+ujMhEP09WAc7kDFqqtnPnnXfy+uuvU1hYyAMPPMC5\n556LYRhxicfu8jD0y9fh8Kby6quvsmDBgricV0RE5EAJV8FqaGggOTl5v9ecSem0hIPs3bvXoqhE\nRAa+I7X+HaijeS/1m5ayd8tKAq2NdIwv5JprrmH27Nm4XC6Ki4vjEXYXR1IaeadeTlLFAh555BEy\nMzM54YQT4hqDiIhIQiVYgUCA1tZWcnJy9nvdkRwZdLFnzx4rwhIRGRQO1fo3767LGD16dNdr5eXl\n1Kx6i9bqHYRDIRzeVDJHH88vH7iRiRMnWhF6F1dqNjdfcTNPPvkkDzzwAA8++CBjxoyxNCYRERlc\nEirBik4QTE1NhcDnr0cTrNraWivCEhEZNKKtfwezZcsWnn/+eRYtWkRTaR2enOFkTTyd1JHH0b63\nGq83diPYj8bYsWO54447ePDBB7n33nv59a9/zZAhQ6wOS0REBomESrCiEwRTUlKg/vPXnckZQGTs\nr4iIxFdlZSUvvvgiS5cuBSIJTEl+AZnHnhq3NVbdFQ6F2LlzJyNGjODiiy/mhRde4LbbbuOxxx4j\nIyPD6vBERGQQSKgEK1rBSk5O3i/Bim42rARLRCR+gu0t1Hzyb+5Z+wwejwfTNLn++utJSUnhw98v\nTbjkCqCjoYYbn67Ck1UKeKhzjOLjJR+S/D//w+9//3tcLpfVIYqIyACXUAnWfhWsfThSMgGtwRIR\niZfGHeupXvEGHY21DDllIjfffDMnnXQShmEcdHhFtHK0rwM/jxdX+udtjgVnXEWgpZENGzbw4x//\nmO9+97vYbJEBuoca3iEiInI0EjLBikwRDHW9bncnYdjsqmCJiPSxUMBP1UevsHframx2B5kTz+Se\ne25m7Nixh/2+/StHEU07N5E0dFxfh3xYhmGQfsw0Nq/ezfpX3+OFNXVkTTmbjr27vzC8Q0REJBYS\nKsGKtghGKlgNXa8bhoHdm6ohFyIifaiuro7KxS8QaG3Ck1VA/hlXEvS3U15evl+l51CVqX0rRxCZ\nQJgIDJud/NOvonb1fJrLNuPNG0FSwTFWhyUiIgNUv0iwABzeVBobG/H7/eqhFxGJseLiYn7+85/j\nb6gha+IZ5Ey/EJvdQUPxmoSsTPWUzemm8OzrKHvrD9SsfoesKQHgNKvDEhGRAchmdQD7am1tBcDt\ndn/ha46kVABqamriGpOIyEAUDAYpLi6muLiYxYsX8/3vf5+qqiqyJn2J3BkXY7N//vwtWpmKfrjS\nsi2MvPecyekUfvl67C4PtavfZvPmzVaHJCIiA1BCVbDa29uBgydYdm8atFdSU1NDYWFhvEMTERlQ\nopsKG3YHlR+8SNDfQvKwY8keNTUhpwPGijtzCAVf+jql8//A73//e8aOHXvE9WUiIiI9kVAVrLa2\nNoCDtgA6vJEK1u7du+Mak4jIQGVPSqP2k3cIh0Pknz6b9GOmWR1SXCTljyZn+vm0t7dzzz33sGPH\nDqtDkgEu6G9l79ZVLFiwwOpQRCQOEjLBOlyLoBIsEZGjFwqFqFk5D39jHVmTziRj3AyrQ4qrpIKx\nXHDBBVRVVfGDH/yAhQsXUlxcTDAYtDo0GWAad25g+z9+xZ71i3jhhRcoKyuzOiQR6WMJl2AZhoHT\n6fzC1xzeyGbD1dWJMZVKRKQ/mzdvHq3VO0guPIbs42ZaHU7cdTTU8OT6IMUZJ/Deup1c+l8/4pwf\nPklJSYnVockA07RjHaFAR9fkykWLFlkckYj0tYRKsNrb23G73Qft/7d7I5sPa8iFiEjP7DvQori4\nmIULF/Liiy9i9ySTf/psDFtC/SqIG1d6HrknnEfhWddgGDZq17zHypUrrQ5LBphgWzMAOSech8vl\nYtGiRYTDYYujEpG+lFBDLtra2vB4PAf9ms3hwuPxsGfPnjhHJSLSv0UHWrgy8giHglQs/BstVVUU\nfOkb2N1JVodnubTRU7G7vZS9+zRPPvkkoVCIq666akAP+5D4CbQ1Y3d7sTndHHfccWzYsIEtW7Yw\nblz/2upARLovoR5bHi7BCodCOBwOSkpKup7CqldeRKR7XBmRUeutVdsJtDaRWjQJ75CRVoeVMJKH\nmhSceTVZWVn87W9/Y86cOV17M4ocjWB7C3ZPMuFQiJEjR9LS0sJLL72k+xiRASyhKljt7e2kpaUd\n9GsdDTV8sKOJQFMdlzy8iEBjHfPuuozRo0fHOUoRkf4p2NZM3bpF2N1e0seeaHU4CceVnstPbv0J\n//znP1m1ahXf//73+eEPf3jQybZFRUXY7XYLopT+JBwKEWpvxpWWQ0dDDf/3kZ/d5S2s/vtbPLPN\nzfy7L9d9jMgAlHAJ1sEmCEa50vMItbfg9KbstwmmiIgcWe26hYQ62smdfgE256GvtYNZSkoK99xz\nD3PnzuXvf/87t956KxsZQeakM7taBv311XrAJ90S9LcSDoPdkwyAO7OA1KKJNJZs0L9BkQEsYVoE\nA4EAgUDgkC2CAI7OC1SgpSFeYYmIDAgdTXvY+9lyXKlZZIw7yepwElI4FGLnzp1s376dGTNm8J3v\nfIdgMEjDtlXs3fwhrrQcPFkFuDLyrA5V+onogIvo/QuAd8goANpqNa5dZKBKmDJQe3s7wGETLLsn\nMkkw0NKIMy07LnGJiAwEezYuIRwKkT31XAy1th1UR0MNNz5dhSertOu1vcmTcaVvpqF4DR1N9RSe\ndY2FEUp/E02w7O59Eqy8IgDaakoP+j0i0v8lTAXrcJsMR0VL7IFWVbBERLpr69attOzagidnGClF\nE60OJ6G50iPDQKIf3tzh5J02i9SiibRWl1D69p+6bppFjqQrwdqnguXKGILd7aW9RhUskYEq4RKs\n7lWwlGCJiHRHOBzmpZdeAiB32vkaPd4LNruD/DOuInP8Kfj37qZy6Us0NOj3kBxZsD2aYKV0vWYY\nBt68IgKtjdrbU2SASpgEq3stgpEnQMFWjc4VEemODz/8kOLiYpIKx3a1JknPGYZBzvQLyDz2VDoa\navnpT3/K+vXr99vAWSO35UDB1iYA7J7995uLrsP67LPP4h6TiPS9hFmD1b0KloZciIh0VyAQ4Omn\nn8Zms5E54XSrw+n3DMMgZ9r5tFTt4J0Vm1j6nz8h7+RLMWw2TRaUgwq2twCRClag5fOHw0qwRAa2\nhKlgdWcNlmGzY/ckE1AFS0TkiObPn09FRQVnnXUWzpRMq8MZEAzDIGP8ySQNHUf7nkoat63GnZmv\nyYJyUIG2aAUreb/X3RlDsDlcFBcXWxGWiPSxhEuwDlfBAnB4U1XBEhE5gubmZp5//nmSkpK46KKL\nrA5nQDEMGznTL8SdOYT6z5az17fM6pAkQQXbWzAMsLu8+71u2Gy40vOorKzsuv8RkYEjYRKs7qzB\ngkiCFQp0EOrwxyMsEZF+6aWXXqKxsZHZs2eTmppqdTgDjs3hovDsa3F4ktm9ch4tlapEyBcF25qw\nuZIwbF+83XJl5BEOh1XFEhmAEibB6nYFKylyoxDsLLuLiMj+qqur+de//kVOTg6XXHKJ1eEMWM7k\nDArPvhbD5qBm1Xx2795tdUiSYIJtzV9oD4xyZeQDsGXLlniGJCJxkHAJ1uHWYAE4vGnA533NIiLy\nuWAwyEMPPcTevXs555xzKCsrY+fOnVaHNWB5coaRd9LFhDraefzxx/H71V0hEeFQiGB76yETLHfn\nur1t27bFMywRiQPLpwgGg0FKSkrYuXMnLS0t1NTU0Nraesj3O5IiCVZ09KmIiHzuvffe4+HnXsWT\nM5wHVwUxVi+laecmkoaOszq0ASttzAk07FhHaWkpTz75JLfccovVIUkCCPkj9zKOQyRYjpRMPB4P\nW7dujWdYIhIHlidYJSUlXHj/KzSX+9i7o47bX95AR1P9IW8G7F0tgs3xDFNEJOGFw2Gef/55bE43\nBWdciTe7EAB/fbXFkQ18WVO+zPD6RbzzzjtMmDCBmTNnWh2SWKxrRLv74AmWYRiMGDGCsrIy2tra\njrhEQkT6j4RoEXRl5GF3e7E5PXiyC3GlZR/yvQ6v1mCJiBzMu+++S0lJCcnDxmtT4Tiz2R1897vf\nJTk5mccee4zt27dbHZJYLNhZwTpUiyDAyJEjCYfDahMUGWASIsECCAU6gMhkpsOxuyO7oQf9Gmsq\nIhJVX1/PX//6V9xuN5kTz7A6nEEpNzeX22+/Hb/fzwMPPEBDQwPFxcVf+AgGg1aHKnEQ6tpk+NAJ\nVlFR5EGI2gRFBhbLWwSjwoHIwmDjiAlW5EIV7W0WERF44oknaGxs5PLLL+fhTYcfFiR9Z8aMGcya\nNYuXX36ZOXPm8MaeAtyZQ7q+7q+vZt5dlzF69GgLo5R4CLYfuYI1YsQIQIMuRAaaflfBMhxODLu9\nq/QuIjLYLV26lKVLlzJhwgTOPvtsq8MZ9K699lqmTJnCp59+SnvdLjxZBV0frs7JcTLwRR8ERztv\nDmbIkCF4PB61lIoMMAmUYEUrWM7Dvs8wDOzuJEJqERQRobq6mkceeQSXy8Wtt96KYRhWhzQohUMh\ndu7cSXFxMSUlJcyaNQvDMNizYTHtGjIyKIWCAeDwD44Nw2DUqFGUlpZqxL/IAJJALYId2OyObt0c\n2N1JBJr3xiEqEZHEFQgE+MUvfkFzczO33norQ4cOpbi42OqwBqWOhhpufLoKT1Zp12s1oaGEgjVU\nLnmR4Rf8FzZ7wvzKlTgIdyZYhv3wD45HjRrFpk2bKC0tZcyYMfEITUT6WMJc7UMB/xGrV1F2dxKh\ngJ9AINDHUYmIJKZwOMwTTzzBZ599xtlnn80555xjdUiDnis9D09WQdfnaaOmEA6HaK3Yyp51i8ie\nqtHtg0k4GF36cPB7m2jV0+v10tLSwtKlSzEMg6KiIux2ezxDFZEYS5gEKxzwH3H9VVS0n7m5WXth\nicjg9OKLL/LWW2+Rk5PDBRdc0LWGY+fOnRZHJvvKnHA6gb3V1G34gJSiSVaHI3HUVcE6RIIVrXoa\nhkHFjjp8zy4kZdFuDUERGQASJsEKBfzYPSndem80wWpq0l5YIjL4LFiwgOeee46kpCSWBsZw1Z9W\ndn2taeemQ27ULvFnc7rJPekSdr3/HFXLXiF3xiVWhyRxEuqsYBmHaQ11pefhyRxCtctDsL1FQ1BE\nBoiESbDCgY5uV7BsSrBEZJAJBoOUlJSwceNGfve73+HxeJg1axabP27fry3Nr4EKCSdlmEnqyMk0\n7lhH884NwOlWhyRxEK1g2eyHv7exOZw403Jp31NBOByOR2gi0scSYopgOBwiFAz0aA0WKMESkcGj\npKSEc370J679fz9j+fY9+LJO5UcvfUpIm9b2CzknnIfN4WTPxqW0tWkK7mAQDgYwbDaMbqyncmfm\nE+rwE2jRAC+RgSAxEqzAkUeZ7ksJlogMNrW1tezZsBgMG4VnX0vGuBNxpWVbHZZ0kzM5ncyJZxBs\nb+bNN9+0OhyJg3AwcMQJglHuziq0f+/uvgxJROIkMRKsI0zaOZCGXIjIYNLc3MzDDz9MsK2Z3Gnn\nkzpSwxL6o8wJp+HwpvLuu+9SV1dndTjSx8LBjm7f17gzlWCJDCQJkWCFAp0LQTVFUESEYDBIcXEx\nxcXFbN26lTvvvJNt27aRNvp4Mo491erwpJdsDhfp5skEAgFefvllq8ORPhapYHVvqbs7Mx+ADiVY\nIgNCQgy56G0FSy2CIjIQlZSUcOH9r+DKyKNu/SIatq7C7k6hcPrp3dqMXRJX8rDxeNo28PLLL3PC\nCSeQlZUFoL2PBqBQoANnN+9rHN4UHN5U/Hs1pEZkIEiIBCvUlWB1d4qgF1CCJSIDlysjj0BzPU0l\nG/BkDyVj/KkYtoRoOpCjEGjaw6eBAhq3reJrP3qU7ONm4q+v1t5HA1CkgtW9+xqIVLEaS9bp3kZk\nAEiI39bhHrYI2pweDMNGY2NjX4YlImKZQGsjVR+9gs3uoODMr2Nzuq0OSWIkwzwFd2Y+rRXbcCal\nae+jASgcDhMOdX86MnzeJlhWVtZXYYlInCREBatrr4huXogMw8Dm8mgNloj0e9H9rfZVUlJCzaq3\nCLa3knfSxbgzh9C+p9KiCCXWDJudjPGnsHvlfOq3rCB5qGl1SBJjfr8fAFs3pwjC55MES0tL+yQm\nEYmfhEiwejrkAlCCJSIDwr7rraJ2r5hH6+6dpI06jvSxJ1oYnfSV9GOmUbvmPfZuXkZS/hirw5EY\niyZYPapgKcESGTASo0Uw2PmkpwcJlt2VREtLC6FQqK/CEhGJC1dGHp6sAjxZBTi8qTSXbcZmd5J3\n0iUaajFA2Zxu0sdOJ9DWTHPZZqvDkRjrqmD1IMFypmRh2J3s3Lmzr8ISkThJkAQr0iLYkyc9NpeH\ncDisxaAiMqDUrH6LUKCdjGNPw5mcbnU40ocyxp+MYUDD9jVWhyIx1lXB6kGLoGGz4UrLoaKigo6O\njr4KTUTiIKESrJ70KttckUmCDQ0NfRKTiEi8tdWU0VC8BldqDimjplgdjvQxZ3IGSYXj8NdXqS1s\ngIkmSD2pYAG40nMJhUL6+yDSzyVEgvX5GqzuX4jsLg+AJgmKyIAQDofZvXI+AOnmDAwjIS7P0sfS\nx04H4IMPPrA4Eoml3lSwAFzpkbWY27Zti3lMIhI/CfEbXBUsERnsmnZuoHX3TlKGH4s7s8DqcCRO\nkgvHYXcns2zZsq6bcun/ejPkAugadqMES6R/S5AEq+dTBO1uJVgiMjCEwyFqP30XwzDIOeErVocj\ncWTY7aSMmEBraysffvih1eFIjPRmTDuAMy0Hm83G1q1b+yIsEYmTBEmwerYPFnxewdq7d2+fxCQi\nEi/NpZvxN9SSNuYEXGk5VocjcZZSNAmAhQsXWhuIxExvK1g2u4PCwkK2b99OMBjsi9BEJA4SIsEK\n9aKC5fCkAFBbW9snMYmIxEMwGKTetwzDZidr8pesDkcs4EzJpKioiE8//VTrigeI3lawAIqKivD7\n/ZSVlcU6LBGJk4RIsHqzBsvuTQWgpqamT2ISEYmHZcuWEWiuJ/2YaThTMq0ORyxy4oknEgwG1SY4\nQPS2ggWRBAtQm6BIP5ZQCZZhd3T7e2wuDw6HQxUsEem3AoEAr7/+OoZhJ3PSmVaHIxaaNm0aAIsX\nL7Y4EomF3k4RhM8TLA26EOm/up/R9KFwsAPDbsewdT/fMwyDjIwMJVgi0m+9++671NbWkjpqijYV\nHuRycnIYP34869atY8+ePaSlpVFSUvKF9xUVFWG32y2IUHqiq0WwFxWsYcOGadCFSD+XEAlWKBDo\nVZ9yZmYmu3btIhAI4HAkxI8iItItHR0dzJ07F6fTSdroE60ORxLAmWeeyebNm/noo48YP348F97/\nStfYbgB/fTXz7rqM0aNHWxildEdvK1jhUIjKykrS09NZt24dW7duZdSoUUqqRfqZBGkR7OhVn3JG\nRgbhcJj6+vo+iEpEpO+8/fbb1NTU8KUvfQmHN8XqcCQBnHTSSQAsX74ciOyJ5Mkq6PrYN9mSxNbb\nClZHQw03Pr2cd0uDLP2skvPuevqglUwRSWwJkmAFsNm7P0EwKisrC9AkQRHpX9ra2pg7dy4ej4cL\nL7zQ6nAkQeTl5TFy5EjWrl1Le3u71eHIUTiaIReu9DxShk/A5vQQCujvgUh/lCAJVu8rWKBJgiLS\nPwSDQYqLi/nzn//Mrl27OOWUU9izZ4/VYUkCOfHEE+no6GDTpk1WhyJH4WjGtAN480YC0FarUe0i\n/VFCLFwKBwO9WgiamRkZaawKloj0ByUlJZx/7wtUL38dgKodaTz60TskDR1ncWRipXAoxM6dOwHI\nz8+npaWFRYsWgWO6xZFJbx1NBQsi7aF2l4f2mvJYhiUicWJ5ghUIBAiHQ70aZRpNsFTBEpH+onV3\n5EY65/hzSRoykkDzXosjEqtF1t1U4ckqJRwOUbqrleXb3mPE16ZaHZr00ucVrJ4vf4DIpGRv3kga\ntn9KXV2dBpuI9DOWtwh2dHQAvRtlGm0RVAVLRPqDxsZGGratxuFNIcM82epwJIG40iMDLbzZQ0kb\ndRzhUBD/3mqrw5Je8vv9YBjQg+1nDuQdEtkPa8uWLbEKS0TixPIE62jK6Onp6RiGoQRLRPqFefPm\nEQ52kDX5LGzO3j3ZloEvuXAsAO27Sy2ORHrL7/djszsxDKPXx/DkKsES6a8SJ8HqRYug3W4nMzNT\nLYIikvAqKytZuHAhDm8a6cdobY0cmjd/FABtNUqw+iu/349hP7pVGJ6sQgy7k88++yxGUYlIvCRM\ngtWbFkGA7OxsamtrCYfDsQxLRCSm/vjHPxIIBMiceDqGNg2Vw3B4U3EmZ9JWW044GLQ6HOkFv9+P\nYTu6BMuw23FnFVBRUcHevVqrKdKfJEyC1ZsKFkBOTg6BQICGhoZYhiUiEjMrVqxg+fLlmKZJ0lDT\n6nCkH3BnFRIOBWit2Wl1KNILfr8fw3H0c8S8uSMAWL169VEfS0TiJ2ESrN7uFZGdnQ1o0IWIxF50\n36oDP4I9qCq0tbXx5JNPYrfbueaaa45qTYYMHu6sAgBaK7dbHIn0RqRFsHf3NfvyDom0i65cufKo\njyUi8WP5mPbobvW93SsimmDV1NRojKmIxFRJSQkX3v8Kroy8rtf89dXMu+uybl9v/vKXv1BVVcWs\nWbMoLCwEdMMsR+bOzMfAoKWymOzjvmx1ONID4XCYjo4ObPakoz6WMy2HzMxMVq9eTTAYxK72YpF+\nIXEqWL1MsHJzcwHthSUifcOVERmfHf3YN9k6ktWrVzN//nxGjhzJNddc04dRykBjc7pxZeTRVlNG\nqMNvdTjSA9HtZ452yAVE9sOaPHkyTU1N+Hy+oz6eiMRHwiRYhqN3I4ujCdbu3btjFpOIyNGqq6vj\noYcewm63c9ttt+F0Hn27kAwu7uxhhENBTRPsZ7o6c2KQYAFMmTIFiKzlFJH+IWESrN6uwVKCJSKJ\nJhAI8Mtf/pI9e/Zwww03qH1ZesWdXQhA624NuuhPPk+wYvNQZfz48TidTq3DEulHEibB6u0arKys\nLAzDUIIlIgnj6aefZsOGDZx22ml87Wtfszoc6afcmZFBF21KsPqVWFew3G43kyZNYseOHezZsycm\nxxSRvmX5kIversEKh0Ls3Bn5peNyubqmexUVFWkRqIjEVTAYpKSkBIhM+/r73/9Ofn4+N998s6YG\nSq/Z3Um40rJp3V1KOBSyOhzppmiCZYtBghW918nPz6elpYW3336bGTNm6F5HJMElTILV01J6R0MN\nNz5dhSerlIqSVtr3VLD2vn8w/+4r1I4jInEVnTZo2B3sWvg3AHJyTqW6upqUlBSLo5P+zJM7goZt\nn+DfW211KNJNRzsdeV/Rex3DMKjYUcemP7xJ2r/LezTJVETiL3ESrF4MuXCld073yh5KR2Mddk9y\nrMMTEekWR2oWuz/+F4bNTsHps3GkZndV2aMO/FzkSLx5RTRs+4TW6hI8nZvOSmLrSrBssbnFcqXn\n4c0upGblPDoaans0yVRErJEwCVZvx7QDOJPTAQi0NMQkJhGRnqrfuIT2PZWkj51O6qgpNBSv6aqy\nRzXt3ETS0HEWRin9jSdnOBBZh6UEq3+I9RosAMNmwztkJE2lm3WvI9IPJM6Qi6OYtuNIzgAg2NoY\nk5hERHpi48aNNGxbjSs9h9xpF3S93lVlj+6hlZZtYZTSH7nSc7G7vbRWq/rZX8R6imBUUn6kJVBD\nT0QS34CoYDm6KlhKsESkb+07YAciI9n/+Mc/Agb5p83G5uzdnn4iB2MYBp6c4TSXf0awrdnqcKQb\nuoZcOGJ7i+XNHwNA627tiyaS6BImwTqaJz3OpM4ESxUsEelj+w7YAWjYuorqFRvJGH8Kns59i0Ri\nyZMbSbDa91RaHYp0Q1tbGxD7CpYrPRe7J5m2mlLC4XBMjy0isZVALYK9z/WiLYKBVvWEPOkOAAAg\nAElEQVQli0jfi7b+OZLSaNy+BocnhfTxp1gdlgxQnuxhAEqw+om+WIMFkWqmN6+IYFsTtbW1MT22\niMRWQiRYht15VHvF2FwebA4nQbUIikgc1W/+iKC/jbQxx2N3ea0ORwYoT85QANr3VFgciXRHtIJl\n68V05CPx5kaGnmzbti3mxxaR2EmQBOvonvIYhoEjOUMtgiISN6GAn72fLcfu9pI01LQ6HBnA7C4v\nrrRs/Hsq1RrWD/RVBQvAk1sEwNatW2N+bBGJnYRIsGKx27kzOZ1QR1vXhU1EpC81bPuEYHsr6eNm\nxOQaJnI4npzhhAJ+KipUxUp0XWuwYrDR8IE8WYUYNocSLJEElxAJViwWgkYnCe7evfuojyUicjjh\ncJj6TR9h2OxkmCdZHY4MAp6cyDqs7du3WxyJHEnXFMEYD7kAMOx23JlDKC8vp6WlJebHF5HYSJAE\n6+if/nqyIz3qW7ZsOepjiYgcTntNKf7GWlJHTcHhTbU6HBkEor/jlGAlvr6aIhjlziokHA7j8/n6\n5PgicvQsTbDC4XDMKlhJnftDbNq06aiPJSJyOM3lkRubtDHHWxyJDBbuzAIMm53i4mKrQ5Ej6FqD\n1QctggDuzmR748aNfXJ8ETl6liZYHR0dhMPh2KzBSs3C4U3D5/MRCoViEJ2IyBeFQ0FaKrbiTErD\nmzfS6nBkkDDsdlzpeZSXl3dtbyKJqa2tDcMwMGz2Pjm+O6sAUIIlksgsTbBi/ZTHkzuClpYWPeET\nkT7TVlNGqKOdlJGTj2p7CZGecmfmEwqFNKI7wbW1teFyufrs+mB3eRk6dCibN2+mo6OjT84hIkfH\n0gQrFpsM78uTOwKANWvWxOR4IiIHaqmMPMBJHTnZ4khksIlWLrT2JrG1t7fjcsV+D6x9maaJ3+/X\nunORBJUYFawYJVjRDfiUYIlIXwgF/LTt3okzOQN3VqHV4cgg48rMB5RgJbq2tjbcbnefnmPcuHEA\nrF27tk/PIyK9kxAJVqxGmdo9yRQWFrJhwwYCgUBMjikiEtVatZ1wKEBSwTFqD5S4cySlk5KSwmef\nfWZ1KHIY7e3tcUuw1q1b16fnEZHeSYgEK5a7nQ8fPhy/309dXV3MjikiAtBcHmnH8Wi4hVjAMAxG\njRpFdXU1e/futTocOYToGqy+Eg6F2LNnD5mZmaxatQqfz0cwGOyz84lIzyVIghW7UaYZGRkA1NbW\nxuyYIiIALbu2YLM7u9bCiMTbqFGjALUJJqpgMEggEOjTBKujoYYbn17OexUOPtxSxfk//hMlJSV9\ndj4R6TlLE6zPN+OLXQUrMzMTUIIlIrHlb6jB31iHO3ton41fFjkSJViJLXpf09ctgq70PNJGT8Xm\n9BBsb+nTc4lIz1maYLW0RC4KNmfsnvSogiUifaFlV2d7YPYwiyORwWzkyJEAWoeVoKKdOX09RRDA\nO2QkhgGt1apeiSQaSxOs5ubmSBDO2D3pUYIlIn0huv7KnaMES6yTkpJCYWEhW7ZsIRwOWx2OHCCa\nYPV1BQvA7k7CnT2M9j27uh5Yi0hiSIwKliP2CZaGXIhIrISDQVqrtuPOyMXhSbY6HBnkTNOkubmZ\n8vJyq0ORA8SrRTAquXAshMNs2rQpLucTke4ZUBWscChEQ0MDra2tbNu2jf+fvfsOj+s877z/PdMw\nM+idAAWCAMthkVgkipKoLlGVtiKXvPHr98prOZIVO5aVWLuxHTtKnKytKF57195410nW0UpWJMsy\nbUqRqF4psYkUe8EhiQEIgkQhGtEGwLT9YwCIBSBBcmYOyu9zXbgInDnzPPeAmGfOfZ4WCAS0so6I\nXLS+tqNEI2F8RRV2hyIyvES35mGNP0MJViqGCAL4S+cAsHfv3pTUJyJjMy56sIwEzcEKdbbwtX//\nmI+PBfnN+iru/uEarawjIhdtaI6Dr6jc5khE4j1YoHlY41Eq52ABePOn43B72bNnj4aMiowj46MH\nK4FDBD3ZRaTlFEM0gju7MGHlisjUFWyqBeKTykXsNnPmTFwul3qwxqFUzsECMBwOfEXltLe3U19f\nn5I6ReTcxkUPViJXEQRw+jKJhkPEwgMJLVdEpp5YLEbf8TrcGTm4/Fl2hyOC2+1m1qxZ1NbWMjCg\nz7nxJNVzsOCTGz9bt25NWZ0icna2J1gOhyOhGw0DwxdB4WBXQssVkakn1NlCZKAPX7HmX8n4MXfu\nXCKRCNXV1XaHIidJ9RwsAF/RTAzDYOPGjSmrU0TOzvYhgl6vF8MwElruUIIV6etOaLkiMvX0tR4D\nNP9KxhfNwxqf7EiwnN50Zs+eTVVVFR0dHSmrV0RGZ3sPls/nS3i5nyRYPQkvW0Smlv7W+LwGJVgy\nnmglwfEp1XOwhixdupRYLMamTZtSWq+IjMz2HqykJFi+TADCQfVgiciFi8Vi9LXW4/Sm487Mtzsc\nkWHTpk0jMzNTPVjjjJ0JFqAES2ScsC3BikajBINB/H5/wsvWEEERSYSWlhYifT34isoTPpRZ5GIY\nhsHcuXNpamrixIkTdocjg+xY5AKgoKCAyspKdu7cObxCs4jYx7YEKxgMAuD1ehNe9nCCpR4sEbkI\nQ70Dfi3PLuNALBqlrq6OQCBAIBAgJyeH3t5e9u/fb3doMsiOOVhDrrnmGsLhsHqxRMYBl10VDy3R\n7vP5oD+xZTs8PhxOF2H1YInIRTh48CAA3qKZ9gYiQnxFywefasKbdwSAYFMnDQcb2LBhA1dffbXN\n0QnYk2ANJd6VlZUEg0F+/etfU1FRQXl5OU6nM2VxiMgnbEuwhrqwk5FgGYaB05dJuOeEdjYXkQt2\n6NAhHC5PfPNykXHAk12EN68EAHd6Nk0b1xAIBGyOSobYMQfrk8S7hKbeLD5692PeOfFL3vrHB6is\nrExZHCLyCduGCJ7Sg5UE3rxSIgO9NDU1JaV8EZncOjo6aGpqIi2vFMNh63pAIiNypvlxpedQW1ur\nm4njhF2LXAwl3vmLb8Xh9tLfdjSl9YvIqWy7ajilBysJ/KWzANi3b19SyheRyW2o7UjLn25zJCKj\nS8stIRgMcuzYMbtDEeJDBJ1OJy6XPQOE0kvn4E7PofvI/uEb2SKSepO2B8tfMhuAvXv3JqV8EZnc\n9uzZA4BXCZaMY2m50wDthzVe9PX1JWXxrrEyHA6yzeXEIiFeeeUV2+IQmeps78FKxjLtAO6MXNwZ\nuViWRTgcTkodIjJ57d27F5fLhWfwAlZkPEobnI81dENA7NXf35/y4YGnyzGvxuXL4q233lLPpohN\nbO/BSuadHl/RTPr7+6mqqkpaHSIy+fT29lJTU0NFRQUOp21rAYmckyeniPT0dLZv3655WOPAeEiw\nHC43uZfdSCQS4Ze//KWtsYhMVbYnWMkaIgjgLSoH4De/+Q0/+tGP2Lp1a9LqEpHJY//+/cRiMebM\nmWN3KCJnZRgO5s+fT0tLC/X19XaHM+XZPURwiK+4kpKSEt5//32efvppAoEAkUjE7rBEpgzbhwgm\nNcEquASn08mOHTv44IMPWL16ddLqEpHJY2iBi9mzZ9scici5LVy4EIDt27fbHMnUFovFxk2CFe5q\nZUO4go+P9vDQ3/8PbvurJzh8+LDdYYlMGZO6B8vh8vD1r3+dhx9+mJKSEgKBgIZQiMg57d27F8Mw\nlGDJhLBgwQJACZbdwuEw0WjU9iGCQ/zTZlF64xcxnC46rI0MDAzYHZLIlDGpe7AALrvsMm677Tbm\nzJlDMBiksbExqfWJyMQWCoU4cOAAlZWVSW+fRBIhLy+PsrIydu/eTSgUsjucKcuuPbDOJmPGAnLM\n5YS62njxxRftDkdkypjUPVgnmzUrvi+WdrwXkbM5ePAgoVBoeNiVyESwdOlS+vv72b9/v92hTFl9\nfX1AchfvuhAFS+/AlZ7Dm2++qb1BRVLE1gTL5/NhGEbS6ohFo9TV1REIBPB4PPT29rJp0yZN9BSR\nUQ0td60ESyaSK6+8EoD169fbHMnUNdSDNd4SLIfbQ8HldwDw05/+dDgRFJHksXWIYHp6elLrCHW2\n8OBTH3Hv/1zPt187xpbaNv7hmTc00VNERrV7925ACZZMLJdddhnZ2dl8+OGH2vvRJuO1BwviG6bf\nfvvtNDQ08OSTT9odjsikZ2sPVrI2GT6ZJ7sIb14J6SWVpGUXEenvSXqdIjIxhUIh9u3bx8yZM8nO\nzrY7HJExczqdXH/99XR2drJz5067w5mShhKs8TQH62T33HMPZWVlrF27lh07dtgdjsiklvIEKxKJ\nUF1dTUtLC6FQiLq6upTVnZZXQqSvh87OzpTVKSITR1VVFQMDAyxevNjuUETO2w033ADAunXrbI5k\najpx4gTAuLw5E4tGaWxs5POf/zx9fX18//vfZ8uWLZoyIZIkKU+wDh8+zF1//1s2B1p4cfdx/uR/\nvkE0RW/wtNyS4RhERE63a9cuACVYMiHNmzePoqIiNm7Uktx2aG9vByA3N9fmSM40NGXiL9Ye5WDG\nZbyz+zC33/cIBw4csDs0kUnJliGCrvQsHG4vnuxCPFn5Kas3LS+eYKWy10xEJo6dO3ficDg0/0om\nJMMwuPHGGwkGg7z//vt2hzPltLW1AeMzwYJPpkwUXnEXuQuuI9LXzRNPPKH9QUWSwJYEKxqKr7Tj\ncKd2nLK3qBwMQ2OPReQMwWCQAwcOMGfOnJTMDxVJhrvvvhuXy8Xq1auJRqN2hzOljOcerJMZhkHR\nlZ/CW1DGtm3bePbZZ+0OSWTSsSXBivTFF5pw+TJTWq/Lm46vsJza2lqOHj2a0rpFZHzbu3cvkUiE\nRYsW2R2KyAUrKCjg1ltv5dixY2zYsMHucKaUjo4OYPwnWACG00nh8k9RUFDAc889p78VkQSzKcHq\nBsCVnvqJoOll8wF47733Ul63iIxfW7duBWDJkiU2RyJycT73uc9hGAbPP/+8hn+lUFtbGx6PB5/P\nZ3coY+JwpfHZz36WcDjMY489xkcffaRFL0QSxJYEK9zbBYDLn5Xyuv0ls0hLS+O9997TB4+IABCL\nxdi8eTMZGRksWLDA7nBELkpJSQk33ngjNTU1vPTSS3aHM2W0t7eTl5eHYRh2hzImoc4WHn3tMFXp\ni1i3/yh3PvBtAoGA3WGJTAr29mD5U9+D5XB5WLp0KY2NjVRVVaW8fhEZfwKBAC0tLSxbtgyXy2V3\nOCIX7f777yc7O5snn3xSK+emQCwWo6OjY0IMDzyZJ7uIgiUryZ59BeGeDp5//nm7QxKZFOzpwQra\n14MVi0aprKykt7eXn//851RXVxMIBNQtLjKFbd68GYCrrrrK5khEEiMnJ4dvfOMbhEIhfvSjHw3v\n0STJ0dnZSTQanXAJFgwuenH1vbgz83nnnXdYu3at3SGJTHj29GAFu3C4PTg93pTXHeps4fEPWtjT\nm8XTr23gjkef5u4frtEdPpEpbPPmzbhcLq644gq7QxFJmKuuuop77rmHuro6vv3tb9Pc3Gx3SJPW\nRFlBcDROj5fiaz5DZmYm//Iv/8Lvfvc7TaMQuQg29WB12zI8cEhaTjHTrvtDnGl+TlRtpH3fh/z4\nxz+mt7fXtphExB7Nzc0EAgEWLVo0YSani5xNJBIhEAgQCAS45ZZbuOGGGzh48CDf+MY3ePHFFwmH\nw3aHOOlM9AQLwOnN4A//8A9xuVz8r//1v/jmN7/Jpk2bNMJH5AKkfLJBf38/0VCfLcMDT5aWU0SO\neRXt+zcSPdGMZXWyfv16brvtNlvjEpHUGlpR9JprrrE3EJELEItGqaurO+VYXV0dX336Yzw5RYNH\nptHuqsTX08Avf/lLVq9ezY033shVV12F1+vF6XSe8vzy8vIzjsnZDSVYeXl5Nkdy4UKdLfzdWxHc\npXdw/NgrbHnpff5tzdt858Ev8Kd/+qcUFBTYHaLIhJHyBGuoEbI7wQIoWHoH6WXzCXV3wOHXWLdu\nnRIskSkkFovxxhtvkJaWxg033GB3OCLnLdTZwoNPNeHNOzJ8rLtuP/7pc/HmlQwfy730Bn74/y9i\ny5YtvPPOO7z44ov8+te/ZktNK/7pc/EVluMrnkl0oI9XvvcZKisr7Xg5E1ZbWxsQn/s2kXmyi/AX\nXoL/Uw/RfXgPjRvXsHbtWt5//32uueYaPv3pT7N06VIl4CLnkPIEa2gjPreNQwSHGE4n/uIKgs6j\nFBQUsHHjRnbs2EFWVpbu4IlMATt37qSpqYnbbrsNv99vdzgiF8STXXRKMjXQceZcq1g0Snt7+/CQ\nwb1797Ju3Tq2N2+n7/gR+o4foX3fhzjT/Dz3XD933303ixcvxjAMIpHIiPOU9Tn5iYm0yfBYGIZB\n5szLiIZD1B07SLCxmk3//h/8t6fW8F/+4k946KGHtOKqyFmk/N0xdJfHlW5/D9aQUGcLH7T46K5t\n5d6/+xXevOm6gycyBbz22msA3HHHHTZHIpJcI/Z01fsoueVLuL0+eo8doufYQbrr9vHyyy/z9ttv\nU1BQwM0330xlZSUP/2b3SUMO40mcPic/MRmGCI7EcDjJmX8tJdf9IZ2BHTR/9BIvvfQSNTU1PPTQ\nQ8yZM8fuEEXGJRuHCNrfg3WyrDnLCB6z6G+pJ7Niid3hiEiStba2snnzZmbOnMncuXPtDkck6Ubq\n6TIMA09WAZ6sAnLmXc2JQ9s42tZAqLOFnm1VvLr1ANFwiPzFK8mceSmGQz1WI2lra8MwDLKyxs/N\n40QyHA6yZ1+OKz2HBcZu9uzZw1e/+lUWL17MqlWruOmmm9SbKXIS24YIjoc5WCdzeTPwTZtFb0M1\nrdteZ2Bgud0hiUgSPfPMM4TDYe655x4Mw7A7HJFxwXA4yaxYjL/wEiL9vbTv30DL1ldp2/MefS2H\nKVp+D77CMrvDHHfa29vJysqa9MPmIsEuXo+WwfQS2vevZ8uL7/Kvq1/ji3dex3333cfVV1+Nw2HL\nAtUi44p9QwTHWQ8WQPE1n6Hh/V/TfWQfP/jBD3jooYdYuHAhHR0dlJaW6u6MyCRRW1vLW2+9RXl5\nObfeeqvd4YiMS840PwVLVuL0ZtBdt49gUw31r/8rWbOXkTFzkd3hjSsdHR0UFhbaHUZKDC2EkT33\nSoJNNRzf+hp79uzh0UcfJT8/nyuvvJK77rqLiooK0tPT7Q5XxBa2DBE0nG4cNmwyfC7u9Gwuuf1+\njr3/HDU1h/je9743/Fh5eTk/+clPhiew7tixg9dff52vfOUrk27MtchkFovFeOKJJ4jFYnz5y1/W\n3VaRc3B6fOQvvR2iIZo3v8SJg1vprN7GluuyqKiomPI9wAMDA/T09Ey5ocaGYeCfVknugmtp6Gpl\noPUo3Tv288rGPaxZswafz0dWVhYlJSVUVFRw++23c+mll+pmtUwJtiRYLl/GuG2QHS43WRWLONY1\nnf7jdUT6g8SiYTa/uYnW++7jq1/9Kn19ffziF7/A4/FgGAbf+ta37A5bRMZo9erVbN++nSVLlnD5\n5ZfbHY7IhOEvrqB81ddp37+e41tf5V//9V+xLIuvfe1rU6b3ZiQHDhwAoKio6BxnTl7ppXMpXHwL\n0XCI41tfobqtAXojhJraCO86THTgPdasWUNeXh4LFy7ksssu4+6779YNapm0UppgtbS00N3djdOX\nmcpqL8hQYwHxO95HXv/fvL1tD+8+9N34sUiET6+4lA8++IA777yTRYs0XEJkvNu4cSO/+tWvKCgo\n4JFHHhm3N3pExivD6STv0htwZxUyz1XFli1b2L17N5/73Oe499578XrH3+iUZFu7di0AN998s82R\n2M/hcuMrmknmrCvwF14CQDQ0QMv2NzjUfJhwbRvr9r9B9NmXWPyLXzB//nzmz59PRkYGDoeDWCyG\nYRjEYjGcTifl5eVkZmbi9/vJyckhPz9fW2rIhJDSBOvZZ58FIP2Seams9qIZhkH2nOWkly0k0t1G\nX9sxMiuXcP+XruZnP/sZv/jFL/jrv/5rpk+fPvycoUZCROwXjUZZvXo1zzzzDGlpaTz66KOTZr8a\nETu4M3J45M8e4fDhwzzxxBM888wzvPzyy6xatYq77rprwm+4O1ZtbW1s3LiRmTNnsmDBArvDGZcc\nbg/egjIyKpbgK5jOwInjtGx7nf3H69j93jZ492MAwsFuHE43Dk8aANGBfhaV5eDz+YbLisVipKWl\nkZubS05OzvBXZmYmfX199PX10dvbC4DX68Xv95OVlcWcOXPIy8sjNzeXrKwsPB4PLpcLh8OhazVJ\nipQlWHV1dbz11luUlpbSXDjxGiHDMPAVleNfeC0AwZajOJ1Oli9fzrvvvsuXvvQlSktL6ejoIBgM\nEo1Gyc3NZe7cuaSnpxMKhWhvb6e7u5trr72W66+/nsLCQqLRKLW1tfT19VFWVkZxcTFOp5M9e/Zw\n4MABDh06RH19PQsWLOCP//iPJ+0SsCLJ0NXVxcaNG3n55ZepqakhPz+fb33rW9q7RyQBDMPglltu\nYcWKFaxZs4YXX3yRZ599lueee45FixaxfPlyFixYQHl5+aRdXe/1118nEomwatUqXaiPgWEYpOUU\nkTlzEbmX3Uxadj79rUeJhgborq/C6c/Bm1eMgUF3vcXhUB8ubwbRUD+R/h6CjbXEYv1Q00E01HdG\nUgZjT9QAHA7HcJLldDqHf3Y6nXg8Hvx+P5mZmfh8vhH/f6PRKABut5uMjAz8fj+VlZVkZ2eTmZlJ\nWloaLpdr+CsWiw3XfXIMHo/njK+z/T3FYjEGBgYARjw3FosRCoUwDOOU1yipcyEtnhOgsbFxTCcf\nOXKEjRs3snv3bgYGBrj55pv56O0aCHYCMNBSR7S3A6O/a/g5YzmWqHMu9Hk9xw5x308+wpOeQ79z\nBt31VWza+wEub8bgJsoGkZoDvPbhVgyHE4fLDcSHFu7Zs4ennnpq1N9ZJBJhZ6ARx0kfSNE1r/Ps\ns89SWFiIYRjDb6z8/HwyMzOJRqPDX7FYbPjfSCQy3JuWnp5Oenr6KZP6o9Eo4XCYcDg8/GZ0uVw4\nnc7hBsHtduN2u3E6nSO+iUf69/TvI5EIvb29hMNh3G73KQ3IUIMzFOvprwM4JZah54z0eof+jcVi\npzSUQw3n0HNHi3Wkxm+sj518jsPhGG5Yz1YOcErDZxjGKV9Dr2c0IzWYJx8b6fHTywuHw3R1dZGZ\nmUlWVhZer5dYLMbixYuZOXPmqHWf7tZbb50J1FuWFR7zk8ZmzG1OV1cXL774Is3NzTQ3N9PS0gLE\nfw/Lly/ni1/8Iunp6dTX14+p4qamJvobq4n1dgwfu9B2RcfGTxw6loBjna00Nc3G4/EAcP3117N8\n+XI2bNjAhg0b2LJlC1u2bAHi77/CwkKys7Px+/3DX0OfRYZhkJuby8qVK8d8ETge2huAV155BafT\nyZw5c05pV8bSdqTyWmc8n2MAToeBy+XG5XLiGvy7cLlceLNKSTtpg+uezDxc/izScoqIRsJ0H96H\n4XTG5/a73Dicbvpaj+H0ZeLOyCEa6qP3aDWBUC+OPhfRgX6ioX5C3YMLrrk9EIsR7unEcLlwuDyD\nn5FRwj2d8c/zwUU5IgN9p1zPjXQsGg6zoCw/IUNlnU4nbrd7+Prn5Gu1SCRyyrkulwuPx0M0Gh3x\ncWA42Rr6d+i6aOgaaehr6Ppj6Nyh7+HM65WR6hiL0c47nyRwKLbTnzv0utxuN4WFhWf8HoeuiVes\nWEF+fv6Y6zvfNsc428XbSEzTvA744LyeJCJTRYVlWbWJLFBtjoiMQu2NiKTSmNucC+nB2gJcDzQA\nZ6bIIjKVja1r6PyozRGRkai9EZFUGnObc949WCIiIiIiIjIy7bApIiIiIiKSIEqwREREREREEkQJ\nloiIiIiISIIowRIREREREUkQJVgiIiIiIiIJogRLREREREQkQZRgiYiIiIiIJIgSLBERERERkQRR\ngiUiIiIiIpIgSrBEREREREQSRAmWiIiIiIhIgijBEhERERERSRAlWCIiIiIiIgmiBEtERERERCRB\nlGCJiIiIiIgkiBIsuSimadaaprnsIp7/gGmafzb4/VdN0/xOIsoVEfuYpvmGaZoFCTjnJtM09yQ2\nOhFJBNM0/8Y0zT8Y/P5J0zT/c5Lru8c0zf+RzDqSwTTN+0zTfHnw+1+aprnyAsvJNk3zncRGN2pd\n75mm+XnTNEtN09yQijonG5fdAciUdx2wB8CyrH+2ORYRSYzbEnSOiIxftwD7UlWZZVn/AfxHqupL\nBsuyHriIp+cCyxMVy1hYlnUMWJHKOicLJVhTgGmaNwE/A3qAdOBvgO8CHqAX+M/AZuAw8BnLsrYO\nPu854H3gl8B/A24FIoPnftOyrK4x1v8ksMeyrB+f/DNQDdwD3GaaZhAoBAosy3roYl+ziNjDNM3/\nM/jtu6ZpPgR8H8gHYsBPLMv61Wnn3A0s5pM2qQh4yrKsR8+jzj8B/nTw+XnA45Zl/WLwsb8CvgSE\ngYPAfZZlnRjt+IW/cpHxZ/Dz/x+AY8BC4p/5fws8DJjA7yzL+qZpmg8OHosATcBDlmUdGPy87gQu\nA8qAKuALxN87y4D/appmZLC6FYO9HcXEP+O/aFlWj2mafwd8BhgAWom/1xrOEvM04FfAUA/3Wsuy\nHjVN8z7g85Zlfco0zfeAjcC1wAzgA+BLlmVFTdP8FPAD4qO0eoCvWpa10zTNFcA/Er8OigLftyzr\n5XP8/rKJXz9dBriBt4G/tCwrPFq7Mxjn/YP1nACeOqm894CfW5a1erR4Rnv9wP8BfKZp7gCusCxr\n6Pc+UtxntG/E/w+G47Is6+azvfbBcmYSv37LME3z+8BMoAQoB44Df2RZ1jHTNKcDPyf+f+EGnrMs\n67FzlT+ZaYjg1HEp8P8Cnyfe8NxtWdZS4EHg94APeIL4mxDTNHOJ32F+FvhroJT4RdBi4n83//Vi\nA7Isaw3xu1H/3bKs/3mx5YmI/SzL+vLgtzcTb1P+ybKsRcBdwGOmaV5z2jn1wEjYn7gAACAASURB\nVH8ifnG0DLga+KtzDR8cYppmBvAVPmnT/gj40eBj9xBv066xLOtSoAZ4aLTjF/XCRcavK4EfWJY1\nj3jy9FfAKuBy4OumaX4R+BZws2VZi4l/7r9gmqYx+PwrgDuB+cSvBf5w8DN7K/FkY83gedOBlcBc\n4BLgs6ZplgF/AVw5+P5+A7jqHPF+BQhYlnU5cD0wZzDROd0s4Cbiyc8twI2maRYD/048iVtE/Frl\n8cFrmv8D/PFgufcAvzBNc8Y5YvnvwMeWZV0BLCWe9DxytnZn0ELgptGSmHPEM9rr/zIQtCxryTmS\nq7O1b2eNawyuJ/7/Pw9oJ55gAjwNPDH4e1oOrDRN8/+5wDomBfVgTR1HLMs6PDjfqQR42zTNocei\nwGziF0NbTNN8hHgy9tLgnd67gO9ZlhUCME3zn4AXUv4KRGQiWQB4Lcv6PcSHmpim+TviF2obh06y\nLCtmmuangU8NXujNBwzid1nPybKs7sE71qtM05wDLAEyBh9eCfzWsqz2wXMfARicx3HGcZFJqsay\nrO2D31cT770YAFpM0+wkfuP1N5ZlHQewLOtJ0zR/Rry3AuA1y7L6AUzT3E28t2YkL1iW1Tt43h7i\nvdFHgZ3ANtM0XwVetSzr7XPE+xrwymCy8RbwncFrkdPPe8myrCjQZZrmocG4riXe47Jj8LX8Hvj9\nYE95CfHEcej5MWARUHeWWD4FLDdN8/7Bn32D5Z6t3QHYZVlW51nKveYs8Yz2+nPPUt7JRmv37htD\nXOfy3knP3w7kmaaZDtw4+P1/GXwsg/jv5PmLqGtCU4I1dXQP/usE3rYs64+GHhi8w3TMsqyIaZrb\niDcoXyZ+1wnO7Ol0EO8CHqsY8QumIZ7zCVxEJqToCMfOaDsGP5y3A2uID/N5AriXU9uMUZmmeQnx\nhO1fgQ+B1cTbMIgPj4mddG4OkDPaccuyasdSp8gE03/az6HTfh7pvWrwyXs1eNLx0z/PRys3BhiD\nQ/ZuJD6ccCXw303TfNeyrD8fLVjLsraYplkxeP4twEemad47wqkjxRXi1Pe2QbyHywnstyzrqpMe\nKyU+zO1snMR7bPYPPicHiJ2j3YFPrrnOVu6I8ViWFRrl9R87R5lDRmv3xhLXuYz0O3cO/rvipAS7\nAOi7yLomNA0RnHreAW43TXMewOBdnV2Ad/Dx/w18G/BblrV+8NjrwFdN03SbpukAvg68eR51Hife\nuA696a4/6bEw55esicj4FyE+p3PANM3PwvDFw+f4pO2IEH/vzwGygL+2LOsl4ndC04h/aI/FMuJt\nzA8sy3qdwYsc0zSdxO/+ftY0zazBc78PPHKW4yJT0fvAH5mmWQhgmuaXic+VOnSO553z89s0zcXE\n52PttyzrH4gPuVt8juc8DjxqWdYLwJ8De4kPOxyLzcB80zQXDv78B8SHDG4iPtTuhsE6lhCfm1R6\njvJeB75pmqZhmmYa8WkND3H2dmcsRo3nLK8/DDhPGro5mpS2b4M9WpuG6hhM6NYT/91PWUqwphjL\nsvYSn3f1nGmaO4H/AtxjWVbP4Cn/QXxYwL+d9LQfAI3ADmA/8QZ11LtPI/gnoMQ0TQt4BnjvpMde\nBR4enJApIpPD74m/z+8F/tw0zV3EP/T/3rKsd08650Pid89fBqoGe9DvIb4y2ewx1vUG8Xlclmma\n24lPsj4OzLYs6xXi8xzWDw5tmkZ8uPOIxy/uJYtMWO8ST3zeMU1zL/HFET41OPzubF4Cfmya5pdG\nO8GyrJ3Eh4ltNU1zK/AnwDfPUe5PgSWDwwy3Ep9D9OuxvBDLspqA/w94anAxiEeALwwOf/wc8UU5\ndhKfM/THlmUdPkeRDxMfrryb+M3o3cTnWo3a7owxzrPFM9rrbwC2AftN08w/S9l2tG9fBK4erG8z\n8GvLsp5Jcp3jmhGLxc59loiIiIiIiJyT5mDJRTPjMzR/M8rD1snzvUREzpdpmn9J/K70SP7rVL9T\nKjLRmKb5AZA5ysPXj3UbmATEcTPx3ruRvGtZ1rl622xxsb+/ifq6JxL1YImIiIiIiCSI5mCJiIiI\niIgkyHkPETRN00V8A7l6y7LCiQ9JROQTanNEJFXU3ohIIlzIHKxLgJq33z7XPnEiMsWMad+iC6A2\nR0ROp/ZGRFLpvNocDREUERERERFJECVYIiIiIiIiCaIES0REREREJEGUYImIiIiIiCSIEiwRERER\nEZEEUYIlIiIiIiKSIEqwREREREREEuRC9sEaVyKRCIcPHx7xsfLycpxOZ4ojEhE5fyO1ZWrDRGSs\nRrseUjsiknoTPsE6fPgwd/9wDZ6colOOD3Q088r3PkNlZaVNkYmIjN3pbZnaMBE5HyNdD6kdEbHH\nhE+wADw5RXjzSuwOQ0TkoqgtE5GLoTZEZHyYFAmWiMhEc/pwnrq6OhujERERkURRgiUiYoPTh/N0\n1+3HP32uzVGJyETX23CI1p3v4skpxJNdDFxrd0giU44SLBERm5w8nGego9nmaERkMmjd+S7B43UE\nj9cRDfVRX3+j5mCJpJiWaRcRERGZBEJdbQSP1+EvmUXxNfcCsG7dOpujEpl6lGCJiIiITAJdh/cA\nkD17GVkVS3B609m0aRP9/f02RyYytWiIoIjIBKG9skRkNOFwmJ4j+3Cm+Ugvm4fhdJJetoC2tj38\n9re/ZcWKFYDaDJFUUIIlIjJBaK8sERnNvn37iPT3kj13OQ5n/PLOm1fKrn0f8s2f/ZqS7TG1GSIp\nogRLRGQC0T43IjKSQ4cOAZBxybzhYy5fJr7iCkJdbbjTs+0KTWTK0RwsERERkQnu6NGjAKTlTjvl\neFr+JQAEj2uvPZFUUQ+WiEiCaa6UiKTakSNHcHrTcXrTTznuzZ9Od+0ugk21ZFYstik6kalFCZaI\nSIJprpSIpFJPTw/t7e14sgrPeMyTW4JhGPQdr1OCJZIiSrBERJJAc6VEJFVqa2sBcGcVnPGYw+Um\nLa+UvtZjRCPhFEcmMjVpDpaIiIjIBDaUYHlGSLAAfEUziEUjDLQ3pjAqkalLCZaIiIjIBDacYGWf\nOUQQwFtUDkBf69FUhSQypSnBEhEREZnAampqcDgcuDPzRnzcVxhPsPqVYImkhOZgiYiIiEwwQ6uV\nxmIx9u3bR2ZmJk2OkVcqdfky8GTm0d/eSCwWS3GkIlOPEiwRERGRCWZotVLD7eGo1YA7I4+SGZFR\nz0/LK6Gv7Ritra3MmjUrhZGKTD0aIigiIiIyAXlyijAMA4fbizd/+lnPTRt8/PQ9+kQk8dSDJSIy\nQcWiUerq6k45pg2NRaaWUFcbAE5/1lnPG9o2QgmWSPIpwRIRmaBCnS08+FQT3rwjgDY0FpmKhhIs\nl+/sCVZaXimgBEskFZRgiYhMYJ5sbWgsMpV9kmBlnPU8Z5oflz+buro6YrEYhmGkIjyRKUlzsERE\nREQmqFB3G840Hw532jnP9eQU0d3dTUtLSwoiE5m6lGCJiIiITECxWJRQd8eo+1+dLi2nGIBDhw4l\nMyyRKU8JloiIiMgEFAl2E4tGcGeMLcHy5BQBUF1dncywRKY8JVgiIiIiE1C45wQA7ozcMZ3vyVYP\nlkgqKMESERERmYBCvYMJVmb+mM53pvnIy8vj0KFDxGKxZIYmMqVpFUERkYsUiUROWfr49L2pziYc\n7Ka3sZrehkO4ejrw5hThcHuSEaaITDLhng4A3Jm5hHs6x/Sc8vJyLMuitbWVgoKCZIYnMmUpwRIR\nuUiHDx/m7h+uGZ7f0F23H//0uWd9TmtrK80fvUR/61Fi0Sjh3hMYLg8de94nY8ZCMmYuSkXoIjKB\nfTJEMO+8E6zq6molWCJJogRLRCQBPDmf7Ec10NF81nP37NnDD3/4Q3qPHcZXPJOsisX0tTYQi8UY\naDtKZ2AHnYHtrL8mQ5sGi8ioQj0dGE4nLv/ZNxk+2YwZM4D4PKyrrroqWaGJTGlKsEREUmjXrl38\n7d/+LT09PeQtupWiZXcC0BnYiSszH1/BdE4c3ELzphd58sknCQaD3H///TidTpsjF5HxJtxzAk92\n4XltGjxz5kxAKwmKJJMSLBGRFGlsbOTxxx8H4M///M/57rvtZ5xjGAY5c5fj9GVR2rOZl156iaNH\nj/Ktb30r1eGKyDjW09NDNNQ35hUEh2RmZlJQUKAESySJtIqgiEgKhEIhfvjDH9LV1cXXvvY1FixY\ncNbz3enZfOc732HZsmVs27aNv/zLv6S5+exDD0Vk6jh+/DjAmDcZPtmsWbNoa2ujra0t0WGJCEqw\nRERS4vXXX6e2tpY777yT22+/fUzP8fl8PProo/zBH/wBR44c4bHHHqOvpT7JkYrIRNDS0gKA5wIS\nrNmzZwMaJiiSLEqwRESSLNRzgldeeYXc3Fzuu+++83quw+HggQce4KGHHiIYDNK0/nd01exKTqAi\nMmEM9Wi7Mi6sBwu04bBIsijBEhFJsvbd7xEKhbj//vtJT0+/oDLuuOMOHnnkEQyXi8b1v6UzsCPB\nUYrIRDI8RPA85mDFolHq6urweDz09vayadMmAoEAkUgkWWGKTElKsEREkqivrYHexmpmz57NDTfc\ncFFlmaZJ8YrP43B7adrwO4LNh8/9JBGZlC4kwQp1tvDgUx/xx7/axY6WGE+9tom7fvC7UzZKF5GL\npwRLRCSJ2vd+AMCqVavOaynl0aTlFjN95X0YDhftez8g3Du2zUVFZHI5fvw4Tm8GDpf7vJ7nyY7v\n2ZdRNh8MB4ahS0GRRNO7SkQkSULd7XQf3o0nq5CFCxcmrFxv/nQKl91NNNxPy7bXiEU1vEdkKgmH\nw7S3t+NOz7ngMryF5QD0tR1NVFgiMkgJlohIkrTvW08sBllzrkxI79XJsuYsw1dcQX/bMTqszQkt\nW0TGt6amJmKxGK707Asuw1ccT7D6W48lKiwRGaQES0QkCaLhEJ2B7bj9WaRPn5Pw8g3DIGfeNTjc\nabTufIdwX0/C6xCR8amxsRHgohIsd0YeLm86fa31xGKxRIUmIijBEhFJimBjNdHQAJmzlmI4nEmp\nw+nxkWNeQzTUT+v2N5JSh4iMP0MJ1sUMETQMA29ROZG+HlpbWxMVmoigBEtEJCm66/YCkDVraVLr\nyZi5iLTcYk4c2sZAR3NS6xKR8aGhoQG4uB4sAF9RfJjgwYMHLzomEfmEEiwRkQQLB7vob6nHVzQD\nT2Z+UusyHA4KLr8TgPaqDUmtS0TGh+Ehgv4L78EC8BVXALBv376LjklEPuGyOwARkcmmt6GaGDGy\nZl2ekvr8JbPwFZXTc9QiEAhQWVkJQCQSGXF/m/LycpzO5AxbFJHka2xsxOv14vB4L6qctNxpONPS\n2bt3L7FYLOGL8YhMVUqwREQSLNgYwHA4yZiRuKXZz8YwDPKX3ErPUYsXXniBlStXAnD48GHu/uEa\nPDlFw+cOdDTzyvc+M5yEicjEEovFaGxspLCwkCMXmRAZhoGvuJyurkYCgQCzZs1KUJQiU5uGCIqI\nJNBAZwuhnnZ8ReU4L/Lu8vnwF1fgLZzB/v37OXDgwPBxT058U9Ghr5OTLRGZeJqbm+nv72fatGkJ\nKc9XFB8m+PHHHyekPBFRgiUiklDdh+OLW/hKZqe87uy5ywFYs2ZNyusWkdSoqakBoKysLCHleYtm\nYBiGEiyRBFKCJSKSQN1H9mEYDnzFqR+C5y0oo6ysjPXr1w9PgheRyWUowbrkkksSUp7T46OiooKq\nqip6erSfnkgiKMESEUmQUHc7fa3HSMsrSenwwCGGYXDnnXcSi8V48cUXRzwnFo1SV1dHIBAY/opE\nIimOVEQuVCAQABLXgwVw6aWXEo1G2b59e8LKFJnKtMiFiEiCdB/ZD4CvaKZtMVx++eW88cYbvPnm\nm1x77bVnPB7qbOHBp5rw5h0BtOiFyERTU1NDdnY22dkXtwfWkFg0SmFhIb29vaxdu5bS0lKtNCpy\nkZRgiW1GW0J6iBp4mWh66qsA8BbOsC0Gl8vF3XffzVNPPcXGjRuBM3vSPNnxhS9EZGLp6emhqamJ\nJUuWJGxJ9VBnC3/7RpjWpjAfr3mX3zfm8uqjn9dNF5GLoARLEiISiVBdXU1zczNNTU00NzcTDAbp\n7++nt7eXcDhMRkYGfr8fv99Pbm4usViMv3ntMGkjXOjprrpMNNHQAMHmOrx5JTjT/LbGctttt/HM\nM8/w7rvvEpt2p62xiEji1NbWAlBRUZHQctNyismecwXt+zcSDfUntGyRqUgJlpxhrD1LJ06cYPfu\n3ezevZsNGzbwwrrtGC73GeeHg904nG4cnrQzjrt8GXjzp5OWV4qvsAxvQRme3MQsPSuSSn0tR4hF\nI/hL59gdCtnZ2Vx//fWsXbuWPtcRfPmldockIgkwNP8qGTcf08vm075/I70NhxJetshUowRLzjDS\n5qRDehtr+JOFLqqrq4fvpEE8KfMWleMrKseTVYA7Kx9nmh+HK43ehkO4MgtIy8wlMtBHZKCXcM8J\nOqu3Ewn1E+nroat2N121uwEwHE5c/ix+U1DPddddx7x58ygsLEzVyxe5IMGm+Mpe6dPnEurusDka\nWLVqFWvXrqWrZie55lV2hyMiCTC0gmBFRUXCF6fxFc7Ameant7GaWCyW0LJFpholWDKioc1JIb5r\nfLCxmo6qTZw4uJXH1kVxpHnxFsR7nLyFZYQ62yleMg9/4ZnLxg6cOI7Ln3XGUEBnWjquzHx8BdMJ\ndbfRd/wIfS1H6GupJ9hUy1tvvcWGDRsAKC4uZtGiRVx22WUsWrSIEydOjBi35m2JHWKxGMGmGpwe\nL96CS8ZFgjV37lzKysrY8uEuQj0ncKcnZkK8iNintrYWl8vF9OnTqaurS2jZhsNJ+iUmHVUbqamp\nYdasWQktX2QqUYIlo4rFYvQ2HKJt17sEj8dXHHNl5JJZuZSCxbfg8mUMn9sZ2HnB9RiGgSczH09m\nPlmVSwDoba7jW3dPp7e3l71797Jnzx7efPNN3nzzTYLBINuPQ9bspfhL5+D0+ADN2xL7NDQ0EA52\nkT37CgzH+EjwDcPglltu4fcf7KTz4Fbyl9xqd0gichG6u7uprq5mzpw5uFzJuXzLmLGAjqqNbNu2\njZUrVyalDpGpQAmWnGHobnzz5v+gr6UegIxL5pF32Y0MdLbiysw/JblKBsPhJC0tjTlz5rB48WKi\n0ShHjhyhqqqK999/n1CNRfveD+nYv4GMGQvIXXAd7qyCs97RU++WJMvevXuB+PDA8eTKK6/E4U7j\nxMEt5F12E4b+/kUmrI8//phoNMry5cuTVod/2iwMp5vt27cTi8UStlKhyFSjBEuGxWIxtm7dyj//\n8z/TtHEbDreXjLL55C26eXi44EBna0piOX2vnk9k0B2ppPTWFYS7W+mq2UlX7R66avfg9KbzpepL\nyZp9+RkfCurdkmSyLAsA37Tx9feVlpZGxoyFdNfto/vIPjJnXmZ3SCJygTZu3Ehvby/Tpk0jEAgk\nfIgggMPlxlc8k+bmZurr6xO6mbHIVKIEa5Iby4qADoeDjz76iOeee45Dhw7R29uLv3QOxVfdQ5qN\nK/qNtlfPQEczLn8WWeXxnqtgYzVtez6gM7Cdgc4WBtobyF+6En9xYpexFRlJNBrlwIEDuPzZuNNz\n7A7nDJkVi+mu20eHtVkJlsgEFQ6H+fDDD/n4SDdff6EWwzhMd91+/EnoNfcVVxI8Vs8LL7zAqlWr\nAI0AETlfSrAmkLMlS6M1fmdbEbC/rYG/ve0SNm3aRE1NDYZhcN1117FixQq+/kKtrcnVWBmGgb9k\nNv6S2bTsmEHP0QMEj9dR/8YTpJfOIX/pbRe8oeqF/L5l6qmtrSUYDOItGJ93et0ZuaSXzKKnoZr+\n9ka7wxGRC7B3716CwSAZ5QuHt10Y6GhOSl1ufxa76k9w4Mm1/O/aHI0AEbkASrAmkNGSpXM1fiev\nCAgQGQjSeWgbrTvf5t/q3KSnp3PDDTfwhS98gbKyssF9NmqT+EqSw5NVgH+6iUGM1h1v0nPsID3H\nDpI581IyZlx63uVd6O9bppbdu+PbC3gLzlxBc7zINq+ip6GaEwc+wltYbnc4InKeNm/eDKRmGLLD\nnYavZBahE8dxedNhhBu0InJ2SrAmmNOTpbGKRSL0NBykK7CD7voqYpEIsWiEW265gwceeIDi4uIk\nRGsPX2EZ01d+mWBjNS3b36Krdg8nDn7ML7Lr+dKXvsTChQvHPHH3Qn/fMnVMhAQrfbqJOz2bzsAO\nPDnjv2daRD4RDAZ577338Pv9KWtnfAUzCJ04Tm9TDZ5sJVgi50sJ1iTW3NzMhx9+yPGtrzLQ0USk\nPwiAJ7uArMqlePJKufbaefT09AzvDg8kZeJsqg0NHSybNovuI/s4vvVVtm3bRlVVFSUlJVx//fVc\nccUVzJ07N2nL3crkF41G2bNnDwUFBRz3Z9kdzqgMh4PsOVfSsuMtehsO4ckrtTskETmLk4eov/rq\nqzQ1NXHttddyuCc1Q9PTCi6B6o8JNirBErkQurKcJDo7O/n4448JBAIEAgEOHjxIU1MTvb299NS3\n4ckqJHf+NWRWLCYtrxTDMOgM7OTBpz46Y6W+ZE2ctYNhGGTOWIgrPZf/fFsh+/fvZ/369Tz//PM8\n//zzuFwuysrKqKyspKKiYvgrMzPT7tBlAqitraWnp4elS5eyv8fuaM4ua84y2na/R3fdXrLnX2d3\nOCJyFkND1F0ZOdS/8QzEolTv7iWjPJKS+j05RTg9XoJNNWSbV6WkTpHJRAnWBBQN9dPXepS+liP0\nHa+np+Eg/2lHGn6/f/iczMxMrrrqKoqLizm6pYfMGSMPixtppb5kTZy1k2EYmKbJXXfdxZ/92Z+x\na9cutm3bxoEDB9i3b9/wPkZDcnJyyM7OprM9B6fHizsj16bIZTwb+ruZM2cO7LA5mHNwedPJrFxC\n68636W04RHrxDLtDEpGz8OQU0dtwCGIx8hfdgjszP2V1G4YDX1E53fUW4d7OlNUrMlkowZoAIpEI\nBw4c4LXXXqPh/TcJ97QTi33yuOF0smjRIpYtW8asWbOYNWsWBQUFGIZBIBDgn6rWa7PAk3i9XpYv\nX87y5csJBALc9YPf4XB5GDjRzMCJ4/Gl3hub6W/fhdObTsf+jfiKZ5Iz50oyys9/sQyZvKqqqgCY\nNWsW7Ki2OZpzy11wLa0736Gr+mMKFt+sdkFkHIuGB2jftx6HO42cedfQc/RASuv3Tauku96ir+X0\n/ShF5FyUYI1T4XCYHTt28OGHH7J582a6u7vp7e1loKMDf+lsfIUz8BaU4S24hHBfD9/4+rVa1e4C\npeVOG+zFW3jK8ba96wn1dhLqaKC3sYZgUy2eXe+QWbGEWGyFPcFKyo22XH95eTlVVVVkZmYOLhIz\n/hMsT1YBvqJy+tqOEWyqxT9Ne8WJjFddtbuJ9PWQf9lNONN8Ka/fN7iXpBIskfOnBMsmI120xWIx\nLMvCsiw2b95MT098Ukd+fj7XXXcdxcXFfH9dB/7imac8L9w3zid/TFAuXwbeonL8hXcx0NVK+94P\n6KzezvGta/nxj1v57ne/q13up4CRlusf6Gjm1w/dQnNzM1deeeWE6gnKKL+MvrZjtO/7UAmWyDjV\n399P58EtONwecuZfY0sMabnTcKb56DuuBEvkfCnBssnJF22hnhN0H95Dz5H9DJw4zqKyHEpLS1mx\nYgXLli2jsrISwzCoq6vD4Q7aHfqU5MnMp/jqe8ldeD0N655n586dPPDAA3z2s59l5cqVp1xgaxPi\nyWek5fqrq+M9VvPmzbMjpAuWllNEWl4pPUcP0D8J51uKTAbr168n0t9LwdLbcab5z/2EJDAMA29B\nGV21uzhx4oQtMYhMVEqwkmi0oUUQT7AioT7adr5Dz9EqYjFwuNykT5/L0aJKumYs4UCLwa9fawQa\ngcm1ul+qxaLREZefP98l6T2Z+eTOX0F9Uw1d1dvZ+KN/xvtvL1NwxZ24/FnahHgKGdraYKIlWABZ\ns6+gbcdbtO/7kJx59twdF5GRxWIx1q1bh2E4yJlv73B0b8EldNXuora2lqVLl9oai8hEogQriUYa\nWhSLxeipt2jZ9hqxaASnx4c3fzo55lVkzFhI95H9uDLz8eWfuU/NZFzdL1VCnS08+FRTwpakz5q9\njIKlt9G86QW6j1TR+OHzFF/1B6f8X8vkVl1djWEYzJ07l2PHjtkdznnxFVfiycqnq2aXFm4RGWcC\ngQBHjx7FN60Slzfd1li8+dOB+JYUIjJ2SrCSbGhoUTyxqqJ1x1v0dzQTC/WTPmMhhVfcia/w4ubx\njNY7A5Nj0+BESfSS9C5vOiU3fpGuwHaaP3qZhg9/S/r0uQwMXHmxoco4F4vGe6fnzp2L1+u1O5zz\nZhgGuQuupWnTf9AV2AHcaXdIIjLo7bffBiBjxsJznJl8SrBELsyUTLDONnQPEjuHJhaL0dtwiJYd\nb9HXchTDgKxZS+MX+0Uz8RVectF1jNY7AxpWmGyGYZA163K8hTNoWPccXbW7ePzxx3nssccoLT2z\nF1Imh4ETxwmFQhNyeOCQzIoltO54m66anQSDmtspMh6EQiHee+89srKy8J22oJUdnN50XL4sampq\niMViE2pBHxE7TckEa6She0MSOYfm0KFDNK1fzcCJ4wBkzlhI3uJbSMspojOw86LLP9lIvTOgYYWp\n4skqoOzOP6Vh3XMcOXKEv/iLv+Dhhx/muuuuszs0SYKBjiYA5s6duDcvHC43OfOuoXnLy6xbt46F\nC+N3y0e6AaWFW0RSY+vWrXR1dXHddddR3Tw+3nNpudPo6Wmhubl5cEsKETmXKZlgwcirgiVKIBDg\n3//933n//ffpa2kjc+Yi8hffineEeVUyeThcbvKXrOT+K5ysWbOGf/zHf2T37t3cf//9eDweu8OT\nBOpvbwIHzJ492+5QLkr23Cs5vu113n77bb7yla/gcrnOuAGlhVtEkm/oleXR9QAAIABJREFUxsar\nr75Kb29vfATEOLk/6skthp4WDh48qARLZIymTILV09NDR0cHAwMD9PX1JaWOgwcPsnr1ajZs2ADE\n724fKSsnZ86ypNQn49PVV1/NjTfeyOOPP84rr7xCVVUV3/nOdygpSU5CL6k30NGIZ5pnwu+D5kzz\nk1l+Ke3th9m0adNwj2syb0CJyJkOHz7MXT/4Pcc/eo9oOETgxX2kX2LaHRYAaTnToGcvBw4c0KgM\nkTGa9AlWc3Mzq1ev5s033yQcDgMQDAY51hqfO5MxYyFpeSUXPK44FouxZcsWnnzySSzLAmDmzJnc\ne++9ZGRksP7l+oS9Fpk4pk+fzk9+8hP+5V/+hTfeeINvfvObfPe732XRokV2hyYXKRoeYKCrlbIr\nl02KYXOZFYuh+jCvvPKKLp5EbGS4XETDITJnXkpadoHd4Qzz5BRjHDM4ePCg3aGITBiTOsE6duwY\nP//5z+np6aGkpIRFixbhdDrZtWsXWw5vpW3POtr2rMOdkUPGjIVkll9KzHCMWNbJ8xJisRhHjx5l\n27ZtbNy4kSNHjrCrvoP06SZZc5bRnF3Glvc66K7bqAUmppjTV3RctWoVOTk5PP300zz66KM8/PDD\n3HrrrTZGKBerv60BYjEqKirsDiUh3Jl5mKbJ7t27OXLkzIVyRCQ1+ppqAfCXzLE3kNM43B6KiooI\nBAJa6EJkjCZtghUOdvHTn/6U/v5+HnzwQe6+++7hu82BQIAPPO8R6eui+/BeeuqraN+3nvZ96zGc\nTn7h3M0VV1xBYWEhGRkZABw4cIBvP/EG0XA//W0NRPq6ATCcblz+LEpv/Qw5s0/dhE8LTEw9o63o\n2Om/jFldO3jssceora3l5ptvPuVxLSIwcfS3xfe8Ki8vtzmSxLnpppt4+umnefXVV1m5cqXd4YhM\nScHm+E3c9NLZ9DbW2BzNqWbMmMHevXtpampi2rRpdocjMu5NygQrFotxfMta2rP6+NrXvsanP/3p\nM85xuNz4Zywkc8ZCouEQvQ2H6K7bS1ftHrZt20ZVVdUp5/f29tJd14bD7cXpTSdjxgLSS+eSUTZ/\neHNgERh9v62A20PHnnVseezn5K/dHx+ahRYRmGj6Wo8C8aHAk8WSJUtYu3Ytb7/9Ntdff73d4YhM\nOQMDA/S11OPNL8Xlz7I7nDMMJViBQEAJlsgYTMoEq+foAfrbjrHkhlv43Oc+d87zHS43GWXzySib\nT/a8FfzDF+bhcrk4fvw4fX19RKNRurq6qHujlowZC3H5s9RFLuctfbpJ1szLqH/zCTr2b8BfMov0\n0vE1FETOra/1KA6XZ1KtpuVyubj99tt57rnn+Oijj4CRh0qLSHIEAgFi0TD+cfqZMLSgTyAQYMWK\nFTZHIzL+TboEKxaL0bYzvgv6lVdeSU3Nmd3sJ8+ROZ1hGBQUFJzRmxAIBPDtNHCnZyc2YJlS0nKK\nKL3pi9S/+QQN656j7M4/PWPe1uk0fHD8iIb6CXW2xCd9T7KbLHfccQfPP/887777LrG8W+wOR2RK\nGVpAwlc0Pocez5gxA4hfC4nIuU26BKunvoq+tgbS8qbzN6/X4d0SOuOc7rr9WnxCbOMrnMG0az5L\nw4e/pXH9b8kxrxlx3hZo+OB409/eSCwWHwY62RQUFLB8+XLeeecdBoxGfNq3TyRlhhIXb+EMmyMZ\nWWZmJnl5eUqwRMZo0o0Dad+/AcOArMqlw3NhTv/yZGm+lNgrs2IRWbOW0t/WSFfNztH/VnMm34X8\nRNbf3giAJ7vQ5kiSY9WqVQB01e60ORKRqSMWixEIBHCl5+DyptsdzqhmzZpFa2srJ06csDsUkXFv\nUiVYoZ4T9DXX4i2aiTsjx+5wRM6qcNlduP3/t707D46rPPM9/j19em9JLbUWa98sWcZ4AYwwZjVm\nxzPBcxkKyISikhk8E2bIHZKpmqQguZksNwnDhCIDk9yEkMFcLjBAXDAYLxCzGBsbG2x5lS1basmy\nJGuXWlJLre4+9w+5BTY2yLKkt0/386lyVZC72z9BdHye8z7v86YRaKgh1NehOo6YgJGeVuDcC6xY\nG2h9ff34ry9qC1Vl0aJFZGdnM9h8mMhIUHUcIZJCc3MzQ0NDOOL8cO9YJ8WZtl4IIU6VUC2CA037\nMQxILZmvOooQX0q3u8i5/HYa33iS3gMffG7Mv4g/Iz1taBYdW6rvnN53pvH98diqrGka1157LW/u\nOER//S4yLpDN7EJMt9jUYkdGfLblxh4QORwOhoaG2Lp1K2lpabI/WIgvkFAFVsC/F03TSCmZz1DL\nkUl9xtkGDsTj02Zhfu78Shy+fIIdjQy11ePOlb1W8cqIRgj1nMCeno1mOfebitPH98frOXlXXHEF\nmvZ7+up2kj53qeo4QiS8WIF1+vEe8SL2gEh3uDju7+bAi+/zq4+Dsj9YiC+QMAXW6EAPw53NuPNm\nn1cP89kOio3Hp83C/DRNw1txKR2frKdz1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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from statlearning import plot_histograms\n", "plot_histograms(train[continuous])\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The descriptive statistics further highlight these patterns. " ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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SkewnessKurtosis
funded_amnt1.090.95
int_rate0.28-0.49
installment1.141.28
annual_inc4.9352.37
dti-0.02-0.83
revol_bal3.2515.23
revol_util-0.05-1.11
total_acc0.830.68
months_since_earliest_cr_line1.182.12
\n", "
" ], "text/plain": [ " Skewness Kurtosis\n", "funded_amnt 1.09 0.95\n", "int_rate 0.28 -0.49\n", "installment 1.14 1.28\n", "annual_inc 4.93 52.37\n", "dti -0.02 -0.83\n", "revol_bal 3.25 15.23\n", "revol_util -0.05 -1.11\n", "total_acc 0.83 0.68\n", "months_since_earliest_cr_line 1.18 2.12" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "table = pd.DataFrame(train[continuous].skew().round(2), columns=['Skewness'])\n", "table['Kurtosis'] = train[continuous].kurt().round(2)\n", "table" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, we plot univariate logistic regressions of the response on the predictors. All the relationships are as expected, with the interest rate and loan-to-income ratio displaying the variables with the largest coefficients. " ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "image/png": 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OQJkwVa/FiutBSLWtISmdASeDshywPeKJY2at8SMkZw7X588iCbHXmLnoav2t\n9MwwqqW3fnQ9M3zlPuoSs3u6TX+QQPquiquEnbaYWWjwiTgGx8Fua8VqaUVlM6avVLVKcPw4yraJ\nBgfROsHp7SUaHcXKZrE7OsxPV1d9UmDAbBtGJrQBSbEEUUz15QNU979EePw4OklMuIoiM0FzuWTK\noU0fUR2G6FIRMOcQt78fjh1Hd7SbbqRRRPn55wmPHKG8d685tm3jbb6OeHoaXa3i9PSQueH6KzZ6\nYI0ELCHmiOOElpzL+HSFODGT9cWJplyNyGVc0IpYJyTJ7HAWiYZSNaCjpW3BfW5c3QlAX0eeo0OT\nlCohlgLXhsoCTQoBNvd3AdDdnmPfkZG0v6iqD5SRy6Qj9aTMqIe6HqJOjUxx6FRMS9Yln3Hpbs8x\nMllieKxIX0eeM9NlRiZK9HTkePdrtwEwU5ambIvOss/e2VeIKy4G5WJGxVFQOWP6Yai0Vqt2QyCq\nmGVJhJ4+DWERjQIng544ga5MEp981rxne6hciK5OkxRH6s0BnYE7UR3r0BMn6kdXHeuW5VMvhfwd\ndzDxqU8vXBsgxErkOAuHq5okNk3wggAdBuB6WBmP+PQwVmcHdncP8egZosEhMtu3NQ0eMfPoo8Tp\n6ILhiZPoOMHK54nHx02Ism0IAqovHUDZtmnCpxR23kyQnNu5g+Jjj1Pv4K5NzZbK5lCZDOHRo4DC\nam0ht2E94alTZs6rJCYcHYXRUdNVo8OMUtryutcSDQ7hXbfpio4eWCMBS4g54iRhphw0NNvT6XIz\nsEXGtResYXcsi672LIcXGGV467ouOlqzPL3/JHGSYFtpi5nEzEcVL/AHOowS+vvaeOD26/ivX3yG\nobEZipWQrGeTdR2CMK6fg2bNvqgEMTOVgHzGpVgJmS4H3LJlNX0deQ6dGqenPcedfj8AhwYn2Nzf\nzVP7TiAWmZKxhMQKo5P0B6gWwXLSYNVwMnGzEEfo2qAUSWT+L8chSekM0cGHTfiKq1CdQkcVUyNm\npZcVlSni0y+Se/vvU336L9GTJ1Ed68jc/dNX+tMuGau1FSufJxm/wEmghVhmyrZR+RxJuWImGZ4r\n0SSTk1gDA6iMh45jojNjEFTNoBetrah8DkvlsXt6qOzfT76lhdLu3RQffwIcB10uEw8Pg9bEtt0c\n6OIYXSzWRylEWSSRR2bDetx1/WbwDWVRH3jHcXD712J3d9fnuwqPHkN5npmDK5zTNjcdvEOXyyjP\nI3fH7VczZnBAAAAgAElEQVS031UjCVhCzFEJorPekKyGMaVqkTCKsSxlOnmmUzk4jnXWSYMLx86w\nub+LUjWkXI2I09qvWt+thfT3zQ6r/rod6xkZLxKEMSdGplCWojXncejUONUwNsOdWqppHivLUpSr\nIYcHx7EshefYHMp7DPS1MzFTYXiiyJHBCXIZl87WLKMTJQ4NrtwLhcpXfpXymlVmDp1sByrbgcq2\no7KdqFxn09w6K0oizYfESqLSodpTlp3+H51z0gurZpntztbAKlW/YZBMD6HjwAziYntmioo4QDlZ\ndNX0G9WlM6hMO7m3/u7SfywhxHnZHR24W64jPHR4tq9TI6XMSH+dHcQjIyTFEiqbxR0YIAmqxGNj\nWJ0dKNcjOHKE6ksvUdmzl2SmSDw2TlycMc30ahdR8Zwh4OtzcJl+6FhmEmSrvZ3W++5j+qGH0JWy\nqVW3LexVfRAnBAcPonI500QwPVepXA5sy4womA4R73Z3EQ+PmBq4OKH7R3946X+pZyEBS4g5dC35\nLBCyytUQpRSObaEwTQWVrg3nnk4EvNA+0WitKZYConSi4URrlFJ0tGYYm5p/J2l0olR/3jhp9903\nDbDrxgEAjp+eZPDMDCjTZ2vz2i76uloYnShRqoZkXMdMgJxogjCmWA45MTLF2HSZKNa4NpyZKlEs\nB9ywsRc9d3KvFSQe3ENUvKjxy1YGyzVzDQmxEnit6eTC6UTDmXYonZm/nlKAAicHSQDahWw7AFam\nzcxX5eZQYdWMkpltRyUt5rmbDpbj5ufNe3XNsaWGWlw9NJpkeoakVMJqbyeZnjZzVoH5zjsOKptF\nBwFWRwc6CLGyWdy0SV4yOYWVz6OjCF0qYeXzBIcPo7XGbmsjGhtbuEm845hBK7Q2j2C+O66L09OD\n02v6WFm2g9XWZmqmXAdKFXRXgtYaXSoRnjpFbscOVDYDUUQ8OUWiprE04LlEI6OobAanpxssxfSD\nX6fn3/6bK/PLnfuRl+WoQlwB85vPXRjXsXEdi+ICk+466R/T1lyWIIqJ44Su9izVICbRmtasxwil\nedsN9JkLE8+zzSAagKOgLZ/hnbu28TcPPj9vm8YR/eYOuV7zKz/wOj71yIsMjxVZ1d3Ch+6/idXp\nUO3PvzzERLFKOYiwLQvXsWjJupQrEa5t42Qt4nRy5MZyfvulUxf3C7tCVGsfuEUIy8tdlIvjZCVg\niZVB2ah8F+hOdGUKdIxq6UFXJhruLieAZfpKacz6tkcycdScK7JtOP7bcbY8QHTyu2jrJHitWN3X\nobLthIWvoZIQMu3Y614zb96ra43T3UN0/IT0wxIrn+Ngd3ZCGGK1taOjEBVk0Gm/LOU4WO3tZP3t\nxDMzppldS4sZ7MLz8DZtIrtzB1YmQ/HxJ7Dyedz+fqJTgyYQ2TbKddFh2FRbpXI57N4eU9OUJNjt\nbURj4xCGOH19ZHfuwFlrJhh31qxBN7Tq0eUybn+/6WtVLqOUouX192C1musc9dnPmYEyUsXHHjef\nMRWNLDzP6JUgAUssic7WDBMz8y8qLWWGOo8XqCmxFcSL9DfKtiDnuWZwiuDixs/t7cwRhglRFBGm\nowRaCjzXxrYtbKXobsvS3pLhlm1ryHoO395/Ep1oBvraOXp6gsaPZyvwHDNyT0c+S9Zz6GqdHb3v\n/ffewP948Pl5FWa1WqpzWd3dys99390Lvre2rw1lKVCaMExozXls7jc1XPuOjDCUjnY4PlMm45hT\ngecuPCfXSpD/0P+kbWAAHYfoyiS6MoWuTJiLw/IkujJB9fH/e7mL2czOo3Jd6OrkcpdELKmzVHmf\ndV2FCTJne5+G/aX7Vk7zUOrKNu/ZrvlJm+UtyHLAy0PndahMG0pBMnkC5WRxb34f1T2fg4ljpumg\n1pDtwNlyP8nYYVAKZ8MuU6LKZH34dZXtIPvGXzFDtTcMy27lu+tDtwOmpusalr3tFqqHD6Mn5Tsu\nlpHrpqEm7Vup1GzzPNs24SebAUzgyd96C/GpQaKhIbSGzObrcDduIH/HHZR27yZKB6vQQUAyPY2y\nrPpAEbVwU1vHWbWKeGLCDDDR2QkteTO8e5JAJkN+191ktpuBtMKTZmAK059Kkb3ex1m7pj4ARcf7\n3svk579ANDKC09eHu3YtSRDgbdpkjtXXWz8+pCN5puUAsLubRwp0+voW9dd8Ma5owPrgfTfw6Uf3\nX8lDrliWgstpjWWr9M+zBhuI07/Jc2+iZRyLTMYlimJK1flB4+ZNfRwamqC0QG0NgG0p1vW2UQ1j\nKmHEdLHaVO6sA5WG3ToKPnD/jTxw+2a++ESBr+8+VO+vtGlNBz0dLZwen+Ho0Pw/Rvfs3EhXa4Yv\nfutA/dJidWcLuazL2FSZShCmE+oqcp7DxtUdjE2VmSxWCEIzF5RlKzpaMqztbqM173HLltUcOjXG\nN545Mvs7cW2ynkNfR47DpyZobNSXdWBtTxtjU2VWdbUQxjEKM4HwhlUdVIKImUqAY1ns3Lqa+27d\nRGvOa2rC9867t/HYC0cpBxH5jMtPvus2gjhhdKLEG27ZwNGhSbP/tMYJ4Je+fxd//oXdVKOYjGPz\n7957B605j8vxoftv4lOPvGiq1oGt/d30dbWw68YBdly3ql7ztW1dDxvXdBBGCb2deW7ftprvvny6\nvp+7rl97WeVYbMp2zbDPDUM/11QP/yuceGp2gdsO4TkuPAFaVpkR0851gXopbA/V2W/2bWchntMM\nVOVBz63ttGi68FZZ0DKP1sWzgXOMlHU+yk23V6aPUVShHnhyPWTu/VXCA18jOf4d038p04EaeA36\n8OMQ1vphWrPb2BlUz2aIA/TUIJBAy2qISlCeMANIOHnoWIfV2kcydgQqk2A7kO0ytbfjR9BhydRC\nta0B20U5WahOm+9D5waSQ4+hy2MQR+B4qEwrjv8OrLY16MpkvZ+irkzibn9rPSjZ/bdSeeSj6JnT\nkO/B2XQPSlm4Wx+or7Pgr2mBSYKdLQ/MC13XstZ77oFKlYmvfBUkZC0fhelPuNAIeRfanEWlNzPO\nt6pSWBvWo6IYPT1NkiRQKqWDNijo6Sa7cSO6WiUan0CXS1iOi7YtM8perd+TbZm+RnNZFrS34/X2\n4K5bR3jyJNHEJEmphLIUynGx8rl03ioLu63V9DlKEpKZGXNxqTVWbw8qTs9jaFQ2i5XNkdlxE633\n3NMUVBrVQlZ8ZgxnXX9TqFponeztt6I0JMUiqrUFpSEeHyeenMTu6GgKUCV2m6Hd54S1Gmf16qYm\nfbV5reIzYwuOBNhYDrunm+4f/WGmH/x6PaB1vO+95/nHXDpKX2S1tu/7m4DDDz30EAMD57/DLoR4\n1ViSDlLLcc7RQQldGiUpjqKLI+jiKLo4SlIcMa9nhs1w1JXLuKDKtGN1rMNqW4tq78fqGMBq70d1\nrMNqH0B5MumzEOdwzZxvhBBXhYs650gTQSGEmEN5eZS3AatzwznX01HVhK2ZYfTM6fT5afT06fRx\nCF0cYcFbotUpkuEpkuGFa/VVrhvVMWBCWOd6rI4BVIcpk2rpPevok0IIIYRYXhKwhBDiEikng+pc\nj9W5/qzr6Dg0wWt6ED09ZB6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n+nfibL4P5coE3kIIcS2RgCWEEEIsIpVpxb3+XbjXv8vU\nbB18hPDA14iPPmnm2opDU9t16FFwczhb3oR7/TuxN74WZcmfZSGEuNrJmVwIIYRYIirThnvj9+De\n+D3o8gThy/9CVPiqmdgYDWGZ6KUvE730ZVSuG8d/G+7178ZaswOl1HIXXwghxCWQgCWEEEJcASrX\nibfzg3g7P2hGIzzwNcKXvkJy+kUAdHmM8Lm/J3zu71GdG3FveJcJW53rl7nkQgghLoYELCGEEOIK\ns1pX4d3+w3i3/zDJ+BHC/f9M+NI/oyePA6AnjhI8+ecET/45dv9tODe8B3f721DZ9mUuuRBCiPOR\ngCWEEEIsI6trE5nX/Qzea/8dydALhPu/TFT4GroyAUB86lniU89SffSjOFvux73xe6W/lhBCrGBy\ndhZCCCFWAKUU9tpbsNfegr7314iPPEG4/0tmMIw4hDgwIxMeeBCV78W54d24N70Xu2fLchddCCFE\nAwlYQgghxAqjbBdny/04W+5HVyYJDzxIuO+LJIPPA6BLo4TP/DXhM3+NtfpmMx+X/3aZX0sIIVYA\nCVhCCCHECqayHXg7vx9v5/eb/lr7vki4/0vo6SEAktN7qZ7eS/Wbf4Sz9U2mVmvDLpSylrnkQgjx\n6iQBSwghhLhKWF2byNzzc3iv+1ni498mfPELRK88BFHFNCEsfJWo8FVU21rcG78X96b3YnWsW+5i\nCyHEq4oELCGEEOIqo5SFs2EXzoZd6Oo0YeFrhC9+gWToBQD09CDB0x8nePq/Ym+4G/fm78PZ8gDK\n8Za55EIIce2TgCWEEEJcxVSmrT6/VnzmoKnV2v9FdGkM0MTHniI+9hRkO3BveA/uze/H7t223MUW\nQohrlgQsIYQQ4hph92zBfuMvo+/5OaLDjxHu/TzxkcdBJ1CZJHz2k4TPfhJr7U7cm78Pd/vbUV5+\nuYsthBDXFAlYQgghxDVG2S7u1jfhbn0TycxpMzDGns+ip04CkAy+QHXwBarf/CNc/524Oz6AvfrG\nZS61EEJcGyRgCSGEENcwq3U1mbt+Cu/OnzADY+z9HNEr3zBzawVFwj2fJtzzaaxVN+Du+ADu9e9C\neS3LXWwhhLhqScASQgghXgUaB8ZIyuNE+79EuOezJGOHAEiG91N96HepPvYnplZr5wexV9+0zKUW\nQoirjwQsIYQQ4lXGynXh3f7DuLf9EPGp5wj3fIbowIMQVyEsE+79LOHez6a1Wh9Ma7Wkr5YQQlwI\nmYVQCCGEeJVSSuGsu43c23+f1p9+iMx9v4HVs7X+vqnV+h1m/vJ+Kt/4HeLhl5axtEIIcXWQGiwh\nhBBCoLIdeLd9GPfWH1ygVqs021drzU68nR/E2f42lJtb7mILIcSKIwFLCCGEEHW1Wi1n3W3o+36N\ncN+XCPf8I8nYYQCSoReoDL0A3/wj3Bu/F3fHB7F7Ni9zqYUQYuWQgCWEEEKIBalsB97tH8G97cPE\nJ58h3PNpopf/xYxAWJ2uz6tlD9yJu/P7cba+CWW7y11sIYRYVhKwhBBCCHFOSimcgTtwBu4guffX\nCV/8AuGeT6MnTwAQn/gO8YnvoPI9uDe/H3fHB7Da+5e51EIIsTxkkAshhBBCXDAr303mzh+n5cf+\nmdz7Po6z5QFQ5nJCl84QfPu/UfzEOyh94WeJDj2GTuJlLrEQQlxZUoMlhBBCiIumlIWz6R6cTfeQ\nTA8R7vks4d7PoYvDoBPiw9+kfPibqPZ1ZgLjm9+Hle9Z7mILIf5/9u47TK77vu/9+/c7Zer2XZTF\nEgAJkiOwgiIpUp2UKFm26CJLtizLzuMktpzHSZybXMf2dXKT65Inyb1P7Ovu65Y4bpIfW5IjW8UW\nJVpUoUiwiCQIDAvqYrG9TT9zzvndP34zs7O7s4tdcFD5feHBA8zMKb+ZOXv2fM6viYtOarCEEEII\n8Zronl0k3vLPyfzTz5N8+Fdw9t7fes0snyX42q9S+r2HqHz2pwnHn8IYcxlLK4QQF5fUYAkhhBCi\nK5Tj4d30EN5NDxEvnCJ47i+oH/k01JYhDgnznyPMfw49dCPenR/Ge8PDqET2chdbCCG6SmqwhBBC\nCNF1emAfyXf+W7Ife4Tke38Jvev21mvx3CvUvvSfKP7eu+0ExjP5y1hSIYToLqnBEkIIIcRFo9wk\n3q3fjXfrdxNNvUj9ub+gfuyzEFZWT2C8+xD+nd+Pe9N7UW7ichdbCCEumNRgCSGEEOKScHbeQvI9\n/xfZjz1C4oGfRQ+uTFAcn3uW6ud/jtLvv4faY79MvHjmMpZUCCEunNRgCSGEEOKSUoke/Ls+info\nB4nGD1N/7hOErzwCcYipLBAc/u8Eh/8Hzv634N/xYZzr34HSzuUuthBCbIkELCGEEEJcFkop3Ovu\nxb3uXuLiDPUjn6T+/F9iCpOAITr5NSonv4bq2dUY6v2D6Mzw5S62EEJsSpoICiGEEOKy09kREvf9\nOGba7GcAACAASURBVJl/8jlS3/VrOPvfCigATGGS4Ou/Qen330Plb/7N5S2oEEKch9RgCSGEEOKK\nobSLe+BB3AMPEi+esYNgHPk0prJgh3p/+e8vdxGFEGJTUoMlhBBCiCuS7r+OxNv/DZkf/SLJ9/1n\nnNE3Xu4iCSHEeUkNlhBCCCGuaMr18Q4+jHfwYaL545e7OEIIsSmpwRJCCCHEVcNpG9pdCCGuRBKw\nhBBCCCGEEKJLJGAJIYQQQgghRJdIwBJCCCGEEEKILpGAJYQQQgghhBBdIgFLCCGEEEIIIbpEApYQ\nQgghhBBCdIkELCGEEEIIIYToEglYQgghhBBCCNElErCEEEIIIYQQokskYAkhhBBCCCFEl0jAEkII\nIYQQQogukYAlhBBCCCGEEF0iAUsIIYQQQgghukQClhBCCCGEEEJ0iQQsIYQQQgghhOgSCVhCCCGE\nEEII0SUSsIQQQgghhBCiSyRgCSGEEEIIIUSXSMASQgghhBBCiC6RgCWEEEIIIYQQXSIBSwghhBBC\nCCG6RAKWEEIIIYQQQnSJBCwhhBBCCCGE6BIJWEIIIYQQQgjRJRKwhBBCCCGEEKJLJGAJIYQQQggh\nRJdIwBJCCCGEEEKILpGAJYQQQgghhBBdIgFLCCGEEEIIIbpEApYQQgghhBBCdIkELCGEEEIIIYTo\nEglYQgghhBBCCNElErCEEEIIIYQQokskYAkhhBBCCCFEl0jAEkIIIYQQQogukYAlhBBCCCGEEF0i\nAUsIIYQQQgghukQClhBCCCGEEEJ0iQQsIYQQQgghhOgSCVhCCCGEEEII0SUSsIQQQgghhBCiSyRg\nCSGEEEIIIUSXSMASQgghhBBCiC6RgCWEEEIIIYQQXSIBSwghhBBCCCG6RAKWEEIIIYQQQnSJBCwh\nhBBCCCGE6BIJWEIIIYQQQgjRJe7l2nGxEvDosyd57pUpqkFIsRJQrtZJJlzuO7iHnkyC+aUKs0tl\nitUAV2vuuHEnDxzaTzblA3B8Yp7f/czTzC6WcV3NLfuGQSnSvsfJqUXyp+eI4phM0uMnP/gm7j04\nBsALx6f4tb96goVCFd9zeODQPnYP91AsBxgT8+Sxc0wvlnC0xtGg0IwMpHnXXfv55GPHWC7V6En5\nvPm2MZK+x3B/mvtvGVtXrpmFMp6nGelLc/TULGEck/Rc/sl3HEI7mtnF8qp1i5WAx18cZ3axTKFU\n4yvPnaIShKQTHj/6/rsIopjZxTI9aR8DFMtBa/1SJeBP//55XhqfIwwjRkd6OXRgFwZD/vQcYRxT\nqgS8PD5HbCDhOfzz77mXdxzav6rMc0sV+rMJ3njzbupRzGKxSn8myehIz6r3uPY7RMGdB3byzrbv\np7lM8z21v9cXjk/xG596kuVSjd5Mgn/xgXu57YadHZefXijyu595mumFEmEUM9iTIJnwuXHPIPt3\n968r11rt722oL8XHvvON3DA6CMDUfJFPfPkI0/Mldgxm+PCDt7JzMNvNQ31LrpRywMbf2bUuXjpD\n7Zu/i1k6i+rbQ+K+j6H7rlu3nKkuEb76JeLCJLpnF+6Bd2Fqy5uu22kdlezbVplI9RNVlmH6CJgI\nNXADqff8B0ytSPWLP4+pLICbsiuGFVRqgORD/xHdu5vqY79CfPYZDAa163a80UPES2eJznwTogBS\nQxgvBTPHIKqD0uCnwU2ie3ej3CQke3F6dqOyO4gWThEe+xsIA0gN4hx4AArnMEEF6mXQDnpgH97B\n7yR4+o+Jxg9DHKEGr8d/4z8iePZPMYunAAX916Eaf9AOqn8vZvE0ALp3FNV/HfHkC5jSLCozgt51\nK/H0MaLJF1AY9J67cfbeT/D134CgCH4W/y3/gujEV4jOPAlxHRK9eHd8CO/AgwRP/U/iqSMYN4mX\nex/eze8lPPEY0enHMYC7937c699ONH6YuDCJSvYRFSaJ8p/D1Kvo/r0k3/3vcHbcsuH3ZqpL1I/9\nLeHpx1GAs/d+vDe83341a46D9ueax0RcnCJeHCeePwFxHWfnrfh3/yOiiWdXldN7w/tb62x0jF3o\nsSe2Ly4WKX7ta9SePwIYEjfdRKyg/tIrxLUqplIB18XbtYvUvfew/L/+hmhuFlyPZO5m4ijEFMt2\nY46mPjFBvLgEjoNxNFRrEIYQx3YZ18Xp6yMOQ0y9DkEAxoAxqEQCd89uBj7yEaqvvELl648TFQvE\n1RpEIUQxyrWXgCqVxMSxLV8YoTIZ/BsP4O/YQX1mmuDV45hK1W5z/z600qBA+T6mXCZaXLLH5NAg\nyYMHIZnAFEs4jcfVo0eJ5uZRrktw+jTR4iLuyAh9H/ge3J07iYtFyocPE56bJJydJVpYoH5u0r5X\n10GnMziDA+h0mrhUJlpahNiAo3F6+1DZNKZYJi4WcPoHSB26k0TuZgpf+DvCmZlV+2r/rsqHDxPN\nzaMzGaKgRvDiMcK5Ofsz29+HSqcxtQDlOiRvu43M296Kzp7/93L7tp2hQdL33NNxveDECeb/4A8J\np6ZRvk/mgXfS++3vQ2ez6z6TuFTesBxrj7uNytqpXMCWyiq2TxljtrVCLpfbD5x45JFHGBsb29a6\nxUrAF554ha8+d5q55Qr1MEIpqFRD4maBFGBAKYVSBoUijG0ZtYK9O3pJJX3i2PDKmTmiNfvoSfmA\noVCpr36jwDvu3MfccpkjJ2Ywa14bG+mhJ5Pg6MlZ1n4iSoHnOATh2r1BOuGiFGSTPpEB39UUKwFa\nK8rVkDCKiNdsUAF33bSLc3NFKkFIT9rn+t39zCyUODOzTBwbyrVw3b6G++zFU2wMmYRHXzZJOuHx\nhn3DPHlsgvHpZcq1gCheWcd3ND2ZBFEUs1iqrdvmgT0D/IsP3Muv/tU3GZ9ebpy3DT2ZBLnrhlgo\n2JBbqYVopbj1+h3csm+YYqXOi6dmmFkosViqEkUG19GMjfRy894hTk8tEUeG5YoNo/3ZJGMjvezZ\n0ctDd9/AD//SJ1kuByvldBV33TTK2bkClWodpRQ9aZ933LGXJ45NMLtYZrlcI4pjFIr+bJJU0mW4\nL81CocqO/sy6AN70k7/6Wc5MLxMbewxdt6OXX/tX3wHAf/2zr/LiiRmCMMJ3HW65foSf+cG3bTlk\nbDeMbLT8//n7X+LIyRni2KC14tb9I/zij75rw+1cTL/1qSd45KkThLHB1Yp33309P/GBN21lVXUx\nyvNazjntF5ftF6JrLzTjpTOUP/nPMOV5cBOQHkKFVXR2JybVjwJMadZuVDvEy5NQnAIT2TDiZUA7\noF0bflKDJO7+YZyxe4jGD1N/9ctgDHrwelQztPTuaQUjlRrAu/tHCI9+BlOcQmV3knzwZ6n8wy9j\nzh4GE8O6MxPQO2YDUml6g09AgZeyoaedl4GwBmb9eWbVumhYdZbV9v2uXU854CUhKLUt6ttyr9tH\n8zDZ3u+eVfsy7WVygLWfj7Om3I39Kmd9eZwkxFHju4SV9xg1vlMP6pWV7Ts+eiRH4m3/atX35xz8\nbsJn/hiqy3ZZ5TTWMeD4eIc+Am6C+pFP28/JTaF33oJWijioYMpzmNIMuD7Ky2KWJyAObFmax1hQ\ntIGx8X7UrttI3PF9uAfeZY/z5XOtt6V7d+Pd+gGCZ/+M8NTjrfDp7rMBsv2GgH/oI8Qz+asthF1R\n55vgxAmm/9svE8/N25AD4GjwfBuIgsbvO63B9yGs28MjWn9dcdXyfXQ6je7J4vT0YJQinJzCFIuY\nMFz/XrVCpdKgFMpxUNksplolnm/7DNdSqnGhiP0swS4bx42LNQ9ncIB4uWD36Tgo18Hp6yfztrdS\nO36c8Mw4plbD3bULd9dOqs9+i7hUxDS2rbQGxwUMOp3G6e9Hp9Ok772H5MGDLH3q0x2DWzPAVA4/\njTEx3ugoyvdxR4bJPvDAurcy+R9/nvr4GUxQb4Xi/u/7ED3v+zaKjz5KODNLcPIk9fFx4moN5TrE\nYYRuXCjrnl7Sb38rOpGg8vQzhNPTEASY2KCzWbydO/D27SOcnCRaXMSEEf7+fSjfpz4xgVIKnc2i\ne3psWA4C4kIBd2QEnclgFK2gLMEL2OY555IGrL/5xkv85ZdfbFwoX+Av1ytMIw8CNlwZoB7Gm6xh\nJX2HWhC11tWKdUGs075cVxNFMbGx5xKtFD3pBI5WFMo1gg77VgoUiniD77on5VOoBOuev3lskBOT\ni9TD2F5qafBdl11DWQZ6krx8Zn7degpIJ117vjOGKDI4jmKwJ82uwQy5vcP8wLtv47t/7uPr9jc6\nnG3UUq2Uc6Q/DQbKtTqlar1tHx71MCaVcIgiQyrpkU36PHTvDTz85ptXbfcD/+7j6z7bf/nBN3H/\nLWP84//8KYJw5UVHwfe/6zamF0ukPJfpxRLlWp3B3jS5vUOrag2zKZ8vPnWcmYWVi8qRgQwP3X1D\nx88Z2HD5tWXUCj71n35gw+1A92uamtv79b96YtXzWylLwxV1wQOsuriMK4sAqFQfys/i7nsz/qEf\nBKD8uZ8hevlL2At1ZYOBaoSToAyOh0oNYGoFe3FsOlwUKXflhKAdnH1vQflpdP9e6ie/ilmasOGr\nZyfu2L3Uj/4tVBfaNuBAdhjqVbtcehCzON4ok7iiuCkbvoLySijbNKw2lnH9lcAU1e02MkOwPGkv\nElXbsRdH2INpk0CqHPz7Pobu3U00+Tz144+tBKm99+GO3U3tyT+0F2+pAdAuyktiqsvEi6fsNhK9\n6NQA7v63AhBXl4inj6K0u2lN7hXgijrfTPzM/0F46tTFKNLrg+N0P2zaiyRbM55M4PQPEE5NQb3e\nFoIdG87Ody2sNbqvF53OYMK6/cmMYpyhIXb82/8dgIU/+VPC6WmihUV0jw0u/v79KK3p++D3rqtB\nWvrLvyJaWl61b3fnTpTnEs7No/v67P8nztnPRisI6uvKhe+D66JMbMNaHKOSSdw9o9THz0IUoRwH\nU6uBo1Gej/J9dDKJ7u1BZ7P4+/cTnDyJqVRIHDxIcPIkAP7+/bZcbSFxqzV0m+nGNi6DbZ1zLmkT\nwa8/f4aFYvVS7vKia/+R7BRuNlINVp9ItpI314Y3YyAyhsViFddRxBtsxBgwm9wt7hSuAE5OLrX2\nZ4AohkoQMrdcZmq+uO49NJer1CIcR7XWDWNYLleJ4piB3hQff+SFjvtbKtZWhSuAmcUyu4eyBKWV\nfRls4PJcTaUWopQiZaBYDXjulal1AavTxzKzUOLxF8dXhSuAyIAxhqn5IqVKnXTSA+DVs/NUa3Vu\nGB1orfvQ3Tcwu7i6ZmDt47U2Wn5tGbdyPDz+4ngrrLWX6UK1b2+7ZblShacft6EIbO0ABuVniOdP\nECycRHkp3APvIp56sXFR26hxaNYSRPXWY1MvY2t0Nvg5N+HKCSEymFqBeOEEun+v3Xdka5BNdZlo\n6siacAUQtcIVgCkvbLwvcXnFEYRVwDS+8618T7GtbTRxoyYttpVsy+cgboSz5vFj2m/dbfIDaCKC\n5/6iUZ5a426ahqBIeOIxjPYwhXMQ1TGFc6jBA1BbJl48Y8sCENaIa8uADVjR2aegWoDeUcziOLVv\n/i6p9/7i9j6f16Hw7NnLXYSr28WoybMXSRDFmHqdsFC88P3GMfHCIvHCYiO4afA8TLXK8uc+T/WF\nI9TPnAHPRfsJokKh1QTTGbJdEsqHDxPO2JYQ4cys3UYcr9S+aU00P4/KpMHExIsLmNjYppLG2Iup\nDuUiCGzNleO0moiaMCQuFKBaBa1tE9I4hijChBGmXkclE5hqjRhsTdnp06h0hrhYJJw4hwnt70Fv\ndJRobr61y7Xvo3z4cMcaus10YxtXuksasE5OLl7K3b2urA0m3dCpSSTAcqlzIGuKmye1BmOgWLE1\nUHEcs1GtaTXofAf4jgM7+cq3Tq0Pl5GtbdAaojgmNobxmWW++NTx89bmfPPoWW67fkfH1144MU2p\nWqdQCVoBC2yoC8KI8ZlljpyYASCb9im0Nb0c7k9vuM/m6+0h5nzLw8Y1VdsNd+fb5nbWv1Kt7W9i\ngopt2hcFjQtiiBdO2BZcqT7i5XOEr37JrhwGrG9WtvZY3Wrgiex+ncbxY+JGM7TGRXa8wS/1OLR/\nTQx+aov7EpdcvPk5cEOmcfy014DGnc572zifV5dt09TG8d2qIYtjolNfbdwdVxCFmKVx27curNGq\nHYvqoD2i6WMQFDGFKUj02HJVFokKE9SPfGpdf7GrqCnhpXEtNfUTm7MXIBBFxFqz/Om/btQ+GwgC\noloN5SeIlCKamyP7jrcDEJ6bJBgfJy4WbbAJI1uDZkyrbsTUapg4QiWT9q52GIDnrTQx3ag8YAOU\n44DWKMchbgZKE6/022vWwYQhxIa4WISavYbR6Qwq4VM9lrc35V2XuFymPjFB+t57WrtrD1udHm9F\nN7ZxpbukAWsrTefEtaHj5YGB549Ps/Pu7VUDn55a4uaxIV48OU0Um1aNShTbZpmxMVRqIX2ZBDsH\nMluqzZmYLbDcoU8aQBjGJDyHqqtRyjZHTPgOrtaMzyxTqtTJJD1mFkr0ZhKMDGRWBZW12sNMNu3T\nm0lQaGtqeD7/8OxJnn1lknLV1qjVgpD3v/nmCwprTY8+e5JvvTxJuVYnnfCoBuG67V2N2vuhxMvn\nGn2PjP1loRrt9WPbt8Y4CfuwMInpGK5eK9vnSvfutjVTYcU+Xa4Rexv8DLgJ2wTE9VE9ezDVIlKL\nJQAbojqFMWMaz5uVx80+aUHJ9iFrXlXFIcbx2/rRGVtbluih9XOiXSjPYYpTdlt+hvorX8LUKygv\nternK3z1S3i3fuCiv/UrXVws2gvb8HzNRMW1p9H/q/kzpjXUAttXt1aj9LWvU/rG47h7dtuWDcUC\nUbnSqk1Svm8HMWn2I7N3jO2gIqkkynHRvb02lFUqK0FpbRkSSVTCR/sJO+hJc1nXXTkumzVvTWEd\nle1BJxO2z9bNN1OfmCBaOIM3OmrPB3XbJ745IAbYGrlm7VPz8WY6DcKhshnMcmHL27gaXdKAFW10\n11Zs2Vb6al1uGnC0ph6tPhEYYKlU47HnTm95W56jCKOI+eWKrUk3KzeKABK+RqGpBiFhHFOs1Dh+\nbmHVvh2t1vX5i42hVu/8y7BQCdgz3MO+Xf2EUcz0fImB3hTX7+rj6ZcmySQ9xkZ67bLlgPevaZK4\ntnaoGoStWq65xTJL5Ro7+jMAlBrLbuZbr05RagzaUqrU+darU7z/zTdz/y1j62qhtuq5V6ZafdqW\nyzUeOXycu27azWLp6m7CG828RDj+JNQK9qLRKPCSqKiOcZMoL207LxtQCRtydM+uLfSfgdU9Ls9P\nD96A7h/Du/UDVP/u51e/uDTeYXsK/7YPEM29igmKKD9LtDRuQ+JGg1yIa59yGp36deNv8yRI45rO\nrLnoiu1x72dsYC/NNJol2o77KrvDDqDRLqrh7DgIQL00jwlKjVpWe9FoakWi04+jBq8nnjvR6udl\nohoeW3Mtj2ZYPnwYlUxiih2aoIlrW/PaQrEyAIfrglLES8u2pkspwrMToDQqkbB9wDAQG9tUz3Ub\nfaz0qtoxPTKM09tLtLhol0kmMZXy+l8FjXOBCepElSo6lUL39mDqIfFyWx8v3Sib1iitUNkeUnfc\nTn1iAlOpoHwff/9+nJ4enKGh1ubdkeFV/aPS99zTcTTCjZQPH6b63PPEZdtKpvL886Ruvx13ZHjL\n27gaXdKAdaUHg0tJq40HndjM1fAZGiCMOt91N7HZsOmho1Wr+eDKIEyaqYUS1SDCcTRRGLVecx0N\nRpHwHcLIPj+9UGGhWKNQDihWArIpv+OAKq7Wdv0OBnqStq/VUpmRvjQjfbZmqDeb5IE37l9Vy9OT\ntgNdtIecz3ztGF986gSVWkgq4bJzIMvtN9jmiOMzy5RrdUb6bG3RJ04eYaQvvSo0Nj+LVR9omzCM\nV+3z4bfcvP3BLdo2v1yqoZTCc3XrvV6toqkjmOqyfVBdhnq17SJOYbwU7uidhCe+ipk/QQgkH/xZ\nlJvcQnzZ3g+fs+MNtvbKlmxtSWldLLcJzz5tm2gle3CGbyYyT3YeUEO8vigNbtIGm0YfC7S3UqPV\nDFz2gf2ntoze8Sbi4qwNS8qAcjHF2ZWmis3l65WVJoKVOTsiZHOZRn9EA1Ceb/VppLJAdHaR2uO/\n3XHI+bUham3t8rVU+xXNzaOTSSIJWK9fxjap0729KKWICoXVNUfNG7quCwnf1nJhw5Rp1io1whiO\nvaniDgzYcUjLFfuaozf+NVStrQyUk81iagFxqWSfc5zW/Tx3cBB3xw7qc3OYcon6xATu8DCmVkNp\njTM0SPYdb28Nrd8p/Ohsdlv9paK5eVuj1vyoKhXiUom+933blrdxNbqkAesC8sQ1a7NBJ64GzeH0\nO72Lzd6Z4yiK5c5tiROei1YR9ShujcIaxTGVqm260h6UtALHDo+I52oySTuPWBjFZPCIonjTZoLV\nesjuoSyLxfXNBF86M8/ccoUbRwc4PrHQakZXD2O+950H19VOrR1o4u8Pn2C5XMMYGyYrtbAVsMq1\n+qp+XdPzJRuw1nxuui1g3XHjTr718iSFco1qEFGsBHzzyDhjI70XPLjFnQd2tpodKqXYOZjZ1vpX\nMuUkbD8nx8cEKxc8yrfhMZ58wc7r1LMLlEPw7J/j7r2P+uJGNauq8Tdm00Eu1tC9u1sXnR057spA\nAwCqcYPB8aFaIJo+un54dfH6o7QdoTIK1vwSbYSs5vFo2p4HiOrEE8/Yk7Xf+Pk2ka3R2uDMbWiE\nNxOBk2gMzKJQiR7cvfdjitOYyiImKGLCiq0RNmZVX8aNQlRcmFy1t7WPr2bO0CCxNA98/dIatMbf\nt5fETTdRfuqp1T+rzb5RrmuHjm/OV9ao7dKZDKZex1SrjZsntiar9tLLbX20lA1sa+/GNjWfM4Z4\ncaHx+yVa+fXVHB23wUmnMI6DqVQwtRoDP/TRVbVU2bY5w7Zr7QiBKptBpVKYRg2WSqUua5PASzWC\n4WWbaPj17moPm57jkEq4LG3Qj6ldMytorQjqMUp1vkB1HEUlsOEqm/Kp1MJVIyOuruHRuK5m384+\nJueLFMo2XCV8B891qNZDnnjxLLOL5Q0bdp2ZWe783lxFGMY8d3y61RSwVK2zWKqSTfmrwszaERFn\nF8uUqvW2mjhDGMetflo7B7P0ZRKt5Xc0gs3asT+dticeOLSfpO/yxItn6ckYCuWAUrXO+MwyN4wO\ntAan2M6w7e88tJ+E7zK7WGZ6sURfOtFxuauN7h0lKreNzqd9TFBojLCmwSxiopq9MPVSKN/FLJ3F\nveU7qT//yQ1qixojC7b+vzXnvzu/tgZVQWWxFQ63uz9xtdvgTKUaoT6sd3i9ObfXBv0yorBRA2Xs\nMo152laLITmw0kQwrGKWz6EcD0MalRnBu/HB1lxbzWMzHH8S5fesbKUwiQmr65oQus2mgdNH7bXj\n4PUoN2Gb5l4j0vfcw8Kf/tnlLoa4WDZrHa5Va5j3+vhZ4krV5qFEAtPoZ4Ux4Hko38OUyitNehvN\nAeNyGYyx82+5buNGB3YEwOYIgs0RB7VaNYhYR7EBtTIyIc1WQ1oRxRHRSy+hHAc9MIB33XU4vb1d\nDRhrRwh0enpI3nH7qj5YyYMHKT766GUZpv1SjWAoAUtckJuvG+Tm64b45FeObbpcsxGUMbZ5YPP/\nnbiOJum5VOshhXJAKuFy3Y5eJmaLlGsrFxcKSPgOvekEg70p5perOLpOFEMUxnhZTT2M7f3Y5ug8\nZv2+NtIsXz2MyaS81uAS/dnkumU7DTSRSXosl+1oiUopOz9XI5RNzRf5xJePMD1fYsdghofffBPH\nzy2i1p401UrCaoa62cUyxhiOn1ugVKm3RjVcKtX4+CMvtIKS7znnrdlqD4prg9nVTA/fRDT3ih3l\nzUvYGqBawV5UttcWRQHMvozpG0PtugVnbCvtv3XjWNpC6FHO+nXX1nxFa/q7mciGP7ADYpRlcItr\n1wY1oesmUabtuF1z3DWb/Wnf9rWqrZkfB2wYioLWFAEba2sdkB6G9AjOjjfYx40Ji4GVCY0Lk+i+\nMVRq5S607tllm7g2mxDWCpjyfKtpoBrYj5k/QbxwAu/Ag5vX7l5ldDYrowheq5QC17GhpdN33LwJ\nbAwmCAhnZ1HJBBiD7u9DoVCZDKZWJZpfWL+NKLLByfPs9UqjlgfHWZmjK45X+kRutZ+I49h+gY3w\n1mx6aJYLKMeBZIK4Uqb64lESB25g6a8+2bWgs3ZEwLhUou+D3wvfttIksPjoo9TPTtj+X0eOUDuW\nX1eLdrFcqhEML2nAch11UYYTFxfPRjduzs4WeHn8/Ael0ivzc53vuvShu2/gC0+8QiWwNT/1MKYa\nhPSmE9SCiDorc3JVqnUwcPjYuUZtkbHTRBhDqVJHa0WhFHD01Ezn/bY6iK/XbJ7Xk/ZXVVwM9q4f\nNrt9oIls2qcWhOzoT1MsB0QYEq7DO+7c21r++RPTq/p1HT+3yEN338Affe5ZltuaTvru2gv0lTA3\nNtLL+MwyCsVSqUZfOtGau6tQqnHD6AAAEzOFdf3DOtVora2VWzvR8FUlCvDaLtyCZ//UXmSuDVgA\nJsSEVZz+vUTjhyGRtfP/bNQEcKOmGZ007vI3O/bb2rPzByaV7LUXpihUsk/qr645zTPqBjVOG9ag\nNldvD2AbtNFuFzWHY2+I68D6cwtRjbhwDrN0FpXdgR6+CaKg1Y8qXjpD7Zu/S7xgJ9J1dt6KHthP\nNPsSFGdQfXvwbvlO4oVTrSaEys+i00OtpoDKTdpaMqWumb5X7cwGfYvFFaDV5+kCOA6k07DcucXL\nOiZGaY0J6rbvVCqFv3s3lWee2Xio9cbw7quEoa19at1sVXa49uYgGJ1GE2wOstEst2r825hsmSiy\ntWI9WRuyopi4sIzu6cHEcddqc7YyymA0N099YqI18EU4PX3B+242+QvPTRItLeH09eHu3rUqFWSv\nYQAAIABJREFULLY3CwxnZtA9PXYExw3K1w2XNmBpTSh3ea4qG/3+LlWCDQfpUNhBGmJDxwEmNvKt\nV6ZYLgetdcIoZm65wq37d1Ao16jVw1Z5YmPn6XIcTT2KVgX3YiUgk/TRWnFqamnD/aWTHksd+mCl\nfI/h/jQH9w4zPrvcelOKldqeiZkCi6Uq/dkko8M9PPyWm1sT9fqeQyrpEkWG4f40vW3N7zaau6pa\nX/1z0Xy80RDv990yxv23jPE3X3+p1RwxnbS1bU2LpSqea2vqujER8VXB8annP7cyiqCTsM2iwDZb\nWkP3jRHP5DHlOQjKbNq/agsBae2yrY79Wm9pFPhmOIzGD6/0mxHXiO2NQtnR2gCmVNu8Vp2W73QR\nBtAe+DUEFRuMMFBdIp59GdWzk/Ds00QzL1F/9cv2Zyqqg3aJl87aPo1uAnffW1Bukmj8MHpgX1vz\nVlqDvDT7ZQHXVNPAVeodahDF5decfNvzLuw7CkNYOk+4ags2KpEE7aB7e9HJJGhF5dlnt7/vZlBq\njizYDFbNkNX821w2kUA1y9GcWDiM7HOOi2k2VdQa5XqgNc7goB0O3l/5me1GbU7y4EGWjn2acGYG\nd2SkNQ9YO2doEHPkyMrbTaU23fdm/aaaTf6C8XHictkOMOK6qwJbe7NA3dNDXCjgjoxc1BEML2nA\n8j2HWj2Su7LXgNgYtNrggkHZ/lREZlujHp6bL6wKZHFsCOoxQRiRTnmt/l660WEzig3ZlEsUx6ua\n18WxoVwLyCS9DedeGxlIb/ja7//MdwG2f9UNuwdazxfKQStEnZlespMRl2t4jm6FIIDZpQqe4+C5\nkE54HDs919rGRnNXhWvufjYfN/cHUCjVGBnIrBoWvn17YyO9LDVGBBzuT68bJr893G2nv9bVJJp4\n1k6mGtahugTpETsBa1DE3rlvqz1wbI2kAaLT39xgwtd2jV9eWxnVr9Ekq9WRf12Twc50727iwiSq\nbw8qNUB0/NEtrSeuBhfhN58JwfFRmWHMzEvr99Fpl8oBL2UDE8aOTqg18fwJTBRAFKD8DDooY2oF\n24ex1LgbbWIbsqKgMblxQDx/AmfHQeLCJP6hH1w3iiCsH1nwWtK88LvqO1ZfqxrzSynft6GjE8e5\n8CaeTmNib61Rnkdi/z5wXbyxMZTvUz781IXNj+Zo8PzGXFaO7UfVNpDFquPNGFQqRWLvXkw9AN8n\nnJpqBa6oXEHVaqikh7NzB6YWYMplvJ07cUZ3r6rd60ZtTvXoUZyhodZQ79WjR9cNmpG+5x5qx/KE\n09OoVApvdHTTfW/Wb6oZzJojFTb/bQ9s7f9Xvo87MmKbLV5ElzRgDfSkqAZho3+MuBpp1WzOq3Ad\nTdAhpCjAd13qREQmajX/PV/Nluc661phaW37Sw1kU8wtVag37sgoZQe6SPouQRgT1Nc2/6I11PqJ\nc4vr9jWQtRfXM4sbj9LWKQw1Q0q50d+hWWPUDCrty3uOs/KBNGw8d9X6OZGa22239nH79vaM9PLB\nd64EpS8+dXzDiYjbg9u1VLsVT74AKHAbYbE8gzP2JgiKxMZgCpNQW7ZZqXesNTpabfpoYwvNL8u0\nPW7eJdTgpSHYYlMR7N36ePkcqnc3Zv54+yt0qi1rNp1qNi2Ue+JXqi7URm2X9oGo0RejWQY7aqZy\nU5jWYBdx2+sdyuinUT07MdV0a31TnbfhCiCs2bWaNb61gj3247DR1LVuB4lpNoNtLKd7dqGSfR2b\n/12LTQKbWhd+zUEIxJXHGDt630bfkaNbQWzLGs33dDaLt3sXmQfe2apVKT76aCsMmHJpddO9tdqf\nb5ZPKfB8vNHdRHNzODt2EE6cwwQ1iCNUNmvnXGsGLcdBaYXB4O7YQTgzgzc6ijc6CkB4brLxMcR4\no6ONgDFM9oEHOtYMvVZb6eOks1kGfuijW973ZttsNklsjlSoUqnW82uXaX98sV3SgPXAof387eMv\nsVisXrS+WBf6a89zNUpBUL9yT5BarQwY0ek1hSLapNlep1d8V28r8Gqt0EqR9D20VpRr6+/MqMYJ\nw3U1UWyI4hjX0aQSHvt29lGoBB1Dj+fo1qTArtZorehJJVpDivdnkyyXazha0Z9NkvBdEq5DEEYE\n9ZWJjTWKgd4kSineccdeTk8ttQJdM5g1B6/YTKcw1Awm6YRHqboy5Hr767uGsswvV0h6DpmUx50H\nVu7crO3v1NST8Vku1VphtCfjt7a7UUjabHsblb/pfMHtarVu+oM4bnXWdwCU6niXvZ7/PHF5sW3E\nNeyd/eYde9Vow641WzrLNC4+m4MCuPveQr00C/Wq3U5qEAoTq7fT1rSqeaFafeQ/Q1RhU45vR4vb\n4vDx4ny28P36WaiXGoGmWbOpwU9C0Px5vYDfRMoB7eDd9UPUj3wa6hV7vMSRrXVK9MLyWVoTB/fs\nQg9eb8NQasDWaAWNuW/8tD3eouYomAaUi+7dgx69k+jsU1AroLwkZG62tVRRgHES9kaCn201tVUo\nTKVxzlYKlRpE94/ZFkeud/4pCa5hzQs9lU5jmvMOiUsrlbJN8LRuTdLb4jho38cohTExBGvOk0qh\nHBeVyRIvLa2vHWpbDlgZ0S+RQDkO/o0HSN97z6q+Q6sm4h0cIg7qmGpldSDyPHAdlOdjwrodDMPz\ncHfuwAQBynVxd+0iHh4mWl5G92SJlyLwfHQ6Zc/29TokE3ZSYj+B9vxWaGkPE6l73rjh5MDbndNq\nK7YaZraz78222XxvhOFKH6yR4VWBbbuTI3fDJQ1Y733TARK+w98fPs7pyaWOYcBzNXEcs8E8tR01\na0fSCQ/X0RQqtY7re07n2hNHQybp4WhN0QTULqCzqrMm/GjdqOHZoElkNulTCQJ706Qx0F1zrkhH\nKcIONTy+5xDFhjiK7TrKhpkd/Rl6MwmWyzWm5ourmuW5jmLvzj6MoWOo6csm6c0kUMbwysTK671p\nj1o9IooNjtbUG5+JozV9mQS3H9jJT37wPv63X/88J88trrrf35P22TmQpVCuEfgRfZkksTEM9qR4\nY243Cvj4l15gubRS6+Qq6MskqdVtDWfCd/EczY1jQ60hxa/b0cdisUp/JsnoSE+rWdsXnzpO/vQs\nT790jkotpDeT4Kc/8hZuGLU/gEdPzfL8iWnqYYznag7uHea+W8aYXSxz5MTMqu+nfXj0TuGlGVrq\nYdzqgzUykGmV5aG7b9g02Gzk2990I1986jjlakg66bb2eyHb2qz8TZsFt5v2DPDy2YVVj68Wes8b\nicefXhnqvH91s4SN7rInH/xZKn//85iFk/YHa8ftOH6SePIF+0s52YeKQ/DTmDiyF76m7eaC39OY\ns8qAdtHXvQlYCUoeoPuvIzz1jdYAAHHvLszUERvgHA/3tvXNFfS++4lPfc1eYJuY9cNhpnFv+x6i\nk1/FVBZsP7L25ZqjyG1qO7elGiEzPk/dmnJs4OhUB+f32D5DcbDxvltzMHV6PmRdhzY32WiyVrPf\nX9yYsLPV7FOhhm603111wT72s5iwCtVF+x1gbLmzI/Yucm251QzOlrVJ473xo0TTeeIzT9jPQnuo\n0bvwb3i7ncS6NENcnLbbNo0RdVIDkB6Awjm7v7ituaqJQLmQHkTvvBXlJXHG7iGeP2GDnJexw5tn\nR4inXrSBB4PecQs6aaeScPa/lXjh5KqBKOLiFNHEt+yx6SbQQzeie3ejk33oRiDSvbsx9Qrhqcdt\nUHMTkOxF9+zClOft51RZsBN4Oy7O7jtRXgpTXVo3mfDrUfPCz9u/j/qZM1CrYWpr5yzbzgZfQ3O1\nK1F7MFmrOVJep8EfOq3X3sSl+bpSaN9H9fehXBedSBCcPmOb5XkeOp1CZ3tQSZ9wqjHoVaPJnkok\n0ENDeCMjJO+6k+CllwmOn8DEMSYMW/M2kUigPA9TLmOiCOV5KK3Rfb0k77h904l4M297K7O/9TtE\n83OoZArd24P2PFQ2Q7y0TFwo4vT3kTp0iMzb3rpuFL1waoqlT30aZQyhASebQWeyxFFIdG7S1kbt\n2IG39zq8PaPobLZjmLgYQWojFyPMbLbNrby3S/n+m5TZ5kkgl8vtB0488sgjjI1t/WKvXbES8FeP\nvsjnnniFai0EBQM9Se66cRc0mp6dnloijg2VWp3Z5TL1MMbRiuG+DFEc05vyGepPYwwUywEDvUlG\nh3o4MblIGMYsl6q8cnYBg63dOTA6QDrps1yukUl4BFGEqzV9WVvTsVioYIDdQ1nGZwoUyjUwtqz1\nKCKKDMmEg1aakf40e4Z72berj3oYM9yfpi/t8weffZblUo3eTIIffPdtfOPFs7x4coZqEFINVi7G\nrtvRy7vuuh6DIX96jmI1YG6xTBQbBntTHLppJ0dOzPDCiZnWOqNDWRK+S29jGO7pxRJhFHPj6CAf\nfc/tZFI+j784zosnpnny2ASVICTpOTx0zw186IFbAfijzz3Dl545SRgZtILR4R5ye4f58IO3snMw\n2/pu/uHZk3zr1Sk7aW/Soy+dZHa5zMRcYdU+dw5mOT4xz2988knGZ5YxxjDSn+Ghu2/gvW86ALBh\nP5/24coHe1Otz9J3NScml1hYrrBjMLOqbJsdT5v1J1o7NHr7Nr/y7El+69OHqYURCdfhJ77nHt5x\naP8FHdevxaXuE7XZ/jb7vM5jgzYQr812zjnN0c7M0llU3x78Qx8hnsmvqq3azsVgs6leXJhsrRdO\nvUh04jEbAJwE+vq34Q7eQDz7MqY4jerbQ+K+j6H7rttwW7pnF87YPUTjhzctW7x0htrXf5No6oht\nlpUaxCy8CrUSJHvwb/te/Lt+aKVsZ56g+sWfx5TnwfHR178NtE987G8bgUXDzltx/DRxcdoOqe0m\n0P17MeVZTGHK7ic9hNYOcWXBXpxrB2fP3fh3fpj60c8QTR/FLI7TCiWmEQDTQ6Te+ws4IznCV79E\neO55ojPftDUjqUGc7A4ISisj1dUKBGefhXPfAmL7eb7h/bg9O8DxiWdfJl6eAGxgcEZuRvXuofqV\n/9YKw2rsXpJv/GHqRz/T+t6d6+6n9vVfh/IcuD7ubR8kee8/tZ9R23egR3LUnvojG1rcJG7uffi3\nf2jVcnG9Qpj/AlTmV23L1JZXHWtrv/PmdxdPHcG4Sbzc+3D3vZng2T9fPVpfrUBcnkOlB3EG9reO\ng7XHy9rj43yvN5epH/tbwtOP2ybae+/Hvf7t6467tZ/LVRKaLvv5BtaPYKZTKYLpacLjJzBhiB4a\nxB0awpRKxGEEGOpnJ2yoaEw0qxrNyXUmQ+KmG+l9+P0Ep04RnpsknJ0lXFggODMOzRoypcD3UY6D\nSiRssEiliGs1VMK3fVCMIa6HdnCDet223hgcIpG7mfIzz2AWl1a2lU7jDg3aC/flZeJyyY6KqDUq\nnba5plLBhJGd26l96O+BftxUGqNAxTFxtYb2ffwDN9D//d+HzmQoHz5M/dRpqseOEc7OQRjiDA6S\necfbydx/H8WvPEbxS1/GVCqodIrsgw+SfcfbKT/+TYqPPWZrCT2P5ME30PNt76X0lccIZ2Zw+vvx\n9+7FhKGdzNbYIcFxXYLjx6mfOYPyE6Tecj/Z++6j/Oyz1J4/golCdDqNOzy8arS5uFik9NWvUX3B\nNjVP3H4r6UOHzrveVo+R1zrn09rtJA8epHr06GWZS+p1alvnnMsSsIQQ16Qr4oJHCPG6IOcbIcSl\ntK1zzsazrQohhBBCCCGE2BYJWEIIIYQQQgjRJRKwhBBCCCGEEKJLJGAJIYQQQgghRJdIwBJCCCGE\nEEKILrmQebAcgMnJyS4XRQhxNXv3u9+9HxjP5/PrZ59+beScI4RYRc43QohLabvnnAsJWLsBPvrR\nj17AqkKIa9gJ4HrgZJe3K+ccIcRacr4RQlxK2zrnXEjAehJ4O3AOuIamGxdCdMH4RdimnHOEEJ3I\n+UYIcSlt+Zyz7YmGhRBCCCGEEEJ0JoNcCCGEEEIIIUSXSMASQgghhBBCiC6RgCWEEEIIIYQQXSIB\nSwghhBBCCCG6RAKWEEIIIYQQQnSJBCwhhBBCCCGE6BIJWEIIIYQQQgjRJRKwhBBCCCGEEKJLJGAJ\nIYQQQgghRJdIwBJCCCGEEEKILpGAJYQQQgghhBBdIgFLCCGEEEIIIbpEApYQQgghhBBCdIkELCGE\nEEIIIYToEglYQgghhBBCCNElErBE1+VyOZPL5YbPs8zv53K5hy5VmYQQV69cLvehXC73aC6XuzeX\ny/1O47l7crncX17usgkhrk25XO5kLpe75zzL/I9cLvdT29zuj+Ryub95baUTVzr3chdAvD7l8/kf\nvdxlEEJcdW4FxgDy+fxh4EOXtzhCCCHEehKwrjG5XE4DvwLcD/QACvhR4MeAZeB24DrgGPAD+Xy+\nmMvlqsB/Ad4DjAK/ms/n/99cLvcjwIfy+fzDjW23HudyuZuB3wSyjXWeBT6cz+erWyzno8BvAIeB\nR4DPAvcBg8C/y+fzn8jlci7wfwMPAyHwdeAn8vl8cMEfkBDiqpDL5X4B+CgwB7yMPW/9AtCXy+X+\nO/BHwG/k8/nbLl8phRCXUy6XewD4VaAEZID/APwc4ANl4KeAbwKngA80bsyQy+U+DvwD8PvALwPv\nBqLGsv86n88XtlGMt+VyuQ8BvcDfAT+Vz+fDXC73T4Afb5RlEPgv+Xz+t1/TGxZXDWkieO25Dxt4\n3pzP52/BXoT8bOO1u4H3AQcby3xf4/kEMJvP59+KvSP8X3K5XPI8+/kx4I/y+fybgRuB64H3X2CZ\nbwC+kM/n3wT8DDZUAfxEo8x3ArdhA+OHL3AfQoirRC6X+27gg8Ah4C1AH3AGe/H0WD6f/8eXsXhC\niCvLbcBHsNcvvwR8Rz6fvwv4GPBJIAX8IfAjALlcbgB7Q/nPgH+PvR66s/FXA//PNvc/hg1ohxrb\n+LFcLpfFXic1y/JhVq5txOuA1GBdY/L5/Ddyudy/B348l8sdAB4ACti7wJ/P5/M1gFwu9zz2jkrT\nXzf+fRobuDLn2dXPAO/J5XI/DdyMPUFlL7DYdWwNVnP/zXI9BPxxPp+vNB5LuBLi9eEh4JPNu8i5\nXO4PgZ+8vEUSQlyhzuTz+VO5XO4ngN3AI7lcrvlajL0J/IfAk7lc7t9gw9hn8vn8Ui6X+3Zsq5k6\nQC6X+3Xg09vc/x/n8/lSY/0/Ad6fz+d/O5fLPQy8P5fL3YQNXxd6jSSuQlKDdY3J5XLvB/628fCv\ngd/BNhMEqLQtatqeb72Wz+dN47HqsIzf9v8/x94dOoVtkvj0mmW3I8jn83GHcoWNxwDkcrmduVxu\n9wXuQwhx9Vh77gkvV0GEEFe8YuNfB3gkn88fav7Fdpd4IZ/Pn8JepzwM/GPg9xrrrL0O1oC3zf1H\nbf9XQD2Xy41hu07sA76KrSkTryMSsK4978Hemflt4Enge7AnnQsxA9yWy+WSjf5Q39n22rcBv5DP\n5z+BvRi67zXsZyNfBH4wl8slGn3Lfht750kIcW37PPB9uVyuv/Gz/8ON50O2f/EjhHh9+BLw3lwu\n9waAXC73HcBzQLPLw+9hW9+k8/n81xrPfQH4Z7lczmuca/458Pfb3O8PNK5TkthmiJ8D7sFeQ/1S\nPp//AjbYkcvlun2dJK5QErCuPb8DvDOXyz0HfAN4Fds/6kK+67/DdgI9BjwGPN/22s8Bn8rlcocb\n+/wHbDV8N/1/wFONv88D54Bf6/I+hBBXmHw+/1lsk57D2E7nS42XvgG8IZfLfepylU0IcWXK5/NH\nsC1rPp7L5b4F/CLwXc3me8D/AvYDf9C22i8Bk9japqPYGzj/apu7PoGtpXoG+Aq27/vfAeNAPpfL\nPQPsxQaubl8niSuUMsacfykhhBBCCCGEEOclg1yIiyKXyz2I7ZvVyZfz+fy/vpTlEUIIIYTYjpwd\nLeMTG7ycz+fzMviW6EhqsIQQQgghhBCiS6QPlhBCCCGEEEJ0ybabCDZGkxsDxvP5vAydK4S4qOSc\nI4S4VOR8I4TohgvpgzUGnHjkkUe6XRYhxNXtQudBOx855wgh1pLzjRDiUtrWOUeaCAohhBBCCCFE\nl0jAEkIIIYQQQogukYAlhBBCCCGEEF0iAUsIIYQQQgghukQClhBCCCGEEEJ0iQQsIYQQQgghhOgS\nCVhCCCGEEEII0SUXMg/Wtr06N8OfPP0k5Si6FLvrKgVopdiZ7SHt+ZTqNZaqNWITk/UTjGQyxAai\nOKIWRThKM5zJ8K4DNzGUzlCuBxyZmmSpWqUvmeTWnbtIe/5593uh610Jruayi6vbibnj/OHjv8P4\n0mlqYXVb6yoUjnJIeimGMsO42gMME0tnCaIakYnalnNJeUmKQRGDuQjvRIjVFBowr+l487RPT7KX\nnkQvb97/Nt5183vJJrKA/dn5n0/8HvPlOQbTQ3zfXR/lzOIpjpx7DgPcNJIjqNd4avxJKvUSxkBf\nqp/B9CCgWKosAoaxgb2EUUhfsp9dfaPcc919rX0Ua0W+duIfOHLuOcI4JO1lGM6MrFvuajH/xHdc\n7iJ0l78bt/dOwoVvQLQM6441BcoDE9r/O/2oxACmOgFx0FjGBccFE9v1lY/bcxup0Q9TX36G+tyX\nietL4GZx/FGgRlxfxJg6JqyitIf2+tDJ64hrEyvbcVJ281HF7hsD2sfUCxgitHJQ/jB+3z34ww8S\nh0WqEx8nrk2hvEGc9PUQFYnri2ivH+0NEcc1wsLzmGAe5Q3h99+NP/wgAPWFbxAHs2h/GG/gzWi3\nhzgsdHx+reZyUfVsa39Ocg9Oz+1EhefPu/75bLUcl2J7ndaFzp/fpXK+99Ptz28jypjtnaxzudx+\nGpPwjY2NnXf5uXKJX/nqo9Tj+MJKeIVTgKsdwBDGMQqF62hGMhk+eugePps/ysmFOaLY4DmaHZks\nh0bHzhs6vnz8ZZ47N0GlXifledyxe5T7rtt3VQSXJ8dPs1iptB73p1LcO7b3spTlcoS98+3zGg6g\nF2Xiz+2cc/7lX/4oc+XZi1EMIa45STfJcHYncRwxXZzCmBhQxI2bCY528bSH67goFGEcAlALq0Qm\nQqEAhVKKpJtEKYXv+Ozs2U3aTzPaO8Z8eY4gqmEARzmML55mubpEPQpIuEmuHzrAgeGbGM7u4IEb\n372d4l/28801F7AuOqfxrwLixr8eUAc63YB3bKBTLkrb+gAThzbgmRrrAqDqwem5mUT/vdTmv0Jc\nm8bEAcTVlWUN4GRRXhZTXwbTCIY6gZPaj/b6iSuniaMyxHWMMhCHKCcNJkIldqJ1AhNXwEQ2+NWX\nUV4f2u1DJ0YIKxPE1bOYcA6Dg07tw++5lTiYQfsjjfcRYMIlnMTOLYW19jBQX/gGUeO9xdUzgMYf\nuG/VNtrXw82iDMT1uY6Brzb/VeJgHqU9TFxH+4MkBt+2peBRm/k7otr0yjeW2AGw7rnEyHs3fE/d\nCp6dytTpc25+fhuVbxPbOudc1Bqscj3gj585fM2GK7A/q7GJiRpB1WAIooizy8v85je+ShRHBFFE\nDFRCqIYhsYGTC/O868BNHJ+f63ih/fTZcRYqZcI4ZqlWpXDyBL7jUA7qACxWKhyZmlwVXC7kwn2j\ndV5LCFiqVjd93C1bKeORqclW2Ov0mV2MsixUymR8H087Hfd5Mcp0DYe2bZFwJcTWVcMq44un0Mpp\nhap2YVwnjOuocOW6wlFOqzbX1qYZjIFyvYSrPSr1MtW6rWU4PvsK1bCCQhGbmCAKVtXA1aIaz008\ny0xxmjv23HWx36647DqFqM1aNkU2xBhlK7LOV3trCsTlV6lUz2KCWWyI63D9GS39/+zdWawkV37n\n9+85sWTkdvetNrK4Ndlks5stdavV05a1zWhGlt2QDUgejGADA8iAYcCGYcPAAPPgB/vNb/aDDduA\nAQ9Gli1b0IynbZhWq9Uat9jspshmk8V9q+3W3bfcYjvn+CEi82bepereqry1kP8PQeTNzMiIyKi6\nWfHL/zn/wJlWue1ynTbFdD/GoMB0Du1XsbzG5Tu4YAZsgjNdihCooXcNlI+uLGHznXK/DZBjOx+Q\nY7HZNsHUJADZ7utFRS16DHCk268eCknASBgwyRr0QxNg4+vYvINSavBcPyiMvG73jcGRc3kHl++B\nCshaf4wO57HJLZxJsS5DqQBjeyTbr5Bu/xiv8WVM91NctoWuLBKd//v40bnB/vX3Zfi+swl57xqY\nLng1cMV5az9YpduvAhYdXcIla2StP0b5k9j4Om7vTbLWFWqP/eGpQlYe3xpULF3exmt+Be03inWa\nLjqcP3T8jnsP43KmAevK6gpr7dZZbuKhYI+pAnaz9NDHQWoMG90Oq50W762vkZkcrRSNSoX311dZ\nak4yGUXsxL1BMHXO0UoT3lld4fL07GBdB4PLwRP3N5ZvEHr+bU+631i+yYcb6yR5TsX3SY3hO48/\ncU8hYDKKRipYk1F0otcdZbPb4S8+/pCtXpeZam0w9PKo93vUPt6vsDe8L1vdLp005cLE5B33ITOG\nd9ZW7xiM7hSg7leQFEJ8/hwVroYNh6Lc5ccul9viRKpXhqpe3gVAo7FHnegCDstqa4WfXv0xj01f\nfiSHCoqzdvKRVi7fBXbv8JqjgpcB06ZoTXDU31VHP/C5dG1ouazclAOXY+NrQ68pl1EaaxKc6ZFu\n/DmgcC5D+c0i3AA6mCTZ/P/orfxZMQDSgQ7ncOQo/KJiV4YVL7pQVLBMt6hipZvk3c9Qysdku0QL\nf68MOikmvo7tfgo6QPmTgB68Lm+/i/JvYtOt8i0a8CfA9HB5B5Qi3fwhzvRAaUy8TNa6QmXmO3jR\nhSIMhnMj1SD8Btnm68X28Yr3kq6TRBdwJsbmezjTxjkH8XW82lPFMM58D5t3ij+dZIVsKCzCnYf1\nxTf+CVnnQ3BZUbXae5Nw5jtFCPZq++spXz+8zzqcu83flbt3pgFrN46PDR+fJ8e9w+PTYU7MAAAg\nAElEQVQez0xObi15P0BRVLb24piZWp2dXg9rD796rdMZCVgHg8vBE/fXbqwQaI/MGgLP47PtLX7n\nuedHTszfX19js9shMwbjLGudFqHnsdFpl0MfD6/7Tl5YXDoUBu7WX3z8IZvd4peuH7Z+78WXjtyn\no/ZxnGHvdoa3HWjNeqc9CK3PzBVDAvoh6ebeLs45FhoN1jvt4jtg524bjO4UoO5XkBRCiJMYDmXH\nhavB884Q5z2Wd2/wGpx2qKAQQ+7lnLMMUfe0XP/ven8YpAM8XLYFyi+GLGLBWlzeLYKW8nAqwNpt\nXLZdrNu5/cqK0qhy/mXevUpl+ltFcFA+NvkUTDEE0ilNvPLPsd1PcTYuQoTysTYF00OZtAgTXo18\n93VstgfpJth8sN8ub4NXKYZD+hO4ZBVneigd4WwCtldUp1QA268Uc66Ggo/NdrHZTjFXzmY4XYFw\nGpOsYbqfFnPhvBrkHWzegu7HuLxTDFH0m6A88GqHqkpHVfKGA1je/Wh/HqDycfkupvtJGRRDnE1R\nOkR5DZyJMd1PAYc/+dJg3ti4nWnAmoyi/lREMcS4Yr7W8HFxFNWtDzbW+dLsPKHnkeej/ygZa9mJ\ne2z1ukxUInxP8+cffTAIMcNhYr3TJjXF8MTcGnzj4anuoRPzVhKT94cxFjV4dno92mnKVFQdLNcP\nJicZilYLwrFVT7Z63WPvnyQ83S7sjXNY3cF9CbwinBYDa4o/6X5Imq/VWe+0We+0QcFCff/b2uOC\n0Z0C1P0KkkKIzzdVnsrdT1ppPO2zvHuTyJfPLvGoU+xXuBSUJ/VgQYegIjDbYFJA4fBxbrOcD2bL\noYXszw+zFqfD8vE2JlnBrz2FsxmDhh+4Mve1yDsfEUz/MnnvKi7bKRqFOFdUu7Bo2ywCh1fFZXG5\nCgVeBaUC8Go4m6CjS6h4BZfuFuHKmSKs5C2cTUm3fzwIVpXFfwPtN2l98F8U2wLAgI1R/QYl5aNe\ndKmorCWrGBODDnD5Hi43eNXH8KJLI1Ulm7dIt3+MMx2UVysaoBwIYEqFOJLy6BfNWLzak+joYjGU\nMr5JOP0trI1x2V4R9ACtozNrwHGmAeuFxSX+7/fepZOnd174C0IBntIYtT9vq88B270ury9fR+nR\nuXSeUnhaUw9CumnKjd0d1totnpmdH1Q0hsMECmZqNdbabQBya6n4/qET81oQsJfEpHleNAhSiuW9\nXaarNaaq1UPB5CRD0cYZXGaqtUEFq3+/72B4enJmlp/euHZou8eFvbsZUnmc4X3xPM2X5uYJygpg\nLyuG1PSPfeB5nJ+YRCl14mB0p+XGWTUUQnwxafQD6YpZNMyAdtJipn42w3WEGHWWX/87BuFKRWBa\nxX0VFE/l20DZiRFdfhPrygDlRvdLFVdTUl4dZzrFa0zxRbPLtoo5VPGNoa6NRdM1pUOUDsuKWf+9\n+ihdRfnNorJj2qCLahXaQ3lVvOgSADZdxcY30NFSEWZcuR6li6F+8fVy9I09UFEaPqZFExxnc/Lu\nx+AMJlnGZW2UDoq3rStQdoDEJgT1Z0a6EUJRvXI4nHPFfsfX8ae/PTpssHIOhykqdV4Dgony8IV4\ntacG88Cyrb9GedUiPOrwzOZfwRkHrFoQ0owqdNoSsPocxRAyY/VgonCfApI8J1Pq0O+9rzSztRrv\nrBehILcWX2uqfsATM7PsxvFImPjpjWusd9q0kmTQiXCh0ThUiWqnxZ+N1grnHFop1jptduIe5yYm\n+FuPXx4JGScZinY384GOC2W/8dQzh+Zg9R0MT8PdC0+y3YP7/tHmJuebE6fa76P25WAXxf4xPyok\n9YPRTq/HRBTx7Nw8SZ5jncM6W946LkxM0k4S9pKEehgyV6tzc3enGEleLjtbqzFdrWGd4/rODtYV\nQwr663D9W9j/+YjHEpNzq7VHnGVUfJ/5egNfe+UHHPsfdOXP/WHA//pzL5zoWAkhHj5qMAjpwYw5\n2Yt3qQZVvrz4lQeyfSFO7rhwphjtkKiBvAwwIaiw7H5o2G9IZ8pw1Of2n/OqxZC2bKcMVxTD67wa\nNm9j0g2wSfk6W2xPV1HhfDk3a2doP90ghCmvhhpUycptqxDtRaDAmQQdLhX/vsc30cFkMWfKZeAM\n2qvibLGuvPXWSCMLf/IlTO8GeFWc1yg6LmabaO8Cunq56HqoPbzaE+S9zyDbKY4NHsqrHHm0bbox\nqHoV4VIf6gaoo3MovzboFtivVA3Wke0UAderFnO9yvlfZzX/Cu7DdbAaYQX4/De6OI1enpPbw+N3\nHcWvo3Pu0Ij11BpSk7PV7Q4qX7m13Nzb5eLkFPONxkhI8bRmN+7hK42nFL7WdNKUry1N8NMb13hn\nbRXnHIHWRH6AsQ5PK0w59yvwju6Ad5KKy93MBzoulM3W6oM5V3fS305mDOudNp9sb+Kc49n5BQLP\nIze2nPtmBrd75TzB1Bhu7e2y3m7ja00jDPlsu5ij1s0yAk8zU6uhUYMhnsZaUpOz3euR5Dme1tSC\nAOMc7SQhswatFIH2+MEnH5FbQ5Lngz8/Bfyzd97COHfquYr/76mWvj8kYAnx6HrQ13NzOOphk3dX\n32axufhA90V8Hh0MRe6Yx497rS5O0AeB6OA5nC6qTSpkP8wMDfezKShTLucXXSxG1mFAKVAVlFcH\npajM/homWS+uE5at45xD+U386mNkrStF+Orvu/IgmEGpEPId8r3Xi/1WVYpGHAaUh46WUOEi5K0i\nKHkRfv2ZIqA4SLb+qqhQZZvFvjpbvi+FDpfQfp1w+ttkrSuY+Fax7WyPLL9C7+YfobwGfv3p4vVe\nDS+6hI1v4NWeLA5D3sLlu+SmCybFuRxFiLM9lK4cURErm30ka/i1p4Cirbr2myPVJ6VDdGWR6oV/\nAIx2FVTBDC7fwSYroCqgKzjTw6ssnNn8K7gPbdpbqUy2P8i4g/FpX/97j4McsNM73DQkt4arO1so\nBd977x3qQUjgeVzd3sJaiylbyOfWUg9C/vraZ0xFVeIsKyZMOkezUkEBjUqFVpLQrFSo+gFwOByd\nZCjacSHsYJXq+YVFfO2R5DnLe3vk1mCtI3eWtU4brRSpMWTlXLLBre3/nJMZO7i/l8Tk5f1+deaT\nrc0T/ZkIIYR4sHKbstWRSy2Is3BciOpfh+u458trdanizEx5DZztgrWMhrQyjLi0CDuu7DA44A2q\nPMVX6f1qV38fyjV5dXRlCe03qF36h/Ru/hEuOr//vNJUL/wD9t79RzhyXLZLceFnXS5hUf5UsZXA\nFB31TAtshvLqRaXa7OE3nkfpYnTS8HWg8u7HmN4NbLpeNLrwqujKRVy2ifIilK5i4ptlMw6K632Z\nHsqrYGpPo53Faz5HoKP9xheVuUE1yZkYay0u3SquJYYqmnw4g3PJoBnFcHg62EijH4pu1w3QtN5C\nh/PocJ68+zE22yuvZRaj/DqV6V856bWv7tqZt2nf7vbuvOAXjEaBgvyIqsXtrmJmncNTmnwooEVB\ngFYaX3ustPZopQmaIpg4HNY6lCqG/6132sQmZyqqEvk+vTwj9DwiPyD0fJqVCqHn4ynFfNl44WCF\nqhaE/OKFS8R5RpwV1Ztbe3vF/TwnznNaSczNvV06aYpWisgP+Jub19nudUnyolthbi3/+9u3/9bo\n5yvLJz+oD4iimLemlUJR3NbCoLwop8E6CDxNPawQaI2nNVopPKXRWu3/XK6j//zh/497vNz2wZ/p\nP8bI86p83CsfU4qhZYvLhv75Rx+wm/To/22cjqr89rPPl+sp3vf+uvbfvxBC3It20qEuLdrF/aT9\nsove7Yb8mXJ4X1ZeuPhg9aqsRqkq6KBs924pglN/3eUQvkG46q/DAy8q5iKZDq68vpY39c1i944J\nEcqfwLbeZT/EuWK4nd8shsDZFDB40fmiiqNDvOZXsPEyZu9NlK6jK4vo6AKm+8nggsQ23SgqPTYt\nux4muHQTv/ll/NpT2LwMSsorK0J+MS/LWUzrbfT0L0PepnLhu4N9Hm2xPoNL1sH2iqGBNsehUX4x\nPcMeMXRP+80jw1Aw/W3cxg/I9t4EHCqYwOatQXVruE29Ux46mAaXolBnWrnqO/M27fltqjVfRBoG\n3fqOooBaGA7mRg2rBgHG2sHngK81xjp6ecYnW5tsdjtY56gGAakp5vGEnl9eCFkR5zkztaJJxHy9\nwVq7hcFxaXKK8xMTGOdYa7d5e/UWN1u7BNpjplbjjeUb9LKMXpbRzTOS/PjroNwvWikCzyP0PALt\n4XuaQHsEnkc3TcswqtDlPLVzExP4uljGL4NO4BU/+1rz2fYWvSwbhJSJKEIrNQiJGsVUtcovXryE\nrzRaF0MvX7t5faRaN1Wt8s2Ljx2ah9V//FGw0Cg66sR5TuT7zNUbzNXrD3ivhBAP2ll2GNRo4rwn\nbYfFGTlmKKBNQNfAdjnUYGJ4+J3yywpVxn77dUaXdSkQFtUuR3nrMZhzpbwiqCmv2K5S5X3AWVQ4\nj/brKF0j3fs5yeq/ABuDVyeY/DpB41m85osk6y+T964yWiErtu9waF0Og7MJSgd41UuocJ689TY2\nWQMb43SMNW10uoEO58k6H2LjGzhTVuD85mC+E8qh/EmyvTcH3fe86BKm9W4ZDos5VM4UTdUOzmsa\nDkh6/WWSje/jXBOcwSa3UC5DR4/hXI7pXccm6+TBLM7EhHO/Pujyd/BaWF7zRfLux8V2vRo22Rhc\nP0uHc0VzjLyDU0VDDaV9vNqzgyGGZ+3M27S7L8B1sE6jHwyMtSNzb/pHyfc8Hpuc5v31NcyBX+D5\nep1ulhIpD68ME3GWEuii81Mxf6q47dcT+o0LelnGcmuXrV6XrJwL1L8O1wcb68fu78ZQB7+7fb8V\n3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8xmZGl2XwJdpR/GymDWv1/xw5Fgtv94P7ANLV8uV1xPLBxZtuJHeFqGdovx2b3yn9DY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\nAgAAAICUEGABAAAAQEr+D+A3kxtjBqZ6AAAAAElFTkSuQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from statlearning import plot_logistic_regressions\n", "\n", "with sns.color_palette(crayon):\n", " plot_logistic_regressions(train[continuous], y_train)\n", " plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The class-conditional distributions are mostly similar accross predictors. " ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "image/png": 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Mgip25lSwKD2W0dFd7DTdX6PmQmCE6trUj6LoiGA/frVsPF/es4grpidw8GQ1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\nX4e/lxuvrzzMS98fxGKVBduO5LQJhKZp/HQoGS83I9e3CoWkHeAVBME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8B8kH9GOBjdE6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\nR6GmaQ5VVTdjfOCvx3jqASfXPJkwqtg6Sse44WgW0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dp/dlPkQgh3qi7OZbxpH03Jk4wm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9vgux6xGjQxPilIRvAjGkgfq1Qy3YLCpLZfqSMMJgH8ThwaXJgwmE7ECMhdpgaViBlDB9qCiz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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from statlearning import plot_conditional_distributions\n", "\n", "plot_conditional_distributions(train[continuous], y_train, labels=['Default', 'Fully Paid'])\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, we consider the purpose of the loan, which is only categorial variable that was not pre-processed. Loans for educational and small businesses purposes seem to be riskier than othre types of loans. " ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "debt_consolidation 9183\n", "credit_card 2460\n", "other 1953\n", "home_improvement 1508\n", "major_purchase 1051\n", "small_business 899\n", "car 753\n", "wedding 461\n", "medical 353\n", "moving 264\n", "vacation 201\n", "house 187\n", "educational 152\n", "renewable_energy 54\n", "Name: purpose, dtype: int64" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train['purpose'].value_counts()" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "image/png": 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NcrySJKmFOYXau2VUU5hzImI58ABwetl3CfAbYLs+HmsG8NWIeCvwB+DZIR6r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\n+8kAmXlBRHRmZtsK2y4t/X4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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "table = (1-train.groupby('purpose')['fully_paid'].mean()).sort_values(ascending=False).round(3)\n", "fig, ax = plt.subplots()\n", "table.plot(kind='barh', ax=ax)\n", "ax.set_ylabel('')\n", "ax.set_xlabel('Default probability')\n", "sns.despine()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Data Preparation\n", "\n", "Feature engineering is often the best way to improve the performance of a machine learning system. For example, domain knowledge may suggest that the size of the loan relative to income is important information. " ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": true }, "outputs": [], "source": [ "train['loan_to_income']=train['funded_amnt']/train['annual_inc']\n", "test['loan_to_income']=test['funded_amnt']/test['annual_inc']\n", "continuous.append('loan_to_income')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the EDA section, we have seen that many numerical predictors are highly skewed, which can be detrimental to the performance of methods such as logistic regression. Log, Box-Cox, and power transformations are effective ways to reduce skewness. " ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def log_transform(x, train, test):\n", " train[x] = np.log(train[x])\n", " test[x] = np.log(test[x])\n", " return train, test\n", "\n", "from scipy.stats import boxcox\n", "def box_cox_transform(x, train, test, shift=0.0):\n", " y, param = boxcox(train[x]+shift)\n", " train[x] = y\n", " test[x] = boxcox(test[x]+shift, param)\n", " return train, test\n", "\n", "train, test = log_transform('annual_inc', train, test)\n", "train, test = log_transform('months_since_earliest_cr_line', train, test)\n", "train, test = box_cox_transform('revol_bal', train, test, shift=1.0) # we add one to make the variable positive" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As an additional step in data preparation, we need to code the remaining categorical variable using dummy variables. " ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": true }, "outputs": [], "source": [ "new_dummies = pd.get_dummies(data[['purpose']], drop_first=True)\n", "train=train.join(new_dummies.loc[index_train,:])\n", "test=test.join(new_dummies.loc[index_test,:])\n", "\n", "dummies+=list(new_dummies.columns)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Building the final design matrices: " ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "List of predictors: \n", "\n", "['funded_amnt', 'int_rate', 'installment', 'annual_inc', 'dti', 'revol_bal', 'revol_util', 'total_acc', 'months_since_earliest_cr_line', 'loan_to_income', 'emp_length', 'delinq_2yrs', 'inq_last_6mths', 'open_acc', 'pub_rec', 'pub_rec_bankruptcies', 'term_60', 'home_ownership_own', 'home_ownership_rent', 'verification_status_source verified', 'verification_status_verified', 'purpose_credit_card', 'purpose_debt_consolidation', 'purpose_educational', 'purpose_home_improvement', 'purpose_house', 'purpose_major_purchase', 'purpose_medical', 'purpose_moving', 'purpose_other', 'purpose_renewable_energy', 'purpose_small_business', 'purpose_vacation', 'purpose_wedding']\n" ] } ], "source": [ "predictors = continuous + discrete + dummies\n", "\n", "print('List of predictors: \\n')\n", "print(predictors)\n", "\n", "scaler = StandardScaler()\n", "X_train = scaler.fit_transform(train[predictors])\n", "X_test = scaler.transform(test[predictors])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Logistic Regression\n", "\n", "The basic command to estimate a [logistic regression](http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) with Scikit-Learn is as follows. " ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n", " intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n", " penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n", " verbose=0, warm_start=False)" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.linear_model import LogisticRegression\n", "\n", "logit = LogisticRegression()\n", "logit.fit(X_train, y_train)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "From the output, we notice that by default the method includes an $\\ell^2$ penalty with an arbitrary penalty, which is not necessarily the behaviour that we would expect. The tuning parameter C is the inverse of the regularisation strength. " ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "LogisticRegression(C=10000000000.0, class_weight=None, dual=False,\n", " fit_intercept=True, intercept_scaling=1, max_iter=100,\n", " multi_class='ovr', n_jobs=1, penalty='l2', random_state=None,\n", " solver='liblinear', tol=0.0001, verbose=0, warm_start=False)" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "logit = LogisticRegression(C=1e10)\n", "logit.fit(X_train, y_train)" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "LogisticRegressionCV(Cs=20, class_weight=None, cv=None, dual=False,\n", " fit_intercept=True, intercept_scaling=1.0, max_iter=100,\n", " multi_class='ovr', n_jobs=1, penalty='l2', random_state=None,\n", " refit=True, scoring='neg_log_loss', solver='lbfgs', tol=0.0001,\n", " verbose=0)" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.linear_model import LogisticRegressionCV\n", "\n", "logit_l1 = LogisticRegressionCV(Cs = 20, penalty='l1', solver='liblinear', scoring='neg_log_loss')\n", "logit_l1.fit(X_train, y_train)\n", "\n", "logit_l2 = LogisticRegressionCV(Cs = 20, penalty='l2', scoring='neg_log_loss')\n", "logit_l2.fit(X_train, y_train)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It can be helpful to plot the estimated coefficients." ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "image/png": 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2uyhVkYBHgbsl3UEGZPcDhwLHlNHVy6m/oOkE4OtlxPRrkNWngKbqUxNKvx8r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\nk8DF5ItTbwBfqJUfNSLOoPPFC5YFriB/duaRRQT+HhGPApO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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from statlearning import plot_coefficients\n", "\n", "plot_coefficients(logit_l1, predictors)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "All logistic regression specifications have very similar performance, so that we do not present a model selection exercise for conciseness." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Model evaluation\n", "\n", "We first compute the predicted probabilites and the corresponding decisions. We need to implement the decision function manually, which is simple to do. " ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": true }, "outputs": [], "source": [ "methods=[logit, logit_l1, logit_l2]\n", "\n", "y_pred = np.zeros((len(y_test), len(methods)+1))\n", "y_prob = np.zeros((len(y_test), len(methods)+1))\n", "\n", "# Constant Probability Model (as an additional benchmark)\n", "y_prob[:, 0] = np.mean(y_train) \n", "y_pred[:, 0] = (y_prob[:,0]> tau).astype(int)\n", "\n", "# Estimated Models\n", "for i, method in enumerate(methods):\n", " y_prob[:,i+1] = method.predict_proba(X_test)[:,1] \n", " y_pred[:,i+1] = (y_prob[:,i+1]>tau).astype(int)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We evaluate the models in terms of their investment performance on the test set. As a benchmark, we compare our models with a strategy of investing in all loans. " ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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LoansReturn on test setReturn given loan
Invest in all1.0003.273.27
Constant probability0.5153.002.71
Logistic regression0.6314.745.58
L1 regularised0.6254.765.63
L2 regularised0.6264.745.58
\n", "
" ], "text/plain": [ " Loans Return on test set Return given loan\n", "Invest in all 1.000 3.27 3.27\n", "Constant probability 0.515 3.00 2.71\n", "Logistic regression 0.631 4.74 5.58\n", "L1 regularised 0.625 4.76 5.63\n", "L2 regularised 0.626 4.74 5.58" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "columns=['Loans', 'Return on test set', 'Return given loan']\n", "\n", "rows=['Invest in all', 'Constant probability', 'Logistic regression', 'L1 regularised', 'L2 regularised']\n", "\n", "profitability = pd.DataFrame(0.0, columns=columns, index=rows)\n", "\n", "for i in range(len(rows)):\n", "\n", " if i==0: \n", " # invest in all \n", " profitability.iloc[i,0] = 1.0\n", " profitability.iloc[i,1:3] = test['return'].mean()\n", " profitability.iloc[i,1:3] = test['return'].mean()\n", " else:\n", " profitability.iloc[i,0]= np.mean(y_pred[:,i-1])\n", " lp = y_pred[:,i-1]*test['return']\n", " ln = (1-y_pred[:,i-1])*(-loss_tn) # the return from all the negatives is the same\n", " profitability.iloc[i,1]= np.mean(lp+ln)\n", " profitability.iloc[i,2] = np.sum(lp/np.sum(y_pred[:,i-1])) \n", " \n", " expected = y_prob[:,i-1]*test['int_rate']+(1-y_prob[:,i-1])*(-loss_fp)\n", "\n", "profitability.iloc[:,0]= profitability.iloc[:,0].round(3)\n", "profitability.iloc[:,[1,2]]= (profitability.iloc[:,[1,2]]).round(2) \n", "profitability" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As additional information, we can assess how discriminative our models are by comparing the average predicted probability for fully paid versus charged off loans. " ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 0.8596, 0.8702, 0.8693, 0.8699])" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.mean(y_prob[y_test==1, :], axis=0).round(4)" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 0.8596, 0.8042, 0.8047, 0.8056])" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.mean(y_prob[y_test==0, :], axis=0).round(4)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, we consider the traditional classification metrics, even though they are of secondary importance in this application. In order to make a more meaningful comparison, we give higher weights to defaults when computing the error rate, the area under the ROC curve, and the area under the precision-recall curve. \n", "\n", "Based on the specificity, the classification models avoid around 57% of defaults. " ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Weighted error rateSensitivitySpecificityWeighted AUCPrecisionWeighted AUPRCCross-entropy
Constant probability0.5970.4850.3020.5000.8100.5510.406
Logistic0.3800.6630.5680.6980.9040.7350.375
L1 regularised0.3810.6570.5740.6980.9040.7340.375
L2 regularised0.3820.6580.5690.6980.9030.7350.375
\n", "
" ], "text/plain": [ " Weighted error rate Sensitivity Specificity \\\n", "Constant probability 0.597 0.485 0.302 \n", "Logistic 0.380 0.663 0.568 \n", "L1 regularised 0.381 0.657 0.574 \n", "L2 regularised 0.382 0.658 0.569 \n", "\n", " Weighted AUC Precision Weighted AUPRC Cross-entropy \n", "Constant probability 0.500 0.810 0.551 0.406 \n", "Logistic 0.698 0.904 0.735 0.375 \n", "L1 regularised 0.698 0.904 0.734 0.375 \n", "L2 regularised 0.698 0.903 0.735 0.375 " ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "columns=['Weighted error rate', 'Sensitivity', 'Specificity', 'Weighted AUC', 'Precision', 'Weighted AUPRC', 'Cross-entropy']\n", "\n", "rows=['Constant probability', 'Logistic', 'L1 regularised', 'L2 regularised']\n", "\n", "results=pd.DataFrame(0.0, columns=columns, index=rows)\n", "\n", "weights=y_test+(1-y_test)*5\n", " \n", "for i in range(len(rows)):\n", " confusion = confusion_matrix(y_test, y_pred[:,i]) \n", " results.iloc[i,0]= 1 - accuracy_score(y_test, y_pred[:,i], sample_weight=weights)\n", " results.iloc[i,1]= confusion[1,1]/np.sum(confusion[1,:])\n", " results.iloc[i,2]= confusion[0,0]/np.sum(confusion[0,:])\n", " results.iloc[i,3]= roc_auc_score(y_test, y_prob[:,i], sample_weight=weights)\n", " results.iloc[i,4]= precision_score(y_test, y_pred[:,i])\n", " results.iloc[i,5]= average_precision_score(y_test, y_prob[:,i], sample_weight=weights)\n", " results.iloc[i,6]= log_loss(y_test, y_prob[:,i])\n", "\n", "results.round(3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The next cell plots the ROC curves. " ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "image/png": 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DpJgR5VdGRgYvvfQSLpeLbt260alTJ6xWuUeQvzIcedg3zCJtytfk7zq+0Z7F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\nDw8PmjRpQnh4OJ9//nmWQXczZsygefPmjB49GhcXF3r06IGDgwNffvl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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from statlearning import plot_roc_curves\n", "\n", "with sns.color_palette(crayon):\n", " fig, ax = plot_roc_curves(y_test, y_prob[:,[1,2,3]], labels=pd.Series(rows).iloc[[1,2,3]], sample_weight=weights)\n", " ax.set_title('ROC curves (weighted)', fontsize=14)\n", " plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Investment strategy\n", "\n", "In the previous section, we considered the performance of our models across the entire test set. A better strategy for an individual investor participating in the peer-to-peer lending platform is to invest a portfolio of loans that have the highest expected return, with sufficient diversification to dilute the risk from defaults.\n", "\n", "We implement this strategy for a portfolio size of 1000 loans, where we assume an equal investment in each loan. The gradient boosting model achieves the best rate of return. " ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
ReturnSE
Constant-0.511.22
Logistic8.200.71
L1 regularised8.140.72
L2 regularised8.250.71
\n", "
" ], "text/plain": [ " Return SE\n", "Constant -0.51 1.22\n", "Logistic 8.20 0.71\n", "L1 regularised 8.14 0.72\n", "L2 regularised 8.25 0.71" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "columns=['Return', 'SE']\n", "\n", "rows=['Constant', 'Logistic', 'L1 regularised', 'L2 regularised']\n", "\n", "strategy = pd.DataFrame(0.0, columns=columns, index=rows)\n", "\n", "loans = 1000 # portfolio size\n", "\n", "for i in range(len(rows)):\n", " \n", " expected = y_prob[:,i]*test['int_rate']+(1-y_prob[:,i])*(-loss_fp)\n", " expected = expected.sort_values(ascending=False)\n", " strategy.iloc[i,0] = test.loc[expected.index[:loans],'return'].mean()\n", " strategy.iloc[i,1] = test.loc[expected.index[:loans],'return'].std()/np.sqrt(loans)\n", "\n", "strategy.round(2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The rate of return depends on the portfolio size. Below, we plot the investment performance of different models as we vary the portfolio size. The tree-based methods perform best for smaller portfolio sizes, while the logistic GAM performs best for larger portfolios. The linear stack is a compromise. " ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "image/png": 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57H3v8rcJlmzB1HsSxozGjRIdqJAdN+p3qAld8K37HH/BquYLuhGGnDucr+O6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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "models = ['Constant', 'Logistic', 'L1 regularised', 'L2 regularised']\n", "\n", "sizes = np.arange(500, 10000, 250)\n", "\n", "returns = np.zeros((len(sizes), len(rows))) \n", "\n", "for i, loans in enumerate(sizes):\n", " for j in range(len(rows)):\n", " expected = y_prob[:,j]*test['int_rate']+(1-y_prob[:,j])*(-loss_fp)\n", " expected = expected.sort_values(ascending=False)\n", " returns[i,j] = test.loc[expected.index[:loans],'return'].mean()\n", "\n", "returns = pd.DataFrame(returns, columns=models, index=sizes)\n", "\n", "with sns.color_palette(crayon):\n", " fig, ax = plt.subplots()\n", " returns.iloc[:,1:].plot(ax=ax)\n", " ax.set_xlabel('Portfolio size')\n", " ax.set_ylabel('Estimated expected return')\n", " ax.set_title('')\n", " plt.legend()\n", " sns.despine()\n", " plt.show()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.2" } }, "nbformat": 4, "nbformat_minor": 2 }