Repository: PacktPublishing/Hands-On-Artificial-Intelligence-for-IoT Branch: master Commit: b07c275e49c9 Files: 78 Total size: 11.3 MB Directory structure: gitextract_21uunvxe/ ├── Chapter01/ │ ├── Readme.md │ ├── matrix_multiplication.ipynb │ └── matrix_multiplication_keras.ipynb ├── Chapter02/ │ ├── Hdf5_with_PyTables.ipynb │ ├── NoSQL with Python.ipynb │ ├── OpenPyXL_example.ipynb │ ├── Pandas_xlsx_example.ipynb │ ├── Readme.md │ ├── SQL with Python.ipynb │ ├── Txt_files.ipynb │ └── read_csv_file.ipynb ├── Chapter03/ │ ├── ElectricalPowerOutputPredictionUsingDecisionTrees.ipynb │ ├── ElectricalPowerOutputPredictionUsingRegression.ipynb │ ├── Folds5x2_pp.xlsx │ ├── Images/ │ │ └── Readme.md │ ├── LogisticRegressor.py │ ├── Sigmoid_function.ipynb │ ├── Wine_quality_using_DecisionTrees.ipynb │ ├── Wine_quality_using_Ensemble_learning.ipynb │ ├── Wine_quality_using_GaussianNB.ipynb │ ├── Wine_quality_using_SVM-with_GridSearch.ipynb │ ├── Wine_quality_using_SVM.ipynb │ ├── Wine_quality_using_logistic_regressor.ipynb │ ├── readme.md │ └── winequality-red.csv ├── Chapter04/ │ ├── Folds5x2_pp.xlsx │ ├── LeNet_for_HandrwittenDigitsRecognition.ipynb │ ├── MLP_classification.ipynb │ ├── MLP_regression.ipynb │ ├── Readme.md │ ├── activation_functions.ipynb │ ├── single_neuron_tf.ipynb │ └── winequality-red.csv ├── Chapter05/ │ ├── Folds5x2_pp.xlsx │ ├── Genetic CNN.ipynb │ ├── Genetic_RNN.ipynb │ ├── GuessTheWord.ipynb │ ├── Optimization_surface_convex.ipynb │ ├── Optimization_surface_non-convex.ipynb │ ├── Readme.md │ └── dag.py ├── Chapter06/ │ ├── DQN.ipynb │ ├── Images/ │ │ └── Readme.md │ ├── OpenAI_practice.ipynb │ ├── Policy Gradients.ipynb │ ├── Readme.md │ ├── Taxi_drop-off-NN.ipynb │ └── Taxi_drop-off.ipynb ├── Chapter07/ │ ├── DCGAN_CelebA.ipynb │ ├── Images/ │ │ └── Readme.md │ ├── Readme.md │ ├── Vanilla_GAN.ipynb │ ├── VariationalAutoEncoders_MNIST.ipynb │ └── loader_celebA.py ├── Chapter08/ │ ├── Boston_Price_MLlib.ipynb │ ├── Readme.md │ ├── Transfer_learning_Sparkdl.ipynb │ ├── Wine_Classification_MLlib.ipynb │ ├── boston_price_h2o.ipynb │ └── wine_classification_h2o.ipynb ├── Chapter09/ │ ├── Heart_Disease_Prediction.ipynb │ ├── Human_activity_recognition_using_accelerometer.ipynb │ ├── Hypoglycemia Prediction.ipynb │ ├── Images/ │ │ └── Readme.md │ └── Readme.md ├── Chapter10/ │ ├── Electrical_Load_Forecasting.ipynb │ ├── Predictive_Maintenance_using_LSTM.ipynb │ └── Readme.md ├── Chapter11/ │ ├── SF_crime_category_detection.ipynb │ └── readme.md ├── Chapter12/ │ ├── Readme.md │ ├── Video_to_frames.ipynb │ ├── audio_processing.ipynb │ ├── data_augmentation.ipynb │ ├── text_preprocessing.ipynb │ └── time_series_data_preprocessing.ipynb ├── LICENSE └── README.md ================================================ FILE CONTENTS ================================================ ================================================ FILE: Chapter01/Readme.md ================================================ # Chapter 1: Principles and foundations of IoT and AI This book deals with the three big trends in the current Business Scenario, __Internet of things (IoT)__, __big data__ and __artificial intelligence (AI)__. The exponential growth of the number of devices connected to the internet and the exponential volume of data created by them necessitates the need of using the analytical and predictive techniques of artificial intelligence and deep learning. This book specifically targets the third component, the various analytical and predictive methods/models available in the field of AI for the big data generated by IoT. This chapter will briefly introduce you to these three trends and will expand on how they are interdependent. The data generated by IoT devices is uploaded to the cloud, thus you will also be introduced to the various IoT cloud platforms and the data services they offer. The chapter will cover the following points: * Knowing what is a thing in IoT, what devices constitute things, what are the different IoT Platforms, and what is an IoT verticals. * Know what is big data, understand how the amount of data generated by IoT lies in the range of big data. * Understand how and why AI can be useful for making sense of the voluminous data generated by IoT. * With the help of an illustration, understand how IoT, big data and AI together can help shape a better world. * And finally, learn about some of the tools that would be needed to perform the analysis. ================================================ FILE: Chapter01/matrix_multiplication.ipynb ================================================ { "cells": [ { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 1.05043888 0.57121533]\n", " [ 1.45642126 0.61392534]\n", " [ 0.52326083 0.47913069]]\n" ] } ], "source": [ "# import libraries and modules needed for the code\n", "import tensorflow as tf\n", "import numpy as np\n", "\n", "# Data\n", "# A random matrix of size [3,5]\n", "mat1 = np.random.rand(3,5) \n", "# A random matrix of size [5,2]\n", "mat2 = np.random.rand(5,2) \n", "\n", "\n", "#Computation Graph\n", "A = tf.placeholder(tf.float32, None, name='A')\n", "B = tf.placeholder(tf.float32, None, name='B')\n", "C = tf.matmul(A,B)\n", "\n", "\n", "#Execution Graph\n", "with tf.Session() as sess:\n", " result = sess.run(C, feed_dict={A: mat1, B:mat2})\n", " print(result)\n", " " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python [conda env:tensorflow]", "language": "python", "name": "conda-env-tensorflow-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.4" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: Chapter01/matrix_multiplication_keras.ipynb ================================================ { "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 127.55612946 125.59892273 122.93148041 ..., 119.09933472\n", " 120.31015015 123.70563507]\n", " [ 126.91525269 120.60913086 122.53115845 ..., 114.7818985 117.73587036\n", " 119.22080994]\n", " [ 125.19042969 124.79894257 128.43052673 ..., 121.96895599\n", " 116.95805359 123.73403168]\n", " ..., \n", " [ 126.55613708 125.86196899 127.1171875 ..., 123.17983246 123.4691925\n", " 123.82902527]\n", " [ 130.45053101 133.09960938 136.35180664 ..., 130.31216431\n", " 129.79626465 130.48962402]\n", " [ 130.10887146 127.49655914 129.2310791 ..., 126.10153961 123.6025238\n", " 126.6700592 ]]\n" ] } ], "source": [ "import keras.backend as K\n", "import numpy as np\n", "A = np.random.rand(20,500)\n", "B = np.random.rand(500,3000)\n", "\n", "x = K.variable(value=A)\n", "y = K.variable(value=B)\n", "\n", "z = K.dot(x,y)\n", "\n", "print(K.eval(z))\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python [conda env:tensorflow]", "language": "python", "name": "conda-env-tensorflow-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.4" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: Chapter02/Hdf5_with_PyTables.ipynb ================================================ { "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# Import module and create a random d\n", "import numpy as np\n", "arr = np.random.rand(5,4)\n", "np.savetxt('temp.csv', arr, delimiter=',')" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "arr = np.loadtxt('temp.csv', skiprows=1, usecols=(2,3),\n", " delimiter=',')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "import tables\n", "h5filename = 'pytable_demo.hdf5'\n", "with tables.open_file(h5filename,mode='w') as h5file:\n", " root = h5file.root\n", " h5file.create_array(root,'global_power',arr)\n", " h5file.close()\n", " " ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " (4, 2)\n", "[[0.66775171 0.5654945 ]\n", " [0.31355147 0.37325779]\n", " [0.25665679 0.79335901]\n", " [0.96372349 0.45597874]]\n" ] } ], "source": [ "with tables.open_file(h5filename,mode='r') as h5file:\n", " root = h5file.root\n", " for node in h5file.root:\n", " ds = node.read()\n", " print(type(ds),ds.shape)\n", " print(ds)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python [conda env:anaconda3]", "language": "python", "name": "conda-env-anaconda3-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.5" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: Chapter02/NoSQL with Python.ipynb ================================================ { "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: pymongo in /Users/am/anaconda3/lib/python3.6/site-packages (3.7.1)\n", "\u001b[31mdistributed 1.21.8 requires msgpack, which is not installed.\u001b[0m\n", "\u001b[33mYou are using pip version 10.0.1, however version 18.0 is available.\n", "You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n" ] } ], "source": [ "!pip install pymongo" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import pymongo\n", "client = pymongo.MongoClient()\n", "db = client.test" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "from sklearn.datasets import load_breast_cancer\n", "import pandas as pd\n", "\n", "cancer = load_breast_cancer()\n", "data = pd.DataFrame(cancer.data, columns=[cancer.feature_names])" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "import json" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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mean radiusmean texturemean perimetermean areamean smoothnessmean compactnessmean concavitymean concave pointsmean symmetrymean fractal dimension...worst radiusworst textureworst perimeterworst areaworst smoothnessworst compactnessworst concavityworst concave pointsworst symmetryworst fractal dimension
017.9910.38122.801001.00.118400.277600.30010.147100.24190.07871...25.3817.33184.602019.00.16220.66560.71190.26540.46010.11890
120.5717.77132.901326.00.084740.078640.08690.070170.18120.05667...24.9923.41158.801956.00.12380.18660.24160.18600.27500.08902
219.6921.25130.001203.00.109600.159900.19740.127900.20690.05999...23.5725.53152.501709.00.14440.42450.45040.24300.36130.08758
311.4220.3877.58386.10.142500.283900.24140.105200.25970.09744...14.9126.5098.87567.70.20980.86630.68690.25750.66380.17300
420.2914.34135.101297.00.100300.132800.19800.104300.18090.05883...22.5416.67152.201575.00.13740.20500.40000.16250.23640.07678
\n", "

5 rows × 30 columns

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" ], "text/plain": [ " mean radius mean texture mean perimeter mean area mean smoothness \\\n", "0 17.99 10.38 122.80 1001.0 0.11840 \n", "1 20.57 17.77 132.90 1326.0 0.08474 \n", "2 19.69 21.25 130.00 1203.0 0.10960 \n", "3 11.42 20.38 77.58 386.1 0.14250 \n", "4 20.29 14.34 135.10 1297.0 0.10030 \n", "\n", " mean compactness mean concavity mean concave points mean symmetry \\\n", "0 0.27760 0.3001 0.14710 0.2419 \n", "1 0.07864 0.0869 0.07017 0.1812 \n", "2 0.15990 0.1974 0.12790 0.2069 \n", "3 0.28390 0.2414 0.10520 0.2597 \n", "4 0.13280 0.1980 0.10430 0.1809 \n", "\n", " mean fractal dimension ... worst radius worst texture \\\n", "0 0.07871 ... 25.38 17.33 \n", "1 0.05667 ... 24.99 23.41 \n", "2 0.05999 ... 23.57 25.53 \n", "3 0.09744 ... 14.91 26.50 \n", "4 0.05883 ... 22.54 16.67 \n", "\n", " worst perimeter worst area worst smoothness worst compactness \\\n", "0 184.60 2019.0 0.1622 0.6656 \n", "1 158.80 1956.0 0.1238 0.1866 \n", "2 152.50 1709.0 0.1444 0.4245 \n", "3 98.87 567.7 0.2098 0.8663 \n", "4 152.20 1575.0 0.1374 0.2050 \n", "\n", " worst concavity worst concave points worst symmetry worst fractal dimension \n", "0 0.7119 0.2654 0.4601 0.11890 \n", "1 0.2416 0.1860 0.2750 0.08902 \n", "2 0.4504 0.2430 0.3613 0.08758 \n", "3 0.6869 0.2575 0.6638 0.17300 \n", "4 0.4000 0.1625 0.2364 0.07678 \n", "\n", "[5 rows x 30 columns]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.head()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "data_in_json = data.to_json(orient='split')" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "rows = json.loads(data_in_json)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/am/anaconda3/lib/python3.6/site-packages/ipykernel/__main__.py:1: DeprecationWarning: insert is deprecated. Use insert_one or insert_many instead.\n", " if __name__ == '__main__':\n" ] }, { "data": { "text/plain": [ "ObjectId('5ba272f0d82f8a68a1fa33ab')" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "db.cancer_data_2.insert(rows)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "cursor = db['cancer_data_2'].find({})" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " _id \\\n", "0 5ba272f0d82f8a68a1fa33ab \n", "\n", " columns \\\n", "0 [[mean radius], [mean texture], [mean perimete... \n", "\n", " data \\\n", "0 [[17.99, 10.38, 122.8, 1001.0, 0.1184, 0.2776,... \n", "\n", " index \n", "0 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,... \n" ] } ], "source": [ "df = pd.DataFrame(list(cursor))\n", "print(df)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python [default]", "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": "3.6.5" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: Chapter02/OpenPyXL_example.ipynb ================================================ { "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Collecting openpyxl\n", " Downloading openpyxl-2.5.0.tar.gz (169kB)\n", "\u001b[K 100% |████████████████████████████████| 174kB 393kB/s ta 0:00:01\n", "\u001b[?25hCollecting jdcal (from openpyxl)\n", " Downloading jdcal-1.3.tar.gz\n", "Collecting et_xmlfile (from openpyxl)\n", " Downloading et_xmlfile-1.0.1.tar.gz\n", "Building wheels for collected packages: openpyxl, jdcal, et-xmlfile\n", " Running setup.py bdist_wheel for openpyxl ... \u001b[?25ldone\n", "\u001b[?25h Stored in directory: /Users/am/Library/Caches/pip/wheels/a7/88/96/29c1f91ba5a9b94dfc39a9f6f72d0eb92d6f0d917cf2341a3f\n", " Running setup.py bdist_wheel for jdcal ... \u001b[?25ldone\n", "\u001b[?25h Stored in directory: /Users/am/Library/Caches/pip/wheels/0f/63/92/19ac65ed64189de4d662f269d39dd08a887258842ad2f29549\n", " Running setup.py bdist_wheel for et-xmlfile ... \u001b[?25ldone\n", "\u001b[?25h Stored in directory: /Users/am/Library/Caches/pip/wheels/99/f6/53/5e18f3ff4ce36c990fa90ebdf2b80cd9b44dc461f750a1a77c\n", "Successfully built openpyxl jdcal et-xmlfile\n", "Installing collected packages: jdcal, et-xmlfile, openpyxl\n", "Successfully installed et-xmlfile-1.0.1 jdcal-1.3 openpyxl-2.5.0\n" ] } ], "source": [ "# Install if OpenPyXl is not already installed \n", "!pip install openpyxl" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "A\n" ] } ], "source": [ "from openpyxl import Workbook\n", "from openpyxl.compat import range\n", "from openpyxl.utils import get_column_letter\n", "\n", "wb = Workbook()\n", "\n", "dest_filename = 'empty_book.xlsx'\n", "\n", "ws1 = wb.active\n", "ws1.title = \"range names\"\n", "\n", "for row in range(1, 40):\n", " ws1.append(range(0,100,5))\n", "\n", "ws2 = wb.create_sheet(title=\"Pi\")\n", "ws2['F5'] = 2 * 3.14\n", "ws2.cell(column=1, row=5, value= 3.14)\n", "\n", "ws3 = wb.create_sheet(title=\"Data\")\n", "for row in range(1, 20):\n", " for col in range(1, 15):\n", " _ = ws3.cell(column=col, row=row, value=\"{0}\".format(get_column_letter(col)))\n", "print(ws3['A10'].value)\n", "\n", "wb.save(filename = dest_filename)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['range names', 'Pi', 'Data']\n", "15\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/am/anaconda3/envs/tensorflow/lib/python3.5/site-packages/ipykernel/__main__.py:4: DeprecationWarning: Call to deprecated function get_sheet_names (Use wb.sheetnames).\n" ] } ], "source": [ "from openpyxl import load_workbook\n", "wb = load_workbook(filename = 'empty_book.xlsx')\n", "sheet_ranges = wb['range names']\n", "print(wb.get_sheet_names())\n", "print(sheet_ranges['D18'].value)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python [conda env:tensorflow]", "language": "python", "name": "conda-env-tensorflow-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.4" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: Chapter02/Pandas_xlsx_example.ipynb ================================================ { "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "df = pd.read_excel(\"empty_book.xlsx\", sheet_name=0)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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idname
01Belgium
11729England
24769France
37809Germany
410257Italy
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idplayer_api_idplayer_nameplayer_fifa_api_idbirthdayheightweight
01505942Aaron Appindangoye2183531992-02-29 00:00:00182.88187
12155782Aaron Cresswell1896151989-12-15 00:00:00170.18146
23162549Aaron Doran1861701991-05-13 00:00:00170.18163
3430572Aaron Galindo1401611982-05-08 00:00:00182.88198
4523780Aaron Hughes177251979-11-08 00:00:00182.88154
\n", "
" ], "text/plain": [ " id player_api_id player_name player_fifa_api_id \\\n", "0 1 505942 Aaron Appindangoye 218353 \n", "1 2 155782 Aaron Cresswell 189615 \n", "2 3 162549 Aaron Doran 186170 \n", "3 4 30572 Aaron Galindo 140161 \n", "4 5 23780 Aaron Hughes 17725 \n", "\n", " birthday height weight \n", "0 1992-02-29 00:00:00 182.88 187 \n", "1 1989-12-15 00:00:00 170.18 146 \n", "2 1991-05-13 00:00:00 170.18 163 \n", "3 1982-05-08 00:00:00 182.88 198 \n", "4 1979-11-08 00:00:00 182.88 154 " ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "players = pd.read_sql_query(\"SELECT * FROM Player\", connection)\n", "players.head()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " id player_api_id player_name player_fifa_api_id \\\n", "0 1 505942 Aaron Appindangoye 218353 \n", "1 4 30572 Aaron Galindo 140161 \n", "2 9 528212 Aaron Lennox 206592 \n", "3 11 23889 Aaron Mokoena 47189 \n", "4 17 161644 Aaron Taylor-Sinclair 213569 \n", "5 20 46447 Abasse Ba 156626 \n", "6 24 42664 Abdelkader Ghezzal 178063 \n", "7 29 306735 Abdelouahed Chakhsi 210504 \n", "8 31 31684 Abdeslam Ouaddou 33022 \n", "9 32 32637 Abdessalam Benjelloun 177295 \n", "10 34 41093 Abdou Traore 187048 \n", "11 35 564712 Abdoul Ba 225050 \n", "12 39 191784 Abdoulaye Ba 204826 \n", "13 43 189181 Abdoulaye Diallo 197233 \n", "14 45 40005 Abdoulaye Faye 100329 \n", "15 47 37280 Abdoulaye Meite 41745 \n", "16 48 439366 Abdoulaye Toure 210450 \n", "17 51 419681 Abdul Aziz Tetteh 190193 \n", "18 56 31012 Abdulkader Keita 157191 \n", "19 61 24385 Abel Aguilar 177974 \n", "20 64 172875 Abel Issa Camara 208005 \n", "21 66 240556 Abel Tamata 202153 \n", "22 68 37422 Abella Perez Damia 159580 \n", "23 71 181344 Aboubacar Tandia 138675 \n", "24 76 208635 Abraham Kudemor 202600 \n", "25 79 41489 Achille Coser 163901 \n", "26 80 25571 Achille Emana 112392 \n", "27 85 26331 Adailton 158505 \n", "28 87 495841 Adalberto Penaranda 231638 \n", "29 89 68840 Adam Banas 189047 \n", "... ... ... ... ... \n", "4846 11005 274787 Yvon Mvogo 206003 \n", "4847 11006 210682 Zacharie Boucher 201138 \n", "4848 11009 33076 Zak Whitbread 138155 \n", "4849 11011 208688 Zakaria Diallo 200067 \n", "4850 11018 172489 Zander Clark 204269 \n", "4851 11021 181312 Zarko Tomasevic 193690 \n", "4852 11023 115585 Zaur Sadaev 187590 \n", "4853 11025 32633 Zbigniew Malkowski 139865 \n", "4854 11026 30861 Zdenek Grygera 40988 \n", "4855 11027 36547 Zdenek Kroca 185004 \n", "4856 11028 89251 Zdenek Ondrasek 189655 \n", "4857 11030 25772 Zdravko Kuzmanovic 168650 \n", "4858 11032 34104 Ze Castro 157947 \n", "4859 11033 128047 Ze Eduardo 188001 \n", "4860 11035 306167 Ze Luis 203757 \n", "4861 11042 157284 Zeljko Brkic 195090 \n", "4862 11043 30850 Zeljko Kalac 51883 \n", "4863 11050 244953 Ziggy Gordon 223263 \n", "4864 11053 277772 Zinho Gano 204227 \n", "4865 11056 27305 Zlatan Bajramovic 31667 \n", "4866 11057 35724 Zlatan Ibrahimovic 41236 \n", "4867 11058 12574 Zlatan Ljubijankic 176103 \n", "4868 11059 39494 Zlatko Dedic 141111 \n", "4869 11060 17233 Zlatko Janjic 188405 \n", "4870 11065 39801 Zoltan Szelesi 157283 \n", "4871 11067 56929 Zoran Rendulic 188593 \n", "4872 11070 282473 Zouhair Feddal 205705 \n", "4873 11072 111182 Zsolt Laczko 164680 \n", "4874 11074 35506 Zurab Khizanishvili 47058 \n", "4875 11075 39902 Zvjezdan Misimovic 102359 \n", "\n", " birthday height weight \n", "0 1992-02-29 00:00:00 182.88 187 \n", "1 1982-05-08 00:00:00 182.88 198 \n", "2 1993-02-19 00:00:00 190.50 181 \n", "3 1980-11-25 00:00:00 182.88 181 \n", "4 1991-04-08 00:00:00 182.88 176 \n", "5 1976-07-12 00:00:00 187.96 185 \n", "6 1984-12-05 00:00:00 182.88 172 \n", "7 1986-10-01 00:00:00 182.88 170 \n", "8 1978-11-01 00:00:00 190.50 181 \n", "9 1985-01-28 00:00:00 187.96 179 \n", "10 1988-01-17 00:00:00 180.34 174 \n", "11 1994-02-08 00:00:00 200.66 212 \n", "12 1991-01-01 00:00:00 198.12 174 \n", "13 1992-03-30 00:00:00 187.96 174 \n", "14 1978-02-26 00:00:00 187.96 218 \n", "15 1980-10-06 00:00:00 185.42 181 \n", "16 1994-03-03 00:00:00 187.96 170 \n", "17 1989-02-10 00:00:00 182.88 190 \n", "18 1981-08-06 00:00:00 182.88 172 \n", "19 1985-01-06 00:00:00 185.42 176 \n", "20 1990-01-06 00:00:00 185.42 179 \n", "21 1990-12-05 00:00:00 182.88 170 \n", "22 1982-04-15 00:00:00 187.96 174 \n", "23 1983-10-05 00:00:00 193.04 185 \n", "24 1985-02-25 00:00:00 182.88 179 \n", "25 1982-07-14 00:00:00 185.42 174 \n", "26 1982-06-05 00:00:00 180.34 170 \n", "27 1983-04-14 00:00:00 190.50 187 \n", "28 1997-05-31 00:00:00 182.88 172 \n", "29 1982-12-25 00:00:00 185.42 174 \n", "... ... ... ... \n", "4846 1994-06-06 00:00:00 187.96 185 \n", "4847 1992-03-07 00:00:00 180.34 174 \n", "4848 1984-01-10 00:00:00 187.96 172 \n", "4849 1986-08-13 00:00:00 193.04 194 \n", "4850 1992-06-26 00:00:00 195.58 170 \n", "4851 1990-02-22 00:00:00 187.96 187 \n", "4852 1989-11-06 00:00:00 182.88 172 \n", "4853 1978-01-19 00:00:00 187.96 181 \n", "4854 1980-05-14 00:00:00 185.42 172 \n", "4855 1980-09-29 00:00:00 193.04 196 \n", "4856 1988-12-22 00:00:00 185.42 174 \n", "4857 1987-09-22 00:00:00 185.42 176 \n", "4858 1983-01-13 00:00:00 182.88 181 \n", "4859 1991-08-16 00:00:00 185.42 194 \n", "4860 1991-01-24 00:00:00 185.42 185 \n", "4861 1986-07-09 00:00:00 198.12 209 \n", "4862 1972-12-16 00:00:00 203.20 209 \n", "4863 1993-04-23 00:00:00 180.34 170 \n", "4864 1993-10-13 00:00:00 198.12 205 \n", "4865 1979-08-12 00:00:00 182.88 174 \n", "4866 1981-10-03 00:00:00 195.58 209 \n", "4867 1983-12-15 00:00:00 185.42 176 \n", "4868 1984-10-05 00:00:00 182.88 170 \n", "4869 1986-05-07 00:00:00 187.96 183 \n", "4870 1981-11-22 00:00:00 182.88 176 \n", "4871 1984-05-22 00:00:00 190.50 179 \n", "4872 1989-01-01 00:00:00 190.50 172 \n", "4873 1986-12-18 00:00:00 182.88 176 \n", "4874 1981-10-06 00:00:00 185.42 172 \n", "4875 1982-06-05 00:00:00 180.34 176 \n", "\n", "[4876 rows x 7 columns]\n" ] } ], "source": [ "selected_players = pd.read_sql_query(\"SELECT * FROM Player WHERE height >= 180 AND weight >= 170 \", connection)\n", "print(selected_players)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: mysql-connector-python in /Users/am/anaconda3/lib/python3.6/site-packages (8.0.12)\n", "Requirement already satisfied: protobuf>=3.0.0 in /Users/am/anaconda3/lib/python3.6/site-packages (from mysql-connector-python) (3.6.1)\n", "Requirement already satisfied: six>=1.9 in /Users/am/anaconda3/lib/python3.6/site-packages (from protobuf>=3.0.0->mysql-connector-python) (1.11.0)\n", "Requirement already satisfied: setuptools in /Users/am/anaconda3/lib/python3.6/site-packages (from protobuf>=3.0.0->mysql-connector-python) (39.1.0)\n", "\u001b[31mdistributed 1.21.8 requires msgpack, which is not installed.\u001b[0m\n", "\u001b[33mYou are using pip version 10.0.1, however version 18.0 is available.\n", "You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n" ] } ], "source": [ "! pip install mysql-connector-python" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import mysql.connector \n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "connection = mysql.connector.connect(host=\"127.0.0.1\", # your host \n", " user=\"root\", # username\n", " password=\"reddel17R\" ) # password " ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "print(connection)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "('information_schema',)\n", "('mysql',)\n", "('performance_schema',)\n", "('sys',)\n" ] } ], "source": [ "mycursor = connection.cursor()\n", "mycursor.execute(\"SHOW DATABASES\")\n", "for x in mycursor:\n", " print(x) " ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "connection = mysql.connector.connect(host=\"127.0.0.1\", # your host \n", " user=\"root\", # username\n", " password=\"reddel17R\" ,\n", " database = 'mysql') " ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "('columns_priv',)\n", "('component',)\n", "('db',)\n", "('default_roles',)\n", "('engine_cost',)\n", "('func',)\n", "('general_log',)\n", "('global_grants',)\n", "('gtid_executed',)\n", "('help_category',)\n", "('help_keyword',)\n", "('help_relation',)\n", "('help_topic',)\n", "('innodb_index_stats',)\n", "('innodb_table_stats',)\n", "('password_history',)\n", "('plugin',)\n", "('procs_priv',)\n", "('proxies_priv',)\n", "('role_edges',)\n", "('server_cost',)\n", "('servers',)\n", "('slave_master_info',)\n", "('slave_relay_log_info',)\n", "('slave_worker_info',)\n", "('slow_log',)\n", "('tables_priv',)\n", "('time_zone',)\n", "('time_zone_leap_second',)\n", "('time_zone_name',)\n", "('time_zone_transition',)\n", "('time_zone_transition_type',)\n", "('user',)\n" ] } ], "source": [ "mycursor = connection.cursor()\n", "mycursor.execute(\"SHOW TABLES\")\n", "for x in mycursor:\n", " print(x) " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.5" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: Chapter02/Txt_files.ipynb ================================================ { "cells": [ { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [], "source": [ "data_folder = '../../data/Shakespeare'\n", "data_file = 'alllines.txt'" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [], "source": [ "import os\n", "\n", "#f = open(os.path.join(data_folder,data_file),newline='') \n", "f = open(data_file)\n", "contents = f.read()" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\"ACT I\"\n", "\"SCENE I. London. The palace.\"\n", "\"Enter KING HENRY, LORD JOHN OF LANCASTER, the EARL of WESTMORELAND, SIR WALTER BLUNT, and others\"\n", "\"So shaken as we are, so wan with care,\"\n", "\"Find we a time for frighted peace to pant,\"\n", "\"And breathe short-winded accents of new broils\"\n", "\"To be commenced in strands afar remote.\"\n", "\"No more the thirsty entrance of this soil\"\n", "\"Shall daub her lips with her own children's blood,\"\n", "\"Nor more shall trenching war channel her fields,\"\n", "\"Nor bruise her flowerets with the armed hoofs\"\n", "\"Of hostile paces: those opposed eyes,\"\n", "\"Which, like the meteors of a troubled heaven,\"\n", "\"All of one nature, of one substance bred,\"\n", "\"Did lately meet in the intestine shock\"\n", "\"And furious close of civil butchery\"\n", "\"Shall now, in mutual well-beseeming ranks,\"\n", "\"March all one way and be no more opposed\"\n", "\"Against acquaintance, kindred and allies:\"\n", "\"The edge of war, like an ill-sheathed knife,\"\n", "\"No more shall cut his master. Therefore, friends,\"\n", "\"As far as to the sepulchre of Christ,\"\n", "\"Whose \n" ] } ], "source": [ "print(contents[:1000])\n" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "4583798" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(contents)" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "data_file = 'household_power_consumption.txt'" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['Date;Time;Global_active_power;Global_reactive_power;Voltage;Global_intensity;Sub_metering_1;Sub_metering_2;Sub_metering_3'\n", " '16/12/2006;17:24:00;4.216;0.418;234.840;18.400;0.000;1.000;17.000'\n", " '16/12/2006;17:25:00;5.360;0.436;233.630;23.000;0.000;1.000;16.000'\n", " '16/12/2006;17:26:00;5.374;0.498;233.290;23.000;0.000;2.000;17.000'\n", " '16/12/2006;17:27:00;5.388;0.502;233.740;23.000;0.000;1.000;17.000'\n", " '16/12/2006;17:28:00;3.666;0.528;235.680;15.800;0.000;1.000;17.000'\n", " '16/12/2006;17:29:00;3.520;0.522;235.020;15.000;0.000;2.000;17.000'\n", " '16/12/2006;17:30:00;3.702;0.520;235.090;15.800;0.000;1.000;17.000'\n", " '16/12/2006;17:31:00;3.700;0.520;235.220;15.800;0.000;1.000;17.000'\n", " '16/12/2006;17:32:00;3.668;0.510;233.990;15.800;0.000;1.000;17.000']\n" ] } ], "source": [ "data = np.genfromtxt(data_file,dtype='str')\n", "print(data[:10])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.5" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: Chapter02/read_csv_file.ipynb ================================================ { "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "data_folder = '../../data/household_power_consumption' \n", "data_file = 'household_power_consumption.csv'" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['Date', 'Time', 'Global_active_power', 'Global_reactive_power', 'Voltage', 'Global_intensity', 'Sub_metering_1', 'Sub_metering_2', 'Sub_metering_3']\n", "['16/12/2006', '17:24:00', '4.216', '0.418', '234.840', '18.400', '0.000', '1.000', '17.000']\n", "['16/12/2006', '17:25:00', '5.360', '0.436', '233.630', '23.000', '0.000', '1.000', '16.000']\n", "['16/12/2006', '17:26:00', '5.374', '0.498', '233.290', '23.000', '0.000', '2.000', '17.000']\n", "['16/12/2006', '17:27:00', '5.388', '0.502', '233.740', '23.000', '0.000', '1.000', '17.000']\n", "['16/12/2006', '17:28:00', '3.666', '0.528', '235.680', '15.800', '0.000', '1.000', '17.000']\n", "['16/12/2006', '17:29:00', '3.520', '0.522', '235.020', '15.000', '0.000', '2.000', '17.000']\n", "['16/12/2006', '17:30:00', '3.702', '0.520', '235.090', '15.800', '0.000', '1.000', '17.000']\n", "['16/12/2006', '17:31:00', '3.700', '0.520', '235.220', '15.800', '0.000', '1.000', '17.000']\n", "['16/12/2006', '17:32:00', '3.668', '0.510', '233.990', '15.800', '0.000', '1.000', '17.000']\n", "['16/12/2006', '17:33:00', '3.662', '0.510', '233.860', '15.800', '0.000', '2.000', '16.000']\n", "['16/12/2006', '17:34:00', '4.448', '0.498', '232.860', '19.600', '0.000', '1.000', '17.000']\n", "['16/12/2006', '17:35:00', '5.412', '0.470', '232.780', '23.200', '0.000', '1.000', '17.000']\n", "['16/12/2006', '17:36:00', '5.224', '0.478', '232.990', '22.400', '0.000', '1.000', '16.000']\n", "['16/12/2006', '17:37:00', '5.268', '0.398', '232.910', '22.600', '0.000', '2.000', '17.000']\n", "['16/12/2006', '17:38:00', '4.054', '0.422', '235.240', '17.600', '0.000', '1.000', '17.000']\n", "['16/12/2006', '17:39:00', '3.384', '0.282', '237.140', '14.200', '0.000', '0.000', '17.000']\n", "['16/12/2006', '17:40:00', '3.270', '0.152', '236.730', '13.800', '0.000', '0.000', '17.000']\n", "['16/12/2006', '17:41:00', '3.430', '0.156', '237.060', '14.400', '0.000', '0.000', '17.000']\n", "['16/12/2006', '17:42:00', '3.266', '0.000', '237.130', '13.800', '0.000', '0.000', '18.000']\n", "['16/12/2006', '17:43:00', '3.728', '0.000', '235.840', '16.400', '0.000', '0.000', '17.000']\n", "['16/12/2006', '17:44:00', '5.894', '0.000', '232.690', '25.400', '0.000', '0.000', '16.000']\n", "['16/12/2006', '17:45:00', '7.706', '0.000', '230.980', '33.200', '0.000', '0.000', '17.000']\n", "['16/12/2006', '17:46:00', '7.026', '0.000', '232.210', '30.600', '0.000', '0.000', '16.000']\n", "['16/12/2006', '17:47:00', '5.174', '0.000', '234.190', '22.000', '0.000', '0.000', '17.000']\n", "['16/12/2006', '17:48:00', '4.474', '0.000', '234.960', '19.400', '0.000', '0.000', '17.000']\n", "['16/12/2006', '17:49:00', '3.248', '0.000', '236.660', '13.600', '0.000', '0.000', '17.000']\n", "['16/12/2006', '17:50:00', '3.236', '0.000', '235.840', '13.600', '0.000', '0.000', '17.000']\n", "['16/12/2006', '17:51:00', '3.228', '0.000', '235.600', '13.600', '0.000', '0.000', '17.000']\n", "['16/12/2006', '17:52:00', '3.258', '0.000', '235.490', '13.800', '0.000', '0.000', '17.000']\n", "['16/12/2006', '17:53:00', '3.178', '0.000', '235.280', '13.400', '0.000', '0.000', '17.000']\n", "['16/12/2006', '17:54:00', '2.720', '0.000', '235.060', '11.600', '0.000', '0.000', '17.000']\n", "['16/12/2006', '17:55:00', '3.758', '0.076', '234.170', '16.400', '0.000', '0.000', '17.000']\n", "['16/12/2006', '17:56:00', '4.342', '0.090', '233.770', '18.400', '0.000', '0.000', '16.000']\n", "['16/12/2006', '17:57:00', '4.512', '0.000', '233.620', '19.200', '0.000', '0.000', '17.000']\n", "['16/12/2006', '17:58:00', '4.058', '0.200', '234.680', '17.600', '0.000', '0.000', '17.000']\n", "['16/12/2006', '17:59:00', '2.472', '0.058', '236.940', '10.400', '0.000', '0.000', '17.000']\n", "['16/12/2006', '18:00:00', '2.790', '0.180', '237.520', '11.800', '0.000', '0.000', '18.000']\n", "['16/12/2006', '18:01:00', '2.624', '0.144', '238.200', '11.000', '0.000', '0.000', '17.000']\n", "['16/12/2006', '18:02:00', '2.772', '0.118', '238.280', '11.600', '0.000', '0.000', '17.000']\n", "['16/12/2006', '18:03:00', '3.740', '0.108', '236.930', '16.400', '0.000', '16.000', '18.000']\n", "['16/12/2006', '18:04:00', '4.928', '0.202', '235.010', '21.000', '0.000', '37.000', '16.000']\n", "['16/12/2006', '18:05:00', '6.052', '0.192', '232.930', '26.200', '0.000', '37.000', '17.000']\n", "['16/12/2006', '18:06:00', '6.752', '0.186', '232.120', '29.000', '0.000', '36.000', '17.000']\n", "['16/12/2006', '18:07:00', '6.474', '0.144', '231.850', '27.800', '0.000', '37.000', '16.000']\n", "['16/12/2006', '18:08:00', '6.308', '0.116', '232.250', '27.000', '0.000', '36.000', '17.000']\n", "['16/12/2006', '18:09:00', '4.464', '0.136', '234.660', '19.000', '0.000', '37.000', '16.000']\n", "['16/12/2006', '18:10:00', '3.396', '0.148', '236.200', '15.000', '0.000', '22.000', '18.000']\n", "['16/12/2006', '18:11:00', '3.090', '0.152', '237.070', '13.800', '0.000', '12.000', '17.000']\n", "['16/12/2006', '18:12:00', '3.730', '0.144', '235.780', '16.400', '0.000', '27.000', '17.000']\n", "['16/12/2006', '18:13:00', '2.308', '0.160', '237.430', '9.600', '0.000', '1.000', '17.000']\n", "['16/12/2006', '18:14:00', '2.388', '0.158', '237.260', '10.000', '0.000', '1.000', '17.000']\n", "['16/12/2006', '18:15:00', '4.598', '0.100', '234.250', '21.400', '0.000', '20.000', '17.000']\n", "['16/12/2006', '18:16:00', '4.524', '0.076', '234.200', '19.600', '0.000', '9.000', '17.000']\n", "['16/12/2006', '18:17:00', '4.202', '0.082', '234.310', '17.800', '0.000', '1.000', '17.000']\n", "['16/12/2006', '18:18:00', '4.472', '0.000', '233.290', '19.200', '0.000', '1.000', '16.000']\n", "['16/12/2006', '18:19:00', '2.852', '0.000', '235.610', '12.000', '0.000', '1.000', '17.000']\n", "['16/12/2006', '18:20:00', '2.928', '0.000', '235.250', '12.400', '0.000', '1.000', '17.000']\n", "['16/12/2006', '18:21:00', '2.940', '0.000', '236.040', '12.400', '0.000', '2.000', '17.000']\n", "['16/12/2006', '18:22:00', '2.934', '0.000', '235.510', '12.400', '0.000', '1.000', '17.000']\n", "['16/12/2006', '18:23:00', '2.926', '0.000', '235.680', '12.400', '0.000', '1.000', '17.000']\n", "['16/12/2006', '18:24:00', '3.452', '0.000', '235.200', '15.200', '0.000', '1.000', '17.000']\n", "['16/12/2006', '18:25:00', '4.870', '0.000', '233.740', '20.800', '0.000', '1.000', '17.000']\n", "['16/12/2006', '18:26:00', '4.868', '0.000', '233.840', '20.800', '0.000', '1.000', 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'0.000']\n", "['19/12/2006', '10:30:00', '0.410', '0.204', '244.650', '2.000', '0.000', '1.000', '0.000']\n", "['19/12/2006', '10:31:00', '0.392', '0.220', '245.360', '1.800', '0.000', '2.000', '0.000']\n", "['19/12/2006', '10:32:00', '0.408', '0.230', '245.950', '1.800', '0.000', '2.000', '0.000']\n", "['19/12/2006', '10:33:00', '0.390', '0.222', '245.610', '1.800', '0.000', '1.000', '0.000']\n", "['19/12/2006', '10:34:00', '0.410', '0.232', '246.280', '2.000', '0.000', '2.000', '0.000']\n", "['19/12/2006', '10:35:00', '0.386', '0.218', '244.740', '1.800', '0.000', '1.000', '0.000']\n", "['19/12/2006', '10:36:00', '0.598', '0.298', '243.780', '2.600', '0.000', '5.000', '0.000']\n", "['19/12/2006', '10:37:00', '0.580', '0.272', '243.140', '2.600', '0.000', '5.000', '0.000']\n", "['19/12/2006', '10:38:00', '0.546', '0.222', '243.170', '2.400', '0.000', '5.000', '0.000']\n", "['19/12/2006', '10:39:00', '0.510', '0.168', '243.600', '2.200', '0.000', '5.000', '0.000']\n", "['19/12/2006', 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'0.000', '0.000', '18.000']\n", "['23/12/2006', '23:12:00', '5.120', '0.096', '238.690', '21.400', '0.000', '0.000', '18.000']\n", "['23/12/2006', '23:13:00', '5.152', '0.098', '239.690', '21.400', '0.000', '0.000', '17.000']\n", "['23/12/2006', '23:14:00', '5.172', '0.100', '240.290', '21.400', '0.000', '0.000', '18.000']\n", "['23/12/2006', '23:15:00', '5.162', '0.100', '240.040', '21.400', '0.000', '0.000', '18.000']\n", "['23/12/2006', '23:16:00', '5.150', '0.100', '239.790', '21.400', '0.000', '0.000', '17.000']\n", "['23/12/2006', '23:17:00', '5.224', '0.202', '239.420', '21.800', '0.000', '0.000', '18.000']\n", "['23/12/2006', '23:18:00', '4.880', '0.150', '239.740', '20.600', '0.000', '0.000', '18.000']\n", "['23/12/2006', '23:19:00', '2.964', '0.092', '242.330', '12.400', '3.000', '0.000', '18.000']\n", "['23/12/2006', '23:20:00', '6.012', '0.058', '239.430', '25.400', '39.000', '0.000', '17.000']\n", "['23/12/2006', '23:21:00', '6.932', '0.050', '238.710', '29.000', '38.000', '0.000', '18.000']\n", "['23/12/2006', '23:22:00', '6.958', '0.054', '238.350', '29.200', '38.000', '0.000', '17.000']\n", "['23/12/2006', '23:23:00', '6.972', '0.048', '238.380', '29.200', '39.000', '0.000', '17.000']\n", "['23/12/2006', '23:24:00', '6.936', '0.082', '238.360', '29.000', '38.000', '0.000', '18.000']\n", "['23/12/2006', '23:25:00', '7.018', '0.180', '238.710', '29.400', '38.000', '0.000', '17.000']\n", "['23/12/2006', '23:26:00', '7.010', '0.180', '238.590', '29.400', '38.000', '0.000', '18.000']\n", "['23/12/2006', '23:27:00', '6.944', '0.130', '238.100', '29.000', '38.000', '0.000', '17.000']\n", "['23/12/2006', '23:28:00', '5.578', '0.086', '238.580', '23.600', '39.000', '0.000', '18.000']\n", "['23/12/2006', '23:29:00', '4.638', '0.100', '241.070', '19.400', '32.000', '0.000', '17.000']\n", "['23/12/2006', '23:30:00', '4.808', '0.104', '240.950', '19.800', '2.000', '0.000', '18.000']\n", "['23/12/2006', '23:31:00', '4.836', '0.130', '240.210', '20.000', '1.000', '0.000', '18.000']\n", "['23/12/2006', '23:32:00', '4.778', '0.112', '240.580', '19.800', '1.000', '0.000', '18.000']\n", "['23/12/2006', '23:33:00', '5.032', '0.102', '240.290', '21.000', '2.000', '0.000', '17.000']\n", "['23/12/2006', '23:34:00', '6.954', '0.086', '238.780', '29.000', '39.000', '0.000', '18.000']\n", "['23/12/2006', '23:35:00', '7.144', '0.078', '239.240', '29.800', '38.000', '0.000', '17.000']\n", "['23/12/2006', '23:36:00', '3.184', '0.148', '243.140', '13.400', '4.000', '0.000', '18.000']\n", "['23/12/2006', '23:37:00', '3.528', '0.130', '243.540', '15.000', '0.000', '0.000', '19.000']\n", "['23/12/2006', '23:38:00', '6.504', '0.084', '239.620', '27.400', '5.000', '0.000', '17.000']\n", "['23/12/2006', '23:39:00', '8.334', '0.064', '237.950', '35.000', '38.000', '0.000', '17.000']\n", "['23/12/2006', '23:40:00', '8.304', '0.066', '238.750', '34.800', '39.000', '0.000', '18.000']\n", "['23/12/2006', '23:41:00', '8.284', '0.064', '238.170', '34.800', '38.000', '0.000', '17.000']\n", "['23/12/2006', '23:42:00', '7.714', '0.070', '238.660', '32.200', '38.000', '0.000', '18.000']\n", "['23/12/2006', '23:43:00', '7.232', '0.074', '238.880', '30.200', '38.000', '0.000', '17.000']\n", "['23/12/2006', '23:44:00', '7.232', '0.072', '238.490', '30.200', '38.000', '0.000', '18.000']\n", "['23/12/2006', '23:45:00', '6.138', '0.086', '239.950', '26.000', '38.000', '0.000', '17.000']\n", "['23/12/2006', '23:46:00', '3.432', '0.116', '243.100', '14.600', '12.000', '0.000', '18.000']\n", "['23/12/2006', '23:47:00', '4.716', '0.120', '241.370', '19.800', '1.000', '0.000', '18.000']\n", "['23/12/2006', '23:48:00', '8.240', '0.060', '237.630', '35.200', '0.000', '0.000', '18.000']\n", "['23/12/2006', '23:49:00', '8.570', '0.096', '237.240', '36.200', '1.000', '0.000', '17.000']\n", "['23/12/2006', '23:50:00', '7.946', '0.054', '237.870', '33.400', '0.000', '0.000', '17.000']\n", "['23/12/2006', '23:51:00', '7.970', '0.052', '237.750', '33.400', '0.000', '0.000', '17.000']\n", "['23/12/2006', '23:52:00', '6.978', '0.086', '239.180', '29.400', '0.000', '0.000', '18.000']\n", "['23/12/2006', '23:53:00', '5.490', '0.076', '240.660', '23.200', '0.000', '0.000', '18.000']\n", "['23/12/2006', '23:54:00', '3.660', '0.100', '242.790', '15.000', '0.000', '0.000', '18.000']\n", "['23/12/2006', '23:55:00', '3.528', '0.104', '243.350', '14.400', '0.000', '0.000', '18.000']\n", "['23/12/2006', '23:56:00', '4.450', '0.060', '242.550', '18.800', '0.000', '0.000', '18.000']\n", "['23/12/2006', '23:57:00', '5.574', '0.054', '242.280', '23.000', '0.000', '0.000', '18.000']\n", "['23/12/2006', '23:58:00', '5.480', '0.046', '240.060', '22.800', '0.000', '0.000', '18.000']\n", "['23/12/2006', '23:59:00', '5.458', '0.046', '239.620', '22.600', '0.000', '0.000', '17.000']\n", "['24/12/2006', '00:00:00', '5.376', '0.046', '237.570', '22.600', '0.000', '0.000', '18.000']\n", "['24/12/2006', '00:01:00', '5.340', '0.000', '236.810', '22.400', '0.000', '0.000', '17.000']\n", "['24/12/2006', '00:02:00', '3.684', '0.054', '238.620', '15.600', '0.000', '0.000', '17.000']\n", "['24/12/2006', '00:03:00', '3.566', '0.052', '238.360', '15.200', '0.000', '0.000', '18.000']\n", "['24/12/2006', '00:04:00', '5.388', '0.000', '236.980', '22.600', '0.000', '0.000', '17.000']\n", "['24/12/2006', '00:05:00', '5.248', '0.046', '237.470', '22.000', '0.000', '0.000', '17.000']\n", "['24/12/2006', '00:06:00', '3.338', '0.000', '239.690', '14.000', '0.000', '0.000', '18.000']\n", "['24/12/2006', '00:07:00', '5.124', '0.056', '237.530', '22.000', '0.000', '0.000', '17.000']\n", "['24/12/2006', '00:08:00', '4.914', '0.054', '237.620', '20.600', '0.000', '0.000', '17.000']\n", "['24/12/2006', '00:09:00', '4.034', '0.000', '238.700', '17.400', '0.000', '0.000', '18.000']" ] }, { "ename": "KeyboardInterrupt", "evalue": "", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0mcsvreader\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcsv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcsvfile\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mrow\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcsvreader\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m~/anaconda3/envs/tensorflow/lib/python3.5/site-packages/ipykernel/iostream.py\u001b[0m in \u001b[0;36mwrite\u001b[0;34m(self, string)\u001b[0m\n\u001b[1;32m 374\u001b[0m \u001b[0mis_child\u001b[0m \u001b[0;34m=\u001b[0m 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378\u001b[0m \u001b[0;31m# newlines imply flush in subprocesses\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m~/anaconda3/envs/tensorflow/lib/python3.5/site-packages/ipykernel/iostream.py\u001b[0m in \u001b[0;36mschedule\u001b[0;34m(self, f)\u001b[0m\n\u001b[1;32m 201\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_events\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 202\u001b[0m \u001b[0;31m# wake event thread (message content is ignored)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 203\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_event_pipe\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mb''\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 204\u001b[0m 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\u001b[0mflags\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mflags\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrack\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtrack\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 393\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 394\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0msend_multipart\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmsg_parts\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mflags\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrack\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32mzmq/backend/cython/socket.pyx\u001b[0m in \u001b[0;36mzmq.backend.cython.socket.Socket.send\u001b[0;34m()\u001b[0m\n", "\u001b[0;32mzmq/backend/cython/socket.pyx\u001b[0m in \u001b[0;36mzmq.backend.cython.socket.Socket.send\u001b[0;34m()\u001b[0m\n", "\u001b[0;32mzmq/backend/cython/socket.pyx\u001b[0m in \u001b[0;36mzmq.backend.cython.socket._send_copy\u001b[0;34m()\u001b[0m\n", "\u001b[0;32m~/anaconda3/envs/tensorflow/lib/python3.5/site-packages/zmq/backend/cython/checkrc.pxd\u001b[0m in \u001b[0;36mzmq.backend.cython.checkrc._check_rc\u001b[0;34m()\u001b[0m\n", "\u001b[0;31mKeyboardInterrupt\u001b[0m: " ] } ], "source": [ "#It is a big file so we will interrupt in between.\n", "import csv\n", "import os\n", "\n", "with open(os.path.join(data_folder,data_file),newline='') as csvfile:\n", " csvreader = csv.reader(csvfile)\n", " for row in csvreader:\n", " print(row)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python [conda env:tensorflow]", "language": "python", "name": "conda-env-tensorflow-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.6" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: Chapter03/ElectricalPowerOutputPredictionUsingDecisionTrees.ipynb ================================================ { "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# Import the modules\n", "import tensorflow as tf\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "from sklearn.preprocessing import MinMaxScaler\n", "from sklearn.metrics import mean_squared_error, r2_score\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.tree import DecisionTreeRegressor\n", "% matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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ATVAPRHPE
count9568.0000009568.0000009568.0000009568.0000009568.000000
mean19.65123154.3058041013.25907873.308978454.365009
std7.45247312.7078935.93878414.60026917.066995
min1.81000025.360000992.89000025.560000420.260000
25%13.51000041.7400001009.10000063.327500439.750000
50%20.34500052.0800001012.94000074.975000451.550000
75%25.72000066.5400001017.26000084.830000468.430000
max37.11000081.5600001033.300000100.160000495.760000
\n", "
" ], "text/plain": [ " AT V AP RH PE\n", "count 9568.000000 9568.000000 9568.000000 9568.000000 9568.000000\n", "mean 19.651231 54.305804 1013.259078 73.308978 454.365009\n", "std 7.452473 12.707893 5.938784 14.600269 17.066995\n", "min 1.810000 25.360000 992.890000 25.560000 420.260000\n", "25% 13.510000 41.740000 1009.100000 63.327500 439.750000\n", "50% 20.345000 52.080000 1012.940000 74.975000 451.550000\n", "75% 25.720000 66.540000 1017.260000 84.830000 468.430000\n", "max 37.110000 81.560000 1033.300000 100.160000 495.760000" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "filename = 'Folds5x2_pp.xlsx' # The file can be downloded from UCI ML repo\n", "df = pd.read_excel(filename, sheet_name='Sheet1')\n", "df.describe()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "X, Y = df[['AT', 'V','AP','RH']], df['PE']\n", "scaler = MinMaxScaler()\n", "X_new = scaler.fit_transform(X)\n", "target_scaler = MinMaxScaler()\n", "Y_new = target_scaler.fit_transform(Y.values.reshape(-1,1))\n", "X_train, X_test, Y_train, y_test = \\\n", " train_test_split(X_new, Y_new, test_size=0.4, random_state=333)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "DecisionTreeRegressor(criterion='mse', max_depth=3, max_features=None,\n", " max_leaf_nodes=None, min_impurity_decrease=0.0,\n", " min_impurity_split=None, min_samples_leaf=1,\n", " min_samples_split=2, min_weight_fraction_leaf=0.0,\n", " presort=False, random_state=None, splitter='best')" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model = DecisionTreeRegressor(max_depth=3)\n", "model.fit(X_train, Y_train)\n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "Y_pred = model.predict(np.float32(X_test))" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "R2 Score is 0.9077326633644909 and MSE 0.004748858361843866\n" ] } ], "source": [ "print(\"R2 Score is {} and MSE {}\".format(\\\n", " r2_score(y_test, Y_pred),\\\n", " mean_squared_error(y_test, Y_pred)))" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0,0.5,'Predicted Net Hourly Electrical Energy Output (MW)')" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.scatter(Y_pred, y_test)\n", "#plt.plot(X_test, Y_pred)\n", "plt.xlabel(\"Actual Net Hourly Electrical Energy Output (MW)\")\n", "plt.ylabel(\"Predicted Net Hourly Electrical Energy Output (MW)\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python [conda env:tensorflow]", "language": "python", "name": "conda-env-tensorflow-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.4" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: Chapter03/ElectricalPowerOutputPredictionUsingRegression.ipynb ================================================ { "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# Import the modules\n", "import tensorflow as tf\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "from sklearn.preprocessing import MinMaxScaler\n", "from sklearn.metrics import mean_squared_error, r2_score\n", "from sklearn.model_selection import train_test_split\n", "% matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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ATVAPRHPE
count9568.0000009568.0000009568.0000009568.0000009568.000000
mean19.65123154.3058041013.25907873.308978454.365009
std7.45247312.7078935.93878414.60026917.066995
min1.81000025.360000992.89000025.560000420.260000
25%13.51000041.7400001009.10000063.327500439.750000
50%20.34500052.0800001012.94000074.975000451.550000
75%25.72000066.5400001017.26000084.830000468.430000
max37.11000081.5600001033.300000100.160000495.760000
\n", "
" ], "text/plain": [ " AT V AP RH PE\n", "count 9568.000000 9568.000000 9568.000000 9568.000000 9568.000000\n", "mean 19.651231 54.305804 1013.259078 73.308978 454.365009\n", "std 7.452473 12.707893 5.938784 14.600269 17.066995\n", "min 1.810000 25.360000 992.890000 25.560000 420.260000\n", "25% 13.510000 41.740000 1009.100000 63.327500 439.750000\n", "50% 20.345000 52.080000 1012.940000 74.975000 451.550000\n", "75% 25.720000 66.540000 1017.260000 84.830000 468.430000\n", "max 37.110000 81.560000 1033.300000 100.160000 495.760000" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "filename = 'Folds5x2_pp.xlsx'\n", "df = pd.read_excel(filename, sheet_name='Sheet1')\n", "df.describe()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "X, Y = df[['AT', 'V','AP','RH']], df['PE']\n", "scaler = MinMaxScaler()\n", "X_new = scaler.fit_transform(X)\n", "target_scaler = MinMaxScaler()\n", "Y_new = target_scaler.fit_transform(Y.values.reshape(-1,1))\n", "X_train, X_test, Y_train, y_test = \\\n", " train_test_split(X_new, Y_new, test_size=0.4, random_state=333)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "class LinearRegressor:\n", " def __init__(self,d, lr=0.001 ):\n", " \n", " # Place holders for input-output training data\n", " self.X = tf.placeholder(tf.float32,\\\n", " shape=[None,d], name='input')\n", " self.Y = tf.placeholder(tf.float32,\\\n", " name='output')\n", " # Variables for weight and bias\n", " self.b = tf.Variable(0.0, dtype=tf.float32)\n", " self.W = tf.Variable(tf.random_normal([d,1]),\\\n", " dtype=tf.float32)\n", " \n", " # The Linear Regression Model\n", " self.F = self.function(self.X)\n", " \n", " # Loss function\n", " self.loss = tf.reduce_mean(tf.square(self.Y - self.F,\\\n", " name='LSE'))\n", "\n", " # Gradient Descent with learning \n", " # rate of 0.05 to minimize loss\n", " optimizer = tf.train.GradientDescentOptimizer(lr)\n", " self.optimize = optimizer.minimize(self.loss)\n", "\n", " # Initializing Variables\n", " init_op = tf.global_variables_initializer()\n", " self.sess = tf.Session()\n", " self.sess.run(init_op)\n", " \n", " def function(self, X):\n", " return tf.matmul(X, self.W) + self.b\n", " \n", " def fit(self, X, Y,epochs=500):\n", " total = []\n", " for i in range(epochs):\n", " _, l = self.sess.run([self.optimize,self.loss],\\\n", " feed_dict={self.X: X, self.Y: Y})\n", " total.append(l)\n", " if i%100==0:\n", " print('Epoch {0}/{1}: Loss {2}'.format(i,epochs,l))\n", " return total\n", " \n", " def predict(self, X):\n", " return self.sess.run(self.function(X), feed_dict={self.X:X})\n", " \n", " def get_weights(self):\n", " return self.sess.run([self.W, self.b])" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "N, d = X_train.shape\n", "model = LinearRegressor(d)\n", "#loss = model.fit(X_train, Y_train)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 0/20000: Loss 0.21545326709747314\n", "Epoch 100/20000: Loss 0.19007785618305206\n", "Epoch 200/20000: Loss 0.17610767483711243\n", "Epoch 300/20000: Loss 0.16699175536632538\n", "Epoch 400/20000: Loss 0.16001276671886444\n", "Epoch 500/20000: Loss 0.15404105186462402\n", "Epoch 600/20000: Loss 0.1486041396856308\n", "Epoch 700/20000: Loss 0.14350160956382751\n", "Epoch 800/20000: Loss 0.13864654302597046\n", "Epoch 900/20000: Loss 0.13399846851825714\n", "Epoch 1000/20000: Loss 0.12953655421733856\n", "Epoch 1100/20000: Loss 0.12524846196174622\n", "Epoch 1200/20000: Loss 0.12112526595592499\n", "Epoch 1300/20000: Loss 0.11715959757566452\n", "Epoch 1400/20000: Loss 0.11334507167339325\n", "Epoch 1500/20000: Loss 0.10967571288347244\n", "Epoch 1600/20000: Loss 0.10614560544490814\n", "Epoch 1700/20000: Loss 0.10274960100650787\n", "Epoch 1800/20000: Loss 0.09948232769966125\n", "Epoch 1900/20000: Loss 0.0963388979434967\n", "Epoch 2000/20000: Loss 0.0933145210146904\n", "Epoch 2100/20000: Loss 0.09040451794862747\n", "Epoch 2200/20000: Loss 0.0876045748591423\n", "Epoch 2300/20000: Loss 0.08491037786006927\n", "Epoch 2400/20000: Loss 0.0823177844285965\n", "Epoch 2500/20000: Loss 0.07982300966978073\n", "Epoch 2600/20000: Loss 0.0774221196770668\n", "Epoch 2700/20000: Loss 0.07511153072118759\n", "Epoch 2800/20000: Loss 0.07288788259029388\n", "Epoch 2900/20000: Loss 0.07074767351150513\n", "Epoch 3000/20000: Loss 0.06868772953748703\n", "Epoch 3100/20000: Loss 0.06670496612787247\n", "Epoch 3200/20000: Loss 0.06479640305042267\n", "Epoch 3300/20000: Loss 0.06295915693044662\n", "Epoch 3400/20000: Loss 0.06119049713015556\n", "Epoch 3500/20000: Loss 0.059487733989953995\n", "Epoch 3600/20000: Loss 0.057848405092954636\n", "Epoch 3700/20000: Loss 0.05627002194523811\n", "Epoch 3800/20000: Loss 0.0547502376139164\n", "Epoch 3900/20000: Loss 0.053286805748939514\n", "Epoch 4000/20000: Loss 0.051877569407224655\n", "Epoch 4100/20000: Loss 0.05052041634917259\n", "Epoch 4200/20000: Loss 0.049213312566280365\n", "Epoch 4300/20000: Loss 0.04795442521572113\n", "Epoch 4400/20000: Loss 0.046741828322410583\n", "Epoch 4500/20000: Loss 0.04557373747229576\n", "Epoch 4600/20000: Loss 0.04444851353764534\n", "Epoch 4700/20000: Loss 0.04336448013782501\n", "Epoch 4800/20000: Loss 0.04232000559568405\n", "Epoch 4900/20000: Loss 0.04131360352039337\n", "Epoch 5000/20000: Loss 0.04034384712576866\n", "Epoch 5100/20000: Loss 0.03940926864743233\n", "Epoch 5200/20000: Loss 0.038508571684360504\n", "Epoch 5300/20000: Loss 0.03764042630791664\n", "Epoch 5400/20000: Loss 0.03680361062288284\n", "Epoch 5500/20000: Loss 0.03599691763520241\n", "Epoch 5600/20000: Loss 0.03521919250488281\n", "Epoch 5700/20000: Loss 0.03446930646896362\n", "Epoch 5800/20000: Loss 0.03374623507261276\n", "Epoch 5900/20000: Loss 0.03304891660809517\n", "Epoch 6000/20000: Loss 0.032376404851675034\n", "Epoch 6100/20000: Loss 0.03172771632671356\n", "Epoch 6200/20000: Loss 0.031101975589990616\n", "Epoch 6300/20000: Loss 0.030498268082737923\n", "Epoch 6400/20000: Loss 0.029915807768702507\n", "Epoch 6500/20000: Loss 0.02935374714434147\n", "Epoch 6600/20000: Loss 0.028811315074563026\n", "Epoch 6700/20000: Loss 0.028287792578339577\n", "Epoch 6800/20000: Loss 0.02778240293264389\n", "Epoch 6900/20000: Loss 0.027294518426060677\n", "Epoch 7000/20000: Loss 0.02682344615459442\n", "Epoch 7100/20000: Loss 0.026368530467152596\n", "Epoch 7200/20000: Loss 0.02592921257019043\n", "Epoch 7300/20000: Loss 0.025504859164357185\n", "Epoch 7400/20000: Loss 0.025094928219914436\n", "Epoch 7500/20000: Loss 0.024698833003640175\n", "Epoch 7600/20000: Loss 0.024316098541021347\n", "Epoch 7700/20000: Loss 0.023946207016706467\n", "Epoch 7800/20000: Loss 0.023588668555021286\n", "Epoch 7900/20000: Loss 0.023243017494678497\n", "Epoch 8000/20000: Loss 0.02290881797671318\n", "Epoch 8100/20000: Loss 0.022585634142160416\n", "Epoch 8200/20000: Loss 0.022273028269410133\n", "Epoch 8300/20000: Loss 0.021970639005303383\n", "Epoch 8400/20000: Loss 0.02167806588113308\n", "Epoch 8500/20000: Loss 0.021394945681095123\n", "Epoch 8600/20000: Loss 0.02112092636525631\n", "Epoch 8700/20000: Loss 0.02085566148161888\n", "Epoch 8800/20000: Loss 0.020598839968442917\n", "Epoch 8900/20000: Loss 0.020350132137537003\n", "Epoch 9000/20000: Loss 0.020109230652451515\n", "Epoch 9100/20000: Loss 0.019875869154930115\n", "Epoch 9200/20000: Loss 0.019649747759103775\n", "Epoch 9300/20000: Loss 0.01943061128258705\n", "Epoch 9400/20000: Loss 0.019218187779188156\n", "Epoch 9500/20000: Loss 0.019012251868844032\n", "Epoch 9600/20000: Loss 0.01881253533065319\n", "Epoch 9700/20000: Loss 0.018618833273649216\n", "Epoch 9800/20000: Loss 0.018430903553962708\n", "Epoch 9900/20000: Loss 0.01824854500591755\n", "Epoch 10000/20000: Loss 0.01807156577706337\n", "Epoch 10100/20000: Loss 0.017899734899401665\n", "Epoch 10200/20000: Loss 0.017732910811901093\n", "Epoch 10300/20000: Loss 0.01757088117301464\n", "Epoch 10400/20000: Loss 0.01741347275674343\n", "Epoch 10500/20000: Loss 0.01726052165031433\n", "Epoch 10600/20000: Loss 0.017111871391534805\n", "Epoch 10700/20000: Loss 0.016967371106147766\n", "Epoch 10800/20000: Loss 0.016826853156089783\n", "Epoch 10900/20000: Loss 0.016690172255039215\n", "Epoch 11000/20000: Loss 0.01655721850693226\n", "Epoch 11100/20000: Loss 0.016427835449576378\n", "Epoch 11200/20000: Loss 0.01630190573632717\n", "Epoch 11300/20000: Loss 0.016179287806153297\n", "Epoch 11400/20000: Loss 0.01605990156531334\n", "Epoch 11500/20000: Loss 0.015943588688969612\n", "Epoch 11600/20000: Loss 0.01583026722073555\n", "Epoch 11700/20000: Loss 0.015719830989837646\n", "Epoch 11800/20000: Loss 0.015612168237566948\n", "Epoch 11900/20000: Loss 0.015507183037698269\n", "Epoch 12000/20000: Loss 0.015404795296490192\n", "Epoch 12100/20000: Loss 0.015304889529943466\n", "Epoch 12200/20000: Loss 0.015207395888864994\n", "Epoch 12300/20000: Loss 0.015112233348190784\n", "Epoch 12400/20000: Loss 0.0150193115696311\n", "Epoch 12500/20000: Loss 0.014928566291928291\n", "Epoch 12600/20000: Loss 0.014839913696050644\n", "Epoch 12700/20000: Loss 0.014753288589417934\n", "Epoch 12800/20000: Loss 0.014668602496385574\n", "Epoch 12900/20000: Loss 0.014585818164050579\n", "Epoch 13000/20000: Loss 0.01450483500957489\n", "Epoch 13100/20000: Loss 0.014425638131797314\n", "Epoch 13200/20000: Loss 0.014348134398460388\n", "Epoch 13300/20000: Loss 0.014272273518145084\n", "Epoch 13400/20000: Loss 0.014198007993400097\n", "Epoch 13500/20000: Loss 0.014125277288258076\n", "Epoch 13600/20000: Loss 0.014054032042622566\n", "Epoch 13700/20000: Loss 0.013984225690364838\n", "Epoch 13800/20000: Loss 0.01391582004725933\n", "Epoch 13900/20000: Loss 0.013848748989403248\n", "Epoch 14000/20000: Loss 0.013782992027699947\n", "Epoch 14100/20000: Loss 0.01371848862618208\n", "Epoch 14200/20000: Loss 0.013655201531946659\n", "Epoch 14300/20000: Loss 0.013593104667961597\n", "Epoch 14400/20000: Loss 0.013532143086194992\n", "Epoch 14500/20000: Loss 0.013472294434905052\n", "Epoch 14600/20000: Loss 0.01341353077441454\n", "Epoch 14700/20000: Loss 0.013355768285691738\n", "Epoch 14800/20000: Loss 0.013299047946929932\n", "Epoch 14900/20000: Loss 0.013243299908936024\n", "Epoch 15000/20000: Loss 0.013188476674258709\n", "Epoch 15100/20000: Loss 0.013134590350091457\n", "Epoch 15200/20000: Loss 0.013081605546176434\n", "Epoch 15300/20000: Loss 0.013029444962739944\n", "Epoch 15400/20000: Loss 0.012978123500943184\n", "Epoch 15500/20000: Loss 0.01292763464152813\n", "Epoch 15600/20000: Loss 0.012877939268946648\n", "Epoch 15700/20000: Loss 0.01282903179526329\n", "Epoch 15800/20000: Loss 0.012780825607478619\n", "Epoch 15900/20000: Loss 0.012733322568237782\n", "Epoch 16000/20000: Loss 0.012686521746218204\n", "Epoch 16100/20000: Loss 0.012640411034226418\n", "Epoch 16200/20000: Loss 0.012594972737133503\n", "Epoch 16300/20000: Loss 0.012550188228487968\n", "Epoch 16400/20000: Loss 0.012506021186709404\n", "Epoch 16500/20000: Loss 0.012462472543120384\n", "Epoch 16600/20000: Loss 0.012419519014656544\n", "Epoch 16700/20000: Loss 0.012377124279737473\n", "Epoch 16800/20000: Loss 0.012335298582911491\n", "Epoch 16900/20000: Loss 0.012294023297727108\n", "Epoch 17000/20000: Loss 0.01225326769053936\n", "Epoch 17100/20000: Loss 0.012213014997541904\n", "Epoch 17200/20000: Loss 0.012173268012702465\n", "Epoch 17300/20000: Loss 0.012134003452956676\n", "Epoch 17400/20000: Loss 0.012095203623175621\n", "Epoch 17500/20000: Loss 0.012056862004101276\n", "Epoch 17600/20000: Loss 0.012018988840281963\n", "Epoch 17700/20000: Loss 0.011981600895524025\n", "Epoch 17800/20000: Loss 0.011944635771214962\n", "Epoch 17900/20000: Loss 0.011908081360161304\n", "Epoch 18000/20000: Loss 0.011871925555169582\n", "Epoch 18100/20000: Loss 0.011836149729788303\n", "Epoch 18200/20000: Loss 0.011800757609307766\n", "Epoch 18300/20000: Loss 0.011765792965888977\n", "Epoch 18400/20000: Loss 0.011731193400919437\n", "Epoch 18500/20000: Loss 0.01169695146381855\n", "Epoch 18600/20000: Loss 0.011663036420941353\n", "Epoch 18700/20000: Loss 0.011629480868577957\n", "Epoch 18800/20000: Loss 0.011596305295825005\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch 18900/20000: Loss 0.011563439853489399\n", "Epoch 19000/20000: Loss 0.011530875228345394\n", "Epoch 19100/20000: Loss 0.011498632840812206\n", "Epoch 19200/20000: Loss 0.011466740630567074\n", "Epoch 19300/20000: Loss 0.011435122229158878\n", "Epoch 19400/20000: Loss 0.011403791606426239\n", "Epoch 19500/20000: Loss 0.011372771114110947\n", "Epoch 19600/20000: Loss 0.011342059820890427\n", "Epoch 19700/20000: Loss 0.011311582289636135\n", "Epoch 19800/20000: Loss 0.01128139067441225\n", "Epoch 19900/20000: Loss 0.011251499876379967\n" ] } ], "source": [ "loss = model.fit(X_train, Y_train, 20000)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0,0.5,'Mean Square Error')" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(loss)\n", "plt.xlabel(\"Epochs\")\n", "plt.ylabel(\"Mean Square Error\")" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "Y_pred = model.predict(np.float32(X_test))" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "R2 Score is 0.7688640977806545 and MSE 0.011896210533449397\n" ] } ], "source": [ "print(\"R2 Score is {} and MSE {}\".format(\\\n", " r2_score(y_test, Y_pred),\\\n", " mean_squared_error(y_test, Y_pred)))" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0,0.5,'Predicted Net Hourly Electrical Energy Output (MW)')" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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9VA0i8j7gH/A6tf1IVZeVvD4F+DHQ6e+zWFVvz1Imw4ijkoqotaiimnSMSs0HwUC2pzjAnpSwVSMZxd2UpMBFN3nRXuUM4KXVbDs7crz08r7BaLF65kKkrhREpF1Vyy6wLiLtwL8D7wGeBu4HzlLVh0L7XA30qOr/FZGjgdtVdWrScW2lYGRFJRVRq6miGs4/iCKIhqlXR7LRRCX+gnJWDHEVU11XlbWozltKLVcKD4vIzcC14QHdgeOBh1X1UV+gG4HT8JzVAYrX8xlgIvBMGcc3jJpSibOvUgehS+LUM30Frlo4q6K6/kYyQb5HOde1HBVS2oWtu6eXy27bmljdNqww4j6rHo5nF6XwJuCjwI9EpA24BrhRVV9IeV8X8FTo+dNAaRb0Uryw188C44F3uwhtGFlQibOvUgehS5P6QzvzwyKZjNoQZJOXZorXkiDpLamjXWlpFJd7ImtcqqS+qKo/VNUTgC8BS4BnReTHIvLnCW+NCqIuvepnAdep6muBDwA/8RXP0AOJnC8iG0Rkw44dO9JENoyKqKQK5qKTp5NrH3qru7R8TJvxhT93wewu1i0+qeICb5WQz7VF/oBHAsG1Da7r48tO4ZErPkBnBhVke/sKXL/+ycTBvrev4FQ8sV4VWVOVgoi0i8h8Efk5ntP4W8DrgNuAJKfw08BhoeevZbh56DzgJgBVvRev3efBpQdS1atVdY6qzpk8eXKayIZREUkJaYmUTnUcJpxJM764z61nieZCcWBE1kmKu7aXdG9xKgFSiaJ0uY5Jq5Sy7sUa4GI++g9gLbBcVe8Jbb9ZRN6e8L77gSNFZBrQi2eC+ljJPk8C7wKuE5E34CkFWwoYDSMuIS2O5Wu2D8uSDRrUJx0nrurppI5crPNxwewuvnDTJiwxuTLaRSKdtN09vVzv2Hmu3pe+GsdypTj5FFT1pagXVPVzcW9S1X0i8hlgDV646TWqulVEvgpsUNVVeF3cfigiF+Jd73O11RInjFFNUhmKaYtXx0aYBM+Xrto6ZIa6c0+RC1ds4oIVmyKrqJpCqJxgNl4aAbT7lX2ZD/blJsFB4xr4uISkfjdi8y68gf0XmUiVgIWkGuWSZfVJl+SypIbuae8vfW+1yWyjGRGYOC77SrFBmYyAfK6dM47rKqsPdpvAtz9SfhJcEq4hqS7VTscBs/DMSP+BF410EHCeiHynKikNI2OyrD7Z3dPLbofKo0mFzdIczkEz+aD1pymEyhGoS+nwdhEmdeSG+AIuXzBzsOmPC41cEboohT8HTlLV76nq9/DCRt8AfAho3o4ShkF21ScDZeM6yMQN/i4hhn2F4mDJZ6MyhPoNtMUBZeee4aUpyg15bUSFVHDzKXTh5RDs8p+PBw5V1X4ReSUzyQyjBmRVfTIuzyAu3r1NJNLH4Npms9SZXUskoS3lSKASe34tCCenQflZ1I2okApuSuFKYJOI3IV3fd8O/L2IjAf+NUPZDKNqsqo+GfeDjcuUDQaD0izWZkhOG8kKAbJTCOfMncIN9z2VONAH5r9X9g3UrQx3tSSaj0REgDuAE4Bu/+9tqvojVd2tqovqIKNhVIxrQlpgs5+2eDXzlt2Z6nOI+8EGNuQg1yHKjlxqvgonUU3qqH0ClZENgUJI8xT0FYpllynJtaUnQGZFolLww0O7VfVZVf2FqnarqtUnMloGl4S0SpzRScomGOQfW3YKAzGzw7iVxpJTZ6R2bTOag2Dmr1SW1BZHZz7H8g8f05D+zOBmPlovIm9W1fszl8YwMiAtIS3JGR33vtJSx4d25jnxqMksX7OdC1dsGvQdxJmvJkaUVAhCZ634XeuheImHLxcHhnx/+Vw743Jtzv0sGpGsVopLnsJDwHTgcWA3vt9GVd+UuXQRWJ6CUWumLV4da3ee1JGjLyKSpJSoqqdBfPqKPzw1zFFcGofuUjXVaG6CBjulOTHgVuwuKZ+lJvLVsHT2+2sgj2E0LUl9kIMZXm9fgQtXbGLDE89z+YKZw/aLW22s3bbDa4dZohQG1MtmDq84TCG0NkFV27hBPS2YoF61jdJwqZL6BF5hu5P8x3tc3mcYrUKUfyAKBX66/klmXXbHMId0UuhrXOezcI5Do8IPjdqQVpIi8DPFBRJM6sg1hUIAh5WCiCwB5uCZkK4FcsBPgXnZimYY9SH4MV6wYpPT/lEtEpNCX5Nmh0HuwsR89uUXjGxoFxkyy08qq7Lk1Bksunkzxf79K8dcu7Dk1BkNkT0KF5/CJmA28ICqzva3PWg+BWOkUWkZiUkdOZacOiPSp3DF6TOHddyKor1N6Ldqdy1HqR8gzjc0fmw7e/b2DwYkrN22I5NaXEnU0qewV1VVRNQ/8PiqpTOMKsmiyJ1rdnEpwYB/xekzI6ORXCJPTCHUhkl+w/ssMsAFOHvulMQBPc43tHuvt623r8AtG3ubxn8QhYtSuElEfgB0isgngU8AP8xWLMOIp3Q2VpolXLqvq/IoDTMdl2ujEOMPKOWimzYzoMqhnXnOnjuFX25+tqyqmMZwyh3gBU9BT+rI8Uqxf5gvJ9c23OFfDgqRQQZhXHxDaeHOjSZVKajqN0XkPcALeH6FS1X1N5lLZhgxuOYVlKM8AkqjR8JKpbMjFzvrD5exMGVQGzrGjklcZeXahFeN8/YJ1zfauadIPtfOOSWz+qmvzrPukedjj9eZz7GrUKQtpkaRSzvUNB9SQDMHFjhFEanqb1R1kap+0RSC0Whci9zVokJqODu559L3ZtLH14gmbXAd4ztouzrzw/JMCsV+brjvKRadPJ3Hlp3CopOnc0+CQsjn2lg6fwaHduYjS1cIcOJR6a2AF5083Sm7uVF1jVxwiT46HfgG8Bq8axMkrx2YsWyGEYlrkbu4QaWawnNL589g0crNmVYtNdwoFAe4cMWm2MTDftXBleHyNdsTC+MN6NAEs6i227ds7GXO4QelmihPOOIg7nnk+djPa1RHNVdcVgpXAvNVdaKqHqiqE0whGI3EtchdXFOTcpqdRFLLQjejlHPmTuGcuVOqPk6aai4U+7nstq2p5ppX9g2kBhhErTKj6mY98OQuzp47ZbDeVmc+N6zpTrP6E8DN0fxfqvqnzCUxDEei6g5FOZDjShWXW8I4zPI124fEmBuVcf36Jzl77hRybeDoy6+YnXuKdNYoD6R0lZmUyd7oGkaV4qIUNojICryy2YNNdVT11sykMowUosoJlC7j4wYCF4dhHM3sIGwlguzweiFCZJ+Lwddx67sgePdZcO9l1cSpkbiYjw7EK23xXuBU/++DWQplGOUStYzfvXcfubahtp5q7bnN7CA04unbU+SK02dGlpnItQlnz53iXOokbEKKux9a+T5xCUn9eD0EMYxqiFrGF/uVSR05OsaOqUmSW3dPL7tf2VcLcY06Ey5WF5e7Mufwgwa3x4WlwtBVQFTCY7M7ktOIVQoicpOqfsR//A1V/bvQa3eo6nvrIaBhuBC3XO/bU6TnUvdbNW7AsNLWrU3fnr3MuuwOdhWGl0EPuu4F3/lVC2dxYUIdrDYRLuneMpgD0dmR44AxbZHHbkWSVgpHhh6/B/i70PP0gF3DqCO16MWclOyWVNq63IbsRv3xykwM/16BISHGvX0FLlixifFj2wdLU5TSrzrEHxIky121cFZLK4OAJJ9C0l1uvwCjqYgrf71n777UfssBScluSY5DUwitR/C9Ll21NTLnZPfefnLt7rHH5SZFNjNJK4UOEZmNpzjy/uMgea11vSjGiCSYoS1dtXVIxNHOPcXE0hZhc1Hc0B6YFapJejOaj7Tvc/zYMYw/YIzz997bV2Desjtb3nwUWzpbRNYmvVFVT8xEohSsdLaRRFz566jet65+gs58jg8ec4jVNBplCPDYslPKLqkelNOG9FyaelJ16exGDfqGUQ3lxI27tsDcvXcfqx98tmrZjNrTkWuj2K+ZlB0J/FGLTp6eWE6jlCCL+uXiQFnFGJsFa6tpNDVBZEhp+8s4yokbd00wKvarU08Eo/4owsLjDxssKVF1CZMQQQG8BbO7OLvMkhw79xSrLsbYKEwpGE1LVELaxbduSVQMcQ7n3a8MdzhnnWCUa/MyaY3sKBT7h6ziaun0v+G+pwbvmcsXRCe+lUsrZDpnqhRE5H0isl1EHhaRxTH7fEREHhKRrSLysyzlMVqLcktfB07jQrF/2GDcVyhywYpNzP7qHYM/9DgFUiuKA2CBSdmzc09xcOJQS4Iqq8H9suTUGcPul1ybDItSyufaY0ust0Kmc1Ly2rFJb1TVB5JeF5F24Pt4OQ5PA/eLyCpVfSi0z5HAxcA8Vd0pIq8pR3hjZJPmHyhtgBPu0hU3GEdFI7n0UDZGJ4ViPxfdtBmIL8QYt61VM52TQlK/lfCaAmklAI8HHlbVRwFE5EbgNOCh0D6fBL6vqjsBVPW5VImNpsa1/aXLfkkJaaWRQ+UM6uEubVGlDybmc4iUd0xj5BLuyxBViDHYHkUzRR+5EhuSWvWBRc4E3qeq/8t//pfAW1T1M6F9uoF/B+YB7cBSVf11xLHOB84HmDJlynFPPPFEJjIb1REV4hmE5yW1yaxkv+VrtledN/D4slMiz6EWxzayp6szz+5X9tWkJLYLkzpyZZVMaTaqDkktOdgbgaOBccE2Vf2XtLdFbCvVQGPwymm8E3gt8DsReaOq9g15k+rVwNXg5Sm4yGzUH9feya77JS3Xqx20BYbUu4lb8hvNg0Bk3aJ6fWc79xSHlM0eqbi041yCN2gfDdwOvB/4PZCmFJ4GDgs9fy3wTMQ+61W1CDwmItvxlMT9LsIbzYVrjkA5uQSly/VgEHChDYjr3xJEM8H+qKZxuTZTCE3MxHyOZ/oKg4EG4Xvjops2O0cezTviIB7/n8KwiUWuDfoH4u8ZYNjEZSTiEn10JvAu4D/9MtrHAAc4vO9+4EgRmSYiY4GPAqtK9ukGTgQQkYOB1wOPOspuZEi5+QHgniNQTQ1614QzgPZ2cQ4jLBT7zYfQ5PQVipGhyQtmd/GtjxyTGknWLsI5c6dw/SffOpiDEKaYohCgNUJKq8VFKRRUdQDYJyIHAs8Br0t7k6ruAz4DrAH+BNykqltF5KsiMt/fbQ3wPyLyELAWWKSq/1PJiRi1o5L8AHDvney6XxTl/CiL/YoqZRU2M1qD0tDkBbO7uOL0mbHJa12deR654gNcvsArP3HDfU9V9LmtEFJaLS5KYYOIdAI/BDYCDwB/cDm4qt6uqq9X1SNU9ev+tktVdZX/WFX1C6p6tKrOVNUbKzwPo4aUmx8QEPwwg+zSuCblrvtFUe6Psq9QZEzb6FEKo+dMh08QFszuYsChMQ5UluTWKiGl1eLSee1v/If/JCK/Bg5U1QezFctoJNX0nY0L2at0v1KiOl2lUXDoDJ/PtTMu19byJqTRFIUxMSJBzLWvhmsPjKB3c1cLhZRWi4uj+UPAnaq6S1UfF5FOEVmgqt11kM9oALVoWFMN3T29QxLKOvM5ls6fMUSRlJbIroR2EQZUh0QfXZDQcctoLnb7vTLCA7Vre8yz3nJYatXb0aQIwriYj5ao6q7giR8uuiQ7kYxGU43Nv1ou6d7CBSs2DZmx9xWKLFq5eYhjcfwBTtHUseRz7XzrI8dw1cJZAFy4YlNLFCsz9lPs12Hfmatp8vIFMzknocidAOsWnzTqFAK45SlEKY7qfpFGUxOXH1DtDyQti7m7p5frY2ZvxQHlAn/gXnTy9KqiQIKVBzCs/abRWriEMcdx+YKZrN22o6Gr4mYkNaNZRK4B+vDqGCnwWWCSqp6buXQRWJOd1iQqySjXLowfO2aw4fmevfucbPr5XDsHjGmrynzUVcbnGc1LVPOkAJdSKq7Z9SOBWmY0fxb4CrACb1V1B/C31YlnjDaiIpqK/To4sJczSy8U+xmXayOfa6842cxWBa1D3PecZNIsHezjmtxktSpuZVyij3YDkWWvDcOVWif99O0pctXCWZEVTgU4e+6UWNNAtQQRKUb2BM7eoB5VEDWU5gR2LaUClUfCjVSSSmd/R1UvEJHbiPgNqOr8iLcZRiS1bnx/aGd+8Md8SfcWrl//5OBNqsAtG3s547gubtnYW/vSFaYV6kKwEqhk0K4NJkF5AAAgAElEQVQmrHq0k7RS+In//zfrIYgxsnHNL3AZb0vNBqsffHbYewrFftZu28EVp8+seZipNc7Jjq7OvJMZJ81f0Oiw6lYmVimo6ka/Uc4nVfWcOspkjEBKbbelTXEg3nYcptRs0N3TG+ssfqavwILZXaOqFPakjhyvFPvZ45Cw14zEOY3DuPgLXPMVjOEk+hRUtV9EJovIWFXdWy+hjJFJVMXT0tle3ACez7Xxp6+9f9j2pNyCNhGmLV7NxHyOXLtQ7K98ij+pI9cSkUrNIuO8Iw7ioWdfLEse1xIdLv4CcyBXjkv00ePAOhFZBewONqrqt7MSyhgdxNmK40IEo0iyEQdlDKoJXQ2c1nMOP8iyncvgnkeeH2bS68zn+OAxh3DLxqcjS4/kc23DMpSjJg6u/gJzIFeGi1J4xv9rAyb428yq2oK4tspsJOXO8GrtwA4zqSPHklP3J7kZ7kQNEC++vI85hx/E5QtmesEB9z05xD+zpzgwxAwUZybqjFm1mb+gNrgohYdUdWV4g4h8OCN5jIxwjdtuJKVK66qFswYHh9IuaUm242ppExhQeKGwjwtWbHIunmYkE+51HJdNHDYDxZmJQIf5n8xfUDtcah9d7LjNaGIqLYddL+J6OFzSvWXY9gtXbGKq3/wHGFLrJq6efjkEvu9AEZhCKI+kbyB8z6WZgeJeLxQHOHbKxIpKrxvpJOUpvB/4ANAlIt8NvXQgsC9rwYza0qxx28HqIMoEVCj2c8N9Tw0blINngeK44vSZg1Er0xavzlpkI4GuzjwnHjU5MT8kuOfSwkaTTIP3PPL84ErSqC1JK4VngA3Ay3jNdYK/VcDJ2Ytm1JJqWmBmRXh1EEfaLL10tWN25fpQuhrI59r5zsJZrFt8EpcvmJnYBS34jtKq8SaZg5TkyDOjcmKVgqpuVtUfAzOBn6rqj/3nvwBeqZeARm1oZDnsOFz6LbuYg3r7CoO9pKN675YymrqT1YLSdqb5XDtnz52SaL6J65scvufSylwvmN2V2GO70avckYqLo/kO4N3AS/7zvL/thKyEMmpPo+O2ywktDGgDxuXa2L033Ykc+Btu2dhLPtcWGfIYlMy+aOVm+gfMT+DKwjcfxtptO8q+b1zuubSw0SWnzuDCFZsio5lsVZgNLqWzN6nqrLRt9cJKZ7ceceWJR0L7y5FCUoRVLUpJVxMOXVrbqlYyjTZcS2e7RB/tFpFjQwc+DrB1m+FMXOSTKsPMC0Zj6FeN/S6qjVKLiywLOumlcfmCmVy1cJZFG9UJF/PRBcBKEXnGf34IsDA7kYyRRpyZaFfBK39dSW2iIJfAqA1BTam4rO1qEgTLKWMdh2Un14/UlYKq3g8cBXwa+BvgDaq6MWvBjJFBd08vbQlRKAtmd7Fu8Ul0lWEfbhexSqU1JFyiOu57EHCe2ZfSrOHQRjSpSkFEOoC/Az6vqluAqSLywcwlM1qeS7q3cOGKTZG26tLIp6joqDgGVK3OShVM6sjFmmIWnTw9MjqrmhDQZgyHNuJxMR9di5ef8Fb/+dPASuCXWQlltB6ljsQTj5o8zDkY0C4SGcK44YnnI5PVSsmy3tFIJ59rZ8mpMyJNMcF3GHf1K53ZWxnr1sJFKRyhqgtF5CwAVS2I1KCWgDFiiKqrFKcQwJvph/shLF211bmSqQAnHjWZWzc+3bI9AxpFlDIOiIoQK6XSmX2jw6GN8nBRCntFJI9fXUBEjsCS14wQUY7EpLl+MLh09/SyaOXmIY120lDgZ/c9Sa7dJXBu5BKXixG/f3IIZ1oiYbUze3MUtw4uSmEJ8GvgMBG5HpgHnJulUEZrUa5ZIcg6Xr5me1kKIWBA4ZV9o3eVkM+1UU5edlCPaPma7Vy4YlPkTD3pOyztdmeMbFKVgqr+RkQeAObi3YmfV9X/zlwyo2WIq28fx9ptOwCLPqmUclYIwYCeVjY9zk/T1Zl3apFpjBxi1+AicmzwBxwOPItXJG9KOJktCRF5n4hsF5GHRWRxwn5nioiKSGq2ndF8lBseGq6SaWRHYPJxKZvejLWxjMaQtFL4VsJrCiROH0SkHfg+8B68iKX7RWSVqj5Ust8E4HPAfU4SG03HrjLbXYarZFqLy+wIfAhx1zi8UjNnsBEQqxRU9cQqj3088LCqPgogIjcCpwEPlez3NeBK4ItVfp5RQ8qpVRNnehCBMSJD/Abh2eeGJ57PRniDSR25wa51QrTjv3SlZs5gA5LNR18KPf5wyWt/73DsLuCp0POn/W3h48wGDlNVy3loIrp7ell08+YhtWoW3bw5NqM1LvFMlWGO5NdOGseC2V1c0r2Fn65/MgvxRyzlZH0HvaXj8g6E5H4FxuglKa7vo6HHpe033+dw7LjESO9FkTbgKuCi1AOJnC8iG0Rkw44dOxw+2qiGy27bSrF/6FBS7Fcuu21r5P4LZndxxnFuM8z/eG43UxevNoVQAa7lQM6ZO2Vwxh/nzFeapze30VwkKQWJeRz1PIqngcNCz1+L56gOmAC8EbhLRB7Hi25aFeVsVtWrVXWOqs6ZPDm9iYpRHXGRRHHbu3t6uWVjZXVxDDeCZkNp5UA68zkuXzBz8HmcM7+cVUcr0N3Ty7xldw42W6q0TpORrBQ05nHU8yjuB44UkWkiMhZv5bFq8ACqu1T1YFWdqqpTgfXAfFW1ZgkthksHNaM6gtIfQbeyqI5k+Vw7S+fPGLJtNEQVVVua2xhKklI4RkReEJEXgTf5j4PnMxPeB4Cq7gM+A6wB/gTcpKpbReSrIjK/JtIbmdCZj26BGLfd8g3ciLt+LoRn9gtmd9Fz6Xv5jkOPgbSWlyMBl5Bbw53UzmvNhnVey56o8hO5NmH5h4+JHEzmLbvTCtQ5MKnMJL+AXLswfuwYdhWKFioawbTFq2Od6Y8tO6Xe4jQttey8ZowyFszuYvmHjxkyu4xTCGBRLK5UohAmdeRAoa9QNNNIDFaau7aYUjAiWTC7i0UnT2diPkdvX4ELVmxi9lfvoLund5hTD4i0cRvV0dWZp2PsmGFhvWYaGcpo8JvUE5eCeMYoJMqEtHNPkQtXbBqyVO/tK3DRys2Mbbdq6rUkGNQudMhGHu1YNnZtSVUKIvIZ4HpV3VkHeYwmIa6CaZTttn9AKVjD5JoR7nsQ17+6XNNIORnqrYhlY9cOl5XCn+HVLXoAuAZYo63mnR4F1OJHHz6GfcGNIdcuLD9zv//mxKMmRyb6BeXHXYhqglRaJdUwAlxKZ18iIl8B3gt8HPhHEbkJ+GdVfSRrAY10qvnRB4qgt68QWyPHqB/jx44Z8p0FZcZLidseRVLIpikFoxQnR7O/MvhP/28fMAm4WUSuzFA2w5FK47QDv0FgnjCF0Hj6CsUhkUVxvoNyfAq1OIYxekhVCiLyORHZiFfJdB0wU1U/DRwHnJGxfIYDlf7ol67aWlHnMyNbwiGntQi3tJBNoxxcVgoHA6er6smqulJViwCqOgB8MFPpDCfK+dGHw0n7yuyDYNSH8CqvFuGWFrJplEOsT0FEDvIffqfkOQCq+ryq/ilD2QxHStstQvSPvtT3YDQvwSqvFuGW5RxjpEcpGekkOZo3st/MXBqErsDrMpHIqIgDxrQNDvaTOnIsOXXGsB+zFa5rHQ4tqXVU7cDscgyLUjIgufPaNBERvCY4Vvw+YyqdoUXN/l8uaewejjAymo9cW3x3unpiUUoGpPgU/Kijn9dJllFLNaV/0yKPwsc2mg+BYXWmGlXFNC4wobevYH0KRhEujub1IvLmzCUZxVRT+jct8ijNZGTFKWpHJYXEFAZLWVy1cBbrFp/UsFl5UjSSFeMbPbjcxycC94rIIyLyoIhsEZEHsxZsNFFNHHla5FHSMSZ15DjhiINiXzfK42Nzp1T0vmYZcNO6uoEV4xsNuJS5eH/mUoxyDu3MR5p3JuZzg70K2kXoV6WrxN+QFnkUd2yAl17ex9ZnXszgjEYnN9z3VFXvb7T9vjRKKS6DxZLeRjYuKwWN+TNqRNQMLdcm7N67b3BAD9oxls4o0zprJc3+igNquQo1pN+hJFjQazmO3r5CQ1cLC2Z3sW7xSTy27JTYPs6W9DaycVkprMZTAgKMA6YB24EZSW8y3ImKI9+zd19sU5bSGWVSuGGw/YKYEsxGfZkwbgxL589IjAZrljBQ1/wXY2ThUhBvSD9mETkW+FRmEjURWSfyhI/f2ZEjPNFM69JVzhJ+wewuUwpNQl+hyMW3buGM47q4ZWNvZBBAo81IAdanYHRSdpMdVX1gNEQjZZ3IU3r8sBJwqViatIQvVWZTX23L/WaiUOxn7bYdXHH6zFhl3Sx2e+tTMPpwabLzhdDTNuBYwL1ub4uSdSJPWqhoYK+LUgxJS/goZWY5Cs3HM32FmjbRMYxa4bJSmBB6vA/Px3BLNuI0D1mUG76kews33PeUk0MSPIXQmc8NcQZ35nMsnT+8hEXAZbdttVIWLUAw6LeS3d7qIo0OXHwKlwGIyATvqb6UuVRNQFwoZ6UzuEu6t0R20EqiM5/jlX1DS1aUPi/9jDRfhFEb2gTCVcfF/8dF34cH/Vax21tdpNGDpHXWFJE3Aj8Bgiyn/wb+WlX/mLFskcyZM0c3bNiQ+edE1RTK59orLkFwxMW3O68QwBtkOsa2s3tv/Kw/nLPQ3dNrzuQ6ERQcLB3IgdQqtKV5Jq1CkC9TSldnnnWLT2qAREa5iMhGVZ2Ttp+L+ehq4AuqutY/8Dv9bSdUJWGTU+sZXDkKATzTUZJCgKGztctu21qRXEb57NxTZPma7bH3Q5xyFmjZAdS6t40eXJTC+EAhAKjqXSIyPkOZmoZaRl4EGcm1plDs5+JbH6RQjDcrGbUnznwyUp3HtTanGs2LS0bzoyLyFRGZ6v9dAjyWtWAjjbPeclhmxzaFkExWRf/i6gA1qtNZuKterSuaWve20YOLUvgEMBm4Fa+M9mTg41kKNZIIfqjXr3+SXMLVtmql2TB+bDtXLZyVWuitUqLMJ2mlR7KgmvLrLjTinIzGkOpobjbq5WiuBd09vSxauXlIA5UwQR5CV2eeE4+aHJvhalSOAI8tO2VYOOWJR01m7bYdTomCMDw0OKBdhAHVhkcNmSPYSKNqR7OI3EbCb0VV51coW8tSbpz20lVbYxUCeBe3XWRwgDKFUHsUb8BcdPL02MHRJVx46fwZkZFFpYUKoTEhmuYINmpFkqP5m/7/AvwQ+F/lHlxE3gf8A9AO/EhVl5W8/gX/uPvwsqQ/oapPlPs5tSBtwK8kTtulAmm/atn5C0Z59PYVWLRyM5fdtpW+PcVh3+/abckJ+l2d+WHRaG0RgQONrFnUzI5gS3prLWKt3Kp6t/93F/BS6Pndqnp32oFFpB34Pl4/hqOBs0Tk6JLdeoA5qvom4GbgykpPpBpc7LHVdEcz6sekjlzk9uKAsnNPMfL7TZtNhxPNgrLSAzFm10bNzJvVEZy1r8OoPa4F8SpxPBwPPKyqjwKIyI3AacBDgwcNhboC64FzKvicqnGpcxRXPyjoXzsxn0OEITPRSR05yzCuMy8U9jntF/5+kxoRTerIRc5qm21m3qyZ0VnXEDNqT5JPIdynsV1EJhEKklHV51OO3QWEW1E9DbwlYf/zgF+lHDMTXOyxSXkGylBTUTAbOuO4Llbc/xTF/tZy5rcy5eSCBN/vopOnRwYE5NqFJadGtw1pxppFzVjR1HwdrUfSSmEj+4t1AjwQek2B16UcOyrKMvIXKyLnAHOAd8S8fj5wPsCUKZX1wU3CZdZXbuJZUB55+ZnHcNltW23F0IQE328wkC5dtXVQuQelLNKaFzXbzLwZCPsQonwv0By+DiOazEJSReStwFJVPdl/fjGAql5Rst+7ge8B71DV59KOW21IapTTC4bXrCmtcxQX8lcLSourGbWlM59j9959Q1ZsuXZh/Ngx7CoMdzwblRNVM6yUamqIGZXjGpLqkrxWKfcDR4rINBEZC3wUWBXeQURmAz8A5rsohGqJc3oBqYk5Sb2Oq2VAvUGqIym7zaiIrs48m5a8l+VnHjP4/U7qyIF6Jj9zftaWuD4h7SKW9NYilN15zRVV3ScinwHW4IWkXqOqW0Xkq8AGVV0FLAdeBawUr6H5k1nmPyQ5vdYtPinxRl0wu4sNTzzP9eufrMjrnkaxX9lnvoeaE44cCq/6Ss155vysDXG+ggFVHlt2Sp2lMSohM6UAoKq3A7eXbLs09PjdWX5+KdU6vdZu25GJQggwlVBb2iQ6hyTu++7tK9Dd02uKoQqaLSrLKJ9Ue4WI/MRlWysQd2Om9TsOioxZW8vWIs5Pk/R9mxmpOpo1X8Jwx8WIPSQmz09KOy4bcbKl3Bu21AdhtBZdMYN/kn/IEhKrwwrntT5JeQoXA/8HyIvIC+wPMd2L12Sn5Sg3jDDOaWY0H+1tQn9oaZA2Ox2Xa4v9bi2GvjqaMV/CcCdWKfiho1eIyBWqenEdZcqUcm7YLAaHuGqbxnDagANy7U7hjeCm7F1CJs3+bYxmXBzNX/aTy6ap6tdE5DDgEFX9Q8ayNZyk8geVYgqhDATOOK4rtmBgu8gQ04SLsk9b/Zn92xjtuPgUvg+8FfiY//wlf9uIxwaHxjKgsOL+pyJfawMOzI/hwhWbyuoylrT6M/u3Ybgphbeo6t8CLwOo6k5gbKZSNQk2ODSeuLpRAxBb9TSJONNQ0IzGvnNjtOOiFIp+xJECiMhkvN/kqCAugsVoLlyjhixk0jCScVEK38XrzfwaEfk68Hvg7zOVqolYdPJ065+cAflcW+p1bZfyrrxLYICFTBpGMqmOZlW9XkQ2Au/CC0tdoKp/ylyyJiEob+HSHS3XJrxq3Bj6fLOGEc+4XDuFYvyCM59rL7v0uGvUkIVMGkY8TmUuVHUbsC1jWZqWyxfMTFQKAsPCIKcuXl0n6ZqPNtLti0mlxLtC13LO4QcNKT3emc/xwWMO4ZaNvU3Vy8AwRgpJyWsvsr8cj4QejwHGqmqmdZOaja6Y8NTAQVnO/h1j2/iP53ZnImczUI3DqfR6xs3q5xx+kPUyMIwMcO6nICITgL8BPgX8XFUvylKwOKrtp1ApUUlPSYlTkNyj4ZLuLU4mqdGE1dk3jOyoWT8FEekUkaXAZmAC8OZGKYRGEnZQgucELRT7WbpqK4tu3lx2j4a123Y4fW7bKPBym8PXMJqHJPPRwcBFwELgGmC2qu6ql2DNQmmnthOPmjzEnh2VoezSo8G1hMZI78gWZ34zDKMxJPkFngB2ANcCe4DzJBQiqKrfzla0xlNqMurtKzg32Ukb9Ds7cqO+b3OuTcw5bBhNRpJSWM5+5/KEOsjSdETVyXGduJeGR5auOF626qu8atwYMxcZRpORVCV1aR3laApKB+5Ki+EJQ+smXdK9ZcgKw5r1ePSFVkql196iiQyjMYyqsNIkokxF4TjcMHHbA5T9dZO6e3pbJsoon1KmutYEq6moax84600xGEZ9cSlzMSqIMxWVBv/kc+2cPXdKYk2k8GuX3ba1hlJmR1CGuha1nro683Tmc4n7hJPNoq69dUAzjMZgKwWfOMew4g1yUWaNuNyFsOmoVZzJA6qD55XWhCYOAR5bdgoQfW2CFVZXyXWMu/bWAc0w6k9SSOoXkt440qKP4nwISSGT5bb3zBoBTjjiIB54clfZg3pgyglkv+imzfQ7JjaWHiN8HJdrE3ftrQOaYdSfpJVCEHE0HXgzsMp/firw2yyFagSLTp6eOuuPIq24mmv7zaAA3NptO3imr8DEfA6R9JVG1Oy7u6e3rEG99DzjVgzhDG6Xa+VaeK7Sa28YRu1Jij66DEBE7gCOVdUX/edLgZV1ka6OZDXrXzp/BotWbqYYykLLtQkLjz9sUAGU21M4zgxTei5RZqBJHTlOedMhqZ/tcj1qda2abcVlGKOZ1NpHIrINOEZVX/GfHwBsVtWj6iDfMBpV+6gaqg23rPT9FuZpGEaAa+0jF6XwZeAjeI12FPgQcJOqNqTRTisqBcMwjEbjqhRcmux8XUR+BfyFv+njqtpTrYCGYRhG8+EaktoBvKCq14rIZBGZpqqPZSmYYbQ6Zr4zWpFUpSAiS4A5eFFI1wI54KfAvGxFM4zWxbK0jVbFJaP5Q8B8YDeAqj7DKC2QZxiuWJa20aq4KIW96nmjFUBExrseXETeJyLbReRhEVkc8foBIrLCf/0+EZnqemzDqAfdPb3MW3Yn0xavZt6yO+nu6XV6n2VpG62Ki1K4SUR+AHSKyCeBfwV+lPYmEWkHvg+8HzgaOEtEji7Z7Txgp6r+OXAV8I1yhDeMLAlMQKVd9VwUQ1w2tmVpG81OqlJQ1W8CNwO34PkVLlXV7zoc+3jgYVV9VFX3AjcCp5XscxrwY//xzcC7JNzJxzAaSDUmoEUnTyefax+yzbK0jVbAxdH8DVX9O+A3EduS6AKeCj1/GnhL3D6quk9EdgGvBv67RIbzgfMBpkyZkiayYdSEakxAlqVttCouIanvAUoVwPsjtpUSNeMvzZRz2QdVvRq4GrzktZTPNYyaUG2hPtfaT4bRTMSaj0Tk0yKyBThKRB4M/T0GbHE49tPAYaHnrwWeidtHRMYAE4HnyzkBw8gKMwEZo5GklcLPgF8BVwDhyKEXVdVl4L4fOFJEpgG9wEeBj5Xsswr4a+Be4EzgTk2ru2EYdcJMQMZoJKlK6i5gl4j8A/B8qErqBBF5i6rel3Rg30fwGWAN0A5co6pbReSrwAZVXQX8M/ATEXkYb4Xw0dqclmHUBjMBGaMNl4J4PXils4M8hTa8Qf3YOsg3DCuIZxiGUT6uBfFc8hQkbNJR1QGsjadhGMaIxEUpPCoinxORnP/3eeDRrAUzDMMw6o+LUvjfwAl4zuIg1+D8LIUyDMMwGoNLP4XnMAewYRjGqCBWKYjIl1T1ShH5HtEJZZ/LVDLDMAyj7iStFP7k/2+hPoZhGKOEpDyF2/z/fxy3j2EYhjGyiM1TEJHbiDAbBajq/KyESkJEdgBPNOKzYziYkgJ+Iww7v9bGzq91qfW5Ha6qk9N2SjIffdP//3Tgz/BacAKcBTxelWhV4HJS9URENrgkhLQqdn6tjZ1f69Koc0syH90NICJfU9W3h166TUR+m7lkhmEYRt1xyVOYLCKvC574Be6aarZuGIZh1AaXchUXAneJSJDFPBX4VGYStR5XN1qAjLHza23s/FqXhpxbakE8ABE5ADjKf7pNVV/JVCrDMAyjIaSaj0SkA1gEfEZVNwNTROSDmUtmGIZh1B0Xn8K1wF7grf7zp4HLM5OoSRGR94nIdhF5WEQWR7x+gIis8F+/T0Sm1l/KynE4vy+IyEN+971/E5HDGyFnpaSdX2i/M0VERaRlIlpczk1EPuJ/f1tF5Gf1lrEaHO7NKSKyVkR6/PvzA42Qs1JE5BoReU5E/hjzuojId/3zf1BEsm1boKqJf3i9EwB6Qts2p71vJP3hNQl6BHgdMBbYDBxdss/fAP/kP/4osKLRctf4/E4EOvzHnx5p5+fvNwH4LbAemNNouWv43R0J9ACT/OevabTcNT6/q4FP+4+PBh5vtNxlnuPbgWOBP8a8/gG8LpgCzAXuy1Iel5XCXhHJ4yeyicgRwGjzKRwPPKyqj6rqXuBG4LSSfU4Dguzvm4F3iYjUUcZqSD0/VV2rqnv8p+vxem63Ci7fH8DXgCuBl+spXJW4nNsnge+r6k4YLHLZKricnwIH+o8nMrwXfFOjqr8luTf9acC/qMd6oFNEDslKHhelsAT4NXCYiFwP/BvwpawEalK6gKdCz5/2t0Xuo6r7gF3Aq+siXfW4nF+Y8/BmLq1C6vmJyGzgMFX9ZT0FqwEu393rgdeLyDoRWS8i76ubdNXjcn5LgXNE5GngduCz9RGtbpT7+6yKxJBUf6a7DS+reS7e8uXzqjpS08rjiJrxl4ZtuezTrDjLLiLnAHOAd2QqUW1JPD+/xexVwLn1EqiGuHx3Y/BMSO/EW+H9TkTeqKp9GctWC1zO7yzgOlX9loi8Fa/v+xvV6xI5Eqjr2JK4UlDPoNWtqv+jqqtV9ZejUCGAp5kPCz1/LcOXqIP7iMgYvGVs0pKwmXA5P0Tk3cCXgfnaWmHJaec3AXgjXj7O43gToFUt4mx2vTd/oapFVX0M2I6nJFoBl/M7D7gJQFXvBcbh1Q0aKTj9PmuFi/lovYi8OSsBWoT7gSNFZJqIjMVzJK8q2WcV8Nf+4zOBO32l2gqknp9vXvkBnkJoJZs0pJyfqu5S1YNVdaqqTsXzmcxX1VYoG+9yb3bjBQogIgfjmZNapaWuy/k9CbwLQETegKcUdtRVymxZBfyVH4U0F9ilqs9m9mkOnvGHgH68CIAHgS3Ag4322Nf7Dy8C4N/96/Blf9tX8QYP8G7ElcDDwB+A1zVa5hqf378C/wVs8v9WNVrmWp5fyb530SLRR47fnQDf9n/LW4CPNlrmGp/f0cA6vMikTcB7Gy1zmed3A/AsUMRbFZyH1wb5f4e+v+/7578l63szNaM5Lh5dVZupfLVhGIZRA5LacY7D01Z/jqed/lm9qBrDMAxjhJLUZGcF3nLmd8D7gSdU9fN1lM0wDMOoM0lKYYuqzvQfjwH+oKrZplcbhmEYDSUp+qgYPDCzkWEYxuggaaXQD+wOngJ5YI//WFX1wMg3GoZhGC1L7EpBVdtV9UD/b4Kqjgk9NoVQISLyIb8K51EO+54rIodW8VnvFJFhZRv87Soip4a2/VJE3lmpPCJynYicWbLtpQpFdzp+yv53+ZU1N/l/N/vbl4rIFyv4/E4R+ZuUfe4p97hJMvnbe0PnsElEOiv5jFojIm8TkT+IyDb/73yH90wVkY9V+bmJvwkR+Y6IvN1/fJeIPBmuQSYi3cF9KSI/F5EFode2i8gloee3iMjpIjJTRK6rRu5WwiV5zagtZwG/x0vCSeNcoGKlkAW6tycAAAfHSURBVMLTeNnJ5XAu2ckTi4i0V/jWs1V1lv/nrFBi6MSrhDuMQD5VPaHKz4jiqtA5zNIalKao4noG7/8z4Gd4cfRHAW8DPiUip6S8dSpQlVIg4R4UkYOAueoVmAvoA+b5r3cC4UJy9wAn+K+9GniJ/S0C8B/fo6pbgNeKyJQqZW8JTCnUERF5Fd4Neh4lSkFEviQiW0Rks4gs82fFc4Dr/RliXkQe9zNSEZE5InKX//h4EblHvHry94jIdAdxNgO7ROQ9EXIeJyJ3i8hGEVkjIodEyVPGeYuILBeRP/rnuNDfPmQlIyL/KCLn+o8fF5FLReT3wIdD+7xLRH4eev4eEbnVVZYSuY4QkV/75/m7YPUmIv+fP4vc7P+dACwDjvDPfbkv+1rxehNs8d/3UujYQ75Pf9snReR+f9st4jWwqkTuc0XkVl/2/xCRK0OvvVdE7hWRB0RkpX/PDbueIvJm8Wrz3xt8N/5+vxORWaHjrRORN5WI8Ld4tYYeAFCv9M2XgMX+e4as6kLXZRnwF/41vNA/j1/457FdRJb4+0+VUG8BEfmieKumtHvwTLzinWFuZP9v7XQgfK+sw1cK/v+/xOtJL+L1oi+o6n/6r9+G20Su5TGlUF8WAL9W1X8Hnhe/WYaIvN9/7S2qegxwpareDGxg/2y3kHDcbcDbVXU2cCnw947yXA5cEt4gIjnge8CZqnoccA3wdUd5lkvI1BHafjowCzgGeLe/n0vp35dV9W2qemNo253AG0Rksv/843iNoKK4PiTP8ojXrwY+65/nF4H/39/+XeBu/7s4FtiKN+A94p/7In+/4/EybI8OHzTq+/RfulVV3+xv+xPe5CCNC0PnsDa0fRawEJgJLBSRw/wJwyXAu/1IwQ3AF0LvCV/Pa/Fm+m/Fq1gQ8CP8woAi8nrgAFV9sESmGcDGkm0b/O1JLAZ+51/Dq/xtxwNn++fzYUmoN+VwD86LkOvfgLeLtzr6KLAi9NpG4I3ilc84AbgXry7UG/zn60rO7y9Szm9EkFgl1ag5ZwHf8R/f6D9/AG+gvFb9fgWqWm4hvYnAj0XkSLzqiTmXN6nq70QEEQnf7NPxisP9RjxTbDteCr4Li/wfLjBkhvg24AZV7Qf+S0TuBt4MvJByvBWlG1RVReQneKWSr8Vb4v9VzPvP1pj6Rf4M+gRgpew3OR/g/39ScExf5l0iMiniMH9Qr8BcKXHf5xtF5HI8U9SrgDUxcoe5SlW/GbH931R1l38uDwGH+8c9Gljnn9NYvIEuYIW/fycwQVUDH8jPgKDF7krgKyKyCPgEcF3EZwvRVTorqfX1G1X9H1+uW/Hule4KjgOeaai05lE/nrl2IZBX1ceD71tVXxGRrXiKfy6e8n4d3n0xG8+8FPAcDTCdNgJTCnVCPJvlSXgDg+INtioiXyL+R1bKPvav7saFtn8NWKuqHxKvDehdZYj2dTzfQhB2LMBWfwZZK+KaDYXPB4aeE+yPfivlWrzl/MvAygpDptuAPlWdlbpnPHHyxX2f1wELVHWzbyZ7ZxWfHa5S24/3Wxa8QfasmPeEowkjUdU9IvIbvMYuH8Ez15Sy1d8eLkx3HF5tJQh9r+KNwGMTzqP0Oinp90UchZh9bwR+jtd3oZR78DqfTVDVnSKyHvgMnlL4pxIZklbrIwYzH9WPM/G6Jx2uXjXOw4DH8GZGdwCfCGzM4jnMAF7EK+sc8Djejw/gjND2iUCv//jccoRS1TuASXimHfCWz5PFq0uPiOREJDALlMrjym/xTBztvtnn7XhFA58Ajhavv/VE/EqXDjI/g1c6+BKiZ7Iux3gBeExEPgyDfo/gGvwbXstRfJkPpLxzj/s+JwDP+ia6syuRO4X1wDwR+XP/czt8E9AQ1OvA9qJ4FTdhuK38R3gmtPtjVq3fB84NfA/+hOcb7DeTPc7++/Q09q9co67he0TkIN8/sADPZPNfwGtE5NUicgD7VzFxxwj4E15ZnlJ+B1yBV3iulHXAp/B8bOAV/ZwLTMFTfgGvByJ7KI80TCnUj7PwZithbgE+pqq/xpt1bfBt8UF44nXAP4WcapcB/yAiv2OoHfhK4AoRWYe3AimXr+O311Sv5eGZwDdEJKg6GTjjSuVx5ed4P7bNeD6BL6nqf6rqU3h18B8ErsfrI+zK9cBTqvpQ0j4he/y/Rrx+NnCef55b2d/m8fPAiSKyBc/uPMM3cawTz1ke5Z8YJOH7/ApwH/AbPD+QC2GfwiZ/JRj3uTvwJgU3iMiDeEoiLvT5POBqEbkXb+WwK3ScjXimvUhfjXplm88Bfigi2/Bm29eo6m3+Lj8E3iEifwDewv4VyoPAPvEc7Rf6234P/ATvPrtFVTeoahGvCup9eM7f8LW6jvh7cDURqy/1+KZG94K5B89kdK+/7z48U9EGHdqk50T/+COe1CqphtGMiMg/Aj2q+s+NlqUVEZFXqWoQr78YOCSobSZeHsBdwFGaYfcy34Q2R1U/U8Nj/h74YC1Cd0PHPAC4G3jbaKjuYCsFo+UQkY3Am4CfNlqWFuYUf7b9R7yomssBROSv8GboX85SIWTIRXimn1oyBVg8GhQC2ErBMAzDCGErBcMwDGMQUwqGYRjGIKYUDMMwjEFMKRiGYRiDmFIwDMMwBjGlYBiGYQzy/wB98XGKpVCWkwAAAABJRU5ErkJggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.scatter(Y_pred, y_test)\n", "#plt.plot(X_test, Y_pred)\n", "plt.xlabel(\"Actual Net Hourly Electrical Energy Output (MW)\")\n", "plt.ylabel(\"Predicted Net Hourly Electrical Energy Output (MW)\")" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The weights and biase are [array([[-0.34623718],\n", " [-0.31071773],\n", " [ 0.73499143],\n", " [ 0.06738219]], dtype=float32), 0.37082589]\n" ] } ], "source": [ "print(\"The weights and biase are\", model.get_weights())" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python [conda env:tensorflow]", "language": "python", "name": "conda-env-tensorflow-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.4" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: Chapter03/Images/Readme.md ================================================ Images used in the chapter ================================================ FILE: Chapter03/LogisticRegressor.py ================================================ import tensorflow as tf import numpy as np import pandas as pd import matplotlib.pyplot as plt from sklearn.preprocessing import MinMaxScaler from sklearn.metrics import mean_squared_error, r2_score from sklearn.model_selection import train_test_split class LogisticRegressor: def __init__(self, d, n, lr=0.001): # Place holders for input-output training data self.X = tf.placeholder(tf.float32, \ shape=[None, d], name='input') self.Y = tf.placeholder(tf.float32, \ name='output') # Variables for weight and bias self.b = tf.Variable(tf.zeros(n), dtype=tf.float32) self.W = tf.Variable(tf.random_normal([d, n]), \ dtype=tf.float32) # The Linear Regression Model h = tf.matmul(self.X, self.W) + self.b self.Ypred = tf.nn.sigmoid(h) # Loss function self.loss = tf.reduce_mean(-tf.reduce_sum(self.Y * tf.log(self.Ypred), \ reduction_indices=1), name='cross-entropy-loss') # Gradient Descent with learning # rate of 0.05 to minimize loss optimizer = tf.train.GradientDescentOptimizer(lr) self.optimize = optimizer.minimize(self.loss) # Initializing Variables init_op = tf.global_variables_initializer() self.sess = tf.Session() self.sess.run(init_op) def fit(self, X, Y, epochs=500): total = [] for i in range(epochs): _, l = self.sess.run([self.optimize, self.loss], \ feed_dict={self.X: X, self.Y: Y}) total.append(l) if i % 1000 == 0: print('Epoch {0}/{1}: Loss {2}'.format(i, epochs, l)) return total def predict(self, X): return self.sess.run(self.Ypred, feed_dict={self.X: X}) def get_weights(self): return self.sess.run([self.W, self.b]) ================================================ FILE: Chapter03/Sigmoid_function.ipynb ================================================ { "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "import tensorflow as tf\n", "import matplotlib.pyplot as plt\n", "% matplotlib inline" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5,1,'Sigmoidal Activation Function for weight 0.242349 and bias 0.0')" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plotting Sigmoidal Activation function\n", "x = np.linspace(-10,10,50)\n", "X = tf.placeholder(tf.float32, shape = None)\n", "W = tf.constant(np.random.random())\n", "bias = tf.constant(0.0)\n", "h = W*X + bias\n", "out = tf.sigmoid(h)\n", "out_d = out*(1-out)\n", "init = tf.global_variables_initializer()\n", "with tf.Session() as sess:\n", " sess.run(init)\n", " y, y_d, w, b = sess.run([out,out_d, W, bias], feed_dict={X:x})\n", "#print(\"W= {} b= {}\".format(w,b))\n", "plt.plot(x, y)\n", "plt.plot(x, y_d)\n", "plt.grid(b=True)\n", "plt.xlabel('Activity of Neuron')\n", "plt.ylabel('Output of Neuron')\n", "plt.title('Sigmoidal Activation Function for weight %f and bias 0.0' %w)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python [conda env:tensorflow]", "language": "python", "name": "conda-env-tensorflow-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.4" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: Chapter03/Wine_quality_using_DecisionTrees.ipynb ================================================ { "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# Import the modules\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "from sklearn.preprocessing import MinMaxScaler, LabelEncoder\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.metrics import confusion_matrix, accuracy_score\n", "from sklearn.tree import DecisionTreeClassifier # The decision tree Classifier from Scikit\n", "import seaborn as sns\n", "% matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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fixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholquality
count1599.0000001599.0000001599.0000001599.0000001599.0000001599.0000001599.0000001599.0000001599.0000001599.0000001599.0000001599.000000
mean8.3196370.5278210.2709762.5388060.08746715.87492246.4677920.9967473.3111130.65814910.4229835.636023
std1.7410960.1790600.1948011.4099280.04706510.46015732.8953240.0018870.1543860.1695071.0656680.807569
min4.6000000.1200000.0000000.9000000.0120001.0000006.0000000.9900702.7400000.3300008.4000003.000000
25%7.1000000.3900000.0900001.9000000.0700007.00000022.0000000.9956003.2100000.5500009.5000005.000000
50%7.9000000.5200000.2600002.2000000.07900014.00000038.0000000.9967503.3100000.62000010.2000006.000000
75%9.2000000.6400000.4200002.6000000.09000021.00000062.0000000.9978353.4000000.73000011.1000006.000000
max15.9000001.5800001.00000015.5000000.61100072.000000289.0000001.0036904.0100002.00000014.9000008.000000
\n", "
" ], "text/plain": [ " fixed acidity volatile acidity citric acid residual sugar \\\n", "count 1599.000000 1599.000000 1599.000000 1599.000000 \n", "mean 8.319637 0.527821 0.270976 2.538806 \n", "std 1.741096 0.179060 0.194801 1.409928 \n", "min 4.600000 0.120000 0.000000 0.900000 \n", "25% 7.100000 0.390000 0.090000 1.900000 \n", "50% 7.900000 0.520000 0.260000 2.200000 \n", "75% 9.200000 0.640000 0.420000 2.600000 \n", "max 15.900000 1.580000 1.000000 15.500000 \n", "\n", " chlorides free sulfur dioxide total sulfur dioxide density \\\n", "count 1599.000000 1599.000000 1599.000000 1599.000000 \n", "mean 0.087467 15.874922 46.467792 0.996747 \n", "std 0.047065 10.460157 32.895324 0.001887 \n", "min 0.012000 1.000000 6.000000 0.990070 \n", "25% 0.070000 7.000000 22.000000 0.995600 \n", "50% 0.079000 14.000000 38.000000 0.996750 \n", "75% 0.090000 21.000000 62.000000 0.997835 \n", "max 0.611000 72.000000 289.000000 1.003690 \n", "\n", " pH sulphates alcohol quality \n", "count 1599.000000 1599.000000 1599.000000 1599.000000 \n", "mean 3.311113 0.658149 10.422983 5.636023 \n", "std 0.154386 0.169507 1.065668 0.807569 \n", "min 2.740000 0.330000 8.400000 3.000000 \n", "25% 3.210000 0.550000 9.500000 5.000000 \n", "50% 3.310000 0.620000 10.200000 6.000000 \n", "75% 3.400000 0.730000 11.100000 6.000000 \n", "max 4.010000 2.000000 14.900000 8.000000 " ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "filename = 'winequality-red.csv' #Download the file from https://archive.ics.uci.edu/ml/datasets/wine+quality\n", "df = pd.read_csv(filename, sep=';')\n", "df.describe()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "5 681\n", "6 638\n", "7 199\n", "4 53\n", "8 18\n", "3 10\n", "Name: quality, dtype: int64" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['quality'].value_counts()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "#categorize wine quality in three levels\n", "bins = (0,3.5,5.5,10)\n", "categories = pd.cut(df['quality'], bins, labels = ['bad','ok','good'])\n", "df['quality'] = categories" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "good 855\n", "ok 734\n", "bad 10\n", "Name: quality, dtype: int64" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['quality'].value_counts()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "# Preprocessing and splitting data to X and y\n", "X = df.drop(['quality'], axis = 1)\n", "scaler = MinMaxScaler()\n", "X_new = scaler.fit_transform(X)\n", "y = df['quality']\n", "from sklearn.preprocessing import LabelEncoder\n", "labelencoder_y = LabelEncoder()\n", "y = labelencoder_y.fit_transform(y)\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 323)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "classifier = DecisionTreeClassifier(max_depth=3)\n", "classifier.fit(X_train, y_train)\n", "#Predicting the Test Set\n", "y_pred = classifier.predict(X_test)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "cm = confusion_matrix(y_test, y_pred)\n", "sns.heatmap(cm,annot=True,fmt='2.0f')" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy is 0.703125\n" ] } ], "source": [ "print(\"Accuracy is {}\".format(accuracy_score(y_test, y_pred)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Ensemble Learning in Decision trees\n", "\n", "The code below implements bagging technique a way to implement enseble learning. To know more about ensemble learning refer to the section __Ensemble Learning__ in __Chapter 3__." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "from sklearn.ensemble import BaggingClassifier\n", "bag_classifier = BaggingClassifier(\n", " DecisionTreeClassifier(), n_estimators=500, max_samples=1000,\\\n", " bootstrap=True, n_jobs=-1)\n", "\n", "bag_classifier.fit(X_train, y_train)\n", "\n", "y_pred = bag_classifier.predict(X_test)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy is 0.778125\n" ] } ], "source": [ "print(\"Accuracy is {}\".format(accuracy_score(y_test, y_pred)))" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "cm = confusion_matrix(y_test, y_pred)\n", "sns.heatmap(cm,annot=True,fmt='2.0f')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "## Random Forest " ] } ], "metadata": { "kernelspec": { "display_name": "Python [conda env:tensorflow]", "language": "python", "name": "conda-env-tensorflow-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.4" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: Chapter03/Wine_quality_using_Ensemble_learning.ipynb ================================================ { "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# Import the modules\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "from sklearn.preprocessing import MinMaxScaler, LabelEncoder\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.metrics import confusion_matrix, accuracy_score\n", "from sklearn.svm import SVC \n", "from sklearn.naive_bayes import GaussianNB \n", "from LogisticRegressor import LogisticRegressor \n", "from sklearn.tree import DecisionTreeClassifier\n", "from sklearn.ensemble import VotingClassifier\n", "import seaborn as sns\n", "% matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "filename = 'winequality-red.csv' #Download the file from https://archive.ics.uci.edu/ml/datasets/wine+quality\n", "df = pd.read_csv(filename, sep=';')\n", "#categorize wine quality in three levels\n", "bins = (0,3.5,5.5,10)\n", "categories = pd.cut(df['quality'], bins, labels = ['bad','ok','good'])\n", "df['quality'] = categories\n", "# Preprocessing and splitting data to X and y\n", "X = df.drop(['quality'], axis = 1)\n", "scaler = MinMaxScaler()\n", "X_new = scaler.fit_transform(X)\n", "y = df['quality']\n", "from sklearn.preprocessing import LabelEncoder\n", "labelencoder_y = LabelEncoder()\n", "y = labelencoder_y.fit_transform(y)\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 323)\n", "\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([2, 2, 1, ..., 2, 2, 1])" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y_train" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "good 855\n", "ok 734\n", "bad 10\n", "Name: quality, dtype: int64" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['quality'].value_counts()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "clf1 = SVC(random_state=22)\n", "clf2 = DecisionTreeClassifier(random_state=23)\n", "clf3 = GaussianNB()\n", "X = np.array(X_train)\n", "y = np.array(y_train)\n", "eclf = VotingClassifier(estimators=[\n", " ('lr', clf1), ('rf', clf2), ('gnb', clf3)], voting='hard')\n", "eclf = eclf.fit(X, y)\n", "y_pred = eclf.predict(X_test)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "cm = confusion_matrix(y_test, y_pred)\n", "sns.heatmap(cm,annot=True,fmt='2.0f')" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy is 0.740625\n" ] } ], "source": [ "print(\"Accuracy is {}\".format(accuracy_score(y_test, y_pred)))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python [conda env:tensorflow]", "language": "python", "name": "conda-env-tensorflow-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.4" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: Chapter03/Wine_quality_using_GaussianNB.ipynb ================================================ { "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# Import the modules\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "from sklearn.preprocessing import MinMaxScaler, LabelEncoder\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.metrics import confusion_matrix, accuracy_score\n", "from sklearn.naive_bayes import GaussianNB \n", "from Wine_quality_using_logistic_regressor import LogisticRegressor \n", "import seaborn as sns\n", "% matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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fixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholquality
count1599.0000001599.0000001599.0000001599.0000001599.0000001599.0000001599.0000001599.0000001599.0000001599.0000001599.0000001599.000000
mean8.3196370.5278210.2709762.5388060.08746715.87492246.4677920.9967473.3111130.65814910.4229835.636023
std1.7410960.1790600.1948011.4099280.04706510.46015732.8953240.0018870.1543860.1695071.0656680.807569
min4.6000000.1200000.0000000.9000000.0120001.0000006.0000000.9900702.7400000.3300008.4000003.000000
25%7.1000000.3900000.0900001.9000000.0700007.00000022.0000000.9956003.2100000.5500009.5000005.000000
50%7.9000000.5200000.2600002.2000000.07900014.00000038.0000000.9967503.3100000.62000010.2000006.000000
75%9.2000000.6400000.4200002.6000000.09000021.00000062.0000000.9978353.4000000.73000011.1000006.000000
max15.9000001.5800001.00000015.5000000.61100072.000000289.0000001.0036904.0100002.00000014.9000008.000000
\n", "
" ], "text/plain": [ " fixed acidity volatile acidity citric acid residual sugar \\\n", "count 1599.000000 1599.000000 1599.000000 1599.000000 \n", "mean 8.319637 0.527821 0.270976 2.538806 \n", "std 1.741096 0.179060 0.194801 1.409928 \n", "min 4.600000 0.120000 0.000000 0.900000 \n", "25% 7.100000 0.390000 0.090000 1.900000 \n", "50% 7.900000 0.520000 0.260000 2.200000 \n", "75% 9.200000 0.640000 0.420000 2.600000 \n", "max 15.900000 1.580000 1.000000 15.500000 \n", "\n", " chlorides free sulfur dioxide total sulfur dioxide density \\\n", "count 1599.000000 1599.000000 1599.000000 1599.000000 \n", "mean 0.087467 15.874922 46.467792 0.996747 \n", "std 0.047065 10.460157 32.895324 0.001887 \n", "min 0.012000 1.000000 6.000000 0.990070 \n", "25% 0.070000 7.000000 22.000000 0.995600 \n", "50% 0.079000 14.000000 38.000000 0.996750 \n", "75% 0.090000 21.000000 62.000000 0.997835 \n", "max 0.611000 72.000000 289.000000 1.003690 \n", "\n", " pH sulphates alcohol quality \n", "count 1599.000000 1599.000000 1599.000000 1599.000000 \n", "mean 3.311113 0.658149 10.422983 5.636023 \n", "std 0.154386 0.169507 1.065668 0.807569 \n", "min 2.740000 0.330000 8.400000 3.000000 \n", "25% 3.210000 0.550000 9.500000 5.000000 \n", "50% 3.310000 0.620000 10.200000 6.000000 \n", "75% 3.400000 0.730000 11.100000 6.000000 \n", "max 4.010000 2.000000 14.900000 8.000000 " ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "filename = 'winequality-red.csv' #Download the file from https://archive.ics.uci.edu/ml/datasets/wine+quality\n", "df = pd.read_csv(filename, sep=';')\n", "df.describe()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "5 681\n", "6 638\n", "7 199\n", "4 53\n", "8 18\n", "3 10\n", "Name: quality, dtype: int64" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['quality'].value_counts()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "#categorize wine quality in three levels\n", "#bins = (0,3.5,5.5,10)\n", "#categories = pd.cut(df['quality'], bins, labels = ['bad','ok','good'])\n", "#df['quality'] = categories\n", "\n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "#categorize wine quality in two levels\n", "bins = (0,5.5,10)\n", "categories = pd.cut(df['quality'], bins, labels = ['bad','good'])\n", "df['quality'] = categories" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "good 855\n", "bad 744\n", "Name: quality, dtype: int64" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['quality'].value_counts()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "# Preprocessing and splitting data to X and y\n", "X = df.drop(['quality'], axis = 1)\n", "scaler = MinMaxScaler()\n", "X_new = scaler.fit_transform(X)\n", "y = df['quality']\n", "from sklearn.preprocessing import LabelEncoder\n", "labelencoder_y = LabelEncoder()\n", "y = labelencoder_y.fit_transform(y)\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 323)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "classifier = GaussianNB()\n", "classifier.fit(X_train, y_train)\n", "#Predicting the Test Set\n", "y_pred = classifier.predict(X_test)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "cm = confusion_matrix(y_test, y_pred)\n", "sns.heatmap(cm,annot=True,fmt='2.0f')" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy is 0.7125\n" ] } ], "source": [ "print(\"Accuracy is {}\".format(accuracy_score(y_test, y_pred)))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python [conda env:tensorflow]", "language": "python", "name": "conda-env-tensorflow-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.4" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: Chapter03/Wine_quality_using_SVM-with_GridSearch.ipynb ================================================ { "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# Import the modules\n", "import tensorflow as tf\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "from sklearn.preprocessing import MinMaxScaler\n", "from sklearn.model_selection import cross_val_score\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.metrics import confusion_matrix\n", "from sklearn.svm import SVC\n", "import seaborn as sns\n", "% matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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fixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholquality
count1599.0000001599.0000001599.0000001599.0000001599.0000001599.0000001599.0000001599.0000001599.0000001599.0000001599.0000001599.000000
mean8.3196370.5278210.2709762.5388060.08746715.87492246.4677920.9967473.3111130.65814910.4229835.636023
std1.7410960.1790600.1948011.4099280.04706510.46015732.8953240.0018870.1543860.1695071.0656680.807569
min4.6000000.1200000.0000000.9000000.0120001.0000006.0000000.9900702.7400000.3300008.4000003.000000
25%7.1000000.3900000.0900001.9000000.0700007.00000022.0000000.9956003.2100000.5500009.5000005.000000
50%7.9000000.5200000.2600002.2000000.07900014.00000038.0000000.9967503.3100000.62000010.2000006.000000
75%9.2000000.6400000.4200002.6000000.09000021.00000062.0000000.9978353.4000000.73000011.1000006.000000
max15.9000001.5800001.00000015.5000000.61100072.000000289.0000001.0036904.0100002.00000014.9000008.000000
\n", "
" ], "text/plain": [ " fixed acidity volatile acidity citric acid residual sugar \\\n", "count 1599.000000 1599.000000 1599.000000 1599.000000 \n", "mean 8.319637 0.527821 0.270976 2.538806 \n", "std 1.741096 0.179060 0.194801 1.409928 \n", "min 4.600000 0.120000 0.000000 0.900000 \n", "25% 7.100000 0.390000 0.090000 1.900000 \n", "50% 7.900000 0.520000 0.260000 2.200000 \n", "75% 9.200000 0.640000 0.420000 2.600000 \n", "max 15.900000 1.580000 1.000000 15.500000 \n", "\n", " chlorides free sulfur dioxide total sulfur dioxide density \\\n", "count 1599.000000 1599.000000 1599.000000 1599.000000 \n", "mean 0.087467 15.874922 46.467792 0.996747 \n", "std 0.047065 10.460157 32.895324 0.001887 \n", "min 0.012000 1.000000 6.000000 0.990070 \n", "25% 0.070000 7.000000 22.000000 0.995600 \n", "50% 0.079000 14.000000 38.000000 0.996750 \n", "75% 0.090000 21.000000 62.000000 0.997835 \n", "max 0.611000 72.000000 289.000000 1.003690 \n", "\n", " pH sulphates alcohol quality \n", "count 1599.000000 1599.000000 1599.000000 1599.000000 \n", "mean 3.311113 0.658149 10.422983 5.636023 \n", "std 0.154386 0.169507 1.065668 0.807569 \n", "min 2.740000 0.330000 8.400000 3.000000 \n", "25% 3.210000 0.550000 9.500000 5.000000 \n", "50% 3.310000 0.620000 10.200000 6.000000 \n", "75% 3.400000 0.730000 11.100000 6.000000 \n", "max 4.010000 2.000000 14.900000 8.000000 " ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "filename = 'winequality-red.csv' #Download the file from https://archive.ics.uci.edu/ml/datasets/wine+quality\n", "df = pd.read_csv(filename, sep=';')\n", "df.describe()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "#categorize wine quality\n", "bins = (2,5.5,8)\n", "categories = pd.cut(df['quality'], bins, labels = ['bad','good'])\n", "df['quality'] = categories\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "good 855\n", "bad 744\n", "Name: quality, dtype: int64" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['quality'].value_counts()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "#splitting data to X ve y\n", "X = df.drop(['quality'], axis = 1)\n", "scaler = MinMaxScaler()\n", "X_new = scaler.fit_transform(X)\n", "y = df['quality']\n", "from sklearn.preprocessing import LabelEncoder\n", "labelencoder_y = LabelEncoder()\n", "y = labelencoder_y.fit_transform(y)\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 323)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "classifier = SVC(kernel = 'rbf', random_state = 0)\n", "classifier.fit(X_train, y_train)\n", "#Predicting the Test Set\n", "y_pred = classifier.predict(X_test)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "cm = confusion_matrix(y_test, y_pred)\n", "sns.heatmap(cm,annot=True,fmt='2.0f')" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy Mean 0.7107344980314961 Accuracy Variance 0.018812075979578975\n" ] } ], "source": [ "accuracies = cross_val_score(estimator = classifier, X = X_train,\\\n", " y = y_train, cv = 10)\n", "print(\"Accuracy Mean {} Accuracy Variance {}\".format(accuracies.mean(),accuracies.std()))" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.73964034401876466" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Grid search for best model and parameters\n", "from sklearn.model_selection import GridSearchCV\n", "#parameters = {'kernel':('linear', 'rbf'), 'C':[1, 10]}\n", "classifier = SVC()\n", "parameters = [{'C': [1, 10], 'kernel': ['linear']},\n", " {'C': [1, 10], 'kernel': ['rbf'],\n", " 'gamma': [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]}]\n", "grid_search = GridSearchCV(estimator = classifier,\n", " param_grid = parameters,\n", " scoring = 'accuracy',\n", " cv = 10,)\n", "grid_search.fit(X_train, y_train)\n", "best_accuracy = grid_search.best_score_\n", "best_parameters = grid_search.best_params_\n", "#here is the best accuracy\n", "best_accuracy" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'kernel': 'linear', 'C': 1}\n" ] } ], "source": [ "print(best_parameters)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "classifier = SVC(C = 1, kernel = 'linear', random_state = 323, gamma = 0.9)\n", "classifier.fit(X_train, y_train)\n", "\n", "#Predicting the Test Set\n", "y_pred = classifier.predict(X_test)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "cm = confusion_matrix(y_test, y_pred)\n", "sns.heatmap(cm,annot=True,fmt='2.0f')" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy Mean 0.7396222933070866 Accuracy Variance 0.03402727407384125\n" ] } ], "source": [ "accuracies = cross_val_score(estimator = classifier, X = X_train,\\\n", " y = y_train, cv = 10)\n", "print(\"Accuracy Mean {} Accuracy Variance {}\".format(accuracies.mean(),accuracies.std()))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python [conda env:tensorflow]", "language": "python", "name": "conda-env-tensorflow-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.4" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: Chapter03/Wine_quality_using_SVM.ipynb ================================================ { "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# Import the modules\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "from sklearn.preprocessing import MinMaxScaler, LabelEncoder\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.metrics import confusion_matrix, accuracy_score\n", "from sklearn.svm import SVC # The SVM Classifier from Scikit\n", "import seaborn as sns\n", "% matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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fixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholquality
count1599.0000001599.0000001599.0000001599.0000001599.0000001599.0000001599.0000001599.0000001599.0000001599.0000001599.0000001599.000000
mean8.3196370.5278210.2709762.5388060.08746715.87492246.4677920.9967473.3111130.65814910.4229835.636023
std1.7410960.1790600.1948011.4099280.04706510.46015732.8953240.0018870.1543860.1695071.0656680.807569
min4.6000000.1200000.0000000.9000000.0120001.0000006.0000000.9900702.7400000.3300008.4000003.000000
25%7.1000000.3900000.0900001.9000000.0700007.00000022.0000000.9956003.2100000.5500009.5000005.000000
50%7.9000000.5200000.2600002.2000000.07900014.00000038.0000000.9967503.3100000.62000010.2000006.000000
75%9.2000000.6400000.4200002.6000000.09000021.00000062.0000000.9978353.4000000.73000011.1000006.000000
max15.9000001.5800001.00000015.5000000.61100072.000000289.0000001.0036904.0100002.00000014.9000008.000000
\n", "
" ], "text/plain": [ " fixed acidity volatile acidity citric acid residual sugar \\\n", "count 1599.000000 1599.000000 1599.000000 1599.000000 \n", "mean 8.319637 0.527821 0.270976 2.538806 \n", "std 1.741096 0.179060 0.194801 1.409928 \n", "min 4.600000 0.120000 0.000000 0.900000 \n", "25% 7.100000 0.390000 0.090000 1.900000 \n", "50% 7.900000 0.520000 0.260000 2.200000 \n", "75% 9.200000 0.640000 0.420000 2.600000 \n", "max 15.900000 1.580000 1.000000 15.500000 \n", "\n", " chlorides free sulfur dioxide total sulfur dioxide density \\\n", "count 1599.000000 1599.000000 1599.000000 1599.000000 \n", "mean 0.087467 15.874922 46.467792 0.996747 \n", "std 0.047065 10.460157 32.895324 0.001887 \n", "min 0.012000 1.000000 6.000000 0.990070 \n", "25% 0.070000 7.000000 22.000000 0.995600 \n", "50% 0.079000 14.000000 38.000000 0.996750 \n", "75% 0.090000 21.000000 62.000000 0.997835 \n", "max 0.611000 72.000000 289.000000 1.003690 \n", "\n", " pH sulphates alcohol quality \n", "count 1599.000000 1599.000000 1599.000000 1599.000000 \n", "mean 3.311113 0.658149 10.422983 5.636023 \n", "std 0.154386 0.169507 1.065668 0.807569 \n", "min 2.740000 0.330000 8.400000 3.000000 \n", "25% 3.210000 0.550000 9.500000 5.000000 \n", "50% 3.310000 0.620000 10.200000 6.000000 \n", "75% 3.400000 0.730000 11.100000 6.000000 \n", "max 4.010000 2.000000 14.900000 8.000000 " ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "filename = 'winequality-red.csv' #Download the file from https://archive.ics.uci.edu/ml/datasets/wine+quality\n", "df = pd.read_csv(filename, sep=';')\n", "df.describe()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "5 681\n", "6 638\n", "7 199\n", "4 53\n", "8 18\n", "3 10\n", "Name: quality, dtype: int64" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['quality'].value_counts()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "#categorize wine quality in three levels\n", "bins = (0,3.5,5.5,10)\n", "categories = pd.cut(df['quality'], bins, labels = ['bad','ok','good'])\n", "df['quality'] = categories\n", "\n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "#categorize wine quality in two levels\n", "#bins = (0,5.5,10)\n", "#categories = pd.cut(df['quality'], bins, labels = ['bad','good'])\n", "#df['quality'] = categories\n", "\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "good 855\n", "ok 734\n", "bad 10\n", "Name: quality, dtype: int64" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['quality'].value_counts()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "# Preprocessing and splitting data to X and y\n", "X = df.drop(['quality'], axis = 1)\n", "scaler = MinMaxScaler()\n", "X_new = scaler.fit_transform(X)\n", "y = df['quality']\n", "from sklearn.preprocessing import LabelEncoder\n", "labelencoder_y = LabelEncoder()\n", "y = labelencoder_y.fit_transform(y)\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 323)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "classifier = SVC(kernel = 'rbf', random_state = 45)\n", "classifier.fit(X_train, y_train)\n", "#Predicting the Test Set\n", "y_pred = classifier.predict(X_test)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "cm = confusion_matrix(y_test, y_pred)\n", "sns.heatmap(cm,annot=True,fmt='2.0f')" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy is 0.659375\n" ] } ], "source": [ "print(\"Accuracy is {}\".format(accuracy_score(y_test, y_pred)))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python [conda env:tensorflow]", "language": "python", "name": "conda-env-tensorflow-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.4" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: Chapter03/Wine_quality_using_logistic_regressor.ipynb ================================================ { "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# Import the modules\n", "import tensorflow as tf\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "from sklearn.preprocessing import MinMaxScaler\n", "from sklearn.metrics import mean_squared_error, r2_score\n", "from sklearn.model_selection import train_test_split\n", "% matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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fixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholquality
count1599.0000001599.0000001599.0000001599.0000001599.0000001599.0000001599.0000001599.0000001599.0000001599.0000001599.0000001599.000000
mean8.3196370.5278210.2709762.5388060.08746715.87492246.4677920.9967473.3111130.65814910.4229835.636023
std1.7410960.1790600.1948011.4099280.04706510.46015732.8953240.0018870.1543860.1695071.0656680.807569
min4.6000000.1200000.0000000.9000000.0120001.0000006.0000000.9900702.7400000.3300008.4000003.000000
25%7.1000000.3900000.0900001.9000000.0700007.00000022.0000000.9956003.2100000.5500009.5000005.000000
50%7.9000000.5200000.2600002.2000000.07900014.00000038.0000000.9967503.3100000.62000010.2000006.000000
75%9.2000000.6400000.4200002.6000000.09000021.00000062.0000000.9978353.4000000.73000011.1000006.000000
max15.9000001.5800001.00000015.5000000.61100072.000000289.0000001.0036904.0100002.00000014.9000008.000000
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" ], "text/plain": [ " fixed acidity volatile acidity citric acid residual sugar \\\n", "count 1599.000000 1599.000000 1599.000000 1599.000000 \n", "mean 8.319637 0.527821 0.270976 2.538806 \n", "std 1.741096 0.179060 0.194801 1.409928 \n", "min 4.600000 0.120000 0.000000 0.900000 \n", "25% 7.100000 0.390000 0.090000 1.900000 \n", "50% 7.900000 0.520000 0.260000 2.200000 \n", "75% 9.200000 0.640000 0.420000 2.600000 \n", "max 15.900000 1.580000 1.000000 15.500000 \n", "\n", " chlorides free sulfur dioxide total sulfur dioxide density \\\n", "count 1599.000000 1599.000000 1599.000000 1599.000000 \n", "mean 0.087467 15.874922 46.467792 0.996747 \n", "std 0.047065 10.460157 32.895324 0.001887 \n", "min 0.012000 1.000000 6.000000 0.990070 \n", "25% 0.070000 7.000000 22.000000 0.995600 \n", "50% 0.079000 14.000000 38.000000 0.996750 \n", "75% 0.090000 21.000000 62.000000 0.997835 \n", "max 0.611000 72.000000 289.000000 1.003690 \n", "\n", " pH sulphates alcohol quality \n", "count 1599.000000 1599.000000 1599.000000 1599.000000 \n", "mean 3.311113 0.658149 10.422983 5.636023 \n", "std 0.154386 0.169507 1.065668 0.807569 \n", "min 2.740000 0.330000 8.400000 3.000000 \n", "25% 3.210000 0.550000 9.500000 5.000000 \n", "50% 3.310000 0.620000 10.200000 6.000000 \n", "75% 3.400000 0.730000 11.100000 6.000000 \n", "max 4.010000 2.000000 14.900000 8.000000 " ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "filename = 'winequality-red.csv' #Download the file from https://archive.ics.uci.edu/ml/datasets/wine+quality\n", "df = pd.read_csv(filename, sep=';')\n", "df.describe()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "columns = df.columns.values" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['fixed acidity' 'volatile acidity' 'citric acid' 'residual sugar'\n", " 'chlorides' 'free sulfur dioxide' 'total sulfur dioxide' 'density' 'pH'\n", " 'sulphates' 'alcohol' 'quality']\n" ] } ], "source": [ "print(columns)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/am/anaconda3/envs/tensorflow/lib/python3.5/site-packages/pandas/core/indexing.py:194: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame\n", "\n", "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", " self._setitem_with_indexer(indexer, value)\n" ] } ], "source": [ "X, Y = df[columns[0:-1]], df[columns[-1]]\n", "scaler = MinMaxScaler()\n", "X_new = scaler.fit_transform(X)\n", "Y.loc[(Y<5)]=3\n", "Y.loc[(Y<8) & (Y>= 5 )] = 2\n", "Y.loc[(Y>=8)] = 1\n", "Y_new = pd.get_dummies(Y) # One hot encode\n", "X_train, X_test, Y_train, y_test = \\\n", " train_test_split(X_new, Y_new, test_size=0.4, random_state=333)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " 1 2 3\n", "count 959.000000 959.000000 959.000000\n", "mean 0.011470 0.953076 0.035454\n", "std 0.106539 0.211586 0.185020\n", "min 0.000000 0.000000 0.000000\n", "25% 0.000000 1.000000 0.000000\n", "50% 0.000000 1.000000 0.000000\n", "75% 0.000000 1.000000 0.000000\n", "max 1.000000 1.000000 1.000000" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Y_train.describe()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "class LogisticRegressor:\n", " def __init__(self,d,n, lr=0.001 ):\n", " \n", " # Place holders for input-output training data\n", " self.X = tf.placeholder(tf.float32,\\\n", " shape=[None,d], name='input')\n", " self.Y = tf.placeholder(tf.float32,\\\n", " name='output')\n", " # Variables for weight and bias\n", " self.b = tf.Variable(tf.zeros(n), dtype=tf.float32)\n", " self.W = tf.Variable(tf.random_normal([d,n]),\\\n", " dtype=tf.float32)\n", " \n", " # The Linear Regression Model\n", " h = tf.matmul(self.X, self.W) + self.b\n", " self.Ypred = tf.nn.sigmoid(h)\n", " \n", " \n", " # Loss function\n", " self.loss = cost = tf.reduce_mean(-tf.reduce_sum(self.Y*tf.log(self.Ypred),\\\n", " reduction_indices=1), name = 'cross-entropy-loss')\n", "\n", " # Gradient Descent with learning \n", " # rate of 0.05 to minimize loss\n", " optimizer = tf.train.GradientDescentOptimizer(lr)\n", " self.optimize = optimizer.minimize(self.loss)\n", "\n", " # Initializing Variables\n", " init_op = tf.global_variables_initializer()\n", " self.sess = tf.Session()\n", " self.sess.run(init_op)\n", " \n", " def fit(self, X, Y,epochs=500):\n", " total = []\n", " for i in range(epochs):\n", " _, l = self.sess.run([self.optimize,self.loss],\\\n", " feed_dict={self.X: X, self.Y: Y})\n", " total.append(l)\n", " if i%100==0:\n", " print('Epoch {0}/{1}: Loss {2}'.format(i,epochs,l))\n", " return total\n", " \n", " def predict(self, X):\n", " return self.sess.run(self.Ypred, feed_dict={self.X:X})\n", " \n", " \n", " def get_weights(self):\n", " return self.sess.run([self.W, self.b])\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 0/5000: Loss 0.8243454694747925\n", "Epoch 100/5000: Loss 0.7768917679786682\n", "Epoch 200/5000: Loss 0.733469545841217\n", "Epoch 300/5000: Loss 0.6937313675880432\n", "Epoch 400/5000: Loss 0.6573486924171448\n", "Epoch 500/5000: Loss 0.6240147352218628\n", "Epoch 600/5000: Loss 0.5934458374977112\n", "Epoch 700/5000: Loss 0.5653812885284424\n", "Epoch 800/5000: Loss 0.5395837426185608\n", "Epoch 900/5000: Loss 0.5158373713493347\n", "Epoch 1000/5000: Loss 0.49394670128822327\n", "Epoch 1100/5000: Loss 0.473736047744751\n", "Epoch 1200/5000: Loss 0.4550468325614929\n", "Epoch 1300/5000: Loss 0.43773695826530457\n", "Epoch 1400/5000: Loss 0.42167842388153076\n", "Epoch 1500/5000: Loss 0.40675684809684753\n", "Epoch 1600/5000: Loss 0.39286935329437256\n", "Epoch 1700/5000: Loss 0.37992337346076965\n", "Epoch 1800/5000: Loss 0.3678366541862488\n", "Epoch 1900/5000: Loss 0.35653403401374817\n", "Epoch 2000/5000: Loss 0.3459485173225403\n", "Epoch 2100/5000: Loss 0.3360198736190796\n", "Epoch 2200/5000: Loss 0.32669389247894287\n", "Epoch 2300/5000: Loss 0.31792140007019043\n", "Epoch 2400/5000: Loss 0.3096582293510437\n", "Epoch 2500/5000: Loss 0.3018639087677002\n", "Epoch 2600/5000: Loss 0.2945026755332947\n", "Epoch 2700/5000: Loss 0.2875416576862335\n", "Epoch 2800/5000: Loss 0.2809506058692932\n", "Epoch 2900/5000: Loss 0.2747027575969696\n", "Epoch 3000/5000: Loss 0.2687731087207794\n", "Epoch 3100/5000: Loss 0.2631390690803528\n", "Epoch 3200/5000: Loss 0.2577802240848541\n", "Epoch 3300/5000: Loss 0.2526777684688568\n", "Epoch 3400/5000: Loss 0.24781419336795807\n", "Epoch 3500/5000: Loss 0.2431737333536148\n", "Epoch 3600/5000: Loss 0.23874199390411377\n", "Epoch 3700/5000: Loss 0.23450538516044617\n", "Epoch 3800/5000: Loss 0.2304517775774002\n", "Epoch 3900/5000: Loss 0.2265697568655014\n", "Epoch 4000/5000: Loss 0.22284895181655884\n", "Epoch 4100/5000: Loss 0.21927960216999054\n", "Epoch 4200/5000: Loss 0.2158527970314026\n", "Epoch 4300/5000: Loss 0.21256034076213837\n", "Epoch 4400/5000: Loss 0.20939455926418304\n", "Epoch 4500/5000: Loss 0.2063482254743576\n", "Epoch 4600/5000: Loss 0.2034146636724472\n", "Epoch 4700/5000: Loss 0.20058806240558624\n", "Epoch 4800/5000: Loss 0.19786246120929718\n", "Epoch 4900/5000: Loss 0.19523243606090546\n" ] } ], "source": [ "_, d = X_train.shape\n", "_, n = Y_train.shape\n", "\n", "model = LogisticRegressor(d,n)\n", "loss = model.fit(np.float32(X_train), Y_train, 5000)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0,0.5,'Mean Square Error')" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(loss)\n", "plt.xlabel(\"Epochs\")\n", "plt.ylabel(\"Mean Square Error\")" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "Y_pred = model.predict(np.float32(X_test))" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.94375\n" ] } ], "source": [ "accuracy = sum(np.argmax(Y_pred,1) == np.argmax(np.array(y_test),1))/len(y_test)\n", "print(accuracy)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python [conda env:tensorflow]", "language": "python", "name": "conda-env-tensorflow-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.4" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: Chapter03/readme.md ================================================ # Machine Learning for IoT The term "machine learning" refers to the computer programs that can automatically detect meaningful patterns in data and improve with experience. Though it is not a new field, it is presently at the peak of its hype cycle. This chapter introduces the readers to the standard machine learning algorithms and their applications in the field of IoT. After reading this chapter you will: * Know what machine learning is and the role it plays in IoT pipeline. * Know about the supervised and unsupervised learning paradigms * Know about regression and learn how to perform linear regression using TensorFlow/Keras * Know about popular machine learning classifiers and implement them in TensorFlow/Keras * Know about Decision Trees, Random Forests and techniques to perform boosting. We will also learn how to write code for them. ================================================ FILE: Chapter03/winequality-red.csv ================================================ "fixed acidity";"volatile acidity";"citric acid";"residual sugar";"chlorides";"free sulfur dioxide";"total sulfur dioxide";"density";"pH";"sulphates";"alcohol";"quality" 7.4;0.7;0;1.9;0.076;11;34;0.9978;3.51;0.56;9.4;5 7.8;0.88;0;2.6;0.098;25;67;0.9968;3.2;0.68;9.8;5 7.8;0.76;0.04;2.3;0.092;15;54;0.997;3.26;0.65;9.8;5 11.2;0.28;0.56;1.9;0.075;17;60;0.998;3.16;0.58;9.8;6 7.4;0.7;0;1.9;0.076;11;34;0.9978;3.51;0.56;9.4;5 7.4;0.66;0;1.8;0.075;13;40;0.9978;3.51;0.56;9.4;5 7.9;0.6;0.06;1.6;0.069;15;59;0.9964;3.3;0.46;9.4;5 7.3;0.65;0;1.2;0.065;15;21;0.9946;3.39;0.47;10;7 7.8;0.58;0.02;2;0.073;9;18;0.9968;3.36;0.57;9.5;7 7.5;0.5;0.36;6.1;0.071;17;102;0.9978;3.35;0.8;10.5;5 6.7;0.58;0.08;1.8;0.097;15;65;0.9959;3.28;0.54;9.2;5 7.5;0.5;0.36;6.1;0.071;17;102;0.9978;3.35;0.8;10.5;5 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6;0.58;0.2;2.4;0.075;15;50;0.99467;3.58;0.67;12.5;6 5.6;0.31;0.78;13.9;0.074;23;92;0.99677;3.39;0.48;10.5;6 7.5;0.52;0.4;2.2;0.06;12;20;0.99474;3.26;0.64;11.8;6 8;0.3;0.63;1.6;0.081;16;29;0.99588;3.3;0.78;10.8;6 6.2;0.7;0.15;5.1;0.076;13;27;0.99622;3.54;0.6;11.9;6 6.8;0.67;0.15;1.8;0.118;13;20;0.9954;3.42;0.67;11.3;6 6.2;0.56;0.09;1.7;0.053;24;32;0.99402;3.54;0.6;11.3;5 7.4;0.35;0.33;2.4;0.068;9;26;0.9947;3.36;0.6;11.9;6 6.2;0.56;0.09;1.7;0.053;24;32;0.99402;3.54;0.6;11.3;5 6.1;0.715;0.1;2.6;0.053;13;27;0.99362;3.57;0.5;11.9;5 6.2;0.46;0.29;2.1;0.074;32;98;0.99578;3.33;0.62;9.8;5 6.7;0.32;0.44;2.4;0.061;24;34;0.99484;3.29;0.8;11.6;7 7.2;0.39;0.44;2.6;0.066;22;48;0.99494;3.3;0.84;11.5;6 7.5;0.31;0.41;2.4;0.065;34;60;0.99492;3.34;0.85;11.4;6 5.8;0.61;0.11;1.8;0.066;18;28;0.99483;3.55;0.66;10.9;6 7.2;0.66;0.33;2.5;0.068;34;102;0.99414;3.27;0.78;12.8;6 6.6;0.725;0.2;7.8;0.073;29;79;0.9977;3.29;0.54;9.2;5 6.3;0.55;0.15;1.8;0.077;26;35;0.99314;3.32;0.82;11.6;6 5.4;0.74;0.09;1.7;0.089;16;26;0.99402;3.67;0.56;11.6;6 6.3;0.51;0.13;2.3;0.076;29;40;0.99574;3.42;0.75;11;6 6.8;0.62;0.08;1.9;0.068;28;38;0.99651;3.42;0.82;9.5;6 6.2;0.6;0.08;2;0.09;32;44;0.9949;3.45;0.58;10.5;5 5.9;0.55;0.1;2.2;0.062;39;51;0.99512;3.52;0.76;11.2;6 6.3;0.51;0.13;2.3;0.076;29;40;0.99574;3.42;0.75;11;6 5.9;0.645;0.12;2;0.075;32;44;0.99547;3.57;0.71;10.2;5 6;0.31;0.47;3.6;0.067;18;42;0.99549;3.39;0.66;11;6 ================================================ FILE: Chapter04/LeNet_for_HandrwittenDigitsRecognition.ipynb ================================================ { "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# Import Modules\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/am/anaconda3/envs/tensorflow/lib/python3.5/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n", " from ._conv import register_converters as _register_converters\n" ] } ], "source": [ "# Define your Architecture here\n", "import tensorflow as tf\n", "from tensorflow.contrib.layers import flatten\n", "\n", "class my_LeNet:\n", " def __init__(self, d, n, mu = 0, sigma = 0.1, lr = 0.001):\n", " self.mu = mu\n", " self.sigma = sigma\n", " self.n = n\n", " self.x = tf.placeholder(tf.float32, (None, d, d, 1)) # place holder for input image dimension 28 x 28\n", " self.y = tf.placeholder(tf.int32, (None,n))\n", " self.keep_prob = tf.placeholder(tf.float32) # probability to keep units\n", " \n", " \n", " self.logits = self.model(self.x)\n", " \n", " cross_entropy = tf.nn.softmax_cross_entropy_with_logits(labels=self.y, logits=self.logits)\n", " self.loss = tf.reduce_mean(cross_entropy)\n", " optimizer = tf.train.AdamOptimizer(learning_rate = lr)\n", " self.train = optimizer.minimize(self.loss)\n", " correct_prediction = tf.equal(tf.argmax(self.logits, 1), tf.argmax(self.y, 1))\n", " self.accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n", " init = tf.global_variables_initializer()\n", " self.sess = tf.Session()\n", " self.sess.run(init)\n", " self.saver = tf.train.Saver()\n", " \n", " \n", " \n", " def model(self,x): \n", " # Build Architecture\n", " keep_prob = 0.7\n", " # Layer 1: Convolutional. Filter 5x5 num_filters = 6 Input_depth =1\n", " conv1_W = tf.Variable(tf.truncated_normal(shape=(5, 5, 1, 6), mean = self.mu, stddev = self.sigma))\n", " conv1_b = tf.Variable(tf.zeros(6))\n", " conv1 = tf.nn.conv2d(x, conv1_W, strides=[1, 1, 1, 1], padding='VALID') + conv1_b\n", " conv1 = tf.nn.relu(conv1)\n", " \n", " # Max Pool 1 \n", " self.conv1 = tf.nn.max_pool(conv1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID')\n", " \n", " \n", " # Layer 2: Convolutional. Filter 5x5 num_filters = 16 Input_depth =6\n", " conv2_W = tf.Variable(tf.truncated_normal(shape=(5, 5, 6, 16), mean = self.mu, stddev = self.sigma))\n", " conv2_b = tf.Variable(tf.zeros(16))\n", " conv2 = tf.nn.conv2d(self.conv1, conv2_W, strides=[1, 1, 1, 1], padding='VALID') + conv2_b\n", " conv2 = tf.nn.relu(conv2)\n", "\n", " # Max Pool 2.\n", " self.conv2 = tf.nn.max_pool(conv2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID')\n", " \n", " # Flatten. \n", " fc0 = flatten(self.conv2)\n", " print(\"x shape:\",fc0.get_shape())\n", " \n", " # Layer 3: Fully Connected. Input = fc0.get_shape[-1]. Output = 120.\n", " fc1_W = tf.Variable(tf.truncated_normal(shape=(256, 120), mean = self.mu, stddev = self.sigma))\n", " fc1_b = tf.Variable(tf.zeros(120))\n", " fc1 = tf.matmul(fc0, fc1_W) + fc1_b\n", " fc1 = tf.nn.relu(fc1)\n", " \n", " # Dropout\n", " x = tf.nn.dropout(fc1, keep_prob)\n", "\n", " # Layer 4: Fully Connected. Input = 120. Output = 84.\n", " fc2_W = tf.Variable(tf.truncated_normal(shape=(120, 84), mean = self.mu, stddev = self.sigma))\n", " fc2_b = tf.Variable(tf.zeros(84))\n", " fc2 = tf.matmul(x, fc2_W) + fc2_b\n", " fc2 = tf.nn.relu(fc2)\n", " \n", " # Dropout\n", " x = tf.nn.dropout(fc2, keep_prob)\n", "\n", " # Layer 6: Fully Connected. Input = 120. Output = n_classes.\n", " fc3_W = tf.Variable(tf.truncated_normal(shape=(84, self.n), mean = self.mu, stddev = self.sigma))\n", " fc3_b = tf.Variable(tf.zeros(self.n))\n", " logits = tf.matmul(x, fc3_W) + fc3_b\n", " #logits = tf.nn.softmax(logits)\n", " return logits\n", " \n", " def fit(self,X,Y,X_val,Y_val,epochs=10, batch_size=100):\n", " X_train, y_train = X, Y\n", " num_examples = len(X_train)\n", " l = []\n", " val_l = []\n", " max_val = 0\n", " for i in range(epochs):\n", " total = 0\n", " for offset in range(0, num_examples, batch_size): # Learn Batch wise\n", " end = offset + batch_size\n", " batch_x, batch_y = X_train[offset:end], y_train[offset:end]\n", " _, loss = self.sess.run([self.train,self.loss], feed_dict={self.x: batch_x, self.y: batch_y})\n", " total += loss\n", " l.append(total/num_examples)\n", " accuracy_val = self.sess.run(self.accuracy, feed_dict={self.x: X_val, self.y: Y_val})\n", " accuracy = self.sess.run(self.accuracy, feed_dict={self.x: X, self.y: Y})\n", " loss_val = self.sess.run(self.loss, feed_dict={self.x:X_val,self.y:Y_val})\n", " val_l.append(loss_val)\n", " print(\"EPOCH {}/{} loss is {:.3f} training_accuracy {:.3f} and validation accuracy is {:.3f}\".\\\n", " format(i+1,epochs,total/num_examples, accuracy, accuracy_val))\n", " if accuracy_val > max_val:\n", " save_path = self.saver.save(self.sess, \"/tmp/lenet1.ckpt\")\n", " print(\"Model saved in path: %s\" % save_path)\n", " max_val = accuracy_val\n", "\n", " self.saver.restore(self.sess, \"/tmp/lenet1.ckpt\")\n", " print(\"Restored model with highest validation accuracy\")\n", " accuracy_val = self.sess.run(self.accuracy, feed_dict={self.x: X_val, self.y: Y_val})\n", " accuracy = self.sess.run(self.accuracy, feed_dict={self.x: X, self.y: Y})\n", " return l,val_l, accuracy, accuracy_val \n", " \n", " def predict(self, X):\n", " return self.sess.run(self.logits,feed_dict={self.x:X})\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "def load_data():\n", " # Read the data and create train, validation and test dataset\n", " data = pd.read_csv('train.csv')\n", " train = data.sample(frac=0.8, random_state=255) # This ensures always 80% of data is training and rest Validation unlike using np.random\n", " val = data.drop(train.index)\n", " test = pd.read_csv('test.csv')\n", " return train, val, test\n", " \n", "def create_data(df):\n", " labels = df.loc[:]['label']\n", " y_one_hot = pd.get_dummies(labels).astype(np.uint8)\n", " y = y_one_hot.values # One Hot encode the labels\n", " x = df.iloc[:,1:].values\n", " x = x.astype(np.float)\n", " # Normalize data\n", " x = np.multiply(x, 1.0 / 255.0)\n", " x = x.reshape(-1, 28, 28, 1) # return each images as 96 x 96 x 1\n", " return x,y\n", "\n", "train, val, test = load_data()\n", "X_train, y_train = create_data(train)\n", "X_val, y_val = create_data(val)\n", "X_test = (test.iloc[:,:].values).astype(np.float)\n", "X_test = np.multiply(X_test, 1.0 / 255.0)\n", "X_test = X_test.reshape(-1, 28, 28, 1) # return each images as 96 x 96 x 1" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# take subset of training data\n", "x_train_subset = X_train[:12]\n", "\n", "\n", "# visualize subset of training data\n", "fig = plt.figure(figsize=(20,2))\n", "for i in range(0, len(x_train_subset)):\n", " ax = fig.add_subplot(1, 12, i+1)\n", " ax.imshow(x_train_subset[i].reshape(28,28), cmap='gray')\n", "fig.suptitle('Subset of Original Training Images', fontsize=20)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of training examples = 33600\n", "Number of Validation examples = 8400\n", "Number of testing examples = 28000\n", "Image data shape = (28, 28)\n", "Number of classes = 10\n" ] } ], "source": [ "n_train = len(X_train)\n", "\n", "# Number of validation examples\n", "n_validation = len(X_val)\n", "\n", "# Number of testing examples.\n", "n_test = len(X_test)\n", "\n", "# What's the shape of an handwritten digits?\n", "image_shape = X_train.shape[1:-1]\n", "\n", "# How many unique classes/labels there are in the dataset.\n", "n_classes = y_train.shape[-1]\n", "\n", "print(\"Number of training examples =\", n_train)\n", "print(\"Number of Validation examples =\", n_validation)\n", "print(\"Number of testing examples =\", n_test)\n", "print(\"Image data shape =\", image_shape)\n", "print(\"Number of classes =\", n_classes)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "# Define the data values\n", "d = image_shape[0]\n", "n = n_classes\n", "from sklearn.utils import shuffle\n", "X_train, y_train = shuffle(X_train,y_train)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "x shape: (?, 256)\n" ] } ], "source": [ "# Create the Model\n", "my_model = my_LeNet(d, n)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "EPOCH 1/50 loss is 0.006 training_accuracy 0.926 and validation accuracy is 0.929\n", "Model saved in path: /tmp/lenet1.ckpt\n", "EPOCH 2/50 loss is 0.002 training_accuracy 0.953 and validation accuracy is 0.954\n", "Model saved in path: /tmp/lenet1.ckpt\n", "EPOCH 3/50 loss is 0.001 training_accuracy 0.965 and validation accuracy is 0.962\n", "Model saved in path: /tmp/lenet1.ckpt\n", "EPOCH 4/50 loss is 0.001 training_accuracy 0.971 and validation accuracy is 0.969\n", "Model saved in path: /tmp/lenet1.ckpt\n", "EPOCH 5/50 loss is 0.001 training_accuracy 0.976 and validation accuracy is 0.972\n", "Model saved in path: /tmp/lenet1.ckpt\n", "EPOCH 6/50 loss is 0.001 training_accuracy 0.977 and validation accuracy is 0.975\n", "Model saved in path: /tmp/lenet1.ckpt\n", "EPOCH 7/50 loss is 0.001 training_accuracy 0.981 and validation accuracy is 0.977\n", "Model saved in path: /tmp/lenet1.ckpt\n", "EPOCH 8/50 loss is 0.001 training_accuracy 0.983 and validation accuracy is 0.978\n", "Model saved in path: /tmp/lenet1.ckpt\n", "EPOCH 9/50 loss is 0.001 training_accuracy 0.983 and validation accuracy is 0.977\n", "EPOCH 10/50 loss is 0.001 training_accuracy 0.982 and validation accuracy is 0.978\n", "Model saved in path: /tmp/lenet1.ckpt\n", "EPOCH 11/50 loss is 0.001 training_accuracy 0.985 and validation accuracy is 0.980\n", "Model saved in path: /tmp/lenet1.ckpt\n", "EPOCH 12/50 loss is 0.000 training_accuracy 0.987 and validation accuracy is 0.980\n", "EPOCH 13/50 loss is 0.000 training_accuracy 0.987 and validation accuracy is 0.980\n", "Model saved in path: /tmp/lenet1.ckpt\n", "EPOCH 14/50 loss is 0.000 training_accuracy 0.986 and validation accuracy is 0.980\n", "EPOCH 15/50 loss is 0.000 training_accuracy 0.989 and validation accuracy is 0.982\n", "Model saved in path: /tmp/lenet1.ckpt\n", "EPOCH 16/50 loss is 0.000 training_accuracy 0.990 and validation accuracy is 0.982\n", "Model saved in path: /tmp/lenet1.ckpt\n", "EPOCH 17/50 loss is 0.000 training_accuracy 0.992 and validation accuracy is 0.982\n", "Model saved in path: /tmp/lenet1.ckpt\n", "EPOCH 18/50 loss is 0.000 training_accuracy 0.991 and validation accuracy is 0.983\n", "Model saved in path: /tmp/lenet1.ckpt\n", "EPOCH 19/50 loss is 0.000 training_accuracy 0.991 and validation accuracy is 0.981\n", "EPOCH 20/50 loss is 0.000 training_accuracy 0.992 and validation accuracy is 0.983\n", "EPOCH 21/50 loss is 0.000 training_accuracy 0.990 and validation accuracy is 0.982\n", "EPOCH 22/50 loss is 0.000 training_accuracy 0.992 and validation accuracy is 0.981\n", "EPOCH 23/50 loss is 0.000 training_accuracy 0.992 and validation accuracy is 0.981\n", "EPOCH 24/50 loss is 0.000 training_accuracy 0.993 and validation accuracy is 0.984\n", "Model saved in path: /tmp/lenet1.ckpt\n", "EPOCH 25/50 loss is 0.000 training_accuracy 0.992 and validation accuracy is 0.983\n", "EPOCH 26/50 loss is 0.000 training_accuracy 0.993 and validation accuracy is 0.983\n", "EPOCH 27/50 loss is 0.000 training_accuracy 0.993 and validation accuracy is 0.983\n", "EPOCH 28/50 loss is 0.000 training_accuracy 0.994 and validation accuracy is 0.983\n", "EPOCH 29/50 loss is 0.000 training_accuracy 0.994 and validation accuracy is 0.984\n", "EPOCH 30/50 loss is 0.000 training_accuracy 0.995 and validation accuracy is 0.985\n", "Model saved in path: /tmp/lenet1.ckpt\n", "EPOCH 31/50 loss is 0.000 training_accuracy 0.994 and validation accuracy is 0.984\n", "EPOCH 32/50 loss is 0.000 training_accuracy 0.995 and validation accuracy is 0.985\n", "Model saved in path: /tmp/lenet1.ckpt\n", "EPOCH 33/50 loss is 0.000 training_accuracy 0.994 and validation accuracy is 0.983\n", "EPOCH 34/50 loss is 0.000 training_accuracy 0.991 and validation accuracy is 0.981\n", "EPOCH 35/50 loss is 0.000 training_accuracy 0.994 and validation accuracy is 0.983\n", "EPOCH 36/50 loss is 0.000 training_accuracy 0.995 and validation accuracy is 0.983\n", "EPOCH 37/50 loss is 0.000 training_accuracy 0.994 and validation accuracy is 0.983\n", "EPOCH 38/50 loss is 0.000 training_accuracy 0.995 and validation accuracy is 0.985\n", "EPOCH 39/50 loss is 0.000 training_accuracy 0.994 and validation accuracy is 0.983\n", "EPOCH 40/50 loss is 0.000 training_accuracy 0.993 and validation accuracy is 0.983\n", "EPOCH 41/50 loss is 0.000 training_accuracy 0.996 and validation accuracy is 0.984\n", "EPOCH 42/50 loss is 0.000 training_accuracy 0.995 and validation accuracy is 0.983\n", "EPOCH 43/50 loss is 0.000 training_accuracy 0.995 and validation accuracy is 0.985\n", "EPOCH 44/50 loss is 0.000 training_accuracy 0.996 and validation accuracy is 0.984\n", "EPOCH 45/50 loss is 0.000 training_accuracy 0.996 and validation accuracy is 0.985\n", "EPOCH 46/50 loss is 0.000 training_accuracy 0.996 and validation accuracy is 0.986\n", "Model saved in path: /tmp/lenet1.ckpt\n", "EPOCH 47/50 loss is 0.000 training_accuracy 0.996 and validation accuracy is 0.985\n", "EPOCH 48/50 loss is 0.000 training_accuracy 0.995 and validation accuracy is 0.983\n", "EPOCH 49/50 loss is 0.000 training_accuracy 0.997 and validation accuracy is 0.986\n", "EPOCH 50/50 loss is 0.000 training_accuracy 0.997 and validation accuracy is 0.986\n", "Model saved in path: /tmp/lenet1.ckpt\n", "INFO:tensorflow:Restoring parameters from /tmp/lenet1.ckpt\n", "Restored model with highest validation accuracy\n" ] } ], "source": [ "### Train model here.\n", "loss, val_loss, train_acc, val_acc = my_model.fit(X_train, y_train, X_val, y_val, epochs=50) " ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0,0.5,'Loss')" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(loss, label=\"Training Loss\")\n", "plt.plot(val_loss, label = \"Validation Loss\")\n", "plt.legend()\n", "plt.xlabel(\"Number of Epochs\")\n", "plt.ylabel(\"Loss\")" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy on training dataset is 99.658% and on Validation dataset is 98.607%\n" ] } ], "source": [ "### Calculate and report the accuracy on the training and validation set.\n", "print(\"Accuracy on training dataset is {:.3f}% and on Validation dataset is {:.3f}%\".\n", " format(train_acc*100,val_acc*100))" ] } ], "metadata": { "kernelspec": { "display_name": "Python [conda env:tensorflow]", "language": "python", "name": "conda-env-tensorflow-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.4" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: Chapter04/MLP_classification.ipynb ================================================ { "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/am/anaconda3/envs/tensorflow/lib/python3.5/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n", " from ._conv import register_converters as _register_converters\n" ] } ], "source": [ "import tensorflow as tf\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "from sklearn.utils import shuffle\n", "from sklearn.preprocessing import MinMaxScaler\n", "from sklearn.model_selection import train_test_split\n", "% matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "class MLP:\n", " def __init__(self,n_input=2,n_hidden=4, n_output=1, act_func=[tf.nn.relu, tf.nn.sigmoid], learning_rate= 0.001):\n", " self.n_input = n_input # Number of inputs to the neuron\n", " self.act_fn = act_func\n", " seed = 456\n", " \n", " self.X = tf.placeholder(tf.float32, name='X', shape=[None,n_input])\n", " self.y = tf.placeholder(tf.float32, name='Y')\n", " \n", " # Build the graph for a single neuron\n", " # Hidden layer\n", " self.W1 = tf.Variable(tf.random_normal([n_input,n_hidden], stddev=2, seed = seed), name = \"weights\")\n", " self.b1 = tf.Variable(tf.random_normal([1, n_hidden], seed = seed), name=\"bias\")\n", " tf.summary.histogram(\"Weights_Layer_1\",self.W1)\n", " tf.summary.histogram(\"Bias_Layer_1\", self.b1)\n", " \n", " \n", " # Output Layer\n", " self.W2 = tf.Variable(tf.random_normal([n_hidden,n_output], stddev=2, seed = seed), name = \"weights\")\n", " self.b2 = tf.Variable(tf.random_normal([1, n_output], seed = seed), name=\"bias\")\n", " tf.summary.histogram(\"Weights_Layer_2\",self.W2)\n", " tf.summary.histogram(\"Bias_Layer_2\", self.b2)\n", " \n", " \n", " activity1 = tf.matmul(self.X, self.W1) + self.b1\n", " h1 = self.act_fn[0](activity1)\n", " \n", " activity2 = tf.matmul(h1, self.W2) + self.b2\n", " self.y_hat = self.act_fn[1](activity2)\n", " \n", " \n", " self.loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=self.y_hat, labels=self.y))\n", " self.opt = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(self.loss)\n", " \n", " \n", " tf.summary.scalar(\"loss\",self.loss)\n", " init = tf.global_variables_initializer()\n", " \n", " self.sess = tf.Session()\n", " self.sess.run(init)\n", " \n", " self.merge = tf.summary.merge_all()\n", " self.writer = tf.summary.FileWriter(\"logs/\", graph=tf.get_default_graph())\n", " \n", " \n", " \n", " def train(self, X, Y, X_val, Y_val, epochs=100):\n", " epoch = 0\n", " X, Y = shuffle(X,Y)\n", " loss = []\n", " loss_val = []\n", " while epoch < epochs:\n", " # Run the optimizer for the whole training set batch wise (Stochastic Gradient Descent) \n", " merge, _, l = self.sess.run([self.merge,self.opt,self.loss], feed_dict={self.X: X, self.y: Y})\n", " l_val = self.sess.run(self.loss, feed_dict={self.X: X_val, self.y: Y_val})\n", " \n", " loss.append(l)\n", " loss_val.append(l_val)\n", " self.writer.add_summary(merge, epoch)\n", " \n", " if epoch % 10 == 0:\n", " print(\"Epoch {}/{} training loss: {} Validation loss {}\".\\\n", " format(epoch,epochs,l, l_val ))\n", " \n", " \n", " epoch += 1\n", " return loss, loss_val\n", " \n", " def predict(self, X):\n", " return self.sess.run(self.y_hat, feed_dict={self.X: X})" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/am/anaconda3/envs/tensorflow/lib/python3.5/site-packages/pandas/core/indexing.py:194: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame\n", "\n", "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", " self._setitem_with_indexer(indexer, value)\n" ] } ], "source": [ "filename = 'winequality-red.csv' #Download the file from https://archive.ics.uci.edu/ml/datasets/wine+quality\n", "df = pd.read_csv(filename, sep=';')\n", "columns = df.columns.values\n", "# Preprocessing and Categorizing wine into two categories\n", "X, Y = df[columns[0:-1]], df[columns[-1]]\n", "scaler = MinMaxScaler()\n", "X_new = scaler.fit_transform(X)\n", "#Y.loc[(Y<3.5)]=3\n", "Y.loc[(Y<5.5) ] = 2\n", "Y.loc[(Y>=5.5)] = 1\n", "Y_new = pd.get_dummies(Y) # One hot encode\n", "X_train, X_val, Y_train, y_val = \\\n", " train_test_split(X_new, Y_new, test_size=0.2, random_state=333)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "_, d = X_train.shape\n", "_, n = Y_train.shape\n", "model = MLP(n_input=d, n_hidden=5, n_output=n)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 0/10000 training loss: 0.7775369882583618 Validation loss 0.7752965688705444\n", "Epoch 10/10000 training loss: 0.7724501490592957 Validation loss 0.7697949409484863\n", "Epoch 20/10000 training loss: 0.7675266861915588 Validation loss 0.7644182443618774\n", "Epoch 30/10000 training loss: 0.762906551361084 Validation loss 0.7592542767524719\n", "Epoch 40/10000 training loss: 0.7585335969924927 Validation loss 0.7544096112251282\n", "Epoch 50/10000 training loss: 0.7543628811836243 Validation loss 0.7501053214073181\n", "Epoch 60/10000 training loss: 0.7504240870475769 Validation loss 0.7462259531021118\n", "Epoch 70/10000 training loss: 0.7465348243713379 Validation loss 0.7425655722618103\n", "Epoch 80/10000 training loss: 0.742756724357605 Validation loss 0.7391285300254822\n", "Epoch 90/10000 training loss: 0.7392124533653259 Validation loss 0.7359191179275513\n", "Epoch 100/10000 training loss: 0.7360482811927795 Validation loss 0.7330106496810913\n", "Epoch 110/10000 training loss: 0.7333075404167175 Validation loss 0.7304995656013489\n", "Epoch 120/10000 training loss: 0.7309179902076721 Validation loss 0.7282122373580933\n", "Epoch 130/10000 training loss: 0.7287744879722595 Validation loss 0.7262550592422485\n", "Epoch 140/10000 training loss: 0.7268343567848206 Validation loss 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0.5422225594520569\n", "Epoch 9880/10000 training loss: 0.5339469313621521 Validation loss 0.5421978831291199\n", "Epoch 9890/10000 training loss: 0.5339263081550598 Validation loss 0.5421744585037231\n", "Epoch 9900/10000 training loss: 0.5339056849479675 Validation loss 0.5421503186225891\n", "Epoch 9910/10000 training loss: 0.5338852405548096 Validation loss 0.5421308279037476\n", "Epoch 9920/10000 training loss: 0.5338649749755859 Validation loss 0.5421403646469116\n", "Epoch 9930/10000 training loss: 0.5338448286056519 Validation loss 0.5421309471130371\n", "Epoch 9940/10000 training loss: 0.5338248610496521 Validation loss 0.5421156287193298\n", "Epoch 9950/10000 training loss: 0.5338049530982971 Validation loss 0.5421100854873657\n", "Epoch 9960/10000 training loss: 0.5337852239608765 Validation loss 0.5420910120010376\n", "Epoch 9970/10000 training loss: 0.5337655544281006 Validation loss 0.5420719981193542\n", "Epoch 9980/10000 training loss: 0.5337459444999695 Validation loss 0.542055070400238\n", "Epoch 9990/10000 training loss: 0.5337263941764832 Validation loss 0.5420383214950562\n" ] } ], "source": [ "loss, loss_val = model.train(X_train, Y_train, X_val, y_val, 10000)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0,0.5,'Cross Entropy Loss')" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(loss, label=\"Taining Loss\")\n", "plt.plot(loss_val, label=\"Validation Loss\")\n", "plt.legend()\n", "plt.xlabel(\"Epochs\")\n", "plt.ylabel(\"Cross Entropy Loss\")" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "Y_pred = model.predict(np.float32(X_val))" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.778125\n" ] } ], "source": [ "from sklearn.metrics import mean_squared_error, r2_score\n", "accuracy = sum(np.argmax(Y_pred,1) == np.argmax(np.array(y_val),1))/len(y_val)\n", "print(accuracy)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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8mIZSiWWrX+fpFa+xxaZDcodVV7rrlGVK6YWIeC4itkspLQamAo9WxnTgjMrX62s9RmcJ+EZg45TSn969IyLm13rQ/mDcuLFcfNE5DBhQolQqcc01v+Gm396eOyxlcPKtD/PH51ew8o23OOCSuzlh920YPriBMxc8wYrX3+QbN/6J7UZvwvnTPsaDy1ZyyQPP0FAKShF8Z8r2jBw6KPePUFdaUrf+0j0LuDwiBgFPAcfTeu7sqoiYATwLHFXr5JG6N9j3sAWh9qyePS13COqDNpp1/gYv/DjuA5+uOudc8cyvsi408UIMSYXipciSlEk9XbZiApZUKD4RQ5IysQUhSZl08yqIHmUCllQotiAkKRNPwklSJvaAJSkTWxCSlElPX93bnUzAkgrFx9JLUia2ICQpE1sQkpSJFbAkZeIyNEnKxEuRJSkTWxCSlIkJWJIycRWEJGViBSxJmbgKQpIyaUn1c0NKE7CkQrEHLEmZ2AOWpEzsAUtSJmVbEJKUhxWwJGXiKghJysQWhCRlYgtCkjKxApakTKyAJSmTltSSO4SqlXIHIEndKaVU9ahGRAyIiAcj4sbK+60j4r6IeDIiroyIQbXGagKWVChlUtWjSt8EHmvz/kzgxymlicAKYEatsZqAJRVKd1bAETEBOAS4sPI+gH2AayofuQw4vNZY7QFLKpRuXgVxDnAisEnl/WbAypRSc+V9EzC+1smtgCUVSurCn4iYGRGL2oyZb88TEZ8CXkop/bHN9NHuIWtkBSypULpyKXJKqRFoXM/uPYHDIuJgYAiwKa0V8YiIaKhUwROAZbXGagUsqVC6qwecUjolpTQhpbQVcAxwR0rpc8CdwJGVj00Hrq81VhOwpEIpp1T1qNFJwLciYgmtPeGLap3IFoSkQumJRxKllOYD8yuvnwJ26455TcCSCsVHEklSJj6UU5Iy8YbskpSJt6OUpExsQUhSJt4PWJIysQKWpEzqqQcc9fS/Rb2LiJmVa8+ldfx70X95KXLvmtn5R9QP+feinzIBS1ImJmBJysQE3Lvs86k9/r3opzwJJ0mZWAFLUiYm4F4SEQdGxOKIWBIRJ+eOR/lFxMUR8VJEPJw7FuVhAu4FETEAOA84CNgBODYidsgblfqAS4EDcwehfEzAvWM3YElK6amU0pvAL4FpmWNSZimlBcDLueNQPibg3jEeeK7N+6bKNkn9mAm4d0Q721x+IvVzJuDe0QS8v837CcCyTLFI6iNMwL1jITAxIraOiEHAMcANmWOSlJkJuBeklJqBrwO3Ao8BV6WUHskblXKLiDnAH4DtIqIpImbkjkm9yyvhJCkTK2BJysQELEmZmIAlKRMTsCRlYgKWpExMwJKUiQlYkjIxAUtSJv8PN4yP+JLbyM8AAAAASUVORK5CYII=\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from sklearn.metrics import confusion_matrix, accuracy_score\n", "import seaborn as sns\n", "cm = confusion_matrix(np.argmax(np.array(y_val),1), np.argmax(Y_pred,1))\n", "sns.heatmap(cm,annot=True,fmt='2.0f')" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1 855\n", "2 744\n", "Name: quality, dtype: int64" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Y.value_counts()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python [conda env:tensorflow]", "language": "python", "name": "conda-env-tensorflow-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.4" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: Chapter04/MLP_regression.ipynb ================================================ { "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/am/anaconda3/envs/tensorflow/lib/python3.5/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n", " from ._conv import register_converters as _register_converters\n" ] } ], "source": [ "import tensorflow as tf\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "from sklearn.utils import shuffle\n", "from sklearn.preprocessing import MinMaxScaler\n", "from sklearn.model_selection import train_test_split\n", "% matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "class MLP:\n", " def __init__(self,n_input=2,n_hidden=4, n_output=1, act_func=[tf.nn.elu, tf.sigmoid], learning_rate= 0.001):\n", " self.n_input = n_input # Number of inputs to the neuron\n", " self.act_fn = act_func\n", " seed = 123\n", " \n", " self.X = tf.placeholder(tf.float32, name='X', shape=[None,n_input])\n", " self.y = tf.placeholder(tf.float32, name='Y')\n", " \n", " # Build the graph for a single neuron\n", " # Hidden layer\n", " self.W1 = tf.Variable(tf.random_normal([n_input,n_hidden], stddev=2, seed = seed), name = \"weights\")\n", " self.b1 = tf.Variable(tf.random_normal([1, n_hidden], seed = seed), name=\"bias\")\n", " tf.summary.histogram(\"Weights_Layer_1\",self.W1)\n", " tf.summary.histogram(\"Bias_Layer_1\", self.b1)\n", " \n", " \n", " # Output Layer\n", " self.W2 = tf.Variable(tf.random_normal([n_hidden,n_output], stddev=2, seed = 0), name = \"weights\")\n", " self.b2 = tf.Variable(tf.random_normal([1, n_output], seed = seed), name=\"bias\")\n", " tf.summary.histogram(\"Weights_Layer_2\",self.W2)\n", " tf.summary.histogram(\"Bias_Layer_2\", self.b2)\n", " \n", " \n", " activity = tf.matmul(self.X, self.W1) + self.b1\n", " h1 = self.act_fn[0](activity)\n", " \n", " activity = tf.matmul(h1, self.W2) + self.b2\n", " self.y_hat = self.act_fn[1](activity)\n", " \n", " \n", " error = self.y - self.y_hat\n", " \n", " self.loss = tf.reduce_mean(tf.square(error)) + 0.6*tf.nn.l2_loss(self.W1) #+ 0.6*tf.nn.l2_loss(self.W2)\n", " self.opt = tf.train.GradientDescentOptimizer(learning_rate=learning_rate).minimize(self.loss)\n", " \n", " \n", " tf.summary.scalar(\"loss\",self.loss)\n", " init = tf.global_variables_initializer()\n", " \n", " self.sess = tf.Session()\n", " self.sess.run(init)\n", " \n", " self.merge = tf.summary.merge_all()\n", " self.writer = tf.summary.FileWriter(\"logs/\", graph=tf.get_default_graph())\n", " \n", " \n", " \n", " def train(self, X, Y, X_val, Y_val, epochs=100):\n", " epoch = 0\n", " X, Y = shuffle(X,Y)\n", " loss = []\n", " loss_val = []\n", " while epoch < epochs:\n", " # Run the optimizer for the whole training set batch wise (Stochastic Gradient Descent) \n", " merge, _, l = self.sess.run([self.merge,self.opt,self.loss], feed_dict={self.X: X, self.y: Y})\n", " l_val = self.sess.run(self.loss, feed_dict={self.X: X_val, self.y: Y_val})\n", " \n", " loss.append(l)\n", " loss_val.append(l_val)\n", " self.writer.add_summary(merge, epoch)\n", " \n", " if epoch % 10 == 0:\n", " print(\"Epoch {}/{} training loss: {} Validation loss {}\".\\\n", " format(epoch,epochs,l, l_val ))\n", " \n", " \n", " epoch += 1\n", " return loss, loss_val\n", " \n", " def predict(self, X):\n", " return self.sess.run(self.y_hat, feed_dict={self.X: X})" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "filename = 'Folds5x2_pp.xlsx'\n", "df = pd.read_excel(filename, sheet_name='Sheet1')\n", "X, Y = df[['AT', 'V','AP','RH']], df['PE']\n", "scaler = MinMaxScaler()\n", "X_new = scaler.fit_transform(X)\n", "target_scaler = MinMaxScaler()\n", "Y_new = target_scaler.fit_transform(Y.values.reshape(-1,1))\n", "X_train, X_val, Y_train, y_val = \\\n", " train_test_split(X_new, Y_new, test_size=0.4, random_state=333)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "_, d = X_train.shape\n", "_, n = Y_train.shape\n", "model = MLP(n_input=d, n_hidden=15, n_output=n)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 0/6000 training loss: 108.84590911865234 Validation loss 108.71923828125\n", "Epoch 10/6000 training loss: 107.54991912841797 Validation loss 107.42478942871094\n", "Epoch 20/6000 training loss: 106.2693862915039 Validation loss 106.14578247070312\n", "Epoch 30/6000 training loss: 105.00411224365234 Validation loss 104.88204193115234\n", "Epoch 40/6000 training loss: 103.75394439697266 Validation loss 103.63336181640625\n", "Epoch 50/6000 training loss: 102.51868438720703 Validation loss 102.39956665039062\n", "Epoch 60/6000 training loss: 101.29817199707031 Validation loss 101.18049621582031\n", "Epoch 70/6000 training loss: 100.09220886230469 Validation loss 99.97598266601562\n", "Epoch 80/6000 training loss: 98.90062713623047 Validation loss 98.78582763671875\n", "Epoch 90/6000 training loss: 97.72327423095703 Validation loss 97.60987091064453\n", "Epoch 100/6000 training loss: 96.55992889404297 Validation loss 96.44790649414062\n", "Epoch 110/6000 training loss: 95.41049194335938 Validation loss 95.29981994628906\n", "Epoch 120/6000 training loss: 94.2747573852539 Validation loss 94.16545104980469\n", "Epoch 130/6000 training loss: 93.1525650024414 Validation loss 93.04459381103516\n", "Epoch 140/6000 training loss: 92.04374694824219 Validation loss 91.9371109008789\n", "Epoch 150/6000 training loss: 90.94818115234375 Validation loss 90.84282684326172\n", "Epoch 160/6000 training loss: 89.86566162109375 Validation loss 89.76158905029297\n", "Epoch 170/6000 training loss: 88.79605865478516 Validation loss 88.6932601928711\n", "Epoch 180/6000 training loss: 87.73921203613281 Validation loss 87.63766479492188\n", "Epoch 190/6000 training loss: 86.6949691772461 Validation loss 86.59466552734375\n", "Epoch 200/6000 training loss: 85.66317749023438 Validation loss 85.56410217285156\n", "Epoch 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Validation loss 74.11516571044922\n", "Epoch 330/6000 training loss: 73.31790924072266 Validation loss 73.23341369628906\n", "Epoch 340/6000 training loss: 72.44564819335938 Validation loss 72.36217498779297\n", "Epoch 350/6000 training loss: 71.58378601074219 Validation loss 71.50131225585938\n", "Epoch 360/6000 training loss: 70.73219299316406 Validation loss 70.65072631835938\n", "Epoch 370/6000 training loss: 69.89075469970703 Validation loss 69.81027221679688\n", "Epoch 380/6000 training loss: 69.05934143066406 Validation loss 68.9798355102539\n", "Epoch 390/6000 training loss: 68.23784637451172 Validation loss 68.15930938720703\n", "Epoch 400/6000 training loss: 67.4261474609375 Validation loss 67.34855651855469\n", "Epoch 410/6000 training loss: 66.6241226196289 Validation loss 66.54747772216797\n", "Epoch 420/6000 training loss: 65.8316650390625 Validation loss 65.75593566894531\n", "Epoch 430/6000 training loss: 65.04865264892578 Validation loss 64.97383880615234\n", "Epoch 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5850/6000 training loss: 0.1215355396270752 Validation loss 0.12168289721012115\n", "Epoch 5860/6000 training loss: 0.1204107329249382 Validation loss 0.12055912613868713\n", "Epoch 5870/6000 training loss: 0.11929944902658463 Validation loss 0.11944887787103653\n", "Epoch 5880/6000 training loss: 0.11820153146982193 Validation loss 0.11835195124149323\n", "Epoch 5890/6000 training loss: 0.11711681634187698 Validation loss 0.11726823449134827\n", "Epoch 5900/6000 training loss: 0.11604513227939606 Validation loss 0.11619752645492554\n", "Epoch 5910/6000 training loss: 0.11498632282018661 Validation loss 0.11513969302177429\n", "Epoch 5920/6000 training loss: 0.11394023895263672 Validation loss 0.1140945702791214\n", "Epoch 5930/6000 training loss: 0.11290674656629562 Validation loss 0.11306202411651611\n", "Epoch 5940/6000 training loss: 0.1118856742978096 Validation loss 0.11204186826944351\n", "Epoch 5950/6000 training loss: 0.11087686568498611 Validation loss 0.11103399097919464\n", "Epoch 5960/6000 training loss: 0.10988020151853561 Validation loss 0.11003821343183517\n", "Epoch 5970/6000 training loss: 0.10889548808336258 Validation loss 0.10905441641807556\n", "Epoch 5980/6000 training loss: 0.10792264342308044 Validation loss 0.10808244347572327\n", "Epoch 5990/6000 training loss: 0.10696147382259369 Validation loss 0.10712215304374695\n" ] } ], "source": [ "loss, loss_val = model.train(X_train, Y_train, X_val, y_val, 6000)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0,0.5,'Mean Square Error')" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(loss, label=\"Taining Loss\")\n", "plt.plot(loss_val, label=\"Validation Loss\")\n", "plt.legend()\n", "plt.xlabel(\"Epochs\")\n", "plt.ylabel(\"Mean Square Error\")" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "Y_pred = model.predict(np.float32(X_val))" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "R2 Score is 0.6767650953865371 and MSE 0.016636404989961568\n" ] } ], "source": [ "from sklearn.metrics import mean_squared_error, r2_score\n", "print(\"R2 Score is {} and MSE {}\".format(\\\n", " r2_score(y_val, Y_pred),\\\n", " mean_squared_error(y_val, Y_pred)))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python [conda env:tensorflow]", "language": "python", "name": "conda-env-tensorflow-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.4" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: Chapter04/Readme.md ================================================ # Deep Learning for IoT In the last chapter, we learned about different machine learning algorithms, the focus of this chapter is the neural network based multiple layered models a.k.a. Deep learning models. They have become the buzzword in the last few years, and an absolute favourite of Investors in the field of Artificial-Intelligence-based startups. Achieving above human level accuracy in the task of object detection, defeating the world's Dan Nine Go master are some of the feats possible by deep learning. In this chapter and few subsequent chapters, we will learn about the different deep learning models and how to use deep learning on our IoT generated data. In this chapter, we will start with a glimpse to the journey of deep learning, and learn about four popular models, the multilayered perceptron (MLP), the convolution neural networks (CNN), Recurrent neural network (RNN) and Autoencoders. Specifically, you will: * Know about the history of deep learning and the factors responsible for its present success. * Know about an artificial neuron, and how they can be connected to solve non-linear problems. * Know about the backpropagation algorithm and use it to train the MLP model. * Learn about the different optimizers and activation functions available in TensorFlow. * Know how the convolutional neural network works, and the concept behind kernel, padding and strides. * Use CNN model for classification/recognition.. * Know about the recurrent neural networks and the modified RNNs, the Long Short-Term Memory and Gated Recurrent Units. * Know the architecture and functioning of Autoencoders. ================================================ FILE: Chapter04/activation_functions.ipynb ================================================ { "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/am/anaconda3/envs/tensorflow/lib/python3.5/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n", " from ._conv import register_converters as _register_converters\n" ] } ], "source": [ "import tensorflow as tf\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plotting Sigmoidal Activation function\n", "h = np.linspace(-5,5,50)\n", "x=tf.placeholder(tf.float32)\n", "out = tf.sigmoid(x)\n", "dout = tf.gradients(out,x)\n", "init = tf.global_variables_initializer()\n", "with tf.Session() as sess:\n", " sess.run(init)\n", " y, dY = sess.run([out, dout], feed_dict={x:h})\n", " gradient=dY[0]\n", " \n", "plt.xlabel('Activity of Neuron')\n", "plt.ylabel('Output of Neuron')\n", "plt.title('Sigmoidal Activation Function and Its Derivative')\n", "plot1 = plt.plot(h, y, label = 'Sigmoid Function')\n", "plot2 = plt.plot(h, gradient, label = 'Sigmoid Derivative')\n", "plt.legend(loc='upper left')\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plotting Tangent Hyperbolic Activation function\n", "h = np.linspace(-5,5,50)\n", "x=tf.placeholder(tf.float32)\n", "out = tf.tanh(x)\n", "dout = tf.gradients(out,x)\n", "init = tf.global_variables_initializer()\n", "with tf.Session() as sess:\n", " sess.run(init)\n", " y, dY = sess.run([out, dout], feed_dict={x:h})\n", " gradient=dY[0]\n", " \n", "plt.xlabel('Activity of Neuron')\n", "plt.ylabel('Output of Neuron')\n", "plt.title('Tangent Hyperbolic Activation Function and Its Derivative')\n", "plot1 = plt.plot(h, y, label = 'Tanh Function')\n", "plot2 = plt.plot(h, gradient, label = 'Tanh Derivative')\n", "plt.legend(loc='upper left')\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plotting ReLU Activation function\n", "h = np.linspace(-5,5,50)\n", "x=tf.placeholder(tf.float32)\n", "out = tf.nn.relu(x)\n", "dout = tf.gradients(out,x)\n", "init = tf.global_variables_initializer()\n", "with tf.Session() as sess:\n", " sess.run(init)\n", " y, dY = sess.run([out, dout], feed_dict={x:h})\n", " gradient=dY[0]\n", " \n", "plt.xlabel('Activity of Neuron')\n", "plt.ylabel('Output of Neuron')\n", "plt.title('ReLU Activation Function and Its Derivative')\n", "plot1 = plt.plot(h, y, label = 'ReLU Function')\n", "plot2 = plt.plot(h, gradient, label = 'ReLU Derivative')\n", "plt.legend(loc='upper left')" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plotting ELU Activation function\n", "h = np.linspace(-5,5,50)\n", "x=tf.placeholder(tf.float32)\n", "out = tf.nn.elu(x)\n", "dout = tf.gradients(out,x)\n", "init = tf.global_variables_initializer()\n", "with tf.Session() as sess:\n", " sess.run(init)\n", " y, dY = sess.run([out, dout], feed_dict={x:h})\n", " gradient=dY[0]\n", " \n", "plt.xlabel('Activity of Neuron')\n", "plt.ylabel('Output of Neuron')\n", "plt.title('ELU Activation Function and Its Derivative')\n", "plot1 = plt.plot(h, y, label = 'ELU Function')\n", "plot2 = plt.plot(h, gradient, label = 'ELU Derivative')\n", "plt.legend(loc='upper left')" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plotting Leaky ReLU Activation function\n", "h = np.linspace(-5,5,50)\n", "x=tf.placeholder(tf.float32)\n", "out = tf.nn.leaky_relu(x)\n", "dout = tf.gradients(out,x)\n", "init = tf.global_variables_initializer()\n", "with tf.Session() as sess:\n", " sess.run(init)\n", " y, dY = sess.run([out, dout], feed_dict={x:h})\n", " gradient=dY[0]\n", " \n", "plt.xlabel('Activity of Neuron')\n", "plt.ylabel('Output of Neuron')\n", "plt.title('Leaky ReLU Activation Function and Its Derivative')\n", "plot1 = plt.plot(h, y, label = 'Leaky ReLU Function')\n", "plot2 = plt.plot(h, gradient, label = 'Leaky ReLU Derivative')\n", "plt.legend(loc='upper left')" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plotting Softmax Activation function\n", "h = np.linspace(-5, 5, 50)\n", "x1=tf.placeholder(tf.float32)\n", "x2=tf.placeholder(tf.float32)\n", "out = tf.nn.softmax(0.3*x1 + 0.5*x2)\n", "init = tf.global_variables_initializer()\n", "with tf.Session() as sess:\n", " sess.run(init)\n", " y = sess.run(out, feed_dict={x1:h, x2:h})\n", " gradient=dY[0]\n", " \n", "plt.xlabel('Activity of Neuron')\n", "plt.ylabel('Output of Neuron')\n", "plt.title('Softmax Activation Function')\n", "plot1 = plt.plot(h, y, label = 'Softmax Function')\n", "plt.legend(loc='upper left')" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "# Threshold Activation function\n", "def threshold (x):\n", " cond = tf.less(x, tf.zeros(tf.shape(x), dtype = x.dtype))\n", " out = tf.where(cond, tf.zeros(tf.shape(x)), tf.ones(tf.shape(x)))\n", " return out" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plotting Threshold Activation function\n", "h = np.linspace(-5, 5, 50)\n", "x=tf.placeholder(tf.float32)\n", "out = threshold(x)\n", "init = tf.global_variables_initializer()\n", "with tf.Session() as sess:\n", " sess.run(init)\n", " y = sess.run(out, feed_dict={x:h})\n", " gradient=dY[0]\n", " \n", "plt.xlabel('Activity of Neuron')\n", "plt.ylabel('Output of Neuron')\n", "plt.title('Threshold Activation Function')\n", "plot1 = plt.plot(h, y, label = 'Threshold Function')\n", "plt.legend(loc='upper left')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python [conda env:tensorflow]", "language": "python", "name": "conda-env-tensorflow-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.4" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: Chapter04/single_neuron_tf.ipynb ================================================ { "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/am/anaconda3/envs/tensorflow/lib/python3.5/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n", " from ._conv import register_converters as _register_converters\n" ] } ], "source": [ "import tensorflow as tf\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "from sklearn.utils import shuffle\n", "from sklearn.preprocessing import MinMaxScaler\n", "from sklearn.model_selection import train_test_split\n", "% matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "class ArtificialNeuron:\n", " def __init__(self,N=2, act_func=tf.nn.sigmoid, learning_rate= 0.001):\n", " self.N = N # Number of inputs to the neuron\n", " self.act_fn = act_func\n", " \n", " # Build the graph for a single neuron\n", " self.W = tf.Variable(tf.random_normal([N,1], stddev=2, seed = 0))\n", " self.bias = tf.Variable(0.0, dtype=tf.float32)\n", " self.X = tf.placeholder(tf.float32, name='X', shape=[None,N])\n", " self.y = tf.placeholder(tf.float32, name='Y')\n", " \n", " activity = tf.matmul(self.X, self.W) + self.bias\n", " self.y_hat = self.act_fn(activity)\n", " \n", " error = self.y - self.y_hat\n", " \n", " self.loss = tf.reduce_mean(tf.square(error))\n", " self.opt = tf.train.GradientDescentOptimizer(learning_rate=learning_rate).minimize(self.loss)\n", " \n", " init = tf.global_variables_initializer()\n", " \n", " self.sess = tf.Session()\n", " self.sess.run(init)\n", " \n", " def train(self, X, Y, X_val, Y_val, epochs=100):\n", " epoch = 0\n", " X, Y = shuffle(X,Y)\n", " loss = []\n", " loss_val = []\n", " while epoch < epochs:\n", " # Run the optimizer for the whole training set batch wise (Stochastic Gradient Descent) \n", " _, l = self.sess.run([self.opt,self.loss], feed_dict={self.X: X, self.y: Y})\n", " l_val = self.sess.run(self.loss, feed_dict={self.X: X_val, self.y: Y_val})\n", " \n", " loss.append(l)\n", " loss_val.append(l_val)\n", " \n", " if epoch % 10 == 0:\n", " print(\"Epoch {}/{} training loss: {} Validation loss {}\".\\\n", " format(epoch,epochs,l, l_val ))\n", " \n", " epoch += 1\n", " return loss, loss_val\n", " \n", " def predict(self, X):\n", " return self.sess.run(self.y_hat, feed_dict={self.X: X})" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "filename = 'Folds5x2_pp.xlsx'\n", "df = pd.read_excel(filename, sheet_name='Sheet1')\n", "X, Y = df[['AT', 'V','AP','RH']], df['PE']\n", "scaler = MinMaxScaler()\n", "X_new = scaler.fit_transform(X)\n", "target_scaler = MinMaxScaler()\n", "Y_new = target_scaler.fit_transform(Y.values.reshape(-1,1))\n", "X_train, X_val, Y_train, y_val = \\\n", " train_test_split(X_new, Y_new, test_size=0.4, random_state=333)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "_, d = X_train.shape\n", "model = ArtificialNeuron(N=d)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 0/30000 training loss: 0.28809911012649536 Validation loss 0.2842104136943817\n", "Epoch 10/30000 training loss: 0.28804391622543335 Validation loss 0.28415507078170776\n", "Epoch 20/30000 training loss: 0.28798866271972656 Validation loss 0.2840996980667114\n", "Epoch 30/30000 training loss: 0.2879333794116974 Validation loss 0.2840442359447479\n", "Epoch 40/30000 training loss: 0.28787800669670105 Validation loss 0.28398871421813965\n", "Epoch 50/30000 training loss: 0.28782257437705994 Validation loss 0.28393319249153137\n", "Epoch 60/30000 training loss: 0.28776705265045166 Validation loss 0.28387752175331116\n", "Epoch 70/30000 training loss: 0.2877114713191986 Validation loss 0.2838217616081238\n", "Epoch 80/30000 training loss: 0.28765586018562317 Validation loss 0.2837660014629364\n", "Epoch 90/30000 training loss: 0.2876001000404358 Validation loss 0.2837101221084595\n", "Epoch 100/30000 training loss: 0.2875443696975708 Validation loss 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"Epoch 1090/30000 training loss: 0.2816450297832489 Validation loss 0.27774184942245483\n", "Epoch 1100/30000 training loss: 0.28158146142959595 Validation loss 0.27767816185951233\n", "Epoch 1110/30000 training loss: 0.28151780366897583 Validation loss 0.2776143550872803\n", "Epoch 1120/30000 training loss: 0.2814539670944214 Validation loss 0.2775503993034363\n", "Epoch 1130/30000 training loss: 0.28139007091522217 Validation loss 0.2774864137172699\n", "Epoch 1140/30000 training loss: 0.2813260853290558 Validation loss 0.2774222791194916\n", "Epoch 1150/30000 training loss: 0.28126198053359985 Validation loss 0.2773580551147461\n", "Epoch 1160/30000 training loss: 0.28119784593582153 Validation loss 0.2772938311100006\n", "Epoch 1170/30000 training loss: 0.28113362193107605 Validation loss 0.27722951769828796\n", "Epoch 1180/30000 training loss: 0.2810693383216858 Validation loss 0.27716511487960815\n", "Epoch 1190/30000 training loss: 0.281004935503006 Validation loss 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training loss: 0.07870473712682724 Validation loss 0.07881271094083786\n", "Epoch 29540/30000 training loss: 0.07869075238704681 Validation loss 0.07879884541034698\n", "Epoch 29550/30000 training loss: 0.07867679744958878 Validation loss 0.0787849873304367\n", "Epoch 29560/30000 training loss: 0.07866283506155014 Validation loss 0.07877112925052643\n", "Epoch 29570/30000 training loss: 0.07864886522293091 Validation loss 0.07875727862119675\n", "Epoch 29580/30000 training loss: 0.07863490283489227 Validation loss 0.07874342054128647\n", "Epoch 29590/30000 training loss: 0.07862094044685364 Validation loss 0.07872957736253738\n", "Epoch 29600/30000 training loss: 0.078606978058815 Validation loss 0.0787157192826271\n", "Epoch 29610/30000 training loss: 0.07859301567077637 Validation loss 0.07870186120271683\n", "Epoch 29620/30000 training loss: 0.07857903838157654 Validation loss 0.07868799567222595\n", "Epoch 29630/30000 training loss: 0.0785650834441185 Validation loss 0.07867413759231567\n", "Epoch 29640/30000 training loss: 0.07855112105607986 Validation loss 0.07866030186414719\n", "Epoch 29650/30000 training loss: 0.07853716611862183 Validation loss 0.07864644378423691\n", "Epoch 29660/30000 training loss: 0.07852320373058319 Validation loss 0.07863260060548782\n", "Epoch 29670/30000 training loss: 0.07850924879312515 Validation loss 0.07861874997615814\n", "Epoch 29680/30000 training loss: 0.07849529385566711 Validation loss 0.07860489189624786\n", "Epoch 29690/30000 training loss: 0.07848134636878967 Validation loss 0.07859105616807938\n", "Epoch 29700/30000 training loss: 0.07846739143133163 Validation loss 0.07857721298933029\n", "Epoch 29710/30000 training loss: 0.07845345139503479 Validation loss 0.07856336981058121\n", "Epoch 29720/30000 training loss: 0.07843950390815735 Validation loss 0.07854953408241272\n", "Epoch 29730/30000 training loss: 0.0784255713224411 Validation loss 0.07853570580482483\n", "Epoch 29740/30000 training loss: 0.07841164618730545 Validation loss 0.07852189242839813\n", "Epoch 29750/30000 training loss: 0.0783977210521698 Validation loss 0.07850805670022964\n", "Epoch 29760/30000 training loss: 0.07838379591703415 Validation loss 0.07849423587322235\n", "Epoch 29770/30000 training loss: 0.0783698707818985 Validation loss 0.07848040759563446\n", "Epoch 29780/30000 training loss: 0.07835593819618225 Validation loss 0.07846658676862717\n", "Epoch 29790/30000 training loss: 0.0783420130610466 Validation loss 0.07845275849103928\n", "Epoch 29800/30000 training loss: 0.07832808792591095 Validation loss 0.07843894511461258\n", "Epoch 29810/30000 training loss: 0.0783141702413559 Validation loss 0.07842512428760529\n", "Epoch 29820/30000 training loss: 0.07830024510622025 Validation loss 0.07841130346059799\n", "Epoch 29830/30000 training loss: 0.07828634232282639 Validation loss 0.0783974900841713\n", "Epoch 29840/30000 training loss: 0.07827243208885193 Validation loss 0.078383669257164\n", "Epoch 29850/30000 training loss: 0.07825851440429688 Validation loss 0.0783698558807373\n", "Epoch 29860/30000 training loss: 0.07824459671974182 Validation loss 0.07835604250431061\n", "Epoch 29870/30000 training loss: 0.07823071628808975 Validation loss 0.07834227383136749\n", "Epoch 29880/30000 training loss: 0.07821687310934067 Validation loss 0.07832852005958557\n", "Epoch 29890/30000 training loss: 0.07820302993059158 Validation loss 0.07831477373838425\n", "Epoch 29900/30000 training loss: 0.0781891793012619 Validation loss 0.07830102741718292\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch 29910/30000 training loss: 0.07817532867193222 Validation loss 0.0782872661948204\n", "Epoch 29920/30000 training loss: 0.07816149294376373 Validation loss 0.07827351987361908\n", "Epoch 29930/30000 training loss: 0.07814767211675644 Validation loss 0.07825978845357895\n", "Epoch 29940/30000 training loss: 0.07813384383916855 Validation loss 0.07824604958295822\n", "Epoch 29950/30000 training loss: 0.07812002301216125 Validation loss 0.0782323107123375\n", "Epoch 29960/30000 training loss: 0.07810619473457336 Validation loss 0.07821858674287796\n", "Epoch 29970/30000 training loss: 0.07809237390756607 Validation loss 0.07820484787225723\n", "Epoch 29980/30000 training loss: 0.07807855308055878 Validation loss 0.0781911313533783\n", "Epoch 29990/30000 training loss: 0.07806473225355148 Validation loss 0.07817739993333817\n" ] } ], "source": [ "loss, loss_val = model.train(X_train, Y_train, X_val, y_val, 30000)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0,0.5,'Mean Square Error')" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(loss, label=\"Taining Loss\")\n", "plt.plot(loss_val, label=\"Validation Loss\")\n", "plt.legend()\n", "plt.xlabel(\"Epochs\")\n", "plt.ylabel(\"Mean Square Error\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python [conda env:tensorflow]", "language": "python", "name": "conda-env-tensorflow-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.4" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: Chapter04/winequality-red.csv ================================================ "fixed acidity";"volatile acidity";"citric acid";"residual sugar";"chlorides";"free sulfur dioxide";"total sulfur dioxide";"density";"pH";"sulphates";"alcohol";"quality" 7.4;0.7;0;1.9;0.076;11;34;0.9978;3.51;0.56;9.4;5 7.8;0.88;0;2.6;0.098;25;67;0.9968;3.2;0.68;9.8;5 7.8;0.76;0.04;2.3;0.092;15;54;0.997;3.26;0.65;9.8;5 11.2;0.28;0.56;1.9;0.075;17;60;0.998;3.16;0.58;9.8;6 7.4;0.7;0;1.9;0.076;11;34;0.9978;3.51;0.56;9.4;5 7.4;0.66;0;1.8;0.075;13;40;0.9978;3.51;0.56;9.4;5 7.9;0.6;0.06;1.6;0.069;15;59;0.9964;3.3;0.46;9.4;5 7.3;0.65;0;1.2;0.065;15;21;0.9946;3.39;0.47;10;7 7.8;0.58;0.02;2;0.073;9;18;0.9968;3.36;0.57;9.5;7 7.5;0.5;0.36;6.1;0.071;17;102;0.9978;3.35;0.8;10.5;5 6.7;0.58;0.08;1.8;0.097;15;65;0.9959;3.28;0.54;9.2;5 7.5;0.5;0.36;6.1;0.071;17;102;0.9978;3.35;0.8;10.5;5 5.6;0.615;0;1.6;0.089;16;59;0.9943;3.58;0.52;9.9;5 7.8;0.61;0.29;1.6;0.114;9;29;0.9974;3.26;1.56;9.1;5 8.9;0.62;0.18;3.8;0.176;52;145;0.9986;3.16;0.88;9.2;5 8.9;0.62;0.19;3.9;0.17;51;148;0.9986;3.17;0.93;9.2;5 8.5;0.28;0.56;1.8;0.092;35;103;0.9969;3.3;0.75;10.5;7 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6.8;0.47;0.08;2.2;0.064;18;38;0.99553;3.3;0.65;9.6;6 7.1;0.53;0.07;1.7;0.071;15;24;0.9951;3.29;0.66;10.8;6 7.9;0.29;0.49;2.2;0.096;21;59;0.99714;3.31;0.67;10.1;6 7.1;0.69;0.08;2.1;0.063;42;52;0.99608;3.42;0.6;10.2;6 6.6;0.44;0.09;2.2;0.063;9;18;0.99444;3.42;0.69;11.3;6 6.1;0.705;0.1;2.8;0.081;13;28;0.99631;3.6;0.66;10.2;5 7.2;0.53;0.13;2;0.058;18;22;0.99573;3.21;0.68;9.9;6 8;0.39;0.3;1.9;0.074;32;84;0.99717;3.39;0.61;9;5 6.6;0.56;0.14;2.4;0.064;13;29;0.99397;3.42;0.62;11.7;7 7;0.55;0.13;2.2;0.075;15;35;0.9959;3.36;0.59;9.7;6 6.1;0.53;0.08;1.9;0.077;24;45;0.99528;3.6;0.68;10.3;6 5.4;0.58;0.08;1.9;0.059;20;31;0.99484;3.5;0.64;10.2;6 6.2;0.64;0.09;2.5;0.081;15;26;0.99538;3.57;0.63;12;5 7.2;0.39;0.32;1.8;0.065;34;60;0.99714;3.46;0.78;9.9;5 6.2;0.52;0.08;4.4;0.071;11;32;0.99646;3.56;0.63;11.6;6 7.4;0.25;0.29;2.2;0.054;19;49;0.99666;3.4;0.76;10.9;7 6.7;0.855;0.02;1.9;0.064;29;38;0.99472;3.3;0.56;10.75;6 11.1;0.44;0.42;2.2;0.064;14;19;0.99758;3.25;0.57;10.4;6 8.4;0.37;0.43;2.3;0.063;12;19;0.9955;3.17;0.81;11.2;7 6.5;0.63;0.33;1.8;0.059;16;28;0.99531;3.36;0.64;10.1;6 7;0.57;0.02;2;0.072;17;26;0.99575;3.36;0.61;10.2;5 6.3;0.6;0.1;1.6;0.048;12;26;0.99306;3.55;0.51;12.1;5 11.2;0.4;0.5;2;0.099;19;50;0.99783;3.1;0.58;10.4;5 7.4;0.36;0.3;1.8;0.074;17;24;0.99419;3.24;0.7;11.4;8 7.1;0.68;0;2.3;0.087;17;26;0.99783;3.45;0.53;9.5;5 7.1;0.67;0;2.3;0.083;18;27;0.99768;3.44;0.54;9.4;5 6.3;0.68;0.01;3.7;0.103;32;54;0.99586;3.51;0.66;11.3;6 7.3;0.735;0;2.2;0.08;18;28;0.99765;3.41;0.6;9.4;5 6.6;0.855;0.02;2.4;0.062;15;23;0.99627;3.54;0.6;11;6 7;0.56;0.17;1.7;0.065;15;24;0.99514;3.44;0.68;10.55;7 6.6;0.88;0.04;2.2;0.066;12;20;0.99636;3.53;0.56;9.9;5 6.6;0.855;0.02;2.4;0.062;15;23;0.99627;3.54;0.6;11;6 6.9;0.63;0.33;6.7;0.235;66;115;0.99787;3.22;0.56;9.5;5 7.8;0.6;0.26;2;0.08;31;131;0.99622;3.21;0.52;9.9;5 7.8;0.6;0.26;2;0.08;31;131;0.99622;3.21;0.52;9.9;5 7.8;0.6;0.26;2;0.08;31;131;0.99622;3.21;0.52;9.9;5 7.2;0.695;0.13;2;0.076;12;20;0.99546;3.29;0.54;10.1;5 7.2;0.695;0.13;2;0.076;12;20;0.99546;3.29;0.54;10.1;5 7.2;0.695;0.13;2;0.076;12;20;0.99546;3.29;0.54;10.1;5 6.7;0.67;0.02;1.9;0.061;26;42;0.99489;3.39;0.82;10.9;6 6.7;0.16;0.64;2.1;0.059;24;52;0.99494;3.34;0.71;11.2;6 7.2;0.695;0.13;2;0.076;12;20;0.99546;3.29;0.54;10.1;5 7;0.56;0.13;1.6;0.077;25;42;0.99629;3.34;0.59;9.2;5 6.2;0.51;0.14;1.9;0.056;15;34;0.99396;3.48;0.57;11.5;6 6.4;0.36;0.53;2.2;0.23;19;35;0.9934;3.37;0.93;12.4;6 6.4;0.38;0.14;2.2;0.038;15;25;0.99514;3.44;0.65;11.1;6 7.3;0.69;0.32;2.2;0.069;35;104;0.99632;3.33;0.51;9.5;5 6;0.58;0.2;2.4;0.075;15;50;0.99467;3.58;0.67;12.5;6 5.6;0.31;0.78;13.9;0.074;23;92;0.99677;3.39;0.48;10.5;6 7.5;0.52;0.4;2.2;0.06;12;20;0.99474;3.26;0.64;11.8;6 8;0.3;0.63;1.6;0.081;16;29;0.99588;3.3;0.78;10.8;6 6.2;0.7;0.15;5.1;0.076;13;27;0.99622;3.54;0.6;11.9;6 6.8;0.67;0.15;1.8;0.118;13;20;0.9954;3.42;0.67;11.3;6 6.2;0.56;0.09;1.7;0.053;24;32;0.99402;3.54;0.6;11.3;5 7.4;0.35;0.33;2.4;0.068;9;26;0.9947;3.36;0.6;11.9;6 6.2;0.56;0.09;1.7;0.053;24;32;0.99402;3.54;0.6;11.3;5 6.1;0.715;0.1;2.6;0.053;13;27;0.99362;3.57;0.5;11.9;5 6.2;0.46;0.29;2.1;0.074;32;98;0.99578;3.33;0.62;9.8;5 6.7;0.32;0.44;2.4;0.061;24;34;0.99484;3.29;0.8;11.6;7 7.2;0.39;0.44;2.6;0.066;22;48;0.99494;3.3;0.84;11.5;6 7.5;0.31;0.41;2.4;0.065;34;60;0.99492;3.34;0.85;11.4;6 5.8;0.61;0.11;1.8;0.066;18;28;0.99483;3.55;0.66;10.9;6 7.2;0.66;0.33;2.5;0.068;34;102;0.99414;3.27;0.78;12.8;6 6.6;0.725;0.2;7.8;0.073;29;79;0.9977;3.29;0.54;9.2;5 6.3;0.55;0.15;1.8;0.077;26;35;0.99314;3.32;0.82;11.6;6 5.4;0.74;0.09;1.7;0.089;16;26;0.99402;3.67;0.56;11.6;6 6.3;0.51;0.13;2.3;0.076;29;40;0.99574;3.42;0.75;11;6 6.8;0.62;0.08;1.9;0.068;28;38;0.99651;3.42;0.82;9.5;6 6.2;0.6;0.08;2;0.09;32;44;0.9949;3.45;0.58;10.5;5 5.9;0.55;0.1;2.2;0.062;39;51;0.99512;3.52;0.76;11.2;6 6.3;0.51;0.13;2.3;0.076;29;40;0.99574;3.42;0.75;11;6 5.9;0.645;0.12;2;0.075;32;44;0.99547;3.57;0.71;10.2;5 6;0.31;0.47;3.6;0.067;18;42;0.99549;3.39;0.66;11;6 ================================================ FILE: Chapter05/Genetic CNN.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Genetic CNN\n", "#### CNN architecture exploration using Genetic Algorithm as discussed in the following paper: Genetic CNN" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Import required libraries \n", "1. DEAP for Genetic Algorithm\n", "2. py-dag for Directed Asyclic Graph (Did few changes for Python 3, check dag.py)\n", "3. Tensorflow" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/am/anaconda3/envs/tensorflow/lib/python3.5/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n", " from ._conv import register_converters as _register_converters\n" ] } ], "source": [ "import random\n", "import numpy as np\n", "\n", "from deap import base, creator, tools, algorithms\n", "from scipy.stats import bernoulli\n", "from dag import DAG, DAGValidationError\n", "\n", "import tensorflow as tf\n", "from tensorflow.examples.tutorials.mnist import input_data" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Extracting MNIST_data/train-images-idx3-ubyte.gz\n", "Extracting MNIST_data/train-labels-idx1-ubyte.gz\n", "Extracting MNIST_data/t10k-images-idx3-ubyte.gz\n", "Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\n" ] } ], "source": [ "mnist = input_data.read_data_sets(\"MNIST_data/\", one_hot=True)\n", "train_imgs = mnist.train.images\n", "train_labels = mnist.train.labels\n", "test_imgs = mnist.test.images\n", "test_labels = mnist.test.labels\n", "\n", "train_imgs = np.reshape(train_imgs,[-1,28,28,1])\n", "test_imgs = np.reshape(test_imgs,[-1,28,28,1])" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "19\n" ] } ], "source": [ "STAGES = np.array([\"s1\",\"s2\",\"s3\"]) # S\n", "NUM_NODES = np.array([3,4,5]) # K\n", "\n", "L = 0 # genome length\n", "BITS_INDICES, l_bpi = np.empty((0,2),dtype = np.int32), 0 # to keep track of bits for each stage S\n", "for nn in NUM_NODES:\n", " t = nn * (nn - 1)\n", " BITS_INDICES = np.vstack([BITS_INDICES,[l_bpi, l_bpi + int(0.5 * t)]])\n", " l_bpi = int(0.5 * t)\n", " L += t\n", "L = int(0.5 * L)\n", "\n", "print(L)\n", "\n", "TRAINING_EPOCHS = 20\n", "BATCH_SIZE = 20\n", "TOTAL_BATCHES = train_imgs.shape[0] // BATCH_SIZE" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def weight_variable(weight_name, weight_shape):\n", " return tf.Variable(tf.truncated_normal(weight_shape, stddev = 0.1),name = ''.join([\"weight_\", weight_name]))\n", "\n", "def bias_variable(bias_name,bias_shape):\n", " return tf.Variable(tf.constant(0.01, shape = bias_shape),name = ''.join([\"bias_\", bias_name]))\n", "\n", "def linear_layer(x,n_hidden_units,layer_name):\n", " n_input = int(x.get_shape()[1])\n", " weights = weight_variable(layer_name,[n_input, n_hidden_units])\n", " biases = bias_variable(layer_name,[n_hidden_units])\n", " return tf.add(tf.matmul(x,weights),biases)\n", "\n", "def apply_convolution(x,kernel_height,kernel_width,num_channels,depth,layer_name):\n", " weights = weight_variable(layer_name,[kernel_height, kernel_width, num_channels, depth])\n", " biases = bias_variable(layer_name,[depth])\n", " return tf.nn.relu(tf.add(tf.nn.conv2d(x, weights,[1,2,2,1],padding = \"SAME\"),biases)) \n", "\n", "def apply_pool(x,kernel_height,kernel_width,stride_size):\n", " return tf.nn.max_pool(x, ksize=[1, kernel_height, kernel_width, 1], \n", " strides=[1, 1, stride_size, 1], padding = \"SAME\")\n", "\n", "def add_node(node_name, connector_node_name, h = 5, w = 5, nc = 1, d = 1):\n", " with tf.name_scope(node_name) as scope:\n", " conv = apply_convolution(tf.get_default_graph().get_tensor_by_name(connector_node_name), \n", " kernel_height = h, kernel_width = w, num_channels = nc , depth = d, \n", " layer_name = ''.join([\"conv_\",node_name]))\n", "\n", "def sum_tensors(tensor_a,tensor_b,activation_function_pattern):\n", " if not tensor_a.startswith(\"Add\"):\n", " tensor_a = ''.join([tensor_a,activation_function_pattern])\n", " \n", " return tf.add(tf.get_default_graph().get_tensor_by_name(tensor_a),\n", " tf.get_default_graph().get_tensor_by_name(''.join([tensor_b,activation_function_pattern])))\n", "\n", "def has_same_elements(x):\n", " return len(set(x)) <= 1\n", "\n", "'''This method will come handy to first generate DAG independent of Tensorflow, \n", " afterwards generated graph can be used to generate Tensorflow graph'''\n", "def generate_dag(optimal_indvidual,stage_name,num_nodes):\n", " # create nodes for the graph\n", " nodes = np.empty((0), dtype = np.str)\n", " for n in range(1,(num_nodes + 1)):\n", " nodes = np.append(nodes,''.join([stage_name,\"_\",str(n)]))\n", " \n", " # initialize directed asyclic graph (DAG) and add nodes to it\n", " dag = DAG()\n", " for n in nodes:\n", " dag.add_node(n)\n", "\n", " # split best indvidual found via GA to identify vertices connections and connect them in DAG \n", " edges = np.split(optimal_indvidual,np.cumsum(range(num_nodes - 1)))[1:]\n", " v2 = 2\n", " for e in edges:\n", " v1 = 1\n", " for i in e:\n", " if i:\n", " dag.add_edge(''.join([stage_name,\"_\",str(v1)]),''.join([stage_name,\"_\",str(v2)])) \n", " v1 += 1\n", " v2 += 1\n", "\n", " # delete nodes not connected to anyother node from DAG\n", " for n in nodes:\n", " if len(dag.predecessors(n)) == 0 and len(dag.downstream(n)) == 0:\n", " dag.delete_node(n)\n", " nodes = np.delete(nodes, np.where(nodes == n)[0][0])\n", " \n", " return dag, nodes\n", "\n", "def generate_tensorflow_graph(individual,stages,num_nodes,bits_indices):\n", " activation_function_pattern = \"/Relu:0\"\n", " \n", " tf.reset_default_graph()\n", " X = tf.placeholder(tf.float32, shape = [None,28,28,1], name = \"X\")\n", " Y = tf.placeholder(tf.float32,[None,10],name = \"Y\")\n", " \n", " d_node = X\n", " for stage_name,num_node,bpi in zip(stages,num_nodes,bits_indices):\n", " indv = individual[bpi[0]:bpi[1]]\n", "\n", " add_node(''.join([stage_name,\"_input\"]),d_node.name)\n", " pooling_layer_name = ''.join([stage_name,\"_input\",activation_function_pattern])\n", "\n", " if not has_same_elements(indv):\n", " # ------------------- Temporary DAG to hold all connections implied by GA solution ------------- # \n", "\n", " # get DAG and nodes in the graph\n", " dag, nodes = generate_dag(indv,stage_name,num_node) \n", " # get nodes without any predecessor, these will be connected to input node\n", " without_predecessors = dag.ind_nodes() \n", " # get nodes without any successor, these will be connected to output node\n", " without_successors = dag.all_leaves()\n", "\n", " # ----------------------------------------------------------------------------------------------- #\n", "\n", " # --------------------------- Initialize tensforflow graph based on DAG ------------------------- #\n", "\n", " for wop in without_predecessors:\n", " add_node(wop,''.join([stage_name,\"_input\",activation_function_pattern]))\n", "\n", " for n in nodes:\n", " predecessors = dag.predecessors(n)\n", " if len(predecessors) == 0:\n", " continue\n", " elif len(predecessors) > 1:\n", " first_predecessor = predecessors[0]\n", " for prd in range(1,len(predecessors)):\n", " t = sum_tensors(first_predecessor,predecessors[prd],activation_function_pattern)\n", " first_predecessor = t.name\n", " add_node(n,first_predecessor)\n", " elif predecessors:\n", " add_node(n,''.join([predecessors[0],activation_function_pattern]))\n", "\n", " if len(without_successors) > 1:\n", " first_successor = without_successors[0]\n", " for suc in range(1,len(without_successors)):\n", " t = sum_tensors(first_successor,without_successors[suc],activation_function_pattern)\n", " first_successor = t.name\n", " add_node(''.join([stage_name,\"_output\"]),first_successor) \n", " else:\n", " add_node(''.join([stage_name,\"_output\"]),''.join([without_successors[0],activation_function_pattern])) \n", "\n", " pooling_layer_name = ''.join([stage_name,\"_output\",activation_function_pattern])\n", " # ------------------------------------------------------------------------------------------ #\n", "\n", " d_node = apply_pool(tf.get_default_graph().get_tensor_by_name(pooling_layer_name), \n", " kernel_height = 16, kernel_width = 16,stride_size = 2)\n", "\n", " shape = d_node.get_shape().as_list()\n", " flat = tf.reshape(d_node, [-1, shape[1] * shape[2] * shape[3]])\n", " logits = linear_layer(flat,10,\"logits\")\n", " \n", " xentropy = tf.nn.softmax_cross_entropy_with_logits(logits = logits, labels = Y)\n", " loss_function = tf.reduce_mean(xentropy)\n", " optimizer = tf.train.AdamOptimizer().minimize(loss_function) \n", " accuracy = tf.reduce_mean(tf.cast( tf.equal(tf.argmax(tf.nn.softmax(logits),1), tf.argmax(Y,1)), tf.float32))\n", " \n", " return X, Y, optimizer, loss_function, accuracy\n", "\n", "def evaluateModel(individual):\n", " score = 0.0\n", " X, Y, optimizer, loss_function, accuracy = generate_tensorflow_graph(individual,STAGES,NUM_NODES,BITS_INDICES)\n", " with tf.Session() as session:\n", " tf.global_variables_initializer().run()\n", " for epoch in range(TRAINING_EPOCHS):\n", " for b in range(TOTAL_BATCHES):\n", " offset = (epoch * BATCH_SIZE) % (train_labels.shape[0] - BATCH_SIZE)\n", " batch_x = train_imgs[offset:(offset + BATCH_SIZE), :, :, :]\n", " batch_y = train_labels[offset:(offset + BATCH_SIZE), :]\n", " _, c = session.run([optimizer, loss_function],feed_dict={X: batch_x, Y : batch_y})\n", " \n", " score = session.run(accuracy, feed_dict={X: test_imgs, Y: test_labels})\n", " #print('Accuracy: ',score)\n", " return score," ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "gen\tnevals\n", "0 \t20 \n", "1 \t5 \n" ] } ], "source": [ "population_size = 20\n", "num_generations = 3\n", "\n", "creator.create(\"FitnessMax\", base.Fitness, weights = (1.0,))\n", "creator.create(\"Individual\", list , fitness = creator.FitnessMax)\n", "\n", "toolbox = base.Toolbox()\n", "toolbox.register(\"binary\", bernoulli.rvs, 0.5)\n", "toolbox.register(\"individual\", tools.initRepeat, creator.Individual, toolbox.binary, n = L)\n", "toolbox.register(\"population\", tools.initRepeat, list , toolbox.individual)\n", "\n", "toolbox.register(\"mate\", tools.cxOrdered)\n", "toolbox.register(\"mutate\", tools.mutShuffleIndexes, indpb = 0.8)\n", "toolbox.register(\"select\", tools.selRoulette)\n", "toolbox.register(\"evaluate\", evaluateModel)\n", "\n", "popl = toolbox.population(n = population_size)\n", "import time\n", "t = time.time()\n", "result = algorithms.eaSimple(popl, toolbox, cxpb = 0.4, mutpb = 0.05, ngen = num_generations, verbose = True)\n", "\n", "t1 = time.time() - t\n", "print(t1)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# print top-3 optimal solutions \n", "best_individuals = tools.selBest(popl, k = 3)\n", "for bi in best_individuals:\n", " print(bi)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "--------------------------------------------------------------------------------------------------------------------" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [conda env:tensorflow]", "language": "python", "name": "conda-env-tensorflow-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.4" } }, "nbformat": 4, "nbformat_minor": 1 } ================================================ FILE: Chapter05/Genetic_RNN.ipynb ================================================ { "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/am/anaconda3/envs/tensorflow/lib/python3.5/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n", " from ._conv import register_converters as _register_converters\n", "Using TensorFlow backend.\n" ] } ], "source": [ "import numpy as np\n", "import pandas as pd\n", "from sklearn.metrics import mean_squared_error\n", "from sklearn.model_selection import train_test_split as split\n", "\n", "from keras.layers import LSTM, Input, Dense\n", "from keras.models import Model\n", "\n", "from deap import base, creator, tools, algorithms\n", "from scipy.stats import bernoulli\n", "from bitstring import BitArray\n", "\n", "np.random.seed(1120)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "data = pd.read_csv('train.csv')\n", "data = np.reshape(np.array(data['wp1']),(len(data['wp1']),1))\n", "\n", "train_data = data[0:17257]\n", "test_data = data[17257:]" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "def prepare_dataset(data, window_size):\n", " X, Y = np.empty((0,window_size)), np.empty((0))\n", " for i in range(len(data)-window_size-1):\n", " X = np.vstack([X,data[i:(i + window_size),0]])\n", " Y = np.append(Y,data[i + window_size,0]) \n", " X = np.reshape(X,(len(X),window_size,1))\n", " Y = np.reshape(Y,(len(Y),1))\n", " return X, Y\n", "\n", "def train_evaluate(ga_individual_solution): \n", " # Decode GA solution to integer for window_size and num_units\n", " window_size_bits = BitArray(ga_individual_solution[0:6])\n", " num_units_bits = BitArray(ga_individual_solution[6:]) \n", " window_size = window_size_bits.uint\n", " num_units = num_units_bits.uint\n", " print('\\nWindow Size: ', window_size, ', Num of Units: ', num_units)\n", " \n", " # Return fitness score of 100 if window_size or num_unit is zero\n", " if window_size == 0 or num_units == 0:\n", " return 100, \n", " \n", " # Segment the train_data based on new window_size; split into train and validation (80/20)\n", " X,Y = prepare_dataset(train_data,window_size)\n", " X_train, X_val, y_train, y_val = split(X, Y, test_size = 0.20, random_state = 1120)\n", " \n", " # Train LSTM model and predict on validation set\n", " inputs = Input(shape=(window_size,1))\n", " x = LSTM(num_units, input_shape=(window_size,1))(inputs)\n", " predictions = Dense(1, activation='linear')(x)\n", " model = Model(inputs=inputs, outputs=predictions)\n", " model.compile(optimizer='adam',loss='mean_squared_error')\n", " model.fit(X_train, y_train, epochs=5, batch_size=10,shuffle=True)\n", " y_pred = model.predict(X_val)\n", " \n", " # Calculate the RMSE score as fitness score for GA\n", " rmse = np.sqrt(mean_squared_error(y_val, y_pred))\n", " print('Validation RMSE: ', rmse,'\\n')\n", " \n", " return rmse," ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Window Size: 36 , Num of Units: 2\n", "Epoch 1/5\n", "13776/13776 [==============================] - 34s 2ms/step - loss: 0.0188\n", "Epoch 2/5\n", "13776/13776 [==============================] - 34s 2ms/step - loss: 0.0089\n", "Epoch 3/5\n", "13776/13776 [==============================] - 34s 2ms/step - loss: 0.0068\n", "Epoch 4/5\n", "13776/13776 [==============================] - 34s 2ms/step - loss: 0.0060\n", "Epoch 5/5\n", "13776/13776 [==============================] - 34s 2ms/step - loss: 0.0057\n", "Validation RMSE: 0.07637882615270873 \n", "\n", "\n", "Window Size: 56 , Num of Units: 8\n", "Epoch 1/5\n", "13760/13760 [==============================] - 53s 4ms/step - loss: 0.0132\n", "Epoch 2/5\n", "13760/13760 [==============================] - 53s 4ms/step - loss: 0.0064\n", "Epoch 3/5\n", "13760/13760 [==============================] - 53s 4ms/step - loss: 0.0058\n", "Epoch 4/5\n", "13760/13760 [==============================] - 53s 4ms/step - loss: 0.0057\n", "Epoch 5/5\n", "13760/13760 [==============================] - 54s 4ms/step - loss: 0.0057\n", "Validation RMSE: 0.07416215025613665 \n", "\n", "\n", "Window Size: 60 , Num of Units: 9\n", "Epoch 1/5\n", "13756/13756 [==============================] - 57s 4ms/step - loss: 0.0185\n", "Epoch 2/5\n", "13756/13756 [==============================] - 57s 4ms/step - loss: 0.0074\n", "Epoch 3/5\n", "13756/13756 [==============================] - 57s 4ms/step - loss: 0.0060\n", "Epoch 4/5\n", "13756/13756 [==============================] - 57s 4ms/step - loss: 0.0057\n", "Epoch 5/5\n", "13756/13756 [==============================] - 57s 4ms/step - loss: 0.0057\n", "Validation RMSE: 0.07466883465012926 \n", "\n", "\n", "Window Size: 49 , Num of Units: 9\n", "Epoch 1/5\n", "13765/13765 [==============================] - 48s 3ms/step - loss: 0.0148\n", "Epoch 2/5\n", "13765/13765 [==============================] - 47s 3ms/step - loss: 0.0066\n", "Epoch 3/5\n", "13765/13765 [==============================] - 47s 3ms/step - loss: 0.0059\n", "Epoch 4/5\n", "13765/13765 [==============================] - 47s 3ms/step - loss: 0.0058\n", "Epoch 5/5\n", "13765/13765 [==============================] - 47s 3ms/step - loss: 0.0058\n", "Validation RMSE: 0.0740184976178182 \n", "\n", "\n", "Window Size: 60 , Num of Units: 9\n", "Epoch 1/5\n", "13756/13756 [==============================] - 58s 4ms/step - loss: 0.0227\n", "Epoch 2/5\n", "13756/13756 [==============================] - 58s 4ms/step - loss: 0.0066\n", "Epoch 3/5\n", "13756/13756 [==============================] - 58s 4ms/step - loss: 0.0058\n", "Epoch 4/5\n", "13756/13756 [==============================] - 58s 4ms/step - loss: 0.0057\n", "Epoch 5/5\n", "13756/13756 [==============================] - 58s 4ms/step - loss: 0.0057\n", "Validation RMSE: 0.0760438347342536 \n", "\n", "\n", "Window Size: 60 , Num of Units: 9\n", "Epoch 1/5\n", "13756/13756 [==============================] - 58s 4ms/step - loss: 0.0135\n", "Epoch 2/5\n", "13756/13756 [==============================] - 57s 4ms/step - loss: 0.0064\n", "Epoch 3/5\n", "13756/13756 [==============================] - 57s 4ms/step - loss: 0.0058\n", "Epoch 4/5\n", "13756/13756 [==============================] - 58s 4ms/step - loss: 0.0058\n", "Epoch 5/5\n", "13756/13756 [==============================] - 58s 4ms/step - loss: 0.0057\n", "Validation RMSE: 0.07489840863581337 \n", "\n", "\n", "Window Size: 48 , Num of Units: 9\n", "Epoch 1/5\n", "13766/13766 [==============================] - 47s 3ms/step - loss: 0.0144\n", "Epoch 2/5\n", "13766/13766 [==============================] - 47s 3ms/step - loss: 0.0068\n", "Epoch 3/5\n", "13766/13766 [==============================] - 47s 3ms/step - loss: 0.0058\n", "Epoch 4/5\n", "13766/13766 [==============================] - 47s 3ms/step - loss: 0.0057\n", "Epoch 5/5\n", "13766/13766 [==============================] - 47s 3ms/step - loss: 0.0057\n", "Validation RMSE: 0.0774579464584324 \n", "\n", "\n", "Window Size: 61 , Num of Units: 9\n", "Epoch 1/5\n", "13756/13756 [==============================] - 59s 4ms/step - loss: 0.0135\n", "Epoch 2/5\n", "13756/13756 [==============================] - 58s 4ms/step - loss: 0.0065\n", "Epoch 3/5\n", "13756/13756 [==============================] - 58s 4ms/step - loss: 0.0057\n", "Epoch 4/5\n", "13756/13756 [==============================] - 58s 4ms/step - loss: 0.0057\n", "Epoch 5/5\n", "13756/13756 [==============================] - 59s 4ms/step - loss: 0.0057\n", "Validation RMSE: 0.07473174433542637 \n", "\n", "\n", "Window Size: 49 , Num of Units: 9\n", "Epoch 1/5\n", "13765/13765 [==============================] - 48s 3ms/step - loss: 0.0116\n", "Epoch 2/5\n", "13765/13765 [==============================] - 47s 3ms/step - loss: 0.0063\n", "Epoch 3/5\n", "13765/13765 [==============================] - 47s 3ms/step - loss: 0.0058\n", "Epoch 4/5\n", "13765/13765 [==============================] - 47s 3ms/step - loss: 0.0058\n", "Epoch 5/5\n", "13765/13765 [==============================] - 47s 3ms/step - loss: 0.0058\n", "Validation RMSE: 0.07458360715108725 \n", "\n", "\n", "Window Size: 49 , Num of Units: 9\n", "Epoch 1/5\n", "13765/13765 [==============================] - 48s 3ms/step - loss: 0.0159\n", "Epoch 2/5\n", "13765/13765 [==============================] - 47s 3ms/step - loss: 0.0065\n", "Epoch 3/5\n", "13765/13765 [==============================] - 47s 3ms/step - loss: 0.0058\n", "Epoch 4/5\n", "13765/13765 [==============================] - 47s 3ms/step - loss: 0.0058\n", "Epoch 5/5\n", "13765/13765 [==============================] - 47s 3ms/step - loss: 0.0058\n", "Validation RMSE: 0.07410878901036914 \n", "\n", "\n", "Window Size: 49 , Num of Units: 9\n", "Epoch 1/5\n", "13765/13765 [==============================] - 48s 3ms/step - loss: 0.0151\n", "Epoch 2/5\n", "13765/13765 [==============================] - 47s 3ms/step - loss: 0.0065\n", "Epoch 3/5\n", "13765/13765 [==============================] - 47s 3ms/step - loss: 0.0059\n", "Epoch 4/5\n", "13765/13765 [==============================] - 47s 3ms/step - loss: 0.0058\n", "Epoch 5/5\n", "13765/13765 [==============================] - 47s 3ms/step - loss: 0.0058\n", "Validation RMSE: 0.07407377472540635 \n", "\n", "\n", "Window Size: 49 , Num of Units: 9\n", "Epoch 1/5\n", "13765/13765 [==============================] - 48s 3ms/step - loss: 0.0176\n", "Epoch 2/5\n", "13765/13765 [==============================] - 47s 3ms/step - loss: 0.0062\n", "Epoch 3/5\n", "13765/13765 [==============================] - 47s 3ms/step - loss: 0.0058\n", "Epoch 4/5\n", "13765/13765 [==============================] - 47s 3ms/step - loss: 0.0058\n", "Epoch 5/5\n", "13765/13765 [==============================] - 47s 3ms/step - loss: 0.0057\n", "Validation RMSE: 0.07401475117886316 \n", "\n" ] } ], "source": [ "population_size = 4\n", "num_generations = 4\n", "gene_length = 10\n", "\n", "# As we are trying to minimize the RMSE score, that's why using -1.0. \n", "# In case, when you want to maximize accuracy for instance, use 1.0\n", "creator.create('FitnessMax', base.Fitness, weights = (-1.0,))\n", "creator.create('Individual', list , fitness = creator.FitnessMax)\n", "\n", "toolbox = base.Toolbox()\n", "toolbox.register('binary', bernoulli.rvs, 0.5)\n", "toolbox.register('individual', tools.initRepeat, creator.Individual, toolbox.binary, n = gene_length)\n", "toolbox.register('population', tools.initRepeat, list , toolbox.individual)\n", "\n", "toolbox.register('mate', tools.cxOrdered)\n", "toolbox.register('mutate', tools.mutShuffleIndexes, indpb = 0.6)\n", "toolbox.register('select', tools.selRoulette)\n", "toolbox.register('evaluate', train_evaluate)\n", "\n", "population = toolbox.population(n = population_size)\n", "r = algorithms.eaSimple(population, toolbox, cxpb = 0.4, mutpb = 0.1, ngen = num_generations, verbose = False)\n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Window Size: 49 , Num of Units: 9\n" ] } ], "source": [ "best_individuals = tools.selBest(population,k = 1)\n", "best_window_size = None\n", "best_num_units = None\n", "\n", "for bi in best_individuals:\n", " window_size_bits = BitArray(bi[0:6])\n", " num_units_bits = BitArray(bi[6:]) \n", " best_window_size = window_size_bits.uint\n", " best_num_units = num_units_bits.uint\n", " print('\\nWindow Size: ', best_window_size, ', Num of Units: ', best_num_units)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/5\n", "17207/17207 [==============================] - 60s 3ms/step - loss: 0.0147\n", "Epoch 2/5\n", "17207/17207 [==============================] - 59s 3ms/step - loss: 0.0062\n", "Epoch 3/5\n", "17207/17207 [==============================] - 59s 3ms/step - loss: 0.0058\n", "Epoch 4/5\n", "17207/17207 [==============================] - 59s 3ms/step - loss: 0.0057\n", "Epoch 5/5\n", "17207/17207 [==============================] - 59s 3ms/step - loss: 0.0057\n", "Test RMSE: 0.09183506777697063\n" ] } ], "source": [ "X_train,y_train = prepare_dataset(train_data,best_window_size)\n", "X_test, y_test = prepare_dataset(test_data,best_window_size)\n", "\n", "inputs = Input(shape=(best_window_size,1))\n", "x = LSTM(best_num_units, input_shape=(best_window_size,1))(inputs)\n", "predictions = Dense(1, activation='linear')(x)\n", "model = Model(inputs = inputs, outputs = predictions)\n", "model.compile(optimizer='adam',loss='mean_squared_error')\n", "model.fit(X_train, y_train, epochs=5, batch_size=10,shuffle=True)\n", "y_pred = model.predict(X_test)\n", "\n", "rmse = np.sqrt(mean_squared_error(y_test, y_pred))\n", "print('Test RMSE: ', rmse)" ] } ], "metadata": { "kernelspec": { "display_name": "Python [conda env:tensorflow]", "language": "python", "name": "conda-env-tensorflow-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.4" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: Chapter05/GuessTheWord.ipynb ================================================ { "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import string\n", "import random\n", "\n", "from deap import base, creator, tools" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "## Create a Finess base class which is to be minimized\n", "# weights is a tuple -sign tells to minimize, +1 to maximize\n", "\n", "creator.create(\"FitnessMax\", base.Fitness, weights=(1.0,)) \n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This will define a class ```FitnessMax``` which inherits the Fitness class of deep.base module. The attribute weight which is a tuple is used to specify whether fitness function is to be maximized (weights=1.0) or minimized weights=-1.0. The DEAP library allows multi-objective Fitness function. \n", "\n", "### Individual\n", "\n", "Next we create a ```Individual``` class, which inherits the class ```list``` and has the ```FitnessMax``` class in its Fitness attribute. " ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# Now we create a individual class\n", "\n", "creator.create(\"Individual\", list, fitness=creator.FitnessMax)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Population\n", "\n", "Once the individuals are created we need to create population and define gene pool, to do this we use DEAP toolbox. All the objects that we will need now onwards- an individual, the population, the functions, the operators and the arguments are stored in the container called ```Toolbox```\n", "\n", "We can add or remove content in the container ```Toolbox``` using ```register()``` and ```unregister()``` methods" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "toolbox = base.Toolbox()\n", "\n", "# Gene Pool\n", "toolbox.register(\"attr_string\", random.choice, string.ascii_letters + string.digits )" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Number of characters in word\n", "word = list('hello')\n", "N = len(word)\n", "\n", "# Initialize population\n", "toolbox.register(\"individual\", tools.initRepeat, creator.Individual, toolbox.attr_string, N )\n", "toolbox.register(\"population\",tools.initRepeat, list, toolbox.individual)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def evalWord(individual, word):\n", " #word = list('hello')\n", " return sum(individual[i] == word[i] for i in range(len(individual))),\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "toolbox.register(\"evaluate\", evalWord, word)\n", "toolbox.register(\"mate\", tools.cxTwoPoint)\n", "toolbox.register(\"mutate\", tools.mutShuffleIndexes, indpb=0.05)\n", "toolbox.register(\"select\", tools.selTournament, tournsize=3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We define the other operators/functions we will need by registering them in the toolbox. This allows us to easily switch between the operators if desired.\n", "\n", "## Evolving the Population\n", "Once the representation and the genetic operators are chosen, we will define an algorithm combining all the individual parts and performing the evolution of our population until the One Max problem is solved. It is good style in programming to do so within a function, generally named main().\n", "\n", "Creating the Population\n", "First of all, we need to actually instantiate our population. But this step is effortlessly done using the population() method we registered in our toolbox earlier on." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def main():\n", " random.seed(64)\n", "\n", " # create an initial population of 300 individuals (where\n", " # each individual is a list of integers)\n", " pop = toolbox.population(n=300)\n", "\n", " # CXPB is the probability with which two individuals\n", " # are crossed\n", " #\n", " # MUTPB is the probability for mutating an individual\n", " CXPB, MUTPB = 0.5, 0.2\n", " \n", " print(\"Start of evolution\")\n", " \n", " # Evaluate the entire population\n", " fitnesses = list(map(toolbox.evaluate, pop))\n", " for ind, fit in zip(pop, fitnesses):\n", " #print(ind, fit)\n", " ind.fitness.values = fit\n", " \n", " print(\" Evaluated %i individuals\" % len(pop))\n", "\n", " # Extracting all the fitnesses of \n", " fits = [ind.fitness.values[0] for ind in pop]\n", "\n", " # Variable keeping track of the number of generations\n", " g = 0\n", " \n", " # Begin the evolution\n", " while max(fits) < 5 and g < 1000:\n", " # A new generation\n", " g = g + 1\n", " print(\"-- Generation %i --\" % g)\n", " \n", " # Select the next generation individuals\n", " offspring = toolbox.select(pop, len(pop))\n", " # Clone the selected individuals\n", " offspring = list(map(toolbox.clone, offspring))\n", " \n", " # Apply crossover and mutation on the offspring\n", " for child1, child2 in zip(offspring[::2], offspring[1::2]):\n", "\n", " # cross two individuals with probability CXPB\n", " if random.random() < CXPB:\n", " toolbox.mate(child1, child2)\n", "\n", " # fitness values of the children\n", " # must be recalculated later\n", " del child1.fitness.values\n", " del child2.fitness.values\n", "\n", " for mutant in offspring:\n", "\n", " # mutate an individual with probability MUTPB\n", " if random.random() < MUTPB:\n", " toolbox.mutate(mutant)\n", " del mutant.fitness.values\n", " \n", " # Evaluate the individuals with an invalid fitness\n", " invalid_ind = [ind for ind in offspring if not ind.fitness.valid]\n", " fitnesses = map(toolbox.evaluate, invalid_ind)\n", " for ind, fit in zip(invalid_ind, fitnesses):\n", " ind.fitness.values = fit\n", " \n", " print(\" Evaluated %i individuals\" % len(invalid_ind))\n", " \n", " # The population is entirely replaced by the offspring\n", " pop[:] = offspring\n", " \n", " # Gather all the fitnesses in one list and print the stats\n", " fits = [ind.fitness.values[0] for ind in pop]\n", " \n", " length = len(pop)\n", " mean = sum(fits) / length\n", " sum2 = sum(x*x for x in fits)\n", " std = abs(sum2 / length - mean**2)**0.5\n", " \n", " print(\" Min %s\" % min(fits))\n", " print(\" Max %s\" % max(fits))\n", " print(\" Avg %s\" % mean)\n", " print(\" Std %s\" % std)\n", " \n", " print(\"-- End of (successful) evolution --\")\n", " \n", " best_ind = tools.selBest(pop, 1)[0]\n", " print(\"Best individual is %s, %s\" % (''.join(best_ind), best_ind.fitness.values))" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Start of evolution\n", " Evaluated 300 individuals\n", "-- Generation 1 --\n", " Evaluated 178 individuals\n", " Min 0.0\n", " Max 2.0\n", " Avg 0.22\n", " Std 0.4526956299030656\n", "-- Generation 2 --\n", " Evaluated 174 individuals\n", " Min 0.0\n", " Max 2.0\n", " Avg 0.51\n", " Std 0.613650280425803\n", "-- Generation 3 --\n", " Evaluated 191 individuals\n", " Min 0.0\n", " Max 3.0\n", " Avg 0.9766666666666667\n", " Std 0.6502221842484989\n", "-- Generation 4 --\n", " Evaluated 167 individuals\n", " Min 0.0\n", " Max 4.0\n", " Avg 1.45\n", " Std 0.6934214687571574\n", "-- Generation 5 --\n", " Evaluated 191 individuals\n", " Min 0.0\n", " Max 4.0\n", " Avg 1.9833333333333334\n", " Std 0.7765665171481163\n", "-- Generation 6 --\n", " Evaluated 168 individuals\n", " Min 0.0\n", " Max 4.0\n", " Avg 2.48\n", " Std 0.7678541528180985\n", "-- Generation 7 --\n", " Evaluated 192 individuals\n", " Min 1.0\n", " Max 5.0\n", " Avg 3.013333333333333\n", " Std 0.6829999186595044\n", "-- End of (successful) evolution --\n", "Best individual is hello, (5.0,)\n" ] } ], "source": [ "main()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python [conda env:tensorflow]", "language": "python", "name": "conda-env-tensorflow-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.4" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: Chapter05/Optimization_surface_convex.ipynb ================================================ { "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from matplotlib import cm\n", "from mpl_toolkits.mplot3d import Axes3D" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "x = np.linspace(-7,7, 50)\n", "y = np.linspace(-7,7, 50)\n", "x, y = np.meshgrid(x, y)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "z1 = x**2 + y**2 + x*y" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(30,30))\n", "ax = fig.add_subplot(111, projection='3d')\n", "ax.set_xlabel(\"X\", fontsize=20)\n", "ax.set_ylabel(\"Y\", fontsize=20)\n", "ax.set_zlabel(\"Surface\", fontsize=20)\n", "surf = ax.plot_surface(x, y, z1, rstride=1, cstride=1,\n", " cmap='RdYlBu', edgecolor='none')#ax.plot_surface(x, y, z,cmap=cm.coolwarm, linewidth=0.1, antialiased=True)\n", "fig.colorbar(surf, shrink=0.5, aspect=5)\n", "ax.view_init(35, 60)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.contourf(x, y, z1, 30, cmap='RdYlBu')\n", "plt.xlabel(\"X\", fontsize=20)\n", "plt.ylabel(\"Y\", fontsize=20)\n", "plt.colorbar();" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python [conda env:tensorflow]", "language": "python", "name": "conda-env-tensorflow-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.4" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: Chapter05/Optimization_surface_non-convex.ipynb ================================================ { "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from matplotlib import cm\n", "from mpl_toolkits.mplot3d import Axes3D" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "x = np.linspace(5,10, 50)\n", "y = np.linspace(5,10, 50)\n", "x, y = np.meshgrid(x, y)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "z = x * np.sin(4*x) + 1.1* np.sin (2*y)\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": 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lSRIzMzMxMzMTq6urERHFZNSvv/5axKjuyajTiFEmn/gU/fga2R+jOr/v/smoSqVS/BKjAAAA8QkAYETleR7tdjvSNI2Ig9tnDbOTuM4sy+LevXuxvb0dN27ciOnp6b7/GRFRxKgrV65ExJ8xamtrK7a3t+PMmTPFZNTCwoLJKMZOkiQHYlSapj2PRWIUAABMNvEJAGAE7d9mbxQXdrMs69vv9f79+1hfX49Lly7FjRs3TvV+7I9Re3t7Ua/X49mzZ0WM6kxG9StGmXximBwWo1qtVk+MKpfLUa1Wo1wuR7lcHsnHLAAA4NOJTwAAIybLsmg2m5Hn+ciGp35dc57nsbW1FU+ePInr16/H4uJiX37f45ieno7Lly/H5cuXI+LPGPX8+fO4e/duVKvVYjJqcXHRZBRjZ//jUmdKs9VqFR/rnowSowAAYPyITwAAI2L/tNMoR4t+TO6kaRq3bt2KarUaa2trUakM51Pb/TGq0WhErVaL3377LTY2NqJarRaTUZ8ao0w+MUqOilFpmvacKVWtVsUoAAAYE8P5Ch0AgB5ZlkWappFl2chOO3U7bjx5/fp13LlzJ7799tsi6oyKqampAzGqXq/H77//Hvfu3YtyudwzGdW9nRmMg85jWCe05nkeWZbF7u6uGAUAAGNCfAIAGGLdEwIRBycIRtWXxqcsy2JzczPq9XrcuHEjpqenT+DqTtfU1FRcunQpLl26FBERzWYz6vV6vHjxIjY3N6NcLvdMRnUW4k0+MS7EKAAAGD/iEwDAkMrzPNI0jXa7PTbRqeNL4snOzk6sr6/H+fPn4+bNm2N1P7qdOXMmLl68GBcvXoyIP2PUy5cvixgV8SHEnTt3zmQUY+eoGLW3t1d8TidGlcvlKJfLI70NKQAAjCPxCQBgCGVZFs1mM/I8H7vwFPH58enZs2fx6NGj+PHHH2N5efkEr2z4HBaj7t27F2/fvo3/+Z//iVKpVExGLS0tiVGMncPOjNofo0qlUs9klBgFAACDJT4BAAyRPM+j1WpFq9Xqeef/uPnU+JSmady+fTuSJIm1tbWoVDx9PXPmTMzPz8f09HRcunQp0jSNer0ef/zxRzx48CCSJInl5eVYXl6OpaUl92zCjVu4jjg8RuV5Ho1GIxqNRkR8iFGVSqWYjhrXx1IAABhWXokCAAyJLMsiTdPIsmwsp526fUp8qtVqcfv27fg//+f/xJUrV07pykZD9/2rVqtx4cKFuHDhQkR8CHZv3ryJ169fx8OHDyNJklhaWiomo8Qoxs1RMarZbEaz2YyIgzFq3B9jAQBg0LzyBAAYsDzPo91uR5qmEXFwIXXS5Hke9+/fjz/++CP++c9/xuzs7KAvaaRUq9U4f/58nD9/PiL+jFG1Wi0ePnwYEVFMRi0vL4tRY+xzz1UbF4fFqIgQowAA4BR5pQkAMEB5nkeaptFutydq8fOoyafd3d1YX1+Ps2fPxs2bN09kq6xxuMefc2bW/hjVarWiXq9HvV6Px48fR57nPZNR1Wr1JC8dTl3ne75zHlp3jGo0GsVj7/4zo8bhsQIAAAZFfAIAGJAsy6LZbEae5xMVniIOjyfPnz+PBw8exI8//hgrKysDurLxV6lUDsSoN2/eHIhRnckoMYpx86kxav+ZUZP0GA0AAMclPgEAnLI8z6PVakWr1YokSU5kumfYdcenVqsVd+7ciXa7HWtra2LHJ/icyaePqVQqce7cuTh37lxE9MaoJ0+eiFGMvf0xKuLPqdTu7VArlUrxS4wCAIC/Jj4BAJyiLMsiTdPIsmzipp26df7eb968iZ9//jm++eabWF1dndj7MUz2x6h2u12cGfX06dNot9s9MerMmTMDvmLovyRJxCgAADgG8QkA4BTkeR7tdrtYuJzEaadueZ5Ho9GIO3fuxE8//RRzc3On+meP+iJxPyefPqZcLsfZs2fj7NmzEfFnjKrX6/Hrr7+KUUyEw2JUq9UqHtMjomebvnK5PPKPMwAAcBziEwDACcvzPJrN5sRPO3Xs7e3F+vp65Hkea2trEx/iRs1hMert27dRq9WKGLW4uBgrKytiFGNr/2N55w0GrVar+Fj3ZJQYBQDApBGfAABOUGfaqTNtM+mLj7///ntsbm7GDz/8EHfv3hWejuG0Jp8+plwux8rKSqysrETEnzGqXq/H1tZWtFqtWFxcLCajpqamBnzF0H9Hxag0TXvOlKpWq2IUAAATQXwCADgBnS2ZWq1WJEky8ZGl3W7HL7/8Es1mM27evGka5phOc9u9z7U/RmVZVkxGPXv2LNI0LWLUysqKGMVY6sSozmN/nueRZVns7u6KUQAATATxCQCgz7IsizRNbbP3H2/fvo1bt27FtWvX4urVq0NxP8bh3KdRUSqViqmniD9jVL1ej9u3b/fEqOXl5Zienh7wFY+PYQ2Uk0iMAgBg0ohPAAB9sn+bpUmfdsrzPB4/fhzPnz+Pf/zjHzE/Pz/oSyoWgEd9UX6U/w7dMepvf/tbZFkW29vbUavVium4hYWFYjJKjDoeAWM4HRWj9vb2is/pxKhyuRzlcnnif6YAADBaxCcAgD7I8zyazaZpp/9oNBqxvr4e8/Pz8e9//9uiKUcqlUqxtLQUS0tLERFFjKrX63H37t1oNBpFjFpeXo6ZmZkBXzH032FnRu2PUaVSqWcyyuMqAADDTHwCADimztlOna3cJj08vXz5MjY2NuL777+P8+fPD/pyxtIoTz59THeM+uabbyLLsnj37l3UarXY2NiIRqMR8/PzsbKyUmzTN+nfc4yfw2JUnufRaDSi0WhExIfvlUqlUkxHiVEAAAwT8QkA4AvleV6EJ9vsRbTb7djY2IidnZ3417/+FVNTU4O+JMZAqVSKxcXFWFxcjG+++SbyPC8mo+7duxd7e3sxPz/fMxklRjFujopRzWYzms1mRByMUd4MAQDAIIlPAABfIMuyaDabpp3+Y3t7O27duhWrq6vxww8/TPz9OGnjPPn0MUmSFDHq66+/jjzPi8mozc3N2Nvbi7m5uWIySoxiHB0WoyLi0BjV+eVnFQAAp0l8AgD4DKadeuV5Hk+fPo2tra24fv16LCwsDPqSJsakxqf9kiSJhYWFWFhY6IlR9Xo9Njc3Y3d3t5iMWllZEaMYS52v6XK5HBG9Map7m779Z0b5XgAA4KSITwAAn6izxVGWZd5BHh8WNW/duhXT09OxtrZWLHpy8ib9a++vdMeoa9euRZ7n8f79+6jVanH//v3Y2dnpmYyanZ11Pxk7+2NUxJ8/wxqNRvEzbP+ZUb4XAADoF/EJAOAjOgt2r169irNnzwpPEfHq1au4e/dufPfdd3Hx4sVBXw4cKUmSmJ+fj/n5+Z4YVa/X48GDB0WM6kxGiVGMqyRJDsSoNE0jTdOIiNjb24tqtRrz8/NRqVTEKAAAjkV8AgD4C53FuUajEQ8ePIjz588P+pIGKsuyuHfvXmxvb8eNGzdienp60Jc0kSb5zKfj6o5RV69ejTzPY2dnJ2q1Wjx8+DDev38fs7OzxWTU3NycBXjG0v4Y9fr16yiVSsXHOpNRnV9iFAAAn0N8AgA4QpZl0Ww2I8/zKJVKE7/Y//79+1hfX49Lly7FjRs3LEIyFpIkibm5uZibm+uJUfV6PR49elTEqM5klBjFuOr8rOs+N6rVahWTURHRs01fuVz2vQAAwJHEJwCAfToLbq1WK5IkKcLTpManPM9ja2srnjx5EtevX4/FxcW+/t4WLz+fyaeT0x2jvvrqq8jzPHZ3d6NWq8Xjx4/j3bt3MTMzU0xGzc/PD+3XsK8RPlf31/L+LWbzPI92ux2tVqv4WPdklBgFAEA38QkAoEuWZZGmaWRZ1rPwNqmL/Wmaxq1bt6Jarcba2lpUKv17+ti5pxYrGWZJksTs7GzMzs72xKh6vR5PnjyJ9+/fx/T0dDEZNWwxapiuheH2scfjo2JUmqbFx8vlclSrVTEKAADxCQAgoncRLeLgItskxqfXr1/HnTt34ttvv43Lly/3/fcf1D0dh3/Lcfg7jKruGLW6utoTo54+fRrv3r2LqampnsmoUqk06MuGj/rcNwN0fk52vr7zPI8sy2J3d1eMAgBAfAIAyPM80jSNdrt9IDp1TNKCWZZlsbm5GfV6PW7cuBHT09Mn8ucIKIyDw2LU3t5e1Ov1+PXXX4sY1T0ZJUYxjo6KUXt7e8XndGJUuVyOSqUyUT9bAQAmjfgEAEy0LMui2WwW7/ie9IWwnZ2dWF9fj/Pnz8fNmzdP9H6IT1/OvRteSZLEzMxMzMzMxJUrVyIiismora2t2N7eLmLU8vJyLCwsiFEMhX5vg3rYNn2HxajuM6N8LwAAjA/xCQCYSHmeR6vVilar1fNO7Un27NmzePToUfz444+xvLx84n+egMKk2B+j9vb2olarxbNnz2J7ezvOnDlTTEaJUQzKSZ/Bd1SMajQa0Wg0IiKiVCr1bNPnewEAYHSJTwDAxMmyLNI0jSzLTDtFRJqmcfv27UiSJNbW1qJS8RRx2Al3o216ejquXLnSE6Pq9Xo8f/487t69G9VqtTgzanFx0QI8Y+mwGJXn+YEYValUiq36fC8AAIwOKwsAwMTI8zza7XakaRoRBxe+JlGtVovbt2/H3//+91hdXT3VP1tAgQ+mp6fj8uXLcfny5YiIaDQaUavV4rfffouNjY2oVqvFZJQYxUk56cmnjzkqRjWbzWg2mxFxMEb5OQ4AMLzEJwBgInQWsEw7fZDnedy/fz/++OOP+Oc//xmzs7Onfg3i05dz78bb1NTUgRhVr9fj999/j3v37kWlUinOjFpcXIxyuTzgK2YcDNtjymExKiJ6YlSSJAe26Zv0n+8AAMNCfAIAxl5n2qnzru5JX5ja3d2N9fX1OHv2bNy8eXNgUxQCCnyaqampuHTpUly6dCkiPiy+1+v1ePHiRWxubka5XO6ZjBKj+FLD/POxc22dr+/uGPVXZ0YN898JAGCciU8AwNjK8zxarVa0Wq1IksRWVRHx/PnzePDgQfz444+xsrIy0GsRn76cezfZzpw5ExcvXoyLFy9GxJ8x6uXLlz0xKk3TaLfbznHjkwx6273PtT9GRcSBbfqSJIlKpVL8EqMAAE6PVyEAwFjKsizSNLXN3n+0Wq24c+dOtNvtWFtbi2q1OuhLElCgTw6LUW/evIlnz57F//7v/0apVComo5aWlkxGcahxeDxOkuRAjErTtOesRzEKAOB0iE8AwFjJ87zYZi8iTDtFxJs3b+Lnn3+Ob775JlZXV4dmoU18+nLD8m/IcDpz5kxcuHAhnjx5Ej/99FPkeR71ej3++OOPePDgQSRJUpwZtbS0ZDKKwrg9thwWo1qtVk+MKpfLxVZ9YhQAQP94lQEAjI3OdjsnOe00StsS5XkeDx8+jBcvXsRPP/0Uc3Nzg76kHuLT8bh3fIrOpMeFCxfiwoULERGRpmnU6/V4/fp1PHz4MJIkiaWlpWIySoyaTKP08+1L7X9u0HnDSqvVKv7+3ZNR5XJ57O8JAMBJ8aoCABgLnbOdOotHJ7FY1Iklo7AQtbe3F+vr67G0tBRra2tDOQE2qPs4Cv9+HyPccRzVavVAjHrz5k3UarWeGNWZjhKjJsOo/Hzrp6NiVJqmxcfFKACAL+NVBAAw0jpb6LRarUiS5EQjy6gs+P/++++xubkZP/zwQ5w7d27Ql3OkJEkiy7JBXwZMvGq1GufPn4/z589HxIeYX6/Xo16vx6NHjyIierbpG4Yz4+AkdGJU57nEYTGqe5s+MQoA4GjiEwAwsrIsizRNT3SbvW7DHp/a7Xb88ssv0Ww24+bNm3HmzJlBXxInZNi/FhltlUrlQIzqTEY9fvw48jzvmYwSo8bDJE4+fcxhMSrLstjd3RWjAAA+QnwCAEbOaU47dSuVSkO74P/27du4detWXLt2La5evToSi18CCoyGSqUS586dKyYpOzGqXq/HkydPxKgxIT593FExam9vr/icTowql8tRqVTcUwBgYolPAMBIyfM8ms3mqU07dRvGbeLyPI/Hjx/H8+fP4x//+EfMz88P+pI+mfj05dw7Bml/jGq328Vk1NOnT6PdbsfS0lKsrKyIUYy1w86M6sSozmP0/jOjhvGe6pzHAAAgAElEQVQMRgCAkyA+AQAjofvchYiDCz6nYdgW/BuNRqyvr8f8/Hz8+9//HrkFrUHeT+/wh/4pl8tx9uzZOHv2bET8GaPq9XpPjOpMRtkSdDh5XDy+o2JUo9GIRqMRER+mqLu36Ru1n90AAJ9KfAIAht7+bfYGtTg2TPHp5cuXsbGxEd9//31xLsuoGab7CfTPYTHq7du3UavV4tdff412ux2Li4vFZJQYNRzEp/47LEbleX4gRlUqlWKrvkE+zwEA6CfxCQAYalmWRbPZLBbFBrkgMwxnPrXb7djY2IidnZ3417/+FVNTUwO9nuMQn76ce8coKZfLsbKyEisrKxHxZ4yq1+uxtbUVrVYrFhcXi8moUX5cg79yVIxqNpvRbDYjQowCAMaH+AQADKX9007DsC3NoM982t7ejlu3bsXq6mr88MMPI78YJaDAZNofo7IsKyajnj17FmmaFjFqZWVFjDolJp9O32ExKiJ6YlSSJAe26fPvBACMAvEJABg6nXcBZ1k2VO/4HVQsyfM8nj59GltbW3H9+vVYWFg49Ws4CeLTl3PvGCelUqmYeor4M0bV6/W4fft2T4xaXl6O6enpAV/xePKYMnid5zvlcjkiemPUX50ZNSzPkwAAuolPAMDQyPM82u12pGkaEQffETxog1jwbzabcevWrZieno61tbViQQrgY0Y1JnTHqL/97W+RZVlsb29HrVaLX375JZrNZiwsLBSTUWJU/wzTz1wOxqiIOLBNX5IkUalUil9iFAAwLMQnAGAo5HkeaZpGu90euujUUSqVTnXbvVevXsXdu3fju+++i4sXL57an3taTO98OfeOTzWMj6Wfq1QqxdLSUiwtLUVEFDGqXq/H3bt3o9FoFDFqeXk5ZmZmBnzFo8m2e6MhSZIDMSpN05437ohRAMAwEJ8AgIHLsiyazWax8DWsiySnteCfZVncu3cvtre348aNG2P7rv5BBZRh/fr6HOPwd4Av1R2jvvnmm8iyLN69exe1Wi02Njai0WjE/Px8rKysFNv0+Z75OPFpNB0Wo1qtVk+MKpfLUa1Wo1wuR7lc9u8MAJwK8QkAGJjOAkmr1YokSaJUKg36kv7SacSS9+/fx/r6ely6dClu3Lgx1gtEpneOx72DD0qlUiwuLsbi4mJPjKrX6z0xqnsyapwfW7+Ux5TxsP9NPJ0tjVutVvGx7skoMQoAOCniEwAwEFmWRZqmkWXZUE87dSuVSie2OJfneWxtbcWTJ0/i+vXrsbi4eCJ/zjARn4CT0B2jvv7668jzvJiM2tzcjL29vZibmysmo8SoP7kP4+eoGJWmac+ZUtVqVYwCAPpKfAIATlX3okfEwUWRYZYkyYmc+ZSmady6dSuq1Wqsra1FpTIZT9HEpy/n3sGnS5IkFhYWYmFhoSdG1ev12NzcjN3d3WIyamVlZWJjlG33JkPneVdn2jzP88iyLHZ3d8UoAKCvJmNlAwAYCp1Dsdvt9khFp46TWPB//fp13LlzJ7799tu4fPlyX3/vYSegAIPQHaOuXbsWeZ7H+/fvo1arxf3792NnZ6dnMmp2dnbkfl7BpxKjAICTIj4BAKciy7JoNpvFO6tHceGin7Eky7LY3NyMer0eN27ciOnp6b78vqNEfPpy7h30T5IkMT8/H/Pz8z0xql6vx4MHD4oY1ZmMGtcYZfKJiKNj1N7eXvE5nRhVLpejXC4P/ZmdAMBgiE8AwInK8zxarVa0Wq2exYxR1K8zn3Z2dmJ9fT3Onz8fN2/enOjFPgEFGDbdMerq1auR53ns7OxErVaLhw8fxvv372N2draYjJqbmxuLx3HxicMcdmZUd4zK8zzevHkTly5dKiajRvm5HgDQP+ITAHBisiyLNE0jy7KRnXbq1o8zn549exaPHj2KH3/8MZaXl/t0ZaOpVCqdyBlak8DkE5yeJElibm4u5ubmemJUvV6PR48eFTGqMxk1LjEKDnNYjLp//34sLy9Ho9GIiA8/3yuVSjEdJUYBwGQSnwCAvsvzPNrtdqRpGhExNosOx1nwT9M0bt++HUmSxNraWlQqnoZFhPgEjJzuGPXVV1/1xKjHjx/Hu3fvYmZmppiMmp+fH4kYZfKJL5UkSZTL5Yj48HWU53k0m81oNpsRcTBGjcMbkgCAj7PqAQD0VWfBYVymnbp9aXyq1Wpx+/bt+Pvf/x6rq6sncGWjaZy+Nk6bySc+ha+R03FYjNrd3Y16vR5PnjyJ9+/fx/T0dDEZNcwxalivi+G1P1oeNhkVEWIUAEwg8QkA6JvOtFNnIWLcFhI+d5u4zlY0f/zxR/zzn/+M2dnZE7y60SOgwMkbt8fhUZAkSczOzsbs7Gysrq72xKinT5/Gu3fvYmpqqpiMWlhY8O/EyPrYxFzn/+uejIr4EKMajUbxfLFarfacGeV7AgBGn/gEABxbnufRarWi1WpFkiRjs83efp9z5tPu7m6sr6/H2bNn4+bNm2N7T45jUPFpHBa0hDsYHYfFqL29vajVavHrr78WMap7MsrPDEbF527X+Kkxav+ZUePwsxsAJo34BAAcS5ZlkabpWG6zt9+nLvg/f/48Hjx4ED/++GOsrKycwpWNJgEFmERJksTMzEzMzMwUW7F2JqO2trZie3u7iFGdySgximHVef73pfbHqIgPQSpN0+Ls0E6M6vwSowBgNIhPAMAXyfO82GYvIiZiYexjsaTVasWdO3ei3W7H2tpaVKvVU7y60SM+fTn3DsZLJ0ZduXIlIqKYjHr27Flsb2/HmTNniskoMYphkud5378ekyQRowBgDIhPAMBny/M8ms3mREw7dfurM5/evHkTP//8c3zzzTexuro6MffkOAQUgMNNT0/HlStXemJUvV4vYlS1Wi3OjFpcXBSjGJjP3XbvSxwWo1qtVhGjIqJnm75yuex5GAAMAfEJAPgsnbOdOosNk/Ti/rBYkud5PHz4MF68eBE//fRTzM3NDejqRo/49OUm6fsO+BCjLl++HJcvX46IiEajEbVaLX777bfY2NiIarVaTEaJUZym04hP++1//tmZxm+1WsXHuiejxCgAGAzxCQD4JJ13mbZarUiSZCIXtvbHkr29vVhfX4+lpaVYW1ubyHtyXOLTl3PvYHJNTU0diFH1ej1+//33uHfvXlQqleLMqMXFxZ6pEein45751A9Hxag0TXvOlKpWq2IUAJwi8QkA+Kgsy6LZbE7ktFO3JEmKbfd+//332NzcjB9++CHOnTs34CsbTZP6dQTQb1NTU3Hp0qW4dOlSREQ0m82o1+vx4sWL2NzcjHK53DMZJUbRLydx5tNxdZ6rdq4rz/PIsix2d3fFKAA4ReITAHAk0069SqVStNvt+Pnnn6PZbMbNmzfjzJkzg76skWXbveOxUAYc5cyZM3Hx4sW4ePFiRPwZo16+fNkTo5aXl2NpaUmM4osNYtu9zyVGAcBgiE8AwKHyPI9ms1lsp+JFeMTOzk789ttv8d1338XVq1fdk2MSn+DkeZwi4ugY9erVq7h//36USqVoNBrx+vVrMYrPMgrxab+jYtTe3l7xOZ0YVS6Xo1wuT/wbsADgS4hPAECP7n3yIw7uoz+J8jyPx48fx9OnT+PcuXNx7dq1QV/SWBCfAAZjf4xK0zT+3//7f/HHH3/EgwcPIkmSnsmoSsXSAYfLsmzkw8xhZ0btj1GlUqlnMmrU/84AcBo8gwQACvu32Zv06BTx4RD39fX1mJ+fj+vXr8fW1tagL2lsiE8Aw6FarUa1Wo3vvvsuIj7EqHq9Hq9fv46HDx+KURxpFCefPuawGJXneTQajWg0GhHxIUZVKpViOkqMAoCDPGMEACLiwztXm81msYgwbgsJX+Lly5exsbER33//fZw/fz7evn0rlvTRIOOTr2+Ao1Wr1bhw4UJcuHAhIj7EqDdv3hQxKiKKGLW8vCxGTbBxjE/7HRWjms1mNJvNiDgYozyXBgDxCQAm3v5pJ+/cjGi327GxsRE7Ozvxr3/9K6ampiLCpE6/uZ8Ao6Farcb58+fj/PnzERHRarWiXq9HvV6PR48eRYQYNak6Z4NOksNiVEQcGqM6v8QoACaRZ4QAMMGyLIs0TYuFAy+KI7a3t+PWrVuxuroaP/zwQ889EUv6y/08HvcO6KfPeUypVCoHYtSbN2+iVqvF48ePI8/zWFpaipWVlVhaWopqtXpSl82A5Xk+8W9c6jxXLJfLEdEbo7q36dt/ZpTn3QCMO/EJACZQnufRbrcjTdOIOPgOzkmU53k8ffo0tra24vr167GwsHDgc0qlUmRZNoCrA4DhValU4ty5c3Hu3LmI+DNG1ev1nhjVmYwSo8bHJGy797n2x6iIKLbpazQaxfPu/WdGuY8AjBvxCQAmTJ7nkaZptNtt0ek/ms1m3Lp1K6anp2Ntba1nsaCbSZ3+GuT9HIfFslG/fmC49PMxZX+MarfbxWTUkydPIsuyYjJKjBpt4/Dz9DQkSXIgRqVp2vNGsO5t+sQoAMaB+AQAEyTLsmg2m8VCgRe1Ea9evYq7d+/Gd999FxcvXvzLzxWf+muQ99PXPsDpKZfLcfbs2Th79mxE/Bmj6vV6PH36NNrtds9k1JkzZwZ8xXyqSTzzqR/EKAAmgfgEABMgz/NotVrRarUiSZKJ35s/4sNiyb1792J7eztu3LgR09PTH/1vSqWS+NRHg4pPFm8Aep32Y/FhMert27dRq9Xi119/jXa7HYuLi8VklBg1vJz51B+HxahWq1XEqIjo2aavXC57PgPA0BOfAGDMZVkWaZoW70z1QjXi/fv3sb6+HpcuXYobN2588j1JksSZT300qPi0s7MTL168iOXl5ZidnfU9ATBg5XI5VlZWYmVlJSL+jFH1er0nRnUmo6ampgZ8xXTYdu9k7H/O3jmvtdVqFR/rnowSowAYRuITAIypzovU7u07Jv1FaZ7nsbW1FU+ePInr16/H4uLiZ/33tt3rr0Hcz+fPn8eDBw/i4sWL8fDhw3j//n3Mzc0Vi54zMzMT/30CTJ5hCwjdMervf/97ZFlWTEY9e/Ys0jQtYtTKyooYNUAmn07HUTEqTdPi4+VyOarVqhgFwNAQnwBgDHX2jW+326LTf6RpGrdu3YpqtRpra2tRqXz+0yDb7vXXacandrsdd+7ciVarFTdv3iwWWvM8j/fv30etVov79+/H7u5uEaOWl5fFKGAiDPvPtlKpVEw9RUQRo+r1ety+fVuMGqAsy3q2i+N0dJ7fd8JfnueRZVns7u6KUQAMDfEJAMZMlmXx5MmTuHTpkvD0H69fv447d+7Et99+G5cvX/7i38e2e/11WvFpe3s71tfX49q1a3H16tWIiGg0GsX3x/z8fMzPz8e1a9ciz/N49+5d1Ov12NzcjL29vZifn++JUcNi2BeLgdEySs8XumPU3/72t8iyLLa3t6NWq8WdO3ei2WzGwsJC8dj9Kec68mVMPg2Ho2LU3t5e8TmdGFUul6NSqYzU9zwAo0l8AoAx0TmYuNVqxcOHD+PKlSuDvqSBy7IsNjc3o16vx3//938fOxx4kd5fJ30/8zyPp0+fxtbWVvzjH/+I+fn54uNH/dlJksTCwkIsLCz0xKharRYbGxvRaDRiYWGheHe9BU1gHAzbtnufq1QqxdLSUiwtLUVEFDGqXq/HL7/8UsSoTrAapjcSjLpR/9oZV4dt09eJUZ1/s+4YVS6XRUQA+k58AoAxkGVZpGkaWZZZAPiPnZ2dWF9fj/Pnz8fNmzfdlyF0kpNknW0Wz5w5E2tra1+8JVB3jPr6668jy7IiRnUvaHbOJjnNrZ58TQP9Mm6TlN0x6ptvvul57O68kWBYp1pHjeeeo+GvYlRHqVTq2aZPjALguMQnABhh3YcNR0TPi8RJfifqs2fP4tGjR/Hjjz8W50MwOWq1Wty+ffvY2yweplQqxeLiYiwuLhYLmp2tnjrnjiwtLRWTUWfOnOnrnw9wUsb5OcNhj92dLVa7Y1T3VOs4349+su3eaDosRuV5Ho1GIxqNRkR8+L6pVCrFdJR/ZwA+l/gEACMqz/NoNpvFO067X0B2ztKZtIWTNE3j9u3bkSRJrK2tRaXiqc4w6/eZT3mex4MHD+LVq1fxz3/+M2ZnZ/v2ex+l+931nXNH3r59G7VaLZ49exatVisWFxeLd9eLUcAwmrTnDN0x6uuvv+7ZYvXevXuxu7t7YDJqku7P55i0r51xdVSMajab0Ww2I+JgjHK2LAAfY0UGAEZQZ9qp84J//wu/Uqk0dlvofExn8uTvf/97rK6uDvpy+AT9jE97e3uxvr4eS0tLcfPmzYG9O7dUKhVnikR8+F7txKhff/012u12LC0tFQua1Wp1INcJsN8kLyLv32K1E6Pq9Xpsbm4WMaozGSVG/Ul8Gk+HxaiI6IlRSZIc2KbP1wIA3cQnABgheZ5Hq9WKVqsVSZIcucDeOUvnS8+5GSV5nsf9+/fjjz/+OLVpF/qjX/Hp5cuXsbGxEd9//32cP3++D1fWP+VyuTgPKuJDjHrz5k3UarV48uRJ5HneE6NM6wGDMGlvWPmY7hh17dq1yPM83r9/H7VaLe7fvx87OzsxNzdXPHbPzs5O7KK7+DQZOv/GndcW3TGqe5s+MQqAbl7dAsCIyLIs0jQ9dJu9/UqlUmRZdopXNxi7u7uxvr4eKysrA5124cscd0Eiy7LY2NiId+/exb/+9a+Ymprq05WdnHK5HGfPno2zZ89GRESr1Yo3b95EvV6Px48fR57nxTvrl5aWxCjgVAgIfy1Jkpifn4/5+fmeGFWv1+PBgwdFjOo8fk9SjMqyzPOvCbQ/RkXEgW36kiSJSqVS/BKjACaPV7MAMOTyPC+22furaadu/T5LZxg9f/48Hjx4ED/++GMxVcLk2NnZif/93/+NS5cuxffffz+yixmVSiXOnTsX586di4gPMaper0etVouHDx9GkiTFNn7Ly8sHFnkA+kF8+jzdMerq1as9Merhw4fx/v37mJ2dLSaj5ubmxvb++tqhI0mSA89T0jSNNE2L/1+MApgs4hMADLHOOwg/Zdqp2zhPPrVarbhz50602+1YW1tzZs4EevbsWTx8+DD+67/+qzhbaVxUKpU4f/58sX1gmqbx5s2beP36dRGjOouZ4hPAcDgsRu3s7ES9Xo9Hjx4VMaozGTVOMUp84iiHxahWq9UTo8rlcrFVnxgFMH7EJwAYUp2znTov6j/nxdi4xqc3b97Ezz//HN98802srq56gTphusPjv//974nYkq5arR6IUfV6PV69ehW7u7vxP//zPz3b9Nn6CPgSAkJ/JUkSc3NzMTc3F1999VVPjHr8+HG8e/cuZmZmijcTzM/Pj+z977xBCj5m/+uZzu4Of/zxR2xvb8fVq1d7JqPK5bKvLYARN/6v2AFgxHTeFdhqtT55m739xm3bvTzP4+HDh/HixYv46aefYm5ubtCXxCl7+/Zt3Lp1K77++uv46quvJnYxolqtxoULF+LChQvx5s2b+K//+q948+ZNvHjxIjY3N6NSqRQxanFxUYwCPsk4PWcYRofFqN3d3ajVavHkyZMiRnUev0cpRuV57mcNX6QTo7Isi3a7HaVSqWer8YgQowBGnPgEAEMky7JoNptfNO3UbZwmn/b29mJ9fT2WlpZibW1tKBY4vEP89OR5Hk+fPo2tra34xz/+EfPz84O+pKGRJElUq9W4ePFiXLx4MSIims1m1Gq1+O2332JjYyOq1WqsrKzEyspKLCwsDMX3DzCc/Fw7PUmSxOzsbMzOzvbEqHq9Hk+fPo13797F9PR0cd7fwsLC0P77eE7EcWVZVmy51/3Gu/3n3kZEzzZ9YhTA8BOfAGAI9GPaqdu4xKfff/89Njc344cffohz584N+nIi4s+pMi92T16z2Yyff/45pqamYm1trefcAA535syZuHTpUly6dCkiIhqNRtRqtXj27Flsb2/HmTNnihg1Pz8vRgERISAMWneMWl1djTzPY29vL2q1Wvz666/x7t27mJqa6pmMGpbHb187HFcnPu13WIzKsix2d3fFKIARIT4BwIDleR7NZrPYM78fL5pGfdu9drsdv/zySzSbzbh582acOXNm0JdUGPV7OypqtVrcvn07/u///b9FSKHXp3wtTk1NxeXLl+Py5csREYcuZnbHKIs2MJn8XBsuSZLEzMxMzMzMxOrqakREMRm1P0Z1JqMGFaOOCgfwqT71a+ioGLW3t1d8TidGlcvlqFQqntcADJj4BAAD0r2VRMTBQ3iPY5Qnnzpn+1y7di2uXr06dC8aS6WSRboTlOd5PHjwIF69ehX//d//HTMzM4O+pLEyPT0dV65ciStXrkREFGeOdG/z1IlRc3NzQ/f9B5wc3+/DrROjOo/fnTcTdE+2diajTjNGmXziuL40YO5/7dQdozrP1fefGSWUApwu8QkABiDP80jTNNrtdl+jU8coxqc8z+Px48fx/PnzoT7bp3MwMv3XOd9reXk5bt68aYHgExw3hHa/s777zJHHjx/Hu3fvYnZ2NlZWVmJ5eVmMgjEmIIye/W8m2Nvbi3q9XsSozpl/y8vLsbi4eGI/U33tcFxZlkWlcvzlyaNiVKPRiEajEREfXiN1b9PnuSbAyRKfAOCUZVkWzWazeLF+Ei/YR206p9FoxPr6eszPz8e///3voX4haNu9k/HixYu4d+/eUJ3vNez6/dhx2JkjOzs7Ua/X49GjR/H+/fuYm5srFjNnZ2ctOMKYEBBG3/T0dM82q50z/3777bfY2NiIarVaTEb1M0Z1to2GL3VSWzceFqPyPD8QoyqVSrFV30m9NgOYVOITAJySPM+j1WpFq9Xq2a/8JIzSdM7Lly9jY2Mjvv/++zh//vygL+ejxKf+yvM8fvnll3j//v3Qne816ZIkibm5uZibm4uvvvqqiFG1Wi0ePHgQOzs7MT8/XyxmzszMWLABGBL7z/xrNBpRr/9/9u40OI70PhP8k3XfVbgvggC6m2w2yWa7mwQor0djzdoKh/Zw7Hgc4fXuhOTwV1v7YWPDK481K2sse9qeHZ8K2d6N2bV8zMTKXtvyyDs67R5Lakl9qZuFg7hvgDgzgbrzevcDupJVRYAAClWozKrnF9EfGgQKL5NZWZnv8/7/r4KtrS3Mzs7C4/FYe0bFYjG43e6qfg+DS7qoywowTwqjVFWFqqoAGEYREdUawyciIqJLYJomNE2zHq7q/RDjhLZ7hmFgZmYG2WwW9+7dg9/vb/SQzsQJx9YpMpkMstksBgYG8Pzzz1/qw30zTCRcdhBaGkZduXIFQghkMhnIsoy5uTnk83mrMqoYRhGRMzBAaH5+vx89PT3o6ekBAKiqClmWsb29jbm5Objd7rLKqLOGUZycp4uqV+XTaY4LowCUhVGSJD3Rpo/nOxHR2TF8IiIiqiMhBAzDgKZpAC7vAd3u1TmpVArj4+Po7+/HjRs3HPUQZ/dj6xQbGxtYXFxEIBDA8PBwo4dDVZAkCZFIBJFIBIODgxBCIJ1OQ5ZlzMzMoFAoIBKJWGFUIBBo9JCJ6AQMn1qPz+d7IoxSFAU7OztlYVQikUA8Hq+6MoroNEIIW7TcLl4Di+d6aRj1tD2jeO0kIjoZwyciIqI6EUJA0zQYhnHpq0LtWp0jhMDq6irW19dx+/ZtRKPRRg/p3Bg+XYyu65iamoJpmrh//z7eeOONRg/Jsex2LkqShGg0img0iqtXr8I0TSuMmp6eRqFQQDQatcIop1Q7EhG1Ap/Ph+7ubnR3dwN4HEbt7u5ifn6eYRTVTaMqn05TGUYBeKJNnyRJ8Hg81n8Mo4iIyjF8IiIiqgPTNKGqqrWS+LIfQlwul1VtZReqqmJ8fByBQABjY2OOnbSw24S/kxweHmJ8fBxXr17FwMAAH84vyO7Hz+VyIRaLIRaLYWhoCKZpIpVKQZZlTE1NQVVVxGIxtLW1IZFIMIwiaiBWPlGlyjBK0zQrjFpYWIAkSUgkEtZ+ph4Pp5eoOnYNn44jSdITYZSmaWVdLhhGERE9xrsDIiKiGhJCWA/hkiQ17EHKbgHJ7u4upqence3aNWsSw6nsWlVmZ0IIrKysYGNjA3fu3EEkEnnizy/7wbxZJlrt9D4/jcvlQjweRzweB3A02XR4eAhZlrGxsQFd18vCKJ/P1+ARE7WOZrkmUv14vV50dXWhq6sLwOMwamNjA++++64VRhUroxhG0Vk5KXyqdFwYpet6WRjldrutVn0Mo4io1fBugIiIqEZM04SmaTBNs+GbL9slIDFNE7Ozs0ilUrh7925T7Plit2DP7k6reCseTz6Itx6Xy2VNVAJH14uDgwMoioK1tTUYhoF4PG6FUV6vt8EjJiKiomIYtbS0hHv37kHTNBwcHGB/fx+Li4sAgEQigba2NoZR9FRODp8qVT4DFvf/1XXd+lppZZTb7eY9MBE1NX76ExERXVDxoaJ0hVujHyLsED5lMhkkk0n09PTg7t27DT8mtcLw6ez29/cxNTWF5557ztrQvBKPZ/Wa7di5XC5rP6iRkREYhmGFUaurqzAMwwqrGEYR1RYXAdBFeb1edHZ2orOzE8DRHo+KokCW5bIwqvgfwygqaqbwqdJJYZSmaWV7ShUroxhGEVGz4ac9ERHRBRQ3nbVDtVOpRk5KCyGwvr6OlZUV3L59G7FYrCHjqBdJkhoe7NmdEALz8/PY39/HK6+8gmAweOL3NluAQrXjdrvR3t6O9vZ2ALDCKFmWsbKyAiEEJzKJaoTXYao1j8fzRBhVvIYvLy9DCGFVt8bjcS4oaGHNHD5VKj4vFv++QgiYpolcLscwioiaEp/QiIiIqlRctVZcLWynB4NGVT5pmobx8XF4vV6MjY015WSwy+XiJN1T5PN5PHjwAG1tbbh3796pkwmNDkrt9L49r1YL7irDqNKJzKWlJUiSVDaR2YzXH6J6cvL1kOzP4/Ggo1WDWHEAACAASURBVKMDHR0dAB5fwxVFKQujWN3aeoqL+FoRwygianZ8IiMiIjqn4kayuq6XPSzYSSPCp2KLtWeffRa9vb2X+rsvU6tN+J/H9vY2ZmdncePGDWty6TSNOJ7FB33+OzrbcROZiqJY+41IklS230jlfmNE9JjTw3hqjIt8jlZewyurW03T5L5/LUIIYcvnqUY4KYzK5/PW9xTDKLfbDbfbzWNHRLbG8ImIiOgcTNOEpmm2a7NX6TKrc0zTxNzcHBRFObXFWjNgaPEk0zQxPT2NbDaL0dFR+Hy+Rg+pJfBcLFfZ4knTNCiKgt3dXczPz8PtdlthVCwWYxhFVILXEqpGLUPLk1qtlu77V1oZxXuN5tHKlU+nOW7PqNIwSgjxRGUUwygishOGT0RERGdQujksANvf1F/WvkTZbBbJZBKdnZ0YHR1tiQfHRrU0tKtMJoMHDx6gr68PN27cOPc5wACF6sXr9aKrqwtdXV0AAFVVoSgKdnZ2MDc3B7fbjba2NiuMsvt1naieWPlE1ajneXNcGHV4eAhZlrG2tgbDMBCLxazKKIZRzsbrz9kcF0YJIVAoFFAoFAAcPat4PB6rOor3N0TUSAyfiIiITiGEgKqqtq92KnUZAcnGxgaWlpZw8+ZNJBKJuv4uO2FY8tj6+jqWl5dx69YtxOPxql6Dx7N6PHbn4/P50N3dje7ubgBHYZQsy9ja2sLs7Cw8Ho8VRkWjUU7WUMtxwv0N2ctlhpalCwaAx2GUoihlYVSxMsrv91/KuOjieO2p3klhlKqqUFUVwJNhlFOeZ4moOTB8IiIieori3k7Fh2un3KjXc1Ja0zRMTk5CkiSMjY3B42mt2wlO+B+9LyYnJwHgwucAjyc1is/nQ09PD3p6egAAhUIBsixjc3MT09PT8Pl81op6p4ZRfG/RWbHyiarRyPOmNIwaGRkpC6M2Njag6zqi0ah1HWcYRa3guDAKAMMoImqY1potIiIiOiMhhBU8lW766hT1qnySZRmTk5MYGRlBf39/zV/fCS6rpaFdHRwcYGJiAkNDQxgYGLjw6zF8qh6PXW35/X709vait7cXAJDP561JzFQqBb/fXxZGcaKGiFqdaZq2uUeurIwyTbMsjNI0zaqMamtrYxhlI7yXqZ/ivUpxn8vSMKpQKFjBU+WeUbzHIaJaYfhERERUwTRNqKrquGqnUi6Xq6YPckIIzM/PY29vDy+//DJCoVDNXttpan1snUIIgeXlZWxubuKll15COByuyesyQCG7CgQCZWFULpez2jul02kEAgFrEjMSidjys8KOYyJ7YuUTVcPO543L5bJa8A0PD8M0TaRSKciyjKmpKaiqWlYZFQgEGj1koro7axhVuWeUXd/nRGR/DJ+IiIje5/Rqp1K1rM7J5XJIJpNoa2vD6Oioo49LLbRiWKKqKpLJJEKhEO7fv1/Tc6AVj2et8NhdrmAwiGAwiL6+PgghkM/nIcsyVlZWkE6nEQqFrDAqHA5zooYcxc4hAtmXk84bl8uFeDxu7VFZDKMURcHDhw+tMKoYWAWDwQaPuHU45RxqRpVhFHD0vtY0DZqmWd/j8Xis/xhGEdF5MHwiIiICrI1ZTdN0bLVTqVq13dvc3MTCwgJu3rxptTFpda3Wdm9/fx9TU1O4du0auru7a/76DFDIiSRJssKo/v5+CCGQy+UgyzKWlpaQyWQQCoWsFlChUMjxnytERJWK981OVBpGDQ0NwTRNpNNpyLKMmZkZFAoFRCIRqzKKYVR98B7QfiRJYhhFRDXD8ImIiFqaEAKGYZTdTDfDzfNFwydd1zE1NQXDMDA2Ngav11vD0Tmby+WyzpdmZpom5ufnIcsy7t69y3Y0NsTgzj4kSUIoFEIoFMLAwACEEMhms5BlGQsLC8hmswiHw1YYFQwGm+KzhpqHEKLlK5vp/JrpvHG5XIjFYojFYmVhlKIoZWFUscI1EAjwOl4Ddto3jI53XBil63rZ81Bpmz632833BhFZGD4REVHLqmyz10w3yReZlD44OMDExASGhobQ39/fVMelFlphwj+fz+PBgwdob2/H6OhoXc+BVjie1HokSUI4HEY4HMaVK1cghEAmk4Esy5ifn0cul7PCqOKKel5rqZGc1D6N7KOZz5vSMOrq1asQQliVUbOzs8jlck9URjXrsagnhk/OU/ncXFzMqeu69bXSyiiGUUStjeETERG1JNM0oaqq9dDcbDfE1fx9hBBYXFzE9vY2XnrpJYTD4TqMzPmaPSzZ3t7G7OwsXnjhBbS3t9f99zX78awnHjvnkCQJkUgEkUgEg4OD1iSmoiiYm5tDPp9neycicpxmDp8qSZKEaDSKaDRaFkaVXsfD4bBVGcUw6mwYPjnfSWGUpmlle0p5vV6GUUQtiOETERG1lMpqJz7sHMnn80gmk4jH4xgbG+NxeQqXy9WUE/6GYWB6ehr5fB6jo6Pw+XyX8nsbFaAwuKFGKp3ELIZRxY3vi+2dihvfF9s7EdVTK4UIVDutHBwcdx0vhlHz8/Nl7VYTiQT3/jtBK59DzaoYRhX/XYUQME0TuVyOYRRRC2L4RERELcM0TWiaZm2OzJvcI1tbW5ibm8ONGzfQ0dHR6OHYniRJF9pPy47S6TSSyST6+vrwwgsvXOp7gyFQ9XjsmockSWXtnUo3vn/48CFUVUUsFrPCKL/f3+ghU5Nh+ETV4Hnz2HFhVCaTgaIoZXv/Fa/jDKOOMHxqfgyjiFobwyciImp6paX/wJOtAVqVYRjWpOZlVro4XTNN+AshsLGxgeXlZdy+fRuxWOzSx9BMx5OoVo7b+D6VSkGWZUxOTkLTNMTjcWsSk9dvuiheh6kaDJ9OVtputXTvP0VRsLi4iEwmg1AoZFVGhcPhljyWxUWB1DpOCqPy+bz1PcUwyu12w+12M6AkcjCGT0RE1NSEENA0DYZhMHQqcXh4iPHxcQwODuLKlSs8LufQLGGJruuYnJwEAIyNjcHjacxtYbMcz0bgsWsdLpcL8Xgc8Xgcw8PDME0Th4eHkGUZ6+vrMAwDsVjMmsRkGEXV4L0AnRfDp7M7LozKZrOQZRlLS0tWGFVcVNAqYZQQgsFCiztuz6jKMMrlcpVVRvGcIXIOhk9ERNS0TNOEqqrWg3ErPMCdRgiB5eVlbG5u4s6dO4hEIo0ekuM0w55PBwcHmJiYwPDwMPr7+xs6FgYoROfncrmQSCSQSCQwMjICwzCsMGptbQ2GYSAej0PTNGiaBq/X2+ghk80xRKBqsGqlepIkIRwOIxwOl4VRiqJgeXkZ6XTaCqMSiQQikUhTHmu23aNKx4VRQggUCgUUCgUAR/dBHo/Hqo7iOURkXwyfiIio6QghoOs6dF0vK+lvdYVCAclkEpFIBPfv3+dxqZKT93wqDR9feuklhMPhRg+J4dMF8NhRkdvtRltbG9ra2gActVU9ODjA9vY2Hjx4ACEE4vG4VRnVqEpHsi9eS6garFqpndIwamBgAEII5HI5yLKMlZUVpNNpBINBqzKqWcIohk90mpPCKFVVoaoqgCfDKC48JbIPPnUQEVFTMU0TmqZZKzF503lkZ2cHMzMzeP7559HZ2dno4TiaUyf8VVVFMplEKBSyVfjo1ONJZGdutxvt7e3w+/24e/cudF3HwcEBZFnG8vIyhBDWBGY8HmcYRQDYdo/OjxVz9SNJEkKhEEKhUFkYpSgKVldXkU6nEQgErMqoaDTqyH8Lhk90XseFUcDRs87u7i6y2Sz6+/vh8Xis/zgvQNQ4fMogIqKmIISAYRjQNA3AkzelrUrXdczOziKbzeLevXvw+/2NHpLjOTEs2dvbw8OHD3Ht2jV0d3c3ejhUI048F6kxPB4POjo60NHRAeDos0FRFMiyjMXFRUiSVBZGud3uBo+YLhtDBKoG2+5dntIwqr+/H0II5PN5q91qOp2G3++3KlwjkYgjQh2GT3RRxWuQ2+2GpmlWNZSqqmVt+ir3jOK1i+hyMHwiIiLHE0JA0zQYhsHQqYQQAm+88QYGBgZw48YNHpcacdKeT6ZpYn5+Hoqi4O7duwgEAo0e0hMYoBBdPo/Hg87OTqsSVtM0KIqCvb09LCwsQJIkawKTYVRr4HWYqsG2e40jSRKCwSCCwaC1f2exMqo0jCqtjLLjvxXDJ6ol0zSttnul9y7FNn2FQsGaL6jcM4rPykT1wfCJiIgczTRNqKpqrdjlTePRzfXq6iqy2SxGR0eRSCQaPaSm4pQ9n3K5HB48eICOjg7cu3fPtu8Nhk8Xw2NHteD1etHV1YWuri4Aj8Oo3d1dzM/Pw+12l1VGcaKwOdn1c4LsixVz9lIMo/r6+gDAqoza2NhAKpWCz+ezruV2CaMYPlEtGYZx7Pl0XBilaVpZ15TSNn0Mo4hqh+ETERE5khACuq5D13VIksSHlvepqorx8XEEAgHE43GEQqFGD6npOCEs2drawtzcHF544QW0t7c3ejhP5YTjaVd8KKZ6qQyjVFWFoijY3t7G3NwcPB6PNYEZi8X4GdwEGCJQNVj5ZG+BQAB9fX1lYZSiKLYKo9i6kWrJNM0z7WPJMIro8jB8IiIixzFNE5qmWQ8rvBE8sru7i+npaWtfn7ffftsRFTpOY+ewxDAMTE9PI5/PY3R0FD6fr9FDOpWdjycRHfH5fOju7rb2jCsUClAUBY8ePcLMzAy8Xi/a2tpstZqeiOqv2OKKnCEQCKC3txe9vb0Ajq7lsixjc3MT09PT8Hq9l76wQAjBc4hqxjCMqvY4Pi6M0nXdCqMAlLXpK7b2I6LTMXwiIiLHEELAMAzrJpCTW0dM08Ts7CxSqVTZvj4ul4vhUx3Ydc+ndDqNZDKJ/v5+vPDCC455IGpU+NQM7w8Gd9Qofr8fPT096OnpAfB4ArO4mr64z0hbW5tjNr1vdax8omqw8snZ/H7/E2GUoijY2trC7OysVeVa3P+vHv/WbLtHtVSr86lygWtxHkLXdetrpZVRDKOITsbwiYiIHKG4SSirncplMhkkk0n09PTg7t27ZceFE9P1Ybc9n4QQWF9fx8rKCm7fvo1YLNboIZ1Lo85TvjeIaqdyArO4z0jppvfFyqhIJMLPcBti+ETV4HnTXI5bWFDacrV0/79YLFaTiiWGT1RLJ+35dFEnhVGapllfd7vd8Hq9DKOIKjB8IiIi2yve2BUfcHkjVx443Lp1C/F4/InvaYbKDjuyU6in6zomJibgcrkwNjZ2ph7ndmOn4+k0PHZkV5X7jORyOciyjNXVVaTTaQQCASuMCofD/FwnciiGT82tMoyq3P+vGEYVK6OqCaMYPlEtXVYr0OKcRPHcFULANE3kcjmGUUQVnDdDQURELaPYa1nX9bKbu1anaRrGx8fh9XqfGjgwfKoPu0z4HxwcYGJiAsPDw+jv72/0cC7EDseTiOonGAwiGAyiv78fQggrjFpeXkY6nUYoFEJbWxsSiQTDqAZhiEDVKHYkoNZQuf9fMYza3d3F/Px8VWEUwyeqpXpVPp3mpDAqn89b31MMo9xuNzweD6+d1DIYPhERkS2ZpglN09hmr8L+/j6mpqbw7LPPWu2NTmLXvYmcrtGhnhACS0tL2NrawksvvYRwONywsdSCXcI8J+KxIyeSJAmhUAihUAgDAwMQQiCbzUJRFCwtLSGTySAcDlthVCgU4j3AJWD4RNXgnk+trTKM0jTNCqMWFhYgSZLVpu+kMIrhE9XSZVU+nea4Nn3FMKr4eVsaRrndbr4PqGkxfCIiIlup7J/Mm7Ajpmlibm4OiqLglVdeQTAYPPVn7LY3UbNo5IR/oVDA+Pg4wuEwxsbGmuL90cjztBkmWxk+kdNJkoRwOIxwOGyFUZlMBoqiYGFhAdlsFpFIxJrADAaDjn/f2hGvJVSNZvgcpdrxer3o6upCV1cXgMdh1N7eXlkYVayM8ng8DJ+opuwSPlV6WhhV5HK5ytr08X1BzYLhExER2YYQAqqqstqpQjabRTKZRGdnJ0ZHR898XBpdodOsGnVe7u3t4eHDh7h+/br1UN8MWL1TPV4jqRlJkoRIJIJIJIIrV65YYZQsy5ibm0M+n38ijKLa4DWFzovhEz3NcWHUwcEB9vf3sbi4CEmSoGkaotEogsGgI/cuJXtpVNu98zoujBJCoFAooFAoADh6lvd4PFZ1lBP+XkTH4ZWdiIhsobi3U/Ehlg+yRzY2NrC0tISbN28ikUic62c5qd8cilVvBwcHuHv3LgKBQKOHVFM8T4noaUrDqMHBQQghkE6nIcsyZmZmUCgUEIlE0NbWhra2tqa7Rl4WhghUDVat0Hl4vV50dnais7MTwNHz33vvvYdUKoVHjx4BgFUZlUgkGEbRuTklfKp0UhilqipUVcXOzg5+67d+C5/73OcaOEqi6vBKTkREDSWEsIInttl7TNM0TE5OQpIkjI2NVfXwxcon58vlcnjw4AE6Oztx7969ppwYZPhUPR47akWSJCEajSIajeLq1aswTdMKo6anp1EoFBCNRq0wyu/3N3rIjsBrCVWDoSVdRLGqY2RkBIFAALqu4+DgALIsY3l5GUIIxONxa88or9fb6CGTzTXLPnSVYVQ6nbYCWiKnYfhEREQNY5omVFVltVMFWZYxOTmJkZER9Pf3V/06LpeLk0kO9ujRI8zPz+PmzZtoa2tr9HDqhgEKUf20wnvL5XIhFoshFothaGgIpmkilUpZn6WapiEWi1lhlM/na/SQbYv3YXReDJ/ookqr5zweDzo6OtDR0QEAVhilKEpZGFWsjGIYRZWa9ZqUzWbZZpgci+ETERFdOlY7HU8Igfn5eezt7eHll19GKBS60OtJksTKJwcyDAMPHz6EqqoYGxtr+gdrhk/V47EjepLL5UI8Hkc8Hsfw8DBM08Th4SFkWcbGxgZ0XbfCqEQiwTDqfc06YUf1VdynlahaT2vdWBlGGYZhVUatrKzANE2rMophFDWzbDaLcDjc6GEQVYXhExERXSohBPL5PJLJJO7cucMH1vflcjkkk0m0tbVhdHS0JoEc2+45TzqdRjKZxMDAAAYHB1vi/cEAhah+WuEachqXy2WtkgeOJi+LYdTa2hoMw+DkJVqjSo5qr1laXFHjnGffMLfbjfb2drS3twN4HEYpioLV1VXrel685nNxQetp1vsehk/kZAyfiIjoUgghYBgGNE2DEAKZTKZpbw7Pa3NzEwsLCzVvr8bwyTmEEFhbW8Pq6ipefPFFRKPRRg/p0jTqOtAM1x8Gd0Tn53a7rRZ8QPnk5crKSku3dWqG6yJdLlbM0UVd5Bw6LoyqXFzAStfW0qz3xZlM5sJdUYgaheETERHVnRACmqbBMAy22Suh6zqmpqZgGEZd2qux7Z4zaJqGiYkJeDwejI2NweNpvdsznqdE1CjHTV4qilIWRhWDqEQi0bTXaIYIVA2eN1QLtTqHjltccHh4CEVRysKoRCLBPQCbUDNfj1j5RE7WnHfORERkG6ZpQlVV62awWW8Iz+vg4AATExMYGhpCf39/XY6Ly+WCpmk1f12qHUVRMDExgWeeeQZ9fX2NHk5D8JpQPQbMRLXndruP3fBelmUsLS1BkqSyNn1ut7vBI64dXo/pvLjnE11UPStVSsOokZGRsjBqY2MDmqaVVUb5/f66jYXq7zwtHJ0ml8ux8okci+ETERHVhRACuq5D13VWO5UQQmBxcRHb29t46aWX6rqCiW337EsIgaWlJWxtbeHll19u6YcJto4jIjur3PBe13UoioL9/X0sLi5CkiRrFX08HndsGMXrMFWDez6Rk1RWRpmmeWwYVbymM4xylmYOnzKZDDo7Oxs9DKKqMHwiIqKaM00TmqZZqyG5IvJIPp9HMplELBbD2NhY3W+OXS4XJ5NsqFAoIJlMIhqNXsp5YHcMn6rHY0d0+TweDzo7O61JIE3ToCgKdnd3MT8/D7fbbU1cxmIxx4RRzdyuiOqH5w1dVCPPH5fLZbVUHR4ehmmaSKVSkGUZU1NTUFUV0WjUqowKBAINGyudzjAMx3zmnhcrn8jJGD4REVHNCCFgGIbV6o3B02NbW1uYm5vDjRs3rNXT9caWXPVVzYTL7u4upqencf36dXR1ddVpZM7CAIWInMzr9aKrq8u6pquqCkVRsL29jbm5ubKV9rFYzLYLDhgiUDV43lAzcblciMfjiMfjAFAWRj18+NAKo4oLDBhG2UszVz5xzydyMoZPRERUE0IIaJoGwzAYOpUwDMN6WBkdHb3UjW3Zdq9+ioHJWc9z0zQxOzuLw8ND3L17lw+rJRg+VY/Hjsh+fD4furu70d3dDeAojJJlGVtbW5idnYXH47HCqGg02rQTZdQaGD7RRdn5Pua4MCqdTkOWZUxPT6NQKCASiViVUcFgsMEjbm2sfCKyJ4ZPRER0YaZpQlVV6wH0rA+hzf7Aenh4iPHxcQwODuLKlSuX/nflxHT9FIO9s0waZrNZJJNJdHZ24t69e019zleD5ykRNTOfz4eenh709PQAOGq9KssyNjc3MT09DZ/PZ4VRkUikYWFUs9+TUf3wvKFqOe2643K5EIvFEIvFMDQ0ZIVRiqJgZmbGCqNKK6Oc9PdzumaufMpkMqx8Isdi+ERERFUTQkDXdei6DkmSznWzV5y8b8bVSUIILC8vY3NzE3fu3EEkEmnIOFj5VD9nDUwePXqE+fl53Lx509rcmMoxfLoYHjsiZ/H7/ejt7UVvby+Ao/0gZVnG+vo6UqkU/H6/tYo+Go1e2sQlryVEdNmcHhaUhlFXr16FEAKpVAqKomB2dhb5fB7hcLisMophVP00c+UT2+6RkzF8IiKiqpimCU3TYJpmVW32mjV8KhQKSCaTiEQiuH//fkMfqFwuFyeT6uS0wKS03eLY2Bi8Xu8ljs5ZGD5VjxMYRM4XCATQ19eHvr4+AEetdRRFwdraGtLpNAKBgDVxGYlE6vq+5zWFiC5T8TmyWUiS9EQYVayMmpubs8KoYmUUw6jacnqY+TS5XI7hEzkWwyciIjoXIQQMw4CmaQBQ9f5Obre76apydnZ2MDMzg+vXr1sbjzeSJElNd4zt4mlVZalUCslkEleuXMHg4CAfKqluGNwRNZ9gMIhgMIi+vj4IIawwamVlBel0GqFQyJq4DIfDNfuMcVr7KyJyPiFE0y1ELCVJEqLRKKLRKAYHB8vCqPn5eauapbjAIBQK8Tp8Aax8IrInhk9ERHRmQgioqlp1tVMpl8sFwzBqOLrGMQwDMzMzyGazuHfvHvx+f6OHBIBt9+rpuEl/IQTW1tawurqKF198EdFotEGjc5ZGBSgMbojI7iRJQigUQigUQn9/vxVGybKMpaUlZDIZhEIha88oTlwSkZMYhtFS16zjwqhMJgNFUbCwsGAFDMUFBrymn08zVz4xfCInY/hERERnUqx2Kq6MveiNcLNU5aRSKYyPj6O/vx83btyw1QMC2+7VT2VwoWkaJiYm4PF4cP/+/aZddVcPjQyB7PR+rQYDNKLWUhpGDQwMQAiBbDYLWZbLJi6LYdR5Wjqx8omILpsQomnDgrOQJAmRSASRSARXrlwpC6MWFxeRyWSsMCqRSNS02rUZNXPlUy6XQygUavQwiKrC8ImIiJ5KCAFd16HrOiRJqtkDgtvtdvSkqRACq6urWF9fx+3bt21Z5dIsAZ8dlU76K4qCiYkJPPPMM9aeHXR2DFCIiKojSRLC4TDC4XDZxKUsy1ZLp0gkYoVRgUCAE5dEZBvNXKlSjePCqOICg9Jq13q0Xm0GpmnC42nOae5m3CubWkdzviuJiKgmTNOEpmk1abNXyclt91RVxfj4OAKBAMbGxmx7I8i2e/VTPH8XFhaws7ODl19+mavRqsTwqXo8dkRUqnTisnR/EVmWMTs7i3w+b4VRiUQCwWCw7GeJiC4Tw6enO26BQTabhaIoWF5eLtsHMJFIIBKJtPS1vJkrn1r535Wcj+ETERE9QQhhtdkDUJeHAqcGI7u7u5iensa1a9fQ3d3d6OE8lVOPsROYponJyUm0tbVhdHSUD84XwACFiKg+SvcXuXr1KoQQSKVSUBQFMzMzKBQKiEajaGtr4/0CEV06hk/nUxpGFVuvFvcBXFlZQTqdRjAYtBYYtFoY1aznU/E5qZX+Lam5MHwiIqIyQgioqlqXaqdSTgtGTNPE7OwsUqkU7t69i0Ag0OghnYp7PtXH7u4u9vb28Nxzz2FoaKjRw3E8hk8Xw2NHRGclSRJisRhisRiuXr0K0zSRSqUgyzLy+TzeeOMNxGIxq6WT3+9v9JDJxvj5QxfVrGHBZTluH8BcLgdFUbCysoJMJoNAIGBVRkWj0aYOMJq58onIyRg+ERGRpbi3U3HT6XrenDopfMpkMkgmk+jp6cHdu3cdc9POPZ9qqzSA7O7uRjweb/SQmgLDp+o55VpERPbkcrkQj8cRj8exvb2Ne/fuWWHU5OQkNE1DPB63wiifz9foIZON8LObLqq42JFqozSM6u/vhxAC+XwesixjbW0N6XQafr+/rDKqmcK/Zg0zDcPg+4QcjeETERFBCGEFT5IkXcpNmxPCJyEE1tfXsbKyglu3bjkubOBNau1ks1kkk0l0dXXh7t27ePjwoe3PXyIiovMoDaOGh4dhmiYODw8hyzLW19dhGEZZGOX1ehs9ZGogIURTTvTS5WnWsMAuJElCMBhEMBjEf/iLVXzkw89iZDgMRVHKwqjiNd3pYVSzVj7lcjnuLUyOxvCJiKjFmaYJTdPq3mavkt3DJ03TMD4+Dq/Xi7GxMXg8/MhsVZubm1hYWMCtW7eQSCQAsFqnlngsq8djR0S1ctz9n8vlsto1jYyMwDAMK4xaW1uzwqjiKnqGUa2l2CnhLJYfpTHUG6nziMhpGD5djj/5f8bxpb+dxeuvr+EPPvsR9PX1oa+vDwCsNn3r6+tIpVLw+XxWGBWNRh3179Os51Mmk2H4RI7GmTQiohbViGqnnwMD1QAAIABJREFUUnYOn/b39zE1NYVnn30Wvb29jR4ONYhhGJiamoKu6xgbGyubVOOkf+3wWBIROYPb7UZbWxva2toAHH1OHhwcWJvdCyHKwigu3GluZw2flrfS+LU/S+Ln/ukN3Hmm7RJGRk7B6rn6m1uU8fk/fgAhgL39HD75y6/hd3/zx+D3HVUIFSujimFUPp+HoijY2NhwXBjVrJVP2WyW4RM5Gu8GiYhakBACqqpeerVTKTuGT6ZpYm5uDoqi4JVXXkEwGGz0kKhBUqkUkskkBgcHceXKlSfeIy6Xi4FJjTQyfHJ6a0oGd0TUSG63G+3t7WhvbwdwtHdoMYxaXl6GEMKatIzH4wyjmsxZqgw297L4P780i20lj6++ucHwico0a6WKXRiGif/5f/0GdO3xM/f0zD7+zb/9Dj75i//o2J8JBALo7e21FmAWCgXIsozNzU1MT0/D6/Va1/VYLGarf79mPZ+y2SzC4XCjh0FUNd79ERG1ECEEDMOApmkA0LDgCTiavC+Oww6Ke/p0dnZidHTU8ZPSVB0hBFZXV7G+vo4XX3wR0Wj02O+TJMl24alTtXqAspNR0RX2NXoYRNTianEd9ng86OjoQEdHB4CjMEpRFOzv72NxcRGSJJWFUc24Qr2VnFb5tHdQwB99eR4TSwoA4HtTO1DSKhIRfubREdM0GUrX0S9++h9wqOSf+PrX/34JIyMJ/I///e1TX8Pv9z8RRimKgkePHmFmZsYKoxKJBOLxeEPDH8MwmjJ8yuVyXBRLjsarPBFRi6hss9focMVOlU8bGxtYWlrCzZs3rT19qPUU9/ny+XwYGxt76qRYqwcmtdSoY6nrOlKpFKLRaMOuh9O7WXxt/gAfGonjdvf522nwPCQiO/N4POjs7ERnZyeAo89ZRVGwt7eHhYUFSJJktehjGOU8TwufDrMafv+LD/HuvGx9TTcEvvHOJv7ZPx66rCGSzTVrpYod/O1X5vDGd9dO/PN/90fvYWQ4gf/iA1fO9bp+vx89PT3o6ekB8DiM2t7extzcnNWeNZFIIBaLXfp1vRnPp0wmw8oncjSGT0RELcA0Taiqaj0kNjp4Ao5atRiG0dAxaJqGyclJSJKEsbExrrxrYbIsY3Jy8sz7fHHSv3YacT06PDxEMplEMBhEJpNBMBi09jEJh8OXMqaNwwL+fvEQAsBriwfwuIAbneznTlQNIQT++u1NRAIeDHWGMNQRhN/LIOM8LuO65/V60dXVha6uLgCPw6jd3V3Mz8+XTVo2egU9ne6k8ClX0PHrf/YAW0oehll+r/S1tzbwEx+8aotnEWo8hk/1sbWdxm/+7htP/R7TFPjMq9/G537nxzA8VP3iy8owSlXVJ8Ko0sooLjI4P7bdI6fjLBsRUROrrHay0819o9uWFcOGkZER9Pf3N2wc1FhCCCwsLGB3dxcvv/zymTdz5Z5PzlTaVvHOnTvw+Y5a/+RyOciyjKWlJWQyGUQiESuMCgQCNZ8kk3Mavr5wAO39STkB4OvzB3BLEq51nL2tBkNQoiN//Poa3l54XGEhSUBvPIChziCGO0MY6gyhLx6Ay8UJbzupDKMqJy09Ho9t9xYhWHvHllI1A7/6xw+gGQJ7h+oTP7Ml5/Hu3D5evtZxWcMkG2P4VHtCCHz8f/k6DO305+xsVsO/+NRr+IPf/QhiMX9Nfr/P50N3dze6u7sBPL6uly4yYBh1Ptls9szPqER2xPCJiKhJCSGgqqr1YGi3FYZut7sh4ZMQAvPz89jb2ztX2OBkp/Xkb1X5fB7JZBLxeByjo6PnevhtdHhK56dpGiYmJuD1ejE2NgZJkqCqKlwuF0KhEEKhEAYGBiCEQCaTgSzLmJ2dRT6fRzQatcIov/9iD+dZ1cDfLx7ioFBe+SkAfHVegdsl4Zm2wIV+B1Er+ebsPt4pCZ4AQAhgU8ljU8nju3NHf+bzuHC1I4ihzpAVSCVC3kYMmU5QOWlZubeIz+ezwqhoNMpJ6wYTQpT9GxiGid/4D+MQEjC7njrx577y5gbDJwJwfIBJF/Ovf/O72H6UPvP3b2yk8cu/+k38xq/9l/C4a39NrbyuV1a8ulwu7gV4ClY+kdMxfCIiajJCCBiGAU3TAMCWwRPQmD2fcrkckskk2trazh02OFWxQseO50Aj7ezsYGZmBs8//7y1F8V5sOLEWQ4ODjAxMYGRkRH09fUBOHnCQ5IkRCIRRCIRDA4OwjRNpNNp7O/vY3JyErquIxaLWWGU13v2yWvdFPjWyiHWU0+uBgcAUwBfnpXxX19vx1Di9JCL5yG1unU5h//3jXWc5V2g6ibmtjKY28pYX4uHvPjB59rwX710ertVunzH7S0iyzI2NjaQSqXg9/vLwije61yu0vtL0xT4nb+Ywo6cx/p+7qk/99b0HvYPC2ivUaUFOVdlgEkX8+3vreOrX5k/98+98/1H+NwfvI3/6edG6zCqcie1Xy3dC7C0Mopt8Y/Cp2g02uhhEFWN72IioiYihICmaTAMw7ahU9Flh0+bm5tYWFjAzZs30dbWdmm/t9GKFTp8sDtimiZmZmaQTqdx7969qqtYGhGe0vkJIbCysoKNjQ289NJLZasGz3p9dLlciMViiMViGB4ehmmaODg4gCzLWF1dhWma1uRnIpE48SFZCIHvrB5iei//1N9nCOD/m93Hf3u9HVfinJgjOklBN/F7X1uEaVYfwHpdEr6zdIAfvNaBNlZB2Z7f70dvb6+1N2M+n4csy1hbW0M6nYbf77cWBkQiEVvfBzeD0vDp//iPM3hnZg8+v/uJfZ4qGabA197ewE/9k5HLGCbZGJ9RaucwVcAvf+Yfqv75v/ziNJ55JoH/5iPXajiq0x0XRh0cHGB/fx+Li4uQJAnxeNyqjDrpPruZq+iy2ay1eI7IiRg+ERE1CdM0oaqq9SBo95uvy5q813UdU1NTMAwDY2Nj56pSaAYMSR7LZrN48OABenp68Pzzz1/oPcK2e/anaRrGx8fh9/sxNjZWszYeLpfLmtwEjq4xxTBqaWnJWrFZ2T7k7c00Jrafvhq8SDeBL83I+PEb7eiP+k78PlY+USv7va8vIFvQq/55v8cFb8CDTE7H+HoKH7zWXsPR0WUIBALo6+tDX18fhBBWGLWysoJMJoNAIGBdr8PhsO3vjZ2mGBz86Vfn8ZU31vHslRjmN05ut1fq629v4id/eBhu7sPW0hg+1c6nXv02qv9EPPI7n30TVwfjuHO7uyZjqobX60VnZ6fVmULXdSiKAlmWsbi4CABWZVTpoq9mPpdyuRyCwbPvCUtkNwyfiIgcTggBXdeh6zokSXLMTddlhCLFVltDQ0Po7+9vyUkHhk9HipVvt27dQiKRuPDrcdLf3g4ODjA+Po5nn33WWiFfLx6PBx0dHejoONq/orKXvdvtRibYibmcD9o5KjQ0U+A/Tu/jv7vRjp7IyQEUUSv6y3c2sbKTvdBrDHaFsH541AJzfIPhk9NJkoRgMIhgMIj+/n4IIZDL5SDLMpaXl5FOpxEKhawwKhQKteR9YS0JIfDaeAp/+6aCl59vx/Tq4Zl/dveggHdm9jB64/ytj6l5NHNgcJm+9toS3n13CwAged0QmnHKTxxP00x86l/9A/7gsx9BT7c99hjyeDxPhFHFRV/Ly8sQQiAejzd1tWsmk0EkEmn0MIiqxvCJiMjBTNOEpmlWmbmTbrjqGYoIIbC4uIjt7e0nWm21mlYPSepV+dbqx9WuhBBYXl7Go0eP8PLLLyMUCl36GCrbh8zvpDC1lkZaO//5ohoCX3y4j3/6Qge6wk+euzwPqRWNrx3itYmdC73Gtb4I1g4f7722vJdDKq8jGmjNx+NmvI5IkoRQKIRQKISBgQEIIZDNZq3V85lMBuFw2GqZyjDq/F57b/coeLrejoerh3imL4LJpYMz//xX3txg+NTiGD5dXC6v47c/91bZ16JxP1IHhapeT1by+KVPvYbP/taPIWDDz8TKRV/FMGpnZwepVApvvfUW4vG4VRnVDF1PcrlcS89nkPPZ70pCRESnEkLAMAxomgYAjguegPqFT/l8HslkErFYDGNjYy3/QNPKlU+pVArJZBJXr17FwMBATd8jLperKSfrnKyyzd5Z3vul+1XUw25Ww+Sein319O89SeH9AOonXmhHO/ekoRanZFX8399cudBrDLQFsH5YAPD4vS9wVP30g8+0zp6Qpep9LbQDSZIQDocRDodx5coVCCGQyWSgKAoWFhaQzWYRiUSstqnBYLDpj8lFfOvBFv7k62t4ts8PJauhoJmQ09q5XuP7s3vYUfLoSgTqNEqyO4ZPF/cvf+1byGbK33turxvxuB8HVQZQc/My/vW/eR2//MkP2v46WAyj/H4/TNPE9evXcXh4aLVgNU3T2jPKqWFUNpttyII6olph+ERE5DBCCGiaBsMwHBk6FdUjFNna2sLc3Bxu3LhhrYZqda0YkgghsLq6ivX1ddy5c6cubQq455O9KIqCiYmJc7fZq+f1M60aeGs9jbRmwu+WUDCqfx/mdBN//XAfP3GzA4mSVaisfKJWIoTAb391AZpe/bU3GvAgLwCBJ9/74+utGz61IkmSEIlEEIlErDAqnU5DURTMzc0hn88/EUbRkben9/Dbfz6JZ/tDgCSwvJUBADzaz2GoN4zlR5kzvY4pgK+9tYH/4UefqedwycYYPl3Md95ax9tvbz7x9YODAkaG4shkNehadZ+Z//mbK/jjP0viY//8zkWHeSmK55LH40F7ezva249a6RqGgYODAyiKgtXVVRiGUVYZ5fPZv7V1Nptl5RM5GsMnIiIHMU0TqqpaK1SdGjwBtQ2fDMPAw4cPoaoqRkdHHXETeVlaLSRRVRUTExNW9Yvb7a7L7+Gkvz3Yoc3ecTTDxOsrh9jOashqJhJ+Nw4KBtQLBFAZzcRfTe3hn93sQMzPW3hqPZ//9ir2U9WXEbokoD3mx3bm+OqMhd0sMgUDYX99PjfsrBUqn04jSRKi0Sii0SgGBwetMEqWZczMzKBQKCASiVh7RgUCrVmtM7mk4Nf/fRLPDERxkClAyehlfx485+fT19/ZxE/9k2G43QwgWlGxdTydn6ab+MxvfOfEP19cPsCLN7vw4L2tqn/HH/3JAzwznMAH/9HVql/jshiGcexzn9vtPjGMWltbg2EYiMViVmWUHecRGD6R0/HJlYjIAYQQ0HUduq5DkqSmWCFWq4qcw8NDjI+PY3BwEFeuXOEDTIVWarsnyzImJyfPXf1SDYZPjaeqKsbHxxEMBm3VYtMUAt9aPURGM5F9f7WpUjDQFnBDzhnQzOrPm7Rq4gsPtvCjAx4MdLXzPKSW8f2VA7y7fAiXywXJJcHtkjAQ9+Ewp2M/reIs74Ln+iJYPTg5vDIFMLmZwuhwonYDdwiGT08qDaOuXr0K0zStMKq44CkajVphlN/vb/SQ62527RCf+eMHGOgKY2Ung86YF1rFooqFjRQiQQ/SOf2EVyknp1S88XAPP3irqx5DJpsTQtjm/s1pfuV/f/2JdnuVkpO7eOmlHrxXZQAlBPCrv/E6fv9KDCM2/2w8axXdcWHU4eHhE2FUserVDmFUsS0skVMxfCIisjnTNKFpmrUyjJMDR4oVD5ubm3VrrdYMWqHtnhACCwsL2N3dxSuvvHIprXFaKdSzo2Kbveeeew49PT2NHk6Z762lkFVNbFVMCMh5A+0hD/ayOvQLBFA504W/29DxwsY43ObRZ8Pe3h7i8Tg8Ht7aU/PJqTq+8MaG9f/CFNBNgeW9PICjSovOiA9+jwsF3cBeqoCMWn59HukKPTV4Khpfb83wiU7ncrkQi8UQi8UwNDQE0zSRSqWshS+aplmr5+0yYVlL+6kCfu1PH6Aj7sfWQR7PDcQwuXzwxPdphsC1gQgmlpQzv/ZX3lxn+NTC+Gx7fu9NbOOb31o90/eOP9zD9evtmJnZr+p3RSI+fPu9LduHTydVPp3G7XZb1+2RkZGyMGpjY6Ps2p5IJBqy0CCXy9mmuwNRNfiESkRkU0IIGIYBTTuawOSqsMcKhQKSySQikQju37/PY/MUzd52L5/PI5lMIh6PY3R09NLOBVacNIYQAktLS9ja2rJVm72iB1tp7GR17JywEnU/p6Mz5MFORsMFOvBBhxvegWu4EQUePnwIWZaxuLgISZKsB+hYLFa3tpNEl+kP//MKcgXjxD/PaybW5HzZ19ojPiRCXrgkCbphYO8pP19qdieLvGYg4G2t9w4rn87P5XIhHo8jHo9jeHgYpmlam9xvbGxA13Xbt3I6KyEEPvl/vQsJgJLVEAt5MbueOvH7dw8LkKSjqomzeLAgY3M/h7527qtFdBohBP63X/0WzlTyC8A0BTa2s+jtCePR1tn2YytyuSR0DMexuHFYxUgvV632DysNo4qvW3ptL4ZRxcqoywijdF2H1+ut++8hqheGT0RENiSEgKqqrHY6xs7ODmZmZnD9+nV0dXGV5GmauUKneC7cuHEDHR0dl/q7GT5dPlVVkUwmEQ6HbdVmr2hRzmFRKaCgi6e21tvL6eiJePEoraGaAigJQE/Mj+WUhptxP/x+P5577jkAgKZpUBQFOzs7mJubg8fjsR6go9Go7Y4Z0Wm+ObuPpe3suX9OyepQsjrcLgnP9Iah5fKn/xAAwxSYepTGy4Pxc/9OJ2P4dHEul8vawB543MpJlmWrlVM8HrfCKCdNJP7eF2fwaD+HeNiLbFZDe8wHTT/53nJbyWO4N4LFzfSZXl8I4GtvbuCjP/ZsrYZM1LR+4/fewOFB4Vw/k8loiEV9CIU8yGbP1hITAH5gtB8PtzMwPfa/f6y28uk0ldf20qrXqampshasiUSiZfcDJHoahk9ERDZTrHYqTgRwMuCIYRiYmZlBNpvFvXv3WqK3fi00Y9s90zQxMzODTCbTsHOB4dPlKrY1unbtGrq7uxs9nCfIOR1Tu3lIOKpuOs1OVkdvxIdHafXcAdRQWwCH75dN7RXMsvPQ6/Wiq6vLCuYLhYK1UjOVSsHv91thVCQS4ecL2dpBVsNfv715odd4tieE1YMCuiJe7KSfvjdG0fh6quXCJ6q9ytXzxU3uZVnGysoKhBBlYZRd26Z+e2IHr31/Ez6vCwcZDbeG4pg4pt1eJd85qwe/8f1N/PSPjMDrgEluqh3eS5/PzPw+vvzVhap+dvNRBs8/14bp6T2IM9x8PvdcO6a30wAkbO5koemmrd+fpmleynW0tOq1+HuLYVTpfoDFyqiLhlFcHELNwJ53OERELUgIAV3Xoes6JEniCvUSqVQK4+Pj6O/vx40bN3gDdg7N1nYvk8kgmUyip6cHzz//fMPOhWauKGuU4x6uhBBYXFzEzs7Ope3ndV4F3cRbm2lkNQN751hNupPV0Bf1YeNQPWvnFAzEfFbwBAAbWRNPOyJ+vx+9vb3o7e0FcNQzvjjxmU6nEQqFrMnRUCjEayvZyu+/tgzjAv0pe+N+rB0WAEiIBc8ePk1vZaDqJnw2nmSrNU5u1d9xm9wrigJFUbC8vAwhhLW63i5h1O5BHr/3l1Nwe1xQNRM9bQHMrJ2t/db8RgqxsBeHJ7ShrXSY0fDdyR188I699nEksgvTNPEvfuUfztxu7zjTczLu3OnBe+8+eur3RaM+ZPxuiKz5/u8WWN9KY3ggVv0vrzPDMBqyIPGkMEpRFExPT6NQKCASiVgLDap9luFnNDlZ4+9oiIgIpmlC0zS22asghMDq6irW19dx+/ZtRKPRRg/JcZopJNnY2MDi4iJu375t3eA3Ciufaqt4PEuvfcU2e5FI5FL38zoPIQTe3EhhI63CjfPPB2xnNAzEfFg7VE/93raAG5VNVjayBp45x3kYDAYRDAbR398PIQSy2SxkWcbCwgKy2az1cNzW1mbLoI9ax38a38Yj+Wyt8o7jdUuAR4LQj64pB2eoSCzSDIHprTRetPEkW60xfLp8brcbHR0dVttgXdetyqilpSVIklQWRl32Hn5CCPzLz78HA4Chm5AAeD0uaGcMhA1TYLA7jIlF5cy/86tvbTB8ajG87pzd578wCfmc7faO82BiB7dvd2N8fPvE7xm+2YX57fL9oVYf2Tt8qtWeTxdVGkYNDQ3BNE2k02koioKZmRkrjCqtjHra+4DPm9QMGD4RETWQEMJqs9eK1U7Fqpzj/t6qqmJ8fByBQABjY2OX/tDdLJohfNJ1HVNTUzBNE/fv37fFamCGT7VVeTzt3mav6MFWFvOKirjPjUdnrKqotJXRMBjzYfUpAZTfLSEY8CBf8VbO6gI5qbp9QyRJQjgcRjgcxpUrVyCEQDqdhizL1sNxNBpFe3s72tra4PP5qvo9ROe1dZjH15InT4qdxXB3+P2qpyP7WQ3tYS/2z1iFMb7eeuETNZbH4ykLozRNw8HBAfb397G4uGiFUW1tbYjH43W/L/7tv5rG3mEBxvt7O/3A9fZzBUkAsCXnIUlHezqdxfiigvWdLAa6QucdLjkQrztnd5Aq4M//6iEgAMnjgnjKnmtnMbuoYHgojqVjWmj+wN0+zFQETwCwspEG7l7o19ZVvfZ8uiiXy4VYLIZYLIarV69CCGFVRs3OziKfzyMcDuN73/se7t+/jxdeeKFsbiSXy3FBGDle42dviIhalBACqqq2dLVTMRipDJ92d3cxPT1t+4lnJ3B6SHJ4eIjx8XFcvXoVAwMDtnmfOP242k3xeAohsLCwgN3dXdu22StaUvIY380h7nfjUer0yqWneZTRMBj3YfXgydeRAPTE/Ujrx59vB67aTNJJkoRoNIpoNIqrV69abUP29/exvr4OwzDK9ifxeqsLvYieRgiBP/j7ZVxkzcRgewBrB3mg4vOiLXT28GnqURq6YcLjbp1FQXb5fKUjXq8XnZ2d6OzsBHAURimKgt3dXczPz8PtdlthVCwWq+mk62sPtvDdqR3o2tEbMRH3Y3othWf7o5g6w35PRXuHBTzTH8X8eurMP/OVt9bxsx+5du4xk/Ow4vLs/tW//Q4KBQPA0Ueby+uCoVX/QamqBg5zOtrbAtgvqTIevBLDgnJ81fHqo3TVv+8y2KXy6TSSJB0bRv3N3/wNfuEXfgFra2u4du0afuiHfggf/vCH0dXV9dTnoZ/92Z/Fl770JXR3d2N8fBwAsL+/j5/6qZ/C0tIShoeH8YUvfMHaf7DU5z//eXzmM58BAHzyk5/Exz72sfr8panlMXwiImqA4t5OxZvuVr3xrqzKMU0Ts7OzSKVSuHv37oU36KSjY2wYRqOHcW5CCKysrGBjYwN37txBJBJp9JDKNENFmd2oqorJyUlEo1Hbttkr2s/peGszAwkCql6b99ej9PEB1NW2AFInBE8AcCjVJ6ArbRsyMjICwzCsllArKyvW/iTFMMqOq03Jef7i7U3IVVYRAkDA60LOxBPBEwCk8mdvvVfQTcxuZ/FCn70+e+qFk8D25/V60dXVha6uLgBHn5mKomB7extzc3PweDxlYVS1n6HbSg7/7j/NQVOPPtt8QS98XjcOszoy6vk/79yu851Xf//9R/jnP/oMfF5+pjQ7p4QFjfbuxDbee2+r7GuiBpdrWc5j+GoMqbQGTTPg97vh7gxCP6G13+qjs4fIjWDXyqfTFMOoX/zFXwRw9Pd488038Xd/93f4xCc+gZWVFbhcLvzhH/4hPvShD+H69etln9c/8zM/g5//+Z/HRz/6Uetrr776Kn7kR34En/jEJ/Dqq6/i1Vdfxa//+q+X/d79/X18+tOfxltvvQVJknD37l38+I//+LEhFdFFMXwiIrpEQggreGrFNnuVSifwM5kMkskkenp6cPfuXU6C1IjL5YKun33CzQ6c0HKRlU+1ZRgG3nnnHTz//PPWxJpdFXQDr6+lkNVNJPxupFQTEZ8LafXiYWRlANUf8yF1yv4aqssLpaAj4a/vbb3b7UZ7ezva29sBHC2iUBQF+/v7WFhYgMvlsvaLisfjLf/5Rue3tJfF6zP7F3qNK51BrJ/QwnI3oyER9EA54/5P4xuplgqfyFl8Ph+6u7utDgGqqkKWZTx69AgzMzPwer3WNTkajZ7pmmwKgU//aRKZ7FEA7Asefa7svj8Zvbabw0BXCOs72TOPc34jhbaID3L6bBXC6ZyO1yd28KEf6D3z7yBnYvh0Nr/6m999snWlAFw+F8wL3nsurRzi1o0OjCe38cIP9B7bbq9ofSsDwxTnDpQvS7OcT263Gx/4wAfwgQ98AAAwPj6OT3/601BVFb/0S7+EmZkZ3Lx5Ez/8wz+MD33oQ/jgBz+I5eXlstf44he/iNdeew0A8LGPfQwf+tCHngifvvKVr+DDH/6wdV//4Q9/GF/+8pfx0z/90/X/S1LLYfhERHRJTNOEqqotX+1UqliVs7a2hpWVFdy6dQvxeLzRw2oqxX21nGJ/fx9TU1N47rnn0NNj302nGT7VRrHNXjabxdjYGGIxe++xIoTA62tpbGc1DEQf79MU8roQ9gKZC7RAKXqUPtoDKqUaOGszv9VDDYmuy72t93g8ZS2hSlfhz87OVjXxSa3LNE38yXfWIbkkiFMC15OMdIVODJ6K2iO+M4dPk5spGGavbSfaao33pc7m8/nQ09Nj3TsVCgXIsoyNjQ2kUin4fD7rmhyJRI69Jn/uS7PYfD9Y8gY80HUBV8X6n3jEd67wyRRAf1fozOETAHzlzXWGTy2gWcKCevr8Fyawv5c7/g8FEG8L4EA+vk3eWU083MMP/eMhfH/16S01Vc3E1m4W/d3hC/2+enFq5dNpCoUC+vr68PGPfxwf//jHYZompqam8Nprr+FTn/oUHj58iMHBQezv72NiYgI3b97E1tYW+vr6AAB9fX3Y3n5yH8319XUMDg5a/3/lyhWsr69f2t+LWgvDJyKiOmO109NNTk4iGAxibGwMHg8/lmrNKe3hhBCYn5/H3t6e7fcp/QUbAAAgAElEQVT6AZxzXO2sUCggmUwiFoshHo/D7/c3ekin+v5WBvNKAQNRL3ayj1uDZTUTEa8LQQHkLrgJNADs5XT0JwLYL5ytxdFqSsWLXY19z1Suwq+c+AwEAtbEZzgc5kQ3lfnrd7ehZDRIkgS3R4LXI0E3BIQATMN8ctV3hUjADeUMLcGy52gbllVNLOxmcc2mE221xLZ7zcfv96O3txe9vUchTj6fhyzLWF9fRyqVgt/vt9qmRqNRTK0e4rV3NgEAHr8bhiHg9bmgVVRWLG1lEPS5kTvHe2lzL3cULJtnC5YfrhxieSuNoZ7WqDxsVQyfni6dVfHv/3zyqd+TSqu4dbMTE5O7Vf+eWNQH1Xe2f4eVzZRtw6dmPZ8ymQzC4cfH3OVy4datW7h16xZ+7ud+DkIIfOMb38BHP/pR/Mqv/AomJyeRzWbx2c9+Fj/5kz9pfQZUOm4RJe8DqF44y0dEVEdCCKiqCtM0We1UYX9/H/v7+3jmmWcwMjLS6OE0LZfLZfsKnXw+j2QyiUQiYfu9fopY+XQxe3t7ePjwIa5fv46uri68/fbbtj+ei0oe7zzKoj/qg2EK5Cv2YUprJqI+F0wc7RdzEb0xPzzus39eyAUDKdVA1GefFZ+lE59CCGvic3l5Gel0GuFw2AqjgsEgPx9b2HaqgO/Oy2Vf00reXy63C7GgB4mgF+7/n703+5FkTc/7frHmvtW+nj49Z+vTZ5thL2NeGJAB24QBG7zRja4MEIL/BkKABBgyIMKURVuSJUoaipYIiZspjSibIjniWNSQw+HMmTNnurr7dFdXb7UvWblnZGZsny+yM7uquqoyotbMrO8HDDCnKzIyKiq++CLe53ueV2kLSNuVFu4eh9R4NspmtbezYqdqk4pqVJvBCuf316pXQnySDD/RaJTp6enuavhGo0GxWGR1dZVyucI//p6L7wt0U8P3QVV5Q3gCaDo+N+ZSPHxeCvzdxZrNuzMpnqxWAn/mj360zv/0378feHvJ4DGsYsFZ8T//3b/ADeCof/ayzLW30rxcDj6+9nL9xijbAd1TKxs1/ovPTvQ1586wOp8syyIejx/5c0VRePfddxkZGeG3fuu3EELwta99DcuyqFQqCCG6C8P2Mjc3143mA1hdXeWv/JW/cg6/gUQixSeJRCI5F4QQeJ6H47RXxkvh6TW+77O0tESpVGJsbEw2tTxn+j12rxPRdePGDUZHRy/7cAIjxaeT0XG4FQoFbt26RTQaBfr/fNZsj++tVJlKGDi+YKfuHLpd1fZJm1p74cEJo8OmkgYVV2D6PgoQdC8rVZubo/3pGFQUhVgsRiwWY2ZmBiEElmVRLBZ5+vQplmWRSqW6YlTnupBcDf75n6/i9XBEVBoulT1xeZqqMJ2OkIhoRHWVpwELZwIYT0aoNoPFhj1Yr/LzX59EHfJnOOl8unrsvSf/yrcfUatvohlad85RtaMd3mXr8DnwOETg2azNn/50i//xv32HSB8tqpCcLZ3FmZI3WXic54svNgNt27I96k2XbCZC6VV/tqDcuDHKk606igKmoWL3ELuWN2qh9n+RDOv1ZFkWyWRwF6iiKPzVv/pX8X2f999/n1/6pV/i53/+59/Y7ud+7uf4G3/jb1Asthf//PEf/zF/5+/8nTM7bolkL3KZgUQikZwxnZg9x3G6MXvD+CB0EizL4kc/+hGapnHnzh1M0+xrYWQY6Nd4uE5e9crKCnfu3Bko4Qn6XyzpR1qtFp9//jlCCG7fvr1PYOjn8ymE4E+XK6RMjXLL6xnbVbE90lEdI4RzqUPcUHGU9uO57QuykeBFt5Vq+GLgZaEoColEgrm5OT755BPu3r3L3Nwctm3z6NEjfvjDH/Lo0SO2traw7eB9QiSDx//3eJedkMUyAE/AdtVmp2pja+FeaZshnInVlsfLo/ptDBFSfLq6LLws8WdfbhKJaKCAEKAb6rGOi41Ck/mQjsDnGzVG0sHjda2my/cWtkJ9h2SwEEJI59MR/C9/9y9CbV8oNhkZi6Ebwc9nLKZTeXXbFwImR49213RY2ayGOq6LZhjnsUajcWwc/V/7a3+Nn/3Zn+Xx48fMzc3xa7/2a/ziL/4i3/nOd3jvvff4zne+wy/+4i8C8Pnnn/PX//pfB2BkZIS/+Tf/Jnfu3OHOnTv8rb/1txgZGbmQ30ly9ZDOJ4lEIjlDfN/Htu3uS/wwPgCdlPX1dV68eMHNmzfJZrNAWxjxvOCZ8ZLw9GPsXr1e5969e0xPT3Pjxo2BHCeDeMyXSSdm74MPPmBsbOyNn1+W+BTk73hv26JuezhCIRvV2Kj1FnlKLY9cTKdoubgBe1wA5BImtT1xY2EErHzDxXJ84iEKD/2Coiik02nS6TTXrl3D930qlUq3P4nneWQymW5/EsMwLvuQJWeAZbv84cKbTbDDMDUSZaNqk47qVJpu7w8AW5UWCVOlfkik2GHcX69yfax3UU4iGTRcz+d/+92HKJqKoqmIV4KTF8C5qxJuwYMvYHo0RqESXGz+ox+t81/fmgn1PZLBQcbuHc6/+jdfkd8J5s7dy4vlCh/dGGVhYSfQ9jc+HufxVr3738lE72erlc16z20ui2F9NzvY8+kgv/mbv3nov//Jn/zJG/92+/ZtvvWtb3X/+xd+4Rf4hV/4hdMfpETSAyk+SSQSyRnQcTu5rtt1O0naOI7Dw4cPURSFu3fvouuvp55+FEaGjX6L3euIkB999BGZTOayD0dyzgghujGbe2P2DtKvzqe85fBgp4Guq8QU2AwgPHUoNj1G4joFyyGI0WIuE6F6sI9UwOJ4h5WqzQcjgx9Zp6oq2WyWbDbL9evX8TyPcrlMsVhkeXkZIQTZbLYrRg1jxv9V4Ne/v7avb1NY3hqJsVFvC07j6Uhg8UkAk+kIz/LBHE3316r8D59OnvQwBwLpfLqa/B+//5ia5aAZWjduyzA1nB4OX4DNqk/EgFYIDWotb6GpSs+YzQ5La1Werld5ZyYV/EskA4MUn96kbjn8y996cOLPP3i0y2efTvDTe8cv7Hj3nRyPt2rA6/u+EcBF3Gi67BQajI/0X8xzP75HnAWNRuPQhXsSySAh7/QSiURySjpup47wJF/eX1MsFvnhD3/I+Pg4n3766T7hCUDTNOl8Omf6JXbPdV3u3btHPp/n7t27Uni6AjSbTT7//HOAN2L2DtKP4pPrC/7zcoWYqdFwPDw/bMcKKDQ9RhMmvQxMmahG7ZAifNMTZEJF7w1nRJ2maYyMjPDOO+9w69Ytvv71r5PNZikUCnzxxRd88cUXPH/+nFKp1Bf3O0lv7q9VebZ18hXUhqbQ2vMm2/LC/d3D9GQrNVxWCsMdvSfFp6vHvRdF/mJhG83Uu04nTVcCCU8AjiuYyoZzoZbrDu/MhhOS/vhH66G2lwwOUnx6k7/9Kz/ADTgGj+LB410+eP/o+DTT1LBjOnuFJ4CWG+x7Vzb7t+/TMGJZFvG4dF9LBhvpfJJIJJITIoTA8zwcp73kTwpPrxFC8PTpU3Z3d/nGN75x5ANTvwgjw0w/nONKpcL9+/e5du0as7Ozl3oskoshn8/z+PFjbty4MXD9vDr85VoVU1fJNzzGY3qguL3D2G24jCdNtms2hy321hSIRXQaR7zzRzWFcsDv2rZcWq5PRB/uYo6u64yNjXVXgtq2TalUYmtri8XFRUzTJJfLkcvlSKVScm7uM1zf5zd/eLqC8vWJBGv112Nyu+aQjGjUWsGKZ5uVFjFDoeEEE6Hur1eZ78OV3mdFv4n/kvPF9Xx+5fe+Qo/oB3o7KRBimUW1Ff66cQIWuDusD7nwe5WR4tN+Fp8X+fGXm6fej+8L1rfqTE0l2DwkJu+jzyb2xe11KAdcwLS8XuVnbo6f+jjPkmFeQGFZ1rGxexLJICDFJ4lEIjkBQggcx8HzPCk6HaDRaLCwsEAul+POnTvHvlT0gzAy7Fymo0QIwcuXL9nY2ODTTz8lmUxeynFILg7f91laWqJcLnP79m0ikWDNxS/rOj3qZXW53KJi++QbHklDZbt+MuGpQ77hMpk02azab5T1ZjIRKsekhdWPafp+EAGs1hzeyQZv6j4MmKbJxMQEExMTQNt1VywWWV1dpVqtEovFumJUIpGQc/Yl89ufb9I8xcruiZTJet1m76ptAUxkotS2g7mpfAGT6SgvdgNG761X+e8+njjB0Q4OclxcHf73bz+i4Yp9wpMZ1bCb4cZlqSG4NpngZQgX44sti3RModLoPeePpE0ahkrZcsjEZa+/YUOKT/v55X/8Y9BUcP0wGvCh1C2HVNIkEdepW68fMt+aT/PkiHkyX2yiaUrPnm/96Hwa5mtJik+SYUCKTxKJRBKSTsxep2gpX9Zfs7GxwbNnz7h58ya5XK7n9qqqyti9c+ayBD7btrl//z6xWIy7d+/KnixXgGazyb179xgZGeH27duh7o39FLtnOT6PC022LBchBCpwirY0XXYaLtNpk43KawFqPGFQdo5frWm5grShUgkoQi1X7SsnPh0kGo0yPT3N9PQ0QggajQbFYpEXL150Gzd3xKhYLCbn8QtkrdjkJy9KJ/68okA0ZlA/pL+TE7CPTIcwm+drDhvlJtOZwe+pdhjDvGpcsp8vnxX44mlpn/CUTBrYrh+439NeotHwJaW5yQwPe9wHNFVh7q0Mu02PzXJTik9DyDALBmH57vdXeLHc9rnrcQP3lIueADa367z3tRxPnhTwfYGmKRgjUfxi89DtPV8wNRpjY8c6dr/LG/0nPnmeN7TXUqPRkOKTZOCR4pNEIpEERAiB67rd3k7D+oBzElzX5auvvsLzPO7evYthBHtBVFW1G1soOR8uQ3wqFAp89dVXvPfee10ngmS42dnZYXFx8cQxe/0kPn25VWf9VcTeTNKg2DjGlhSSbastQK1XbCKaApqKEmB4xkKIT5t1B8cTGL0aTV0RFEUhHo8Tj8eZnZ1FCEG9XqdYLLK0tESj0SCVSnXFqON6k0lOz69/f5XTDPV3JhJsHDEmt2s2cVPDClg836y0MDUlcP+n+2vVoRafJMOP7Xr8o/93CXvPGInHdVwBQnR6PoXb5/OtOqm4TtUKPleu7tTRNQX3mLH3/rU4u6+cWJvlFh9Mh+sVJel/fN+Xi9No339/9Tfudf/b9wRqRMMPGCN7HE+eFfnkk3F++tNtPv36FI93jncpZlKRnuLTykb11Md11gzztVSv12XPJ8nAI8UniUQiCYDv+ziOg+/70u10gHK5zIMHD7h27RozMzOhzo2M3Tt/LrKoL4RgaWmJYrHIrVu3ZBH3CtCJ2atUKqFi9g7SL+LTk0KTZ+UWAphM6Ow2PTRVIW6oWCHi745j23KZTUdQVYWKG+x3bgTcDtpujrWazduZq+1+OgpFUUgmkySTSebn5xFCUK1WKRaLPHr0CNu2yWQy5HI5stkspmle9iGfin4YVx3+6OEOxVrIyvYe0lGd3dbRBW5ftGMsn/conHVwfcF8NsrLgD1l7q/X+G/6rM/FWSKfbYefX/3DpxTKre5/R6IaiqrgvZrfPE+gqAoihC3Q9QTvzaR48Cy4o7HacHl/NsXjlcqhP39/Pk1FfV1Ivre4zIxWJJvNks1mAy9yk/Q3Qgi5mBP4v37vIcWDbiRFQTFVhH36Z8+Fh3nu3Jnmcb733GgE6BlaqTuUqy0yqf55zhxm55NlWTK6XjLwSPFJIpFIjkEIged5XXeOFJ5eI4Tg+fPnbG9v89lnn53IDi7Fp/Pnos5xJ3Itl8tx+/btoX0BkLym8zcfHR3l1q1bp7o3Xpb41PleRVEoNV2+2Krj+jAS1VB47YhIGCoxAQ33bMaSooJQgzd2rzk+SUOh5gTbfqUqxaegKIpCOp0mnU5z7do1fN+nUql0e0Z5nkc2m+2KUbouX59OQrXp8mdPi6iagn/CHMvxXJTNHlFEoUdoiNvWZqXFTtVmPDXYguRhyNi94efpZo0/v7fd/W8jouGLdrHZ3tv7KaLRCun43SnbKAqhXI2tI+bTkXQEN27sy7v1I2lGR0coFossLy8jhNi3SEDelweTYRYMgtJouvzu7z859GeKqqLoAj/EAqSjMNIR3AC92VwvYMTzRo1P+kh8GmbnU6PRkM4nycAjZ2mJRCI5AiEEjuPgeZ4UnQ7QbDZZWFggnU5z9+7dE784SPHp/FFV9dyL+tvb2zx58oQPP/yQkZGRc/0uSX/Qidk7q7/5ZYhPe+/pvhB8b7VKwxUkDRVTU/cVueuOT9JQEQKaAV/MjyKiKbiKStpQqYZY0ZowNGpOsILges3B8wWaKuetsKiq2l1df/36dTzPo1wud3tGKYrSFaMymUzfFzv65dnlt7/YwPUEuq6iGO0itQDwBa7r9yxaXxuL9RSeALaqNhFdPbKwfZDNSgtdVXADOj3ur1f5rz4IHy3a7/STQ05yPvzKtx/jvbrODVMDBTRNpX5AaDrJlZCvtLg+neLZevA4rpdbdaZyUTb3OD50TWHurXQ3bq/DVrnFyMhI93nDdd1992Vg331ZilGDgXQ+wd/9pz8+vs+aroHnnmxgvuLmzTGeBXQEV+rB3MnLGzU+eb9/5sJhFp+k80kyDMhZWSKRSA7B931s2+6uBO2X4k0/sLW1xdLS0ol7u+xFik/nz3kW9X3f5/Hjx1iWxZ07dwY+nuokXLXV4r7v8+TJE6rV6pn+zS87du9BvsFuwyOiKRi6eqjDqeb4pEwVYQtaJ3RuAEwkTSoeNENG+TdDfKcrYL3uMD+EDo2LRtO0fUVPx3EolUrk83mePn2KpmndflHpdPrKF9IO4+mOxdPt14WvzlBXAFSlXQhv/1+SUR1dwE7V7hbKI7pKI+D9wRMwm43yIkC8EIDjCeayEZYLhzdgP8jC2nCKT9A/QqXk7Pn2X66x+WpM6Ibadt+Kw41/whfMjcdZDVis7mAY4e99I9n94tOnH4yxdcjkWGm4NGyP2Kt7ha7rjI6Odt9DXNelVCpRKBR4/vz5wC0SuKr4vn+l58zNHYvv/WCt53ZqRAfbww8Rh9khEtEooVBrOKSTBpXa8Ys4dgrNQNGb/db3aZhddLZty6hRycAjxSeJRCLZgxAC13VxXRdFUYb2IeYkeJ7X7YdxVkVnKT4NLvV6nXv37jE9Pc2NGzeuZNFqb2TbVaDRaHDv3j3Gx8dPHbN3GJclPlVaHl9uWWgKZKI6mgIbR0QOVW2ftKnh2x7OCQSo8bhO5VVdre623VS1gL2kqrZPTFcC939aqdpSfDoHDMNgfHyc8fF27x/btikWi2xubrK4uIhpml0xKpVKXZn7w1EIIfi9LzcDbeuLdpEZ2gWzkYRBzFAxdJWX5RC9okKecjWEQ3Ct1KRoOeTiw1UIukpz2VWj1nD4nT99CYCmq6i6gvDbYpFzxPyTPMH1/WyzTiZpUO5R3N7Ly80ahq7guIIP3sqw1XLhiOtws9zk+vjhEd+6rjM2NsbY2BjwepHA7u4uz5496zpaO4sEpBjVH1x18elv//2/DNxfLTmRoLpVCxVtCXDzk8lur6exXKyn+OS4PuO5CNu7xy/IWN6shTuQc2aYxSdgqH83ydVAik8SiUTyCt/3WV9fJ5VKEYlE5Ev4HiqVCvfv32d+fp65ubkzOzeapknxaQBZW1vj5cuXfPzxx6TT6cs+nEvjst06F0knWvHmzZvkcrkz3/9l3W+FEHx/rYYnYDplUrU9Wj3EoIrtk43olJpu4KguAF2lHZ+yZ/cJUwssPgGkTY2GGyx6b63q4AuBKueyc8U0TSYnJ5mcnATasbSdflHVapVYLNYVoxKJxJV7tvjukwLFAHF5B3F9wXbVZixpkouEe2XdqjkYmhJYIN6q2KiqEnhV+f21Kv/lezJiVjIY/Mq/X8S2PQxDRdXV7nXuHhNN+WK7TiauU7aC937yfMHcRIpyrRj4M1bL44P5NNvFJk5M39fn6SCbpdaR4tNBDi4ScByHYrHIzs4OS0tLXcdqNpslk8nIwu4lcZXFp5882GHxSSHw9rVSk08/neSnP90K/JmpiQRLhdcOxnhAUTmXjvYUn/rN+TSssXtX5T1TMvxI8UkikVx5hBB4nofjOGxsbBCNRolGo5d9WH2BEIKXL1+ysbHBp59+euZ5w4qi4Hkhs6ckl4brujx8+BCAu3fvXvlM/asgPvm+z+LiIvV6/VyjFS/rXC4WmmxZLrMpk3zTI2sq1Fq9j6PU8shFdYpNh4CtZZhORbqupw52yAiVMNvbvmCr7jKdHC6HRr8TjUaZnp5menoaIQSNRqPbl6Rer5NMJrtiVDQaHWoxqml7/KfHu6faRzyms1N30JRj69L7cH3BTDbGy91gsWEt12cmE2G1GCx67/768IlP0vk0nNx7WeKnTwqor+ItnVcT1nGuJ2gLSfMzKcrPS6G+b6vURFXaLsagNFveoX2e3th3Odj4PAzDMJiYmGBiYgJoO1ZLpRLb29ssLS2h6/o+Z9RVFUQumqssPv2vv/p56M/cX9zlww/H+OqrfKDtR99KU8k3Xv9DwHu8afQWcXZLLayGQzzWH8+Yw+x8ki0gJMPA1a4aSSSSK48QAtu28X0fRVHQNE2KIa9otVosLCyQTCb55je/eS4PdKqqDn3xflgol8s8ePCAa9euMTs7e9mH0xcM+/XbidmbmJjggw8+ONcXn8sQn+q2x+ebFtNJg3zTI22obIVwaBRbHiMxg13L6VkUz0U1yu6bxd2q4xPXVayAClal5RNRFVoBK3vLVVuKT5eIoijE43Hi8Tizs7MIIajX6xSLRZ48eUKz2SSVSnXFqEgkctmHfKb89k82cU/RH+3aaIztV86LqUyEtVIr8GdVLdz9ygix/cvdBpWGSzo2PK/SUnwaPoQQ/P1/t4iiKETjeldsUjXlWOGpw06lFVpIKlRtvjabYmk1uCtidDRGPsDxbJaDj/9emKa5T4xqtVpvxKd2xKhUKjW0Re3LpvP+fdX4/f/4jHzInmrQ7tP2cr3K3GyK1bXjx9iNG6M83ys80V4QEoSgLuDlzRo3rp99GsJJGGbn0zC/a0quDsPzxCyRSCQh6bidOi/cnR5PUnyCnZ0dFhcXef/997uRFeeBjN3rf/a63z777DMSiWCRJ1cBRVGG9vo975i9g1yG+PRny2UyUY1iywchaAW1MO2h0PQYixvsWM6RBTpVAdPUaR6x+1QkuPgkgExEY/uInlQHWa3a3J2KX8niTj+iKArJZJJkMsn8/Dy+71Or1SgUCjx8+BDXdUmn010xapAbTK+WmjzaOHlPCFNTqO8RrkwtXPF3u2ajq0rgaMztqh240C6AB+tVfvad/ii6nQVSfBo+fuM/vaRcbRGJG/vEpvbfufeFXqjZfG06ydJ6uHGshBBqZsfiFHwYTZps9RCXNk/hfOpFJBJhamqKqakpoB2fWiqVWF9fp1qtEolE9olRcqycDVfR+eR5Pt/61wsn/nyz5dGKC9Jpk0rl8F6IpqFS11Vgf02jUAkm4FatYAuxltf7S3waxkQO27aHbmGS5GoyfKNTIpFIeiCEwHVdXNftCk4drroY4nkei4uLWJbF7du3z/1hR4p9/Y1t2ywsLBCPx8/N/TbIDGPsnu/7PH78GMuyzjVm7yAXfS6fFhqUWw6OaJfgRqIaGyGapO9lt+kxkTDZqtmHlvNmUibVY6YVJ+SvHcZI0vQEOw2XiRM0j5ecP6qqkk6nSafTvP322/i+T7lcplgssrKygu/73YJnNpsdqMLKb36+carPvzUaY63+WmTN151QLgzbE0xno6wUGr03BhqOz1QmwnpAd9XCkIlPkuFit9riD/5yjWjc2NfbSdfVY3s9HUQzwj/3PduskUuZFKuHF8a7+1YVRqeSlGyfREQHjh97uzUbx/MxQgrRJyEaje4ToxqNBqVSidXVVWq1GtFotHtvTiaTUow6IUKIK/du8X/+xj0aIXqpHcZuscn1+TR1q4R3yHj+6NNJHu++OfeVqjbxqI7VPP77d4oNFKXttDqOlc3+6fvked5QOp/q9TrxePyyD0MiOTWD8wYjkUgkZ4Dv+ziO07X5H3xZuMqxe9Vqlfv37zMzM8ONGzcu5EVKVdUrLfb1M7u7uzx69Ij33nuvG0si2c+wiU+WZXHv3j0mJycv7B7Q4SLPZdP1+WKjAoqK6/mYKuycshCQb7hMJU02DwhQaVOj5gHHnMqK7RPVFZpusN+/1PIwVAUnYBV+pWpL8WlAUFW163qCdjGlVCp1e0YpitIteGYymb4ttHz/eYlC7fjC83FkYzobB8Zkw/WZSkdYDxG9ZejhippRPfj5fJ63qLc8EpH+/BuERTqfhotf/jePMKIa7oE4u6BxWh1ebNXJJQ2KIRZnCAGz44me4tNnH4ySt9vHpwcQlISA7XKL2ZFY4GM5K2KxGLFYrNvLr9lsUiwWWV5eplarEYvFuvfuRCIhx1JArprzqVpr8Qd/8vxM9vV8pcLHN8e4d29737+Pj8V5VjraJTg+GuNlj8i+Zssjl4lSOGY/AMuncDefNcPa88myLCk+SYYCKT5JJJIrgRCiG7N30O20l6sohgghWFlZYW1tjY8//phUKnVh330Vz/dlEbSw5Ps+T58+pVgscuvWLaLR6AUc3WAyTD2ftra2WFpa4qOPPiKbzV7491+k+PTDtTK6rlFptRcaxDSFauv0373TcJlKmWy8KrgpQDKmUw+wniEd0Wi6wQQwAWQjGjsBo/dWqg63JgNtKukzNE1jdHSU0dFRABzHoVQqkc/nefr0KZqmkcvlGBkZ6Zu+JLbr80cPdk61j2zSZOsQQTgSUkzaqTtoSnC34E7NJlggWduB9XCjyp23L/5+eR4My1wmgT9/lGe92HhDeDJNFdsO98ztC5ibSFCslUJ9bq3QQFWVI8WuTtxeBy+gKLZZbl6K+LQXRVG6YtTMzAxCCLuvJ7EAACAASURBVBqNRneRQMep0BGj4nEZfXsUV018+nv//EuEenbXwv3FAp9+MsG9hdcC1NTbGZ7mj3b8JhPBEg1G0pGe4tNKH4lPw9rzybIsGXkvGQqk+CSRSIYeIQS2bR/pdtrLVXM+2bbN/fv3iUaj3L1798If2uTL2MXQKez3Ot+NRoOFhQVGRka4c+eO/Pv0YBh6PnVi9hqNxoXG7B3kosSn1XKTiu13hae0obB9StfTXnYsl5mUyXrVZjplBhKeIFxDdwhWHO9Qd3x2Gy6jMfnYP+gYhsH4+Hi3F6Nt2xSLRTY2Nnj8+DGRSKRb8LysKKjf++kW9gn6p3WYy0UPFZ4Adi0nsDgEbZfjdDbGajFY9F7d9phKR9gI2Bfj8VZ9aMQnkM9kw4Dr+fyrP325r8cTtIUnIThWEDqKjWITTVUCC0QA5brDu7MpFlcqb/xsb9xeh1or2GS5GcL5eFEoikI8HicejzM7O4sQAsuyKBaLPH/+nHq9TiKR6ManSjHqNVdJfNrYrvODH2+AL4gnDawTRj0f5KtnRd57d4QnSwXef2/kWOEJQNWCXXuxaO+awPauhe14mMbliz7S+SSR9DfyLVQikQwte91OQE/hCa5WD6J8Ps/jx49lrNoVoOMwO+6hvON8+fDDDxkZGbnAoxtcBj12rxOzNzU1deExewe5iHPpeD6PCg12XzmGYlrbBZuNaJQCFr6CsG25zKdN6iHq72Xbx9AUnIAWjVLLC+XoWKnaUnwaQkzTZHJyksnJtrWt05ekEwUVj8exbbu7Ev+8x/h21WZh9c1ic1A0VeG4cpzl+EymTDZ7xHntJWKGK0bFAsbo6aqCGb8csf48uIq9V4aRX//uC4oHxNN00qTl+viuj2GotELOd2XL4Z2ZJIur4fq7HPUte+P2OpSsYD3dtsrHOzH6AUVRSCQSJBIJ5ubmEEJQr9cpFos8e/YMy7JIJpNdMSoWi11pMeqq/O6//K0vusJvs+URqonhMXieYKvYYGY6QSuqg3P8/Niyg43/IIfmC1jZrPHOfCbQPs+TYXU+yZ5PkmFBvoVKJJKhRAiB67q4rhtIdOqgqiq2ffI+BYOA7/s8efKEarUqY9WuCMfFw3mex+PHj2k2m5fqfBlEBll82tzc5OnTp5cWs3cY530uF7brrL0qWuuqwkjcYK1qY6gKmYhG+QwFKN3QUB0fL6AAJYBcRAvce8oXMBrVQ0Tv2Xx9Qr68DjsH+5JYlsWXX375RsEzl8sRi519dNW//ny9Z4Py43h7LMZ6/fhrOh7RIEQNfKcerKjdYbceLHrvw/k0FU9gOT5xY/BFm0GdyySv2am0+N79/f1fzIhGy/HwXq1UCOt66iBOIBC82Kwzlo2QL70Www7G7XXwfMFIwiTfo1dcPzqfeqEoCslkkmQyyfz8PEIIarUapVKJpaUlGo0GqVSq28/vPO7Nksvl4VKBh4/y+/5N1RQ0TXnDpXgSanWHmx9Ocy+AQNyrF1sHK+jz5Ub/iE/DuICi0WjI2D3JUCDFJ4lEMnT4vo9t292YsTArqjRNG/gYreOo1+ssLCwwOTnJrVu3rsxqs6vOUfFwtVqNhYUFZmZm+PDDD+X1EJJB7Pnk+z6PHj2i1Wpx9+5dDMO47EMCzl/Iy1sO93csoL3YdCyus2O1PRaOL1A1hZSpUQ24IvQ4xuI6NV8hGwkuDgHtJlHnRMX2Kbc8MgFdHZLBpxMFZZomn3zySbfgWSwWWVxcpNVqkUqlumJUJBI51ff9dK1CwXLRdBXh+aEXdCciGtsBxkuh4YaK3ms4PlOZKOs9eld0qDY9JlImW8cU6N6bStB8tcK62HSJG8OxaEM+Aww2//A/PMHZE3lpmBq+J9D0139XISAW1Wk0w8XNvtyuM5aOkA8YSQntMTo5Eu+KT4fF7e0lGdN7ik/b5Ra+L1DPsG/ORaMoCqlUilQq1RWjqtUqpVJp3725I0bJRYKDzy//sy8OXZih6CqcgfiUTprknWDPr4VKi0hE6+mAzAecM5f7pO+T53lD6XySsXuSYUGKTxKJZGg46HY6yeqXYY3dE0KwtrbG8vIyH330EZnM5a9Qklwcndi9Dnuvh48//ph0On2JRze4DFrPp70xe/0mNp7nuRRC8IO1SrdYPZE0Eb7A3pNZ1/IEUU0haajUTlEIUJW268kToIQsjpVtH12FoO1yyi0vlKNjuWLzybhcUX1V2VvwfOutt/B9n2q1SrFY5OHDh7iuSzqd7opRYYRpIQT/4eHuq2tRQdE0NF6lCvkCz/d7qkWTmSgb9d49MGotj/GkwXaIfhlBo/Q6JKI6HCE+TaRMlJjZ/XUKDZfZ1OCLT0H6Qkr6l5++LLG4/DryUjdUhC+IRTQaB4rMuh7+7yyA6bFYKPEJYDVvoWkKnicOjdvbi6n3HqeuL9it2YynTyeW9xOKopBOp0mn0917c2ehwKNHj7Bt+0wXCkgulj/78Qbr64c7klzXR9HbGcqnWX/1tfdHWNyxiJoqzWPGGLQF6ImRGCs9RKOa5ZBJmpR7CMIrm+HiOM+LYe351OkZJ5EMOlJ8kkgkQ4EQAtu28X0/tNtpL8PofHIch/v372MYBnfv3kXX5a3/qrHXoeO6Lg8ePEBVVXk9nJJBit3b3Nzk2bNnfSs+n+e5fLKnz9N0yqTh+pQPcVg0PUFMV4nT7i1zEqZTJpZozz8Vxw/Vl8kXkI1o5BvBFkC4AkYiGvlmsO1XqlJ8krxGVVUymQyZTIa3334bz/OoVCoUi0VWVlYQQnRX3mcymWPniu8sFqgdsoraF4CioGkaqgIRs11c9nyBbbvdKLBkTA8kPHVIRvVQ4lPBCueWKlmH7ztqqIyPJ7D32BQLIR0k/cqgzGWSNxFC8E//6Gn3v3Vd7V7vh/1ZG00PVVVCR/CtFRromoIbdFIDqg2X92bT1Czn0Li9vfgBr8HNcnOoxKeDqKraFaOuXbv2xkIBx3H2LRSQcdn9zT/4F18e+3NFUTBzEZqFk/Uzm55MsJRvO/vHR2KsbNZ7fiaVDHbNjGajPcWnfnE+DWvPJ+l8kgwLsuIkkUgGGiEEnufhOO1CwWmEJ2iLT8PkfCoUCnz11Ve88847TE1NXfbhHIlccXu+dFwl5XKZBw8e8PbbbzMzM3PZhzXwDIL45Hled+XsnTt3+iZm7yDndS6brs8Xm+0X44mEwU7DJa2rRxahG64grqsIAY2gFqRXxA2V5p6itC8gFw0uJgGho4TUEPfNYsujZnskzeF7OZecHk3TusVMaC9UKJfLFItFnj9/jqIo3Z+n0+lukafp+Pzgebnn/n3BfgeGohKJKOi6SiKiUmkFH2+lkIJPzfaYzEQC94spNVzGkgb5PQKXArw/l6ZxIB+zGCZas8+Rz2GDye9/vk7h1bWt6SqK0hadNEOleUSUrKar+CFjZqsNl3dmUjxeqfTeeA+OL46N2+tgBTyezXKLT+ZDHcJAc3ChgO/73YUCa2treJ7XFaOy2awUo/qIf/udp5SKvUUlu2qTmYhT3rZCf8fITJJyvgFAMh7sGV/TgjmEYrHe5eKN7Tqe5wfe53kyjHOYZVnynV0yFEjxSSKRDCxCCBzHwfO8U4tOHQ7Gkw0qvu+ztLREqVTiZ37mZ/q6eW2n6DyMD4z9gqIorK6uUiqV+Oyzz6R9/4zo9/tFvV7n3r17zMzM8NZbb/X1GDsv8emLjSotT5CL6ZRsj5GIxnYPh4XlChKmho+g5QY/ppG4QV3sP8eaqgLBC3xlO5xbqmz77SpjwL/tStXmw9H+nQ8k/YOu64yOjjI6Ogq0XdSlUomdnR2WlpbQdZ1cLsd/2tSwQwq1HVxPEDFVUjGDSit4nFel6TGWMMiHdEsRUHwCSMf2i08fzqZoKG8W14pNdyieYYbhd7iKNB2Pf/v9VeCV40lVEK8cTcpxzqYTzrfuCT6Xy0QoBJhLS5YTyKG4GbAXzbCiqirZbJZsNsv169f3uVZXV1fxPI9MJtMVo/p10VG/L946LUII/sX//VXg7WulJkbCwAkxr717PcvzV8ITtO8BQQg6ZweZE1xPsL5jMT+VDLRPSTgajYZ0PkmGAik+SSSSgcT3fWzb7r4sn9UL8zA4nyzLYmFhgbGxMe7cudP3xYROAX8Yc5r7Adu2KRQK5HI57t69K8/zGdLPzqeNjQ2eP3/etzF7BzmPc7lTt3lSbJI0VFqeACGoBVxZXXd8kqYOwm1/tgfjcf0N4Qmg6vih+jJ5AnJRnXxAN4XjCyK+TUsLFkG0XHWk+CQ5EYZhMD4+zvj4OACtVoun67s83Q2/UruDqoCiKfv6rwUlHdVDiU9hHUqVPe6quVwUxzz8tdkTUGl5ZKKD/VotxafB5J/+x2e0bA9dV/dF6emGintMfKwQvbc5jOUdi8lclK0Abg6AkZRJRVHIJXTyR/RR62C7gnRcp2wdP1a3QojIV4GDrlXP87qu1eXlZYQQ+8SofonbHvZ7zj/73Yc0johwPQwhQPgC1VTxe7gEoT1/iqQBpdfjoeUEe8Yt148fix0aAV3Gy+tVKT6dE5ZlyUWjkqGgP2YeiUQiCYgQAtd1cV0XRVHOvJDe706GXqyvr/PixQtu3rxJNpu97MMJxDD22eoXdnd3efToUbfBvBSezpZ+FJ86MXuO4wxUT6+zPpe+EPxgrYqpKaiaStP1GYsabPbIrt9LzfFJRXX8hotzjHqkKaAa2qHLtV0BuYjGbsC+TABhh6lK8PtnvuHScHxihrwXSE5HJBLhu6veqZqkJ+IGAig3PXJxnWKPovNeKiEjwyotj4l0hO1KsMJ1oe4wkjBwPJ9ULobL0UXSQtMdePFJMnhsFBv85cM8iqoQNbV9EXteAGdDmNjWvYyHEJ+uv5Uhb7fn0l7iE0AmZvYUnzbLV9v51AtN0xgZGWFkZAR4HaFaKpV4+fJlt59f53+X9Zw4zAsPW7bLv/vDpdCf810fParjuE7XwXgUH90c52lp/3xWCjDGAPKlFrqu4va4T+wGHGsrm/3R92kYkc4nybAgn5IlEsnA4Ps+juPg+/6Zup32MqjOJ8dxePjwIYqiDFTBGdqC3yCe836mE7tYLpe5desWL168kALfOdBv4lOtVmNhYYHZ2Vnm5+cHakXpWZ/Lx7sNyi2XbNyg3PKIago7AVd67qVq+2RjOsWGi3tEIWAqZWId4nrqoGvhoveqjo8KgSUlRzUDRRV1WKnavD8SDXw8EslhPNisBe6hdBi63pZNOyMnFzNCiU+lhstIXKcQ4jPpmB5YfALIxQ0SqcgbfZ4OUmi4XB+M9T5HMuwuhGHkH/zBEkIIzIi+T3jSDQ03gAPC98U+t1RQVvIWhq7i9Chcvz+XJv/KwaFrwa6taICFEU3Hp2Q5ZAP2t7nqHIxQdV2XUqlEqVTixYsXKIqyT4zq9PM7b4ZZfPp7v/7T0K7CDm7TxUybtEpHz1URUyN/yBgvVFoYhorT47t9XzA1GmN9q37sduWqTSKmU+/hHF7eqB77c8nJkc4nybAwONVJiURyZRFC4HkejtO2rp+X8ASD6XwqFos8fPiQ69evD2RDykE85/1Mo9Hg3r17jI2Ncfv27a5DUJ7js6efzmvH9fjxxx+TTqcv+3AulYbr85OtGmMJk91XkSFxTaUmTiZyV2y/XeSuOxxsW5Ew1J6F6ZrrhxKHHB+yMY1CI9jx+qpOxlQptYJtb/c4XomkF0IIfn9h51T7iEX1fWPCOkGhLhMzQolP5YBjpEM6YVILMF4KIZyN/YoUnwaLHz0t8GKjSiSq7ytyKyqBhKcOuq5ih3QRWi2Pd2eSfLVcOXIbQ1cxUma3b2LQZE0R8BrcKjel+HRCdF1nbGyMsbExoL2AsVwuUygUeP78eVeMyuVyZDKZcxOjhlV8Klaa/Ofvr5xqH07dIZqL0jzCYXjz5hiPdxtv/LsvYDoXZW27dxxuJmX2FJ8AxkZi1NeOF5dWNi7X+dRZmDyM1Ot1KT5JhgIpPkkkkr5GCIHjOHied66iU4d+Kib3QgjB06dP2d3d5Rvf+MbAWrIH6Zz3O1tbWywtLXHz5s1u9jv0n0NnWOiH8+p5Hl999RWe5w2c63EvZ3kuf7xRZSxusPNqpWbGUNkOkbt/GOWWz1jCYLvu7OvflEsY1P3j5yXHh2xEoxii8G2o4dxSkYCryt/KRLCHsNgjOZzzuj99d6lIPaSQs5eIqb0hxhYaLklDoeYEP+Z6iCI7tN1SY0mDfK33/WA6E8GP6O0B3IOw/aQkktMghODXvvPsDeEJQNNU3DDP1EIEit86SLPH9p++myO/Z7VG0PtF0J41m6UWH0ynAm0rOR7DMN4Qo0qlEvl8nqdPn6JpWleMSqfTZyZGDatg8Ev/5Meh3YSHYddsbrw/wqPFwr5/z2UivDjGdZxOmoHEJ0MP9ndMxHqLvKubta6T8jIYViET2otKpfgkGQYGs0IhkUiuBL7vY9t2dzXmRTygDspDcKPRYGFhgVwux507dwb6gUuKT6fH8zweP35Mq9Xizp07mKa57+fyHJ8Ply0+dWL25ubmmJubG5j712Gc1bncqtvUHZ98p0myEEfG5YWl2PKZSJhs1218AeNxvafw1MHUVQhRrK864dxStQAF8vG4jhE1cAS0fEHkkooEkovlrO8LTdfne0vFU+0jYmqHxkomDUEAXahLwXLJxnRKIcSfTMLsKT6ZmsLoWALbh5iu0uhRaK/aHq4v0Ad4TEnn0+Dwu3+xSsv1DxGelNBRX76Ad6eSPFo92sV0GKu7DSazJlulN+NsJ7JRigemu5LloCj07BFXCuhklH2fzg/DMBgfH2d8fBwA27YplUpsb2+ztLSEruv7xKiTvoMKIQb6/fUwXqxV+On907mC97K8XmVuOsnqHmfRtXdyLO4cLS7perBz6gS0I2oB5rWW47NdaDA1djkLYT3Pu7C4yItGxu5JhgUpPkkkkr5DCIHruriu240Mk7xmY2ODZ8+eveFuGVSkMHI69vb5+fDDDw8tHqmqeukOnWFEUZRLu3bX1tZ4+fIln3zyCanU4K/+PQvxyReCR7tWN2oPYDRmsFUL3+vpKIotj8mkSb7uoAZcNQpgHczr64Hti1BuqYYrSJsqFfvw6zFhqGRSUXh1f6i5gogpC82S8Hz73jZu0AytQ4jH9CP7mSlGBAjXRyqXMEKJT7UAEWM35tK0XsXtJc3e4pMAik2X8QGOAZPPCIOB1XL57v3tN0QmRVUwTB2vEd7l2wzpIOyQTRmHik9zsyl2D8xFri/IxgyKPVzIlu2RMDXqPcbp1in6zUnCYZomExMTTExMAG0xqlgssrm5yeLiIoZhkMvlyOVypFKpwO/tw+hW+ZV/eQ/F1BDNs3HDNlsedtwnnTKpVG3mZlI82anDMXGwdkAXY8UK9mzcDBjLubJRvTTxaRivpQ5SfJIMC1J8kkgkfYXv+ziO07Xiy1WYr3Fdd1+8lmEMbpFjL1J8OhlCCNbW1lheXu7Z5+cyRZJhRlVVPO9ie314nsfDhw/xfX+gY/YOchbi09Nik03L7bqFkrqKphLKQRSEQtPjrWyEQojaQssXZCJaqJ4zYd1SMf1w8UlTYCYX3ddLo+4KRs03NpVIjiVft3lwit4OitKOBTtqPO5aLqmIRjXEdd84QnA9ioLlkkvoFOuHD+D5kShNTeuW9gwtWEGr0Bhs8QkGx/1/lfnWnzynXt8v4KiqghnV8b127FXYyK/V3QbTuSgbR/SXOYq1QgtTV7D3LK748FrmDeGpQzqu9xSfAHJJk3rhzX42e5HOp8vDNE0mJyeZnJwEoNVqUSwWWV9fp1qtYppmV4xKJpNHigLDJhgsPNll8WnbFZzMRKmd0TWaLzb52nyauuWQGo9T6DE2KvVgAvROsRnoflEM+Hssb9S488lkoG3PmmF2PjWbTaLR6GUfhkRyaoajYiGRSAYeIQSe5+E47QcmKTztp1wu8+DBA65du8bMzMxQnRspPoXHcRwePHiApmmBBAh5js+Hi47d67jc5ufnmZ2dHar7wGnPpeV4LOStbsReRFNIRjQ26w6TKZOtqn1mAlRMV7AVFY70bxz1OTWU+FQP2YPjKHfV27kYQt3/Ul47hXNFcnX5nS+3esZmHUcibvQch2NJk2rr+OLaXvKWQ0zzaXjBi5gjycih4lPUUEnn4rh7VpUH/X0LA973Scbu9T8bpQZfPNnf/0VVFQxT616nRkSndQL3Uy5lhhafWq7grTGTp5ttF1LU1BBxA46Yi4IKuVGzdxG50nCxbI94gG0l50skEmFqaoqpqSmgXSwvFousrq5Sq9WIRCL7xKjOfWbYxKd/+K8Wuv/faroouoII6Xo/imcrFe7cmmYhwOKPfKmJpil4PZ7zXE8wPhJlK3/8fFuotIiYKq0eCz1WNk++MOW0DNu1dJBh/t0kVwcpPkkkkktHCIFt29LtdAhCCJ4/f8729jafffbZUNqupTASjnK5zP3797l+/TozMzOBPiPP8flwkeLT6uoqy8vLQxOzd5DTnssvt+vdaCxNgZGYwVa9HSmSb7hMpkw2q2cTv5eLG9R9jo25OwwrpODT9ASZiEq5Few76o5P0lD39X+aT5so5puP+3VXyGKzJBSLOzXWiyePutI15bikoC72CYTRiXSMlyGOzTqiL84Hs2maBw6y5QUbf8Uzilm6LOT9oP/5J3/8DO+ASyGVNGm5Pn5n3JxwHl3ONzB1NXBkV4e9muvH7+bYcY7+/qBHpga8DjdLTb42MXzvRYNONBplenqa6elpoN2nuFgssrKyQrVaJRaLkcvlhuqd/89/ssHyanXfvymahoqPF3JMHYaqgBcNVrr1fMFkLsZG/ui+UB2y6UhP8UkIGB+Js9pDXFperx778/NkWJ1Pcl6WDBNSfJJIJJdKx+3UmVz7YYLtRJRd9iqTZrPJwsIC6XSau3fvXvrxnBdSGAmGEIIXL16wtbXFN77xDeLx4LnaMnbvfLiIa9d1XR4+fAgwVDF7BzmN+JS3HJ6/6v+gAGMJE8fz2VujyzdcplMmG6cUoHJRjbrfnqeimkKYFu1NT5A2NSoB8/MBoroWWHyCdm+njvg0GtOJxA7P1vOBhg/x4XtXl5wTf/CoiBnR0FUF1xP4QqD4InDT8list+sJIF93SBga9RB9aI6pdx/5HZmYTnlP5fz6WJzmIcWrmuMHiu4sDLj4JOlvHqxUeLa2v7gbSxg0W94+UVcIMCMadgiXLbT7urw7neTRSphZDXaqLvPjcXxfkHd8jlOYGwHHdFABbKssxadBIBaLEYvFmJmZQQjRFaM2NzexLItGo0E2myWXy5FIJPqiFhCWf/Sb9w/9d19V0HT11ALUzQ/HeLpjoSoQJFUznTLYyPfezjxkcdKh+0v2jpSVzqfzYxDHhERykOGsYEgkkr5HCIHruriui6IoffXA0CkoX+YxbW1tsbS0xI0bNxgdHb2047gIpPjUm1arxcLCAslk8kRCpDzH58N5O5+q1SoLCwu89dZbzM3Nndv3DDJCCD7f88I7lTRxfEH+kAisnVMKUApgGBqtV3/yo2LujiNuKFRCfH0joOuiQ/PVMcV0hVwq0m6ycwR1VxDX5AutpDc/eFmm8kpccX0Byit3gqoQMxUMvT0nCU9Qb7pv9JCImFpbrApQQBHAeMqgXghePN+uOSRNlVoIJ2I6olB+teA7YWrE01EOk498AamISqWHCNx0BZbjEzf653k2DHKFdX/za999tk8AjcUNPFdgGCrOASefop7s71g/oYCaThoYcZNij/FXsoLtvxLwODbLJ3diSi4HRVGIx+PE43F0XceyLCYmJiiVSrx48YJ6vU4ikeiKUfF4vO/vS//Pn76gUDg8slJRFIyUiVdqnLj5qGmoFAQ4rs9INko+QDymqQdbWXTQSXkUWoD3znrDZbfUZDR78f2JhtX5JJEME1J8kkgkF47v+ziO07cxe6qq4nnepTgMPM/j0aNH2LbNnTt3MM3h7wjfOd+Sw8nn8zx+/Jj333+f8fHxE+1DVdUL7U10VTgv8UkIwdra2lDH7B3kpALp83KL3VeFqqmEQb7pEj2m8HYaAWoiodMUr/fd8iFlqFSPiPA6jGbIW53lClKmSjVgUb3q+CQNhelsHNGjWFBzBeORcMcjuXp4vs/3npeO+bnA2+Pm0w0VQ1fRFAVfCDzXxzC0UB3STtImYzxlUtsN3rOmucexNTdq4B7zLBrTtZ7iE7Sj9+LGYD63SfGpf/nug2129hS3o3EDzxOoKm8ITwDCb/eCOigC92Kj2GR2NMbabvCea9B2TxQCzFEt1ycV1aj2mAgrDTdQBOBmOVyPKkl/4fs+mqaRSCRIJBLMzs4ihMCyLIrFIs+ePcOyLBKJRLdnVCwW66v7lBCCX/83j47dxrYcYiNxGru9Y/AO48MPx3lSaQutuXQkkPjkBhz7tYD94Ww32MPrykbt0sSnflrIfFY4jjO0iReSq4e8kiUSyYUhhOjG7EH/Nk/UNO1SXCKVSoX79+8zPz/P3NxcXz1cnyeqquK6Mq7mIL7vs7S0RLlc5tatW0SjJ3+Yl7F758N5iE+dmD1FUYY6Zu8scDyfn2zXARiLGRRtn5GI3u31dBQ7DZfppMlGLbgAZagKrvLmnBXVFaohertbnnijL1Mv4kZw8QlgNh3FC9DYveb5gFwpKjmeP3hUoBHieoX2Cu3OsEjFdDyh4Idw8e3UHGKGGup7Q6RZAlC2IRXRmciYEI8du60W0ElSaLjMpgZTfJL0J57v8zt/ttL972jc6PZ3ikV06oe4fAF0U8M+gZMpHTdCiU+mrmCbGglfUA8wT2XiRk/xCWAkYfR0Nm2VpPNpkBFCvFELUBSlK0bNzc0hhKBer1MsFllaWqLRaJBMJrtiVDQavdT35d/494tY9d4Pga2aTSQXpRVAONpLIm6w1ny9U55yCwAAIABJREFU/0gk2DNbpcdzcIftYjNQlF8poGV/ZbPK1z8cC7TtWdIRMocNy7JCxexLJP2MrGhIJJILQQiBbdt963bay0U7cYQQvHz5ko2NDT799FOSyeSFfXc/oGlaV5CUtLEsi4WFBcbGxrh9+/apx4uM3Tsfzvq8dmL2rl27xuzs7JntdxA4iZB3P2/RdH0yEY2656MqUGoGu5fsNF2mkiabAQWo8YRBXbw5DkO21QDaEV9hxKcQLZ+YTBgk4iaVAPtveOALEbi5u+TqUWu5LGycvI+DAqAoGJpCwIXTQDudaDJp8iJEoW67bhPTVRoBe2sIYHY0ihI1e7qyrGaL43rZdBjkvk/S+dSf/M5frFG32vPaXuFJUTlSeDoNL3csYqZKI+CCh/fmklQ9wUTSpG73Hq9RM1iBOBHVoYf4tFu3cTwfI8BiC0n/ESTiXlEUkskkyWSS+fl5hBDUajWKxSKLi4u0Wq03xKiLwnF9fu+PngbfvuFipEycEM779z4YZXHPPBh0DcdOqRnI/Wg7waL88qUmutbu93gcy6d4XjgNvu8P5WK9er0uxSfJ0DB8I1QikfQdnd5OnRfbfn+5vUjn095ePt/85jf71g12nsjYvf1sbm7y9OlTbt68SS6XO5N9SvHpfDgr55MQgtXVVVZXV6+kAA3hz2XV9nhUaBDTVXxFwfMFOVNlK4T9Id90mUzobNWPL+AlDZX6EX3UG54goavUQzSTtkNGIdUcn4ShUu8hKEU1hUwyQlBdSwB1T5DS+3tOlpyc096fvv0gHzi+5zDiMR1fgHKCXYR9KhACJtMmL47ovXEYI+kou07vg3NQCdKwo3gOYsBFIcWn/sNquXznJxsAZNMRrD2rHTRVxT3uuU6AYWo4IS2Btuvz3nSSr1YqPbfNxDXqWrucFDvjXmdB3IZCwFa5xdzI8c5FSX9yEsFAURRSqRSpVIq33noL3/e7YtTjx49ptVqkUqmuGBWJnF+28D/+7QfYYVYgCRC+QI1o+AE+N5KN8ryyX4CtBVzg4HqC8WyErQDzYRDxyfcFU6MxNnaOjw5cuSTxyfO8c/1bXxaNRkOKT5KhQYpPEonk3BBCdIUnRVEGRljRNO1CxJCdnR0WFxdP1ctnGJDCSJu9/b7u3r2LYRhntu/z6k101TmL8+q6Lg8ePEDTNO7evTuUsRFBCHsuf7xZQ1MUYq9cRFFNYccK76DcbfmMxzV2rKPv+amojnWI66lDwlDooV/to+4K4oaKFcL9lDS1nuLTfC4KqoojQFeC9c2pu4KUfBsYak4qKKxXWjwP2ftlL6qqoNCWbDxfBFo1vZedukNEV2mFEHbD9Iq6PhrD0zRweg/elq9gqOD0EOKKTVeKOJIz41vffYHj+ETjxn7hSVNwA4wLNWBc5EHKAefS2ak4tVerMoJe882A817QYb9ZbkrxaUAJ4nzqhaqqpNNp0uk0165dw/d9qtUqxWKRhw8f4jgO6XSabDZ7pmKU1XT54++9DP053xXoER3H9RE95sP5r+V4Utg/B+fLTRSlLbz2IpMOJj5FIsEeAjMps6f4tLxRDbSvs+YsrqV+pF6vk0gkLvswJJIzQb5uSiSSc8H3fWzbHhi3017OWwzxPI/FxUUsy+L27dtDuVInDFJ8aset3b9/n9nZWebn5898vMhzfD6c9rx2+ry9/fbbzMzMnOGRDR5hxKf1ms16zWY8YVJ6tarbDJBZfxRFWzAe09hpvClAjcaOF57gZNF7qZDiUy+31ExSR9kjWEdVhVqAQn+QbSRXk28/yAfw+hxNPKrvK5AZuoobYmGPL2A6ZfIyTPRezQ4kWEV0lXgqguX6gYXaVESj0MPZ5An4/P4jZkbS5HI5YrHYwDz/StGsv9gqNfjx4i6RmN6N2guLEGAaKnbInm3b5RZvjcdZPqbQ/PZkgpryes6xA+aBVQI6N4I6PLZ6RPNJ+pfzEAxUVSWTyZDJZHj77bfxfZ9KpUKxWGRjYwPHcchkMuRyObLZLKZ5sh59f+9f/BQvzGqHPbgtj0g6SrN49OKOmckES4f8vOX4jKQj7Aa47iMBIy79gM/eht57f6WqTbVuk0pcbO9Dz/OGcvFeo9EgFpPiumQ4kOKTRCI5UwbV7bSX83Q+dUSGmZkZbty4IV/0udrCSCdubWVlhU8++YRUKnUu36OqqnQ+nQMndT4JIVhZWWFtbe3KxuwdJOi59IXgx5s1ppImu69Un7Shkj+B62kvJUcwFtPJ7ykuqwqoutozbcvyBHFNwQpRIAwbzlV1/CP72ZjCJXHgGtKUjufkeOonLJ5IhpsHW3XyAfuhHYahqfj+fjHjJFNQ2I/4AmbTJi97rPZ+byqJ88qxkTQ1SgEU5EjAvjLJ8Wl8p8rS0hKNRmNfBNRF9iMJixSf+otf/c5zdEN7Y9xouooXwg2IqkLPrmZvEo8eXSZSFEjlYpT3iFrVgGJRveURM1QaPQSxkuWgq0rP2M/NUnBxWtJfXIRbRVVVstks2Wy2+53lcplSqcTq6iqe55HJZLrOqCCpE7ulJt//8capjsu2HHJTSYqbh8fU5WZSlI5YeJHLBBOfvIArsuoBx64TUGBe3qjx0bsjgbY9K6TzSSLpf6T4JJFIzgwhBLZt4/v+wLmd9nIeYsjeYvPHH398biLDIHJVxSfHcXjw4AG6rvPNb37zXFdsKYpyJc/xeXMS8cl1Xe7fv4+u61c6Zu8gQc/lYqFBwtS6wpMQIlQs11EIoOL4jEY1dpvtfU8mjJ6upw4JQ8UKsWih6giiukIzhPiTjrwpPqnAVCaKoux/6Q56Rpo+uL5AP2E8k2T4EELwh492T7WPaER7w4no+QJNVQIXxADyloOpKdghhN1eW06lTZw9QlJUVwPZF4M+0lpC58P5eebn5xFCdCOgOrG6Z7HqXjLcPFitsLxTfzUv7v+ZOEUPtjC82K6TjGrUmm+OjY/ezu4TngCqLY+IodIK4LLKJU0avXrMCMglTHaqxxfZN6XzaWDp1AsuElVVu4sBrl+/jud5XTFqZWUF3/e7YlX2/2fvzWMkye47v+97cWfkWXcf09M9PUfPNDnc4XT3CBQkLQ3ZhmxjZYla0MIubCzstWHYsAUYhiH9IcL6UxANLwwbPgAZOpcWvJJ3l1pdlkhRlChxaXLUVX1V39Xd1d11ZOQZGed7/iMrs7OyszJeZFVlZWW9D0CAXRMVGRkVL17E7/u+31+xOFCM+uVf/cGBjMOq40EvGAj6ruHzb+TxcMj4MAVj8uqCi7I2Bd3FdcEFKU+e18cuPk2r88l1XSk+SaYGKT5JJJJ9wzlHHMcIw/ZDznEWnoD2g+lBOp+CIMDKygpM05TF5gGcRPGpUqngxo0beOutt3Dq1KlD/7yTeI7HQVrxScbs7Y3IufQihufNsCs8AcCMoeJlc3SHRi8MQD3mmDHb/ZU8iM9j4Qh1iIKuwIvEPVCDPuNc0YBuvR7dGqQ4nkbMUZTik2SHbz2sohmM/gxk6K8LTx10jaKVIqcyYsBSzsBaCnfDRmNvwYoSYL6UQdDzjCq6WDoUFMDKPcV6Qshr/Ug6EVCdVfedFffFYhGqenSv5tL5NDn81l88BqEEvO+xTdUoopQReqP+XhRzXDydw4216q6fW7qCQFPaGZN9lDKakBhkaWLvQTlLTRSfNms+GOMj97eSHB2c8yN3qyiKgpmZGczMtMWSjhjlOA7W1tbAOd+1YODphovrt7YO7PPjIIZqqYh6XPf6jAUMGUeiI3mz4oEKRFK3/BilggEnYexuOK2d+9LwHa49H+zmOkym1fnkui4ymcxRH4ZEciBI8UkikeyL/pi9aXhxVRTlwAr1W1tbuHPnDt555x0sLCwcyD6njZMkjHDO8fDhQ2xubuKjjz4a2wOljN07HESv3V7n4+c+9zm5im1EVp0Wyj1FcZ0CrfBgI1IZB5oxx2JWRzXFrpsRh6kQeCkcGmmPvBYwGAqBv/MZM6YCzRzsnIh5+/wEArfWZsRRTE6akZwAgojhrx5V9rUPTaV7RuyNMg2RlEXliHGczhtYG7Ca+92l7C7hCQBEa/INwQ2dIX2heiOgelfdO46DR48egRDSFaMKhcJYFytJ8Wky+PPbm9isBgOFJ6oQYISEWTpi9N523Ud/gOv7F4rY2sOxmxHsMSNaI1YFoi4jxrHVCLCQP9n9c48jkygY9ItRURTtukf/77/vgOoK2CjNPgfAGUA0CqpRsJBhZsHG0wQRyPXFFi0FIcNM0cSWgLNptmgmik9RxLFQMvFye+9eVQDw5AjEJ+l8kkgmHyk+SSSSkWGMIQiC7gvrtLy0UkoRpViNPgjGGO7evYt6vY6PP/54onP+j5qTIj75vo/l5WXkcjlcvXp1rC9cMnbvcBBx63TiFTVNk87HISSdy4of4WEt6K7gVAhQMlXUAwZDPZjovQ62RqFpFBDMt++Q0yi8FK7ZWshTR4oVTAUbzQgaBeby1tAsMIMQBAJdcxojNrOXTB+/f3tb2OEziIypDhWYYsZhqhReivG61QyhUYIwRczRoGFRMFVAf/3V1xU8lpBxZFSauH09iIWjLAcVOh3HwdbWFu7fvw9FUboRUfl8fuIKtZKDhXOO3/ubdbC+a11RKRSVgu1jbOoaRZDS/bRVD3B+0cbDl00AwFzeQHnIMYi6j0TnvP7zsBcvKp4Un44hkyg+9aOqKmZnZzE7O4vrd7exvrkBQgDTUuC1DkaAYiGDmtER1n2oBSNxRcRWiqjJUt4QEp/MIT3eeinkjETxaW2PPlaHyXG4lkbBdV3Mzc0d9WFIJAeCFJ8kEklq+t1O0zbZK4qCIBg9xqnZbGJ5eRmLi4v4+OOPp0aUOyxOgvjUccC9++67mJ+fH/vnn4RzfBQkCSbVahU3btzAhQsXxhKveJxJOpc/2HQR9BSiFjIaXjRDcAC2prR7Dh6QiJIxVLRGGC6jfHzRULDhii926JyCc0ULSFgVTikROqhmir5Tkuml0oqwso+iESEAJSRR7pyztcSV3b2EjGMpr+NJRfx3NpohVEoQ9dwzzi3Yr7megHYhXLT/mq0ni08cgONFmM+ktxOqqor5+fnuc0IQBHAcBy9evMDq6ip0Xe+KUblc7kCfL6Xz6ej53e+to97cbW0ydAWcoCs86YaCYATXRaFgYnPLTf17uvZqnjl3JoetIXbaSFAsqntic55o/OdL2ffpWHLcBIP/5XdudP9/yNFuunlAr1aRF8FasIUctq4fo5jTUakn1yoMQTeiaFNDkf1tOS20vAiWoKB1EEjnk0Qy+UjxSSKRpIIxhjAMu01Cp/FFVVGUkXo+cc7x7NkzrK2t4fLlyygUCodwdNPHNAsjHQdcrVbDlStXYBhHszJTxu4dDnudV8451tbWsL6+LmP2BBkmPj2tB9jqibJasnX4cdwtcjcjhpyhou5FuwSqUZizFPggAAcKGn2tqfow6hGHTkmqY0h7tNWA4UxOg2IkF7ZFxbCQAz7jMGTPjBPNv1rdgqJQsJgl9ogYRMbUhK7neIS5SElZoAxjjlN5A092ekVdmLUQDNlHVhPrv6YJxIABQLk1mvjUj67rWFxcxOLiIgDA87xuv6h6vQ7LsrpilG3b+3oml+LT0eKHDH92fWPXz6hCQBQCAiDeuaGrCkWQOrQVaLghFIV09yPKow0X+YyKubw5VHgCgLpgHFitFUFTKcIEIddxQxCSHNf5oireE04yORwn8enPv/8cT/vi5DRTRZhi8dBQCKDkdMSCz5wzRVNIfBJ993MFBeFY4OGAc+DJiwbePV8U2udB0KlLTRuy55NkmpDik0QiEYJzjjiOEYbtFXnTKjwBo4khYRhiZWWlG611lE2jjxsH2WNrknBdF8vLy5ifn8eVK1eOdLzI2L3DYdB57dwLDMOQMXsp2Et8ihnH9Z7V2osZDRHjKHu7i2/1kCFvqqh4kfDq634oAaiqdMt6upq+v0ZBp9j00kXvaVS874yuEMzmTHgCX9FneK1fx140Ig5Dn845XZLMk4qHNScAJQRUbd+zVLpTaCIEfoIDQaGdKy35Gtp2I6H4ul623BAKJUKFr93HBBgqhZU1hpbrNUXs2hfVzcpD+j7tB9M0cerUKZw6dQqcc7RarW4vkmaziWw22xWjTNNM9dwhF6gcLb/xl0/g9TiaKCXQNAWM8139n8KIgSokdQRfHHMUSxa2U7qfYsZxbj4LWBpqCRNVrRVBVQiihGPjAGZtFS+rw4vnUcxRtDQ47vCJ+IV0Ph1LjpP49H/8s5uv/YwxDsNW4Tf3f783S5ao+QgAhF1FDcG5SDTKr94US4YZt/g0rXWpVqslFzBKpgZZHZVIJIlwzhGGIeI4ntrJvZe0zqdyuYxbt27h4sWLWFpaOsQjm06mURh58eIF7t+/j8uXL6NYHN/D916I9CaSpKf/vFarVaysrMh7wQjsdY2uVjw0d4rUs5aKahBjrzpxLWQoWSrKbjhSBN5CRmu7nnZojSBipV2PzgEUTRWbgqtnz+YN6AoVigjjAAwKeAK312bEMasLHYJkCvnDu85rP2sPu7YL0NAVmJoCBiCOGYIw3lVgthJ6PfXCAcznNDx2xAvGQcyxlNPxLEWReaMZQCHAO0tZhAf03OoJ9oFzBFeR7wdCCDKZDDKZDM6cOQPOOZrNJhzHwd27d+F5HnK5XFeMEnFeT/vz/aTiNEN8985W99+EAJqhgMUcVCHgfUsIVJUiGCWhYcRoWt1QsSWwQoIDKGU0bAo4MmxdBZC8XT6TLD69lM6nY8lxEZ9+9xsPUdlj7olijsysCXd79GuQUAKaUcE4UMxqqDRSrnoawpbg2Gi4IXK29lrsZz+bjifkRlx7Xhc9xANhWt9xZeyeZJqQ4pNEIhkKYwxBEHTjOE7Ci6mo84kxhnv37qFSqeDzn/88LMsaw9FNH9N0TcVxjFu3biGKIly7dg2atv/YnYNgms7xJNERTDjnePz4MV68eIGPPvpIRiSMwKBr1IsYbpfbjY0LuoJWzJHXFWwMKURVA4bZjI5tN0glQBkKQdAXOxcwIKdR1FNE7zVSOpkAgAi4RQBgzlKh6FqqFgM6pfAE5rPGAfXLkkwWIgWZT9cb2E4oOHEOtIJeVwaFpREYKkXMOGKWLrItzfjooKvpipRBzPHuoo1AoYkjTDQqsxkyUILEWMLyGMSnfgghyGazyGazeOONN8AYQ6PRQLlcxs2bNxGGIQqFQleM6n8+kbF7R8ev/vnjbhweIYBuqmAxh2UoaA3o7zRqnbXhhjg7l8HTFO4nQ6OIDBXEDYVctLapYFOg7qwIug0NLXnceyFDpRmiaE/GM7dEjOMQlRbHDL/9+3eHbuM3QxTmLFS3WiN9hjFjtq33ACyNoSLwO65gP7SWH6OUN+DUkhduzM9YieKTH8SYKRjYTujB+OT56P0jJa+QsXuSaUKKTxKJZCCcc0RRhCiKQAg5FiuTDgoR51MnUm1ubg5Xr16d+IdnyeFTr9exvLyMN954A2fPnpXXxAmgI1R/+umn3Zi9k3SvPGyWt11EHLBUCkYIKOdCjoJKEGPW1rHVDIR718xaGloDStSmSlBPsQiVA8hrCrZTNISvRQwKGd6jSSVAKdt2LiS03diF6G2oGXFZfJ5Shv1NGWP484cipa7XiWMON46RtVQgStcnatsNYaoUXsroPRHhpwMBMCPQpwYAGiETiqhkHMjqFDV/+D69iMMNGTIChfPDglKKfD6PfD6P8+fPgzGGarXa7RkVxzGKxSJKpRKKxeLUrhyfdB5vu7j1+NUYNEy1K0QNi6/TdAWhYAG6l1zKXmTvny/CiTiKGRWOgENXtC9aKDiQRS/LF1VPik/HkEl/Zv7Vf3kHnsBzp+uG0G0NQYJ40w9VKUhPhF4xb+O5U0v8PdGYPACYLYiJTxlLbPzMFMxE8WltzOLTtD67uq6LbDZ71IchkRwIUnySSCSvwRhDGIbdFUnTOqHvRZLzaX19HQ8fPpyYSDXJ0cI5x5MnT/D06VN89rOfRS6XO+pDkoyJSqWCRqOBt956S8bsHTCOF+FRLYBGCSyNohlx5FWCRiBWiar4MeZtHRuNILGgnNMoXAzuVuON0j8q5ZTJOFAyVWwNyeZ/I2+A7BRpGNpxegn1bwCAQDpfd58tBmRki7ITxZ8+qKI1ig1pB9tQwEGgawq8FIVwxoFTWQ1rCQWsXvyIYylvYF2w6HZxPtNuXCUgPnVEpbrAtpamJIpPQPseltEmJ8uSUtp1PQFtp3alUtnVM+rhw4eYmZlBoVCQ/QrHxP/5zcddgcXMqIh3btqaRhEOGZujvpqtbbnIGApcgQUShYyGOqEAB3KmJiQ+ifZcbAou0BC9P72oerh0Wj5/Sw4OP4jw+3/+WGhbxjioSqDoCuIUc6ExY4L0uO6JoHjb9CKhmDwAME3BezkVdCMaySXkF1suwjCGph3+PDLNCydarZZ0PkmmBik+SSSSLpxzxHGMMGw/yJxE4Qlov6APcj6FYYibN2+CEIJPPvkEqipvoSedMAyxsrICXdfxySefyGLNCYFzjkePHuHly5fIZDJSeDoEPt10QdDuh1QLGSyFDBVnBuH4MRazOl4mCFC2qezq9dSLz4CsRtAIxV9uGyGHSsSFH6DdXH4vioYC1di9IlUnBL5ACJLPAAoIRfU1I46MYBSS5PjjBjE+Xd/f6mRKCRhGW3U8iq5rCEbvmSqFZulopRiEliYmPimC37XcinAmNzniUz+KomB2dhazs7MAgO9+97vI5/PY2trC/fv3oaoqisUiZmZmkMvlJt6hcBz527UanrxsAgAKeR2u9+rdgycMEM7bvZ+iFO5BAAhjjrcXs7i5Vk3c9q1zBZR3DkkTdPE1BQvvFTeEQgnihO9ZSej31OFlCieIRCLCP/naDUQpFmewiEG1VLCIJY5fAFB0BTB2vzcOitncC0tnqDeTtxOdBUUXkIiIPYxxPN1o4sKZvOCnj85x6R02Cs1mUzqfJFODrJxKJBIA7QeJMAwRx/GJFZ06KIrymvPJcRzcvHkTFy5cwOnTp4/oyCSTROeauHjxohQfThBBEGBlZQWWZeHatWv467/+66M+pKnjST3AlhdhydbhdF6GeX/LdTHKfoylrI7njcGNzecsFT6Gv7RaKkUjTOHqQLtHVZrovcYevWQIgIWc8doy9zRTtKkQuAI9nRoRx7whvl/J8ebrd8qJhd9h2KYKtiPatns+petFs+WG0FWCIIVAVHYjoc+5uGAjIgR+zKErBIHA9a8KrvqOBb/kUfR92g+EEMzPz2NhYQFAe65zHAfPnz/HnTt3YBhG1zmVzWZP9HvCQfGbf9F2VVj2buFJ1ahQ0ds0FDRSik8AUGklCzpLJRPliHcnG9GxXRWMx2QcKNkatuqD5+YOXsiQMxXUveHz6YuqJ3aAEokATs3DX3xvPfXvRX4Es2igVU6+HvWS+dp9dEsgHq/D/GweG46TuJ2bMHY6lAUF3KbA/QNo930ah/gUx/HULv70PE/2FJdMDVJ8kkgkYIwhCIJuv4eT/kLZ2/OJc4779+9je3sbH330kbQ+S8A5x8OHD7G5uSmviRNGR3B8++23sbi4eNSHM5XEjOP6loslW+sKT3mNYFMg7mcvtv0YpwYIUJS08/aTXsuDEQr0aafRiAMlQ8F2X5HgjbwODHipFqild9GISDcboBEzANP5Ai/ZzctGgIfbozVHB9rXN6FkV0HaSBm9F3NgKavjSYrovVbEsJjV8WJIwXrO1hCpr67jrEZRTujj2TkeEVzBlfBOSqfmJND7/K/rOhYXF7tzXavVQqVSwZMnT1Cv15HJZLpiVCaTOfHvDmn5s5ubKFd9WLaGqG9xg6ibKRhBeAKArVqANxcyeLzh7rnNqaVcW3zaQbQ/W8yBYkZDWSAOLGuoieITABQzOure8PvVixT3EYkkia/+5rKQe2kQgRvCmjGHClCqqQL66wufml6EXEZFXeSZV9Dts+nsPc57qTQCZEwVbsLCiS1HTOh9Mqa+T9PsfGKMTa2wJjl5SPFJIjnBcM4RRRGiKAIhZGon7rQQQsA5R6vVwvLyMkqlEq5evSrPzyFzHJrd+76P69evI5/Py2viBNGJ2dvY2JCC4yGzWvFg6xTOTvwV4RxNwT5PwxgkQC3aOjyBBk0tBtgqQTOFQ6MecSgknUikKhTokcKyGoVhDo7t8nnbFSWye9FDaMUA4xx0wu/Dkv3z9dvbIzkJO9im9poTggg6h3oZ5RgsfXgh5tRsBkGviCIYJSlaXG9FHJpCECYMbseLjsVzTYekKCXLsmBZFk6dOgXOOVzXheM4ePDgQbcpekeMkiu1hxMzht/7m2cwbe01h5Oo6wnYid5LsX0v1pC+LW8tZXcJTwBQTSGm5ixVSHzSVLGxYSaMeQCoexFcP0JGoB+NRDKMpxsNfHprc1/7CNwQRl6HXxssrmrF111PHeYKJupusnDTCsTGpOsz2CZF00u+T8zPWnj8rD50m2YrQiGro7pHokCHtefD93NQTKvzaZp7WUlOJnJ2lkhOKIwxhGEIxph0O/VBCEEYhvj+97+PDz74oNucWXJ4UEonfnXP5uYmVldX8d5772Fubu6oD2ckjlMhbFIIggDLy8uwbVsKjodMTBW8aIZo9hR187qCDcGeD0ls+zFO5XQ8rwcwFIIgxVDIaBTNKEX0HgcKOkXZFy8KNiK2S1A6lTeGjleTtoWxJETbVXEAzZgjJ1gQlBxPbm262GyMPqZUhYANmEvieITovWYoJOT0UhlSBD8/ayEgu+/RXEBgBgA34tAoQSiw2j2vK9hOKMbHHKj5MQrm9L1uE0Jg2zZs28bZs2fBOUej0YDjOFhdXYXv+8jlcl0xyjBknmcvv/u9F2AEiPtEI0KRWkga9ZlubauFnKWi3ncdEwLYJQu1PvEpiDlypoq6QJzf5wVIAAAgAElEQVSkpog9J4kOe9Hv+KLq462F6RtvkvHyy792fbSVEX3EEYNiKoj7HO2arYEMcD11sATnjPIewtYgFudsPHiaLAZlbS1xGwCYK5mJ4tOTF9L5tF9kjU4yTcjZWSI5YXDOEccxwrBdeJjWyXpUoijCrVu3EIYhvvCFL0DTxB7CJPtjksUnxhju3r2Ler2OK1euHNsiSsfRJx9ixenE7L3zzjvdPhiSw6NhzQCco1N6UwlQFsyWF2XbazugKCVwUxQXRAWcXpSUYy1k7ei9sh/jdFYDUYc/pqsijTXQPnaVtKP9kmhGHDn5djC1cM7xJ3eTe0QMwzLUPS8721DRSNHrKObAYlbHU8FeEwDQDNvRey/7Cl8aJchkjddiNNMIW1mdwhHoj6ELFtfLXnRsxKf9PBsQQpDL5ZDL5XDu3DkwxlCv17tzaBRFyOfzXTHqJD9be2GM79zdRjzghqwoFBFL72LKZjQ0Ui7SiBjHhUUbN9aqu37+/rnCa8JTh4KlCYlPTFCBdgVjOn1BV+KLqoe3FmyhbSVHS39v5Ulh5f42HjyuHMi+OONQNAoeMrCeeUgrDH+PFL0X19wQWUtFQ8CVKCpoeZ5YpJ7I/r7wd8bTD3lanU8SybRxPJ6GJRLJgcA5RxAE0u20B9VqFTdu3MCbb76JWq12ol+Ox01HfJo0XNfF9evXsbi4iI8//vhYjxlKqbTwC9Lb1+vzn/+8jBAaAxUvQmwX0bkNUAIUdAWhQrF5wAJUwDg0xAARfwx2Yw5LIWilKGQ3Ig4KIM2dTVMpzIjBtg5W5DYVgoaA+tRIkxMoOXZ8+3FNuOA7CF2jQ/XOUSIbR5lWM4YC9C2qfnvRRjzA5dTscxQOw1B3R1/uhegxl1sRLhTFtp0mKKUoFAooFAo4f/484jhGrVaD4zh48uQJOOcoFosolUooFApQE4T2aeJrf/0U7oBisW4qI7stdFMFRnAIbzWDXWNDVymYqe+5qMHQxETXlqB7q+KGQm7JmuAzgOz7dHzgnE/kAtj/6XdugqgUfMR+av3EIYOW0+FXfYADek4H1OHfWzRODwDmZiw0EmLygBSLC+jB3Is/uFjCz/577x7IvpKYVudTHMdT+b0kJ5eT86QnkZxwOm6njvPhOBfRD5pOoXljYwOf+9znYNs2Hj9+fNSHdaKYRPHp+fPnePDgAS5fvoxi8fhXjwghE+sumyQ6MXvZbFbG7I2RG44Hpr7qbzRvKnjZapeS5ywVWyn6TSShEQZEAaClewzWeIQWxMdPxIGCQeGkiN5rRgxn8oZQ/5wULaigCJbf0/S1khwvgojhb57U9rUPXVOGFor9FEJPh203gkoJohTN3at97qSCqYLpg8dzzHdiMwUK4qJtqwJBkbYs4KI6CSiK0nU9Ae2UgWq1Csdx8PDhQxBCuv89n89P7XNKuRngXw9wHmo6BfjoPT4azQCariBMKSw7jRAXlmw8eNEEALx/vojKsHEoOD4qrVDoPhDGHAVLRcUdPr/XvRimShP7sr2sirk2JEfPJAoGf/q9day/dAGQkcbTXkReBLNownM8KPnBfTx72a6Ji6i2JfYc2/LFnqFrrth3bnp7C8K2peK//Y8/gjJCH8hRmFbnk+u6ssewZKqQ4pNEMuVwzhFFEaIoAiFk4h70jhrP87C8vIx8Po9r167tOj8yomx8TJL4FMcxbt26hSiKcO3atalxwE3SOZ5UyuUybt26JWP2xsyzZoBt/9UL75xJ0djJueNoCyIzpnIghVwz9sG1HEzTgJ+ysBCPMB2oKeeQnK4gY2oQ0avSxOmJjnyPteOY1DEVDSTj4w9Wy4j24WyzDDXRocABaJqCIBQfWyHjWMxqeJaif0UjiDGf1bq9q87NZxAMqYxbqpj4JNpuR2RfAOAcoGg+TaiqitnZWczOzgIAwjBEpVLB5uYm7t27B1VVu2JULpebmneXX//2E8R94k7W1hDGDPHO2LRMFa0U0ZVA2zmUzxnY3nZTH5OmtYu2OUtFg9KhipEvuDghjDkKmWRRCQDylia0XSmn47kzXFx6kSK+U3K0TJr4xDnHr/3L1e6/D8j41CVshZg/l0dTYNt6KxrYj20QRPAcigpa5ZoPw1Dg+8Pn8I3tvb/Jf/kPPsTi7PhEk0m7lg4K13Vl8oZkqpDik0QyxTDGEIahjNnbg5cvX+LevXu4dOlS9wW4g+yPM14mRRip1+tYXl7GuXPncObMman6+8vYvb3hnOPBgwfY2tqSMXtjJmYcN8uvCkpzpgqAoxm9evFlAFo7/ZCchBfioXCGbDaDGIAXc9gKRTMWv++EVIMBDj9FAb8Zc2EnCCVAMaNDI4BoCc2gQCRwSoIUt9dGzFGU4tNU0Lnnl90QDyoeDI1CpQStgAn3ZQHahgeFEqHreJTVzopgD6VecoaKzUaIs0UDAR2+6llUTBUVlULGkVEp3IQKZT2IpZgrgKZpmJ+fx/z8PADA9304joP19XXU63UYhtEVo7LZ7LF8Nnuw2cSdp7udh7qpwAviXTGOqjLad/P8KLXrEAAeb7oo2hounC2gnHD511KIYjlBUUkXjPKz9WRnQ7kZIIgY9IRYM8nRM2mCwW/80X04feKlYWnwDyr2mQBqVgMaYvubLRhC4pMnuNCj1hTrD8U5sDBj4cnzxtDtXI8jm1HR6Bvj1y7ncfmCjiiKxhanOs3OJ9uWPewk04MUnySSKYRz3o3Zk26n14njGLdv30YQBLh69Sp0/XULfEcMkeduPBy1+MQ5x5MnT/Ds2TN8+OGHyGazR3Ysh0Undk+yG9/3sby8jFwuN3LMnhSqR+deze/2UVJDF65qD4y0YrwtyBQNBZURBaiFjIawp0id0dKJTwCQ0yn8VhpXB1DQKSoC6s9pWwOhdKcQKVZCVIhYuTFGW6gScVQ1I47idBg+JWjf+//wntPtpxbEHIpCoIDAUCmoQsAYEIQxgj3ElIylChe1+50dImy7IRTSjsgTpebH7d5wBQtJJTrRQwoZh6VStASWvWf1ZPGJA3C8CPMZOaDSYBgGlpaWsLTUbljfarXgOA7W1tbQaDSQyWS6YlQmkzkW8+9vfPvJLuegbihgO2Mx7rnwWztiVNq1QkEQo1Q0UE7Z94hx4MKpLBzGkZSr1woZbF1BU8A1bKhifxPR7ynybMY5sFHzcXZGLiCadCbpHdsPIvzzbzx67edByJDNG2ikiMHbC7NgIo3ZPmOJzRnlurhjeLZkotEaLioBQM5OjgYEgLmShYb7qt/U6YUM/vHPXEKlUsGjR48AYCy9/SbpWjpIZOyeZNqQ4pNEMmVwzhEEgXQ77UGtVsPKygreeOMNnD17ds/zoygK4jg+UU2Qj5LO+T4KwjDEysoKdF3HtWvXpnL1FHD0At8k0onZe/fdd7srrtMiXZKj04oY7u2sNM2qBB4xYSoU7h5WnpgDIYCCrqCaMjLPUAjivpfTcIQi+Qi/gqDlAoo5dJuMSmAY7WJDmgJ8msPRKYEv8AUa+4hmk0wem7GJ583BBSo/YuhVbkxdgbrjQopihiCMR7q36Smj94KYYzGnYz1F9F7Nj3H5dHZo3F4HL4XInNXFxCdV0K1Vbknxab9YlgXLsnD69GlwzuG6LhzHwYMHD+C6LrLZbFeMmkTn8vceVfB8u9X9t6pTMMahKQThgPutolFEaeyqHUYswOYKJso1MUdGIaMKiU+is4gn6jYUHMMvKp4Un44BkyQY/JPfuYlgj0VNTTeCVTDR2kc/MUIJ1IyWKvZWdN6tNALhqM6s4DykCDoH7Z79qSrFf/effB5nThcBtGPLoyhCpVLZ1duvWCx2/3dQ79vTWq+R4pNk2pi+USqRnGA6vZ06hVBZDH0F5xyPHz/G8+fPhZwtRymGnESOShhxHAc3b97ExYsXuytsp5WOSCJ5FbO3vb2Njz/+GKY5XBgYhjyvo3PT8RDztjDECQEJA5T58JfeiAOEAjlNQT1FcXvW0tDq+zO5MYelkK7zSoRGzKHTdDF2MKydBgJ7z8mLWb07ZwccoBDr05TmOKigS6op2NdDMvlwzrEaijt5Y8YRs1fjSlUobEtFkPKaGCV6L23cmKVRFLIGNr3kQeBGHCoV6+Mhehyit/2y7Pt0oBBCYNs2bNvG2bNnwTlHo9GA4zhYXV2F7/vI5XIolUqYmZkZmG4wTjjn+J2/ftb9t9oTM6eqFOGgd40Rb8H1RgBdVxCkWJxxds5CJcU8YmkqRIJhPcGmORXBWLO6YM9H2ffpeDAp4tNmxcO3v/9i6DaeGyJXMFAf8doyi2ZXGKYKARN45vRC8XljvmRiLSEmDxAXtESFXtqzv//w772Hd94s7vrvqqpibm4Oc3Nz7f3u9PYrl8t48OABKKUolUooFosoFAoji1GMsalcOCpj9yTThhSfJJIpgHPeFZ5kzN7rdGK1stksPvnkE6HzI10i42Xc5/sk9viR13Qb3/dx/fp1FAoFXLlyZd/3S9lLazS2vQjPmiFUApgqRSOMhQtuIWu7eLIaRUNg1XReV+Cywe40W6VopVxokNMVbAsWwgAg5AR5XUFtD6XIRghF3f2CqVNAoKaOGACNQzAleUWrqMYWcsBnHIbsU3Ps+aunTfjx6H9HVaUCvqLXGSV6r+xGoETcXXhhLrNTcBKb12yVoiqg1orezkXdVE6KPjmS9BBCkMvlkMvlcO7cOTDGUK/X4TgObty4gSiKUCgUukVOTRuvC+2PVjZRa7QdfapKu5F6ikLQGhIhq2oUkaArqJdsTke5x2WVxMxcBtWQIaMrcAVEKyL4yFQVFF29kCFrKmgkzKnVVghVIYnukZf7cKhIxsekiE+//JvXwZMmHQ74AYOiK4hTuu6pSkHNV/ccRaFgAs+caeL0RB1NvuCCrYrgZ3tBe4x/9P4cvvRvvZW4fX9vv44YtbW1hfv370NRlF0xfaLXx7T2fGo2m9L5JJkqpPgkkRxzGGMIgkC6nfZgc3MTq6urqWO1pPNpvIxTGPE8D8vLyygUCiP3+DmOSPEJ2N7exu3bt/Hee+91V+LtF9lLKz2cc6yUPRAAeUNBLWQo6gq2mbgDLWAcOqWwNaCZUKDL6Ar8PWoLo5h8+AjTrKkQ1Ab8nAJYKA5Y2RhHABF7TKecCZXffQbhhvTNiMPQ5fPEccYNY3z6wt3XPgxNAQhgaAR+ykK4rlEEKX7HjzkWszqeCxS+SpaKWFMRphD+TVUREp8G9ZwbRCNgQmJZWYpPY4VSikKhgEKhgPPnzyOOY1Sr1W7PKM55t8B5kNFPg4gihj/4tO2q0DUKTkm30K1QOvQ9Y9TXOc+LhHtGvX06i+rOIRQsMfFJdHx4IUPWUNAQ6NFYzGiJ4hPnwIytYyOh/450Ph0POu0BjpLbjyq4fc8R2jYMGVRDAYtYsljVg1k0QXoW8oh+52ozRMZQ4AqMHyro1i0L9q7aqvpQVYoowb1Yrvoo5HT8N//o74z0t+wXo4IgQKVSwcbGBu7duwdVVbv36nw+v+f7+qQImQeNdD5Jpg0pPkkkxxTpdhpOHMdYXV2F67q4cuUKDMNI9fuyUD9exnW+O2LkpUuXMDs7e+ifN0mc5Hg4zjnu37+Pcrm875i9fk7yeR2VJ40Q1SDGvKXACRhUgj1dQcMIGIepUFgce/ZombfUPYUnoB29Z9C2MCNKM+LQSNshJEprj/rB6awGRX191WoURYAmKD4JWsY4AJMStET6PkUcM0ebViXZJ/9q1RnJgdQhZ6rdpMhRYvQUKhoe+QpdsNfEmdkMQkLgplCPRWtjzVBMVOJouyCrCcVBL+JwQ4aMJp/TjwJFUTAzM4OZmRkAr/qQlMtlPHz4EJTSkVbbi/C1f70O349BFQLSE7elajSxJxrngKZShILxdR3CkCFfMFGtDHcAUQIYebPbc8rSxUS4WgrXbyGjColPpib22VlTTRSfNms+GOOg0rk70XDOj7x28T/805VU20d+DCNrwKuJuesUXQEx+p7jUog0c0UTay+biduJLgxx6gFMncJLeN5mjGNp1sJ6wmc7NR+/+J9fwUzhYN6pdF3HwsICFhbaPaN830elUsGLFy+wuroKTdO6vf1yuVz3+plW51Or1ZLOJ8lUIcUnieQYwjlHEATdVUNHvXJo0qjX61hZWcHp06dx6dKlkc6PdD6Nl8MWnxhjWF1dRbPZHEmMnAZOqqDaidkrFosHErPXjxSf0hExjlsVD3NmW3gC2nFYaWLsevFiDo3wgdFzCoFQA3ZbpfBTiF8cQN6g2BbJxes5zpxGUe8pElgKgWEMjktRTUu8OTUVf+nWRMWnFH2wJJPHet3HY2cfDdIJQCnpSkdsBBGLMZ4qRg9o90dKcuedKRgIdwpNMQcyGoUrUHwLBQ+EAcjqFDUBRdpSaaL4BLSj9zLaZKu5J2UeG9SHxHEcbGxs4O7du3sWONNSa4X4zu0tUEqg68ouF0EseH9VVYIUrV+6iLz1XDpX2HWfF31XagYxDI0KFbxNQUFLNNtTE3B4RIxjsx5gsXDynvOPE0ftVvnmD57juYCw04/vhjALJjyBeEejaL4+rlLcZ7OWWKnWaYpH9C3MZLD2Irk/VD6nJ4pP/8a1M7j22UXhz06LYRhYXFzE4mL7M3zfh+M4WF9fR71eh67rKJVK8H1/KmthzWYThULhqA9DIjkwpPgkkRwjOOeI4xhh2G7OKoWn3XDO8eTJEzx79gyf+cxnkMvlRt7XSS3UHxWHeb6bzSaWl5exuLiI995778SOmZN4TR9GzF4/J/G87ofVigdbpajsFK4yChlZeOoQgiJvW2iFMfyeYtpCRoNI+X2kv94I9xFLJaj39FZfyul73o8iDqhELBaQUbVd0BA4JtGjbka8G+crOX78wapYlNBe2Ka2a1xwQLjg3Ps787aGl40wcdsOXsSwkN37dwiAYsFEbz0+oxK4Ah+RxiVlqYqQ+CTqrii3IpzJTbb4dFLRNO211fa9BU7TNLtilG3bwvfEX//LJ2CMQzfVXcKTqimIBHuvhCMuAmi6IUxThbdH5KOhUkSGukvlFY3TA4BSRhOKtyOCM44v6O4SNYG9rHpSfJpwjlJ84pzjf/vd2yP/vt8MYeUNtIa48FRTBRng6Euj8VNF7Pw49QCGrsAXiM3M2mLlX10dLhwXczr+8d//QGhfB4VhGFhaWsLS0hKAdoy+4zhoNptYWVmBZVnde3U2mz32z6+tVgunT58+6sOQSA4MKT5JJMcEzjnCMEQcx1J0GkAQBFhZWYFpmrh27dq+7deyoDxeKKVdUfUgWV9fx8OHD/GZz3zmxK8eopSemJXNnHPcu3cPlUrlwGP2+pHOJ3GaYYwtL4K7U+TinIOP0kBpAK2Yw9YVcD/eieMjEO384MaAToE0yX/NiEMhbeeFKL0a25ypQFGHP4brguITKAVvuSCGlbipaFQgA9BiQGb6kkymnu8/b6DSGr3PkKoQEHD0S5WjRO+147TSze3DfuetOQsR2V2QUykFkFx0C3fuC57AoBVsoSEca3gc+j7J94o2/QXOVqsFx3Hw+PFjNBoN2LbdLXBaljXwvD1zWri5VoPRJzwRAkRRusUWikoRp4zeAwDb1vYUn957s4Bq36XbCMSvUdGIPj8WO+6q4P2q6Ytt96Lq4UOc7Gf+Secoxaff/pMHaAj0FhxG4MVQTRXRXgJvYYDraQfaE8E5DE9ATALagtZ8ycRTASeXIihoJTmF/9O/fxk5+2gXVJimiVOnTuHly5f44IMPEMdxt7dfo9HoilHFYvFYilGy55Nk2pDik0RyDGCMIQiC7irk4zZ5HjZbW1u4c+cO3nnnne7Kxf0iY/fGy0GLfVEU4datW2CM4ZNPPoGaUOQ9CRBCToSg6nkelpeXUSqVcOXKlUO/X0rxSZy7VR8+511HRVGj2E7TbCmBZsSRM1TU/AgzGQ2tFLvOqhTlFOoTA1DQKcopjr8Vc9gqRStmKGSSX9oVkhRA9gpLV4VcXgEHFIiU6tvnMyNahZdMBBFj+MtH1X3tw+rvUbEDG+E+53jJMXr9VPYq5ikEesZ4zamYZt8ZTYEXJxewRUVlV1AUcPYhBkqOFsuyYFkWTp8+Dc45XNeF4zi4f/8+XNdFLpfrilGdhS6/9u016KaCqM8pqKj0tZ8lkTEU1EcQn9xW1I7O7Csi50wVzQFCcjNgMFQq5EISFaJrgqJr049haRSthHPjuJFQlKeIK0tytByV+OQHEf7Z//tw3/tp9xWjICoF7xszmq0NdD11UBQKJlBjcBriAllbCBLpDyVW26gNifK79tkF/NjVyXHkdHo+6bq+617dWTjQEaMymUxXjErjYj0qXNeVPZ8kU4WsxkkkEwznHFEUIYoiEEKOvDHnpMEYw927d1Gv1w/c3UApleLTGDlI8alWq2FlZQXnzp3DmTNnJv7hclycBDdfR4i+dOkSZmdnx/KZUnwSY6sVwQlidGpLtkIQcMBWCZop4rCSaEQMC7YGN+WlPsoRjNLQ3NYISqYGIjCfH9ZVpYEhRvLnNyKOeZlcdKz4k/uVVPFZ/RhDHA2cA5pKEaYohHsRx7ytYaMp7n5yw/YY7v+dt+YzYAPmcz9FUyldUExtCX7HVsShKyTxnDteJGMspwBCCGzbhm3bOHv2LDjnaDQacBwHd+7cge/7KPMsNuvsNZFJ01WhgnM/rSBGxtLgttI5CKOIYa5kYWPb3fXzi+fycPZwHBctFS8FHCGicYBNP4apUXgCglvJ1tGqDF9CETOOkq1jO6Eo/zJhP5KjhzF2JAsD/9d/cQehoKMoCRYxaIaKIA52PbDpueEPTqLzgNMIYOoUnsDCKEUV22dF0PG16XgDxeuMqeK/+NnPCO1jXAwSMgkhyGQyyGQyOHPmTHfhQKVSwaNHj9BsNmHbNorFIkqlEjKZzMTNz9L5JJk2pPgkkUwojDFsbW0hl8tJt9MAevv4fPzxxwd+fhRFOZQYOMlgDkIY4ZxjbW0N6+vr+PDDD5HNZg/o6KaDaY7dY4zh3r17qFaruHLlCgxjfBXzkyDq7RfGOVZrPlo7BStTITBUirIfQ6cEloLuf9svBG1RyCQQitfq4MaARsRj6YC2OCOyCrsXxgHD0IS2DVLslxPxxSlx4AN6ckRfI2Zo+6Qkx4FKK8KtDTd5wyEYKhkqeqoKQZjSxGPpCpBCfAKATN/v5E0FXB88btwUEZiiQ6oVc2iUJEYPAUBOV7Cd4GyKOVDzYxTMyXz1ntZng8OGEIJcLodcLodz586BMYb//p/fQdjnLtB0BZQSUEVFnFJEAgDLUlOLT212/13ncjoqQ/7UlqEA9eS9NlIU70sZDc8FnEiiUX55S00Wn4b04pFMBkfhfHpedvHN778ENVWwA4pCDf0YRs6EX2sLnkZOB0nol5SmZ+h8ycSTl8nzeiC4iKtc86FrFEGCIBzFHAszJl5stXb9/D/699/DXCn5+XHcJNWBehcOdMSoZrOJSqWCBw8ewHVdZLPZrhi1V6TqOGm1WlJ8kkwV0kYhkUwYHbeT7/tYWVkBIHPYe+Gc4+nTp/jbv/1bvP/++7hw4cKhnB8Zuzde9us0C4IAn376KZrNJq5duyaFpwFMa+ye53n43ve+B0rp2IUnQDqfRHjcCFDZKVapBMhpbeEJAALGQSmBdUDxbnOWipC3Y/TSwNE+rjR0ovfSUMpoO3F6YvvXRHvPEEX4od7KiL3MtuLRotYkR8PXV8upmpn3kzFVcAy/4HgapXWHqp9+bq/09XZ5Y9YGHzJu7CERR734KQTpnODY1gV7aExy3yc5hx0M37rrYLu2WxixTAWKSsEYB2M8Td25S8MNoIwwRzr1AIuFV89EZ0/nho5x0Ti9uhdDE9zWMsTGpqgOoSUV9gF4IYMzJDZMcvQwxsZe3/jq1252nTyWLbYISITADWHkDBACaFmBd5AU99usJXacVcHrnXFgfkYsKaaQ3/1dLl8s4d/90TeFfnfSIYQgm83i7Nmz+OxnP4tr1651FxDcu3cP3/3ud3Hjxg2sr6+j1Wol7/AQkM4nybQxmcuvJJITCuccYRgijmMQQqAoinwh7CEMQ6ysrEDTNFy7du1Q7frSzTBe9uPKcRwHN2/exNtvv43FxcUDPrLpgVI6dW6+zc1NrK6u4v3338fMzMyRHIMUn4bjxwx3d1Y9EwAFXUG9b8WlzzhMhUAJQ8R09Pu6RgkYOACCeEx/E9FiHQAUdQpN00A5gyc4vegUEIroJwQG4UJ9rkQdVRzAsy0HS8UcNO3gCjWSg+duuYWqH8PQFRDOhWKueqGECBV/OdJH77khw7ytYTOF+6kZMMzZGraaIZZyOqKEgrOpEtQEam/NkIECr/WNGoShUoh0RxO9A5RbES4UBTc+AuRCt/0RxQx/8OmLXT9TNQUMBKwjevK2yNtM2QMsjjnyeQOOkz5OrpQz8LLq4+ysBSfhwo8ExWUOoGAp2Gomfw/ROVI0LlR0QcSLqo+SndxbUXI0cM7H6nz6q5UN3F171Q/RjzmELbMCBK0IuXkbTGAxAudt85PIpUwFFzds13zhuTlv63iGZDeVrr96HtdUiv/qH344tfNEr4v1jTfe2BWpurq6Ct/3kc1mX+vvd5h0ogElkmlBik8SyYTAGEMQBN1M+I741GmieNIpl8u4desWLl68iKWlpUP/POl8Gi+KoqQW+zjnuH//Pra3t/H5z38eljV5MQCTxDTF7vX2ezsKt1Mv0+ooOyhuV3x00kBmTRWc84FxeF7ModL26ueUdfMus6YCf2fXbsxhUJKqH0wzbjuz0rSgasZcqJhNAMzuFMJU0eo32qKAaFiYRglaAt834uLfs+qH2Lh+HQC6cSSFQkE+l0wQMWP41qNaN/6Rg0DXFKgUUCkFJ+0m48NidjKWKiyiqJQg7TKG/hg9EbK6gq1miNmihaQSNxUsiHEAtk5RF2r4jHkAACAASURBVOihIXo+AsF7TNmb3GdK2Y9q//zO956j2eNuU1UKRSEgdPfNNslduBdsxCL5M6cFXaWYmbdRTdhHmjg9S6dAM3m7IBab7OqCzsCWwNgFgJdVD++fzgltKxk/44zd45zjf/6926/9XDNUhK3wQBpsEkpAbR1M8AG2aOtwEuIjASAQWn3UdjSdKpl4tpksKqmCyQC9i7j+g594G2eXTk6qyKBI1Y4Ydfv2bQRBgFwu1xWjDuM9VMbuSaYNKT5JJEdMJ2YviiIQQnY9iEkBpP1wev/+fTiOM1aBQTqfxkva2D3P87C8vIxisYirV6+OPTf8ODItIonnebh+/TpmZ2cPpd9bWqZJ1DtoHD/CM7ddcJ43VfgxR33Ii3SsaDApwDlDtEdD9L3IaRQe211AzWoUforIr070niNY3ALai2bzBkXFH/47C5YKuiPYEIgLSmlSztJchaZC0BBQn8zSLD5zdhFhGKJSqWBrawv379+HqqoolUqYmZnp9qaUHA3feFyDO6DgFTEg6t7zCSxDhapQABxhzBAEMRhvr2juLsUWgI9QqauNEL1X82Ocn7EQCczvaQRjSxUTn0RFpYZgcdBJ6XYZJ3IO2x+OG+Jv7m53/60oBFSh0BQKv29shjEHVUhqManZCmHbGpopRVwvZPjchQI2BT6v7sfQFSLkQhKNj60Liq61VgRNIQgTPrviin3/FxXZ92mSGaf49Kt/eA+NAeMmZhyZvIFWzd9XZC0AFOczbSeV4PCcLRhC4lMlRXxkLqsDAuJTIOhcru+MtfNncviZf/ui8HGMk3HNXZRS5PN55PN5vPnmm2CMoV6vd9NXwjBEPp9HqVRCsVg8EDHKdV1kMpkDOHqJZDKQ4pNEcoQwxhCGYTf3uL94c9LFJ9d1sby8jLm5OVy9enWsxa399iCSpCON2LexsYG7d+/i0qVLmJ2dPeQjmx6mQVCdhJi9fmTs3mA457ixExE0ayioBDFMhSSWrT0G0DiEqujpCsoaRdi3/Sj9ikaZZZJ6X6gEyPXk9qf5XgGHsFTV//2HoQjutblzsJqmYX5+HvPz8wAA3/fhOA6ePn2KRqMBy7K6K0AzmYwUo8bEdivEnU2xfgQx44jZq+caVVWgKQSaRhGnKIRzDmgKRSjoaADacXdzGRVbrrgA04oY3sgb8AQOzUtxLKIuqaZggS5igK1RNBNWvNeDGBHjUFNEdY4TOWZH59f/6ml3DFFKoGjq0P5oqqogiNOLkbqupBafKAHsgonNqlgRu5jRsFFP3lZ0Hqt7ETSVIEz4BQ5g1tbwMiE/048Y8paKWoKY+6KaPqJQMj7GJT5VGwF+/9tP9vzvnh8jM2OhuT16Xx+qUnBDSbUCyNLFyrDlWgBdo0Odyx00wd6HVQHRCwA2HQ+UEvzX//DDnYUrk8dRuXYppSgUCigUCjh//jwYY6jVanAcB+vr6wjDEIVCoStG6Xr6CNA4jmXktWSqkOKTRHIEcM4Rx3G3/8og4Qk42eLT+vo6Hj58iMuXL6NYHH9I/igxcJLRERFGGGO4c+cOXNfF1atXR3qQO8kcZ5GkN2Zv0v72x/m8HiZrjRC1kKGgUTQijrymYNsXK7YxzYCtUjSDWKjANW8qA4UXN+bQKRF2MLR/J30bADfmQ6WcU1kNpKfIEoNAJVzou3G0+z4lGKsApIvTE53dPIaBBXPDMLC0tISlpSVwzuG6LhzHwYMHD+C6bjeOZGZm5khjMaedP7hbSeWO60dRKFQl/WIbVSFivch6sI104tNbsxZsQ4Un4JwIGGAopN3HIwHRfnARQ/s+JCBCiYhPHIDjRZjPTF4xScbujc69jSbuPqsBaEdvaboKxjhU7XXXU4dRHxmabghVpYhS9Fx792xeqBdgh4wuVsBuCapPHMBsJllUAtr9sESatxUzWqL45Kc4R5LxMy7x6Ze/tpK4uKLVDDG7aGP7pUCO5AAyM1bXOSzay0l0HuIA5oticXqiEZdbVR+KQhLPSxAyfPkn3sa75ye3WWEcxxORfkIpRbFY7Nas4jjuilFPnz5FHMe7xCgpKklOIlJ8kkjGDOccQRDs6Xbq5SSKT2EY4ubNmyCE4JNPPoGqHs1tahpcIseJpPPdbDaxvLyMpaUlXLp0SRZJRuC4XtOtVgvXr1/H/Pz8RMTs9XNcz+thEsQMqzUPttJ2I6kEqAbpVnk3I4asrqAexEOFII0CbMglkdUIyr54pY8ByKkUlRSNp0IO5HWK6oAoL1slMAascNUpEAlO7xoBRAOEDMH9iohZHRoxR3GIW4MQAtu2Yds2zp49C8456vU6yuUybt68iSiKui/dpVLpyOb1aePTF819RbkplIBSII65cMGswyjRe2n6yWQ0CmrpSLPY2tYofIFnZtGiOQBkdDHxSXRVeLk1ueKTZDR+6ztPAbTHk2qo3Tg9lnDdaLqCMMWYAADGOHJZHU5FzNWjqxSxoaGWot+Yoog9YzUCBkrEomEzhgogWVQSdQUaCQ4PTSG4+NYsGOfCTkfJeBmH+HTzUQUr9xyhbZ2qDyOnwxdw/fWimgoU89X1SBWKWGDOaAr2OAOAnOCcUROMpGSMY3HGwvMEQWu2YOBn/s23hPZ5VDDGJrIHqaIo3WdeoC1GVatVVCoVrK2tgTGGYrGI9fV1XL58GXNzc7t+X87JkmlEvv1JJGOk43bqrDBMKqKetOi3Tm7uhQsXcPr06SM9lpMo/B0lwwr46+vrePToES5fvoxCoTDmI5sejqNI0olY/OCDD7oP8JOGdD69zu2qDwICSgl8xpFRCJoj1MgbEUPeUFDz9xagZk0VKbQlIZQRYrH0PQp2C7YOQl4vsIjG3gGdOCyxbRXBbRnaQpWICNWMOIop6uWEkG42/vnz57sv3Y7jYG1tDQBQLBZRKpVQKBQmsnAw6QQRw3ee1Pe1j6ypoFMj0xQq3AcCaAtVWUNBI0Uvp3rAMGOpKAsIZm/OZcAIAUvR+01XKIDk4/FiDo0ShAJV86RIzQ6iU4CTouA4biZtYcdx4C/ulrFZ8UAIYFpaNxpL1SiihAUMo57vMMU4fWNWhb9znXfm0iREHRmMA1mdoCYwAauCglYkaONMOndXLs2D6ypaEYetyet6EhmH+PQr/9eNVNvHMYdmqghT3KetkrXreqQKgUii5lYtEBZvRRc3iDqaAKCQ1RPFp3/0996DaUx2uTiO42PxDKkoCmZmZrqR8XEco1Kp4Ld/+7fx8z//82CM4eOPP8bf/bt/Fz/+4z+OYrEoVCsEgDt37uDLX/5y998PHjzAL/3SL+Hnfu7nuj/75je/iZ/8yZ/EhQsXAAA//dM/jV/8xV884G8pkQxnsu8mEsmUwDlHFEWIogiEEOGHLVVVj12xeBQ457h//z62t7fx0UcfTURzRSk+jZdBwkgURbh58yY457h27ZpcLb9PKKXHRiRhjGF1dRXNZnPiYvb6keLTbip+jOduiJymwI05sipBOUWBup96yFAwFFS8+LWouKxG4bHhcVFuxKGRdP2Q3IiDQjyaDgBaAyoIMwaFqg2+b6WoqafqEZXmmI0dcTCJRpoMwgH0v3SHYYhKpYKtrS3cv38fqqp2I/pyuZwsggvwh/crwoXaQegaRRi/GjujnPOsoaYSnwAgZyaLTyVLBdsZN16aXlQpjsPWKCoCxy46nlqCgoCI8HYUyNi99ESM4V98/zkAIJPRu8KTopBE4QnY6Z2m0lRiEgB4foRsVkcjoW9L1lTAbLv7bxr7ECn9NFO4sWxNTHwSvVeJ3k+8IZmfl87moRfb75GtiMHWjj6SS/I6nQSYw+L/+fYatp10fb86cZk0JF0H4zAMW4PSF1Mp+pWCiGEmb2CrmuxrF43TixnHQsnEi63k/lWaOnxcXL5Ywo99fEroc4+SccU3HjSKomB2dhZf+cpX8JWvfAWO4+DP/uzP8I1vfANf/epXQQhBEAT4oz/6I/zwD/8wstnsnvt677338OmnnwJoi1pnzpzBT/3UT7223Y/8yI/g61//+qF9J4kkCVnJk0gOGcYYwjAUitnr5yQ4n1qtFpaXl1EqlXD16tWJeYA4ji6R40x/Ab9Wq2FlZQXnzp3DmTNnZFHkACCEHItruhOzt7CwgPfee2/i//ZSfHoF5xw3nBaKhop6yEDB0Uyj+uxBLWQomQrKXryrwGxrFEHC7jmAnEZRHhCJtxfxzu9UU0TvBQzIaxS1nd8hAEqZvUXTNKcl5BAWw1J8TRBB91UzjfolgKZpmJ+fx/z8PADA932Uy2U8ffoU9XodmUymG1eSyWQm/h4wbp7WfDyuiAYxDkZX6S63TsyG9y0bREs0N7KHpL5IAHBqJoN452/uMw6NAiJDMU1vN0MVy7IU6SEFtL+XSK+48oQ6n6T4lJ7f/f9eoOVFyNivhCcg3RjCCC5bANAEBJW3zuRR6VnlUMhlUREoxtf8GColQoKRoojNTKKiUsUNoVCCOOGzq3uIuLNZHYtnX/Wnae1z4YTk8OCcH9o7vx9E+K0/vj/S70Yhg5434AmMFaNojvQZHUpZXUh8qiQIzb0UcoaQ+DTM+UspwX/2pfeFP/MomdTYvbSUSiV86Utfwpe+9CUAwJMnT/DlL38Zf/zHf4yvfOUr0DQNP/qjP4ovfvGL+MIXvrDnQu0//dM/xcWLF/Hmm2+O8/AlEiGk+CSRHBKc827MHoCRHrCm3X3z/PlzPHjwYCIjtY6TS2Qa6BQ9OOdYW1vD+vo6Pvzww6ErfSTpOA6C6suXL3Hv3r2JvCfsxXE4r+PiSTOErrwSbWyVYnsfrqdeqiHDjKWg3GoLUHOmkig8dRmhqCrae6IXQyXATuT+UkYFHfJCzEGgEy78HQwKoabxDO1+UiIilOhfJuRtEcAYsVCahGEYOHXqFE6dOgXOOVzXheM4ePDgAVqtFrLZLGZmZlAqlWAYxqEcw3GBc44/vl/Z1z4ypjowJk5XKfwULgwvZMiaKhopBJWaH6NoKai0Bl99p3I6YnX3uMloFFWBfMhWJC6gtYXXZJqhWF8bDiCnK4luKi/icEOGjHRjHGuqrRB/eWcLGVtH2CM8icTt7YYIR2/10mgGQz8rm9FQHfEVhnOgYKnYbib3j+GC46jaCqFQIMnAwThQzGjYTii2N/0YGV2B2+PSUijBZ96fR2+jOFFHomT8HKZj5X/8v28jSLMSp4+gFaEwn0F1SCydWTBAB7iH0pQOzAH9QAexXfeFXZJaQj+0DvXm3mPs3/nhN3DhdE5oP0dNHMcTs3D5IDEMAwsLC/jqV78KACiXy/jWt76Fr3/96/iFX/gFZDIZ/NiP/Ri++MUv4od+6Idgmm0h9Gtf+xp+9md/duA+v/Od7+Bzn/scTp8+jV/5lV/B5cuXx/Z9JBJAik8SyaHAOUcQBCO5nXpRFKUrXk0TURTh1q1biOMY165dg6ZNXvNlyfhhjOEHP/gBTNPEtWvXpmIl0yQxyYIqYwx37tyB67oTH7PXj3Q+tQlijk0v6gpPeY2kXAKeTDVgmLXaEXw8xbzqRgwqSRdf58bpnSDeTgVRp4BtJs9rOgVEE47UFDmAOiEIBI7cZxD+js2Iw9AP3xlBCIFt27BtG2fPngXnHPV6HeVyGTdv3kQURSgUCl1n1EmLY/2rp3U091FUUygBAQcGFI1VJZ34BAAZXUklPgFAwdQGik8EwEzRQv/edEHRk6HthmwIFP9FXVJtUUlM/DJVCgiI7Y4XIaNN1hwnnU/p+I3vPIVharuEJxAgHkHsKOZNlKvp4sE4B3JZHc4e7oylBRtB3xj300yAUQsiZSKRnoFAR1TSE0UloB3NKbJdydbhBq8cHlcvzQH67nlXik+Ty2Hdc55sNPCd5Zf73k+9HsDMG/BqrzuTCAGM3N73cELERCgm+O7AOTBfNLG+NbxHE9COAxVhs+KBUgLWNxfmbQ3/4Cf+f/beLUaSLD/v+845cc97VlV3V99nZme2e6Y5szPT3QNYBGkCFAnQBLUwIQkSYYF+8j7sk03A0IMBmhBtwQ8GBBMgCUOmBMrcJb0kDcleiqRkkuJtOcvd2enq20zf75eprqyqzIzIuJ3jh6yszqrKzPifrKqurKz4Pe32ZEVGRMY5EfH/zvf9v0TaxiQwreKT7/sb3E31eh1f/epX8dWvfhUAsLi4iD/7sz/Dt771LfzCL/wCyuUyfviHfxi/93u/h1/8xV/csr0PPvgA9+7dQ7FYxLe//W189atfxY0bN17V4eTkAOimeOTk5OwgSZIgDMNtC0/AdDqfVlZW8PHHH6Ner+O9997LhaccAN0VPb7v49ixY3j77bdz4WkXmNTYPd/38fHHH8NxHHzwwQf7SngCcvGpx/1WhMZaUbwour1almOJWWdnx/JyJDFfNLV6G/Wi93RIlf7fhLLbh+pIwQIjvAzrPB8ojSZRVIOSAuAQP9za4eg9KowxlMtlnD59Gu+//z4++OADzM7OYnV1FZ9++im+973v4datW1haWpq656XNNMMEP3jS3tY2XFtgkPAEdHtQ6KIrVgGAP0QcOlV3kAwYNzpP0S5xzA7bh0E4Bm0O48SxNIl9n3Lxic6dRR8PljobhCfGAMsWYGO4Q3XiIvsJh6xcKBYtRAPG0WpIv+6qZZrroRUp8v2mRHwWMAzaBt2+XjtvHS3BrhW2fCbYo/tWDo3dmHP+xe9f13IfjSKKU5jeVhHWrblgYvi9ho/4b/3o9EwsD9iPQTR92jhPUoW52tbYwH/y02+i6O2f2sy0xO5txvd9FApb57Qes7Oz+Nmf/Vn8yq/8Cv72b/8W3/jGNxAEAUqlEu7evbvl8+VyeT1N5qd+6qcQxzEWFxd3a/dzcgZysJYL5uTsIkopJEmCJEnAGNuRVRjTJD4ppXDnzh08f/4c77333sgbas7BQSm1Xjj0PA+HDx/e612aWiYxHq4Xs/fOO++gWq1m/8EEwjlHkkxeMfFVsholuO93XboOZxCcobNWGFuOJMywjdjemTm/aHBIpetJAvgYRQ5zjLpIyeSwLNqLsE7NUadHlE6bC5MzBIQdaU1I7wwhBOr1Our1OgAgjmMsLy9jcXERt27dgmEY6xF9pVJpqgrq3765rB3P1Y9jiZFFOQWQo316dOIUBVugrVFEWwlTVByBlc7LvzE4g1t0BorKOte+IFbCEwV4BodPOFZiHZHUIweY3L5POTS+9f2nG3o8cQaYtoE0lTAMjkjzvS0MU5RLNlaben3coihFqWihucklVK27gz+fKhQtjhbBOUmdAVIFlB0DywRB1SAOJOoc1xN7awUT86cGPz/mzqeDxf/znYe49bAJYXGk23AIr6MAxRm4ySHXxjw3OMzCaHGGC4aUMM2/WA3J7nNjQMTfIL5YGexoGkSlZOHZi5fuwS+dKOPvfnSc9D2TQpqmUyk+BUEA1x08lw/i8OHDePDgAX7pl34JP/IjP7Llvz99+hSHDx8GYwwff/wxpJSYmZnZyV3OyckkF59ycnYAKSXiON4Rt1M/0yI+dTodLCwsoFwu4+LFi1Npj87Rp9Pp4NKlS6jVajh//jy+853v7PUuTTWTFLvXi9kLgmDfR29OqqPsVaGUwrWVCFIBJu/2efpi0wpr5RYxa3EsdrZ3P2Po9n+JFOAKjiCrgUQffiIhmJ4wE2jE0vWoOAZSRrvHxaBvPwXI0YGR0t/vLNqJmkh3hGmamJubw9zcHAAgDEMsLS3h4cOHaDab8DwPtVoN9XodrutO3P5T+fyFvy3HDENX4Mm6JsY5PwXb0BKfAKDqmhvEp9dmXcgh3x1oDFodcc4zaeITtX5N2RYANHLn077lr28v43FfsRYAigUDwVqhWyn9uFYAMIjuus1wsfE3q1Yd8BHbKtkGWlF2pJ2OM1Co3p1sNJJ4VgJiFm2USAjO8O6ZQwAffMw6c0fO/qYVxPit/3AbQDemrlg00Wptv3WBTBUM10ScRlBSwas72Q5H4lQaxhK1koWlZvaYjInXcpIqzNYcPN80Tw3C7lsoxRjw3/zsWbKDd1LYzd5he0m73dZaqO37Pv74j/8Yv/7rv77+b7/2a78GAPja176Gb33rW/jVX/1VGIYB13XxzW9+M7/n57xycvEpJ2cb7IbbqZ9pEJ96zoYzZ87syxUW+Qv57vD8+XPcuHFj314X+5FJEUl838elS5dw5MgRnDlzZt+Pr4Meu3e/HaMZS3B0XUnL8eB71nIkMWsLLGoWqfuZdQzEa/FzjmAY0DZmKBJA2eTr0YAUEgWUDI5VYlG5bnFwQyCVg3vqbIXB5god4i7ZHEgIx6zWPkvZLjWVSKIrxnkTvsDUtm3Mz89jfn4eSin4vo9Go4Fbt24hCAIUi8V1Z5Rt23u9uySiROIv7jfBAJiCweAMiVRgDIgTRSrpeq5B60ExhrVqnLi+Tt/feCaHcKyhx5EqwDMYfMLF2tEoNhvEIhtVVOokCrZgCDP2odFJJu7Z8iDfw6hEicT/++nGXjKOayCVL/u7KAV4noE2MfqqR8uPYFkCEbUJ4BptP4ZlckSxBGNAqbI1Rqsfm+ieWO0k4Iwm5tbKRbzoZMeB+sRja/gxScBrdhJ8+NYMMKK/Yu58Ojj8829eRtD3fOmHKRhnUNuxC6+RRCnsso20E0M42eVTHad9vWyTxKcVny6kVYsWSXxK++5VP3b+KM6c3n8JFGma7usFjMPIit3bjOd5ePHixYZ/+9rXvrb+v7/+9a/j61//+o7tX07OOOTiU07OmCilEEXRjrud+tnP4lOaprh+/TqiKMKFCxf2XR8X4GVReZIKBPudnuPF9/0t10VPHJnGFUyTwCTE7j19+hS3bt3CuXPnUKlU9nRfdoqDLD4FicTttZfmqi2QSIl4xIv+ciwxYwu8GEOAcgTrupbWpuNxElXGid6zBABCHZEBqDgmFBgsphARLwliewsAANdYU29yhg6h6BIpkB1h7UTBE/vnfsgYQ6FQQKFQwPHjx6GUQrPZxNLSEq5evYokSVCpVFCr1VCr1WAYk/la9Ie3lxH1/UC9eDelAEN0Yy5Ng0OwrgASxukGockUHErSn2UMwZFouAqDOEXBEmhrFM4bnRQlS6AZpTg16w11PfVwDA6foLx2UgWTs5HzUA/qEYapgiXYht9gGEVLIMxwNqUKWA1TVAhFzFdJ/qw7mt/53lO0+yITHdcAQ9e90M84jwNKAbWSg+cv9Hu6eQUL0XIHMzPeyD40AMiOjFQBFddAgyCiUcdRo00TleJUoeIZWM747lrRgjdTGLk9qYAolbCo2Zk5+5KPP1vEldvLW/6dCQYotSM9oKJOguKMR5ondWZSx6LdB16shhCCbRCMhmHZxP5QQVfQ8hwDP/8zb5H+ZtI4qD2fcnL2I5P11JuTsw9QSiFNU8Rx94a9W8ITsH/Fp9XVVVy+fBknTpzA8ePH9+0Lbe/852LIztBut3Hp0iXMz88PdLz0xJH8fO8OeymSSClx/fp1hGG472P2NjNJcYavmusrIVIFzNgCqVJYJihCK2MKUBVLIFIv54xQKrjEfkU9/LTr0NLRrajuqsOuAF97ATYYyOJTdx6kfVhnv7WKH5yhTShotBKFuf1hFhoIYwzlchnlchmnT59GmqZYWVlBo9HA/fv3AQDVahX1eh3lcnkiChqfvQjwaHX0quhUKqTRxv5JpsEheG/Oz47b64dz1s151MBzDC3xCQDqBQOGYJBm9uuojkhbtDgahIjPkGr7A1CyBF4Q4vJsoji71EkmSnzKF1qN5vFyB5/caaz/f8c1kCQS5oDfO0okuGCQmpFvQZiMFdkXhgkMg8MtZk/OWte8TROfOsSIvlQBBROgJKGVXXOk+GQbHCdP12AKnul2DBIFYhvGnH1IKiX+xe9eG/rfudFdfKE7HjdTLtswXRPxEHf/xn2ifxf1k6lUOFR18JTiaCJ+/xfLHXAG/KOffAO10v58uJvWOo3v+/A8b693IydnR5mcp96cnH3A5pi93X5R22/ik1IK9+7dw5MnT/Duu++iWCzu9S5ti975n6ZC+V7x6NEj3Lt3D++8885Qx8skOHOmmb0qLLXbbSwsLGB+fh5nz56dugLXpMQZvmqeBjFehCnqVteR4EfJ0L4Lm1mJJeq2wBJRgKrbG4WnHrbByf0hgO4q6LLFSSJZj1gBRYOhNaJoZzLAs1/eJ3SucZ16SLxLnzU4zfrUTiWA6aniCSFQr9dRr9cBAHEcY3l5GV988QVu3rwJwzDWI/pKpdIrn7s6icSf318d62/jRCIG4NoGhGCINHq4jBO9p+OU6hEmCkdnPCSE86qzdUsjTo/q+rOIopIiXiM64/NVcFAXUFD5ze88Xo+gc5w14cnkiIeMK2FwSM33t06UolR2sLra0fq7OJE4dLiQ3YcGQFNj0Ydp0gq6Kx26aFYvO2i9yD4+O+O7v/JmHcoQsAUjiE8SFXt67lvTwE7ON//b738Gf8TCAAXAKVnwl8NtfY9dsrTuQ1QBut9NmUWlYJLEp56jKYsolnjnS3X89I+cJO/DpDHNzqdcfMqZNnLxKSeHiJQSURStrw58FUWI/SQ+hWGIhYUFFItFfPTRR1OxCiUXQ7ZPkiS4evUqAODixYsjY43y8z19PHnyBHfu3BkpOu53DmLsXiwVPl+JUDE5mnGKOPChbL2XpGYsUbMEGhnikcHQze0fcIrHaecgxrh3O2K0+HTYM8HYy3uejqCUgEFAkYwmEt1eTiHhuGONOD3q5TvjAgoKTMtXtX8wTRNzc3OYm5sD0H2uWVpawsOHD9FsNuF5Hmq1Gur1OlzX3fXnwD+8vYxkG6u1TcHAGMZqIK4bvedHKTyLw9cQdm1TQBmCVLXWidmk/i4KXZfUCmVAEaFE852qOih6o3vz7AXTtjBkp/ir2w08bXQLvrZjIEklhGBDhScAY/eZGWesWpZAqexs6HczjE4i4RqcOACauwAAIABJREFU1AuJeghRqlB0DDQJRXTLoBWJW+3h8YMnZ12YFRcArW9bsE3HS87Os1MpF7ceN/GfPn2a+blOO0Z91sXSYrZwM4hCxUYKpmVL5JwmPr1o0kUxk9izbXElJPds+4c/8TqMfRxLOc3iU+9ZNCdnWsjFp5ycDDa7nV6lqLJfivFffPEFPv/8c7z11ltTdaPcT+LfJLKysoIrV67g1KlTOHbsWObn98v1npPN5p5v0+wePIixezdXQxgMCJIUMvAhNYUnoPsO30okqpbA8ggBasYxBrqeAKAjFWzOEGoU+oJUakcbdUZMS55gsMyNL74JAA4FSRRpbAH4xFuNyQBqqcLmtO1SIgLnbKDsSqhkesWnzdi2jfn5eczPz0MpBd/30Wg0cOvWLfi+j1KptO6Msu2djaz57EWAJxlxe1l4joFYgtQjYjMFS2Al0LsfVxwTfkS7OhmAWsWBITiaBFdWkCqymKrjKrIFLYgzIs4xWdGDVUfg6GyJHMv5qshj9wYTJRL/7pNnAIBywYSv4bQ1LY5Yszlh249hWwbCiO6GmJsraIlWFddA0MyeWzqEHmsvt2mSxCfyZW/YGHSnMwXD6dO1dYch5bApQlvOq2WnxKd//s3L5MUzyysh3JJFuvb74YLB8vT7VlOciADQiSTqJQtLhP1KiPehKJGoVxwsLo92GX54ZgbvvTlD2uakMq2xe0EQwHXdvd6NnJwdJRefcnJGIKVEHMeQUr4yt1M/k/4imKYpPv/8c/i+j/Pnz+948WWvycWQ8eiPX3zvvffIDTPz8z0d9Hp7HTt2DCdOnJj4eWy7HDTnUyNM8UUnAdIUgELqePpNKtZQ6EZfVSyOlQFFurLJEUpg1CXkGhyhRkEwVd3trmjEkEUSKIjBfZHmNrmeujCYXJEcSgAgNOQwnR5RBvGzieqKWsOK9kUBHCp2D0YyBX5wLvd1GGMoFAooFAo4fvw4lFJoNptYWlrC1atXkSQJKpUKarUaarXaSJdvFp1E4s/vjRe316MnPAHdK8AyuVb0XjSGSJto3L9P1RykgqMgGJq0hCAUTI5VQjFfp9jMifendkw7H4kCiiZHa8C5NjnDl+cr4JxNZOzetN+rx+G3v/cEnSiF44h14WlU3N5OUChaCJdo4lOhYMFyTLJLCQAcontitZOS54CsmLweAaFfTu+7B/HmvA3V1yOO8uwVaPS5ynk17IT49H/+xztYbOhFVEZRCss1EBH69/Uo1JwNQtI4/dyyqJVskvi04hNvlgBqFXuk+MQ5w8//F2+RtzepTKv41G639337ipyczeTiU07OAJRSSNMUcdy9ye+F8DTpNJtNXL58GUePHsWZM2em8vxwznPnkyZRFGFhYQGe52nHL+bi0/7nIMTsbeYg9XySSuFaw4eKIijDgiU4OttcVSzRLQ6VTY7VvoIeQ7enUzLE9dRjnBqAOUa0kWNsFZ9qFh8qMuiEgGQc4gZ0jlfn1NiCIR5QpDMZcLwi0dPXJFNji43TBGMM5XIZ5XIZp0+fRpqmWFlZQaPRwL1798AYQ7VaRb1eR6VS0boX/vtby+QVzoMwBMNmc5punFecKpRdAysaRbrVMB0qvPRjcganZEFhtLC8GWovp0QBnsnhEwSChLhwIF3bZpuwTc8afA7emS/BWCucJ6obiTTGVLQrHKQFFFQeL3fwyZ1l2LaxHkGZFbfXj1LjCVVBJwFjtDjUmZmu61irTxu1L5lUKDliqBC0cZu0r6bOJ51YomgLtPqiBI/PuKgfn93wubbvA3x0hGXufJo8tis+vVjt4Pf//J723ynZzSPmJockjEvTMWBYG5/xOOOQpKBkOi6xJ9niStiN8yOMd8ceXeb98QtHcfLI/hc3pjV2LwiCvOdTztSRi085OZtQSiGOY6RpmotOA1BK4cGDB3j06BHOnTuHUqm017u0awghDkxReSdYWlrCtWvX8Oabb+LQoUPaf5+LT/uXNE1x7do1JEmS2dtr2jgozielFD59tIg4AeB4qJqMFJdFQQIIpYKjUnRY9yVyzjEQE1SZIFWwOCPHYnX/Rt/VsdlwwQBUnVFxkvRnB52F2ZECed913BVigEuKAThZVuhv0yFz5WkgQgjU63XU63UAQBzHWF5exvPnz3Hz5k0YhrEe0VcqlYY+W15bDPBUMxZoM45tbHFCjBO9ZwiBbogknZpnoLUyev9Pz7hQa2qmTgmP6lICAM9goCwS13FFFIjikzGgqPpG3UWhsLFIHivAnqBXjPx9ZyO/+Z3HME2BdBvPpePMlkkiUSk7WF4Z7eqo1VyIvshX0+CICUJLpNHLrewaJPEpJI6jMJEoOQJNwjarBXNdfDIFw+uv1dfj9nrYbiGz11UuPk0evUSZcfnl37o81j0NAJJYwnIMhEmUKfB6FXvLfu7GNKmIz4upVJit2HhOcHylIw7OsQV+7iffIO/fJDOtziff98nJMTk5+4WDUx3KySHScz3lwtNWoijC5cuX4TgOLl68OJUrTfrJxRAaUkrcunULjUYDH374IRxnvEba+fnen7RaLSwsLByYmL3NHISeT0mS4NJnN+CX5gDHRcViWAolTA64gu1IQ+9UAZwL2DIBN8yucEK8lDyDI9KI3ksUUNrktMoilN3+Tv7asc45AnzEPVCnZC/BYDFF7gNj89F9qNb3ISNOb8M+DPjMUQ9wrY3/QbJ8jqZgmibm5ubW+2CGYYilpSU8fPgQzWYTnuehVquhXq/DdV0wxtBJJP7y/vbj9gb9lgqAY3F0NHrQ+Bp9Z3rEGSJw0eLg7kvRtqMxd1BdSkBPACIUzVMFWzCEhP0wBG1Ckpv2c9Y1cGhm6wrzSHbH8iSQx+5t5C9vN/CiFW64B1kWR6TZwwkA2cXUT9bHOWcoVzY+awvBuotDMmhliDX9WAbtPW+V0O+pR8UzSeKT0yesfeVLdShz675kzTfdfYtw+/Zt1Go1lMvlqX933Q8opcYWDP7D95/gzqPmtr4/6iQoVh00R4g4bsmCIF7/w6BG9AUhffxUixZJfGqNGJP/5Y+eRrU0Ha0SptX5lItPOdNILj7l5GyCMTZxKyh2qjHndlhcXMRnn302tqtlPyKEyGP3MgiCAAsLC6jX67hw4cK2ihe5+LT/ePz4Me7evYtz586hXC7v9e7sCdMeu9dqtfDppUtwTp1BKmwUTbbedyWW3SgshwOdbcSE9ZCMgXOBqmWgo7G5cbQvSzCAHp8PAHCNrvhkMKBoj3I9dQUlAxIJUUEzOUDVzwyNadbmAKXNxub+VHULqBW2XtcKgIIC03B25QC2bWN+fh7z8/NQSsH3fTQaDdy8eROdTgfFYhFX5MyOx+1t+O+co+szpBGnCmXH0CosNzrpyMi7EzMeZN9zQqIARzCSCKUjVOmcxYLJERKe9ag/TafPBWILhjfmKwNdW5PW9ymnS5RI/OHlRaBPNPIcA1EiIQTTdlxUihaWNd2MfpDAdQwEQ8be7IwHLja+F1KdgX4sYQuOUMMBlUUQSxQsgTbhJuYMEJEGsnY4x+oO7Nrg+KmQMCglN1AolvDFF1+su1B7vflKpdKev18fRMatazT9CL/5/93ZkX1otyK4JQvBgLHJOINTtAb+nc7o55wmPi2u0ucHi9hfbXElHPjvMxUbX/3RU+Tvm3SmdeFELj7lTCO5+JSTs4lJu4H1ot/26uFYSokbN26g2Wxuy9WyH8l7Po3m+fPnuHHjBs6ePbseNbQdcvFp9+lFxG13nuvF7KVpeuBi9jYzzbF7PXHx6JlzuB8JuBxIUrUhJi6SCjZnsEErBGXhMQnb1HNoBKmCyRlpFXSPzhiFt149/bBrghHuySYHqIk/fEDs3VA0PkvdrgTWRETAFcCR0pAdZ93oPZGLT2PDGEOhUEChUMDx48ehlML3HyzhxXNNNXQTg+L2+tGJ2+phGvrRezOeAX9A9N6MZyA1t145rkETn2IFsktJZy4yiY4mqvjVjiUE64ri78yXYQxZPU91Or4KprWANw6/+8kzhEnaJzwJxGu/vWHovxeMKygXPWug+GRbAl5xq2tBp39YxRV43sqeD3QEqopnksQn6r0rTCQMzvDG61vj9npIBdK9v1ibweFDL12ojUYDjx8/RrPZhG3b62JUsVjMx8ErYNy6xj/7xmW0/BjC4kjHcCFuJuwksD0Dob9xnBWqzvBnPJ32asRBGUQpqkUTy63sZwDqfNKJUtQqNhqbRKif+8kvwbamxyk0rfeuXHzKmUYObrUoJ2ef0HPf7EVxt91uY2FhAYcPH8aHH344lTf3UeQ9nwaTpik+++wzdDodXLhwAZY1eHWYLrn4tPv0XDrbiSjoxewdP34cx48fP3DzwmamUXySUuLatWuIogjvfXgBnyxHsDiDKYDGgBf+UCo4gkEBWr2XNmNCwrAssmDTT8HgWNaI3oslUDIZmhr2g44EqiYnv7hzDYFG55B1ekTpbNfkDLFSOFmWGFUXkkxBTNclv6e0YolLyylMs3vFGAzoxGpLfNsohsXt9SNVt3ieaAywQGNM9RhWHDtcc5EOuF8YGlVzz6CJT0Gi1gWgbOiOEYNljz8FoGQJHCrZcL3h0UY71DJvR5jWAp4uj5YDfPpwBXLtGrZtAUNwREl3HMgx7m+dKEW5ZGFV0/3UCuKuc2LTdx45VBhY1Nb59VxTgGL91XE9Uh1N1P5Qq0GCr3ypBmWOfv+1RfZYChKJwppjxLZtHDlyBEeOHOn+tyBAo9HA/fv30Wq11iNRa7UaPM/Lx8UuMI749K2/uI+ba3F7SgHFookWQazJIk0VDIsjWXu2NUwO0xl9zVHj9FxbICbGXNZLNkl8agb0MVkvbxSfXjtawo99OE/++5y9IwiCXHzKmTpy8SknZwCTVEzci+g3pRQePXqE+/fv45133kGlUnml3z8pCCEQhoNt6weVnvBw9OhRnD17dkdfynLxaffpneNxxadHjx7h3r17+KEf+iGUSqUd3rv9ybT1fPJ9H5cuXcKRI0dw9uxZLCx358CCwbA44iW6kyq4gkFivJXeDEDJ4FBcIFxzU+m4F8bRvGzO0ST0helnxjUARiua6ApKVD9TokAqggPdvjJUOGM4WVTIqPVBMqWXPZMzFKUU/uD2MnoGA4WuI4YLBr4WbmhyAIwjjJKBYgq1FxFAj+bqEaUSJcdEs0Mv8jU6KVyTI+irCB+r2EiHOIB0LiVqdKACUDAFVgniWUR0NCkARUtgmVBMnC1YmK2NLhxNkvMppzsWf+u7T9Zj9SxLgDMGv+/3VgpwbIGORt8koCv66pKkCuWSjeWVl/1dPM8EswZP0HGqwBntXkh9dg9ThaItSH2iOFFEXiEKWgXHQHG2kCkgW4T+bsEIwd11Xbiui6NHj26IRL19+zZ830exWFwXo1zXJe17zmh0xaeHiz5++0/vbvg3P0zBDQ45zmqlDfuiYJgCPFWQqYJXdTPHhyE4IkJtRmlIwp4zOsq5x+JqOFCUHoS7SUT7r3/6TfI43S9MqzgcRdGOLe7NyZkUcvEpJ2fCedXiUxzHuHz5MkzTPPBxWrkY8hKlFB4/fox79+7tWn+fvMfW7jOusJ6mKa5evQql1IGfFzYzTT2felGa77zzDqrVKh62Y6xEElVLYCnKLhgFawJUoJSWOwcAZh1jQ38kR+iJT71eTDrfqxsTWDEZCpaBNnGa6p4xBcqadAUGi6stvZeGYRGPVaLb94my3YKhUHCyPyi1ZLWcUfzJ/SZWO8MvqK6bEAAkmOBwBYcQDEoqxIlElMjMuL0N2xtj/rcNDp327grArGfg4Vr0HmdAuWwPvWp2q/eRYzBQWmm0E0kWfh2TAxmFeEswnJgpIMooisWqK2ZMQu0sdz4Bf3BtEc/XXAKWxcEYEA8obI9TvG22I1gmR6Rpd9s8rmdmhztxFICKI9AIsm9QiU6cnmuQxCdqrKcfpfAsDn/EyggG4K3XarCFgJ8hLgjC7xEQBeZBkaitVguNRgOff/45wjBEqVRaF6Nse7izMWc4OuKTUgr/47+5NFhsYeOJwZtJ4hSWZ4IrBYPgbLcMjoiwsKETJmS3MfU2GCcSM2ULXyxnL47t/9YPz8zivTdniN+yf5imxX+bOej35JzpI68e5eRMOK+yIL+0tIRr167hjTfeWI8jOMjkPZ+6JEmCK1eugDG2q8JDfr53n3EE1VarhUuXLuHkyZM4duxY/jC8iUlyyo5Lr7dfq9Vaj9JsJxJ3mhEqFkcrScnF7SBV8AwOP5FkIahg8O5n+y4tzZ7uAICiKbSi9yKpUDQYWoQdZQDqrgV6mborKJlMkYvrJgOoXluh0ffJYgxhxmddDswWOpDKyKyGS7a/r/dJ4Uajg9uNTvYH+4hTibjvEp8pWQg0FFeFMaL3Yv37cv98cbruQI4oNHY0HBuRxlxLFQmkAgomR4sgDFBuf2cOFWAaHFlTkUJXQDYn4JZ60MWnxVaE/3T9BQDANDikBIYZxKNYkmO3eijVjdV6thRo7VfQSeB5Jnw/RrXiwMiwpRZsgyQ+tTQssbZJEwlGieibqRYs+NHwue/s8TKYY8HigJ+xLUa4D45yPo3cNmMolUoolUo4efIkpJRoNptoNBq4evUqkiRBpVJBrVZDtVqFadLcKwcdHfHpV/7d51haHf5klCgFxoDtPobHYYLK3M7HnBWLFpaXs+/zOhG31ZJNEp96rk3OGX7+p98kb3+/MK33rWk9rpycXHzKyRnAJBUTX4X4JKXErVu30Gg08MEHH+SxAmvkPZ+AlZUVXLlyBadPn8bRo0d39bs454jj7ed35wxHV3x6+PAh7t+/n8fsjWCS7hfj0Ol0cOnSJczMzOCDDz5YP57PlkNULIFYSnQ0lSA/VevF3Kw/ZQA8kyNRG1+0OlLB4kyrh9Q4P4MjuuJaFrO26N4TAHDINREqG5PRnR1MQ1DSOdSsd1gG4FgpAuMAk4RSHgMklFZPq5yNNKMUf/FAx0+0FdvkiOXaKmyN4qqucyNMJIq2gVZI7zWxFCSwDQ4GBcuzRl5TCl0BukkQf4JEgRa8pydguwZNfMoydxwrWSgVXUjiCI0VkJer955//TePkErVjcdjgGkIxCNEV2rsVj9tjfHTj20bCIIYlVr2u5kpaAX9VpTCFAwxYZBQi6DtKIVjcnQI48gdIWi5JsfsfDddgdIPjhJtpiPQj4JzjkqlgkqlgtOnTyNNU6yurmJpaQn379+HUgrVanVdjNpOf9VpRkpJuq4u3WngT3/wdPS2FOAVTPh+ArWNnqMzsx5gifXeT6OgjJsetk0rty416TH/VEF4cbUrev3di0dx8nCRvP39wji9w/YTuQCVM23k4lNOzoSz2+KT7/tYWFjA7OwsLly4kN/o+jjIsXtKKdy9exfPnj3De++990qaXh7k8/2qoAolSZLg6tWrAJDH7GWwn6/bFy9e4Pr16zhz5gxmZl7GcdxrxWtOBIUVzZigHu1EobTWc2XUFmYdY4vw1MMVeuLTONF7lO0bDCj15fFbDOhQBSUNgUanWK7TLybrfMy7KWxzbYWsUkgJzwGKKWDI75YzGqUUvn1rGek2CmWcMZhCQIEWO7Xh+8f4XscUWuKTAjDnGSi4BhShOGQJBhDWnih0xWqKUKTjdqCew/aIbTqC4cRst8BHHcuRAjzaR3eVg7zS+k8+X8LTRge2ySHBIAQbKTwBIPVb2UwnSlEuWVhtErIg+/CDBHNzBXCCsCQ1VmBUHQNftLMHXagR0Vf1TDxdyS6isxHj7d036sDasVJ61KWEYx7X+ZSFEGI9gg/oPjsvLy9jaWkJd+7cAed8XYyqVCpTXSjXQSmVeS7COMX/8jtXSNsLwhSFio2WppO4h2kLwBTkp7VU42HNNIj3lk6Ksmdg1c++z1K/3u+kODLj4h//xBu0P9hnpGk6lQLvfl7QmJMziryalJMz4eym+PT48WPcuXNnvb9HzkYOag+iKIqwsLAAz/Nw8eLFV/aytJ+L+PsFyjluNptYWFjAyZMncfz48Ve0Z/uX/eh8Ukrh1q1bWFpawocffgjHcdb/WzNO1/o7MaxoxIAMopVIVKxuFN6gM1QyOeIRPU90Z4Oug0JgRSMmLJQKnmDwR7zNH3KNDfOg4Cyrv/k6OmcwAciuDgm6qypSw7dbNBSq7stiKLX+LCEhkBfSxuFP7zfR3GaPioLzss+TroilALgmQ6DRbGmc6D0hGGDTfD06+pkjGFoEoSqSgC0YQkKljiwWpQqOwdAZoOh++XARwugWwiQYOFTmWB5T299xDqr4tBLE+KMrzyEEA+cMUipyYdk0OWLNH3CU6DIMwRkqVRcJYb8GXZfDcE0BiuKrM1d5hH45QDc+dBDzVQdWRS99IybMfzvlfMrCMAzMzs5idnYWQLePcqPRwPPnz3Hz5k0YhrEuVpVKpQMrRkkpMxe1/fI3LyPQuPb8VoRCxUabIH5uplh1tZvvMc5oCzk07s+zFQerfivzc82AnhLyMz9yCtXSdPYmS9N0KsdQGIZ5P7mcqSQXn3JyJpzdEEDiOMbVq1fBGMNHH32UuxqGcBB7EPWcEG+++SYOHTr0Sr87F592n1HnWCmFR48e4cGDB3j33XdRLE5fRMNusN8KdlEU4dKlSyiVSjh//vyGFzepFG43I0gJcNbtwzJO76V+molE1RZYDjcKUIIBthAjxZkgVTC5ZoF2jJ/DEYA/ZEdcweBa498juzKeIkUDAQw2VwiIx2txgKoJ2BxbtisYcLQYgm14d5cAsguIkim97L8cAMDNRge3xlyd3aNfeAK6cZO60XtFx0QQ0x0YYSJRsAXaGsXAWslGQJwfdYY4xQ3Ro2ByhITnuI6Gu6NoCnSSjavTT1RsFAvOhn8zORBmbFbHwbib7LcFFDvFv/qbR5BqTUhKlXY/NF3afgzb4gg1ei7NznowBEdCuI6bYULuf0NM6EMQS7gWR0DYZ2qk52pnq7uDMeDN12obRADKWaK4lwON8b2TmKaJQ4cOrb9PhWGIRqOBx48fo9lswnGcdTGqUCjsu+fJccmKS/uj7z/BlTvL2tsN/Bhu0ULQot/bCmUbwujui840yDkjLfzwB1zrw3CJEX2LKyGpT2KtZOHHz+9uZP5eIqWcSueT7/t5C4ycqSSvOOfkDGCSHv52WnzqNUl97bXXdr2Hz37nIPV86vX9Wl5e3uKEeFXk4tPuwzkfWGTqxewxxnDhwoVckJ5SevP/MHH5XitCnCokCghThcJaH5TtjspmLFGzBRp9AtSMbVBStuAJjhWNecFPFISmaDZqUfQh1wDbqM4gBUO3LEYTlCyuMovQPQyq9Qm9IjztQI0BlYqjXgLT2PhvitG2J4mfy3lJM0rx59vs82SZHGqAW1A3es8fw3LjmAZZfDpcNJEaBiwlQamz6/SVG+yjHIwlOCj+wyBRMDhA0R1MsfFcewbH8Zmt0cSU2r6G+WzXmaR3n1fBX91exuOlDixTIEklTJNpOZmUAhyLo6MhJAGA51kII5oAXfBM2A69K1iqgLItsNLJvuZ1poCqayCIsgv61F44zU66RTB/50Rli1MyIRT3Y4nMQrxU3fhAm6q47RK2bePIkSM4cuQIACAIAjQaDdy7dw+tVguFQmFdjHJdd2rH5CjxaWm1g3/5BzfG3nYcp7AcgYgwBoTBYRetDf/Wc0BmQY1UbvkxLJMjIgw46u8dJRL1so3FDJfXP/yx07CJbsT9yLT2fGq326+k3UFOzqsmry7l5Ew4QghEhAf+LHoxSy9evMD7778Pz5uElPnJ5qDE7gVBgEuXLmF2dhbnz5/fs5edXHzafRhjW85xL2bv1KlTOHbs2B7tWc5uopTCvXv38PTp06Hz/3KYYCns9mcK1168u32bOFZjuW2Ty+qaALUUpqiYHDHxxX2c6L2iwbV6VYWy637aXKuomBymubXwp8BggtSiBgDFR7Rx21RBaRstg1A1FcruoCNQGKhubP5uqDU/13QWx3aanenzBFhrfZ42o7vdMJEoOQaaGquyOwnteYgzoFpxu/F+giMi3NdjBVic1uNNR6jSaUtWNLsOTV2+fKQIPmD1NUUP1NQtdo2DFrvXDhN8e+EZLEsgSSRKroEgltC1c3b7MOn9iIGGO6le765+13FkFG2DJD75GrG6jkm7i7U0tlkrGHi20n2/LdgC9SOlLZ+h3sYdweBnROsFiYI9YXV413Xhui6OHj0KpRR830ej0cCtW7fg+z5KpdK6GLUXiwJ3i2GigVIK//PvXCVFTA7ftgI3BbihIDNWE5Rq7hYnLRc08YmKAnCo4uDRop/52UBj/FRL1kjx6eiMix//YJ68vf3ItPZ8CoIgr9PlTCW5+JSTM+HshAASBAEWFhZQq9Vw4cKFqVwlshscBDHk2bNnuHnzJt5+++31hrl7xUE433tN/zlWSuHhw4d4+PBhHrM3xcRxjMuXL8O27aE93JJU4l4rgkI36q6fVqJQMbt9lHZCgJq1BRRj5HJdkCoYbLQ7aQtjFFE9wTYUtRmAujt8xbnJmcbKcfr+6NRcIgWyVNV//kwGHCkOWXnPAK4I5VSGXHzS4E8frO5AnydzqOA4TvSeZeg9C4axhGcL+BnHcbLqQK3NM4aGI8szGaIw+2qOZG/8EVwRGgPKFsRV533fe7pqw3PH782Qoisij9EKaEc5aLF7v/ndx+CCI04kLEsgiFJIBe3YvSiW9El4jSSRKJdsrKyOdi3M1N31HmI64rJl0C6mZpjC4IzkLqLG6TU7KUzBSOOuYBkAuuLTu6/XgQHPJqGkLcewOIOf8algLQJ4UmGMoVAooFAo4Pjx41BKodVqodFo4LPPPkMYhiiXy+tilGVZ2RudUIaJT//r71/HnadtcMEhtxGVmMQpbM9EpxkOFW6dggljkCtoF+bikkcruS416YuNsxxNP/fjr0PssdNvt8l99mUmAAAgAElEQVSdTzk5+4tcfMrJmXC2Kz49efIEt2/fnghxYb8xzT2f0jTFZ599hk6ngwsXLkzES0wuPu0+vdi9JElw5coVCCFw8eLFqVw5lgOsrq7i8uXLeO211zA/P3wF5N1WjFgC7SEFo2YiUbEEVqLtC1Cm4EiUnmunoOlkChIF3fXomw991hYjx4VOsVjnLpKCwWSKFMel0O37RIn0ixXWRbzjxRijhjz10CRT4AerZj0WVxYD3F2OYAi+dm6VligCbO3zNAhLMER0IxM6Y/S2cc3R4pPJFCxvvOcJU2NQFQyGZULDJD+hewmpzp92LMEAeCbH/IC4vR7UXzhSgDMBGu5BcT5978EKHq+EiBMJ0+SQUq2PLarI0o8h9PtEZV0bhsFRLG0UNQ3BSI4Qqo6oAFQcAy/8bA8vdb5SAGoFE89Xs4voYk3sPV53YJSHu3rsTQtDBkERuQOtFSx7D2MMpVIJpVIJJ0+ehJQSzWYTS0tLePToEdI0RaVSQa1WQ7VaHejSnlQGiQb/9jsP8ddXFwGsma85g9qGAykMYhQrNprLW0Vexhm8IdeczgxAdTAaxIUNrU6CkmugGWTfyEd975eOlfCfnXu1fZv3gml1PuU9n3KmlVx8yskZwCS9gI3bdyhJEly7dg1pmuLixYv76qF0UpjG1TQA0Gq1sLCwgKNHj+Ls2bMTc73n4tPuwxhDq9XCjRs3cPr06bzv25Si42p7HiRoxCn8jOJOM5aoWgINjViQzczYAgkYPINhVUNM0p0VJIDiWlwglY4E7DUhx2BAKaPPhtRY7p6CQUCu9YrKxuJATDzNJgNGr59/ic2BqiHh2aMLGwwKlBKMZEo3perAcX81xHcft6DURncJZwyMdYumcdqNMBxWTHKH9HnaDMXB0E+YSBRtA62QrliFGUX2oxVzg4OhEyXYjaXkm/suDUMCKJjd3nVZUJxUQFeoLlocbx4ugvPhhS+qvhhLwNnjx02l1NQ+8/bTiVP8+6uLiGMJw+iOwbTvh0oSSe750sPgDBqaLwDAD2J4rgF/SJF5btbb8mxuC46EsCBOJ5bSszheZKeBoalx3+93NI0iThU4A14/VR85uVkE8YnyGhOMIbZPEpxzVCoVVCoVvPbaa0jTFCsrK2g0Grh//z6UUqhWq+ti1CQX5TeLT1fvL+M3/+OdDZ8RnCOR21sA2m7HcMsWgk1iaKnqjCU0b6boWWi2s6/1SEP4nK04aAatzM+1RkTm/pOfeIP8ffuZNE2n8r7l+37ufMqZSnLxKSdnwhFCIEn0XmtWVlZw5coVnDp1CkePHp0YcSFnb1FK4dGjR7h//z7OnTuHcrm817u0gVx82l2UUlhdXUWr1cIHH3yQx+xNKUmS4OrVq2CMZbra2onEvXaU2Suhx2osURtTgCqZHOlaSJvUjHjqjBG9tznHn0LBYAgjhUOukflCm6Ir0ihiYd3kADVFprvvtINlGp+1ODBXoEhVxJ5TufI0kqUgwZ/ca46Myus5ChgYDMFgGhwcXSEpTLpypWEIklMwlV1nYawRV+RYQkt86sQS7lpM2WZqjgGruLFgEjMOyBRg2QUiittvHY3x7RLFJ1+jOH284sB1RsftUWdJgoErZ4f4xvefoBUk3XEmGOIB14VhcEQa97hEKlRKNlaa1GUAXQpDxKdK0YJlby3RkKPvNOI9qbGY7SiFbfBM8Rl46WjKohUmOHeyAgw41n5ojkiC82kbMW6TiBAC9Xod9XodQPfZb3l5GUtLS7h9+zaEEOtiVKVSmagivZRyvTbRaEX45W9c2bL4IpVq2/F7ABB2EtieiXDN4Wc5Bkxn+DWn83jqeCZJfFohuAt7eCP2rZ/F1cHRye9/qY4fev1gJN1IKSdaZB2XIAhy8SlnKsnFp5ycCUfH+aSUwp07d/D8+XO89957+Y0rZ51ezBrnHBcvXoRhTN70n4tPu0eSJLh8+TLCMMTrr7+eC09TSs/VeOLECRw/fnzkZxOp8NlySBaeeowjQFmcwRJ8zS3UdRnpiEkKgGfoOZn8lB631SNVgCsYXIsyPzJYDCC0qAEACA33h84+6/x8s240qK3G1u9nRPGJ+LmDSJBIfPvWslavllQqpH3jioOhWrTQ0bjuBWdk1xzQdYLo4lrGQPHpcM2B3CwKMYaCIYZGevYTaIxZqksJAARRqIol4AqeWaT2DI6jVRdDuqb1wWAwlTlGtUS3XUIpNfUL1f7qzjI+f9KGIRjECIEp1YzEBLrjTpemHw90WdXq7kBxlXrJJ1KhZAuSCKVjBqp6Bp4R4vSoWkGqFOqHS5nOZspCEspCiP0Wu6eLYRiYnZ3F7OwsACCKIiwvL+P58+e4ceMGLMta7xdVKpX2dLz3nJaplPinv/EDhEPucUph+wKUAlIpIUwOmUgUqk7msVPdj4Io6DVaESyTd3vEZUD9XTqRRK1kodHXJ4ox4L/6iddJfz8NTHPPJ8/z9no3cnJ2nMmrPubkTACT9AJG7TvU6XSwsLCAcrk8tKl8zsFkZWVlve/LJMesbbe/Wc5g+vv+RBG9mW3O/uLJkye4c+cO2dV4fSVEe8wYGh0BigGo2AKJ2nhf9QTH6i7G4EgFFA2OpsZ3dCQw7xpgBJcGsFbUJtazdI5UpxgdK4BDrQt7w5ixUjhWBAnCKlG21pQr41loLSxuzc+W0yOREv/2xjKiMYrY/RQ9UztKT9tVmEgUbYGWhlsiHjCmjpUtyCGLWmzBSOKTAuBwiUBmjz8doUpn7Hlmtvj0pdlC9xmbsGGTILJHE7DmZtrFp8VWhG9ffp4pPAHdc2EYen2cmu0ItsmHFtEHkaQKpaKFldWXjqnZuothKwR05oKSY5DEp0BDfPYsmsOgRVyY8s7pGixTZEfqEbZF0Sb2e+yeLpZl4dChQzh0qNv7JwxDNBoNPHz4EK1WC47jrItRhULhlY7/nmjwP/32VSyujHYM7kT/J5kqmJZApe5Aiuz7i2lyhITxkxJFMQXgUNnGoxdB5md1FoTUy/YG8emHzx3Ca/Ml8t/vd9I0ncq2EnnsXs60kotPOTkTDqUg/+zZM9y8eRNnzpzBzMzMK9qznElHKYW7d+/i2bNneP/99yd+FU3ufNpZlFJ48OABHj16tO6EfPDgQX6OpwwpJa5fv44wDMmuxlurIVpRqt1LqR+qADXrGFuEJ0C/VVCQKghG76MC6K9Gr5gMBcsgu5l0ajXdcCVaLyUFBosrclHahEI4YrsmA2YL7bWNE5oHAeAKkFkfY90V5zqurmlHKYU/uL2K9jZ6owFA0THWnQ6WwRERC6dKAYbgSLSi9wwt8SmIU7gmR7BWaBcMKJacoWNaZxiyNAGYlfk5qQDX4KSoPJ0eOFm9pI4UTRQLNhRxBqMc+yQ4n6YZKSX+j+88AgMgBB8YtbddFLpxec8b2X64fvqdkYbBUSgOv/Z1XJS2QVtAsRqm4IzmqqLG/q12EgjORu7v4bINp+LB5tnjk3LUEeEApt35lIVt2zhy5AiOHDkCpRSCIECj0cDdu3fRbrdRKBTWxSjXdXdVjJJS4nf/6jE+vdUg/gWDY3N0NO5Tg7A9e6BrdzOmwRASUjQ7I/oubabomQBBfFpq0hcJOvZLQdgQDP/4xw+O6wmYXueT7/sTvVg4J2dccvEpJ2fCGSU+pWmK69evI4oiXLhwAZaV/cKeo89+XBEahiEuX76MQqGwb5xw++0cTzJxHOPKlSswTXND3x/GWC4+TRG+7+PSpUs4cuQIzp49SxpDd5ohngYJLMFgpNmRUKPIEqAqFkcyRJgIpJ6YpAAUNKP3dNwRHEDNtbrFYrKbif5hBQaTKXKh2WKUlu1djIz4vyOFzvpieiYZBmiBW6BOx5IpiINd09vAnz5o4Ys2vb/DIDxbQOKlTEk04q0jOEOiE72n8+E1XMtAEHev0FN1F2rEM0ZKueDWKHgu/IC2P67B4BNqf51UweTdWL0sRn3E4MDJmULm5/qhHHmsyJrwrrEfn3OpfOvT51jxY7g2R0icgKVU5OitHu1OAsb0esZ0wgTeWu+nuVkPLONZnbpP1H2QCqg6BpYG9J7aDNV5JVU3ou9Fa/g8+OXTNTDGQNHIUsLBhIQHiUgqpFKNFZE4bTDG4HkePM/DsWPHoJRCu91Go9HAzZs30el0UCwW18Uox3F29Ps/fxbj//7bR1p/k0jAdQwEGoLPOgwo193sBTWaBJ2E7JI0iYJwM0hQdAy0CMfZf9X/xPmjOFJ3Sd8xLaRpmvd8ysnZR+TiU07OhMM5hxrw4N2L0ur19pjWl8a9pufG2U8PNy9evMD169fx1ltvYW5ubq93J+cV04tZfP311zE/P7/hv3HOEcfbK4zmDOZVF+96Of7vvPMOqtUq6W/uNiM88hModAuyjmBQqdJyE21mmADlCAbB+UhpxjM4mhpiku7ZTTWi92YdASEElKLvjwQDh8yMvOthMrrLoXstEYWtEfF/FVOiaPf1BCBulRFdWpIpfRvblPL9Z23c0XQ+bMY2efe375tLEs0BOuiZcRSdWKJgC7R1ovfWCtGeyWE4oxc+UVwJPXSWRqRJjK5snE3BEljuZB/fqKjEN+reurM0RXeMqIwxQjlyhW40n5mLTzvOlSctfPpgFYJ370dS0gvXQuiJT2EsUS7aWGkSLBN9WJYAYwyWnV2WEUTxKdJwPnqWIIlP1Dg9ACg65lDx6Y3DBQjPBkDrx0Z5RFAAbM4QZpybIFUo5uLTFhhjKBaLKBaLOHHiBJRSaDabaDQa64tcK5UKarUaqtXqtha7Pl/u4Pe+H2qJtEC3F5tlibEi+Co1t9vGgNo3TeOeWy6YWMqIDgT0xuRsxSaJT+21zziWwN//z0+Ttz8tTLPzadLTanJyxiEXn3JyBjDJL2BKKdy7dw9PnjzBu+++i2KxuNe7NNX0nGf7QXySUuLmzZtYWVnBhx9+uOMr1XImG6UU7t+/jydPnuArX/nKwFVTebTh7sAYe2XFOyklbty4gVarpeV4vbUa4vGmAlMnVXAFQ5AqcjPzQazGElWTY3mtSsQBlEwx1PU0Ln6qyBFBPSirnG0OFOxubvxLNxNt3y0GdKiCksb50CrCD9muAHCo4G/eCSLEVe7bCm+cHm41QvzgqZ/9wREYgsE0xJYzr5R+9J4luF4B2tQTn4IohWdyHK+7mdlysQIsTuttlFU87kcnhswiFpzbsRw4x1RsgXq5/5mKQbBs5yj1F4gUsJedK3QFy/1AO0zwf33/CYRgYJwj1LT5pmOsyhjnEcDvJDhyiNZzhxO/oKkhFJmEHjgA0AxTmIIhJpyXYfGVgjOcOPZysQzlcKhzgiUI4lMiUTSnr1i90zDGUC6XUS6XcerUKUgpsbq6ut4zKk1TVKvVdTGKEvcMdK/1f/bNK9oLKnoEQQK3YMHXEHgd14DZE3aJ85zO/tm2ASB7f1Z9+sK/gku7G7xY6xf39/7OCVRHRHZOK/ulPqNL3vMpZ1rJxaecnH1EGIZYWFhAsVjERx99NJWrPSaN/VKsD4IAly5dwuzsLM6fPz/RAmrOzhPHMS5fvgzbtkfGLA5zUuZsj948sdtzcqfTwaVLlzAzM4MPPviAPM4/WwnxfMgqyiBV8ASDv00BqpkoVC2O5UhixjFIwlNHs49TL3pPxy1F6fcy55ovfzvGYEKBWiYQnHVtEAR0ws1i1RXxKEeaggFJBBgbCxaH3RiG2LwFYt8pJgHFM6uDiuXzyeNWiL9+3NIIYdyK4AyubQwdg7q39JIj8KKtM070n3MOFy3AohXJHMEREZ6lIglyRB5MCyAKZtSivQJQMDmafUoZA/DGXAFsU/6hwZApPlH1jkno+zRtz42/8TePuvGVjK8LSdSIrB66n2/5MRxbaPWmqVcdOI6JiHLRE3+iKFUoWoLkVpIaz4Q1z8RzQk+aYfPYuRNlMOtl+YnyzVIBJmfrbsthmIIh68YdaPyWOS/hnKNaraJareK1115DmqZYWVlZ7xnFGFsXoyqVykBBoBMl+O/+5Sd4sRJCCI50jHsOAATtGMWyg9ZqtsuYc4Zi5WX/Kp3XH8YBihGeERc2LLUimAZHTLgGqf3V/DDFiTkPf+/vnCB9ftqYVudTHruXM63k4lNOzj7hiy++wOeff55Hqb1iOOdDe25NCk+fPsWtW7fw9ttvo1ar7fXu5LxiejF7b7zxBo4cOTLys3nPp92h53zaTXpxmmfOnMHMzAz57640OljKKED5qULBYGjHals+lmascNg2EBErZAqAJxiaGivSdV8zk7XovdaQF/6yyWFvKqALjXg8rX0BLaqrC4PNFQLiD8LSGKpPfCoIhYo3oLk1k+h6ojK/HpREPcWwdkTTVbim8rAZ4U/uN5HKbhGKs67QoVRXzKVEZDEAJdcYKbhQ+630iDVdHkEs4VocAcWehO4+10o2WsSv0YmU8wyOFcJ+dFJFEoAAwI8iUCv3jrFRfDpZseHYW1eVU+YiBQYBlSk8E0/7rjFtsXt/eG0RXzQjgLENDrluoZJ+ssdJaPNckyw+2ZZAqWiTxVEddbtk08QnX2Mxh2fR7sD+gAu6YAvUD5U3/Bt14YktssWnIWarDQTbaXKZs44QAvV6HfV6HQCQJAmWl5fx4sUL3Lp1C0KI9X5R5XIZcarw3/7vXeEJ6M43uj3V+mm3InglO9MBVa67W8Qh6vdyxpASBlxCvKaUAuYqNh6/GPBctomOxg3hZ374JFxCZOc0Mq3Op3a7ncfu5UwlB3OmysnJYJJewNI0RafTwf3793H+/HnYtr3Xu3SgEEJMbLE+TdP1LO6LFy/CNPcytCXnVdOL4Hz69Cnef/990oPqfnHy7Td2U3xSSuH27dt48eKFVpymUgqfNjpkl1A7USiYDK1Yje3eKJkchuBaPV6g0dsI6Dq19MqHgDGkgsgA1N2tRWWdXepG3knQCtsMFlMIids2NA6UKbm+ywzAkeKQCDimyLvLFZASPieZglCT89z0qri7EuLPHjQ3rKZWCkhVf7GbwVornCoFKLl1fJULZqbTRynANBhZVAoS/T5OnmUgiLJdDQAwX3UgBadbezSeq02Nin/BpAlVEQSgUtJ+9PehcQTDfG3YvZW2nwYHshb577XzaZoc0feWfHznzjIUGDY/7sSJ1LpfJ6l+kdwPYvLfHJotgHMG6uJ9nf2wifFyq50EjNFcIYIY0bfcSbbEV547XQM2/X2WoNSDciiU0Zg7n3YHwzAwOzuL2dlZAEAURWg0Gnj69CmuXf8M/+pjiUZr072IdaNmx43g6/gxCgUT7fZgu5tXtGBaW0udjJjdzDjNlu8TejP1KBdMkvjUaNHuw4eqDn70vcPk7582ptX55Pt+3lYjZyqZvtGakzNFNJtNfPzxxxBC4L333suFpz1gUov1rVYLH3/8MUqlEr7yla9MlfA0TUWQ3SKOY3zyyScIggAXL14kr5DKY/d2h906r1EU4Xvf+x6SJMH58+fJwpOUEt9/EWjF0wFdAapk8rE8LJ5gMDhHR9FWIPforIlJVCQAz9Dbw3BIAWHONQYW02jOpJef1Zl9dc6NRoMmgL9c/TnnJLCM4aID32G94CD2fbq5HOLP7jdJBdsoVeufY5zBtQSKroGCbaDsGaDWQ8nuiDVcU29FcEQsAloGR9GzEEn69ZxozI86h2kRd0CCwSOej37x8M25Avg2V1ZT/jqagNvyJC28G5cokfit7z9BoobXl03Nnj+65yVJFcqE/iv1qgvL0ru2FOjzAHW3UwWUbf2xMfJzUqHsviz8HypZ8Kpbn1OpBo9hC0j6oexZMKbQkaOHZVk4fPgw3nzrLfz2JXOr8AQABGf1KJQCkhQQxtbxLAwOrzS4ZrLT01ynk5BFWUG8X634MTyCm+kf/Ogpcs+2aWRanU9BEOTOp5yp5ODOVjk5GezlS5hSCvfv38fCwgLOnTsHz/MmUgA5CAghJip2TymFBw8e4NKlSzh37hxOnjw5FQWDHq8ivmy/s7y8jI8//hjHjh3D2bNntVZ95bF7u8NunNdGo4Hvfve7OHnyJL785S+Tf+cgTvH9Fx34YxZZWolEWbM4Z3MGxxRQa3ORq+Fc6PZx0pvDhOacF6tur6h+LM5QtAfLRl1jEP38UQpj62jsu05CELO6hRaHK9TdIa4nbWg7IA9Y36fPljr4ywfNsQtnYSLhhykMg8M26SEUqWZEUai5yj+IU7iEsT9fc9fzyBzitd9J6VKq1nWvMZ5cYvZfM+yuZJ9zDZQKwwV/6tml7KLmOoEdZ1pi937jbx7BD9VIUVj3Vj3OM2lWHyXT5KhUXhbHdRxN1AI2VUwGgBIxtqutEQdWdl/eX798uj5wICh078VZUGJdKYebO59eHUop/Pe/8SkeLw5/HlEKEJrPf/1EUQrDNLZcWpW6O3w+24V5ruDRliDpuLzmqqMXHJ+Y8/Cj7x5c1xMwvc6ndrudO59yppI8di8nZ8KIogiXL1+G4zj46KOPIISYOAHkIDFJPZ/iOMaVK1dgGAYuXrwIw5i+KbznNJvGh8ntopTC3bt38ezZM3LM3mYm1cm339lJ0XScOMUeT/wYt5tR1x0k2NgCVDORqFi0OCuDAUVLrMXPdZHQi9JjY0Tv6f1Ft8DV7vv/hzxzZHHChCT3rtJxM+n8JCkYDKZIxXjGBWwoHCl0wDKmT8YUKFKAYhKUdWoHSXy6vBjgb5+0sz+YQdk1kYLuJAC6Tg6dmKJOLOFZAj6h70sPzzYQxMMjf6qeCbuvUG1yILOhEbpj1SXOSTruBJ1eWCan7WwCDgMJjlZGu1e6W6KNpSy614KuM3LnmIZFP7/76VM8Wg4z3YipVHBtgUAjkrLoGmgF9HgtP0hQcA20h/zN4dnCBgeTUtgSUzcMqvOpqTHuDeKFtxomEIQYSQCw1hwZrx8qwCgML6TbgulF9Q6BEuGXi0+vBqUU/um//hT3n2XfK5UEHFuQ+6RtJgoTuAUbfqvb/2lu1gOMEW4YjbmOGp9p2QaQ0X8KAFaDwRGBgyg6owWtf/Rjr2m7oaeRaVg0sZkwDPO0o5ypJK/u5eRMEIuLi/jud7+L48eP4+233163Eufi094xKT2fem6Xw4cP49y5c1MpPAG5ODKMKIrwySefoNPpaMXsbSaP3dsddkp8iuMYP/jBD+D7vtbvLKXE5UYHN9eEJwDwU6XtYOqnGUtUM5qLcwBV29ggPAFAR6pdj97TdUt1+goIJTPbcaLjrpIaxecUDEJDNiP2dwcAVM0UrpVd3KB39VKkQo2E0tjm/uXTL3ZWeAKAOFVazjmu47IDyFFzPeIRwg9nwExloxNIp/BjkyPy6NGaOkKVzhX61lwRhcLo+VeBkSL1qN+7132f9nMR74+ufYFPHrRI4g2gf6zj/DTDYrPmqs4GAbeHOSA+bBDUXQ8TBY94A6E+vigF1Fyay0OiK6aePF4d+TlKjzfKG/CweN1+OonKn4F3GaUU/od/s4Dbj1vkv4liqX1v6yfwY3gluysCZURZ6vz8hQLtWhfEfV9qRuRxzkfcL986VsJHZ2ZJ28nZn+SLcHOmkfyqzsmZAP5/9t70SZLkvhJ7fkRE3llZV1efQKOXmMEMB+BMzzTWaKB2V2tr+1WUadf2+E8k0xcdJjOZTPqwZlrJuEsu9xSMXBEEKBAASQAEcWMGwHRP393T91XdVVmVdxzurg+eWZVVFccvqquru6rjwRo2VhkZ6Rnp7uHxe/7e01rj6tWruH37Ns6ePYvFxcUtrxfk08vDy772xhjcunULV65cwbvvvoujR4++tLbsBwryaScm9mu7sdnbjsJ278VgL0i9TqeDX/ziF1haWsJbb71F/p17gcLPVkZox+xy7oT5LfS2vz+NgJotSUQJxEs5B/u0mxwn6sP+BKEGKuOd2LPl7DyOPGIGhXw2fXl+EkFsiIlCut0e07QKDCM+KLDny244CPjwSR+/evz8xFOz4uwopMocmQ15rfcCikRhCoNQoZRQHFuaKe/Itsij3MpDSJeIB0cGKBPnDp947aoOx1KTlq8X+aPMY6j8WA5Hsz3HQbbd+/qFZfzg5jqqJTrROgw0WI57yNBXqBML0RN0BuGO+5QUHPWEvkVVMeRZalCznEY5FEHVHOd8+1QTzM3Y6EGY/igKx4igHNTAnqisCsRDaY3/5Y8u4dq9zq7e/zxT0GgYoTFbBmXxRiW64kjiOETE+6w2wHyDsP6EVS4n4Z/9l6dJ5yhw8FCQ4wUOMw7n1vkCBfYA+5U90+/3ceHCBRw5cgRnz56Nffh72QTI64yXSYb4vo8LFy6gXq/j3Llzr8UumFdFafYqYGKzt7y8jPfeew/lcvm5z1kon14MnofUM8bg/v37uH//Pr74xS/m8vm+2w9wp5eudOmEGk2HY32XoSITAmptW2V0PoV4AqwqIA8lwXNa7412Yb3nCYaqI0nF/jxqJoDBZYBPbAyVUAIAQzy0wdYh+E4VWiwYwAyNLqO2VDMNbg5f8LMxBj97PMC11eFzn6tVdWIJhqx8mK3tyWe9Nww1Ki7HIAezUS1JjHpbrffKrojNtcjjlKSpnRn5yOWyZBgSvCmHkVVkZl26M3MVOx8RLnHJdZFltmTJ6ezTvUzl00Ekn5TW+IOfPsTtVUsA5rHRAywRFOo81nQCAN02S2mDWs3FemezhxxZqKbYvdLOm2e+KDm0NndGe2/R50cas4uNzOM44YtTCaOSYOhnzAXDyIDInxXIgc4gwH/7h+ex3LbWv2YXS07XEYiUhtqFZXRrroJK2cFwlG2PyYgel+R8tRxzT6Pq4tFq9qaFtV68/e2XPtfCO6db5M8rcPDAGDtw9+MCBSgoyKcCBV4SjDF48OAB7t69i7fffhvNZjPx2Fcpd+h1w8si/p49e4arV6/i85//PBYWFvb9818Wir5uEQQBLly4gGq1ig8++GDPiIW/IP0AACAASURBVMdC+fRisNvNClEU4dKlS2CM4dy5cxtWq5nv0xqftH10ibuV18cKqM4eEVBznkglnoCxlR4xw2JyfB4ySRlrvdejBCKNERlgxqPtXjeMQUDTyByMbfqITcnzK1C+XplrHJ0xAFOgLu0ZGMkqjxEzbfQh1D6NIo1v3e5gbSq3hXMGzixZGuRQCzQTiCfA7uoXnJFVTZwTGJQpVByJQZCc47QdcdkpSzPxAe4KgMcBn3ApgjwkG/lIQDIOyqgyAKqOQCclC2ehLFGveOQWCCEIH80goRFmjKPg8A2hF4b1YYh/+cP76E0VfbUBXIcjIN7n8qoIO30fjmQIc9xz1JQiolH3UCo9f+nFGJDnC2oBM9QGDU+gQyiiU2e9zx9vwJE88x5G4aRDTcvEcgTLvGkOI42Zgn3aU1x72MH/9B8vYjSZW3c5l4WRRqniYNgP8in8WiVIh4MRGS8hGBQhwo2S9wQAw1Fkz0m4L1Nt99b6IcquwHDqfsUA/PNC9QSgUAgVKHAQUZBPBQq8BIRhiE8++QSO4+DcuXOZ+T1SyqIg/5LAOUcY0nc6Pi+01rh+/To6nQ7Onj2LUolm/XJYUNjuWZu9S5cu4Td+4zd2WHA+L4rr+2KwG0VZr9fDhQsXcPLkSZw4cYL8vqfDCNc7PikDYRqdPSKgAECx7IdnA6Ai6OTQxHova9fyNGxWDv34licgOb14JnPU+PNsUrQ1Dyqpw+Ayk1qYPlLpjW1kjP1ypLbkyH0iQDNzqLz3HvRCfP9uZ0cGktZm3H/s39mYiJIcCJTZUTBjABoVN9M2yhF08ilv0TwtxykOg0DBkxz+mFybq3lwUnI0PM5IlnaBtmOKMsSpFnlAvm5XkgydBB6OA/jsXDXfOan5O8MBeLmaeswup+Y9wUFSPl1+3MP/89Hj2Lk5z9gwxhaCQyKJbAxQq7lor2dp3TYx8hUqFQf+KMJcK309n2cNUZIc/RQSdYI8tpu1Eo18GhA6aqsiUZ2pwOXZKk3qT1biDIOMc0lCH6aoJAvQ8Ze/fox/9a2bW4gaY+wmCSp5M43RIESt4aFLHGflqgNvTOoyTtuIIDkDZTuGTxhjE1QrLjrd7DaHOcbk/EwJ95Y37X7/9hfmceZYnfz+w4yDdM/KgzAMD22ud4ECRc8uUCABL+qGtrq6isuXL+PMmTNYWloivacoGL887KcSZzAY4MKFC1hYWMD7779/KBdVWXid+/ok3+vp06d7ZrO3Ha/z9X2RyKt8evToEW7duoXf/M3fRKORbUsDAGtBhOudAKNd2JFM0Ak1GpKjk0OxMQ0GQHJONh7KuzFR7MJ6j4q6w1FyBTjoxa483JbOodsyYJDMkIrwAOByIKkG0nIilNzNMgozDIZRTkwjv2jnGpNPhwS/eNzHxac0mz1jbO7RdD1JcqsqcwW3NkKEgZDn6uW13huEChWHk4rGE9RKEn4vgOAMrYyMCpFjoFQEbf7xXxBRlZb1carpwR3n01BXfdQ5rl6rop9x0pepfDoohbxvXnyKH99aT3xdKZNrbOQNmRnltPYDAFdyzCxUMxXsxtDsGQHAkwx9QvW8n8Nu0yVmz3VGUWY73zzZBOPM5htmXDLyfVBkk0+U3KzhLtc/BXbi//rmdXz3109iX9PaoFySJBu87eh3A9QaHnqddDLHcQVqDW9j7tpr1eNwGJJVhq4nAQL51B3Sr0e9vKnU5wz4p3+vUD1NoJQiu0UcJPT7fVQqlZfdjAIFXggK8qlAgX2C1ho3b95Eu93OXVguMp9eHvYrg+jx48e4efMm3nrrLbRar6+X8+tKjkxs9mq12p7a7G1Hkfn0YkC1M9Ra48qVK/B9n6R6BYB+pHF13Ud/W8Ekj6XdNDqRRt1h6OYMGGm5ApoxeJKTbYdG2qoJqCN6pAyM0WAEZRVgi1ZVglqKAZgd74zNM7LyXCENBg5NzopyiIV1YLIZZufBAgbzla3B3mQagGkAhAd3ZmxHyyjqmfH/WK6srFcLQaTx7TsdrAzyF8umEWkDVzA4jrC/HWHODZXJtVM8r/Ve1ZUYhHTrvYlS62irTCia09vh5BiAFUlTauax+EzadO4JhqXW5rrcEMczdWVOGRWB1rj/4BFmWy2Uy/E2h68r/Ejh93/yEA8Iaog8YyNSBo7gZDWCHyg0qg46fbojAmOMbLcnBEdEaItDXCMOI42SwzEijCPqMFYGqHscnQSvzaNND17DjiUKMU3Nc5KEHDiKlewwh/KkQDzCSOO///fn8enDXupxw1G0awXUoB+gXHEwHMSPNcYZmq2t86TSBpRbLpWcNgaoVR2sd7PvnWkbG6ax0vXJBPl05tTf+60lHJ8vSIkJtNaHMgt7MBgU5FOBQ4uCfCpQYB8wUbTMz8/jgw8+yP1AKYTYV+u3Apt40conpRSuXLmCIAhw7tw5OA4tj+Sw4nUknyZqyP3I99pt5pPWGo+6Ae6sD9EZRfAjQBmNSBtE2u76t7v/7cOUIxgE53AkhycYSpKjLDnKjkDVEah5ArMlgRoxf+dVB0X5NBgMcP78eSwtLeELX/hC5n3AjzSudnysJxSNtLG2dkNFKbdsRS80qEmOHnEH8KwnoJmNBY/Lg0mCAVDOYaWnAcAfAqV0e6ppUKz3FsuCHBy9tT2Tc9Pe6zJgRFVV5SBpkn6lxbIPIba+yqgKJCKpBBAJRGZzn8QBJZ8e90P81Z2dNnu7QdnhEJJDGat6CDVtDeEKhhFxfJnc1nv55v1+oDBTcVAmFM3z8Nh51r+uYKDILA2AquToEgrsSYXnM7NlCL6VjJUsW42kwcCgYTL6PeUSMcahwHDjxg2MRiPU63W0Wi3Mzs7CddPVZ8+LV1n59L2rz/CDG20EmtZ/glCTCtATOA6dfAIm1l40SMExO1MGF7QCvOBARJgu8mTsNTyJEYF49nPY0VVchiRRypmTmxnGlB6lDCAIfCHlt6dMi4Xt3vPhydoI/90ffkwmYLXOt7FiAwaIQo1SSWA02jkoWvN2XG0H57TsJeocUS5JEvmkiWsHbYD5pofHq6PMY/3xPc2VHP/4v/gM6fyvCw6r8mkwGKBapT8DFShwkFCQTwUKvGA8fPgQt27dwttvv42ZmZldnUMIgdEoe5FSYO/xIpVP3W4Xn3zyCU6cOIETJ068sg/++4nXiXwyxuDTTz/Fs2fP9i3fi9LHVgYBbrdHeNT1sTII0QsiBNseqoQ9Wep5pGCpu9I4sw+JruSoOAI1V2C2JDFfcXC07qFMDOV9FZClKFteXsb169dJ94FIa1zvBHhGyV5QBjVJz1WawMAqqigE1JwntmQ8hcaSLC/KIorlVOb5GQ/8JcFQmcqrsVeVSCgxBgeGbDMoOCNLIfLMcmGMHVOJGzS8uF3HGlR9l7XoIxxHOpu13hMHsK73yyd9XFge7klkVdUTAOcb54py3M/y9AllgJon0CPagPVDjZLkGBEJZwZgcaZEIif9HArHPFNVniWRJxi6hIEaaHvs9Lwx4wnM1Lwdx1LdBCXLJuCov+380jF85vgxaK3R7XaxurqKTz75BEopzMzMoNVqYWZmZs8zIV5FRfSN5T7+6JeP0Rt7jnqSg+oi50hOtuCiqIKm0e0H8By+URhOw5GFKrhgEMTiO1U9MczR5jJRbtjx6YpPJ6Gdp+crkOX8RGlJ0BTMWaDML4Xt3u5gjMFX/+YuvvPRI3QHYa5nVq0NHMnIqvkJlNJwHQnO9Zbxc+RIFSbBJpIRlY9UkoqyRgIAP6CPn2bVJZFP62OC7x++fwzzzdcrAzoLhfKpQIGDh4J8KlAgAc9LBIRhiEuXLoExhi9/+cvP9aBY2O69PLyIa2+Mwf3793Hv3j288847qNeL8NAJXhfyyfd9XLhwAY1G44Xa7FEQKo2PHnRwZbmP9VEETZj6FACesVM6UgYSyQ8H2gAwVuETKIO1UYT7U1tpBQOqrkDNk5gtSxytujhac8m5BPuJJEWZ1hrXr19Hr9fDBx98kLp7/dkowv1+iF6kqYIUAEAvMqhLjm7OgsqEgEqzrZvzJFRMQ1zBEFCt91Q+6z3hlclWVoAtNlVSsiAWKnJLPzWg7bLeaA+hsLwbhADY2KguGwweNxhNXcQjlR5i3QmZstUSQgeiWvSxPPlQr14NOxH3uwF+fL+HXqDAOQPnDAwGWuf/GowBnivHjMUmQmXgcIBSK46UIedLAIArGUDLZAcAVD2JUUSz3ltqllB2JblQWxYMfcKgypPTlqdOSS3aA0DV4fCn1nWfm6vEWn1S7jSR0giGAdr9EINRhMEownAUYuRHGI2zTkolB62Gh0azhNlmyfaTBAQGqMKuhZrNJprNJk6fPg2lFNbW1tBut3H79m0wxtBqtdBqtdBsNvdkDfGqbIDqjiL8+58/wP21rZ3bjzSE4DR7xRz9zBhrd6dyqJ8qFQd+hgXgXKsMd7zxgeXonxSMIoOS4BgR2kwdG4EyqLkcPQLDF1eQ5ww4eXxrhiX1ijoEyRflXBQLv0L5lB+X73bwf3ztClbHa3QheW71bQ5T4C0YjiKUqg4GY/VRte5C82SDX/JII2aMUh9Jh8MIQtAILUfSWtnuBWjVHPzXXzlFa8RrhMOsfCrIpwKHFQX5VKDAC0C73calS5dw+vRpHDt27LnPV5BPLw97TYaEYYiLFy9CSokvf/nLh3Lh9Dx4Hcin/bTZS0KoNH75sIOLj/tYHYRbHr84pxUrNIgEFDOJIdBa22Krwc7zKAN0fIWhMlgZRrg+3iUoOFCRAk1PYLHi4HjdxUzp5dr3xdnujUYjnD9/HnNzc3jvvfdir9N6EOFuL8JaoLYUVhjG4ePEp+hupFF3aLZT0zCwhZjt5A0DMFeSiBIe46O81nvEwjQAMClRYcgMFp+Gk7DTddYTtqi1DQI5slpy1EtUruIKg8sMfOLh07WKGSdC2U0ofDLQt+qSqzRU28SDUdRrjyL84F4PK1NZElqbLTurJ8pMjMe21slznRAM3iTfKQauFAgDuvXekBqGnnO8U8dtxRWoVxyybRkwznIi2YXZ+WBIIapyzDMqT+6U2Gzs8ZqLcilbqWGMwd0HHVy6/gy37q9h+dkAw1GEUGvwjA0RXPAdfYNzq4aplh1Uqg7mW2UcO1LHb51qYuZ4ZcfxQgjMzc1hbm4OgF1LttvtDVWt67qYnZ1Fq9VCrVbLTSS9CrZ7Smt8/fwyPrrbSZxJHElTHCltICVHRN2UkfO7D4ZRqm1XuSRRq031K2L3zKNAq5cERv3s75fnfl33JHoBwaIvZvy+ebQGvo1YpfI8lFo8ZYz7ymTehQNtLaLFHhOChxGDUYT//WtX8Osb7S1/V5GGlDyXlV4YaTAOmF084o36IWoND4EfoVJzU+eqvV6F+MR7NwBUKi663ewdIeTMKQC/+5VTaFQOh0X5XqJQPhUocPBQkE8FCuwhjDG4efMmVlZW8O677+7ZzaMgn14e9vLar62t4eLFizhz5gyWlpb25JyHDYeZfJrMD6urq/tmszeNSGv88kEHv4oW8eMf3k18QLOX35AIKAMAWQRUpOHIncW3CTaKAAmnCMfvnxBYSgPdQKEbKNzvBvj46QBScLiCoeoItEoCi2UHR2sOOZT7ebGdfFpZWcGVK1fw5ptvbhQLJxhEGnd6AVZ9lViYMbDF71wEVKjRcDg6OQvSGoCvNwkoDmA2hXgC7O78PNZ7LA+Dg2QyKbE9MQUQhwENL57cz1NyoimTNo+V0KnXbhoi5rJEWsP3FfxIwQ+UVQaGCjqyfd6TAqdbQ0QBx2yNo+LFKDYMgyZlP1EVTRowPLMzavZqz92jSOOH93u4t+5n9kZtJhkOm0cKhvHlYtBGA7C2oY6Tvokkj/VensgpPzKouWLDliwLg0DBExx+ilqCATg6U7JqzhyF8Dxj3OM08kkZa8dFUUtRzrcdkgHHZ+PX6UM/wp//+DY+ubGCZ6tDDEYJlkqUoR4zzCZk51rXR3cY4Gl7iMufruKvfmJf9xyOmaqLpVYJbx6v470zszgxFTbvOA4WFxexuLgIwG52WF1dxd27d9Hr9VCtVjfyosrlcnYTX6LtntIaP7rZxveure6w+N2OMFTj+yJN2UmF1iaX+imMNJo1D2sxhWbOGBbmKls23VCvb56fwSNaE1Pnh1zn3GZx6QqGhaWdLg7UjMikDUpbz5X8WuBHuH3xCe5efoa1J130+yGiUEFHBlpZyzYz/gcAv1eS4AxwXIFSSaJScVCvumg2Pcy3Sjj9mRmcfWcRJ4++vs4UX/vpfXz1+3cQJhC4UaQhBMvVZ422G9x285jnjyLMLlQyPy+/Iisdw1FIzqzyXI4u4ZzdpPvJNjQrDv7+bx0lHfu64TArn4rMpwKHFQX5VKBAAvLuABwOh7hw4QJardae22gV5NPLw16QIcYY3Lp1C0+fPt1TUvIwgnN+KPu67/s4f/48ZmZm8P777+/rbq1BEOHb11Zwa3WISBswln3r19pabWVZxRjYHdwmRRUA2EJNyeHQCeUgpQ0Ekq1pthNQ298LpmHA4asIq6MIN8d2PYIBNVeg7gnUHIEZT2DOk6i5e/vAMsl8muR4rays4OzZs5CuiyfDEG1foRNae8GIuMNcG2pyzyY6uySglLEEVFUylKQgkSf5rPeydyNPw89ZPAjNTjXFQkUmEqh5zm4pBqo9niXOsjbbG2PwdG2Ie0+6uPdsgNXOCINRCKUMuGBjMhOJapsfMWsTxTgDY7ZlQnC4Eqh6HM2KxOKMg6MzEp9dcDBTTejvTIH0KMAAiqOeYRhfqVdrR7nWGj9/PMSVZ8NchMp2bCoIDIRgqHiSlokUGUiOzH4x+Qyeg/hxHQ7kKC7XShJ+P1nZsNQsQUrbX/JYVOZRHuWJ8ytLGvkU6vGcRDrWHnO6VYIjN8eGMQY/+ugB/vond/FkZWCLjTzjecBkq4a00RBIvufYcb/1b36o8WRthCdrI5y/vYY/+fkjMAbUSxLHZ8v4/PE6zv2tWRybs8RSqVTCsWPHcOzYMRhj0O/30W63cf36dYxGI9Tr9Q1lVJL9634rn24/G+Dbl5/h3uoQarzRIqsNShu4rkwsiE8jjDQ4A8mmDwDqZYm1Hs2WEkDiGD2+WIXYpoajWmkCNi+TooygCncGoYYnGXzK/Zp4TmWAmsvQG+9AeetkAyymEBwZkH4DStebqK38UYi7l5Zx5/IzLN9dQ3dlgHC4tZjPHJ5KUkajCNzhiEKNYT9Ee2W49f2uVbJyzlCtuVicL+MzJxp46zdm8cE7izi6WMtu8AHFzYcd/G9/chVP29mZRFobSMmQ57FNa6BWlej16RlJQjDMLlTguAJBRs7hZNMW5ZZEOc4YoFpx0CXMDa6kPVusdAOSxe5/9dsnUNrj55XDgsOqfOr3+wX5VODQoiCfChTYAzx69Aiffvop3nrrLbRarT0/f0E+vTw8Lxkyyfap1+svPdvnIIBzjjAkpIYfIExUMG+88Qbm5+f37XP9SONb157i2vJg48Hfqmlo5IcaB7NnFRWVNmNrsvTzjkINN0UhoLRBSbBENVAYaTiCxxIK1mNdQ2wbX8oA675VjHDOtrRPcgZPMFQkR9MTKEsOj3N4gqEsGUqSw80Yr5HWCDQwYhLd0ODTKzfBq03gM0v4STuCMvEP1zLGZjAOGkCZs1xkzG4JKI8z1FwJn/i2KEexeWK1RbXSy8pxioM3RT41HI5SWl9DDkKJMTjQCIhVubiCYGcQ4NfXnuFJ2xJNQz9KLHhs310rJN8kZY0thBiDHXZSQhiEIYMfGiyvR7j20BaOpORwpFVIVT2OZlXgeEvizBFLThHFT+AGUITjNAzEK0I+RVrjwtMhrq6OMPBVbmutOHDG4LkCMg+DAkAKTlZAOYJYKAYwCvOtT9KKXRO7vWlIln0PAECeN4B8akKZJ8uJSD4NIoOqwzHftMTN9dtt/Pn3buLmnbWd14cwPow2YOI5CKox0ZxIlpvNQltnGKHzoIvLD7r4058/BGPA0VYJsw0Pnz9Ww9nPtXCsVUatVkOtVsPJkyehtUa328Xq6ioePHgApRRmZmbQarUwMzMDKeW+2e4Nggh/cXkFH9/v7LCNpCt9cxA5kiMg3gt7IwXJGdmmrjcIUfIERlPF8JmGB+nFl1eo5xacRj6FOe6PzZLE8na5Ugyo8w5gs9N6gULNE5iZSy6WUpSOWZfFGINLP7uPj/7iBtae9BBFKnWTgwk1mCuSu4oBdGTABGLPYwIF5gloDXQ7ProdHzc/XcN3f3AXAFBuemg0PJw52cR7b8/j77x/DM26l/4lXmEYY/DNDx/iz372AGtdf9y3CKpoAyiV37azP4hQrjgYDrL7JBcMrcWqXQcRP4JzWvZSteKg189ug1cS6PYIH0wcPkobLDRdPG4nW/QtNj38g3cLl5QkHFbl03A4LDYpFzi0KMinAgWeA1EU4fLly1BK4dy5c3CcF+PJW5BPLw9CiF0rn549e4arV6/uO+lwkHGYbPcmNnvtdntfbfZCpfEX11Zw8UlvxwO9NvTdsoDdfS1F9gPf5k7D9AfQIFR2Z2DCIaNQpxZ1Q6VR4iL2+U4pA4b4nXDa2P/jU7vYI20QaYN+qPF0GEEKFlv8Y7DXzOGTIizb+fnChmzzRh0MDCzjoVcZQBAJqKEymHHzkUl5CaiWK2AYg6/twpCyHzXQ1touJD5s85zWe3KX1nsT28DM8+dou2CMntkx1niNggi/uLKMK3fa6EztmGXMZsBMbBW11qn9QG0jmbhgVvXErOJQjdVRShkoZTDh7jlnkONQcD9QGAxDrDAGIRiuPuD4/iUGyYGKJzDXcLA04+DUnMRnF1zImDFErStpZiBenoOXnXfXfFx8NsSzQbiF5ONsMv/Zi59HYMcAuK7YYSFKVdvkUT7kuXyjyKDqcvQD2ljvBwqu4Ai22YsxAEtju71pcKJ0RBnA5XZeyEKcTWYS8lwLJ40AmoIGcKou8R++dgkfX1reQh7sqgEaMDx9Ps8iqLRKt7rV4/vXjvMa4OHqCE86Pq486OLrv7AKqUZZ4mirjM8freHd0zM4MddEs9nE6dOnoZTC2toa2u02bt++DcYYgiBAp9OB67p7vkmq70e4+LCLH3+6hifdZPWA3cOSfV8MAgUpOSgOeeTMJ9gxOl/38HQ9W/ExQcmTG/3HdQRmGsnrPOpYoljQAUA/x5qg7NB+026GqmQarmPb+fapJmI75xiSkAeXNIWuPu7hR396CbcvPEE03TbBMvvKhEBC0mXSBuA8NnMUALSvwEsydg4Yrvvw/QhPnw7w018+wv/57y7A9QSOLFTw5uda+PKXjuDcby6mbrh6FfBodYB/993b+OX1FYRT/YkLlppzOA1rWUk/HrDzVhgqOC5HmHLT4JxhdqG6MTdS9zwx4vqxVJIk8mn7xrYkjHy6moubdCXVf/M7pyAz8gRfZxxm5dPETrdAgcOGgnwqUGCXWF9fx8WLF3Hq1CkcP378he4YLMinl4fd/K5aa1y/fh3dbhfvv/8+PO/g7obbbzwP2fcqYbvN3n7sKFZa469urOL8o25qUUYbwBU0CyjABuNSCKgJsZVVEAgjBUcm58dEkbYPXAmn8EMFN+H9kTKQOQione/FjgKggX2GVcpMFa/jYa+B9SfLCkS2jk60h/W1QO+KgGo6HOsp75EMmPG25ju5RMsfwKqNwhdkvZenQG2PB0qcoelxcEIROtcjK7HhYaTxk2vL+PjmCtqdUWyhxJidhJIQbOw5ZWzOUMpnaWWglQLYWNXkcquOAoMrgVBZK74NyyFjYAB4ngPGOeR47DNja3I936C/EuDuSoCf37T9u1HiqJYFFhoOTs66OL3goCppO6E104DZ/4Lb00GIXy8P8KAbJPZfrc24DmlfZ8wWlSbWO0rFE4FVT4CJ+Nw6KqkaKkO2sLPWe3S7MM8RZPIJAOqewMpg6/FHGlst6HaDEmekcetrO/dQpo4cX4ukqDLG4PzP7uE/XlpGEKUTv9PvyTqOI70XTGxZk85jdPrrWXkjRgNMTD4LWB9EWB90ceVBF9/48BGka9WgzbKDxaaHk3NlnFk6gi999nOAUfjVr36FZ8+e4fbt23Bdd8Oir1ar5Vq/+JHGxYddXH3cw/21EdYG0YbaJ8tWz8BuOqD0eyk4FGGtqA3gyBz3qZyK4f5wMw/myEI11arYUC9jDjs9KvlNLdKOIo2KwzGgXAfGMF9zUG6mZ4pJwhwZTt0so1Dhw+/cwIUf3kHv2SD+DcqASZ7JRhhfpSqgTKTBXZ74uvYjcC+egNKBBnM27wuBr3Dvfhf37nfxlz99AAaDasXBsaUa3j7Twm9/aQlvn2ntu73ldhhj8J1fPsY3fnofj1bir69WBkJycjaTGh+fJ28pCjVKZYmI6difkXOG2cXKlvWc3kW2Xxq222MmgZL3BADdQQhHcpItaGumgYdr67GvnZgv4ytvL5A+83WFUirRQvYgo1A+FTjMKMinAgUSkPiAOM7vWV5expe+9KV98WU9TGqQw47BYIDz589jcXERZ8+efekPGQcNhyHzaaJ4e/PNNzE3N7cvn/mDT1fwi3tdsl1MEE12UdP6514SUAZWwSTHBfP4z0tWQBkAgbIZUHHvj5RBiZvYfCltABgDjmQCykFy9pQ24yys1HwPGgGlbVPICqi1QGPG4ejk2Mm9nkJA1SSHK/mOfKc82S15jt2N9d72HKcsVB2Gygvwx0/KKpvgyt02fnb5CR5NcmJyYvvufM7ZOPvJFpUBQDrWfo+PyRJga/8SwmZNsbGyZ+MlZkeJUgZSAMqMX4sZzo7kMAC6oUEvUnjSUbj4wAdgUHIEqh5Dq+ZgqengM7Mu5uvOjr6r2f7InrTWeNALcb/r4/Z6kIt8mcAYO9dMg7NNApoB8Nzk7DDAFtuppKojORSxuO0IDp841vMWzLcPmhtjkgAAIABJREFUqbIjUK/GK/fzZGQJTqeXy4KjS/h+oxxEXJjR1quXlvGLn95FNHW9klQP08gilgBr3ZV1jCMZopTljS34pqgglU4slmptwBI2VhjY8T+Cxij08aTj48K9DoAnAABPcrjSRaXMUXZb8CSDfDKENOsQOkCr6mK2WYXwqggNxyDQGAQKo1BhFGrbTxmw3AtyWcHFYUKYZyHYsJskkIc5Pr83ilCrOOgR7MAAe13rVXdXVpxJMDnGXMOTeEZoK3VtCACNksQgzM63CZTB50+2MmWxFLV9MF4QfferF3DlZ/fhE/J1TGTJn6zJwUQaECxxvakDDe6J+PMYQIcKLG69qQ2gNEzMxoSJ6qrfD3H9ZhvXb7bxte98CsYZ5mZLOH6sgZNLVbx1uoX33phDo/riiuijIMLPr67go+uruPGgi/4oQmcQZOerRRrS4eQ8JxVpVMsSwxzqudEwQrXuor9NEckYcORobYdgbvJskWeMpIG6XvOJuYrGAAsNF49Ws9WTaVPll09qfPThh2g2mxv2qC/KXeeg4rAqnwaDQZH5VODQoiCfChTIgdFohAsXLqDRaODcuXP7dtMrCIyDgUn219tvv42ZmZmX3ZwDiYNMtBpjcOPGDaytre2b4m19FOGPPn6MFWKhZBpaAyJ5w+cO5CGgHGFtwJJJfFtwdx2RWGeKIg1Hstjd7MaMM6ASCKhRqBPVUVoD4MkEVJiggJqAYpmnDSCm1ScJmKiqpGWqEo+bYC3cHQG13YJv1hNQYLGkiq8BgUyXnI1jX6T1nkPIipiAAZjxGAzxfplnljGMQUBDbbted5508P/95A5W161v/8RSb1I/zbLUS2ybZTkhHQEhGcolkVq0BgBPMowyfghXMKTxFLaAPZWNxjY0QhiGGqMI6Aw1bj/z8dFtDmMMPIeNFRUCjbLAYt3BmRpHg2B9SEWkNe53Q9zvBng2CNHx1Q7CxZJGk2u/aUe4GwjOIDgnFZMNLHkzJGQv5bLey9H0UaRRcS0pQMG09R6DzQxKsvoad0XSqM1TDyS6gAEAqpKjSyDYhlG8uvLxwy5+8N2bGPSzC9pxmNwz0vpTFGkwwVKVL0GYXixjSN+4oXUy+QSkZyZZW7/4c/uRzS70TYT2YNo2igHwgDaABwMIkV5QFZKl3+8It7kw0vBckakc0NqgVnbQJxS6o8hAcJBs+gBASgGAvqYSnKFSyS4MU8eHyTHmSsSBNMiRDVcikmglR8Ct7c0699LP7uFb/+ZX8MdjlDmcJBM1IYGAmuSSpmy40r5CqeYgiOtPygDcwMQo98xkI1fMabWvwD2+ZbFhtMGzZ0OsrPk4f+UZvvnXNkNKSI56zcGR2Qo+d6KOY4sVzDXLWGx5WJqtoFndudFjSzuMQXcQYrUbYLXr4/ytNVy6u46HzwYYDHdawQlplYNZ98go1PA8Sc5NGwwjcMFy3Qv63QD1hoduZ3MdNXekmrgG5QJQGe52VAUWlVQajjYVjlmolx08Qjb51B3GzzFvnGjgH/3Dd6CUQqfTQbvdxt27d2GM2ZLVdxjzjvKgyHwqUODgoSCfChQg4smTJ7hx48a+qhkKHAwopXD58mVEUfRCs79eBxxU8mk0GuH8+fOYnZ3dN5u9j+6v43s327kKmtuhUkLO4xApA5myM3uCUBlrn5NSAJ4QUEKMZRhx54kMpETs6xMCypXxT/5BpOE48eSU1vYBNqnQFymDEmPQCV+VQhgpA1AiSCa/Hke8Wms71kKNpksrxk4wyYAaKoOmKxClRnVbOz2qQimP9Z6f03ovzNG350scXJiNXK4saNCvOWBtwiaXZHltgG/8+DYePd1qWRNnqee43NbFJpZ6KWCcwXH4mMDKN4cElN9Ap18bpQ1cAegE5sMYAJyBg40LuQzDABj4EdYGlppjbIjvoguHA1IwuFLAkxyCMziCoSQ4Sg5HWXJUHI5IG4yUQaA0Am0QKoNIGYTaQBuDyFilo85ouzaAnqouMwCMW8LTYPxawjVlDHCl2EFYU0PUDbFH57LeU/ms98oOnXwCgEZJ4Flfk+z2JGeksZilPJoGNdcGsHNMl8AFaAAVydEfj8Fu18f3/+IGVp72k99Emy5oyPj6RhubDZXwgWp830xqjxkrb5PsBU1GqGMa+WN0xk5ykz0eKOSS5ATLX+LvQbHdm4Bzmk0fAHT6ARzBERLYKtcRmJkpkYrteVSxUjCSioy6fOsFdIs+asDfZ481wJG9kSPtE1ced/D//ouf4dm9rfZjJtTWVo9wzcyY+E37IKMMhCNS78HhSCXKHE2o4VacWEtXHSjwkkBcx9WBHqumYto8pdJWkcbamo+1NR9XP22De2LHe7hgkIKjXnUQGEtmq/G9cbr/MQZwh6f2SRVpCIeT+mQUqbHaKPNQe6+dkH05JtZux0el5mLQCzC7WAVPIdkZzw4R21gGZrR5MKSRSsYA1YqDLkGRR81pWukGEJzteIb7Z3/3MwCsDX2r1UKr1QJgc8bX1tawurqKW7dugXO+QUY1m81DqQJKw2FVPvX7/UL5VODQoiCfChTIgFIKV65cQRAE+OCDDw6lv2yB3aPb7eLChQs4efIkTpw4UajUnhMHkXzab5u9INL46vnHeDhWW0xj0vvy0FFaG6tUIr4pisxYkZRxnLZKKZ2igFLalm1t7TP+mCgycJ34HIgNBZSIVzmFKQqoiZIrqaA2GiuvktoeGUtIpEEZA4Fs9WpogJrgCLQm5ZasBxpNh6FLJH0YLBHWcgUCCjHzgqz3FIBSDiu90NgcmVFGYaAsGKquBsDIqi0AYFoBnLYU5gzo9gN8/ce3cetBh/gJQLBtZ+3EUg/YtFeSrthQNMQqEwINztNVBZGyod9pP8cwUBCOSD2PYOmZK2asjprGpDBlYIkKwTm0MQg0EAUK/VBvZNpMvoeZIgkFZ+O/m43zxIEzay/IGABt+2mavadSBmrqbJzZz2Kw7xWCw5EcnMVfW3stswkoP8xhvSf4DoIyCZ7kGBJJ5izV23YonW63Nw3BGELCt/M1SMVoAIhyzBt5llUlydALDX7yg9u4duUp6T0kklHDEkdp5IsN8ks/VwZDo5UZ2xfGI23DiMkgiLTS4DyZaJy0P/H1DHIpi/wCLAGbOZ58ZdcOGV1k6CtIKUgbcLbba6bBGKBed7G6nq5e4Jzh6GIVjNk5iVLIp6onKi7H+jD7TpZng0azJPG0n83iBoRrdazK4FXL4LCbB9IQt0wJgwjf+L2PcPXn95LzmJQGOEOmk6uxZLYyOnVbjRpF4CUJk9BeFWkwycaWtDEbmgYhREXCxPwseqTA49S+BoA2MHznOSfWfLGkla8gSnKLXE4rg0AprATWBtAgwWbTADocf5e06xHSCCilrLrL3rEJmzHMpF35mP3RMMT8Ui0zs5N6Rs5Y5lrWGKBedbDezSaVPE+g28v+XAppDdi5fL7p4Ul7c545+7daeONEI/Z4KSXm5+cxPz9vPycM0W63sby8jBs3bkBKuUFW1ev1Q0nMTOMwK58K8qnAYUVBPhUokADGGDqdDj755JOCWCiw44HeGIN79+7hwYMHeOedd1Cv119i6w4PDhL5pLXGjRs3sL6+vm82ezdXBvjTi8uJO2J3q4EKCYXrLcdTCShlIAWHVsn2HlobREgnoIJQw3V4bDHK2u4biIT3B1E2AZWEMDJwJRIrbpEe71RPAZWA6kUadcfmvFCs49ZDg4bD0csoZDccDsE5FBhKRCnFKKf1nmTxBaY4iJzWe65IJ58YgIXy5vUVTEMZ2gPpoN+DV8+2SPWDCH/yw09x+c56LjuZOGhtNmz1SiUHgmCbZIwtWmZ9dlYWgjGbSqAkBBnF4UgbOCz5mEhtfX3M34wLtPa3tyQS36JmisY7uCeFXMkZpLQKq4k9kDYGeltHk8ISSpPCe9IcIgSD4Aye5BtEu9yFwiwOBlZ5RCGK8hC7OUSpGOa13gsVTrYqJAVSnktUFgx9Arns57jN50mBHA0D/OkfXcTa6jBfw/cKGfVWrQy4SLPWM+ApBJIa30uTXvckT1S3ZJFTShmIlMoARVlFUUdRCF3piB3EfRw40U7PGEBKhoh4oxoQ7PyOLdY2FBpScASa1l7KErfqSRL5lCfrruwKgEA+9TK+O2fAZ05YNYbDkGkwtp1o/tm3ruMH//kTRFnX2Nh/VuuXsc4aK6VMhpWcziCgTGTAXZF4HjWIwMsylmHfIJO2ndooA8Z57NjQ/pi0immOGkUQpZ3ns+3UVt2U8D2NgW1jyr0asNeNS56ZoaS1sVZ9BPJ4crzr8FilWByE5GgtVFAqycxxT1UaU6f/kidJ5FOa5ek0OoMMT8ApNKrOBvnEGPBP/s5nyO91HAeLi4tYXFwEAPi+j3a7jYcPH6Lb7cLzPMzOzqLVaqFarR66GtZhVT4VmU8FDjMK8qlAgQRMCstf/OIXUavVXnZzbOHlkN5oX3VwzqGUgrT+XwjDEJ988glc18W5c+cO5c6bl4WDQj5NbPbm5ub2xWbPGINvXHqKy8sp9kFTyFfet1C7IKAoFnyR0pkKKK0NFGwGVdITYxBaG7249kUZCqog0ig5PNZiLVIGjkj5XAoBxVhqwVEZmyOV9TTcndjjRckWYduPrzmbNlPTqEkOV3BEYBvF2yDHLuk81nslztAjHuvntInMUkgslDiE2Dwmz0is1xvIKjn86OJj/PXHDxGE+rmJpwlcT2xY68UpieIwyd5JPYZiUZRRQFYq2eoSoFlrOYKlWGtZlZMGtrSDG0Bwbq3ywDbOwWAg+ESdNLY21MqWI8dcqlHW5oexMcE0fi/nNoOHYZOc1JhqP6WzEDdvU7tGkMNObxQZmk3ZGJ4UZPJpqVFCxZMICPfb3FlOBLZIGTvH+IR5I0tZMcGd26v43nduIAxp8+cGJgXurPcQ+oIZD9TnWRMYbayNWNxrGQTPwFdjdWDC+1PODVhyKz1XKv06OZxlbkQoOwyjjPpsGCqSIi0M1fi3Jigyciwt/UChUXXRScgJO3GkBmfKNo36c28qQtJBHXODUMMhWvRlbZSZPqcnOPwEVu+NIzV4lfLUOdM/O9D21wmCCP/pf/0bPLzZtqomCrQBk2w8x6e330Qa3OWJxNLGKf0IzJWJk/CGjV7CebSvwN0YkkkbIFBAjN2zCTVkSW6xhp1uD3fjS3I60GAOi7ePDrUlyhKVYwYihaCabjcX2aSoijSkw6GIuwGCUGNmxkOnk77KqlYcVGZK1GFMznMCcawJij826LaZq70AUjAS8eZN5av9ztsLOLmwe9LB8zwsLS1haWkJgFXQtNtt3LlzB71eD9VqdUMZVS6XDzwZdViVTwX5VOAwoyCfChRIAOccZ8+ezdwNtF+YFOUL8mn/IYTYIETa7TYuXbqEM2fObCzwCuwdDgL59PTpU1y7dg1f+MIXMDs7++I/rxfgqx8/Rp8YjAtsrZHlmcHUWAlEfbaLCATU5NWSJ+FOCsrjP05UDpMjJ/ZbfGxhY4wtFhtjxv/sg6KKeahTY595GyG1s01+qOE6IvZ6hBkE1CR7KtmCz8ABUhVLkTGoS4aszcydUKPpcAwIBJQB0NtGQJUFQ0UKhGDYXt+zNnZAQPh983BEKkcvU8a2kWq9F+hk672qZKiM7fYm0NCwuq1s6AmbEUtYKvzb71zH3cfdLX+fEBowtr9Rd9IDgOMKCLlVbWPGXFDWUsNaZqVb72mdTSBHUbb1Hs+y3kN6jUhn5G7FvbyhlDKA4XYsJakZhZBwBEu0qIzG80RWnh2FSDPjA/faes8nMEoGQMPjaA9p90SKXRYAzJQlamWHrKzKo9bKU84qcxr5FBlk5tX86K8/xaVPlre2hTCucsEgu6hobA5QmPL7Gq3BUopmKjJgKRZ/WlsbrKTPT0UGeZVVqs4il0aBhkixqwWAQGePFKUMSp5AkKEmNAbw3OzjgLFCOiZjJQks4SLPzZTgeFtLKNR+Tz0uifiJQ8MTWCGoLaI8Fn1lgeXezjY4gmFhYXNDJvWJtP2oiz/4H7+H0SQzJ0eg3YYaiTBn6kCDeTxdim0AEylr6Zfwi1gbvQQCShvbFrHz/UYZMAGYGMVRNIriz2kAHSpwZ2d+qdEGUAxImA90oFIJKBVqCDc9/8loA8OSLai3fIdQo1pxMBjRnkfW1nzU6h4GCSRuvemhXHU3vjbl8W9i65dZnyFO/lR1lk98BjMGWGh4eNTO0gRiSoXN8I9+5xTp/FSUy2WUy2UcO3YMxhgMBgO0223cvHkTg8EA9Xp9g4wqlUp7+tn7gcNaEyvIpwKHGQX5VKDAAYEQYov6psD+YaJ8unnzJp49e4Z3330XlUrlZTfrUOJVJp+01rh+/Tq63e6+2exde9rHNy4tk3e+T8OAVtTeDq0NGmUHnaytyZPPUQZcWCsvwdmWf5zzjQIwgy3cy4yHW0emW2ExZi1u7C5Eg0hpqHFYutJmrBDZ+RBtAAShSiWg3AQrQYNsRUhorAVN2uXuhhqznkQno1C2HmrMjC31Mnf7AuiH1rLPFRyBAcKUEpdkDAGhU4y0Iee3+LYOQ84N2wvrPQ5grhxjYwP7+1Oyswzi233nSRf/4S+vYxRjC6S12VI0YwzjjAJbDIkLNJcOh8wgfDLbavbPek/p9CLUJGct6ZhRoFPHsVJ2p3VacR0ii+JKv5YTMjqLrGNZGT0ASoJnKvYMgJLDMdpj67080/cw1Jn2f67gmG+U7Hcm9kcDq2iixE+pHLpbqhIDACoynnzy/Qhf/8+fYC2m0Jfr3kdUuEWRyTwuCFSqmjFtE8XmMenkU/rrQBK3pSZWsQnNCyMDJ4X48kMNnqKssm1P71pRpOF5wirUUkD9/VSkQKV1eA7yqdMPUHI5RlNqwnrVQaO+c+1H5XWoxw0CReZnypJWgB3kWEiWHQFgp0XfW8fq4HKqcxEu+8d/cxt/9nsfbr03TqS81OsWKDCHkxYZxtdgbsaxyioATcq9TvsK3IsnoEykwYWIHYcmSMh/wpjU8sTOH1YbeIJjFLPmM5EGc5PJah0qMCfeng8AVKBRrkj4KeNNK2NVTYQO1x+E1jaX+NsNhyFcTyLwtz5TtOYrkC7f0ofilGFx4IJBZWz8oc4fQ4LFJgAMRyE5s61ecUjkU3dox9g/eHcJC80XRwAxxlCtVlGtVnHixAkYY9Dr9dBut3H16lX4vo9Go7FBRh2EfPPDqnwaDocol8svuxkFCrwQFFXsAgUOCCbkU4GXg48//hizs7P44IMPDuVOm1cFryr5NBwOcf78eczPz+Ps2bP7Ylfwywfr+Mtrq7vOcQLs860nOcJIk0iEyXt6owgLFQdPB+n5ADVXYL7mQkqGNV+lVpyo34NnHDuxHbKkFoMbMx6ZDZmBUhowBkFkoMbWfEGk4Mp4AmqSDxX3mjaTrCv7CXEICRlQq36EWU+gE6ZfkbVQY8bl6IXJBJRgQMMR4NwSCqFhmfVkCvE0QSmn9R4l6wXIZ/8HxFvvLZYZktyhJDMIDW2MSmx1Cfv2h/fw4wuPyYULYzAugmy+gQsGzxEIlYGUPNNWT6vDZ72XRpRNiOr0pmZcM8IubUoPECybYN1L8QxgiW5qYbnr57PeKzkilXw61ipvrGHyKPtLnCEkNDjP2M5zXZ2Y/vrwwTq+/Y0r5J3r2e3JzpaxYyedsDRqfEzKuaKsjQUaqbKSicoxqY0mpY0q4z6Vdm4KsuYXIP3aTOAHCq7DEGU8+kTKwCHmy6ixDTC1z9QrLkaBLSJ7rsDcbCV28lPEdSvVussYoFYS6BByn7IUnhP0fAXJGUkBFZcFV3E4Zma37sjPmkK+/q8+xMffuxX/Yk5pohnnOlEmThOOlUlpip9Ag5cETNIlNmO7PJfHEh3aV4n5T3oUgVec2LbqUIPJnW0bDsLE/CcdKNSaHoZxm8IMbPDZeE0cB3+krH1hyvWIJt+VMDb0xmIku++pSENKvkG4MwbMH6nF2n9SxwdlvYRJ8zJOSSWVjAFqVQcdQj6UJNYJVroBKp7A7/72SdLxewXGGOr1Our1Ok6dOgWtNbrdLlZXV/HgwQMopdBsNtFqtTAzMwPHcfa1fRQcVuWTMeZQkmoFCgAF+VSgQCpIsu59QkE+vRw8ffoU7XYbb7zxBk6e3N/F4euIV5F82m+bPQD4/s0V/PxuZ8szUw6Xki3wI43FqoOVQUhWpkTaYHUYYq5i3zeNqiswX3VhONAJFNYjDURA05PohelzVFnyzPyOiFD4yioYGzC4ksMZ7wguwS7otR7/MwYwdkPr9ik+jYAyZmw1mGLRF2mDmpP+PVd9hZYn0M0ioAKNlsvRmyp8MAANh0MKjlAbKMbs72oAlxlEGcWAQAMet0qtLOTpb3m6ZmQssUXNcrFtZhvqk5pkKKdszMxOOdiJwSjC73/7KpZXBrnfux1aGUTSwHF32ujEwZhs0tWe9wBZ72Wop8xWt8SdbVQaMiV7RmmTmPG20YbsGjiJoBpF2lqGZvxAaTvLt0MKjoBovdcscawOqNZ7yY082izBdTaLGnk4G0K8HwA7tqkqqechqn7+k7v4+JcPye/fS1A2nzCTrC4CxmNZ7syGmUCN7buSPkplzAVpBLHJUk5lnTsjN8qYjAYAGPkRpGSZNltcCFBCZqj7gbQBZqouVjs+6fjuMLLqVsawtFBNvd5kYoeonig5NPKJaqdn5xKaRV8Yo0D5wvHGDivCpNb5wxB/8D98D0/vrid/iDb5JNOAzYri6aSSPRD2mAzyQY8UREVCJyyIjDLgjCMyUbzt3TCCV3cRxmTt6UEYT0BpA2gWmzOnRxFEWcbmk/XWfYhyfFaVUXZDVtKmDK0NWASwjHtmFGgIhxOIGLPRjynzoT+KUK27CAOF2YVq6twoHY4o4wZCHe92HZNNKlUqDnq9bFKp7EkS+UTZqAHYsftPvnISjcrLJXc452g2m2g2mzh9+jSUUlhfX0e73cbdu3dhjMHMzMwGGfUqkCOHkXx6VWqOBQq8KBTkU4ECBwQF+bS/0Frj2rVr6PV6WFxcRL1ef9lNei3wKgWgTmz2er0ePvjgg32zIfjGpWVcetLf2R6z+wyL5X6IIzUXT/sBmVAIlcH6MMRsWWIYaSzWXIAzdAKFjtKA2vp7VRyeST4FapKLkqK60AY8xfZn0rYsgooZsyWDiTEGIdiGHRFndneiNgZKWVJKjf87jDQcGZ8po43daZ1GQA0jjZojNnKY4rDmK8y4At2MinZ7TEABDCXBEMJev9Ds/HyXM5JCQjIa+eTnsN4baZOrjkQtZk/gCUs+CWbt9lIzhXKQTwbA5btr+OPv30zNaaGCMaBa9aDGA5U8Zgk7dA+S9V403u2c9Lodw+nqDCHSlSgMlO+Qfl/xlYEgWO95jkCUkflgQLfey1NkyEMCD0MV24ZGyeY8bQe1YJ7ntkNVSfmavqlier76q29dw6c3VsgVSNfhpEwgZgAusnNHHCkQZshxtNJgGfZ0ma3PIHCESCZvtErv+2kE0kRdnEa0ZOXDuI7IJFcdKTJzVIJAkaSW4djqkGK3GmenmgQ/1GhUXTTqHngKGQ7YLLdIU5RKtGwbqi3lIAfpXSHmQ/W2kSkzZYnazE678bif+NHtNv7t//zXCDKU8wAAZeDVXPiEwj+AjYmIkk9klIEoCahApR6rBhF4WcIkrMX8QWgVTgn9OeyHQEnGLn70KLI2e9teMpGOt98DoIbJBJQaRRAx5wOsokp4IrFvGW3sBr8Y0mvLZ0Ta5qhljF+tTK78Jyk5WvMVRBnndWU2+US9fzqSwSeEnJY8gV4v+3ySqAjtUPo+gGbFwd//0quXHy2EwOzs7MaGyyiKsLa2htXVVdy6dQuc8w0yqtlsHjoS6GXjVaqFFCiwlyjIpwIFDggK8mn/MBgMcP78eRw5cgRvvPEGrly5Ulz71wwTm72FhQW89957+7IQNMbgP/36Me6tJfuET2owuyGgnvQCHK27eNT1Qc1HqLoCraoDPVLoqLFUKOFa9AhhvIEyWKg4aGcUf2oORz+FlNEG4BnFhywF1eQacsbAtzEhxtjsKIdbokkZa/M1yZTSxio3pDDjjJOtUAbohQpVRyTmLBgAa4FC0xVblE0cQEVyeIJBMLs71iqFbJ5TGmgpXTTiadLGkmAYUq33BEv93aYRs1E4FRMyZ7HMkPWcG2kFMNoS949/fAcfXn6arzEJEJLDccRGWwG6gvqwWe8ZZBNlr4r1HuUgRcyioCJUdGK3G1iCjNqEiiO2kE+OYFhslmKvlWAMEYFaIg5rABjn+tHeUBFsy/yXhNH4en3rm1dw59Ya9RYGgB4obwzgeBL+MH0mDfwIblkiTLvnGaBSduJtssbQSoPxFAJ3bIOV9HoQKsiUHehlR2CUcP9RyoClbPLQyqTOs65gqeq24SiCdHjq+A9ClUkiaG1Q8kQmeWhg8yJDQl8a+BEaVQedPq1A3Kh7kA5hpz/VCYw4PqhDrhdoMolMJbR6gYIjGMLx2HnzeDP2dwrN1n0Tv/7hbfzZ//2hvb8Q4fcCawlHnWSUAZMcRmVnYqqRstZ6Gf1H+8pa+iU0QQ8jiIqMPY/WBixQgOQ7FVnaQBogjLH01L5tWxxppXwF7saQTGbKti9u7emrVAJKRRrSFelqIIPxRpz0TT7AOP9J8lQy1StJLC5WEBpLQGWRT3GWjzuaSOxftbILP8jOXsoilieg5sW1ewHJ3vN3//YJlFLyvF4VSCkxPz+P+fl5AEAQBFhbW8Py8jKuX78O13U38qLq9fq+PDMfVoLmsH6vAgWAgnwqUCAVr9INoCCf9gcPHz7ErVu38Pbbb2NmZgaAvfavmhVcgReHyWL6rbfeQqvV2pfPjLTGv/nwIVYIxRBjgGMNDw+JtjHTeNQNMOsarAbpc1uzJDFbc7HmR3g2UpitSKxl7G7sBopkvUcXvePEAAAgAElEQVSpfVCKlzIjT1obIO2RzsAWr+MedBljEMwW111n60OpMQYOY5DMFnIMDJTSGIyGKFeq0NqSRcoYBEqjvKHYsecUjIGPbXw4rOvMQskWGDVsAcn+96Qwbe2bHM5SLbUAW6B1GGIJsWnksd7LI3nIQ4pGxiokRsSHeV8Dsy5DieBOwjgHNxo6JTTFGIN//a1ruPWwa22VuP3BjTFbg9GJcD0BHqMEypPxQbHeU0pDvCbWe2qfrPcoGVaDUMMRLFPZl8t6TxKt94y13mvv0nrv+FTO03ZQl7m+BpksyzN6XE4rxGtj8K0/u4w7dyY2XvT1udYGbkkiSCGCJgiDbEIEoD0fZBVZDaFvpo4vZWBS1IF9P4JIGT+e5In3FK1NqhhzFGjwFGWXMYArBfyU9YBSBiVPIshYM1CX31Gkx2M5+7ehFLgB4NhCBeWSE2tDtx1Z9l4TUHuun0OJ2/AEVjNIUyCf1eZMSeJpP8SRuguv5iUe53C7pvjoe5/iz3//lyDGLW6BiUwuCz4Tabg1ByFBxaUpBJQ2gDHje1qCGneskIpTQJnIjoe4/heMIvv5MfOKHilU6i5G2+cmbWCicW7VtvYYZVWLcbZ9AKACS1zFKacAIAoUpCdS1yZaGQjJoTSB4Is0XFfsIH4ZA+YWq5Au31hrUpZDlPWXMTZbM+tYal+krvmGhE12gP2e8w0Pj9vJxNex2RL+7juLpPO9anBdF4uLi1hctO33fR/tdhsPHjxAt9tFqVTaIKOq1WS70ufBYbSoU0oVKrIChxoF+VSgwAFBQT69WERRhMuXL0MphXPnzm0J1yyu/euBidViv9/fV5u9QRDhDz58iF4OK5iHHR/HGx4e7IKAWg04Fsscy8OdT6Y1T2Cx7qHtR1gL1EZl0uMcye7+m6BY77VHUab1XnsYQkoOnXJMRfBUyzozVkfpVHlGetXZYWyHmoiN/xYZAMqg4QhE4ChXrTUnF8B0z6kIlqpYUgAqnGHycyTucieSGJ5gGBC6kuSbu5rTMMooQm4/Nk82maRWswE4DGiVTGqf2HJuhsTrHmmNf/mNK3j8zOY7GYNxyPaUAk2wDZVhWmGCcWaJp5QHxr203oMBpGSZMSics9Tg8MJ6bxNaGzCC9Z4jOFQWoQA7D1KssHIVT3IcOggVSpJhFBksNTy4TsrjXo7zljjDgDC4oxzfi7TL3Rh87Y8v4uGDDvm820ElBlSo4VYchBlElT8MwSVLLPDaYyK4FZlqIeW6AkGKDFRFBsJJI3CTFZNGA0Ykkx1DX0HINHI3PVOjWpIYpKxbKMqm9NG7eR7H4anzGWDHseMIktJtvR/AzSB/Ty1WIT25MYdkdSHqfY963DBQ5HtH2REAgXwa5iDHy44AEOLM8Ua6xSJn+Mlf3cQ3//VHm/NJziwnAJuyXuLbgl4I7nKSYkr7CsxJ37FkQg1RcewNmwOlkrT9Y7LZgwFMcEv8aAOjDEoOx3AYwWgDHWkIT8LE2u8piIoTq9gZDUIwh+94nyW0EkirQEOUZHznMPa7QDAkjf7IV/DKMtVqWEUawhXQBOJVKQ2vJOGPN6nVGh5qDW/Hx1PO5WdYmk7gORzDjIXQYEhTN2bZf06fj5rZ1ig7qeTTP/7KKQiiEvFVh+d5WFpawtLSEowxGA6HaLfbuH37Nvr9PqrV6gYZVS6XX6nN3a8SBoMBKpWd9qYFChwWFORTgQIHBAUB8uLQ7XZx4cIFnDp1CsePH9+xKOKcF8qnQ46Jzd7i4iLeeOONfVsYtwcB/vCjR7l2uE7woOPjRNPD/fX8BNTyUG95b8XhWGqWsB5EaE+RThOs+xFpN3g/g3gCaNZ7kQFmXWkJsASs+Qqe5Kl2UHVHYD3l2rqcp1rVjbRBVTL4KT9PlgX8QBmUBEtt50CNqbi0bBpt4HGWqVaiWnPlCSkv57DeK+ew3qPWwRiAhbKG4IAmbmVNKmoGkcK/+NolrGaMm+2EE+OW0PGkwCiIoLUNxpYZ6iFg7633JCNQwYTP44i3jZzg0FjvEeZzyjFUFRsVoaITu7mt91wJRxjUK+mbKPLUhx0BElnsa3oNWWX0U2MM/virF7D8mBDIkYLIV5AuR0Tx+6RU+w3gEiz6BOeIUi7a/8/emwXbcV1XgusMOdzxvYcZBEBSFE2TTYpUUQQ8llqh7rYlOezWR3WFFRXhCIfCg74c4fCPo6O7I7r90e2vrnLZLbm7PFfZlkd5kCzZEiXZZYsUTZngAJLgjJEgiDfdKTPP0B9578Mbbp69L94D8ADmioBEIPOeHM/Jc/baa+3hoECcRpWkpnMeceC5G+MQRQElZKCTunFNv6o33xoHEWh7mIXJpaJwaDQ1isBAP8x4xJKSkrkG4itN280IVyoSeI6MiacJtBKkpR93fGDv58uEoFVGTR2maxh6uWXzQkoCdy6kiBvVqicAeOrx1zcST8DMRBIw3ldyMzXGhykcRDkxoNu2Hl7gqjWeFJCpgowVVKwhE1W2tS4RYdroKSK19p32ANK5dYcZ9wdnHHzhkGqBfi+Hyx3soIBqRVtIprI+Wyl93mzb5zKLuB1PJbDtyKDRicu6aJsv1/mSJAnUd8ozWxLooeSo3ELHirSdtdZDO49GU6M736isJ0epQSdtccAhbvLCIYklMmLcH454pJL3pZ1qr0/XKNMBYv+7j3TwoffvIdu4FSGEQLPZRLPZxJEjR+C9R7/fx+LiIl555RWMRiO02+01MipN05mPwVmP3oro9/s1+VTjtkZNPtWocYugJp92Ht57nDlzBufOncPDDz+Mdrs9dT/JXvTWuBXx9ttv45VXXrmhNnsAsDws8LtPXxvxNMHZ5QzH5hKcuQYC6uxyhjvnU2glsWJsSTpVhKEGhWNZ761kFnOJQo9gFTjJflSw3vmSXFoMEFSDwgVj1wPr0I1U0PotURJZgHweGEd6VyUy7P1uPdDWkrSgixlqpaHjWe9lDojlbLVcOJhFzFH48t5kxHXvS4FIO1Yx+bXzmBL16o8MfuXPn8cqs9bHhvZcqaIZmDLYHCcKSvO8+mex3osUQCX+DjIHSVjvFcaX6sHAsfPcQhLkWawF8kB8vbQpqsaNsN6LFYKKOE6gIpaCVBeODM96bzjDmK41HdQGyvGum2gsMdQNQHlfDs5Pr/O0HpN4L+cV5ZrBTAjrASOIGCL2nXP4/H85iXfHCsVp0ImCYaqGy7oe9LPJhwYqlnDEcykI8gUAskEBGYmgdVwUhRV1UilYd21zUGs8lK4mV4PKKQ804o31w9bDGIdmU4cDu4yuoBU9x85yM/7Mht/nonBQSrLe535FXzpyoIUk2RgiUVKiICj/CdnOGe8jKVAw9mvGPPKJO+Q4D3QbGosMuzrjPO6+Yy64z1OPv4Yv/Pq3p9YmmpVImpzgTPWf/Lj+j9hK3GzZ1XvoTgKVauhGxJuITm8IVe+hEAJQEipSQKOsw5mMEwC8dXDGQQjA5RYus2tElDcOMlHwU2zu8l5eqqamjKejXg6VTrfYc8atKfemXobzpTpSVFsNAqVNn4rCc4lmK0Z3PkXSjKaSYevBqfukNK0y5uaCtBh1n7wHms0IvR5NKiWJQq9PHzc05//Uh++iG7hNIIRAu91Gu93GsWPH4L3H6uoqFhcX8eKLLyLPc8zNzWFhYQHz8/Ms15Hb1Z5uOBzW5FON2xo1+VSjRgC7KatCKYU8pydFNXjI8xzPP/88kiTBiRMnoAJFm2vi78bjRmQ1Oefw0ksvYTgc3lCbPaC0Rfqdpy8gs34c3L12BuDMcoZj8ynOLNFFddfjYDtGIYDCO1YWLN96jyafdsp6zxCKxL5xmEtVULUTS2AUaGZEZHxmzqOjgTygyOEEmQQVOcHOW+/FBCk2wWgGhUY2o/VeJMMB6G4ENOMyUFbST55FQll4rKdGlnoZ/uMXTmHIqPtCYUI8se30wLfeK5VddPatUuGANkAHQp0vg6ChZ0UdYzdY7ykZvhfGlrWhQt+UkbFQUpDvVqwlaV3lfVlPh5NYwOj2M0MIYH8nYQdntJDIQ/5xY8wyC4p4nwsYP534s9bhD37vX7AUsC0CMJOyohgZthojihQyEx4rbOHQnovR71WT2c55NJIYw4D9U7a2rUJh1M/RaOjKxAMpw2NLEsnKOYazDlqpyjGAqklGjQ/DkUGahq3wSsurMI3tPdBINEYMeyylBEkcAkBWWMy1YyyvCzjfMYV4QvDMNoJLPnUaElf6HEUT78iz2Om1YsUinzppBJVUF1msJJ4mcB5xQyNnEuYTeOMRNTWrntPkOELLqcQNAMhUQbViqGYEIQSEFrRkN7TZE1+JCnJKKAmlJGQkITrlduEcTO7gCguXW0it4aa843ZYQKV6C8nkPWAzBxnLqc/BZAY61ZVqImd8aa1H1HZyxpdFVNc1IwQwv6eJpKHHMzMelBIghlZIhrqbU4cNAJKY9x1MEoUeQ2ArKbuDMVYG08f877lvD95/aHqy63sBQgh0u110u13cddddcM5hZWUFi4uLOHv2LKy1mJ+fXyOjtN46HjvngnGbWxW18qnG7Y6afKpR4xZBTYDsHBYXF/HCCy/g3nvvxcGDB8n9pZQoitkz5WtcGyY2h9dzYjkYDHDy5EkcOnQI999//w0lmo1z+N2nz2M0DuoYVwYs4D07YL8ZZ5ZGODaX4swyTUBJAIfaEkMBwHocase4yMj2Y1vvMYJDXOu9vbEOKpuWMotYC9gA8aOcRShnf2DD0ouBYVrvBZ4dy3pvvDGYRT+2YDKE9RzXSotDigGzWe95AA0p0GeeRChmFktgPt0YpVCCr9ZSwsN6gbcXB/jsX76IfIYAXeU5pRqK63G0DlrJYH2FCSb2NqRihXEPOBY3ZdZ4gJTJHSJdTcp4H7bO8yiJmDzwPmgpg/ZrkRTBzP7cbt96z/mSOK9SeUxQlUW+5XisvcpzjyTPgrKXW0hJB/yPLTSgIwUJXl/hCgA4jnVrbfJ3RUNtJJ+KwuL3f/dfsMqoaWhyy8qSB8p3lRsMz0cGtK6vVBZRKIhvojEOrW6KUUWw0vvS4q+o2J5lFvPdFIMKYr0wvvLb7T0QxRpZNv23o5FBsxlVkkejkUGcqKBVVhop9Gz1PXfOl/V1iPvEVZAa48ZugxyJ9dX/vGN/C+kU4gngJxlw+1Kp5qTnStwpYS+30JKngIoYJ6kkcPRQG1Uzw6e+ThBPY+RDc031n4qBKQklpqTLGwcRq6s3QAnoVgzViiCjjWsJygqWxDanESV5NbbtkxIqlWX9pjGEFPCFBQqHIjOwmQWch8stRKS23PNJ/SnI6QomMzJQqa6sXWlzC52qYG1L7z2kF3C+rKvWasVIOzE2GxyH2li7Pk6/ZOzDtecLJa6sh2Ima3DXaVd6ObTamOCllcC//YE7eQ28RyClxPz8PObn5/G+970P1losLy+v1YwSQqyRUXNzc2vxsNuRfKqVTzVud9TkU40atwhq8mn78N7jtddew+XLl/Hoo4+i0Wiwflff+xuL600+TWz2HnzwQczPz1+XY1TBe4/fe/oCVqbYqOxvx3i3X7Br8WzGmWWagJpLNZoxMMTVe5szswfZ1nv5zlnvUUXiS+u9cG2o5UEO1aj2FB8Yj24UtrxLZNh6b2jDAfyyjZ2y3pOkWmloPbQIW5EBY+s9gWDdq2vCDFxuUaF8ECjrPG2+rUWWQcQ8j3gFj9cv9fCfvvQyi4ihkKR6bN1VYpYAFpe08A5QRB0GoAywUtZ7ztHWe0VhoQjrPSVlcFyirowKGksiNlkQ5JJ1pbIprJxgqGkZj2hUOGhF1akCMm5xJgDtRGBxSB+cY713x1yKNC6Xd5Irt2P2V+vBJsrcDAVf1gfDjbH4/H/mEU8TSCb5BIC9n7MenbkEvdVwYsawXyBKVHB8yUcGaTtGXkHwAIAh6iX2+0Wp+ax4h0PK2aJw6HTiStVnRiSWCKKDUrZ5vUFRWnuFxiqOAje3iOPwvQbK8abTjNBj2NUt93M0YoWFuRRpWh0WoWqTzQxmc5TybH1znURjkUGsclq8/1AbQmso57c8+qe+/hq+8DmaeFqD9ZipYN0Y3riZiCufW+h2BNWOIRMdfJ+nWdhNoFU4GYIcU6ntDkBgeSOUgIpKJwaN8tvljYPLS5tPVzi4wpWJI5MmCwedqspDu8xARNXbzcii1YkwnNJnhAB0rKBjhTjVaATqCBaFLS0QQ32d0Zc8N8mD8YnLGbVoAT65TSUTrLXngX3dBBfXqXf/u4cP4sD87DWO3ktQSmHPnj3Ys6esiWWMwdLSEt599128+uqrUEqh1WrBGAPn3G1lv9fv99FqtW72adSocd1Qk081atwiqAmQ7WE0GuHZZ5/F3Nwcjh8/PtNkpa75dGMxIZ92Gs45vPjii8iyDCdOnEAUVduJXC/84cmLeKei3sw7/QIH2jHe6eXsQPVmlATU9BpQdy6kGDiP0aamrwwNUi0wYmRw77T1XplYHrDeG5ky0BuIjlL3yuoYbSkwDOxHWu8R72PmgMgMIZNqQptlvceI5nDJyZRpvVeqShgWRTNkLo9sWcie24tjuVVVsb8BaLW1BWMNuD331Jkl/NbfvsEOKoTQ6cRTs8rZdnrOs1QrZZuCrHm2U9Z7lHIJoPtYXljogPXeMLdB671R7qAUKseC3DgkUTgZobxn1ds51nuD3JQqVIKNSXbYeu9ax/vN2NeK0W5c7R1cQe8scfWUWauGquO24fhr5+HxR79/EiurOcbSFdbvTYDU2QxbOESJQsGoE0UpcSaIGIQIdSXZsECjFSOvOK8it2jPJcgqCKSllQxpGlWqAUIz3iK36LQjjCqkbcNBgSiuDlzn2djOsOIqjfVoNnVQeZoXPGKJm9fAVbMLAAf2Nsj6fdZ61ljP7UtU7cYJBrll07jNSGJxSO9H1aRLtcSBfaUtWCw3Kp5nJp4msA4zTQomYB5HKIFoIUUyl5KKGFu4yjpnAF1v9OqJVbThERy/yPYdNnRYIQREpNYUXFKXSSfe+dKuz5S1pFzhyvpyhduqjvKAGNd3qzp8v1dAxQpSipJsihQaDV0Sh8xz935cwy6UaMVYa3BVTVIKct9ewPJ0PTLmeD8cFWx7zW4jWiOfmonC//g9R1jHqHEVWmvs27cP+/btA1CWTTh//jwWFxfx1FNPIY5jLCwsYGFhAZ1OZ1eVzJgVg8GgJp9q3NaoyacaNQLYTR+wmny6dly6dAmnT5/G/fffj7179878e6XUdSFDakzH9SCf1tvsPfDAAzelb3/h+bdxZimczX2pl5c2eKvZtVvwLWc4Opfg7JiASrXE4fk0qA6aTyJcNDtovcfINGRZ7zmPPY0oeO5LmSGt92KCfBoQi1eO9Z4zBjIJtGE9UhW2zBtaP+bjAjZoO2y9Z5jRMgegIYEho2t6lFZaXOu9zbGItjBoVDBMaTNlXdubl/r4vcffhFACWpUBF+c8O6N2AiFKq70dEE6Ng1GcwMvust4rjEOkZai0R5AI86Ct+RRhvUdFYJ3bhdZ7zM/MasZXFPVyO7WmWjfVWGhvzEifRZjAhWZek5lBJVW40hbuz/7oOSyOA3VxqiuJmM3wHohSXdZ0YoD7/c+GBlGqYYigZGmXFwhGo6zbpJOwtZUk5MBhW6xSnWArlC8rqzniVK+NLVsbr267rFulMKhQEhnr0WpFGAWel2M8SiUFqcLNcgulaGXoSj9HqxlVnjMARFri2IEWkjRCxpizaClI0oib6NBnelg6D7QShR6jL3DrQ/UyGyTS/ps7OmsK30gAEz7r5Wcu4K9/+zuzE08TXMvvnIdQAaWSAHQnge7GEDJMeqydhg/XELQTC7vAOOHHRE4VtA4o1EkCk9oBpbpICqhEQ22ad8axKtVS7uqfydyn24zQz0tFuZCi/CNESRiNpwE6kOiRRIrkDxWhhLTWkUSu955Vn49SZQJAXjgksURG9DkuqeQ90GxE6PXpNZNeR9z96PE70Kma2NZgI45jzM/PI89z3HfffRiNRmv1onq9HtI0XSOjWq3WrorlURgMBrXtXo3bGjX5VKPGLYKafJodzjm8/PLL6Pf7OH78OOK42ioghPre31jsNPl08eJFvPbaa3jwwQcxNze3Y+3Ogq+8fBkvX2akpAK42MtxuJvg/Ep2zb7055YzHOkmZTBXyyB5AwA5836zrfeynbPeoxbiHOu9ARGAHxiHbqS2Zb3nFL2oTAjLPON51ntUO8D1sd4TTPJkVuQOSIRA5j2kybEwX60+8aATqC+vZPjNr7y+do8mZ1zax4xJlDEZFQroClFa7YmAUnaWPsolvkrrPUm++zfWei+skCOTuSuKsF/dHv69tS5Yk4FjvcdKvGc8olHhEClBkjYc1dPkkO0ELNWC80A3VVgaXh3vGpHEwbl0y/ObRVHVUDmGlp4jzdL7+Sop4It/8SIuXrxa7X3WGYCbgR3ORwZRJFEwmDGlJUk+OevR6MSVNZsAAB6IY41RIAt/0M/HfbViey9HoxVVEsrDQQ6B6eOBRxmUHlZkD6wODNKGrgzel5Z91X2LIrlHmUGShmtDjTIDKVBZX26CSEtW/b5GVE2YtRsR9u9pAFLAMuc/WkkUxFrAg1abAmViDVdx3op55BPX1c56YG7TGDJBN1GYX7ga/FTjR3HlUg9/+H//I8zQhMmgEDwwlTmnfmY9RCThNz1z1dCI5tOyDtJkX+dJ2zfeILa9gHVhLISo/l6FbgNHXRQ6Ozme9wklttj75QJI21vH+cmjoZAbB63D6mBOsD+OFKk0Uopvp0qh0YiQ5eHkP++BdjPCCqMGbpIo9Pr0cSdzpr2dGD/0rw6zzrUGDWvtmoNNmqY4fPgwDh8+DO89hsPhWr2oiY3dwsIC9uzZgzTdOk/aTaiVTzVud9w+Jpk1atzmuF5WZLcr+v0+nnzySSRJgkcfffSaiSegvvc3Gjt1v621eOGFF3DhwgUcP378phFP//D6Ip650KN3XIcLqzmOzKXXvPz1AOJIAkqw7NIm1nscJEzLyiZhkwUASyNDRq0XRwbVmosSVPCoXzg0iJV1rMLbQ8QTAPgoRkLcGo7KiPMUCibjkRDXNAGnADlQ2ulxMXJ+pklmpAQkgH1NRyoAVKC+yWBk8P/89SuVwUlrS8JpEhyUSkBHEq2mhtJi7QEIKRCnUZB4mmAWezPq2mZpc2K9R4E65sR6LwSO9V4oaFYULrzduOBYUBKJ4XOgggq5daS15SA35HEAIFL0e+E9EOudLWIObLxOLQWOLDQrCQfm64ZE81RDjFj51XNjdowv/83LeOutpQ3/ZgsHHfFHEGscdMyvE6mY+2aDgtUXOUHSfFgE+4B3HkkjnBeqA++TKRzSwO+Hawqt2dvOMotoG9sBBMljYKwoSOkkjsKEx5IJlnr51Pd//0KK/Xsba8oVKpFjAm5f4o7xTWrCMEbE/I5Tdnrr0Y6nvycPHO1uUPQIAeS5wef+l79DPlbVeev5N2MznIdgjonr4QuHpFOu44SWiPc3Ee9vbSCe+I0x1EXU5VGvDJlMEZL9hH+bEONi6FvtGddOrb80YVHJsi1kvD7X+opNQ0Sc8wSNQN239ZDMPrkyTjb4N99/jD0XqEGjqi60EALNZhNHjhzBQw89hBMnTuDuu++Gcw6nT5/Gt7/9bZw6dQoXL15ElvHrSt4o1MqnGrc76lGwRo0AdlN2hNa6Vt8wcf78eTzzzDN44IEH8L73vW/bz7Gu+XRjsRPkU7/fx7e//W20Wi188IMfvCn1nQDg6XPL+NZby9f02/MrGY5eY2Hae/Y1sVQ4LMxg8TCf8PadWO9R4FjvZdZjgVjsGefRJQKFi8MCwoffmZhYyVKBm74p7e5CUJQdofHQRGRhaOnA2sjS7QB8VQ63mLoDkDJnjh5AOkNcyHiPA03PXvxPbcM5/Me/fGVq0ewQnPMYZnaNHNKxRJIodhCRzT4B7IRqywyw7qj1XgCFCWvoJtZ7le2DJrio4DR1nzl2OY0o/H45XyomKHCKpgNXlQMUVkZ8S7teVlrvSQEc29MIvqfUmDQBd0GYu+1qAjbiG994DS++dHnqeyxnDNbN0g1zpkWfdx4x4zuaDw3iJPzelORQuK18FP6+DvphAqnSVg/lOJAGyJ3RkEe8VoEaL0cjuv2CUyPNesSMPmqsw/w6lYcAcOxgG512smVdEHE6H/PWcD8bHAK7PC7vwL3cssebaYTW/naEVmfrnPPX/7evYbC5jiglvwnAG3dNBFTWy6HnEySHWlDX276Mcr6jsgW2KRAPktQkeXR9z41KeOFZH9LH8YwBfSfqea4H17qSO7de7BW452AL3//Avm2cVY3NsNZOJZ82QwiBdruNY8eO4eGHH8Zjjz2GI0eOIMsynDp1Ck8++SReeuklXLp0CUXBqw12PTEYDNBut2/2adSocd1Q2+7VqHGLoCZAaBhjcOrUKTjncOLECWi9M0NcXfPpxmK75NOFCxfw+uuv31SbPQB46Z0+Hn91cVvrvHMrGY4tpDgzroNBQQrg7r1NLI7tLDiWRxPsbus9YruQmE+iteueBppccuhEClngnqVSBu/T0DpQEZlE0dZ7VH0pAIiJdsrz8VAM672RK2s7cL4wcgbrvVls+tqRR6oNabkEAB4Om/1kvPf47F+/hiWGZUoQAohiXVonMTPiZ6kjxQ2WlKqm2npvPTjWe7EKv+8copUTWBoVDloK0h5uFuu9TiqwOKQPbsfWe3ONmMzo5gbCLZN+8ijt9EI19CagFJpPPnEGz558u/K9LHILIQW7fxWZhZClbSUFZ0uVUVZRI2k9KNu9Cco6OYQtG6UgzCyanaTyvKxxaLaSMZGzFYN+gWYrgimmHycUGDbGod2JkVVYvA0GOZJUV9oCDofF2C50+nbnPNJUBy3zRrlFEiuShOKOuBNrrySSuGN/u7Qim1bv8GMAACAASURBVAIlJAri2YXGvmsBt7Wc+R1yHugkipybVR37vju6W8b/3/j338Lbry9ObUBqOZPd5YafG1cy81zVWSQR7WlARmpnJDHhT9GYXAvsQBI8VCJEuO5UqLZqljuogPqJLhlFHTv8e2qOawpLWh+yyCdmf0siiYxYZ3BIbYBvVZszvwnWefz4h+8az5tr7BScc2u2e7NASolut4tut4u77roLzjksLy+v1YxyzmFubg4LCwuYn5/fsTgSF8PhsFY+1bitUZNPNWrcIpCSDkK9l7GysoLnnnsOd955J44cObKjqrXadu/G4lrvt7UWL774IoqiwPHjx2+a2gkA3l7N8DcvX2YVzKVwbjnDnfMp3loKE1CREjgy39hAwFwZGqRKsOzSJtZ7nBoEpfUevfhqRjT5VFrvIbjQXxwZaCWCQWUqMNQrHLoJUddJiSD5FNoGlIqkrhbIArtxrPc4BA+nnUmgeMDoTokSGDDek1ms9zLrWV1gLgLaSQ6bWciY7rcOEwu2q+/D737tTZy/PGCfWxXiRPMVT+vADpJ7Xk0QAGRRbuAqSUW1Rx1zYr0XaoZjvaeVrPz+FoWD1oHtxpV1myq2G+uhZDhqqKSoDI4DwCArx5IQyTnIzfgdIKw6tcSICGo5X47NBaPfzPLZm2toJBXWWdeCnFHvaYJYARWlgzYgc9WfwJPPXMC3v302rMrzQJSoNbsvDqJYs1VNXC7B5BZxI0KRhdsd9QsIFSa/Rv0CcVPDhL6LRKenalUpJcsA8BQMBwUaTV3ZRsg+0PvSvqqqVov3QBIrjAL1iQxjfsGZv+e5hdbhvg4AvWGBA3ua6LYT+FCzjCGf04cBviqC295qZthTyWbMI582z/OOzadIW8mGf/v6X72If/7GG5VtuBkJpK0NeFYhPhFJpIfapRKGxVqAfJ4U+TOx5gsTKIHt1O+35s9s+X0IobY5NaNCoLoCR2EcRTJIdHPqrDlmEbN2K0ZGrI+GgVp7G/Yjxvi1/UYFax73wffN44GjXVabNfjgKp8oSCmxsLCAhYWFtXaXlpbWakYJITA/P4+FhQXMzc3tyDFDqGs+1bjdUdvu1ahR45aG9x5vvvkmnn/+eTz88MM4evTojtslKqVq1dkNxLXc70mNr06ng0ceeeSmEk/D3OBPnr8E40oS4VDn2uuNTXB2JcOdC9UWfI1I4vB8iuVNAScPYH4GG7MFRr0FYHda7y1lBopYsSfbtt5ztPUecYy+8eR5jhiWa1zrPW48m5vV7QA0mLNHB6BBrNVaGuimpaVPXvBVS+ub/cK3zuPFt1bYv61CnGqodVZImuthNCt22HqPqmMEvHes93LiGpwHUob1XrqD1nvcOclK5lnWe0fnE3QaCb0j+OdYOI1I8gJ03MXjhPzejNMvX8Z//Yc3SRIFCNvITUMV6TINxciw60qxarA5H7S1m4CqmTLo5VABW7LRoEAU+B4O+jlC0etQAG04NMHaTSPCFpB6pnlukRIf0VFmWOIWaqzQSuDOwx0c3NcME0/gE0acbwJX3TrIee+2g0AseO81t87aam7XxnIB4H13dDZsf/nZi/ji7z5DN2Q93y90M/zk6NVQzQjJwRbLgu1Wgnd+W9Z6wbapnxI7ULWJCkO/i4qwlOSoyrm3IGKM4YVxSBh1/gYjw0o+8h5oEtaPWgp86l8fI9uqMTuqaj5tF0op7N27F/feey8ee+wxPPzww+h2u7h8+TKefvppfOc738Hrr7+O5eXl65KUXNd8qnG7oyafatQIYDfVfKqxFXme4zvf+Q76/T5OnDhx3Xxya+XTjcWs93tS4+vBBx/EnXfeeVP7rXUOf3DybQzHARjngXcHBQ62d4CAGiugNqOTKOxpx1itCPrMYr2XMQN9g8JhgSiMDpTWe23GwnAnrPecBzrE4pJDLlE1wFNiUT1i3EOqjcIDDUaQK2HUjBhaD8FYxY9cOBF3PWbpYyG7kVQCC2m2FthttfkZf5N35pvPvYMnTl1m/64KUay2BEz0DHUprpf1HhWoBcrACqcOBFWroSgs2dE00Vl51nuh7cTviaCVtR7UY2MRMoxdRoVjEX8Fc1z1ANpJ+P4em0/RTGJw3a5mESU0NFMxxG8Sm8sgnTmzjK997dVK9cyWYxmPiKiltGF/6xHPkHChGCQjAGSDglVkvsgsnTjQp0n2mOj3oXHVWo8kQIIN+nmQTAtdZ1HYYBA3zy1i4puvGOx7xHguhXGVA86hPU3cdWQOUawhGH2Ao8QAwvd9Ag+6/hVQKp9SJnGzp82r/zlLskln3K/u3d9EtK7m55VLPfzW//n3M0gDPb+43WY4D1FxD6KFFPG+JsRmkpFlnUp9SBjnRj1r4hjbWonQ3nnEz8Mq52v9LYCwanMMzhwxRLBPwCKCmHeaIouAManU5CXhJcR36X/44EEcmLu2ur01wtgp5ROFKIqwf/9+3HfffTh+/DgefPBBNJtNXLhwAU899RSeeeYZvPXWW1hdXd0Rd6Ja+VTjdkdtu1ejRo1bEleuXMGpU6dw77334uDBg9f1WDUJeWPBJZ+stTh16hSMMTta42s7+NPnL+HKJosg60vbuP2tCO/0t1fQ9OzKRgu+Pc0IUayCdmm3i/XelUFeBs9l9YKDtODhWO9JiSzQUMaos9TRAvlust5TgmWTFSuBIeM9oe7BeowqrPciAexJRxuCCw5uXGqBQ/p4nHxjGV9+6gL7XKoQxQp6SrAzn7GexfWw3rOOfg+cKwMhlEqEqsHlfZnZH77s7VvvlZnK12a9V1iHJDAGAKVqqRew/uJY7w1zU14D8f1PtCKt97wHuonHSsbJqK5+RncupEjX2VJqIVhjgBZlfJjej9evmYINABtv36VLPXzpiy9iOION3pZGGJglCJSPDNseN040RoPwN7zILdJWhCxg/eesR7eboBeoTzdas4mafu2DXg6dyEqLv6IIHN95tNK40opq0C+go3DtphCCdmYAVgcFVKB9oBxHKNsz5zz2dhMsrruP3VaM/XsawDrCglPHhaqfOAF3PdDQAv3QJGCMZiwxYihJIkaiCQCsDDPWfgDQSTUGhcORQ1cT94rC4Ff+579j1zlbg+VZ6E2DNx4ikvCTeaIUiPc1oaaRyFzrPc5xifeLbuD6te8dZa23vZJVobZz46E0EDpCFMmgypEzBispyBWEkIIkQQum2pWrck1ihR5jPxUgjueaEX70+B2s49WYHdda82m7iOMYBw8eXIs7jUYjLC4u4syZM+j1emg0Gms2fs1mc+b+PxwOr1sidY0auwE3P1JXo8YuhxBiV9Va2vZk+RaH9x6vvvoqrly5gkcffRSNRuNmn1KNHQaHfOr1enj22Wdx5MgRHDt2bFf0ia+cvoyzy9MX/sZ5rOQW+5oalwczBt424exKhmPzKXLrYKUgbaY8gPmGxsUej/haSCNcCATFJphY71H3nmu9t78ZYTEQMLYQ2JtGWApEPpcyujZUIkWQfBoSAfy+cWhogRCfFkkRLBLeNx6JLK+p8jyMG/NxgX2sRyTC7QD8smPcRGeLUrU0YgSaHIBUAcN1j1YBmI8GU+2nnLHBYtoTvH6ph89/48y241BKy2AG7g7GujYgiQBOnNAYVxYIJ/oZZ57CsTArA8shUsYhjmTlu+I8oJWsDPh6ALGSwf6hpIQNXA/1TKhA8sR6bxgYm6wHWrFCRtZ04r0cXBHdSu62lFERKImnZFM9NCUBRty6JLJZRDXvO2p8SR4XjEt3YzJteXmEv/jCCxgVDlGiUATG+i3Hyy2kEiyrJqBUH6lIwjIy9L3zSJoRMoJUAkqiivPNo6wlgTJO32zHkFJASAEpJaQEhJQQsiSn01QjLxy89bDWwVoPZx2sKf+704yxUvGtHg0NWu0ERQWJkAfIBec8klhjVFH/ZDAokDaiShJqOCyQJKqyppHzQEMrZKH+Zz3SRCMn5g+TcSaOJI4daMMruSXqzlV0R1qg4LC0DLQSAYbADVrxkni4GFkJAcvqy7ESuP9QC2pd4tav/q9fQ5+on1MFIQT8NRY59YWDiMpnF+9rQBLWlDuC8KeO3O6dh9hG3aZtHd+Hf+yJ7dSxo1ihCMy1y8B/9XbL6Uc7tGzrMes5cV9NLqkRytX4N99/FA2GzV+Na8P1st2bFWma4vDhwzh8+DC89xgOh1hcXMTrr7+Ofr+Pdru9RkZx4lX9fr+23atxW6Mmn2rUuIUwCcrvhg/uzcBoNMLJkyexsLCAxx577KZkvdS4/qDIp/Pnz+ONN97AQw89hG53dxRyfersMk5fHgaD94X16MNhTzPCFUagKwTrPRqpwnLGC6pwC1sDs1nv7Wlqsrj1SmYxl9DqJ471HrV4tB7YE6sgQUWpFPrGoRNJhG5tQ0kUgXe0JLDCF5QqiX7guRQeaGkRPA8ASJTAgIhdDa2HBMi6CUNb1pzhvAEcZdYEat2+Ah4dsYpGOr1mjbcOIFxPBrnBb3/1bNm2lmMywsM7vqUdUGa4Cl2t0gHGaiZuofkZjp1zIvcoryeKw8W7gTL7V0oRvBZrPVkIPM9tqQILtKOULIvNV4AKQFJWoNSdMdYF6730MoM0kkEF104lFY0KByVp4mY1A+hoYxlT7KQCS6NJfwHu3NNAMrVOFbcPMkkbx18WpooXqM+dx2CQ40/++Lmylp3zEDPYWU6gY4V8BsWU0jzyCShrq3H3a7RjUv3U7+XQiVo7fpQoJI0IcapLlWUsS7IpoBAEgDhRiAJ9pZkodAsHZyyM8TCFRZ5b5JlBnlloBVSdaTYyaLVjZBUEE0X6RFogq1D2eJSWpUWgdifVPsDro6vDAscOthEnGhDTNVfeA5ES5DxICYmCIIL43xdmZJ25G0WCT+AAdFONZWJeBpTftv37rmbZf+G3n8b5V6/wTmgKvPXlRG6G7+CG81ES0b7G7lnbUfKhcd2myj5MvL/e+VLZU7UdvlJF6D2tjAohicIJIFpJFNciYxvDGAuIcPIUx+mSMwYY49BINYYBtSkwtulkgJtQUkXgv+9ACz9w/15WGzWuDdba3TNOjCGEQLPZRLPZxJEjR+C9R7/fx+LiIl5++WVkWYZOp4Pl5WUcPnwYd91115Y2BoNBrXyqcVujJp9q1LiFoJS6YT63uw2XLl3C6dOncf/992Pv3npSdztDSomi2BoymdjsWWt3jc0eALz2bh//cn6VJDWAUuEjhMN8qkuruWvA3mYEIwUWYo3ljJFai9mt9xpaYLgLrfcWRwaSsMuiFrSrhUMnUci2Yb0XWrQDY+u9SATtqTh2WYpB8HB4kVms9xIpMGQEj0L3bzOuWu95NIpldPduT7H6//3NGQzHygnvNlIdSomxYrkMXFQFC7UWJPF0LeBa71mHmdQcFLwHtKat96Qo8+KD7RDWe9QxstxBq2oiLC9saSMVsOZTAes9Yz1iXd7DKlDWe32m9Z5kWO+lWpH15AonMJd4VIhjN2DydIQA7l5IEU0lnvggs+DHyGwEAQfPKAnMLfHSzwz++I+fw8jYNTKmmFHJBIzrjPD57pmVVVw1Fifc1WhGaM2n0ImCjmRlkEwqUWmbB9AB5Nw4SCkgYw0dA+tZe+89FICuL+93PjIYDQ3Muvc0VgJVr2M2MpjrxBhU3JPR0EAogar3apSFbfOKwqHR0MGAcDauHzVNydhtRti70ICMFNqprjzPCZQKk2EAWEQQx8IP4Cf8cPfrMa3FAKAVKRb51FIGcmzn99KzF/Gtv30VMlZwM/SdLXAeQvETNiaQqUa0r0FaNrLBGPK8ryZ3Jm1sy22E+j1HGXWN4NSA3A6oGmnel3XbQnMFx1Zi0+CQT1U2o5tRRchvRn9YQEsBs+4cBYB/99/e3NrD7wXsRvJpM4QQaLfbaLfbOHbsGJxzWF1dxT/+4z/iF3/xF7GysoIPfvCD+MhHPoIf/uEfxoEDBzAcDtnKp7vvvhudTgdKKWit8dRTT23Y7r3Hz/3cz+GLX/wims0mfuu3fguPPvro9bjUGjXY2B2Ruxo1djF2k+3ehHx6L8E5h5deegmDwQDHjx9HHMc3+5RqXGdMUz5NbPaOHj2Ko0eP7pqJ/bv9HP/w5jKWZ1isj4yDjCS6icLKjIv8uVRDxQqF8+gzs7qB2a335tMIwx203hswrff2NSMsBe5J4TwONPS2rfdSJYLkyYhYFPeMQ0OJoO2UFgJ5IHowMB7xbrTeY+5n/WzWe8rmkGaIffvDxJMkVBF//q23cfFKtS3QhHSaQK0jQZwrySghgDiJ2PdkFuu9WEtkzHoZ7YbCSo8OdFjjd8x6zzBIcsp6Ly8o6z0PrVS19Z4vg8Eha77QdqAkl/qBsYJjvdeMVHAcdR5oMKz3uHNEZtkWrGYekQSOLqSIdLUMkBsI5/ZpD4mGzjAw01WJs8Jahz/4/LNY7edbitSrWMHNoGTyzqPRjDBk1k30ziNuaLZaSjITKfq9HFGqt9TEkUqgPZ+i0Y6hIsWqL0Jxac66oDLCWA9dUZtFCAEHIEk14kaE1tzVNm3hSjs+4xDHEnnF9zQ03hTGodNIKsc6YxyarQhZaI7DeH2llMB43SME0G7F2D+fQqyzZePMBjnKam4/1kqQ48uAOT8bMguoGQd0YolVxv6awQzPpQp3HtmLkS/ri/2Xf/9Pa0kTMtkeAeVtqW70TKXJhHiSUsI7WjXOPg9qbsp53NdxqeEJ5dR2Yg+UMmpU2Km2xxNQSuvN4/k0KCUQClmwiCXmLYgY421hHNJEYUS828ORYdXkdB7Y243x9tJVCv97v3sv3n+oVq5cb9yKLkBSSszNzeHTn/40Pv3pT2M4HOLrX/86vvrVr+Jzn/sc8jyHEAJf+cpX8OEPfxidTods8/HHH8e+ffumbvvSl76E06dP4/Tp03jiiSfwmc98Bk888cROX1aNGjOhJp9q1LiF8F4jn/r9Pk6ePInDhw/j/vvvv+mEw80qcPlew2by6dy5c3jzzTfxgQ98gDUZu1HIjMOXT7+Ly8xg2HoMCodWrNBJFFaZi/xWrNBs6DX10mpu0Y0VVpiB7ptpvbfMtN6DY1wLx3ovUlgKHGtEnEePa70XCK6MCOu9Uo3EsN5TAhllocKw3ltTH90s6z0zxMH9Kd2mLgNQYspY+9xbq3jyxdlsgTaTUVIKJKmeKal4Fuu9vLBQMqzKmWAw5PVd5zw6bY0+sT/Les/tDus9ytqGVH1QfZhlvRc+BhfDwkGyrfdoKAHcvSeBUGH/SQeey5WHQCxy5J5O3omVBacsISWM9d7jD//oWby7NJyq7rGFnUnJBMweB+Zk1k+QjQroSLKCqlEkS/JJAM1OgmYnRpTqDf3OWQ/JlYdVwFoPRVhXlfOl6pu4mTiXqrT7i9IyBLAQt2Gdg8ltqZDKLLJhgSyzWF7NECeqUk1siIJjlLJtODKIExlUK+eFRRxLdNsp2q0IagqDG6oPNwGnr3PJXM53r7BAqgVGREfJrWftB5RzQA75xLmM++/owksFWIf/9H/9/caaZ86XCijm3HIavHHYUrxuCmSqNlrtcccDyhIPpfVm8B1kHCuNFbLQPSfOYzsjAKmgJtRdWonKeYgi7p0hEsaMcYgiGexXnDW71pJMiOGoZLnf8kYakeQTADSbEXqMBLxOQ6+RT0kk8T99/1HeidTYFm4HF6BGo4GPf/zj+PjHPw4AWFpawsc+9jF8/etfxy/90i9Ba42PfOQj+OhHP4rv+77vQ5rS66f1+MIXvoCf+ImfgBAC3/u934ulpSVcuHABhw8fvh6XU6MGCzX5VKPGLYT3Evk0qevz4IMPYm5u7mafDpRSNfl0gzAhn4wxOHXqFLz3u8pmDygDa3/14iUsDs01O2P0c4tOrNCKJfpEQCHREgvtGP1Ni7R2wiefbgXrvcVhAaH09q33iNNmWe8pwnqPOMjQerQjgdAlc/gMxZDdcNpxKGtIcfiOnbbea/kh9u9PwNEgCCHgrN1CPi0PCnz+m+dZxwshShSiSLH9/2eF90CjGaPHqDZvrEe7GaHHqAGnpALVz7wHYsa17RbrPR2oeVMYB62qCZ1eZtBKdVCBQFnvrbKs92ypmgiMSR5ArGmFVGm9h6D1XieRONRN0YgkGM5ZpcKSEXlLdIG8oMkn7gwnJ1zw/uwLL+Dtd/qQUsBMGUe9B6JEo5jBfnY4NFARv5aTyR10rLaolKbCA61WjOWlalXlWruFw8LBFpJmtGZbthnOeQgZDsB6QmEIlGR5OMBLnCxTXRSnEnEaoTX5mfflffYeJh/Xkhr/mWA4NGi2IpiKecJoZBAnKkh0R0ohW5d0opRAmmgksUIr1ZBaQWsRnHtzxnIOsUSpmSbgJsI1Y4kRQdCV+ymMDN0PYs07LmUBfbgTo9VO4QH8/V+/iDMvX966k59NvTQV1kMnCqZiDE7nEvhOvPHZcu4t8/4LBklIqaPKc6u+B2qsMLzW9oMEEqFe8j48etjA+GKdRxKLyr5ZFA6aIJe0VigCJBVH2SQZkkTOc+TUkAOAiFlvMIkVejO29yMfOoyFdu3OciNwO8Zj5ubmEEURfvmXfxlCCCwuLuIb3/gG/vRP/xS/8Au/gPn5eXz0ox/FRz/6UTz22GMQQuCHfuiHIITAz/zMz+Cnf/qnN7R37tw5HDt2bO3vR48exblz52ryqcZNxe6J5NWoUYPEe4F8MsbghRdeAIBdRThMs4KrcX0gpcRoNMKTTz6JO++8E0eOHLnpqrfN+PIr72J5ZFl1nkJYzS26iUIzqrZp0VLg4FyC1Snbr6f13txNsN6zQmFfqm+I9V5DEtZ7xLPtGYdUiaACIBYCRWDR3DcOkUTwPEcMG5qraiWqyEF48wSzWO8lEpUKMe89FnSBbtOjNBCU4OQCb8749d7j17/0FssyLgStZVkX6RrqHaSpxogZKOcWrAYQtNVaj/6gYPUzDvm0W6z3dMh6zwPtJMJqoP5CoiVMYE7Esd5LI4Vh0HrPoxHrG2K9d6ij0WkkEEKSyrEJOIE5AFCC13csk37yANIKkvpvv/Yqzp5fhVQySPzM0k8mUJpPPgGYSX00IEhgKQXm9jfR6CSQOlyvCRj37cAlWufJ4Ctl/U3dQk8oM1zFmCKEgI4VYi0h2le3e+/hrUNROBSm/OIY49YsTb3zMNatBbUjLdfWLUKU91BKASVLQkkpiQPtCEJLxJGGnhIcpq6xsB5aimDSCTfZQGtRSabNioCr2ab9uEFU3ru8mtkgMfz+w10AwNvnVvDFP3i2+mgC8BxpZQC24r7LRAHdZKwiW39QXrtRrErryACMsXQNKUoJTqhkwtQUTU5RlxtSLwlC+mRd+HujIwVrq7+vUaQ2kM2bIZUAAkMmlaACYMdsDfvMek7cpSSX2JjUe9rfTfCxf3WI13iNbeN2JJ8mmHyPFxYW8MlPfhKf/OQnAQAXL17E448/jt/4jd/AZz7zGdxzzz34xCc+gQ9+8IP4+Z//edx///348Ic/vNbOtHnDboul1HjvYXdEdWvU2MXYTQP17U4+rays4LnnnsNdd92FI0eO3OzT2YDb/d7vJrz77ru4fPkyjh8/vqts9iZ44q0l9DIDzQwaU1jJLOZTDef9FusVKcq6H8sVwbZZrffyGaz38utgvddNwjVWANoOBADPei9WQYJqyCCXKOu9ppJY2ab1XoOw3ssd0FIgrfdShvXecHIc4h6PrIcE4BmPQlUEv7332J8YNJOrwQ0FGVTcrGHTcX//m+exuEoTocEmZRlMXfv7DHWcgDKISmsiSgwHxbjeAX2AATNoUhiHTjtCn6hh0+vnpVoisI91Ho1EIQv0RZb1ngzb4WzXeo+KeeZUH76B1nvZNqz3lACOLSSIo6tZ0zstzKPOa4Lc8jO3YwUMN53nU985j5devgznfGXgeQJnHHQsYZh1bwCgyG1pg8kMiOcjAyFBEkWTtpOmRjbFd7A1n2JuX2NNkUmpBwFAQAT7gHcekLT6KQRKXWCthwwoZpzzUAGCznoPvW4MEEJAaIVEKyQox4CqdVJ5bmVtjpAdaBxQOAK8PppoGSSRPYBICdJ+mGOpx6pVA2CnCwZx7ZCtBzoVNUXfv7eBOI3gvcev/dLjpJ2s1AKu8DPZY66Htx5CbbStFYlCfKBVSXpykixuVC1mT9Rdy3MLEatqdZLz5QBfgVjLoIpeSxFM0ABZMyr0/a7cVG4niHvq7TaFI9WfHPqA86yt9Wg0NIbE/KhgJi5w+3hvnJD0b3/gKFtVVWNnsJviczsB51zwmg4dOoRPfepT+NSnPgUAeOONN/D444/jN3/zN/Hd3/3dePLJJzeQT0ePHsWZM2fW/n727Fnccccd1+8CatRgoB4la9S4hXC7EiDee7zxxht4/vnn8cgjj+w64gkos6Bux3u/m2CMwcmTJ7G6uop9+/btSuLp9OU+LvRynF/JMSgs9jV3JodjaWTQSjTiTYu9u/c2K4mnCdoJ3/d6cWy9x8HEeo+DhJmB1oroc10aFWS0aXFkEA6v86z3EuLyEuJeFcR5DqwHVQeZZb3HIDo5a2UHsJ6/Z+4HVNgPeo+DDYNmspFY4S4V5bpF/JOnl/Dc6yvMX1YjTjbWZJl14ZrNUP/CA2g2wvV6JrBj6z0OFKOfWefRYrSXJuGxa2K9Rx0rhCx3wcCRMS7Yj1eHJvjO9EaGPMc0Cl9nPzOgIquj3JJjkgcQM2oQFA7oJlf/3oqA9+1tbCCeAMB6MmcfAF89VDjeO1Y4DS14bW5+G1997Qr+6YkzKHLDJocEW/Uxhi+tM2faP+V/pzffzqQZ4cBdc5g/0NpoBbpDcS9K+UTdR0pdYG24D1rrg9upWiuhMaBUOYWJJ4D+dnGGamocKPeh3zXOsazjUbkFsw9w9+vNQNK24q19REuBowfLefXv/Mo/YfXKkG7IA3JKW7PA29LCDxgTT/ubYUKC8RA4tdlYwlGrGwAAIABJREFUhBkpHWSQH6F32/kgAU2RIVnAMYAqe0WNHdsQtLEb0MScn6XCZibNNRjjPFchleU8lfu7qzkeOtbFY/fuYe1fo0YVhsMhGo0Ga99+v4+9e/fiJ3/yJ/G5z30OZ8+exUMPPbRhnx/7sR/D7/zO78B7j29961uYm5urLfdq3HTU5FONGrcQbkfrtzzP8fTTT2M4HOJ7vud70Gq16B/dBExqPtW4PlhdXcWTTz6JvXv34r777rvZpzMVl3oZnr/UxztjK7rceoyMw0JjZwioxaHBXCNCNA5G3bO3gUVGwPtarPe4mEt5AcuJ9R4FjvVeZj3miUVk4Ty6REBkaWQgiehDSgSjqPpYq4VDRMRJYiKQ0jeOPM8RI+N5aD3ZDsCPmXLjEmZsvXf1hx6HmgZpNG2RzwxqKwkB4NJyhr/4p4vMM6lGnGhWbYEQjHVozUA2c1RPE8xivceRAXD4iAHDQjAlfKPywiGU7Ou8RzvA8Frn0QqQYM57tIkxKCWyjQ3Rd5wHGkSAzHqPlEHEc63yJqe8vyVxx3wLUk2/B5xEau5rNrIxJNN6j5tMsD72+M67fXz5q68gHxnEacTOHDdjJdMs4AYjJ7AzBO3zoYGOJHSssPdIB/uOdhFNeUc5fYx1D4jvA0XwludBqFcY1n6V7a/738BZENu392uOypzzPFiiaualRIzxYMB874bMxAbjPFpUNssY0RQy7v5DLSitcPLbZ/HMf32L1Q4AwHk05xJ6vwC8cZCpRry/Sdplcchllp0mgzjiKCLJF4c4RujXxoTJYXIMIeXpgeQPYu1AzWE4CTlU4pSxnjUpbTDeCQ65XBiHhNHWcGSY9aiAH//Xd5L71ahBYTAYsMmnt99+Gz/4gz+IRx55BCdOnMCP/MiP4GMf+xg++9nP4rOf/SwA4BOf+ATuuece3Hvvvfipn/op/Nqv/dr1PP0aNViobfdq1CCwm2S9SikYRlHaWwVXrlzBqVOn8F3f9V04cODAzT6dIG5X1dnNhvce586dw5kzZ/Dwww+j3W5jMBjsunvdzy2+dWYZuXEb7Noy6yFESZYszVA4vQrvDgrsb0ZoRAKLTFLpVrPeS2BgZDigzLHeo5LzrQcWYoXlQACIrOtUOLQjiVAMqaFksJ4Ex3ovVRIDwnqvqRE8j4laaaes94YzWO+ZPAd0DAGPQ80CkZ7eFzwcuHlPznn8v3/z1gwWR9OhIwlVEcVvpRr9GfptpBUA3v6D4U223lMiGBPLcotuK0Iv0NZKP4eOVTDBuZVGWA7UyhFSI3TPyMRz4vlTtVx6mUGiZZCk4QScKQsjAMhMScZZotMMCuDO+Qhpkgb343IyWggY8iIEUjXCwNDBDc2sI5WPdxkOC/zZX7yIYb9AlCiMiNpJW46XKBTEO70e1jjoRMEQ9VjW7x83NHLmMTp7G2i0kyApxlF2UZZ2ZUOEOsF6CEFYV0mBUG4U9TR5TzvwexEea8jwONG1CusRR+GaTobxPDj9nJs0oKVATty1wgKpFlvslDcjtx6xEqy5WYdhXQxsvdZWLLF/Twv9Xobf+w//RP5+M0aDAiKS8DMkO62H0BLRngarTotuaORE33bWQ2gBT9zbKNHBunNwHp6whhOzevRu/j2h0w+Sv9YD1yg88x6IYl1ZGyvPDGSAsKFqahXGIY5lcOzhEEtKSdKitdWIyPpb3CfUSCNkjG9HsxmhR9S9/e8fPogje3mEQY0aIfT7fXYC9j333INnnnlmy7//7M/+7Np/CyHwq7/6qzt2fjVq7ARq5VONGrcQbhf1jXMOp0+fxiuvvIIPfehDu554Am5P1dnNxsRmb3FxESdOnEC73Qaw++61sQ7ffGMRzntcnLIQGRkHB4+5dHvWJBNEWpKWWJsxq/We8rwg3PWw3uumdE2RnbLeozavFm6jamcKKPs5yjJnYD2pjuLwK5pByN0s6z0LASWAwwHiqWzTsyeev/F3Z0mShYKUImj7QgfsN4KjFlqPJtPuy1qeVR7At96ba9HHTogseus8uoRSkiJ/VgYFQh1xZVgE+/EKYb23wrHeI1SSLOs9y8ii90A3Cd+P/S2JowtNtBnjIBdc57pI7mzykvGA9A6f/7Pnsbo8gk7kzComAHDMJIf14FhzzQqlJfYd66LZTcnrYBGWLmxpB/DUUXTAniK4yENsCwznsm1tB8raOCFwEmU4BBWlNLsK3vvXjHmds8mcw222Zq7C5sSaBw53IaTEr/4fj4fJmAAEAME8/uYfxgdaG+x0Q2BZ6gFTFYmboTnf4O0OJaQ68do7iCfUW1TbITWQc74yMQcoVVnU0KMIq1mOsozzyaD6PwAUDGcFANDcNQ0xb9jXifGjH6pr6Nxo3Kh6bzcaw+EQzWbzZp9GjRrXFTX5VKPGLYTbQX0zGo3w1FNPQQiB48ePI03Dmb+7BXXNp53FxGZv3759+MAHPrBhAbPbyKcnzq6glxucX6nOgBsWDgICbWagoQrdRMEIwM8YWJvVei/x/Mz0nbbeyxlSGq713hyxOFzkWO8Rq+uMst4zDOs9YnXdJ2rfALQFIDCD9R7z9eIu8eI4wqFGBq3o4Danks0/nlrCm5dH0JG6drs8sbXO02YURZm5y4WxjlVXYIJZYurc6+wTZM4EnPV5b0A/L0oJsDIogoGhwjp0AkFC6zzagXu6E9Z71DWwrPecJ8k6oPq+dxPg3r0JFtotSKmgJSMQzuyAvOpQ/H4/C0X1J3/5Ii5f6kNFEkJeK/nkkTLJ1wmK3M4UCM+HBirw/JJmhH3HSos9bnCLk29BEUcc4m27PNt2CTCajyFs/6hfM+zRlKDsNek6TBRRPgEnOM3lqBhlLsf7Mb9DzJdhdZ1Mel8rQqeb4q//8CQuvrHEO07V4aWY+YWM9zehYgUhBWt8MMwkDw6ZxTnTtBVOBKCt+yhyaZvkVADke0PcgIi4h0kcnu9QYyClaALAep84t4hbz4kLysbv3/3rO1lzgho7C+/9rnIl2ikMBoOafKpx26MeMWvUuIVwq5NPly5dwj//8z/j3nvvxb333ntLTR5uF9XZzYb3HmfOnMFzzz2Hhx9+GEeOHNmyz24in158p49zKyNkhSezZvuFRaQk25N/MyIp0G3FML4kTSjCYj1Wc4u5GdRPbobP/yzWe5z6V8uZRYsRNOAofagYvPUga0NlxPWtFg4UP0Gpw0bE++xR2veFkDtPngdXrTQynrWaH1pP2ht2I+BAcwBuTXLqyS/1Df72X94dqwYAISV0pNb+kDZWY8SJZgW6IqKu0WZoZvY2UNrpUaqc9ftyUBiHbosO1C+tFmRG8TC3aDfCbS33crKdFjX2EA1Qwz0VnMuJAFc/M9im+xkAXiBzaSSg1nWaVAPv26NxcK4Fqa8GOTltMWPlbJLYOt67njve+X3pq6/gtdcWoSIJFSlY41DkNkjyVJ7bNQRgI+6gM9m/ggnYd7CNhcPtNQsqDhkC8Gu1hcCp2URtpsgjS9U9o8inbZJTbkcy1ek2YsZ7N60W0mZwVHWCeU3cRCLucog7HzPOozOeMNx3uINLF1fxzS+/wjsIATlD/+4c60I3r457EcMhwFnPOwZH6c0hdwkGhXxlCIWjt4QCchv9Iy8sQjeC6rvUGBYT81Lq1I1xoB4UR/3JqS9lrUeTsQYpmMl6IVLxsfcv4KE751jt1NhZWGtJxd2tiJp8qvFeQF3zqUYNAruJILlVySdrLV566SWMRiMcP34ccbxzdjM3CruJELlVYYzB888/D6UUTpw4UTl53C33+p1+jlPv9KGlwMVh2Pd7gl5u0U0UnPcYEl70m3F0IcXyeFHkPLC/oXGxz8/ka8UKy8waGCMRo6EF6xwn1nucfUvrPfocWrFCn4isLo6Kcs0aGIOXRgZCimCAh1ocr+QOnUQiC5xOKgXywEKUWssOjEdbCxTbjD9oQdeZ4GRkWwBNJTCigv4oyazhlEYFPPalDo0oow+4oc1wDazffvwCik3v2oZ7IySUBmItUIyDOs66DfuUJBXT4ocb4R8jy2ezLms0IqwSdQOAMnDSbkXoMfq8IFQAwNh6r5NgpR8+dhIreJSFwZUUkGr8/0Ig0mWWexJrGOtgnS//WL/2d2Pdlue1GcuDoiQdK/rpypikq9IurAwMlKoOYa2ODFqpLouXT4FHab3XD4yP/cxCK1SeAwCMxh09NC90XmA+AVYzj8NdhUbSmPq8HFOtpEVpbxcC1ypsZHlzLw8gkQiOD0/88zk8c/IipBKQUsCsszySWsLOWBvG5BZSC7gZvpumcDMVLMqzjX1XSIGFgy1EU5QPUobrGI1bIA/OOTWqZhNF3ljrguOdtR5CVWeLW+chA9nk1nkoWb3dOQ8RqJtjHaDE9rLVOe84p1akVhIFsY7inGZuPaSgv7cFU77I3a8fKvy4Ce1YY6EpkTYT/If//eswuYWIFDzTnqwS3iOdSzBaDn/7G4fbsJveyyjRyBmKWxUrOGIMcYxvd8GZE1PkrfFQiZxpbNqM4DfDeoRi6YmWlfXAvAeSRFfWMKJqZ9G166ixh74nWqvgPItDEK4y6wg2Uo0BYdc8ZCrrRhVzvTiS+PEfOMZqo8bOwznHqh13q2GWmk81atyqqMmnGjVuIdyK5FOv18Ozzz6Lw4cP44EHHthVZN4suBXv/W7CysoKnnvuOdx99924446wR3ZZvPrmejqPjMW3z61gVDhcJoK3m7GSWcylGs4b0rJtgrvWEU8TzEq/zWK9ByEwl2gMDW9BN5dGGDIC6BPrPaqfDxjBj8x67GtGWAosnnPncaChsRQIyCyNDGQgsA2U1ntZIPqXEQv0XuGg4ABZHUGIpQgGmHrGQctw8JvzPpXWe3TGNXcknnbERAJ70xxaXX02jvnGlvvJqWfwdyev4J1FHpmVrwWCBOQ4cpNEErlx0DNkZpdFsxVyZh0MM86upQIcE1Cqg/Xg1rHp9cc10Yj9N4+jaaLQakRIE400lhBSIYkVqepsp5oMjt4hyyC5NR6FcciNxSi3GOUGuXHY20nQqwj6WOcx34qxWrHdeY+5NMbqqHq8SrVEL/CNpoLXzns0Io1BYBy1zqOZqHXv3lZo6dFOJPZ3GxCiepllHO9ZKwkY4tV0ACsQbr1GLHPkjiahYikwqmjw9Kvv4ut//0Y5zkux5eDWuDIpgF0/p4SOFHLDJ3e982i3Y/RWed9oZz2SpkY2MIgShYVDbagKNRTnzDnzFO/8tm3znPWQAfnFZCgInY6SIvh+SBE2fqWugWqfgkf4m5RNiMYQGPeZI0Rl1fMC0I4FVrLwzkMmWTRkEkK582hFkjXfi7XAXYc6+PKfv4C3z66s/bvQEn7GpIst5zEoIGMFV/HdTPY0ME0OzV0DSkbySJExbGNzCxlJ+MD3i1MDK0oiZKZ6nOl2kmCSSbB/rqkfp98boSUQ+LZprSrJpywziAMKUUO8d5SquCgsaRkcKRkknzjjV2EdGonGkHjmHGW6MQ5pojAiiLnB0IwTAzae2A984BDmZrSJrbFzuF2VT8PhcK32dY0atytq8qlGjVsItxoBcu7cObz55pt46KGH0O12b/bpbAtSSpgZgiI1Skxs9s6dO4dHHnmEldVzswlK7z2+9dYyzq2MoImATBWWRwYLDY2loanMWJzgQDvGqnFbojtXhqX1Xkhxsx4T6z2u+omym9uwLzNQMSgcGiiQiXBwczmz6DLUT9dS23ozjAf2xypIUHGs91qRrFQ4eQDK5rCyUX0MF1b8TKz3BoH3JXMeDS2CSquyHYEB8chGE1s7or+VtaauEh1zkUcnGW5xUvMz9BQJsYWqemc5xzefvcJuYxpy45CkirRx24xkBvIJAFtVBZQBDK1EpSpnPfrDggzCAGPrvU6MHpENLKXA0UNtRJGCkmKqvVBhyqBu6D3gqA5irVC4MoM7ToD1I713DrEqicG8sBhmFv1RsYHQotQd27beGxnEWgbrKF279Z7HniYw39BQOkWkAOeJGlJeQMAHyeZZkMoMA5uQ+yWqYJFP5fuw9Yb0+jn+6sunAaBUyUVqagAzShRyJkE7gSks6/1fj1nzVJz1aM4l6O5tbts2j0OuOeugZrDqnNqG8+O8hurzvdlJO0oCLjCEUnfaE+xTYR3iSAYJLq4CkAJHzQEASSQAgnzKLZAoQSaO5MYjVoKcLwJAO1Es8mmuGaPfz/HVPz+1cYMQEEoECRkWvIeIJPymc9GtCKo7fSxiK405xK71ULGEJQi+Um1V/a0shgWgRbB2ntYSobQYavbQ7sRYDdSNbTRiDCusd/ORCbK/1JIplFyT5SV5VPX9HxHHds5Daxm0zlOMcVYqSdaHajZo8ok7BKRpRJJPHqVyvb8uAXFhPsX33b+Xd5Aa1wW3q/JpMBig0aheQ9aocTugJp9q1CBwswPh63GrkE/GGLzwwgsAgBMnTkDrW3+oUUohz2dTwLzXYYzBc889hyiKgjZ7uw3/cmEVL10ZYm8a4fzKbJZi67E4NNjTiHBlWFSqBlqxKrMap6yYPIA9TY2Lvetjvbc0smw7vcUR33qvmUTIGKfMsd5bziyp8Fga7oz1XjuRCMUwGlKgCKxsqTpa/R2y3ouEQLEDVk/W86z3HAA3HCBqNrE/tUij6nFQQrIUUCX5dPUsvff47a9dmJk02owokpBSIopEZSbwNPz/7L1Zlx1XeiW2zxDDnXJCZmIiyCLBoQaUmiPkJXv5xf/Ij37tNz+2l7t7dbtlt1tya7UGU22pSy5ZqpaqpFKBBKtIFqo4guAIEGMmMvPeGxFn8EPkTdy8eeN8X9zMBBKJ2GtpIDJujOecOPHts/c2NS3CquxYqlDHeq/XjVnbVhV0Ou0IywsJojiCkAJprILEl0eZ6xBSPxUmbOsFjFRb0/chpIRSEolWSNIIvV757855OONQGIu8KK0TB7mdupeNgYGU1WTNQVnvSYngeDNuvdfSwEpPIoliCPlgJbRxHkrQxFIkHXIih4nLJ8QqZ5FPSjIVilOO673HH/3ZFeS5hdIS3vvKlfOzEAHeAzrVKGqQVv1+AR1LGKbCpNWN0VmkCzzeMwkT1jZhZsVZWsUohQwStOSnCnGuntQehUGtZaH2zmktsZY7/W8aCo4NG4Nw4bZdLnfZSSSyPv0+aieKZUmXMBY/tLTEuZUO/vn/9P9NL+qrsJqGC6kEZBKj2H5n6UQhWelUPs8ioF4dB9e2U8Ualvg2kwT56z2QpBrFsPp+UMSuJRREjmh3IWVjkVvE7ahyH5aYlystSxZ0CrzzSFsaWcX4aaxHnMggIau0rFTAAbxMJykFSeBpRrvnLiLSRE7rCEmisLVV/v9CAD+8cGonS63Bo8FxVT41tnsNngQ8/hXhBg2eIDwO5NP6+jquXLmCZ555BmfPnn3Up3NgeBzu/VHCqB08++yzOH369KM+HTau3RvgVzc2sZSqfRFPI9wdFFhuR7i1Vewp7ioBnOjG2AwUTGqIkwCU9m9cePDt9FBj24GT8N4diPXe0DiW9d5KS2M9ZL2XlfYZoWJwS0nk+7DeK2SE2Hu4wHVT1ntbxkGJsGUex3qvz7Te4xbP5nttLKQ5lCSyMmY08/uLy7exVoNknYZWsk3kAoi0rEU+ZYVFEitWqDXwINj6kVvvbZd0k1hhaT5Fqx3tIYmqFCzj0EoEySfjPCIdXlVMKpOsg9a7CwZSCshYQccKLQDtbQtAaxyMcTDGYiuz6Geldd9ce5/We0Tfsd6jHetggVsKYL4VYaGtEWlVkb8lEEmL3IU/szjKTm7MiPe8dsPdbhpJ/uP/ehV3bm9BxwqmcNCJqlyt7p2HThRMjX44+l1dqEixyKe55TY6CylLXeWdZymjptkyTaJUJVX/3W6v5A6+M4luTJ2Dcz444HsHhKLkqFHEEdIlyiVUAKRlrxISIUNi4zw0Yf9HKSRH4KhVucIhzZRwx1xFLWN33z/Txd/9zVVc/3xt+gYeELGCr6H4nbob64EIkJGEMw7tp+aC980ZD6EFPDGwWYalHhAmbepAaYUiQH9QZFg+MMH+VVD3mWhLcaQwtNPvSV5zMcy0fVeRT0Cp+gp9/1Jzlny/GWPb4Cg7+4OCFwPI7LvjloLPPbuEuV6Czj6VrA32h+OqfBoMBlhdXX3Up9GgwaGiIZ8aNHiMcJQJEO89Pv/8c9y4cYNtr/Y4QUoJt99l+U8AvPf44osv8M033zx27eDeoMA/frkO7z3WGKtPubjdL7DajfDtRr6rWPH0UmtPztOecxrWs97brGm9lx+S9d5SSwcJI6BUNfVihf4BWO9RmxgHLCcqeL+p69soHDpaViuXhEAMhyGqV+RRBJYD0NW09V5bCeQhIgBAqgQGxCMb2LD1nnQWqx2PVBeAYNjgkFuMtntQqPzqzhCXPlhn/nI6pCiLX6PaRFFTyQQAUcQnn4D61ntJJJCFZG/b2NzKWcVxYx1On+yilWooTRSuSTCL7IF2SWcq0blEAmVb1JGCjhSACN0dlZSD8MCCiVFYB1M4DAqLYWF3jk1a72X1rPe0FOjEClGkkEYKSSShpIQuTzR4LF5NlN7IebrwDwCF52VQFI63nfGAFg/Ir08+u4tfvXsDaTvCcGAQpZrMC5nF1s4aV0vJBADF0JAZU/MrbbTn09GZgTNaldo1IjuPcYlSijDx6ctV/cE+RJyuc2HixloHFcgktNvETeX+CfKI4La2C8fh/bciFZyTcIrPkZZlPlTVPgBEKrwIBNguOhPbcBTeJZj9gLlZQczbVjsRtFL4k//wq/COPCAiBb9PcsAODaJeDNWOWPayUayRE3mj3gMqUbDUXJYx6XCM+QD1/swrLPFGsIVF1Ikryfh8UAB6etYlQGcvheYbeWahY1k5Z6AWXVC3kBrHyYUnhSNtQznqKM78zDqPFmNhEEclOX5e7VaEF58/ASXKuXWDR4fjqnzq9/tot9uP+jQaNDhUNORTgwaPEY4qAZLnOd5//320221cvHjxWK5IOcrE31FBURT49a9/jSRJHiubPQDIjMXfXbuHzdxiuR3BeY/sfravAO1x3NoqcKoX4/pGDueBcwsJSTwBs1nvtWtY790bGKRaYHjA1nulLQx9Dl0G+cSy3hv50u+jAM+y3lMCReD6qSveMiWBFbqFnCanJS8bgkKV9Z63BkuJw1zL1LqljvHMAcDDQUDBOoc/+MmN2rktk5ifS3aRh9Z5VqD0OIqaBbhhbpgl7BLtVCMr6H5sncdcN8b9gMpwaTHF4kKKditCTowjljFn4NhMUc0gty6YHQEASoigdVioHUgpoaVANCU83TkHZz2McRDbhEHhHKzzcK68B9Z5WO/RjRWGxkNJASUFpBRQQpSFd1H+23IkkES60uYnVgY5QeJwMtAsU4WkJYI5bwAwtMm26WV4/jW0MaRwcJ6epyWyVH/0BwX+/C8/QJQoDAcGUgmyWAqURJLUpSKiDqQKK1wm4T0QpxpZRa7L/GoH7YoMmhCEEGQHFwySQgqB6WaSD6CU2FdmkbVl3lpV/3POQwcyxpzzQeVRYRyiAMntnIeQ1cf3nlY2RVoGySfO/VGMF5ZSEgUxn+e89/q5Jwl1gGf1B4BF3ABhhbsA8OKZOfzL//mnbOu6uB0FM5E4UIlG1E1QDOmFW4KpHFERTT5ZBiFRMFRUjiD0nPWIWhomcLw4URhUKUF9mTOUVdyfbGCCKxYogidJdJnPNAU5cf2G45kZACcjTelqlSxA338A2CQIwBHShCafqvK1JjEivC784CSUkuhG+13o02C/OK7Kp36//1gt2G3QYBY05FODBgSO0iTjKJ3LCHfu3MEHH3yAF1544VjLhY8q8XdUMLLZe+6553Dq1KlHfTq14L3Hz75Yx/WNHM8spLi2NgQAnJ5L8M16xi4uU7i5VeDMXIJ+bjFw4VXA46hrvccJoh7BA1hII9w4YOu99cyQhSbgAK33rMdKWwcJvbXtPJeg9Z4MW+9RCrSBF0iFhwkcY1TQrcJWQVvvcYingS2vlLLe2/Vn7xAVmzi1pDFZd5dQLHJJQLAK7xIC/+nnt7BZI99lGnrdeKpqTWsJ1CGfjKttvcdZYTtCxvVPAyqHhoVehMWlTnlt4GXOFMZBijApVBhHDkch0mh0LloKog4f3ochrM6M81BTCtxSSkgJ6Egh1TJYDI4YahxK2WQcvQ8OsVQOV/S7gCcgEkhVhr6l8oy2tzMpsR2ghYD3Dn/0Z1cglUS+3Z+kkmwrSRXVJ5+K3JJKpklUnc/CyQ5avUniibdfDrnMKYFxVvVT/ctaRyoQlBLB3DzK/k8FLOs86G8Q6hOFasZUO8+M3f9BmGDleQHoxAIbGTEvYM7JuNvl1qMVyanbn19u4/1fXsdnH9xm7QvYVgpzWLQKqFRDtCNEqWKRT9zVJiEbyBFYxJLZtggN3F+OPahOVJB8ovqnjiSy4fS/WeOQJBFMxVhpzT6UUblFmqrKsYFSiodIo/L3dpc93TRQY4f3ZaZbSL1srUc71egTbUwxyE1jPdJUYRjI+QKA4dDgqbNzWF0pSYFuY7n3yHGclU8N+dTguKMhnxo0aDATnHP49NNPsba2htdeew1pShcyHmdIGfa8flIxbrf48ssvH6hknENeHAR+dWMDH97u4+mFFN9uPSBWvt0qcHY+wVfr+89+GuFuv8B3llu4scUvuN8bGiRKsLJ+gMO23uPt86Ct9xIlMJ+oUq2wrU5QolxRXhbWy7yXBZT1E7etcrDOwzgP4z2MA7qxxFpA2kSROvcp6z0ItLTCRuB6KAKLY703dL5UYQV2xdkPUGZIeQ+ofBMn5wSS+elTQ25PlJCwDJLq0xt9vHt1g7nX6YgiCevd1LOjFEHT93e41nuRliy7l62tYpf1XivVWF1uI052PxtyxTK27Yt0uCjtvEekRJDoNpYejxVhL0at3yicQyTCK4us5AZzAAAgAElEQVQpgotqp857OleL+HPuJCLhCYJYAgjfMw8BLTyZ68QtCWtpOIJTRER22zj+9qfXsH5/uEM8xS1N55eMwVnHdbnbfY6JKnNUmLCFQ5QoFGPvnMVTHaTdvYonrtJSEQsFAFQWi8eR5xZShQlgmnwq+2hQHShK7dthgbIDpcgtCrTVqEcShfs/Rx3FOceQxeg40ogmn3LrEStarZwZh0gKFIxr6MV7yadEC6zMp/hf/u3b9IlPQCgJ723tfgoBJKsdeICtRuCoXIAyh4zeCNAtDUOMFTrRsEX1wqliaKASGTwm+b4nGhb12okTVTmelMqo6uOHLDOB0uqwSnWVZ2absJm+j3x7MUAVnPPlXCzQvjnzx7lOjNvrFezcNlppRJJPHHtOAEiTiCSfokjhey+t7Px3Nzp6i4CfNBxX5dNgMGjIpwbHHg351KABA+UH1UHpHx5/DAYDvP/++1haWsLrr79+JBVZBw2lVKN8msCkzd5BTgZH9/uwVzd9s5Hhn768j7NzCYz1ez7mb2wVeGohxVdr4Q8iLs4upDBMq6URPIClVoTrTHUSAHRqWO/drWO9NzBIJJAxrmEW6z0BYC5R6MYaaSShtYQXAolWQaIFKEO9qRbY0xJL1kN4D+c8jHMorEdmHQbG4X5u0U5k0OKqpWVwpagl3hWbB2S9F0k6t4JSIEkAKQy0vY+llbBigqNm4sIYhz/8+xv73k+rpSuLw855pInGkBlcDtQPxh5mhpXPNEIr1SgY/XhkvTfIDE6udJCmeup71lqPctglVlwTir7RNiGLvtw4JFoGK2jUqFBYt22pNh3eh9UXnGM4IlvGepTWfKF5CylGEoiURe6qP6M8BGLlYFz4Haakh7Hhq+IKErgRS4LZl3/7+T28/5ubO8RTFMtaxBNQPtMoUSiIIt8kuMqqcYgx26qVsz3oFi/faj8YZSGFxgDvywyh0DYl2UEo4KjsKOr5E32DGsf2+xlEXSGVZQSU1nwhBQuH3OdYkRpmp+P2uXYikffpPtBOFNYZpGuiFYDd233/VA///l/+AjlHfTQFQtfPf2qf6u2MJtw+y1EZAWWOEgdKS1BXLBk5Pa1OhP5GtR2boghiIqfOE/O1WCv0Mf34xrhy/lDR9ilCL6RM8r4kWSr37TySSAaJ+CRS6Nvqp8BRf+qI/obUjOfIncdFDBXThe+vIhlb9NNlnGODw4W1FnEcP+rTOHA0mU8NngQ05FODBg1q4dtvv8Unn3yC733ve1haWnrUp/PQ0Nju7cba2hquXLmC8+fPH4rN3khpdpjkU24d/ubTu1jpRDDe49uN6UXhG5s5zi2k+HKfBNTpXoJ14wFj0dKy0ht+GriFkBE2a4S1A8BCEuGGYZBbQmCxE7Ns+jjWe5EUaEUSzy+3oZSEm7AHG1115jwiAdhA6WpoPRIVttUrRqHsQkBKIIZCDGBnrZn3aEmP3GMXKdU3budcKKXYRuEQy/C5UtZ7fePKX4eUFYw2sZPPNbGfrga6kUOkCiglIG0XFFHIXVHPIan+8Ge3MKhhiTcNc72YVCUoLYAawkVjXC3CyjqPXkfh/ibvWjhKpRF63QSrqx1ycYfW1aul64CziEQrgdChqMszziNR1bkzQEmIhncTltGMyIAQJBHlwzFFVYK+51p4sigqGGvCuY/XeN77krPdYFjgzf/3453V8lIKtLsJ7hMr06dhFs7CWb9HyUShGJYKo/mVNkk8aS3JfjMiMqi+IZUkramEFMHiszUlMRs6liDaPmVT6JwPsiWeyFak3ueeQW6FbmU/tyij1wLjA/EsjPOICHUkN1tJKYLsA58YjpmLcWKmrdfkbTjR1rj+5X389p3rvBOqgEw0HPP9F88lcPrBCMay3AMAD6iEkeeUWZZqkrK7A+i+AQBxVE3+ALTKMe/n8IE+bIj7Kqhcp7iaIKrKe+JCE4uqlJYwAfcPRZBCHGKSpUhkPMf+gLcoiJqrnj3Tw6nV7q5/a2z3Hj2Oq/Jpa2sL3W6X3rBBg8cYDfnUoMFjBiHEI3nxWmvx4YcfYjgc4o033jiWq05CUEo1tnvYbbP3yiuvHNoqnYdB9v39tTVIWZIWnrDour5PAqodSdixiudCqlnZSSPUtt4ralrv1bjX0/J1pqHKek8J4GQ3QTfVcNsf6mo7iyn0+Zoo2kIulhJZ4ON0uE1iVdplCQEpJRQApYAUwDwA+DKM3ToPYx3a3mNgy/+ZhAfQ3qf1nvXAXERY71me9V5HSwysR6qAXuTRigykHP2ovA9CSXhWnpOEJ6gBiqT6+HofV78dIkpUeV8dz7pqHEmsYNx0u71xFIWr7filaxJWEWMl7gj9gUEcyaAlYKQlzpzuodXSrCBvzsp7ytKLu01ZVKvermDk0mgpgspC6iyo8yycRzQlF2oXRJh9cj6Y/Q6A2aYE475z9oMyh4oSp2Z2r8Xc9O3o+dsf/+UH2LhfdgQhgKQTY2urgFSCbUk2grMeKgorVqZiBlX9wskukjateNJKwBD1Wu/LoirVNXinSY9ESslgcZVyYKAs56z1IecuGOvL8S+w/5DygOC2SgIgZN/lgVasgrkvHCRaok+0Na1oS8WIUpoBGFZzFTOBq6SatOZ7/mQX//x//Kt9H987j/ZSC/27g+B2QkuohQTj72BrHIQKk6wjqEiS5BOwnbNEqCY5xBJn7KH6+WCz2B7Tpz8kaz3itoKpWPw1HBjIRFVa++XU/QgMNMPMIk5VZXsl51jUgg2iYVKkkLMeUoUPlDNUtRlD1eScL+35BuHOaQJtopVqfP+lvTnWnUb59MhxXDOfBoNBo3xqcOzRjKANGjBwlGzlHoUCZ3NzE5cuXUKn08Err7zyxBFPQEM+AUCe5/jlL3+JwWCAixcvHuok6bDb+Sd3+vhifQgpJXqxwgbjo2dEQM2C5V66iyTgqFbG4QEs1rQQ6sT8yfnIeo+De0ODFnPbZLvSJQVwshPjhZU2zp/sodNN4LXaGVtbjOwcTo2fM6mJiY/oYloFQggIKaG1QppEWO0mODvXwvmFFp6dS/F0N8aitOjAIlWCZb2niMI013qvCloAPS3Q1R5PdQ1OdXN0kmKMeHoAx6RnJDP5SVY8Cecc3rx0F96XBaPRQnkdSehYQkcSMePjPk4VOCkCznm0WvXWWdXNitoYuFo18jSpPp+5Xoynz80hjquLSHvA2IxjRcXNjwrBOs8unlaBKmJxVKBqnyfhQRf6jafbqWPYk3Lr7GROFQDrNWJBM6fWK8SyevHDT35+DV9+sVb+hwDidgSzXfTT0WxFHznDinFTWJZd1ghzyy20urz5KddCjjP/5+yLY91NHYrsG8YFj1P+nvh74PfWeXL/ob97ANTwHhFzAY5qiSqUA4BiLOBrRwyVRc4b8zg5TgBflbU1Nmd97kQLf/HHV2otaAphsJEjmg8T2a3TXUx7ByvmvJOjVgIAxRhvDGP+XgwNqXTJCPLHOY+YmIdP5jJOIk2r/761lYf7D9GG4sC9zwnVFbWggBp7OPMmijDYJMgiANjq89jeNKXbTRU5JQD8zoVT0BPvrFSJ4Jy7wcPBcVU+9fv9RvnU4NijUT41aPCYQWsNay20Pvzu673H119/jS+++AIXLlzA3NzcoR/zqOJJz/0a2ew9//zzOHny5KEf7zDJp83c4qdfrGG+FcE6h2/u82UO1zfz2hlQ5xbSPSqY9cyireVO1hEHnCDtcWzWzOZYTPm5UvNpxCp0OO/x/HIbQktAlB8L066C8z1nGP2PIn12jhXYLHdAIn21OgoPfi+EgFICSkmcmO89+LNzkNvnY71HYT1y55Fbh6EttUOpktgKSBnqWO9pAbSUQKKASHlEapQF5EsaiLAI8zu6s4MZ46o0bP/58ho2BhXtcvvQDru9/70rTdpGRbm5uaSWfR2nEDmOutZ7xnq02xG2tnhFkapzP73aRacb7Sp2ayXJa+WMC9Z5xDqcpWS2VREhSzyO2kxLgTxQyKK6aMFQV0TwQVtLCs7T1CVlzZdbSWZHFUSWU3kuIz0hsS3zchNtkBe0AipRBrnbS9R8ef0+fv6Lr3YuPm3HEBKw22NVXdXTCNaUqjiOSmEcOlbIGRk47bkE3YVwbt04OIQsANZ958wNnfWzCLl2wRgHRZB4lHpKCiJTTYQTwUgLQqLfpJFGERhbqf3nxpIsHaeJcZ4FJxvNA+jEAhtZeNsB0wp5Mne0Cpn1SLWE8x4pgEs/vQYZS7ialstVsJmFamvY/t5nla60K+dHoUy/cVSpf/aAQ7pnllRkeucRt6OgNSBnnNGxDm5HzTdK8n767631aLWjSgVQQah+VODeF4ULqqmpfZMKwMxAShG2zJQCoTWchXFoJRqDwPhgnUenFWGLIKpC92IEYz3SRGE4QTp+//kTWFrc+y5pLPeOBo6r8qkoiidycXeDJwsN+dSgwWOGURbOYcMYgytXrkBKiYsXLz4Ususo4yip3x4mvPe4du0abt68eag2e5M4LPLJe4+/+fQOltoxbm3laGtZu9x+YzPH2fkEX6/TpNV8qtF302065lON/qFa7znMJxrrzCJ6VqOgT1nvdWOF0/MpnJaIlQxawwEI5jOMMLR+OzOiui8OrCczIxyRSwGUiqI8VKgnwiuElLtynyY/Y53zkN5hKZXw3sP5Un3kfHmNbvvfRt+6QgBKCAhRFvdG1yjhEUkHqapXtDsAkr5kSCg4IqGGm/s0DTfXc/zqs43avxutkG4lCkmskeWm1nicPwTrvTpKm0nrvUhLPH2mCxntfccqCRjidV+YUnlF3ROlBBzh2xbpsOVVYRz0PnNpjA1bb1nnEaswGSOFDBPNDIWWJp6ZIErPzguk2sG46iKI9RJa0MSSliXpHQJ37YsSvPmhknsP6JzDn//owx2CKO3G8N7DjA2G3gNREiYQqhAlPCJpHMY40rEuaWvMr9Sbm3CyQwBmDgnjBWaNg9Lh4ixFYo1eO6HNqKGRGqk4WSn0Dqr/TNWEKVtNYz2SiMh0YjxbzjUyhBgAgDSiyafcesQqTMwDwNA4RFKwlFK9ROLMQgv/+7/4p502eFAElLceMlIQkdxlS61bGqKmCn8aHJP89cx5adSKkBHzaZ2oIPlkcouorYMWfaT9HHVdxGONYlVJPmWDAiDevyHoSMHa6defZ3Z70c/0fee5Dc4zvC9zoYIEIGMW1m6FyScAaKWKJJ+4ixzSNNpFPs31Erz80jLuTulD3ejJrAMcNRxX5VODBk8Cmp7boMFjhodh/7a+vo5f/OIXWFlZwQ9/+MMnnnh6UpHnOd555x1kWYY33njjoXoRHxb59O6NTUgpcatfYKUdYW3GkN5vtwqcJWxJpAB6raiy8PhwrPf4r/m7AwPtefejynqvpSWeX2nj1Ik2fFTa6iUM26QBoxBhfWl7QW1D2eqFMqFGoM44d3ShMHTnpRQQSiHSEkms0Uo0OkmEXhpjrhVjoZ1gqZPgRDvGQlthvqXQTSU6iUQrlkgiiVgLaC3JIjoA9Lc4Sj2GvRSTwplGUv1f/3Ab++nSReEAKZDE9d5Hzvmg1c005DWLd4OhqWe9l5b9eK4b4+mn5qcSTyV4O40YK3I5BSuqKZWEKKU6oIqwjlz2rvZZQacUkCUXS21Dt3VNKAoBQE8heSYhGdlQlikV8MxPu2mWgD/6yadY31b1pt24zMQ4QMF3XfUuUBYRo4CVlY4VFk92JxSDzM7IUTUxz1kwbjtVMOOQWJT9Yjg5kUdwhUH9nrDvIvbPyXuKiFXvBYOwsIyXUb9g2v0yB/92wuubPaZ13WIrwtUPb+Prz+49+EcPaOZxKNihRdRNHvQTAcQrneBvuPN2m1uW9Z5h5pZy7Dk578DQWAMAjmhblAWgJVaThNqStR5xUt02QhlGAE2cxYG5lXceCdEuKbUR5xVG2W5yt8kZ2VBAuShnBCkFXv2d05UjXLfJezoSOI7KpyfZWafBk4VmFG3QgIGjpHo5TPLJe4/PPvsMv/3tb/Hyyy/jzJkzh3KcBkcf9+7dw1tvvYVz587hu9/97kNfZXQY5NOdfoEv7mf4tl+gE0nc2KghbZiCm1sFzsxVE1DnFlvYChST1jOLTk0bB86K3nFwsqzGMZ/yya3xbWMlcP5EG2dXOkCkdy2/5oyfxgMcnozjt66J4xkPaKLYSwhE4FFa3YVAtV4PehLG+R4RjKlcGtNZZdyWVZXntHtfu/f2sw/u4+79/SWzdztxqVipkWU2AseCZRzGulqElXUenTa/75jC4tRqB6urnWDRjFus51gLcoronPZG3UqOJeIMj3AXqEvhjZPUPeOQ1ByygGPdxbPnixh5Trnl2bYUbnd7/er6fbz73g0AQNopiScdy6l2S8560v5tGrzz0IHiaeXvKhqmVAJLp7t7rL64Yxnn3UTlGO2cC2OORBXbrQ1nNpU7Cf+ZIn8pxRf5d8a7MQSKGBoWlhyIqDGIkz3HzVbiRIhxp6ox8z0UMzM1T82n+NP/81fTz+eAPluLzRzJiXLhWft0j3y+rkZmomKMBc44KE7Rn9WXGQsBCBKrGIbn1Fm/QKgXZITykyKgo0AGVkYspqOGFkVce0I8B+oRWAaxzMrPY7zf+wPmgqCxXX33hWXM9RIMK55B3e+1BoeD46p8EiKsjG7Q4Djg+PXcBg2OOQ6LfMqybEflcvHiRXQ64dVtDY4nRgTkRx99hFdffRWrq6uP5DwOmnyyzuOtr+/j9qCA9x4KPKu3EDyA24MCp3t7qxPLnQj3GR/hczUVGWtDQ6p/xrFVOCzUOIaX/MJgZhy0FHh2qYVnVrsQiZ769ZkznyOHWOI8Mp5igciVYBQcyeIW42SpSZgB/TFOZsUAUBFNjFg41v2lVtaPIFG2pf7Q4m/eX2P9pgpaS9jts6uT9zRCXtjaeSuc1bXj4AaoCwGcONFGr5eQH5qWe62MB8cptnLILknQqoWttoDc2cc+lUsUueTH/nclqP7rQHY+O0U9tPdcGJklzHdRomib1szFkAzrvcxGO9s55/Bnf/kBvAfSTgRjXJnzFHiHzUI+Afx+Mg5rSiJs946ApdO97QyV3eAuIuaeCU9VwdkTfWIkUU5ZSlIZccSLicp2ocgpqjA8JBbEeA/EgQL7aBsKmjF+c0gejtB2yHnZA+wGx1FSnT/Rwpv/6dfbZMeUfRxgoTxfz7D4nQU4Rj/wzkMyFSLcMSRhKP45Nn4sFRWlzBsUQZWjdx5JYBGXyW3wuvM8TCCF2oYxDjqwb1MjY3bqsYmxm5yrOg9BLMbIGIqlAWNRnfMebUa7GWX/nVhq4fx3FiFQ2ndPQ6N8OhpolE8NGjy+aEbRBg0eMxwG+XTnzh28/fbbePrppx+JyqXB0cCkzV6rxQ/vPmgcNPn0yxsbuDUo4Dyw2o5wu+KDvS6cB+4MDU6OLY+NpCgLYowCwsOw3mvX+GC6O5hupzcNaSTx4qkuVBpVhk8DwMB4iAOaWFN5CeU29H6oK7QeoG4b9eisByRp/0VdjwD1icUhqCw8q2AnyaPVUBVs/9//+I+3yYImhVZL7+zRWo+4pnTGOY+UsNOZBNe2ZQSO9Z6UAufOziNNNbnKGNhW2DG24+TXlIqD8HaFcTThwjgWtcLfuvDfKXLJOk+eJ1XApfqDh0CswoXAgrgOAODwh2X9a//EXwmBlEFSlduV78Ef/eQqNu5n6PRimO1CulIyeI+s8SzF3d7fOSQz2IKJiTa1eLKLuGphxQEvHuasRuat2Gcci1L1EGOpteG+4bwPKvZK5VT13y2xMMN5BI9fWI+IGNMo4p+jbOTk8GmOWo3Rlvo5rbQCRsQ8Yzvi+jqRRAKFf/y7a8HtZA3b5RCEEpC9mL0qXxHk4YMdMxeyMEiqUJbTzjYDQ1a+KNs8AIhbYTncHqJ8AklA8UUpp6jvotDcKCOylKixhVrAwiG3FLHAbbNPv7s2mN9vCWPONxgW0ErglR+ehhCicnGfANBmfhs1OFwcR+VTlmVIkrCVf4MGxwHHq+c2aHBIOEoy2IMkn5xz+Oijj3D16lW89tprWFlZOZD9HlcIIQ4lh+go4FHb7E3iIMmnr+8P8cm9ITLr0YlEmTlygHAeWMsMVrslKXR2IUXGPMR6ZmuRQwBdmJjEQVvvKQG8sNJGp5ci0TyyosUoHnCIpcx5KKJIO3Th4hrAu4eKqGByiMMDUUcxwkQko9rKscvjqJoceO3Jw+M3X/Xx+U1O3lQ1uu0IkzWNkPVMFThkzzgK49CqoRp0hPWeUgLnzs7tFJ64OSGcsbjg2HWBzobyAGKiTxP1KwD0tVEEVkEU0AFG4ZhULTEUkkReU+EkSQjlTjCYCQGOCICjogKASPL6aCQNvr6xgXffu4G4pZFtZ51pLcn8EABo1bCa3HXcGaz3TPEgI6Z3ooVWyA+N+Yo8yNXGHCsoKi+m3FH4zxxrPko9RfXP/f69RTzfhGjs1DcXRx3BAYcw4j6ydszItmRmCfaJvve9M3P4d//rz+k25wFxAGqN1moHRY15pGC+az1zLs5RADvjWIqrmJjf5gND9i9N2c8R43RI+eQ9ECfV51gQ6q3QggBrXHBsKIh+RakWTWFpxwDSttejQ8y7qLnWCJw5n7Ue/+zCqR2VVBVv2NGCPWdrcLg4juTT1tbWQ83VbtDgUeF49dwGDZ4AHBT5NBgM8NZbb0Ephddffx1pSmeCPOk4jByiRw3vPa5evfrIbfYmcVD3OjMWb1/fxGZuESuBXhxhYD0WWvVUEBSsB+7nDs8ttbDOtWDZxnxNRcZhW+9lgdWLC6nG86d68FG5P64zmWZUeQbWQzKqhgnHTof4MMkYygnqTAxD2URdtSFWiZfnwbF84hBLnIfFtI5jbJc7gzcv3WHtrwpSCrgpbYcTKL/nfPL61nsc66ZxVFmKaS1x7ux8bcUWAFaf8J4mlgAe4UUpBgpHF22pmj5nAQB1HtSlUEegFBpcRIo6kkCkOFagDFWn4+U5cZu5dw5/+he/hY7VLiUnlRs0gqn5rhthMLT11Uke0IlCZz5Bb/FgVNncx8/ZjqM+tJa2c6X/TttfzWJtWAdU3S8iLJGo86fan3WeJGs5Ck2OEGnAFMmnDOIjtx4xY+42MK7ShvjphRQf/uYWvv7sHuu8BPhk0DRE3QheC1jj+Pth9quQrecs20WMea4iVEnOOHI/5LuHUgARf44CbWk4NAjdYGqsCmVmFrkN9u0iD5NL3gOaWLzSZsyBWozs2YSxH86Y/PRT8zh7em7nv6suv7HcOzo4SgvCDwr9fr8hnxo8EWhG0gYNHjMcBPl048YNvPPOO3jxxRdx/vz5Y/kiPwwcVt7Wo0Ke57h8+TKKonjkNnuTOCjy6edf3cetfgEpgJOdGDe2cuTWw3pgfobV1yEoAcQz7PNhWO+1anw43RtOt957ZjHF8mILdmy84GZLc1QGAJAyiv2cuhq1Fw8gIXbEUjZRBTCW1d3+99Hvc9RFHFUT7zlxVFRvXrqHIXOldxXme/HUYoq1Hmla03rPo5aSCQAyIn9hEoOh2UPwRJHEubO9PQUlTmGkBG879RBXglLHMsTY7Tzdj6nWSlpNHkj/ZSgKGaSRYmzD6Z+ZSyAYykPjeX3jT/76OgZDUx56+xSjWJKh9zvHMS5YzKyC96i2zAsgjiXmlnkFGu68lrMZS7EEXjaUpCwpGceiVn2TlpIMgit4fFIZRR0//Pf8AOy7OAsUONv0i3JuR4HL93WYVni9Kf0qUQLnVrr4o9+/zDvYNoQU/BPc9UMgWmzt9CWunR4nf2m0nWLMTQuO3BZ03wJ4nHdEBH1RuWk5YQFoCIVRqH955xEHFqzR+w7+OWhP7D2tOqfURhzFCsdqmDPfoayTF+dTXPju7gWXVdOGhnw6OjiO+Uj9fr/JWm/wRKAZSRs0eMywHwLEWosrV67g+vXreOONN7C4uHjAZ3e8cZzIp7t37+Ktt97CM888g5deeunISdiVUvsmn66tlXZ7AHCqE2Nt7IMwsx6QcuoH/qw4NZfiXubIPINJPAzrvfsMP/xxjFvvpVripZNdRK14T6VuwCwycLdj2eAwPjw421DHyh3orCriz4Xf/4eSgyCPoyJGIDfrWB4HEZjyzb0c73++ua99pInCIMBuJjNY79XNqDHW17bea7cfbJ/ECufOzk1dDcwhRwB+TginiG4YxVbOeVHHyg3DGozKZCLOgerjlqMqJMilzDBsRQ+oEMJrDgKpysitMktnB3x1fQO/+s0diLFsJ6mAoiZhPOvipbr3TUiBhZXOgZJKo/1S8J6Tz8e7F6RqwoYzlzig7i2V7UIR45aYm1GZTAUxHxgWFtQ9oHgG6zxJGlEZNiN0GVEc3OkqR6EKAEm09+S/d3oOf/of38Ngk5PpNnHcRCGqqfhvrXZ2TQc42UsAYAu+sjFhnJO3HoozV2c8Too4AugxISfkcPmgCGa3ZcTvKQI6CqiLsmHYNpAadqnvl5BlIADyuW8y8po470LO4p3+oKic57cShddePrNnTphX3KAus+03aDALGuVTgycFzUjaoAEDR0kZNCsBsrm5iUuXLqHX6+Hll19GHPPsWxo8wHGw3fPe49NPP8Unn3xypHO+pJT7Ivoy4/CPX64DAM70yrY+mX00MA5KS3RnKGJPYrUT4b71cAAWZ1jRPYv1XqsGyTWwHnOMTIIRRtZ7J7sxnl7pwFZUehyANuM8Cl/tpT4ODkc1YFgXcbbhWEtRdoGGYd1HZRRzzoMq1EsdkYUXB88qznBUTVQh9o/+4da+Hc10rILv3owruxv/zQzWe1R+yiRGhatOS+GpM3PB3xMLrAGUmSOcU+AUYzikUG7o3AbqWN4DEbmggbbWCsEw+jil0KCtNSW0CL+HCkc/HE6N2zhew9SCXkhgvUIsqwvUzjm8+VcfQki5KzdmlkUoznqW4mfPOVoPzZ+4aTYAACAASURBVFSBAMDCShuiTl884Gk7596wxhZGW6AsJ6msH6p4Tf2dIqlzooBPZTJR2THeA0kUHhw57xeObSpHZcEZpzOmBSW3WU5a257uxXDG4x/+5lPmHnbDGoeopdmWjLqlgWTi/tXoUyyyCIBk5IYCpeUmBcvIAjOMhVieIlczC0lM7ELqJGscosD9oZRToXHGWo840GApZRS12IVUJBPTsmFukRJtg6M452zjPXaynMYhBPD6y2emqrwGFf24UT4dHRylmtxBocl8avCkoBlJGzR4zFCXfPLe46uvvsJ7772HCxcu4Omnnz6WL+6Hgf0SIo8aWZbh8uXLsNYe+Zyv/RJ9l765j75xONmJsZlb3KxYbdcvHKJI1lYejUNLATlGYM1Sc8+YK3DHj7FQ03qvGwgxnsR6ZvDCcgvdXgJHjBdV2QSTiBnbDRlh6s6DzLyynj5exlF4EH/PHb3KnGpZnJoVJ6+JQxpJcAo99H5cQEf1t79ex9pmPaXdJLrdmAx6L6yvnaHkH5L1Xqcd4fTpHlmU52ShATwCjFITANukELEvx9iGY1dFnTI1vBfEWODBv3+V58CoYEcy/M7PrSQr4QWDWHLgfZQJloUfkKjqFeY//vvP0B+aXSRipxvBzEDoAoCecQEHV0nRnkvQ7m3LTw56+sp89XKmzRxChLMNNUeniFlrPQSRCxOqnXuP4O+t80Hl8CC3wcdknCcVFhQpxFFxcpSuCYN84jS5fh6+JyNwVevj6jEtBZ47NYd/+y/+kSQeQxhu5IgXGDIuAPFyaw8BVufY3L7NtZ/lkGYFg1gyOU0cmYz+zouJXCJqTAzNXbKBIQimcNuPA6T+MAsrowwxj6CeF/V7AGgTc7BNRtBaf1Cw+uU0EvDlH6xifn6vzbwW1f2zO0WJ2ODh43FfAFyFwWDQkE8Nngg05FODBo8Z6pBPRVHg3XffxdraGi5evIher3fIZ3e8cRBWcI8Kd+7cwdtvv41nnnkGL7744pGz2ZvEfsinbzYyfHhngMVUYz0zSJUM2sBsFQ6tWCOd0Vbh7HyCbOxU1zLLCpUex/3conPI1nuTyq8qKAE8v9JBr5OwiGruWbBswTyvGMQhvDRxQOMBTRRyqVvMUTZR98eBtvfjPWpO3gFH1cTDNLJra2jwTx9vIIoVolghraFsGCHSkp0RRoVbT0Nd6z1rPVksGUccKZw93YUI+e6M9s2spFMKHoAuCI9AZTIAtGKgzGShVkiHj0MRWM7TChBa2UT3K1o9RakbBSJFXYuAYvQszivIOl5bVGL6OX3z7Qbe//D2Luu1KFHIjUM0Yw4iNyNqEqZwZH/UscL88oNCIXvtFPOU+NlrjH0x7gMn04m0nDSOZLHifWazUMX+FiEHahELAxJi7CYXfnDIdnILoBVxrEjp/XgAbYa6fMC0teyPXd93T3bxm/e/xZef3mX9NoRiI0e6FM53TVfaUzuaq0FOcxVWBXNeyrHp9M6zFFIR8T7PB4Z8d9BZVcR7heh/SUA5RWVgicD3nbM+mOtE5SRZot8VDMvMmOj71np0icV1zgNthmXj5K145ql5fP/ZpanbVn17RJK3eK7B4cM5B6UONq/5KKDJfGrwpOBoVx8bNGiwB1zyaW1tDZcuXcLJkydx4cIFaF3fCqzBbjyOyifvPT755BN8+umnR9pmbxKzkk/Gefzsi3V0IonCecwlqlL1NI6N3KKbasQ1LbaWWhr3J6QrHrNZ783V/M29oUGLYj7GsFU4LBDHiJXA+ZNdOK3YC8yHzMJjztyO85HH2RMnoyMiKpkcdRR1ugVL2RSGBSMknvHEOPfNbSc/UZhGLrx5ea20Q3Pl/xgHaC2hI4kokiyLo7QVgStv4Kx+n0SW29rZ61zrvSiSOH2qyybFuEMc18JQH1AuASuUnVjAQNrmubA6A9j/Rwo15HgAiSaKbRybNIYaSUv6YXOIlaHjKRfclLvnvcebP/54l8IpShSiSMJZX9uS8sF+MTNxFSoWCwEsneocicUyHNWH9/Q9LO8z1TDpthIRxW+KmCVJN+LP1FgeEXMTinSkhnbrPEnW8ohFehuGEAMAkDIWEGXWsxYn9QsHLQVOtCMszKX4g//tLd5JMJBvZIjnp48jKlGQFUV97z0kc5EUVyVlmeSTY5J2HPKJsvrzziOhnAUom2OCqKPuT+hdPhya4EvaEy+t0L7LBQHVv81zmlyi5j+cltFJ6ecYItFGGL/NiwspfucHq5ULxqr+vaNl4xhzROCcOxLzgYPG1tZWQz41eCJw/HpvgwaHgKM06aDIJ+89PvvsM3zwwQd45ZVXcPr06Yd4dscbj5vyKcsyvP322/DeH3mbvUnMSj5dvr6BgXFItEJhHfrMD1sAuJ9ZzLc0W7UkBdBKo6kVw1koSm5mwDgWCOuPSYTsBVta4tmVLtz2xL5gVrxz58FxpBhYD8n47OQclkNkcR49ucLcA5qhnqD2QSmbKKLMQyAfZuHjgL53lnP/wbPwm8S1WxmuXh9U7tR7AEJAa4lWotDrxljoxYjH2mSnHZFh9eOwziOpWfQurffq9ZshsdIYKFU6Z0/1oJUk1TIjcMkz7naSQRtxCrIcOzqqeJwzznm/tnmk/Zijc6E0YavHyXTilNM400gRsLMcwXqNSFTnOY1QuL1t/O8ufYn19QfjiI4lPPx2QbFccU4GylditmcZao/zKx1EnMCdCnBVjgyRIlshRSmOgFLdGTwWq78Tyl5i/kRNr/ab2UedH1V859iIahm+15wxaMgglvpFqQinwB3O2ky72PlE4cXTc/gv//kDMierLuzQQLf3jhHJStj6STEtNrkqKe88FOMdTql9Rgipfh4clG7c1HVa4vpygrHkKr6mwoeJl4JQL3niZURZBkZUGyD6Qc64dslY8MMZ30fHSmKFi6+egVLVs6Sqf2/yno4OrLXHUvnU2O41eFLQjKYNGjxmCBXlR5k+eZ7j4sWLzYvsgLHfHKKHiZHN3rPPPosXXnjhsVspNMu9vt0v8JtbW1hqRdjILU60ImzWzLBYzywW2zHL1u2p+RT9ChJkfUbrvbofOXWt9+5XfPT1YoVzKx3YseseGI6WpkSLUbD0zO1yxjVlzpM2VkOGsoJDdCjiQ53zDKimwFFXtNOwVY4HQOnVPADhORkX9XOf3nzrLtuyr7Aeg6HB5sDA+TL/oNuNIWr2GYCvShoHp9g8Dut80OJFCODs6d5OUYZbuPWezkYaHZ+VOcN4Ahy7Kk6mE0VQcWwAKWs+jnoqBA+6/1JjRGYVSQpZRp9iqQ4dT2IRB/KcRshsDCkejPcbWzne+uX1nf8e2UYpJXe1Vz1joc3NSFw556eqplq9GJ25veqMw1gMRrVDgN9XOQVRR7QXY2vYC1aAVA5Rtpf7JIeovkuNMUOGfRc1rXUMddRWTo8BANCJyU3YSlaKfBzh7EILzgN/9+NPAAeImhmHIXjnAYFdSqZkqUUyaNx3tLOOzFYagZUZ5wHNsFnjKK4c4x1IjTM54ahQZOFsqaxfBBdGUPZ2obE2GxqE+g5FblN2t7PMucaxyXCjoMYPgLcgYDAsIJXAG6+e2Vl0VDXfrrJ77h6QorzB/nGclU/dbvdRn0aDBoeO49d7GzQ45tBaw5i9K8Bu3769k+nz0ksvHcuX86NGnbytRwXvPT7++GNcvXoVr732GpaXlx/1Kc2EuuST8x4//WINp7ox7g4N2lri2y16dfg03BsanOjEwVX5c4nCZuDDZ1brvR7DRmIc94YGSY2u3p9ivbeYapw60Yab+Nj2ADrM4gH3FKhCMMBXSKWMD+CYGAcz50mmgDqTgqFsomA8aMaCwZhwcoYkOAUsTjH9wfn+/QcbuN/nrUyeBuc8tJZIZlA5FDPkzWS5rZ39FNr+zMnurowG4/jFY8V8V3Ms9QxDPWmdJ4voxtLEEUeNRSmbKAKLIpecB2usCMGSyiaBWIXf+7llPBvHWKXtYwiGblYJzjxEoKUeqJze/OuPd8LglZYQomzTZmKBhjFuZuIjmrU4PtFOdCSxsHwAi6cOmKfirLjm5NVxXhdUJhNVeDVE8dpYFyx+U/sfEOoFSqkzyG3w+B5AEoXfB5y1N9T46iHQZZgCJIy50Czq9SqcaGmcPdHGf/j9t5GPVD8eSKaolWaFyy10JyrHgkhCdQ/Wnp20rtsGl9DiqK44Nn75gJ6rUASNNQ6ayjULzOe980gC3wglgVSN0C1zziMOzKUKyhJwn82Ycg3NCktmwg0y+jly1IDeAy9//ySWlx68T7KKOWPV7hrl09HBcVU+DYfDZsF4gycCzWjaoAEDR8l2b7Io75zDRx99hM8+++yxyvR5HHHUM5+GwyHefvttAHjsbPYmUZd8eu/bLSRa4eZ2ATxSgqUmqcLdocFKN64swM61YzJjZ5aWws1PGkdkw3Zskxi33lvpRDix2Kq04dDMsY+TawQAhvlVyyGWONwBJxI6IXZ0EMomTpGMuh7OPrznKNAYK/OZBX0FhWHu8LPfrrO2r0IcSRjH0e3shZvZeq9eoS3L7NSi/MnlNtrtvcviqdXDI3DnFxzitlQTcPKH6HYSEX0wM+HiNUDbAFLkEicXSlOEK3HbhgziSAta+aSIbQoHRlVPoCWH5Pl4htIKeGAp+OFnd/HVV/cBlEH3Uongqcya32QKN9N82RoHuU2uCgEsnuqGbZe49mbMxRxcJQpnhGLZWjK2oe6jJeYKjlBLlkqu6g2s80hDuTOFDZLLw8IG34vWeSRENgxFXlPqLQCstsKIl2ERsv2cJu0BetyLpMB3z87j2283cWVMrQgAeeH4/n4MmK0CyVKKdLUNzs3iWUKW4JJKXLKD8wyKLJyHBJTKJ4o4KgjyBwAiYg5BKYRCc5Ais9ABwpPKdQpZ4w0zE1Zd7VMVyemXbYKY3GIErfUHBdkmXnxuCU+fm9/1b8MKknhQcd5djq94g4eC46x8ajKfGjwJOH69t0GDYw4p5c6ksd/v46233oJS6rEnGx4HHOXMp9u3b+Py5ct47rnn8MILLxwpwnQW1CGf1ocGX21kuL1t5bCU6p3/fz+4MzA41Uv2fOefnUuwxSCJ1jOLpKaN2EZu0YvrvZqTmhPWkfXenCwwP5cGv+i5BFrfOFY9cMCwOwF4tRUOkcXZhjpW5vavbCo8yEI9FRNkATrkmnEuvG34RND//fZdmP0wvQDiREMIgcI4Msh+Gmay3qs5Rlrn9xSLlhZSzM1Nf+9ylVVUu9jZjrUVEBEFXQCsgix1/t4DmrjvlLKpYBBYJOlGWVpS9l9OQRNKIg7ZQxFUgECkGMSgoN9duWN4gG0f0zqHH/3kKoCSeFJalkrDSFYSGLMOd96DLOpWQW+/9+aW24gJ0qjb5pFKGTNXhVIJjcBZAEDZZXG3ofpFec5E36Es1IhmnRBKk5SYq6REW4gpRQH1bmY+NwqcWibnUB5AO6bHiiGRyfOD0z3EcYR//a8uwU3po2KftmeT8AAEk6h1hWOTvxzrNACwxP0YgSJcyo1oUggANME4FgMDScxFqPZLTQGoBR6hbMohEVYWmt945xEH+iaVyWSI52WM2/e1O+fRJVR+3gOdAIn13NML+O4Lu90/Ejl9DpzIaju+TmO7d2RwXJVPTeZTgycFzWjaoAETR62Yf+PGDfzyl7/Eiy++iPPnzx+58zuOOIq2e845fPzxx/jss8/w+uuv48SJE4/6lA4EXPLJe493bmxgPTPb+R7ARj67/dckbg8MTs+lO9/a7UgiY/Y1D2CRaTsyjl5N67G1oUWbaY8HlNZ7zy0mWFldJKtPXLLIA2gxSAPjwbIJ5Bx2YDxZoBtaehuqQA4wVmATu/AAqEdEBcQDgizE8wgqzyouc+z5vro3xMffDOidBdBuRbvuX8wMNB/HbNZ7ZgbrvQeNd64bY2kxnMPFASd3DKCVDiNwrolDeHG2oYrbFCnpAUT7XMVK3T6ODVpE2OoZhqJQUOwxDu6jK3MxJGNpgPUaP/7pNQz6BaQU0JHcWbUeIkCc80gZ+SrTMCtxZY1D2onQnacXUXlmaJtlqiKt82gxiu8UmQKU10+dHqVKAjCVdJgEadlJHCQhCsBk9gtxfIqcpt6JFHnhnCfPkUOAcMQ8A6a8O6QWGyEzvjJX9Nx8gsX5Nn79/nV8eW2tch/RAdnvCQHouXRHeciBYhLMnDYMADazEIz3lmGSyZzz46gn2sQ9ppS9jiBpqG+c0D0ZDEyQNKX2HbIwtMYFbT+LgrZmnSNC0jgtI2V8C1VZG54/N48ffHevE0xccU+rMnpbSpDz/wYPD8dV+dTv9xvlU4MnAsev9zZocMxhrcVgMMD169dx8eJFLC4uPupTemJQ1wrusDGy2RNC4PXXX0eS7A3nflzBvdcf3x3gVt/skBWLqUaf8DOvi1v9Ame3C2LLvaSWnR/XZm4cgxkK6fOBFZKTODuXYLGXQjAm8FyyCCitDjmgcpiA0v6CJI0AtIjqlfXVH5sjZBwLJOLvmaNJLuqqhwVDrcdQYEjG1E4xiCXB2M8f/+I2uU3wGAJ7lgfPar2XPgTrvWFmIATQbmmsLHfCq4uZF8IpRAO8jCXugQsGu8tSsxGb5AwbQKquQypAGLlQlCWeJIijoZXkeXDsLDkw4LRJgVTR9nxf3bF478pNQABpW+8QmDqSZDOZVcvonZ9J/SSkwMIqr/iSZfwFJtwcuZgxfvQHBYuUjYmsIoAufhtDF9rrkueTiAnCgSRu9psNQ40fjHGKUlBwxk1CRAIAGBS0vS4AMk9vhPaU9taJJJ49NQcA+IPfvxz8vSkcxAEoMnpneoAQLPJnBC5RxclfGkFzSOLccqIvWYsxOf2YyrAriHFosBVuWEMie4o6xVCmVEHce+oOURmT1CIhatzNGIsEqdw7YPrCsKdO9/Df/rNTU9tBVTOvOlST93S0cFyVTw351OBJQTOiNmjwGGFjYwOXLl2CUgovv/wyoujggmcb0DhKmU8jm73z58/j+eefP3bKNw75tJVb/PZ2f0eds7xtxXMYi9Ru9gs8f6KNjZqB0uuZRVKzQDCL9V7OLEyvdqIyn6ZGe+HkLwE1VrwzFALWg2VZyMmkorYxHlBEFY1Tg6NWcVNXraKYtPfjFLgFSxXAKM4Qf/+njzewtrk/lWGnE+85TmEcWfiYBjnDB2ndYdM5j7leglOrXbLwy7UcAmiFwAhUoZV73MLSWUo5w9bLMhYIxMQ5U0VA6nqM8+Tgk6pwEZAau5wXiBWR6WTpxsSZPWQ2BS+3i97bn/7Vp2UmWksj2w5wl7IsXFMwxs1MbswyHzmx0sV8l2cn6JxHzHxHcgqY5XaMvmU9mVUCAL0OvQ11i7zn2cGFQGWvDIkCdUYoN6h5B0X8ULZ5WeHIcYq6Rc7Tc8KtHJCMPtdhrO/irhGYJP4EgB+enYdSCj/+0Ye4f48mlyEE1Iz5bAAgIwmz08j4fZZLVHnnIZnFe+52EWOhlWc8BA4xRr0Xtjbz4PuryG2QwOpv5cHbTim9VGCelA0NQu8RKreJHPuJAYwauzYIYg7gZeNNjnGnV7t4/XdOV7sEVOyy6jE25NPRQqN8atDg8cbx670NGhwSHmVx33uPL7/8Eu+//z4uXLiAOOb6/Tc4SByFzCfnHD766CNcu3btWNnsTYJDPr11fQNr2wW1+UTB+9Im72QnPqA16A+gpUAhRG1iywNYqKmuAIAu039/hLWMtt5bamks9MqMp+E+M3qmgbvPjLldlS3NODh74qQXPQx1FIe3pCZlFrxCOQ1GsHjgvuXG4SdX1hnHqUYcyUrVCrUifxoKY2uTSVlmaxXYpQSWl1rsjCnKEmpnv8wTpzIogNISh7Wqm3GPI4LQ4ygTqGujyKWcYZ1JZ9uEf585+l5oGb7W3AmycxrLINugEYuMPB8K7/zmW9y8uYW0rXeRTXUKN2rGwpuzntVWR1hd7SDtxJAMxdAIXEUTd1GEZ5LFHIUUB72UvrfU2EQWZ6ni+aAIkjtbQxNsr31C+UGRW/0svH+AVpGF3lMjUOS+h0A3odsrZ0FMVnOB0ggvrXbQaicwxuEv3/wN+3fOA1Fvtm/CZLm9c/e47R/g5xQCYJNj3G9sjuqqyGhiKR8Ycm6YEftx1iMhyOgocP3eA2ko14lQRoVO33sgCXx7UMoo6hFTpHBBLHAojEOH+DYaMhSugzHZ4uqJNi6+fBpSisps06o5Z9U8pFPD0rzB4aNRPjVo8HijIZ8aNDjiKIoC7777LtbX1/G7v/u76PV6R87+7UnBo77vI5s9pRRee+21Y2WzNwkpwzZHX97P8Pl6WaBLtUQnVriz/aF2e2BwunewBNSpXoK+9VisSQoBvMyRSQxnsA4MWe/1YoXl+RR++wPfeEAUOWu/ObMokTsPzmL0gfWsVcacTXIGkcVxfqH2Yj2gia2ofRgPhrKJVj5Rn10cdYVl3NzyXKY/0D+/fA/FjEW2EeIkqiw4zTLKOg+0axK9HvV+c+5km2WpNQK3AM8WDTL3xSGWOMU+KuvAOM+wzQv/nVJPeMZ5CCp7gziHzCjyRBmaQ1Id5SDA4VVjSZNPxlW3w7yw+NufXUPajnYVAZWWMMwcP6BekXkSmpnd1m5pJN1yLkMpdcbBVTQVhHpnhIybJ8Mg7zj7yg19/vOd8FhDiYgL45AEnoPzQDswp7HOox1QbuTGQQeeQ2ZcsL07D6REO9lvrhzAU7hypnac/fRzekwEdhe7T7Q1Tp4oC49/+O/fYZEnO/CAMx5xu967L+rFgH5w712NcaHOttzFHZ67IIpzb3MLFYU39M4jITKdsgGtzomI+QM1XkQBgt8UNqgCN8TYpgNF+tK6tPqeU+8JatHIFuPeUTl7JTkeRn9YLjo6sdjCf/Pq2Z2FQf3h9O+bqq5VtXiu1yifjhQa5VODBo83jl/vbdDgGGFtbQ2XLl3CyZMnceHChZ3VHkqpI2P/9iThUd73W7du4fLly3j++edx/vz5Y2ezVwfGeVz6+j6A0uqslyqsD3evkLvVNzjdOxhybi5R2Nj+EAsVWqqwnllWCPU4NgqHxNezNKsq4ra0xJml9p6QdmEZQQcYkUU8cC36WKHcDNIrc560zBs62mKMyo0BUG3jsY2CtY/w3zn1F0nY6nkAglA/eWDm/Kib6wU++KpP/jaEXjsKkrJmRuu9mTw3mT9ZmEsRt1K4GgV5ihQZgWNfV27HOzZHcbUPXmEXqMdEWW9xbLH2+7GSWVr5kKjwu53XN+nnyBkiOdc7dAmqiof/z99+Ciixh3ipSyZ5D+iaFrAjOGYx+dTZuZ2qfp7z1Yvc8vcop43CYEgXOgHAMQiyPqPwusXIj/LEGD3MLTknaRHKE8oWmMp2aZF/DxeYKStR6pnkDKUnp37MeXVwOBcPoM044GCbFNZS4Htn5yGFxNpaH5f+4XP6IBNwxkHEGqLG/FTP750fSyZh7K1n5z5xhxyKSBmBS3xRiiQA0IR60mSWVH9S5Jon3u2US3IcILeyYfgbgcqkjAPXnxOZTJR160a/IMcmSkHuPNAlCEIP4NRKF7/32tldc0Yz5cYKTCeZBKqzdrsEidng4eK4Kp+yLDvWC4obNBihIZ8aNDiC8N7j6tWr+OCDD/DKK6/g9OnTu/7ekE+PBo9C+eScw4cffojPP/8cb7zxBpaWlh7q8Y8i3ru5hc3tD5/lXgwFga0pH0K3+gXOHgAB1UujneLYemaPtvXexIdyJAWePtGBm/IRWucy2sxCA7dwyMlqGlqaWAJ4hFdMrJTLGLkx1JkUHrCGCJgehnMcCs8pEB9M7pMkNVTTj/Unv7izL+JCCLCS2+MZVpzmhauteBxmllRRpInGiaXWzn+zV3Mzb5RlFuopImcEztlx9mUY7ztFEC6FpYvD1HhAKhMJUm5gFCRBV2hJ2f/R7ZGVycYY0xyjbzqvEMu9q7u//nYDn3+9vqc4GEWSTQiNY9aFLt6HLacAYPVUF8UYweI9kASUNuPg5FaN9tni5MR4oNWi37kcVVOZDUUUto1DmzgvyvYLAGldRaoGiedLLbqgxkKqAE0tYqf6tvOetNWTgl7Iw+E0BgWv/ySMd1dmHCIp8L0zczv5vf/uX1+qZX83jnyrQDTHm++my+2prIesYTHGJaW5ZJErHIs8MwThsgPGvJCTXRUifwCaZKdUbNT9CS0kKXILFXhmFFEeyoxy1gftj41x5HyfIo44mU4p8R20utTCqz88iWiMOFUCsNPIJzNdDdWqaHdnO9V/a/BocFyVT0A9S+QGDR5XNK28QQMmHpbSJMsyXL58GUVR4OLFi2i323u2acinR4OHfd8HgwHeeustRFGE1157rcn6ArCeGVy5tQUAODOXYCt3uN2vLvjf3CcBdbobY3Ps49J4YHEGIomjipmEVXTBbBILYx9qSgDPLXfgqj5ea+yfK0IpuEV0ZlE+YRQQODwAtRcPOveJ8wxNFrbL4lz1Qaij4DkPrH7u0y+vbeH2fZ5irgrdTswq9M3C8zsi56AKoQKHlAKnTnZ2FVnZ6owa/Z5SAADbShRGMYTDURXGAZ7OZaDVGVRxGIiZisgqUAVo46hcKIFEhYuWlOVl4SRJYHH6Jmfoyx3vXT+NfPqrn36GfIIgiWM1M2HsnA8WKcOobqtJqpF09l5nxFRfZDlP0QSU18/ajrHgoz8oWMeliqYAkBLnVVpXEcQtsQqcGoJoey3CFpPYPzUUUeN8xnhZRMSYuMVwGOZwGoOCtW6CtQ0AnDvRwvJcuajh00/u4NMPbvN+WIFis0AytkhiGmQkISqIUVGn+Mnc1uaWvdJJM97dznroA8pd46gYJfHuKohcoqyfB99NGZHrRLyig2R9zshMCkETJCr195RQlnGI/BC5/dy5efzeG+f25P8lFb/pptO/BW2+d1FYSwEvn7rPWsjV4OHhOCqf9mNv4z9xlQAAIABJREFU3KDB44ZmRG3Q4Ajh9u3bePvtt/Gd73wHL730UuUqiIZ8ejR4mPf95s2beOedd/Diiy/iueeee6Jt9sbxi6834Dxwqhfj9tCgJQVpmXazX+DMDIHMkRQoptx3aiXwNKzn9a33NnOLXs2P7Gz7Y1oAOL/cgQt9OEcxu0jCXTA/sJy1/8CAUdQGeMQSixRiHIu6F5kDWWmzNvyxL+OUvm7iz5axDYe34TxSN1aSd87hv36wiSjRiFONbieqtVIaALSWbOLR2Nms96hi0TSEruLUageR3t0PJXMM8OCrpLg1Dk7mTMEoqnkAlCDEeQRXPwO8oi1lA0hZGfJyoahzCO/DOPo5RUSmU27DWYUAz2Ix9xEk6BsrJ0J//undb3Dr9m5LTKUElBZkoTB4nBlD1531mKt49548PYdpPY+7+td7IGGqg7l9kNOvvQfaDEsvzjGpsco6T2fSEe2NKvAOiAL1MCfavAnvnxqLqN/nhlazUn8vrEArCp9HP+fNNzqMtUycNQe9VOE7y92d//4//s0l+kcM5Bs54oW08u/JchtVd6xWEbTGkKCZ5C/33U2pkQCe4oqtogog6xfBe+GsRxJQQeZDE8yGNIT9XeieZVnYxpRSXFOtYb9KjU2CmAOmLzwRAF7+3ir++1fOTB1nq15XVbcimiQzvMerJ+8jaoinI4fjrHxq6jwNngQcz97boMFjhpG12rVr1/D6669jeXk5uH1DPj0aPAzbvVFb+PLLL/HGG29gcXHxUI/3OOHa2hDXN3MstyOsZRa9SOJmQPU0jlt9g7M1CahTvQTTXFbu55ZN2oxjIa2/WqvHXAU+wsh67/xyG14TvxUCbebHfp9pneIBtBmFSuOBlGOxwjjs0NKWeUNLKSPoAjhQEpJByPA996j+MH6wDW27RxGgFvSKdAvPUkOM7Pn++somhttWV94DmfFQSiFKNFrtCDpW6HZ0kDAqi7b8zjOb9R4/N2aEquyUpYUUnfbecaPOOkVO/hLAz4fiXJtzHpyhg/MRrynLSsOwkiP+TBWoOblQbR2WN1DPbMiw1VOCGkMEad9XeAXpqfeWQKrCKsryeA/OOS8Mfv7O17v+LqVAqxXBGg9nfe1+sXMc69kEziTiKQznyskufMX+6hTAE671F1OBaJm2lhwlFWeqyFFeUOqBnHhJbhLZUv2hCfatrWER7N99krwKf6sMc0u+rKj3AOfxtiK60N5lEEsJY96SGeJ6lMCFpxZ21K7/8NNruHNziz44E2ZgoKdYnulOBB+Y89Wx5axjDyiZ5BN3hBGMeWtOKIpG21D8gqGsLz2QTJkjjCMirj+k1h72iWw44jmE9l0QxBY1blJdoSDGJmM9ac03nBhftBL4715/Ct89f6I2yVR1G6XefQ7Pdoc4OZfjxo27eO+99/Dll19ia2urUagcARxH5RPQEE8Nnhw05FODBo8Y/X5/l7UaJ3CwIZ8eDYQQhzr5HLfZe/XVVxubPTwoRBXW4dI3G+jFCgPn4bwnP2wmcbNv2AqohUTjfsX+Z7feq/0TNukzjnPzKQQR8j0CV1hiPd/7PGJOojlWXANGXowDkBKMjvW0rV7GKKZQZ8xRNlE1XM4j5xAVijHFk4xtBAQ2hxbvfdmv3MZYDyFESUQoiVY7xuJ8gm4n3inUtlJd236yjm3dCN4DKSPjZRKTuTCtVGNpcbqFUZ3CG/ebkrKWG4FDkgK0FVW5L8bzZ5w/ZRloiXMurCcJLEm0eUnZ5llqjJCIZHhexbPNZORYqAG5jRb0HC93D9rsj/7+2q6MICGAOFG7SCOOrVwV4hmtrtbXM+gxJjRJNNJu9Xu4znudCqwfISOKrDvbMTKWAF6RnHNMipgBaAVVnyiyl+qp6vHQA2gH2oXzQDtAgBXWB9/luXHBsch5ICGYcooANwzSJFL0NpzuwRkP+7mrfM8LAD98agHp9hzNe4//8he/pXdaA96VS1jkxH2NFlrBIqe3npWDBAC24H+Dsm1qmeQvZztnHMueLyaIo5yxwI0ilyhFZRSYiFvrgwrPgviwCOWhZUSGLWXJSdlib/Zpv8sWQa5vDYqdc2ynGv/D730HZ0/1AFSPw1XDQdX8sxibnywlwO+cLYngk6tP4fnnn4eUElevXsWlS5dw5coVXL9+ncxvbXA4OI7KpzzPd3L/GjQ47pj9K6RBgycMh7Eq4fr167h69Sp+8IMfYGFhgf27hnx6NDjMlSk3b97Exx9/jO9///uN2mkbI7JPCIFffbsF5z2UVsiMw1KqcWODYeQ/gVt9g9PdGNc3w79tpQr9QBfj2m6NYz23aGmJQY3i2mbhMJco3GcWxU52InRbEe4TK29HqJM/H0uBAeMH3JGJQ+Ra//+z9+7PlRznleDJV73uA7hoAA32kxQlUaREPcZiR+xuTMTG7v6bs44YexwxP2x4Z9YzMQ6vx5Zn7PWasqllUyRFqVtUs5uv7gYawH3VIzP3h8IFLoDKzK9ug2yyu06E1EHculVZeTOzKr/znfMBPclqyzsPJGMI1sZg7NQm8ywqC8TMQntCi6HbZ0JAwMJnFBRWJAHSWlhfkMiyYAS09qr3dxwHhw4cY8HwH97ZJwX4Fqi0wXgxEBhDkiokqcScOI5PzmOhJG9NNK+yVi+PRyEYdrZ7zvNY1AE1Cg9E7bWKGHij/g4WHKHZSCG8KAQgD8y/ojLBoKbkzEvQh8ipyvrPP6sEODPeGg6Ka5TGHUgMEVgALdAqCJZ6FBQmBofGF7s5PrzzeKkRi4x3hvkFWEsB9PF5FsZYjEYJHh3Vaty+2my3t0BZGnDOSOOOmgyU55p0znleQXAWnBchuyrgpDaUr4mTaXkklvU8cwL9npcaWSZReCZPojhmHiFdyFozlhwTD1EWK+61xowVR+nZsyjJSbWdXCgrDcaZn1gJrA8AjVSkNNMCyBTHuMGy8AcvDTA8IjwsgP/rP36AgydziFhAt3w++mBKg7gfYf5kVqtzNlKSryBXnNYOWx9rCFlV1GSNoMroCJqYySVjGTynVBw+jamuDOK+Quk7T6Bb2yjKmqAica6O3wL5rASeoqZikkgngV0U9brpmlfTwLNlMquQJQKFZz8SSiCwFhj2IwjJ8a9/fh3pUuKfaxS4SCbX/mWxJ5MM+PmV/ZPutApZliLLMly9ehXWWozHY+zu7uLDDz9EURRYW1vDaDTCaDTqCISvAc+j8mk6nSJN/bX6OnR4XtCRTx06PANorfHBBx+gqircunWr9QuLEOIrt3/r8PXAGIOPPvoIk8kEb731Vqd2WsLC5nA/1/hod4ZRprBfaCgG7BHt9prweFZhpxfhc0cV6iuDGJPAHnj/yHqv7Z5yLRGYjdvN3V5EI5/6kcCwH5MUPAvMn3JT3HhOYhCpILZTcY48VPSccJ6wnd0Rwea5VG5s0EZtcniIdLjm/LyyCBNH8N9TFT4FDIGgolTourc7xyePwhZgPiSxPKpV1D64plR78qmoNJkcWqC23uOotMFL233IgG2l4IxEBLVRb0kRPqcxFoIDIS6Acu95acCYn6wL2XoBYTWWNhaxYN71kgdGfa2ecrdzVvl/L205UmlQGHewK0Rw5ZrXyiZPf1F+bsqIqCxtixaLEn/xt3dOBXjTREFrc84F1BoLFQmUBMXNWVgLCMXJgd9lzI6Cm5vbfRI7F8cSs1n4GU9VKgFAlkqMJ+FzpqnC2PFusECoThJQ91c/Uxh73lWMsRj0Ikw8QdyztlNNSCOJonS3OfTMCon1Qsk2IXVWaO0Phc5DdT2NBRLBvccRupH0Pjdr8mJuQNxAPt3YSLGznp20qajwd//1zvF/Z8MY04One9YuIx/X9Z/KwwI8YG+2QG1pR5tXQgkS+URdM6yxUIlEGSA1Qp8vwFeoGdkEFUsv+RSqLxVqrw583ze/qsogSSQqRx8/zbmtsZCxdJ5jOq+QZRK5py5cL41QHLrHNOX96OWdAb73vc1zKi4nydTwd8mAeUMzJQNmRz/tT7dz9OOl9frMM5gxhsFggMFggJs3b9b70/197O3t4ZNPPoG1Fuvr69jY2MDa2tpzR5J8E/A8Kp8mkwl6vd6zbkaHDl8LOvKpQ4evGYeHh7h9+zauX7+Oa9eurZShzTlHVV1MNmuHZ4fpdIrbt29je3sbr732Wuf5ewacc2it8Y+fTrDVV3g8r3cIA8nx+VNkiFoAu/MKl3sRvjgTZIoE82ZBLqCPrPceEXzllzEj2v+c+g5h4y4YcGU9hWEMpQVijqBaCDix06MomqikFvX6M20RMXiVRgAtuz0ntL/QaFUkuwnaAhH86qis3/cTRxaQ8Aewj7Q1nk8ZuAV8ydzGe4bFMeF++4tf7baqcXQWi+BGWdEVDct4Gus9SgB7GUkikcQCaRoO1DGC2m7RFs4YraaYACrC0ialgA4QCAXhRMZaJEp4Ca/KWKgAcVQS5p/k3KviCPVPqS2Ex76r0AKp1CitO+CjuEZh3FsfHVBHWDAooVF6zlEaSs278Pia6xihdQAA3r+zhy+W6sWkmYKujJMoEoJh1dQNztkK9DEwn1e4tNVDSrS9VZIjbExYE8ZcMJKyQCkBEO48IhRLm88rKMWDCqg4ll7yaXGMj3yazEoIKbxkss9aCwgrHMvAWhGqyRZ6Rod+nZAtJ4UAF4J5yadxHp6Xc8LEmJUgJR6dXaou9RRe2R6c+tuf/MmvkC/99nle0U7eAuW4RLrT9yqpl0GtPQgAjGjFbCoDJhgsZZ4mIkjWUEkqSgZGiJwBwjWmQvWl8mkBFkvnRMgDa0So32IlnORTiKAPJbFIyaA9tzfMIjws3BZ0ofXUR65nicStN1/C968OGt0cmt77Iw5MG04Zc4Z5ww+QCIZZBVzvAzdGh8d/txbnyKez4Jwfq54AoKoqPHnyBI8ePcKdO3cgpTz+fDAYPHekybPA80g+zWYzZFkWPrBDh+cAHfnUoQMRT0sMWGvxySef4MGDB3jzzTcxGAzCX3JACIGyXF350eHZ44svvsDvfve71paLLxI457j7JIcUDI+OiKeeZPiCkL0cggXwJK+wnSl8ubTxu9yPcUDc+K9ivTfRQCoZZkRbPIBmvffKpQxmKYMxFoxMFsVE8mmuLSRAMoyKRVitBACJ5JgE+oJyH4WpiazKEziZG4tEwGuJV2dSBrK4mT8Qr8GCmdyS+ftRh7gpAJz5LfMMAGkZrCet3QDgljlT39/5eIwnk6dLdOgl8ri/0lhg0pKwrbRFrDjyloqLVR7ZSjCsryWkY9uEB7lgMIQ5Tyy3QVp7Km0hBQsqcSRBwaWk8Nb4yCsNJbj3PSnU5Mr4B31lLKSwXrVeLCqUHgUUC0g8co8qagHJrdcesDIMivvbOTcpLKy3vwwEYj5Hbtzj0RiDv/hvnx7/94J4AtxWT+aIxKNYx52/Xl0Tpk3NswXWRimJpATavW+nicSE8E5APifxsDQJK6ko8zl0jLVAL1EYe4j0EPkTqi01nVfeuTcNfD+0Nodqx/gsA4GafIqlfy0L1YQrDUMvspiVvvusf4/Q8O5FDAd5WI21QBZxvH5l/dS6vX8wxzu/fHDue0JyGGtWmmNNEImE6EeoiO/N1JqCrdsRCVSUZz/xISiiMElVERSeJaFNod+inFcQMXc+4xfJMLljDpeFhsqUk0AqAgSSjwQsC404FagcbQvZqYbeNShkvQ/jaXn0/nlyHQbgtVc28LPXtxEp0WiXLRgwa7iliDFMG97OXGVhJQMyCfzkpcenP7ASbbPVpJTY3NzE5uYmACDPc+zt7eHTTz/F4eEhkiTBxsYGRqMRsizrEk5XxPPWb53yqcOLhI586tDha0BZlnjvvfcQRRFu3br11FJsIURX7PIZYlGHaBUYY/Cb3/wGs9mss9kLwHCB+5MSj4qTjQQz7YK+3vPb2j5vK5N4OK0wSiT2K0P+bVey3mPsyHqvHYHW91jvXV+LgTMWYaEaLKea1GKDlUqOQ0qmKPF8lDjDXFvEPKyQigUPBtEjzjH3WfMYW2csP8XmprRAEqjZFEJl67pP/rFIqN8ABh0YBxwcpkHPoI3B335wELyGD0rWVkiL+xAr9kkSidbkU1GZVtZ7nDFsbmTk355ab2ZxbgqogT/qtZUMk3aUloUS3K0FVMD2KtTmojKQ0k9gJcJgpt3vT6HSF1VAlVRogZgbGA99HLLmAxYElZ9YSnmO3PqJzogXXvLpF2/fPyZeloknqbgzkLn4fNW6oVJxf/2TBqyP0tq6imiX20btSFU0hQiQtsdRrkux5XQFhZcRRxxjjxSs8BDDQF17xVdbalZoJKl0qiCm8wpRxJ2E6jSvvLVhasW3h4wt9ZGq0X1MaC2jrJ2pqpVLLlgAgxg4CGyvIsmAAPmUH/2ukgM/vrYOdcYC7o//zS+dqps4U5gH6pJSEV1qV0skZCF36tgWdeCoFnjU+kihGoIAjViqCg0ZC681YEWw+YtShbmnDq2KOHLPHI5it3ppPivBlXt+hJRRSSQxrpoHfpFr7ytPcC0OfBxSYGtjMewrjI9+q9Ewxv/wkyvY2jhRgjTZg0cMjepY17uK6xYZA956aYJInLkG0fbWhziOsbOzg52dHVhrMZvNsLe3h7t372I6nWIwGBwro5KElvTU4fnDbDbraj51eGHQkU8dOnzF2Nvbw/vvv49XX30VOzs7F3JOIcTKgYMOTwfG2Mrk03Q6xbvvvoudnR384Ac/eO6ydy4ae2KAvSXiaRRxfNGStAnBWOCwMNhMFVQkMG3BJGkLbCQSD1sqOTz26E5MHJvSjVQiTs5bOLm80JtAsaxbgOiyQq7nRI1zxIIHfxvKdAodYlHXfSo8lyoDCg2g7iffqAiUr4FFTcz57pgg0oIFh7ss8wLNJ/mr9w5Idkc+JLE4FdCcl6tZ7/mK2btQZxvL43ozIWxdyiCVgDaGRFhZG67NtXQ06aicuLyFspUXoJB9IcsrgFbHSHC/7VVZ2eCYDxFYkSi95JMJkEtzHQ6ARlJjXrmPCxFYAMBDRXQASFYGyScf0TWdlfjn258DAAaD6MReiYUJlKcRN7RVRnDOMNhIggTJMtocS0VOKfoDYEbxXgONtKXUa6LUjxKB8TSZVt7CScZaDBJ/bakskjh0fG6PPp84lCSVsRjGErlj3JXaIo24U/lmLZAogbmHAKjrRnkIAsKaKHn4oRkTIiOUd/dpYcAZ8KOrQ6Tx6Xe0+/f38dsPHzq/W5YaPOIwq7wsLkENIjAlyIQOAMACTHJYSq2/0pAfgtTtTpv6UJRjVCqDJJRKFXTpNtzOpyWYh7wFEKwRGbp96SHnrLGIk+iUReMyytB66SHqylIjSQRcRgW+JAYAyAPqMkqdvSRWmBUaP3ttC6+/unmqDpViaLRCd73buEaFawrs9Apc6p2nsSzBGrcNGGPIsgxZluHq1auw1mI8HmN3dxcffvghyrLEcDjExsYG1tfXW9cC7/DtRad86vAioSOfOnT4imCtxd27d/Ho0SP87Gc/u1A/1458enZY9H1bz+HPP/8cd+7c6Wz2iNidlXiiBsehhlQyCMEv2g4fQH2+Xiyw38IKb4FVCMSDQiOTHNMWgf1JabAWC+wvZZwnkuPSMGlU2My0JdnHALXaR7JabRMCtYvmmkZ7zHQd6A/1I0UhFSpKDgAVIXAqAsGU3AASBjwks/CgtHVg2Xff3ALac98atVuJN2uVECJtymY/nFW4fW8c/K4P6RniaYFECUyJgeAFyspCSY6yJRlGnZ+9TGE4iAHUKiUKIQMc1cAhjDuCA2V9nK1JnNA5K21Jqi7K/K8JxoBlFYnsCiibtIGSzGtHF5rnoX7MA+RSoQUyqVF56kIJFrAA05xA+obBg6tjrZBy4T/94i6qyiDN5Km6HkI213o628AoEigItlRN390Yxdjdo1RHBC6/NADAYC0QRRwFIaCutUWkBImE8tURW0ZZGSjFUQb6piwNIsWDVnCUtWg2ryC4v67KdFpCRdw7V0Pv+6U2GCR1ANeFRAkv+RQFnmeR5E7yCUBtjerpk1gJlJ7iMYpzzD1zIjSnisrUykfmCeJfEElCedewAH6w08Oofz6j/Y//918Gb4gxhvRSitljSvWzZsj1E3KbS05WNQkpyApAEQloghKSSoBVeUUitCqi+lLFYfLJV0sQOCJ/BhGKpmJCR2CMIUok0lTCCg6pBGQkjv+NUonRFYuUA4fTClVlUJUauqz/lVJ4n+lKCSf5NJ+VYAHbWx+UEs7aUEVRgXvOvT/xPwdmeYVeKr2qxc1Rgv/l1vXGWpsuuzxXYobr3a1oGH9DBbwycqj7PXUdLwKMMQwGAwwGA9y8eRPGGOzv72Nvbw/37t2DtfZYFbW2tvbUjjkdvrmYzWYd+dThhUFHPnXoQESbl7o8z/Huu+9ibW0Nb7311oUXR+zIp2eHRd9Ts5KMMfjwww+R5zlu3brVZTMRYK3FL7+YQB8FERRnWI8lPpuU2OxHeDguSEFVKiLBUDKOUQLsztvNq/1C1zVTWjZomEhMW9qq9NQJ+cQA3BilMJ51KRMMYyJblEmOA0LG6ZQY6LMAepJjHAhgaFvX8Qp1OyUOcmzN4emT+REp5621EpIlAeCmAoTbMlMHTmFQ24b4bssg1A4GCTQY5i21AzWJ5Xt8adhzJNZ/fGfvqeeYEI6A6oqCT6Xak09FaYLBXymA7UsnGz/WxrKSyPAaSyOLgKMC3z7p3REigqUeRQ1QaYsoUE9lEdj1EUcUizHFuV8VGeig3MfGAphVPFgXKhIalacuVOg3qixHzA20db/XUZbJkI0oAOS6eY359ItD3P3DXm2VtrTGc8HIygGKbZULlLYDQJoqyPikr5USJPIJANKERj65gqbN55Qoy/BzN0kUCo8SAgBZUdnvKewfeuo1ARhkEvue2noTwntJEksv+cSf4vcGwst2aI8kAtcPbbFCNcqsBfqxhe+1ijJUKHN3VobXuu9sJrg6ys4l7Lz33hf4/D7NzrYYF4jXY+RPaETvMoY7fVRLhCJXdPLJV0PoLLjkJPJJU4luC6gkTBhV+ZHaL3RLF7T9lpE4RT7FmUL/UobBpRSDjQzZegLOGa6tx/jcM9/XUompY43eziQ+eTTDbFJgOi4wnRSYHf3rmz9GW2SZe20NqZfqRKrm38ceJSq4SPt5rtHP/MR3L1XIz6ynSnJ8/+UR3nj1EtYHMWKOxvqvrp/PtRy4huLZOrsMwE8v74Fz131/vXt1zvkx2QQAVVXhyZMnePToEe7cuQMpJUajETY2NjAYDF5I55Q2VtffJkwmkwtNUO/Q4ZuMjnzq0OGC8fDhQ3z00Ud47bXXjotOXjQ68unZgXMOQ0xjX7bZe/3111/Il8VV8Pv9HI+PMvwYgM1M4ctJHVHYyzW2LpiA2u7HmFhg4NmAuaAtsB4LPGptvde+8ZOlwMErGylMIFNZcnoQnVqLx1ggFQwzQqCZKgxSnGMeiPjMjj73kkaoCbe551TaAolg8MWOcoKtXhxF3pIPhbGQ3N9eEeAtKgOEkh0ZC0dfOBhsYBwIcJij89x7nOP+4/bBrmX0UuW8t0pbkrrnLELBxyZYAHEiMfVkLG9f6nktby4KUtDIM2oNNkowuagM6twX/7GU2lCxFJh72p9XBjIw6XmArKsCXpKTUkAJ41Q3WDCkssK0cgeOQpZ4pQmPBcUNtEdlVWgGzvzKxtzEweuUNoJACY3T9/MXv7gLFZ+302pDnFpja5XUCtaa43GB3iDCxFPjBAA2d/qn1tI2BIggPkAqTVM0AX5rq9PHEeZWqZHEIqiQSiOG/cC5lJTwGbVOZhpxLL0Erwz0bYgcLgO1WVyWeQuErFSDKs3A90O1YwAgUQbjwv3QHOfhmq2+mlALzEvrVeG/fCnBza018IYipX/2p++EL7AEnWskg8hbT+gsGGcwiTz3t68C1PNaYyFjQVIsCcVREgRfUapQBGzdQvWQgLruUwhxFmG42cPOlQHEIEbUoNIBgImHXI8Fw6TQzvEXK4HeIEZvcP7ZsBYx3H80w/ggx3h/jvFhjvFhfvwM8BH7IcvR0NwcpAq7HjK+n0WYFe4fLFqyJBwNYrz+6ga+d3OESJ383TVrXU1r2kMxoLGma8yByZm/fWdYYT3Nj6ypmy78bEOkUkpsbm4ex5HyPMfe3h4ePHiAg4MDpGmKjY0NjEYjZFn2QsQXjDEXnsz9TcB0Ou2UTx1eGHTkU4cOFwRjDD766COMx2P8/Oc/RxyHAwuroiOfnh04pxXq/vzzz3H37l388Ic/xNra2tfQsucDpTb4/x6ebBMuxQylNqc2+Xu5xnY/wheHBTHM5sYwFpgckQ3jyrSo4XKCr9N6bz0WSJUAj8KP7za1Oag2Y0BdE4lCPlGtxijZbNoCfeknloCjAFwgiCUZQ+m5ZmWBmFlvdn/o9g1quxDfShG6bQ1AWttoq3hyDhZMSeeMQwdJ1RMS6z//ai9wrB/imPR0NyyOBKaEIt7L0MYikrx9HSqPDdNaX6LXe4pndYt5IzgDpZrMRWd3RlIE+4wSPwwRB9pYxMqvHAjdWlkZCE/tDGMZUmkw8xA/kvvvNbQuzitR19jxzLvQkm/BkLApcrgDCqWNELECFdwKSgBIRI6JPgl0vvPBlxiPi1pJt3TcKkSSXJF8AoBeKr3k0+Xt7NzAajO023DTCVHRBEsMlBOvTVFIXVRMMEskDjyB9hCZH6otNZlX3sbOCv/3fZZ7QNi6M7RGldoiDig0Q5xhZRj6ETD1LMTTAiTL4l7McdDwQnLzUoJXtpvf+X/xi7vY321no2eNhWVHyiWiqjHZzM6npLQYh21qRLV5XgnFSeQTIwaY62eF/6lKIZaKWdVISnLJsXljDduvbqC3nkAsEooc80RxhoN55dwTrKUSex4Vo89BoR8rrK0Da+snNo7WWkwnBVRR4fPRk6KTAAAgAElEQVQnOdT+HJPD/BwRX5UGcSqdVoqhZ0CWSOweuNc5FSD1LYCXrwzx5vc28d2dPsYtxpfLKnvecI6YA01TJOIMk6WnZSaB17Ye1uqmBptdawF8zcqnEOI4xs7ODnZ2dmCtxWw2w97eHu7evYvpdIrBYHCsjPoq40/PEs8z+bS9vf2sm9Ghw9eCjnzq0IEIX4B5oXC5fPkyXnvtta88A6Ujn54dhBBe5ZPWGr/5zW+Q5zneeuutzmavJW4/mh5vKta5RmkE9huCJru5xs4wwucHT0dADVKF6dHPWRpgI5HHqisq9gsNbg2MJ8jdhFWs94aJRJwo0j1TCKLjYyu7EvHmvz4tUJITo4wUYokSBwmpgIAjgs3T/NzYsI1Q4BpV2N0v+JtQngLWcoCFjqwb8s+/H2PfoxKioJdKkk3iKpArkE9FocE5O5dZLwTD5qVmcoA6F2re2tYkYADUuUWxr2tzXGicAu0C/aFr+YLgVYCR1hbo8QpzT70FxSvMtGf7EriXIlAXyoAhFRqFpw2U/lKsQG792awxz1EZP/kkuD6e6FVl8A/vPGi0cFyFtLSGbgd5Fk8OcgjVXF9KCIZkEJ8LgFNsIE+ObRHUJqqkQuqe4+OIQf5YhdtYEizaKIrISPnvMQ8E2SfzEkq6a0vNC41ephprowDANK+VXq7vT+YVpGDO/c8sD5FbGkIy+B6KkeSYe34byvBKlZ98AoB+DBzM/cdEDdZ0NzZivLI1OP7v5a6y1uI//Pn74QY2oMo1VE8h38+D65uIBJCcX7uoyUAAans+4kOQauVXN442Ty21sYStdjkL2/OZyiDKFMqjd/9sLcH2qyNs3liDWFLnjFKFJx7ybD1ALiXS767gq6nW6GDMGHr9GC/1+4g2TgZ1nleYHOYYjwvMxjkODwrIiDvJpzyvvMklIXLq7LOHMWB7lOKlrT6ubPdwdbuPLKn3woLbxi5wPYOaFE6KAU07J+Vwezh7az/ZOoQSFkbzRvIJVmBlf+ivAYwxZFmGLMtw9epVWGsxHo+xu7uLDz74AEVRYG1tDRsbG1hfX39u4hDGmOey9lWnfOrwIqEjnzp0eEp89tlnxwqX9fX1r+WaHfn07OCz3ZtMJnj33Xdx5cqVzmZvBRzkFX67V+/4t1KF/ZJBagPXJuDxXOOlYYRPD9oROAts906IpwUU1StuCcYCAwXst4zZ522iAag3UOu9GFNipLi2mEOwnhJQ78tTyTAl1IhqyjhsQmWBhCOoVppri4iF64hQAqQUIqvQeOp9pbZABL86KtSSygISIYs/P0NlwCCsP6GfcrsGFpUx+LsPQwZRfsSKo9R+WyNgdeu9trXVgLoHk0RieibiuH2pB86bN7K8RduUYAiIAgDQlYjaWFLmfdv6V097LgoZwAIRy9oG0B2gBoBEVpgXq29PShOqCyWguNu6DwBUgHwKXQMAWLAYCcBZeOAsE5t/8/Y9lIU+txZKxYN1PVyIInexeR+0tlhfT/D44fTcZ5evDGEaVp2yNOCMkeZCEVDqnAJxWZgRE0tmc4pGkXbd8SzctxQVaGjEjaclmHAfaS2QJQpjj69cGkkUns+zWGLsaKuxFmkkneRQZSyy2K3CtACSgLVnnfXu/nxWhucl5RUvJiw/Z5ew66MY39keHlnhnsef//n7yEOslwfVrEIySjDf9bNi8WbaKGW1xtKzKgAIJUh1mqy2YIKR7O2oLHdFrAtHJb7iVCEP2POpVOKl6wP0r69jcKm5/kqmhJd8ShX3kk++u48lw6Q0zmdj7mFWZ2f6IY4l4lji2kuD4/pSWhvM5xXmsxLzWQVbVnhyWGA+L1HkGmkqndbGvjWjPrfF1e0+rh0RTVe2+liLOcYtSKYmhVNto3f+WBcP75z9S6e+1jPYHhwetcVhcvwNUz2FwBjDYDDAYDDAzZs3YYzB/v4+9vb2cO/ePVhrj+tJra2tfWsJHK31c6l8ms1mXc2nDi8MOvKpQ4cWYIwdZ/hUVYUPP/wQVVXh1q1bX2tmSZu6Qx0uFi7i77PPPsPvf//7zmbvKfDPX05gAKzFAhNtkFqN/UD9jUdzjSvDGJ96LCGaIBgAyc/tBg9LTQr8nkUUxUDVLrBwWBj0FMeEGDS8uZagBAOHJYQ1a8SEekrLx04JWprcWCgGb92k43MKjjlhrUokbyw2vAwK6VUYi4gDlYeNmRuLRKAxOLrAtCjAZECRwJjXfq8yNlgPQTJfpY+a5Apx2LWtnruPDQARUFkZAP/1/UMwzhFFALM2WAOoCWksQK0rvor1njEWkeLBOivncKYTh/0I/Z77921TO4cTa6tpTVcXSkm7R4oNIYU0KioDKfyqg/xINeKtu0ZQJkaCNVrjLBAibcrA+JqWAvDUW7JgSKTG3KeACqqnGBTzW2JWNhzgsQQWvDxSRj05zPH+bx+f62OlBLhgK5NPT5MkM2+Yv71eBBG5+zaKBOYEYslYII5pxFhJTMYyxiJJZGO7l6G1RRLLYDuL0GAEMC9McM2azSskifDO1dB6oI3FWqYw8fRXooQ3kCwb1DzLCCXnRFJ4lUmx8q9XUnA8jWz2cM7AAsRyTXz675MS3lxOhLg+ivDq5fPE04I4zvMKf/vXvyOc1Y9yUtYE1F4zAaV6ClYJ593xSMAQiWYmOagPcxEJVIS6p1SySBcajLOaMPOgIhLUIhKAh3y6dGMNV3+4hVeu9vDFofueQ+YGoXqNPmJ1LZbYdRBXDMD+vELTuOUM2Hd8rx+JY/JJCI5eL0Lv6L1HMSA/Wm+MsSiLCtpYJAw4mFXYSCWubvRQVBpFWb8fRLFClkiksaz/TSTWEo65Pd8xLocBl41e034i4s3Jc65lyjVaFnM14sCPLj90HLV0nmdc7+lpwTk/JpuAOl715MkTPHr0CHfu3IGU8tiibzAYfGsSZZ9X8qlTPnV4kfDtXl07dHhGODw8xO3bt3Hjxg1cvXr1a39wf1teFJ5HnCX+tNb48MMPUZZlZ7P3FLh/mOPzSYlUcljGwKzFRNPG+aN51ZqA2hnEmDYQFJUFRvFq1nuSs9bKjEEsMSHUqthIJXgkYQFkkmP8FfiatWl5IviFKi8oNWdyY5FwoAps8GPOgwH3iPPGwsQLaMYhLYH58aCwQBw4B8WaTwYKpIcCHgDAwWE8Qf3xvMIHn06Px4BlDFFUB88XG/28NF5yIY0lmXgCVrd4lKI9+ZQX+lhpJQXD5sbFZRnWAUaidaRgKAlkECeOOyEC7CVqqzGL8HtDTWS522ZtHbwuPGR2od2Z2wsobr3KoSqgKpqUAg7BGgBAW46e0sg91nySG69nZdUQTDsNBiUMCuNuyNwmwQlemvD7Qm4iAAZ//Y9/OGd/pJSAkBzmKezzjLGQ0m3J5MN8XmFtPcH+k5Ng+KXLPS+pJhUHiI/qWEkS+ZTPNZnYjWMRJJ8AIE3C5NPhVDfWijmLXhqhKP2KlV6isD9xvwtMA+oDAIgj6SWfgrXKQgMo8HnoOS4DDQg+DwNrp7YM6zHgczOm8BX1GuS/1uwoWn51XeHVy2uNiqfFjPrTP32n0Z5yFZTTEtEgQtFQb01tpN4fWQgOQzLrbbfPZAHScoGq0GT1lUolioBaSZcGXPIgqeW6lXQY48ZPLqN/pHQa5/7z+NRHwHkF0jJqkqiZQAKA2GPJN0wEDh1qsEEsnOSTqxRTpjjGS+sE5wxxoiBYPccu92L8b29eRqSWnqHWofS3pvH3dL0qNnWRAJA3HO96ujrVU46LLm71zc0JYrnUV02We8C3TvkUgpQSm5ub2NzcBADkeY69vT08ePAAh4eHSJIEGxsbGI1GyLLsGxtjep5t9zrlU4cXBR351KFDC1hrce/ePTx48AA//vGP0e/3n3WTOnzNWFY+LWz2rl69iuvXr39jX9i+6dDG4p0vJ5CcIVUCk8pgXXFMCnp/tiGgMsXhCwOFsn+bYI5Iq4eEANEyQptZoK51dKmfHGfZKwpTc4Qmv3R3W1rU2CA2gXp9atwzEmFiiTINg4cwjogzFJ5LlSZMToWUTaHbtqg34L7jTDiRGyxQ9OAv39t1BgsW3S0lR6R4rfgyFvO8OvVbCOEvBn/uvNpCCOa0enFhFes9AIiPrPcub/WCNWI4o18jkYKsEosVjXxalZhrgrEndog+CILaSwrmJRiLyiBV3KsqTESBaeUuiD0rhXc8l4ZjoLSX+Im5gY+zCNV9ywN1oYCj2hWen720MRJWoIJbYTc3Sa308qTUW3A8fPQY9+6ftsQUkkOpk/UwiiXylkkTx+cSDNWKpd740sNgc7vnVYMB7fh8TnzQGGsRExRNACCImdNShq9dVRa9TAXt/FSgXlN9jD+oNi800lR5ifdQfbdQwkiI1C8D6uTQ2hyadzpAbuVVyJwXSKT1kk+TAkHCcEaQdc9Li2sjhe9eXnda7QEM+09meOftB8HztYHRBlFPnSJn4vUk7CnY4t2xDZFN3vtYQMYSFWGechdzcgYqEcjHAVXgmXHPJceV1zex/Z3RcdtjyTDO/ckTBwF1177nvtYTiUPP/PJZkWaKO8mnVHLsO0grl3VwPxKnyKcFhonE40mJW98ZnSaeYJ2UpWtJaJxC1ja+UysONPF+rp/C9SrTlFDGULsmXE4trq6dsZV21EK1Hsvd5wFxHGNnZwc7Ozuw1mI2m2F3dxd3797FdDrFYDA4VkbFsftd7euG1vq5JZ865VOHFwXP9+raocMF491334UQArdu3XouH4AdwuCcQ2uNTz/9FB9//DF+9KMfYTgcPutmfavxwe4Mk9JguxfhSaGRCoYvV/DGrwmocA2ojV6EiWevOi7NStZ7q9QROizD1ns3RwnsUtCgTf5sbiwEc2/WllEYi4gxFISoA8VyDwBm2kLAKzIAAEx1nT0ZCmJQYicuW4+2x4hALL4wtYrjadRRpQGe1kWiAsACzfCRAQ/25niwS5MiLAcnueDIIgbBGTjjKIn2jsuIlcBUt7feixVfwRaQYW0QI0v9dooAYCyD5DSia1LQVRdZpDCeh/taE/uSWpdKCh60JrsowiuSFvPKp2zyf39aCWSR9trWxaLykk8sQB5WAVVtaThSYbwKKEpwNmIzVNY93iw4UjHF3PgDD//5v3966npcMAjBa8XSBdT/tBYkm6smHBzktWVcZZH1o+A4Clkznm0XFVFEUzQF1T2L44jLSxLLIPkUIl0AWruyWHgJolBixjQg+5nMSi+BMQ0Ut8sDNoQhciv0/UpbJIrBtzyK0Nw3DIPI68KGaYnge9Mrl2J8Z2sAE/Bi+5N/9w4CuR+tYbWFzCTKeVXXWmKAGIafa21gWjzP26wbQnFUfhEgADqhRSGpyiVl0OjaENd/tA2VnA6Brfckdufu+xjGAlPPgFhPJMae8Z1Fwks+zTyf+ZTQvs9c+wpXlyWC4bXtDJvrp1UY9Tt803VsI/nEYBv3CExXOCpMdwquX9A1rJpquwqGxoST5Kge2Y8vPzr1jmwtd78zP2fKJx8YY8iyDFmW4dq1a7DW4vDwEHt7e3j//fdRVRWGwyE2Njawvr7+TN1djDHPpe3ebDbrktk7vDDoyKcOHVrgtdde+0ZlgXT4+sEYw/3795EkCW7dugUpu2X0aTAtNd5/PMVOP8Lu0c6BY/VA6KO5xkuDCJ81WJIAwKVUeoknoM7k20gkHhE87Jexn1+89d7lngI7M8baKJSA2qbPt+ldRioZCgKz5LMXOXdOxb2bcqDeZPYlxyxwbyWhb2fVkemTZ1M+1xacBerXBGpDWNQqNF93hVRJBoCE9ZJDdeDSF4hhQXWUhpug+qtf76083yptYQzQz1Yjn1a9rpAc3sJBDTDWYnODnl2YRRwHnuLhy+fNIoEJwXMwVJ9pgepofIaGO9X6khLGo9SGopBdIbHKpPArmwAglRqHpZtc4i67nCOErPtmlQiSvkoYVJX7IMoyLJgODnLFSq8S973fPcLj3ZMjGGeQRyqZ+VK0zVpARQJlG+/L5XYojoJYD+YseoMYSaqCqieAPmbbHsuJqo6C2D+UulRAWG0EACVhraIcowKB9lCbp/MKSSSc7yhFZdCLpXOdmhcaWSKd35/kFSLJ4ZrgizXS9dzNSwMlmde2UUkB7fkNKfMyUX7yCQB6EdAkpOcM+OGVHjaHfXCu4Stpef/TA3z00SMwKQCjVyJ3XcinJaJhjHxvjuRSRspiaXN9qy3YUeA+BEOofXYM4jyl1oeivEToQmN9K8XW9zcx3Gp+B4hdBYaOMEgkpp5B04uE9z3Xd9uCAQe5dr6z+lTLrrmqOMPYoZZyPesTJfD69fVzf3eNLAmgaeciGdDUU8yaRvLJhaZ1hqF5DxRz1vh3xYCbG3Nk0ekWWSs9tnsv7r6eMYbhcIjhcIibN2/CGIP9/X3s7u7i3r17sNYe15NaW1v7WhOxn1fyqbPd6/Ai4fmbwR06fIVI0/RZN+EYjLFTtYc6fPUYj8f4+OOPEccxfvKTn3TE0wXgnS8n2MrUMfE0VBy7K1oHLfA419gZnM8CZQBURPvNKAGlszAARkn7MTF3BO0TwTDonb+PygJJi/a1cRGk1poxADLiiak9QnFZmVU2mA5vUfedD9rWVh8+UCwDQ01uytA8i9DWLeB0VLcjkH1tAfCGgN67n4yxP326+ZYlElrXaqS20NquZHO5Sn2aq1sx+gn9WsSlAkCt4KJgoZKiYJiEz2ktoAj9RyGN8koH1RcU8kwyf1Q31xyx8J9H8rB6wYe5hzQC6ppOivsDpoxgzRfqL9NQV/A83OcwxuC//fL+SZtYTTAZbSEbFCo8ZLvlgWB0AucsrAUY0SZLa1uTxwQUhSa3iapomucV6ZyTWUkStfrsshYYE5TcE4Jlb+hS41kZXF+ywDtKFvnXndTzubX+72tjkQTWytBaGgWGzoxg10yZJlGD7WIiGf7o5XVsDuss9dCV/vTPfnX8m0X9i1UmAUA1LZFcSsF7NDWCMZZcnwkAuKQ916wFOPX5T7ViJpK/Zy31mjC6NsTP/teXncRTjUCtwkC/hd5jfIlTa4n0JksdehSHB45+WvO8P4wbyFvOgO9e7jeSCa6WuX5x50hwLGBtFE4Rb35iulxSM2nwnY3dhrY0t9JagS48egLOOUajEV599VX80R/9EX76059iOBzi0aNH+Jd/+Re88847+Pjjj3FwcEB+Bq+K59V2bzKZdMqnDi8MutW1Q4dvKTjnHfn0NeLTTz/Fu+++ixs3bmA4HHb1nS4AX05LTCuDJ0fZecxaTNtkUHqwl2vsnNns7wyixqK2TTgsDfgKuoxVXr3HpUG/YeN+bZQ6a4HELYKMVGsuoF09nYhaZJp4Ssq+xSBMLAF1nawQZuOx9/PSAiJYo8J/DYPwOUK3rS0IhFv4fs/WpdDG4O9/u+84mgYl+LGNVhKKCjoQEYmbZRgL9GL69S6vJ4jjBLGkBwDbrPDUALk2FllMu19FnONrSfg4CmlkLRAHCDdtbJC0zQOWdkCtbPIhFNCfl/5G5FpABtRREQ+oMb2f1sSSEgGyzmO5t4D22Av+/a8+xWyJuIgSVRNPkjcSsNbYlQkkbYDRKFnpu+uX0qAqZxltiOokNCiPUBDfHawFemn4nPVx4aA+RSFVVQZp4D7yQiMK9EvoHq0FegFyKQoQCqFnZyjIHgXGQejzJlJ1GbH02yof5kBo9lIIw7PdsJFx/PyVDfQTmvvFrz/4Ep/84cnxf5eFBiOu/W0gM9XK+le0eN62IbNlgLRcoCLOU6MtBOGdovQ5FDDgpTe2cOOnl3E494+JeUB5GHo3DimHDzxJdaln3meKO+sX9hSHQ9yEyPHbuRRRt26uY9hvHtsX5UDuysVossHmaLb3JpTiO9WO744OHNPDcaLnvN7T00JKia2tLXz/+9/HW2+9hTfeeANJkuD+/ft4++238e677+L+/fuYTCYXTkZprZ9L5dN8PkeSrPbu1aHDtw3P3wzu0OErxDeJcJBSQl+A138HP7TWeO+99/Dw4UPcunULvV6vI/0uAMZafLg7w1SfVENYi4W39lEbWAB7hcZ2vw4exYKhDKhDlqEtEBt/kKMJ+7mGWiH41z8TmLo2jAFPhlebpWimKdTEybHUXqLyVDOiHducaCdIIZYoe54sC1uwhX5Lijoq1FqSfdcFnONspucvfrNPtoFzIY7E8XNx1cfRyvtT4iZUCYbR2tHGrsW8aeNAVrWwHExXINt8oJAN2lhEAaIEABIZvo9B7O+Yw5yDBYK/MkD85AHl0rTiEIFKcokMkQIB4ohAooUIrrlJAOtv59w0Bx3yosI7v/7i+L/jVJGy/OVTjK82iQoLXNrMwARHRAw+A4AgKiqA2g6Qgjynq6RioqwxIhw3nZZBlRwAZATCK0v8ZNeEoKCKW/wOTQjV5Aqt1yywyIZ+o0DJJq8FGVCTwiGREUVUszzVro8U3rx+CaqF28Gf/ft3z/2NMYb4AhVQIhKwsQBr8c7ZRvnU5nnJiESVKQ25DZJA1lljkWTnfxehOF556yo2X17HqCe9CTqMAfsBi12Xhd0CBx51Uj8STpII8DsODDzzue/5zEWwDhv69LXtDNe2+k77Z1fTXbXsXDPUOHYXTX3j4h1dQ71p2brRrzCMm01tXeuYfYHqPV0E4jjGzs4O3njjDbz11lv47ne/CwC4e/cu3n77bbz//vv47LPPkOe0urI+GGOeS+WTtfa5vK8OHZrQ0fsdOnxLwTnvyKevGOPxGLdv38b169dx9epVMMYghOj6/QLw8X6OcWWOg+aKA49b1lgKwQI4KAy2MoVYCUxaxtWGgz5mocIADdfcSCS+JASJlrFsvddXHEkgCNWGo1uohUL1lIC6/ZlkGBPkSlSySFsgJVw/NxYxB6qAVRUlPpobg1DkRBNotlCIpLJACusoxlwj1NzS1nWffIxiqO6TAYOw7szS+pgTTPMKv77vV36FkETi1Aa+1BZK8lZ1WoA64O1ScvhAvc6Ny4PjWjRVi8SBWUGruwQAsxaKTaq1ZU6tD0UkDAaxweNpwPKKQFCFBC7GMmTKemuq6IBt3rjgkMI4lZ8AQyI1JpX7fkQgih1avnLNETHrrWPErYZvK2XBkfAJ5tZtqVJZhZgVKHE6KP1X/88fUB0t9ElPoTqK0KmIQ1PlpC0xnZQYDCIcOmomngXnDP31mjyjWunVoLe/TdJXkghMCTai1DwUCr9tLDDoRUFiSBCC8yHVT1EZ9LIIc0/NoyZ71WWE1s08sJaFvh8khwLrVYgAPZyH+zGRFmPPEB4XOPKKdPfVrKzpitdfSnB5fa3xGJet5t//4z08eTxt/KysDITi0BeQaBVtpABj4BKoCuKcajGfWtWoapFFImOBkjBPqcqrOJWYL50v7kd4+edXEGX1e3Q/4Tj0ZJOsZwLjyt0vsWA4zCvnWpQqjmlhnJ8PYoHcs7/x1XH12f35umfumKdnHQsu9xVevz6CAJzpHK7muf7uejzphv6RDI01D50kk2PanLU1VAz4waXP4H4+u9inLjS6KhhjyLIMWZbh2rVrsNbi8PAQe3t7eP/991FVFdbW1jAajbC+vg6l2hF9z6Pt3ldtVdihwzcN3QrbocO3FB0J8tXiwYMH+MMf/oA333wTg8Hg+O+d3eHTI68M7h7kp8iLUSRwf8Vi5z4YABAMJUNr74jD0kAwoqpk+ZorvEuOS4NBxDEuDF5aT4KF23NjIRnd0i4mkk8AoDiHext6gsJYRAygxDwi4vUTwYPEl2+jftI2IOLWS2TNjUUi4Mz2BIDS+kkfAJCMecdIZW0wgCoZ4AvFmHAzwBmD9gxyDUAcnee/vLe30jhdhhTsXLxpFfIJqK2Y2pJPxtZZ/rknELveiyCWlBN5acAYLU5mAWSRxJiQJl9qi0TxoHUPQCeLxnlNaYaOnuS0fgvVUQKAIkAKAbU9WwixNJiU7iDBtAwRzAyZMph6FFAioDoKdXOuQ4FNhkhq5NqzVTJTAH4bLsnK4I+oeInSnJBPuwcz/Pb3ewCAXj9CfvRsZKy2owpBRQKlZ174ECeSTD5d3ukfr21t1LhtFFZtXreUEvCvpDUq4oMzZKW1QByJIPlECTJRjklj6SWfysAEncz97RzPKkAw5zNrmlcB0sY/7kJq2xD5Na8YBrHx1nUTgbVOm1odFcoR+lc3+xh6FNJNV7HW4s//z/e952VKQCqO6inqLcpUwaqaamScI2wUetI+KihKywVMC/UvJxLV5KYuMRXDyz1c+8nOKTI8VMO1lwiMx+72r2fKmxy3lkg88vyWoXpRh553DN9P4HqX4B4l1/LvnymOt17dhOAcHM0JWwwWlePl0/W609hmY2DY+XcCl42eq8dcS/f8TF/8YDRBJAwMdPPZHDUfremUTxcFxhiGwyGGwyFu3rwJrTUODg6wu7uLe/fuwVqL0WiE0WiEtbW1ILFkjHkubfcYcz9vO3R43tCRTx06fEvRkU9fDaqqwgcffABrLW7dugV5xmaj6/enxwd7cxws7VouZxJfTCtc7kf4YlysVDfJhywSiATHo4CtxlloW6uYHrZUZO0XGpFgKFqyVv1IYiPjsJyW2ZUKjkNicKBNclWbVieSo6Bk8F5gUm5+RBqFFFIx58HgYcT5uU3rqWtpC8H8DQvdWmGAmPuzrEO3XVpABkgshjBpyMHx6f4MnzxutiKhIkskbEP/r5rEt+r3Ygnkjjg5Y8DOpewUkWtR295NiYH5WHGMiW4hqRIk8mlCvLaxwCAROAysW3llkUiLuSdzGwA0wUZunIePCRFHNfw/6LTgiJSBdhT9BoBIaEwrn6ooZJsXqgvFkQjjtAICwuqp3CbByctI9WVOj5u//O8fwxqLNDshnoDaUo8SEF617hMAjA8LRBFHEbCZimNxrCwA2hFKRQtijFrLCaCrpOYei6xTx1H82UBTM+aE5A7CAKoAACAASURBVBrKMaGaSzNPbRkAmBcaWSKdJFClDYZp7CSR8tKgl0qnwmmWV4gVd9qczYqq7i9Hn+WVQaS4lzzuBcgnymtRqlgj+cQAvLIZ4cpogEQy7xO1Vj6dzgz5T//lI8wmfvLWGAsRCXBpYFa0vlWj5KQPW0x3Cnl9DAswyWEJbdSlASlbAiAz1dS+KY+eu9vf3cD29zbOrQOhhCURmFNJgCwL1WDVnmfAIBaYechwXx3ciWONHsYCTxzvDdOjvuIM+B9fvRS0IHXR+Qy2cW5wWDSOflMB7Py1XD3n4jLzhmwExYDZ0p+HCnh5/WFtodfw01rLwJjDHaFTPn1lEEIck01AHW/Z29vDo0ePcOfOHUgpMRqNsLGxgcFgcG4eP6/kU4cOLxK6FbZDhxb4JmUmdCTIxePw8BC3b9/GjRs3jm32zqKzO3w67OcVfn9wEs29lIjjzdBerrHTj/CZzy+lJbYyhbkFVs1lWyWIZwGM4vbWe5YBiliLAqDVPlogbxEYnLfJYCU2gXp9ahyGQiyxUPEIhGM2FkDEmVfdVRo/sQQQlE2EdojAcRR1FMDxl+/thg7yn4G5L2PM6tZ7q3yv1MxpjXd9qwfbMEAjycnkU5uoHsVWC6iVCanimBGIqkQyHBLO2YvC5BOFNMoroB8I7E4KIJIWlUclFYrZWzD0lMZB4emzAGkTrAtViloB4Zmbsagw06vXYMltghglNHM/YUpCMGs5UP/xpwf47PMxkkyhXAo8cs7ISgRr64CqbivbRT2HL40SfPZFs23YAts7p4NDbZSL1oJEcC3OS7XlDFm6LZDnGlKw4DNkPq9I/Uip+Taehd8HxtMSlvmTDEL3OJlXiGPuVShmsURRud+z6vXJvUamSqLUzfdjAaSRdK6xxgJpLJB71r9YCcw8a3SoDNisCK91Tcv1Zo/hu9tDxPGRmpHpAJnCgCXFSFlq/NX//bvgtQFAFxpqEKF4kre2XoqH8Sn/07bf54rDEG3/hBLkuS1jgYqQ5GWJ75jlvCIplavS4Ma/eglrO832pvvTgJVkoMmhRAcTTLZw328vEphVzQ9MxRkOC924HkSCOWvkppLjSQM1xBlwcERw37q5jrXBSb1B1x24pporRU4yNJNPWgMtaqaVDT86BzBvrA912lnhzUuPa5WwEfUcPgsr3cXluppPXxuklNja2sLW1hYAIM9z7O3t4f79+zg8PESWZcdkVZZlz6Xtnta6I9Q6vFDoRnuHDt9SdOTTxcFai/v37+P27dt48803ce3aNefmXwjR2e49Bd59NDve5KzHApwxHC5toHZzjZcuqCCzYIv/Aw4rg2gFImlhvdcWbS3NGIBRFgXtQZbRJq5YGAtFPHVpgJjYDqq6a64tKdtlWhmSDIaSB3A4mQWPqQjXCv3+hUGwzUFlE2FJCZ2jCjcD7316iCeTp6utliXK25qoVe2XE6gVvldqg43+eduzNBJI0+YgQpskEkpQeYE2yo+UTDLT2hoKxgLAuGAk670sGHth6Ef+84zzcINUoC1lQKk1KQVg3b+PBUMq/e9IIXuukmBDGHP/OjM3ifdzACjMSUDlr//hY8SpPK73tEC7mkq1SmpVTANq3+EwhjhT7L4mlOjXVIoeiExi2nlzoqIJgHN9OIssPCFICiljLHqBc1XaHK2vblDusRc4hwyMpdB7SEh9FapdFXpGhK4fes6Nc4AFUjqW1SiJBH58NcEb1y6dEE+gJbAsj8x//3+8h6qFqq+cVRhsZ+TjF+CD08+8Vmom0G3vgPBYOXVe4ppD7iMLpD3/WOaC4eqPtp3EUz/hCE2ZwyZGYwmu+kkL+MilSNQEkgvKM9aHiXC+rww9a6LrFWcYCxgLvLad4erW4NRnbXe1rlHh+rt1PKtdQ7dJrRY7Tr5s3Xc107jUO0rZcSirrW3uO2s5utDos0Mcx9jZ2cEbb7yBW7du4dVXXwUA3L17F//0T/+E3d1d7O7uIs+JdgTfAkwmE/R6bmvXDh2eN3QrbIcO31J05NPFoKoq3L59G3t7e7h169ap+k5N6Pp9dTwYF3h4ZAnTkxyVsdhvsJm5KALqci865Q++5tq5eLCw3muLhfUeFdeGMSznSFt8Z94y4JC2CCIkxHbMtMtcZ7XrG9R2giGcLS7ciCgORqnm2gazhk3gcwt/AAEIE4UGtV2J/5jQPTNnNipQW1b83UdPp3qSggUz71etJbWq9V5TUP76dt8ZgWlDEs1KTR7f8xb2YKHg7QIua6uz8NnXLWPgL08EwF/EfIFIhCzvGJIA8ROsZVX6G2Js+BqKP31dqND6IJg/sllBQcFPUOUmBrMa73z4JWaFhj5jw8QFXfW0gLXAqgm1s7nG+rqbNBttNQfMk6hFoLpF26jZzmVpIF1FRM6ASpRFhID6dFaR5k0ah98lQkTbeFYGF8tgAkDomRdc40OqY//lgzaFgXkZcBaEsXVNJx/y6shi75LEH72ygdFgCMZO9xtlJC1uZTwp8P/+4z3CN05jvJ8jHoUJ6gX6W73GjJg2hBJr8Z7ZRv1Pzeuw2oJTMiZQq6mc1+MMP/+fr6F/yU3grWX++ZRFHHOP7R1jwBPPgBMM2Pd8vpZIb8KLrwakz87Pt7/IHfeTSI7LfYXXr4/Ot8MlBHJepR1ct9mUACZZs4G0cPSjPf4ceGPz86W/u/rI8feu3tM3BowxZFmGa9eu4c0338StW7eQJAmqqsL777+Pt99+Gx999BEePnyIsmznMvJNwnQ6RZqmz7oZHTp8behs9zp0aIHOdu/5wsJm7+bNm7h69SrpO5zzTvm0ArSxuP24DsApzsA5QywYxmXzpm0319jpKXw+We2lMpUc+Rkv/hUciADUG9y2aGO911P82HddMqpp/glRMyOqM9rcBqWGBVC3tCcYJoTOpcY7lDjt396EWWXr+/HWYmLIBPOeS9va2synPMoJtnqhMEph7dFv60bIVk/b8G/IGYdxnOUffrfntTqiIImlT2wCoA5cRpIHC8ufhV7xewfTApyfkGIvjSIwTyAur+jPTWNrFRXFpi+vDLnWG5UAGxNqwABorF3ShFDhc8Bda2EZFKIwlQbzyh34ywM2gYXmGEiN3LjPEXEDv8tTmCTzQVuGTBqUnjZQluuIFyiNL8DAoNgEv/z1F42BfyHYOUKKgl6mcDhe7RnqCjrvXO6BOZgjKvEDtAtqtnn1ThOFwwu07qVeOsui4HUpgXwRYOW0thj0FaZzDcEZIiUQKY5ICSjJoSRHP1MY9mOU2qDSBlVl63+1QVmZsN1gYL0L1XkMKUZD61/oHfuQoKxMlMWhqx4ggH7M8cZLfaSJe162SYj4t//uV60VSAuUswpqEKF0NfgIjDMgk42ThwsGQxT+0VOG0Ir0btNfMhakeqGuocIYcPXNy1jf7mG27x6vkeLwvVmtZQKPZu6Gr6UCY4/v8ihVeOJ5TieKA57PXXWbQvD1tYsMiwXDD1+5dG6N4bCoHGPC1TqXFaHr97Ksec423b7LpYE5rrlQMX5vbY5ULT3vHMpFa5vXddvVe/rGgjEGIQRu3LiBKIqgtcbBwQF2d3dx715N+q+vr2M0GmFtbe1bY883nU475VOHFwrdKtuhw7cUHfm0Oqy1ePDgAT755BP8+Mc/Rr/fbNfQhK7m02r47f4c08qAM6AfCWhj8Thg7bNXGGR6hqlonxU0SiXO7ifHlQHXJYxol912WJq6Zk/LuAI1DvHS4KRwdNuRFQuGGfFLvgzLsyApi46gOCPdLLX/KJc2ADLBMQ8cLFwFgZYgGUPpCYVWFkiYe3MOhG9fWyCChfGcI3TblQUi6yfCXEGleVHhg08p1YPcyGIRJJ4WUCuQSEBt8dP2e6W22BjEeLQ/hxQMa2upty+NBbnmEgAkikY+AUAWSRSE2i7Ua1fGohfVdZZ8mBR1CZBQ11GCg6FrAWHlARC2rBrnHIwbwBGUAoBEauSFV8/nvUZp/EHqeSUgA22Q3E8+5Tas0uWElf0X7zzEfFaeWwik4isRT0A7ld9ZjMcFklRivvScFoIhpsjnCGhTI6pN7SqqTRj1+tQ6dBS7UUpNqqZjYiWwsZ5gOIjRyyIMBxHAuDMxJpH+EbfgoIuiQlFozHONWV5hOisxmdf/g+DOZ81kXnnVM756TQBQBBIAQkkSecUwTAxmHnUkb1h/Egnc2JDYHAygVATB/HpiKknz8NEYv37v8/CBHhhjIRIJ7Vlc443E+a7RRqFkW6wLRa7rSD3lvayF+pcRayQ2zn0GXHnzMvqbGQ4ClnmhXMFYMZzbLCyhF3OMPeM5VdxLPvl+lVgwTMrmmk4AMPfMk6ljXepHHOMGRkdyhu9fGR4nui3D90u4Hj2uZdHVYsPPX1c5aqG6hrJr7Bca6Eng1dHZOehqjYsh65RP32Qs10cSQhzXgwKAsizx5MkTPHr0CHfu3IGUEqPRCBsbGxgMBt+o5PFldMqnDi8aOvKpQ4dvKYQQKIqLy+58UVBVFX79619DCIFbt261zo7pCkO2x7Qy+M3eHABwKVHYKzR6wpXDdho66WMn4q0UUKNYOPeS62mEXU8WYxOMrcmsh9N2tXL2C41YsEbv8gWu9KNTPle+Y58WM23hzwE9faygcUpkr/gpUaFFtROUhJuhBNspxbolZ97AfmFsMPhDUTaFYl2hW3ad429+8xja1LU4IslQVBbG1paD1GxlJTmooiFKkLXxe6ta7x31/Y3tASzBUTpWgkwAUVWAAD3wPS81FGckkjeNBCYE8msQM+x5gmj1dcODbFIAiTKoPMTNYc7AmPUGZ0NjRVuGYWQw9QaQA+RSQLk0q7j3dg0YYmGQe+419OvPTArB/ARWyBZxOi/xLx/sn5uLKhK1qm/FhJdK12OyDdGzjH4vOkU+XbniD+KULUiysjSnFIs+5C3q6FBFHTMKgwpgRiCTAZozwoxQG2qeVxgNE2ysRej1EmQ9BXUmYBwrgcLTbzqQpKAtoCRDmkVIM2DtzOfGWkBrzOYG01mJw1mJw2mJ8axO0yi1wSBWyB3jalZopJFwruezQh+t2c1tzCuDNPKTx5nyk0/L689WH7g2StHP+uBL81Qwg8ozN42lDaY//re/emp/MqstRCxgSg3b8A7EJAfz1CnjLaz0rLFgnJFJKKEENGEOGm3BJYehrDfEF49z9aEYcOVH2xhs9ZBGDOO58c69kLIoZKwbqj8WckbwvWesJRK7DukuA45syRssFhlw4PheT4lz5JNgwL/+7gaGWdz4/sjR/F7AYJ39Uzp+vqbbFQDQsNd2CWVdQ8P1vjQ3Fm9t7Z2yPrUWAGvuP7jseDvl0zcay+TTWSilsLW1ha2tLQBAnufY29vD/fv3MR6PkabpMVmVZdk3hozqlE8dXjR0q2yHDt9SdMqn9ljY7L388su4cuXKs27OC4NfP55BW2A7ldgtNNYUx2Ni4AeoFVCXewpfEAgoBiCOBHLXvpZztNcXrW65uR5LfOHwxIoFO1dcvLbSY5gRCRhfAOosLIBUMkyIAcJMcBwSgggzYmDTWNq95cYi4cy5uV0+Xwi5MQhFIymuJ6Ffv7JAElA2hS5TWkAibPHngwEDPxNHeHiY497Dk5ozxdHvzxkDGEOkOCRn0NYiL3SjJVMvkWTiCah/myTimLe0lDHGIo2AWcu8iv1JgY1+BEmsOeOqHdCENopBCpG5QC+ReELwyyMmiCOS4fT0wxxB0ggA+jHwxFOmyFiGtdjisPAF/cJ9nAjjtQysjP8c01KAefJHKsORKY3Co1ySwiAPWHP6YMGR8Cnm1h1AKALqqL95+/65DH+pOISs7fZkJM4HYYngFEmcA9NpeUwQJamECNQsyksDxuj2W5ESmBMIGWNs/Vwn9AGVaKsqQzpnURpEEUcRWMsoCqnptIRLjDvoRbh2ZYBLl7JzZNNZhDgRY2yQjBCcOZ+hnDGkcYQkBkZrJ/WItLHI8wqzeYlSGxxMamKqSenkU4xaCySR8D4fhonB46l78QuV4qoM8OqmwNawhzhqzixnzHqXTGNZ8HF85+Mn+MPvn66W4gI614jWYuS783OfxZdS77tBW8s/rjg00daVSV7LSwgQEY18IhFUAKpcnxBlDLjyxjaG27VbxWgg8HDsvm8pgP2pW1kEANPQvA70q0+pzQAc5BVcb5C1WrK5X4eJwKGjbcNY4ImDfJJnyDDOgP/p1Q1cGqatrccFHMok2Mb3WQHbeLyLZHLNbu14gORNqlAO9CODnf7+qb9bqxqni7UMzEFK2a7m0zce/z97bxJkSXaWC35n8ulOEZExZGZlDVkTNUhPoqokWmphCAnxutmA0Rt6g2G9kMkwGSzYYCwwVixkYMYKls/66b02eE+vQQ2N2t6TEN0NUiNKlJCqSoJSlWqUSpWZMd/J3c85vfB7I27c62e6kVkVEemfWVlW3OvX/fhwBv+///t+3wTgOI5x+fJlXL58GVprDIdDbG9v45VXXsFgMECn0zlSRsXx7VFzL4PBYIAsM9esa9DgoqEhnxo0CMBZyZQAGvIpBFprvPnmm3jzzTeDbfYanA63hiXeOMxxKeHYzhUoNPoB1hxT7OYKm5lw1lDabAkz8YTKei9hxFtdM8VBribql7Df2Xiea92kNpggaAj5hKB2VS+mIdu6UWggon4kTuR5bjEjziz6w3EOyu0vi7kCYqpRWKJ1I6mRMFiJo0K7FSOCEOuzV2rtnEO4wYZkCuXIaAcqYkXO3OOvfvem9Y7nhcIx10OQRAx8okzMC4lSKlBiDlSaEPNw8gmoanEMA38nlcY9my2vZxAwBzfqMAoYr0YBNbUc8eUj+JJ+PmekNLCSAPtj+3Y+teBtNVUAYFBQtGNlVS+49K/DwkUcU7SFtNrixcxOPrnrQrkvhiC5lXwaqQSCSOgapuzm7hCvvXEycEYZgRD0iJA61cpTA2nKMXTY3NahLBVWVhJsbw+xudXxIsbjiGHkGdTmggKOZ3GKJOZe5NMoILElSfz2mSUCeW5v6MBDIaVRkUz7k9pQlBJcu9zG5mYHWUsczQ/UkchQ+Z/alU3UMVc4XDErgmtuG0YJslQgSwViRo6C2VIqjMYl+qPqv8NhAcGI1a5UMIqRJSUj5g47tRNt0+ilwHqLoptFyJIUsYgBEOuc6xp/NAiI4zr+u//wLes+QlEMSvQut7H39uHRZyxiQGRn28oyjPitbO88yaeAd1/qa3uZS+/2ipQj7xe48vgGupeP398SYT+HtTazJlEwCuwPlfX+7jvGMlN9JQBYSTkOLWsCW7JKJqiRfEo5xa7hvIsZn0EC4CPXV7Gx0gKFNlviGT433UnTk2jiu039zHT2dUl1nACDmh9ElOB96z+u2TmbKJ/mP+fmwa9RPp1phCR3zYIQgizLkGUZrl27Bq01Dg4OsLOzgxdffBFlWaLX62F1dRUrKysQ4t0jIfv9fqN8anBXoRllGzQ4p2jIJz+UZYnnn38enPOlbPYaLA+tNf751gC9iOFg8gLWixhuLBEEA4D9QmEjE7hhIKA4JZAeL8mdiGLkWyhpAgVgLWF4ZwnrvYRRjOYs5zYzAcLrn8XQQGPGCPY9mYGQtXsI2ZAwitxlrh+AKthhb4CiHBHRKB1p4BGlzuzViNrrR40nNoT2ekt2TIkwa0DQsY9SA8xBYhEcB2S+96NDbB/6W1YCmBSmP/57pR17FQafR+FpsziPKtgf9turlzJkSYR84CeZGgUoQUqlkXDq9ZthUdlJedXa8eyMvgXJRy6p4ASxIM6Av0/TfMapTCjsjc3BSJdt3qColEulMdxVkUs28qmu9sssXLc1VxQJKSGJ+ZWJOJ9XgoQOMawhqP7mH944YT1HKYEQDEIwlGU13yilwTiFXFLB1GpFS5FP02P3VhJQH0YSABcM8FVUBEx2vpaWUmkksR8B5rtPn+3GuUQSM+dYGUcMa12BK1dWsLqagNWsAzizC020RzIEo8SqdCAhTEVdG2aPxShaWYRWdqzwiybjYFFIDHOJUTGpLVVU9aVcdmZ1E6pgGgkHEgFkEcHjlwk6aYIkzsDmanlqAJwoKMvY4TOKUcs+vvWdH+HWtoXdWBIHuyNEvRj5XjVQR2t21dMUhFNoz7naZRc3i7DHxHO/GuAJR+ExLvGIYe2+FfSudOZ3YUUWU+wOzddjtcWwZ1HophHB2LJ86sYMA0uSVBZRK/lkcw2w2f3aHoWDmXHvv3tgBZfXKrLOZK0HhNsdmw4fakpvunR1SuSIAoOabTeSEVrx4oJGo56Y1JrWklJaU5hptQYXCYQQdLtddLtd3H///ZBSYm9vDzs7O3j99dcBACsrK1hdXUWv17ujcaPhcNiQTw3uKjTkU4MG5xQN+eTG/v4+nn/++cZm7z3Cawc5cqmhSRWaSxiC7PbmoQEcFArrKcfNmhfWzZaw1Q0+wpK127Fs/nkvYRj1j9+mOCXoZubMqhArPSDMQmyeBLtd2/q2wFdx5lMLBwBiRlE6bqjP5XFtolEF02zlwkrlViU5lU2OdigAAvagi5rEGJRS+Pr3T2cHxFlFAsaCOovAz0OqytIvlLgaFhorrQi7fT8iSXCKTicOKrkhlUbMqbFmyTySiGNU+rWnHTFrNvQUvgTYqNCIOeByJzsca6+68D591afMjY8LEzfVVpign9trMgEEKR/joDTbklBHr5EO675RyZxjREL76Ov5yjjHKDwypjnJgTny6dUf7uOdG/2jvwkBRMyglV5Q8FBGIJecPvuDAoyRBWs/HwwGBa7d3/OmgwNi2kvXeHMhirmf+srz+L7B9zQRyAszs7vWS/DQA6uIM7sNo6t8j3S7yRpCr7cRTjvKqsYgZxRpsrjeYaQKsEuloZSGVBpaS3AyhtIanJR4YEuAMQ5KGSgRIPQ4CMhJCRB7hjojCkqbA4c+t5USbXxO/8tfvAhoDSL8SR9flLkEzzgAAi2o17hNqZsGnyJEReBrkQcAKmDdyARF4cHddbbaWLnaWfh84Kjf6hrXs5hhz5Lc0UsZbvbN37djhkFpHpRta3NGpkSRoaaSZaweG9a8MSMYTr770H09XF1fvGZ1MI0T5haEDdzK8KzVvetEhtyYul4cUeDBld3afWtNDFfWMDI2qqczjzvlPsQYw9raGtbW1gAARVFgd3cXN2/exMsvvwzO+ZFFX6djr3sZisZ2r8HdhmakbdDgnKIhn8zQWuONN97AW2+9hQ984AN3JKtEe1ho3c0olMa/7ozAZmzWIkJgsWf3ggbQlxprCcf2THCuJSiGHsF/AOiXCik/fknzxUGhICjxJkammI9J3NuNrcXpCw2ElOkIOY1SVx7pthons9smlFgVQVP4EmZjpSGIuVjxFINSVVmUjvvp0wN9LAlLj0CMy6VmrABOdFBG8UI7lPXRAAAnwSBRbfC1l3eCCaN5ZAmviDduzwA2gVOKPFDFBFT1QNB3bwcA9220QAgJVlolgnmTT74WlAAguKlSwkn0xxqUAj6CwVbkJsqUBroJsL9YLuQEqppf9vM5GAOMqkn2sGkb9zVx3ZKxpGjFEmNpqcnkUC65+JRRae9QhaLIuERpCVIzx/0cqcQ5GM3X2dJa4//+xzePPyBAnAhIqcAFRTnXd5X0f17mIaXGykqKW7fqcsftuLSeIUkjL1s5ICwsWQRYWoZsy13szQS55z59t+MGdVgSMzx8fQ0rqykSwZxrCNcyplAAow4FrEPZ5CIflENd5UyU0G4ikhBS3aujrscBVPU3YpZDanMtjlIzZ41En5pOLpi2+G9/+zJ2dyrmhBJA+jsa+0EDYBSiF/lLBEOI35BahkqDMALtQV7LgH7qs1bqXelg9Z7uwueUALsDM3kDuBM2XOVjBLersIWjIKOtn/cSjn0L8XWQmxtvSm7pxAzjQYmnrnVx3+bJa2ZqCYVGabiGoTZ9ptOtW+8Tw+em+lB1u36kdwBODdfJsG4wjWpNvaezj2Vt90IhhMDGxgY2NjYAAOPxGNvb23jzzTdxeHiINE2xurqK1dVVZFl2qlhQv9/H6urq7Wp6gwZnHg351KBBAM4S2cAYg7qNNlcXBUVR4IUXXoAQ4o7Z7FFKoZRqLPws+N72EBGn2J8E0LqCniCLTgOlgZHSWE0YdiZFdzMOlAH9sy0ohr7FVKbHBbAaM7wTaF+0P2O9t5pwUIPd3ixSSnDgGRwYST/FwxQxI7WFe+uQMD/yaSSrMLXPiJQw6izOrlH52rvqQ/kQgcPJ9ZkP4FBUhBInACMaEavqGh39h5OBbdehNACmJZRlaaXs8TxIAJHWUNZn2b4TDYI8L/HiW4fGbXwQR+zomVrS7cuY8epC3xU1mqCbCYi4ut6l0hB0kew1wWn7NIOQ8/DdUgPoxBx7PrZDzK93JYJgf2RvwcHYnTyhNNCLq21NKCRBO1IYWMidUem+xhkvreSTtfYN3OTSSFLETEFaiLSYSZTl8gqJQkdIyAglEss2J8eFb//rTRzMXOAkFZClAmNkgXiagjK69LqvXMIGk1KC1FCb0ATX2D4LKf3tBIdj/xoxvnH14aj02qcv8Ta/I0qAB+5bweXLHbCjQLVbtqQ9SBHusNVzwTUXucgj1xiiXfOUo+2F5A5ygIDRElKbg8aumk4+5FMdpFL4b1/+/tHfWle1jtRtVj+xmIFnEUpPG8sg6MrOrvSRsAKggkH6SC81QCMK5WEX6yLA0l6MrUcv1X631qHYcyRA7Pbt5JTrlcQV7HYlNh1Y7lsqqJF8agnz2rcdURwafhcxgn9zpY3rlxdVuqZTsaXKmBLbTMO1afu65kaGtZppSJm/HC2ucX/vbSgjQW16/gyNbJRPZxpKqfcsBhfHMa5cuYIrV65Aa43BYICdnR288sorGA6HaLfbR8qoODYnTNRhOBzi2rVrd6jlDRqcPTQjbYMGgSCEvGvZFzYwdlwPoEGFvb09vPDCq8J3bAAAIABJREFUC7h+/TquXLlyx45DKYWUsiGfDDjIJfYKdUQ8QWtvhYEvpK5eaHoxQ5mPUbIw2frS1ntLLn57MUM+VLjU9stipR41j6aYEjUDz2scsm/fxb4G0OIUhx5tcCSLHoF7+AYNSnMtppQRZBxIuUbCJChVoFSBEQVK9MJPKFStF7zSlR+81gxaA6XiKBRHqSgKxVBogkK5FRgAkGsN7rimjNhphtIjo/wr370VlNlch1jQo6dEKg3OKcrAfqw1lrLeGxcKq60IOw7rvSuXTmYdpoKgGPv2G//rMwpofx4Q5I9Nab5z8CW/iAcNXUigHQOG0nkzbbOTTwCQRhq2MngHY4KI2xVUzKFsGkv7q8qgoEiFtNR2IUh4ib6FpCKuNmi7TRoAJHSEQ2Umn4YyASFVwL6UCt/49tvHv83EMQFjGZ9P06OHwxKdTnyC8HJhY6sdvOZVgWNFLPzmLq0rWzsfIshXJaWURpJwZz0sKTXShGPoiFbnM1HWrfUMD9y/gig+SY74DA9Su/sxJQTSqmyyH0NN1CzGNigNavteAwxmAqqaNy3fw/U9BYetXwOMSCv55FICSU1BLOcA1N+FL3zxRQxrBlCRMBSj20cU8V4CQv0NFFUgGxklAeQTo942jlywE33BBGmZV5OM4+r7tozqqE5KsWeZ63sZxdCR/HAwsrfRZet3aCGXWoJibLkftppO7YgZx6SWYEbyaauT4PqVentYc0vrSWIKDWmyBKw9LW1UMtWt4ky92qiemvvi8dUdEAIQlJhvfyXaXPx8coT6A9jGkQbvOc5Kwi8hBK1WC61WC9euXYPWGgcHB9je3saLL76IsizR6/WOlFGcO9avg0FT86nBXYWGfGrQ4Jxiqr5pUL3Avv766/jhD394x2z2ZtGozux4eX+M7ZmXsvWU4cf920+UlhpQUqItuIfJ1Uksa723X6iq9k+o9Z7WuNax2+3NIjR8EVFSW4y3DiFtD9nWs3a7tzJlnOfOmg4aQMqqujFHRBOXiLg8QdBwUChiD1oScOiaK08JAKJQhcMkEkOgWimKcU4xyiV43EOuOHLJkKvjOgxSAxG0Vc3huuKlrtRRJhLzx3tDvHHKIuhTu71ZCE6wTL7DstZ7scN67/JquqAiZC5vxBmEEOK5VBCMeln7DfISlHiqLzy57KEjCDZF7hl8zCLiJJ98muZyN1O6Ukcd5ObBwSVAPcwpOLP5URIkXGJgUS4xR+0p120dqAyc2D0xXaFZBYYWHWGsU3ztWz/EeKLua3cijCfzpZO00fBWCtUhihlw4LltxCDSavwtQ4Pawp98Yp42ldV+mRf5NBr7KZoAII7d5BMAJLGbfOoPC3RaHA9dv4ROt56ILBWOSEgTfJaXrrwQF2FdKg1h6cAaqC6gzdbO3oQlq2Qeg9MSuTL3a+ogjd33n4ARO8E1r44aDHJ8/euv124rpUbSEhj1l/ConUPUjUGYqybeIigj3iQU8c0ECoTvfmUua+38CCV48qcu49BGPDI7QdtNKYYH5u87KYWNJ2QU2B9K4/MfM4LDXBr7cTepr007hY2YstmGmiyAn7m3h+ub7VrVPLEQSaYjMQB1TzE1pO1w1I/ioXSByQJ7Vgm2FilstW9Ba1GfOKJ57X2pSKl6NZxulE9nGkopUJdP5nsAQgi63S663S4eeOABSCmxt7eHnZ0dvP56NU+MRiPs7OzgZ3/2ZxfiU8PhsKn51OCuQjPSNmhwTkEpPRMKrPcaRVHg+eefRxzHd8xmbx4N8WfGjwcFXj88fmVZixl2xhKXUo5bw/K2WuIDwErM0M5i3FzC0q8tGIaB0XQNYCXheMcVuZ2D0lVA35frGgda6YVc15HU3sHxkayURT6xDF/Bx9AzKDmSALWsUnoRwWpM0IsUCLPfD1/diBsC9XmcAKUKidBoZyMAJwvvlFKgUCkKGUErgQIChaIoa2x/pMMOCYDV4vCr33vHdRJWEDIN7JzEsvPNstZ7h6PS2Ac4JVjpLdpbaFMx6RoUUnsTSgDQihh2h34KjVbEceBhHZh7Dgj9XHn1w0OH5d4UPo6DtuYzorGSAasZw1qbQjAKRgkYpaCUT/5loIShxUfIVQqtJBQ0lJZQSkFrDaUryeBlQjAqJUaFxrAABgVBoapAg9IEmbDb+3EHueR6BMfSHtTQoEhoHyPdNm4jtTswImiO3QHHCy/dAgCkLXFEPAF+dVhO4zxzeJgjihlyDxuv9c32UQCvUjMRlJ7PK/PNRAiEbz29EJWUr/0m81ApXrvSwX339iZqFTNclnlS6VMzN9JBHFXTjF6oRTYLSol1neD63l0Hzb4Jq1EiL+7BDB9bPUq09Rzmv/r3f/ZtK/mb5xLpSoLhrqP4ng0EYJ1KbRk674aQTzKw7tOdgEg48jmybvPRNZRC1LMfE7imV+GQzndThlHffE4rGcOeZT7tpRzbQ/P+bcQuAOv6wLYkKebePQUl+OiDa1hfyaAM/YEhvH6TjZSq/dzwAxNBbHqs66y0IwrMCvSeuPQOCAGU4iA1SWVa81oXg4qUqj8umppPZxrnxW2GMYa1tTWsra0BqGJUzz33HL74xS/i937v99DpdPCxj30Mn/rUp/DRj34U/X7fK2H6jTfewK/+6q/i7bffBqUUn/70p/Gbv/mbJ7b527/9W/ziL/4irl+/DgD45V/+Zfzu7/7u7T/JBg1OgYZ8atCgwbnF1GbvwQcfxOXLl9+14zLGIOUd8GA/51Ba4/nt4xfu7sS2S2pgN1dYTzluBNZLsiGigBbcq+5PHYolg+LL/OpyJ0bCmJct3fQYKSMYeAYRbFmUdcgYwaFnILE1U7vLhqFnEF9Nju86N804BDlp8dHiBGtJpabgTKMKC7sqO/jdMz+hiiOoSnVVx2TuTZyzApwVSAVAdAxGK/mBUhSFylDIDIWOUUiBUjEoTaGXiDK/+NYu9gOJ0Xm00/qXcKWrgE5ITRegCjLEgmEcUIgcAPJSYaUdYedwkey7d7MNUqNCcdVgmEcaURQehBIwrbvkh1gwL/LpMKCOTSehTvKrUEArAhxuhZPr5AqOAVorUELQTYHVlKKVcGRRDC5iUEIhaAkNV9BGgxAKwqih9ygIShfup5QFinKEXCloXaJfSBzmGoc5PSKmjo7guH65g1ya1oWy2QNGGGMEM/mUe1jzESj8zTfehCwVkpY4UduJC2qs9TQLrSsSZplgsNZApxPj1tiuk221BGh0MtATC4rSs05iyNAlAxJ5QqxEo8hPJeW7T1tAn1GCJx5bx9paC4IRFI65jVICadlG6smTeAqmsZQVYWgDccydp1UuuRJoqIN+ctlhuu6cDyFMiLbuSGpyRNbfuNnHC8+/bd54gvEgR9wSGC+pgIpXkiMCU4fmuQXUMgyx0Q1SWwb0aTpHVK/c28XV+9sYOOawvYH9GK61gHAop9KIYs8ijUoc5Jbt8O2IYWTp/33LWulwhoXpRAwffXgd7SwCtSjqiaWfhdaNMz1dps9Nc3OdwokAqHNCFDM7v5qWWEkn8l1D/zYlIWnNakmpqsbe2VPVNDjGWVU+uTCtP/7hD38YAPDaa6/hS1/6Ev74j/8Yv/Ebv4E0TbG1tYX19XU8+uijRiUl5xx/+Id/iKeeegoHBwd4+umn8alPfQpPPPHEie1++qd/Gn/1V391x8+rQYNl0ZBPDRoE4qzUfLqbobXGa6+9hrfffhsf/OAH33XJcqN8qsfL+zn6kxfUlBPEnODGTGbgTq6wkQncOGVwfIq1VGCkKzuGlFFv8mOKQamQcYJBoPXeQaEQMeJtcbWVCVDGvG3pphDUU3KEyootpgRjz0BaZd3hmcXuGQArNZDQ+pfHefieW8wIiAIuJQTdWCHiFeE0C3f5dkBBgdgTwR1meNNtiHUbQoBiBNhLkB23n1KFmB4i5ocn96MZNEqUKoNUCUoVo9QCpRaQmoGAgIDN7ImgVArfeGXbcQZ2cDbNhTe9ABEUS/DHjBFrFrMJiVjMdGzFHHFSv3zNpQZz1EKZBQ94mQ1RcPnGjJUGWhHF4djdaSJu07sdI4so+o56G31H2Z+EA9dWOS51YkQiBaX1GaeFYmAOCzH3VaNgpITCSfKGMQHGBBIAMR1A4jg7tJQ5RkWOUVlgWEpIBeRKGRVMw5JVGdjGdhLETGFkIalcNcKGKoUgEpqYs3Pf3inx6uu7SDK+UO8kJLjLGAkmWqcYjdyE5+p6a+Ge0oDaMyGKityz5gwA5AEENvUMwvsef2zYLksF3vf4BtKsen6JR01Fn9psjBGrAsJFmnkpm3ztQQ04LTmV8BFGcvn1u3Yom3xqOrlTV8iR/eC/+9++5Z3JIpVGlHDkgap8QglYdpLQP43Vpg1KajdDOIUGKGdQHgR0MZb+BPnMrcnWUqzd38Nqi1rJp3ZC4OKVXSpg1/LTXe/U/v3AkkjQjihGBtWUoGY7v5gRDCbZWFttgQ8/tI5IVGshv9XBPOqMpiuY9mWaB02X07QWq6sPFVHUWiHSmX8fu/TDmWPWr1S1NqxgtWEO0wKnH80a3EmcF+WTC/fffz8+85nP4DOf+QyUUviVX/kVxHGM3/md38FLL72Ep556Cp/85CfxyU9+ElevXj363ZUrV45qmXc6HTz++ON46623FsinBg3OOhryqUGDBucK8zZ770UmTKN8WsSoVPjXic0IJ1M/9MXXl52xxGYmgm3r5tGNKIbqOKjQEuHkE1BZ7w2Wsd6L/az3OAF6EyVJaIZhaEwoZv7kU0gg3TeQX7WBYuRBzPrssc0pLrcoEiGtGdAKGtRBLAEAsZqPTF6qNQUh5vb7EFSux0l71TfhYHQERvcB7C/uQ3NwWhVEUgrQ4Pjid1rILXVvfFBX62kWy1roqQCrx1kcjBZrt9yzsRgYn0USUfQ9bMUAN6Ewizwg+Odr5QcAqWBe5JNvW31i7sMCSIXGWB5vTAlwpUex1UuQJS0QQpGw3FpzBSDgpICEWfUjtfuZJERaB4X5ADpnEdosOtIhEUgwCkhVYlyUGMoCo0JhUAL9gmBUUrSExFCaX3sY1VZupXTWhCBI6QCDGWs+Cok2H6EjhsgihT/6m1uIEg45l/Dgq3qaYuqotkx3LAqJ3kqC3Z16W7DeSgLCF+9ZyPwVoqjQGkhiipFHHygK5a6LNYGvomo4Kiv7OMfgNBgWC9d8cz3DIw9fAp+5Xj7Es882jBBIS6eo5mX7jhihxhoqtwNOxbGjidQy11a/d5BLyrX+J+BEQQZXnjkJSjT+9ZUdvPHqjvdvVKkQZQJFLoNUivFqAsy91xBOq2JhHgi93VQwKE8ClgrqRT4BAI8YCg/iTU3mSpEJbP3EJRBCEAk7I7bSpvixpXZdzAkORsq6Tug76iiOHUlpA8t1EJRgf1wajy8stSm7CcOOQXHViRnGgxIPraV4/wNrYDPPiU1FaDoTBhhXtKazMz1fpke8jmRiAMaGz2uPOfn3gc4IWTSu+cb0i7lPSf0V0qoJh551SCnPpfLJBkopiqLApz/9aVy9ehVSSjz33HP48pe/jF/7tV/DrVu38NGPfhQ/93M/h49//OPo9XoAgFdffRXPPfccfuqnfmphn1//+tfxgQ98AFevXsUf/MEf4Mknn3y3T6tBAyua0bZBg3MOre1ZfRcJu7u7eOGFF/DQQw+9qzZ786CUNuTTHF7YGWGaRNlLGIgGdg0B2O2xxGYq8I6HJY4JqWAnXl6Wtd7Ll1Sw+R7tnm58FEjIlX+SabV92DnRgHFgGBBJHAbWn/KBzXIkYQRX2xTtSIMTv2xOOjEdsYGAWvI8p9tUr+MmaEhXDXZrpnl1EAWtOQgxB2Zs9l8AQEgJpTkoKUEpsDeU+Oc3TxdgSyLmFUyMOA0iYoDq2Vltx7h14JDczCEvFdZaMW4dVr/b6CVgNWqoWdgCO/Nw2WPNYlQq+MYA+wFqDt/36XFdFKcGvremFROMB8BKCtyzGqHX6oCxk68FfkFyaSUmSiXgciwkDm8ppak1iK3BAD0GoxxZzJEhOfG9VBJS9ZHLErmsbErHEhiXBCNZ2fi5grZDnVrbwCCR0BFaLEdLlMgEEAs+qVPE8M8/KPDObrngSEUpCSKepmilDIeD5dYhJmUjIUBnNa39LmSq1LqyvfNVFQnBMRo7fLYmSGKOw9K9rUmpVIcs5Th0WKRpDXRaAgeT7R6+voqrV7tLrb99erJLQSWVdqq7nCIsR0NcSxBXn1Gwm1m5ajLV1UQ8+T1zjlGUKkgLge4iuIDKmu/zf/bPzu3mMRoUiDsRRnt+856IGEiyaGMqOPVKWQEmtZkCFmxMUG/yybfmGgDQiM6XvqyFHEtQTnHliY0jCz5X7kYi7Dqf1TbD9sB8ASJOsO8gp/Yt9ZwoAfaHJUwTQi/l2LMkwNiUoTY7v4gR/OQ9XVy/HDbumA5n65umOd3EyZmWJ3UibEGBulwD0xkVqlJFPbL21twPDNfYYde5AN3UezrrUEpdCOXTPIbD4VHNJ8YYnnnmGTzzzDP47d/+bYxGI3zta1/DV77yFXzuc58DAHz0ox/FF7/4RXzuc59Dt9s9sa+nnnoKr732GtrtNv76r/8av/RLv4SXXnrpXT+nBg1saMinBg0CcZaInqn920WckGcxa7P3kz/5k++6zd48GGON7d4Mbo1KvDUJyGxkHMNSOW2ftnOJjSVrQG2kfCFrbig1MkYxCLXekxotTtBfwnovZsRaa6ktKGJxPM1qVMSKL/ETaqUXwgdIDSSs3uJiHkr715/yJcxypcGhUc68bgoKXG0x9BI9CSgRYwHleVQWdK4L4BFkAoN2+MMRcMASCqLMrrACAK2FlXzyody0TgBS2fX9p+d6p7JPAgAh/FgQxont9M37dxTgNv5u0i5KgPWV+sD4CQTM0eNSgVN/C7N27K67BFTBpVbEvEgoXyexw7Hyiin2PRQklAAbHY6HtxLEsaXYsUcaPSHK2igFBoESyvLK4TqK1NxY0HwKSpSx1zDKEDOBzPCMK61QFhLQfWiloKChdGVtplBdBqUBRkowJiCYBiUEnBFQSsEoB6UUXI2hWQbU1MH637+2V3s5fVQ3dTiNC1d/UCDLBAZz6t1L662jWjMLxwucW4Wg3uSTr0UeAHBP/9o8l5V1nce8VVlXuZNhophDjCWefHwTvV5Su42P8sRLCePYkdIAc9jqOe1oHcok5UhucymblNbWpJjSoVwqrarLSlXJoYCa+n9TUMdc6tYyA//w7FvYvtF3bleHfFAgWYkx2nUTUNl6hqKmL4S+d1JOoXwJ7ZB9BwxTvm1WUuPKExsQ6fH8cOAgrVxdOo0IYCGfVjKGHcvt6CQUtleTXsJxYHnHSTm1kk91rhBTmFqdcopHtzq4tFI/X9suiaklplpQVZpWrUYI9YIxXUtKCQLUpQmYeqvpquRK4ZHeAQQ7uYVxDW1cWxuuhFPV3OC9xnmt+eTCYDAwxrSSJMEnPvEJfOITnwAA3LhxAz//8z+Pzc1N7O7uLmw/S0b9wi/8An79138dN2/exPr6+p1pfIMGS6AZbRs0OMeY2r9dZPIpz3M8//zzSNP0PbPZm0ejfDqG1hrf2R4CADZSht1cIqXES62ykytsphzvBBBQnALK8FKbcYJlEsEzQdH3tBKZQgPoOaz3rnTihRf7iPqTT0CYld5IaTD/MlGIKcXIM6AYe5JPQ6kd5nbHkKMBkLTACHA5Y1hL9UQFcnzNNAA6qVxhg1NtdFvBYGNfoohBK2mLh8FsMDJFAaUdFmqTF+bv3xR451AgiSp7oHGhnJnD81hpUU+t0NR6z235NI+DYQlKSLB138GwACXAfRuZl6dcSL0ZoFJRHoz9xqCI+VdWSIQf+eSrklK6CoodOIqqjcqqZpPJ8ehqj+G+jS66CcFYme3yAM9AusdzQCdKPROUyQ/n6BgMRJfQxPba4pJpmNVTlFDEgoLSdv0GEwi1DcVtBGh9v/6bbx/i1l6xcD2jmCH3tIicR1FqpDHF0INsrEOaRSfIJ84I0nZs3F5rQHCGwnOuDCGUQrpsSCA+SQT6fbdKyj5Wz+wvYnjkJ68ijm3Psvtk/KxsPepCEWJUIsD560lbLdezIlwtv4cGtXRc133NFbMO6aXioI6acpxKlHr59wLp+K3WGv/5i99bev8AkPcrBdT4wPwsspghp/WaxFDil3quwUMRQpL7Wg2u3NNBunJM5AoG7Bwo63PpssxzEWppTLFjGTc7CcPw0DzOtSJmJZ9sh48ZQb+or+kEAKMa0vDR9QyPXVtBws3rNBOJSixm0TY7vro3G9Oq1ZSPFBqZqHOwIJP939d7+8TnSvHa66xVvRqyIsol6hYBulE+nXlc1FiXUgqcu8PxWmv81m/9Fn7mZ34Gf/RHf1S7zdtvv42trS0QQvCNb3wDSilcunTpdje5QYNToSGfGjQ4x7jotYemNnsPP/wwtra23uvmHKFRPh3j1YMc+7nCWsKwVyh0OMUtHznNBNu5ClJAracCpjrCrvdRE0Lt7aaw/WorEyC3YaEcEL8DAKSM4vAOFKYOIRpS7teGbqeNhFOsZxpVyYz6Y1DYa18Afgm5yiNM7rON3awEoIygUj6YiUkfaz73PqoX6i+92D0KaktNwBkDZ4CgVX2UQhIMx6XZeoUSKMc5nTiurjKLh34uWUcolcZqOwq23iukxtZKgjQzB8ZnMS5VUD0cwQng2SSXHeIsfIPvhdRIBcHQw1YvEcSZFQ5Ulnqjuaj0akbw4FYbWVJlWUoP+ZpPUFd5bONSR5WaOZVNhJbQ1gxlh1IE1KqC0CQG9BCwEFyukK4ifKFnl0rhS88eLDyPWcogQU5FQCVphOHY44GowWCQn1AGbV5uOycczqk3+RSCkBpRMiAQ76vm1B77XOsJPPzQJWdg24f8lsptl+1DYhFqzzbRjn1IBaslptTTeor1ba0c3sznomG3Bi8VR0TNyiUNAkFLlJbgMHWMLS4oTazn8H995fsYHAZOdjUocwmRchSGtW6ylkIb2iBl1T7X/ZwiRMwUQiipUoFQ4kUsSQ9Jb9yOsHpv78Rnl7oUNw9sZCOw01ewrUmHS9iYnjiGYzJijnFyZBnPugk31nSiBNgbH9v5rSQMT9+/hpVORc5Za8CZ2mr5zrQ309mZ3mhMnxPDEUyPT12iXUyBR1e2F8cpzWsVTlrzyfp5cXtjv2hqPp15XFTlky/+/u//Hp///Ofx/ve/Hx/84AcBAL//+7+P119/HQDwmc98Bl/4whfwJ3/yJ+CcI01T/Omf/umZcmtq0ABoyKcGDc41Lir5pLXGq6++infeeedM2OzNo1E+Vcilwvd2x+iIyu6OQuNwiZe+HU8CqsUpRsqcqTuSGhmnGASSL8Mlrff2DdZ7nADdtD5Y4lm25Xj7wMvJA9iqENLNZi+40AaPJqSM4J42gxA+Chr3DpUHLaChQTSx+sFrVIEse00nH4LKRRx5jB86AojFCopIfPX7bewN68+8UBRFXh1NcIZIUDCqUUiC0bg8CpJ2Mh5s2UcDFECzCHk+Z3GpJyBDFA+cYejrZxdArIb0gxAFVhZzDAu37ZdvS6vrXB0/FcAjWym67TbITJBXOiyvAKDU3BqYBSbWWKd8v5Wag2p1on3zoDDb6gFuYlA76rkBAFUjKGZWP7nqwyhEoPpkofkv/P0BBnP1Q0TMQDlFmasghdA8Dvo5hKAolph3pdToraTYvjVAHDOw2J397asQAsKe/7yQ3qrIcQBR5ztkjBzKx143xhOPb4Az6rTo9FIL6kqlaptJfK6f63ZIe4zeSS4BVUDc1hLX9y4IVqBQ5sQCRqSVfCKO+i4eFB4YUbXqkKKU+PJXXnbuwQd6IiPjUdXvZyEyAc3tyUqUUy9CBwi7H1ppb0IJAKKEY2xR/E+hpK7s/wzrcUIJNh9ZW6gj1Uoobh6Y27LWodgZWawmCbDrIqcceReuPm6reUkA7I/N9aBiS03KbsywnytQArz/SgfXL3fBJgF3Yll5Uou6yWStB/i7JLhg5HQMn5c1gyQDFuzUAaDFJbbatxY+17pec6l1/TyvNautEVXN6RdPUXPRIKX0UgidJ/gmEwDAxz72Mef2n/3sZ/HZz372tM1q0OCO4u6lkBs0WBJnKYvgIpJPeZ7jn/7pnzAej/GhD33ozBFPQKN8muJ7u2MwAmhSZbK1OQsKzs5iJ1eIiqF1m07MnNGkzLMexDxanhnS81ipsd+51o3NdTN0Zb3ni/HESs8XISTCWGlA+inOxkpDeI59Lg5vI6F4aJVMiKfbAw1UD6IDxOMlk9bUa5mF8jKocz1PpTOQ7QqmD/IC33jVowbSBHmhMBxrlKUCZxStRGClHS1Xb2bJaedgVDqzhufRywRSj8D4LKKAcaAIGMuHufRWIw487fSAKpvbB7nn+Ko0wKjGo1sRnnpwHb1Od4HYkZqCaHedNEHtY0SpmUd9GjfBzGxEK9wv6krb+7YGhXacL9H2c9Wu8YNQ0BnSeX8o8fUXD09swgUFo0A+CUArpcGWnLe0BjodP0VgHaaKo63LbU+mxr/vlqWy1vuZRxz7BQCl0ogiz209+8soV5UCsgYr3Rjve3ITjHNvy1Afkp05sril0s5n3tUaLwLLcY/qjeBObnEasJqA8InvqWuMco0L7r5F65QSAP7svzyPMmAcd0EWCnEWLVwy0XP3YRqwEAyd05lnfwIAHrAts/TpS9dXIGqStFxdp5Xa7+dqi1rnG4JqXLbh0GFlum8hwLsJg7TNdxayNBUUVzoRfv6JLTx8deXEGGG76kvOHhYLP9Mv6ttuOiOTvWidY6HpFezB7na9jZ7hrM2EvuEAmuPU2TMN7jiklBdS+UQIOVNxxQYN7jQuXi9u0OAuwkUjn3Z2dvCP//iPuPfee/HYY4+d2YUGpfSuJ5/2xhJv9XMkgiJXGikjQXZ7dVBJC5fghQ0NAAAgAElEQVSS+symSwk32u3Nwjc4Ow/fukrzmD/jjqAQwp6dFQcG35OAoEPoeVDpbyeT+kiaAAwNFkYUwPUuw9UuOSIgfPbostybwodYIl7LHg8NlSvQ7WoyAYDlg8YA8BffaeE0DoulVGCMIo3Dx1kNIFqCsJVKY7VlrzM0j821DLkM6zPz2dQ2jAvlTShpAK3IL/sylwqJJ6ng6yTW96zvkwqKDz+0iq21VVBqflY5c4/Z7uAvAad24shVVwWYWPNZ4NIb2mpKHR/D1TEdgWzioQ7Sx9f03391D+UMG884BWMEyVwA19cerg7DYRlkszWL0ajExmYLytMiNrT2TBQwtvCAa2CruTSLkanwWQ2ybHFcWukleN8TW+CT6+M7vfqo2VybaLjtvVzzjNTauZGTfHLZYZ4ybmYifo7hIuDsDfCxDq07h72DIb793RvO34Zi2M8RzxDGUScCHKonAGEXWldKKV+QkAyngEC9iVRf2Uxx+b4O6l7xXHa+rj7RTuzXsteyk0MRJ9Z5thXZk+zaDnLOZAm4lnE8stnGRx7dRCtdHItMFnau72x1nUIVUaGf150qJ/XkVt1t3YgL9OJ+/c4D9ZbGrZt6T+cCSqkLV/NJKdUQTw3uOlws/WKDBncZLgr5pLXGD37wA9y4cQNPPfUU0tQ/m/+9wEW57qfBd7YH6MUM+4WC1hpK2V5//LFfKKzFHNszNjgEuno59jjASGm0PWsOzWIoNdqC4DDQF+9gznrvcid2LiZtNRbqwMmxhZYLUgMJJZU9oQdIgOyfeS6Spa5s9YYzb6QtTnBfl2I+bs9AUTqssHyM+Sq4t/IxzPPZD7E66fvZ6mldXxj5GObA6au3OF7fPh05n8QMWmsIRjBcoveGKpimCLEZ21pJQVlVqafqZ379OqgwOoAsos5s5yliQeFbtiqLmbX+wxQDT9u0QgKtiBgLrhMC/MTlBJe6bauF3RS2GkhH+/R4Nlx1V0rFwYijzo1DlaS03d5PgwI6B4g5QEGMudvTfdiDG4okoKq0k5uT83jtRo7vvTo4+pgyAi4oCAEGc4kaSukT9ZdCkBcS3W6Cvb3laj91V1LvpI2q9kxIPTWGkZdS1E2CzIJ7TqKlVIgjgrFHQUgxFyhf7SV48vHNhQA6JW4SyudUiMe8zhxWhD5KLEqJtb13Vtfkcy1cNoYu0tn9PXV56dbgP37hBRS5BE04VACJ6YNiWCDuRhjv5+CeysUQiyagGm+Ub7MDrk1Ifba1Xowf7p2cLNvdGP/z//JhpFkV9FdKAkpBSQmtJKAkHhsVGAxzHAwU9vsFdg8l+hNPtpFjje7i8doJRf/QvI+VlGF7aKnZFDPcGJgvrG1dRMlUNXWcfHX/aoIHN9tY6SSICVAa7sWy/dBY69P4C21ceZqmiTq3A4L6OrymHLb5jwmAn1h9w/Jshuu2aj9t6j2dC1zEmk/D4fDMx7saNLjdaEbcBg3OMS4CCZLnOb7zne+g3W7jQx/60LlYXNztNZ/ePMzBKcHuJGDaixhuOmwsfKEBHJYKqzHDzsTaokeV08ptFskS5BNQWfYdeteJOcZKwvHjfoHLLQHikZkVWFoquLJOwilGvlYxAZlkdT7txt3qY8/9yxnDRksbMr399kmdFV8An9fz+soOy7TIZb+loBUDoTaCyjHWkRJKM9A5WyKtgb984XR2pIRU1lAaQL5kXM3XgmoeB8MCnBGUjoA3owS9znEGsOAEvuVefEmqKWJOcOhJKAVlfnvOZ6NCIWLEiwRII4p+Tf9OBcET1zpI4qo4OacS0qEG8os5+pCxLgKZgpESymJp6bQQ09xpQUqgrASSi3zWrtciwkAxhIZZvTc9xue/undE0lBKEEUMSulJvbTFs00ThsP+cp3RTqmZ0VtJIBKOvO+u4TKFEAy55/ziI+iYIpQw9kUrpRj7tHfm0VhdSfDkY4vEEzAhc25XsRRXkxyl9UrlIHRRBZnvqE7fcSkSNsRImucrt6LRZU/oGmMJmMdYOItXXt/F979/a9pAxK0I476/StwHxbBEup55ZyMFT7chQqmAfRcBNoTjeX81AvwPv/zkEfEEoFLmUgbKBThVABjSDrAyty8lJcp8BJkXuG+QY/cgx839Ejv9k8dwLeE5s5O+cUQBC/nksvS11YvqJRwHeZWw9shmC/dvtJHMWArbEyOWg+lymGpBVWlVtdWUamvWEsPnggB1SyrT1Ztfsl1Jx+jEhyhU/dhhXFsb19yGK9gon84FpJQXTvnU7/fPZGmJBg3uJBryqUGDQJwliex5J592dnbw4osv4pFHHsHm5uZ73Rxv3M01n0ql8aNhcUQ8cQLseSoGfKEADKRGL2IYFiUQhdl0LVt3ylctNA+pAUEJujX+9XXIlV/29BTLno8PNIvgqy0alVWYyKc1/cEAPG3hgS5DK4Zx/75n5q47Ub1kexjmwZUErSBBHNvYS8VPIWBXR3n0Gx0B5GQttP/35QSHy4kcjpAl/OjaS1Vl/ReBhO3Uei/3VO1MoTSw2opwY9/O9ly9lJ2onRYy92oNJIJh5EkmU6cd2zFcxchnEULQtWKG3JJRPUUdAbPVZbi+1QNjx8t6TrSzoLjPU2ytXxGwH0oklCXQozR1KpuILqAt1neEKEcAr75I+RSSJs4xhOjSSj4BDM++PMSPbownbQKihEHJqraTNPSz4hTjfL9fIE0FhkN/EokQoLOSQAYStZxR5J5qppAzygPGnzJgzKkUTe72TvdpI54AP8LWp9v7jA3EMeNKpcGofWykDqmas66Utq8P3HfCvn+p7BfUZdlZKurkb6ijUuO8euo//ufvnLhkRalABIUOnOusIEDUjVEE7JOyO0N8qoAxQCuNNBMYDtxjzcHe6IRS8iMfuwf3Pbhm3D5mCmNZH2CmjCHNUrBWGytrBPcetV1iNBphMBhjv59jVEhkowIDg9rRtSRwWlk65v8DCzl3KRN48mqKrbXsyMpz5sgoLStdW9qU6Ts62WcdTGdh6kocQB39yglQ9ySEppEWMxeeAnh09VVoTUBIifnxp3INWDwDranZWrdmP9VvmlDoecBFVD4NBoOGfGpw16EZcRs0OMc4rwqc82azN4/zTvqdBq8d5rg1kR8QAF3BMCIKux62NiGQuiKDVgRBHrjgHC9pvTeSGh1BcRAYYDgoFO7tRpM0ZT+kjKLv2T6FMCu9oOZTigTaq56WApBxgr6HdKvbaeNalzgz3+2h4jBUxBKxFnQGAApuMRap4LbVcyuonOoolNAuN6C5F+NBDvx/r4aRsfPgjEDrkxnznFIUS+THM2aIPDjgIpISwZDNkbmh3HDECUaebQvpM4O89CZhhwFKysizptrssEEAPHI5wXpv0WbPJ0juCvxWx3Nnm7qsr6r22K35pGZuZROV0KfIVLYGpwBoEgN6ANgILm2/pxIMX/i7HQBVkkGU8qOAsU3do3VV9yiEWJlFmvIg8mntUgbCKJRGkOVfSP5VCDFdlspKzs1inJfe9n++/O9gVGJtNcUTj22AWdkMd+/3I5/82uUCc9jqueDglioC20YKa7v6qnAok1zjS6k8SGlIq+KREG29ZbP362v/+Ca2bw0WtiGUgnBAnabQ4gySlRSE06DJh3IGJf3UkaFKKSoolGdb2t3Ei3wqS4V2L8bB7hgblzv4uX97P/qWQ3CqrermmOmFZAjKGLJWC1mrhc1NDUYoPgBAliUGwxEOh2PsHebYOSxx66DEgSNZbuS4Bn3L96mgGBZVHRcCYKMtsNWJsdZJ0G3H6AqK0vCeQAHr4GqbdUzf2Xqe6SxMLTDty9TrTHa9pmlmNPPFfa0BUjGGUlGtxa1WvFbhpLUAamrIacXMY1SjfDoXuIjKp8FggFar9V43o0GDdxUN+dSgwTkG5xx5fnutIO408jzHt7/9bXQ6nXNjszcPSuldqXzqFxKvzBQ7WY85boxLMAJ0I4r9eYuNU6IjGGJWKYVCkS5pvZfycPKpLSh6sbC+VM8jtL58zPzJp7HS4MTf3o+TCWnjgYhS9B1Z5CsRxbWeX40o3/iI76UlYB7EEnUel4Bb6zZpqElWpm1Prki6BhChPp90epyT+/iL77QX7ElCUdV6OvmZXHI8U0oHqfimOBgWEIwY1R5X17OFQEwReOIsgAweFj4h5QpKA1nEaq3v5jEqFAQlKDwukO817E+icwkHnrjWQZrUJ2/4WLGVDlUBUCkPKJSVXC+Uq36ZG1Kzyq7TchziGHuUJtZup8BBHWwpVWMoZrMHtJ/oX/3TGAeH1RjU7QoMRhOVsAexxBhBuQSZCwCHh7k3icQYQTpTa0Zw/0SmkL5eSg3OqXedmCTyS8rQGkhijqFHLR5fArqVCbzvic3b4m7go2rysRn0qfPDHDaAPkoOYmF9S60ndSfroXS9GnOKseSwOZUViluVS6Wm4I7xh1FpJbFct1TqqhabVAr/x5f+xbwfTkEJgVrCnvnEfhgBb0fQgRNnyKMZuu8Q8knE/kHgVifG8LDAv/2fngTn1LbcAXMkDnGqYBumIqqgJnQIFwJdIdDtdnB1q/qeQEEWJfKyxHBcYDAucTgqcTiU2B9JDMYK+8PjmkzzEAzYH5UnxgiCqmZkKhjWWxFaKceldoJOO1pQN9mW2bZ0J2LR2lNLKpTJWg8wk0C3Kx3M9CTV2XfPWvRxAjy8+mq1Dy1AatbzGqx2LaA1rSefNDN8DkBfLELjouIiKp+amk8N7kY05FODBoE4S7Z75035tL29je9+97t49NFHsbGx8V43Z2mct+t+u/Di7ujohWUtZtifvIBXny2nGjKBolICFJrBLQ9ZxHBJa5LhEpH9zSwCDYzA366sZxNSRnHgGfALaYpr20sxxdVuZfXDJvnILvhsN1VIuZ4CH2LJxyLMr86NgJU40h71pbQAIZZozEy9p9e2OV67dbqXryRaJJ6qdlS1DEKsr4DqeVhtRbh1GJYEUVnvxXhnf9E/sJcJcLEYEFAaEIx6k1AyIPVbaSCNGAae9Sxi4Uc+AUAr4dj1yBJ3ZVwfbVcCV3sM9250wbnNxs6jBpqm4EQeBezqQSBYaVUwSM2dAWJ3ewgYLSB1bN5EKYcKgjn6HAVUDlCzepBoF6FhvlajQuGrz+0BALL2MfFEiJ9VnNaTmkJLTBBSaXQ6MXZ33Z6clzZaJzLKK6WP3/PsSyRNEUXM+zeMM8CRPHC8Xz/yaTCSTpVUEnM88dgGOKNOWy0fQsi1j+k2lMCudvDYj6tXuYgwqTW4zdJLw7oGq5Z/5u+lZohgHmOkphCW7wECTksrUc6grE+N65ZpEBCt8ed//S8YW54prYEoExjvy/AaTDNIV9Oj/hdkpRe4Dqaceiu1SEBgN2R44pzhYz//CNbWW1Davk5wrc3q64Yeg9FqijAhYoBmMRLE6LYXvyeqhNQUSklIpSGVglL66P+hNSIqEHEKLhgEp+D8WFWTUo3CpjeyqAhtp2YbFRmWqelmJqxMt9aUzBJCMgH1yYSzou/r7X0INjlbU583rSWMY4Sh8p0WCCqO1uA9w0VUPvX7/Ub51OCuQ0M+NWhwjnFe7N+01njllVdw69YtPP3000iS5L1u0qlwN9Z8+tGgOLLbW4kYoCuFzRRSV9YmbU5w6Cu5sWAj5aic/AiEzFHwMKuxXGm0BcVhIBk2VmEk2nrCQRj1Vhkdty9w+8BIB3e9pc+g8FQ9AcDYEvjfTBkut48teHz36qprMQUDhXK8ZvtcJR9FiN/VtgdrNCS0IiDUkpXudMYvoDQBgcZfPn86b3CCieWe4XsW8MzMgrq80gwwtWNzNTMmeUTcn3zKA4nkmFNv8slH1TeF8CwoP8gVGF0svD2Pe1cIHrraRaHsdjGlw+5qCk4Vcof1Fa3JGl7cT4lSm8dp6ZFh7FI2uS3COBiUNUmIqRGkhXxyQVks+f70/9lBXihkbXGCbKLMz04OqBRSuc17yoLcQ5ERRQxRNncOAV24sumj3rWi+JLjgwvUc7xSSltVUpwRvP/JTUQR94rt+wbeGTGrC462YcTa36UKT7xZ2IcjecdFLgFV0p1V4+v4XrDCWMsHAAQtMbaMQYwqlLZH26GY0R5rnEF/hK//wxvO7fJRiXQ1xWB76Ny2DkxQ0Jn+xziF8nyP8yE+Z1FnWWZCpx1h2zOJZDT0I4iBqq7cv/nQNQCVQtYGtw2s/XtGtFXXyoi2rtUFI2CEwxQei6mEtITOXLVJrY+w5ac2BZPtO+NaDzbSqP5z01hm+rzuHUeQehJt+pjGVOP6yutHn2vDFTWdsfFzbbgzTb2nc4OLqHxqaj41uBtxsXpxgwZ3Gc4D+TQej/HNb34TUko888wz5554Au4+271SaXxvkk3dmvinbNcEaUsNaErQ8qxdYkLKyFzx2eWudeIZ9F04vs0jZgaUAN2kenkpHNYz81AAkoAfFApW65p5hAi/ShAIz6bkCohq3pKvZgxXOssqQz1rjXhESH2eFAXlkQl92jLqAAhA4ApyO/Yyseb7+x8kOHALGqzIUm49mk+WfR0Ox6Wz4HsdDkYFormHenMlAbU86CH8mJqouXzhG8wGzFm9dfDdVAPoOh6X+9Yi3Lt1yctSUGq/ojjUEbgFzPUbTu7HvhYpFfcInroUkC4CizoJLDiUTdqRlycR11pa3dgv8K2XDpC1lieeTovxWKLdtj9ElzZaC+N0aFBbhExGAXOCb90pwM/aboo4Nt/T9z22jiyrrpnP/OWrSvMZT6jjeEfE0ClQ+tgwOtrhuiyuM2WOscFFbi+7BpxCediL/q9feMFbgTTqF2ivLxc0TNbSE89ZyLzjIt/nEeA8G1Sf7XB/5EVsiYjhIx9/sGoLNEalffwupCOhx0Eiuu6eWzl1un5gOz6DneC1dfNl36hMj7P5KpvNpk1TWN3n1HBs06vh9Nwf6u6A0dmilqaLYnpWDdsbj9vUezovuKjkU7tdI8Fs0OAC42L14gYN7jKcdfLp1q1bePbZZ/HAAw/g0UcfvTALh7N+3W83XtofY6w04smL2ciSqlsoDRANKpevRdaLGfTMS5rm0VIvX6M7bL13pSVOWJXEgcqRKHD7NGD7kfQzlzvad0AwMZoLFN3bZtioWT/7hjJuZ1hWQXvF6qgjiK2hK/94xzYuuALZtrpSU4xyga+9srxSA5ioDxwXZmq9FwqpNNba4RmkWgMrrePzooRgpWuxXEN4HDapse8zIYR8G3oqpABgbE3ZP4mYm9vwwHqMaxtdEEK9yCCAgDOfY/s8xz42lO59MGInfly3oETsJEqIh0rL2gbi6GuE1daN+vzfbCNOeY3FXPg81G4tby/DLH04ywRYTb0WH4LixDECEidCEnV8lFtTFAHyYVN7H35wFb3VcNsbL+NWj/nah+xyXWsXGaYBpxntacklF9zKSVf7Tqdsko7v3/jhPv71pVvWbeYxOMgRd8LmZZ5wkDkiNKQvAfb+fRoM+jm4ZzFSKTU6jrkaAD7y8QfR7lYJhzGX1geNEY3codZ1KadcazYX3N3R9ZyZv3ON6DaTZtvTb+tZthpSdWCTb+u2r1UsGVawJpLJdHek1siYxr2dN+cOXH92hBrOzPS56SqpRvl0nnCWyl7cDjTKpwZ3Iy5GJLhBg3cRZ2nyO6skiNYa3//+9/Hyyy/j6aefxvr6+nvdpNuKs/QM3Gns5xKvH+ZgBIgYRcyIk9QpNEGWxEHKninWYobx3O41ZWgv8cKdK42u58v0LMZKoxvZf5cwgljMBRECTzf0OQqxRVOo6j75IoQHGw4GAKpX1Ac6DGuGtbPyDLr6bucbHnURS9OtXCBOgsqn7oMrqierYsgW/NULzGnF5kISMy8FQkgW9snfLfWzE9fvnksZiCNVuwgMkoeczihAnVIqjdRzbOnn0vu6mkiehzYT3LPeO7o+7mpiFVwF3AHf2lDubXzujJt8cl1TCurYh6sl2tGvneQTAKJPkk//8tYIP94rFhROXFD/ei63CYeHOSLD/LWybrG0DJgrQ1IbioB+pZQ2tn0eeSG9A/d1Kqmrlzu452rPu22z8LEa9WqZlzLRFfD22cfpXvddR3BPLQ5yyfFrFyntGp8UKKDNz+F/+E/fXoYjRllq8MQ/gB2vpouqw8C5nQSs6+oUmja0e/7OFGnLPk5evtbF+5++5+jvmNlPNGbSuiamRKN02PJJx/zhek5OpazS9lQi+xLA/lvbfG/6ji5hx2e6eqYZ00QyMcMRTMcdK41HezdOrCMrJ9DFuV5rClKjpDR9DuBE7dSTv2mUTw3eOzTkU4O7EQ351KDBOcZZJJ/G4zGeffZZaK0vjM3e3QqtNV6Y2O11RVWSesezFkWugYgTxAGMDCNmgkUsWTciWtJ6L3GQXZdb0cKLcig/UAYGBkJjmCHKqpC2pO0OCIAHuwy91L6t79VnHqE66aU18juqj4Wfi3yqtnEomzwCg9pSJ+cH2wyv7RIIThEJijQKJ3o6CfFWDKlAxdwU+0O1VM2og2GBWFDEgiKbr0NTA6l1UA0Z3ycGqJRPrn4/iyTyDzq2a9QmdRjLxXN7ZCvF5bXuic9cmfxT+CikfPZVOjLOq/14XDsHGVZ61IWiHmpBG1zkkiYC0LbKIVggDb74jd0F4okxEhxYniIvgXZr+azsVmtRldDtxmAWJSALmCt9reeAiaLS8/kHgCigX6WJX/BwPLduWV1J8NCDawvb+doP3r6X59Nb8/koNp3kkMuG1vF9zOyesG5yyA4XKV16jD0n7Lxm8Oy3foSbN/rO39dBKw0qGKhH32n1EtCoTnWowqz0ApKWHKW8FhCn/n3Pao/LCD7xPz52oq3cUvsSAASzfx8zey0/Ajc55XoOXXOhtCizdFk42mcGg/2+mmc8s0G0Te1o+o2pBaazNn1uOrKpnlRMFS633z65Dy1qn92KMFr8Qmtu+Jya+0BT86nBe4jGdq/B3YiGfGrQYAmcFeXLWSOfpjZ7169fxyOPPHJhbPbuVrzRL7CXS6zFHAeFRKl9NSoVRlIj4cSbBNlIea21AwAMS99c/7nfLSkZGViytXsRA+OLQYQAFyAAVZ2okPJYoVZ6IRsPpfZeEORK46EeQduDV6aejfBtqt/+PJQcnsdzw0Md5TxY/T6UAr78vXiynyr7u1AUlFIITpDGBJ1MII2Z8eWaIDBTGkC8RDKo1MBKK/yHGkAvi3DPpZZ3lCyEUA5VSqU1wUETeADZ5mtneJifzJB+7EqGzdXOwnZVkM3HLs+NUjFn0F1qBuLoNT4ElQtKc0C71jT2djgtuKg705Qqf9vYv/vuId7ZPrk94wRMMGuA1oXYk1ipw2BYLHSn7po9U8BHzTNFETgfh9SICiGxhadaazQuj9SHWSrw+E9s1KoRvTk1j7HKh8cKrbVVB6XdJPNplUXudrrGBvt9kqdUtEiPMawuGK+1xp//n9+1/s6FMpeIPOz3qGWbEOI3FCSg75GAdzZbfbanP3I/1jbC7CxdtQeFg7yKHOQUhXaQS9qa4EUdNZuSyD5e22rU2a66TcFU/a7+O9uZ3mkxrmk8yWu+iAjw2OoPFy6tSZWkDesMk4OANhBMWqMhnxq8pxgOh0hTRwZngwYXDE1kuEGDc4yzQj5prfHSSy/hlVdeuZA2e3cjcqnw0t4Il2KOvUKhG1EMTGlrFgylRhZRCEdAqcUpxpYISKmBzhIWeoXCUtZ7udLoGex/LhkUGhpAEqj+SAKJgTQgQGi7nrX79mTC7u9QdGPfdt9eot6HfPI5ax9FjB/V6rgORAOwByVMlOJXXkpwMDbZphAUkmCYSxRSgzOKbkbRzgTShB8FVluZcAb2Fve9HIngU+OkDpxS8ADSJyQ4XaowsirEdjCglI2X0g6ogjbtSd964moLl3qmrEgC7mWp5z53DQJuUAXMghvrKVSoCCr7Nj72fZQ6bPUcu1AuNSKJQNTYcQh7G6Z9JC8V/us/7Z38LSVoJQKy1EEKoXkc9nNEATXLZlGWCt2Zmixrl9IgEtoHcYCaKaSuTcgVC0kEy1IOzine98QmhOW63q7cMp9771VnzoOgYqdM9LIFxgF3sDqX9j7nqtVTnLbWDwi4yXJrgrq6UX/5X1/CaOBQOXogH5ZIV81BxGw1ASz9L4T4DR1TfAlaACgCaq4NDusJ+rX1DM/89/cvfO4c+x2n5SKnXByboHZyilNttf1lDvLLtXQY5+aEBjv5tNx3JhBoo1Wf6e6b1sumR7GsGU+I1rXKp7YocCnbXjymiUwyLQBMz5dpDWRQSjU4e7gdSRpnEf1+v1E+Nbjr0JBPDRqcY1BKg4o53wmMRiM8++yzANDY7F0gfG9vjEww7BUKCSXednt1GJQK7YhaVT7tiDqjPnzJqNCy1nt1loFbmbBmh4byXKGJ8S4SbxaFCtveR8lxf4eiFftnvfvanvm/Wnja87kyuaHNL6VH2yi//TjhCIbXvPLf7BO88CP/4K4GMMiBUS5RlAqUErQz4QyY1GHZgPnBqFjKHnOtFwdbCYUgCeiUIac+LP3HxLx01Sk6RiIo3netjdWuPXPc594WnsQjcwRuAYB6bMMd9Zh81FHEoXxyWnhptwqCuJRNjt9rUhHKf/4PexiMjttLCMA5RT5VzmoEEavzTeh03YoKE6ZLQ8YIsq57XRYc1A6R7QYgpEaUTX0xjyjiePLxTaSpPRnAayzyOKwPsSSVdj6rPnOMa9g9Lbnk+v1Ycut5VN+b72uh7N8rTZ3ENiVh70LDUYm/+9prQb+xYTwo0L20qKoklIB3Fm0wT2wT2JVCEj16AXWc+n1/xWf/MK8lcX/2Fx6rVXI51W+O9ZgrgcNV39A1X7qSOVzkl6t9PDbfh9KSRGpTNdq+M31jm41M44BpSDa1us4FwnR9H2j/uPbzUANo0/kaldBNvadzA6XUhXTSGQ6HTc2nBncdLl5PbtDgXc7ywt0AACAASURBVMBZsd17r9tx8+ZNfPOb38SDDz6IRx555D1vz7uNi5qNsz0usZ9LDKWeehMsU4v5BPqlQjf+/9l7kyA7jvNc9MvMGs7YEyZiIAhwAidL4gD4Sgo/P1+/G3FfOGwvHFrZ3mjjIRxeWguHvbDDEdraEQpHyN7o3WdfS7asd+0XepKsS4YsWr4iRVICR4AkCJCAAQLC1N1nqiHzLapP9xkq8//zdAMNNOpbdZ/KysoaMrPq//L7flUaJNldU0gYB+jmM1rvzWjZ15n4Egsk0IwpIsEPvvYXvvX7KKWox/lAQ2KuVnxVcmvNmS3mluOev2TlfaLLSEK1pFn5Z6jgczb1gfxPrze8iJAyFDmiZnvN87HJGsIYYN7Tem/fXAAZKC+C2DdXmo+l0cCLUNIImXV3Es0OMB5YjDHfYljDcdR7RpJ2eQAdWAN4fV4QAeDMuIPMRSXutmgyL5SEMj2ijBuUCWmOGq7cSPHK6dWx38M4hFIS6cgSb67qrQyDQT6zEqfbS9FohNi3t8EKVufakKv3R+ETHMw8JrokydntGAz4pO6+vU0sMALxnGNTZAwAh1nWeBlqqGWpo4gjUVVQJFhxLq7tApGyK4gMpFNdWagv3WMvtd23n3z1n96cytO2WXQ6CcIJZXx9sQ6zxd9GPnaemcc5JoMcdY85vL0w3p9+5pmD2H9ovrRsn1THuc+JVs26t1P9mrpFnL5sg4Jxfh8nqZ30cx13Fms9Wz8WMMgtNaZWG73p3xTKTTjL7u7uaIBW2C2v3HpythPzey+02fFVuPOwU8mnbreLZtPPnrRChbsdO68nV6hQ4ZZDa413330XH3zwAZ577jns2rVru5t02yGlvCMsD7ca2hi8d7MPDQFtgLlIYnWLPtBXM42FWI19BIYS7Jkon9V6z8xmvZdOWO/tb0YkwerrTJj45Zou9Ux3gUpWPoq+Iz/WnprErubGdp/sX/x8TjT4x2UoLFhHpMoYqz3IegmqyQIANhQO/+tchBubi52jEQfeCqFR+Dw34+DvJwUwP19YFUUeJGmmjZf1ng9jm+aGnZ8JAJpM6zFtgCaDCHx0Xx272ryVkAwHOwBgWeqxjse4t5wSgXDbXFHPbW7cKgsAkPkmySdBBGCFxH9/8fqYWiiuhdBaTwV7tTZe1lejSNLcS7kwibgWQNX56qnQQ6XlI7rP1tSYXNRrvMBglhuWNeHe3Q3sv286d1oZOIuouCoxjt1gSfrIMWQ5Z6GTezuHsKfGfIpEjQhyKNzkdkWQ6FTeq1Hy4uz5G3jz7SsQHtaRHBhT3Am51o9kICEZZI7vXO2jfFq12OPZ0CBUWqOIR1SErXaMT//Cg+XlVO6cPwQMktw9RmaUUn2Ti3U2O7u5+HVqGIhr9jk/Se3zpYvOtfUmW1NsPUHClB5HWRYl2gSxk3dPAHho7oxjwUr578KqwLbUY6tfV8qnuwV5nkOprR2r7wT0er2KfKpwz6EinypUqOCFoc2eEALPPfcc4pj/obKToJTadsvDW4EPVxNkRiDRBjGAG8nWEmwrmcZSTa1PPrtqAZEEeByzWu9xFQqTGFrvNQOJkIoSoSCffNyIDICaT+DdAJFH4MFHWZUZAOl0LpTFWGJfayKgikIRx4FiEhL8fE4sDQazrs3XIwiiyx4GGCmzZgGyOgB+eHZzH8VSivWg5yATfkTNGrT2NT0psNJP2eTNfYsNCFkEmX0C0wAQe/TnxHOcrnvk2Qk82lEj6j28qLBnoc0eD612MhPgqJo4uZhyYmU64AqGjbSHsD+i7JcAAUUQWHZDoAJUXrNcuJNAv/VRFx9+3F//P66FyDINJWVpEDSaUYEIbC4HUasdeVXAsV4dwleBGHsQWz5lawRRVYsDPPzQ1i6QYitwGdeeuuSFOspdiGpPrg0ZnSfbSmxWhOqRsvakbD0FRaKTdpwb2//2G28Wl0MDkSWH56zQuUEQBxBSoL5UZztCzJozkUKWaTSafAI68iHkRpr88//1UUQWZ4BYue9tLcid10nAIN0sOeXcSi+uoMwxXWdItcy1r8u3wbatOF75Ntt5+JJSts8WmyJ78td9tR6aUQfCQjoLOT3HGyMgSnJCFr9brqJlXKmUT3cPdqryqdPpVORThXsOO68nV6hQ4ZbhypUreOWVV/DQQw/h4Ycfvuds9kZxJ+Tb2mr0shyX+zm6eRGaCwKBxWjrX9CXU42lukI7FOh7BrC6uZ5p4upuwnpPANjbDNnPe+wZRPAhkwA/sqqfUwZS45irj6+yb4cCB9rllmEcssgPzPw0W0QscVRUXHrNCaFhiMD9MPDxT2824BCgsdCqj/dZjjKgtJ4Z1hUYAPOMYF4YSLRaGwEx30XLHEXBEGluvILqgU/eKo+Gu5qwtx3h0J5FAEPCmK44Y+ZzohQBAB24K45HP0ccgoqiqHJNzzlU/hfqlCllkxEhYMoVA8YY/OO/X1n/f0g8BYG0WlwNBmZmEqnTTdFo+M/D9XqAsBZ6HdcnAJ7nxos0DjzUXz5xpsBBdgsBPP7oboSB2rQyYhIc3plD/HLmPeo6c6z5qEUIVCuo0yXP1TMn0xSovFTE7kNS+7svfoBrVzeUkWmqIWawmXUhS3LECzUIpoIP8JvTfNHwsNLzeSVP1nLBPv7EEo4+sttaLiAWHITE9lhpkpyi5sOcmL+oRQ+uuS0QbsWkq2UK7n1lWH7vjDHWxWUuS15bP7G1wPa77WrYjjxq6ScBPDj3PowBBKZJJq2D0nmrWKQ1vaEgksp+l/b5r8r5dNdgpyqfKtu9CvciKvKpQoUZcCeRLkKIW06CaK1x+vRpnDt37p612ZuEUmrH2e6dWUmxnBYkzUIs0ckMbqYa4aCz5cdaSTV21fmEzhCzWu9lBpif0XrvYCuE9HjxvdULtHwumQFQ9wisjAa56krg/jl7HhAu+cSNZfAzizFskVjEkiHVIxo5GbTktdu98tjA4M1LIS7d3NzcUoumg6yzxlyjgL9aehSca39gqTHW931VFL7wUTP5BKn7HpakqeUc52sBHtzXhhDDfipY6skiWMZ5zunKMi1Jay8NCUmQPhmDOKKgoSAMNbcSbRXuduSCkftHl5NP3/7xDax0ioBZrR6uE04uAsAAUDOSwADQ8LDOG2LXniaEECzF7hC+3dDHTtBnrvfJEeV6bI8eXsDcnJ9tIXsWEvS581SHnHo2Tz6RPCFRRV1ZcrOs705OlMR2SlvCVzbZ9u/3E3z3ex9MbxQCckZrTBtijwVLgJ/615dIVR7n5pNHbXVlgLgW4P/8pSPOctRlUEQ/CZV7e6S0sz8KGEJRbJyLfiSM8ySoEdaV88t1Z6Qjc5wrj5TrTG1Dq3v24iO3PJyjeX0P1FdRC/owJkLZbTMWYshmcW3/vfw9oEhlXCmf7hbsVOVTZbtX4V7EzuvJFSrcY7jVCpyhzZ5SCs8+++w9a7M3iZ2mfLrcy3CxV3x07ooVrg02zs3U29hlsdOYFbtrgVfC8lGoGclfH4usIQSABY/Vq4Cf1R0w/lHGgW8KrtAjqDHMKRVJ4Mi8ca7u5igqAH6eJp88UhQK8QhjRTkrNxRlq8e5Ie46kizFC6dnI3uGEKI8GKy18VPzrGFlkCGcYb/Vfua03mvGAeKJPqUNvI7lE5wG/Pp+4iE966c5uNxuZzBdbz2UeOzg/NSqTkWsBB8iZORz4imkBJlzBQACokxBULmDl7StHiApWz0iWKkJcsmIAEJPW4yOHaKEaOsMcnz/jesAgFYzRJoW1z8MJQxFAGxieOt0Ey9lxNxcjGBNteyj+ss932mUR0CIzlu0gX5ZJnsLbGqzxYUaDh2cH/uN8+rAbSfvNYRBDjOORx0r04ash8rZRBuLEYpFSjlCzMc5EZKgyKWMMd//t398A1lafh5SScgtUh9F9QAyCvzsZD0PLT0WFXFzlAHA6krCbncyyPHZ/+Nh1AlbP/JdnySn3NupeTAklFOBME7yipqPSetMx+6uXV132PUdtLy8YjmWsVr1+WVesi9USEt+N3m2/l2khMGRuTPF77CQQ7bfbWOM9+/lSqkKdyZ2qvIpTVNE0ea++ypUuNtQkU8VKtzlCIIAWcZfqeaDoc3eww8/jIceeuiOUnxtN3aS8inVBu/cLAJxS5HEcpJPhSFuphpLW5Scua4EMgP0tfGykBuik2l6FW8JZrHeu68RegXYACDRfpNr7pnHaaAN+TE+Ci8lR24QCeDovCATofMVTfxynNPihiV5xBJ9pzjkE51/x73926cCbDa9WrMeWi+2j/phCGOA+bq/NQllvbdvQvU0RORBEKWefcCn4w8y7RWwb8a8a5Rpg3q0UW8gBZ48NI+g5N5wD0+tGC+Oy7uuikFkSYZ1lhLu9xGOfZ89CXkBSi2SiQYMQaQIi63eOkoGzr//tyvIMo1aPUBvjUxUSqyTUFR1aoZ+CBTKlvk5/sKfhaWRBPaeigofotbnnTD1UQlmGooZXB8M8qnuHYcCxx7ePdU+Tnv58yVdkMNjs3gBRhmfMWuWdlDjCLmdtDUjyCWi/txI58374KMbOHX6mn3/3CBqRFuSeymeL8hv31yGPvAhyvp9/jei1gated5Ys29/G488sZe8t9QCCIpYpKY56jIHRAU0ubQ5Ytf1aucaklwLvFzbWu2WpSH2lmSW6mzDdhnJBAClU2G2sZjk/sYyIlXMvXbFkl+/sZOblt8r1dNdhZ2qfKpQ4V5E1ZMrVJgBdxIJcysUOFprnDp1Ch9++CGOHz+OpaWlLa1/J0BKuWPIp9M3B0i0wUIoMUgzqxJnOTVY3AICqh0F619cjRm89jWAuRksjDIDzHskMo+kQCMKkBr/NXKxJ6lWu4V5nwYeq14FgCPzAlHAsAti00C8PE0Az8pPM43uONmueOo7DkFF5JAx9mt1/rrABz9lNMOBMHAH3mYVXcyaJ8WmYltsRQgtfZDKSTKJ2GPs8FVKNXzGCY92NNbUKALAU4fmEEe254Z3LVhKDghIRl9lKRkZ7xpUoC43CnD0B4BWg2hL0GodQkGh7ywijTsgayZI549+2sc7H64irgVIbZE3ApsJbKdMRd6uXY1xVYRnJw5Cfhu1R91aG2d+pknUmH1QG4PahJLyiUeXEJeotblnxiENXHlVhshyhuUl475ypnCqzVQVNrusIQaEpeYgd8+BSe6+nylBSqdaEeOCgHQoM//m62846weAQT9D3YPkLUNtLlrvf7fUSs+DJO52EkiP8rUGvRpfCOCzv1gsSuxn7ns/yNzPDkUsUsvGqKtMzUnUbdqMZtBljwfM/o7l2m2aji+QJuVqX5ciqnyqM6VkVSDK22XWvpVDYXC4/f7Ykb1gu4+W323X1mbrV+HOxE5UPvkowStU2EmoyKcKFe5ybLUCp9fr4eWXX0YYhnjmmWcqSbAFSqkdYbt3tV/Y7bUCgcGgjw6xgnE1NVj0CMxOYnccjH3MMBaMl2LWEJ7P6uD7Ghue/b4KLU7OllH4LuoKfPJnGD65daApQDiorIOrVAI4lj/DcjyoLXp94dBYvE8Ed3sMcpiS1b/GAN96e/MfwrVIOQMchfWe/zVb6WeIZrHs62eoTVgACgC7F/zyr7jgE3wb5H5qyXCGa8XB8FI+fqCNZt0e5OTyxdwVwgFD1cQ6HmvOoxovEEiK+CFWkpvASegCgNBu6z6qlVqM98uvvngFYaiQjUTdwlAi98lPpM3MaojBIEe77R6cpRRozo/3MU4+oFH4KH191EwAEHu8O/gQVXG0Edw+dGAOcwttr3ZNgnOLOMEjzqNR3B6CbOXkdKKOQ9RBPSeDXEE4+lyqFYSD5E61m3SmthtIBIQiUlmu4798/wMs33TbbA7R66RoLs42TwkpELVm/2bysdZscl/UULxnUGPHWDsYfe+xn7kPe/a1EMncabkYSu20VBQwGGTucYGydKRt/TZHXlElXP2cuqX+fgwFXEuNbL2kUa+X/p4NyvuGtLwhB5bj2+7icKw83Lw+/i5iJZNsZ2CLddh+t7FPlfLpbsJOVj7dSYvZK1S4HdiZPblChXsIW0k+Xb58Ga+++ioeffRRPPjgg9Wk6MBOUD7l2uDtmwPEEkj6ffSJJO1A8Sq/mhkszEBAxUpgMv4+0Ab1GYLb3Uz72W6toZNp1sTXCuWYVZkvmeS7pslmebFV9Y/aXtiwGAssNvyeaY+MH1tYikdmccKiGppcecrJ6US2WwDAdPDnX98P0PVN+jWB+QbvLoQzqAwNgAVm/ZOYbNe+hTqkY/Vi5hkk94WPmsknHpR4BOBzDTy0p4HFdnkQaL0ck1TiXjKOXR7nmLklF8MoKAslABDCPc7QeaEElO64SxC2era8EkOM5o364ekV3OhkY9dbCHu+IRe4dnJlCAnF7569zSlyq7DSuzW5ZworQf75KJ+J1KPo0IKs3Ypw9IHFTdfJefflco6cBS8BEVjLOKQv0WZK2aQNCBmGQBS4SeNIud4zBELl2p/aDihiHCuz6+z2Ezz/r2ed+03ts5qiybSeG0V9IYYYuZe+M5qPOqnnYaUHALGHhS6lxotrAY5/9gEAQC1wj+XU9khp57MrYJAQCkKSnCLmJOo+UdnQXGdIL0uyw0VM2bYVM0T5Ntt5REH5XGgjc23TiVUNKgRiaXCo9cHEz+VnL0oUjMYAosTS1xgAtvcJixLSECrOCncWdqryqYqxVbgXUZFPFSrc5dgK8klrjXfeeQcfffQRjh8/jsVFx8d7BQA7Q/n03kqCNNfIkwFkEMI4Eu6OwgDoZgbzod8UMh8FpSsU6zOoDDSA9gxBvNwAc4wg9J56uKkXQ4986evlfeKDfU8bsTx3BypqSuC+VjGOcMiWIbhN5tZos2ubBRqGZWlC5YYyMPZExyNlaIwf59KywNsXJUIlEAUSzVh4W88FSoBHp85u78J0+5rCKJkUSIE5YuV1bvyUib6KDh/lV+Jx0t0kZ6uqapHCvsUmWU4bwbLB4+Zz4lwpTl0pIlLxkVOWeKAt/jJGcEjpnrsAFWwX7uCyESFgEmSZxndPXocx41WqQM7UpzbjtrLaSRBZrG+jSFntsnxURD5WeoAvqe3Rv70UZYVq5PFH97iVZcwqOX2PO0RwxnSqiDaMNhH3jWM9SnGDlPIodNjeAUBAbFcEKU3n3pnG//0PbyKfgSTu93OENX7AUwUSwQTBYzznKOYrOAAgSfIpu0kXfIQD3a57sdJznzmM+lpex1C5ry2luo2IfEux0hDOC2OQ5Ix8YA5QOQS1Y24MhJusdnUpBfe+9t5gf0t32YHahgBbYL+zulr6u63FtiMbqXCkdWUqt5YU08+ZMaqURDam/JvM/ruw546sbPfuKuxE5dNgMECttnVOEBUq3C3YWT25QoXbhDtptcJmyaehzV4cx5XNngduRa6t24nrgwznVxPItI9mHKPvOR1oAL0cmGMSUEuxsiaonVX4MWs/pIJBu2vB1MeYrzJJA4g8Z9iaR3Bcwy9PVFR3BLy1xuE5g+HhCwJoa1UoXFKJe1RuOYpYGpaiIEiCKmcElTeOk2vgO++E6+dhjEGSFUHIUAk0IqAVA81YOZ/XVl2BG9DVxqA2wzf3ct8gmsWyb2BQW+sE+3c1xlaE2+CTPynJDSvn0RA+40U/5SsrDYBmRAcBm5HE0T0tSGY7OJdCM/M5cez5ciPpXEyQZICZzs1CK7YMJATchLlwrhmfts2bRC7qZDulzvDPr15DbzCukFyYC70sskZhDDDXmj341bbYeu3e07TmlOI+c0BB+vicmU9QyOedaZDw32uTNMcjD+1CnVB3sOcNhlIQEOAMi5xxh3N/KDtEilsqbHLdhRqhm9BVBPlDbaeuKzWWWYPJa5g8+pkPb+C9968597FBawNICcGck+qLtdJ7LWccJzhotPjqLB+LzF43RWghuZf2NPDEJ/ev/0+9hlLbA4J8ositiCCnBAzyEsvjDRhkju2CWI5FvWEaR992PVnSYWztUje54GlOh2arVfr7wGLTZ1NXSmQ40Pxw7DdtAoiSe691+RhubFZ5tsUuJoD1GlW2e3cVdqLyqdPpoG6xwaxQYSejIp8qVLjLsRnyadRm7+jRo3cUqXanY6tzbd1OJGmKH3+8jDhPUKs3sDzjaWgAAw20CQIqlML5bCXaoDFDcLuTaW87PKCw7LN9EEsBzJckKp+FTIp8VSye5xJ7BDUG2liD6btVD1Ew/nnNzanko1RSzI9lTrmcqTUSLGKJ4/jPUXO4P2hHA93/83SEjiMFRZoLDDKBZO1ZbdUU2vUAzZpat9BqxAERVJnGLHmfAGDOw7JnFO16iHqkyKDwED7KJwCIPcgqX6VUM+K3pUbYoUkBPH5gDkopVsB6uA8HVPAO4Nr40bZXAK1OKHKzuOuhVpoDgMTmcjblwr2q1IgA0rjzwFxdTfHa+6tj+XJqdYXuIEeez56/iWurWIZeP5siXRuNEKFDBeH7bhduIq+jC2nGz3CitUHMnHTn2jH27SkPlN5KSJZUZWsWcpC5YxjEniLaS5I/mzQoI/cnyCsyT9tIvzLG4G++/gaxhxtZqhHVQzLO32hFkJY+40P8+qoibQRRGbodtw3pJFpz5ePnZ//zQ2PjHkVVb0bpCdCEZkiRV1I7x79AGCd5FUi3PRY1BbjO37Wrm5jyR5G/qfyIVlLKljLJUk9ZDt9AAIcbF6fmLGPKF1FY7XAt7wzGcjWMhZQqfq/Cn3cTdqLyqdPpoNmkXRBuB1ZWVnD58mVcvnx5u5tS4R5ARf1XqHCXYxYSRGuN06dPo9vt4vjx45XaaQZIKZGmdB6dOw0rKyt45aMrqC8sIZUBEr250EhuCsu4ViCxallZuRQHpLqpFgh0PUkwg4L4uu7pcZcbYD5SuD6YPuB99dCq0IikQOIRwPYNL3rfB88DNJTEysQ92hMD++amV18JwWvQ8HOWs1ae21wB3sGL9OZUsGprckPxoACHWsMgh9HAuRsS71/xq3kw8lUvADRrClEoWVZKo0g9yaohZs3HlOYG9y01btnChlBK9Jl3cLAW+OaeSRRKoGSMKAVxesf2tVCPi3lWCddTsoHCYoqjmqDPKNUSArTHPKcujipEyRy5ZQUzAGQmoMk14Z6cNPEJk4sGlM4gpD1AK02CHHaS6qv/axXZiOy1VivaPewPYSQx6Puv3ugPcsy1YiyvusmvMmSZxvx8DTdu9Nd/27W76by32tuiUoAbqvYhdY0BolhhwOxXURRgkLhbEgYSDx9dYrdBCDogzs+lRpfhVMVaSEFMypmmyVBqXhdE/6cVi26Q+28yN88o+fSdf/0AK8v+/WsSST9Dfb6G3kh/m0Q8H0Nb+p/v1Md5PofwIXYGgxxxTbHHq7g+Pb4+/NgeHDg0P/YbZde62XxM1OVTwjiXKQTSOJ0LlDRO1aAU7u3U4qUcjnNw7OpSPgnHNrsdnx2262O9bmE01RmN1kj09DtGXaY4uvvmVBV2ckiWnpntjI2x3QHLxa3yPd112InKp16vh0ajsa1t+OlPf4rvfOc7+Pa3v42LFy8iz3N897vfhRACly9fxqlTp/Dss89uezsr7CzsLBq5QoXbhDtJIeRLPnW73XWbvaeffroinmbE3ah8On/+PH5y6l00du3FQIaoBxKDGYPKo8hN8ZHSKFEhLEaKZavnQ+qMws8gaAOqpA/HSqDusM/ynTB9rfp880Slntds0r6tFQjsaW2eein/VNwMeOfFUSzxAn90KZ7Ci3hChEGSBfifpzb/8RtHysuibghjZlM/dQaZl8poiEAJ1D3yUvj2GZ+p2ACoeyk6+OfrIgEPzMfYPb/x8cYXy/AK8q6BQCBpyouT64anOHSPK7lRgCHmT+JAmSAsS4SEMl3iGPY2vHJmGR9e3iA+4pqCkEAyQgT7kjqjUDP0pyFGx6yF+RoU8Vzn2m+mtNn3lSHzUDMBQESoBEchGWPVQ0eXEEVcXS2dtwiA04ZrDBzyifGIbOY5Wq+DkReKaq4mzpta65BvkoigFIFU/dla/Z1ughdePOcs64N+J0V9rtzirj5nJ55mAeeZHyJJOMsYNtD0sOmbnFiCUOI//W9HpooNcnd/TojtVF+jRi7q0lMLKqihjrqzrtoVteBjxm7v2s3WR225oOyKKFNqlS5QTuZFSpae625x1nKPtqjPWO6vVbVV5Xu667ATlU/dbndblU9Xr17F7/3e7+E3fuM38MILL+CHP/whXnjhhfU+/PHHH+Pnf/7n8Xd/93fb1sYKOxM7qydXqHAPwocE+fjjj/Haa6/h2LFjlc3eJiGlvGvIpzzP8frrr+PqtWtoHXkMAyPQDgRWyzwSZkRm1oK7Ix/OgeD73ScaM1nvdWe03lstyelyXyOaMflvOVJDW+WMQgOoeeyQaD+rvtH4ViCAQ3PamoSaZ2pXgEMCAXyFEf+pZNrzkau1jdXSY6OM5tVD4FvvBPCMF02hUSu87H1t5IYIZsxB0Z4hYdSexTpCT2s8n+blnkOYD2HnMzx2LflpWhFwZEY7MO7t1UwLt4ChauIckmOZx4EiyDCK0shFA9DuOqSm9Dvlx0gyjW/9aEOeGMUKWhtkE8pRY4BoRou6ldUE8Yz79noZGs0QQgBzS7y8ASEzP+MsCL0IJR/G2P1E7lqsY9+eIoDDfZ3lqPsAwbNUZFTFGac5ZTitpvJCUUgJcofanmj3c0DtT6loMmLs0Wt56/7bN96A2OJcS/1ehqgxPgcKAURt9wI+X9s5HyvPTif1WoDhY9OXTMxpz/zs/Wi2x8krJbSTXFJCIyHuKUVe5cQzRSunNqesokq4CFnSKtNRt+v1w35IY63Tto/t6irLPrZvjrJ65tQAS7XV8h1sp25bHGC1+7WVt7FPlfLpbsNOVD5tZ84nrTX+/M//HF/72tfwh3/4h3j++efx+c9/fqw9jz32GI4dO4ZvfetbWju7YQAAIABJREFU29LGCjsXFflUocJdDg75pLXG22+/jQsXLuDEiRNYWFi4Ta3buVBKeSXP3i50Oh289NJLWFhYwOKDj6GbG9QUsJxq1JTwCvRSSHWhCqytfTzvqgXswChQWO/5Ymi95wsNYG4kYDYXKoQBsULT0MmFJxF75gTxzRPlQ1b1RwJcD8wBgbJ/wmpo8Jdl8spx80Nxy3FbJ1l5n+g7K+EmXzRBT779MfAfNzc3ZigloNbuuTbwInaGmHWNfeo53u2ZqyEI1JTijoIPx5V4sk8++Td6ac4O7OXGoDkR2BvN8zQKLqnEVV9wx1hO0J1DLOW2BN+ebaLyy5BtEQLKdIijUPmpys/lGz+8jP5a8DWKJLQ2UEqWB5JnnEMNgGbTQ4kwgXocYPfuBlsp4UNM5J52nspn7vaoOrVY+QKFgvORh/h2e0NwhyNOOc7owyGWMm3GcgKWHotRT5miexTUMSiVSp/YPsjdgV2q/pSwxMq0JM/hlTcu4uy5mzDaQPkm6iSQawM18t7YWKyzWE8fgsinrNZmihBywqPvrY5Ygs4v1vCJ5w5OlamHuXPBVi1w51uKVQ7jPGGDJN8kIUnlpHJupbYb5wxDLWly7etSfOWWba7eZTsP21Fsbbef0/QRHmx+gMjiJtHv20ip6atiDCBK8kgaA8CWX9JCYhmHHXCFOxOV8mlrsby8jC9/+cv4pV/6Jfzpn/4pjh49ijRNEYYbfSPPczz88MN49913t6WNFXYudlZPrlDhHgRFPnW7Xbz00kuo1+t4+umnxyaXCrPjblA+Xbp0CT/5yU/w5JNPYum+A7jUyyABDHQRNOnmBo1AethA0Ui0gZISu2KFxDOp+sAz4DXErMH00RWmuxu8fhF7snW+5J6vGrHfpYKvG9CmUKYdaEjUIzpsxiFtAD+VFPelg0PycUkqDrHEU29RrTdWH/tuYvCDM5u3VhqqnoZQM7DHxgCt2H+/7iBHnUn0KgksrAXFfI/kwyUbADVPZRW7bgM0PJQp9Qn1x7H75lCLp/MJcZtgIFiEUUFScZQT9J1IiVXmwzJUADhjEFTWlcnDOnQAQxCeUlM5XSj11HTg9sOrfbx5dhlAoRYyKJQ9k6qnIbQ2CGaR3wLo9vyUC6MYJBmac/Z8VZPwCmob40UaS+FjUcknjJPETgA/dGQR8Uhgkzt3uoPdG+DUx7XL4wzTAXG9Z1W6jh1D2PMWAUA/D2CM/f7kRiGwKhAKwjiwBYPX9leOkHtuJEFKCwTSvn+/n+F/fPv99f+HOca2Cjo3kJGCkAIqkFAleZHK4GOl53uX68x3VwDoD/iy6yzVaM0VdX/mf38QquQcIunuy5F0n02k3N9QkdKAc2wxyAhyirJypBY5aIdyKxDuccJ1ZAXXvi5iymaTZ7fWA/jvHUNYhUnWVo1jV9jHXHQNsPTXRmO672gNyJLyxgTl84AJSq+hMQLCNk5Vyqe7DjtR+dTr9baVfLp8+TJ+8Rd/EQDQ7/fR6/VQq228TxpjsLCwgGvXrm1LGyvsXFTkU4UKM+BOsqtzkU+XLl3Ca6+9hscffxxHjhy5o9p9t+NOVj4NlW4XL17E8ePH0W638d5KgiTXkFKM5VfqZBqtLSagcmPQ8rDhGSI1QGsGJUc3M14B6yE6a9Z7e2sBe1WVb4zR9wnJPL8Qg8hv5fx8JLDY4AUguM9EQQLx2s3ND8XN58QjjXh1bUU9NqLrm28Jb4u4STTr48QTMHuukHo8W66/ZswLdB1YjNcDbrmv75DnPOVjIzjwvAkxoYYcxaiqav98DbstxEBBKvHq5FnlCSjGSMN5VLSRkKTBqEBIWOalOiAJKtrGUkKZHtEWQvVNqBVz0RhrpzEG//Bvl2AMUIvkenfLMvdxxAyWsUCh7FnwIJBGsW9Pw8vuzhc+Nn3UvR5Fmmm2tZgBUIung4WLCzXct29WO0su+USX4RJCHNUZ9Q7CUUdRoFQk2gjEJCGQEtvdY0NAbXeQV0Bh5WbD33zjjSm7uKxIhrVlyJIcUTNCfbHG/qbyfb/2UeiWkUI2dFYTr+/A9lyEww8u4vCD5QpDRZBL1OIJan6jyS03OSVgiDxhxqkwFjDOmZUafV1Et6tVcu3osxzTBk/TOuuiMtsdy0bGJgHgaLNQTKhSxZKCLCGHsmz6HbcoX04Y2X4vCCbLta9yPt112InKp06ng0ajQRe8Bej3+1BKod8vFqOkaTpFPg0GA1y8eBHtdntb2lhh52Jn9eQKFe5BlJFPWmu89dZbuHjxIk6cOIH5+fltat3OxZ2qfOr1enj55ZdRq9XwqU99CmEY4sNOipuJRjOUWC1Zvb2aabQDLi1AY6mmMJjxez+aRcmB2UgrDWAxVl65bHxDP4n2uw6pAUKfnDcyYNcfCmBvM5t5pb0L/HTvW1uO89Ry6AaOiooZZpz65ZWPBK51Nhc0DIPyZM7GANEMzGs3sRm3uJEyyJtaKFFvbHiH58Yv+Ma1mxvCR3WhDdDw6GA++TeGxHErDvDgXndgnDvMcY8fEEFAgM6nslEXPa8pIihYKBTcQWqOfZ/SXbKM8xiCIHaEgjQbeaFeePM6ri8nCAIJSAFjYLfbG4HZRIB7FlK6Xgug4ohUy4wfx9NKzyOonXmqlmMPNUo4oT5USuDRh3Z5HW8U+RblLBuCcws4QxSnHup+Uyrkfq4gHMomAIgIYtmlfALc5FCx3b2/JMYWaan/9Xcu44NzN8r3CaTXWM5B6KE48h0cmkQeqVH49OvCpo9fdxwrfOYXHmSXnwJx2tS7KDWvhQ7raAAIpdv2LxAGwtE5A2mc+1OPlGveoMkn/222w0mHWspKSlkqsw31ozky90YdNIIVGBNAltwjbcLSmy9V+WK65eXy9wBjuRrGMsYXv1ehz7sNeZ7vOPJpO2334jjG/fffjxdffBEAUKvV0O/3x8iwoXPOM888sy1trLBzsbN6coUKtxF3iopoknwa2uw1m8118qHC1uNOVD5duXIFr776Kh599FEcPXoUQggsJznOraZYiCSuJ/b2rmQac+HmCajFSCE1ArkBmjNYEfVvs/VeOyy3bbAh9TyQgX/ep9iDSDMA6sxI9qF2DkkElsbr5p8s9wy5dfKPzSGfOMSSIRNXa+QMtcZ4getdg1c/2tw4IVDkd7E9p745lQAgzQ3mvYJnBbpJTlrR3bfUmGpr5BHITjVfGQQUFmE+8Mkp5ZP7ppfkUELgsf1z5Icyl3ziXgbJIZ+MpOVGsAd3x8owKF0qyMyy5jMEgSXcgdRc1GE0oVoyxfaVXobvv351Pa9alhuEobTa7U3CRyk0ik4vRbvpZwW0e3cTQggIj45ijB+Z6vOKm2vjZQFK5Vd0tePBBxZL1VAFGGP95t3rxsAZfzkKXc7VoxRUNBFBK5tIcohQq1Djh9jk/mVIkgzf+OYpZxk1Y/8sQ3OpyGfIhc+7FFCQy1z0eu4xcqpuj3n/0KEW5hfq1u1uVRGQEwtJyKeV6BSKeJYoZdZmlVvufu3O6eTa1WWfJxzbbGOb60m1veJkHr/Lkd8lDB5oFn1Ro3x+tiuZyltqU18kie17xvJcVvme7koYYyryaQtx4MABfO5zn8M3v/lNfPnLX8bZs2exvLy8rnw6d+4cvvjFL2IwGOBzn/vctrSxws5FZXxaocJdjlES5NKlSzhz5gyefPLJSu10iyGlvGPIJ6013nvvPSwvL+P48eOIouKFP9cGb98cYCGSuDqgV7OvZBrzkcTNhJtNZxw1Jcbe+Yvk1341ZWvWe2UKLRc6mUEoBVKPVaB1JdCMAvR98sCgIJMGHvuEEuh7nI4vZxdKYf+CXMPuGGhEydrdkOCRNnqt3NYEzoo6edctn5lOnIYBACPIHDMSCgZucq6w1XO58W+ktzYG+P/eFpu2SpprBs5Qvw9BMoooUAD8AlcA0IgCdJPyazBfDxGXWPP5EmRxoNBLecrSxFM6UgTgedeM2wYASLXBY/vbqFsD4xtgk7VGY3ajnemjhipHqt3t47SNkz/KFSQDAL2WP8Zs4vxy1NwtERLKdKFhtw4Ra73ra//2MbQplDZ5riEl2MTTZhFFAdDhLQyYn6shsCRxpxAqiQFBxg3hTepGEp0er26fONLoa9b8XIz9Frs9IQQkeEpXIWgSijvFC9Z7ztYoa6lhVOsMEO5nI1I5+o4grCvwDfgTKWU1zL61HP/9n96estubRK4N2os1rFx3572i0JiPoQLFUrOtw/OklMcCpF4vQxgppMT5b9TNm31q9QDPPrcPrquVEDkCE4KcovI1gVgQRJNT7ldj6tWZulKu2+rO6bQ2vsyw2s91TNsTIFB+MAmDcg28KV1oJyy/jz5S+2srqKniqSlIprJ3TMt997U113lpXcZCC1pt+ipUuM3YzpxPYRjid37nd/DCCy/gt3/7t/HJT34SFy9ehFIKX/jCF/D1r38dZ86cwW/91m/hl3/5l2GMWwFaoYIPdhaNXKHCPQilFNI0XbfZO378eEU83Qa4cm3dTgwGA7zyyiuQUuLZZ59dJ54A4N2VBLEEllM+mbScFgSULwSAdjROavRyTohyGuEM1nuAv/Xe3kYELYQ32eN7eXyDKb5cAlU8lsDu1kbuFL5FHjenEp9U8vne5lxmLklly8Xke0SqHgO9bqP1g7MC3QHjsA7UIkkmzDYA4hlWda8OZrNgTBwB+T2L5aukfY/jYyemjZ+yilqNPVbWGDSY13ZvO8beOfsq8VFwuzg3AM7NY0NZYgFMm0rG8ThNV8JNfmoikJ7LBqDddUjt7oQGwJsfreLDj7uI4oJ4AooFJj78izEo7PpmwPJqygo4CwEsjPQx35xvPvnRfEltn3P3abfJC1IuVMAnHl1yBkEixSMLOYEUbhs5YxvnOdrsIgWgUI1StnqUrSbVCqrvb3a7Lyn4zvtX8e57V907raGzmqC5MFuONQCQSqC+ZlvHfTda39fHwtVzQUWrzc/9mdkkLRP41HOHYJRdWSpg0Mvs70MSBoPc/b40yN3jO2UVS6nVqad5s+FU1xBJDbWuryPX3bftJ8aWPvFguzu2u2L7VhoqxAJhcLh+eqRRlh2szbRd0PIxK66VP5+9nsWut8r3dFdiJxIf3W5323I+AcCRI0fw1a9+Fb/5m7+JmzdvrrfpL/7iL9Dv9/Fnf/Zn+Mu//EsAO/P6V9g+VEsAKlSYEUJsfkX7VqDX62FlZQX79+/H448/Xk0Stwl3gvLp2rVrePvtt3Hs2DHs3r17bNuVfobVJIOGQOIZoFpONRYJm75J7KopZBMfghqF9d4q82N3iMGMSg4fvdZCpNatf0IpvHJW+HYxX6u+YZ4o7m7U/T3UzsY+hH2aLyFYBM/GtadrVxDIGHUKxlXgklmCXM8NZk10gFVnwLkVgVOXi2CVgigssmCQZoatKBACqIXKsjJ1olUzWO9l2mCpoXC140ei99Ic7VhiZTA+Puydq0FZrIh8u7RvH4uUAHPhN/p850kAQC1S6KbusTBUAkd2t8AVe3IvR2FNRz/llP2Rz5E5dWXEincA0IzcOkJnzlPLEJNPvzJd5HAtuHHfu1Qb/PNLlxHXFNI1YrVZU+j0/ReXCCVAiCfLW2iAdivCjRtuVcbu3U3IEaLVmEJVyM374sHRwqDINZcy1V90IHgD3DoBoJcY1EKJpx5qQkZum8VGrNBnpAkLZY4B4znnKKQ4yBmV+ObkKodALUjQy+0EC6VIpMj5lFKzbNKKjbRyGxlXskzj7//5bWf5SfS7CeqtEL1Vf9Vva6kOsSbb81UGSinYhKavlV7kkUONU/fCYh2PPb4b/Uxa5+J6kCMx9v5YCzLn9lDm0EQYKiXmGXquIsgrYm/38rlCNWQrIeHWyLv2dZFItjpdx7ONtq62l8F6N9Yu5MHadYRqI4ei9QC2RTAWy09hswsX5b/XaiHK7m6WCrblcYU7B3dCrG2rsZ22e0DhmHP06FF85StfwdmzZ3Hy5Emsrq7i4MGDeO6557a1bRV2NiryqUKFuxgXL17EBx98gFqthgceeGC7m3NPYTtJPmMMzpw5g6tXr+LZZ59d9+kdop9rnFtNoKTEMjciO4GbHgRUK5DITHnAWM5ovdcOJVaIgO8kukzrPQFgqb6xAs7XHtDXiSk3hfUe93QMgJoCmA5GyAwQyXKScV8dqIXjwQaf0/UpK9cNrLYGfIWUXLMItINzChw7IU49yz2BF8+M7zP8eJJKQEIgkAa5LgLHfUsfazdCFvEEANmaytD3E81HCTGKWjROPikpMO9YfZ0bv/b5xmF9yLfcFEqxAbNDSoa/0iP72ggDfs68gozjUKcCgdBTxP4kiu28+iikWpHkX2ZUkffJcW0yreiE8yZ3k0+igUDr9YBvGaQewL3A3t2If3qlAy0E0rXnIY7VzOOY0QZBINjqglEMiLk2DCWarek+FiiJnG2l59emIOSTT6kHw5xrg1ZNYZVJ8B090EJjkVb0c4eBuRi4wiAJpRAkccSJi3GIpVxbXqLGS4F6nmOZO98dqJZQAf+EULPQVmzu7SlBWmd6gxD52v/7NgaeqwmMKVRtQSiRebxjRrUA4WguJgMoJdgKQZ9PhsEgRxQrJAy7bN/Ke92UPPfn/tP9aEU5Bg6bslhpWFPuoLB3dG2PlYar+4cyh4FbqZJq6ewOlGqcWiChHfUrYSAc85/ryAqAsd4z4yCLjJUQk475f8b1fGwYA0TC4NCo6gmwfhHIEtLIGECIDJPnYCzvI8bYfgeEHFqGj+Pdd89idTnFwsICFhcXsbCwAKW2yta4QgU+er3etiqfhjm08jzHkSNHcOTIkW1rS4V7C5XtXoUKdyHyPMebb76Jjz/+GCdOnKhenu4hJEmCV199FVmW4bnnnpsinowxePfmAKEUuD4j8TTEkIByQQkgDqSVjOvnZqaJJpiR3GszrH/21sOx5KW+AbkcQOirzPA8n/7qqlf5WgmJ0FDAUqNXUtontMq/ONzQO7dGbjmO/Q3LSgyaDCTawwJrdWiDfzkjSIIy0wIGRbkwlJhrKLQaIepR8UFdjxUrr84oajPYZS4PDDtgO4rJxdP7F+tjiowyRB62XKn2O3vfPlz3SBZPrXDfNxdjsVmQAh76SQTudOTroJKiD+sLGZZ6VDAOAAwkFNm2In+UC5lRAGEBZnK30scIBWXcUhZBtFU7Apgf3+jj1MXeOvEUhhIwQJqZmS30whn3S5IcbQeBu2d3q3Se9cmnlnqSYtJj3vJRMwHAfJNngSQlcPjAnDPQOwTfCo1XrhnRKhGOAsYY+oja0KqkgLCpBIBAuu9Dlrtb0udYpTn6NbU9yYO1XHblSHPl3J6vjSvvf3gDb71zxdlW6zFSjSBWEB59p7lUm+p/PrmZfNFqulV+o6DyXU3V7Rhn9h+cw6HDC4gDd53UcxYQ81ZI7B8TFpqhdC9+AMymVHiCeNuTRA5RFyHoapVcO7rvfnYYb0WUbSGW7T0rNQb31y9DTdzTMsWSMQKyZBwzJiqd37TFKs/YcleawPo9+tijT+FTn/oUFhYWcO3aNbz66qt47bXXcPbsWSwvL+9Ihc3dDq312Pf6TkGn09k2ddH169fxV3/1V/jWt761Hj80xow9/+fPn8f58+erPlFhy7HzenOFCjscq6ureOmll9But/HJT34SQVAJGO8V3LhxAy+//DLuv/9+HDt2rPSF7KNOCgODa9wVkwQoAmoXsUq8sN7zn2r6nsHnIahVypEUaMXjfSaD/2QYe0bsfQP8zZbfS+nkt5YAcLCdlh5XIwc3TO5jZUgFznzr9Dk2py7OO7QkczoZwLFa9vn3gG7i125jin36SY5UG8SRQqPmP677WGoNkWtgvsEPcg0xSPV6P6qFCg1GHT55nAC/PFapZ44Mn4B9P7WPpZGSOLKrtf6/DwlGBe+G4DZ1MvBTBioYt1EXrSZQFoucDQgERD056OdGmTICfQNUnotc2PNw/fPLFzBYC9yGoYRScl2lombpUCgUQLPYYAJ2RXWzESKyjQkehzIolFL8BnnU7ZnzKop4BPCTRxbQZAbiuSv8uf20xkgIaZj2y4qhMo0Dd8N6KX3NKEJlQCqbgkLVaIGBQKRc/dq93UA4iQcDQY6NUqT4u//nTWcZCskgX8/fRKG5YLeU5cI3hhfF/HeATsfPpi+2jCVCAMc/fRgAY9HDJs0fKPJms+RVKLXToUIJN3kVCOPcnxriXWOMa5R0PWUc8+jy+vwUUdbfLceQQmN/7f2x34yRVpKpjJizkUy2q2VM+ZWy/y4BKARBgN27d+ORRx7B8ePH8cQTTyCOY5w/fx4vvfQS3njjDVy4cAG9nvu9o8LtwU4ln3q9HlqtFl1wCzFMFfG9730Pv/u7v4vvf//7678LUdjDD8t88YtfxGc+8xmcPHnytraxws7HzuvNFSrcJmyH7dp//Md/4OTJk3jyySdx+PDhKr/TPQJjDM6dO4d33nkHTz/9NPbu3VtabiXNcWOQYTk1WGAGdjiwEVALkULK+AKd5THNDdDyCD4P0c0NIsdX4b56WNpvIk/7Md93YV8qMBd+929y0fn+hkEU2ANAfJUSnwTcakUTN58Tl3agiKVhKQrCUs/rFw0u3Ni88WA9CryTmQNAks1G2PoQMaOorY0x+5carLnIdxwIPDpZboq8S1z43KVUG8SWoPqj+1pjAXef0BD37NhaDkbB3PDMMclV3QA4ZyuNOyiaSppkF8ZNYGnhVtDkog5TYkv34jtX8PH1QnkVBBJhoJCNDKR5rmcmkebn7MoCFzrdFLWS/C1Lu5rWPuYb1Pax2uTmqBlikalmAsDqLO16gF172/wcacyO7eno60RuBBkoB4B2mJBlmiHRX7REQJC+1C3rpzSpESt3O0LpbgO1nSKlqXP8+2++h17XP2fTJPrdFI15d19VgUStZXuuPccHr+IeNpa5RqPlsYjE0o6Hj+3B0q7GWhHKso5oE2VpR6hwqUtFkWNUn6S2S2K7u312tRG9s/24rt1s98N+F4w1PaFtHC0bNxWAw/X/mPom0ohLX0qM1crRRjJZfrddDdtzZzluHMfYv38/nnjiCZw4cQJHjhyB1hqnT5/GSy+9hFOnTuHy5ctI082PNxX8kef5jnT36Xa7t912b6hievnll3H48GH8yq/8irXsz/3czyFNU7z11lu3q3kV7hFUkokKFe4C5HmOt99+G3me48SJE6VqJ2Pcq7Qq3J3IsgxvvPEGwjDE8ePHrS9huTY4u5JgJTPIDbCcaixGatPWe0NM5oCKpYCUvM/jXl5Ye/laY4UzBv5agcS1kvNuhRJRWH79lGfGHN9zSXTxacWNeeWOPE5lGGgDJYr92gGwUHfbWfm0RUIgZ1ybIu8SU1nBrJNz7KFCis52I6mQAHjtVwDGP0QvrRi8dn7zEc1WPYQ2gJ6hzxgIhIFA4ml/tTrIoKTwTnjfS3LM1QP2Cm3fq+Nj+QUU9p9pzhvvfK9RPVIYTOxz31wN883pwKUUgmfFJRXrolBBuo0KecUCmRe5MjYNBuFoMmcxLUJEuotc2j/CqdPKRMPda4WAQh8aG0TXtdUB/u3NwrIrUBJBIJGUKNzCUGEw8MspAxTB4FlRr0foDzZWXS8t1hFY5i3AnyDyyo+mDYTgE1z1KAQwYJXtMt5Nnnp4CUJKaMK+cQjurRpkvGvA7Xv1QGMlcQfIQkLVBBR5ZMhjhZnzWJS6MTMSDZEhdeTzCWWGXm4nZRRBLlEqTElYhLq2v3fuGt5897pzfx/0uynq7Qi9lXJycHFv03+1kQVSCmimPM/XSq/RiNBdpQlOAEhL6g4CiaefO7j+P/XsU7m/0pywvCPIKWp+ofaWwq2EJLfDvWjMNSYquBeoasfKKtdZu54cW1uFZRmXAkpzigoLKSUtv9dUhj3xhyVbAqBkD2NZvGWMje70dBIQ5dfQaHphhBACrVYLrVYL999/P7TWuHnzJq5fv46PPvoIxhgsLi5iaWkJ8/PzO1KRc6dhpyqfut3ubVc+DcmnCxcuoNFoYGFhAcD4WDX8e9euXUiSBN2u2/a6QgVfVORThQp3OFZXV/H666/j0KFDOHToUOkLrZQSWusduTrkTsetJP1WVlbw+uuv48iRIzhw4ICz7PsrA6ykGqMpHW4FAbUQStxINeYiZV01NwmDwnpvxXO5cS83npRQgczyVbjHonoC4L2ANTV+BA4AxEqg55H5t6b45BMA1Nfq398ekCoI4XFhuWWHn7i8DDW8ktxyCnKN/HK1j5Pvxt/WpJ8ZPH+aZ+vnQiMOxuqohcLbws8nh8UQ2gDz9RDXOrzA1RBJpvHgPv7HU6b9+jOHwBkFJ3A7RKoNIiWRMEmCSRVWHEg8sLtctaPYpCHvg5qTpwmg7eeGUMKAWsNrGMekg4e8e61MDzns5FNOKBa1qEHqBJD2lf9SJ9BrKitjDP7+3z5ClhsoJSCVsBI4vsTOEN1+hlYzxKqnHRYAdHppEaTWhX1fe95uGzhEGEjvnEtchKFiB8J9+uxyL3MS7If31lFrF+fucL4cQ24EQmXI8qkWUFI7c74My3EQKs5ctjWIiFw4CRH0L+rIkGb2EAA1llIWu+R2qn7L9v4gwze+eQpZpqEihXyL3m2TJEe9GaI30V+jegCjZtEhl0MKwbYT7nRTL+JXeTgFdErm+qc+tR+NEXvLxGnPaNDPXcH8te2OC0eRV9T8Ql2WzVFbNFzZbJVwt881V7ttzMv3EzClRBJgb4et9ba7Egig7A3xYHy+9HvDen1sp24dE2xXxHO+c5DtNkgpsbi4iMXFRQBAmqa4ceMGLl++jHfffRdRFGFpaQlLS0toNu3q5AqzY6cqnwaDAaLI3/J8Mxg+n8YYdLvd9es6Gj8c/t3v99HtdlGv0++fFSr4YOdRyRUq3CbcjpeMCxcu4OTJk3jqqadw//33W4+plEI75Cn/AAAgAElEQVTOXPFdYetwK6/7+fPn8frrr+MTn/gESTxdWE3w036OtOTdfZnI2eSL5UzjvjqfeBpitvxNQHsG671ebqZyMu2uBc4XWM887AAKMskHjPQRY/DlEZQUONDUCIngVIFbE6jk3y3eBeeSQZzwEO+q0KVGg0fGAN9+p8jzshmEwbQRIocAmGrbmlLBF7OQVnvnaqjXPGy2UJwnF5l34N+vfM3DmnTyOXxkX9uaO4eb+4xLKuVGsOrk5nPiXKeMQSxlRO4YANCW/AujEIQ1X44aWUdgOsQxNmas7711BT+9OUCgBJQU60RPGYwpcrDNAm5Oo0nkucFcu1Cd7NndZCmVfKwzfQm1wGPi6njkmsy1wYLFKkxJgaMPLK3/n2j+OwQ3VU6dUY6rkOLY7nEuO6cMRQz1HKTSEJEi7hPBeFDNJLcTBWzb/6+vv45eP1srYyBneD8sPZ4uRng5MT+1Fuvkt56XNadHUa0NO9fZsDwXaarRam08J41miKc+cd9ICYOew56xFuRO8qUW5E4fWCU0qb5NCXJKk+QUZYznhnt/45y/qZbNQjAV28rhpgnLYTuKre1lv8+rAeYCiwrRcv+ttr8WtaOwqSyt6kvL79acUnyEYYg9e/bg2LFjOHHiBI4dO4YgCHD27Fm89NJLePPNN3Hx4kUMBjz1bwUaO1X5BOC2n9dwLnvsscdw4cIFvPzyywAwFhsZ/v3888/DGINDhw7d1jZW2PmolE8VKtyByPMcb731FowxVpu9UVTk0/ZgqDjbSvje++VBhrOd1EmeFDmgJG4km29rO5RQUvIze6+hl2/YwvlAzUjyNgO5nkheCWAudn94GACRLIJcXIQC8ElJ66/g8isfSoO5Gu+jx3hY5BVlefeBe7e4l3kWJZL9mAbSMHLjGGn9EC7apAEjIITBD84a3Ohurl9JAcShmgq4DbLZ7CrjQKBfxkQ70BlkCKRgEz6BEphvR96kciBF6erZMhgUCqNJuzsbfBfBBx5Bw8GIavPAfIz5hiMoaHK4w0EFtCmIKlqRJ9as8uiV4pz6OArAVCsIuFW9uVFQyGCEfY7KGOQTNTKmooFQ5xDSXpcw7qfKrCWW/+nyAP/+9k8hRGEzJaRESshkAiWYRnLjWOkkCEOJdIYEQ2luUIsV6szgs08AXK+NgVxFhc9Cq5V+CqUEcuZE36oHuFZid/bUgwuQ4ehzJRAHBn3GqhfuWptCrURZiwlIqaGZCih3XXTDkpxhZUlsz7REPcyQanu/lJSVLUGM5wRxQG2n6i/b/sIPzuHSx6tjvwlRLJwwMyoUR5EkOcKawqCjAQM0F2pTZFQZXMrJSUSBQuYxHtQbIVaZVnp9TucYQaMZYXW12Ofp44fGrD3rQYYMdtvFmsqx6jhcsd3+zl0Lcgwc5FIgNTQRoqLnQ+oZo55hi38bCgJYCPv+riMrAMY6ptqJKdfcbrPWA+zfEZvvMQaHa/Z8MMJCApWRScYAQpRY9BkJIXJMnlvx+/QZGAOgpHyxbetDnvV6HfV6HQcOHIAxBqurq7h27RreeustZFmG+fl5LC0tYWFhgfyWr1COnah8Mpu1yZgRQ7Lr137t1/ClL30Jf/zHf4xWq4UTJ06s558KggBf//rX8dd//df4hV/4BTzyyCPb0tYKOxfVSFihwh2Goc3e/fffj4MHD7I+/CvyaXuw1de90+ng5MmTTovFUfTyHG/cGLBIipXUYD6UuLmJTNuxFAiVRF8XKh4fcmRovbe8DdZ7++ohKzgXCoHE40i+bSqILTrgNVqeS9hJAext8Kmw4Wcs5xz0etmtU3tyrWf4JBUPEpJYdwpIKFIBJRHgvasp3ruyBYRuPbAmeA6U9M5PVAsl+lyfqjVoA8w3QlxlBrruW2ys5WLxOoy3mi9UfPIp1QKBnEUxRWOQa4RKQAng8K6t8mkvrL8yRmBbicksY+UIZY7EEXQGQFqNDdsWyhwZEbAJkSBxfEpkooZQJzAOSzxNEXVCIjCryDFnL0PkBNIIYYzB135Q5G1o1ENkuUHG6CeDVBcBZs+VE8YA860YV6/7LFEo0O9nOLC/dcsU9mGgSnNclcJnnjfAQiPC1RUeXReUqIfnGwEWd0/3sUiBRT5xFxNzRZiNwGA18SdLJjHI6AMOMjrIxnkMayp3kk/UTaXGJFK1QhADdF6q8eNfuLSMF1+azitjDCCUKIJ5WzDsp4Mc9XaMQSdBrcVTShQBPeYcleVOpeUkAg+lcLebQgUSOXO+lEFxj5Z2NfDwo7vHttUCN7kUEjm9qJxfodRwiSRjpa3vREBBThlnCMsg09K50MhFLgkYaIeeXgpiWZTjwK5cUrY8TMNt/tkH7W+79t8tFrQTP+8OO2iq69Z5uZxMAoRIME0mhSjj8owOSxVOxoTlC8SMss+ZjJxPm4EQAu12G+12Gw888ADyPMfNmzdx7do1nD17FkKIdYu+dru9Y9U8W42dqnwSQmyLTaMxBo8++ij+4A/+AF/4whfwq7/6q/j0pz+NT3ziE0jTFD/4wQ/wzjvvoNls4o/+6I+wf//+297GCjsbFflUocIdhAsXLuDcuXP4mZ/5GbTbbfZ+Ffm0PZBSbtl1v3TpEt5//3089dRTmJ+fJ8tnWuPHVwelVns2dDKDudCfAAKKoHE7VuurCWtKYtUzKD5LbECjUFvNQlrVZfFxWY+YU51PIiTA69oDxbnowQAypm2khqgpiQ7jOt9Xz6FUCmMC9hn4fMxKSOSMmrUHuaYgWHVyyg230xoSRZJPHJLtesfgBx9svu/P1UNnkGWWBXLdxMyYA4Z335pxgHqt6FO2/Go2+LbIl6yKA4mMKYFKPMmERqTwwGIMZbHbGyL3MJ9UgtcHudeBk/cq1YplzaikRkZcSinoax2iiwR28sm1wn79OGbgTv5OPLuZqOH51y/j+vIA9XqALMu9yPRASSQzzPW9QTbT4omlhRjNZryu3qXga6XnY9Pn2/p6HABM8qmMKH7y4V0QJcGmgqji5FZiXnFm4Cdi5HPi5IbqZ2Jt6YO9bC+TUNKtXkwZCqrQaklVgFKFDHLlnA4GeeC8fIPcPcYkWjnHtGxkjMoyjb/9xpvOuVCGEnoLlP0AMOilmN/bLH0GS+HRlYwBms0QKyVqv9Lynv261Y5w83qfVXZY9fFPH54KgoZKOycmMmcXcWxJ7B9IN/kUSe1c/BZKtzJJCQ04FLsUuSWJRWHa8RrsujYuYsqlbrI1RcFu42drv+330cdBwuD+6I3ibzG9LMYYASHTqfYaE0GW2IIbE6LszI1RpQoqY3EnMCZYUz5Nt+d2ZxpRSq2TTQCQJAmuX7+Oixcv4tSpU6jValhaWsLi4iIajUaVL8qCPM93JPm0XRCiWKzx+7//+2i1WvjKV76C06dP48c//jGEEIjjGD/7sz+LL33pS3jmmWe2u7kVdiAq8qlChRmxlS8KWZbhrbcK+TrHam0SFfm0PVBKbdp2T2uNU6dOod/v48SJEwhDenWW1ho/utpH4vlhagB0M4N2KLHiSebsioOx1aiziAt6ufFWTAGzW+/VQ4l2FLD7qu85GQCxBAYel7LdqKHj0VU56TaaATBXK3KeFCQRr0E+V5XLy20oqjZvH+Rbbmg65sJWqLcGmcF330ughIRUReBXz8AS1SOFTBPWZtoUSco96jcA5msBfspUMQ2xOsgQKkHmr9o3kQcjlAIps/PkM4xZPgikK3wzjiTXXlaDS80I7WaDLGcgIaGdQeYhuBwA+zow6jMQCJAjJxRH1twMo2UY44LSiTPuk4oGlNbuYK9xU3RauO3pLi0DL5++ino9QJpq1CKFvodP4zCXmm83T9Ici/Mxrt3kG/dJKbCwUPPK3WYMvCw6fepOc4NmrNj5nHziRJ3+eJ1H7msgbpUvzuBWy70G3HvJcfzh5YYSqEcpOonrHU+gEWToOCzL+gx1lHLYxgI0gTXIA0ilrQH8VCvUVG5VLeZGIRb2MSbVCjWp1+0wp7dLRFIDQuJv/8eb63merDBrBNQmlP1DxI0QtWaElDs+eI4JtThgk0/keU8groUAeORTv5fi0OEF7D84rVyh+pomSlCWdtRERe2tpHEujAikRu6wfC22O44vjHM7ZWybO1RTrrHXRTC5jmob8+zX0Vjfkmyk32jX2hddR6y6BTkks6m2GUSQJeo3gxAoMbG1kky262h9vizvfyacauPtRhRF2LdvH/bt2wdjDHq9Hq5fv44zZ86g1+uh3W5jcXERS0tLiCJ+rredDq31jrPdS5KEFeu5VRgSUJ///Ofx67/+6/jRj36EixcvQkqJJ598EseOHdu2tlXY+ajIpwoVthkrKyt4/fXXcfjw4ZkT+1Xk0/Zgs8qnXq+HkydPYu/evXjsscdYJIkxBj+62sfAN3nScH8A/cygFQisMlmgXbFCNvHi3tezEUmNGa33oHPAkfOjDIEQCAP+Pjn87QRDKTDwCKhLT3UVVVIAuK/ZXf+g9fq8EgZg2AX5Qlrd3m8tJEMh5WMzWIZMG3zr7Qy9dLy2Yf4ggyKoSZFFgRJQSrICoIESSDw72izksAEwV49wddUeKN83F4zlhgAKFQWXfDIAIiXYqiOKCJuu31OlESqsDOgAX6QkDi002MotPhHAJca3lqVSUiMnrLFYhxOKcUz3aGBEgABd5Gg6CrmPkImGNdBmjME//vs5xLFCmmrEsUKfSaSMIggUmR+qDL5rJ/buaQJyOgcchTCQY7nJXPCtu10P2eRT6jFWrfQzREExvgVK4IHDi9ay3C7AFXxyX0M4BHE/E6x8a7Ey6BB1RYFGxzEk9TMFSaijqGvVzwlrTiPXrPvs4etYZeg58vaEKkfuyuujMqTaFmQVCFWOF1+9hLMf3nC2dRTthRpWbvDIl9KjCqC1UPNe9OBDTEtCOTuKbi/1yhsnPBSN3W6K//xfHi7dRs03m7VVpK1fCXKK2F06yJXh/k7yCe5Zy3WvFdyLU7Wx2/3Rb68WFZNlDxuZZbP3E5brpmDWlU+BMDi4pnrSiKFK7fVsJFP5c2ElmSywXQljLL8TVsS3G0IINBoNNBoNHDx4EMYYrKys4Nq1a3jjjTeQ5zkWFhbW80XtNPLFBzsx51On01nPsbSdyLIMcRzjs5/97HY3pcI9hDtrNK5Q4R6CMQYXLlzAhx9+6G2zN4mKfNoebEb5dOXKFZw+fRpPPPEEFhftAZdRaK3xk2v9gozZBDQKtU4zEOgQgaK5UFpX8dWVxMptst6T6QA65r+sSQALtbAgAzyOFUqBzOP6+poB+Fr1UU4ye+oaUTD68edDbOXgvgYUVnW8z0Ou7RG3pfwnjG6fhiHvmUFByk1ayxgDfOedDKslUrdJokEKASGAUBYkdT/N18sIAI04QM48sVms91YGGeKAny9p/ViObUoKzLXrU7/79gEfC7OCrJJImBeLS4INEQayLEYyhaO7m1BKsu3NJFcpyGxuTq4k9ytH2R4BPMIrT1NQKZuoVfIAoEzPST7lDoskANAihtIDGDlt4ffdkxeR5HqdeEpT7ZV3ZQOzzbs3Oynq9QC9Hk1y1msB6o1iRayvSjAKBAacxGDwt+mLQn7wZ8VTrbHUkrh0I8dTDy5AOlT/WvM6FVewkrDUSnwSuRHm6KTu6xRIujKqjIFAPcjQdaijNBHcH2QKobIrjwAglhlSR54USl0VyNwZwQ+kdhKA16518C/fO+M8xiQ6nQSNdoQuU1k0ifZSA1JJb7s7qfi5lrjlhmi2ItxgWullHnU//MhuLCyWv1MnBLnUT92r9nsEuTmg8hISc48htgshnEPFppdc6RSQ5eeg3Id2vs+69ytv9TA/lU99tl4foPx1aDQ138HoIgKZrdUfosyf0Qjb8+PrRWi7WpYzs73XmO1TmXAghMDc3Bzm5uZw5MgR5HmOGzdu4Nq1azhz5gyCIFhXRbXb7XvKok9r7e0GdKej2+1uO/kkhEAQBOh0Ouh2u1BKIQxDBEEApRSCIKjsDivcEuys3lyhwl2Coc2eEGImm71JVOTT9kBK6U0+aa3x3nvvYXl5GcePH2fL6zOt8eM14mkhkrixSX97bYqVv00l0LGQLbEUCJS0rrD1VT0Bs1vvScExgdrA3noIqSQkDEngjEJ5KpN8EwBnxk9dpQHUlSglHGMJLNZXx34rCCXeC2OxdpperV20Y3h8zkcP04KNWc6WAHmWoxqgWBZOBN/l2FrPAs+/m+F6l/8wGbMWCM2L1NX1SEJJgUAK9qp7YDbrPQCYqylcWfUbJzqDDSXCJPYv1iFK1Ie3Oo9ToZTilU1zAyWFR+CeLrdUD7CrFXtRD9x1vFSQbQhtBKTQJCFErTjfAH02GSOPTSroD+gcdOBHGDdrkokaOapJ00U+kT/q4vUuTl24WRBPkVpXEASBROJhuwcU/TkIpFeAd4h6jUc+7dnTHAssKSWQMxdDSI+AlAH8+olHn+0lOeqRQo95fRuxxGJLYmGXewEWd8xMtYCShiT3B3lBwtJ9infykaLJJ87YxyGGY5U7yaeEGAcMBOIgQz+3v39S5BKVX47aLh31a63x1//wzgwEMZCmOWqNAP2up2VdPUBU3/gW8yGopeSrvXt9JkM8bFfM/z7sdHh1h4HEJ58+YN3ez+3PVqRy55geq8y5XQqNNJdORWhGkFPkwohNKvrd78QGGWJrCSpvk3a8Hbisem11Ksc2Ww/zNfYbjiaxyLEvfKdkC7Mm65hQ3lJhGyOsOe1s5NPdFe5USmHXrl3YtWsXAGAwGOD69es4f/48VlZW0Gg01vNJ1evTC8J2EvI8RxzTeUHvJnS7XTSbDqX/bcCZM2fw/PPP44c//CEGgwGyLEMYhuuKvCER9Sd/8icVCVVhS3F3jcYVKtxBmHXlydBm74EHHsDBgwe3pC2btX+rMBt8Sb/BYICTJ09icXERzz77LPsZ6mcaP7neWydRllO9JQTU/8/em8Vqlt3l3b817L3f6Yw19+Ru3G5sMG6M3QT8CbCjIJCQuAMJgUgUkVyB8E0kBErEBUIIruAiETdE+pJAiGM+HD6wQzyAPyfGpo3pobrdc3d1V49V59QZ3mkPa30X+7ynzrD3Gt5TXdVd/T43pTp7vWuvPaxh/5/1PP9qz6KgidyQApYy5QyOTo0lEYIiMije15KtSOs9kXXrj6yAU3WUoLf3wa4i073H3tEqkkwCyJSgjPhBKpvJpwuD6bFglhXG4Yd+HDEWeXUmmxBFk8u3/midYfc8pFwoSSUaiKXmM17H/36h5PXt+cdYC0wLw1I3QWsZnk9iD4kWTCNlc6G5jA7CAksdzdXdw0GsXqbodpsDSrHniW2VjGSrMiUZmdD7O8El3VHCcP/Z69etKILIlHDqK4xUAtDCkDvyWEAdvKrzTXmCzwHnK61GUuddaT2fSEjsmEq0B0AK0fOPBj5bPXqkpmzdbQ4gbX5oLDPG8IVvX2aaV6SppDzARlShssOj54hlTvewOyq8RNKptW6jpWUo+RRLTidaUgWOQ+NIu8HlXhJMPgkh+Z73r3otw2KGzI6GoVf8Iuhoy8gTrw/L5wRahagJ/fWElNHK/f6G5IVKZcXEcU9d5BDcCG1z+/H/8T+fYWcnjqSZoaosSaqQSmAC+44QMDiSy1BpgclDfx8+Lkyn9XiUB67fo/KzFVWQyvLBj5zeV1geRaZKKttOSnY9OckyVTFykFcdVTmVVUoYSqM85JR/HnQe96yRXQri1Awxup0od51ZAab1wqyDLGrfJObKE9X2+re/1e73/Z7OS4dz+rVxTC1jh2iw6AP28kYdaYkVzfmkrECIqvnkonlAs+9w5ZMPWZZx/vx5zp8/j7WW0WjExsYGTz/9NNPplOXlZdbX11lbW7uluYTeDhhjbjvy41Yrny5dusS//Jf/kq9+9avcddddvPLKK3Q6HdI0ZWdnB2vt/j3/rd/6rVvWzgVuT9xevXmBBd7BsNby8ssv8/jjj/ORj3zkhhFPAFrrBfl0CxCjfNrY2ODhhx/mvvvu4/777w/+WN3OS/5hY3xMvTMjoE6K0tY7ujtHAj+nMh20K7+j4oNx87ypQkoGSdj1nu2l+/c3NtBdtniGu5BFBiSjn1rDu7KeWbpJs1+YjLiCmLaEXqaJMDsMbWtIufqM/nIi8gl8+5WKF6+efHztdzSVtRRzBL7ncffczQ0dHT9GNBEh51Z7zjFLR/SBt5us6srwnB/DPEE3JMae4QOnS7S+vkMxlQEefYCw4W3QAUoHgNBUIb7ANEAZRFALUsbeUol1l6lEhjTu++Gz1UMIlPVky7GHr/tLj73OxvaUJBEYc9ji0FpIAueTgzDGRudwmv1ueal9967WkuWV48dj1Eyx6ahiiLSdcRGlWMwibPo6nYS03/GWs0CoCCQNPH0aQBhN9/I5+RDyNoWoqELK+M41KRU+rbhr3AO/JajvuI9Qb/v9xaff5DvPXHFX7sFkXNIZhLkKACytdY/lYtIRuZli0e+H7+QvIpWWvYG77izTfOh7L7Qe72o3cZV43pvE06d8xzNlnGsNJXybK6xXAew6LnCrIRWeud3Rdler1N7Z24/FwWXH1/YE2/5eWRjIgnX94pFztEw6LSSTbPi7sfqYxTXMCKPj7W/9u4uwfIflfDoJhBD0+33uvvtuHnzwQT7+8Y9z/vx5dnd3efTRR3n44Yd57rnn2NjYmDstwDsJVVUtyKcbiKqq+J3f+R2++tWv8gd/8Ad8/vOfB+DTn/40f/VXf8Wv//qvs7q6ys///M/zta997Za0cYHbG7fPaLzAAu9glGXJxYsXUUrxgz/4gzc8eeI89m8LnBwhyidrLc8//zxXrlzhYx/7GJ2OP8gyw1uTkqe2pq1hjxulgCpsra7JZK1mOpUpykBSoJwjIc2ksmgZnhh8hhArq7VUkejr/WseyiCTgklEgDyWf4tt09FgvRZwurfbUromakJVQAh7YouSJihkoK1eqDItrJxCeM8bcrbxZEKnK/nOG4YnXo81VzyOXqrq/alCYCzR+ZgqM1+f6Xc0k924/BfDvCLVknzvZKcGGYknmptEtM0CiRIUgbvSY8mqJDVBeZzqtghWsoKr4+MBu+WsYG3p3KG/qZadtUdRkrGXrc5btk7Y7Q+UhvZSn90VQGEkAuvdBKHtiFy47UGkxzIPQDMip33uK/EHY2U1oVIrrccPZnN7/o0dHntxA60FME9+p3YoNZ/1XlG2vztnz/QRTQGWiKHZ2jibvhgYC8u9lGt+OVHdlsB6lRTcdecagQITMgXTgOE4nKgN2dQg6GjD2KOACrmEaeVv2LT0l/G/zoKOKhk7bPV8hJpv81F5wuNN6pI3r4z46795ESEEdp5khwcwGRX0VzsMr7nJgm4vIW1QAb2deVWSCHJ25JPmHa3bM1d//0fPIRzqiEQZ5yLVR0L73ivf/JRIg+uzKlXW6YCQSAOOzQxKuI9rabCO8JiyJa4nYsKF/4fgs+trq9SV16mtvrb71zatlRbu6z51jNxpUjJZC1Iev0PGJsgG4tGaBBo2zFirGhVU1qpGcsvWurKG8oL56Lt3B6SUrK6usrq6CtTxps3NTa5cucJzzz1HkiT7qqjBYPCuyxdljLnhMbNbjVtJPu3u7vKZz3yGn/qpn+KXf/mXuXbtGgAXLlzgE5/4BJ/4xCf44R/+YX76p3+af/Ev/sUtaeMCtzduLyp5gQVuIoKVK9vbfPOb3+TMmTN8+MMfflsm0UXOp1sDn91hnuf8wz/8A0VR8NBDD0URTy8Pc77jIJ5m2C4Mq3Ps4D6KfC+vzFoqqSK+nKYG0jkWs/05dpXO8kW1QQlYPWINZsH5m8Z6Ips2ngZGuvdQmLhv08JCcuAHF/oFypmUPDxo02700VQ2vN6QneIxdYaf239nQ0ixJEt4YcPwDy/PZ/1zEL1UIpQ4NGfMY9/VGJz2oJwzEL3UqfuRkoK1Zf+45csPchQhQd8ZjK3JqlCEWE4dRCdpbvsHzmSII4mzQ/KxQE2EpCJsXEhpJ5IP13kjIUgCGLoQU86Qvq6Mm7QoRB/r20Bj3axDJepA+3Ba8FcPv4KUYm+Obm5fVVnUHMpdKebrv5NpxVKDGmPQT8k6zcHOWNJMR4wRsZcwaGljEyaBtrofunuAcVgpHoUKbHRozrXQW9DRAWrCgHxr40Lh68ljT94oCMsVl2nfxij37wsPUebLK1VU7usojvx+NCn5kz+/SJ5XXgvGUIyHOb0GReFBLJ3qNn7PlZEK5TRqHR7er8vS0GuxyGuCa8gYLKV89PuWnb/3DYl+M8WTPTvfEOZTCvsUfb7jXnLNedw6Z0zXb13zqOuUbc+7PRuTbf3Ga3Z2tvTliGX95uG/WoEUx9fHlrTRqbfN+s62EEO2TZ3dNva1ltfMxQa+S6G15syZMzzwwAM89NBDfPCDH0RrzaVLl/jmN7/JxYsXefXVV5lMwtX5txJVVd2W5NOtyvm0s7PD1atX+dSnPgXA1tYWwCF12Y/8yI/wwz/8w/zBH/zBLWnjArc3FsqnBRZ4mzCz2bt8+TIf+chHGAwGb9u5FuTTrYFSijxvDqpdu3aNixcvcv/993Pu3LnGMm14ZnvK6wHJyWfYLmsC6lpkHqWj0AIGqWY3JoERdQ6jPPI388TELTBIFNdakj+c7SaN8vzYnEzhapwaMkkRJibPS21zOI4ILHa0pCgMywkMMrfNVU0ohX1sWQzWiCBiw+zXe+M+5EIVWmEqqjBYZr7x7XW+uWv4+gvFic+aaonU8liOnbwykW8Z2DnUG6OioptIxpFjQ7UXmTy/2kUGBOhjm6YiCetUS4rAOa620quCgsFAY0TofatTsuy4NW5boKQJiSjIrT8ZdHCd1QTwf7CKagoBealShpQONdJebd56TFD7PS+IkCTsUtKeS8P3ipWih7SW//5/XsIYU+dW87z3SlzChrAAACAASURBVMXnyzQWVpcyNrbigzf6yE4IIeBUS/B7di4h/ETBDDH8dFHaOqdUYOeNIeq2R4V3fOtnivUzdSA8VWE5nUKbEDMXh8AXsIZQxZKgoyomDmKmspKurpg6ykwDCHbfhgCfMmlaaecW1WmpUNIgWnLCTSuFFO3qyrxSzFxhjTH83//tUSaTet1rLSgtqeZQGB5rx7ig00+YDI8HypdPdVvJkmgFYQRhFpvzsddPghVQU4c08MEfuJNuAiPH6X3kkS/fkm/e9ec3dB/33WYl3MooKdzfH9JDIBmXKgr3xlRrRevlzbudLFYtpaAx46ls2eKlBbyv80RDm7JmZRIJcPy72Lbkq7Q2xiicVg2YtS1bDt7l+Z5Oik6nwx133MEdd9yBtZbd3V02Nzf5zne+Q57nrK6usra2xtraGlq/88LCt2vOp1tFPk0mE9I03c8NVhQFUkqGw+u21kIILly4wGOPPXZL2rjA7Y133iizwALvIrRZQ5RlyeOPP47W+m2x2TuKBfl0a9CkfLLWcunSJV577TU++tGPRkmrrbU8fm3aSq64sF0aVhLJ1pwEVEcJOoliOsfPi3ms94wllYL8Btkh9ZSgmzZPafXHYMRO09gmCUFH1cqsUGhJu8F6A5So40Dn+p68J8z2XiqCCaiqClbVhGW/mCmVwvI03UjjvdBbKlHYxk9wuDY2/O2z0+CAbxu0EmSJaiRmrIXkgLVdCIwlKlg8Qz/TjIs4671RXrHSS4J3XBcBeUoOIvbWDpIxw2lYDg+LYCkt2ZyElc+rw+NGpirOr51qLFvZ8GWzDLToqwKIIoCCLu2hlwP1Bdpoypb3/yDKgLaVwm+Z587RUUOZCaVsJ5+McD9PK1K+9OgrbOxMWeol7AQEa8vSIGW8LV85p83xzrAgSyXTPavc06d6KO1eH8bY/IUqfqB+k1a6CRuBVnox9pelsSz1ErYdz+BD964hZH3tqRLkIfNn4OWFDquh4paQ6XFcSkL6ZycxTvIJoOMhn8YB5JNPpZl72jCtFJk0h+wsD8Ig6arqmIJpBoskVRVFS76VykoyKioUf/o/nmRr+7AS0xjo9cJJlzZYW9vWpqkiP7C2TjJF2nWPbzE2ljHrheGoiCKVtWeMOFT3MG8c09ZP9bj3vjWUGDl/78sHOK3cc+DEc9z33pnItcRR+G0BT4ZKtm/YUGI+ogjcv2vL3SSxrRbpLju+JrQ9lRW5TVdtN9SfQkP+K2s1TeRT9J1vG79a/978Z3sb5Xs6KYQQLC0tsbS0xD333ENVVWxtbbG5uclLL72EEIK1tTXW19dZXl5+R5A+C9u9G48LFy7w+uuvA3Xc6cyZM/zjP/7j/vGtrS2ee+65fSvHBRa4kbj1o8oCC9xmmNnsnT179m2z2TuKBfl0a6CUOpRrqyxLHnnkEXZ3d3nooYeiFhfTquLbV8dzEU8z7JSG5ST+0yqVgp5WGASVhW6kFVFu6jpiEXseaLfeO9NLW3ccxm5gtUAa2bRYa7/Y4Htl4VyvQquA90OAjJneI6ImoYqVWtEUuKM+8KM0ZH+kCTToEy335+qw4ivPThFSkGgZbPN0FEoKepl2kmHzeK/P056goG4Dzq62KzKOwiLQEW2LzeMUG7jIAmyyZtjJE+SBHbwPnAHVEmAqbEZ47w1rc247QXUaFIkv2Tns5Va6MTkga8LLd74e1npUFgE5nZr3Yx9oi3TPp8+9vs1jL23TyVQQ8TRDN4tfo43GJYP+fDuq+/2aREtTxWDgvy8xfT42T07MtQ9Dki0dwMy6swlnljN6y9ffrdCxI5RYDV1G5VXg+BZwXmNFkD1f0pD35HgZT38yklS6L9KnMBmXvqCsIGvI3XIQqXS/E4nnuFYlf/23z3PplePBbYDxpELOYdF8FFVp0B11aD5bPtXzzm8x9n8yYj43xtLvhW2OADAR/dpa6C8dH1c++vE793JO+t6L9n6rhXGSoomsqFoULlCrcVy/Byg8yipf+9uImlA4lV/WUDjUx643VXhMrl1252093XW+2BVA09kVljuS5xvLtyq22/pBK2nU1tK2q24r31L/e1z55IJSivX1dd7//vfz8Y9/fN+l5/XXX+fhhx/mkUce4eWXX2Y4HJ44B9+8qKrqHUGC3UgMh8O31Q2pCbPnt7y8zN13382Xv/xlAE6fPs0nP/lJPvvZz/Kf/tN/4plnnuH3f//3+fa3v82P/MiP3NQ2LvDewGI7wAIL3CActNl78MEHb6qkdkE+3RocVD7t7Ozw2GOPce+993LHHXdE1fPGuOS5nSmVrT8mThI2HJaWpUSyE6iASgQsperQh4+KVAoBZHOomKIVRjRb753KlHNnqKHe1RfTQ7QM3Ik9J2IFalJYVjq7hAa0RYSp29uVf1YiblAIvEZoMxVyzyKwHU135tWtiv/94pSjogYpBVrWyqPZLmpnOwUMOgml52OtnMN6L1b1BDAuKnqpYhRBbJ8apAw6CcMi/DdKCMrgHF51HwslofyB0sOIeaeNlaykYzanHc70pgx6F1rLWhSJmOyRUL56wwL7FkUqRkEWfamYUnjKWRQpQ3KPRV+I4qoUHbTNnaojKzQZQ2dQrhA9Px3ueRUq0UWZAuTxdu+MC/7Xty+TpZLJNG4tlM+pFs4Sxa4z9XwzRpMSKeHsmX5QcDvuXX77bPpGeUWayOD7pRw7Mj7wvtVDgf/QawxVNOVVrYDwDS+Tsg4K+6zGQkmvTFkmHo4uJG+cCijT0SV53j7GlB4FSWkkPVVROMapVFVMHPdcnTC/zrcee5NvP/aGs4y1NQk0j+3sQUzGJd2llNH2lKW17g0htQ5CaUlAGr19dLua3UDVoctKr63una3r/79wxzIX7qgtLl22eFoY8kq19sduUjJ2KJs6uvIeL0z7vCOFpTTSOR74bP+Mz/bPMxNVDuVXakcY1a7OdfU4BZjWC2sfgyS2lVBrs9aD9k137b3o+JE7kldQouXda7uUlrGryaIPQDSQ6LUtdnnsJNaKvfINJ29pp41Qq7/XkSQJZ8+e5ezZswCMx2M2NjZ4/vnnGY1GLC0tsb6+ztraGlkWsqHo5LgdyafxeHzTlU+z9dba2hr/+l//ax5//HGqqmJtbY1f+ZVf4atf/Sr//J//8/3yP/qjP8q/+Tf/5qa2cYH3BhYj8gILnAAz272iKLh48SJJktwUm72jWJBPtwYz5dMrr7zCpUuXonN7VcbwxLXpoVxNFuhrwXAeZmbv96PSMNCSXU+kRglYyfQx24ZpZaOD4vkcu6KmN8B6TwtY7vh3kGoZbrEDb79VXwWkslaNheBsdwclZHCOpDBzvBpSxlxpRL2IoPbe6PwcIdZTeVGQHHhtXrha8vcv561B24NdaUZGVXtk1FF7m+VeGkSqWCCJCObC3q7mVDDM4164GPJJS8H6Uha90zFWlJWocPJpWmkyPWUaSEL5dlcfRTcxbOWW+04vt+YwmSEVeRD5FFJmhkTkQeSTCqTQNVMv+VQEqZEgYcQU9xir7IRCtJ/PiITEjKgc6qVK+MkwZYdUHLYCsdby5994CSEleWmjCBKoCV2tw63tZtge5iRaUkT+riwNd5ztkWZh73Ls1KpVeJti89qs9FLeCsx11Tbf3neuT9KdL3AVzp8LOsoy8sTsLYLMk4OpPm9Y8Cv1KJZuJBIPsTMNaHOmSgqHhZ8vb5RvneE6/sqr2/zVV15xN3C/IoGQ8+U9PIjJuGCw1iGbU7XoQlka0kyRB5LfMeTXaFTs5acLtN88UvdHP349f6GLHOolBWOHTVmqKsaOy0uk8R53Dc2ZMs5NG0oYSttOjsEeOeU6XsnW4wKLsaK1fsUE48hL6GqYa2Oha3PcPBsSBbZVSdVW19Gu1RElZ9WTGLHSeo7Gv7fYDUtxfKOGtXKv/FGSSTfmZLVWNz67up7G0y6UTydAt9vlzjvv5M4778Ray87ODhsbGzzxxBOUZcnq6irr6+usrq6+bXEva+1tRz7dzJxPo9GIjY0Nzp07R5IkJEnCL/zCLxwq84lPfIKvfOUr/NEf/RFXr17ln/yTf8LP/MzPsLLS3PcXWOAkWJBPCyxwQmxtbXHx4kXuu+8+Llxo3zH9dmJBPt0aWGvZ2NjAGMMP/uAPRiXr3JiWfGdremx3mqW2luspwWhO5Y0FJpVhoAW7LayIBNYaiCeoP4K6kecvDHSkYBIZHOiqePJpVFkSWZ/zbC9BBkS9ZSSdFhvjMMSRSUAw8baaVnSSMdhuMPlUG3wEqqQUGNueOPwgakXRjf0QCL2m0HIhEFphbYkQ8OQbJY++GpcT6SgZlWmBkpIk8n2eR3TWSTTDPE5xMY0IkF9Y69aqThuej4uIcjPEWggupVUw+bSbJ0hhA5Kb15BKcv96gU7O+MtKExQNKkmRlM4E5TPowPxQoQghqXK6SFuCcLdP2dz7oirrfx+1HVPRTj6VtOfSmEHa/NiVffHRVxlOqn0iM01UtJppHvWntbA6SLlyLYyMmUErycpKh3FgF47NR6WkCNZj7UaqxDppeIBpNDneCiUFd91xPI9A6CUaC4mCEEFmovE5OQKQacvEU9+kDJvzQoa0KiCnjU+1BKCl+6ZNCu2dqpWnDt+o7jO5bTu6vTPhz//yqXBi1bLXSU++BugNUq/Sbf+0kacb9FM2puOwuiP79WCQsLUVJq06SFLd+13rrJ+qx10lDJNSt453qTKMXUo3z23zqfp872wqjbMvpso48y5qWTnnMyWM87iWBuuYr5UtnWOraRciOd84STv55FI3td1NF5nVtlHu6N/vSZ7ae0+ar1i0KI2aSSaFaCDmjU0blVI1+dRcDw1rpZqUOl6/tUCgAn0BN4QQLC8vs7y8zL333ktVVVy7dm1fGTWz8FtfX2dpaWkuW3HXuW8n3Ezl0xe/+EX+/b//9/zu7/4uH/nIR3jyySd55JFH+Kf/9J/uK9wAHnjgAX7nd37nprRpgfc2FuTTAgucAC+99NItsdk7CinlodxDC7z9GA6HXLx4EaUU3/d93xe8ODLG8PROwVsObxZja1VQVwnGcxJQBphUtlFFJYD1TjPxNIOc40M/mYN8mlPgxUArcmPpJGHTWGzvqIi36otVcYW8MUrAqc7m3v8iFDL7VxBi6yQweYVK/QG22WdwuLnajSp1Y8sJIZAovnV5wjNvniyhOUBeWpZ7Eq3koaTmPhSVjbLJAhhGBowBJqVhkClvsHm5k9A7kKslUeH2k7GWgLFdP4mIIRgrWUonbE3D8moYK1lfPusvSAxhKEjElOkNtH0JIbLCIcgYMWXZU+rGrC2kh6AqRJfEVCAdD9oefn+funyNZ1/bOaSyGU2r6D5VVRYpRTTRM8+5zp/tYyP2s1tq0ia0f8XknpkUhk6imATaa8bEgLYnFVoJygPjx4fuWUE2dOQY8VgWSD7pwD0SiZeAqW33MlV51UQhTygkz1QRUMZH/JRW0pMluUPJojx1ePPrOCzK2n5flob/+mdPRCsNsdDpJkxCWdsGrKx3EVLWqt7APhtj+ZdGTFLjyOvIugkEkk+jcf19IaXg+3/gug14TxcMy3aS32ej6L8LJwsS+8krg2ufZSKNM8er9hyXwjqPu8e/Wm3UKsDx/Lbt3rl+1tbWNsLKpYgqDtS1okYsq8sASI5vzLIWhGj6u0Y0vEPGpqgGEstaTeNWibZxxbbQdFY2546yCSd9JxdohlKKU6dOcerUKQDyPGdjY4NXXnmFnZ0der3evkVftxueP/a9gJupfHryySf5m7/5GzY36zjC5z73OX7rt36Lv/7rv+bs2bOUZYlSCmst1tr62/g2U5ot8M7CgnxaYIETYDAY8NBDD910m72jWEzqNxevv/46zz//PA888ACXLl0Kvv87ecXFrSlFwIdsZaG4AQRUbo6rqE55iCeoiato6705LFGmxpJJwTTytwY43UuC7/3M2CHqekZDVC98gRi7XAsh3s50pmhVf2yJGNWRAGnDbfpMWaLSMHsKiaAKqDdGJaUC6wwpNzvuezP+z0tTLm2enHiSAgbdhNKCifF23EOqJdMIpUZeWZYyxU6saiHRTvJJCjizcjgwpYUgf5vyOFkb137rCXQeRTcxobE67lpJkTKUyA6f77UomQbcjlBSqfTY38XWpxoCS0dhA/pwFXQ+z40QEs0OpYMMO6hYuDac8uXHXj9m72YtJImKIoEBlJIYE5svqmJ1KWNzO+xFW13OyDIdTdQqFU4+xc7Cy90kmHzKIwmDtX7CW9v1O9bPFGunm22JYx5VosJm8tANNKFxlk5i8Q25IUrLicPmboZx4S/jI36gttVzkU9eAsujwCo8+XWO5hey1vJf/+wio9F88+50WrG02mXnWpi66CA6vYRkz+pSSRFsOSkjiN+YOPd4UkbZfcYIhaeTkjTTfNf71xksXbe4zHTF0KkG9OQ+8z5vH1npPu67RB85pTzkkRTthE3I+V3zqsLzLW7FXKoo1xW3q6WaoWgWgyrs/kpAYLk7eQQAQ4psyslEhmwg7Q1tJJOiSUFlrWxsaxsVZ1vIPWtbSL9FvqebhjRNOX/+POfPn8day2g0YnNzk2effZbJZMLy8jJra2usr6+TJO9tK8TRaHTTlE/dbpdut7tvoy6lZGlpiXPnzgHsO/Ys4ogL3CwsRuUFFjgBTp8+vbC7ew/BGMNTTz3FZDLhoYceAuCFF17w/s5ay3PbU17zebscQWkBY8kkTOfcfF5bZ1m6CsYVnA4gnqAmd6Kt9yx0pWAcGVjrqHjyqacFiZJRgbYk0hav1+3E5I+OUklBfb+U42O4o2A5vbb/f0sJhO/kE1F0W/iNCRXFxaikQtsaWk458k0ZY/j/Xpjw1s7JiSclBUtdvZ/HwFjoJJJJpO1XLFLlMmppxtgTYL5jNUUfkQvEfpBoEeR0BdQ78zOVM3XkoDgIV66KJoQ2/Vx3RL97N6WZUFk/uVMGlJlBenKmXK8zLAdOTmePfnUHp4sACzsAGfAOVfgDBaXwny+EtFN26nx/Zm0py5I//dqLrXmF5uCAqSoTrWKC8PJaS9ZWr+f1OqoKciFRglCnzVg7rywJJ3V3Gqz0XOhk15/5h+5da91Rawm30wsNwocrZcMqTJS/xhBVU15JtDCUDvJoWikSZagcZULOlTQEjg/C96rknlxYvtx6B49ba/mL//k0G5vxxNFB7G5PWVrO2AkkfKHue/2Vzv58JiPIp5gpMI9ZYAKDQcq1QNvOWKXY6lqH73vwsBW88pA3rvcNYOogMkOO+8hKP3nrIa88P/flKJO2Asd8V8n2eU4L9yra1RON47rajkls67G2q2y7+wf/flZfpSN29upJgeP9tf778f5nrabZpi92a16bpCtSXb/I93RLIISg3+/T7/e56667MMbs54u6fPkyxpj9fFErKyu3fAP3zcZoNIrKD34S3HfffUgp+bf/9t/yy7/8y3zlK1+hLEu+8IUv8AM/8AP7z6rb7dLpdOh2u2RZRpqmpGn4t84CC4RiQT4tsMACCwRgPB7z6KOPcvbsWT74wQ/WVmXGeMnHy8OCV0ZFkNqpCaWtP6hi8wkdrQPgTEdRRGzNnMd6T0sRnTCpiIz2pVKw1JFIDltF+BC7vFVJEuUHVHrIpCZksp3gO9fd5lAaJmERVnp3K+8XD29GUN6s6wi/wLadlvPXGXrtze9uaQxfeWbElisrdiASJehm+lgC7VjCpqgssd1mN6/ilXyVYbkj2Z4cf6d7qaLXPR5YsZF9U0SOGf20ZDoOW4pOSk2qcm9AdIYioJzA8r71WmmTyoJxFUY+SapABVTYu1CQBdYpSRkxxa3IzOlw3Ty0HSGB99yRp+lgGWmrOoFcC0IIKqx7tChFF1sZ/ujLz1M4BlpjLEkiKSJJYK3jf7M7Luh2NGOHlS7A+TP9Q+Os9G3DP4iIIcUC3UQyDryOmB5eVJalrmZnHDaqz8bCsysZveWus2yqhPOZXq8z6NTBBGQVmBfOl+8GYFyEBVm7ScVO7i7b1RW7jvpCFFTSs6nEt7yZlBrlGJ7zSpEp06qOLIwikQZjBX/2l9/h8uWd+vmdMH3TaFwre/Jp2Hu4vN49bEf5Nu3yHo2LKAI7xqZvFGnTd89962SduDCPK6eiwDIpVeutU8KQV+3Hwdb1O2596cnP41f7nZC8qoagWuYpayhEv7UGV81ijyhqK+NajbYdkw6rvthPxVktiTDcoR/d/3tb/qvWzS9teWNbb1obydR2BS13o628hwxd4OZASsnKygorKyvcd999lGXJtWvXuHLlCs899xxa6/18UYPBYH/dEPv98W7BzbTd+2f/7J/x6U9/mt/7vd/j537u5/b//iu/8iuHykkp0VqTpim9Xo8333yTP/zDP+Rf/at/dVPaucB7B4tReYEFFljAg7feeounn36a7/me72FtbW3/70KI1sXRa6OCF3cLygPH5/3eLkxNuKTCks+5FlvNNKmSFBFJlsZVTGaKGrEKJqhJtU5EvqizPYUQAoWlCAwcwSzQGt6+0sY/MxeZ1IS2gNZqWtFJju84VAjKYBu08CenU7VHaoWo4sLKxSC0paHlqsoei7tvTyr+7sUxw8JEkz1HkWpBlujG2HFemej3Jom03isqy1JHsR2ppky1oukuXlhr9mSvIvtA7C0NybdyEEtpxdVxWMBuJ08QWCfB8l1rOUl6GgDdkMi6GYJUTpkYPylTBe+8FaRiwsT6P0gTkTP1lpvlc1pyty9A1VSJlMSOqYSDPBCSxO5QiPbz5fSQxiAcPmc+MmxcJfzx3z5DHjKPidjZa/5RzUc+ra10yLLDn1wxfH9sLqrlbsK4CFOGxIw7AEvdJJh8mq037r9n1UvKaxk20tTqCH+50MsqPHmcYlAYv6oJIA1QUWXKsOvgGyalQkuDcakKrPsm+JRLlZV0REnRal8lSGXB1LQpNwWpzPkvf/4cr762WzfJ1haXVWzOpwMwlUUpiVTSa3W7drqL0oev8+0Kahpj6fdTdnf9dqZA1IBTFIZORzPxkNwA3a7m/vtPHfv7URvEw02xTKp2cqiXlI7nDF1dMnEcz5ShctigCSx5JZ28YGk85JTP9s+jwCkdc1xqRxjVPr+5zqwA03JhrjWKS93kOl/b9Nj+1tdH7kheQomD71cbmdT290gySRx/l+t8UgVHr9BaQFTH/u6qf6F8emdCa83p06c5fbpee08mEzY3N7l06RK7u7v0+/19VdTtmH/oZpJP3W6XT3/60/zQD/0QOzs7/Lt/9+94+eWX+dmf/Vl6vR5bW1uMx2NGo9H+v1VVURQFS0vu74cFFpgHC/JpgQVuE8yUOLfjRH2rYK3lmWeeYXt7m4ceeuiYBLkpmPL6uODFnaJRzRNjQ3YU+V5upAQbpfaRwPrMai8ygGWprfeGEWRKaZkrT1WmwsintVSS6L1dUZF3ch6tSxppeagjo5dNVSsBpzqbLb8Iv2ZLRf0G+BsllcRaERSqtfs03o0joIJzUwWWG0/GdPvXd7A+9daUi69OjnUBJUWdB8DY4O6RaUGSqNZ7ZS1kiQrOowLzBb21jLfeGzVEZM8uZ2jdHtRJlWUaYPEE8UFyXzDoWFt0eP2VlQySETtF807mjqo4u3Rm//8xm+FVQ9CkCbkNs78D0CKHAPIpxCoPICH32obmuNUo1+uaUHnKajuhcJBdVig0Qyra7UZcZNhoWvCnX3speANFWRqkFFHvZFnZ6N8A7I6KOi9Tw7yntWR1Nfw9aELEUALUZHYodqdllFojpu7tccl3ne+TdP22kqFkXChnMS3dgd0ZJmXg2Ba40aWXGLY9qiYdQD4p6btQQUcXjMp2taZP1TUpNVqZ9mAykKqSwqGISVTVukay1vJf/p9nePX10eG/GxuV76gJRV7R7SWMhtPWZVHWTdDp8bbH9O9YnqrbTYLJp1iVZb+fBpFPH/7w+WMWuuAmG3tJ4ZyvMsdzBkikoUFUfej3I8d71NEVxmFpK4WhNC5lFRTGrayqKtl6XNqCUnYd+ZImGNdmDkfDXNsgXKu4+O0T11VWTWg7T2WhJwtOy2eOHGk7e/Pf274gZCPJJPbySR0lmXTjrbQ2afm7RLSRXoucT+8KdDodLly4wIULF7DWMhwO2djY4Omnn2Z3d5ennnqK9fV1VldXb4t8UZPJhG43bO19I7C0tMSP//iPA/D1r3+d1157jT/8wz9cxAsXuCVYvHULLHACvJMS9CmlMObtzTPyXsJ0OuXhhx9GSsnHPvYxr/ftm+OSv3trxDPbudNGzgLLETkWDrXJWJQUwbsGtIC1AzmeSgud2FPP8YrrOfpFHvCFL21tGzZD/bbHkDHx5JCOsqOLJxabrBTPdKZo1fyZGEYPzQpbZMQ035z692RlQ8kiCLdFVAHn7uwlc81Lw988O+Sxy8eJJ6hJp6K6TjxJIVBS0NZFEyXQifIGNGO7QL5nvReD3WkVfZ5aMXV9BEmVZKXvDgzHDFcGUBGNmlSxH5JxF9zT7YG6+0+BVAfPH36hKjCXk0GjRJgCpZ3OPIqweyACSKqSDGn97VPWH0wNIcW0dec3adt5/tKbu/zJV1+MDla7SNU2iNiOSB3MXh4096MLZ/qHLb/2EJubSYf4vs3qjqi6Mpblbng/jGl2WVnuumM1/AcByAOJOIsgCyB5KitIAxSYuUdVMUNIbqgQhAyjHe3uD74cOxZBR7nJDJ8itG0stNby2c89eYx4mmFmjXkSjEcFg+VmwkRKwdJKh8bx0oKK6E9xZcOvaTTKo2a0JPXXvbSU8V3vXz/2d4FlVLR/x3S05zl7yFDl6UPaM2cmnvpTZZzf3FoaNwEkDNZBsiZ26KxfeSxhXeOi6xm7nqgrq23bEdeM17Zvo7KWu5OLx26faNlk07a2EPJ4+cqAFMclnDXR2DAvthFGLZaMtvXvLMindyGEhIrn2gAAIABJREFUEAwGA+655x4+9KEPsbq6ytmzZ9ne3uaRRx7hW9/6Fs8//zybm5vv2piXtfamEz9FUffBc+fOcffdd/Pmm28Cdf7UqqowxmCMuW2tDhd452AxKi+wwG0CpRRlWaL1olufFBsbGzz55JN893d/974svAnWWsxglW+8NSKPiMhsF4blRLIduesRYFJZukpgK+sM9WVS0E8V1ZHFfWxOpkkVv/su1D7vIArjV0ytJfZQzowZmRThJBhdPlarVsxhU5ZJsW9X2FGwnF5zlC+A8CSgbbmPGssKG9xwEbhVPkYlFdrWoGCNgDd2Sv7uxVFQHpEZjK3vwaxvSXE4j1GSqKAW5KWJUhMAaCXJY3KMGctylrA1icsFkR7YEX3HWteb7ys2FK8leFLh7WNaaVJVBOdxmlRx81vbta13cpZ75w79Ldwij9YcJ01IyRnjV36E3ukQq7waYW1MGTPxtM+XqB3C+rgMyOmUmgIrr1/jVx9/ne9c3gYgSxXTUPYBKKv4fmiNJUvibDABivJ4u9ZWOqRZ8ztrLFFti8kRNYkk6XqpZmsUNo6M8zDVH8B337XEoKsZBsi1Q5cNla3HmJBLzHS9hvGhkxjyqXsMClVI+YLwUO/69yHkfigPMRRC7qeqcipWZJuqYB/Hjxtj+O+fe5IrV5qJpxmqOZWGBzHcyekvdxhuHya2V073nUSyFIIqcMGTJNKb33WGmGupKlurtwL7Xkhg8CMPnm+c97q6ZOLIaag9z7k9Y9H1Es6jnvq1ME6lbiINU8cjSGRF5cgZlUjrHD61zZ3fVE4y2Boq0b4ly9XdxZw21m3X0lafxB77HtxrHX25y5J8s+E3zZtOmsgka+Xe3w+fo8glqtdMMommjLC2OVOsbdOIWdVo31cTT++cDcILxMMYg1KKtbW1/ZQHRVGwubnJm2++yTPPPEOWZayvr7O2tka/339HbQpvwq0id2Zk1ze+8Q22trb257NFzHCBm43FG7fAArcJFsqnk8NaywsvvMBbb73Fxz72MTqd4zsqrbW8Oal4ZZSzkxvMmbupKtO4s9mF7cKwlAh2Yjz09jCuLD0lmFTNWX16WpBp1Wi9EEOSQR1W6GnBbgRjU1noqbjcR1DntWojnzq2ZHlwfFe8EpYyIu/TQSIhBJGXsE8mxRBwB639zna3XQ44ICwRHFHkp9d8H8E+yCANxo2DtZZvXx7zwpUwxYkLxgLW0k8FnVQyCbSfs0CmJZOIAPY8nyTzbJ4b5XUIYrWXHMtD04Q2C5fWNkX2sX5SBpNPo0KjZenMXXEQZePOV8t9p7qIIx2tshmhfcBE7KhVogq6HVXgkrwgzMKtDCSpFP7AZxWgSzQB5wuxStWMKFhhPC353DdeZnt8vX1FpP+ctZClmsk0nDABGPRSpltuldZRTKYVy4OU7T3LrURL1lbcz0orEWwjGBNUGU5KlKx3nYcgiVBr7EzLIPKnk0jOnBkQ6tIXw5dlGsoAZ7MkUPgWUq6oJFoaSo8CKuQxFQHzSEguKml95JMmUcZJlmuP4sQXLDtKpBlj+MyfP8HVq8dzVrYhliA+ivEwp9dPGQ3rl2Kw0mm0nTuImNPFqHnHkZtBut1w8qko3OPYmVMd7rmnWWnY8ZBPvmnPZ+HoO+7dnOC5xy4VENTfAq71uhTGfdyzSjWO+TlhAqLdMtda0Xp/XVfV3jPbNx+22wbSONNrLHcljzScQe8pn470b5sg1fGWWVJEAxleta3VWvLita3ArG2jP9tu7Lvfnu29jqqqUOrw5JwkCWfPnuXs2bMAjMdjNjY2ePHFFxkOhywtLbG2tsb6+jpZFrLp69bgVpFkly9fZjAY7N8ba+07nrBb4PbCgnxaYIET4J00YEsZvjNvgePI85zHHnuMfr/PQw89dEgSPSOcLo9ytnNz5INA7H3Q2GgCaqewLGnJzhy+96PK0tOCcXk4nLecSKSUrR96pa3VNSG7gWeYJyagIgPQ0E6MKQGn+80fErE9sKhmeZDCYIBEEJVnK5E4dxIfxazq1dTQTfwBG4mkCtaixZAfFaHLgpr2DLX/CnsXQm+x64p2pxVfe37I0LVFNgJS1MFrpNgjYWLe6bi3szLxu8B3JxWRYkZKY1npJZxusSs6Xj7uurUoieljaZRNlWApLdmchKn/dhtshu5ZzumkxxWtFkkqR+TGf18KG/5R69vxvV9nYP6lkgxJ7iV7QkmqECvPMkBtWQj/+VwBvBmkmfLilR2++I+v10rEg7+31CRwk19pC8rYHQTA7rhACnHs/D7IAyTO+bMDr4WfEoJirhnWDQssdzSbozDSrYy4Tmthtae4suseY7/nfStIJYPJ6BihWahbW+iaLJR762jDbu5mqkL2gE0CiKVp6S/jz0Ml6OrSmRfKNz75znFw809lDP/tz55gczOceAL25715CShrIS8qdCIRUtDphRDh4YgZB6bTijRV5IEKzRg7zd1h6VwjPPj959vP4yEZTQsZMEPuUR3nxt0vCs8GE68a0EdO+X7uPuytv5Lt85u2E0raySfXnXeRcm3HajeK5mNtb2rbWU7JN8nEcYWiIUOJ4xswLClNNJYloUmZVI8fx1tlhV9Ldwgt45S1bWTVIsT5bkdIHvNut8udd97JnXfeibWWnZ0dNjY2eOKJJyjLktXVVdbW1lhdXX3HqHxuRexwdh8/+clP8id/8ieMx+Nb1pYF3tt4Z/TCBRZY4MRQSi3IpzmxtbXF448/zv3338+5c7UVk7WWN8cFr45Lto4RTsdR2XrnXYj10EHsloaBluzOQ0CVlr6WjMo6s85aqjBCeD+idCQxNKmIDnBPjI0O1RctiqkzXdWaGyAmpxAAUiGNiVJzJFJEWbfFLuVKWxNspzobgfVH2MhREZpN6bpFXki9s1IhVxvW3lALnLZn/tyVCY9eHke9py4kWpJoub84zytLIsODpHllovuNViJKnVhZy3In4do4brf1Wi89FCh3Q+xdd+BzjIwgxo6ZHR1hg2kUvWTKqKiDkVoaLqwez4cxQyIK8gDSxqDRYkrpSJA+gw3sfxUpmmkQ0ZOJMWPPzt7rJJW7vhALwZweWHNMLXYQBV2ULbCivV2lh6Cy1vLFJ3Z58rV269FYMqkyFq1lVL6oojSsLKVsbsepJ3eHOVkqWeqnpGnAc4949WOtyXqpZNPteraPUaQyrJcJ2G0/vtqTrKz2ouo0EXZ6St7YDQ2hyLRl16O48uVZApgUEp/KclyquoxjPedTYUFtq+fiIK3nfvuuZ6ZazcuKP/vcE2xuxikGoQ4gSymo5iCKZ6hKS9rRDJbTsIBaxDwVs/4D6PdT8jyMgIvp19ZCf5Cy0zAu3XGhz5lzK62/9d0RN3lkGZe6tRKBZVJq56eHzzK38JBX1kOO+a0sPcosx7yFNRSi31qDdOREFFiMw7Sw/au9Xd0kHWNHzKuaiYJ18XzL2ZufV/33JvKp+fm0bz5r8w1su+q2gaqlHrNQPr3b0aR8ckEIwfLyMsvLy9x7771UVcW1a9fY3NzkxRdfREq5r4paWlq66XmXoM6xFHNNNwqzOfFXf/VX+fKXv8x//I//kV/6pV/iwoULCwJqgZuKBfm0wAK3CRbkUzystVy6dInLr7/BXd/7IFet5sW3RozLw/YMofNybmrf9JiJ3ALD0tDXgmFcMiLY++1ACxIpsLLdc/wgpvNY76l4673uHNZ7yZG8FgMt6CTtV2WoP8RiyaRpBNcXayMW+xhLC2c6E7QK678iQnUUa9PX4qreUlYGkX82ws4vNL9YPpmSdmr1yWtbBd9+ZcRwWquApKwtTqyZP/iYpQrdQM5opShM+DibxlrvzdHg2M+GlU7CoJtEkVxaimDyKTeaVBbkJmyJGZvHKXhA3sMgLffJpw+sV2jVToAoT1L0g0hFHkQ+lRH2L6mYBNWpA6zyADLGjD3kUxFAdlmhyRhROHZ3IwSpHTEV7QHQnB7aVCCPf3yPpyX/79+/zO7YkxeqsmglokioeT6u51FMASz1M5aXAvPyRZzCErcJxJfL7SBGeUWqw3PO+YI2H7xneV/1FbOtJlOCMuACQx+nXxk0KxdWX4iFYEhuKIugl5SMivaxz1hBLymcVmnTgLFTe8Y0n23xtNROIeu00mxujviLzz/NNJLEPAhrQSqBOQEBNVjO0IkMqiMqD5yFblcz9oxNM2QhxPMeYi1Bux3dSD79Xx8/5fydry+My/Z5qqsrCse81NUFuUMNnKoS45gHBZa8Us5+7SNBfdfnU3aVop0sT5hgpWPuc0BBvSmwAYL2DYsudZPrSsOpG8td8h8d6tyW+9W6AaW5ntY4e8u4JBryN1nLXl6nhnPI5iteKJ/e/QhRPrmglOLUqVOcOlWPjXmes7m5yauvvsr29jbdbpf19XXW19fpdrs3hYQZjUb0enEbc24k/vN//s8kScJv/uZv8o1vfIMf+7Ef495772VpaYlut0uv16Pb7ZIkCQ888MAtIegWuL2xGJkXWOA2wYJ8aoa1lmllmRqz96+lMJatacW18RSj17F3n+KZETQlOZXsyfoD1ySlhY4ID+BDHVQal3buPEkdrUikYBwY5amJIRhHNLLNOsGFeaz3DhJjEljvKu+CUEtLHrAD+Hq9ce2KjYVUxFn1ZdKymg6DW2QpITCfC8TZ9EWRT4GB0BiVVKhFX6IUV4YF37o0YvvAi2wshwJPQtQEUGXrscAXcOpog9Bpq11TGZlXLzaMVhlLR9vg5PZQ52AJtQjTUnB6JYsKCEM030NPl+R5KPmUkMjCu/N5htD8UDNoWd+XpbRgbXDGWTbm41O17tA9jMJ2mNHk/jrLoJcmVP0Yks8pD7T703ZC4chrAaAcO8ABEJKEXQqWDv35qZev8fXvvBmsgpNHNin4UFYWIQU2gnAdjgsGXe0lw45ifbUT3LQqciOIUhITSBDFiqmXupqrOwGJlHCrP8+vdugtD/b/H6OEDFY0BdYZyvuH5GACgpj+SSmdQeUZOtrgS/XTke48PeMq8bbJZ6vnG0+nlSIRpjXo/NgTV/n6N17e71tKnUDBZGEwSNn1ycsasLLeRWtZ95HAb6EYm9teNwkmn2ImzPG4jFJmygabvve9b5ULZ1LecojOXM+5qwtK2smjTJcUDRa2+8dVRe64NR1VMXKQW5mq9uzcmiGwlMZNTpXW/R5XRrYrt2xJIbutXUnb3LPxon2t49pQ5Vpvh27EOvwbS9mmiDry/3W5SV+8QUVzjrBYNOV7AlANt6b+pi44+kBs2zO2unFtZq1AiJbNeIucT+96xCqffEjTlHPnznHu3Dmstfv5op599lkmkwlLS0usr6+ztrZGmgZuIIrEcDi8peTTb//2bzOdTjl37hxf+tKX+PznP99adnNzk5WV9s1kCywwDxbk0wILnADvJKnqgnyCa9OKRzZG5MZiPGoPJQRC+Rens5BhDAGVW8tAS8YRH+CGmniJIaBWEolUkhJBGkn0xBJDE2NRcfE+xlW89V55wHrvTFehAvzwY/flxH7QzbJExe7gDk0kf6a7hZAR90kYhBXYwF9E+6oH7haPgUIG2eqFnHlrXPGtl8dc3fUH1q2F6YEIpBB1gFNJS2nqwNNsHM8SiVRusrOoLIkKt2Esqvg8ToNMMynDx3JjYaWj2Qyw3ruw2kXt7WTTMkxlAESzaDoqjxMMkpLNadhH5rBIUKKk8uxknqHcI7XuP50ghCdfS8SyODSXk0WSiiG59X9w+pKqHywZVspfnyEhtWNK4SahpPW/XyKAulZ2sk8+vXplyNcuvs7upAQhwufYwiBEnHpBKxmlWgRIUw0R5NP5M32kklQRzE9MsD5GzTSMkfcC3UQDYUH/nWl7e99/z+FgRRFxy0OdQGvSzn8vahLff2+ngeST39oLQNBNKkaFe6yZkeLuMu6bV1lJV5dOBZRviB+Xql3MQK3SylTJ1BwOxllr+dJXnufFFw9bZFaVPREBNZmU9HoJIx8zdwCrax2SrL7fMZ9lIkJKqENkb3soYl56oN9L2doOsys8el+lFHzkwfNI4b5fM/VvEzq6YtcxzCWeuU553mWf+i5VBleazkxVGId9rRLGaW8rhcE6CKLUDqlEOwkjPfOaKx+U63WUWKpW5VP7GOcis9pw8LXRGC6Iv9/7X/ODbyOT2ub4pvLWQqIrjpFMNm1UXBmbtCifdLMdn9WNuaCs3Tu2wLsaJ1U+uSCEoNfr0ev1uOuuuzDG7OeLunz5MsYYVldXWV9fZ2Vl5YaRYOPx+JaST3/8x3/Mzs4Ok8mEPM/J85zpdMp4PGY6nTKZTJhOp+zs7NDvz6f2XGABFxYj8wIL3CZ4r5NPo9LwyOaY3IQRJJWtP1VCCEQDaBFOPhhb2+H1lGQSlb+lJqA6SjBxXIQSsJZpygP7313lmzCZgxjqRlrvGea33usB3TQwIBQZGZ99CsX8KvU8k6MIjREuaUM32QGHZUlj/YjgHEkxV2oxxOSICkWoUsNVapQbHr405I3tuPxGh+q3tQqi5nbqs2klSPZ2TIdAR+YAS7VkEph8HGAyh/1myC/Wegnd7PqyTwtBGZqPK7JJYUHa60i1hcD0OhbBUlJwLQ/rM8Mi4cJgSi+7w1u2CrC8u96O8I/RhLzOm+StM5BQC7DKAzCB9SVMKH0KqACmJ0S5JGzJ1jDnK4+8ysbO9YeuIuZYgDRRTCP6VazKCGB7dxps8bc8SOn1EixEKRn0CXPdtGFaGjqJYhIYCI8J2k8KQzdVjI/c//vO9ck6h4PcFqJy5YUgVOlsrCBVhtxDLuWVRElD5ak3VCGVKeMln0LWCAF7b+goN/lUeuzKSqPoqsqpHElldciqeDTK+Yu/fIqdFqVcVVmkhEihMFDnQKqMRWlBFTAXdnsJaVczC3DHENIxs1TM+BFDnAGkWfhcMj6yyeT9968zGGQYx+aATJUUjhw4iXQ/KN/Y4Dvue9d9drdaGnJHkVQZKsf7q6V1cozaTp30krP51lA68kG5lkLzbvNqmy7aCCt1hOS6IJ9G7ZGVsmXhJVo2IjSTQwIh82PnLkpFmjWRTLqFxGoeq2yLRsxatWfHd/TA9fFggXcvbrTyyQUpJSsrK6ysrHDfffdRliXXrl3jypUrPPfcc2it9y36BoPB3JvPb7Xy6Sd+4idu2bkXWAAW5NMCC9w2eC+TT4Wx/OPGmMLUZJIizOolhoAqbR0ICP38rCyMK0N3DgJKGEtHwqThY2ugJamWx6wVDNCVBFvv1cQQjKKs98LLzhCbLwlqAu5MVwYv7moyyW9zcxCJxPkxexQ6kq4KuVcCONW9sve/khgNVyiZA7O0xWF119mzZNC9NPu5p27cR15TDqndacXjr415eWN6Q88lpSDdI51iviNik4+HWkTNMCos/VQwzMN/tzMtazKp5VypspxaOkzWxH47xSilJmXc8tJGNqajSwgknwqjeN9aGFlT2RRJHqSAisnlJEVYArIqkFQKtcoLJalkgD2fa+f5DL7250XFXzw24tLV7eO/NRatZXCQt4j0lbMWkkRFqRKMhZWllM0tNzOaaMmp9d7+nKWkaNlPfhwxr76WwRwtAEsdHUw+5ZHj2nIvOUQ+KSm4+47lxrJJYM640DVGYSRKVFQBJHdHWy/5BNDVht3cZ0EX9rBClJ8h00LIPJwodz+YVv65v6NKdkuHsuQAOfHyK9f40pdf8JKr8+QvnKHIKzrdhHGVO+tJUsVgJePguiBGZRzTxJh8VpWx9PsJw2EYCRUhaGSaV6SZIp9WaC358IfPAThta7u6pMjb5yvf+X35lHwqZN/vfWt95bEeUMI4N8go4SaffMom41BMJ3YIqnncg72NOC2X53r/2i/Xth5rq+/g0xmIEWvy2b1zpPXa5Fg9EimOk0nWSqRssMsjRTYQmFUpIWtqlaKZTBKNt6rt7+03dhHevB1gjEHrW/MstdacPn2a06dPAzCdTtnY2ODSpUvs7u7S7/f3Lfq63bD1ONx65dMCC9xqLEbnBRY4Ad5ptnt5Hu+T/m6HsZZHNyaMDuyQfLsIqMrGfSSWFibGkEoRRXSUexZ/6QGCRAJrmaJEtCahJZLoEdHWe7UCLEaYMY/Cai0TZDrEwOk6dESOJQjV9lxHREopoG6Lz6pvPc1J1F4oUVRgVKOFRBPEPvHjhxUGYWMMvcIs8uqyIkitYB0WIofL1aXGpeGpNya8vJkz3PNjEewFIURNNZrKzjUGK2lJtEZKcf33Eb6apbFkCqdNzEEUlUVJqCLGgK6GYcRwbi2sdAQb4+bndudqdsy+IjY4GEM+TU2CluW+5Z0PuWPnfhNinvp9qzn9bIlJ4PNKVc4koD2lzQjN5RTa4ppU8vcVg0YzpqTd7iemvhAyuxDucwHkLdZ9xhi++dRbPPPKlvNMWolg8skYS5pI8ghJTVsuNxfGk8o7h91xbnDIEu/tWhoWlQ3OtQeQRViF7U7i1BpZcrjuB+5cRifN/V0r2tydDiGG/8o03pxJAEmIfAhIA4aqaSmQwnqD6SHrxDKAOCsDVirKs2YYF9ppqwd+e7/Z+PDNh1/h0Uff8LYJrk+p85JQk3HBYDljp4X4lVKweup4knhrwvO7xeSAG41LlA631Ox2w8mnWCJ90E/ZmI754AfP0NlTGro2fPiUTa3fFHvIPfN47iE4Sw85FauUPgqvMuuEFVSifVOFthNK2skn15133fe2Y8pxrO1tFvv/Wu6UDx8onwHjhnoyhDi+ADV06ryUR8vbhKZtEcY0z5ztZFIbmq/M2uZnaxf5nm4L3Ezlkw9ZlnHhwgUuXLiAtZbhcMjm5iZPP/000+mUlZUV1tbWWFtbI0na37/hcHjL7OyMMXz961+n1+uRpilZlpEkCWmakiQJSZKgtd7/9+2yPFzgvY0F+bTAArcJ3ovKJ2st39mastlgvRNLQOn6R96yxtakUCgBU5h6cZxGKm0KA6kUpMKipKCXKCrcC/ZYomc6BzHUUZLdiA9lQ53DaRgYVeprQS81KCGCdjXPMB2PkJ3wBV3slc/hhEamRGveLy1gtfPWob9JFCZwv3ytZorIT0N4jii5V3tQWSEwAX3suqKp/ZmWxvDMW1NeupqzPT5uCGdhj/y4fkTPyCgLVWWcZJQQtVWXkiCOROMsdZQslMxKlA3eBQ/17vthHl5+PMcLZ2xzvzyzlJGkx1VCbSqpNsR+hvSTgq3APE6jUu8pGcLOUgQGFzJVcX7pNEqMgsoD6JZcB0dhkaQMg+z0QnNJWRQJI4oAZVMqppTWTQgZNAljb30hqqZc9JG2Ascu8Ep00HaKEfX7Ni1KHn1+g6dfuRZEvuZ5uTcPBxLrkd0kL010Dra8qFgapGzvNrPB58/0W0mXEMQoI42FQUezHZiHKkZRWFSWQabYDWXVD1TdSSTnzrbPv6EK6JhUOakKI58CuaegfFMWQUf78zmFPNJp6T+hL6hft8mNykp6siQ37WOQz/ZsZ7fkL7/0FK+9tuttz6G2WaL720EMd3KWVpoJqPWzvdb5WqnwjRJCiOA+OOglbG2HaQ9DbXwh3qYvSRVZpvngh87U5xKGicNWT3oIyqJyv8+T0jXfWiZl6hyypx4ltG+TyknJKR+McFyfNZSi23p5LtWU2PMSaPvtPOomZy4oz2t8Vr5KJq6rjm3LusSS0Jz/L6FpF4FtWRO0Nqf1fWy56rbybX9fKJ9uC7ydOZ9OAiEEg8GAwWDA3XffjTGGra0tNjc3efnllynLks9+9rN86lOf4lOf+hSdzvV1+mg0umXKpytXrvDJT36Sc+fOobXeJ6CyLKPT6dDv9+n1elhruXDhAv/hP/yHW9LOBW5vLEbnBRY4IWI+XN5OvBfJp0vDgldH7UEYIQRZOWWq/NZDZQQBlRtYSyVbgTuuc2MRCJJIdU5lLeuZprC0JqU9CEudY6mN8DiKeaz3Qsi8owhVWCkB61279wgiA+OhEaY9zNNTYglE7WjS6c6w0aYiHHHkU61QCrynwrqN6udEk0rKGMOLmwXPvTVhc1RGB5NrC7zrP5KiDnZZW9c9+0jPUoU6qHRqgBaGKlATF+HAA8RbVo5LsWe9F/6O7EyPq5M6iWS159i1K4x3V/K88CUpPwzBIC3ZmobZxA3LbO+Ndr+n968bpEqidtmGawQhETm59X9IFhE53VKRU1g/+aQDieqEiZd8Kghon5Ck7JKz5G6XGfHo5RFPXrrG1p58T4jA/Iq27quhaqaiNME5mWaQSmLMjVkrzfI8HUVMsN2YuByE3VQFk095GXedg24STD5NDzyjD96zinQE20M5sMqCFjZIFaRCZeihxQLLhSiuygCp9KTwj7tjhxXeDL48VVDb6rnIpzYYY/i7b7zCs89exRiLUiJ6jjbGnpiA6g1SRgfI3/UzfWdQUkRYFEgJoZ9OMbmZYq63LA3drmYc2K8R8L0fPkuyR3r3dMFO0T6G++bJSdVOvmSqdNqpdnVJ4ciVmMiKyrlWteSVcvZTVz4yAHNC27+qRbELkNohxmGr56pZAaZlYHFZhdemdLFrcNu6IjBAJgrOiG8faURbjqU2MqntPrdcYyTJJGTLFTTldQJoIc2tg4hd4N2Dd5LyyQUp5b7qCWqC6dlnn+Uzn/kMv/Ebv8Hp06f50R/9UX7iJ34iSvn0hS98gV/91V+lqip+6Zd+iV/7tV87dHw6nfKLv/iLfOtb3+LUqVP86Z/+Kffee6+znT/5kz9JmqZsb28zGo0Yj8fs7u7yxhtvcPXqVSaTCQAf+MAH5rsZCyzgwYJ8WmCB2wTvNfLpzXHJM9tuX6rElIi0QwZMA7ZdlxEWfJu5iSKgpsbSUSLItk4JWEkVlRVMraCj4kiPGIhIBdDUEE2ijSvrtaADONMV+7kF6nxC4YvOJMv2ckoE7pgn3kIwEYI8khRrQkdZBsnG8QOe3ceHy4JgVt+kAAAgAElEQVS0cu8+hSAmD0LMvY+4H8by6k7B61sFV4cl25NyP8hc510SIOyJckVUe8nKoQ5OpolCCREYjAonYQojSJUIzpMyreReAvbwi+skKop8sgiWMs21AwnJL6x2ndeeRJBPtcItpo/FBU8ybYIT2hgky9nUSVYtpzlr/TN7/4sg2CLUIlJUQV2gJENSBCmglCiD6gzN+6YCSKqcLlhzTBF4FNpOyEUz+fT6xpBHn7vKm9fGx8jWCFdLqrLaKx/2AyFFlF9bVdloO7DdUUE3U4wPEDNpcjjP00EYG64KhT2lRuA1xGwE3p2UUdeaRKg1tvfqXuokrK25yc2Ye50qQxlAuoS+T6FDbmg5Lf0FQ/IslVaSqYqpQ3VSVOr/Z+/dYmS5yrPhZx3q1Kc5z+zDeIPB2GAw+vjATvLzI4hjFALIVyhEoEQCbpCCcpncRIhEITeJogiRKEKJRC4ikcMNJCTk5yAI4QvYwTanOGA+yLa9bW97H2emu6uraq31X9T0TE93rbXetfG29x7XIyG8p1dX13lVvc/7PA9ioZzF95LwW5FQcMW6NZ16P3rsAh78z3MoZ5wFtJ42B4Xd27U2iGOBosGlgIJiUiHNJPJxhcFKChm5tzlk7ULsp0PMwiaB29rJIjL5ZAxw221rB/9OROUkn1zKppgrZ35hKisMK/s8GwuF0rHaiVBOG9tEKMBBbjEYlMqdy6k8yimtjPURgJkKJe9Yj6w0OQqHrZ5h9m1zvfe4nEg5whvk6t9q3gplgDPiOw12oIEP25adxCzvLkI2L7+JZDKGN5JVxojGY28MA2OqeaWIds8tbmzcqMonHzqdDt7//vfj/e9/PwDgRz/6Eb7whS/gD//wD/HYY49hc3MT29vbuO+++3D69OnGZSil8Ju/+Zv44he/iO3tbdx99924//77ceeddx6M+au/+iusrKzgxz/+MT7zmc/gd37nd/C3f/u31vVaX1/HP/7jPzb+1oULF/D5z38ef/RHf4R3v/vd+NjHPvaz7YQWLSxoyacWLY4JXkrk006h8P0ruXNMBAMZRaj23zkSwUkElDIGEaMVTi8XGqkuUQhal1WuDDLBIGFQNSyfM2A5ru31KrCDZ2oR8HIcar2XaxoxNItE8CCPegOgIxn2HEzPUsSQRvrId/i+ZQX1N0LJpNDxoZlaNoJuI71saTisEFIk5w5rjnmYkIyogxwbglJhLvOm0hrjwmBUaIwKhQtDhYtDhd1coSjt2is9cz4xBkhRFxsYaqs9aiGaM4ZIcsTCHFiJUbugS10rhQpiOBPVSmqKSDBMQjImQjyo9jGbl3NikCKS7pdwe2fqIgwYOrLCyGnBc4hJaI5TYFBOFmlY4kAAAK9ci8H27WAU6J2wOiAvIKQYGbMcuekRlkkDxSqPCgOBBEOUcK8fM0eLRpd2czx69jIef3bPOyfUVqr+861SdaG6JN6cq4DMpyn6nRg7IaFqANJUHpBPDMDJzaM5T/OQkqEgdmkIxrBoNNqMEDGTNkA/xKYvRJmtDQZZhDvOLHvvsSGKaQ+vcADqIkuiPSpFrQTQyL9xyUEh6rPITT5Nx+wW9jFjj6UZ4M+gmiWwLlwY4t++/j/YabCXm5LCJsCidoqiUGESvxloZQAJ9AYxktS/vSFNHiGrE/Lcm+cVooijJN6fZEA+26tuWzti6+fL/XLlQWVSYeRQivjyovyfK2feYsy1s7kuFgrGQU4Jpp1zIYeGYnb1c6z3oMSK4/vue6fi/izEJjDH/cH1WTiZZdBjV9BjzzT8TvM3bGSSteHFYlUcx4vboXXU+P5jdAQ0ZM8ZLYGmc8zIZtu9AoDm4T7RLW443CzKJx9uv/123H777fit3/otfPKTn8RoNML58+fxgQ98AJcuXcKb3/xmvP3tb8db3/pW9Pt1c9cDDzyA2267Da94xSsAAL/2a7+Gz372s0fIp89+9rMHJNF73vMefOQjH7mmuVkIga2tLXzwgx/EmTNn8Ku/+qv4lV/5Fdx7773Pzw5o0WIGLfnUosUxwUuFfMqVxiOXcmeXKgeQSo585nmVM0YmoCptIBktLyFnEpkuMeG0guVYGXQEgzDmwFaBo1Y6GcYaSamJMuTO8VDrvel4aiYTcG3We66qQ8SBpWzxuAhCsPf8eIpdz8EaBZIHIWHo2F9ywmu12BSDSCGNhpYVUnXHHpEQoCof6nVRqF9PaftHgFk7L40xePiJCZ7drbAzViiUhtJ1wadpjaRgtUUSZzCeXKZ6+UeLPDUZxeoOfky7r9mRzyPJITgHQ31c9ZHl0R/GQ9wbqSTVFKGuQ5MqMIMFwLBQiAVDJDj6DXZg8zCBlntpAPmUKwnJ3F37syg8Rdh5uAqqW9kYvfSwo1CZqU0fRZGQgKrwcmZFzIFqk0fNhyJZ5YFOUkWYOMmn3VGBHz6V4+zlx3Fld4LxpKZLhHDbWR6sr9JOsuZaYVCrkKhWffW6hM9he8PywD7s1KY/5yloWwOGjkIVFQmdfBoHEt4nVjP0Bv7zMIQfFARlEYD9TEiKCok2riCSVJTHH20YUl46c3gAICJsayzcx2SiBCTX0I57m88afKIE8rzCv339p3jqqV3nWK0NhOQw12ijd60QgmNprYOSMB/qkOs74Hl2NA7LZup0Y1z1NModrgftIlleTnHLLUtHv+oYL5lGrprVIwAQCe3sPvPlRfmuGt9tUHLjJJ8iDzkVcTf5FOtdVNJOLklMnCoj5+objYp1rWPc06L9Q9cet53aNsIqgsYp/mDjd7hFas4sMknGmv/O2WIjhzYRuGhSMkk0nXDGNL91GCOs4+ft+IxhiB//FtTpX21czxY3F25W5ZMLeZ7jNa95Dd73vvfht3/7tzEej/GNb3wDX/rSl/AHf/AHSJIEv/RLvwQhxBFV1Pb2Nr71rW8dWda5c+dwyy23AACklFhaWsLFixexvr5u/X3b+/B0X9922224/fbb8elPf7oln1pcF7TkU4sWPyPazKcXDpU2eORijsLz0juIeCOZMiWgCuVOvzGoXxriOeKgEYxhbCSWJMcusStypAy6koFpg34sYRhz+ntXps5m8q7L7AaEILAeONFAzBiKgPN+rAw4ay7Ab2bNL6ihZcrQ8aH1R41wy8GIH6pdOIC19DnneF73aZKWfUgoEcAAbgTZps9lx3hlrPHkTq14ShIGqQ3KStf/U3qhllMpc8RSSoq6MCs56q7gayCj0gjgQoAz7s2TqdeHRkBNKk0mq7QBYslREK97pQ0EZ0fUST7EkpGt6KboxhKr/YS0DVWgesaVY7YIhk5UYcfRtT+LURWBM+XNb5iitJBaDAYvW5u3yWGI+ASF9mcpGQhEbEzKaaoCspwY02SLPgpKZCRCjby8yQQXJ2PsjUoM8wrjSYnLwwKXdyYYTex5bNMsGApq60n/uKJQkJKDyu+GZD4BQF4oLPcTXNmlX1xKGyz1YhgDJBmFdHz+iTagvkclkmNCvO9EAcWbvbyC4CDv99PrtOBsbQDJAcoqM2LLOpXQqjSD5NqrbMorBk5oeFHEBpdUKuSF+zyhEG2S0IySRRWGpV0d4lrnPK/w7e8/gx89domsGFKVRpLKI5Z8JOw3UYW+MnX7MTr9eF9tTAM1Z8oErFNoNlMS08ssVJu+u153YmFur7T9mulEJfZKuzrHd+v2zS8+dwLfbvUdUp+qy0deSVM62z64ce93V4NJZIaAIw9KG+aYBmgNLvPfobpBTMdvsh9DNpBDBnJf+cTm/g6whofO2uauWBivTdSoBjUmQi1Dmv+7BGv6u0VHbvt7076TT/84zMajxQ2N46J8msV4PEanc/jclGUZ7rvvPtx3330AgAsXLuDLX/4yPvWpT+Hhhx/GpUuXcN9992E8Hi/c95tqj773PtvnU5JPSomyLHHhwoWg7WrRgoqWfGrR4pjguJNPxhh8/3KOPU/1YjXm2HXY9awmAlcKhrHHu6bQtX1dxJm/yMEYdkqNfsS96wcAPcmRSY5EMIypVi8BfiXjQCu93EEM2RALhiLAs84A6IhF6721hCGSltDYOUs3H9yUYtN4tyd7EyLOgrrmZ0m11WQCKXydsyEvllObPtp3QpZcF8qbv/H4FTUzjkEKBik4sqS+TpU2iDmws2+1N597UikDKHPw+inFYXi81ov2gLVyih8oLGbJpqnSyb89VOslgyzimFDzV0ijDiFFGPk0uoawt34WHbHkccGAQTC6mlEFKqVijyXP/Lp0owq7hb2IOotREe+TL0eP7cuWciTR2sJ4yaqGkkczJC9RKgL5FJDlRFFdAUCJFAzKGvo9u7wYe5jAHV48zXOa9br54dkrePC/zqPSZq5Ae+XId7lgXgWPMXR1YRpLjHJa4VYKDkVhqlATYFJyVIF2sKHQBlhbzUjbGtKUFGITBgD9lGOyRye9qTAAlrIYlwiWhGfWO1jqJ7hKVGbGnKEirItLwTOLQoFEFgFAJg12C984hkRqjEv3uKKizSVRQ9f/NYHwc4lQGDoeLeZzoYwx+OlPL+HRR5/F5ctjwAAiwPYNACZ5BSl58LlrDNDrxdjbo92NO70Y3X11HTVDDajvXdR140RLUCAsmynE/Wg4qiAEg3I8e2xt9rC1tahOdVncxp68L5/FuIvYAoDSk6+jPN83vuvXW0x1Hzefcst3gSlufw6QJkflyINy3RldJJKtGbFZAzRd3iKWcBk9PGsZn0CwBpIJCXhTJhMS8IbnudoSsYlkaj4v7GRSGBaou1GB+Py3USz/7+dh6S1uBBxH5dNoNEK3a39mX19fx3vf+16cOXMGH/vYx/DHf/zH+OIXv4ivfe1rePOb33xk7Pb2Np544glsb2+jqipcvXoVq6ur1mVrrVGWJTivM5an/w/URJ+UEg8++CAefvhhfPjDH35+NrhFizm05FOLFscEnPP9wu3xxI92ClzwFDlWYo6d0m7rtRRx7JYakjNkkmPsKVRNtEHKgIgxr9rFANgrNboRx7BhuV3J0ZEcGvVLRwUAml60m2i69R4ApIJhFGq9F0AmVdeg9ptPo8oEQy+xH4O6rGxI+Vv1+LCcKKC2/CMryhCe8zPdpREDlhK36gkIy+Cp1UycTLqF2PTZujKVNjh3xV54mZJR3ZSDR/UjRqU0ilKjqBTKUkPpmuycEknTbmbGGKQUEJwhEvsvqcxt6cU5I3UsC2goYlEz5Bhfb+u9Qhn0E4FdYoF3tRujm0ZB1piCA9S+hdwRPN4E6rU7RRpp7BIZospwdOMCezPqgogrnFxqtpzglhyDJlAt8gAg5jly7c9youdOMSRsTMqHkqzAxLjJJwOO2OyiYLWX/P88vYP/892nSWe40cbvmwS6+mk8qchz3kFGzHVSEO0MS3QSidGEdqyFYFhbySA4I13HIaQPQFdqAPuKSCJCs+OyRAAWZ9gpOANedrIfdD+jchsV0U4PYEgEQOEBJFGZlwgDn7PauKLlOVGuB8ojmiY0KEkPyT+u6kLwzk6O73znaZx78uoCUasqDS451f0NQK0EEoI2B89ib69Af5BgtyFXahadXoze4HDOUQHzbaBIl4yQbKagbFQDdLtxY9bWFHfdtbXwNwaDocMK16ccqjwNJbnHZtf3eeHJfvQ1tHjJKQ98TRxO61yjUbKe9fTgDnqJwcAS7grARUzZs1y5474zfy+JoHAC/wfAUuN4gwhNsvqaTGoinyzjjUQT+RQ+b4fQavUaHfyX4kh//IX6vxN78b3FzYXjqHwajUZHlE823H333XjssccghMCHPvQh/MVf/AXuv//+I2Puv/9+/PVf/zV+4Rd+Af/wD/+Ae++912mp95WvfAWf+MQncObMGSRJgk6ngyzL0Ol0wDnHQw89hL/7u7/D9vY23vGOdzxv29yixSxa8qlFi58RocF+1wuc8xvC/u964MlhiSdcbZ0A+vvEku149GStSoo4w2oisJEmKJTGwxfHzjJHvp/RJNkhkWCDBjCqNLr7GUodwdCJRJ1T0xDtWtvpMRL5URkg48CEeIhDi9yhp07xM1rvMQDrmfGSaZPRCHHHXVydRW3BEWDNEpiAHUrvKlNblW1kO+AEix3TYIPhAt/XflBQv9JSiTy1P/bo+Gd2lNc6qRuzIwVBKTik4OjsP3KMJ5W3yFqrm/xFHnLovK6JPcr9Oq80qEyvNgiywFLaIBaGnC8C1HlWIJBPWSSw1ktgUBNo1C7xkL7CygikYoJc0YgUX1f0wroEzqfdWB0hn165qiGFrVOZvuwQopZKVJWGHkwuXa3qMxBEi87I5CjQx7nnhvjqQ0+Rt46qamKgjVPKIE0EKZ9JG4MkluQsp6rSZFu/KTopjXxiDNje6kMIjlgy5MR1koKjIhbMRcC6h5C640IF2YNywn33lSf6iCIZZF1bK+j8XwjJh4olkXwi3uQk4XalDUMqFHJPRh3lGM0rkhrHEJ5nXCOqqsKj/30JP3nsInY9NpNaaTDOg2SBdQajby0WsbszQacXY2RRQB0QT7P3FAOvOmhmaADoz4EhpPI4MCMqSezn1JkzS1heXrSNzWSJUWBTyCwmjvNYcoVCS+ujUCIqZ1OFYBqFtudNARRyykMeecgr5cllrIS9EBybIbTDVs91xrtUSq6mOgG78skOM/ckYHAK34ZABWVtumnebzayzkCi0QPadnBtpKdlfRizPMtw2zNOvRxjgOjxb4OrOltNx/Z8rxY3F0Lyem8W+JRPU0gp8clPfhK//Mu/DKUUPvjBD+K1r30tPvrRj+JNb3oT7r//fnzoQx/Cr//6r+O2227D6uoqPvOZzziXefbsWXz+85/HiRMnMBqNUJYlqqpCVVVgjCGKImxtbeF3f/d3F4iuFi2eL7TkU4sWLW5oXMwr/PCq+4U5EwyFshNPqWBYijluTWIsx3ymwCnwv9aARzwE1EhpdGVN7tleChiAjqwzpSLG0I2AsjE+9SiIDbkA9guzxIJyrs3+SwwNuQ633ksEUNAFAgCAjqhJwI2MQQh/lSnZJ++oCBXoh9K1lQm36htECt34KvUXABOR6zgsYE0OM6LoVo/zS3/coXqaIo04Jg6WNBKHOVg2lAqIpL+7XBtDtN5zK6hmobRBLwFGYXUjMrqxQTGmX/jDifKWxgQDTq1kYPsqFclrWyoK5hWJPnREQSafxlUEwTTZrq8MIOWAumA/RS8usdZzBO2GPPJ6Cl6z8Nv61NCQkBijgp+EEkwRDwnxWmYaz14e48sPPhFsk0VRNWkDcKIyN4Qc0oHKQik4ioAfuDoqSYqj7a0+ZFSfE4KgBJtCcAaPw+8BQhSXkwCVMgD0U4mLRJuzyrP/IsFwaqO7P5a+HtS9VmkGwQyJ2JLEY0GtX1EPbRppL/mkCKTRuPTfZ6aqJRdmrQevXB7h8cev4tnze7h6ZYwir8AjTnt0NADMVGVP2xnGAIwzGB1eKJyMS8SJRDFHAHf7MbqDGE1nDZecZDFuAs7NJOYYjWn3jQlRKQnUZHu3E2FIfJiw7T/Bgdfeuah6AoBUVE7yqXTY3gmmMVF2cqgjK4wcc30i3J+nssLEYV8bc7WvqLHB1ASt47RSPts/Zp9vmalQ8Y518dLkKBy2eobZnylc7wkCTdqiw+/Zzm7bGV0TXYdbscrOo2ueAoCGtsd9WPepjUyyDW9eK2YjmZos/QwD49OGt9m/80Y3CGMAsHo8v3oF0ZXHDj7Tcat8Oi44bsQTUGc+9Xp+VwMAeOc734l3vvOdR/72+7//+wf/naYp/v7v/967nGlj+rve9S587WtfA2MMk8kE4/EYVVUdzKcnTpzAG9/4RiQJPcu2RYtQtORTixYtbljslQrfvZw7yzHR/nvJ9ME74kBPCvQijp7k6EUcmbAXnreyCHetAt+9NHauy7DS6EkOow04q237Ys5gtEKlFERUd2ga1AYEPcFAcbsZK/qLfh5ovZcEWO8BQMZrxRYV1xBHAwOgHzF0YmJYeixRBNTZTCCdNNUZhXwrFgx5wH5ajnfoC2cAM2Khj9GOKaFEW3aITd/8y/O41HjOkzPCAAwLt8JquSNx/qq/EFoHHBPWk2i9F9JFx4yf+JqCqiiYou40pqvQSm3QTwV2cvs5sb2SHMl5ClH0heRoAH4bn1kYMHRlhZ2SmONUxUFWm7N5FLetReDMfi0oQ+8OV4z+8kXNcgKAiBWoSAoo2jIpWVMAcGFP4f/71uN13logjAYM9187hnjNFKVCHHHSupSVRhwLlESyJSTzCajz55Z7CS7v5NYxJze6iJPD/RyyBwkiosOxAcvdIxDSs0gjOpk69BTX7zi9BDEjEapzMZ9fJUpMtNOjPgtReQjqrTAiZNlR1K0TJbzkfF6KugA7t7FFUWFvt8DlSyM8+NxVnH9ugtGwgG64rlShIWIaAWW0ARd8X9FEVEorgywTmIR4GKMmZ4Q0dT7T/np3+zE6FuIJgDeDborpszJlm0Pui6NxBSE42QIwy+jkk+3+deftS+h2m+cvn+WiS1nUkSVyY/884srZwRZx4/w85top2o6FQqnt5FMsNOAgpzjqa8d2mgpToOKJ9d4a6yGUWHYs330TUtxBbDlOU+awz3N9ZjuXZ+8eCUpsmm/OfNY8t1lbI21kkuUOblMscbZ4zmsjLGRS1KiIsv0dplbjmRJIfvrFwz8zCSPtZGGLFi82hsMhyXbv+cTUuvDEiRM4ceLEwudFUaCqKsRxDClbaqDF9UV7hrVo0eKGxERpPHIpX+h+Zai7XSWr/38lFkgFQzfi6EuOWIRqX4BTnajOshmV4KzuQJ4ajjE2LZowGGOwJjlG+8/CGgCEhBCLt1Lqu6wyQEcw5IT3WGWATNAzikJyX4BwFVBpgIT7VSyzqLTBZodeoFB1Ky6opbm6F45evAbqPKYQgksElP0GUYFEjgISZAAWYPpV2/RxUPcPC1j3+Ub8Jy77CbHljGPXU2cRnJGsc7TWRwgVG6hFb2Pqrn1Kt/6w3N94QvHNAOhEGqOSdu/JK2CQaOxM6AVhV4f/iUGy0KkWci0b1Mop6j3LZ3MzD1/g+ZFlg6MX59gtiGTVPqm10SnQTxdfqo4uO4JgBZTxq7YqpGBQJGJJk7OcAMEq0sFRxMfzkqCi2h0V+Pw3d8n2dY0gXGLG1Nk6lEIu5wEnXAC0qTNZQkioiWO/rC9n6HSOnosUy69DhKik6EvVBuilErs5bWYJ6SDOS400Esgbume6icD66tHCiSSSTyHPIxGRfKKiIlrxKmK+DOVY5QTFEgBkkcJe0bzA733naTz5+BWocj8vsdJQyhxR9dSqJj9RpEsNJqdPtm5opZFmESbErEEAGI8VhGRBmVEAUEzUgf1et5+gM4j2n1OaEWIvzgWDIhDXVaWRpRJj4vXU70a44smrmkIEZEQ12fRFEcer71i3Thuuo8lgMCrtSvpEKuSOuVl4rKJ9ql/f5xE3TpvNiGvn55IpwNFwEushSmGfIyVyZ5uX80oxGhXrXlMioes7rj1mW9cpYcVgcBrfPHhq0YghbAoky0OZXSnV/PcmkskYAdbgbGF0DDT93YhGpZQx3EJKCRhoJP/3K/s5WDV0vELvSGjR4kXAaDQiK5+eD+zs7OALX/gClpeX8Za3vAVZlh1kae3s7OCf//mf8bnPfQ7nz5/HXXfdhQ996EO46667XrD1a/HSQ3iVtkWLFkdwo8mCj0vuU6ENXr2U4M6OQXruh3gVdvGLJ7q492QXbz3RxZu3uvi5jQ5uX0pwphdjLZHXRDxNsd2NkEgBwzgUGCowlGAoDENpGEoDVKiVRJR9PFYGVMO4kDOIB4ye6LAOg1ybIBtAAIgDO7s3M404MD80NG40IIu9Xn7oJUy18AGwkjyN5jBe1/IDrmEG8ICpPGxTD1/6jDF4gmC55/uBWAA7E42IcK1qQ1Oy1RlitH1GvUdoU1s+Xi8kgSfdXqEaLaEGqUS/s0h+hNhhAWFWYhMdaskQtq2ppFcvSy3QjUq8fJWWCxdx6rXIEHNacbE09P1B3RMlFrM9mscl4MZePRzlJb7wH49j5OhY6Kb+WYJq1Ud9HpoUilwjKiznvg0hdltAnUHXb1AVDHoxBoPFYxuikgixOCwrQ84mAoAsYCItA+0LB1kzoXrH9tKC8oRqfRciSqPeHinWdgBNhRQyjtLcUigOac1bOURiud9VlcLZJ65g9+oEezsFJuMKVakXzm9dapIayBgAmv6OkI9L8MB5SlUm9HYPABjtFdjY6qLTdxNPABqVXTaEvJ91LOd8E+KY/mQd8kpWlhppevS6fs2r12GkfT5wEaYdWTmL8X4Vs0ftek3Uy8zSPV/3kV++W6A07jmcGze5qh15UZEZ1vlotu86jotrq+wkozlirdf0nQ08jhQXZ/7eTLwZMLCm/CYA3Pr3xWcnYyRYg/JOW1Tm1jwpW0OTtdGJQZw/CzF+7ujwNu+pxQ2O8XiMLKM93/8smM7zDz30ED7ykY/gm9/85sHv8v371p/8yZ/gfe97Hz7zmc/gkUcewSc+8Qm87W1vw1e/+tXrvn4tXrpoyacWLY4ROOfQIYEKNzD6kcDk4nn8z399D//rNXfgZadOQnB6bksoGGNYc4T9TlEZICMWsKkF5rEy5DfUeixpKIDaIi4EWUiVD2HWexspIKUOnnhCbL6uZXxotYQat7ESDyF4WXfuBalF6N3GQBghaQIyovTM2EsjjaFHHlYTS+4x3ZiDMQZJrbJSbZAI+WFAYBE24DQal2Hn0G4ZctTqwu0gOfqNSDBsLqVgDd5eBvT8EgCQAdq80kRIBD0Qq/BkMszDUc9pxPZAIY36pLHCFmrdANnQ0dsEDQnJ7LZts7AVX+ahEEOSSGuG2IwaP5kUCv/6zSew67B82lrNMC79x94YGqmTTyoSqWQMEAWQuzKgo0ARMqrmMa+w7GUS6ytZ4/OGAd36SxtDPp8NgF5K36D2BvUAACAASURBVCchhPFeQE4NAMTR4kqvdgWWBovFTOojmTJ0Uon6nEcV8xWKls2WVzRlsKuwPItO5L/fSEuB/amnd+smiH58kOVngyq0dwxQEzchM48qFflcn/2NUKxtdhGnknTctTakbQ2FDGleC/j5PPDa62SHhfssi/D6V7vntokjeywRnt/27O/KkRcFuPOkAH8eEzzXke+I+K5pb+quZ/sVtzeWSOOe812/bCORXJ+59oUBkCHHGh4++oElk8ogtcxtdjKpyUJWw0IyWTIzrSSTBdajO95D8vQ3F/7c5j0dH2htz/G+maG1fkGs7abk09e//nWMRiO85S1vAQBUVQXGGL7xjW/g05/+NDqdDj72sY/hb/7mb/DhD38Yly9fxqc//Wlcvnz5uq9ji5cmWvKpRYtjBCEEKYj3RodSCt///vdx4cIF3HPPPS+YRJlCPgF0lQXZxgpASqzK1GNpy63XIawQoALN90oDpIRCwHIEZHH9IhyayxRqCHgtuU9hy/crviJmMIifPvg3C5puy7B22YDtrbOk6KZ+07WmqJ76Kfe+LEz2mTvGGCThnFeapjQsFW3/TipNJmTzUpGvdQOGOEC2UCiGfhL2YsX44YXPANyy2jnoYGsCVZEAAFEAmQQAHRlgx1RF4AQFwBRVAFklmMbJhoK4DSF7PITEjhhNUVUF5E7FREJLNgR4l5XGFx94Apd37Z3fG8spLu7k4NyemTELWq4anVQqS0VWYZSlDlJ1h5YsdvYKJPuESxxxbK13nV3tIYVqispzijTgHhIyt1fKoEd8vgGa99/tp/uN9/eQXg/qvZe6yEJRn8cYCAI/aMOQEhoZCuJ8E0v/utn2yJNPXq0/5wxJ33/fUBOamlBVOsiZSisVfEHVWUv+bReSY/NkDyLiKAOkcRQ7XgBBj44h11PIuuZ5RW+2ASBniN/X3rmJricfdVQ51DncPU+7ssYAYKLdF81EudViE89c7vt9H8lrvCey+/edlrlGo4T93dOVB8U8ls32o2JXN3HHyWyMwWn8+8I3bbbBxkIaaTSTbbbxxpoXFlhetE4ii383mkM++VDj6JZ8Oj7QWjvfb1rQ8IMf/ACveMUrcMsttwA4nJc/97nP4fHHH8fHP/5xfPSjH8U73vEO/Pmf/zne9a534V/+5V/w9NNPuxbbosU1o72qW7T4GXEjdWYcB/JpOBzigQcewGAwwOtf//oXNPywKzmJSBkRlUpjZSCDiRYKwqz3ooBTdKLDbesamqSPIBPAID18UVOgK72Aowoc2nggjJC5hm32XPdr6eUjnYJBOhcGsADDRG9355HBJsimj6POSHrqqv83co8krBcz5DOLoRRkjJnmVLlRE720Yy6JLzQGQCIDmN5AojcKVGfsTg7tx04tZ5CedQt5wPMVkeZh69ZvggFDR9LJrbqYRlv+y5dKxA2Ze3aE7HP6WElUVBVIwYj3M2nJWJjHPLGntMFX/vNJPHt5bP3O6iDBlWFxUCAjqSa0aQwLn0cxoZFKShnERKJKa4MkwOO1UgZJwGlhAHQ7MQRnOL018MrvgqxyA54RQwQd4yLsWa+b0q/xfK64fnI5Q6fXbG0ZMjtTa/BUgWpN+tPGUrk3mw3eLPKKtiE+2zCgedoYjUpcuHCoaOSSI+75CShdGVDunarQ5PPS7NvahpC/ShkkiXR+J+tEWNvsHjnpBfFBTBCPZYjtZU7MewKAUUM2kwvdLv3am+6ypUGCl71s2WndmPAKyqIyoaBwqKY408g9xFbl+G0O7Vx+/fvui9dHTvlOSc3c10wl7PZXscdWz/Vc6trqaT5T6PfsMFjHY4ixZ/mtpm9YlEmW9w7b3xvlUPUPW/7evD7Wd5gG8pQ/8z8Qk6uNw1vy6fhgmkt0nPBixGKcPXsWm5ubGAwGAA5rlg888AAA4L777gMAFEXdQPeLv/iLuHr1Kvb2Fu8nLVo8H2jJpxYtjhFudvLpmWeewSOPPII777wTZ86ceVGIvVVChUIFWO9RM2bGyoBdJ+u9KNCihKrCmsIRJwLJgPVssSM3H9kLo/MwcHf9NY0PJZNCx7u6GjOh0IkuzQ0Puy4phMsUBhVCToiQZTNm8PRV5c3r6CcME09exjzZQlUPUJVs1PtFEdC1rANeFgoVloCwV4SN16bOeFrtxOhQ2vgDysKVkUjIeUjh9ikJ0RYRqAtO3chfCExlhfXuCrS1+3YRIWOtBZcGBNALiIiKJqrqbvZRXhuDf3v4HM49N7SOXu7HGE7KI0unWmvFkX+fVEojCQ32IyDUzqvXDcsmG+YVTm/1gnNufNAB9+YyYBvHJS07b4qQsXv5UfvEV5waWMeGRGxRV4FqpwfQSSUhaCsaE8aVmkM22FPNg3ImNVmXnTu3WFyViUCUua8/UwclkopcVUnPXdPKIAlQzgE1mTNYalalLq1m6K8kCzuIqmgKsQKkjh2PS7KVZVVpZKQ5uEYSwIQX+zZ9r3vdCTDmngVS6Z4nXeQNg8HYQS5lsnI+U/l+O5XK+X3JFdwm3Aalh7zy2fqVomP9jJkKFbNnRfps9Vx5UK6tcq3xtRTletjFCh5t/IzZGlisx8VGJtn+biGTLO87TetjDAMsf59fjpkA4twDYNVu4/J1m/l0bHCclU8vZG1rPB4jiqIDIo9zjosXL+KnP/0p7r77bqyvrwPAQaP32toaiqJAFIU1JLZoQcXxvKpbtHiJ4mYln7TWePTRR/HUU0/hnnvuwdLS0ou2LlTrPWq9pSJWZgzopI8BkAUUyKpQ673A8ZVpXncGYDPT4A3dv53AwM3rnfsUQsgAbkvFteR8w1/DOmWDvIyYCbL1C9tSQ7Lciz3sHWfA7lxAWG2951/v2nrPX+ijEkWF0kgJdkgAMCl1AAmAIOu9UgODNOwxTHCOtUFCenkJ4NgA+ItJs8gDlVKhL1sZISvl1mUDziVUgJVdhRTUu3dl6OQFNcsJACJSlpPdMmce1b51kDEG//HdZ/DTp5oLMwDQ70SYVKpRWULJVcrziqxqomBSKHI+U1lpklXnFHujknyv4ww4ud5Fp0M7r1UA4xJCMQ9DQhQB9AM8eI2hPxsqbTDI6n3x8s0eEkehnfqMA9DnnlLTVWBUe1Gq0od6r+pElDnJv5zJnIrKGIMnnmzu7JeZhPAQu7oyiCiKXUNXSgHAJK+Cs9SuXsnRGxzeRxkD1k90kWQSzWcD8Rk44DKhkskGQK9Ln0syDxE4i5D5bzQusbnZxcmTddaTcuQqRR5lnUt51JGlnVQAkAj3/SLykK+x5/u+5cdcO9ePM43KQa5xU6BiDmWTHjqPi8tWDwA0t9v9ug63+3nS/lnTvSRGiS3zbcdvNT9rBLklONer+RxoJpk4WIOSyZjmrDdjIszeD4xhkD/+KhANGvevERngULK1uLlwHJVPLwahdvLkSTzyyCMH5BLnHN/4xjfw7LPP4ud+7ucOFFFTDIdDRFH0gsVdtHjpoSWfWrQ4RrgZyafxeIwHH3wQaZriDW94w4vebdGRnETsjCpNstnKtYEkEgnXJ+UHKK7Bei9kPADIhreB9RSILBY2oSHWIQRAPT4MgXV6aDSrpZaiCWI5WvyAKSBILRJ2HYswDQ155F5R4cLQPZ6zWsXjwiDhjf79EdV6z1GEmB1H6VoHAEFYHhBuvReiAgDoSgCgVo6t92nEEwBo8CAbL4pF1BSllv4w8yPjQ0k29+dLSYGltO5yNRAQxMylWnlkz0GahUYEQSSKQrKcqPlXytDmwhJ1weXb//0cfvj4Feu4bsqhYVA22GMyBqwt+7OzlDJICV38IaQSOb8FYbZ0k1JjdeDfh5wz3HKyDyE5BLEwUFsQ0tYjxPqrVAZZwORLtS0EgDzQpq8TSwjOcGbLXYQwoBNAITQVVWhDreWQe2qIB5aS50TJhhpXArN75urVHHt7lqIxY4h7ESLPzinyivSMVSulGNkOqCoVeX9PsbszQdaJEMcCGyf74I7rnZwBV9HP5ZAnoxB1lwh4LqgCOkGMAV5/19bBv3OHNR33vFuMKvu8lHhyG33KPt/zgq8BzEecRV5yyq2s8pFLEm5lk+u8YUahgl1V5doy53Idn83vDQ6D0/pr1sY5A2Eln6xJtxallI2s4g15k8Zw8IblaBOjaQttuVFmztKRXbwAMXoWRjbv99Zy73jhOCqfxuMxOh37feP5xHTfve1tb8P58+fxp3/6p3jsscfwox/9CH/5l3+JPM/xrne9C9l+I/D0Xvm9730PGxsbL2oTeIvjjeN1Vbdo8SKgzXy6djz33HN46KGHcPvtt+PWW2+9YfYlRf2kEWC9R3yACrXeCxHHhFrvJcHWe0dXZikCOrG9OB2a40S1XjtcfqDaC+ET4vw+5QCWE3tIZ4g6CagCM4RCMrTo94jHL/vHLmUcyhMMbTva1AI1tSgVEfN/xhU9wyLEeq9UOqhAvlsY0njBgDOrHTDOwgilgHuqDrTSywKUUuMqCiKQfVkPL1+OjhCSdPIJkAH2gjFxbImEnOVELYcWyEDKb0GM7//4PL7744vWMWksIKTAxOJndnqzh+VBilUCAUUtpFIbDCYTum1oUSryWKC2AHRBcIYzJ3oHBFjIrBeiwqLaeQFAL7k+KtZhYYLWQwqOV50cQBIILipfFkLO+7IkQ+Gbo6ZoapJoAkVdPa9qsv1eNkME2FRPU0SRQDqIvTlt1YRGFOlKgxObMQBAVdolSFmA0QZxKrGykXpPWEW8txgTpmi6HjABJ/M4pyvft7eXsLw8LVCa/QzEZrjO1VSUTls7n3LJd7AM8TqxwXcrcmVdAX7yKzLuJhPuUYK6bPWkGTnzoFz7xrXW2rLPGczcZwanzCNIYL9XGKTWd2qO5n3DLQ4NnC3+vSaZFv+uTdxI4NtJJst+nNmHpuSQP/3K/so0L6cln44XjqPyaTgcHpA9LxQ+8IEP4LbbbsPv/d7v4Td+4zfwnve8B//0T/+EN73pTXjrW996MG56r/jXf/1XvOY1r0G/339B17PFSwct+dSixTHCzUI+GWPw2GOP4ezZs7j77ruxsnJj+TTTrfeIXZrXyXovJJuJug5ThFv1HVoBpgJYSt2FaXvkbjM0DIymn9saYTlRQHixa97GZyXeg2joBJwiyNqPGbAAKy964Xu6bEJXtDE4e9m/XN9eTiSwawkGY4whIwg8lKKRRTmxI7pSBilRNZCXOsjGMSRbRRGs9xiAM6vpQYE8hEgO4fMniq7eqdcjgPA0HF1ByzoC4Cy4bXVzdOKjXXk8IFNNEJVHACDJy+WIiVlOmpglZZhE5OnOBoD/PnsZDzx6yfp5LDnSRGA0qbdFcIbVQYJbtnp4xS0DvPa2VaSdCDwSWF7xvxhPJopkl5nnFemOZwwQx8R9YmhqySmuDhV6Fsu4SDLccuKoEiOEaA4p2IfYlYVcV4WHXJuFAbBEudnOjN9ap3Xp+grFU4RkOVFvo1SyqPBkEoaOozRL5RUnke5TFYrWBufO7TjHLi2lYJwjW/LbglaFJs0BqqQrFYGaeKEQW1knwtapPqJYICLMtyGKQvL6higPAxRKeUFvvqgqQ7LpE4Lhzjs3D/6didJpv1o5FMW+5hDffvY1gLgs7yjf98G3fj7Vl/eZ2PMDymGr58uDcj012Agm1/fm9+QazqFnfgLArkoyFvJMIwFr2HcGHLyhiccY2WyXh2aSCVaSybbdNuUW2/8eIH76gPd9rs17Ol44jsqn4XCIbteeM3c9sLm5iT/7sz/D/fffj7Nnz2J3dxfvfOc78alPfQppevQe96UvfQlPP/003v3udy981qLF8wW6YXGLFi1ueNwM5NNkMsF3v/tdrKys4I1vfOMNo3aaRSo4upJh2GBPNIthZSAZYDzbMNEGKWfwOJMBCLN/CxlbGiBmIK0DUFv1hYwHapWFYAbrGS3IWoChClHsFAVESu8aktyg0PTzi4MhpE92tk4ZMYNB/Ix7+SxM78XAYYjfMKgA0AuLHAzKs60XhwYjT8NuIhl2J8Z5HS/FFS7k9nVbzUqcK93rbkAr9pXKoBMJ5IQiEjUDBABSaTAsqZ3zgRaRnvU4s5oesSO9XrfMUkvEfIJC00goa8eqBYnQ2CNOT5UW6EQTjObOCwaD7cGiDVioiSd5ZMCCJSsxIRz6MiBLKmYTlMZ+z/vJUzv49+/Y1ZZSMGyudSAkw8ZaB1waVIYfnHMatZKi2Cf2k1ig24kw9Fz4nDOvM6gxQBJz5IQcoxBbqqrS+1acRHu0SAD50UJsEnGc3Oot3ANKYlZVMAJOpBCV73AS9ryXxgIY0saeWE7Jlog1Oe/fRmVqxQOFD6iPr38gldCaVPX9w5fBlRPUSgBtGwwYOlGFUel+3Z7axT777B4Kjz3iVB0k41oBle841JkGMMrsV6/d213mCjLh0IT9abRBHDOMLXO/lByr6x0YdngE44jvKxfdkJKjpBxUqv2sqYdSpmXffW8W43GdgUXNt+tkMcZjNyH0yleuoTNDEKeywtjRFFJbNjbDp2zykUOuvCgAmDjsAAGg1O7vK8/nPmUV877r+tT4jmdOo1GyrnUJ3DH5MV0Awj7H277pujfxmXa9Lvawrr91+D1TWja1ef8axADGDX9PwBrIJ40YosGmzyBC09bYyVLb3rScp/sNDWxnCHn1JzN/br7ftcqn44XjqHx6IW33ZvH2t78dP//zP4/nnnsOALC9vY04XpxXOOf4+Mc/jve+9703ZG2uxfFASz61aHGMcKOTT5cuXcKjjz6KO+64A+vr6y/26jixlkgMK/eLqAGQSoYxYZdLDlAiF8aqJrRc3XFT5MpAMDoJFXGGIqC4FguGwkPAzWKiDTYzHZAdE5rjFGbVF9ozFVp2rIADumotvUSwoinD1irEV5FpMMPIhUvKYyVF9dRLGK56InRGnsKyYhGpOGSMm+Sagmr3lZeKvMyAiAmUyiCVCrmjMDSL3YkG52gs+m0vp0jiuSJJwGlBKSTOIuMFmXya6LBHSJdNTRM6kVogn84MJojlohe5qzt8cWzIegeoW4jqjwoJuCmctj4Hy3SEnl/ezfGTp3dw6/YAEa+gpp3IrCYwDBj6mcRov+BrUJeK5k/3+SlhZSXzFmHH4xIy4t5rtiwViSiqKo0kFiTlgdYGUSxQEeemK3sFIsEOiKU0Fji9mTU2jWhdz6mUaTKEYw55jZ+UAbaC2qCbCDIJRSXc+6nExkq2YKdrRcBcFQuGnHDsqEusn6v85JcBQyq1s2APAJVmiLjy5tSVxMaWRGhvE8d0yvJZ7nUygd29ycH1FGcRdKVRjBwWx8pAcA5NmOtUocEJ1zUAjHMNIY+SVYwBK2sdyLjex7O/Rr0GfHaCBwi5/hgt16qqNLJUYpzTVE2dToTdXZo1q/RI65NE4vZXHX0vihxzimDKOQf7lCKl47sMBuMqsh40wTQKJaz8H4NBXkknP1h5yCdtPM9P5RiI7CoCzdzPMZWwN3XEZggtBtbPXWdohAIVLOSTVgBv3i4Of9JrhAqn9Ffmriu6ohywP//UpFSD8gkRmjKirM9cVoGT5Xy0nuMaRnFE//dLR4dXe42jTUs+HSscR+XTaDR6UcgnAOj3+14rvXvvvRf33nvvC7RGLV6qOF5XdYsWLwJupO6AG5V8MsbgJz/5CR577DG88Y1vvOGJJwBYi2mFY2ptpgiw/gix3ksCLLhC1gEIt+pbTzRiS2BtE0JznMhFiWtcPn3ND5Fwho5Q6ESX/YNZdcTH3I9Qsu35W3alDM7tUDIt3J+vJmOMKnsX6EoyxlgliAjh3RXVem+fVPJBaYOMaL03rhhCYtB6UYglHbDUkPNyYhCjky4SFCGWmBr+bIVZCG8OxCEKFSFqsGOxofIVkxbW5ei/I66w1VtuHKsNXfWnDN1eUCPEipC+o2NNk5+4Hi+e2y2ASMBIgYInUJxB7QuS9L7ZF/coVxgW7aN63QiRp1BqzL6iyINJaRAT59IgBMxN2hgMuvVxzBKBUxtdGGZfJ0G09VMBlnchasi9IizXrUO0CQb8GVhT3HF6CWCMnBkXkv9CtbelHmJtGKinWELknTPp30+UPCcAiASNaCsKhfPnmwurU9yysZjjkvTiA7LHBlVqkk2d2VdKUZ+dVKWxvFTfe/uDBKe2+9Z1ITcyUY97wD0gCug36ARYUyZEu1DAn1v5mtdsQM7de5rs0aboSDej6WtgGzusbTNZwdVNlcrK+e7r+1ww7SV3C5+yirtdECphL/Iyo1DBTlz5bPVcjSPc2MlIVdqPmWtv1NS6wWn97xAzuUw2Cz0AgM0y2HJc7Q08YaVCa5Ngw/rUjSlNeVIMYAriyR+Aq8PuNsMTMNWk2gJ01Pxs2OLmxHFUPr2Y5FOLFjcKWvKpRYvnATcKAcU5hw5tdb/OKIoCDz30EIqiwN13333T+MjGgqFPKEKNKgOt/LRFoYGYeMcNcf4Jtd4LyDI/sOqjYD3RSOISoxHR0wd1jhMlD2EKEQkYQ99iBQQtH6AHp08hObCaniePZ0GqizA6LGRbfXZ+564a+OqUg5Qh93APkXAvZGoNQ81xIamUAkilIFu1gCwnqnWTDWvdCP2smfjQJpBQCthIb7fxHDqeXIlZ1MWusJyoWdy6XEGI5n1SE0o0IkwjBideW5VJQF1nFXBti4YO4ya48qGujvzLKD2SvSwWmCfNGGNYWfbP05MJjeSl3rInhSLlyAB1NktIU+wwr9BNBU5udL0XDzWbz4CuJDKmniuoy+1bcqqaIAN2xJAgv94YJFjqJ/vLpm2fClEIUo9xwMMNlVQiTgsksiKvuDd3BqDdq5VmeOqpHS+hcnFnsVDLGEO2lIB7yKVqokmqYK0MOGhKIQAoFXBye4CsF8PlqlcQPZyppJIxhtyQ1Mvo85oM6DIJaYiaOK69wSDBy84sFs9deWaJcF/LhUNZlIoK2nHNJsI9P8ae3449TSk+ciriCsZBfnFToeL2Ii43JUpm/zw2e85j53s+cOVBMcd308TezFIWdgsBbYAT5gdIcfHI341NYQWAw0Z0BXo82NRtlnsfa8i9NYaBNTQmGhM1ngfGSGCsIJ/93tG/R/3m8dESwFszp+OE46h8ejEyn1q0uNFwvK7qFi1e4hBCoKquRb9xfXD16lU8+OCD2N7exqtf/eqb7kFijdBRbADA8dIwC0l8Tx0rA0F8QajH0iEDidKY8CK+Eml0kvpFxyfrnkeIWodxDhN4flP3+RQh6hYAiPkEsRiRxwctft9Kj77sgPB55u6LpVjuRZ4qomQKF3N7AUAyjUv7n0vBSURQ7CGzpqBa740LRfbPomY7AMBeydGhN1BjpzAQ++u8lEqs9hJPcSYgyyzgpJtoeh4RAMQBSilleCBZdVhM6IgCKx2XrQqDZLT7MABEnDbWgCMmLrcKyHKiFK0BWO17lDbYGbs73yVnyD0ZKzbSd3kp9V6PVaVJRd18UpGvRxnQ6SoCTuwsETi91SeyzfTrfF6p4EI/pW9bSpUHIUgEhqLSXmL+tpOHdlPU5wWqBR1AJ/wLRZ8vqfdD6m/TCDKGLPKTeZTppVDca7m3vZHgyl7z/ZNxhu0zff81O1EkwkSVGsLxvM4YsLaWYOv0ErJegojwrKyUJu3/kPw3ipoLAEoVYMsa0OAUsq51RlTzetz1uq3G+d6Vq+SzeR07cjQzj2pKeuZ14Zm/pMd626eY9pFbid5zPh/Feuj8XGr3nO46q5hRUA7VlOvbruW61LF9PI0l88OFvxtms9BjYGjeRhs5xhz2d83jLSRTA+llTGwhmSy5VJojeuzLi38XzaRfm/d0/HAclU/j8bgln1q85HFzVYJbtGjhhBDihlA+GWNw9uxZ/Nd//Rfe8IY3YGtr68VepWvCaiJIxQ9Xh94sJkTbMCDMTi8JYEzIGQ778Fnv9YVCPzt82TAhOUW4hklIh5FPvpfkRQQU9WGwkj4RuPSw65MFUIuGqPw4XHbztu5NDC6O3PtNsDqryIW1dOzsrl1Njn4eE6z3RoU/ywAAxiWNVNIGSInt8IXS5K59AAgQL8AYYJAKdGOBjcGitdI8QkhMYSlCNKE0EaKGzlUbmnJzXEgDyKdCy4Pxty4ZcIdVGgCIAMtPYbOkaUDEaSH0CvERSxwXbEWjeZTIwBqu691x6T29s0RcsypbCI6lgZ9M00QbN6qaoCgVqORPWSoSiXd6o4vlpRQx1U4vYI4M2btxgKw2hDAeewjGefQdtmJn1jpHbMeo/UIGLEjZRQPdTo+8v4jNHNTFJZKQI0Qg5p67rHD58qKd1Cw6HvJycy1FZ8WvWNQlkQQq1AK5IyOOjY0Mayf64ElyWDAnHlQqWUsdR1UeDcclPQuSmPcEACNPA8A8ut3Fa29rq4eNjV7jeJc1nuv+H3HlVD75FOm+8+NnNfvwTQfSQ6xJ7bbFk8b9zONTNrls9aTJneed67uuyyRKLMTK3tPY1P9h+ZbNQs/ePMKtpFTzucwttniMNeRDmbhx3xhjed6xkE/s4jPgRQMZb3kGbMmn44fjqHxqbfdatGjJpxYtjhVuhMynqqrwne98B3t7e7jnnntu6ok24gwDQgeyiRJwQqG7NEBGLMJR7fGB2maMPNYAaaD1nm18agosZUdfTOqYezp0aO5T4PjQ3KeQq2c5vgLBc8DQd2hTt6D7CyFjw659m+rscYLqaSnj3kyB0hMoPe/5T7HeMwbICP6VShtk1CyUEOu9gJehgBpWvRoMOLmc0opkAevsIgCb4OuKnkWhAuRdCFcWdqMKK2mBQXfDO5Z7ClbXOtbXYT4LqqJKcapKiiHBYlH6ytBvuUc5V0tHBsXKijtTAwB2R5pkR5lPaKSS1oaUJQXU94K1vt3GKI0Ybj01QJLKmoQjnntViO9tEJVCP/lD5vRRocj2eAAQWS5CzoCXnTiqXA5ZZ6r6KKQ/imqTuigVsgAAIABJREFUR11N6qFVRJLKp/AAgAlBdfPTJ3bdvyMYnrpov7cwBjxzaYIokcgG7pw6o039P8K5W+YKXHCknQgrG12sbPSAaNEqi5olRi0mylDJugfG1Fl2FAzHikxUVZVGFtBlksz5QzIGvO61zc15Ea9QOmxwK8dzZyZLZ+OBb/PmLW8XftuT1+T7vg8+ckt4ntS553NfU4bLVk96LHNL3kwkAu4crqYrKMMIr0y/G/gtwLDme4CBbFQ+1ZlSi/cXY1gz+YQwkslYzoemt0VdcrDzjzWOh2k+ri35dPxwHJVPre1eixYt+dSixfOCGyXz6cUmn3Z3d/HAAw9gc3MTr33ta4/FgwPFeg+MQRIVLdQaUa4NJLGwlWsEJQlRcyqmaCpsiTLHeq85QyAoYwYGIQU8TlDHHF0+gpZvQLPqi5hGL3oSQGiOUxlouR6mlKJp9aZYXBFjDB6/4v9NXxGvF01wubAXr3vRBFfmPheCk87N51vdmReKTGpSi2wAMCoBYr0Lvchgo5+Si3MhxelCR5ANBQQbKAXVKSYqgvRY6MzCV7RaWBdh8DJC/hAAsqqhRgBhHLBU6n4u4Sd2Dpe5WOy6Qsh7Uh77qE7kDn1PE4le139vI+XIBJBKIfaWo0nVqCZe6Sc4vdUDmyFaQpZLVWqZkOswZLsI2Uyz6GUhc1Dztt12YrBwjEKaN6gEWEhjDdV9kJxPRLQHpKiVANrz3LgUcE36xhg8de6KcxlnTnSQOzKTzmxm2BvX50zSjRF7zgddGXDGG/ebiDjSboTVzQ5WTvSwvN5BfzmDdFy/JdG+lnqcyCo64vIAIIlp14gBnagCgDTg2pu/V955Wx/9fnMjgs+i1mXJl3jm5J+9cSissWgeykNOeVvYvNedh1yC4/gajdJhq8cc86rQY8ChfLIdFdbQtpcgx6nqC+AOlfRkbCOtm7+jkTQyewYJmgw8DNLm8aZ5G20m8Pbjubgv+U8eBtPNzzfMYpdo4hXL8lvcrDiOyqfWdq9Fi5Z8atHiWOHFJJ/OnTuH733ve3j961+PU6dOvSjrcD2wEtPK+RXxJTgPsN6j5C0djA0JPg603ivmx09GOLEMb8A1FSJgKuJSkGzXpqCSSbOgjF9Nng4KvD8AuwayKmjx9I1tsul7bmjgc5LJIoY9T3h4LyqcpHxXNn8eEaqNu7khKWjGBVFtYUDOZyqVCVIZJIRDPUg4Ti73wBgnn6va0HODgDA1kyvkvAndoBynCCHsayYZssjeSTwLHWBRqV3Fp4WxAcVF4r1dQ0LCbR00hZgr0BhjcHXoP57jwn1cljM3OdVLFJaX/crlfFyRbJhKYj5KWWmy7dbeWGG5d7TLe3uji+WlZMEOtwggjsnKB23IFlTDgv77RWWQBExcIRafk2rxvp9IjlPri8c65FGBelusNH0sdd9So3fyioNy/8krIvlIuFcqw5A6bM5ksYO94c+W1TqvFMqWEgiPQliXGoN+hCiV6AwSLG10sHa6j9UTffRXOxBJ5CScZmEMkBIyzagEMJXU1QGEbkjTVUwkqgAgCmiIms2IiiKON901sI6NHfa3DAbDyq5w8z0bVMq9zmPHshmM0w4QMMidnwOlRzGtfMoqi7KH+rmyZAcBQGyGYJ68MxuksVtn8n19URPmj0aMAtvVv0JAOYm4LG3+zNYkZSzPPQY2pZRlvMcCeQG283Hu73qoIC7+CEwNG4fzsplsa5VPxw/HUfnU2u61aBHWLN+iRYsbHC8G+aSUwqOPPgqlFO655x5IebxuK5IzLMcclz2Fo8JwpMyg8hQjKgN0BENOKJbUeUvEQOWADlC1b6VHWQfg0Kov14CZjLG9yp1BzwYaYdlJYXZ3gpmgArkgHJdZ1ASOfX92RIEsunTwb85U0PqzEPNApgEt7S9vC8umFzgNNIzGkRdtiuVeN2YoHe5iDAZXHaonBoOdovnlvxsbTDyiDgMgiYRXHaAN0EskSUUQcPlAcEZWHo08HMFKxrHez8D2C+WCM7Ltl2SAhwM8HBugTgq10os9+RGzqIxAJgqMCb/BmcapXkS+VpShWtlNx9Lur8q4C1mzCDEdjdgElSGouhg7cjsaTRRKD5HSSYSXbPG55fVjhWE/hpT8SNF0HtoYZJHExHOdlWVtUUUhoULEudOhseS12snCbBhTW86VhOuLTuIzCE6z6lMa6CYcQ09W3hS9RGJS0UjjkIaM4aQCZ0eJpTtODSAaToiQ/KsQxIJmS2o8c/EUE0W0/DMMiTDe8YXi4FxDexRQVIVUIjXyhYK/weu2Snz9QfeE100Fnjg/gu1elUQcTzx7tOjNGEN3OUOZl+hmESZKg3EGzhnY/v845+AMSAm2mcYYr8ODlBzwnNtVqeoGHM+y8jKAKBaMREKVDaSrDUHW0QHXyHjmpL/j9o1aaWhZLReBlMkSIwdB5JuHcsf8m4gKykJGTH+7cMy1qahQOeZMzjRKLZz3+Eq7nw8q4S7guj5nRqFkPesekiZHATspaFP3AAA39muZw+5jwGaeRSJU2K6+COGx9zOQ4CjRdLaWxRhp4y602d/ZFEu2bW3ee8xKMjVvOZtRihvDwH/0VRgeg1fDhYcALbsQDVlehkkY2V/4e4ubG8dR+TQajVrlU4uXPI7XVd2ixYuEl6rt3nA4xAMPPIDBYIDXv/71x454moJqvRcTH5SoXb8TDUgi6VCPpS23Xoewc7YqJjDlxEs8AbWVXkg2U2guU2juU+jV6Sp7MAAryeNzf/RbYB1dRtj68wBFh/EEKc+tCMrJ4ctfqQzOXfWv28jjm7SajjBxFA9WkxEm+mhxQjKFW5fGuH2zj17iP2JUFQVVZTgs6MeF+ttAXWDtWeow611xhHgC6PcGAEe+54N2ZEfMo9ARpKPregGBSqlU0uaoM/0JhMhApabrYgztWjQQjXZ2TVBIvAHlh2PpRJUgL/PotXSVYLkXE5RDw8I9hqEuOK+u+AmyolCka41655tMFJmAurw7wcZSgu0TduJpCko+FRBm5xUyl3YIeXVThCifywArUm2AwUxlcpAyrFnyvWrlMPFeS16DgHwockYTR0S0C6WoUQGgI/3bTclzAhYJ+ohr/D8vMzi9tIz//O/Lzu+e3sycZMItmymaeBUuGDqDFEgkkk6MOI0gYwkhxUFRTxu/jS1jDAnhGZhyrIwBJEEppBQabcCa4HsenWLkk3TPIGSO95Hus6gqjTSV6HYi3PbKVad9neuYp8I9d7jsbSOuUDh+N/MomX3NJolw749UVM53ZsmVM6eSmQoltxdwualQMjv5FJs95zzhm+tdeY2uZ0j3ZzUEFE5XX4bEaOaz5vWxWeIBQMci5c9zm9rasj9sf7a9lzY0ORlzlGSaQmt5ZPXNhcsQ40sw0aDZ6k82K+B1vBrWrdLipsBxVT615FOLlzpa8qlFi2MEzvnznodiwzPPPINHHnkEd955J86cOXPDEHDXA8sxzRhuwZ7OgrEyZKlFiJ0etaAD1PZ/ITAAtlcA6rMgD5heNDTCcpnC1l0HjlewT46DaA+RmO8yroCAgGXGfjaLHTfCyOcsOyw6PnnVeAtISylD4ekaFx7CdPY05dA40x/hVRsZsmwTjEus9fzF7lGhvMoNABiVtDwnAyAh2udU2iAR9HOqycFnqy+x0k0XCKQQoUHIWT0JVDN1Amz6XIWsJghCkTgRFTY6XdQmlTR7OqA5H8k6ltPHxtwh9ZtBaTJQjwz1bl3i6PVwZRhGdjchFgY7ufu4jcr63FxZzry1nbLUpEyVPK9Adami2FllMcfLTw2wtd4lFaDINm4BFn0hhS8i9wUg7PoOPSWyGTLhjlN9J4FGzd8JefSk7ocADgCppO2xiHjvjgnj8oqT5pfZ7e0nFd7yighrvQG+8+MrGE/cc/Zw7Mn+caiEuhnhYiPsDkoRsCKGeVHJoiYl3s+yvKLUR857F0KIqtG4JK8DUBMDr3vtFjjnGFX2e2bpIDZ9SubckQeVSTf5E3GPYtbzfCc934+9n7u3LdF7zvWPPZ/LBvXMLFxHkhkF5chrDLajm34Ptb3udvVVxNiZ+01LzpEjW4qj+TtRZNu65mNqvbc1vMMYg8bmH2OixuNhzOE5qpWA+PFX6r9Ly/M/b27s0W3e07HEcVQ+jcfj1navxUsex+uqbtHiJY4XggDSWuPRRx/FU089hXvuuQdLS0vX/TdfbAjGsBL7XypyZWB9tp+BMkBGfLEOiInYt+mjQQPIqJ2lusKpZR1EboWcigYAD9AnhSqrNMJsiYDmgptgBkvx2cbxLECdBBRhlUVi5/n+igQRf7P7kWK55yuyxLzCxYm9syvmFS7lHQAGp7pD3LERodfdAtvvJk1YTiKfACAhFMSMATJifkMI8eMrwMxiOFcHOL0UYamTNSqXQoqtOkCdUegYIoD0lEQVAQDkKoYIOEd9eQ4A8PIlBc7r40tVMwG1BSYVIessifvOMIGISJa57HtmoZAcseC56vNyBJB7LKZWMuUu4AmNq5O6uCUlx2DgtzSk5rRQi8plaVdTCc5w5kQPp04MICJBfvahXuN1phptLHW7Q34fACYBN4NChamqxH5RZ2OQYDBwZ6pRM+5C7l3U54NaVEIllWjLpBJfFNJNG4aUoJCa4lS/wP97aw/dpC5g/8f3LzjHbywneOaSvVi+3JN48jn7/SYlZCg+X8aKZUm999IOPvWaDnm262S0JoypQomKboc+dmkpxenTS5BMoXAoxMcOAsm1ZzjTzkwmnzLJqmo5+G33cfF939d84nv2iIx7fvWRS9y453LtIHUiM3SqphSjW//OgkHjtPp3JLh05O8GHNzyPGGsFnoxmOXZJrLcqyaTveb1alAsAQBvaPIxJmpUKxpj2Z8zDXvs8R+Cm/3zklnOe8v9wLR5T8cSrfKpRYvjiZZ8atGiBRnj8RgPPvgg0jTFG97wBkRRWDf9zYxVYsckmaAhFq8LbRBTCzUaJPLrYBUIL/cJ0zg5KCC47VWnGaHqpBDyCQACGk0B0Lu3D5bfsD4r8QVwa9dmWEgJC4pcDFNKhR2nent2c4NLI19RANiduMesJGPrSzEALCc5Njo5XrPBsNw/ATbnzS95gSQS6BMKPwWx2kklafJS0S0xK7o110QBde3e4JblGN3UTq4Z0M/tkAI5AGSCTuKYICs9hm5E7xR35U0AwCAusJQcnhdUyzvAX/g6MpY8Mmw/R0T1VQl6oSreL0CVlcZw4t4fieTeDCKfddJypo/MD6sWW7ZZjPMKgrCj8lyRlL9KG8RzTR8MwMm1Dl6+vYQoiQ4KUtRsIld21TyoFn0hmS95SR87Kuj3IwDoUP3kcLi/XnXKnm0yBbWxQiPA+pe4GwwYEmKmXIiqjAIqQZYQyKdKM7x6dYT/fcsypKiP086wxA9+ctX5vdVBs2pgis2VxPp5EnFc3iPclwnHQhGUgEoZUBrVzfPszkDJcJuCYvk3RSejX08RscEFAF7x8rpYnjnUxZxp5zypHEr7jiyd54yvccY392vPzKkDXACa4Mq6AgDhIY+4R/3vu64rbn8+E9pNfFUOO0DbfuMw2KweQGrOL3zmstaz/07zc4UBA7M08nTSJmUSGsdrG8mE5vPVWCyfp7aSOgfk0w/PfGI5frr5uOuWfDqWoOQM3mwYjUbo9dzNPi1aHHe05FOLFs8DjtsE2YTnnnsODz30EG6//XbceuutL4ltnsVyzElF4QnxRXhc0a33QogTGXBcfNZ7PWGw2c2DCmBThFrphecgXd/x86MTrtCLn7EvP0BxAYQE2gNgKihXhzlTq47CQAEwePyK/ztLGfMGWduyngTTONUdYasXY31pC0w0BwRPz4P1vr8wPy4ViewdF4qsVEqIF1upGQY0gRbw/7P37k9yG2e24MkXHlXVb7L5JiVbtmTZlh+ytLExv2zs/s0bMXEjbtwbce+EZ7wax5jSjGzJkiyLlClRIrvZXYUCkI/9AVXdXV3IzC9JSqJaOBEOi10JIAEkEonvfOd86J7hl3ZylHn8vKhKAyCNhKWqdwAEM7L7ELPSOYvWCuTcH3i7s8VXVGGxOj4rSAh8UZVHi16QW1IVZi0K8rMqFxnIB4R6TwVBpdvo8PmctyYblQolgRCmEDYdqURUI54hdvY2C/z49jZGk3wtINdElF5LaOvIsTzqGseBXvdp1jry820dMCHcyyVUAvsyawxuXxqh7K9Kv4KUtR41+SblbZklqDApsMR3KXVGi98ihzs7DD+5tr8yr/3hv74OKuEYgC8Ows/710/8v9/cL2BMWv0yHxqiqkkS5GdUAtgSrS+NocfnU9TCVIUmQH9G7tzextZWt3DIAnUVR6JF6J1TG/91jimbXKSvoTpUANAEjk3ZPl5/MnYtI8qrGDnmIUkAAM5AI1BPKpAII+wM8KqmXO+cJ2Fw0/xvjHC/fyvmryHJfLOopw8ORe84tVBgPes3Y1Xv2su5/j7FSKb+bRj4h/9y7sD9yjVuqt6/D+TTxcVFizMNyqcBAwbyacCAARE45/Dhhx/i008/xVtvvYWdnR+mvzInWu/V1oHifmMBlMRAN5XQAp6f9d62ctgpq9WCsIn1hGiVspZ9SQ2QpGXPpiqxzn9i7ub3wht47Cm8zZPJs5SgesJ9YgBzjGS5F4sZbao5DptTRobBYb+s8Opug59enmB74wqcCM8fZuEDvzfJSeF+SsDVARj5v+FXj5/w/CgioVVI4NJEIctoSpeU7y1q0BsIBwHOozYKMoFQTf1IHKn+fV8ZVRipVZWN89mw9MCEAkvnYD1BlN62CfulE8scCv0BlbWWi7F2SCjuE7sVDO7EUs+Hvrpuu7s09RNFfUatqdS2FuOC40c3t7C9U3oDp9YBisjEKnI2R0KgOoEgnRT0ubxMIJ9S3oiNNnjpSn8CwHmkvKmoxFpKSS3q7aLOb22kZuESmtiOR8ix3143uLmzHnD6/XtfB7e7daXE8cwf7L5+KcfjI//vTaAW1HnEasVa4wL1Yk5BeR8Z43qVE33tqODEZ7+OqEbPIkXRSCHUMiXw6k8vnfw7ZPuai3A/Z61//pYRO1kdsb0N14d0qAN2gABQByz/uuOH57SQqgsAHA/v3wRs8wBAc/97LLMzsIB8L1xLyv8u7/a4um2OGrf1PyNz/ZZ3QDhBxlcLykfOOXhII8/fmUcB9uRolnRc/7Rs4Q5nEEefr/yV66OePnKwnr8DQ82nAd8fzGazoebTgB88BvJpwIALBsZY9EOSirqu8c4774AxhjfffBNZRg/WXUTsEa336Ooj2sdt60AitACgcSDb9AFYj1Q6h73MYrOo1n9KCl+nBdFT6zgZOKSExQzSCZ9lrGUi58jlk2Bbxto0dVKCCmWxRUJbjZRr8+UxMI90Z6SA40jce2n3spPP8cp2hdf2S1zaugKRXQLjEjmfA5GCzLXtCBolODYJ9RlqsuKB1Azz1pLVRE9qRNWLl8YMt3bHAC/I4+85JKr3orFp83fIFug8WptiI9lf14Ezi+sb6wEP46sZ0APjMlDD8AaZP3v4HLSjW+TZBEtNqkXf0srygFDvyZpwm52RDQb4OHN4XK2fw+ZmHq35Zq3DiPCebBoDGdgXY8B4pHD72gg/urUFRiCZqcofQXwxpRDRKeo8qroSAMnGbIk6gWz48ZUNsk1fSp0q6mVoLf2NRt2ntkQCwlCTfmjtQqui31zXuLm7uVAFnN6f+w9n+PsXviBuhzxCPI4DSsSuFlS97OBzgSJY1lEPJYm1PKjqI2ryw7RqwYkDKoWoqmILKACvvnoJ2RllWGjchOoi5VzDJKl2V1EH3tWSWzQBcqgQOqicyriGDYSWGFxQtQUAOkJuaRFOgmi5P7jLnEXL/MqDEIEE+K3lAIC7gI3iuX+PcISb+v+FxAwuQpb194OBeWtL9o8dH5HltcvztN/Y6K/zrI3vGfCtsSzEB/999ZhiBNZzHZ3a7P2etGIEiAQLggEDvkNorX/wcbQBAwbyacCA54AXSRoshIAxaQqVPjx69AjvvPMOXn75Zbzyyisv1Dl+V9hSnFTTYE5M6620A6Na7yVc/pQs7LlxgFv212G/NBjnfl/zvlpIfqRFPdL2jeTP75RrCHSWZhwO2/nfSO3T6ji1SSxDSi0bMEDX9Dngb4/jbUcZ884BnFns5HNsFAI/2xe4trOPrLgCxlcX2bFAu2L1isKEYr03by0yQoBq2tAz6DNiw8YwbBX994Uz4M6OxO5kcmK1RKj9DoBOlAGASRhDtS0gEtRMKsFKb64VeCTj+iz6yI9bGzUysf5h5iDBQSXCOCQLFxw/BYPitLYOEgq0ttqTSdyHWH2KJQwyWOfwJGK7JzkwjVyqURav99RnTcYZw842Qf1U08ZB33ySZwKXdkvcubmFq/sTqDwHdaZPscmjIFY36yx4gu4oZS2VohCa1pqkPCmVwNXdkrxOSCLhEt6x1DI5jiKTAVBTFU2WQYr4OdWakRIGjIf0+vV1jVu7i5pajK0knMRUT5niuPfQHwiXguHzr/y/n60FRbl7JYEwpqQeUS31CO58AOiKJvJYcsDWmHbwWaXJRJXWFkXgGm5tFbh9czVo3wYUSKHvglhSSDixwGIeUCaVQgfnp0KGSTbK7yzwPAtmoQP9Z86gYf6aKczpYN0l5aqgsilWXzJEfIXmirO239vuAW7of4ZYvPtDNVJ9yTE+Cz0AYL41nve++hRLnr97iFHn+tcmrMde0jkB9uAzcL06h1nVf2+d7CcU3WC5N2DAgAHfKwzk04ABFwzPSj455/Dxxx/jww8/xJtvvolLly7FN/qBgDGGXUpWtwVywjerBVBS7UKMI3v3NwlBMwcAbQMO4GqpUahwcDOFv0mt+5S293jh5Gdtz8CwrQ4hA/Vpzm9B37lLIqtc5KP4PCxxDmi0w+dP4gGjZUCboyOabm1M8ZPdOX52Gfjp/gR39ibIy2tggQ//WCAvO0cE7I4zUjCTavWUEYuNJwWdew49zoAfXSqRZ+W5tkTlE+i1nKyj1xgCgFLSlDZdP9Is/caRwNNZzPXq2M+Fxn7AjkJwer9FggVmSh0sKlGlUZAVVdQ5o0WJo6qJqlB2R+HgIQC4iEKkVP75YHeniD6TTWNJ1nbzWoMzQAiGve0MN69v4Ob1TWxtFiuKh5bIwFDfjyl2Woo4RaeoalPez7OGvpZzADYIdbl+cn0DQtCNXFPnIyqo9aEssY6btiyoGDmLUsbHlANDEXgWluiz8fv1dY3bS+JpgWWw2VqHf/vPMPl0+0qJVvvP5dZ+iarx//7oyZk5kHBJOEGJRKmX1DaGdMBUFXoMKfWZyoKetjQZ0xMJyoBS+5ev76/NyyH7uhABo3h4Tgjtd6lO9yET4fEuIwkpsd+zSN/zCPmV2+MgeZTbacQa7+m/b5jTMAHVlI2o+gGHy/ZD7Jv/RV5ZMU9/HfMnZjF4SKBE23Tfc+zbT9Hz0VvX/TUWbcMgPv2X9R98KibeP6YHy72LiedRp/BFw0U8pwEDngZpPikDBgx44SGEeGrbvaZp8O6772I8HuOtt94CT/F8+YFgLxf4ch5fxAvOAEKQiXqnWtcRVZSk8tYBhehIMArGZYnNvIaK+MwDgIMFNWDq0BE41CVXyr5Pj5DSOnHxxywm2Wf05iytchUDT6jPpAGnyJdnPMpJWpHPDlYDhpJ3BdSlYFC8CwwXkqGQXWDCiQkYO2sd00GxBtqFC6laF15ysHNPgxQc26MMjyN1bipiMXRNjIzW2iKTjKRAOpx3gbQlUXN1g2OjLHuze1Nrp1BrXpSixrGmLedkJPhzFk2ilV4uLI6IvE9jJTLenhzjzqYGD9Ri4AmEUooCi0oIAkiogcWQsQq182doL0GtUWUhcTQNW38CQKEcvG48Czypw/c1xPUoJbCzmeHR4dMH8ThnKHKJIhcYlQqjkQxmw9faLkje8ORHJanaBGmh4iDNo7VmEMxRXvlJhFKtLTLByIRVkQkcVv4e744z7BAUpechOYchXN8U8onKF3R2esTzFw5Tgv0e1ca4EEDgcgIAKr26s19dWyeegNP325///gQHx+GdttH77f/9+l6OB4/TalBSarBpoq2jUhJt5J2sA8TaWTjigJrNDThnJGI5ZYWZUQcKAOEh3G/d3ML2OcUog8MsoECqA9Z34XdWeL+5sAi5Ccbeh7HVfIxPltz5XdgWv4eclJWbB/XH0s2DaVoxpbENWOApO4UT24FtQ/OqxXVzFxP30dovzPl77LfW81noiQX51Hcj+o/jswD3kUy8JxnPOgneoyblnrpRzScfoC/VyGvp6Anc20H5dCFhrb2w8afBRWjADx0D+TRgwAUD5/yplE+Hh4d477338Morr+DKlSvfQM8uBjYk7wJSke/wSndERGyhURkHwQBL+CSmZh8DSwu7+If4RALbeQVOtNjq1Ewc1E94wRg0NSP9pL/EjGhYpJjvdWfoyPvfye4tiBba85Rc9ymFjmAAcyKBrFrep8hueYZXr8jOYoOx3iDwWJqTOgG+s+PMRYdbbfPgpe/LcL80yaPkU6MtNguGSoev/bw1yAQnkVCFcDgmBDG1ZdgZOUwb4M42h1B+As5nz9SHlO+TFELJEVUEQFd4XDAD46i2Z+RdAwBGUqNpJDayBttFmLhMsTVL45iTqtiRWyrWoib0Q4NeryBmuQcATaROzSQ3QdslADgMkFOlstjcLqPk06zSyHMBax3yXCDPJcYlh5Bqpe5JN+3Ex2SuRLSmUWsc6a3nACjBCAH+pd0p7b5PcobDeXyf2gKjjGNGzA6ZFAKPpjSFXsx27yfXN5Ps2JaQHKA4uRI5BQB0643uMtHuQybitpMAQCw5RLLn05afEOm/uqZxZ2+deAIALN6Rv383rHramijc/7LyTqjjguPvX1bwXY9RIXGeMnXOBdeiTW3f6joSAAAgAElEQVS6NUagTdsaCMGibsFcsChjO29s9HgASIQn0MWmJyOFJ7HClADqhEHKUtS/PbtViuNnr667R5SywXHrV1VNA7/1WaKe7Fdo1IHajjFlUuwZi60fnjW5PzYniABR023/bJbzxkOWAIBAHSS2NO9PNlFocUX/K0o86P2deeg0ixzcY1Xtu8wOBbjHdph7juNTSvUd2zkB1jOGnFPoe+i7Wner96R+omG+vtffF9d///rqQAED+XRRYYy5cOTToHwaMKDDQD4NGPAc8CJlMkgpk8gn5xz+/ve/4/PPP8dvfvMbjALWRwO6e72XCTyIqJ9aB0wkwzzyrecAFIJhRrhlc+PgHG281XbZtv93BuBSblBm9eLffKE8ikOAwzxn25TTfdPNIRyw6DVdiSUZLUBW8hqF+grGjAGy4qEBXJhgOQuvN7uvfZKKTANQCHXGOaBxOViE1eSYAwirOGKqJsVqGIQz7hu7/vvOOANnLGr5Q82izySHJigPOqUB7UaWkmF/owTj4aC+dmxFJRWES1AYJhBKjU0pbs0wkg2O2nidHwBoA9nafVgGdu9s9pOeZ5FmAZhiZ5myDKaf33kVnw8aOTialVpnfXDO4dE0vE8Gh4Mq3MfN3OFhIIK2kRtMW/812SoMqjbDaKRgtAUXDEJwCMEgOIcQXQIMFwyZEsgzEXxfUb/HJfeF6FaRK4E5QQkpOEdLWCeliMhzSVfopJBPlJp2S+hAh2/tjVCeseVLeYNTakkBSxtQkuib/J60jkEJgCJwpZJK1NmEWj6zkAav7cJPPC2OOW8M/vjB4+C+ru7m+Ohz/8neuJzhr/f7nwYpGD7/OiJ97IG1DkUp0UYIXpWJjqgKgHLJnOtUlDFyyTmAc9pzWOaSRD4dURbbC6SoJOtmfWJ99SeXkPUUpCq4xrGnNmAhWsyN/30QqhVVSI268ZNPsXtjIslTbSQRJbYGiFpoNhUQrKsUQ4Q8C73DnUHLJv4EKw8xAgDCzuDEagINcwbb+Af2zL969+nAvOqmzlrPZ6HXPy4dU0DPm7Ijstb77yAWqvLVHlqnepVMFjlEL8m0Tnh3++c4Sz45B2Qf/39QRd6r0G7nh71Jlkwfr/8RA/l0UWGthaC+zL8nmM/nKEvat9SAARcZA/k0YMAFQ4rySWuN9957D0opvP322xfuZf9NYS+XUfIJWGZNEqz3iFEg7YCRiBNay7YlR2/2fcGBvbKGOKOY4Amkz+rnRBipVnpdP+hhMcFcMBO0r72OtGdw2Cn+uvhveuCvi8/1ZwD2owGcpEtGUmpWMQfuOEJGgK1l0WADAGiC/Vpty+BtzniNqodcWkKghe4hpwRn2B1n+Oo4HHqe1o6kNKRac801Q6ZYsP7TRu5wZTNHobJgjYazUJxmh6kTVFIphFJtFDgzsEQ1U4qqaq4zMFgyUeQssD+aY5xtxNsmED/G0et0aEe3IDOeYGE/6PNGhgrzCPlUNQbziPhlZ2TxpAk/qzFbpUlug8qR5fY3b2xGa6NxgrIBAJyzcfUT8XEQRLaAOuX2xJOfeZ9AZylK3m+C6s9n6Sc4w+3LqwkEJsEjL8mqjDNUFMvhhNdZTiSfqP2krhmoXfzxHnArQDwt9/XHvzxGEyF4YpZ8R7MAwbhf4NMvet6VhFwKIXiUfKI8X9RxxTkD5VOFC06yEmfEZ79tLcpCoCKs4Wcxz8UzmM/1ivXf3pbCndv9Nm0ioEAqpQ6ST1VAuRqrB2Ui65R5oF5U7NgA0AT6DQA6Qk45o4M5HsZT+2eJkG1et73/HZ7ZGawKPMPBWlIVWpyST6X5B/btn5CzI1jkXnWTQwGOyrdXf19c2/s8+9ZJDhnQcxyLHKJHKdW1p5NMvmtz/pm0hw3k4X2Y8nLPMRnyHkbKMgVu1pWgDgwu89sgDvj+whhz4eJR0+l0SO4eMAApX8cDBgz4XkAIQSKfjo6O8Ic//AH7+/v4+c9/fuFe9N8kJoojJ3zoVsaSpNYz4yCIYY6UINBatrJz2M0cLo9mK8RTKlJqJ3WhaHr71LpMqYWrKddvSx1A8sUHWUL9mG7/CXeIOaTlgKTds9gLfq7jz7xAiyYSoFesjn70x2zTcu7P1r5EqFHSGKBQ8fNZ1nOiQHo+qAvp8KM9ies7G5CygHacbAtHrTFkwSCI0eza5hAe3/51MJQinh1+goTHy4FhrOj7ro3ATWL9GeMUqBXyLBQ4kQB2kBCxIkkLaBRkij6mZDoLSbh3BxHrSQAYZ/HrU7XhZyQ25GYNJ7UD6EOHon6i1j+jIqakPDmuY2RSiVh6Lun4QGcrSkWtLfKe2jM/uboBpVb/7kBX9qRcfSoBmHBa8JTTWQOV+KaS+xSr1Je2arx8KT6POXD863thy73rezm+fuKfuy5vZ3jwyD8X+HifS9tx4pz2TMcbtQk1zSigjqc2VCzoHMqCNj9r3RFVFDgHjEan+/0/f73lJd9D69YQgSSYQR1INok9JnUgkYgzizpAPmVcwwaYIckNdCCphcGhiZBbrQjb7xoeDt7q0O/OoGX+36XzkUAnLby/8IUtHHcN9pt/w033v5Gzo+6wgVpQjvmfy9Czxj1rFr/Cytf3/rHkbe+dJPrH89nECec4xJ//JwCA6/X6lU5tLmxuV1G7ovc5cmoLYEPc4iLiItZ8ms1mA/k0YAAG8mnAgAsHCvl0//59vPvuu3jjjTdw/fr1b6lnFwt7eXzRqx1QEjOcc2JBp7l1ZK+iuXUn5JdkDtfGLSZ51fv9YBOyqy0MUsJRPOFVY2CT9p1KVoWUQACgmMVEfXLyb+bxXPeBJZJVKdcGaJ/dVP8M5pH6MABQ8HlUvZDzuCGWjZxnnyXIElsjRQo+UgOpkvhRY8wqKaCEw8u7DncubSDLRitqDSWItSkSyElqwBUASkG3WqL2FQDaJJs+oEjY9+VinmApxiETnkXpqXvQB0XeL0NG3G+bUMuJomyJ1T0D4mMrEw6H8/D1njb+3yV3OJh37z2KDZtzNJ97ypRWE5kKa4mq7wQySxDH6DGlyNcC84S6M9MmZdYAJsVq4HCUCezv9Nu9UAnuFJUStTZla+mJKdS5nVpXrza0dk2k3aWRxuvX4spNAPj60ODPn64HXc9iMg7Pt9sbmfd9PC4E7n25+h7Y2VB4+9eXcelGXCFgCc8EpQaTtQ6CMAgscVAJYsLGNEGlJBNeruOSHuDOFxZ7N29s4vKun3QIKc5Dz8RIhs/RBGYKBhdULo1kG1zrlZFjlyKcRJELDQQUrsI2aIPkkUXjqasEdDZ3IXIps1OwwNqPBys6AVr4983gMNL3cKf9Z2zzv6/chZC1b9j2t38cOGT+7wzv3/vP2/nW5b775LNx9xyXidP3sX14AFY/gZUTsJ7aXU72X99stNX79ydNhgcPHqBp0r7RBrz4uIi2e7PZDONxmFwfMOCHgIF8GjDgOeBFqvkUIp+MMXjvvffw1Vdf4e2338ZkEq7jMsAPCvkE0JU51FiYcV2NKGpb1tbYlA5XxxVU4OMwlcRJIU1SHw+eEGpLVVZZdHWifNjJPsPZ71PG2qSaOoysQFm0T2wcq7ew2jzcdq7j50VRZVBUP23Acg8AXCAYwxnDpUn8eZvVhhTJpioJasMwySw4c7i1Dfzo8gR5vtVrESaIpCPR9Q9AmoouXkj8FKFrfR5zm4MnEKrUd2EuNK5MSghGDxj6im/3t6U/JyELpPOgEmAWCpJUpSgQADoDivLpqA7vZ6c0QXu7TFg8mfufs+1SYzljPc8lD0n5ZB0U4b1XE0kdYx2Z0KDO/toCI0U7ftVYMrlsHDAmrjcAQJ3b8U+vb4J7TpaqKkmx6KNPW10tJ1pLIllEJJ8aw0mkRqU5fEHgsTL47c0SnAtQTvr3/3kYXKlwDvwjUK+JAfjysX9OuX6pOCGglWT47es7+NFr+9BZjsvbWfSZbQjSvbYxJEKZQu4Y4nuYShQ3LV2lRCW+AJCV0kAXs5eS4/Wf7QdVRiGFUcgSMhfhe9QY//mXsg2SP1lk37E1RszyL7b/3B4H1w+5PQ6SRzFySblwgk7oLjOnYTzEVmYPsW0/xA38Hqo3EevpXpbMQ4bZQO1U7qkRRVWNx+EhmXq+E9rWnSiZrBEQH/xPAIBT/XEH57NE9IxZObmCqqrw3nvv4Z133sFHH32Ex48fkyw6B7zYMMZcOOVTVVWD8mnAAAw1nwYMuHDwkU/T6RR3797FjRs3cOvWrReKMPs+YiQ5ShGvbTAzi3BA5HpXxiFjgH7mkrqnkM5gf0shkzE7iQ4CAoZoK8VB/5xJJ7bSPpUkA9qEQ0gGND3tR2KOQvXY4jgFkJUUDeAy8k1iCUHyVDhoIFCnhmK7RzkRx3hQrCag0bo8+Ag0gTo9DBa7kzEePAlnjWvrsFFKzCN1KxpjUSgRJaEUd7g0YiiKERCxFaQK0rRjYHAkBVTKU0MhMJZoCTW8TsEwki2OW5o9XqymwxIvbTYQIu8IJeKJphBKKYR0ypuQmoUPdHaUlJpSsVpS2lgcRQo+jTOL44YHn7FMhvu+XVp8NfPfv+xMAXLGOju62LgnlJpBJh1pvleCo42ouq0DlGBoCQFrKXm0/g6Q9hyOlMOspT3b41zgsKKN6ZESOE5IPFji0kaO7YC1JXXWcOgUTRQeIOV6ZaKzTI2BeuaN6axNKTWdCmUxbcLzlXUMOddrRILkFm/dVshk9/fYfO6cw+/fexxsc+fKGJ899K/Vbu6XuP+VP4B+tBhLr96ZYPPyBIbLk3sxyjmKUqGa+cl+3VqojCMUu7XWIcsEdOTd6SM7z+9LKhY8HgA0CYu7calItZzqhGcphXjVxuK1n15CkUvMAlP2NKBAagLv0FiSS6X975JC6KA1qIzsO2YZHLvlkrlgOoZydVB7pFyFBv0qGACQtoKBX+G3tMbzIWQdrewUTqzuuzAPsWffx0Q8XJAvvvvmv26hBDHu5v0vT08/u3dt/xX2Hcf3/eFrz3uShZzj4Pw0MWWJpmE4cbf8/B6YWRBjIk8pG9UVB+2BnFzFyzsv4+WXX4bWGo8fP8bDhw/x4YcfIs9z7O7uYnd3F6PRaIh3fM9wEcmn4+PjgXwaMAAD+TRgwHMDY4yUEfhNQwixJkN/8OABPvroI/ziF7/A1pZ/8T4gDXu5wL3QFya6YM2G5KgIH7CZ4KQszxPCy7Ogdm2Ny2OOMrdkW51UpKmNLLoPk2+yL/R9c+aAc8EpBoft/CPP/lOUT8s6TlQFVIOUV3FR5AvbQ0pnHJjr//TVFmht/Lx0JDAOAK0NW4zlvMHcBfzyodG6fluobvsKm+WIFlQmDkvJ0ZsjygBslcDeWILLAiNlSdZzlGu5hOJAQ4i264SgV6zQ91nMTQbODGygPsNZxDKaV/atVZRc287n2C66gDjFcm6JtPpuKR+uKfYeCWo0pknjsUWJEE1zMIurnrZLiy+m4fNoIzabSoQ7256zIOOMwTyHNY91nHRZKQFtAJAEkgqgWQcCaSqJFKvMQnEy+USxMVtiaVHIALxylWYJR4HkjFR7KyXxnKz+sgy0yZ2hEA4zHb9euQCmhD0WwpwjnxzevMEwyU9JvZg6+tMHM3weII6A+Pg+X7PrLC5vZ2AA/o83r6AVam2FIBhDGSGfumOIKDEjFYF8Is7VQgiSVaYQtLGnJG0un1YtOGekZ7ua0we04Bwv3dmB4hq1J7kn5xpVQKFUtf53eWi6zbiBDqyzFLepZUNXjx0jWCPbx6Zb4cKdEz1Wbau/x1TU4Q4Y7l/HCiyIMWexoT/FLvsQJT86XToEFGUs1C/POTkIMDSePnu++1CAe5wKuJeU6u8b85FMYp1ksi7rrTnaaqAAYFsB/vG/nN157zHh+tc5zPb33WW7J/8tpcTly5dx+fJlAJ3K5NGjR/j4448xm82wubmJ3d1d7OzsIMvi3zQDvltcRNu9Qfk0YECHgXwaMOCC4azyyVqLv/zlL6iqCm+//TaUSqvfMSAMCvkE0DOBqcFmC2AsGdbiVkZjhAp7OxnYwiLDwi1s7Ai1N5IJJXqkTURN4M7uO41MQmJf+s5zSz2G9NUuSqzjxMihl8W+LU84Rlr0gIMv6mitwhcYWekaLOa2DAYNBNqoqokzExx+OZ+jsn4LUMk0GGPYm+R4cBgO4E1rg+6bJTx+am3g3KlNXKkENkcKk0JA8NNrU5uO9IhlTmonweFgCeO2yyCmqSM4Y7CEAH9tM3CmYQPBp1MwlKLBVPsJv9XmKTaYHCM5x9STgc3gcGfz6QiLFHWXTVjeGk/R7f4+0NtSyTILCYkK2lMn6vFxnHyKlSVicDiows98qL4Ng8Pjc6oozuM2ktbFM+K1cSgyHn3/UUmg551vkaJ8oChulkjJxk7pw7TWYAx4aTdDUYSfg5QnUXKGmkAAJJSzIlsfUtRRS2QSQdXJEtRyc0phJVP/F1cMLm+sknouQmD//r1Hwd/LXOCzL2fe3zPFcc/z+7W9HK+8vIWZyNF6xpS2DuVIAT3C7rPoss3DF5tyy1qipR4VjNNkdylk+GSs8OQoPrceV4ZEVDEG/OJn++CcoRStd41ViNZLPilu0ASUyTpAcpZS46j1bxuabhgccmlxRR4hF8BINsilQSkNcmGRS4uc12CsW9tay+AYg3PdnNf9zaKxCnMjURuJuZaY6wxzI1AbGbf7jcyH0eky0sDxwLvbGbRs4h3bwrXYaN/HDvsY2fn6ms4Gj83gX7Ny9CsdHUow1k+N+77THMvRl/TmIHsVTg5soZRa7btzolfJ1NWa6pkbXH+y3UkSwif/tbonDxnHdP/8xtuj3r/bM+TTeZRliRs3buDGjRtwzuHJkyd49OgR7t+/D2stdnZ2sLu7i62trQunsLkIMMZcOPJpqPk0YECHgXwaMOCCYUk+VVWFu3fvYn9/H6+99togO/8GUAiOsWSYRqItM20hGOAi92BuHTLeWXNFceaQAg6ifoL93RxcrAd9OThJLWMTVAhuEQqmbsEoHk1n9s0SdA4GDjSDp2V7rKgzFDOYqL952zPWJgXpGEszGmQL2oIGDThBjrD6rmIVUUEAQMErtAjXhSvEHHXUWix8NfqyJvu2vkQgn6xzmCiJKmKn1RpgIxcoM4lJqZB5Mqat4yilIVnVKW5RE2znKJZ7p/vsCLA4GEaiwbGmLeuUsGRxXhuoT9GHQlpMPfu+PqlQyDOKgRRCiUSsdejs7miktAG9rQ7UWziPWED6LBTmXvKJUu8pZvW2M7I4qv3BN84cHs/813erMDiYr/7erSnCzzY1Fqwkh44wDA2xYBr1mCmEjhSMpkomWO4tQbEGXGJOqMezhHXAzijDjf3NaNs0LSFRKeboFn3kd7ZjkNxCE2o6UdVUVOLrbF2s29stXtpbv67ds94/h2jj8If/CpNPN/dH+OQffh3Wrf0Sn/zjNDhbZAyv3N7EeHuElkuMNwSqqf/5qFqLsowT5xT3BgoJPK8tSan4vM0i5jW95maeSfTrn9cxHikcRZIAXr6zg62tbg4PqYVVoPZRKVu0ASvIUK0oxSPk/crYdNjJ57g8muPSqMVOaZHzJqjg4WCny06+XHOfgqEFC9REtFaj1gpTXWKmc8x0jupJjRk2MWcjGBZWpDgWIdIj1sg6oGzK7AxWrT7XuT3AxD3ABF9hxL6A9KlPuyym/p/AwT3qIodsoW7q+S1wLr5aUL6QnkUO0aN8csjB+XrfLPLeNXmXeNNHYvWPGcY4TMUg//Hu6t97SCbHFLg+XruOVo4hXE/fmYKTtJrVjDFsbW1ha2trxaLvyy+/HCz6XlBYay8cKTiQTwMGdBjIpwEDnhNeJNu94+Nj/PGPf8Trr7+OnZ2d77pLFxp7mcBUhz94vUqlHuScbr0nGTDiLca5htoMWZ/Fs1lPW3IyCcXBFiolCtKeDQEGnbCNgINJssQ6rRO1k32G8Dq3gXPqpHhuDP6Pw37wBFVYZ6UnyGQVO7E8XAWl3lPG2mgtLRFRNQGI2tbFyJillcxGqZBLfmIr5UNfbExyhjITKJRArgQK2Vl1UIKfgllP+GAVnMjGEmPoAABGVC0CgBSG7vaYoNCojQJntrNHI8AXc1Tc4Np4ddnZEUo04sdCgaElqo84BCoYUGwuODI2RePibS0yCMyjdZqAeC2nsxC27b0Ezrmo7Z7kC1VT4JaOlMVRoODGdmlwMPdf15GyODjH+1KD987FlYMUV7lmmcQReWZb4gMWsw47CwrRBnQkoGCORLzMKN6bC1StJdeyAoCbl8rF/BZGCgGXEpPLuCUlOCQcHrno7GJjoI/LtCDj3kjj51f9waNuxbL+zP/nx4c4ikixqkiGwbI22c39EteubcBmCo6dhrWrwFgSDDiu0SmfIqA8Ey2RCJWKQ0eSQAyVUCYOlGnVki36ksZzFl4vjUcKP31l7+TfodpMPLBIyAKkFYPzKoqBeE2mTBj8eOsIl8cVdkuLbGXSZYuULx9iyV3x682ZxVjVGKszL6IdAPgCxjrMDytU5u+o2gxzXaByI1RsAxXfBBiH5mGldssD7++Iskm6CsYqTOw/MMFDjOUhcnXmhdcYeENmznkvjYP0EkyW5RAeq7lQ4gpzjed4PgJMAb3kU4be4kse23BvopDn3Llg4B/+YW0frH2yto3LNsHM4fox5QRo1/tus52nljgPFn0vPqy1pPXL9wmz2Wyw3RswAAP5NGDAhYJzDvfu3cPR0RH+6Z/+aVg4fQvYzQX+TrHeIwZZGsJH9kgAYwWI9hjFJEOsZkmKAUro83O9LR3mG677RM+0XrZ3aB3DSFQoVDgrmTEANgNYqFzy2Q1qwCq6OslT9NfbPkEp5aCBnmD9XBOIBEq8NRLA4zBoXNi6TwfJKYfangYd9iY5Pj/wF2UXvFMoTHKBcQZImSOXHLInHT6QsPpUoD7j2q0q74L7TDo+/WRC1j7rfWAoZYtpS1P9GI/6687mHGIt+5iDsxY2qp7rIHlDqsEFANI1MIz2sSdZi4Z4sTPeoLKEWmgoQSXWmKeo9lHVRMnK3bHBo+rZCN5COQTcgXrVDoylUKNhUOsE5UpgHgloa+PAWZzYcAByCVDEErHA7tl9buQch4Q6Ma11KBWPqjSXmOQSjyM1ewBgs5S4tjMiWeR11p40Eiglt0pyR8p3SXFnC5Q8WgFVWUpSmKNTXY2UwW9vFiuWrOfh05fHLPf2NjP84+vKS9Be2sqwu1Pg5ku7aLlYO4bkwHHjJ3gnBcNh5SAER5YJNAGFYVNrcBFep2ltISWLPrNCEMgnbcEJNrkUMgnoxujWWODxk/hD3RKfOyBukfnLn1+BOOPjGLLfDB01RFqVssUsQD71PZ+cWdzeOMaPd1vslBrsRNl0vn8mco42ulYK/e5c+CuFw2G8qTCGAVAt/ve465kF6jlQ1wKtFWisQmszNC5HiwINK6FZDs39xHBmp3ByAmWeQKFGhiNkqKDYHBmfI+NPUEjtP4fYyXl/Y4GhHVI3eaz1IBb1m3p26h07/VfeRyalKLYBf93O9uAQ/PGnq/vONsHtuo2eEyXQQz6B94/3kOVeKs5a9FlrcXR0NFj0fce4iLZ70+kUe3t78YYDBlxwDOTTgAEXBHVd4+7du5hMJtja2hqIp28JueCYSI7jSBRlqh0UR7QmTG0dCs7WAqGFADYUUEgDvrDXoDrx2MVRKaGWFMM451HV+MC9nynPvu/UEGhnG+iwnX9Eas8S7ME6hZQCXYbSIkYgnutMQlsL5tjKfbUOqAlZ6cbFg/xtxIYs5xVq57fHYLConV+1l/P5Cvl0eTPvAqa8K+4tuQWYAGcMnLOTAIricdtGqlGjIQYpKZZQHdiitkP8nnfF2Gn7bYikDADMTZakZsq4gd8UahWVUTh/dTdUg92if6wIaFiinR3vKYTtb0sndXlCXTcZsYlcwoEjwxQNQX1lfQGi+gFCQSoAKGR87juqIwkKkbH7pPZY6/Cu5EUIlOesIbIQgihrUZKjJgSXlXCodXyfihnUxHdAJukT9Dink0850U/up9c2wYlKLQAQxLpyKUksgnjs7tRpMzE17kdNQgnVODsLbRneuiWRy/BzyJhbu+TTSuM/PjwIbndpO8fhIoGJAbiym+HS7gijsYLLFHY2FB7NnFd9O8k5ntT+ky4kw+GiY+VIBckn54BMiROllQ9KSdQx1pbIVkopSIqr8UhhSiBfs6xfuXEes4r+LgmpvW7f3MLe7uoc31r/YG0D7/3QFSuEDpJP+swxC9Hix9vHeGnXIlcccO4M8bSO2Mqctpp6BrQ14FE6CA6UqsEo9/ffNTUY72pQOXea2LNMyHF1DZkLL4fk6hngs/1zLmhHGIb/uoRJ8v7x61CAsf7kK+abIbyJE57jey6Sz/mB8R6VlOOYfPbv63+XI6DpqeHkI/V9dobZN+PowjkfLPpeABhjLhzRN5/PB+XTgAEYyKcBAy4EHj16hPfffx+vvvoqNjY28O6778Y3GvDcsJeLKPnkABScYUaI4EjeuTxk/JRwkmJ94V+UJamWE9CZULnnXPepI7U4OdOYE4Nc3b6X7Yh1JmCRQuBYAJvya0hO8/z3f8B5moPTP8eZQdsASlH7n6aU6mz9TnvTEU+x67pQHAWaMVg0tggmhUqmEYiLIeNz1Na/IJesQY1T8qlUApvj50esUyzBasM7U5pIO+04WdEkiOPJgCNjczQEZVBtMnBmYB1lHDGUosFUh+w6z7amf2RbxzESM8zMct8OL20ZMNbfL0awblwiZFnUs+OEmFgKiU6fCxRv0ATG9xJ9lkIb7Cv8fd6vXDyLUKATACaZRRUoRA8ATwLk1DizmDb9feAsbr3qCNxC3RoIGU+RoFqfCnJQiEjsEsj6kz0mxKOkeL4Bllu7I0xKlRQKFpyhJUif0iz6aBfBgSETDpFyX90+yfeK1m6u2cJkONz+xwvl2N0AACAASURBVLsOGwVlnly/Pu/8+bHXRjlTHONSIs8FfvP6HlShoLmAXVy75cqkGyP+C1QoFiSfzt6LslQ4PO+feQ5C8iUr6AWlnhNVrcSIhPK4kCTyiUoUtNqiLCSqeZyoqqr+NkUu8dqrl9b+Pg/UXpwH5uIQMSUjCRJzI7GTV/jJ7gzXNhmEYFiqbjtj5xj5FPr92W33gtCtl3wCALQNkAeeQaMBrsDY+bmn65fjrXf9EYW1kUk9pIqygZ8Dz6zHWs+xDJ0qbB28x1qv651n7vCMJ1971lcHyjEw1uJ8Z9tHDbL2yXp77lvLeK6F7X/unqfyKYSQRV9VVdjY2Dgho5SiJ38NCMNaeyGVT0PNpwEDBvJpwIDnhu8iA8Y5h08++QQPHz7Em2++iaIooLWGMWnB6QHPhr1c4NPjJhp1in2LSwaUgqGUwHZhoUTEsgRdpi1l6HVkyDdR94mvEBvPEyKhFpIDFmZ0tOdQMoNJ9g9yXxhrks6SsRQNGcCICpQOLeAE3dbvXE8o9Z5yNo9alhW8QouNYJvYNVCsQR1QhpzvO2MMgjGYKIlJy6anXELrOEpp0BKs6jJuURMUTSljIxcNGk1RBjGUosZUE63mejJWfWhJhNYpSmEwWzy8V0dzjJSfMIxnVZ+CSnQDi6LdxF3T6kgt29KvhSDOoy0bLSjibt8Kc7y0fR//7f1YkMXhoArPHZuFxcOAbG0jN5gGAqKT3GDa9B9DcEBHJmlKvoEDUEiBeSSJw1D9+YjDhGr71higkAAhTk1WJAMgJ2N0+w13NhMcd/a74IaLydHOgPrmcaDb26Y8p5kAiXyinlFj2KIaZawPDIW0mAXehzfHFW5uT2CcQZI6eYGvjjV+98ZlcME7f0MGWMahFzV2RhlHa7tr60uDiZF+sXfYWfKrINR9os2Z8UZLtVD024iaeEDMhE+x0ytLGvmkjUVRSMzPtf3F6/tQcnVccNiF+ncdnNmgZWtobRYi3i+XM/zico3dCetVOMXIpWclj57ZQDjmLWsjE4Q1CCZpxE4vNLZchHwKju9ADS/nUzcxMI8Hrt8qT3nV276aU77atP0kE8B4g/N32rkMXKyeo7MC7pM/9e7bO1BsP3HGTT/R9m2RT+fhs+i7d+8enHODRd9zwkW03auqalA+DRiAgXwaMOB7i7ZtcffuXYzHY7z11lsnCx3O+UA+fctQnIE3FWweXljMjEPGAQMGxTo7lFwAijtI4dAlQXdfSVSDOkEkipKIkG+o7pNNtNJLpXMFc0Gv/bPYyf4KBon+grt9aOCcImfe+z7sfMhUBkvtC3NgjpMtEtm5606p95TzBjMXHs+KtWgjXdAx677o9uvLFMnjBcUtQdEEAAVvMLdxYkcwg5awZOIcpCipjqhVVo9ND6apQMHy82AJge+5VouALq3ffKHoEMzi+iS2TQKhlBAANo6ukDNE2z8AsAlEFRmMI3Mz1BgDcHhp/BHmmuFwHj7fndLiKKJqEjx8nydZmHwK3b3t0uKLNnz/Oho4rggUgkfZoHnjFrXank+NGArpscQoo5FPs5r+vNaa/gxOI5396bWNEyVVSv3DFFDm3u749PmN6lLY2ZpS6lgx5ESiMJcOvpKdI2XxixudIrFbZcXmnvOBWYevtEAbsAub5AKPq/B9rSIv2RjZOTtDxpRlfO6iKNxsjCxAF7BWisfHC5EoNYRjAsA0wU4vRXlYniOfrl+d4Mr+uqVwKVscNf3vnpFo8MSz3mBwqIz/ndX3TGVc41f7R7i9bcECwdqUBI9+PJvyKXZ8xyJfHdGkg8j5xeaYKPnkscV1Diywbei8mUep5FCAs37yycvdoADvUUR1RNY6aQT0K6U6JVMPyYQcnK/fA+ckzhNs5osDQHuILdtPhPH2uKfvHEwf9Z70d0U+ncVg0ffNwVp74ci72Ww2KJ8GDMBAPg0Y8L3E4eEh3nvvPbzyyiu4cuXKym8X7YX9fUHWzFDnJRTvlBmCddY/XcIrA2dd1nDGAS7MCtHUBwEOQ4hid7WI4u2WBNXzX/7SCSW3+BT6huJi5D1PxBPk4hDGbtH3zQBYBTCqTV8DWElWJ3GWYngIMAg4IsHVKd5OX/cVQfnEmX1mJ5XOli9s3acjgfzGrlutSM5QRwJuxnVXKPrBRySLqGQwlc+hFrsHAJOgOkpRHLQJNaIcOEZyjilJgXXa5zubFZQIW1bRbAKX+1VANIt7sV9kYNBwhKWuhQJHQyKWNEpQlXU2YZktUAMY45r6GzZGLd7/In6tJ7nFUSTWWrXh6xsb21WAXJrkFl8QCMEik6hiTA/h4TGuq33URgLaDTFQvXRUojy3xJJLaC1QKhYlDQBg1lgIHk/8BzpSYJwLTHsmv91xhr3Ns+OFLVRKFLKGjm7uJaheHHFiBb2WU4qiLBcOc0otL1+pETj89pqFEt180FmDhueG88T8cW3RRK6VijBvggPT2gbfY7OA0kdyYHZmyZLnElww2EC/mtpEp7b5ok0sUUnIeDJc23bGnbF3NZVUSrHTswlWkuIMUZUpjtd/tt/bLhcaR+gnkXKhgbZ/Xh/JJvh+rc+t2+5sHOGXVzVyJcBdG07McO6bWPwnICo9Cv/8rIH7gBWjsyZs1RiaQ0PLgJNrvt7AgXtt8jprPZ81ps9Cr3+d0RFZ68+NRQbek6jkJ5kU0EdWnRtz1kiw9/8H3Ohafzd1D8kkSjDXs2+1CW7X21sxBgQ9WejbwmDR9/xwUcmnQfk0YMBAPg0Y8NzwbWS1OOfw2Wef4f79+/jNb34zvMheIJSmxrXteKa9AmAZJYpCJ3Ro7TpCyyUQVRTYk0Awrb+CMWhihN6cEFvP71pIWGypDwBgEZimgyWoLhiz6F6x1IhZC7oBEtICCcwACwWRc0BNUD6ZHsXRWptIoD7nczRB9ZRD3UMuLaHYHK1b/10Qa0RwFqcjG0O7p1SlUpedTwGDipfWAIDgNTqPFEJpbvMkNVMmDKZEQd9cK4xEg70yHiRwkGSSCOAQrIHpGRd9kGyO1q1np/dB8Ro14fo5cGRsisat12k6D52gqBKwmLCvcXXzAADDvQNKX8LjTYnOli+0PPFZ6gGdKvcgoL7StkusiMVwJeGZ9dXGWeuT4GgjAW3nACVYlKQCOvVDS/DfS4hTY5RxVAS2xAGY5BKHnpoy6/tdJ58YgJ9c21hbg9It8ujg1BqMjn586stMOwbJLIm4l8RXte+5ePWywc749NmlJB+4cyT6o+P4PY0thTYKgeNAPSclgKrxJ1lsFByPzxUaLUuF6bE/icYYi6wQMAFVnnMdkdVGCGXKd5G1gFQ8SgTN5hqZ5GgIz+qIaKc3j2WxrPTztH9v/3IHedb/vgpZrYZqPRaR9+tMd98XE9XgN1ePsb8hsbSCjFbei9wHF6UR4zVCwwjfMxZToMUC0bHfRWBtYQzwtLX3HIO3HmxAFeVQgGHm+c3fV+Zaz63wHSdDn7tD9/c+pVQ/8eWI9bLM/QcQpgU36/OL4wpcT9fGolUbEPrRens5ApoesirbIfXlu8Zg0ff0uIi2e7PZDJMJ7VtkwICLjGG2GzDgewKtNf70pz/h6OgIb7/99kA8vWDgABzB7pD6qTtv+rPi1vfnyGoLRpzyHeFT9CyoAamnQcq+uyoP4Yuxk/3tJLOPsTlcgvWY9yPT1zylRgTT3YcsGSk6qa5+FgA0lpHqYjXR4L5DbcNzkIyoxDI2D2brZrx/e0G8TJS7pa2AJJDBtZGksaIdJyvwiBwatFPIGG0+mJsMnERudyRKKekWRSkJFsYJvLzdghO3ET3Zuf62tGvRtaXbX6a0VcT+ahTgjtaWMYuXt++BLQYGhXw6ChBHALBbmt4aIEvkwuKJxyIKALZKg1Dg8bASJ/0NgaLImxOlLdRhSLXUosZ9KDZuS1AJcgDIqJIq9JN4L10eo8zXA5bUZ49is7ZESo6VIp5WCqnnifevgX751xteGmm8sqfOtSJY0UHg7FvnawJTX0eIlEKFT2Qj58F5ue96lYS6T+frGPWBMsapyiJOvGGTMc1Klfrsz6qW/KzWdXc/r1we49U7ofVR6JwDbgcBy9yca1gwvLZ7gP/nR7MF8ZSCmGVe+Pfwuz9iqecQZ1lj401Exiz3Xw9nTNAaDy7y3gmcevCsAnaSnbopDd1d6q+B5N/GYxfoIbioiUhLsDOW0FZL8A/+FwCAmx7SSG31v0B8yniPmutFsNxLxdKi7+WXX8abb76JX/3qV9jc3MSXX36Jd955B3/605/w2WefYTqdpn2TXlBcVPJpsN0bMGBQPg0Y8L3A0dER3n33Xbz00ku4fv36d92dAT3gnMM1DVgZzoa3AATBsUkoRaiSsWhLrvuUQihxGCJVxuN5l2f6kFb3SSCNZpEM3jpEYz5DIb86+XdnpVOCWveJoU1TSjGqLm25f7qVXtfnBCXW4v9DBa2XUKyJ1sApWAXNwllcsbuseIPG+J8XX7Y5YwyKO7QRlZEj1n1S3EBHFFAWHIWw0DZ+/TJuURPapSDnNRpDUdEwlKLGVNOSE1JqRFHOfYlLxQzbuQO1pA1nml54nkjmAGm1LlLCLoJI8AEdyTon2PldLr6Ekl1/WwN8cRReoo+UxXEdVjVlMnz+26XBw1mAABb+2XcjNziqBTiz0TdFS/CVM9ahUDyqgKKSFWSihLi/aY2Fdvj5Bd0BeuAdWLfRKxTHzb3+Z516/g4gqdeWbamQnCZ9SqlPRSW0YA0oT/R5pariFr++LoOErR8MgMby05qifOpUbP4bFSMQMxmug9W3NaXuE2nsENpogkqJui+ATtRS7CaX2BgLHBzF71U118gzgV/94mqQBNCBemeh30KXYDuf45dXDbZKn8IlpmwS3hYMYVtHiuov6m4cInCMDtarctaCBWzKYtvD6rDyKTZWnmouiCFUn6v/encWep6ELl/ijPfGeP7ua+/785n1o/nb3yCchZMluOtRPskSMI/X/+69vv335ftIPp2HUmrFom82m+Hx48crFn17e3vY2dn5QVr0XUTbvaqqBvJpwAAM5NOAAS887t+/j08//RRvvPFGkmSXGngd8HwghIBtGvAI+QTQ6jkxxiDASKQSVaVkk+inFNA/9E9JKqqVXhqYJ4uTw2Er+3PPAQRAVV2wGs5JclCPkXVuy/YJoXJmFzWoaMGd5Z6bQP2WJXI2xzxCPineRIkFEyHHYmcbqnWUc4PWhpcwS+unGDhR0SaZhSYQfpxY7oQalwMAQbzPQBqhlDIfzLUCI1Dikmlcn7guK5asyky4GCmZodUMyIk2LXVDXhWnKEMFa6LXYYd/jq1xBSxIqs8PFWxECbldWnwZII4AoDWRexUZzrXxBwAm2ZJ8CgfAAaBqDBihBlsmOHREQdxo2vim8j9U5Y8DsFEAT3zlOM5gTmVdQbcbBID6nE/nq9c2V2rRPC0EY7CU+lAJj16nKIlv0Dm30WqoUU+VSv41556PX193KH2FoAg4mwYUUz5Nch6tXagj5xGbh/rsJCnKp4Bo4wShulFLGG0hBIuOG8rxAPp6MMVOTykJEJN+fv7aPkalgrb+SSBk5TvX/heMj5i6NTnCWzdmYCLwbRGaV52JzLuxix+76rGby8KPdlMDWUAJ1NaADIxZ3QJB8smEyacQrH0G8ilgWxncrv97xLEcQD/5xD1/93+D9PfAtyZnPeSWcwBbfDuZRkJ88m8AAJttQuh1kgm8/x4x1//sMU+Skf2e2O6lYDQaYTQarVj0ff311/jss8/gnDupFbW5uXnhSBkfLlr8qq5rZKF5bsCAHwgG8mnAgOeE5/2iNMbg/fffhzEGb7/9NqSkP66cc1hrL5xs+UUG5xxmPofc2oq2dUQ9Dz3ASa+J1O2TYiFDDwa7k7pPNIgEWsYSg1Jnt+jry466D8HXP3KSSicxADYDInZypxvU0SLlq31Js9LjRMVbh67uU9NWAMKWeoKgQqHc7dqUwQscIpcAoLF+AiwTBggEcoDuFCgkfCzIf3Z/pHbUoHdCIDdG5K2CPqpbQm2vJSw4Slmf1J7w4dZkCim+wY8s3ZJXr5bT62VZRn9WbYTUOQsRGTnSVbi59Y8uyOW6YNe9w3hfYsQRg8NhFb5QdeA8GBwOguRWty1l6WOswyQXUZuxUE2UJVrjIDmLkktU1UUK+ZMTPT+rxpHVRDOi3SAAzBoDyRm0dbi8kWNnwz9HpiRuCM7QEjqbZNFHbOfAkHGHhnC7qGuillijryNXOzX2S9saV73Xk3bcJeHunMPjafi+jnKBehZuUwXqPQFxReG0Xl8/FUV8bjEEgrcljlupRLQ2lCEoIwGgjuxniapqIQSDITzbfuXFKq5cHuP2zW593wTG10z7r+/M+H9relTpP987wKv7HCJSrdUGlE0cYRUgi6zffQldp4jY7hkTfl/rNkw+aR0mn4yJkE+xMRAm7kK/hxOoQtv5xzF3895N/RZ6HAyN53j9BA7zKbc9Cirek6DnXAa+UEa7j/7r9OiiBPrIJw/DzGy/ipDpae/fL4LyKYSlRd/WIpbQti0ODg7wxRdf4IMPPkBRFCdkVFmWF46kucj4oRCHAwaEMJBPAwa8gJhOp7h79y5u3LiBW7duJS8uhBAX0jP3RYYQAnVdo8Czlt49BTXME1NRnQWHgCVkeaYYxrmFUzi1F4wxcoR+SZjRr4XD+SzqgtcYqc/7+wKdbI1HbsssjA5/F6+210ijw+hw0CjNEWZ2n9A23mETIS0yVkEjrAKsrZ8YkKyBdv6ARMapdZXiYyek7jgLE7H5W+K8lVMIigMt4cEJXavzaCKKsLPo1EyW7PWfC4NZYPrYVHNsL2ydXEINs5RaAzaUAX4ORm4sU3SjbbXcJGc6GyfJj2rs3G6NPkKXW8LgtAZkhvuEek+VDndgp7Q4avz74czhYOYfK1ulwUGAvDqul+QTA2VKV5JHySdHlEBkkmMeeXC0dRA05zdIwUgkFCOovLBosZFzHM7j59Mah0JyzIlk2SQXeDLXeOXaRrAdRcm0BNnNDh1BaAjPdsp7VQmQyCfqGqN2EhQ1lXUMhXBQwuJnVwLWXsQrtEwgOZpbtJHxpCJEphIMszZsiXbc+I+RS9Zbp4xzhqKQmAeKmM1rA6l48Jk2xkEpHiV4KLaS1riFMCLc9mhKS/xxADZGGQ6P4vUBNYFoy3OBX//y6sm/Kw/BlPMWlScpphQNpgFianomqYMzi7euHuDmTrcvZgPqIWcBFlqzxZLDKMqmEJESBqtngAyQS7E530buT2z7WAdD49O68O9Pu1zvsaUDAAfVqzLq4LNc7LfjcwA4+sc/S1BQWafAe9bbXd2oBqYS4PfePf27R+GEHrWgA8Dao/W/8wzMzNbWbQ4MLtvu3/8FRZ9F36NHj/DXv/4V8/kcm5ub2N3d/cFa9H0fMNTxGjDgFAMFO2DAC4YHDx7gP/7jP/D666/j9u3bT5XVsiSfBnx7WKrNMsL9MqBxL4ZIATkAjBzoTan7RCdaeNJXWNpCzJ/T6Wt/un8Gh50+u73l7ywenFjdIK3vkqd8DLRplybBio2BgzVfoHHxukEtoU3jwgRAxsNBIsVq2EAdnIyFva0coYZO1y4O7QSk94P/FI3hpI8I7TpNGgXUci/aZVBExd3c5ODEmkQOHKUkKvkQnj0YHG5tNifvLAqJuYR1kuy9ZMUIzNKeW8cVhFkPbvSCSyhiWyM3wA2tDyZgAbVjP8X25Mx+dAPngHsR8kkyi4MqfH1HWfh67pQmWL9opPxjvZAWR81pkHV3FB/IFNVK7SvYdw6C+OAURB6WWneJKLwAsKzHQ8Mopz8rueJ4ZT9HHrGHSxApJUESr1XK8YmlfEAUyMCCI4/UO1uizCx+e4NDBrKSU8hxIG65B8TXgBsFD67/C+mC43Gc+belWO8pgv2gJNw4auCNkixnrMMmoe9ARxhRMKvi9+o3v7yGPOsmE8mMN8GjkH4L51z4j1OIFnYxxgrR4v+6c0o8AYAL1mTSwdyKlLqHT4dnTLd71sBsdHv/xXHOee3govu2NpzTEphPuKdmmGX+JCOfWsqxfmLPIe/tn4PoVTJZl/Xm3Th49r9IQLN/+eNqP11/P3kfyaQ2ex0fnNrsnfuc2o4QrRcfo9EIN2/exBtvvIHf/e53uHr1Ko6Pj3H37l38+7//Oz7++GMcHBzA2jQnjQHfPAaV2oABA/k0YMBzw7O+VKy1eP/99/H555/j7bffPpFcPw0G8unbx/KaK+I4cMQsZ0GcpjmxnU34EE2ypEto2ym1UvqRSFad6cyW/BKS+4PEjOmTjyhaX2i1AU73n3BlWJqyilqnAA4o6k8wteFMeQAQ0GhcWGWTsXnUoixmIagipB8PEGscGi0KUhCUGvjKeiwZz8OCQQmiMoOwv1QUZJKIoRR0UpV6TkC4YPr1yRT5GXucrsg59TpwCNNvs9IHMqEEJO53Rm6r2kNSO6363+VSH+HG9oPVP1ZTfDUVmOvwfL5bNIQi8+Hfi0hwPhTo3yq69QWDw69vCuxuxOtRUizbqpZWl5D6RqCW66Dauc0CSpO1fSZM/YrKvgBQguPq7iapraCuR8hHp5NPSTXtiKffJqhKc0E7q5e2HTajLCUjzmVd/x4dx9vGVIAx8jJELgGACmxflnECh1JLjBFshCjWd0DcRnSJgsgoU0eK1ja4zx+/vIP9S6eF4kMEkwqsW0K1GEvR7XMnr/B/vzzFzmi1PzawLozaNT8z9xS7kpG6ZLH54lmtqGJzXIhcsjq4fTD9L5Qs45z35WORB75pAs+lpzaS346vnzSyyHvP2U8y+a+fnjKIh39d+RvrWUM5OQLrUXs5OV77GwA4j13yRaz39CzgnGN7exs/+tGP8Oabb57UBn/w4AHeeecd3L17F/fu3cNsNvveqG++L/1MwVCDfcCAUwy2ewMGvACoqgp3797F/v4+XnvttWd+SQ3k07cPznkS+TSf1xipuH3U8677ZBchSVqAj74ItIu6CVR02hDa/tPrPnX7zVmLjexTQnPp9TpfA6vhXDgjeaU5ubrVsj2Ho27DDGBFVI0lrYHSDzC1b0R3WfAKNcKBzYzVmGMUbGMjhF5MGRSqw5TzOSo7gWBx00Si4xo4UdEmmYUmEIRURRM1kx+g1cNZIhToOo+UJ2uuM7CFGeZZFKLF5R71C2c6atF40tbNYBAnSAFA2IpOa3kKV/fuN6GtNHOPqc0qrMgh6yfQavXcbsk/Q+Xr14ZiuVdkzlfO4QTH8/A41ZHhdBjYXnAHzhx+fVNhPNpFTVCizYk1YnJFsNQjBrS7eYRgk0cMeDSmU1MFHMtOkFDKKSngcmtvTK4dQLUdTKoPRbQeNAvHKpoCilrLiUEwS7L9UwTyabfUuL1Ns/PjzBKuZdevryPkEwNwNA/Xk4m9I5WM1A4N9HVjEp9jKGOSpARePAixNdM456gJ3ovUtVeTwH6WHhvC7c0CP/vJ5ZW/ZUwDnkB9KFEq9JvkBjcnU7x5o4U8T/o5h3C4JnIPIpfLJXxlPBVirKKIrA9iiriQYtG58PbGAMF6yoF51lnAtxYMWCE6lgOu/30ZUlhy9LsBeN0YvWNGAT0rl6WN3voBfH1ywH/+fr15j8LJqg2ItueYIutfx3ju6UWv9/SsUEphf38f+/v7cM6hqqrvnUXfRSRq2rZFFqprN2DADwgD+TRgwHeMhw8f4oMPPsDrr7+OnZ3nk9UzkE/fPoQQsNZCovsYiH2SjyebcLHoYQJSFE0CApZAcNiEWlIOFmzF8C4MelDqlDCj5cR3/WYQ2Mk+ILVP+fRmzAEuQzTye7JBAwQyB9f7kmaV0JF4gXvpGIrqPwEAxyZeN0gyjUiN806VFGkTs/ezEaVeG6hxJBaWcpIzBBzNFscBFDMwkXtACWYC9CBtyM7sLLRz4DCwhOWYDaiOzoP6rABAaxPs8cBRiBqVWf2QurM5B2fr55AynlNIohRSN0nBmfDRKxJsL5U+WiGfduYfYPtWz/2UEvcO40EBHSH0xpnFzFN7ZImj2v/7JDM4bvzjotEcv72Voyy7+gsbBDVCrS0KxaKEK0WtU2sDuPj9otRTAwBrw0TAWYwyhnmMuQMwoxQxWqAm7A8A7uyNsDVSmBPJN04lihI88lKeJ8URnaOBNIu+jBtUJj7eYtaMnDn88srCOot0fMKaaTFHP4rY7k0KHiUwtY4k3UTGfhMYU5e3c/wlfHgYAnmjCQ+Yc52Fn4k8+MbS3m+aSCpNZwmJBD0qLyEYfvura2uWnCFVdrBGVmCNcXXc4Cf7Cqwn0M9g4Hrerae/x9LEYvWeQr+Ha445FxEuGQOmIs+qigRlA/WinHNh8sjosDovRp4+bRA8uN90IrGrBeWZf3zj0UMaeQku37l6kp7ckyOIw/srf7PZZj9JJvLeTybv1fWoygbyiQ7GGEaj0YlNn7UWT548waNHj/DZZ58BAHZ2drC7u4vNzU1yQss3DWvtC9OX54XpdIqypNeqHTDgImMgnwYM+I7gnMNf//pXHBwc4He/+x3yPF5nhYqBfPr2sVQ+McYgGUMb+ajRADhBlWES1EGMLBDioARSupacTEJ1JlvfjGSeJ4SbHYBN8RWUoNltpfaaQYJKPjGmAavI9Zm8H5iB3oSQ6Scn/vJTG1YrURGr5SNZA+PJDF6isf75TkCjhf/35b2i2j9lQqPS4T7XhGAmQFcqaTKhw1CICjMTV/zUgWt2Hq2nJkXvfj1qJh9yaVCdGaaXyxnGWf/xUiwzk9pS/dTQFa/+JtoG7XzOQdjTjGKpj3Bj/wC9WdNZHq33BAAHAeIIALYKi4eB6W8jN5i2AfIpt17yKRMWL+2PUOanhb9zJZBJHlUb5JJHSRlSnUMHFIoHA+zAMoAfh7YMhXSY6/gzQHXIaw1QKqAivCpmjYkmZIwygTuXJ0npCdTYqQM97jAtQAAAIABJREFUISTlXak4Q00gyohcGgAgE25l/nla/HSvxSQXoNrXUuYnBwHrXJR8Gmdx9dy0DS/oYmP/uPaPlMlIQCmONkAeNY3prID/f/beJNaS47oWXTsiu9PdW/dWX8Uii62KFFmk2JT1DNl+MCA9y/ofhvxgPBie2RrYAwOeeOCR4YFswDPDMxuQR4bhoRsY+AL0BTx84dtqvtiToihSlEiKLIpVrLr3NNlExB/kOfeee05G7J3VkarKNSHrnjiZeTIjIjP3irVWoBOVpZmTDwzRpwncK8nOuAAEKvfxTPbsVRmLfi8SZTrZho7/2CPHMRys3w9CquwicN8vTfNnn9r6CJ86UsF5rIwJJqBiYRaaOAcXSGQlmJubF1XMQJn/fubKPEgOuaoEhZRLVRl+FrCGYcdCtnquHdMu3G5I3eSz9rTIoNF8Q1dNaqX5t24Emt68nCPQK/97/e/xECjXySfnu0a2eSyTbVZ5uY58umYsLPoOHaqf28qyxOXLl/Hee+/htddeQ5Zl2N7exvb2Nnq93semPjLGiPL/fpEwHo8xGDRbTHbocKfh9qKWO3T4GNHmRp3nOb773e8CwA0nnoCOfPo4sFA+AUHX7oPfEbzZ1K/isr4lzYdqR7S0UAW12G4bVVXbbcdk0Nfvy7dNzS86frR7IW+X41S02jwFrNjIKST5ywCAymnMHE8+VQHSZ4GSyYRKmfOpUQX3k6jmMOa9/dt6hEmt7SSWepXTiATEX2G0zGrIKXHhJhLSqqWNEZMs9yk3CWvbtICFapEndfB8RmRwchha7i1ffR5a3b0Kq+X3TKPlL30m4rOL9o9BTuYu94Uz0SuI0+Y5YUwDXJqEz8OhXoXChOd6rcJ9b5iG+4ZvaB1KCnz2vt4B4mkBifopFkyFlZDh1YLVsQ5ApGUTRT+RWvmJmgEABrGssQMwbLBgXMbDpzfnOTwE4U9qBWk+VJvfv6oa8aGNRaE0HypEEmykFe7dXmxIaqErI5+uTit2kQLnRJZGxBKhIXKpFxOr+uNyn6x1iAUDNvYsPFiGpGtVxmGjz5P/s9wgjWWdQJJtVW/zYNH//jM93H26OauvCvSraUBtOqnWj+WBzSv49AnFZGcxFzJwcmtyKXTyuT7N3SeY71fMc0XJfF4xzw+GIRYtdy8JnBvLnbtrnYT9x0QeOz54no3q98Pm7/iW6/kWuPnaK7V+DarLFdT00npjz3MZefKqyJPHqcrdxr93mU83DguLvnPnzuGZZ57BAw88AAB4/fXX8Z3vfAevvvoqLl68iLK8cQ4tEhhjbjvl03Q6Rb9/YxZ/dujwi45O+dShwy3GpUuX8Morr+BTn/oUjhw5clP20ZFPtx5KqT3yKSHCRFCgJpKtulVQMKICtexlyMxNwW507pNrkfvk5k7zN1onRQC2olehSK5gIKrgXCJWHTlb+uzcA0clb0pOi3OfHKrm7Tsgy9/aoyPHdoM9DoJFbnvBZholSpcGi0mKTLCmkagC04AyRweuA8GgcLV9AREhUoSKqYZaYUh9rAwqz+rkvW2BkGkrUjbFCpA4b7WxyMuiEmXJ920HhSyaYVLxVovA3MpKeAxm6bffvTFBpAKrsoV2hgBgtexYAcDoIeCMN1fh4HYHIFvCKb4QaaMhVDETkVtVHM5GW8Zi37Xdnv+Y37nCX9tRarHL1AOmZfi8cFdlXK63uO+wxcmtE+glBSYNt61RL8aHu+FioiTaaFZYkOIX9EjX+0RaoRI8D0mIMQAiy70FpEQJUGddeWI9cM/h/oHCvFYEI5ALtcpyUoRSwCy1yalLzSUA60TlKiwICc1Yu1YAUK6CZIlP6Zn3CQ7nT1io+dzRZv6V4BIzBgDAGYvQ+s9hqnFlFiCXEkIeeHTsJ4Rp6b+WRQVk/RhXr4az2uJIsTZ3WhOrA29SFjWhl8W4KrDLG/QT5Ff4RUNSdfR0VtVjyjr0+zE+98QmLnsOY+YhmDRZ5Ka5X8ZkkK8889y3cRXnTxGI2lnqrsIGlrHVtrchpQ33OQfmunLkEPd5VYVt+azBdeVhBTk/G1ZNBdXXAULQYy/soOaWdevf9bkNOGRQnrxa5SWlmucn1bCwyTkNUnblbwp4/dnGbfhuymTWny4daahqd+07Vg+gmzKpVAzXYnFQBzk+SRZ91tpO+dShw22Mjnzq0OEGgoi8q+Kdc3jzzTfxwQcf4KmnnkKWyQttbdGRT7cey+c8JppbNoRf3ayj67B1WEebQpOCjOBoo1Cyey+yQqUWESph0LrZI7bC297QFxGpSfuXeRcDUss7msG5SKx2VCSjDvc2DyUmn4BqfuwH/6qtRVy9u/fvseML5RlNUVL45S5TM+SMOipkKwIAiiFcQ4RFqmaY2f2HeAn5NGMIpb3jkhazyaISqNmU0AezcHKiNETMrSJWMjspoF20QZ335LCR5Nj0KHgWsLoHuMq7cncZTvdBJodTAlWTiqCLMUwkIICIEFVXUSaH+bYAovIKCn2Mbed0imh25UCWkw8mHobt9uZ4R2C5x12sWDtcmapg1xuX/jGaaIurs/3rFSmHR09FGA3qc0IehmQUsFdaIBfktVjn0I80CoZckRa0pV1beCvCOK/vcpI7Y0h9swqfSmhht7cMEiVL1udSvH9hO4sWmY0t5pVM5SiMgHwyYwB8fkLuUQ7dtzHFZra8HwKYInwbXGYs9wCAc45LovCJGyQqOJZiRhq3m1uZKugGPZ9KsqEA+T04Fnpfmhb9fzBIsLOb48JnTkFp43FWdo0KJgDo6QKlp//2ohJ5sf/ZPaOreOKUq58hnUOoHBN8jHCWWYDB9Wvu/HDPMJLvB8ApkzzZP+Lvcx04dO5c4NxxYVdBNVozKeSQgci3BKj5dzpK0LSA0SFqXExXL/krsHpenItAen0fFgn0yvari2PAN/c0EGsOAJVX1v8eb4Ls1Ya/D4Fy/RzZePvaM7g6tMLHadF3O5JP0+m0y3zq0GGO20vX2KHDJxRlWeL73/8+iqLAM888c1OJJ6Ajnz4OLCufLl68iNmYzxuSXiEp/WPgxAU0ajH9t2krtf67Fvid62ukVGAQvQXAXyD1oY29oNYEMJlGBzfezraglR6MGmz9HCGbvXzgT7uWL9LHil+xrT2rLJcRynOqEf59pfMXxaKV/Uusokors9QLBYIvQ3p1pCvqc5uxhNwC1rV5KZP3aXlGVb1KuxeVODOq+JdOUlBmIt62Ns12K41trbytcvL5QBt521h4vGW8gTPRy167vQV++hE/r+wW4Tl2q2eCq7LTyOLqzH8cm5ld+n+Dp88OMRrsE3c+cnnU4wnGSeFELE8kkAwVQgmOlICZNqi9muAADIWPcJIMqQV8JPrDpxZ2e/uQFunbWOS1Wb7SI1meYuXkaxwTwb0FACqhsbFxCslKoXeoZnjw2Or3ZUSedD7/cCd8H1UE7FynkxFHLoWuexYTCgP0+gIlqCSvS6AEtNaFo3fmKAXkNCAf0zMuWGsJSazw8ENHsL3VQ+5ZsNLTpbcfpNq/r2Tps7uGO3jytAPNBzGhChbVXZDIqG5uZhMD4rJM2ecz5nPuOrObD5xXyxBIoX2HSDHnvPdfB+3NaHIBtwZ/4m3z/Go9C8Qc0r1+J2q/Mn9bq6Fe+Ib391G1/qzn4o3GfugiTzHek7tp0y7v6ePCrbToux1t9zrlU4cO++iUTx063GRcuXIFL774Ih544AEcP378luxzOX+ow62B1hpVVeEHP/gBdnd38cD58571bftwAMgRHJNJY1sYw2gokVqpnUrK/+qz3lZOqrWx6Vts2/fLFIBD0T7hQlTB2R6owa+8CYSqZRaWhmdp7HpbKgGbzZewStrLCyb1sRx8tUuqXWh3sDg4FpBPssd9JmsGFUqXBWsOhQ2/ZC9s9Zpx8BxKbXUSVaAy4ZVnOZOjs4DUdkpYRwNAyPQUE8MraPLAuVvfv5xQmlW1mkk6Ho/3p0gj2bEoO4OFzKJOWW7WXGrrsa9pbitfjKFa6BQjJ8vK2pi9gc3DMwB+ZWFlgZ9dDReEe3FNHIXGFxMdhEOZwQcTf9+I5yug79myuOvIUagVW0UfSTtII1YRYx3QTzVyRgkh6YWVcYgVTxpXggI6AIwLKQ0BJFrWclzIVULTYr3f3XO4j41Bi4UODdBEQgWI/H6cUAkJ/SSx0VtAc4XsOXImc3AZPTVbWgzh8PixMbRetwFUxQ5swtgDGie6SX64WyHUcJRpTAKWeABQcqo/5nLmAUJokBBmpUOaRlCKggrCoqFPrqIsDZxz7EKE+r0kvL1dgeUeAOS5bI6ezCpoLbOo7PdjfOqBmmSfedRNNcHks93z70PN54pTg108fdoeUDkSDFyAULUBpSzBhmch526ou0LDDhDcQcTcjLhFBop5hgla3zHft/bal2Bb6z/2gCrKIQN5Zk6ftR4wz4JqdNf27ScGmqzrkKDpvcUhavz7qjLM/uwyVDlrVjiRBpU7a8fp4iFQNPxmn12zj7iLu7ynTwJutkWftfa2I5+6zKcOHfbRkU8dOtxALNvuOefw05/+FO+88w4+85nP3NIbj1IKVdWugN3h+lAUBSaTCaIowpNPPoncOeQCAlCRLM9JQ82t58IgoSGQnec+3Wi0yYhakGo3InPhUPRT6BWiySKGFhNEOWsXdz0gRHDCYwFKwEVyi4mlogc5hSR/6cDH1hHGjic2KsEjQcVYxCVqijywLwWDMmCZtGqrt4pVVZQiWcGYBMSfcQqJMqgYdVFhNIj4YptxNS0o6d+RkPAobYKYcpQCq76ZSebFKb5fWyhkeoaZwPZqEOU42i9hhHaBcup6vgpc2lZI5gKA44pUy9sNZFittRW0UWaGU1vvA3kO9Pzk03tXYxgmn2yrZ3AxQBwBQGnC24iYel5pFB6/S2HUP9rYx0vTfH6ICBuZxkfT8PWOlELO3KOMULKTRcCYy6S3TkQAWUcYZcCOQPgmVR4BwDBVuBrI71mgMA5ptG+n1mS3dy3QVPMmHFo4lImJohIpNEoYgU2ptEqeo1aKhoryCyRLquP7sg9waLPZelO5gn1qcjoBTAkEsuOsdbg8Df+OXqIwKcNjZMyEBYbIJQDYCfS3hWqKiJD1IkwCA6iqLOJEBd3NrHXIUoWCIdQkU/BkVqHf05gxv393UmDPsY7BsJ/gyk54UUO/F+OxR46BiJCoCpOquW+FbG99lutA/XxyrDfDhbuqNRVjkCDiLPk48ol5RnEhzT9rHS44+QnzjMCSU8wYD5BLzlpQ4PvO2kYV0B6u9bcH7PocJYCTqUb396TmVn3rx0PeWcuXEeU7H57BubRLayLQi9+s/9ykcEo2ody6EtyXtel9V7TN81GnfPpkoo1Fn6QOZoy57Wz3OuVThw776MinDh1uAqqqwosvvog4jnHhwoVbfiPtlE+3FpcvX8bLL7+MNE1x//33A5CbsjmnhFlDN9oSDFBQc/VRGO1ynwza5D4pKBjhUe9nSh1EnybI9Htrf6cWuU9EJZzrN/qkN6OtrWWLaiU5kIta5D7tt8vyn+6tsl1g6obBVZUL5LYfPEwFg9z1gu/kERnkgcuZqClm1l9QjYIWhQ65PUhcERFiZVHY8LWWWurFyqBiMqIsCD1dobS8bVGsAKaOBqAdAZtFBcqSn2EcFHrRDJNKphRItcGM6XIEi7s3Jq1sKttQy9SCJHIks98CAKfkagkbt1goEihEL3C8ehFJpmDzMLH2tiDvKdJMgRcOVwKWeoA/CwcAtvsG9x7dQJo2j1FFBjZgi7nZVyz5JOHUC6FsULpANo4Uq7YCgEwDO4LtMdzBASRamhAFDFK9Rz412e0t0Eq5TDKVVtUin6rNmM5UjrHlx5SETFrsPaMZJo4v5ixIsj5N8OAp/6IIEiojlZnBBsb8lalh7zWaYS6zmNj+tZsb+K7CICGMiwARskSS9HpxkHwCgDjWrNJIacUOCim5OezFmBVhssg6YDRIsDPmladJEu5XUaTw2adPI03qkkiqS0w8fTFkY1cFnkH6cYXHTjYQT8w2CQZOkJfoB5f3FP48vMCG+bwqoSL/WHHWAgH1tHMOiMPfpxB5ZaoweeUsQvmLIra0JUILgXyLdBwyKGpeEeF1SWixMGe+cw/2j8n89D1oW9tONmc4DYBinXzydRHyqNyVac69sklHPv0iYGHRd+zYMTjnMJ1OcenSJbz++uuYzWbY3NzE9vY2Dh06hLhhfHe2ex063N7oyKcOHW4wdnZ28MILL+Ds2bM4derUx3IMXebTrYFzDm+99Rbee+89PPnkk/j+97+/95mi2piNuwr+EsLKvoTHZGH5BYtzkJB8cnvWGrKjUFDcesz9Y5BvtvEoNBw2olc832ip/nOxkAislVLWkTholUh6RubtoVqQT7VSSjsgrt5e+1SS95TSFIbCBcJUTVEy9mncb4zaXpOV/ecrRcyYchzOdvGzSfi4ciMjKqSKBk1WpGNTwbTwfUiUTMv7liJWbZREfJvTwx2kkQJMLrascR4P/ybYNm21PMDXRLzyb69tvFFXNwUnxERhZUqWv4/DR2eozUvDo+NtQd5TXoVP+qGexW7h7+uKHD6arj/692OLh45pbI22UQWs0mJVevNQAGCzx9uRSmzwisoiiZVIrSSBEs/TombB4v7avlvUURaqlLsZu702WU7S3+RAiJCjEiydkSxmWCCmAgBPPkmUtwtklIvIp8VPP39kjCgOFS+FZKedwcI/l9SWe2FweUXDVOOjqf94hqkK2vb1E4VxwC5vWTXV6ycAwpl8dSGQexYRzJVCQjmKZAOml2rsSEQkzKE9/cRJbIz257w48AxYBZSpuUcRuhHP8NgJi1g3fx5aeFJb8vnHRXDRinNz1Yx/2+G8KFYLGP64mAG9gJK6zMPkUVkElUuoivDkas21k0/WMhP3jfeNIFc2W+tRAngybJXH3N1HZPkzupr/ruaLwWwVQ736v+vjSTehmtRb2vPc4VEyqapBJQUFqtat+wDAdrZ7v3Bosui7cuUKLl++jJ/85CcAaou+w4cPYzQa7WVn327Kp+l0ihMnTnzch9GhwycCHfnUocMNxDvvvIM333wT58+fx3B4/XYp14qOfLr5qKoKL730EqIowoULFxpX6sSCrAULQAvq0wt1EPfK00bR1OYFqrb9E64OhrSU086mrz6Og+lMW/GPoFTz3ohN3Vpt32LlN1nAZZASXEQlcJNWloMA5SJks+cbP5bkPSWUY8oUCGOqwDjrsLZ83NUOqYkiKg9cUY0K94zexdUiw8/C9TOUViPTvKWeVCElJ0xl25vZbG6syb90WeY3XCs4p9ZRNMHhrJ6srErq4o1gdXAbksjqgXy70QBU5XCKtwp0KoEudmBinoRyKkFUXEUlaFvFI1BVwTVZ9TmL09nr+/eGkEWQ45VPmhwuNxBHyxgkFrsBMcBWz+DybH8/kXJ48Bjh8OgoSGnENEHI0YsjPkcZ/1oxE8qGEq0wYwrWhZGtXpDeZaQ5bYWpLf9mgum/jUrKgdCPgbMCuz2pnV4bpKpAJciVqwIZNauIbkKWU8j+bBkWGnenH+DwNrdqXkhOegqpC1wa8x1iytxEFwSkD704bNvHxejszJaVT/x4DdnJLRDKjVqgquy8nh/+fUws1B6kC3/KwLl69NxRnDh2cKyFSJfCm6PoMGnIiYqVwWfP7CIOFVOD9zpm7AS+W5NLoXPEZbyFX0y4xRSoSgAh8qkM2+5VZZg8MiZMEDkmVzCkDrJGztq3QEhhSWhW/PiIfocYyjMPEnwPAR4iiNbbO6egdAWAYH78Y+j5+XTJEMjXySffuxxVDW11BjKztXPs4hGUXW9vowGg5fmBHT6ZUEpha2sLW1s1kbiw6Hv33Xexs7ODXq9+V9jYkGXE/qJgMpl0yqcOHeboyKcOHW4ger0eLly4gIjzsb7J6Minm4vd3V08//zzuOeee3D69Glvu5gIM8GLuyYtzn2SWOApYcpKOy1OG7TJfVq8AAuLP0vbHqqPkKjL/rZUwtkMJFR/GJOzhZuDxxJBrq4qarJKaIfhtdPwIKquQNv1lYQAsOv4B3lNtp2XUwMIBrkN2/JVAcsuwCJ3fqJi+doTLO4evoM0chh6X7QPItYGlSfLYYHcyDpAaAX0wXaiZgAImZ5iYviicy4oDu/vX34vmtkMvoKTIoszwzFooUwiDVVNYBnlD1CTOVRO4SIBCaUi6HIsVipps4NKQD4BgK52ReQTAERmV0Q+gTSS6jLyBkuYrdmrGJxcOpcBC6GPphrjItw3twcHiaMmcGRnFtu9RdT3bhuc3N6GXirqKKa4zhUcexnfN0vj0IsVKqZgzdmTAbVCigC2UCjNkNptsV6hnxBmTPYOAEwkvptz5MbikdMjr93eMrQiGAH71GZaj4S5hEVAHbcK6S21QowYOUqB8mrVWtYHQxHOnRLMgSQj9Dl7vg8Z8kkRMJ5xhW3Gto851FBXX7Xky3o8iVgJ7CpL4Y1u2NPYDai6AGDK2JO23ed4Ujbq9u+5axMP3Lc+b4fUlJOquS/1dInx2sIZh186dRmj1MEGRkHoMw4WOqhsCtnKEWeRwKi22TVzHItomevMft8AIRKcDQINDCRrwwMtsJAkdE59C+Is0kYCKASLFKpBEeWgoFCg8Tmu4VnZOQWlqrX2DgmIDEwZQ//w/93/u26en8k2bJs0VLXTSDJRtf7u5qI+UDSQT53l3m2JVYu+yWSC119/HRcvXsT777+/Z9G3tbX1sdfVrgfT6fSW5r536PBJxu1lqtmhw8eMw4cPfyJukB35dPPw3nvv4fnnn8ejjz7aSDwtrxKNhSvnnDCbSKrMkaotLP/6uLRNeQlLprrah2pxK1oQZjEZDPUPBe1b2Hh5vMb9aKOUAtqt9yjlIQlOIy7WM6+AehNju8luwgqOzTCr3VOaBlfaEkMupaq2J/Pu3y2O0eHM4B30k3qO60UGkWAlvKRgaZwSbSu3kWhFuHEEJVQMSvYLAKVNEAlVfTOTCOxzalhopLq5+HxX8j6SlfBw5ZqtYJqgjDxkW1lGxrYEbdscg5xZ0E5eCIoajldXuzh5+NLBPyYZnOe+/FNB3lMv5q/jTs4o+yzh+NDgl+7t466jJw8QTwA/d4cKswQLpVJkMV/ITwRtJNOfQ53nxEFaqF4omiQQuoPVZFsku1ccGSYYDWRFCr+h1kFwNm8HtilUKRnEYqLKtchO6SnZGJVu897RGHHCK6qcILsN4MnXS4zt3kZPs0RpYbgxGPwYswBZ1E8O7ltrhTQNj8WiqMBV8k1lRUKRYZ+/FjtzsojDeCLrf8Y69FZItpNHUjz+6PHG9qVnAUpMFUrPYo5Ur1/380cv49gGY77tHILPhaHT7kwwI5Hrqyw3w7RglU/c59y8dL2fBzqkc46RCAbGoLUMaefP6fOqm8hP5nuf3zyZlw5p4/FZJCC1fs587d38ed+9/oOV42n+7Y0Kp2SzedvaMw/4flNHPt32ICIMBgOMRiPce++9ePrpp3Hs2DFcvXoVzz77LL73ve/hzTffxJUrV37hMs075VOHDvv4+KvkHTp0uOHoyKcbD2stXnvtNUwmEzzzzDONQZmrfsURkciCTm5RJzzWFkSRgoYVqHckiqsF3Jz+kh6FInmGhYVDMctxbPQjUZYGCYk9AMgyoHIq4Iu+inZjjASv6/uNLeASSHpHkl8EueaiXe56IoukgrU8snXeUuCdO6YSoSiUhKbIAzkdEcogpTKzNXF1qvcuRunBwtMwzvFRES7aSi31JAopB0KsnUgB1aMxxgL1mZQ0BoCMZtgVqA8cFHrRDJNKZmmVaoPVbPlNuoyt0TppSE6uzlMoxaNFtSB+qFWGmHxebGO4oxpWcJ+0LyFKDs49pBRcMQV662oxSd4T138HicWk0N662NGBwZntPnq9LS9J7JwK/viQ5WOiS+QmxagXsdZ6kmy1kinCL6AVoRQogPqxw6Tkd9xPZHZ6bXKXeonCtAqfk81ehLsPj8S2tVJXqDbH2abnpypHFbBJXaBy8lfNWLj6n1sIAQDb6jJOHNmCdRVA4WNwQksnFzjpxjp8NAlf414czmMCgHEevvp5wLaPcNBWbxVN9fZeP0Ge+xfeOAf0Mo3pLHxccaxRML9Nkr1mrcOhjRRXx+G+kJcGWaoxW71hNaCXRZhM62eGQT/G5z+7jauee/fMQzBlUYVJ1TxPxyvWz3ePruL+I/X2a4VR83ERKjhPwb1uELbVCxNXNjic29hMN26+sqA48HzNPaBfr60d9/2QOslU4ZtQkPSzftVUQE3mkHozmkLXkVzhuY4+O74ETfZ6Dima8t1qkml9DDkomDyC+vH/t/JBg8JJJVDVeF3hFPUBc2W9vVBpukCnfLpzYIyB1rrRou/SpUt49913cfXqVfT7fWxvb2N7e3vPru+Tik751KHDPjryqUOHGwipB/nNxoIE6XBjkOc5nnvuORw+fBif+cxnvNd5Qfoth2XGRMiZFXoGstwnO9cf8blPDuRIaPEm77NKaPtXt6W9nCoe7fzekvIniJRUHdGShHUxQLLV10R5K8KAqJ3RYU0MMr71ViHJ34CDhiO31jcllnsxFahC3vyoyY6Kri/HLqISeeAEhDz4E8pRuBTH0vew1Vu/PsOkYMknqaWe9IpqciLqQ6tK1A1DeVfr27Tirh0r+RhYVYdplLhrowApeR5LE1rQru3atlBVwGMX0wSftYzkGPrTn2D7pOecF7mHfOKv/Uez8CP7Zmbwwfhgm15scfcWYXs4RJr2vPkRC1SBFdgAUAT66CKHZ5TF+OBqeA6VECKz0tTWe8xzlfS5a5gYTEr+tUeqaJpxAXgHtqkQGrCRIjx8ahNKUZ3YeIMdcSPi8+4Af75IExKqINEzli0s+rhMsQU42z+CxSPHSpBKocoZaw/qdDovSDPGrZfbAAAgAElEQVT9I0BifTQx7HXj3BT7iUKIvyECdnID311qmFGQfGo6vl4vxkeXw6rvOFKYMs8iSsAo5ww5tUCWaFwVdK5BFovIJz3P0Yoihc9dOIMsKXC1QTSrYDFryG4CuOyypRzSdIonTpWie1P9lOyfU0OWfPV3Q/01fD0ccS24FxIm04lzAQmRQ4CAvGI+D9nmWRMe66F7irPw2hkGiKn6OjffF0PvEdRgrbf4VvNffftv93cAcK++sHZkVDYonNJNUAPJ5DvHvkVD5Fl4ZJMt7zF2uL1grW3O0I5jHD9+HMePH9+z6Lt06RJee+015Hn+ibboG4/HnfKpQ4c5Plmjs0OHDjcEnxQS7HbApUuX8Morr+DcuXM4fPhwsG0T6SchnwAZsePQJvdJwQqq023okDYrJdv0wDb2fz2aAtHVFscht+Wq28uL2UQG1qYgoV0aUVm/8YuPhYED0umP5m0NHI2wurJxbHnyKaUZZgz5VBNUYXCr0TlSwQRWyEeqwlDv4Ei/mXQcxrwFT2k1Ms0XYUPWYgchGztSW82ZzaBgYAUFYMes5D+AFoNx9dzcnb6HOG3uQ44r1B5oKy9qSy2wAMC2IIlsLH/5s5G87bKFDNkKp0c/gdc+0qyPollJ+GCXJ5bGnryRBfT8vk9wOH3I4vhmD1m2CTUv0sU0RcFkqpWBeSCiKqhiWYzvkSBHphDY4DkHZDGxKiQjtJWTKGUX+5VgXECsUuKs7x45tYE0rs8tEckPQoiMxqKFCBKV7AJioggJFCrRvCa9+86QgWDhPN84m17EaFj/3qYsksZ9mymsCue8hUjpS0zeEwBwYr5BqlBM/I2GqTqQ2bSKXqywM/M/jzQRplmfv+YiC0xBmytj6cIe2U0rEjLFxtbl/f/25GlsjlKUHtVupksUHnVTiFtbPDOkusIvnZkgWmIZg6cloIriLPmIM8O+ntdAJg/KOQeVMPfelFmwEshABABo5vkiUGB2VQkKTfiOm7uu8eQFB0Fom83fq7OgPM+2vlxY73Xzye88Sq1JDv3uSwf/RhFUeVWucPL8LjLN72VN1n1Ap3y6k7C6gLcJC4u+wWCAM2fOwFqLK1eu4NKlS3jrrbdARHuqqNFo1Ehm3UpMp1MMh9e3eLNDh9sFHfnUoUOHDg1wzuGtt97C+++/j6eeegpZxq/8b7I7lOY+SctYUgJI2s7O19xJWrfJfap/izxPSmJJF8FiQ7+E3RYPkkQlnM1ASmrPdS1KKel3CsCl4vdajtTSxiKqfr5/KJSCXHvyKSLD8ih1QTTUwtW2eIHfVrlwsWFm/WMsUzlO9K80F6WcxTCRXV+JpZ5UISWx3AP4Vfr7IGR6ionhX1Jy1iZxH5WVEz+5ibFIJ9im97G56e8/Xt/+prYtlFO2xXaNHs5XG/PXzEQjkC1F5JaJRqBK1raK98/RkeIl9Lbb5XC8cyUGNylsZAZjhqDSivDoKcLG4NBalhMAaFcC8JNPsSpRWH9BMVIVKhMo+M3/OxKEJs0Kg1jzVpOx5i3wpHlOpZGN16nwVuEADFPCTkjOOUde+dvcvd3D1nCJwJTtvhU/pYX2lPK5qk15lpBRjonjbWes0I7JQSGjCaYN20yR44Hj+/1cag9KNgcQJp9s5P8NHzJ5TwAwCeQxAUCkw2eVs+0LESQ+S77VPKQmFMxxA0AlsMksSouNQYxdZpCVgv0BtUWfBNNZhfOPHMPJ4/W9Nfc8AySqAjw5oaFdFUaD4PDZ01fQT1bn/2vLZaoXFF17mcaFUuGcBQWVajac35lPQL0A+VTMoAKWfM5UoMjf75y1oNi/fVdVYXLJVIAKHF9o8nROvlKhFfx92qcEqq3ymskn5fk7ed9hPERQk+WeA/DD767/PTvkUTg1jycvyVTurG9bJSAzWSe2oOBiPre2w+0Bn/IphE+6Rd9kMuls9zp0mKMjnzp06NBhBVVV4cUXX0SSJHjmmWfED0JNyqcIEOUftbFvk6Bt7pMTqaTkVo51WwVpiUoToQq8ECoA29HLUArI0narwi0ScfGNqGhFsbVRGdZNI0CcU1PCb+9BSKcvr+5hrZlktbuk71kXXuWa0hQlQg/XDnlAdZHQzJs7tRldwon+z0FeCw+DLLKIyKJiVEYSSzfjFFIqUTIqgMIQVIPV4Spy14NGAeMpai0jEhKZpU0QUcESegAwq5KgSmAZxmmkbhcWGqe2GNselYJMARcq8sxhdQ8QZK8AgNN9kMnhlKAQrmLoYgwThYvGAABS0NVHqJKwgnXRNq6uohC0tVEfOt+FcgWOHdlFOGhj/Rr8VJD3tFqQUeRweGCx3ScMY0I22ESsUqjAanFyJnhoEVUoAn2UU7qYuWqul2hEilAFqrUOQBrz2VCS2dVYh1gTqyrZLWT38XEhu2cDQBqpuQ1aGJPCNmYbbmQR7jki6LsNaGOsLLWytIgQIUclmKvaWfQVIvKpFMxnC6SUN5JP5zY+RJQc2vs3Sa2CXcU/BakIVE7hovV72SWGfIpUnecUvF9cp22fCYw5nyVfkmjoSMEESNw8N6z9ZTUfy9z9cNjTLPm0M5Gp1aa57Fnq7tMb+NT9i7ncYWqa+1lojisDi00mVYQnjl3B4eH6BQramgXt1sK2esHnNucQJr24eZdZPFbmQIh8KnMgDhR5ixwIKafKHAiQUzAGCBG112M7b7m+fm3vaqH7r3Kz5s88z0s1sZh7jsVDSnmUUk2L3MxEQ118Y23zLh40Kpx8dyNV7a63jIZQDY4ULhpBNamnkkNeK8MOtx8kyicOnzSLvvF43CmfOnSYoyOfOnS4gejs7n7xsbu7i+effx5nz57FqVOnWn23SflERIiJUDDLlCsAKux0AQDiHCULV0c+CbpknTIhK3q3yX3SUC1yn8I4pN+CVvULS5I4WBtBSbNshLZnAEDIYV0WzB86iHZKKVpL1Qk1NoCN0JTdFZdjKLuS0+AOvnSWLkYuKvjxRf6CUWzFVKAM7Ksml0KqixyFWSeftuMPcGJwlbke9WfDpMBHeVg1I7XUi6lkC6EOhFg7kQIqUzOMA6qS5W1KkUUldkuBZRIUsmiGaSVTFGUY40gvRxTzxKUyExgB+QRSUNUENuK3CQDa7KKSkE8AtNmRkU8AtJmKqd/ITiErgQJxtYPj6RvQEXP9GlZzv32Zv4bWEc5uG2z2IvSyDFE0gFpabZyoEpZ5pOf6Fj/nhT8v5wVdIsIwi/DRJGyFGWny1cmWtskc0hyxVjBMwTGvCFnkMKsYUtUBGxkaM2FWIX3kc6gt1XZm+8e4nPO02lYKTSSzHWzxbJqqApVgrqpavELGQlK9gFz12LTNbXUZJ4+srpIXnlHWiquGMlOYBvLpQ8Z2b5Rp7AYs84CwQg7gbfsmQUs+fx7UcBDjyhW/JZ4xDmmqUJkAoeyAJNWoWHUX/zyWFwb9XoQZkxE1npaNpO4yHrp3G+fPHdv7d6ZLFB4FZ+jsz6rm447J4PRgjLOHG77tXJikDSh2WdKU+W74fXSxOMwH5uXBMBN3xXmllvApzPa+H1RGOVCIfOLGcmhBobXhz4Mq69A596mbNAhF43d992yHDIrWn05qyrF5HKuGpxnnCEQHSSznCO4Hz0M1PQ/4rJbN+j6dzkBmuq5kiodA2UA+6ayR2Orynu4sXIvyKQTOom+hmrqZFn15novcczp0uBPQkU8dOtymIKIbfhO/3fGzn/0Mb775Jh577DGMRu1XIyul1sgnACLyCQD0XJ8QQv1ywbq918cjJIpaKX1uUu6TC6y0HKlLSPUHB/5mbAKlwiHZC1RlHsweXgYRAJsAJM0maKuUamdeSIjgViq05DTiNdUTQHZyoJ4gsdxTqJC7XrA2mdAMlsIkFreyPqaCzZtZxdHkPRwdTOa9IvS4Mief4pIln6SWekRONDA0ORGZocWKJvnKf6lKCgBiVUE2WoCBnmK0cYhvCEC5Qky/KjuDhYx8UlY2/lq3FVpw1W3l53fk3sfmlqDDpCmcNaA5cWQtEGmNB48ZJBEhjhQipRFpDQWCilNoFUGpOFhMjFSFwobJOnOdj/w2UKzUVB3IbBv1YpZ8khCts1KwIgNgLKT2MUh48gkAUuGpKgPF+PVtKuws3Y/PnRwhS9Z31MpOTxGM4BhsIKtrFRHHCM5RtrD+lCqvSiSIUIiyp1aLo+QsHjlWglZJa4ElZw1hdphdn/Mq43BlGp4vskRhlyFTdnOD0JPTNEDsKEIwD0oFxtHmKEw+AYCONKqGzLoDbbRiySfpmBn2YpZ8cg4YDRJcHTcvE3jw3m2cf/jYgb/V1nrN48F4FpIQHGYewmorm+Kxk6Zxfq6t8/wkS2hODeZBAbDQQVu90LxJDPnETbnseObunRyLapnvc5Mk9wPYPCjP55wlX/Cc+8inDETNWaa+OanuU03kUwrV8FzoEIEaFus5JFDq4D7MVQdMm4/H9/aimmz04g1Q9WFDY8948BBbXd7TnYUboXwKYdWirygKXL58+aZa9Dnnulpchw5zdORThw63KRYqnO6Gx8Naix/84AeYzWZ45plnEHNBuB5ordds9wCII7yJNCSWbGpuyMFuT0j/GNgWuU9tIG+9yJ5aLUimVGAYvbG+ZdumX0/R5nZH4tjz2srC2pTNZ9pvX7Y6iQrr2qpk9l6jfopQwKE/t+MAdgXkU09NkTOEQEI5ZkFLPbBh8twqXruiTjuZvYvt/nx1pDNhuzZHANXKJw6l1ci0QeXCx2uYz5d2Lmwl61Mzm81HN7//YOFqBdKWPXcVJzaKVgabUlALMsdXqGnebguLnTb2LcKiNdkS28k7APhcAlIaLp8BvQEAILcJTh8/HfxOqnNUjGJPC5SaJaMqMYxCNJQdFqsKZqk4O8oEWVmCjJjSOKQJb6nnhIxNJF2EIGuGcSHve8v82F1bGQ6PmgsbbQyjpOO6FNjo7W2TsVdcoEI8t+gTqC/FBBDQUzPsiEj4g1fpbPIeRsMG0lw85oV5mnadnLs8qdh6OBPnhGGqgvlmimrbPl+Be5QpXJn6r13Ikm9zxPePG2XssDuRze1aoJACattAjNf/fu6+TTx67tja35uK8wsUprmv9KICRYNVX0QGTx6/Cu3NKQzc85xD6Lk0bKtngplHta3eTSzxcO+VbF+5PpUti8B846zxZhTVDUJ5UIF8SRdYKOH89sw1kdRM9vgJq+bjd0iAhmVGFil0k9UdEmBJKeWcAp7/v+Hi5sVH1LDQx0UDkGsgwqKs+XXWe46ax0pHPt1ZcIGxcjOQJMlNteiTPpt26HCnoCOfOnS4TbEgn66VSLlTMJvN8Nxzz+HYsWM4d+7cdT30NGU+AbXySQI7L6LzkG2vzSPPzcl9MmiT+6RWbPoiOBzSL3q3LkWaAM7qxpV/17ttAICLa4s8EYp5e2Gha2W7yirExY/9h0I9kKtfEMeCvKeIKuRs0cywnSm3vbBLC0Om5HZRiHW4q/c2Nnv7BT5OD7gobA5j2Yr9WBtUnsDxveMRZo9ILPcAsKqUfRB6eoaJGfDbNPKCciVQPihX4t7Nq1BR1oZSkrdsMbVSi2K1U/J7nNXS6zC3gRFgK38RSdrCfrPYJ59mFX88sTKomM1zpzaiirXlq2xYXRhS5a0WdEc9vr/NCiPqE4nWmDIr4UN2YMsI2XMtgxFv7LcztUpKEj2zsBAcZRpnj4bnZs5KbAHpmCqRinPf2ozpVOWoBESRES/BARKSzePLtn8Jctx/snnOlM4PTpBHBzQvpLjEWO4BCGagAUA/UZgFcpdGmcJO4GadRYSmNJYFJgGidDgQEIiCIpoVdNqdSYkkDlv4SbcFNI+BT927hUfPHRd9fxmTqvk8JMqgaRnZE8cuY5Ba75MzBdRLhAqOAuc9aKsXJpekSsNrBqdMCGQPAhCQV9dJboXIJWP48DQfrPV+1znnJwSd9ZLgoYVGyuWNv9X3830ZYf6/H9y3uVRA7X4It9VMPjUpnGwygq4urW/bS9J5sqdss89tRz7defi4Iiw4iz4i2lNFbWxstMx87mI5OnQAOvKpQ4cbik/SzaUpf6jDQVy6dAmvvPIKzp07h8OHBSH0DHznnIgQgdc0GdTxPlw3khaGDWrrMEm3JPHrqpu3FVrUiOO+58fpFscDbEWvQqnmb2slTWOp4ZCCPKsL14+jpZVeqwK8Q13EkCo7SmDxguiAdPJDpv3+y6RE+SQpNHKWTTHNYBhVRWH9n0dUoHIJCBZn+j/FKFvJTXMWIeHH4sU6iwxiZVAGFBoARKlbxikkildIFYagiF+pl7sMGqWoCBt5gqHX9m0TaCoP2J35MK2S+aj1H+fZ7B2k2RDO2bpAICjGOkne0xz2ZrWNeaJuAWk2FABUgsyrqLyC7UNXgLLF43S1X1yfVvzvbMxeWAGXZRarCnmAXCJYVAHCNVZVkHxa3fswi7E0nTeisg6DVKFgcm4k96+isnNlUbjxrJTN1WO5kyP6iUIeIA0WmBQWWgEPnzoEzdgEKsju8/Iis0JCE+QB69MFJKrLBWLpXCXIFVxAbFHq9sfOw6OfI/Zkgzgh4ey0cM5p6JCXdvljDuUxAXUGWAhZrLCT+/cTGie1JV/g8zgCUVj0UQoY2VIY0rYxSHGJCVWbhmRgy/tcGXsP3buNXz5/CDseDtN45spEVZh4FqU09cm7hldx5hD2lNdtUSuc/XNqSNlMjnsCv05bOg4JM6Y4hQBHXoXII4RtVp21oND+LUM+Bc9N6LyGHlL93/PN4Q6RNwsKPnWql7Tz/H2pvXMa9Pz/Vf/Dl+FUTdbPj0fh5FO6U9X8HqbK3ca/uy7zqcPHhJBF36uvvnpTLPo6dLjd0flxdehwm8KnwulQr1B788038cMf/hBPPfXUDSGegPA5TwQvew4IubjvgcuFWoYWTvOS/I0FVCu7L/l2l1+nD+l3EKkGL5U5tK7gxNZoczsJIQizVrkb3hdB7/Zb3HrJAPNj18ZAm/UVhgcwf+EzTmHihuzmK/CFuYLJ9kiYfKyYcpiA7VNCJRQqnB38ZI14ApgyijMHXqAl6ifOwmyB2EN8Htg9CLGWdZZMhYtt+9uUoxfJSFgHhVT72x5Xb2Nzc95fSEEZWUKUjXp8vsPiGLT85czqAR8avncMA5AVngedQVXNRY6mtrryz0EAcNS9DB0REMuL67Q0uUwFyicJQpZ4QNhqCgBiFR43HCG62me1IvQFwUlJJLjfCafXROCpt5vLyOfCAJmQT+RIgwVK4/DpEz30GnKeViFdyKRduH8uIxYritrkzskuToEUSpgOJ31imKEHwGFbf4STRwMZdaRBhp97XdQTzTmuQcHw4W54fMSagsojgFe6cV2iDGxglPmfORbEVMZYZZYCpaI1DlnCP9+kCT9WdyYFJENrvJQt99C923jy08cR65C1XvPxpTp0DQ+e254u8fiJWU3YBU5K+Nkl0B8YSz4WzIUKKiBdnSzrhanC9ztreXIqZojeyN8XnXMIdgzDzHNsXtQ1lsaCz5XBZRieb2Te60jeXL627/z7x1Vd3AHN6mcjKtafkVy62Xg8XoVTw5zrSIGqq2t/t3rQaDHoVAKn5YuLOnS4mVhY9D388MO4cOEC7rvvPlhr8dprr+ErX/kK/uAP/gD/9E//hEuX9t/Tq6q6Jru+P/3TP8W5c+dw/vx5fPnLX8ZHH33U2O7s2bN47LHH8MQTT+Dpp5++5t/WocOtQkc+dehwmyKKok751ICqqvDss8/u5TtlmTw0m0NIbSa13pPaTUlJJakqpw2h1Q5tcp8sAIeBuopM/4zfcqvA8xZ5MwSgxUptonYqLBIoGQ60hwKcQjp+iW/r6lWFEzcCd4snWOQBRRKAefh7+FxwRcWYIac0GZwdvY1+6ntxDtnPHPyOJPcp9+Q6rG1bWAWV5O3U7YQrua1c9ROJ7R6B1FOQG9oPcWL7YLFHNXj7N4K0mKhyKgEJ20JF0EZYWCeCbiho+BAxhNIyYuMnqnqzt7GxVfc3iiK4QnjOlqx5JOQTl8VEcAdUII1tmD4aMZakXA5Qk2XfSMDecDk4AFAJn2M4NRFQk8Uj4W1jIByGUiu/ew9nOLoZzs5rC9OiQC1VFLVRKbXQ/CIlIfkuzGiy0MhohkeOlkElBAAoAfkEJSSp9HphnLPdG2WKV8cyCkAu92yS+xukAZJ3mNaf9fo86RjH/LU5NOQHjsTCzzlgJBiEZWWRpRoP3buFJz9dW+2pQBF+ZprHTBwYH6tz8NMnP0IaLf4Wmp9Dzy5+EKrrUieFySUT7IsEG951Pg1/XsygQoRcWYACyiZXGVBIGVUW4bHE3i8Cfc9axhIwRDSGr6j3k4a8JABeS8aaGmx+1vBmZXr69qK9tRHU89+st6/iRns9FzcvHvIpnFS5bgLq4s3Ga+fi5oVyNtm+cWFzHTrcQCws+s6cOYPHH38cf/u3f4vf/u3fxn/+53/ii1/8In7lV34Ff/Znf4ZvfOMbGAzaE6if//zn8eKLL+L555/HQw89hL/6q7/ytv3mN7+JZ599Ft/97nev5yd16HBL0JFPHTrcQHySbPeUUh35tIKdnR18+9vfxokTJ/Dwww9Dcb7jLRFSPknJJ7lCR5r7JG3HrHZcQpvcJ9dyJV5GJTai14TblhfeavsKOdqok4jKlsoqqeXeoj0hLne8L5wH2ropnIsxFljuZTRhV3mmasbOa5axaQqRUz21ixP9D9CL/f0kHFa/Qj5JlE9WIfKuHN2Hz55nHVLDSlkfmdlMTJaGrHlW0WTvErkc9xyaQq0Ug3wFhSYoKySUAGgjs76stytvq1scg/JkCzS39Yw5Z3E0e+Pg33LhMSzlMErIJy5TJ9Elu2iBm584+zauhxcNxzjqCbKABMxNXlrBEciRRrJtCYRUAICZwI7sxEaMM0eGIgtFQE7qVEjFc4XUos8iQiS8X7ax6EuF5Hsb5dXZ3ocYDfnCjlQZSQ12U6tYzYIrjcOVafgaZDEz/ghBSz0AmAaUU7FymAZuaSFuLovn5JNgvCoBwZtE/D1pVsj6rEQhBQAP3LOFJz99gm0XkUHpsR+lAME+W1qw8tDWZRwd7p+HUJZeaHyEDdyYOSX0TBbIFxJtm/u8YsZIyX3OjEXuvbVint04qWzo3DHZgmG0t+RzqJ0Wmj/zZEsh9S4mUZ552/v3+cI58+6HoKo+Dpcd8iicmvtU04KiAs3PsC7yLL7w2Cx3eU93FiSLEj6p6PV6+NKXvoS/+Zu/wX/913/hn//5n3H//ffja1/7Gl588UX8zu/8Dv7+7/8eb731lmh7X/jCF/YUU5/97Gfx9ttv38zD79DhlqEjnzp0uE3RZT4dxLvvvosXXngB58+fx8mTJ2/KPkLnXBGJyjTSKyZ9RLOwYgs5qZ2eNO9p0VZ6o4ngMCLZgxkgeYleaks5nG1zy2tpXxHIS1k/mJy3/liCchXi6SvyQ6G+KO8pFii2IgFRVjIr5X0EyZHkfZzdeB9JyO7G1WaU/s8P/lOifAKAhLEZA+o8Jwmk82xhpYoCQk/LCJLCyFVSa/lVzuHe/ntImtSfbYJ0WxBV5GTWXwCgPCuCG9u2IHTbrBHxWbRtzF5Fb3DwM1cJjyHtwVkL64AZc/0U/MXSBSLF79cwFqXcbBQiryIq0fQ6MeoJsshKgQICMks9CZEFyK8/pzRZYFKE73GHehoPnNgEkRIrkeV3B0IsWJSwaCtFKsxUrFosABFn2QmVVzEK3LMlPFNWtm8lIKmczupMvDkuM6onAEElCACMUo3Q9YkUMA6QT8MkfB5K4/9ck1z5JEEl8Mm8ultA0su58aIU4enHTuDRh44e+Ltv4Uiq/fefUG7edJ7pt5nkePjI0vV21r+Ax7kw+RRU+IXPYcjqj2AYDU542yxJzRE03OeMLZ7jnqeuiyBCeMEV13cDiq0gH+j50CHzfs+3OedxInCIQA0KJwe1RzItw7oYpBysiaFe+uZ+e0+GZiOZBAKV66rzkjzyYm+OaPOvtV3e0x0Fa+0NXxT8ceHUqVP4yle+gr/8y7/Er/3ar+Ev/uIvMB6P8Ud/9Ef4zGc+gz/+4z/Gv/3bv2FnZ11luIqvfe1r+OIXv9j4GRHhC1/4Ap566in83d/93Y3+GR063HBch6Fwhw4dmkBEn4jVGx35VMNai1dffRV5nuPChQvX5L0rBac2i4lgmL5hAeh6OVwQZv6CyJWTaj2TEiqQ2uQ+aVghVaZArK2fBrBFz4N8Ly0NMGYSdshYgUMKgkydQFS2WmvfTim18PMX2klN3xfllOzvIMLY8eST5Go70sEakUaJ0vlfoAGgXCFdNJW4q/8uhqmpjyJQGCcYOO8LK9be+FNtESuDksnAIUG2h3EKiTLrpM0KCqtBZFn1Se4yaJQwgpX9kTKi7lHYBJpKGMfPa7MqOVAsOxW9jeHIoxpQbchUedM2pC61uI+G1XGrbVvcAxrOgzJTHN24iNUfLj1e0hqumCGPDq1tYxUJzVBROLtNC3J3ChMHd8WRU6FMqVhVqEyD8onJkAGAWemQRsRa10XEa1dLM59P2HayDjsrBTdi1NPjMCXs5Os/ohcrPHL6EPT8RiVWIreY7hMqUQjahxQaq5AoQwF+4cEypCO0QDqfJ8PH++DwYj0/C7Ypme/rdoKtEUEVU9hkBIDPewLC5A8A9BKFSemf8IeZwtWZfxuJDp/dUN7UIisqEyifqoo/j7sT/nxUxmJzmGBnEu5neUAh1c80fvnJu3Bke11NUXisdaOA8rD0ZEElqsLYaGiyeObUDvSSV2hdkPcoqWDgyL+4IERMkXPBqccF0lRDx1TDIjgaGXUmS05xY40jeKwBQs9IbGZT6KsOCFn61W9gno9smGHyskXO+z1HKeBdPODp+57nF4sUumFbtVJq/e7pkAAoYd96G2qZ0FOevlM1ZDglG1AN71TGZxnoPUee3KtO+YgTQ8IAACAASURBVHRHwRgDHRyfv3iYTCbo9/t45JFH8Mgjj+BP/uRPkOc5vvWtb+H3f//3cfHiRRARhsMhhsMher0evvrVr+K3fuu3AABf/epXEUURfu/3fq9x+9/61rdw6tQpXLx4EZ///Odx7tw5/Oqv/uqt/IkdOrRCRz516HCboiOfgNlshueeew7Hjh3Dww8/fNNtEbXWXts9oCafZoKqkiIZsaOhRBZ4SmjM0yb3qc2Z5F5WFYAtehGacnFxDgDiqIJzOmiXsgzHFFiXQZjBukSukhAew/72NZzkqhiNaPZDON0HCVeOO2dFyidJXkjFZMmkaopZQPWlUaLEPqE41Fdwavgh9qIjXBVYDVkfZehRpUlzMIxLXM6ZYrqwFBori4rJiLLQGNAEOXj7p0zNMGZs1AB5kRoAejrHbsVfSwuFDFcwwyY28QGObfkLx1Y3+/s3wfmKFU1o0Vaa/QIATssVYFbLc3dMg03M4fJFRBsN16cNE57PMGWy1AAgRsUX1xl7yEiVsIwys/TkoACLTCn/9315UGmskUSEgsmySWLN2nBJHDCNBYaJxaQMX4cdoVBoXNT3JsnMnkZqzTYtUoTH7tpEvKTakuqA29xNNBmRVIrLBVsGl/G1QIUEGoXoXhJSa6wioxnGzk+6DmkXp4/0eQuvBcT7lj0DKTuDRU0+cXlPADBhrBm5vLJUM8el/D011gha8i2IqShSiBONMjAWi8KAKGwxfmVcItKKVSL2ezFLPu2Om6/v0a0e/s9fPowJNc/l0wYyHAhn380899BMlxiXKc4fvYyNtbVRoesamNOcCz73hO7/5KrgvYYjrq4b3H3uet+zOPKK23zo+KxhJEqB7YbsDJ1/8VH9NZ/yKXDfdUXjb/Xb8cVoIrLqvzeRTzFMRVA/+H8O/t1z/lWDwsnFQ6BcJ5988wN5bIx9uaGd7d6dhdtJ+bTAZDJZy3xK0xS//uu/jh//+McAgIsXL+Ib3/gGvv71r+N73/se/vEf/xEffPABoijCv//7v+Mb3/iGd0ydOnUKAHDs2DF8+ctfxre//e2OfOrwicbtNcI7dOiwhzudfPrwww/xve99Dw8++CDuvffeW5LHxSmfEukxCPODpBY+0jdRC9lKb6Cd9V6I1CIAh+gHiGiR7zJrZY/nnFwp1c6mzwEtinVEcjuxve1zcIRk5zUQAKfkBfOZIdEq98KFt6lgkLswCaGZ0niiFqslHU6k7+Lu0c+xnFnOrkhnyNqml3eJ9V5uZddWOmQlFoYAoIUEYikgqBaIlHwsppghcRPcvVWCQi95Kmr08m+Ca0VUyZUSbQgwG4XVQcsw8QgQWgWaeONA26T4OQ4dGjc31i1IuKoQ5T1J3sO53K+Y6XOKTHC+iFWJa61mbvZ4ApErvAOAYZQjC/QTfo4vDaEXS+z+CENhd12dJwjA+btG6KUHx3HI2msVgtMybyc7NyXSFvfAdkSRBBLF5wIpY4t67tBH8yKV9CRJiWzZ9sjuHx9HPqURBfOaAMAyRA2nfA5xW8M0YJmpgMnSrYvLfXLWIWZymJwDNof8/VULbq5FZdHPDs5ND96zhf/xubPoeeaWRFWwnmdo3/hTsJh5CKtIGZwc7OJso8Vj4Nk28OzC5X6GlLz8UjJuPmDZm/DHETOOOYtUTtnAzWecylkHjo99J2aytHwILDp0V3lbrbXvgLwuDf7FfM3n1auwI4L90Q/X/9yQe2fjYaNlsouax7lqsP8DAFXtNhyfAlXN56iz3buzcDsrn0I4duwYfvd3fxf/8A//gOeeew5//ud/jldeeQV//dd/jX/913/1fn88Hu/Z9o3HY3z961/Ho48+esN/Q4cONxId+dShww3GrSA5JLhTySfnHN544w386Ec/wlNPPYXt7Vu3copTPmmSmbNJy0PSkrNpQRRJc58kiqsFHKz3le4QvYGEruz9m1D7oMu3LS/48sZNq2jzEFzAtSgscsUHAKDKQFWX5v+S/86xgFhJaQrrscZYbsNZyXEr+TUMYspx3/AtHB7MQCsV1TaE4PrOTWMhYhjzRGDlNCLPC/IyfNkRDQcjbCUbXzOTCvWK7VRSBIt7hz9HFPN9xLcadW3/KgEZWQHa6p7XYmVtu/HAu1K26Ri0p4CxBtKIyit8u3nbuNhf8XtUvwrlWcWMVE6skXMi8gmMnRYAVAxRydnyxUyhX6twPwwRKps9QX8XDJ08YEm2jEj4VjNgMnIWSGPZ2Fq1VXv45BCj/vp9rI2Vq99Ya3Wb0lc5QkLC8dTi3hcLMseAdsqr0Nx8VH+Iw1tzslmopHShgvQyhCTVsj3fJcZ2b5Rp9r1gxqgDK4Z8Hef+MZ5E/n0Ps4N9R5L7FAnmpCzhr0spsPADgF5ab0spwn97/CR++YlT0Ep5781JINfJt8te5P9OrAw+c2LavBClxTPfMrh7e3j8XcczEwSHzNnipczzeczc15hFGsQt4gh87kwVXlTDqqr8Jyc4AkOLpALqTB+RVGdBedRDvoVuwkUIe/uYldBvfHft76pYfzZySbOTgm8BZNSkwFJJ44ImF48aT7uNhoCS3zM6/OLjdlQ+jcfjNeVTCESET3/60/iXf/kX7Ozs4POf/zyeeOIJ/OEf/iGAOr/8N3/zNwEA77//Pj73uc/h8ccfx4ULF/ClL30Jv/Ebv3FTfkeHDjcKne1ehw63Ke5E8qksS7zwwgvo9/t4+umnb/lDjFIqSD4BtfVezqg5DGS5T3auP+JznxzIkfDlREGaRaSEtn8AoEGoVl60DtHbSOnna23b2eO1eeGawdkEpIT5D62UUgBsAgiLe6ACsJH/ZdcpxDvPL/1bfiySvKcEM0wRXo0VU8kmf3AFxUzPcLr/ISJfwZ5DaAWwc42FFInyCQBibVBVTJ6TJwdiFSFbsgPbs1KCgtDTM0wM/9KSG/kL+la8i/5AtpqUjJysVXYCowWkMSmoagwbbYq2q6tdVInsnGmzCxONZG3tVJQVsziGMt3CYPoGhkf8cyNFMVxZgATEHhTVGVwMuCwvgkVp4+tyOuLIKcXMg5X1H+Nmnx8/hRHkr1mHNOKtvIT1bAmnB0Cu/xkvKVvuO5Lh6KZ/blUENuMKmN8abnCWU0wFq2YFgKqFSikSPi8Uc+WVhCzz39ctHjpSAKh/g9Ty00nmJshJqsXxFZXF1Vm404XIH6BWru3OwnZgu6X/szQi5AHyKrT3dOX2xymfbiR2JkK1sFbopRH++4UzOLaU71R6c5381yP33M/jAMF+z+YUWeyxOrtmssKGM4JCeVCc70BowZBz7OcUUh6UOShELJoKFPv7kHMOCNwf2c+NAYVye6sKCOw/SBI5x0iNr/F9MnixmsdAnRXmsajz/N1HaHptyX/0wvp+o17zQqIoQ+PLgG1QQ0EhcdO1/u3iTZC5vN5e9wG7ribvLPfuPNyOyqfpdNqKfFrg9ddfb/z7qVOn8B//8R8AgPvuuw/PPffcdR1fhw63GrcXvdyhQ4c93Gnk087ODr7zne/g1KlTOHfu3MeyekZyzmNhlVCiQKpfUWW/U65okkNu+7f+nr1B7yOjdz1tW5A+LdRMRHX4rnzbLa30WtxS6xdC/0u0mu1CLak+yMqUJQAwtnxhX5LpwXVVBYPCU8jsqR2c7b+J44OrQeKJU+0EC5WzdQ96AEi1RcKoOQBZYdk4Eimkprbn9cpfRu4yKGG/ihjFyQKFTUTbPKl/gu1DbWwqWygmGyxZvG1b9GXl2rRtMRc0FE180LYE2RJHBz/lG+fC440SkfKJUzUlumRVFdwY4+ZbrogaIl5Hq9XtBswKUxcdGSQCAnsaKNIvQ0L+AAATRbWH0gBZBJzaVLjrcNgCUnqX0E6mPKxa3NO0YC4DgMK1uE9K3YShxBZ9Ptuxe9L3MRzs33OcSmULM3TSWChd26+UpJof36Uxfz6587ORuGCjRBNmAaZ8kIR7VChzbXW3EuUTZ4ULALlg4ExmFVLm2AGgn0X4P/77fQeIJwCYenLq/Pcth5nnO75xcXpwFSeHoX5zbWRFqEsQquvKTQrP9zZ8v5h5LGUXKJjxW8zCd5siD++/LNjPg7DX8c7L5UFda5BW4F3U93zjc3RwiECevup7F2p6j6mmMdR768Vtl3reHTzzMZnJ2t8qPWw8jS7yzK2qec5xHfl0x+F2VD5JbPc6dLiTcHuN8A4dOuzhTiKf3nnnHbzwwgs4f/48Tpw48bEdh0T5JM59Ek7PUgJI2s4GE5oOok3uk1sqcA7pMvr0lrcteVb2NWMGJ8zIAlqqqmgmKoruf6GdHQr5VrfaCNH45ZW/7Yht/XYFyieJrVLFqJpSmqwVChKa4kzvLdy7+QEGmfMHNC+OI7gK1wZDuUNl8aHmC7fGys5nrPnraqGRKVmxuKeEBdgWxQ5um0foXRzbSuF0ClghSSNUFdSQ930lzFsCAGrTtpVSscXjryIcyl9GKomgqmSklk16mDGKNUUGZUBVBIRX7O8dEqOe4uZPG8jgq7POAqqMJGGzi6wD0pifjySWxrs5QQsUvtNCNrbGhXz+PzKMcP/xbbZvSR8BIqwX9ppQQW7RyWUHLWARIRIu7Ghj0ZcIs/Ga8qEilLjv6EpfJiVWaKqGLJNV1POjQBM5nxu5vCegJiZDyHT4mgxTFez7MaOs2g1Y8hUrh58kGooZsKWAWLqym4uenUZ9/xzYzyL8xmeP4deeOYN+drA/RIFcJ9/RZbr03lObDjVWBp8+thPs3yHVYeh7oXs7b8nH5BIFnrm4bVPBzDkVM9Y4cqhkxiD3fcOMt+uw1WPtBkPfDXxGqYdcQewlknz3VJ8luYOCapiva2eMg+fcOcC++GyjwslFnoecprwnikDlutWxUZ5jbPPMhU75dCfidlQ+jcdjDIfyTNoOHW53dORThw43GF3m062DtRYvvfQSPvjgA1y4cOFjv8FLzrmGbP1cm8KzBHJKCVDCYlKb3Ke6rUOfdjGk9ZDbg5jBGiGpRu0yotoVqB3QZgW42Mhrsf3mv0eTd9eKhAQHR7x0v3ApSsH58CmW9mGRu/D+Itr/vRolTqZv4/7Nn2GjZ+aWUcwKXucQUn+xRZhAkWWo+cLtTGipJx2JsbBYq0mYkcIQD8uIAlaSm+4iTh9We3lbSpjP5F2p2gSGZDyw3RZFiDb3U6kFFwBY1WJc6xiHD63bxTQfhGx+yWkArmelAlUTZ4kHOBQmrGYwDPlUBsirKJj341C5BIOUvy6SHBkrkCs5EEYZfw3GBUQkVWmATCAGOdTTeOD4SLRqV9qj5XZ6bbKc5GMvVbJtNhFFPsTCBRp5w333gcH7SJL1fZGAVAIgzo9TFX/vcLomTT5k8p4AYJwzz4QM2cORS6FHuyyioHpvUhy8HkTEqp+K0iJj1VYWGwPeVjSO1u8bioDHHjyC//Ubn8KnzjYrMZLAvFN57uuJ9p8I00Cwf/rIh+hFgTHjrN/Czrkw+cQtugkg9NxDqBhVFTPnccohbjEI932OPOK+zxFE3Ox6rXlQzq3llR7cbeCzXvNzlCX/85Xv2dd5clod0sZjcEjXuqjZUaDd5nxM58m8o4Y50aWbzVlNnucw8pxf37xsE5k9dIfbB7ej8mk6nXbKpw4dlnB7jfAOHTrs4XYnn6bTKb7zne9gMBjg8ccfRxTyAb9FkCifiEhkvVdB5G4CI17J7ETbA9rZx0nt/ACghyk26GW2HQGYTFuoqpiV/Qcht+aq0WIVFuVidRLgIauMRjTzqMKIL+bsCiz3IhQoGaumjKbhbIDFIcHgaPIzPLj5E2wPigPv9rxqhbM5YQoN2v8bhhFfaDROiSz1fMHm65D1WWkBeGYysaLBeRRifXsZdx8+GMIttZxzOpOpADC3vxLCeWxWmtCGJLKR/AXPxrJsKAAYubeguQLwAkKyTGK5J7JdZPYXK57AKgLWfgoWJmCrp0KKDFUrDYYC9kZy1gphoFPKKEmAOUkl7Fr9OHx0232N82e2GgvpzfuWoY2dXuwLoV+BaZEPFQntQVtZ9Al/fYEUamnu69MYZ442L5iQ2uNK5z0SKEPtXCHAKZ96kWOtGyuG/OVU66G8p0EayopCo52fJPdpY8Bf84FgO6uE8skjA/zPzz+EX37iFJJYo/QsQgrl1OWeLKiQ5eTqd45kY9y9WQHwE0yhezPBBJ+fQsRU8GozpBa/uKqdOv//Z+9d2uw4rmyxtSPycc6pU08ABEFQFEjwoW6SfSXx0e57B/7s0Z3YU0888R/xX/LQ9nevr++gv75tUmJLfKlbaLVEUmSTIAkQQNV5ZGbE9iDrcapOxt47C2ATLOQaSERlZmSezIjIyL32Wnu9fQ3K+NY+QlRySTleqsEVIyCpKqRrk0gxoY4WLyuhBpZUmyo1VyXOg+5vg7N/Z3bAr/8ruEjMpR1rdgaB6nvrf0+tt1IkXsJuucu6DxiUT08iLqLyaTabnavm04ABFxUD+TRgwAXFRSafvvnmG7z33nt4+eWXcePGjcdGbWa9DmvdJ2/81LNa6lnrQ/UwmjOfe4Q5pu7P5nbHY7uKrU9tGsICbLRb6902ARACtesHnAlyMZA/EFRhBvbwwGC5N6K52ldzxR6JEDH2M7y0/Qmems7hO+uxKESsRk4pxaE5T6u3NnJboDEXsqGPkCpSfhZS7ZtVVNEarCWMvS1bv+6wSCx5Hy/sHcCvEfPGABQRfLRZf0U/apVupn17kETZhllNFP2GKXDc7js2KRzyxVeYbh2AjXZ68LY+MA+6qsxikRYVG1HNls9TIwYzc6/8bmEaOSJ2p2Od8AiGua1qYq/5WEOR29rKhOF/acPjtZ/stgGTR3dpAPrZ6VlrOWlWqqvQVXWHbaKAt6p+zWs1wnilPtTPtu/CpbLyjddp3c9Uv87loLDEHUX5NC0MFpCNRg7L170vKKtyoU5aqlaUpe5TIQ2KQ2iKLgCYL9v7Nxll+B/f/gn+5//hJva2T+bGZUL9m3qWhIhlTATihTlm3pz8ZoeIf3f1Hoi0PiNt08gKya5PtuST1m4pdYkVpClQtcCw4Zk/1PHa/CFdn6a6kiCqomL6uuZplbmUhOSQqgWVaqv7d5/9e/imAu1/CyTmUjTr1tFcbne/dxNrnVT9Uddh0ccu7ySfGA6c64l0Ay4WBuXTgAEXHxdrhA8YMOAYF5F8Ymb84Q9/wB//+Ee8+eab2N39ccryreST1crK/YB1nyyY0H1suY97kkR9rsCuZupv02cMOh/v36emVABWsp6pYfjm2/QBrBMR+6x/sFls31KLg4l7gOvln/HKzid4anMOqVQLKQFl/RkL/bWpxMzirChRGJQjlhERmUwqgHkcgw3BnyWP4IzBWtnWbKXNkMOtBKAzXuKFrTvIio5AXA+LPCuZA3ImMgcA4HK4RLbr+r4ZfFCKoB9fA8E39237AsiabuuZVezxx20NlMpYh66wEYvzYNnPoJAVVEsA4BSbs1zpXxqpIVn20eG5LcqnZW0LmuYGe76l1bLV+IpJuf1dmWZ49dk9+MNAXrS+X82vNkKeCEKehfVjrkbZw3rWHkgeGW3/7FaCQOHaueeSv4Mre1Iyiu2Gmn+Nsc5cvTgQ6ykBQKkoJjMHHCivlplwjnFOkASB0iu4SDwKi/IpGDpxHQz2l/Mab/xsB//Lf3wFL/309Ho+o5Cu65RQI498ej5LHVO6+tQ89srePUyPp+fzrYSltY9mzyxZ8lnJaKEBGZlCTufK9kzpO5pDRYoUMWxnZsAL7WuWfqpFdP9tXMu2tN1/LY7fnWeRrBGVvPSTvhQ5A977z4cn6W7HLb9bv56ie+5N1fz1cZ3AitlGZ7/nbKuTTOVix+S8MOBi4SIqnw4ODgbl04ABK/jhfaoGDLhgeFxUOBeNfKrrGu+//z6m0yneeOONH3V2jFUbw0zGiImtz1k/oxmtSooNQSqt7tOUvsGEWhu5s4VvZfQglLAAs2trNFmgqAVOtU0LRM7s49p6DUe7IwOjAtghf/Bbed/wAOy9+I18YCCfLLZvq3U8clpiN7uD7fEC5ZEFFUeQ8nGo1S1T75SU4RsWaeVTswT8BjaKGtVCftbBqILLqUGjKJsiPDbcTK2VBQBjt8BBtKj7eigFcIADbMFxg5sb/4py3J1td1SvxNaqPZPa8dK8twszswLKhRlCZrPJ83Furrym1YoZz/8Fk83DyLBWCP2ozTwH1xVICdLNG50AD8o85SigjpldTNIBjVzSCOJGqEt2dOR0pH9qVE3EKCdo8WrvCAjyNd1fEJhZtxs0Lo/m9fr5rm5meOWZXXi3Gtx7tO9hoLW+s/Q8ey0nQkELLFkfe5Ii7ixyV4tijyNUPZRXOQWAGa9cWgCQrtd43x/xmvHufmU4t3zOzZHHg2W6R4xyuWbTpHCY14KdnGDJl9oyGucgYTvQkkYa9mfpfbanBV5/8TL+8oVL2Bo3OGjW+1qbeNH9zm06ajQBQOYDUHcfU8Xu/lz6BgeH4ZCtYoEX92Y4ea4SISFvToEQwMJXgDzuHk7ZxFoK2kioBcoMlMp7S0u+0MgpiTwCZPKqqUHntdUDFPJJOE6060s/r1RiW0SZVJK6xNsguU5bIbHC53fhqpYY6qrhFItNuA7iKLVe7EpMYlcg58XaveRs2q7L19oeA2Hd0m+w3HsyMSifBgy4+BjIpwEDLigeFxLsUeD+/fv44IMP8OKLL+Lq1as/9OU8NIgIOaBqKQJaLkN7lNbP0QAGDO0Bbd0nC/nEh5+zXVlwW/QFRvSvK39ZgOFMAe3WHi8DOT1URwREjEEwqil6fMATRYAL6E/rcH/U/WwLqf2udcsDuIQn+knbAew2Ae7+nQ17zFknNCy1RBousZt/jZ3yAJPiqNjyScchjqqCJlWH6AQaOXU+CxU6JJ+meYO7inBgEZxpPHheABACM4fIUWEJnXzyrjF1w0oI7p9F1hwAbornR59hPE1fA/uRXCx9FT3eI32ysslibXUIx3Yi2qooa9uVJAMBe8W/rPy7R9BvuVAzxC22e3WQA3WFMxCiSj0ZjQphgQ0iRPH88ZDYzb3DKHdYKOqmIvOYK4yQVOvlCE0kbBaMg1ruu/tLW9+eVa0b1JHY4+mtlnhya+OH4BBNCqjV9uT9GhNb1UdRlFMNge84RmNOkwEy49ivMAIhmsgyAuPZ/EtsTpXAjTVDXn0f9Wvv21mA9hldKYRkmZNIPm0UHtU8fW8zwVYPAPYX6fFSdZCqAOAcYTTOMJ+n59J7Dyr4jMQ+vD+rMSo9qsNxTwQ8f30bf/XiZTx3bfP4G6VOBOjFuk4J8skJyT+LDoILWK2tx/h3V79rla5HfxEIJpbqCyW3KFs5iv20e6W9uoOi3pH6djUHlcKYrxYgKZGgXsIJde84NGJSBocgb6+rU7Ur1xCCbguYapsZJKmuxGctEHqiReKyu29R9zNoz7NE10EpIutIbRRDAfrt/3v8d7fsqOFUbAGLdfIp9etcs9/ZBjV313f2JTqXZon+OJBPTyYuovJpNpthOrWXERgw4KJjIJ8GDBjwWOPPf/4zPvvsM/z85z+/UNLlnAi1konHALLDUI2EqGpMTuAPw2Ma7C0CDg5hJfhEALbpExT0zan9CEckkW6jRWBEjEBY/xjqvuCsRxZqPys9sAeMBd1BFZhlddKp3RGAmCHb/8h2KSiTJJtF9eTQYMmTtetzaDBxC2xkBxhnS2yOeKWOU8eP4QhomfEqOSUEEgzkVhKx/cq11H0K7DDyAU0iK/q4SXJGyYKNerQqFZahNAdrmRyeyz/B5rYyT5ID1TNwrn8QsetRw6wXvicLzh5kGQv9a3P+OxRb58wyV+pDRSYsg0xOeQpolHkkcw0aJeav9WutX0m2ermrxfplq0qD6SjHopaVZpYaMW0wTf90mRQRB7X82+pImBQtuSSBAWyWwP0FcH3b48VrXcTT4fVpkpFDONhSIKy92ZJQcITMWB+q6mVPawPDoaQ5FqwT+QDhxauGec84R7GxHhs726fxtwY30H1F7O202osKuSTVMZoUhFmV3p4ivQrPKEe5SD5FZuxNR7j7QP6BWxsF5suAn7+0jVdeuIrNydl5j5OKpNQIIUQsQuIZJX5uRgGzxDFHfNUL2/ewNz57Tqn/SesXlxwUJBBarSrq/Jn/LFSK1epFoZoDEvlULwGJfKoWQCaM62oJdNkAr7YvJW00NZAL12e0y+xECPIkJpFPNaf5woRSi+HgEkRS6vkzxnAdtVjb7tQ9Dt3h38O/fAZ3aDvI5RZcVwJblniHdCT/sC9b9dRZhZMfA13kU3Kt3z3GB/LpycRFVD7NZrMLFbsaMOBhMZBPAwY8YlwkxdEPiRACfve73yGEgLfeeguZ5hX+GMFi+ZMT6TYQwKG1mf5R5eEQTIoia92nPvTTCRyAbbqFnLprr0h2I+s793nmfYLZdlVVu78dRHyolLLWyqmQze7Ama8/fTUHvKUePaI5KtrEmPax4Q8wzmqMc0aRu0N1E+BiDVICdcSydQy4kTPNuaVW0+1HkZwSA4SH42pqIJ8AIKeIRiHS5nHcqvyUcV0rSpQjVImi6GfBcJj4GWZBt23Yyvaxt2ezp3NhiWAhn/zYrJLqRVRZVQiwB4MBIHp7ED7m3ffUhTl2Jl+c+WMPIlRRSS1CCW1WKXyNWlG9SVn+QEva1TEXCSyNnKqR7qeZC6hSbj+IqFfqUU1HGb5RAtUWJdD+gsCkW34aSkMBAMaZTj4Bbe2en+x63Ly6K57b4AoIwE5SWQPRDUo41Ig9rPI0RGTIsEQj9IEjSETuWZRUmcinvXyGstTfaVYbUavik41zyLdzuZ1J4aCVMtPIY0XngmVCvdSeP00+TXLCLHHsZh4xGmdARwz5VBtlliSfLu+M8ezVKV64vo3nn9lC4SssOgj3zAVUIaF+SMyRBSosEn0yZX1Z+hqzJnUMP7j5QwAAIABJREFUMM5q/OzyAc7Oy5L6WhprkupbeqK6JZ+kbArKvBghvXdISZpQayY1NUR1eF3L5FPTyORTDBBNy7X5VApqR0U1Jb3/6wWQqJNGo+65hDkHudSLp3vSYOr+rmCUp+p9nvw9A7mIUJdw//h3J62Xm3DLDvIplVBRdymctkFhvT5U8j5xN5FNsXv+iMWPs57zgIfDRVU+DeTTgAEn+PFEcwcMGPDEYDab4f3338e1a9fw3HPP/agIvaNaWxpZlht/E7MDTJnKj7ru05GZhH7EkTLLA9ih38FT2v6uXi5hjw/3MbCzq5mIGBElyFjMndD0s9LrEwAMBL/4Qt/vCLFKPuqDuIUcS+RUIaMGOTXw3rX/dgzvgIIWKEb3V2qVrH9wEisf+YDa3YiDGIAhyNuxeABMhA9QoRMdBUJzzyh8QBXk52GpFRaQY0xzLMXaIy1JRYbg+DKO4NCY7LIy18j8MzN+kv0Ru7sbdju9RDBgDeThmjlipn88RW9RMxyevg9J1KPdmNntLUK+1Xm/tpcfIjsb85Yyrs9Cmdst9Z5yF9TAtVZPJnc1GqXGThXTv8tTQBDIVEmRlrsa9QrBOh3p969q9OSJOhJ2Jox9hTAyNAUAEFyiTmF3kuGZvR11XNsrtNkQeyRrFLTEwlDLqU86RemWaAxEeejxOWlRXpVY4Ke7xveuy1sbVo2kJgdqlmBJnQGAMxtJ9c1Mvo/T0uHuXO6IM2WQV0LNJgB4sEwfnwlKwkmRJp9yHzFOBNNP4bB55wjXLk3w3NUxrj21jetXphiVp59Fk1BQtjXnurc1iXqMnmsgQT6liKzcpftcHR3+6qk76yoz5jT5JCmzmUXSWFd8C5uFY1vbW2m7olbXyCUNQn2jdruy5tDOr7WvTWtCUJtiTG+PQZwLaNw9n3AIwDhBPgVKPqq0fXH39TEKoMMhoq0dtUD83UenT5WN0CmUiuvfUOwyuPrBusIpGwNd5FMi8YZCt0zUNQ86/86D8umJxEVUPg01nwYMOI2BfBow4ILDosJ5nPD111/j97//PV599VXs7Oz80JfTG845RO0jCa3dioeuaQqwhYqs5Ihm4bcKB49oqKHCiMjA2KYP4FV7OsWH5hTstV4ICzCTiUgA0EtVRbRAZN9jHBmt15jgv7sFdhudxXs7ryXuJ+MH17YYV/PVj7kj8vAkkOTDzFB4Xb9+MYCCQ+WS2EBQlFHCyIgBnKcD+KuZ8NO8xh2FfEoFuc6icAsso/wREeGx4WZYsk7WjN0CB9GgPhI3Rvy0+BO2dw7PVx8AuSHLrk8tJ14ChjpWcDmombdqKQXRj9tglEHVxNkEFJZgpxNW7Aq4at9GQrkMfvkdwkqWbVbdxdbGHZyddSkvwE0N0gqmA2rR9HnQf4fFajAoqqXWlk9QLlEt1mzKXY0gjB3pCv0ZgmFzrD/nRRWQOah9c5xF7Ffybz+obP1bKGkFAPAEvHp9gqe3x6gTdWbOB5t9YN3DTi+nykQ+9SKKjDXUKrZfpze8o29Ob8OXI3MFNxcWiE4f8y4uELTafeRBzQKcpe/loo5qTbE809SNhHkVxXXFvkAubZQOB8J2SUkoka7MWCOfRoXH5iTHdJJhMi4wHefY3Rrhv//ldTxzZYo8c3CoEmQ2JxWWTlDrp8ab80h6Vqbs+KR14U65wNPTrvVGTNbfaQmCxLkQDlUq3ZDUVGKPYQZTJtjqRcgJCcq409YEalJLnxSt7wGSUj40yu8Trj3GpJSWF8ukugnzBShVe7MJyfyuZC2oxJPnRD9k5GjmEe6TD05vSKz/qVlPGuRyBxQ66kOlFE4ddWsZDlQ/WLt89pPOep7sSrAfgvVPIi6i8inGeOF+04ABD4OBfBow4ALjiAj5Mbz4mBl/+MMfcPfuXbz11lsoJHuGxxhHyicLCiLMFeu9CCBjQKlbjXD48aSF3BiAYwKbSBpbAG+EJSb0qYF4AsqirV9DJovAudker1Uzje01ovrY9FEAeITuirkd+xuVUlQxXP0tYn7Nfi1cgbHVqdpib6nRYamrooe+pSAIYKKvlIuQvMIWQJG2l4srQcONvMadhXxflsGZuBhr7aEcFZYGssa7xlT4pU6oU4gDbhR/wubOSdDVxSWi4dzs7POr48Zg/Hm4bzNDsCiVyMGFfURnSzDwzT6awhbgzsIDVEYFVBYOEHBCPu3Gj+BSdVaqJWAhn5TrtCifLOO0EVRLAFQrz1whn84SSGcRWAii0umOPco9vCMEISLOAEaFw0KwEgNk96QjHFQOpWfUCrE8E15Z05Lw2rPbGBclorGeiPWt4ngBQO+j4dhOT/9c82STe2lquFU4a5so4VEZiS1FMUT7uLabAe5QqWRRSbJN+Uwd2f2d+4WlSD59u//wavTpyOH+QrLNc1gIyqdxTjgQcnkWgqpKIqaqQNjeyPC//U8/w850hOkkR3YcfG9QJ4ikVH24jELS1laaDaoEkZRKfMldg3mTOE/iRLlr8PKl7jVjr1qDpyCMGY5i0o1kq0dQFDjMsmqKZdNt0r4TNS9T7XiV3FK2axO/lPQRgiJzFW9cetM8TT7xskqST9ws0ZVFJtWCQmouTvYJAv/2/1tvqUvhBAIt15VMnG8AHeRT6vvN1et261xswcUO675sCjR31v4ei71eyVEDLg4uovJpwIABpzGM8AEDHjEeJ5VRHyLkh0RVVXjvvfcQY8Sbb775oyWegJbws95zq/UeGespeOOU7oz7aSopArBFX2LLfajaER0fQwBrmcfH7TO4R9FzGOvttLCrqtq2e+Rq0FIsBA4AzB7uu48O/9FvjDKt3xOGt9XcMfQlVRkVa8Pzfrh5UCJHqBGeXb04paaZ5jphGNghN/SHxtwHjMo34zhchnLtY9+hwQvjT08RT8ChfYzl3Ir11HlBxiAwAPiE338X2mC9cd9gJaDb+mZHKBd/xnSatgyFVhPjEEcqqRQsyqdGIHaAlhjS9tGGoDOqLVOoE8FhYD1eR0SYSsXqD5GliL8VaHVyjjAt9bEwq4Augcoz2x6//OkljA+JxFRg/SxYyxI5RDDUUTpCblQLW2fcGqVg7XTeVoGSbNepEVQvbd05DkBRR2Z8F8hoI0rGd602Nr4xkE9LxfuxzPSaURIkWz0C8EAgtqRaUfuVw1/f3MWNa9vY2SxXiCcgJMlcTo4RicBMkWAFVUj1vZhYexRestbrbuuVnW9T5XogvseFTSSs/bRxx8I3gT5mlXVHrby/tOQOre6uZk2rHa+RV0LdJQ5BJM/U8rrCWBLvq3RPhRdVDN3HMcZJ4oWS1uKJGlH39uG+/tPa312zboHHo51usjWlTA8dyW/ZpHP9lyLxU0kFQ72nJxcXTfl0FAd4nOKCAwb80BjIpwEDLjB+DOTTvXv38O677+LZZ5/Fyy+//KN/SXvvTbZ7gJ18gjHwJec1nt7TgiiUu84RsUcfYUSfH7ZoLLIBiAWV13fuI9DtoWbCImVPnti/x77ESNUkON5ndgB3aFFBUQh4d6GjP0RnI+miRlAdWrtIsAT6NFs+9Y4KtnrSg6NwOgC6UdgIg4z03zSPE5VUBIDKSIJWhloqQEtSjfzJ7/LU4Obkz9jY7Piot84pPgd1BBA6YbDGOz59j0FlD4Dbg8YAxP6x3vDh/WLGnv+9vG+fmhjL9L21KJ/qIPehwtXquzJq7w3lPkWBSPEURDVOlyrKUvfJQt7sL402md72PpiuPA4C8JdPZ3jlmctngiBkese1e1hqyBVwxjqFFkVxe1brJx2hJNvYt7fZ9kkLakF5tefv4PLOyXazUsm8/rD1Ce0dd+dAnwv2F/I+Wi+WyCUACMK7aFpKuh1OElPjnPDK1RK7HYoNAiMm+kOruksEzQXyKUVmeSGJoQ7dx0i1xJbN+ly1mS9wY3tdkXEC6Q6ej6xQySeRmO3xXus8uUSmMVAKCSkxAIUwfzPr5FWukVuCVWGMIIncSpA5J9AyMSQfSuE4zbc1gSxBLM8FG02XqFFLHY4MzB78u1+v/z2pcOpW4aa+AF29Xqsp5t1uBOmkuAS5PNR7emIxKJ8GDLj4GEb4gAEXGI87+fTZZ5/h448/xs9//nNcvXr1h76cR4I+yidPZJqEH21YpV/dJ99hDTGh+9h1/wC/EsDqsoFLoZ+dSZ997aoLon6qqq4PPHl/QRUQc7j7H5/8Ie6De5Fs69diIZ8oVrpiiYO6jxroY07WSjjeRVJgNUvA91GxreBMUevcMUohI/oIFs4mIMOIdFXNIo5NJNUylnDGfpW75vD/a7y48QXGG4ki1xb12yGow5+/u017PZdkLYCufXtQur2SEnqQZUe/bWP+e4zG/ca4iLpbORGZsFRIx4wa1WbNO71PN1FuIyrKKUntkylEQ5dllkX5JDiFHeOgcsidvqP1zVEc3oZRBrx5Y4qru5c697NZ0JGcRL+C3GgR640kbeyR1JEZCa2mh0LLG1cqS4wSxCfjpd3TZEAv0tmAR1W3UbPd2ygdgkKk1pL3nXoFEO0pR3l67G7mnHxSlzY8bj5zpXOb1P+t9oxnUaVqQQnPPVnXKXnHGPOOY1679K04VmUiPL3tvMSUZsmnrpuldVsMQCGQS/USJAV9l3M4aexUCzFozHUlK5PqWrb9S7xPj/Ew37laPSipk0j3TPg9VHY/Z5dY90aUybplrkO139yewz24vfb3lMKJE8QfdajTYz7tVrhb7FFPnbT7mfFAPj3R+LEnIK9iINMGDFjHMCIGDHjEeJxenI8r+RRCwAcffIC7d+/i7bffxsaGoZj9jwR9lE+ATf0UAFMkTVIqnd1PLSJ1DHfqv3boXzClW2t7ESola3MVfeye7PsSFrq9xio026rVto2Z4iuNJ7e4+5+fqsdCANjbxwCFdduM6HQbNYuFUVcB4P4I8sc8sxxkaeR7LRIsHf16I9cDrVZLvdzp/SDCY+QtajbC2NDeEQpX4ebmbZTj9Ed+7GGnR9FGtrAvQUaLNs7shC57e2A79iDAYm4fSyHfBMUKu6NP9Z37fEQmVEXzpoSWhV14vb/qusKYrBV2hEYhiGuBJJPqCzkKneNp00A+LWvbesViqbds7GuxK1PCmy/sYTpO9x1nJFesvcT3TGjQUMM+RjIjoVWxvU2txtgRGA5lR7LKtfxrbG2c7XPGF7rR9ldMejjdoLjVQj5pOKjk/iTZ9hEBDxbp7VJ5nnHefU+nJeGNG3twiXspJZ1I21Icm0NM1o3jxPzpKSRJ8Zh4ZiNfrxFC1yb7uLKhzbMCmSL2N8E6T7Tke7j5QFQpLvblBBvNkq9WLDWrh9zeKNuDcm+0hb/04zVFs/Ssc4FgSijFeL4AJQZonlB3HcwTRA2ytibtCmIsgN/8LVCtfyeg7FYnpe5Ol8KJUwqn1LyRWDu60L1GHpRPAy4KZrMZJpN07cgBA55EDOTTgAEXGI8j+TSbzfDOO+9ge3sbr7/++oXy9wVa5VMf8qkwkpWWOk0MwBvVBNb6UEefdCUq7NFvUNDd5L51bcu8JizMqgfC3Oyi1aqZ7MH3PjwVUeilTkraq9QZ/Pyzjg09FCs8X7MuNCmfDMFTW6a5/Ow0yyI1yKIEGiSCoyu4ODWQT4uYmdRK1gB0brXUcvaA083tOygKpQ+6vNOPvxv2EWCto8R+DBjrPkVv/yiL2YbZTi/mUzNZFrMNbM/fR14Y7kXepxZh9xiZB32cZgZVj4bC14qaj1FTeq7MqBaDmVIWfp5QRW2McnXWbwLDG+o+FU5/XveXpAYlvQMub+b4y2cvI1fUlqnM8/X9TLvBGQkgq/Vda+X3aAmtiAyZMQFEt1o9wVnyySHg5t56ANrsTKxYxZ6cyLpfel06qyLmguoIAHKJ/QFQZoRKeVSSbd9UIbeCoKrqGl65A/7Di5dRCoop2YYujRi728wE9WbKMrQQjmkSFn5na0E5inj1UruOldah3KH6P9n26I/T1mfi+FIU66SRR5pt3cNu1+olaoX8+ljedkGaRyQFIrM4F5CQCIRJ4l2/SD+LVB8YT7oJn6YjwST882fgfNKZCJtSOHU9P/YjUNNBEGXdvzm17u9KlmMQqFkntgAgFjvd1zhgwI8MBwcHA/k0YMAZDOTTgAHfAx4X9dPjRj7dvn0b//AP/4C/+Iu/wHPPPffY3KdHib733Fz3yThd24z82j0tYERs0m1suw/gBE99AJjNrIXR7SRRu69dTQFjvZ22bbtNX9t2D2s8Wq6RGcwE990/JdruF3BmOr2gtdV8spxDD7BqQUYpu7dtQLsO4fjYiLUDYgcxtVHoAdnIhMJABDXG/mXTINoCy5fLO3hue19Ksj0FF2zjUKxzcAYW1Vy7IyUzWtfPn3cWv+7eN4PvCGJ0X4ODb+7bmm0eYLP4wtZuloMbY3A/UVzdQj5ZSI6gzEVS/RPgiCBKvwMyZSxo9aA6/+4IG4WBWFIC94CNNg2RMBVig8/uePz1Czu4urOdVHv0P2uP+oDGHUOPxISCjGO/D1HkbG32uc78DEn2XPkVxl01wazXabRpjUZrKBba01RPgD7/T0fy3DstXVLJAwCjTO48EjkWOt45f/PiDqbjkcjVSr9IsqhrEkSStJ5sEgSOZDe6bBLHnDnPyzt3MCnav0nv3+Q2jmlWlFkmn0QCSSGfhGtViWyldhop5M5Dr+keertyfsEaj2MESQkN0rmF+8IhgMYJ++NlDUqsAaQ1RHqd1f3sF2fUj/WyAP3uv4HzVMA7UZetS+FUbnf385TCqSNBiV3RucbjfKuz6ZhtAq5Pks+AAY8vBuXTgAHrGMinAQMuMB4X8omZcevWLXz66ad46623sLNzcTOb+iqfPGwxqD71USzQ6j4R2tpOl+nXKOkbU5ubW91Fa7vR4wOjB6FkDfoDhwqsHpxPnyfQFtk+/RtpGeGa9WK/AEDRpiw5xhm7GovtnukXaGQos5plrj8DbbuUwbtA8ndw7FRFWZRPAFCyVHy8xTxOTAopK0lVCdZmjgJuTL/EM1t1GxxvjH1ECTQdoU8tpz512szkFwDXo9+bSS0A3rjv9vJDeM3y5xBEBNRGVVlCJdXa7smQa420kCzxAF19KKkOANlWD0DSLqtFuq9sGTjycaYTfHNj3sCow2JsbwK89fwGbj59CXlWIDKZxjQLNbDOAylIvYqmh51eTjZyVCMvV5EZ1VR1D4u+VX4xR4Ubl1LebLZ7xK5I1hA5feLCND+yT3dUC/lUKcqoQlH3jQu5r3kh2O6Isb9Mn39enz72F89t4MpWq6yQCC+JcE7VtyLBWk96pyyT68PuYwiMZaLG3erYnmQVbu6cvOfTBBOng+yISAbxEUTC1Drmu65HPFbp+yp5pCUmaYXsvu9EQq19aZ7QLPskSB8IB+m1AEuyxoTSitE6PXQhNVbGk5NvLmZg8c7fAQDmVTeJRby+1mHK4Kr1ZB3OE98UHWsLBoHqex1tbHU+O06o3gfLvScXljXYjw2z2exClZUYMOBRYCCfBgy4wHgcyKeqqvDrX/8aAPDGG2+gKC52VlPfe05EJvVTAz35DwCCMUgcwcn2xpjhMv0GW/SPh9mptmfmrFnXAPpYfvUjlOwfmkQR3COwl7TSS+5/8kHMnMHd/SC9c3xgCjqf4ORaGF6ug3R8QYbAh5Zpzo1o79JCr0gjniITfktIK3CoXnT+xswxSqcrdyzPNyDDiHRiYx5Gpo+pZSw7rbIm2Qwv73yNrfHJvXJWOz3jcGE/stvZ9FBK9BknnYWrE3AdgZP0NejzQL78GhuTu/1Uh1ptjKPzFyW4w2rIonwKUR6nnhpELYgpWQnBbh+ZglRPSgpSbxlq4XjS7/H9hTM9t9WY6Thn/NX1Eq/95DIm5WpAgFSlGADEnla1GqxKodZOz9bvNHXyEfoQWk4hIo9Qo7RbCa7UT3thcht5QtZpeq8BADmQ8G5YhWUeZV8mSapvD/Tf+GCpkAHKq14ilwCgEcb3pmAh6gh4sHKbXnyqxPNXdo//LRGsUi9I1WGSSO7UVTqKCIn6oamfXfq0knOVGHv10rcnxCcz0msV0UjwfNs4irUuxVpRaESnCDU5ROlPpNmfJ2oRHUM7XiORtXqKwnZmBrxwX7XvMVGNJlhNJggeAOBGeB6JZ8EYJecFSiUVrKikmwce4zt/BgDkiXNQvZ5gxaOdboIo8b3QqXDKtzr7YNIiOzGvD+TTkwtmvnBuOPP5HOOEOnLAgCcVA/k0YMAFxg9NPt27dw/vvvsunnvuObz00ksXbmHRBedc73tutd6z1Glqs+fO116JJS7Th9h2H8LTyYeVvT7TEpwIGqzDaOMFgHrtOzeRdMfoU8eJrLV0Tq7m+L8OvhMDiAQGO7tybFUpZbPcA6IWyGNWg32aogJIf7Qeb9eGRyrjEpAZWCH4OPW6wqY29oXCYEMV4THyFvUNYexO96unxnfwws49FGetlczFz4zZ1UQgo0LIHAQ+bNe8ax9iuYei1PKu2Y4fwRH1y9ruU3tiud7n5o0+VjVVU+F1IiKlRLBCUkBk1IgWUE2ixgsAbIz0MXZQ6e+5EAmbpUGBGFslyEtPebxx4xJ2N7c7yXMLwdIqOPRzSuTbqWvrYSebGxM7rB91NQozUdRH8zsyviPj4TphTDNcvyS073KzkpOiUcFo3M911TuBrnzaHDm1D1RB7kdRIY/nVbq/jrxATK2Qv1e3Mrz+7OWVuZLFtV6KFCfEZI0maVyl5qhcIKxCoq6TdEx1eN1Xxgd4emP12ber5S7I76X0NklhpCVlSJZ8fROf1tpO1Ok5hpYUWCjHa/UQExZ0x5DII217iPL7Xls3ScSc1A2EOlWic27RvZZiSt/D9DfQoX0ke+Dd/3Jyio4EjggPWnapk7qVGV19juFAnSqphLojRbamzAuK3e4NAy48QggXrgb4wcHBoHwaMOAMBvJpwIDvAY8LyfJDkU/MjE8//RQff/wxfvGLX+Cpp576N7+GHwre+162ewBQWPuLUX3gzGRR216BBnv0T9h1v0XWoeroUxvJWp+JsOhBatnt8Yh61ojqAaKm/cgzoyVqOOTIHiRqPZ06QQ9VYDw4vhYL+URRVywR1/o+mmKCWcnuBcQv+nqhFIUX+oxADFjIpzlvmNRKVsIkN46bI6WHpwbPb32Fp6e1sQZNN9hY1wQAKNqIXfZjm60V+hFVLD7r8+8bFUvB0fwTTMaHfaJH7ateOJMZHdiJNotAW2tJUzVlBqJEU7aklArHxycsrAC9HpT0Gy1ZoPcXDt5Q92rcYam3iswxtscef31zB8/sXYKXApimVxGZxj6v/K+EiBwONiIkM84ldlsvQmEkiix16Y5QONt11of988XpN/DK+DOTSsb5yZJAAQDUYR/KzCr5NFEs8wDgYCGP4YWgnPAEHFSSuim9rczbjr5VMt5+4TLciqJEIooIMbleky0609dSJ26j1N4yQWx7lz7PovEgMF67fOdMnoGgaumRFHH2yBRU8kkYu32SNDoxEtaIMQClUJckyHU2EQOoTG9nZpHcYmaRvOKmkZVZ4SHqRcUovv81i2nhpMktlHgWnFDCMnzyG8wdklLNlweg/bsn5+ggmTDZ64xNLFIDsVl/P3C51VmTkpN1mrrbTqlUB+XTk4sY46n30UXAUPNpwIB1XKxRPmDAgFP4IcinEAI++OAD3Lt3D2+//fYT9+I9j/LJ+nljt2WzKp8q7NIfsefeQ0EdHyvHrc3Nn75W5ROBwbDJ0dvARw9CqUeNKPSw6evbNlEFZsDd/9TYtv0yCAymdmyZyCeDvVkfC7R0G42sJIkByNPXS0r9HcmSTyIzp14nWayWeo1RIWUNYDEDG9kML+1+jU2JMzCSOpyNgWgMsFrt18jBGWtORT8xq7Ssqj0AiJn9XRJzQUXIEdvu1sm/JZvHs+jzcXzmHiwM9Z4swfuu4M8qHAWVfGpEaz8WbfW8YO2WUZNUQXgKyPISuVLvhkHYGun91yXG196E8VfXc/zNzct49vJl5N5C6tvGqhTYPwEhM6p1CyP5ZLVJTNmVdSEz1odqetRn9EaFRoURttw9XN3Rx56zkkVm8sl2L13HHDqrGEvJUgtQ+/c4J9TCJTgC9gXbvulInoPqhDroqO3cA//+5iUUZ+Y9ieAQiSmJfJLyTBIEd/qdxKiCXtdpFRkF1DHDC9t3sVmcvacSUZSGtM6QE6qEm6Fa8inzjpSs0lSApHxazmTxr7p9IX9xVAs5qaleygmbjTyXsqZGli5OOVas3SQRYmdV60ft1Q2oTM2nKTu+cef9YTg4VIixAH71n07+Xky7ifOiW4GRWtK4zhpO3euq5JokUf+T4kHn3wfy6cnFRVQ+DTWfBgxYx0A+DRhwgXEeIuRhMJvN8M4772B3dxevv/76hVtIWHAe5RMRmao/BNjqPklnz8CYuu/wVPYBdvMPUdLX+vUhIgSbmsIczAYAY80LAL1IH6uiCjgk1hSLm9P792ibAqh28IsvbAf0qGnTnqC9J9HpJB6xoeaRKSin/X5lvqmVbHvFYom98FsFZcwkj7AEmC1qgHmcmBRSjaHPOgRs5Qd4fuc+Ci8vyWLKP/8siDp9+bv3tS8DzTWXXGauT8V+bLbV4nzDrIJgX8I16/UNAGBj9k8oy5NzUpaBGyMJ3YeoOjNWLPWeMqePQakuC9ASWFIsjxDFvpm7dP0UDZlAnuWH26Yj/R6WmT6+qpWpJveMm5cJ/90LU7z+3NPY3dyD8xkAEuvOHMGa2GG9Kznb7Cx9D1WxBX1qOXlj4kXFPepDGfeL8Hhl+w5IqUUD2JVKstfV6n623bpWUt8oqidALbeGjVJeF09LB+kiy0Rg+wizJv0UQmD8zc09bEy6AmISGXO+bam75SkkFXWpGbBwTXJtl7LwK32N0jd4eXfdKgzCPCqvIaVeLtRlEi35eiZBnYGomlLII6qItadsAAAgAElEQVSUd3WtvHPV7craU6ujGJR7o5FPUsKIciwVwlgt0+8xStSww3whJGd193ymlCKqBIgQfv8n0Mrv4NF2d/O+u52iQzVY0ahz3k2q2hNrONc86GyDOixNmTw43+puf8CFx0VUPs3n84F8GjDgDM6rJx4wYICAx8V2L8uy3kTIeXH79m3cunULr732Gra3E4vfJwDOuXPd85wItRLQZgD+UDMk4axBigMwogOM/Rco3OlsNsYIBD1QvL9fYcfwWMmYyd0XfWxQ+tgEEkVETMx1pQiNXQXGDrj3mf1awgOwI3sJmkOyyKYesVy1wVJKqSdELD8pahbg8pyL8VABAgETBWLKlWOUPmIZNEJcvwcBGcZujiXLSpx5GLUZ450ED+NyeRdPTZbIfGYLiPqyzSQ12OpRrG1PvIdFXp8AmYtzRBiUSkTwYR/B7Zja9c0DNFr9iUNkzQNU2elMXQpLbBcdSsR6qdelAIC8aG2ALPue2WduUD6l1DyrCIr1p1cULbmrUcX0tWQUxBlUqmcjqVSO7LSmowx3D+x1/FLYXxKuTAKu742xOdmCS9gnOYpQSuyY6zQRwTSVOuNYIYqm9qx2egEFPC8REgHLVdiJogwZliYFlFS3ZhWX/NfYnQTTHGVVKplZJTPhvn51muUeIFvmAUChKKNGucN+lT6PlOCTOWBWp9u/cXkDV7bO8/6VLOrSCEmbvIBUCCJV16klkbvHQZ04T+4C/nLvDrq4gCiMAGm8Sbao0nGSfWWr7k9DHFes1DyqlwCEZ66RO9/3do08UthcNeFNsluNnOpS4EUFkgimSfd6k5sAGne/X7mqk7MUcZ2YwrqfPaNAqBzon945/fd8BHTmHnXfR1evE7N+Yw+o1hMTq6rqHLXUrCuZ2E86k94424IL66qqWFzulQw14GLhIiqfDg4OsLm5+UNfxoABjxUG8mnAgAuMfwvlEzPj1q1buH//Pt566y0UWuHaC47zWh3mRCZZE5EDG6xtPBxymmPsvsLI307uFzGCN5BPm5tbANLWfMfXh+WhR7nlHvQgiXrtuwAz20lgzgEr+URLew2Mg2WndU+ybTRgtwNwtyXF2v5xATgj+WS5Fdr9YgZTdl5jmcMdlECBVHS7WYB94rfG0H50d6FZAH4T07xRyac62oiggiqVfIrwGPsZlvF04Gc7v4+nNw5Q5g5HgQVCBTYEd9t7YCBfLBJJHNVyShFka2c3tdk2bCcXXFyYy6lbFVUA4Hh9383ZB8g2O/qgUflERODKSFSdIcksyicpIHqEWqkbpVnDZS6gEm2/5DEq2U6KCrLD69oY6fdu0RFAd8S4tMG4tOGxOSkxLicofKYT4gZFTGBnIv2tiQf2/CPbjn3s9ApaYm5QFVuJIgAo3RKN0u8AIFgsSTni5s49ULQmchjvurV2m7V2XMf9uXOgKCUI2F8ESM9V4zmdogZrmphsf1o6fDfvHr+vX5/guStpkl96ZUhKIIm4TdWOk0j2FJEkzWvLpvs846zGs5upd4ZEPiW2cQRS8w2zTD5JkwJHcSoQiavlHBhJNZm0tbM83+tzmcrsK4dr41vZLqglODSKVa5Aqh7MQGV3xh3PF6Bx4n0+mwNbCdtfoT6VS3yHpa6Q4cHv/d16t0nM69SxdmI/AtWz9YecWGN2JSAxZa3C6UwbMZ/CN3fW989GQDibBOlR3R4Bz3eedsATgIuofJrNZrh27doPfRkDBjxWGMinAQMuML7vmk9VVeG3v/0tdnd38cYbbzw2iq8fEg+jfDKBHdBRc8ODkVOF3B2goLvwCMjcd2pzRM4U2yGlyPypS8QEhHW7hbU2MQcbi7i39njOFCNv1Uxjk6KrL4hqME9AQt0TAOCYg+78Csgv92qfqQQZySfEfUQaCcV+V2ApnKyqmhpDgE/rx0q9F6k2gZBBS/V8Ldh/vK1ZAn4TG3nAt0qXmPMGAJ24tNpL5qiPtYBjP8MzG/ewUZ6QTqvtWUKsLtY2osYaWHYeVC/bOlHqvvYlY783gX2+tJHaR/uevqO+foDNjdvovDqzugLgugJJmeRH5y9KcGhAh5nXFuVTEGsxtbZ2rFirabajD1e8Xq4HFQKlM8kPTzs1kE/3Fh6lj3hqk7E9zjCdTFAWkzV1U+Zr1Mo9s/RFhmtrBilzoNWez5qgYFU01T1qHmauMQ2pXvWhjEqu2mDRdy3/EtOJB5bGMWcMSFlVnKb3ZaI9Tfm0OfKY1/L4qqSCTwCCEqyfCVxCypLv9esT/OzartgtojBSJOVgyvLOxwpNkgRNh9SrZIJIoq6TC1g03cfc2PouSZ4k50mOSIVH2vd+alsAU7pvWcf6+oFK0k8lk0/qjKWNr4fe/pDfhVr7kloiNEqiiHBtQr0nXlRJ8omXVbrVZF/Mk98Uqb/Huw/gvvpjR2Pd1+2qdYUTj7ZBy287Ttp9T0taTyqaxxIT37Gw9qNESd31tqvvJohKMteAi42LqHyaz+cYj221rQcMeFIwkE8DBlxgfJ/k03fffYePPvoIL7/8Mq5cufK9nOPHiPPec0eEDIm1+gqOjEcyalDQDIX7Djl9C3+GHIoxkXl3FhRs5BPmYFgDebYFJIERsQGCTrYQjuzxjIQSF4Chfk+Lnn77nHcSgKugO5/DgcFhv2d1xT71qiIab7Mriy6XW2ZuvdjFfRokI8tHuyjkFCd854+uARIJImXIhrTShg7VZxu5/pwDMmzQAZaQx4+k/lgFH5LC10ZfY3viQYlAyqOm7XvZ6cUFGPoHUkypzjrP3+MjskfSQp8Eh7PXsLV8H36aOL5P4oRmI7SK+RyYtrYbFuVTJRA7QFv3RLLMA3QCS5vuo1ALJXe1qLxKB5txTBJNygwbZYaRb7A9CRhlhMJ75HmGLPPwLoP3BSY5EBSiTVNpAWYRIDxqBG1+M5JP0VjPMBjUjkftOSwRDft7wz0BbETRESz3GQBqGsGhTlqTOTR4/tIM7XvESD4phOARzKSSLwEL0XgmEYKZ8a2ifJoUDvNa3md/mVYuAcBcIKdyT5hL009Hs69fn+Dla5faILYwFqRxL6mbUvNNaz3ZPQ5S/FruAqrQfR0p0UjpGyya9Wf/9GQfO6MluPMapPpLMkV3rm0clQQgoVYUGrGv0sPUPAJAmTJucmUuk2ztAD1hSQs4S8qmGEBSHcaYtnds2xbmcyGJkKVbLqibkHX/1ogSHt11Al2H60PkHPy733TvX3dZ4JWtNd6ZdQ5nE6CLfOL1c7LL2zqaZ9oop7vA8l/X9q/qpntVeUbB2NAe4n/6v8H/6//etfeAJwQXUfl0cHCA6dQYixkw4AnBQD4NGPA94HFRAH0f5BMz47PPPsMXX3yBX/ziF5hMhmylVZxX+QQApYtwHOEowKOBoxqeanhawtESnhbwNAcxg0i20iAj8WKt0dSSP2NQt5n42r522IPkrT3e96BmwuLQecxadF5R71QZ/OyTdt84B7tNey0s1SLlzO5OH38UK5BXAhBcJ4mR4300dQ4z1GVFyhoPAOo5UAi/R6x7oEeYJ3kDGCjUgpZYsvzBMI9jsKKQGrs5dssZticLeEU1ZK4jZg3EZuP2nhjeRSRGUlbgClCzNNn+Je0Ru/Z19iB4tFgOHu970pfy6mtsbNxD8tn3IMt6KYealhQN0aFSbMtyV6vWi14hvQGIyiRADjK3x6fvReYC0rFxRiVYgNahvS5HhL9+cU9V52VuIcbxANuzCMrvPYJDo+rq2svR55BGIc2O0EfRlFOFJRusOY3tNSjh0Ig1bPq3CoxogRl3KwN/kn2OUXF4PuM62Uyku6xNQtBIKHKgegHOZfUi+9EpS9L9ZUSl1HNSXrPYKByWQifzxDhYpmv4TEuHu7N0A/UZidIR8dRCI1xSz4OTY8hRQJPIrnHCOjBV16klTrvbS9nxZR3EqEPEz3a/EhSILMw/YvWl5BZiTt5CQhDVkJIFZrvuksaoPElyXsijt1TmqlKZowplfErkkLKdmeXj61omx7R1ofTeF9fD5/vGpzIxNyVq9LXOEIu18zV/uA3av9vd1GL97zzegavXSaZUkhA160QYF9uguK6eSiniOfEts2r/x1Sg/q//0P7j6Rud+w94MnBRlU9DjGzAgNMYyKcBAy4wHjX51DQNPv74Yzjn8NZbb124hcKjwMPc883sHvLslrpfCGN4pQYSUQOOBajDJmFtP4yMiqICiUq2p9u0Ei0A7GH3VkViR49aSxQOVVXGuk9IF0pndqBvPjr9N7cBikaSLz7opZSyZHu3RX/lAANxre6jn6eRA4WhBgRbPQoVGOmFuqSakqzGjgibzAFjmmOu2nsYAtrIMHFzLM605anB5eIedsc1xkUGsDeSmkZrKSv5Qh5o5rKS7BxwYYZgIZ/cqCVSDYHj6Cew1p2K2dS+bz5t+5zLsRM+giuE56AFyFbR5713+C6YB/2eFa7GUiGotHi9pwZRWdrXIb2dENFw+l5IAeXc1WgSxFeGCtVKosHY7WPOW+J1OgPRZkFguxLXspdDFC3KACDQCBRrsGJ3yvDwWCAYSKhsxcJTa9OKgpZrc1h3m/aXUkE1Zh23MkeF5y7XOFbPmq1Bc5NSCQBcXCIa3okuLhE060znW5Iqa++PZrkH6LHuSemwnKXH0GbOuN8IykMv97uDFSvD08STDCJGqhiVEwghaRscJXmROklmpe/NMkGKd9V0u7F9FxtFhSDMZY8eErkXEgqsoyOFeUKblrRxNBLWAE2tJAQtQRK51NSgXLAaDAEk1AHmoCiX6kpeP8WAXglsqwiN/EIthPekREwl1E0cGZh0P4vU/MoYwZ1JNAzVCPjN34IKWuPAeLQNF9YtzznfADrIp67EIwaB6vX6vpxPgGWHdV9ikI+y0P0Ztrx3PGUsPw3A7dvg7SvAeLOznQFPBi6i8mk2m2FjQ7foHjDgScLFGuUDBgw4hYdR4ZzFwcEB3n33Xezt7eG1114biKcEnHPnJp84WhcptryBaCQTLIXEATtNRFj2CIDZlT7Ua995mzVpRY8gBZEQAjxYwjVnP9B6qCq4ApOdMIiGOjwWZYtN/aJEvpW6OVQrxGWUCUPOBWJKIDmOVDh+cQfbrsNi5Aw05cgR8uN+wNjO7uGFyed47dIdPLPNLfEEtAHThAf/KiJnJvUWZ+PDgIsOF4wkcJ9aTobf0u5IcMFYu8xlcKHbcqZrX9/sG6/BIWvuYzT/E8Zjue9RloEb428Tgm3rDbdjxmK55zusbs5CIwFyJ7fhEBCEYF17vDTO09sygSwqmpNg1m78HJsjG9GvQbIDO9nHqXMTYCdYXEewuwt5tPXp3Jj0IBF/q+hTyylXklOO0BjtAYF0P7gx+Vfk+cq70KjiBBEoGhNDgm2NQEaFMTUnSTkW8mleyX1DI49GmXy8dHSZEZaHU9jrT3sz8dS2KxEn6W1O2JYiXgmMJqmu7G7PU0wec9bCL3cNbm5/c/g6PU+YQ0hkOec2cfXM0T4WupCodQmgJY8kW73lTE5oWCprtmohrworJamtVtYojTJOte9biZhTjqWxcF8TBBMAUJl4v84XqrvAWZytIcZMCP/t78CTnc7nxmWCwEkpvJr158vldveYT6wTqWOd2RJY6yRY9BN41973ew/G4L//ewDAcu86Guv6a8CFRAjhwpFPg/JpwIB1XKxRPmDAY4LHxXbvUV3HV199hd/85jd49dVX8eyzzz6SNi8qnHP9SI8VMMZgQ5Y2GesvkHGK39+3Bcmoh5pIUrCcbnPeg9Sat9mDln0pmurYrLZu3pMqcEfmLsccdOf99QOsAfuj3XuQTzYbtvP1x/7nUu6hMTDYiWYpkiQxS/Q3ZsRsDHDEaP57bLnv1FPNeWIaw7lr8Oz4K7y69xVe2KuxPS07P57IQCqAHNAY1IdEnZYonTDa6ZnVVOhnqemMAWMAcEFXVJ60a/z9AHzYx47/Z9vOS6OlZ17YiarDJI15o99jCwEcokwsZE5uI/fydWvHB4Hskd5Ljtu+4HmB5y9/g9LrxKgczD26Hts7Tvtd7fmsZIhtN28klbxiodsXfaz8rIRW1aM+VFfgckQzXN890/ek+n9n27TOJcZ3rZVEP+q3ANR6T46AfclTD+laRydtyJ2rCukGNop2LLx2fQMvP/vM2na5Xtn5yCfpuJAYKNJYTJHJuUs/r7MWfi/tfoPCy3W1RC5IHODntGIT1hPaulq8Ho5AKay1l3ICCDUa+aOMu1rZrpFH2ntUSUhS17bnrenUNKBxeh6lIqFuYgYSwWZepu91ug+cft833wTQ7U+BBMnEWfc1p9ZtrupQMhXdltMp5wnXkQzE+VanIpHztu3oNpD/7XvHf9+fXsZvfvMbvPfee/jTn/6EBw8enPs7esCPEzHGC5fUPNR8GjBgHYPt3oABA5KIMeLWrVvY39/H22+/jVwrPDvgIQk/AvMGiDp8tU+dozLyCbbA0ubWBIB8zvbq5oZqFy2sgTwCH1re6QHltu7Uht3Wj3PAWPuqj03fSdunr4Puft4ZzKNw0DPVw75ztNTWsTwwrd8ygylTmno4ZZSY/dss0hZyoQZSH93NHMi2UTz4Z3iqsEU6+RSQYYMOsMTJR4NDg01/gI1sgY2CUBYeJVWAgbihGGGJkbtmgZjrxGNrT2iAcSpiXx7b06noVRvJrgA1EXRHl9CD1BqF2yjGtra5qY1DhcDVAsgMH5WHWekW5ZNFeVOFQnmuSk0axcpOs55rBPLL0iefL/4ZRUEYGZ5hSNR4OX09HlBqrwGHdl7KBQbFIu8I1je8ZCF2uj1bkM1WmwmIyOGxFBVuR5CUK6fbzJDxHA2drx/fnH61TsyTs9VoAuC4Ma1m7PXYjEksK8FvTfm0NfY4UJRPWs2oWpkDZgK5lWeE165v4JVre53b4znzTSW74yiwaanxK1n1NalaUC4gFbJYhpNxsZFXeG6zuxbOKmQFU3qcSWNQXvNqNZ0ECBMOLWegkUCSaORR0Mgf5R2ubddcIDQ2VtsuKJuYo2ynK61HDxbAVrcTBTcNaCSomxLWemjS94K4Ul8sMZbgv/0/2t1SarbUe7BL4ZRPQXH9+4h9d9td1uHsR91tZBOg7iA+XQkGUH30DWj/hLTa/cs38ebLb2C5XOLOnTv49NNPsb+/j+l0ikuXLmFvbw+FYN844MePi1jzaTabDeTTgAFnMJBPAwYM6MRyucT777+Pvb09/PKXv3xs1FwXHZE34BQiiKhuVTZKxjQZiRfvKhNP1ZI/Y5Cp7lMfu8cepCYXgLmmVA81k6nm1er+pz96ucrgDz7p3jfut3WfrLaBxkA8w4OdISPdElRVVE3EtWrPpobzlNoELNWDEmLH1MyBovuDn0IF4n2Uzb8CBGy67w6vNN03PBpsuH1s+QU28oBxQSiy4rD2wEmgJ7IxnGfN4DQ+d/PYShSx7oILc0QD+WTqb8fn7zP+7Fmu1n0pLLHBnwJGFaZq4bMKLeP7CEUJDg3mjR60b1gmlnJXgxUrVU0tpN07aSshiPWgQp2+fwEZduOfcWmv3af0+v2rY6bz2XDIKahWc5aeGNmZuqxZqWvu/7b9Yo/3ZEFLzA1WsmytuwSg4IWJfDpLem26+3hqq3vOpVib6hZaVZzmV775d7dPm5lxR1E+jXKHg0oIMIOxvwjiRc6a9LbWVi/d+67vjPD8U7uJrfI7T1JFSf09TT4xmkTGhfT+qhOEVWreIjAWKzXsfrb7FRRnw8Ork35vom9I9njMIvnE0lzAURFpUXpzNQME8klXDsnQpzBtNlTeNyT8Ngskm66mAbywXfhxvKxAqZpwBwtgO6EOWlRp8imxDmSmw2+1jutZSWBoPrh1QiYmrF9TClFXrddwiuUW/GL9uyf1RFyHjR7nW6Bm3co6OaeTQz3fBX/47um/P30DAFCWJa5du4Zr166BmfHgwQPcuXMHH374IWKM2N3dxd7eHra3ty+cRduTjotY82mw3RswYB0D+TRgwIA13L17Fx9//DFeeeUVXL58+Ye+nCcKzLYsmYgRvEJmEDXgWLRKKW0/jIwETAGYyCcrQdTXFK7P3vYP7/ZejUFKzZQTnASZmB3om4/EvdlNQVHPyAXsSqnobfZ80eWKWILByj7EQS6KDejkkmSzxBGQVD8SOSZl78aA8eyfji1AMgp4Jv8CETlyYmQZIXeA9w7ee3ifwbsMeWgQsjEkYpSpAPMSpBB3NmtEwBo5tQaMTaq4ozNbCU8/aq2tDISmKah8vK99OWrddzr/GN73UF/1SbAw1vUjIvB8hkXQSbtKsUsrXI1llNvRbPm02TMKEr3cNaiStVqAukGyvF0kj+cvfwM67LsW8qlhjwxRnVe8C+rvtoBBcIiqAk22L1vZzzjurYqmPnZ6GTWmV6VFHXXcJtsI1/oMQXpz6+tkrRPixvRGt5PTxuBVj3pTAHB/EVErQz6TLL4ATEvCXCCXcg/MqrSKb6N0WHaoJzIHvP3CDq7vTJK0jkMU+1kU3j3SnBDYdb62MopoeiqtHCIWifkllb9R+ID68Ji90QxXJydqinMTTIl1TkuapbaFtfo8p88nzU8aMSXUF1Js7bSZSq1BpCkRtOO1tYq2XSKPAFnZFIJ+fAqCSqklphKohbVo4l5y9HBZ98g9suNrDgrQ709s6pCo50lVlwXeBlyHainlFoCOeZ79uFv5lI06P7NSSymugeb/+i+n7h9PtoDNdbUmEWFrawtbW1u4ceMGmqbB3bt3cfv2bdy6dQuj0Qh7e3vY29sbAvwXABfRdq9pmsExaMCAMxjIpwEDvgc8TiohIjJnlDAzPvnkE3z55Zf45S9/ifG4T82cAY8CHG3kExmn74jSVHuCqQSxTj7Z6zMtwfAm6y2zIghAL0IJCzDrlkxHaO2KjOoTWrE+O6jgGs22sIddGc/B2AApzy06g0VbXIKUD3Di2lAIWVGFcNQt2XIheFrPgSKRaQotc1goiF7fR0an7T+e26xQ5d3WREeIRgsux7UaZGarAslIqphJHV+0tlYJG5XTjRpHNhFcM0PMttRdox+3igVDoNdKpAJCfa8V+Po+NvLbQB9S4nv68G2qgCrKz6BwFaKiavIuqsOwDrkYcQxKTcE6pO9X5gKqxPmJG9S++91FHHB99AWK4mSO8S4iowYNy0HZ3NeolXtnsbeTgutn29LqSBnLDpqJnQDbeI7I4HmOYFAfeaPlX812gtjcJko4NIjwuOS/xd6mYDlmVjQ9WsJPTIQ4dd62Pc1yDwCC0jEmhcNcWL5MS497i/Q9zjskPZsjh7958TI2RyVYmByIWFi8cbLWEiDVeeNkH5es9VJlq3IXsAjdz69J1oKKOFpb/cXeV6e6SZp8SqvAZFXxObcpBJJoyTe/D2yk37Xq+NHeaZqVmRY4lcgfw/lJuz4v2Lw2yrpVtXl+9MSUdE4qun8LN5yMxjkswOwR//b/OdVjqepQIZEDLddtpXm0DVS3O/fvvM66g8AqtkBhPXkumQjVRWDBofn7D0Bn63xde940v2dZhitXruDKlStgZsznc9y5cwe3bt3CcrnE9vY2Ll26hJ2dHWTZEN78sSGEcOGUTwMGDFjHMDsPGHDB4b03vdSbpsGHH36IPM/x9ttvD4uAHwiMEZg9SKnNAXM9CetztNZospM/jDEI6x8y620uzLWkCDNwpEMLNGVfCogYmVVYZCxe317HEpFHAGegO7/WD9A+hM/u7jbUIuuWgH2raJGD2pZ9VEQly7RZApKtXlOBBfJJIraSH9HNAuXy8zalfAU+ykW42zZtQVlipV4DDskibo5VH8n9UjWtuvZjNn2wU1gkPfxPocd872JlM/5zGVw1R8x1Qr3NrK3BFuu/bAJqlm2tqgQ2lx/Cle01cGhAQhDrGH1qCvTIqF4Y6j3lBlWThoxq1ZqtDuntDkEkTCT1SRH3UdN257Zd/jMu7a0fW/oKTSM/F2eqG6azQZJ64/T5WD0jw4G4VknlxqhUalBCs0U7Qk4VgqFda/pTs0IUqTDPEYSSFpjzBDd3voOsRrKS3rZzm+Y6HCo4e7RnIZ/mgu0k0KprJZKiyOSndjY/4PpOgV/euIwia+ueaZabKZDU9zgmx46niCb17hWea5OqBbVCJJ1FlSCljmqWXZ/ew055NnlKIp9S/Unqj+ltxJw8HaEBCwSzZN5L9QIMIdFDW1PkhdwrSmXNMVK2l8o7S3unZsK6LgSQRG41jdK+8Cw5ysSYcF3iDZXmyFQyZ1OjKxwXUcBTRPXJPdD9E2s7djloeX9t/cejHbimg3zKx+jKY6MOS0Z2WWuvd7btfAx0kE8p8pOa9Rq+9WwX/MdfrSUD8tXnO9uQQESYTCaYTCZ49tlnEWPEvXv38O233+JPf/oTnHPY29vDpUuXMJ1OH6uE4AHduGjkE1sT+gYMeMJwcUb5gAEDOnFEPknY39/HO++8gytXruDVV1+9UAuAHwJHarNzHg1mIQh/fA6bBY4eSjtq0GgjhXmPkt7WAu4RbKzL0u5rtx9Cj8zufjZ9bdt093M4Syg+rn+MyXg0ahFLZrlFnaaFM2mxnol5anutqOqk62QW60GllEDlvX8EufXe6oNOiEaUbcFqBXYzKL1vcTZqlUpqYxkQbDXKHNv6dB+LPLNaAYALxn5PBFqsB05S8AfrdQaOkH/3KSblSn+c6zahAEBZDj6blZuClvG9grlhPs+coa8pBEru5Gv3FESSIffnrw/iO4qOA0AZ7+P57c86edLS60kBlKhtsQpL0D0V8F4/oW233GA9G6mAMyhpGR6ZseagJ+N47qG0LciWnGGpCXfSZoWn89uYTh7NWtJCSgMAfGmrc+NzkGGu5UNrKq3ekyfG/lIew5K6CNBzCaoVydBrVz3++uaVQ+JJIZAUSMpBLyTAWMZmF/rWdQIYywT5BLTX//LuurLjvPfjfGWOh34AACAASURBVBCIKW19KFoKK/OHpvAYCevqeimTO8s5SFKzVws4gcDhRk764LqSlUtaXcWorUMkCbB8LBXCfZFIq8RxvKzSbSZsoxkjhGYEeuc/n/77ZLeTTOFys7v9lOVps56ExeVO50SUTPAK6+/BCA/XnF5jR7+F5te3ukmgp/uTT2fhnMPu7i5efPFFvPnmm3j11VdRliU+/fRTvPPOO/joo4/w5ZdfoqqMtToH/JvjItruAY+XE9KAAY8DhgjzgAEXHBr59OWXX+L999/Ha6+9huvXr/8bXtnFhXPuIcgnIBrqPhHVYENB8Rh0lQcAuzoIEQybQkP96D6FHr7Iht+9ehX2Pe11qgAAVQ138Imt7fAArFhenYIhwG+x3TPRI5ZdNDsjLeCnBVGk59QsROVTlw1b9uDPyOL9zsxgi/IJRPBs6Q+2ZZSN4AOothElLuH5vwZj9l1by8k4Xnt9TNkJDacQmKcuoUo8Q47YCv94+k8z2z0FAD6wzZcoSrBUa2wFc0oEhU6fWd0jiBZ1be0jCblSz04jNiTLPuogOYkDXtr6A3yi71vqPllgUTUxHJyhrpk1WdUb6x9ZSaXM+O5xRrq7Ty2n3JjIYrUHbNts8PyuTvKb5xKzTR6BjHNjV+D0LNiXQKzxjaJ82h7pQaZFLT+7OuVHd4j9RUDmCP/hxV288pNnTilpz0sEtRCIE+G9LqmbkrWnKK2kSg2+woUkwRwZeH7rDibZ6etsmzqPuikNuYbU+WyBW0u+8ysDuRASsZYzWfG7VBJDKmV8VMo4q5X5ViOXtDWjtl4RiC3Wvs+m6bU1jQQF/yjxPBbCeyC11ieP8M6v1v9eJpJZUnNkl8IJBFreW/973t02Je411et2442frs3r1YdfJ223+RGQT2dRliWuXbuGV199FW+//TZ+8pOfYLFY4MMPP8SvfvUr/OEPf8Ddu3cf6jt9wKNFCOFCkU99LP8HDHiSMNjuDRjwPeBxeuGkiJAYI37/+99jNpvhrbfeGooiPkIcEX7n9Z1mA/kEABFjtUZRngMcC1UpRdSAMQKZgmUFYMj87kPm9AsH9BlfdpUGUQ2OI5CiIADQWiN+97m9bTCin4Li+gdf5/5xpv5MU50ck5ehshMzmDJN+6ScRHnCArlEzTL5UYxmCfjT26g6QLH4BIixU6Hi44HNts5CAFJu643GxHSKtW0smBVNxo85cqBmDs50lQ56KCCox8d9L0VV4vePvvkY5c6ZO9gnwHAwB7a77eNWQUTg/Rmwrde+WsBgO6gSKIwqFA+VzO8UdasUTAaASqgH1WUd9dP8H7ExBTjB51nIJ00tAgDBqGryaFRbQqt1mZVMzlAbKi4CGYXzxsQ7UfewUfXGJJGqh+J4N59jXBqei3l+8odzvf67KC7BMKjHo3F9Us9U5dMoJzwQlmJEwP4yQBrAs1RBNQDjnJB5h39/8xKm4/V7oPVayzjqRE+74COE2H2+jAJSqu5UbanMBUhE0s2dtAq2GxKJJPVZaZt0f9PbNEs+URUVQ5qIAEDVHBgJNqrNQ5I/oQaERDQOSqW9pgZKYU55WGJAUmUFl7y1fDAHTYTr2kiQKFUNGnU/S66a5L2ghN1m/PYB6PN/Xt+QUqslTuA6LPC43IbrcgBI1R2NHQqnfBOO19sOrjz10q/DFfD7/yfw079Yv45iDOxd7T7nIwIRYWtrC1tbW7hx4waapsHdu3dx+/Zt3Lp1C6PRCHt7e9jb28NkYnPgGPDoYa1N/mPBYrEY6qYPGNCBgXwaMOB7AhE9Fp6vWZahOWMltFwu8dvf/haXL1/GK6+88liRZRcBzjnV6lBCjIYAMAAyWutElPCGEBhTCWKdfLIH6JZgeFOgjgz2RCew70sGkmwVEQW8Qa0R789B1boHunwxPSwA4z4CCnjfPYcwPNgZAoxkeM1rAUAOsj0KoBdwV+sdSUGIdP+hZnGafGLG/8/emzXGbWRZg+cGgNyYTJJJUxarZFtyu6tr6aqubi9vM/94nr/5ph+m5+vlq267usqSXF7ksmXZlmxKTIkicwEQiDsPSG6ZETduarNM4zzZQjCABAIB4J4453Qef1xbCQXmX4KDcRO4RHefSXBowXAZzXPSC82f7TNjFTs943JUiqKtivQ83r9d4fdoC9GAv7BkCwza3+CpGJpKX/By4zESBfk0VZxTG1FztkwJFyn+c8zWK3IMbMO/3VAFJyivqoW5bQvf4dL2/JkTeL/QkE+VQtVkOQEhvspUkx+lLdJrR5jKkhV6lbAqmwmAQ4YUM1iVWkl3jzqk8z7l506KAj9ffwRAofgLFTt9Te0MTkU+6Yh5Ldn9eFygcvJ5r+3HwudxvWMwEYZ7OyXMyvAY/vlmG7/6+fDEZm9VSKTKk6Uc1aqjEGzgvq0t/vy/IWTHJ9kCXuk/Qua1LJWUSNKm8HmS7j3J5lLqs3YSEJAJKpt8DOoKxfIYuRSzrYuRP0/797Hv49h24Z2LKysrnx49Bi75F5nwNA+STzyZhYmp6SxouyedC5+SyrkO3Ed/9o+qkAopoPikwqNwaq0D02XyiQOj0RTLqnTO+kDhUc9RenKPsemg/B//DgJAU4+y/dU3ot8GzxppmmJnZwc7OztgZkynU4xGI9y6dQt5nmNjYwPb29vY3Nx84gWkDVbHRVMKjcfjhnxq0MCDZlZt0OCCY1H5NBqN8PHHH+OXv/wltre3f8Aju7hIkuQp5fydWlkTy2ESPsrPNVMXvnWFjVWIIkYXhLj9DmGmTi0gTOduJfHWRBWY2+qMLFIUO51tw+z/NxgETlrx63SMFVYREwDLHSQB8swpA9OdiShzmMER9Q5xCcRW0gtFEgByELyrgpYcUSxYn2WPvzw9Z0LxIqmOFOST4r4hguESTPLv1xaN1R/iSvVRbadXySuoT6AcxyatVVIKEoo7OhUnAHBLv+qUW8v7XnvwJ6TbyyOZVikgKOaUE5RK270qNrbjqqbMWEQiZURbPCBOTlXOBId8Vh4iTzeDf1vQ6bVru8e4duneKecUIBU15FPpUsUlIWTGRgk8jcVbxaQistT3sxKy4uIUq1jftaiA1WQerlD8a1MBy/Jc93r3W2SpcjYxaT2HK0goEvKHzrXTEn7K53GtepKvd630CT9vupkRlU1rbYNyurx90Enw+9c3cHmjE7m/5cwhkXwS5gXJbjdE1Bq4IPkkHWco1ymUvdhLc1xZHwX7C0Em4kKWgC78DGWWySfp/jo8BAbh54Nrd8OKmWIGSORTjFyNTIfR6TI6nT7lQprYAUgWXZWV87Ckc1OEv294GiafuCjDpyTwEKv7O38NGYTq/esgDzkEAAiSTMvkjmtvIPFJjwPv4z41qMvWYXwuFoGFTWctQIuvK9Djx2CTgB59v9SWd5+95d4qICL0ej30ej1cuXIFzjkcHBxgf38ft2/fhjEGw+EQ29vb6Pf7F4oceRlxkc7veDzG2trTL3Bs0OCioSGfGjS44Di2gGNmfPXVV/j+++/x9ttvoxPypm7w1HjazCeAwNwHkWzRRpQrv++0WS466x/CVE0UsfIxQ3Bw6KqUSoQKDmtqWz/HbSRK8ilWMmMmYO+z+XEwXLoOqh7puna6/I9jZK01r90FoMt7IpeDErmwSFzIodKoV4iLw6KyQBY5Hml7OQXaYZKCNeotACY/QKv49mRgMlFwjCbVOEqhxiy6ThtaIKJCY2QAu+jHFStW9tftlPM3GVA5DtsWnoO+CG3cFJWGfGr3QcUMrCAXXXegJspcd/NcWzN7jP7gIbyzUneFZ50UNP4EsC5BGSEAalWTfN0T4yLTeJzACheEaxQCgZryFDn85FPiZsjn89FxzlN29pYNFLpq8kl+kjg2SKmKkj210lFsoiR4CAm56P4WlV7hfWqVSro5rlzB+i4lq3qer5IPlVIp9tnBGD/bzIEV8g3JFWAV+fTkavKnwYNINA4AjAViCQCSCIPaWrDeSg3wm5/18ealjbkVkeK9JAAiFkUkktqvEubikE1ePVcFiKRAXylVKEK5ToFT+7ebewi93jxRPhM7IEDu1oRmaIw6gKR7SJh3rPBOaAv5vamKvNPGyKWY0ji2aCP297FCcsxiS7LNcw6URRY0CXNqyB7vuO8gpAUnkmo6dC5nObBAPlX3GXTnUyCQ52V8WU0mqzOcFs45t9eB3EM+Ba6NKT0Kp9Y6UHq+tUJ9zN+sLW3D/dv/rP9x61XQ42XyCc8h7+lpYIzB1tYWtra2ANQuMaPRCHfu3MHR0RH6/T62t7cxHA7Raq3gZNHgJ4fpdNrYODZo4EFDPjVocMGRJAmKosCHH36IVquFd99990L56r6MOCb8ngaO12AQI58s2GUgksvoRDrSQ5f3tCpRtMrqR12WFACAM0BJPoUpCF9b+Ry4McPMzmYM6LM1yD4GJ6k+HFxYme2S+EutRrFEbKFVvAX7KCbgrmA/Vs5EZRO5iPVMKhQKjotj7NA+/PT897BAWiUer/pFOGqDuIqvxtOsoCcClROgJZNAnPVUBAwn7br4JCnKjnftClUGiqYAfNJnVUBbs6bZI3B2Od4wacFMHsN1t+Jt0xbM+ACuNwQA9B/9CYlH9QTUtjZclCAFsUQ9vU0GZfHzFVc91eTTzOnnER8yU6KKkFylC29PyIokhKQSydwY+Zx8fT2tc56OwYHcNaCuXbVMiUI4ruNjcxEyQ7LmOoY7YwckgYij7ayQdXIWWmJHZ49Xk1kJpqgUJJTmnABAGVEyneszspDlWv8ejCHArTCXsFUb/+k6VD7vle/B+xO5XZYAk0JWy1WSRx1wbry9Pmzh769soXumuBlzIJPSdcR3MK5EUrYKkCoEhguQ2TVRE8h1CmVBmTBZUHrsPjdaU7zafYwQwxImn8IqJVkxJ9imMSPI39US/XC3wjOX8jGQhfMHY8o9SiL3YKx4LpE7ir+PLXwKZhdptttSJsci9xsFcpsAyBbTsfs41KfHWg8AuDyfBVVVXfD/+r+Adh9kl0kjTjugcrxMMnU3YQpP9lnW8X8meVSknHRAdrI8fyZtv9M5+78702oCphTl/7px+o9rA8BDPvGrLxf5tIh2u43d3V3s7u6CmXF4eIjRaISbN2/COYetrS0Mh0NsbGw0dZUG5zCZTBryqUEDD5qZskGD54SXRT5srcXnn3+OS5cu4de//nXzgvQC8PTKJ4BZZ1c1UXA1RBasKDARVWD1ymrtqi9lqDf0WVI1VmmrJwKJSnCgcOaqNmjvzwv/qv8YJVRgo7cho4DqCVAqnxSZFqrci9hcFlmBS9Lq3hjYgYXVv8dKodbB50gWx1po5Sw7JKy5cRIY1mSl6eZUYzX7NKBSR8Bqz6s220StpgJAM/01NcrfAwAm9+QCBJDMHgMA0sd30RtG9jFRyBcwJ6pypb2XQlGlIZ8SDXESUS1lRrYATMmKRebY30tj/Pge2XJf49VXFs6dLRCcq6sS7SRu4ZoYzfiNz8Oa/CgAMIrFAY4yGI4/27QklUVbbcvaUqt4dc9IixaMIucw1uc6PcIr6/N+jKkVoZoelfOTxma33reS+FJalz6IkE/rHRN9359Fsu+KijHoGPyfvxji3TdfPUc8AbF3I60OfRmmCt9/BBe87yViUzoVNkA+Sf0VHhXV323djfzkMPkULn08afqVZHkYUaylQqaTT21yFrEczlbknb8jFEadAzrCexczIPTPzICwOIOdE8ktLguZBKoic4sU+TUtZCteaVGJdExZQO3nXFh9fYZVZhi4//0+yFXgbiCPqrfpn2tCi5oC54FKT95Te8N/8wZuaLLL71T1s6RA+agH3L17usFDRHKSAjtX/Af4EoKIMBgMcPXqVfzTP/0Tfv/732MwGGBvbw8ffPABrl+/jm+++QYT5btmg4uN8XjckE8NGnjQKJ8aNLjAuHfvHu7evYvd3V387Gc/+6EP5yeDZ6J8ckqSghL4l6Ut9IfWcnHeA6Y2iOOFZS1RVGc5JdGP8LqtPktK85vPHsMqqM/V+Y9bZoAf3Fle+V3pC+v1wbQBVhbY3WFtp+EphjqF5ZnSQzF+GJREcqNiYTSxIqTQezkFWoHgema4tAszGyErvzv/kcwcrjFVDkmiJCO4BEfUY4xIrtYxlIR0rVTStCtV7bTkGEwGsjMdCZXorbq0xWVgrqhSt63vvfX8I5huxM4wz9XlWT4ag9oKcn2tC7ZWLGRpyCfNOpUqkmcUU7mkpoStwsdpIrl1loSV4lyh7R7jzZ37y4WxyoYVcrZEOylwWD69L77meVSxUeU5adcTZJghj8wNjlIYnkVtFQGDjMcoKX4u9ESRXtHaphxTj8JkEZKN4LXB3jmCiFypVFMqF+rEbL7mEPMFV2znHGM0lcdLNzM4FALZEgKOZmHL1VZCuPpKD9d2wqvnpfFNYHVm2NLfCnNz/d4hkUWBYw3eP+wlko63hfaTV+f3s9M9wnZnHMnAepGL/6RrU9WWuwFwW7jfBWIQgKx6ZgbaQuEzn4jWc8gnoJ4wZxU5TEsYc8VMJnjKHBRTNmXC9ti7lDDH86wECcSZpIpCS1CqtQPqppmFWQvs78z9bu/moHtf1v8TcgporQHFg+V9hN7HPAsAmBJQ/nhp2HLWhffRwr4+DKhc7qNAF6AE9p//5dwmmnms/3ZeA2LqvJcYaZpiZ2cHOzs7YGZMp1OMRiPcunULeZ5jY2MD29vb2NzcRLpK7uhPEByT9v4IMZlMmsynBg08aGbDBg0uIJxz+PTTTzGbzfDWW2+hKPQFvQZPj2ehfAI6YE5BJBeaut02oCBXSF2I0rXTEkUEzC36AuG559rqs6QI07mjSbz1sfKLtCvGPSvk3SyFGX+73Ld9DE6M3kpvBRAYzvRBHrJKRT6p6i8KS7lAsPBpF7HCl3xuWFo5bAsE3cTsDDBddI4+Wy7sOQdKA7+NGQYW5HJwJKsp6ncEwJk2jCLPKX6e5s3UZI1yzClX+AOoz4mCfBKLZouQgsGfAsSM1oNP0d1UzLWr5MXkSjtPY+COjkCb/iwkQEc+xVRNBEZRZWo3MR+SCPkfs0ctkvBiCAeDt9b/ijTzHKD0HKwqlfJJg8pp85zi+VHamTxRPgM1JBUApJihVNhjxqzvjvEss5yOYQN9bid72OwvdLAC6ayCkvDmpB23OztpJ1ucPpq6YLbRMWL35XonwVGxfHK3+yneurSG3c0eMtGCTH4rimU6SXlQ0uN/NcvkU4ScyepcJ/81Cf1NZipMz92vjF9s3js5Qh/qcyEpn0KQLuQTTr6Hj4FB4BkQsSOOvpq1O+Em+RjUlcijGSCRT2UO0bK5KIKZRCfbRfKplG31KiuTT7GxKRDVbMPzJ5dWVDSHtrFzQC/wd4UFQuTT/De60SHw7//faX+h4w+8S4UsGI1P4dTZhCk9uVGBeZCq5YVanG3AuMdL/26RAv99B7SYf3Vwf7mP3Te9+/sxgojQ6/XQ6/Vw5coVOOdwcHCA/f193L59G8YYDIdDbG9vo9/vvzTOOC8LnHMXzpVnOp025FODBh405FODBs8JP9TLxWw2w4cffohLly7hl7/8Je7fv4/pdEV1RoOnwrNQPgEA8xqIYrlPubJaplwtTZVSMKMnilj5qFktS6rCNE/QVUa0OG4hUZJPiyotdhno+z8F27pkA+T5EPMfiN6GsN5Ba+l6MEycNAEgZR6dILKanFwZLfpFV7dH8oukTCepgEk2R2v69UnA8fm/Ewq080pd4iawsfOoIYwoAaFALASJYyTecTu1UklH6uhUcjXI6Qrq3FoDbA4ItkGnbVewnojlTJxr28YGfa5svMLzeIWFAzyZAWHuCTMbJ59sRNXUSgpULF9DjhBYHFEfSsqJ1E1gTfga7rTvox8QJ4r7ZEYric/JseJ/3UZ3Lxhy0fwovf2rbpwsqmhD0CuadPNDuUIeYYycPEbhseUldri2OcLiPUYcyfI7aag736wl0cnUixbSyL1HBqacwmVhYvXBUfwaC7VsAEAnMzgq6vObGODqMMW1S5vYWKvv6Zhq0YAjmU4yxPEsPptFRiu4yQbu18SE1VIhS8yUzudH/XztAIOW5nsm9JsFRYy4LXzPiee3DM9vlE/AEvkkPd8rKyqbqJgBAvlENjLvRlRXtUJZOPaY2j22PfoMFq4Vs7zgReibx1PQhn8+qImpwDmdzkBrgethBWvLbgdcWlR/eP882Rs8/4Fjr5YXIDIZUOEhmbI+4CGffO4UdR8+lVQPyJe/ecqHObLPbp1vu7kDMx4tH/Pllzvv6WlgjMHW1ha2tur80jzPMRqNcOfOHRwdHaHf72N7exvD4RCtWPbaTwBVVSF5TovUfig0mU8NGvjRkE8NGlwg7O/v45NPPsEvf/lLbG9vA3h2REgDPYwxz+ScO+7DIEY+WbBrRVU9RMrV/EqLutWIolVUYC1A0ScA5AXU5BOtYEtDC/aE7uE+jCeg9/QPOgB05BNVj8GkU2wB8NZ/tGSCMxE7OGYwxdpYxEgVcfUuAJbIpaqM/H346Ew5RlZ5QpaByALnus+Ex7DYEhrqiVODCi5ynlzW05W1tSSVsh1MCtgpkD4rtRwAIiSzx6j6O9GmrjOo7V8UFlyurWcx0tn3yF7RtSUpx2Gpsb6pVFgCNMqnWtUk7TMzFWKPExuxTOMKoqjVBu2wgEwgn7bdF3jllSLcuUgskEr5JB3bMSpOkKAKrt4+dzgRRkRDdq0Co1SQaBcsxZRbp+1SpJjWeVKxfat6PO5zdq7Py8k36HV8PSiVM1qyPcnq54WChCKXq/IryeUAwuTTvoJ8mgiWe0DtrLXeMXjr1TW8NlxHlp6/fiRqfwEQR06lTBKxZEGWJME/l3YZJsM4eL9K74FlFQqnOT0KQw5/u3HvzMbQ8icp8EfaJBFM0j0njF+BQCKby0lSwnsT5WNQT7Lsi5BLEVUixRTfMfIoZsX8tDZbEjFnS2/GkGrfswLwxy0B4ymw6X8/4VkRJJ9CCz+4tKB2C/b9D0Hj87Z0VHhs6gBQ6beLNj6SqbPpVScFF5N5MkS5tQHDnmPxzMFsOuDrt5fbrm8BHvKJL1/1H8cFRLvdxu7uLnZ3d8HMODw8xGg0ws2bN+Gcw9bWFobDITY2wtarFxlVVV24393Y7jVo4EdDPjVocAHAzLh9+zb29vbw9ttvo9M5/ehuyKcXjyRJUJZPbyfEbk3lgufQQYIY+WTBrh0loYgqMDpKEkpHFFHk2M5Cv+ocYLdK0PYKuTNU1OfKVHBFG+Ygpq7QHzNxiYL7aBmtBeDy9XImTiKQy0HSBzgA4gIUUz7FCmNR8ghAFj5eKqdhv3oIqqpyhuzoS1Ag5FnEvNiZOP8H/lk46ujsh9jFh0HSAsqxeD4AwKXdukASKUZz2q3t5BQKKOMKOGjIpxVIWqtU1CYpzPgxXG8Ybcqd9doSKDKmaHqItd4DsDVydsQxJAuiRayyElUYGtYlKCOqprYpUEWK5DFlxDGBJQ2XkF3aMYoqDY5fw/65ar26hze290ASuSwQjkwJ2grlk3UpDMWzmlIqUEbGuOZedky1CimiHNUS02qYMBFwFhX04zOjApbjY9+tsDijfabPBBZvDMfwFeDV4n9VLtS8zypXKaC0Ck6KKDz2j+T3hlYCTMrlsUmobfUuDdr42VYbQ8FqKcaHKoyFw1tcKWZbSYoqJ5CwIYI2cQVsaHzOpt6FFQSH3PnHwFme4I3+CN2sPPn3JzGXkAmmwDbmsBqZWVZMSUremBq1sxbuuZgCEvkUI39iJy+2OCq2PWpBHOs/Mh9Jln6ukskn4XWJbfh+56IMXw8bvpbB9/DpDO7BCPjq6+W/mT709+VRG3HaqcmqhXPKrXVg5mkfylfzWfFla4CHCPP1UXyfoHXgWQzmeb9nEHDpDe9xXHQQEQaDAQaDAa5evQprLR4+fIi9vT3cunULnU4Hw+EQw+HwJ6Occc5dSOXT8SLwBg0anKIhnxo0+JHDWosbN26g0+ng3XffXVo90pBPLx7PJvOpVj5poM1zcmghQVwBxdQGcZx80hNFMzCMSgGlzZICgNToV08uqpliYLRr4ue7G/HGTqcWO0baWgdcQLGzAKoOwYbOfVdqlE/EJUTPfhxb6sXGjnyOqZiCu0IxsJgCLeF4I8U/r3USO7Qe/mVuExI4fql4Mf/NGvKJKQXxBKCIpZ7yXqByWgc7S9AqlciAikOwRi3kCY32wQWKgN7dr5CjZIojFfkEAGbyCG7jstim9/DPSDYJmMyAgaIY3enUxaOWom0ou8HXVrh/NHlPmbFYjEdYFS1TwgaD0QCAUZrwsaScw5Jg0eSZA9ruMd7c/mZe5Bf2LVl2JhkyU8FQJVrhMQipsagiRJ7Gtk53nxIynqIk+b4KFtkX4JSfWjHl5DFqOz3dwovMVJgqblPtMQLn86Fe63yLVjsw16rtQ1PEspdOmnKpWkaizc2LvZPElE+DboKDGcMQsLOeYGe9g+F6B5trHaTzuSEzqyySWR2SkMM4i0ognyS7TpF8qtj7kzJ3iNz4C27EE/hkJZk9Qk7+d93juTEzFd7c+O70uEEInVPZPi+UA+kQnsfOW/+dQz4G2oPw/kTLWcmuLwe1hDk5pjyKkDuURIj1LKKIj703xhbExP5e2M6VFZ+7Mcs+0xZ+u/SjBWJKfE0OLGbhWY7qgw+XdsmdAahcznnl1hqMz16vuwkz21veQWBRDnn6cNk6jOebL6SupwX78CrZgv3PPyIrlxckkUdRhe2fyZlhPyGkaYqdnR3s7OyAmTGdTjEajXDr1i3keY6NjQ1sb29jc3MTqUS6/ohxETOfGtu9Bg38uJizWIMGLwFeRObT4eEhbty4gWvXrmF3d9fbpiGfXjyexTmv1Wz38DdvRnJ3ASC6Mn7eTL26WUdmaYkiAuYWffFi/ypZUp3OCuQTlWBugUiZY8tr2AAAIABJREFUacMGfDiBscuBvUt928dgE1+Zf+YvlO0AggWbTeCM9YVK+cRVdBG9hgyM5g9F7F3I5mCJfJJW6LrKq4BJH95CwhO5uBn6kGE+WWmfOL+FyVJXroSL5V5pc82cVZlRGTurFVCx/uxMST4pdgoA7bWaEIz8XgDx1clnQDEboLPdlhNxZCYPv0FnYwKAwEUOgtKqbzIGWkJA0xzU6YDzAtRWkAsCUaUhnxLF3M2RjKLUWNgqfKyZKVE6YXt5ANu6FNy+SEykboK/3fwcWUZggdlg5+RQ+bliqm1KTCv5NyZURcknzawqFdTPwnAORMaVhU6hqFUqacksRjK304uPL6O0vC0VfS322cIMP9+cInjmteQTEciW4DT+3kFKEl0/4YXPT+UYDyen2wlAr03otRL0WgbddoLNboJBr4v13inZtHQkTDH3ySeNV5r/gvB5JuH5SlKWFHNN/Hg2J24CS/5nU0glCYQzyDLMELI+tLMpkLTwZusrtJLT38KOglzlk2Q3ie9CVRUu5JczcIh8soW88EYgUCiXn1WxdzeKFanbkfs9Rgy0I2rK2EeLoAiLPjdsKWdDCjcMWwtaE36bRGpJN7GgtKI1/xhwn3wOypfvF+5u+Mmn7iaQf7/871kPXqOK0LRcLn/TcHuuONf2sfBdVHz4HbCxDex5bP4O7i/v7ydkubcKiAi9Xg+9Xg9XrlyBcw4HBwfY39/H7du3YYzBcDjE9vY2+oKa9seGi5r51NjuNWiwjIZ8atDgR4q7d+/i9u3b+N3vfod+P6yQacinF4+nVT5Za3Hz5k20Wi0YswFEc59yZa1FeUxUqfpbhShi5aruVbKkshRwVQajyA0BAMdtJEryCbaE2f9I1ZS4nBNEcaKqPhB9IR44VqKdIZ9UmU+aARFvE8+Eiowp9+QrdKmcLhV2zOE9pOX9uuYrEkyBD5mqOiFWEs5BbMERey3NKnqHTEXtqulS5cr96Pk/htbaigg0PQD342FKIqno6VfdVCows8Na/jFMe97fCs82zgs17ctHYxX5RP1eXdDyFPs05JPmtJQRNVqMwEqNFcmnJLKI4KwqirjEW2sfo9Oej2Rp/NkCyIQQ9qTe1k7K6LmKWw/OifJonpOOEFGpqChBwlNUEau/miSKPykt2iBUkXyZGikVsKwYX9EWx/tuIUGJSvVZWPd6de0ukkTYwyp2eq7QZTRp50X1OpDweCgqg//jl1toZynaiUHWaoEW2rcTWZFH4KW/WYSkPgJiar3IuJKer3AI5RUlPEFJ/ns35SnyAPkkZQUVob9xZXC908ysoesO8cYrhzj7O814BB74n1GcW5BP3eI4eL25dMFoSzoaAZs/828UMv8oH4u5TU6wlo3lQcWURZy1w6OCnUw+VRYUsQuUVFlcVZHtVrbKLQvZMrqq5BhSKfLrcAoaCDbQHeF3S+R4gGzj6QzUW95f9dFn4Ht73t/JaeDcZV34DBxC1tm+BT+ctGCKo+V5Ie3A545Onm8VNi0YOznpw+IS+Ob/Dbz5m+W2/U2Y2TKRht03vcfc4DyMMdja2sLWVp1Nm+c5RqMR7ty5g6OjI/T7fWxvb2M4HKK1il30S4aLqnxqyKcGDZZxse70Bg1+AnDO4S9/+Qv29vbw3nvvicQT0JBPPwSe5pyPx2O8//772NnZwa9//WuwEIZ9DCILFm2Xjtvp7OF0eU918YI1OTLwWzeFoX+JdpHV8OePQffIY06Avb+q+wUAVqiRTo6j8nyMyX9x7v9copDyq9ylIueDnaKAGOsjsllS2Cx8PFNxhNb4i/qnMcLKG+cE5dP5IrbKei/aorbnU1nbRYiulaHIewIATvSZR5Trspy4s64myThbIXNJsARqPfgY7cEThpWvYBOIQmfTScaAD/2k89TGi+kuUngmOJE4AuLjU5MZFd7kUCRzBRAz3mx9hH5fef4la6gzdpstRe6TBhrSxrGJS0nmvWmQqmxsE5XdLUBzFYhmv0obTaWKGQAy5fsBI8EaHeLVQeS6mURt96lXNCmhsPADEJk/E7yyMcB6r4dWuxMgkeQHrSFdxpgEiXwykfmXhbnUa4c1RyLYLkvqppCSyrgCNviO5D9HmRvDUYq/7d1eIjmlJMrga81UWMQ19mfs1DuT58fgcdjIPd8WCpOxRTsSeeMqoCO8Ixa5TIjOJvKiiHwqj3qfgmZh/yIiVszxPCvpt0Uyb/tPRkyFrHp56slsvbcH99GnYWI4RJCGlCGBOcCrnmpv+fcbyqOznryn1uCkPVOK4v/977qt77ptBAjiy9e8/95ARrvdxu7uLn7zm9/gvffew2uvvYbZbIabN2/igw8+wF//+lc8fPjwmdj+v0hcROXTdDptbPcaNPCgUT41aPCc8Dzk0LPZDB9++CFeffVV/OpXv1LtoyGfXjyeVPl0HDj629/+FoNBrfhg11e54Dl0kPiWrp0BkQW7dq2UEttVYHSUJFQLUKiUVstc0t87rLRRqqG7Ju7RGGZ6D5x2QZFzeooVLMg4B9MGiHVFfpwJT2cYsFEU8jUkR6xAV/kVHWfBUhEEEXIJqFdzBv/4zPVyFq2HH4OOi3lSTU8qTiz8XeLGsMlyDsX5P1Fa6qGKtuWQEmQRStVAcJXsYrukU48joyBrlTaeSDKYySFcN25lxx2lNR6EnIxyhn76Dc7NDwqrrhOs8EyWgseX2k6mwNbyOdAon2KqplZSomK5TYzAisEJc0Xmxijn5NMV8xdsbS4URaVCn/AM5DPvJG2FclVDBVVKoiUxVfScxtQoJ30pSaAMOSqFsic5k6ckQbuYI2ZVeBYZWcwU3VpkuDa4CzKK+6mygCqnQqvKVi4g0cxzCGeaAICN2F1qEDtFcWWUrGwiZ0WyngVyLcNjlPDPtYbL8G6F52tF/vOZlodAuuX/m8B923ITrPMEP7vkGZSBdxeuqvA7hRXuVeFdSDqH4niUyPdiFrG2k29EbvfCoyKfyLmFZQl0JFu7HJAWlZUF0BXeO8oimHNU928BQRm1iprZC+GdgMvwNeHxNEwiMQMB+zwuqnCW5MKY4/EE1X/+CSTN4AFnhCBBb5e/I9ikoOLx0jsPZ12vwsm3SIBNVtuOL/aRdAFbk7Xl4QD4fp43deghcEPXubHde2oQEQaDAQaDAa5evQprLR4+fHhSR+h0OhgOhxgOhy89CVJV1YVTPo3H40b51KCBBw351KDBjwT7+/v45JNP8Ktf/QrDoS64HXg+JFgDGasSfsyMzz//HAcHB3j33XfPyecdx5VPAEDKwlttPadZrd0GCatfT9qpiaIZGEaVMwRllhSw2oppDZnmijbM/p8AAJyug+y+rvOVrfR6avKJ3OEJt6Wz3AOcidvlcaQNzR4D/chcI5FHACCRIzaXt585umz0KcxZAtNZBFlZqW6zMB9qcp8cCRY25/brorwpZz2gykGxDKlEl8HCilyoY1A1UxVlQzYu3rbFGNCQT60eaDYGS6u953C9jXo170JBsLf/ZySDhRMcy544e6zZKq+8Kzw3S/98FSOfCA6Fa4ucWGZstBZnI3lJzskf9WXADgsAMp6ixDp2+BYu73jmK002mA9n1ARthfKpivwGALDOqPhFA0bs6azNadI9z/QkVRI9stM9a7BKlpN2310zxbCvzJlUZA/WUKrpnrHSk9Pw+bGKMRdXLkoaHZwupgjASLlMQE1wCKdEyoMSIRBModwkIGytl9hZsOJQkv9aJa7Amxt7/m+YEOFWzoB2oNAqkmZp+CwLC2xYXCQiqLOKMVgin6R+bSkSV1TMxCzCqAJYIs0022P9x7bHlKkC4cdVFZkjhDyoySxM2k1mwewmzktQK7BPd7o/ripU//5HUFGCmYPkPeV+JTWV/ndVkz9aPqbOFkzlIYMC48rXN7c3QZVHLTg/vy7po/znf60j61od0OMHS+/X5LGl5I1XgK5+MVIDHdI0xc7ODnZ2dsDMmE6nGI1GuHXrFvI8x8bGBra3t7G5uYlUtSDkxcE5dyGVTzFnogYNfop4uWafBg0aLIGZ8eWXX+LBgwd4++230enoiwkNfhisonwqyxLXr1/H+vo63n77bc+HdhvMGSiWVUS6wpGejNS9CJKSKCIADj0Q4rlIq2RJGaNXVBGVYG4FzyVzBtz98My/rGDpVx2uaGS7ilKqANMAxFM4hb0fuYhn/nGb2AdIzPrFFoCQaQBmMRiaypmo3DlWTSUHd5AufEivZuN4Bgur61S2e2ZOxMaUYmUBtOPXx5TTqCKM005tPxMr7pu0XvmqIKHIlToLwVjY+NndV4W+XD49UJFPSDKY8QFc75T4NEcP0O0fYGlWaLfAtpQzJI6xwrMzuJJZidIlsBHVSa1qimUdyVeM4FBySyRdrBPGLbsTZZN3/1xio/oGr116BO+MLN3/kirgzAFrlE/WJXFiFwYJqqjVHBFHuY6wRdjiPnXPU1IQ08AKiibl5xtTigwTFQml+yUO17a+Axzp1JmxZ8jJznV7jyppj5Fk9fwZI9tNWufreMiqOPnEcEyrCCqXECOnohAL9QzpPcMJY0girUJjj9iiNH7ihzh8j+eBbKmumeCVYeDkht4byhIIbJJIMymjyEkLbKTcQ+H+oFLOdGLheCg/AqVC8d6T9XPu7yPkTnwOimx3TzWi4xORVKS2ZR0G+yR/K6mipnmYfCqFN6Az75vujzeBR2fIHM/EwUSgqd8CkvLlf+fWGkzlUT611gBfPx6LPgaBymWSidMu4COf5n0UX85A+fz7a3gZtH9nue3RaPnfXm0s9543iAi9Xg+9Xg9XrlyBcw4HBwfY39/H7du3YYzBcDjE9vY2+v3+D75I+aJmPr3sirMGDX4INORTgwYvMcqyxI0bN9Dr9fDOO+9cuIfzRYVW+XR4eIgbN27gzTffxOXLl4PtHK8hoeXVbWdBlCsXD2sDuyul/Y+eKNLblzk4pe2fIQt2XZDRkWC18suzGo8B9+ABzFkLi5i3/NljdjMU3EVLUUit+9aruwCAqVuTTwrlE7kCiKz8rhVjkesR+SChcioGaqOYyLkGEW9/Trug2SNk0zvLA0w6NmnbwqpYDfkE1MUzjpBPbF2w8HWuL1eoblWyE3DEEhAAjMvhNNlrQmHuLOosJ6ezuFrhm5U89jAhmPzwHPnUH9+AWV/eGREBkykwUCi6Om1wUYAUwcyhQpO3rYfEnVbxv28Zi2l0OpZPcCspUDrZvqkU8gBb7ghFMghuzzDDGzsjGM8qba4qOXtEWIF+Np9Jo3yynCBFfEwmxsFJZJsSFbWRcAEO2IgdQyrgn4PyISkVyM+iWmFhREYlygjJCehUMrvZXax1XF3fVpBP5JTKJ6WdHkzmVUV6m1YzOI3S006fiHxKKGaZB8QuOiEm1I0RpUIelM3hJGVXQHEEABWFz1tJ/j4zN0ZJ/rkkRNKmboYicI9d6e+DPPtiV4Vt3SSrT2m8hs5TZcMEE7u6yB/an6Tqjqh/uL0WvLJU5gDC5FNUjRmbh2LfmdHtMa/JyHbpucEsK9GcvByA2uH5nCXSTCCmKKB8BnBChFWffwW+/bVwZHN0NkCFhwjqDGA8Kn3ubADTFRTJ1fJiPW5vwLDnPTh0v7gclXkF7j/+n9N/6y4X2bnTAx2Olm37mrynFw5jDLa2trC1VVuf5nmO0WiEO3fu4OjoCP1+H9vb2xgOh+ecV14UqqpClj3dgq+XDZPJpFE+NWjgQUM+NWjwnPC0K0m0xESDlw/GmCj5dO/ePXz55Zf43e9+F31BYe4DiJFPFdi1QBTLfdKFiuvyno6Joi5Ilfu0yorINqA8Boc2ErVVn//D2eUtmIMvzv9jFbdkO4u0tQFUD1RtqTp6IqWUSvnEJTjCMpCGhIwVFyPkEdk8onQRxkNVAkRoHXzqr1VIBZBQ8YJ56TcZnqqKmYar6BlzaVd3SWO2MnNolUqhwOklaHOMkgw0PQJ3wqTECZSZU4DeogxAraqbI9v/HO318FjjvNBzYJOJnEcxB3U74DwHKWz9yJN/ocl7MopsrZitV0qVOPO1TIHChX9D6qZB8qlTjXBlcBdpiPCunPwVIalUzthmGWJkpkTppOIDITMFSpavh1GMMW1OYIYZioj9XqVhc4GoGuu0P10BplYyOWgeIqkyRyq27xQF3hjun7RWQTnXaTPuQAZkC53daFWoxMtUzrykvI3ce4biqiW9LXHo7yPbpUwnOkIuKN6qgOIIAGzo79jBkv+Znrg8KJgPkYCZmwAe8umSu41BrwB8x1EWQbtVabgFySBbhJXDxSRsK5tPgHb4Gckh+z9AJmCKmUiSRBftkJFHXRIZk4JaHUA8w03IIFNtl/q3JUhQL/HMQeBUQeuCXaFEmokDKzwXUrcDt/8I7s83dQv0sk6AfNoAZh7yKTgP+p+DJvf03eoDuW8Rlv83m/IQs//aO/dv3m+7zUug/WXCjXcb8umHRrvdxu7uLnZ3d8HMODw8xGg0ws2bN+Gcw9bWFobDITY2Nl7IoueLqHwqiuLCEWoNGjwLNORTgwYvIb799lt89dVXKmJCC2b+waXVPxUkSRK03XPO4bPPPsN0OsV7772n8l5m11e54Dl0kHjTZE9Rk1TtWikVa6dUHwEtQEU+6S3yVpFUkLKYWPe6fF2ca4Pu/nG5rX0MTjMdUQNAa1UIAOQmYNMHRa7XCeZKKV3m01Panhz3knWjuVEiYpZLwqpxKqfIDm/DhErrkq9/yHKwqpYK4gQgcVNUydPPs5z1wK4ARQqpTM/41Ut5ubmlt4CgfKwin1xnhfO2gs/8yaOqsujTF2LbVQLKOc/VMwsfTlTkE9bXwNaeU0BN7bOxx7UiIRMnsFJjRfLJBDLzutU+fjH8K1JhbmVXBc8lu4gqaqEQ3IqST4CBRVS6F7EpBIDwUS/sj/PoY6hER6UStAG1yFI7ZdYUYOZ2egqbT8U5AYAycm5f73594milznLSvnMmmVptWStHFYswlM9t8hTzHceJX0OMKnISOGrLF/WSFOESwdZWyBlL3CSY9SbZ52U8hiX/nC9Z65WBInniuceIK1zb2gtbelpBwSstIgkpmIqZV7kBzFVGgT+jcgYOkU+2ADIp0yl8nFSMAYF8it1RFCl4UoxcitnTxp6JgmVgvV1QH1kLksgpa0XrPD44ADYu+beNp6COcG5akl2fMBek/uPh0gKGUP3HH0FqK0L/1eVWx78WL1CwJ5/CKVsDOc+/h+YQXx/pGmyxDv7rH85vGHvs+TqBhWeN8umlAhFhMBhgMBjg6tWrsNbi4cOH2Nvbw61bt9DpdDAcDjEcDp+bjVxVVRcu8wnAhSPUGjR4FmjIpwYNXiI45/Dxxx+jLEs1MaHBcQbRRXy4v4wgotoeYgF5nuP69esYDof4u7/7OzUZ6FiRkwKAlOQHo60igpjmWTeKPeswA8MoFRDKrIgV2y7+bmYCf/81jFsunBAYLhmAfMG9PgQKucHmZq22yFOAqjFgdMonVcEv1qaSQ63rPp7uxTr40QvAjL9D4gL5YK4KF6ZcBWSh0Ad/ASDhMSpE1Ieae4sMzGwM7spWeaIdz0J/KihVA9zp1+dHDOSe71o5LtHqgYopWMrFON6/YFG01HZ+DTsPPoQUbwFgJes/MZ9hEYWOLCdj4I7GoM3T665RPkWL22xRuuypMmViSiDfHbFW7eGt4W1kGcCCZZ+ouLMlECqC2hJIzhcx2kmBsZULG6rnhmIhQuWM6pwaje0qGaQ8hoU8ti11oVEqVWjDwKrs/FIqUbKGhNENoAotJCi8CqguHWF38xCnN5tSvRlTOZw0NPOMu/jcaLhUvUGQ1jbXY4EWz3tSkACI2/LFlFHydpZJDEGtkfAsSD4lbgKQf9JN3Awwqy/UKIz//iDPHLKLL9HrAjBPYK0XGm9lAaSBe9QKzzppAY0Nk22Uj0VLYicQU7E8qJjtHbeERUOVlcmhMpfzDsscRrK9K+UsUS5yUblUq9CEOSMy7VAmEFOTmUg+UVd4ZgvnxKd8BgBMZqj+/BHIZ4sXegCFVG2h9oHxaTwZTq49QDK7v9x1YLQYe7jcR9pH8T/+cO4vOElBj75f7sCXL9VbB9aHy20bvDRI0xQ7OzvY2dkBM2M6nWI0GuHWrVvI8xwbGxvY3t7G5ubmM6tRXbT6lK/+06BBgxoN+dSgwUuC6XSKDz/8EJcvX8Ybb7zxTFVKxxlEF+nh/jLDd+0ODg5w8+ZN/OIXv8DOzs6KPbbBnIE8WUXnd6wtrGrHlna86CzvCIBDD4QAqXCurT5LSmsRCABExfxc1h9tbkIw42+FP1jBVswermilt4IShKdwWAcnihX0GmVNhLCgYgJ05aq/RB5p9oEAYWEe30U6+TZcJJFWkQrbHPsvT+Lx0l/6WyED4xwiYd8AwGkPYBcvTCoKsXU7pcrGpMD4EbAWsBE6B/3zx8wOUWnIp+4AcGWd2xJr2xmAZo+x1n0QP5bAymMfVnmustamEPWqapwhn2YK8imm9GnxFDaSOVRxJIeskgvwi+O6X32Ht175CmlCYFuCQkQuICsfKxu2PKvKpUdLW5GVp/mgd4pxyzAwXMUz3JRDJUUeJZ9ABhmPUSK+cjjFDEWEDAeARLnoQp1LBSCjHBUvX7hr61+fy/0iheUcANl6cQHkSuWctwKBrNqxZw8K8ilGHGkUZy6ijJL2YcDieHfC+DYcfk7VaiT/s98I6iYXmItSN0ERUP4tHr/hEle3R7W9Wsc/94nzQOg5ZAXyKZK/FIQw/0UJpLZwf8eOR1IuVVZeNJRPQGvCHJRPAWmRSD4LWxQC9YINSTlVFoCk3rdWJJ+YI1R6iAhCbc8b3FZaUE8gBAP9clUBgb+rPr8N7OlsuE9g/d8yFFjYZspluzxOO6ByskxYha6bZy7gtOdVSdl9BzpYILaGr4Ie3Vs+5rHHKv7yNb0atsEPDiJCr9dDr9fDlStX4JzDwcEB9vf3cfv2bRhjMBwOsb29jX6//8R1q6qqLqRKqHEbatBgGQ351KDBc8IqD50HDx7g008/xa9//euTQMhniWPyqcEPg2+++QZff/01/vEf//GJZeuO+0hIVuAQ5coFycqxQJWqv1WIIlY+dggVnNL2j1CCXQdkdMU45jaILFzVAd37Q6y1qk9gbqWXrIFYaS+ozeqZo0o1pEFtxROzy2PK5DYKEiVoY3O8G0nhU868tjTm8HukR1/Iq/VF/31hW6AQmjif3/15OLRBXMQD5pVqCVMcgaUCFOZ+/oo8Kk7aalLHlBM4xMeRtGp7EVTGLTfrnScwRwdwa6/E99/pY+3bf4PZVMwqHT1BfOIbpoCYAbGIM6HjzHHlE8GhdC2xDpOijNIL1sm/p5oVolPdWXut9eou/uaVr5EeZ4JUNqwiROQdR1IneN5F2omCtFUw+xrSAAAynqAIFNiPoc1pMsrnaYpCRz5RiUL13NU9m7Q5UgCQUYnZQrebyQNsry9cH7WdXqqawwDMswoV0K4q1ipHPWoZGyF15zsQt8bIp5gyiuDEMU/OhtVBAJxo4Rg+NsmuT/pFIcvIlKfwZjcBsAvvg6/RX9FpE3gqkNGBc8bOhYkXSd0kgKUxJG2TFFPFVH53ijkxtnvhJvlYJJeozAFpDrIFIBHpZYGgDyEwV4MJz74q8kSLvT8J8w47BxoIv01YiMFHU9Cm/32MixIUescYz0CD5fNVfXQL7tMvYbLlMcLMwXcLCrx3U+FfsEf5MsHDnU2Ycpn0Co1lKpf7dq0BEjs6/2/pOsqP7y7PSGsDYIF84jQDHSyrrPjVxnLvxwxjDLa2tk7qVHmeYzQa4c6dOzg6OkK/38f29jaGwyFaimzVY1y0zKdG+dSgQRgN+dSgwQ8IZsYXX3yB0WiEd955B21NvsQToCGffhgc2yhaa/Hee+89lfKMeQ1AjHzS5jnplEJaRRHBwaELUuU+rfJS1oLf6HwZDm31SnBGAmYD3PssejxUKQvrx32bvtdv3du3G69mGabIeyKXgyR/etSWapI1CoD46ltbypkuzokFFrIz8AL5RJMRsqPP6/rCc1gxRoFCXlIeivWS+o8JCZdwESWcyp4PtSc/x9QNZEDFUR0IHe1vClaQT9pV3jFi7En6BABTjFXkU/LwDjpmH0DMcw+gdhtcltGsCwCgNU1m2hyS/dAizhTNLKewLN9f7aSAjVimxb7FDaoogWWFVerEFnaedTaovsHfvPItkrNh9LHruoKq5Rw8H+YthfKpUnyyWDbzor48fyRuAhh5bFXK/CXtM01LUsWsEo+hrW+U6ADK5SHJ0r4d3txcXtWuJnaI6oKz4t3HZ8Hm71L3bGClHeli/higs91zjsRTSpDJGhNRjxGxeI0zO0LVuhzcbk34oSaqtoSdOoEMKxJ/4T9xRVARXpyx/ks5x+s7jwGQPPeE5p2yCL9zSP1JSnFhEYZ6fC3uLp+AJfJJzDyS1ahUzABJ2RQjf2Jz/lNvj8xtsUlNWBDChxPQuqBekhaT5DkQeB/jaRm0IqwzJM+TT9XnX8Hd+FR+hfVsZGbAFUvbGASaeUim9gDGY03NWc9rRuGbX9mkMOWhRyXVWXIzL24XwNGynZ93Pt66DHp0d/nfdxvy6SKh3W5jd3cXu7u7YGYcHh5iNBrh5s2bcM5ha2sLw+EQGxsbIrl00Zx5iqJ4bvW8Bg1+7GjIpwYNniNC2T8AUJYlrl+/jn6/j7fffvu5rvpoyKcXD+cc3n//fVy+fBmvv/76U8uv2fVVLniMTjTPSU9SVfP+NARQC1CRT6usQNXfExTJTzl/DA7ucQkz2483Lg/gDEULwadYoSBbHdb2Flr7JIX1G7kCiNiw1RYescd/ZLzaXCafyolsLbNQBKH8CK2DT08LueKHiFTxE7YFrmFiD+uiR/Qejc+hnHZ0fKIyj4RcoSptk7O6Erh2Hmr36iDJIWUEAAAgAElEQVR2KTT9GIoMqROEMg3OtbHo289q2zdtv5MJsCFnbQEAddrgvAAJge4nbXt6oorOKKo0eU+ZsYi6+qUpJB6ilZQonJT5Up6QS96/r46Qp5vYrO7g2s49JIvFuVghUFTHiZW3pX/RKJ9KlynmYUJqKlQR8k+TH1VCyE45A40iC6jJBJ2SWMcqOU5UixcYCTJMUUoSuAB2s7tY63jO1Qrvq+Ss0k5PyaZp55uYLewxPGqZGPlEc8u7p3yri+wjAuE0EJfggNUdADjhPUVS/IVsQBM3gzWhaxyY6NihoFOi5PXWHWSZ4oyGyCdJ2SKcTTmbSXgGSASSlMVlZUs+STFORcT2TlJc1Q3ErdGz/7RrgmLvH5Ht0sIqnsxE8klSPbMVzou47fzYdnfuofrgRn2aQj9FuPje78TeJsgt5y9xZwBMPIqowNgju2wvze1NULVMKC2qpKp0CPvv/wJ4lFyYeY6h1wc8rnt8uSGfLiqICIPBAIPBAFevXoW1Fg8fPsTe3h5u3bqFTqeD4XCI4XC45ABz0ZRPk8nkiV1uGjS46GjIpwYNfgA8fvwYN27cwFtvvYVXX331ue+vIZ9eLEajESaTCd5++21sb28/kz4da5UIuhc4RjtKUgEAUxvEGvJJ+1U6BcPowuOVpEyNFfJZSgtz/7qqLcFh5vroGqUCKuDN7u8bcKYPcp6vNF/XiqIbcZyEUJ17iVgCQOUM3AmPSbJ5RD1z5ijLGbLRR6D5NWTn5FBq6SMldI6Yg0OUuILhGVwggP20nSLDo9WHUeQ5aZUDWjWAWgYhWSEu7nt2uKRO86Kj/8jSWNl19j5E2me4iY6gAwCXl+qEOh6PdeRTt6MmqnAmrHxq4+dMo26RCsQAkBgrk1PVEfI0bOOb8Axb1Ve4tvP9uTwfDbiyMvkkrN73zWMtY6M2Yw4GKaqoHV5CDlXkdtDcLkwpUp6iiswLels73T2vtfuzK5BJGeUoOd7+7PlPuMAbQ/8CDTJJnc+iUH8QV0piXNNIrzjhpL2Cben53xIjnwzJlnk1IoV0KHOzApAsjFOeoKQwGW8FYipkn1dv898LKU+RB1TBIbVUy41RUK0aabsjXBmeeQ8KkBC1tV4gf0fIe5TGDIcIpsqGCSZ2YCEbSbQcjjzTubsWHjllDsn2LvZuFx2zEdV8lPiNynUjfy89N6pKVvUX8sIW6j2ZEkH7auW+uw/7v//79NqFiLQVSSluD4Cph3xK/fdw6J3Rq57K1gAP+bTYR/7hd8DwEszB9+f/ngj0aM+zs+V/4lYHGIaVmg0uFtI0xc7ODnZ2dsDMmE6nGI1GuHXrFvI8x8bGBra3t7G5uXnhlE/j8bghnxo0CODi0MwNGvxI8M033+Cjjz7C73//+xdCPAEN+fSiwMy4ffs2bt26hbW1tWec39UCe4LAl0Da66yd/rUvhApFA46taHSKguMsKV1bpT2fa4G+/gAcWMHrQ7szULelKp4fdB56pZRTFd00ZyzeJko6xEiRmP3KcUGwsmg9uAFzZvyQc/IKWKkAESp+VBUotI2AhJdXhS5CpXAwKaiIE5UcXCW+0E5rb6Ws3LpO3MbupMdA+PUiuDtQrLiet42MKzMeodutC94rqUXtCkS1EDy+CD7S3c/UXwPPj0GjfNJoJkonz1Emch+nTr5+6zTCtZ3vwvVCqVBoI/O9pDrxKBeIdNZ7ieL5RqQg9iSrrbP7U+T3VUoSSGMbCABO2c5SR7mIA8iU7wVnibTXu3fkiLSYhdcJlCSydq4Tco7O90egUjGHEcEszHU2oqROInlOQMTaDlCoX+V9OOEdxrAwx3ElEqql8W8zrkAVsPKT7pMq8N5aZ0HVuNb5AunZUx561ykLYZxI7wyBc1WV4Vy7YhLuM5+I8yO3pcKjcJzFDCTYmZKLzJGxeyhmTRvLaYn9fWTREgS7Z2YWt8dyu1jKGbSVbLmbPaHSfj4G3P4B7L9+cLJAiYXFTquCs8A1Cb0fueX3T9feOFncdQ6B+4yq0/nQmktw1z8C+p6s0M1LIM91oalHDfXqG/o5vsGFAhGh1+vhypUr+Id/+Ae88847uHTpEh49eoQ///nPODo6wtdff43Dw8MLkZc0mUywtiZk5zVo8BNG8xRo0OA54mzxrKoq3Lx5E/v7+3j33Xdf6IOpIZ+eP6qqwvXr1zEej/Huu+8iy7Jnfs416ieiXLlST1k4UhatViGKWEm4ECqwsrBHKMBO7pfZgO9+CbJTcKIvwq/0FVkdqn/f/A/ULV0gU+EcNAX72AdgVcbt1p5y1TenLYAdsgc3YBYVeNIAdi5c9HEubNcnFPqYWjrySVmwPvvhHoLLurqPLGWhlRUZJQDq65orCRWlNSBMAjN9rGoaI7/WDq/DHOcOiZXvp8Aq8/JMmd+WGPBRPYY05FMVVe9U0dyoWHE7uAKaLV6n6/h5/66seJLIboFcZlfJRcSABVvbxElBY/RqOAlVJLvtGInCJtairVK8akmqMhpANwcZpNpcRgUhV++7PsYujvCzLX/I/QmUKl/101OboWMSQJurqJiLF9s5BlyEfCIN+cQx8unp/r6k8PuAERaHpDwNvicYzoO5hqkVxoPwrCgCKiszHz9r7iEuby1cp5ACRiK9hXcSbgXOVTFFaISSDY8xkdS0ufz+JKh7fIX8c4gs8ggSFcdoRxZ+dYTtzEBHyBFjDqrSgDk5JB2fFRYIAfHntmTJdyS/34nq5kS4DzsZ+NEh7L/8AXT2+FhYPBN8R/dPCKF+yPnHisk9NnqZ/73Lp71kEKh8fPLf+X98Um/wnd/1ZUKKyQAeNVRjudfgGMYYbG1t4a233sI777yDTqeDdruNO3fu4L/+67/w0Ucf4bvvvkNRrGLT//Kgsd1r0CCMxnavQYMXgMlkguvXr2N3d/eZ5P+sioZ8er6YTCb48MMP8dprr+HKlSsA6pcrFwvXXRHs+oB5KLYhquAUlnpEM1XMDWGmiionODh0Qarcp1VWNnUAhT0gADi0kAgKLPdwAjOeh6evoHyCsoAFnLXSk6/TSftqqsztMGAhQPy0Q8VjPVLko2ICdGWSQLSVAWTVBDOQdZHd/wsS5yFCYuTTk2yThlyVI+H4uK3QBnEVn781973JQHYMSJkSAFzaVeVRcWe9JgQ01oz5GNxWLH6Qgs8X+yzGwNow3rDVBc2OvJaM2f1P0Fo7c/+uENhLrRVeZ1ewmOOYgu9s2/EE2ByoyKfSycfbMgXKiNLVRQhH9oyZVnWIN3ufoL/O4GlslbwwR0rEpC3CK+RtAST+sdeiGQB53tE8O6JFfwAlrQHM0XtZY7UJImTVBGUiK2Qt2vMFFfI96pDBoIzaLgJARgVKjhc5SLnWsEILCQpcW/sqasVIpLSN077vaskn1Cocp8iRiipFjtvxabuY5R4Qf2QbxG35YkPLCXtJInaQ0i2Q8Awg/z2YuinywLuRiSgpfSCukIdIsvkc8qZvrIXUSBLpHTrfwpwjklmSsk/4O8on4vuRC9ilRfcJyHbEANAW+rZF+LwCQD4BdYQ5p5jBtIT9FzkojWyXlFNVBaTCwI28V1Fb6HuaA+v++4WZQX3BylD4zcRA+S//CSoXx8MTqDdC823lL8BTsUwGc6vvv08D45E8JD631mHmixpstQP+8r/rDfkygUe+RSZbl0CH95f/vSGfGgRgjMHu7i52d3fBzDg8PMRoNMLNmzfhnMPW1haGwyE2NjZ+FNlQje1egwZhNORTgwbPGffv38dnn32G3/zmN9jc9MjWXwAa8un54fj6/v3f/z02zgTeP49zzqxVy8UJG6IKjjUkVQWHjtLWrgUoyCcoVpOfOQJ9U2G1cjXNYO5/cKat/tqQPQAbfQ1tNWLrEJxk0ZXULumqDsAlLfmMsQNTJreJrb4FgNBK4uPdSKRKOUV6+A2SKpB1Jf1OqWL3pHYN7Q4SF1c+gRKY8ii8ivq0oWq3ZGfyeQLqgqydyiHjQE06jR8Ca3Grz1jg+TFYClRf3L0r1Ro+MztAtUg+lVOsma/P/RNlGbgoQDErIADorJDnELMOOnsMqywUKUswx5VPBg6la4nDPCWLMnKRiogt36K6Z2C/wbXh3VNOScrXsCVIKlSKO67CbqK2DDq5tqFQHyoGrlOQT0wJWpjAIjKPxXcHoFZIxWkOQoYZCsSf4xlmyBXkkyY7DNDnSAHAdrqH7U2NqulZ2+mlterWxH93aNX/UjulevOsSlBDPsXmeBNVRjGckZ7VXDNIgQYJz2TySbjeJCjWDOfhnyYQIyFLycyNkZNfse9gsOH28MrG+TuHbQkK2dZJpzVkV1fMgG7gnpPe0aV9CeOKypk8bwhZmVG7YlE5VMiKq3wCyoR9FzNAIp/yGSDkXKHMgVSYT20pPnu5qvxkhhK0JqiySmE+m8xAPf/fsnOgrv+Y+XAM+28fgGa+b6NAZhkZ4V0/oHAqPXlPZEC5J8OpvVFfx6VOAkpHu6xY52wdKGdgk6H45zPfTL5sJ9+++puAh3xqlE8NNCAiDAYDDAYDXL16FdZaPHz4EHt7e7h16xY6nQ6GwyGGw+FLS/BMp9OX9tgaNPih0ZBPDRo8R3zxxRe4f/8+3n33XbQ0BbTnhCRJkOc69UgDHZgZX3zxBUajkff6Pg/lk8Z2b753Zbs2VKoiagOsIZ+URXdMwTDKvAp9lovX0xyAqzqgO/9xvq3VZzMRW3CyCbjlj0AvtHZlmCvGzDqIZdsyjeUeuVwOZEZdtIt+4MeIuWIaDuIG6gKKsD05vIuk8HzInhzkc1CGBlbLcVGAemsq2z0AoHIcJZ+iBOBxX86qitumymsFVKxdPoFTkE9arKKmWuWa+eyMevf/BJ8TJk9mKvKJOm1wWYBitkNAsNDkRWsFC03nUHKKKmKX10py2IhaJXY6E7KoOBXblcdzBjN+ho+x++rR+fapcK6kLBRAJhSk7A2huNomhV2l4s6qIpZpx0g4hxWsywB9NpRR2tPWNn5x8imhUsV8aVXEFrr3T4LDa/09aN4h6vxGBTTzx3GfVQHWkE9aqvsJHiUVx483anmJClJeJkWUUeQqsKAEEzOdELOVlBZwPNmCqTJAhKU8AwLkU0kt/GJwe3kOsyVCDpXB884uPF9VOvXbUpfSCntx/hPOYTEDWis8fxaPqb0WHHk0G4PWw98IVEYI21iOX+w8xnIXYxlxse8lQYnJeSE/14VVCyyQTxjPQOvLzwg+OKoVT1P/Yrvgc5kS+L5pmBnksfdjSkCzg6V5jDubMNZDPmUd/9o+jwKU0y7Ieiw4kxZQAuXRAPygzt/ExjbMxOPm8Oj75X/zqPPYJMDOFc+BNWggI01T7OzsYGdnB8yM6XSK0WiEW7duIc9zbGxsYHt7G5ubm0ifgrx+lmgynxo0COPluEsbNLigePXVV/HGG2+8cJu9RTTKp2eLsixx48YN9Ho9vP32214Z+PM55y0wt0AU+YhUFsL0JJW2eKS0uAHmFn1xAug4S0pVzPeos5hT4M7NZaLLPganbZCS3OKkB1KST6RR0Zz7g1a0iqcjnwogkQsb9arnyKM/slKdypmoiqFiUhMXHqT7t5AcfS0TCpK1jDSXSttChbyiAHprMLAgLsAx1VqsgAKAs3CB6DyUzwVltkosD+IEQqD5OZgEND4A9+KEFq+gklm8F5NH36LdP4L3fBQrFA7HU2BTSVTlOUhh60c9vfoLaYKpjRcWM7Kw0aq9PDYyU6IQrPuMy1GZHhI3xZvtv2Bj4/wzgW0JksLho/kaq+Tand1xeFMr9lwDYF38WVRxUiuCIvNYwvGxZZU5TVp1T6IkTbSKJlY+w+vfEX+SXs6+Ri+bARFFGBDt6hQmrckBxTkiLnWEllblqn33PnNsGuVT5cKqJKDOXHLSdpIf+Zl7jMKEbUxjpKMVVVFCNo7wo1yIFGSHIkDiEpfB87SGA2yte+4HyVovDSzsKIuwha0TzhUJ80kSfpZIxKAEKnOwQD6REa5sMQVJiyFsDkBSVUXeD2LEY2xRVXTRVeyejZFP4WvFhxPQUPjtqXBPC6oonuVL5JPbG8H+6/u1lV+o3xBHGnJQZng3cG8LxuMQwO0+4CGfQnMslT6LvgHIjjyNCS7pofif/3b6bxvbwAL5xOtbMLPlfCny2PPh0utP/s7Q4EJDlX07BxGh1+uh1+vhypUrcM7h4OAA+/v7uH37NowxGA6H2N7eRr/f/8Fqb03mU4MGYTTkU4MGzxH9fv+lIH2MMS/FcVwEHB0d4fr167h27Rp2d3eD7Z6H8gmo1U8JeT4YzoAor2s90fcuZbGaKuUqbD1RxMrHD0GXYVW3LcCuAzL172ImuO+/hymWP9IIjJw7aFMkVP3kgFd4ibWPwUkLpCYB43BJvAhOHFfSqFbKx7J+YjkaNocvuyW9/xekvo/ds2AOqpQAyAXM0N8xhwsXZ+bFxE1ghYJTdP/Hu0vaQHkkW+DgyQtYQSgVBqq8p+MuiwkqDfkUIBu9bc+OL1dhLf8EpiesaNf2W+TqejiPJzryqdupV1VLgeTHbXsdTCuFQi1qyYWoeio1FQrh1LTcERJ3gDe3bvujs2wJSORTrCAgqabEqxDelqXx55F1CQzFs5pSU8FGFVDxsVWiC2IXz+/RWssp5ULaceyUz1FGbTNYIDwntTDFaxv39V6D6t9satI+NrfivP2d2G4V4kvV7nTujJFPBAaDItdIPol1H8LhkPyMlXLNDOdwgvKpEuwcK2GhkQ28g2Q8gQ1kSIV+o+ECr2/s+zdKc09gDHFZBK0+Rfc8YUw66fktKb+lMRf5DmNhTqZ8ArQ2gtvji08iYzIeAPuUiGXIRf5cODc8y0EC8UYdgUgUyEkuzp9Td+ce7B/+BHJOPpuhH8PVauex3QcmHpIplCnmWVBRW/R51FNZ1/8JyCWK7xPgrJ2gT1G+8QrgIZ9wsOxqwJev+o+3wU8erMjeDMEYg62tLWxt1d8oeZ5jNBrhzp07ODo6Qr/fx/b2NobD4Qt1HxqPx43yqUGDABryqUGDnwDSNG3Ip2eA7777Dl988QV++9vfYn1dLrY+L7UZuzXAxMgnXU4T0Sy8Eu9sO8xUpBLBzRVN8dyn1V41lfaAABzaSOZfVO7IwRx8GWxblAaKmvK8Y31OFYHh0nVQKNNoqW+F3ZRC+aSpGnLFoMgCRM46kdyoyH4WCQN2yPY+QuLmH6rSgKsqWfkk2QpKBJPCPi3hCSzkXD42OiUE2WltgSL1pbDSqzvTrRiN7e+kXXutttDRrETVqqlaXdDsqF6RG93/aZv23g1kawIJELGRPNdvtQLZn+vvZz4a68in9TVMC82EEp/9ykiek0gic4Ut3MXlSxMkIauiWLaIcI9yZWXySRhXEuGapgYJVaLtGYOQmipKzhni6HSoUg2RQYYJbMQqzynymdT7hD6jSWunBwAZFSg4PEdcXbuNNPHWL/1YIfi7XhihOdZnmyMlkQvn253O6zHyKSGO799BFpZHSMhYVpUTFDsJT0XyyZrwc8cKf1cm/nsg4RkQIJ9cgADe5q8x6AdOQGDuYebwc1yy+hSsM4MKbmeBYO6UAwvZRyzNjZHryp1e2FbPY1d7bnvkvYxii1NillWxRUlPvV2Y98sCJL0XlvLzjNYEpb5g53f2Xbf65AtUf/5L9OnNzOE+XbXSvBnO+Pr/2XuXWEuus3p8fbuqzus+u9vtdrfbuAMBozgEx0lHYsoEKSAiAUJMMolggBhBEGMGDCIhZkGIMRAxQEJIf8wjggD/OInTTuzYbcdtt93tfrsf930e9drfb3Du45xTe3/7q3v7ttvdtaQo7lt1qurU2XvXrr2+tZb7tzZF9d2L28vubCfPGMIlUPzXd6f/mFfbnsu9oJhbRpI5HCKavKcGHpRliUjq2zXQbrdx8uRJnDx5EsyMzc1NrKys4Pz587DW4siRIzh69CiWlpacjjX3C4PBAKdOnTq04zdo8ElGQz41aHCI+Ljt9nZwWCqcxwXMjHfffRdbW1s4e/YsEkVg/WHdc1bnPrWBIPlUwnJYVaQls8ZoAQryyW1M7kONSeL2YofNOzA3vivuOtfrAo6XMhcoXwebOhVaNWzIig1wZERORkU+aa4tHwEdKRQ6EFoNKBQ2E9dhSyR3ziOatCyUrlNaQLHWX/1qrf9FXVrwmXgB0eQ+2da8jjhV5Exw3AXKESiwQMq+KtfZ/bpK9REZ0HANPO+3ddrbV/8MM6NNlBryqbsAlDko7aPXvg2JjKGkxjS1zuM2RL5MYqQjvimKMByFnw02kCkTUwEbWKj32WMtltfwzOItdOwayCwKRwhVHAhjbpHL5JOkqBLyfDjqoBUVGBby/RkTVHK70OQJavOcxtlQMvmUC4qiqXMqSaqSdfsVaINQghVkVUSFl/BYMnfxxPy2Da5aVRSN+5FkXbaDOv1Ne24F1GNnvPf7hRRzhjjYuoIk4wGJUUv+9hZx5jVAJs5RCp/1tfPIDlF4PmcEaz0XmUVc4Nnuh/Bmn/naU5GBEs9npHmDb6wqC39OVDoAup5ClHQAtP1jax1l8fRx+6BOIEtTQmgBN1T5H8pLDBXwhN6LhO1sS5D0+aII2DEHMp3awlgvfa/t4pfizUvgt2aIJ984yf53f38WlO8C3N+LyuqchEGgtJrLxK1553uOKzePKUJ+4U6VzN24W72IovoOZ+eXgZUq+cQN+dTAA2vtoRBBRITFxUUsLi7izJkzKIoCq6uruH37Nt577z10Oh0cPXoUR48eve8WecPhsLHda9DAg4Z8atDgMUCT+bR/ZFmGN954A8vLy3jxxRfV5MNh3XOrJp+0kzmlqojaAGvIJ23azWjbukbj8aNUX2BbfWXbwOVz4X1LfTYTcQaOFgEbzqkao4aPNQqwOQqw2wKQYcBGsbipWUwNhF1TNgBiadE6XE3OO8qHMkdy580qqSMtIoTIp31sYxh/q5xY8NCQTxx3QflmmKBT9gNTjBT3swOUWdi2Km4Doz7QCS9+UT5UtVCtmgoAqNSMDwDIwAzW0Nt6G2Y+cJ86Nc5fJ2y4BqnGNRbOh7YT/OlzKy/SJSZDzvJvXc5kH7WLVTzT/gDLx8e/qmco2QVTJF+mdC+lTBZbeivcmdm/wFlkQLSAlskxDBD3ujykcOsulISRUTx/LFog5OAAuaTNkFJnTYGQYIQsoMwC/HaPhBKfWro29RfVmYnGChFF1fKYIFNAuQjFRqn4MjGoSKeUTe79IlA+RBl1vWqdHVBQVcewxpNNtLuHfI+tpHZli8KTsQTIxGvMA5Tkfr5HZR+557gxD5H6SCvhmZ06sqdO4SLaJoOXfPKR10UuWOsJOVa+LKhs6H2+SCojykdgH/lU5H5CCxiHffmOmw0AiXwKdEtO5DaHtkRsWfncthTnjmxLUMv/vbko5IzBLANiYRwJPYOF5xX3h6C2vzCHekIGVxIj/79XgRu3qhu9v6UkafR8xncoB8kEAORQF3FnCcbx4GefErmoztfK+AmUr/z39B97C6Ctteq1bzocOBxEL4OAE8+6r6HBY4/7qXySEMcxjh8/juPHj4OZMRwOsbKygvfeew9pmmJpaQnHjh3D8vIy4jrvEg4MBoPGdq9BAw8a8qlBg8cADfm0P6yvr+P8+fP4+Z//eTz55JO1Pnt4OVsJmFugUDi7Om9IuwirnRzqiCICw2IOhDCZo1NcbYNT4MYlkFUQavk6OIpBpAx3N3MgLflUatRfk8fugEr3irGNuqrFchsFFh9sAbQC1VgBaxcAQKiyN+kARYrWnTdgZohNLkvZPmW/qihB3cRZAa+b0MTiU2R1ZKTJB3IeBAAWFB6TIJurFmUp64O74QVXk27BKsgnbZYSd7Rkd9j2ZxLJ+mW0l8LqR0ricZ5DR7EY39UTVRobxl0ovxYzMAyQAAYlck7EZh6bMuQihKyIAAMYm+IUvYvjJ0bTFnshZUjINmy/1lFF5q9wl3J/yhyIgFakeH4oHllcFmESEB0YDit3dAUSQIIhsgD5VKINgzyY1WQpQcQpSgUJFVOOTJUj5f7NT7WuoteauO9KVRGAbfJJkZ2mPZ5GRQWMrR3LXFTS7R6yGIXJJ4wLAdKAwg0It4dxJpn8PZhJvCmSOiniIUoj2L5J12Yz71RubJ/nnh8YzrzX6yN+Ipsim3nwRpzh9NJdoBC+vG/8kIpPfHaeRQ7EnjmPQ7mxC2nuXgiq5jyVs4mEcZWKTG5ZoX4pKdqzEagt9JV0AOoJfWQ0hJHmH+lIVlZlI6AtPJ+LXCSfmIUCIgDoCZlOqf/34qwAdd3fm4cjlD86D6xW7bPZ+q31fAWKzAzy2Qh7PmMyh12eibcznGY+01oA0uo7hO++mbya15Redsz/jzwJuj393sOdOdDmSuUaqHT0qWMn5Yy0Bo81Dkv5JIGI0Ov10Ov1cPr0aVhrsb6+jnv37uHy5cswxuDo0aM4duwY5ufna7sYNcqnBg38aMinBg0eAzTkU31cv34dV65cwQsvvLCvCpYoig7N6tDyPCIK5T6NwFbDWSjbBZWqRVjCQJUPBSBYJb53zAKMsD0gM4HvfgQaOkJwnce1sPGiPptJTcBNWulpV679m6wn6HvqfDb1v9ju7JP2Qb1AWw6REtlAJrDKHGBG6+5PYBzmP2RtwD5ln1alUrGpp2qWsxw08YIQIRtnk4QUZApLPRv3dNpDJWFDwz64eyS8o+vl33lA5cte0lFnOQUzI3ZOnQ3RSS8DgXytXYxGgIJ8onYLnGXOLILKvjWIKlISVTknsmIBQDvKkLP8UmoCA23EGZgSPFFcxKljq2i1CJURN1BJSsLLPhc5SKrel1CWXnUCitw/hG7PT9qRwq5S0WWsLcPDNRnE2EIhBNUDgGWl2llZeBFjhCxwTgBIKEXJCtJE+Qx35Ui1aYCnl+5gsv2QMWBb+Bf0J/14bXYAACAASURBVEBsldScclxXnHP3iEUKVvRNsroQK+IsmPcEACitKCwP9V+At1Xfvs0lSodiaAdjUlIin/z3kIQ2Kiv8/POCwjPmJdzHcGaMPx19gCRmcOlZoC9LkE9hI3R8r71iPgJ8lsXSO9E+C12oDBBIvhwpQCbXAJBU8BIil7IhIKh/kI0AiXzKU0Ain/JMJp+KQnaiLuWxk9MS8EVwMYMWpPsqKHX7A9By9b6Ul66jfPVNkO+6WPuWM/kZ36HcRBZHLVC6WZkTc/cITHavun/cdptYOOaENlmAmXGyKKITKG/crA5tLkXc0SdBdz6s/DnqO2z/Gsu9BgIelPJJgjEGR44cwZEj43esNE2xsrKCK1euYGtrC/Pz8zh27BiOHj2KluL9olE+NWjgR0M+NWhwiHhYMp8a8kkPay3eeecdZFmGs2fP7lt+bYxBnmuTu+uB7TxgQuSTVeU0EY3AHF7zH9vkhV+3CLrzjvfVKyUs2ogE8okZsGsjmNX3YdsnQLlSLWU6av4NVp9TRShhoyWQ1WVKScfW5D2RzYAoYKlXjOC1utnbS96aj8AC+USjDSTr78GQ56U9RMjul5iSKud8Lwtltf1FPEDhsSWqA0564HII8lme7OynXWy9z0S214rIAUr7KvJJm3XRvfsajNFbaXJRqJd5eDDUkU+dNjhNQW0FqTWnq2AcluF7mpgiqGqS814Yi9lVnFpewdyCg3TagaRcAmSLozJgHSURl6I1pv835+1nQUtBPpUBWzQAKITF+0nEnKKgAPkUIBR34MrQcJ4TmSrxUEtmaftG4cgRO9O7hNi18FmUoJZibFpfBY6Hs+Z4OAJaS+HjmRjgQmUhazjT3XFW3kdboAzksQHb9pHSdRGLMxsCg4Q+FPMQhfG3SVdRxyQk1ZQMybrW38p8eWezaqm23cKp5Tvjf/ieiwIJwT4SmNlvgyuplIRfiaX5hDT+SSRK2pdV44FJOHfmvL8CZQORXKI8MOIIYzOAIDkkEnnAwTPf0hS+uStvDkDz/ueV+B4+mr4vPEpRvPImcPMWUFgg9vzWcgf3fMbHPsH523NvGSZ3vOe15gAH+eRrP06LvvbCmHDcvQTC4PvvAZnj+7qKrRwkatGeQzyskmVoyKcGAj4O5VMI7XYbJ0+exMmTJ8HM2NzcxMrKCs6fPw9rLY4cOYJer4cjR46g47BuHQwGjfKpQQMPHq7e3qBBg0OBMebQVDiPEkajEc6dO4dut4tf/uVfPpDv72ESfvrcJ8UiLJVghbXPeD/tooa2ar4OmRMI4N4CzJ23xv/Q5kHUBBXVlzj5A3XycvwklYp8Uiyw+RRAUwgtWguLEGb9+thqz0c8hRBiQcVt7vbB1gI+gsExJqpyn5Tti3LNsZR9xZOjU0Ho99s5b3dBr7rSWDEC4M5CUBUW33sf7e6gVj4T1Xl2pfoxhbd0NovU64AVxx1ahUJRQbiXXL03CWU4OXcLv7TwOj599MNt4skNLrKwskl62Q89twKEqv/E0qbx9bQUpGRhwwSBpRYiDlufGsW4qc9f0kGXWSVn90xCJiv3MCYJ9o55JLqNY/Oee7SlUw+rIeW9TIIIGCr7ZWjBfGc/pcUoSqtSPoXIyBAZ6Mve2kEUyNYUvw+zSLy61G972/xXXnrkjMQ5CuOenxBPjyM/k7yHXXG2b/yXSA6f7Vye+bdJzw7JGlEY48SCEUkVlQXGI+mZmPZlu+I88IwOzP2CfSSwPfhUC801AsQbzQlz6UGg0CwR8qCKvftSfngT+b/975h4AtTzowpqF516ztNyv+d5M5wcBWxsEpjc8d4ST9/Pgk7AXrwEWrtT3Xer6gwx27cBIJ9bdhJ9/NQZ9/U2aICHQ/kkgYiwuLiIM2fO4MUXX8QLL7yAxcVFvPzyy/iVX/kVfPnLX8Zf/uVf4u233979zGAwwPy83rIcAP78z/8cTz/9NF544QW88MILeOmll5z7/fu//zuee+45fPrTn8Y3vvGNA323Bg0+DjTKpwYNHgM8LAqshxmrq6t4++238Yu/+Is4duzYgY93mIQfs1bOrZ3QteH2a5gBtYHAwsj2jqqzjtVUpFRA+V+ey2ECc/OV3X9n6UBNk9XJZiI7BEfzIMWiZl0QZ2Bach5bQz4pPRHDR2l3A7s5zmMLxCvvI8rvqBdMnbDWn/cAyKoon+VgloE8eQBcpCBMVwtHdhDsNuzLkJiBKbPg3eC4C9gSFMhzYMmuZ3K/lnJsiBJgtAF0NCov5SIMGZjhGuz8E+7NaR+98tK47EmhONo9uwnkPUzuW6fCOqtBVG1ugdpHxX00yicNsrK93VcZS8k6jvfuYrG1vpfptBn4PYpcJiuLXCYDQotuEsEpqQKEeQhvLwSPlU+yxrbgCDFs0Doy5pFoX7Z95sB2oEBnTFIF1Disfd6S6rTQPkdD+VF7MGhhiAxdEJc4M3fFv2tI6bANipT6ZZd1k++Ywz64Fx6XeLMPKA7LaRkW/ALg9T6KYwHrWjAsRfIvE/h9TTpA2fZ/v5CCjoV2P86D8j8rpAwxH8EE+NVNiR0g9yiFJ4eRnl3B8eVNAAS21m/rKZEcvjGryLxWn1LblPKXRGWwkF9D5O8PoUwnFmz1KBvKz8wQERu0LAjsEFImhPKoQp8X5nacFzCCrR6nmfj1fJlOe8cvUbzyE+DajanjMNc21gPDwBjPr+y9x56/e57hvhZmimreE3eWQVnVCm/yWpgMhv/zJrB0DDSaKTpotUFrt6vX3q8SUtbXnxrlUwMBD6PySUIcxzh+/Dh+53d+B7/927+Nt99+G//6r/+Kr3/967h16xZefPFFDAa6AppZ/PEf/zH+9E//1Lu9LEv80R/9Eb797W/j9OnTOHv2LH7zN38Tn/nMZ/b7dRo0eOD45PT2Bg0+gWhIn4cfzIwrV67gwoULePHFF+8L8QQcttVhAlZkQYC059e2Uy2ZpbULYrDPyL2yr5vwWbubwVx9ZXrfou/c13ncfM1v5+IARzWqmWrY9AEAe8K+VeSTYqzhkHomT0G+7IQdzFT90mgDyUc/Qpzf0bUi6SVDtOwSFputFaqi/Uocl/pDo3yyyRxYkfsUsmfavgiYQqGQ6iyGF5gAcGdeDlKfgBkpX5C0qgUAlPn7Xvfea7sF5dRKwEpbUpIIycrO+l1ZucAOABiFyXkN+WQdqqZJxJQjNiVO9q7jc8fO4xeOfoAjnY094kmDg1ogSSRRWcgksLAIKWWpcTQedwwBiQk/t5Io/NsZDvcDlWqICInCRrZUKI3H59Q9R7X75TWUWcn2s/Hp9A10heZK2qp/5dhAcQKkSitc5fhFyvGDM2U/j5Og8ikiG57XB+YTMVUXiac/L5csWIEkCqmmCuP/0UtfyQ5bFJ65ScT+cbGcIEXPdN6H2RnDrBBGKt3bxFNEIox3LKibOPbcC1v6iz3YigUeEqEFh1pkbxuDO/7jUkDZFLTNDikLQs/YgFKZQnPL0DghbOcNeT7PwryRyxI072/zvL6F/KX/Aa7dqG4UGS3f3yV1ft2/e75XUe3jTBEorapVOfG0qQnFb148CXv9BuiIo2jo6FOV8Y7jBHAopFzuC7x4DFAUEjR4fPGwK58kEBGef/55/Nmf/Rn+7d/+Dd///vfxW7/1W7h9+zZ+93d/F7/6q7+Kb3zjG3jttdfuSzHyD3/4Q3z605/Gz/7sz6LVauH3fu/38C//8i/34Zs0aPDg0JBPDRo0eGxRliXOnz+PjY0NnD17Fl1pNaYmDtvq0CrUTzt5TuEdlSSVcj/CoEaakzZPowDPLO7ZoouFe29V9m2ZEZiU1mhcguNwZsUe6gSir+sIiF1UH8kMAhuFjkuRkeHNRdg5RKYgQSbIKbN6BcnKG4gmMyhCqyDSS4Z0r6QFK2lbWa+S2mSKjC4iRALJsgNpsX3qcAoiC8aAhjrbRxppc8aUi7fKLCcAMB6CLLn7LtrdmUUTBaEDAAhULk+CBIsdx97qPSfteXzQ2O5ltjouESwWkw083buBn1+6hM898SZOL3yEduz5fUJV5qExJ2SxJCmKxAwVyKoo45MnMDAxrmhynzTWdUZa7N1GqXxOGIUyuFDqbSWFyf72a8MoCz6i4SY66T08/XTgHmsLp2ooGJHqikK0dnris2QSLd01cruDIpAn5lU1TB4n9God6r8BRV8h2PlKeVDGjsA+y0AukXtUggn3/XkyQrvLtq9zyd7EkbmJsd8K99BDFnGRVQpf9jYKx4t8eY8F0PLcx3QA77MhG8oEu6RQFvoUpX1Z/Rwas0PkT6gP+O7F7vbAOBkir4Tzc56J5BiHbPWEwgweuIlse+028m+/DPv6edDQXdgmksy+c4pt20e4evb3zDGNi2TqLLsP7+kzO5bQbBIM/+vV8d9cv3HPMfc7+pRTfRWPHKR6o3pqEMAnTfkkodPp4Nd//dcRxzF+8IMf4Fvf+haefvpp/NVf/RVeeOEFfPWrX8U//MM/4Pbt287Pf/Ob38TnPvc5fO1rX8PqalWxeP36dTzzzDO7/z59+jSuX79+aN+nQYPDwKPR2xs0aNCgJobDIc6dO4fl5WU8//zz973y5nCVTwDbsAKHyKpymgg6kmpsk6fZT3feMfTkjJ04prVd4PI5Z34CAeC4RrWdYFNTAesW7IFxHgJMDWLLUalvo65qIdD6Fll2UBZAK/A9y0DFOdvxMcoC8UdvoDW6Un0Hl/pRWYbtV7znlgJjpFwH4Zjdahs1195TKYwodK8AcKTsA8pMEg05CISrpCf2VO3F7flgltPeIau/L4220OWqxReHQtB3Ph/H4JFSNeEI//Uet1VD0RVQHjGHlU8RFSg4RkQFlltreGbuGp5fegdnnzyPzxy7jGcW72Kp3d9TCHgPFLBoDC1uh353aSFTUE2xtf7PMgM+q60ym1p0bplwW6NAbg4AsGLczDW+bQBMwAoNGNvfGYVCSktSja3OdM/HhHR93sDiTPtdRHHIBktJ4rY7sqJjEkqlkpoTDi2I78C1gOqA7c7Dhsin4O/BsAHlUygzykoKOi5hxfmK//pioW3GPPTOMyR1k/d0zMi21VJnepenDs3SD+wb2wTSWzqe1z4vG8Lb0IRnHeVC/87TKRK9ci0+8h0AAs/2oNjON7buQLK9LAuZHCoKUIg8igXyKEvljMHQPCCX52PUFlRT6fTYVH54A8W//x/s934IrKyIxJVvG1v2E1OeuSNb9s8hPMdyFUJx3AZyh71e25MP5SjSYBhQPiawsuER8N174w3O38HRwXvVdxpuddAaOkixhnxqEMAnWfkkwRiDp556Cl/96lfx93//93j99dfx7rvv4k/+5E/wqU99Cr1eD8ePH8enPvUp/NM//RP+8A//EO+//z5ef/11nDx5El//+tcrx3QVszYOSw0+aWgynxo0eExARI9UhclBcPfuXVy4cAHPP/88lpeXD+UcxphDJZ8sa+3f2kBgQYyohOU2KFDdTVTCogNSLLBpzjuG3pqOtuslLLeByz8BSbZ25v6GxO9eQ7FVz9rLdEFlwGZn59jlVqUkRGO5RzYF+TKPdvZJt0Bzcpsha+WltWwIsgXi1XcQUXWBhpllexdrA5Zd/u/AoZwN7zE9ldRZBppZlOTBALR2F9FwFeXccfGwGkWbbc3DcA6Sgs2BYKX73gGV44l2MVhSqUyCCDRcB3uynKZO7aig7q28hsi19lVHGTpKVcQStVvjDIh2+LvRXA2la4CoyrgFu2OTxhatqEDL5GiZHO0oR2IKtKMU860UvSSrn0k+idBC40Hbm8feCoBMlBalP9y9yPztrcinHF3bCks9XUpg+BWnpDYizsJKWTmGahcJUqQBcskigeERLAWySBAhwshvhzaBSFC8TKJDfRw9oSBdlVaXZAwwGAGCZdjuvmWuo9K0c1RtjlR3bkx8Bay/ivYCwrdRbgSGGBToX6WgXAKAQlA6xzxEQcJzXCC+DGf+eBkeAeT+DQ3n3s9ZjzVkwn1k1MMT9kMs9GrYm/rGCGku7SN1ysKv9pasHSX1ikCCUTYQSSCWsqICfYOjWG55Ul9IB6COML6NBqI1HfJcntelI4genlkKdIV5bFkAAuEammuJz/HCgplh378Ge+EiqD+hJir9JJJIFrH/YeB/rru/w3jO7HA8YAYVo8oBubMMk1Yt73wKP3JZ9LWXYHgLHHUw+s/v7+27ca96gEFVbe/8jkdOgFauVc/VkE8NAnjU1qV845UxBq+8shcRsLGxge985zv4j//4D/zFX/wFnn32Wfz1X/81Tp8+jT/4gz/Ab/zGb1SOcfr0aVy9enX339euXcOpU6fu/5do0OAQ0ZBPDRocIh6mioQdJc6j9JCvC2bGpUuXcPfuXXzxi19Eu45lTE1EUXSotnusJp+0v3cbUFgLgTpAIFdge0fVWcdqKvKG6E6jBHMCXLkwJoEkaBfgAaBUqioAUNkHJz2QVA08BX1/IzsEm4UpElBHPmVAQGUzrtiV2wwH8piijeuI8tv+YlELORYs1B+k80vZDdTytzZfdXyWVSvir30IAIhGK0HySWVzSAYmH4JbofuuHIeCipad/XRTO9vR55dRrlM9cm9pTFBsL8Amd95Bq+vuK3Wej1wUavKRBwMd+dRpg0cpqBO+/zQn90NDhF86ehHtqERirHvhivN9C/92kWegUP7GQcLdywKkJSVnIfXvsvC/ccyQqhrbPU1On2RPNomER8gC5JMqGwqy7dnUOTFCqshqSihDyRryKfy8i4oBnp6/CUCRaxmy4ZpENtSRTxwobtjZTyI/J9GdGysNFOMdDTbBi0fFfQp78OrrkDKKuEBJXX/MCxewHvs7YEwSFcJz3ErPJWFORFx4p2zSNyo8bTi2QxDa+JkFhyWQh5xjW/ozJwXSm32q72zkJ2WkQg7puSRcBxWp/14xA5J9bWhuJFkmpwNQRyBW0yEgkU9FCkgK0KKAOLErcvnzoWzFwHeXVVU5aM59b3gwgr19D/YH74GGg0rzZsugSLDP85JP0sXW/Izwd9f8iFs9z2uaZ8HbpZ5qzQPpFtK1OfDm9ntUbx7YXJ2+fhMBqx9VD+qyf+562vZTZ9x/b9BgG4+q8in0frO4uIivfOUr+MpXvgIA+N73vocnnhgX+P3zP/8zPvvZz1Y+c/bsWbz33nu4dOkSnn76afzjP/4jvvWtb93/i2/Q4BDRkE8NGjwm2CGfkjrh7Y8QiqLAm2++iU6ngy9+8YuHTsIdduYTEIO5DQrZ7WjznNRLu9r7pqt2JTAseiAoLMU4g71xBSZdCR+31FmUAQDl62AjWGnMXka0ACqU5JOkznId28yBbE3yiYvgop4qR8Oz6GzWryPqX4VJt0CS+kSxIOz/LAfIJ+HYRQa0q9MZZvZnksxUUvPqCjDYAphh0qrX9iysL6y8cp7w72/jLsDh9he01tndT3ltrR6QDwHF/lRDTUXDLXBnETTcQI+q1bB7+9bIT5Oq0WfAqZYYBrg/0JFPvY6oqIoiwkKy08d8+SgK+UyoCxVZ+L4Ji3Xj7dJCZC5vl1QdIUs+37aZn7alUD4VVmOp1wOxDSpRIkXRhTZ/KWzLtn1O5XNZq2jiQR/oyqTSM/YNtBaVOXRJAptnOiJIaZ9533OkjAG2+sDcUnjf0QAIkE+5gnwSLeMwVj5JLSDmIUrjtwSOeYCc/NspQDKWAnElX7v/qq3wucxjAWg4x1PmA/Ta1ev1PueK3EuysGSH6CNKJXWTpKYRxwvhHkqEVjaQySfJ/o0Z6PjngZQNgI6jD5QlMNyAWb8NDFfHx2EeE2g7/x21QcNVoL86VjvGrbHyded/URxWXIccHmrlnjrQFVRRmwPQkb37au+uwV65Ab59G9jaAKcEYzzXZ9nPqe33mkOWudUT1Ty+J8PJVp9hHHdBjsI6jhLYeB6jb7+89/ljJ0C3P5ze8egJmPVb0581BrTmIKQc94u7C8BiWC3f4PHGo6Z82i/+5m/+Bq+//jqICGfOnMHf/u3fAgBu3LiB3//938dLL72EOI7xzW9+E7/2a7+Gsizxta99Dc8///zHfOUNGtRDQz41aHDIICKVRdNh47AziB5m9Pt9/OQnP8GZM2cemET5Qdxva+cRRSGrvNFYiBB6J9KSVFSq3pcIQ61TEaBY2GNOwDcugQfVSj4n8nWwiYOLNcA4m4mjZcDq7PE017t77HKzZrri9NuwdfqVzUL1g4SP0pquyKb+CuKNDxBhBJDiLGyx7yhJa2WrJyO0Jp/VV5qCep4q8YkxmZnBt26Oj24tIg35lMyDuAgubKsUeCYG5VvBTC7uLI6vO0RSdRfH91PxQkejLR1ZVePl0Iw2UXYW0Vt9HUY6tEKdtAtfhbILZQ3Svw5Rtdn3K6q01okHReCZwoVskcRFDpLsx0JV6pHwWamtC3Mgnrl3bUXmU2GjcJMkgxb3kUs2ZdARq9qcJu1Mz5BVDtvKIwbI2cXB+3jiNIOLGoqm0VC2YNwG2XDxAwB9jlSnN17wVig9adQHK8gnygVVyjYKG+7DpTXis5RIXreOOBVnJMaR+zh1fDH7sBTJJ0kVVQrKltJjiRbZoVddSFzgmQXHIjUA+PLGJJLD19nzFPDle0rFX5KSWshm8qqsxlv9p8sGYIl8EsZVSvv+oh9mUH8N1L8DGvVB6RaQDcaEVJmNm2qWy8/vPPfPKYjAcRc2orGKrN0Fdbq7/41OV7Zi1SAwkJtF/33j4Qh2YwN8/Sb47l1QNiZbdr9NHQeEyeOy0M194ibLMInnu9R0QfH+HJ57TXlVjcTtJVB6t7ovCOktM7Yy3oGL3JxfAmbIJxx5ErRxu3pMR94TnjpT+3s3ePzwqCmf8jxHXKOwbgd/93d/5/z7qVOn8NJLL+3++8tf/jK+/OUv7/v6GjT4uNGQTw0aPCY4bBu4hxW3b9/GxYsX8dnPfhaLi/6K0vuNw1c+7VjvOXy6J0BkYdEFYSjvh5FmXXvbJi/MZRDq5EPJsNwGrl6AGa1gI5/HooL7ITBssgTKwyopAOBoDqQln2q80JIdgaNFECuVWDy9+KtRPmle8DhO5N8sH+1Vuad9xCvvIcLW9GdC5ylzOY8mYOvnBbPXSkwMw5YC7iePd+smKB3snisahcknmAgmWwO35TGFRR/CPVB/Y2ypIiFuAaNNoFMNfJ69NgzWgF44z06zKAvUUFNhnDOQ3DyPVjewkJokats7NvvM/Aodt06BwMg/lnEoZ2m8F/QKU98hAs+UIoM7YGsbZSj7JtAaRFWUtCov5blNv4rEpgTBilZ3FhFilN7MmR1EnCIP2I1qfpOSWqpsKKt+rdK2A91+pfCbR8UQzx69CSACxfE4785nRzoJraJJC805gbEtZH/gDLafBWW68UuTlxey3SPwtkXw/kEBRXhouxX6UVz2UfiIGAClYPNohYKa3ENoJTxE6iGfluMVtBKHGoLZT0JKBKqPnMlz70qGpEGTSCSWxkfJ/k6wf6QikOkk5EEhG0znHWZDmJXrMBsfwfRvg4Zbstr1IMWPzKDRmMjCxnheVDlaUcJGCajXG5NRnS6ou0dQcZGPc6HiGOT67aViif4Q1G3Brm+BVzfAG5vgrS1gMBgf984KqD3uExVbPWa5aEmAPNWVsqA88CmiBNLPiaL6HsemBco2K5/huOu06GMLpP/18tTfyDU+unJk55eBGfKJoxi0WiWkmrynBho8asqnwWCAubmwDXGDBo8rGvKpQYPHBFEUoShqBP9+wsHMuHjxItbX1/HFL34RLe3Cx32CMebQFW9WnfvUAkLkE5Ww3J7KG/LupyaV2oBqPz9JYG0HuPLm+OUKwNzCEjBSkkRCcLdjZ/WeVGzVep9l01PbAFLZ370UBoFF6cj2fpr8IWlxA9vWLQRE995HVNxz80ShF4TQLZQ+L/WVsvQvWGUZ0PZVBQs/0jbRxkUBvnNz6ueMRquYzC3ygfIUHOBNOBLyqCYhWQRNwKR92BD5BMBkQ1gF+aTVanB3QXVPAACDEXrdm9B0Eu6PlLZ3XbC14TwjQK+wgCIfaQJcSDkhiuOEbgcLIedahJRLQbLNf34uC5l8EpQqEjk3uxBMNLbeS0v5mR3bATIj9wVNBlOIwNpBghEyjwpkBz6VyCy0pLR2vyLx34fT5Rto9yaOk410RFCoLe1A22Z9+TsujJTkk/YaFcg5QGQai9Brc8iWL6RkC22XSCIj5VAyoyB/cUPuIZEMZ8iEba6vm9g+nuzeBlxkV1mAlPax0wd1f4aF31+aF7GPRLIl4CtmYSsWiFhfXtXOcb3bLLjT8+eAFRlo/SOY1RswWx+BRhtTXS78BA/0z1DVWRkYQ60d276tp8B6laDirNh9F2KicZFTuzX+TVut8TOJGcwEXt0ak3hFOr4voxxE5ZTihyb+v2QL8k08CytbGe9jGwO1xf0s3V/f3z1Kb5OuVY/fXYYpqkV27Hkep+/drqjDaat6XKTVdxanqvrICdCsQgoAGvKpgQKPmvKp3++j11MUjTZo8JiiIZ8aNDhkPEy2e4+L8inPc7zxxhtYWFjAF77whVrB9p8kMM+p1Er6t6U2PGm206AOwBpSSXffxxZ9VFl4sWUXuPyjKd9ydfYMgFp+6g7PdC/KTXDcDlYp76HGxNpujXOfkI8t90I/LnOY4Chz2dItHyHavIVofWVsB+VrLiErAckmyVqxwlX8ntK4JRAC4n3pjhef+NoV0OTxDYG4hMk2YduynZOmLdp4DkaR56RmM5UkFTQZX4BYrT2FKAENNsHdgMVVWaDdfx9mQfnyleq+DyUxbH8IWghXFPoCyF3wLdA49y2FSnpVHw+yT+FDhMiy0DM+pJySSLRQHpS0+OpTGjA7P9eO8iD5FNk+ECCfNLZ1ZUDNtAODcFsd2/OFq+21JFWh3K+MezDFEDaabvsLg0s4fnrmN9cqmpR21SPzEQAAIABJREFUWhTHqicttdrjYgGNlZ9SkQnSPeMlK8odhJRPhjjkbghI2UQIjxOh7aVQjGIE6+SIh7AeBZPhzEs+xXaAjNxjvs8C7HT0DiKfEmcf1npc5IAvY1H0OPQQdbb0F+NkQ6DjUY9lQ6AljDeSrZ4wrlLaB3qOzxYp4psXEN9+B9HkXLPu64wYJxWYmzBDfC4F3m15Zh9iBrJ0/D9sjn/ztNi9zNkrKQcpqLe/nGIuGBR7SCRm2crXt40ir5W392gMkPd4+vNzMgdTOpRPrTnAQT655qdl6wlk//e/07950gbWqsolrDpsMx2EFOYWq/Z8aJRPDXR41JRPw+GwIZ8aNBDw6PT2Bg0aiDDGPBaZT5ubmzh37hxOnz6NX/iFX3hkiacxYrAmi4K0JIn2XmkfHbrzju1spidrtugCH7xSCcylQpn5BIAKpdUdAModnuW+fQFwpAg53wHrq7MJANN4IUJjuUecB3OHKO1XuR1mmPWbSG6+hva9VxEPr42JJw+4LMW+NH6ZFxbODmvsERY/fNfLWQaKE/BgAKzN2FZuf4doFLZrVFmtmQTksEqpQLLzmYR6PNPtF7T6m8RmNVdgFq3LryKZq1HkUKMggvs6C0/qtsED3b5mvoYaw5NJxoCYITLe6T5Y7gF6snC/kFRjwsIxl4W//zP7Sasic967lgmPmSryV/GsyqFrA0aRH8iIECvUvrkyQ2qPzAojzqafjaZM8eyR69UdlWOx2mCujqp8qHwmh9QW29AqBTlAeFkm2ABxZIK/A8NKalsANkB0SqQkcQ7rIYlCiIRiocj6n0+RQLi6Ru6evYdjvVU/SS4Rmr5CgMLfFiSlmdcqNhvBNxZT4b9PlAv9Oh+JxDxLmU75zP0f9RFfeR3dt/4/tFfegZHOC4jKw7H1nEB8harXOKTlCyA0Xwn13wDb6yV1AEAoFkHpJ93YSoTcfpbN3NfBIOf4xR6baW/hj29O4Jh3Dq85vtsTT1V/4+UnYDJHu1t3kFSOe8VJGzj6lPu6GjSYQKN8atDg8UJDPjVo8JggiqJHnny6ceMG3nzzTXzuc5/DiRMnPu7LeSBgG7beI0p1tu9C5ex+9hsrmrTYe4GyWQf44HsgB2lD5UBlRQcAyNfF3JCp49oUbGr4NAuh1JVjF+FF++kPjI9tpeyWnV3L8ELn1KLJaBPxnQto3/o+2qP3EZu+zjUpNHYEyKkgySC+fOzTOsV4Wl82XlDjax9OHZkZE+RTOPeJI91CIBVhVZ3GSg+ArDyZgNdWaHa/zrxaJRX6Hub6u+gsbQHdtmiHNP2hGktbmW5BGgDshs6ak3odNalllnwvlKQgBa2CN1SMliHVSMj+L7Rdal/SwnGRw9tPi8x/Xg/J0IrCv7VmWVSyKdvdhxJxcX73fAFiYQeRQj3MiBApSCqGQVzo2nIys9j4dP4TdOYc46pWia9dEKpjp5cpiHjoFGsAAvllE+jKi0GF1cwT5N/fUFjhWnjUR7vbhblNVPbFz7LQtw37+5Ok6JMI3tLRt55pXQBxWcvOdBe+sUd6lvis9Wzpz5+UnmPSPEcgwSiTSVVuCc/jbTUz9VfRuvhddC+8hPbqu3tkd6joR7QzPhi5c6C8KBWCIbNeMAOIpXYmKJUlYkq4J+Lt8BFhvs/4SCPP/hz72pD7uTxr0VckTyL/sJoRTHOOuefS0er5l46B0ur4TS4b9BNnZCeEBg220SifGjR4vPDo9PYGDRqIeJTJJ2stfvrTn+Kjjz7Cl770JczPa7OQPvlgVthQkVUppAhD1bsmYaSz2UGpU2ZNoBwmoMvfExefONGpjghWvS8AcFSDfKqh2CA7AFONnIPtBR+d8incp8lmMPcuI7lxDp31nyCxd2DMzPWHJv8hCyZFoLsI6fzSNo8VIDMDviyhsgSvrYIGMy/Nttw9l0b5VCbzKktVZ5jzLNo9cGABCwBsWze2cUc5BpoIGCoz1ATQxgp69OH4vw2pCZ1aqok6C2EjpbUY9ESVme/BDh0LlxoFnGLE5JAFYZGH7cNCeVdSuHtZiMcXLcGkBWJJxeLpGxrySUMs+ezEZqFRK2mzoSKl0jVR2PgBQJzq1L6TxRrzgys4/pTnOrTqSY/Sr3K4KAIci5LOfbW5o9rFKC3x1Zvbfa66kAcs9zSIZhUsMxgrl/zzAOJcnCdEgioHCPUH//PbVeSzA2nIne1by/YqlrrDwFzAY61nLchHJAoXwT7CKhfIcKnYQvrCwhyIJDtctqIlH6WbaF34b3QvfhtJ/0ZVYScVaATf6ULzugOSTwclp0Kfl1RdeSDTab/nlbZJbbvmtXiV8z7SxvN3VwGabS1U+vXg9VvONuy8bFe/WjxW+RODgPU71b8/dcZ5rQ0azIKZHynyqd/vY26uxlpCgwaPGR6d3t6gwUOKh8X27VEln9I0xauvvop2u40XXngBcSiX5gHjsPO+LGuJtjD5oSapqA6ppCVdchSbgLn6SnjXGqojaFVSAOpkM1Gpt/QDADZ6QpTs+Nga8gnWMb4UGcz6DSS330Ln1itorb+NdnENcZT6C0lDi9qhZhwMwxBQlvsnn3zkRZqBfC/xzODrV6t/n/gORkE+IWqprB1Z+QwwQ7myHcB4EStkwwOMbfxSxfEAGAXpBcAfxF7k6Ky8BtPa+56sJH9orgYpW8Oag2s869hFKHlg16oqRo39okrJ4bK5mYQmqydEGEjKJqGyH4BcySwtEAtEve9j7ShMUmiIpYK6KttTSRmyA21Ok0RyTCJS2uFGyjzCnd5nygxnlq/A+BZutf3I199dcGWBuKDNbNTO47rKRZ4oBoTxNZT3BAA2oNJIWFY4xzwQ3wdilu8hWbmNSnlQkgJcXGv3jW1skdPe/ITY4pnupfABfc/yMvcu3osjp0fhK47/Eu8gKkMlZbe/L1M6cI+dRYbWB99D+9oPkYzuOo/O1soqsmAR1H3IGTzIx0PvPyFbvUQohsjk8VO05JSeV9IleYos2LL/fDXXAErfY97zHDNZ1TKcW9O5ZUXyFPLX3gIG1UIGGjjGLccck1xzi+XjbuK1yXtq8JhiMBg0yqcGDQQ8XKu0DRo0ODREUYQs01eDfxKwtraGt956C8899xyeeOKJj/tyKjDGwFp7qH7GzHMI2bYD8uLDNFqAogoc1AEUVkWanBPmGMXd24hW3tXFogQWYfYNW6N/FJvgKAIJOUnTqPG4LTfBUVtHPmV9kCGY/m1E+RoMD2GiHIZoXF5iEMpAH+c5hdroQTl00TpGSBcRsqS4LEE+8inPALgXJrm/CcrlBV2N7R4AmGIEm8gLoKwkS6lIdYrC0ZbKVo/SPlgKQd8GD1PgSPi8POcOYm99eA7J0syV50orv1YC7o9Ac4rv062hkqpj51eHqNpyjHkhK7vxJxHqRNQKtJMyB4R8orFyyT/OhLYH70NIVeU9sbDJc+9aJjzG59SDCS3OAmjxABm52+4ONORgjo5uGFTa82mt5bT72e1x5lT+E3SOC+O50qqOWm3YIvcrUiYhKT8mj6n8NtRWFrckyThHqqNQCQ/64J67HajIJ2vELmx8Nq/biDgTU8MiziC2euHwxIWYByWporwEE4DcUzyU8GjKQvBJvIduSzEv87Ulaezxja9FBkQeu1qJ1JCKBYRsJo6k54/QMLIhMKNENms30b72A0Q2BUvjV1kGM5kkcCizKfRYOqjySbK3A8Clf+7HuQVJtnqlha9gjJllSz7hnkq32/uoFwlXnx2fdd57Hg6Bxeq8zeRVcpqTHozLejue7rf9Vy6Pn98rH80c1AArt6qfX79b/ZtrzrxwBOhXrfy4IZ8aPKYYDAaN8qlBAwEN+dSgwWOCR035dPXqVVy7dg2f//znH9oqkwdBPgExGB1QgDAiZZW1nmXQklnyeW3ZRXnlPJJ8HUW8DFOuifsDACkzMAAApc4OaHzcDfXXJ1jY+AhIcb0A9BXfGC82ltHCONuKS5gyBXEKsimMzcb/zxmQZzCDu4jy7fYV7R1hCqH2VxRhxUTIvz3EforXINl5lf5F7zTzV+eXblKQiwK4d9e9IDDxN1OmoLwPDhBLpBhTbdTT9RalSpJype1lkemWrbWVub35ykJvdO0ddJaqiyJ1in25P9CRTws9sC39irbJfTuHQ1S5sqxEO7o6CFkQhqrc8wyQFu2LXCaQQtaakhpln4oB9mS2RFQiohIlC5XvZJCgjwKyqjRiTQZTGEwxYh6hDCiurPrV6v4+a23cxdzwGp48KViOAUC7hqJpNATmw+QTFYVurImVfSWQ0TR17uEWWEM+ZX5yP5T5RLBhe80ACR2yyA1tt8K4F/MAuUCwFkKbLQR1+qS6aRIRMuwQ4RGPcGru+sRWoe35xh9p7PEVb+SZd+ogtUWWCCapqEPaJvw2VE48h/MRWlfOIelf37tLIWWTNHcKzBkoNKYHhVEh5dIB7ZiLzK94y0pQa5/P1iJgybePbeN8rZrWekJlIHnejajjsddzFERxewnIq7Z3k2NRHp1E8fZ/gk6cBq3fnN7v2FOgjdvTf5tbgnEopLDpIKQccwI2EXD8Ged3aNDgUcdwOGzIpwYNBDTkU4MGh4zGdu/+wlqLt99+G9ZafOlLXzpkYudg2LnniTI7Yb9gOw9EIfIpHRfahZqjlqSiUrViRxg6iyuZATuMQNe+h3j7QCaZAzTkU7kFpg5IobyifA0MApEim6ccglsLIKskrJR5IuNj9+uphyjGwtrLAAlmPwRwSz4oWxu2MNLkV4X6mRh67VcvhcAs3DZtfsjk8a5dhbfcd+Yao9EqipCqSTG+c9wBl31QJC/6Soti0ztqrXKU+7X11pTUX99d6KW1u+hGV+G8l4l+ehmyz9k9dxzBbo1Ajqrgyr4LNQoS2nqiyvk812Q+kVxizmUBc1DLWCl3SbNdKrAvclAitF9feDoAlrb5+kSRohUVGBbyvY3tEEXA0tS3yDcJLWEUYYQyYDkrLfRPn1M3JlpRcbGHMu7hZ7sXYALkLMUJOM9AieK4GqtHAFKm0BQ05wS2FU1DXaZTyK5yB8J3yQWSEwCigKoJACwFvltw7hU4fuRvV0YgWIlL0ZLPSzDZobctE5e713vKXEASTdwfzwI9lwXIZ/sp3V7PuOMqBJi4Qu8Wjj330Zbgluc+sQW3/M8UK42N2wRQdPcSWjdfQ8TZxCYrq85Dz/qg5YFbXaOGZKsQUl0BogKNKRadA7i0IGmMlNT0hQV5iG5mBiKJIPVlhTEocs9z/cp9gHzH87yQxb3qPJDbizC2WnTHSRdOueS2OwSD0P/uhfE1Li4BM+QTLSwDs+TTkSeAj6at+Li3ANpar1wvucbe48+o1bUNGjxq6Pf7D6UTT4MGDwuazKcGDR4TPArk03A4xA9/+EMsLCzgl37plx5q4gnYUz4dNliR+zTOcwov5BBGqrVtglJ9gWo+FHMCe3sV5to5taXQLGY9zb3n5xKcLOmPG+mzmWr55ZebYDEQfBrEBUginoDxAmIoqDXPwwR4aBGhLMPnkRbOg+OOtAARILV8cCzAcr8P3L3t/C5jm5ZZ8imc+8RGt5hqFDlNGos8AHr7MyWZxfPLuuMBYyUEAOQZOus/gUk8Vb11LPJqjJHc1xHD1GnBbun2NUv6KkXX95JzQpTQEKmh84SqzIMLmZLFUcBSS8qS8pEnbCv2QLuXUuZoGU1WU30C2gVJ/TGJSEFkjRfsw3MtbYZUkeieSU8V59HpKvtSqsw5C+WA7UBbZNXRF2xgpMuso1Db3IGgAgnZ7hlF8YpkeweEFZIhMrKI/e3ACKqpSMiSiuzQqz6MWRo/x/eybTfwZG9GEeEjm0VrPT9h5Z1bSMSGj4C1JeAjmLKhfwzMhrL6u+V/hlBZoP3e/6Bz45Up4glAeG4Ucr0LWPKJRVc2UI2m8fOWEPis3Qw8n0N5UBKBJN3Wkr1zYrb+bbKVo+fvnmeuLyOqHBXOnCvueN53PO/AO1mkOZ1E+f6H3n2dxKdLPX3kuPu+rN+u/Kmx3GugxWFnYn8caGz3GjSQ0ZBPDRo8Jvikk0/37t3Dj3/8Yzz33HN49tlnHxpFmYQHdc8tayc64cWuMUmlsMCiKqnkx97Cni26sJfOw6xfqu5mlRXMAKBc9AcARHplR71sJv31EgAOVOhPYXaRwoVcseh2P9pf4BhcFnJ/DJILwvaRYLEondNhYcZXL/sXIB3ZMRryycbKvqchnzoLYMVCqlSBPbWflsxqtYG+w2bFgR0bn9aVc0jmhBfHbhuszX3yVBM7oVRJAYDd1C1em14bVklqGZfqKqR8YkZwuu2xiZyC1rLMfyHyZomsFMYALgvZTstDTHFR+Bd7bYlWpPmtw/dNY4uYowsOWVRh234tuJNBoshNLNCBpoChSBZAVr4XS1vv4skTGTA6uApoClorUG3b7HTDCrydYyoVTRQiXXd3dD8vLBNsIBgxKFricioDyQUOKKNKYbvhVJzzSDFjku1kJKjHjTAHsdsnfCZ5B5Xh20c+idZ6ns8UfgtJMc3Ip7zIR97jUSGox6Tndz7yjnFm/RZa188hGd5ynzVAsIhqVOawZZ+Eg+Y5HRTZ/uembBlIpD4rEJNSDpX0e0i3o6Ydn+/e2tR9T3zPMJdVJ5MBpWtgitD/zht7+w4dc+nUQUy7xmeHOp4XjoBcn2/IpwZKlGX50BcR18VwOHxooyAaNHgY0JBPDRo8Jvikkk/MjMuXL+PixYv4whe+gCNHjnzcl6TGg1Q+ad4TWT3kK4kdte0cgZlQbgL44GWYwr0ovGORp0KNDKVaviNWWUUNgPJ1sDJcfvyBGvZeipwSFIp7oFmYCxG5oXEjtHAeyiUQFi5FSxrPgjczA51pJYNd2wQN+vC2BceiQ+Tw2K+cK+4ChWKRdKRY7CXj9tqfPWdnAQgsRgMAt+fHmQoK0ECZoxbHiK68jc6STNYQkVqlhE4di5YaC2JDpboDgF3XfX+z0IOdOC6Dwook2HABuYL4ENVFQHgBLHSdklWOdH2SOqbIBIJJOCZDRT5ZxTOt0DzPlISRdj12nIkTOBYM4lJBkJJBnPvHhSRbxc88cWP8D61SSTkXVD/dQnllO8cjGufGaaBUNLkUBE54rjGU9wSE70PMg2BBVCFY3wFA4bG/A4BIVCHJBKtkOykRTBLRWiLGgr2FI90Ze66yEFTSQufxjT1Ce/aqjm3ptepDLjwTJFJUuA7K3O05uv0+Olf/fxiW2vEBbPVs4LkSzGM6YJ7TQcmpkOJeKErhvNx/AaJwXtEi1ns4QSFWl5PyZZWOPP2/qLY9bi+BwMjsk7DX9mz2ePWj6uWtOfKiNqvzXnL1jcVj7mt96oz7Whs0mIG1FibkqvEJQ6N8atBAxqPV4xs0eAjxsCh0PonkU1EUeOONNzAYDHD27Fl06li2PAR4cPc80lnqafOc7nMQOpcl7K2PYG7+WDzy2CJPZwFGHgLLiVK/CE2FchEe25aCsc7+bww9Ealy8rpflakHrTw7CMFalnL1bktYEPcteGYZaMIih8sSuPL++B81xmON8gkAqAiTLNo8J7HCegfGgEab4f2IgM1whhoAfW4KGN3WNd2eqZL4muuOs8k0qKOSqjH28lCvYrRrE/de11EPvk9ZhHMUhIWz0HYuS5Bo5yhZHAnPFWmbtAhIRmW7J6lFdpAHFCk7kFQgO9BmQxmF7R4AxKN13X6+550t8an4DSSt7XaoVBWpyE5A39802Uw7SJXWmeprrKG6ciAPWO6pLiFQLGI4A5Pf2tHYEUD+/l2xa5uB2A/EocV/jyXT34LaeKZzsfo4Fcdxj4KJ2T+2SXMcn51nnnrPJY9HEhnu30aOAo/k+uvo3H4VxKVcQCMgqGw6YNFPrUKO/Xw8dP4AaWwc9nO7yOXvLhLSol2z/357p44sna/u+79n/9T9XmLS6vODk3mwSdD/zx/t/fHok1Ul6dKxqnKp3QWtOwipLUchloPgZRBw4ozzWhs0mMWjqHwaDAaN8qlBAwEN+dSgwWOCTxr5NBgMcO7cORw7dgyf+cxnPpHVMcaYB3bP2Wpyn0Y6pY6WpKKAHRsnKDcs6NIPQBu6BWtEyklbsRG0sdkB5Wtqb2kqt8RFouoH9IQolcqKb0C3qK15rw2QLWwtSMpruh/YpyUfWwu0fWHjFtT2kU/TlcZ8/dreApFvHHEsHpi8L1rx7ECTOWLbSstFJQlD2gVc5X4qbK2jm76r41sAnTIPACUxMNARxNStUYBQh6gq9ATqZJYUhyz3AFWuXdDOLU/D3T1ETiWSrV7g/NLzVyKRpP4tORyZWKV80hBLllowCmIpUhBG2mwo7cJuZHUErfHsd3L0Y8wvT7QMLWGjXRNVBsdTkgCZsg+rc6R0u/meERXMucfgUN4TIBMxQDgLLBZyl4CwsokCbVNSTUnqQElpXnoyKg3nWMRNzLcdv7doree5z0XuVUuJvSjxPP+Vat/KuaRMJ+n3n1SX2xKtS99Fe/XC+BOh+b84NxLyh4BwXz8oOXTQ7aHMplhol4UVM51YyvtiBoRjy3NiYZuPYBLJrHqKKPJYCboKnLi1AHKo6jhuIUuPge/c2/v88tHqMY88UT3R0ROVNsetDuAgpKh09LOjTzkt+ho0cKFRPjVo8Pjh0erxDRo08OKTRD7duXMHr732Gj7zmc/g9OnTH/fl7BtRFD0Q2z0AYEXuExHr8pwwUolqCCPn+y2zQTmIwe+/DnPrdZBNkZNyMqZUphAATpZ0+3IOxLp9AYCjBfW+tSobi01wIFti77CK/TQVY6F9CgXRGHo5CP1m+1y4Rpr5LXzS1H/eiXGOh0Pg7thuhAF/bo7nPhmF9Z5mnZm7i7o8J3EBbALanBNNlhDgtynaQTZC586riHpGTxTVmGHaLR0xS4tdsPK7U6dOLlyNi53KstL8XooGErIOC/RTLguQQBRwWcjKptBzSlRFSRcmbRMWEKMOOoNrwQXWgroghVVqoiCfNMrUHB1VNpT29Uqr7nERmAuDSzjx5Gy/UT6PtONMq0Yhhiv/wwXt2KUtiugq5xbtDpBW24GKfArsEyKYZes1IIK8XSKJDKeiasoK20pByZd75ooJ9/FM90P3h6Tb4JuLiGo9Tz8qC39GndS+pIeSEcZPn8pqEvkInYv/hVb/+sS1HIDU16oY943AWBEknwLfTZh7MDOo5e9T7Mk+2oVEbBVWJu2Ebb5+PFah1VMxMch/Ks/vbtrVe8KWEbWq18Vtj+sCG/T/45WpP5GDqCWXc4BrLHUQUgCAzaozADd5Tw1qoCzLR458Gg6HDfnUoIGAR6vHN2jwEOJhsd17WK5DAjPj4sWLuHz5Ms6ePYulJT1h8DDiQWU+AYBlpbJCkX9BZHUkFZVT+zETbNYBf/guzLUfguzeInXcUf6WZQ2lhtEvjHFcQwYvLNZUUMfSDxYca4gt0pFwgcW5cQVoYAFP0z5DBFaIXJI+Ly1w5MLii7Rt8vBXLu8tsTCcZNb4PrmvMUoV5JOwaLULbZ5Toqwa1b6wKS0teE5ol2WJ9tUfINl+n+KRss0nNQiLTPd7UhSBN5SqryV9nzfdGkTVxL3n+2W756vi30Gon+aBav+A2iSoDI2FsVa6B8I29rVhWwJRjNbKBbQ5bIMac9iCVZvBFAQZJAGVCuBXjVTOqSWBZhR2cb6JZ5c/hJldENVa2ITa2w5abbB2EVyrfNIdzb046kKnK2f5TB5zWLUrDWU+GdiwgjiggKRARmVoOwskURxoj4WgDix88zy2XjXVEVxFO/Fcr3SbfGSRRGL4SG9pvBMVlQLBlAhzXmmbiUDDdXQv/ifibGa+ICp0IPfXg747hDpaMIcwRD4FNkmq17QUrfE4D5BP0mcFFTMzA4KiCj4Fo6BC8x6NIue4Mc6Icl9bNFdtn3ZYgBzFMRsj97VmtwbgtZm5pkvB7+xDjh/VQUhxZ85txdeQTw1qwFr7SNruNeRTgwZ+NORTgwYNHgrkeY7XXnsNZVniC1/4AlrahYeHGA9SbcY8p1IrqRbYAGhIKgC7tnO26MLeuAG6/D1Q5lhkV8ogKF+v4URf5+W8xgQ3sBA0CSrW1ZZ+YyhswxQVllyWYbu8Qgr/3kZggUNlyye9PJSlvHC3T0s+cdv2d+aVFVB/YrHRVxFtrZuUsow4DWcmWS2xqchV4u6iSlkhLpRNoqu8trlF7+Jx69IP0FqYuHdaO71eDYu8GgttPFCST+0W7KZSUbWkf1mcIqoUtntBIrksgTg03ioyoQ6yXRjzuMjlPi5lSUnbIg+hVaSI1q/CFAO0bTjbLA7k4YxPFm5fWsJIkw2lteezRvecLaOJvsQWz9LraHUcY7tWLaRUNBER4Au7n91XoewEoFcZascuAOgrMvAAYFj9LjnLfdiY8POdAwUroSEgdIZSyosS2r/hDNb3WbZe28qEB86LbtlNnIgv+y/UM88TM+Wk+ZOH9JYIUYkI5NjzTLIW3PKQdGzBLX9bpKyP7gf/hahO4RQwLoYRbfUCnxc/y/Kcm1nx+X3mJoWuDQBnAbI1MFxLlnyiQ2XpJ5G4FMw1JaWVn33y/DlyEm8+ksl6VGDzi9WCocJ0MPjOq9Wd125XL2PjXnW/QTVDyqkGO/Kk8z42yqcGdfAoZj71+33Mz2uLgRs0ePzQkE8NGjT42LG5uYlz587h1KlTeO655x4ZGfaDVD4BEZjDqgnS5jkp65O5AMrb66APXobp3/TvqMy2IC7A8bJu3zoZSraGQqkIV9vv7ss5EHnsL5zQ+Bkq2n+muJ8aS73Q4nnQ7quUCa4D2M6Ijl3S4kbSAlsLvn5Fd0DfwkJRqJRPNu6BreJea+ymogQ0Crc/7iyEF4AA8NySntjZqhJt8Qc/QntxdrFdaY3ZaYFTZf6Gzw7RBaVKCgDsZlgVAwCm257KchL3XdwjqjSZT8E+n4+E3cFWAAAgAElEQVTCQsfg6nUo/yMQ0C5dY4hUkIgzn12Vtd6FZSozJCsXAQBtG1YLunIvqjuF26yWMDKBfB8AKNEGafZLdIsUk/s9OfgJlo56fq+QfeY2KEnGpKIGqfLZqc2bkrLHJtHuhBV9O9ASZA4FQMh2L6LwOFsGssc4UPwSIq+kTCcpL07Kkop54J1rROz+zU+b8zDS/fAp+aTno4+wstbfViRVjY/wthbwEUyZ/14gG3q/l1m9htbNV4O2it7rkRAasqQiJRtQ64XOXaugqj7Y7t+Jgy0Dnmyk7T38W0rhe0kEk3Q7vFlQ7ntcDt3zQOsh5NhjX0hc7VMbd7vA2sycdX4R2JohlXrzwObMflEMrHxUPZFLsd/2jEdPnXH/vUEDBx7FzKfRaIRut8k9a9DAh0erxzdo0OATh1u3buHNN9/E5z73OTz11FMf9+XcVzzonC1WWO8RjcCsePETSCrmGOUgRnntGnD9LZi198OHy6sVdV5olST5enBRZ1/nLzbAyip4AGBTo0JbUx2rIZ80xJKm7YUWz0OLFIFzBJV2QqU+CRVxJJEVnTZw4wZoduGy7ksOA5Em84kMoixMckgqkKnDpQrCJG4BqYIkjROgr2v7lE63TXPlbXTnHcqvZUdQtQcsLJpOnbuO7V0NbSRG+vB5u6ZTT5jFHnjHqivUV5kRXEXUkAChzKUDWCQBkBUzQh/nsvBeG1vrJ6aKDL77YjY/2iWUNMonKQ9nB1bxnCjQGVdTKM6oQQyFQipZACmI6yKeA9kcveE1nHrST8jVI5U0OVgIk4+7J9fthnYNRaSCiAcAUyj7+czzzDLBBnIYQ3lOxAVKkr9TSfL4JimbIh6Kz2k5D8p/X3wEEwAYRwbVor2OpdZawFpPIH68J/Pc/yKH72TSd2afRWg+9I7XLlJyd1vu7ie0tYLOtZdlIZ9YmBOYnwnkEYcU5RKRAiieFwd8noQQshuNZUu+fdvIS99LyiD0fsSvEGPPc7Psu98BvMSY56uaYnqeaJNFlD+9W7k3w+585W90zJHjdPQEzAxhxlEErDoIKYdSuugtAXOfbKv8Bg8Wj6Ly6VG0EmzQ4H6iIZ8aNDhkPGxZS/Uswg4P1lpcuHABN27cwNmzZx9JmfKDVT7pcp+IWJfnhNHUuxgzweYdlHe3wO/+EObqD2D6t8a2c4prI5sq846gt+gDo4x1LztkU3Cks9YiQH+tAOo8SqnYCJN/mu+vaVeavh461QFDp5mERW1r5cWZlkAAemw5OU2BogTfuVXd6DuXp3KVCDDZhly1vYNSkSkjWPdMnVdDLALAlo4soYFSyTfxU9JwhG6natUCYEzuKeENxZ4BLS/qi61rZEn5FoBcsEO9OpLRHo97QeVTIPwc0Nl8xgEyPDRmhIhXSb0k9XGJ6CgLeFfOfIRG1ke0enn3nxrlk6YAQaVqIkKCcHGATu0GxI4F/Oo5DeI8/B1BBu3RHZxZuAgjZpZATyppVUXKOYxYEDCJblenAgVA2u+iPN7sQnEo72l8DbLCOuFBsI8XJM89pFymyMr3QGr/LnXE3jZ/+5x9TyAu8XRyYXz7PMQB2xLkJRWEwd2XBSWRnr4+aK0/n0lq75ItqWOMM1t30f3wf8LPajHvcv+q8AMrlz7u7QHyycTCd88DKl5JEbY/7snP87H/fCHSugLfcRz3gkGgdLqoaHDDgBxKwbmjxyp/G7pu4bxjrnbkBMjRTqlfLUrKn3jGcdAGDfx4FJVPDRo0kNH0+AYNHiM8aDLEhyzL8KMf/QhxHOPzn/88Eq0NyycMD6PyaYzwQhyRBaMDa9soNxl8+R3QB9+FuffOlM0L2Qy5Ut3AsfL6Sv0iMIy+ipqjGoSSUIlcQR1LP5Th61CSb+GTBXz3g+HLChzE+l9SVDADbQ/BZC3Q9vw+WQ6+8mHlhZmZ/dZuvgUiE4HAiFKFckjBnNjukirPSU2+KjKkAOiUNQD45M+P/8MSev0fwXjCE4iHOvUkUINItuCAemB331M1Fjnm9EUN5mc/o96XEUOVI6f5vUNNx5ZAElCGhSothcU+Lgt/HgsAUeogEbPSXMNzX+KP3platOsoyKcioCoBgBxdVd+LFPlRNmCRtgON7R4AxIXCPpYtTtE76PQU/SlXKpVCOWB1EWqj2yATqW3yoFQ0kcIaD0DlGZAHLPcAwEC+BklBBACGR4Dxt5nIDgGhSMOlQpqEpJqSZUr+ezarFHwSF9CNt7+n11pPmusK1+EjviVrPR9hlY/8zxxprBIVMdPXQYNVdD78v7HVnjCmsA0UHgSUTeJnD9s2L/j5wPaA8kqMoyosKBGeV8Kxx/M84eDizyFs9FrrSaSqh0zyWAb6/h51q2MDd5an3sPK1hH0//177jHd0e5bDqVz6fou89XiPk7awNqdyt/zJ05XP9+ggYBHTfm0U7TxsBWdN2jwMKEhnxo0eAB4WB5ED5oMcWF9fR3nzp3DmTNn8HM/93MPzb05DBhjHjD51FO9c4pZOhyPFU5bBHtnFfTeyzA3fgTK/CqLrFRaZkkqmMndcp2aarxzjZdsZcU6AH1+BcZqploILbJLlZt19gkFuxdFuP8ddLuQRyE21jTzV1GnmT9najQCbTqs4pidnxFJqe2/a3KfVG0rSkBDhY2YMrdFTVJqKwvJAkzorv4IRqi2JzDY6FSEKlXPzq7KzJ0x+aU7pjmht3JVL2CP9wYr7r+q+jnUh7I07GgWUkZJ20PkpFiBL2Vo+De5fj/q34XZvDV1PzTKpyygKgHGaqVEYYOnyXPSZkNhpCOHjSIP8cTgx1hqKcYhQE8qhay5tiFm+k2iU8NO7z4rmiSL1im0pn+7UN4TAFhfbtk2fCT9DmIrE21SLhMgZzoBch6UFZ5LVlgCKCdshxPbx4nWOD+Rmf3PZYkQ8eU6laX3txNbp89aTxrLJFs1cT61t42GG+hc/l9dxpMnq2fvYMI5D/u94aDKpaAds7zdRAJplwa+uzRuFfsn/Lz2ecy6+fbMZ3ynijru96CoW31Gc2lheo7929NFNf33M8Ba99x3NtsJQDKqzkMLx2ed9/LICed3K574meofGzQQ0CifGjR4/ND0+AYNHiN83OTTtWvX8Pbbb+Pzn/88jh8//rFdx4NCFEUPWGkWgTmsQiIatwFmwNo2bNpGuZ7DXrsKvH8O9OH3YW79GJTqbL16c0pFkVIhRJwBsc6uy2gylHbPr89/oVKRu7Ozr031i/GqAyoezVJGi3Yfjb1baFFPqt4NKKtEuyypet+zja0F37zh/oxvwcJaNyk1sSimyX2ysS5glkZhlYPtKPuTUmkwu+AqnBmdtZ8iLsPfl5XKQFIoSfZ2Vua3gcGCTdX0+fXjg2RDVQGz8noVC/yhPhYgh9iWIIFcYmvF7UGyIpQ35TuvlMsye++YEd96u/KJhEfBhV5LyVg9EkDMSsIjgAJtFanKAXuoHYQIyuX+T3HyxJY+Z+V+WyuHiM1tULutViohV84FtF/aY8NaQXf6Oa0hn0KWeaFrDKnpQu1bFFfYgTgOlfDfl5L9v2s+USDztDmP2GxfhBWyhqTb4BvjJJLCq2AqBbWU0C+leZXx3yfeJh8p7aNz+TuItEr3UAGTND8LEsMHkZwDsiIOB8p8YkAkp+yokJVPoXEzoJrybmN456PjIiTPPbHsJbS8d9G6s6C4tDAO8oktYLrVPlIOCvf8dIJ8LVtPYPjfrwCtNnhlJp+p1QFWZ+yTkzawVrVUbg+rCv/B2t3K39Bzq8nLJxvyqUE9PGrKp0ft+zRocBhoyKcGDR4jfFzkk7UWb731Fu7du4ezZ8+i19PZtH3S8aCVT0DVeo+ZwDaBLdqwWRvlMAb3Byjv9sEfXgB98APQ1R/A3DkPGs28aATsZPb2U1rjaLItdg6ptOijYkNbwF1PoZSvq7JEdlCLfJJIMAXxxNYCAavKsZVW4PoDxOj4hTyw8CydI2Ad4w1YBuRFKd+2Gzf0FfU78DWeiXNolE82ngdrqvSlHIsdJF0gkDPy/9h70y65jSxL8D4AvoTHwli4UxtVmVmVeym1TJ+e7jOT1XMq/+/MmS/TU6emq6uyKiUxJZISKe77ztgjfAFg9uYDPDx8gb33PIIiqSDuFzIccMAANxiAd+3eCwA8ayNoefmsYSVCc/Mq4tyorrASRVOQP9NEJJhVUnDIciupZe87hNyW/WOxJ9RIRK2IqWX3aCSzVmgMqQwAZbySlAaj40q0cR/R3mSHsWKbRf0UG4glzb4MmLQbK4ctG8olNiWQ1I9a7Qd4f+VJf7evWCWuKWP3ELI4LYPRTo+s6ixrIadpI6PRmh0hWzMl8ynmLliwzOuvJS7VlEvacsnmUev3kioqC4yhEWdwffJpzj/BUn1tf6EYjCM0JGCTJ2bQhhRWWS+8M1HdFH6WYelarTWBrIPm7X9G7MbOt5RdJBE0mq2eejM8hCJdU/JwOXEyslyCkufkFWWTlktM0rglbVpSRQkEk/iCMSUR6zp5aUaU67pykikNHNDQJna+L0ij6OSZiV5BJ05PHtfx05O9Z+kEorEJAUyEVnfy3pumk++Frj4Dnp/MlqpQQcJRUz51Op13pr5VocJBcbDpjBUqVJgKRKQ+UL8OvAnyqdvt4uLFizh16hQ+/PDDI22zN47Xr3wCfDcGOhtAtgvKtkH9PIm9s773r6eFwbIQKN8sgm2VF2ErqUO+C47nQG7HsLI9K2bXzWAu0Yte5DrgZA7k9cI+geHjBZBBBVLATlRRvgmOAi+7luPOMj3cPcuBulI008akPC8NMB583XuZ4PIO0mMGuRQIzMxmhMsrTDSxjHs5+NmTcFFl2hecoVNjUT4hihHtroLnlpTNGvt1bwfcUF5iZuaAXgdoKIVX4oIECdnKMWFm4zsk2VO4aBlgQwaNkSkiTsHUAim5KcUmnVpT24f9eoviGQD6mEPIwTwDIr0wTuiBLXZvWQ8kFTa9L2YiixvRrtNMVrcd4p7PeSa3TyjoSmMZx0PnxOVInl8LrtvwW+jEcmErtvQvQ5+VlCLD0HKAACCv2chhH5f3j3pvDeeP3UC8l2GiFHUHsBI2RkUTGo1CXWfZf9oFMJkTMg4C20YQaxtbs0W2idQfgeI+sL0NLBTjtKZ8qvkO8lgeX52WOaZlLyqDnpTpJBGqse+MKJhGmsQ5XFx+f0m4jRQLAHu8V7s61vzpC/HsPchqJTuMgN0h53nosUG8vwYJJu/BoXsoMzhO+sRTiRr+oIVT7+3XaRkk8sh7uc/9yHlRTInco42K0NJtewYC2Uj9NcJLnA+PYY6neaTYx5R2fD4rvxf7boqyLN5QvhX54rrP66fQ/fM/FZ/NzQNjUUw0Ow+slny2NvoZFpaBnbEVF0+AdiaVT1F38jrYPf4RokrxUWFKeO+PlFJod3cXMzPGiTAVKryjODp0c4UKFVS8bvJpfX0dFy5cwM9+9jN89NFH7xTxBBTKp9dNPrGfQbR1G1HnmUwuBQoPwyDOTfZ35Hvg2GYVZlU0TWORl3v7PApOjJZmABBPUTDxdssu4gyIytvBkWGflkB5y3WuXY/aNrTlmiStIRTtQrY6KM/34Lt3gxZ6AMJFokDxYPjUxL0NUwYYWdRKNWMuiqZm2cPupFVK+Y4DL3hMmFn/Fkn2tPjbmss2hUWd1zLOBtucRrU2TYHM/nLrzYqq1KSMJCUPBlnP4JCkWSQpfVMbm6QCqqbUk1RRAbXDuG1WtHoXtLefkmNtet3+lSwZfQYVWgZbX9VyfoDCijNSJngAQF6bvCdGrovzjYuoNYZ+m5rxPmexZAXMdpxEkV3RpOWH7cGcI2Us5MQx0LZZ5Q5bn+ahcXFvswaS0Udyn/HKPE8vjbns4QT1kqSaigUlZyJMMNizCTyBm2glRvU7EFbDulwYw0I2aD5MPArXOocsQtkD9UBfStthojzronnnX5Bkk2NQkX91wHyhQxA87L2Sw3dIcukQlnvFcuXaVpVTglo+0xRjAqTnUUkxJ30+ZVOCav9Q2wKnkvr9cfurR/uflbZlcrul65Vda/OTE6k4ilEbI6nyWhO3cBJ3797F48eP0etNMWZUeKfhnDtSyqd2u43Z2VdowV+hwhHE0bniK1SooOJ1kU/MjHv37uHatWv49NNPsbLybsrx34TSjOvH1Zm0AMwWPlxSGCtfz0jqqBY2BSgzFtWnhbG4DmCqAgHltnyswaaj8kIIdw3FO4u9m8XuS3voP2zotLTp3IEEZRZJGR5jy/yz56Dttb66J3DcIaVYaNbd0AxZYocoNfy+BispP2NTQ1hrGmS2GSz5rX2EmfULSPISX39tv34KokiwjRpZjTvmS45gtO3ClHU+pRg92gjDWKIV+C0ZOZri5LDXqaQWEe5f7PLw9eOF4nGeYq+HU28H8cvrYvMstnuWe54z9ENPdUQmu1lbp0pSve2uNt9Xge5t2uFD9xfMzI0dk1U9YlyP6o1CRWJBidVSKQxZWADsBFmrZRpXAYBKZuSXrte3l/JM8EqhXCOPaciiLgRJuQTI5HzCHVlBKLQ/EuyQpT5O7JBwG2fqd0oWCtdZkPgRzmGo+XkWPG4Wnm04RIan3SA5Rnn4XNQeXUaSbpQvVMdVKZxII2AOQS5pk34OTT7Jy31buacp7x6RpGxSVFMiGajlVIW2GVwQh/cX+jy0scDvHdVKJloxQL0tZMkZpN/+sP/5bsm9pj35GbVLnmWzyckFpTmRSycnJnp06SRqn/xnnDt3Dnme48qVK/jqq69w8+ZNrK+vv/YJmBV+OjhqGUntdruy3atQQUFFPlWo8A7hdZAhzjlcvnwZ29vb+OKLL9BsGmf6H0G8CeUTKAHXlvX1jCqD3BqopNnd7MGoaCLfM6uUapG9EI0pFBuU2/NqyLfBRoVHgcnbr8eSaoUIwFT3lPINBrAWAIM7OUToda8nL68HCk/eA839AhNnGfD4YfFHyKYnoIhiRpiUGvvckvtkykGqt2x5TkaSFs0plHzD8BFa639BMpbxZCZ1uAM2EzU2Ko3gwWRUOkyhklLVR2Nrm3YP6AR+1gOFCrJ7sBDJmvWY1g6NZJYypyRFkaRyGSKYJjBEJiTPriAaHtBKjsVCPjmDi7hV1ZQY+pY1D3AiIyaA2lAe4rnOVzi2XDJjPUmK8U4B1Wp2UqlnvMdZ1LaAnTVvGNWQUQR0bKSSmSDr979cyXvqt0BcWvNtVYnhIqEYxV7MZdIynaQ8KOnaJYEQYgDn6AqSqOT7QUJIsDQTnhMoRKxJ/Tf0HfZFPlPp9oS+ESA3k8ffIdl8EP6e8GwfeuYYQMrC1L57aGWTvFifrSEv5658HZJALnHuQSWEy2C5kBNaZJQK2xZ/DuEaDhJawgYD3ykjkwCAAs+7UWvyvuY6DiDC9v+8NbRiBLx8OvblGFgf/ywC1sY+A4CNkglIacm9YXbUUjU7/jfIonn4RgOzs7P44IMP8Mknn+CTTz7B4uIinj9/jq+//hqXLl3Co0eP0OlMkQFa4cjjqGU+VeRThQo6js4VX6HCW4y3xW7uxyaf2u02vvzySywtLeE3v/nNkXqoOAjehPIJALh2Ql2H8i1T/kzaNSp6rKSSMR8KsFv0zcRdm9oL+3YVJmSb4izbcVitBwFMnC9GDNy+BljIWsN44pUQc85zOa/JuB8R0vUf8L4HiuJFsJDVS0cKM3z33iC8PpirFyJQQ6SUcxPnxpL75Gs24oQM15SvGa0bFs/Y1hsuGHnC7NqfEbuSa9GoaCIALBVVR3ZtHwNZUQkM9s9d+7UpKAAmYZwsQJGJfFKhWuZ5XT2lKaOE65xdrhBkwjFKihRpWb94Gu08R7QzHlJRRj7p10tuIP5zNAHWSZlYyNHZg4NRzedtJFDcn+iwsnMJJ04K16CVYLEqIlPjtWEhSVFuiVqKaXIRupYMuiks//r3iUzJewIAVkjNGPLvEfuOqJCMFWWTNhlAUlVJz0QSfVDHDpbrL8oXBjNz3NTWegDCE2AkYic0MSPrydZ/wQ1O7ivaeITa8ysHfwbSCCJpu9o7w6Hzg39c5RORfOxRQ7gf9bRxRth37sV3bZHQC5BFzAyKQ2RneVuYOfjzRs3yY49bk33aZx5Rc/L68O0UKZ9Adv324DM6cWbwHDz47Php0HhfWjkNGh/L5xYRdUrusVslY8Dws/fcMtb/+QIaf/zTBImQJAmOHz+Ov/3bv8Xnn3+On/3sZ/De4/r16/jyyy9x48YNrK6uvpH34wpvDyrlU4UK7x7e7cpwhQrvGH5MMuTly5f45ptv8Mtf/hLvv//+j7KPnxreFOnI9ZPqOgQHNuQ5zdZzk8GQlVQi17EXrS1KEgARMbi2aNz/LljJaBisazxH+7AriciNvuxxOgvafAo0DMX3kFpnCHHohXkPlpnxGnmsLZdeKqSieyoUEYdm4PPGJmhzPzmZpsjdAhAuELnJz+NewHpneHMtWx80Wa215sAG8oKtqp5+QYgcYXbtXxH5cjUBccdM5JryyQCQycZsD7bHUgKbVVLEUygYrUSVZWxyhm1Z1FPaNjRllJCfJqqXAMU6SigQS8v6342fXplceEDyKcNMoYqUQIQaLH1BJyBzYzaYVQoUcYb53Vs4d0KxwLQSLNbMOKOlnRk1IymX1MAdG6lkInEBEBmL8v17Y24gn7wyxkUKmZkoY4+mbCKp0K/kQUlZUz6k3GOP48n90mGJmcPkk3TtBcY4mfgWjjsJPL9JfV7K9Bkby6m7jcaDL/t8xAGJHm0skp6N3rRNmWrjKquPIuF243t5mMxBoXwSIbnq5YoqKg4TTKFl4rEGCfm4tM9z7hGXkUm5RzRT8nknK3+HdB7b//3SyEfRwuSzJy0cm/xsvuQZdfH4ZFvnjoE6O5Pf76v2GYTN5w5ca6L+yeeigoWI0Gq18P777+P3v/89Pv30UywvL2NtbQ0XLlzAxYsX8eDBA7Tb7fAEsgpHEkdN+bS7u1tlPlWooODoXPEVKlRQ8WOQT8yMW7du4fbt2/jss8+wuGgswFb40WBRPgEAEv0hiTgDksmXmIn1fBcc2x66uGYkdKYhE2L7bKOpFEqBbKbyDdsLeeS7g+I5Uwu49hXQaMizM/egFPjYy9YlAPTZtYBuyye0lZ0Tj4UPOvO3Twyx9+D7d8faE5puOuWjTsn7r0X5hFoTKJtBOgFLJlsE2rYQus5YH3OAA1pr/4JIUDcVpI715clGDpM3FpmBqWp9zlA8BgoS2bNQERtZ16YYGS9YlsJSRNRs+RSyobjWw8fG3pVnN+xBIx/E9knElLCIYsTr9xClNju1GDlqSh9iilCD3s8SCxFqmDTiUDcRlRzbyJia7+DD5XuItEkDRiUVW5/zrIVu6xjatJJyAIzk0/iM/iCsbexfLxbyKVPGQlLG8kix+dXIK+k+mXBb7Ks5wuNCaNkJ/gGtOPC7sGAHJ00mCSmVhN+VQ2ow78I2oaI6T+gbw2Ocy9C486+I+s+eZFXCjkMiuzRbvcMW4KXxi/lQy7WJKdzJxdwl7srjEpdM/BmGSFxJzyOZoIryLC4LtiWg6M575d9xnbz03PpOXrp/n5afi96LNtyTsUkKZc/qJZ+VKlPLLFAXA++Qm88BAOny36B76Sqa/9v/AYqTqUiEOI6xsrKCn//85/jiiy/wi1/8AlEU4ebNm/jqq69w7do1vHz5slJFvQM4auRTp9OplE8VKig4Old8hQoVVLxq8inPc3z77bfIsgyfffYZGhbVRoUfHVw7blMvGJVZXLPZ35lJJcGGZmS1bAqLPvOagDg18xCgfHKmoASOisKWX+0UodeGHAzOMp2gslgpKQUOznOZPPIeJJFTyjgjfVec7N3vs/zwEWjIgkq0RwkdR6BIUnZZmDKfAFBX7wMcyKaagCWPhQALCRTlOWbWL4gh9HuwKgNVy7g+CDnYqBShKQjcnR177hMLhdiR/SMHs4EwMIxhZBmVDpsJpSkYNQWMVuhMBFsvKZdMsBJjAPHz6+ULA9eqJffJktVk6V9BVcgYapl+reeGSRGN7nOcbdxAImShDGC90VkL2EZxtjjWD6PRNKupyGghaNWPk1F1tWf5lymZT7HrqFmWGgmtkRfaci/YO8YSkcoeLgoTZ3mJarTut3Ayunkwgkl6ljyAtV5QWZoJWXJSnxdIYE72z0X97n8gTocnkAjbPGjRVCFYDpWl6b2iVjWQT4eA7ypk6mGUTZ4BaYwU7GdFUkvIkZJ+fgqopfx2+aQKH7CaDn1e9lv43KNzffJZlHol++yWfFaWN1qm0C6x+uVjx0FZD37xNNb/+58BAI0//qnfVIHAUzAzM4Nz587hd7/7HT777DOcPHkSGxsb+Otf/4pvvvkG9+/fx87OTqWKOqJ4W2IpXgXa7XalfKpQQcEh08YrVKhgwdtyc32V5NPOzg4uXbqEjz76CGfPnn0l26zwihDVwMkSKF+T17PaYRnt77RCzf5+jcUp3wHHsyCnz5AnY7D7NPsHAEyxXXI74GQGBGvGTAxPS4ju/Y/iT0veU5bq1nx5DtSVQpw2JjknK5+cO7AlFwCxfdLsVopjcLsDPH8yusALRZWQTWHI+qakiBG5HijdBdeVmfB5ptaHfWPONPOHvLPVmjkGKFTAAGqdl2juXISLJ+1VymFUFE2RpeSpKRdL98Bdc7V5fn4OgI0UtB4TAHjU1dwfk/JJK04y69eyVnBxHmL80CFs1TjPgJrQPoE4Y+FeEK8/NGch7aHht7GD0+I6JuLS0LeckaiMultAfVlcJ6/LEzIa3Wf4ePYykszKAhnXy43PeVpe2B603LE+iKjIaJo1TERxRmVzyBJrHIbJGwCAVqF81pRPCXfgIJOHnuS+ouVqasvLSKI9SHlQCXeQBiz5Iu4iK8mKeo++QQSHYGlAHIuEzJyQ8vIAZBGLailBcRMi0dmDG8U5TpoeJ2QAACAASURBVB5/h9rO/nMFMwdVPIUFofDbiQTPwfOgCos4Yb/eQxzkFKUjszJtTZkMwZnQf9AnkCRIE54yJ6qqxIZLuxUnO5VvtpjsFFLaB/pMMHs08HnJdtpXV+G7+YRahFefTbZz7dnE92mjJMdpq4TMKhub55fBvR1sXH8JZDni9z9C8rO/LQ7hFSlYoijC0tISlpaWAAC9Xg+rq6u4c+cO2u02jh07huXlZSwvLyOxToioUOE1od1uY2FhGqv+ChXePVQjd4UK7xBeFfn07Nkz3Lp1C7/97W8xPz+FhVmF1waunwQU8onyTTAi3VrEWmS2kkrGfCgA4GTeRj7lm2DY6tbTKJQo2wBH9pofJ/OgfNW2ss+B2zf2/7aQT7mDKiKxWClpB6RtwznZ/k96oXce1BCKdtJ26zXw3TuTv3Mo/DlgccPMQC3wCBQgq+LeBnKFfLKwRdyYB+dtkKAqKdY75Aw6H2Fm63sk6ePibyuJbJRXTJOlBEpMmyXugDkx5bdEZCcw2Do4AH0yT9u54VxqxZGsJ18HgHqdsvdKsVC5jqWCkTs4+YQ48L3ONmjjYfky4VgbrN8zLEozrdgPFPlRMMzktmTMcVxHnG7D1Sefkxrtp/h4/jvUGgQWGcQhGMO5yZALCEDvo3toNAsLR0u/73VN5BPBONJYFU3WGce1GrC7i5xlMj5BqibqOSW/UiOppVwmsIOXbH8FEiPiLhAgnxLuojf2ELHsbmC2vimrQCSE+oXLw2NIUMDE4d9cID+YwiQX1wPnMe0AjXlEG49Qe35ltE3eh8c4LyitAVH5eShlk3Pyvclz2H54b9/iuBYBQq9Xb6HKsWljaiQpmzIPNMLXk/geI4qbwoRbsLmeQUl5W6N6OPuoFIF+FI0dq+/m2L2yOkHy0NIKqD2WSbp0AtQZe/87tgLqbo5+1pgBbb2cPNDtknfHpIbuzDmkt/dUT/84OKbDKJ8kNBoNnD17FmfPnoX3HltbW1hdXcX9+/cRRRGWl5exsrKCubm5t2aSb4V3F+12G2fOnHnTzahQ4a1GRT5VqPAOIYoi+EOE2TIzbty4ge3tbXz++eeoWYsC7zh+rAdzcZ/1E0D7B3EdgkPb1dGKldDrfMtE7Fht8si1wdEMyBuK10Y1FXEOnxwD5Zv6uvk2OG6alBvEOThZBpyVMLNfE9zJEW083v/AQj4Zrc5UHHbWoFpAEZb1ekBNKBQGzgMzgze3QO0y8nBKz37nQY0SUso5RGWWI7lDnG0gx7lQq4v1aobfkAhRZxs8r7CIy+cARYETQpR7zKz/W1GEHDTOSirZ9kncA1MdZGqjbfwjMDzNgAz5PcQ9u22YWkYeXVuDSfmkqUXyFNDIJ63YH8idGFrh4NsXJqpwnoFCBJN35XZ9zIgeXQmfXeEe2fR6lpo3EEtSDs4eGDFq6MBhirw/AXG2M0E+NTtPcX7uMmp7Y1CtrmfBAPZxu2609jSqhSiKwL0eMKPbCJJLbaSSVCQfRtP4O9TqRY5UU28jd9vwSr6WNikn4hQ+qstcgGJh6gRyKeE2chImdwljkJQlFXE6MsQlvIvTSd8GUxQ3CUcaIkWl8SlE0OQZUCv/DUVL3lAWVJ4G800p7wGI0Hjwpbk7FjvzCCUXqKooabPM5bk8e/BeVj5p93jlQZ61FDNNuaQR9omg6nJyVim7MElUKLaEnK0ofNjiu1nodxTOQ9wqfwegACkV1QOTnca2s3P5BTjzE6QXLZ0AxsgnWloGxsgnWjoOPBl7P1o5BXpxf+QjbrRAW6sTv6WnBJv/758Hfzf7lnuD7f/I77hRFGFxcXGQK52m6YCI2tnZwfz8PFZWVrC8vFzVJiq8EbTb7SrzqUIFBVXmU4UKrwFvy4ycOI6RaxkRAaRpigsXLoCI8Ic//KF6uDPisITfQcG1k6b14pIZ2eMgToHEMJPZd8DKTOA9mPOhprFnChQYyvd/zL6uNPt4YmXbb83RHHD538CNoXZYyCfLzHOliqLmNQF2qVewDVL4s2Cd00vDs/a7PeDJo/Jl07Y3VKQJZQOkOaL1p+pmfdPYrzPdgo7hbHwRDbWZgVpnA7Nr/zxKPMFukzeNosl8bUxhdcmKldX+ih0wWyWJdotAaeY30K/PauRTnok5I8VuDOdE2UYo9Hx/BeUxX1I2SWNZLhCOeXkuC60/mpyhPby70DXMjAZ08skZiP/cSCgl0K9Pb8wOjP3odVgQT9+h1tz/bSiKAEsGkvR7DcNIPlG9Abaq4XtGC1rj8445R8pAeA22WToxYRJZ7/DPZIlvy8/2zMhIeCZhByfY6mk2pVIeFEQVyOjvfQ7fIo76n0nDaWAsYebweCiRFaGJRZIdY2g/zEASOJfSO0+eonnnz4jKVIwi0yUsc17uF5qyScJhc2+U2+VhlU0IqIH2EDUES77eISZSZF48rSKpH7RWhGzzVwLXyxEFCLS4VT4mx7OT17HvuRHlU76don1jvfQHohLrXioZ/8s+Q7NkfFo+OdF/ud7E2v/8dvB37befID4p2+D+2KjX6zhz5gx+/etf44svvsB7772HdruNS5cu4cKFC7hz5w42NzerrKi3FEfxd6kynypU0FGRTxUqvEOI4/hARMjW1ha++uorfPDBB/j5z3/+1pBpPwW8ypytacC144KZxD5qxiwHrtnsFbluJHWksPohTGPRpxZaR9Y1FrgBTHOrtFgEAoB/vF7MEK8v7X9oIJ+8hXzSrJkyg1JFm7mrjQHa7N0Q0nDb+P59UMh2JzQbeNoZyCH7vtwhenBH/3pzHpwZirQma0TA1vdcUZdhQnPrJprbF8o3ZySViLtgY0YSG/NxyJovBxS2d5ZtAuCAtdTk/jvml11d+UX6WJN1dT5UI6rZ6ySCot6QQ9hzxUZNyg4R7mll97s8BT2+Jo8bAqHXYJ18yoRC/qBpVEfk9eszFvJ0BtsKFbvHMDxmNTtPcL5vtTeB3KDETRJwZlivVgdbJxr1jGSzoX0FjEUlK0GWJEDbdl+1EmRa3hMg2Lj1ESvZjjF3QMI4kXBbvB6kTCcAyAVVlWQvObxswT/GQjJsEyypQEIEk1Dcl8bAoLVeeFwMZsllPaF94e3Fq7cR9wK5gQd9z9HGdWnM1Z4LDluw1b6vZDpp34+b4WPzPRe0qgMAzrQsLKFZudBnBIu8IkMrtOHpS2S+W37+fOoQ1Sb341MHKrES9N3Ra3/n2+dBPrlQ742h7B5R9llZXy0hpLrHPoZ/9nzwd+OP/1jemDcEIsLCwgLOnz+PTz/9FL/73e/QarXw6NEjfPnll/j+++/x5MkTpOk0k5Aq/JjwXiHpf4KoyKcKFXRUtnsVKrxDOAgR8vjxY9y9exd///d/X91UD4A3pXxCVAMnSyAl9wnWorA1L4aMtxXtJXdvc9NY9LlpCtxTXAfOOOMbAPJtcFIXC0feL4Me/n/9v/ovuHFsmgkeKVkeYl7CHrS8pn57DrqcnROtY0Rrlzzw8v78JbC+DjQm212Emgf2FyqKBWa0hoqFvN1GbCzSRu0t8DGZSLSSO/AxEGtFGQZlQGvz3xH5cJGWuAdGzWSTx9EsyBuIXyPhS75dhHebXjbtBTZrVg7BgTEDUgrGxbpdiJUuy1iYpwA0Mlk5F1kKCllJ7UFTVyXC8jwDEmH7EnErFFi55H5HT68XIebSNimcAlTnXRA70e4wpyZqPgMrExsS10aq2KGJypE+svoxm+tj/xppdh7j4/nvkQTsl0Jj3wS63cJiTkOvCyRz+nppD2gZ1rM+O1rVAlMomtDdBVr68ye5nmn0sByKj+SJOZHyDJFwBxnCbY65hxzhST0kFPrJZ2IelKQCdP1X/5h7OBtfGtuwpBAJLJMIidD91Ltw5qG0vZDVZ94DQhOkAtuLVu8iXr8ftrIUu7FEzDNCt/eC7BDOsfeGMVJqlmBrx8r9l1l+LlaIJ9/NZXKplwMz4X5Zdt8YhpSxxY7Djte5YFUoZHcxxUHrzZAiymfl5891ckQzk/3MdXPEC5N92g+RadlqB917/Wexkt3y5urkx5svJ9u8VZJF2558xqOxe7tfOYfd28/2P6jV0fhf/zi5rbcItVoNp06dwqlTp8DM2NnZwerqKr777jt477G0tISVlRUsLCxMZGhVeD3w3iM2Zlj+VNDpdCrbvQoVFFTkU4UK7xCmIZ+897h27Rp6vR6++OILJIfNiHlH8aaUT0A/90khn8ht9ckAzSvemj1jJJVyfSb7YNe1YyBD4Z/yTVM2FWBXKA22S7bcLgLDx/MgVz6blqkBvnphv429vk2QIdeCndOD5LNMX0d5yVfJI2Y5e8Q7mbyqCzO2eVKvx70UePgw/MN6IUQ7dC5C7Ssp1nMvA69uIYqpCCgPhZf3QbleAPVJyzSvlrS1mFBrP0PSeSQST4PVoxbI67loTEZ7L6PNJMHB01yf2NG2mZmznECxmasqVFoW8ikHcwtE5WNecPb9MCzkuvbinWciycDeK8udTF5ptn+xcJxWu0MAaG+AVh8W/xeVHh5lP7xHDREYdd5Bj2RlbYI2MsjrxBby1ZL7FdeRpNsTeU7j8HEdM51HOD9/JUw8AeYJGZymtssj6wEwkEoHtGIOwvisSI0G0EttCiiLmhQwW/65XN+eaJkHnQeIFOUSKVakXiBaY99GHoX7uaSK2ss9O4OLqEVj42GgEFtY9ZZX951z4WJCqLDrciCQjxi89tiHbSenfcbu7aL28JuD5f0Ayv1JsuRz8iSjw2RpqllTJHfakmevaeA7GeKWYKuXeYiup8I9hRlA7WBZV+w4PF/EhYlCUfkUUksFmsEBUiqo9hrq59t/3Sd+JoiSRhPYGMtnmp0HttdHP2vNTX4WJ0CZlfSQNS5ThI2728jv3B30jcb/8l8QzdmcMN4GEBHm5+cxPz+Pjz76CHmeY319HU+fPsW1a9fQarUGWVFNi/V5hVeCo0g+VcqnChV0VHR/hQqvAW+LtNiqwun1evj666/RbDbx+9//viKeDoE3pnwCTLlPxDk40a3yyNns76w2eeR2wGR80Lda9HlbNhUAINsCG+dfkE+BeIqXLaFw79c9qDNEvO08A1Niy3uyWEZYLPU0aDPw81yZQSufV6oLy0uKJ3zrlqyaCFnlMZXaARXkWUgpNXlc7tHLfYHa1ovy7w1DC+UGwHOLYGOxuXwDQNxrY/bFv6C5exlkIUQwRZ6SVdE0RZaSldDKUzsxPY0NkVltBsBLdoIG5ZNK5gN6kV4jh7JUvg41ezY1v0P4vYQi54g6iT2ih98bFUKBtbaKYpjFei8xKHkp0n8bOU9nH7Ghrya+g/PzV2XiCbATrtbnCWtB3ro943MsNaYo4HVsk0BIyhgbXs+guoruXkVek2cnJ74TziTqQx9PtPOqKEmEMSim8LVNnMOHLCjZI6MW5vxjLMZjheci5CbwvXBfiqVzHnp2k/pmyJYuS4X2CeRDyXdq9/5S5DwF+nRh0XpAYkpURWkqZomAkckl1p47tGFPux+o89OUSU1a+4R+xJ4OnqMlZndJDQo9V3K4rUJ+1DS735tA1nu0g/RZO9hCajQnzgsdPzX52crkZ1g5jWisYZzUgI19e71s5W/gUQcNvVe8bZZ70yJJEpw4cQJ/93d/hy+++AIff/wx8jzH1atX8dVXX+HmzZtYW1t7Y+/t7wqcc0dOdVaRTxUq6DhaV32FChVEWEiw9fV1fP311/j4449x/vz5t4Y4+6nizSqfdPIJAJDoMnHyPXCiEzB7NnkWcN1IFJlVVwBbbIZQFIe5ZsynAsDxNFL68ldKT0ugm6N5PMQO3DoBNA2FeQuxZHlhUh/4leVKf+Z2WKVWqDXC26faaMHPP34K2u2rw4LFicDnIQtI58tJKTdp7eJ3OuCN3cE4GG09n/jexHZiA8ETJyBDjgnTZF+Kco+Z9b8WNnt7Sh4zB2N87LMqmny4MDK5so0gq8c5mI3kl0HFMsA0cRlC7pRk/TaA5b6p5O2pvJqWwaORV9K+naJelOz+hor2tPpglGwPQTpf/Wuy6fXtkMVO1fDb5MYss0hR0Cxvf4uPVu6o7ohFu6zXprEjm0kl42rWSUitGTuxntoUTZIF3ch6WhvzDI2da8jqi+JqiSEfT7Pl09WB8u8tqZdIUJgnHB6TE+4ggsO56PLkZcBCBod4/oUifUg9KVrrlX+HBQJSUomNq5njpz8g2X1ZqKyF4w0RmezLnx8GkMg4rRtLY5P3Csmi9LdD5jlpjWen2OZpjoECOc9O/rI4dIrck2BtGbqX+HC/iUrym4oFgX0EFFSUMJgZ2988K12+By55J6AyF4VmyfvLbMn71/KpwVjL88tY+6e/jExioYVjqH/6n0bbcNgcsjcIIsLs7Cw++OADfPLJJ/jDH/6AxcVFvHjxAl9//TUuXbqEhw8fotMx5iJWMMN7fyTJp7k5Ww2iQoV3FZWcoUKFCgCKB8gHDx7g8ePH+MMf/oCZGRuBUEHGm1I+MTNu3t/AL5uGWpvxAZCTeZNdHtePgbqGh/XIGDie7ZjWK1ae4mFWKx6NwK6aoHyy+MOIwdd/KK3zcTwDNA3ttvQjy4uganWgWb1podVC0bubguYEIm9IAcadLvC4sOtiF7b9CXZw71H6u4WUUp0uUB99cfAPX446lWw+U+kObhhfPmaPQ7eBGyqAeEJj5ybq3bsTa0nFyFEYSSUj4UvIwGiCYMhbM9YoiABPTRB0You4ay+em89RsXZ4kWEskCzrACDrgeoKI6Gp2bRiojZeSON+7uS8KCkrak8xlXVBT66PLjvAZBbq59M1WFfVWrqYN7z6ZJhBxF6/nwQKlORSnG3/B1ZOZQBicK8HNJT7jdZn9rYtZcaMrGhbzXr/18jSwW6jCGh3TDlSlGe2YcGaI6Wc4/jWt0jqOVKFfIo5VUzzAEfyhBRNZeqF5cSZXRk+hojT4G8foYdTfA31pGS8PmgROWiH60AhmzwJIcWlNJ6FniWZwUPbo/YGkiffDZYFxyPpXGi5TCHlVrHh8BLNNs975TqIIN7fvVOIMU2JV0Mk3EM11aGYB+U8oppw3pwPVqzEzE8AHIW7qDy5Mkw+hRC3yu+ZUb28fSGyKq4D3dubyDeGrtOypqYdIBlbUPJcQGXPCmXba80Dfcfw7Y0Y1JxFfufGvuXef/1vg/vxYHdHiESI4xjHjx/H8ePHARRkwurqKq5fv45er4fFxUWsrKxgcXHxyFnGvW44547cOWy321XmU4UKCiryqUKF14C3XT3knMOVK1cAAJ9//vmReyB4k3gTyqc8z3Hp0iXMzs6Ca0ugvDx/aABvtM4yFsespJJZXeF2wFET5A2zpK3HUjTAvuo02823wHEEGlKt+E4LtFk+i5FcDjSNKjANylij5jUB5tysYBOk7QvqLU4z0DD5dOv2/ox36acKhkkXhV8zxooKfnUL6KYjBZvYoHzyzQWQS0Hq9WJ4YScG+wj17nPUdy4hCp0Iy7UBu00eCbPnJ3YdNRF7A/k0BfnD1AAsbeAuGDUQWYhZSxv3EG6rSfmk5dhkKaCST8p+DjlTXdy+QCBzngFxqEDsBsuixz+MFr0kSymi8JjT6JNPBuWTN1zvNlUTIeEd5KSofcssvXrr+DD+K1qnhj7MMp18kvK5hqFl+u1B6z+D7dksBtFo6qqPPfRs5JP1GcCaI4UZofDT3kHT3Yer1eEEVRGwlz8WRsQ9cCxvwymTW3KBXIrzXeS1MEEmWf6RQJvVeQcr8X2U3uEPKmAI9TOJHA9lS+UZkJRPfJMUFhwiw7MuUJsftKd29z8QmSZgSHIZKV/IgSTCWno+c4LyDOiTT5Iil1AilN7ftfZkp80p2thBNCdch+MkyBgiwW6Zew4kkU9ZHt5+5kXi6yAqNfZh5VsIrpsHyafg57OTn7tOhni2hu3LL0cXlDSneMcYW7CzMbleyWfoTirv9/pffvJvsPt//itqv/sE7ur+d5v/8KeJ7xwl8mkcrVYLrVYL77//Ppxz2NjYwNraGm7fvo1arTbIimq1Wm99nedtw1G03XPOoVYzPk9VqPCOoiKfKlR4x9HpdHDx4kWcPXsW77//fvUA9YrxupVPu7u7uHjxIs6fP48zZ86A1+4DCvlEbhMM0nNKvNHiSgnSHuzXoKIabLJ2DNTTC+yUbZq3WaZQCm93y+xYRvDwyRLIFS9tPloArvw5/IXdl0DzhL5hy4O6Nis+y1RrokIhcsACCjAoFpeBcx8uf/TSgfLJP3gMGs4DCeY6cfiYQzNtDX797BnuyVphvzO0HYvtHqIY0e4meG5FXE2aeA0A5AlJbxVJ5x5q7mV4RRRkkeUaJm/MWOEUjDpIVWYBMOZIkberlKwXGwHwNAOCflzEHTDHpnucqPzS7AO9GwkML4XBEo+0SSDqcSjLJfWSpF6UyKc8BWpNYOclsDGWKSORT1n5aeU0A7WKMaFpyHzKDbliGWaK61o5f3FnC/msTD75MYvN2d3b+GDhNmqNsW07w72zXreRO1ayyErYWBVNcQzudgHDzF7KpiF6DdCIuz3MtIC0V3r91e5cQFRjdCKDfbCyvOY78BL5xB45tcKKC87hBXviKO8AAvnkJAI1cK+MOMNJ/m7qojoAWQEYUmhKxGKIRBHIJ7ENobEsT7EX35Y8uoS4Z8skPTC8l5/BpGPwzq5CLMVh7c+UZwelaXEjPN741CES7JY58zL5xDkQyOHjzIMCyiL2HFRcFc+OIdV8ONeJkxrKzpUPkE++lyMq+dx1cySLk2OI7zp0H2zD74w+e43fr5gZ0fg9rN4A1scmudUaIzlO/Y1NrgcAnR1wYwbrfy4mo/LG6mBRfPY9JH/768n2HmHyaRhxHGNlZQUrK8VzfafTwdraGm7duoVut4tjx45heXkZS0tLVU62Ad77aqJzhQrvIKrRsUKF1wQieiu8kYlo8LC4urqKH374Ab/61a+wtLT0ppt2JPE6lU/Pnz/HzZs38dvf/hbz80WBhWsnAVwTv0ecwycLoFwmbsjZXtytpBLl22Cq29QYZCu4ke+B4zmQM1j1ZZvgKFJnORfb7YCTBXvGTdQEHMAg8L3HIKEQQ+m2rQBomfGuzbpyTi9Khl7IB8uF2bd5DmoIhTFp03lxnfBuG3j2eOx74SIBhYoaIeVRoP3DpJx/sgbKXZ8g2t93tLNaFJKVEBfqdcHKxH8GT54OBuI8Q71zB7X0PgDAxToxSWB4mgWxTMIQ8r5Nnk7kcjQDMin+bMXMgiCLbM5rVkUEAEYdsJBP8GDMmiwCCR2UEWAepBcI0y5oRiHklOMrrISU61RTtgjKO3bOoMwLQCKmnANih+jh1anUk+RSlL6SZPv7siifMsyotCVTjDo6yCGTKHGmj/Uu2Q+XPr79V5w+voqorPhsmIBCRIU9X1lmxzCMZNE067F3IINSijsdkMVWxvjMQ0liK5lLiqZx7GwDS2PHvvYc9fg5AEIa6ypjrzxvxOiJTw0Jt+EFkivhNjIS2iHkGwGAE4irkPrvlPsr6tRGqIgvDsyBvsHehccp6YcNkUWCWoqjQLuzHhDI+qQ+yU8764hf3Jhi8sMBJ+Fp73jSM5xi56cp0sl7ZQXFck+YDcPOI24J5FI3Dz+HAeBuDswIk5IEKzsAIqEnfjcXyEAXJqYkaz3vym+tPi8fEVynnHzyvfL7qO/l2PlucrJR2WSJcdKHjp8GrT0cXen4adDao9HPlk4i2n4x8hFTBNp4hk7jHPKXtxGfex/+yf62Gn/8U2kb3hXyaRwzMzM4d+4czp07B+89Njc3sbq6irt37yJJEiwvL2NlZQWzs7PVpN4SHLV+8zbU9ypU+CmgIp8qVHjHEMcx8jzHo0eP8Pz5c3z66adoNg/mLV9Bx+tQPjEzbt++jbW1NXz22WeoD1k+cd2gqAGAZBbQyCffAydzoFwmdsjtmm3yuLYISg1qkmksu2rzJvKJ4OFrS6BMsSXc2248ayef+s+hPl8EPbsor9tsqi8nzKxaeXGe62oJpS+y93rRW9pHNwUkaxapiNAvgIzY7e0hNFs7pIhyvjTAmpmBWnn7qFkUCDjN4V9uFHWc+gyGc5mIGdH2S/jFM8HjAKDn8QDgofLlnsqp0b6CiMevG9uLK0dNwOkkjI9nEDvDtWlUNIVyb8ZB8AVBBj0Pjjiz1/8oMk/6LogqC/nkwNwA0WgR2HcbUDiLYra9au2mNDhLJ7IVJqAQoKJCJk/l5eIMfcF2ynvQi7ug3mQ/ZIrCP2nAgpBzN/hOgh5i7sEJ6iZPNdRcGy5WiCX0VPLJ0qey+gLivIP3sq+wdCqDWR4bQqqTT5Qk4CwFKTZ9VKuDXa6SjETUVzTNiusBGAmfl7fJtktSs6fc216jAXS7QMPwrFqSN9l49FdEfTVaz0A+aZZ5pIx5MffgESafIs0CVChkRdyDF6+ByXM67+5iIX4s9+kDkE/ifS5EZLAHxeGMpiBC38lTIQ/IAy5D7d5/TD5CiMcrSZIPlptUEP6SukezSRXGFmbIeU9eb7ew3O+miJvhtvtujnhGmJSUeeCAMcLMwgSjYoXwIsfhiEaJtBKvvwylHS7kiBwgpTgv/0L30TaQ2SYrTHw2twCsjX02Oz/xGeaXgDHyCUsnwPUGNv/vfwcARCsrcM/3SavmH/+xtB1HjUQ4CKIowtLS0mASb6/XGxBRu7u7WFhYwMrKCpaWlipbtj6OYuYT8PbHbFSo8KZRkU8VKrxjiKIIly9fxszMDD7//PN3/qHxx8aPrXzK83zwe3766acTvyfXTtgs9TRPjb3tJQsq+VTs12aTFywojMGyz/2Vp7i1RTMYJOyqmGK7rgOOWsCVv+jrzhjeytOeTgplGSCpjgC9wKFkk3CeK5lOsp0YScXGKALffwjqlZATmp3gOEKFBedBjRJSqpchahW/g3v0cpCdQK47Yt/Hueuz7QAAIABJREFUzIi3X6jkE4esiMYQpRka3X2VU6DRpm3Z862sL79G6ztjjhQAOK4hIgv5NIVF31QqKXs/8qgjxj75xJwAT64Dy/9J/qLBUk9VLWWpqGJk9mLhnr2XyQntniSONcIPk2egZ7cCX4tR1pc5d6CZEKkweh03/DbaIcu/PmLX0cknA7npDUqgmd5TfJBcxsyKMu4an7M4y23dPk1tGVG9ri17Ke3ZyCcDqQ7Abh82zcSnzq6JfKKsN9Jr6OEt1Bv7zw8W5VNO8jnTCkyRZNsJnbyS7h8xd0TyKafR54nE7+A0XS76lWifF7Yno9D9V3qeCFrr5UBS3v4SLfCgDUErU0mJiQi1BxcQpyWqSdHz9qC2w0pm02GKrUoelNgsTVmkPBf6rhPJJ04dIJFPyv4lK0hOnWwVeUAyUCStUP5LeseIGoHjDDkGTCGIyLdTtK9vIBo/3rJNl342ubPS01OmwJtbwvqlu4Mv+ScP9lf/5W8Rn3mvtM1ssLB919BoNHD27FmcPXsW3ntsb29jdXUV9+/fRxRFA1XU3NzcO3vujhppWV0HFSrYUJFPFSq8Q9jd3cXm5ibOnz+Pjz/++E03551AFEXIMmNW0pRot9u4ePEiPvzwQ5w9ezbQgBqQLKq5T7AWkI1FdSupZC0cT2XRZ7IKOwCUgtIwKN+CX/Og1EDABQuvQ8hy3S7PQnJqD8eaSk+x7WMfznRiZmBGKBynGfD86cTH7D0ooIywkqZDjSj/OCvOnd/pgjd2CjWA50n1FDPinRfQegLXJ4vfLneowyPJN1HLHqOWP4ejBcRufFrqKEibJT8tzC9IxmvT7xYWgobt7ux2sWSohRN3+8ojvU9PpZLSvItG1o1H1nUbKShWaXyTjad6LYsFVfSVUcIYm6VyoV4bd6XMKqFgQC/uCxajgTOX5UAr8KOM9akGb6ON4+G2oVCdaNB/RcDF4UkBcb6L051vsXIqBe10oSrdrEVnZyRSFVu2AdJUV+oBNsIU9qGDkthWc23OFPu2WECmOmkNFMTOYN/eo7n+PTDUnVMl8ynx7UIJLoAVol/N3lOWs9T3OA3efyLuIR9WPrHHWf8l4njv9xUUxCGJCAuqGekwQn1eGttCbcgzIAmMZwKxEbXXEK9PTuxgyWJOINuYWSZWD0qEKN9l53RySdy3QfkkgHOFLFWeG9VxI2R/B4BTDxKIr2kfAQfbRfhRIER2ud0U8VJ5P4xCtoKBbZVZ/m19/bS8ZSWbKDtuak9ao1O7hHgteS9Je4T01j0AQPLxz+Hv3xwsa/7xT5Pb6OOokQivGlEU4dixYzh27Bg+/vhjpGmKtbU13L9/Hzs7O5ifn8fy8jKWl5dHXEuOOpxzR+p4O50OZiwTSStUeMdRkU8VKrwmvOnMp+fPn+PGjRtYXFzE8eNy8abCq8OPpXx6+fIlrl27ht/85jc4duyYuK6vn0SskE/ktmwKKSsBY7XiynWbsMEmjRZ9lE8RKu1sBS0AoGzbXLhmPga8+MG2XcsDq6UPWewdD2nLpy6XqgzdFDRbfqzsPfD0UfnplWbNhmabTjsDrb8P/+jlPolSOpONEG/rfdA3jyHaeolao4WYd5D0HqDmS65Bg7Ud+bbp2qRXfW0aSS+Cg6cZU47U/NwcJj1gysHRDIgN9plTqKQItiL73tp78FwH3fhn8Ee/NHzN0Bgtj0e7zvJMVr64DMDB7HQ5z2TyKWT3t/YYtCP8tuzKz41EuIyNV02vj+0WYilMke8jr5eQFOyxvPM9ziy/QG0xKvZmGZsli8PRHdhWM5JF5vWsiiZrpbdpy5siIqC9C8zLzzAAQFlqOjs0NPs/vn0ZSWN0MoqmfEq4gxwy+eQVWz5NYamRV3k9zNBL5HbMnRHyacV9j1bct1NmHyb5RHWTcH1Kl1Eoo0m8nwcscV0WttYLjbdZD9GTcPYchQrmXsgJ8l62zlOt8QRI3/VetgJUbPNMyyUoy9WJJxK55BhRXTinziOk6i6yEYXvHsRKEgj+/j5gh8fMSGbKO2hIKTVOqPWe7qJ7fwtxUy/NMfMk4RNF4LVno/09ioC1yQld2Bh9huXZY9j88vv9tg1PiEsSNP7rPwTbUpFP06Fer+P06dM4ffo0mHmgirp8+TIAYGlpCSsrK1hYWDjSShrn3JHqN+12Gy1LHmaFCu84KvKpQoUjDmbGzZs3sbm5ic8//xw3b978UW3gKowiiqJXer6ZGXfv3sWLFy/w2WefoSFYpA2+UzsB4Jq4DnEGTuYBhbghp4e+A1PY5OWbYKrZCudWi76+5Z0ln4myjaKwX2JXMbndXXBtVi3Kc9SC//YrYOEcqG3Is7JYD70q4lpTXBwSYmEmzYAQ+XTzDiikEAwcOjOHybRAsYNrM+XFKIrg17aBztBvWzbbtNlAtPUclO4i6m4jynYR5buIuIcIPUSUIYodotiDOCqKZgJ2dnZwTPn5i6ykFohlopbYqgwwkkrWfDMURBEMGW8RuSnymWwFe0IPzHWTSsqs8AQwbBHnn60h8hm4abAmUwhedg6Rch2qJQftnqKSxMJLv0Y+JSXjcNYD7v0AoLzIyUTBQgpzyGwLE4rHBuv3n2Axegje0LdcbQ5x3gX3FRcz7Ud4r3EDs2eAEUtKy9hcb5hsWcRC6giMF5E1b9J6e7G2rzlTZPtYyKq0A0Ann8z2mnvXX6+LRu/WhMuoRj7FnKkUdU4KOaVMKnAU7n8R9+AFa0npp4o4HQweM/45jkf76gXZgkw4t2JOVOA+6xxIswAuQ4BQZ4EcDVkUxve/QeTSclJHvGaFZZ6D7rbsfZjQAhRlk/Jd72Wy5LDPh5ooS7E9ptAkoD4kcsl3c5l8kpB52ZJPWBZqM3sObzNAnLpOjmSuvO/Gs+XXejL0OTNj86snwRt/WZF+/DM6fhq09Wx0pZXToK2xd5D5JUTd0XzfTvMs/MvrxR/1Bty9/XGj/vl/RrQQHp8r8ungICIsLCxgYWEB58+fR5ZlWFtbw+PHj/HDDz9gdnYWKysrWF5eNr3r/5TgvT9SmU+7u7sV+VShggEV+VShwhFGlmW4dOkS5ufn8emnn4KIEMcxvLUgUeHQeJXn2zmH7777DrVaDZ999pn5gZ/rJ23rJXOqaoh8F5zMqoolcjvgqAHycqGbAPjaIih9Ia5XNHCKbJfaAqhnIJ/YwdeXQfmGbbvxPCgPHxMD8E+2Qb3dIpfFAovyyTIDTikQqAUOQM/q0NoRssdDvyBV8rl/9hJ4+SKcVxXapxeCqIMzqFOUWWRREsHdeznSvvGCS1Ekz5HsPMdS99+KQnK9dHMo9EDzqqXebKsJeEOGWtQEnEY+dcGIQUpGVGGTpxMchByMpknRZM5Z8x277d0U+UxMMyBYVFIdMMemGaV7hLh3M6C7/6P40EI+aSqXLA1mngyg5Q2pY4tSTZRe+qVCb54BZYXxez+AXB4kJ7ibg2YD+wyRUp6BxvTkk1NUKQCQw1bISXqb8HA407uI5RO9ySwOwNSfKYrAva6eWWQOIzdeG9ZrzZipJ2b2Da+XJMBuF5jRizGUZUZFk5Ej67cxuXUBcW30OnFUh4vk30CzzYx9F0jke7a2jzwKn5fEd5CK5FN4rN3Lkoo4xVm+MMoNHSDjptho8GthEluy1gsU9tm58rEFUBi3kvv55lNEa/dBFjvHqSARUwpBJGY2BVShrw3ylRXMOepDssXjzCGqycshkU+SYCvzoMB32XOptR0g2y7CCb9joO/6bg6UkE+ukyOemxzTXQYkQ+esfXMd+Vr3cMP6/CIwRj7R/CIwTj4tHgee7pNP/vj7aD9YHfxd+9nP4W5cGfzd/OM/im2pyKdXh1qthlOnTuHUqVNgZuzs7GBtbQ3ff/89vPcjqqif+jk/av2m3W5jdtbwflChwjuOinyqUOE14XXLp7e3t3H58mV8/PHHOH369ODzV63EqSAjiqJXQj7t5Tu9//77eO+98uDXELh23GapZ7TU4WTBZJfHtWOgnkH5YygWAtNZ9JmzqQAgngGM5FPQSqYP75aB+/1CdVvJ2dqDJfPJMuNcUzXlDNSUcUibiSa8LLDzICnTqaT78fYucP+eUlwScipC7RjPakK/4NCYPEfMDN5sg7L9gllp3pPngrzzHuj29N/NQMhEZFTiGMkdjmZBijXZvpJKJ2d9PIPYGcgnY5WbuANGYlIaTkU2U81UmSZ4MGZBsOQCdQBE8A/uINrbuEY+MQPaDNUslbPPAJ3A0p4nVBskgUSQznsZ+bT2GLTxTN5nmgKzgWMOjTlpBqqPFvkbXiefctLvJxkaiNghmDEDgHyGxc5NnFzsDSz2ylc0FlCyTCefrBkIRrJIJTEH2zOSXhaV7h56bRP5ZLb8M86SppkWsL2BBj2ZWKblPfW3IC5NuA2PMPlE7OAooLAFQK4HFsilCPI9wYn9u9jraXcBtbhbtii41/AmhS8Gbfwkq77A7+h8WFUkNJ7HVZouR3Lvgn2OwzSQ7jEa+SQS/l7p3wfPizItlzKwPCMKWMoBgO/J5JLv5oib0qQkbaKE8MwpWTjmgn1i7kGhNgubjAKTnXxe/iXfKyeffDb8f4ftvxbvSaWTw2q1SavkUvvayXGj1IlgzPJ3894W8vuPhhq0vy+anUf98/88uY0hHDUS4W0BEWF+fh7z8/P48MMPkec51tfX8fTpU1y7dg2tVmugimpOc19+S+CcO1LKpyrzqUIFGyryqUKFI4gnT57gzp07+N3vfoe5uVHv+CRJKvLpNeJVZD6trq7ihx9+wK9//WssLi5Ov4GoDiSLgJL7ZLakUgiYAYQCyyisIetTWPTxwbJdVAgzejmaB3/zH4OtUXsVrjmLWMqVqtdlq7o9KDPi2XuQNmve5eJ2mFmfeS+1tdMF5oRi47iSKHfgGzdA3slWWdZC6x5CBYmMQY2SbfVy+NXN0V6gWWS12wbyyaKu6YARqbPtt7e3sWh4r2FD4b1YrwkYyKcJz6rgBo05UmD4SLcQBPrKox9DJYUaYCKfHPLeLKKnRXYbEwF15UdIuyAls4G8YfzSSAiN8BCKoOycogiQZuiPjX8Duz0o/T1c0aNaoC3Z5Fjb4G01vyTFDGLVAitCDTvIMElG1HrrOJ5dx8pyG8ncKqKGkpFpzXOy5C/V6rp91zT7tNqsSvlhQ6CkBt/rgjQSDQBlNntPM6y2Q61Z1L7/t9I5LZrlHgCwQvTHnImjdcJtuCi8nzjbQS48G5EyluYUvsd6inHM3cJCXJLzIl3X0ngSstbzHhQisSU+IdB3RSIhNJkoT4F4dEJA/Ph7UG+KiUrjkMaxgw1x+jWtTpw4BLmk5T15ebnfTRGXPTftLe9miEXlk1fiBwVFHgvq9mKF8CLH4bkFjsOPNlIkWSs0NgTIp0BGlO/2sFeC27n8olBOAeWPMXEM5KNjQukluVMyga7M/WGIpMpO/Rz5jdUidw8ALRxDfvvGoLc1/ssfQUo+ZUU+vR4kSYITJ07gxIkTYGa0222srq7i6tWryPMcS0tLWF5exuLi4k/i9zhq/WZ3d7dSPlWoYEBFPlWocITgvcf169fRbrfxxRdfICkpPFTKp9eLwyifmBn37t3Ds2fPzPlOIfj6CcQK+URuy2THBTURoQ+rcsHZsmWmseijzJZNVezfouzobzeQZcWI4O49nbDka9Ms5iGQTwbVE2eZTlClqWh5B6D/0imsk2WirVJRtBaKDGkuO/SMtY+v3wRl/RfhwEsIez/xvf0NTjdrjtMUVGK35Z9tgsYLXyXFmBELl902sLIs7k8rIgJDZIxivTc/2wAsGUxWFUZUs3G+RsUusf0aYqoDJvJpCou+aVRSxkdfBoHv3dj/oDGj21Lm+0Wlg4KzVM2E0kliYXmeyuSF1IfGr5N7Vwq7PUDuK9J5a4SK0X7i54/gUeNdZDRX+p2iHRFq2EUOYR0AMVIMaED2mNu9ixO1hzi27BHFEYAYbIkutKqVDM8BRATuGuzqlILgAEZSCY0G2DuQRSnV6egKLsBGtqGwNzXZ6Vlnd68+R90/B0rG+p6BfNJt+eRxPeaeuEakTPKRzkXMHTiSVFc5TtL34vYDrQovCt1nJcWakO8WHJskIiGU95n3Rsgnam8genZdbAMAMQtIHKuEsZGjOHy78l65dyhXgNRe5xVySRl3lHun7+aIhfwuTh0gkU+Kskki5Th1cqaTRroJi0LfZCrX2fnUIQocZ8jeL4j+b5Jvp9j5ft/yrmzCE+WTk1VK86p2NyfX2yh5V9oqrKC53sT6n79DdOLMYFHy0Xm4q5cGfzf+4U/hY+jjqJEIPwUQEWZnZzE7O4sPPvgAzjmsr6/j5cuXuHnzJhqNBlZWVrCysvLWqnGOmvKp3W5XmU8VKhhQkU8VKhwRpGmKixcvYnl5GZ988klw1v6rUOJUsOOg59s5h++//x5xHOPzzz8/9MM9104CuC6uQ5yBkwVAy33KbcQOKRk1g/WyzcKKy0JqWS363C44mgF5gfgZ2b+xzp5vg5MZEEZfCH06D3p6cWL11twisPEyvL2m4cUgTYGmctx5rpNP2gFq/dQ53TpGwpDVmH/wGLQ99LIcJygtwEizoWu18u9MYXHKnRS8ujmhvqBkdBvMPJItwO22ITPJNvOfqQkoeUWRcVtmEsaaT25VNPk2GIpabACjfRb3wNwEkT4uTKWSkipPQ/CdOtAbKhRb8p6cQdUkFdOAQk0kkE+FQlEoBnovkw4aKSASX0NtX3syWtySaoSBcYOz3JwjtIem30YWycRSgp5OPnGOyHexvH0VJ5Y2MHN2r41D91qTKrUOdrlNwWpBSbFxHJQk4CwFKeQS1Rv9tilqvCgqSK+WoY9nNoU0MduGGStBNtMqzo2i+koeXwJ6Wam1pUX5lJN8DrQxjhTVdRTJYzRT+Phi34WLA88M7HAS3yGmwJgtPEOGCvwFWRSyJ5Os9QL7cllYLSUhlJE3/MzCjPju1yCBdNhH+HhFskNSYUukvdYmoU+xRlyxh6xWVcgppW2cac+FCrmkjT2CsolTL+ZJWefalGw5vM3AuXKdHHHAfjCYiVVGEgGI+vbXW18/HXnGHd83M2OcmmdmRGOTBNgz4vHrcfE4qDOmhmq2QFurABG6s+/BdV/C3b4+6D28tb9+dOoMar/6XflxjbXndccKVBhFHMc4fvw4jh8vVNrtdhtra2u4fv06er0eFhcXsbKygsXFxbeG8DlqpGW73Z5wGqpQocIkKvKpQoXXhB/z4WxzcxPfffcdfvGLX+DEiRPiunEcI02N9moVDo2DKJ86nQ4uXryIs2fP4oMPPngl7eC63C8G6yWzII188h1wPKuTS/kWOKqDvNzfCAxfXwSlAkkzwBQKh9o8qGcgnzgD1xYBJx830FdfJQugfH+2oo8WwZf+XPr6T5m8f7LkPVnISwvpoD3oa/30EKQ1d1NQqzhW3tgCHj/cX+YZFAcKKIEaATOj6Asl3wnNQi156XIPVifOC3uezDDYy3vaQ9ugQuIOGLE6U96U52TcVre9GYzWGdmlUb1oVTQRPHq+jkZsIF/MzBfAURNkkJ9MpZIyHDsjAa79Baiv7H9oIZ8s16FSjFPJoSyVLTbzFFQXxhXtOh/PUBnG3jWQ9YB7V+XtDKNut9YbIFBZbOQb2E7OlC7bQyT8xgl3MR+t4ph7iMW5LpKlQuVUCu23Ql+tlPZ0tZK10GJUDCFNbcRNrwcE7aKGt9ezkU/W9mkk6x6MiiYiKuyhFgTr4Qe3Uau14XbKx0kt8ynxbSCRzwErVqRatiYpNrJSplOE8Ph6Kv8KM7E0MegA7yAs2MWJEq1QrlP4/sUUaGGehi2ch8ZbWn2AaHdtaKFEMIWOiYPjTsEBhc9h8Y4XfGAJfq//5fAy7+WfzrN8rSl2Bo7riCHc5zVySstvFPKg2DGikhzO/cZ5hMbmwpJPKKQfVPkWuD59gIRjz0hmy8fhECkVzdSQrjt07w+9cxyiDl/2C9DiCjBOPi2fAr24B144jvV/+nfUf/kb5FcKpVN0+izcw3uDWkXzf/9H3f4VBYlQ5rJS4c2h1Wqh1Wrhvffeg3MOm5ubWF1dxe3bt1Gr1QZZUa1W640Rh0dN+VRlPlWoYEN1t6hQ4SeOhw8f4sGDB/jkk09Mkt9K+fR6Me35Xltbw9WrV/GrX/0KS0tLr6wdXDths9SzFMLRJ3YU8mlgk9d7rm8wNhagjBZ9AIqsKyM4mQUZyKeiEfvbZUrgb94OW6xtPwNHFC5IWR5WLeSlpZ6vWXVp0PqGVFRLU6DVBKcZ+NbN0X4oZQ6EPnceVAssCxSMaawA7nYdeHMXNF78sMyc3tX7IQF9S70pLCDFbc2CvNxHWw0jsWNQBAJ7iiZb2TKpzQN+TV2PrNlyAJhtfZaQgrlhVEnp+3cbOaLOJmhYKWEgn0yv8Zplmna955l8Lec5cABxAQBwnsnk055l1v2r+3Z7e99F+Tgn5dGVWesNUC8vTDSyDSVDBCPXcMw9zPnnWGjuYL6+g1ajaLdffYmormQoWlVZuUW1a3zlktSeI/u0EL0oiELofZfzzNZ/re2zFiSbM4WFm8Xyr9sOkk/sPeqrVwtn2cA1pCmfEu4gV86VV54rWLGD9cqrt5TpFJp8sJj/gGO4j+BrvaJgCqpTpHuh1FlCuaDS2BYqdOdZkBtm7jcibSO+d0Fo0MiXws8VwvESRQhNfmKT2iqwSy0Pynv52Urdt3wHdy/WER8XrBwVsjTSlifhfftujigwzqvIvFw4l4jCQJvZh5Vvod/YdTIk85P3TGZGMjt5HbBnxK0E6//Pg9E2lVnulezPShaUKor7EyS2dxIgy8G7+8+myclTyFf3s+IslnvA0VOwHDXEcYzl5WUsLxc24Z1OB2tra7h9+zY6nQ6OHTuG5eVlLC0tvVYS8aj1myrzqUIFGyryqUKFnyi894OgyS+++MI8g6Qin14vrMonZsaDBw/w5MkTfPrpp2ha8w3MDakDySKQl4TSjjTEWBi2EjtCsPbofo0v79NY9BktwwpM8RA8tF2/2wStPgquSi4F5s8Au8/KV7Aonyzvmsosc84NtlDKSy27ci/8wdelXIDcgdDPeRov1Eo/fei4ApcUOw+qT/6W7BkYIpmYGf7OY6DeAtFYEbdknxPWMN2umoEFAEwNADL5ZFUhsTAjfn9bGRh1EBS1IXeCZMHo9jw8tUBsyZuyFte79kn4FJmFUkxNkGJfCADEbTDXgt3dcwN07Z+LdYey49iifNKuMZcDiu2UWlvSlCdSFgsgexVp5FPSANafgtZLJhR4V15ATvMwiSPVtgMZh/VsMtti0Dzuok4dzEQ7WE6uYr6+i5l6WqpYMNnk1Rs2WyGLGshM/hs7vFGBxLmcxTeAVaFtVDRRo246EopjYLcDtHTLGsp6wW3SrStIav1xL7CSRj7FnKqjsWbL5wXbPABwFL7+Y9+Bi8JEAPPk9dVyj3EC30HqN8weFGZwgt+T+2KgSO+drMwMbS2QBcXSeNYnLJP73yDyY/fxg8zoF3OCKLxJ7/vkVABSW5yibDoEsWWBlldEjYMv58yJ5BNnLjjJoNh4eBFnfsQKeWQZh4+rsJIMT2oKkeAhUsp1HZISQaXv5IjnJ69118mQPuwiezH2rFK2/TJCquSworLjKbNH9R7u5EfY+b/+HdGJk8jv3h7c29yz/feY5Be/RPLeh5PfL8FRIxGOOmZmZnDu3DmcO3cO3ntsbm5ibW0Nd+/eRRzHg6yo2dnZH1UVVdhHHp1+0263VeehChUqVORThQqvDa/yJt7tdnHx4kWcOnUKH3744VTbrsin1wvLb+O9x5UrV8DM+Oyzz340KbqvnUSskE/kNo1KB6P1jjGDxqpoKiz6joHSVX3d3JIW34c3ZuoAoLxQfPloGfz9v6jniuvzoCD5ZFA+WR7QtRlrWQYEirnmbeQpQpIK9r40Y2MY/u4D0O4kERO02wHkGb+lOwlVHXNQc0ix9mIb1O2/nI8pn8bDnNnzhDqKPYM6XWBOISQMhAyxTYVkDTjwUQuxweqym9fRTHSymakJWMgnY9G8IL7qILIo+qaw2aSaqQkEBqMRzOTyz9b2C5m9TXBSLxQHBvKJk5o8HqQ9UEu55rXf2TTTXYBkJScUejlPAU/A3SuTy7xQxJSeN0KzzJ0DBTLsWm4VK/QAjaiLRpyinuRoJDnqNYekf+1y1htScwR+EW08BIospE5bt4azdP16XVc5ADYFkHmnALTMlj5sWTmw5WABtntbH9xpgwzkU5BwyzLUd29j4IhXcood1eEi+XeMFFvf2HeARHYYcCQfdx6Fvx9zBw7h7/ux+0nNb+I0fwnJ8a3AwTN/ggj1Y++mVjKzd0A8PUHNcR208QjR+uQEoAO9cx30Nc2zHGV4GGWTisO9W8aCJSd7RtwML/e5RxSyVEWfgJkJE5Gs5EVJ54UFBSZzHJ7Uk/uwXZ/QnDhEdAWOwfUc4gAptfVNyftA2c9YcvilJFgJ+URbJe9I7S1sXC/szZNz7yFf7////N/AP7wzWK3xR5vqCajIp58yoijC0tIS/n/23ixIkuy6EjvveWwZmVlZmdVVXdULGmgsjW7s3awebhiQFEhCpCAJNNNgaDLRJM1IoqihzGiUBOPHyIYjcTQmUkYzmfGD+OIfjfigGfkBSCBFECBANKobtXftVV37nnvG5u7vXX14RGQs/u69kVlVnVXl56er092fv/Dw99zjnnfOmZ+fx4c//GF0Op0+EdVoNLBr1y7s2bMH8/PzKG/XOeMJR2G7V6CADgX5VKDAY4aeLdurr77al1FPgoJ82lnoEYn79+/HBz7wgYe70qiyF2idY/cxlIBKs0AqqDWUxI7aJi9Z1eXjAIBQQOqfO10HmYrKZsskK/rg3HQNVJ6BP/2uqBwBEF5RHtl8a4xRCKQQEcmU/pUOAAAgAElEQVSr6p0Qck4knodTNqHVgZlmVmy32jC3b+av5AwU28lTcGWrOk+k11ZXeQVk6ih3czH7/5GCeW7eU859QT7L+oqYDI7unmLfDHWUaj5lkVBYed/frVQHBIUUgMxCScEBBa0nR/dD145QleUUT1Bb0xdACBUgh3xyrg773nc3zw+Cr87BdJaAqkL5VBXmpjQGmOIygE1ruy1DuGBcThBH9qUJcOPSmN0egCxXqJJfVCcfVkyaUmB8dxIgMN/UzRo+uvd2zpaBs2gInGoN5FNesQBkK8gl8klxjxprQe1WZjPHIXRNxqC837XkgpL0Uj2zAJhyOZv3pTEBwHR0iz9CHIu5eBxReXP+yVO1dQTVEwCQcE1L1IIHY4vnEzhbC94O1rfhmfcXS/zzxJnNYy3FeN79I0pWsxBoq8VhLhsn8L7AWusF5rY0CdqRcnprsiWULx8On29isOFKzDaOCCGefNqusonNe+LP7VsJa3vnmwmiHCV5v/kOv77GJx7RFuuxWabTFq+bo/At7wih2Dai8N0W1QMHhRwc0/z7pXV1DdQef1caJXCICNaM/2108QIRIRol6eozwPry8Pt2qYy0NIvOxWy8+Du3Ns89M7N5d9sItX/6xfwPlYOCfHpyUK1WceDAARw4cADee6yvr2NxcRFXr16FMaafFTU7O/u+ZUXtVDQaDczMKBbQFCjwlKMgnwoUeExARLhy5Qru3LmzLVu2gnzaOVhZWcG77767ZSJxUlBln26/aBZGIp98ExTVZXIpXQXZMsyoJcpoe/DwlT0qRdMkoPJumFjOnDI+BpV3AU5REAehfT9Fee2+rhPN5fy/S0VI9BQAQrEv9WNqnfGGpByZlLXKodQNKYfGtsdJkHyiZgvmxo3wj5VQ37kiVsizPyqJdWB/axkm9dmP+9F28goaOQ36+nMwcx9GlJxhz2VEcqp7WjsN48N2YsAkWUm6QkApqkLD9Wo5L3gmtHy0SVPRqZR8i19RPtToBCqpwDWiKxfHvm4q1UGdJaAmZCrG7WDAeB+SJR4g5wxJq+QZEiGziuRe/ZnR01iFWcpXcJJjspu4NkNEGPOOYgzJCwVslNl/MfsYa0HNjqzQ0djbadVKaQKRfOTIwUFo1RLawqA6o2mCd892UyZkAX1+VZ66rtVEJb42NE+M5vsBQGwV5JNA3EcUsxREyTfgonBWZ0QteCawzHCtk99UVZHHgfQfUbF87mb/UJitjc+QUok8TIhI4ib2UigLipkXQxlaaYzo1lmYRKkc7re3xZwgVoLFnM97weJTeBBy/fVe3s7ANWPY2fA979sJIuZ55L3h3zaERU8c70+xC1rdZQczpKTzwbY5ggnlGvJcHVwnRSlAPlnBtnAQyUobG+eWxkilUIdyc6BGF0LR+N9cO0FpJBOVFp7F0vcy4qn00ofgb1zJNpTKSK9c6u9Xef1N2N36vOGCfHoyYa3F3Nwc5ubm8PLLLyOOYywtLeHatWvY2NjAzMxMn4yqaLMxn2C0Wi1V7nqBAk87iqdFgQKPAZxzOH78OBqNBg4ePLitPKCCfNoZuHbtGs6cOYPXX3/9kRBPAEDlvbo6srKQRmW5mGOQEUAqaBVNWjUVELZyyQFFulVL7WQOjavhnKdRmOYiqJzzUqrJe4oVhEOitEDkIM0JLWFleqDIQHECf/ocQvlb5CkcaB24WTPP/sAxoXu3W/yhOIW/sxJs3+Ssvh61EyNv4f6D/xlkcjxVRo8lHSGjynPS2vOpGCVAyyq1Wmuq/bIsJe1qSN3rp0EMIqWSS03OIfeju3gK9k6OMtREWVGKJW0AJJrvmr/mlCYCOQS5HxyJkMZ8sTKkQuy0gCtn+fOGEChMkScgYNnEWSqhWhYJIWOMjujTzK+aLKRQUX0Uo3l3eShn9nwP6pwh+8IxqBVNFZDmugEwsdLOVktw5xBz9tJR2GikgWpp7BrGeR5YI3CWn4clpbMVlKSRMEdxKp8Stfrjc687gmk7svhlq5lDHELPU4n0CCFEwjD3O4Xe4VprsHcu5G9j57jJ579s2xYVStostRyw7zqatrl5FAB1hDkykdrnN0uS0KD9HQASzs0KVrVk9ghcO3++cs3wvG1rgYyoHFJq+e3b+e+dyrGUS0bl7OfWxxfRdfw0/Gq2qNDunuv/vfyRjwGtTRK7+gtfUvWlh4J8ejpQqVSwf/9+fOITn8Cbb76JF198Ea1WCydOnMA777yDS5cuYXV1NZuznkI0m01MTyvcEQoUeMpRKJ8KFHhE2KpEudls4tixY3jxxRfxwgsvbLsfBfn0/sJ7jzNnziBNUxw8ePCh5TvlwlaA0m5AyH2CtohrlcSOklRSI1kFwfKrhHuYQA0RXGE7gI6fRnTibczufhHghSrD3ZjaC5NcGf6jxh86TQCpeJh2ID7OpR+HQhGDPLdyGrmKIfI+I57a7bB9nqeJ8yHgwnYsxub/8OmthHfXFvs1pNxVtZRi8Od8Xt5T8sp/DFTrIKcgnxCDTBlGsFPS3HuGYpWNpCGlhZXUpy5qZWWWEwjezqjs9CbLcqqpFGSGWhNY9A0XkwgWdO5w/uHeyaonQFfwEi3eOkAknKscLpATeXa7qOLJIa7Ie+D8UZ6g5uq5IeukJIGpBfoatDJyMLUa4BKZfPFeVM2RSxW3jOKm0q78VRRmjDGgTkdWxgZsysagUR519yPvYDSLT9ot3Wd2ujnGRFbHP40u2FhbQdnfGeOyjbWgTgpMbfYxlmz3iJCYafbblu4EK1inSu8snlFeRdRGimnsSi9gt3lvfAdO5cd1nPu+OfIpeLIA2ezSIBHKkW4o5d/n9uqJidVc3D1GFF4IIyotuW3SmOeOdU5wAiTBglhIcJXsmAXySnpvNCVGncTZKgNA6oGAkjiz5OPuW6ZTzPUKOd76QG4eOR/MtBolpRoXVxDfbeYPj5w+5d5vublQ43+0I3/zlWmsHj/f3RghvTIwfwzsaqbqqP6Tn805SRje+8KC7SmDMQa7du3Crl278KEPfQhJkmB5eRk3b97EmTNnMD093VdFVXOyNf02CPmdimazWSifChRQoCCfChTYwbh37x7OnTuHT37yk5ibm5MPUKAgn94fEBHiOMaxY8ewb98+vPTSS+/LC7sv70UkkE/G6ZQOaoWFcsWocTplh4GHL++BSWSLPpPqbGkAAJ4v6qdUAp29COtTUHNJ3y6Qq8AyGuWTUBwAoMsMkCyVpDZIKJqP5JQQEfzpCzCtJt80ty1UJAgURCjxMNPj15k8ZSvhGx3Q0vrmuBtVNDkPWx65Tn646OTKC6BPZCtDvZXJJwAgW4dxElOp+zFGpi6TT74plZyy/ZRKKot217ZJvs/IVAFVlpN+ZTKFAhpG20QCohqM0RBVwwSd24hg1/PtOU3SBDQrGjVKG2mxgaCMoSSG4cZyEsNUmHlF+tGfR1xdOwdsrLKKK9ZWKkSep8z1Cq3278QZaa8iNRTzombuVNjbmXJF/m4mQRKL5JMplbJzSjZ9lUpW+BcUcxlh0wGmFAUUpaLJkFeRSqZa1pFP9elsnHVJkejy0SA/Qp0YZgLyqURNQFA/c+QQIM+50md0jALWIMWUu4N9ODZ+HiKB2GaeCCFrPe9hggQvl+sU+EI44jvU9zQBohw18v0rMBvLwb4znQtfJm4u8J6fu7eo/BDzoLzn5x/xvY1Z3AOIN6y0VgLGsY2weVLtdDxfcxDcR0t4wiOopme2udgjCvU30Je0maI8N35/EmGIlPIdh9UjmWVtrkoor0t53FMeSZWzuCMaWTC1fPI6aCN7byx/9GNwFzMVs5mZQfreuf6pKj/zc9nijglARIXy6SlHuVzGvn37sG/fPhARGo0GFhcXcerUKTjnMD8/j4WFBczNzcFaC+/9o114+wjQbDaLzKcCBRQoyKcCBXYgiAiXLl3C0tISDh48+ED9dIsVSo8eURRheXkZp0+fxiuvvIJnnnnmfesLVfYBrfPsPoZiUEmR+6TIRwIAaAmgdAJFU1SDKk4nXdUpTwCYJEy6EQHx7Q6qjYx0Mu1VUH03TEcpf4pzrAI1yicN2JWv3SISk+cEQC7iuAQAUxgbsc/y596DWe9eGyEqIXhmKcdqtK0koGKIU5haBenV+/35Lz/vKefYgWtLZJD+1H+/uc1UQajACFZLKks9dTZUReSpDBw8pmDAk0uG2iBEMAKJbA3gzbRK0dSJU9QVb5aGmhOolPT3wSQqKaIyjMnILXP2h+F9OyugmpyXp+olp0oCgvaUfSSxYKuXABz5xJ06Tcb6Ryv3gFuX5YNHSdt+my6obiIf1juYQHvohbhryCeVykhuJrgcfhSdjkzya4t0mpyp3jkF8ilTUrWzEHqxvbaOfNLYBwJ6c3dlwdNEEbDRBKZngXu3UbJLCKpcRtQKUuZTmVpIIJBPgoqbBKkdGeb+IA9nwtc+og6exVHYvMU8xChgOAKCKEwWcepUzl41RHJy7YX6kCbj6kWXwLx3LNwWB2MRfoByLyrhbSQpPwRlE3vsQ7awshV+gLLKJKBrdxkYf6mHrYbvd4odsEX1EiU+2DfylGt5J21zjRhR4NkZylT1cf7z2rUcSrObn331yB34kMVhqQw7otgnojGiKY/kyf423Bx5QjSgGIs3YrSW2yh1SSp39/bmqT/0Ebgzx/v/X5vQcg8obPcKDMMYg5mZGczMzOCll15CmqZYXl7G3bt3cf78eUxNTWFubu6Js+drtVqF7V6BAgoUT4sCBXYY0jTFkSNHkCQJ3njjjSLI8QlAkiQ4c+YMPve5z72vxBMAUHmvbj9F/pFxDVAkEygmXeOLLr39yOnzoZTIMqd0qkHjW6CA5VXc2YXqjeHME6rv0Xdk/c64tYym4CYQSwBkNUWiKNRKbRgmk4Fo6LP4967DLA+o0tiIBaZIEOpT4Jo4kz9XUurglzeABp/Jk7uqdKDAke77HLDw4nDbKvWTQjnhtTlmutc2sjpik6zuxxIJWSibOypVjtQBkXblo16pS4IyoX9+EAjZPesW2zBxmCA3PgbqinlJUsh4J1qliSJRiZSQVM3cUvYR20CK28D5rMhLQPC7JaJwEXGreXSB9x7qFbBVFocPYO5k+jIKUs2zyjV/Slsa6igzlZQZTWpSSWubqf28k9jVtLK5snzzBCz3HY8oZKXMJ6tYoJIy5BAA+MAzqAfHLOAYzHQaRURtPONPoGRC3yNPnORazAIAMcQJN5eH1FJEMCGylmsvkOtEOWpSc+MMTMLf98H3Cu5lZKsKbWmssvfow7WfIuF9wU7x4zNitlPqWfLKt6RnlfCwY56nnB3gKOk8fM7w9Q5Z6wGADdn/BT6Da22O0869JhoXM7eJ3FshcHuMETo0bqeHUmVsDnSxGzp25cp6n9slIvjV5c0mNzYX3Nln9qH8qc/ld4ZBQT4V4FAqlbB371688sorOHjwIF5++WWkaYpms4m3334b58+fx9LS0mNvxVdkPhUooEPxtChQ4BFBozja2NjAoUOHcODAAXz84x8vXugec3jvcfr0aSRJgk9/+tM7wg+YKnt19jbKopGG2DEgPamkILMAAF5nGZa1qVcCUE6BKjXziN49NPZ3VTZGb18XA9MD6gkDHfmksXGSVE2agqJwHlNjimrtuG9l4m/eBe7cGj6WI9CC6iZm7gsUJaKQGoKyrCeuT0QEjOQTkKd+/7ypwf/Ufz3WtLcamwX5R5VBqlJIqYkYqxy/qnMCWqF8LVCkyT23kiCTbAaHG51AJYUyPNVgzv+jvO+UgmSUxmrckfkQiWzW2Cxx4Ob1gUIvkc+Ipx7ZZaLwO0ySBou9rG1oQAVGcQJTCt1H3T5olE8a8knKjQKASjXLvZIgqdYAfTaUVqGutVDWkHUASG3JrOufqeo+rymV+6SSiLgF3HgPpZKgxBwYC6mpwAmqJUlxHaUb4jM/tfw7XsqQ/RHlL46w1MYL6XdRNcznZYf91hQ9LEIk9hYyHMn78Nw52r9OE2ZkEdADw1ZdIcQ5l7lnpEwlCUyfiYhdzeA7KatMcm0XVAkBgGvwcwoFVEH97dw2Cud6dncIb+NILW4bs6k0HXhOBLMJs7mEPGHl0C1+fy3hn3doe/w7GCTEWkttdNbioVpC79/kCe7aZvZT9Qu/GH6OMyjIpwJaGGMwPT2NZ599FgsLC3j99dexsLCA+/fv45133sGxY8dw/fp1NJvaxXg7B3EcF4vFCxRQoLDdK1DgEcIYE5Qa3759G5cuXcKnPvUpzM7qskQK7Fz08p327NmD3bsfrJpnW7BVoDQHpIJdnLbga5UvW6UaBHeyDMoCgElW1Tk0ExVYRhRa3taBkydh8lZ6dybIkwJAlVmYRub7jmpN/KGXKQr44iglCZ+3AoiraylN2awSch6YCpMUWbZGFX5xBXT1ytDv62wldKjoFA6NJlvOtYMjonAxJ9TWRhsmHvmBP0pg5YVfd/OeiID001/NJXTIyHO1ZMvXP52ZRkTCim5hex9aEkYMdeifWLfbBKRw6kuoKLppfHPceimICVRSiEA3rsFqVBxVBVFWFcjkNAYg7CNZvInklfB9chZtg9fhxkVgfXOFNEyEIInKZDcRMVqD0NzGqaV685TGlk5TEJO+M3Tf2+KOmMGkQrmS2XRJfdMWwrXPNuWqYtXzFNApxgC1nR4AoN1UWf6ZNEFp+bz8C3bgGkp5Txn476SUNuEr4fneUgxvqsH7PfJNOIacyrMGtr6DF9zfo2rWwQ1+TcbfxOAaDH3/rLVeKNcpDisdRzphLx+B2YZdE3t/c5+XWxTAWfI5J7yfCZ9FsuRj5hHXdiiVw8f7Rgw7Ex5Evpkgmgn3nTopwCxKCqmCeuAWJVHs+EVLzOfm7FzZ51Fo/UQrRWkm/1llQ+Rc93vbOLuEZEV4Z8tpIu9rz31VcynGXo4GrKVXrq5nj+7+JjOy6+b/V3/+l/h+BlCQTwUmhXMOURQhiiLs2bMHe/ZkLiLNZhNLS0s4f/48Op0Odu/ejYWFBczPzz8WGVFFrEWBAjIK8qlAgfcZRIRz585hY2MDBw8eRFlSMTzA8xYPyoeDtbU1nDhxAh/72Mewd+9enDhxYkdJyn15HyKBfJLynjahLPZq6wVOV7w25ODL8zDJsryv05NE5DaJAoKFv74C0wxcq/U7IBvBaFa8A8MFwClFUS7xbHBytk9+IPdESFNetdFqw8wwRcHUgdYaoAsXx3/YMx777Crp0Hhx4RWxZmp87qTUw99fGybEfE7eUx66xQ9fOwB6+afyu6mw3TPU1BUHbVnOc/LKtvBg7bM0mWlZey0QSjBGHhOtVhsVhRjUIAVRHSZoOTWwLyXqKizFCaJryuwQkVhK5Fw1DWEi5PeIZCGjzCDvYFhFa7dotboIXL84fGw7BgIWTFyxjyueBnM5nA9nQVW611ilfNJY6lVBSSwT+Ekikk9GQeT285ckIkujyAL0GVJaKNvrfw/SftUqqNOBkbLOgCy/SgHbWEapJO87eH9JeU8ARGvgyKTs9FzyTaSl8OeMqA0HhnwaeUmy1MFz6XdRs+tgc5sA4V5/sOqmTFGzFfIpcH1zrPU2+zBwntU7MIs3u383EyusuidjzsXc+w9LFcUeyuR4AbyqFACttoBnwvOM76QAQz5RJwE48kk4v/Qc5lRVlHgYRkUtztdb6FNUyb8WrpWEyafAM9GWDVzbY+34veG/58yvuSRbzv2Wt1+U8y4cda0QG3dbSFtpfz3L6EKwwX/bD38c5Q99JPezSChqCQUmRYiwrNfrqNfreOGFF+C9x8rKChYXF/Hee++hXC5jYWEBe/bsQb1e31H33JOWX1WgwMNEQT4VKPA+Io5jHD9+HLt378brr7/+yB6m1lp47x+LlSSPG27duoXLly/js5/9bN//t3e9dwqoshdonWf3MdQBlWZgUt7aRkvsaDNtTLKiVzRFdUBBPiFZBSHKVdKMIm0v9Z2pfHsG5ubJcF99Cpp7Hli7KfcBAJoDfdWsok9TeYV5mgJMjgQAuaAoKaM6CQznLpek8O+9l68O26LNCShB7itKwKaG4hRmZrxw72+ujIt2lD/sTa2aqZ7+6f8Y7qaGfIKHN1MwJBGr8vxv4OBNDSZg0dTfT9i+uZ+OVDJe2R4ytaAhmbyema4Dyn56U0OkUJAZaqnIJ4IBrl9VnZuiMqsMBJBZgZWFMS38QCWXZvZjHCQ7VE45lcRAjdluLCjpAOePjm/rtIEpjcXkCELZK87BhPrCXafeSnvvRAWRiUqguMN+d31Vk6S40ZBd2vcpBZElWql2YcrKn3Eash3Q2bwCKsVYH80mMCeTT8al4lOf0hTRvYvAHoVibaCYLOU9AUAq2IBay/fOCmpxKVNqMJ/HUozn0+9iyq72tz4UcoQ7LkRkexcm2tlcp8AxzPsH2XJ/H3vpyOaGrRJxARKPmFwsEom/LdrDAXy73gvKJ8+em80+AmTLv23+ZmGVTZ5Ycikj1gJKdiKea2XOyy0QiALWej7Nvw4+9SiFyKepEpYP3ejb74VARGM5TqF7cdRBgIgQ1aKxv5VqJXjnsXptHcYgaLkXda8/lcqY//f/N9tPCTuJCCiw89FTPnGw1mJhYQELCwsAgHa7jcXFRVy6dAmtVgu7du3Cnj17MD8/j5L2veUhoxgHBQrI2BmjtUCBpxA9dcxHP/pR7Nu3Tz7gASKKItXDv4AePQVbs9nEwYMHh16Getd7p4DKuvuNSrMK8mkDZKswXliJnKyBjKwSMuTgS/MwqYJU0lr0geAru2HiRXHfqk1Atg7CDHDye+L+VJ5R292Y5iKoPguTNGBUyqdYtvvSrLiSxrnUBnPvUrsDf+0arJ/ct55T8ISs+oJtxeNqCWqncIutsRraaP0hzxqQPGCtR/LCzwPT8+HzmlmVEonslELVpyv2ZG0J5JNvqkhcn67rxA7UUhO4ZCsqQeREWU7K11WDBERTokrKt2qw946w+/QxpQgRTmMAwliV8pziDoxESjCe8kTEZwoJyiuKIuDC8dx8IPZHNVfsC5EjcQpMhZLWA0XgTgwzM0CAuSSzkeXQbgEzAvGQJOJXpxqaWrUSp/TooVzJlGpSrqBWKa/tm0ahhEzR5BOF2g+AibVWoYpsvAvvwkJhXwkA1VKfoBRt98gjNdP8PC68b1hBbSrNnd5k19JQkhFPZkBxLZIY3DbWT445LrCNVTcFxq5LYSr5g4z9ZF0lmbl7EaY1sKCBJYNMfqtEYSU5MeQeEW+TKVnjceDa9V62++O+Wmmxg/ReKNzv0juaZcglaqdBy2UA7HxLsR8jbIb7xeWF5h/nOh5RNfT95//ZNRPY3ePzEDmPZKmD9rXhBTi5VnrG5D5bR1Uh5Al25Fngk3Hyy8Ue5VoZa9fX4RM/9O47RGh1/0kE7Ppffh+2rnjHKVDgAWErVo21Wg3PP/88nn/+eXjvsbq6iqWlJVy5cgXW2r593/T09CMngQrlU4ECehTkU4ECjxC9zKcbN27g6tWrQ+qYR4mdRoY87hhUsH32s58de/HZkconDTS2RQCovBumc4dvCh6+vAAT35cbLNUBDfmktOgDAAirmgfhoz2gH/+DilQymhXxA6D6MzCrDWBK0R+XQKyKCi/ZmtwomcQLFJSabfhTZ2DiGAh63zOthlQRTBZUsK85L//plfsw3g0toCUimFJOttNI3hOVp+BRgn/jnwX7n/UnApk6DAnKPqMo0mrznKBoCwRvpmGIVyaWrAOhIuZS9RVNXmPHqZs3DDXVFnlra+uY18S2APC2hoghtghlmNM/BDrroOqUrP6qKZ7RmudpyHaq3wY/3ikV8t2SGKbCkAfSM+j+LWA1QNAzBT22TwErI+56BdsbzW1ziUiWUJzIt5jGDlFzn2rDphXvApv2fIIii/u+B6EklVCr6kgvAGi1dORXqiSZpWdZp43yxtXx+TvUnLXw7QSmXhXJp5JrABV+HzLS9yv0X8iUSk02Fz2ffBdTdkU412DD21DmBNU+PqzC5AptoXOxi1NCYTspEE0DSRvm8gnm+BFIpEz+QXx7wU0CMcWd0Tm+SOo9a6MqxkUxtnZA2Gauf3yFP34sI3MA5DxslcmTih0ijnziTp14IEBskQvbPBNjAe2cDb61hMgsH+c/w9JGguUf38ppKHCCLYKqdYzaK/vUwyUOazey977ecCQiRN15c3CxVfngz6D2M194sB0rUEDAdhc/W2sxPz+P+flsYWCn08HS0hIuX76MRqOBXbt29VVTjyLKIo5jVKvbtL8vUOApQUE+FSjwCOG9x6lTpxDH8Zg65lGiIJ8eHNbX13HixAl85CMfCSrYdtz1tlVQNAfj+NwndW6MNnMo0lr1KBVNyeoEQdu6lUlka/A37sLGOjswNBRk2iB6JIRG+aRZvaXIhBItw6TiSc45aKMJf/psppJgVsCyodGh45wPF62DJNfw3/1qC1htAWWDoUoGCStje0g7SD7/P6i+AzKzgEQ+aSz1qKWznFSu6iNbAxS2mN7WEXm5QEymCkBBPilXARqKQWYKBjKBu2u2DkCar3rg73e/FMO21rJ/V+dg2sIYVtjNGY00RvoRLJESSSezGg0hjfVkxAiynKcLwe2GK1QGtlGcwtTz5znyzIgItTeaBaUhjVLFPpr7VUHGmFIZlHRk60QtOrFIPplSKcuskrLCKpVMfSLYNhobgToKG0IAFLdhoGCEtbly5RI789nzJ2CtBwlF80H4Zgxbr4qZT1GnASeQT05Q2XmBXPJMppQhB29KeCH5HupWsfBmCFLmCrNgI7TAaKuLpUL3F9deqAiZxkBlGubqyWwRySC4LLkHveidmx88b30nKpu2o5oSYGoSucRfqChgKQdkczG33TdTRNz5OTtmgH2vJc9Y8qUuTE67MJnHiVFtLXCuwC3dOL8M31D+dsqz18vZLe828USAB7kAACAASURBVM0GMFLwNgZYu7aREW1DlnsDDRCyzNOpacz9r/+Hrp8FCjxAbEX5xKFareLAgQM4cOAAiAhra2tYXFzEtWvXYIzpZ0XNzs4+FFVUo9FAva4IsC1QoEBBPhUo8Cjx7rvvol6v49VXX31fvWF3HBnymOL27du4dOkSPv3pT2NmJlyg3GnKJwDwlb2IWnwx16Rr2tZ0u2lveW2+DKXwpTmYVC5KG4VKikwJ7voaTEtvIWfaa6Dp3TBtZWE86fZDo3zSkNPSPmkq7yNsN9XhwqbfaINOnYbxnl1pCiBYRMhVIPU3Bv5MFC5uDayiJSK4K4uZn/5oYUuZ9+T3vQrs/3j+uUb7ZWcBLyn/5HvKwHfVRRJhpJ27la94ClVWtp9ScaAgk3ogU1PlTknKrJFGg5s8TcGc+ZvNP0TyOCSN8kn6IU00VigahZGKjRKRIj3TA0VSardB53JynnrbfXiskvdhaz2uv6F6uCegkn8/jh2iUp0qCriadzEtoRTH8r7KogslCtUWAMQdQCCfMiVVB6gr5gRNBhYAdJTjXPvcZ8YHbayj1LkFmGzxAHUSmKr8nVCS3YNS5pN1HX5WVdjyecMvKHHM9hI18Gz6Dup2iW0jv2+caseHn5mcDR07bphspGDmHNOezT+GnAMayzB33mP6Mtq1R/y7ynt+AZBEPnGr/6XPwmwnomCGEQBQ4hAx5JNvJbCMMsk3EtiQTV23fTDkE3vLEsGUuRyt8CakFH7tcRQUjVMcA8ifQ0uB65h3+Tt3m1g/u5Sf2ZT37pm3ACuv4Zz2bCknu8wTNu5mC6F6PNuYrbTNrv/uf/t/jb8fFyjwCOCce2iLr40xmJubw9zcHF5++WXEcYylpSVcv34d6+vrmJmZwZ49e7CwsICKVq0uoNFovC8uRgUKPI4oyKcCBR4hPvGJT7zfXQBQkE/bBRHh/PnzWF9fx8GDB0VZ90683lTZB7TCK92BzAaMomkYQT1hHJ8LtbmfzibPJCt6RVNpGlCQT0hXQbBBhQLBwN8HzI0LoPKU6GQzdGx9j558Wr8DqpRhNS/ewosxOSb4uwfhvssKy0yRwBNQ3ywK+qVV0PkLML1ln0xoNTkPG7JmcdRVJeUch8B37wkmcIypbV4Hf3cdaOcXRkeLbRkJNpr3ZOE//5v5/c7rlpXD7AGdpR6ZKQDCeFOSszDaldPKFYhaRZOXVGADTZqyjh/wLa2bH5qNNcwGfgfS1RuwA0uWSUOoacgnaTzHbZiaoEqSJhxxAYO0mnz8s1LSgT/99uZ4zkOShueZJA2SReQpPIcHrfWScDaMNcPt5WRTjUFD9GgKcNoihaZPWiJLO3wTJQkUdwBNtoe2PeU7jYms7qMwhJe9cBx2YD7Tkk/wHqmpwFmeGGLvfwAlasFEvAIy5VSJ5OFM/vbIN7E/+SGmIu1inwnAztlbVNaEVIDewQTtHcP2fsHx4D3speP5R07ygtbvgpQTtQVwlnzdvLGtHJv1iTs0y6gK7eLbHhFD4LiNGKVphlxqJrCz4fHlO44nn1L+s3F5UZQaXjHPgCj8zOG2ZQsZxuf4tJmgPJs/949aQ/vUY+mHN4J9GyWfiCj/tsu5rLZSxugiv1KOIqu5YfvDevAa9lQmvffd2n/4n6Ly2qeCfS1Q4GHiQSufOFQqFezfvx/79+8HEWF9fR1LS0s4efIkvPdDqqit9qnVahXKpwIFlHg0I79AgQIAsC2P2weJnUiGPC5IkgSHDx+GMQavv/66yk/YWrvjrjeVdblPVJYL68ZtgKTgdwBIVkGcRUmvPUpBpTlN93jLk6E2Hai8O7jdNaaBS8ezfZMWMPOM7vwAjFIRAgDGxcDCc+J+lCRytkWsUINI9aVEUFO0Ov0Cir+3DDp3frhQx7XPWatwsRGBwgQh/x6jeDO8mpyHu5HZFo0WMIho3LbPj2c2pK99GQgUwHPPbxRjhFpi5ke2o0xKGmqCKaMM7KdVICmLyEyO0vB+HRBp1zYpxy/S4Pc/iqmp/HHjkmnY6yeH29WoZxS2e6LdnSb3RrBEk79yYYcRdQx5BzpzGKYtKO1SpjDvuGT48CDfilpqjCRXfHei5SgAlGRiyVSqII3Nn0bhrM0g0BbEOc+oAZCSVHKrukUixN0XAzBK4s5Uq0Anh6RfWkSUDltjUqL8zLET854AiLaKJc9fE+vbAJMJFVEr912l0r6Nl9zfYAqC4ulhKHq2auvGkE9BhN7T0iS8bWMZZn08g46MCV+PrV6nLRNTQmbTdsAUQik1bLd8ix/rvsPPZaE8o83zb+/3zOiCn6G2Pf8dcsQVf2B4UymQT+Xa4esUjahIVw/fQboxQQasMWPFbgLG/0bZ75ehfiV+LDOrs56gdTN797XT9T7ZNUh6EQH2mWcx+6/+J30/GdA2rSELPJ3YbubTVmGMwa5du/DBD34Qr7/+Oj7zmc9gZmYGN2/exNtvv42TJ0/i5s2b6OS9hzAolE8FCuhRKJ8KFHgKUZBPW8PGxgaOHz+Ol19+Gfv371cfF0URYg1R8AhBFR35pCmGAwCVd8N0JOsxD1/aDZMorGW0iianf0lsxsBMzg9Qly7AnPr+0N+othtGm+fU1im/+phWFMM6MTAlFbRToCoU9qQVpMIPferEMNM1+NuLoMuXxn6/k+FoEGZVMJhaQMjGptUAquOryyjZzIJxN5ZhekXR0QKHD6w0HdylMg/67Jf5nUbPr1A+GQDeTsN4RWaS2BbBmzoMSQoppdJQTSrp2gMAiuowXrGSn/TPIY8qIoWCLG8f7w3i429hTP8Qy5lYovLJpUBwxf/APhIkUkIay0wBnbyDGbDEIiLQhRPAhmKOZYqorLqJK+gH5q2xXKehY0ausYY4lLKQ8toNoRPLtnWaWly5kn0fAuGhyqYD1CSCX2nBah77SlJJGeUEmiSIuzWeY2Ivnxivw3Ok5wB8SmLeEwCQQEBaSliKvkQtpAgvWIioDYfheWQ2vYh99ggijUJVuchmDFstDrNkTKAv3LlC97pL863QXApz/Vyga9y1mJyYIElFtdUV+tsozJNzbJ/cWgd2jlGsNzvALmauksjbbRJnrLLJE0yA7AEAYmxziQgmpKYH2GckZ3UfsigMEezeAVF1sx/tWxvYON/NalM66eVmO5WrwMiiIapUYczw+4PP6dfazc13GRM3AZspTgeHi61WMP9//skDs/1/lAqWAk8Odsp9Uy6XsW/fPuzbtw9EhEajgcXFRZw6dQppmmJ+fh579uzB3Nwc299ms4kpjZV+gQIFCvKpQIGnEQX5NDnu3r2LCxcu4FOf+hRmZzU2W5vYiZlPsDVQtAvGSQViZf5RpCwwleqAhnzSKpqSVbVFX94PLo+9wJHvju87SeFg4w7IlmBIea0qis+WpoCk9NDcU8LqMlpvwEwxKrPUwd+4C7p2JRCEzBWpmE2BF3nyNGZnMnCy/L93rf+oncDfXoMB8vOecjo0qI4iAtI3/8twpwMgUwchghEURGSqAHjySZMNBQBka4Bkh0ltOG8QWcECxzdUY8hQB4SyKtNJq1LSEl/ZziWdRR9SEE3BmM22qVlGrb08vnN7CVSywVuLjAVqgp1G3IapC59XqNQTa1nVRSAbpQ/Ozi2JgQFrSrp+Hli8zbfX25cdyMyBAeKHkhRmejKvffI+U8YMQkM+1eSCgKlUQZ22rJJKYwDCvaCwi+rnL0n3laR83WzwQe7GZ9gMtifZSPb2m6rBx7FKAeWWNxDtXtj8/yvXUME4QZqstBHtk9XRphKJeU8A4Cs8wWwMP36tMI/ZwYIyeTyTHsG8uaR6zSEaV+eO9I7ZtEXVTkDNTd6Fxwl3KsZaLxc3LwOdgL0sex8HnmQcwcRkX4nWedz1ld4hpTwoZi6R1EHbtmiVJgtBfRS0WwZA7TT8jgfwVoaxh+UIJm7uCqht00YSzHUKwbVS2F3ZMT52WHrr5mYf1ExTzt9KpTHbU7feBOZGVMsjh7VXY8TrCawdVk8RAdGA5d70f/EvUTrwPPfRJsJOIREKPF54v5RPHIwxmJmZwczMDF566SWkaYrl5WXcvXsX58+fR61Ww5EjR/DTP/3T+OhHPzp0bLPZLJRPBQooUZBPBQo8Qjyo1UbbRUE+6UFEuHjxIlZWVlT5TnnYqdfbV/YhavHkk3FapYaSXNOOAaWiyVAMKu0CUlllMVUBMFAj8tEz8D/6fn4to6XMcAJgvAPtOgCsh/3eh/aPFNfqQZGVUr6IYNlEK2vA4p3cuYsI4zZ2A2BXKIeKF84DlfxXE4mUcleX2LrU6GpcwrD9i597GXhhC7l8xoDMNAwJ96DKUk+p5FMqEr2ZQgQ+g8nAgVADIGdJka3DeMXY0BbEfUNvAM2siB6FtzVE3YIwoTqmbOzBkkPHTKOKgKpLk/eUdCASxRK1F8fj5MoomOI9EfG5RAPKK7p3A7h+aeR4GyRP2fwNZowHxytn9RkqHnYSoDqiWyMSFUSmXAa1WzLxEbeBkmCvqJmTBTVTH0mCcRneCCQrxy582+vi0CRbxy6MpKbtws7qMg6MtfBrG4ieWRD3pfbw/GcvnwByuCNtJkxUr8i2e+SRRjPCCOUnqVFbrLHt3fcjSzEOpD/AtBlVVW+DxHgYVnOhgjI7BgJ2ud7BlAKWuXl/TDqg997NppbcQv4WCbVgkZyzB2aIEKItq6J0eVDb+L0oWdMx720AYJm8KACwzAIqch62Fp5rfOLGLOOGd+DyBz1QDRCjzsMEPhd5Cs4ZrpUGyaeQgss1Oyh3yaeVH9+Ga4afaYT83/65/cn5nZj3iB09duVao38rDm4aPG308U9i9p//RrCfWwERFeRTgYnxOJCWpVIJe/fuxd69e0FEaDab+Na3voXf+q3fwtLSEt5880186Utfwhe/+MUtZT599atfxdmzZwEAKysr2L17N44ePTq23wc/+EHMzs4iiiKUSiW88847D+TzFSjwfqEgnwoUeAoRRdHEnrZPI9I0xYkTJ1Cv1/HGG29smTzckcondHOfWhfYfYxvZxZaTihgCyqMPpzSCmwCRROVZmAU5JNJVkAwmW1ZNA//zo/CRaONe6BqHSZV5l9UptWlAlNW7KlpTHh5pySBkVaXBYoAlDr4s5dA9+4zhYQIxjBFt1AhgBAujgRqPUQEhPpRLsGvtUArm/doXt7TWDE89f3sGUIE94Xfym9fAbKzgKgiVMh2qAVCSa2AkmBLdUAYuwDg7RQiryCfmFyT4R2VeTBIQajDQKOAmuSabN4r/s46bBIex6X6AtAMEMdTCvLJK/oljUPBRozSBIYjD5IYhiMrus8fWlsCXTyZsz0Nr7Rn+s4V+0K/MDhrvWDOXSgLKk0AzoYJyBQUEvmkyUPyivGrXZyiWIxiKhWQc+IcriWLTFXXNzMlsWLd/aanQHECU5HbpUYDUJBPg4sh0lNnUZ/N/15Y660BRLNVdATbvbJvgMr8PmSkzyjY18Ki7FfxfPp9VGzOfMy8W5rtqFS2YJ9HRMxcw1nrBd5HXAqUQ/dUTv+unukqlQLtGbMtS7sxcE1x5/GeX2DDXXvneb4Rll9II9RtTYCg6SGq8dsts10kl1opIsZWD6n03TmEiUwPBKh2SnxwwQNR1CeAR+EZC8Io8Dl7rzet6+toXBIW4xDBjnxhFFLbpZ2x53ApZ64rDXy/zaU20laKKOoqncqD58jaImMx/2/+Pd/PLcB7v2MW1RZ4fLATlU8cjDGYnp7G1772NXzta1/DxsYGvv3tb+Ob3/wmfv/3fx+zs7P4wAc+gIsXL+LDH/6wqs2/+Iu/6P/7d3/3dzE3F1Zyf+c738Ezz+hzqAsU2MnY2bRzgQIFHgp2qhJnJ6HRaODQoUPYv38/XnnllW29YO/U602Vfbr9hMIMAMCtg6wiuD1dBSkePYZioKQ4L6C36CMHKs2Boln44ydh0jABa0DAjO76AIDRWED1UFd4Q2tezAM2In2ECrYDyLNOovUm/LF3YdZXeBsTpmBLzjO+/6Ww7V6ovU4aPMbUSnBXR6wc8/KeRj/LQGHJvfCTwPTuwNlleFXuk+L7QKYuEvcj5f0WsE8a30+r6NSONb2dnjc6r3S1Kgzoq6S8n4a58CN+X87OriaoYaC06JQUiFIbifDZU4UVYqsBOnN47FxEhlWSmIASEQAQIh6S8HhlEcyCClwfzbyrIpY0qibFO4BSraQunsfyPW9mlAqkaeU4m613i7zCfl1FkwrKxU699yxyHqXF/MwfALA1JZFWihAbfgyXFKS7s/y1I8s/iyt+FR9wf5dPPEmZQ9y9wpE0wNa2ZVKN8LYQQoQVp64eVQq2NoCrFzYr6BPikRbCJTKatdUTfg8I95MkfI7qjErWedh6+Pr6TgrLkEe+mbLXmWJBTc9lgRLxH5275KFnBIBkY/LMXfIUVkTZbL3G0o9ujWzI2TdApI0qP4jGM0nJE6IRlZl3HlF100pv9Xqzf5wxw5Z7vX9P/Wf/OUp7Hnzx+nFQsBTYeXjc75uZmRn82q/9Gv70T/8Uhw8fxpe//GVUq1X8zu/8Dj73uc/ht3/7t/HNb34Tzaa88I+I8I1vfAO//uu//gh6XqDA+4/Hd+QXKPAYYqesENqpZMhOwb1793D06FF88pOfxIEDB7bd3o5VPlU06eNQFaYNACrLxXtDDlTRFflJskHqwU/ww7I0C3f2MkxTYR0mhJAPYWPURicAa3UB98IKevJeXmUvjHFyHhjJqvE37sKfOgWTdLrqBabIwKk1mMIMlyETJKyS/HNRnMIvt4DmQL5PHtGUd86ujZdHBf6nt2dHQkZBPpFSRWdl5YG2La0CSW/dpFQ0eflH1+bOSjsw38oUcCo4EAC6dEFUDhhmbiaN8klz7YQxL34uJxCXUjHTe9CZH+e30woX38n5YPYQpY5RKjH9CVwvtr3QMauy4lVDpKjIIIWlnolKoETxPFLex9SR2zKVMoj5DvuoT4E0CxLKJVBDOVcp92Pvh8Fzd4lOd+IEKtPh7y2aUZJ8AOISnw1lpQw78nCGJ/h8QBFqKca++BCexTFEJnTtBfKJ7ZtETIVIJMb2bauiohBrwPVxlGS6dIJvi2tvywqw8CYW27Lk48svvhEma4kIUS3cad+M2cwltxGzv0V9gx8PvsPPISQomzi1GHWc2lJz7Fjm+3DN8PwYWmDlWmlwm61GWDp8D76tUD3nOkfm5ZKN/90l48qiwb8177eRtl3/Vhu8tP1srLkFzP3Lrav6OTzuJEKB9wePm/JJQqVSwZe+9CX89V//Nd566y185StfwXe+8x184QtfwC//8i/jj//4j3H69OncOeof/uEf8Oyzz47lSPVgjMEv/dIv4Y033sDXv/71h/1RChR46CieGAUKPIUoyKd8EBEuXbqEy5cv4+DBg9i1S6m8EbBjr7etgaQ8BACQijM9RMqCUKT0RtYqmhJdPhPZKbjr92GW7+jOnygKer0+dNZAtXl5x1pNJKHJOdnKKElkMltamdtsD600d2cvA9eubCo5UsFSgzs9V3DiirMhpVXIji92cDdGVE95P/ZH856IYEwKIsB97tf1WS0BkEr5FIOgWdGtKHJTG6TaTzl2SWdpp1U0GSQgMQdpc2/dXg40gUrKd+qwd87LO3PWmprMpxBh0kPcYXOJAMUVEGz5WLLXpaCr54B2PiFI3DyRuPAcwBAKLOETKjokSbhInFPgIudAt+S5XKVM0xT/JfVaD7FijCifbbSqI3E1JJCxBrSus8f1G8rztpULP6yO0TD1GiiOUWle4fcrWbgNWU2Vmip8xJP5ViDoy74hqvhSOz5P1DfO4YPpt7DbXOZvr21lOm2VfAofl3JzTYgE5rLXQscQDS/wWVsE7t7M/s0REFshnzg8jEWBnnh1kDD+TYUjh2JWke4a/JiklkAuxQK5xNjUdfdgtxomT0pqO6ym58HGmwUsBh1DLHXut9E6f2/8PDn3be65lR8jr1jdm67IE1ZvNPuPRqLN19hsAZYBEbD73/6h7mRbQEE+FdgKnrT7ptVqYXo6eweoVqv4hV/4BfzhH/4h5ubmcPnyZfzRH/0RfvInfxJTU1NYWFjA7/3e72F9PcvT/vM//3NW9fSDH/wAhw8fxre+9S38yZ/8Cb73ve89ks9UoMDDwpMz8gsUKKBGFEU7UonzfiJNUxw7dgydTgdvvPEGKlJGxATYqconQKd+Mm5d25puN+2PfaWiyfgOYgi2ONEM0rM3gHuBbJc8rN+ZaBEwTSnIJ43lnsaiSGMlJVxmamdt0HoT/shJmOXhH9NsURr5P7RVJzf57eYrlroInMuvtmBGVFF5RBNGCx62CmMNfOUZ0CufD/dVCY3tHqCz1NOOI8opeI7CkK6IrLW0M16Z7QbAKxRcANTEFwA1oWUogTl1SLdveyW8UaN8Kkt5QgrrNPFtXCpQB45yDnT6x8A68xm54je3aIIlxBh1Yyh7iCGzTI7FKF2+CVpVLDzQFDm4PK0etJZ6kkoN0BNZyvcGauvGLzWVSiWNkgrQfVYgrGgbgZ2dhjt2FKWaPAd6BfkUKxbXSERARPy1iHxrSB1edmt4Pv47vFA7hpKJRVs9om2QH+xlEoipAKISMxZCBBOnvAxdX5dsbiMCzh8b7GCgLfPgySLu+99ygZTvI6s86qSwlfB5vUQudXgCh8s4AsDa12Un4DdzxBgRsXltlDIqZOFYjrAMKcGICOWAtV6oL+lGjNWjugVsRJR/KyjVXXn3Sc9JeeNuCy72A/sOWO7BwBiDyk99AdVXX1Odayt40kiEAo8GT9p902w2Ua+P/7b727/9W5w9exY3btzA6uoq1tfX8Vd/9Vew1uIXf/EX8fWvfx1/+Zd/ia9+9avBtp977jkAwL59+/CVr3wFhw7pftcUKLBT8eSM/AIFCqhhrUWqsF95WtBsNvH2229j7969ePXVVx/4S9GOVT4B8GU518j4NihSFLqd0mrL6QpbJllRkz/NmPlBG+1CevIizNItYP0uKFIGtCdtYFppTQjAKPJ1VGHumrEp2WwBcuHEOfgb9+DfPZWffyXZypTC7bNRE6HjnA8XlnJyZygh+DvDmSNEBIyujvU0tnKdkJFd7uf/Vbijk8BUQZDvK1UumlJpmHq5YG4oVvXL+KZqrBmkIChJpYAV1dhuk2Q5KS36/HJHR9ACMGkTVAqQwlLmk/d9+8YgNAV6ifyQlHk52ylN4E8fAdaW+WO5Yi6nqOJIq0BxjYiCOVEs2V0bvpeoE8O/dwNoKshQrpjeg2KxiSmXQYpsLRVhFMi2Gj+pbjckuvc56ujGBCnb03bQTimJO2tRia+rdvWCggMAYkVuZMgyr4dIIMejngUqOczHJ/CS/xtM28WBPaRqvfCcVqrkxqC2KJ0AoXmIU4+FjhmcFxdvjMxTgfebB/xuTkTBRTTZNu5FhmlXLK+EvxuJVJXUQZLNqGRDKw1p6TWXUzb5jucXLXF8acwr8TnSqxTIwHLNhM81He2DJ9z7/nVQrF9MmPd7Mk/BlXddounxeTOqRPDOY+1ms9t+9zwD34uNACpXsftf/2/qfm4FTxqJUODRYafEUDwINJvNvvKJQ7lcxuc//3n8wR/8Ad566y288MIL+PjHP44XXnghd/9Go9FXSDUaDXz729/GJz/5yQfa9wIFHjWKJ0aBAo8QO+VhWyqVdqwS51Hj/v37OHLkCF577TU8//zzD+Uc1todSz5pc5+opFB2uDWQJh8qXVH8OAeMjwFl7tNUPX8/H+1GevQUzFqWyWTIAbPPqtoEAKrxeRFD6CgUYhrlk+Ze0dSVGEUGxQn87bvAtcvhYgQ3X7lw0QZAOCeGaHJrPSKYnADs9L37uX3U5D0ZSuD2fgpYyH/p3wp06icFQel16oQGk2MwCK9QWxl4kFGofADRxmqwVdVevjlBlpNCEUHTMO/+A3xFb5tK5fF9CQBqwrWL2/JzXSCKyXuZ/JD88UeUNJQmoFPvZPOepBbgVqpvMRAlqHSJk3BBN1BkpySFGcm38xeuZTlWGvJJo1iSCMQeNJZ6Gpe/ShWkmee1uQjabDctqaQVMZd1/TMzOqtdd/SwTvkLgBQ5Uhrlk4v4Z7IRcu4sJaild/BS8v9irz0Da0b2F+Y2dowJqqkt5xVxysQQGeN92FqP+4ijuU499H6DeAecPz7ch9DvE3auZSwGg/lWTMel30jcggBO6eYJXHailwjibSnSeZIGAAyT8wnwY548wdbCZH9PbR9unNkWyP0EMtvo0KImHztEIWu9Vng+zCPRlg/fRnw38H6WZ/ec80ciGiNs8hZNERGiERKWPCGqWqzfasGnNGK5lx3vffZuPvs7vwcr5cJuEwX5VKBAZruXp3yS8I1vfGPMcu/mzZv4lV/5FQDAnTt38LM/+7P4zGc+gzfffBO/+qu/ii996UsPpM8FCrxf0C0hLVCgwBOFnUyGPCoQES5fvox79+7hJ37iJ1AVAuG3A2vtBMXVRwuqyMonAIBVkEoAfGUepnOX348cfHkXTMJYQXXhS7sQpRvifhbjPyJ9tAD34x/DdIYVWVSuq2s2qryQHjbugqIyjA//wDYa8kkDTW5UTgGYPMFfuQncvQ00O8BU+HtlixipDyqfyFHYNial4MpYQqD20HEwMyPF55UW/HILtqogc0Z/1MMAxsL/3G+Kx04CsrOAX+R3Gi1M5u2CGGQqYr7S7Ow0oFEbKghhACBTA0hjq6dUbSgL4gYO3szAQEGmeSesOAfowvksx0WpcAQAyiPUqlOy4ijhxxAA2SYqbsNI5Idk7TewndIE9O7bQGNNdX7OEo3N2OCKmKHMOo4wCJFEcQIM/K6nRht0o2t71G52i+JMX2pT3Yw3puBeKoPaTRjRQlGhHFJaKqHTBuo84WtqyntYWwBULoDSqgG0/bNz03BpCsOo0Nz9ZZRxF64Z6WEfdgAAIABJREFUA7sVhRwp0xBAHAmLR8ghNcL7AKM8qrolzLtz2FW6s2WBEntujjgR+rb1DKRQX7aQBUUeJjQP996tbr0HtEcK+t7lt2nMg1V0cW2xym+z5cWEfq2FaIYZN4z1HIBxZfcIQhZzPZgAEdNvntlOnmCnwmPYt1JErK2e8N0xavpM0RVY1JT64HMsaSSoBvrkGUJr9Do0rqxi/dTSZIRv7i2c80cC7MjfXeJRHiHynK3AuBTrt1vdtrpt2sF2DaIPv4LpL/7yBB3dGqTnaoECTwMajYZK+TSKP/uzPxv723PPPYdvfvObAICXX34Zx44dG9unQIHHGcVyhQIFnkLsZBu4RwHnHI4fP45Wq/XQiSdg5yjecmFroEij2FCumNaqIpSKJo2VHQBEbpig8tEzcIcOjRFPAIA8i7kQmjJB1oPxDpgRyLwpBfmkKSRKVlI5K/T9nSX4Hx+HuXMThsI/1oFATtLo9hCYFcPcccEV1yM2M0SE9OpK/g97Td5TksJ94j8CyloFjw5k5HtaIpR68AoVklXa86lf9ZSWduoC9gR2emR034UR8ld8ewr29vleq+rz5+4qWe4BWW6JBGk8C1ZulKYwTEYQJZ0++UJJPEw8CcjGB0M+MfNMaP7IVqEHtjGEQfCYEcLKXezm9vXyNEYL16PtRpFOTaNRNWnsTjX5UQD8mkwcqxcr5GRi5banzF7KsznNg1Uqmoy18Ov8IhL/7mHYkgHFyhwpBcknKZ/KvskTlwD8KHlPhOnkCl5Mv4MP2u9iztwQ6tHSPMRtlyzSOFUUsy1AqhOntGKt9UJWummwPdciUKsNOndy+DSd8DG+kX9v9DJuJsZDUI4BYPPwRLtIyfZOIJc4cog8IWIWS/jEwdaY7c2EHXdS3pRk5cwSZ9xQYEgtL/Up71Sehqz6krUO7v+g+9wJfPzc66K9v3IWXvic7C3XaGF9Mc3yUQcEzT2BLBHBVktY+Hd/rDzx9lAonwoUyJRPWyGfChR4GlE8MQoUeAphrX1qbfdarRYOHTqEPXv24LXXXitenKFTPxmnsJTLWtPtpi0UeF2x3lIbHZ/9gHN2L9yPvp+fYwQA6/d05waAxn1QWS+np7LwAqrJfNIUCKVi40DBluIU7uhp4L2LMC67nuTCCqTseN5fX5SfbGVbyCJk5Bh3ex3opGP9IwAYtYzJyXvyU3tAn/tPmI5sDd7KFk+GWroRoshLMl6ZsRbKzxiDcuySrj3jG3rFp5JkNtQK1q8IFZjj39/cN9HZEgLoj4shTCl+TGrUXRJRLGVCJQJx0lXjhIgn4q5t4vjiO0dChLYl4eIxP/7lY2h5DbjdC3vvFpybinEQK8inB5WDyRCFQ1CQLKZeU+UvmZpyAU1V1zct6WVm6yDtddsIqyrTazdRLq8CyMhL1blD6toBSJlPJYXFac+21FCC3fFpvOz+H7xYOozpaEVrLLr1zeyzNKgV7ra7BVUUl3PE9SVAZnH5RcYT6MSPYUatxbhMo7x5GgAxRK5rMIthwIwHThTlGAKGCMYyJIuQ2SRZWUbT4XcD30lhOXKpEbNqVt/g37d9mx/r4tjliKvYqQjl3GM5UiuHxOkhpPB0MfWvk0897v79VSClLjmr71fuMMv5jHlEU97B3nusXc3mSdt9/hIA2/0cRMD0P/sNRAsL+k5uAwX5VGBS7FQXmO1Am/lUoECBgnwqUOCRYqcoYHZKPx41FhcXcfjwYbz66qvBgMenEb4s5z4Z3wJFiuwYr7HtAuB1qgiTrOraAxD7Chz2wr/1XRhG2WfiDVB9j+78IGBal4sFAIZTQlQr2Sp8BkQk5r9QkojtwHuQc/DXb8AfPgrTHll1LoVWMz/YATVXMI5gngRTsBk4hhIHd3O165E/sl8O0TR2HiL4n/2vJumxGmQVyic4kFEUdhUeTgYJyMhFZ63ayijVjVpFk4EDNJ91AmTZVPlt+tvrMJ3N+9x0JlAtxjnkukL5pHqUVnjCWbT2lNRV3oHiDujdQ0Az53Nw9xIzD1DqgvMMJWl4DuKs9QLFPoqZ4PeB8e/OXd78e/fPqtwnQV0GQKdq0nzhlaquwKJcAEQMadODNlNJSyrZ2QkUTWu6Zz61mXnj4nHYCQvPdkom6CXlkyV+zjPkYOCwL34HH/Hfwv7yGVSiUVJbIpf4TCf+lhJs4RgSiTssOCdos8NGEbBlNo2w2s11YpiNW+MbOHVkSJXD2KehFSamaDmsEHVL4eP8YngxFgkEj2QbaDjyqJX0SYfc7QzZ1zueg6RcIskSUHhvYfOihHOz15QDM6+EVGLebX6OxR/eRLqy+Q6lJVvysp2A/M9BOYsjopzsreZiDIOuRXV30Uo0s/meEj3/Inb9i/9O1b8HgYJ8KjApnsR7ptFoYGZG5+ZSoMDTjidr9BcoUKBADogIV65cwYULF/DGG29g9+7d73eXdhS0uU8krCIGAKTrIIV9l0lWVGH2xrfhI/mljgB0VlPQj/5eldNE9Xlxnz5K+uwYbDCqKo3lXhyLBIpkD0Xeg9bXQEePA9dvZgTa2D5CAUS6hJw6i1twHfT0L4ULaQPFlvTqShabRJCvE8ZXtvrdLwMvfko8bisgM6u6p8lqCsC6IqA3OkJYs9bQeJ1SyPim2tDOW6W1oVJNBSCXcPNuBvb8W0N/M/FGfpZTHtorYwoh0iifJDI5iVnrugzCPSORFGnSJZ5ChV6mEM21zRHUHGnFWWuGrgWnAupeY3/rPrA68Bl7l40pcPf7pFTTiFBY6pko0imtlGQLS9r0zlkpg1ry+DUzUyqlkqmU4Td0ykpS7hciAJOzF1GpbSqQws+IYUTTFfZ7TU0VTph/TN48S4Qpdw9705P4oPsuPmz+PyyUryGygXte+h65grxE9myVmAKTFUXcAjSmzVCuE7MYhp0Lrp/P70fwPA4mpPjm3mc4W9F6+N2OI4Hgw2PIt/j3M+7+ptSzeVBOIpc6gjJJUFuK5JL03sg86zJLOIZ8YsYyEfF2g8wYjAIKSfKE8nT+d5x0r9P62SU039tcABccN1rHPWNy1V2lHBeCUnX4b0nbodUlwXotEKG/4IZshPl//b/n9+8h4UkkEgo8XDyJ90yn03no8Q0FCjwpUBr8FyhQ4EHBGPNEyo53KpxzOHXqFIwxOHjw4BP30vMgQArlE4Dg6tZBGBB8ZR6mw1vbGUrhy3MwiaxO2GgDuzh3FFtDeqeD0o3LYluDPVUjlq15+q121kEzCzCt5fFtmlXncQyUhP0Cqi5qNoG794D79zPrq21ESZAx4dzx1LMFlNC2TK0UKFK1Y5gcxRcRwVSzVxW/3oFfbGQFAGXe02DBgmDhf+43g/3eNkwEMlMwJBRjRzNE8nZRqpVgyyJPZeDgUYOBUJymJgg2vyA72p6ZgiHFuFB8VgBiltMwRkgiWNCZ47n3K1V3wzRvy+cHwVd2w3QWN/9YU5BPIavIXrtxG6gI5Llos8mQR4010MVTQF62XQ/OTTTdbR7HFAOdDzfJSTmq+T87yLlge6ZSAXkPf+FK7nlIYbunUqipsvaUlnpJAlQF4lOZDYVYl+1GG00YwdY1Uyo1YeblhSS00QAUiioNOdbdc/wv3sPePAUMPPKkTJv+fuUIrpUgmskv+kiqp6yR7Fxlt44ZdxP1zi3smm2gVOk+Y5MYbBYeeZZcIpbogaiC2drAldrdYpuhz9loBi1KTciO79p1RK3AO2JIHdlJYaYCBT6GfDC1fDKHPMEGyCcigp1mxlKOKqV/bCwoeLhMpY027GyYfJLahkAebVXY1oNki8dZOVM75TPnOJFf7FllZFA1CyCq51/vtJGgHLjW1jl07qdYOpSjzMvtQN7fchROGJ8PXOJRHlFgucSjOmKRunq7BWuzoT34Gt37TVt+45+g+vFXdf19QHgSiYQCDxfOOUSSc8djiGIcFCigQ0E+FShQ4IlFu93G0aNH8dxzz+HFF198X+0GjTE790U9mgJFs4pcJ2W2g1ZtEE0DCvKpOjUDpPmr2320AHfyNMzafdRMGUIKwiZaejs/rN/JAq2Veg+ayiefVMonzrKqf4LNfpD3wNIScOcusDFwjaSuCrehKZcRZDWcB0IEkyeYUGGGyZmikE1ix8HMZD/C06vL/TE8lvdElJMBEg2po/z+zwK79BaKWwHZWcBJxXDFHeo3WFelidpCV20lKJsMAG+mYUjOdyMzBWjIJ20qim+CjNWRBCOFVb9egl26lr9rNMFqxNIUMFhHn1LYaEgEg8buTSI0AkVff+8WcPGEvBodHsHvgS2Mc60yGwPPOHI+nE0UyvEiAqpl0NXbQGuE5OiRzQrlUyiTZrg9xU8i7epWxva1h1BRfAxaez4lCeQbTVgN+TR6vQMwysynvAJxevxdVKeGjw8Vi/PgN9ph8ilHqW3IoeLXUaENVKmBaVrEtD+OSjnOYvZGH9FStd57eQxt57Vzy++sW1zgxp0v4LVLcRz+iNX8e9yfOQ4bGm4hpRL3bhQgNSj1wQwk6niY6QAZ3oxhp8NjPURaZQdzlofEKpt8K2HJJ25BAADZAVKYBoPvbr3tFYZ088SuT/Mdh4ghn4g7deKBgGqKfHjBk+s4lALWeq6dBsknpCnu/eD2+DDKOQ0RwSpskgF05/Lhz5GX9+RGvue4maK1Eme8rNl8xPa+T7Il7Pk3/07XhweIHfubtsCOxZN2zxSLyQsUmAwF+VSgwFOKHU2GPAAsLy/j1KlTeO211zA/P4HF2kOCtXZHX2+q7IVp8UVn4xRFvkmgvBbliMZ4LwLg3R74Qz+A6Vp2lSgBzT4LrN8Zb2QUG3dB5RpMqrAqSjuguQPAhqJdACb0Q7SuIeUUL7LGDKucRgqdKos1ZqUoEQGWUT14psbhTZDkJceY0gUKTpRkigh3dwNoJv3+jRVJcmz4Bs9GKMP//H8TOvsDA5lZANJ9IhemrSF4W5dVVEp7Pq0CiWwVEEloALakO7XSTs/AZ8QXZELLUNq/AT2mYE78HbOzfr4dtN0jQFY+xW0YxkYo66CQoUEEUxEIjRFShLwHXT4N3M4n3IZgLFuQZOcBrhjIFKmDuR5xAoSUOaFV7XECVCP4S9eH+4ZN+yJV5pOgUAMgZu0BgIlKoE47V6U51D8fVnL121LmL2kJCBJy/ProKJVUgkXXpBgl2yhOUFq9BIzc/rZeyTLFQhZrA/CtcB+jyGN/cgwV00I5baBWdyhH6ZCCguI2INpiMhBXCPQSWoIN8O1PMH+pwRXLgs9uBxOyH2bUNiZnwY27eBForAJz+ao6E1BHso6AATURtWKYSv55KGVIolYCBMgnShyfN8YodPxGB1GADMn6JIxhYS7gMpUAsLZ32XbBNo/No0oRMapFydKPtSPMIW02t1kYk3/d0maCKERaBfpDRFg9eTff4jBXXp3bTO5Xlaccyy1ej/xp5Warf1v1/ku0ybnW//lvwGoWtz1g7OTftAV2Jp5U5dPTmqVeoMCkKJ4YBQo8YuyUB1QURXCK1bmPI65evYqzZ8/ijTfe2BHEE7Dzr7cvy7lPxjdBkfwDx3hlBoTXWYuZdDgUmmwN7p4FvfO9PvHUR01htQNkuVCzuqwrACBluwCAdn6IdV4hRn1+70Gra6Br10HXbgInTgJ37qhW2I+3RQBjj4LEy/W0EKIJ8rEGECx6UFYkSG+sDP1Nlfc0UIhxH/sloPTwPbm9nRX3Ee3vutBkQxnSWl5pnzvKH4UsMzFw1gns9PKynPJ33GyTrt6CceF5xDhdoR3AMFFUqsiKpHgSq8BQG50wWd3vy2Y/KG6DTh7SEU8Ako7AEDIF/pBlFgA+6ypE9DCr9oOFxzjNiKdRdU1U2nyX0pBPEsEHANWabhVrIj+3qCHfd2aqCtJY6om2jN32tJ5a2meGdkWvsn92Zng+S44cRVTN77Nb181rXLF+ttrAczM38cz0MubmYlTLbph4YmxgN3eSOiBc8+3Y6qmIrcm3GcP0OSSNabbCfQn8mbwH6sPEDzkHXDkTVjomLmzNxpwnSD5xBCpnK8oQub7B35tB8gyZooqFcLvYKv+s4MghgFcVUuoR1cN99zFvu0dS3hRnq0cEy9ltcscypSzXmfz9ePGtW4jvjC+CISI10UJE+dcqGv/bqF00ANiBv3U2ErTXEhgzPiVYa0Ezc5j7F/+tql8PGkS0Y2oaBR4PFIRlgQJPNwrlU4ECTyl6ZEhZsyL4MYH3HqdOnYL3HgcPHtxRq2t6yqedCvr/2XvzH0mSNDvsmXvcEXlUZlV1V1f39HQ15tzume7qrt7VcnVwtSsewIIgIQGERAmCrtUSWggSJIAghZUgLpaA/h4BukCKHO5yNZzp6amr677vyjsuP80+/eARkXG4ffZFZWZVZpY/oLqrItzNzT3c/PievfcqMjsyKi1CaYc6IW2DVClTKDBQybbIlUbpAOQ3oHR/YLP3LVR7w7GWG1Sqi0vyyqFgmEDnJahUhjJThUXJTPfBLGwyBuh0gHYH1OkA/V5GmAFOaz7yqlCaKYwnmi+QaAPFkBCsyxB3jlsKF6QNVMXSH09BP96GGp+lLM17UhqZpqYO+urv2Pu1j8iUTzwUhaJsJVE2lOkLLSGlKgaphYTQasv0BzkHkoVLMuEfBSAqwaRNeA/+Bb9wnE8E57Ybj5EYEss9CbHleslOIlZxQ1pDlbPvqb0FuvGNiPwYwg8Cq4KLjGFnynP5HLb1KEmtuUNEjPKxYikeJwnoUU7uBo1ZCYZhdpy4+321lqmRmN9DeT4oDN3WehIrRakdZq8PVVniW7JYl83Akceyu1HZYq58l9Fywv55Sy1oo6E8H6bTQyV5bH0LNYGQNOYsJ13ksdHuC9ObrKuSATtH9JVVUcxO2SwzI8Zaz2ZXGYRQi5Pntrl5A0qH1rkLQ6VzLmzWekECb9FyDuZYmu12hrn/ctZ5YQIs2p/lPIsNJAAYh5qQy0wCeLs/SjT8pv2c170YPtO+7sUo1RnlUqIALnvVpWziiKtY89ebVx2HzO+fN+Fh69cv0bu9ld8Xayih5eMca+i8onsp515aGiPitp4EuzZ7Y5Z7np+dpsv/4E/eGAFUEAkF5sVxUz4lSXKs6mgFChw0CvKpQIG3FIddiTMvwjDExYsX8c477+DDDz88dLOxDvvxJoHyCQDgCayJQDCVZahonV+OElBpCUjd+UtUWoKJ6hM2e7nobzrbGkFgubfbbk6GkwWKTGb/tzNmFeX7VqsmihMgDIAoArXboF4vI5uG7Y0vS+QkGqgbQFWZF33O/g5w26lxRWkdwfZoYbP4okhDVS3FZ0PQa12osR6L8p78KpRnQATov/Kf7iE7Yz6QSPk0sNQzDhtLQZ8VNIyqQTnyl6QKJAVZ0VfenoHxmgL7QIiVFgoEo1pQV/7SvWy4DfJLbqIPgAq3QP7gsLss9wCnpR4At6WXdpB4SZQRJ88egO5fnz1Gw+nQNrA5Gprvn2UmOhEBNrKYI8YtIS9kjJV8Mk/XcwmG7Pcc61+/ByzY1anK80BBF6g7ftckdpNPgkkkyrI/06DQTSSqplAxKyyAcKTiBIT7oFqy/qlyCbTVh1pagL74K/bUk1r+sQVr39F/rQWqrb0WVg+KQAI/trmCsG0SCJFd7cjlellUhdQPJsgnimPgye3sHzalI6eOtJ2Psf2aQ2yRk7tWMc9PDMliwgSeRYWVLeDI52MmBZk4ZdvW3RilBcYWrx/DX2LIqzAFOPJJG558cjlIMsQXRRqKsSNkiSu7Ix+7nl+bXKl9fQOdq+uDZ+wchZJlwOW9a9qWnSZqjCb4lenPDMqDYxG0E4TddHQpHm6KCCiVFLxz30Pjt38nd1uvAwX5VGBeHLdzpt/vo9HIt3YtUKDALAryqUCBtxSHnQyZB9vb27h69Sp++MMfYnV19U13JxeHXfkEvw7yF6CceS9CBYXAng8AqNyCcpBPxl+CXutBXftLt0qqtwGqNCdVDDa0X7idbcbbrS/I2gVApcZkX8slmGfPoeIYFEdAkgBxDKS7M4rJEJTrHHEUL7JlUuytSsCsmhrWm99WYKBxg/ppMN77+nln4kVenPeUJkDZB1VPAR9fsPZ3v0GqAYIP5ch1IlUDwJNPLuXgqC2vDjjUiCMFkqMtqWWmXHE12FcB+aTE6izAbHThd93qR0UGpnYCKhQsaxJQfQVIOm6SArBbVI3DZffmGotJDPPwJrD21L2tPHAv+QxRREkKr2bJZ4pTqKZF3aQNk+tmKXzGSW5hmdo90PPNXFui6Y1QvwfFkE/ZdmL37zqPTSMDm/prBon7nFf1GshyjCaXE9pWWoj+mfZEGYWAt9AcqMrc48F0uqA4QVnxE1NYBe14H6cnHYzDRfxyVeshmEw0GRjyyfXwwfqUGSuZm8EmLSLrdYs6XaiVFct6zJZs5OiUQtBc+xaKss+seV42Oz5DQM1CXnP2ecw5oBibN44E4m6iphezBFHutWwAMgSvZSeHTDeGt2gf4xQmAEM+kcOCjjuOAG9xCTgIIiI+T4r7DYnY30pHMfyGhRiy5T0ZQqm5e6x793ew9fVzK2mUNWbp2zwqqSlov4LSVF6VTgiVwf5sPemPbuETLqEKgPKw+qf/m2xDB4TjRiQUOHhorY/VOdPr9QryqUCBOXB8Rn+BAkcEh0WRc1zIp8ePH+PatWs4f/78oSWegKNxvKnstt5T2qHWmBNhZJ/5TV4Nur8A8/OfQ73MsV6yQZjlpJIAWJDZDQIAmvJlZ3JoTAr15CGw9hxqZwuq34VKp6xsJJkdAnUIRw4BcNozcQUSNifBUK6nfbYeWS2vbLtkNgNgOjtFmvdUKWeqqd/7Y+ey+wqlQMpt2UYS4kKa56Tc84gUCKTchIqiCCSYl5S1J3zhEvQPAGBkaipjWsCTx+4FB6CS/MXQlDPlGtUEtnsSVVOZJwS4kUhba6Dbl16deAJ/LSCGyDap/Tu2CMldnmx9seSr6BsP7Qdo+homyX2S3H8l5L6kstisywgUYU4TdR1WtwBUU3aeS5VU3oKwvZIPsyPMeQwCmKvfQMBTieDVGSW2zQ5uCNexN8atTHI8z/PP+8KZL5ZVrV8R7KQz2e/D4PLHbGqpNIWykdRjY4n6AfDyXvZ3Q3a7T9uzSZRa+80RBj5HBDEWdpy9HWfvSRFPXrPKpl5kVYcDA3KJgYsc2nM+Gec4ALCZTRQ6bPU4B8TYsOPIU/Z+lS0ZVmkvGfUneNbF+r96snse2baV99NQPumW20TOh6afny0FAP3tGHFfj4aEUrtN+CWg8m//Hspn3svv62tCQT4VmBfGmGNlu9fv99FsCiarFShQAEBBPhUo8NbiKJAhHIb5ThsbG/jqq69Qrwutad4QDr3yCYCpuEkbZfogzz0jWhl3sSzb6Cz5RPChzUnoX18Hrv48C1LvvABJCzUlYUYGAKrxeRsTcNn4jKOzNvFPrqgwguT0cNYH+BwXUV+Y9dn6RcoUCdhsjtn+UGqQPJ89N3Lf6WfyngClUpjVHwGr7zMdPhhIrPe6XZfCEFm2kegxTZgvIxi32XKyFylpe1Io6oNsYSDDbcKHuXpJZnk3BKsQmF52MMYlyicX+RSF7tpyThWeogjm+q9A178GAp5UcZKYQpXLNBSXN8aNZW7iuE0VmXNfNOvboPW2PGejJyCfhESPEy5SA4MCdU9ApgrvaRQKyKdqGRS4t6ladZDL7hGAqlZgerL7OHVlimCztYVy1W2z65xAMYDfrOSrJkrlvU/0clxjiIgnpyTfs3AQV/u9Hqd8sakW+wFTrN/dd/PtpV3r04TJ2rLlOnEKQeZ5xmY/TEFstZ+kKLXaAAOAqr+6rR5Hapk+b8HpylRywkWUcpOOACjFq5M8izINAEzEX2+IG+6Jfbs6NihZ7Pp0lMK39EmHWX+ijQBrP3sENfjZsqzQnP4Ria8n2XVh9vM8tV8eceV5CkSErSfBZMbTmALKq9aw8g//Z1F/DhIF+VRgXhy3zKd+v3/o608FChwmFHeMAgXeUhwFMsSGKIrwy1/+EvV6HT/5yU+OxIPMUSD7qCJT9lDZYW8EAGkbJFA81EvRRDnEeKegb68BX/8MKtqdTa3SUKxoQiychQ04yZwJRPJ2VdwF1U/sfiB6QZN0xpH3FDusU4gAzns/ccxQZbbPKSm4OlueHUv6YBuqm6Oy86aJJgLKU/1VJYA8mN//b+wbPUAYz62aaXIz9gdQIBERJLerk5EwpKTkraw9qX1gtr+82kJvEbzNJ/PltTlmbE8uOzhRJZlPrkyc1J3nM57TQ0aDHt6C+fXPgK2X7nUBcIVmSjRPNLMB769YuLfciylJZQQ8BnabNx4Dyq4cmbHZlCifJCStRJJjK8RPQUQEuQjMIYQZSNQV2Ft6Hqgtu5eZjtBmVrCvAIDn9+FJsuwsdlkzy5X9/KJ9aR8CwF1kZZK6bfNe1VYPOBBVlDRXb6YrFnUTS4gMrm200wY2n+yuw+Y62az17P22rhPGdrU1oyIyfbvimAyxBBJryRckvPUck13lahuAXVk6XN0xpri+AbMZSeMwQcrb7jmIM1bxxbynJh37PTbt2a+ZlBoknRhr//whMK7yJeaek/e5lXed3Z88cs/P+c1KrRr6HYMk3FU9Ee2+QigFtP7zP4InzPg7SBTkU4F5cdzOmUL5VKDAfDg+o79AgSOCw2K7VyqVDj0ZkoednR388pe/xLlz5/DRRx8dmuPpwlEg+6gsJHc8SeHcgMrL7uVMDJQWYfxlpM816Of/Amrref7CdaFKqf1CqBgBEGzLlgMy9dUct02qj+UnSAqvorqQYyHX7NiEtzBxFgkcln1W2Gx7tJkpiph2BP2iC5RyiKbp+kde3lMSQ3/8V4Hqm/HhJuUmZ0uegJgABtlQDgjt6kbTe50QnuPS9qT9A098GdOCuvSzbNPBlrhNNQcZrYb+bvbQAAAgAElEQVSkVt1BICYxPNekB4k6a5AJRZsvQN/8OejJHajx9VzFYu6eYrGzG4Kzj2IL78z1w5rlwikXpsfv03Wg3Zuv+C5RPkkmqUjIoGp1ZI3EgTgbsyFqwpwmqT1fKLPqpBy7p/zlZGOXVaYMEF+5Ba8vG7c+V+Cfgu7m7LNAnea01HP8xtxki8ECjg441n/Vey27RWYM2JRAxgC2/C/uMjEgrMy1S1Dj9wrLOmQIYKzbrNtp5BfhKWTIB+7ayJBA5LDGs17/kNnqsXCcby5Fu03lM/reohACsucwz2JRBwC6n/AWrqHL8o//2uOIMU6sx2zXML+jSQkv/+kDmKn1bdf1eQipPBtIUh68qR0hQ/ArauYzj1JsPeoCnje6RKkxEsqsrmLrt/4Kut2u6D50kDhuREKBg8dxVD4VmU8FCsgxhxdJgQIFjhM8zzty5NPTp0/x4MEDfP7550fuZn8UlE/w6yC/Jch1EpJovluKbvxlmLYCrv45lNNzXvaipXQMWjwDtAU5Ud11ULUBlQhmjOsYtPwe0LGQY9PLjxfxHZYmAAZFWE495Lb+sDh+7H6vHRNoXT8tU4DgiClbwYYiPWFxQ0RI7m4O2ptaR5j3hFoL9Dt/z73cAYEEyieFGISSW7UkyIbK7PkUlKPCMwx7d0M2vsWKJupn/RORVZbzBD7oysXRPqqkD2q0ZOM23JI6E0JFOyC/BFQcpF8cABXHPchVvI4jKJ3A3L0G7Kyxy9rBFEq14XebKZQq0gAsM6tt9nlE1ja5vowXVUkb6JuDPC/bUM+5zoiUTxLFUsVNBinfB/V6QMNxfxPc75WtqD+7pGgpCQkEZKoQESQEGuC8d1OSQj29wVq6jsOrl2HiFJ5F1TLRdp6KRaJ82mPRNFOtucKXXhEu1RS/MmznC221gdOWbFSbkjMIoRbyJ1QQR+A1mzCbW1CdF5Of23YrTqFqljFqm7wSp/DqNlUWl03HPGcxyiwTJPCa9muE17Sfd9l5yox3xzOix+RXkTbwmszEjdiwmUy6l6BUs/+WJkjhtRhizZU3xZwmJuJVU9ylj1XbW75LgxQ7325BT+eJcrBZ8eX1O29Zv4zpe3WaGFSnxlwaG4TdFOmAFBsKcUeXKqVw8k/+MTqVCu7fv49er4fFxUWcPHkSJ06cQEmqpN0nzGNHWKAAcPwIy0L5VKDAfCjIpwIF3lIcCTJkAGMMbt68iSAIcOHChdf+gL0fOCpkH5VPO8knNzk1gC3zWlVgaBH06BHUi2+Bk99zE0+YT+2A2oKIfFIgoHUK2HogapaqC1BC8glhO/u/p9ykkSEneSAqZrme6UtVgCM8uBd9bfhZvxZiiojss6qnlFb6SRsI0vyOlCuYfoGfyXsyBPrtv7uHwt3eYQSZTwBAXhPKuPNPXFAgGNWEIse4FCqQFMkK09JcNwWC8ZpQJLPwyoPeJHhbTyc+o8qykDSOQPUTUIkgZyvuwqwKQrzTGICDfOIUhkEPeHoP9PLRnorUPJHMF1htmScAn7mjbHY/SWpVIbCXtupu0dQ8eA4MiRHb8cv7WEI+VQVET6UK0qmT5KZuAOUinwSEkapVQVEMVXUQYwxRONGe9FySPou41D1DOGYyx19fQqWioR3WXuPQ3RDeipvIN3kqFgf5RGRkSji+EdcCju8dlnzcOciqthiLOq7JWv75TFEEZbmlWW3t+gHUwiLMtV/P7qWNSEoZgtpmrRelgG0Ysl6/3IGwf8UppijV8Jr2awxHagG8LR6lBn6LIZe6Mfwml7lE8BhencKEVWFm+82MXZeDJHOvoVhDMaotdlITNwxy1ovbEV7+s4fQfS2djzLYUM5nZOlbzkdpL0B5afL3yxMtG03YfpY9pw1PUUO7c76qn3+B1pcX0AJw5swZGGPQbrexsbGBBw8ewPd9rKysYHV1Fc1m88CJoeNGJBQ4eGitj9U5UyifChSYD0evglugQIF9ge/7iGPh7Nc3iDiOcfHiRaysrOAHP/jBkZ1l5fs+kmSOmXZvCKZyCl54l11GmR7Iq0E5itlKTxanjX8CZicG7l6GisPdd7RYRmap3gZMpQUlWX4Oi0Mq1cQvokpipTVE9yWoVIHyBetI+isoBLpzVfZg9ZPm2N4NWzUEVbKsqwmKyZkatRGmSB+3oZAv+FE0a5GiKlOWXfWToB//O85tHShUFYQKFBwB4l7FKTJSkF0zyKsBLtKYAhB8KEYtAwDKCG3qKADB2w2S5xZVNUBAPuWps4xZgLr8f8y26UtVI4CpLsAXkE8AMjLaBS1QmEwRBkSUZTk9fwi0N+a6Rr0SuItaktpVSkRM9oqxfgdu9rvlvk3prvKR4gTmzhjBaOt/3jUqSUBxDFWxF2hVpZqpzZjJK8rzQEEEWArxo35Lnp2YgutEW92+k3xyklNDSK3apCSVsD2rYgWA6fRR6j4CSpkyhIwRKVhNIFRd5d0XXROUtBbsm+N7MuBnezjWZ59l95AHxShMbeOD0tSa68RaZtomlQQBqNOB6m/O9sE2LphzUtlUP5x9HneOMSogzjqP66PpRvCX7UVIl8ujzyiqdDdCiSOfgoQlnyghgCGfTOK4FzmePdm8JyJ4DLlEKTNJggiK+a04Ndf0d+F6gLX/9yFMaFda5X1OmG8uU240VI6qLa/JsJtCD36L4aP8KPdJeVj5X/7XieU9z8Py8jKWlzOL8yiKsLGxgXv37iEIAiwuLmJ1dfVAVVFH9Z28wJuBMebY2e4VyqcCBeQoyKcCBV4zDsuD2lFQPrXbbVy5cgXf+973cOqUoCB4iHEUMp8AiHOfqLQEFTuUFGkbsS5Bpw2Unz+FWvs2vxzTfQnyy1BaUGxaOA1sCMin/oZ7mSEE6okRerPFFBsUGVDrNBC/cC+8J5udQROGrJZYI5gYVgYJYEmiTJ1lQWqsRRsyTBlurECU3N2CokHBYapAldl7TBX7DE0UMYkA/bv/9XwzWg8IKRooO8gn7ncYQpE0z0nQFgDjNaAMT8IoJCBUocBnVGTtNZ3tSfsHICO0x37AzG7vG4sqcI5fmpv2PY2GW7km2nJ5kOeUxMDzh6D1Z1DRGAFnd8faH3BENDcLP2YUTEkKVC0ZK5zNn63gHCfAgLgwd59OElhWGYTl86AHMORTtr0QKDnUNHHsJJ/Y4zeAstmYTYEiN5Glmm4LWwCAMISePOF4FO4Dp/hIvv4Vhnyl8j3oIIXPWISN+hjJLARzSSSX7Z7RgOd4BXbejx3kk5PbYtblyEEidl3FtWtT6wWh/ZxniAfr+REnoLtX8w+B0HpxCNKAsinmmMPEZSRxFnaKyUbirqm59o/C/ph+zCufggRgyCc2wwoAaQ3uhHQqJl08KrNvFGpWSctaIEYaHnPMS0xOVXnMArH/pIv1P38EpARjmUNltZAjgie8XpIh+Dn3ulJldh+8qclaRhO6G7v3AqWyQzOs09d+999D5ST/HlytVvHee+/hvffegzEGOzs72NzcxP3791EqlbC6uorV1VU0Go1DU4so8HbhOGY+HfX6VIECrxMF+VSgwFuKw04+PXv2DPfu3cNPf/rTYzGr5LAf7yGoInyI8i0zaOGBvCXEnRjRi2eohjEq7ae5yw6hjAYtnAW2H7m3W5LNAFf9LVBtEWpofceh/VJcB1b9TVBjESqSqbWo3ICnBS1LOuAIdKJY80UA4hVIlBq+SMDlUXEzY0s12CQ+w4KLXuuBdnaJlpmZ8YagpgsAU8fCrH4f6uwP7P14TdjY2IDfT3BmxbGgEhSwKZJlQzktngZLqSoAN1lk/Dp87QhIn6M9MaifqcYGx8ZsGqgti31m6u7fbrtyazuqCJSQLoWh0UAcgh5cB7ZeAJRH3B4s+8RaHTmIZOt6jDUW2xdbkXpANlE/hHkwSdJbQ95zFA1kKLPeWzrBd0SgPqY03ZdfRVmyaGbgKBxnbcns+VRdRrKKl2vIlvMWmyCjZ67R6bM1lM36BEFk+rGMfGLOw4k+5qkffBf5xLed5bnwBTJ37dZhq8eql3gliVVpwtj1URTDq+crcyhO7L21qRaNASznEbV3cp+RKNVQNns3W79TgrKdhpwlm0WNR4m2KqlIG/ac9xjbSNf56jUY5VIvhs/YgrL5VYDsfsSUfPKUORPfc8+NRPBrDLEWp/CZ53ZibQ6Ndd/SMEXJoqjSYQp/8F3nzjY2f/50VxBI+fddMvlzZKyPDkLnMKMJpersZKppQqqzHo2eo4e1eUI2LMgv4dQ/+p9kGxx2z/Nw4sQJnDhxAh9//DHCMMTm5ibu3r2LIAiwtLQ0UkUdJzKgwOHGcbNqLGz3ChSYDwX5VKDAW4rDSoYQEW7evIler4evvvrqSOY75eGoKJ/gN0B+S5DrlBXDSZVAahEUGmBzDfT0LpSOUUYWV29WvwcI+B+qNmXkTzJHbkzr1G7uEtdmGgKL7wJdgUIJABqnACH5pNLYXRgArC/EUwvxy6SGn1WcGNbChBIH+eS/4lgMAqAyW9AhbaCqPig1SB5sj+2Z0OppbF8JPszv/9EbVz09evQIz58/x1effAiYb9llpdlKkmyoPLu6/AWlRQahzZdU0STsX6amqkNRD9osADl2e6NlI8GFZbhsKlSQwW5NNYG8Yk0SAzvrmaVeexOIXj3jCsBchFku5sjXmdgsd58i5jtb8ZjIrpYakGD65qNJhcXcA5lA/b57Nckzj0DVJMoLatZBWkO5luWO6fhivcBNPjVlRRBPSiotyCb+qEoZphtALU6qysyVX8MvT+XyiRVNssX8+tS5pTz3MXeR9Wnq/o1dPmpcgc1W6d5dgNswsxpDaoURYCGf2HFhewbvB1DLs4QJGQN6eCt/9xID2DgW27MJQ+rYCPbMztOixA5iqKqFhOvH8BbsJBBHIHGgRPPkU5TyOmiXMMmhUPQcE1041RWAEZGTBxOk8LlJT4ytHgDWKprLyUq7KfwT+dtN+xn5tH1lHe2LL4W3k/zna2v0oFA5pDWhPLWsTgiVxm7fdWqw8zIacanTlnutv/v34FXnUG/noFarzaiihhZ95XJ5pIqq1+uFKqrAgaGw3StQ4O3G8ajqFihwhHBYHuoOI/mUJAkuXryI5eVlfP7554fmWO0HDuPxtoHKp2bIJ1K1TOVg/OxlshcgXY+AF9egxvJPpn+x6ZweG5QR5o91XoA8D0pC5AmtMgBkKikp+TQPCdN9CSwIqmiueAmbJcj4MpyaAQPVAkM+OX39yx5sVRDO8x+WgilFGl61jOTeJtRY5kCuP/5UgSKz5tv9zJw9D7WwyvT+YDEkzeM4xvnz56HoGRyudVAU8AsM27ZO+56/LWmhW2wBKWxPGWH/AJCqgCgELv/KYrc3QLgDqlagjJvYUhFP3k3AQpRMLlMDdJrN8N95AXS2gLA3OmyUpgdrqQeweVsUp2wOD1sYZ8HslK1IHadQtsKZAsxOF/Rsc+bz+TavMuWTC9Lz3wGJHZ3yPJh2F2rJYfMnHGsUupV+qlqG6YfwGrzqSrUaImJM1aswQQhPoOIy7R68MfIpuX4X5XKOpa1LxTGAx6pwd+G3qtlEhuE9Yh8mLFGcQNWZY0PGQS7x1nhuYpk7JxhVFPMtcao/zr7OolSiKMrdFt2+BYR9ICfDiHtGsWbJ6RS2wCJlI7WDxDpGKbaTnxQmgIV8MkHCKp/AnK+mG8Jf4EKXHOSQYyywz3UAvBpvp+zbbFaRkUus8inS/KQnx3Mll9vEnZc6sp8XOtLY+MVz9G7mZI5Jc/GGy+dcn4ko1w4wf9nZNo2e/HDnxe5YGj1DDCz3qN7Eyh/9/bn67MK4KgoAwjDExsYGbt++jTAMsby8jNXVVSwvLx8roqDAm4fW+lgpn4IgKJRPBQrMgYJ8KlDgLcVhI0M6nQ4uX76Mjz/+GO+8886b7s6+48gonwBocwrUeQb0u0B3G2ivQ+kxL/LB/8lfmCCeciHNXuquiRZTOgEtnAF2eCs/AEA8R5bTPCqDORQNKu1DefysKJLkPUn652rDUQRgNz9e3MvbdCl/25nVn8XmJDUw7Qj6RXf00j5NKu1+NrU+qd11UIL5/T98Y6qnNE1x+fJlLC0t4fvf/z6UUiAjyQ3SIFVz5zpJ8pwoAqkSFPHjUayQkpLGTjvAYXsBCP7ITo+HD7OhobafO7ZNoOoJqOClu59xB1RpQhmBVd+0/ZFOgbCX/Yn6UFEfCHaAoAMPFrXOPmS4Oce8Ibs6JNF2hQH4YiZLcnM2V1ZrPeYc8T2YGzl2q7Y+2ILigSzzyQWXWgUAygJ1gy07ZwoURoCLfBLmNCGWjV3T6bnJJ9+DafehTrivU6bdE5FPFOxex0gb4OG1fAGl8DakGLXFxHJlH7ofwx+SBq68p2wt9lunzZkx/Ph2Kpsc4Gz34NkJpm4ItWw53zhFH1cMtBXWktlxTWkKenjTfn2x/PaUaijrOZu/Emljt89jCCb2ODC/O/UjVk3K5UiZIOHJJwchwo2FzPbOvr6JUniccqmXwK/a19dBglKT2X7iuKcz+2ZizVr+cZdrY3me1ZFG59o6omez9wMigme7h9jGXG5AVP6ief3NJbvG1teJQWc9Hgktxx+zPQ9Y/uP/7sAnYtZqNZw9exZnz56FMQbb29vY2NjA3bt3UalUsLq6ipWVlaLIXmDPOG7kU6/XK5RPBQrMgYJ8KlDgLcVhIp9evHiBO3fu4Cc/+QlaLUeh5ojiMB1vN1pQN/61YLFVIHTYgYU7oPoSVOBYLu6BmqtQPQFZVVsAJEKG9nOQ8mXqq/6WoMEBOi9AyoOSzKKXWO4Zt6pJQj65fPud7Ay3PuO9T4asxBRFGl7LUpQhILm7ObPvorynSh1ARojq3/gbUMIssP1GEAS4dOkSPvzwQ7z77rujz0m1QFC8cgeA8RrwtcsSTmjLpZpQ5LLnkxGyimR5SsoI20O2r4rcdpUUp1BX/lzULpWbgFT0VVuG6vPqRgKgnt0BkYGKu0AaQZnJDKAsE2iPFpkCcopVMQDIMsPseSxs6zaVAQAwWVFWmytjoCwkCqd2oG4A2sixT7RdNhlSiiTKJ4FqVaRqatazfXYVUQQZU6xCbRzSySsChRQAmF4fnoB8op7QsnKMHIu/voxKxUIACJUH/hwWZ7obzUc+ufrgUk0bw9vyOe/XLh+1VyzOhX0AtmdoZp9tYzeKoWxWfTn7aG5cB5IIKFvGmeW4k2Z6Z3uGChO7RSo30Ya7LNu/4hVTRKxdH2cdCACq4lAmtezElenFfOZSL4bXYsinMIHP2HlSrAGGfHKd69xEB0rVYHKSZV3m+dnLuRf012Js/Ow+0l4MP2dde65Tfo4aGWItBafbyFNDTduOApOTtbafRxO3teEtRXkAVk9j8W/9LdH29wue52FlZQUrK1loaRAE2NjYwK1btxBF0UgVtbS09Fr7VeD44DiRT4XyqUCB+VCQTwUKvKU4DGQIEeH27dtot9u4cOECytIZwEcQR0n5RCfOycgVof0cNU86yScAoMaKjHwyUiu/FLR8Fth+4l62tw6qNaEEaimlk6zdzjN3J0Tkk2MWNeCsVRGRPTNB2Be2SMBZ+qUGylJs4mbFmp0QCKYKOsIZnkoHQMmHQQ34rX9ftM5+Y3t7G9euXcOPf/zj2Rdx5YNUA4ocBXElKIaLs6EqTp5KUQxCBQp8m8r0XfTJoL0IhDIU3EV28qqAI0uOUAXduDGpKGI7MMdLbMmt3lC+D+Ua14bcxWtn7dk5oPljrx1kM9M8aZrIS5uGjUQCYLckjFNAkpU13g8imHuWY23bNdvP7Qtt9ySqJkG2hvI8mJ3uTMbRDAT5UaopU1GJlXQ5ipQ8UCi0uhUqrobntOmH8HceZKGPOeAsvMbh1cswUQJPYINJ4VgfJc8lDnLJahM52qB7/PJwqKaY/rGnAXM9ZJXLNmu9ILSTT1MFRIoi4MltQBuoqmVbtj4Qs78WopwS5jmQO0Ycuc7mD9l/U+rF8BiCiBw3BDYPqhfN5pqNf99PePIpTAGGfGKPI7JnPxaOe6HH5EkRPAD5289U8/Z1x9s12mDjly8Q3N4adMmuxM89OSQPO+PI9ZucVU8RFPzK7MKlAdmYRho76/GIq52w3POAk//oT+bo1MGgXq/j/fffx/vvvw+tNba3t7G5uYk7d+4gDEM8fvx4lBVVoMDbhn6/f2wnTRcocBA4PtRzgQJHBIclx+hNk09JkuCbb74BEeH8+fPHmngC3vzxngvlGrB41rlY3JWphWZUK9blhMSD1MoPAKpzPBS25HaPJGzXZkc32Zhkg/xCFGvntYV7kafU8LkCzPbZ4oTlOxMk0C9miYi8c2Am7wkKKHsgIuh/6z/bu73ZK+DZs2e4efMmPvvsM+sMUPIk54i77+I8J+EjHXnuWXqZJaDQWkzQXgb+OkBQ0PfWoNYfCtvDIA9EBpIcH6YwuduQYBLBQec99V1qFIeK0dauMdb8Dkq1XfnE2ZRZZrnS03Wgm78fNkVR3uejWetBf1BcZFATEJCVKkhA4FAgURgJxne1ImvLpiaZhtRCVjh2SDppZnCdTv71r+AzagY2N2cKuiNUcY0X0B3KJzLamXlmy1Iba2WP33OrulRRzPecSsNCDpMxgM1WMWYIyqlndnPtWyijAYaksV0/rNcIIrvVHffcwRFtHJnJnJvc85XpOyZzMPcVE6fwGHKJAp78ZS0GAScB7iKXXI9XHJlswpQl9LjrI0WafScoN7NjFm4EePq/3x0RT6S8uXOdbPcN25GRvsen8azFmE4N/MGz9tbzCJ7aPcbjh6p07mM0fvM3Rdt5XfB9H6urq/je976H8+fPjwinmzdv4he/+AVu3bqFzc3NIzPRskCBvaJQPhUoMB8K5VOBAm8p3iQJ1u12cenSJZw7d27Cquo44ygpnwCAVj6G2snJ4hhDNdkCeSUo43j5jTuyjYY59ks5UME2TH0ZKth2L6yFs7YBkF8W14yVlEiUKJ8k8EoAl+dDJXAFL0o0Sz4hNWxgttQqSbIeESG5vT1jXSbOe0o1VK0MUzkJ9f3ferV+vSKICHfv3kW73cb58+dRYsLtSS0A4G3eJJZ6CqksG0pszycj+knVAQHxRUqoeHEUVXW3BnUvs9ujRgsqEVj6xXPkr0muBUI1557hmmntql0zBV4A7HWH4hjKFjAfayibOihJAZtFHFPAzCu8kiHoW48tK1ibysdw08YAUQjU7KSpKpVBYR+qwitbqNeHWl7ktytRBEnITADUD6DqfJ/E9nxCSxuOw5hYTjoppFKBXttCWa+xPLPXKDszBIcwjsL77oJjO+Oy3dN6/+7LVrgq9q+4fYddJ6fYUjbiNYygFi3rMc+samycUa8HPL+f/d2ikiZDdnLMtk9RCmWz7WVgI5jIEFTTPo68JjMGuck7DvUQm7nUjeAvMaqp2PGsuReyEm67ZpVjGzcOn9u3SMNvMPcipl1irJ7TMIVX8bB5cQ2dq2uTu1iuALFlUsOc7725VnxEuccsr+W84aMTQrkGxIFGZyPB+CPk8NLtlYBTf/pnc/X1dcMYg3K5PKOKWl9fx+3bt1Gr1bC6uorV1VXUBJM+ChQ4iuj1eoXyqUCBOVCQTwUKvAEopdwzdI8pXr58idu3b+PTTz/FwoI7b+C44Egpn5CRT7j3z9llFBnQ4nvAtqWIOET3JcivQGmHzU9vDVSqQaWCjInWKiAhn7rr7mWGEFjujdATtisqcvGVaCICTMoWnUiVWOszSgyvfOJs9cBb8nEv9HnbTB+3YUKVq0OR5D1RrQEyMfRf++ODFphMQGuNq1evolar4bPPPnMWMoznvr4pCGf2C7KhpPZ8YtG7VxLyWcJiN2P1Z2gB+MX/Pfo3VZdF5JMKNkGeUPwmIcFFyifH10QCAmCP7NMecjbYdZl7FGlHjpQNOYVg82gDiJL842TbiNWKb6yNfo8lnwAAcQS4yKcgApb5ZkSWekyWysT2BDlNqiFTItpsymYgJMZslqrT8Jo1pJe+YcU3QKZk1b0Y/qK7IOlUdYzaHDs5mAkBADLbXtd9ec/c1B7y3rjxacmmyb4iq4KJ0tRKTFEUMb1l+jl2PprrV3fVYrZ9S7U9T82zKE8SPXcWFBFBWWzsKIit2UwmiOHVGWLKRtgDLJlIxvCWfGEKcOST613RQQ6z9wIAnmPAckpFE6Tw2EwnR9aVxXIPQGYjbHkAiTYjbH/zAulGMHN+EFlc8YjsP5PlnM1dPsdeD8Dg3J7qb06zqtEEEGPraTix6eFtjAioff4F1NmM0FFKHcqMHGPMRL+GqqjV1VUAmR3ZxsYGrl+/jiRJcOLEiVFW1GHcnwIFXgVBEBSWkwUKzIGCfCpQoMBrwVAxsLW1hS+//BKVOfMhjjqOGuFIKx/LFqw2nYuMSKotXkmliEBL7wCbD9zbFSoUVNgGSVVS7RcgeALLnUx9RY1lqIhRayk+QwAYFBaclfMSlHIQl2nC39Fdvv0OWz02I4axFlRTs2JNL0b6qJ29k0+3KZyV6qUhzJkfQr3zkWj5/UAURbh06RLOnDmD999/X7QOScgnCmTnnECtpIyUPBUqMMWXK1l7yuSrqAgVmK9/OZExRyVhod2kWVZc5B7fKtwC+T4Ut2PCgjyL16FwZbklxqoK4McZd53g7l822yxtZvJkKEpgrt+DtbBt65+t6B4RMJyd3+8BKyft/QRAceIm0SS2e84lANWQzfhmbQuHbdWroCh2ElouBdVoORsR8IrtmZ02Sl4bEumaCRIZ+SSxuMTUJAffsV+uNo1xWwW7lEssIUCvrnzizrowgmpa7H+CCKpiOd6casdms9kPoBYzu1na2QHWxiYg2cZpaievle1wc4pKGykaJFAtCwkXJYCFfKJ+DFjIJ9IEr8Goypj3GdON4DPEFTkmpbmUh4rJVAJ41RUA+A1OuZTCY7JETZjCazHXI5cZtEwAACAASURBVMdzp+fbv4+DdJSNNPqsHWPr4hq6d7dhe+w0UZR7O8rmhOQomZB/yyEiMUlCRFCkZxryc4g50+8h9H30dlJ4/hj5NPi/qlRw+h//EyjfH7llpGkKz/NGfw4DpsmnaTQaDTQaDXzwwQfQWmNrawsvX77ErVu3UK/XR0RVVZCxWOB44Ci5v0hBRPD9fXhvKFDgLUFBPhUoUODAkaYpLl++jHq9jvPnzx+ah+fXicOS9SVG8ySotgQV7vDLGZktDlUaslnzZWHROZzNC7KieVKkklI6BpbPAO3nsnYbqwBHPklUT4ac5wZ1+lANx8MtpdjTLZ0bk4m2FrPzrPKGMFEKr7lb6CFDiG9tZnZ7eYIHSd4TZRkD5q//t69N9dTpdHDlyhX84Ac/wMrKini9zHaPhwLBeA0o4zqf3eeS1J5PkUxtZQsCn21PprhSFIFQhlK7hX0CoO9vQLXnUChOgSqLgIR8IgNTPQkVbdoXkpDarvuXber1PHCR0tzLbqJ5izammOnQU9m/sZF2STqjxtC3H2eqoUoFoJxzbM7Z6TApgGx/qd9zH3oB0SPJTVKSgkOjnqlOHKSmck0OGHar23eTTy1Z/oBqCe+1i+4JJqQ1cO9bGJOyhfYhTCRUNAkHkj9Uuni+NS9sCEoNWKdQowEX+cRtgxzkkvP68IqqqCgGLOQTcdlNHBlny4kKwxH5ZK5dniD0rcffco4TjZHHc0DZiKI4tR9BTonD2NtRaKDqzG/KEEAUJACrmnKQSy7yiLERpESz41H3YvhcZlM/YckpYvIDAbD7ZqKUJdYoCIGl7HyO2jF2rmwguL+VWexpgo19Usbk2z0byv1clcrZ5K3p5S0Kp9yTq1SBNzVBjIhQyjkvShUfaw+yiUJ+yR/dAz0v2+biX/9rqJ7anUBhjIHWevT/oYOG53lvVBXlIp/G4fs+Tp48iZMnT4KIRqqob7/9FmmaFqqotwTGmGNF1BylCcUFChwWFORTgQJvAIdJBUPkLn7vBb1eD5cuXcKHH36I995778C2U2D/QSfOQT37hl1G9WVFY6WFBW+HtdgI3ReyvClAnIEBAFRdhIKQfHIVqoXkk6u+RtqAC9EgIufsV1dfuNBo0owlX2qsxRGKDTBWt0wf7QD9NJtROvUCkpv3ZAhqauYrUg39w38TqunIY9knrK2t4c6dO/jJT36CZtNdhB0HqQYIPmstky1XBeAin2REkPHqbns+03eavmXLycaiXHEFkN+AMruEre41oO7+xeyCiZQgA+DJM0Go0gQ48slBEmTWPRLy6WCpUTa/LdEAIyphVYzcvnHXkJrlN5gieqgbgB6+zP5OFiWE1V7P8nFtbH8CwbkoefYS/H6SDCalFEyvD7XkIKKFGUQUuMekqlZA/dCpulKtOkhrJ4nmNWqgIGIVUPEvL6NcipHsxCLySUQAwm0ZNoTfqmQqO5u6Z6JRR5vG8OSTmVU4TMBlu7kHGzVCyTo8KGFIF84ikjkeymYnNFAGmvV1qO2pXEPbs4TtmMXaTqjaFJVRAs9iQ8mqCDmFN/ddasBNAPFrzPOZIw+Ke/YCxojVHJh+wiqTdC9GiZm4ZPoJ/CWm/Viz5JPT/pUjtiINv25vu9QoIW7H2Ly4hujRDtTYo7J1DBA5eePZDeWTT1aVXs55bEjNrKBTQnWKsDSakEQGYTc7J9SAePLrFSCNoWp1nP4H/3BinXG1kzEGRDQio4A3p4p61dqBUgrNZhPNZhPf+c53kKZpoYp6S3DcyKchjtzk4gIF3iAK8qlAgbcYnucd6MPA2toabt68iU8//RSLi6+nWFxg/5Asfoiqi3yKuzCNVaj+Bt9Ydw0ExVteAUDnJUgpKNdLrdGgpfedVn4AgFCQ9TKEmSOXK+IJA1HhTFCodj3XUmyc22LJJWNXL2ULMF9p5suxmc6mEyF90slmsuetUq5CeVMFspyXW6o0QH/1vzhw1RMR4eHDh1hfX8cXX3yBcnn+0HMoBVItKHKoB5X7UUysVpLY8yEFoQbApZDqi8asQgJCVZRfRdgtdE3nPE20GTKKwmnMY+XhOtaue6HWcNN2jgw3qD29rJIxUIytnjObibFb42xCbeodSvWMtd7ou6ksOX394Wj8284rGwGW9zmlBmqs8Ev9Xu66E5AU6ATjXTVlyiEKIsBFPglzlUhgBwgAptuH7yKffB+m04dadis0TbsL30I+mZ0u/PaDbH4Eox6ZgHDulcsybAhV8pH2YpQWBM+ZJcdv67qeGOMgl3iiwrnzXLbj9g7UyRPMdm0rMtu0nOuUJHYyb3CM6PrlifFNhuzXJpsdH3e9sjyXUJQCNoEfd3i5340j/RjFlAkTeDbyHXDaPHLKJtOPedu7fgSPIVxNkAIM+eRULnHPd4Azb8rjFGHMMQ3WAuxcWkP0uD1BOo3atTRrDOD7lj5ZhbM6f7TmWvHlc7W6HwKtyd8xz00xjQ22nmbPScNhTgR4SQwoYOk//k/gMYTLkFwavrO/SVXUftUOSqUSTp06hVOnTs2oorTWI1XU4uJioYo64tBaF79hgQJvOQryqUCBtxi+70Nrve/kExHh3r172NjYwIULF966fKfjgJ2dHTx4EeO8ZOHmCcBBPqmkD2qdBLpr/HJpCGqdBjov2OUAABWZvRA6L0FeGUpiEdhzkGjjaL8Aef5o9uIMJLPZJUU428v0EIm2FmmAQYGW+55KvDrnVevkg7f0zG5va2ShlDvROoncRUEA5qu/DeUKlN8jjDG4fv06iAiff/75nl6WyFsAtIN8EiDLhhKQt8Ify3h1+A5lkwLBqCYUuQv6xq/Dl6gbh+cEKjC/+jqzyMlDuAOqVqGMwNIvyc+SygVXCPR9Nylkse6Z3IarEx5YJZuLkI5SazaJCxSnvGKnyoxBW1E5SQEL+TR+OprNNujlwB7RdgwtH9voPIo0vPHZ8wLyictnGS1j259xCC31JPlRou0B7uy+IQIZWW26ATwJ+dQPrdrb5OuvR/F9LpXHCK572gBeU076m24ouoe4HUxd+YgOcmkPrgZuNQHXNrMedw+rWciLIASqFoJVKZinT6G6UyrS1AA1Sz9shC6X61S3KSqZ6zhzfeZIe1VhniuYSUnUj+3KT4B9DiRt4DG2eaYXw6vYSW4TubJAHd+7iFbHMOWeK01s+MzTqXGStiOEz0NsX36JZDuE7+ebbhptHyNkyHpt8WzrRFGusUCeJSAZgpezz57wehYFGlF/oHYabFMhu91TrYFTf/hfidoZbXdKFTX+B9gt9h8EEWWM2XfFh00V9fz5c9y8eRONRmOkiirqCkcP81g1HgUUZFqBAvOjIJ8KFHiLMSSf9hNpmuLKlSuoVqv44osvihvzFA7a5nA/8PTpUzx48AA//fJ3Qf/s/3GTNtJQ3voylIN8ypZbghKQTyLLPWTWFrT8nkglpfpboPoiVORWSymTZuqr9tP8BSTkk6uOLbDUY23xkBUEWWWTNpyrH7sudyoPLfPS+9tAOPZb5WY7ufOeTGUJ6sIf2De4D0iSBJcuXcLq6io+/PDDPY9V4y3Ad1xiFQTFaZhBNpSruC68ngsUUgBAXg3QAjUJhIUAMlnO04MtqB37tUCBQLVlqP5LZ5MqdOc9jZaNmX2RTMJwqhogE0btAZQ6lE0cGEs+0gbKpoJIyW6vmWp7fwbHlIigrz1w98/WULkG5IwTmi6eSpRPAisfSW6SUgqmK1AOCZR54vwl4fWIEmEWYyRUVMb57SV3H6FEW6MbAQlPbo+zjRxfrlaGCWN4AotDEyaAz1/XiATksfOm7OrJq08QUGQAjyNImOPAkKC2cQ3AaqdIUcwoksqgG5dmv2fOddYqNG/7iWatHq3bYUgkxVjYqYZ9Wwr2/SJHfhn3/Ga6EbyW/bchl5LQeW1xKZf4r12Kep9TbQWJ04Iz2Q4Rbhj0rjxBsrM70cRo++3YaLKrmywgQ9Z8KU/NTm0gotxnUtvxLOVklnkzeaWEnZe719Hha9NwP1f/6O/v6XlzmogCMFJFDZVR2fb80fJ7wesgEqZVUb1eDxsbG7hy5QqMMVhZWRmpog77e3UBHMhk5zeJfr8/tx17gQJvOwryqUCBN4DD8pC03+RTv9/HxYsX8cEHH+D999/ft3aPC4ZZX4fl958GEeHmzZvo9/u4cOECSqUSsPwdYPMOu56KZBZZ0v12qzsGEBBZI5SFKikAaJ0EBOQTkGXI2Auvgv11LRJpKEuwshgu65Q4tlpxkSF7hgPsVlxAZidjdkKkz7qj396e9zT12VTeExGgf+8P3ZPW94Ber4fLly/j3LlzOH369L60ScqtKlAky1YiVQfAF9fl9nzSc0r4mCgd2xRnOU93cnKepkBl2UudSvqgxgJUIlC9hNv2MedSsOwXbErJ0feO9bn8FoBXQXIWV3FqLW5nBJPlXODiZAYFZ3q2AeyM5THZumibnZ5Q/uz06Q/CAGQ0FBf8Ua0578OqVgX1+s5cJwojAI4xLsmPKpdBvcBt5cepMyY6JryHpkIbvxxFE2kN3LoCNVZ4ld7jPSbrZRq6KyOfKDGASxWrU2sRegTnLuyRPX5lZROAut1ijSOmrLaYYQTVauWvxLwb0PYmVDD73EfIV6uQZqxCLZN0KE7t5BM7dm3XMLISU5Ro1uJxJntyfF2HGtFnSC0TJDPkE2kDvR3AbAdIX3SQPtjMrvnlGkynkz3PpRqUEnQQIyx5gO9lk3VKXnZ++9lnpDXSl1141RJUrQRvoQHlZfvqVUtOQtCrMcqmMM1VAY32I9EAsn0jQzC9BGk7QrIdIdkKET7rImlblM1kt67lLm1WRZQlWtVKSpGcnElTQjUn86tcnWy3s5kgiQyUmrTc830Ai8tY/Y/+Q9H2JBj2PU8VpbUe3f/2oop63SoWpRRarRZarRY+/PBDpGmKzc1NPH36FNevX0er1RqRUYUq6nDiOJJPjcYctYUCBQoU5FOBAm8z9pN82tjYwPXr1/HJJ59gaWlpX9o8bhge78OoBhsqPhYXF/HZZ5/tkgUrH0M5yCf01kDlGlTiKKJLFQqBbDkVdWAaK1D9TffCWmDfNQB5ZbmyQFuKd77nLMSRwLbCJBq+qyg+5yzQGXCnY6KtRSMiWIkxijRQK2d2e87Qqpw8F68EpcYyo1Y+gnfup3w7e8Dm5iZu3LiBTz75BAsLbsJICuMJyCdEgyB5RyFYkg1lZDlNIFnRGUpYaHURKuOb/cU/FW5b/pJKlSUZ+aRDUP0EVJJDLkvsHF2HlcitBDWpY5m9FbdZW0qucseRWkkCwG2tN4NKJSum3phWnVpWshyWzH4x53zIK34GfaBpH3fK90HdDuAoGkjIJ5siaLKPwjynfugkn1RVWFSTPmNIT7Wca3jy9RWUypP3VS5bcBxeowLSGkpQiDKB8N5tSJDnpFllEYC5rjuWBhxfv2K+YhxDVZkcL5uCyRCUxVqPJZ9s/TAGeHJ3rnWQGPvlw/Z8w1yPrPlzSQplm0gTGyuJRP0Yail/7BERr4pinm+cmU07AeKtLsxOCIoA2tgGgnhE4ekwnVHQjLaLAXnlDa1sZ6HjFKmNkFEDF9maD69Sgqr6UNUS/MUmlEqhKl427H0F5XtQvsrUQL4HVfKguzH8qg9KDCgd/Bn83SQGuh1BJz1Ez7eQbEYz9oqay5tih5CdmLJd9shCZpn8OQ32YZhzLE06u7ROCZWxDChjCFvP49HqI8u9wb9P/Q//o22L+4JpVRQRzRBSWb/kqqg3baFWKpVw+vRpnD59GkSEbrc7o4o6efIkFhYWDu2Ez7cNb/qc2W8U5FOBAvOjIJ8KFHiLsR/kExHhwYMHePnyJb788ktUBZY2bys8zxvZIRwm9Ho9XLx4EefOncO777478R2tfOxcX4FArXeALYetUm8dVK5DOTJaVH8DVGlBxV3nttFcASTkU2cOlVQksRnLoLrr+V9ICCFJfodXgctKzWln4+gLmwfFWfqlBGWZGUuxRvq8NxNA/yovgQQP9Df/+NWtxhx48uQJnjx5gvPnz+/79YsE5FO2XBPK7EM2FAyMqkNRn19OqJBSJLPvkrRn0AT95b8ESg1AMra1bNsAQCWmKDvdj8oC/DzySTIj03USSizW3FtxNGBvgYgAptjJqi44Yor7znL9oDSFajSg7z0FpASCLQrKwil4ecR4nyefAMB0Avgu8ikU9FlwDZcSRhS5tye25xMqpNhMljF45cn2zE4X3s6Dmcpt7u+Rt11PIe0lKC26x5zTemzYp1JFQD4Jnr9c1wHXPcw1xl61EBrGgIV8ojix54aFIVTLMh44e0bLcaA7d+wTjSzPGmQYxaXtesVxEzZ1U5BYxxsxOVEU2ydjUD+Gx2XsMaoo048mMptIG6RPd5A+3gS9aEPvhPDHrp9q7L/Tf53pl0O9ScbhsqAJSAxMpGEwfu3ZGrWvYy5by4NhLAfJZCRUHpSvAPbUs/fblt1kDMFzWmpOwUJK2Q5bXvu5pN8UIbWzFsOkNGrX87JNl0uAOvkOlv/m35iv33vAsPg/JJrGCahhPUCSFXWYiASlFBYWFrCwsIDvfve7SJIEm5ubePz4MTqdDlqt1igrqsxYkBY4WBymc2Y/0Ov1CvKpQIE5UZBPBQq8ARyWWTh7JZ+01rh69Sp838eXX355rB4qDgIHkbG1V6yvr+PGjRv49NNPsbi4OPM9rbrJJwBAxV38VSDQwjvA5n13e4ungHVBgZqzVxrfdtwFNVehehvuhTsvQMqHEqg5VLgDap6ACqeIA4dXPoDBtFPJLGnG/iZ25DmBt8YjTVBcsZopOhNKsFWITJDCvMghQPKss3LUUzS2nDn7GdSJd+x9fEUQEW7duoUwDPHFF18cjB2EqoBQhQJPzpByF6gl2VAAQF4d0A7yyfSZ+cPjy8ksAV3tESowv7oMFXRgTnwgIpZVJBj/ow3McU/1LUVaie0ep1oA7P4+88BVoObGe6yhuEIpc49mk+M4myubsidOQaUE5pYlEy+vrZz+ZZZYs78ZpQYqx+6I+j33TyAgekSWdIJip2rICCOkgvtNrZqpVWxEw3A5YVaOlBhTzcn2kq+/RtmfvTd4jXJGNAieBakfA4sC0lhoIVj/wceA73itdbWl9d7IJYBXnZF5ZWUVxYn9vA4jwHZORDFgEzdx5GlOkZbSFOb2dXiW+i33rJEHSjVUw3IOWMZWZuFnUTfl2EOOwD17MyorChKAuaZ6TI4URSko0UgebcFshDAPX0ANtqVG/7Gsa4h9pzLaTT5xcH/Pfj149rZfIw1D9nH7rTXBs5BPhpT9O20fevO+c+ctT4bg5Sg7XYSUTglbz+JR38b7qBTwzp/8yVx9228MVVGlUsmqihqSUOPn42EmEsrlMt555x288847I1XU+vo6Ll26BAAje75CFfV6cdxs94IgKMinAgXmREE+FSjwFmMvZEgQBLh48SLOnj2LDz74YJ97djxxmJRP44q1Cxcu2D2yq4ug5mmo3ku+QS0rUqMsVJaUhMWzadKHQ2MFEJBPSifA8lmgLSuaUmN1ph+yGeXu8r8yEThfPEoMuy3ShrVCokRDcQU7prhKvT6QY+dDsUbyqD2zZ0QEb4oszM17IsBTBoCCNh7+v1MXsHTtGk6dOoWVlZV9eeFN0xRXrlxBq9XCp59+eqAvoMZrwTcOZZCL1ADE2VAiez4YGNWEIofKjwIQfCiX+g4aBnUozKoaCT70zWdQm8+yD6QqpWAL5AssBAEgFWZdDXqUC0fhmojcRXUXcbQX5cMAnNIxy0lhsl+YdfMD1gfgisq2QrA2oDtPZ0kVhfxjYNk8WWbPU6zzs4O6ElXdPk0CERTbVaMGiiKrHdgIQpKFun03+dSSFURUUzYWvYXd/LXk3mOUaCv3N1S+Bx2k8JuCjKZIaNUpza9CLLB4dbRlNH8dIOInvBCx13IiluLlrw1cBlNiJ6ZImOs105Wce7u5eQOIAqBsIVSt48HSO856zZYFFSbwrJkuzG9jmOPAXbIZQotSDc9iyZc+30F89QmSjc7IvXZ6M5xSx2hjSdAabFubnBbHvjfE34pc5JNjrDiHJfc9851JCV45v+M6MXNHM2b37fzvrPlvOduwHY9STl/H2916PvlsMiKhFOB/50Ms/Pa/kd+HN4A8VZTWekYZ5XketNZHQkU0ror66KOPZlRRCwsLWF1dxcrKypHYn6OMw0xYvgp6vR6aTVk2bYECBTIU5FOBAm8xXpV82tzcxLVr1/DjH/8YJ06cOICeHU8cFuWTMQZXr16F53kixRqtfOwkn1TvpSxrxmG5N0LMKzdG6K6B/CqUFhSf5yj6UrUlFjCovJnMEuWTYAsuHoFSxs4Gg5yDOtMXLucFPImW90JPRIhvbQGJQNWVrTBb0C9VodTAk+XzP8BXv/072N7extraGm7fvo16vY6TJ0/i1KlTrxQsHIYhLl26hA8++ABnzpyZe/15Qd4CYBykpyRbiUJZNpS0X6oGOMgnBcB4DSiTY1M33Z5XB8zk+CYA+kkI9fD63P1TJgU1T0KFbltNFclJaGUjqlxVLa3hHrMOQllAPpHyrNdR0oa3VGMKikQEMLZoXFaUjbSiOLUrF+IU5v6L2c9t1wW7bC4flmsXtd3nqkieJphAoIQ2ndQL3OSTsCgjsQNU1QpMN4DnsOlTrYYoe8lr1TNbwHIJuHXZmvUHABTEgIR8Eii9AICEVlpKCa6LTnLKMTnIOJRRTmXTHlVVNnD3cY5ksIx50hqqPnnuUBQBD2/Dtg9klDVTyUokafvzi/U6x6mbuHw1bjxzz2sMy2J6MfyprKj0yTaSq4+BrR4oSuBZ7ism5TM/SRM7IcA9z8FhuyfJL+S+5/LunMOMOaac+6shxkY6/95LBrlqqez45G/Dd1lZD6ANUM4hn0qDcZDEBttrycSpN7TcK5WAM3/6p6LtvCnYsqK01uj1elhaWkKapjOqqMOMaVVUp9PBxsYGHj9+DAAje75Wq1WoovYZWmuUhBmYRwFF5lOBAvPj+FwBChQoMDfmJUOICA8fPsTz58/xxRdfoGYJMS6Qj8OgfArDEBcvXsSZM2fwne98R7QOrZwDHv0lu4xKo8xSr/Ocb6z7AqQUlGvaZPcFyPOgHMdLkQEtvwNsPuTbA4BgDpWUnqPAn2cPJszS4EBx6rTUc8J1vrExLwQwqqk8RVX6NIBpx7mkVf6LXM5ncQDUSjBeHfid/wCeUlhZWcHKygqICL1eb2ShQURYXV3FqVOnRC+LOzs7+Pbbb/GjH/0Iy8vL7LL7BVLu3CdJttIuEdR2tCU8d4X2T6SqAAQFfa8048KodypQ1/5i8kNbXkjetisLgIB8QtgGVStQRnAc89SSg3wDvjMC1ZJzNrhbGcUS+HHKWkCx09FdlnyWYm+WI2UrBNvPNf1kXazmAQD4ZeTaeM55CTSdLqd/yCBRLAlmQaumzFKPQsHkCGFOk+6F7v0DoNs9N/lU8pHu9FFacV+j0q0O9L1HqJR48stIFU2SzEMAXk2YIyWyzXSMX1efXOSS83znSY6MeLbgVZ8pOKWWbfJGEAKLk8SquXEzmxBg6yDnsjavHR9HlLNWgYyykzmPbDZ+AKOOQabCwoB8Sp53kVy8B7W9O3HKSS7Nm1E00QD2ZPHKkVMEsL8nAFl+mgWaUby96i7ZVGRkyXWykVLzII0NKlPXbWMI/iALdfNpNCH0Hef4Kz/8ERq/8Rt72v7rxJBc8jwPDx48ABHh9OnTUErNqKK4rKjDBKUUFhcXsbi4iI8++ghxHGNzcxMPHz5Et9vF4uIiVldXceLEiUIVtQ/QWh+rXPB+v18onwoUmBMF+VSgwBvAYZlN4/s+okhmVzRUywDAhQsXjsSD5WHDm1Y+7ezs4MqVK/jhD3+I1dVV8Xq0Isx9qi86ySel44ykaruWS0AL7wI7Aus7mwXMNLovQaUaVCoofkuyoYbovAD5JaihrYunnGOcjLvwQJGGqjuKNq7ihbMWxqyfGGseFBFB1SdfxvRmgPTRzqA4R7PL58yWz/2sXMpmWP67/+XMcVRKodVqodVq4bvf/S7iOMbGxgbu3buHXq+H5eVlqz3fixcvcP/+fXz22Weo14XnzD7AeBLySaYIzIig/WlLLu0TFg6nzjUdLUD94v+cbW4eq0xbuMh0myBQ7QRU32EPCkDFbVClAWXGiuiS2ZiGWFcnACDlOKwuYRRKUEwSOyUGijt12fGsAcu6tlwlAECcWu3ZbMoFs9kBrXXyd9VW8Ex07kx/KwFvK8b3BHlikqyjuiDHsC601IvdhLASbA8AoGWkjenJSF6z0wUk5NOLLZTaDwDHrqZBAokeVcpLlhaExaqyYKt7zZtwddqlFlF2IzUCQXH2q1zxkxn3ijkutjwyiuKJflK3Bzy5PdiWRcVkNFROWSGbxGI57rZuRylUa/5ir81imIyBquUfBzIEZbHOA8CTwqlBcn8d6bWnQDuYPGaG4HGEoYUUEcOpenJ9z9mslgEhiZwLhiBUnmKfS7khYOsykT0nygYrKWXbds5vmSeU1MZD2fMQBRqdzXRiuAz/7pc8vPdnfzZXfw8DiAg3b96E1hqffPLJ6BwaqqKGFn0ArFlRhxmVSgXvvvsu3n33XRAR2u02NjY28PDhQ3ieh5WVFZw8eRLNZvPQ1HGOEo6b7V6R+VSgwPwoyKcCBd5iSMmQcbXMBx98UDx0vSLepPLp6dOnePDgAT7//PP5H5YW3wOV6lCpq6gt2zeqLUI5yKdsuQUoQZ1aaUF4PABFlBFfWw/cywbboMYSVMirTICBPdjSB0D7SfaBRK0kOA8oNXBVu7k8JwBOcopTVlHKkE+hhjdWHDJhivjODhQAsgRRT9vr5eY9GYIqEUzzXagf/CbbdyB7WTxz5gzOnDkDY0yuPd/q6iqePn2K7e1tnD9/ieyq0wAAIABJREFU/rXPYCQJ+YQUpGruXCcJEUQBCB6UYzyKFVLO5PEhdu8l2iyC/uL/yq2PqbANqtRkVpnibQNUkl/XqLY8SVRJZuZLquXO7C6+4Ei9EKrB5ZY4jgeX/2YYi840BZBfgKVUz1UiJSLoqw/nUz0BUF7+9U5V84+H7XOEbstW1XSfK9LcJOr1oSpLjoXcx0I167JJCcIfgyK3ChAAKBBOQLp3B/6ipEFRc/bfbwp+vQxjPHie49wv8dd1MubgySfHzrM/XQpwrB1r8cjd0yzrUZJA1Sxs9FROlLl2GWpwLbZZ8SrbtTo1dhWh5feghLnm2NYxBGXJCqJ+DG/RQrQFMbym/dh6jfwfRW90Ef3qAbx+/j2btOFVU45ziVvXaIdln+HJJafq0PGc4eq74WwgWT6OrEOU+46LasvsZOXPEXnHnSjfAjHvdq8HkwM2nmbX1eHPoFT2hwionz+P6nc/FPfpMICIcO3aNfi+jx/96EcT55ctK2pISA3/7vv+kVJFLS0tYWlpCefOnRtNdLt//z56vd5IFbWysnKsrOQOEseNfBpaTxYoUECO4mpZoMBbDAn5tLW1NbKpWllZeU09O554E8qn4Uy1fr+PCxcuvNpDsvJAK+egXl7lFwu3ZM0JC8rKCI9VR5g3BQAVueIly5txk08AMiXF8B8SexwjyERyzaJOjTWLZQiWXDK8rR63fYo1hlPgyRCSm1tQwxn5JnnlvCcq1aAoBv3Bfz/3nODhzMRxe761tTX8/Oc/BwC8//77CMMQpVLptRLopFqi89N4dfjapVYQFLEBGK/ltuczMoWUxBIwWy7ru0ET9K9+xlpmUn0FqvvM3ag0+w3AXI+0/tR1YK9Fabhnm4sQRcAeZlKyChzOYpNT03CEV84Om8froHYgJheAgZtZNWdmeWrgtWaLv9ny+e17ngGlCRRHRjTroDRl7cBUuQzqdO2ZVsO+CDKYJCeGKpdgOj2oBd7GxamyGnVMxgIRY504RPTtXWDtCbDoVkw7+dcBSjm/qxWVBSB1zERxkE/QqfvevOcB7Mp0sn9F29tQp/OPL2kNZVMmAo7vLMcliIC65VozRk6YrS3g5WNAKdaK16rqShmLNZuqiFPO2NYJE6iWPX/OBgoTwEI+UZjMbI8MIb78CPrWcyBKrRMXyPGMxz2DSMglDmSIzwJ1re96T3EJAIXqzGnohOBb8uSy4TuntR4RlMo//2zHNzfL1BC8nGfpUt7zNRn02yn6OxpeowYVZ89Fw0cMVS7h7D85WqqnofNJo9HAuXPnnM/PeVlRx0EVNT7RrdPpYH19faSKGmZFFaooO7TWI4LyOKDf7+Ps2bNvuhsFChwpFORTgQJvAIflwcRFhjx69AhPnjzB+fPnX6tN1XHF61Y+JUmCS5cuYXFxEZ999tmezjta+RhwkU/BFkx1ASpy5MMEQku7vmw5lfRBrf+fvTf/sRzLs/s+l2+JeLGvlUtlVmVlVldXTy2dWQtky/KCgQyMAEmjZSzYEjSQbMOyBNuyFsMSBMuCAcs/GP5P/INt2BZkGfJsPT09092TkfueGVm5RsYe8TaS9379A+O9iPce7xK5RkTxAIWqCl6Sl3zkJfk995wzB9sv/I2TQEsyyDJsAqH0boHeNjN4L6Q0hBJP0dJjIyJt7cwxEC1W5RIAiUbZchXAm03T3cyDDaSRFXcy5dLL5z2pdgPz0QXU/Cn7vgOglKJarbKyssLZs2c5duxYsD3fa4cqIWoEJXVPu4CMGYcl216E2POFK6TCCCBlGhiGMb+4hGp5jjXQKlM1VsOdiQLynjqQ/gpTCCnv68fbGNudBUm7UjFb17HImfLu6E+/cjHV6BuP7f3cG4DRs4tSbp1WLNkgoqP89qlBDZeg2YBx+4xUFUWY7QZqyq1KlEbDTz7FAdddJTDPaatB5CGfIpdF2B4E58n4VAzNNty/ErYtIHJdg3tQGq1iEk3kmUAB2fMyR0zbC5/tntF+8ulVnwXeZ6ZjuWv8aLZhPP/54CKmpB0TWQgmiWN7b/acB7l+eff5rSWXJIZBZXN3fcknQpxZcg702/12txendktDBwFGav8GMo2Y0p4+6u2E+Peuw1Yr25frenFN3vFY8on2KJe0JzvQQ3z5JsNI7H43dZJXvjlVpSEg/53CGLvW3xixkk/WfZn8yDMRySeZVEQUDR6bqlbZq+zOti2Uq4P9KVcinj/MVE/SbGUHVC4RRdn647/6q1Tm5/Z1HO8SxhguXbrE1NQUZ86c2ff6R1EVFUVRVxUF0G63WV1d5cGDBzQajZ6sqEIVtYujpnxqNptFbaxAgX2iGBELFPgeo1Qq5ZIhxhiuX7+O1ppvv/32SM1UeZd4m8qner3OwsICZ8+e5fjx46++wZmzYe3G5sBDPqnmema951EVqdYGUptENQO890amw8inraXwnOaWh0Tbi+3l3f8OsN2TtnZa7ABEHks9SR02WuwUYyyFmmx9wcVR5OUxdZftFD7SFw3M0h5CL6cosZ+8JypV5M//3VdJQgBge3ubK1eu8IMf/KCbb+az55ubm3tjYbgSjYP2EDIuP5odhKqVQuQHmUJqFGU896vECFUUPoVHGX3zGdGa31IzNEdKpa3M/jIJuBfb/qyf7nZ1H2EQ8ozznVNjwhR/DohvfdfY0tYw6nitd6kgXft15sn07k/febqjBrCRT5a/t9uQR6rYipztFuSQQtJOiWplaNSd5BOANJrgI59CrOt8Fla4lSk9aPhVVNFkWMC1LeNmAB7Lyfbv/4KhIYMOsZMFSiOBOW2RIt1sEc2GHE/A56pP+eQjh0Xs3l0d+MYAD/kUTAj2rxon9mdiy05M0XKom1zWaDs2eebZc9T6nvcq17UeeH10EWt79pqNyGqnRBYyWFzH44KLfN1RTIkIybUnpDeedIl6n62ec6JAGmCb55h89IppUTvXsf1bxHsuX07YBEBSb1nfdGwKpmyZfZu2c2lMfhaUCER59nom/13VRBWiPieGJBFqfUS7iNBqQtw0O/vodiR7vStXOPlP/4n9QA4YtNYsLCwwPz/P6dOnX8s2j6IqamhoqOf7opMV9eDBA8rlclcVNTIycmAmH78LHEXl09jY2LvuRoEChwoF+VSgwPcYURSR9nm7t9ttFhYWeO+99/jwww+/1y9KrxtRFJEk4bPzXxbLy8vcvHmTL774gomJkJAGP2T6LKIiv2VeObDgNToHAZZ2MjoXRj4FQiVNZGw+kKh6jkRllAkIi29tImOzqOZakO2e8uRXSKLdKoYQpC8fju4MCQeoRphmSnJ/o7dUkDdclKuoqG/WaF7ekzbIhV9DDb1agOvy8jJ37tzh888/z/0wyLPnW15e5vLly4gIs7OzzM/PMzY29trGP1HjgI+U8RPTigRRQyjx5bQEWm6pIcBP7EhUQxl7YVyooK8/hkbg+NZP/rj2PTQRRD6p1hpSCrTfjPuIKk8BXnIsInMa4fbV8sxWx5/hZrWcAm82Uz9R1LvMQY64bKX29Ecabczd5zsbdCif8re2r/a2oatj9ySNur84G2A351JFdBCidKXmVk91IKm/T1FtCNNoWYvw3XajYfuMHCRVfPs7qvo5RIpoOFDRNFbNrsWAHDUjofl7Pjs7tZPt4oJnXNDar3zyZPgoFzklBucEAxfx5bguXMSUvOT7ZocslZuXe7ZtG+HEiF1FbSOStGPyjIXIkjiFl3k9cBFjzpw8wWw0if/wLrLe6D0XHoIoj9zowBih5FK2e6PFPM8S52NIwGGL58tz8sK1fhQRObImXWSeslx9IpKrbgL7abRZIorJzx00jQYM914nea7gSSysPcvek5TKLv3MqTLrydiv/RnK4/4M0IOANE25ePEiJ0+e5OTJk29kHzZVVOffncmaURQdKlXU1NQUU1NTnDt3jna7zcrKCvfu3aPZbDI5OdlVRR0lIiYExpgjdcyNRmP/GdoFCnzPUZBPBQp8j1Eul3uUTxsbG1y5coVPP/20qxYo8PpQKpVotXyZLi8PEWFxcZGlpSW++eab16viqAzD+EnYfORsphKfumMHvlnKne35ZiJ32rXWw/YLUJsKIp+U0TB5Ejbcx9yB1GZQrXV/YLw2RGWP535bo4ZfvhAGAUUEp21LhCrlry8iRENl2teWGbDTzyvkxG3otwfMy3uqjMKv/qa7zx48fPiQpaUlvvrqK6pVPxGqlGJsbIyxsTHOnDnTDRXut+d71Q9FE/lnxyl8hNLOttQIJQ/5FJrTFKpAEmU/l0KV9Moi0eM7yHs/DNttYJYagJTDiujKpMjILKrtz57LiKpol6jyjTNBs+oDyF4f+eTI8xGNO+PN0ccs382Rb+TKPrL1KUl7lqXXv9stPNoO03b8tuKw5e+uoi+AfrJK9JGzSZhNYlBWk/9ZFo0MYxpNoprnmRzIdZvNup98ClRIReP5tjESJ3B7AbVjLVUaCZtYokoRST2mMum3o6mcOgOtJ/6NiocEDHyfcMJ4yCdj3Monwf1M9jyPXcSyc10XieoatyzPMxGBkRrm4UNUvW/ij+15oY31XrGSs65cJ5ti23U8LuLCcW5dy/TzDeI7z/NtSfN5imyRNk7yyZvH5rnMnKoqFbknzQjuCRqvyj05iK24pZ1u0uVKZO2Ajawzxr5sv1OH8k6bAJUce728jTe2NGksA9uKIpBSmdP/9B/vs0fvBnEcc/HiRT788EOOHTv21vbrU0WladptcxiIKMhUUR0CzxjDxsZG9xujUql0VVG1Wu3IT/bVWh+a3y0EjUaD0dGw96wCBQpkKMinAgXeAQ7KC0YURd2ZRY8fP+bhw4dcuHChmMnxhvAmM586gbBRFPHNN9+8kRc8mT2H8pBP1JeQqJQRNy70Kw9etd32MlIZQSVh+TShkOpI8AesUlGY5V4zzXJJXG0SPTDTcmB/vn15yCmXrZ5ICWUJ2ZC2Jn2yDc0+NZOWfOVGbj96/yYimD/9HxO95NhojOHWrVukacpXX3310td/f6jw67Lnk8g/21VJ05vFAEBUxRPTFJzT5C2C7e40f3WGSC/dJXp6P/uD777voLmGlEooX1E520kwpDoOIeSTaMzQLKq9ClHkt8N6ZZ8jvOSTxKnznpbUPdPe2b84RVmygjIVomWf2liLs5LsKq3MyibydM8EgH2ST7bcn2go/+82VagqKfRmG918QeXfsvTB05feDgSMI4GWemaz4SWf1PhU0Lak4i94RMNVzHaTaMxNApUmR7PcoD4yovWTXzBU3X0GlGplTDslcuUE7sA0Egggnway16wb9JDpvrwn8P/evrFQdJCdqWMD7sWu68i5X9d2HcdsI5WbLRgZRW5fHVzbRvK57Pg8as5+iMauonJZr7kIJgdxqmqDy8QIyc/vo+88t5NnyqEe0uLJ7PQp6FzkUhmlHJZ5ngeVM68J/6Ql3/rGQRCaxFDKI3KANBWrCE1rsZJ5YuznOs9yD7Ce/rx9pLFQrQ12rNz3/qy1sPYiAVTm4FnqbDP799Rv/AbRG7J1fp1ot9tcvHiRc+fOMTf37rKpjqoqanp6munpaQBarRYrKyvcuXOHVqvF1NQUMzMzR1YVdRQznwryqUCB/aEgnwoU+B6jXC6TpinXr1+n3W7z7bffFuGYbxBvKvOp87Fw4sQJPvjgg9e+/Q5k5mO4/9vONsqkyPgJ2Hjs3th2IEm1vYSUKoMZLf37RZCJ92DlgXt7AM19qKT2YQ9Ge8tTcMiQEUueDwtfAUAbry2fy8IrC/t2rJ8k1twWvdrCrOQo+HL6LJJfMO8nvmTqA6LP/pS9Pw4kScLly5eZnp7mhz/84Wsj9/vt+RqNBi9evHgpe77Mds8NhWCiEZR5DdlQEiOqihJ3hoxv+S4GC0rCEOnF20TPF3f/GIeRXkoMpjaLaiz7G6f7UItG4QoIqYxCe9VrubezYbyMn3eHuO2Q2inKZVsapzDkOD5HgdppyRdrlC2nJ3Hsc2cihYigrz6092tvP8xg7o0YQdUGfwPRJrdfmcWXRRG1o8iM3g+YQBOgWLJm0uxtMxoWOC0+G1SgdOJ9wD/hIpp5DwhQD45OA+77R1XK6Hqb0sTuOYu/W6KaPB2YOJDWE6oB5FPl3I+g4XkHgHBiWXvGgBDlk9c203N/G7EqXbL1fc8dB6HQpyIcgGuMctz3/YRizzIL2SWtNvL0GaqdM5aX8pUpIhY7Pm3sx2W143OcKZe6yWIfKa3ESj5JO0H1jW+SGuKf3MIsbXgm8LiU427yyaWKElFu8snmMddZnqbuLFAf+eSNRvORtK5F9oegTsVKTBmdn93kgjF2wipvkpMtm1TnKLlEhHJfX1efJ8iOALDzjwiUyyCVKif/23+wr/6/CzSbTRYWFvjkk0+YmZl5193pQUhW1GEiogCGh4d5//33ef/997uT3fJUUUdlQvBRI5/q9XpBPhUosE8UVeYCBd4RlFKv7q39ikiShO3tbY4dO8ann356YBRZRxVvQvn0Nq0SZeZcWMPhMfDENCmTIhMnYd1doFJGI+Pvw/p3/v0G2nPtSyW1HVAc72DzOUwEzGwMCKj3Wuq1tX12MDsFWhe5FBuURVUA7JBugwUbvdYiXdwgyiuG5XU5px+C6vnIFyLk1//rlxKWNBoNLl++zJkzZ96oPYhSitHRUUZHR1/Knk/UCEIJ5cl1EjUM+MinsDFEVC2AfAoli3pt/oRh9C9vEL3ovS9VYzVoewBUxyGAfNpX5tt+xtdo5/4JsPYUIpRXbua7gj3sky9fKIkBV4Ha8VHvsqty2Hb5cqQAzKNlZHMPORDlF08lquRm3UmiiUYGfwOJDdFoznbaae4MctEG/bwOjRRZ8xMztuJ7T5uArCY1VEUaDW/b6ORZwDfxIXCyg6ggJZ6EfuaNztAhqUQgWl/OVQPK8Q8g9lvWSqCdp/Ipmjrt0m33xI4AItEaDLO7F/dicfishcC1+WYrGw9tq7osZF3ElIX4EW1QNQtpGsdw/0Z+P6L9ZbORGhiyWKLZJsc4bNts9qASpy9HPjXjHvJJmjHtnz2A5c3s3cUyptsm1YQgc290kUtllHbkK/peH7V2P4u87rCeBq7HoOeUOLtl7M9H161n26YtYi1vAkSnfSmwMJ4mMDyy2zZuG9ZXUso718TezUQRzPzmbxIdcCVLo9Hg0qVL/OhHP2JycvJdd8cJlyqq89+ddoeFjNo72Q0yInB1dbVHFTU7O8vU1NShVkUdpTpTs9nMzRUuUKCAHQX5VKDA9xSbm5tcvnyZoaEhzp49+667873A61Y+PXnyhMXFxbdnlTg6hwxPolqeYnBgQUmGRoMIh9B2Km0G7Xc/KinV2kRGZ1DNMCsvNTQMFru6PR3wb8tjVSOpQTlqpxJrov6cpYH1HR8wOYVsU49Jbq/mF5ZFnMHdvRsS1J5CoDnzLWp2/4HGa2tr3Lhxg88++4yJiYl9r/8q2Lc9n1JINIYyPlbW/1rWTwTZIEH2fDFCBeUpeiuzS1IZaphfXEEtD2a1qLSF1KZQcYAqIwrMc2tvIsM1lA447iRsDIBMfQUEKp8CrIpetcDhm5HuWd2ZGeNSXbj261oWRUiSoq/1TSCwnAapN1F55Lyt2Gzbd5p/UZt6QvLdZmZcub5l6fQughRLYyOI0QP5dP3Qmw3KPqIqJD+KOCQajGDJkESBdpG794Be10TPb+W2SjQEGUeFzqtKw8YyZRKkPG4f+1yKQUC0J88p20tQX6z78G7BZbmZuNd15Hup4ZdY1mqBxQJMXiyh0kHyQ4yglOUobWSKi/S2jVdGYyPZ+1VK3b61Ezv5lDgs6uLdZWajQfx7t6CZHbs4Ir7EYQMHbpdEUSX3RAaftWPqeb/0wGXrJyLh927uBtz7tWUzQZgLau56lt/BSDbdZ7Af+7vT8xRX/WqoF0+S7i2w91WgVAKGRzj+X/7tfezx7WN7e5vLly/z+eefMz7uV+kfNPSrovb+07neDxMRBVCr1bqqKK11Nyvq7t27DA0N9WRFFXg3aDQaR0aVVqDA20JBPhUo8D1Eh7Q4f/48CwsL77o73xu8LuWTiHDr1i0ajcZbt0qU6bOop3/sbKOaK0HbUjrsIzp0djRbz8MycwBK4d7roeQTAMr/+/qymkQba9bJbiPPTiwF2t31HXkFIgOqKtNOia+vZoXivI99A6oSVpTaO7PWREPw5/6Ou685ePLkCY8ePeLChQsMO4pvbwOh9nyVyjiRVxLo35+SRuB1HvaRLdGIlxRTpAjDCArzR5dRq0/t26tNhJFPoflQgAxPo+rPvO1UK/A+BegoHwPIJyXG/du8oSy/HjiKJqINypKbBDgre47oEjfKJfTdp4PKKVth09qB/f3dtp10MSOesv9Jke0GasxeGFAjNaTVctqdqVIJs7WNGndbq6ip40CohaVjO0oQqeBXQL1u+97svBldQS38C2urWm0kxBUQZQKf7Wk9dJhCSjWUjazyKZ906r/PfblvHigf/eQqejomJUmrbVcpgTUrStIUZSOY2nG+TV6zidy/g8qzPzN2VtRm72e140uNNYPO9lxwWfi5CCanVGiH4NZLm8Q/vY3aux1x/J7G8h7EDgnpupZUGdf9LeJObZLETlR685o8eVKvDMf+08RN2Lls9awkoCoRRfm/vfsc5Sy1rFCxWAF20NjS1Dc1lZ33X7WHhCqVYO4//1sHWu2xubnJ1atX+fLLL4+EhViePd9eMqozAbSjHjoMZFSpVBpQRa2srHDr1i3iOO5RRR2G4zkq0FoXURUFCuwTxR1ToMA7wruw3TPGcOvWLZrNZpHv9A7wOpRPaZqysLDAxMQE58+ff+sfNTJzDnzkU3sLMzKNangKwSF5LwDbfpsfAJW2aVWnGI4DCtD7UEgEV8cUKA/5JEZQDkUSgLRSa6bJ3n359vPS6ye9eVJiILmxikpNVkzK+7jZR95TNFKjow4zf+I3iDyz1vu3effuXer1Ol9//fWBs59w2fM1xjY543EGVOIv2ioMJqr1qJHyEWrPF3b+DWPIz36CWl9yNwy0v1Sxz15wF1IOm12okiYyMo5K/NvuElUBtnvZuXRlfJhXLl77lBlONWQ7hYrjOByEtzOqxqVm1BpzN+dasG3Pdn5s+7D0OW/8STfBbLZRe7Zl1jcpOcgnICOoZjwWQ802eMgnqiP4yaewZ7+hSsmnRPSpa7sIVUhlQ7i5dYPIoUZSvkCYHSStrTCFVNpEqlW/pSUgpapdVOzLfDIar2Wez5bvVWw1XTKazqo2tNpgIZ+k1UaNWIipZhtVtYzFFvWMuXJth0jPOReun8g2vtjOmYsssmRN0k7t9oOuvjmJIEgXl0l+fm9fJLzr002iCpHzPdC9I5U6iCkjzkkn4rAK7Cx3wbvc881qHJOedCpEeROUyPKZypbfXYxYHxFSqaLi/Hf5/X4b5RGGWiLK5cFjLu20FRGWnsTdR3eHcOpuc3yCY3/zr++rH28T6+vr3Lhxg/Pnzx9JBU2ePV8/CXUYs6JqtRqnTp3i1KlTaK1ZX19neXmZO3fuMDw8zOzsLDMzM0fyNy1QoMDhRlF5LlDge4I4jrl06RLT09P88Ic/PNAzsY4qXlX5VK/XWVhY4OzZsxw/fvw19iwcMhuY+zQyAx7ySbW3kNoMqunOiVHxNjI6i6r7FVVpZQRCyKf9qKRafgsnIMh2TlopUdWjfEoM+Mgnl8oBz8xbcPZVVKVbDBQjxNdWoJkVq6zltdC8J8ms3lARujpN9G/8WXc/90BrzZUrVxgZGeHLL788FGPYXnu+KB6H5I+c7ZW0nMs7EFUD3OSTL+9pF/7r1phx9LXblHzEE4Tb9jRWAy3G8FsR7d19dSqMfOpYBHoUERLi0+OaJd9Z7jtQj9WmM+MtNc4uuiz5XMdvzVjRBn37aUAAyd5tWcgky3gYWTLr+tuLNqQ3XwycflnbhFPu56S0/LZvEofcRyGWeqGEUYASL1hlFTrZRTBbED2+7NlcoAo5qTvjyTpQCKY8ikoDnrEuS1Kf8smbY2P8tny++9cpV/GMIa4MoDS1r9pqg4V8cl63OafDrG8gjx9ii+uyjXAuRZItp8t1OpTlt3Sqm1w/neNdST/dwNx5ZunLy1rEea4jh+LfaOMkwYzLxpAOOeVe7lzfN5z7BPWO7Wc5cPkbSBPpKof6obV0yZ6B7pj8sy0iVi7Z9m6cd90nbU0557lV3lFDba6mxC3pIZw6w0i5DMf/3t/N78QBwMrKCrdv3+b8+fPv3D3gbcGnikrTFKUUpVLp0BBRpVKpa8EHmRXcysoKN2/eJEkSpqenmZmZeeeqqHedcf66cdSOp0CBt4WCfCpQ4HuAra0tLl++zMcff8x7773Xs0wphTHm0LxoHWa8ivJpeXmZmzdv8sUXX7z1fJseTH2YBcf7rPACFSkyOu0lnwBkZCaIfAoVH6i0jYwfhy2/lRdbS0gp4JgD7LskTsEVHg5eCy8x4rbYAmc4u8ggKdSz+2ZMtKOiiG+vwfZuESs3qHk/eU+VIVSksyLHr/+9sHWAVqvFpUuXOHXqFCdP7j8f6iBAShNeNy1FG6HsL1Qrf2VXmUZADgn4KkppfRR+/1+iQonnQFWh0jEyOg2tdX9jx2zwfkg53FLTVMf9Q5XWAUSnN8XdW7x2kkuJdt/zjjHDZ8lnK/YCYMlYMWvbyDOLVWPOYYrkk0yZEjTn79qgRgb3nbXvPZb04RbSSgaUYSYg92nAMjAPnoLvTs+8LcIJI/9YqpRGpIJS7veJYMLLJHDx//G3S31qywxVFSOEKppqEEI+2UayqNSTIZi/E8/vo7XznSWzK3Mpl8SZCyY6cvPnLtWi4/qTxEFMua7bvOf4pUs70g3LFm07smW24SC2LXl/YgRs7yaO39BFoEe1/DEsuf4Efeup/Z3G8Xs5J/e4xmIRpyWl6BBRv4SqAAAgAElEQVTlkmuSg2exbx5FgG2fFZ5HZFQtg8kfA12CSqPtr5fSauVWsoyGUo6SyvYYNkYo5zwjTc61rVOhOlxCa2H5WfZe0rET7LHcm51h9jf+ouWo3i1evHjB/fv3+eqrr6j6vkeOKGyqKK11959Ou8OkihoZGWFkZITTp0+jtWZtba2bR9tRRc3Ozr51wvGo1pkOwyTIAgUOEgryqUCBI45nz55x7949vvzyS8bGxgaWdwiRo/hScNDwMsonEWFxcZGlpSW++eYbhiwe/m8NURmmPoDVu85mqh2mFvIWjbrtwl7wKkmgSgmgNhFEPinRMH4SNh6523mynABnoWZ3Q+5jlXbqVDLkZTb1oM9WbwBpApSJH2zA2q4SR4RcGz1sf8/7zeI2lMuY9z5FnfqBvQ970PGk//TTT5meng5a5yBCorAg50ZbMeq9zcOUFqJqIG4ySEm++kMoox81UFd/N/t/hx1Xz/aaAWRSZx9DE6gA8il0PMk2uo+PwfIQKvIQW9oEk+n2PrnJJ2mnTlu9bPnLZToR2y35JEed2F2WatTwYGFKjCG9+l3+FRjlf4xLKrlKJkkM0chg3yQ2RKM524k10dBun0wjIX28lX87bATkjoXMXg0pLoRk/SmDmJKXMAqFUEF5lE0hhJcIyL27RLGfWFLxdpALrYJgRZNI4L1ly4jzqZ46HXJ2wmPL5yOPs2q3ffH6BqX5KXv3LLlNO0sd+3Vcd65ru89y2zx5BqsvQJXsJJnlG8GmthERu+rINp7GqTODzQZrFlSqUTkEevpoi2ThIZH1nS2yWiiLiPt91KEOFO1RJvkymbyzSTwNHLld4CaBupt/SUTyunPq2CG3LeRh3nkoV1B5v4/luPI+T3SpCmhWlxJ0Sq7lnlLQ+g//Cj/72c+YnZ1lbm6OycnJA1GofvbsGd999x0XLlygEjJ2fk/QUUWVy+UeVdRee74OCXVY6iWlUom5uTnm5ua6ebSrq6vcuHGjq4qanZ1lcnLyjR/TUSOfjDEH4n4uUOCwoSCfChR4R3jTDy0R4fbt22xvb/Ptt99aXzI75FPxEvrmEUXRvqTaxhiuXr2KUopvvvnmwLy4ycw5lId8ovECKQ/ZA8I7aAcUCAFaYe2qyRZpZZRSgO2W/0t7T9PqqL/cH0I+BUBZ7Ee6fYkNzpieWPstuhzkk6qUSJ5uI896z6Fog8rLicuxWhERVJ6H/3AVYwT+8j+0938PlpaWuHfvHj/+8Y8ZGQnL/TmwUFWEIRTue2J4ZAq0O+csVNFgohol7SGfcrKjDCPoizeIni/utmtalC7922tvIUOjqDRAAVUKnHXbXEXKUZCSgkCSDIAoYDx+HfYavhnpcYqqOM5F6incuWy7tGPXcYoatTz7kxRyyCd97zk0Y3K3anuvSTX5U9Qtv6eNpE962yd317Nz2zeciQhmI8TKLeA9rOrPTVA+SeMOsvvfR/KEXW8S8AmnlPEqpPSaRi3+HImU31nOxEhlHOUZUwAohymathtNpoJi1yzn2Jf3BH7bTq8tH27SzbO+L8cH1yx0l6rYaeVnX0/t2Z+IYK5eRgGiImxKWBWqbu4gNXZVpY3ISrXdjs+yf9EGLDma0kwGJuro71aIf3rHeZtJVEZZVDpocY63ymjrcl8mk19A61nfs+6rLA/YgXWR1kLkeN92HZJtmYhYOUxbT6RSzSWfbO3LORNCdKNNOjHM+otGT/+Uyi5rEah9eIof/e3/jDRNWV1d5cmTJ1y/fp2xsTHm5uaYnZ19J4qjx48f8+zZMy5cuFBkPjuwVxVVqVS6JNReZVSn3WFRRe3Noz19+jRpmrK+vs7S0hK3b9+mVqt1VVFvYpKr1vrAZfS+ClqtVpGpVaDAS6B48hQocASRJAmXLl1iYmKCCxcuOD9WXsUKrsCbQ7vd5uLFi5w4cYLTp08fqBk2MuO331JikPGTsPbQ3XB7CSkNobSnYFx/gZSHUak/E0ePzFDaCCCf6n67vy7SgBnk5YqXoPKpo8QIaujVLIQkMSjXt4PH99+0UvTiBqrvaKxr2fKe+qaNZpPHNebHfxZVG3X2oaP4W11d5euvvz4y5LiJxikZz7VuC93YAzFh1nYhr3mKFGEYRXZv6XQC+clvEbV67yHVWkdKVZQOuBdGplCbAX0MLHgpMUhtFtX0W2+qVhhJlm03xivleC3qGDf7JD5rN88967T8tFhcAc4cFdGDOVLSbKNvPrVbc9mKrtZqoOXPAY87vdzArO/cS3nnf3Pbv5EAlYU6cRrETaKEk09h92MYwgpeLoWUToZQv/xfs/urMgmp/5xJeSSMfIrCiqvjY6Nh1pu2fYY8G3zFQd8t7l3uaeDaf6uFGrVPrHAqgRzFPKedZm0P+XT3Pqreub4dN57t3cW2SuogG2zddo1zNnVTKyEat+Ve9d5L+tk68U/voMT9k8rA28/ehfaxXIxHFWUctoZYN7uzW59qyU9sOS39XpV7cqj601j6xXZ79itW/lhErNyrTqFsmahlPU7Lt27eb6a15G5fRfD8QT1TOo2NErV7SSgUvP/f/WMAyuUy7733Hu+99x4iwvb2NsvLyywsLAB0VVHj4+Nv/Bvv4cOHrKyscP78+SNFArwN5GVFdcgoOJyqqHK5PKCKWllZ4dq1a6RpyszMDLOzs0xMTLyWYzpqyqd6vX74J0QWKPAOUJBPBQocMWxvb3Pp0iXOnTvHsWPHvO0L8ungYWNjgytXrvDpp592Q0QPEiQ0+yVoxrggE8e8JJUSQSaPweqisx2ARGFEhWqsIkNjqHZAkXJrybm4HdUYLvkTdnzEkrRTIo/yyQtfodqBdL2Fvr+eW3rJm3m8n7wniSoQDaN+9a852xljuH79Okopzp8/f6Q+WCQaA7PsbhRg4RVJC20UJZ9yJ7CgYaIakWmj1yvws/8bZSnNyegsavOpf4PlwI+yxE8md/ddGYMA8on2JjJU9We0Acq0gVebvZjlg7ivUaMqRC6LtMAxywan0tFFZrpsu3LGkfTKd9nfI5U/1JWHQAbJSavwxFKIzVVNQpdvEW1I7u2SjP2FO6UUsu5Xy6paQO6BSr3kQ2apV0YpH3EUMpYFkk9i+Q36m1k+9URK8Me/g9pRJEhlBBVCPpXCZkWLT220A6XDjlcl2/kESNlNcsleP6w3Bs8F4sp0asfgIJ9cqigXMWWz8pNmCzWeWcBKkmJuXd9zGVnGfW1yM9sA6/NfxGXHF5ZG2LMt2xjnym3bM76Z5S3i37vVPUQnT2OzeMQ9jUC0CbaIHljXo2oKy4NybN/3Xuib1OSbtOQgn4wWsIzpOhVKlueA0bt5SgPLHPlXVkVUu5U7BEc5fUsToZxzzaeJUN/Mrg9Tr2frlkqZPTcw/MMfMPGn/s2B9ZRSjI+PMz4+zkcffUSSJKysrPDw4UO2traYmJhgbm6OmZmZ1z7Z6v79+2xubvLjH//4SL1PvwvkZUVprbuEVOe/S6XSoVRFffDBB6RpytraGs+ePePmzZuMjo4yOzvLzMzMS6uijpryqV6vMzrqnkRZoECBQRTkU4ECRwjPnz/n7t27fPHFF4yPh+WLFOTTwcKTJ09YXFzkwoULB3dWzdAEMjqPqrutwfCpmXYg1VpYGaISViSOAkPRARh7DwLIJxVvI2NzqMZg8TtVFVg1qPc8dnmt1K98irW7UAVulQP4CQfL9vV6m/jaMqWcjyUxkp+PICo87ymJkb/0D50FlDiOuXz5MvPz8wdO8fc6IMo/LisJIE0UqPIYGI+tVWDOglBF31lH3b3kblgN/NgKLTw318K2B8EEjUKQ4WlUw00YZ20DCt++azAgx0+abcjJNtrdh4eUdtnqpdqTk2Kw59mEz8I3S+uYZ25VmbRb+UVqy7hns/+0Zdp12qffbSHtnWs7b9ORQja3s2KwgxxXY/7rWZEgolDKU5yl6r+eAsYzReyNGOrsMQgS5f7M+uFzoo3dzMNQUgkVWBgNlVLoMAJaSUpialSivjHNV6jVqdMmLdu459ltIVL2bMC9dMhOkEmSuskM171tIabEmB5rvZ5l7XaXfDI3bqLi3fc0JSm5N5RjiFO+95F+JNp+PmyF2jhFjVjUTT7VKGDWG7R/50avnafjN3flE3kjlxx4o+SS5xr1k1POxe71owjENfbZiSKt7WSR1pJLDIF1cxgtRDnvpCKyMwyonL8Pts97rBsjrC2lO+vt9lvSzqQxxQf/7J/kd6wPlUqF48ePc/z4cUSEzc1NlpeXWVxcJIqiripldHT0lawW79y5Q7vd5osvvjgURMhhw1FVRc3PzzM/P4+IUK/XWVlZ4erVqxhjelRRodfmUVM+NZvNg1ujKVDgAKMgnwoUeEd4nUXVzgvm5uamM98pDwX5dDDQn9F10P24ZeZjL/mk6ks79iW+PITAjJbAIlW5uYJEpcx734dS+L0itekB8skQYbZHiRqPgRPu9dspypdP4SmkZDOAPcUe28f6DvKKw+lqi+TWCqQCOXUhm6WLlKsDllO2vCd5/xOiT76y9qter3P58mXOnTvH/Py88xgOK0wUQj6FWepJNOwnnwLs+XQ8gbl3h9Kih3iCYFIpNHdJxXVkeBSVBBDGIffzDqQcSJJVAgqnIeSTp7gdeYgLlFsJ4MpoI9ZO+7jMcs2yvuvY9pA2og3JpR11apT//iKAynlsiUhu/7OxLJ/oVsO29iVMIyF9tOe67+uKRCVUJCCCbG2jpibyj49MOSLb29bCNmSnyFD1ZrWFWOqFQCnBSBWFz97y5d/b9HaJ6NZP+nYcRiKEZsMoE6poqtu50T5E1XFI+yz6fM9Uo53Wk1kn3OOa8tyfPjs0XAo7V1G/lcCYRbmWpHZiqtVGjVkInh2lkDQayP3bPUdlI1hF8o8+UzHtc2xxvOMoy7Yk1mCt87msAkuYrRbt37rekxcnju6JEWzDdaZydXrjWfvjs+QT7VluPIy0j7EOyJNyN3AsqtZ2cgDz4bq9xKFgcu0zjzCCncdxzmVkDLkKK1Gl/NOW5+S6ponbWaeyjKesUaemXvn4Y8bOf2nvtAVKKSYnJ5mcnOTcuXO0221WVla4d+8e9XqdqamprioqVD0iIty8eRMR4bPPPjtyE7kOInyqqDRNu8sPkypqbGyMsbExPvzww54csxs3bnRVUb4cs6OmfGo0GgX5VKDAS+BgVzcLFCjgRZqmXLp0idHRUb766qt9v2BGUdSdoVPg7aDfXiNNUxYWFoIyug4KZOYsfPdTZxuVtpCxedj2KBC2w0gqtpYQpVCej+TIpJiJE7D+xL09gDggG6qDnN9FcwJz85eUz017VxdHBsLuPjzbaGtrgaa7CYtqALIiTn8hOF1tkdxcQUm+tZ6zP3EL+gvLeXlPBtRf/nvW7aysrHD79m0+++yzYNXmYYSEkE+kiBpCiY/ACciPkUbXJa0fhjHM7e9Qiz9BTX3g3RYAOWHduftt+S3POpDh6TDyaV/3auB17CH5Qyz1XAXH7jYctngASmlcv6dL/SDpYDZT77quXBjHsj3nRt99Bs2d3972fEoMajinn6nJH5MSg6oN/l1iTTQyuB1pa6LxKvG9lZ6ipOr/fbRG7RQ5ZH0LHOQTgNRbTvIJQKiAh3wKY1BClYgV8JBPoTlT/RVco6vw8//T18yK+vYmkyG3V6iiSTeRcm1HdeNBXo6Ux3bPq0wMsuV7efKJZgs16ri+XMRVO0aNWdZttsCmIGrHMGbZaGdG/pWrqP5zY7PBtHVRm1wCGezvEmIcCh3bOOmalOPKUBIh/q3r0O69tpSI/V3HNZ77cpVcXdHGlSS1o1xyLHdv3guXrZ+IJwTLg7iVWp9eIkLZMSHK/XjNP2oRoWTLkLKtY8vbGqrlTtLp73OaCivPd6+jTr+NEarV7Ovl7P/yz20Hsi8MDQ1x8uRJTp48iTGGjY0NlpeXuXfvHpVKhbm5OWZnZ622XyLCtWvXqFarfPzxx4fim/Io4qiqovpzzFZWVrhy5YpTFXXUlE8F+VSgwMuhIJ8KFDjEqNfrLCws8NFHH3HihFt1YUO5XO7Oxinw5tEh+zozgDq/4dmzZzl+/Pg77l04ZCYw92lk0ks+qbSJjM3BtkdJlbaQ0QAyC2AokMDYeo6oqJt74USz13IqHf4I/ft/AEBkm2XcA//XvfJY7knsJp+krZ3KKEl6yad0C5Ibyxn1ZyTfQg9ycx1ELFZ8OTBf/RmiyXw106NHj3j69CkXLlx4aT/xwwJRY0FEq4lqlLy2lf7rKVJg1CjILnGT6oj284Sh6/+qe92rdhhZpNoepVUHzTWkVAkrKFcCcncA1VjLYm5C6iiBOTI+8omd4oAbnt+hlTgzmSBfjdjdepw6CWWXckJS7RxTnJZeO32Wegt9a9eezfYDiKXIL9qS/WIrKNuyQ1KDXm5i1vbcFzkb3ts92dgE3s/fXqdN7FMYQbA0x4Nwwsi/v/Bt7RJeIgpz5Y+JksFia6hSaaRaCoqlUkk4WSzlUVTitnQE8hVMPpta37PdaD9Z7RsC8qQWnd23Yzf55JrwkdivTUkSu12f651eKczqGjx+2HOziIOQEWWhRRw5P3ZVqWX8SLU1p8oF2xgmqZD80T2kEXJ/710R++/tEumIX7n0snlQIft2rurJk3oV4gmA1E6qp7FYc5uAXIu87jJb3pOGkoXQcv10eYi36pRHe697MUKl2rulF0/SnQyqjJ/sr6PXfvxjRj4J/C7aB6IoYnp6munpbIJbq9VieXmZ27dv02q1mJ6eZm5ujqmpKUqlEsYYrly5wtjYGB999FFBPB0QHNWsqE6O2ZkzZ0iShLW1ta4qamxsrJsVddSUT0XmU4ECL4eCfCpQ4B3hVV8IX7x4we3bt/n888+ZmHDP7HWhUD69Xewln5aXl7l58yZffPHFK/2G7wQTJ7PZymmYRZgPUptCecgnABmZQgWQT8GWPzpBJk7CZoBKamuJNCpTJkWPnyX97V3lVxD5FKAqchWhAbdFDzsqCJct355ikd7UJFeedmfb2nKdrH/XQpTX376CgRmeJPq1vzm4vgi3bt0ijmO++uqrI/VhYoUqIWoEJZ7CrPJfT6HFZ4mGQdcRQLfHkV/+AcP1Puuq5gZSqqK0p1DXWM1sajxZUkoEMzKDqgcQxbmhPTnb1G0YnYI4gCgLIcmiaFA10w+bh88+IO3UST5JK3Gqk7zkk+tVwmHJJ9qgLBa9EqeokawQnFx+SE9+zz5fXaxZIdY/26y/ItJ7fZl7/ZZ7Ij25erIecB2ETL4JUtIFTC4IJowCnhVKI1LZUc259rl7fHqpQbR0J79hYEZj2avI3Nlv2kAqw0EEtETDQAD51H+KS2WU77fx/SzGuJ/NxrjHCWPc8o3EffzKYVXkzM1yFPyd7wmlMnL50uA3iEPVYzt+W86QGLGT3jayIdZgm4fg+I1z1ZZAvPAE2bSo71xqMxwEhkOdJDo/O6hnww748p68xNYr5kW9ynKStnXISrVQtfRda8m1woMsu8m6zAgly+9gI6xsZyfvtTNJhKHK7gE16obNNd2dq6JU9ntlZAE7qqf/0bKH14vh4WFOnTrFqVOnMMawtrbWJaOGhoZotVrMz89z9uzZt9KfAi8HnyoqTdNum8NAREGWY5anirp8+TJxHFOr1ZienmZ8fPzQk6LNZrMgnwoUeAkU5FOBAocMIsK9e/dYXV3lm2++cXrshqBUKhXKp7eIzvl+/PgxS0tLfPPNN4dT7aEiZOYj1NI1d7PWWtjmfJkonXbB3kAr/jYdDI9BQD1bITBxAkNE8js/610WQD5FrtwWwLRSbzSFt/LrC9/eWT1dNyTXnjltXjqwFjZyChJZ7ovqaSK//ncH1k/TlMuXLzMxMcEnn3xy6D9E9gOJxkH7VAEBxeeAPKesYTmz2Lu1iHr4k9xfXCHUoxqjHvJJicGMzQcRxQyNQoj4ITTzDZChSVQA+aRa60jJozArB5BKRkK5MTtcRWJ2yCnX2OCz63QVJVNtv8Pj1Fr47qiV9NM15EWg2s1WVLbd2zaLL0uhRdczZWdvY/f6mfLJDRl+PRM/fCRQ1sYgpoxSr+edS6iicI8Dnewo3a6iLv0f9nbxdthO4y0kUkEKxGBFkwrMXuxXMYXkm/qsbI244646cgdnnzzklAsjjve/ly06uoiIzQ1Yy3k/cr1a2cg5235SO+mtLONuZktn2Y0tC6qdoGqD5y+59hjzaB/vgD0bxXrNOC8lm61bAIw2TnLJhGSBupZ7ySfnYnDs3mihHDk24FhXJ0JUze95mgoVi7rJ1l8xYlVS5f3d5CicAPbGIIsIzx8lPbdih7DKHDsVo3/yTzL0/sn8Tr1BRFHUzdtJ05Rf/vKXjI6Osrm5yc9+9rOuBdrU1NShITC+j7Cpojr/7uRyR1F0aFVRi4uLNJtNHj16xNbWFuPj411V1H5yyg8KCtu9AgVeDgX5VKDAIUKnYFur1fj6669fywtIqVQiDrKdKfA6oJTixo0bVCoVvvnmm0PxEmmDzJwDH/nUXMVUx/yFrea6e/k+26nWBqY2hQppb8KD21V1nPi3f7+noKSGy96spLyspYE27dRvA+bwzs86415MKSJ9uk16f2OgabTPvKe8wpOkhmjPjGfz4Xmis1/0tGk2m1y6dIkPP/zwUFlNvi6IGgeeeVoFFLKJEVVBiV1NYRhDL20Q/fJfe4nb2uQcLAcUiofGvRaZWQfDXjFVK2CfO5BSGFGvTIqMzKHaq/ZGvnsNAqpyeH0AvYU/H7nkWd9ub+Ve173fEpJq0isPc1Ykn/SxWHCqiuXvw/nEV55y07RS0sUXAyX+fqKqn8QOUT6p907hVd0oEyBsClQiUu1RI+UjTI0uIfZ8ymBMDX7x/7rHgKSOlMsoz76VaKQyAak/p03KNQghn0LldKbvXdWX9wReAscYj8mhT9kUYtlrWzVOULbcJnBbCjr6pCxjmxhB7tzZl3hRRKz3sHXsexk7PsdYq4YsCs12OkA+6WfrpFcfQ8VxXl32dc4APZdiys5aeS35dIhyydEx417sOyyvot6xPGkb9zwOV7cdu3XdVjYhnDaQ9xgyJt/6L01h2JJZ1sHasiZuSfd2yyz3sm0plVlSfvQ//w/ObbxpJEnCxYsXOX36dNeCX2vN6uoqz58/5+bNm4yOjnazog7lhMfvEY6iKkopxdTUFMePH0dE2NraYmVlhUePHgF0SdSxsbFDMRmx0WgUyqcCBV4CBflUoMAhQaPRYGFhgQ8//JCTJ1/fDKtSqdSdVVPgzaLdbrO+vs6pU6f4wQ9+cChesJwIzX0an4cVN/mkGitIdRQVu6USWbsAMgtgbDaMrKov+9sAMv4+yfXH0O5VaoSonqSVokZ8+TIBtk0eAstn7adXmujvBguyNms92zb77a32LOj+pykNo/6Df9CzeH19nevXr/OjH/2IqakpZ1+PKkzkzyPrqBV8EDWKkt5rXFCYZBxZfJApnSZOhSkGbQne/fv0y/MyhOYuNTeQSgVlAor3IWRQp2l1DF6VfPJ2Jyf8oQ/ecf4VbW9thVkvnPtV6NtPoZX2/zn3eETslqF5hWsRyXWWFBHU8OAYlzzYIOr/7fMlfD0w6wEqOeU//yqIDE6y8+BT2uD/vUL2l8FfeBIBfecBpbrjXiBTP5rqOCr2k0VSGkUFkE8qCrs2gzIXAfptfkNmTHtsM73Wm76++YYk1zjTaoODfFKuIrEry82yTfPgMapt+d1s1622X9TKYpVrtePTxko6W7PkUm1XaPYR6Ga7RfwHd3f6bXn+iNjVomIfr30Ekgtecikkk8m5a08DcS93PlY9A5pODWWH4svlpvyyX0DWDCnLcdr4Y2N55+6opJJYWH6W7uyTnn93LPcm/v0/TWVmZl/9f52I45iLFy9y5swZ3nvvve7fS6US8/PzzM/PIyLU63WWl5e5fPkyxhhmZmaYn59nYmLi8H+LHmEcFVXU3swnpRQTExNMTEzw0UcfEccxq6urPHz4kO3tbcbHx5mbm2N6evrAqqIajQbz8/k5xgUKFLCjIJ8KFHhH2M/LXicb6PPPP2dycvK19qMgn94ONjY2uHLlChMTExw7duxIvOzL9FlERf7CUcjsZICxeVgN8OkKILOA/HDyHKjmBjI8hWrZiSozcZr4969A3IbJadTWrp1gSN6TpBr/I9ddxTJtbVUXdGAjp8QI6f0N9PP882ub2brfvKe99jjy7/5VoupuiMOzZ89YXFzk/Pnz1GqOAPYjDgkhn6RJli7hmZGsdouTwhB6S8HNBdTmcrcEE2p9SWBO2vbmOiFPIRWH2bUpBFObQdWf+xsn+8iY8xW+g8in10Acee5ZLxyEsiTaTT45yWhHMbKVoO/m/B6Wop8Q5doaSWpQtZz+JQZVGzz/0tZEfe31Zhu93Bwch/r+V0QGFKhBmU9eFRKZTZ43s0UwUgnIdQqw1AzNhvIWpUG/aMHK07DtlUcgiHwKe6a7zcD2IIR4BlRa772fyu57XIxxV78B5fs9vHy3p4Ejr03ixJ4vZARyLOU6sJI4ADnPV2m1Mdeu2o/WRiC4mIn9KqISDTXLMts4Gad2Em7PhAlJNfHv3872Ac6x2fr+7SJpjJtZduU9+TKZfJeQl5zyrOsaJ7z3qDcPyr4sTe25TYDT7tJWQzfGvc1cWH7XaGIccmxLK0NZ26UnCWLoUT3ttdxT5TJn/qf/fn99eY1otVosLCzw8ccfMzs7a22nlGJsbIyxsTHOnDlDkiSsrq7y6NEjNjc3uxZoc3NzB7bYXyBDvypq7z+QkTwHkYgyxlj7U61WOX78eFcVtbm5ycrKCg8fPiSKoq595EFSRRW2ewUKvBwK8qlAgQMMEeHBgwe8ePHijWUDFeTTm8fTp0958OABFy5cYHFx8eic78owjJ+EzUfOZiEzpLPtBV7fge1UOxnneocAACAASURBVDCrBDKVlIV8MpNniH/3l5BkBTI19z7sk3wKUmz4Pqhj7SxkS6JzsxFMKyW5sYo0HQW+coU8q6d95T3tsefREyeJ/sSf6f793r17bG5u8vXXX1N+DYqTw4zMds8NhcGoGko8946KMDKOebYGt/4QpQd/YxXXkcooKvGoCgPvl/GqgpCYpvoqovwEGgDVsHwoF0E8AB8xFHIdemUsPj+jALWij5xy2OpJ7CGfHH5INsWHiJDefJhfELWpAuIEaoPjoKT5OS5iyzDJmYme3F3Pt/nL6X//38xWIyvwW/JnAJTKCoyun1qpFJHIq5ISqnjt94JqJ3GQisoHXS8TXfyX6OkwlbKEZi+Fqh9DlYppK6iZkhSJxlFmZwByWatBpn7x2clGHgbPq2SL3E5ttWH7Qte7YKuFstj6iDEwnL9dabeJaoPr6ctXod2EHFtLwKrqEclPhhTjsOOzKVMcPmvKUvSWxHHP7SGfkl88QDYCJif47Olsyx2ZTmI8qihfHpSzT+6BIITYci4vlXER8OK5h12HnSbGnttkxPp46qiK8qAdEWzZ++8+1NGNOoz0bixJhOpwxPamZnsju/Y6p0+pXdVVFMH0X/jzlN9RAbrZbLKwsMAPf/hDpqen97VupVLh2LFjHDt2rGuBtry8zMWLFwGYm5tjbm7uQBX7Cwyin4gCuqqojjIKdlVT75KM2qt8ckEpxeTkJJOTk5w9e5Y4jllZWWFxcZF6vc7ExEQ3K+pdfksWtnsFCrwcvt8VoAIFDjC01ly5cuWNZwMV5NObg4hw+/Zttre3+fbbbymXy0RR1H1JPAqQ2XMoD/nE9lKYQioJJKniwHbbS0gUaOdlKaaZiY+If+uPenKhpFHvqRWE2O45v9A7TXyZUI5A7mx5NJDbrldbJLdX91jnWGxGAjNGdlfIUTlQIlLZv9V/9I+y/WvNtWvXqFarnD9/vviQBUTVsnPksdaSaBj04LUulDG6hmzUkbX7RPd/4a1ny8g0asPD7jRWkShCecYn1VgNEVxkuUtjc9B0231ljf0fpQCqvY3URlH99lt58I0nng9XEfEXro1xK5NEYNhd0M8jjHuWD7n66VnXtW8L6aUXXyCrW/n3qlMxkPNn26UUykks1THbie8wd7qW0zdjkM0t1JzdFkkpweBXLGVZTT6SJOQ6DrBXVWBkCOVlee1jiE6G4A/+t84WA/oFW/UGQWaogb9f0LMXUOl22KkDpDSySz55lE8Y7b+Hvc8kT6abVgPP3e4yD/Hp3HY7Bltxq9VGjdmWxdBHPpmVVXj0nZsMtJEjVjJG2e34LKyC1Y7PCNjef1zBQDtjWHr7Gfrhir1db+/sixxEj+uZlxFAgbsf2KU/78mfB+XYvi8z0Ge368plEnHegsYh+E9ToWwhptIUyjYy1JJ/JSLW12wbMZjnTqkTwVSFpccZIadUL/nU2VepVuXDf/aP8nf4hlGv17l06RK/8iu/8spuKHst0PYW+x88eMD29jYTExPMz8+/82J/ATc6NaI8VZTWujvOvCtVlEv55EK1WuXEiROcOHEiVxXVyYoaHR19q9+XBflUoMDLoXiKFCjwjuB6SDabzW546KlTp95oPwry6c0gTVMuXbrE+Pg4Fy5c6P7eR+18y8w5uP/bzjbKJMjEcdh44t7Y9vOg4ndoO2U0MnkK1r5zbw8gR/VhJs4S/9bPBiuozx4go+OoRrZOiPLJWQRmR8Fgm0XcbeRZHJXpFCNFhPThJvrxrj1hRgAOXnuiDZGtAG7Le8or6JWzGbTms3+PaOYE7XabS5cuceLEiTc+jh0qKIVEYyjjsbfaqWgKCjGjSD1Bnj9GPbqFMikKMKOBnuNVv82hEoOMzML2C3c7nSC1aWj67fwaUiHo8yxHsWWDDE+htv3kk2quuevGzpR0QOuAj1lP4a+ZEA07bLdEUK7l7dRNPjlIO0lSa/YL5I9JptEmvfbYvr+oCiYnj8w2dNlOn61KuOfvog3J/SyzaaBokVfEyCPEoyiz3nOQTwAiFVAe8klV4DWQT6F5TkIZn8TQRpgZU4E//FcovfNbmbB9Dg8NhakaJTDPLYQkBpRuI+URlPjHAYn2XNM+iyjfu4QYf46PZ7mSFOvndLMFLvLJodySRNuHr3YMFvJJksFzaBYu7XTWsi8jRLZZ6TaFpCrZ+2cbW23jaZzaSTqX1V1FoV9skiz0vePZ59m44diXU1zkUTY5ySNfHpQnC9RG6PX0zbO+c7lj/SQWSo77QzkoOxcxZbQjl8vyZ63z55OISO4lnMTC0OjgdarGx1h5tkUSZ8e9Szj1Wu7N//W/SvQOLOq2tra4cuUKn3/+OePjfhX9ftFf7N/Y2GB5eZkHDx5QKpW69nxvu9hfYH/oV0WJyAAhBVlN4m0QUaHKJxf6VVHtdpvV1VUePHhAvV5ncnKS2dlZpqen3zhR2mw2C9u9AgVeAgX5VKDAAcPKygo3btzgs88+Y2oqaA7qK+GokSEHAZ1ZaR999BHHjx/vWXbklE8zYXY+DI15myidIGPHYdOdT6F0gowHkFkA1bCXQ9l8lil3dgqDeuwsyf/30/zGxqDeOw0PrkG15LXVktQQeYglaaWoEZ89l+dDL9VQzYis+NYqsrmnSByVUJYC5GvLe0JjSiNEf+5vdT+QP/nkE6cX/fcVRo0TMUg+CSVEhhAdYZptZHkb9d0NVLuBYrDuopprmbWdz94qsEggQxMoD/kEIMMTGbnjQXmo5q/XAyoOyHDr7LsUlhem0hYyMoFKcratFMr3IWwMRK/2sUw7AQe5RCtFWWywACR2k0+uXz2z5LMsSzVRjo1vsrAIBpStkBq3chVTtiw6ZRmzrH/fM5amj7eRWA9c9CKCysl76i+eC1lwvGwE2EmGKO+kFCog8iCQuAn4RFMMEoEiEebSRaL67v2pdAijZBefDOw3cHsq2Q4+Z1IaQaX+vKkuwacUyqd88jEQWrvvcRHncjHGaasnceo+fFfx2vGeKIlju32WlvruA9jIzquVaHM8P6xjQf9N2NlUqu2EukWFZrPnzPZvWUcbEEX80zvh9o5gJ+B8uUouYsqRFeWz5PMqm1w5VFkDDzNmXwRgEs945Mp0ig2u+LfIRUxVK1jHQsc+bdlaxqKIMhpKOc+bVEPe4zFe22Jtabdfnbr8Xsu9yvQEp/+b/8reyTeEjY0Nrl27xpdffvlWVBdKKaamprq1iFarxcrKCnfv3qXRaDA9Pd21QHtVYqHAm0OHXOr8RnsJqM4/nXZvShX1ssonF4aGhrpEqTGmq4paXFzsEqWzs7OMjIy8dqK0UD4VKPByKMinAgXeIZRS3VlnIsLi4iLPnz9/Y/lOeSjIp9eL5eVlbt68yRdffMHExMTA8iN3vkfns4J1e9PdLkdxk9tseBzlIZ+67QLqVKG2P5EYZOoUrD9Cj5wl+W0L8dRBnBX9olH/zEdpJU51A2TFF6+FVsW9DWVi9HZEcv0F9OUkGKIusZazZn6f9pX3RJZT9Jf+PsvLy9y9e/etfSAfRhgzjm6PQjtGGtuwtY7aeI7azizqFKDG3ydadav2lEmR2gw0PJZDgcVir41Vt13Y86kSWpBorIXnQ+2DATBDk5TyyKeQWZFG7IqeQDjzSuiQS45zlPqUGxprJ13rxin0vWPoxWXkxbY91wSVS0YDuWQ02C0F1fDg3zMV2E5xpK1JvtshjXI2PTgs5aieEk00VEbWPc8m2078O81p459YooLznPz7U8ogUkGpjuIV9P0nREt3etuFWtomgUqlwO0p3UYqIyjJUcv1QUo1CCKfdhA6VrngI5+Mdv9Q202YcDzjPO96qupSNXoIB/uKu83iBHP9+u5frLaZjs3ZxnCbja9j3LG+w7gmZFnOkbQS4l9+B+0cAsN2PDbVNuzY++3zXagD10+lDfmpWXu27VBNvWr2m9MuUARix7uBZ8c2CzzIyCCXo19ULmWZbPvYrYhjm5bf3Ebe2RRfqy909/Yqj41AKxvrOnVzEeH4f/o33rrqZ21tjZs3b3L+/HlqtbAJOK8bw8PDvP/++7z//vsYY1hfX+++71erVebm5pifn39n/SsQho4qqlwu96iiOrUJvaP636ueelW8DuWTC1EU9RCl7XablZUV7t27R7PZ7FFFvY5+FORTgQIvh4J8KlDgAEBrzdWrVymVSnz77bdv1Yv3yJEh7wgd8nBpaclJHh415RNk6if19I+dbVQzzI8/zxYut12gjZDPQqwHQ2Okwx+R/q6HeALkyX0YGkGN+R+jEhtwZI7vtHIvTdy2fCKCXmn22Oz1oNWyTmu3qRCsyLO3KlUxJ3/Ao8oUyw8f8tVXX1GtBmRhfU8ha03Uxf8LsBeGVGs9bFtDEygP+aRagUXd0PsqdKZ57MmZ2oEyKenINOV2wDHvw6KPkuXG8xC5QNgx+gpQPkuj1HO+PeurkuNZ4lhX+grE0oxJruxk99kKf3GaaxMmqSHKsfCT1KBqOX9PNNFwznZiQ7TTPnmw0c166S/y5RX9VKUCfbl1ncPXbYWfpgghlvCqCEIs9ZQSjPgzpkKRZVFlpJFeiYnu/GywUbyVFaE9h5mrEsxDvI2USijlv0ekMoKK/eQTKvCTtGODG0Q++e5Pz7uYMXbyBZB2jHIZi7ruQWPAZanpKpC5VDh71jNXr6OSeKcrDpLDYcenLMQylt/emuskEjbu9m9rKP931vdfIEthz8iwnWG/XIzDBg6HoozsErOIxF65X767z2fJ59tAIhVKjjHNte00FkoOUi3Ks2/dge3Sz6z1LKpZW0aUbf85fVtf0bSbnQmhYBqNjHSqlHfsNaE8N8eJv/U3rH1/E1hZWeHOnTucP3+e4WHvx8RbQRRFzMzMMDOTWdo2m02Wl5e5ceMG7Xab6elp5ubmmJ6efuv5QgXCsVcVValUuiTUXmVUp92rqKLehPLJhaGhIU6ePMnJkycxxrCxscHKygr379+nXC4zNzfHzMzMS6uiCvKpQIGXQ0E+FSjwjtFqtbh48SInT57kgw8+eOv7LzybXx3GGK5evYpSim+++cb5glUqlWi3A1UIhwQycw585FN7E1ObQjU9xQKfgmOf7VR7GzM6i6q720upQroJ+id/ELZ/naKOf0I0EkBuhZCNnlwJaWtULf+6Mlsxyf11ZNtezHRZ11jznoaHBhRrtrwnMXDrwl/AbG9z4cKF4mPTAxk74W2jknpWuPWpDAKKsKq1gZSGvHZZwfZ3ocqH+mrY9oC2Ggp6KVWtECXLDmzF39fgBy8hU9J9j1dfGLxv864suX0oJ5KFxT12XZYinuVcGm1ydUM2K61+4quLnWwTvRWjlxrdrgy8o/Rb7tEhffpIqnKEiYaI/p2/BtzL32fPVnwIUTWFEUpCFbxtwyapyI5iVjcqRL/83/P7JRoZGofETQartIlUhr3ZSwqDVCYg9Y8XmU3mayQJOkXrgLwVKUWvVHz3kU/4yGPXc7DZQllymwB7BhK48+p21jPrG5gH93ePX0DZ+mM7SY68IaUMuYpB2yqJtmfQ2foVp6iRwfOgV7ZIbz5l3z6YHnc662KXrZ42r2Sb51xXRe5MMlXqqh5z13/FvKd4vU4tJxepA5eySWs7+ZQmYieYUrHa9dms9cAl0MtvXxnq/XsSC8+fpD2Pza7aKU6JqtmC9//+33mr381LS0s8ePCACxcuHOgJXbVajdOnT3P69Gm01qytrfHixQtu375NrVbrZkUdFPKsQD5sWVGvqorSWr+z78IoipienmZ6ehrotY9stVovpYpqNpuMjfnjBAoUKNCLgnwqUOAdYnV1lWvXrvErv/Ir3YdigcOFdrvNxYsXOX78OB988IH3o+RIKp9mA3OfRmfBQz6p1gYyPOlVaqjWBlKbRDUDFB2jM+Agn2RklmSxjnn8ExiqQTvMekhSTTQW8DEY8KFqs63a3dfgDFaJNcniBrLSchYRRBs7+eTKezLpQN9teU/fffJvU5ua4cMPPywI7QDI2DEEv82cjEyjNtxET6gKUEZn/JaWzdWgDCnV8Oc9AVnuUm0SFftzd2ojE9B67t9ocw2plsNUkqmFbAsinzzXsTH+e9tiUxe6C1tGCoC0U1TNFbgRll+iv1vBLO39fWwFvv1Nv7eNSdZLq1oDhOSe/RmR5T31nhOpDBP1ETnZmFeC/+SfIxV/KLurgNvTxstRJYioAEVQSJ5TqCK9hEmH4A/yiacOpDKG8pBPWbtxVOwnjaUyigogn4jCPjW9uXWddunOeFgOePa+amab7wf39dmh9JFW7CSfGLEXaa0kDqB2irvm4qXeO9bRVRsp5VQx2Q7NNu5olx1f/u8kyeA9IIkm+endICVizp72u0K2T8eavkwn1x4zcsmR7RWVcZHU4vmW8F2evuWuWrFOxapCyjbuWNdBTOlUiCr7e9bYtpfZ9A3+XadCebj34J48THosDvcee6ceHc3Oceyv/MX8TrwBPH36lEePHnHhwgUqAWT7QUGpVGJubo65uTlEhEajwfLyMlevXiVNU2ZmZpifn2diYqKYqHaAYVNFichLqaIOym+dZx+5urrK/fv3qVQqPVlRNiRJcqjuyQIFDgoOxihQoMD3FMvLy3z99dcF8XRIsbGxwc9//nM+/vjj4KL7kbQ5nPpw5yPZg8AZRTI2F9ZuNKydq0BsJj+i/fP7mIeLmZrpxJmwbQI8vY8a82ffWG1rdiCpQXmT3ne/usUI6aMt2n/8HFlp9S8eXNMxe9l2boy2FNZzqhWbQ3OM/OpvcObMmYJ4CkWpCrUZf7uq/eOni9A8l6rfIkLpBIan/O2SBjIUNutPav7tZQ3DSHmFIMMB5w6HSsqlHOjAo0YM6a9y2WqBk1wCe1EWdmzwXOs6xpROsVlaMclld65Yd519WnbZFtiHCE263MBs7rFj6j89Sg2OMa3B698YkD//X6BOnEEkYIxWfsWSwm8dp1RH1eSD//MrZH+QEW3yh/8aZSNaO+2isJw2KQfOTI9CCy+BzwQddryk9R0LN/f+Rad2pU+3a76++ZSJ7vVdJJFLNSVxsmMlaUEt/zcSbWCoinn4CNb6CUTHsViHChuRNEgCd9ewEVkuFY5tnMx5d0kWHv7/7L15kBzXfef5eVln30CjcTQuAiRIggQviARJa9aW7fFI8iFZmvHKHk/Y8kqWLcsj+VpbEz7WhzySpbA8YXsd2o0NR6zD/yjCXq0cEyFfCq92PLHmKRzESZAAGlc3gG4AfdWRx/vtH9XVXVWd7+gDRKOR3wiJ6MqXL19VZb7M+n3f9/tFZurLIJ5YLvdkzWyyPnvhsORzPDe7bjES2+8BTnbJoYzKW6yeo9A+OBsBbzsXbEM2KqIMl5Lo9K88itszvG6NJ8zOSEuu08KlLQK5nEIE9v7n3zIPbpVx+fJlrl69es8RT51QStHT08MDDzzAs88+y7PPPsvAwABXr17l5Zdf5tixY1y9epXQx5o1w11FEAQUCgWKxSLFYpFCoTC/oDZJEqIoIo7je2qBbdM+ct++fRw6dIhHH32UIAh46623+PKXv8wnPvEJvva1rzEz077QRsSRAziHv/qrv+LAgQMEQcBrr73Wtu0LX/gC+/bt49FHH+Xv//7vU/c/f/48L7zwAg8//DA/+qM/ml0nGe55ZORThgx3EY8++mgmQb9HMTo6ysmTJzl48CCbNm3y3i8IgvVHPgV52PCAs5mvpZfyXK2sbJ4fre1SCtAS5IgLDxD+00tQbVE6+WbZAOiIoMtVPBBnAVpqjgICzBfCk5s1wqPXSC5PmyIXlgRz0LXh9Y4f/4lW8JP/C1u2bFn5YO4z6D639Z6Pak5Vb+JIdmjAc9WhdA14tvNcNJH3K3r75kMBTNc9iar6JBKkFIAdyicRcX9eLksjLXZbPEDZlBEiYMg7AcBkX9eEZd8mqRUdu9jeT2C2KVIGFZfpdUxzi6mgKRHRSPs9onOlbNrQUlWdz/8Awbu+pzkQRFz3lBgRB5GgNCIeGX8+phJeRfDIQ7mQR589jZoZd/Y2XfFT9JLzK3SKZ0aT8iSVSWqe/SWQ63LbjboK8+AxJ7qUjRZyWGsjSQTY54+qmUiUeh1lmr9qISQJyfET5r47+xMxX6um+49t7BbCPPX4iZjfTweS0Vskb12f23FJh7E+24k2FxIb9wJbt7bFPTm7rZ6TXHKQ4q75wWm7Z94WhzpVNdRE4rj/2Pa1P9aYB2X6aWDaQwzqSK0XBhDWhbEOuz2lFk8NxT0PMPivv9M4ttXEyMgIN27c4JlnniG/ChbBawn5fJ4tW7bw+OOP8+KLL7J3717q9TrHjh3jlVde4e2332ZyctJpCZnh7iIIAnK5HMVikXK5TLFYJJdrzHdNIioMw/n8qHsFXV1d7Nixg6eeeopPfepTfOADH+Af//Efec973sN73/tevvjFL3Lq1Cnv/p544gm+9rWv8V3f9V1tr588eZKvfvWrnDhxgr/7u7/jU5/6VGpt6LOf/Sy/9Eu/xNmzZ9m4cSN//ud/vuL3mCHD3URGPmXIkAFwe39naEBEePPNNxkdHeXQoUNWWXYacrncPfUg5gsZ9LDem72O5DxWhfvmzvi2m75O3FIok64NRDe7iF96ZVFTuXrez84HvCz3pBrZPftJt5ZZ1CYRwlPjxG/ehI7Cu+vateY9GbalFZSloy8RIXrvx+gf3uEYfYY0SO82dyNHRhOASkIoexBG2i+PxpssKnT5tfO8tfha+QH09vurhaWc0tZV0HHkeACIsl//Ug2dWSCqbBlHPbbPHbZiat2h+igVSK7cRI91EPOmAqwWI8lksgwNyumfT1BKryAmN2tIpWVVZ8dQ0orAaeNK+ofJ/fDPdOxrP6cbiiWPDCGPNn6O5u6LwqWiEsmTHDlMcOuqx/GgXPK8rn1/GnqTSn7zjoo87+eA5LrdmU8+dqSuhS4u8t9GDtdC+/VrIzQiy2dWsxFTIfrUm6h6CpFnJJIsWVCG8RstNbWgTIScSRGV+NmDSj0ifGkuuy2nvBZmLOrO9H2sgEAKrOSS4zp3XBvW86DRwrxFxK7Kcjw3upRNVls9ckaVEpgVTLZtOigYt5nsi7Xh+lYtp+LVi1FDIdVBPkFT9dT4777/8p+NY14tiAjnzp1jcnKSp59+2jt/5l6FUoq+vj727t3Lc889x8GDB+np6eHSpUu89NJLHD9+nLGxMSLndZDhbiNNFdWsd4RhiNb6nlNFdXV18cEPfpCvfOUrvP766/zJn/wJQRDwi7/4i1y+fJlf/uVf5h/+4R+o1cwLZx577DEeffTRRa//zd/8DT/2Yz9GqVRi79697Nu3j1deaa9JiAj/9E//xI/8yI8A8NGPfpSvf/3rq/smM2R4h5GRTxky3EWsFYuq9UqIrDbiOObw4cMopTh48OCyVqStx8wn8COflGjo2+rubOYa4rP6eua6VzuFEAw0VCa6fzf1I1fRb7+d3jiqw4697mMDyod8Cj0KYJYigJ6qE14KiU6OI5NLl9s3sk/MBWXT60Gaf37cnm2gh/dTfvcPLHlMGRqQXrfySdUNtnGdfXmQT759wSrPT7GnoiGuIcV+vz61h6phDpLvWCCgVCMPyNq/+7rVs4735bDFo2Ynpp2ktK3oGZmPLZGGWBMdS7HbM9Vm43QyThKdag0oiUYFi+cXidPb63pMfKWDfEhTOXXmPXUoFrQogo9/bvFxPaz3RDzIJy+1j4da0TPPyUQ+ieRJjh0jGL+AivwUTUVbPkvr2HztL32vwdhvfEqH/taAQcG9SMT1nJUkbuWTY7vqNhPwEjmOb1NuW9TxtmubasX4bGOea8zPH6aFK0bEiXFeMiokLYrD1rkifO081Ofe+2r/drLxMBZrvAYhvrx5GOy2eS5yyKbWajSw/8Z0OvLF9gbWPCiLJadOhLwh00lrc46UiUhqjMWQHzWTbkncPP7NGzGVWZnro7GtVfTctNzrfvopep983Hj81YCI8NZbb1GtVnnyySfXTD7OO4lCocC2bdt44oknePHFF9m1axezs7McPnyY1157jfPnzzM9PZ0tll3jaFVFFQoFTp06xa5du+55VdT+/fv51V/9Vf72b/+W7du38/73v59vfOMbvPvd7+YDH/gAX/nKV7hw4YJXX1euXGHXrl3zf+/cuZMrV660tZmYmGDDhg3ztaa0Nhky3GtYX1reDBkyLAtNK7j1vspqJahUKhw9epQ9e/YwPOxhl2XAusx8AmSTh/IJoORWiinRSN8w3L5sb6cTpG8H3HZnlqhyD3HXXuJvvez81a1U4CXWCPo8FFKWkO2FA7b/KYkmuVElvlaBaoyo3PIzBxJBmS5rS95TLq2grDXNNSs6KBL85G+aD5zBCR/lk6pPIvmSM8+FgkfRtnobCfLuorFnMZvIk1Sq3PbrjwaJpkI3Sabq/hZ9M7MV2qg5n0UDWrtVEU5FoyMMPkzslpwuWz1TMRes847UI+Kz1yGV3DIQ1fkSaRONGMYokUaVU8gqw2cSj84uznfp3D1tvgralZjyg58iGFicB6alaI61mYfP889qkU++K7lT5mHJkxx7g+D6HMkQTjVUqa6uXHNIE77ZS742edGMv5gq143SHuMM8m4C2QWd2Akgsc8BUq2juszzrjVrEVA21ZRtV9u1/fZ5M3lo+g4MxxLBbJGpDK9b7fgMn7Xleaz5GcUXbqAvtShjl8M92YgYW5c2EkCLnQdrDQ9atMlulusil3zIKXsD13OwbVcxfp0AOtLG2TSOhYKBfEpiIW9YLKXrUeoUrbUYLf7SSC6thUJRUa9rrl1t3ANbv2LVIqoLAhAU+/7k84Z3szoQEU6fPo1Siscff3zNLEy9m1BKMTAwwMDAAA899BBhGDI+Ps758+eZnZ1lYGCAoaEhBgcH15014XpBHMccOXKEHTt2zNdNtNaISBvx0O36cwAAIABJREFUlCQJSimCILgnSNd6vU53dzfve9/7eN/73sf3fd/3cebMGV5//XV+7dd+jTiO6enp4TOf+Qyf/exnKaWoztPm787r3qdNhgz3GrLZOkOGDOuWEFktTExMcPr0aZ588kn6+z1X5huwXpVPlPqRns2o2Rv2dtozRL3U41VfkFK3s53uHSYeq5K8vthmL7XPsZFG0cmhfvCx3fN5E83VvboSEY/NkoxXUa2nSBQtOyibrm6jdVuaumluROkvt6wGlR/6JEF5aZaTGdohPplPgHQPoqZGrW181ApKNLp7C2rmmr1d1c/+TtX8SCVVn2pcpz4KKF/Lv+pNxNN5qbtYgFY+zadQ4cE+K0MBbaEPRycOZZNrd1W0Fc7Nm/StWfRVw3esDGXYIIAUtY5pjEZVZcoOuhoTXZtdVHRYlPeUy9H6xgQIWorjeteT5J7/PsNxPc4rU0F9qVA+9/cIEYVyBvd1FiNyJG8cJ7j+1kILSZBCLzhs63wzF4nS1QKL+ov8CGClQyTfi9Lu699X+YSPIsc1NyQO8smhjJKanXxyziHdljyopSqOAH3hEnLtBqqYRliK2VrPIogy7WO08tXphIokGmWw4TQpbSVOUMUiulIneu2CYd/OgeE1dy8+GObPwfa87mB9V0QuucgjZ56Ti5yyd2+7NKK6JmdTfNVrmBxKdYJ9m5G1ilKvC224TBNy5POL71lRBOWi4upIeqZeq+opn4f+7/5Oyju2Gwa1cmitOXnyJKVSiX379mXFZQOKxSLbt29n+/btaK2ZnJycJ6Py+TxDQ0MMDQ3R09Nzt4eagQWnmF27drFt28JCu+ZzXXOxs9Z6nohKkmS+FhUEwTwhtdZQqVTo6lpQPX/zm99s216tVvnWt77FN77xDV544QX27NnD6OgoY2Nj82127tzJpUsLC2cvX77M9u3t88zQ0BC3b98mjmPy+XxqmwwZ7jWsvSs6Q4YM7zgy8ikdIsKFCxd4++23ee6551ZMPMH6/qy9rPdmb2Bf7znXznP1tbLk2Eipj0jtJPzno+ijr0OvRy4OQK0COx50NvMhn4xWM80xJhqZrlM/Pk54bBx9vZpSu3T4+tsQpqtYGnZ8S8h7EiGYy3LQDzxF7pn32I+bwY18GSlvcLcrevyY9lUrld19qaiKlPrc7WpTSN5SQG2BdC1Wo6Q39Gumkgi6PD47IBd3FNNNmSRLGEjDbs4xj7mesF3XrsVWTETAppoyrbav1IlPWGw7TEMyqdxMNn1LGFZ9ZHJxwa3cbmcmsIjUkVjPW4npoEzwU79lOCqI+OX4OeGzmAC3HZ1S4pkftQCRHMmJkwTXzi7e5jVHzCIeP/tUOO03oKiCeKnFUqwvTbBYnLXCqOZta+R4r07fMUd1PlxBHk8tRNlIcFueVYriS+p1kmPH7USSqahtynWyqbZNzzWm40dmOz7j5zRnWxq+9LZbBeoD21xlu65tz5i23D0Ru62qizxaIbnkupdqmzWeFvIm1RsQRw5LPiepvnSYFktpw+cUVtK/tyQRbt5IqFYW9jNZ7ql8jn1fXmzjulrQWvPGG2/Q09PDww8/nBFPngiCgI0bN/Lwww/zwgsvcODAAXK5HGfPnuWll17i9OnTjI+Pr9vf2msdURRx+PBhdu/e3UY8paGZFVUqleZt+pqLdJMkIQzDNZcVNTs7ayU5u7q6+P7v/37+9E//lMOHD/OlL32JJEn4y7/8y/k2H/zgB/nqV79KvV7n/PnznD17lueff76tH6UU3/M938Nf//VfA/AXf/EX/PAP//CdeVMZMrxDyMinDBnuItbKg+Z6JkSWC601J06cYGZmhueeey5VNr0crFvlE57kU1SBniF3Zy4FVRMz44te0gTE3XuoH7lC8vrrc1Idjdq6069PgLyjMKhA9XiQT+XFBSY9XSe6HlE/fp3ay1eI3p5EZtJ/KIs4fP0t0LEmsKxQTn09MeU9NQq9utBF8OP/aVnjybAYPrlPzlwS/NVKvsVd6d7o2c6TVCqYs1Ha4JkPBaCLboIMgOqtdsLby6LFYalXDd3XpYvkcu2fYn05j3psVjJA6gpx0Zrw1REwZXko8zNJYCg2m/LkjGPr6CeZDoknqouP22kNWSinWJLM/VcDH/1tlGXO9lI+eWWduZ+TfC31THlO7WiMqUE8nSYYfTO9r5ybBFYI4nHNKB0hOff12uiv19nOd3yNhp73upQ8scVtVmjL55KGuCx1bW+lZrcWVCXzuZGmeEyOnECFkdkK1GZtZ9zH8LIW86Ia03VveeZVBpJDYk1ydgy53kGGqvn/S9nJdBBlIebMz1gigrKN3bjFbbvoIodWGmtjI68EGvmmBkS19Iw/n76TxJzbBPbHGdMhbVlQxnEYvrYkhuujC/N4p+Ve6+ub/t2HyPd7PmcsEUmScPToUTZu3MjevX45sxnSUS6X2blzJ8888wzPP/88mzdvZmJigldffZXDhw9z6dIlqlXPBVoZVoQoijhy5AgPPPAAW7d6ZDy3oDUrqlwuUywW5y0Vm1lRURTd9ayoSqXirbD7+te/zvd+7/dy6dIlvvWtb/G+970PgAMHDvCRj3yExx9/nPe///382Z/92bwa7Ad+4Ae4evUqAF/84hf5oz/6I/bt28fExAQf//jH78ybypDhHUJmu5chQ4aMfOpAvV7nyJEjbNu2jd27d68qSbieP2sf8gmAng1OckmFs0jPJtTshKPdTFu7uHcX1TfOUbj18uLGkWeWBcC1S41fooYKgOopWFe1AuhqRFAI0JWI5FYNmayTTNdRrUWJ2B5Y7bJWceU9Ge/yprwnrcmlLCuXXAFBkA9+mqC4OkRsBtC92wjGTzkauYvZKq6iS32oukO14Mp7aqLgWSwueioaPC3NVNU/HwpPiy4lCVIaQtVvNl7wIZ9ceU61GNVl7ydIsb9qg9H2cm5/y+4SJijLR69SiKv49BhyqwJ5RWrZtFAEWXx+iBZjgTjN4qvxenr7oNTevnb+duo8qnTcNkdJEi+yY2oqN/WhHyT3wKOpx5vf34PoUcqDWFKxs2isVIzoAOW033OTI4q4QTydepNg9LS5oSepTKEbwklnMyn0oBJ3sa5BUrn78x6f+D0bKQf5JCIe5K9rLCvabFQWA0gYmbmQRKO6LPNvuX2bHr0Oly7brfVsWGJRn0Qbnx9MOVwi6R+3aDGS7FINiQ6n5Hk65uZU5Atmlbzti5Tl++q5bPX8gtpMw3Kf/7Zj1yuxMVsJIInMuUzAnF1o+vY4FOMtVkSM67pEzPslMQSGKdz0HJ56vgHjtwKkZZ5pfkwiC6JCESj0lNj7u59NP+gKEccxR48eZdu2bezYseOOHON+RRAEbNq0iU2bNgENomB8fJxTp04RhiGDg4MMDQ2xYcOGNWnpdi8jDEOOHDnC3r172bx584r7a82AMmVF3Q17vkqlQne332+fD3/4w3z4wx9O3fYbv/Eb/MZv/Mai17/xjW/M//vBBx/klVf8IgMyZLgXkJFPGTJkWNeEyFIxOTnJ8ePH2b9///zD62pirajd7gj6tyP5LlS8OivMpHujk3wCqARd5PL9BNN59LdfM5sZjV2Arh6oemRVVKZh+4Nw9Vzq5qC3vfAtIkgtRqoxUo2QWoyuRMhMCNFCETJFU5T6qu9mKyz7LTXvSRXy6D1PkTvwwjIHkyENPrlPqj7l11fXBif5pEK/nBavMCXwz8mJ7Sv957urTyOlLr98KI+cq/mmxT7wJJ9ExKmakNiR16Q1lO3qSRNx09hfjLkYgFV1kWbJl1yfInnzunU8Ok519EJiTZBi8SdaUKUUotpAVkmiUS39xBNV9FTYltvUROd9UklMq1mDaCEoBCS9W8h9wL0SVKTYKIDb6sgqchNLRM5+AIQCCtc573PtJCSn3yK4ctLaamZmBi9TWVMFtwO+SiVlse5aDnztdp2/XpPYTVK45i7XFOjIZVJdls/Gtmq7WoO+dEWZJEmbKkrimOTbRxtDtVnrGQVRFsLKqGKyXCQmqzbTuKIYVVz8OYkW4hNX0xf/LId8CvLmRRw2gsm26U6SS86+V5YXlYRCvsuyv4PcshJXFuVTHJm3JTHGHClt+I5shFU+hTwbvZxQm9Ztp2On2qmJ4U9+jMBmf7lMNJUhnVk4Ge4Muru72b17N7t37yZJEm7evMm1a9c4c+YM3d3d81lRq+Vucr9itYmnTtiyopr/bra702TU7OysN/mUIUOGdmTkU4YMdxFrhYjIyKcGRkdHuXDhAgcPHsweLJYDFSCDe1HX7YUyVfdYKY3f9VHL9RPNBOQOn0NrxzmcxKidjyDnTngdvzN3pL0vTXhmHAkTqMdIlKA6fhzremL3/AePIr+5guEqEJgssTQBuZxB0ZWW9wRQKBP8+1+1Hi/D0iG9HsWH2m0kl0clDtWST/5S9SaiFMrl6eNrf+ebzVbzI9AApGsjanrU3dCQZ5YGTW5BZ1JwPPqKcs89rjyOakRgKT6LFjs5VY+txWvrtV+PUd0LhRSpRUSvjzT+sFjrUatCT3ohOHUMsSZQKaRUlKSTVZEmmDu2aKF2vnEfWDSejilItCxSTEkiUMwT/OwX0t/LIgRzhJBZRahUguicVQHVyGrKgyPXqaG0cpBPrlNMF9FHX0NNXLY3BEqFnGtIjXH5WtGlfK+p/XmFLwGue3MTngtXbBaLjX5iSCE12jtx5DGSt35FyjKPSD1ClZZXuLapoqjHbXlQ+o3TKA87KeM1byE5TM8PpicSiRNU2VDANTEEhiyn5PwN9K2qaSlMel823ImfWtog55qH5dlNrzQPyj40d5yZ49nRcmnEsZCzqeUsZJ5OMFaekkSM5JMJSUIqmaW1UOzg2W/f0ty+pdsIpny5AHHjntB6GRQ3b2Lnpz+xpLH4oFmg37NnD1u2bFn1/jPYkcvl2Lx5M5s3b0ZEmJ2dZXx8nOPHj5MkybwqamBgYM3UZ+4FhGHI4cOH2bdv3x1ZtJuGTlVU6/+ays87RURVq9WsRpQhwzKRkU8ZMmS478knEeHs2bPMzMxw6NCheY/hDEuHDD4EDvKJykTD1idyqDBq6SSVqBxJ9w5m3r5K15U36eruR7zyOvC3HQO4ccW4SeoxcnOh6NNJPM21wlqdyBVQ2ly8X3HekyGXQWINxZRVpImkqhAkV4J/+wvugl+GJcMn80kh6O4h1PS1FR9P6RjpGoSKw87S0/5O1Rw2f01UbyG5wpyCxYGC348675wrYHZ6ig0AShmtoebhM5W4Lst6DDblQy1KzYNrQkI7+WQtXEbJ/PBEhOjbI1Cfu7/binum1eyW7JfU1w1ZJ61ERXxttmFdGKQQfZ3xT1oWaYREKdQHP4Xq88smAxq5T8puYdkgqOzPQpoSOSfT41PssOSmhCV45R8IqlPo8kZUzX6uFwPP5zfvIBnP+46n+tB2n2trF8/42cAVHAounWCVDrZ6bJmaRBGqYF4NbyRaAKnW7OSTrRgWmc8tifXCtT1xC/32Ob9vyvQcYTsdjMouQ18GIgnSc6pgjoTpfK0aEh25bLX3WzJsz33LVSg5JJArUSa5yCGRxZ/borFZ4IwbNCnYgKieYPg6AfupLS6VmbFPk010en9hCOXywuu1mjB6pX2OFGGeeEIpAiXzr+/67GdWnXyo1WocPXr0HS3QZzBDKUVvby+9vb3s2bOHOI6ZmJjgypUrnDp1it7eXoaGhti0aRPFTiYzwzyaMQV387xOs+drJaOa9a2mamqlZNRSMp8yZMjQjqzCmiFDhvuafIrjmGPHjtHb28vBgwez1U4rhUfuk0KQ/s0w4SCfZseRfHnegkvKG0hkA9HxUwRTrzCvS6pMobbtRQwWeW0YvQDFMoQeyo7p27DtARgbWfweTIHbcxAfb6bIkeXjKkAUyijD+7DlPSnD8lNT3pN+8F3kHzloH2uG5aHYgxT7UKGDxCn2AnbyycuqDpByH8pFPtWn/Sw0q7cQlUM5slqUCLp7I8qR9daAZz5UVEG6e1FRxdm2p6Agwp0DA37FSEtxDuYIXtv2MLaST7ZCLmAvnLdY8iVvXUdfn1nYZrPiNL0nE2G11BpioQQkSKypjzSUcGkkWmdhIHUV/v5DBM9+r+lI6eOSEjBjb+Pzs8hL7eM+h5WBwIqnFOqVr6HmiB3JdwN28snXTtOYe9MJH5J4Cf35WvEqHSNBL0o7VGMu8slFsiWJvUqOmTCBOfu7bovSNHR8LrbqvW3sc9e2aE38+tG2S9A6ZXkK1OaHYMl5M80HJkJERCx2fItfir590W4rZ3q2UpgnnzhKPZbtOc2Zq4T5M3fZ5vnY6q3EjrlB6pkb2PKcolBblU06ESNB3LDBsxBylkvO9HnYrPVM33cSL3xAOhEuj8Ro3X7bbD2c1gtZVKXdO9n2P37QPNBloFqtcvToUfbv38+GDRtWte8Mq4N8Ps/WrVvZunUrIsL09DTj4+McPXoUgE2bNjE0NERfX19WJ5hDrVbjyJEjPPLIIwwODt7t4QDp9nydJNRKs6KWkvmUIUOGdmTkU4YMGcjlctTrfpkc6wmVSoWjR4+yZ88ehofdCoQMbsjGB2n87HUUf/Juf22FoPu3IZEmuVFBf/skiE4v63V5rkKKQ9Tu/cj5U37tu/sWv5ZXVuVB4zjaw7rLUQBzFRhqNfOPeVu2ibFwvninpHuI3L//FXNnGVYM6d2Guukgn1xqHUDV/NRK5D1zX3oGUZNm9R+AEo3u3YKasecJAQ0CzYd88s19AaS0wYt8yoWTjdPbR9WqE2ex1mmp5bQ1dJBTrpXrKbZ2ndA3Z4lPetgX0lBK5rrS+zQS7aagd9PcqEMgR3hlep6cc6meRGSRglOX+sl99NfTj2GBiMd5LzkPFYRPJd+tMOq0ABRRJFenCE5+q+NwHuOOZhEClEu250sC+V6DvvmO4QwS+AlZJN+DCi3Po7m8OafIF457r9RCeyZbLUb1WLY7MuFsqinrhzS3IEWffgs11WFlaiGFApPdoukj0GZSxjgfGIkfjSqZCOz215PR2ySXbzfIqjQFZaDMn48KjEo840dqu0xtGVrYpwlJ7Mokp6rduQDCZcfsIJcsiyfiuiZXtsqXjP3HoRhVSgA5S1ZUrpDOHiZxen4T+An6rl5JCMP2tp3Cx+aQBXjw879pHONyMDs7y7Fjxzhw4AD9/f2r2neGOwOlFP39/fT39/Pggw8ShiETExOMjIwwMzNDf3//vCrqfnVKaRJPjz76KBs3+ivQ32m4VFFxHKOUIpfLeRNRmfIpQ4bl4/6cMTNkWCNYK6tn7kfl08TEBKdPn+aJJ55gYMArqntVsRJLtTWNQhn6d8CUPafCthJaSn1IYRN6JkaPxsjx19zHHb/S+HXpbSvkiZtji15SBXfx0SV88lJG2QoMKkcQmFenG/OeYk3OUFTrLCrpXBH1E7++8kJfBiukbxhunrU38rCLVOEMutDtJmM8rbIoeq7sK/WCD/kU+D1yelv5AZK35LK19hnXkO4BVN5jftAx5GyqB40yEDULB7Rf205yyWXZZCmMoxQSxoSvXvDhQBp9JunnhIgQFNOv/8AwD5rmxyAv6FgRXrEosTrmGtFC0PJeBVAf+UWUb3ZRa1/iESi+Wvdk5XONRYioRo6UFEhOnSG4kpJH6MgmgrmFGqUBVN1OQDsVls12HoRuo78ZL8syJTGS74PETVaJcnxPLtUTuL9H7SB/K1VUsdfcIPKcQ9P6TizZSOAgyDUyPYM+/ebij930lm0KaiNRbBmCST1q+swNcwu0zxUSJ4SvzqnMTe/FtujH9D6tLJBtm0MV5Xi+W+7zvYjYVVWOvnVQRGFWQUe1hGLJQrzmC2CxHrXlMsWxUEyxdIaGGslEPiXJYmvVJkzWeoBRodU8RSfGE6YmJfWrbN5qWomorv37GfzOFwwjWTqmp6c5fvw4Tz75JL29lvkkw5pGsVhkeHiY4eFhRITJyUnGx8cZGRkhl8vNq6J6enrW5+/6DtyrSj6TKipJkvn/NdvZVFGVSoUdO3a8M4POkGGdISOfMmTIcF+RTyLCxYsXGRsb47nnnqNU8ihIrTKCIGhYnHmoGe5FyKaHUA7yidnriApQopFCF1LejK6CvnQVuXoWmCvGD2z2O+j0LdTWB5CxC+7xjZ2HXAESD8ug2+OweUdb/pNZOdR6EAe55LJlcRUYaqHRJsua92Qo0OiOvCdBkbzwIQrDe4xjyLA60L3DTi2Fb9FYujeiJh2FY0+LLpct1UI7z0dJ37y16i0k57byA7zJFZhTzOR93rujMF0N3eSTY45w1idstp71CNVjsfzKB0RHLkHV02INjJ+jxJogxSJMhFQ1g4igUsgqSTSqO09tZHa+SJye99ReaOzk6PTup8jvf9b+XgzwUj75nFBKnM1cuVHQOAe0lJBEkNf+G8FUOoGrfBdU5LvBRT7FtYaVrSuDKZz2UiqppIYUulDiPtck34PyIJ+ctoarQT65PtO6IxvMQqgA9gUblRr0mwvR9qwoSF4/ikojz1xq7LRjGYgkU7lfEo0qpo9PGTKiRGszT9Myt0Snb7jnLFNHimUuPFpm4JPTNs/crevZzseyz4bqzVm6us3XUGLI5GsiKBTAcO9ttaczj81MPgUWYiowWvmlHytJ0sksEaFQVFQrmmujC9dJ6yXZqYIKgsYz72P/x5fTD7YMTE5OcurUKZ5++unMomsdQSnFhg0b5kmXer3O+Pg4b7/9NpVKhQ0bNjA0NMTg4OC6/I3fJJ4ee+yxu7JwdzXRVEXl8/k2VVSrPV+ThGoloqrVanZNZ8iwTGTkU4YMGe4b8klrzcmTJwE4dOjQikMnl4vm570eH0wBZPAhOP//pm8r9iGFXoQSOsijL11GLl4AfT69s8kbVHsH6Zq56T5wT4pFXgpUWEd2PAwX3/RqT//GdvLJmfeER/ELe83DmfdUBENmiDXvydhfGdRC4Sce2kfhvT9mHUOG1YH0bnM3qt2aJ2utKLiVQKp6088a09d6y5NUUnV73s58O9Ho7s2oWQ811RIs+siVIe9hL+u4L0gtdpJPQckxtzuyp2y2ehIl1qkjuTaNvjqZ0qlFUWUqlBoKlRImBF2LSQAJDWRVpJFKTHx5YR5fZM9XKhN0kCJBV5nmPKfJk/uflm+J5Kd8citanNZ2gMLvvNQVUK/8V1RsOS9dRFETHla2AFLsRdXs91MlCVIYgNh9zUqhFxXaM6kAJFilhT4+5JNPtpsF4noediinKFiu33pkpzu6zXO4XLsB44vz+qzWejYs1bbXRlwYFeEGUiFeUIDpyQrJiZYFSzYVU9qm5arel8k9ufaz8lIrJJdc79M0ZzfheruBJe8tqmvyFpLTumDKcskYYkgbfRrOUZ2kuxFHYeP1SyML17DVcm+u/43v/z66dm03D3IJuHnzJm+++SZPP/00XV1+6uwM9yZKpRI7duxgx44daK25ffv2PBlVLBYZGhpiaGhoXZAVlUqFY8eOrQviqROtqqhCoTBPQonIIlXU7OxsZruXIcMykZFPGTLcRawVefb9QD7V63WOHDnCtm3b2L1791397JvKp/UKPbgP1bMLkTxST5CZCjI5idy4AeG1hYYPHEAunHP2VxjaCj7kU4pFngkqn/cXTdweX/h3zp33JLkiKjbbnsy1wu7bYt+sktgcPbKMvCepVaCrsaQ1KQ6sqMibYWnwIZ8ahMwganbc1dLdVxIiXQNQdagkalPW7fPtPEklZm/iw8sCUPD7YaeqKSSLCSLOorSIgKOI68pzEa1RZXuB3JonozXYMp0sxUV9a5bk+FXDQS1FQ4NqwaiIMty/jPZ9iSa83H4+dc6jul4naMn2EFRDoRMEje/l3/1HVHH5BIb2UD75KJZQkVv5pBJEBygDmaWlgL5wFa5ftRNPQDg7iUXnNg/xsOcDkHw34L6fSqEb5UM+5ct4cW2+5IiDYFdF+/coWlttMxudOAfh2OwYo+36t8wfUqujDMVqqdTQZ1Ls9sA+XBOxrC35PAYCXiQ9y0gEMM0hpmNECZQb4whfvdC+LTEox5cjbrIokJYLm3rJ1a/oxtIP23bbCdr4DixzuWMasF2GOhHyhkVN0FAo2SIj78R6urwpJ6pYBBYr5aJYGL2qiefehs1yT2soFBpWg4/8l99blfE2iYeDBw/eFWeNDHcPQRAwODjI4OAg0FDIjI+Pc+bMGer1Ohs3bmRoaIiNGzfetcWvy0UzI/t+yS5Ly4pKkoQ4jvnmN7/J008/fZdHmCHDvYmMfMqQIQNBEKxr8mlycpLjx4+zf/9+Nm3adLeHs+7JPtUzRHRqDG44rPcmb3j1l5+1F8kX+huHLTvhuuO4ANcuNn4p+3wPE2MwuBVuXvPKe7IVl6CZF7D8vCcdawJLcPNy8p6a+S1aFZDv/2lyniqyDKuA8gBS6EFFdks4KfeBg3xSnkogKQ+gHOQT1dtIroBy2VNW/JRUSkdI7yaoulUS3lZ+tUmkWEBZVmvPI6478lSAOZsNK1yqxGqE6jJ7E4kWO7lUi1Hd5gqfqbYp1ZDw5Qvmfi1vy2ThaVJGGD8BwwZdjUlut5Asc576bYcK2uc8HcXkC43PUW/dR/7Z7zEd1ROF+YwlE5QPsUTkdFUFEEoo2m3mREDPBPD63xKEsyQbHnKOuiiuhQwLR/RCYPHNau0t51k4VX79+UIlDnWiS/kUR27lk7NC79huOYdEa+i20IU2cqJWBwP5lLz8bZTxecVyTpvIH8s4jGS0CRIYrQZNSnFJGnZ8yfkbyESLVWxOpX+8Si1fqWSChUCy3QesMVIrsORrHNs1twTYvu+8xfJVBAqGDD+AsK4pFpZHjCWxGDOYwE56mbaZrPUAJMiDXvxsMj0lzM60fz4my73m3zv+40+T7/Kh+O24du0aIyMjHDx4kKKDJM+w/tHV1cWuXbtWuDcEAAAgAElEQVTYtWsXSZJw69Ytbty4wdmzZymXy/OqqHJ55efencTs7CzHjh3jiSeeoK/v/vtt2EoU/sqv/Arf/d3fzY//+I/fxRFlyHDv4t6i3TNkWIdYC+qnfD6/bsmQ0dFRTpw4wcGDB9cE8QTrX/kEoA68293o9nXY5GFzcXMMBj2syQDVv9GrHbVZGN7r1xZg45ZG/z55T5GjeLbSvKfYfO40iClTsceU99Qgs0SEePdz5A/+K2P/Ge4MvKz3cu4ir6p7KoE8LLoUgnQPutvpGLr9rjspea6Y1H73I4VQUX52Jqo2iXKSTx7zsuuWXbcTYVIN7dd/aH/vaXVziRPCfzlvP7bhkDrWxgKxMixlNxa0U14XEcKxdgWN6mr/zkTLonmr+RlpCch94nfTj7ckKGfuk1IaEYclogLBXViUjvV9OimRnDyL+pf/GzWfu+YmjAKJkLyHnaZPhiF4Sg8B5UkA+/bne03HjlwoF/nkcxyHREOlWEd6b6/U7M/1lkcIidKv3+TUW8iYbbHOMjzgbKeeyVrYRNbYpk3TnCuNuTA6fKn9ddPcks9Z3ouNUF7G+3ddliuwTF6WeqtlV5PqFCCOtJ1cCu2/OZPIfv8LLKRrZNlXxHIaiBi5YtvPUqktnidu39SMX28fR1q+UxO5HKiBDTzwiz9jPpAnrl69yqVLl3jXu96VEU8ZFiGXyzE0NMT+/ft58cUXefjhh0mShBMnTvDyyy9z9uxZbt26teZqAzMzM/c18dREkiT8wi/8AgMDA/zxH/9xdo1nyLBMZORThgwZ1iUZIiK8+eabjI6O8vzzz68pv+X1rnwCUI97kE+A2jjk1+HGzX7tbrtsyVqOvRRLjOmGWsNFPnmpmlzFhxUUL2x+/8YazJzFTVTeSuGn/mfH4DLcCfhZ77nnDFWbRHIeP4osRaQ2lHq9mknZk1TyGRtLsPIDSj1+xJfSFXcjn/ugaw6wkMMAhA6Vluve0KG+FBGi10aQqdqyVAEmqzyAwFDINNoGphSt44kqutr+nlXcodArLSZX5ufa930U1b06RQ+f3CcXQQUg+Kh9grn+AuIbdeT/+b8Irpxsa+FLGEnB4zp0kTbNY7py4+YP6jdH+PbnVDQ1Ec3Yrcuc5JNjPFo7LQCbWUTG7T3mlepSd6hPbQR4yrUotybRb5yyj8dmLbfEXCdJtFnFZFKEGZ5ZRGuzWlxBdPjiIhtR4+OT7Z5l2mSVKNn6synbXJlNlmM64Fp4VJ+NzVaJuNc9RVXHfOO45G3KJhvnm+SKxvcVRxYLQ8N5pTWUOqaB6WnNlctJG7mU9jW2Wu4FATz8pd82D9wTly5dYmxsjIMHD5J3LXDJkAHo6enhgQce4Nlnn+XZZ59lYGCA0dFRXn75ZY4dO8bVq1cJwyVkmt4BzMzM8MYbb/Dkk0/e98TTZz7zGTZu3Mgf/uEf3nOWiRkyrCVkd8gMGTKsO+VTHMccO3aM3t5eDh48uCbUZa1Yj2RfJ9Tw3oZayZXD5JPlBDDjYdUFcHOMeMNm8rc9LP2uX26ssnWRPc22AxtRgd0CSWLtcb6tMO/JYrm3rLwnIJIS+Z/47Jq7Vu4XeCmfQrst33xf3YOoacd1F/kVqr3t7zxJJe/KXPWml7UZgPJQhAFQcL8XUUW3fWDJcTxX5oeDnHLV/DtVF/GJq+ixaftONpgKfLEml2IfKCIEpcVzSeP1DmJMC7WLU4vnFR23L0EPq9BCaOlEkyvnSQaGyb/nQ0t4M3Z4KZa8FD8+bQKSsIQc+e8Etw05XJ6EkZfyKfQ8BzxJIKU9rCwBPAk0FXuQvzRIdsn3Q5L22SjIr+z6I4mtmVCSJCiLbZ5U66guCzllUC81oTqr5hZIkhD/y+soEfusZFH3GAkj09xqVTGZyKeY1BDKSEPZQCzcqpBcWmz9KrJEDl2xIjXRkrHSrCgLeWRbPAQQ1zVFW15gvgiYr28TmdOEjdiKQ220wGt0bn5wFWNAaWOthdEl03C4el3o6lrYWJnVXLoojXt83H79tZ7+nfZ75Yf3MfS+7zaOzQcXLlzg9u3bPPPMM1lROsOykM/n2bJlC1u2bEFEmJmZYXx8nGPHjqG1ZtOmTQwNDdHf3/+O/Uabnp7m+PHjPPXUU/T0+GWwrkckScKnP/1phoaG+NKXvpRd4xkyrBAZ+ZQhQwaUUuuGDGmGYu7Zs4fh4eG7PZxU3A/KJ2ion+S/f83eaGKUZGCI3KRDsTR+BTYMeSmbcoNbwYd8qkzBtj1w9by7LaB6e6DuyN/QYraNYZXynkwFIJaX90SQQw79EMHOJdgQZlhV6D73XKVqt7yylSi6fygqn9wlAJ8sJfAnlULPAnQSIT2DUPMYZ+I5xoKbpNK3ZwmGzCpZSTSqy/HobLn+G504yC3bqvpEt5FP8YUJkrda5kQLcW3s17Tq3ECSSaQJUj4DiTRBBzEWXZtFwmSRrV/rD3gRQXWsqBctiEDuE59LH/My4aN88jKFcGQG6biEvnKZ3OlvWgvp3oSRj+VmUkeCEko75Q9+x/RVKnmSSkQzSBB4EcqS60alkU+FwsqLb1qn8iTzx56toXpXQD65FrPYsmU6qvD62ydQ03MqUNspZ9pmGYqJlDKRPyKySHW50JnhQAZVpcQJ9WNX068Nn8VAbcdW5jnVqnyybLdusz/DWeOeXORSUCZIJV3n9nd9No5rw1U3LRQtqqpQU7RMQzYxoQ6jZZ2jJrIriRe+oHpNGDmfkMSgAt32EXRa7jXdNkUa62r2/+9/aD64AyLCuXPnqFQqPPXUU1lROsOqQClFX18ffX197N27lyiKuHnzJpcuXWJ6epq+vj6GhobYtGkTBY9n2uUgI54aaBJPW7Zs4Q/+4A+yazxDhlVARj5lyHCXoZSyW4y8Q2NYD5iYmOD06dM88cQTDAwM3O3hGHE/KJ8A1IHvcJNPQKXYSx8ednmbhr3IJ+WrkgJUd4/3olkV+luBGeGR92Td3UIi2YgpY95TrIk3PUz5h/6D9bgZ7iy8bPd0hHRvhIpDLeiwlAJQUQUp9rRkzxjga38XeZJKrrG3QEp9KA/ySdX9CvjOvCdAWZf9z+U1ucgnQ+bawkEc91tTgRcamU5zCq7kxjTx0cv2vpqwEWKm1eyGqchk09c5x0isqY1MLlrp30mQixZyHeesygXof/VvyfvarXrCj3xa3vOQCOhaATl3guDqKVTvDveRfAkjzzFJqQ9Vtfelwimvvpxzw3y7GT++TjRS6IPY3a8EBnWQy3IPPMhfx7NX5FgYFHpma6UdulZH2cin0sI2fWUMOXdhYZuJJBZBGclQy/OEYRozTk+xRqUoHgFjNpwgqWdu/NYNqEaLD5YLGuRg2iAMyhqdLxGEhkVBKkealEtQ9nnYIfAxCs188jwtndfGp+geMBeUA4flq9IWkgfIW/aP6nZlk04ECmZ7vLzFko+oDqZzx5RF6OoTiCLhwrl43qm29dJXxSJEC7ZlquUrF4GN3/seeh56wNq/CU1b9yRJeOKJJ9bNb+gMaw+FQoGtW7eydetWRISpqSnGx8e5ePEiQRDMq6J6e3tX5Tycmpri5MmTPP3002sqruCdRpIk/PzP/zzDw8N84QtfyIinDBlWCRn5lCFDhnseIsLFixcZGxvjueeeo7SULJ+7gPtG+bTzERjYDJN2FVJf3pP+qXoWzcavIhs2e6mf4rGLBD5qEkAltZWrmhyWei5yyjZMG/mUXssRQt1D8ed+xzKgDO8IugaRfBkV25V1utxPzkHgeOfIdG90FphV5SaiAmeui5r1I5VUVEW6BvwUHznPebx6C8l5XMMetnuU7G2kFjvJp6DsIP+MHkMNGPOUAIkSFKBnakSvXFg8H5hyNXJFCqSfF4Elk2VJ6FiVX78yg8SCKnWSTx65eRs2k//Bjy5xAD5D9LE887gfKZlvJpIjmUrgzCsEk6MLH5unIigMSpRc5JOnAtHLnk/HSLHXbYMXzSD5vJOQVTpECr2N+6NrfLlulAf5ZJQmeZFPjiKRK1LR8Wgmrmc3y/XdUE2Zyadm1pRUaySvHmm/BC0qHaO1nmkXETNhpAz0iE11Y1Jjp8xHUgmJT11Pf84pllAp6nJBNcRVKbskN6cIehefFzqay/9JOU40XafQnU7yJDHkbARmvgg6fS7VuSKBRTFYndZ095nPD4sbJACFUh6TL6JoIU+C6USJ6tqa2RSFmlx5eQXWKBQrcZU3kFZgjkBLYjBx0Pk8JIkwci4han4VHYdIamFb362XSL6ni/3/6+eNY7JBRDh16hS5XI7HHnssI54yvGNQSjEwMMDAwAAPPfQQYRgyPj7O+fPnmZ2dZWBggKGhIQYHB5eVPTY5OcmpU6d4+umn6epyP0usVyRJwqc+9Sl27NjB5z//+Yx4ypBhFZFdTRkyZLinobXmxIkTTE9Pc+jQoTVPPMH9o3wCUI+/6G4zfhn6N7k7u34J+jb6HXhwq1ezfHWacKNH25yyevXDUvKebMex/2Cw5j3Z9ktZ8VqfEXIf+TmCnvs3SHYtQXo8zsO8R/HV18qr4FGolgS6B93t4hpS7vc6rJQ9VameVn5Kx0h5g7uhB/kUdNs/X4nthWdJNKps76MzF2nx/uaV76IFCWOi/+98I0+lFbaF/NV0YkCSxZZ38/uYCsqGeVBasnh0PaF+ZQqVU4vmxKDz7w4ySkSR+/jKQ+BTx+ihfFLKvTBEkaB1gfh6Ff3P/0Dwyt8QTI62twmnDbqPdiTKY0yxpwWeZ/6ZFHvdx0SQot+9QfJ+1jzimw1ngg/55Krg32nYMp0seVASx6g5Ijh5+TCqvqDacGUFGWEqmlkWuZiOYxJlS6JRJsIt5fjh0SvG6oPW6f0kaSqp5iEMap64nhjfo+6cO1sQzZhJVBEaKh4DahN2YjVQ5uOKCAXLwoY40uRz5v3DWmLNbIrqrnuX/dk0lzNvTyz7xpEYxxXH5m3aQHaKCIWiYuRCQq0mc68tvv215Tt1dc3/LQLbP/Zj5HuWrupo/t4sFos88sgjGfGU4a6iWCyyfft2nnrqKV544QW2b9/O5OQkr7/+Oq+//jojIyPMzs56uevcvn07I55oZIb/3M/9HDt37syIpwwZ7gAy5VOGDHcZ2cPr8lGv1zl69Chbt25l9+7d98xneb8onwDUgXcj//Jf3e02b0emJuyNRGDLDpj2sOKqTnvb6ZU3DSG3xuz9OexOgNXJe7JkibjynkxFmDSrvvpkHXn8f6Dw7HfZx5PhHYP0DcPkiLWNS4EEjTwnCXIo7ZhjPOdLKfejZt12l1LegKp5qBMLFtupVvhm00CjSO6y6HOsBNWzdQKH8smVRyLVCGVYUQ8e5FItRvVYitdaCF++gFRCc5vUjjVpahIda/IGpZZpPjHNhapUoLkqv35pqvHPzrda6oIWBZZoISh29Pfsv0EN70k9xkohPsonFRvXCOikgJ4J4cbbqHOvEFiuMaVjpNANkb0gXerpc6p0/dRC4G3P56sqzHdDOLl6/Sm3JSgAhs9VOcgnSWK38mmlz4mO/u15UJb5u1KDgSLJmXNwLeV8WMa4G+qmlJPZNo0ZFFHG48caDG9Zdagqk9shycgto3pUV2up3KGJiNCJJmeYsyW2vElLMda2X5IE5C3KzaBUAsxzQtFyb4iqCYFlcVFUTSh1mY8d14V8t8U2Lxbj9wT200sndgu8RhaVmWCyZTflLKqoNIQhXBtNqMy0f0+tl6WWdgGirlTnefn8pkH2/qdPL+mY0CCe3njjDQYGBtizZ8+S98+Q4U4iCAI2bNjAhg2NhVi1Wo3x8XHeeustqtUqGzZsYGhoiI0bN5LrmONv3brFmTNneOaZZyiXPZ/P1yGaxNMDDzzA7//+72fEU4YMdwAZ+ZQhQwagQYJpre+Zm+3U1BRvvPEGjz76KENDQ3d7OEtCEARE0fJzA+4p7NrfUCu5CKOaZ3Gt7hlu3lRJeRBV3LrubOJFPrngk/eUmIvKq5X3FM2GxOVNdP/sr3sMOsM7BZ/cJx8rLyUa3bUZNes4rx0Wf/PIe4Ya+5JKnpAZBxndCpM/TyscdndSjZy2e67avrXgCUgtQnVZPs8oBtLfi4iQXJhAHKvr02Cy1jOtiJVYExjGaVJuBUEMBCSViHCsMcZFeU86blM9NPJqFtroXJngIz9vehurgDwiOau6SRHP57qIBOh6Drl1E66cJrh9ZX74PqomKfaiHOSTF2EUzjSsx5yecZ6K6sDzmva5rgDU6gafK9N90KV8imO3wtH1jOtQGKmihTyuhyib8skCCSNkuoo+dnLxGWH52q2jXeJji4iYn3UML4tOvxJEpO27EC2Er14EIIlUatScSqJUCz/TY1NSTwi6TZZ/6S+D3frTpi5PYrHeDk28HUBUjclZFg9FtYRSj/ncTSINFvJJO5RLLrgs+fKW68L6XGshpmzTVRoRJyJcv5YwNdn+Xq1O1bLwvYjAvs//+pIXKiZJwtGjR9m8eTO7du1a0r4ZMtwNlMtldu7cyc6dO9Fac+vWrXkyqlQqMTQ0xNDQELVaLSOeaBBPn/zkJ9m7dy+f+9zn7plaWIYM9xoy8ilDhgzAghrnXrjhjo2Nce7cOZ555hl6evzsXtYS7ivlUxCgHnsReeVv7Q1vXIS+DTB9295ubAR6+mHWobAQgc1+Kilu32i0vXHF2KRzBe/iw61C3lO+hDIFZzf3N8Ca99SyGjeuxdSmofu3f++eUQreL5DeYWcbVfU4nwFKPeCoeauaW9EA+Be0fbWGkR/plUtqSFevM5cKMCol5lHIO893CT1ydZwktEPZUY/BQj5JYsjzECE+fh195TbGScTwsmhBdaqLmrsYFT6SWmvWUUIuxVZQR8m8ZWHtwuT8eNo/c4Uioa2K3bJZRFAf/iSBrYK7ChApolS6qk50Dh0HyEwMNy6irhxHxWF6cb3Yj6rZs84k51HMcYUM0SCUpdgPof2+pyx5M+3H9GxmUeK2H9izmfbLrlJxJb1PF/mkE1w/bSWXtw7XZEM5v73LPAap1u3kk+35OopJvv0aaqmWzIZ5TUTm8rrSWB5DX4n5OcaUEWVErFGlhfcbjcwgE43z10QymVQwpucvic2fVc4w5wFGtSdAYLFN1dUaFNLnb9FCPrDY+dUScr0rII+WL5oHILBcFkksFCyfVxJp8iXzAGyZTrbbrjIEeQmLhcpaC5cuJkzdlrY+Ox+9pcOAQGThsut+4jG2/tC/Ng8oBXEcc/ToUYaHh9m+ffuS9s2QYS0gCAI2bdrEpk0Ne/tKpcLExATHjh1jZmaG7du3U6lUKBaL90QNaLURxzE/+7M/y0MPPcTnPve57Ldxhgx3EBn5lCFDBmCBECkYflitBYgIb731FtPT0zz//PPLCtRcC7ifMp9EhIu9O3CuFRRBbdmJuMgn0ciWnajzJ53HVvVZb+s9NTCImMgnj7wnYruqqQE7+5TMVMhbCgDLznsiBhRJlFC5UaH4oZ8it/PBZfWV4c5BeyifVFJHygNu4sgj+0XVp5BcyV2w9iF/wJtUUlXHNd4CKW/0I59cFn0+9wqHpR40reXMcKlhTORSSweLXxIhPnyZZORmo6pmOoRhepFYm3OmTHNKsYs0+yhTsVfmMlTiihDfbJwHnfOViBAE7eMISgWab1oP7qTw/NIKg8uB1iXQMRJqpFptLGSYuoG6fRUVzqAAKQ0RzNqtWKXQBa5T3uc6tKhd24/Xg3KRT75WleJHAtnsydrgIn+b8CXH4hkkn1tcuHY9n7qeq5IY5ajS25SJkmhUtyUPI3Qo2i2qKX3xKuq2YXGBlS0zvC6WZxLT84zt+zYtwDESWQvfRTIbEr32Foo5BXeK7Z6ONLmUeUpEUl+3QURSjwGQJMqsBNWNPCETbLa3UTWmYFCmw8rJJZN1XRN5y7h1YieXorqmaNnfdlrEkbZyqrZxm/KeEgnIt+RjJYkwcj5hdkZSyazW4wvQKmybz3pSAU/9xR+bB5qCKIo4cuQIu3fvZutWvxzZDBnWOrq7u6lWG88K3/Ed38Hs7CzXr1/nzJkzdHd3z6ui7oUM7ZUijmN+5md+hocffpjf+71sUWaGDHca92blNkOGdYS1cqNb62qcOI45duwYvb29HDx4cM18bsvBWv+sVwtRFHHs2DEGdjwK3f1QcaiVbKqfFniHr49d9FNJAUyaM218LPdE2wPBfZRRNkulleY9aQ2V8Tq5Pfspf+gnrePIcJfQvQkJCihtL2BKl5t88lUYSM8gamrU3lflppflly+ppGpTSLHLT6WR97MBUZWb9uKdy4rLA5JoY1bJ/DhEY/W6chBcnXNIg3i6RDIyV5S2LyNPfdkWNm2aN9ANwnpRX4Z+ZO59Vc8sWD0ustzr6E4L5FkIjM994reN41xN6DOnCa69jMJyyvicdz6qJg+4bfmax/MoBIVTToEt+CukXHPRQn9+BJrve1WikVwf6BabURWgfC1ADZAElOUSllodVbQ0qFShz6y2l9j+XGfKg0reuoi8dcGssLZd90YiyTIOAyEgotJVfok22w0amIckzs0XGeqvX0bNkS9ap+tDdZxOPulIkzN8J6b3kYSaQreBfKpFBAblU1yNKVjmeBsJFtcTK/nk+sliI49EC4WyJe8p1BQsz6lhLbFmNiWxBsvYbWuv4kgoGJRPOhGjKsqWI5WEMfk51VwcCRfOJdRqkjq3dX6urX+2Wu4Nf+zHKW3bbH4jHajX6xw5coQHH3yQzZv998uQYa1jfHyct99+m4MHD1IsFunu7mbz5s2ICJVKhRs3bnD8+HGSJGFwcJChoSEGBgbu6bpLGuI45hOf+ASPPvoov/u7v7vu3l+GDGsR95+2MkOGDKlYy4RIpVLh1VdfZXh4mEceeeSef0C4H5RPze9sx44d7HvkEdRjz7t3uj4C3X3udmMj0NXrbicatnj6s98cg03pKxuVhfTxhjh88RNtLgTTKMzYtgWGDAOdaEQLlWszUO6l61e/6D/mDO8sVID0ulfXSt6nCD3jd8yi27ZUJSF0Dbjb1acbahAPSPegVztfqLiGFMxzgvIhnxzXuVRDj3uPg/RzKShbcqkWEU82LOOWKFrMFmOJgXQwvX+liMYr6NmW/TqLgp1KqBZlhH7yuwiG3LaTqwEpbXC3sTEUTbi+S0B5WOoRTnspdCVwqz+UJGC5Dubb+aoZYz8llfLIogMgmiXd0HExJN/d/oKPKt/lfDtrJ91k1r4ARmp+JFvqvnGSasmnb9wkef2Y/XwyCpXE6zxcBJMaxWbHZ4AyZOklk417UHRtGq62LEwopt+/TCS57dknZ8joE0ummdTNc7SOzNerjjWFsnle6MzW7ETe8nyXxJqCJW8wrCVGlVBzuw1xuHTFbStswmGb6DGKzB3Htlvl3G71uvD2WzG1WuOFzk8gzXJvkQVfAMHABh7+nV+2HLAdtVqNw4cP8/DDD2fEU4Z1hRs3bnDu3Ll54qkVSil6enrYs2cPzz77LO9617vo7+/nypUrvPTSS7zxxhuMjo4Shsu/D64VRFHET//0T7N///6MeMqQ4R1ERj5lyJABWLvk08TEBIcPH+bxxx9nePidKU7daazVz3q10PzODhw4wLZtDSsx9fi73Ttqjdq226NdAls92gHK0woMQG3ckv66V96Tve/EEdyehNr+8OvIezJBKUV1vIqOofyJzxL0eJB7Ge4avHKfPH4jqeotxKehp7+7dLkL9rAEUqnQ7W4D4KtyBKRkIcg8bPeUZeU3gNTcajJTttL8dgfBpeYKkKnE03J/GxuKlhJrlOH7DwyfRWAiq3KK6vkWNV4uaJvPRFhEkDeVUUlQIPcf/AuDK4UP+WQMw2pr4m4TVac9+tFQXL15WYoeCzOiGadFJIDyJbHDaS+HPoVA3i+nUzrvmQUP0t1hqSfT9u9D6g7VaWSfA6z2vPVo0eQtlRrxP7+G0g69multORa1pO6iBWVYrGL6/EzEkIgYSfvmMcLXL7e/biCrTJ+d6bxKwsSsBrcQtWJ7zLIoU+Oq/bsPLPZyOtHWnKmw4iCP6nbySFtIHnC7Z9qs8WKds74328Optnyetm1BdxfVqnD+vBDN1blN5gFpZNN8P0Hjtcf+tz/wvk4qlQpHjhxh//79DA6u7iKZDBnuJq5fv8758+dTiac05PN5tmzZwoEDB3jxxRfZs2cPtVqNo0eP8uqrr3Lu3Dmmpqas6vq1iCbx9Pjjj/M7v/M7GfGUIcM7iMx2L0OGu4y1ctNba4SIiHDx4kXGxsZ47rnn1pX38HpWPl26dIkrV67w7LPPUi632BI99DSUe6DmWHFtWnHfCU9bMa5d9DsuwEyKbVig3FlLHnlPzsXJDjsuKebxTolvQTgdNqxk/s2HKTz9wpL3z/DOQjxyn3zUCErHSNcgVCbsDT3tsrwKvwCeyicvBg1QVUe2VQvEZpXmoXwKeuzv0WWpJYkm6LL/oFeW1e0Sa1Qpj4gQffsS+mKn4mnpygiAoFwCFt9vTIVWSbRxnKbsqPh2HakvfD6qq7vtPJUg10FGCUFBNf77w58kyC0t02Ul8CGflE+GkXZfO4GnIqiR52QnRnwt8MTDnk+JRooDEDmOGVeRQhkl9mMrSZBCP3i8X8l3oWI3KbeICPFRPrnUYa5bqFWOgTsXzpLpJJFutwRLEuL/9gqqXkdELFZ4gvJQvXXCOMVaCoVGJaRpl1ijDN+LKheon7mOmmq/XymJU+crk3rbRErpMCFnWjBgURzmbPbFlme9JEqwlU3ylsULYSWhaCGfklBTKJnHZSPF5lpYt9rWmIiIPS8qyJPDvAjERlxZ7/KWIVen6lw+H7dFuKl8Djp+o3ae423ZT9JYc9L3wnNs+k6/Z9+ZmRneeOMNDhw4QH9/v9c+GTLcC7h+/TojIyMcPHhwWdneSin6+vro6+tj7969RL1ps0QAACAASURBVFHE+Pg4Fy9eZHp6mv7+foaGhhgcHFzT2eFRFPHxj3+cp556it/6rd9aMzW4DBnuF2TkU4YMGYAG+bRWCBGtNSdPnkREOHToEIHn6vx7BWuN6FsNaK05c+YMYRhy6NAhch3FRJXLox49hBz9lr2jsREodUPdUcS6dgGKZXdOVBLDjn1w4ZTzPXDjMmwYgtsL+U+rlfekktBeO7bFxMSavKV/k11f7VaNuBIT7HmYrp/4tPkAGdYMfMgnVfPLVpJSH8pBPqm6Rx4auJdOz3foOVfHnhkxtUmkWPLOlDHCQT7pSmhU+yw0chDElRDVYyafJNGosqU4XYugp2ggnlietZ4IiiR1Z5vNVb68uL2ONbmU8esoIbzSrpDpzOULpL1wqxOhUMyRDAxT+I73eryT1YMUPZRPiYdi1oMEzku9Qby5yCyfjClPCzwCv592UuxBOcgnACn0ocKb7na5Hj/7PYcKeKHDjr8L9v0aKhzHNeza7prmHKtIVNkyxo7H6+TlY3Brcv64xiLYcheVm56bbf2ZSGATAaTzqflN0FCSxidGOzJ4hICYTimXaDE+x5jmZW2xuQskJi1ZSrSQs1jnmbKlGjubNyVhQt6SB5WECVjIJ5dywLV4ya5Mslv+RXVN3qbairXxO9banOkE9scB0+k5HZW4+Hb7/UQESBJjnlPz7zbyCSCf58n/84/Mg2jB1NQUJ06c4Mknn6S310M9miHDPYJr165x8eJFnnnmmVUjhgqFAsPDwwwPDyMiTE1NMT4+zsjICEEQMDQ0xNDQED09PWuG4ImiiI997GM888wz/OZv/uaaGVeGDPcT1ldFN0OGDMtGLpcjdq36fAdQr9d57bXX6O3t5Yknnlh3xBOsP+VTFEV8+9vfplgs8tRTTy0inppQB3ys92Jk+AF3uziC4T1e4/Naxd5su6m9+O9DPjmhxSr0kMRceIFG4LZxmyHvqTpRJZwOobuX7v/05SUNN8Pdg/R52O5FFT9rrZxHkbd6G8l5FKsjz8K3J0kUTTsUWS2Qro1+DSOLRZ/Ddk+qHuN25clYskQax3AoV6LETDy5YKhd6kgbiXGjzZWhqCsGe8/66OzibR3KgzR7LK2F489/iHPnzjE9Pf3OWbeU+p25Qypy282paNppXacQpOheQS/KI8/J1wLPF4GfmtGqKGxt5zPfAOJLUHeom5XLJiiO3YrKsuO9uFTOFvJKtIYuc/+t53dy8m1k5HLrRvtxDbCO1vQxmzgumx2f4Tk8nkhfvCBRQvTmDVTYPg9IIqlWnzpOn6dEC4HByjQtP6t5jJxhn7gWG+e9JEzIWQgkG8ETVR3PmC5yyXHe5UqWxU1arKqpqJ6QMynagNhl2VczE/FRaN5XRCgYjisii27JWgtjt0pcOLF4nkt1tu54QWi//HMB7P6lT1Lod1ua3r59m5MnT/L0009nxFOGdYXR0VEuXbq0bMWTD5RS/z977x5kR3bf931O933M3HljZvBePBYAl8vFLrFPLrMkI/Eh0WuJlCLxseSS3AcpUbRIySGlkLRjm07RIeUkli3bUZXs2EwlVlKuOGZKRdN2lDAsmaa8IhcDLN5YPHeBwbyAmbnv7j6//HFnBnNn+jwwA2AGQH+rtrYwfbr7dN/u092/7/l+v/T19bFnzx6eeuopHn74YQqFAmfOnOHHP/4xx44dY3x8fE0n3c4TT48++mhGPGXIsIbIlE8ZMmQA1ofyaWZmhsOHD/PAAw8wNDS0pn25lbiblE+VSoWRkRHuv//+hXwnI/Y+6qVWUuJ7HXoWbEbP+amkAGpLZvA7yCefvCe0WItaSZRYZ6bairI61m2FXRGhPlknqkaoIKDzt75BUMo+pu8USGkYUSHKYhvUatfvLEa3FC92KARdGkTNXrG3q/kRIqruZ5NXiMpILuc8TgDyfhkxqmZQhOVyzg9Nafr0w0EQWLLXAGjGUEr/+JdYE58cQ1+ynL8VfCybiCSw5U+ZqtPLt6UbCfXLswSLK91Bu4pDtCwjyMNcgOx/F/t/5gNMTExw9uxZKpUK/f39DA8PMzAwYJzEsGqosJWx1DSfa5XUkbBgVdwpHSO5EsR2S1fJl6C+AkJx6f7iGhLknfZ7ytOSVnyt3HyVSp6KK1+iZdm5dyifSGII7cW1oNNRfHMoJK0EWLWO6jZn2ak56ZO+NEYycnTFEW5tMBHIWghM45VpxysaK9IRT9fRZyeW7UonkqqiMY1TLUVR+m8S5kPS7ESTZkze8DvpZgKG7SX1mLDb/PvmO83Xhm2CEGAm9eaQs5BHcaTJ2yz9agk5C7kUNTQFm6Wf5XcHCEgwMZlJLEbVVByJsV9xoli8qF7TvPGG0Kz7Eewic/mDi8aSxbeCFiht28Tu337Zua2pqSlOnjzJgQMH2q3CM2S4w3H58mUuXbrEgQMHyHnknt4sFItFtm7dytatW9Fac+3aNSYnJzlz5gz5fH5BFVUqeWa/rhLNZpOXXnqJxx9/nK9//esZ8ZQhwxoiI58yZFhjrJeHYBAEa6p8Gh0d5cyZMxw4cICuLr9i452Ku0X5NDk5yfHjx3n44Ye9/NFVvoB6y+PIa//B3nDsfCtjxqZkALhyHnIFt4VXHMF9D8D5484+MnYBegZg9upc3pOj6KLyKLVKS7BEwFITM2UhLIWIUJuoEVdbs3sLv/Ac+QcfXV3fMtxeBCHStRFVvmxv55Ot1PTLm6HQDTjIpzm1lVN9UbvmVyBH0KUNqMq4u38eqhBoWQhKRycqWTJueOQ9kbjHY2WZFQ9u6yQx7ENXI6L/eAaZ8SDHUzuGLeAl/a9iURSYiPKUP1fPT6NEtS0LOjrA8vtrLQTFIuHzXybIXbdumS9SjI+Pc/r0aTo6OhgeHmZoaOimZz5KsR9lIZ9gzm4usSv0pNiNcpBPyieDyTfPqdCLqrty3DyvI18SyG9r3nNBfMkxFVfbd+4in3SC7UEqcWK3xcNhmweoLouyqd60kk8okNkK8Y9+suycWj8DVrLM9tsaVExCkLo5ETES76b3o/rhUcLULpjHo9S/m1SYIgSBYTy1TAKwzWvSsfmcxfXYOkHIhbzl2dGsxdYcqqiWUOy0kFNNM8kDLYIIyxBkyzRNErEft82KMLYIjvNFiOuICFOTmiuX54hZvfxeEEn5G6AWXTNCS+k0jyAM2P8/ue32xsfHOXv2LI899hgFl7IyQ4Y7CJcuXeLy5cscOHDg1k3k8UAQBGzYsIENGzYAUKvVmJyc5OTJk9TrdQYGBhgaGmJgYOCWON00m01efPFFnnzySb72ta+tm5pbhgz3KjLyKUOGDEBLjdNsrrKIvgKICKdPn2ZmZoannnrqts7OWSvcDS8/Fy5c4PLlyzz++OM3NFtQPfSMk3xScYRs3ecmi6IGbHsLXDjh3i+etTER1MatyOxVv7wnFbqLcy7Dfst3gY611ZJvfpmIUBtvEU8EEO59kI6PfNbVswzrENK9GRzkk3gMIao2haBQrivf88NUSgNutZUIumsDqmwns4AW6eVDPiV+hXkA6RhAVUbb/9jhtt1xxrlrQTlUE7bcNyA1MyoZm6X5n86hHDPnVyyTMDxrJNYEhfTjMSomlhRIk0pE40qV3JJZ+ZJEbbtdWtwULfDhXyPIte9/aZGiUqkwPj7O4cOH0VozNDTE8PAw3d3dq36GSnEAZs/b2+RL4CB6JOcmgb1s5mI/wshnf6ppJ8MW2nmSQN6sknhubyk5bEJUQfLh9WvJRT65yN9KDdVjJodEaze5ZCOnXJO3AiH+4SuoKK2dLRDSsmwFuU5GLt9kv5oIyqCcUSkqomiiRnLxWqqK6EYtQE2nRTcSa3aTCbZbUSnzSUvqdnV6YFMe1RNCW+ZSXVMsWfKiYo0tJUE7sghdQ2WukCNNQQYt1ZRN8BsEgulHahGKhp0HIVEkXLqYUKksIpHSiCZZfpkvI2/z+YX3BBEY+vmfofftbzN3nNaEx1ttR5Yhw1rgjTfeYGxsbM2JpzR0dnayfft2tm/fTpIkXLt2jYmJCU6dOkVHR8eCKupmqBDniaennnqKr371q3dF7SVDhjsdd3+VN0OGDF5YCyu4OI45dOgQ3d3dPPbYY9mLwR0ArTXHjx8niiKeeOKJG3+xfcvjrSJS5CA6fa8FT1UQV85BLt9SQbkwZ8/nlffUqFvrRpJo63UtiVhnvepIG3MIkgTCMGgRT2M14lqLeFJdPXT8zt919z3DuoT0bIHRV61tVOwu4KqkiXT2gcmObh6+ReiC58dgsRt8yCdPmy7VmPXbL7Rs0JYgqga2id+tfThspaTaRHU6xjqXLd8SEiY6OUZ85BLKi1laWXHadFxGRYEW47gXLCGZKmenCZYWjYsdBOr6GCsic/ZYi7B5J7ln/pKxz/Po6uqiq6uLXbt2EUXRTbXnk2K/u5HDwg0girVt7gCAm/zlBvKcfHKVosqcisVBaMY3N8dtmeLQ1C7yJMfQSK4XkgqEIcrXJtAAqTbAQj5RqaO6zCOF1Op28slCAogIyavHYMYwltmGHxO/JLL8/puH0TnTkusUhqSxVoJ5gk3Yvfx81f/igtGe0ERsLxsjFhak91Vb7jvbWG7NdLK9h1mUsSJCvsO83ageE3aZx5KWUstybTuGD+fcJstjNon10jkFbdCxNlpGi4g1S8pG9FUqmksn4zYlmm/s2VKCSgRUFC3cJ2FvDw/9429at/Hmm28yOjrKo48+ek9MeMxw72CeeHr729++7oinpQjDkMHBQQYHB4HWhKPJyUmOHj1KFEVs2LCBoaEh+vr6blgV1Ww2eeGFF3jnO9/J7/7u72b1pQwZ1gmyJ26GDBmA208+VatVRkZG2LlzJ1u3br1t+82wckRRxMjICAMDAzz44IMreplThY5W9tOxP7c3HDsP+TxEDrLoyvlW0cR17TYbsG0fXDzp7uToOSh1o/L2fbdmdmpshWEpdKIi86z21eQ9SRQjQY7qWJ1kjnhCBcx+6rcpBqGzKJphfUK6HdlpgGr4ZStJsdechTQP38K3j4IDwLdI7Et6VaeQwEPBBcv6GBd3zBFhdm/5tBn8i6FrTcJOu+pEOWbizxdFJdFEP72IfuPaAvEkKrBn3dkI7lwBZbAeDQ3WeqZKn450asaKJNJmOxhdqxNN1ZerMuMm5BflPQltRQNBkXvxq6ZDMSKfv7n2fF7kk8f13mg2cVKyHso9FVcRlUeJo63HM1ch6GIfqmG/71Xked9HfiSVijxtPuNq65r1yKSTsBOVVNyqJ3CfG8e7hNQaVvKJxsp/m/jHh+CKReVp67tpkUPxkgoBZSriqXR9eDxVoVDqW777RkywRCnWOHsVmaoRFPMsVdOIFlTKeKQTbSR+TGSR7b0oZ1hHR5qwkD5Giwg5y/ht+3miWkLe9g5nsfNrbdy+2EbwAIQF83ItypoXFdUdeVCWrseRhfyE1CyoalkzNppQKS9/XplUT2l/W1xTX/xvEdj333yFXKd5VL5w4QKTk5PrUhWSIcNqcPHiRSYmJu4I4ikN8xOOduzYQZIkTE1NMTo6yvHjx+nq6mJoaIjBwUHne16j0eCFF17gmWee4Xd+53cy4ilDhnWEjHzKkGGNsV4eireTfJrPCtq/fz99fcs/ajOsP1QqFUZGRtizZw+bNm1a8XZEBN76tJN8UlED2bzHTRY1arB1L1w85dy3yoV+BkJaozZuRZUv2tsl4rx/9WyZwFLUkNXkPQlURyskjQSUQilQf/k5om33c/jwYURkwaqqq6tr3Yw1GezwIp+as0i+E+UqDOfcaiVVnUKUastQSIWH2grwJpVUw6/4rUQjpWGoTrgbL1I2SqGHyp/+hO4P7HWuFjgs9Yjsz0aJNUGHw5avmEdXmzR/fBamlxDSQeiVO5W670YjNbdDR5qwZBh7DEVDYy5VMyHX1dqWiFA90yI2lg8p7bP4l6q69P0HyG/dld4nT9wMez4v8slGBs6hu6sDHNwusR8pI8UeVH3K2sZKUC5GrgRO8qmKhAWUwy5PNWftypx5NMtIGFrty2COHMt3oaIZ9zaDOdLJh3xyzYx23F/SdJBTrvdjA4ESv3IYOXPeqDgSEeNzXkRWpPgy2thZxnilDOfHQHLpatRGPokWGiOXWooYEpayKjrR5FI8/3SkU5VPOtbGdyfT8enIbMcX12PypfQxOqnHqaT7PIxWpLQs+Wzkkws2S76WqsqS9xRj3XfUEJNwCWhlOln7ZjmsOBIKBuIridtVUbWqZnw0oVIWI5GnZfljKc1yz3ab9z55gG0f/5Bx+dmzZ5mZmeHtb3/7LcmXyZBhrXDhwgWmpqbumms7DEOGh4cZHh5GRKhUKkxMTPDTn/6UL3/5yzz99NP84i/+Iu9+97vb1IuNRoPPfOYzvPvd7+YrX/lK9t2bIcM6Q0Y+ZciwDqCUcoaV32rcDvJJRLhw4QKjo6M88cQTNz1EPMOtwTxZ+PDDD9Pb27vi7YgIWmv0vsdQYQ6VOIrUvnYYOU+/9tELrSKvdl/nPjZIkogz58WW1wSgigUwzAC35T3pRFOfqqPns2ICUHsfpOdjn6UH2LVrF81mk4mJCV5//XVqtRoDAwMMDw/T399/V3yc3K2Qrk1uJQwgnQNO8slpvQUoSZDSIFTs5I6qu6rsc6h72uRVplrB4R7fhlLoRnmQT6p5fd+1SwHMzhL02Ak4XW0us5Rbtn9XnkytieoyF8glTtAz9Va+UzPlfneNhabtouZyN5ZDJxZrKl+70vn96OvXUXO8RlyOUYFq+7BPK6IvzkLRosh/7us3tF8frMSeTwpu8klpt92cj9Xc4mvShlbGlJ18wqNPAOR8FWA9qPpVaxulm0i+G5W4xxrJ90Ls8ewMO8GDfJL5K9iHfHJd066BxkX+ul7RU6zm4p8eRV4/b193pctskhkj+WT4swgoQ0aPgVTTjfZxrH5sDKpR670o5VyLgcQy/V3HmsDwm5nIoKRpJp8kNv++SUMbbxlJhLzFclVr+3VjI5d0rMlblEdRXchZlE8JeXIWBWFUiwhLK3vXa9nqWZZrc6ZTHLeIqXqtRTqVZ9tznZbvyy/WcCkZ1fbvQoFH//d/ZFhPeP3116nX6zz88MPZ+2+Guwrnzp1jenqaRx555K68tpVSdHd3093dza5du/jud7/Ln/zJn/CHf/iH/OZv/iYPPvggP//zP88HP/hBvvKVr/Ce97wnI54yZFinyMinDBkyALeefNJac/ToUUSEJ5988q58QbpRiLiVM2uNCxcucPny5VWThSJCkiStY+7sRt3/CJz6qX2l8Yt+lnrjF1tfoI4iAI0qbN0Db5x2d7g8CSbLqnk4ilFJM7HmOYlOnyE8D1PeU1yPqY3Xrue2KFA9/XR97e+1tSsUCmzdupWtW7eitWZqaoorV65w4sQJuru7F6yqMs/7dYYwj5SGUJUxe7uC3UoO8M51kY4elJN8mkFyHajYbCMJQGUSwW2Tp3SEdA9CzV78BiDwJJirU0guICluJ3rlB6Bwkk9Sa0LBbqnnKlxLIwYD+SRaE58YIz4+ai6wiXZYbxnGiFjfsLUeQGBYx0imz+1fNFTOzqmeUjbRbrEXtFtGvetDBB0e1+wq4G3P1zHg3JbyUCz5WNcpHSG5knt7gU+ek6eKytMiU3KdgPv+k3yXk3xaaOdBPimfY21tsdXeQT6J1vZwG3BPZnG8irkIW1VqH2fikRPIyTP2jQJuVsuwP2uu041lQaHNE2kCgyJoMWmkmwnRMcfzytRh03GYhFiJJjRMFjARWWC3kBPLu2NUiyh0WlRRtkxPwUpcNSNFzjKBSQd5wEw4u155XUFKts+wqCGEtslVlkXNhjA+GjNzbfn+0/qsUx5/aaqnpZNV5i33ROBtv/8Ncl3Lny8iwsmTJ9Fa89BDD637b64MGW4EZ8+eZXZ29p4iVYeHh3nxxRd58cUXSZKEH/3oR/zrf/2v+f3f/32UUjz99NMcOnSIRx55JLvfM2RYZ8gqThkyZABuLfnUaDQYGRlh48aN7Ny5M3sZ4Pr5Xq+Ff601x48fJ45jnnjiiVX5R7cRT2putvyD73SST6pRRbbcD284LPVqZdiyG950F3pUvuhV6klxh2mDiDitiCTSxlnDAEkjIWchuNLUFo3pBo2r7bPtVbGD7m/+EwJLgS4IAoaGhhgaGkJEmJ2dZXx8nAsXLpDL5RaKsp2OXJsMtwfSvRlc5JPHh6byIXYAQj9yR0obUDOXrG0CNNI15FRSwXwmlUcfPdSK0LIl013bqf771tiiuorOorGkKZGWbtdlq2SYVZ+MzRKNvIHMNrxmdqfv3LxIosRMkhsJKyE0FESVQVGg5kikxmSMNPTc5tu3v7R4LUmCUq3rKgk7KPzK59L7eYvgsud7RhUIxaIkisoIgVU9qOIqEuRQDqtJKXS7ySefPKfmLGa9QdsenS0AP8ILkNBz4oknSexLji2cV5fyKY6tz1oA5bDFdK2PIS8IWmOIWrQ8fu00csxtBdxa2bLM9kNb1E32XKcUWEiMwGBVt/j+rx+6DPPjqIljMhBiRsLbcAi6kRAY1Dy2b4vAoiCyKdh1U4Pltcimbo+TkNBkZwhIkAfM6knXfaLrDbDcmrZXBBEhb3n/jCNNaLntwiW/Z9QUZqcTZioh9Zk4lWQy/TxBsYA028fitLynpT/T/PK+dz7Blv/ig8u2KyIcPXqUfD7PW9/61uzbM8NdhTNnzlCpVNi/f/89QzwtRRiGPPXUU/zBH/wBX/ziF3n++ef5d//u3/Htb3+b1157jXe84x08++yzvP/976enp2etu5shwz2P9Vn1zJAhw23HrSKfZmZmOHz4MA888ABDQ0M3fft3KoIgcNp1rBWiKOLgwYMMDg6ye/fuVX2wJUmycJxtL8dvfQr+5A/dReUUK5tUFD1Jk/GLrS9YW1i38rCl8sh7csIRGL7Ywkq0UJuoEVeXFDlzAaW/8Q8J+jd471YpRW9vL729vezZs4darcbExATHjh0jiqKFzJSenp7sY32NIN2b4cohe6PEwxYsqqEL3W4bSd8smaKfckWKbiUVgLW6tRieig+AxqUYmW5ZegW97swrn6wlZckDgeVEsdQjmkfGkIsT8w3c/TDv3Xu/bWsZiq06SlLJJxExKqKCQohuJlROXjF2a+n+Fg8d4a/8xpqPJUvt+eKRHxI2zdeoEo3kuyCy2+ZJodeZ1SR59/PJJ89J6bhlz+e4H1Rizy+63tCzYKV8SSXPz0rf8Wbe1tBFPiWR02pQddt/g5YFrmV5h3n70ohQc2qc5NgZ5LXjS1a2bNd2W6xkmW1MMBBAsiydrQXdTAi6DMc9f7zlJtHpyYX1A8M+AgO5lzOQesrQ3pRL11rHfMJMdnyAUUkFWN9BdSJWVVQSC7Z5W+JgklUSWys1gcRgNFfFOrkpqmurpZ/NVi9JWnaAcSTMziTMXNXUqvNK/MSoyDJZ7ulGc9l9kKaEWnwuF8ipYpEDf/wHy7artea1116jq6uL+++/f82fPxky3CyICGfOnKFWq7F///57+tqu1+t86lOf4v3vfz+//du/jVKK559/nueff544jvnzP/9zvve97/Gtb32Lnp4enn32WZ599lkeeOCBe/q8ZciwVsjIpwwZ1gHu1syn0dFRzpw5w4EDB+jq6rqp277TcTsytlaCSqXCyMgIe/bsYdOmTSvezkK+k9bX1U6LUeqBnQ/BWUeBfeINv5ymiTdaX6Ku+6g661RJmYoei3Ez8p5syqnFeU9JM6E2VkMvVVeEitKX/ja5nXuc/bWhs7OT++67j/vuu484jpmcnOTChQvMzs4uZKZs2LDhnp1ZtxaQ7i3ONqox7bet0oCTfFLNite2CDwVkJ5KKt8itKpOpc6EXoqosJPozOT1bvS4i/6uJ69ojTLM/l/o36KxIDo93rLYs2SMtG3fNWatRP2AORfFRHpLpAlSCsGiBdUZUn39GsxZfapwSd6TCtpmwovIQkE36dlI8Z3vtxzE7Uc+nyfXNQQW8gkgDkvkXeRTvhMcTpReiiDPPCfJ96BcZKyn3aYP4TXX0q+Z7/2s/cgxFVVbz0kX+eR4P5B6A1Vw3MOdFnIp0VAyE9nSiKCng+TkefTI0ZSN31z2qWWtZ7LbtGzOMCQkcZj6OqJrEUF3+nEHc0qy2quXUIvGlDQCSCc6lfzRoozkjNFaz3JOcgZ1W1yPCQ3jodaQsxBTrTExXdkYVyPylnV1vQFdlmdm1ADLpa2kielFUbRdudSsJ0YiECCO7HlSqWQkAfVyRLWsmb2WUK0sv9hMW1Qq3UE7SVLs9VKe9Usfk/O2fG/7R98k19l+jSZJwuHDh+nv72fXrl2GHmXIcOdhPr+s0Wjc8zaS9Xqd559/np/7uZ/jt37rt5adi1wuxzPPPMMzzzzDN7/5Td58802+//3v8/Wvf50zZ87wrne9i2effZaf/dmfzVw/MmS4TcjIpwwZMgB2u4obhYhw+vRpZmZmeOqpp9attdxaYj0qnyYmJjhx4gQPP/wwvb29K96Ok3iax9ve6SSfVK2MbNnpttSrzMDmnXD5nLN/qthpLzi7SCNwTln1ynuyBVHP5T1F5YjaZG15QUkpOp77DfKPP+Pu6w0gl8uxadMmNm3a1JaZcurUKUql0oI9X8FXkZZhRZAeN/lEYwbJFVCxo2id81D/1Ka8cprwVVT4FrWbfoomFTeQUh80ZoxtdGkjlf/rz1Bbdl6fhe+hfFIWOy0AqTRRJcczLB+STFWJRi7CdH35yOBgzlb6/A06iqR5ZulYE5pm5Jtq1kn6b6+bCQqhfuk6Qbm08C1LxkPRQhAGrYLwS1+zHsNaQYru3KfZehOnptTXks4F3zwnj/tZNe2E2QK872dP20uH/eACXLlxC+0qSCHvznwSEyUxt7xcR20wk0/SiFAdln1U66gei+qzdzsBiAAAIABJREFU2SB5/RrJTw8t0xCJiEPBZFloekWwWusZVhFBGZgeqVahb/k7n26kj+MiQthdIJqskly8unDMoiX1fd88tsSp41QSJYSl9N/DRKgkjYR8V/pvrJvaSD4lTU1QMP8GOWW+9pNIk7fdjklCHAUkjRgda3Qsc//X6ESI6jEql0eSpKW4n7sWlLr+ypcrdUDcJAgVQU4RhgFBThE1EnI2cqmhKXTY30GtEKE8o2nUWv/VqkIcyfwiYxSr6bNGBYGXyhhAFYvQvG5HmJb/pALof8/TbP6F97X9PUmSBZv37du3e+0vQ4Y7AfO1lSiKeNvb3nbPE0+f/OQn+eAHP8iXvvQlr3Oxbds2Xn75ZV5++WWazSZ/9md/xve+9z02bdrE448/fht6nSFDhqwinCFDhpuKOI45fPgwpVKJxx577J5+ObJhvSmfLly4wOXLl3niiScoFldeTEvNdzLhwXfA9/7IXaj2Danv7PZrN3HJqpJyKZ9aeU/261piR95TMyFnIZ8kX6A2WSaaTS8OFt73ixT/0kesfVgtFmemiMhCZsrIyAhKqQV7vkzVePMh3ZudZJBC0J2DqNnLq96fSppIZx/UrtnbWcifNngqqVTVble2GNLRb9y/hEUq/+kNiCJkrJVJpfAknyx2WgC6HhFayCdpRMSvj6MvThnMq24NRIRAJaRVm23WVJistwzjoWihdq5dZaeCsI2QCNTSaemt/+kdD5Hf9RZzX9YQUux3tunp7YcZeyaZ8rGu8yBvvPOcAvenm0oaSFBEaXOeDACuHKpF2/NC4kcqqajsJaZSCLqj3+M90vE8btgJeqnWUH3m55jUm1bySSavoY+cSL//xZ4pZCSLLMtWlhMVGM9jmuIRMKokdV0T9uSp//TNtmM2ERpGdadp+7EYzeSCokHBFFnGvHw6SQ+g600opD8DkmayLNuoDXPHJVpoVmOalYioEhHXYuJaRFKLjec8jmWOqElXKUbR/M+crlqO49Z1FRZCEI0KFEGoFv6vF6nzc709JLOzC9eGUi3yqnw1IkmEJAYKRaLZGkksxPHc3wwwfbooZSafEglY+htona56otFo42TTLPfCUgcH/pffb1s3iiJGRkbYtm0bW7Z4TODJkOEOgYhw6tQpkiThwQcfvKdrK7VajU9+8pM8++yzfPGLX1zRuSgUCrz3ve/lve997y3oYYYMGUzIyKcMGTLcNFSrVUZGRti5cydbt25d6+6sa6wX5ZPWmuPHjxPHMU8++eSqrNVuiHgC6O6H+94KF1JsahZj8lJrmqOLpJq67Ge9V74Gm3bA6Pnly3zynrSHUqHYgcmqBWjZVxkmYifNhPpEBV1PXz985Ek6X/yr9v3fZCil6O7upru7m927d9NoNJiYmODUqVPU63UGBwcZHh6mr6/vnv4oumkIC9C5AWqT9nbFEjhEDr6FY+noQznIJ2rXkCDnVDio6pRXIV1FNaSz151JBdZ8qPpML8nFY3P/qKI2bYWpUS/yKSg67vcovdKmp2vEp8ZI3ryKSkypKSyMhyuGYV0dpVtZgX1We2jKWDEUyJN6THNiEamgQC0iU9KyooJcgBbIf+6vGfux1vAhnwLXswDQktgcVAG/e9A3z8kXUuxB1ez79VVIqciTTPZtl9SRfCdKPJRXBffkE1F5+1gTu2z5mmAjnyLzeBcfPoUeOYrKGegSi0paRMzXmApRgek+tkxKMNzHrTyu9HeowGQrajiputIgmWki40t/b0O/TOTbDZJrSTMx5zPZ3vvCENOx24i8pB4TpqipdKKpTdapjVWZuVglKteWjf9xrAks477r9V87bGZFWo1iw3tio7Fo/bH29wgRIV622vU/WF+18zlSVrYiLHUQlZcT02nkkxZYekvke7uQ6vVrTZTi4T/6PcJFE+WazSYHDx5k165dbNy48Yb6lyHDeoaIcPLkSUSEt771rff0N1atVuMTn/gEv/ALv8Bv/uZv3tPnIkOGOxEZ+ZQhwzrA3fDwnJyc5Pjx4+zfv5++vr617s66RxAEa658iqKIgwcPMjg4yO7du1d1HWqtSZIEpdSNEVhve9pJPqnKNLJpB4yes29rdgo23gdXLjh3q7p6UmsOPnlPSRIQWqxYAIKkaVU+pVUrRQv1q3WaM01zDWfHHrp+99vOPt5qFItFtm3bxrZt20iShMnJSd58802OHTtGb28vw8PDDA4OEtrStjNYobs3E7rIJ48MJlWzKzcWkHcrHpVoqmEPJW3Pm1JxA+nog7o7l0o6+v3IJ8M9EXfsoPFvftD+x96BFvnkyHzS1SaBLWye5bP24/EaybE3kam5PifaUSW0bn7F0LE2R82bCCuLHZ+JdK9fKi9p177XliVSsOjfc3lPT3yAoMdN8KwVfMgnHxu5emUGl+bW6/rGL8/J19pOcu4MA5U0kXwXKnFkRDXLSODOXCOqtqxAcb/bSL4b1XSPTcqgSmnfbwSdlkwnl8WYg5xKYwok0cR/PgIX3rSva9v1CpdZfwYT+WRYS0eaoJR+jpXBqk6ihMaRFNWqSVllGFuMxJvhGHRkJp+URaEUWCYCpWVULexvkYJUx5raZI36VJ3mbBOdyPUMvLRza2GPRMTqxKodjxTX8iRxr2+DdXmYxzSxyrReY7buMgu4jpRsp6RcaSOphv/SzzL8c+++vv1Gg4MHD7Jnzx6GhoY8d5Qhw/qHiHDixAmUUjzwwAN3Rb1opZgnnj70oQ/xhS984Z4+Fxky3KnIyKcMGTK04UZnaYsIFy9evCmWbfcSwjBcU+VTuVzm0KFD7N27d1WzBL3znUx48Gn4/j/DWaHt6vHbXncvXPFoNzWa/ncP8kmaDSiaH58ryXtqzjapX6u3shEWn4pcHuLW7HA1METX3/pH6+6FOwxDNm7cyMaNGxERpqenGR8f58yZMxSLRYaHhxkeHs7GhhuEdG+G8SP2RtqtHFDNMjrXiYodBWZPliTXNQDXPEilzn6UB/lkD81YhGh5/3XnIOV/8+fL285ZoQU99m1LrQkFR5FeKSTWxGcnSM5OQHWJhZcGMwtEiyC0eRhZ921ZZpnpb1Q/xOZnTpqdVWOiSlyJ2sYcVSxCvEhRszQYPhEkLJJ/7ovGfa0HSMGDGPOwkSsVFLhuw6iCqADlUO/65Dnha4EXWsiYxfssdKFq9rFBSYIU+iCyk2gKQRe6UU2P8cE3K8sjM1R1OjIIbdZp4K7GL3nmSr1J/MNXYGqOPLNNuLEpcmzLdIxxYLGQGqYsKG0Y33UtIuhKv+6UgahOJmvI1HLVXJDy3iMiBCkEj4gQGKyHlyopF9axkIimSQSihSAwq89ylnc5NFTHqtRrAfXRdltVScw5Y6LtGWSxgxxaARd6Q8tXekkCxJX0scJkuWc68ybLvWVKKA35RUNZbmiQt/+Tby38u1arMTIywlve8hY2bHAm9GXIcMdARDh27Bi5XI59+/atu2+/24larcZzzz3HL/3SL/Ebv/Eb9/S5yJDhTkZGPmXIkGEB84SIr1pBa83Ro0cRkVVbtt1rWMvMp4mJCU6cOMEjjzxCT48nqZOCVRNPAH1DsG0vvHnK3u6qp6Xe1TG//U5PtlRSYxfb/qwsOUwwN6vflQl1A3lPSSOhNlVDN3VLYbH0432OeKLYSdff/scE65zAUUrR399Pf38/+/bto1qtMj4+zuHDh9FaL+REdXd3Zx8PDkiPO7PA1zZLShtQM44Z+inkThpyvtdgzrOdpzJoqSWgBHkqIxOQUjiX2WmCroJTyagbsZU3SibLxBcniV45izJUBAWz5R7QIgEM5JMxC2UBtqwYy0x/Q/FWJ4YCdJQQltoL+JIIlQszy+/TZr1NmbCssKwU4S++SLDeVY+FHkSFbRaCS6EcZEurzaxfPluhB9VwkDI+eU6e1nZeoUqAhH7kr+RKXudDwk7Ag3RWfuQYJju7+f3VGigbgQAtqzAbXPaKi/qgr80S//A/oaqtcadF+KzsPl0puWyUkVgmj+VMWW9NM1MRdi0n9STRNE+OL+u6yPJJNQBCQJCybx3rVOJHtJmUMh2bJJrQkAUV12MjwZQ0YsIUdVfSSCi/OUvlShklrX0uG+Mt0qUkEbvlnoN8sqmiXMtF7MqoedWVbdtWGJab1kvidG421XJPt2c7QftxCIoD/+IfEMyxUZVKhcOHD/Pggw9mjhsZ7iqICEePHqVQKLB37957+nupWq3y3HPP8cu//MsZ8ZQhwx2OjHzKkGEdYL08SOet4HzIp3l/7Y0bN7Jz5851cwx3CtYi80lEuHDhAqOjo6tWqd1wvpMNb3unk3xSs1eRjdvhykVrO6bHYWgbTDgK7YDq6UcWk08eeU+6mXjlgNggiUYH0LjaoFluXj93psshl6fzb/x9wg3Dq9rvWqBUKrFz50527txJFEVMTExw9uxZKpUKGzZsYHh4mP7+/oy4ToF0ewRm168hQYjSDiLbpe4BVC3FRikNnrZf3qxS7FaXQEvBJR1dC7Zkjdogyes/St/z+CWC7T7KliUB6PWI5PI0+so0erKMimIk0tbisnPkc6hdrGOnbZGpmJwIypBjZSxIxst/q9rlcuvYF69U7CBYJPNpkfHt7wvB0BZyP/thc8fXC1SAzvcQNs05ZyppIGHRmtmkdIzkShDbSaFqFDjt+bzQLCMEKOMDYw6ujMR5+CqkPJVKKvAklTzfGWx2agAyW0UV7ZNoVKej7w51lSq2jil5c4zkP/4UtTjzJhGwWLdZt2tdaFlqJJ9s20u/HkxqoqQaEfYvz9uqHR1H1+Nl973o9PwqCQukSQPFSIRrQgNZZLLIS5oJeYPySccaDD9/0kjayKd50qk6XgVLjp9OHA4RWszWgQ4LSB/LvNUut8G23DZfzrReGinlItcWt5t/NRSBPX/9S/QfeBvQcm84fPgw+/fvX9UkugwZ1htEhCNHjtDR0cGePXvu6fpKtVrl4x//OL/yK7/C5z//+Xv6XGTIcDcgI58yZMiwgFwu56XGmZmZ4fDhwzzwwAOZv/YKcbuVT1prjh07RpIkq1ap3VTiCVrWe//+f3a36+53k08AfQNe5BPXlqikPCz3dKQJDIXdeaRZz8xDREhqMbWxVq7T/Lkz2snkC3T9nT8it3WHs2/rHfl8ni1btrBlyxa01ly9epWxsTFOnDhBd3f3Qk5UPu9ZvLzLId2bnW2UaHRpCFV2KP487lEV1ZBiN6rhsNZqeiovPJVUquahkpiDdPSjoipxaQf1pTlPixHHBNvc5w+BeGwGfS1GX7iMzNYW7knFnH2SK6zCdW5jtzXiSmDKPtGxJtdhykUx2Fnl2hUOuplQfWNm+bE367Bov0tterUWCp/+sk/31xyzs7PkmiEDjnZS6EXVxu1tit0oB/lU6hmAaXvGkZNEZk5FVexDNcykGWAlzNogns9v5ffJKMpP8aacEov53TqUxo2mXUCkNarbTr4rW14UoEodJMfPkowcQS3ttus4VqqKMllnWqz1rBCTjZ+BJKkvV4XqRkzjyJX0MfEmZdtpnZ5lJ4KRlDIRWa0NmpfNr5c0E6rXhPLZMZS0zoi2qMgk0cZlWguB5Td3kUvJKi31XJ8V9jyn0Or5Z7rUbSqstLmMaQSZy3Kv75knuf+3XgBgenqao0eP8sgjj9DV1WXsb4YMdxq01hw5coRSqcSePXvWujtrinni6Vd/9Vf59V//9Yx4ypDhLkBGPmXIkGEB88onG0ZHRzlz5gwHDhzIXvpXAZ9zfbPQbDYZGRlhaGiIXbt2reoFTkSI52b93jS1yobNsHk3jJ61t5v2tNSbmfRrd3UMBjfDZCv/yVXkAlAdJZCmcbluJqnkk441UV3RvFpGovbfXUTSCzfFDnq+9c8INnoU0e8wBEHA4OAgg4ODiAjlcpmxsTEuXLhAGIYLOVGdnW7Fzl2LfCdS7HNbdRW7wUE+qdh8zS6GdA44ySeqU175NapqL44vtKtPI4VOv2J5rhPd0U/53/6Fs2nYf11nIolGz9TQ0zVkto5U6ki1STJVbit0Lh0bJbGTTyJilS+IiHeRPRWmAmekUzOaoFVsN8Fkx9dSflwfl6rnp1tKTEXb8S1VoSwtgqvdD5F74BHj/tcLpqamOHnyJE8PbIOr9ueFTw6T5NzjlHg8L+P6LI70ohbyXeAinzzJXyy2g+3tPK9jX8VV4jcmOW33IrsSUyp1Y6YRzN2jJfPvJ82Y5NVjyNkL6be61cJsFZZ8pstFlo9TLuhYCDsMaiKDxZ1Ey3/H2mtX0I2EvIOsa0MSpXJe5mMwjHmEhOrG35ltRJ1ozczZa1TGqgukE3jYoZojpNCJGH86cCuTXJZ6q1oeBCwe55f1LVrZN4nRci9Jt9xL62Oa5d78ukF/P0/8H/8jAFevXuXEiRMcOHDg3n4/zHDXQWvNa6+9Rnd3N/fff/9ad2dNUalU+PjHP87HPvYxPve5z2XEU4YMdwky8ilDhgwLsKlxRITTp08zMzPDU089Rc4jBDqDGWEY0mx6Fl9WgXK5zKFDh9i7dy8bN25c8XZuSr6TDQ8+7SSf1PQE4mOpNzUKGzbB1BX3fvuHr5NPnZ0gdpWCiuvWfAgd6zbyKa5FRDNN4mpru0tjqFNzngA6S/T83ncINtz9ykKlFD09PfT09LBnzx7q9Trj4+McO3aMKIoYHBxkeHiY3t7ee+4DRHq2uMknD9ss1Zjx22HeXWhXOkZKA1CxF+1Vs4zku7wyaqQ0gJoddbdTAbVjs1B2Z8/oiWnqP3wTag1oRssslBSgEm3Pe7HYJ80vd9ov3QLoODGSTybY7PiI6lBoXUdxuUl9vIYK2sd5ESFcUkkM8ouXKwovf/WG+rQWGB0d5cKFCzz66KPkrsyAXYwEoQcdFLh/C1sm1MJmkqp7X+DXp+aMrUa+AF+FlPK021TaU+mXeNhtKoVyKWFdREG1DhbyiUodVUo/n3pymvgnx1FTZuWb9Rxr0sVG2Ikpq7rJcrymoUhXmoTdBvKpI/38LiVgkmpE48SEed+G7ioS0k6CSSFuthPFWLWwWTOGBsK9crnM7LnpFpm3dF8W1ZNO7DasNvZHa7GrnlZpuedaXxU7oJH+PJ7PirJt29YvbyiFWiYfZNmPME9GiQp4x/f+OUEYMjk5yenTpzlw4AAdHX5ZdRky3AmYJ556e3vZtWvXWndnTVGpVPjYxz7Gc889x2c/+9l77rsvQ4a7GVnIQoYM6wDr5cFqIp/iOObgwYNorXnssccy4ukm4HZkPo2PjzMyMsLDDz+8voknaOU++aBvg1+7Ab/jVbNTyOAW6l1bQNvJwKQRe+U9iRaa0w0qb8xQv1IlqbWKdlrL8sJR2jd4Vw89/8P/ek8QT2no6Ojgvvvu47HHHuPxxx+np6eHixcv8uMf/5ijR48yPj5+Wy0r1xLaw3oPn2JvfRoJ3AXrxHNMko5ev3Yll6nZHPJ+KtpoKiE+bs+Hm4caH0NNV1DNODW7w5RR0tbGZwb8apbbYKtvWn4mU2FUR4lx7F5coC2fnU7dv1Lt7yrLzt+DTxIObTJ3bB3g/PnzXLp0iccee4xisYgUPXLBfJ53Pm08yJswqWPXTbQgpkr/4i5JAnl3ytR8hpoTPmQR3FCGmxMFj5wplzKqaR8fpba8v5Jo4p8cJf6//wzGzYQLOH562/gh5nu1xWit4D3LQFhpi5VaWEonn1Sh/XlRG7mMsuQgpSnHRYQgJadJtBjzmwKDnag0zCSpSdWlo4Qg3769pJEwdXRigXhKX9G4K6vFn06EwPK7WX6GVt9WY5nnWC4ixDVPK87U9dP/biQ8dfrlGMfLCTidMgdkfnfb/saX6Ni9nbGxMV5//XUeffTRjHjKcFdBa83hw4fp6+vLiKc54ukTn/jETSOeXnrpJTZu3Mj+/fsX/jY1NcUHPvAB9u3bxwc+8AGuXnXNQsqQIcPNQEY+ZciQYQFp5FO1WuWVV15h06ZNPPDAA+uGKLvTcSszn0SEc+fOcfbsWZ588slVhfHeFuIJYHg7DN/nbjc75be9svtFUjbtpCkd1JqdyOXzzmPTKTY0C9sSIa7FNGeblC/O0LxaR2JZtDzF0ivNbq+nn+6/9y8Iej2KovcAcrkcmzZtYv/+/bzjHe9gy5YtXL16lVdeeYWRkREuXbp0WxSEawWf3CcidxFXIUiXm7ite9w3AOS8zMEg72mL41FMj4o7qf0//9Fve4ByWDRJ7EG0ucY7BznlJK9s6+YthXfb7Pa8yY4vvS860gsF2sZEjXjWcD8tPRed1wlDLYri575m7tQaQ0Q4efIks7OzHDhwYGECjRf55GEj55PVROwmeRSCFN3EbtNSiF8MKbjJJ5qzXo56qulHUqnmrF873UACB7mU9xhnCo7JUI77fCk5pUcnib/3A+TUGZQIEjtIQzv7tLJlNnWTrS8Wq740JLXYOF4Endd/m3i6TvP11rtX2lBtIpl0nK7g0nF6ZpJOtDkzM00tg9nqGFpE02LUq4qJw2NEs03jeGizUnXdJtqWPYV7KLGvbX8WOedJiN2i0k5c3fgyY3ZVSvu0bYQB9Lz3GcL3P82PfvQjjhw5wqZNm275pL0MGW4ntNYcOnSIgYEBdu7cudbdWVOUy2U+9rGP8clPfvKmKp5eeOEFvv/977f97Vvf+hbve9/7OHXqFO973/v41re+dVP2lSFDBjsy+UKGDBkWsJQQmZyc5Pjx4+zfv5++vr417Nndh1ulfNJac/ToUUSEJ554YlW5TCJCkiQLFiS3nHh88GkYv2htoq6OIhs2t6z1bJh4E/qH4NqSWcv5InrTbuqXxkhePdL6WxjSef9mwDFje8kHchJpdBIST5fRzRgdydKIlNZqWpbZuLQKHCFt/vuDG+n5vX9O0JH52KchCAIGBgYYGBhARKhWq4yNjTEyMgKwkBN1N2XRSfcWZxtVu+qVwUTBfV66Ak/LLN9cF98xw5H/knRtp/yv/r9WhWx4E0w58t8CZS5izkESbc0DAQ/uCXtJ0C6KsFv26SgxOgIa7bq0EBgyXKz7IY8kmsrFOSJTLSfLlxWXGxWYUxwEz/xlgs7SDe33dmE+wLujo4OHHnqofRwuuMkn5VDEAl6qIC+lDyD5kjPPyUvtCEjoVg4pSVqEl4vEjspILoeyyUIAldSRfCfKYWELILkuVNNCpBXc5JPqdByjawiaq5JLFJP85Cj63Pkl6h5HVd9mwXYrYBizWve+QQVmsoGrx4SGV/ug4/p+qq9eXuB+UhVOiaS+axoJHgNJoyNN0GFQZxrGcx1pQsMrk54jHnWsmT03TX2ylYNmmxQgFitVVeywKgVVGBhZHI3Bbm5+eZhHKfM9o3Fb8tngIpdsBNPNstwzORKGHXmIrh+71tCxdSPv+N/+gEuXLlEul9m3bx9Xr17lyJEjxHF8T9sxZ7g7kCQJhw4dYmhoiPvu85h8eRdjnnh6/vnnefnll2/qtt/znvdw7ty5tr9997vf5Qc/+AEAn/nMZ/iZn/kZvv3tb9/U/WbIkGE5MvIpQ4Z1gPXy4ryYfLpw4QKXL1/m8ccfzywObgFuhfKp2WwyMjLC0NAQu3btWtV1JSLEczN+V0Ng3RDe9jT88F+62w0Mu8kngMHNC+ST9A+TdG6gdvwEnP+L9nZJQhhXnPY9KlBE5SZJPUY34jZrFkmWpjnN/V0EnVZk1rCYeAq27qDr7/wRgc9M7wwopejq6mL37t3s3r2bZrPJ+Pg4p06dol6vs2HDBoaHh+nv71834+tKID0e5JMk6M4BVNWeweSTSxM0y0iuExXX7A09VRDEnnkydXMmlS4NM/NvfrJQ5VJ9Q4iDfAoMOR+LIVqs+n8RR94TK3PG8oXSsbHQbCoy60iTM2VBWQgrgOqbZXStRbQsJbdEhGDR9SMiBHP5UToo0PHcF8wHsoaI45iRkRGGh4fZsWPHsuU+yiefzDIVe7RJGkjYgXIQVcqDMCoGnu8Ogd9nXiubzU4+KQSd70ZF7vw4yXejmh7qY9exOmz3JNGobsf7qYdVbnLxCslPDqPq9RRbOfNNLtqc2+SEsozHK2G0LepRZRgPTepPiTVBZ+vaicYrJBdbVpytcSClAzeq8DQpiwzHoBNttOOzTuJSisZ0nZkz19DNRe205bvLMqPANudCa0FZMv4Si3IeIGnYydoksudFuSz3ViHCNUKFKjXXMEnSb7skWf5I0xqCqP3Yg1In7/i33+HixYtMTk5y4MABwjCkt7eXnTt3Escxk5OTvPHGG8zMzNDT08Pw8DAbNmwg78qIy5BhHSBJEkZGRti4cSPbt29f6+6sKcrlMh/96Ef59Kc/zUsvvXRb9nnlyhW2bGl9X23ZsoWxMceEtgwZMtwUZORThgwZFhCGIXEcc+TIEbTWPPnkk7ePeLjHcLOVT+VymUOHDrFv3z6Gh4dXvJ3bZrOXhs27YcMWmLpsb1dxzAqfg1RnkS17aJabRMeOg5xNbxiohUJqy5okIYkSJNLoWCOxbtm3GKxiBBAJUmeEp82iXTobOLj/rXT9zX9AkMs+mleKQqHAtm3b2LZtG0mSMDU1xeXLlzl+/PhCYWJwcPDOy6srdCOFbqdqQjp6wUE+qcRTLdE1gJq2k0+q5md/qerTXu2oXkXCsJVTs7gvhW5mf3QOKouK+x7PJFOhsn3jjsWJXZkEWNknm32TC1pjVDDZrKm00evIvD2UImnE1C5dv8aWld9L3aCvkyaLlQ65X/wMQehxvm8zGo0GIyMj7Nixg82bDfaVYaGlvrGRR1HFqSxUUQUJci3C0AIp9qCqdvJJbKTE/P48re2ajQY+OlofhRQAuRL4kE+5DvBxQ3WQY8pmPQlIpYbqtrdRRfOEDj1dRp84C+NjK+F0WoV3G/lkGz/CHGC4XmzrmUjkIEebknrxKoZMJKMdXzUiV2qd19pPL11vbiJtTEohkxAehYnLAAAgAElEQVTLcAyq2EHaMeimJuhM/yFsKtDaWIX6+PJnmUl1aptwICJI1TzpQnT6BKR5aG3+WUXcy21wKZdc69vmwdnW1SpP2o2uNfg+EpZtPwh4+B9+g9FmjdnZWd7+9rcv+w6dt2PetGkTIsLMzAwTExOcP3+eMAwZGhpiaGiIUql0R08+ynB3Yp542rRpE9u2bVvr7qwp5omnF154gRdeeGGtu5MhQ4ZbjDusCpMhQ4ZbCRHh/Pnz7Nixg507d2Yv7bcQN1P5ND4+zsmTJ3nkkUfujHwnGx58Gv7D/2ltoiYvIf3DcG182TLpG0K6h0iqDaLT51BbdxOfOGbdXpBTNMYqSCKpCibRYiSeoGVblRbCrfVyj5GlOU/5d/0cnZ//anav3USEYbhgwTdfmBgfH+fcuXPk8/mFZXeKolO6N6OmTtsb+RCXngVrH3s+FTeQjj43uVSbRsICymGrp0SjS8OoyvV7WoI8lVN19OUrbW2l4j4OH/LJqVjQAqGFXCKw2ihhsW9yQTciAkPhfXFGU0qn0v+sxagGC3IBlfMz12ewK7V88n+9AovP6dxCXeyl4+d/1Xgca4VKpcLhw4d5y1vewoYN9qwzKfZbySeFRvLd0LSTLlLoRdXtpKzk3FSQ0z6TORVVUERph7LQQYYtwEMVCYAvSaV8J1I47g+H7Z5U6m7yqbR8ub4yRXL0NIyNInVz7hFgV065qvqWMcY6NBgWioiR6FUqII24ES2EJcPnvoHI13NZSc3xBsnYonvD0OfAkLsVGNTkoeF8m8gquUFSXceaayemaE6n3B+2E2+bL+BQFpHyvjePRLtVS6tdbsNK85zATEypQgFdT3+up/VV6+WXm0j730Rg95d/jfJDu2hUKuzfv985AVIpRV9fH319fezZs4d6vc7k5OSCCn5gYIChoSEGBgayyZQZ1hxJknDw4EG2bNnC1q1b17o7a4rZ2Vk++tGP8tJLL/GZz3zmtu5706ZNXL58mS1btnD58mU2btx4W/efIcO9iox8ypAhAwAzMzOcPXuWgYEBdu3atdbduetxM5RP82Th2NgYTz75JAWPfATbttaceIKW9Z6DfALmLPXGoaML2bCVJFHEb15CHz8PnF9oJqdfI/eW/cQnX0vfjoIwHyBzlizLiCcRkkinW80wF5ydUq5o2Zyk5DzN/+RBQOev/1cU3vVz7mPNsGIsLkzs3buXarXKxMQER44cIUkShoaGGB4epru7e90SgNK9BRzk01LFUGqb2lUkCFHa0dbzPEip30k+KQRd2oCaddtkSqEb5sgnQVG71kt09JXl7cYugeM4XOSTiNsuyyVckjixqyJcgVAW2ILrTXkpYCbUWnZ86YRAEiU0JxapA5RquwREhHBJgTfIB4gI+U//VWNf1grXrl3j2LFj7N+/32syhhT7ofKmvU2+G+Uin/KdzthAQg9SxjfPqdiDqtnJp2JOwMv50vOet17wizfneeG73oFctntN+7mSKEaVrk8ySM5fRh87jZq+unDEYiGXJJGWtZixgWWRI9PNymaYhi/b/gy/oa7H5ErpEy2UYZycJ3tqr7ar0I1ZSPmQpccjWlLt/nSsCQ37DYi5kUFTRAiXEF86UUy9NkFcNVwbhaLRVtb2m2md/q4HFkXYQp+Mi+a27V5+qyz3VmrH16w0U9VNadZ6kE4+aWnndrc892H0h/9zSJJl+Xy+6OjoWFDBa625evXqwgS9Uqm0oIoqFj2J9AwZbhLiOObgwYNs27ZtwfLtXsU88fTyyy/z6U9/+rbv/0Mf+hDf+c53+OpXv8p3vvMdPvzhD9/2PmTIcC8iI58yZFgHWOui5+joKGfOnGHPnj1UKu7sggyrx2qVT1prjh49CsATTzyxqhl9IkKSJAsf3mt6PW7bB31DMD3R9mdBQXc/lPqQXJEkUsSlbeizp+GsxatZBDl/gvD+B0jOnFi2OFcM7UUDC/EkWtJt9WTO/3/pdueFBT19dP2tf0i4+d72+V4LlEolduzYwY4dO4iiiImJCc6ePUulUmFgYIDh4eF1N0NWd2821iIXYAlBn4cSTdwxSK46YW/oUCktIOepHPNQUgGoRRZcTbWNxn/4YXrDqInaugOuXDRuy0k++VjquapyadW0pevf5rFU5QxjlUE5oBNN7WK7kmzZFrS03Q+ihbAYIhu2kX/0P1tNd286xsfHOXPmDAcOHKCz08dwzi/3iZxHodJLFeRxPcR+eWpeKqqm3/tcVK+4xxjwrlT7qLcAlHaMNa4JNY53KCnXoLeEPnURffJ1VGW27RcQcSggHf5hVn7ZopwUkdaEAYP1mzIp0SznX5oRaR6L8yqmNASdBjJUKepnr6KvtFscp50r0UJIwtIzoeP0/DmdaMKUq82m6FQGglA3EnJd148hrkVcPX6VpGYmJaWRzhCL7dzOK9aNWVBmy73WxC6H5Z7lUeHzGFoNuWQjrhxxWunr2AXD7VjUt753PUX4Gx8hDEP27t17U75DgiBgcHCQwcFBRIRqtcr4+DiHDx9Ga83g4CBDQ0P09vau+Xd4hrsb88TT9u3bzTbA9whmZmb46Ec/ymc/+9nbQjw999xz/OAHP2BiYoLt27fzjW98g69+9at89KMf5Z/+03/Kjh07+Jf/0iNvOkOGDKtGRj5lyHAPQ0Q4ffo0MzMzPPnkk5TLZWZm3H7+GVaP1Sifms0mBw8eZOPGjau2RxQR4jhe6NN6gBx4H3LqIKJB1+ro6WvoiQm4fBG4XnBW9+1tFX9d5zFJ4PIFwvvuJ7l4ZuHPQT4wZqfAfIaCIZBbpKV6Sjv3xQ5UvbasPQLhgwcofe2/W5cZKfca8vk8W7ZsYcuWLctmyHZ1dTE8PMzQ0NCaB1hLj3uGpKpNISiUI8ioGil6Xdtq+D4DPKdM+1p6zVmERZ07qf6r/9faVHX3IRbyKW3G/WKIxUbz+kZWV4yyFaZdqghTsRUAwyKRFil0I2hO1YnLSwq1hTzE1/9myqwr/tpfu6F93Wq88cYbjI6O8thjj93QPetDPonHNax8VEE+CkVfe0wfFVVUadlD2lQ2tGw0feAki+bhmS+HQYECtNSN4co/U/XUDMmpc/DmJVSjnnovCiHKlLuEO8fHOkTYhkdtJorRthUtO0yawPJrwkQ8S6IJUywJoTX+1P7ijeV/T+lz2gQcsJAehktRJ9o0tBEaxnMdX99Yc7bB1WOTSCwtwjCFmGxTny9baJ4IaDrGhW1aJhq4VEuJa3k6R9m2fRtWbLkXBG3ndzFig7pJZO5xv2S7aWqoxZZ7nfvup+MbX6Czs5Pdu3ffEiJIKUVXVxddXV3s2rWLKIqYnJzk4sWLzM7O0tvby9DQ0J2ZDZphXSOKIg4ePMiOHTvYtGnTWndnTTEzM8NHPvIRfu3Xfo1PfepTt2Wff/zHf5z69z/90z+9LfvPkCHDdWRP1wwZ1gmUUtaZdzcbcRxz+PBhSqUSjz32GEqpm5pDlMGOlZJP5XKZQ4cOsW/fPoaHh1e8/3Vjs5eGd/0y0U9fQZ88ZG0mF08T3v8gyaljLd8TG6IGXBsn2LwdPfoGKjAXNGDu/NQbZguW2FBw0YKuVZfb7aEofuRFOn7p9rxsZ7gxLJ0hWy6XGR8f59VXX13IkJoPsL7dkG73LEmlI6SzH2pXre26evthcnlWWhtq15Awj3IVkGOXx9gcPG3EVKNC0rWV8nf/zN3Ycr+rfOAczyQRMBV/57fjPX3bsP4q1rUpt0zqAB1pcgUDIZGiWNCxpnxhpr2fSkHUbKt2qvySdQOQnQ8R7txr7OPthIhw5swZyuUyjz76KOENEvteyievdzN3G1f22XwbCTtQiev+cl9hCkEX+1CNa9Z2BTxJJd973lO9paKykUx1qp4AFmUKSaLRb46hL0/AWItwknKToGj51JVVTrhx5AetcOEN705ECDtv7LpPajG5znT1anyljJTbrwnRQngjE5RMp8ZEnhua62ZiHg/nVqpP1rh2amqBWFLFDqR6Yy4OtgkBLRJyZZZ7rk+qVbpv3zLLvaCzA5rp97FoUq0hTZZ7adzcvKgwNzxE6e9/lf7+fnbu3Gnu0E1GPp9n8+bNbN68GRFhenqaiYkJzp07Ry6XW9N3vgx3D+aJp507d97zuULzxNPnP/95PvnJT651dzJkyLAGyMinDBnuQVSrVUZGRti5c2db4GVGPt0+rITsGR8f59SpUzzyyCN0d3eveN/rmngCVL5A4QvfoPHf/w5y0Z51I2ePEb51P8kxO1EFQK2CyhdQA8PkGlftdnsWZYRO0me6igg6pYChSr2UvvxNcm/Z7+5jhjWHUoqenh56enq4//77qdfrTExMcOLECZrNJoODgwwPD98+q5aOfiTXacypmIfu6CN0kE9BSiD9UlzPabpib+fY10K7up+SQ7Qw+4MjELnJKrlm3rfLcg9wKsR8bPlsdl3iCoyy7Vsvz1havMyk1hTDLHUAlbJO9cIMxNJGTKmOEiq6fp2JlrZ8FhEhKObp+PW/7jyO2wGtNcePH0cpxSOPPLKi+1EKbvJJiVkdc70zHsRS5FcQl0IPquYgejyt7ciVwEE+EVVaeXAOhZTUZ8xk0SKopoVUWtxOR608rTSizZH31NqAEB87i1y+AtcmUTppv+1cHI+LVHStv1KCeqXzzEzvJPWYXK/hfBlUlJKYx5H6yYmU/EtDnwy/s4m8NynsjXl1sTaP6aGiOlpm5kx79qA0Dfei4fK2Ek9Oyz1LFtQc+WOz1LMtX42qybW8ZQ9tXp4Y8uQWK5bSlqXBdHyqq4uef/xfs2nrFrZvXzsbaqUU/f399Pf3s3fv3rZ3vkajsWDJ3N/fv24cIjKsf0RRxKuvvsru3btXNVn0bsD09DQf+chH+MIXvsAnPvGJte5OhgwZ1ggZ+ZQhwz2GyclJjh8/zv79++nr62tblpFP6xMiwrlz5xgfH+eJJ56g4DMj2LKt9Uw8zUN1dlH80jdp/N3/EhlzhMGffo3wwQMkxw66NzxzlVx3yVo41rGjmJCkE1NLrVlEINz3EKUv/7cEXSsnCzOsLTo6Oti+fTvbt28njmOmpqYWrFr6+voYHh5mw4YNN6y4uBFI92bUtbP2RjmPccEzA4ZiN7jIp2YFKXS5c2WqU4gKrDkwujTEzA9OQr4EuEktmRiFzk5UYzkhF3hYzzlr0lqjLEUm0WLPirFYOLmQNBNyFnVT2Jn+6m5STossV3nGlYjqpfKyoq7UqkustZaMaVoID7ybYGDQ40huLZIk4fDhw/T19bFr164Vn28pDrgbOYhf7zZR2XkvAH55TomfVZ5PXpVC0IVeVNNOUoW6QaLyhMph46ebZlJpCSTXZSCfro9n0ozQM3VkfByZnoVKGalVYHYGFQYrVhlKolG24cJGMLvGALVChsl2MIYhyZrrVDSQTEG6SrJ6bBzVXP77mm4vUzE+jUAXEQIDsa5y6T+EWGwIa+MVaqMp6px4+QQGq7OEbZFlIoKLmHJa7jks9VaT1+RarnLp1oQLfWumL7Opm9L+nnaMIhAWc3T/wdfY/tZ9bNnitha+nVj8zpckCVevXmVsbIwTJ07Q1dXF0NAQQ0NDq/oWy3B3Y94ePyOerhNPf+Wv/BWee+65te5OhgwZ1hAZ+ZQhwzrB/8/emwVHktxnnp9H5H3gzAQKdaGqu5pV3V3VVUB1874lSiLFQ2JXN1sSuVpRq9XMiEuJlChqdFIamiRKlDQaiZKJD7u2D2O7Y7a7sw+rNc7smiRyVzs6SGsUUCjUiUKduBJHIhN5RIT7fx8SmUAiw90DV+Hyn1k9FMLTwzMy0jPCv/i+/9OI3Xvw4AEmJydx+fJlxGKtcRtbqUNk2BmEEBgdHQVjDC+//PKWnrojInDOG0957lXhqQ5r60Tk538P1T/8ArA4p2xLd4Zhn3sJ/IbGAWWp61uQIHW+vyJur+kOOxpH/Ke+iMg7v089HsO+IhQKoaenBz09PSAiLC4uYnZ2Fnfv3kUsFkM2m0U2m932RQlK9wEa8Unn5gFWakMxBqb7rQlYp4kSnVrxiZEAJTPAcs53u0hksfTt2xBzC7DfckwrNtd2LGBl+0CPxls2BXE+WbpYPgGoSvhoF57F5p1PQlKjpb5N+u6kC6RWy8dZuLMAZvn8BljNK6ktAlwkjuhP/5J0fE8Lx3Fw9epVHDt2rMm9vSkiKZAVAhNydxNzi9pumKt3+DESoEgaqObVDQPUOmJusGg7UqorawjHAY34BAAs2g44AQTiUCKY+GT5i2N83oU39m2gWoK1Jq6wcXZyIXX1NFB9R4FaJKiiD+U1kiB1/6qabkqBSbFRsk3lerQTweufiSpHeTTnb+iS7NtaH8uJ2gM8fiK58ARCPjGIJOSilGweLT5c8heeNoHO+SSP3FM7ZBVTOQBNeS+oxSudc0l7PxmJAtWNHz9ZtxuJ3EM4guTXvoBn3/7Kno8is227ITYREZaXlzE7O4vh4WEQEbq7u5HJZJBOp/f8PZXh6eA4Dt588008++yzyGQyuz2cXWVxcRGvvfYaPve5zxnhyWAwGPHJYDgMCCEwNjYGIQRefvll6RP6oVAInhcgXsbwVKg/OdXT04P+/v4t3dgQUeOz3U+xEVbmCKKf/11Uv/5LQEm9uEfjo7CeexHi9qi8P9kCxwrquD1J4e61cXuMIfyeDyP6U1+AtYNOGMPuwxhDZ2cnOjtrzon6osTVq1cBAJlMBtlsFslkcsuLEkHqPgWps8KEB4p3AqV5dcOAdZoQCVYPgWJpMB/xaa3wBAC0kTyqWNL3z5ailhtQdztscQ7ULTyrHofXoToEigVFJqlhJaou7OjqAnRlpgR3yQFbNxeSINih5oi99e6F0Ic+CSviXyfmaVEul3H16lWcOXNm2xZ2KNIBVvEXR4EVJ08oBqaoecSEV3PxeGoxlsJJMJ34FOTccQoBz7KADxRZwQRzCsUDiU/CigZJ3gMsHzGCWeDXbiLk/xWvtXGE9JyvoxSIEUCwV/WvFfA3LiLVBiUXOmT13mQIV8BKSW71hQegWZgqX5sBXA74CPh+x5IE+V7nCEG+Ijlx/2MmPAFbUpvLL2a0NL2M5amN1XTabOSeTHgC9K4oUohHQVxLO+mK8pblTk2PhQC03guqIvf88BOeiFmI/fJnce6jH0J39+47aDcCYwypVAqpVAqnT5+G67rI5XK4f/8+isUi2tvbkclk0N3dvaNOeMPepVqtYmhoCGfOnNl35/d2s7i4iCtXruDzn/883njjjd0ejsFg2AMY8clgOOBsRMB4Gu4rQzAKhQKGh4fxlre8ZUuW/f0Ss6fCOnYakc/9Gzj/9suAo4gaIgIe3ob1zFsgxm+19hO21E+punLhqRa357/QIFZcT9bJM0h84auwuvf2k5yGnSGZTCKZTOLUqVNwHAe5XA53795FuVxGZ2cnenp60N7evinxl1L6WBpWCVaDiaJtYBrxiVX1To9aw4DziU/Ek0hksfS3tyEWVsdN+WDvAYC0NpTO+USKWM0Gto3NF2ZR1/HQ9aqO8pJvky3eEueoLzILT6Bwd1G/HwAggrVGpBOxNBKvfVb9mh2mUCjg2rVreOGFF1pig7cCRTsAhfgEABROK8WnWj8prfgUJAYPFKA2G3FQOAVoXFmMBxOSKaDbMahItVyqon2TlxuemwHz7gNQHCtPAJKoNmDFlSKpj9ZAIUJrRWplihtTT42qGDdZHSnVxCGrl1TxYKf9nU8s1NwhL7ko38r5jo0AWD7jkkbiycYqay7phwS1zOeVXBmFiTwQCq0IaHpU9zVKp7tELANqIgoUNRR1kXo7Wc8JAAQYZAecCFLbFRdMelxZMglWbJ3fhJBE7onmU5NsG/F/83lceO1jjYd29jPhcBh9fX3o6+uDEAL5fB65XA737t1DOBxuPIAUj+tjVA37n7rw9Nxzz6Grq2u3h7Or1IWnn//5n8enPvWp3R6OwWDYIxjxyWA4wCwtLWFkZARnz54N9ITwfhQm9jOMMQghWhajZ2ZmcOfOHVy8eBGp1OZrBR0E4amO/ewLiPzsb8D5i68AXLHgwD1g+iGs46chHq3GlDGbSQtgAysCkmTFmIikjighCCyRRvxnvoTwy+/Z0HsyHFwikQiOHj2Ko0ePgnOO+fl5TE5O4saNG0in08hms+ju7kYoFOwyTARwPjGvgqoVR1Roas8EqQ1V1tdpAgAoIiybEM2LdCLRg/zf3gQtNMd80ewkEImCqUTmets5n5pUNtO6mrSReVgRZrbwIIaydzsEppjDZLVPALS4lerIYq5qL1odzfL9pVpEF2v9vW89Js3/D3/yp3b1N2Rubg63b9/GxYsXkUgEc9wFhaId+jYB6jAFahMgBi9oPScKp/SRgEFqUQG1rMkgBDwHUuk0UNQ4LIGWuYGsKCr/6R8Qf07tsCNB6u+Zp7aOEBf66EzFR0VM8T1nIYD8RT8i8nXz1Db6RF2u3ShB5vYkSd0eALCTzb8DpeFpMA7/N2WHYVkbmA9lCXqy2EBJN8LhsGOr81p1sYL83YXaGJ9CSoPK+cRicTBHPgYKhQFHLvzyAM4m1QMM2sg9xXbVaz2XIDPtuPll323SBMy11sxIBImvfwkv/fCHtvXBgb2CZVlNTvhyuYxcLocbN26gWq2iq6sL2Wx20w8gGfY2lUoFQ0NDOHv27IEQVrfCwsICrly5gi984Qt4/fXXd3s4BoNhD2HEJ4Nhj7DdizpTU1MYHx/HpUuXkEwqsksMu0a9xlb9RoSIMDExgVwuh5dffnlLdWMOkvBUxz7/VoT/61+C+99/Tb0w7FSBpTmw3mOg6ce1hVZFfA4RAVYYjDn+27n/AgRFogi958OIf/pzB+L4GnYG27YbtaCICEtLS5idncXExATC4XBjm18dvgbxLpAd1S5Kh9LdQP6Rsg0TQVwVApTIAMuz6nYVXXzYSrs1daFEogf5v7kBWvR5rRCweo6C1gjHMig/D9bRCRRWBawg9Z5IkHRxtL5dVxNL+3VXble4LYhgRyULyUSwJbFbwhWwJboHW6nJ4lWB8pOaULFeoCOiFnHeSsQbC+gi2YHk939cOu6dZnJyEg8fPsTAwACi0QDOoQ0SRHyCHaB2TiD3UIDabG6wSDEK6SMQmaOvRQVAWfOquZ1+/gDQIipJWSdgOwsJ0OIiWOSY+nWa7yh5AkwRU0eeUAq9OnFLGbrJGJhUUVFaphQ7lO/RivmfmzLHj6hyWOnV60tvqYrq3QXAZx5Q9SOriycT12TXYTJRam2NO7fgYPFWTcxkYBuLaJVF7kF+bUzMAlMoOOT4Xy8CK055iTMXWCkJuEnhCdDrxLrnJjbzXIXM3aTaR/09sGQS8T/917j0/e/b0gN1+4l4PI4TJ07gxIkTjQeQpqamcOPGDSSTycYDSNtdH9Tw9KkLT+fOnUNHR4BriQNMXXj64he/iNdee223h2MwGPYYRnwyGA4YRIQ7d+5gaWkJr7zyCsLh4MWGDU8X27bBOUcoFIIQAqOjo7AsC5cvX97Sk3FEBM55I8v+IAkjobd+ECguwf0Pf6FuWC6ChUKgzgys5XnlMSCPAEiEJ0GtsSzROELv+gFEXv8ZWDETp2EIDmMM7e3taG9vx5kzZ1AulzE7O4vR0VFwztHd3Y1sNttavJoxUKoXLP9AvYNIgPMxQG0ooF6nSS8+kR0B4/KFOABAaR4EgJK9yP/f10H5JXnbePDFKdbVC9qg+KSbDWuOCLVApKrZoitCr3JuCkfATvj/ZguHw45LFpkVj7LbkRCICIU7q8epZdGYmmsBEhgsXgUsC0SE6E9/Sdr/TjMxMYH5+XkMDg4GdgpumCDiU5Df0QBtAok8TrG2MK5bYA8gdjFeBVlRMKFxUwV1SAWsB6edE1Zwy3nYKx8rt9Oofus7YFFbf92i2a5dYOekvgvWvV7lmlLWbVN1qthoh323ExewYpLzQDJEXvZgpVf/X7o6BUYr8Xp+c5/nAj73En6xhjVnl6Q+lESUks7bK2/XK7lYuDnX+P9G4sFVbVk0Bir7/x6SwllFRKCK/Pu005F6XKHr1q7/Vdvl2zwPUtcT55JtkShsn7mF85pYZXd1Iv6NX8PFd7790D4Iuf4BpGKxiFwu11QfNJPJIJVKHaj7tcNAvQalEZ5Whadf/MVfxJUrV3Z7OAaDYQ9ixCeD4QDheR5GRkaQSCQwODhoLmL3OHXnU70uV29vL06ePLmlz42I4K3cNB/UaIfQB38EVMzD++t/r25YWARFospoHWWm/5q4PQJg9RxF5BP/FcLv+tAmR24wNBOPx3Hy5EmcPHkSrutibm6uUby6s7MT2WwWnZ2dsCwLlDoC6MSnIOvjpYVgC9tBnB4AKNkNtjSp3id3wNtPY+lb31ULT8DG4pTCzU8Ny+KnNkItlk/RQCcu6RwZ3JNGAwpPSJO+VNtkC/HC5bATYZQni3Dnl7XtV8fIYUVrnz9lTyBy6W3K9jsBEeHWrVtwXReXLl3a0d+zQM6nIHWYgrh9Aog8jAQo0gY4mu9KQCiaBiurxSdWDeaQghtMpGIBRe4oc1ZcEgyLowsIuy6sZAB3m+500MUb6gQM5VdcLUDXVt7V3W8Ugn+NIV72EI75387Lal6Ru9qPu1CBcz9fc3dLDonl816JCLaf+ATLt73whK94LLiAJalXB4uBVzzMj82tXqvJSxn5ozKaqeJdVQKQZv/qSDz9dtWpqduuQ1ULSiowKXCXq9LXhI4fRezf/QoGXnnZ1D1agTGGdDqNdDqN06dPw3EczM3N4d69e1heXkZHRwcymQy6urpgb/TDMDxVSqUShoeH8fzzzx/IKMmNMD8/jytXruBLX/oSXn311d0ejsFg2KMY8clg2CNsVSgqlUq4evUq+vv7cfTo0S2Nw68OkWH7sSwLS0tLuHPnTuC6XDIOYsyeivDHf7j69GYAACAASURBVBJUzIN/+/9Qtgspnr4mIgjuX8sJWBGmLBvWC4OI/sTPwT56cktjNhhUhMNhHDlyBEeOHIEQAouLi5idncWtW7eQTCZxhlLQJckzr6LdD+NVUKwdqCyqG1JAESiqf5qZJ0+gMDSlF54A0NxMsP0CQKV5gVvnfKrFy2l+23SRRcI/mqrp9YrNzA5DVqhe9ZQ+KeK6LJmY5Qowl6M4sRpxyKzW3wcWjTSJK/XNRIT4z/2mdL87hRAC165dQzwex4svvrjjv2cU0YtPgZw8PMD3L2AMHoWTYBrxiQV0IQWpRcV4BRSKgQn1+2ROIZiw4hRAFtOawRhxULgNHAmEv/ufa3+LBnB0SeqfNbbbNmTfM2Alck3ZgboelHIeIQ/SSUCZ5aeaV/z7JEeuZMgcUWtj20qjC6tHQrZ7vyg+Qb73CWRHALSel7L5S3gCVkwSx8eA+RtztTp1m0U1n0ssQjpnle6hJZ14tBW25JoKh0FlSS0yAFYIvsdLJUr5nbJCALEXn0P0a7+AwcuXdyQq9aAQiUTQ19eHvr6+xnVfLpfD3bt3EY1GG64oI97tLeprLi+++CLa2tp2ezi7yvz8PF599VV8+ctfxic/+cndHo7BYNjDGPHJYDgAzM/PY2xsDOfPn9/y0zf1KDgjPu08juPg1q1bGBgY2FIO+mETnuqE3/gcsFwA/+63/RtoTuG6q2k9BACdPbAH34Poq5+FFTaZ7Iani2VZ6OrqQldXF4gIy8vLWB6f1otPOkFpBYq1aduurdOkHqx6odgJ96P4v30H1lvOBxvbYg6srQMo6utJiZma46r+LdaKT3wb6itq62nonFFcvsireJ2qT0tWJ0oQiveWVqJFV1jnViDLhiW8pn1b9YX9Z84j1P+MdL87geu6GB4eRjabxcmTT0fwp2iH1g3IPP33IVCboDF4doAFW0/TR6OvgC7GSAqsMq9sw4RTE8Y0QltdVAoS80l2EpV/uN/4vyVx8jTae0IvIm+pLpu8DlFtALqu5Qq0uhyc5EEYAqyQf5/KyE1JhGf9vTlTRXgTudW/+10PEfk7MGTHYIPTq6x+kfAEFu/mIarrBKKNiDcWq8Ur+u13k0K/Tpja6cg9tWuKlNurRVceq+fJPzoh/MWnerRe8xiA9o98APi5T2FgYMDUNNoAa6/7ADRimcfGxuC6Lrq6upDNZtHe3n5o7vP2IsvLyxgeHsb58+eRTqf1LzjAzM3N4cqVK/iVX/kV/OiP/uhuD8dgMOxxjPhkMOxzHjx4gMnJSVy+fFldsD4gdfHJ1IraOYgIExMTKJVKePHFF43wtEmYZSH82S+DSkWI699r3mipF2sFF83PPdshsL6TsC+9E6EPfAxWW9cOjdpg2BiMMaRSKaSfvQg8/I/qtu5ybWHY1SyChwIsbJfng8XzccmT1HYEpYU0qv/f39X+UAlYUwYAy/SCAohPKC+D9RwB5qYBpndDEAuBSeq7NVAtOgdA9WpdEXtZTRRAHqFVi63yX1HkFY7KdPO5wCKRWg2X+phcF2xN7BUJQigaAhEQ/9xvSMezE1QqFVy9ehWnTp1Cb2/v09uxFQLCKcBVuJLcZRCzwVTxe24JZIW0dZ2CxOBRkHpOblHbZqVloFYUSgBQi08AQOGUVnwCaqJSkPg9rxoDH73R+D+T1TCq9+sKMJ+6QmvRzVvKOF4iX7fPmgbq16rmEFW/kq8/L7kId2zs+p5XOULtkoX/UO34lt6c0u+fNijYc9e3H+nxlrznwr08+PK6uXqjkXsKh5KsHyJSRu6phClAV48JUL18pyP3VNOAEK1CUuNlkte1iFJ2CEe/8vMoXz6HixcvmvvILbI2ltnzPMzPz+PJkycYGxtDKpVquKLMcX56GOFplbm5Obz66qv41V/9VfzIj/zIbg/HYDDsA4z4ZDDsU4QQGBsbA+ccL7/88rZlQ9fFJ8POUI8Tsm0bvb29W3KY1QoL88aT9odJeKrD7BAi/+I3Uf3jXwZN3Fz5o3qxhASBBAPryMA+dwmh938U9rMvPKURGwyb4/Gig5OwYSuipADAiaQQ1YhPWkEJAOMuKN4BlBfU7XxqxYh4F4rD8/Dufnf1bzNPtPtsEA6+0Mrau0Fz07DCtn4O1NSBISK14wHY8JP9a+GOQEixTmQr4sZkApNwBWwfpwgJwvJjn9g212laTVx/zOpP9rNL74Hdufko2I1SLBYxMjKCc+fOobNT5/HbfijaAaYQnxgIIpwCc+SiKANBRNrAKurvDIUTQHAtVr4/d7kmqOoiMmX2kvVYAR1SQVxZACgUBTTmLLLCqI48bh6GrAZQHS4aAopvn4LAmK6mneLaSxOrR6SYBnSRfLKYXyIwyfUgOfI5X1rXicvHYCXCqD7Mg8+tnoREkhg92X4lIpoFAT9FyZI8GOB3rJYni6gsBPyCMCZVZHRCkf+LNNs1ziNl11rXrGbXOlcUydU5z5Of8rWaa5I+7VDNGevzmrWnS/TUSRz/5lexIFwMXLxo6hVtM6FQCD09Pejp6QERoVAoIJfLYWhoCIyxhhCVTCYP5b3g06B+jXLhwoUtPTR6EKgLT7/2a7+GT3ziE7s9HIPBsE8w4pPBsEfYyMWi4zgYGhpCT08P+vv7t/VC04hPO0e1WsXQ0BCOHDmC/v5+3L59e9PHmojgebUbwsMekejAwvV3X8Fzxf8BodyTplUhAsBCYSDZBtbZA3bkBNjZS7AvvxeWahXYYNgjEBHGx8dRLBbRn+4FCmoRx2VhaJeGA9SGAgCKt4NpxCeUF0CWDSZqcxlPHcfS/3W1tb5TeRnI9AK5af2Oq3q3RANWm/9YRD8Pkma+Ja6JzEMAx4Tq6fKKA0hiPLnLYcf85yThcumCvKz+SOlJEbzkNb+fdfWeiKjFQWJZDAIWkv/iy/I3ss0sLi5ibGxsVxd1KNoBFB+qG0WSgEJ8Amq1mqARn4KIPIHrOUXTWrGL8YDxfEGvJQOKVDqxFwCcQjso3+zA0TqfBKk1YNcnD2zt6z2hFpk5AZtdO1c5VwRJRZjaxYqi/qQEWX0sInkUpx0PofB36+ZhicNJVhnL8nGekSDfeVh4ArYkEnX9352lKgoTef+HiHwOA4snQKWAEbH1bnYock93Oc814tGWxCfLBnc8+Vc4GgOq/r/7nif/6nPH8zXr1SP3CMDx//bTSPzMFSwsLOCiEZ52HMYY2tra0NbWhmeeeQaO4zTqRJVKJXR2diKTyaCzs9N8FttEoVDAtWvX8NJLLyGZ1Nc5PcjkcjlcuXIFv/7rv46Pf/zjuz0cg8GwjzDik8GwzygUChgZGcFzzz2HbDa77f0b8WlnKBQKGB4extmzZ5HJ1J4mtywLQvco4zoOc8yeH8ViEdeuXcNzL5xH6vzX4fz7fwsWT4KdOAPrzHmw/rfAMjdfhn2KEALXr19HOBzGSy+9BBr6nlZ8SiQSgCaNSyso1bH19RoYCYhEN1hxFk6kH8X/9TtSp4XVkYEIID6JmcnABiMq1kQuXb0nAIDnKGvBERdgWkeFSl1Sb1ctXtYcTP7buCtgbaBmu1dyUXy4BGvdWFgsBqwRNYgI9pp4NyKCHbYQ+sAnYEW3HuMbhJmZGdy7dw8DAwPbEh28WSjaoW8UxPETClDjJMjvthfM+VGLytOIT25AMTeoQyqooUS3mB5uR/mvvw3We3L1+84Apvsu6xwmHoEpPgbt95zU4hZT6dy63LTNbFMcSDsuEaUFh99kJ8oenPkSRD6YIOmnHxIRbJ/jJ7hAyOcFggvYPmqe8ASs+KqQyR2OhZtzsGyrZa5kjPnPn0Jyv7KJw0lEWvFQuk1Tb2mrkXq67ZWSJ63nBACiXJHqsUKSMkkk/7kkAlgyhcv/4c8x351CPp/HSy+9dOgfhtsNIpEIjh49iqNHj0IIgcXFRczOzuLOnTuIRqPIZrPIZDK7+vu6nzHC0yqzs7O4cuUKfuM3fsMITwaDYcMY8clg2EdMTU1hfHwcFy9e3LELICM+bT8zMzO4c+cOLl682PRU90aPtRGempmdncXdu3dx4cKFxvch+rmv7vKoDIbtwXVdDA8PI5vN4uTJkwAASh3RvzCAy4G5JXihJGxP98R4sMVoinegNEWo/L9/p24YdGGqVASyvcDcjH7fM08AOxRIfLJq9gJFZ5p9cZLGTQHq+CJA45pSLWyqnA/rxkNEyN+WiBHr4pPWuxpIEBCJIfbpfyXd33by6NEjTE1NYXBwcNfrVgQRnyiAk4ep1M1GR/rvFXMC1nMKIBDDKejO/No+vYAOqcAilToOsHKjCDgOqLDYGBuLBonPVG/Xxq1xUt4Ba6+slAKz4vWbrdcjyUsTDoedlHxvPAdA6zZR8VAe9nkAQDJov1g8mQgvFUckp4uwwo1vCwnC4s05kOv/2ZDk4FHF380jPQcsSypYsVgCVFT9JlqQvZmtRurptvNoAlCIyFYsUotU9cHz5D+9QshLlHGJgZAYQ+f73orL//O/w92JCbiVCi5cuHDo70n2ApZloaurC11dtbq1y8vLyOVyGB0dhed56O7uRiaTQXt7u/m8ArC0tITr16/j4sWLtQe7DjGzs7N49dVX8Vu/9Vv42Mc+ttvDMRgM+xAjPhkMewTd09J3795FPp/HK6+8sqOLNLZtb9iNY/CHiHDv3j3Mzc35fm4bcT4Z4WkVIsLDhw8xMzODwcFBRCIBFt8Mhn1EuVzG8PAwTp8+jZ6ensbfg4hPrLIYaB+OnUBcJz65evcFTx5F6XYR7j/8s7Zt3aUUBNaRAQUQn+C5sI6ehBVRx6EJXdQWAhRzJ/USPguFAS6PS5NFT9VerFKt5JusdTFXpcki3IIDa71IFoms1GNZ063P8Qi/+tM7/vtSv6ZZXl7GwMDAnogGCiI+aesI1XrS9xNEIPbKICsCJvwXlFcb6sUuRhwUTgGuRtDygjmkgsb4qdp5di+c//Kt2n/y86BwCExwWD71y1rQvWXtSr9+s+wbQIB8tX6HsMKSem8VTyo+rY/TrOPMlEDLrXOUv5hEsP2EL8nxkx4V2QY7DKA2lsL9PNyCCzCJuOW3T1mJIzsknYfdiouQpE6WWyxL0xZ5OA6mqKXosiiYosAZD8cALo+7FVa4yZW6Hm+pJBWQOCflPME55K6oaAysLBHwfP4WymZx7g9/DdkffC9u3LgBxhheeOGFQ31PspdJJpNIJpPo7++H53mYm5vD48ePMTY2hnQ6jUwmg+7u7l1/+GMvks/nMTY2hosXLyIej+/2cHaVuvD0la98BR/96Ed3ezgGg2GfYsQng2GP43keRkZGkEgkMDg4uOMX+LZtN2oJGTYP5xyjo6MIhUK4fPmybxSFbdtwXX1dByIC57wWBXPIhSchBG7evAkhBAYHB03Eh+HAkc/ncf36dbzwwgtob29v2kapPu3rmVMAhWJgmrpO0XQHUJ1V91Wel26jcBKVpSQq3/p7sJNntOMCVl1K4PrfGGbZwY0CqTZYlnphnTwRoJ5T0B1K9mHZYJJFT8HldU8Af4fB6jZJLRhqriHDK568XorPa9f3a2WOIPZDrypft1WEEBgbG4Nt23jppZf2zO8ZRQLE7okA10Y6sQgACyjyUDQNVp5T96VVTFf6iqTANOITcwqB+goa4ydrRyyE8t9eW/MHAbRngIVpaQ2jpn4l4sF2oT4lbTAmd6wr5xBVvwpBy07IasHJH17ye41wBUqjOX/tzueYSk8tqZjkv0HuFq2NvzxbQmlyebXtuv1KI/ck43NLVV+BiSwbti1xUAmCpbCt8cIyZEmNRAQ4VelrhQCEW5EeNk5M+dCC6xIsxfmhcjYRqc9Jb9k/jo+o+ZS02trx3O98Ecfe+DiEEBgdHUUsFsOzzz67Z+Zwg5pQKITe3l709vaCiLC0tIRcLocHDx7AsixkMhlks1kkEolD/5kuLi7ixo0bRnhCLb3lypUr+O3f/m388A//8G4Px2Aw7GOM+GQw7GFKpRKuXr2K/v5+HD169Kns0ziftk61WsXQ0BD6+voacVl+BHE+EVFDDDzsQovruhgZGUFXVxf6+/sP/c2R4eBRr39z6dIl3xteSvaAmA1G6rhOSnSBLalrQwWBeRXwSAr2mggwAoMXO4nlv/keKF9zMtHsZG3FVrcQ7rlgR/tBT+5r9y024pIiT+9q4gRI3ACNfrY4xzJZ/REAwhGw4vL+7Yhqm/+qJ69yhFecn424PSGJ9/NcYE0/JAh2uLneU/wn/zvpGLYDzjmGh4fR0dGBU6dO7ak5nKKd+kZB6jAFaeMUQGBgGnmVQgEWvQKIXQBAAepVMeHVRCqdOOYUQFaA0lVOEWTbLY4xt9wF/qDZKclSaWBhWut8qkVfqr+n2rmAaaL1VPOALr9QcVCUx0uyUbi8qS5S81gkQgonWPHWOaM0NgeqcmCdk4qIZLqRLy2uyhXssJ+A1TzPrIWRC6/Ekb+jrlnmG7kXDgM+D2+RINiS8fGKC1vyG+B58mNQq40n/+BdV/3Zui4pz0nPEdIxA/L4uyB4niJWT8j7re+TxeI4/cv/Eqf+1WcA1B4eGBkZQVtbG06fPr25QRl2HcYY2tvb0d7ejmeffRbVahW5XA537txBuVxGR0cHstksOjs7D929Z114unTp0qGvk1UXnn7nd34HH/nIR3Z7OAaDYZ9jxCeDYY8yPz+PsbExnD9/vuXp953EsizjfNoCS0tLGBkZwdmzZ5HJZJRtVTWfTMxeM6VSCcPDw3jmmWeaYsgMhoPCgwcPMDs7q65/Y9mgZBasOKXuLKLPpg8anVWx4kiiJj650S5U7xThXvvb5kalIlhXD2jOp5bI+v2m2gI5mmj6MWDZSkGn0efyAqAzrmiEMeL6WD5lzSYiZWyS8OQPGggCLImbQ3Ah3UZr+ixPL8PJ1z5Tv8Qqa/2i6/r/HnsG0VfeLR3jVnEcB1evXsWxY8ee2sM0GyKc0Mbc6ZxDtTZ69xAjAYqkAJ3TKEg9pyBiFwBYwW75gohPjDgo0gYoYsgAgEGAwm2At3rcRCiN0l9/u7VxqCaO6ZxPVPXAfESOpv1uQZwiIqmDBwCIczDJoay5CRX7Vs0vspo8FQ47ITkPZKJCxUU42SxcioqH5dGcr3Akmxpl151+zjPBBexw64ERnkAo2vp3EgRmAXM3F1YdTJZP5J4kWo+Xqv5iVzQBVpGdvwrlUMg/d88l2IprcKEQcQBNHTCohStfx9fasXmk3LeViIPK/nME5/JT0kolcPyn3sBzv/65xudRf3igu7tb+WCdYf8RjUZx7NgxHDt2DEIILCwsIJfL4fbt24jFYshms8hkMohG9Q8x7GcWFhZw8+ZNIzwBmJ6expUrV/DVr34VH/7wh3d7OAaD4QBgxCeDYY+w9mbrwYMHmJycxOXLl5/6xU8oFILjBHuS1tDM9PQ07t69i0uXLiGZTGrby5xPRnhqZmFhATdu3MCLL76Itra23R6OwbCtEBFu3boF13UxMDCgfcqUUkcAnfgU4ElVVg5WGyqe7gC5eSyXO+H8538E4/4iCuvKBBKfENRZ67mwjvaDph5om7JKHoB+zlVRc1SoF6WV4pQg5QKjahGRVzxYKf8FZuFwac2Xep/cJRTGFTWviFrOq7X/JwDJn/2y/PVbpP7wwJkzZ7QPZewmFO0AK8vrjDHhgEJxMIXgw4QHCiXBNPXUKJIKEHOn/+0PGpUXFLKDXXNSKKmsgdNoF06CrRGfKvccwLfGTO1c1jqfPKEUn4iTMpaPiHwj5hpoXg/PA0KSMSoEDNX8QZALYsJVRPzJ5oVq62uKIzmQK3yPr+ws85sPSRBCPvlzxOUuLD+EJ5B/sARRXhXsLctqmSetWBzCRzyRXRbTctFXABScYMsEfo14ZCUSQFkSIYl6VKDk/YcisGz5PZUnmHLfrqfezuIJUEkyNgJEqSwXt9ZpcQQgevIkjv7Ej+LEv/w07DXnued5uHr1Ko4cOYJjx47JB2TY91iWhe7ubnR3d4OIUCqVkMvlcO3aNXDO0d3djUwmg7a2tgN1fzo/P49bt25hYGDgwItsOqanp/Hqq6/id3/3d/FDP/RDuz0cg8FwQDDik8Gwh6jXQuCc4+WXX96VItyWZUndOAZ/iAjj4+NYWFjAK6+8Erhwq5/zyQhPzTx58gSPHj3CwMDAoX8KzXDw4JxjZGQE6XQab3nLWwJ932t1n4Y0HQeoO+MUIDQL6cQscDeE0vcWICZvKJfCg4a10mIuYEsA6XZAo7MBaKp7JEMbxSUITPGTS1wtPtWe5FdEbqleq3BFyRZvgVWHx9LEMkistPOp97R+3ySoKcqPnX4B4TNnpfvZCktLSxgdHd0XDw9QtANQiE8AQJG08jtT60cvPiFADB5I/61iXC+IATVRLBBWsOvOIDF+tf5Wr4d4qBfOt7/l31+1VhOHxTTOJ8X3AQDI5Wr3ESf1PCs0vkzlJEiAbPikmAME5GqKYjyWxCW2/hjxooPSzXn5+5b9WSI++e5TNkjJhvJcGc5CdV3T1sZeYbnF+Slzg6rm4Jr4JBH/mA3G/L9rRASUlqWfj+sSmOKUccvq32JiFhgUAmMkAqr690FE8IolqTjlefLTqh6rRwSEerLo/eRHcPoLP41wW7r1PbguhoaGcOLECRw5ckT5fgwHC8YYkskkkskk+vv74bou5ufn8fDhQxQKBbS1tSGTyaC7uxshmSi/D5ibm8OdO3eM8ARgamoKV65cMcKTwWDYdvbvr4TBcMAQQuB73/seMpnMrtZCUEXBGVrhnGN0dBShUAiDg4MbysZe73wiInDOa0/IHnLhiYhw584dlEolXL58eVeEWINhJ6lWqxgeHt5wDBml9Is/zAlWL4kSnWBLrYvWFIrBYz0o//NNEK5BTOodTc7CHAKEhIHmZoBECijpI8yCuqRki7CNfQqCpYni0o+F1LFZmjVrWXSe7qWqbVbYQmmqiOr0qovNb/G1xckRTwK85j4hAtK/+NuKvWyeubk53L59GxcvXkQioY+C3G0oqsturH039G30tZooQAweCyAiAwCFU1rxqf556wl43aFSatdAKxl1xGyUvnNT3q6wCMYAJqlvFhiuEIBQd07JGxCpHYw6EVvRsXyTFZYLEJLrQCKCHZecQ+teUhiaUQokfu+JBMHeSK0XmYDl07dbclF8XGxtt26MJMh/3gxHfSNOhSfk87xq6nYc6efKPYKlitzT1GMiUri0iECuvF4hEUFUq9J7AV0tKOm+LQuh3l50vOtteOZLP4v4Mfk1heM4GBoawunTp5HNZuU7MxwKwuEwent70dvbCyLC0tISZmdncf/+fdi2jUwmg0wmEyj9Y6+Qy+Vw9+5dDAwMIBIJchV7cKkLT7/3e7+HH/zBH9zt4RgMhgOGEZ8Mhj2CZVk4f/68b5H5p4kRn4JTrVYxNDSEvr6+TeWfrz3WRNSotXXYiruuh3OOa9euIZFI4KWXXjrUIpzhYFIsFnHt2jU899xz6O7u3tBrKd2nb1TJg+wwGJfXIAIAhJsFARHrgLucQPk7bwLL12t/tEO1f1ztnIguzYECtAMRrGwfxP3buncBWpzTtgECiE+uCLqkvgXk9URIEGzFGFVxf6qFSTCGwr3muL31rYkI1nrnU6kIrNRhsV9+H+zO7Y/Cm5ycxKNHjzA4OLhvFnQo2qlvZAVwNgdpEwDmqmsv1QkidjFHH5EHAAjqkApSuG1NQ9fJgI9/V94sPwekovrfe81mmTOngW677n0p60Uphqfo1ytUEOnyP2dkc4OoeAi1SZ7QX/MaN++gci8PBiaPX/O55JQNl0kuT2XiOlvnNiJByI/nW2o7sVgMVG0WSAUn2H5iuuf4fg6yMQsuYMuOI2+dH5v6VKhHQiEsATVxSLddte+ac0lda0pqmFvZRgCsZBLRkyeQHriA7g+9F13vfwesAA90VSoVDA0Nbeo6xXDwYYyhvb29UZe6Uqk06kRVKhV0dnYik8mgs7Nzz97Xzs7O4t69e0Z4wqrw9Pu///v4gR/4gd0ejsFgOIAY8clg2EMkEgltcdmdxohPwVhaWsLIyAjOnTu36ZuyesQh59zE7K1QqVQwPDyM48eP782i9AbDFpmfn8fNmzdx4cIFpFKpDb+ekr0gMDDFaiYDQSS6wAoax9LKggBP9MF5UkH1H95sjfriHtiRftDjCXVfngd25AToib5GE2LBXDCUmwLicbCKwtXBIK2JVKe2+Khe/Ni0oyEA3OEIKWrZqMYvW9QVDkfh/pI2hgzrfleIVt0EAjbSP/cr6tdvECLC/fv3sbCwgIGBgX0VxRPE+aRcTW60CdDEx7nRgrOkkDTXYAcQu9xlECwwTUAm86rK7Y12AUUqxl1QKInS//7/qBsKAasrwPHXfU9119C6S2yVQ0aQOtJvs6aoahWA/2doRf2/P8JRxfGtvqb43Umw+sBkDief+UdeB8on7o4Ilk+kHVHr/FV8XAR3eMu1riiVW4Q2P6FLeP6Re4KTdI6vfW4K8UnmTIrEaj+R8ThYPAGWTsNu74DV0QU70w275wgIFkiI2u+mqP8jEAkQGLyFRfBCEXy5CFEsQZRKtRpWgoNVPfDFPMh1QK4H4l5NkVpx3wkS/uczQ20esgDYIbBIBFYiATudQqizHeHubkT6T6D95QF0feAdsAJGga+lXqfv3Llz6OgI8L00HHpisRiOHz+O48ePg3OOhYUFzM7O4tatW0gkEshms+ju7t4zsXZrhaegcfkHlbrw9LWvfQ0f+tCHdns4BoPhgLJ/7ggNhkMAY8yIT/uAqakpjI+P49KlS1uKFrAsC57ngXMOy7IOvfBUrw1y7tw5dHYGeALdYNhnTE5O4uHDhxgcHNz8DbgdBhIZoDSrbhdJAZCLTxRJg1cslMZteDf/XtkVS7UFMjpY6Q5w6MWn5YU5BPL4EsHKHgU9vCvfp8b1BEBdJ2McMgAAIABJREFUiwUrUVs6cUpnyFA9oa6q6SQIdkS+aGpJYsiq+SqqC81OARaPgbnNUW1WMgnwVUGB+Opic+TDr8OKbl8tPSLCzZs3wTnHxYsX9+zTzjIo0h6gkf76iIkA11CePgaPEQeFU4Cri6jUXzswEES0Hay6qG7nBnRI8WAiFXgFlYcAivp+rVQCgFrU0tZ32+JllLYelKqumzKvT7FJ8Z6k0Xq2/Cl9O1FbSHWmluE8WT3uMteSL5L36Ssycf95SrjNortTdFB8Umx1Q3GCtW4OFNw/Qk96ixSKAJ4kplL1wRCBwIBQGFZXBqHTZxAdGETiPe9H+Nhx+esOMMViESMjI/uiTp9hb7I2go+IsLy8jFwuh5GREQgh0N3djWw2i3Q6vSv3vjMzM7h//74RnlC7L3nttdfwB3/wB/j+7//+3R6OwWA4wBjxyWAwNGHEJzlEhPHxcSwsLOCVV17Z0gUrEcG2bYTDYfzTP/1T40K8o6PjUIpQ09PTmJiY2De1QQyGjUBEuHfvHvL5PAYHB7fsBhGpI7B14tO6WB1iFkS8F17Jhnv3EfjdYSB5D2IxL+lgFRZgwb02sGA1muLlQrD+ALB4Ql33aKs1YrBSB0YhlBCgjttSFLqvb5fBHY5Q0n8hmTscoUTr74xX8Vri9gCArVtdJgDMKQNrFnHrD7iIcByJn/gZ6bg2Sr3+YSKRwNmzZ/fl71iQ2D0WRHQJUF+JaQWlGhRJ6dtSMBcShROARnyCWwQxtbMSCC5SkWej+jdqcbtOTUiWvxfyRGv9svXj0igsZGn0KUUEpjayTzGHqL4PsjpXvOIhlPZ/SIEk71M4HFa6NmcU3lx9+KAWvxl8rvSbzwQXCMVa5yMhyFfbXzvvCS6wcHsBzGIg3hy5Rz7nGvk8MEBEsCSfDzlV6Zj9ou0oHEVk8G1Ive3dSLz7fbCNuwcAUCgUcO3atU07sw2G9TDGkEqlkEqlcOrUKbiui7m5OTx48ACFQgHt7e3IZDLo6up6Kk7p6elpPHjwAJcuXTLC0+Qkrly5gq9//ev4vu/7vt0ejsFgOOAY8clgMDRhxCd/6nWIIpEIBgcHt/RENxE1jvGFCxcghMD8/DwmJydx48YNtLW1oaenB11dXbAD5LLvZ4gIExMTWFhYwODg4KG/ETAcPIQQGBsbg2VZ2+YGoXQfMDOi2bELiqThWV3wpgtwRm8CxXUOouUCWFcWNK8WsmghF2xc+WA1mlDIAx0ZYFHfLznqeLJAzieNEFITjxTbubrofK1+iWr3m3RF+WwjQVi8OV9za63rl6rl5kVdQS3ugfr/45/53LYJRK7rYnh4GD09PThx4sS29LkrRNu00XRBRBfmBWlTBlkRMCFxa6xAtt4hGTgqL0hfJECRDsBdUjd0iqBQSHmsiIVQ/JvroGgcrKKvX8VstXhNDle6hGp9qM9ppUhM/rFyje2CpMIVkVqAVonXobj/dY+ociAtexWHn51TVDzYbRFUHizBy62KoNLvul+9J2qdN2obZGPRU3hYgHBEzd1WWo1Rle3LjkVb6gcSJ2nknqx2EnFqOK0oFEH4xUtIv/EZxC6/dfNv5oCyuLiIGzdumIfADDtKOBzGkSNHcOTIERAR8vl8IwIvHA43HFM7cQ5OTU3h0aNH+y4SeCd48uQJXnvtNSM8GQyGp8bhnnUNBkMLRnxqpVKp4OrVqzh69OiWF9aICJ5Xu6GuL0Lbto1sNotsNtu4EJ+ZmcHdu3cRj8eRzWaRyWQOXDFUIQSuX78O27Zx6dKlfRfRZDDocF0XIyMj6O7uxsmTJ7dtsZ9SR5r/H4qDwu0QIgJR4uCLyxDjU/DGrmv7Yl09evFpfgZIpoFltWOJ5qaBRAoo6V0dVncWIoD45E0/ViXmBXI+qYq6A9AvqnIBqBwXoTDA5SKZrG6Tbt9+MbzFB3l4y67vIvt6gWx9CxK1hVhqyyD2fR+R73gD1H8fT58+jZ6enm3pc9dgNhBJA47CDeiVQMxWuwHdEsgKaesiUTQNVtYItpb+Vi2I2AUAxII9zEKRBJhGfGIgiHBK2a663Al+73uwjp0EgohPluZ4uUIpPhFXO6OIqMkF2IqNmqizCQRJXVO1WE//bbziwU77X9v5ic91LHLhKz65AiQIhTdnmjdIHmTy/U0i+F+PSaZRmXBfF+Oq+SpK07XPXxRLTSJdLVrUJ3LPzwEnmSuJC/9aVABYLI7QufNIvfEZxN/6Dv8ODJifn8etW7dw6dIlxGLbF8VqMKhgjKGjo6NRV6xcLiOXy+HmzZuoVqvo6upCJpNBR0fHlu8RJycn8eTJE1y6dMkITyvC0x/90R/hgx/84G4Px2AwHBIO98xrMOwx9kJMzV4Yw15iaWkJIyMjOHfuHLq7u7fUF+ccQgiwdQXg17L2Qryekz07O4urV6+CMYaenh5ks1nE44EqpuxZHMdpPCl/8uTJ3R6OwbDtlMtlDA8P49SpU+jt7d3Wvnm8D6JyBDy3CPFkCrQw4d8w2QYsaxaRQ8EWpK1sH4RGfAIRrJ6jEBO39B2GgrkcreUluMk2hCVRfTrnk3C52pEQAF0tRl4VSo1g/eJq80ZFHNe6bdWFCpafLAeua7O+torgAqFwCImf+9VgHWio1wZ5/vnnD0xReop2gCnEJwaCiKSVtZNqbdrAKgvqfYUTgE58CoJTBEEflQeFS6mJAA4pAEAoIXVIiUg3Sv/T39X+Ew8W32XZAqqTWxVfCayIUypxydPEY9Lm6zYp68qJ1u9iY1OVw5a5mxTzjhWT7EwQyncXIZaaHXUsGgF4899kopjM3yU1T0l+Q6ywBeEJLIzXzhESPs4lvz593jZBfgx9r6fjKaR+8meR/pHX/AdtaDA7O4vx8XEMDAxsvhalwbANxONxnDhxAidOnADnHPPz85iensbNmzeRTCYbrqiNPoz55MkTTE5O4tKlSwc+UUTH48eP8frrr+OP//iP8YEPfGC3h2MwGA4RRnwyGAwGCVNTUxgfH8elS5eQTCY33Q8RQQihFZ7WszYn+/Tp06hUKpidncXY2Bhc10Umk9nVgq2bpVgs4tq1azhz5gwymcxuD8dg2HaWlpYwOjq6c4vy7Ufh/P33AE8TSZftg9CITygFrL8U8GloFgsojAdwR9WJHDkGunfDd5vO+USaBWcAgcUc6T4qZSAlr9tkK8aocnKsdXFwhyN/Z0XM8Bnv+kV3ImpZFGaMgR19FpHzA9J9BmVhYQE3btw4cLVBKNoBFO6r24ST2tpJFE4CGvEJtn4BTeeeAupReW2AoxGaFe68tQR1SMnGT7Cw/I9PgBWXN+wAt5sMYDo9WutQVAhAWHHIqOoeceEbQ1dHOY8oxybfqIrdlAnT5AlYMf9jSgCKV2da/s78hCyJw4lFY/CrveXnKiNBYD7iuuACdtjG4sMKyKn1tV7E94vck8UXEodvvSfBRXP7WALJz/wM0q/+WEtbQyv1+jcm9tqw11ifClIsFpHL5XD16lUAaNRKTqVSynvgx48fY3p62ghPWBWe/uRP/gTvf//7d3s4BoPhkGHEJ4PBYFgHEeHu3btYXFzEK6+8sqUbss0KT37EYrHGE2Ge5yGXy+H+/fsoFovo7OxET0/PtkQT7CRzc3O4ffs2zp8/f6AWLA2GOrOzs7h79+6O1k1goTDY8WdBE/6CTIOoXjCi2Se1SCZd3KoTrK4MnIq+DQAx/bj2KL3GVQRA6pJiYUsrLJEQgGLBufb0v6aOjGbathQCEvdIqjEQEUISYYoIsKOhRrv87QUIV1GLyGo+luSzsGyFbKS++BVpH0GZmZnBvXv3MDAwcOAimijaqW3DAohGQYSlQKqnF+z7ROEkmEZ8gldWb290Fqy4j0ykcr0euNf+0+ofAkQ5s2hIf32ku7QRAkr1SeOcgucCYcWtsaoeFDapYSuOtcyVyssewnH/cTqTRVC5+XgTUc31tL5GnGy/NmvZSESwJfWWQj6fG3mEsmuh/GRVgLVikaZzwc8JJavrBBLwOwGIr7i3IjEkf/yzSH3qM/vqYazd5MmTJ3jy5Impf2PY8zDGkE6nkU6ncfr0aTiOg7m5OUxMTKBYLKK9vR3ZbLalVvKjR48wMzODixcvHnrh6dGjR3j99dfxp3/6p3jf+96328MxGAyHEHOlYTAYDGvgnGNkZATRaBSDg4NbEnKICJzzRmH47bwhDoVCjYKtQggsLCw0ognS6TSy2Sy6u7v31A3lw4cPMTU1hcHBwQNXv8pgAGrn+PT09FM5x1n/Wb34VA2wcO25sHqOQ0w+UDaj3GSgcYncdKB2cKpgvcdA04/1bcv+NW2C1HvSuSWIk3ZuZorfAeKEUFQxz0biAPwdJ8IRsBP+whp3PITitW2lJ0U4+VXxr2W8tt3ibFjfRnCB8MA7YB/dWt3Chw8fYmZm5sA+KU9RvVORVO6ZOkGuHUgfg8fcgA7BAFF5zAnmcgzitgLgK5yIcDuK/8t3mptVSlphxpKIKU3j0gnNOs1Mt11lbBJqB6VyCrFD8p0r+pRF6wnHX8wTDkfpzoLv21hfD06FXz0zEuR7PSyLJOWhOBbfXP1NIUGw1omQQa+J5fGAAEskkbjyaaQ//VkjOm2Ahw8fYnZ2FgMDA4d+Ud6w/4hEIujr60NfXx+EEMjn88jlchgfH0c4HEY2m4XjOFhaWjLCE2rf90996lNGeDIYDLvK3lmVNBgMe+rGqS6YHCYqlQqGhoZw7NgxnDixtQU6IoK3Ejmz004ky7LQ3d2N7u5uEBGWlpYwOzuLiYkJRCKRRmzBbmW5CyFw69YtuK6LwcHBQ38TYDh4EBFu376NSqXy1BZz2KmzwLc1jeYmA7mLWFsHoBGfsFwA6+4BzbVGOjVRzIN1ZECLOc3gANbWGUh8EjOTgE9NG129JyCAG0EQ4LOwWYfWxzqtw6t6CKvG4bnSq23ucdjwF3CEK4A44BYcFB6scbT4Re7FYkC12dXSUh8lFEHqF35TPk4NdUdwqVTCwMDAnnbYbgWK6MUn3wizzbRZV4PHF7cEYiEwUgtCQQQxxqsgKwomNC7GgA4pJlpF1dJoASg3v54W5/XiU5DvssJhWGug60Hzmahez9UOSqX6ZNnwi7ED/KPsgJpYY0ui9WoOr1aKI7OA11q4SjYy33g7IljEW9+P5NCxaBR+tcSWxp6oDzdjYOvenlRk8nFDEQHRD30MHb/wrw/dvcpWmZiYQD6fx6VLlw7sPG44PFiWhc7OTnR21lzL5XIZt27dwsLCAmKxGMbHx5HJZNDe3n4oz/eHDx/i9ddfx5/92Z/hve99724Px2AwHGKM+GQwGFqwbRuc8z3lmtlp8vk8rl27hueffx5dXV1b6otzvi0xe5uBMYb29na0t7fjzJkzKJVKmJ2dxcjICIioIURtpYbVRvA8DyMjI2hvb8fZs2fNIoHhwME5x7Vr15BMJnHhwoWndo5b/WehDbMqL4N19YLmptTtAjgwAIB1ZvTiEwCW6QkkPgXOqaqUwHqPArPN7qtAziedW4LZLaJW03bhvyBaR7gCUIpPDgCJC0512AVBeAKLdxabF3F9hkKl5aZIMBIEe92YIh/8KKzk5qJOhRAYGxtDKBR6quf4bhDE+QQKUDvJR5hZD/NK+jYgiEgarKqpHxUQiqbBymrxiTkB3VbrIgFd1gfnH7/V2q6YB6WSYFW5qKVzPpErpEJNHcbU24kx5ZSj7F83RW7SFSUT3UTFQ6jdP9LSb7byig6Wby7A9hPoJAPwO1wkJA/CSd5DLRqw+eAsTy3DWXYaC71+dZwE57DDze9dGrm3nmgCnb/zx4iev6hva2hQf4CgXC7jwoULh3Ih3nDwmZ6uue/f+973gogwNzeHyclJ3LhxA6lUCplMBplM5kA6t9fz4MEDvPHGG/jzP/9zvOc979nt4RgMhkPO4VlZNhgMgbEsC0LyZOVBZGpqCuPj4xgYGNhSjZbtrO+0XSQSCfT396O/vx+O42B2drbh0KgXa21vb9+RsZbLZQwPD+PUqVPo7e3d9v4Nht3GcRxcvXoVfX19OH78+FPdN8v0Ack2YFld54V1dGvFJ1qaD7bPgA8kBG1H+eAL6lZbF8R68UnjliAuWgrat7SxQ0oxgQQpy8iQ5rfSVghkpJp3GbB0dxG8rBExLAts3eIvoXkBmaJJJH/68+p+JNQfIOjs7ER/f/+e+F3bSSimr/kUyBkUQFiCUwD5OPpaxhROABrxyc+F5NtXKK5tw7wSyI5o3VZrY/wolETxP/4Xedv2bmDmkXy7zOVT79/lWudTi9tv/XZVfKZQ134jIqlwpYvkA3F/0VgQbInoJovWA/xFssKbM1KHq6/IRP4xejIsiQBvkdf03rjLsfRgqblvQouAHzhyb434T0QID7wdnV/5Q1iHYOF4OyEi3Lp1C5xznD9//sDP44bDyb1791AoFJrE1Z6eHvT09ICIUCwWMTs7i6GhIQBAJpNpPJB50L4TDx48wKc+9Sl84xvfwLvf/e7dHo7BYDAY8clg2EvslQufUCgEz/MOfF2e+lOA+Xweb33rW7fk9NqLwtN6IpEIjh07hmPHjoFzjrm5OTx69AhjY2Nob29HT08Purq6tuVpyMXFRYyNjeGFF15Ae3v7NozeYNhbLC8vY2RkBGfOnEEmk9mVMbBTZ0Gj/6xuFCQCcH4aiCeltZXq0HKwmjFUCuacoNlJIBIFHE0MGPyFGp3zSXgClsZeRUIEd2D5oJrrhSeU4pOlWCx3lhxU5taJHKx1f+Txln1Y6xbCYz/+s7A2EQXpOA6GhoZw4sQJ9PX1bfj1+xI7CrJjYFxeLy1IHSbm6r8rjDgonAR0/dkBrsUCRuXBDrZoT9E0WEUjeHllUDgKRh7K9wFazMsbx9Vua0snPnlCKT4RF1rxCCpxShurpxicIKnzqSZMCd8OeMVDqN0/Dpk8uahtrasx5+RKqNxfkgpgvn+njTqcWjcITrDDzYJX/l5e6xb1c0IRka+oRqLmhiI7hLYv/CYSH/iQtF+DP0SEsbEx2LaN559/fk/enxgMW2V8fBzFYhHnz5/3vY9ljCGdTiOdTuOZZ56B4ziNOlHLy8vo6OhAJpNBV1fXvo+Hv3//Pt544w38xV/8Bd71rnft9nAMBoMBgBGfDAaDD4fB+cQ5x8jICGKxGAYHB7d0M0ZE4Jw36mTthxs727YbT4MJIbC4uNhwRSWTSWSz2U3HEkxOTuLhw4cYGBhALOYfG2Mw7GcWFhZw48YNnD9/Hul0etfGYfWfBdeJTxpnFACACFZPH8T9O+pmM49rYhZXB/7RzBPAsqS1SRoIAdZ7DPRwXD/EwmLzHywGS1cDxtMsOAOw2dZ+61QxUbzKldvtqP9luFt0UHi4BHVIWI0WMYoIVjyCejgXtWWQ+KFPaPtZT6lUwvDwMJ577jl0d3dv+PX7GYp2gJXkbkEmXFAooYzNY8IDhZJgnkbQjaT0YpYmTg7YQFReQKWV7AQAvTORImlwHkPlb3zi9taiEb204hPXuMNctfgEwZTxd8QJTDlE1YuVxY2k14Qqd5Nqf3ai+VgtDeWkL6kJPT6ijqTv9cI1sOLQ8lmMJS6w1hZaXayiPFtuEqr8hCaIVjGLBMndVf3PofsPvgErtXu/tfsVIQRGR0eRSCTwzDPP7Iv7E4NhIxARxsfHUS6XpcKTH5FIBEePHsXRo0cb98G5XA53795FNBpt3Afvt/vYiYkJ/NiP/Rj+8i//Eu985zt3ezgGg8HQwIhPBoOhhXrNp4NKpVLB0NAQjh8/vuWoLCKC59WiafZrfrplWejq6kJXV1dTLMGbb77ZEKmy2az2ArzuJCsWixgcHDxUNcMMh4epqSk8ePBgT4irrP+cvlFuEgiFAE8ToZUIUAfOc8F6T4CePFC3c6pg2WOgaXnM1up+U5rQsRo0MwmEwmBeLV5MF7kHrLgdVNu5PEqrjt9i7Nr+Q4pxCIV7gbscdqx1tZs7HPM35vyFJ78/rf/ZCUVg1YUnIqS++NvSMcjI5/O4fv06XnzxRbS1tW349fsdinYACvEJqIkuuppNFNWLTwjp5xCmOY+BFReSFdbH7wWs7xbYIRVKofh/agRwQC1EM4Bpv8+aY+CJprpnra+2waCYAzXHWKn/KV4qKARLUp2PPMULJfOOqHqwU6tOuPL9PNypFeFRMsaNCA4y95FfH7Tm4SQShMWHJTDGtJF7qhjTJsJRpH7yv0H69U8HfIFhLUIIDA8Po6OjA6dOndrt4RgM2079vrNareLFF1/ctLi69j4YqD18k8vlcP36dbiui+7ubmQymR2Lqd8uJiYm8MYbb+Cv/uqv8I53vGO3h2MwGAxNmJVBg8HQwkEWn/L5PK5du4bnn3++cZG5WTjnezpmbzOsjyUol8uYnZ3F6OgoOOeNfOxUKtX0njnnGB0dRSwWw8WLFw/M8TAY6hARJiYmsLi4uGfEVdb/lloxedXCKffAek6AnkyoOwsQfQcALN0eSCxiHZ2BxCd4wWrVgHuw+k6BntwHoI/cC4LQ1IQiUteB4VWOkCoKTPG5CFfAXqc7kCAs3JiDcEXrHGrbrbWdBLVE7pFTBcK1xWn2zHlEnj8vHYMfuVwOd+7cwcWLF7dUA3E/Q9EOfZsAolGQ+kpkBZhHhKNvA4AibWCVOWUbxoP1FcRtBQDVR1WIJ9PadlRelgq9LBrSXzPotmsFui0+HKQUtuQ+JXehgFCv7HskH7OV8I/j4xUOO7XyakFYenOmsc3vGMoOm9zh5Pc+/ccpHAFEau0LDwvghTJYPAZ4a86xdbupzak+kXvrY/jsEDp/788Qff6C/xswKOGc4+rVq8hmszhx4sRuD8dg2HaICHfu3IHrunjhhRe29b4zkUjg5MmTOHnyJDzPw/z8PB4/foyxsTGk02lkMhl0d3dvKh1kp6g7nr75zW/i7W9/+24Px2AwGFrY/ZUTg8HQYK8s2B9U8WlychITExMYGBjY0qLafqjvtF3E4/HGBbjrusjlcrh37x6Wl5fR1dWFnp4exONxjIyM4OjRozh27NhuD9lg2HaEELhx4wYA4OLFi3vG5cgSKSB7DJhRizws1aYVjGhO7fRYbRhEegrm1gAAmp8Ntl8ASK66cKxogCgy3Xo1J+VT+MR9IqPWIBwOKMQnlbAlfGLEFm8vwC26/g6naAyoqp02a/dJBLR/+ava9mt58uQJHj9+jMHBwQNf81EFRTv1jawAi05B2gTB1X/uQDCxi7kaJ1a9XQCHlBfuQ3Us2LxB+QWpQKOL3AN83DPr+9dMN0T6+UD6Wq6uYaTqVzkPKvq0Y/4TE7mr1+bLN+YgiqvivX8NJ5n65NO3bKySPix4ACJwSy4Kjws1jcqpNGL+yG9MPrWmiFNzPGk0gcw3/keE+raWTHBY8TwPQ0NDjUgxg+GgQUS4ffs2OOc7XscsFAo1YuqJCIVCAbOzs3jw4AEsy0Imk0Emk0Eymdy19YB79+7hx3/8x/HNb34Tb3vb23ZlDAaDwaDDiE8Gg6GFgyY+1Z+OWlpawiuvvLIlx8JhEp7WEw6H0dfXh76+PnDOsbCwgAcPHiCXy6GzsxPhcBic831fqNVgWIvneRgeHkZnZydOnTq1577z1qmzEDrxKUgNv+UlsM4s6P9n782j22rOM8+nLjYuIMAFAClRJLUvnzaSspz4dPq40x27bX/unuNkJpPJ4iwe29njOLGd2GmnlyzH6Um6neVMz3G2OUkcJ453x+3pTrod7/78xeImURS1kRJFkQAJEvtyq975AwSI5d6qK5ESQLJ+f0m4davqgriFi/ep93njCjFoU55ZUYYSG+pGACgeA7q6gfqaTlbwbdssVeYTEeQ1YMqNZIeFAAz7cVR1aKQ1qeo+RsnFBHJrWfuxcpmGGLDR1l6TFUNEFTtC9z99HVw9zrJ767P6Dvoa7iTzyVHpJAdtmJDbYQIAKyQdDAbA5UAwLCSlmToVFBlS5O5E4lPfBLoHnM0tuQHq7AAr5BoOGTZCSzVMVd9NhTClQrNU3BKiVOvO9mSJMCWpOWd4rPsUpoDRZn29JEprjihwJKdj2wfc1n1ZTY3A4LK8XhuRyWIdJSK4t0TDjTsbYGAgUO3GDKMxW9NSzK96iQX7EPovH4ZxAO0+d4NCoYCJiQmMjIygv7+/2dPRaHYdIsKtW7dARDh79uxzfSZnjCEQCCAQCODEiRPI5/OVOlGZTAY9PT0IhULo6el5bs9Rd+/exfd///fjD//wD/HKV77yuYyp0Wg0T4MWnzQaTQP7SXzinGN6ehptbW0YHx/f0UMqEYFzXrIIOWDCUz0ulwtEhGw2i2/7tm+DaZqIRqO4e/cu2traEA6HEQ6HD/Tuec3eJ5fLYWpqCsPDwxgYcBhkfc6wkTPAS38vbUObMenxSl99avGJ1laA9k4gK8+goOgy4GsD8o3B5nqMUD+EA/GJ4tvXoar5RGQo12jlEu4secv6VEFwSeZYLYxlYxmkHm4LDJbzrkvdIAKYmQOqgr3ES8FfYXgR/Ml3O5snEebm5sA5b6msvmbiRHxipH5GctIGXH1/MFEEuTuUNaacpPYw4iCPHyim5A1NeyEUAFJzBYjYOpi705EOB5SEBUSXGl432uU/R6kolEKyUmgWptQ6T2qrJxS14STCleGzvza76xZZE652m6y5LfEmOR0FitvCDvP5ALPROtVS7PF4ATTandpleRqextd5nsPT5UX6cRrFZNH6fNMEqjKaSFCDGEdiO7vUOHIMfb///8JoISurvUQ+n8fExAROnDiBUCjU7OloNLtO+XmFMYYzZ840/Xe4z+fD4OAgBgcHIYRAPB6vWBe3tbVVsqKeVX1YLTxpNJq9hBafNJoWotkPUWX2i/iUy+UwMTGBI0eO4MiRndl3EBFMs7RD+aAH54gICwsLWFtbw5UrVyqe193d3Th16hT5oawzAAAgAElEQVTS6TRWV1cxOTkJxlhFiDqo9UM0e5NkMomZmRmcPXsWPT0ObLiaBBs5rW4Uj5Ys69IJeV9uB0E/IrD+w6D784p2Akb/IMTiHXWfXuv6Jg1drq0C/i6wTEqZ+SQKXBYTLiGx1HOCLFvCzJvw2AWQgUqtpkKygI3b8apObcaqn6sQMDx1j/FbzxDtP/jjMBzsuuWcY2ZmBn6/vyUCOa0CeR1kPnEHNdKcCEsFhQi0BXn9SvHJiVVepS+F+CTLtsqLw8h/5fOlvuIxx+ITOvzWYyls96jIlZlPKls+2dGSrd7TPdcRManm57ITmExha60nCvbP34bHgJksIH1zveaarOz9SrWULK7LcAHCmfhEzIBh8d4KU4AXORILicpY1dZ5VnWc0NEJVqwVNUmUznNffAV6f/N39Rr0lGSzWUxOTuL06dM7rmer0bQiRITZ2Vm43W6cOnWq5dYKwzDQ19eHvr4+AEA6nUYsFqvUTO7t7UU4HEYgENiVud+5cwc/8AM/gD/6oz/C1atXd9yfRqPRPGu0+KTRaBpwuVzI550Vn29VNjc3MTMzgxdeeGHHgWPO+YG02bOiuvbN2NiYpRDX2dmJY8eO4dixY8jn84hGo5ibm0OhUEBfXx8ikQi6uroO/HupaV3KOxcvXbqEzs7OZk9HChs8XtrJXpTbZBmhQxAK8QlZZ/ZeRls7nGxPYB0O37ucPMuiZuzwIWD5jnr9KBYBiUBVKnyvCGh7PADsA/puSWaTKArApgSPIMDlMcDzJuI312RD2GLlGGh4DYiuPnS8+D3K84vFIiYnJzEwMLDjzRn7Dm8XiLmkmUtOaic5amNmQIZbab9HLgc7p4V8DdjuSy32Ml4AeTrBeO29Kbx9SP7Z/9x+IZ8F9fSApTbVA9vYAqpqPpEppOITmfLMKBIkF6+4wlZPlgHJXACs/3Y8Z8LdaS1Ai5xpm/lUttazwmhzI3FtBayuCfGCpcBmuU6ahYaaT0RkaRNKLg+sro8EIXEvUWM9WjOWAFhdxhTLZRqzxBiD77veiO6ff5/F7DVOyGQymJqawrlz5xAMBps9HY1m1yEi3LhxA16vFydPntwTvx87OzvR2dmJkZERFItFrK+v4+HDh0gkEujq6kI4HEZfX99TlQKYn5/HD/3QD2nhSaPR7Cm0+KTRtBiMMfuiv8+JvZ75tLy8jPv372NsbGxH2TYHub6TFcViEVNTUwiFQhgeHnb0fvh8vkrmmWmaWFtbw8LCAlKpFHp6ehAOh9HT03Pgs8k0rcPDhw+xvLyM8fHxPWEbyVxusMEToPuz8oYObD8o+qhkP8XlaggphK4KprqeDQCI1UfO+gPAfO1giqwnAOp6TqawzgqonE6lTBKbdY4XOFxuyboleQt5XgBtBtZvrpdEqios11WLl4y6sQUnuH0u+H/+V+0H3iKXy2FychLHjh1DJBJRtj9wMAPkDYLl1+3bOBGNig6FJW8ALCcZCwBcDn6yFRW2fGUMZz//yNsJlt0Wn8hwI/mlRaBuc5IR6AU5EZ+E9XOlUnxS1FZTik9OxCnZAJIMSWJGgxBUhue5vfhUeHLFmQSBZ4rIL9RuEiAiy3JWds9ojJs1dp2lTmyy+m2y6cy8iWxs+7PRkHlW371oFLcEM5D8rteD/28/jPZ8Hj6fswxYzTapVArT09O4cOECurq6mj0djWbXKQtPPp8PJ06c2JO/xT0eD/r7+9Hf3w8iQiKRQCwWw8LCAlwuV8Wez8lmt7Lw9Md//Md4xSte8Rxmr9FoNLuDFp80Gk0De1V8IiLcvn0byWQSV69efardRNV9CSHAOYdhqGuH7HfS6TSmp6dx4sQJhMPhp+rD7XZXHr7L3tjRaBS3bt2C3+9HOBxGKBTa0d9No3layutHJpPB+Pj4cysWvBuwo2fU4lNBbQGGYgEscgS0vChtRmuPHc1LrK04aodsGgj1AzF1e8rnpLWUKigs9YgTmEQ7IrhhMHvRgOc5XB2StUqmSxWKSEUJZrrR+soJJauqutopRGBHz8F7/rL03HKw8ty5c+judmAvd1DxBQGJ+MRAEJ4usHxc3sYbAMvZtwEA8nQAKvHJgbkdKyRBjlo6oz7bKhcPojj3rcaGbTYpfvX9ZTONc2MAU97Pig1ZppDXc+KK4wqhWvr8JzmVivbP0STsxSdmI2qLnInktVWL+dl1ZPO6xdtt9xYwlwv1SjoJQurRtm0jEdV8X5brotb2T3X/B7p+9KcQeP3/gmg0iunpaQghKkFYnR2vJpFI4Pr163siQ1ujeRqEELhx4wba29tx4sSJZk9nV2CMIRgMIhgM4sSJE8jlcojFYpifn8dHP/pRrKys4I1vfCNe97rXNdSKunXrFt785jfjT/7kT3DlypUmXYFGo9E8HTrCp9FoGtiL4pNpmpienkZHRwfGxsZ29KOViMA5L9mQaOEJ6+vrmJub29WdldXe2ESEZDKJaDSKhYUFeDweRCIRhMNhvRNW81wQQmBmZgZtbW24dOnSnrvn2cgZdaPYcilKqQi0GoEg+LKir+QmWE8IFI/J222ugwV6QAl58B0AjO4QhAPxSUSXYQTV4pOhEp8U70MxkYGvyz7zTZgcssfo+sykarKxLHJRC6tBh/WeSBAMo/Y9YF4vAu/5ddsxASAej+PmzZs6WOkA8qmFOfJ0AhLxqdJGIT7Z2dHVYJM1VA0TZknIUmRAqTKxKhjbWTum5xDSn/r/bDp0JtRTIt7wEWc+t3q9VRxXmQUoM5tUuCVnC24rNMvs82QYNmJcPppB0WrdsLk6K3GdBMHlaezf9i3OZYE6kT39OA1RsLHbAywt9wxXbT2o9jf9APz/6/cDKNlTHT16FMVisZINkEql0N3djVAohN7e3j21GeR5sLGxgZs3b2J0dBTt7c7EX41mL1F+Lvf7/Th+/Hizp/PMaGtrqziEnDlzBp///Ofx6U9/Gu9///sxNDSE1772tXjTm96EXC6HN7/5zfjTP/1TjI+PN3vaGo1G88Ro8Umj0TSw18SnXC6Ha9euYWhoaMe1K4gI5pZVlLaCq7Uge1ZCEGMMgUAAgUAAJ06cQDabxerqamUnbDgcRjgcRmdn554TBTStT6FQwNTUFPr7+zE0NNTs6TwVxtEz6hpM2TRYb7/jrCUVrDesFp8AsFC/I/GpwQbKjlQCrsMhaROhsNlyAimsB6UZD0Rw+6wfsTcXNpFZyTQISrZ9GawxYF/3AhHB9+rXwtVn/76srKxU7Gjrd9NqGiGfg1qRTkQjJ21kKXjlJtxB5iIA8nSBqez3HPZVFjXI3YnEp1+2H7PoMIMvEQd1tIFV2XaqLPcAC0u3elQ2gioNSJbYxBVriUR8kj6vSMRpV0ejVR+ZAslrNuK8Q9EakAh1Nm1dvnp7TyAZrbNdrb+Uess9TjC82y/6Xv06BN/6Mw3jeTweHDp0CIcOHYIQAhsbG4jFYrhz5w7a2toQCoX0piQAa2trmJ+fx+joqF7LNfuSsvDU1dWFY8eONXs6z4329na86U1vwpve9CYIITA5OYlPfvKT+L7v+z4sLS3hu7/7uyvlAHSMQqPR7DW0+KTRtBi65tOTsbGxgevXr+OFF15AT4+DYJEEzrmu77QFEWF+fh65XO65W5C1t7djZGQEIyMjKBQKleBDNptFb28vIpEIgsHggf8baXZOuVD3TuwkWwHWNwD4g4Ci7grr7lOKT5RQ2X9t9eVxVg+LeZ0FCimVcNQOAAwPgyyiTEWhFneU64f8uKx/XuCW4lNiIYHUw5RtMN1qTWNuN8BrM1UaguG+DnT99Ltt57O4uIhoNIrx8XF4PNY1aDS1OMl8Yg5EIyeiKpMVCCu3cVjPidzqYDQrpB31BSo9B6ZuFSGia/bNMknHmUUsGAJi2zXejDb1swXzKN5DlwFpkTVV/1LLPrK0qaucCwE79Uk2b7vsJp434fY3rpmpGzHwjFmTQVQZx2I9KWXuW4zxBEIVL5jwtNfOZfP+BkQuX8ksbbDcE9QwH6paq92XrqLnPf/WehJVGIaB3t5e9Pb2AihZP2t7PiAajeLevXt7pialRvOkCCEwPT2N7u5ujIyMNHs6TcMwDIyNjaG9vR2f//zn8bGPfQyPHz/G7/7u72JiYgJXr17Fiy++iNe85jW63ptGo9kTaPFJo9E0sFfEp0ePHmFhYQFjY2Po6Oh46n7K9Z208FTCNM3KjrOLFy829f3wer04fPgwDh8+DM451tfXsbS0hNnZWQSDQYTDYW3JonkqNjY2MDs7i/PnzyMQCDR7OjuGjZwBXX9J3sjJfbL2GGjrAHLyYDdlktLjlXaKfirtVh8BrkahpR7mYmBMvkGDuACsAq9VWAVxa47LrLYAuLz254uCAOrix4nFBFJLpToplmuq1XCGAVb3fpAguL211+b+gbfBcFtkS2zVMctmsxgbG9M7ZZ8AJ+KTOqUGYE42E3EHmUOFJAgMTDWmKgsIAIppEAyl6MV4HgU6jPyXPy9tR/GYc1u7jtogmdEuny8VuTKLUfmIohKiJf0T2Vv2EREgycpySYQ1l811ixwH/LWvmekCUtdjlmuEVX0lGVZNichyPSSz9vNRSBeRWcnAqBLVjE4/UKyyAqTa9a26b2PkFHp/44OO51pNZ2fngbfne/z4MR48eICxsTG9iUCzLxFCYGpqCr29vRgeHm72dJrOzZs38cM//MP4sz/7M4yOjgIAvvd7vxecc7z00kv427/9W3zgAx9AMBjEG97wBrzxjW/EyZMnmzxrjUajsUaLTxqNpgGXywUhKYbcbMpZOalUClevXoXb/fRLWVl44pzr+k4oWRhOTU1haGgIhw4davZ0anC5XBULPiLCxsYGotEo7ty5g/b2dkQiEYRCIf2jXKOkbEG2n+olGEfPgKvEp7QDwYgIRv9hiIXb8mbRR6WgrcKejlYfOao1BbMIdngE9GhB2swuY6B2UPlYwlRnRrkk4wgubG31gMZaL4nFUsYToLDiqsfbVhvYtei72B3Bg5FTmPv619HX14dwOIxgMAgiwo0bN+DxeJq+iWAvQl4H4pNwIBo5aWOqBVoGAnmDQEGe3egEBoLwBcHyG9J2xIHEX/9PdYe5LKinB0yReQkAqBNJmeQ+AkpCriERegF15phMvFLa6smWEk5gNvZ5JAiuNutnEWECRrv1+kLFxo1fiW+tgri90GUlKtvd73ZWfFZ91F/65r3NGgGMiIB8GqzqXMv6dF4DLHQIfb/3J7uyDh1Ee76lpSU8fvwYY2NjO/rNo9G0KpxzTE1NIRQK7VkL7N1kdnYWP/IjP4I///M/x+XLl2uOuVwuvOpVr8KrXvUq/Nqv/RoePHiAz33uc3jnO9+Jhw8f4ju/8zvx4osv4ju+4zt0hqRGo2kZ9NOLRtNitEKAyOVyVeoetRqmaWJ6ehodHR0YGxvb0ftFROCcb9mTaOFpc3MTN27cwLlz59Dd7WTXd/NgjKGnpwc9PT0gIqTTaayuruLatWs1ItV+ERY0uwMRYXFxEWtra/vOgoyNnFE3ij0C3G5Asb6zjk51X4U8WHgQ9PihvF0uAxY+VBKhFDB/QJlL4kh8UuRhEBdgbvt+eIHD5ZEcz3FHdmFArfDkYGo1UCbdYN1ltHlRthgjAkL/4T/h0PBRcM6xtraGpaUl3LhxA6Zpoq+vDydPnjzw321Pg6PMJ+4gq8+JsOQwq4m8nWAK8YkJZ89u5OkAJOITuTuw8fnrIJM7+sgagR6QE/GpbmOTKvMJpgAk4lPJYtP+dBL2AhEApa2e9OKF/d+L50x4uqyDfoIzuzJRDbp5/nEKuYXS+2otMtl0ZJclZSVg2Yj1RlXbTCyLQqLQ8F7VzMnjBavP4mMA/EGE/p8Pw3gGWUkHwZ6v/MwyOjq6rzO7NAcXzjkmJycRiUR2XLt5PyATnqwYGhrC29/+drz97W9HNpvFF77wBXzsYx/D+9//fnzxi1/UWe8ajaYl0OKTRqNpoBXqTlmRzWYxMTGB4eFhDA4O7qiveuHpoPP48WMsLCzsyUwQxhj8fj/8fj+OHz+OXC6HaDSK2dlZFItFhEIhRCIR+P3+PRt80OwcIQRu3boFzjlGR0f33X3PRs6oM4y4CRYZAj26L++sWJAfL48Z6FGLTwBYsNeR+FQfmLbCifikLMUjCRoDpcCxy2+/W1QUOCARn8oB3uSDZI3wZGdvxdragULOtp/K+QAMYVbqCLlf9c/hGT4KoLRppFwPb2JiAv39/RBC4Jvf/CZ8Pl9FkN9P2QDPFJcX5O4EM+3rI7FCSikasWLK9liljTBLYlBRUYvJ5WAHM8+r2wBgLvvPATEDiZkc+MJDGJHDQGxZ3WGbM+tjymVrdBGjTWG7p7hXqcjBJEIxFYVUfCIuwKRFneyfGWSWfCLPAZsyHLzA4bb7CV6dWSQIG99UvffOM5xgk+Fkd43urb8NcUJioVSTr/r8+ucpkcvViPZEBMPfhdCHPgKjTV2LbDfYb/Z89+7dQyKRwOXLl/fdM4tGA2wLT/39/Tv+bb8fuHHjBn70R38Uf/EXf4FLly498fnt7e14/etfj9e//vXPYHYajUbz9GjxSaPRNNCKAfqNjQ1cv34dL7zwAnp6enbUF+e8Ut/poP+YIyLcu3cPm5ubuHLlyr6w82hra8PQ0BCGhoZQLBaxtraGe/fuIZ1Oo7e3F+FwGN3d3Qf+b3+QKGdMBoNBHDt2rCXXuJ3C2juByBFg5YG8nYPsIlpfcTios00KzOG9RhsxZRvDqw5Wq2rEqPZWkDQVAsrNGW6fG8kHSSQf1Nkc2nzsiBoPEacGuzEyRSVYL1xe9P7i+2uOZzIZTE1N4dSpU+jr6wMAnDp1qiYbgIgqtlSdnZ378l7YLcjXLRefyAS5OwBZG1EEuTvAFBlQ5PWDKcQnYg6EV5WA5aCvbLwH+Ze+UOrPHwA5EZ+Uiu/WuIn49medAUwpJivEJ5MAWQIrF4As80mBzJ6T3O0ArG0V6+sl1RzL5WH3E7xaSEvfWgff3NoIYDcNi0sjVPTp2tftMpysrPg4wd1RmkvyUQo8zxss91hdXbx6YZ35gwj9/p/CFWhOJv1etucjIty5cwe5XA4XL17Uz6uafQnnHBMTEzh06BAOHz7c7Ok0nbLw9OEPfxgXL15s9nQ0Go1mV9n7UUaNRrPvefToERYWFjA+Pr6jrJxyfaey8HTQg26cc9y4cQNer3ff7qr0eDwYGBjAwMAAhBBYX1/HysoK5ubm0NXVhXA4jFAotGd2wWqenHw+j8nJyZasY7bbGCNnIFTik2isKdJAcgOsuw+0sSZvtxl3NC9KyWvLVNqtrQIdfiBjny2iynwSRQ5DsbYr136fD4C9fZnsdF7kyKxkGoUnAMwugpzPAvW1UiyC7uVXiAj+t/8ijCrbyLJt6oULF9DVVZtyUZ0NUCgUKgHYTCajBXkJ5AsC6SVFG79UoCq16VKLTy4nmSEOxN5CCgRDWQcJNscLbBCpT39++wWvs8A8ObVqTsRBbT4wswDmc6vvRdVxVWaUsM9OUkEE2zpLAMBTWbiC1j+lre7f6qN2uLbEZZ4zkZhc3T7wBDWcmNsDw7D4+9pcCvNtW3mW4UUOD3OD5zlSS1vrcd3yUL1ekCAYVSIfAej9N78J96HWCCir7PnK9fJawZ6PiDA3Nwciwvnz55s+H43mWWCaJiYmJjA4OLjvn82dcP36dfzYj/2YFp40Gs2+RYtPGk2LoX9kbENEmJ+fRyqVwtWrV3eUlVMWnjjnur4TgEKhgMnJSQwMDByYwq6GYVT8/4kIiUQC0WgU9+7dg8/nQyQSQTgc1sVZ9xGpVAozMzM4ffp0Jei0n2EjZ4CX/k7ahjYVglK5r75+pfhEsceAr70knsjarT4C3B7AtM4S2G5IMMKHIBbmbSaFhmyghi5MAtyK9V2SzQCgZIHnsf++MST1otIrOaQWG4UnIrIMFNtZ8Rkd7YBZa3/o2mrH+ofR8S9frLwei8Vw+/ZtR7apXq8Xhw8fxuHDhxsEeb/fXxHk90MW7E4hnzrL2oloRG4HwpJLXX+OCcX9A4CBIHxBMEk9JwCNtXkAcG8/Nv60bv0gtRUmAFA66UzkIQLrDgGxR47qplnWKKrt0MmokgFk/RtgTJLBJEzYZjBJMsHq67ht90dwbdXASkysAEVRdY4HqNs4QETWz7IuF6zERcv1hxkwiDe8D4KXzt9cSFSsD2WWe9VZVUSEzh98G3yjV6wusyVoVXs+IqpsCtP1+jT7lbLwdOTIEQwMDDR7Ok2nLDz95V/+JS5cuNDs6Wg0Gs0zQf+y1Gg0LYlpmpiamoLf78fY2NiOfoDV13c66D/mygH5amumgwZjDMFgEMFgECdPnqzsgp2cnASASn2Uzs7OJs9U87Ssra1hfn4eFy5cgN/vb/Z0ngvs6Bl1o3gU6AwA6YS8L486GA4SYP2DoMXb8nacgx0+Cnp4T92npHaM4XWp128HmRCGwobL5bLvg4jgtgiaC1MgfiuOfMH+0Vpm4VUzBhhYIV8jkpEguDwGiIDuX/2PldcfPXqEpaUljI+PP7FwXi/IJ5NJRKNRLC4uwuVyVdbBvVYHcLcgnwO7MMPBfeKkjROKcpG3gqcTUIhPMGv7Ep4A4p+8BtRlMFGusRaZFRRfc55h1FFaj1X1ngB7oWa7wc6e56S2eooMMpdEPJOJ5Haim8iZcAfbUFjLInM7XsmUJCLbjFXLbEVuwso51OpaeZHgtpgrA5BPFJCNZbdf2KLecq9eQPdcGEfgh95iOd9WpFXs+YQQmJmZgd/v37f2wBpNsVis1G/u7+9v9nSazszMDN7ylrfgIx/5CM6fP9/s6Wg0Gs0zQ4tPGo3GEsYYhBBNseHJZrOYmJjAyMjIjj2gq4UnbbW3vUP+IAXknVC9CzafzyMWi+HWrVvI5/Po6+tDJBJBIBA48J+fvUI5ID82NtaS9RyeFezwMcDjA4p5aTsjdAhCIT4hZ299V9NXRwccGPnB6Oxy1A4F+2C3ynIPgDIRgkwhrQkluIDLZ/94zHO8IVhrZk2sza6B5ziIFS2D8HZrh5UVH5m84VqFILgZg/ufvBaeI0OVen2JRALj4+M73qHPGEMgEEAgEMCJEyeQzWYRi8UwOzuLYrFYCcC2gi3V84K8DsQnJ2+FgzZMqG3rWKExo84Sl1qErO6LDA82v74KEWvMdKyp0SQjlwF1d4Op1hUAcJfmpxKfRFF+rwIOMqNUz7AycUuQpYgDAMQF3O32oqKdMEVEtufxPIcbwOY3lxvWBes1xWZw09zKfqod1+p5XuQLQGejuGx4XNi8t1n1Qt3x6r6o6u/Q1Y3e3/p9m4m1Ps2y5+OcY3p6Gj09PRgZGdm1fjWaVqIsPI2MjCASiTR7Ok1HC08ajeYgocUnjabFaJWgjsvlqljUPU/i8Thu3LiB8+fPo7t7Z0WKOeeV+k4HvZYFEeHBgwdYXV19qh3yBwmfz4fBwUEMDg7CNE2sra3hwYMHSCaT6O7uRjgcRm9v74H/TLUiRIS7d+8imUzuSkB+r8FcLrChE6C7N+QN2xxYhUUfAS4D4HLbLVJZ6ZVxUmsKgIg9tj3mSHxS3JYkCEzSDc+a8HTYB5V5oVZ8ysVziN+KgziVNjkoa+1UzcXONssCBkC4fQj8wq+AiHDz5k0QES5duvRM1qL29nYMDQ1haGiosg4uLi4eqHXQSeYTI/Xn2kkbcLlgDACM50GGD0zI25LE8s2qr/QDH4rXv2Xd18Y64HaDcbU4ZgR6QE7Epy2LNqYQn6hggnnl9zzzKI5LLDjJFGCyLEjObcUnM2PC02X9HEVc2AprImfCFbDeEEGmQObuBoqx2qw02zXCTtC2mDORdT92mV+FjIlienttl1nuVcZgBsIf/EMY++h793nY83HOMTk5iUgkgiNHjuzi7DWa1qFYLOLatWs4duwYwuFws6fTdKanp/HWt74Vf/VXf4UXXnih2dPRaDSaZ44WnzQajSVl8cnjxHppl1haWsLi4iLGx8d3ZPNTru9UFp5aRdBrFkIIzM3NgXOO8fHxfR0s3G3cbjf6+/vR399fsWOJRqOYn59HZ2cnIpEI+vr6nut9orFGCIEbN27A4/Hg8uXLB/a+Z8On1eJT3oGVViEPFjoEWlmSNqO1FUfzonjUUTskN4HuELARazhkKALRACzrJ1UjvB0wyD54z4sCsruZqsS41FIKmwub21kKBMsUBbt6T4B18NcqIG64Dfh/4l0gANNblrTHjx9/Lp9z2TrY0dFRqRO13zY1OLLd4w7uJQdtWNFZpiH5usCyKqHKWR0k8nUhn+hF5u//m6SRAOsdAKLL6g7bnNnUUjYDBsBoV4hPRQFI7nkqcrltHid55hQXgFR8sq/pJPImYCM+8awJj9daYOI5DlfAfr6b37IX3xuwWWusn/GsPxNWaw0vciQfVmXZVSc5WVjuMYOBCAi++9/CfXj/iifPwp6vWCxicnISg4ODOHTo0DOcvUbTPAqFAiYmJrTwtMXU1BTe9ra34SMf+YgWnjQazYFBi08ajcaSsvj0PCAi3Lp1C5lMBlevXt1RofOy8FTO2jqoAegyxWKxYuVx9OjRA/9+7IRqOxYiQiqVwurqKhYXF+F2uyv1UdocZJVodpdisYipqSmEw2EMDw83ezpNxVHdpzVnAU6juxdcIT4hEQcL9oA249JmtB4F/F1ASm0dZvSFISzEJ5ci80kogtEAwDcTcNtkHpQ6UQfuSRA27mwgG83W2GNRqVqT5TmW87IYSnCCq87WT3ABz9ET8Lz6u3Dt2jUMDAw0bYd8/TpYXS+PMVYJwO6LenkeP8hwSy3xWDGt7MZJGxQzyrEAgNzqjTmMO8tGNEU3Eh/5lLq/zgDIifhkOMz+SJTqUals90hVv60opJlPqswmEgpHRIkuRab93ETB/tlZmPaZkdRHg8sAACAASURBVLmlBChnca5d4pPNmvIkG4zcFmtqeiUDKl+DYYAZtdda078AmIeh7TX/Ch3//LWOx93r7IY9Xzkgf/ToUW1Bptm3FAoFXLt2DSdOnEAoFGr2dJrO5OQk3va2t+Gv//qvce7cuWZPR6PRaJ4bWnzSaDSWPC/xyTRNTG3t4h4dHd2ROFJd30kLT0Amk8HU1BSOHTumi7ruMowxdHV1oaurq1IfJRqN4vr16+CcIxQKIRKJoLOz88B/Dp812WwWk5OTOH78uA7gADBGzqprK2VTYH39jrOWVLC+AaX4BABG+DBEak7dods690hlu0cOasQYskwHQFJIZeswAbGZGIqpxgC/bV0nu9ctgsdEFkFtlwdtv/Rr+Na3voXjx4+3zM5hxhj8fj/8fj+OHTtWqZc3Pz+PXC5XCcAGg8G9uQ4yBvJ2g+UahdBKE9OBaFTMgAwXmMR6koEgPAGwvOI+cjnIsjWzyibc24/EV++WFBgVNlk89Ti14KTEOtDuBXNioykdTwAy2z1VZpPVvVaFbC2RnSm45KjNoeJGDunbcRhWipfFvWOf4WQNs2jLixxef+3flhc40itpGFtjEhc12aQNaxYDjMERdP/C+xzPZT/ypPZ8+XweExMTOiCv2deUP+cnT55EX19fs6fTdCYnJ/H2t78dH/3oR3H27Nld7/+DH/wgPvShD4GI8Na3vhXveMc7dn0MjUajeVq0+KTRtBitEqB5HuJTNputFB49fPjwjvqqFp601V6pdtbNmzdx/vx5BAI2Hi+aXaO9vR3Dw8MYHh6uBB7u3LmDTCZTCcB2d3cf+M/lbrO5uYkbN27ghRdeQDAYbPZ0WgLW1w90dQPJDXm77pBafEqqBSUAgEO7NeZzmBWYabQgY25DKSyRINsaLUAp1uxWZFu4PPZj5OI5bMzHIYqNAXsS9tZ6tlZ8VuuBxUvGq1+L6eUVnDt3bse1EJ8l1fXyOOdYW1vD0tISZmdnEQgEEA6H0dfXt6dqsZGvG5CITwBA3gBYbt32OANBeANgOUV2oLcDUIlP8lydUouCPLuQeyNY+/DXYBw5oeyrNDGHNn7ppIPZlfoz+vqU34fKr0tVZpQg+XwkB0mQNGtKWlZLMi2r84gI8W8sAwIN2VaydcKyhpNtllTja6LYONHEw1SNfWj1eQRWk9xGRGAdnQj93h9bD3pAUdnzBYNBLC8v49y5c+jp6Wn2dDWaZ0I+n8e1a9dw+vTpSobgQWZiYgI//uM/jo9+9KM4c8aBQ8ETMjMzgw996EN46aWX4PV68brXvQ4vvvgiTp06tetjaTQazdOgxSeNRmPJsxaf4vE4bty4gfPnz+84mMY5r9R30vWMgEePHuHhw4cYGxvTFnBNoDrwwDnH+vo6lpeXcfPmzT0bgG1FVldXcffuXYyOju6oRtx+hI2cAc18Q97IweePYo8BXzuQV2RSZB3YigEgJ7WmAIiVpVIUtSrorcp6Kg0gP8xNwCWrEUNkKU4JDmzeXkM2mnVaTqemT8s6VAQwF2to66oLeAt/Nxb+6Wtw6dKlPWVl53K5EIlEEIlEQETY3NxENBrF3bt34fP5KjalT1IfpRk4qftE7g4A9uLTdhtVVpOD94LUz2WM50GGD0w01obi3hDW/+qboGTK8f1IuYyzdvE1Z+ITACPgB6DIlFJlKe4UmbrFhdRGUFZ/rv6+rsHivPR8HIVYtuHer4xl8VxrZ+8Jm2xKq+eN+izLYqaITCwL91ZNJyKquRYSAoxV9WO40fuB34PRsXfWpedNvT1fLBbDjRs34PV6MT8/78ieT6PZa+RyOUxMTODMmTNaYAVw7do1/MRP/MQzE54AYHZ2Ft/+7d+Ojo4OAMCrX/1qfOITn8C73/3uZzKeRqPRPClafNJoNJY8S/FpaWkJi4uLGB8f31HQuFzfqSw8HfQfbkSE27dvI5PJ4MqVK1rcaAFcLlclyFofgG1ra0MkEkEoFILXYeaIpsTi4iKi0SiuXLkCj8eBDdUBwxg5A64Sn9IJdUckYAwMQizeljeLPmoQi2zbOaGQB+sfBFXVm3IkPim+AsxUDi5JvSee4w01UNLLKSQfpkFFs5QN8aTfM3btrYLEgmpsAYmAx9/9gxgbH295kUYGYwzd3d3o7u7GqVOnkMlkauqjlOtE+f3+lvsedyI+we1g/XY7+PtJ02m2mvCCuh8A5OsCy9aKT8Lbi/W/uQaxsVlqk5BnR1b6Smw4E5VyGVB3N5iDtaV0P8vFJ0OShQhAnRqlyqySiETECUzy1eJqt/8JbXjt5+2qE7d5nrB5bdV+jrapTDZjW1yTpZUnGi30Nh8kUatz1W7oMurad/3oj8N37oL1RDQNJJNJzM/PY2xsDF1dXY7s+TSavUZZeDp79mxLZ2o/L771rW/hJ3/yJ/E3f/M3OH369DMb58KFC3jf+96HtbU1tLe343Of+xxe8YpXPLPxNBqN5knR4pNG02K0SuDlWYhPRIRbt24hk8ng6tWrcLuffgnSwlMtnHPMzMygo6MDly5dOvDvRytSHYA9efJkpUD15OQkGGMIh8OIRCI6i0dCeQ0pFosYGxvTmY42sKMOdlbGlgG3GzAltWoAMCfZNvksWPgQaFUhLmVSYKF+UExda4oFemrFJ0mmQeUcSVYTUAooy+CFbfGpmCli83bcsrZTQ79EtsFg1tYGFBszUJjHU0rFqumntk3q1CVc/J7/fUffla1IR0cHRkZGMDIyUgnA3rt3D+l0Gj09PQiHw+jp6WmN+9vrIHjmQDSCg2thDtLqWNFhFpK79ntEeHuw/skZiLXt7CuKrwGMgalE4411wO0G4/K1AgCMQC/Iifjkkj9figJX2mxKM4yABgu7aojw1PWgRJHD5bO/J93t1qoVcQFXe+06Fv/m41Ltql3QtInIsrYT83hR8vSrxVW1puY288hvFmoSYlmdxV61GGgMHUfX9735ySZ9gClbBFdnsKrs+cqi/F7eeKA5WJRrsGrhqcTzEp4A4Ny5c3jPe96D17zmNfD7/bh8+fK+e3bUaDR7G70iaTQaS3ZbfDJNE1NTU+jq6sLo6OiOxBFd36mWXC6HqakpHDlyZMe1szTPB8YY/H4//H4/jh07hlwuh1gshtnZWRSLxUrQQVuxbFMWWP1+P06fPq3fFwls+LQ6E4mbYOEh0PJ9eWdFZ5kWLNirFp+wVWvKgfhUH4x1KTKfiAtre7sngEwBEoTkgwTSj1K1FnuyzxtZC19EBGYhPIEZloF8o60NoJLYZbq8OPp//f6+Dx7UB2Dj8Tii0Shu3boFv99fsSltVoYjtakDaCrxxmkbOMlqKiSrS/LY49p+v4Q3iPXPzoGv1NWu4iZYuB9Yt8+8AQCQAOsdAKLL6vm1dajbAGCGXMiiAgdT3PPMozguXQ8MMNYoyGxPwP4Qz5gwgtZjiwKH0WZ9jGdNuKuskDMLm8gtlMRA2+XF7hKshDey3sDGBVD/VhBRzZqaeJDcmse25V61+EtVfZPhRuh3/m+biWnqicfjmJubk1oE19vzpdNpxGIxzMzMgHOu7fk0LU8mk8HU1BTOnTuna7AC+Md//Ef81E/9FD72sY89t7pLb3nLW/CWt7wFAPDe974XR44ceS7jajQajRP29y9ajWaPwhiztcl4XhiGAVOxI94pmUwGk5OTGBkZ2bE4ooWnWhKJBK5fv46zZ89qX+09TFtbG44cOYIjR47ANM0aK5aWywRoAvl8HlNTUxgcHNQCqwNYeycQOQKsPJC36wqAFPFkiisC0+W+FFlHlXZuZ3ZCtFlbH0dluyeKAobi+8BQ1JAxcyY2725C5Bu/+wS3t9wjIusaLHYJUV4fUKytt0O0ZalmlL7/e3751+E+YJaShmGgr68PfX19ICKkUimsrq5icXGxxsL0eWaHkpPMJ+FANBLqDDqYitpqABhxkLcLKCRVLUvDerqw/l/vgi89tm4V6AapxCcAzB8AORGfJHWSavpjBQD2n28yBSC556nApWsOcSEVnwgGmEU2UAVJ36JgvzGLZ01bSz5e4JUf3qLAsfGy9d+kBos1hwC4nuRZoFgoZbnWzEXA3VZ6/zOxLIoZszY5r+75umznR0QI/ux74ArqrAYnrK2t4fbt2xgdHX2iGqydnZ3o7OysyQ7V9nyaVqX8O//8+fMIBALNnk7Tefnll/HTP/3Tz1V4Akq1cCORCBYXF/Hxj38cX/va157b2BqNRqNCi08ajcYSt9uNQsHZjncZ8XgcN27cwPnz53ecgs85r9jsHdQgfDWrq6u4e/cuLl++XCkwqtn7uN1uDAwMYGBgwDITIBKJoK+vb99nRJRJp9OYnp7GqVOn0NfX1+zp7BmMo2cgVOKTkARfyyTipaymzXVpM0o6rB+TclBrCigFur0+oJAHmNp2j7gAFMKWyyYjIbuWReZRErn1vGXNFACgre8eK550E4TIZOCqq2cjTAH31vxcl74NHa/6jifqc7/BGENXVxe6urpw4sQJ5HI5RKPRSnZoORMgEAg8000o5OtGSVq03xDETAdWeA7aMKWgtDUnT6e6LQmQx4+Nv38Ivrhk24x5fA7M/gB4nNUlJNOByAbA8Cj+ZkJhBWgKaeYTmUIudAsAsuVClujI7ddNmTBF5vY1bXxrBSK31ZYxWKVaEREMC0tH5vUBaBTI7dwf6+vYAYDYugYShMTDctaTdV8kqFLHyn3uMjrf8K+tB9LUsLq6ivv372NsbGxHdT21PZ+mlUmn05iamsKFCxfQ1dXV7Ok0nZdffhk/8zM/g49//OM4efLkcx37e77ne7C2tgaPx4M/+IM/0JtSNRpNS3EwIlcajeaJMQxjx7Z7Dx8+xMOHD3HlypUn2vFXj67vVAsR4f79+4jH47hy5UrT7Ig0z576TIBkMlkJaHi93komwH4NOqyvr+PWrVu4cOEC/H5/s6ezp2AjZ4Bv/J20DW3GpMcrfYX61eJT9BHg8VnWN6ppt/qolB0hFN8vQoD1D4Ie3FVmPZXayw/zPIerKlhNBGQep5BeTkHkeCnAKqkhw2y8zqT1nqys+AAYbuvXAUC4vAj/+9+SXMnBpK2tDUNDQxgaGoJpmlhbW8ODBw+QTCYRDAYRDoefTSaA4QY8fqAoEXuKKbVAVUwph2KiCHJ3qMUst4PnKbOI+D88RvHOgmJQh89TTpPxM+rrBFNnMqqGI5NkiVMAJ/mvXMGl4hOTZklKMq6kolnpWH41jcydKrHecMGwsQC0fN51uxvqxQHW6w0vcHg6Ld6orWmmVjLgBVEzVr3lHtiWI4Pbi9AHPmg5T00ty8vLePjwIcbGxnb1GV3b82laCS081fLNb34TP/uzP4uPf/zjOHHixHMf/0tf+tJzH1Oj0WicosUnjUZjyU5qPhER5ubmkM1mcfXq1R0Fg7TwVIsQArOzszAMA6OjozoD7ADBGEMgEEAgEMDJkyeRyWQQjUYxPT0NIkIoFEIkEqkUs97rLC8v48GDBxgbG9u34tqzhI2cVTeKR4GOLiAjz6JgTnZtCwF26DDo4T15O7MINjAMeqQIigNgHf6SWONAfFIFq82cCa/fC2EKpJdSyKzlQbnt7F4SZGvjJTsmrfdkdY63DcxsFOhcHqNkafXOfwPDqz/vMtxuN/r7+9Hf3w8hBDY3NxGNRnHnzh20t7cjHA4jFArtKNugGvJ1g0nEJ0Yc5OkEJAKTU2GJvH51Gyb/+Sa8vdj8wn0U5+5K2wEA5XPKNgBAObUlIACI+JptmaIyhs+ttOlUPuspsjZJkLwullkEPNbvIwmSWvYxiUgtg7kNEBdY/0atfSGZJuCxyHCyew9shHureQluvTIytwFhCiQflT6zMss9ZjAQAd3v+zUY7TrLXsXDhw+xsrKCsbGxZ56hru35NM0ilUphenoaFy9e1JvDALz00kv4uZ/7OXziE5/A8ePHmz0djUajaTm0+KTRtCCtUPPpacUn0zQxOTmJQCCA0dHRHYlFur5TLYVCAVNTU4hEIhgaGjrw78dBp6OjAyMjIxgZGUGhUEAsFsP8/DxyuVxl92swGNxznxMiwr1797C5uYnx8fEDYy+427DDRx1lIhnhQxALCguvbNrZmP6AowQJoysAR98uWxZeLp/6M8B8PoDs6xTyHMfG4zhysYxllpQkgakUjLar9wRJvSerTIR0pkFME5zg8hpgpy6h4599l+01aBoxDAM9PT3o6ekBEVVE+cnJSQCoZIfuRJQnXzeQkltYktevzG4iX5daWPI4qWdlf5eZ3kNY/8hXQYlUySqvKLdPpoRDu8xEXC7mlMmmQd3dYGl7e03DpiZSDRbZgTXzcTIXGbLuOUlrPsksQJlk3szrQmImBp6o/ZvYnmF3oJgH6jKzStlKjfOyy8Ryt3uRWuegLXHKMKrFptrzXT4XPK/4J+j4J6+2m6lmi4WFBayvr2N0dPS5Cz7ank/zvEgmk5iZmcGlS5f2zYa3nfCNb3wD73jHO/DJT34Sx44da/Z0NBqNpiXRER2NRmPJ04hP5YKjR48exaFDh3Y0vhaeainXvTl58iRCoVCzp6NpMbxeLw4fPozDhw+Dc461tTUsLS1hdnYWwWAQkUgEvb29LZ8pV53Zd/ny5ZafbyvDXC6woZOgu9flDdvUwW6KPiptjVdkGzDurN4LHG6uoPUoAAf1nggwhNkQrM1v5pFbyyK/mUdhMw+XLJtB9hVjY7lXOs/ugPXLhkVQWwgCY270/8bvSCahUcEYq2QCHD16FIVCAdFotCLK9/b2IhwOo7u7+4meKcjnoF6lSx3QJZcDuzxDbdHFhPV9lqdBbPzRfwPM0rMb642AVh7K5xSPAYahrP9GG+uA2w1mYfdWjxHoBe1QfDIk9ZwAa2G3toEis0qSvSSYBy6JPO7qsP8bGRKhnIocieuxxqXBbip2NeasljGbJdUuG4oRR+J+aS7M4waodL1EVCOaEAC0daL3333AZpIaYHvTTCqVaolnF23Pp3lWaOGplq9//ev4+Z//eS08aTQajQItPmk0GkueVHxaX1/H7OwsLly4gGAwuKOxOecVm71m/4BrBdbW1jA/P6/r3mgc4XK5EIlEEIlEKpZUq6urmJ+fR2dnZ8WSqtVqhRWLRUxPT6Ovrw/Dw8M6ILILsKNn1OJTwYHtViEHFj4EWlmSNhPrq47mRRtrztrFY0BXNwxFXN/MmHC7GQQn5NayyK3nUEjkAXM7oG5IPk+CC3kw285xj+wzoqxet6srxQyGrp98F4wOHcjZTbxeLwYHBzE4OAjOOdbX17G8vIybN2+iq6sL4XAYfX19yuxKJ+ITuRysp65d+tll1t6zxAxk471IfuK/1rzO/F2gFUVfnMPoGwCtKRqSAOvtB6KP1fNTWLOpxCdR4EprO7X4ZH+IiBoyh6oprKfQHrEW5XnOhKvNfv5uq/pKAESRY/Paaql2XP1UbcWnxpeICC6LDCdbsdviOnmRI72aq5zChLk9ibrJGB4Xev7Db8NoseeFVoKIcPv2bRQKBVy8eLEln11k9nzPtGaeZl+RSCRw/fp1XL58GR0d2oLz61//Ot75znfiU5/6FI4ePdrs6Wg0Gk1Lo8UnjaYFaYUfLk8iPj18+BAPHz7ElStX0NbmYGevDbq+UyMPHjzA48ePMT4+vmv1KzQHh3pLqlQqhWg0imvXrsHlciEcDiMSiezovt0NstkspqamcPToUfT39zd1LvsJY+SMlcNcLTEHwWQArLtXKT5hYw2sKwhKbkqb0doK0N7pyM7PCEVg+KzFKiGAfDyHfCyDYqIAM2sCvPGKBZfUbAJAAmA2MTfB7QUmu4woIrIM+gpOcFvYZblOn4P/Df/adn6anVNe78LhMIgIiUQC0WgU9+/fh8fjqRyzWgsdZT45wTJlpX4w9XMXK2zb+5HLh+RNgeyX/0djO4fPDMwfVItP5XZOxCe7m2kLQyLeAAAVOJiizhtTZUbJjptCflzy7CsTn3jOtBWf0nc3UYg11s0iIhgWGZm2IpMNdmK3y9fYt5njyMVzW+PUCvPM565kuApB6PyXb0Db5THH8zhoEBFu3rwJxhheeOGFPfG7RdvzaZ6Gzc1NzM7OYnR0FO3tTuxh9zdf+9rX8Au/8Av41Kc+hZGRkWZPR6PRaFoeLT5pNBpLnIhPRIS5uTnkcjlcvXp1RzvmtPBUCxHh1q1bKBQKGB8f17sRNTuGMYauri50dXXh+PHjyOVyiEajuH79OjjnlYCD3+9/rvdfeSfluXPn0N29S0FeDQCAjZxRN8qmSvZciqwlpx8JFh5Qik8gghE5BLFwW92f1w0SBDNdBM8WYaZNmJkCimkBypfsxwo5Ey5JJoPgQmq5J/XVI7K/eDv3QJvXyUqocnkR+Y3/LJmbZrdhjCEYDCIYDOLkyZPIZDKIxWK2ayF51esSE2o7OiapSVZpw+U12gCAmRkQ84A8Hdj4whKKc3etGyqs9Cr4HG4+8DgLRpv5HGRPLKrMJzIFIBGfRIFb2ldWzleJzZzAJIk8Uku+gv17yrPW4lNxM4+Nayu2c7L+vn2y72ArXZMXBby+xvmko9uZc9VDCyK4qz4zrv5D6HnP+59oHgcJIQRu3LgBn8+HkydP7snfLdqeT+OEjY0N3Lx5E5cvX9bCE4CvfvWr+MVf/EUtPGk0Gs0ToMUnjUZjiUp8Mk0Tk5OTCAaDuHz58o5+lOj6TrWYponp6WkEg0GcPn36wL8fmmdDW1sbhoaGMDQ0VLFhuXfvHtLpdE1tlGdpfRmNRnHnzh1t4fGMYL0RINADJOLydj1hpfiExIazQb3OAtms3ZnFXPFxFOn7j+R9Pb0DV+l7x+WCnWJkZ61HAGB3a9gMWC+AEYCun3k3XEEtujaTjo4ODA8PY3h4uGEt7OnpQX+vH8oqlg5EI3C1xWV1VpMMs20QG3/9ZYjYum0bymYc9eW0BptTMSsfX4dsNVfWfBLy+VBRAJI6cCQMMEMyV0X/0hpzkvdAFBufmYkTYl9ZAgm5IFaPbSk5iz5KmZaNcxa88TpziQIKqSIMo/Rnr9nXVDUogSH067+tnz9tEEJgenoagUBgX9V50fZ8mnrKwtPo6GjTXRJaga985St417vehU9/+tMYHh5u9nQ0Go1mz6DFJ41GY4nsB2cmk8HExASOHz+OgYGBHY2jhadayvZjIyMjO35vNRqn1NuwrK+v4/Hjx5ibm0NXVxcikQj6+vp2NeDw4MEDrKysaEvJZwwbPgOa+bq8kYNaNLS2DHh9QEERZM87DHgXC46a8VW5KCa4tW1VGSIHWRA2p8tqOsEmmGw3nuAEl6d2INelb0PX6/+V7dw0z5/6tTAej2MlGkUYLrhhvyGHFdWiESuqbSZRTIOYAUbWIgcxF/L5CFJfmpcKTwBAG/LjFfIO6r4BIIft2mRrANuydpONoxqAC0CSW0XMAJMZjirENneHLC1KspZYdLsxtYpiPGcrVNuuL3bDWPVDdjXtGie0+TCN6v0k5QyykoC13Ufbd74WvlOnbSZxsOGcY2pqqlKfcr+i7fk08Xgcc3NzWnja4stf/jLe/e53a+FJo9FongItPmk0LUgrCzDr6+uYnZ3FhQsXEAwGd9QX57xis/cssyv2ChsbG5idncULL7yw4/dWo3laDMNAKBRCKBQCEWFzcxPRaBR3795FW1tbpTbK0wpGRIT5+XnkcjmMjY3pHbTPGOPoGXCV+JRJqDsSAsbACMSDO9JmFH1UCtAqArzCYa0pkZOLVNwUMCQ2WSSr2YQtscgm0kvC/lzb88gmO6Eu20K0dSHym79jOy9N8zEMA319fejr64NxrQ/I2guhjGdBhgdMFO07LGZAhgtMSEQsEIQvAJZrzDTkvjASX1pA8eYEjNMXlfOnRLxkqacQjYTDrEZKxJ2ZwWXToGAQLJNsOGT43MoMIOUzsCoBiwx5uqOkf5434ZKIY0xi71nfbfZxGsnZrXp1T/pYb5VtSWT9rNzWDqDxc8fq2mbW8zBzvJLtVD0EAXCV2/va0fu+f/eEEz4YlF0fBgYGMDg42OzpPDe0Pd/BY319Hbdu3cLY2JgWGLEtPH3mM5/B0NBQs6ej0Wg0ew4tPmk0Gsc8ePAAS0tLuHLlyo52QOn6To0sLy9jcXFRF3LVtBSMMXR3d6O7uxunTp1COp1GNBrF5OQkGGMIhUKIRCKOLfM455iZmUFHRwcuXryo7/3ngKO6T9FHgNsNmPKaNKzTr+4rmwELDYCiy/J2yQ2wnj5QfM22ieCiQbSph0hSrwmAEASXRJySnSsr9+S4CNYWRmcnYJZEAEEMof/4BzC08LpnIF+3VHwCAO7uhLtgL+QwEIQ3AJaT22DC3Qlgux8y3Mgl+5D8i/+xlfXjfJMS6wmBHj+UtqF4DHDJRTFgK5PK7Qbj6tpVRrAXZCU+qSz3AMAtvzZlZpTKRlCyHvCMXHwy2u03XRhVmY08z7H21aXKZO3+XnZZl3bLkpX4JLJ5oKPxdZdv+zUiwsZSurJs1VvuledHBPT80q/qtcmCYrGIiYkJDA0NHXhnAm3Pt79ZW1vD7du3tfC0xZe+9CX80i/9khaeNBqNZgdo8Umj0SgRQmBubg75fB5Xr17d0Y8JLTzVQkS4e/cukskkrly5ArdbL8ua1qUccDh69Cjy+Tyi0Sjm5uZQKBQqO18DgYDlfV0oFDA5OYlDhw7hyJEjTZj9wYQNny5FOG1svAAA3AQLD4GW78s745Ksjuoxu3vV4hNKNamk4pOprjGjzKKQHCNB0qwpZqNrlez4bM5xN9aPIgCskAW25trxf7wFXm1ptacgn7ouV164lT+syN0BQCE+ubaDfdzXj82/vwXz7rXafhxa4DF/QC3WCAEjFAGtrcjbkYDR60BYBoA265puTsQnwyN/xlQ+NgpT5soHJrHptKrbVIaI4G6z79ho27629ZceQWS3RTrLQhaPEAAAIABJREFUunFEMCzUJyKCy7AYx+4PyU0AtaKY4FQjPqVjOfCCgKfq7a+23GNbgp/79Avo/Gf/wmagg0uhUMDExASOHj2KSCTS7Om0FNqeb39R/tuNjY1pS2wAX/ziF/HLv/zL+MxnPqN/u2g0Gs0O0FFOjaYFaSVBplAoYHp6GsFgEGfPnt3R3HR9p1o457h+/Tra2tpw+fLlA/9+aPYWPp8PR44cwZEjR2CaJtbW1vDgwQMkk0l0d3cjEomgp6cHhmEgnU5jenoaJ0+eRCgUavbUDxSsvQOs/wjo8aK8XVcApIgp07o886PSl8MNCswjD2yIolx84qaoBFCtIJu6TJX+ub34JDuXBCzPK1nxCdQrVsIkuL1bgeDhU+j+kf/Tdk6a1sSJ+NTmDwAbMWkb5lYHYMkwQC4vsmtdSH3m7y0zeSjpzCqPeST1i6rbdQXV4hMA+AOAE/HJSjyBWnwSBQ4mzVRERSixh8OuyBIJklrnEbeX6njWtK0HRYLgbi8dS93dQHaxMevLCsvnvifMknJ1tqHei5AXeUUoE4Kw+SiL6uXMynKPmIHQb/0nR/M+SORyOUxOTuLkyZPo6+tr9nRaGm3Pt7eJRqO4d++eFp620MKTRqPR7B5afNJoNFJefvllHD9+fMcWE1p4qiWfz2NqakpngWj2BW63G/39/ejv76/sfF1dXcWtW7fg9XqRTqdx8eJF9PT0NHuqBxI2ckYtPgl1lhES62CBnlItGQlOA+OUSdkfYwxCyL8nhEkwJIFowUkuTsnmJqn3tJXL1Piqrw0Gb6xRJbasA4XLi4H//F8ko2paFfI5WLtsPdS2yRYKsM4J2obnGDb++yPwh/YiT9kqD1xulVe26VPidZiV4HHWjmwsPKuzgyzPK3Awn714TURgqswomXhlCsD7dNn7MvGJZ014Al6YqQLiL9f93ezEJJtxbGdvlT0lCC6YqBfbqhNdUytZ8KKAe+uy7Sz3/D/4Y3B399qNfiDJZrOYnJzE2bNn0d2tFqA1tWh7vr1DtfDkcbhpYT/zxS9+Ee9973vx2c9+9kDVd9NoNJpnhRafNBqNJWtra0in0xgfH9/xTj/OOcRWYNOyWPIBI5lM4vr16zh9+nRld6BGs1+o3vm6vLyMe/fuob+/H7du3YLH40E4HEY4HN5R3TjNk8GOngG+8d+lbWjT3v6upq9Qv1p8ii4Dbg9gym36KPqoZEVXX9epvROZQjtcXFGrRjVXrw8wG8WgMlLHPkkpKTtRyq4+leFipVoqv/pbMDpU0oOmFSFv0EEjtdBj2H12YMB0DSDz1Xnw7JRUeAIACAHW2w+KPpZPKZtWzmlrAs6aKepCVdplkpa3jyrziUwByMSnorB9D8vHZdmOxElqxSnLipJlYoq8CSIPYl9dAhVr30zm82xZ49UPZjMTjwf1mUyAtahmFgU8lu/pluBtCmw+zoIxoPz4Xfp3neVeTxjdP/Z22+s7iKTTaUxNTeH8+fMIBALNns6eR9vztS6rq6tYWFjQwtMW//AP/4D3ve99+MxnPqOFJ41Go9kltPik0WgaePDgAZaWltDT04P29van7kfXd2okGo3izp07uHjxIjo7dRBSsz8hIty/fx/xeByvfOUrK7XMstksotEoZmZmIISoCFGdnZ16fXiGsJEz6kbxVaCjC8jI7aKYk8AQN8EGRkBL9+Xt8jmwyCDocZXIFDqEjfsJYO0eXAH7IAgR2Tl7VTWSBIu53JLPLkIts+MzzAJQFyAWguByMXj+xRvR/spvV0xY07I4sN1jQl0TzVeXqSdgYD3RCfGNeRjLE6V+HGafGF1BcJX4tLnuqC84rCGVWV9Hh4N2FF97KvGpQYiu77coz1wik4N5JWNYWBhWY0iEL9m5whRIzMRQiGYtDjrMPtuCuVwNGWtEZLl5y07wdvk7AZhIPM6CONVlOlX3y2AYBkK/+dtPNMf9TjKZxMzMDC5evAi/39/s6ew7tD1f67CysoLFxUWMjo5q4QnAF77wBfzKr/wKPvvZz+Lw4cPNno5Go9HsG7T4pNG0IM160BZCYG5uDoVCAVevXq0EiJ8GLTzVQkRYXFxELBbDlStX9AO+Zt8ihMDNmzcBAKOjozUBs/b2dgwPD2N4eBiFQqGy6zWbzaK3txeRSATBYPDArxe7DTt8tGSrVchL2xnhQxALiloluYyzMf0BR8kULNCzLT4NnUbsK9fBUyl09srrDXBFhoPgBAZr6y9gS0Syq/fEDDBmPftSXafG8wQnuLyNwWHBCSxyCH3vep/tXDStD/mCIBhbNb2sYaaDe2OrDbl8KOR7kf67fwRWYzWmaWIjDnJ7wBSZg/Cps0cpsQH42oG8hShSPeamPJuxMmTRmUiFbBoUDIBVW2sygP3/7L17dFxZfef73afeUqlUpVId2bJlybb8tqxHu3klQychDwJtQsIQSCbkzrph1p25N1kT7pqQTCaXxxAIpAMJpGkCgUwCIYSGbje0HehuAoQ0oROGtiXZluSXbNmSrKrSu97nnP27f5SqLFl19ik/JZV+n7VYq6nzq31+VVbts8/+nt/353OofHIa15IAVOITQaimDocT2NnqAVD44QFmxsDcYGJVCBFBsxHBK7k0EhFgFipeA2/nuqjJAixoWIwX/71KUyXRzQqoUg6+H/tJ+PYdqHrsWmd+fh5DQ0Po7u5GXV01Uitzt9xqzzc9Pc32fA+AGzdu4Pr16+jt7S0/JLaZ+c53voP3vOc9OHnyJLZu3brW6TAMw9QUfJVhGAYAYBgG+vv7EYlEsH//fggh4HK5YDn1E6gA93daSWkznojQ29vL1oNMzWKaJgYHBxEOh9HR0aH87Xu9XrS2tqK1tRWWZWFmZgbj4+MYGhpCKBSCruu82XCPEJoLoq0TdOmsOtDnXOlKiYnijqWDxZio0p6r9Cdi7TiE5PP/Uq4SqCTkrMhDUYEELIlBqt4vKl89rx8wq7QrK51P2myJ+wKIffwvN/11cMMjXIC3ASjM28cYKRSlSYXCQUAu24L08/8Kml+ofCoQZGMTXNNTDklV55UnmppBk9fUI81NAy6X4+9WLMyCPFUIYwC0xuiKvm7C51ZXGwKOxwkO84JDZZNKQLKyJlyKyizNU/ncsmBhfiCBinq1TTpEBFel0k2yt2asmFOFnneWIeGpc2N6dLHYEypYD5EvzmerLPeC9Yi+94NVn6/WmZmZwfnz59Hd3X1Xzg/MnePxeLBlyxZs2bKF7fnuI5OTkxgfH0dPTw8LTwC+/e1v473vfS9OnDjBwhPDMMx9gK80DMMgnU6jv78fu3btwpYtW8qv34n4xMLTSgzDwMDAAKLRKNrb2zf998HULrlcDgMDA9ixY8eKeaQaXC5X2YKPiDA3N1e2qAwEAuVjXDF454j2/c7ik6GujAIA5LMQsS2g+IQyjOYSVeVFqQXkmnZj/psvll9ze7W7nyvJXlwichCustmKW9zK91WsYgDC//Xd8MR053yZdQ/5whAK8UmQBfLUA0ZqxevSG4Wx4Eb+zCjMoX5QoBG0UFl4KuENN8FyEp+qtMrT6hvguJKTElqsBZRU2/iBCFqkudjXzQn/yqoRR8s9YJVt5erjbqhFN4f3K373ppP45F99jIiQfPE6rEWjciXlbU5jdp/Mbjp0e1YLWJYhgayJVDJffF8mXVbGV1juCYHof/sf0Pi6CgBlgaO3t5eFjXUC2/PdHyYmJjA5OYne3l5+wAvAP/7jP+J973sfC08MwzD3ERafGGaTMz09jeHhYXR1da1qqHu74pNlWWWbPq7uKd4kDQ4OYteuXdB13nxkapdSf4T9+/cjEonc1VhCCEQiEUQiERAR0uk0EokETp06tUKk4qeSbw+tfZ/CMGyJaYeN5yVEOOosPs0kgPoGIK2w8dveidlz12Ddshx1+9TXD6mwzANK/VEUxyXZblQREYSUlXd7qXJlBhHBXSEf78/+Ahp+/lHbPJiNBfnCwOJVdYw3CBgZSE8MRtJE/tQwrGv9K2JEuAm0oLa5Ex617SQAFKbjCgO6ZVQpLohgo7P4BADBEFCN+ORa+buuRnzSFP2cAEBoGqCQ0pz2n0UFsaYEGfbjkqSKlnxzp+PITqRsRWm7eea2t8krjC8tgiew+vNISVgcz0CIlTZ7RFjRJ897qAv1P/P6282kJpmamsLVq1fR29sLr9f5t8esDWzPd/eMj49jamoKPT09/D0B+Na3voX3v//9OHny5G0/OMcwDMNUD4tPDLMOeVBPb42NjWFychJHjx6t+JRfteIT93dazczMDEZGRnD48GE0NDSsdToMc99IJpO4ePEijhw5gvr6+ns6thACwWAQwWAQO3fuRC6XQyKRwNDQEAzDQHNzM3RdRzAY5HnHAdGxzzkoswjRpINm4uqxqvyqtdhWyEriU7AROU8UC8//GwDA190DY+ymLZjb57Ah4g1AKHpPSctJfCq6qFU8ZqmEqcqbxtIC3L5bjuzcj+bf+X3bHJiNB/nClV93BSBFA6y8BnPCQv7FUdDMKdtxRKCKPjIOtpYA4E4vQrpcEE7rtGofIqp2070KYQwAyFjZc61S5dByZMGqwpbPwVZPJUo7iNZS2o9tZgx4gis/d/rqPBbPTS/lZTOu3ensRCk7EavC65Yh4a1b/Z1ahkR2rlA+TelUtNzSz+NF7A8fs0luczExMYGJiQn09fWx/dgGgu35bp/r168jHo+ju7ubhScAL7zwAj7wgQ+w8MQwDPMA4BUWw6xThBDO3vV3SKkHkWEYOHr0qO0CtBrxiYWn1YyPj5dvZPmmh6llrl+/jsnJSfT19T2Qp4X9fj/a2trQ1tZWfup1dHQU6XQakUgEuq4jHA5z5WUFRCQGhJqAhRnHOCfxCSlF35vl+G+pThMC1LYfMz8YhDU/Wn65MDIMraEBcnERQgNcNr1VSkjTVFYOkEVAhV4oyyJgb8lXvbi2fLQV+dVHsPXxz97eIMy6R3ojkJ4oLNMDa96AFZ+FOXoNNHXz9yI6u0Azc+qBXM7zEynE1ZtBElrzVtCUQxVipsr+ZdUuOatcm1ImteJX5lT5RAULQiE8F+2c1edWVjZJAaH46lU/e5kzgWXiU2HRxPQP1N87gIp2nID9HGNXWVnpmkY2YlkqedM+dfmf2vIhAj//JrhjscpJbCKuXbuGRCLB9mMbHLbnc+batWtIJpMsPC2xXHhqaWlZ63QYhmFqHhafGGaTYRgG+vv7EYlEcODAAeUi3El84v5OKyEiXLhwAblcDn19fby4Z2oWIsKlS5eQTqfX7G/91qdeZ2dnMTU1hZGRETQ0NCAWiyEajfKTzMsQHftAAz9QB1Xxb0nJCcDjc+4Rlc/e/O/mLUjPA+lvfn/1eLkcfD0HkX35ZbgdbLcAgPJ5ZQWD6lLk1O9J/b7Kx7Q6P2AVKw2k5oH+2b/lPio1iDHrRuZ/fUsdVE2VkWE4htC8WiQuoTU0wnIQn4wq7fkol3UOup242eTtiU+mBFTiU8FSPlggDWvJlq8ylimgue3FK6EQvaVxsxLNyptI/OMoYKmFMCKCS1v9eezmoNJaenViNj3wKryWXTBQSJtly73SdE4ElC6F5K9D9Hd+T5n7ZuDKlSuYm5tDT08PP7BSY7A930rGxsYwMzOD7u5u/lsH8Pzzz+ODH/wgC08MwzAPEN6RYZhNRDqdRn9/P3bv3l3VYsvlciGfr7y5yMLTSkzTxJkzZxAMBtHV1bXpvw+mdpFS4uzZs/D5fDhy5Mi6+FvXNA3RaBTRaBREhIWFBSQSCYyOjsLn85X7RG32SkRtx15YTuJTRtGjqYSU0FraIK+PKsNo+gbg9sDa0onp7/ybctPdnBgHNOHc78mU0Bz6PanEJZUlH0nVMVQ8LwkBzcgDmoAkoOkjj8MTbVZ+BmZjIvRtzkG5KqqM0lVUDi7MAb7ASgG3ElVUnLqyaUivH1ohp4wzZpJV3RjS/Gx1PYuyaVBjCCKTAlBFzyeF7R0AkCEBxfxAhoRQHC/MLMLTGrQ97goovsul1IgIyX++Dit9cy6zvQYqPk7FDWCyed0XALB6La5VqO6cXer1BKxsE1Ws6Cy+EH7X766L6/ZaQUS4fPkyMpkMjhw5wpvxNc5mt+e7evUq5ubm+G99ieeeew4f+tCHcPLkSe7HzDAM8wBh8YlhNgnT09MYHh5GV1cXQqFQVe+xq3ySUpZf54UskMvlMDAwgLa2NmzdunWt02GY+0ahUMDAwABaWlrQ1ta21ulURAiBxsZGNDY2orOzE5lMBvF4HAMDAwBQFqLudX+qjUBVfZ+Sk8VH5E1TGSaCDtcRTQM1bcNiPo3s86urnW7Fik/Bt/8Q3MmL6jiLlOKTNNX9noqWYZWPS0k3e6KsfmPF91kFCY9fAxHQ8F9+B4HuHvtzMxsa0dQCuD2AaS+i0ty04zg0mwSE5tjXSWtqhpy8powRVVrguWMtkONX1edbnAN53BCW+rdP8zOAxwOh+B7KY4aaQJkUIADN51D55DAWWQ59sBwqkVQnICK4VeLY0pwz+6Mp5KeqsEQE1D5+lXKwed1cTMHdsLqS8lahPj1bgJmzyvZ6y4uuSq+JrdsReuOjt5dYDUFEOH/+PCzLwuHDhze1CLcZ2Wz2fKOjo1hcXERXVxffrwP45je/iQ9/+MMsPDEMw6wBLD4xzDrlXvZ8Ghsbw+TkJI4ePXpbT3bdKj5xf6fVzM/P49y5czhw4ADC4crNyBmmFshkMhgYGMDu3bsR20C9Iurq6tDR0YGOjg4UCgUkEgmcP38e+Xwe0WgUuq4jFAptivlM7NjrvOltGhD6dtCEeqMals3Gs9cP2bITC4OXYAx+H97u3qrz04wsNKd+OArxCFjqg6ISn5SefPZD29l50dKWsfenjyH0S//efmxmwyM0DaJ5K+jGmH1QNgXUNwBpRQWhaUCEm4oilOp89Q2OOVVrgSfqnMV2QQQ0NgFOPd+IoEWaQYlJ5xPXFSuNhM/taHfpaIfpoD05rZlVorWZMeGpt7fK1DwupEbnkBpZaYeoqrS0u6bc7pWmUt6WKeFbli8Rrah6IrrZ76lkuUcA9A/+8W2evXYgIgwNDcHlcjnajjObg1q257t8+TJSqRQOHz7MwhOAb3zjG/jIRz6CkydPbqh7GIZhmFqBxSeGqWGklBgeHoZpmjh69OhtL56Xi08sPK1mamoKV65cQU9PDwKBwFqnwzD3jbm5OQwNDeHQoUNVV06uR7xeL7Zt24Zt27bBsixMT0/j2rVrWFxcRDgcLm801OqNuvDXQWxpA02qhSURbHSuQphNrHwh2Agz1IqFfx2AderF8svGyBC0YBAylXLMT05cBUL21lfV9GtSXZqkwlYPUGwKu+yrQVxuDdi5H82/+wfKvJjaQOitavEJgBaOQqrEJwCiMeIsPlXRN4wWZh1jABQrtqqg4A2gqkeUgiGgGvFJFNedjpZ7gLKP271AU/STsrJq8cnKm5h5qUJvLSHs5yS7j2MzSdnNTa4KVoLSXDlDp6cLMHISpT+ZFZZ7KAph7odeDd/eKqpfaxApJc6dOwe/34/du3fzPQyzilqy57t06RKy2SwLT0v8wz/8Ax577DEWnhiGYdYQFp8YpkYxDAOnT59GNBrFzp077+hGqyQ+cX+nlRARRkdHMT8/j4ceeghuN0+lTO1SqyKry+WCruvQdb280ZBIJHDhwgXU19cjFouhubkZnio2gDcSon2fs/gkHUoMAGB+GgiFAY8fBdGA+ZdeBvKXV4VRLgdvz0PIvfwjxyFdXvUDEtJ06OckSVk5JS2Cy2aDW1qKsb0+ILtafJIWQYSb0PL4Z5V5M7WD0Lc7B1VTZeSvYi6t4ndYtMDzAkZBHVjBQrkSgVAj5I0qAj3VbcDSkmhblfjk8PtXVjTCuXLKXWc/l0vD/rs2MwZmX5qoWHklvD7AWv3dE5GtmGa7hK6Qv2VKeH2r815e5UWSMDOeRen5slKlUwmXBpDmgv7BP7I5cW0jpcTg4CAaGxvR0dGx1ukwG4CNas9HRLh06RJyuRwOHTq0rnJbK06ePIk/+ZM/wcmTJ9HczP04GYZh1greMWWYGiSdTqO/vx+7d+9GS0vLHY9TEp9YeLpJ6elJt9uN7u5ufqKMqVmICGNjY0gmk+jr66s5EWY5yzcaiAipVArxeBynTp2C2+0u94ny+/1rnepdI9r3AS89r4yheYe+NXVByKZtyBc8WHjhu0tWePZYE9eLG6tSHef2qudTaRE0t8Jyz7Tf8C0GKA5Je/HJWEzDU2HFLP112Pq5L0Kr4d8GsxKhb3OOqeaBlCrWUpRxrhYEEUSzDrpx/e7HWhqvGizDQDWrH0qnIABofvV3IvOmo3ik+u0DuOkzVwErb8GlqHwim7nJyluI/+NVWBmbPlhud0XxCbDriSoAsfpcRFQx3jIr57V8Lb6YyMMsSHi95TOUezxJCXi9AnVvewdcQWcbx1rDsiz09/cjFout2z6VzPpnI9jzEREuXryIQqHAwtMSJ0+exEc/+lEWnhiGYdYBLD4xzDrlTheNyWQSIyMjOHLkCBoa7u5GU9M0zM/PY2JiArqu1/TmczUUCgX09/djy5YtfBPL1DRSynJT7t7e3k0lsgoh0NDQgIaGBuzevRvZbBaJRAJnz56FZVlobm6Gruuor6/fkDf3oqMK26XZeLFXy/IN66YWWIEm5CamkfnhCGBdgadzT1Wb1VZ8Cr79h5A/d9Y2RvNozj1fHCBFP6ji5q7y3RXfW+ydsvoYef3Q//RT8ER5Q2MzUY34BNNGqFiOkXcMofkZxxgA0IINcKprkrMOgnLpnFX2kCosLqAaKZ7mpotiiEPlExUsCIVARZIgPArxiAjCY/8DNzOGUnwSFd4qDQvx71xFYTZnX1FpY8dpJ3STywWXqFBCRZXX/XbTq7ZUJSYtwsxEdsXcdutnofoQmv7v36w8UA1jmib6+/uxdetWtLa2rnU6TI2wHu35iAgXLlyAaZo4ePDghlyb3mtOnDiBj33sYzhx4gQLTwzDMOsAFp8Ypoa4evUqbty4gaNHj97VorfU38ntdqOnpweJRAKnTp2Cy+VCLBaDrus1UQFwO6RSKZw5cwZ79uxBNBpd63QY5r5hmmbZouZOLTtriUAggB07dmDHjh0wDKO8yZDJZNDU1ARd1xEOhzfM9yS2tgNeP1DIKeM0fRtkvgBDepE5P4bCwGrhyLh4Ae4d7TDH1DZ+AACp3pB3qnoiSaX2MbaoxCtS2OqVKnsrIS2C65aKC3J7Ef3oE/DvP6hOiKk5tGrEp2wVVUap+SpiFlaLwBUQ3irWe+lFUF09RCatDKu2h5SvUJ1IhUwKFGpwFp9MtcUgFSylpSYZEkJluZlXy3PaLcKVNCXi3xmDMa2eJ1HIV7YDtJmKyDCACvaCdhJ+pTmLiOBeGmMhnoM0qWyzRwS4lw3vcgHR33/Phrk+3StK1uM7duy4KwcIhlFhZ883ODgIKeUDsecjIpw/fx5EhAMHDmy633olnn32Wfzpn/4pTp48yffsDMMw6wQWnximBpBSYmhoCJZl4eGHH76rKoWS8CSlhBACwWAQwWAQO3fuRC6XQzweL1cAlISo+nrn/gYbmWQyiYsXL+Lw4cMIBoNrnQ7D3Dfy+Tz6+/vR1taGrVu3rnU66w6Px4OtW7di69atsCwLMzMzmJycxPDwMEKhEGKxGKLR6JparzghNBdEWyfo0pmbL9Y1gEJRSJcfZtaEMT0L64aB3L/90HE8d1NTVeKTcX4E7m3bYY5XtgdzOYhPlinV/Z5IQFP2g7J35SKpEJ8kwbVsJ1kKF6J/8kn4Dx5W5svUJiLcDPj8QN5elKC5hOM4NJss/kFaatFFi0QhnSzzqLp+Tnl/PfxO4tPsDOB1QzhUbxV7TXkgTMPxvFpjFFrAQVxysOQkU/EDXjquFJ8s9fiuZeIYSULie9dQSKgFNiJ7QdtuPhE2pUx28a4K1VyWQfD4BSxTYvZGHsLjhibMpXFWWu7593Si/pGfVH6OWiOfz+P06dPYtWsXYrHYWqfDbCIetD0fEWFkZARCCOzbt4+FJwBf//rX8fGPf5yFJ4ZhmHUGi08Ms06pdgFZsoKLRqN3XaVARMr+Tn6/v1wBUCgUkEwmceHCBeRyOUSjUei6jlAoVFOL37GxMcTjcfT19cFbMtRnmBoklUphcHAQ+/btKz/FydhTqgSNxWIgIszPzyORSODy5cvw+/3QdR3Nzc3rct6gvQ+hMJ+HMTOP/PVJWNOXAVxeESP8AYj6elBavVltjAxBC4UgFxYcz+vWY5XFJ1F5k3U5UhJcKls+XwDI2ucqbGz1AIBkZdstYGXlgYSG6EefQKCrW5krU9uI2DbQ9Uv2AfkcRCgMWpizj5EWRCQGSk6pz1XvbJ9M2eqqkAKRZtBM3GEwCa1pCyg+6RBH0JpioPiE43kXTYmQ7+5uOclBPILTcVUrOIvgrivaShMRkv97GvnJm3OJ7ZrWxioPAOwaYtmLVRWGJ6pYEWotVYnNTeaKoptG5Tv65eMIjxv6h/+kciI1Si6Xw+nTp7F3715exzBryv225yMiDA8Pw+VyYc+ePTV1732nfO1rX8MnPvEJnDx5kn//DMMw6wwWnxhmA5NKpTAwMIDOzk7oun5XYzkJT7fi9XrR2tqK1tZWWJaFZDKJsbExpFIpRCKRshXVRu0VU+p5Y5om+vr6NuznYJhqmJ6exoULF9DV1cXVfXeAEALhcBjhcBh79uxBOp1GPB5Hf38/hBBlkaqurm6tUwUAaEdeg/nHPqrs10S5LLxdvci//CPlWJTPw7v/EHKn1HEAYIwMQwsGIVMrKzlcHs3xmuO0r0LZrO3+MhFBuG5/Y4Yg4FpaKUtoiP7x4wh09972OExtIfRWtfgEQDQ2qcUnACLU6Cw+VfGEfNW9oTwex95QACAN5GoUAAAgAElEQVTqQiA4iE8AUIUwBgANTSEIzUEgc+r35lAZZetbt4SqKsrMGPA0FB8SSHx/ArmrNy0RVdVNZHNSlY2n7URWYX6yTILbXyFvAsyCxMJUsfqu2JOu+BW43aKYGQF1P/lT8LTtqHy+GiSTyWBgYAD79+9HOBxe63QYpsy9tucjIgwNDcHj8aCzs5OFJwDPPPMMHn/8cRaeGIZh1iksPjHMBiWZTGJkZARHjhxBQ0N1GwB2EBHMJYuVOxFZXC4XWlpa0NLSAiklZmdnMTU1hZGRkQ1jRbUcwzAwODiISCTCNgZMzTMxMYHx8XH09vY+0AbJtUx9fT127tyJnTt3Ip/PI5FIYHh4GIZhlKtE72cPACfc29rge8VrkP/X7yvj5NRE0cNJqi2zrPFrVcVRLgtvdx9yp15emY9PfW2QFqn7vRBBkLTd2JUW2VryFTeKK49rmRJut4CEQPQjn0DgoaPKPJnNgdC3OwfVOdsRC1/AeRzLWS6ihVlHK8DiWGorvTLV9JACAE91cZrL+TMIt8Pa00l8dlCf3H77OcbKmfA0eJF8aXKF8LQ0sOO5K1FpLU1EcFX4nEQEl7Y6PzurQOESmJ3Ilp8dKJ+KAG3pexD1Qeh/8L7bT3yDUqrcPnToEEKh0FqnwzBK7saej4hw7tw5+Hw+7N69m+9RARw/fhxPPPEETp48iUgkstbpMAzDMBVg8YlhNhhEhLGxMdy4cQNHjx69q83iW/s73YsFrKZpiEajiEajZSuqeDyOy5cvIxAIlK2oPB7PXZ/rfpDJZDA4OIiOjg5uUszUNESEy5cvY3FxEX19fRtGHN5o+Hw+bN++Hdu3b4dpmis2GSKRCGKxGCKRyAOvrgz+0tscxScrPgXv/gMonDurjksm4DtwCPmzZ5RxAGDdmChWOSyrZHDq9yRNqaxckqa9uAQs9ZOxE59cHghZuW8NSYKEhqYPfRyBo69Q5shsHjR9m2MFkbhHv2dy6vdUOl9TDDR57Z6MpaqIXBmnFptLaDILQH190bwO1x+HyihVPzgAZVu9SkhDYvpHU8hcml11jIggbNQnlR2f3euV3lPt1718nMVkfimHmy8v/wpCv/Jr0Pz+2xx4Y7KwsICzZ89y5TazIVHZ8/l8vnLlvM/nAxHh7NmzCAQC2L1791qnvi54+umn8alPfQonTpxg4YlhGGYdw+ITw6xTKt2gSikxNDQEKSUefvjhu9qsvB/C060st6IiorIV1csvvwyPx4NYLAZd19dNtcXs7CyGh4f5yUmm5pFS4ty5c/B4POju7uYnJx8Qbrd7VZVoIpHA+fPnEQwGoes6otEo3O77vzzzPfwquNvaYV67qowTqG6DGUZlAedWrKkb8O47iMLQueL4GipWAyxHEsGlKD9QiUuAQw9Ft8c2d+F1o+kPP4q6V75KmR+zuRD6Nucgo+AcU3Du1USzySoyArRgg6MgJqsci3KZ6uKq7DUlZBaAvSgg86ajeKQ5VUYpjptZE+6A/ZyauZ5C5kJ11oXLsbfWs3mDjcpkF17pOyFJReFpaajSMyNEQOmyQfUhNL3zP9mMWlvMzc1heHgY3d3d68bWlmHuFCd7PtM0EYlEsGvXrjXOdH3w1FNP4dOf/jQLTwzDMBsAFp8YZoNQKBTQ39+P5uZmdHR03NVm8YMQnm5FCIFgMIhgMIhdu3Yhm80iHo9jcHAQRFQWotbq5nFiYgLXr19Hb28v/JvkaVFmc2IYBgYGBhCLxbBjx+bpB7HeuLVKdHFxEfF4HFeuXIHH44Gu63fVjNoJIQTqf/GXMf+Jx5RxxoURuLZthzV+XR138TzcbTtgXhtzPveyigmXU8UDnKsaVLZYJAmaomqKMpmKtnsSGpre+xHUvfrHHPNjNhdViU+ZReeYRXVPKABANg0RDIFSC+qcPN4qckoX+zSl1bnR3OoKoIpxC7NVOdK53A52nAULwm9/S0qSlLZ8ROrjZqay+ESSkHxpEqkLM44CeMVz2swrt7umFnV1gLVarKxUEVrIWkjP3hTLlz+DVvrv5v/2u5vigZKZmRmcP38ePT09vG5napKSPV9bWxsGBgbg9/thmiZeeuklR3u+WuerX/0qPvOZz+DEiRPc441hGGYDwOITw2wAUqkUBgYG0NnZCV3X72osIoJlWeWGyGt1gxoIBNDe3o729nYUCgXE43GMjIygUCigubkZuq4jGAze9/yICJcuXUI6ncZDDz20KRfwzOYhm81iYGAAO3fuvOu5hLl3CCEQCoUQCoXQ2dmJTCaDRCJRFudLc2J9vXMfmduh7vWPYuFzT4DSaWWcu0V3FJ8AwN0UrUp8Ms4Pw71tG8zxcbgdLPcsUzpa6qmOF/tF2fSCklSxYIoCQUQ//HEEDncpc2M2JyLY6Cji0FyyWNansKajuZliuYqp7sUkIlFH8UlW2c9JizRDOolP8zOA1wNhqqsZaX4G8DjHaQG1zTKZzuKUsuebISEUx6386powq2Ah/k/XYMQzynWmrfBtY6EHALBJxc6KURYKuLXlE0mCy7c6fm4qf3O8CpZ7Qm9F6Od/vnICNUQikcDo6Cj6+vrg9VYhvDLMBkVKicHBQYTDYXR0dJRfU9nz1Tpf+cpX8NnPfpaFJ4ZhmA0Ei08Ms04p3dSWLJmOHDmChoaGuxqTiGAubXI86P4iKrxe74qeKMlkEqOjo0in02hqaoKu6wiHw/dciLIsC2fOnEFdXR2OHDmyKZ4UZTYv8/PzOHfuHA4ePIjGxsa1TodRUFdXt0KcTyaTuHDhAnK5HKLRKGKxGBobG+96ztICdah7/TGkn/p7ZZx5YQSivt5RpDLOD0FrCEEuqjfKAcCjt8AcH3esfCLLvsIAWOr3pKpsUvVT8dcD2Vs+k74NLU98Du5IkzIvZnMjYttA6WH7AKMAEW5S2+aRhGhqAcUn1ecKOIvO2ZlpVLPlKOqqELCJoEVioMSEc1xTDBRXx2l16ttNkuqmR2RIQCU+WQShmEZuHd9IFRD/zhishULxQSyboUsPaVU8ZncuItv1daXzEAANErcqVqZJ8AdWvpZdNJFbNMui062We0RAy//8gE1mtcONGzdw7do19Pb2rtv+sQxzL5BSYmBgAE1NTSucCuzs+c6cOQPLssrrxIaGhpq7t33yySfxV3/1Vzhx4gTfyzAMw2wgWHximHUKEeHKlSuIx+N4+OGH7+rJvrWw2btT3G73isar09PTmJycxPDwcNliIBqN3rV4lsvlMDAwgO3bt6O1tfUeZc8w65N4PI7R0VH09PQgEAisdTrMbeD1etHa2orW1lZYloXp6WmMj49jaGjontiuBH/xl5F++stKlYZyWXi7epF/+UfKsSifh3f/IeROqeMAoHB+CK5gHTSXw8YzEYSq3xMRVL57qkuFtbgI9zK7Le1QH/Q//SQ0roBlHBD6NtAVhfgEQIQijj2bREOjs/hUxd+jL19dn6ZVJTZ252wIOYtPAFAfAqCIE4CmsNSrBrLUlVEmeeBV9aZbNj3kElkk/mkMtFQNRRL24rXbA9hWlNnMWzYVUURUsXpLSsDjWf06WavHn53Irax2usVyz7X3IOp6em3yrQ0mJiYwOTmJ3t7eB9IbkWHWCsuyMDAwgObmZrS1tSljS/Z87e3tMAwD09PTuHr1KlKpVE3Z85WEp2effZaFJ4ZhmA0Gr9oYZp2Sz+eRyWRw9OjRuxJaNpLwdCuappVtBIgIc3NziMfjuHjxIurr66HrOpqbm2/7BnRhYQFnz57F/v37uUEpU/OMjY0hkUigr6+PnxLe4LhcLui6Dl3XIaXE/Px8eU6sq6srz4m38+/s3tYG3yteg/y/fl8ZJ6cmijucUr0RbI1fqyqOsln4d7cDC3H7GEVflRKqa5q0pK1tVrFCoXiMAAR+8VfR9Ju/rTwXw5TQ9G0quQMAIKroQyO8VdQrOdjyAYBIL4ACdUDWQYSy1BZ5ZdxVPvDk0GtK87mde7Y5HVfr0yjMpODdat8vVFsSmNNTBUx/5wqwTNhRitcer6345PiZbkVWnquk5ikeXHWClf83PWegkLXKgtOtwhNBYOsf/fHt5bTBuHbtGpLJJHp6ejb8JjrDqLAsC/39/dB1Hdu3b7+t93o8nhUPcdaKPd+Xv/xl/PVf/zULTwzDMBsUFp8YZp3i9/tx8ODBuxpjIwtPtyKEQCQSQSQSARFhcXER8XgcV69ehdfrha7riMVijhVi8Xgcly9fRnd3N+rq7DcrGGajQ0Q4f/48DMNAb2/vurLaZO4eTdNWzInpdBrxeBynTp2Cy+UqbzBUU+kW/KW3OYpPVnwK3v0HUDh3Vh2XTMB74BAKZ884f4iFGeVhadrbXgHqfk5AUf+y26OUmgeaZkBCQ/i/fwDBn/4Z53wZZgmhb6siqJo1l4OyAoAcejSV0JpikONXHcZKVTWW2q9yeZxagnOy3AMA4b67a5Nw+A7dfjdmBpNYHIxD3BKqemex6tLmnHb/trb/5JXPZGXzQIWHBZaL7kSEGZuqJyLA7QJ8r3ktPNtqt4p/dHQUCwsL6O7u5rUMU9OUhKeWlhZs21bFdUZBrdjzfelLX8LnP/95PPvsswiFQmudDsMwDHMHsPjEMOuUu10EEhEsyyp71q/nReXtIoRAKBRCKBRCZ2cnMpkM4vE4+vv7IYRALBaDrusrNl2JCFevXsXMzAweeughrgBhappSP7NgMIi9e/fW1O+fWY0QAsFgEMFgELt27UIul0MikcC5c+fKGwy6riMYDFb8W/A9/Cq4d3TAHLuiPo9jrcdSnFFddYVGhnKDXlqyXLVgd9yl6AUjPF5AVs5F5g0gUAf98c/Bu3t3VfkyTImqxCcj7xyTyzqGOFn3lRD1QccYWeVY5FRBVY5T568FnG81NYe+b07Vj1oF27oSJAmzZxLIX5mvqAspR85X/vezs9ADFBaJNvOc3fTnXvaZUjMGzLxcIaQv11+E24UtH/jDygNtcIgIly5dQi6XQ1dXFwtPTE1jWRZOnz6NrVu33hdL+I1oz/d3f/d3+MIXvoATJ07cde9rhmEYZu1g8YlhahAigrlk07IZbtTq6urQ0dGBjo4O5PN5xONxDA0NwTRNNDc3o7m5GWNjY3C5XOjp6dkU3wmzecnn8xgYGEBra+tdPzXJbEz8fj/a2trQ1tYGwzCQTCYxOjqKdDqNpqYmxGIxhMPh8lwohED9m9+K+U88phzXuDAC17btsMavq+Munoe7rQ3mtWu2MZpHu3tRVFG2UKxasLcrc7XvxpYnPgtXff3d5cBsSqoRnyi14ByzOOt8sny22D9qQR0r3FU8VJPNAMFGIDWvzmu+irwA0MKsUsDRAuqcZN50tLBzqoxy+StvlOZmckh8fxy0YC8Cqs4tyKqoDimLwtxaxWowu/O4KghnkgTcvmK8lPZVTwDg0oDgm/99Tc5jpeptKSUOHTrED9EwNY1pmujv70drayu2bt163893qz3f/Pw8EonEurLn++IXv4gvfvGLLDwxDMPUAIKqtVUoclvBDMPcHYVCAbfzG60lm717gWEYuHHjBi5dugRN07Blyxbouo7GxsZN/90wtUk6ncbg4CD27NmDaDS61ukw6wwpJWZmZhCPxzE/P4+Ghgbouo5oNApRyOPGW98ASqeVY3i6epB/+WXHc3mP9CB3+pT9OHVueOvtN6ZJEixL2s7VRAQyyXZT1zJluafTciQE/G/+VTT9l9/i6wBzV+R+71fU1pGaC9K0AEtRMSgEJNxAwaFKavsuyLFLyhCt8xCsEWe7S61tJ+S1UXWQEIDPC2EUHOOE1z6u/kgMvjZ7myRrsQDNRjwCivOAsOzXwSSpGLPst0ySMDOQROpcAlaB4PbYzyF2c4CUVHH+KB2rJBoRka2oLgRWVUsREYRbW/VAVCEnEWgszo3zU3lMj+fgct3UwdzupT5PBHjqfdj53RehraNKhXsBEWFoaAhutxt79uzhuZqpaUzTxOnTp7F9+3Zs2bJlrdMp2/Mlk8k1s+dbLjwFg85VvQzDMMyaUdWFgSufGKZGYOFpNYVCAePj4zh06BCampowPT2N8fFxDA0NIRwOQ9d1RCIRroRiaoKZmRmMjIygq6uLb9SYimiaVq4GJSIsLCyU++D5fD7oP/6TwHMnlGOYF0Yg6usdRSpjZAhaQwhysXL1h8vBassypbIqQVr2m8NAcQMatx7fthP6hz4Gz9ba7Y3CPDiE3gpSiU/SggjHQNNT9jFE0KIxyBvqasI8NDjWNWXVv8kSIlBFlQwRtEgMFB93jgs3gxITFQ87VT6RaQFQiE8FC0JhrWnkBTzLWn3mZnKY/sEErLkcBLD0AJeN+CQBYXPq23s28+Z7Kq29SVLF+c40CD5vhconWTy5ZRFmEwYgYFv5FPnP/0/NCU9SSpw9exZ1dXXYtWsX388wNY1hGDh9+jR27NiBlpaWtU4HwNrb8/3t3/4tvvSlL7HwxDAMU0Ow+MQwNQALT6uZnp7G+fPncfjw4XKpvq7r0HUdUkrMzc0hHo/j/PnzaGhoQCwWQ3Nz87ryuWaYapmcnMS1a9fQ29sLv9+/1ukwGwAhBBobG9HY2Ig9e/YgnU4jYfwc3M+fhFDsvFIuC29XL/Iv/0g5PuXz8O4/hNypCnEC0Nzq6xRJQCieCyCLAMUYYtkOrXR5EXznb6LxLW9TnpNhbgdN3w7rorrSSIQa1eITAASdG6hXo4XIuekqogBUuc4R9Q3VWV4EQ4Cd+FSnvtUkqT4DGbLoLWeDVSB4vEvVToNJpM8lUGVrOrUwpThmOy/ZzZs2r0vLZpyl085N5iELFrSAr9w/bLnw5Nmio+k//JrNIBsTy7IwODiIcDiMjo6OtU6HYe4rJeGpvb0duq6vdToVedD2fJ///Ofx5S9/Gc8++ywLTwzDMDUEi08Ms44RQjja7hERLMsq24ew8ARcv34dk5OT6Ovrq7gY1jQNTU1NaGpqWvH0/+joKPx+P3RdRywWg8dTRf8EhllDiAijo6OYn59HX18f3G6+rDN3Rn19Pepf8SokX/ljyL/0ojJWTk0Wd0GlepfXmrheMc5VTb+nuyhIJQgIjUAEuA71Qf/AH8PF/QKYe0w1fZ+Ez/lhAOF1XmsEvG7YaRVl0otFwSi9qI4zDMfzAcCKkqI7iROA5r+7axKpLAsBWOk88gaQ/JdxWHO51Sk49JO6E2znLts5zUbgsol2uTWYBYn5eL6oW+XzxfnQ7YYmin3siIDYu3/vdtJe91iWhf7+fui6ju3bt691OgxzXzEMA6dOnUJHR8e6FZ5uRdM0RCIRRCIRADft+c6cOXPX9nxEhC984Qt48sknceLECdTXYB87hmGYzQzvUjHMBoaIYJrFG1G2jrvZnLhQKKCvr6+qKqZKT//H43GcOnUKLpcLsVgMuq5zNQmz7pBSYmhoCJqmobu7m+cA5p4Q/MVfdhSfrPgNePfvR+HcOXVcIg7vgUMonF1ZHeJkuSetyv2aShCt7p+yHNMCXHUNCL3r9xH8iZ9Snoth7pRqxKeqcBBxAQBOgtISIhJ1FJ8cxakS1eSliNN8bkfxx1EcUjx/ZRUspEZnkL8yB1ToC0VE1RZ5rc7L1qqv2NepEnZzlq1WZRPv8QlMT9wU0kqXdjJMaEvPU3l3dyL0Ez9ReeANiGEY6O/vx7Zt27B169a1Todh7iuFQgGnT5/Gzp07EYvF1jqdO+Ze2fMRET7/+c/jq1/9Kp599lkWnhiGYWoQFp8YZgPCNnurMU0Tg4ODCIVC2Lt37x1/J/X19di5cyd27tyJXC6HeDyOs2fPwrKsshDFi2JmrTEMA4ODg2hqakJ7ezvPAcw9w/fwq+De0QFz7IoyLp1KOfegASAqVFm4bDZvS0iTIFyKfk+mvTglhQu+n/hpNL/7D6Bx9SpzH6lKfCqsrsZZRS7jGEIzyaKK4VANL+qcbYrkTNI5JwCUdc4LACifrfi6k+UeAMDt8NBEhZ+5lbcwe2YamYszKKQMeGzmEykBl2IeUQlfml0vKFWudh/F5jxu7+rXLbNoM7iYNCDESpu90v4tEdDyvvepMtlQlDbiN1IFCMPcKYVCAadOncLu3bvR3Ny81uncM+zs+YaHh/HhD38Yr3vd6/CWt7wF7e3tK95HRPibv/kbPP300/j617/O99gMwzA1CotPDLPBYOFpNdlsFgMDA2hvb8eWLVvu2bh+vx87duzAjh07UCgUkEwmceHCBeRyOUSjUei6jlAoxP8GzAOl9Pfe0dGxbpoTM7WDEAL1b34r5j/xmDLON3kd2NoKOVm510sJ4+J5uNvaYF67VhxfE9AcNpyJyLbyYCkAt+5KS5cHsqUd0d/5HwgcOKAcn2HuBSLWWmwARPYVQrQw6zgOzc84n8zIQzQ2gZz6OlVjvZrPAqEI4JBbNbkDxfwr/Vq1gEO/JyJoDlWQywUiqyAx059A9vIsYBa/c02x/iIJwE5EIrIXsKX9MftzEVye1ScjImgVBDApCe4Kn900CAs3cmWdsVz1RDfFJ+/Bw6g/dPC28luv5PN5nD59uuY24hmmEqW/987OTkSj0bVO576x3J5v7969CIfDOH78OH7t134NuVwOjzzyCN785jfj1a9+Nb7whS+Uhae6urq1Tp1hGIa5TwinfjK3cFvBDMPcHaZpwrJuuvyz8LSaubk5DA0N4eDBg2hsbHwg57QsC8lkEvF4HKlUCpFIBLquIxwOs/UZc19ZWFjA2bNnceDAAYTD4bVOh6lRZDaDG299AyidVsZ5unqQf/llx/G8R3qQO30KAOD2u+BrsO8lQyjaaamqEizjZuWTZQFGXRT1b3k7Yr/+DsdcGOZekvuDXwdmpuwDhIAkzbHPknT5nSug2nZDXr2oDNE6D8EaOaOMAQBtxy7IscvqICEAnw/CyDvGCa8XwiiseNnfGUHdvibbt8m86Wi7JzPF7y01VsDsD69ALLPXIyKQRbZrYaMg4fFWXpNZFtlWRVkmweW2F6YqVW5KS8Jdob8V2YhMhbyEP7i6MjM1b2L6+s1quZKWSAT4fMX5cefXnoVv+z2yfFxDstks+vv7sXfvXjQ12f+dMEwtkM/ncerUqU3/955IJPDMM8/gG9/4BoaHh2FZFv7sz/4Mb3jDGxAIBNY6PYZhGOb2qWpTmiufGGaDQESwLKv4RDgLTwCAyclJjI2Noaen54EuWF0uF1paWtDS0gIpJWZnZzE1NYWRkRGEQiHEYjFEo9Gqek4xTLUkEglcunQJ3d3d/HQgc1/RAnWoe/0xpJ/6e2WceWEEor7eUaTKD5+DCDaAUouOlntweSE0e6uyUlWCWZAowA/P4V5s/6MPwc1WLcwaoOmtkCrxiQgiEgPF1RWCFI5C3FCLT8JfxbyfSTnHVDsWEbSmZtDUuHNcJAaKr4xzOdjuyYIFVwXBpoSVMZA5P4u5M0mQKVfd2Vom4FKIV8plsscLSDtBcHVlZXlM2/PZNXayE7Eqhy9O38xp+VtLy0n/K15dE8JTOp3G4OAgDhw48MAeHGOYtSKXy+H06dPYt28fIpHIWqezpsRiMbzzne+EpmkwTRPvfve78cILL+BDH/oQtm3bhmPHjuGNb3wjWltb1zpVhmEY5h7C4hPDbACICKZpAgBX1qD4fVy+fBkLCwt46KGH4K7GZuY+oWkaotEootEoiAjz8/OIx+O4fPkyAoEAdF1Hc3MzPNx7hLkLrl27hqmpKfT19cHrta8aYZh7RfAX34b08Sftd0kBUC4Lb1cv8i//SDmWKBRgtO+Ea2QYLgebLSuft318SkpCIWMhn5VwxbZgywf+EPV9vU4fhWHuG0LfDgyfUscEQ47iU1YIOMlB1Tx0JGer6+eEKh+OEfUN1dle1K/uNeVku1eyzluOlSogN55C+loK5nQaZtaq8MYiUpKD+KQ45vEAeXU12q0Qkb34ZHeqQD1gVu6JdSvpBRO5tAVNq2y5R0LD9j/64G3lvB5JpVIYHBzE4cOH0dDQsNbpMMx9pSQ87d+/nx0LUJxHP/e5z+HEiRNlq72f/dmfBQBcuHABJ06cwDve8Q5kMhm8/vWvx7Fjx9Db28sP3TIMw2xwWHximHWOZVlss7cMy7Jw9uxZ+Hw+9PT0rKvvRAiBcDiMcDgMIkI6nUY8HsfLL78Mj8eDWCwGXdfh8/nWOlVmg0BE5T5jvb29XE3HPDDc27bD94rXIP/Si8o4OTVZ3CVViFQA4J+bheVxOdpskSUhltlhSVPCLEgYOQkjTyCPB9Hf+A3E/q//XP2HYZj7hNCdq1CE1/maX9/YCJp0CHKyvwOAbBqiIQRaXHAYq6A+XsJd5cMOntWfUQuoH7qhJQs9cyGP3PgiMqPzKMze/IyajWVeGcWUU+y1pHivZS9q2ZZMkX3lk12PKDOVgdu/+titfaCICNMT+bLgJARW/LcQQN1P/yzcG3zzumQdfOTIEdRztSpT45SsJVl4KlISnk6ePImvfe1rq1xL9uzZg3e9611417vehbm5OXzzm9/Exz72MQwODuLVr341Hn30Ubzuda9jez6GYZgNCItPDLOO+dKXvoSdO3eiu7ubK55Q9MseGBjA1q1bsX379rVOR4kQAsFgEMFgELt27UI2m0U8Hsfg4CCIqCxEsX0aY0dJaA0EAujq6lpXQiuzOQj+0tscxScrfgPe/QdQOHdWHZeIw7ujFUjP2MYUKwsACQ1GqgAjJyEtKvaBsoD6h4+i9bHH4GabJmadUI34BFILswCgEUEhhxSHSTkISqWcwlFH8YlSi1WN5SQqq+IqVT4REWTagDmfR24yhczYAswFGyHM5QFg2p9T2NvjSQnbnk4AikJehcNEBLvlNqmEcxuhS7PJ0e1d+drCjAmzQOWCtFst90hzY9v73mN//g3A7OwsRkZGHrhVNsOsBSXhia0lixARPvvZz+Ib3/gGnnnmGcc5IPAyhU4AACAASURBVBwO4+1vfzve/va3wzRN/Mu//AueffZZvPe978Wjjz6K97///Q8oc4ZhGOZewOITw6xj6urq8PGPfxwjIyN45JFH8KY3vQmvetWrNmX1w+LiIs6cOYN9+/ZtyEatgUAA7e3taG9vR6FQQDwex8jICAqFApqbm6HrOoLBIAsMDACgUCigv79/QwitTO3iO/pKuHd0wBy7oowT1RlzQeRX97SRloQ0CdayCqfle/WmBbibwmj7wAcQfM2P3U76DHPfqUp8yldhu5ZV900DAJpNApoApPr3JuqcK0rkTMI5JwCUVfehKsfd8hk1vwsggjGTgzmXhzGbQ2E6C2MmCzKL+ROKlY22OTrcptpVGwFLep/NUrnUN67i+8h+XM0XAMzV/ehIElye1ScjWi0yAYBlEdy+m/HSIkxPFMqCUyXLvdxrH8H/HhhANBpFLBZDQ0PDhlovTk9P4+LFi+jp6YHf71/rdBjmvpLJZNDf349Dhw4hFAqtdTprDhHhL//yL/Hcc8/h+PHjty0+u91uvPa1r8VrX/taAMWecQzDMMzGQhBVt2GwxG0FMwxzb8jlcnjhhRfw1FNP4Yc//CFe9apX4dixY3jkkUc2hYVbIpHApUuX0NXVVXM2HaZpIplMIh6PI51Oo6mpCbquIxwOb6iNBebeUWrE3dnZiebm5rVOh9nkpI4/iflPPOYYJ/RWWOPXbY8TEXwhL8jlhZnOlgWn0srSvKXAQQv4UMgUUPiJR+B9539CS0sLwuEwVwEz6wqyLOR/+02ApajQaQhDzthX/AEA6oKQ886VTRRuBjkIR9q+bljnTjuOhcYIMD+rDBGhCJCecxxKhKMQCzc/ozQlCqmC8s7RsqTyuCld0KzKfZlIkvK9hkHweGys8EyC2135mGUSXDbH4K+DMFYLidIiuP2rxSejIOGvWy2gFXIS/oabloTTk3nMThlY3hp0eStTT9CPvS/+MywpMT09jUQigVQqhXA4jFgshqampnU9L8bjcVy5cgU9PT3cs5KpedLpNAYGBrin2RJEhM985jN4/vnncfz4cRafGYZhao+qNi1ZfGKYDYZhGPjnf/5nPPXUU/je976HQ4cO4dixY/iZn/kZBIOrGz5vZIgIY2NjSCaT6OrqqvmbVrlsY2F+fh6NjY2IxWKIRqPremOBuXfMzc1haGiIb1qZdYPMZnDjrW8EpVPKOE9XD/Ivv1x5DEm27l2WVXy6v4SmAZpbwNf3MGLv+Z9wNTdjdnYW8Xgcc3NzaGhoKM+LbjcX8DNrT/59vwGK2wuvACA1n2MFlPTVA052eDs6Ia9cUIZouw/COq+2wQQAbcduyLFLjnEI+CEKDv2mhIDweiGWekmZORNmViHIaYBVsK96IiFABXsjQtOQcCkqnwxDwuOpvG4yChIem35SlinhctustzweCLn6M0m5spKpRD4rUdeweo7KZSXqQkWlyTQkrpzLQBNYYblX+m+XC4j95m8i9s7/85ZzSszNzSGRSGBmZgb19fWIxWJobm6Gx6PutfUguXHjBq5du4aenp51lRfD3A9YeFoJEeHTn/40vvWtb+Hpp59m4YlhGKY2YfGJYWodKSV++MMf4qmnnsILL7yAtrY2PProo3jDG96wIa3pliOlxPDwMIgIBw4c2HTiCxFhbm4O8Xi8vLGg6zqam5t5w7VGuXHjBsbGxnDkyBG+QWPWFXOf/BjSX/2SMkb4A5ASoGV2KERF0anSUpOoKDyV3y8Ad8AD/7/7STT/zu/BXaE5NxFhYWEBiUQC09PT8Hq9iMViiMVim6IKmFmfFJ74/yDP/JsyhqLbQDeuqQfa0g55bVQZIvZ2wRoeUMZoW9tgXR9TnwuAa18XrJFBxzittQ00pRbXAMC1ZTsoPg4AMNIGLIV4JFwCZt7+uFGQULVsKuQlPHYVSlBXMKmEKTvxqdiPTkBUELxIElwVxKdcRqI+tHq9Zrjq4NOKYt7UWA4L0ybc7qLwTlSsehKi+N++pgbs/afvKivhiQjpdBqJRALJZBJCiPK8uJZ9RcfHx3Hjxg10d3fzupWpeVKpFAYHB9HV1VVzD4PeCUSEv/iLv8C3v/1tPPXUU3xfwzAMU7tUJT7xSpBhNjCapuGVr3wlXvnKV4KIcPbsWTz99NN4y1vegmAwiGPHjuHYsWPYsmXLhrJwMwwDA0ve9u3t7Rsq93uFEAKRSASRSAREhFQqhampKVy9ehVerxe6riMWi9V8NdhmgIhw5coVzM7Ooq+vjzdpmHVH8M2/jPTTX4Zt+RIAymXh7epF/uUfgYhAVDlcCIDcHljZm3ZannAD6l7/RjT/1n+FphCRhBBobGxEY2MjOjs7kclkEI/HMTBQ3IwvbbjWmj0rs74R+nYAavEpAw1OXS5EwPnvVlRxfydnko4xxcGqe6hH1DdU9/RhsAGIL+WgmCuKg6oPS5PgsrHNAyoL2jePETRFa1TVmtLuGMliRWbF99j1iLJRzyibAepdKJgCC9MmhLjZ40kIlHs/CQHE3vX/Oq6BhRAIBoMIBoPYuXMn8vk8EokERkZGkM/ny32iGhsbH9h6emxsDNPT0+jp6dmUfWqZzUWpLzELT0WICJ/61Kfw3e9+l4UnhmEYBgBXPjFMTUJEGB0dxfHjx/G1r30NlmXhjW98I44dO4Zdu3atazGn1O9m165d0HV9rdNZl5Q2XBOJRPkJV13Xb7uBK7P2lCr8AGD//v2brsKP2Tgk//u7kH/pRWWMS9+CwvhksZdTBTTt5hP9hQLgiUURevuvIvLr//Gur0uFQgGJRAKJRAK5XA7RaBS6riMUCq3rax6z8TG/dwLm3/+5MqbQtg/uy0PKGLHniGOvJq1jL8zLI85J1TeCFtS9mly7D8C6pM4JAFwHuiHPq6utAMC1/wjo/CCICPn5vPqu0SVgKSqfcmkLXq/CVi9nwW1bvaTo24RipVLFCqale+JK84VlyorWekQE4RYVr91SoqK9H0HA5dEwfimLzIIFl0fAJUrnLlrtEQG+bVuw77l/sP0c1WBZVtnOeWFhAaFQqGxber9EodHRUSwuLuLw4cO8pmFqnpLwdOTIEX7wBcU58YknnsD3vvc9fPWrX+WqdIZhmNqHbfcYhikuAqempvDMM8/g+PHjmJ6exs/93M/hF37hF3Dw4MF1dWM4MzODkZERHDp0CKFQaK3T2RDk8/myEGWaJpqbm6HrOurr63nDdZ1jmiYGBwcRDofR0dHB/17Muib3w5cw/e7fcowz8hboFu2p9GS/EAJSaHDv3oOGX/l1hH7u9fcl19KGazwex+LiIhobG6HrOpqamtbVNY+pDazhUzA+8XvKGLHzIKzzZ9Qxuw7AGlH3ahLRFliJG445iW27HPs5aS3bIKfGHcdy7euCvKjOHQBce7tAF8+A5JL4pIAASBuRGnAWn8yChMumskhlqyclQbOpVJIW2VYr2YpPNpZ7UhLcHm3Vdd00CN46NzILJsYv5QAAHs9NUX655d7OT34cDY/8u4r53AlEhPn5+bJtqc/nu6e2pUSEixcvIp/Pr7v7C4a5HywsLODs2bPo7u5eU4vL9QIR4ZOf/CRefPFFfOUrX2HhiWEYZnPA4hPDMKuZnZ3FiRMncPz4cVy6dAk/9VM/hWPHjuHhhx9eU2uM8fFxTExM4MiRI7xYvUMMw0AymUQ8Hkc2m0VTUxN0XX+gVitMdeRyOQwMDGDHjh3YsmXLWqfDMI4QEeL/8Zdhjl1RxkmLYBZubiprrmI/KGvXHqRf8++w/x3/xwO1lpRSYm5uDolEotw/LxaLobm5GR6P54HlwdQuNBNH/g/eoYwRW9thjTn0c2rZ7tyryeWCtKTSAhMAtM7Dzv2cPF7AKKhjAGg7doPGL1cVh+uXYRkWjJShjLUsqbyrLOQsuG2EICkJQvFeoyArVhwBRfHHbWPnZ9fvqZgvwV1hTGkR3P7Va+dCXiJQv3qey2UlAg1ujI1kUcgW/w1L7slaIADNyBZf6+zEvuNPVszlXpFOp5FMJpFIJEBEaG5uLtuW3u6akYgwMlKsyNu3bx+vOZmaZ35+HkNDQzhy5AgLTyjOAY8//ji+//3vs/DEMAyzuWDxiWEYNZlMBs899xyefvppnDp1Cq95zWvwpje9CT/+4z/+wHoJEREuXLiAXC6HQ4cOsTf8PeJWq5VwOAxd1xGJRPhp1DWmZNGxf/9+RCKRtU6HYaomdfxJzH/iMce4QoHgjsXg6+pB6FffgVEIENGaW0uW+ufF43FMT0/D5XKV++dxTwLmTiEi5H/7FwBDUe1TF4ScX1AP5AtAptLOJ4zokNNTyhDXvm6YDhZ+AIBwFJibVoaIUBhIzzsOJcJRiIUZmDkTZtZUBAKWYS+eSUmAZX/LaRoSLpvqJQAwTMBjo2+rqqJMQ9pa+REq93CSloTbX0FkykjUh1a/nk1LWBYwNVb8W3G5iv8DblruQQjs+sL/Qn33kcof4j5QKBTKQlQmk0FTUxNisRjC4bDjnE1EOHfuHLxeLzo7O1l4Ymqeubk5DA8Po7u7my3PUZwD/vzP/xw/+MEP8OSTT7LwxDAMs7lg8YlhmOopFAr47ne/i6effhovvvgienp6cOzYMbzuda+7b090maaJM2fOIBgMYvfu3XzDep8oPfkfj8cxOzuLhoaG8pP/LPY9WJLJJC5evIiuri72hmc2HDKbwY23vhGUTpVfIwAiUA9N3wLP7j3w9fTB/2OPwNPUBMuyynP8euw3mM1my32iLMv6/9m78/i4zvIO9L8zq6QZbTNzRrIka7MtWbY22wlwk0BuEhywjZQLDS0fCrdcKKVwW3pDacNOFkiAQqBlS8sOhTZNLRkSEpJQIA1J0yTEWmxLsrVbtqQ5M6Nt1rO87/1DnrFlzTkj25JmJD3fv+w575zzzqIz57zP+z5Pcua/0+nMur6S7Bb/3F+Cnzde2cSseUAkZNiG5xaCz88YthGq6qCNGNd9Mu1ogHb6lGEbADBV7QQbG0zbDnm5EOIx4zaCAMFmgzobhibr13MSTAJUg+1KnMFsEO+QYwxWndVLAMAEM0w89f6Ngk96K5845xDMQspzAuccZtvy66hYlMGRnyL4FGHwnZWhKou31Jem3EssxLTVN6D+P3+q+/rWGmMMwWAQkiRhdnYWTqczWSfq8tWijLHkOb6mpobOm2TTo8DTUpxz/OM//iNeeukl/Md//MeaTF796le/iu9+97sQBAFNTU34wQ9+QBOGCCEke1DwiRBydTRNw4svvoiOjg78+te/Rm1tLdra2nDo0CEUFhauyjESace2b9+Obdu2rco+SXqcc8zPzydn/ufk5CRn/lMKqrU1MTGByclJtLS0rNvKQkJWW6jjEcS6XoW1uhY5r70Btj2NKQccFUVBd3c3SktLUVFRkYGeXplL05YmZv57vV4UFRXRgCpJS/6X+8C6njduJG4HOz9m3GZbTfpaTXXNUPu7DdsIJeVg5yeMj4XFek5p0/MBMJVVgk+fTb+/0grEhobBVYNbRrMALa4ffIpFNNgMgkvxGNPdzjlfDILpPFdVOSyWK6v5xDQOs04aPwiAKUXASo5z5DqWB6X8Uwrm/IurwgThYsApEXziAHb9ohO5NVU6r2B9cc6xsLCQrBNlNpuTdaJsNht6enrgcrlQVZUd/SVkLc3MzGBgYACtra0U/MDi+eFrX/saXnnlFTzyyCNrcm9z7tw53HTTTTh16hRyc3Pxx3/8xzh8+DDe8573rPqxCCGEXJUV3SivX9J9QsiGYTabceONN+LGG28EYwy9vb04evQo2tvb4XK50NbWhiNHjsDr9V7VoNzk5CRGR0fR0NCAoqKiNXgFRI8gCCgsLERhYSF27dqFcDgMn8+H48ePJwcVvF4v3VStIs45hoaGEA6HsX//flptRjY059v+BM63/Ylhm1gshu7ubtTU1MDr9a5Tz66N1WrFtm3bsG3bNmiahmAwiMnJSfT396OgoCA585/+fkkq4bxCpJ0D73Cm3Y+Qu5KV5unnAvIZ/8UlNYYHXFkaTMHpBDfO9Ld4XIcT3CBlHgCApdmersuL6y1TbtM0wKiknN6fL+dc963Qm6iZWBGVitW2/HFV4ZgLKEj0/dK+JDLb5R64LmsCT8DiNWNBQQEKCgqwY8cORKNR+P1+nDx5EvPz8yguLkZxcfHie0FBerKJBYNBnD59Gvv27aO0clg8/331q1/Fq6++umaBpwRVVRGNRmG1WhGJRFBWVrZmxyKEELI2KPhECDFkMpnQ0tKClpYW3HvvvRgcHERHRwfe/e53w2Qy4ciRI2hvb0dlZeWKbjx/+MMf4jvf+Q6efvppSjuWBRwOB2pqalBTU4NYLAafz4eTJ09C07RkIIo+p6vHGMPJkydht9vR3NxMgzNk0wuFQujt7d3Qkwsund3POcfc3BwkScLw8HBytajH46EVjAQAMDo6CmbJSxt8EoyiIglG+eYS0qW/AwA5DqGwGHw2aNzOqE7Vkn6t8LvOhbTBo7RJN9L8TJoM6j0xph+Y0lvZtNgnAbqljfQOx5GyHhLTOCwpVkoFpuXF9+eCxFM5XwyYcQio+tIDOgfLDrm5uSgtLcXU1BTq6upgtVoxPj6OhYUFFBUVQRRFuFwuqi1KNpVAIIDBwUEKPF3AOcdDDz2Erq4u/Pu///uaXguVl5fjox/9KCorK5Gbm4vbb78dt99++5odjxBCyNqgtHuEkKvCOcf58+fR2dmJY8eOYX5+HocOHUJbWxsaGhqWDbIzxvDJT34SL730Eh555BF4PJ4M9ZysRKL4tM/nQywWg9vthtfrRUFBAQVQVkiWZfT09KCkpATbt2/PdHcIWXOJmcGbuaZZYrWo3++HIAjJINVa1UYk2YtzjsHBQcTjcey2M6hf/TvD9kJ1A7QzJ43b1OxOW6tJKPZAC0hp+yds3wE2alzPSfCWgfvOp92Xua4RbMi47wCA0irIA6cNm2gqM9yuxDSYdYJEjHEIBnejcpzBZk8d+FAVDotOuj5mtcOkE4jTGIclRZ0oxgBLimPJMYZc59JAYyyiYWIonrx+SpVyz3Hz/4nabzyk+9qygSzL6OrqQnV19ZJVrYnaopIkIRgMIi8vL1lblIL0ZCPz+/0YGhrCvn376LuMxd+9r3zlK+jp6cHPfvazNX9PZmZm8Ed/9Ed45JFHUFRUhLe//e2488478a53vWtNj0sIIWTFqOYTIWT9BAIBPPbYY+js7MT4+Dhuu+02tLe3Y//+/YjH43j3u9+NgoICPPzww3TxvsFompYMRIVCIRQXFydrodDs1tQikQh6enqwY8cOiKKY6e4Qsuamp6cxNjaG5ubmLZO2Mx6PQ5IkSJIEWZaTQfr8/HwK0m9ynHP09fXBbDajrq4OCM0hfrdxOkqhZDu0CeOaT4JYBi1drSbBBCYIgKoaNjPvaoLa32O8L4v1wn6Mb/FM22vBz48Y7wsAyyuCOmmQn08ANEU/+MQYBwzS9qky0w1MAYAiM1h16jMZbWM5Dphi4ZTbOJByxRTPdcCsRpc9HoswOAqWBp/Gz8Qgx1jyvGCxLK584nwx/Z5gNmHP738LS0G+7mvLtEQ61Z07d8Ltduu245wjHA5DkqRkkN7j8UAUxU07KYFsTpIkYWRkBK2trXTvisW/7X/4h3/AiRMn8G//9m/rUiv40Ucfxa9+9St873vfAwD8+Mc/xosvvohvfetba35sQgghK0I1nwgh68ftduM973kP3vOe9yAUCuHJJ5/Eww8/jOPHj0NVVbzxjW/EF7/4RVhWknaGZBWz2YySkhKUlJSAMYaZmRlMT09jYGCAaqGkMDs7i76+PuzduxcFBQWZ7g4ha258fBySJGHfvn3rMhiRLex2OyoqKlBRUQFVVREIBDA2NpYM0ouiiOLiYgrSbzKapuHEiRPIz89HTU3NYkAhvwjIdQDR1AEMAOCz/rT75rMrqNXEGUzubWDpViwZpKdLUhXA5QGCxn3jc2nS913AFhYMt6cLyqoKh8Xgz4Ux/bpNQOog0UpwRUn9uEFdJzUahznF6e7ylzgfVBGPsiXpAi9NuWcyAQVvOZLVgadoNIru7m7U19ejuLjYsK0gCHA6nXA6naipqUE8Hoff78eZM2eSK+lFUURhYSEF6UnWSgSettp1jR7OOb70pS+hr69v3QJPAFBZWYkXX3wRkUgEubm5+K//+i9cd91163JsQgghq4dWPhFC1sypU6fwp3/6p3jb296G8fFxvPDCC7juuuvQ3t6OW265ZcvMjt+sErVQfD4fgsEgcnNzk7VQtuqN2vT0NEZHR9Hc3Izc3LTl5wnZ0BJpx2KxGPbu3UtBlgsSQXpJkjAzMwOn05lMQUUTMDY2VVXR3d0Nr9e7LJ1q/At/DT5unHKO5RYA87OGbbizGHwmYNhGqNkNbajPsI2pdje0M8ZtAMBUvQts9EzadsjLgxBfvtLnUkpIBjdYuQSzAC2u6W6ORzRYdVLjAYAcZbDadOo2MQ7BIOCmqhwWi146P6Ss+aTp1G8CAAYTLCkudRSZIydvMULGNI6R/ig4Q8qUewBgtlvQ+L/PwZSltWTC4TB6enqwZ88eFBYWXtO+NE1DMBiEJEmYm5ujCUwkK/l8PoyNjaG1tXXL3s9cinOOL37xixgYGMC//uu/rvt78tnPfhaPPPIILBYL9u3bh+9+97tUe4sQQrIHpd0jhGTOU089hY997GP46U9/ij179gBYHLR5/vnn0dHRgd/85jeor69He3s73vSmNyE/P3tnfJL0EmlWErVQLBYLRFGE1+vdEjcInHOMj48jEAigqamJblbJpscYw6lTp2Cz2bBr1y6awa6Dc46FhQX4fD4EAgFYrVZ4vV6Iorglzo2bSaLeTWVlJUpLS5dv//6DYK/8zngnpdVgZ4eN25TvSBsMMtU1Q+3vNmwjeMvAJs8ZHwuAub4Z2kCa9HwATBWV4JNndbdzzqEsyIZ3iywnDzwU0t0eC2uw6QSXAECOabCmqL8EGNd0StzvpjpPGQWtNJWnrOvEGGC2CbrnPfOF5Vv+SRlBnwqTCbop99z/97tQcfdHUu4n0xYWFnDixAk0NTXB6XSu6r4TE5gkSUIgEIDdbk8G6WlyGsmU6elpjI+PU+DpAs45vvCFL+DMmTP4yU9+Qu8JIYSQy1HwiRCSGd/85jfx6KOP4tFHH9Wtd8MYw/Hjx3H06FE89dRTKCkpQVtbG44cOQK3200DmRtcNBqFz+eDJEngnCcDUXl5eZnu2qrjnGNgYACapqGhoYFWf5BNT1VV9PT0wO12o6qqKtPd2VAikUiyThTnHB6PB16vl2qhZLlYLIauri7s3LkTHo8nZRvl8Z9Ae+JfjXdUswfs9AnDJsKORmhpajWZ6lZWz4nLinEKPwDm3c1pjwcA5l17wIb1V1JxdiH4ZCAWNV7ZlC74pMlMN7WeUU0nTeO6taKMVkRpGoPFtnxFjhxnyHEuX8WoaYDtQrBKjjOMDcQAYEnKvUTpGM4BqzMHe//3OZiycNXP3Nwc+vr60NTUtC7np8S50e/3Q9O0ZJ0op9NJ9wRkXUxNTWFiYgKtra20ShmL9zcPPvgghoaG8OMf/5gCT4QQQlKhmk+EkPWlqiruuusuzM3N4amnnjKc1W0ymXDgwAEcOHAAn//859Hf34+Ojg684x3vgN1ux5EjR9De3o7y8nK66dyAcnNzUVVVhaqqKsiyDJ/Ph4GBAciynBxs3QwDCpqmobe3FwUFBRdrfxCyicXjcXR3d+uu/iDG8vLylpwbqRZK9kukHWtoaEBRUZFuO1NJOfQTyi0SVjI5YSW1mqKR9G1UBUKRG3wmTT0n2ThglJRm4JGz9HMU08VYzEb1niAY1nRKUyYL0Dn2Yr9T71fv71BjqfelMgEXYkuQzsnJmk4X97f03+Jf/HlWBp5mZmYwMDCAlpaWdUshfOm5UVEU+P1+jIyMIBwOUw09suYmJydx7tw5CjxdwDnHAw88gOHhYfzkJz+h94QQQsg1oZVPhJBVMTc3h3e84x14/etfj49//ONXPXDGOcfZs2fR2dmJY8eOIRqN4vDhw2hra0NdXR0NyG1wqqrC7/fD5/MhHA7D5XLB6/WiqKhow322iUH47du3Y9u2bZnuDiFrLhwOo7e3F3V1dXC5XJnuzqaiaRoCgQAkScL8/DwKCwshiiJcLhfVQsmgubk5nDp1akVpx9joAOQvfdiwjVBVD23QuA6TUFUHbbDfuE2hC1qaulAAYKrcCW3EOIWfIG4DlybT7stc1wg2dFJ3uxZXocX0w2+KzFLWVUpgGgcMAliKzGEx+FMwWvl0tdsAQEgR8IpGGRwFywdjo2EGZ6EFEdmCib55ANBNuWctLkDjC7/Numsfv9+PoaEhtLa2ZkVqUL0aem63m1ZikFVx/vx5TE5OorW1lX5vsXgv/rnPfQ7j4+P40Y9+RIEnQgghRijtHiFkfQwPD+NP/uRPcPfdd+POO+9c1X1LkoSf//zn6OzsxOTkJA4ePIj29na0tLTQ7McNjjGWHGydm5tLDra63e6s/2xDoRB6e3tRX19Pg/BkS0gMwjc2NlKNvjXGOcfs7GyyFkpeXh68Xi88Hg8Ntq6jQCCAM2fOrHj1B4+GEf/btxm2EcQyaOcnjNu4S6BNpQkGCQKYyQooccNmpromaH1pUuqZLQDT0qbnM22vAT8/qrtdjSpgss6SIACxiAarUT2nOIPF4Kc/HmXGKfmMUuspDBbdWlGpt3HGYdJ5jixz5DiWD1JHIwwOpxkjAzGoMoc53wlEwsntiZR7jAFVn/80PHe+Vff1ZILP58Po6ChaW1thS3Q2i3DOEQqFkjX0zGYzRFGEKIrrtkKLbC7nzp3D9PQ0WlpaKPCEi4Gns2fP4oc//CEFngghhKRDwSdCyNo7ffo03v72t+N73/serrvuujU9O+TdpwAAIABJREFU1tzcHJ544gl0dHRgYGAAN998M9ra2vC6172OLo43uMRgq8/nQzAYhMPhSA62ZttnGwwGcfr0aTQ2Nq56AW5CspEkSRgaGlrXFExkEecc4XAYPp8Pfr+fBlvXyfT0NMbGxtDS0nJFqz9if//HQGhOv4HVBhaNGwd6zBYwWV2MUBjxloNNGQeyzPXNUE91G+8HgOD2ggd8xm2cBUB0QXe7EpLBNf3XFVnQYM81qPcUYbAZxFZjUQa7TvCJMb6krtLlNJXDrFPXSe+5msZh0VkRxYCUAStF5ggvMASmlORjiYCYIFzMXGgtL0fjrx/T7W8mJNKOtbS0bJggdywWS9bQUxQlmbq0oKAg61aUkewzMTEBn89HgacLGGP43Oc+h3PnzuEHP/hB1t1/EUIIyUoUfCKErD1VVSFJ0rqnHYvFYnjmmWdw9OhRvPLKK3jta1+LtrY23HzzzVmRJoRcvcTM1unpaQQCAdhsNni9XoiimPGZuOfPn8fExMQVD0gSslGdO3cO58+fR0tLS8b//sjywdbNVEMvW0xMTGB6ehrNzc1XPAgf/8pHwA1S0wEAdxSDzxqnzOMFnvTBoNoGaIOnDNuYaurTpvBbbFcHNnI6bTs4HBBiy+tNcc6hLMiGd4qRBRX2XP2lTbGwZriyKR5jsFn1VjZxWHS2Je51U/19GAWtNJXDYl/eX01bfDzV/jQNGBuIgfPF+KLZvHhczjlsdgECFh+v/vpX4Dp4i95LXXebYRBeVdXkavqFhQVKXUoMnT17Fn6/H83NzfT9wGLg6f7778f58+cp8EQIIeRKUPCJELI1KIqC5557Dh0dHXj22WexZ88etLe34+DBg7QyZROIRCLw+XyQJAmCIEAURXi93nWd9c85x/DwMBYWFtDU1EQ3qmTT45xjZGQE8/Pz9J3PUoqiwO/3Q5KkZA09URRRVFSU9alLs9XIyAjm5uau+juv/PjL0F58xrhRWS3Y2KBxm+27wIYHDJuY6puh9hmvahLEUrB0KfwAmHc3Q+tPk54PgKmiCnxyfNnjnF0IPhmIRlTYDGorxcPGafk0hekGiuQ4gy1FoAhIk45P5bDorIjSW/kUj3Hk5i//bigyx4xfxcLMxbpXieNyzmG3L/7btmMH9j7+aMpjZsLY2BhmZmY21XmeMYa5uTlIkoRgMIjc3FyIogiPx0OTKAjGx8cRDAbR3NxMv5VY/Hu57777MDU1he9///sUeCKEEHIlVhR8ol8WQsiGZ7Vaceutt+LWW28FYwwvv/wyOjo68OUvfxkVFRVoa2vD4cOHqTbPBpWXl4fq6mpUV1cjHo/D5/Ohr68PqqomZ/07HI41m/XPGMOpU6dgsVjQ0tJCqwvIpsc5R39/PzjnNDiTxaxWK7Zt24Zt27aBMYZgMIjp6WkMDAwgPz8fXq8Xbrd70wworyXOOc6cOQNZlq/pOy94K9K3yclL32YlK2vTpeUDwIN+wCQAbHXmDwp5zpQzEXma/XOuH+RJMBl8TTXNOK2e4bHNFgBa6m2MQ++eWdD5CjCdiZtynC0JPF16qZD4OnEObP/Mx9N1eV0kJtWEw+FNd543mUwoLi5GcXExOOeIRCKQJAnd3YvB2kTqUofDkeGekvU2NjaG2dnZTfedv1qMMdx7772Ynp7GD37wA7peIIQQsiZo5RMhZNPinOPkyZPo6OjAL3/5SzgcDrS3t6OtrQ2lpaUURNjgErP+fT4fotEoXC4XvF4vCgsLV+2zVRQFPT09EEURlZWVq7JPQrKZpmk4ceIE8vPzUVNTQ+fJDYhzjvn5efh8PgQCAdjt9qxJXZqNGGPo6+uDxWJBXV3dNX3ntePPQfnO5wzbCDsa064yEnY2QUu3qqmiFtr4UPpOFYlpU/iZaurBRoxXWgGAuaEF7PTyvmtxFVosdYAHABSZwWicl2ncMEAmxxmsBsErRWaw6qyq0mx5MMvLUwWmex4EQEgR8IpFGfIKls7f5Jzj7KAMReYX/r805Z7VCphMAmy76rD3F/+u+zrWSyLYqigK9uzZs6XO87IsJ1OXxmKxJStGt9L7sBWNjo5ifn4ejY2NFHjC4m/fPffcA7/fj+9973sUeCKEEHI1KO0eIYQkJFJIdXZ24uc//zk0TcORI0fQ1taG2tpauuHc4DRNS+b6n5+fR1FREbxeL4qLi6/6BjMajaK7uxu1tbXwer2r3GNCso+iKOju7kZpaSkqKtKv4CAbQzgcTg62AkimLs3LS78CZ7PTNA29vb0oLCxEdXX1NV8LsIlhyA980LCNUFkHbci4DpNQuQvaUJpgUH4h2Nxs2j6ZqnZBGzau5yR4SsD902n3Za5rBEtR00qNKmCy/kqsWMQ4pZ6iCjBz/eBVPMoM60ExjcOkk1pPs+bCrERTbtNLu8cYh9ma+tpBVoCcvKXbgpKKwJS6JMB2eco9DqDhsUeRu3OH7utYD4mVrYIgoL6+fktf/2qahmAwCEmSMDc3h/z8fIiiCLfbTanHNpmRkREsLCxQ4OkCxhg++9nPIhgM4rvf/S4FngghhFwtCj4RQkgqnHNMT0/j2LFjOHbsGPx+P26//Xbccccd2Lt3L92UbHCMMczOzsLn82FmZiY5mODxeFZ8czU3N4dTp05hz549KCwsXOMeE5J50WgUPT09qK2thSiKme4OWSPxeDy5YjQej8Pj8UAURRQUFGy5QWhVVdHd3Y2SkpJVC7ZyOY74XXcsLn3R4y5JW4dJKBah+dIHg5g1B4inDqwkmOqa066igtm82Oc0qfxMFTXgk6PLHldCMrim/5ojIQ32HP3vlyzkwKKzOgkA4hEGmz318xkzTsmnwQQzUr8uvefq1XsCAC4AZsvFbYrMMDIgw3xJc0FAcr+CwGG1CsjdfwC7f/od3X6uh0Qa4ZycHOzYsWPL/c0bSawYlSQJgUAAVqs1mZ4vJycn090j12BoaAiRSITu8S5gjOEzn/kMZmdn8Z3vfIcCT4QQQq4FBZ8IIWQlZmZm8Pjjj+PYsWMYHBzELbfcgvb2dlx//fV0Qb7BXZ5+KicnJ5l+ymq1pnyOz+fD8PAwWlpakJubu849JmT9LSws4MSJExRs3WJUVU2uGF1YWEBRURFEUYTL5dr0A3SyLKOrqwtVVVUoKSlZ1X3HPvGnwKxfv4HZAqaogGYQ6BFMi3EgVTU+WGkF2Pmzhk3Mdc1Q0wWfAAjuEvCAccBLcBYA0YUlj3HOoczLhs+LLKiw5+p/p2QhFxY5rL89xmC1pr63VRUOi862xH1uqiCLUdBKEyywmJavxFJVDlvu0uvCs8MyoiGGxOUi50iupkqk3BNMJjQ++xRsXk/qF7gOGGPo7e1FQUEBampqMtaPjSIajSZXjGqalgzUO51OCtptEJxzDA0NIRaLYe/evfS5YfE88OlPfxrz8/P4l3/5F7rPJYQQcq0o+EQIIVcqEong6aefxtGjR3H8+HHccMMNaG9vx0033US1MjaBcDgMn88HSZJgNpuT6acSs1rHx8chSRKam5t1g1OEbCbBYBCnT59GU1MTFV/fwi5fMepwOOD1euF2uzfduTCRUnXXrl1wu92rvn/5H+8GG+gybMMLPGnrMPHiEnBfmhVStXugDS5Pg3cpU3Vd+hR+WHndJzidEKIXA0WccSgLxsGnWETVr60EQJZNsAj6gTZNYbqBIllmsOnsWy+tHmActGI5DpiV5Sux4jGG3PyL6djmZzRMjiswmWCYci//1luw85tfSXms9aBpGnp6euB2u6l+5VVI1BiVJAnhcBjFxcUQRfGaUjuTtcU5x+DgIGRZ3nJ1zfQwxvCpT30KoVAI//zP/0yBJ0IIIauBgk+EEHItFEXB7373Oxw9ehS///3v0dLSgvb2dtx2221UK2MTiMViyUCUpi3OcLbZbGhubqbBBLIlTE1NYXx8HC0tLbDb7ZnuDskSnHOEQqHkilGLxbJp0k+FQiH09vau6So/5Wf/CO33Txg3Kt8JNmpchwmVdWBpakOZ6pqh9huvahLcXrAVpPAz726G1t+Ttp2pogp8cjz5fwYz1Dn9lHmcc2gKg2CQGk+JacmAzeU0jcPoF9ko+GS0TZGZbkCM2XNgVuPLHo9GGRwFi8EnTeUYGYhDU4FLywNdmnLPZOIw2yxo/t/fweLMTHA/kV6ytLQU5eXlGenDZsIYw8zMDCRJSgbqE6mdN1ugfqPinOPMmTNQVRUNDQ0UeMLi9/aTn/wkIpEIHn74YQo8EUIIWS0r+pGlSpqEkFVRXV2N/Px8mM1mWCwWvPLKK5nu0jWzWq04ePAgDh48CE3T8OKLL6KjowMPPPAAamtr0dbWhje/+c0oKirKdFfJVcjJyUFlZSXKy8vR03NxwO2ll16C2+2G1+vdknVQyNYwNjaGQCCA/fv3U2F1soQgCMjPz0d+fj527NiRTD918uRJaJqWDEQ5HI4NdX5M1PJramqC0+lcs+MIJenrRwkrCOIJthUEhNny1HCX4zP+xZpOWvq2KyHkOZfMRozwfNigH3xSlTQ1mTSuG3gCFoM8Jp3VS0DqlHpJRtMmdZ7GAQjxGJCiT5c+Ik2q0NSlK544X7rqyWQCXG/7vzIWeFIUBV1dXdi+fTtKS0sz0ofNxmQywe12w+12JwP1kiTh+PHjMJvNyfR8NEktMzjnOH36NDjnFHi6gDGGj3/845BlmQJPhBBCMoJGGwghq+a3v/0tPJ7M5bNfS2azGTfeeCNuvPHGZN78o0eP4o477oDL5UJbWxuOHDkCr9dLNzobiCzL6O7uRllZWXJGsKZp8Pv9GB8fRygUQnFxMbxeL4qKimhFFNnwEjOC4/E4Wltb6TtN0srNzUVlZSUqKyuhKAokScLQ0BCi0ShcLhdEUURRUVFW//YFAgGcOXMGra2ta17LT/CuzuoSYQUJJ3hUv05SEmMQXCXg0pTxvuKxlXXMcnF1h1y+F9HfvwSbS/+WUlM5TDb974aqcFgNxkK5QWksADAaRzX6Sup9X5nJqpsC0GxdPF9GQgxzweXBvMt3KdjtqPzM3+t3Yg0l6prV1NRAFMWM9GGzuzRQX1tbi1gsBr/fj4GBAciyDLfbDVEUaSLTOuGcY2BgAIIgoL6+nt5zLAaePvaxj0FVVXz729+maz5CCCEZQcEnQgi5QiaTCS0tLWhpacG9996LwcFBdHZ24t3vfjdMJhMOHz6M9vZ2VFVV0Y1PFguHw+jt7V1W98NsNqOkpAQlJSXJ9CrT09MYGBhAQUEBRFGE2+2mmYNkw2GM4eTJk7Db7WhsbKTzE7liVqsVZWVlKCsrg6ZpCAaDmJycRH9/PwoKCuD1euFyubLq/Dg9PY2xsTHs379/XWo3rij4JKcP9PB4NH2b2cBKugRTfiG0dMGn+dkV7Quqsti+fCcG//N/UF5lfDvJ0gSPmMZTrjJaCaZxmAyea7TNrDMGK4dlWJypN1rtAhjjmJ5QLh7jQlPOL6/7BHjf+2cwZSAVWywWQ1dX15rVNSOp5eTkoKKiAhUVFVBVFcFgEBMTE5ifn0dhYSFEUcy68+NmwTlHf38/zGYzdu3aRdc3WLzmu/vuu6FpGr71rW9R4IkQQkjGUM0nQsiqqKmpQXFxMQRBwAc+8AH8xV/8Raa7tO4455icnERnZyc6OzsxPz+PQ4cOoa2tjVI/ZJmZmRn09/dfUfolzjnm5ubg8/kQDAaRm5sLr9dLef7JhqCqKnp6euDxeKjgPFl1qc6PiToo6xHw0TMxMYHp6Wm0tLSsW3pJrqmI/02bcdSlyAMm+Yx3VOgC8/vTHo/lOIBIyLCNqa4ZWp9xbajFyImQNpWfqaIGPB7BmecmoczMo2ZPrmHavMiCCnuu/qBnNKTBbtd/vhxjsFpTb1cUrruNM65bZ8ooaBUJMzgKlgcHFJnD7jBjJpwD3+BioM5kWhp8slguptyzFTmw7w/Prfu1XyQSQU9PD3bv3k1pobME5xyzs7OQJAnBYBA5OTnJ8yPVW7x2nHP09fXBarVi586ddL+FxcDT3//934Nzjm9+85sUeCKEELJWVvSjS8EnQsiqOH/+PMrKyuDz+XDw4EF8/etfxxve8IZMdyujAoEAHnvsMXR2dmJ8fBy33XYb2tvbsX//froJyKDJyUmcPXsWzc3NyFlB3Y1UOOcIh8Pw+Xzw+/2wWCwQRRFer5cGEkjWicfj6O7uRmVlJdX9IGsucX6UJAl+vx+CICTPj2ud8u7SPoyOjmJ+fh6NjY3rvtIg/tn3gEuT+g0E02JsSk2d3m2xjQDGLYASNz7Ytkqwc2OGTUx1TdD6egzbAIAglqZNzycUe3BuVMPcqRGYLQJqGow/02hEhc2mf80TCxtv1xSmWzNKjjPY7KmfqyocFp3AlNG2aJQhz7n8+xKNMFhsJoyelhcfMJlgArtstVNinxzV938CJe98e+oXtUZCoRB6e3uxd+9eFBQUrOuxycolzo+SJAFAsk7URqujlw045zh16hTsdjt27NhB7x8WA09/93d/B0EQ8I1vfIPuOQkhhKwlCj4RQjLjnnvugdPpxEc/+tFMdyVrhEIhPPnkk+js7ERvby9uuukm3HHHHbjhhhvWbTb2Vsc5x8jICObm5tDU1LSq73s0GoXP54MkSeCcJwdaqeA0ybREesm6ujq4XK5Md4dsQbFYLDnQqihKcqA1Pz9/TQYKEwXnVVVFQ0NDRgbe5G9+Cuzky4ZteFEpuHTeeEfuMrCpCcMmwo490M6cNGxjqtoFbfi08bEAmGp3gw336zew2eGLubEwMQP53CRynSaU1+hP4uCcQ1O5Ye0lJabprpzSVA6duNPic2GBFakDeIrMYNUJahltkxXAnrP8oOEwQ1DSEIss3g5zDiQWPSdS7iWCZPbyUrT+9xP6HV8DCwsLOHHixBWt6CaZJ8sy/H4/JElCNBpFcXFxso4eBQ2Mcc5x8uRJ5Obmora2lgJPWAw8ffSjH4XZbMbXv/51+g4RQghZaxR8IoSsj3A4DMYY8vPzEQ6HcfDgQXzmM5/Bm9/85kx3LSvF43H85je/QWdnJ1544QUcOHAAbW1tuPXWW696JQ4xxhhDX18fTCYT6uvr1/RmTJblZCBKlmV4PB54vV44nU66MSbranZ2Fn19fWhsbER+fn6mu0MIVFVNDrSGQqHkQGtxcfGqnJcZY8lZ8JlMv6T8x7eh/e6YcaPtdcaBHgCoqgcb7DNsYqpvgdrXZdhGcInp0/wBMO9uhtavs0LKYkUQ2zD9XA+c17Ui9EoXCt0WiGX6aRUVeenKoMtpKodgcC8qxxmsFv3PUDPbYNZk3efqrYrSCz5xvpiqL1W6Pr9Pw4x/aUrCxBwWU14ehAs1ujgHdn37y3C/6Vbdfq+22dlZ9Pf3o7m5mSa9bGCapmFmZgY+nw9zc3PIz89P1hmliWpLJWpYOhwO1NbWZro7WYExhr/927+F1WrFP/3TP1HgiRBCyHpY0c0WXcUQQq7Z9PQ03vrWtwJYHFh65zvfSYEnA3a7HYcOHcKhQ4egqiqef/55dHZ24r777kNdXR3a2trwpje9iVKmrBJFUdDb2wuXy4Wqqqo1H4y02WxLCk77/X6MjIwgHA7D5XLB6/WiqKiIAlFkTUmShOHhYbS2tq5bqjNC0rFYLCgtLUVpaSkYY8mB1tOnT1/zQKumaejt7UVRURGqq6tXv/NXQCgpT9/Glj5Fq2BdQb0sTUnbhM8EFiMlRmn+gMXISSomE2Zt2zH9X68uaWfLMR7cVBUOm0E9J1XlsBpkROQGZbMAwGTw2o1+YvVqQSkyYM9bvk2R+ZLAE+fApZkctXAkWe8pZ0fNugaegsEgTp8+jdbWVprAtMGZzWZ4PB54PB5wzjE/Pw9JkjA6Ogqr1QpRFCGK4pb/nBljOHHiBPLz81FTU5Pp7mQFxhg+8pGPICcnB1/72tco8EQIISSr0MonQgjJEowxHD9+HB0dHfjVr34Fr9eL9vZ2HD58GB6Ph4IVVyEWi6G7uxvV1dUoKSnJaF8YYwgEApAkCXNzcygsLEwOtNJNIllNExMTmJqaQktLC6yJvFCEZLFLB1oDgQBsNltyoHUldfQURUF3dze2bduG8vL0gZ+1pvX9AcrXP2HYRqjdC22g17jNjr3Q+tO0KauENmFc8wkA4N4G7jNO82eq3gU2euayAwiYL9yFc0++lHzIVuqFPOVDea0duQ796FFkQYU9V//3LRrWYLfpX9vEoww2ne2axnXT9RltT9z7prqmikQYHPnmZe3Hh1XIcX7JY6lT7nEO7H70+yg60Krbr9V06SQDqje5uUWj0WT6Uk3T4Ha71zR9abZijKG3txeFhYUZn2SQLRhjuOuuu5CXl4evfvWrdE9BCCFkPVHaPUII2ag45xgYGEBHRwcef/xx2Gw2vOUtb0F7ezvKy8u31I3m1Zqfn8fJkyfR0NCAoqKiTHdnCc45Zmdn4fP5EAwG4XA44PV64fF4KLUKuWqccwwPDyMUCqGxsRFms8GSAkKyWCQSSQ60JuroiaIIh8OxrG08Hk9OMvB6vRno7XIsMAX5039m2EYor4U2OpimTQ200SHjg+U6wMKhtH0yVddDGzJO8ye4RPCgtOSxsKcB44/9z7K2Vq8HFZ6wYQAobfAppMFusDJKiTFYrKm3yzLXDUwxxpP1ly5nFLSKhBkcBUvPmwFJgzSlLVnpJAgXVz4xBlgv9DGnsREtP/+x7utZTdPT0xgfH0draytNMthiFEVJTmYKhUIoKiqCKIpwuVybOvDAGENPTw9cLhcqKysz3Z2soGka7rrrLjidTjz00EOb+vMnhBCSlSj4RAghmwHnHBMTE+jo6MCxY8cQjUZx+PBhtLW1oa6ujgJRKUiShKGhoQ1R/4BzjlAohOnp6eSMf6/XC1EUYbOtIOUSIVgclOnv74cgCNi9ezedF8imIctyMhAVi8WSM/4LCwuTq1vr6urgcrky3dUkzhji/187oBqkxMsvAgsGjXfkLASbmUl/vLx88NC8YRtTfTO0U93GOxJMgNmUTM8XK92Dkc4XUjYt2LcXXnXEcHexiJqytlJye1iFzWA7U5huirx4jMGuk/ZPVbhu0Eoz2WBmqetERaMcec6L+4xFGUYHVQgCkrWrOF/MYJg4xS7+XwCHgNb/fgw55WW6r2e1nD9/HufPn0draytNWNniGGOYnZ2FJEnJyUyiKMLj8WyqoKSmaejp6YHH48H27dsz3Z2skAg85efn4ytf+QoFngghhGQCBZ8IIWQzkiQJP//5z9HZ2YnJyUkcPHgQ7e3taGlpoRsPAGfPnsX09DSam5s3ZPAmEonA5/NBkiQIggBRFOH1eqluD9GVqHVTUFCAmpoaCjyRTUvTtOSM/9nZWciyjNraWmzfvj3rfv/i978ffHLcsA0TbIAcM25jzgFiEeODldWATQwbNjHVNUHr6zHeDwBBLAOXzkOu2IuhR5/XbefeX4diZUJ3O+ccmsoMz0dqTINJZxWSqnIYLKpCPM5gt6f+zOU4g01nm2ZzwCyHU25TVCRrVDHGMTqoIh7juDS+o5dyr+CNt6Lhn7+s3+FVcvbsWUiShJaWFlrdSpbgnCMcDkOSJPj9/uQ1pCiKWT8Ry0gi8CSKIioqKjLdnaygaRr+5m/+BkVFRfjyl7+cdb9/hBBCtgwKPhFCyGY3Pz+PX/7yl+js7ER/fz/e8IY3oL29Ha973eu23GxYzjnOnDmDWCyGvXv3bopBmXg8ngxEqaoKj8cDr9cLh8NBAQYCYHFVSHd3N8rKyrKi1g0h62F2dhZ9fX2oqqrCwsJCVs74l//5XrDu1KuGEri7DHxKP4ADABArwM4bB7GEnXuhnT5h2MZUtRPa8BnDNgBgqt0NWTXjzCP6gScAcNe6UOzQD5wpcQaTwc+wpnIIBvehcpzBajFIyafwZLq7VM/VDT6Z7TBr8WWPM8ZhsgjJ39bp8yqCfrYkxR6QOuUeN1tw3fHfweJY2wH+0dFRzM3NoampiQabSVrxeDy5ajQejy9ZNbpRriE1TUN3dzdKSkroGucCTdPw4Q9/GG63G1/60pfoXEAIISSTKPhECCFbSSwWwzPPPIOOjg68/PLLeM1rXoP29nbcfPPNm74QtaZpOHnyJHJzc7Fz584Nc1N9JRRFgd/vh8/nQzQahcvlgtfr3VCDCGR1RaNRdHd3Y+fOnfB4PJnuDiHrwu/3Y3BwEK2trcjJyQFwMX1pYsa/2WxOrhpNtFlvSsd3of36UeNGVbvBBk8ZNhGqG6CdOWnYxlTfDLXPOKWeUOQGC/iN+wNArX8NzvzomcVlPQbEchsKXfqTXKJhLbmKKJV4lMFqMEcmFmWw69R0AozrOiky0033pzLAkmJTPM6Rk7e4IRxiGB9eTD24uLJpsY1eyj3v//Mu1HzqI/ov5hol6vlFIhHs3buXBpvJFbt01ej8/DwKCgogiiLcbnfWTtbSNA1dXV3Ytm0bysrWPp3lRqBpGv76r/8aoijii1/8Ip0LCCGEZBoFnwghZKtSVRXPPfccjh49imeffRZ79uxBe3s7Dh48CKfTmenurarEyo9t27ZtmXQclw8iFBUVwev1ori4mG5Et4iFhQWcOHECe/bsQWFhYaa7Q8i6mJqawtmzZ9HS0mKYVjUajSZn/GuaBo/HA1EU4XQ61y1Yrz7/JNSffs2wjbCjCVp/mqDRrmZop7oM25h2NEA9YxzEgiCACxZASV3vCADiZXvhG55H6Hiv8b4AlNfakevQH7SOLKiw5+r/HkXDmmFwKR5lsOls11QOs8GqKL3AVOK+N9V3IBJhcOSboWkcw6eVZLmutCn3HA68pve5Nftecc5x+vRpaJqGhoYGmmxCrhnnHHNzc5AkCYFAAHa7PZmeL1smq6mqmlzVvW3btkx3JyskAk9erxdf+MIX6HqfEEJINqDgEyGEkMVixK+88gqOHj2Kp5/e5aXtAAAgAElEQVR+GhUVFWhra8Phw4ezqkD71QiHw+jt7d3SKz8SxaZ9Ph9mZmaQn5+fTD2VrbNZybUJBoM4ffo0mpubN3QdB0KuRKLWTXNz8xWllU2sGpUkCeFweMmq0bUcvGNneiF/9aOGbYTaPdAGjNPlCTv2QOtP06Z0O7Q0qfkAAJ4y8Olzyx+3WDHvqMa5X70MS1Eh1Nm5tLuq2ZMLs0FRpkhIhT3HIPgU0mA3WBmlxBgsemn1ZK4bmDJaEaWqHBadoFUi+HRuXMX8LAOAFaXcq7zvE9j2p3fqvo5rwTlHX18fzGYz6urqKPBE1kQ4HE6eIxljyUBUplI8q6qKrq4uVFRUoLS0dN2Pn400TcNf/dVfobS0FA8++CAFngghhGQLCj4RQghZinOOkydPoqOjA0888QTy8vLQ1taG9vZ2lJaWbqiBjUTNj8bGRuTn52e6O1mBc475+Xn4fD4EAgHk5OTA6/VCFMWsqIFCrt3k5GRy5Ue2zFAmZC0lUo6FQqFrrnWjaRpmZmbg8/kwNze3pqmn+FwA8Y+/07CNsK0a2vhwmjZV0MZHjA9mzwGLRtP2yVSzG9pg39IHcx2QIoXwv3hx5ZS9sgLxcf1aVGaLgJqGXMNjxSKqbuo7AIiH9bdzzsGZ/t1sPMZ0A1tGtaAUzQSrmaXub4xDVjjOj2vJxy5PuXf5z6it1IvrXnxKp5fXhjGGkydPIi8vD7W1tRvq+oxsXLIsJwNRkUgELpcLoiiiqKhoXQIeiqKgq6sLlZWVKCkpWfPjbQSapuFDH/oQysvL8cADD1DgiRBCSDah4BMhhBB9nHOMjIygs7MTv/jFL6CqKg4fPoy2tjbs2LEjqwc6pqamMDY2hpaWlozV89gIwuEwfD4fJEnKihoo5NqMjo4iGAxe8coPQjYqzjkGBgbAGFv1lGOXp57KyclJzvg3Sul3JWIfeSsQi+g3yMsHm0uzyijPCTY3n/ZYPL8IfG7GsI2pvhnaqUvS/LlETJyJY+H00lVTzuv3IfTycd395DpNKK/R/x3hnENTmeHnpcY0mHRWTqkKh9lgfFWOM9jsqRvIMoNNJ6glW/JgU1N/HrJgx2hfCOxi7AmXn2YT/0+k3Nv942+j+PWv0+/oVWKMobe3F4WFhaiurl71/ROyEowxBINBSJKE2dlZOJ3OZLB+LSY0UeBpOVVV8aEPfQjbt2/H5z//eQo8EUIIyTYUfCKEELIynHP4fD4cO3YMnZ2d8Pv9uP3223HHHXdkVXFrzjlGR0cxMzNDA/BXKBaLJQNRmqYlA1EOhyPTXSNpJGp+KIqCPXv2ZM3fIyFrKbHyIycnBzt37lzzCRGJYL3f74cgCMlA1LWktow/+P+Cnx00bMMseUA0ZNzG5gDCC8YHq6gFGx8ybGKua4La17P4n21VGPn9OGK+5QEr5/5mhF7t0d1PodsCsUw/QKfEGUwGC8lUhcNkcFsZjzHYdFYvAcarm4yCT5EwQ55j+TZN45gY1xCNXOxTupR7zr31aH3y33X7eLU0TUN3dzdEUcT27dtXff+EXA3OORYWFpLB+sSEJlEUkZtrvApyJRRFwfHjx1FdXQ2v17sKPd74VFXFBz/4QVRXV+P++++naz9CCCHZiIJPhBBCrs7s7Cwef/xxdHZ2YnBwELfccgva29tx/fXXZ6yOEGMM/f39AIDdu3fTTdg1SKRV8fl8iMVicLvd8Hq9KCgoyOoVb1vReg/AE5INNE1DT08PiouLM7LyIx6PQ5IkSJKEeDwOj8cDURSv+Bwpf+8BsD88a9xI3A52fsy4TUkl2MSoYRNhVyO0gV7DNqbKHdBGBsEqd2PwF13QorGU7aweNxR/QL/L5TYUuvQnf0TDGmwG9ZziUQarwdyRWITp1oNK3LvqfQ6qwq+4VpQ0rSHgvyQdn8kEk8CQKB21LOWeyYx9vz6K3Noq/RdxFRK1bsrKylBWVraq+yZkNUWj0WR6PkVRkufI/Pz8K75OkWUZXV1dqKmpgSiKa9TjjUVVVfzlX/4lamtrcf/999O1HyGEkGxFwSdCCCHXLhKJ4Omnn8bRo0dx/Phx3HDDDWhra8PrX//6VUtNlI6qqujt7UVRURGqq6vpJmwVaZqWDESFQiEUFxfD6/WuW35/ok9RFPT09EAURVRWVma6O4SsC0VR0N3dnTUD8KqqIhAILDlHiqKI4uLitOdI5bEfQXvyZ8YHqG4AO3PSsIlQswfa6ROGbUz1zVD7ug3bCAXFiDnKMPSfzy9GVAzYykohn59Kua281o5ch/5ElMiCBnuu/u90NKTpBpcA4+CTqnJYLPrPZYzDZFq+nTEOQVgetFqYZzg7puHSjzJVfadLU+4V3nwjGn/8Dd0+XA1KOUY2qsQ5UpIkLCwsoKioCKIowuVypT1HyrKM48ePY8eOHfB4POvU4+ymqio+8IEPYMeOHRR4IoQQku0o+EQIIWR1KYqC3/3udzh69Ch+//vfo6WlBW1tbXjjG994TamJjMRiMfT09KCyshKlpaVrcgyyiDGGmZkZ+Hw+zM7OoqCgIJnfP1Mr3raqxPe+qqqKBiLJlhGPx5Mz4LMx9VLiHClJEmZmZpI1UDweT8o0sNr//hrKj/7BcJ/CjiZo/WmCRruaoZ3qMmxjqt0NdbBPv4GzEEHZBX/3KNRA0HBfAOC8rhWhV1Ifs2ZPLsw69ZoAIBJSYc/RH3ROF3ySY0w/rV6c666qYgzQG+uOxThycpY+Lx7nGD6jJms4XerSj9NkES6mCbTZcf1LT8FaVKjb/yuV+N7TADzZ6BhjmJ2dhSRJCAaDyMvLS54jL5+wlvje79y5E263O0M9zi6JwNPOnTtx3333UeCJEEJItlvRDxUVyyCEELJiVqsVBw8exMGDB6FpGl588UV0dnbiwQcfRE1NDdra2nDo0CEUFRWtyvEWFhZw4sQJ7N69G8XFxauyT6LPZDLB7XbD7XaDc465uTn4fD4MDw8jNzcXXq8XHo9nTQpNk4vC4TB6enroe0+2lEgkgp6eHtTV1cHlcmW6Oyldfo5M1EAZGxuD1WqF1+uFKIqw2+0AAMFbkX6nTE3fRlPSNuGhef2nb9+NkV/3QZZG4djXvKLgk96UQ7NFMAw8AfoBoARB4NC7V+Wcw6ic4+LESZ3nWmwAk1Nu07Slz9M0jrOjKhhbWtuJ86X/ZwywXPJmeN/x1lUNPMViMXR1dWX1956QlTKZTHC5XHC5XOCcIxwOQ5IkdHd3QxCEZHo+i8WCrq4u7Nq1i773F6iqive///2or6/HvffeS4EnQgghmwatfCKEEHLNGGPo7e1FR0cHnnjiCRQXF6OtrQ1vectb4PV6r+oGamhoCJIkoampCQ6HYw16TVYqMYDg8/ng9/thsVggiiK8Xm9ykJWsjtnZWfT19aGpqQlOpzPT3SFkXSQmGuzduxcFBQWZ7s5ViUaj8Pl8kCQJjDGIogjRkQvLPX9m+DyhdDu0s8Y1n4SS7dAm0tSFstrAZHlpOj1HAWaYB5PP/CH5UP71+7Hw8qvpXg6spV4oU75lj+c5TSirydF9HuccmsoMf/fVuJYyNR4AKAqHxSB4JccZbPbUDVSbAxY5nHJbKMTgdF583tkxFQvzi++VUcq9S/8vOJx4TfdvYTaKjl2BRMC1oaEBhYWrF9AiJBvF43H4/X5MTU1hdnY2mVK4sLBwywdaFEXB+9//fjQ0NOCee+7J2vfjbW97Gzo7O/HQQw/hrrvuStnm5Zdfxg033ICKigp0d3dv2N90QgghK0Jp9wghhKw/zjmGhobQ0dGBX/ziFxAEAUeOHEF7ezuqqqpWdEP1ta99DceOHcNTTz1FwY0sdOkgK+c8GYhaq9SLW4XP58PIyAhaWlqQk6M/uEvIZjIzM4OBgYFNNdFAlmX4/X5IkoQdP7wHlnhEv3FOHthCyHiH9lywUOqgyqV4oQt8JgAAYNvrMfpfA4j5ZpYerno7YqNn0+4LAKxeEYpPWvJYodsCsUy/3qMcZzDK0qpxEwRVf7VXPMpgs+lfJygK103Jp5jssLJ4ym2RCENe3mKUSZrWIPkYOAcEQT/lXmIVVGJ79ec+ibJ33anbtysRCoXQ29uLxsZG5Ofnr8o+Ccl2iZV+O3fuBOcckiRhbm5uS6d5VhQFf/7nf47GxkZ85jOfydrAEwAEAgE0NTUhGAzi5ZdfRlNT05LtkUgE+/btw+DgIJ599lncdNNNGeopIYSQdULBJ0IIIZnFOcfk5CQ6Oztx7NgxzM3N4dChQ2hra0NDQ8OyGyzGGD7ykY/gzJkzeOSRR2jlxwYgy3IyECXLMjweD7xeL5xOZ1bfQGebiYkJTE1NoaWlhdIaki1DkiQMDw9v6oBr/Et/Az7ab9iG5eQDC3OGbXhuAfj8rPHBtu8EkyYxJ5Tg3FOvpG4jCDAX5EOb00/Tl+A80ILQH5bWoxLLbSh06a/8iYY13ZpMABCPMVgNxpZjEaZbDypx36r326KogFWna7LMYbMJWFhgGB/RIAjLU+xdHmxiDEiUqbGUleM1Lzyu3/ErMD8/j1OnTm2qgCsh6USjUXR3d2P37t1L0nMn0jxLkoRAIAC73Z6sE7VZfxcSFEXB+973PjQ3N+PTn/70hrhufuqpp3Do0CE0Njbi5ZdfXjJJ8IMf/CAefvhhfOITn8DnP//5DPaSEELIOqHgEyGEkOwSCATw2GOP4dixYxgbG8Ntt92GtrY2HDhwAPF4HO985ztRWlqKb3zjG1tu5uNmoKoq/H4/fD4fwuEwXC4XvF4vioqKNsQNdSZwzjE8PIxQKITGxkb63pMtY3JyEhMTE2htbd3UAVf5R/8A9r+/NmzDvVXg50aMd1RaDXZ22LCJuvs1GP3Fy4hNBQzbOVobEe46YXw8AM7r9iH0yvElj1XssCMnT/88FVnQYM/VP99HQ5pucAkAYhENdr20eiqHxaL/XMZ4ynR+msZhNguQ4xzDg4t1nhJWknKPc6D58X9DftNu3WOv1OzsLPr7+9HS0oLc3Nxr3h8hG0Ei8LSSFJORSASSJMHv90PTtGSdqM02qSkReGppacGnPvWpDfXaPvzhD+PrX/867rrrLjz00EMAgCeeeAJHjhzBgQMH8D//8z+b+nedEEJIEgWfCCGEZK9QKIQnn3wSx44dw6uvvgpN03DbbbfhC1/4At2wbAKMMQQCgWRKlcLCwmRKFVO6avRbBGMMfX19MJvNqK+v31ADD4Rci/Hxcfj9fjQ3N8OySvVzspX65M+gPvYjwzYhTyXyJkcN2wi1e6EN9KbcxirqMHUqAMXiwMIrXWn7lP+aA1h46Q9p29kryhCfOL/ksZo9uTCb9c9VkQUV9lz9c3w0pOoGlwBAjjHdtHpynOuuqtJUDrNOYCoW5bDZgOEhFfKFrHypUu4JwsWVUJeugsq/8f9A00+/pdvnlQoEAhgcHNzUK/0IuVyittmePXuuuP6PoijJFKbhcBjFxcUQRRHFxcUb+lpSURS8973vxb59+/DJT35yw13/xWIxHDhwAH19fXjmmWfQ0tKCxsZGLCws4NVXX0V9fX2mu0gIIWR9UPCJEEJI9hseHsadd96J9vZ2nD9/Hi+88AL279+P9vZ23HrrrTRAswlwzjE7Owufz4dgMAiHwwGv1wuPx7PpB571aJqGnp4eFBUVobq6esMNPBByNRIr/cLhMBobGzf04OFKaX94Fsr3HjBsI+xsgtbXbdxmVxO0U5e0EQSw8jpMnvBhtndxRZS11At5cjptn3JqqxEbHk3bDgAsbhfUQBAAYLYIqGkwXq0Ti6iw2vQ/11hYhU1nO+ccXOO658N4jMGek/q5ibR6qYRDDMEAw8L8xVvZVCn3LJbFABRwMeUeN5nx2q7fwlJwbXWZJEnCyMgIWltbYbPp18wiZDMJh8Po6elZldpmjDHMzMxAkiTMzMzA6XQmJzVtpElrsizjfe97H/bv349PfOITG/b6r6urC6997WshiiKam5vx5JNP4lvf+hY++MEPZrprhBBC1s+KfsS25ogPIYSQrPDiiy/iAx/4AL7//e/jwIEDABYH5Z9//nl0dHTgvvvuw65du9De3o43velNVzxjkmQHQRBQXFyM4uJicM4RCoUwPT2NsbEx2Gw2eL1eiKK4ZQbkZFlGd3c3ysvLUVZWlunuELIuOOfo71+sfdTU1LRhB9yulOCtSN9IVdI3iUUX7+4EAVp5PSZ7pjD33y8uaaNM+WD1ilB8kuG+YqPjMDkdYKFw2uPaKyuSwSd7jvFnxjmHRWfVUoJR2jxVASwGq6qMbm8XJ1SmbhBa4EsCT8DFIJPeY4l/l/3Fe6458DQ1NYWzZ89i3759G2qQnJBrsZqBJwAwmUxwu91wu93Ja0mfz4fx8XGYzWaIoghRFLM6naUsy3jve9+L66+/Hh/72Mc29O9ga2sr7r//ftx99904d+7c/8/enUdHcZ5pw796l7q1q6sEQoDEjtDSCIPBOzZeWNQkMYvt2HFsmMST+ExwFmeSSd757CRje5bsnkwyZ5K837yzZBJJgG1MvL3x5/HEEBurJSEhARICiaWqu9X7XvV8f8gta+muFiCpW+r7d05OjrqerrqF2iVVXfXcD7Zv307BEyGEkIRo5hMhhJC0aGlpwXPPPYff/e53WLRoUcIxsiyjtbUVTU1N+P3vfw+O42C1WrFt2zaYzeZZfdFGhgUCAQiCAFEUoVKpwHEceJ7P6JsH1yO+7sGyZctgNpvTXQ4hM0KWZXR0dMBoNGLp0qVZde5m4RDCT+1UHKPiFkC6eEFxTLSIgzeqhaPTgdDZS0nH5TXUw/tB6tZ7ptpq+Ns7U47LX98A759OAACKzFqY5yd/SCAma6CSkwdpsagMpblu4aCcdPbS8PuTh1vhsJywnd+Qi2GwPzZhbafJtNzTFBVho+2t6/q8Dg4O4vLly6ivr8/amb4k+/h8PrS3t6O2thZ5eXnTfrxQKARRFCGKIqLRKEpLS8FxHAoKCjLm900kEsFjjz2GG2+8EV//+tczpq7rIcsyFixYgMuXL6OzsxOrV69Od0mEEEJmFs18IoQQknkYY/jhD3+IV199Fa+//rriwsNqtRoNDQ1oaGjAd7/7XXR3d6O5uRkPPvgg9Ho9duzYAavVigULFsyJi7hsZDQaUVlZicrKSoTDYQiCgK6uLsRiMZjNZvA8D5PJNCd+vh6PBydPnkR1dXXKBbcJmSskSYLNZoPZbE76oMFcpjLkAIWlgNuRdAxzicPpx/iHAlVqsLLF8Id1sH/QC/8VN+RgSPmA6smdK9XGyQX8UfHjWVR6hbWaAEDW6KBRDJ8Y9Aozo2SFxxwZY9AoXLmqNRNrc7lkDA6MDZlGxo8Lo8a34FOpgBXff/a6fvecP38eDocDFosFmtEHIGQOm+ngCQBycnKwcOFCLFy4ELFYDA6HAxcuXIDX6x1Zc7SkpCRt/x1GIhF89rOfxaZNm/D000/Pib9pgeHrNIPBAABz9qExQggh149mPhFCCJkxkiThqaeegtfrxc9//vNrbrPGGMPAwABaWlpw8OBBBAIBbN26FVarFStWrJgzF3XZLL7ItCAICAaDKCkpAc/zKCwsnJU/X4fDgdOnT6Ourg5GozHd5RAyI6LRKFpbW1FRUYH58+enu5y0Cf/ga2Cn2xTHMFMRmMsJqFQfBU4GiH/qQeiifWSMYdUq+DtOKe5HN78MkYuXU9aUu3wpgqfPpi5epYKmIB+S24OKpQbkGJPfvA3JBujkYNLtQZ8EgyH5+TsUkBLOXgKAmKSCVpP4UpR9lBaN3rPXI6O/T5oQLMWND5/Gd8PLq1mFhqP/kbTWVPr6+uDxeFBbW5sVa5sRAgBerxcdHR2oq6uDyWRKdzmQZRlutxuiKMLpdCI3Nxccx8FsNs9Yq+dIJIJHH30UN998M772ta/Nyr9hlVRWVqK/vx99fX2orKxMdzmEEEJm1qR+qVH4RAghZEb4/X489NBDuOGGG/Ctb31rSi++RFHE4cOH0dLSgosXL2LLli3YuXMn6uvr6abPHCBJEhwOB0RRhMfjQVFREXieR3Fx8az4+V66dAkXLlygheZJVgmFQrDZbFiyZAk4jkt3OWkV/bcfQnr31eQDtDpIFdUI2AMQ3j+N0GDiNZtMN6yD+70PUh6P5ecBHq/iGJVWA+h0YKlmUgEwWWrgb+1AVXUuNAprMgUDMpROcUFvDIac5OfsSFiGLsmaUDFNDrRS4lolaKCBNPK13yejr1cCkxO019PpgFh0wvpOY1ru6VRY998vI7fi6tfkY4zh7NmzCIVCqK6unhW/owiZCvHZ3fX19Rn5kA1jDIFAYKQ9H4CRdaKmKygLh8N49NFHceutt+KrX/3qnAueAAqfCCEky1HbPUIIIZlhaGgIO3bswBNPPIFHHnlkyvfPcRz27duHffv2wePx4MiRI/jRj36Erq4u3H777bBardi4cSOttzBLaTQa8DwPnuchyzJcLhcEQUBPTw/y8/NHnmLNtLZGjDH09/djaGgIDQ0N9PkjWSMQCKCtrQ0rV65EcXFxustJOxW/4OMvdHqw4nmIaUwIeaPw9gvwdfcjt34+PCmCJcnrmdTx8pZWwfdhiplWMQnGlcsROKk8kwoA1Ho9tDqVYvAEACrIgMKqTiqFHIbJDFqFUzhTqzEqXxojJquhUQ9vDAYYzvVJI49Mjs9+pEh0zHEYA0afmhkDzDu3XXPw1N3dDcYY1qxZMydvNBOSiNvtRldXV8YGTwCgUqlgMplgMplQWVmJSCQCURRx+vRphEIhlJSUgOM4FBYWTkloHA6H8ZnPfAa33347vvKVr9D5gBBCSNaimU+EEEKmnSRJaG9vh8VimdHjhkIhvPHGG2hqasLx48dx4403wmq14vbbbx/pUU5mL8YYPB4PBEGAw+FATk4OeJ4Hx3HQje+hlIbaenp6EIvFsHr1anr6nWSN+NPvNTU1yM/PT3c5GSHcehyef/s1vP0CvN3ngVhswpic6tXwtXUp7kel1yMWA1gkojguf/1aeI6fSFlX/o3r4D2WeiZVztJKqK+cR3lVTtIxTGaQJDnpDVbGGKSIDHWSNamiEeXwKQo9dEj8fXu9MvLz1QiHGHrPxCBJ8WOmbrmn1uugHrVOlbogHzfZ3oT6Kn+HMMbQ2dkJnU6H5cuX041mkjVGB0+zdd0fSZLgdDohiiLcbvfIg02lpaXX9OBQOBzGI488gs2bN+PLX/7ynD4f0MwnQgjJatR2jxBCCImLxWJ455130NTUhLfffhvV1dWwWq24++67Z2xBZDK9/H4/BEGAKIrQaDTgOA48zyMnJ/kN0+kgyzI6OjpgNBqxdOnSOX3TgZDRhoaG0N3dTWubjRNzuXDylruG05Ak1MZcRAIRIJZkes9H9CtWINDZozjGsGgBQv0DqQtbXAFMZpxGg+IyA0pLkw+JhOWEQU9cLCorzIkCQkEZBn3yc2UsxqBN0pLP55Oh16vQezo2kut9tAzUhLWd1GqMbbmnBjTqj7cvf/6bKH9kt0KlE8XP+Xl5eaiqqqJzPskaLpcLp06dmtXB03jxB5tEUYTD4YBOpxtpzzeZvyfjwdOdd96Jp556as6fDyh8IoSQrEbhEyGEEJKILMt4//330dTUhNdeew0VFRXYsWMHtm/fjpKSknSXR6ZAKBQaCaIkSRoJoqZ7AexoNIq2tjbwPI+FCxdO67EIySSiKKK3txf19fUzHvjOBt2f3INQzxnFMdqqJQid7lUcY1q/Du4/pp6tpC4qgDTkVhyj0uuG87BoVHEcAJTXcDCq/Em3B/0S9Ibk15+hgAS9Tmm7DEOS9zOZASokvYnr98sYPC9h9ISwRLOeZIYxs6tkGWPWqMpZvgwb/u9vk9aYSHxmd3FxMRYvXnxV7yVkNos/bGCxWOb0OT8YDI6sE3X06FG43W586lOfwoYNGybMag+FQnjkkUewZcsWHDhwYM4HT4QQQrIehU+EEEJIKvFWOc3NzXjllVdgNBrR2NgIq9WKefPm0YXjHBCJRGC32yEIAkKhEEpLS8HzPAoKCqb05xsKhWCz2VBVVQWe56dsv4RkuosXL2JwcBAWiyXtLS8z1cD3XoDj33+jOMa4rgGeY8rt8nJXr4S3vTvl8Uz1NfC1tqccl7t6BYJdyjOpAGBRfQn0cijp9oA3BkNu8rlNQZ+UNFwCgHBAgt6Q+P3RCIMuyayoWGy41V4kPPZ1ho9nNI02+l6xzAC97uPxDa/9F/KrlyetcTxJkmCz2cDzPCoqKib9PkJmO6fTiZ6enjkfPI0niiJaWlrwyiuvoLe3F+vXr4fVasV9990HAHj44Ydxzz334Etf+hJdPxBCCMkGFD4RQgghV4MxhnPnzqGlpQWHDh1CNBrF9u3b0djYSO3T5ghJkkaCKJ/Ph+LiYvA8j6Kioutal8nn86G9vR2rVq1CcXHxFFZMSGbr7++H0+lEXV0dNEp917Kc6/dvoP/LTyuOya2phre1U3GMKseAWFgCi05cN2q0/A0N8ExiPafJrvu0pMYEtSr5pWDAF4MhRyl8isGQJFwCgGhYTtpWLxySE+47HGY4dzY2YeJWspZ7oz+e8RZ88ddKP7kda37y3aT1Tag3GoXNZsOCBQswf/78Sb+PkNnO4XDgzJkzsFgsWb1+aigUwtGjR3H48GH86U9/gk6nw4YNG/DDH/6QuigQQgjJFhQ+EUIIIdeKMQZBEHDw4EG0tLTAbrfjnnvugdVqRU1NzXUFFSQzyLKMoaEhCIIAl8uFgoKCkQWmr+Ym+tDQEE6dOoXa2lpaP4xkDcYYzp49i2AwiDVr1tA5MYWow4nO27YojlHn5SHi8Q9PyVGgX7YMgVPKLfxyKhch2Nefsi5j9UoEOpVnUml1KlSuUl7PJRyIQatP/hkIB2LQ6RJvl2UGhVwLoaCMnHGzqvx+Gef7JESjY0MmIB8zvN4AACAASURBVEnLPRnQasd+HW+5pzKacHPX21BP8rwfiUTQ2tqKyspKmuVKsko8eFq7di30o3tWZrFgMIiHHnoIa9euhdFoxKuvvgqj0Qir1YrGxkYsWbIk3SUSQggh04XCJ0IIIWSquFwuvPzyy2hpacGZM2ewefNmNDY2YsOGDfS0/xzAGIPb7YYgCHA6ncjNzQXP8zCbzYptxARBQF9fH61zQ7IKYwxdXV1Qq9VYuXIlzQqdpFON9yPc26c4Rru4EqGz5xTHmG5YB/d7KWYrqdVQmYyQPV7lYbk5kKNRICYlHWPMU6O8Kvn5jckMkiQn/RwwxiBHk2+PRtiYtZjGGz/zye2SceG8BCYPfz1+t5NpuccYoNMN/3/1r38M7u5bkxcwupZwGK2trVi6dCnMZvOk3kPIXCCKIvr6+mCxWCh4+kg8eGpsbMQXv/jFkXPc4OAgXn75ZRw+fBiCIIw8vLZ+/Xp6UIMQQshcQuETIYQQMh0CgQBee+01NDc348SJE9i0aROsVituvfVWuiCfAxhj8Pv9EAQBdrsdWq0WHMeB5/kxLWYuXLgAQRBQV1dH69yQrCHLMjo6OmAymbBkyRIKnq7CwLN/A8dvfqc4ZlLrPq1ZDa+tK+XxTHVr4LN1pByXu2Ipgj1nk24vMmthnp/8d1skLE+YaTRaNCJDo/AxiUha6BBNuj0WYyMt+VyhHAx0DwdqMgPU44OnBC33ZIYx4VZ8ZpRaDZjWN2Bdy78kL26UYDAIm82GlStXUntVklUoeJooHjxZrVZ84QtfSPq70Ofz4fXXX8ehQ4fwwQcfYOPGjdi5cyfuuusu5OYqzyglhBBCMhyFT4QQQsh0i0aj+MMf/oDm5ma88847qKurg9VqxZYtW2A0GtNdHpkCwWAQgiBAFEUwxsBxHILBIKLRKNasWUMz30jWiMViaGtrg9lsxqJFi9Jdzqwz9Orvcf6r31Ack1tbA++HyoGR2piLaCACpjBbCQDyNjRMaj2nVOs+8Qv0KCjRJt0e9EvQG5Jfe4YCEvS65NujaiO0UX/CbbLMoFarwBiDw2/E5bOejzeq1cP980a5mpZ7TKPFTe1vQVeQn7S2OL/fj/b2dqxevRqFhYUpxxMyVwiCgP7+flgsFnrQ5iPBYBAPPvggPvGJT+DP//zPJ/0QhiRJ+OMf/4jDhw/jjTfewKJFi/DMM8+gvr5+mismhBBCpgWFT4QQQjKLy+XC/v370dHRAZVKhV/+8pfYtGlTusuaMpIk4b333kNLSwtef/11VFVVobGxEVu3bkVRUVG6yyNTIBQKoa2tDeFwGHq9HmazGTzPIy8vj2aAkDktEonAZrOhoqIC8+fPT3c5s1JUtKPzjnsUx2gKChAe8qS86tItWYJgT6/imJxlVQieVh4DAKbaavjbO5Nur1hqQI4xecge8MZgyE3eSirok2BQCKeiMEArhRJvizBotcBlpx6OwcDI64wBUCW+4k3U1SpRy73K//VVLPrcp5PWFef1etHR0YGamhrk56cOqgiZK65cuYLz589T8DTKtQZPiXR3d6OoqAhlZWVTWCEhhBAyYyh8IoQQklkeffRR3Hrrrdi/fz8ikQgCgcCcDWXiramamppw5MgRFBcXo7GxEdu3b0dZWRkFFbOQJEloa2tDcXExFi9eDEmSYLfbIQgC/H4/SkpKwPM8ioqK6OdL5pRQKASbzUbr3EyBU9s/ifC5fsUx2oqFCJ27oDjGdEMD3O8pt+eDRgOVwQDZn3hWUZzaZIQcDE2YRRS3pDoXaoW+eQFfbMyaTOMFfTEYDMm3R6OAVp34MjMYkHFFAHzusbO8JHniuk5X03Ivp2oRNr57KGlNcW63G52dnairq4PJZEo5npC54vLly7hw4QLWrl0LrTb5zMdsEggE8OCDD+L+++/H5z//efpbjxBCSLaj8IkQQkjm8Hg8qK+vR29vb9ZdrDHGcPbsWTQ3N+Pw4cNQqVTYtm0brFYrKisrs+7fYzZKNetDlmU4HA6Iogi3243CwkJwHIfS0lJaXJrMavF2Y6tWrZqzDwvMpAt//R04f9eiOCa3oQHe48rBkrG2Gp4Pk89WGhlXs1pxVlNcztIqhM72TXhdq1OhcpXyuiThYAxaXfLzXDgQgy7JdllmUCW5wgwGZAwOSAgFJ26TpInt9dQmExAcG7SND59kGdDpVVj/7mEYF1ckrRkAhoaG0N3djfr6elqbhWSVS5cuYXBwEBaLhYKnjwQCATzwwAPYvXs3Pve5z9Hf7oQQQsgkwye6G0IIIWRG9Pb2guM4PPbYY1i7di32798Pf4qnsecKlUqFZcuW4emnn8Y777yD3/zmNygoKMCBAwdwxx134Pnnn0dnZyfkJE+dk/QKBAI4ceIElixZkrTdmFqtBsdxqK6uxsaNGzF//nw4nU4cO3YMbW1tuHz5MmKx2AxXTsj18Xg8aGtrw5o1ayh4miJ569elHhSLphwS6jsPqFNf72mMkwtNdKXFCV9XWssJGA6PNNrkYxhj0Cpsj0UnJk+yzCBckdB3VkI4PPG9jCVurSf5/BPHjXu7Sg2UPbw7ZfDkcDjQ09MDi8VCwRPJKhcvXsTFixdpxtMofr8fe/fuxZ49eyh4IoQQQq4SzXwihBAyI95//31s3LgR7777Lm688UZ86UtfQkFBAb7zne+ku7S0cjqdOHz4MA4ePIhz587hrrvugtVqxbp162jGTAbweDw4efIk1qxZg4KCgqt+P2MMPp8PgiDAbrdDr9eD53lwHAe9Xj8NFRMyNZxOJ3p6elBXVwej0ZjucuaMyOUr6Lprq+IYTXERwqIr5b60iysROntOcUzuiqUIdJ9JuS9TfQ38to4JrxeZtTDPT36uioRkaBTuT0cjMhQ69iEUkMesBxUOyRg4LyEcBjQGLaLBiaG9LE8MnxK13INOB7UUHTNGzxXjlvY3FW8eC4KAc+fOwWKx0HmaZJXBwUFcvnwZFosFmvFTC7NUPHh68MEHsX//fgqeCCGEkI9R2z1CCCGZ4/Lly9i4cSPOnTsHAHjnnXfw/PPP45VXXklvYRnE5/Ph6NGjaGlpgc1mwy233IKdO3fipptuooWe08But+PMmTNTevM9EAhAEASIogiVSgWO48DzPD1ZTzJK/OZ7fX09DAZDusuZc7rusyJyYUBxjKZ8AcLnBxXHTGbdJ5VOC6ZWg4XCyscryIfk8U54nV+gR0FJ8nQp6JcUZ0eFAhL0uuTbgwEJOQY1GAOcdglXLn88A5jp9GDhyIT3JFrvSZaB8ZM0xr8mq1RY1/TPKL4p+eyzS5cuYWBgABaLhX7vkqwyMDAAQRBQX19PwdNH4sHTQw89hH379lHwRAghhIxFbfcIIYRkjnnz5mHhwoXo7u4GALz55puorq5Oc1WZJS8vD7t27cK//du/4cSJE/jEJz6BlpYW3HzzzXjiiSdw5MgRBIMJFr8gU+7ixYvo6+tDQ0PDlM76MBqNqKysxPr161FbWwu1Wo2uri4cP34cvb298Pl8uMoHgwiZUoODgzh//jzWrl1LwdM0Md3QkHKMvoxPOYaFlQMlAGDRGHKXVKYcJ3m8yKlcNLGOHOVrSianOF8pTYvC8BVrJMxw7mx0TPAEtRpyaGLwlKiVHjA862nCuHFXuua7b1cMngYGBkbajVHwRLLJhQsXIIoiBU+jxIOnT3/60zTjiRBCCLkONPOJEELIjGltbcX+/fsRiUSwZMkS/OpXv0JxceJ1JsjHJEnCu+++i+bmZrz11ltYvnw5rFYr7r333mtqBUeSY4zh3LlzcLlcqKurm7GbMNFoFHa7HYIgIBgMoqSkBDzPo7CwkG54kBlz7tw5DA0NzehnPxs5D76EC3/114pjjPV18HzQpjhGU1iAsNOT8got/8Z18Lz3fsq68jc0wHt87EyqJdW5UCv0zQv4YjDkJH+eMcIM0MYSPzTBGIP9igTxiozxl6SSnPhRSkkCxn80E7Xck2RANzr3ysnFbV1vQZubk7CW/v5+OJ1O+uyTrHP+/PmRzz61ex7m8/mwd+9ePPLII3j88cfTXQ4hhBCSqajtHiGEEDLXyLKM1tZWNDc34+jRo+A4Do2Njdi+fTvMZjMFFdeBMYbu7m5IkoTVq1en7SaMJElwOBwQRREejwdFRUXgeR7FxcV0Y4hMC8YYzpw5g3A4jOrqavqcTbPI4EV03bNDcYy2tAShK86U+9JWLETo3AXFMcZVK+Dv6k65rzxLLXyt7R/vW6dC5SrllqDhYAxaXfLPS1TSQiOPncHEGIPXLcN+RUKyybyx2MSZS0Di9Z5StdxjAOr+/UVwd940YX+MMfT19cHn86GmpoY++ySr9Pf3w+VyjczEJh8HT5/5zGfw2GOPpbscQgghJJNR+EQIIYTMZYwx9PT0oLm5GS+99BL0ej22b98Oq9WKiooKCqKugiRJ6OjogMlkwtKlSzPm306WZbhcLgiCgKGhIeTn54PjOJjNZno6n0wJxhi6urqg0WiwYsWKjPnsz3WdW7Yheumy4hh12XxEBi8pjjGua4DnWIp1n/R6MMbAIhPb2I2mLSlCzOn6eN95apRXJZ4pBACyzCBLctLPDGMMcpSNtMSLh07iFQmR8PBsJUlK9L7h8ChRK71Eh2Js7Gyo8TOhzDvvQ93Pn0tY35kzZxCJRFBdXU2ffZJVzp07B7fbTcHTKD6fD3v27MFnP/tZfPazn013OYQQQkimo/CJEEIIyRaMMQwMDKClpQUHDx5EIBDA1q1bYbVa6YZyCtFoFDabDfPmzUNFRUW6y0mKMQaPxwNBEOBwOJCTkwOe58FxHK1PQq5JPHTNz89HVVUVnSdm0PlvfBtDh19RHJO7di28f/pQcYyxvhaeD9oVxwBA7splCJw6nXKcfuECRC4MAgCKzFqY5+uTjo2EZMUlnaIRGRrVcBjk80gQLg+HTnGMYUK7PQCQoIZKlie+LgOacffI42s7jf7ojm65J+flofS3Px45V+r1+o/ex3Dq1CmoVCqsXLmSPvskq/T19cHr9dJsv1G8Xi/27NmDxx9/HI8++ui0HKO7uxt79+4d+bq3txfPPvssDhw4MC3HI4QQQqYZhU+EEEJIthJFEYcPH0ZLSwsuXryILVu2YOfOnaivr6cbDaOEQiHYbDZUVVWB5/l0l3NV/H4/BEGAKIrQaDTgOA48zyMnJ/lMBULiYrEYbDYbeJ7HwoUL011O1nE0HcTA/3pWcYzRUg/P+zbFMdriIoREl+IY4CrWfVr/ceDFL9CjoCR5uhT0S9Abkl9zBv0SIkEZdqcGIe/YWVfJZj0BgKTSQJVg49W23GNQYf1bv4FmcTlEUYQoilCpVCgtLYXb7YbJZMKyZcsoeCJZ5ezZswgEAlizZg39PfiRePC0b98+fOYzn5mRY0qShAULFuDYsWNYvHjxjByTEEIImWKT+iNa4Vk1QgghhMxWHMdh37592LdvHzweD44cOYIf//jH6Orqwm233YbGxkZs2rQJ2vF37bKIz+dDe3s7Vq9ejaKionSXc9VMJhOqqqpQVVWFUCgEQRBw8uRJSJI0EkSZTKZ0l0kyUCQSQWtrKxYtWoR58+alu5yslHdDQ8oxkcGLKcfEhlwwLCpH+LzyWMnrm1RdbFToo89Rvp6UZYZE15zhoAyvW4LbKSEUBIAkKVOi4zMMp1IJWu4luk+uSjITCgAq/vxRFFQvBzB8vqysrEQwGITNZoMkSQiFQlCpVOA4DgUFBRRCkTmNMYazZ88iFAqhpqaGPu8f8Xg82LNnD/bv3z9jwRMAvPnmm1i6dCkFT4QQQuY8mvlECCGEZJFQKIQ33ngDzc3NOH78ODZs2IDGxkbccccdMBgM6S5vxgwNDeHUqVOora1FXl5eusuZUpFIBHa7HYIgIBQKobS0FDzP081VAgAjN9+XLVsGs9mc7nKy2snN9yImiIpjVOYyRC9fURxjbFgLz3Hl9nzq3BzIkQhYTDkI0nFmREU7AGBJdS7UmuTnjFBUDZ0qBgCIhGV4XRI8QzIi4eFLxlgs+XGStdyLxRKHTDJUUI+7FE3Yck8CdDpAv3gRbjl2aMx4SZLQ1taG0tJSLFq0CLFYDA6HA6Iowuv1oqioCDzPo7i4mGaEkDmF1jdLzOPxYPfu3fj85z+Phx9+eEaP/fjjj6OhoQFPPvnkjB6XEEIImULUdo8QQgghycViMbzzzjtobm7GH/7wB6xevRo7d+7E3XffPecCmdGuXLmC/v5+1NXVzfkWdZIkwW63j9xcLS4uBs/zKCoqopurWcjv96OtrW3Wzvaba/q/9k24jhxVHJNrscD7fqviGKOlDp7321IeL2f5EgR7zqYcpy+fD1m8jMpVuYrjgjE9fKIPniEJ4eDEy8Rk4ZNSy71k4RNTT1wHKlnLPY1eg5tPvIqcedyo/cZG1vZbsGDBhP3LsgyXywVBEDA0NIS8vDxwHAez2ZzVM4TJ7McYw+nTpxGLxbB69WoKnj4SD56eeOIJfPrTn57RY0ciEZSXl+PkyZMoKyub0WMTQgghU4ja7hFCCCEkOa1Wi82bN2Pz5s2QZRkffPABmpqa8Pd///dYsGABGhsbsW3bNpSWlqa71Clz/vx5iKKItWvXQqfTpbucaafRaFBWVoaysjLIsoyhoSFcuXIF3d3dKCgoAMdxKC0thUajSXepZJq53W50dnbOydl+s1Xe+oaU4dNk7hNHBgYndTxdcRGCkxinn18G2Tt2tpUsMYSDMkJBGaHA8P+CgcCkjjtZMkv8/coyoIY84fXxIVV8JtTy7319TPAUjUbR2tqKhQsXJm0zqVarUVJSgpKSEjDG4PV6IYoi+vv7odPpwHEcOI6b8w8skLmFMYaenh7IskzB0yhutxu7d+/GF77wBTz00EMzfvxXX30VDQ0NFDwRQgjJCjTziRBCCCFjMMbQ2dmJ5uZmvPLKKzAajWhsbITVasW8efNm5c2LeMuZYDCImpqarJ/1wxiD2+2GIAhwOp3Izc0Fz/Mwm81ZEcplG4fDgdOnT6O+vh65ucqzWcjMCfWdQ/eOTymO0c0rQ3BAue0eAKjL5iEyeFlxjKlmNXztnSn3lb/OAqmnHcY89XDQFJQRCU1seZds9hIwtS33JAkYn4+rDAYgEh7bck8GitbX48ZXfz3yWnx9s8rKSvA8n7woBcFgEKIoQhAEyLI8EkSZTKZZ+fuQZAfGGLq7uwEAK1eupM/qR+LB0xe/+EU8+OCDaanhgQcewL333ovHHnssLccnhBBCpgi13SOEEELI9WGM4dy5c2hpacGhQ4cQjUaxfft2NDY2YunSpbPiZoYsy+js7IROp8OKFStmRc0ziTEGv98PQRBgt9uh1WrBcRx4ns+qdcDmqnibyfr6evp5ZqCTt9+DmN2uPKiES7k2lLHBAs9x5fZ8apMRUiA4PJVIgX5eGUIXlQMvSUocIMVNZcs9WZ74enxtp9HU+fm4re0otCYjgOE1DuPrm03VDN74mnqiKCIYDKKkpAQ8z6OwsJB+t5CMwRjDqVOnoFar6e+eUdxuN3bt2oUnn3wybcFTIBDAwoUL0dvbi8LCwrTUQAghhEwRCp8IIYQQMnUYYxAEAQcPHsTBgwchiiLuvvtu7Ny5M2NnE8ViMbS1taGkpASVlZXpLmdWCAaDEAQBoiiCMTYSRBmNxnSXRq7SwMAArly5grq6OprRlqHOfeXrcB99XXFMTl0dfCeU13Qyrq2H50+2lMczLK1E6Eyf4phkM5NGSzWzKVnAlCx8SpaHJQqe4sa8rlKj7n9/H/x9twMYPo/ZbDasWrVq2tY3kyQJTqcTgiDA4/GgoKAAPM+jpKSEWpmStGGMoaurCzqdDsuWLaPg6SMulwu7d+/GX/zFX2Dv3r3pLocQQgiZCyh8IoQQQsj0cblcePnll3Hw4EGcPn0ad9xxB6xWKzZs2JARN97C4TBsNhsWLlyI+fPnp7ucWSkSiYy0m4pEIjCbzeB5Hnl5eXRDK4PFZyy63W7U1tZmxH+PJDH7f/wXBr/7vOIYY8NaeI5/qDhGV8YjOCikPF7+hgZ4jn2gOCZV+HRdLfdUGrAEb76alnuyDGjHrVzM7W5E/YvPAgD8fj/a2tqwZs0aFBQUJC9mCjHG4HK5IIoiHA4HjEbjSHs+Cn7JTIm3TTYYDLNmdvpMcLlc2LVrFw4cOIA9e/akuxxCCCFkrqDwiRBCCCEzIxgM4ve//z2am5tx4sQJbNq0CVarFbfeeiv0ev2M1xMIBNDW1obly5dPWbulbBeLxWC32yEIAvx+/0i7qaKiIrrBlUEYYzh9+jQikQiqq6szckYi+VjozFl079ytOEa3oBzB/osp96Uyc4heVm7PZ6pbA5+tQ3FMiq5819xyT5tvQsTtT/qea225l7NsCW75nyYAgNfrRUdHB2pra5GXl6f0bUybeCtTURQhiiI0Gs1IEEVrrpHpwhjDyZMnkZubiyVLltDv5Y8MDQ1h165d+PKXv4zdu5XPtYQQQgi5KhQ+EUIIIWTmRaNRvP3222hqasI777yDuro6WK1WbNmyZUZat7ndbnR2dqKmpgb5+fnTfrxsJMsyHA4HRFGE2+1GYWEhOI5DaWkphR1pJMsyurq6oNVqaZ2PWYIxhpO33gVpyKU8sLAEMYdTcYjRYoHnfeV1nzT5eYh5fUnTo+lsuReTVVAnuJy8npZ7KqMRt598HVqTEW63G11dXairq8uoNqGhUGgkiIpGozSDlEw5WZZx8uRJmEwmLFmyJN3lZAwKngghhJBpReETIYQQQtJLkiQcO3YMzc3NeOONN1BZWYkdO3Zg69atKC4unvLj2e12nDlzBvX19fSE+QyJt5sSBAFOpxMmkwk8z8NsNkM7vjcWmTaSJKG9vR2FhYWorKykm9qzyLkvfRXuN95SHDOZdZ9MDRa4jyuHTwBgqFyEUF9/wm3T1XKPAZCl4TWfEr3nWlruMZUKG177dxTWr4LT6URPT0/Gn/uj0SgcDsfIDNLi4uKRGaQU3JNrIcsyOjo6kJ+fj6qqqnSXkzHiwdNXvvIV7Nq1K93lEEIIIXMRhU+EEEIIyRzxGyRNTU149dVXUVRUhB07dmDHjh0oKyu77pvlFy9exODgIOrr69PS6o8MB1E+nw+CIMBut0Ov14PneXAcRz+TaRSLxWCz2VBWVoaKiop0l0Oukvh//gMXn/s7xTGTWfdJXz4PgfOXUx5Pad2nVC33lGY9KW2PSYA6ySk+UfjE2PCF5/j3SDKg0w5vX/HCN7H48d2w2+04e/YsLBYLDAaDcoEZRJZlDA0NQRAEuFwu5Ofnj8wgpeCeTIYsy2MeOiDDnE4ndu3aha997Wu4//77010OIYQQMldR+EQIIYSQzMQYw9mzZ9Hc3IzDhw8DALZv3w6r1XrVszZkWcaLL76IjRs3oqGhAZrxj8qTtAkEAhAEAaIoQqVSgeM48Dyf0TMTZptIJILW1lYsXrwYZWVl6S6HXIPgqR703P+A4hj9wgoE+gZS76y4FDHRoTjEVF8DX2v7hNens+VeNAZokqzplEiilnuMDc+cUqsBc+M9sPzLC7hy5Qr6+/thsVhmdcDNGIPH44EoirDb7TAYDCPrRM2mQI3MHFmW0dbWhuLiYixevDjd5WSMePD09NNP41Of+lS6yyGEEELmMgqfCCGEEJL5GGO4dOkSDh48iJaWFrhcLmzduhVWqxWrVq1SbEUUi8Wwf/9+xGIx/PKXv5zVNx/nunA4PBJExWKxkXVPTCYTtYi7RsFgEDabDcuXL0dpaWm6yyHXiMkyTt58JySPR3lgXiFiLrfikNz6Ong/UG7Ppy0uQtQ5NLGOFOGTLCvPjEraco8Nv2+qWu7pFy3ErX86hEuXLuHixYuwWCxzbqbQ6OAewEgQZTKZ0lwZyQSSJKGtrQ2lpaVYtGhRusvJGA6HA7t27cJf/uVf4pOf/GS6yyGEEELmOgqfCCGEEDL7OJ1OvPTSS2hpacG5c+dw1113obGxETfccMOYIMrn82Hv3r2orq7GCy+8QOtlzCLRaBR2ux2CICAYDKKkpAQ8z6OwsJCCqEny+Xxob29HdXU1CgsL010OuU59Tz4Fz/99W3FMTm0tfB9OnLE0mmndWriPKbfnAwD9ogUI94+dSXU9Lfc0JiPC7kDS9yU7PSfbFp/lNL4+bV4Obmt/DVc8LoiiiPr6+jk/2zUSiUAURQiCgHA4jNLSUvA8j4KCAjpfZqF48GQ2m7Fw4cJ0l5MxHA4H7r//fnzzm9/EJz7xiXSXQwghhGQDCp8IIYQQMrv5fD4cPXoULS0tsNlsuOWWW2C1WrFixQrs3r0bu3btwlNPPZXuMsl1kCQJDocDoijC4/GgqKgIPM+juLiYAsUk3G43Ojs7UVtbi7y8vHSXQ6aA+Ov/g4t/933FMcZ1DfAcO6E4Rr9wAQJ9gymPl7d+LbzHP97X9bbcUxuNiHgSh09X23JPkieOZwxQaVRY/8qv4eYK4HK5UFdXl3XniPj5UhAEeL1eFBYWgud5lJSUZN2/RTaSJAk2mw08z9P6fqPEg6e/+qu/ws6dO9NdDiGEEJItKHwihBBCyNwRiUTw1ltv4V//9V9x9OhR3HHHHXjggQdw55130hpCc4Qsy3C5XBAEAUNDQ8jPzwfHcTCbzXN+dsNkORwOnD59GvX19fS5n0MCJztxes/DimP0ixchcPZ86p0VFCOWoK3eaHlra+E98XF7vutuuSch4ZXiVLbcW/mdL0O+7yYEAgGsWbMm68OW+PlSFEU4nU6YTKaR86VOp0t3eWSKSZKE1tZWzJ8/H+Xl5ekuJ2PY7Xbs2rUL3/rWt2C1WtNdDiGEEJJNKHwihBBCyNzS3t6ORx55BC+++CIkSUJLSwvefPNNLF++HI2NjbjvvvtQUFCQ7jLJFGCMwePxQBAEOBwO5OTkgOd5cByXtTdWr1y5Ly59QAAAIABJREFUgv7+flgsFlrfbI5hkoSOmzZD9vmSD1KpwIx5kNxexX3l1tXCe0K5PZ/WXIKo6Bj5+npa7iltv9qWe/FL0/FhFfeJ+2B6+jHEYjGsXr2a2s2NwxiDz+cbOV9qNJqR82VOTk66yyPXKRaLwWazoby8HPPnz093ORnDbrfj/vvvx7e//W0KngghhJCZR+ETIYQQQuaOt99+GwcOHMB//ud/YuXKlSOvy7KM1tZWNDc34+jRozCbzbBardi+fTvMZjPdpJwj/H4/BEGAKIrQaDTgOA48z2fNjdULFy5AEATU19dDq9WmuxwyDXr//C/g/f/+W3GMYU0N/LYOxTGmGxrgfk+5PR8A6MrnITJ4KXXLPbUKsUjyAUylhhRNnF5FoxNnMQHDs5sSnZoTzXrKa6hF8Y++AbVajRUrVtA5fRKCwSBEUYQoipAkCWazGTzPw2Qy0b/fLBOLxdDa2ooFCxZQ8DSKKIrYtWsX/vqv/xo7duxIdzmEEEJINqLwiRBCCCFzw29/+1t8//vfR3Nzs+LNF8YYenp60NzcjJdeegl6vR7bt2+H1WpFRUUF3XSbI0Kh0EgQJUnSSBBlMpnSXdqUY4yhr68PXq8XNTU11H5wDhP+5de49P0fK46ZzLpPhsWL4J9Ee768dRZ43/8wZfikys1F1BtMslGFWDTxm6ei5V7OsioU//MzyM3NxdKlS+kcfg2i0SjsdjsEQUAwGERxcTF4nkdRURH9e2a4aDSK1tZWLFq0CGVlZekuJ2OIooj7778fzzzzDLZv357ucgghhJBsReETIYQQQma/n/zkJ3j55Zfx29/+9qpa6jHGMDAwgJaWFhw6dAh+vx9bt25FY2MjVq5cSTfd5ohIJDJyYzUUCqG0tBQ8z6OgoGDW/4zjYWq81Vi2r3Ez1wXaOnD6wc8ojjFUVcJ/+pzyjlQqwJSPmMujOCxvXT2877embLknq7SQo4n76kkqLViSbcg1QvYHEm6aTMs9Xfk8FP3yuyg2l6KyslK5SDIpkiTB6XRCFEW43W4UFBSA4ziUlpZSsJ1hKHhKTBAE7Nq1i4InQgghJP0ofCKEEELI7CXLMr7xjW9gYGAAv/rVr657jRu73Y5Dhw7h4MGDGBwcxJYtW2C1WmGxWOim/hwhSRLsdjtEUYTX6x3zhP9s+xnLsozOzk4YDAYsW7Zs1gdpJDUWi6Fj0x2QA4kDGwCAWg2mz4Xk8yvuK6emBr5W5fZ8ujIO4UuCcss9KK/3FIkkX9PpelruaUqKUfS//wZ8xQIsXLhQuUByTRhjcLvdEEVxzLp6ZrOZ1pRLs2g0ig8//BCVlZXgeT7d5WSMePD07LPPYtu2bekuhxBCCMl2FD4RQgghZHaKRCLYt28f5s2bhxdeeGHKgwOPx4MjR46gpaUFXV1duPXWW2G1WrFp0yZaT2eOkGUZQ0NDEAQBLpdrVj3hL0kS2traUFxcTDM+skzv574I77t/VBxjqK6Gv61Tccxk131Cbi4kX/KwS5aRdGaULCcPkpRa7kVjgEah5Z4qz4TCX30PFSuXo7y8PPX3QKZEfF09u90OlUoFjuPAcRyMRmO6S8sqkUgEra2tqKqqAsdx6S4nY1y5cgW7du3Cd7/7XWzdujXd5RBCCCGEwidCCCGEzFYPPfQQNmzYgAMHDkz7sUKhEN588000NTXh+PHjWL9+PaxWK+644w4YDIZpPz6ZfvEn/AVBgNPpRG5u7sgT/jqdLt3ljRGNRmGz2TB//nwsWLAg3eWQGXbln3+Jyz/8qeKYSa37VJW6PR9jDNAbIIfCSccoznqKAuokl5zJ1nQCAEmeeKU60nIvx4CCXzyDqhss1GosjcLhMERRhCAIiEajI+1M8/PzaRbmNIpEIvjwww+xdOlSmM3mdJeTMeLB0/e+9z3cd9996S6HEEIIIcMofCKEEELI7OR0OlFSUjLjx43FYvjv//5vNDU14e2338aqVauwc+dO3H333cjLy5vxesjUY4yNecJfq9WC4zjwPJ/2sDEcDsNms1GrpSzm/9CGMw8/pjjGsGwp/KfOKu9oEu35GGMp13u65pZ7CrObkrbcM2hhevHbWHHbJprxkUFisRgcDgcEQYDP50NxcTE4jkNxcfGsa2eaycLhMFpbW7Fs2TKUlpamu5yMcfnyZezatQvPPfcc7r333nSXQwghhJCPUfhECCGEEHKtZFnGBx98gKamJrz22msoLy9HY2Mjtm3bRjeG5pBgMAhBECCKIhhjI0HUTLeaCgaDsNlsWLFiRVqCV5IZWDSKjk23Qw6Gkg/SaiCr9JCDQcV95ayphs+WvD2fLDPF9Z6uteWeLA/PZEq0LdmMKKbTw/jcAayx3kuf/wwWb2cqiiKGhoaQl5cHnudRWlpKLWuvQzx4Wr58OX3+R4kHT88//zzuueeedJdDCCGEkLEofCKEEEIImQqMMXR2dqK5uRmvvPIKjEYjduzYAavVivnz51MbojkiEomMtJqKRCIwm83geR55eXnT+jP2+Xxob29HdXU1CgsLp+04ZHY4u+8J+N47rjjGsGoV/B2nFMeYblgH93sfJNw2nbOekgZMydaB0uth+ObnUP/w/SgqKlIuimQMxhi8Xi8EQYDD4YBOpwPP8+A4Lu2zSGeTUCiE1tZWevBgHAqeCCGEkIxH4RMhhBBCyFRjjKG/vx8tLS04ePAgotEotm3bhsbGRixbtoyCqDkiFovBbrdDEAT4/X6UlJSA53kUFRVN6c/Y5XKhq6sLdXV1MJlMU7ZfMntd+dk/4/JPf6Y4xnjDOniSBEtxOcuWwHeqN+G2lOGTRoNYWEq6WdLmgIUSz85KFj4lnCml1SHne38By24rCgoKFAoimS4QCEAUxZFZpPHwns5rycWDp5UrV6K4uDjd5WSMePD0wgsv4O677053OYQQQghJjMInQgghhJDpxBiDIAg4dOgQWlpaIAgC7rnnHuzcuRM1NTW0HsYcIcsyHA4HRFGE2+1GYWEhOI5DaWnpdf2M7XY7zpw5A4vFgpycnCmsmMxmvvc/wNlH/0xxTM7yZfB1nVHekVYDWa2HHJjYni9Vy72IpIaaJU6nUrXjS2Z8KKXKMyH3B1+H5e7NtKbeHBOJREbC+1AohNLSUnAch8LCQnpA4yPxVqurVq2iGX+jXLp0Cbt378bf/u3fYsuWLekuhxBCCCHJUfhECCGEEDKTXC4XXnnlFbS0tKCnpwebN2+G1WrFhg0boNFo0l0emQKMMbhcLgiCAKfTCZPJBJ7nYTabr2rNk0uXLmFgYAD19fXQ6/XTWDGZbeRIBB0bbwcLh5OOUel0iMlqxTEAYFi9Cv72se35Us16YgyIRpO31ZuKlnvaeTxyfvyXsGy8ccbXVyMzS5KkkfDe4/GMhPclJSVZ+3sxHjytXr2aWq2OEg+e/u7v/g533XVXusshhBBCiDIKnwghhBBC0iUYDOK1115DU1MTTpw4gU2bNqGxsRG33XYbhQ1zBGMMPp8PgiDAbrdDr9ePrHmi9DO+cOECRFFEXV3dVQVWJHuc+eyfwf8n5bZ6+hUrEejsVhxjWr8O7j+O3U+q8CkSBdQKl5KK4ZOU+L2jW+7l1KyC9ntPomHdOprxl2XGh/dGo3EkvNfpdOkub0YEAgHYbDasWbOGWk2OcvHiRezevRv/8A//gDvvvDPd5RBCCCEkNQqfCCGEEEIyQTQaxdtvv42mpia88847qKurQ2NjI7Zs2ULrYcwhgUAAgiBAFEWoVCpwHAee55Gbmwtg+MZrb28vfD4famtrqS0jSeryT3+GKz/7Z8UxxnXr4DmWYt2nFcvg6xzbni9Vy71wGEg2IUWSkPS9qVruqVRAwb23QXPg07BYLBTCZznGGPx+/0h4r9FowHEcOI4bOWfONX6/H21tbRQ8jRMPnr7//e9j8+bN6S6HEEIIIZND4RMhhBBCSKaRJAnHjh1Dc3Mz3njjDSxevBiNjY3YunUrLTg+h4TD4ZEgKhaLobS0FH6/HxqNBtXV1bTuCVHkfe84evc9oTgmZ+UK+E72KI5R6XSQZDXkj9rzpWy5BxViMZb0SlJp1lM0BmiStdxjALd/L6T774LFYsmaWS5k8kKhEERRhCAIkCQJpaWl4HkeeXl5c+J8GQ+eampqkJ+fn+5yMsbg4CD27NlDwRMhhBAy+1D4RAghhJDZqbu7G3v37h35ure3F88++ywOHDiQxqqmnizL6OjoQHNzM44cOYLCwkI0NjZix44dKCsrmxM33MhwEGWz2RCNRqFWq0duqhYWFtLPmCQkh0LD6z5Fo0nHqAx6xCJMcQwAGFauhP/kcHs+7bwyhAcvJx0bjX7cHi8RxfApmnjGlKzRYf43v4DITbWor6+nVpMkpWg0CrvdDlEU4ff7UVJSAo7jUFRUNCtnjPp8PrS3t6O2thZ5eXnpLidjxIOnH/zgB7jjjjvSXQ4hhBBCrg6FT4QQQgiZ/SRJwoIFC3Ds2DEsXrw43eVMG8YYzp49i5aWFhw+fBgAsG3bNlitVlRWVlJIMUtJkoS2tjYUFxejsrISkiTB4XBAFEV4PB4UFRWB53kUFxfPypuqZPqcfvhxBD5sVRyjX74cga7TimNM69chdElEWJsPn60DuQrLLE11yz11fj7m//BbCFVwqKurgybZzglJQpZlOJ1OCIIAt9uN/Px88DyP0tLSWfF5ouApsYGBAezZswc/+tGPcPvtt6e7HEIIIYRcPQqfCCGEEDL7vfbaa3jmmWfw7rvvpruUGcMYw+XLl9HS0oKDBw9iaGgIW7duRWNjI1avXk0hxSwRjUZhs9lQXl6O8vLyCdtlWYbL5YIgCBgaGkJ+fj44joPZbJ4VN1XJ9Lr0o59C+MUvFccYb1gHz3uJ131S5+ZCv3w5QhEV/EMhuD48hZLi5DOXGPt4baZErqblHmNAyb2bUfT/fAE+v5/WOCNTgjEGj8cDQRDgcDhgMBjA8zw4jsvINcS8Xi86OjpQV1dH6zuOcuHCBezduxc//vGPcdttt6W7HEIIIYRcGwqfCCGEEDL7Pf7442hoaMCTTz6Z7lLSxul04qWXXsLBgwfR19eHO++8E1arFTfccAPd0M1Q4XAYra2tqKqqAs/zKcePv6mak5MzclOV1sfJTt7/eQ+9f/YFxTE5q1fC19498rVKp4Nh5QqEI2qIJ7ohBcIj2wrXLIFqoDfpviJRQK1wCRlT64YHJdoWGxVMGXJQ+4vn4F1egVAohOrqajpPkWnh9/shiiJEUQQAcBwHnudhNBrTXBng8Xhw8uRJ1NfXZ0Q9mSIePP3kJz/Brbfemu5yCCGEEHLtKHwihBBCyOwWiURQXl6OkydPoqysLN3lZASfz4ejR4/i4MGDaG1txc0334ydO3fi5ptvppAiQwQCAbS1tWHFihUoKSm5pn34/X4IggBRFKHRaEZuqubkKPRMI3OKFAiiY9Ptw8lOEqqcHMQiMgxLlyCqzoF44jSibl/Csbk5gMGQ/HjX2nJPkoZnSzEGFNzYgIb/+il6L5yHLMtYtWoVtQwlMyIcDo8EUeFwGGazGRzHoaCgYMY/g263G11dXairq6PgaZTz58/jgQceoOCJEEIImRsofCKEEELI7Hbo0CG8+OKLeO2119JdSkaKRCJ466230NLSgnfffRdr166F1WrFnXfeidzc3HSXl5XibZbWrFmDgoKCKdlnKBQaCaIkSRoJoqiN09x3+qFHEbC1j3lNZTBAv3AhmKkAkUAUUejh/PAUYp7EoVNcQX7ytnkyA6RrbbkXBTQGLVb9/bex4MFGdHV1QavVYvny5RQ8kbSIxWIja+t5vd4ZXVsvHjzV19fT7+FRzp8/j7179+LFF1/ELbfcku5yCCGEEHL9KHwihBBCyOz2wAMP4N5778Vjjz2W7lIyniRJ+J//+R80NzfjzTffxPLly9HY2Ij77rtvykIQomxoaAjd3d2ora2dtmAoEonAbrdDEASEQiGUlpaC5/m0PN1Ppt/FH/wErqOvQ11iRjQG+C8NwXvmAlhMGjNOV1qE3KWVcL7XmvCKTasF8hQ+kqla7imFTzlr1uCG3/4U2qICnDx5EkajEUuWLKHPI8kI49fWM5lM4HkeZrMZWq12So/lcrlw6tQpCp7G6e/vxwMPPIB//Md/xM0335zucgghhBAyNSh8IoQQQsjsFQgEsHDhQvT29qKwsDDd5cwqsizDZrOhqakJR48ehdlsRmNjI7Zv3w6O4+im8DQQRRG9vb2or6+fsdZ4kiTBbrePPN1fXFwMnudRVFREa+zMETGvD6e/91MM/Op3YJKUcnxe9XJE/CEEei+MeT03FzDok79P0ueCBYKJtyVpuacxm7Hyb76OedYtkCQJ7e3tKCoqQmVlZco6CUkHxhh8Ph8EQYDdbodOpwPHceA47rrP2/GHDywWC7VHHSUePP3sZz/DTTfdlO5yCCGEEDJ1KHwihBBCCMl2jDH09PSgubkZL730EnQ6HbZv346dO3eioqKCgqgpcOnSJQwMDMBisaRt3S1ZljE0NARBEOByuVBQUACO41BaWgpNsoV8yKzhsXWh6+nn4P6gPeVYlVaDwvX1GPqwC5J/OFAqKEg+s4mx4WWlJttyT1NSguXPfBnlu7cDGA5BbTYbeJ5HRUXFVX1fhKRTMBgcWScq3tKU4ziYTKar+t3odDrR09NDwdM4586dw4MPPoh/+qd/wqZNm9JdDiGEEEKmFoVPhBBCCCHkY4wxDA4Oorm5GYcOHYLP58PWrVthtVqxcuVKCqKuwfnz52G321FfX58xIQ9jDG63G4IgwOl0Ijc3d6TNVLrCMXL9GGMY/H+bcfq7P0F0yJ1yvJ43w7BwPrwn2hVb7kWjyYMn4OPwSV1YiGXf+hIqHvnkqPdGYbPZUF5ejvLy8qv5dgjJKPGWpqIoIhgMoqSkBDzPo7CwUPF3o8PhwJkzZ2CxWGAwGGaw4swWD55+/vOfY+PGjekuhxBCCCFTj8InQgghhBCSnN1ux+HDh9HS0oKBgQFs2bIFO3fuhMViobZtKTDGcPbsWQQCAdTU1GTsvxdjDH6/f6TNlFarBcdx4HmebpTOUhHHEE4/+yMM/vvhxP3wxsmbVwj4kodVSus5xSRAZcrDkr98Eov37x33vghaW1uxePFilJWVXdX3QEgmkyQJTqcTgiDA4/GgoKAAPM+jpKRkzEMG8eBp7dq10OsV+lpmmb6+Pjz00EP4xS9+gRtvvDHd5RBCCCFkelD4RAghhBBCJsfr9eLIkSNobm5GZ2cnbrvtNlitVmzatGnKF2Wf7RhjOHXqFABg1apVs2rGWDAYhCAIEEURjLGRIMpoNKa7NHKVXMdb0fX0c/B29CiOM5mSz2yS5eE1ncZvZwAMlYuxYP+nUfHorgmf8XA4jNbWVixduhRms/k6vgtC/n/27jw+qvrQ//87ZCEhezJzWARZZJE1oeJXtC4owgNIZqKtvb2P1mutrfbe9tq9dm/vvXaxt4tt1dpWFIu1LWpmJhsUcUHEgoKShEQW2YSCcGayE5JMMufz+6M/uJbSliXJmSSv559C5rxhSHzkvHLOiW9nupLU6/Vq2LBhOnjwoAoLCwlP77Jv3z596EMf0vLly/X//t//c3sOAADoO8QnAAAAnLuuri4999xzKi0t1WuvvabLL79cfr9fCxYsGPJXyziOo7q6Oo0YMUKXXHLJgApPp4tGowqHw7JtW9FoVB6PR5ZlKSMjY0D/uYYSE4vp4CN/0J77HlbsePvf/HpiopSW9vc//t233DNGSh49Stb7lmnC3XcoKfPM9+rr6OhQTU2Npk6dqry8vN74YwADwskrSQ8cOCDbtpWZmamRI0fK6/Uq7R99og0RhCcAAIYU4hMAAAAuTE9PjzZu3KjS0lKtX79e06dPl9/v16JFi5SZmen2vH4Vi8VUU1Mjj8ejiy++2O05vaqnp0eRSES2bau9vf3U805ycnIIUQNA19Gwdn3zxzoaXPtX/334cOkfPeYr2i0l5ebKs+xGTfz8nRo+yvsPj9Pe3q7t27dr+vTpys7O7o3pwIBi27YOHDiguXPnKhaLKRwOKxwOq7u7Wx6PR16vV5mZmUPu6+bevXt16623avny5br88svdngMAAPoe8QkAAAC9x3Ecvf766woEAlq7dq3GjBmj4uJiFRUVKT8/3+15faq7u1vV1dUaO3asRo8e7facPuU4jhoaGhQOh9XS0qLs7Gx5vV7l5+fH7bOt8BcNG17Vjnvu04k9ByT97S33jJESRoxQ6viLlT3/Mo368PuUPmXSWb328ePHtX37ds2aNWvIhWdAko4dO3bqVnvJp1Xd7u5uNTQ0nAr4ubm5pwL+YP+6uWfPHt1666167LHHNG/ePLfnAACA/kF8AgAAQN8wxmjHjh0KBAKqqqpSWlqaiouL5ff7NXr06EH1U9+dnZ2qqanRpEmT5PX+4ytDBhtjjJqbm0897yQ9PV2WZcnj8fAssDjlRLt14KGVevv+Xykl2Sh55EhlzJmhvBuulqfoRiWln/vzvVpbW1VfX685c+YoPf3Mt+MDBrOjR4/q0KFDmjt37j/92uc4jpqammTbtpqbm5WZmXkq4A+2r5tvvfWW/u3f/k0rVqzQZZdd5vYcAADQf4hPAAAA6HvGGL399tsKBoMqKytTNBrVsmXL5PP5NHny5AEdok7eZmzatGnKzc11e46rjDE6fvy4bNtWJBJRSkqKLMuS1+tVSkqK2/NwmlhnpxJTUy/4dZqamrRr1y4VFBTwXBsMSe+8844OHz6swsLCc45Hxhi1trYqHA4rEolo+PDh8nq98nq9A/4Zirt379Ztt91GeAIAYGgiPgEAAKB/GWNk27bKysoUCoVk27YWLVokv9+v2bNnD6jbD5282oPbjJ3ZiRMnZNu2wuGwEhIS5PV6ZVkWgWIQaWho0J49e1RQUKDUXghZwEBz5MgRvfPOOyosLFRiYuIFv97Jr5uRSETGmFMhaqBdUXgyPD3++ON6z3ve4/YcAADQ/4hPAAAAcFdzc7OqqqoUCoW0e/duLViwQD6fT1dccUWvnMjrKyev9pgzZ45GjDj325QNNV1dXadCVE9PjzwejyzLUnp6+oC+8m0os21bBw4cUGFhIVe2YUg6fPiwjh492mvh6XTRaFThcFjhcFidnZ3Kz8+XZVnKysqK66+bu3bt0kc+8hHCEwAAQxvxCQAAAPGjo6NDzz77rAKBgF5//XXNnz9ffr9f1157bVyd3A6Hw9q3b58KCwsH/G2R3NDd3a1IJCLbttXR0aG8vDxZlqXs7Oy4PqGK/3Py+TaFhYVKTk52ew7Q7/785z/Ltm0VFBT0yw9KxGIxNTQ0yLZttbW1KTs7W5ZlKS8vL66uGN65c6duv/12/eY3v9HcuXPdngMAANxDfAIAAEB86u7u1oYNG1RaWqoNGzZo9uzZ8vv9uvHGG129/dCRI0dOPduDk+4X7uQJ1XA4rNbWVuXk5MiyLOXm5sbVCVX8n5NXexQUFJzz822AweDQoUOKRCKaM2eOK1foOo6j5uZmhcNhNTY2Kj09XV6vVx6Px9X/L50MTytXrlRhYaFrOwAAQFwgPgEAACD+xWIxvfbaawoEAlq3bp3Gjx8vn8+npUuXKjc3t992vP3222psbHTthONgd/KEqm3bampqUmZm5qkTqvx9x4eDBw+qoaGBzwEMWSc/BwoKCuIikBtjdPz4cdm2rYaGBiUmJsqyLHm93n59DtuOHTt0++2367e//a0KCgr67bgAACBuEZ8AAAAwsDiOo/r6epWWlmr16tXKzs6Wz+dTcXGxRo4c2Se3bTPGaO/evero6NDMmTPj4oTjYGeMUWtr66kTqqmpqadOqHLFmTv279+vtrY2zZo1i88BDElvv/22mpubNXv27Lj9HOjo6Dj1nKhYLCaPxyOv16uMjIw+u60p4QkAAJwB8QkAAAADlzFG+/btUyAQUHl5uYwxKioqks/n08SJE3vlRJsxRjt27NCwYcM0bdo0nknkkvb2dtm2rXA4rMTERHm9XlmW1a8/2T9UGWO0Z88edXV1acaMGXF70h3oSwcOHFBLS0tch6fTvfv5eidOnDj1fL2cnJxe+3/Zm2++qY9+9KN68sknNWfOnF55TQAAMCgQnwAAADA4GGN09OhRBYNBhUIhNTU1acmSJfL7/Zo+ffp5nSzs6OhQbW2tPB6PJk2aRHiKE52dnadCVCwWOxWi3HwW2GBljNGuXbskifiKIWswXPUXi8XU2NiocDislpYWZWVlyev1Kj8//7xvoXkyPP3ud7/T7Nmze3kxAAAY4IhPAAAAGJwaGxtVUVGhUCik/fv364YbbpDf79dll112VifampubdfPNN+sjH/mIbr/99r4fjPMSjUZP/WR/Z2en8vPzZVmWsrKyCCUXyBijN998UykpKZo8eTJ/nxiS9u7dqxMnTgyqW64aY9TS0qJwOPxXtzX1eDxKSUk5q9d48803dccdd+jJJ58kPAEAgDMhPgEAAGDwO378uNauXatgMKjq6mq9973vVUlJid773vee8flBR48e1U033aQ77rhDd911lwuLcT5isZgikYjC4bDa2tqUm5t76hZTg+WkcX9xHEd1dXXKyMjotVtYAgPJyWf9dXZ2aubMmYP6c+DkbU0jkYja2tq0ceNG3XLLLZoxY8YZf399fb3uuOMO/f73v9esWbP6eS0AABggiE8AAAAYWqLRqF588UUFAgG98sormjt3rnw+nxYuXKi0tDTt3btXH/jAB/S1r31Nt9xyi9tzcZ4cx1FTU5Ns21Zzc3Ov3GJqqIjFYqqtrVVeXp7Gjx/v9hyg3518zlk0GtWMGTMGdXg6XSQS0cqVK7V69Wo1Njbquuuu0/ve9z69973v1bBhw1RXV6ePfexj+sMf/qCZM2e6PRcAAMQv4hMAAAAPoSbiAAAgAElEQVSGrlgspj/96U8KBAJ64YUXNGbMGO3cuVP33XeffD6f2/PQS07eYsq2bTU2NiotLe3ULabOdOXbUNbT06OamhqNHDlSY8eOdXsO0O+MMXrrrbfU09Oj6dOnD6nwdLqmpiaFQiFVVFTorbfe0qxZs1RXV6fS0lIVFha6PQ8AAMQ34hMAAAAgSVu3btWHP/xh3XDDDdqyZYs8Ho98Pp+Kiork9XqH9AnIwcQY81e3mEpKSpLX65VlWRo+fLjb81zV3d2t6upqjR07VqNHj3Z7DtDvjDHavXu3HMfRpZdeytf9d3njjTf0xS9+UZMnT9b27ds1a9Ys3XTTTVqyZIkyMzPdngcAAOIP8QkAAABYv369vvCFL+iZZ57RxIkTT/3keyAQUHl5uZKTk1VUVKSSkhKNHTuWE5KDSEdHh2zbVjgcljHmVIgaMWKE29P6VTQaVXV1tSZMmCDLstyeA/Q7Y4x27dolSZo2bRpf59+ltrZWd911l1atWqXp06fLGKPq6mqFQiGtWbNGHo9HN910k3w+H+EaAACcRHwCAADA0BYKhXTfffeprKxMI0eO/JtfN8bo8OHDCgaDCoVCOn78uJYuXSqfz8dPxg8y0WhU4XBYtm0rGo3K4/HIsixlZGQM6ve5s7NTNTU1mjx5svLz892eA/Q7Y4x27typYcOGaerUqYP68/1c1dTU6BOf+ISeeuopXXrppWf8Pfv371dZWZnKy8sVjUZVXFysm2666e/+fgAAMCQQnwAAADB0rVixQk888YQCgYBycnLO6mMikYjKy8sVDAb15z//WTfeeKP8fr/mzp2rYcOG9fFi9Jeenh5FIhHZtq329nbl5eXJsizl5OQMqhPTHR0dqqmp0bRp05Sbm+v2HKDfGWO0Y8cOJScna/LkyYPq8/tCnU14Ol0kElFVVZXKysp04MABLVq0SDfddJOuvPLKPl4LAADiDPEJAABgIFi+fLnuvPNOLVmyRGvWrDnj7ykqKtLq1av10EMP6ZOf/GQ/Lxx4fvSjH2nDhg1atWqV0tLSzus12tratHr1agUCAb355pu65ppr5Pf7ddVVVykpKamXF8MtjuOooaFB4XBYLS0tys7OltfrVX5+/oAOju3t7aqtrdWMGTOUnZ3t9hyg3xlj9Oabb2r48OG65JJLCE/vUl1drX//93/X008/rWnTpp3Xa3R0dOi5557Tq6++qu985zu9vBAAAMQ54hMAAMBAUVJSovLycj344IP61Kc+9Ve/9vDDD+uTn/ykli5dqtWrV7u0cGAwxuirX/2qjhw5okcffVTJycm98rpdXV167rnnFAwGtXnzZs2bN09+v18LFixQampqrxwD7jPGqLm5WbZtq7GxUenp6bIsSx6PZ0AFx7a2NtXV1Wn27NnKyMhwew7Q74wxqq+vV1pamiZNmkR4epdt27bpP/7jPy4oPAEAgCGP+AQAADBQ2LatWbNmqb29XW+88capE0K7d+/W3LlzlZaWprq6Oo0aNcrlpfErFovp3//935WRkaEf//jHfXbVSk9PjzZu3KhAIKAXX3xRl156qUpKSrRo0SJlZmb2yTHR/4wxOn78uGzbViQSUUpKiizLktfrVUpKitvz/q6Wlhbt2LFDs2fPVnp6uttzgH7nOI7q6+uVnp6uSZMmuT0nrpwMT88884ymTp3q9hwAADBwEZ8AAAAGkrKyMt10002aN2+eNm3aJEm66qqrtGXLFpWWlup973ufywvj2759+1RaWqovfvGL/fZT7o7j6I033lBpaanWrl2r0aNHy+fzqaioSPn5+f2yAf3jxIkTsm1b4XBYCQkJ8nq9sizrvG/r2Beampq0a9cuFRQUxNUuoL84jqO6ujplZmZq4sSJbs+JK2+88YY+9alP6emnnyY8AQCAC0V8AgAAGGg+9rGP6bHHHtM3v/lNSdK9996r22+/XStWrHB5Gf6Zkw+2DwQCqqqqUlpamoqLi+X3+zV69Ghu+zSIdHV1nQpRPT098ng8sixL6enprr3PkUhEe/fuVWFhoYYPH+7KBsBNjuNo+/btys7O1oQJE9yeE1def/11/ed//qeeeeYZTZkyxe05AABg4CM+AQAADDRtbW0qKCjQwYMHJUnjxo1TbW0tt3MbYIwxevvttxUMBlVWVqauri4VFRXJ5/Np8uTJhKhBpLu7W5FIRLZtq6OjQ3l5ebIsS9nZ2f32Ptu2rQMHDqiwsDCubwkI9BXHcVRbW6vc3FyNHz/e7TlxZevWrfrP//xPBQIBTZ482e05AABgcCA+AQAADESPP/64PvrRj0qS1qxZoyVLlri8CBfCGKNwOKxQKKRQKKRjx45p0aJFKikp0ezZs/vs2VTof7FYTA0NDQqHw2ptbVVOTo4sy1Jubm6fvc/vvPOODh8+rIKCAiUnJ/fJMYB45jiOampqlJ+fr4svvtjtOXFl69atuvvuu1VaWkp4AgAAvYn4BAAAMNB0dHToPe95j3bu3ClJuuOOO/Too4+6vAq9qaWlRVVVVQoGg9q1a5cWLFggv9+vK664QomJiW7PQy9xHEfNzc2ybVtNTU3KzMyU1+uVx+Pptff5z3/+s2zbVkFBAf92MCTFYjHV1tbK4/Fo3Lhxbs+JK1u2bNGnP/1pBYNBTZo0ye05AABgcCE+AQAADDR33323HnzwQX3mM5/RSy+9pOrqapWXl8vn87k9DX2go6ND69atU2lpqV5//XXNnz9ffr9f1157LbdPG0SMMWptbZVt22poaFBqaqosy5LX6z3vq5XefvttNTU1afbs2YQnDEmxWEw1NTWyLEtjx451e05cee211/SZz3yG8AQAAPoK8QkAAGAgefbZZ7VkyRLNmjVLW7Zs0VtvvaV58+YpJydHdXV18ng8bk9EH+ru7taGDRtUWlqql19+WbNmzZLf79eNN96o9PR0t+ehF7W3t8u2bYXDYSUmJsrr9cqyLKWmpv7TjzXGaN++fWpvb9esWbO4bSOGpFgspurqao0aNUoXXXSR23PiyquvvqrPfvazCoVCmjhxottzAADA4ER8AgAAGCgaGxs1e/ZsRSIRbdmyRXPmzJEk/fCHP9Q999yj97///XrmmWdcXon+4jiOXnvtNZWWlmrdunUaP368iouLtWzZMuXm5ro9D72os7PzVIiKxWKnQtSZgqMxRm+99Za6u7s1Y8YMJSSc1fd8wKDS09OjmpoajR49WmPGjHF7TlzZvHmzPve5z6msrEwTJkzos+M0Nzfr4x//uOrq6pSQkKDHHntMV155ZZ8dDwAAxB3iEwAAwEDxwQ9+UE899ZT+93//V1/60pdO/XfHcbRgwQK9/PLLeuKJJ3Trrbe6uBJucBxH9fX1CgQCqqqqUnZ2toqLi+Xz+TRy5EgCxCASjUYViURk27Y6OzuVn58vy7KUlZUlSdq5c6cSEhI0bdo03ncMST09PaqurtZFF12k0aNHuz0nrmzevFmf//znFQqF+jQ8SdJHPvIRXXPNNfr4xz+uaDSqEydOKCcnp0+PCQAA4grxCQAAYCB44okndNttt+naa6/Viy+++De30dq/f7/mzJmjpKQkbd++nWdbDGEnb7kWDAZVVlYmY4yKiork8/k0ceJEgsQgEovFFIlEFA6H1dbWJsdxlJWVpRkzZvCMJwxJ3d3dqq6u1rhx4zRq1Ci358SVTZs26Qtf+ILKyso0fvz4Pj1Wa2urCgoKtG/fPv6fAwDA0EV8AgAAiHcHDx7UnDlzZIxRbW3t3z1ptHz5ct15551atGiR1q5dywmfv+P+++/X8uXLlZCQoNmzZ2vFihVn9RydgcgYo6NHjyoUCikYDKqpqUlLliyR3+/X9OnTeRbQIOE4jmpra5WcnKxhw4apublZWVlZ8nq9ys/PJ0RhSDgZni6++GKNHDnS7Tlx5U9/+pO++MUv9kt4kqTq6mrdddddmjFjhmpqanTZZZfpZz/7Gc8mBABgaCE+AQAAYOg4fPiwrr76ar355ptKS0vTv/zLv2jZsmW6/fbb3Z7WL5qamlRRUaFgMKj9+/frhhtukM/n07x58wgUA1QsFlNtba3y8/N18cUXS/pLdGxpaZFt22psbFRaWposy5LH41FycrLLi4He193drW3btmnChAmyLMvtOXHlZHgqLy8/9TWir23dulXz58/XK6+8oiuuuEKf+cxnlJWVpXvvvbdfjg8AAOLCWcWnpL5eAQAAAPSXnp4edXR0KDk5WSdOnBhSD6PPzc3Vbbfdpttuu03t7e1au3atHn30Ud19991673vfK7/fr6uvvppAMUD09PSopqZGo0aN0kUXXXTqvyckJCgnJ0c5OTkyxqi9vV22bWvbtm1KSkqS1+uVZVkaPny4i+uB3hGNRlVdXa2JEyfK6/W6PSeuvPLKK7rnnntUUVGhcePG9dtxx44dq7Fjx+qKK66QJN1yyy267777+u34AABg4ODKJwAAAAwaP/vZz/T1r39daWlpWrx4sZ588km3J7kuGo3qxRdfVCAQ0CuvvKLCwkL5/X4tXLhQaWlpbs/DGZzvs206Ojpk27bC4bCMMadC1IgRI/pwLdA3otGotm3bpksuuUQej8ftOXFl48aN+vKXv6zy8vJ+DU8nXXPNNVq+fLmmTZum//qv/1J7e7t++MMf9vsOAADgGm67BwAAgKGjqalJ73//+7Vq1Srl5OToAx/4gG655Rbdeuutbk+LG7FYTH/6058UDAb1/PPP65JLLpHP59OSJUuUnZ3t9jyo9670iEajCofDsm1b0WhUHo9HlmUpIyODZ8Yh7nV1dam6ulqTJ09Wfn6+23Piyssvv6yvfOUrroUn6S/Pffr4xz+uaDSqSZMmacWKFcrNzXVlCwAAcAXxCQAAAEPH008/rT/+8Y969NFHJUkrV67U5s2b9Ytf/MLlZfHJcRzV1NQoEAjoj3/8o/Ly8uT3+1VUVCSv10ugcEFnZ6dqamp6/YR7T0+PIpGIbNtWe3u78vLyZFmWcnJyeJ8Rd06GpylTpigvL8/tOXFlw4YN+upXv6qKigqNHTvW7TkAAGDoIj4BAABg6Hj11Vd1xx13aMuWLUpLS9Ptt9+uefPm6e6773Z7Wtwzxuitt95SIBBQeXm5kpKSVFxcLL/fr3HjxhEo+kFHR4dqamp06aWXKicnp8+O4ziOGhoaFA6H1dLSouzsbHm9XuXn52vYsGF9dlzgbHR2dqq6ulpTp04lPJ2G8AQAAOII8QkAAABDy7e//W2tWrVKSUlJmjt3rpYvX67hw4e7PWtAMcbo8OHDCgaDCoVCOn78uJYsWSK/369LL72UENUH2tvbtX37ds2YMUNZWVn9dlxjjJqbm2XbthobG5Weni7LsuTxeJSUlNRvOwDp/8LTtGnTuIXbaTZs2KCvfe1rqqio0EUXXeT2HAAAAOITAAAAgAsTiURUXl6uUCikQ4cO6cYbb5Tf79fcuXO5UqYXtLW1qa6uTrNnz1ZGRoZrO4wxOn78uGzbViQSUUpKiizLktfrVUpKimu7MDT015V/A9FLL72kb3zjG6qoqNCYMWPcngMAACARnwAAAAD0pra2Nq1evVrBYFD19fW65ppr5Pf7ddVVV3GlzHlobm7Wzp07NWfOHI0YMcLtOX/lxIkTsm1b4XBYCQkJ8nq9sixLaWlpbk/DIHMyPE2fPl3Z2dluz4kr69ev1ze/+U3CEwAAiDfEJwAAAAB9o6urS88//7wCgYA2bdqkyy+/XH6/XwsWLFBqaqrb8+JeY2Ojdu/ercLCwrj/++rq6joVonp6euTxeGRZltLT07kNIy7IiRMnVFtbS3g6gxdffFHf+ta3VFlZqdGjR7s9BwAA4N2ITwAAAAD6Xk9PjzZu3KhAIKD169dr2rRp8vv9Wrx4sTIzM92eF3fC4bD27dunwsLCAfdMsu7ubkUiEdm2rY6ODuXl5cmyLGVnZxOicE7a29tVW1urmTNn9uuzzgaCF154Qd/+9rcJTwAAIF4RnwAAAAD0L8dx9MYbb6i0tFRr167V6NGj5fP5tGzZMnk8Hrfnue7YsWM6ePCgCgsLlZyc7PacCxKLxdTQ0KBwOKzW1lbl5OTIsizl5ubyPDD8QyfD06xZswjUpyE8AQCAAYD4BAAAAMA9xhjt2LFDgUBAVVVVSktLU1FRkfx+v8aMGTPkrpQ5cuSIjhw5osLCwkH3jCzHcdTc3CzbttXU1KTMzEx5vV55PB4lJia6PQ9x5Pjx49q+fbtmz56tjIwMt+fEleeff17//d//rcrKSo0aNcrtOQAAAH8P8QkAAABAfDDG6ODBgwoGgwqFQurq6tKyZcvk8/k0ZcqUQR+iDh06pHA4rIKCgkEfY4wxam1tVTgcViQSUWpqqizLktfrHfBXe+HCEJ7+vueee0733nuvKisrNXLkSLfnAAAA/CPEJwAAAADxxxijcDissrIyBYNBHTt2TIsWLVJJSYlmz5496G7ZduDAAbW0tAzKP9vZaG9vl23bCofDSkxMlNfrlWVZSk1NdXsa+lFbW5vq6uo0Z84cpaenuz0nrqxbt07f+c53CE8AAGCgID4BAAAAiH8tLS2qqqpSMBjUrl27tGDBAvn9fl1xxRUD+iohY4z27dunEydOaObMmUMyPJ2us7PzVIiKxWKnQhQxYnBrbW1VfX094ekMnn32WX33u98lPAEAgIGE+AQAAABgYOno6NC6detUWlqq119/XfPnz5fP59N1112nlJQUt+edNWOMdu/erVgspunTpw/62wqej2g0qkgkItu21dnZqfz8fFmWpaysLP6+BpGWlhbt2LFDc+bM0YgRI9yeE1fWrl2r733ve6qqqpJlWW7PAQAAOFvEJwAAAAADV3d3tzZs2KBAIKANGzZo1qxZ8vl8WrRoUVxfPWGM0Y4dO5SYmKipU6cSUs5CLBZTJBJROBxWW1ubcnNzZVmWcnJyuGJsADsZngoKCpSWlub2nLiydu1aff/731dlZSXhCQAADDTEJwAAAACDg+M4eu211xQIBPTss89q/PjxKi4u1rJly5Sbm+v2vFMcx1F9fb1GjBihSZMmEZ7Og+M4ampqkm3bam5uVlZWlrxer/Lz8wf0bRiHmubmZu3cuZPwdAZr1qzRD37wA1VVVcnr9bo9BwAA4FwRnwAAAAAMPsYY1dfXq7S0VKtXr1ZWVpaKi4vl8/k0cuRI14KP4zjavn27srOzNWHCBFc2DDbGGLW0tMi2bTU2NiotLU2WZcnj8Sg5Odntefg7mpqatGvXLhUWFio1NdXtOXFl9erV+uEPf6jKykrCEwAAGKiITwAAAAAGN2OM9u3bp2AwqLKyMhljVFRUJJ/Pp4kTJ/ZbiIrFYqqpqZHX69W4ceP65ZhDjTFG7e3tsm1bkUhESUlJ8nq9sixLw4cPd3se/n+NjY3avXs34ekMqqqq9KMf/UhVVVXyeDxuzwEAADhfxCcAAAAAQ4cxRkePHlUoFFIoFFJjY6OWLFkin8+nGTNm9Nmzg3p6elRdXa0xY8ZozJgxfXIM/K2Ojg7Ztq1wOCxjzKkQNWLECLenDVkNDQ3as2ePCgsLCYKnqaqq0o9//GNVVlYSngAAwEBHfAIAAAAwdDU1NamiokKhUEj79u3T9ddfL7/fr3nz5vXas4O6u7tVXV2tiy++WCNHjuyV18S5i0ajCofDsm1b0WhUHo9HlmUpIyOD5271k5Phae7cuUpJSXF7TlyprKzUT37yE8ITAAAYLIhPAAAAACBJ7e3tevbZZxUIBLRt2zZdddVVKikp0dVXX33ezw5qbW3Vjh07dMkll3BCOY709PQoEonItm21t7crLy9PlmUpJyeHENVHIpGI9u7dS3g6g4qKCv30pz9VZWWl8vPz3Z4DAADQG4hPAAAAAHC6aDSqF198UcFgUBs3blRhYaH8fr8WLlyotLS0s3qNvXv36gMf+IBWrVqlKVOm9PFinC/HcdTQ0KBwOKyWlhZlZ2fL6/UqPz+/z27DONSEw2Ht379fhYWFhKfTlJeX6+c//7kqKyuVl5fn9hwAAIDeQnwCAAAAgH8kFotp06ZNCgQCeu655zR58mT5fD4tWbJE2dnZZ/yYuro6ffjDH9b999+vG264oZ8X43wZY9Tc3CzbttXY2Kj09HRZliWPx6OkpCS35w1Itm3rwIEDmjt37nlfQThYlZWV6YEHHiA8AQCAwYj4BAAAAABny3Ec1dbWqrS0VGvWrFF+fr58Pp+Ki4vl9XqVkJCg119/XR/96Ef1q1/9SldeeaXbk3GejDE6fvy4bNtWJBJRSkqKLMuS1+vl6p2zdOzYMR08eFCFhYWEp9OEQiE9+OCDhCcAADBYEZ8AAAAA4HwYY/TWW28pEAiooqJCiYmJuvzyyxUKhbRy5UrNnTvX7YnoRSdOnJBt2wqHw0pISJDX65VlWWd9G8ah5ujRozp06BDh6QyCwaB+8YtfqLKyUrm5uW7PAQAA6AvEJwAAAAC4UMYYlZWV6e6779a0adPU1tamJUuWyO/369JLL1VCwll974UBoqur61SI6unpkcfjkWVZSk9P572W9M477+jw4cMqLCzkdoWnCQQCevjhhwlPAABgsCM+AQAAAMCFev755/XlL39ZoVBIY8eOVUNDg8rLyxUMBnXo0CHdeOON8vv9mjt3roYNG+b2XPSi7u5uRSIR2batjo4O5eXlybIsZWdnD8kQdeTIEb3zzjsqKCggPJ0mEAjol7/8pSoqKghPAABgsCM+AQAAAMCFqKio0He/+12Vl5fLsqy/+fW2tjatWbNGgUBA9fX1uuaaa+T3+3XVVVdxcn6QicViamhoUDgcVmtrq3JycmRZlnJzc4dEdDx8+LCOHj2qwsJCJSYmuj0nrjzzzDN65JFHVFFRoZycHLfnAAAA9DXiEwAAAACcr1WrVunBBx9UeXn5WV3J0NXVpeeff16BQECbN2/WvHnz5PP5dP311ys1NbUfFqO/OI6j5uZm2batpqYmZWZmyuv1yuPxDMow8+c//1m2baugoGBQ/vkuxNNPP63ly5cTngAAwFBCfAIAAACA87FixQo9+eSTCgaDyszMPOeP7+np0caNGxUIBLR+/XpNmzZNfr9fixcvPq/XQ/wyxqi1tVXhcFiRSESpqamyLEter1fJycluz7tghw4dUiQS0Zw5cwhPp3nqqaf02GOPqaKiQtnZ2W7PAQAA6C/EJwAAAAA4Vw888IDWrl2rp59+WmlpaRf8eo7j6I033lAgENAf//hHjRo1Sn6/X8uWLZPH4+mFxYgn7e3tsm1b4XBYiYmJ8nq9sixrQF79dvDgQTU0NKigoGBI3FrwXBCeAADAEEZ8AgAAAIBz8f3vf19vvPGGnnzySaWkpPT66xtjtHPnTgUCAVVWVio1NVXFxcXy+/0aM2aMEhLO6vs4DBCdnZ2nQlQsFjsVotLT092e9k+9/fbbampq0pw5cwhPp1m1apUef/xxVVRUKCsry+05AAAA/Y34BAAAAABna/369Xr88ce1fPlyJSUl9fnxjDE6ePCggsGgQqGQurq6tGzZMvl8Pk2ZMoUQNchEo1FFIhHZtq3Ozk7l5+fLsixlZWXF3Xt94MABtbS0aPbs2YSn0/zhD3/Qb37zG8ITAAAYyohPAAAAAHC2Tn5v5EYIMMYoHA6rrKxMwWBQx44d06JFi+T3+7nyZBCKxWKKRCIKh8Nqa2tTbm6uLMtSTk6O6+/1/v371dbWplmzZrm+Jd78/ve/18qVK1VZWcmz2wAAwFBGfAIAAACAgailpUVVVVUKBoPatWuXrrvuOvn9fs2fP1+JiYluz0MvchxHTU1Nsm1bzc3NysrKktfrVX5+fr+/1/v27VN7e7tmzpxJeDrNk08+qSeffFIVFRWEJwAAMNQRnwAAAABgoOvo6NC6desUCAS0detWXXHFFfL7/br22ms1fPhwt+ehFxlj1NLSItu21djYqLS0NFmWJY/Ho+Tk5D497r59+9TR0aGZM2fG3W0A3XYyPFVWViojI8PtOQAAAG4jPgEAAADAYNLd3a2XX35ZpaWleumllzRr1iz5/X4tWrRI6enpbs9DLzLGqL29XbZtKxKJKCkpSV6vV5Zl9Wp0NMZo79696urq0owZMwhPp/ntb3+r3//+96qoqCA8AQAA/AXxCQAAAAAGK8dxtGXLFpWWlmrdunUaN26cfD6fli1bptzcXLfnoZd1dHTItm2Fw2EZY06FqBEjRpz3axpjtGfPHnV3d2v69OmEp9M88cQTWrVqlcrLywlPAAAA/4f4BAAAAABDgTFG9fX1Ki0t1erVq5WZmSmfzyefz6eRI0cSFQaZaDSqcDgs27YVjUbl8XhkWZYyMjLO+r02xmj37t1yHEeXXnop/0bexRijJ554Qk8//bTKy8u5qhAAAOCvEZ8AAAAAYKgxxmj//v0KBAIqLy9XLBZTUVGRfD6fJk2aRGQYZHp6ehSJRGTbttrb25WXlyfLspSTk/N332tjjHbt2iVJmjZtGv8m3oXwBAAA8E8RnwAAAABgKDPG6NixYwqFQgoGg2psbNTixYtVUlKiGTNmaNiwYW5PRC9yHEcNDQ0Kh8NqaWlRdna2vF6v8vPzT73Xxhjt3LlTw4YN09SpUwlP72KM0W9+8xsFAgGVlZURngAAAM6M+AQAAAAA+D9NTU2qrKxUMBjUvn37dP3118vv92vevHlKTEx0ex56kTFGzc3Nsm1bjY2NSk9Pl2VZikQiSklJ0ZQpUwhP7/Lu8FReXn5Bz9ICAAAY5IhPAAAAAHAufvazn+mRRx6RMUZ33nmnPvvZz7o9qc+cOHFCa9euVSAQ0LZt23TVVVeppKREV199tZKTk92eh15kjFFbW5vq6+sVjUaVlZUly7Lk9XqVkpLi9jzXGWP0+OOPKxQKqaysjPAEAADwjxGfAAAAAOBs1dXV6V//9RMQ/qYAABVgSURBVF/12muvKSUlRUuWLNHDDz+sKVOmuD2tz0WjUa1fv16BQEAbN25UYWGhfD6fFi5cyIn4QcAYo/r6eqWlpWnSpEnq6OiQbdsKh8NKSEiQ1+uVZVlKS0tze2q/M8ZoxYoVKi8vVygU4t87AADAP0d8AgAAAICz9fTTT2vt2rVavny5JOnee+/V8OHDdc8997i8rH/FYjFt2rRJwWBQzz33nC655BIVFxdr6dKlys7OdnsezpHjOKqvr9eIESN0ySWX/M2vd3V1KRwOy7Zt9fT0yOPxyLIspaenD/rb8hlj9Oijj6qqqkqhUGhIxjcAAIDzQHwCAAAAgLO1Y8cOlZSUaNOmTUpLS9PChQs1b948PfDAA25Pc43jOKqtrVUgENCaNWuUl5cnn8+noqIiWZY16OPEQOc4jurq6pSZmamJEyf+09/f3d2tSCQi27bV0dGhvLw8WZal7OzsQfdeE54AAADOG/EJAAAAAM7Fo48+qoceekgZGRmaMWOG0tLSdP/997s9Ky4YY7Rnzx4FAgGVl5crMTFRRUVFKikp0bhx4wZdnBjoHMfR9u3blZ2drQkTJpzzx8diMTU0NCgcDqu1tVU5OTmyLEu5ubkaNmxY7w/uR8YYLV++XGvWrFEwGCQ8AQAAnBviEwAAAACcr6997WsaO3asPvnJT7o9Je4YY3TkyBEFg0GFQiG1trZq6dKl8vl8mj59OiHKZSevWMvNzdX48eN75fWam5tl27aampqUmZkpr9crj8ejxMTEXljcf4wxeuSRR7R27VoFAgHCEwAAwLkjPgEAAADAubBtW5Zl6eDBg1q8eLE2bdqk3Nxct2fFvYaGBpWXlysYDOrQoUO68cYb5fP59J73vGfAXyUz0DiOo5qaGuXn5+viiy/u9dc3xqi1tVXhcFiRSESpqamyLEter1fJycm9frzeZIzRr3/9az377LMKBoNKTU11exIAAMBARHwCAAAAgHNxzTXXqKGhQcnJyfrJT36ihQsXuj1pwGlra9OaNWsUCARUX1+va665Rn6/X1dddZWSkpLcnjeoxWIx1dbWyuPxaNy4cf1yzPb2dtm2rXA4rMTERHm9XlmWFXdhxxijX/3qV3r++edVWload/sAAAAGEOITAAAAAMA9XV1dev755xUMBrVp0yZddtll8vv9uv766zn538tisZhqampkWZbGjh3ryobOzs5TISoWi50KUenp6a7sOckYo1/+8pd68cUX9cwzz/BvDwAA4MIQnwAAAAAA8aGnp0cbN25UMBjUiy++qKlTp6qkpESLFy9WZmam2/MGtFgspurqao0aNUoXXXSR23MkSdFoVJFIRLZtq7OzUx6PR16vV1lZWf36TDBjjB5++GGtX7+e8AQAANA7iE8AAAAAgPjjOI62bdum0tJSrV27ViNHjpTf79eyZcvk8Xjcnjeg9PT0qKamRqNHj9aYMWPcnnNGsVhMkUhE4XBYbW1tys3NlWVZysnJ6dNnghlj9Itf/EIbNmzQM888o+HDh/fZsQAAAIYQ4hMAAAAAIL4ZY7Rz504FAgFVVlYqNTVVxcXF8vv9GjNmTL9eJTPQ9PT0qLq6WhdddJFGjx7t9pyz4jiOmpqaZNu2mpublZWVJa/Xq/z8fCUmJvbacYwxeuihh7Rx40Y9/fTThCcAAIDeQ3wCAAAAAAwcxhgdPHhQwWBQZWVl6uzs1NKlS+X3+zVlyhRC1Lv09PRo27ZtGjdunEaNGuX2nPNijFFLS4ts21ZjY6PS0tJkWZY8Ho+Sk5Mv6HUffPBB/elPf9JTTz1FeAIAAOhdxCcAAAAAwMBkjFE4HFZZWZlCoZCOHj2qG2+8USUlJZozZ06f3q4t3nV3d6u6uloXX3yxRo4c6facXmGMUXt7u2zbViQSUVJSkrxeryzLOqd4ZIzRAw88oM2bN+upp55SSkpKH64GAAAYkohPAAAAAIDBoaWlRatXr1YgENCuXbt03XXXye/3a/78+b16u7Z4193drW3btmnChAmyLMvtOX2mo6NDtm0rHA7LGCOv16uenh5Nnjz5736MMUY///nP9dprr2nVqlWEJwAAgL5BfAIAAAAADD6dnZ1at26dSktLtWXLFs2fP18+n0/XXXfdoL7FWjQaVXV1tSZOnCiv1+v2nH4TjUZ1+PBh3XXXXWpoaNCCBQv0gQ98QFdcccWpK+CMMfrpT3+qrVu3Ep4AAAD6FvEJAAAAADC4dXd36+WXX1Zpaak2bNigmTNnyufzadGiRcrIyHB7Xq85GZ4mTZokj8fj9hzXNDU1KRAIqLy8XPv379f8+fN18803q76+XtXV1frDH/5AeAIAAOhbxCcAAAAAwNDhOI62bNmi0tJSrVu3TuPGjVNxcbGWLVumvLw8t+edt2g0qm3btmny5MnKz893e07c6OjoUEVFhR577DHV1dXp5ptv1i233KIbbrhhUF8BBwAA4DLiEwAAAABgaDLGqL6+XoFAQFVVVcrMzFRxcbF8Pp9GjRqlhISz+p7ZdV1dXaquriY8nYExRj/+8Y9VW1urJ554Qlu3blUwGNQLL7yg6dOn6+abb9bSpUuVmZnp9lQAAIDBhPgEAAAAAIAxRvv371cwGFRZWZlisZiKiork8/k0adKkuA1RnZ2dqq6u1tSpUwf0lVt9wRijH/3oR6qrq9Pvfvc7JScn/9Wv1dTUKBgMas2aNbIsSzfffLP8fv+QelYWAABAHyE+AQAAAADwbsYYHTt2TKFQSMFgUI2NjVq8eLFKSko0Y8YMDRs2zO2Jkv4vPE2bNk25ubluz4krxhj98Ic/1Jtvvqknn3zyr8LTmezZs0fBYFDl5eVKSkpSSUmJbr75Zo0fP76fFgMAAAwqxCcAAAAAAP6RpqYmVVZWKhgMau/evbrhhhvk8/l0+eWXKzEx0ZVNHR0dqqmp0aWXXqqcnBxXNsQrY4x+8IMfaNeuXfrtb3/7T8PT6d555x2VlZUpFAqpublZK1as0PTp0/toLQAAwKBEfAIAAAAA4GydOHFCa9euVSAQ0LZt23TVVVfJ7/fr6quvVkpKSr9sOBmepk+fruzs7H455kBhjNF9992nt956S0888cQ5h6fTNTc3KzU1Vampqb20EAAAYEggPgEAAAAAcD6i0ajWr1+vQCCgjRs3qrCwUD6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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(30,30))\n", "ax = fig.add_subplot(111, projection='3d')\n", "ax.set_xlabel(\"X\", fontsize=20)\n", "ax.set_ylabel(\"Y\", fontsize=20)\n", "ax.set_zlabel(\"Surface\", fontsize=20)\n", "surf = ax.plot_surface(x, y, z, rstride=1, cstride=1,\n", " cmap='RdYlBu', edgecolor='none')#ax.plot_surface(x, y, z,cmap=cm.coolwarm, linewidth=0.1, antialiased=True)\n", "#fig.colorbar(surf, shrink=0.5, aspect=5)\n", "#ax.view_init(35, 60)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.contourf(x, y, z, 30, cmap='RdYlBu')\n", "plt.xlabel(\"X\", fontsize=20)\n", "plt.ylabel(\"Y\", fontsize=20)\n", "plt.colorbar();" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python [conda env:tensorflow]", "language": "python", "name": "conda-env-tensorflow-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.4" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: Chapter05/Readme.md ================================================ # Genetic Algorithms for IoT In the last chapter, we saw different deep learning-based algorithms, these algorithms have shown their success in the field of recognition, detection, reconstruction and even generation of vision, speech and text data. While at present deep learning is on top in terms of both application and employability, it has a close competition with evolutionary algorithms. The Algorithms inspired by the natural process of evolution, the world's best optimizers. Yep, even we are the result of years of genetic evolution. In this chapter, you will be introduced to the fascinating world of evolutionary algorithms and learn about a specific type of evolutionary algorithms, i.e. genetic algorithms in more detail. After reading this chapter you will: * Know what is optimization. * Will learn different methods to solve an optimization problem. * Understand the intuition behind genetic algorithms * Know about the advantages of genetic algorithms * Understand and implement the process of cross-over, mutation and fitness function selection. * Use a genetic algorithm to find the lost password. * Learn about various uses of genetic algorithm in optimizing your models. * Learn the DEAP genetic algorithm library ================================================ FILE: Chapter05/dag.py ================================================ #!/usr/bin/env python from copy import copy, deepcopy from collections import deque try: from collections import OrderedDict except: from ordereddict import OrderedDict class DAGValidationError(Exception): pass class DAG(object): """ Directed acyclic graph implementation. """ def __init__(self): """ Construct a new DAG with no nodes or edges. """ self.reset_graph() def add_node(self, node_name, graph=None): """ Add a node if it does not exist yet, or error out. """ if not graph: graph = self.graph if node_name in graph: raise KeyError('node %s already exists' % node_name) graph[node_name] = set() def add_node_if_not_exists(self, node_name, graph=None): try: self.add_node(node_name, graph=graph) except KeyError: pass def delete_node(self, node_name, graph=None): """ Deletes this node and all edges referencing it. """ if not graph: graph = self.graph if node_name not in graph: raise KeyError('node %s does not exist' % node_name) graph.pop(node_name) for node, edges in graph.items(): if node_name in edges: edges.remove(node_name) def delete_node_if_exists(self, node_name, graph=None): try: self.delete_node(node_name, graph=graph) except KeyError: pass def add_edge(self, ind_node, dep_node, graph=None): """ Add an edge (dependency) between the specified nodes. """ if not graph: graph = self.graph if ind_node not in graph or dep_node not in graph: raise KeyError('one or more nodes do not exist in graph') test_graph = deepcopy(graph) test_graph[ind_node].add(dep_node) is_valid, message = self.validate(test_graph) if is_valid: graph[ind_node].add(dep_node) else: raise DAGValidationError() def delete_edge(self, ind_node, dep_node, graph=None): """ Delete an edge from the graph. """ if not graph: graph = self.graph if dep_node not in graph.get(ind_node, []): raise KeyError('this edge does not exist in graph') graph[ind_node].remove(dep_node) def rename_edges(self, old_task_name, new_task_name, graph=None): """ Change references to a task in existing edges. """ if not graph: graph = self.graph for node, edges in graph.items(): if node == old_task_name: graph[new_task_name] = copy(edges) del graph[old_task_name] else: if old_task_name in edges: edges.remove(old_task_name) edges.add(new_task_name) def predecessors(self, node, graph=None): """ Returns a list of all predecessors of the given node """ if graph is None: graph = self.graph return [key for key in graph if node in graph[key]] def downstream(self, node, graph=None): """ Returns a list of all nodes this node has edges towards. """ if graph is None: graph = self.graph if node not in graph: raise KeyError('node %s is not in graph' % node) return list(graph[node]) def all_downstreams(self, node, graph=None): """Returns a list of all nodes ultimately downstream of the given node in the dependency graph, in topological order.""" if graph is None: graph = self.graph nodes = [node] nodes_seen = set() i = 0 while i < len(nodes): downstreams = self.downstream(nodes[i], graph) for downstream_node in downstreams: if downstream_node not in nodes_seen: nodes_seen.add(downstream_node) nodes.append(downstream_node) i += 1 return filter(lambda node: node in nodes_seen, self.topological_sort(graph=graph)) def all_leaves(self, graph=None): """ Return a list of all leaves (nodes with no downstreams) """ if graph is None: graph = self.graph return [key for key in graph if not graph[key]] def from_dict(self, graph_dict): """ Reset the graph and build it from the passed dictionary. The dictionary takes the form of {node_name: [directed edges]} """ self.reset_graph() for new_node in graph_dict.iterkeys(): self.add_node(new_node) for ind_node, dep_nodes in graph_dict.items(): if not isinstance(dep_nodes, list): raise TypeError('dict values must be lists') for dep_node in dep_nodes: self.add_edge(ind_node, dep_node) def reset_graph(self): """ Restore the graph to an empty state. """ self.graph = OrderedDict() def ind_nodes(self, graph=None): """ Returns a list of all nodes in the graph with no dependencies. """ if graph is None: graph = self.graph dependent_nodes = set(tuple(node) for dependents in graph.items() for node in dependents) return [node for node in graph.keys() if node not in dependent_nodes] def validate(self, graph=None): """ Returns (Boolean, message) of whether DAG is valid. """ graph = graph if graph is not None else self.graph if len(self.ind_nodes(graph)) == 0: return (False, 'no independent nodes detected') try: self.topological_sort(graph) except ValueError: return (False, 'failed topological sort') return (True, 'valid') def topological_sort(self, graph=None): """ Returns a topological ordering of the DAG. Raises an error if this is not possible (graph is not valid). """ if graph is None: graph = self.graph in_degree = {} for u in graph: in_degree[u] = 0 for u in graph: for v in graph[u]: in_degree[v] += 1 queue = deque() for u in in_degree: if in_degree[u] == 0: queue.appendleft(u) l = [] while queue: u = queue.pop() l.append(u) for v in graph[u]: in_degree[v] -= 1 if in_degree[v] == 0: queue.appendleft(v) if len(l) == len(graph): return l else: raise ValueError('graph is not acyclic') def size(self): return len(self.graph) ================================================ FILE: Chapter06/DQN.ipynb ================================================ { "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/am/anaconda3/envs/tensorflow/lib/python3.5/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n", " from ._conv import register_converters as _register_converters\n" ] } ], "source": [ "import gym\n", "import sys\n", "import random\n", "import numpy as np\n", "import tensorflow as tf\n", "import matplotlib.pyplot as plt\n", "from datetime import datetime\n", "from scipy.misc import imresize" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "MAX_EXPERIENCES = 500000\n", "MIN_EXPERIENCES = 50000\n", "TARGET_UPDATE_PERIOD = 10000\n", "IM_SIZE = 80\n", "K = 4 # env.action_space.n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def preprocess(img):\n", " img_temp = img[31:195] # Choose the important area of the image\n", " img_temp = img_temp.mean(axis=2) # Convert to Grayscale#\n", " # Downsample image using nearest neighbour interpolation\n", " img_temp = imresize(img_temp, size=(IM_SIZE, IM_SIZE), interp='nearest')\n", " return img_temp\n", "\n", "\n", "def update_state(state, obs):\n", " obs_small = preprocess(obs)\n", " return np.append(state[1:], np.expand_dims(obs_small, 0), axis=0)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "class DQN:\n", " def __init__(self, K, scope, save_path= 'models/atari.ckpt'):\n", "\n", " self.K = K\n", " self.scope = scope\n", " self.save_path = save_path\n", "\n", " with tf.variable_scope(scope):\n", "\n", " # inputs and targets\n", " self.X = tf.placeholder(tf.float32, shape=(None, 4, IM_SIZE, IM_SIZE), name='X')\n", "\n", " # tensorflow convolution needs the order to be:\n", " # (num_samples, height, width, \"color\")\n", " # so we need to tranpose later\n", " self.Q_target = tf.placeholder(tf.float32, shape=(None,), name='G')\n", " self.actions = tf.placeholder(tf.int32, shape=(None,), name='actions')\n", "\n", " # calculate output and cost\n", " # convolutional layers\n", " Z = self.X / 255.0\n", " Z = tf.transpose(Z, [0, 2, 3, 1])\n", " cnn1 = tf.contrib.layers.conv2d(Z, 32, 8, 4, activation_fn=tf.nn.relu)\n", " cnn2 = tf.contrib.layers.conv2d(cnn1, 64, 4, 2, activation_fn=tf.nn.relu)\n", " cnn3 = tf.contrib.layers.conv2d(cnn2, 64, 3, 1, activation_fn=tf.nn.relu)\n", "\n", " # fully connected layers\n", " fc0 = tf.contrib.layers.flatten(cnn3)\n", " fc1 = tf.contrib.layers.fully_connected(fc0, 512)\n", "\n", " # final output layer\n", " self.predict_op = tf.contrib.layers.fully_connected(fc1, K)\n", "\n", " Qpredicted = tf.reduce_sum(self.predict_op * tf.one_hot(self.actions, K),\n", " reduction_indices=[1]\n", " )\n", "\n", " self.cost = tf.reduce_mean(tf.square(self.Q_target - Qpredicted))\n", " self.train_op = tf.train.RMSPropOptimizer(0.00025, 0.99, 0.0, 1e-6).minimize(self.cost)\n", "\n", "\n", " def copy_from(self, other):\n", " mine = [t for t in tf.trainable_variables() if t.name.startswith(self.scope)]\n", " mine = sorted(mine, key=lambda v: v.name)\n", " theirs = [t for t in tf.trainable_variables() if t.name.startswith(other.scope)]\n", " theirs = sorted(theirs, key=lambda v: v.name)\n", "\n", " ops = []\n", " for p, q in zip(mine, theirs):\n", " actual = self.session.run(q)\n", " op = p.assign(actual)\n", " ops.append(op)\n", "\n", " self.session.run(ops)\n", "\n", " def set_session(self, session):\n", " self.session = session\n", "\n", " def predict(self, states):\n", " return self.session.run(self.predict_op, feed_dict={self.X: states})\n", "\n", " def update(self, states, actions, targets):\n", " c, _ = self.session.run(\n", " [self.cost, self.train_op],\n", " feed_dict={\n", " self.X: states,\n", " self.Q_target: targets,\n", " self.actions: actions\n", " }\n", " )\n", " return c\n", "\n", " def sample_action(self, x, eps):\n", " \"\"\"Implements epsilon greedy algorithm\"\"\"\n", " if np.random.random() < eps:\n", " return np.random.choice(self.K)\n", " else:\n", " return np.argmax(self.predict([x])[0])\n", "\n", " def load(self):\n", " self.saver = tf.train.Saver(tf.global_variables())\n", " load_was_success = True\n", " try:\n", " save_dir = '/'.join(self.save_path.split('/')[:-1])\n", " ckpt = tf.train.get_checkpoint_state(save_dir)\n", " load_path = ckpt.model_checkpoint_path\n", " self.saver.restore(self.session, load_path)\n", " except:\n", " print(\"no saved model to load. starting new session\")\n", " load_was_success = False\n", " else:\n", " print(\"loaded model: {}\".format(load_path))\n", " saver = tf.train.Saver(tf.global_variables())\n", " episode_number = int(load_path.split('-')[-1])\n", "\n", "\n", " def save(self, n):\n", " self.saver.save(self.session, self.save_path, global_step=n)\n", " print(\"SAVED MODEL #{}\".format(n))\n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def learn(model, target_model, experience_replay_buffer, gamma, batch_size):\n", " # Sample experiences\n", " samples = random.sample(experience_replay_buffer, batch_size)\n", " states, actions, rewards, next_states, dones = map(np.array, zip(*samples))\n", "\n", " # Calculate targets\n", " next_Qs = target_model.predict(next_states)\n", " next_Q = np.amax(next_Qs, axis=1)\n", " targets = rewards + np.invert(dones).astype(np.float32) * gamma * next_Q\n", "\n", " # Update model\n", " loss = model.update(states, actions, targets)\n", " return loss\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "[2018-07-01 21:57:38,273] Making new env: Breakout-v0\n" ] } ], "source": [ "# hyperparameters etc\n", "gamma = 0.97\n", "batch_sz = 64\n", "num_episodes = 2700\n", "total_t = 0\n", "experience_replay_buffer = []\n", "episode_rewards = np.zeros(num_episodes)\n", "last_100_avgs = []\n", "\n", "# epsilon for Epsilon Greedy Algorithm\n", "epsilon = 1.0\n", "epsilon_min = 0.1\n", "epsilon_change = (epsilon - epsilon_min) / 700000\n", "\n", "# Create Atari Environment\n", "env = gym.envs.make(\"Breakout-v0\")\n", "\n", "\n", "# Create original and target Networks\n", "model = DQN(K=K, scope=\"model\")\n", "target_model = DQN(K=K, scope=\"target_model\")" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Restoring parameters from models/atari.ckpt-2750\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[2018-07-01 21:57:39,914] Restoring parameters from models/atari.ckpt-2750\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "no saved model to load. starting new session\n", "Filling experience replay buffer...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/am/anaconda3/envs/tensorflow/lib/python3.5/site-packages/ipykernel/__main__.py:5: DeprecationWarning: `imresize` is deprecated!\n", "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", "Use ``skimage.transform.resize`` instead.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Copied model parameters to target network. total_t = 0, period = 10000\n", "Episode: 0 Duration: 0:00:03.697166 Num steps: 209 Reward: 1.0 Training time per step: 0.014 Avg Reward (Last 100): 1.000 Epsilon: 1.000\n", "SAVED MODEL #0\n", "Episode: 1 Duration: 0:00:04.108321 Num steps: 346 Reward: 3.0 Training time per step: 0.010 Avg Reward (Last 100): 2.000 Epsilon: 0.999\n", "Episode: 2 Duration: 0:00:02.908623 Num steps: 208 Reward: 1.0 Training time per step: 0.011 Avg Reward (Last 100): 1.667 Epsilon: 0.999\n", "Episode: 3 Duration: 0:00:03.344515 Num steps: 250 Reward: 1.0 Training time per step: 0.011 Avg Reward (Last 100): 1.500 Epsilon: 0.999\n", "Episode: 4 Duration: 0:00:03.885956 Num steps: 268 Reward: 2.0 Training time per step: 0.012 Avg Reward (Last 100): 1.600 Epsilon: 0.998\n", "Episode: 5 Duration: 0:00:02.406549 Num 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"output_type": "stream", "text": [ "Episode: 879 Duration: 0:00:02.484271 Num steps: 160 Reward: 0.0 Training time per step: 0.012 Avg Reward (Last 100): 1.129 Epsilon: 0.726\n", "Episode: 880 Duration: 0:00:04.028181 Num steps: 258 Reward: 2.0 Training time per step: 0.012 Avg Reward (Last 100): 1.149 Epsilon: 0.726\n", "Episode: 881 Duration: 0:00:05.474800 Num steps: 369 Reward: 3.0 Training time per step: 0.012 Avg Reward (Last 100): 1.158 Epsilon: 0.725\n", "Episode: 882 Duration: 0:00:02.711716 Num steps: 185 Reward: 0.0 Training time per step: 0.012 Avg Reward (Last 100): 1.158 Epsilon: 0.725\n", "Episode: 883 Duration: 0:00:02.397803 Num steps: 180 Reward: 0.0 Training time per step: 0.011 Avg Reward (Last 100): 1.158 Epsilon: 0.725\n", "Episode: 884 Duration: 0:00:03.507889 Num steps: 234 Reward: 1.0 Training time per step: 0.012 Avg Reward (Last 100): 1.168 Epsilon: 0.724\n", "Episode: 885 Duration: 0:00:03.171233 Num steps: 232 Reward: 1.0 Training time per step: 0.011 Avg 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"Episode: 940 Duration: 0:00:02.366674 Num steps: 173 Reward: 0.0 Training time per step: 0.011 Avg Reward (Last 100): 1.277 Epsilon: 0.706\n", "Episode: 941 Duration: 0:00:03.935361 Num steps: 280 Reward: 2.0 Training time per step: 0.012 Avg Reward (Last 100): 1.277 Epsilon: 0.706\n", "Episode: 942 Duration: 0:00:03.922774 Num steps: 273 Reward: 2.0 Training time per step: 0.012 Avg Reward (Last 100): 1.297 Epsilon: 0.705\n", "Episode: 943 Duration: 0:00:03.641000 Num steps: 245 Reward: 2.0 Training time per step: 0.012 Avg Reward (Last 100): 1.317 Epsilon: 0.705\n", "Episode: 944 Duration: 0:00:02.496292 Num steps: 159 Reward: 0.0 Training time per step: 0.013 Avg Reward (Last 100): 1.297 Epsilon: 0.705\n", "Episode: 945 Duration: 0:00:03.395513 Num steps: 236 Reward: 1.0 Training time per step: 0.012 Avg Reward (Last 100): 1.267 Epsilon: 0.705\n", "Copied model parameters to target network. total_t = 230000, period = 10000\n", "Episode: 946 Duration: 0:00:04.271262 Num steps: 327 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"Episode: 1235 Duration: 0:00:05.312473 Num steps: 381 Reward: 4.0 Training time per step: 0.011 Avg Reward (Last 100): 2.386 Epsilon: 0.600\n", "Episode: 1236 Duration: 0:00:06.460765 Num steps: 431 Reward: 9.0 Training time per step: 0.012 Avg Reward (Last 100): 2.465 Epsilon: 0.599\n", "Episode: 1237 Duration: 0:00:04.962931 Num steps: 360 Reward: 4.0 Training time per step: 0.011 Avg Reward (Last 100): 2.495 Epsilon: 0.599\n", "Episode: 1238 Duration: 0:00:02.403929 Num steps: 184 Reward: 0.0 Training time per step: 0.011 Avg Reward (Last 100): 2.465 Epsilon: 0.599\n", "Episode: 1239 Duration: 0:00:05.724790 Num steps: 376 Reward: 3.0 Training time per step: 0.012 Avg Reward (Last 100): 2.485 Epsilon: 0.598\n", "Episode: 1240 Duration: 0:00:03.944176 Num steps: 255 Reward: 1.0 Training time per step: 0.012 Avg Reward (Last 100): 2.485 Epsilon: 0.598\n", "Episode: 1241 Duration: 0:00:02.510585 Num steps: 186 Reward: 0.0 Training time per step: 0.011 Avg Reward (Last 100): 2.475 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0.538\n", "Episode: 1376 Duration: 0:00:04.690068 Num steps: 313 Reward: 3.0 Training time per step: 0.012 Avg Reward (Last 100): 3.495 Epsilon: 0.537\n", "Copied model parameters to target network. total_t = 360000, period = 10000\n", "Episode: 1377 Duration: 0:00:06.568530 Num steps: 433 Reward: 6.0 Training time per step: 0.012 Avg Reward (Last 100): 3.525 Epsilon: 0.537\n", "Episode: 1378 Duration: 0:00:04.057050 Num steps: 278 Reward: 2.0 Training time per step: 0.012 Avg Reward (Last 100): 3.515 Epsilon: 0.536\n", "Episode: 1379 Duration: 0:00:06.505225 Num steps: 419 Reward: 5.0 Training time per step: 0.012 Avg Reward (Last 100): 3.525 Epsilon: 0.536\n", "Episode: 1380 Duration: 0:00:06.018175 Num steps: 414 Reward: 5.0 Training time per step: 0.012 Avg Reward (Last 100): 3.535 Epsilon: 0.535\n", "Episode: 1381 Duration: 0:00:08.489316 Num steps: 570 Reward: 7.0 Training time per step: 0.012 Avg Reward (Last 100): 3.584 Epsilon: 0.535\n", "Episode: 1382 Duration: 0:00:05.237333 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target network. total_t = 420000, period = 10000\n", "Episode: 1495 Duration: 0:00:11.987094 Num steps: 777 Reward: 11.0 Training time per step: 0.012 Avg Reward (Last 100): 6.752 Epsilon: 0.459\n", "Episode: 1496 Duration: 0:00:09.592837 Num steps: 626 Reward: 8.0 Training time per step: 0.012 Avg Reward (Last 100): 6.802 Epsilon: 0.458\n", "Episode: 1497 Duration: 0:00:06.394079 Num steps: 448 Reward: 5.0 Training time per step: 0.011 Avg Reward (Last 100): 6.752 Epsilon: 0.458\n", "Episode: 1498 Duration: 0:00:06.780902 Num steps: 434 Reward: 5.0 Training time per step: 0.012 Avg Reward (Last 100): 6.762 Epsilon: 0.457\n", "Episode: 1499 Duration: 0:00:11.197134 Num steps: 754 Reward: 11.0 Training time per step: 0.012 Avg Reward (Last 100): 6.851 Epsilon: 0.456\n", "Episode: 1500 Duration: 0:00:11.826198 Num steps: 764 Reward: 13.0 Training time per step: 0.012 Avg Reward (Last 100): 6.950 Epsilon: 0.455\n", "SAVED MODEL #1500\n", "Episode: 1501 Duration: 0:00:08.954032 Num steps: 741 Reward: 12.0 Training time per step: 0.010 Avg Reward (Last 100): 7.030 Epsilon: 0.454\n", "Episode: 1502 Duration: 0:00:12.139689 Num steps: 765 Reward: 10.0 Training time per step: 0.012 Avg Reward (Last 100): 7.059 Epsilon: 0.453\n", "Episode: 1503 Duration: 0:00:07.103989 Num steps: 498 Reward: 7.0 Training time per step: 0.011 Avg Reward (Last 100): 7.089 Epsilon: 0.453\n", "Episode: 1504 Duration: 0:00:08.310342 Num steps: 572 Reward: 7.0 Training time per step: 0.012 Avg Reward (Last 100): 7.099 Epsilon: 0.452\n", "Episode: 1505 Duration: 0:00:07.419538 Num steps: 488 Reward: 5.0 Training time per step: 0.012 Avg Reward (Last 100): 7.119 Epsilon: 0.451\n", "Episode: 1506 Duration: 0:00:06.333071 Num steps: 426 Reward: 5.0 Training time per step: 0.012 Avg Reward (Last 100): 7.119 Epsilon: 0.451\n", "Episode: 1507 Duration: 0:00:08.236699 Num steps: 579 Reward: 7.0 Training time per step: 0.011 Avg Reward (Last 100): 7.119 Epsilon: 0.450\n", "Episode: 1508 Duration: 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Training time per step: 0.012 Avg Reward (Last 100): 7.347 Epsilon: 0.444\n", "Episode: 1515 Duration: 0:00:09.116916 Num steps: 597 Reward: 7.0 Training time per step: 0.012 Avg Reward (Last 100): 7.327 Epsilon: 0.444\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Episode: 1516 Duration: 0:00:07.708254 Num steps: 538 Reward: 11.0 Training time per step: 0.011 Avg Reward (Last 100): 7.386 Epsilon: 0.443\n", "Episode: 1517 Duration: 0:00:10.575859 Num steps: 693 Reward: 12.0 Training time per step: 0.012 Avg Reward (Last 100): 7.436 Epsilon: 0.442\n", "Episode: 1518 Duration: 0:00:08.317468 Num steps: 546 Reward: 8.0 Training time per step: 0.012 Avg Reward (Last 100): 7.416 Epsilon: 0.441\n", "Episode: 1519 Duration: 0:00:07.217388 Num steps: 496 Reward: 6.0 Training time per step: 0.012 Avg Reward (Last 100): 7.416 Epsilon: 0.441\n", "Episode: 1520 Duration: 0:00:10.049280 Num steps: 676 Reward: 10.0 Training time per step: 0.012 Avg Reward (Last 100): 7.515 Epsilon: 0.440\n", "Episode: 1521 Duration: 0:00:10.906394 Num steps: 757 Reward: 10.0 Training time per step: 0.012 Avg Reward (Last 100): 7.584 Epsilon: 0.439\n", "Episode: 1522 Duration: 0:00:09.581490 Num steps: 691 Reward: 13.0 Training time per step: 0.011 Avg Reward (Last 100): 7.624 Epsilon: 0.438\n", "Episode: 1523 Duration: 0:00:09.360553 Num steps: 627 Reward: 9.0 Training time per step: 0.012 Avg Reward (Last 100): 7.683 Epsilon: 0.437\n", "Episode: 1524 Duration: 0:00:09.435588 Num steps: 614 Reward: 9.0 Training time per step: 0.012 Avg Reward (Last 100): 7.713 Epsilon: 0.436\n", "Episode: 1525 Duration: 0:00:08.903059 Num steps: 590 Reward: 9.0 Training time per step: 0.012 Avg Reward (Last 100): 7.762 Epsilon: 0.436\n", "Episode: 1526 Duration: 0:00:12.958585 Num steps: 816 Reward: 16.0 Training time per step: 0.012 Avg Reward (Last 100): 7.851 Epsilon: 0.435\n", "Copied model parameters to target network. total_t = 440000, period = 10000\n", "Episode: 1527 Duration: 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"Episode: 1534 Duration: 0:00:11.070759 Num steps: 699 Reward: 9.0 Training time per step: 0.012 Avg Reward (Last 100): 7.960 Epsilon: 0.429\n", "Episode: 1535 Duration: 0:00:08.535151 Num steps: 563 Reward: 8.0 Training time per step: 0.012 Avg Reward (Last 100): 7.970 Epsilon: 0.428\n", "Episode: 1536 Duration: 0:00:07.414143 Num steps: 519 Reward: 6.0 Training time per step: 0.011 Avg Reward (Last 100): 7.970 Epsilon: 0.427\n", "Episode: 1537 Duration: 0:00:09.521469 Num steps: 634 Reward: 8.0 Training time per step: 0.012 Avg Reward (Last 100): 7.970 Epsilon: 0.426\n", "Episode: 1538 Duration: 0:00:08.278269 Num steps: 569 Reward: 7.0 Training time per step: 0.012 Avg Reward (Last 100): 7.960 Epsilon: 0.426\n", "Episode: 1539 Duration: 0:00:07.772386 Num steps: 501 Reward: 7.0 Training time per step: 0.012 Avg Reward (Last 100): 7.990 Epsilon: 0.425\n", "Episode: 1540 Duration: 0:00:11.667356 Num steps: 767 Reward: 12.0 Training time per step: 0.012 Avg Reward (Last 100): 8.059 Epsilon: 0.424\n", "Episode: 1541 Duration: 0:00:10.152084 Num steps: 697 Reward: 10.0 Training time per step: 0.012 Avg Reward (Last 100): 8.129 Epsilon: 0.423\n", "Episode: 1542 Duration: 0:00:10.961583 Num steps: 700 Reward: 10.0 Training time per step: 0.012 Avg Reward (Last 100): 8.139 Epsilon: 0.422\n", "Copied model parameters to target network. total_t = 450000, period = 10000\n", "Episode: 1543 Duration: 0:00:11.502602 Num steps: 745 Reward: 14.0 Training time per step: 0.012 Avg Reward (Last 100): 8.218 Epsilon: 0.421\n", "Episode: 1544 Duration: 0:00:13.870281 Num steps: 940 Reward: 14.0 Training time per step: 0.012 Avg Reward (Last 100): 8.347 Epsilon: 0.420\n", "Episode: 1545 Duration: 0:00:11.037120 Num steps: 728 Reward: 11.0 Training time per step: 0.012 Avg Reward (Last 100): 8.386 Epsilon: 0.419\n", "Episode: 1546 Duration: 0:00:10.987938 Num steps: 717 Reward: 12.0 Training time per step: 0.012 Avg Reward (Last 100): 8.416 Epsilon: 0.418\n", "Episode: 1547 Duration: 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Epsilon: 0.412\n", "Episode: 1554 Duration: 0:00:10.345467 Num steps: 696 Reward: 10.0 Training time per step: 0.012 Avg Reward (Last 100): 8.703 Epsilon: 0.411\n", "Episode: 1555 Duration: 0:00:11.362696 Num steps: 726 Reward: 11.0 Training time per step: 0.012 Avg Reward (Last 100): 8.743 Epsilon: 0.410\n", "Episode: 1556 Duration: 0:00:10.160322 Num steps: 656 Reward: 10.0 Training time per step: 0.012 Avg Reward (Last 100): 8.772 Epsilon: 0.409\n", "Copied model parameters to target network. total_t = 460000, period = 10000\n", "Episode: 1557 Duration: 0:00:10.029301 Num steps: 688 Reward: 9.0 Training time per step: 0.011 Avg Reward (Last 100): 8.812 Epsilon: 0.408\n", "Episode: 1558 Duration: 0:00:09.023976 Num steps: 588 Reward: 8.0 Training time per step: 0.012 Avg Reward (Last 100): 8.832 Epsilon: 0.408\n", "Episode: 1559 Duration: 0:00:09.834929 Num steps: 631 Reward: 9.0 Training time per step: 0.012 Avg Reward (Last 100): 8.871 Epsilon: 0.407\n", "Episode: 1560 Duration: 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"Episode: 1567 Duration: 0:00:10.983777 Num steps: 701 Reward: 14.0 Training time per step: 0.012 Avg Reward (Last 100): 9.139 Epsilon: 0.400\n", "Episode: 1568 Duration: 0:00:09.872795 Num steps: 648 Reward: 8.0 Training time per step: 0.012 Avg Reward (Last 100): 9.158 Epsilon: 0.399\n", "Episode: 1569 Duration: 0:00:12.962256 Num steps: 825 Reward: 12.0 Training time per step: 0.012 Avg Reward (Last 100): 9.208 Epsilon: 0.398\n", "Episode: 1570 Duration: 0:00:08.572053 Num steps: 588 Reward: 11.0 Training time per step: 0.011 Avg Reward (Last 100): 9.267 Epsilon: 0.397\n", "Episode: 1571 Duration: 0:00:07.829303 Num steps: 549 Reward: 8.0 Training time per step: 0.011 Avg Reward (Last 100): 9.267 Epsilon: 0.396\n", "Episode: 1572 Duration: 0:00:08.029299 Num steps: 541 Reward: 7.0 Training time per step: 0.012 Avg Reward (Last 100): 9.267 Epsilon: 0.396\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Copied model parameters to target network. total_t = 470000, period = 10000\n", "Episode: 1573 Duration: 0:00:08.415054 Num steps: 549 Reward: 8.0 Training time per step: 0.012 Avg Reward (Last 100): 9.277 Epsilon: 0.395\n", "Episode: 1574 Duration: 0:00:08.382916 Num steps: 582 Reward: 8.0 Training time per step: 0.011 Avg Reward (Last 100): 9.198 Epsilon: 0.394\n", "Episode: 1575 Duration: 0:00:13.778623 Num steps: 940 Reward: 15.0 Training time per step: 0.011 Avg Reward (Last 100): 9.287 Epsilon: 0.393\n", "Episode: 1576 Duration: 0:00:10.212970 Num steps: 657 Reward: 12.0 Training time per step: 0.012 Avg Reward (Last 100): 9.337 Epsilon: 0.392\n", "Episode: 1577 Duration: 0:00:11.805722 Num steps: 762 Reward: 18.0 Training time per step: 0.012 Avg Reward (Last 100): 9.446 Epsilon: 0.391\n", "Episode: 1578 Duration: 0:00:11.827982 Num steps: 803 Reward: 13.0 Training time per step: 0.012 Avg Reward (Last 100): 9.485 Epsilon: 0.390\n", "Episode: 1579 Duration: 0:00:09.363281 Num steps: 586 Reward: 8.0 Training time per step: 0.012 Avg Reward (Last 100): 9.455 Epsilon: 0.389\n", "Episode: 1580 Duration: 0:00:07.961734 Num steps: 531 Reward: 8.0 Training time per step: 0.012 Avg Reward (Last 100): 9.446 Epsilon: 0.389\n", "Episode: 1581 Duration: 0:00:10.355274 Num steps: 668 Reward: 10.0 Training time per step: 0.012 Avg Reward (Last 100): 9.455 Epsilon: 0.388\n", "Episode: 1582 Duration: 0:00:09.744994 Num steps: 676 Reward: 13.0 Training time per step: 0.011 Avg Reward (Last 100): 9.535 Epsilon: 0.387\n", "Episode: 1583 Duration: 0:00:10.194128 Num steps: 637 Reward: 9.0 Training time per step: 0.012 Avg Reward (Last 100): 9.564 Epsilon: 0.386\n", "Episode: 1584 Duration: 0:00:08.527806 Num steps: 528 Reward: 9.0 Training time per step: 0.012 Avg Reward (Last 100): 9.604 Epsilon: 0.386\n", "Episode: 1585 Duration: 0:00:10.691575 Num steps: 705 Reward: 11.0 Training time per step: 0.012 Avg Reward (Last 100): 9.653 Epsilon: 0.385\n", "Episode: 1586 Duration: 0:00:09.357543 Num steps: 623 Reward: 9.0 Training time per step: 0.012 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100): 9.851 Epsilon: 0.373\n", "Episode: 1600 Duration: 0:00:15.108786 Num steps: 995 Reward: 20.0 Training time per step: 0.012 Avg Reward (Last 100): 9.941 Epsilon: 0.371\n", "SAVED MODEL #1600\n", "Episode: 1601 Duration: 0:00:04.981432 Num steps: 408 Reward: 5.0 Training time per step: 0.010 Avg Reward (Last 100): 9.861 Epsilon: 0.371\n", "Episode: 1602 Duration: 0:00:08.165176 Num steps: 585 Reward: 8.0 Training time per step: 0.011 Avg Reward (Last 100): 9.822 Epsilon: 0.370\n", "Copied model parameters to target network. total_t = 490000, period = 10000\n", "Episode: 1603 Duration: 0:00:11.655574 Num steps: 748 Reward: 11.0 Training time per step: 0.012 Avg Reward (Last 100): 9.832 Epsilon: 0.369\n", "Episode: 1604 Duration: 0:00:11.822819 Num steps: 749 Reward: 14.0 Training time per step: 0.012 Avg Reward (Last 100): 9.901 Epsilon: 0.368\n", "Episode: 1605 Duration: 0:00:10.427565 Num steps: 668 Reward: 14.0 Training time per step: 0.012 Avg Reward (Last 100): 9.970 Epsilon: 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"Episode: 1619 Duration: 0:00:07.901635 Num steps: 534 Reward: 8.0 Training time per step: 0.012 Avg Reward (Last 100): 10.277 Epsilon: 0.355\n", "Episode: 1620 Duration: 0:00:10.999451 Num steps: 719 Reward: 13.0 Training time per step: 0.012 Avg Reward (Last 100): 10.347 Epsilon: 0.354\n", "Episode: 1621 Duration: 0:00:08.570987 Num steps: 589 Reward: 12.0 Training time per step: 0.011 Avg Reward (Last 100): 10.366 Epsilon: 0.353\n", "Episode: 1622 Duration: 0:00:10.630969 Num steps: 671 Reward: 11.0 Training time per step: 0.012 Avg Reward (Last 100): 10.376 Epsilon: 0.352\n", "Episode: 1623 Duration: 0:00:09.973185 Num steps: 629 Reward: 9.0 Training time per step: 0.012 Avg Reward (Last 100): 10.337 Epsilon: 0.351\n", "Episode: 1624 Duration: 0:00:12.805583 Num steps: 848 Reward: 13.0 Training time per step: 0.012 Avg Reward (Last 100): 10.376 Epsilon: 0.350\n", "Episode: 1625 Duration: 0:00:11.199248 Num steps: 688 Reward: 11.0 Training time per step: 0.012 Avg Reward (Last 100): 10.396 Epsilon: 0.350\n", "Episode: 1626 Duration: 0:00:11.901004 Num steps: 785 Reward: 11.0 Training time per step: 0.012 Avg Reward (Last 100): 10.416 Epsilon: 0.349\n", "Episode: 1627 Duration: 0:00:14.422096 Num steps: 922 Reward: 16.0 Training time per step: 0.012 Avg Reward (Last 100): 10.416 Epsilon: 0.347\n", "Episode: 1628 Duration: 0:00:10.116768 Num steps: 639 Reward: 10.0 Training time per step: 0.012 Avg Reward (Last 100): 10.455 Epsilon: 0.346\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Episode: 1629 Duration: 0:00:11.641790 Num steps: 808 Reward: 15.0 Training time per step: 0.011 Avg Reward (Last 100): 10.574 Epsilon: 0.345\n", "Episode: 1630 Duration: 0:00:11.229220 Num steps: 719 Reward: 11.0 Training time per step: 0.012 Avg Reward (Last 100): 10.574 Epsilon: 0.345\n", "Copied model parameters to target network. total_t = 510000, period = 10000\n", "Episode: 1631 Duration: 0:00:12.917099 Num steps: 834 Reward: 13.0 Training time per step: 0.012 Avg Reward (Last 100): 10.604 Epsilon: 0.343\n", "Episode: 1632 Duration: 0:00:06.970956 Num steps: 439 Reward: 6.0 Training time per step: 0.012 Avg Reward (Last 100): 10.564 Epsilon: 0.343\n", "Episode: 1633 Duration: 0:00:12.449170 Num steps: 818 Reward: 11.0 Training time per step: 0.012 Avg Reward (Last 100): 10.574 Epsilon: 0.342\n", "Episode: 1634 Duration: 0:00:09.630119 Num steps: 612 Reward: 9.0 Training time per step: 0.012 Avg Reward (Last 100): 10.604 Epsilon: 0.341\n", "Episode: 1635 Duration: 0:00:09.481689 Num steps: 628 Reward: 9.0 Training time per step: 0.012 Avg Reward (Last 100): 10.604 Epsilon: 0.340\n", "Episode: 1636 Duration: 0:00:09.070082 Num steps: 564 Reward: 9.0 Training time per step: 0.012 Avg Reward (Last 100): 10.614 Epsilon: 0.340\n", "Episode: 1637 Duration: 0:00:08.789603 Num steps: 539 Reward: 7.0 Training time per step: 0.012 Avg Reward (Last 100): 10.624 Epsilon: 0.339\n", "Episode: 1638 Duration: 0:00:07.149586 Num steps: 464 Reward: 6.0 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Num steps: 670 Reward: 13.0 Training time per step: 0.012 Avg Reward (Last 100): 10.762 Epsilon: 0.332\n", "Copied model parameters to target network. total_t = 520000, period = 10000\n", "Episode: 1646 Duration: 0:00:12.604658 Num steps: 800 Reward: 10.0 Training time per step: 0.012 Avg Reward (Last 100): 10.752 Epsilon: 0.331\n", "Episode: 1647 Duration: 0:00:07.337578 Num steps: 498 Reward: 8.0 Training time per step: 0.011 Avg Reward (Last 100): 10.713 Epsilon: 0.330\n", "Episode: 1648 Duration: 0:00:14.289500 Num steps: 917 Reward: 14.0 Training time per step: 0.012 Avg Reward (Last 100): 10.743 Epsilon: 0.329\n", "Episode: 1649 Duration: 0:00:07.492182 Num steps: 505 Reward: 9.0 Training time per step: 0.012 Avg Reward (Last 100): 10.733 Epsilon: 0.328\n", "Episode: 1650 Duration: 0:00:10.054828 Num steps: 621 Reward: 9.0 Training time per step: 0.012 Avg Reward (Last 100): 10.713 Epsilon: 0.327\n", "SAVED MODEL #1650\n", "Episode: 1651 Duration: 0:00:14.008683 Num steps: 1061 Reward: 29.0 Training time per step: 0.011 Avg Reward (Last 100): 10.861 Epsilon: 0.326\n", "Episode: 1652 Duration: 0:00:09.923531 Num steps: 632 Reward: 9.0 Training time per step: 0.012 Avg Reward (Last 100): 10.802 Epsilon: 0.325\n", "Episode: 1653 Duration: 0:00:12.229060 Num steps: 791 Reward: 12.0 Training time per step: 0.012 Avg Reward (Last 100): 10.861 Epsilon: 0.324\n", "Episode: 1654 Duration: 0:00:08.761175 Num steps: 593 Reward: 9.0 Training time per step: 0.011 Avg Reward (Last 100): 10.851 Epsilon: 0.324\n", "Episode: 1655 Duration: 0:00:10.167512 Num steps: 687 Reward: 11.0 Training time per step: 0.011 Avg Reward (Last 100): 10.861 Epsilon: 0.323\n", "Episode: 1656 Duration: 0:00:07.976732 Num steps: 525 Reward: 7.0 Training time per step: 0.012 Avg Reward (Last 100): 10.822 Epsilon: 0.322\n", "Episode: 1657 Duration: 0:00:12.291552 Num steps: 795 Reward: 13.0 Training time per step: 0.012 Avg Reward (Last 100): 10.851 Epsilon: 0.321\n", "Episode: 1658 Duration: 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0:00:13.378065 Num steps: 840 Reward: 13.0 Training time per step: 0.012 Avg Reward (Last 100): 11.832 Epsilon: 0.293\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Copied model parameters to target network. total_t = 550000, period = 10000\n", "Episode: 1685 Duration: 0:00:13.342748 Num steps: 863 Reward: 18.0 Training time per step: 0.012 Avg Reward (Last 100): 11.921 Epsilon: 0.292\n", "Episode: 1686 Duration: 0:00:10.503974 Num steps: 663 Reward: 10.0 Training time per step: 0.012 Avg Reward (Last 100): 11.911 Epsilon: 0.291\n", "Episode: 1687 Duration: 0:00:13.087383 Num steps: 859 Reward: 15.0 Training time per step: 0.012 Avg Reward (Last 100): 11.970 Epsilon: 0.290\n", "Episode: 1688 Duration: 0:00:08.138711 Num steps: 492 Reward: 7.0 Training time per step: 0.012 Avg Reward (Last 100): 11.931 Epsilon: 0.290\n", "Episode: 1689 Duration: 0:00:11.029399 Num steps: 683 Reward: 13.0 Training time per step: 0.012 Avg Reward (Last 100): 11.941 Epsilon: 0.289\n", "Episode: 1690 Duration: 0:00:07.347004 Num steps: 474 Reward: 7.0 Training time per step: 0.012 Avg Reward (Last 100): 11.921 Epsilon: 0.288\n", "Episode: 1691 Duration: 0:00:09.266301 Num steps: 598 Reward: 7.0 Training time per step: 0.012 Avg Reward (Last 100): 11.891 Epsilon: 0.287\n", "Episode: 1692 Duration: 0:00:09.583877 Num steps: 675 Reward: 11.0 Training time per step: 0.011 Avg Reward (Last 100): 11.901 Epsilon: 0.287\n", "Episode: 1693 Duration: 0:00:09.589617 Num steps: 702 Reward: 12.0 Training time per step: 0.011 Avg Reward (Last 100): 11.911 Epsilon: 0.286\n", "Episode: 1694 Duration: 0:00:14.135026 Num steps: 1008 Reward: 16.0 Training time per step: 0.011 Avg Reward (Last 100): 11.980 Epsilon: 0.284\n", "Episode: 1695 Duration: 0:00:12.480091 Num steps: 876 Reward: 22.0 Training time per step: 0.011 Avg Reward (Last 100): 12.109 Epsilon: 0.283\n", "Episode: 1696 Duration: 0:00:08.466503 Num steps: 565 Reward: 8.0 Training time per step: 0.012 Avg Reward (Last 100): 12.059 Epsilon: 0.283\n", "Episode: 1697 Duration: 0:00:09.251010 Num steps: 640 Reward: 9.0 Training time per step: 0.011 Avg Reward (Last 100): 11.990 Epsilon: 0.282\n", "Episode: 1698 Duration: 0:00:15.581656 Num steps: 1038 Reward: 25.0 Training time per step: 0.012 Avg Reward (Last 100): 12.178 Epsilon: 0.280\n", "Copied model parameters to target network. total_t = 560000, period = 10000\n", "Episode: 1699 Duration: 0:00:07.443283 Num steps: 464 Reward: 7.0 Training time per step: 0.012 Avg Reward (Last 100): 12.119 Epsilon: 0.280\n", "Episode: 1700 Duration: 0:00:10.258603 Num steps: 649 Reward: 9.0 Training time per step: 0.012 Avg Reward (Last 100): 12.129 Epsilon: 0.279\n", "SAVED MODEL #1700\n", "Episode: 1701 Duration: 0:00:07.488678 Num steps: 613 Reward: 10.0 Training time per step: 0.010 Avg Reward (Last 100): 12.030 Epsilon: 0.278\n", "Episode: 1702 Duration: 0:00:14.411715 Num steps: 936 Reward: 16.0 Training time per step: 0.012 Avg Reward (Last 100): 12.139 Epsilon: 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"Episode: 1716 Duration: 0:00:11.241784 Num steps: 725 Reward: 11.0 Training time per step: 0.012 Avg Reward (Last 100): 12.455 Epsilon: 0.262\n", "Episode: 1717 Duration: 0:00:12.021350 Num steps: 782 Reward: 13.0 Training time per step: 0.012 Avg Reward (Last 100): 12.446 Epsilon: 0.261\n", "Episode: 1718 Duration: 0:00:12.758584 Num steps: 873 Reward: 16.0 Training time per step: 0.011 Avg Reward (Last 100): 12.495 Epsilon: 0.260\n", "Episode: 1719 Duration: 0:00:13.227691 Num steps: 872 Reward: 14.0 Training time per step: 0.012 Avg Reward (Last 100): 12.545 Epsilon: 0.259\n", "Episode: 1720 Duration: 0:00:11.065369 Num steps: 712 Reward: 11.0 Training time per step: 0.012 Avg Reward (Last 100): 12.574 Epsilon: 0.258\n", "Episode: 1721 Duration: 0:00:13.481810 Num steps: 880 Reward: 15.0 Training time per step: 0.012 Avg Reward (Last 100): 12.594 Epsilon: 0.257\n", "Episode: 1722 Duration: 0:00:10.687766 Num steps: 678 Reward: 14.0 Training time per step: 0.012 Avg Reward (Last 100): 12.614 Epsilon: 0.256\n", "Episode: 1723 Duration: 0:00:11.351178 Num steps: 745 Reward: 16.0 Training time per step: 0.012 Avg Reward (Last 100): 12.663 Epsilon: 0.255\n", "Copied model parameters to target network. total_t = 580000, period = 10000\n", "Episode: 1724 Duration: 0:00:15.723071 Num steps: 1055 Reward: 26.0 Training time per step: 0.011 Avg Reward (Last 100): 12.832 Epsilon: 0.254\n", "Episode: 1725 Duration: 0:00:11.908486 Num steps: 764 Reward: 15.0 Training time per step: 0.012 Avg Reward (Last 100): 12.851 Epsilon: 0.253\n", "Episode: 1726 Duration: 0:00:12.087756 Num steps: 758 Reward: 13.0 Training time per step: 0.012 Avg Reward (Last 100): 12.871 Epsilon: 0.252\n", "Episode: 1727 Duration: 0:00:12.827378 Num steps: 779 Reward: 14.0 Training time per step: 0.012 Avg Reward (Last 100): 12.901 Epsilon: 0.251\n", "Episode: 1728 Duration: 0:00:13.469371 Num steps: 924 Reward: 21.0 Training time per step: 0.011 Avg Reward (Last 100): 12.950 Epsilon: 0.250\n", "Episode: 1729 Duration: 0:00:11.288679 Num steps: 721 Reward: 11.0 Training time per step: 0.012 Avg Reward (Last 100): 12.960 Epsilon: 0.249\n", "Episode: 1730 Duration: 0:00:12.112658 Num steps: 780 Reward: 12.0 Training time per step: 0.012 Avg Reward (Last 100): 12.931 Epsilon: 0.248\n", "Episode: 1731 Duration: 0:00:15.832014 Num steps: 1025 Reward: 16.0 Training time per step: 0.012 Avg Reward (Last 100): 12.980 Epsilon: 0.247\n", "Episode: 1732 Duration: 0:00:10.549286 Num steps: 680 Reward: 14.0 Training time per step: 0.012 Avg Reward (Last 100): 12.990 Epsilon: 0.246\n", "Episode: 1733 Duration: 0:00:12.907884 Num steps: 838 Reward: 14.0 Training time per step: 0.012 Avg Reward (Last 100): 13.069 Epsilon: 0.245\n", "Episode: 1734 Duration: 0:00:10.925879 Num steps: 726 Reward: 12.0 Training time per step: 0.012 Avg Reward (Last 100): 13.079 Epsilon: 0.244\n", "Episode: 1735 Duration: 0:00:10.779411 Num steps: 653 Reward: 14.0 Training time per step: 0.012 Avg Reward (Last 100): 13.129 Epsilon: 0.243\n", "Episode: 1736 Duration: 0:00:12.236813 Num steps: 784 Reward: 17.0 Training time per step: 0.012 Avg Reward (Last 100): 13.208 Epsilon: 0.242\n", "Copied model parameters to target network. total_t = 590000, period = 10000\n", "Episode: 1737 Duration: 0:00:11.949114 Num steps: 751 Reward: 21.0 Training time per step: 0.012 Avg Reward (Last 100): 13.327 Epsilon: 0.241\n", "Episode: 1738 Duration: 0:00:13.291612 Num steps: 868 Reward: 13.0 Training time per step: 0.012 Avg Reward (Last 100): 13.386 Epsilon: 0.240\n", "Episode: 1739 Duration: 0:00:13.225922 Num steps: 879 Reward: 18.0 Training time per step: 0.012 Avg Reward (Last 100): 13.505 Epsilon: 0.239\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Episode: 1740 Duration: 0:00:14.386357 Num steps: 887 Reward: 17.0 Training time per step: 0.012 Avg Reward (Last 100): 13.574 Epsilon: 0.237\n", "Episode: 1741 Duration: 0:00:13.217459 Num steps: 874 Reward: 14.0 Training time per step: 0.012 Avg Reward (Last 100): 13.594 Epsilon: 0.236\n", "Episode: 1742 Duration: 0:00:08.614660 Num steps: 537 Reward: 10.0 Training time per step: 0.012 Avg Reward (Last 100): 13.584 Epsilon: 0.236\n", "Episode: 1743 Duration: 0:00:12.101299 Num steps: 743 Reward: 13.0 Training time per step: 0.012 Avg Reward (Last 100): 13.505 Epsilon: 0.235\n", "Episode: 1744 Duration: 0:00:15.466081 Num steps: 974 Reward: 18.0 Training time per step: 0.012 Avg Reward (Last 100): 13.614 Epsilon: 0.233\n", "Episode: 1745 Duration: 0:00:11.267105 Num steps: 733 Reward: 12.0 Training time per step: 0.012 Avg Reward (Last 100): 13.574 Epsilon: 0.232\n", "Episode: 1746 Duration: 0:00:12.967653 Num steps: 834 Reward: 14.0 Training time per step: 0.012 Avg Reward (Last 100): 13.584 Epsilon: 0.231\n", "Episode: 1747 Duration: 0:00:11.339979 Num steps: 690 Reward: 10.0 Training time per step: 0.012 Avg Reward (Last 100): 13.584 Epsilon: 0.231\n", "Episode: 1748 Duration: 0:00:11.804908 Num steps: 768 Reward: 12.0 Training time per step: 0.012 Avg Reward (Last 100): 13.624 Epsilon: 0.230\n", "Copied model parameters to target network. total_t = 600000, period = 10000\n", "Episode: 1749 Duration: 0:00:13.705515 Num steps: 858 Reward: 14.0 Training time per step: 0.012 Avg Reward (Last 100): 13.624 Epsilon: 0.228\n", "Episode: 1750 Duration: 0:00:12.875589 Num steps: 846 Reward: 18.0 Training time per step: 0.012 Avg Reward (Last 100): 13.713 Epsilon: 0.227\n", "SAVED MODEL #1750\n", "Episode: 1751 Duration: 0:00:10.033249 Num steps: 790 Reward: 13.0 Training time per step: 0.010 Avg Reward (Last 100): 13.752 Epsilon: 0.226\n", "Episode: 1752 Duration: 0:00:13.531064 Num steps: 853 Reward: 18.0 Training time per step: 0.012 Avg Reward (Last 100): 13.644 Epsilon: 0.225\n", "Episode: 1753 Duration: 0:00:14.468068 Num steps: 945 Reward: 19.0 Training time per step: 0.012 Avg Reward (Last 100): 13.743 Epsilon: 0.224\n", "Episode: 1754 Duration: 0:00:13.705661 Num steps: 841 Reward: 12.0 Training time per step: 0.012 Avg Reward (Last 100): 13.743 Epsilon: 0.223\n", "Episode: 1755 Duration: 0:00:11.274385 Num steps: 737 Reward: 20.0 Training time per step: 0.012 Avg Reward (Last 100): 13.851 Epsilon: 0.222\n", "Episode: 1756 Duration: 0:00:13.608168 Num steps: 899 Reward: 14.0 Training time per step: 0.012 Avg Reward (Last 100): 13.881 Epsilon: 0.221\n", "Episode: 1757 Duration: 0:00:10.100158 Num steps: 680 Reward: 11.0 Training time per step: 0.011 Avg Reward (Last 100): 13.921 Epsilon: 0.220\n", "Episode: 1758 Duration: 0:00:15.302237 Num steps: 945 Reward: 16.0 Training time per step: 0.012 Avg Reward (Last 100): 13.950 Epsilon: 0.219\n", "Episode: 1759 Duration: 0:00:14.173849 Num steps: 879 Reward: 16.0 Training time per step: 0.012 Avg Reward (Last 100): 13.990 Epsilon: 0.218\n", "Episode: 1760 Duration: 0:00:13.211292 Num steps: 845 Reward: 16.0 Training time per step: 0.012 Avg Reward (Last 100): 14.050 Epsilon: 0.217\n", "Copied model parameters to target network. total_t = 610000, period = 10000\n", "Episode: 1761 Duration: 0:00:13.296315 Num steps: 853 Reward: 16.0 Training time per step: 0.012 Avg Reward (Last 100): 14.069 Epsilon: 0.215\n", "Episode: 1762 Duration: 0:00:16.992856 Num steps: 1099 Reward: 23.0 Training time per step: 0.012 Avg Reward (Last 100): 14.218 Epsilon: 0.214\n", "Episode: 1763 Duration: 0:00:12.498384 Num steps: 803 Reward: 14.0 Training time per step: 0.012 Avg Reward (Last 100): 14.287 Epsilon: 0.213\n", "Episode: 1764 Duration: 0:00:11.771790 Num steps: 727 Reward: 15.0 Training time per step: 0.012 Avg Reward (Last 100): 14.307 Epsilon: 0.212\n", "Episode: 1765 Duration: 0:00:15.624220 Num steps: 989 Reward: 18.0 Training time per step: 0.012 Avg Reward (Last 100): 14.347 Epsilon: 0.211\n", "Episode: 1766 Duration: 0:00:12.100609 Num steps: 795 Reward: 12.0 Training time per step: 0.012 Avg Reward (Last 100): 14.356 Epsilon: 0.210\n", "Episode: 1767 Duration: 0:00:14.569745 Num steps: 931 Reward: 22.0 Training time per step: 0.012 Avg Reward (Last 100): 14.446 Epsilon: 0.209\n", "Episode: 1768 Duration: 0:00:12.597536 Num steps: 841 Reward: 14.0 Training time per step: 0.012 Avg Reward (Last 100): 14.376 Epsilon: 0.207\n", "Episode: 1769 Duration: 0:00:14.147376 Num steps: 975 Reward: 15.0 Training time per step: 0.011 Avg Reward (Last 100): 14.337 Epsilon: 0.206\n", "Episode: 1770 Duration: 0:00:13.258364 Num steps: 845 Reward: 15.0 Training time per step: 0.012 Avg Reward (Last 100): 14.356 Epsilon: 0.205\n", "Episode: 1771 Duration: 0:00:11.618501 Num steps: 726 Reward: 12.0 Training time per step: 0.012 Avg Reward (Last 100): 14.396 Epsilon: 0.204\n", "Episode: 1772 Duration: 0:00:11.646424 Num steps: 722 Reward: 13.0 Training time per step: 0.012 Avg Reward (Last 100): 14.327 Epsilon: 0.203\n", "Copied model parameters to target network. total_t = 620000, period = 10000\n", "Episode: 1773 Duration: 0:00:12.999582 Num steps: 799 Reward: 11.0 Training time per step: 0.012 Avg Reward (Last 100): 14.277 Epsilon: 0.202\n", "Episode: 1774 Duration: 0:00:12.021459 Num steps: 800 Reward: 13.0 Training time per step: 0.012 Avg Reward (Last 100): 14.317 Epsilon: 0.201\n", "Episode: 1775 Duration: 0:00:13.170049 Num steps: 865 Reward: 14.0 Training time per step: 0.012 Avg Reward (Last 100): 14.386 Epsilon: 0.200\n", "Episode: 1776 Duration: 0:00:19.823397 Num steps: 1235 Reward: 28.0 Training time per step: 0.012 Avg Reward (Last 100): 14.485 Epsilon: 0.198\n", "Episode: 1777 Duration: 0:00:07.740656 Num steps: 516 Reward: 9.0 Training time per step: 0.012 Avg Reward (Last 100): 14.475 Epsilon: 0.198\n", "Episode: 1778 Duration: 0:00:11.898996 Num steps: 776 Reward: 11.0 Training time per step: 0.012 Avg Reward (Last 100): 14.327 Epsilon: 0.197\n", "Episode: 1779 Duration: 0:00:14.094922 Num steps: 904 Reward: 18.0 Training time per step: 0.012 Avg Reward (Last 100): 14.327 Epsilon: 0.196\n", "Episode: 1780 Duration: 0:00:12.849440 Num steps: 792 Reward: 12.0 Training time per step: 0.012 Avg Reward (Last 100): 14.307 Epsilon: 0.195\n", "Episode: 1781 Duration: 0:00:09.471969 Num steps: 591 Reward: 8.0 Training time per step: 0.012 Avg Reward (Last 100): 14.218 Epsilon: 0.194\n", "Episode: 1782 Duration: 0:00:14.255242 Num steps: 910 Reward: 14.0 Training time per step: 0.012 Avg Reward (Last 100): 14.188 Epsilon: 0.193\n", "Episode: 1783 Duration: 0:00:14.853503 Num steps: 947 Reward: 23.0 Training time per step: 0.012 Avg Reward (Last 100): 14.238 Epsilon: 0.192\n", "Episode: 1784 Duration: 0:00:15.120003 Num steps: 969 Reward: 17.0 Training time per step: 0.012 Avg Reward (Last 100): 14.297 Epsilon: 0.190\n", "Copied model parameters to target network. total_t = 630000, period = 10000\n", "Episode: 1785 Duration: 0:00:10.815431 Num steps: 658 Reward: 13.0 Training time per step: 0.012 Avg Reward (Last 100): 14.297 Epsilon: 0.189\n", "Episode: 1786 Duration: 0:00:15.080648 Num steps: 950 Reward: 23.0 Training time per step: 0.012 Avg Reward (Last 100): 14.347 Epsilon: 0.188\n", "Episode: 1787 Duration: 0:00:17.961561 Num steps: 1151 Reward: 25.0 Training time per step: 0.012 Avg Reward (Last 100): 14.495 Epsilon: 0.187\n", "Episode: 1788 Duration: 0:00:13.233367 Num steps: 840 Reward: 16.0 Training time per step: 0.012 Avg Reward (Last 100): 14.505 Epsilon: 0.186\n", "Episode: 1789 Duration: 0:00:11.384845 Num steps: 741 Reward: 11.0 Training time per step: 0.012 Avg Reward (Last 100): 14.545 Epsilon: 0.185\n", "Episode: 1790 Duration: 0:00:17.466275 Num steps: 1055 Reward: 23.0 Training time per step: 0.012 Avg Reward (Last 100): 14.644 Epsilon: 0.183\n", "Episode: 1791 Duration: 0:00:10.406002 Num steps: 694 Reward: 14.0 Training time per step: 0.012 Avg Reward (Last 100): 14.713 Epsilon: 0.182\n", "Episode: 1792 Duration: 0:00:09.673981 Num steps: 600 Reward: 9.0 Training time per step: 0.012 Avg Reward (Last 100): 14.733 Epsilon: 0.182\n", "Episode: 1793 Duration: 0:00:12.491438 Num steps: 915 Reward: 17.0 Training time per step: 0.011 Avg Reward (Last 100): 14.792 Epsilon: 0.180\n", "Episode: 1794 Duration: 0:00:10.901792 Num steps: 773 Reward: 13.0 Training time per step: 0.011 Avg Reward (Last 100): 14.802 Epsilon: 0.179\n", "Episode: 1795 Duration: 0:00:16.252594 Num steps: 1054 Reward: 18.0 Training time per step: 0.012 Avg Reward (Last 100): 14.822 Epsilon: 0.178\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Episode: 1796 Duration: 0:00:11.367618 Num steps: 748 Reward: 18.0 Training time per step: 0.012 Avg Reward (Last 100): 14.782 Epsilon: 0.177\n", "Copied model parameters to target network. total_t = 640000, period = 10000\n", "Episode: 1797 Duration: 0:00:15.504905 Num steps: 968 Reward: 22.0 Training time per step: 0.012 Avg Reward (Last 100): 14.921 Epsilon: 0.176\n", "Episode: 1798 Duration: 0:00:10.405946 Num steps: 641 Reward: 11.0 Training time per step: 0.012 Avg Reward (Last 100): 14.941 Epsilon: 0.175\n", "Episode: 1799 Duration: 0:00:10.805441 Num steps: 656 Reward: 10.0 Training time per step: 0.012 Avg Reward (Last 100): 14.792 Epsilon: 0.174\n", "Episode: 1800 Duration: 0:00:15.714914 Num steps: 998 Reward: 19.0 Training time per step: 0.012 Avg Reward (Last 100): 14.911 Epsilon: 0.173\n", "SAVED MODEL #1800\n", "Episode: 1801 Duration: 0:00:11.680526 Num steps: 915 Reward: 15.0 Training time per step: 0.010 Avg Reward (Last 100): 14.970 Epsilon: 0.172\n", "Episode: 1802 Duration: 0:00:11.516179 Num steps: 727 Reward: 10.0 Training time per step: 0.012 Avg Reward (Last 100): 14.970 Epsilon: 0.171\n", "Episode: 1803 Duration: 0:00:16.742991 Num steps: 1133 Reward: 19.0 Training time per step: 0.011 Avg Reward (Last 100): 15.000 Epsilon: 0.169\n", "Episode: 1804 Duration: 0:00:12.665010 Num steps: 761 Reward: 14.0 Training time per step: 0.012 Avg Reward (Last 100): 15.000 Epsilon: 0.168\n", "Episode: 1805 Duration: 0:00:15.653682 Num steps: 1053 Reward: 28.0 Training time per step: 0.011 Avg Reward (Last 100): 15.188 Epsilon: 0.167\n", "Episode: 1806 Duration: 0:00:08.416522 Num steps: 535 Reward: 8.0 Training time per step: 0.012 Avg Reward (Last 100): 15.149 Epsilon: 0.166\n", "Episode: 1807 Duration: 0:00:17.457704 Num steps: 1113 Reward: 24.0 Training time per step: 0.012 Avg Reward (Last 100): 15.218 Epsilon: 0.165\n", "Copied model parameters to target network. total_t = 650000, period = 10000\n", "Episode: 1808 Duration: 0:00:14.769992 Num steps: 907 Reward: 16.0 Training time per step: 0.012 Avg Reward (Last 100): 15.267 Epsilon: 0.164\n", "Episode: 1809 Duration: 0:00:17.749289 Num steps: 1151 Reward: 27.0 Training time per step: 0.012 Avg Reward (Last 100): 15.386 Epsilon: 0.162\n", "Episode: 1810 Duration: 0:00:10.656997 Num steps: 708 Reward: 12.0 Training time per step: 0.012 Avg Reward (Last 100): 15.317 Epsilon: 0.161\n", "Episode: 1811 Duration: 0:00:09.768579 Num steps: 683 Reward: 11.0 Training time per step: 0.011 Avg Reward (Last 100): 15.307 Epsilon: 0.161\n", "Episode: 1812 Duration: 0:00:09.209629 Num steps: 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parameters to target network. total_t = 660000, period = 10000\n", "Episode: 1819 Duration: 0:00:13.449967 Num steps: 838 Reward: 17.0 Training time per step: 0.012 Avg Reward (Last 100): 15.564 Epsilon: 0.151\n", "Episode: 1820 Duration: 0:00:12.414516 Num steps: 761 Reward: 11.0 Training time per step: 0.012 Avg Reward (Last 100): 15.535 Epsilon: 0.150\n", "Episode: 1821 Duration: 0:00:12.499532 Num steps: 857 Reward: 14.0 Training time per step: 0.011 Avg Reward (Last 100): 15.564 Epsilon: 0.149\n", "Episode: 1822 Duration: 0:00:15.492913 Num steps: 964 Reward: 21.0 Training time per step: 0.012 Avg Reward (Last 100): 15.624 Epsilon: 0.148\n", "Episode: 1823 Duration: 0:00:14.825377 Num steps: 922 Reward: 14.0 Training time per step: 0.012 Avg Reward (Last 100): 15.624 Epsilon: 0.147\n", "Episode: 1824 Duration: 0:00:12.184961 Num steps: 812 Reward: 12.0 Training time per step: 0.011 Avg Reward (Last 100): 15.584 Epsilon: 0.146\n", "Episode: 1825 Duration: 0:00:13.317358 Num steps: 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parameters to target network. total_t = 670000, period = 10000\n", "Episode: 1832 Duration: 0:00:14.692484 Num steps: 914 Reward: 17.0 Training time per step: 0.012 Avg Reward (Last 100): 15.545 Epsilon: 0.138\n", "Episode: 1833 Duration: 0:00:17.880474 Num steps: 1142 Reward: 19.0 Training time per step: 0.012 Avg Reward (Last 100): 15.594 Epsilon: 0.136\n", "Episode: 1834 Duration: 0:00:09.014738 Num steps: 582 Reward: 9.0 Training time per step: 0.012 Avg Reward (Last 100): 15.545 Epsilon: 0.135\n", "Episode: 1835 Duration: 0:00:11.690954 Num steps: 726 Reward: 11.0 Training time per step: 0.012 Avg Reward (Last 100): 15.535 Epsilon: 0.134\n", "Episode: 1836 Duration: 0:00:15.412968 Num steps: 1003 Reward: 17.0 Training time per step: 0.012 Avg Reward (Last 100): 15.564 Epsilon: 0.133\n", "Episode: 1837 Duration: 0:00:12.492302 Num steps: 792 Reward: 13.0 Training time per step: 0.012 Avg Reward (Last 100): 15.525 Epsilon: 0.132\n", "Episode: 1838 Duration: 0:00:10.676363 Num steps: 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step: 0.012 Avg Reward (Last 100): 15.554 Epsilon: 0.125\n", "Episode: 1845 Duration: 0:00:12.792795 Num steps: 793 Reward: 13.0 Training time per step: 0.012 Avg Reward (Last 100): 15.505 Epsilon: 0.124\n", "Episode: 1846 Duration: 0:00:15.261916 Num steps: 986 Reward: 20.0 Training time per step: 0.012 Avg Reward (Last 100): 15.584 Epsilon: 0.123\n", "Episode: 1847 Duration: 0:00:14.334739 Num steps: 865 Reward: 16.0 Training time per step: 0.012 Avg Reward (Last 100): 15.604 Epsilon: 0.121\n", "Episode: 1848 Duration: 0:00:12.758569 Num steps: 795 Reward: 12.0 Training time per step: 0.012 Avg Reward (Last 100): 15.624 Epsilon: 0.120\n", "Episode: 1849 Duration: 0:00:15.908954 Num steps: 990 Reward: 20.0 Training time per step: 0.012 Avg Reward (Last 100): 15.703 Epsilon: 0.119\n", "Episode: 1850 Duration: 0:00:10.284505 Num steps: 647 Reward: 10.0 Training time per step: 0.012 Avg Reward (Last 100): 15.663 Epsilon: 0.118\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "SAVED MODEL #1850\n", "Episode: 1851 Duration: 0:00:14.403221 Num steps: 1138 Reward: 27.0 Training time per step: 0.010 Avg Reward (Last 100): 15.752 Epsilon: 0.117\n", "Episode: 1852 Duration: 0:00:15.358638 Num steps: 998 Reward: 18.0 Training time per step: 0.012 Avg Reward (Last 100): 15.802 Epsilon: 0.116\n", "Episode: 1853 Duration: 0:00:14.903593 Num steps: 943 Reward: 22.0 Training time per step: 0.012 Avg Reward (Last 100): 15.842 Epsilon: 0.114\n", "Episode: 1854 Duration: 0:00:14.430271 Num steps: 865 Reward: 15.0 Training time per step: 0.012 Avg Reward (Last 100): 15.802 Epsilon: 0.113\n", "Copied model parameters to target network. total_t = 690000, period = 10000\n", "Episode: 1855 Duration: 0:00:15.304759 Num steps: 968 Reward: 16.0 Training time per step: 0.012 Avg Reward (Last 100): 15.842 Epsilon: 0.112\n", "Episode: 1856 Duration: 0:00:13.538196 Num steps: 926 Reward: 20.0 Training time per step: 0.011 Avg Reward (Last 100): 15.842 Epsilon: 0.111\n", "Episode: 1857 Duration: 0:00:17.543876 Num steps: 1105 Reward: 26.0 Training time per step: 0.012 Avg Reward (Last 100): 15.960 Epsilon: 0.109\n", "Episode: 1858 Duration: 0:00:16.959592 Num steps: 1042 Reward: 21.0 Training time per step: 0.012 Avg Reward (Last 100): 16.059 Epsilon: 0.108\n", "Episode: 1859 Duration: 0:00:11.077576 Num steps: 770 Reward: 12.0 Training time per step: 0.011 Avg Reward (Last 100): 16.020 Epsilon: 0.107\n", "Episode: 1860 Duration: 0:00:18.853798 Num steps: 1224 Reward: 26.0 Training time per step: 0.012 Avg Reward (Last 100): 16.119 Epsilon: 0.105\n", "Episode: 1861 Duration: 0:00:12.916033 Num steps: 803 Reward: 16.0 Training time per step: 0.012 Avg Reward (Last 100): 16.119 Epsilon: 0.104\n", "Episode: 1862 Duration: 0:00:14.840334 Num steps: 1000 Reward: 25.0 Training time per step: 0.011 Avg Reward (Last 100): 16.208 Epsilon: 0.103\n", "Episode: 1863 Duration: 0:00:13.851871 Num steps: 891 Reward: 18.0 Training time per step: 0.012 Avg Reward (Last 100): 16.158 Epsilon: 0.102\n", "Episode: 1864 Duration: 0:00:15.672310 Num steps: 976 Reward: 20.0 Training time per step: 0.012 Avg Reward (Last 100): 16.218 Epsilon: 0.101\n", "Copied model parameters to target network. total_t = 700000, period = 10000\n", "Episode: 1865 Duration: 0:00:12.066512 Num steps: 775 Reward: 20.0 Training time per step: 0.012 Avg Reward (Last 100): 16.267 Epsilon: 0.100\n", "Episode: 1866 Duration: 0:00:10.140434 Num steps: 638 Reward: 10.0 Training time per step: 0.012 Avg Reward (Last 100): 16.188 Epsilon: 0.100\n", "Episode: 1867 Duration: 0:00:13.786614 Num steps: 965 Reward: 15.0 Training time per step: 0.011 Avg Reward (Last 100): 16.218 Epsilon: 0.100\n", "Episode: 1868 Duration: 0:00:18.136526 Num steps: 1093 Reward: 27.0 Training time per step: 0.012 Avg Reward (Last 100): 16.267 Epsilon: 0.100\n", "Episode: 1869 Duration: 0:00:18.773232 Num steps: 1171 Reward: 28.0 Training time per step: 0.012 Avg Reward (Last 100): 16.406 Epsilon: 0.100\n", "Episode: 1870 Duration: 0:00:15.902762 Num steps: 1006 Reward: 19.0 Training time per step: 0.012 Avg Reward (Last 100): 16.446 Epsilon: 0.100\n", "Episode: 1871 Duration: 0:00:11.469522 Num steps: 725 Reward: 12.0 Training time per step: 0.012 Avg Reward (Last 100): 16.416 Epsilon: 0.100\n", "Episode: 1872 Duration: 0:00:14.003833 Num steps: 898 Reward: 20.0 Training time per step: 0.012 Avg Reward (Last 100): 16.495 Epsilon: 0.100\n", "Episode: 1873 Duration: 0:00:14.097145 Num steps: 909 Reward: 15.0 Training time per step: 0.012 Avg Reward (Last 100): 16.515 Epsilon: 0.100\n", "Episode: 1874 Duration: 0:00:16.782351 Num steps: 1003 Reward: 21.0 Training time per step: 0.012 Avg Reward (Last 100): 16.614 Epsilon: 0.100\n", "Episode: 1875 Duration: 0:00:15.691959 Num steps: 1039 Reward: 23.0 Training time per step: 0.012 Avg Reward (Last 100): 16.713 Epsilon: 0.100\n", "Copied model parameters to target network. total_t = 710000, period = 10000\n", "Episode: 1876 Duration: 0:00:10.990681 Num steps: 702 Reward: 10.0 Training time per step: 0.012 Avg Reward (Last 100): 16.673 Epsilon: 0.100\n", "Episode: 1877 Duration: 0:00:14.301851 Num steps: 917 Reward: 20.0 Training time per step: 0.012 Avg Reward (Last 100): 16.594 Epsilon: 0.100\n", "Episode: 1878 Duration: 0:00:19.900811 Num steps: 1268 Reward: 29.0 Training time per step: 0.012 Avg Reward (Last 100): 16.792 Epsilon: 0.100\n", "Episode: 1879 Duration: 0:00:24.623940 Num steps: 1560 Reward: 41.0 Training time per step: 0.012 Avg Reward (Last 100): 17.089 Epsilon: 0.100\n", "Episode: 1880 Duration: 0:00:11.602385 Num steps: 773 Reward: 13.0 Training time per step: 0.011 Avg Reward (Last 100): 17.040 Epsilon: 0.100\n", "Episode: 1881 Duration: 0:00:11.291875 Num steps: 676 Reward: 8.0 Training time per step: 0.012 Avg Reward (Last 100): 17.000 Epsilon: 0.100\n", "Episode: 1882 Duration: 0:00:21.477830 Num steps: 1372 Reward: 32.0 Training time per step: 0.012 Avg Reward (Last 100): 17.238 Epsilon: 0.100\n", "Episode: 1883 Duration: 0:00:17.685580 Num steps: 1115 Reward: 20.0 Training time per step: 0.012 Avg Reward (Last 100): 17.297 Epsilon: 0.100\n", "Episode: 1884 Duration: 0:00:10.271905 Num steps: 629 Reward: 10.0 Training time per step: 0.012 Avg Reward (Last 100): 17.168 Epsilon: 0.100\n", "Episode: 1885 Duration: 0:00:18.418996 Num steps: 1186 Reward: 26.0 Training time per step: 0.012 Avg Reward (Last 100): 17.257 Epsilon: 0.100\n", "Copied model parameters to target network. total_t = 720000, period = 10000\n", "Episode: 1886 Duration: 0:00:11.321993 Num steps: 708 Reward: 12.0 Training time per step: 0.012 Avg Reward (Last 100): 17.248 Epsilon: 0.100\n", "Episode: 1887 Duration: 0:00:12.538892 Num steps: 795 Reward: 13.0 Training time per step: 0.012 Avg Reward (Last 100): 17.149 Epsilon: 0.100\n", "Episode: 1888 Duration: 0:00:16.899015 Num steps: 1048 Reward: 22.0 Training time per step: 0.012 Avg Reward (Last 100): 17.119 Epsilon: 0.100\n", "Episode: 1889 Duration: 0:00:19.780616 Num steps: 1244 Reward: 26.0 Training time per step: 0.012 Avg Reward (Last 100): 17.218 Epsilon: 0.100\n", "Episode: 1890 Duration: 0:00:17.012853 Num steps: 1083 Reward: 26.0 Training time per step: 0.012 Avg Reward (Last 100): 17.366 Epsilon: 0.100\n", "Episode: 1891 Duration: 0:00:12.593148 Num steps: 803 Reward: 17.0 Training time per step: 0.012 Avg Reward (Last 100): 17.307 Epsilon: 0.100\n", "Episode: 1892 Duration: 0:00:17.113130 Num steps: 1083 Reward: 16.0 Training time per step: 0.012 Avg Reward (Last 100): 17.327 Epsilon: 0.100\n", "Episode: 1893 Duration: 0:00:17.788340 Num steps: 1074 Reward: 25.0 Training time per step: 0.012 Avg Reward (Last 100): 17.485 Epsilon: 0.100\n", "Episode: 1894 Duration: 0:00:15.145819 Num steps: 992 Reward: 18.0 Training time per step: 0.012 Avg Reward (Last 100): 17.495 Epsilon: 0.100\n", "Copied model parameters to target network. total_t = 730000, period = 10000\n", "Episode: 1895 Duration: 0:00:21.383976 Num steps: 1336 Reward: 29.0 Training time per step: 0.012 Avg Reward (Last 100): 17.653 Epsilon: 0.100\n", "Episode: 1896 Duration: 0:00:16.110888 Num steps: 1039 Reward: 16.0 Training time per step: 0.012 Avg Reward (Last 100): 17.634 Epsilon: 0.100\n", "Episode: 1897 Duration: 0:00:13.282518 Num steps: 821 Reward: 12.0 Training time per step: 0.012 Avg Reward (Last 100): 17.574 Epsilon: 0.100\n", "Episode: 1898 Duration: 0:00:15.460567 Num steps: 995 Reward: 16.0 Training time per step: 0.012 Avg Reward (Last 100): 17.515 Epsilon: 0.100\n", "Episode: 1899 Duration: 0:00:16.194060 Num steps: 1049 Reward: 21.0 Training time per step: 0.012 Avg Reward (Last 100): 17.614 Epsilon: 0.100\n", "Episode: 1900 Duration: 0:00:16.522353 Num steps: 1043 Reward: 21.0 Training time per step: 0.012 Avg Reward (Last 100): 17.723 Epsilon: 0.100\n", "SAVED MODEL #1900\n", "Episode: 1901 Duration: 0:00:15.037512 Num steps: 1183 Reward: 27.0 Training time per step: 0.010 Avg Reward (Last 100): 17.802 Epsilon: 0.100\n", "Episode: 1902 Duration: 0:00:13.979053 Num steps: 878 Reward: 20.0 Training time per step: 0.012 Avg Reward (Last 100): 17.851 Epsilon: 0.100\n", "Episode: 1903 Duration: 0:00:13.366622 Num steps: 864 Reward: 18.0 Training time per step: 0.012 Avg Reward (Last 100): 17.931 Epsilon: 0.100\n", "Episode: 1904 Duration: 0:00:14.928465 Num steps: 994 Reward: 16.0 Training time per step: 0.011 Avg Reward (Last 100): 17.901 Epsilon: 0.100\n", "Episode: 1905 Duration: 0:00:12.213862 Num steps: 810 Reward: 16.0 Training time per step: 0.012 Avg Reward (Last 100): 17.921 Epsilon: 0.100\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Copied model parameters to target network. total_t = 740000, period = 10000\n", "Episode: 1906 Duration: 0:00:15.124239 Num steps: 955 Reward: 23.0 Training time per step: 0.012 Avg Reward (Last 100): 17.871 Epsilon: 0.100\n", "Episode: 1907 Duration: 0:00:13.458148 Num steps: 875 Reward: 16.0 Training time per step: 0.012 Avg Reward (Last 100): 17.950 Epsilon: 0.100\n", "Episode: 1908 Duration: 0:00:13.472179 Num steps: 875 Reward: 15.0 Training time per step: 0.012 Avg Reward (Last 100): 17.861 Epsilon: 0.100\n", "Episode: 1909 Duration: 0:00:13.389437 Num steps: 855 Reward: 16.0 Training time per step: 0.012 Avg Reward (Last 100): 17.861 Epsilon: 0.100\n", "Episode: 1910 Duration: 0:00:19.669828 Num steps: 1242 Reward: 25.0 Training time per step: 0.012 Avg Reward (Last 100): 17.842 Epsilon: 0.100\n", "Episode: 1911 Duration: 0:00:12.120708 Num steps: 792 Reward: 13.0 Training time per step: 0.012 Avg Reward (Last 100): 17.851 Epsilon: 0.100\n", "Episode: 1912 Duration: 0:00:16.615873 Num steps: 1054 Reward: 31.0 Training time per step: 0.012 Avg Reward (Last 100): 18.050 Epsilon: 0.100\n", "Episode: 1913 Duration: 0:00:16.063901 Num steps: 1066 Reward: 25.0 Training time per step: 0.012 Avg Reward (Last 100): 18.198 Epsilon: 0.100\n", "Episode: 1914 Duration: 0:00:22.351640 Num steps: 1420 Reward: 34.0 Training time per step: 0.012 Avg Reward (Last 100): 18.416 Epsilon: 0.100\n", "Copied model parameters to target network. total_t = 750000, period = 10000\n", "Episode: 1915 Duration: 0:00:21.325103 Num steps: 1309 Reward: 26.0 Training time per step: 0.012 Avg Reward (Last 100): 18.406 Epsilon: 0.100\n", "Episode: 1916 Duration: 0:00:20.945105 Num steps: 1328 Reward: 29.0 Training time per step: 0.012 Avg Reward (Last 100): 18.535 Epsilon: 0.100\n", "Episode: 1917 Duration: 0:00:15.657764 Num steps: 964 Reward: 18.0 Training time per step: 0.012 Avg Reward (Last 100): 18.545 Epsilon: 0.100\n", "Episode: 1918 Duration: 0:00:17.461505 Num steps: 1138 Reward: 41.0 Training time per step: 0.012 Avg Reward (Last 100): 18.822 Epsilon: 0.100\n", "Episode: 1919 Duration: 0:00:14.748836 Num steps: 964 Reward: 23.0 Training time per step: 0.012 Avg Reward (Last 100): 18.861 Epsilon: 0.100\n", "Episode: 1920 Duration: 0:00:16.121740 Num steps: 1040 Reward: 18.0 Training time per step: 0.012 Avg Reward (Last 100): 18.871 Epsilon: 0.100\n", "Episode: 1921 Duration: 0:00:17.156747 Num steps: 1061 Reward: 18.0 Training time per step: 0.012 Avg Reward (Last 100): 18.941 Epsilon: 0.100\n", "Episode: 1922 Duration: 0:00:14.552888 Num steps: 952 Reward: 19.0 Training time per step: 0.012 Avg Reward (Last 100): 18.990 Epsilon: 0.100\n", "Episode: 1923 Duration: 0:00:13.950840 Num steps: 895 Reward: 16.0 Training time per step: 0.012 Avg Reward (Last 100): 18.941 Epsilon: 0.100\n", "Episode: 1924 Duration: 0:00:24.013595 Num steps: 1511 Reward: 37.0 Training time per step: 0.012 Avg Reward (Last 100): 19.168 Epsilon: 0.100\n", "Copied model parameters to target network. total_t = 760000, period = 10000\n", "Episode: 1925 Duration: 0:00:17.043160 Num steps: 1088 Reward: 24.0 Training time per step: 0.012 Avg Reward (Last 100): 19.287 Epsilon: 0.100\n", "Episode: 1926 Duration: 0:00:18.762903 Num steps: 1164 Reward: 25.0 Training time per step: 0.012 Avg Reward (Last 100): 19.406 Epsilon: 0.100\n", "Episode: 1927 Duration: 0:00:20.465435 Num steps: 1337 Reward: 31.0 Training time per step: 0.012 Avg Reward (Last 100): 19.604 Epsilon: 0.100\n", "Episode: 1928 Duration: 0:00:17.320746 Num steps: 1091 Reward: 21.0 Training time per step: 0.012 Avg Reward (Last 100): 19.653 Epsilon: 0.100\n", "Episode: 1929 Duration: 0:00:15.301649 Num steps: 993 Reward: 20.0 Training time per step: 0.012 Avg Reward (Last 100): 19.743 Epsilon: 0.100\n", "Episode: 1930 Duration: 0:00:14.840358 Num steps: 892 Reward: 21.0 Training time per step: 0.012 Avg Reward (Last 100): 19.762 Epsilon: 0.100\n", "Episode: 1931 Duration: 0:00:23.050714 Num steps: 1455 Reward: 39.0 Training time per step: 0.012 Avg Reward (Last 100): 19.931 Epsilon: 0.100\n", "Episode: 1932 Duration: 0:00:13.850815 Num steps: 867 Reward: 18.0 Training time per step: 0.012 Avg Reward (Last 100): 19.960 Epsilon: 0.100\n", "Episode: 1933 Duration: 0:00:16.992090 Num steps: 1064 Reward: 24.0 Training time per step: 0.012 Avg Reward (Last 100): 20.030 Epsilon: 0.100\n", "Copied model parameters to target network. total_t = 770000, period = 10000\n", "Episode: 1934 Duration: 0:00:09.076539 Num steps: 585 Reward: 9.0 Training time per step: 0.011 Avg Reward (Last 100): 19.931 Epsilon: 0.100\n", "Episode: 1935 Duration: 0:00:14.005275 Num steps: 915 Reward: 16.0 Training time per step: 0.012 Avg Reward (Last 100): 20.000 Epsilon: 0.100\n", "Episode: 1936 Duration: 0:00:14.797608 Num steps: 968 Reward: 18.0 Training time per step: 0.012 Avg Reward (Last 100): 20.069 Epsilon: 0.100\n", "Episode: 1937 Duration: 0:00:14.822427 Num steps: 956 Reward: 20.0 Training time per step: 0.012 Avg Reward (Last 100): 20.099 Epsilon: 0.100\n", "Episode: 1938 Duration: 0:00:20.857864 Num steps: 1262 Reward: 22.0 Training time per step: 0.012 Avg Reward (Last 100): 20.188 Epsilon: 0.100\n", "Episode: 1939 Duration: 0:00:07.729574 Num steps: 514 Reward: 10.0 Training time per step: 0.011 Avg Reward (Last 100): 20.188 Epsilon: 0.100\n", "Episode: 1940 Duration: 0:00:16.735363 Num steps: 1065 Reward: 22.0 Training time per step: 0.012 Avg Reward (Last 100): 20.228 Epsilon: 0.100\n", "Episode: 1941 Duration: 0:00:16.766402 Num steps: 1079 Reward: 25.0 Training time per step: 0.012 Avg Reward (Last 100): 20.287 Epsilon: 0.100\n", "Episode: 1942 Duration: 0:00:16.510748 Num steps: 1032 Reward: 33.0 Training time per step: 0.012 Avg Reward (Last 100): 20.416 Epsilon: 0.100\n", "Episode: 1943 Duration: 0:00:18.913423 Num steps: 1208 Reward: 41.0 Training time per step: 0.012 Avg Reward (Last 100): 20.663 Epsilon: 0.100\n", "Copied model parameters to target network. total_t = 780000, period = 10000\n", "Episode: 1944 Duration: 0:00:12.738696 Num steps: 765 Reward: 13.0 Training time per step: 0.012 Avg Reward (Last 100): 20.693 Epsilon: 0.100\n", "Episode: 1945 Duration: 0:00:16.660635 Num steps: 1064 Reward: 21.0 Training time per step: 0.012 Avg Reward (Last 100): 20.743 Epsilon: 0.100\n", "Episode: 1946 Duration: 0:00:12.410620 Num steps: 793 Reward: 14.0 Training time per step: 0.012 Avg Reward (Last 100): 20.752 Epsilon: 0.100\n", "Episode: 1947 Duration: 0:00:10.271526 Num steps: 652 Reward: 13.0 Training time per step: 0.012 Avg Reward (Last 100): 20.683 Epsilon: 0.100\n", "Episode: 1948 Duration: 0:00:13.791051 Num steps: 841 Reward: 16.0 Training time per step: 0.012 Avg Reward (Last 100): 20.683 Epsilon: 0.100\n", "Episode: 1949 Duration: 0:00:16.568118 Num steps: 1050 Reward: 23.0 Training time per step: 0.012 Avg Reward (Last 100): 20.792 Epsilon: 0.100\n", "Episode: 1950 Duration: 0:00:20.154296 Num steps: 1293 Reward: 32.0 Training time per step: 0.012 Avg Reward (Last 100): 20.911 Epsilon: 0.100\n", "SAVED MODEL #1950\n", "Episode: 1951 Duration: 0:00:18.148265 Num steps: 1373 Reward: 30.0 Training time per step: 0.010 Avg Reward (Last 100): 21.109 Epsilon: 0.100\n", "Episode: 1952 Duration: 0:00:10.818189 Num steps: 686 Reward: 11.0 Training time per step: 0.012 Avg Reward (Last 100): 20.950 Epsilon: 0.100\n", "Episode: 1953 Duration: 0:00:14.265136 Num steps: 885 Reward: 17.0 Training time per step: 0.012 Avg Reward (Last 100): 20.941 Epsilon: 0.100\n", "Episode: 1954 Duration: 0:00:16.080016 Num steps: 1010 Reward: 17.0 Training time per step: 0.012 Avg Reward (Last 100): 20.891 Epsilon: 0.100\n", "Copied model parameters to target network. total_t = 790000, period = 10000\n", "Episode: 1955 Duration: 0:00:18.521844 Num steps: 1173 Reward: 31.0 Training time per step: 0.012 Avg Reward (Last 100): 21.050 Epsilon: 0.100\n", "Episode: 1956 Duration: 0:00:14.132201 Num steps: 912 Reward: 20.0 Training time per step: 0.012 Avg Reward (Last 100): 21.089 Epsilon: 0.100\n", "Episode: 1957 Duration: 0:00:19.102083 Num steps: 1284 Reward: 24.0 Training time per step: 0.011 Avg Reward (Last 100): 21.129 Epsilon: 0.100\n", "Episode: 1958 Duration: 0:00:21.404299 Num steps: 1340 Reward: 33.0 Training time per step: 0.012 Avg Reward (Last 100): 21.198 Epsilon: 0.100\n", "Episode: 1959 Duration: 0:00:15.591565 Num steps: 1006 Reward: 22.0 Training time per step: 0.012 Avg Reward (Last 100): 21.208 Epsilon: 0.100\n", "Episode: 1960 Duration: 0:00:17.589054 Num steps: 1144 Reward: 27.0 Training time per step: 0.012 Avg Reward (Last 100): 21.356 Epsilon: 0.100\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Episode: 1961 Duration: 0:00:19.636002 Num steps: 1255 Reward: 38.0 Training time per step: 0.012 Avg Reward (Last 100): 21.475 Epsilon: 0.100\n", "Episode: 1962 Duration: 0:00:16.322971 Num steps: 1042 Reward: 25.0 Training time per step: 0.012 Avg Reward (Last 100): 21.564 Epsilon: 0.100\n", "Copied model parameters to target network. total_t = 800000, period = 10000\n", "Episode: 1963 Duration: 0:00:20.357159 Num steps: 1273 Reward: 31.0 Training time per step: 0.012 Avg Reward (Last 100): 21.624 Epsilon: 0.100\n", "Episode: 1964 Duration: 0:00:17.819582 Num steps: 1186 Reward: 24.0 Training time per step: 0.011 Avg Reward (Last 100): 21.683 Epsilon: 0.100\n", "Episode: 1965 Duration: 0:00:17.959590 Num steps: 1135 Reward: 31.0 Training time per step: 0.012 Avg Reward (Last 100): 21.792 Epsilon: 0.100\n", "Episode: 1966 Duration: 0:00:13.717864 Num steps: 817 Reward: 14.0 Training time per step: 0.012 Avg Reward (Last 100): 21.733 Epsilon: 0.100\n", "Episode: 1967 Duration: 0:00:20.705655 Num steps: 1331 Reward: 30.0 Training time per step: 0.012 Avg Reward (Last 100): 21.931 Epsilon: 0.100\n", "Episode: 1968 Duration: 0:00:22.954306 Num steps: 1518 Reward: 47.0 Training time per step: 0.012 Avg Reward (Last 100): 22.248 Epsilon: 0.100\n", "Episode: 1969 Duration: 0:00:16.683684 Num steps: 1025 Reward: 19.0 Training time per step: 0.012 Avg Reward (Last 100): 22.168 Epsilon: 0.100\n", "Episode: 1970 Duration: 0:00:14.050147 Num steps: 934 Reward: 25.0 Training time per step: 0.011 Avg Reward (Last 100): 22.139 Epsilon: 0.100\n", "Episode: 1971 Duration: 0:00:13.902361 Num steps: 859 Reward: 14.0 Training time per step: 0.012 Avg Reward (Last 100): 22.089 Epsilon: 0.100\n", "Copied model parameters to target network. total_t = 810000, period = 10000\n", "Episode: 1972 Duration: 0:00:15.462132 Num steps: 992 Reward: 29.0 Training time per step: 0.012 Avg Reward (Last 100): 22.257 Epsilon: 0.100\n", "Episode: 1973 Duration: 0:00:21.072735 Num steps: 1356 Reward: 28.0 Training time per step: 0.012 Avg Reward (Last 100): 22.337 Epsilon: 0.100\n", "Episode: 1974 Duration: 0:00:19.344371 Num steps: 1167 Reward: 31.0 Training time per step: 0.012 Avg Reward (Last 100): 22.495 Epsilon: 0.100\n", "Episode: 1975 Duration: 0:00:17.976447 Num steps: 1217 Reward: 24.0 Training time per step: 0.011 Avg Reward (Last 100): 22.525 Epsilon: 0.100\n", "Episode: 1976 Duration: 0:00:14.638873 Num steps: 923 Reward: 15.0 Training time per step: 0.012 Avg Reward (Last 100): 22.446 Epsilon: 0.100\n", "Episode: 1977 Duration: 0:00:11.603280 Num steps: 712 Reward: 11.0 Training time per step: 0.012 Avg Reward (Last 100): 22.455 Epsilon: 0.100\n", "Episode: 1978 Duration: 0:00:14.969685 Num steps: 955 Reward: 16.0 Training time per step: 0.012 Avg Reward (Last 100): 22.416 Epsilon: 0.100\n", "Episode: 1979 Duration: 0:00:14.180881 Num steps: 885 Reward: 19.0 Training time per step: 0.012 Avg Reward (Last 100): 22.317 Epsilon: 0.100\n", "Episode: 1980 Duration: 0:00:15.548599 Num steps: 1011 Reward: 22.0 Training time per step: 0.012 Avg Reward (Last 100): 22.129 Epsilon: 0.100\n", "Episode: 1981 Duration: 0:00:16.079681 Num steps: 1011 Reward: 19.0 Training time per step: 0.012 Avg Reward (Last 100): 22.188 Epsilon: 0.100\n", "Copied model parameters to target network. total_t = 820000, period = 10000\n", "Episode: 1982 Duration: 0:00:19.055616 Num steps: 1163 Reward: 24.0 Training time per step: 0.012 Avg Reward (Last 100): 22.347 Epsilon: 0.100\n", "Episode: 1983 Duration: 0:00:12.855943 Num steps: 855 Reward: 19.0 Training time per step: 0.012 Avg Reward (Last 100): 22.218 Epsilon: 0.100\n", "Episode: 1984 Duration: 0:00:17.946713 Num steps: 1130 Reward: 30.0 Training time per step: 0.012 Avg Reward (Last 100): 22.317 Epsilon: 0.100\n", "Episode: 1985 Duration: 0:00:16.885770 Num steps: 1114 Reward: 22.0 Training time per step: 0.012 Avg Reward (Last 100): 22.436 Epsilon: 0.100\n", "Episode: 1986 Duration: 0:00:15.823458 Num steps: 946 Reward: 17.0 Training time per step: 0.012 Avg Reward (Last 100): 22.347 Epsilon: 0.100\n", "Episode: 1987 Duration: 0:00:14.902028 Num steps: 925 Reward: 17.0 Training time per step: 0.012 Avg Reward (Last 100): 22.396 Epsilon: 0.100\n", "Episode: 1988 Duration: 0:00:19.940871 Num steps: 1285 Reward: 23.0 Training time per step: 0.012 Avg Reward (Last 100): 22.495 Epsilon: 0.100\n", "Episode: 1989 Duration: 0:00:20.776826 Num steps: 1326 Reward: 29.0 Training time per step: 0.012 Avg Reward (Last 100): 22.564 Epsilon: 0.100\n", "Episode: 1990 Duration: 0:00:17.951335 Num steps: 1153 Reward: 22.0 Training time per step: 0.012 Avg Reward (Last 100): 22.525 Epsilon: 0.100\n", "Copied model parameters to target network. total_t = 830000, period = 10000\n", "Episode: 1991 Duration: 0:00:11.995927 Num steps: 732 Reward: 13.0 Training time per step: 0.012 Avg Reward (Last 100): 22.396 Epsilon: 0.100\n", "Episode: 1992 Duration: 0:00:11.748325 Num steps: 770 Reward: 13.0 Training time per step: 0.012 Avg Reward (Last 100): 22.356 Epsilon: 0.100\n", "Episode: 1993 Duration: 0:00:16.267248 Num steps: 1044 Reward: 28.0 Training time per step: 0.012 Avg Reward (Last 100): 22.475 Epsilon: 0.100\n", "Episode: 1994 Duration: 0:00:13.310178 Num steps: 876 Reward: 21.0 Training time per step: 0.012 Avg Reward (Last 100): 22.436 Epsilon: 0.100\n", "Episode: 1995 Duration: 0:00:18.671700 Num steps: 1173 Reward: 36.0 Training time per step: 0.012 Avg Reward (Last 100): 22.614 Epsilon: 0.100\n", "Episode: 1996 Duration: 0:00:18.815052 Num steps: 1155 Reward: 24.0 Training time per step: 0.012 Avg Reward (Last 100): 22.564 Epsilon: 0.100\n", "Episode: 1997 Duration: 0:00:13.841812 Num steps: 933 Reward: 18.0 Training time per step: 0.011 Avg Reward (Last 100): 22.584 Epsilon: 0.100\n", "Episode: 1998 Duration: 0:00:15.087649 Num steps: 951 Reward: 19.0 Training time per step: 0.012 Avg Reward (Last 100): 22.653 Epsilon: 0.100\n", "Episode: 1999 Duration: 0:00:18.203514 Num steps: 1119 Reward: 31.0 Training time per step: 0.012 Avg Reward (Last 100): 22.802 Epsilon: 0.100\n", "Episode: 2000 Duration: 0:00:14.494751 Num steps: 976 Reward: 25.0 Training time per step: 0.011 Avg Reward (Last 100): 22.842 Epsilon: 0.100\n", "SAVED MODEL #2000\n", "Copied model parameters to target network. total_t = 840000, period = 10000\n", "Episode: 2001 Duration: 0:00:17.399177 Num steps: 1312 Reward: 33.0 Training time per step: 0.010 Avg Reward (Last 100): 22.960 Epsilon: 0.100\n", "Episode: 2002 Duration: 0:00:13.944653 Num steps: 866 Reward: 16.0 Training time per step: 0.012 Avg Reward (Last 100): 22.851 Epsilon: 0.100\n", "Episode: 2003 Duration: 0:00:19.775484 Num steps: 1251 Reward: 31.0 Training time per step: 0.012 Avg Reward (Last 100): 22.960 Epsilon: 0.100\n", "Episode: 2004 Duration: 0:00:20.228638 Num steps: 1283 Reward: 34.0 Training time per step: 0.012 Avg Reward (Last 100): 23.119 Epsilon: 0.100\n", "Episode: 2005 Duration: 0:00:12.771303 Num steps: 796 Reward: 13.0 Training time per step: 0.012 Avg Reward (Last 100): 23.089 Epsilon: 0.100\n", "Episode: 2006 Duration: 0:00:14.858084 Num steps: 949 Reward: 17.0 Training time per step: 0.012 Avg Reward (Last 100): 23.099 Epsilon: 0.100\n", "Episode: 2007 Duration: 0:00:11.966496 Num steps: 829 Reward: 17.0 Training time per step: 0.011 Avg Reward (Last 100): 23.040 Epsilon: 0.100\n", "Episode: 2008 Duration: 0:00:16.615139 Num steps: 1003 Reward: 23.0 Training time per step: 0.012 Avg Reward (Last 100): 23.109 Epsilon: 0.100\n", "Episode: 2009 Duration: 0:00:22.754262 Num steps: 1445 Reward: 41.0 Training time per step: 0.012 Avg Reward (Last 100): 23.366 Epsilon: 0.100\n", "Copied model parameters to target network. total_t = 850000, period = 10000\n", "Episode: 2010 Duration: 0:00:20.330432 Num steps: 1295 Reward: 26.0 Training time per step: 0.012 Avg Reward (Last 100): 23.465 Epsilon: 0.100\n", "Episode: 2011 Duration: 0:00:18.024176 Num steps: 1137 Reward: 27.0 Training time per step: 0.012 Avg Reward (Last 100): 23.485 Epsilon: 0.100\n", "Episode: 2012 Duration: 0:00:10.706301 Num steps: 700 Reward: 16.0 Training time per step: 0.012 Avg Reward (Last 100): 23.515 Epsilon: 0.100\n", "Episode: 2013 Duration: 0:00:11.813407 Num steps: 770 Reward: 14.0 Training time per step: 0.012 Avg Reward (Last 100): 23.347 Epsilon: 0.100\n", "Episode: 2014 Duration: 0:00:18.136115 Num steps: 1158 Reward: 27.0 Training time per step: 0.012 Avg Reward (Last 100): 23.366 Epsilon: 0.100\n", "Episode: 2015 Duration: 0:00:13.889817 Num steps: 947 Reward: 17.0 Training time per step: 0.011 Avg Reward (Last 100): 23.198 Epsilon: 0.100\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Episode: 2016 Duration: 0:00:17.495505 Num steps: 1090 Reward: 25.0 Training time per step: 0.012 Avg Reward (Last 100): 23.188 Epsilon: 0.100\n", "Episode: 2017 Duration: 0:00:13.811196 Num steps: 873 Reward: 35.0 Training time per step: 0.012 Avg Reward (Last 100): 23.248 Epsilon: 0.100\n", "Episode: 2018 Duration: 0:00:23.795202 Num steps: 1452 Reward: 41.0 Training time per step: 0.012 Avg Reward (Last 100): 23.475 Epsilon: 0.100\n", "Episode: 2019 Duration: 0:00:17.657353 Num steps: 1128 Reward: 35.0 Training time per step: 0.012 Avg Reward (Last 100): 23.416 Epsilon: 0.100\n", "Copied model parameters to target network. total_t = 860000, period = 10000\n", "Episode: 2020 Duration: 0:00:18.961290 Num steps: 1178 Reward: 21.0 Training time per step: 0.012 Avg Reward (Last 100): 23.396 Epsilon: 0.100\n", "Episode: 2021 Duration: 0:00:16.610352 Num steps: 1041 Reward: 18.0 Training time per step: 0.012 Avg Reward (Last 100): 23.396 Epsilon: 0.100\n", "Episode: 2022 Duration: 0:00:13.945197 Num steps: 911 Reward: 26.0 Training time per step: 0.012 Avg Reward (Last 100): 23.475 Epsilon: 0.100\n", "Episode: 2023 Duration: 0:00:08.485567 Num steps: 577 Reward: 9.0 Training time per step: 0.011 Avg Reward (Last 100): 23.376 Epsilon: 0.100\n", "Episode: 2024 Duration: 0:00:21.449350 Num steps: 1380 Reward: 27.0 Training time per step: 0.012 Avg Reward (Last 100): 23.485 Epsilon: 0.100\n", "Episode: 2025 Duration: 0:00:16.148010 Num steps: 996 Reward: 23.0 Training time per step: 0.012 Avg Reward (Last 100): 23.347 Epsilon: 0.100\n", "Episode: 2026 Duration: 0:00:15.396262 Num steps: 950 Reward: 23.0 Training time per step: 0.012 Avg Reward (Last 100): 23.337 Epsilon: 0.100\n", "Episode: 2027 Duration: 0:00:15.357058 Num steps: 1010 Reward: 15.0 Training time per step: 0.012 Avg Reward (Last 100): 23.238 Epsilon: 0.100\n", "Episode: 2028 Duration: 0:00:21.843932 Num steps: 1421 Reward: 42.0 Training time per step: 0.012 Avg Reward (Last 100): 23.347 Epsilon: 0.100\n", "Copied model parameters to target network. total_t = 870000, period = 10000\n", "Episode: 2029 Duration: 0:00:20.489321 Num steps: 1287 Reward: 23.0 Training time per step: 0.012 Avg Reward (Last 100): 23.366 Epsilon: 0.100\n", "Episode: 2030 Duration: 0:00:09.299341 Num steps: 569 Reward: 15.0 Training time per step: 0.012 Avg Reward (Last 100): 23.317 Epsilon: 0.100\n", "Episode: 2031 Duration: 0:00:16.490995 Num steps: 1060 Reward: 18.0 Training time per step: 0.012 Avg Reward (Last 100): 23.287 Epsilon: 0.100\n", "Episode: 2032 Duration: 0:00:15.879917 Num steps: 1021 Reward: 25.0 Training time per step: 0.012 Avg Reward (Last 100): 23.149 Epsilon: 0.100\n", "Episode: 2033 Duration: 0:00:14.181500 Num steps: 928 Reward: 20.0 Training time per step: 0.012 Avg Reward (Last 100): 23.168 Epsilon: 0.100\n", "Episode: 2034 Duration: 0:00:16.702541 Num steps: 1030 Reward: 19.0 Training time per step: 0.012 Avg Reward (Last 100): 23.119 Epsilon: 0.100\n", "Episode: 2035 Duration: 0:00:18.375299 Num steps: 1170 Reward: 30.0 Training time per step: 0.012 Avg Reward (Last 100): 23.327 Epsilon: 0.100\n", "Episode: 2036 Duration: 0:00:12.991327 Num steps: 820 Reward: 15.0 Training time per step: 0.012 Avg Reward (Last 100): 23.317 Epsilon: 0.100\n", "Episode: 2037 Duration: 0:00:17.183587 Num steps: 1107 Reward: 23.0 Training time per step: 0.012 Avg Reward (Last 100): 23.366 Epsilon: 0.100\n", "Episode: 2038 Duration: 0:00:14.755466 Num steps: 910 Reward: 19.0 Training time per step: 0.012 Avg Reward (Last 100): 23.356 Epsilon: 0.100\n", "Episode: 2039 Duration: 0:00:09.064065 Num steps: 555 Reward: 9.0 Training time per step: 0.012 Avg Reward (Last 100): 23.228 Epsilon: 0.100\n", "Copied model parameters to target network. total_t = 880000, period = 10000\n", "Episode: 2040 Duration: 0:00:17.476874 Num steps: 1096 Reward: 29.0 Training time per step: 0.012 Avg Reward (Last 100): 23.416 Epsilon: 0.100\n", "Episode: 2041 Duration: 0:00:20.153016 Num steps: 1251 Reward: 32.0 Training time per step: 0.012 Avg Reward (Last 100): 23.515 Epsilon: 0.100\n", "Episode: 2042 Duration: 0:00:11.581322 Num steps: 774 Reward: 14.0 Training time per step: 0.011 Avg Reward (Last 100): 23.406 Epsilon: 0.100\n", "Episode: 2043 Duration: 0:00:30.311142 Num steps: 1900 Reward: 56.0 Training time per step: 0.012 Avg Reward (Last 100): 23.634 Epsilon: 0.100\n", "Episode: 2044 Duration: 0:00:15.709236 Num steps: 1008 Reward: 17.0 Training time per step: 0.012 Avg Reward (Last 100): 23.396 Epsilon: 0.100\n", "Episode: 2045 Duration: 0:00:24.211530 Num steps: 1562 Reward: 39.0 Training time per step: 0.012 Avg Reward (Last 100): 23.653 Epsilon: 0.100\n", "Episode: 2046 Duration: 0:00:13.112840 Num steps: 828 Reward: 14.0 Training time per step: 0.012 Avg Reward (Last 100): 23.584 Epsilon: 0.100\n", "Episode: 2047 Duration: 0:00:14.749952 Num steps: 898 Reward: 19.0 Training time per step: 0.012 Avg Reward (Last 100): 23.634 Epsilon: 0.100\n", "Copied model parameters to target network. total_t = 890000, period = 10000\n", "Episode: 2048 Duration: 0:00:28.557771 Num steps: 1838 Reward: 45.0 Training time per step: 0.012 Avg Reward (Last 100): 23.950 Epsilon: 0.100\n", "Episode: 2049 Duration: 0:00:23.807802 Num steps: 1432 Reward: 32.0 Training time per step: 0.012 Avg Reward (Last 100): 24.109 Epsilon: 0.100\n", "Episode: 2050 Duration: 0:00:17.464795 Num steps: 1209 Reward: 28.0 Training time per step: 0.011 Avg Reward (Last 100): 24.158 Epsilon: 0.100\n", "SAVED MODEL #2050\n", "Episode: 2051 Duration: 0:00:11.296304 Num steps: 906 Reward: 15.0 Training time per step: 0.010 Avg Reward (Last 100): 23.990 Epsilon: 0.100\n", "Episode: 2052 Duration: 0:00:09.311415 Num steps: 651 Reward: 11.0 Training time per step: 0.011 Avg Reward (Last 100): 23.802 Epsilon: 0.100\n", "Episode: 2053 Duration: 0:00:13.029865 Num steps: 801 Reward: 16.0 Training time per step: 0.012 Avg Reward (Last 100): 23.851 Epsilon: 0.100\n", "Episode: 2054 Duration: 0:00:15.505649 Num steps: 1012 Reward: 21.0 Training time per step: 0.012 Avg Reward (Last 100): 23.891 Epsilon: 0.100\n", "Episode: 2055 Duration: 0:00:17.473439 Num steps: 1099 Reward: 23.0 Training time per step: 0.012 Avg Reward (Last 100): 23.950 Epsilon: 0.100\n", "Episode: 2056 Duration: 0:00:19.061013 Num steps: 1194 Reward: 27.0 Training time per step: 0.012 Avg Reward (Last 100): 23.911 Epsilon: 0.100\n", "Episode: 2057 Duration: 0:00:16.693912 Num steps: 1110 Reward: 29.0 Training time per step: 0.011 Avg Reward (Last 100): 24.000 Epsilon: 0.100\n", "Copied model parameters to target network. total_t = 900000, period = 10000\n", "Episode: 2058 Duration: 0:00:15.907586 Num steps: 998 Reward: 21.0 Training time per step: 0.012 Avg Reward (Last 100): 23.970 Epsilon: 0.100\n", "Episode: 2059 Duration: 0:00:17.784536 Num steps: 1139 Reward: 21.0 Training time per step: 0.012 Avg Reward (Last 100): 23.851 Epsilon: 0.100\n", "Episode: 2060 Duration: 0:00:17.958872 Num steps: 1169 Reward: 33.0 Training time per step: 0.012 Avg Reward (Last 100): 23.960 Epsilon: 0.100\n", "Episode: 2061 Duration: 0:00:13.628962 Num steps: 887 Reward: 14.0 Training time per step: 0.012 Avg Reward (Last 100): 23.832 Epsilon: 0.100\n", "Episode: 2062 Duration: 0:00:20.685856 Num steps: 1285 Reward: 29.0 Training time per step: 0.012 Avg Reward (Last 100): 23.743 Epsilon: 0.100\n", "Episode: 2063 Duration: 0:00:19.139576 Num steps: 1210 Reward: 28.0 Training time per step: 0.012 Avg Reward (Last 100): 23.772 Epsilon: 0.100\n", "Episode: 2064 Duration: 0:00:15.616919 Num steps: 1033 Reward: 28.0 Training time per step: 0.012 Avg Reward (Last 100): 23.743 Epsilon: 0.100\n", "Episode: 2065 Duration: 0:00:15.099645 Num steps: 1034 Reward: 28.0 Training time per step: 0.011 Avg Reward (Last 100): 23.782 Epsilon: 0.100\n", "Episode: 2066 Duration: 0:00:21.050469 Num steps: 1291 Reward: 25.0 Training time per step: 0.012 Avg Reward (Last 100): 23.723 Epsilon: 0.100\n", "Copied model parameters to target network. total_t = 910000, period = 10000\n", "Episode: 2067 Duration: 0:00:16.734200 Num steps: 1057 Reward: 18.0 Training time per step: 0.012 Avg Reward (Last 100): 23.762 Epsilon: 0.100\n", "Episode: 2068 Duration: 0:00:20.405845 Num steps: 1247 Reward: 27.0 Training time per step: 0.012 Avg Reward (Last 100): 23.733 Epsilon: 0.100\n", "Episode: 2069 Duration: 0:00:17.235372 Num steps: 1094 Reward: 21.0 Training time per step: 0.012 Avg Reward (Last 100): 23.475 Epsilon: 0.100\n", "Episode: 2070 Duration: 0:00:17.746377 Num steps: 1105 Reward: 23.0 Training time per step: 0.012 Avg Reward (Last 100): 23.515 Epsilon: 0.100\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Episode: 2071 Duration: 0:00:16.760809 Num steps: 1079 Reward: 24.0 Training time per step: 0.012 Avg Reward (Last 100): 23.505 Epsilon: 0.100\n", "Episode: 2072 Duration: 0:00:12.823897 Num steps: 820 Reward: 15.0 Training time 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100): 23.752 Epsilon: 0.100\n", "Episode: 2092 Duration: 0:00:12.747033 Num steps: 802 Reward: 12.0 Training time per step: 0.012 Avg Reward (Last 100): 23.743 Epsilon: 0.100\n", "Episode: 2093 Duration: 0:00:19.525873 Num steps: 1213 Reward: 23.0 Training time per step: 0.012 Avg Reward (Last 100): 23.842 Epsilon: 0.100\n", "Episode: 2094 Duration: 0:00:16.913098 Num steps: 1128 Reward: 28.0 Training time per step: 0.011 Avg Reward (Last 100): 23.842 Epsilon: 0.100\n", "Episode: 2095 Duration: 0:00:16.240287 Num steps: 974 Reward: 16.0 Training time per step: 0.012 Avg Reward (Last 100): 23.792 Epsilon: 0.100\n", "Copied model parameters to target network. total_t = 940000, period = 10000\n", "Episode: 2096 Duration: 0:00:12.892434 Num steps: 803 Reward: 17.0 Training time per step: 0.012 Avg Reward (Last 100): 23.604 Epsilon: 0.100\n", "Episode: 2097 Duration: 0:00:19.962074 Num steps: 1299 Reward: 31.0 Training time per step: 0.012 Avg Reward (Last 100): 23.673 Epsilon: 0.100\n", "Episode: 2098 Duration: 0:00:12.402833 Num steps: 815 Reward: 17.0 Training time per step: 0.012 Avg Reward (Last 100): 23.663 Epsilon: 0.100\n", "Episode: 2099 Duration: 0:00:11.739603 Num steps: 720 Reward: 10.0 Training time per step: 0.012 Avg Reward (Last 100): 23.574 Epsilon: 0.100\n", "Episode: 2100 Duration: 0:00:14.431763 Num steps: 909 Reward: 18.0 Training time per step: 0.012 Avg Reward (Last 100): 23.446 Epsilon: 0.100\n", "SAVED MODEL #2100\n", "Episode: 2101 Duration: 0:00:10.341187 Num steps: 835 Reward: 15.0 Training time per step: 0.010 Avg Reward (Last 100): 23.347 Epsilon: 0.100\n", "Episode: 2102 Duration: 0:00:15.606984 Num steps: 1052 Reward: 19.0 Training time per step: 0.011 Avg Reward (Last 100): 23.208 Epsilon: 0.100\n", "Episode: 2103 Duration: 0:00:10.872503 Num steps: 705 Reward: 12.0 Training time per step: 0.012 Avg Reward (Last 100): 23.168 Epsilon: 0.100\n", "Episode: 2104 Duration: 0:00:14.808666 Num steps: 923 Reward: 20.0 Training time per step: 0.012 Avg Reward (Last 100): 23.059 Epsilon: 0.100\n", "Episode: 2105 Duration: 0:00:10.240519 Num steps: 659 Reward: 14.0 Training time per step: 0.012 Avg Reward (Last 100): 22.861 Epsilon: 0.100\n", "Episode: 2106 Duration: 0:00:16.912827 Num steps: 1037 Reward: 22.0 Training time per step: 0.012 Avg Reward (Last 100): 22.950 Epsilon: 0.100\n", "Copied model parameters to target network. total_t = 950000, period = 10000\n", "Episode: 2107 Duration: 0:00:17.293868 Num steps: 1078 Reward: 25.0 Training time per step: 0.012 Avg Reward (Last 100): 23.030 Epsilon: 0.100\n", "Episode: 2108 Duration: 0:00:16.438881 Num steps: 1038 Reward: 27.0 Training time per step: 0.012 Avg Reward (Last 100): 23.129 Epsilon: 0.100\n", "Episode: 2109 Duration: 0:00:12.438127 Num steps: 767 Reward: 13.0 Training time per step: 0.012 Avg Reward (Last 100): 23.030 Epsilon: 0.100\n", "Episode: 2110 Duration: 0:00:13.788958 Num steps: 911 Reward: 20.0 Training time per step: 0.012 Avg Reward (Last 100): 22.822 Epsilon: 0.100\n", "Episode: 2111 Duration: 0:00:18.168883 Num steps: 1230 Reward: 26.0 Training time per step: 0.011 Avg Reward (Last 100): 22.822 Epsilon: 0.100\n", "Episode: 2112 Duration: 0:00:14.607105 Num steps: 964 Reward: 20.0 Training time per step: 0.012 Avg Reward (Last 100): 22.752 Epsilon: 0.100\n", "Episode: 2113 Duration: 0:00:17.956205 Num steps: 1116 Reward: 24.0 Training time per step: 0.012 Avg Reward (Last 100): 22.832 Epsilon: 0.100\n", "Episode: 2114 Duration: 0:00:20.691990 Num steps: 1333 Reward: 32.0 Training time per step: 0.012 Avg Reward (Last 100): 23.010 Epsilon: 0.100\n", "Episode: 2115 Duration: 0:00:19.058335 Num steps: 1176 Reward: 21.0 Training time per step: 0.012 Avg Reward (Last 100): 22.950 Epsilon: 0.100\n", "Episode: 2116 Duration: 0:00:17.650629 Num steps: 1124 Reward: 22.0 Training time per step: 0.012 Avg Reward (Last 100): 23.000 Epsilon: 0.100\n", "Copied model parameters to target network. total_t = 960000, period = 10000\n", "Episode: 2117 Duration: 0:00:18.485241 Num steps: 1145 Reward: 22.0 Training time per step: 0.012 Avg Reward (Last 100): 22.970 Epsilon: 0.100\n", "Episode: 2118 Duration: 0:00:23.286673 Num steps: 1521 Reward: 42.0 Training time per step: 0.012 Avg Reward (Last 100): 23.040 Epsilon: 0.100\n", "Episode: 2119 Duration: 0:00:11.904092 Num steps: 777 Reward: 12.0 Training time per step: 0.012 Avg Reward (Last 100): 22.752 Epsilon: 0.100\n", "Episode: 2120 Duration: 0:00:14.727232 Num steps: 938 Reward: 23.0 Training time per step: 0.012 Avg Reward (Last 100): 22.634 Epsilon: 0.100\n", "Episode: 2121 Duration: 0:00:18.677597 Num steps: 1148 Reward: 32.0 Training time per step: 0.012 Avg Reward (Last 100): 22.743 Epsilon: 0.100\n", "Episode: 2122 Duration: 0:00:24.488317 Num steps: 1536 Reward: 44.0 Training time per step: 0.012 Avg Reward (Last 100): 23.000 Epsilon: 0.100\n", "Episode: 2123 Duration: 0:00:13.567536 Num steps: 875 Reward: 19.0 Training time per step: 0.012 Avg Reward (Last 100): 22.931 Epsilon: 0.100\n", "Episode: 2124 Duration: 0:00:16.556541 Num steps: 1090 Reward: 40.0 Training time per step: 0.012 Avg Reward (Last 100): 23.238 Epsilon: 0.100\n", "Copied model parameters to target network. total_t = 970000, period = 10000\n", "Episode: 2125 Duration: 0:00:22.238671 Num steps: 1388 Reward: 33.0 Training time per step: 0.012 Avg Reward (Last 100): 23.297 Epsilon: 0.100\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Episode: 2126 Duration: 0:00:15.535089 Num steps: 949 Reward: 22.0 Training time per step: 0.012 Avg Reward (Last 100): 23.287 Epsilon: 0.100\n", "Episode: 2127 Duration: 0:00:12.596946 Num steps: 831 Reward: 14.0 Training time per step: 0.012 Avg Reward (Last 100): 23.198 Epsilon: 0.100\n", "Episode: 2128 Duration: 0:00:15.916714 Num steps: 1011 Reward: 26.0 Training time per step: 0.012 Avg Reward (Last 100): 23.307 Epsilon: 0.100\n", "Episode: 2129 Duration: 0:00:11.798874 Num steps: 773 Reward: 12.0 Training time per 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23.119 Epsilon: 0.100\n", "Episode: 2136 Duration: 0:00:10.183315 Num steps: 644 Reward: 11.0 Training time per step: 0.012 Avg Reward (Last 100): 22.931 Epsilon: 0.100\n", "Episode: 2137 Duration: 0:00:16.160437 Num steps: 1033 Reward: 22.0 Training time per step: 0.012 Avg Reward (Last 100): 23.000 Epsilon: 0.100\n", "Episode: 2138 Duration: 0:00:15.160794 Num steps: 972 Reward: 24.0 Training time per step: 0.012 Avg Reward (Last 100): 23.010 Epsilon: 0.100\n", "Episode: 2139 Duration: 0:00:16.923037 Num steps: 1025 Reward: 23.0 Training time per step: 0.012 Avg Reward (Last 100): 23.050 Epsilon: 0.100\n", "Episode: 2140 Duration: 0:00:22.386466 Num steps: 1440 Reward: 33.0 Training time per step: 0.012 Avg Reward (Last 100): 23.287 Epsilon: 0.100\n", "Episode: 2141 Duration: 0:00:26.037420 Num steps: 1635 Reward: 41.0 Training time per step: 0.012 Avg Reward (Last 100): 23.406 Epsilon: 0.100\n", "Episode: 2142 Duration: 0:00:18.027346 Num steps: 1174 Reward: 25.0 Training time per 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100): 23.099 Epsilon: 0.100\n", "Episode: 2149 Duration: 0:00:15.578006 Num steps: 977 Reward: 22.0 Training time per step: 0.012 Avg Reward (Last 100): 22.871 Epsilon: 0.100\n", "Episode: 2150 Duration: 0:00:18.192510 Num steps: 1209 Reward: 25.0 Training time per step: 0.011 Avg Reward (Last 100): 22.802 Epsilon: 0.100\n", "SAVED MODEL #2150\n", "Episode: 2151 Duration: 0:00:12.876486 Num steps: 1039 Reward: 22.0 Training time per step: 0.010 Avg Reward (Last 100): 22.743 Epsilon: 0.100\n", "Episode: 2152 Duration: 0:00:13.266673 Num steps: 868 Reward: 21.0 Training time per step: 0.012 Avg Reward (Last 100): 22.802 Epsilon: 0.100\n", "Episode: 2153 Duration: 0:00:11.197363 Num steps: 696 Reward: 11.0 Training time per step: 0.012 Avg Reward (Last 100): 22.802 Epsilon: 0.100\n", "Copied model parameters to target network. total_t = 1000000, period = 10000\n", "Episode: 2154 Duration: 0:00:13.455327 Num steps: 800 Reward: 16.0 Training time per step: 0.012 Avg Reward (Last 100): 22.802 Epsilon: 0.100\n", "Episode: 2155 Duration: 0:00:10.929001 Num steps: 699 Reward: 11.0 Training time per step: 0.012 Avg Reward (Last 100): 22.703 Epsilon: 0.100\n", "Episode: 2156 Duration: 0:00:08.991369 Num steps: 578 Reward: 9.0 Training time per step: 0.012 Avg Reward (Last 100): 22.564 Epsilon: 0.100\n", "Episode: 2157 Duration: 0:00:12.670396 Num steps: 774 Reward: 13.0 Training time per step: 0.012 Avg Reward (Last 100): 22.426 Epsilon: 0.100\n", "Episode: 2158 Duration: 0:00:18.132021 Num steps: 1119 Reward: 24.0 Training time per step: 0.012 Avg Reward (Last 100): 22.376 Epsilon: 0.100\n", "Episode: 2159 Duration: 0:00:21.432836 Num steps: 1382 Reward: 28.0 Training time per step: 0.012 Avg Reward (Last 100): 22.446 Epsilon: 0.100\n", "Episode: 2160 Duration: 0:00:23.676386 Num steps: 1546 Reward: 36.0 Training time per step: 0.012 Avg Reward (Last 100): 22.594 Epsilon: 0.100\n", "Episode: 2161 Duration: 0:00:22.760476 Num steps: 1395 Reward: 33.0 Training time per 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100): 22.119 Epsilon: 0.100\n", "Episode: 2168 Duration: 0:00:12.970049 Num steps: 782 Reward: 12.0 Training time per step: 0.012 Avg Reward (Last 100): 22.059 Epsilon: 0.100\n", "Episode: 2169 Duration: 0:00:19.618963 Num steps: 1266 Reward: 30.0 Training time per step: 0.012 Avg Reward (Last 100): 22.089 Epsilon: 0.100\n", "Episode: 2170 Duration: 0:00:13.567145 Num steps: 847 Reward: 14.0 Training time per step: 0.012 Avg Reward (Last 100): 22.020 Epsilon: 0.100\n", "Episode: 2171 Duration: 0:00:11.361219 Num steps: 725 Reward: 14.0 Training time per step: 0.012 Avg Reward (Last 100): 21.931 Epsilon: 0.100\n", "Episode: 2172 Duration: 0:00:14.113962 Num steps: 881 Reward: 13.0 Training time per step: 0.012 Avg Reward (Last 100): 21.822 Epsilon: 0.100\n", "Episode: 2173 Duration: 0:00:22.121030 Num steps: 1396 Reward: 24.0 Training time per step: 0.012 Avg Reward (Last 100): 21.911 Epsilon: 0.100\n", "Copied model parameters to target network. total_t = 1020000, period = 10000\n", "Episode: 2174 Duration: 0:00:14.152964 Num steps: 870 Reward: 15.0 Training time per step: 0.012 Avg Reward (Last 100): 21.861 Epsilon: 0.100\n", "Episode: 2175 Duration: 0:00:17.874197 Num steps: 1096 Reward: 32.0 Training time per step: 0.012 Avg Reward (Last 100): 21.842 Epsilon: 0.100\n", "Episode: 2176 Duration: 0:00:12.467644 Num steps: 778 Reward: 14.0 Training time per step: 0.012 Avg Reward (Last 100): 21.772 Epsilon: 0.100\n", "Episode: 2177 Duration: 0:00:10.534713 Num steps: 703 Reward: 10.0 Training time per step: 0.011 Avg Reward (Last 100): 21.505 Epsilon: 0.100\n", "Episode: 2178 Duration: 0:00:11.186118 Num steps: 721 Reward: 13.0 Training time per step: 0.012 Avg Reward (Last 100): 21.426 Epsilon: 0.100\n", "Episode: 2179 Duration: 0:00:17.347113 Num steps: 1080 Reward: 22.0 Training time per step: 0.012 Avg Reward (Last 100): 21.505 Epsilon: 0.100\n", "Episode: 2180 Duration: 0:00:12.009617 Num steps: 739 Reward: 16.0 Training time per step: 0.012 Avg Reward (Last 100): 21.475 Epsilon: 0.100\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Episode: 2181 Duration: 0:00:14.293873 Num steps: 920 Reward: 23.0 Training time per step: 0.012 Avg Reward (Last 100): 21.465 Epsilon: 0.100\n", "Episode: 2182 Duration: 0:00:16.158735 Num steps: 1069 Reward: 27.0 Training time per step: 0.012 Avg Reward (Last 100): 21.604 Epsilon: 0.100\n", "Episode: 2183 Duration: 0:00:16.668879 Num steps: 1064 Reward: 37.0 Training time per step: 0.012 Avg Reward (Last 100): 21.762 Epsilon: 0.100\n", "Episode: 2184 Duration: 0:00:14.628559 Num steps: 886 Reward: 12.0 Training time per step: 0.012 Avg Reward (Last 100): 21.762 Epsilon: 0.100\n", "Copied model parameters to target network. total_t = 1030000, period = 10000\n", "Episode: 2185 Duration: 0:00:17.390537 Num steps: 1086 Reward: 25.0 Training time per step: 0.012 Avg Reward (Last 100): 21.891 Epsilon: 0.100\n", "Episode: 2186 Duration: 0:00:15.208170 Num steps: 945 Reward: 19.0 Training time per 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0:00:14.748848 Num steps: 950 Reward: 16.0 Training time per step: 0.012 Avg Reward (Last 100): 21.218 Epsilon: 0.100\n", "Copied model parameters to target network. total_t = 1050000, period = 10000\n", "Episode: 2207 Duration: 0:00:24.660899 Num steps: 1540 Reward: 41.0 Training time per step: 0.012 Avg Reward (Last 100): 21.406 Epsilon: 0.100\n", "Episode: 2208 Duration: 0:00:14.691086 Num steps: 884 Reward: 16.0 Training time per step: 0.012 Avg Reward (Last 100): 21.317 Epsilon: 0.100\n", "Episode: 2209 Duration: 0:00:12.505929 Num steps: 839 Reward: 18.0 Training time per step: 0.011 Avg Reward (Last 100): 21.228 Epsilon: 0.100\n", "Episode: 2210 Duration: 0:00:10.948560 Num steps: 686 Reward: 11.0 Training time per step: 0.012 Avg Reward (Last 100): 21.208 Epsilon: 0.100\n", "Episode: 2211 Duration: 0:00:12.101688 Num steps: 775 Reward: 14.0 Training time per step: 0.012 Avg Reward (Last 100): 21.149 Epsilon: 0.100\n", "Episode: 2212 Duration: 0:00:10.677405 Num steps: 740 Reward: 12.0 Training time per step: 0.011 Avg Reward (Last 100): 21.010 Epsilon: 0.100\n", "Episode: 2213 Duration: 0:00:16.868547 Num steps: 1054 Reward: 31.0 Training time per step: 0.012 Avg Reward (Last 100): 21.119 Epsilon: 0.100\n", "Episode: 2214 Duration: 0:00:18.229979 Num steps: 1119 Reward: 30.0 Training time per step: 0.012 Avg Reward (Last 100): 21.178 Epsilon: 0.100\n", "Episode: 2215 Duration: 0:00:11.023002 Num steps: 704 Reward: 18.0 Training time per step: 0.012 Avg Reward (Last 100): 21.040 Epsilon: 0.100\n", "Episode: 2216 Duration: 0:00:20.794992 Num steps: 1325 Reward: 35.0 Training time per step: 0.012 Avg Reward (Last 100): 21.178 Epsilon: 0.100\n", "Copied model parameters to target network. total_t = 1060000, period = 10000\n", "Episode: 2217 Duration: 0:00:21.266002 Num steps: 1329 Reward: 27.0 Training time per step: 0.012 Avg Reward (Last 100): 21.228 Epsilon: 0.100\n", "Episode: 2218 Duration: 0:00:17.405131 Num steps: 1127 Reward: 23.0 Training time per 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1000 Reward: 16.0 Training time per step: 0.011 Avg Reward (Last 100): 20.782 Epsilon: 0.100\n", "Copied model parameters to target network. total_t = 1070000, period = 10000\n", "Episode: 2226 Duration: 0:00:12.301235 Num steps: 728 Reward: 13.0 Training time per step: 0.012 Avg Reward (Last 100): 20.584 Epsilon: 0.100\n", "Episode: 2227 Duration: 0:00:10.222087 Num steps: 652 Reward: 14.0 Training time per step: 0.012 Avg Reward (Last 100): 20.505 Epsilon: 0.100\n", "Episode: 2228 Duration: 0:00:16.815249 Num steps: 1066 Reward: 24.0 Training time per step: 0.012 Avg Reward (Last 100): 20.604 Epsilon: 0.100\n", "Episode: 2229 Duration: 0:00:25.154927 Num steps: 1592 Reward: 45.0 Training time per step: 0.012 Avg Reward (Last 100): 20.792 Epsilon: 0.100\n", "Episode: 2230 Duration: 0:00:21.056993 Num steps: 1283 Reward: 28.0 Training time per step: 0.012 Avg Reward (Last 100): 20.950 Epsilon: 0.100\n", "Episode: 2231 Duration: 0:00:13.306934 Num steps: 875 Reward: 28.0 Training time per step: 0.012 Avg Reward (Last 100): 21.020 Epsilon: 0.100\n", "Episode: 2232 Duration: 0:00:16.240866 Num steps: 1001 Reward: 20.0 Training time per step: 0.012 Avg Reward (Last 100): 21.040 Epsilon: 0.100\n", "Episode: 2233 Duration: 0:00:16.271804 Num steps: 1059 Reward: 23.0 Training time per step: 0.012 Avg Reward (Last 100): 20.931 Epsilon: 0.100\n", "Episode: 2234 Duration: 0:00:12.068337 Num steps: 766 Reward: 13.0 Training time per step: 0.012 Avg Reward (Last 100): 20.901 Epsilon: 0.100\n", "Episode: 2235 Duration: 0:00:14.528414 Num steps: 892 Reward: 18.0 Training time per step: 0.012 Avg Reward (Last 100): 20.891 Epsilon: 0.100\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Copied model parameters to target network. total_t = 1080000, period = 10000\n", "Episode: 2236 Duration: 0:00:22.158916 Num steps: 1422 Reward: 29.0 Training time per step: 0.012 Avg Reward (Last 100): 20.950 Epsilon: 0.100\n", "Episode: 2237 Duration: 0:00:20.754336 Num steps: 1293 Reward: 36.0 Training time per step: 0.012 Avg Reward (Last 100): 21.198 Epsilon: 0.100\n", "Episode: 2238 Duration: 0:00:22.181666 Num steps: 1444 Reward: 39.0 Training time per step: 0.012 Avg Reward (Last 100): 21.366 Epsilon: 0.100\n", "Episode: 2239 Duration: 0:00:15.870287 Num steps: 1024 Reward: 21.0 Training time per step: 0.012 Avg Reward (Last 100): 21.337 Epsilon: 0.100\n", "Episode: 2240 Duration: 0:00:14.957817 Num steps: 965 Reward: 18.0 Training time per step: 0.012 Avg Reward (Last 100): 21.287 Epsilon: 0.100\n", "Episode: 2241 Duration: 0:00:22.663204 Num steps: 1492 Reward: 40.0 Training time per step: 0.012 Avg Reward (Last 100): 21.356 Epsilon: 0.100\n", "Episode: 2242 Duration: 0:00:21.823011 Num steps: 1379 Reward: 45.0 Training time per step: 0.012 Avg Reward (Last 100): 21.396 Epsilon: 0.100\n", "Episode: 2243 Duration: 0:00:12.255761 Num steps: 753 Reward: 13.0 Training time per step: 0.012 Avg Reward (Last 100): 21.277 Epsilon: 0.100\n", "Copied model parameters to target network. total_t = 1090000, period = 10000\n", "Episode: 2244 Duration: 0:00:13.647129 Num steps: 836 Reward: 13.0 Training time per step: 0.012 Avg Reward (Last 100): 21.208 Epsilon: 0.100\n", "Episode: 2245 Duration: 0:00:11.679987 Num steps: 755 Reward: 23.0 Training time per step: 0.012 Avg Reward (Last 100): 21.248 Epsilon: 0.100\n", "Episode: 2246 Duration: 0:00:14.992657 Num steps: 947 Reward: 25.0 Training time per step: 0.012 Avg Reward (Last 100): 21.257 Epsilon: 0.100\n", "Episode: 2247 Duration: 0:00:11.229156 Num steps: 683 Reward: 10.0 Training time per step: 0.012 Avg Reward (Last 100): 21.010 Epsilon: 0.100\n", "Episode: 2248 Duration: 0:00:19.037157 Num steps: 1200 Reward: 42.0 Training time per step: 0.012 Avg Reward (Last 100): 21.267 Epsilon: 0.100\n", "Episode: 2249 Duration: 0:00:20.307456 Num steps: 1279 Reward: 25.0 Training time per step: 0.012 Avg Reward (Last 100): 21.307 Epsilon: 0.100\n", "Episode: 2250 Duration: 0:00:13.048975 Num steps: 794 Reward: 14.0 Training time per step: 0.012 Avg Reward (Last 100): 21.228 Epsilon: 0.100\n", "SAVED MODEL #2250\n", "Episode: 2251 Duration: 0:00:08.481947 Num steps: 682 Reward: 10.0 Training time per step: 0.010 Avg Reward (Last 100): 21.079 Epsilon: 0.100\n", "Episode: 2252 Duration: 0:00:07.437785 Num steps: 588 Reward: 7.0 Training time per step: 0.010 Avg Reward (Last 100): 20.931 Epsilon: 0.100\n", "Episode: 2253 Duration: 0:00:22.387321 Num steps: 1484 Reward: 38.0 Training time per step: 0.012 Avg Reward (Last 100): 21.099 Epsilon: 0.100\n", "Episode: 2254 Duration: 0:00:13.624984 Num steps: 877 Reward: 29.0 Training time per step: 0.012 Avg Reward (Last 100): 21.277 Epsilon: 0.100\n", "Copied model parameters to target network. total_t = 1100000, period = 10000\n", "Episode: 2255 Duration: 0:00:17.521703 Num steps: 1072 Reward: 26.0 Training time per step: 0.012 Avg Reward (Last 100): 21.376 Epsilon: 0.100\n", "Episode: 2256 Duration: 0:00:13.185562 Num steps: 812 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step: 0.012 Avg Reward (Last 100): 21.960 Epsilon: 0.100\n", "Episode: 2289 Duration: 0:00:14.106061 Num steps: 917 Reward: 21.0 Training time per step: 0.012 Avg Reward (Last 100): 22.000 Epsilon: 0.100\n", "Episode: 2290 Duration: 0:00:13.450713 Num steps: 857 Reward: 15.0 Training time per step: 0.012 Avg Reward (Last 100): 21.970 Epsilon: 0.100\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Episode: 2291 Duration: 0:00:15.774434 Num steps: 957 Reward: 20.0 Training time per step: 0.012 Avg Reward (Last 100): 22.079 Epsilon: 0.100\n", "Episode: 2292 Duration: 0:00:15.391973 Num steps: 1004 Reward: 20.0 Training time per step: 0.012 Avg Reward (Last 100): 21.960 Epsilon: 0.100\n", "Episode: 2293 Duration: 0:00:12.782061 Num steps: 822 Reward: 15.0 Training time per step: 0.012 Avg Reward (Last 100): 21.851 Epsilon: 0.100\n", "Episode: 2294 Duration: 0:00:11.729912 Num steps: 751 Reward: 20.0 Training time per step: 0.012 Avg Reward (Last 100): 21.901 Epsilon: 0.100\n", "Episode: 2295 Duration: 0:00:17.052775 Num steps: 1088 Reward: 22.0 Training time per step: 0.012 Avg Reward (Last 100): 21.911 Epsilon: 0.100\n", "Copied model parameters to target network. total_t = 1140000, period = 10000\n", "Episode: 2296 Duration: 0:00:10.852216 Num steps: 680 Reward: 13.0 Training time per step: 0.012 Avg Reward (Last 100): 21.881 Epsilon: 0.100\n", "Episode: 2297 Duration: 0:00:14.788845 Num steps: 940 Reward: 20.0 Training time per step: 0.012 Avg Reward (Last 100): 21.861 Epsilon: 0.100\n", "Episode: 2298 Duration: 0:00:13.846099 Num steps: 863 Reward: 15.0 Training time per step: 0.012 Avg Reward (Last 100): 21.871 Epsilon: 0.100\n", "Episode: 2299 Duration: 0:00:21.956164 Num steps: 1410 Reward: 31.0 Training time per step: 0.012 Avg Reward (Last 100): 22.079 Epsilon: 0.100\n", "Episode: 2300 Duration: 0:00:16.046237 Num steps: 1070 Reward: 25.0 Training time per step: 0.011 Avg Reward (Last 100): 22.139 Epsilon: 0.100\n", "SAVED MODEL #2300\n", "Episode: 2301 Duration: 0:00:11.754150 Num steps: 946 Reward: 23.0 Training time per step: 0.010 Avg Reward (Last 100): 22.238 Epsilon: 0.100\n", "Episode: 2302 Duration: 0:00:12.644245 Num steps: 847 Reward: 16.0 Training time per step: 0.011 Avg Reward (Last 100): 22.178 Epsilon: 0.100\n", "Episode: 2303 Duration: 0:00:13.817482 Num steps: 864 Reward: 14.0 Training time per step: 0.012 Avg Reward (Last 100): 22.010 Epsilon: 0.100\n", "Episode: 2304 Duration: 0:00:17.746644 Num steps: 1161 Reward: 21.0 Training time per step: 0.012 Avg Reward (Last 100): 22.050 Epsilon: 0.100\n", "Episode: 2305 Duration: 0:00:16.256543 Num steps: 1027 Reward: 33.0 Training time per step: 0.012 Avg Reward (Last 100): 22.307 Epsilon: 0.100\n", "Copied model parameters to target network. total_t = 1150000, period = 10000\n", "Episode: 2306 Duration: 0:00:17.275512 Num steps: 1054 Reward: 18.0 Training time per step: 0.012 Avg Reward (Last 100): 22.287 Epsilon: 0.100\n", "Episode: 2307 Duration: 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0.100\n", "Episode: 2314 Duration: 0:00:14.476154 Num steps: 907 Reward: 18.0 Training time per step: 0.012 Avg Reward (Last 100): 22.079 Epsilon: 0.100\n", "Episode: 2315 Duration: 0:00:15.031466 Num steps: 952 Reward: 23.0 Training time per step: 0.012 Avg Reward (Last 100): 22.010 Epsilon: 0.100\n", "Episode: 2316 Duration: 0:00:13.942561 Num steps: 905 Reward: 18.0 Training time per step: 0.012 Avg Reward (Last 100): 22.010 Epsilon: 0.100\n", "Copied model parameters to target network. total_t = 1160000, period = 10000\n", "Episode: 2317 Duration: 0:00:10.418141 Num steps: 659 Reward: 10.0 Training time per step: 0.012 Avg Reward (Last 100): 21.762 Epsilon: 0.100\n", "Episode: 2318 Duration: 0:00:10.796537 Num steps: 678 Reward: 9.0 Training time per step: 0.012 Avg Reward (Last 100): 21.584 Epsilon: 0.100\n", "Episode: 2319 Duration: 0:00:11.009594 Num steps: 742 Reward: 14.0 Training time per step: 0.011 Avg Reward (Last 100): 21.495 Epsilon: 0.100\n", "Episode: 2320 Duration: 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0.100\n", "Episode: 2340 Duration: 0:00:18.748627 Num steps: 1232 Reward: 31.0 Training time per step: 0.012 Avg Reward (Last 100): 19.812 Epsilon: 0.100\n", "Copied model parameters to target network. total_t = 1180000, period = 10000\n", "Episode: 2341 Duration: 0:00:12.676112 Num steps: 809 Reward: 17.0 Training time per step: 0.012 Avg Reward (Last 100): 19.802 Epsilon: 0.100\n", "Episode: 2342 Duration: 0:00:19.701891 Num steps: 1229 Reward: 31.0 Training time per step: 0.012 Avg Reward (Last 100): 19.713 Epsilon: 0.100\n", "Episode: 2343 Duration: 0:00:20.721694 Num steps: 1343 Reward: 34.0 Training time per step: 0.012 Avg Reward (Last 100): 19.604 Epsilon: 0.100\n", "Episode: 2344 Duration: 0:00:11.273818 Num steps: 672 Reward: 10.0 Training time per step: 0.012 Avg Reward (Last 100): 19.574 Epsilon: 0.100\n", "Episode: 2345 Duration: 0:00:13.025260 Num steps: 813 Reward: 16.0 Training time per step: 0.012 Avg Reward (Last 100): 19.604 Epsilon: 0.100\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Episode: 2346 Duration: 0:00:11.802228 Num steps: 766 Reward: 13.0 Training time per step: 0.012 Avg Reward (Last 100): 19.505 Epsilon: 0.100\n", "Episode: 2347 Duration: 0:00:20.124077 Num steps: 1254 Reward: 43.0 Training time per step: 0.012 Avg Reward (Last 100): 19.683 Epsilon: 0.100\n", "Episode: 2348 Duration: 0:00:18.774723 Num steps: 1244 Reward: 28.0 Training time per step: 0.011 Avg Reward (Last 100): 19.861 Epsilon: 0.100\n", "Episode: 2349 Duration: 0:00:12.329664 Num steps: 814 Reward: 15.0 Training time per step: 0.012 Avg Reward (Last 100): 19.594 Epsilon: 0.100\n", "Copied model parameters to target network. total_t = 1190000, period = 10000\n", "Episode: 2350 Duration: 0:00:26.998106 Num steps: 1664 Reward: 46.0 Training time per step: 0.012 Avg Reward (Last 100): 19.802 Epsilon: 0.100\n", "SAVED MODEL #2350\n", "Episode: 2351 Duration: 0:00:14.996816 Num steps: 1208 Reward: 22.0 Training time per step: 0.010 Avg Reward (Last 100): 19.881 Epsilon: 0.100\n", "Episode: 2352 Duration: 0:00:13.367601 Num steps: 869 Reward: 14.0 Training time per step: 0.012 Avg Reward (Last 100): 19.921 Epsilon: 0.100\n", "Episode: 2353 Duration: 0:00:13.675786 Num steps: 882 Reward: 22.0 Training time per step: 0.012 Avg Reward (Last 100): 20.069 Epsilon: 0.100\n", "Episode: 2354 Duration: 0:00:20.966471 Num steps: 1329 Reward: 35.0 Training time per step: 0.012 Avg Reward (Last 100): 20.040 Epsilon: 0.100\n", "Episode: 2355 Duration: 0:00:13.395644 Num steps: 886 Reward: 15.0 Training time per step: 0.012 Avg Reward (Last 100): 19.901 Epsilon: 0.100\n", "Episode: 2356 Duration: 0:00:17.732654 Num steps: 1169 Reward: 24.0 Training time per step: 0.012 Avg Reward (Last 100): 19.881 Epsilon: 0.100\n", "Episode: 2357 Duration: 0:00:15.396252 Num steps: 981 Reward: 20.0 Training time per step: 0.012 Avg Reward (Last 100): 19.941 Epsilon: 0.100\n", "Episode: 2358 Duration: 0:00:17.560746 Num steps: 1128 Reward: 25.0 Training time per 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"Episode: 2371 Duration: 0:00:10.021987 Num steps: 617 Reward: 9.0 Training time per step: 0.012 Avg Reward (Last 100): 19.604 Epsilon: 0.100\n", "Episode: 2372 Duration: 0:00:16.138578 Num steps: 1051 Reward: 27.0 Training time per step: 0.012 Avg Reward (Last 100): 19.574 Epsilon: 0.100\n", "Episode: 2373 Duration: 0:00:09.227613 Num steps: 647 Reward: 10.0 Training time per step: 0.011 Avg Reward (Last 100): 19.356 Epsilon: 0.100\n", "Episode: 2374 Duration: 0:00:12.975245 Num steps: 849 Reward: 13.0 Training time per step: 0.012 Avg Reward (Last 100): 19.267 Epsilon: 0.100\n", "Episode: 2375 Duration: 0:00:15.235072 Num steps: 984 Reward: 19.0 Training time per step: 0.012 Avg Reward (Last 100): 19.238 Epsilon: 0.100\n", "Episode: 2376 Duration: 0:00:17.024710 Num steps: 1100 Reward: 20.0 Training time per step: 0.012 Avg Reward (Last 100): 19.248 Epsilon: 0.100\n", "Episode: 2377 Duration: 0:00:09.810324 Num steps: 645 Reward: 15.0 Training time per step: 0.012 Avg Reward (Last 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"Episode: 2384 Duration: 0:00:14.829942 Num steps: 921 Reward: 18.0 Training time per step: 0.012 Avg Reward (Last 100): 18.921 Epsilon: 0.100\n", "Episode: 2385 Duration: 0:00:08.951888 Num steps: 555 Reward: 8.0 Training time per step: 0.012 Avg Reward (Last 100): 18.733 Epsilon: 0.100\n", "Episode: 2386 Duration: 0:00:15.850089 Num steps: 1025 Reward: 19.0 Training time per step: 0.012 Avg Reward (Last 100): 18.634 Epsilon: 0.100\n", "Episode: 2387 Duration: 0:00:20.664706 Num steps: 1297 Reward: 30.0 Training time per step: 0.012 Avg Reward (Last 100): 18.812 Epsilon: 0.100\n", "Episode: 2388 Duration: 0:00:10.785971 Num steps: 689 Reward: 9.0 Training time per step: 0.012 Avg Reward (Last 100): 18.752 Epsilon: 0.100\n", "Episode: 2389 Duration: 0:00:08.262386 Num steps: 538 Reward: 8.0 Training time per step: 0.012 Avg Reward (Last 100): 18.762 Epsilon: 0.100\n", "Episode: 2390 Duration: 0:00:18.450661 Num steps: 1158 Reward: 18.0 Training time per step: 0.012 Avg Reward (Last 100): 18.733 Epsilon: 0.100\n", "Episode: 2391 Duration: 0:00:18.915701 Num steps: 1180 Reward: 24.0 Training time per step: 0.012 Avg Reward (Last 100): 18.822 Epsilon: 0.100\n", "Episode: 2392 Duration: 0:00:09.624784 Num steps: 600 Reward: 11.0 Training time per step: 0.012 Avg Reward (Last 100): 18.733 Epsilon: 0.100\n", "Copied model parameters to target network. total_t = 1230000, period = 10000\n", "Episode: 2393 Duration: 0:00:11.093158 Num steps: 691 Reward: 11.0 Training time per step: 0.012 Avg Reward (Last 100): 18.644 Epsilon: 0.100\n", "Episode: 2394 Duration: 0:00:15.297035 Num steps: 989 Reward: 20.0 Training time per step: 0.012 Avg Reward (Last 100): 18.693 Epsilon: 0.100\n", "Episode: 2395 Duration: 0:00:10.090774 Num steps: 636 Reward: 15.0 Training time per step: 0.012 Avg Reward (Last 100): 18.644 Epsilon: 0.100\n", "Episode: 2396 Duration: 0:00:10.688606 Num steps: 686 Reward: 9.0 Training time per step: 0.012 Avg Reward (Last 100): 18.515 Epsilon: 0.100\n", "Episode: 2397 Duration: 0:00:15.589945 Num steps: 994 Reward: 16.0 Training time per step: 0.012 Avg Reward (Last 100): 18.545 Epsilon: 0.100\n", "Episode: 2398 Duration: 0:00:16.827103 Num steps: 1147 Reward: 26.0 Training time per step: 0.011 Avg Reward (Last 100): 18.604 Epsilon: 0.100\n", "Episode: 2399 Duration: 0:00:17.625513 Num steps: 1097 Reward: 19.0 Training time per step: 0.012 Avg Reward (Last 100): 18.644 Epsilon: 0.100\n", "Episode: 2400 Duration: 0:00:15.659914 Num steps: 987 Reward: 23.0 Training time per step: 0.012 Avg Reward (Last 100): 18.564 Epsilon: 0.100\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "SAVED MODEL #2400\n", "Episode: 2401 Duration: 0:00:14.052649 Num steps: 1137 Reward: 28.0 Training time per step: 0.010 Avg Reward (Last 100): 18.594 Epsilon: 0.100\n", "Episode: 2402 Duration: 0:00:16.850261 Num steps: 1138 Reward: 19.0 Training time per step: 0.011 Avg Reward (Last 100): 18.554 Epsilon: 0.100\n", "Copied model parameters to target network. total_t = 1240000, period = 10000\n", "Episode: 2403 Duration: 0:00:19.054721 Num steps: 1138 Reward: 19.0 Training time per step: 0.012 Avg Reward (Last 100): 18.584 Epsilon: 0.100\n", "Episode: 2404 Duration: 0:00:13.260494 Num steps: 848 Reward: 23.0 Training time per step: 0.012 Avg Reward (Last 100): 18.673 Epsilon: 0.100\n", "Episode: 2405 Duration: 0:00:12.308814 Num steps: 793 Reward: 13.0 Training time per step: 0.012 Avg Reward (Last 100): 18.594 Epsilon: 0.100\n", "Episode: 2406 Duration: 0:00:19.336730 Num steps: 1279 Reward: 39.0 Training time per step: 0.012 Avg Reward (Last 100): 18.653 Epsilon: 0.100\n", "Episode: 2407 Duration: 0:00:15.443281 Num steps: 1023 Reward: 21.0 Training time per step: 0.012 Avg Reward (Last 100): 18.683 Epsilon: 0.100\n", "Episode: 2408 Duration: 0:00:21.662374 Num steps: 1487 Reward: 33.0 Training time per step: 0.011 Avg Reward (Last 100): 18.772 Epsilon: 0.100\n", "Episode: 2409 Duration: 0:00:13.743868 Num steps: 865 Reward: 19.0 Training time per step: 0.012 Avg Reward (Last 100): 18.822 Epsilon: 0.100\n", "Episode: 2410 Duration: 0:00:12.352382 Num steps: 834 Reward: 17.0 Training time per step: 0.011 Avg Reward (Last 100): 18.752 Epsilon: 0.100\n", "Episode: 2411 Duration: 0:00:12.901984 Num steps: 802 Reward: 17.0 Training time per step: 0.012 Avg Reward (Last 100): 18.782 Epsilon: 0.100\n", "Episode: 2412 Duration: 0:00:08.160587 Num steps: 529 Reward: 8.0 Training time per step: 0.012 Avg Reward (Last 100): 18.752 Epsilon: 0.100\n", "Episode: 2413 Duration: 0:00:10.603593 Num steps: 691 Reward: 18.0 Training time per step: 0.012 Avg Reward (Last 100): 18.723 Epsilon: 0.100\n", "Copied model parameters to target network. total_t = 1250000, period = 10000\n", "Episode: 2414 Duration: 0:00:15.133342 Num steps: 994 Reward: 16.0 Training time per step: 0.011 Avg Reward (Last 100): 18.762 Epsilon: 0.100\n", "Episode: 2415 Duration: 0:00:12.721346 Num steps: 788 Reward: 13.0 Training time per step: 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0.100\n", "Episode: 2467 Duration: 0:00:15.386759 Num steps: 933 Reward: 22.0 Training time per step: 0.012 Avg Reward (Last 100): 18.485 Epsilon: 0.100\n", "Copied model parameters to target network. total_t = 1300000, period = 10000\n", "Episode: 2468 Duration: 0:00:17.774101 Num steps: 1075 Reward: 11.0 Training time per step: 0.012 Avg Reward (Last 100): 18.396 Epsilon: 0.100\n", "Episode: 2469 Duration: 0:00:21.049147 Num steps: 1367 Reward: 30.0 Training time per step: 0.012 Avg Reward (Last 100): 18.485 Epsilon: 0.100\n", "Episode: 2470 Duration: 0:00:08.157028 Num steps: 538 Reward: 7.0 Training time per step: 0.012 Avg Reward (Last 100): 18.337 Epsilon: 0.100\n", "Episode: 2471 Duration: 0:00:16.048442 Num steps: 1050 Reward: 28.0 Training time per step: 0.012 Avg Reward (Last 100): 18.406 Epsilon: 0.100\n", "Episode: 2472 Duration: 0:00:14.752034 Num steps: 898 Reward: 15.0 Training time per step: 0.012 Avg Reward (Last 100): 18.465 Epsilon: 0.100\n", "Episode: 2473 Duration: 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per step: 0.011 Avg Reward (Last 100): 17.525 Epsilon: 0.100\n", "Episode: 2512 Duration: 0:00:16.009663 Num steps: 1073 Reward: 26.0 Training time per step: 0.011 Avg Reward (Last 100): 17.614 Epsilon: 0.100\n", "Episode: 2513 Duration: 0:00:16.048436 Num steps: 1051 Reward: 29.0 Training time per step: 0.012 Avg Reward (Last 100): 17.822 Epsilon: 0.100\n", "Copied model parameters to target network. total_t = 1340000, period = 10000\n", "Episode: 2514 Duration: 0:00:20.038794 Num steps: 1249 Reward: 42.0 Training time per step: 0.012 Avg Reward (Last 100): 18.059 Epsilon: 0.100\n", "Episode: 2515 Duration: 0:00:18.075598 Num steps: 1166 Reward: 26.0 Training time per step: 0.012 Avg Reward (Last 100): 18.158 Epsilon: 0.100\n", "Episode: 2516 Duration: 0:00:10.910966 Num steps: 683 Reward: 10.0 Training time per step: 0.012 Avg Reward (Last 100): 18.129 Epsilon: 0.100\n", "Episode: 2517 Duration: 0:00:18.055161 Num steps: 1168 Reward: 25.0 Training time per step: 0.012 Avg Reward 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10000\n", "Episode: 2524 Duration: 0:00:13.178941 Num steps: 826 Reward: 13.0 Training time per step: 0.012 Avg Reward (Last 100): 18.000 Epsilon: 0.100\n", "Episode: 2525 Duration: 0:00:13.351494 Num steps: 831 Reward: 14.0 Training time per step: 0.012 Avg Reward (Last 100): 17.911 Epsilon: 0.100\n", "Episode: 2526 Duration: 0:00:14.299347 Num steps: 960 Reward: 25.0 Training time per step: 0.011 Avg Reward (Last 100): 18.020 Epsilon: 0.100\n", "Episode: 2527 Duration: 0:00:09.669229 Num steps: 615 Reward: 9.0 Training time per step: 0.012 Avg Reward (Last 100): 17.871 Epsilon: 0.100\n", "Episode: 2528 Duration: 0:00:18.361327 Num steps: 1149 Reward: 36.0 Training time per step: 0.012 Avg Reward (Last 100): 18.050 Epsilon: 0.100\n", "Episode: 2529 Duration: 0:00:13.007614 Num steps: 824 Reward: 14.0 Training time per step: 0.012 Avg Reward (Last 100): 18.079 Epsilon: 0.100\n", "Episode: 2530 Duration: 0:00:19.990437 Num steps: 1296 Reward: 35.0 Training time per step: 0.012 Avg 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Epsilon: 0.100\n", "Episode: 2550 Duration: 0:00:11.834058 Num steps: 715 Reward: 10.0 Training time per step: 0.012 Avg Reward (Last 100): 18.257 Epsilon: 0.100\n", "SAVED MODEL #2550\n", "Episode: 2551 Duration: 0:00:12.083259 Num steps: 979 Reward: 17.0 Training time per step: 0.010 Avg Reward (Last 100): 18.238 Epsilon: 0.100\n", "Episode: 2552 Duration: 0:00:14.499506 Num steps: 1076 Reward: 22.0 Training time per step: 0.011 Avg Reward (Last 100): 18.317 Epsilon: 0.100\n", "Episode: 2553 Duration: 0:00:13.183033 Num steps: 880 Reward: 18.0 Training time per step: 0.011 Avg Reward (Last 100): 18.396 Epsilon: 0.100\n", "Episode: 2554 Duration: 0:00:14.276163 Num steps: 916 Reward: 22.0 Training time per step: 0.012 Avg Reward (Last 100): 18.436 Epsilon: 0.100\n", "Copied model parameters to target network. total_t = 1380000, period = 10000\n", "Episode: 2555 Duration: 0:00:19.937310 Num steps: 1217 Reward: 22.0 Training time per step: 0.012 Avg Reward (Last 100): 18.406 Epsilon: 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100): 18.822 Epsilon: 0.100\n", "SAVED MODEL #2600\n", "Episode: 2601 Duration: 0:00:14.940843 Num steps: 1204 Reward: 40.0 Training time per step: 0.010 Avg Reward (Last 100): 19.059 Epsilon: 0.100\n", "Episode: 2602 Duration: 0:00:10.203883 Num steps: 708 Reward: 16.0 Training time per step: 0.011 Avg Reward (Last 100): 19.020 Epsilon: 0.100\n", "Episode: 2603 Duration: 0:00:14.229161 Num steps: 936 Reward: 23.0 Training time per step: 0.012 Avg Reward (Last 100): 19.040 Epsilon: 0.100\n", "Episode: 2604 Duration: 0:00:10.012629 Num steps: 636 Reward: 10.0 Training time per step: 0.012 Avg Reward (Last 100): 19.020 Epsilon: 0.100\n", "Episode: 2605 Duration: 0:00:09.868701 Num steps: 658 Reward: 11.0 Training time per step: 0.011 Avg Reward (Last 100): 18.990 Epsilon: 0.100\n", "Episode: 2606 Duration: 0:00:16.926499 Num steps: 1088 Reward: 26.0 Training time per step: 0.012 Avg Reward (Last 100): 19.059 Epsilon: 0.100\n", "Episode: 2607 Duration: 0:00:20.067895 Num steps: 1275 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14.0 Training time per step: 0.011 Avg Reward (Last 100): 18.584 Epsilon: 0.100\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Episode: 2621 Duration: 0:00:12.369129 Num steps: 782 Reward: 13.0 Training time per step: 0.012 Avg Reward (Last 100): 18.594 Epsilon: 0.100\n", "Copied model parameters to target network. total_t = 1440000, period = 10000\n", "Episode: 2622 Duration: 0:00:19.983441 Num steps: 1245 Reward: 33.0 Training time per step: 0.012 Avg Reward (Last 100): 18.762 Epsilon: 0.100\n", "Episode: 2623 Duration: 0:00:16.346312 Num steps: 1053 Reward: 18.0 Training time per step: 0.012 Avg Reward (Last 100): 18.743 Epsilon: 0.100\n", "Episode: 2624 Duration: 0:00:12.281468 Num steps: 842 Reward: 14.0 Training time per step: 0.011 Avg Reward (Last 100): 18.743 Epsilon: 0.100\n", "Episode: 2625 Duration: 0:00:11.513840 Num steps: 728 Reward: 10.0 Training time per step: 0.012 Avg Reward (Last 100): 18.713 Epsilon: 0.100\n", "Episode: 2626 Duration: 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0.100\n", "Copied model parameters to target network. total_t = 1450000, period = 10000\n", "Episode: 2633 Duration: 0:00:17.311512 Num steps: 1155 Reward: 26.0 Training time per step: 0.011 Avg Reward (Last 100): 18.594 Epsilon: 0.100\n", "Episode: 2634 Duration: 0:00:15.713219 Num steps: 1007 Reward: 19.0 Training time per step: 0.012 Avg Reward (Last 100): 18.584 Epsilon: 0.100\n", "Episode: 2635 Duration: 0:00:19.646838 Num steps: 1223 Reward: 39.0 Training time per step: 0.012 Avg Reward (Last 100): 18.653 Epsilon: 0.100\n", "Episode: 2636 Duration: 0:00:12.177230 Num steps: 828 Reward: 14.0 Training time per step: 0.011 Avg Reward (Last 100): 18.663 Epsilon: 0.100\n", "Episode: 2637 Duration: 0:00:19.418884 Num steps: 1301 Reward: 36.0 Training time per step: 0.011 Avg Reward (Last 100): 18.762 Epsilon: 0.100\n", "Episode: 2638 Duration: 0:00:11.659954 Num steps: 725 Reward: 11.0 Training time per step: 0.012 Avg Reward (Last 100): 18.644 Epsilon: 0.100\n", "Episode: 2639 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0.100\n", "Episode: 2652 Duration: 0:00:11.812211 Num steps: 934 Reward: 14.0 Training time per step: 0.010 Avg Reward (Last 100): 18.574 Epsilon: 0.100\n", "Episode: 2653 Duration: 0:00:13.122784 Num steps: 845 Reward: 18.0 Training time per step: 0.012 Avg Reward (Last 100): 18.535 Epsilon: 0.100\n", "Copied model parameters to target network. total_t = 1470000, period = 10000\n", "Episode: 2654 Duration: 0:00:12.011050 Num steps: 816 Reward: 20.0 Training time per step: 0.011 Avg Reward (Last 100): 18.554 Epsilon: 0.100\n", "Episode: 2655 Duration: 0:00:11.954963 Num steps: 759 Reward: 12.0 Training time per step: 0.012 Avg Reward (Last 100): 18.455 Epsilon: 0.100\n", "Episode: 2656 Duration: 0:00:13.862269 Num steps: 896 Reward: 18.0 Training time per step: 0.012 Avg Reward (Last 100): 18.416 Epsilon: 0.100\n", "Episode: 2657 Duration: 0:00:16.570934 Num steps: 1081 Reward: 25.0 Training time per step: 0.012 Avg Reward (Last 100): 18.564 Epsilon: 0.100\n", "Episode: 2658 Duration: 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0.100\n", "Episode: 2665 Duration: 0:00:13.459430 Num steps: 833 Reward: 15.0 Training time per step: 0.012 Avg Reward (Last 100): 18.505 Epsilon: 0.100\n", "Copied model parameters to target network. total_t = 1480000, period = 10000\n", "Episode: 2666 Duration: 0:00:13.443509 Num steps: 862 Reward: 17.0 Training time per step: 0.011 Avg Reward (Last 100): 18.495 Epsilon: 0.100\n", "Episode: 2667 Duration: 0:00:11.612474 Num steps: 736 Reward: 15.0 Training time per step: 0.012 Avg Reward (Last 100): 18.446 Epsilon: 0.100\n", "Episode: 2668 Duration: 0:00:14.557212 Num steps: 980 Reward: 26.0 Training time per step: 0.011 Avg Reward (Last 100): 18.574 Epsilon: 0.100\n", "Episode: 2669 Duration: 0:00:19.665731 Num steps: 1221 Reward: 36.0 Training time per step: 0.012 Avg Reward (Last 100): 18.653 Epsilon: 0.100\n", "Episode: 2670 Duration: 0:00:13.127594 Num steps: 840 Reward: 21.0 Training time per step: 0.012 Avg Reward (Last 100): 18.693 Epsilon: 0.100\n", "Episode: 2671 Duration: 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0.100\n", "Episode: 2690 Duration: 0:00:18.319691 Num steps: 1149 Reward: 31.0 Training time per step: 0.012 Avg Reward (Last 100): 18.891 Epsilon: 0.100\n", "Episode: 2691 Duration: 0:00:13.911953 Num steps: 916 Reward: 20.0 Training time per step: 0.012 Avg Reward (Last 100): 18.713 Epsilon: 0.100\n", "Episode: 2692 Duration: 0:00:18.924364 Num steps: 1245 Reward: 23.0 Training time per step: 0.012 Avg Reward (Last 100): 18.733 Epsilon: 0.100\n", "Episode: 2693 Duration: 0:00:10.995885 Num steps: 701 Reward: 11.0 Training time per step: 0.012 Avg Reward (Last 100): 18.673 Epsilon: 0.100\n", "Episode: 2694 Duration: 0:00:13.982365 Num steps: 892 Reward: 15.0 Training time per step: 0.012 Avg Reward (Last 100): 18.644 Epsilon: 0.100\n", "Episode: 2695 Duration: 0:00:11.842672 Num steps: 784 Reward: 15.0 Training time per step: 0.011 Avg Reward (Last 100): 18.594 Epsilon: 0.100\n", "Episode: 2696 Duration: 0:00:15.700348 Num steps: 937 Reward: 16.0 Training time per step: 0.012 Avg Reward (Last 100): 18.475 Epsilon: 0.100\n", "Episode: 2697 Duration: 0:00:18.670741 Num steps: 1191 Reward: 24.0 Training time per step: 0.012 Avg Reward (Last 100): 18.604 Epsilon: 0.100\n", "Episode: 2698 Duration: 0:00:11.827133 Num steps: 733 Reward: 12.0 Training time per step: 0.012 Avg Reward (Last 100): 18.505 Epsilon: 0.100\n", "Copied model parameters to target network. total_t = 1510000, period = 10000\n", "Episode: 2699 Duration: 0:00:08.352123 Num steps: 526 Reward: 6.0 Training time per step: 0.011 Avg Reward (Last 100): 18.465 Epsilon: 0.100\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "with tf.Session() as sess:\n", " model.set_session(sess)\n", " target_model.set_session(sess)\n", " sess.run(tf.global_variables_initializer())\n", " model.load()\n", "\n", " print(\"Filling experience replay buffer...\")\n", " obs = env.reset()\n", " obs_small = preprocess(obs)\n", " state = np.stack([obs_small] * 4, axis=0)\n", "\n", " # Fill experience replay buffer\n", " for i in range(MIN_EXPERIENCES):\n", "\n", " action = np.random.randint(0,K)\n", " obs, reward, done, _ = env.step(action)\n", " next_state = update_state(state, obs)\n", "\n", " experience_replay_buffer.append((state, action, reward, next_state, done))\n", "\n", " if done:\n", " obs = env.reset()\n", " obs_small = preprocess(obs)\n", " state = np.stack([obs_small] * 4, axis=0)\n", "\n", " else:\n", " state = next_state\n", "\n", " # Play a number of episodes and learn\n", " for i in range(num_episodes):\n", " t0 = datetime.now()\n", "\n", " # Reset the environment\n", " obs = env.reset()\n", " obs_small = preprocess(obs)\n", " state = np.stack([obs_small] * 4, axis=0)\n", " assert (state.shape == (4, 80, 80))\n", " loss = None\n", "\n", " total_time_training = 0\n", " num_steps_in_episode = 0\n", " episode_reward = 0\n", "\n", " done = False\n", " while not done:\n", "\n", " # Update target network\n", " if total_t % TARGET_UPDATE_PERIOD == 0:\n", " target_model.copy_from(model)\n", " print(\"Copied model parameters to target network. total_t = %s, period = %s\" % (\n", " total_t, TARGET_UPDATE_PERIOD))\n", "\n", " # Take action\n", " action = model.sample_action(state, epsilon)\n", " obs, reward, done, _ = env.step(action)\n", " obs_small = preprocess(obs)\n", " next_state = np.append(state[1:], np.expand_dims(obs_small, 0), axis=0)\n", "\n", " episode_reward += reward\n", "\n", " # Remove oldest experience if replay buffer is full\n", " if len(experience_replay_buffer) == MAX_EXPERIENCES:\n", " experience_replay_buffer.pop(0)\n", "\n", " # Save the recent experience\n", " experience_replay_buffer.append((state, action, reward, next_state, done))\n", "\n", " # Train the model and keep measure of time\n", " t0_2 = datetime.now()\n", " loss = learn(model, target_model, experience_replay_buffer, gamma, batch_sz)\n", " dt = datetime.now() - t0_2\n", "\n", " total_time_training += dt.total_seconds()\n", " num_steps_in_episode += 1\n", "\n", " state = next_state\n", " total_t += 1\n", "\n", " epsilon = max(epsilon - epsilon_change, epsilon_min)\n", "\n", " duration = datetime.now() - t0\n", "\n", " episode_rewards[i] = episode_reward\n", " time_per_step = total_time_training / num_steps_in_episode\n", "\n", " last_100_avg = episode_rewards[max(0, i - 100):i + 1].mean()\n", " last_100_avgs.append(last_100_avg)\n", " print(\"Episode:\", i,\"Duration:\", duration, \"Num steps:\", num_steps_in_episode,\n", " \"Reward:\", episode_reward, \"Training time per step:\", \"%.3f\" % time_per_step,\n", " \"Avg Reward (Last 100):\", \"%.3f\" % last_100_avg,\"Epsilon:\", \"%.3f\" % epsilon)\n", "\n", " if i % 50 == 0:\n", " model.save(i)\n", " sys.stdout.flush()\n", "\n", "#Plots\n", "plt.plot(last_100_avgs)\n", "plt.xlabel('episodes')\n", "plt.ylabel('Average Rewards')\n", "plt.show()\n", "env.close()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "[2018-07-02 04:50:47,488] Making new env: Breakout-v0\n", "[2018-07-02 04:50:47,635] Finished writing results. You can upload them to the scoreboard via gym.upload('/home/am/Dropbox/AIforIoT/Chapter06/save-mov')\n", "[2018-07-02 04:50:47,635] Clearing 2 monitor files from previous run (because force=True was provided)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Restoring parameters from models/atari.ckpt-2650\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[2018-07-02 04:50:49,809] Restoring parameters from models/atari.ckpt-2650\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "loaded model: models/atari.ckpt-2650\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[2018-07-02 04:50:50,049] Starting new video recorder writing to /home/am/Dropbox/AIforIoT/Chapter06/save-mov/openaigym.video.1.1230.video000000.mp4\n", "/home/am/anaconda3/envs/tensorflow/lib/python3.5/site-packages/ipykernel/__main__.py:5: DeprecationWarning: `imresize` is deprecated!\n", "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", "Use ``skimage.transform.resize`` instead.\n", "[2018-07-02 04:50:59,305] Finished writing results. You can upload them to the scoreboard via gym.upload('/home/am/Dropbox/AIforIoT/Chapter06/save-mov')\n" ] } ], "source": [ "from gym import wrappers\n", "env = gym.envs.make(\"Breakout-v0\")\n", "env = wrappers.Monitor(env, 'save-mov',force=True)\n", "with tf.Session() as sess:\n", " \n", " model.set_session(sess)\n", " target_model.set_session(sess)\n", " sess.run(tf.global_variables_initializer())\n", " model.load()\n", " \n", " obs = env.reset()\n", " obs_small = preprocess(obs)\n", " state = np.stack([obs_small] * 4, axis=0)\n", " done = False\n", " while not done:\n", " action = model.sample_action(state, epsilon)\n", " obs, reward, done, _ = env.step(action)\n", " env.render()\n", " next_state = update_state(state, obs)\n", " state = next_state\n", "env.close()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "[2018-07-02 04:51:14,953] Making new env: Breakout-v0\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Restoring parameters from models/atari.ckpt-2650\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[2018-07-02 04:51:17,560] Restoring parameters from models/atari.ckpt-2650\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "loaded model: models/atari.ckpt-2650\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/am/anaconda3/envs/tensorflow/lib/python3.5/site-packages/ipykernel/__main__.py:5: DeprecationWarning: `imresize` is deprecated!\n", "`imresize` is deprecated in SciPy 1.0.0, and will be removed in 1.2.0.\n", "Use ``skimage.transform.resize`` instead.\n" ] } ], "source": [ "env = gym.envs.make(\"Breakout-v0\")\n", "frames = []\n", "with tf.Session() as sess:\n", " model.set_session(sess)\n", " target_model.set_session(sess)\n", " sess.run(tf.global_variables_initializer())\n", " model.load()\n", " \n", " obs = env.reset()\n", " obs_small = preprocess(obs)\n", " state = np.stack([obs_small] * 4, axis=0)\n", " done = False\n", " while not done:\n", " action = model.sample_action(state, epsilon)\n", " obs, reward, done, _ = env.step(action)\n", " frames.append(env.render(mode='rgb_array'))\n", " next_state = update_state(state, obs)\n", " state = next_state\n" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "
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\n", "\n", "\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Display the game as an image\n", "import matplotlib.animation as animation\n", "from JSAnimation.IPython_display import display_animation\n", "from IPython.display import display\n", "patch = plt.imshow(frames[0])\n", "plt.axis('off')\n", "def animate(i):\n", " patch.set_data(frames[i])\n", "anim = animation.FuncAnimation(plt.gcf(), animate, frames = len(frames), interval = 50)\n", "\n", "display(display_animation(anim, default_mode='loop'))\n", "env.close()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python [conda env:tensorflow]", "language": "python", "name": "conda-env-tensorflow-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.4" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: Chapter06/Images/Readme.md ================================================ Images ================================================ FILE: Chapter06/OpenAI_practice.ipynb ================================================ { "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Total number of environments installed in your Gym installation are 777\n", "Acrobot-v1\n", "AirRaid-ram-v0\n", "AirRaid-ram-v4\n", "AirRaid-ramDeterministic-v0\n", "AirRaid-ramDeterministic-v4\n", "AirRaid-ramNoFrameskip-v0\n", "AirRaid-ramNoFrameskip-v4\n", "AirRaid-v0\n", "AirRaid-v4\n", "AirRaidDeterministic-v0\n", "AirRaidDeterministic-v4\n", "AirRaidNoFrameskip-v0\n", "AirRaidNoFrameskip-v4\n", "Alien-ram-v0\n", "Alien-ram-v4\n", "Alien-ramDeterministic-v0\n", "Alien-ramDeterministic-v4\n", "Alien-ramNoFrameskip-v0\n", "Alien-ramNoFrameskip-v4\n", "Alien-v0\n", "Alien-v4\n", "AlienDeterministic-v0\n", "AlienDeterministic-v4\n", "AlienNoFrameskip-v0\n", "AlienNoFrameskip-v4\n", "Amidar-ram-v0\n", "Amidar-ram-v4\n", "Amidar-ramDeterministic-v0\n", "Amidar-ramDeterministic-v4\n", "Amidar-ramNoFrameskip-v0\n", "Amidar-ramNoFrameskip-v4\n", "Amidar-v0\n", "Amidar-v4\n", "AmidarDeterministic-v0\n", "AmidarDeterministic-v4\n", "AmidarNoFrameskip-v0\n", "AmidarNoFrameskip-v4\n", "Ant-v1\n", "Assault-ram-v0\n", "Assault-ram-v4\n", "Assault-ramDeterministic-v0\n", "Assault-ramDeterministic-v4\n", "Assault-ramNoFrameskip-v0\n", "Assault-ramNoFrameskip-v4\n", "Assault-v0\n", "Assault-v4\n", "AssaultDeterministic-v0\n", "AssaultDeterministic-v4\n", "AssaultNoFrameskip-v0\n", "AssaultNoFrameskip-v4\n", "Asterix-ram-v0\n", "Asterix-ram-v4\n", "Asterix-ramDeterministic-v0\n", "Asterix-ramDeterministic-v4\n", "Asterix-ramNoFrameskip-v0\n", "Asterix-ramNoFrameskip-v4\n", "Asterix-v0\n", "Asterix-v4\n", "AsterixDeterministic-v0\n", "AsterixDeterministic-v4\n", "AsterixNoFrameskip-v0\n", "AsterixNoFrameskip-v4\n", "Asteroids-ram-v0\n", "Asteroids-ram-v4\n", "Asteroids-ramDeterministic-v0\n", "Asteroids-ramDeterministic-v4\n", "Asteroids-ramNoFrameskip-v0\n", "Asteroids-ramNoFrameskip-v4\n", "Asteroids-v0\n", "Asteroids-v4\n", "AsteroidsDeterministic-v0\n", "AsteroidsDeterministic-v4\n", "AsteroidsNoFrameskip-v0\n", "AsteroidsNoFrameskip-v4\n", "Atlantis-ram-v0\n", "Atlantis-ram-v4\n", "Atlantis-ramDeterministic-v0\n", "Atlantis-ramDeterministic-v4\n", "Atlantis-ramNoFrameskip-v0\n", "Atlantis-ramNoFrameskip-v4\n", "Atlantis-v0\n", "Atlantis-v4\n", "AtlantisDeterministic-v0\n", "AtlantisDeterministic-v4\n", "AtlantisNoFrameskip-v0\n", "AtlantisNoFrameskip-v4\n", "BankHeist-ram-v0\n", "BankHeist-ram-v4\n", "BankHeist-ramDeterministic-v0\n", "BankHeist-ramDeterministic-v4\n", "BankHeist-ramNoFrameskip-v0\n", "BankHeist-ramNoFrameskip-v4\n", "BankHeist-v0\n", "BankHeist-v4\n", "BankHeistDeterministic-v0\n", "BankHeistDeterministic-v4\n", "BankHeistNoFrameskip-v0\n", "BankHeistNoFrameskip-v4\n", "BattleZone-ram-v0\n", "BattleZone-ram-v4\n", "BattleZone-ramDeterministic-v0\n", "BattleZone-ramDeterministic-v4\n", "BattleZone-ramNoFrameskip-v0\n", "BattleZone-ramNoFrameskip-v4\n", "BattleZone-v0\n", "BattleZone-v4\n", "BattleZoneDeterministic-v0\n", "BattleZoneDeterministic-v4\n", "BattleZoneNoFrameskip-v0\n", "BattleZoneNoFrameskip-v4\n", "BeamRider-ram-v0\n", "BeamRider-ram-v4\n", "BeamRider-ramDeterministic-v0\n", "BeamRider-ramDeterministic-v4\n", "BeamRider-ramNoFrameskip-v0\n", "BeamRider-ramNoFrameskip-v4\n", "BeamRider-v0\n", "BeamRider-v4\n", "BeamRiderDeterministic-v0\n", "BeamRiderDeterministic-v4\n", "BeamRiderNoFrameskip-v0\n", "BeamRiderNoFrameskip-v4\n", "Berzerk-ram-v0\n", "Berzerk-ram-v4\n", "Berzerk-ramDeterministic-v0\n", "Berzerk-ramDeterministic-v4\n", "Berzerk-ramNoFrameskip-v0\n", "Berzerk-ramNoFrameskip-v4\n", "Berzerk-v0\n", "Berzerk-v4\n", "BerzerkDeterministic-v0\n", "BerzerkDeterministic-v4\n", "BerzerkNoFrameskip-v0\n", "BerzerkNoFrameskip-v4\n", "BipedalWalker-v2\n", "BipedalWalkerHardcore-v2\n", "Blackjack-v0\n", "Bowling-ram-v0\n", "Bowling-ram-v4\n", "Bowling-ramDeterministic-v0\n", "Bowling-ramDeterministic-v4\n", "Bowling-ramNoFrameskip-v0\n", "Bowling-ramNoFrameskip-v4\n", "Bowling-v0\n", "Bowling-v4\n", "BowlingDeterministic-v0\n", "BowlingDeterministic-v4\n", "BowlingNoFrameskip-v0\n", "BowlingNoFrameskip-v4\n", "Boxing-ram-v0\n", "Boxing-ram-v4\n", "Boxing-ramDeterministic-v0\n", "Boxing-ramDeterministic-v4\n", "Boxing-ramNoFrameskip-v0\n", "Boxing-ramNoFrameskip-v4\n", "Boxing-v0\n", "Boxing-v4\n", "BoxingDeterministic-v0\n", "BoxingDeterministic-v4\n", "BoxingNoFrameskip-v0\n", "BoxingNoFrameskip-v4\n", "Breakout-ram-v0\n", "Breakout-ram-v4\n", "Breakout-ramDeterministic-v0\n", "Breakout-ramDeterministic-v4\n", "Breakout-ramNoFrameskip-v0\n", "Breakout-ramNoFrameskip-v4\n", "Breakout-v0\n", "Breakout-v4\n", "BreakoutDeterministic-v0\n", "BreakoutDeterministic-v4\n", "BreakoutNoFrameskip-v0\n", "BreakoutNoFrameskip-v4\n", "CNNClassifierTraining-v0\n", "CarRacing-v0\n", "Carnival-ram-v0\n", "Carnival-ram-v4\n", "Carnival-ramDeterministic-v0\n", "Carnival-ramDeterministic-v4\n", "Carnival-ramNoFrameskip-v0\n", "Carnival-ramNoFrameskip-v4\n", "Carnival-v0\n", "Carnival-v4\n", "CarnivalDeterministic-v0\n", "CarnivalDeterministic-v4\n", "CarnivalNoFrameskip-v0\n", "CarnivalNoFrameskip-v4\n", "CartPole-v0\n", "CartPole-v1\n", "Centipede-ram-v0\n", "Centipede-ram-v4\n", "Centipede-ramDeterministic-v0\n", "Centipede-ramDeterministic-v4\n", "Centipede-ramNoFrameskip-v0\n", "Centipede-ramNoFrameskip-v4\n", "Centipede-v0\n", "Centipede-v4\n", "CentipedeDeterministic-v0\n", "CentipedeDeterministic-v4\n", "CentipedeNoFrameskip-v0\n", "CentipedeNoFrameskip-v4\n", "ChopperCommand-ram-v0\n", "ChopperCommand-ram-v4\n", "ChopperCommand-ramDeterministic-v0\n", "ChopperCommand-ramDeterministic-v4\n", "ChopperCommand-ramNoFrameskip-v0\n", "ChopperCommand-ramNoFrameskip-v4\n", "ChopperCommand-v0\n", "ChopperCommand-v4\n", "ChopperCommandDeterministic-v0\n", "ChopperCommandDeterministic-v4\n", "ChopperCommandNoFrameskip-v0\n", "ChopperCommandNoFrameskip-v4\n", "CliffWalking-v0\n", "ConvergenceControl-v0\n", "Copy-v0\n", "CrazyClimber-ram-v0\n", "CrazyClimber-ram-v4\n", "CrazyClimber-ramDeterministic-v0\n", "CrazyClimber-ramDeterministic-v4\n", "CrazyClimber-ramNoFrameskip-v0\n", "CrazyClimber-ramNoFrameskip-v4\n", "CrazyClimber-v0\n", "CrazyClimber-v4\n", "CrazyClimberDeterministic-v0\n", "CrazyClimberDeterministic-v4\n", "CrazyClimberNoFrameskip-v0\n", "CrazyClimberNoFrameskip-v4\n", "DemonAttack-ram-v0\n", "DemonAttack-ram-v4\n", "DemonAttack-ramDeterministic-v0\n", "DemonAttack-ramDeterministic-v4\n", "DemonAttack-ramNoFrameskip-v0\n", "DemonAttack-ramNoFrameskip-v4\n", "DemonAttack-v0\n", "DemonAttack-v4\n", "DemonAttackDeterministic-v0\n", "DemonAttackDeterministic-v4\n", "DemonAttackNoFrameskip-v0\n", "DemonAttackNoFrameskip-v4\n", "DoubleDunk-ram-v0\n", "DoubleDunk-ram-v4\n", "DoubleDunk-ramDeterministic-v0\n", "DoubleDunk-ramDeterministic-v4\n", "DoubleDunk-ramNoFrameskip-v0\n", "DoubleDunk-ramNoFrameskip-v4\n", "DoubleDunk-v0\n", "DoubleDunk-v4\n", "DoubleDunkDeterministic-v0\n", "DoubleDunkDeterministic-v4\n", "DoubleDunkNoFrameskip-v0\n", "DoubleDunkNoFrameskip-v4\n", "DuplicatedInput-v0\n", "ElevatorAction-ram-v0\n", "ElevatorAction-ram-v4\n", "ElevatorAction-ramDeterministic-v0\n", "ElevatorAction-ramDeterministic-v4\n", "ElevatorAction-ramNoFrameskip-v0\n", "ElevatorAction-ramNoFrameskip-v4\n", "ElevatorAction-v0\n", "ElevatorAction-v4\n", "ElevatorActionDeterministic-v0\n", "ElevatorActionDeterministic-v4\n", "ElevatorActionNoFrameskip-v0\n", "ElevatorActionNoFrameskip-v4\n", "Enduro-ram-v0\n", "Enduro-ram-v4\n", "Enduro-ramDeterministic-v0\n", "Enduro-ramDeterministic-v4\n", "Enduro-ramNoFrameskip-v0\n", "Enduro-ramNoFrameskip-v4\n", "Enduro-v0\n", "Enduro-v4\n", "EnduroDeterministic-v0\n", "EnduroDeterministic-v4\n", "EnduroNoFrameskip-v0\n", "EnduroNoFrameskip-v4\n", "FishingDerby-ram-v0\n", "FishingDerby-ram-v4\n", "FishingDerby-ramDeterministic-v0\n", "FishingDerby-ramDeterministic-v4\n", "FishingDerby-ramNoFrameskip-v0\n", "FishingDerby-ramNoFrameskip-v4\n", "FishingDerby-v0\n", "FishingDerby-v4\n", "FishingDerbyDeterministic-v0\n", "FishingDerbyDeterministic-v4\n", "FishingDerbyNoFrameskip-v0\n", "FishingDerbyNoFrameskip-v4\n", "Freeway-ram-v0\n", "Freeway-ram-v4\n", "Freeway-ramDeterministic-v0\n", "Freeway-ramDeterministic-v4\n", "Freeway-ramNoFrameskip-v0\n", "Freeway-ramNoFrameskip-v4\n", "Freeway-v0\n", "Freeway-v4\n", "FreewayDeterministic-v0\n", "FreewayDeterministic-v4\n", "FreewayNoFrameskip-v0\n", "FreewayNoFrameskip-v4\n", "Frostbite-ram-v0\n", "Frostbite-ram-v4\n", "Frostbite-ramDeterministic-v0\n", "Frostbite-ramDeterministic-v4\n", "Frostbite-ramNoFrameskip-v0\n", "Frostbite-ramNoFrameskip-v4\n", "Frostbite-v0\n", "Frostbite-v4\n", "FrostbiteDeterministic-v0\n", "FrostbiteDeterministic-v4\n", "FrostbiteNoFrameskip-v0\n", "FrostbiteNoFrameskip-v4\n", "FrozenLake-v0\n", "FrozenLake8x8-v0\n", "Go19x19-v0\n", "Go9x9-v0\n", "Gopher-ram-v0\n", "Gopher-ram-v4\n", "Gopher-ramDeterministic-v0\n", "Gopher-ramDeterministic-v4\n", "Gopher-ramNoFrameskip-v0\n", "Gopher-ramNoFrameskip-v4\n", "Gopher-v0\n", "Gopher-v4\n", "GopherDeterministic-v0\n", "GopherDeterministic-v4\n", "GopherNoFrameskip-v0\n", "GopherNoFrameskip-v4\n", "Gravitar-ram-v0\n", "Gravitar-ram-v4\n", "Gravitar-ramDeterministic-v0\n", "Gravitar-ramDeterministic-v4\n", "Gravitar-ramNoFrameskip-v0\n", "Gravitar-ramNoFrameskip-v4\n", "Gravitar-v0\n", "Gravitar-v4\n", "GravitarDeterministic-v0\n", "GravitarDeterministic-v4\n", "GravitarNoFrameskip-v0\n", "GravitarNoFrameskip-v4\n", "GuessingGame-v0\n", "HalfCheetah-v1\n", "Hero-ram-v0\n", "Hero-ram-v4\n", "Hero-ramDeterministic-v0\n", "Hero-ramDeterministic-v4\n", "Hero-ramNoFrameskip-v0\n", "Hero-ramNoFrameskip-v4\n", "Hero-v0\n", "Hero-v4\n", "HeroDeterministic-v0\n", "HeroDeterministic-v4\n", "HeroNoFrameskip-v0\n", "HeroNoFrameskip-v4\n", "Hex9x9-v0\n", "Hopper-v1\n", "HotterColder-v0\n", "Humanoid-v1\n", "HumanoidStandup-v1\n", "IceHockey-ram-v0\n", "IceHockey-ram-v4\n", "IceHockey-ramDeterministic-v0\n", "IceHockey-ramDeterministic-v4\n", "IceHockey-ramNoFrameskip-v0\n", "IceHockey-ramNoFrameskip-v4\n", "IceHockey-v0\n", "IceHockey-v4\n", "IceHockeyDeterministic-v0\n", "IceHockeyDeterministic-v4\n", "IceHockeyNoFrameskip-v0\n", "IceHockeyNoFrameskip-v4\n", "InvertedDoublePendulum-v1\n", "InvertedPendulum-v1\n", "Jamesbond-ram-v0\n", "Jamesbond-ram-v4\n", "Jamesbond-ramDeterministic-v0\n", "Jamesbond-ramDeterministic-v4\n", "Jamesbond-ramNoFrameskip-v0\n", "Jamesbond-ramNoFrameskip-v4\n", "Jamesbond-v0\n", "Jamesbond-v4\n", "JamesbondDeterministic-v0\n", "JamesbondDeterministic-v4\n", "JamesbondNoFrameskip-v0\n", "JamesbondNoFrameskip-v4\n", "JourneyEscape-ram-v0\n", "JourneyEscape-ram-v4\n", "JourneyEscape-ramDeterministic-v0\n", "JourneyEscape-ramDeterministic-v4\n", "JourneyEscape-ramNoFrameskip-v0\n", "JourneyEscape-ramNoFrameskip-v4\n", "JourneyEscape-v0\n", "JourneyEscape-v4\n", "JourneyEscapeDeterministic-v0\n", "JourneyEscapeDeterministic-v4\n", "JourneyEscapeNoFrameskip-v0\n", "JourneyEscapeNoFrameskip-v4\n", "Kangaroo-ram-v0\n", "Kangaroo-ram-v4\n", "Kangaroo-ramDeterministic-v0\n", "Kangaroo-ramDeterministic-v4\n", "Kangaroo-ramNoFrameskip-v0\n", "Kangaroo-ramNoFrameskip-v4\n", "Kangaroo-v0\n", "Kangaroo-v4\n", "KangarooDeterministic-v0\n", "KangarooDeterministic-v4\n", "KangarooNoFrameskip-v0\n", "KangarooNoFrameskip-v4\n", "KellyCoinflip-v0\n", "KellyCoinflipGeneralized-v0\n", "Krull-ram-v0\n", "Krull-ram-v4\n", "Krull-ramDeterministic-v0\n", "Krull-ramDeterministic-v4\n", "Krull-ramNoFrameskip-v0\n", "Krull-ramNoFrameskip-v4\n", "Krull-v0\n", "Krull-v4\n", "KrullDeterministic-v0\n", "KrullDeterministic-v4\n", "KrullNoFrameskip-v0\n", "KrullNoFrameskip-v4\n", "KungFuMaster-ram-v0\n", "KungFuMaster-ram-v4\n", "KungFuMaster-ramDeterministic-v0\n", "KungFuMaster-ramDeterministic-v4\n", "KungFuMaster-ramNoFrameskip-v0\n", "KungFuMaster-ramNoFrameskip-v4\n", "KungFuMaster-v0\n", "KungFuMaster-v4\n", "KungFuMasterDeterministic-v0\n", "KungFuMasterDeterministic-v4\n", "KungFuMasterNoFrameskip-v0\n", "KungFuMasterNoFrameskip-v4\n", "LunarLander-v2\n", "LunarLanderContinuous-v2\n", "MontezumaRevenge-ram-v0\n", "MontezumaRevenge-ram-v4\n", "MontezumaRevenge-ramDeterministic-v0\n", "MontezumaRevenge-ramDeterministic-v4\n", "MontezumaRevenge-ramNoFrameskip-v0\n", "MontezumaRevenge-ramNoFrameskip-v4\n", "MontezumaRevenge-v0\n", "MontezumaRevenge-v4\n", "MontezumaRevengeDeterministic-v0\n", "MontezumaRevengeDeterministic-v4\n", "MontezumaRevengeNoFrameskip-v0\n", "MontezumaRevengeNoFrameskip-v4\n", "MountainCar-v0\n", "MountainCarContinuous-v0\n", "MsPacman-ram-v0\n", "MsPacman-ram-v4\n", "MsPacman-ramDeterministic-v0\n", "MsPacman-ramDeterministic-v4\n", "MsPacman-ramNoFrameskip-v0\n", "MsPacman-ramNoFrameskip-v4\n", "MsPacman-v0\n", "MsPacman-v4\n", "MsPacmanDeterministic-v0\n", "MsPacmanDeterministic-v4\n", "MsPacmanNoFrameskip-v0\n", "MsPacmanNoFrameskip-v4\n", "NChain-v0\n", "NameThisGame-ram-v0\n", "NameThisGame-ram-v4\n", "NameThisGame-ramDeterministic-v0\n", "NameThisGame-ramDeterministic-v4\n", "NameThisGame-ramNoFrameskip-v0\n", "NameThisGame-ramNoFrameskip-v4\n", "NameThisGame-v0\n", "NameThisGame-v4\n", "NameThisGameDeterministic-v0\n", "NameThisGameDeterministic-v4\n", "NameThisGameNoFrameskip-v0\n", "NameThisGameNoFrameskip-v4\n", "OffSwitchCartpole-v0\n", "OffSwitchCartpoleProb-v0\n", "OneRoundDeterministicReward-v0\n", "OneRoundNondeterministicReward-v0\n", "Pendulum-v0\n", "Phoenix-ram-v0\n", "Phoenix-ram-v4\n", "Phoenix-ramDeterministic-v0\n", "Phoenix-ramDeterministic-v4\n", "Phoenix-ramNoFrameskip-v0\n", "Phoenix-ramNoFrameskip-v4\n", "Phoenix-v0\n", "Phoenix-v4\n", "PhoenixDeterministic-v0\n", "PhoenixDeterministic-v4\n", "PhoenixNoFrameskip-v0\n", "PhoenixNoFrameskip-v4\n", "Pitfall-ram-v0\n", "Pitfall-ram-v4\n", "Pitfall-ramDeterministic-v0\n", "Pitfall-ramDeterministic-v4\n", "Pitfall-ramNoFrameskip-v0\n", "Pitfall-ramNoFrameskip-v4\n", "Pitfall-v0\n", "Pitfall-v4\n", "PitfallDeterministic-v0\n", "PitfallDeterministic-v4\n", "PitfallNoFrameskip-v0\n", "PitfallNoFrameskip-v4\n", "Pong-ram-v0\n", "Pong-ram-v4\n", "Pong-ramDeterministic-v0\n", "Pong-ramDeterministic-v4\n", "Pong-ramNoFrameskip-v0\n", "Pong-ramNoFrameskip-v4\n", "Pong-v0\n", "Pong-v4\n", "PongDeterministic-v0\n", "PongDeterministic-v4\n", "PongNoFrameskip-v0\n", "PongNoFrameskip-v4\n", "Pooyan-ram-v0\n", "Pooyan-ram-v4\n", "Pooyan-ramDeterministic-v0\n", "Pooyan-ramDeterministic-v4\n", "Pooyan-ramNoFrameskip-v0\n", "Pooyan-ramNoFrameskip-v4\n", "Pooyan-v0\n", "Pooyan-v4\n", "PooyanDeterministic-v0\n", "PooyanDeterministic-v4\n", "PooyanNoFrameskip-v0\n", "PooyanNoFrameskip-v4\n", "PredictActionsCartpole-v0\n", "PredictObsCartpole-v0\n", "PrivateEye-ram-v0\n", "PrivateEye-ram-v4\n", "PrivateEye-ramDeterministic-v0\n", "PrivateEye-ramDeterministic-v4\n", "PrivateEye-ramNoFrameskip-v0\n", "PrivateEye-ramNoFrameskip-v4\n", "PrivateEye-v0\n", "PrivateEye-v4\n", "PrivateEyeDeterministic-v0\n", "PrivateEyeDeterministic-v4\n", "PrivateEyeNoFrameskip-v0\n", "PrivateEyeNoFrameskip-v4\n", "Pusher-v0\n", "Qbert-ram-v0\n", "Qbert-ram-v4\n", "Qbert-ramDeterministic-v0\n", "Qbert-ramDeterministic-v4\n", "Qbert-ramNoFrameskip-v0\n", "Qbert-ramNoFrameskip-v4\n", "Qbert-v0\n", "Qbert-v4\n", "QbertDeterministic-v0\n", "QbertDeterministic-v4\n", "QbertNoFrameskip-v0\n", "QbertNoFrameskip-v4\n", "Reacher-v1\n", "RepeatCopy-v0\n", "Reverse-v0\n", "ReversedAddition-v0\n", "ReversedAddition3-v0\n", "Riverraid-ram-v0\n", "Riverraid-ram-v4\n", "Riverraid-ramDeterministic-v0\n", "Riverraid-ramDeterministic-v4\n", "Riverraid-ramNoFrameskip-v0\n", "Riverraid-ramNoFrameskip-v4\n", "Riverraid-v0\n", "Riverraid-v4\n", "RiverraidDeterministic-v0\n", "RiverraidDeterministic-v4\n", "RiverraidNoFrameskip-v0\n", "RiverraidNoFrameskip-v4\n", "RoadRunner-ram-v0\n", "RoadRunner-ram-v4\n", "RoadRunner-ramDeterministic-v0\n", "RoadRunner-ramDeterministic-v4\n", "RoadRunner-ramNoFrameskip-v0\n", "RoadRunner-ramNoFrameskip-v4\n", "RoadRunner-v0\n", "RoadRunner-v4\n", "RoadRunnerDeterministic-v0\n", "RoadRunnerDeterministic-v4\n", "RoadRunnerNoFrameskip-v0\n", "RoadRunnerNoFrameskip-v4\n", "Robotank-ram-v0\n", "Robotank-ram-v4\n", "Robotank-ramDeterministic-v0\n", "Robotank-ramDeterministic-v4\n", "Robotank-ramNoFrameskip-v0\n", "Robotank-ramNoFrameskip-v4\n", "Robotank-v0\n", "Robotank-v4\n", "RobotankDeterministic-v0\n", "RobotankDeterministic-v4\n", "RobotankNoFrameskip-v0\n", "RobotankNoFrameskip-v4\n", "Roulette-v0\n", "Seaquest-ram-v0\n", "Seaquest-ram-v4\n", "Seaquest-ramDeterministic-v0\n", "Seaquest-ramDeterministic-v4\n", "Seaquest-ramNoFrameskip-v0\n", "Seaquest-ramNoFrameskip-v4\n", "Seaquest-v0\n", "Seaquest-v4\n", "SeaquestDeterministic-v0\n", "SeaquestDeterministic-v4\n", "SeaquestNoFrameskip-v0\n", "SeaquestNoFrameskip-v4\n", "SemisuperPendulumDecay-v0\n", "SemisuperPendulumNoise-v0\n", "SemisuperPendulumRandom-v0\n", "Skiing-ram-v0\n", "Skiing-ram-v4\n", "Skiing-ramDeterministic-v0\n", "Skiing-ramDeterministic-v4\n", "Skiing-ramNoFrameskip-v0\n", "Skiing-ramNoFrameskip-v4\n", "Skiing-v0\n", "Skiing-v4\n", "SkiingDeterministic-v0\n", "SkiingDeterministic-v4\n", "SkiingNoFrameskip-v0\n", "SkiingNoFrameskip-v4\n", "Solaris-ram-v0\n", "Solaris-ram-v4\n", "Solaris-ramDeterministic-v0\n", "Solaris-ramDeterministic-v4\n", "Solaris-ramNoFrameskip-v0\n", "Solaris-ramNoFrameskip-v4\n", "Solaris-v0\n", "Solaris-v4\n", "SolarisDeterministic-v0\n", "SolarisDeterministic-v4\n", "SolarisNoFrameskip-v0\n", "SolarisNoFrameskip-v4\n", "SpaceInvaders-ram-v0\n", "SpaceInvaders-ram-v4\n", "SpaceInvaders-ramDeterministic-v0\n", "SpaceInvaders-ramDeterministic-v4\n", "SpaceInvaders-ramNoFrameskip-v0\n", "SpaceInvaders-ramNoFrameskip-v4\n", "SpaceInvaders-v0\n", "SpaceInvaders-v4\n", "SpaceInvadersDeterministic-v0\n", "SpaceInvadersDeterministic-v4\n", "SpaceInvadersNoFrameskip-v0\n", "SpaceInvadersNoFrameskip-v4\n", "StarGunner-ram-v0\n", "StarGunner-ram-v4\n", "StarGunner-ramDeterministic-v0\n", "StarGunner-ramDeterministic-v4\n", "StarGunner-ramNoFrameskip-v0\n", "StarGunner-ramNoFrameskip-v4\n", "StarGunner-v0\n", "StarGunner-v4\n", "StarGunnerDeterministic-v0\n", "StarGunnerDeterministic-v4\n", "StarGunnerNoFrameskip-v0\n", "StarGunnerNoFrameskip-v4\n", "Striker-v0\n", "Swimmer-v1\n", "Taxi-v2\n", "Tennis-ram-v0\n", "Tennis-ram-v4\n", "Tennis-ramDeterministic-v0\n", "Tennis-ramDeterministic-v4\n", "Tennis-ramNoFrameskip-v0\n", "Tennis-ramNoFrameskip-v4\n", "Tennis-v0\n", "Tennis-v4\n", "TennisDeterministic-v0\n", "TennisDeterministic-v4\n", "TennisNoFrameskip-v0\n", "TennisNoFrameskip-v4\n", "Thrower-v0\n", "TimePilot-ram-v0\n", "TimePilot-ram-v4\n", "TimePilot-ramDeterministic-v0\n", "TimePilot-ramDeterministic-v4\n", "TimePilot-ramNoFrameskip-v0\n", "TimePilot-ramNoFrameskip-v4\n", "TimePilot-v0\n", "TimePilot-v4\n", "TimePilotDeterministic-v0\n", "TimePilotDeterministic-v4\n", "TimePilotNoFrameskip-v0\n", "TimePilotNoFrameskip-v4\n", "Tutankham-ram-v0\n", "Tutankham-ram-v4\n", "Tutankham-ramDeterministic-v0\n", "Tutankham-ramDeterministic-v4\n", "Tutankham-ramNoFrameskip-v0\n", "Tutankham-ramNoFrameskip-v4\n", "Tutankham-v0\n", "Tutankham-v4\n", "TutankhamDeterministic-v0\n", "TutankhamDeterministic-v4\n", "TutankhamNoFrameskip-v0\n", "TutankhamNoFrameskip-v4\n", "TwoRoundDeterministicReward-v0\n", "TwoRoundNondeterministicReward-v0\n", "UpNDown-ram-v0\n", "UpNDown-ram-v4\n", "UpNDown-ramDeterministic-v0\n", "UpNDown-ramDeterministic-v4\n", "UpNDown-ramNoFrameskip-v0\n", "UpNDown-ramNoFrameskip-v4\n", "UpNDown-v0\n", "UpNDown-v4\n", "UpNDownDeterministic-v0\n", "UpNDownDeterministic-v4\n", "UpNDownNoFrameskip-v0\n", "UpNDownNoFrameskip-v4\n", "Venture-ram-v0\n", "Venture-ram-v4\n", "Venture-ramDeterministic-v0\n", "Venture-ramDeterministic-v4\n", "Venture-ramNoFrameskip-v0\n", "Venture-ramNoFrameskip-v4\n", "Venture-v0\n", "Venture-v4\n", "VentureDeterministic-v0\n", "VentureDeterministic-v4\n", "VentureNoFrameskip-v0\n", "VentureNoFrameskip-v4\n", "VideoPinball-ram-v0\n", "VideoPinball-ram-v4\n", "VideoPinball-ramDeterministic-v0\n", "VideoPinball-ramDeterministic-v4\n", "VideoPinball-ramNoFrameskip-v0\n", "VideoPinball-ramNoFrameskip-v4\n", "VideoPinball-v0\n", "VideoPinball-v4\n", "VideoPinballDeterministic-v0\n", "VideoPinballDeterministic-v4\n", "VideoPinballNoFrameskip-v0\n", "VideoPinballNoFrameskip-v4\n", "Walker2d-v1\n", "WizardOfWor-ram-v0\n", "WizardOfWor-ram-v4\n", "WizardOfWor-ramDeterministic-v0\n", "WizardOfWor-ramDeterministic-v4\n", "WizardOfWor-ramNoFrameskip-v0\n", "WizardOfWor-ramNoFrameskip-v4\n", "WizardOfWor-v0\n", "WizardOfWor-v4\n", "WizardOfWorDeterministic-v0\n", "WizardOfWorDeterministic-v4\n", "WizardOfWorNoFrameskip-v0\n", "WizardOfWorNoFrameskip-v4\n", "YarsRevenge-ram-v0\n", "YarsRevenge-ram-v4\n", "YarsRevenge-ramDeterministic-v0\n", "YarsRevenge-ramDeterministic-v4\n", "YarsRevenge-ramNoFrameskip-v0\n", "YarsRevenge-ramNoFrameskip-v4\n", "YarsRevenge-v0\n", "YarsRevenge-v4\n", "YarsRevengeDeterministic-v0\n", "YarsRevengeDeterministic-v4\n", "YarsRevengeNoFrameskip-v0\n", "YarsRevengeNoFrameskip-v4\n", "Zaxxon-ram-v0\n", "Zaxxon-ram-v4\n", "Zaxxon-ramDeterministic-v0\n", "Zaxxon-ramDeterministic-v4\n", "Zaxxon-ramNoFrameskip-v0\n", "Zaxxon-ramNoFrameskip-v4\n", "Zaxxon-v0\n", "Zaxxon-v4\n", "ZaxxonDeterministic-v0\n", "ZaxxonDeterministic-v4\n", "ZaxxonNoFrameskip-v0\n", "ZaxxonNoFrameskip-v4\n" ] } ], "source": [ "# List all existing environments\n", "from gym import envs\n", "env_ids = [spec.id for spec in envs.registry.all()]\n", "print(\"Total number of environments installed in your Gym installation are {}\".format(len(env_ids)))\n", "\n", "# List all environments\n", "for env_id in sorted(env_ids):\n", " print(env_id)" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "[2018-06-28 00:44:11,462] Making new env: Pong-v0\n" ] } ], "source": [ "import gym\n", "import matplotlib.pyplot as plt\n", "% matplotlib inline\n", "\n", "env = gym.make('Pong-v0')\n", "obs = env.reset()\n", "env.render()" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(210, 160, 3)\n" ] } ], "source": [ "print(obs.shape)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "% matplotlib inline\n", "plt.imshow(env.render(mode='rgb_array'))\n", "env.close()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "frames = []\n", "for _ in range(300):\n", " frames.append(env.render(mode='rgb_array'))\n", " # take a random action\n", " obs, reward, done, _ = env.step(env.action_space.sample())\n", " if done:\n", " break\n", " " ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "
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\n", "\n", "\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Display the game as an image\n", "import matplotlib.animation as animation\n", "from JSAnimation.IPython_display import display_animation\n", "from IPython.display import display\n", "patch = plt.imshow(frames[0])\n", "plt.axis('off')\n", "def animate(i):\n", " patch.set_data(frames[i])\n", "anim = animation.FuncAnimation(plt.gcf(), animate, frames = len(frames), interval = 100)\n", "\n", "display(display_animation(anim, default_mode='loop'))\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# saving movie\n", "from gym import wrappers\n", "env = gym.make('Pong-v0')\n", "env = wrappers.Monitor(env, 'save-mov',force=True)\n", "env.reset()\n", "for _ in range(300):\n", " env.render()\n", " # take a random action\n", " obs, reward, done, _ = env.step(env.action_space.sample())\n", " if done:\n", " break" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "[2018-06-27 23:55:20,078] Making new env: Go9x9-v0\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "To play: black\n", "Move: 0 Komi: 0.0 Handicap: 0 Captures B: 0 W: 0\n", " A B C D E F G H J \n", " +-------------------+\n", " 9 | . . . . . . . . . |\n", " 8 | . . . . . . . . . |\n", " 7 | . . . . . . . . . |\n", " 6 | . . . . . . . . . |\n", " 5 | . . . . . . . . . |\n", " 4 | . . . . . . . . . |\n", " 3 | . . . . . . . . . |\n", " 2 | . . . . . . . . . |\n", " 1 | . . . . . . . . . |\n", " +-------------------+\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import gym\n", "\n", "env = gym.make('Go9x9-v0')\n", "obs = env.reset()\n", "env.render()" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "[2018-06-28 01:29:29,201] Making new env: Go9x9-v0\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "To play: black\n", "Move: 0 Komi: 0.0 Handicap: 0 Captures B: 0 W: 0\n", " A B C D E F G H J \n", " +-------------------+\n", " 9 | . . . . . . . . . |\n", " 8 | . . . . . . . . . |\n", " 7 | . . . . . . . . . |\n", " 6 | . . . . . . . . . |\n", " 5 | . . . . . . . . . |\n", " 4 | . . . . . . . . . |\n", " 3 | . . . . . . . . . |\n", " 2 | . . . . . . . . . |\n", " 1 | . . . . . . . . . |\n", " +-------------------+\n", "To play: black\n", "Move: 2 Komi: 0.0 Handicap: 0 Captures B: 0 W: 0\n", " A B C D E F G H J \n", " +-------------------+\n", " 9 | . . . . . . . . . |\n", " 8 | . . . . . . . . . |\n", " 7 | . . . . O). . . . |\n", " 6 | . . . . . . . . . |\n", " 5 | . . . . . . . . . |\n", " 4 | . . . . . . . . . |\n", " 3 | . . . . . . X . . |\n", " 2 | . . . . . . . . . |\n", " 1 | . . . . . . . . . |\n", " +-------------------+\n", "To play: black\n", "Move: 4 Komi: 0.0 Handicap: 0 Captures B: 0 W: 0\n", " A B C D E F G H J \n", " +-------------------+\n", " 9 | . . . . . . . . . |\n", " 8 | . . . . . . . . . |\n", " 7 | . . . . O . . . . |\n", " 6 | . . . . . . . . . |\n", " 5 | . . . . . X . . . |\n", " 4 | . . . . O). . . . |\n", " 3 | . . . . . . X . . |\n", " 2 | . . . . . . . . . |\n", " 1 | . . . . . . . . . |\n", " +-------------------+\n", "To play: black\n", "Move: 6 Komi: 0.0 Handicap: 0 Captures B: 0 W: 0\n", " A B C D E F G H J \n", " +-------------------+\n", " 9 | . . . . . . . . . |\n", " 8 | . . . . . . . . . |\n", " 7 | . . . . O . . . . |\n", " 6 | . . . . . . . . . |\n", " 5 | . . . O)X X . . . |\n", " 4 | . . . . O . . . . |\n", " 3 | . . . . . . X . . |\n", " 2 | . . . . . . . . . |\n", " 1 | . . . . . . . . . |\n", " +-------------------+\n" ] } ], "source": [ "from gym import wrappers\n", "env = gym.make('Go9x9-v0')\n", "env.reset()\n", "for _ in range(300):\n", " env.render()\n", " # take a random action\n", " obs, reward, done, _ = env.step(env.action_space.sample())\n", " if done:\n", " break" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [], "source": [ "env.close()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python [conda env:tensorflow]", "language": "python", "name": "conda-env-tensorflow-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.4" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: Chapter06/Policy Gradients.ipynb ================================================ { "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/am/anaconda3/envs/tensorflow/lib/python3.5/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n", " from ._conv import register_converters as _register_converters\n" ] } ], "source": [ "import numpy as np\n", "import gym\n", "import matplotlib.pyplot as plt\n", "import tensorflow as tf\n", "from gym import wrappers\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "class PolicyNetwork(object):\n", " def __init__(self, N_SIZE, h=200, gamma=0.99, eta=1e-3, decay=0.99, save_path = 'models1/pong.ckpt' ):\n", "\n", " self.gamma = gamma\n", " self.save_path = save_path\n", " \n", " \n", " # Placeholders for passing state....\n", " self.tf_x = tf.placeholder(dtype=tf.float32, shape=[None, N_SIZE * N_SIZE], name=\"tf_x\")\n", " self.tf_y = tf.placeholder(dtype=tf.float32, shape=[None, n_actions], name=\"tf_y\")\n", " self.tf_epr = tf.placeholder(dtype=tf.float32, shape=[None, 1], name=\"tf_epr\")\n", "\n", " # Weights\n", " xavier_l1 = tf.truncated_normal_initializer(mean=0, stddev=1. / N_SIZE, dtype=tf.float32)\n", " self.W1 = tf.get_variable(\"W1\", [N_SIZE * N_SIZE, h], initializer=xavier_l1)\n", " xavier_l2 = tf.truncated_normal_initializer(mean=0, stddev=1. / np.sqrt(h), dtype=tf.float32)\n", " self.W2 = tf.get_variable(\"W2\", [h, n_actions], initializer=xavier_l2)\n", "\n", " # Build Computation\n", " # tf reward processing (need tf_discounted_epr for policy gradient wizardry)\n", " tf_discounted_epr = self.tf_discount_rewards(self.tf_epr)\n", " tf_mean, tf_variance = tf.nn.moments(tf_discounted_epr, [0], shift=None, name=\"reward_moments\")\n", " tf_discounted_epr -= tf_mean\n", " tf_discounted_epr /= tf.sqrt(tf_variance + 1e-6)\n", "\n", " # Define Optimizer, compute and apply gradients\n", " self.tf_aprob = self.tf_policy_forward(self.tf_x)\n", " loss = tf.losses.log_loss(labels = self.tf_y,\n", " predictions = self.tf_aprob,\n", " weights = tf_discounted_epr)\n", " optimizer = tf.train.AdamOptimizer()\n", " self.train_op = optimizer.minimize(loss) \n", " \n", " \n", " \n", "\n", "\n", " def set_session(self, session):\n", " self.session = session\n", " self.session.run(tf.global_variables_initializer())\n", " self.saver = tf.train.Saver()\n", "\n", "\n", " def tf_discount_rewards(self, tf_r): # tf_r ~ [game_steps,1]\n", " discount_f = lambda a, v: a * self.gamma + v;\n", " tf_r_reverse = tf.scan(discount_f, tf.reverse(tf_r, [0]))\n", " tf_discounted_r = tf.reverse(tf_r_reverse, [0])\n", " return tf_discounted_r\n", "\n", " def tf_policy_forward(self, x): #x ~ [1,D]\n", " h = tf.matmul(x, self.W1)\n", " h = tf.nn.relu(h)\n", " logp = tf.matmul(h, self.W2)\n", " p = tf.nn.softmax(logp)\n", " return p\n", "\n", " def update(self, feed):\n", " return self.session.run(self.train_op, feed)\n", "\n", " def load(self):\n", " self.saver = tf.train.Saver(tf.global_variables())\n", " load_was_success = True # yes, I'm being optimistic\n", " try:\n", " save_dir = '/'.join(self.save_path.split('/')[:-1])\n", " ckpt = tf.train.get_checkpoint_state(save_dir)\n", " load_path = ckpt.model_checkpoint_path\n", " print(load_path)\n", " self.saver.restore(self.session, load_path)\n", " except:\n", " print(\"no saved model to load. starting new session\")\n", " load_was_success = False\n", " else:\n", " print(\"loaded model: {}\".format(load_path))\n", " saver = tf.train.Saver(tf.global_variables())\n", " episode_number = int(load_path.split('-')[-1])\n", "\n", " def save(self):\n", " self.saver.save(self.session, self.save_path, global_step=n)\n", " print(\"SAVED MODEL #{}\".format(n))\n", "\n", "\n", " def predict_UP(self,x):\n", " feed = {self.tf_x: np.reshape(x, (1, -1))}\n", " aprob = self.session.run(self.tf_aprob, feed);\n", " return aprob\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# downsampling\n", "def preprocess(I):\n", " \"\"\" prepro 210x160x3 uint8 frame into 6400 (80x80) 1D float vector \"\"\"\n", " I = I[35:195] # crop\n", " I = I[::2,::2,0] # downsample by factor of 2\n", " I[I == 144] = 0 # erase background (background type 1)\n", " I[I == 109] = 0 # erase background (background type 2)\n", " I[I != 0] = 1 # everything else (paddles, ball) just set to 1\n", " return I.astype(np.float).ravel()\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "[2018-07-10 13:48:37,032] Making new env: Pong-v0\n", "[2018-07-10 13:48:37,179] Clearing 26 monitor files from previous run (because force=True was provided)\n", "[2018-07-10 13:48:37,187] Starting new video recorder writing to /tmp/pong/openaigym.video.0.24686.video000000.mp4\n" ] } ], "source": [ "# Create Game Environment\n", "env_name = \"Pong-v0\"\n", "env = gym.make(env_name)\n", "env = wrappers.Monitor(env, '/tmp/pong', force=True)\n", "n_actions = env.action_space.n # Number of possible actions\n", "# Initializing Game and State(t-1), action, reward, state(t)\n", "states, rewards, labels = [], [], []\n", "obs = env.reset()\n", "prev_state = None\n", "\n", "\n", "running_reward = None\n", "running_rewards = []\n", "reward_sum = 0\n", "n = 0\n", "done = False\n", "n_size = 80\n", "num_episodes = 2500\n", "\n", "#Create Agent\n", "agent = PolicyNetwork(n_size)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "models1/pong.ckpt-2500\n", "INFO:tensorflow:Restoring parameters from models1/pong.ckpt-2500\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[2018-07-10 13:48:37,938] Restoring parameters from models1/pong.ckpt-2500\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "loaded model: models1/pong.ckpt-2500\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[2018-07-10 13:48:53,406] Starting new video recorder writing to /tmp/pong/openaigym.video.0.24686.video000001.mp4\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "ep 0: reward: 3.0, mean reward: 3.000000\n", "\tep 1: reward: 5.0\n", "\tep 2: reward: 8.0\n", "\tep 3: reward: -5.0\n", "\tep 4: reward: 4.0\n", "\tep 5: reward: -5.0\n", "\tep 6: reward: 3.0\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[2018-07-10 13:50:18,515] Starting new video recorder writing to /tmp/pong/openaigym.video.0.24686.video000008.mp4\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\tep 7: reward: 2.0\n", "\tep 8: reward: -6.0\n", "\tep 9: reward: 1.0\n", "ep 10: reward: -6.0, mean reward: 2.715466\n", "\tep 11: reward: -4.0\n", "\tep 12: reward: -2.0\n", "\tep 13: reward: 5.0\n", "\tep 14: reward: 9.0\n", "\tep 15: reward: -2.0\n", "\tep 16: reward: -6.0\n", "\tep 17: reward: 3.0\n", "\tep 18: reward: 5.0\n", "\tep 19: reward: 3.0\n", "ep 20: reward: -3.0, mean reward: 2.533318\n", "\tep 21: reward: 2.0\n", "\tep 22: reward: 4.0\n", "\tep 23: reward: -1.0\n", "\tep 24: reward: 7.0\n", "\tep 25: reward: -3.0\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[2018-07-10 13:54:11,972] Starting new video recorder writing to /tmp/pong/openaigym.video.0.24686.video000027.mp4\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\tep 26: reward: -3.0\n", "\tep 27: reward: 5.0\n", "\tep 28: reward: 8.0\n", "\tep 29: reward: -5.0\n", "ep 30: reward: 2.0, mean reward: 2.442926\n", "\tep 31: reward: 8.0\n", "\tep 32: reward: 4.0\n", "\tep 33: reward: -3.0\n", "\tep 34: reward: -2.0\n", "\tep 35: reward: 1.0\n", "\tep 36: reward: 1.0\n", "\tep 37: reward: 2.0\n", "\tep 38: reward: 3.0\n", "\tep 39: reward: -4.0\n", "ep 40: reward: -2.0, mean reward: 2.280863\n", "\tep 41: reward: -1.0\n", "\tep 42: reward: 4.0\n", "\tep 43: reward: -2.0\n", "\tep 44: reward: -7.0\n", "\tep 45: reward: 10.0\n", "\tep 46: reward: 1.0\n", "\tep 47: reward: 4.0\n", "\tep 48: reward: -5.0\n", "\tep 49: reward: 7.0\n", "SAVED MODEL #50\n", "ep 50: reward: 2.0, mean reward: 2.189813\n", "\tep 51: reward: -4.0\n", "\tep 52: reward: -6.0\n", "\tep 53: reward: 6.0\n", "\tep 54: reward: 2.0\n", "\tep 55: reward: 3.0\n", "\tep 56: reward: -4.0\n", "\tep 57: reward: 4.0\n", "\tep 58: reward: -2.0\n", "\tep 59: reward: 7.0\n", "ep 60: reward: 4.0, mean reward: 2.081892\n", "\tep 61: reward: 3.0\n", "\tep 62: reward: 8.0\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[2018-07-10 14:01:56,303] Starting new video recorder writing to /tmp/pong/openaigym.video.0.24686.video000064.mp4\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\tep 63: reward: 2.0\n", "\tep 64: reward: -1.0\n", "\tep 65: reward: -2.0\n", "\tep 66: reward: -4.0\n", "\tep 67: reward: 4.0\n", "\tep 68: reward: 1.0\n", "\tep 69: reward: 6.0\n", "ep 70: reward: 3.0, mean reward: 2.073846\n", "\tep 71: reward: 4.0\n", "\tep 72: reward: -3.0\n", "\tep 73: reward: 2.0\n", "\tep 74: reward: -9.0\n", "\tep 75: reward: 6.0\n", "\tep 76: reward: 1.0\n", "\tep 77: reward: 6.0\n", "\tep 78: reward: -4.0\n", "\tep 79: reward: -2.0\n", "ep 80: reward: -6.0, mean reward: 1.824195\n", "\tep 81: reward: -1.0\n", "\tep 82: reward: 7.0\n", "\tep 83: reward: -5.0\n", "\tep 84: reward: 2.0\n", "\tep 85: reward: 3.0\n", "\tep 86: reward: 7.0\n", "\tep 87: reward: -12.0\n", "\tep 88: reward: -4.0\n", "\tep 89: reward: 3.0\n", "ep 90: reward: -6.0, mean reward: 1.587284\n", "\tep 91: reward: -7.0\n", "\tep 92: reward: -8.0\n", "\tep 93: reward: 4.0\n", "\tep 94: reward: 9.0\n", "\tep 95: reward: -1.0\n", "\tep 96: reward: -9.0\n", "\tep 97: reward: -9.0\n", "\tep 98: reward: -12.0\n", "\tep 99: reward: -2.0\n", "SAVED MODEL #100\n", "ep 100: reward: 2.0, mean reward: 1.119059\n", "\tep 101: reward: 4.0\n", "\tep 102: reward: 4.0\n", "\tep 103: reward: 1.0\n", "\tep 104: reward: -1.0\n", "\tep 105: reward: 3.0\n", "\tep 106: reward: -1.0\n", "\tep 107: reward: 1.0\n", "\tep 108: reward: 4.0\n", "\tep 109: reward: 1.0\n", "ep 110: reward: 5.0, mean reward: 1.213144\n", "\tep 111: reward: 5.0\n", "\tep 112: reward: 3.0\n", "\tep 113: reward: -3.0\n", "\tep 114: reward: -1.0\n", "\tep 115: reward: 4.0\n", "\tep 116: reward: 4.0\n", "\tep 117: reward: 1.0\n", "\tep 118: reward: -10.0\n", "\tep 119: reward: -4.0\n", "ep 120: reward: 4.0, mean reward: 1.121684\n", "\tep 121: reward: 5.0\n", "\tep 122: reward: -5.0\n", "\tep 123: reward: 4.0\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[2018-07-10 14:14:25,098] Starting new video recorder writing to /tmp/pong/openaigym.video.0.24686.video000125.mp4\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\tep 124: reward: 5.0\n", "\tep 125: reward: -1.0\n", "\tep 126: reward: 8.0\n", "\tep 127: reward: 3.0\n", "\tep 128: reward: -2.0\n", "\tep 129: reward: 3.0\n", "ep 130: reward: 6.0, mean reward: 1.264871\n", "\tep 131: reward: -9.0\n", "\tep 132: reward: 8.0\n", "\tep 133: reward: 6.0\n", "\tep 134: reward: 6.0\n", "\tep 135: reward: 7.0\n", "\tep 136: reward: 8.0\n", "\tep 137: reward: 3.0\n", "\tep 138: reward: -11.0\n", "\tep 139: reward: 9.0\n", "ep 140: reward: 6.0, mean reward: 1.461757\n", "\tep 141: reward: -1.0\n", "\tep 142: reward: -6.0\n", "\tep 143: reward: -7.0\n", "\tep 144: reward: -2.0\n", "\tep 145: reward: -9.0\n", "\tep 146: reward: 5.0\n", "\tep 147: reward: -8.0\n", "\tep 148: reward: -5.0\n", "\tep 149: reward: 9.0\n", "SAVED MODEL #150\n", "ep 150: reward: 8.0, mean reward: 1.178325\n", "\tep 151: reward: 2.0\n", "\tep 152: reward: -2.0\n", "\tep 153: reward: 7.0\n", "\tep 154: reward: -7.0\n", "\tep 155: reward: -4.0\n", "\tep 156: reward: -8.0\n", "\tep 157: reward: 3.0\n", "\tep 158: reward: 8.0\n", "\tep 159: reward: -5.0\n", "ep 160: reward: -2.0, mean reward: 0.987942\n", "\tep 161: reward: -4.0\n", "\tep 162: reward: -5.0\n", "\tep 163: reward: 6.0\n", "\tep 164: reward: 4.0\n", "\tep 165: reward: -2.0\n", "\tep 166: reward: 1.0\n", "\tep 167: reward: -3.0\n", "\tep 168: reward: 6.0\n", "\tep 169: reward: 6.0\n", "ep 170: reward: -1.0, mean reward: 0.974065\n", "\tep 171: reward: 2.0\n", "\tep 172: reward: -4.0\n", "\tep 173: reward: -3.0\n", "\tep 174: reward: 1.0\n", "\tep 175: reward: -1.0\n", "\tep 176: reward: -8.0\n", "\tep 177: reward: 6.0\n", "\tep 178: reward: -11.0\n", "\tep 179: reward: -2.0\n", "ep 180: reward: -5.0, mean reward: 0.637990\n", "\tep 181: reward: 3.0\n", "\tep 182: reward: 3.0\n", "\tep 183: reward: -4.0\n", "\tep 184: reward: -2.0\n", "\tep 185: reward: 7.0\n", "\tep 186: reward: -5.0\n", "\tep 187: reward: -1.0\n", "\tep 188: reward: 6.0\n", "\tep 189: reward: -8.0\n", "ep 190: reward: -6.0, mean reward: 0.504405\n", "\tep 191: reward: 5.0\n", "\tep 192: reward: 8.0\n", "\tep 193: reward: 3.0\n", "\tep 194: reward: -6.0\n", "\tep 195: reward: 7.0\n", "\tep 196: reward: -4.0\n", "\tep 197: reward: 10.0\n", "\tep 198: reward: 4.0\n", "\tep 199: reward: -6.0\n", "SAVED MODEL #200\n", "ep 200: reward: 3.0, mean reward: 0.682123\n", "\tep 201: reward: -6.0\n", "\tep 202: reward: -5.0\n", "\tep 203: reward: 8.0\n", "\tep 204: reward: 4.0\n", "\tep 205: reward: -2.0\n", "\tep 206: reward: -2.0\n", "\tep 207: reward: -6.0\n", "\tep 208: reward: 3.0\n", "\tep 209: reward: 4.0\n", "ep 210: reward: 2.0, mean reward: 0.620729\n", "\tep 211: reward: -2.0\n", "\tep 212: reward: 2.0\n", "\tep 213: reward: 3.0\n", "\tep 214: reward: -4.0\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[2018-07-10 14:32:54,216] Starting new video recorder writing to /tmp/pong/openaigym.video.0.24686.video000216.mp4\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\tep 215: reward: 5.0\n", "\tep 216: reward: -8.0\n", "\tep 217: reward: -8.0\n", "\tep 218: reward: 1.0\n", "\tep 219: reward: -6.0\n", "ep 220: reward: 8.0, mean reward: 0.475342\n", "\tep 221: reward: 4.0\n", "\tep 222: reward: 7.0\n", "\tep 223: reward: 5.0\n", "\tep 224: reward: -1.0\n", "\tep 225: reward: -6.0\n", "\tep 226: reward: -1.0\n", "\tep 227: reward: -8.0\n", "\tep 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reward: -4.0\n", "\tep 353: reward: -3.0\n", "\tep 354: reward: 1.0\n", "\tep 355: reward: 1.0\n", "\tep 356: reward: 1.0\n", "\tep 357: reward: -3.0\n", "\tep 358: reward: 1.0\n", "\tep 359: reward: -3.0\n", "ep 360: reward: -1.0, mean reward: 0.483370\n", "\tep 361: reward: -3.0\n", "\tep 362: reward: 6.0\n", "\tep 363: reward: 2.0\n", "\tep 364: reward: 2.0\n", "\tep 365: reward: -3.0\n", "\tep 366: reward: -4.0\n", "\tep 367: reward: -7.0\n", "\tep 368: reward: 1.0\n", "\tep 369: reward: 2.0\n", "ep 370: reward: 7.0, mean reward: 0.467308\n", "\tep 371: reward: -4.0\n", "\tep 372: reward: -5.0\n", "\tep 373: reward: -5.0\n", "\tep 374: reward: -2.0\n", "\tep 375: reward: 3.0\n", "\tep 376: reward: -3.0\n", "\tep 377: reward: 13.0\n", "\tep 378: reward: 3.0\n", "\tep 379: reward: -11.0\n", "ep 380: reward: -4.0, mean reward: 0.280868\n", "\tep 381: reward: 9.0\n", "\tep 382: reward: 2.0\n", "\tep 383: reward: -2.0\n", "\tep 384: reward: -6.0\n", "\tep 385: reward: -6.0\n", "\tep 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reward: -1.0\n", "ep 420: reward: 6.0, mean reward: 0.220787\n", "\tep 421: reward: 5.0\n", "\tep 422: reward: -3.0\n", "\tep 423: reward: 2.0\n", "\tep 424: reward: 2.0\n", "\tep 425: reward: -3.0\n", "\tep 426: reward: -6.0\n", "\tep 427: reward: -2.0\n", "\tep 428: reward: -5.0\n", "\tep 429: reward: -4.0\n", "ep 430: reward: -1.0, mean reward: 0.050964\n", "\tep 431: reward: -4.0\n", "\tep 432: reward: 1.0\n", "\tep 433: reward: -5.0\n", "\tep 434: reward: 1.0\n", "\tep 435: reward: 1.0\n", "\tep 436: reward: 3.0\n", "\tep 437: reward: 2.0\n", "\tep 438: reward: 2.0\n", "\tep 439: reward: -5.0\n", "ep 440: reward: -5.0, mean reward: -0.040575\n", "\tep 441: reward: -2.0\n", "\tep 442: reward: 5.0\n", "\tep 443: reward: 3.0\n", "\tep 444: reward: -1.0\n", "\tep 445: reward: -3.0\n", "\tep 446: reward: -5.0\n", "\tep 447: reward: -3.0\n", "\tep 448: reward: -6.0\n", "\tep 449: reward: -6.0\n", "SAVED MODEL #450\n", "ep 450: reward: -7.0, mean reward: -0.284156\n", "\tep 451: reward: -3.0\n", "\tep 452: reward: -5.0\n", "\tep 453: reward: -3.0\n", "\tep 454: reward: -7.0\n", "\tep 455: reward: 2.0\n", "\tep 456: reward: -5.0\n", "\tep 457: reward: 6.0\n", "\tep 458: reward: -1.0\n", "\tep 459: reward: 6.0\n", "ep 460: reward: -8.0, mean reward: -0.425587\n", "\tep 461: reward: -1.0\n", "\tep 462: reward: -5.0\n", "\tep 463: reward: -2.0\n", "\tep 464: reward: -3.0\n", "\tep 465: reward: -4.0\n", "\tep 466: reward: 2.0\n", "\tep 467: reward: 5.0\n", "\tep 468: reward: -5.0\n", "\tep 469: reward: -1.0\n", "ep 470: reward: 6.0, mean reward: -0.456269\n", "\tep 471: reward: 3.0\n", "\tep 472: reward: -1.0\n", "\tep 473: reward: -5.0\n", "\tep 474: reward: -1.0\n", "\tep 475: reward: -6.0\n", "\tep 476: reward: 2.0\n", "\tep 477: reward: -1.0\n", "\tep 478: reward: -3.0\n", "\tep 479: reward: -3.0\n", "ep 480: reward: -2.0, mean reward: -0.577135\n", "\tep 481: reward: 2.0\n", "\tep 482: reward: -8.0\n", "\tep 483: reward: -4.0\n", "\tep 484: reward: -5.0\n", "\tep 485: reward: -1.0\n", "\tep 486: reward: 1.0\n", "\tep 487: reward: -6.0\n", "\tep 488: reward: -7.0\n", "\tep 489: reward: 1.0\n", "ep 490: reward: -2.0, mean reward: -0.798685\n", "\tep 491: reward: 4.0\n", "\tep 492: reward: 5.0\n", "\tep 493: reward: 5.0\n", "\tep 494: reward: 5.0\n", "\tep 495: reward: 2.0\n", "\tep 496: reward: -6.0\n", "\tep 497: reward: -7.0\n", "\tep 498: reward: -3.0\n", "\tep 499: reward: -3.0\n", "SAVED MODEL #500\n", "ep 500: reward: 7.0, mean reward: -0.641601\n", "\tep 501: reward: 1.0\n", "\tep 502: reward: 5.0\n", "\tep 503: reward: -8.0\n", "\tep 504: reward: -5.0\n", "\tep 505: reward: -1.0\n", "\tep 506: reward: 6.0\n", "\tep 507: reward: 5.0\n", "\tep 508: reward: 8.0\n", "\tep 509: reward: 1.0\n", "ep 510: reward: -7.0, mean reward: -0.531671\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[2018-07-10 15:33:34,517] Starting new video recorder writing to /tmp/pong/openaigym.video.0.24686.video000512.mp4\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\tep 511: reward: 5.0\n", "\tep 512: reward: 5.0\n", "\tep 513: reward: -2.0\n", "\tep 514: reward: 10.0\n", "\tep 515: reward: 4.0\n", "\tep 516: reward: 3.0\n", "\tep 517: reward: -7.0\n", "\tep 518: reward: 6.0\n", "\tep 519: reward: -3.0\n", "ep 520: reward: 3.0, mean reward: -0.255471\n", "\tep 521: reward: 9.0\n", "\tep 522: reward: 1.0\n", "\tep 523: reward: -2.0\n", "\tep 524: reward: 6.0\n", "\tep 525: reward: -7.0\n", "\tep 526: reward: 1.0\n", "\tep 527: reward: 2.0\n", "\tep 528: reward: -7.0\n", "\tep 529: reward: -5.0\n", "ep 530: reward: -6.0, mean reward: -0.317416\n", "\tep 531: reward: 2.0\n", "\tep 532: reward: -2.0\n", "\tep 533: reward: -8.0\n", "\tep 534: reward: -5.0\n", "\tep 535: reward: -9.0\n", "\tep 536: reward: 2.0\n", "\tep 537: reward: -2.0\n", "\tep 538: reward: -2.0\n", "\tep 539: reward: 2.0\n", "ep 540: reward: -1.0, mean reward: -0.504475\n", "\tep 541: reward: -4.0\n", "\tep 542: reward: -6.0\n", "\tep 543: 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675: reward: -2.0\n", "\tep 676: reward: 4.0\n", "\tep 677: reward: -1.0\n", "\tep 678: reward: 10.0\n", "\tep 679: reward: 2.0\n", "ep 680: reward: -2.0, mean reward: -0.339806\n", "\tep 681: reward: -3.0\n", "\tep 682: reward: 2.0\n", "\tep 683: reward: -2.0\n", "\tep 684: reward: 7.0\n", "\tep 685: reward: -2.0\n", "\tep 686: reward: -7.0\n", "\tep 687: reward: -1.0\n", "\tep 688: reward: 1.0\n", "\tep 689: reward: 5.0\n", "ep 690: reward: 6.0, mean reward: -0.245666\n", "\tep 691: reward: 6.0\n", "\tep 692: reward: -1.0\n", "\tep 693: reward: 2.0\n", "\tep 694: reward: 12.0\n", "\tep 695: reward: -9.0\n", "\tep 696: reward: -8.0\n", "\tep 697: reward: 5.0\n", "\tep 698: reward: 1.0\n", "\tep 699: reward: 4.0\n", "SAVED MODEL #700\n", "ep 700: reward: -5.0, mean reward: -0.159494\n", "\tep 701: reward: -1.0\n", "\tep 702: reward: 3.0\n", "\tep 703: reward: 1.0\n", "\tep 704: reward: 3.0\n", "\tep 705: reward: 9.0\n", "\tep 706: reward: 5.0\n", "\tep 707: reward: 9.0\n", "\tep 708: reward: 8.0\n", "\tep 709: reward: -9.0\n", "ep 710: reward: 11.0, mean reward: 0.232122\n", "\tep 711: reward: -2.0\n", "\tep 712: reward: 8.0\n", "\tep 713: reward: 4.0\n", "\tep 714: reward: -2.0\n", "\tep 715: reward: 9.0\n", "\tep 716: reward: 7.0\n", "\tep 717: reward: -8.0\n", "\tep 718: reward: 4.0\n", "\tep 719: reward: 3.0\n", "ep 720: reward: 2.0, mean reward: 0.448041\n", "\tep 721: reward: 1.0\n", "\tep 722: reward: 7.0\n", "\tep 723: reward: 7.0\n", "\tep 724: reward: -2.0\n", "\tep 725: reward: -1.0\n", "\tep 726: reward: 5.0\n", "\tep 727: reward: 3.0\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[2018-07-10 16:17:34,934] Starting new video recorder writing to /tmp/pong/openaigym.video.0.24686.video000729.mp4\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\tep 728: reward: -7.0\n", "\tep 729: reward: 10.0\n", "ep 730: reward: 5.0, mean reward: 0.673364\n", "\tep 731: reward: 2.0\n", "\tep 732: reward: 6.0\n", "\tep 733: reward: 3.0\n", "\tep 734: reward: -1.0\n", "\tep 735: reward: -2.0\n", "\tep 736: reward: 3.0\n", "\tep 737: reward: -6.0\n", "\tep 738: reward: 11.0\n", "\tep 739: reward: -3.0\n", "ep 740: reward: 2.0, mean reward: 0.750852\n", "\tep 741: reward: -5.0\n", "\tep 742: reward: -3.0\n", "\tep 743: reward: -2.0\n", "\tep 744: reward: -1.0\n", "\tep 745: reward: 1.0\n", "\tep 746: reward: -11.0\n", "\tep 747: reward: -1.0\n", "\tep 748: reward: -9.0\n", "\tep 749: reward: -10.0\n", "SAVED MODEL #750\n", "ep 750: reward: 4.0, mean reward: 0.324575\n", "\tep 751: reward: 3.0\n", "\tep 752: reward: -5.0\n", "\tep 753: reward: -1.0\n", "\tep 754: reward: 2.0\n", "\tep 755: reward: 3.0\n", "\tep 756: reward: 2.0\n", "\tep 757: reward: 2.0\n", "\tep 758: reward: 2.0\n", "\tep 759: reward: 11.0\n", "ep 760: reward: 4.0, mean reward: 0.519966\n", "\tep 761: reward: 4.0\n", "\tep 762: reward: 1.0\n", "\tep 763: reward: -6.0\n", "\tep 764: reward: 4.0\n", "\tep 765: reward: 6.0\n", "\tep 766: reward: 5.0\n", "\tep 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reward: 3.0, mean reward: 0.949555\n", "\tep 801: reward: -2.0\n", "\tep 802: reward: 6.0\n", "\tep 803: reward: -5.0\n", "\tep 804: reward: -1.0\n", "\tep 805: reward: 7.0\n", "\tep 806: reward: 2.0\n", "\tep 807: reward: 5.0\n", "\tep 808: reward: 13.0\n", "\tep 809: reward: 1.0\n", "ep 810: reward: 2.0, mean reward: 1.131446\n", "\tep 811: reward: -5.0\n", "\tep 812: reward: -5.0\n", "\tep 813: reward: 5.0\n", "\tep 814: reward: 11.0\n", "\tep 815: reward: 3.0\n", "\tep 816: reward: 6.0\n", "\tep 817: reward: 8.0\n", "\tep 818: reward: -5.0\n", "\tep 819: reward: 1.0\n", "ep 820: reward: 7.0, mean reward: 1.276297\n", "\tep 821: reward: -6.0\n", "\tep 822: reward: 2.0\n", "\tep 823: reward: 3.0\n", "\tep 824: reward: 4.0\n", "\tep 825: reward: 7.0\n", "\tep 826: reward: -5.0\n", "\tep 827: reward: -5.0\n", "\tep 828: reward: 2.0\n", "\tep 829: reward: 7.0\n", "ep 830: reward: 5.0, mean reward: 1.292452\n", "\tep 831: reward: 6.0\n", "\tep 832: reward: -4.0\n", "\tep 833: reward: -7.0\n", "\tep 834: reward: -3.0\n", "\tep 835: reward: -7.0\n", "\tep 836: reward: -2.0\n", "\tep 837: reward: -5.0\n", "\tep 838: reward: 4.0\n", "\tep 839: reward: -3.0\n", "ep 840: reward: -1.0, mean reward: 0.958490\n", "\tep 841: reward: -4.0\n", "\tep 842: reward: -2.0\n", "\tep 843: reward: 4.0\n", "\tep 844: reward: 5.0\n", "\tep 845: reward: 5.0\n", "\tep 846: reward: -6.0\n", "\tep 847: reward: -2.0\n", "\tep 848: reward: -3.0\n", "\tep 849: reward: 2.0\n", "SAVED MODEL #850\n", "ep 850: reward: 3.0, mean reward: 0.887107\n", "\tep 851: reward: 4.0\n", "\tep 852: reward: -12.0\n", "\tep 853: reward: -8.0\n", "\tep 854: reward: 4.0\n", "\tep 855: reward: 3.0\n", "\tep 856: reward: 1.0\n", "\tep 857: reward: 2.0\n", "\tep 858: reward: 5.0\n", "\tep 859: reward: -3.0\n", "ep 860: reward: 3.0, mean reward: 0.798036\n", "\tep 861: reward: 2.0\n", "\tep 862: reward: 10.0\n", "\tep 863: reward: 3.0\n", "\tep 864: reward: 2.0\n", "\tep 865: reward: 1.0\n", "\tep 866: reward: -4.0\n", "\tep 867: reward: -6.0\n", "\tep 868: reward: 4.0\n", "\tep 869: reward: -3.0\n", "ep 870: reward: 5.0, mean reward: 0.851438\n", "\tep 871: reward: -6.0\n", "\tep 872: reward: -3.0\n", "\tep 873: reward: 3.0\n", "\tep 874: reward: 5.0\n", "\tep 875: reward: 6.0\n", "\tep 876: reward: 4.0\n", "\tep 877: reward: -5.0\n", "\tep 878: reward: -4.0\n", "\tep 879: reward: -6.0\n", "ep 880: reward: -6.0, mean reward: 0.650932\n", "\tep 881: reward: -4.0\n", "\tep 882: reward: -3.0\n", "\tep 883: reward: 8.0\n", "\tep 884: reward: -8.0\n", "\tep 885: reward: -10.0\n", "\tep 886: reward: 7.0\n", "\tep 887: reward: -10.0\n", "\tep 888: reward: -2.0\n", "\tep 889: reward: -8.0\n", "ep 890: reward: 1.0, mean reward: 0.310026\n", "\tep 891: reward: -4.0\n", "\tep 892: reward: 6.0\n", "\tep 893: reward: -1.0\n", "\tep 894: reward: -5.0\n", "\tep 895: reward: -6.0\n", "\tep 896: reward: 1.0\n", "\tep 897: reward: 6.0\n", "\tep 898: reward: 3.0\n", "\tep 899: reward: -3.0\n", "SAVED MODEL #900\n", "ep 900: reward: -6.0, mean reward: 0.193279\n", "\tep 901: reward: -2.0\n", "\tep 902: reward: -5.0\n", "\tep 903: reward: 7.0\n", "\tep 904: reward: -2.0\n", "\tep 905: reward: -4.0\n", "\tep 906: reward: -1.0\n", "\tep 907: reward: 1.0\n", "\tep 908: reward: -8.0\n", "\tep 909: reward: -6.0\n", "ep 910: reward: -1.0, mean reward: -0.028946\n", "\tep 911: reward: 2.0\n", "\tep 912: reward: 7.0\n", "\tep 913: reward: -3.0\n", "\tep 914: reward: -10.0\n", "\tep 915: reward: -3.0\n", "\tep 916: reward: -1.0\n", "\tep 917: reward: 1.0\n", "\tep 918: reward: 9.0\n", "\tep 919: reward: -6.0\n", "ep 920: reward: -1.0, mean reward: -0.075049\n", "\tep 921: reward: 7.0\n", "\tep 922: reward: -3.0\n", "\tep 923: reward: -7.0\n", "\tep 924: reward: -7.0\n", "\tep 925: reward: -1.0\n", "\tep 926: reward: -7.0\n", "\tep 927: reward: 4.0\n", "\tep 928: reward: 2.0\n", "\tep 929: reward: 6.0\n", "ep 930: reward: -4.0, mean reward: -0.161695\n", "\tep 931: reward: 9.0\n", "\tep 932: reward: 5.0\n", "\tep 933: reward: -8.0\n", "\tep 934: reward: -3.0\n", "\tep 935: reward: 5.0\n", "\tep 936: reward: -8.0\n", "\tep 937: reward: 3.0\n", "\tep 938: reward: -4.0\n", "\tep 939: reward: 2.0\n", "ep 940: reward: -1.0, mean reward: -0.150283\n", "\tep 941: reward: -7.0\n", "\tep 942: reward: 2.0\n", "\tep 943: reward: 3.0\n", "\tep 944: reward: 10.0\n", "\tep 945: reward: -1.0\n", "\tep 946: reward: 1.0\n", "\tep 947: reward: -1.0\n", "\tep 948: reward: 3.0\n", "\tep 949: reward: 3.0\n", "SAVED MODEL #950\n", "ep 950: reward: 5.0, mean reward: 0.040201\n", "\tep 951: reward: -6.0\n", "\tep 952: reward: -4.0\n", "\tep 953: reward: -2.0\n", "\tep 954: reward: -1.0\n", "\tep 955: reward: -2.0\n", "\tep 956: reward: 3.0\n", "\tep 957: reward: -6.0\n", "\tep 958: reward: 6.0\n", "\tep 959: reward: 5.0\n", "ep 960: reward: -4.0, mean reward: -0.063533\n", "\tep 961: reward: -2.0\n", "\tep 962: reward: 4.0\n", "\tep 963: reward: -7.0\n", "\tep 964: reward: -5.0\n", "\tep 965: reward: -4.0\n", "\tep 966: reward: 4.0\n", "\tep 967: reward: -4.0\n", "\tep 968: reward: 4.0\n", "\tep 969: reward: -1.0\n", "ep 970: reward: 2.0, mean reward: -0.140261\n", "\tep 971: reward: 3.0\n", "\tep 972: reward: 4.0\n", "\tep 973: reward: -5.0\n", "\tep 974: reward: -3.0\n", "\tep 975: reward: 3.0\n", "\tep 976: reward: -1.0\n", "\tep 977: reward: 8.0\n", "\tep 978: reward: -9.0\n", "\tep 979: reward: -4.0\n", "ep 980: reward: 2.0, mean reward: -0.148643\n", "\tep 981: reward: 3.0\n", "\tep 982: reward: 2.0\n", "\tep 983: reward: -7.0\n", "\tep 984: reward: -4.0\n", "\tep 985: reward: -1.0\n", "\tep 986: reward: -7.0\n", "\tep 987: reward: -8.0\n", "\tep 988: reward: -4.0\n", "\tep 989: reward: 4.0\n", "ep 990: reward: -5.0, mean reward: -0.395453\n", "\tep 991: reward: 1.0\n", "\tep 992: reward: -3.0\n", "\tep 993: reward: 5.0\n", "\tep 994: reward: -2.0\n", "\tep 995: reward: 4.0\n", "\tep 996: reward: 10.0\n", "\tep 997: reward: 6.0\n", "\tep 998: reward: 6.0\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[2018-07-10 17:10:28,973] Starting new video recorder writing to /tmp/pong/openaigym.video.0.24686.video001000.mp4\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\tep 999: reward: -2.0\n", "SAVED MODEL #1000\n", "ep 1000: reward: -1.0, mean reward: -0.127091\n", "\tep 1001: reward: 2.0\n", "\tep 1002: reward: 10.0\n", "\tep 1003: reward: -4.0\n", "\tep 1004: reward: 6.0\n", "\tep 1005: reward: 4.0\n", "\tep 1006: reward: 1.0\n", "\tep 1007: reward: 3.0\n", "\tep 1008: reward: 6.0\n", "\tep 1009: reward: -3.0\n", "ep 1010: reward: -1.0, mean reward: 0.110672\n", "\tep 1011: reward: 2.0\n", "\tep 1012: reward: -2.0\n", "\tep 1013: reward: 4.0\n", "\tep 1014: reward: -4.0\n", "\tep 1015: reward: -4.0\n", "\tep 1016: reward: -4.0\n", "\tep 1017: reward: 5.0\n", "\tep 1018: reward: 4.0\n", "\tep 1019: reward: 1.0\n", "ep 1020: reward: -1.0, mean reward: 0.110685\n", "\tep 1021: reward: -8.0\n", "\tep 1022: reward: 8.0\n", "\tep 1023: reward: 6.0\n", "\tep 1024: reward: 3.0\n", "\tep 1025: reward: -3.0\n", "\tep 1026: reward: -11.0\n", "\tep 1027: reward: 5.0\n", "\tep 1028: reward: -1.0\n", "\tep 1029: reward: 6.0\n", "ep 1030: reward: -3.0, mean reward: 0.118926\n", "\tep 1031: reward: 4.0\n", "\tep 1032: reward: -2.0\n", "\tep 1033: reward: 1.0\n", "\tep 1034: reward: -5.0\n", "\tep 1035: reward: -9.0\n", "\tep 1036: reward: 6.0\n", "\tep 1037: reward: -5.0\n", "\tep 1038: reward: -2.0\n", "\tep 1039: reward: 6.0\n", "ep 1040: reward: -3.0, mean reward: 0.021217\n", "\tep 1041: reward: -3.0\n", "\tep 1042: reward: -3.0\n", "\tep 1043: reward: -4.0\n", "\tep 1044: reward: 1.0\n", "\tep 1045: reward: -4.0\n", "\tep 1046: reward: 4.0\n", "\tep 1047: reward: -4.0\n", "\tep 1048: reward: -7.0\n", "\tep 1049: reward: -4.0\n", "SAVED MODEL #1050\n", "ep 1050: reward: 6.0, mean reward: -0.150402\n", "\tep 1051: reward: 5.0\n", "\tep 1052: reward: 7.0\n", "\tep 1053: reward: 4.0\n", "\tep 1054: reward: -3.0\n", "\tep 1055: reward: -1.0\n", "\tep 1056: reward: -2.0\n", "\tep 1057: reward: -1.0\n", "\tep 1058: reward: 1.0\n", "\tep 1059: reward: 9.0\n", "ep 1060: reward: 3.0, mean reward: 0.073761\n", "\tep 1061: reward: 7.0\n", "\tep 1062: reward: -4.0\n", "\tep 1063: reward: 5.0\n", "\tep 1064: reward: 4.0\n", "\tep 1065: reward: 1.0\n", "\tep 1066: reward: -3.0\n", "\tep 1067: reward: -3.0\n", "\tep 1068: reward: 3.0\n", "\tep 1069: reward: -1.0\n", "ep 1070: reward: 1.0, mean reward: 0.159093\n", "\tep 1071: reward: 5.0\n", "\tep 1072: reward: 3.0\n", "\tep 1073: reward: 6.0\n", "\tep 1074: reward: -4.0\n", "\tep 1075: reward: -3.0\n", "\tep 1076: reward: -6.0\n", "\tep 1077: reward: 2.0\n", "\tep 1078: reward: 1.0\n", "\tep 1079: reward: -1.0\n", "ep 1080: reward: 6.0, mean reward: 0.228646\n", "\tep 1081: reward: 2.0\n", "\tep 1082: reward: 5.0\n", "\tep 1083: reward: 1.0\n", "\tep 1084: reward: 3.0\n", "\tep 1085: reward: -10.0\n", "\tep 1086: reward: -7.0\n", "\tep 1087: reward: -2.0\n", "\tep 1088: reward: -6.0\n", "\tep 1089: reward: 7.0\n", "ep 1090: reward: 6.0, mean reward: 0.197503\n", "\tep 1091: reward: 1.0\n", "\tep 1092: reward: 7.0\n", "\tep 1093: reward: -2.0\n", "\tep 1094: reward: -1.0\n", "\tep 1095: reward: -1.0\n", "\tep 1096: reward: 2.0\n", "\tep 1097: reward: -3.0\n", "\tep 1098: reward: -1.0\n", "\tep 1099: reward: -1.0\n", "SAVED MODEL #1100\n", "ep 1100: reward: -6.0, mean reward: 0.125181\n", "\tep 1101: reward: -9.0\n", "\tep 1102: reward: 1.0\n", "\tep 1103: reward: 3.0\n", "\tep 1104: reward: 2.0\n", "\tep 1105: reward: 2.0\n", "\tep 1106: reward: 3.0\n", "\tep 1107: reward: 4.0\n", "\tep 1108: reward: -8.0\n", "\tep 1109: reward: 2.0\n", "ep 1110: reward: -5.0, mean reward: 0.065056\n", "\tep 1111: reward: -2.0\n", "\tep 1112: reward: -7.0\n", "\tep 1113: reward: -5.0\n", "\tep 1114: reward: 2.0\n", "\tep 1115: reward: -6.0\n", "\tep 1116: reward: -4.0\n", "\tep 1117: reward: 7.0\n", "\tep 1118: reward: -3.0\n", "\tep 1119: reward: 4.0\n", "ep 1120: reward: 9.0, mean reward: 0.020834\n", "\tep 1121: reward: -3.0\n", "\tep 1122: reward: 3.0\n", "\tep 1123: reward: 9.0\n", "\tep 1124: reward: 1.0\n", "\tep 1125: reward: -4.0\n", "\tep 1126: reward: 2.0\n", "\tep 1127: reward: -4.0\n", "\tep 1128: reward: 2.0\n", "\tep 1129: reward: -8.0\n", "ep 1130: reward: -1.0, mean reward: -0.014818\n", "\tep 1131: reward: 6.0\n", "\tep 1132: reward: -2.0\n", "\tep 1133: reward: -7.0\n", "\tep 1134: reward: 8.0\n", "\tep 1135: reward: 4.0\n", "\tep 1136: reward: 5.0\n", "\tep 1137: reward: 4.0\n", "\tep 1138: reward: 8.0\n", "\tep 1139: reward: 3.0\n", "ep 1140: reward: -5.0, mean reward: 0.216018\n", "\tep 1141: reward: 11.0\n", "\tep 1142: reward: -4.0\n", "\tep 1143: reward: 1.0\n", "\tep 1144: reward: 1.0\n", "\tep 1145: reward: 3.0\n", "\tep 1146: reward: 1.0\n", "\tep 1147: reward: 9.0\n", "\tep 1148: reward: 4.0\n", "\tep 1149: reward: 3.0\n", "SAVED MODEL #1150\n", "ep 1150: reward: 7.0, mean reward: 0.542042\n", "\tep 1151: reward: -6.0\n", "\tep 1152: reward: -4.0\n", "\tep 1153: reward: 2.0\n", "\tep 1154: reward: 2.0\n", "\tep 1155: reward: -2.0\n", "\tep 1156: reward: -5.0\n", "\tep 1157: reward: 3.0\n", "\tep 1158: reward: -1.0\n", "\tep 1159: reward: -7.0\n", "ep 1160: reward: 1.0, mean reward: 0.328921\n", "\tep 1161: reward: 1.0\n", "\tep 1162: reward: 4.0\n", "\tep 1163: reward: -5.0\n", "\tep 1164: reward: 2.0\n", "\tep 1165: reward: -3.0\n", "\tep 1166: reward: -3.0\n", "\tep 1167: reward: -1.0\n", "\tep 1168: reward: -5.0\n", "\tep 1169: reward: 6.0\n", "ep 1170: reward: 4.0, mean reward: 0.299086\n", "\tep 1171: reward: -4.0\n", "\tep 1172: reward: 8.0\n", "\tep 1173: reward: 10.0\n", "\tep 1174: reward: -7.0\n", "\tep 1175: reward: -7.0\n", "\tep 1176: reward: 6.0\n", "\tep 1177: reward: -7.0\n", "\tep 1178: reward: -2.0\n", "\tep 1179: reward: -4.0\n", "ep 1180: reward: -2.0, mean reward: 0.179014\n", "\tep 1181: reward: -4.0\n", "\tep 1182: reward: 8.0\n", "\tep 1183: reward: -1.0\n", "\tep 1184: reward: -2.0\n", "\tep 1185: reward: 1.0\n", "\tep 1186: reward: -6.0\n", "\tep 1187: reward: 6.0\n", "\tep 1188: reward: -6.0\n", "\tep 1189: reward: -7.0\n", "ep 1190: reward: 2.0, mean reward: 0.073011\n", "\tep 1191: reward: 2.0\n", "\tep 1192: reward: -8.0\n", "\tep 1193: reward: -10.0\n", "\tep 1194: reward: -2.0\n", "\tep 1195: reward: -3.0\n", "\tep 1196: reward: -9.0\n", "\tep 1197: reward: -3.0\n", "\tep 1198: reward: 1.0\n", "\tep 1199: reward: -1.0\n", "SAVED MODEL #1200\n", "ep 1200: reward: 11.0, mean reward: -0.135746\n", "\tep 1201: reward: 3.0\n", "\tep 1202: reward: -6.0\n", "\tep 1203: reward: 2.0\n", "\tep 1204: reward: -2.0\n", "\tep 1205: reward: 7.0\n", "\tep 1206: reward: 2.0\n", "\tep 1207: reward: -3.0\n", "\tep 1208: reward: 8.0\n", "\tep 1209: reward: 7.0\n", "ep 1210: reward: 3.0, mean reward: 0.083466\n", "\tep 1211: reward: -2.0\n", "\tep 1212: reward: 2.0\n", "\tep 1213: reward: -1.0\n", "\tep 1214: reward: -4.0\n", "\tep 1215: reward: 4.0\n", "\tep 1216: reward: -6.0\n", "\tep 1217: reward: 4.0\n", "\tep 1218: reward: 9.0\n", "\tep 1219: reward: 4.0\n", "ep 1220: reward: 7.0, mean reward: 0.245715\n", "\tep 1221: reward: -4.0\n", "\tep 1222: reward: -7.0\n", "\tep 1223: reward: 2.0\n", "\tep 1224: reward: -3.0\n", "\tep 1225: reward: 7.0\n", "\tep 1226: reward: -5.0\n", "\tep 1227: reward: 5.0\n", "\tep 1228: reward: -2.0\n", "\tep 1229: reward: 5.0\n", "ep 1230: reward: -3.0, mean reward: 0.178437\n", "\tep 1231: reward: 12.0\n", "\tep 1232: reward: -4.0\n", "\tep 1233: reward: 2.0\n", "\tep 1234: reward: -7.0\n", "\tep 1235: reward: 4.0\n", "\tep 1236: reward: 3.0\n", "\tep 1237: reward: -3.0\n", "\tep 1238: reward: -1.0\n", "\tep 1239: reward: -7.0\n", "ep 1240: reward: 1.0, mean reward: 0.155472\n", "\tep 1241: reward: 1.0\n", "\tep 1242: reward: 2.0\n", "\tep 1243: reward: 3.0\n", "\tep 1244: reward: 6.0\n", "\tep 1245: reward: -5.0\n", "\tep 1246: reward: 3.0\n", "\tep 1247: reward: -5.0\n", "\tep 1248: reward: -6.0\n", "\tep 1249: reward: -7.0\n", "SAVED MODEL #1250\n", "ep 1250: reward: 5.0, mean reward: 0.107295\n", "\tep 1251: reward: -4.0\n", "\tep 1252: reward: 6.0\n", "\tep 1253: reward: -11.0\n", "\tep 1254: reward: 12.0\n", "\tep 1255: reward: 1.0\n", "\tep 1256: reward: -3.0\n", "\tep 1257: reward: 7.0\n", "\tep 1258: reward: 9.0\n", "\tep 1259: reward: -7.0\n", "ep 1260: reward: -2.0, mean reward: 0.173832\n", "\tep 1261: reward: 6.0\n", "\tep 1262: reward: -1.0\n", "\tep 1263: reward: 6.0\n", "\tep 1264: reward: -6.0\n", "\tep 1265: reward: 2.0\n", "\tep 1266: reward: -6.0\n", "\tep 1267: reward: 1.0\n", "\tep 1268: reward: 1.0\n", "\tep 1269: reward: -3.0\n", "ep 1270: reward: 4.0, mean reward: 0.193417\n", "\tep 1271: reward: 7.0\n", "\tep 1272: reward: -7.0\n", "\tep 1273: reward: 7.0\n", "\tep 1274: reward: 1.0\n", "\tep 1275: reward: -2.0\n", "\tep 1276: reward: -9.0\n", "\tep 1277: reward: 6.0\n", "\tep 1278: reward: -4.0\n", "\tep 1279: reward: -5.0\n", "ep 1280: reward: -1.0, mean reward: 0.102977\n", "\tep 1281: reward: -5.0\n", "\tep 1282: reward: 3.0\n", "\tep 1283: reward: -1.0\n", "\tep 1284: reward: -4.0\n", "\tep 1285: reward: -6.0\n", "\tep 1286: reward: -7.0\n", "\tep 1287: reward: -2.0\n", "\tep 1288: reward: 2.0\n", "\tep 1289: reward: -3.0\n", "ep 1290: reward: -2.0, mean reward: -0.145648\n", "\tep 1291: reward: -11.0\n", "\tep 1292: reward: -4.0\n", "\tep 1293: reward: 6.0\n", "\tep 1294: reward: 1.0\n", "\tep 1295: reward: -1.0\n", "\tep 1296: reward: 6.0\n", "\tep 1297: reward: 3.0\n", "\tep 1298: reward: 1.0\n", "\tep 1299: reward: -5.0\n", "SAVED MODEL #1300\n", "ep 1300: reward: -1.0, mean reward: -0.176244\n", "\tep 1301: reward: -1.0\n", "\tep 1302: reward: -9.0\n", "\tep 1303: reward: 3.0\n", "\tep 1304: reward: -4.0\n", "\tep 1305: reward: 1.0\n", "\tep 1306: reward: 1.0\n", "\tep 1307: reward: 1.0\n", "\tep 1308: reward: 3.0\n", "\tep 1309: reward: 3.0\n", "ep 1310: reward: 3.0, mean reward: -0.143349\n", "\tep 1311: reward: -4.0\n", "\tep 1312: reward: -10.0\n", "\tep 1313: reward: 2.0\n", "\tep 1314: reward: -4.0\n", "\tep 1315: reward: -3.0\n", "\tep 1316: reward: -7.0\n", "\tep 1317: reward: 1.0\n", "\tep 1318: reward: -11.0\n", "\tep 1319: reward: -4.0\n", "ep 1320: reward: -9.0, mean reward: -0.600955\n", "\tep 1321: reward: 1.0\n", "\tep 1322: reward: 6.0\n", "\tep 1323: reward: -3.0\n", "\tep 1324: reward: -4.0\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\tep 1325: reward: 8.0\n", "\tep 1326: reward: 2.0\n", "\tep 1327: reward: 1.0\n", "\tep 1328: reward: -8.0\n", "\tep 1329: reward: 5.0\n", "ep 1330: reward: 3.0, mean reward: -0.438528\n", "\tep 1331: reward: -7.0\n", "\tep 1332: reward: -1.0\n", "\tep 1333: reward: 7.0\n", "\tep 1334: reward: 2.0\n", "\tep 1335: reward: -3.0\n", "\tep 1336: reward: 2.0\n", "\tep 1337: reward: -7.0\n", "\tep 1338: reward: -1.0\n", "\tep 1339: reward: -3.0\n", "ep 1340: reward: -1.0, mean reward: -0.512436\n", "\tep 1341: reward: 2.0\n", "\tep 1342: reward: 2.0\n", "\tep 1343: reward: -1.0\n", "\tep 1344: reward: -6.0\n", "\tep 1345: reward: 4.0\n", "\tep 1346: reward: -3.0\n", "\tep 1347: reward: 2.0\n", "\tep 1348: reward: -2.0\n", "\tep 1349: reward: 4.0\n", "SAVED MODEL #1350\n", "ep 1350: reward: 9.0, mean reward: -0.353897\n", "\tep 1351: reward: 1.0\n", "\tep 1352: reward: 5.0\n", "\tep 1353: reward: -8.0\n", "\tep 1354: reward: -4.0\n", "\tep 1355: reward: 1.0\n", "\tep 1356: reward: -1.0\n", "\tep 1357: reward: -5.0\n", "\tep 1358: reward: 6.0\n", "\tep 1359: reward: -2.0\n", "ep 1360: reward: 2.0, mean reward: -0.366615\n", "\tep 1361: reward: 4.0\n", "\tep 1362: reward: -2.0\n", "\tep 1363: reward: -4.0\n", "\tep 1364: reward: -1.0\n", "\tep 1365: reward: -8.0\n", "\tep 1366: reward: -2.0\n", "\tep 1367: reward: 6.0\n", "\tep 1368: reward: 8.0\n", "\tep 1369: reward: 3.0\n", "ep 1370: reward: -7.0, mean reward: -0.359137\n", "\tep 1371: reward: 4.0\n", "\tep 1372: reward: -3.0\n", "\tep 1373: reward: 2.0\n", "\tep 1374: reward: -4.0\n", "\tep 1375: reward: -10.0\n", "\tep 1376: reward: 6.0\n", "\tep 1377: reward: 5.0\n", "\tep 1378: reward: -1.0\n", "\tep 1379: reward: 3.0\n", "ep 1380: reward: 2.0, mean reward: -0.284006\n", "\tep 1381: reward: -4.0\n", "\tep 1382: reward: -3.0\n", "\tep 1383: reward: -1.0\n", "\tep 1384: reward: 4.0\n", "\tep 1385: reward: -6.0\n", "\tep 1386: reward: 2.0\n", "\tep 1387: reward: -2.0\n", "\tep 1388: reward: 2.0\n", "\tep 1389: reward: 4.0\n", "ep 1390: reward: 5.0, mean reward: -0.240785\n", "\tep 1391: reward: -4.0\n", "\tep 1392: reward: 7.0\n", "\tep 1393: reward: 7.0\n", "\tep 1394: reward: 6.0\n", "\tep 1395: reward: 14.0\n", "\tep 1396: reward: 9.0\n", "\tep 1397: reward: 6.0\n", "\tep 1398: reward: -2.0\n", "\tep 1399: reward: -2.0\n", "SAVED MODEL #1400\n", "ep 1400: reward: -3.0, mean reward: 0.140431\n", "\tep 1401: reward: 7.0\n", "\tep 1402: reward: -5.0\n", "\tep 1403: reward: 10.0\n", "\tep 1404: reward: -5.0\n", "\tep 1405: reward: -2.0\n", "\tep 1406: reward: 8.0\n", "\tep 1407: reward: 4.0\n", "\tep 1408: reward: -8.0\n", "\tep 1409: reward: -2.0\n", "ep 1410: reward: 1.0, mean reward: 0.199377\n", "\tep 1411: reward: 14.0\n", "\tep 1412: reward: -3.0\n", "\tep 1413: reward: 4.0\n", "\tep 1414: reward: 4.0\n", "\tep 1415: reward: -3.0\n", "\tep 1416: reward: 7.0\n", "\tep 1417: reward: 3.0\n", "\tep 1418: reward: 1.0\n", "\tep 1419: reward: 10.0\n", "ep 1420: reward: -5.0, mean reward: 0.482086\n", "\tep 1421: reward: 2.0\n", "\tep 1422: reward: 1.0\n", "\tep 1423: reward: 8.0\n", "\tep 1424: reward: 5.0\n", "\tep 1425: reward: 4.0\n", "\tep 1426: reward: -5.0\n", "\tep 1427: reward: -2.0\n", "\tep 1428: reward: 1.0\n", "\tep 1429: reward: 3.0\n", "ep 1430: reward: 6.0, mean reward: 0.655232\n", "\tep 1431: reward: 5.0\n", "\tep 1432: reward: 7.0\n", "\tep 1433: reward: 3.0\n", "\tep 1434: reward: 7.0\n", "\tep 1435: reward: 4.0\n", "\tep 1436: reward: 3.0\n", "\tep 1437: reward: -5.0\n", "\tep 1438: reward: 4.0\n", "\tep 1439: reward: -5.0\n", "ep 1440: reward: -1.0, mean reward: 0.794760\n", "\tep 1441: reward: 8.0\n", "\tep 1442: reward: 1.0\n", "\tep 1443: reward: -4.0\n", "\tep 1444: reward: 2.0\n", "\tep 1445: reward: 8.0\n", "\tep 1446: reward: -6.0\n", "\tep 1447: reward: 8.0\n", "\tep 1448: reward: 10.0\n", "\tep 1449: reward: 2.0\n", "SAVED MODEL #1450\n", "ep 1450: reward: 5.0, mean reward: 1.046500\n", "\tep 1451: reward: 1.0\n", "\tep 1452: reward: 1.0\n", "\tep 1453: reward: 3.0\n", "\tep 1454: reward: 6.0\n", "\tep 1455: reward: -7.0\n", "\tep 1456: reward: 5.0\n", "\tep 1457: reward: 6.0\n", "\tep 1458: reward: 6.0\n", "\tep 1459: reward: -2.0\n", "ep 1460: reward: 5.0, mean reward: 1.177934\n", "\tep 1461: reward: 3.0\n", "\tep 1462: reward: 2.0\n", "\tep 1463: reward: 3.0\n", "\tep 1464: reward: 2.0\n", "\tep 1465: reward: -4.0\n", "\tep 1466: reward: 6.0\n", "\tep 1467: reward: -3.0\n", "\tep 1468: reward: -2.0\n", "\tep 1469: reward: 4.0\n", "ep 1470: reward: 1.0, mean reward: 1.178439\n", "\tep 1471: reward: 6.0\n", "\tep 1472: reward: 6.0\n", "\tep 1473: reward: -10.0\n", "\tep 1474: reward: -3.0\n", "\tep 1475: reward: 2.0\n", "\tep 1476: reward: 8.0\n", "\tep 1477: reward: -8.0\n", "\tep 1478: reward: -5.0\n", "\tep 1479: reward: -3.0\n", "ep 1480: reward: -5.0, mean reward: 0.944023\n", "\tep 1481: reward: 6.0\n", "\tep 1482: reward: -7.0\n", "\tep 1483: reward: -11.0\n", "\tep 1484: reward: -1.0\n", "\tep 1485: reward: 1.0\n", "\tep 1486: reward: -11.0\n", "\tep 1487: reward: -1.0\n", "\tep 1488: reward: -2.0\n", "\tep 1489: reward: 1.0\n", "ep 1490: reward: 6.0, mean reward: 0.676474\n", "\tep 1491: reward: 1.0\n", "\tep 1492: reward: -1.0\n", "\tep 1493: reward: -1.0\n", "\tep 1494: reward: 7.0\n", "\tep 1495: reward: 5.0\n", "\tep 1496: reward: -2.0\n", "\tep 1497: reward: -2.0\n", "\tep 1498: reward: -11.0\n", "\tep 1499: reward: -5.0\n", "SAVED MODEL #1500\n", "ep 1500: reward: 1.0, mean reward: 0.529902\n", "\tep 1501: reward: 1.0\n", "\tep 1502: reward: -6.0\n", "\tep 1503: reward: 3.0\n", "\tep 1504: reward: 3.0\n", "\tep 1505: reward: 4.0\n", "\tep 1506: reward: 4.0\n", "\tep 1507: reward: -5.0\n", "\tep 1508: reward: -6.0\n", "\tep 1509: reward: 1.0\n", "ep 1510: reward: 8.0, mean reward: 0.548253\n", "\tep 1511: reward: 5.0\n", "\tep 1512: reward: -3.0\n", "\tep 1513: reward: 6.0\n", "\tep 1514: reward: 3.0\n", "\tep 1515: reward: -8.0\n", "\tep 1516: reward: 4.0\n", "\tep 1517: reward: 1.0\n", "\tep 1518: reward: 6.0\n", "\tep 1519: reward: 9.0\n", "ep 1520: reward: 8.0, mean reward: 0.797946\n", "\tep 1521: reward: -8.0\n", "\tep 1522: reward: 2.0\n", "\tep 1523: reward: -3.0\n", "\tep 1524: reward: 2.0\n", "\tep 1525: reward: 5.0\n", "\tep 1526: reward: 6.0\n", "\tep 1527: reward: 11.0\n", "\tep 1528: reward: 3.0\n", "\tep 1529: reward: -4.0\n", "ep 1530: reward: 12.0, mean reward: 0.979610\n", "\tep 1531: reward: -4.0\n", "\tep 1532: reward: 3.0\n", "\tep 1533: reward: 3.0\n", "\tep 1534: reward: -5.0\n", "\tep 1535: reward: 5.0\n", "\tep 1536: reward: 2.0\n", "\tep 1537: reward: 5.0\n", "\tep 1538: reward: -5.0\n", "\tep 1539: reward: 3.0\n", "ep 1540: reward: -4.0, mean reward: 0.913943\n", "\tep 1541: reward: 5.0\n", "\tep 1542: reward: 1.0\n", "\tep 1543: reward: -1.0\n", "\tep 1544: reward: -6.0\n", "\tep 1545: reward: -5.0\n", "\tep 1546: reward: -5.0\n", "\tep 1547: reward: 9.0\n", "\tep 1548: reward: 7.0\n", "\tep 1549: reward: 11.0\n", "SAVED MODEL #1550\n", "ep 1550: reward: 1.0, mean reward: 0.994902\n", "\tep 1551: reward: -4.0\n", "\tep 1552: reward: -6.0\n", "\tep 1553: reward: 3.0\n", "\tep 1554: reward: -4.0\n", "\tep 1555: reward: 2.0\n", "\tep 1556: reward: -6.0\n", "\tep 1557: reward: -5.0\n", "\tep 1558: reward: -2.0\n", "\tep 1559: reward: 2.0\n", "ep 1560: reward: 1.0, mean reward: 0.721236\n", "\tep 1561: reward: 2.0\n", "\tep 1562: reward: -6.0\n", "\tep 1563: reward: 2.0\n", "\tep 1564: reward: 4.0\n", "\tep 1565: reward: -4.0\n", "\tep 1566: reward: -3.0\n", "\tep 1567: reward: -3.0\n", "\tep 1568: reward: 2.0\n", "\tep 1569: reward: -1.0\n", "ep 1570: reward: -1.0, mean reward: 0.575215\n", "\tep 1571: reward: 5.0\n", "\tep 1572: reward: 1.0\n", "\tep 1573: reward: 3.0\n", "\tep 1574: reward: 8.0\n", "\tep 1575: reward: 6.0\n", "\tep 1576: reward: -7.0\n", "\tep 1577: reward: -3.0\n", "\tep 1578: reward: -1.0\n", "\tep 1579: reward: -4.0\n", "ep 1580: reward: 2.0, mean reward: 0.609705\n", "\tep 1581: reward: -6.0\n", "\tep 1582: reward: 9.0\n", "\tep 1583: reward: -5.0\n", "\tep 1584: reward: 6.0\n", "\tep 1585: reward: 7.0\n", "\tep 1586: reward: 5.0\n", "\tep 1587: reward: -7.0\n", "\tep 1588: reward: 6.0\n", "\tep 1589: reward: -8.0\n", "ep 1590: reward: -6.0, mean reward: 0.555812\n", "\tep 1591: reward: 11.0\n", "\tep 1592: reward: 14.0\n", "\tep 1593: reward: 2.0\n", "\tep 1594: reward: -2.0\n", "\tep 1595: reward: 1.0\n", "\tep 1596: reward: 4.0\n", "\tep 1597: reward: 2.0\n", "\tep 1598: reward: 6.0\n", "\tep 1599: reward: 4.0\n", "SAVED MODEL #1600\n", "ep 1600: reward: -1.0, mean reward: 0.887895\n", "\tep 1601: reward: -3.0\n", "\tep 1602: reward: 9.0\n", "\tep 1603: reward: 8.0\n", "\tep 1604: reward: 5.0\n", "\tep 1605: reward: 5.0\n", "\tep 1606: reward: 1.0\n", "\tep 1607: reward: 6.0\n", "\tep 1608: reward: 8.0\n", "\tep 1609: reward: -1.0\n", "ep 1610: reward: -7.0, mean reward: 1.094159\n", "\tep 1611: reward: -6.0\n", "\tep 1612: reward: 8.0\n", "\tep 1613: reward: -2.0\n", "\tep 1614: reward: -2.0\n", "\tep 1615: reward: 10.0\n", "\tep 1616: reward: -2.0\n", "\tep 1617: reward: -5.0\n", "\tep 1618: reward: -7.0\n", "\tep 1619: reward: 9.0\n", "ep 1620: reward: 2.0, mean reward: 1.038940\n", "\tep 1621: reward: 7.0\n", "\tep 1622: reward: 10.0\n", "\tep 1623: reward: -5.0\n", "\tep 1624: reward: 2.0\n", "\tep 1625: reward: -3.0\n", "\tep 1626: reward: -8.0\n", "\tep 1627: reward: 3.0\n", "\tep 1628: reward: 1.0\n", "\tep 1629: reward: 5.0\n", "ep 1630: reward: -10.0, mean reward: 0.951078\n", "\tep 1631: reward: -3.0\n", "\tep 1632: reward: -2.0\n", "\tep 1633: reward: -1.0\n", "\tep 1634: reward: -2.0\n", "\tep 1635: reward: -3.0\n", "\tep 1636: reward: 3.0\n", "\tep 1637: reward: -2.0\n", "\tep 1638: reward: 1.0\n", "\tep 1639: reward: -7.0\n", "ep 1640: reward: 10.0, mean reward: 0.807511\n", "\tep 1641: reward: -4.0\n", "\tep 1642: reward: -2.0\n", "\tep 1643: reward: 9.0\n", "\tep 1644: reward: -6.0\n", "\tep 1645: reward: -6.0\n", "\tep 1646: reward: -3.0\n", "\tep 1647: reward: 3.0\n", "\tep 1648: reward: 5.0\n", "\tep 1649: reward: 4.0\n", "SAVED MODEL #1650\n", "ep 1650: reward: -1.0, mean reward: 0.724537\n", "\tep 1651: reward: 5.0\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\tep 1652: reward: 5.0\n", "\tep 1653: reward: 3.0\n", "\tep 1654: reward: 4.0\n", "\tep 1655: reward: -1.0\n", "\tep 1656: reward: -5.0\n", "\tep 1657: reward: -8.0\n", "\tep 1658: reward: 1.0\n", "\tep 1659: reward: 10.0\n", "ep 1660: reward: -1.0, mean reward: 0.776329\n", "\tep 1661: reward: 6.0\n", "\tep 1662: reward: -5.0\n", "\tep 1663: reward: -5.0\n", "\tep 1664: reward: -1.0\n", "\tep 1665: reward: 1.0\n", "\tep 1666: reward: 4.0\n", "\tep 1667: reward: -3.0\n", "\tep 1668: reward: 3.0\n", "\tep 1669: reward: -4.0\n", "ep 1670: reward: 9.0, mean reward: 0.753382\n", "\tep 1671: reward: 8.0\n", "\tep 1672: reward: -1.0\n", "\tep 1673: reward: 2.0\n", "\tep 1674: reward: 1.0\n", "\tep 1675: reward: 9.0\n", "\tep 1676: reward: -3.0\n", "\tep 1677: reward: 1.0\n", "\tep 1678: reward: -4.0\n", "\tep 1679: reward: -7.0\n", "ep 1680: reward: 1.0, mean reward: 0.741225\n", "\tep 1681: reward: -3.0\n", "\tep 1682: reward: -5.0\n", "\tep 1683: reward: -1.0\n", "\tep 1684: reward: 9.0\n", "\tep 1685: reward: -5.0\n", "\tep 1686: reward: 2.0\n", "\tep 1687: reward: 4.0\n", "\tep 1688: reward: 1.0\n", "\tep 1689: reward: 7.0\n", "ep 1690: reward: -4.0, mean reward: 0.721796\n", "\tep 1691: reward: 3.0\n", "\tep 1692: reward: 1.0\n", "\tep 1693: reward: -6.0\n", "\tep 1694: reward: -6.0\n", "\tep 1695: reward: -7.0\n", "\tep 1696: reward: 3.0\n", "\tep 1697: reward: -3.0\n", "\tep 1698: reward: -4.0\n", "\tep 1699: reward: -3.0\n", "SAVED MODEL #1700\n", "ep 1700: reward: 11.0, mean reward: 0.551235\n", "\tep 1701: reward: -4.0\n", "\tep 1702: reward: 1.0\n", "\tep 1703: reward: -1.0\n", "\tep 1704: reward: 8.0\n", "\tep 1705: reward: -2.0\n", "\tep 1706: reward: 9.0\n", "\tep 1707: reward: -3.0\n", "\tep 1708: reward: 2.0\n", "\tep 1709: reward: 9.0\n", "ep 1710: reward: 2.0, mean reward: 0.704239\n", "\tep 1711: reward: 3.0\n", "\tep 1712: reward: 6.0\n", "\tep 1713: reward: -4.0\n", "\tep 1714: reward: 1.0\n", "\tep 1715: reward: -2.0\n", "\tep 1716: reward: -7.0\n", "\tep 1717: reward: -2.0\n", "\tep 1718: reward: -8.0\n", "\tep 1719: reward: -4.0\n", "ep 1720: reward: 4.0, mean reward: 0.508128\n", "\tep 1721: reward: 3.0\n", "\tep 1722: reward: -5.0\n", "\tep 1723: reward: 7.0\n", "\tep 1724: reward: -1.0\n", "\tep 1725: reward: -2.0\n", "\tep 1726: reward: 4.0\n", "\tep 1727: reward: -3.0\n", "\tep 1728: reward: -10.0\n", "\tep 1729: reward: 5.0\n", "ep 1730: reward: 2.0, mean reward: 0.458425\n", "\tep 1731: reward: -3.0\n", "\tep 1732: reward: 2.0\n", "\tep 1733: reward: -4.0\n", "\tep 1734: reward: 10.0\n", "\tep 1735: reward: 3.0\n", "\tep 1736: reward: -8.0\n", "\tep 1737: reward: 1.0\n", "\tep 1738: reward: -2.0\n", "\tep 1739: reward: 2.0\n", "ep 1740: reward: 3.0, mean reward: 0.454089\n", "\tep 1741: reward: -1.0\n", "\tep 1742: reward: 9.0\n", "\tep 1743: reward: 4.0\n", "\tep 1744: reward: 4.0\n", "\tep 1745: reward: 1.0\n", "\tep 1746: reward: 2.0\n", "\tep 1747: reward: -1.0\n", "\tep 1748: reward: -3.0\n", "\tep 1749: reward: 6.0\n", "SAVED MODEL #1750\n", "ep 1750: reward: -2.0, mean reward: 0.588539\n", "\tep 1751: reward: -5.0\n", "\tep 1752: reward: 2.0\n", "\tep 1753: reward: 9.0\n", "\tep 1754: reward: -3.0\n", "\tep 1755: reward: 13.0\n", "\tep 1756: reward: 11.0\n", "\tep 1757: reward: -1.0\n", "\tep 1758: reward: -1.0\n", "\tep 1759: reward: 5.0\n", "ep 1760: reward: 4.0, mean reward: 0.859975\n", "\tep 1761: reward: -4.0\n", "\tep 1762: reward: 5.0\n", "\tep 1763: reward: 3.0\n", "\tep 1764: reward: 5.0\n", "\tep 1765: reward: 7.0\n", "\tep 1766: reward: -8.0\n", "\tep 1767: reward: -2.0\n", "\tep 1768: reward: 4.0\n", "\tep 1769: reward: 5.0\n", "ep 1770: reward: -4.0, mean reward: 0.881398\n", "\tep 1771: reward: -1.0\n", "\tep 1772: reward: -6.0\n", "\tep 1773: reward: 1.0\n", "\tep 1774: reward: -3.0\n", "\tep 1775: reward: 2.0\n", "\tep 1776: reward: 5.0\n", "\tep 1777: reward: -2.0\n", "\tep 1778: reward: 4.0\n", "\tep 1779: reward: -4.0\n", "ep 1780: reward: 5.0, mean reward: 0.810945\n", "\tep 1781: reward: 7.0\n", "\tep 1782: reward: -7.0\n", "\tep 1783: reward: 4.0\n", "\tep 1784: reward: 6.0\n", "\tep 1785: reward: 2.0\n", "\tep 1786: reward: 1.0\n", "\tep 1787: reward: -6.0\n", "\tep 1788: reward: 1.0\n", "\tep 1789: reward: 2.0\n", "ep 1790: reward: 6.0, mean reward: 0.886538\n", "\tep 1791: reward: 2.0\n", "\tep 1792: reward: 1.0\n", "\tep 1793: reward: -5.0\n", "\tep 1794: reward: -4.0\n", "\tep 1795: reward: -6.0\n", "\tep 1796: reward: -4.0\n", "\tep 1797: reward: 1.0\n", "\tep 1798: reward: 8.0\n", "\tep 1799: reward: 5.0\n", "SAVED MODEL #1800\n", "ep 1800: reward: 9.0, mean reward: 0.877132\n", "\tep 1801: reward: 1.0\n", "\tep 1802: reward: -1.0\n", "\tep 1803: reward: -4.0\n", "\tep 1804: reward: 2.0\n", "\tep 1805: reward: 11.0\n", "\tep 1806: reward: 6.0\n", "\tep 1807: reward: -4.0\n", "\tep 1808: reward: -1.0\n", "\tep 1809: reward: -2.0\n", "ep 1810: reward: 3.0, mean reward: 0.898549\n", "\tep 1811: reward: -10.0\n", "\tep 1812: reward: 4.0\n", "\tep 1813: reward: -4.0\n", "\tep 1814: reward: 1.0\n", "\tep 1815: reward: -1.0\n", "\tep 1816: reward: -1.0\n", "\tep 1817: reward: 2.0\n", "\tep 1818: reward: 6.0\n", "\tep 1819: reward: 2.0\n", "ep 1820: reward: -2.0, mean reward: 0.789218\n", "\tep 1821: reward: -6.0\n", "\tep 1822: reward: 3.0\n", "\tep 1823: reward: 10.0\n", "\tep 1824: reward: 8.0\n", "\tep 1825: reward: -10.0\n", "\tep 1826: reward: -4.0\n", "\tep 1827: reward: 8.0\n", "\tep 1828: reward: -8.0\n", "\tep 1829: reward: 1.0\n", "ep 1830: reward: 1.0, mean reward: 0.740744\n", "\tep 1831: reward: -7.0\n", "\tep 1832: reward: -4.0\n", "\tep 1833: reward: -1.0\n", "\tep 1834: reward: -2.0\n", "\tep 1835: reward: -7.0\n", "\tep 1836: reward: 2.0\n", "\tep 1837: reward: -7.0\n", "\tep 1838: reward: 3.0\n", "\tep 1839: reward: 5.0\n", "ep 1840: reward: 4.0, mean reward: 0.544534\n", "\tep 1841: reward: -11.0\n", "\tep 1842: reward: 3.0\n", "\tep 1843: reward: 5.0\n", "\tep 1844: reward: -4.0\n", "\tep 1845: reward: 7.0\n", "\tep 1846: reward: -10.0\n", "\tep 1847: reward: -8.0\n", "\tep 1848: reward: 8.0\n", "\tep 1849: reward: -4.0\n", "SAVED MODEL #1850\n", "ep 1850: reward: 4.0, mean reward: 0.400300\n", "\tep 1851: reward: -3.0\n", "\tep 1852: reward: 1.0\n", "\tep 1853: reward: 2.0\n", "\tep 1854: reward: 3.0\n", "\tep 1855: reward: -4.0\n", "\tep 1856: reward: 3.0\n", "\tep 1857: reward: 4.0\n", "\tep 1858: reward: 1.0\n", "\tep 1859: reward: -9.0\n", "ep 1860: reward: -1.0, mean reward: 0.331023\n", "\tep 1861: reward: -7.0\n", "\tep 1862: reward: 4.0\n", "\tep 1863: reward: -6.0\n", "\tep 1864: reward: -7.0\n", "\tep 1865: reward: 5.0\n", "\tep 1866: reward: 6.0\n", "\tep 1867: reward: 7.0\n", "\tep 1868: reward: 4.0\n", "\tep 1869: reward: -4.0\n", "ep 1870: reward: 8.0, mean reward: 0.403218\n", "\tep 1871: reward: 8.0\n", "\tep 1872: reward: 6.0\n", "\tep 1873: reward: -4.0\n", "\tep 1874: reward: 1.0\n", "\tep 1875: reward: 8.0\n", "\tep 1876: reward: -1.0\n", "\tep 1877: reward: 2.0\n", "\tep 1878: reward: 1.0\n", "\tep 1879: reward: 4.0\n", "ep 1880: reward: 5.0, mean reward: 0.650521\n", "\tep 1881: reward: 5.0\n", "\tep 1882: reward: 4.0\n", "\tep 1883: reward: 5.0\n", "\tep 1884: reward: 8.0\n", "\tep 1885: reward: 1.0\n", "\tep 1886: reward: 5.0\n", "\tep 1887: reward: 2.0\n", "\tep 1888: reward: 2.0\n", "\tep 1889: reward: 5.0\n", "ep 1890: reward: -5.0, mean reward: 0.888875\n", "\tep 1891: reward: 6.0\n", "\tep 1892: reward: 8.0\n", "\tep 1893: reward: -1.0\n", "\tep 1894: reward: 5.0\n", "\tep 1895: reward: 6.0\n", "\tep 1896: reward: -2.0\n", "\tep 1897: reward: 4.0\n", "\tep 1898: reward: -6.0\n", "\tep 1899: reward: 4.0\n", "SAVED MODEL #1900\n", "ep 1900: reward: -2.0, mean reward: 1.007720\n", "\tep 1901: reward: 2.0\n", "\tep 1902: reward: 2.0\n", "\tep 1903: reward: 7.0\n", "\tep 1904: reward: -3.0\n", "\tep 1905: reward: -1.0\n", "\tep 1906: reward: -3.0\n", "\tep 1907: reward: 2.0\n", "\tep 1908: reward: -9.0\n", "\tep 1909: reward: 6.0\n", "ep 1910: reward: -2.0, mean reward: 0.917358\n", "\tep 1911: reward: -9.0\n", "\tep 1912: reward: 1.0\n", "\tep 1913: reward: 1.0\n", "\tep 1914: reward: 1.0\n", "\tep 1915: reward: -5.0\n", "\tep 1916: reward: 8.0\n", "\tep 1917: reward: 8.0\n", "\tep 1918: reward: -7.0\n", "\tep 1919: reward: -1.0\n", "ep 1920: reward: 12.0, mean reward: 0.923804\n", "\tep 1921: reward: -3.0\n", "\tep 1922: reward: -5.0\n", "\tep 1923: reward: -9.0\n", "\tep 1924: reward: -11.0\n", "\tep 1925: reward: -5.0\n", "\tep 1926: reward: -1.0\n", "\tep 1927: reward: 1.0\n", "\tep 1928: reward: 7.0\n", "\tep 1929: reward: -3.0\n", "ep 1930: reward: 9.0, mean reward: 0.655935\n", "\tep 1931: reward: 4.0\n", "\tep 1932: reward: 4.0\n", "\tep 1933: reward: 6.0\n", "\tep 1934: reward: -5.0\n", "\tep 1935: reward: 2.0\n", "\tep 1936: reward: 8.0\n", "\tep 1937: reward: -3.0\n", "\tep 1938: reward: 1.0\n", "\tep 1939: reward: -3.0\n", "ep 1940: reward: 6.0, mean reward: 0.782376\n", "\tep 1941: reward: -5.0\n", "\tep 1942: reward: -7.0\n", "\tep 1943: reward: 4.0\n", "\tep 1944: reward: -3.0\n", "\tep 1945: reward: 3.0\n", "\tep 1946: reward: 5.0\n", "\tep 1947: reward: 10.0\n", "\tep 1948: reward: 2.0\n", "\tep 1949: reward: 3.0\n", "SAVED MODEL #1950\n", "ep 1950: reward: 5.0, mean reward: 0.879228\n", "\tep 1951: reward: 6.0\n", "\tep 1952: reward: 3.0\n", "\tep 1953: reward: 4.0\n", "\tep 1954: reward: 9.0\n", "\tep 1955: reward: -1.0\n", "\tep 1956: reward: 11.0\n", "\tep 1957: reward: -4.0\n", "\tep 1958: reward: 1.0\n", "\tep 1959: reward: 3.0\n", "ep 1960: reward: 6.0, mean reward: 1.156512\n", "\tep 1961: reward: -14.0\n", "\tep 1962: reward: -3.0\n", "\tep 1963: reward: 9.0\n", "\tep 1964: reward: 3.0\n", "\tep 1965: reward: 4.0\n", "\tep 1966: reward: -3.0\n", "\tep 1967: reward: 13.0\n", "\tep 1968: reward: 3.0\n", "\tep 1969: reward: 5.0\n", "ep 1970: reward: 1.0, mean reward: 1.226748\n", "\tep 1971: reward: 6.0\n", "\tep 1972: reward: -4.0\n", "\tep 1973: reward: -2.0\n", "\tep 1974: reward: 5.0\n", "\tep 1975: reward: -3.0\n", "\tep 1976: reward: 6.0\n", "\tep 1977: reward: 6.0\n", "\tep 1978: reward: -3.0\n", "\tep 1979: reward: -3.0\n", "ep 1980: reward: -5.0, mean reward: 1.134004\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\tep 1981: reward: -1.0\n", "\tep 1982: reward: 3.0\n", "\tep 1983: reward: 5.0\n", "\tep 1984: reward: -2.0\n", "\tep 1985: reward: 1.0\n", "\tep 1986: reward: 4.0\n", "\tep 1987: reward: 3.0\n", "\tep 1988: reward: -5.0\n", "\tep 1989: reward: 7.0\n", "ep 1990: reward: -4.0, mean reward: 1.129231\n", "\tep 1991: reward: 9.0\n", "\tep 1992: reward: 5.0\n", "\tep 1993: reward: 1.0\n", "\tep 1994: reward: -1.0\n", "\tep 1995: reward: 6.0\n", "\tep 1996: reward: 1.0\n", "\tep 1997: reward: -6.0\n", "\tep 1998: reward: 6.0\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[2018-07-10 20:27:07,031] Starting new video recorder writing to /tmp/pong/openaigym.video.0.24686.video002000.mp4\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\tep 1999: reward: -3.0\n", "SAVED MODEL #2000\n", "ep 2000: reward: 6.0, mean reward: 1.247070\n", "\tep 2001: reward: -1.0\n", "\tep 2002: reward: 5.0\n", "\tep 2003: reward: 8.0\n", "\tep 2004: reward: -4.0\n", "\tep 2005: reward: -6.0\n", "\tep 2006: reward: -1.0\n", "\tep 2007: reward: 11.0\n", "\tep 2008: reward: 1.0\n", "\tep 2009: reward: 5.0\n", "ep 2010: reward: 5.0, mean reward: 1.351104\n", "\tep 2011: reward: 12.0\n", "\tep 2012: reward: 9.0\n", "\tep 2013: reward: 8.0\n", "\tep 2014: reward: 3.0\n", "\tep 2015: reward: -10.0\n", "\tep 2016: reward: 1.0\n", "\tep 2017: reward: 6.0\n", "\tep 2018: reward: 2.0\n", "\tep 2019: reward: 3.0\n", "ep 2020: reward: -3.0, mean reward: 1.509420\n", "\tep 2021: reward: 3.0\n", "\tep 2022: reward: 3.0\n", "\tep 2023: reward: 2.0\n", "\tep 2024: reward: 4.0\n", "\tep 2025: reward: 5.0\n", "\tep 2026: reward: 10.0\n", "\tep 2027: reward: 5.0\n", "\tep 2028: reward: 3.0\n", "\tep 2029: reward: 4.0\n", "ep 2030: reward: 4.0, mean reward: 1.777608\n", "\tep 2031: reward: 1.0\n", "\tep 2032: reward: -2.0\n", "\tep 2033: reward: -8.0\n", "\tep 2034: reward: 1.0\n", "\tep 2035: reward: 3.0\n", "\tep 2036: reward: -6.0\n", "\tep 2037: reward: 13.0\n", "\tep 2038: reward: 1.0\n", "\tep 2039: reward: -2.0\n", "ep 2040: reward: -5.0, mean reward: 1.570200\n", "\tep 2041: reward: -3.0\n", "\tep 2042: reward: -6.0\n", "\tep 2043: reward: -2.0\n", "\tep 2044: reward: -4.0\n", "\tep 2045: reward: -1.0\n", "\tep 2046: reward: -3.0\n", "\tep 2047: reward: -2.0\n", "\tep 2048: reward: 8.0\n", "\tep 2049: reward: 1.0\n", "SAVED MODEL #2050\n", "ep 2050: reward: -6.0, mean reward: 1.251564\n", "\tep 2051: reward: 7.0\n", "\tep 2052: reward: -3.0\n", "\tep 2053: reward: 7.0\n", "\tep 2054: reward: 9.0\n", "\tep 2055: reward: 6.0\n", "\tep 2056: reward: 3.0\n", "\tep 2057: reward: 7.0\n", "\tep 2058: reward: -6.0\n", "\tep 2059: reward: 5.0\n", "ep 2060: reward: -2.0, mean reward: 1.442626\n", "\tep 2061: reward: -2.0\n", "\tep 2062: reward: 7.0\n", "\tep 2063: reward: -8.0\n", "\tep 2064: reward: -4.0\n", "\tep 2065: reward: 5.0\n", "\tep 2066: reward: 4.0\n", "\tep 2067: reward: 9.0\n", "\tep 2068: reward: -4.0\n", "\tep 2069: reward: 2.0\n", "ep 2070: reward: 6.0, mean reward: 1.452679\n", "\tep 2071: reward: -5.0\n", "\tep 2072: reward: 6.0\n", "\tep 2073: reward: 8.0\n", "\tep 2074: reward: 6.0\n", "\tep 2075: reward: 4.0\n", "\tep 2076: reward: 7.0\n", "\tep 2077: reward: -1.0\n", "\tep 2078: reward: 2.0\n", "\tep 2079: reward: -3.0\n", "ep 2080: reward: 7.0, mean reward: 1.610000\n", "\tep 2081: reward: -7.0\n", "\tep 2082: reward: -5.0\n", "\tep 2083: reward: 2.0\n", "\tep 2084: reward: -2.0\n", "\tep 2085: reward: 6.0\n", "\tep 2086: reward: 4.0\n", "\tep 2087: reward: 1.0\n", "\tep 2088: reward: -2.0\n", "\tep 2089: reward: 6.0\n", "ep 2090: reward: 6.0, mean reward: 1.550768\n", "\tep 2091: reward: -7.0\n", "\tep 2092: reward: -5.0\n", "\tep 2093: reward: 11.0\n", "\tep 2094: reward: 3.0\n", "\tep 2095: reward: -6.0\n", "\tep 2096: reward: -8.0\n", "\tep 2097: reward: -1.0\n", "\tep 2098: reward: 1.0\n", "\tep 2099: reward: -5.0\n", "SAVED MODEL #2100\n", "ep 2100: reward: 6.0, mean reward: 1.299866\n", "\tep 2101: reward: 5.0\n", "\tep 2102: reward: 12.0\n", "\tep 2103: reward: 1.0\n", "\tep 2104: reward: 1.0\n", "\tep 2105: reward: 12.0\n", "\tep 2106: reward: 6.0\n", "\tep 2107: reward: -1.0\n", "\tep 2108: reward: 2.0\n", "\tep 2109: reward: 11.0\n", "ep 2110: reward: -2.0, mean reward: 1.621269\n", "\tep 2111: reward: 4.0\n", "\tep 2112: reward: -6.0\n", "\tep 2113: reward: -1.0\n", "\tep 2114: reward: -2.0\n", "\tep 2115: reward: -9.0\n", "\tep 2116: reward: -3.0\n", "\tep 2117: reward: -1.0\n", "\tep 2118: reward: -4.0\n", "\tep 2119: reward: -1.0\n", "ep 2120: reward: 5.0, mean reward: 1.296059\n", "\tep 2121: reward: -3.0\n", "\tep 2122: reward: -5.0\n", "\tep 2123: reward: 5.0\n", "\tep 2124: reward: -9.0\n", "\tep 2125: reward: 1.0\n", "\tep 2126: reward: 6.0\n", "\tep 2127: reward: 7.0\n", "\tep 2128: reward: -3.0\n", "\tep 2129: reward: 3.0\n", "ep 2130: reward: -3.0, mean reward: 1.165823\n", "\tep 2131: reward: 9.0\n", "\tep 2132: reward: -7.0\n", "\tep 2133: reward: 1.0\n", "\tep 2134: reward: -3.0\n", "\tep 2135: reward: -3.0\n", "\tep 2136: reward: -3.0\n", "\tep 2137: reward: 3.0\n", "\tep 2138: reward: 5.0\n", "\tep 2139: reward: -5.0\n", "ep 2140: reward: -6.0, mean reward: 0.964317\n", "\tep 2141: reward: 5.0\n", "\tep 2142: reward: 1.0\n", "\tep 2143: reward: 8.0\n", "\tep 2144: reward: -2.0\n", "\tep 2145: reward: 1.0\n", "\tep 2146: reward: 2.0\n", "\tep 2147: reward: 6.0\n", "\tep 2148: reward: 7.0\n", "\tep 2149: reward: 8.0\n", "SAVED MODEL #2150\n", "ep 2150: reward: -1.0, mean reward: 1.207496\n", "\tep 2151: reward: -4.0\n", "\tep 2152: reward: 5.0\n", "\tep 2153: reward: 6.0\n", "\tep 2154: reward: 10.0\n", "\tep 2155: reward: 7.0\n", "\tep 2156: reward: 2.0\n", "\tep 2157: reward: -4.0\n", "\tep 2158: reward: 8.0\n", "\tep 2159: reward: -2.0\n", "ep 2160: reward: 9.0, mean reward: 1.447284\n", "\tep 2161: reward: 8.0\n", "\tep 2162: reward: 3.0\n", "\tep 2163: reward: 3.0\n", "\tep 2164: reward: 1.0\n", "\tep 2165: reward: -1.0\n", "\tep 2166: reward: 6.0\n", "\tep 2167: reward: 4.0\n", "\tep 2168: reward: 9.0\n", "\tep 2169: reward: -2.0\n", "ep 2170: reward: 4.0, mean reward: 1.642385\n", "\tep 2171: reward: -1.0\n", "\tep 2172: reward: -1.0\n", "\tep 2173: reward: 4.0\n", "\tep 2174: reward: 7.0\n", "\tep 2175: reward: -4.0\n", "\tep 2176: reward: -5.0\n", "\tep 2177: reward: -5.0\n", "\tep 2178: reward: 2.0\n", "\tep 2179: reward: -7.0\n", "ep 2180: reward: 8.0, mean reward: 1.465885\n", "\tep 2181: reward: 7.0\n", "\tep 2182: reward: 7.0\n", "\tep 2183: reward: 7.0\n", "\tep 2184: reward: 3.0\n", "\tep 2185: reward: -5.0\n", "\tep 2186: reward: 4.0\n", "\tep 2187: reward: -4.0\n", "\tep 2188: reward: -4.0\n", "\tep 2189: reward: 2.0\n", "ep 2190: reward: 10.0, mean reward: 1.580405\n", "\tep 2191: reward: 8.0\n", "\tep 2192: reward: 5.0\n", "\tep 2193: reward: 1.0\n", "\tep 2194: reward: 4.0\n", "\tep 2195: reward: -9.0\n", "\tep 2196: reward: -3.0\n", "\tep 2197: reward: 3.0\n", "\tep 2198: reward: -1.0\n", "\tep 2199: reward: -3.0\n", "SAVED MODEL #2200\n", "ep 2200: reward: 2.0, mean reward: 1.490690\n", "\tep 2201: reward: 6.0\n", "\tep 2202: reward: -3.0\n", "\tep 2203: reward: -4.0\n", "\tep 2204: reward: 8.0\n", "\tep 2205: reward: 4.0\n", "\tep 2206: reward: -5.0\n", "\tep 2207: reward: 6.0\n", "\tep 2208: reward: 6.0\n", "\tep 2209: reward: -2.0\n", "ep 2210: reward: 2.0, mean reward: 1.520551\n", "\tep 2211: reward: 6.0\n", "\tep 2212: reward: -3.0\n", "\tep 2213: reward: 5.0\n", "\tep 2214: reward: -6.0\n", "\tep 2215: reward: -1.0\n", "\tep 2216: reward: -4.0\n", "\tep 2217: reward: 12.0\n", "\tep 2218: reward: -4.0\n", "\tep 2219: reward: -2.0\n", "ep 2220: reward: 1.0, mean reward: 1.411901\n", "\tep 2221: reward: 2.0\n", "\tep 2222: reward: 5.0\n", "\tep 2223: reward: 3.0\n", "\tep 2224: reward: 8.0\n", "\tep 2225: reward: -3.0\n", "\tep 2226: reward: 9.0\n", "\tep 2227: reward: 7.0\n", "\tep 2228: reward: 2.0\n", "\tep 2229: reward: 7.0\n", "ep 2230: reward: -2.0, mean reward: 1.639333\n", "\tep 2231: reward: 11.0\n", "\tep 2232: reward: 6.0\n", "\tep 2233: reward: 7.0\n", "\tep 2234: reward: -1.0\n", "\tep 2235: reward: 3.0\n", "\tep 2236: reward: -2.0\n", "\tep 2237: reward: -6.0\n", "\tep 2238: reward: 8.0\n", "\tep 2239: reward: 1.0\n", "ep 2240: reward: 5.0, mean reward: 1.783672\n", "\tep 2241: reward: 3.0\n", "\tep 2242: reward: 3.0\n", "\tep 2243: reward: 2.0\n", "\tep 2244: reward: -2.0\n", "\tep 2245: reward: -4.0\n", "\tep 2246: reward: 4.0\n", "\tep 2247: reward: 6.0\n", "\tep 2248: reward: 4.0\n", "\tep 2249: reward: 10.0\n", "SAVED MODEL #2250\n", "ep 2250: reward: -3.0, mean reward: 1.834827\n", "\tep 2251: reward: -1.0\n", "\tep 2252: reward: 5.0\n", "\tep 2253: reward: 4.0\n", "\tep 2254: reward: 1.0\n", "\tep 2255: reward: -3.0\n", "\tep 2256: reward: 2.0\n", "\tep 2257: reward: 5.0\n", "\tep 2258: reward: 2.0\n", "\tep 2259: reward: -4.0\n", "ep 2260: reward: -10.0, mean reward: 1.662283\n", "\tep 2261: reward: 6.0\n", "\tep 2262: reward: -6.0\n", "\tep 2263: reward: -2.0\n", "\tep 2264: reward: 10.0\n", "\tep 2265: reward: 2.0\n", "\tep 2266: reward: -1.0\n", "\tep 2267: reward: 5.0\n", "\tep 2268: reward: 2.0\n", "\tep 2269: reward: 11.0\n", "ep 2270: reward: 6.0, mean reward: 1.824723\n", "\tep 2271: reward: -7.0\n", "\tep 2272: reward: -9.0\n", "\tep 2273: reward: 8.0\n", "\tep 2274: reward: 4.0\n", "\tep 2275: reward: 3.0\n", "\tep 2276: reward: -1.0\n", "\tep 2277: reward: 3.0\n", "\tep 2278: reward: -4.0\n", "\tep 2279: reward: 2.0\n", "ep 2280: reward: 6.0, mean reward: 1.704107\n", "\tep 2281: reward: -2.0\n", "\tep 2282: reward: 6.0\n", "\tep 2283: reward: 6.0\n", "\tep 2284: reward: 7.0\n", "\tep 2285: reward: 8.0\n", "\tep 2286: reward: 3.0\n", "\tep 2287: reward: -5.0\n", "\tep 2288: reward: 11.0\n", "\tep 2289: reward: 7.0\n", "ep 2290: reward: 5.0, mean reward: 1.983579\n", "\tep 2291: reward: 1.0\n", "\tep 2292: reward: 1.0\n", "\tep 2293: reward: 5.0\n", "\tep 2294: reward: -2.0\n", "\tep 2295: reward: 5.0\n", "\tep 2296: reward: 6.0\n", "\tep 2297: reward: 5.0\n", "\tep 2298: reward: 6.0\n", "\tep 2299: reward: 4.0\n", "SAVED MODEL #2300\n", "ep 2300: reward: -5.0, mean reward: 2.042155\n", "\tep 2301: reward: -2.0\n", "\tep 2302: reward: 7.0\n", "\tep 2303: reward: 8.0\n", "\tep 2304: reward: 3.0\n", "\tep 2305: reward: 3.0\n", "\tep 2306: reward: 7.0\n", "\tep 2307: reward: 1.0\n", "\tep 2308: reward: 7.0\n", "\tep 2309: reward: 6.0\n", "ep 2310: reward: -8.0, mean reward: 2.149502\n", "\tep 2311: reward: 2.0\n", "\tep 2312: reward: -1.0\n", "\tep 2313: reward: -3.0\n", "\tep 2314: reward: -3.0\n", "\tep 2315: reward: -10.0\n", "\tep 2316: reward: 1.0\n", "\tep 2317: reward: -5.0\n", "\tep 2318: reward: 3.0\n", "\tep 2319: reward: -2.0\n", "ep 2320: reward: -3.0, mean reward: 1.742402\n", "\tep 2321: reward: 3.0\n", "\tep 2322: reward: 7.0\n", "\tep 2323: reward: 1.0\n", "\tep 2324: reward: 5.0\n", "\tep 2325: reward: -1.0\n", "\tep 2326: reward: 10.0\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\tep 2327: reward: -2.0\n", "\tep 2328: reward: -2.0\n", "\tep 2329: reward: 3.0\n", "ep 2330: reward: -2.0, mean reward: 1.781431\n", "\tep 2331: reward: -1.0\n", "\tep 2332: reward: -7.0\n", "\tep 2333: reward: -2.0\n", "\tep 2334: reward: -3.0\n", "\tep 2335: reward: 3.0\n", "\tep 2336: reward: 11.0\n", "\tep 2337: reward: 1.0\n", "\tep 2338: reward: 3.0\n", "\tep 2339: reward: 1.0\n", "ep 2340: reward: 4.0, mean reward: 1.713683\n", "\tep 2341: reward: -10.0\n", "\tep 2342: reward: 3.0\n", "\tep 2343: reward: 5.0\n", "\tep 2344: reward: 2.0\n", "\tep 2345: reward: 2.0\n", "\tep 2346: reward: -3.0\n", "\tep 2347: reward: 3.0\n", "\tep 2348: reward: 8.0\n", "\tep 2349: reward: -3.0\n", "SAVED MODEL #2350\n", "ep 2350: reward: -8.0, mean reward: 1.539606\n", "\tep 2351: reward: -1.0\n", "\tep 2352: reward: 5.0\n", "\tep 2353: reward: -3.0\n", "\tep 2354: reward: -1.0\n", "\tep 2355: reward: -1.0\n", "\tep 2356: reward: 6.0\n", "\tep 2357: reward: 3.0\n", "\tep 2358: reward: -3.0\n", "\tep 2359: reward: 7.0\n", "ep 2360: reward: -5.0, mean reward: 1.459150\n", "\tep 2361: reward: -6.0\n", "\tep 2362: reward: 1.0\n", "\tep 2363: reward: -3.0\n", "\tep 2364: reward: -4.0\n", "\tep 2365: reward: 5.0\n", "\tep 2366: reward: 3.0\n", "\tep 2367: reward: 2.0\n", "\tep 2368: reward: 8.0\n", "\tep 2369: reward: 1.0\n", "ep 2370: reward: 3.0, mean reward: 1.422505\n", "\tep 2371: reward: 3.0\n", "\tep 2372: reward: 5.0\n", "\tep 2373: reward: -3.0\n", "\tep 2374: reward: 6.0\n", "\tep 2375: reward: 2.0\n", "\tep 2376: reward: -2.0\n", "\tep 2377: reward: -6.0\n", "\tep 2378: reward: 1.0\n", "\tep 2379: reward: -2.0\n", "ep 2380: reward: 3.0, mean reward: 1.350149\n", "\tep 2381: reward: -2.0\n", "\tep 2382: reward: -2.0\n", "\tep 2383: reward: -10.0\n", "\tep 2384: reward: 1.0\n", "\tep 2385: reward: 5.0\n", "\tep 2386: reward: 5.0\n", "\tep 2387: reward: -2.0\n", "\tep 2388: reward: -2.0\n", "\tep 2389: reward: 6.0\n", "ep 2390: reward: -2.0, mean reward: 1.196505\n", "\tep 2391: reward: -1.0\n", "\tep 2392: reward: 1.0\n", "\tep 2393: reward: -3.0\n", "\tep 2394: reward: -6.0\n", "\tep 2395: reward: 3.0\n", "\tep 2396: reward: 5.0\n", "\tep 2397: reward: 2.0\n", "\tep 2398: reward: 6.0\n", "\tep 2399: reward: -1.0\n", "SAVED MODEL #2400\n", "ep 2400: reward: 7.0, mean reward: 1.212610\n", "\tep 2401: reward: -11.0\n", "\tep 2402: reward: 1.0\n", "\tep 2403: reward: 1.0\n", "\tep 2404: reward: -1.0\n", "\tep 2405: reward: -5.0\n", "\tep 2406: reward: 3.0\n", "\tep 2407: reward: -11.0\n", "\tep 2408: reward: 8.0\n", "\tep 2409: reward: -2.0\n", "ep 2410: reward: -2.0, mean reward: 0.918453\n", "\tep 2411: reward: 2.0\n", "\tep 2412: reward: -1.0\n", "\tep 2413: reward: 1.0\n", "\tep 2414: reward: -3.0\n", "\tep 2415: reward: 4.0\n", "\tep 2416: reward: -6.0\n", "\tep 2417: reward: 3.0\n", "\tep 2418: reward: -12.0\n", "\tep 2419: reward: 4.0\n", "ep 2420: reward: 9.0, mean reward: 0.842252\n", "\tep 2421: reward: 4.0\n", "\tep 2422: reward: -7.0\n", "\tep 2423: reward: 6.0\n", "\tep 2424: reward: 8.0\n", "\tep 2425: reward: 8.0\n", "\tep 2426: reward: -1.0\n", "\tep 2427: reward: -3.0\n", "\tep 2428: reward: 2.0\n", "\tep 2429: reward: 1.0\n", "ep 2430: reward: -3.0, mean reward: 0.901775\n", "\tep 2431: reward: -3.0\n", "\tep 2432: reward: -3.0\n", "\tep 2433: reward: 2.0\n", "\tep 2434: reward: 4.0\n", "\tep 2435: reward: -3.0\n", "\tep 2436: reward: 7.0\n", "\tep 2437: reward: -1.0\n", "\tep 2438: reward: 1.0\n", "\tep 2439: reward: 1.0\n", "ep 2440: reward: -4.0, mean reward: 0.825472\n", "\tep 2441: reward: 1.0\n", "\tep 2442: reward: -8.0\n", "\tep 2443: reward: -5.0\n", "\tep 2444: reward: -1.0\n", "\tep 2445: reward: 4.0\n", "\tep 2446: reward: 2.0\n", "\tep 2447: reward: 8.0\n", "\tep 2448: reward: -4.0\n", "\tep 2449: reward: 2.0\n", "SAVED MODEL #2450\n", "ep 2450: reward: 13.0, mean reward: 0.871311\n", "\tep 2451: reward: 8.0\n", "\tep 2452: reward: 2.0\n", "\tep 2453: reward: 1.0\n", "\tep 2454: reward: 6.0\n", "\tep 2455: reward: 6.0\n", "\tep 2456: reward: 3.0\n", "\tep 2457: reward: -5.0\n", "\tep 2458: reward: -1.0\n", "\tep 2459: reward: 4.0\n", "ep 2460: reward: 4.0, mean reward: 1.052505\n", "\tep 2461: reward: 2.0\n", "\tep 2462: reward: -1.0\n", "\tep 2463: reward: 1.0\n", "\tep 2464: reward: 8.0\n", "\tep 2465: reward: 2.0\n", "\tep 2466: reward: -6.0\n", "\tep 2467: reward: -2.0\n", "\tep 2468: reward: 1.0\n", "\tep 2469: reward: 1.0\n", "ep 2470: reward: 5.0, mean reward: 1.057228\n", "\tep 2471: reward: 1.0\n", "\tep 2472: reward: -7.0\n", "\tep 2473: reward: -5.0\n", "\tep 2474: reward: 4.0\n", "\tep 2475: reward: 3.0\n", "\tep 2476: reward: -1.0\n", "\tep 2477: reward: 7.0\n", "\tep 2478: reward: 8.0\n", "\tep 2479: reward: 3.0\n", "ep 2480: reward: 3.0, mean reward: 1.116690\n", "\tep 2481: reward: 12.0\n", "\tep 2482: reward: 2.0\n", "\tep 2483: reward: 11.0\n", "\tep 2484: reward: 9.0\n", "\tep 2485: reward: 5.0\n", "\tep 2486: reward: 3.0\n", "\tep 2487: reward: 5.0\n", "\tep 2488: reward: 4.0\n", "\tep 2489: reward: 1.0\n", "ep 2490: reward: 1.0, mean reward: 1.509238\n", "\tep 2491: reward: 4.0\n", "\tep 2492: reward: 5.0\n", "\tep 2493: reward: 3.0\n", "\tep 2494: reward: 5.0\n", "\tep 2495: reward: 7.0\n", "\tep 2496: reward: -1.0\n", "\tep 2497: reward: 6.0\n", "\tep 2498: reward: 3.0\n", "\tep 2499: reward: 8.0\n", "SAVED MODEL #2500\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "[2018-07-10 22:05:59,779] Finished writing results. You can upload them to the scoreboard via gym.upload('/tmp/pong')\n" ] } ], "source": [ "with tf.Session() as sess:\n", " agent.set_session(sess)\n", " sess.run(tf.global_variables_initializer())\n", " agent.load()\n", " # training loop\n", " done = False\n", " while not done and n< num_episodes:\n", " # Preprocess the observation\n", " cur_state = preprocess(obs)\n", " diff_state = cur_state - prev_state if prev_state is not None else np.zeros(n_size*n_size)\n", " prev_state = cur_state\n", "\n", " #Predict the action\n", " aprob = agent.predict_UP(diff_state) ; aprob = aprob[0,:]\n", "\n", "\n", " action = np.random.choice(n_actions, p=aprob)\n", " #print(action)\n", " label = np.zeros_like(aprob) ; label[action] = 1\n", "\n", " # Step the environment and get new measurements\n", " obs, reward, done, info = env.step(action)\n", " env.render()\n", " reward_sum += reward\n", "\n", " # record game history\n", " states.append(diff_state) ; labels.append(label) ; rewards.append(reward)\n", "\n", " if done:\n", " # update running reward\n", " running_reward = reward_sum if running_reward is None else running_reward * 0.99 + reward_sum * 0.01\n", " running_rewards.append(running_reward)\n", " #print(np.vstack(rs).shape)\n", " feed = {agent.tf_x: np.vstack(states), agent.tf_epr: np.vstack(rewards), agent.tf_y: np.vstack(labels)}\n", " agent.update(feed)\n", " # print progress console\n", " if n % 10 == 0:\n", " print ('ep {}: reward: {}, mean reward: {:3f}'.format(n, reward_sum, running_reward))\n", " else:\n", " print ('\\tep {}: reward: {}'.format(n, reward_sum))\n", "\n", " # Start next episode and save model\n", " states, rewards, labels = [], [], []\n", " obs = env.reset()\n", " n += 1 # the Next Episode\n", "\n", " reward_sum = 0\n", " if n % 50 == 0:\n", " agent.save()\n", " done = False\n", "\n", "plt.plot(running_rewards)\n", "plt.xlabel('episodes')\n", "plt.ylabel('Running Averge')\n", "plt.show()\n", "env.close()\n" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "scrolled": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "[2018-07-10 22:18:14,115] Making new env: Pong-v0\n", "[2018-07-10 22:18:14,268] Clearing 4 monitor files from previous run (because force=True was provided)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "models1/pong.ckpt-2500\n", "INFO:tensorflow:Restoring parameters from models1/pong.ckpt-2500\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[2018-07-10 22:18:14,368] Restoring parameters from models1/pong.ckpt-2500\n", "[2018-07-10 22:18:14,418] Starting new video recorder writing to /home/am/Dropbox/AIforIoT/Chapter06/save-mov/openaigym.video.6.24686.video000000.mp4\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "loaded model: models1/pong.ckpt-2500\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[2018-07-10 22:18:26,865] Finished writing results. You can upload them to the scoreboard via gym.upload('/home/am/Dropbox/AIforIoT/Chapter06/save-mov')\n" ] } ], "source": [ "from gym import wrappers\n", "env = gym.envs.make(env_name)\n", "env = wrappers.Monitor(env, 'save-mov',force=True)\n", "with tf.Session() as sess:\n", " \n", " agent.set_session(sess)\n", " sess.run(tf.global_variables_initializer())\n", " agent.load()\n", " \n", " obs = env.reset()\n", " cur_state = preprocess(obs)\n", " \n", "\n", " done = False\n", " while not done:\n", " diff_state = cur_state - prev_state if prev_state is not None else np.zeros(n_size*n_size)\n", " prev_state = cur_state\n", " aprob = agent.predict_UP(diff_state) ; aprob = aprob[0,:]\n", " action = np.argmax(aprob)\n", " #print(action)\n", " obs, reward, done, _ = env.step(action)\n", " env.render()\n", " cur_state = preprocess(obs)\n", " \n", "env.close()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python [conda env:tensorflow]", "language": "python", "name": "conda-env-tensorflow-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.4" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: Chapter06/Readme.md ================================================ Reinforcement Learning is very different from both supervised and unsupervised learning. It is the way most living beings learn - interacting with the environment. In this chapter, we will study different algorithms employed for reinforcement learning. As you will progress through the chapter, you will: * Know what is reinforcement learning, and how it is different from supervised learning and unsupervised learning. * Know different elements of reinforcement learning. * Learn about some fascinating applications of RL in the real world. * Understand the OpenAI interface for training RL agents. * Know about Q-learning and use it to train an RL agent. * Know about Deep Q Networks and employ them to train an agent to play Atari. * Know about Policy Gradient algorithm and use it to ================================================ FILE: Chapter06/Taxi_drop-off-NN.ipynb ================================================ { "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": { "scrolled": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/am/anaconda3/envs/tensorflow/lib/python3.5/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n", " from ._conv import register_converters as _register_converters\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "+---------+\n", "|R: | : :\u001b[35mG\u001b[0m|\n", "| : : : : |\n", "| : : : : |\n", "| | : | : |\n", "|Y|\u001b[43m \u001b[0m: |\u001b[34;1mB\u001b[0m: |\n", "+---------+\n", "\n" ] } ], "source": [ "import gym\n", "import numpy as np\n", "import tensorflow as tf\n", "env = gym.make('Taxi-v2')\n", "obs = env.reset()\n", "env.render()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true, "scrolled": true }, "outputs": [], "source": [ "m = env.observation_space.n # size of the state space\n", "n = env.action_space.n # size of action space" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true, "scrolled": true }, "outputs": [], "source": [ "# Intialize the hyperparameters\n", "gamma = 0.9\n", "max_episode = 1000\n", "max_steps = 100\n", "epsilon = 0.4\n", "alpha = 0.01" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "class QNetwork:\n", " def __init__(self,m,n,alpha):\n", " self.s = tf.placeholder(shape=[1,m], dtype=tf.float32)\n", " W = tf.Variable(tf.random_normal([m,n], stddev=2))\n", " bias = tf.Variable(tf.random_normal([1, n]))\n", " self.Q = tf.matmul(self.s,W) + bias\n", " self.a = tf.argmax(self.Q,1)\n", " \n", " self.Q_hat = tf.placeholder(shape=[1,n],dtype=tf.float32)\n", " loss = tf.reduce_sum(tf.square(self.Q_hat-self.Q))\n", " optimizer = tf.train.GradientDescentOptimizer(learning_rate=alpha)\n", " self.train = optimizer.minimize(loss)\n", " init = tf.global_variables_initializer()\n", " \n", " self.sess = tf.Session()\n", " self.sess.run(init)\n", " \n", " def get_action(self,s):\n", " return self.sess.run([self.a,self.Q], feed_dict={self.s:s})\n", " \n", " def learnQ(self,s,Q_hat):\n", " self.sess.run(self.train, feed_dict= {self.s:s, self.Q_hat:Q_hat})\n", " \n", " def Qnew(self,s):\n", " return self.sess.run(self.Q, feed_dict={self.s:s})\n", " \n", " " ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Total reward per episode is: -275.812\n" ] } ], "source": [ "QNN = QNetwork(m,n, alpha)\n", "rewards = []\n", "for i in range(max_episode):\n", " # Start with new environment\n", " s = env.reset()\n", " S = np.identity(m)[s:s+1]\n", " done = False\n", " counter = 0\n", " rtot = 0\n", " for _ in range(max_steps):\n", " # Choose an action using epsilon greedy policy\n", " a, Q_hat = QNN.get_action(S) \n", " p = np.random.rand()\n", " if p > epsilon:\n", " a[0] = env.action_space.sample() #explore\n", " \n", " s_new, r, done, _ = env.step(a[0])\n", " rtot += r\n", " # Update Q table\n", " S_new = np.identity(m)[s_new:s_new+1]\n", " Q_new = QNN.Qnew(S_new) \n", " maxQ = np.max(Q_new)\n", " Q_hat[0,a[0]] = r + gamma*maxQ\n", " QNN.learnQ(S,Q_hat)\n", " S = S_new\n", " #print(Q_hat[0,a[0]],r)\n", " if done:\n", " break\n", " rewards.append(rtot)\n", "print (\"Total reward per episode is: \" + str(sum(rewards)/max_episode)) " ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0,0.5,'Total Reward')" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "% matplotlib inline\n", "plt.plot(rewards)\n", "plt.xlabel('Episodes')\n", "plt.ylabel('Total Reward')" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+---------+\n", "|R: | : :\u001b[34;1mG\u001b[0m|\n", "| : :\u001b[43m \u001b[0m: : |\n", "| : : : : |\n", "| | : | : |\n", "|Y| : |\u001b[35mB\u001b[0m: |\n", "+---------+\n", "\n", "+---------+\n", "|R: | : :\u001b[34;1mG\u001b[0m|\n", "| : : : : |\n", "| : :\u001b[43m \u001b[0m: : |\n", "| | : | : |\n", "|Y| : |\u001b[35mB\u001b[0m: |\n", "+---------+\n", " (South)\n", "+---------+\n", "|R: | : :\u001b[34;1mG\u001b[0m|\n", "| : : : : |\n", "| : : : : |\n", "| | :\u001b[43m \u001b[0m| : |\n", "|Y| : |\u001b[35mB\u001b[0m: |\n", "+---------+\n", " (South)\n", "+---------+\n", "|R: | : :\u001b[34;1mG\u001b[0m|\n", "| : : : : |\n", "| : : : : |\n", "| | : | : |\n", "|Y| :\u001b[43m \u001b[0m|\u001b[35mB\u001b[0m: |\n", "+---------+\n", " (South)\n", "+---------+\n", "|R: | : :\u001b[34;1mG\u001b[0m|\n", "| : : : : |\n", "| : : : : |\n", "| | : | : |\n", "|Y| :\u001b[43m \u001b[0m|\u001b[35mB\u001b[0m: |\n", "+---------+\n", " (South)\n", "+---------+\n", "|R: | : :\u001b[34;1mG\u001b[0m|\n", "| : : : : |\n", "| : : : : |\n", "| | : | : |\n", "|Y| :\u001b[43m \u001b[0m|\u001b[35mB\u001b[0m: |\n", "+---------+\n", " (South)\n", "+---------+\n", "|R: | : :\u001b[34;1mG\u001b[0m|\n", "| : : : : |\n", "| : : : : |\n", "| | : | : |\n", "|Y| :\u001b[43m \u001b[0m|\u001b[35mB\u001b[0m: |\n", "+---------+\n", " (South)\n", "+---------+\n", "|R: | : :\u001b[34;1mG\u001b[0m|\n", "| : : : : |\n", "| : : : : |\n", "| | : | : |\n", "|Y| :\u001b[43m \u001b[0m|\u001b[35mB\u001b[0m: |\n", "+---------+\n", " (South)\n", "+---------+\n", "|R: | : :\u001b[34;1mG\u001b[0m|\n", "| : : : : |\n", "| : : : : |\n", "| | : | : |\n", "|Y| :\u001b[43m \u001b[0m|\u001b[35mB\u001b[0m: |\n", "+---------+\n", " (South)\n", "+---------+\n", "|R: | : :\u001b[34;1mG\u001b[0m|\n", "| : : : : |\n", "| : : : : |\n", "| | : | : |\n", "|Y| :\u001b[43m \u001b[0m|\u001b[35mB\u001b[0m: |\n", "+---------+\n", " (South)\n", "+---------+\n", "|R: | : :\u001b[34;1mG\u001b[0m|\n", "| : : : : |\n", "| : : : : |\n", "| | : | : |\n", "|Y| :\u001b[43m \u001b[0m|\u001b[35mB\u001b[0m: |\n", "+---------+\n", " (South)\n", "+---------+\n", "|R: | : :\u001b[34;1mG\u001b[0m|\n", "| : : : : |\n", "| : : : : |\n", "| | : | : |\n", "|Y| :\u001b[43m \u001b[0m|\u001b[35mB\u001b[0m: |\n", "+---------+\n", " (South)\n", "+---------+\n", "|R: | : :\u001b[34;1mG\u001b[0m|\n", "| : : : : |\n", "| : : : : |\n", "| | : | : |\n", "|Y| :\u001b[43m \u001b[0m|\u001b[35mB\u001b[0m: |\n", "+---------+\n", " (South)\n" ] } ], "source": [ "s = env.reset()\n", "S = np.identity(m)[s:s+1]\n", "done = False\n", "env.render()\n", "# Test the learned Agent\n", "for i in range(12):\n", " a,_ = QNN.get_action(S)\n", " s, _, done, _ = env.step(a[0])\n", " S = np.identity(m)[s:s+1]\n", " env.render()\n", " if done:\n", " break " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python [conda env:tensorflow]", "language": "python", "name": "conda-env-tensorflow-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.4" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: Chapter06/Taxi_drop-off.ipynb ================================================ { "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "scrolled": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "[2018-06-29 21:51:21,422] Making new env: Taxi-v2\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "+---------+\n", "|\u001b[34;1mR\u001b[0m: | : :G|\n", "| : : : : |\n", "| : :\u001b[43m \u001b[0m: : |\n", "| | : | : |\n", "|Y| : |\u001b[35mB\u001b[0m: |\n", "+---------+\n", "\n" ] } ], "source": [ "import gym\n", "import numpy as np\n", "env = gym.make('Taxi-v2')\n", "obs = env.reset()\n", "env.render()" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The Q-table will have 500 rows and 6 columns, resulting in total 3000 entries\n" ] } ], "source": [ "m = env.observation_space.n # size of the state space\n", "n = env.action_space.n # size of action space\n", "\n", "print(\"The Q-table will have {} rows and {} columns, resulting in total {} entries\".\\\n", " format(m,n,m*n))" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "scrolled": true }, "outputs": [], "source": [ "# Intialize the Q-table and hyperparameters\n", "Q = np.zeros([m,n])\n", "gamma = 0.97\n", "max_episode = 1000\n", "max_steps = 100\n", "alpha = 0.7\n", "epsilon = 0.3" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "scrolled": true }, "outputs": [], "source": [ "for i in range(max_episode):\n", " # Start with new environment\n", " s = env.reset()\n", " done = False\n", " counter = 0\n", " for _ in range(max_steps):\n", " # Choose an action using epsilon greedy policy\n", " p = np.random.rand()\n", " if p > epsilon or (not np.any(Q[s,:])):\n", " a = env.action_space.sample() #explore\n", " else:\n", " a = np.argmax(Q[s,:]) #exploit\n", " \n", " s_new, r, done, _ = env.step(a)\n", " # Update Q table\n", " Q[s,a] = (1-alpha)*Q[s,a] + alpha*(r + gamma*np.max(Q[s_new,:]))\n", " #print(Q[s,a],r)\n", " s = s_new\n", " if done:\n", " break\n", " " ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+---------+\n", "|R: | : :\u001b[35m\u001b[43mG\u001b[0m\u001b[0m|\n", "| : : : : |\n", "| : : : : |\n", "| | : | : |\n", "|\u001b[34;1mY\u001b[0m| : |B: |\n", "+---------+\n", "\n", "+---------+\n", "|R: | : :\u001b[35mG\u001b[0m|\n", "| : : : :\u001b[43m \u001b[0m|\n", "| : : : : |\n", "| | : | : |\n", "|\u001b[34;1mY\u001b[0m| : |B: |\n", "+---------+\n", " (South)\n", "+---------+\n", "|R: | : :\u001b[35mG\u001b[0m|\n", "| : : : : |\n", "| : : : :\u001b[43m \u001b[0m|\n", "| | : | : |\n", "|\u001b[34;1mY\u001b[0m| : |B: |\n", "+---------+\n", " (South)\n", "+---------+\n", "|R: | : :\u001b[35mG\u001b[0m|\n", "| : : : : |\n", "| : : :\u001b[43m \u001b[0m: |\n", "| | : | : |\n", "|\u001b[34;1mY\u001b[0m| : |B: |\n", "+---------+\n", " (West)\n", "+---------+\n", "|R: | : :\u001b[35mG\u001b[0m|\n", "| : : : : |\n", "| : :\u001b[43m \u001b[0m: : |\n", "| | : | : |\n", "|\u001b[34;1mY\u001b[0m| : |B: |\n", "+---------+\n", " (West)\n", "+---------+\n", "|R: | : :\u001b[35mG\u001b[0m|\n", "| : : : : |\n", "| :\u001b[43m \u001b[0m: : : |\n", "| | : | : |\n", "|\u001b[34;1mY\u001b[0m| : |B: |\n", "+---------+\n", " (West)\n", "+---------+\n", "|R: | : :\u001b[35mG\u001b[0m|\n", "| : : : : |\n", "|\u001b[43m \u001b[0m: : : : |\n", "| | : | : |\n", "|\u001b[34;1mY\u001b[0m| : |B: |\n", "+---------+\n", " (West)\n", "+---------+\n", "|R: | : :\u001b[35mG\u001b[0m|\n", "| : : : : |\n", "| : : : : |\n", "|\u001b[43m \u001b[0m| : | : |\n", "|\u001b[34;1mY\u001b[0m| : |B: |\n", "+---------+\n", " (South)\n", "+---------+\n", "|R: | : :\u001b[35mG\u001b[0m|\n", "| : : : : |\n", "| : : : : |\n", "| | : | : |\n", "|\u001b[34;1m\u001b[43mY\u001b[0m\u001b[0m| : |B: |\n", "+---------+\n", " (South)\n", "+---------+\n", "|R: | : :\u001b[35mG\u001b[0m|\n", "| : : : : |\n", "| : : : : |\n", "| | : | : |\n", "|\u001b[42mY\u001b[0m| : |B: |\n", "+---------+\n", " (Pickup)\n", "+---------+\n", "|R: | : :\u001b[35mG\u001b[0m|\n", "| : : : : |\n", "| : : : : |\n", "|\u001b[42m_\u001b[0m| : | : |\n", "|Y| : |B: |\n", "+---------+\n", " (North)\n", "+---------+\n", "|R: | : :\u001b[35mG\u001b[0m|\n", "| : : : : |\n", "|\u001b[42m_\u001b[0m: : : : |\n", "| | : | : |\n", "|Y| : |B: |\n", "+---------+\n", " (North)\n", "+---------+\n", "|R: | : :\u001b[35mG\u001b[0m|\n", "| : : : : |\n", "| :\u001b[42m_\u001b[0m: : : |\n", "| | : | : |\n", "|Y| : |B: |\n", "+---------+\n", " (East)\n", "+---------+\n", "|R: | : :\u001b[35mG\u001b[0m|\n", "| : : : : |\n", "| : :\u001b[42m_\u001b[0m: : |\n", "| | : | : |\n", "|Y| : |B: |\n", "+---------+\n", " (East)\n", "+---------+\n", "|R: | : :\u001b[35mG\u001b[0m|\n", "| : : : : |\n", "| : : :\u001b[42m_\u001b[0m: |\n", "| | : | : |\n", "|Y| : |B: |\n", "+---------+\n", " (East)\n", "+---------+\n", "|R: | : :\u001b[35mG\u001b[0m|\n", "| : : :\u001b[42m_\u001b[0m: |\n", "| : : : : |\n", "| | : | : |\n", "|Y| : |B: |\n", "+---------+\n", " (North)\n", "+---------+\n", "|R: | : :\u001b[35mG\u001b[0m|\n", "| : : : :\u001b[42m_\u001b[0m|\n", "| : : : : |\n", "| | : | : |\n", "|Y| : |B: |\n", "+---------+\n", " (East)\n", "+---------+\n", "|R: | : :\u001b[35m\u001b[42mG\u001b[0m\u001b[0m|\n", "| : : : : |\n", "| : : : : |\n", "| | : | : |\n", "|Y| : |B: |\n", "+---------+\n", " (North)\n", "+---------+\n", "|R: | : :\u001b[35m\u001b[42mG\u001b[0m\u001b[0m|\n", "| : : : : |\n", "| : : : : |\n", "| | : | : |\n", "|Y| : |B: |\n", "+---------+\n", " (Dropoff)\n" ] } ], "source": [ "s = env.reset()\n", "done = False\n", "env.render()\n", "# Test the learned Agent\n", "for i in range(max_steps):\n", " a = np.argmax(Q[s,:])\n", " s, _, done, _ = env.step(a)\n", " env.render()\n", " if done:\n", " break " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python [conda env:tensorflow]", "language": "python", "name": "conda-env-tensorflow-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.4" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: Chapter07/DCGAN_CelebA.ipynb ================================================ { "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/am/anaconda3/envs/tensorflow/lib/python3.5/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n", " from ._conv import register_converters as _register_converters\n" ] } ], "source": [ "import loader_celebA as loader\n", "import os\n", "from glob import glob\n", "import numpy as np\n", "from matplotlib import pyplot\n", "import tensorflow as tf\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Found Celeb-A - skip\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Let's download the dataset from http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html under the link \"Align&Cropped Images\n", "# alternatively You can use https://www.dropbox.com/sh/8oqt9vytwxb3s4r/AADIKlz8PR9zr6Y20qbkunrba/Img/img_align_celeba.zip?dl=1&pv=1\n", "# This function unzips the data and kreep all the images in celebA folder\n", "loader.download_celeb_a()\n", " \n", "\n", "# Let us explore the images\n", "data_dir = os.getcwd()\n", "test_images = loader.get_batch(glob(os.path.join(data_dir, 'celebA/*.jpg'))[:10], 56, 56)\n", "pyplot.imshow(loader.plot_images(test_images))" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def discriminator(images, reuse=False):\n", " \"\"\"\n", " Create the discriminator network\n", " \"\"\"\n", " alpha = 0.2\n", " \n", " with tf.variable_scope('discriminator', reuse=reuse):\n", " # using 4 layer network as in DCGAN Paper\n", " \n", " # First convolution layer\n", " conv1 = tf.layers.conv2d(images, 64, 5, 2, 'SAME')\n", " lrelu1 = tf.maximum(alpha * conv1, conv1)\n", " \n", " # Second convolution layer\n", " conv2 = tf.layers.conv2d(lrelu1, 128, 5, 2, 'SAME')\n", " batch_norm2 = tf.layers.batch_normalization(conv2, training=True)\n", " lrelu2 = tf.maximum(alpha * batch_norm2, batch_norm2)\n", " \n", " # Third convolution layer\n", " conv3 = tf.layers.conv2d(lrelu2, 256, 5, 1, 'SAME')\n", " batch_norm3 = tf.layers.batch_normalization(conv3, training=True)\n", " lrelu3 = tf.maximum(alpha * batch_norm3, batch_norm3)\n", " \n", " # Flatten layer\n", " flat = tf.reshape(lrelu3, (-1, 4*4*256))\n", " \n", " # Logits\n", " logits = tf.layers.dense(flat, 1)\n", " \n", " # Output\n", " out = tf.sigmoid(logits)\n", " \n", " return out, logits" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def generator(z, out_channel_dim, is_train=True):\n", " \"\"\"\n", " Create the generator network\n", " \"\"\"\n", " alpha = 0.2\n", " \n", " with tf.variable_scope('generator', reuse=False if is_train==True else True):\n", " # First fully connected layer\n", " x_1 = tf.layers.dense(z, 2*2*512)\n", " \n", " # Reshape it to start the convolutional stack\n", " deconv_2 = tf.reshape(x_1, (-1, 2, 2, 512))\n", " batch_norm2 = tf.layers.batch_normalization(deconv_2, training=is_train)\n", " lrelu2 = tf.maximum(alpha * batch_norm2, batch_norm2)\n", " \n", " # Deconv 1\n", " deconv3 = tf.layers.conv2d_transpose(lrelu2, 256, 5, 2, padding='VALID')\n", " batch_norm3 = tf.layers.batch_normalization(deconv3, training=is_train)\n", " lrelu3 = tf.maximum(alpha * batch_norm3, batch_norm3)\n", " \n", " \n", " # Deconv 2\n", " deconv4 = tf.layers.conv2d_transpose(lrelu3, 128, 5, 2, padding='SAME')\n", " batch_norm4 = tf.layers.batch_normalization(deconv4, training=is_train)\n", " lrelu4 = tf.maximum(alpha * batch_norm4, batch_norm4)\n", " \n", " # Output layer\n", " logits = tf.layers.conv2d_transpose(lrelu4, out_channel_dim, 5, 2, padding='SAME')\n", " \n", " out = tf.tanh(logits)\n", " \n", " return out\n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def model_loss(input_real, input_z, out_channel_dim):\n", " \"\"\"\n", " Get the loss for the discriminator and generator\n", " \"\"\"\n", " \n", " label_smoothing = 0.9\n", " \n", " g_model = generator(input_z, out_channel_dim)\n", " d_model_real, d_logits_real = discriminator(input_real)\n", " d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)\n", " \n", " d_loss_real = tf.reduce_mean(\n", " tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real,\n", " labels=tf.ones_like(d_model_real) * label_smoothing))\n", " d_loss_fake = tf.reduce_mean(\n", " tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake,\n", " labels=tf.zeros_like(d_model_fake)))\n", " \n", " d_loss = d_loss_real + d_loss_fake\n", " \n", " g_loss = tf.reduce_mean(\n", " tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake,\n", " labels=tf.ones_like(d_model_fake) * label_smoothing))\n", " \n", " \n", " return d_loss, g_loss" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def model_opt(d_loss, g_loss, learning_rate, beta1):\n", " \"\"\"\n", " Get optimization operations\n", " \"\"\"\n", " t_vars = tf.trainable_variables()\n", " d_vars = [var for var in t_vars if var.name.startswith('discriminator')]\n", " g_vars = [var for var in t_vars if var.name.startswith('generator')]\n", "\n", " # Optimize\n", " with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)): \n", " d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)\n", " g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)\n", "\n", " return d_train_opt, g_train_opt" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def generator_output(sess, n_images, input_z, out_channel_dim):\n", " \"\"\"\n", " Show example output for the generator\n", " \"\"\"\n", " z_dim = input_z.get_shape().as_list()[-1]\n", " example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])\n", "\n", " samples = sess.run(\n", " generator(input_z, out_channel_dim, False),\n", " feed_dict={input_z: example_z})\n", "\n", " pyplot.imshow(loader.plot_images(samples))\n", " pyplot.show()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_files):\n", " \"\"\"\n", " Train the GAN\n", " \"\"\"\n", " w, h, num_ch = data_shape[1], data_shape[2], data_shape[3]\n", " X = tf.placeholder(tf.float32, shape=(None, w, h, num_ch), name='input_real') \n", " Z = tf.placeholder(tf.float32, (None, z_dim), name='input_z')\n", " #model_inputs(data_shape[1], data_shape[2], data_shape[3], z_dim)\n", " D_loss, G_loss = model_loss(X, Z, data_shape[3])\n", " D_solve, G_solve = model_opt(D_loss, G_loss, learning_rate, beta1)\n", " \n", " \n", " \n", " with tf.Session() as sess:\n", " sess.run(tf.global_variables_initializer())\n", " train_loss_d = []\n", " train_loss_g = []\n", " for epoch_i in range(epoch_count):\n", " num_batch = 0\n", " lossD, lossG = 0,0\n", " for batch_images in get_batches(batch_size, data_shape, data_files):\n", " \n", " # values range from -0.5 to 0.5 so we scale to range -1, 1\n", " batch_images = batch_images * 2\n", " num_batch += 1\n", " \n", " batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))\n", " \n", " _,d_loss = sess.run([D_solve,D_loss], feed_dict={X: batch_images, Z: batch_z})\n", " _,g_loss = sess.run([G_solve,G_loss], feed_dict={X: batch_images, Z: batch_z})\n", " \n", " lossD += (d_loss/batch_size)\n", " lossG += (g_loss/batch_size)\n", " if num_batch % 500 == 0:\n", " # After every 500 batches\n", " print(\"Epoch {}/{} For Batch {} Discriminator Loss: {:.4f} Generator Loss: {:.4f}\".\n", " format(epoch_i+1, epochs, num_batch, lossD/num_batch, lossG/num_batch))\n", " \n", " \n", " generator_output(sess, 9, Z, data_shape[3])\n", " train_loss_d.append(lossD/num_batch)\n", " train_loss_g.append(lossG/num_batch)\n", " \n", " return train_loss_d, train_loss_g" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/2 For Batch 500 Discriminator Loss: 0.0733 Generator Loss: 0.0750\n" ] }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "ename": "KeyboardInterrupt", "evalue": "", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0mshape\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata_files\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mIMAGE_WIDTH\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mIMAGE_HEIGHT\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0;32mwith\u001b[0m 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"pyplot.xlabel('epochs')\n", "pyplot.ylabel('Loss')" ] } ], "metadata": { "kernelspec": { "display_name": "Python [conda env:tensorflow]", "language": "python", "name": "conda-env-tensorflow-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.4" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: Chapter07/Images/Readme.md ================================================ Images ================================================ FILE: Chapter07/Readme.md ================================================ Machine learning (ML) and Artificial Intelligence (AI) have touched almost all fields related to man. Agriculture, music, health, defence, you will not find a single field where AI has not leave its mark. The enormous success of AI/ML, besides the presence of computational powers, also depends on the generation of a significant amount of data. Majority of the data generated is unlabelled, and thus understanding the inherent distribution of the data is an important ML task. It is here that Generative Models come into the picture. In past few years, deep Generative models have shown great success in understanding the data distribution and have been used in a variety of applications. Two of the most popular generative models are Variational Autoencoders (VAE) and Generative Adversarial Networks (GAN). In this chapter, we will learn about both VAEs and GANs and use them to generate images. After reading the chapter, you will: * Know the difference between generative networks and discriminative networks. * Learn about Variational Autoencoders * Understand the intuitive functioning of Generative Adversarial Networks * Implement the Vanilla GAN and use it to generate handwritten digits. * Know the most popular variation of GAN, the DCGAN * Implement DCGAN in TensorFlow and use it to generate faces. * Know further variations and applications of GAN. ================================================ FILE: Chapter07/Vanilla_GAN.ipynb ================================================ { "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/am/anaconda3/envs/tensorflow/lib/python3.5/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n", " from ._conv import register_converters as _register_converters\n" ] } ], "source": [ "# import the necessaey modules\n", "import tensorflow as tf\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import matplotlib.gridspec as gridspec\n", "import os\n", "from tensorflow.contrib.layers import xavier_initializer\n", "\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Extracting MNIST_data/train-images-idx3-ubyte.gz\n", "Extracting MNIST_data/train-labels-idx1-ubyte.gz\n", "Extracting MNIST_data/t10k-images-idx3-ubyte.gz\n", "Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\n" ] } ], "source": [ "# Load data\n", "from tensorflow.examples.tutorials.mnist import input_data\n", "data = input_data.read_data_sets('MNIST_data', one_hot=True)\n", "\n", "# define hyperparameters\n", "batch_size = 128\n", "Z_dim = 100\n", "im_size = 28\n", "h_size=128\n", "learning_rate_D = .0005\n", "learning_rate_G = .0006\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "#Create Placeholder for input X and random noise Z\n", "X = tf.placeholder(tf.float32, shape=[None, im_size*im_size])\n", "Z = tf.placeholder(tf.float32, shape=[None, Z_dim])\n", "initializer=xavier_initializer()\n", "\n", "# Define Discriminator and Generator training variables\n", "\n", "#Discriminiator\n", "D_W1 = tf.Variable(initializer([im_size*im_size, h_size]))\n", "D_b1 = tf.Variable(tf.zeros(shape=[h_size]))\n", "\n", "D_W2 = tf.Variable(initializer([h_size, 1]))\n", "D_b2 = tf.Variable(tf.zeros(shape=[1]))\n", "\n", "theta_D = [D_W1, D_W2, D_b1, D_b2]\n", "\n", "#Generator\n", "G_W1 = tf.Variable(initializer([Z_dim, h_size]))\n", "G_b1 = tf.Variable(tf.zeros(shape=[h_size]))\n", "\n", "G_W2 = tf.Variable(initializer([h_size, im_size*im_size]))\n", "G_b2 = tf.Variable(tf.zeros(shape=[im_size*im_size]))\n", "\n", "theta_G = [G_W1, G_W2, G_b1, G_b2]" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "def sample_Z(m, n):\n", " return np.random.uniform(-1., 1., size=[m, n])\n", "\n", "\n", "def generator(z):\n", " \"\"\" Two layer Generator Network Z=>128=>784 \"\"\"\n", " G_h1 = tf.nn.relu(tf.matmul(z, G_W1) + G_b1)\n", " G_log_prob = tf.matmul(G_h1, G_W2) + G_b2\n", " G_prob = tf.nn.sigmoid(G_log_prob)\n", "\n", " return G_prob\n", "\n", "\n", "def discriminator(x):\n", " \"\"\" Two layer Discriminator Network X=>128=>1 \"\"\"\n", " D_h1 = tf.nn.relu(tf.matmul(x, D_W1) + D_b1)\n", " D_logit = tf.matmul(D_h1, D_W2) + D_b2\n", " D_prob = tf.nn.sigmoid(D_logit)\n", "\n", " return D_prob, D_logit\n", "\n", "\n", "def plot(samples):\n", " \"function to plot generated samples\"\n", " fig = plt.figure(figsize=(10, 10))\n", " gs = gridspec.GridSpec(5, 5)\n", " gs.update(wspace=0.05, hspace=0.05)\n", "\n", " for i, sample in enumerate(samples):\n", " ax = plt.subplot(gs[i])\n", " plt.axis('off')\n", " ax.set_xticklabels([])\n", " ax.set_yticklabels([])\n", " ax.set_aspect('equal')\n", " plt.imshow(sample.reshape(28, 28), cmap='gray')\n", "\n", " return fig\n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "G_sample = generator(Z)\n", "D_real, D_logit_real = discriminator(X)\n", "D_fake, D_logit_fake = discriminator(G_sample)\n", "\n", "# losses:\n", "# -------------------\n", "D_loss_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=D_logit_real, labels=tf.ones_like(D_logit_real)))\n", "D_loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=D_logit_fake, labels=tf.zeros_like(D_logit_fake)))\n", "D_loss = D_loss_real + D_loss_fake\n", "G_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=D_logit_fake, labels=tf.ones_like(D_logit_fake)))\n", "\n", "D_solver = tf.train.AdamOptimizer(learning_rate=learning_rate_D).minimize(D_loss, var_list=theta_D)\n", "G_solver = tf.train.AdamOptimizer(learning_rate=learning_rate_G).minimize(G_loss, var_list=theta_G)\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Iter: 0 D loss: 1.085 G_loss: 1.998\n", "Iter: 100 D loss: 0.08578 G_loss: 5.416\n", "Iter: 200 D loss: 0.3299 G_loss: 3.09\n", "Iter: 300 D loss: 0.2034 G_loss: 3.862\n", "Iter: 400 D loss: 0.08722 G_loss: 4.015\n", "Iter: 500 D loss: 0.0238 G_loss: 4.706\n", "Iter: 600 D loss: 0.03654 G_loss: 4.905\n", "Iter: 700 D loss: 0.03494 G_loss: 4.825\n", "Iter: 800 D loss: 0.02324 G_loss: 5.374\n", "Iter: 900 D loss: 0.01525 G_loss: 5.616\n", "Iter: 1000 D loss: 0.05434 G_loss: 5.536\n", "Iter: 1100 D loss: 0.02404 G_loss: 5.85\n", "Iter: 1200 D loss: 0.02222 G_loss: 5.645\n", "Iter: 1300 D loss: 0.04074 G_loss: 6.221\n", "Iter: 1400 D loss: 0.02187 G_loss: 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2.554\n", "Iter: 90500 D loss: 0.4961 G_loss: 2.568\n", "Iter: 90600 D loss: 0.5252 G_loss: 2.56\n", "Iter: 90700 D loss: 0.5454 G_loss: 2.645\n", "Iter: 90800 D loss: 0.5431 G_loss: 2.748\n", "Iter: 90900 D loss: 0.5505 G_loss: 2.741\n", "Iter: 91000 D loss: 0.4621 G_loss: 2.709\n", "Iter: 91100 D loss: 0.5716 G_loss: 2.3\n", "Iter: 91200 D loss: 0.4838 G_loss: 2.375\n", "Iter: 91300 D loss: 0.563 G_loss: 2.669\n", "Iter: 91400 D loss: 0.573 G_loss: 2.598\n", "Iter: 91500 D loss: 0.6137 G_loss: 2.863\n", "Iter: 91600 D loss: 0.6003 G_loss: 2.717\n", "Iter: 91700 D loss: 0.5156 G_loss: 2.703\n", "Iter: 91800 D loss: 0.5459 G_loss: 2.742\n", "Iter: 91900 D loss: 0.5363 G_loss: 2.592\n", "Iter: 92000 D loss: 0.4685 G_loss: 2.564\n", "Iter: 92100 D loss: 0.4688 G_loss: 2.708\n", "Iter: 92200 D loss: 0.6248 G_loss: 2.317\n", "Iter: 92300 D loss: 0.4224 G_loss: 2.651\n", "Iter: 92400 D loss: 0.5207 G_loss: 2.43\n", "Iter: 92500 D loss: 0.5117 G_loss: 2.731\n", "Iter: 92600 D loss: 0.5375 G_loss: 2.642\n", "Iter: 92700 D loss: 0.4374 G_loss: 2.303\n", "Iter: 92800 D loss: 0.4899 G_loss: 2.818\n", "Iter: 92900 D loss: 0.5472 G_loss: 2.638\n", "Iter: 93000 D loss: 0.5079 G_loss: 2.552\n", "Iter: 93100 D loss: 0.5597 G_loss: 2.31\n", "Iter: 93200 D loss: 0.6198 G_loss: 2.403\n", "Iter: 93300 D loss: 0.4476 G_loss: 2.767\n", "Iter: 93400 D loss: 0.4883 G_loss: 2.758\n", "Iter: 93500 D loss: 0.4375 G_loss: 2.63\n", "Iter: 93600 D loss: 0.4311 G_loss: 2.577\n", "Iter: 93700 D loss: 0.4585 G_loss: 2.41\n", "Iter: 93800 D loss: 0.5636 G_loss: 2.937\n", "Iter: 93900 D loss: 0.5743 G_loss: 2.567\n", "Iter: 94000 D loss: 0.4221 G_loss: 2.416\n", "Iter: 94100 D loss: 0.5488 G_loss: 3.05\n", "Iter: 94200 D loss: 0.5656 G_loss: 2.689\n", "Iter: 94300 D loss: 0.5288 G_loss: 2.688\n", "Iter: 94400 D loss: 0.4748 G_loss: 2.799\n", "Iter: 94500 D loss: 0.5187 G_loss: 2.459\n", "Iter: 94600 D loss: 0.454 G_loss: 2.572\n", "Iter: 94700 D loss: 0.582 G_loss: 2.512\n", "Iter: 94800 D loss: 0.5412 G_loss: 2.712\n", "Iter: 94900 D loss: 0.5695 G_loss: 2.654\n", "Iter: 95000 D loss: 0.5379 G_loss: 2.467\n", "Iter: 95100 D loss: 0.494 G_loss: 2.528\n", "Iter: 95200 D loss: 0.5245 G_loss: 2.465\n", "Iter: 95300 D loss: 0.5152 G_loss: 2.665\n", "Iter: 95400 D loss: 0.5506 G_loss: 2.651\n", "Iter: 95500 D loss: 0.5408 G_loss: 2.48\n", "Iter: 95600 D loss: 0.5091 G_loss: 2.674\n", "Iter: 95700 D loss: 0.4928 G_loss: 2.769\n", "Iter: 95800 D loss: 0.6571 G_loss: 2.298\n", "Iter: 95900 D loss: 0.6298 G_loss: 2.66\n", "Iter: 96000 D loss: 0.4411 G_loss: 2.929\n", "Iter: 96100 D loss: 0.5124 G_loss: 2.424\n", "Iter: 96200 D loss: 0.6251 G_loss: 2.467\n", "Iter: 96300 D loss: 0.4483 G_loss: 2.284\n", "Iter: 96400 D loss: 0.501 G_loss: 2.722\n", "Iter: 96500 D loss: 0.4814 G_loss: 2.468\n", "Iter: 96600 D loss: 0.5751 G_loss: 2.647\n", "Iter: 96700 D loss: 0.5086 G_loss: 2.308\n", "Iter: 96800 D loss: 0.4959 G_loss: 2.686\n", "Iter: 96900 D loss: 0.6352 G_loss: 2.532\n", "Iter: 97000 D loss: 0.4949 G_loss: 2.72\n", "Iter: 97100 D loss: 0.5548 G_loss: 2.67\n", "Iter: 97200 D loss: 0.5301 G_loss: 2.789\n", "Iter: 97300 D loss: 0.505 G_loss: 2.707\n", "Iter: 97400 D loss: 0.4875 G_loss: 2.714\n", "Iter: 97500 D loss: 0.5051 G_loss: 2.557\n", "Iter: 97600 D loss: 0.6194 G_loss: 2.574\n", "Iter: 97700 D loss: 0.5979 G_loss: 2.64\n", "Iter: 97800 D loss: 0.4831 G_loss: 2.434\n", "Iter: 97900 D loss: 0.5484 G_loss: 2.68\n", "Iter: 98000 D loss: 0.4961 G_loss: 2.68\n", "Iter: 98100 D loss: 0.5347 G_loss: 2.812\n", "Iter: 98200 D loss: 0.5144 G_loss: 2.649\n", "Iter: 98300 D loss: 0.553 G_loss: 2.631\n", "Iter: 98400 D loss: 0.5556 G_loss: 2.557\n", "Iter: 98500 D loss: 0.5233 G_loss: 2.576\n", "Iter: 98600 D loss: 0.4789 G_loss: 2.608\n", "Iter: 98700 D loss: 0.6351 G_loss: 2.446\n", "Iter: 98800 D loss: 0.6554 G_loss: 2.543\n", "Iter: 98900 D loss: 0.5202 G_loss: 2.307\n", "Iter: 99000 D loss: 0.5883 G_loss: 3.114\n", "Iter: 99100 D loss: 0.5 G_loss: 2.624\n", "Iter: 99200 D loss: 0.5332 G_loss: 2.294\n", "Iter: 99300 D loss: 0.6092 G_loss: 2.959\n", "Iter: 99400 D loss: 0.5967 G_loss: 2.729\n", "Iter: 99500 D loss: 0.5029 G_loss: 2.782\n", "Iter: 99600 D loss: 0.5029 G_loss: 2.615\n", "Iter: 99700 D loss: 0.5419 G_loss: 2.247\n", "Iter: 99800 D loss: 0.5293 G_loss: 2.61\n", "Iter: 99900 D loss: 0.5307 G_loss: 2.625\n", "Done\n" ] } ], "source": [ "sess = tf.Session()\n", "sess.run(tf.global_variables_initializer())\n", "GLoss = []\n", "DLoss = []\n", "\n", "if not os.path.exists('out/'):\n", " os.makedirs('out/')\n", "\n", "for it in range(100000):\n", " if it % 100 == 0:\n", " samples = sess.run(G_sample, feed_dict={Z: sample_Z(25, Z_dim)})\n", "\n", " fig = plot(samples)\n", " plt.savefig('out/{}.png'.format(str(it).zfill(3)), bbox_inches='tight')\n", " plt.close(fig)\n", "\n", " X_mb, _ = data.train.next_batch(batch_size)\n", "\n", " _, D_loss_curr = sess.run([D_solver, D_loss], feed_dict={X: X_mb, Z: sample_Z(batch_size, Z_dim)})\n", " _, G_loss_curr = sess.run([G_solver, G_loss], feed_dict={Z: sample_Z(batch_size, Z_dim)})\n", " GLoss.append(G_loss_curr)\n", " DLoss.append(D_loss_curr)\n", "\n", " if it % 100 == 0:\n", " print('Iter: {} D loss: {:.4} G_loss: {:.4}'.format(it,D_loss_curr, G_loss_curr))\n", " \n", "print('Done')" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0,0.5,'Loss')" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(DLoss, label = 'Discriminator Loss')\n", "plt.plot(GLoss, label = 'Generator Loss')\n", "plt.legend()\n", "plt.xlabel('epochs')\n", "plt.ylabel('Loss')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python [conda env:tensorflow]", "language": "python", "name": "conda-env-tensorflow-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.4" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: Chapter07/VariationalAutoEncoders_MNIST.ipynb ================================================ { "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/am/anaconda3/envs/tensorflow/lib/python3.5/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n", " from ._conv import register_converters as _register_converters\n" ] } ], "source": [ "import numpy as np\n", "import tensorflow as tf\n", "\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "\n", "from tensorflow.contrib.layers import fully_connected\n", "\n", "np.random.seed(333)\n", "tf.set_random_seed(333)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Extracting MNIST_data/train-images-idx3-ubyte.gz\n", "Extracting MNIST_data/train-labels-idx1-ubyte.gz\n", "Extracting MNIST_data/t10k-images-idx3-ubyte.gz\n", "Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\n" ] } ], "source": [ "# Load MNIST data in a format suited for tensorflow.\n", "from tensorflow.examples.tutorials.mnist import input_data\n", "mnist = input_data.read_data_sets('MNIST_data', one_hot=True)\n", "n_samples = mnist.train.num_examples\n", "n_input = mnist.train.images[0].shape[0]" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "class VariationalAutoencoder(object):\n", " def __init__(self,n_input, n_z,\n", " learning_rate=0.001, batch_size=100):\n", " self.batch_size = batch_size\n", " self.n_input = n_input\n", " self.n_z = n_z\n", " \n", " # Place holder for the input \n", " self.x = tf.placeholder(tf.float32, shape = [None, n_input])\n", " \n", " \n", " # Use Encoder Network to determine mean and \n", " # (log) variance of Gaussian distribution in the latent space\n", " self.z_mean, self.z_log_sigma_sq = self._encoder_network()\n", "\n", " # Draw a sample z from Gaussian distribution\n", " eps = tf.random_normal((self.batch_size, n_z), 0, 1, dtype=tf.float32)\n", " # z = mu + sigma*epsilon\n", " self.z = tf.add(self.z_mean,tf.multiply(tf.sqrt(tf.exp(self.z_log_sigma_sq)), eps))\n", "\n", " # Use Decoder network to determine mean of\n", " # Bernoulli distribution of reconstructed input\n", " self.x_hat = self._decoder_network()\n", " \n", " \n", " # Define loss function based variational upper-bound and \n", " # corresponding optimizer\n", " # define generation loss\n", " reconstruction_loss = \\\n", " -tf.reduce_sum(self.x * tf.log(1e-10 + self.x_hat)\n", " + (1-self.x) * tf.log(1e-10 + 1 - self.x_hat), 1)\n", " self.reconstruction_loss = tf.reduce_mean(reconstruction_loss)\n", " \n", " latent_loss = -0.5 * tf.reduce_sum(1 + self.z_log_sigma_sq \n", " - tf.square(self.z_mean)- tf.exp(self.z_log_sigma_sq), 1)\n", " self.latent_loss = tf.reduce_mean(latent_loss)\n", " self.cost = tf.reduce_mean(reconstruction_loss + latent_loss) # average over batch\n", " # Define the optimizer\n", " self.optimizer = tf.train.AdamOptimizer(learning_rate).minimize(self.cost)\n", " \n", " \n", " # Initializing the tensor flow variables\n", " init = tf.global_variables_initializer()\n", "\n", " # Launch the session\n", " self.sess = tf.InteractiveSession()\n", " self.sess.run(init)\n", " \n", " \n", " \n", " # Create encoder network\n", " def _encoder_network(self):\n", " # Generate probabilistic encoder (inference network), which\n", " # maps inputs onto a normal distribution in latent space.\n", " layer_1 = fully_connected(self.x,500,activation_fn=tf.nn.softplus) \n", " layer_2 = fully_connected(layer_1, 500, activation_fn=tf.nn.softplus) \n", " z_mean = fully_connected(layer_2,self.n_z, activation_fn=None)\n", " z_log_sigma_sq = fully_connected(layer_2, self.n_z, activation_fn=None)\n", " return (z_mean, z_log_sigma_sq)\n", "\n", " # Create decoder network\n", " def _decoder_network(self):\n", " # Generate probabilistic decoder (generator network), which\n", " # maps points in the latent space onto a Bernoulli distribution in the data space.\n", " layer_1 = fully_connected(self.z,500,activation_fn=tf.nn.softplus) \n", " layer_2 = fully_connected(layer_1, 500, activation_fn=tf.nn.softplus) \n", " x_hat = fully_connected(layer_2, self.n_input, activation_fn=tf.nn.sigmoid)\n", " \n", " return x_hat\n", " \n", " \n", " \n", " def single_step_train(self, X):\n", " _,cost,recon_loss,latent_loss = self.sess.run([self.optimizer, self.cost,self.reconstruction_loss,self.latent_loss], \n", " feed_dict={self.x: X})\n", " return cost, recon_loss, latent_loss\n", " \n", " def transform(self, X):\n", " \"\"\"Transform data by mapping it into the latent space.\"\"\"\n", " # Note: This maps to mean of distribution, we could alternatively\n", " # sample from Gaussian distribution\n", " return self.sess.run(self.z_mean, feed_dict={self.x: X})\n", " \n", " def generate(self, z_mu=None):\n", " \"\"\" Generate data by sampling from latent space.\n", " \n", " If z_mu is not None, data for this point in latent space is\n", " generated. Otherwise, z_mu is drawn from prior in latent \n", " space. \n", " \"\"\"\n", " if z_mu is None:\n", " z_mu = np.random.normal(size=n_z)\n", " # Note: This maps to mean of distribution, we could alternatively\n", " # sample from Gaussian distribution\n", " return self.sess.run(self.x_hat, \n", " feed_dict={self.z: z_mu})\n", " \n", " def reconstruct(self, X):\n", " \"\"\" Use VAE to reconstruct given data. \"\"\"\n", " return self.sess.run(self.x_hat, \n", " feed_dict={self.x: X})" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def train(n_input,n_z, learning_rate=0.001,\n", " batch_size=100, training_epochs=10, display_step=5):\n", " vae = VariationalAutoencoder(n_input,n_z, \n", " learning_rate=learning_rate, \n", " batch_size=batch_size)\n", " # Training cycle\n", " for epoch in range(training_epochs):\n", " avg_cost = 0.\n", " avg_r_loss = 0.\n", " avg_l_loss = 0.\n", " total_batch = int(n_samples / batch_size)\n", " # Loop over all batches\n", " for i in range(total_batch):\n", " batch_xs, _ = mnist.train.next_batch(batch_size)\n", "\n", " # Fit training using batch data\n", " cost,r_loss, l_loss = vae.single_step_train(batch_xs)\n", " # Compute average loss\n", " avg_cost += cost / n_samples * batch_size\n", " avg_r_loss += r_loss / n_samples * batch_size\n", " avg_l_loss += l_loss / n_samples * batch_size\n", "\n", " # Display logs per epoch step\n", " if epoch % display_step == 0:\n", " print(\"Epoch: {:4d} cost={:.4f} Reconstruction loss = {:.4f} Latent Loss = {:.4f}\".\n", " format(epoch,avg_cost,avg_r_loss,avg_l_loss))\n", " return vae" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch: 0 cost=177.7624 Reconstruction loss = 171.4143 Latent Loss = 6.3481\n", "Epoch: 5 cost=110.7841 Reconstruction loss = 94.2649 Latent Loss = 16.5192\n", "Epoch: 10 cost=104.7110 Reconstruction loss = 86.5796 Latent Loss = 18.1314\n", "Epoch: 15 cost=102.3811 Reconstruction loss = 83.8277 Latent Loss = 18.5535\n", "Epoch: 20 cost=101.0192 Reconstruction loss = 82.2603 Latent Loss = 18.7589\n", "Epoch: 25 cost=99.9811 Reconstruction loss = 81.0854 Latent Loss = 18.8957\n", "Epoch: 30 cost=99.1842 Reconstruction loss = 80.1694 Latent Loss = 19.0148\n", "Epoch: 35 cost=98.5227 Reconstruction loss = 79.4611 Latent Loss = 19.0616\n", "Epoch: 40 cost=97.9145 Reconstruction loss = 78.8046 Latent Loss = 19.1099\n", "Epoch: 45 cost=97.4815 Reconstruction loss = 78.3192 Latent Loss = 19.1623\n", "Epoch: 50 cost=97.0883 Reconstruction loss = 77.8916 Latent Loss = 19.1967\n", "Epoch: 55 cost=96.6536 Reconstruction loss = 77.4327 Latent Loss = 19.2209\n", "Epoch: 60 cost=96.3638 Reconstruction loss = 77.1022 Latent Loss = 19.2616\n", "Epoch: 65 cost=96.0363 Reconstruction loss = 76.7655 Latent Loss = 19.2708\n", "Epoch: 70 cost=95.7969 Reconstruction loss = 76.4903 Latent Loss = 19.3066\n", "Epoch: 75 cost=95.5165 Reconstruction loss = 76.1933 Latent Loss = 19.3231\n", "Epoch: 80 cost=95.3297 Reconstruction loss = 75.9827 Latent Loss = 19.3469\n", "Epoch: 85 cost=95.1273 Reconstruction loss = 75.7489 Latent Loss = 19.3784\n", "Epoch: 90 cost=94.9648 Reconstruction loss = 75.5637 Latent Loss = 19.4011\n", "Epoch: 95 cost=94.8310 Reconstruction loss = 75.3959 Latent Loss = 19.4351\n" ] } ], "source": [ "n_z=10 # dimensionality of latent space\n", "\n", "vae = train(n_input,n_z,training_epochs=100)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Test the trained model: reconstruction\n", "w = h = 28\n", "batch = mnist.test.next_batch(100)\n", "x_reconstructed = vae.reconstruct(batch[0])\n", "\n", "n = np.sqrt(vae.batch_size).astype(np.int32)\n", "I_reconstructed = np.empty((h*n, 2*w*n))\n", "for i in range(n):\n", " for j in range(n):\n", " x = np.concatenate(\n", " (x_reconstructed[i*n+j, :].reshape(h, w), \n", " batch[0][i*n+j, :].reshape(h, w)),\n", " axis=1\n", " )\n", " I_reconstructed[i*h:(i+1)*h, j*2*w:(j+1)*2*w] = x\n", "\n", "plt.figure(figsize=(10, 20))\n", "plt.imshow(I_reconstructed, cmap='gray')" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Test the trained model: generation\n", "# Sample noise vectors from N(0, 1)\n", "z = np.random.normal(size=[vae.batch_size, vae.n_z])\n", "x_generated = vae.generate(z)\n", "\n", "n = np.sqrt(vae.batch_size).astype(np.int32)\n", "I_generated = np.empty((h*n, w*n))\n", "for i in range(n):\n", " for j in range(n):\n", " I_generated[i*h:(i+1)*h, j*w:(j+1)*w] = x_generated[i*n+j, :].reshape(28, 28)\n", "\n", "plt.figure(figsize=(8, 8))\n", "plt.imshow(I_generated, cmap='gray')" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch: 0 cost=190.4920 Reconstruction loss = 187.3914 Latent Loss = 3.1006\n", "Epoch: 5 cost=153.7174 Reconstruction loss = 148.1031 Latent Loss = 5.6143\n", "Epoch: 10 cost=148.1305 Reconstruction loss = 142.1184 Latent Loss = 6.0121\n", "Epoch: 15 cost=145.5968 Reconstruction loss = 139.3989 Latent Loss = 6.1979\n", "Epoch: 20 cost=144.0174 Reconstruction loss = 137.7337 Latent Loss = 6.2837\n", "Epoch: 25 cost=142.8392 Reconstruction loss = 136.4487 Latent Loss = 6.3905\n", "Epoch: 30 cost=142.0323 Reconstruction loss = 135.5803 Latent Loss = 6.4520\n", "Epoch: 35 cost=141.3513 Reconstruction loss = 134.8311 Latent Loss = 6.5201\n", "Epoch: 40 cost=140.7743 Reconstruction loss = 134.2062 Latent Loss = 6.5681\n", "Epoch: 45 cost=140.2939 Reconstruction loss = 133.6786 Latent Loss = 6.6153\n", "Epoch: 50 cost=139.8537 Reconstruction loss = 133.2091 Latent Loss = 6.6446\n", "Epoch: 55 cost=139.5072 Reconstruction loss = 132.8231 Latent Loss = 6.6841\n", "Epoch: 60 cost=139.1816 Reconstruction loss = 132.4597 Latent Loss = 6.7219\n", "Epoch: 65 cost=138.9606 Reconstruction loss = 132.2183 Latent Loss = 6.7422\n", "Epoch: 70 cost=138.5709 Reconstruction loss = 131.8063 Latent Loss = 6.7647\n", "Epoch: 75 cost=138.4276 Reconstruction loss = 131.6297 Latent Loss = 6.7979\n", "Epoch: 80 cost=138.1472 Reconstruction loss = 131.3339 Latent Loss = 6.8133\n", "Epoch: 85 cost=137.9909 Reconstruction loss = 131.1626 Latent Loss = 6.8283\n", "Epoch: 90 cost=137.8240 Reconstruction loss = 130.9699 Latent Loss = 6.8541\n", "Epoch: 95 cost=137.6045 Reconstruction loss = 130.7342 Latent Loss = 6.8703\n" ] } ], "source": [ "n_z=2 # dimensionality of latent space\n", "tf.reset_default_graph()\n", "vae_2d = train(n_input,n_z,training_epochs=100)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Test the trained model: reconstruction\n", "w = h = 28\n", "batch = mnist.test.next_batch(100)\n", "x_reconstructed = vae_2d.reconstruct(batch[0])\n", "\n", "n = np.sqrt(vae_2d.batch_size).astype(np.int32)\n", "I_reconstructed = np.empty((h*n, 2*w*n))\n", "for i in range(n):\n", " for j in range(n):\n", " x = np.concatenate(\n", " (x_reconstructed[i*n+j, :].reshape(h, w), \n", " batch[0][i*n+j, :].reshape(h, w)),\n", " axis=1\n", " )\n", " I_reconstructed[i*h:(i+1)*h, j*2*w:(j+1)*2*w] = x\n", "\n", "plt.figure(figsize=(10, 20))\n", "plt.imshow(I_reconstructed, cmap='gray')" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x_sample, y_sample = mnist.test.next_batch(5000)\n", "z_mu = vae_2d.transform(x_sample)\n", "plt.figure(figsize=(8, 6)) \n", "plt.scatter(z_mu[:, 0], z_mu[:, 1], c=np.argmax(y_sample, 1))\n", "plt.colorbar()\n", "plt.grid()" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "nx = ny = 20\n", "x_values = np.linspace(-3, 3, nx)\n", "y_values = np.linspace(-3, 3, ny)\n", "\n", "canvas = np.empty((28*ny, 28*nx))\n", "for i, yi in enumerate(x_values):\n", " for j, xi in enumerate(y_values):\n", " z_mu = np.array([[xi, yi]]*vae_2d.batch_size)\n", " x_mean = vae_2d.generate(z_mu)\n", " canvas[(nx-i-1)*28:(nx-i)*28, j*28:(j+1)*28] = x_mean[0].reshape(28, 28)\n", "\n", "plt.figure(figsize=(8, 10)) \n", "Xi, Yi = np.meshgrid(x_values, y_values)\n", "plt.imshow(canvas, origin=\"upper\", cmap=\"gray\")\n", "plt.tight_layout()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python [conda env:tensorflow]", "language": "python", "name": "conda-env-tensorflow-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.4" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: Chapter07/loader_celebA.py ================================================ import os import zipfile import requests import math import numpy as np from PIL import Image from tqdm import tqdm import os from glob import glob from matplotlib import pyplot from PIL import Image import numpy as np def download_celeb_a(): """ The function unzips the img_align_celeba.zip into the cwd """ dirpath = os.getcwd() data_dir = 'celebA' if os.path.exists(os.path.join(dirpath, data_dir)): print('Found Celeb-A - skip') return filename = "img_align_celeba.zip" save_path = os.path.join(dirpath, filename) if not DEBUG: zip_dir = '' with zipfile.ZipFile(save_path) as zf: zip_dir = zf.namelist()[0] zf.extractall(dirpath) # Rename the directory as celebA os.rename(os.path.join(dirpath, zip_dir), os.path.join(dirpath, data_dir)) def plot_images(images, mode='RGB'): """ Function to plot images in a square grid """ # Get maximum size for square grid of images save_size = math.floor(np.sqrt(images.shape[0])) # Scale to 0-255 images = (((images - images.min()) * 255) / (images.max() - images.min())).astype(np.uint8) # Put images in a square arrangement images_in_square = np.reshape( images[:save_size*save_size], (save_size, save_size, images.shape[1], images.shape[2], images.shape[3])) # Combine images to grid image new_im = Image.new(mode, (images.shape[1] * save_size, images.shape[2] * save_size)) for col_i, col_images in enumerate(images_in_square): for image_i, image in enumerate(col_images): im = Image.fromarray(image, mode) new_im.paste(im, (col_i * images.shape[1], image_i * images.shape[2])) return new_im def get_image(image_path, width, height, mode): """ Read image from image_path """ image = Image.open(image_path) if image.size != (width, height): # Remove most pixels that aren't part of a face face_width = face_height = 108 j = (image.size[0] - face_width) // 2 i = (image.size[1] - face_height) // 2 image = image.crop([j, i, j + face_width, i + face_height]) image = image.resize([width, height], Image.BILINEAR) return np.array(image.convert(mode)) def get_batch(image_files, width, height, mode='RGB'): """ Get a single batch of data as an NumPy array """ data_batch = np.array( [get_image(sample_file, width, height, mode) for sample_file in image_files]).astype(np.float32) # Make sure the images are in 4 dimensions if len(data_batch.shape) < 4: data_batch = data_batch.reshape(data_batch.shape + (1,)) return data_batch def get_batches(batch_size, shape, data_files): """ Generate batches """ IMAGE_MAX_VALUE = 255 current_index = 0 while current_index + batch_size <= shape[0]: data_batch = get_batch( data_files[current_index:current_index + batch_size], *shape[1:3]) current_index += batch_size yield data_batch / IMAGE_MAX_VALUE - 0.5 ================================================ FILE: Chapter08/Boston_Price_MLlib.ipynb ================================================ { "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from pyspark.ml.regression import LinearRegression as LR\n", "from pyspark.ml.feature import VectorAssembler\n", "from pyspark.ml.evaluation import RegressionEvaluator\n", "\n", "from pyspark.sql import SparkSession\n", "\n", "spark = SparkSession.builder \\\n", " .appName(\"Boston Price Prediction\") \\\n", " .config(\"spark.executor.memory\", \"70g\") \\\n", " .config(\"spark.driver.memory\", \"50g\") \\\n", " .config(\"spark.memory.offHeap.enabled\",True) \\\n", " .config(\"spark.memory.offHeap.size\",\"16g\") \\\n", " .getOrCreate()" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "house_df = spark.read.format(\"csv\"). \\\n", " options(header=\"true\", inferschema=\"true\"). \\\n", " load(\"boston/train.csv\")" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+---+-------+----+-----+----+-----+-----+----+------+---+---+-------+------+-----+----+\n", "| ID| crim| zn|indus|chas| nox| rm| age| dis|rad|tax|ptratio| black|lstat|medv|\n", "+---+-------+----+-----+----+-----+-----+----+------+---+---+-------+------+-----+----+\n", "| 1|0.00632|18.0| 2.31| 0|0.538|6.575|65.2| 4.09| 1|296| 15.3| 396.9| 4.98|24.0|\n", "| 2|0.02731| 0.0| 7.07| 0|0.469|6.421|78.9|4.9671| 2|242| 17.8| 396.9| 9.14|21.6|\n", "| 4|0.03237| 0.0| 2.18| 0|0.458|6.998|45.8|6.0622| 3|222| 18.7|394.63| 2.94|33.4|\n", "+---+-------+----+-----+----+-----+-----+----+------+---+---+-------+------+-----+----+\n", "only showing top 3 rows\n", "\n" ] } ], "source": [ "house_df.show(3)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "root\n", " |-- ID: integer (nullable = true)\n", " |-- crim: double (nullable = true)\n", " |-- zn: double (nullable = true)\n", " |-- indus: double (nullable = true)\n", " |-- chas: integer (nullable = true)\n", " |-- nox: double (nullable = true)\n", " |-- rm: double (nullable = true)\n", " |-- age: double (nullable = true)\n", " |-- dis: double (nullable = true)\n", " |-- rad: integer (nullable = true)\n", " |-- tax: integer (nullable = true)\n", " |-- ptratio: double (nullable = true)\n", " |-- black: double (nullable = true)\n", " |-- lstat: double (nullable = true)\n", " |-- medv: double (nullable = true)\n", "\n" ] } ], "source": [ "# DataFrame Schema\n", "house_df.printSchema()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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summarycountmeanstddevminmax
ID333250.95195195195194147.85943780185971506
crim3333.36034147147147087.3522718367811040.0063273.5341
zn33310.6891891891891922.6747617966182170.0100.0
indus33311.293483483483466.9981231044773120.7427.74
chas3330.060060060060060060.237955642816448301
nox3330.5571441441441450.114954508302893120.3850.871
rm3336.2656186186186160.70395157573344713.5618.725
age33368.2264264264264128.133343605623386.0100.0
dis3333.70993363363363351.98112305144070011.129610.7103
rad3339.6336336336336348.742174349631064124
tax333409.27927927927925170.84198846058237188711
ptratio33318.4480480480479942.151821329439083612.621.2
black333359.466096096095386.584566857183933.5396.9
lstat33312.5154354354354327.06778080358578451.7337.97
medv33322.7687687687687839.1734680273154155.050.0
\n", "
" ], "text/plain": [ " 0 1 2 3 4\n", "summary count mean stddev min max\n", "ID 333 250.95195195195194 147.8594378018597 1 506\n", "crim 333 3.3603414714714708 7.352271836781104 0.00632 73.5341\n", "zn 333 10.68918918918919 22.674761796618217 0.0 100.0\n", "indus 333 11.29348348348346 6.998123104477312 0.74 27.74\n", "chas 333 0.06006006006006006 0.2379556428164483 0 1\n", "nox 333 0.557144144144145 0.11495450830289312 0.385 0.871\n", "rm 333 6.265618618618616 0.7039515757334471 3.561 8.725\n", "age 333 68.22642642642641 28.13334360562338 6.0 100.0\n", "dis 333 3.7099336336336335 1.9811230514407001 1.1296 10.7103\n", "rad 333 9.633633633633634 8.742174349631064 1 24\n", "tax 333 409.27927927927925 170.84198846058237 188 711\n", "ptratio 333 18.448048048047994 2.1518213294390836 12.6 21.2\n", "black 333 359.4660960960953 86.58456685718393 3.5 396.9\n", "lstat 333 12.515435435435432 7.0677808035857845 1.73 37.97\n", "medv 333 22.768768768768783 9.173468027315415 5.0 50.0" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "house_df.describe().toPandas().transpose()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+--------------------+----+\n", "| features|medv|\n", "+--------------------+----+\n", "|[0.00632,18.0,2.3...|24.0|\n", "|[0.02731,0.0,7.07...|21.6|\n", "|[0.03237,0.0,2.18...|33.4|\n", "|[0.06905,0.0,2.18...|36.2|\n", "|[0.08829,12.5,7.8...|22.9|\n", "+--------------------+----+\n", "only showing top 5 rows\n", "\n" ] } ], "source": [ "vectors = VectorAssembler(inputCols = ['crim', 'zn','indus','chas',\n", " 'nox','rm','age','dis', 'rad', 'tax',\n", " 'ptratio','black', 'lstat'],\n", " outputCol = 'features')\n", "\n", "vhouse_df = vectors.transform(house_df)\n", "vhouse_df = vhouse_df.select(['features', 'medv'])\n", "vhouse_df.show(5)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "train_df, test_df = vhouse_df.randomSplit([0.7,0.3])" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "regressor = LR(featuresCol = 'features', labelCol='medv',\\\n", " maxIter=20, regParam=0.3, elasticNetParam=0.8)\n", "\n", "model = regressor.fit(train_df)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Coefficients: [-0.010279413081980417,0.034113414577108085,0.0,5.6415385374198,-7.783264348644399,3.085680504353533,0.0,-0.8290283633263736,0.016467345168122184,0.0,-0.5849152858717687,0.009195354138663316,-0.5627105522578837]\n", "Intercept: 24.28872820161242\n" ] } ], "source": [ "print(\"Coefficients:\", model.coefficients)\n", "print(\"Intercept:\", model.intercept)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "RMSE is 4.735492439102997 and r2 is 0.7060968639352195\n", "Number of Iterations is 21\n" ] } ], "source": [ "modelSummary = model.summary\n", "print(\"RMSE is {} and r2 is {}\".format(modelSummary.rootMeanSquaredError, modelSummary.r2))\n", "print(\"Number of Iterations is \",modelSummary.totalIterations)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+--------------------+----+------------------+\n", "| features|medv| prediction|\n", "+--------------------+----+------------------+\n", "|[0.00906,90.0,2.9...|32.2| 30.33845691985376|\n", "|[0.01311,90.0,1.2...|35.4| 29.92694704599407|\n", "|[0.01501,80.0,2.0...|24.5| 27.53450962471756|\n", "|[0.01951,17.5,1.3...|33.0|24.177663554797803|\n", "|[0.01965,80.0,1.7...|20.1|20.945922220637236|\n", "+--------------------+----+------------------+\n", "only showing top 5 rows\n", "\n" ] } ], "source": [ "model_predictions = model.transform(test_df)\n", "model_predictions.select(\"features\", \"medv\", \"prediction\").show(5)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "R2 value on test dataset is: 0.6849428292603987\n", "RMSE value is 5.557545888924286\n" ] } ], "source": [ "model_evaluator = RegressionEvaluator(predictionCol=\"prediction\",\n", " labelCol=\"medv\", metricName=\"r2\")\n", "print(\"R2 value on test dataset is: \", model_evaluator.evaluate(model_predictions))\n", "print(\"RMSE value is\", model.evaluate(test_df).rootMeanSquaredError)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "spark.stop()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python [default]", "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": "3.5.6" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: Chapter08/Readme.md ================================================ The advances in distributed computing environments and an easy availability of internet worldwide has resulted in the emergence of Distributed AI. In this chapter, we will learn about two frameworks one by Apache the MLLib and another H2O.ai, both provide distributed and scalable machine learning for large, streaming data. The chapter will start with an introduction to Apache's Spark the defacto distributed data processing system. By the end of the chapter you will: * Know about Spark and its importance in distributed data processing * Understand the Spark architecture * Learn about MLLib * Use MLLib in your deep learning pipeline * Delve deep into H2O.ai platform ================================================ FILE: Chapter08/Transfer_learning_Sparkdl.ipynb ================================================ { "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import cv2\n", "import os\n", "\n", "def load_images_from_folder(folder):\n", " images = []\n", " for filename in os.listdir(folder):\n", " img1 = cv2.imread(os.path.join(folder,filename))\n", " img = cv2.cvtColor(img1, cv2.COLOR_BGR2RGB)\n", " if img is not None:\n", " images.append(img)\n", " return images" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "im_daisy = load_images_from_folder('data/flower_photos/daisy')\n", "\n", "im_tulips = load_images_from_folder('data/flower_photos/tulips')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "% matplotlib inline" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "42\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "print(len(im_daisy))\n", "samples_per_row = 5\n", "num_rows = 2\n", "\n", "# Function to visualize some of the images of our dataset\n", "fig, axis = plt.subplots(num_rows,samples_per_row,figsize=(15,5))\n", "#fig.axis('off')\n", "n_train = len(im_daisy)\n", "for row in axis:\n", " for col in row:\n", " idx = np.random.randint(0, n_train)\n", " col.imshow(im_daisy[idx]) \n", " col.axis('off')" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "65\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "print(len(im_tulips))\n", "samples_per_row = 5\n", "num_rows = 2\n", "\n", "# Function to visualize some of the images of our dataset\n", "fig, axis = plt.subplots(num_rows,samples_per_row,figsize=(15,5))\n", "#fig.axis('off')\n", "n_train = len(im_tulips)\n", "for row in axis:\n", " for col in row:\n", " idx = np.random.randint(0, n_train)\n", " col.imshow(im_tulips[idx]) \n", " col.axis('off')" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "import os\n", "SUBMIT_ARGS = \"--packages databricks:spark-deep-learning:1.3.0-spark2.4-s_2.11 pyspark-shell\"\n", "os.environ[\"PYSPARK_SUBMIT_ARGS\"] = SUBMIT_ARGS" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "from pyspark.sql import SparkSession" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "spark = SparkSession.builder \\\n", " .appName(\"ImageClassification\") \\\n", " .config(\"spark.executor.memory\", \"70g\") \\\n", " .config(\"spark.driver.memory\", \"50g\") \\\n", " .config(\"spark.memory.offHeap.enabled\",True) \\\n", " .config(\"spark.memory.offHeap.size\",\"16g\") \\\n", " .getOrCreate()\n", "\n" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Using TensorFlow backend.\n" ] } ], "source": [ "import pyspark.sql.functions as f\n", "import sparkdl as dl\n", "from pyspark.ml.image import ImageSchema\n", "from sparkdl.image import imageIO" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "dftulips = ImageSchema.readImages('data/flower_photos/tulips').withColumn('label', f.lit(0))\n", "\n", "dfdaisy = ImageSchema.readImages('data/flower_photos/daisy').withColumn('label', f.lit(1))\n" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+--------------------+-----+\n", "| image|label|\n", "+--------------------+-----+\n", "|[file:/home/am/Dr...| 1|\n", "|[file:/home/am/Dr...| 1|\n", "|[file:/home/am/Dr...| 1|\n", "|[file:/home/am/Dr...| 1|\n", "|[file:/home/am/Dr...| 1|\n", "+--------------------+-----+\n", "only showing top 5 rows\n", "\n" ] } ], "source": [ "dfdaisy.show(5)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+--------------------+-----+\n", "| image|label|\n", "+--------------------+-----+\n", "|[file:/home/am/Dr...| 0|\n", "|[file:/home/am/Dr...| 0|\n", "|[file:/home/am/Dr...| 0|\n", "|[file:/home/am/Dr...| 0|\n", "|[file:/home/am/Dr...| 0|\n", "+--------------------+-----+\n", "only showing top 5 rows\n", "\n" ] } ], "source": [ "dftulips.show(5)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "trainDFdaisy, testDFdaisy = dfdaisy.randomSplit([0.70,0.30], seed = 123)\n", "trainDFtulips, testDFtulips = dftulips.randomSplit([0.70,0.30], seed = 122)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "trainDF = trainDFdaisy.unionAll(trainDFtulips)\n", "testDF = testDFdaisy.unionAll(testDFtulips)\n", "\n", "#trainDF = trainDF.repartition(100) #required when dataset is large\n", "#testDF = testDF.repartition(100)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "from pyspark.ml.classification import LogisticRegression\n", "from pyspark.ml import Pipeline\n", "vectorizer = dl.DeepImageFeaturizer(inputCol=\"image\", outputCol=\"features\", \n", " modelName=\"InceptionV3\")\n", "logreg = LogisticRegression(maxIter=20, labelCol=\"label\")\n", "pipeline = Pipeline(stages=[vectorizer, logreg])\n", "pipeline_model = pipeline.fit(trainDF)\n" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+----------+-----+\n", "|prediction|label|\n", "+----------+-----+\n", "|0.0 |0 |\n", "|0.0 |0 |\n", "|1.0 |1 |\n", "|1.0 |1 |\n", "|1.0 |1 |\n", "|1.0 |1 |\n", "|0.0 |1 |\n", "|1.0 |1 |\n", "|1.0 |1 |\n", "|1.0 |1 |\n", "|1.0 |1 |\n", "|1.0 |1 |\n", "|1.0 |1 |\n", "|0.0 |1 |\n", "|1.0 |1 |\n", "|1.0 |1 |\n", "|0.0 |1 |\n", "|0.0 |0 |\n", "|0.0 |0 |\n", "|0.0 |0 |\n", "|0.0 |0 |\n", "|0.0 |0 |\n", "|0.0 |0 |\n", "|0.0 |0 |\n", "|0.0 |0 |\n", "|0.0 |0 |\n", "|0.0 |0 |\n", "|0.0 |0 |\n", "|0.0 |0 |\n", "|0.0 |0 |\n", "|0.0 |0 |\n", "+----------+-----+\n", "\n" ] } ], "source": [ "predictDF = pipeline_model.transform(testDF) #predict on test dataset\n", "predictDF.select('prediction', 'label').show(n = testDF.toPandas().shape[0], truncate=False)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+----------------+---+---+\n", "|prediction_label| 0| 1|\n", "+----------------+---+---+\n", "| 1.0| 0| 12|\n", "| 0.0| 16| 3|\n", "+----------------+---+---+\n", "\n" ] } ], "source": [ "predictDF.crosstab('prediction', 'label').show()" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "accuracy: 90.32%\n" ] } ], "source": [ "from pyspark.ml.evaluation import MulticlassClassificationEvaluator\n", "scoring = predictDF.select(\"prediction\", \"label\")\n", "accuracy_score = MulticlassClassificationEvaluator(metricName=\"accuracy\")\n", "rate = accuracy_score.evaluate(scoring)*100\n", "print(\"accuracy: {}%\" .format(round(rate,2)))" ] } ], "metadata": { "kernelspec": { "display_name": "Python [conda env:spark]", "language": "python", "name": "conda-env-spark-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.6" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: Chapter08/Wine_Classification_MLlib.ipynb ================================================ { "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from pyspark.ml.classification import LogisticRegression as LR\n", "from pyspark.ml.feature import VectorAssembler\n", "from pyspark.ml.feature import StringIndexer\n", "from pyspark.ml import Pipeline\n", "\n", "\n", "from pyspark.sql import SparkSession\n", "\n", "spark = SparkSession.builder \\\n", " .appName(\"Wine Quality Classifier\") \\\n", " .config(\"spark.executor.memory\", \"70g\") \\\n", " .config(\"spark.driver.memory\", \"50g\") \\\n", " .config(\"spark.memory.offHeap.enabled\",True) \\\n", " .config(\"spark.memory.offHeap.size\",\"16g\") \\\n", " .getOrCreate()" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "wine_df = spark.read.format(\"csv\"). \\\n", " options(header=\"true\", inferschema=\"true\",sep=';'). \\\n", " load(\"winequality-red.csv\")" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+-------------+----------------+-----------+--------------+---------+-------------------+--------------------+-------+----+---------+-------+-------+\n", "|fixed acidity|volatile acidity|citric acid|residual sugar|chlorides|free sulfur dioxide|total sulfur dioxide|density| pH|sulphates|alcohol|quality|\n", "+-------------+----------------+-----------+--------------+---------+-------------------+--------------------+-------+----+---------+-------+-------+\n", "| 7.4| 0.7| 0.0| 1.9| 0.076| 11.0| 34.0| 0.9978|3.51| 0.56| 9.4| 5|\n", "| 7.8| 0.88| 0.0| 2.6| 0.098| 25.0| 67.0| 0.9968| 3.2| 0.68| 9.8| 5|\n", "| 7.8| 0.76| 0.04| 2.3| 0.092| 15.0| 54.0| 0.997|3.26| 0.65| 9.8| 5|\n", "| 11.2| 0.28| 0.56| 1.9| 0.075| 17.0| 60.0| 0.998|3.16| 0.58| 9.8| 6|\n", "+-------------+----------------+-----------+--------------+---------+-------------------+--------------------+-------+----+---------+-------+-------+\n", "only showing top 4 rows\n", "\n" ] } ], "source": [ "wine_df.show(4)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+-------------+----------------+-----------+--------------+---------+-------------------+--------------------+-------+----+---------+-------+-------+-----------+\n", "|fixed acidity|volatile acidity|citric acid|residual sugar|chlorides|free sulfur dioxide|total sulfur dioxide|density| pH|sulphates|alcohol|quality|quality_new|\n", "+-------------+----------------+-----------+--------------+---------+-------------------+--------------------+-------+----+---------+-------+-------+-----------+\n", "| 7.4| 0.7| 0.0| 1.9| 0.076| 11.0| 34.0| 0.9978|3.51| 0.56| 9.4| 5| 1|\n", "| 7.8| 0.88| 0.0| 2.6| 0.098| 25.0| 67.0| 0.9968| 3.2| 0.68| 9.8| 5| 1|\n", "| 7.8| 0.76| 0.04| 2.3| 0.092| 15.0| 54.0| 0.997|3.26| 0.65| 9.8| 5| 1|\n", "| 11.2| 0.28| 0.56| 1.9| 0.075| 17.0| 60.0| 0.998|3.16| 0.58| 9.8| 6| 1|\n", "+-------------+----------------+-----------+--------------+---------+-------------------+--------------------+-------+----+---------+-------+-------+-----------+\n", "only showing top 4 rows\n", "\n" ] } ], "source": [ "from pyspark.sql.functions import when\n", "wine_df = wine_df.withColumn('quality_new', when(wine_df['quality']< 5, 0 ).\\\n", " otherwise(when(wine_df['quality']<8,1).otherwise(2)))\n", "wine_df.show(4)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "root\n", " |-- fixed acidity: double (nullable = true)\n", " |-- volatile acidity: double (nullable = true)\n", " |-- citric acid: double (nullable = true)\n", " |-- residual sugar: double (nullable = true)\n", " |-- chlorides: double (nullable = true)\n", " |-- free sulfur dioxide: double (nullable = true)\n", " |-- total sulfur dioxide: double (nullable = true)\n", " |-- density: double (nullable = true)\n", " |-- pH: double (nullable = true)\n", " |-- sulphates: double (nullable = true)\n", " |-- alcohol: double (nullable = true)\n", " |-- quality: integer (nullable = true)\n", " |-- quality_new: integer (nullable = false)\n", "\n" ] } ], "source": [ "wine_df.printSchema()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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01234
summarycountmeanstddevminmax
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" ], "text/plain": [ " 0 1 2 \\\n", "summary count mean stddev \n", "fixed acidity 1599 8.319637273295838 1.7410963181276948 \n", "volatile acidity 1599 0.5278205128205131 0.17905970415353525 \n", "citric acid 1599 0.2709756097560964 0.19480113740531824 \n", "residual sugar 1599 2.5388055034396517 1.40992805950728 \n", "chlorides 1599 0.08746654158849257 0.047065302010090085 \n", "free sulfur dioxide 1599 15.874921826141339 10.46015696980971 \n", "total sulfur dioxide 1599 46.46779237023139 32.89532447829907 \n", "density 1599 0.9967466791744831 0.0018873339538427265 \n", "pH 1599 3.311113195747343 0.15438646490354271 \n", "sulphates 1599 0.6581488430268921 0.1695069795901101 \n", "alcohol 1599 10.422983114446502 1.0656675818473935 \n", "quality 1599 5.6360225140712945 0.8075694397347051 \n", "quality_new 1599 0.9718574108818011 0.22337381114497842 \n", "\n", " 3 4 \n", "summary min max \n", "fixed acidity 4.6 15.9 \n", "volatile acidity 0.12 1.58 \n", "citric acid 0.0 1.0 \n", "residual sugar 0.9 15.5 \n", "chlorides 0.012 0.611 \n", "free sulfur dioxide 1.0 72.0 \n", "total sulfur dioxide 6.0 289.0 \n", "density 0.99007 1.00369 \n", "pH 2.74 4.01 \n", "sulphates 0.33 2.0 \n", "alcohol 8.4 14.9 \n", "quality 3 8 \n", "quality_new 0 2 " ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "wine_df.describe().toPandas().transpose()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "string_index = StringIndexer(inputCol='quality_new', outputCol='quality'+'Index')\n", "\n", "vectors = VectorAssembler(inputCols = ['fixed acidity','volatile acidity',\n", " 'citric acid','residual sugar','chlorides','free sulfur dioxide', 'total sulfur dioxide', 'density',\n", " 'pH','sulphates', 'alcohol'],\n", " outputCol = 'features')\n", "\n", "stages = [vectors, string_index]" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+-------------+----------------+-----------+--------------+---------+-------------------+--------------------+-------+----+---------+-------+-------+-----------+--------------------+------------+\n", "|fixed acidity|volatile acidity|citric acid|residual sugar|chlorides|free sulfur dioxide|total sulfur dioxide|density| pH|sulphates|alcohol|quality|quality_new| features|qualityIndex|\n", "+-------------+----------------+-----------+--------------+---------+-------------------+--------------------+-------+----+---------+-------+-------+-----------+--------------------+------------+\n", "| 7.4| 0.7| 0.0| 1.9| 0.076| 11.0| 34.0| 0.9978|3.51| 0.56| 9.4| 5| 1|[7.4,0.7,0.0,1.9,...| 0.0|\n", "| 7.8| 0.88| 0.0| 2.6| 0.098| 25.0| 67.0| 0.9968| 3.2| 0.68| 9.8| 5| 1|[7.8,0.88,0.0,2.6...| 0.0|\n", "| 7.8| 0.76| 0.04| 2.3| 0.092| 15.0| 54.0| 0.997|3.26| 0.65| 9.8| 5| 1|[7.8,0.76,0.04,2....| 0.0|\n", "| 11.2| 0.28| 0.56| 1.9| 0.075| 17.0| 60.0| 0.998|3.16| 0.58| 9.8| 6| 1|[11.2,0.28,0.56,1...| 0.0|\n", "| 7.4| 0.7| 0.0| 1.9| 0.076| 11.0| 34.0| 0.9978|3.51| 0.56| 9.4| 5| 1|[7.4,0.7,0.0,1.9,...| 0.0|\n", "+-------------+----------------+-----------+--------------+---------+-------------------+--------------------+-------+----+---------+-------+-------+-----------+--------------------+------------+\n", "only showing top 5 rows\n", "\n" ] } ], "source": [ "pipeline = Pipeline().setStages(stages)\n", "pipelineModel = pipeline.fit(wine_df)\n", "pipeline_data_df = pipelineModel.transform(wine_df)\n", "\n", "pipeline_data_df.show(5)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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01234
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volatile acidity15990.52782051282051310.179059704153535250.121.58
citric acid15990.27097560975609640.194801137405318240.01.0
residual sugar15992.53880550343965171.409928059507280.915.5
chlorides15990.087466541588492570.0470653020100900850.0120.611
free sulfur dioxide159915.87492182614133910.460156969809711.072.0
total sulfur dioxide159946.4677923702313932.895324478299076.0289.0
density15990.99674667917448310.00188733395384272650.990071.00369
pH15993.3111131957473430.154386464903542712.744.01
sulphates15990.65814884302689210.16950697959011010.332.0
alcohol159910.4229831144465021.06566758184739358.414.9
quality15995.63602251407129450.807569439734705138
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" ], "text/plain": [ " 0 1 2 \\\n", "summary count mean stddev \n", "fixed acidity 1599 8.319637273295838 1.7410963181276948 \n", "volatile acidity 1599 0.5278205128205131 0.17905970415353525 \n", "citric acid 1599 0.2709756097560964 0.19480113740531824 \n", "residual sugar 1599 2.5388055034396517 1.40992805950728 \n", "chlorides 1599 0.08746654158849257 0.047065302010090085 \n", "free sulfur dioxide 1599 15.874921826141339 10.46015696980971 \n", "total sulfur dioxide 1599 46.46779237023139 32.89532447829907 \n", "density 1599 0.9967466791744831 0.0018873339538427265 \n", "pH 1599 3.311113195747343 0.15438646490354271 \n", "sulphates 1599 0.6581488430268921 0.1695069795901101 \n", "alcohol 1599 10.422983114446502 1.0656675818473935 \n", "quality 1599 5.6360225140712945 0.8075694397347051 \n", "quality_new 1599 0.9718574108818011 0.22337381114497842 \n", "qualityIndex 1599 0.06191369606003752 0.2839804503421069 \n", "\n", " 3 4 \n", "summary min max \n", "fixed acidity 4.6 15.9 \n", "volatile acidity 0.12 1.58 \n", "citric acid 0.0 1.0 \n", "residual sugar 0.9 15.5 \n", "chlorides 0.012 0.611 \n", "free sulfur dioxide 1.0 72.0 \n", "total sulfur dioxide 6.0 289.0 \n", "density 0.99007 1.00369 \n", "pH 2.74 4.01 \n", "sulphates 0.33 2.0 \n", "alcohol 8.4 14.9 \n", "quality 3 8 \n", "quality_new 0 2 \n", "qualityIndex 0.0 2.0 " ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pipeline_data_df.describe().toPandas().transpose()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "train_df, test_df = pipeline_data_df.randomSplit([0.7,0.3])" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "classifier= LR(featuresCol = 'features', labelCol='qualityIndex',\\\n", " maxIter=1000)\n", "\n", "model = classifier.fit(train_df)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Beta Coefficients: DenseMatrix([[-2.12908685e-02, -2.15649410e+00, -1.14989690e+00,\n", " 6.03485505e-03, 4.34852745e+00, -1.24570841e-02,\n", " 1.85425162e-02, 6.62398057e+00, -7.80430677e-01,\n", " 1.17897956e-01, -2.37305887e-01],\n", " [ 3.81099169e-01, 2.65183608e+00, 3.14959443e-02,\n", " 9.05755732e-02, 8.92807636e+00, 1.78080907e-02,\n", " -1.58814149e-02, -1.31773133e+01, 6.01291582e+00,\n", " -4.15805194e+00, -8.81077553e-01],\n", " [-3.59808301e-01, -4.95341980e-01, 1.11840095e+00,\n", " -9.66104283e-02, -1.32766038e+01, -5.35100655e-03,\n", " -2.66110129e-03, 6.55333268e+00, -5.23248515e+00,\n", " 4.04015399e+00, 1.11838344e+00]])\n", "Interceptors: [2.4452990696315684,-0.717067157664007,-1.7282319119675615]\n" ] } ], "source": [ "print(\"Beta Coefficients:\", model.coefficientMatrix)\n", "print(\"Interceptors: \", model.interceptVector)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Objective History [0.23581786131403523, 0.23580931675532288, 0.2357844684998421, 0.23578153826655315, 0.2357774832561159, 0.23577553882853106, 0.23575592269598752, 0.23571847279018668, 0.2356081652133476, 0.23533935601775696, 0.23465909571090346, 0.233074638221403, 0.2298469418187082, 0.22521317735730695, 0.22277104098335837, 0.21714901864321998, 0.20543939322577454, 0.20541769138199, 0.20540848490538513, 0.20540608996512336, 0.20540526125177352, 0.2054023472615037, 0.2053962830942178, 0.2053790366611294, 0.2053356750249505, 0.20522264991633213, 0.20494261450790907, 0.20427965443798451, 0.20393628442217043, 0.20227740821547938, 0.1981875184173352, 0.19691004381535576, 0.19217931420713402, 0.18984666163417804, 0.1896464650042805, 0.18962716750337047, 0.18962431706375787, 0.18962371892669455, 0.1896232586786512, 0.18962191812859064, 0.18961842034510618, 0.18961075625682117, 0.1895794208848531, 0.18950742839706422, 0.18930640430833076, 0.1888122191161155, 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0.17485446528091225, 0.17485428307538287, 0.17485426559210582, 0.17485425816820513, 0.17485424415787607, 0.1748541144404477, 0.17485392704834307, 0.17485348203923415, 0.1748532646788378, 0.17485282343061206, 0.17485253177478421, 0.17485246807139332, 0.1748524009673578, 0.17485217821142518, 0.17485207203201147, 0.1748517427258107, 0.1748516285557831, 0.1748515944701574, 0.1748515861812947, 0.17485157159831333, 0.1748515599125384, 0.17485155318639267, 0.1748515355484534, 0.1748515245544895, 0.17485151983675878, 0.17485150331909652, 0.1748514918186804, 0.17485148296918984, 0.17485146073277483, 0.17485143059590919, 0.17485142102737342, 0.17485141683778965, 0.17485141170826302, 0.17485140052096299, 0.17485139949461884, 0.17485138699376196, 0.17485137471793383]\n", "Number of Iterations is 563\n" ] } ], "source": [ "modelSummary = model.summary\n", "print(\"Objective History\", modelSummary.objectiveHistory)\n", "print(\"Number of Iterations is \",modelSummary.totalIterations)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+--------------------+------------+----------+\n", "| features|qualityIndex|prediction|\n", "+--------------------+------------+----------+\n", "|[4.6,0.52,0.15,2....| 1.0| 0.0|\n", "|[5.0,0.74,0.0,1.2...| 0.0| 0.0|\n", "|[5.1,0.47,0.02,1....| 0.0| 0.0|\n", "|[5.1,0.585,0.0,1....| 0.0| 0.0|\n", "|[5.2,0.32,0.25,1....| 0.0| 0.0|\n", "+--------------------+------------+----------+\n", "only showing top 5 rows\n", "\n" ] } ], "source": [ "model_predictions = model.transform(test_df)\n", "model_predictions.select(\"features\", \"qualityIndex\", \"prediction\").show(5)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy: 0.9475113122171945 False Positive Rate 0.8985878446501954 True Positive Rate 0.9475113122171945 F 0.9267671517671516 Precision 0.9234410550109313 Recall 0.9475113122171945\n" ] } ], "source": [ "accuracy = modelSummary.accuracy\n", "fPR = modelSummary.weightedFalsePositiveRate\n", "tPR = modelSummary.weightedTruePositiveRate\n", "fMeasure = modelSummary.weightedFMeasure()\n", "precision = modelSummary.weightedPrecision\n", "recall = modelSummary.weightedRecall\n", "print(\"Accuracy: {} False Positive Rate {} True Positive Rate {} F {} Precision {} Recall {}\"\\\n", " .format(accuracy, fPR, tPR, fMeasure, precision, recall))" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "spark.stop()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.6" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: Chapter08/boston_price_h2o.ipynb ================================================ { "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import h2o\n", "import time\n", "import seaborn\n", "import itertools\n", "import numpy as np\n", "import pandas as pd\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "from h2o.estimators.glm import H2OGeneralizedLinearEstimator as GLM\n", "from h2o.estimators.gbm import H2OGradientBoostingEstimator as GBM\n", "from h2o.estimators.random_forest import H2ORandomForestEstimator as RF\n", "\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Checking whether there is an H2O instance running at http://localhost:54321..... not found.\n", "Attempting to start a local H2O server...\n", " Java Version: java version \"1.8.0_191\"; Java(TM) SE Runtime Environment (build 1.8.0_191-b12); Java HotSpot(TM) 64-Bit Server VM (build 25.191-b12, mixed mode)\n", " Starting server from /home/am/anaconda3/envs/h2o/lib/python3.5/site-packages/h2o/backend/bin/h2o.jar\n", " Ice root: /tmp/tmp7hjshd9o\n", " JVM stdout: /tmp/tmp7hjshd9o/h2o_am_started_from_python.out\n", " JVM stderr: /tmp/tmp7hjshd9o/h2o_am_started_from_python.err\n", " Server is running at http://127.0.0.1:54321\n", "Connecting to H2O server at http://127.0.0.1:54321... successful.\n" ] }, { "data": { "text/html": [ "
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H2O cluster uptime:01 secs
H2O cluster timezone:Asia/Kolkata
H2O data parsing timezone:UTC
H2O cluster version:3.22.0.2
H2O cluster version age:18 days
H2O cluster name:H2O_from_python_am_3z4r3u
H2O cluster total nodes:1
H2O cluster free memory:6.957 Gb
H2O cluster total cores:8
H2O cluster allowed cores:8
H2O cluster status:accepting new members, healthy
H2O connection url:http://127.0.0.1:54321
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ID crim zn indus chas nox rm age dis rad tax ptratio black lstat medv
type int real real real int real real real real int int real real real real
mins 1.0 0.00632 0.0 0.74 0.0 0.385 3.561 6.0 1.1296 1.0 188.0 12.6 3.5 1.73 5.0
mean 250.951951951951973.36034147147146910.68918918918919511.2934834834834860.060060060060060060.5571441441441435 6.26561861861862 68.226426426426443.70993363363363669.633633633633636409.2792792792793 18.44804804804806 359.466096096096112.51543543543543422.76876876876877
maxs 506.0 73.5341 100.0 27.74 1.0 0.871 8.725 100.0 10.7103 24.0 711.0 21.2 396.9 37.97 50.0
sigma 147.8594378018597 7.35227183678110422.6747617966182176.998123104477312 0.237955642816448380.114954508302893110.703951575733447228.133343605623381.98112305144070018.742174349631064170.841988460582372.151821329439083686.584566857183937.06778080358578459.173468027315417
zeros 0 0 248 0 313 0 0 0 0 0 0 0 0 0 0
missing0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 1.0 0.00632 18.0 2.31 0.0 0.538 6.575 65.2 4.09 1.0 296.0 15.3 396.9 4.98 24.0
1 2.0 0.02731 0.0 7.07 0.0 0.469 6.421 78.9 4.9671 2.0 242.0 17.8 396.9 9.14 21.6
2 4.0 0.03237 0.0 2.18 0.0 0.458 6.998 45.8 6.0622 3.0 222.0 18.7 394.63 2.94 33.4
3 5.0 0.06905 0.0 2.18 0.0 0.458 7.147 54.2 6.0622 3.0 222.0 18.7 396.9 5.33 36.2
4 7.0 0.08829 12.5 7.87 0.0 0.524 6.012 66.6 5.5605 5.0 311.0 15.2 395.6 12.43 22.9
5 11.0 0.22489 12.5 7.87 0.0 0.524 6.377 94.3 6.3467 5.0 311.0 15.2 392.52 20.45 15.0
6 12.0 0.11747 12.5 7.87 0.0 0.524 6.009 82.9 6.2267 5.0 311.0 15.2 396.9 13.27 18.9
7 13.0 0.09378 12.5 7.87 0.0 0.524 5.889 39.0 5.4509 5.0 311.0 15.2 390.5 15.71 21.7
8 14.0 0.62976 0.0 8.14 0.0 0.538 5.949 61.8 4.7075 4.0 307.0 21.0 396.9 8.26 20.4
9 15.0 0.63796 0.0 8.14 0.0 0.538 6.096 84.5 4.4619 4.0 307.0 21.0 380.02 10.26 18.2
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "boston_df.describe()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 1.0, 'Correlation Heatmap')" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(20,20))\n", "corr = boston_df.cor()\n", "corr = corr.as_data_frame()\n", "corr.index = boston_df.columns\n", "#print(corr)\n", "sns.heatmap(corr, annot=True, cmap='YlGnBu',vmin=-1, vmax=1)\n", "plt.title(\"Correlation Heatmap\")" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "train_df, valid_df, test_df = boston_df.split_frame(ratios=[0.6, 0.2], seed=133)\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['ID', 'crim', 'zn', 'indus', 'chas', 'nox', 'rm', 'age', 'dis', 'rad', 'tax', 'ptratio', 'black', 'lstat']\n" ] } ], "source": [ "features = boston_df.columns[:-1]\n", "print(features)\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "glm Model Build progress: |███████████████████████████████████████████████| 100%\n", "Model Details\n", "=============\n", "H2OGeneralizedLinearEstimator : Generalized Linear Modeling\n", "Model Key: boston_glm\n", "\n", "\n", "ModelMetricsRegressionGLM: glm\n", "** Reported on train data. **\n", "\n", "MSE: 25.29061565365854\n", "RMSE: 5.028977595263131\n", "MAE: 3.5119806236622573\n", "RMSLE: 0.21879597717063684\n", "R^2: 0.6585836959508422\n", "Mean Residual Deviance: 25.29061565365854\n", "Null degrees of freedom: 199\n", "Residual degrees of freedom: 188\n", "Null deviance: 14815.118876113953\n", "Residual deviance: 5058.123130731708\n", "AIC: 1239.662094110731\n", "\n", "ModelMetricsRegressionGLM: glm\n", "** Reported on validation data. **\n", "\n", "MSE: 29.45943429400654\n", "RMSE: 5.427654584994014\n", "MAE: 3.9827620428290818\n", "RMSLE: 0.23155132773489584\n", "R^2: 0.6075220878659529\n", "Mean Residual Deviance: 29.45943429400654\n", "Null degrees of freedom: 57\n", "Residual degrees of freedom: 46\n", "Null deviance: 4379.649896571945\n", "Residual deviance: 1708.6471890523794\n", "AIC: 386.81169393537243\n", "Scoring History: \n" ] }, { "data": { "text/html": [ "
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timestampdurationiterationsnegative_log_likelihoodobjective
2018-12-10 22:21:59 0.000 sec014815.118750074.0755937
" ], "text/plain": [ " timestamp duration iterations negative_log_likelihood objective\n", "-- ------------------- ---------- ------------ ------------------------- -----------\n", " 2018-12-10 22:21:59 0.000 sec 0 14815.1 74.0756" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "model_glm = GLM(model_id='boston_glm')\n", "model_glm.train(training_frame= train_df, validation_frame=valid_df, y = 'medv', x=features)\n", "print(model_glm)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "ModelMetricsRegressionGLM: glm\n", "** Reported on test data. **\n", "\n", "MSE: 58.79022368779993\n", "RMSE: 7.667478313487423\n", "MAE: 4.535525812229012\n", "RMSLE: 0.2716211906586539\n", "R^2: 0.4911310682143256\n", "Mean Residual Deviance: 58.79022368779993\n", "Null degrees of freedom: 74\n", "Residual degrees of freedom: 63\n", "Null deviance: 8748.76368890764\n", "Residual deviance: 4409.266776584995\n", "AIC: 544.388948275823\n", "\n" ] } ], "source": [ "test_glm = model_glm.model_performance(test_df)\n", "print(test_glm)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "gbm Model Build progress: |███████████████████████████████████████████████| 100%\n", "Model Details\n", "=============\n", "H2OGradientBoostingEstimator : Gradient Boosting Machine\n", "Model Key: boston_glm\n", "\n", "\n", "ModelMetricsRegression: gbm\n", "** Reported on train data. **\n", "\n", "MSE: 2.042942663976728\n", "RMSE: 1.4293154529272842\n", "MAE: 0.956157636642456\n", "RMSLE: 0.07545015650988193\n", "Mean Residual Deviance: 2.042942663976728\n", "\n", "ModelMetricsRegression: gbm\n", "** Reported on validation data. **\n", "\n", "MSE: 9.952095863557963\n", "RMSE: 3.1546942583328046\n", "MAE: 2.4723717368870632\n", "RMSLE: 0.14778009630478506\n", "Mean Residual Deviance: 9.952095863557963\n", "Scoring History: \n" ] }, { "data": { "text/html": [ "
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2018-12-10 22:22:00 0.383 sec49.01.44058480.96270642.07528453.16968572.485432210.0469074
2018-12-10 22:22:00 0.389 sec50.01.42931550.95615762.04294273.15469432.47237179.9520959
" ], "text/plain": [ " timestamp duration number_of_trees training_rmse training_mae training_deviance validation_rmse validation_mae validation_deviance\n", "--- ------------------- ---------- ----------------- ------------------ ------------------ ------------------- ------------------ ------------------ ---------------------\n", " 2018-12-10 22:21:59 0.007 sec 0.0 8.606717979611611 6.208675045776367 74.07559438056977 8.68971835638586 6.406206926148523 75.51120511330939\n", " 2018-12-10 22:22:00 0.082 sec 1.0 7.874525631082505 5.695541753768921 62.008153914575324 7.915833228281387 5.837858645576864 62.66041569796373\n", " 2018-12-10 22:22:00 0.099 sec 2.0 7.2269156601718185 5.246859560012817 52.22830995923667 7.352898226995891 5.366629046078393 54.065112336559324\n", " 2018-12-10 22:22:00 0.110 sec 3.0 6.651141048147663 4.840724048614502 44.237677242354785 6.730598133141566 4.920995748741879 45.30095122984873\n", " 2018-12-10 22:22:00 0.119 sec 4.0 6.135254277808832 4.461024179458618 37.641345053371566 6.225317121276851 4.530078803167265 38.754573260462706\n", "--- --- --- --- --- --- --- --- --- ---\n", " 2018-12-10 22:22:00 0.367 sec 46.0 1.4819745996519473 0.9849367189407349 2.19624871401355 3.1864195638216692 2.504840910437171 10.153269636705478\n", " 2018-12-10 22:22:00 0.372 sec 47.0 1.465042926220891 0.9735426521301269 2.146350775669871 3.165845117903913 2.484071976604012 10.02257531055604\n", " 2018-12-10 22:22:00 0.378 sec 48.0 1.4519265690069845 0.9678360986709594 2.108090761788394 3.1687301007707314 2.488529072249958 10.040850451530488\n", " 2018-12-10 22:22:00 0.383 sec 49.0 1.44058476848856 0.9627064180374145 2.075284475201238 3.1696856974092413 2.485432197876908 10.046907420360709\n", " 2018-12-10 22:22:00 0.389 sec 50.0 1.4293154529272842 0.956157636642456 2.042942663976728 3.1546942583328046 2.4723717368870632 9.952095863557963" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "See the whole table with table.as_data_frame()\n", "Variable Importances: \n" ] }, { "data": { "text/html": [ "
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variablerelative_importancescaled_importancepercentage
rm35352.76562501.00.4662487
lstat30494.92382810.86258950.4021812
crim2075.96337890.05872140.0273788
dis1768.76953120.05003200.0233274
ID1526.98986820.04319290.0201386
age1282.94653320.03628980.0169201
ptratio1082.07861330.03060800.0142710
tax746.50695800.02111590.0098453
nox697.57800290.01973190.0092000
chas280.37692260.00793080.0036977
rad250.92456050.00709770.0033093
black182.07124330.00515010.0024012
indus52.52891920.00148590.0006928
zn29.42135240.00083220.0003880
" ], "text/plain": [ "variable relative_importance scaled_importance percentage\n", "---------- --------------------- ------------------- ------------\n", "rm 35352.8 1 0.466249\n", "lstat 30494.9 0.862589 0.402181\n", "crim 2075.96 0.0587214 0.0273788\n", "dis 1768.77 0.050032 0.0233274\n", "ID 1526.99 0.0431929 0.0201386\n", "age 1282.95 0.0362898 0.0169201\n", "ptratio 1082.08 0.030608 0.014271\n", "tax 746.507 0.0211159 0.00984528\n", "nox 697.578 0.0197319 0.00919998\n", "chas 280.377 0.00793083 0.00369774\n", "rad 250.925 0.00709773 0.00330931\n", "black 182.071 0.00515013 0.00240124\n", "indus 52.5289 0.00148585 0.000692776\n", "zn 29.4214 0.000832222 0.000388022" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "model_gbm = GBM(model_id='boston_gbm')\n", "model_gbm.train(training_frame= train_df, validation_frame=valid_df, y = 'medv', x=features)\n", "print(model_gbm)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "ModelMetricsRegression: gbm\n", "** Reported on test data. **\n", "\n", "MSE: 28.403762409118233\n", "RMSE: 5.329518027844379\n", "MAE: 2.8727256028741603\n", "RMSLE: 0.18390281605425915\n", "Mean Residual Deviance: 28.403762409118233\n", "\n" ] } ], "source": [ "test_gbm = model_gbm.model_performance(test_df)\n", "print(test_gbm)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "drf Model Build progress: |███████████████████████████████████████████████| 100%\n", "Model Details\n", "=============\n", "H2ORandomForestEstimator : Distributed Random Forest\n", "Model Key: boston_glm\n", "\n", "\n", "ModelMetricsRegression: drf\n", "** Reported on train data. **\n", "\n", "MSE: 13.535222159406818\n", "RMSE: 3.6790246206578745\n", "MAE: 2.50327929070973\n", "RMSLE: 0.15461658290638441\n", "Mean Residual Deviance: 13.535222159406818\n", "\n", "ModelMetricsRegression: drf\n", "** Reported on validation data. **\n", "\n", "MSE: 10.280927854920087\n", "RMSE: 3.2063886001107362\n", "MAE: 2.559741400028097\n", "RMSLE: 0.13662446955334076\n", "Mean Residual Deviance: 10.280927854920087\n", "Scoring History: \n" ] }, { "data": { "text/html": [ "
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timestampdurationnumber_of_treestraining_rmsetraining_maetraining_deviancevalidation_rmsevalidation_maevalidation_deviance
2018-12-10 22:22:00 0.002 sec0.0nannannannannannan
2018-12-10 22:22:00 0.030 sec1.06.12035673.886301237.45876634.85141293.413793023.5362067
2018-12-10 22:22:00 0.048 sec2.06.06811363.879752136.82200314.26609033.062931018.1995261
2018-12-10 22:22:00 0.059 sec3.05.59841113.671212131.34220723.73754262.806321813.9692244
2018-12-10 22:22:00 0.068 sec4.05.79554553.733799133.58834783.88102912.787500115.0623868
------------------------------
2018-12-10 22:22:00 0.328 sec46.03.64098222.483595513.25675123.22893872.563512010.4260448
2018-12-10 22:22:00 0.333 sec47.03.65242352.494258013.34019743.22394232.565902410.3938040
2018-12-10 22:22:00 0.337 sec48.03.64713552.485122013.30159763.21556292.564637210.3398446
2018-12-10 22:22:00 0.343 sec49.03.66246112.493851513.41362113.21210062.570953610.3175903
2018-12-10 22:22:00 0.349 sec50.03.67902462.503279313.53522223.20638862.559741410.2809279
" ], "text/plain": [ " timestamp duration number_of_trees training_rmse training_mae training_deviance validation_rmse validation_mae validation_deviance\n", "--- ------------------- ---------- ----------------- ------------------ ------------------ ------------------- ------------------ ------------------ ---------------------\n", " 2018-12-10 22:22:00 0.002 sec 0.0 nan nan nan nan nan nan\n", " 2018-12-10 22:22:00 0.030 sec 1.0 6.120356713822716 3.88630123660989 37.45876630443479 4.851412860732422 3.4137930212349725 23.536206745279944\n", " 2018-12-10 22:22:00 0.048 sec 2.0 6.068113634468775 3.8797520645393813 36.822003080825844 4.266090262344155 3.062930970356382 18.199526126467614\n", " 2018-12-10 22:22:00 0.059 sec 3.0 5.598411129904372 3.6712121040948897 31.34220717943715 3.737542558755161 2.8063218237339775 13.969224378506077\n", " 2018-12-10 22:22:00 0.068 sec 4.0 5.795545510591357 3.733799127811724 33.58834776533564 3.88102908675291 2.7875000649485093 15.062386772222128\n", "--- --- --- --- --- --- --- --- --- ---\n", " 2018-12-10 22:22:00 0.328 sec 46.0 3.640982182023553 2.4835954508398244 13.256751249812991 3.2289386504761755 2.5635120150210082 10.426044808538906\n", " 2018-12-10 22:22:00 0.333 sec 47.0 3.652423492900152 2.494258041533931 13.340197371488948 3.22394231044322 2.5659024412808664 10.393804021065968\n", " 2018-12-10 22:22:00 0.337 sec 48.0 3.6471355310726308 2.485122047811369 13.30159758201244 3.215562868530333 2.564637234156159 10.339844561471022\n", " 2018-12-10 22:22:00 0.343 sec 49.0 3.66246107674688 2.493851457895479 13.413621138685915 3.212100604177361 2.5709535751772292 10.317590291356568\n", " 2018-12-10 22:22:00 0.349 sec 50.0 3.6790246206578745 2.50327929070973 13.535222159406818 3.2063886001107362 2.559741400028097 10.280927854920087" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "See the whole table with table.as_data_frame()\n", "Variable Importances: \n" ] }, { "data": { "text/html": [ "
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variablerelative_importancescaled_importancepercentage
lstat170228.84375001.00.2983517
rm146500.57812500.86060960.2567643
ptratio62365.93359380.36636530.1093057
ID30946.30273440.18179240.0542381
indus30772.86718750.18077350.0539341
crim30057.46875000.17657100.0526802
dis24625.07617190.14465870.0431592
nox18071.79492190.10616180.0316735
tax17483.34375000.10270490.0306422
age17011.56054690.09993350.0298153
black8173.18896480.04801300.0143247
chas5748.40722660.03376870.0100749
zn4295.74951170.02523510.0075289
rad4283.29638670.02516200.0075071
" ], "text/plain": [ "variable relative_importance scaled_importance percentage\n", "---------- --------------------- ------------------- ------------\n", "lstat 170229 1 0.298352\n", "rm 146501 0.86061 0.256764\n", "ptratio 62365.9 0.366365 0.109306\n", "ID 30946.3 0.181792 0.0542381\n", "indus 30772.9 0.180774 0.0539341\n", "crim 30057.5 0.176571 0.0526802\n", "dis 24625.1 0.144659 0.0431592\n", "nox 18071.8 0.106162 0.0316735\n", "tax 17483.3 0.102705 0.0306422\n", "age 17011.6 0.0999335 0.0298153\n", "black 8173.19 0.048013 0.0143247\n", "chas 5748.41 0.0337687 0.0100749\n", "zn 4295.75 0.0252351 0.00752895\n", "rad 4283.3 0.025162 0.00750712" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "model_rf = RF(model_id='boston_rf')\n", "model_rf.train(training_frame= train_df, validation_frame=valid_df, y = 'medv', x=features)\n", "print(model_rf)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "ModelMetricsRegression: drf\n", "** Reported on test data. **\n", "\n", "MSE: 26.47634734831408\n", "RMSE: 5.145517209019331\n", "MAE: 2.7435666684468587\n", "RMSLE: 0.16544264104460665\n", "Mean Residual Deviance: 26.47634734831408\n", "\n" ] } ], "source": [ "test_rf = model_rf.model_performance(test_df)\n", "print(test_rf)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "gbm Grid Build progress: |████████████████████████████████████████████████| 100%\n" ] } ], "source": [ "from h2o.grid.grid_search import H2OGridSearch as Grid\n", "\n", "hyper_params = {'max_depth':[2,4,6,8,10,12,14,16]}\n", "\n", "grid = Grid(model_gbm, hyper_params, grid_id='depth_grid')\n", "\n", "grid.train(training_frame= train_df, validation_frame=valid_df, y = 'medv', x=features)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " max_depth model_ids residual_deviance\n", "0 2 depth_grid_model_1 8.582603711148172\n", "1 6 depth_grid_model_3 10.299542166052671\n", "2 4 depth_grid_model_2 10.378852893143074\n", "3 16 depth_grid_model_8 11.350201168324462\n", "4 12 depth_grid_model_6 11.350201168324462\n", "5 14 depth_grid_model_7 11.350201168324462\n", "6 8 depth_grid_model_4 11.386830104743034\n", "7 10 depth_grid_model_5 11.408501214655228\n", "\n" ] } ], "source": [ "print(grid)" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "AutoML progress: |████████████████████████████████████████████████████████| 100%\n" ] } ], "source": [ "from h2o.automl import H2OAutoML as AutoML\n", "\n", "aml = AutoML(max_models = 10, max_runtime_secs=100, seed=2)\n", "aml.train(training_frame= train_df, validation_frame=valid_df, y = 'medv', x=features)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
model_id mean_residual_deviance rmse mse mae rmsle
StackedEnsemble_AllModels_AutoML_20181210_223722 9.827933.13495 9.827932.139170.139589
StackedEnsemble_BestOfFamily_AutoML_20181210_223722 9.944613.15351 9.944612.146710.138903
GBM_3_AutoML_20181210_223722 10.2273 3.1980210.2273 2.241260.14437
GBM_2_AutoML_20181210_223722 10.2627 3.2035510.2627 2.238990.143894
GBM_1_AutoML_20181210_223722 10.2719 3.2049810.2719 2.219910.147681
GBM_4_AutoML_20181210_223722 10.287 3.2073410.287 2.245460.144326
XGBoost_2_AutoML_20181210_223722 10.3645 3.2193910.3645 2.051240.143118
XGBoost_1_AutoML_20181210_223722 11.068 3.3268611.068 2.164750.14958
XGBoost_3_AutoML_20181210_223722 11.3421 3.3678 11.3421 2.263890.147565
XRT_1_AutoML_20181210_223722 12.0748 3.4748812.0748 2.315720.141624
" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "print(aml.leaderboard)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python [conda env:anaconda3]", "language": "python", "name": "conda-env-anaconda3-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.5" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: Chapter08/wine_classification_h2o.ipynb ================================================ { "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import h2o\n", "import time\n", "import seaborn\n", "import itertools\n", "import numpy as np\n", "import pandas as pd\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "from h2o.estimators.glm import H2OGeneralizedLinearEstimator as GLM\n", "from h2o.estimators.gbm import H2OGradientBoostingEstimator as GBM\n", "from h2o.estimators.random_forest import H2ORandomForestEstimator as RF\n", "\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Checking whether there is an H2O instance running at http://localhost:54321..... not found.\n", "Attempting to start a local H2O server...\n", " Java Version: java version \"1.8.0_191\"; Java(TM) SE Runtime Environment (build 1.8.0_191-b12); Java HotSpot(TM) 64-Bit Server VM (build 25.191-b12, mixed mode)\n", " Starting server from /home/am/anaconda3/envs/h2o/lib/python3.5/site-packages/h2o/backend/bin/h2o.jar\n", " Ice root: /tmp/tmpfvwskrf7\n", " JVM stdout: /tmp/tmpfvwskrf7/h2o_am_started_from_python.out\n", " JVM stderr: /tmp/tmpfvwskrf7/h2o_am_started_from_python.err\n", " Server is running at http://127.0.0.1:54321\n", "Connecting to H2O server at http://127.0.0.1:54321... successful.\n" ] }, { "data": { "text/html": [ "
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H2O cluster uptime:01 secs
H2O cluster timezone:Asia/Kolkata
H2O data parsing timezone:UTC
H2O cluster version:3.22.0.2
H2O cluster version age:18 days
H2O cluster name:H2O_from_python_am_5e0any
H2O cluster total nodes:1
H2O cluster free memory:6.957 Gb
H2O cluster total cores:8
H2O cluster allowed cores:8
H2O cluster status:accepting new members, healthy
H2O connection url:http://127.0.0.1:54321
H2O connection proxy:None
H2O internal security:False
H2O API Extensions:XGBoost, Algos, AutoML, Core V3, Core V4
Python version:3.5.6 final
" ], "text/plain": [ "-------------------------- ----------------------------------------\n", "H2O cluster uptime: 01 secs\n", "H2O cluster timezone: Asia/Kolkata\n", "H2O data parsing timezone: UTC\n", "H2O cluster version: 3.22.0.2\n", "H2O cluster version age: 18 days\n", "H2O cluster name: H2O_from_python_am_5e0any\n", "H2O cluster total nodes: 1\n", "H2O cluster free memory: 6.957 Gb\n", "H2O cluster total cores: 8\n", "H2O cluster allowed cores: 8\n", "H2O cluster status: accepting new members, healthy\n", "H2O connection url: http://127.0.0.1:54321\n", "H2O connection proxy:\n", "H2O internal security: False\n", "H2O API Extensions: XGBoost, Algos, AutoML, Core V3, Core V4\n", "Python version: 3.5.6 final\n", "-------------------------- ----------------------------------------" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "h2o.init()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Parse progress: |█████████████████████████████████████████████████████████| 100%\n" ] } ], "source": [ "wine_df = h2o.import_file(\"../Chapter08/winequality-red.csv\", destination_frame=\"wine_df\")" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Rows:1599\n", "Cols:12\n", "\n", "\n" ] }, { "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", "
fixed acidity volatile acidity citric acid residual sugar chlorides free sulfur dioxide total sulfur dioxide density pH sulphates alcohol quality
type real real real real real real real real real real real int
mins 4.6 0.12 0.0 0.9 0.012 1.0 6.0 0.99007 2.74 0.33 8.4 3.0
mean 8.31963727329581 0.5278205128205128 0.27097560975609752.53880550343964950.08746654158849279 15.874921826141339 46.46779237023139 0.996746679174484 3.311113195747342 0.6581488430268919 10.4229831144465295.6360225140712945
maxs 15.9 1.58 1.0 15.5 0.611 72.0 289.0 1.00369 4.01 2.0 14.9 8.0
sigma 1.74109631812769570.179059704153535370.19480113740531861.40992805950727980.04706530201009010610.460156969809725 32.895324478299074 0.0018873339538425440.154386464903542850.169506979590109961.06566758184739490.807569439734705
zeros 0 0 132 0 0 0 0 0 0 0 0 0
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3 11.2 0.28 0.56 1.9 0.075 17.0 60.0 0.998 3.16 0.58 9.8 6.0
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5 7.4 0.66 0.0 1.8 0.075 13.0 40.0 0.9978 3.51 0.56 9.4 5.0
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7 7.3 0.65 0.0 1.2 0.065 15.0 21.0 0.9946 3.39 0.47 10.0 7.0
8 7.8 0.58 0.02 2.0 0.073 9.0 18.0 0.9968 3.36 0.57 9.5 7.0
9 7.5 0.5 0.36 6.1 0.071 17.0 102.0 0.9978 3.35 0.8 10.5 5.0
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "wine_df.describe()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['fixed acidity', 'volatile acidity', 'citric acid', 'residual sugar', 'chlorides', 'free sulfur dioxide', 'total sulfur dioxide', 'density', 'pH', 'sulphates', 'alcohol']\n" ] } ], "source": [ "features = wine_df.columns[:-1]\n", "print(features)\n", "wine_df['quality'] = (wine_df['quality'] > 7).ifelse(1,0)\n", "wine_df['quality'] = wine_df['quality'].asfactor()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "train_df, valid_df, test_df = wine_df.split_frame(ratios=[0.6, 0.2], seed=133)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
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01ErrorRate
0964.03.00.0031 (3.0/967.0)
15.03.00.625 (5.0/8.0)
Total969.06.00.0082 (8.0/975.0)
" ], "text/plain": [ " 0 1 Error Rate\n", "----- --- --- ------- -----------\n", "0 964 3 0.0031 (3.0/967.0)\n", "1 5 3 0.625 (5.0/8.0)\n", "Total 969 6 0.0082 (8.0/975.0)" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Maximum Metrics: Maximum metrics at their respective thresholds\n", "\n" ] }, { "data": { "text/html": [ "
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metricthresholdvalueidx
max f10.19975450.42857145.0
max f20.19975450.39473685.0
max f0point50.19975450.468755.0
max accuracy0.25008080.99179493.0
max precision0.25008080.53.0
max recall0.01210731.0111.0
max specificity0.49848760.99896590.0
max absolute_mcc0.19975450.42898705.0
max min_per_class_accuracy0.01210730.8841779111.0
max mean_per_class_accuracy0.01210730.9420889111.0
" ], "text/plain": [ "metric threshold value idx\n", "--------------------------- ----------- -------- -----\n", "max f1 0.199754 0.428571 5\n", "max f2 0.199754 0.394737 5\n", "max f0point5 0.199754 0.46875 5\n", "max accuracy 0.250081 0.991795 3\n", "max precision 0.250081 0.5 3\n", "max recall 0.0121073 1 111\n", "max specificity 0.498488 0.998966 0\n", "max absolute_mcc 0.199754 0.428987 5\n", "max min_per_class_accuracy 0.0121073 0.884178 111\n", "max mean_per_class_accuracy 0.0121073 0.942089 111" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Gains/Lift Table: Avg response rate: 0.82 %, avg score: 0.82 %\n", "\n" ] }, { "data": { "text/html": [ "
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40.04102560.048708512.187515.2343750.10.05173430.1250.12358340.1250.6251118.751423.4375
50.05025640.03818680.012.43622450.00.04358530.10204080.10888990.00.625-100.01143.6224490
60.10051280.01780554.97448988.70535710.04081630.02670040.07142860.06779510.250.875397.4489796770.5357143
70.15076920.00866432.48724496.63265310.02040820.01230170.05442180.04929730.1251.0148.7244898563.2653061
80.20.00528120.05.00.00.00695410.04102560.03887440.01.0-100.0400.0
90.30051280.00163040.03.32764510.00.00300640.02730380.02687760.01.0-100.0232.7645051
100.40.00045150.02.50.00.00086470.02051280.02040770.01.0-100.0150.0
110.50051280.00016240.01.99795080.00.00029180.01639340.01636800.01.0-100.099.7950820
120.60.00004790.01.66666670.00.00010030.01367520.01367070.01.0-100.066.6666667
130.69948720.00000930.01.42961880.00.00002540.01173020.01172990.01.0-100.042.9618768
140.80.00000040.01.250.00.00000330.01025640.01025660.01.0-100.025.0
150.89948720.00000000.01.11174460.00.00000010.00912200.00912220.01.0-100.011.1744584
161.00.00000000.01.00.00.00000000.00820510.00820530.01.0-100.00.0
" ], "text/plain": [ " group cumulative_data_fraction lower_threshold lift cumulative_lift response_rate score cumulative_response_rate cumulative_score capture_rate cumulative_capture_rate gain cumulative_gain\n", "-- ------- -------------------------- ----------------- ------- ----------------- --------------- ----------- -------------------------- ------------------ -------------- ------------------------- ------- -----------------\n", " 1 0.0102564 0.155307 36.5625 36.5625 0.3 0.24528 0.3 0.24528 0.375 0.375 3556.25 3556.25\n", " 2 0.0205128 0.0877692 12.1875 24.375 0.1 0.125986 0.2 0.185633 0.125 0.5 1118.75 2337.5\n", " 3 0.0307692 0.0591149 0 16.25 0 0.0713336 0.133333 0.147533 0 0.5 -100 1525\n", " 4 0.0410256 0.0487085 12.1875 15.2344 0.1 0.0517343 0.125 0.123583 0.125 0.625 1118.75 1423.44\n", " 5 0.0502564 0.0381868 0 12.4362 0 0.0435853 0.102041 0.10889 0 0.625 -100 1143.62\n", " 6 0.100513 0.0178055 4.97449 8.70536 0.0408163 0.0267004 0.0714286 0.0677951 0.25 0.875 397.449 770.536\n", " 7 0.150769 0.00866427 2.48724 6.63265 0.0204082 0.0123017 0.0544218 0.0492973 0.125 1 148.724 563.265\n", " 8 0.2 0.00528119 0 5 0 0.00695406 0.0410256 0.0388744 0 1 -100 400\n", " 9 0.300513 0.00163036 0 3.32765 0 0.00300642 0.0273038 0.0268776 0 1 -100 232.765\n", " 10 0.4 0.000451516 0 2.5 0 0.0008647 0.0205128 0.0204077 0 1 -100 150\n", " 11 0.500513 0.000162382 0 1.99795 0 0.000291799 0.0163934 0.016368 0 1 -100 99.7951\n", " 12 0.6 4.7861e-05 0 1.66667 0 0.000100286 0.0136752 0.0136707 0 1 -100 66.6667\n", " 13 0.699487 9.33687e-06 0 1.42962 0 2.53534e-05 0.0117302 0.0117299 0 1 -100 42.9619\n", " 14 0.8 4.46625e-07 0 1.25 0 3.31545e-06 0.0102564 0.0102566 0 1 -100 25\n", " 15 0.899487 3.26477e-09 0 1.11174 0 1.08822e-07 0.00912201 0.00912215 0 1 -100 11.1745\n", " 16 1 2.44866e-21 0 1 0 6.04621e-10 0.00820513 0.00820526 0 1 -100 0" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "ModelMetricsBinomialGLM: glm\n", "** Reported on validation data. **\n", "\n", "MSE: 0.016830206080396287\n", "RMSE: 0.12973128412374668\n", "LogLoss: 0.07559378953489561\n", "Null degrees of freedom: 305\n", "Residual degrees of freedom: 294\n", "Null deviance: 52.98982257100096\n", "Residual deviance: 46.263399195356\n", "AIC: 70.263399195356\n", "AUC: 0.881063122923588\n", "pr_auc: 0.08677975193068073\n", "Gini: 0.762126245847176\n", "Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.04515190614038355: \n" ] }, { "data": { "text/html": [ "
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01ErrorRate
0285.016.00.0532 (16.0/301.0)
12.03.00.4 (2.0/5.0)
Total287.019.00.0588 (18.0/306.0)
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metricthresholdvalueidx
max f10.04515190.2517.0
max f20.04515190.384615417.0
max f0point50.04515190.185185217.0
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max precision0.12655880.16666675.0
max recall0.00093831.0108.0
max specificity0.44206680.99667770.0
max absolute_mcc0.04515190.287285617.0
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40.04248370.07823320.04.70769230.00.07934700.07692310.17524900.00.2-100.0370.7692308
50.05228760.04957490.03.82500000.00.06035110.06250.15370560.00.2-100.0282.5
60.10130720.01709658.165.92258060.13333330.03100580.09677420.09433470.40.6716.0492.2580645
70.15032680.00907844.085.32173910.06666670.01255480.08695650.06766740.20.8308.0432.1739130
80.20261440.00495270.03.94838710.00.00639100.06451610.05185410.00.8-100.0294.8387097
90.30065360.00194400.02.66086960.00.00307860.04347830.03594910.00.8-100.0166.0869565
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110.50.00020490.02.00.00.00042570.03267970.02194350.01.0-100.0100.0
120.60130720.00005370.01.66304350.00.00012050.02717390.01826680.01.0-100.066.3043478
130.69934640.00000670.01.42990650.00.00002470.02336450.01570950.01.0-100.042.9906542
140.80065360.00000060.01.24897960.00.00000250.02040820.01372210.01.0-100.024.8979592
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" ], "text/plain": [ " group cumulative_data_fraction lower_threshold lift cumulative_lift response_rate score cumulative_response_rate cumulative_score capture_rate cumulative_capture_rate gain cumulative_gain\n", "-- ------- -------------------------- ----------------- ------- ----------------- --------------- ----------- -------------------------- ------------------ -------------- ------------------------- ------- -----------------\n", " 1 0.0130719 0.200785 0 0 0 0.329776 0 0.329776 0 0 -100 -100\n", " 2 0.0228758 0.119581 20.4 8.74286 0.333333 0.139187 0.142857 0.248095 0.2 0.2 1940 774.286\n", " 3 0.0326797 0.0819314 0 6.12 0 0.101177 0.1 0.20402 0 0.2 -100 512\n", " 4 0.0424837 0.0782332 0 4.70769 0 0.079347 0.0769231 0.175249 0 0.2 -100 370.769\n", " 5 0.0522876 0.0495749 0 3.825 0 0.0603511 0.0625 0.153706 0 0.2 -100 282.5\n", " 6 0.101307 0.0170965 8.16 5.92258 0.133333 0.0310058 0.0967742 0.0943347 0.4 0.6 716 492.258\n", " 7 0.150327 0.0090784 4.08 5.32174 0.0666667 0.0125548 0.0869565 0.0676674 0.2 0.8 308 432.174\n", " 8 0.202614 0.00495266 0 3.94839 0 0.00639104 0.0645161 0.0518541 0 0.8 -100 294.839\n", " 9 0.300654 0.00194403 0 2.66087 0 0.00307861 0.0434783 0.0359491 0 0.8 -100 166.087\n", " 10 0.401961 0.000754556 1.97419 2.4878 0.0322581 0.00120254 0.0406504 0.0271918 0.2 1 97.4194 148.78\n", " 11 0.5 0.000204868 0 2 0 0.000425676 0.0326797 0.0219435 0 1 -100 100\n", " 12 0.601307 5.36638e-05 0 1.66304 0 0.000120529 0.0271739 0.0182668 0 1 -100 66.3043\n", " 13 0.699346 6.67544e-06 0 1.42991 0 2.47167e-05 0.0233645 0.0157095 0 1 -100 42.9907\n", " 14 0.800654 5.87326e-07 0 1.24898 0 2.45512e-06 0.0204082 0.0137221 0 1 -100 24.898\n", " 15 0.898693 4.02051e-09 0 1.11273 0 1.44616e-07 0.0181818 0.0122252 0 1 -100 11.2727\n", " 16 1 2.59236e-22 0 1 0 4.74466e-10 0.0163399 0.0109867 0 1 -100 0" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Scoring History: \n" ] }, { "data": { "text/html": [ "
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timestampdurationiterationsnegative_log_likelihoodobjective
2018-12-10 23:35:47 0.000 sec046.39105680.0475806
2018-12-10 23:35:47 0.023 sec139.47862810.0405123
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" ], "text/plain": [ " timestamp duration iterations negative_log_likelihood objective\n", "-- ------------------- ---------- ------------ ------------------------- -----------\n", " 2018-12-10 23:35:47 0.000 sec 0 46.3911 0.0475806\n", " 2018-12-10 23:35:47 0.023 sec 1 39.4786 0.0405123\n", " 2018-12-10 23:35:47 0.026 sec 2 33.7456 0.0346835\n", " 2018-12-10 23:35:47 0.030 sec 3 30.9715 0.0318906\n", " 2018-12-10 23:35:47 0.032 sec 4 29.7996 0.0307666\n", " 2018-12-10 23:35:47 0.034 sec 5 29.2248 0.0302714\n", " 2018-12-10 23:35:47 0.036 sec 6 28.8419 0.0300152\n", " 2018-12-10 23:35:47 0.037 sec 7 28.7678 0.0299927\n", " 2018-12-10 23:35:47 0.040 sec 8 28.7621 0.0299922" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "model_glm = GLM(model_id='wine_glm', family = 'binomial')\n", "model_glm.train(training_frame= train_df, validation_frame=valid_df, y = 'quality', x=features)\n", "print(model_glm)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "ModelMetricsBinomialGLM: glm\n", "** Reported on test data. **\n", "\n", "MSE: 0.017228193204603934\n", "RMSE: 0.1312562120610066\n", "LogLoss: 0.13988271775187358\n", "Null degrees of freedom: 317\n", "Residual degrees of freedom: 306\n", "Null deviance: 53.187557984070224\n", "Residual deviance: 88.96540849019598\n", "AIC: 112.96540849019598\n", "AUC: 0.6038338658146964\n", "pr_auc: 0.03346490361472496\n", "Gini: 0.2076677316293929\n", "Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.17042651179749857: \n" ] }, { "data": { "text/html": [ "
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01ErrorRate
0308.05.00.016 (5.0/313.0)
14.01.00.8 (4.0/5.0)
Total312.06.00.0283 (9.0/318.0)
" ], "text/plain": [ " 0 1 Error Rate\n", "----- --- --- ------- -----------\n", "0 308 5 0.016 (5.0/313.0)\n", "1 4 1 0.8 (4.0/5.0)\n", "Total 312 6 0.0283 (9.0/318.0)" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Maximum Metrics: Maximum metrics at their respective thresholds\n", "\n" ] }, { "data": { "text/html": [ "
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metricthresholdvalueidx
max f10.17042650.18181825.0
max f20.17042650.19230775.0
max f0point50.17042650.17241385.0
max accuracy0.49848760.98113210.0
max precision0.17042650.16666675.0
max recall0.00000021.0253.0
max specificity0.49848760.99680510.0
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40.04088050.06964130.04.89230770.00.07474440.07692310.20346680.00.2-100.0389.2307692
50.05031450.05394970.03.9750.00.06092050.06250.17673940.00.2-100.0297.5
60.10062890.02075140.01.98750.00.03742680.031250.10708310.00.2-100.098.75
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80.20125790.00331403.9751.98750.06250.00505100.031250.05790980.20.4297.598.75
90.30188680.00117720.01.3250.00.00205890.02083330.03929280.00.4-100.032.5000000
100.39937110.00036842.05161291.50236220.03225810.00067740.02362200.02986700.20.6105.161290350.2362205
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120.60062890.00002820.00.99895290.00.00006070.01570680.01990410.00.6-100.0-0.1047120
130.69811320.00000602.05161291.14594590.03225810.00001620.01801800.01712700.20.8105.161290314.5945946
140.79874210.00000070.01.00157480.00.00000280.01574800.01496960.00.8-100.00.1574803
150.89937110.00000001.98751.11188810.031250.00000020.01748250.01329470.21.098.7511.1888112
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" ], "text/plain": [ " group cumulative_data_fraction lower_threshold lift cumulative_lift response_rate score cumulative_response_rate cumulative_score capture_rate cumulative_capture_rate gain cumulative_gain\n", "-- ------- -------------------------- ----------------- ------- ----------------- --------------- ----------- -------------------------- ------------------ -------------- ------------------------- ------- -----------------\n", " 1 0.0125786 0.268243 0 0 0 0.388896 0 0.388896 0 0 -100 -100\n", " 2 0.0220126 0.14099 21.2 9.08571 0.333333 0.178922 0.142857 0.298907 0.2 0.2 2020 808.571\n", " 3 0.0314465 0.0869027 0 6.36 0 0.109495 0.1 0.242084 0 0.2 -100 536\n", " 4 0.0408805 0.0696413 0 4.89231 0 0.0747444 0.0769231 0.203467 0 0.2 -100 389.231\n", " 5 0.0503145 0.0539497 0 3.975 0 0.0609205 0.0625 0.176739 0 0.2 -100 297.5\n", " 6 0.100629 0.0207514 0 1.9875 0 0.0374268 0.03125 0.107083 0 0.2 -100 98.75\n", " 7 0.150943 0.00726595 0 1.325 0 0.012422 0.0208333 0.0755294 0 0.2 -100 32.5\n", " 8 0.201258 0.00331398 3.975 1.9875 0.0625 0.00505101 0.03125 0.0579098 0.2 0.4 297.5 98.75\n", " 9 0.301887 0.00117721 0 1.325 0 0.00205895 0.0208333 0.0392928 0 0.4 -100 32.5\n", " 10 0.399371 0.000368374 2.05161 1.50236 0.0322581 0.00067741 0.023622 0.029867 0.2 0.6 105.161 50.2362\n", " 11 0.5 0.000103984 0 1.2 0 0.000207139 0.0188679 0.0238977 0 0.6 -100 20\n", " 12 0.600629 2.81701e-05 0 0.998953 0 6.06854e-05 0.0157068 0.0199041 0 0.6 -100 -0.104712\n", " 13 0.698113 6.03315e-06 2.05161 1.14595 0.0322581 1.62411e-05 0.018018 0.017127 0.2 0.8 105.161 14.5946\n", " 14 0.798742 6.86983e-07 0 1.00157 0 2.83121e-06 0.015748 0.0149696 0 0.8 -100 0.15748\n", " 15 0.899371 4.53521e-09 1.9875 1.11189 0.03125 1.62015e-07 0.0174825 0.0132947 0.2 1 98.75 11.1888\n", " 16 1 5.19226e-14 0 1 0 6.77667e-10 0.0157233 0.0119569 0 1 -100 0" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n" ] } ], "source": [ "test_glm = model_glm.model_performance(test_df)\n", "print(test_glm)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "gbm Model Build progress: |███████████████████████████████████████████████| 100%\n", "Model Details\n", "=============\n", "H2OGradientBoostingEstimator : Gradient Boosting Machine\n", "Model Key: boston_gbm\n", "\n", "\n", "ModelMetricsBinomial: gbm\n", "** Reported on train data. **\n", "\n", "MSE: 1.7528245314956014e-05\n", "RMSE: 0.004186674732404706\n", "LogLoss: 0.0007428475623428082\n", "Mean Per-Class Error: 0.0\n", "AUC: 1.0\n", "pr_auc: 0.875\n", "Gini: 1.0\n", "Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.9407067152946088: \n" ] }, { "data": { "text/html": [ "
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01ErrorRate
0967.00.00.0 (0.0/967.0)
10.08.00.0 (0.0/8.0)
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metricthresholdvalueidx
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" ], "text/plain": [ "metric threshold value idx\n", "--------------------------- ----------- ------- -----\n", "max f1 0.940707 1 7\n", "max f2 0.940707 1 7\n", "max f0point5 0.940707 1 7\n", "max accuracy 0.940707 1 7\n", "max precision 0.973756 1 0\n", "max recall 0.940707 1 7\n", "max specificity 0.973756 1 0\n", "max absolute_mcc 0.940707 1 7\n", "max min_per_class_accuracy 0.940707 1 7\n", "max mean_per_class_accuracy 0.940707 1 7" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Gains/Lift Table: Avg response rate: 0.82 %, avg score: 0.82 %\n", "\n" ] }, { "data": { "text/html": [ "
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30.03076920.00409850.032.50.00.00526750.26666670.26077800.01.0-100.03150.0
40.040.00231670.025.00.00.00287160.20512820.20126120.01.0-100.02400.0
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60.10051280.00059740.09.94897960.00.00100250.08163270.08080500.01.0-100.0894.8979592
70.15076920.00034300.06.63265310.00.00041460.05442180.05400820.01.0-100.0563.2653061
80.20.00023540.05.00.00.00029100.04102560.04078550.01.0-100.0400.0
90.30051280.00016470.03.32764510.00.00019410.02730380.02720890.01.0-100.0232.7645051
100.40.00012760.02.50.00.00014510.02051280.02047760.01.0-100.0150.0
110.50461540.00010250.01.98170730.00.00011390.01626020.01625590.01.0-100.098.1707317
120.60.00009140.01.66666670.00.00009890.01367520.01368730.01.0-100.066.6666667
130.70769230.00008880.01.41304350.00.00008890.01159420.01161800.01.0-100.041.3043478
140.80205130.00008870.01.24680310.00.00008870.01023020.01026160.01.0-100.024.6803069
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" ], "text/plain": [ " group cumulative_data_fraction lower_threshold lift cumulative_lift response_rate score cumulative_response_rate cumulative_score capture_rate cumulative_capture_rate gain cumulative_gain\n", "-- ------- -------------------------- ----------------- ------ ----------------- --------------- ----------- -------------------------- ------------------ -------------- ------------------------- ------ -----------------\n", " 1 0.0102564 0.0100441 97.5 97.5 0.8 0.769625 0.8 0.769625 1 1 9650 9650\n", " 2 0.0205128 0.00608 0 48.75 0 0.00744117 0.4 0.388533 0 1 -100 4775\n", " 3 0.0307692 0.00409847 0 32.5 0 0.0052675 0.266667 0.260778 0 1 -100 3150\n", " 4 0.04 0.0023167 0 25 0 0.00287156 0.205128 0.201261 0 1 -100 2400\n", " 5 0.0502564 0.00176509 0 19.898 0 0.00205866 0.163265 0.160608 0 1 -100 1889.8\n", " 6 0.100513 0.000597405 0 9.94898 0 0.00100247 0.0816327 0.080805 0 1 -100 894.898\n", " 7 0.150769 0.000342973 0 6.63265 0 0.000414603 0.0544218 0.0540082 0 1 -100 563.265\n", " 8 0.2 0.000235428 0 5 0 0.00029099 0.0410256 0.0407855 0 1 -100 400\n", " 9 0.300513 0.000164654 0 3.32765 0 0.000194098 0.0273038 0.0272089 0 1 -100 232.765\n", " 10 0.4 0.000127604 0 2.5 0 0.000145085 0.0205128 0.0204776 0 1 -100 150\n", " 11 0.504615 0.000102457 0 1.98171 0 0.000113885 0.0162602 0.0162559 0 1 -100 98.1707\n", " 12 0.6 9.13766e-05 0 1.66667 0 9.89436e-05 0.0136752 0.0136873 0 1 -100 66.6667\n", " 13 0.707692 8.87532e-05 0 1.41304 0 8.88791e-05 0.0115942 0.011618 0 1 -100 41.3043\n", " 14 0.802051 8.87282e-05 0 1.2468 0 8.87477e-05 0.0102302 0.0102616 0 1 -100 24.6803\n", " 15 0.899487 8.36643e-05 0 1.11174 0 8.71169e-05 0.00912201 0.00915948 0 1 -100 11.1745\n", " 16 1 5.8806e-05 0 1 0 7.4902e-05 0.00820513 0.00824636 0 1 -100 0" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "ModelMetricsBinomial: gbm\n", "** Reported on validation data. **\n", "\n", "MSE: 0.012983011364705343\n", "RMSE: 0.11394301806036798\n", "LogLoss: 0.08507280750116293\n", "Mean Per-Class Error: 0.05315614617940201\n", "AUC: 0.945514950166113\n", "pr_auc: 0.13600655538736034\n", "Gini: 0.891029900332226\n", "Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.01627635517717819: \n" ] }, { "data": { "text/html": [ "
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01ErrorRate
0298.03.00.01 (3.0/301.0)
13.02.00.6 (3.0/5.0)
Total301.05.00.0196 (6.0/306.0)
" ], "text/plain": [ " 0 1 Error Rate\n", "----- --- --- ------- -----------\n", "0 298 3 0.01 (3.0/301.0)\n", "1 3 2 0.6 (3.0/5.0)\n", "Total 301 5 0.0196 (6.0/306.0)" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Maximum Metrics: Maximum metrics at their respective thresholds\n", "\n" ] }, { "data": { "text/html": [ "
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metricthresholdvalueidx
max f10.01627640.40000003.0
max f20.00051760.438596535.0
max f0point50.94799630.55555560.0
max accuracy0.94799630.98692810.0
max precision0.94799631.00.0
max recall0.00051761.035.0
max specificity0.94799631.00.0
max absolute_mcc0.94799630.44427140.0
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" ], "text/plain": [ "metric threshold value idx\n", "--------------------------- ----------- -------- -----\n", "max f1 0.0162764 0.4 3\n", "max f2 0.000517597 0.438596 35\n", "max f0point5 0.947996 0.555556 0\n", "max accuracy 0.947996 0.986928 0\n", "max precision 0.947996 1 0\n", "max recall 0.000517597 1 35\n", "max specificity 0.947996 1 0\n", "max absolute_mcc 0.947996 0.444271 0\n", "max min_per_class_accuracy 0.000517597 0.893688 35\n", "max mean_per_class_accuracy 0.000517597 0.946844 35" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Gains/Lift Table: Avg response rate: 1.63 %, avg score: 0.40 %\n", "\n" ] }, { "data": { "text/html": [ "
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groupcumulative_data_fractionlower_thresholdliftcumulative_liftresponse_ratescorecumulative_response_ratecumulative_scorecapture_ratecumulative_capture_rategaincumulative_gain
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20.02287580.014528420.417.48571430.33333330.01531640.28571430.16013430.20.41940.00000001648.5714286
30.03267970.00851130.012.240.00.01113440.20.11543430.00.4-100.01124.0
40.04248370.00469670.09.41538460.00.00615330.15384620.09021560.00.4-100.0841.5384615
50.05228760.00187040.07.65000000.00.00251210.1250.07377120.00.4-100.0665.0
60.10130720.00076354.085.92258060.06666670.00115010.09677420.03863200.20.6308.0492.2580645
70.15032680.00033688.166.65217390.13333330.00049350.10869570.02619550.41.0716.0565.2173913
80.20261440.00026680.04.93548390.00.00029000.08064520.01951020.01.0-100.0393.5483871
90.30065360.00016690.03.32608700.00.00019790.05434780.01321270.01.0-100.0232.6086957
100.40196080.00014050.02.48780490.00.00015030.04065040.00992050.01.0-100.0148.7804878
110.50.00011030.02.00.00.00012760.03267970.00800040.01.0-100.0100.0
120.60130720.00009490.01.66304350.00.00010280.02717390.00666980.01.0-100.066.3043478
130.74183010.00008880.01.34801760.00.00008990.02202640.00542340.01.0-100.034.8017621
140.80392160.00008880.01.24390240.00.00008880.02032520.00501130.01.0-100.024.3902439
150.89869280.00008370.01.11272730.00.00008810.01818180.00449220.01.0-100.011.2727273
161.00.00006210.01.00.00.00007600.01633990.00404480.01.0-100.00.0
" ], "text/plain": [ " group cumulative_data_fraction lower_threshold lift cumulative_lift response_rate score cumulative_response_rate cumulative_score capture_rate cumulative_capture_rate gain cumulative_gain\n", "-- ------- -------------------------- ----------------- ------ ----------------- --------------- ----------- -------------------------- ------------------ -------------- ------------------------- ------ -----------------\n", " 1 0.0130719 0.0292142 15.3 15.3 0.25 0.268748 0.25 0.268748 0.2 0.2 1430 1430\n", " 2 0.0228758 0.0145284 20.4 17.4857 0.333333 0.0153164 0.285714 0.160134 0.2 0.4 1940 1648.57\n", " 3 0.0326797 0.00851135 0 12.24 0 0.0111344 0.2 0.115434 0 0.4 -100 1124\n", " 4 0.0424837 0.00469671 0 9.41538 0 0.00615326 0.153846 0.0902156 0 0.4 -100 841.538\n", " 5 0.0522876 0.0018704 0 7.65 0 0.00251211 0.125 0.0737712 0 0.4 -100 665\n", " 6 0.101307 0.000763528 4.08 5.92258 0.0666667 0.0011501 0.0967742 0.038632 0.2 0.6 308 492.258\n", " 7 0.150327 0.000336796 8.16 6.65217 0.133333 0.000493459 0.108696 0.0261955 0.4 1 716 565.217\n", " 8 0.202614 0.000266824 0 4.93548 0 0.000289982 0.0806452 0.0195102 0 1 -100 393.548\n", " 9 0.300654 0.000166911 0 3.32609 0 0.000197866 0.0543478 0.0132127 0 1 -100 232.609\n", " 10 0.401961 0.000140515 0 2.4878 0 0.000150273 0.0406504 0.00992054 0 1 -100 148.78\n", " 11 0.5 0.000110257 0 2 0 0.000127567 0.0326797 0.00800035 0 1 -100 100\n", " 12 0.601307 9.49465e-05 0 1.66304 0 0.000102813 0.0271739 0.00666979 0 1 -100 66.3043\n", " 13 0.74183 8.87531e-05 0 1.34802 0 8.98718e-05 0.0220264 0.00542337 0 1 -100 34.8018\n", " 14 0.803922 8.87516e-05 0 1.2439 0 8.87516e-05 0.0203252 0.00501135 0 1 -100 24.3902\n", " 15 0.898693 8.36924e-05 0 1.11273 0 8.81297e-05 0.0181818 0.00449217 0 1 -100 11.2727\n", " 16 1 6.21014e-05 0 1 0 7.60314e-05 0.0163399 0.00404479 0 1 -100 0" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Scoring History: \n" ] }, { "data": { "text/html": [ "
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timestampdurationnumber_of_treestraining_rmsetraining_loglosstraining_auctraining_pr_auctraining_lifttraining_classification_errorvalidation_rmsevalidation_loglossvalidation_aucvalidation_pr_aucvalidation_liftvalidation_classification_error
2018-12-10 23:35:48 0.007 sec0.00.09020980.04758060.50.01.00.99179490.12703960.08658470.50.01.00.9836601
2018-12-10 23:35:48 0.093 sec1.00.08338470.02981180.98649170.150941533.23863640.02564100.12518540.07525810.68205980.091265611.12727270.0392157
2018-12-10 23:35:48 0.130 sec2.00.07789910.02520480.99334280.507705040.6250.00512820.12342360.07025620.78006640.083638315.30000000.0130719
2018-12-10 23:35:48 0.157 sec3.00.07328070.02224380.99844880.785110377.55681820.00205130.12208070.06585020.96312290.142026415.30000000.0457516
2018-12-10 23:35:48 0.185 sec4.00.06966170.02020540.99851340.786426185.31250.00205130.12117060.06366290.96279070.146147315.30000000.0424837
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2018-12-10 23:35:48 0.704 sec46.00.00510540.00092041.00.87597.50.00.11385500.08125710.94817280.140456415.30000000.0196078
2018-12-10 23:35:48 0.715 sec47.00.00493500.00088431.00.87597.50.00.11384690.08195010.94750830.138702615.30000000.0196078
2018-12-10 23:35:48 0.728 sec48.00.00460370.00082291.00.87597.50.00.11387700.08315080.94551500.136724915.30000000.0196078
2018-12-10 23:35:48 0.738 sec49.00.00444030.00078961.00.87597.50.00.11391010.08383310.94617940.136699415.30000000.0196078
2018-12-10 23:35:48 0.747 sec50.00.00418670.00074281.00.87597.50.00.11394300.08507280.94551500.136006615.30000000.0196078
" ], "text/plain": [ " timestamp duration number_of_trees training_rmse training_logloss training_auc training_pr_auc training_lift training_classification_error validation_rmse validation_logloss validation_auc validation_pr_auc validation_lift validation_classification_error\n", "--- ------------------- ---------- ----------------- -------------------- --------------------- ------------------ ------------------- ----------------- ------------------------------- ------------------- -------------------- ------------------ ------------------- ------------------ ---------------------------------\n", " 2018-12-10 23:35:48 0.007 sec 0.0 0.09020977816326574 0.047580571062694046 0.5 0.0 1.0 0.9917948717948718 0.12703956850248 0.08658467740359631 0.5 0.0 1.0 0.9836601307189542\n", " 2018-12-10 23:35:48 0.093 sec 1.0 0.08338468300323641 0.02981178689097729 0.9864917269906929 0.15094145626206995 33.23863636363636 0.02564102564102564 0.1251853516426087 0.07525814935673943 0.6820598006644518 0.09126559714795009 11.127272727272727 0.0392156862745098\n", " 2018-12-10 23:35:48 0.130 sec 2.0 0.07789906883308008 0.025204828266284802 0.9933428128231644 0.5077050035274656 40.625 0.005128205128205128 0.1234235987498404 0.07025618650459124 0.7800664451827243 0.08363828788170893 15.299999999999999 0.013071895424836602\n", " 2018-12-10 23:35:48 0.157 sec 3.0 0.07328074946694621 0.022243788293282492 0.9984488107549121 0.7851102941176471 77.55681818181819 0.0020512820512820513 0.1220807156294957 0.06585015660862442 0.9631229235880397 0.14202635599694424 15.299999999999999 0.0457516339869281\n", " 2018-12-10 23:35:48 0.185 sec 4.0 0.06966166639718213 0.02020535699720757 0.9985134436401241 0.7864260835913313 85.3125 0.0020512820512820513 0.12117057378110216 0.06366287584688039 0.9627906976744186 0.14614727342549924 15.299999999999999 0.042483660130718956\n", "--- --- --- --- --- --- --- --- --- --- --- --- --- --- --- ---\n", " 2018-12-10 23:35:48 0.704 sec 46.0 0.00510544125347546 0.0009204070116415139 1.0 0.875 97.5 0.0 0.11385501373130032 0.0812571364487619 0.948172757475083 0.14045636207854634 15.299999999999999 0.0196078431372549\n", " 2018-12-10 23:35:48 0.715 sec 47.0 0.00493504090525195 0.000884300730194754 1.0 0.875 97.5 0.0 0.11384685491215636 0.08195006426995971 0.9475083056478405 0.13870262877478498 15.299999999999999 0.0196078431372549\n", " 2018-12-10 23:35:48 0.728 sec 48.0 0.004603697149121872 0.0008229235258820089 1.0 0.875 97.5 0.0 0.11387700796815912 0.08315082315662917 0.945514950166113 0.13672490531623657 15.299999999999999 0.0196078431372549\n", " 2018-12-10 23:35:48 0.738 sec 49.0 0.004440312810439291 0.0007895610596115889 1.0 0.875 97.5 0.0 0.11391009362598371 0.08383307698218712 0.9461794019933555 0.13669939108019602 15.299999999999999 0.0196078431372549\n", " 2018-12-10 23:35:48 0.747 sec 50.0 0.004186674732404706 0.0007428475623428082 1.0 0.875 97.5 0.0 0.11394301806036798 0.08507280750116293 0.945514950166113 0.13600655538736034 15.299999999999999 0.0196078431372549" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "See the whole table with table.as_data_frame()\n", "Variable Importances: \n" ] }, { "data": { "text/html": [ "
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variablerelative_importancescaled_importancepercentage
sulphates3.31082841.00.3232392
total sulfur dioxide1.58002640.47722990.1542594
alcohol1.09948680.33208810.1073439
chlorides0.81649670.24661400.0797153
density0.67843840.20491500.0662366
fixed acidity0.64428060.19459800.0629017
citric acid0.60023310.18129390.0586013
pH0.54558160.16478700.0532656
volatile acidity0.45835610.13844150.0447497
residual sugar0.27346880.08259830.0266990
free sulfur dioxide0.23546150.07111860.0229883
" ], "text/plain": [ "variable relative_importance scaled_importance percentage\n", "-------------------- --------------------- ------------------- ------------\n", "sulphates 3.31083 1 0.323239\n", "total sulfur dioxide 1.58003 0.47723 0.154259\n", "alcohol 1.09949 0.332088 0.107344\n", "chlorides 0.816497 0.246614 0.0797153\n", "density 0.678438 0.204915 0.0662366\n", "fixed acidity 0.644281 0.194598 0.0629017\n", "citric acid 0.600233 0.181294 0.0586013\n", "pH 0.545582 0.164787 0.0532656\n", "volatile acidity 0.458356 0.138442 0.0447497\n", "residual sugar 0.273469 0.0825983 0.026699\n", "free sulfur dioxide 0.235461 0.0711186 0.0229883" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "model_gbm = GBM(model_id='wine_gbm')\n", "model_gbm.train(training_frame= train_df, validation_frame=valid_df, y = 'quality', x=features)\n", "print(model_gbm)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "ModelMetricsBinomial: gbm\n", "** Reported on test data. **\n", "\n", "MSE: 0.015718138327642\n", "RMSE: 0.1253719997752369\n", "LogLoss: 0.12803113765206706\n", "Mean Per-Class Error: 0.23003194888178913\n", "AUC: 0.7722044728434504\n", "pr_auc: 0.032857432345781645\n", "Gini: 0.5444089456869008\n", "Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.0004738379817841345: \n" ] }, { "data": { "text/html": [ "
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01ErrorRate
0275.038.00.1214 (38.0/313.0)
13.02.00.6 (3.0/5.0)
Total278.040.00.1289 (41.0/318.0)
" ], "text/plain": [ " 0 1 Error Rate\n", "----- --- --- ------- ------------\n", "0 275 38 0.1214 (38.0/313.0)\n", "1 3 2 0.6 (3.0/5.0)\n", "Total 278 40 0.1289 (41.0/318.0)" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Maximum Metrics: Maximum metrics at their respective thresholds\n", "\n" ] }, { "data": { "text/html": [ "
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metricthresholdvalueidx
max f10.00047380.088888936.0
max f20.00016950.180180284.0
max f0point50.00047380.060606136.0
max accuracy0.04008190.98113210.0
max precision0.00047380.0536.0
max recall0.00010551.0138.0
max specificity0.04008190.99680510.0
max absolute_mcc0.00016950.143691684.0
max min_per_class_accuracy0.00016950.722044784.0
max mean_per_class_accuracy0.00010550.7699681138.0
" ], "text/plain": [ "metric threshold value idx\n", "--------------------------- ----------- --------- -----\n", "max f1 0.000473838 0.0888889 36\n", "max f2 0.000169488 0.18018 84\n", "max f0point5 0.000473838 0.0606061 36\n", "max accuracy 0.0400819 0.981132 0\n", "max precision 0.000473838 0.05 36\n", "max recall 0.000105519 1 138\n", "max specificity 0.0400819 0.996805 0\n", "max absolute_mcc 0.000169488 0.143692 84\n", "max min_per_class_accuracy 0.000169488 0.722045 84\n", "max mean_per_class_accuracy 0.000105519 0.769968 138" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Gains/Lift Table: Avg response rate: 1.57 %, avg score: 0.06 %\n", "\n" ] }, { "data": { "text/html": [ "
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20.02201260.00539790.00.00.00.00838050.00.01542710.00.0-100.0-100.0
30.03144650.00271980.00.00.00.00409180.00.01202650.00.0-100.0-100.0
40.04088050.00191630.00.00.00.00241950.00.00980950.00.0-100.0-100.0
50.05031450.00178330.00.00.00.00181350.00.00831020.00.0-100.0-100.0
60.10377360.00063143.74117651.92727270.05882350.00116000.03030300.00462680.20.2274.117647192.7272727
70.15408810.00038993.9752.59591840.06250.00046650.04081630.00326830.20.4297.5159.5918367
80.20125790.00028860.01.98750.00.00033880.031250.00258170.00.4-100.098.75
90.30188680.00016223.9752.650.06250.00019710.04166670.00178680.40.8297.5165.0
100.39937110.00012210.02.00314960.00.00013950.03149610.00138470.00.8-100.0100.3149606
110.50314470.00010241.92727271.98750.03030300.00010990.031250.00112180.21.092.727272798.75
120.66037740.00008880.01.51428570.00.00009250.02380950.00087670.01.0-100.051.4285714
130.73270440.00008880.01.36480690.00.00008880.02145920.00079890.01.0-100.036.4806867
140.80188680.00008850.01.24705880.00.00008870.01960780.00073770.01.0-100.024.7058824
150.89937110.00008210.01.11188810.00.00008530.01748250.00066700.01.0-100.011.1888112
161.00.00005880.01.00.00.00007360.01572330.00060730.01.0-100.00.0
" ], "text/plain": [ " group cumulative_data_fraction lower_threshold lift cumulative_lift response_rate score cumulative_response_rate cumulative_score capture_rate cumulative_capture_rate gain cumulative_gain\n", "-- ------- -------------------------- ----------------- ------- ----------------- --------------- ----------- -------------------------- ------------------ -------------- ------------------------- ------- -----------------\n", " 1 0.0125786 0.013353 0 0 0 0.0207121 0 0.0207121 0 0 -100 -100\n", " 2 0.0220126 0.00539785 0 0 0 0.00838045 0 0.0154271 0 0 -100 -100\n", " 3 0.0314465 0.0027198 0 0 0 0.00409176 0 0.0120265 0 0 -100 -100\n", " 4 0.0408805 0.00191631 0 0 0 0.00241951 0 0.00980951 0 0 -100 -100\n", " 5 0.0503145 0.00178334 0 0 0 0.00181346 0 0.00831025 0 0 -100 -100\n", " 6 0.103774 0.000631404 3.74118 1.92727 0.0588235 0.00115995 0.030303 0.00462676 0.2 0.2 274.118 92.7273\n", " 7 0.154088 0.000389882 3.975 2.59592 0.0625 0.000466483 0.0408163 0.0032683 0.2 0.4 297.5 159.592\n", " 8 0.201258 0.000288573 0 1.9875 0 0.000338803 0.03125 0.0025817 0 0.4 -100 98.75\n", " 9 0.301887 0.000162239 3.975 2.65 0.0625 0.000197118 0.0416667 0.00178684 0.4 0.8 297.5 165\n", " 10 0.399371 0.000122062 0 2.00315 0 0.000139453 0.0314961 0.00138472 0 0.8 -100 100.315\n", " 11 0.503145 0.00010239 1.92727 1.9875 0.030303 0.000109902 0.03125 0.00112179 0.2 1 92.7273 98.75\n", " 12 0.660377 8.87531e-05 0 1.51429 0 9.2516e-05 0.0238095 0.000876725 0 1 -100 51.4286\n", " 13 0.732704 8.87516e-05 0 1.36481 0 8.87516e-05 0.0214592 0.000798942 0 1 -100 36.4807\n", " 14 0.801887 8.84534e-05 0 1.24706 0 8.86798e-05 0.0196078 0.000737665 0 1 -100 24.7059\n", " 15 0.899371 8.20599e-05 0 1.11189 0 8.53296e-05 0.0174825 0.000666957 0 1 -100 11.1888\n", " 16 1 5.88061e-05 0 1 0 7.36226e-05 0.0157233 0.000607251 0 1 -100 0" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n" ] } ], "source": [ "test_gbm = model_gbm.model_performance(test_df)\n", "print(test_gbm)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "drf Model Build progress: |███████████████████████████████████████████████| 100%\n", "Model Details\n", "=============\n", "H2ORandomForestEstimator : Distributed Random Forest\n", "Model Key: boston_rf\n", "\n", "\n", "ModelMetricsBinomial: drf\n", "** Reported on train data. **\n", "\n", "MSE: 0.008894848806863922\n", "RMSE: 0.09431250610000734\n", "LogLoss: 0.08389391894356298\n", "Mean Per-Class Error: 0.29453205791106507\n", "AUC: 0.6283609100310238\n", "pr_auc: 0.019539466164439814\n", "Gini: 0.2567218200620476\n", "Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.10294113159179685: \n" ] }, { "data": { "text/html": [ "
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01ErrorRate
0932.035.00.0362 (35.0/967.0)
16.02.00.75 (6.0/8.0)
Total938.037.00.0421 (41.0/975.0)
" ], "text/plain": [ " 0 1 Error Rate\n", "----- --- --- ------- ------------\n", "0 932 35 0.0362 (35.0/967.0)\n", "1 6 2 0.75 (6.0/8.0)\n", "Total 938 37 0.0421 (41.0/975.0)" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Maximum Metrics: Maximum metrics at their respective thresholds\n", "\n" ] }, { "data": { "text/html": [ "
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metricthresholdvalueidx
max f10.10294110.088888933.0
max f20.10294110.144927533.0
max f0point50.10294110.064102633.0
max accuracy0.35714290.99076920.0
max precision0.10294110.054054133.0
max recall0.01.0399.0
max specificity0.35714290.99896590.0
max absolute_mcc0.00383420.1105805100.0
max min_per_class_accuracy0.00067780.625160.0
max mean_per_class_accuracy0.00067780.7054679160.0
" ], "text/plain": [ "metric threshold value idx\n", "--------------------------- ----------- --------- -----\n", "max f1 0.102941 0.0888889 33\n", "max f2 0.102941 0.144928 33\n", "max f0point5 0.102941 0.0641026 33\n", "max accuracy 0.357143 0.990769 0\n", "max precision 0.102941 0.0540541 33\n", "max recall 0 1 399\n", "max specificity 0.357143 0.998966 0\n", "max absolute_mcc 0.00383425 0.11058 100\n", "max min_per_class_accuracy 0.000677808 0.625 160\n", "max mean_per_class_accuracy 0.000677808 0.705468 160" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Gains/Lift Table: Avg response rate: 0.82 %, avg score: 1.04 %\n", "\n" ] }, { "data": { "text/html": [ "
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10.01025640.15800000.00.00.00.21274150.00.21274150.00.0-100.0-100.0
20.02051280.12523780.00.00.00.14034650.00.17654400.00.0-100.0-100.0
30.03076920.11163390.00.00.00.11944370.00.15751060.00.0-100.0-100.0
40.040.091723127.08333336.250.22222220.10467280.05128210.14531720.250.252608.3333333525.0
50.05025640.07722200.04.97448980.00.08525710.04081630.13306010.00.25-100.0397.4489796
60.10051280.04581420.02.48724490.00.05655670.02040820.09480840.00.25-100.0148.7244898
70.15076920.00078854.97448983.31632650.04081630.00952180.02721090.06637950.250.5397.4489796231.6326531
80.20.00070450.02.50.00.00073700.02051280.05022140.00.5-100.0150.0
90.30051280.00058801.24362242.07977820.01020410.00063910.01706480.03363750.1250.62524.3622449107.9778157
100.40.00051860.01.56250.00.00055270.01282050.02540870.00.625-100.056.25
110.50051280.00045760.01.24871930.00.00048900.01024590.02040440.00.625-100.024.8719262
120.60.00040500.01.04166670.00.00043280.00854700.01709280.00.625-100.04.1666667
130.69948720.00034081.25644331.07221410.01030930.00037570.00879770.01471520.1250.7525.64432997.2214076
140.80.00027710.00.93750.00.00030870.00769230.01290510.00.75-100.0-6.25
150.89948720.00017981.25644330.97277650.01030930.00023190.00798180.01150340.1250.87525.6443299-2.7223489
161.00.01.24362241.00.01020410.00010160.00820510.01035740.1251.024.36224490.0
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01ErrorRate
0301.00.00.0 (0.0/301.0)
14.01.00.8 (4.0/5.0)
Total305.01.00.0131 (4.0/306.0)
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metricthresholdvalueidx
max f10.64009080.33333330.0
max f20.06159910.441176512.0
max f0point50.64009080.55555560.0
max accuracy0.64009080.98692810.0
max precision0.64009081.00.0
max recall0.02032381.041.0
max specificity0.64009081.00.0
max absolute_mcc0.64009080.44427140.0
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" ], "text/plain": [ "metric threshold value idx\n", "--------------------------- ----------- -------- -----\n", "max f1 0.640091 0.333333 0\n", "max f2 0.0615991 0.441176 12\n", "max f0point5 0.640091 0.555556 0\n", "max accuracy 0.640091 0.986928 0\n", "max precision 0.640091 1 0\n", "max recall 0.0203238 1 41\n", "max specificity 0.640091 1 0\n", "max absolute_mcc 0.640091 0.444271 0\n", "max min_per_class_accuracy 0.0203238 0.82392 41\n", "max mean_per_class_accuracy 0.0203238 0.91196 41" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Gains/Lift Table: Avg response rate: 1.63 %, avg score: 1.32 %\n", "\n" ] }, { "data": { "text/html": [ "
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10.01307190.101324515.300000015.30000000.250.30570020.250.30570020.20.21430.01430.0
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" ], "text/plain": [ " group cumulative_data_fraction lower_threshold lift cumulative_lift response_rate score cumulative_response_rate cumulative_score capture_rate cumulative_capture_rate gain cumulative_gain\n", "-- ------- -------------------------- ----------------- ------- ----------------- --------------- ----------- -------------------------- ------------------ -------------- ------------------------- ------- -----------------\n", " 1 0.0130719 0.101325 15.3 15.3 0.25 0.3057 0.25 0.3057 0.2 0.2 1430 1430\n", " 2 0.0228758 0.0982296 0 8.74286 0 0.100162 0.142857 0.217612 0 0.2 -100 774.286\n", " 3 0.0326797 0.081217 20.4 12.24 0.333333 0.0814128 0.2 0.176752 0.2 0.4 1940 1124\n", " 4 0.0424837 0.0764825 0 9.41538 0 0.0802362 0.153846 0.15448 0 0.4 -100 841.538\n", " 5 0.0555556 0.0612672 15.3 10.8 0.25 0.0614085 0.176471 0.13258 0.2 0.6 1430 980\n", " 6 0.104575 0.0402743 4.08 7.65 0.0666667 0.0499362 0.125 0.0938409 0.2 0.8 308 665\n", " 7 0.166667 0.020448 0 4.8 0 0.0274341 0.0784314 0.0691011 0 0.8 -100 380\n", " 8 0.20915 0.0202446 4.70769 4.78125 0.0769231 0.0203137 0.078125 0.0591912 0.2 1 370.769 378.125\n", " 9 0.519608 0.000568452 0 1.92453 0 0.0020387 0.0314465 0.0250435 0 1 -100 92.4528\n", " 10 0.630719 0.000477679 0 1.58549 0 0.000488194 0.0259067 0.0207177 0 1 -100 58.5492\n", " 11 0.715686 0.000447969 0 1.39726 0 0.000447969 0.0228311 0.0183112 0 1 -100 39.726\n", " 12 0.820261 0.000365101 0 1.21912 0 0.000404624 0.0199203 0.0160283 0 1 -100 21.9124\n", " 13 0.898693 0.000313004 0 1.11273 0 0.000337934 0.0181818 0.014659 0 1 -100 11.2727\n", " 14 1 0.000120482 0 1 0 0.000238989 0.0163399 0.0131981 0 1 -100 0" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Scoring History: \n" ] }, { "data": { "text/html": [ "
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timestampdurationnumber_of_treestraining_rmsetraining_loglosstraining_auctraining_pr_auctraining_lifttraining_classification_errorvalidation_rmsevalidation_loglossvalidation_aucvalidation_pr_aucvalidation_liftvalidation_classification_error
2018-12-10 23:35:49 0.003 sec0.0nannannannannannannannannannannannan
2018-12-10 23:35:49 0.027 sec1.00.12997860.50577280.65112990.00205760.50154320.68169010.17141670.74189500.62890370.059515810.20.0294118
2018-12-10 23:35:49 0.037 sec2.00.11198810.31772210.40753620.00376260.00.99480970.12122870.15949280.76179400.165536418.360.0098039
2018-12-10 23:35:49 0.051 sec3.00.11160630.31151580.40.00518811.12847220.15416670.11588860.15840160.64584720.122648012.240.0294118
2018-12-10 23:35:49 0.065 sec4.00.12350800.40179050.41124740.00568860.00.10718640.11251970.14181990.80232560.316427220.40.0228758
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2018-12-10 23:35:49 0.527 sec46.00.09458560.08398000.62454760.02030990.00.04102560.11585500.05751790.91495020.068354215.30000000.0130719
2018-12-10 23:35:49 0.537 sec47.00.09453060.08372670.62196230.01947790.00.04205130.11605090.05779920.91295680.067009915.30000000.0130719
2018-12-10 23:35:49 0.548 sec48.00.09438260.08357230.62713290.01990420.00.04102560.11493000.05445600.93089700.092079115.30000000.0130719
2018-12-10 23:35:49 0.559 sec49.00.09430030.08368450.62732680.01971010.00.04205130.11445900.05333600.93820600.111203415.30000000.0130719
2018-12-10 23:35:49 0.570 sec50.00.09431250.08389390.62836090.01953950.00.04205130.11424670.05280930.93787380.111559915.30000000.0130719
" ], "text/plain": [ " timestamp duration number_of_trees training_rmse training_logloss training_auc training_pr_auc training_lift training_classification_error validation_rmse validation_logloss validation_auc validation_pr_auc validation_lift validation_classification_error\n", "--- ------------------- ---------- ----------------- ------------------- ------------------- ------------------- -------------------- ------------------ ------------------------------- ------------------- -------------------- ------------------ ------------------- ------------------ ---------------------------------\n", " 2018-12-10 23:35:49 0.003 sec 0.0 nan nan nan nan nan nan nan nan nan nan nan nan\n", " 2018-12-10 23:35:49 0.027 sec 1.0 0.12997863045766606 0.5057728397702758 0.6511299435028248 0.00205761316872428 0.5015432098765432 0.6816901408450704 0.1714167287489557 0.7418950395471623 0.6289036544850499 0.05951576032711936 10.2 0.029411764705882353\n", " 2018-12-10 23:35:49 0.037 sec 2.0 0.11198805649414983 0.31772209626856873 0.40753623188405796 0.003762624131431626 0.0 0.9948096885813149 0.12122872624081818 0.1594928216920369 0.7617940199335549 0.1655364259524948 18.36 0.00980392156862745\n", " 2018-12-10 23:35:49 0.051 sec 3.0 0.11160632492657313 0.3115158448952877 0.4 0.00518811050084602 1.128472222222222 0.15416666666666667 0.11588856930053215 0.1584016258414013 0.6458471760797342 0.12264803195227572 12.24 0.029411764705882353\n", " 2018-12-10 23:35:49 0.065 sec 4.0 0.12350800092501908 0.4017905049390314 0.4112474437627812 0.005688610188525884 0.0 0.1071863580998782 0.1125196624083839 0.14181994725348046 0.8023255813953488 0.31642717427603645 20.4 0.02287581699346405\n", "--- --- --- --- --- --- --- --- --- --- --- --- --- --- --- ---\n", " 2018-12-10 23:35:49 0.527 sec 46.0 0.09458558394067364 0.08397996150066123 0.624547569803516 0.020309918834664285 0.0 0.041025641025641026 0.11585495320787315 0.05751789149784571 0.9149501661129568 0.06835419611834706 15.299999999999999 0.013071895424836602\n", " 2018-12-10 23:35:49 0.537 sec 47.0 0.09453058017475768 0.08372670935489629 0.6219622543950362 0.01947794973009856 0.0 0.04205128205128205 0.11605093897589737 0.057799173012280054 0.9129568106312292 0.06700987484564534 15.299999999999999 0.013071895424836602\n", " 2018-12-10 23:35:49 0.548 sec 48.0 0.09438260760210383 0.08357232656002825 0.6271328852119958 0.01990419105942016 0.0 0.041025641025641026 0.11492996787382401 0.05445595808480517 0.9308970099667774 0.09207910413173571 15.299999999999999 0.013071895424836602\n", " 2018-12-10 23:35:49 0.559 sec 49.0 0.09430031346075192 0.08368454280924166 0.6273267838676319 0.019710071983464725 0.0 0.04205128205128205 0.11445898455203439 0.05333598453615175 0.9382059800664451 0.111203440237923 15.299999999999999 0.013071895424836602\n", " 2018-12-10 23:35:49 0.570 sec 50.0 0.09431250610000734 0.08389391894356298 0.6283609100310238 0.019539466164439814 0.0 0.04205128205128205 0.11424665195693184 0.05280930977551538 0.9378737541528239 0.11155988009436285 15.299999999999999 0.013071895424836602" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "See the whole table with table.as_data_frame()\n", "Variable Importances: \n" ] }, { "data": { "text/html": [ "
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variablerelative_importancescaled_importancepercentage
sulphates39.22068021.00.1577585
alcohol31.47262190.80244970.1265933
density23.10702900.58915420.0929441
total sulfur dioxide22.67153930.57805060.0911924
residual sugar22.47064020.57292840.0903843
fixed acidity22.32360270.56917940.0897929
citric acid19.18482210.48915070.0771677
free sulfur dioxide17.96484570.45804520.0722605
volatile acidity17.67375180.45062330.0710897
chlorides16.58775520.42293390.0667214
pH15.93481060.40628590.0640951
" ], "text/plain": [ "variable relative_importance scaled_importance percentage\n", "-------------------- --------------------- ------------------- ------------\n", "sulphates 39.2207 1 0.157759\n", "alcohol 31.4726 0.80245 0.126593\n", "density 23.107 0.589154 0.0929441\n", "total sulfur dioxide 22.6715 0.578051 0.0911924\n", "residual sugar 22.4706 0.572928 0.0903843\n", "fixed acidity 22.3236 0.569179 0.0897929\n", "citric acid 19.1848 0.489151 0.0771677\n", "free sulfur dioxide 17.9648 0.458045 0.0722605\n", "volatile acidity 17.6738 0.450623 0.0710897\n", "chlorides 16.5878 0.422934 0.0667214\n", "pH 15.9348 0.406286 0.0640951" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "model_rf = RF(model_id='wine_rf')\n", "model_rf.train(training_frame= train_df, validation_frame=valid_df, y = 'quality', x=features)\n", "print(model_rf)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "ModelMetricsBinomial: drf\n", "** Reported on test data. **\n", "\n", "MSE: 0.015312985031476338\n", "RMSE: 0.1237456465152465\n", "LogLoss: 0.07774923041444476\n", "Mean Per-Class Error: 0.160702875399361\n", "AUC: 0.7437699680511183\n", "pr_auc: 0.06459019745180232\n", "Gini: 0.48753993610223656\n", "Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.04150581955909727: \n" ] }, { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
01ErrorRate
0294.019.00.0607 (19.0/313.0)
12.03.00.4 (2.0/5.0)
Total296.022.00.066 (21.0/318.0)
" ], "text/plain": [ " 0 1 Error Rate\n", "----- --- --- ------- ------------\n", "0 294 19 0.0607 (19.0/313.0)\n", "1 2 3 0.4 (2.0/5.0)\n", "Total 296 22 0.066 (21.0/318.0)" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Maximum Metrics: Maximum metrics at their respective thresholds\n", "\n" ] }, { "data": { "text/html": [ "
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metricthresholdvalueidx
max f10.04150580.222222218.0
max f20.04150580.357142918.0
max f0point50.04150580.161290318.0
max accuracy0.18012050.98113210.0
max precision0.04150580.136363618.0
max recall0.00014831.073.0
max specificity0.18012050.99680510.0
max absolute_mcc0.00020340.309695672.0
max min_per_class_accuracy0.02041460.834.0
max mean_per_class_accuracy0.02041460.839297134.0
" ], "text/plain": [ "metric threshold value idx\n", "--------------------------- ----------- -------- -----\n", "max f1 0.0415058 0.222222 18\n", "max f2 0.0415058 0.357143 18\n", "max f0point5 0.0415058 0.16129 18\n", "max accuracy 0.18012 0.981132 0\n", "max precision 0.0415058 0.136364 18\n", "max recall 0.00014833 1 73\n", "max specificity 0.18012 0.996805 0\n", "max absolute_mcc 0.000203351 0.309696 72\n", "max min_per_class_accuracy 0.0204146 0.8 34\n", "max mean_per_class_accuracy 0.0204146 0.839297 34" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Gains/Lift Table: Avg response rate: 1.57 %, avg score: 0.92 %\n", "\n" ] }, { "data": { "text/html": [ "
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groupcumulative_data_fractionlower_thresholdliftcumulative_liftresponse_ratescorecumulative_response_ratecumulative_scorecapture_ratecumulative_capture_rategaincumulative_gain
10.01257860.11684060.00.00.00.14011940.00.14011940.00.0-100.0-100.0
20.02201260.09374370.00.00.00.10050210.00.12314060.00.0-100.0-100.0
30.03144650.08081320.00.00.00.08149310.00.11064630.00.0-100.0-100.0
40.04088050.06166560.00.00.00.06788290.00.10077780.00.0-100.0-100.0
50.05031450.060215421.23.9750.33333330.06114230.06250.09334620.20.22020.0297.5
60.10062890.04020577.955.96250.1250.04199450.093750.06767030.40.6695.0496.2500000
70.15094340.02029663.9755.30.06250.02356960.08333330.05297010.20.8297.5430.0
80.20125790.00161110.03.9750.00.01666020.06250.04389260.00.8-100.0297.5
90.48427670.00056850.01.65194810.00.00060070.02597400.01859210.00.8-100.065.1948052
100.52515720.00051340.01.52335330.00.00051340.02395210.01718480.00.8-100.052.3353293
110.60377360.00047770.01.3250.00.00047770.02083330.01500940.00.8-100.032.5000000
120.69811320.00042520.01.14594590.00.00044800.01801800.01304160.00.8-100.014.5945946
130.81761010.00036510.00.97846150.00.00039770.01538460.01119370.00.8-100.0-2.1538462
140.91194970.00032380.00.87724140.00.00033850.01379310.01007070.00.8-100.0-12.2758621
151.00.00012052.27142861.00.03571430.00023750.01572330.00920490.21.0127.14285710.0
" ], "text/plain": [ " group cumulative_data_fraction lower_threshold lift cumulative_lift response_rate score cumulative_response_rate cumulative_score capture_rate cumulative_capture_rate gain cumulative_gain\n", "-- ------- -------------------------- ----------------- ------- ----------------- --------------- ----------- -------------------------- ------------------ -------------- ------------------------- ------- -----------------\n", " 1 0.0125786 0.116841 0 0 0 0.140119 0 0.140119 0 0 -100 -100\n", " 2 0.0220126 0.0937437 0 0 0 0.100502 0 0.123141 0 0 -100 -100\n", " 3 0.0314465 0.0808132 0 0 0 0.0814931 0 0.110646 0 0 -100 -100\n", " 4 0.0408805 0.0616656 0 0 0 0.0678829 0 0.100778 0 0 -100 -100\n", " 5 0.0503145 0.0602154 21.2 3.975 0.333333 0.0611423 0.0625 0.0933462 0.2 0.2 2020 297.5\n", " 6 0.100629 0.0402057 7.95 5.9625 0.125 0.0419945 0.09375 0.0676703 0.4 0.6 695 496.25\n", " 7 0.150943 0.0202966 3.975 5.3 0.0625 0.0235696 0.0833333 0.0529701 0.2 0.8 297.5 430\n", " 8 0.201258 0.00161109 0 3.975 0 0.0166602 0.0625 0.0438926 0 0.8 -100 297.5\n", " 9 0.484277 0.000568452 0 1.65195 0 0.000600651 0.025974 0.0185921 0 0.8 -100 65.1948\n", " 10 0.525157 0.000513431 0 1.52335 0 0.000513431 0.0239521 0.0171848 0 0.8 -100 52.3353\n", " 11 0.603774 0.000477679 0 1.325 0 0.000477679 0.0208333 0.0150094 0 0.8 -100 32.5\n", " 12 0.698113 0.000425189 0 1.14595 0 0.000447969 0.018018 0.0130416 0 0.8 -100 14.5946\n", " 13 0.81761 0.000365101 0 0.978462 0 0.000397735 0.0153846 0.0111937 0 0.8 -100 -2.15385\n", " 14 0.91195 0.000323833 0 0.877241 0 0.000338529 0.0137931 0.0100707 0 0.8 -100 -12.2759\n", " 15 1 0.000120482 2.27143 1 0.0357143 0.000237489 0.0157233 0.00920491 0.2 1 127.143 0" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n" ] } ], "source": [ "test_rf = model_rf.model_performance(test_df)\n", "print(test_rf)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "gbm Grid Build progress: |████████████████████████████████████████████████| 100%\n" ] } ], "source": [ "from h2o.grid.grid_search import H2OGridSearch as Grid\n", "\n", "hyper_params = {'max_depth':[2,4,6,8,10,12,14,16]}\n", "\n", "grid = Grid(model_gbm, hyper_params, grid_id='depth_grid')\n", "\n", "grid.train(training_frame= train_df, validation_frame=valid_df, y = 'quality', x=features)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " max_depth model_ids logloss\n", "0 2 depth_grid_model_1 0.07040736021614663\n", "1 4 depth_grid_model_2 0.0787918395099991\n", "2 6 depth_grid_model_3 0.08387145119986193\n", "3 14 depth_grid_model_7 0.0849816145272973\n", "4 10 depth_grid_model_5 0.0849816950502771\n", "5 16 depth_grid_model_8 0.08498174004732731\n", "6 12 depth_grid_model_6 0.08498183111077036\n", "7 8 depth_grid_model_4 0.08919355715963359\n", "\n" ] } ], "source": [ "print(grid)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "AutoML progress: |████████████████████████████████████████████████████████| 100%\n" ] } ], "source": [ "from h2o.automl import H2OAutoML as AutoML\n", "\n", "aml = AutoML(max_models = 10, max_runtime_secs=100, seed=2)\n", "aml.train(training_frame= train_df, validation_frame=valid_df, y = 'quality', x=features)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
model_id auc logloss mean_per_class_error rmse mse
XGBoost_2_AutoML_20181210_233553 0.8365430.0451932 0.35128 0.08991510.00808472
XGBoost_1_AutoML_20181210_233553 0.8294980.043128 0.4421540.09008210.00811478
GLM_grid_1_AutoML_20181210_233553_model_1 0.8262020.0423037 0.3466260.09021580.00813888
XGBoost_3_AutoML_20181210_233553 0.74037 0.0514577 0.2960830.09086410.00825629
GBM_1_AutoML_20181210_233553 0.6339840.133934 0.2982810.127564 0.0162726
GBM_2_AutoML_20181210_233553 0.6307520.0605161 0.3084280.09226920.0085136
GBM_3_AutoML_20181210_233553 0.5947520.0607896 0.3167010.09239 0.00853592
GBM_4_AutoML_20181210_233553 0.5286970.0610144 0.3285940.09240880.00853939
StackedEnsemble_BestOfFamily_AutoML_20181210_2335530.5127970.0480753 0.3831440.09028790.0081519
StackedEnsemble_AllModels_AutoML_20181210_233553 0.3492760.0480757 0.5 0.09026040.00814694
" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "print(aml.leaderboard)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Confusion Matrix (Act/Pred) for max f1 @ threshold = 0.9407067152946088: \n" ] }, { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
01ErrorRate
0967.00.00.0 (0.0/967.0)
10.08.00.0 (0.0/8.0)
Total967.08.00.0 (0.0/975.0)
" ], "text/plain": [ " 0 1 Error Rate\n", "----- --- --- ------- -----------\n", "0 967 0 0 (0.0/967.0)\n", "1 0 8 0 (0.0/8.0)\n", "Total 967 8 0 (0.0/975.0)" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model_gbm.confusion_matrix()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python [conda env:anaconda3]", "language": "python", "name": "conda-env-anaconda3-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.5" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: Chapter09/Heart_Disease_Prediction.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Heart Monitor\n", "Another very useful personal application of AI in IoT is the in the detection of heart diseases. A large number of wearables exist which can be used to monitor and record the heart rate. The data can be used to predict any harmful heart condition. Here we will employ the AI/ML tools to predict cardiac Arrhythmia, a group of conditions where the heart rate is irregular, it can be either too fast (above 100 beats per minute) or too slow (below 60 beats per minute). The data used is taken from the [UCI Ml dataset repo](https://archive.ics.uci.edu/ml/datasets/heart+Disease). The dataset consists of 76 attributes, not all required for prediction of the presence of disease, the dataset has a \"goal\" field associated with each data row, it has five possible values 0-4, the value 0 indicates healthy heart, any other value means there is a disease. The problem can be broken into a binary classification problem for better accuracy. The code is inspired from the GitHub link of [Mohammed Rashad](https://github.com/MohammedRashad/Deep-Learning-and-Wearable-IoT-to-Monitor-and-Predict-Cardiac-Arrhytmia), it is shared under the GNU GPL 3.0 license.\n", "\n", "The first step as always is to import the necessary modules. Since we are now classifying the patients as suffering from heart disease or not, we will need a classifier. Here for simplicity, we use SVC classifier. You can experiment with MLP classifier" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# importing required libraries\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "\n", "from sklearn.svm import SVC\n", "from sklearn import metrics\n", "from sklearn.metrics import confusion_matrix\n", "from sklearn.model_selection import train_test_split" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, read the dataset, preprocess the dataset to select attributes you will be considering, we choose 13 attributes from 76. And we convert the target from a multi-class value to binary class. Finally, the data is split in train and test dataset." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# reading csv file and extracting class column to y.\n", "dataset = pd.read_csv(\"data.csv\")\n", "dataset.fillna(dataset.mean(), inplace=True)\n", "\n", "dataset_to_array = np.array(dataset)\n", "label = dataset_to_array[:,57] # \"Target\" classes having 0 and 1\n", "label = label.astype('int')\n", "label[label>0] = 1 # When it is 0 heart is healthy, 1 otherwise" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The Dataset dimensions are : (617, 13) \n", "\n" ] } ], "source": [ "# extracting 13 features\n", "dataset = np.column_stack((\n", " dataset_to_array[:,4] , # pain location\n", " dataset_to_array[:,6] , # relieved after rest\n", " dataset_to_array[:,9] , # pain type \n", " dataset_to_array[:,11], # resting blood pressure\n", " dataset_to_array[:,33], # maximum heart rate achieved\n", " dataset_to_array[:,34], # resting heart rate \n", " dataset_to_array[:,35], # peak exercise blood pressure (first of 2 parts) \n", " dataset_to_array[:,36], # peak exercise blood pressure (second of 2 parts) \n", " dataset_to_array[:,38], # resting blood pressure \n", " dataset_to_array[:,39], # exercise induced angina (1 = yes; 0 = no) \n", " dataset.age, # age \n", " dataset.sex , # sex\n", " dataset.hypertension # hyper tension\n", " ))\n", "\n", "print (\"The Dataset dimensions are : \" , dataset.shape , \"\\n\")" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# dividing data into train and test data\n", "X_train, X_test, y_train, y_test = train_test_split(dataset, label, random_state = 223)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, we define the model to be used, here we are using support vector classifier, using the fit function train it on train dataset." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "model = SVC(kernel = 'linear').fit(X_train, y_train)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let us see its performance on the test dataset. " ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy of the model is : 0.7419354838709677 \n", "Approximately : 74.0 %\n", "\n" ] } ], "source": [ "model_predictions = model.predict(X_test)\n", "# model accuracy for X_test \n", "accuracy = metrics.accuracy_score(y_test, model_predictions)\n", "print (\"Accuracy of the model is :\" , \n", " accuracy , \"\\nApproximately : \", \n", " round(accuracy*100) , \"%\\n\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can see that it provides an accuracy of 74%, using MLP we can increase it further. But do remember to normalize all the input features before using MLP Classifier. Below is the confusion matrix of our trained support vector classifier on the test dataset." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "# creating a confusion matrix\n", "cm = confusion_matrix(y_test, model_predictions)\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import pandas as pd\n", "import seaborn as sn\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "df_cm = pd.DataFrame(cm, index = [i for i in \"01\"],\n", " columns = [i for i in \"01\"])\n", "plt.figure(figsize = (10,7))\n", "sn.heatmap(df_cm, annot=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python [conda env:tensorflow]", "language": "python", "name": "conda-env-tensorflow-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.6" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: Chapter09/Human_activity_recognition_using_accelerometer.ipynb ================================================ { "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "\n", "from sklearn.ensemble import RandomForestClassifier as rfc\n", "from sklearn.metrics import confusion_matrix\n", "from sklearn.metrics import accuracy_score\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "data = pd.read_csv('data/samsung_data.txt',sep='|')" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "train = data.sample(frac=0.7,\n", " random_state=42)\n", "\n", "test = data[~data.index.isin(train.index)]" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "def varPlot(X,model,plotSize=(15,6),xticks=False, ntree=10):\n", " \n", " model_vars = pd.DataFrame(\n", " {'variable':X.columns,\n", " 'importance':model.feature_importances_})\n", "\n", " model_vars.sort_values(by='importance',\n", " ascending=False,\n", " inplace=True)\n", " \n", " varImp = plt.figure(figsize=plotSize)\n", " #varImp = sns.set_style('whitegrid')\n", " \n", " varImp = sns.barplot(y='importance',\n", " x='variable',\n", " data=model_vars,\n", " palette=sns.color_palette(\"Blues_r\",\n", " n_colors=X.shape[1]))\n", " \n", " if xticks==True:\n", " varImp = plt.xticks([])\n", " else:\n", " varImp = plt.xticks(rotation=90)\n", " \n", " varImp = plt.xlabel('Variable')\n", " varImp = plt.ylabel('Variable Importance')\n", " varImp = plt.title('Random Forest : Averaged variable importance over '+str(ntree)+' trees')\n", " return(varImp)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.9650213758258842" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X = train[train.columns[:-2]]\n", "Y = train.activity\n", "randomState = 42\n", "ntree = 25\n", "\n", "model0 = rfc(n_estimators=ntree,\n", " random_state=randomState,\n", " n_jobs=4,\n", " warm_start=True,\n", " oob_score=True)\n", "model0 = model0.fit(X, Y)\n", "model0.oob_score_" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5,1,'Random Forest : Averaged variable importance over 25 trees')" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "varPlot(X=X,model=model0,plotSize=(10,4),xticks=True)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "model_vars0 = pd.DataFrame(\n", " {'variable':X.columns,\n", " 'importance':model0.feature_importances_})\n", "\n", "model_vars0.sort_values(by='importance',\n", " ascending=False,\n", " inplace=True)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "n = 25\n", "\n", "cols_model = [col for col in model_vars0.variable[:n].values]" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "oobAccuracy = {}\n", "\n", "for cols in range(n):\n", " X = train[[col for col in model_vars0['variable'][:cols+1].values]]\n", " Y = train.activity\n", " \n", " model1 = rfc(n_estimators=ntree,\n", " random_state=randomState,\n", " n_jobs=4,\n", " warm_start=False,\n", " oob_score=True)\n", " \n", " model1 = model1.fit(X, Y)\n", " accuracy = accuracy_score(Y,model1.predict(X))\n", " \n", " oobAccuracy[cols+1] = [cols+1,model1.oob_score_,accuracy]\n", "\n", "accuracyTable =pd.DataFrame.from_dict(oobAccuracy).transpose()\n", "accuracyTable.columns = ['variables','oobAccuracy','accuracy']" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5,1,'OOB Accuracy vs Number of variables')" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(10,5))\n", "plt.scatter(x=accuracyTable.variables,\n", " y=100*accuracyTable.oobAccuracy)\n", "\n", "plt.xlim(0,n+1)\n", "plt.ylim(0,100)\n", "plt.minorticks_on()\n", "\n", "plt.xlabel('Number of variables')\n", "plt.ylabel('Accuracy(%)')\n", "plt.title('OOB Accuracy vs Number of variables')" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5,1,'Random Forest : Averaged variable importance over 25 trees')" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "varPlot(X,model1)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "n_used = 4\n", "\n", "cols_model = [col for col in model_vars0.variable[:n_used].values] + [model_vars0.variable[6]]" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "X = train[cols_model]\n", "Y = train.activity" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/am/anaconda3/lib/python3.6/site-packages/sklearn/ensemble/forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n", " warn(\"Some inputs do not have OOB scores. \"\n", "/Users/am/anaconda3/lib/python3.6/site-packages/sklearn/ensemble/forest.py:458: RuntimeWarning: divide by zero encountered in true_divide\n", " predictions[k].sum(axis=1)[:, np.newaxis])\n", "/Users/am/anaconda3/lib/python3.6/site-packages/sklearn/ensemble/forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n", " predictions[k].sum(axis=1)[:, np.newaxis])\n", "/Users/am/anaconda3/lib/python3.6/site-packages/sklearn/ensemble/forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n", " warn(\"Some inputs do not have OOB scores. \"\n", "/Users/am/anaconda3/lib/python3.6/site-packages/sklearn/ensemble/forest.py:458: RuntimeWarning: divide by zero encountered in true_divide\n", " predictions[k].sum(axis=1)[:, np.newaxis])\n", "/Users/am/anaconda3/lib/python3.6/site-packages/sklearn/ensemble/forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n", " predictions[k].sum(axis=1)[:, np.newaxis])\n", "/Users/am/anaconda3/lib/python3.6/site-packages/sklearn/ensemble/forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n", " warn(\"Some inputs do not have OOB scores. \"\n", "/Users/am/anaconda3/lib/python3.6/site-packages/sklearn/ensemble/forest.py:458: RuntimeWarning: divide by zero encountered in true_divide\n", " predictions[k].sum(axis=1)[:, np.newaxis])\n", "/Users/am/anaconda3/lib/python3.6/site-packages/sklearn/ensemble/forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n", " predictions[k].sum(axis=1)[:, np.newaxis])\n", "/Users/am/anaconda3/lib/python3.6/site-packages/sklearn/ensemble/forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n", " warn(\"Some inputs do not have OOB scores. \"\n", "/Users/am/anaconda3/lib/python3.6/site-packages/sklearn/ensemble/forest.py:458: RuntimeWarning: divide by zero encountered in true_divide\n", " predictions[k].sum(axis=1)[:, np.newaxis])\n", "/Users/am/anaconda3/lib/python3.6/site-packages/sklearn/ensemble/forest.py:458: RuntimeWarning: invalid value encountered in true_divide\n", " predictions[k].sum(axis=1)[:, np.newaxis])\n" ] } ], "source": [ "ntree_determination = {}\n", "\n", "for ntree in range(5,150,5):\n", " model = rfc(n_estimators=ntree,\n", " random_state=randomState,\n", " n_jobs=4,\n", " warm_start=False,\n", " oob_score=True)\n", " model = model.fit(X, Y)\n", " ntree_determination[ntree]=model.oob_score_" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "ntree_determination = pd.DataFrame.from_dict(ntree_determination,orient='index')\n", "ntree_determination['ntree'] = ntree_determination.index\n", "ntree_determination.columns=['oobScore','ntree']" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0,0.5,'OOB Accuracy')" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(6,4),)\n", "plt.scatter(x='ntree',\n", " y='oobScore',\n", " s=35,c='red',alpha=0.65,\n", " data=ntree_determination)\n", "plt.xlabel('Number of trees used')\n", "plt.ylabel('OOB Accuracy')" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "model2 = rfc(n_estimators=50,\n", " random_state=randomState,\n", " n_jobs=4,\n", " warm_start=False,\n", " oob_score=True)\n", "model2 = model2.fit(X, Y)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5,1,'Random Forest : Averaged variable importance over 145 trees')" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "varPlot(X,model2,plotSize=(6,4))" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "train_actual = Y\n", "train_pred = model2.predict(X)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "cm= confusion_matrix(train_actual,train_pred)\n", "sns.heatmap(data=cm,\n", " fmt='.0f',\n", " annot=True,\n", " xticklabels=np.unique(train_actual),\n", " yticklabels=np.unique(train_actual))" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1.0" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "accuracy_score(train_actual,train_pred)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.9450058297706957" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model2.oob_score_" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "test_actual = test.activity\n", "test_pred = model2.predict(test[X.columns])" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "cm = confusion_matrix(test_actual,test_pred)\n", "sns.heatmap(data=cm,\n", " fmt='.0f',\n", " annot=True,\n", " xticklabels=np.unique(test_actual),\n", " yticklabels=np.unique(test_actual))" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.9437896645512239" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "accuracy_score(test_actual,test_pred)" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(6,6))\n", "sns.set_style('whitegrid')\n", "\n", "sns.pairplot(data=train[[col for col in X.columns]+['activity']],\n", " hue='activity',\n", " palette='Set2',\n", " diag_kind='kde')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python [conda env:anaconda3]", "language": "python", "name": "conda-env-anaconda3-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.5" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: Chapter09/Hypoglycemia Prediction.ipynb ================================================ { "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from sklearn.linear_model import LinearRegression\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# Read the data\n", "ys = pd.read_csv('data/ys.csv')\n", "ts = pd.read_csv('data/ts.csv')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# MODEL FIT AND PREDICTION\n", "\n", "# Parameters of the predictive model. ph is Prediction horizon, mu is Forgetting factor.\n", "ph = 10 \n", "mu = 0.98 " ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "n_s = len(ys)\n", "\n", "# Arrays to hold predicted values\n", "tp_pred = np.zeros(n_s-1) \n", "yp_pred = np.zeros(n_s-1)\n", "\n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "# At every iteration of the for loop a new sample from CGM is acquired.\n", "for i in range(2, n_s+1):\n", " ts_tmp = ts[0:i]\n", " ys_tmp = ys[0:i]\n", " ns = len(ys_tmp)\n", " \n", " # The mu**k assigns the weight to the previous samples.\n", " weights = np.ones(ns)*mu\n", " for k in range(ns):\n", " weights[k] = weights[k]**k\n", " weights = np.flip(weights, 0)\n", " \n", "\n", " # MODEL\n", " # Linear Regression.\n", " lm_tmp = LinearRegression() \n", " model_tmp = lm_tmp.fit(ts_tmp, ys_tmp, sample_weight=weights)\n", " # Coefficients of the linear model, y = mx + q \n", " m_tmp = model_tmp.coef_\n", " q_tmp = model_tmp.intercept_\n", "\n", " # PREDICTION\n", " tp = ts.iloc[ns-1,0] + ph\n", " yp = m_tmp*tp + q_tmp\n", " \n", " tp_pred[i-2] = tp \n", " yp_pred[i-2] = yp\n", "\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# PLOT\n", "# Hypoglycemia threshold vector. \n", "t_tot = [l for l in range(int(ts.min()), int(tp_pred.max())+1)]\n", "hypoglycemiaTH = 70*np.ones(len(t_tot)) \n", "#hyperglycemiaTH = 180*np.ones(len(t_tot))\n", " \n", "fig, ax = plt.subplots(figsize=(10,10))\n", "fig.suptitle('Glucose Level Prediction', fontsize=22, fontweight='bold')\n", "ax.set_title('mu = %g, ph=%g ' %(mu, ph))\n", "ax.plot(tp_pred, yp_pred, label='Predicted Value') \n", "ax.plot(ts.iloc[:,0], ys.iloc[:,0], label='CGM data') \n", "ax.plot(t_tot, hypoglycemiaTH, label='Hypoglycemia threshold')\n", "#ax.plot(t_tot, hyperglycemiaTH, label='Hyperglycemia threshold')\n", "ax.set_xlabel('time (min)')\n", "ax.set_ylabel('glucose (mg/dl)')\n", "ax.legend()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "RMSE is 27.001869102039027\n" ] } ], "source": [ "from sklearn.metrics import mean_squared_error as mse\n", "print(\"RMSE is\", mse(ys[1:],yp_pred))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python [conda env:tensorflow]", "language": "python", "name": "conda-env-tensorflow-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.6" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: Chapter09/Images/Readme.md ================================================ Images ================================================ FILE: Chapter09/Readme.md ================================================ Now that you are fully equipped with machine learning and deep learning knowledge, and have learned how to use it for big data, image tasks, text task and time series data. It is the time to explore some real uses of the algorithms and the techniques that you have learned. This chapter and the following two chapters will now concentrate on some specific case studies. This chapter will focus on personal and home IoT use-cases. After going through the chapter you will: * Know about the successful IoT applications. * Learn about wearables and their role in personal IoT. * Learn how to monitor heart using machine learning. * Understand what makes home "smart home". * Learn about some devices used in smart home * Explore the application of AI in predicting human activity recognition ================================================ FILE: Chapter10/Electrical_Load_Forecasting.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Dataset to be downloaded from: https://archive.ics.uci.edu/ml/datasets/Individual+household+electric+power+consumption#" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Using TensorFlow backend.\n" ] } ], "source": [ "import time\n", "from keras.layers import LSTM\n", "from keras.layers import Activation, Dense, Dropout\n", "from keras.models import Sequential, load_model\n", "from numpy.random import seed\n", "\n", "from tensorflow import set_random_seed\n", "set_random_seed(2) # seed random numbers for Tensorflow backend\n", "seed(1234) # seed random numbers for Keras\n", "import numpy as np\n", "import csv\n", "import matplotlib.pyplot as plt\n", "\n", "%matplotlib inline\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "def load_data(dataset_path, sequence_length=60, prediction_steps=5, ratio_of_data=1.0):\n", " max_values = ratio_of_data * 2075259 # 2075259 is the total number of measurements from Dec 2006 to Nov 2010\n", "\n", " # Load data from file\n", " with open(dataset_path) as file:\n", " data_file = csv.reader(file, delimiter=\";\")\n", " power_consumption = []\n", " number_of_values = 0\n", " for line in data_file:\n", " try:\n", " power_consumption.append(float(line[2]))\n", " number_of_values += 1\n", " except ValueError:\n", " pass\n", " if number_of_values >= max_values: # limit data to be considered by model according to max_values\n", " break\n", "\n", " print('Loaded data from csv.')\n", " windowed_data = []\n", " # Format data into rolling window sequences\n", " for index in range(len(power_consumption) - sequence_length): # for e.g: index=0 => 123, index=1 => 234 etc.\n", " windowed_data.append(power_consumption[index: index + sequence_length])\n", " windowed_data = np.array(windowed_data) # shape (number of samples, sequence length)\n", "\n", " # Center data\n", " data_mean = windowed_data.mean()\n", " windowed_data -= data_mean\n", " print('Center data so mean is zero (subtract each data point by mean of value: ', data_mean, ')')\n", " print('Data : ', windowed_data.shape)\n", "\n", " # Split data into training and testing sets\n", " train_set_ratio = 0.9\n", " row = int(round(train_set_ratio * windowed_data.shape[0]))\n", " train = windowed_data[:row, :]\n", " x_train = train[:, :-prediction_steps] # remove last prediction_steps from train set\n", " y_train = train[:, -prediction_steps:] # take last prediction_steps from train set\n", " x_test = windowed_data[row:, :-prediction_steps]\n", " y_test = windowed_data[row:, -prediction_steps:] # take last prediction_steps from test set\n", "\n", " x_train = np.reshape(x_train, (x_train.shape[0], x_train.shape[1], 1))\n", " x_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1], 1))\n", "\n", " return [x_train, y_train, x_test, y_test, data_mean]" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "def plot_predictions(result_mean, prediction_steps, predicted, y_test, global_start_time):\n", " try:\n", " test_hours_to_plot = 2\n", " t0 = 20 # time to start plot of predictions\n", " skip = 15 # skip prediction plots by specified minutes\n", " print('Plotting predictions...')\n", " fig = plt.figure()\n", " ax = fig.add_subplot(1, 1, 1)\n", " ax.plot(y_test[:test_hours_to_plot * 60, 0] + result_mean, label='Raw data') # plot actual test series\n", "\n", " # plot predicted values from t0 to t0+prediction_steps\n", " plt.plot(np.arange(t0 - 1, t0 + prediction_steps),\n", " np.insert(predicted[t0, :], 0, y_test[t0 - 1, 0]) + result_mean,\n", " color='red', label='t+{0} evolution of predictions'.format(prediction_steps))\n", " for i in range(t0, test_hours_to_plot * 60, skip):\n", " t0 += skip\n", " if t0 + prediction_steps > test_hours_to_plot * 60: # check plot does not exceed boundary\n", " break\n", " plt.plot(np.arange(t0 - 1, t0 + prediction_steps),\n", " np.insert(predicted[t0, :], 0, y_test[t0 - 1, 0]) + result_mean, color='green')\n", "\n", " # plot predicted value of t+prediction_steps as series\n", " plt.plot(predicted[:test_hours_to_plot * 60, prediction_steps - 1] + result_mean,\n", " label='t+{0} prediction series'.format(prediction_steps))\n", "\n", " plt.legend(loc='lower left')\n", " plt.ylabel('Actual Power in kilowatt')\n", " plt.xlabel('Time in minutes')\n", " plt.title('Predictions for first {0} minutes in test set'.format(test_hours_to_plot * 60))\n", " plt.show()\n", " except Exception as e:\n", " print(str(e))\n", " print('Duration of training (s) : ', time.time() - global_start_time)\n", "\n", " return None" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "def build_model(prediction_steps):\n", " model = Sequential()\n", " layers = [1, 75, 100, prediction_steps]\n", " model.add(LSTM(layers[1], input_shape=(None, layers[0]), return_sequences=True)) # add first layer\n", " model.add(Dropout(0.2)) # add dropout for first layer\n", " model.add(LSTM(layers[2], return_sequences=False)) # add second layer\n", " model.add(Dropout(0.2)) # add dropout for second layer\n", " model.add(Dense(layers[3])) # add output layer\n", " model.add(Activation('linear')) # output layer with linear activation\n", " start = time.time()\n", " model.compile(loss=\"mse\", optimizer=\"rmsprop\")\n", " print('Compilation Time : ', time.time() - start)\n", " return model" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "def run_lstm(model, sequence_length, prediction_steps):\n", " data = None\n", " global_start_time = time.time()\n", " epochs = 1\n", " ratio_of_data = 1 # ratio of data to use from 2+ million data points\n", " path_to_dataset = 'data/household_power_consumption.txt'\n", "\n", " if data is None:\n", " print('Loading data... ')\n", " x_train, y_train, x_test, y_test, result_mean = load_data(path_to_dataset, sequence_length,\n", " prediction_steps, ratio_of_data)\n", " else:\n", " x_train, y_train, x_test, y_test = data\n", "\n", " print('\\nData Loaded. Compiling...\\n')\n", " \n", " model.fit(x_train, y_train, batch_size=128, epochs=epochs, validation_split=0.05)\n", " predicted = model.predict(x_test)\n", " # predicted = np.reshape(predicted, (predicted.size,))\n", " model.save('LSTM_power_consumption_model.h5') # save LSTM model\n", "\n", " plot_predictions(result_mean, prediction_steps, predicted, y_test, global_start_time)\n", "\n", " return None" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Compilation Time : 0.010883808135986328\n", "Loading data... \n", "Loaded data from csv.\n", "Center data so mean is zero (subtract each data point by mean of value: 1.091607820345784 )\n", "Data : (2049270, 10)\n", "\n", "Data Loaded. Compiling...\n", "\n", "Train on 1752125 samples, validate on 92218 samples\n", "Epoch 1/1\n", "1752125/1752125 [==============================] - 143s 82us/step - loss: 0.1915 - val_loss: 0.1549\n", "Plotting predictions...\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Duration of training (s) : 165.87630820274353\n" ] } ], "source": [ "sequence_length = 10 # number of past minutes of data for model to consider\n", "prediction_steps = 5 # number of future minutes of data for model to predict\n", "model = build_model(prediction_steps)\n", "run_lstm(model, sequence_length, prediction_steps)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python [conda env:tf_gpu]", "language": "python", "name": "conda-env-tf_gpu-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: Chapter10/Predictive_Maintenance_using_LSTM.ipynb ================================================ { "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "colab": {}, "colab_type": "code", "id": "bFU-T5cNeEe8" }, "outputs": [], "source": [ "!wget -q https://raw.githubusercontent.com/umbertogriffo/Predictive-Maintenance-using-LSTM/master/Dataset/PM_test.txt -O PM_test.txt \n", "!wget -q https://raw.githubusercontent.com/umbertogriffo/Predictive-Maintenance-using-LSTM/master/Dataset/PM_train.txt -O PM_train.txt \n", "!wget -q https://raw.githubusercontent.com/umbertogriffo/Predictive-Maintenance-using-LSTM/master/Dataset/PM_truth.txt -O PM_truth.txt" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 65 }, "colab_type": "code", "id": "CzoL9ofEgJJf", "outputId": "fe452431-6f92-4f8c-ea86-d8af76ee4176" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "PM_test.txt PM_truth.txt\r\n", "PM_train.txt Predictive_Maintenance_using_LSTM.ipynb\r\n" ] } ], "source": [ "!ls" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "4BsYPkY7gYGx" }, "source": [ "# Binary classification\n", "Predict if an asset will fail within certain time frame (e.g. cycles)" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 34 }, "colab_type": "code", "id": "QfpzPSgG-If3", "outputId": "d1ca8986-0b86-48af-8667-d76d3e68b939" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Using TensorFlow backend.\n" ] } ], "source": [ "import keras\n", "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import os\n", "\n", "# Setting seed for reproducibility\n", "np.random.seed(1234) \n", "PYTHONHASHSEED = 0\n", "\n", "from sklearn import preprocessing\n", "from sklearn.metrics import confusion_matrix, recall_score, precision_score\n", "from keras.models import Sequential,load_model\n", "from keras.layers import Dense, Dropout, LSTM\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "EilFg--x-ety" }, "source": [ "## Data Ingestion" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "colab": {}, "colab_type": "code", "id": "JjbfnUZGgc3C" }, "outputs": [], "source": [ "# define path to save model\n", "model_path = 'binary_model.h5'\n", "\n", "# read training data - It is the aircraft engine run-to-failure data.\n", "train_df = pd.read_csv('PM_train.txt', sep=\" \", header=None)\n", "train_df.drop(train_df.columns[[26, 27]], axis=1, inplace=True)\n", "train_df.columns = ['id', 'cycle', 'setting1', 'setting2', 'setting3', 's1', 's2', 's3',\n", " 's4', 's5', 's6', 's7', 's8', 's9', 's10', 's11', 's12', 's13', 's14',\n", " 's15', 's16', 's17', 's18', 's19', 's20', 's21']\n", "\n", "train_df = train_df.sort_values(['id','cycle'])\n", "\n", "# read test data - It is the aircraft engine operating data without failure events recorded.\n", "test_df = pd.read_csv('PM_test.txt', sep=\" \", header=None)\n", "test_df.drop(test_df.columns[[26, 27]], axis=1, inplace=True)\n", "test_df.columns = ['id', 'cycle', 'setting1', 'setting2', 'setting3', 's1', 's2', 's3',\n", " 's4', 's5', 's6', 's7', 's8', 's9', 's10', 's11', 's12', 's13', 's14',\n", " 's15', 's16', 's17', 's18', 's19', 's20', 's21']\n", "\n", "# read ground truth data - It contains the information of true remaining cycles for each engine in the testing data.\n", "truth_df = pd.read_csv('PM_truth.txt', sep=\" \", header=None)\n", "truth_df.drop(truth_df.columns[[1]], axis=1, inplace=True)\n" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "QEpD7amS-lpu" }, "source": [ "## Data Preprocessing" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "#######\n", "# TRAIN\n", "#######\n", "# Data Labeling - generate column RUL(Remaining Usefull Life or Time to Failure)\n", "rul = pd.DataFrame(train_df.groupby('id')['cycle'].max()).reset_index()\n", "rul.columns = ['id', 'max']\n", "train_df = train_df.merge(rul, on=['id'], how='left')\n", "train_df['RUL'] = train_df['max'] - train_df['cycle']\n", "train_df.drop('max', axis=1, inplace=True)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/am/anaconda3/envs/tf_gpu/lib/python3.5/site-packages/sklearn/preprocessing/data.py:323: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by MinMaxScaler.\n", " return self.partial_fit(X, y)\n" ] }, { "data": { "text/html": [ "
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" ], "text/plain": [ " id cycle setting1 setting2 setting3 s1 s2 s3 s4 \\\n", "0 1 1 0.459770 0.166667 0.0 0.0 0.183735 0.406802 0.309757 \n", "1 1 2 0.609195 0.250000 0.0 0.0 0.283133 0.453019 0.352633 \n", "2 1 3 0.252874 0.750000 0.0 0.0 0.343373 0.369523 0.370527 \n", "3 1 4 0.540230 0.500000 0.0 0.0 0.343373 0.256159 0.331195 \n", "4 1 5 0.390805 0.333333 0.0 0.0 0.349398 0.257467 0.404625 \n", "\n", " s5 ... s16 s17 s18 s19 s20 s21 RUL label1 \\\n", "0 0.0 ... 0.0 0.333333 0.0 0.0 0.713178 0.724662 191 0 \n", "1 0.0 ... 0.0 0.333333 0.0 0.0 0.666667 0.731014 190 0 \n", "2 0.0 ... 0.0 0.166667 0.0 0.0 0.627907 0.621375 189 0 \n", "3 0.0 ... 0.0 0.333333 0.0 0.0 0.573643 0.662386 188 0 \n", "4 0.0 ... 0.0 0.416667 0.0 0.0 0.589147 0.704502 187 0 \n", "\n", " label2 cycle_norm \n", "0 0 0.00000 \n", "1 0 0.00277 \n", "2 0 0.00554 \n", "3 0 0.00831 \n", "4 0 0.01108 \n", "\n", "[5 rows x 30 columns]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# generate label columns for training data\n", "# we will only make use of \"label1\" for binary classification, \n", "# while trying to answer the question: is a specific engine going to fail within w1 cycles?\n", "w1 = 30\n", "w0 = 15\n", "train_df['label1'] = np.where(train_df['RUL'] <= w1, 1, 0 )\n", "train_df['label2'] = train_df['label1']\n", "train_df.loc[train_df['RUL'] <= w0, 'label2'] = 2\n", "\n", "# MinMax normalization (from 0 to 1)\n", "train_df['cycle_norm'] = train_df['cycle']\n", "cols_normalize = train_df.columns.difference(['id','cycle','RUL','label1','label2'])\n", "min_max_scaler = preprocessing.MinMaxScaler()\n", "norm_train_df = pd.DataFrame(min_max_scaler.fit_transform(train_df[cols_normalize]), \n", " columns=cols_normalize, \n", " index=train_df.index)\n", "join_df = train_df[train_df.columns.difference(cols_normalize)].join(norm_train_df)\n", "train_df = join_df.reindex(columns = train_df.columns)\n", "\n", "train_df.head()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 76 }, "colab_type": "code", "id": "ulY14O06knOI", "outputId": "d5cd0f95-a5cb-411b-be45-d2fc687aae6b" }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " id cycle setting1 setting2 setting3 s1 s2 s3 s4 \\\n", "0 1 1 0.632184 0.750000 0.0 0.0 0.545181 0.310661 0.269413 \n", "1 1 2 0.344828 0.250000 0.0 0.0 0.150602 0.379551 0.222316 \n", "2 1 3 0.517241 0.583333 0.0 0.0 0.376506 0.346632 0.322248 \n", "3 1 4 0.741379 0.500000 0.0 0.0 0.370482 0.285154 0.408001 \n", "4 1 5 0.580460 0.500000 0.0 0.0 0.391566 0.352082 0.332039 \n", "\n", " s5 ... s16 s17 s18 s19 s20 s21 cycle_norm RUL \\\n", "0 0.0 ... 0.0 0.333333 0.0 0.0 0.558140 0.661834 0.00000 142 \n", "1 0.0 ... 0.0 0.416667 0.0 0.0 0.682171 0.686827 0.00277 141 \n", "2 0.0 ... 0.0 0.416667 0.0 0.0 0.728682 0.721348 0.00554 140 \n", "3 0.0 ... 0.0 0.250000 0.0 0.0 0.666667 0.662110 0.00831 139 \n", "4 0.0 ... 0.0 0.166667 0.0 0.0 0.658915 0.716377 0.01108 138 \n", "\n", " label1 label2 \n", "0 0 0 \n", "1 0 0 \n", "2 0 0 \n", "3 0 0 \n", "4 0 0 \n", "\n", "[5 rows x 30 columns]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "######\n", "# TEST\n", "######\n", "# MinMax normalization (from 0 to 1)\n", "test_df['cycle_norm'] = test_df['cycle']\n", "norm_test_df = pd.DataFrame(min_max_scaler.transform(test_df[cols_normalize]), \n", " columns=cols_normalize, \n", " index=test_df.index)\n", "test_join_df = test_df[test_df.columns.difference(cols_normalize)].join(norm_test_df)\n", "test_df = test_join_df.reindex(columns = test_df.columns)\n", "test_df = test_df.reset_index(drop=True)\n", "\n", "\n", "# We use the ground truth dataset to generate labels for the test data.\n", "# generate column max for test data\n", "rul = pd.DataFrame(test_df.groupby('id')['cycle'].max()).reset_index()\n", "rul.columns = ['id', 'max']\n", "truth_df.columns = ['more']\n", "truth_df['id'] = truth_df.index + 1\n", "truth_df['max'] = rul['max'] + truth_df['more']\n", "truth_df.drop('more', axis=1, inplace=True)\n", "\n", "# generate RUL for test data\n", "test_df = test_df.merge(truth_df, on=['id'], how='left')\n", "test_df['RUL'] = test_df['max'] - test_df['cycle']\n", "test_df.drop('max', axis=1, inplace=True)\n", "\n", "# generate label columns w0 and w1 for test data\n", "test_df['label1'] = np.where(test_df['RUL'] <= w1, 1, 0 )\n", "test_df['label2'] = test_df['label1']\n", "test_df.loc[test_df['RUL'] <= w0, 'label2'] = 2\n", "\n", "test_df.head()" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "57FSFDb4-r3d" }, "source": [ "## LSTM" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 833 }, "colab_type": "code", "id": "zSInZu-EkFtf", "outputId": "37500e7c-45ff-4471-ef9b-bbe54515a425" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(15631, 50, 25)\n", "(15631, 1)\n" ] } ], "source": [ "# pick a large window size of 50 cycles\n", "sequence_length = 50\n", "\n", "# function to reshape features into (samples, time steps, features) \n", "def gen_sequence(id_df, seq_length, seq_cols):\n", " \"\"\" Only sequences that meet the window-length are considered, no padding is used. This means for testing\n", " we need to drop those which are below the window-length. An alternative would be to pad sequences so that\n", " we can use shorter ones \"\"\"\n", " # for one id I put all the rows in a single matrix\n", " data_matrix = id_df[seq_cols].values\n", " num_elements = data_matrix.shape[0]\n", " # Iterate over two lists in parallel.\n", " # For example id1 have 192 rows and sequence_length is equal to 50\n", " # so zip iterate over two following list of numbers (0,112),(50,192)\n", " # 0 50 -> from row 0 to row 50\n", " # 1 51 -> from row 1 to row 51\n", " # 2 52 -> from row 2 to row 52\n", " # ...\n", " # 111 191 -> from row 111 to 191\n", " for start, stop in zip(range(0, num_elements-seq_length), range(seq_length, num_elements)):\n", " yield data_matrix[start:stop, :]\n", " \n", "# pick the feature columns \n", "sensor_cols = ['s' + str(i) for i in range(1,22)]\n", "sequence_cols = ['setting1', 'setting2', 'setting3', 'cycle_norm']\n", "sequence_cols.extend(sensor_cols)\n", "\n", "# generator for the sequences\n", "seq_gen = (list(gen_sequence(train_df[train_df['id']==id], sequence_length, sequence_cols)) \n", " for id in train_df['id'].unique())\n", "\n", "# generate sequences and convert to numpy array\n", "seq_array = np.concatenate(list(seq_gen)).astype(np.float32)\n", "print(seq_array.shape)\n", "\n", "# function to generate labels\n", "def gen_labels(id_df, seq_length, label):\n", " # For one id I put all the labels in a single matrix.\n", " # For example:\n", " # [[1]\n", " # [4]\n", " # [1]\n", " # [5]\n", " # [9]\n", " # ...\n", " # [200]] \n", " data_matrix = id_df[label].values\n", " num_elements = data_matrix.shape[0]\n", " # I have to remove the first seq_length labels\n", " # because for one id the first sequence of seq_length size have as target\n", " # the last label (the previus ones are discarded).\n", " # All the next id's sequences will have associated step by step one label as target. \n", " return data_matrix[seq_length:num_elements, :]\n", "\n", "# generate labels\n", "label_gen = [gen_labels(train_df[train_df['id']==id], sequence_length, ['label1']) \n", " for id in train_df['id'].unique()]\n", "label_array = np.concatenate(label_gen).astype(np.float32)\n", "print(label_array.shape)\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "pWyZJ2mP-1pB" }, "source": [ "## Model Evaluation on Test set" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "_________________________________________________________________\n", "Layer (type) Output Shape Param # \n", "=================================================================\n", "lstm_1 (LSTM) (None, 50, 100) 50400 \n", "_________________________________________________________________\n", "dropout_1 (Dropout) (None, 50, 100) 0 \n", "_________________________________________________________________\n", "lstm_2 (LSTM) (None, 50) 30200 \n", "_________________________________________________________________\n", "dropout_2 (Dropout) (None, 50) 0 \n", "_________________________________________________________________\n", "dense_1 (Dense) (None, 1) 51 \n", "=================================================================\n", "Total params: 80,651\n", "Trainable params: 80,651\n", "Non-trainable params: 0\n", "_________________________________________________________________\n", "None\n", "Train on 14849 samples, validate on 782 samples\n", "Epoch 1/100\n", " - 7s - loss: 0.2621 - acc: 0.8914 - val_loss: 0.0979 - val_acc: 0.9604\n", "Epoch 2/100\n", " - 6s - loss: 0.0974 - acc: 0.9584 - val_loss: 0.0952 - val_acc: 0.9540\n", "Epoch 3/100\n", " - 6s - loss: 0.0779 - acc: 0.9682 - val_loss: 0.0775 - val_acc: 0.9616\n", "Epoch 4/100\n", " - 6s - loss: 0.0740 - acc: 0.9692 - val_loss: 0.0386 - val_acc: 0.9834\n", "Epoch 5/100\n", " - 6s - loss: 0.0687 - acc: 0.9708 - val_loss: 0.1187 - val_acc: 0.9565\n", "Epoch 6/100\n", " - 6s - loss: 0.0683 - acc: 0.9705 - val_loss: 0.0526 - val_acc: 0.9757\n", "Epoch 7/100\n", " - 6s - loss: 0.0678 - acc: 0.9719 - val_loss: 0.0556 - val_acc: 0.9744\n", "Epoch 8/100\n", " - 6s - loss: 0.0591 - acc: 0.9766 - val_loss: 0.0366 - val_acc: 0.9834\n", "Epoch 9/100\n", " - 6s - loss: 0.0587 - acc: 0.9759 - val_loss: 0.0403 - val_acc: 0.9834\n", "Epoch 10/100\n", " - 6s - loss: 0.0590 - acc: 0.9742 - val_loss: 0.0463 - val_acc: 0.9795\n", "Epoch 11/100\n", " - 6s - loss: 0.0554 - acc: 0.9771 - val_loss: 0.0809 - val_acc: 0.9616\n", "Epoch 12/100\n", " - 6s - loss: 0.0620 - acc: 0.9727 - val_loss: 0.0413 - val_acc: 0.9834\n", "Epoch 13/100\n", " - 6s - loss: 0.0515 - acc: 0.9793 - val_loss: 0.0387 - val_acc: 0.9859\n", "Epoch 14/100\n", " - 6s - loss: 0.0546 - acc: 0.9755 - val_loss: 0.0393 - val_acc: 0.9859\n", "Epoch 15/100\n", " - 6s - loss: 0.0515 - acc: 0.9779 - val_loss: 0.0563 - val_acc: 0.9731\n", "Epoch 16/100\n", " - 6s - loss: 0.0468 - acc: 0.9799 - val_loss: 0.0478 - val_acc: 0.9770\n", "Epoch 17/100\n", " - 6s - loss: 0.0495 - acc: 0.9796 - val_loss: 0.0438 - val_acc: 0.9795\n", "Epoch 18/100\n", " - 6s - loss: 0.0534 - acc: 0.9771 - val_loss: 0.0393 - val_acc: 0.9872\n", "dict_keys(['val_acc', 'val_loss', 'acc', 'loss'])\n" ] } ], "source": [ "# Next, we build a deep network. \n", "# The first layer is an LSTM layer with 100 units followed by another LSTM layer with 50 units. \n", "# Dropout is also applied after each LSTM layer to control overfitting. \n", "# Final layer is a Dense output layer with single unit and sigmoid activation since this is a binary classification problem.\n", "# build the network\n", "nb_features = seq_array.shape[2]\n", "nb_out = label_array.shape[1]\n", "\n", "model = Sequential()\n", "\n", "model.add(LSTM(\n", " input_shape=(sequence_length, nb_features),\n", " units=100,\n", " return_sequences=True))\n", "model.add(Dropout(0.2))\n", "\n", "model.add(LSTM(\n", " units=50,\n", " return_sequences=False))\n", "model.add(Dropout(0.2))\n", "\n", "model.add(Dense(units=nb_out, activation='sigmoid'))\n", "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n", "\n", "print(model.summary())\n", "\n", "# fit the network\n", "history = model.fit(seq_array, label_array, epochs=100, batch_size=200, validation_split=0.05, verbose=2,\n", " callbacks = [keras.callbacks.EarlyStopping(monitor='val_loss', min_delta=0, patience=10, verbose=0, mode='min'),\n", " keras.callbacks.ModelCheckpoint(model_path,monitor='val_loss', save_best_only=True, mode='min', verbose=0)]\n", " )\n", "\n", "# list all data in history\n", "print(history.history.keys())" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1393 }, "colab_type": "code", "id": "FpVYSzXkmk5l", "outputId": "50af192e-e9df-46ad-ffa3-0b7bd0e3f29c" }, "outputs": [ { "data": { "image/png": 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\n", 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "15631/15631 [==============================] - 1s 94us/step\n", "Accurracy: 0.983238444036367\n", "15631/15631 [==============================] - 1s 95us/step\n", "Confusion matrix\n", "- x-axis is true labels.\n", "- y-axis is predicted labels\n", "[[12456 75]\n", " [ 187 2913]]\n", "precision = 0.9748995983935743 \n", " recall = 0.9396774193548387\n" ] } ], "source": [ "# summarize history for Accuracy\n", "fig_acc = plt.figure(figsize=(10, 10))\n", "plt.plot(history.history['acc'])\n", "plt.plot(history.history['val_acc'])\n", "plt.title('model accuracy')\n", "plt.ylabel('accuracy')\n", "plt.xlabel('epoch')\n", "plt.legend(['train', 'test'], loc='upper left')\n", "plt.show()\n", "fig_acc.savefig(\"model_accuracy.png\")\n", "\n", "# summarize history for Loss\n", "fig_acc = plt.figure(figsize=(10, 10))\n", "plt.plot(history.history['loss'])\n", "plt.plot(history.history['val_loss'])\n", "plt.title('model loss')\n", "plt.ylabel('loss')\n", "plt.xlabel('epoch')\n", "plt.legend(['train', 'test'], loc='upper left')\n", "plt.show()\n", "fig_acc.savefig(\"model_loss.png\")\n", "\n", "# training metrics\n", "scores = model.evaluate(seq_array, label_array, verbose=1, batch_size=200)\n", "print('Accurracy: {}'.format(scores[1]))\n", "\n", "# make predictions and compute confusion matrix\n", "y_pred = model.predict_classes(seq_array,verbose=1, batch_size=200)\n", "y_true = label_array\n", "\n", "test_set = pd.DataFrame(y_pred)\n", "test_set.to_csv('binary_submit_train.csv', index = None)\n", "\n", "print('Confusion matrix\\n- x-axis is true labels.\\n- y-axis is predicted labels')\n", "cm = confusion_matrix(y_true, y_pred)\n", "print(cm)\n", "\n", "# compute precision and recall\n", "precision = precision_score(y_true, y_pred)\n", "recall = recall_score(y_true, y_pred)\n", "print( 'precision = ', precision, '\\n', 'recall = ', recall)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "oxvEuR4S-6VI" }, "source": [ "## Model Evaluation on Validation set" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 502 }, "colab_type": "code", "id": "6yT96j4rkL0K", "outputId": "f3b3f649-6e83-4795-cafd-1ade4ef4afce" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accurracy: 0.9677419239474881\n", "Confusion matrix\n", "- x-axis is true labels.\n", "- y-axis is predicted labels\n", "[[66 2]\n", " [ 1 24]]\n", "Precision: 0.9230769230769231 \n", " Recall: 0.96 \n", " F1-score: 0.9411764705882353\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# We pick the last sequence for each id in the test data\n", "\n", "seq_array_test_last = [test_df[test_df['id']==id][sequence_cols].values[-sequence_length:] \n", " for id in test_df['id'].unique() if len(test_df[test_df['id']==id]) >= sequence_length]\n", "\n", "seq_array_test_last = np.asarray(seq_array_test_last).astype(np.float32)\n", "#print(\"seq_array_test_last\")\n", "#print(seq_array_test_last)\n", "#print(seq_array_test_last.shape)\n", "\n", "# Similarly, we pick the labels\n", "\n", "#print(\"y_mask\")\n", "# serve per prendere solo le label delle sequenze che sono almeno lunghe 50\n", "y_mask = [len(test_df[test_df['id']==id]) >= sequence_length for id in test_df['id'].unique()]\n", "#print(\"y_mask\")\n", "#print(y_mask)\n", "label_array_test_last = test_df.groupby('id')['label1'].nth(-1)[y_mask].values\n", "label_array_test_last = label_array_test_last.reshape(label_array_test_last.shape[0],1).astype(np.float32)\n", "#print(label_array_test_last.shape)\n", "#print(\"label_array_test_last\")\n", "#print(label_array_test_last)\n", "\n", "# if best iteration's model was saved then load and use it\n", "if os.path.isfile(model_path):\n", " estimator = load_model(model_path)\n", "\n", "# test metrics\n", "scores_test = estimator.evaluate(seq_array_test_last, label_array_test_last, verbose=2)\n", "print('Accurracy: {}'.format(scores_test[1]))\n", "\n", "# make predictions and compute confusion matrix\n", "y_pred_test = estimator.predict_classes(seq_array_test_last)\n", "y_true_test = label_array_test_last\n", "\n", "test_set = pd.DataFrame(y_pred_test)\n", "test_set.to_csv('binary_submit_test.csv', index = None)\n", "\n", "print('Confusion matrix\\n- x-axis is true labels.\\n- y-axis is predicted labels')\n", "cm = confusion_matrix(y_true_test, y_pred_test)\n", "print(cm)\n", "\n", "# compute precision and recall\n", "precision_test = precision_score(y_true_test, y_pred_test)\n", "recall_test = recall_score(y_true_test, y_pred_test)\n", "f1_test = 2 * (precision_test * recall_test) / (precision_test + recall_test)\n", "print( 'Precision: ', precision_test, '\\n', 'Recall: ', recall_test,'\\n', 'F1-score:', f1_test )\n", "\n", "# Plot in blue color the predicted data and in green color the\n", "# actual data to verify visually the accuracy of the model.\n", "fig_verify = plt.figure(figsize=(10, 5))\n", "plt.plot(y_pred_test, color=\"blue\")\n", "plt.plot(y_true_test, color=\"green\")\n", "plt.title('prediction')\n", "plt.ylabel('value')\n", "plt.xlabel('row')\n", "plt.legend(['predicted', 'actual data'], loc='upper left')\n", "plt.show()\n", "fig_verify.savefig(\"model_verify.png\")" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "ElhakcHtnX9J" }, "source": [ "# Regression\n", "How many more cycles an in-service engine will last before it fails?" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "colab": {}, "colab_type": "code", "id": "yWuxznPhATyL" }, "outputs": [], "source": [ "import keras\n", "import keras.backend as K\n", "from keras.layers.core import Activation\n", "from keras.models import Sequential,load_model\n", "from keras.layers import Dense, Dropout, LSTM\n", "\n", "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import os\n", "from sklearn import preprocessing\n", "\n", "# Setting seed for reproducibility\n", "np.random.seed(1234) \n", "PYTHONHASHSEED = 0\n", "\n", "# define path to save model\n", "model_path = 'regression_model.h5'" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "LF8N3B5fAZT2" }, "source": [ "## Data Ingestion" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "colab": {}, "colab_type": "code", "id": "JxowlCjhnkId" }, "outputs": [], "source": [ "# read training data - It is the aircraft engine run-to-failure data.\n", "train_df = pd.read_csv('PM_train.txt', sep=\" \", header=None)\n", "train_df.drop(train_df.columns[[26, 27]], axis=1, inplace=True)\n", "train_df.columns = ['id', 'cycle', 'setting1', 'setting2', 'setting3', 's1', 's2', 's3',\n", " 's4', 's5', 's6', 's7', 's8', 's9', 's10', 's11', 's12', 's13', 's14',\n", " 's15', 's16', 's17', 's18', 's19', 's20', 's21']\n", "\n", "train_df = train_df.sort_values(['id','cycle'])\n", "\n", "# read test data - It is the aircraft engine operating data without failure events recorded.\n", "test_df = pd.read_csv('PM_test.txt', sep=\" \", header=None)\n", "test_df.drop(test_df.columns[[26, 27]], axis=1, inplace=True)\n", "test_df.columns = ['id', 'cycle', 'setting1', 'setting2', 'setting3', 's1', 's2', 's3',\n", " 's4', 's5', 's6', 's7', 's8', 's9', 's10', 's11', 's12', 's13', 's14',\n", " 's15', 's16', 's17', 's18', 's19', 's20', 's21']\n", "\n", "# read ground truth data - It contains the information of true remaining cycles for each engine in the testing data.\n", "truth_df = pd.read_csv('PM_truth.txt', sep=\" \", header=None)\n", "truth_df.drop(truth_df.columns[[1]], axis=1, inplace=True)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "MmS3G0OnAegq" }, "source": [ "## Data Preprocessing" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 442 }, "colab_type": "code", "id": "WRfcnQH8nqII", "outputId": "62d93a40-7de2-47ed-f73d-e7d388300627" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/am/anaconda3/envs/tf_gpu/lib/python3.5/site-packages/sklearn/preprocessing/data.py:323: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by MinMaxScaler.\n", " return self.partial_fit(X, y)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " id cycle setting1 setting2 setting3 s1 s2 s3 s4 \\\n", "0 1 1 0.632184 0.750000 0.0 0.0 0.545181 0.310661 0.269413 \n", "1 1 2 0.344828 0.250000 0.0 0.0 0.150602 0.379551 0.222316 \n", "2 1 3 0.517241 0.583333 0.0 0.0 0.376506 0.346632 0.322248 \n", "3 1 4 0.741379 0.500000 0.0 0.0 0.370482 0.285154 0.408001 \n", "4 1 5 0.580460 0.500000 0.0 0.0 0.391566 0.352082 0.332039 \n", "\n", " s5 ... s13 s14 s15 s16 s17 s18 s19 \\\n", "0 0.0 ... 0.220588 0.132160 0.308965 0.0 0.333333 0.0 0.0 \n", "1 0.0 ... 0.264706 0.204768 0.213159 0.0 0.416667 0.0 0.0 \n", "2 0.0 ... 0.220588 0.155640 0.458638 0.0 0.416667 0.0 0.0 \n", "3 0.0 ... 0.250000 0.170090 0.257022 0.0 0.250000 0.0 0.0 \n", "4 0.0 ... 0.220588 0.152751 0.300885 0.0 0.166667 0.0 0.0 \n", "\n", " s20 s21 cycle_norm \n", "0 0.558140 0.661834 0.00000 \n", "1 0.682171 0.686827 0.00277 \n", "2 0.728682 0.721348 0.00554 \n", "3 0.666667 0.662110 0.00831 \n", "4 0.658915 0.716377 0.01108 \n", "\n", "[5 rows x 27 columns]\n", "142\n", "(15631, 50, 25)\n" ] }, { "data": { "text/plain": [ "(15631, 1)" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "##################################\n", "# Data Preprocessing\n", "##################################\n", "\n", "#######\n", "# TRAIN\n", "#######\n", "# Data Labeling - generate column RUL(Remaining Usefull Life or Time to Failure)\n", "rul = pd.DataFrame(train_df.groupby('id')['cycle'].max()).reset_index()\n", "rul.columns = ['id', 'max']\n", "train_df = train_df.merge(rul, on=['id'], how='left')\n", "train_df['RUL'] = train_df['max'] - train_df['cycle']\n", "train_df.drop('max', axis=1, inplace=True)\n", "\n", "# generate label columns for training data\n", "# we will only make use of \"label1\" for binary classification, \n", "# while trying to answer the question: is a specific engine going to fail within w1 cycles?\n", "w1 = 30\n", "w0 = 15\n", "train_df['label1'] = np.where(train_df['RUL'] <= w1, 1, 0 )\n", "train_df['label2'] = train_df['label1']\n", "train_df.loc[train_df['RUL'] <= w0, 'label2'] = 2\n", "\n", "# MinMax normalization (from 0 to 1)\n", "train_df['cycle_norm'] = train_df['cycle']\n", "cols_normalize = train_df.columns.difference(['id','cycle','RUL','label1','label2'])\n", "min_max_scaler = preprocessing.MinMaxScaler()\n", "norm_train_df = pd.DataFrame(min_max_scaler.fit_transform(train_df[cols_normalize]), \n", " columns=cols_normalize, \n", " index=train_df.index)\n", "join_df = train_df[train_df.columns.difference(cols_normalize)].join(norm_train_df)\n", "train_df = join_df.reindex(columns = train_df.columns)\n", "\n", "#train_df.to_csv('PredictiveManteinanceEngineTraining.csv', encoding='utf-8',index = None)\n", "\n", "######\n", "# TEST\n", "######\n", "# MinMax normalization (from 0 to 1)\n", "test_df['cycle_norm'] = test_df['cycle']\n", "norm_test_df = pd.DataFrame(min_max_scaler.transform(test_df[cols_normalize]), \n", " columns=cols_normalize, \n", " index=test_df.index)\n", "test_join_df = test_df[test_df.columns.difference(cols_normalize)].join(norm_test_df)\n", "test_df = test_join_df.reindex(columns = test_df.columns)\n", "test_df = test_df.reset_index(drop=True)\n", "print(test_df.head())\n", "\n", "# We use the ground truth dataset to generate labels for the test data.\n", "# generate column max for test data\n", "rul = pd.DataFrame(test_df.groupby('id')['cycle'].max()).reset_index()\n", "rul.columns = ['id', 'max']\n", "truth_df.columns = ['more']\n", "truth_df['id'] = truth_df.index + 1\n", "truth_df['max'] = rul['max'] + truth_df['more']\n", "truth_df.drop('more', axis=1, inplace=True)\n", "\n", "# generate RUL for test data\n", "test_df = test_df.merge(truth_df, on=['id'], how='left')\n", "test_df['RUL'] = test_df['max'] - test_df['cycle']\n", "test_df.drop('max', axis=1, inplace=True)\n", "\n", "# generate label columns w0 and w1 for test data\n", "test_df['label1'] = np.where(test_df['RUL'] <= w1, 1, 0 )\n", "test_df['label2'] = test_df['label1']\n", "test_df.loc[test_df['RUL'] <= w0, 'label2'] = 2\n", "\n", "#test_df.to_csv('PredictiveManteinanceEngineValidation.csv', encoding='utf-8',index = None)\n", "\n", "# pick a large window size of 50 cycles\n", "sequence_length = 50\n", "\n", "# function to reshape features into (samples, time steps, features) \n", "def gen_sequence(id_df, seq_length, seq_cols):\n", " \"\"\" Only sequences that meet the window-length are considered, no padding is used. This means for testing\n", " we need to drop those which are below the window-length. An alternative would be to pad sequences so that\n", " we can use shorter ones \"\"\"\n", " # for one id I put all the rows in a single matrix\n", " data_matrix = id_df[seq_cols].values\n", " num_elements = data_matrix.shape[0]\n", " # Iterate over two lists in parallel.\n", " # For example id1 have 192 rows and sequence_length is equal to 50\n", " # so zip iterate over two following list of numbers (0,112),(50,192)\n", " # 0 50 -> from row 0 to row 50\n", " # 1 51 -> from row 1 to row 51\n", " # 2 52 -> from row 2 to row 52\n", " # ...\n", " # 111 191 -> from row 111 to 191\n", " for start, stop in zip(range(0, num_elements-seq_length), range(seq_length, num_elements)):\n", " yield data_matrix[start:stop, :]\n", " \n", "# pick the feature columns \n", "sensor_cols = ['s' + str(i) for i in range(1,22)]\n", "sequence_cols = ['setting1', 'setting2', 'setting3', 'cycle_norm']\n", "sequence_cols.extend(sensor_cols)\n", "\n", "# TODO for debug \n", "# val is a list of 192 - 50 = 142 bi-dimensional array (50 rows x 25 columns)\n", "val=list(gen_sequence(train_df[train_df['id']==1], sequence_length, sequence_cols))\n", "print(len(val))\n", "\n", "# generator for the sequences\n", "# transform each id of the train dataset in a sequence\n", "seq_gen = (list(gen_sequence(train_df[train_df['id']==id], sequence_length, sequence_cols)) \n", " for id in train_df['id'].unique())\n", "\n", "# generate sequences and convert to numpy array\n", "seq_array = np.concatenate(list(seq_gen)).astype(np.float32)\n", "print(seq_array.shape)\n", "\n", "# function to generate labels\n", "def gen_labels(id_df, seq_length, label):\n", " \"\"\" Only sequences that meet the window-length are considered, no padding is used. This means for testing\n", " we need to drop those which are below the window-length. An alternative would be to pad sequences so that\n", " we can use shorter ones \"\"\"\n", " # For one id I put all the labels in a single matrix.\n", " # For example:\n", " # [[1]\n", " # [4]\n", " # [1]\n", " # [5]\n", " # [9]\n", " # ...\n", " # [200]] \n", " data_matrix = id_df[label].values\n", " num_elements = data_matrix.shape[0]\n", " # I have to remove the first seq_length labels\n", " # because for one id the first sequence of seq_length size have as target\n", " # the last label (the previus ones are discarded).\n", " # All the next id's sequences will have associated step by step one label as target.\n", " return data_matrix[seq_length:num_elements, :]\n", "\n", "# generate labels\n", "label_gen = [gen_labels(train_df[train_df['id']==id], sequence_length, ['RUL']) \n", " for id in train_df['id'].unique()]\n", "\n", "label_array = np.concatenate(label_gen).astype(np.float32)\n", "label_array.shape" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "7ps8UixLAj1D" }, "source": [ "## LSTM" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 2417 }, "colab_type": "code", "id": "gPx1AzOan6VT", "outputId": "b037bc16-3ae9-4be8-ada2-36737c8db0f3" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "_________________________________________________________________\n", "Layer (type) Output Shape Param # \n", "=================================================================\n", "lstm_3 (LSTM) (None, 50, 100) 50400 \n", "_________________________________________________________________\n", "dropout_3 (Dropout) (None, 50, 100) 0 \n", "_________________________________________________________________\n", "lstm_4 (LSTM) (None, 50) 30200 \n", "_________________________________________________________________\n", "dropout_4 (Dropout) (None, 50) 0 \n", "_________________________________________________________________\n", "dense_2 (Dense) (None, 1) 51 \n", "_________________________________________________________________\n", "activation_1 (Activation) (None, 1) 0 \n", "=================================================================\n", "Total params: 80,651\n", "Trainable params: 80,651\n", "Non-trainable params: 0\n", "_________________________________________________________________\n", "None\n", "Train on 14849 samples, validate on 782 samples\n", "Epoch 1/100\n", " - 6s - loss: 8708.7517 - mean_absolute_error: 74.5681 - r2_keras: -1.6905e+00 - val_loss: 8120.9259 - val_mean_absolute_error: 71.6563 - val_r2_keras: -2.4368e+00\n", "Epoch 2/100\n", " - 6s - loss: 8033.3967 - mean_absolute_error: 70.6486 - r2_keras: -1.4763e+00 - val_loss: 7602.0601 - val_mean_absolute_error: 68.6449 - val_r2_keras: -2.1906e+00\n", "Epoch 3/100\n", " - 6s - loss: 7523.9030 - mean_absolute_error: 67.7133 - r2_keras: -1.3200e+00 - val_loss: 7111.2714 - val_mean_absolute_error: 65.8128 - val_r2_keras: -1.9596e+00\n", "Epoch 4/100\n", " - 6s - loss: 7047.8663 - mean_absolute_error: 64.9807 - r2_keras: -1.1712e+00 - val_loss: 6652.0097 - val_mean_absolute_error: 63.1799 - val_r2_keras: -1.7453e+00\n", "Epoch 5/100\n", " - 6s - loss: 6601.0888 - mean_absolute_error: 62.4898 - r2_keras: -1.0331e+00 - val_loss: 6218.5047 - val_mean_absolute_error: 60.7157 - val_r2_keras: -1.5451e+00\n", "Epoch 6/100\n", " - 6s - loss: 6174.9351 - mean_absolute_error: 60.0647 - r2_keras: -9.0169e-01 - val_loss: 5815.2786 - val_mean_absolute_error: 58.4434 - val_r2_keras: -1.3611e+00\n", "Epoch 7/100\n", " - 6s - loss: 5789.0737 - mean_absolute_error: 57.9379 - r2_keras: -7.8299e-01 - val_loss: 5444.9836 - val_mean_absolute_error: 56.3772 - val_r2_keras: -1.1944e+00\n", "Epoch 8/100\n", " - 6s - loss: 5432.6761 - mean_absolute_error: 55.9616 - r2_keras: -6.7071e-01 - val_loss: 5097.4074 - val_mean_absolute_error: 54.4604 - val_r2_keras: -1.0405e+00\n", "Epoch 9/100\n", " - 6s - loss: 5087.1771 - mean_absolute_error: 54.1238 - r2_keras: -5.6402e-01 - val_loss: 4781.2311 - val_mean_absolute_error: 52.7429 - val_r2_keras: -9.0326e-01\n", "Epoch 10/100\n", " - 6s - loss: 4790.3006 - mean_absolute_error: 52.5153 - r2_keras: -4.7110e-01 - val_loss: 4494.0189 - val_mean_absolute_error: 51.2102 - val_r2_keras: -7.8150e-01\n", "Epoch 11/100\n", " - 6s - loss: 4520.9393 - mean_absolute_error: 51.1445 - r2_keras: -3.8865e-01 - val_loss: 4238.1011 - val_mean_absolute_error: 49.8728 - val_r2_keras: -6.7618e-01\n", "Epoch 12/100\n", " - 6s - loss: 4265.4276 - mean_absolute_error: 49.8224 - r2_keras: -3.1058e-01 - val_loss: 4007.3899 - val_mean_absolute_error: 48.6987 - val_r2_keras: -5.8477e-01\n", "Epoch 13/100\n", " - 6s - loss: 4049.2607 - mean_absolute_error: 48.7805 - r2_keras: -2.4346e-01 - val_loss: 3804.9419 - val_mean_absolute_error: 47.7068 - val_r2_keras: -5.0854e-01\n", "Epoch 14/100\n", " - 6s - loss: 3862.1675 - mean_absolute_error: 47.8955 - r2_keras: -1.8574e-01 - val_loss: 3634.5574 - val_mean_absolute_error: 46.9117 - val_r2_keras: -4.4872e-01\n", "Epoch 15/100\n", " - 6s - loss: 3700.8644 - mean_absolute_error: 47.1364 - r2_keras: -1.3606e-01 - val_loss: 3488.2620 - val_mean_absolute_error: 46.2746 - val_r2_keras: -4.0247e-01\n", "Epoch 16/100\n", " - 6s - loss: 3576.9094 - mean_absolute_error: 46.7163 - r2_keras: -9.9351e-02 - val_loss: 3373.1049 - val_mean_absolute_error: 45.8277 - val_r2_keras: -3.7185e-01\n", "Epoch 17/100\n", " - 6s - loss: 3468.3598 - mean_absolute_error: 46.3546 - r2_keras: -6.5381e-02 - val_loss: 3289.7875 - val_mean_absolute_error: 45.5561 - val_r2_keras: -3.5564e-01\n", "Epoch 18/100\n", " - 6s - loss: 3395.9189 - mean_absolute_error: 45.9592 - r2_keras: -4.2465e-02 - val_loss: 3012.6933 - val_mean_absolute_error: 43.3690 - val_r2_keras: -2.4243e-01\n", "Epoch 19/100\n", " - 6s - loss: 2969.2829 - mean_absolute_error: 41.3670 - r2_keras: 0.0878 - val_loss: 2449.5799 - val_mean_absolute_error: 37.2000 - val_r2_keras: 0.0378\n", "Epoch 20/100\n", " - 6s - loss: 2490.4717 - mean_absolute_error: 35.3711 - r2_keras: 0.2365 - val_loss: 2399.2325 - val_mean_absolute_error: 35.2970 - val_r2_keras: 0.1324\n", "Epoch 21/100\n", " - 6s - loss: 2337.8514 - mean_absolute_error: 33.7280 - r2_keras: 0.2831 - val_loss: 2032.5708 - val_mean_absolute_error: 32.1575 - val_r2_keras: 0.2116\n", "Epoch 22/100\n", " - 6s - loss: 2083.7693 - mean_absolute_error: 31.0261 - r2_keras: 0.3626 - val_loss: 1836.7766 - val_mean_absolute_error: 29.4539 - val_r2_keras: 0.3001\n", "Epoch 23/100\n", " - 6s - loss: 1954.0267 - mean_absolute_error: 29.7769 - r2_keras: 0.4015 - val_loss: 1782.8279 - val_mean_absolute_error: 29.3565 - val_r2_keras: 0.2924\n", "Epoch 24/100\n", " - 6s - loss: 1881.2816 - mean_absolute_error: 29.4817 - r2_keras: 0.4244 - val_loss: 1620.5137 - val_mean_absolute_error: 27.0793 - val_r2_keras: 0.3642\n", "Epoch 25/100\n", " - 6s - loss: 1699.8900 - mean_absolute_error: 27.5062 - r2_keras: 0.4795 - val_loss: 1560.6913 - val_mean_absolute_error: 27.2027 - val_r2_keras: 0.3648\n", "Epoch 26/100\n", " - 6s - loss: 1579.7842 - mean_absolute_error: 26.2835 - r2_keras: 0.5174 - val_loss: 1442.4307 - val_mean_absolute_error: 25.0639 - val_r2_keras: 0.4853\n", "Epoch 27/100\n", " - 6s - loss: 1473.4805 - mean_absolute_error: 25.2765 - r2_keras: 0.5499 - val_loss: 1333.1121 - val_mean_absolute_error: 23.1076 - val_r2_keras: 0.5499\n", "Epoch 28/100\n", " - 6s - loss: 1378.5738 - mean_absolute_error: 24.1196 - r2_keras: 0.5785 - val_loss: 1427.6300 - val_mean_absolute_error: 28.5137 - val_r2_keras: 0.3677\n", "Epoch 29/100\n", " - 6s - loss: 1307.9167 - mean_absolute_error: 23.7053 - r2_keras: 0.6013 - val_loss: 1150.7362 - val_mean_absolute_error: 22.3446 - val_r2_keras: 0.5404\n", "Epoch 30/100\n", " - 6s - loss: 1209.2905 - mean_absolute_error: 22.3253 - r2_keras: 0.6313 - val_loss: 1204.2452 - val_mean_absolute_error: 24.6296 - val_r2_keras: 0.5213\n", "Epoch 31/100\n", " - 6s - loss: 1145.0836 - mean_absolute_error: 21.7566 - r2_keras: 0.6508 - val_loss: 1010.6109 - val_mean_absolute_error: 20.8401 - val_r2_keras: 0.6362\n", "Epoch 32/100\n", " - 6s - loss: 1091.1834 - mean_absolute_error: 21.1795 - r2_keras: 0.6665 - val_loss: 970.5180 - val_mean_absolute_error: 19.7887 - val_r2_keras: 0.6669\n", "Epoch 33/100\n", " - 6s - loss: 1026.7513 - mean_absolute_error: 20.5681 - r2_keras: 0.6871 - val_loss: 944.7394 - val_mean_absolute_error: 21.0682 - val_r2_keras: 0.6191\n", "Epoch 34/100\n", " - 6s - loss: 1001.6547 - mean_absolute_error: 20.4459 - r2_keras: 0.6947 - val_loss: 807.0192 - val_mean_absolute_error: 17.9429 - val_r2_keras: 0.7055\n", "Epoch 35/100\n", " - 6s - loss: 973.5041 - mean_absolute_error: 20.0967 - r2_keras: 0.7036 - val_loss: 873.8784 - val_mean_absolute_error: 19.6136 - val_r2_keras: 0.6935\n", "Epoch 36/100\n", " - 6s - loss: 933.6586 - mean_absolute_error: 19.6959 - r2_keras: 0.7141 - val_loss: 850.6574 - val_mean_absolute_error: 19.7095 - val_r2_keras: 0.6558\n", "Epoch 37/100\n", " - 6s - loss: 881.7047 - mean_absolute_error: 19.1366 - r2_keras: 0.7314 - val_loss: 1088.2258 - val_mean_absolute_error: 25.0830 - val_r2_keras: 0.4543\n", "Epoch 38/100\n", " - 6s - loss: 880.0293 - mean_absolute_error: 19.1857 - r2_keras: 0.7315 - val_loss: 966.3578 - val_mean_absolute_error: 18.1578 - val_r2_keras: 0.6572\n", "Epoch 39/100\n", " - 6s - loss: 843.4015 - mean_absolute_error: 18.6313 - r2_keras: 0.7427 - val_loss: 823.4788 - val_mean_absolute_error: 20.1654 - val_r2_keras: 0.6481\n", "Epoch 40/100\n", " - 6s - loss: 831.5227 - mean_absolute_error: 18.6448 - r2_keras: 0.7460 - val_loss: 747.4146 - val_mean_absolute_error: 17.7913 - val_r2_keras: 0.7204\n", "Epoch 41/100\n", " - 6s - loss: 814.2008 - mean_absolute_error: 18.4304 - r2_keras: 0.7510 - val_loss: 775.8853 - val_mean_absolute_error: 18.7378 - val_r2_keras: 0.7177\n", "Epoch 42/100\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " - 6s - loss: 797.5138 - mean_absolute_error: 18.1876 - r2_keras: 0.7556 - val_loss: 685.3588 - val_mean_absolute_error: 17.1742 - val_r2_keras: 0.7444\n", "Epoch 43/100\n", " - 6s - loss: 773.5271 - mean_absolute_error: 17.9547 - r2_keras: 0.7638 - val_loss: 692.3455 - val_mean_absolute_error: 18.5788 - val_r2_keras: 0.7079\n", "Epoch 44/100\n", " - 6s - loss: 768.8692 - mean_absolute_error: 17.9141 - r2_keras: 0.7652 - val_loss: 665.5158 - val_mean_absolute_error: 17.3064 - val_r2_keras: 0.7091\n", "Epoch 45/100\n", " - 6s - loss: 757.0849 - mean_absolute_error: 17.6720 - r2_keras: 0.7687 - val_loss: 747.2899 - val_mean_absolute_error: 19.0316 - val_r2_keras: 0.6323\n", "Epoch 46/100\n", " - 6s - loss: 747.5241 - mean_absolute_error: 17.6405 - r2_keras: 0.7713 - val_loss: 645.7469 - val_mean_absolute_error: 16.4030 - val_r2_keras: 0.7482\n", "Epoch 47/100\n", " - 6s - loss: 736.6385 - mean_absolute_error: 17.5826 - r2_keras: 0.7747 - val_loss: 708.1268 - val_mean_absolute_error: 17.9221 - val_r2_keras: 0.7165\n", "Epoch 48/100\n", " - 6s - loss: 728.5000 - mean_absolute_error: 17.4688 - r2_keras: 0.7776 - val_loss: 705.4419 - val_mean_absolute_error: 17.4397 - val_r2_keras: 0.7364\n", "Epoch 49/100\n", " - 6s - loss: 713.6034 - mean_absolute_error: 17.2577 - r2_keras: 0.7819 - val_loss: 580.2264 - val_mean_absolute_error: 15.7173 - val_r2_keras: 0.7279\n", "Epoch 50/100\n", " - 6s - loss: 703.0439 - mean_absolute_error: 17.1042 - r2_keras: 0.7853 - val_loss: 932.7723 - val_mean_absolute_error: 22.0960 - val_r2_keras: 0.4774\n", "Epoch 51/100\n", " - 6s - loss: 690.2173 - mean_absolute_error: 17.0187 - r2_keras: 0.7890 - val_loss: 599.5811 - val_mean_absolute_error: 16.6280 - val_r2_keras: 0.7115\n", "Epoch 52/100\n", " - 6s - loss: 690.6461 - mean_absolute_error: 17.0228 - r2_keras: 0.7895 - val_loss: 780.5881 - val_mean_absolute_error: 21.2900 - val_r2_keras: 0.6417\n", "Epoch 53/100\n", " - 6s - loss: 695.3857 - mean_absolute_error: 16.9946 - r2_keras: 0.7873 - val_loss: 694.5624 - val_mean_absolute_error: 16.1024 - val_r2_keras: 0.7427\n", "Epoch 54/100\n", " - 6s - loss: 686.6690 - mean_absolute_error: 16.9357 - r2_keras: 0.7900 - val_loss: 787.4097 - val_mean_absolute_error: 20.1888 - val_r2_keras: 0.5858\n", "Epoch 55/100\n", " - 6s - loss: 662.0582 - mean_absolute_error: 16.7035 - r2_keras: 0.7971 - val_loss: 763.8014 - val_mean_absolute_error: 15.9823 - val_r2_keras: 0.7247\n", "Epoch 56/100\n", " - 6s - loss: 659.2255 - mean_absolute_error: 16.6379 - r2_keras: 0.7986 - val_loss: 664.1955 - val_mean_absolute_error: 16.8696 - val_r2_keras: 0.6495\n", "Epoch 57/100\n", " - 6s - loss: 659.2878 - mean_absolute_error: 16.5946 - r2_keras: 0.7980 - val_loss: 664.0523 - val_mean_absolute_error: 18.5229 - val_r2_keras: 0.7026\n", "Epoch 58/100\n", " - 6s - loss: 642.7832 - mean_absolute_error: 16.3461 - r2_keras: 0.8031 - val_loss: 1029.9782 - val_mean_absolute_error: 23.5485 - val_r2_keras: 0.3787\n", "Epoch 59/100\n", " - 6s - loss: 639.5910 - mean_absolute_error: 16.3789 - r2_keras: 0.8036 - val_loss: 686.3486 - val_mean_absolute_error: 17.5799 - val_r2_keras: 0.6162\n", "dict_keys(['loss', 'val_r2_keras', 'r2_keras', 'mean_absolute_error', 'val_mean_absolute_error', 'val_loss'])\n" ] } ], "source": [ "def r2_keras(y_true, y_pred):\n", " \"\"\"Coefficient of Determination \n", " \"\"\"\n", " SS_res = K.sum(K.square( y_true - y_pred ))\n", " SS_tot = K.sum(K.square( y_true - K.mean(y_true) ) )\n", " return ( 1 - SS_res/(SS_tot + K.epsilon()) )\n", "\n", "# Next, we build a deep network. \n", "# The first layer is an LSTM layer with 100 units followed by another LSTM layer with 50 units. \n", "# Dropout is also applied after each LSTM layer to control overfitting. \n", "# Final layer is a Dense output layer with single unit and linear activation since this is a regression problem.\n", "nb_features = seq_array.shape[2]\n", "nb_out = label_array.shape[1]\n", "\n", "model = Sequential()\n", "model.add(LSTM(\n", " input_shape=(sequence_length, nb_features),\n", " units=100,\n", " return_sequences=True))\n", "model.add(Dropout(0.2))\n", "model.add(LSTM(\n", " units=50,\n", " return_sequences=False))\n", "model.add(Dropout(0.2))\n", "model.add(Dense(units=nb_out))\n", "model.add(Activation(\"linear\"))\n", "model.compile(loss='mean_squared_error', optimizer='rmsprop',metrics=['mae',r2_keras])\n", "\n", "print(model.summary())\n", "\n", "# fit the network\n", "history = model.fit(seq_array, label_array, epochs=100, batch_size=200, validation_split=0.05, verbose=2,\n", " callbacks = [keras.callbacks.EarlyStopping(monitor='val_loss', min_delta=0, patience=10, verbose=0, mode='min'),\n", " keras.callbacks.ModelCheckpoint(model_path,monitor='val_loss', save_best_only=True, mode='min', verbose=0)]\n", " )\n", "\n", "# list all data in history\n", "print(history.history.keys())" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "F_AqerQKAsaA" }, "source": [ "## Model Evaluation on Test set" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1928 }, "colab_type": "code", "id": "mNHApglLn-CT", "outputId": "1f43329c-c0c7-442b-dd57-978fc205a1f4" }, "outputs": [ { "data": { "image/png": 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\n", 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\n", 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "15631/15631 [==============================] - 1s 94us/step\n", "\n", "MAE: 15.569483895668194\n", "\n", "R^2: 0.7421105207099498\n", "15631/15631 [==============================] - 2s 101us/step\n" ] } ], "source": [ "# summarize history for R^2\n", "fig_acc = plt.figure(figsize=(10, 10))\n", "plt.plot(history.history['r2_keras'])\n", "plt.plot(history.history['val_r2_keras'])\n", "plt.title('model r^2')\n", "plt.ylabel('R^2')\n", "plt.xlabel('epoch')\n", "plt.legend(['train', 'test'], loc='upper left')\n", "plt.show()\n", "fig_acc.savefig(\"model_r2.png\")\n", "\n", "# summarize history for MAE\n", "fig_acc = plt.figure(figsize=(10, 10))\n", "plt.plot(history.history['mean_absolute_error'])\n", "plt.plot(history.history['val_mean_absolute_error'])\n", "plt.title('model MAE')\n", "plt.ylabel('MAE')\n", "plt.xlabel('epoch')\n", "plt.legend(['train', 'test'], loc='upper left')\n", "plt.show()\n", "fig_acc.savefig(\"model_mae.png\")\n", "\n", "# summarize history for Loss\n", "fig_acc = plt.figure(figsize=(10, 10))\n", "plt.plot(history.history['loss'])\n", "plt.plot(history.history['val_loss'])\n", "plt.title('model loss')\n", "plt.ylabel('loss')\n", "plt.xlabel('epoch')\n", "plt.legend(['train', 'test'], loc='upper left')\n", "plt.show()\n", "fig_acc.savefig(\"model_regression_loss.png\")\n", "\n", "# training metrics\n", "scores = model.evaluate(seq_array, label_array, verbose=1, batch_size=200)\n", "print('\\nMAE: {}'.format(scores[1]))\n", "print('\\nR^2: {}'.format(scores[2]))\n", "\n", "y_pred = model.predict(seq_array,verbose=1, batch_size=200)\n", "y_true = label_array\n", "\n", "test_set = pd.DataFrame(y_pred)\n", "test_set.to_csv('submit_train.csv', index = None)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "X-4BShGEAyMP" }, "source": [ "## Evaluate on Validation set" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 417 }, "colab_type": "code", "id": "pDrp7F6Fn-3p", "outputId": "7aa247a4-07fe-45c8-f3ca-1074eb5059e5" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "MAE: 12.224518396521127\n", "\n", "R^2: 0.7951398120131544\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# We pick the last sequence for each id in the test data\n", "seq_array_test_last = [test_df[test_df['id']==id][sequence_cols].values[-sequence_length:] \n", " for id in test_df['id'].unique() if len(test_df[test_df['id']==id]) >= sequence_length]\n", "\n", "seq_array_test_last = np.asarray(seq_array_test_last).astype(np.float32)\n", "#print(\"seq_array_test_last\")\n", "#print(seq_array_test_last)\n", "#print(seq_array_test_last.shape)\n", "\n", "# Similarly, we pick the labels\n", "#print(\"y_mask\")\n", "y_mask = [len(test_df[test_df['id']==id]) >= sequence_length for id in test_df['id'].unique()]\n", "label_array_test_last = test_df.groupby('id')['RUL'].nth(-1)[y_mask].values\n", "label_array_test_last = label_array_test_last.reshape(label_array_test_last.shape[0],1).astype(np.float32)\n", "#print(label_array_test_last.shape)\n", "#print(\"label_array_test_last\")\n", "#print(label_array_test_last)\n", "\n", "# if best iteration's model was saved then load and use it\n", "if os.path.isfile(model_path):\n", " estimator = load_model(model_path,custom_objects={'r2_keras': r2_keras})\n", "\n", " # test metrics\n", " scores_test = estimator.evaluate(seq_array_test_last, label_array_test_last, verbose=2)\n", " print('\\nMAE: {}'.format(scores_test[1]))\n", " print('\\nR^2: {}'.format(scores_test[2]))\n", "\n", " y_pred_test = estimator.predict(seq_array_test_last)\n", " y_true_test = label_array_test_last\n", "\n", " test_set = pd.DataFrame(y_pred_test)\n", " test_set.to_csv('submit_test.csv', index = None)\n", "\n", " # Plot in blue color the predicted data and in green color the\n", " # actual data to verify visually the accuracy of the model.\n", " fig_verify = plt.figure(figsize=(10, 5))\n", " plt.plot(y_pred_test, color=\"blue\")\n", " plt.plot(y_true_test, color=\"green\")\n", " plt.title('prediction')\n", " plt.ylabel('value')\n", " plt.xlabel('row')\n", " plt.legend(['predicted', 'actual data'], loc='upper left')\n", " plt.show()\n", " fig_verify.savefig(\"model_regression_verify.png\")" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "j1T8y5qA91x8" }, "source": [ "# References\n", "\n", "\n", "- [1] Deep Learning for Predictive Maintenance https://github.com/Azure/lstms_for_predictive_maintenance/blob/master/Deep%20Learning%20Basics%20for%20Predictive%20Maintenance.ipynb\n", "- [2] Predictive Maintenance: Step 2A of 3, train and evaluate regression models https://gallery.cortanaintelligence.com/Experiment/Predictive-Maintenance-Step-2A-of-3-train-and-evaluate-regression-models-2\n", "- [3] A. Saxena and K. Goebel (2008). \"Turbofan Engine Degradation Simulation Data Set\", NASA Ames Prognostics Data Repository (https://ti.arc.nasa.gov/tech/dash/groups/pcoe/prognostic-data-repository/#turbofan), NASA Ames Research Center, Moffett Field, CA \n", "- [4] Understanding LSTM Networks http://colah.github.io/posts/2015-08-Understanding-LSTMs/\n", "\n" ] } ], "metadata": { "accelerator": "GPU", "colab": { "collapsed_sections": [ "4BsYPkY7gYGx", "ElhakcHtnX9J" ], "name": "Predictive-Maintenance-using-LSTM.ipynb", "provenance": [], "toc_visible": true, "version": "0.3.2" }, "kernelspec": { "display_name": "Python [conda env:tf_gpu]", "language": "python", "name": "conda-env-tf_gpu-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 1 } ================================================ FILE: Chapter10/Readme.md ================================================ AI for Industrial IoT ================================================ FILE: Chapter11/SF_crime_category_detection.ipynb ================================================ { "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from pyspark.ml.classification import LogisticRegression as LR\n", "from pyspark.ml.feature import RegexTokenizer, StopWordsRemover, CountVectorizer\n", "from pyspark.ml.feature import OneHotEncoder, StringIndexer, VectorAssembler\n", "from pyspark.ml.evaluation import MulticlassClassificationEvaluator\n", "from pyspark.ml import Pipeline\n", "from pyspark.sql.functions import col\n", "\n", "\n", "from pyspark.sql import SparkSession\n", "\n", "spark = SparkSession.builder \\\n", " .appName(\"Crime Category Prediction\") \\\n", " .config(\"spark.executor.memory\", \"70g\") \\\n", " .config(\"spark.driver.memory\", \"50g\") \\\n", " .config(\"spark.memory.offHeap.enabled\",True) \\\n", " .config(\"spark.memory.offHeap.size\",\"16g\") \\\n", " .getOrCreate()" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "data = spark.read.format(\"csv\"). \\\n", " options(header=\"true\", inferschema=\"true\"). \\\n", " load(\"sf_crime_dataset.csv\")" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['Dates',\n", " 'Category',\n", " 'Descript',\n", " 'DayOfWeek',\n", " 'PdDistrict',\n", " 'Resolution',\n", " 'Address',\n", " 'X',\n", " 'Y']" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.columns" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "drop_list = ['Dates', 'DayOfWeek', 'PdDistrict', 'Resolution', 'Address', 'X', 'Y']\n", "\n", "data = data.select([column for column in data.columns if column not in drop_list])" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+--------------+--------------------+\n", "| Category| Descript|\n", "+--------------+--------------------+\n", "| WARRANTS| WARRANT ARREST|\n", "|OTHER OFFENSES|TRAFFIC VIOLATION...|\n", "|OTHER OFFENSES|TRAFFIC VIOLATION...|\n", "| LARCENY/THEFT|GRAND THEFT FROM ...|\n", "| LARCENY/THEFT|GRAND THEFT FROM ...|\n", "+--------------+--------------------+\n", "only showing top 5 rows\n", "\n" ] } ], "source": [ "data.show(5)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "root\n", " |-- Category: string (nullable = true)\n", " |-- Descript: string (nullable = true)\n", "\n" ] } ], "source": [ "data.printSchema()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+--------------------+------+\n", "| Category| count|\n", "+--------------------+------+\n", "| LARCENY/THEFT|174900|\n", "| OTHER OFFENSES|126182|\n", "| NON-CRIMINAL| 92304|\n", "| ASSAULT| 76876|\n", "| DRUG/NARCOTIC| 53971|\n", "| VEHICLE THEFT| 53781|\n", "| VANDALISM| 44725|\n", "| WARRANTS| 42214|\n", "| BURGLARY| 36755|\n", "| SUSPICIOUS OCC| 31414|\n", "| MISSING PERSON| 25989|\n", "| ROBBERY| 23000|\n", "| FRAUD| 16679|\n", "|FORGERY/COUNTERFE...| 10609|\n", "| SECONDARY CODES| 9985|\n", "| WEAPON LAWS| 8555|\n", "| PROSTITUTION| 7484|\n", "| TRESPASS| 7326|\n", "| STOLEN PROPERTY| 4540|\n", "|SEX OFFENSES FORC...| 4388|\n", "+--------------------+------+\n", "only showing top 20 rows\n", "\n" ] } ], "source": [ "# By top 20 categories\n", "data.groupBy(\"Category\") \\\n", " .count() \\\n", " .orderBy(col(\"count\").desc()) \\\n", " .show()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+--------------------+-----+\n", "| Descript|count|\n", "+--------------------+-----+\n", "|GRAND THEFT FROM ...|60022|\n", "| LOST PROPERTY|31729|\n", "| BATTERY|27441|\n", "| STOLEN AUTOMOBILE|26897|\n", "|DRIVERS LICENSE, ...|26839|\n", "| WARRANT ARREST|23754|\n", "|SUSPICIOUS OCCURR...|21891|\n", "|AIDED CASE, MENTA...|21497|\n", "|PETTY THEFT FROM ...|19771|\n", "|MALICIOUS MISCHIE...|17789|\n", "| TRAFFIC VIOLATION|16471|\n", "|PETTY THEFT OF PR...|16196|\n", "|MALICIOUS MISCHIE...|15957|\n", "|THREATS AGAINST LIFE|14716|\n", "| FOUND PROPERTY|12146|\n", "|ENROUTE TO OUTSID...|11470|\n", "|GRAND THEFT OF PR...|11010|\n", "|POSSESSION OF NAR...|10050|\n", "|PETTY THEFT FROM ...|10029|\n", "|PETTY THEFT SHOPL...| 9571|\n", "+--------------------+-----+\n", "only showing top 20 rows\n", "\n" ] } ], "source": [ "# By top 20 descriptions\n", "data.groupBy(\"Descript\") \\\n", " .count() \\\n", " .orderBy(col(\"count\").desc()) \\\n", " .show()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "# regular expression tokenizer\n", "regexTokenizer = RegexTokenizer(inputCol=\"Descript\", outputCol=\"words\", pattern=\"\\\\W\")\n", "\n", "# stop words\n", "add_stopwords = [\"http\",\"https\",\"amp\",\"rt\",\"t\",\"c\",\"the\"] \n", "\n", "stopwordsRemover = StopWordsRemover(inputCol=\"words\", outputCol=\"filtered\").setStopWords(add_stopwords)\n", "\n", "# bag of words count\n", "countVectors = CountVectorizer(inputCol=\"filtered\", outputCol=\"features\", vocabSize=10000, minDF=5)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "label_stringIdx = StringIndexer(inputCol = \"Category\", outputCol = \"label\")" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "pipeline = Pipeline(stages=[regexTokenizer, stopwordsRemover, countVectors, label_stringIdx])\n", "\n", "# Fit the pipeline to data.\n", "pipelineFit = pipeline.fit(data)\n", "dataset = pipelineFit.transform(data)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+--------------+--------------------+--------------------+--------------------+--------------------+-----+\n", "| Category| Descript| words| filtered| features|label|\n", "+--------------+--------------------+--------------------+--------------------+--------------------+-----+\n", "| WARRANTS| WARRANT ARREST| [warrant, arrest]| [warrant, arrest]|(809,[17,32],[1.0...| 7.0|\n", "|OTHER OFFENSES|TRAFFIC VIOLATION...|[traffic, violati...|[traffic, violati...|(809,[11,17,35],[...| 1.0|\n", "|OTHER OFFENSES|TRAFFIC VIOLATION...|[traffic, violati...|[traffic, violati...|(809,[11,17,35],[...| 1.0|\n", "| LARCENY/THEFT|GRAND THEFT FROM ...|[grand, theft, fr...|[grand, theft, fr...|(809,[0,2,3,4,6],...| 0.0|\n", "| LARCENY/THEFT|GRAND THEFT FROM ...|[grand, theft, fr...|[grand, theft, fr...|(809,[0,2,3,4,6],...| 0.0|\n", "+--------------+--------------------+--------------------+--------------------+--------------------+-----+\n", "only showing top 5 rows\n", "\n" ] } ], "source": [ "dataset.show(5)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Training Dataset Count: 614666\n", "Test Dataset Count: 263383\n" ] } ], "source": [ "### Randomly split data into training and test data sets.\n", "(trainingData, testData) = dataset.randomSplit([0.7, 0.3], seed = 100)\n", "print(\"Training Dataset Count: \" + str(trainingData.count()))\n", "print(\"Test Dataset Count: \" + str(testData.count()))" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "# Build the model\n", "logistic_regrssor = LR(maxIter=20, regParam=0.3, elasticNetParam=0)\n", "\n", "# Train model with Training Data\n", "model = logistic_regrssor.fit(trainingData)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "+------------------------------+-------------+------------------------------+-----+----------+\n", "| Descript| Category| probability|label|prediction|\n", "+------------------------------+-------------+------------------------------+-----+----------+\n", "|THEFT, BICYCLE, <$50, NO SE...|LARCENY/THEFT|[0.8711581002180425,0.02115...| 0.0| 0.0|\n", "|THEFT, BICYCLE, <$50, NO SE...|LARCENY/THEFT|[0.8711581002180425,0.02115...| 0.0| 0.0|\n", "|THEFT, BICYCLE, <$50, NO SE...|LARCENY/THEFT|[0.8711581002180425,0.02115...| 0.0| 0.0|\n", "|THEFT, BICYCLE, <$50, NO SE...|LARCENY/THEFT|[0.8711581002180425,0.02115...| 0.0| 0.0|\n", "|THEFT, BICYCLE, <$50, NO SE...|LARCENY/THEFT|[0.8711581002180425,0.02115...| 0.0| 0.0|\n", "|THEFT, BICYCLE, <$50, NO SE...|LARCENY/THEFT|[0.8711581002180425,0.02115...| 0.0| 0.0|\n", "|THEFT, BICYCLE, <$50, NO SE...|LARCENY/THEFT|[0.8711581002180425,0.02115...| 0.0| 0.0|\n", "|THEFT, BICYCLE, <$50, NO SE...|LARCENY/THEFT|[0.8711581002180425,0.02115...| 0.0| 0.0|\n", "|THEFT, BICYCLE, <$50, NO SE...|LARCENY/THEFT|[0.8711581002180425,0.02115...| 0.0| 0.0|\n", "|THEFT, BICYCLE, <$50, NO SE...|LARCENY/THEFT|[0.8711581002180425,0.02115...| 0.0| 0.0|\n", "+------------------------------+-------------+------------------------------+-----+----------+\n", "only showing top 10 rows\n", "\n" ] } ], "source": [ "# Make predictions on Test Data\n", "predictions = model.transform(testData)\n", "\n", "# List top crime predictions\n", "\n", "predictions.filter(predictions['prediction'] == 0) \\\n", " .select(\"Descript\",\"Category\",\"probability\",\"label\",\"prediction\") \\\n", " .orderBy(\"probability\", ascending=False) \\\n", " .show(n = 10, truncate = 30)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.9725282146509521" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "evaluator = MulticlassClassificationEvaluator(predictionCol=\"prediction\")\n", "evaluator.evaluate(predictions)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python [conda env:spark]", "language": "python", "name": "conda-env-spark-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.6" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: Chapter11/readme.md ================================================ The chapter introduces the reader to smart cities. Case studies would be used to show to the reader how the concepts learnt in this book can be applied in developing various smart city components. After reading the chapter the reader will: * Know what is a smart city * Learn about the essential components of a smart city * Learn about cities across the globe implementing smart solutions. * Understand the challenges in building smart cities * Write a code to detect crime description from San Francisco crime data ================================================ FILE: Chapter12/Readme.md ================================================ Now that we have understood and implemented different Artificial Intelligence (AI)/machine learning (ML) algorithms, it is time to combine it all together, understand which type of data is best suited for each and at the same time understand the basic pre-processing required for each type of data. By the end of this chapter you will: * Know the different types of data that can be fed to your model * Learn how to process time series data * Pre-processing of textual data * Different transforms that can be done on image data * How to handle video files * How to handle speech data * Cloud computing options ================================================ FILE: Chapter12/Video_to_frames.ipynb ================================================ { "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import cv2 # for capturing videos\n", "import math # for mathematical operations\n", "import matplotlib.pyplot as plt # for plotting the images\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "videoFile = \"video.avi\" # Video file with complete path\n", "cap = cv2.VideoCapture(videoFile) # capturing the video from the given path\n", "frameRate = cap.get(5) #frame rate" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Finished!\n" ] } ], "source": [ "count = 0\n", "while(cap.isOpened()):\n", " frameId = cap.get(1) #current frame number\n", " ret, frame = cap.read()\n", " if (ret != True):\n", " break\n", " if (frameId % math.floor(frameRate) == 0):\n", " filename =\"frame%d.jpg\" % count\n", " count += 1\n", " cv2.imwrite(filename, frame)\n", "cap.release()\n", "print (\"Finished!\")" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "img = plt.imread('frame5.jpg') # reading image using its name\n", "plt.imshow(img)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python [conda env:tf_gpu]", "language": "python", "name": "conda-env-tf_gpu-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: Chapter12/audio_processing.ipynb ================================================ { "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import librosa\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "# Get the file path to the included audio example\n", "filename = librosa.util.example_audio_file()\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "input_length=16000*4\n", "def audio_norm(data):\n", "\n", " max_data = np.max(data)\n", " min_data = np.min(data)\n", " data = (data-min_data)/(max_data-min_data) \n", " return data \n", "\n", "def load_audio_file(file_path, input_length=input_length):\n", " \n", " data, sr = librosa.load(file_path, sr=None)\n", " \n", " max_offset = abs(len(data)-input_length)\n", " offset = np.random.randint(max_offset)\n", " if len(data)>input_length:\n", " data = data[offset:(input_length+offset)]\n", " else:\n", " data = np.pad(data, (offset, input_size - len(data) - offset), \"constant\")\n", " \n", " data = audio_norm(data)\n", " return data" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "data_base = load_audio_file(filename)\n", "\n", "fig = plt.figure(figsize=(14, 8))\n", "plt.title('Raw wave ')\n", "plt.ylabel('Amplitude')\n", "plt.plot(np.linspace(0, 1, input_length), data_base)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "def preprocess_audio_mel_T(audio, sample_rate=16000, window_size=20, #log_specgram\n", " step_size=10, eps=1e-10):\n", "\n", " mel_spec = librosa.feature.melspectrogram(y=audio, sr=sample_rate, n_mels= 256)\n", " mel_db = (librosa.power_to_db(mel_spec, ref=np.max) + 40)/40\n", "\n", " return mel_db.T\n", "\n", "def load_audio_file2(file_path, input_length=input_length):\n", " \n", " data, sr = librosa.load(file_path, sr=None)\n", " \n", " max_offset = abs(len(data)-input_length)\n", " offset = np.random.randint(max_offset)\n", " if len(data)>input_length:\n", " data = data[offset:(input_length+offset)]\n", " else:\n", " data = np.pad(data, (offset, input_size - len(data) - offset), \"constant\")\n", " \n", " data = preprocess_audio_mel_T(data, sr)\n", " return data" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(126, 256)\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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miDZtHsf5EwMlIMqnnMa4TwC/S+FYpTBAkpJYt92nYactihibL1nGQfxT2MZWxYSwdIgjutWEnymXx0X43ry+cmYCtkL3OjpcqblDRIJ59rjnFbpxrF/CZJoKj5RzOmdhNyHOjSasl3ss1w8/p8NTUxua3DY5jzbvQScs7mnTa2FUtGxI3hyhbarv0AbikXEDHl0pNDCMUxOZ8vqx56JV+HlaJ+F9hXugckXD8ZxvihojmtwL1zVjXkwSxECl6c7JJNMf5dTJfbJF90Qi5Ti/91j+Jy/C94iYGBZtUy6EuH7FtjWR6RHXXnhfINLrnH5G0xWujoi78njUCfOe6z6hKxiR6E7DXGpwjFXugpgvnMNsZ7GBwW46qIakMFX0XwIHt2TZMFdTPifgsHnJsQX2hnoXDeuKe0QCXE21cwP9Zr3TqWwg2SNxMfZj+S5fPJbXU5Quhx/6qK6rwIFhWSTHZDKZTCaTyWQyXSnZjxyTyWQymUwmk8l0pfRS4GpV7d2qfBY/3AaKto/yV1oSg7woBWlbIGz1h6sDObctyeU6iMe1VJzfua8d3F+Xv5PeWJcnK4nJfuvB3XU5SSTstphLnTwdvqbSrekYYWHcOiKKhVGIV2Fso/cQydUO5fMVcLCTIx0WpdvL+B7DkAw9yudH/whJp5DEkWFhioiJak8DklGyeor1QpLH95D4iaHpjZm6WiKxVSl9THe17mM6UIWTs2XDcF8su8DP4BRU7gNJUY5tYZelBO5Yx1+WOaXQK6J0DP87jSRUDYlYFYpCFxPgVMUGDvCBOD5MAEtHJA5oNKO7C3EOojRhHO6j7P8Y6t95G8k6EYZfbtHljK5buBD6cnEgc2l2EOa1kjnahuRlfSSbrJCMbLUd7sfd72tkku5CTCzbfRxOHqdcc4BAsP+Ig/Hc9ol8TiSnfSF1YjsrIBytsfQLXcf6D6T96YWwIMPPkI3UyQ37o/AAs9592W5d+xKo4EjGfLkrE50OQnRKag3h+EeUpAzjE9zz0incLJF0sGwmYlVyS9aJDn5EN+h+R3E8uZ6J5xCx4VxgclNiT2wPUa0K6EnR2vh7ZoPjE/Eb7uMqWW+Dqyb3NI7tHOgenxncGzVCTKSTyItcXyXY3exq5VoX/rzJXYsUDPHdpnZyH1LoIxE4OnKiDdy3+czgdwDiSR9KpkwEmeOGPmNyV1UPOnMprAhIG9abShTMBMANLmKcq8r9EvdV6PtGslqFUROPomslk6HCpWuFMdHzFt8zgClXSfhrMPFQiomVuRdQsUo2qtce+8DXfFaG9yi6lrHeCY7hvr8C7sg5076gZa48NBOgZH7Q8JzEGLRO5XlQp3I857ZzzlVIJpqey2Qvv//D4D0+KVkkx2QymUwmk8lkMl0p2Y8ck8lkMplMJpPJdKX0UuBq1IPV/rq8BcuMr7Qm63I3klBbCyHFtpdw3NlCnM8OWhJH60ba2qKLuOhkdW9dvv9Q6tHuyznLhdy7mKP7kGDQZXAda0DUWA26gaRjXAYoFcPinRP5fCmEnsLTnHOu2pKb+5HUm65lJRxhTr6E8KJ0t3LaYhiZ6M6mw80HuvH7cv2HX0cYFXgf+4vhVZUEbGOmliNaqPAkKc5vIKnaHKF3uAMtEarvASXK6YiknKzo1iIfMyEdQ+90oum/L5WbXQ87pdEBxzmNMYzuSmhYJf8iutYwVkQvmDiNyemIFSmUZkmsJOy6p5IFdjA/gcBlGyYpRICIVhEzSifEA1knYDkZ+16Ome9JJyknM7RNuTTBcagC3rN1XxYrk4qugBFsJpIkikF0Q7mzXUqluL7pzkeMh2ghkbPuCfYbuim9K5tJMZABqrZkYdFliDjH+I5Mhk4PSMJC7zEcE+UQp5zKWNcyeMzkFtDfsg4eQ2ylAEqjcIuCOEz4OvFcOpLjtvL6b36ck3kv/PdAoj7khxTSQ6wIa5KOgpwj3Pfo/sc2ENXxYXPJjaSAzaxogTlNTEa5xTGBbD+87/N+RB/bF+Fx47OO+1P7FHXDXkpELQaWt7n2KL1HhTFQDtAmLrz+XOFgqOs59gzgOb1HxCblOhnq0zmTgVsN6GSHNk/C93XOubaYxmrsFvtYhPtxndQR90YfPIZYt0L9iDE2JO1UDnx4JnE+K2R70/6Rt+ZXq0kYp+P+3jv++GcGsVa6rnE9E4mlljtSVz6flMul2m/0Mz1ehb8sMbkpk2Tqva4KHuPwvAE5rt0ziexOwntm60L2xngRbn88kUXiF/giO9uwcGzLM6d4973gtf48ZJEck8lkMplMJpPJdKVkP3JMJpPJZDKZTCbTldJLgasVVeyejp9xM3+/fGv9+R93b63LX7z9u+tyj8mlcJ2H+c11+V7/fF3egy3LNmOnTuNrN3tIUnRbQngP/pFcN30dMXY4t9RwryLGxrBtrRJtAU+Bu5h20wonsLuULnIFEnLWrY3wIsLwTFTGcHONxFHpCOFfhO0Z5mWCUeIGKuEhQvtPfgWIWpsHIQwPF7Xr/1COGb4q8W+6uzjnXPSe/NtSyEKFBx79kVzr0a/SFUyOSZFskBiTQkDC5I2SchxiqB2fD18jrueC5clt/XeHDhI6Eung/Fnuhp2ZmEgxUhiPfM4kdEQUW0BMWCciI2zzSvLoKinXtHdy9W/EZNgehaIAeyHWSTyQmJ1vQCmIOTCErzEROZ5jkha4EPAP12/GuPj/nTM67aCI9U10TyVOu5Q+W20Dle1wbwgjWsWWbD5VSjZEigqZg7POSmGMwAG7en5yrBS61rB3sV9LHEPMkEjkMuZCCV+HCey6T/UcW9eHjmUNrkmbyUA3/18+D37soiWcj+CKVQCn7ZwCLWQyXKAx831gK8qxLTzm7IvOQ3m+LW50g8c7pxEYPid4rf77slHOr4WTUfO6yo2La/UjEnd+oKphLXA/o/iMSZb6ouzXxS6Q0vCt9Z6Bg4grch8iQkrMkIl4+dynGxefMTqRZLjMNdW60C1Q+OKme15Aap9Q6KMcQ7SSx7TPw/u+SlALdy3iisSk2DaKGJtzen/jWNM9MG5I9k2nxgh7dxUTb4NbGMZHJ0YFGrYkTr3xZWRdN7hFwpGSiYGf3YOWbw1IID7OLqU92RDodKvJwZHPOn6nA74/De+T0YiWf8Dj4STrCiCNjySBZ7Xgw/HlkUVyTCaTyWQymUwm05WS/cgxmUwmk8lkMplMV0ovBa7mXO388zhpJ5Ew2iCRcPkYWa3yKhx4PkqEn/r2kzvr8l+5IyH8fCNJW9tL+K9AbHg4l3hz69PC/aQxwpY5QuErKXd+JPHfxSFwMpWkTD6PkWAxH8AFCwgQ3cjovqJCnKUO7fslkkid0gVGjlkRdWJSReVKImW6xuz/3T+Ta74mSF++I313+SZc3VKgGhFDtlJ8/Ksyzk2uQc41u+Y4YAIP/llpf/sEbj/Ah3belsadv4VknQ2JNyuEyHsgH7vHRFWkTCcWOqoRB+LYMszv3EYyQESSmYSRiMliP4y3ZKMwMlPT1ehcOpz1JjKx3MU14e6jkr4mYdxiek1vN62h1KN9KuuQ4Xz2/XxfMAGOP/EZ4op041KIFhxkFvtSpwXc2Ii3LfZlwdAESDnQbUT/VcJAYDwZXG1qJpkFQhcB74nncuH2Eq5gXVlXRHqKLpP40lGQ2ELYjS3vs54ueEy04fCUjZBwFG48KskmsUTWFXOdiR4VokX8ssFpj8erBH5Eb1Dv1Y6MJ1G8agONotOUxongfNSRdhIT4d7FxK3E0hRKxUSsTESLOtV93iuMdNZAWLKhzB3tAqfXFfEropx0C1PH5+H1qdybsFepfm1ADhVKxaSVcJfket50rKI41r4CptyAk/H5xvmTzPgACjtfpUxoCqOpWCUDxdwjHkuMS7mwoqwSA28gXVjryRzrcIZnKPo7HUql6lg6IMX3j6QBY1KOj3l4jXFtcx6pZKPArRSKWOtnA8etBWSXyBm/o3C9EVEjlhWhHgoZ6+CLD6+JcfZAvdrn2qF3fQxwtWiMB9QGruaAftUJ3AZz8tINqOxQNsooQ715jxyuukDuqp48P/0Ke0Mb39HG3Ij54A/bDr6siBplkRyTyWQymUwmk8l0pWQ/ckwmk8lkMplMJtOV0kuBq7WTwr21f+ycc+6N/vH681dbJ+vy7wy/si7/5b5gUinilD9cHa3LX7z2cF3eTYUrGkQ6vJbh/O1U/u3mliBqx9P+unx6srUu1xt42AeavybhzK0/FjRidpPORxJGLBUPEryki8fyD9e+Lccf/5J8TkcS55yLEUrPt8OIAcsqOds4jAn0/s435dwviM0bEbXVtkyrKAcikNNNCxVFTL19StcwOWQz0enyBkKySMTqcb8WErUxbM9+Ov+MIGpMqjhWTmjoO2TaWuwBAULbUuBGix2giDKNVHidblJb72okgeOjknUi+aZKwjYAMlTSpQk4VB5GQ9hHTGjaOWWbG5APJkbF5zyGCUOd20gwCOew1rmExpk4rcXkuDC4Ue47QD2WW8SvgAzx+kBA8oHMWyYkZR2WcL5imztPdCK0okuHRTmHjnLEtdhPHtldF4fS0Pax7GPESoo+0TW5Dh2X6PxTdohxSiO6K7hJof4KSar1BkVMJnsk7pQZ8InJp2UhE1Hpvyf3Zh/xGJY7D4Wh8/cfSSWuHa6LVReTbBvJ6DphNKwF3G65pVHmAklQmTBPYXArImroY+wBxJXSiUzQ+R7mCPAeuibFdEQCokWXpfk1mTz5FlE8YEgzzf4mUyT9AxLJfZzJJ4klllwbwOYckqmqRxqwn+UOng0NiV6bEq/yXtklkL4NlynO+1iRRcTYmNQ5PLbxQvooHcmeUfSlj/msI6LWPZZz2R7uT+mlnFClbRwTTs7qC/1syIbhuZfMNzbaD45Zyc1bT4ElVeLClxzDkvNIvut0mDwT2JsDbrXaJtbLcZbDOdc88SwPq1q3MT8xDgnKVT+cQJiKpqgrETU6h6ENKhExzyX21eb3GyCtl2HUy1+gT51TSTJVKmHUyQM5owNbPcE9KLpQFjg3Qx+N8AUvQRuAslfTWbDsGl4R+Ysgi+SYTCaTyWQymUymKyX7kWMymUwmk8lkMpmulOxHjslkMplMJpPJZLpSeineyUl85fayZ/zfQSLc9WEiLOOX2++tyxUg8bIhBf0Phwfrcj9BltgNprwbCXc5zIWLPZsLIxpHYbtKPwc7DRtGcr2jt+gHCSY4gx0qbEzdglwz2jmQ459+FVz/FtNm63qWeA8neRzOWE3ek+98kCnPwWBf/NavrMvkjlfg2fk+RzqGZewWbYkbLLFRTIiErvQ453hfh/2tLDOBoPaegpdX/Lscc/Y5cvsNDCrrx9cwcF9lt8kMzXj3htOQ5XjDnZLc+nw/nFG8TgmS4/2hQPWd0/aefC+EmclVn/L9oSXLeMcMHcMx4P/wvs5p3jwFgx3h3ZDlftuFRMfRFS2X0X8J6sdxoLVyMqatKt6jUO+wwH4d9035rsUTeR/FOefqOzJB+W4H+4DWwnyHoXuMBYq+rFp4h6Mj5dWWlFm/bIh3Cs5go78ne9tyXxZAdiGdRyZ+dl2OoR22c86lY1hiw4o0msj7Nr37sqfnuzKe2cMLORd2qHUHkxuKLuU6Nd7/cLSGLcPvCCU9vDOI95w4zquBfreDFt8RLF1pMxwvsI9zXsH6mO+MFRi3NjLYcz4rS+hI6pSOaQ0r1+w8XQQ/zwfSj3y3xzn9PgQzsmenmCe70k+0B+f6obgf8N2wGO9ttYZ4ZmJN0kK+jvDOAtYF3x9TY5Pr5x4zu6uM8hDnd9ELfw2K5nyZBOv2Qjb+5R7eJbyEJf6xHFPdwvsm3A8zWEDDrryH9wSTGfZFWMg7p9/RqTNpQwtrMkIf+wX2lQr36CN1QhvvcKAfE76rMsOeiT0pXkh7+vebbJbRBtgY+0Lv81xjHu/h8F2V+JQPI0w4vm+DfonGzMGAfuU7Oaifv5QvEDX7q8mueYIvLFSs9xW+36P2rjne0cL7M55W0djfKr6fg/0w2tsJ1ru8kP0w3pX8JMXZebjeV0QWyTGZTCaTyWQymUxXSvYjx2QymUwmk8lkMl0pvRS4Wu2cq57jLo9X28Fjvpidfux1bqVMDLMpAAAgAElEQVSCP9zoCeqW+Gb7uxm8eBPExvuZhFsfD2EbXRCNYAZduWbRkVBr9z1kVD+EDWFEDiV8TfW5wlYQyu1JuDRr62zFjNQvkSGd1tLsmq37sPGEzXIBu96t9+R+zGDP+rEvaG9Me2dWjnVIgPAkiMZuUIZucQ02oUT8ONQ4p30qfTO7jgy/tBLF/YgolRlRJ/QRLKEz0Eo7P5Cw9eSOdN5yB1aNWHk1+mi+r/GKrfvSoHQCZAJIIPGWFZ04G6zCaZlK61EqRzbtAvdqXTLzOyxdYTm+hM20sivfjNq3OKjSIbMjQSaI/XROZAzjpRxPq+iUGdKBbdAqWmV/R/NjoCEOFsorZu8GtqLwzv2Bo4jMZLDojFawCaWtOSyUF7AWTifI3g3LamYvL9vEpOQYZuympWkykb1tuddbl6e3Za52TuQYlY2eVspO2xSXA9gPz+V8v0R27UjuURzKvloB41IZ0s+lQTVwC6fGkBbvWFjch2D72nok2FuFfTEb6glK62dihgrZhCVuiczp2RTnokrExlpncu5yL2z7q+pDXAkYCtch52d23GA36zTWR3EMlZU3cVRlLQ376YG0n0g5x4p7D+udPJWx7dQyL5Sl8wVwIFxzeRPHO+fSkcy9FFbbPCceMpWEzEnaFRM/5LnRUOrRPpPrxwugUXPuVeGHI1HZCnPHA5/iczJaaFytwlxnd7cu5N5E7oh6EVHkua7W63t9LjEzomRY5ylwOA8Ure4APySStSMP0Him8bYa+1U0nOAfmiBsHLKNBzOtpZvaBnTTL4BNwkK5xrkR6lav5PiC+xMsl+Md/Z22JuLWl/2X1s+8rsulL6ul9J8nBldjs8Mx9WIZPKa84ogaZZEck8lkMplMJpPJdKVkP3JMJpPJZDKZTCbTldJLgasVVeROl8/CdufgbZ4uJQz9L/T/dF1ug/Pgr7RBJGhDO5bQ3yXSoy8r3eRtWHidLSR0+OhS7t3OJFw4HwuWkl3K3Qf3pU4nX5Ow4OwV8jBhdzWqrsIYV4U2E7GqgbeVhcYtWsTXkjDSkMD9bAXiRrt/yTGju0DUSDwoRy0XFDE5HqPamaA+oBDKDZMtD2ywdQ4MARRC/yEdhOhYFMapuk/pTEQHtrB72XIfGafRYYsDmUesd9ELu2mx63hf55wrOkQXpEgMsKIZFaPzGPMKDmST6zJPFJLDeQHsi65B/YdwKgSqdP4WXHkqIinALRA5f9YGICdwCGM7YzitKfQN3dIaA+laEuOhSxGwIqBLdNpifSJch9nY4yUwCuA2dBNzTme5jvfF7cbvhtGYaCVIx/wQTmMYE2Z2Zxvoisa+X+7KNVe7Mj4tuBLRfanMwgu38xj7aqL/LkaHJ+2qh3p35d5E64qu9EXrDBgGER04IlXAOfyrd+SavfDcK7caEDC0gU5+3fd0ZnJmQl9ekzVNDJAIETGm8q4sfDpVeuJ+QM5aZzJ/IqI7zHaeAnUEJkRXu6bNdxNP8zM4lbXD/RQDdeJaqhocD5MJsJqMWCZdDsOYnUuBaD4UlKbugZXGvl21w2PunMaPEvQl52GN9RMD34ym0i8VHPmIOxLxJeJaYn0W23Iukbk6Qr2Bd8Uo1/sy14iVbYqoZVOf0UlRY2Ybm/H6omEXNc5VX2BPxripz6eybrmeHY6JhtPg58/+EfOe6BbnMfeDHrAvtnOJvp+B5cV1FMYHNEzdF2iYQsCIMWIdEUlTxzvnqoXMsRj9R7e0Gm1zLaxPtK1mX+Dzao5nEdfGC6B+V1EWyTGZTCaTyWQymUxXSvYjx2QymUwmk8lkMl0pvRS42qpK3HujZ8nz7m1L2PWgJa4aC1gzlXCJqBCe/8boc+vyTspMjaIk0mHRHNcdZBLmu74tv/8eXYg7RnxdrrsAJrHaBiqG6Gx2iiSZ23B4SsArIalotAw7hWWXTCiI+gPbokOTc85FMULMSKYZ0aQFP3OZGFIdgyhn96QKHk/MjAk26fZUNcy2ZAaU6pU6+Hl7w1yvakhcSYxpsQc0BtgX8SOCHqNXwslKVbJNYn8dGSAiKRH6mvUhohaRSCFyuJEHcXoDdWrCABGdLntwnSMeiHuvhJ5SdU3hCMU5XMBp7/I1JI+kgxTGnElPmZC1e6oRzdY5cI2+tHPVB04EvCcGMtO/L6H9+Q1BMnIk3uTY1nMk9ARmp8L5DahPdi6D1erFwWNW17W7WgyXqgJJGblOiENxnnB+0smK6zudACUCVpTvyX0TJGHM+8Bz4OrUnUp5BcwyngJ1pSvVRKMXnkgLXJB8V+oRA91IgJCttlGnERC6U3HJLJ4eyzED9DEdikq5TjGAWybaSberqgUXsA5xGz22ycOzdTltNThJIgFgeSjPCWKG3J/SU+mjchvo4ggJPcdwdQKW5YCrOWBM0RkwO2IuHTiltfXG0oQ0RcAP4ykwSGBcSSu8Bugcx/lDXG21BecrknuoH//yyrHidSK4faVnmygenAf35H4ca9av3AJCBgQoBk4Vw1WSiWtVUlU6/gHR4thGTDxKfIhYGj8GbvchxAxIXNWXMfTAo+IJHNga0DIim8TSFBpFpDHlwwR4F9twKMmQ1Q5LlIpzNeVu7dS+7PFvRLRqYINq56ZzKzG4pusQUQPS5tlm4Gq+K31NpM0DK2PZZbptnN++I+tQOdKhb+I96cvq4lKOz7g3YJyZZJXYML43877VGA/pKyiL5JhMJpPJZDKZTKYrJfuRYzKZTCaTyWQyma6UXgpcraq8G8+fhfceRMLSFBWQsYGgADcTSboUIxj6tf476/J/8r3fXJf3uxJ23mlpjK2PzG4XSwlDnk+BwLwrIe/sVQnt5QzbeiZmkmI+wP/wJ2VBPgVhYYaIcfh8AHxEZXZECDLVONBqKuHMGFgbT8/7cKCaAOkCitS+kGPmu3JM7ylC3nSymkqo9dGvST8SaarhUpbvyLnZBbEiOWZ2yykVAyCLRDeYswxoUO+p/EPrFI4rQIamcB1jnVhXhZ6cIfkbsC/ldga2pf8uzoWxTGssdZsdahTk6P8QXOfJ14/W5YKuW4yGIzqvUDTkU4uZHw/uWoMHUo/hPbloCYOjDNhGjHMj5qHFNOQ86j7VuMViDxgXnM06JwjVw42pRHK+4kAqlaL/SqABeZe4YthBKpkDXRwCF6ADFxAetqFKwoiJc84VfTg5YaxSJE9c7eAYzFU6jRFFI25Dp7XZXbgxAZnJhkDamNATzm/VgeyrKRIkeqBHxIQ+5NDTkjbkb91el5noNVPuUsB40Pf+qaBh1Y19qeuRoBq+wY2JiBod65gssYSzVnosezjxJJVI1DlX3Np3ITExIrEvYomrfthJcvYanm9AaLuPgSlvATk8lrFizzNZogMa6OFqtby7j+MbWFenx6rzWPo4ugRadxhO0l1hTRJvK7t0CETCWNyLmBSdwoodGU86PtI58aOcotIy/G+1D/eB+pxudtgzlEMgEgXz2cNkx8Q6ix047QE5rTtZ8Jj5oXzevw90ccNpjf1XEtOkuxjLRdiZjPtKtESbkaA0V0gfEi4j8arDPlm3iFlz/8MaBmK1emXPUXQwJMrqF3i40s0MiBad8OqezFvliggkVOFte5jn50jueSD7UEE3R+W0iH2OiTo3HCkjuEpW23CFO5S9ITkWZLdGnSIgqEQFXYL7tZilGt/RYvn+QDwyPsH+gT6tLqX9PkmCxxSPn7iXXRbJMZlMJpPJZDKZTFdK9iPHZDKZTCaTyWQyXSm9FLha7ZyrnzNU/UxCk7uZhN53IgkvxptsyHOtEC9e5NK0vEKSrkr/rjvPJVx4MpHydCwhvN4bl8HPHVyxqh45IalfdgJkooNEdagDnc+INhAri+dhdzWHZIt1odtGZ7dsCAcvRjy3w45a6jrAXra+K6jH+E04qCAkW3QROkdUeImItHIKQxVYzwKYFNE157T7Gfuboefd70u9J3ApmwF14r0ToFXKUY3CmDMJKdtJd7kliJf5kRyfg5pc7oSdm5xz7uRXJcRMJIxuXGWbdjzhajO5ZwcuZ3SFq5AMksfTkJCOap3HgjdNriPsjmg55+r0hkbG2Abl5kd8gC47+Hw5QLgdbcjGcEcCckWsZHEANAbJKaNluPOWSH643JFjkoX0Y+/diTonOpE9o7whE78EAkI0KN8F0qLwEXaMiBiPxyHJouF44HDVrmTZZYLJHM5n7PfsEbAFJNd7di1xPCO6RCfBssGNi4ieA5JBd6y6D7RjB85cQGYSOGUxIWcFh7sKj7q6xTKwqo39j33DuhIbUjgQ+owoZ9HlngZ0C+uNfVQDDSm6soEkTFrJ5H9bnfDnqDNxRed0EleuE7+UviyuCT5D/Gx6UxY494lkCESrjzXWJdbJfeXjExWWHemXeEGEUo4pOhvzC25xbCdRsfmh7FfZCNcF9sV7qLmBepdo2woOkW2ikrhvDufIAlgiUVyubSb9jSL9fPdc60i+ujoCvoo9sz3CQwrIFF0bE7SNiCPnrUpQnIZxPTpSKhdJoJU1vid8CCUkBkj8rE0nReD4wHp5LWK9RFnrKpzQVmGtQOsq37T+HT5v2uf0uLEPmMhYC5gux4QOeXCYdJgbPCbiMRXaj+8xdMJsFPakuqo+4sCXTxbJMZlMJpPJZDKZTFdK9iPHZDKZTCaTyWQyXSm9FLiad85Fz5Gj8UpCmCex4AkPCwnf3UrEeSICFzGDfdcb+yfr8qKU0Pkk18jMIJUQ+6At5VYC7CWSe0xqok7hhG87fyQhyNGnmAAUYUcmA+3hGGBpvkLYleYZJw1h0ViH/PMtIBM9hqFxTgaXMoSec7gDXb6BsPAdOB8hEhrnYVQjXvJz1hUhedYZRAqM71RiUOecWyFZK5NpchzGt4EMICpcMx8dunKF/nIZnXxwY6Bxqv2LsNMYcbCij/EAG8gkqVvv6lBw+0J6Z7ELHLMi0oWqkppEPeKNPHLrc3E83cjYL8ShiL1Mb0uYmy5qTJyn6rZJJGBudIG+FV2G5HGBJPw3mdaZTKz0KZzDdgTbSCdIQucF1yqBq1Wt8PWJoSy3gAARFY1144gkRJdIKvgYKNYWkBm4/ylnt7FwjeUt2QOZ6LPJdSrfln0vheNh1aerXRjJiuB8Vu4gWeQfP3SUvxCUrX0peJNyDQLqsejJtVwD9cCkh3UPyBlQv0glScREB0oRjaTvFOpDNAqfl32NjqQXSAwIp6XqmuCHFTAhYkzcG7h+WhdhnFDhp0hWm8AdyZd8loTbUxwIPth+DIQy37gvMTu4QlW7sjnObwKDw/6ucCp0GRPRNiUuJgLFdnpgSMSK4jSMBiVIqrr6lKyLTWWYJxEQdrqhsm0Kg0PiTiJ9yRlYY87tPSBju2GHuCZxPLMJ1vZsFTr82TlAsYjlEekiQsk5wHHm/sGElBG+i6Rj9p0UOYY6GaoUo5xPeNQffUfU0TnnImCTdBErjmTv5nOZSBcRLd6be4ZKdApkNUZyZDq2RTM4TxIJbYe/QkdngivXTIC7IToS6jWJhJ5AhImiqcStTACKcaiH8jwssVdHcEjzh2Dq4cBXI6nsX+SEoRbJMZlMJpPJZDKZTFdK9iPHZDKZTCaTyWQyXSm9FLhaVXm3mD+Lex8OJAy9BXe1w2T0ofOccy4HV/MnU0lGlwAxK3P5LXexAG7mdHLQ8QJJ5RC3PuxJneIjue75WJCMxVDOHb2O5IGP5N6zGwgpsxoTGYZkFnYoIq6WA3uqWrjmhgMZ0arkEgnMiM0BcVvt8PywcxjdtRgKVwlGO0jCeYxEoki2WdGJCPdi0spcyAvtAuc20IUpMBtEfz3QKg8Uh65tNVZAOsYxtxHCx81iYIbTWxLm7nok9kObV8CbWM8ExEM6CaOEzmlnonQGFAdz2iPR64us6Ca0kIkx6yO50HwflUI7W6NwvRd7wK1wr/a55pPoTqZdcIiGCOq1eh2OM7xUQzI/Ykyr6zKZ0ktBD+K2jOHiAAn/6DQHnGMRST0VijjQGGzMZGs/eLAuq1mMhHwsRx04iiFZZ1MfpY8FjSDOwcSLCg1DJSrgJkRPlDMdkKzk2iFb4Oop3MzgfraEg2Gch5HYbBzGzJiEMXp6vi77AVyjkPDOE9sYI9kgMBE6qkXnQC9wbkpEZlNwTvLEb9A3TM6YEF/lPkRXOCA6NfrFReHEtUzQShGliZgUcIikp219TSb3jJBA1ymszwXLrRFRSTw/tqWPs0vgl3jGVKAYmQA4mSGZIxBK5SxFhzusW4VrOu20FWEPSNE2zkOVBLfBXUwjjijC/bBKBNejGxn3Ku6HyQgIbQQsM+HejufQhrOhg6seHb+YmJuIo8uBNBHjorseES26dHFuo97zW7KvJjOgTpzPeZhLZSJeP99oG8aKjn9Rw7Ui4MglXBUjYl8z9nf4+kpcF6wrEu5GzHDOZKs41y82WPGyYUyVExrayb3HYb6NsL6ZrDOlE1rYtbCa4UudvNmhrlPnzajkXyRZJMdkMplMJpPJZDJdKdmPHJPJZDKZTCaTyXSl9FLgaq70rhw9C+/NdyXMNysESeh5CZ01JQP9bPfRuvyt07vr8qJAAqlS/65bFHK/rXbYgqoCi3VyKQhEPoO1DBJxdp5IeX69AScDSlYDOavg2KQwJNanCVFLN0K5S4a95WMmVXRw6aIbF8vZGI5NSGzXe9LgmgJigmH7hmFTKB4TcrL9TFTpnHZ/2/mBtLtow+1mH3V9LMfkPeJXqAeG02N8WCauVqLebPPWjyQs3nsk/3D2OTiaoOtKYhsbndR7T/AbIkCNIsfXkFg2WoYHIqEDV4JwOw5vX0r7iaQsdqXz6JBHB7JopdeecqcjKgcESCU6BOoRNSRrnb6J5InAJ5gYsUQ/Zhey5tuncLEBqjK+i6R1cEoihkQEzjmNQJQXFy6k5Po1ue6u4EPEGaILQRKyhiRsTKq33KerkxzTnkln0x2qRALQCP2ePpH75kD9ihvaySoeIpEc6kdEhw55rXPp7whYkQNmRbcvpVPpR39TkuR6JO3jrCh3wUZhPKNjua8v4C63mWyRrkbbMqnpqKRwJSBx2TCcuJLYDxPAphdAKDFWRAUV9sLEg3RlGgnvWyPB6uZqaUKx/ArzZNrQBuzLxLtiLIF0LH23gjtfNkESUuCq8TnG8ED6ukRSTe5hCZ5h6fnG2gNaVME5i/NTJRYlfog5kIzD3weIkHFeqa0Xl6SzIesdXQKDRzt9FkYj67luZzSE2yDaRuc0hT11ZL75CZCrHrNXY85vwVWR7m3ACeMV+nQWdg7kuXROVAk/uxqnZEJgta9gfsb8roQ9wG3L/q4wOOJgmCPsF6pewl0Mbn7xAdzIiM0uGtzbFnrcOI4+k/PrFVwOp9KeaCD7r0efqTYwWSkwtroIjwmxNIWuXUFZJMdkMplMJpPJZDJdKdmPHJPJZDKZTCaTyXSl9HLgarV30XO0ajyXsOVpImHXcSUhxW4UDiOfl4In9DNgEYgjL7xu8hRI3MlQ7pemcC+KEdqmkVPM5Gzy+ey2HJ9dIMEmCQMmc1zA1WjekGwQjmgeVlY1k7RtJEukm1e8JK8k55QNCBnFMPzWfenX7Gk42RzDtk//+Tvha6L9dPgilpbBvWv7XT3mD39N5sPl63CWQXS2cyLnj+/AWYeJQdGvZQt9HM5rttGG8Ofju1I3uiypJKycC6TnxCjr+T+Gz2cCUZ1kFu1pAzME3reZuFKOl0p1T6Qj855gEUwY2jkBuoaxWm2HE5VynJ3TuBeTchJpIq5Bp7lVX8qLHbjp4JrZpSBj2SmSjeL4sitta90/W5dV0rW7cBQjtkN87lQSrTmnXceo5N4r6/LsTUGuVltAxeCi1/2+1MnDacwBpahv3wjeq3Mq7Y+ncBkC9lTvwwUNSRiJ4SRA8RSq45zzY2lnvQMHOzhWMSknsRQ60tEhjTiMh9OaQ4I9hb0Q20A5Hksdqi6wkH1JWkqnJKKRzjkXn6E/mGCwCuM3vofzD+C0hblNB0e1H8CNii56dLD0xG3mDdl9U2CmcJOqe7ptyTHmqw/vB76S8aTbnkoayySZ4/CziG2gG1s6AhONfY4OWhHd6JAANzpBssXOhhsdEKKaxnGY3ykRKjhwcX4SdfJwqnN0skKCWjVWKKfnSCrLezHBJDFBJi4+xQNhqcfcM7Fu/QIP8hV59HAy2ToPO43FZ5Pg5xld2sZc5w0IGJJKMqmm29YJM/kdgtgY+49rnUhYcgwnXuJ3OIaJPn2Dy2V5qfd0aYP0hdq35hjnDi1c9diwParcgJapRJzEHXGPmglKz/CcaFDd8N3lKuonjuR47+947/937/2feu+/473/959/vue9//ve+x88/29zOmKTyWQymUwmk8lk+hnrp8HVCufc36zr+rPOuV92zv273vvPOuf+lnPuG3Vdv+Gc+8bz/zeZTCaTyWQymUymPxf9xLhaXdePnXOPn5fH3vvvOuduOef+qnPun3l+2N92zv2ec+4//NjrPU8YNjsR5Oz+SEKeT+6KU8z1RMKIEXiYm6mEdodLOXe2lPD1MtdNHrQaHNWAU5WV/Bbc2xLXi3MndV0tJcydjphgT65JdxhiL3UPSI7CYcLoWussjB5VCM0/+zcmoXPhMpxyohXxNjmGTkHT63ATKaT9DM+7iggYnXhwX2JMwOoO/0hCvmeflzE8/pIOfxd94g34hyiMg7Qu5PjpLRc8pv+e/M/oEPWmoYlv+LsAmk8kg+5DxP6a4ALibc45FwG5Wb0u+IhCXRqqRCQwanD7yR7KWlrdhBsT3aiIlWGOTW7C1QuImnJyg+FMsuHqlgFXYWJE/+5DKe9JILj20v5sLBNodgQ3NjjVMRlmNhKsKl0pq8F1sfjRfan3F96S4+H4t8K8U+51A6AjzrkaSTxjzkk46xRdqfdyGwgq1nQH6E0ExKS+Kc5slELo4HxEZMxdCs6RAM+p4fBUteW+FZC+5ATohHPKIYlJKR1QweIBxvOXPi9lrI1oKPtqtQ1XNGIYwEfqE2B8cBqLb12Xc4HSRcdyvLsu+GEFBynlOOacc3SzSulYBbwHSf/0Xsx5iLWEpJecF0xamNJdD4hVDee0Jse+eAvJYEcY51s31XH1DGgNE4VijhEbU88umotNgAMBM6SSCRzlBjLH4gWOR9uYnDQB9pYMgUMRYdpwxaM7Ybkr+wf30ugS12pwzkswJ2smcMSDTCFw2Cfo7Mg9hk6FGXE17E+cU9QmzuQ5PzGG0QxjgjlZPH4q9bsridPLPr6kYI3FxOGYSPWpZI+M9veCdW1C42qiskTGNhKdcnw5j2MmNwU2SJy04t7Q5CKGNVxNgOK9APZX0R2NqKzC/hqc3FwzlvZC4j3gxqbwOJPSz8R4wHt/zzn3JefcN51z157/AHLOuSfOufDT2GQymUwmk8lkMpk+Af3UP3K8933n3O845/5GXdcj/lv97G2o4E9j7/1f895/23v/7XISfqnNZDKZTCaTyWQymX5c/VTuat771D37gfNf13X93z3/+Kn3/kZd14+99zecc8ehc+u6/m3n3G8751zr1du1GzwL4bW6EoLb6klY8LKU0OlODAwBNhFvLyRo1E0RCsV925l2D2GiT0YqlwsJGT9aCXrCa5VIAOpbCMlvwSnqFMnmkMCQrmZ+jBD5NIx38efocj/sCFZnzZYZyRCOPbwsXNcSEC3EXuaHUr/eE+m/+RFQGiBNxLWI0NVhmkElKj39hXDYNd7ID6gM9hS6BbRoQHc6nh3mxiZ3gWcApSD2R1e8pAHvI4pHN7KSVAimIcd58H++zYq65Rfu4VoNieeAHMZIdqv6CHWa3MK4HQm6k43gCgiUzAMH61ww4aNUYnIbSfv4pxPUk9d3TieJLIBM+M+9ui6n9wWN4P3orsaklwqDxPXLPUFS/FLX4wMRUSN6sxqE/xZE3Gp1baD+jclHO0Nghkh6GS8x3zaRmw/uwU2JeEYBx8eU81A+X20DpQLql56I+45/JP1LjKuE8xkTWPoxku45pzCZGokXq/clMTORkWgOXOuJIFd0yGJSzXJP9oPW+GBdJgLn0nC/UCUch+IDQWyIj6kEhE47TXleFyiKQkYckvY1bMU1nNNi9AWd8yL0F1EqYmUqQSBwKKI9PoXL2IYzF+eScgJscZOSooczYvqUjl/S/hwuhPEUybuXSMbdgHtW53LNaCC4YoLkw2rOcy1sJskFplnTcRRrj8gQ17pCFpl8lXZU2OCYhLJ9Lt9RYmB8rF+Z8mGF5woSOHIdeqJrtX42KvRrIvXwtwXZVC5nFeYw6sSkvAqlouseE8YSATs5lWPg7BdhjanFgOSXnKuqrz9CnMcRUF43AE4KF7UK/fKhNbC+EK1Ow/uHqkO+Cpb/vKXmsAUKGvXTuKt559x/6Zz7bl3X/zn+6Xedc7/1vPxbzrn/4SevnslkMplMJpPJZDL9ePppIjm/6pz7t5xzf+K9/8fPP/uPnHP/qXPuv/Xe/9vOufvOuX/1p6uiyWQymUwmk8lkMr24fhp3td93zXkSv/5jXczXLkqfhTTv7gvC8Epfym+0nqzLMRiYElVogwF6MtL4yAdKIh3a3u9IqF5FZ4Gi1VPiEECgWmRjpE57/1TOndLUhm8nVeHrVDkTW8KtBxHVqsvEecCHNnA1IlftU7plyTELIUAUNkWciskJl9vA3oh63ZDPc2Biyz0kZxyGHbjmR2EHMqJ76aap0yp8Lbbt+reEv3v8Kx0cQ0RRbtg+k76fH6HvgQRWbfRxw+xPp8Aq4MzHsnLmwsfnv/GGutbeN4X2LN8UHJOIHxMMJrMwfkhUkBhf5yychK9oMyGhnLsEutU9fQGXGLSzzHTgOMrCmAj7o/y0oBclEsW2hlgzwE0K0BnzQ8EZ0o6s4exccIb4UtY/k0GWcFlSyWrpfPYYDk2xblsFzKQaADOh0xCupWWCe2YAACAASURBVJz3ODewKXlgWSoxJJAhv4s27DJRoZSzLaAdqA9dlooBHK4mdATT7azhwqb/ATgM0SrgPcSHiJwRMWL7KyQb9U+BXO4ITqycjJi0kejWI3GZqoj8FBuJELE3JMBsmGxSJSR88FjKn91flzl/mEw1fgBUkNgfEaOioT2Yb3SpUmpw9dusd4Xzk324kQFjIn5G1EklGVUZlIFQAt1q0zluievjvvUpcMptcYujY1f9UYkkkShVJbilU98dScRLNzKFbqkEqki8iKS0TB7JpLkqMWYTogpEje1RCSanYYc35zaQPSbxxJyhqyJBLCKK8VDmXskx5NpgUk0ibdzPgG6qedvkWEZMrNpAmYlTotlcrw6uikQ21bxvQtSo6uMRtZdeL5IM9v+n+pm4q5lMJpPJZDKZTCbTyyL7kWMymUwmk8lkMpmulH4qd7Wflbx3Loo/HDKk8xkRNSYA5a+0cSlh/ru7grpNc8ELVqW2+CqAEBUr4DPAxrIDCflmmYRqF3M4heRE1OBqlCBEDuzJAVHzM7kvE3IyAajLcB12lSd6pX+zZnB2K0lANCSSJCZDRKn/roTMJ3fhfLOQg9IZMRw5d/KG9FcuBIfCmKIOEIFzZk+Va77yn/2hox799a+sy3QtIxJIRG3vu3KP4atwh2HSPtyaCFgNBKwCMkWcLpnJjYn0EZ+LQYyooUJ/bbrIrW7tBOtK17qyDZcekpV9rB/mNUTbchIZu0C04K5XoV9Yh5mHKyDQuBVJUToo4VznNpJBwsFLlX8kLl3zX//0urzYAwZI0yCMD9G61kWDm9JDwWB95xVpww0kiSQ+hv4tmSRzogeu/VA6nPhV2cHewDmA9UY8lO5qygWLmBQchPLPIhkmRGe68pB4F8YA2I7rSz3n12TzqFr6+h6uiuk50N8k/GgpbgBLQr8QlWRd6RZXbslCj998Ta7DhIc9YGlAuuJrso6iM2BCcHVTTlzOuegcxyHZa77fwzkHLiS669Ftki6CxVuSlTg7QTLUjFgixpmJW48whkCGuMLiY8GqyqMdR3FOJ4/lWZnflPFR90YfTz8P9y4ShxMiSjJ/xvdkk1luEZWVMe9gbOm6mAObjMrwvIhnG0ky0X9FT85f7so9FntS8e5T4JjAyZjosWrLdfJ74hxWdoBMoX7ZJXBKujzi+PhTd+Rz1JPP9Ggga49OgM4556dA0d6UaxGJ5LWSV++uy0w4OvmMtKezK/O8YMJMOB7y+hXcArn+8x6cADmPbsuXAJW0tKUToPqltJVYI7GsYl8wRXUPrGPWVSVEVjfjQwrjzwSjSBrMcabTYuO5cAt0TuOI+aH8W/auONXVwFcrzAGPRNZqnIEyVn2sJdRVoc/EI9HXNRFAuHASy/REd+n+V2lkrnwsz9afKgHqTymL5JhMJpPJZDKZTKYrJfuRYzKZTCaTyWQyma6UXhJcrXat1rNw1pOxsC7DpYTpzpDMbyeW0D4xtldaZ+vytytBT2rE5jcNsSLYGrWRiDRP4HCCKFyJZIsMznkkoSSilF4CpegSLcPJAwkXFh6OSMDVmLSz8wROZv0GHM5ppKkrxj8KXauAejGhJd3VVns4Aeq+K8m84rmMz2pbptXwaxJGHmxLuDiGy12MvjsbAjfBT/DLf+VL6t5Ey3bepiuY9NNiT8rD14CokVgkitSQrFQdgzL7nq5mdFE7+MNLfC5ze7EDlBBjULT0DC26TFQWrmsFlLEEftaCo142lGO6x+H+KnHvre8N1+XRHcEZiIZ1zoASAVtg+J9tYzJT55yLt2RM0pEsmuh9may+A1cjJsTdxj2I3JEIhQkS56TvA1X5rGBP0RSJ5oDrLXaBtII8GN2TxvUfbLQNWJpykQKi4bn4cDpxx/a2rL0kBwcIxyYmjyRus9rCmKDN7QfARIB2VF0gNsAciDZU6cbfxZgo9widc/TZdZFOfRXmWPd9IFrAgZJTSWxXEdHivYFP0cmqQr+XHRwfYTzGafj4rnaK88BECmBG7IMYiSd5zKonx3SI3KEvchwTrTDOQyQzJN5IDAfXrFoYQzgWOiBqyz0yvRsuX0CIVlt4/gA5S4dALrFncJ007VV0JOQzsJzgc/SdQ7lAMmVif9yHnTYdc9UAGHmbyZjD7pZFL+wkuQJKFK3CyJlKSgwcV2FCQBe5tqtYECiuVSbWTmZAgrsa6Upa7CfgdLwW+qwD1KkG0jffZYJaPH9Zb1yzfQE0DF262hEnPI55OgUyBnTPoc7VhvNmvMQ8RH8QZeWcifG6APcb9mXUodskcWyuQ3wvmcjzQCUo7nKN4PpYk1yfdK3cvB/nZLIj36E4PnT6dMAa+cxlHxH3prj3JHOMIRI/E0vNiErzOUFHPSY3Hmsnu2gs66e8HLqflyySYzKZTCaTyWQyma6U7EeOyWQymUwmk8lkulJ6KXC1qvJrp7Lbh+L0cgfJQHuRhMJyWBzRb+QPhp9al/spElkhtr0oNHpFB7fZCC4WSACawF1tPoZrCJA2D2yDblfamok3BrrGe42ZPBKHI1Kdb4XvVW0kAyVKMkGf8br8mUsEimFoOtkUh3Kdy8/DiQfhXyZ2JBbQbQkmE3uN1n0gtj9eyMnjVzQOVHbk/NE9ujTJMb3H0tDdbwoC9eg3xdWIzmGcGsv9cKJXJl+NJ8AFOky2KuWTrwoyko0Zdnc4XsqN6XWdxsyIxNUIHxfdMDbJ+6mxRX8RYZnfQeJFDpVCxuR/mGCUfcrx2Bzy7BKuRmNBdOjeUrz/QI5Z3V6XkzkRKqwlzGGiChRD8is4LrUnUof2Y0GmkpsyhqsdONPBvS4bbtjiQct9OCQB1+g8FjeiBRNdAhNgEsZyR/AWhSQgEWB2DsclohDAE6puOIEn3cjocBWtyB7pPlXOZq3w38xWgwYOlAkjgWj5kfR9RFRuK5x4kskj4wvBIuJDwSyZbNRP4ALXkb7wG21TfdwDWjlBAsgL6e/Vp8XxjOutdS5juDjIgscQxVNoCDCZaIH18licmOIUSBsSidZoWzJtdjdi4tf2qYzD9LbMt3gZRoCIryoHP2BiCZCpFLlgFdI0hgtnn/glHdWkw5Kx9IXfSJJZYf/gWNVAFtsXwPXwvIob0MJ4jjVG6q2Qus6O8HWK85OuVuwvfk1gAmQ6eMIVk/VxzrlkGsbvSmJzNCaLwuuTTpqsUwlcjXss+4hjvkBiYM4RtSdPOenxrNpMdNqwdxMVJfblq9DRG05oFO6tklTjcEW10/2R7xrQyI5ObnRBG+nKlX2Njq5vDURNOV3iusTDOG+bcDWOZ4TrqH5he+g0R+e4mPg132VAO+caV6vyn5+jGmWRHJPJZDKZTCaTyXSlZD9yTCaTyWQymUwm05XSS4Gree9ckj4LsU1XEvI8XYjbxA9XR+vyrVQwtgjxwr+09c66/DuPv7wuLxFSTiIdOmzHElLb2ZNYOt1O6Ki2uy8oxXwJFxdk2GR4dvuHcr8z4C1VHka6SCsxGWgqeenc4ojJuNCeTVwtD2NcxKnqOIw3MYQdq0SHEmrd+qH0xfANuKsh4Vtd0lGOIfwGXA0JNpnAkmXnnCvkdsrBSzmn4RYXX7sh1wJ+o7AS1HVxnTwZrpnCje8ECWMv0V/sLob/gSGkc2BvF0wkqv/u0OQIRNxi520ZuCe/TGTAfaxa5zIx2g/FLW9xi0454XOJkqjh5DRswCiccxpXGAlCROTGf+lz63LeCfeFaifLqFPniUwguuYUW7QaRHvysEONElGA4Uz/U6cJCcMaYDJMlegQ9ZgBAaDTFhOavnozeM3WGdoM7I1JBGtgXDEwrvw1Sfio1sipTqjHetDZi/er7sE1iBgPcIsIaEMNdK/cFkQtwj7kF9IvfheJMZHMLj+U6zCRYow2RxeysZYd4G3OufhE0LfWUtCtJVzkYjgfZZfShuWWjP/shmxQCdZ9OgFyRbyH2Aqc+VRyTibzQ51Vvyylv9KZduYi+si1y2SDdEMk6qWc91AmGqYwJiTeTCdElHBfoDQpk3ti7iVwb2I93SbqxOSbW2F0TamBLk/PsX6G8t0ggmNqvCtzu+wAJ6WLGC7augRyt5AOaDPJJdze0iF5Yr1/Kpc/XCsFxka3MM6f1a7se3xepZfA/L3MWwJWCY6pMP/bJ8DRgfcx0SnxTr8iy9zMaZdIUJoA2awSYMCcA17an14Cg52ivJC6RteRoJjfxXC8g8tjPMW6Ja7FpKXH4vQbtfSzgAmElasccTLs0VGNZxTq1GQGy2ePqt8E+z6SG/sLee471LVmHXqyD5cPJEF3jYS58bZ8Z3DOuWo6dS+DLJJjMplMJpPJZDKZrpTsR47JZDKZTCaTyWS6UnopcLW69m45fxbS7LaXwWPorrZSWRul/P/AXW2QSlgvQrB1WTY3eTyFM81ThHPnSIr1poT2qoqxzXCisss3mCws7NjlV2FHsRhkCB3V6NASz3D9lf7N2jqHww0jh0SIgFDt/pn08eJAQsyXnxHsY/8f/GhdrnclPFl05BgmfHNw5aGTnUeZCVkrDA+j8+1zjSQsDtiv6Bs62RAVA61B9C3CVFoyqpyQ4UARiUtXGBMmA83GTBCI+gCTo/tMBiSh+77m8hhuPv+yJO1bIkHl8FWEmDGvOG+7J1Kn9jFwAyBGy5synqN7cDiTyLZLMCfZvymd44BDqsS4Mz2GxJVqhMPp0lJ1BA1qcv/rPQG+iWOIBEZzqQgTTxKrIN5Td5AgjeOPtccxrHratdFXwFJw7/Sx7B/LO+LappymgF7Uj57KdYCTVUeCWBC5Iw7CBcSkkvFQMNN6Gwk8kWCUjlUpnaymG/PzUnCvGO5srF+k8FCsgUW474kfeTo2jRvwByBqFVBHhfM0OQjhvskpmGCncQ23JYuATnqcw+lI5i2TGRI/7H5PXNFYj+JI1h6T0qq6Au+hA2Hdx9ohkjKbBo93zrkEDnbcx4mu1F+U+cnky933kDQXGCCdn3wDfpkspNw6AxrFzZ6PSSbPJbpZhdEe55yLgNCs4GxIdzZigMrNjsjlJfpoBsyqLXuDwlppuoW5x/VPXDOeNbj0HciaJKK36Y5Gly8+J1bbgm+qZJhcq0gynMLRNHkEp8KpoHjcP+gcxnLZAya1wJ7HMRzJnKznmKtTjftGB/Ksi+gweCyvKrTnwFSJq9ElcozrwoWyeCL7akzEs83M3Hh4Yc/0+Nx3kax6Kdcph0iUTpzWORef4t8ugB3jHJ4f7cpe6irMN7pBos1RC88u7BnlCNccy/iXE4xJEUY6/WX2scf8PBN+fpQskmMymUwmk8lkMpmulOxHjslkMplMJpPJZLpSsh85JpPJZDKZTCaT6UrppXgnh8pLvCOA92dWNawKa+EMI/ibvtUTzvJ/ffKZdTmGbXQ/0+/88PwsE9ZwsS8caT6DzSoyzdNausb7Oa2xlPvvCzd5/vmwTWLdljrkyBxftsF7D8nygrVv4b2ADdfaCu+VLOGO2uDe7M4+j+zv53LQ1jvCzuavirXs6FPCo5K7bot7ohtFDTeDYlQo38L7CMi47Ev9e7zokvmWz+Ee6WaH8j+9J8Kyti7AkeMey210YBR+J4ed1zpH/YAE00L74I+l7y4+Lf21wPsPix2w7JV+t4PvsZBnXzBDdgfZm4EU5/KalJvDTtzVYMqrjx8fLD1X4d0mvkfCd36ykayjOd7t2sxKXYHh5/sGDvx7QpvuG3jfAu+u9d8T7po2w7Q3jmEVzPd/aGNdncjE9XjPI50LH17C4rwF2/BNy+lyi9w+3h/AOyC0fVXjQLYfNpzRDlh7vC8Qg9Wvwc6r9wKGeJcGTHUF3r0GU893mOIx3lW52OCu8VJbNQa33sc7LBewXwWfH51jTNAvHuOjMnzD3tQzmzatyPHeUoZ3M/iuioOFdL0l7x3QfvlZZbnIMNYXaCesaOsteZdi8K7cL33vRC7z9Fjqeu+OlGk/+zT83o7vYN7i/QLP/QyfU5vvPDhmS58hFcLFpRyC9zkS7O/pA1kndQZb8yr8rkqykHonU1jOwmacFvIV388rG6zcL/Hu0NG++ieOCd/DUWu9Fbbp5rbP9/L4jl35RMaQay9CXgOubaZgiPgOIN5P4ZyMB3gGoM7xqV57fKfF4b2PdCT3SPGuT3Uu77Ok6NdkT86tMP4Ox0e35LnPPcAP0Ga+M0ZL+KGs82oFa3nubT28G+h02/z7mBv8nO+G4N60V+fTrd5c389Vnsp6476v6sP0Alw7De1R1z871x9s/v/HqDw5+fiDfkyVy3BfNKnOVx9/0Esqi+SYTCaTyWQymUymKyX7kWMymUwmk8lkMpmulF4KXM372iXPUTGSQSXQMNoPUxW8ZH8wO1qXWzGyTwN7Gy01DnS9J2HvfCXH+XNY5gEnW4wRwk6AoUREwxDabgEtyxA8pUXxEiHvJbA0IDn5NmxIH8rni0O5zGYXFbtSj8EPgZVg1Jf7tHd1QZVtObf97belrq8IEshrsuxK4HdA+iJgGGUdbjOtRJc7GquqEyIqzPAsxxz8Uwlt065zuSdjWHSBivEWtPjG2EawAV8cyeedJ1LvrfeA3KE5RD7yHtqfSvnG7wFVcc6dfQUoBrsG2BhxMvYL7W0pWjFzzGktXbYEGZldh9Uzzi2AUxJtmdyUtZMsmOFdoyfRsgFFgS2nAw7C82k5WxMrgh1sRDtd2GQqq2jgBtGh9HUNfIQ22MxYvv1dQTs2sZrkAngQ7U2JZeXE1VywHNM+FGgQMab4HBmric/ATtkdCyJRXgiGkiRAt2DXzHvVsMeOio0xI5YyBQ50/6FUdQ9IGLJ6E62qZ9JfRN2UTSqQoRpYmqdtNJHDm9dQT2BcXBe0MU/147B+KphIlKI/kC28Alrm8Xn6VJCe8hR4F+1nl8Cuh9Ie2r56ZiAnbkN8JGWWcsw71LnaxNVgRRsD0VFIJOY9MTOF0MHWmxnsud46PdlvowmyzsPSuOL6HGGeX5Ps8LSWr7mmPmJOpudo9/IFkBviSg2oHK2Cee8MmFj2nqy3+kzWm7I7J3o1ELY4OcH3ELat2nhAk82GJXhKK3j0cYk1VmEuJVjrZQNypSAu9H1x/4F8Xsvn8Z7sWxVw1yb74Wq2YSGNsjoHY0IMri6wvuuPR7CViLc11E8dTgLyBY43/XxlkRyTyWQymUwmk8l0pWQ/ckwmk8lkMplMJtOV0kuBq9W1c9XaVU3CjnRF60ZhNwi6o73RFdeTxzPJ4kxHtW6iQ9ZZJOHPrYGETGepfL64QHbgDjLIFnA1ApbVeySfb/9IrvPkV8LoFtG1JvSodSbHz27ATatLxxgdzvao02KftjHBosthcBI1RPar18URqHMqfXH5KUEm6C4WAcWrwW4RP/Rkukj0zeXz9qkOQQ8/jVOicJm4W/2tP1mXW1/9hXU5BxqzkimjsDQmHY9joIhA2nhQdikh7KInA7rYBX5JlzKM+aOvC57hnHPZGA5Hc6KFDShaw1yqwBv04EZWdeSgqk2XtwaMEU0mokZ3vSX6cTVo/jsKXYCq94A3MZv7W/fkWtvh7apsE1eUc7vvouJzYDI94FADmfTMRk6MKV5hLmBNMau7n8G9zDlXz8JuZnSCYqZ1opIVHP8UMoM2KBctOo3dEGS32JJ9K6kke30E1Kl4IP0eI8u425FBjJDtu97AWYgNKeQErmvRAhgTUSe0R2EozC6fA3UDouaAqNEtLyqkncwQzwznyZ3bck3gQBUxOedctCvXoqOWvxT8ihhYjL4od2VeRefA6XD9GhiPTwXvqYElVcgirlA3YIYRngEcj3oBV7wNeSBuFVAnD2Qvmcu1opx4NRyl6Ki34IYDxPcdmWOcFyozPbFJrlXggBU+51yLN7AyOkfFXCc+vGdSxOaIsjapPBakMQP2VGK+1Q0OlhHxUzBQxPIqOHF5ZLJ3zqn+K08EnY2rF8CvgCsS8WwSHeuIvfE6VAlnthfCxzaOaXIqM5l+XFkkx2QymUwmk8lkMl0p2Y8ck8lkMplMJpPJdKX0UuBqrvauKp6FkkcXggyMziTkP7uJUC3RNdg9/Y+PPrcuH3Yk3DktJDS/6a52b0tCtctcwseLkdwvPQXS02fSqXD4uwD1cPY5uKmktE1CeLYAwkFHGyT9XMFdjS5tqgobUeHWqdw7Q44volL5IBxKJqLU+Y7gBqvXb6zL528hyd9KrjN4X06evE7sCwgDykwGyrqVbfmcCJxzm1gWysC4Jnekfq3f/Oq6PD+Q8SyZ/7OBbFD1htNahXHjOIzuykUP/lCcr4pfwM1I6OFPDcTTnHNu/49k4C4+L85HrDdRtBrziv2X95H09FDWQHYJZKwlFVn1gJJw2oIw0o5gdHWTz0GDunSqccp4LOs4IipFF613n6zLxVuvy+doc4KceKxHdIG+B/7gX7m5LpdIvBf/SO7lDgUfap3CjQ2IWdVF8sxT4BlOJ7Sk4xkd79L3BMWJrwlCpa5zgCy+7JcRUCegPqsbgpktd2UxZV0kU0a/OIcEg3T1AsZFHMxvIj8T4EoKoZJNkMkz667efz9QtcDEQjnpAEsjeoMknsTSHFAlolfEs+i0Vg/hwHcAztJtYHDMrMt6ENc5gaPWnmC97ggI6jv3pU7bcr/iQK4fD2V8mrAd9kU5GgWP+SjVRLxqOt7JXE/Hco+ESXaZiJTjDPyKSSU9xlBhiUjCSccyhT3SgSzBwwH6qPYzMaRKoNrGvMJ84xymK1jj9YGKEv18EVVMGApEtyIOSNfBDSyPWC/nITFI15DcknoRLK94/8HHHqMv+mM6nJlMn5AskmMymUwmk8lkMpmulOxHjslkMplMJpPJZLpSeklwNefq1bOwajqQsPV2X8LIOa2iHNxTgBL9y7f/ybr8f529sS734Ki232527cgSCT3nfTknR9LPhGgV0Ki6oBuTXDNFJH0JIkfZKdGUJiNuJNfsHAM32gsnzCQW8+x+CGH7cNia2BNxN+JNZ//cvXV5/xs/klv/0qfkXjty/OhVuU66DSSJ1QbfNVsh0V5CdA1udKB/PqQ6XN7+gYx1/ERwovG/KChJgaSc8xtwNVoB12JiUIxbcSj4w8IhoRoSwD75NSTXI21AhzsM29a72kVwcUOwHCYTjXEY55ty7GL+Pt6PyBGwgvY74hRUpeLStdqSScJr0l1t+GqK4+WYhKZjtd5u8t42jpOTkpncZLkDFGs7jAdyDpcLuHr94i35hy9KmfOKSUXTrVfW5exMKp4+AdIFpzG3LWNDNzLnNshRukhNgUch+SbRICbfnb8uqFP7PhAgYDVEUqKV9F2UA8sEilgdCIqXINlkcf99OfeeIH10kas3EiqqBJVITkgnrIhJNuFeVU7CezFRNyb8o/NVTIc89K9y+4JLm0KygCfVDQkfndtA9ph8Ei5XH3K8+qBKI9yjK8fQaauGq1m0LWh2eYH59gKKgF4p7O+jzgFC1oTEcdxdw55RL3E/oG4KUaO7GOZIeSl4X6N4r/wFknluSLmTdfAQQXvqBte2F7tB8/z5sS6zDLvHUptY2YfQ0Q+u9YJz4ANFSETKRMH6IHx/+Bm12WT685BFckwmk8lkMplMJtOVkv3IMZlMJpPJZDKZTFdKLweuFtUu7j1Df2KgQYxyl7CjqvDbjMf8k5FgSNNcMIy8BM7htevHKwMJZ88WSGg5lXJvR7CH5PcEsRm+BZedFE5gO3C1QoLNGu5qERC4umQ2SybSlI+XMFkqW2EnK6JezjlXdyWsnKskhjioavgcKrpyzNN/6TXUg/fGCahTVcn/zJaCNDHSPkdf3/h99COQObbZOY3T0Wmrwjg8/ZqE4VuXGAiKDmE58MAfSuNWO3JQ3odbXAfuQA1/LmifMyOpFBf74cSgZ5/X7lP9hzIoq76ck8zkYj2hjNwSCUeJtGUjOT4bCmaXDAVtWL4qaJROgCnXoXtZsgBaiblQ9PA5CBFiYs45V51JXdMpUNEBkpIy+Sgc/Oi2R+QQ1XY5ErG2hmj/GGuPcwdIV92SfyjaQOmAXtUtYJapxkHjU3CqRLrotLYLR60c/d3gWkd3Nb8l5yrs7aHsZ9FK9qrFYQfHY7FOkUgV2EpER7RW2NXKOZ1YlWgQkTN3TeYVkwrG4FcV9kYBJyNipTaQPJzwsAk9UijdAMlGR3N93LG433kehzEkMqSSdTa4S8W7MiYKn5pLvyQ3r8s14S6mkmQyeSYxJmBFHv2r8EbnnO/LfuiLsMuZ+hNoxUnpg5+XdNqD8xfLTY5lCrlbhhHInwSTIk7oG7DGiMewX5sSaTbdCw5+Py5aV78ArvahhJkvcs6L3Hs+//iDDFEz/QWVRXJMJpPJZDKZTCbTlZL9yDGZTCaTyWQymUxXSi8Hrua8q4pnv7c6fQmdHvbE9aUbSfi3bGCDbrTFlebBZGdd3mnLNbdS7TxCd7bdgeANyzacYnBM/VckeWg6kzB3sZKuzC4F70gRnV8dSlk5dkXAZ4Bb0flp8K58PrkDR7CbCKm3N5KF4R7pEKgTotOrLTlm+x2pRwGHMKJoBRzfEtAgrXO5zvXffWdd/rP/QKzW5vvAJ5hUcwWcsCDq1+yuxsSq0QrXBUnBxJrKRQuoF8vtCznm8k2e68K6pDsW+gVTLMLwTK8zKWT4ksrRyDk3+JNj+bfPywQq2uFrMaFpayR9pBJx4vhiRzq2Qh9Nb0rbSlBCKczFohxzuIFm8HAdTCb63+gKV2VyXHYpnRbPpEFlJogN+5U4JfFQJi7tPcJ6Rh8XQNraj2SxFtvSL/kWFgAQoHiKpH2JHtDirlgp8n5RjgSA57K/VXtIPAknr+RMFmtxCEQtBfYE1C05Fkwu3w47f0WXct96Ios4InoG57hyG4kTb8i+6pxz6bH0WQKErtqSaxVImlrvywClCdY9XduQlLTeFVSQqFtxBDc7IH1+WxLGxkNpW7WL8M7/7QAAIABJREFU9gBFrAv03QPZ259dS+5RD6TeNd3ymCgV2Fy+A1wPc6b6wj2p3xx4G+rhduReRR8JXR/K863awnwZy0SP6RYH1726rZFDP8ZD4JVr62KJ+xVtjg/Q57cEC4/xOfs7R1LavA+HRKCorQtpf+cB5hEcDJmQlW3mGKZPdTLQCv3H3ZQukfMDaWfvCdxakfRUYaNYt3VHzo0fiCMlVe8BFYVDZpXJ3GkfyxhE95+uy/lbkhg4nkl9og2ccnVb1mI8RaJT7B/xGb6AYH7WWN9+jAf5iSCa0aFgplw/0TkcD5H0NWLiYjq9EkXlnGRy1g2nuJrOiMQu6QR3De1/jES82D+U2GbseyoZLNc8Excfy95AVFi59NEVEQ6WHI9nBzKRecOXCyaXPpR+9XPpy/IYc49JsF+/K5/z3ti36vceyanAepObkuy9PJU2E+n8SZIP/zxlkRyTyWQymUwmk8l0pWQ/ckwmk8lkMplMJtOV0kuBq3lfuyR7FkodjSXMf3ku4cXhkYSqia5R//hCwrw5XL3O5nLN0VK7V31qS8Kz47n822opXbO/s8HZPNcSTkt0EZu+AgyB7mXAydJUyiWczwrgakVXyuN7cF1j8+GoFmc6LJpmUo/lPjCEORy4cK3xHbhdocnJnBZX+ByOXcSHzr4uiFr7VE6Yw6WpAkrn4DR3/GXp0xQ56tonGuOaIs8jXeVquPAtdqV8/Q8kxD78NBLvAZNiQtOyDYSw4U8BnSfyDxki1XQd2/qe3He+LwhDDKcwOrzxXOecm70pyADdxSK0eb4n9Tj6h0h6+mm5X9FBqDoGbjGXecgEmMs9OOFhflYJ5iquSewvX2LOY15EGyhe50z+MWE9HgoC4c6lnNwUFKkGDlF0cD/0JTG7Bea/IxnBqX0IzCWcZ88NPwVMKIcjVKJPIHbZPpd2phPOe6A7T2SerI5k38sPZe9a7kgbiHK2LoluwjWrCq/bxWuC0rWeShuIlS33kcASREU20o5T+ZGgG+kpMKNM1vH8htwjx1h1eoJhdL+PRLTXgIPwXnvSF4tDqV86lbmTTFk/IHPAsOaH0k66CA6ONdJVEL+Bqg6TrGIMj4E7o/+Y1JkYsK+kHkwUTAxUJUTOBIFJR0D6gFbSjbCEi169MT8d8EgmB17s4xzgq1ULY9jFvgckNhlg/uzKdZZbcvziAHhPhL1+AhTtmsz/FZIBx3O4XAJ7S3aY0Vr3d/b/sfdmMbelZ37Xs8Y9fvNwxjpVp6pcHjoe2t3uhrjTtAkKiCSEIAQtuGhQpNwhEEIE7rjhgivIVVALBLkACRSJBCESiXQwShpksLvb3U7b5Sq7pjN/4573XiMX59R+fs+utXxsl4fjT8//ptbZtYZ3Wu/a+3t/6/8fw51yW49ZIVg4yjAe+nq9YkCnyja3PP3OQRSPc91ir9ktUgIw2EevrDfHL2sZ+idatu65xU85p8V9vQbHTHhN74Huic7vy2Ptz6DU50TnBJgdMNXFMbDJid6f6aUi1BnwRvZ/PNd+o7NnCBT5B/25vQQeKBiTxCA78SH218+tO6VuxiNty9Ux5kwTmq3bHXx3yfaA7xKzu8u0d9Umgs5/51vNX8FT4LhlCzaawFWz7un2EvXhOGRZOZLChE6i+G4AvK9C/eMRvpjBXbGe2O/JARwTiRCWJ82I509LvpLjcrlcLpfL5XK5rpT8R47L5XK5XC6Xy+W6UnohcLW6CiSfP11uY/Dm/mFzmFveYnf1S7sP19vfPFeeaZAokzVMbIDWqtImCOFyFka6nRV6vfP71l1ID9Blwf4HXPJGgCFcoAKzLkq7K7iATYkGYYnzANgSAinTTouriIhECP3rPWaQIqpAYx6gUZ0xy6qbu9/XtpzCjYtBjUTrArigGceUJfGH5vITcxCxwao83tjp4JCLT+vSqwlQxT7JrLmeRKDqFhwquER7jTB2DnQnYihsF4M2XVqbsu4//OP19uhf++X1NnGLDJjdvb+giM3gEVASIIdEqwK4/RAFiIGGDNT4R5a7HJN02qODWDOuFm9kzg2+o8vWNccDHGTquBl7GLytA+X8lxXjMdgY+pC4Cds7hDNZCIerEM5X05eBjKHdiWgSrRSx90//LUVipc35B843EsAFCLgFsaxsS/GBEghUlepclT7RQkUD4AzXgAYBUStwTuKDFM8pIpIB8asGQLSAcREzqoDoESuiy1AwU6ym2tG2N+gJROQyeaC4ZrWjZaP7VNJrfn4YxzaxuFcM160y0b7Kh8CmEGqcA3XimOE9YDFYYMdZ8zxERKtzCmeuhdYtA+pFt8SNDGzr+Fc1zxNLYLA1QjmJ+NIVcb4NV9E5rcl0k8HHdOqkA9Vqr9O4T4p5tcyBQR/Y/kxQByK1vO/7p3h+MGQTfcJ6crt3Che9gV57iSDm7gXmEtxKBQi1cIXnBLA84mbc3hz/RPHYn7PjZgyy/x7dIFEQ9AlDjVmmDHhsiLYPWuahHO3OOZm4Wr4PZG6jblXSfDyfExxXq33ioQzNhtPcsrm9iIzxGc2AaoNuJrwvtAymbHysLuxrBHzmmOde2Ty/me80KIek7WHUTcfS2ZOucCXmbeMEVzbfI3SXFCBzcglmX0TqIebuafN3+Z+FfCXH5XK5XC6Xy+VyXSn5jxyXy+VyuVwul8t1pfRC4GpSi8gzB6f5FMuOpf4Gu49wpWMkbEZgib72REOQOpEuzV0se43bIiJ3tzVEKsExAVbhuokuW+7d1CW5KcJAyxzL1kcIvMIqYgAELgTeFsNpLYcD24rZWkC9qg5Qg7B5W0SkqpqXdrkdMXMLJA2DFLnkOz/SITN5SetPxGLrntbh5PMYYi2OVXQ9YXkYQlpZ46ONZVgcj3a6+Y+0b6evw9kMy8dcJi+AHozvNiNwAVy9tr8PxISOSFye/+o39diXv6TXQlYggyTzoW2k7K8qomaoRuAD2W7duA+Xlbun7B9tMIbTrYDWEfMgbkN0r3uu/Ty6GzfuQzwnndpl++wlvaeN882FHhQg3LDzSN1bFi8jeHJzbDxTBDJ18I7etwxAXe3rjV4BjRvcA5ICNyleq4c2HTxsdnwUESn3FSGKzuFANkao3t2buj/6hAhD2aErIgIw0XY58JEMrlM8TwBsp8bYSy61wQo4ZYVwIJPCYmzEuIIlg4mbnb0qUhWoZ92Hmxsdi4LmSaP3WCcog38gRDAASlHATYzOZBHKTDxNRCQG4sZyJFPcP0RXgJ7MbjaPGeJgdD4iSmVckEYI2wSWZ4IhEzqQoT476MOVvffMeMAc2LkA0pRqnxDjyTHJGCe4IZE2jmFcmO5vsHiiI5bBk1owoWSi9cy2rLsasc6iBZvqXBL9bK4PEVcTJj3WNloxNBhzcpSxLbRsBi1Nmp8xvLdZHtnEmdBv7Pc6hOsWux1YIp06zfeBC4QDH+nzIGrBq5MJXP4CHegc20SfGapawR2NDmKb1+B8RbFPWAeDfdXNx1Zwf4xWzWhljPmwZl8xeJMZ25n2Ad0cw8zOmRHCe1OMSRPoinkpGNrxvf58zjkQaG2XzqiYGzmXApsOi+bQ6GCGuRRBsrLAl8MevjOM7LsG0RHc5io7//ws5Ss5LpfL5XK5XC6X60rJf+S4XC6Xy+VyuVyuK6UXAlcLikDSJ0+Lkl3H8iLIsh1wLwlsjSKs837l+lvr7T+4eGm9PcSaYjdqdyCbznXpjWGde129dj9BqBECQCcID917U5cFR58gSkS0TMtUFEB9CjiNXWLJm4jaso37siKuRmSATlibrmXr68E5bLUDTGDWvJzPJfZkiv7JULec6+JoFwagKtljMYdN0YyKK9JcqYbjSP/BhrXXMxlUDO5FPL8JG4Wr2wSIVl+N/WR4H7jEn/ucXosuNkHzUnv/xC7rLtH2RNSIu3Fs1CnaEq5zefZ87CM5VVSh7KnTFDGuCGGly30E0HHZHavZ3P8j7ljEFLE8nyHMjBgG3b8YvGfcZ0gVYJWfQZLxBFjWLQR64pR06zH9hvMTT2l1xhGR6B5c5IAPlK+rA+T0jpaP1yBWQ6cl1pkBnclI67Y8wgSK8db7QDE5qvrWd7TMX/6ClhNOaVK235Srm3AwpGshxthyF4gv+wcBe+zzfFt3Sk/gKLYC2gFso+YYI2JhcF2G/rYEB4pIONO2pGsZ1bnQfVjWHPcnxySRTaJIdCok0tR7grl0pPUsDhXXjDCeOQ4ZdLoZVlu0hGwahyiI2PXgIeoM5Miglehb4xaHjjDOUnQRBB5dwi2Q7dKZ6T7JzM6ZlcEjm1HbJQI6iRoX3TYktBm5apNxzmP+L56ffG7xWdoZER/U/q86FulaHOnENLxHbgqbxCDxPCSiRZw0WIFLY44mvily3LIt8BaBeTbUUXM/mzJsjLsKfR1jP+JxBi/l4ehDg8rWRCi13Cyfeb0A+7D/6xhzFRzYIgR1cjtY2O+cRL/a0Fz2Fd0Tid/VSfPX93AJFI0IMVBjomgRkTY4tpm5Hg/H6gJh3VM+HO38Wb71/cby/azlKzkul8vlcrlcLpfrSsl/5LhcLpfL5XK5XK4rpRcCV6sDDbuMT7H89QHWvN/QzRxrp1wIJKJWYmlyukS4WIX0SxF5fVeD+va2FNcJNtPTnulyoQjIHO5qXLYbva4f0yEMeWoS4fxE41bAoQq41YQrLvNjyRfYG7dFRMJU/z3t6zHz61iSJ1oENyp+znC1+IzLvLoPl11Xu9oWW+8DPYA7FJeIiy4cenDO2Li9babZ6abBtXD8w99UR7VkTIwDp6ma8QG2cR2RKyDawG2iZEDMetoWxI2sC5puL/bt3x22PqALil7v5As69ujIVnSYXMoUPmwmzUhKeKbL0AP0z/xAA3DDFqcklq3DYFQE1sXzDcyDS+8kGbHUT/cvjp9sqzmUlJgMHQnNsTuKHrHcdL4q4crEMFwGWJqgxqGdStORDrJ6C3POY51vcjjwrLaIZ+iJe+9ouCUDQ+ev6NguEG6ZoO27T+CUNOegB8KB8Rm9flfLPAWStKXttRmYyfuHLmwsEzEmht0SgzXnAWIRo85EY8ptICMoT31ypvsfqXsfg14j4B8BPpdiwwGoBSvhvRujTOk5XJMmcLhCAYnocEwy2HG5D5QMTkxtf5I0+Aswl+RckZRyaB2UGIDYFrCYDZr7p/uEk4luLg+aHdWIOFsnTNxXQGjppsXrspxETjuJbRgiSqsd4G74akFqPUdIN5913TPMvZiTBA6bZj4kKotrpeONZ9eHdZgSvQIOhOBRg5JtuIwZBLFtbPCZFgGBAqKUA/uqtpWzpGthhABQjuF8l0GSmEsxhxVAzwo4hfHe65xZpIvj2LYNkTvgV0QIa9x7aCPjwkh0ryX4mGM7KPUe4zwXrvA5HA+rbf2eGGw6vOHfdHDk8QFDo4GZ1d1mpzUGg4aYu4ny0uGsxnZ1T1n7EG5pBofL4Si51HOa8NAXVB97JScIgigIgj8MguB/e/bvu0EQfC0IgreDIPifgiBo6RWXy+VyuVwul8vl+snrJ4Gr/fsi8m38+78Qkf+yruvXReRCRP7aT+AaLpfL5XK5XC6Xy/VD6WPhakEQ3BaRvygi/7mI/IdBEAQi8s+LyL/1bJe/LSL/mYj8rR98Iln/3CqOENQ21GU34mcV0IEQzMjLQw1/fG+qSZq7cEfbdFcrcN6sAGKBdd4CLmor7FNiCTdcwPnlHKjPHbiPACeju1qZ04FMzzm4x2V3LXPVxTJvrNtp3O76MqWzGww36EAVtoRuFQbR0fL1nsB9aBtYFvChyUu6fzzT03AZvXvCEDUiDMSQLGJFhMzgVwGXgvVjG8LW7PYzfMgl7xb3LnBV8RzbLS5ixLB4Sgbh0bkmQZlFNpxsiNahqHQNCooW5z3TRMSvgAIASwvg0JICN+FY6J3oPjkwhMUR7qltBhtad6DBfYStAUuLZopWLW4q6rXcJaqg5zFOViQ4TLAf7j1gFUVJOx3dTKbawItD4Jf3mhGmaCNsMYYTllzCdghYweYxTaoQkiloo+4TuAW2BGYSBym/87Z+/sXP6PkRHBdqXqqUgxbcamZDTwME4EXncEHq6fGzV9QJLB0BRZsBE3qiIanBBNgwHC9NIB3aguhJ0IUrHlA84kYsc9WHI9q2hQ7SM+AnmGM4T5Y4nhhcH0gXcSiiSAyhZIClCUKc0zmt2eErWOo+xtQLfVtuOHPx3uie6lilY9XsOgMT9VgGztLBjQGTxLg4V/VOdJ8+nOM4h7GN+JwIjLOU1qfo27qlcOBiSCKfAeY5k/I+5hyLZwzazzhWEQdrmXrTSbOTFxHKvNOSaAzRgVJEJJ3hewP6gdht3TLvE31L4BBGFI/IrQm95RyL6/L8xC/zAXEzfB8wuNkGar9kyCgQKtz3RMWIgCWYe8teSyhnwOc4xmHRXB+ifvw8utS5SmI7DtfK2t3ViBHzPjZOaz08A9iHOK/5RkOEDLiv2QZyVudahpKOcsQbh/Y1j18kfdyVnP9KRP5jUXrxQEQu67r+sBfvicitpgNdLpfL5XK5XC6X66ehH/tHThAEf0lEntR1/Y0f8/i/HgTB14Mg+Ho5nT3/AJfL5XK5XC6Xy+X6IfRxcLUvi8i/EgTBvywiXRHZFpG/KSK7QRDEz1ZzbovI/aaD67r+XRH5XRGRziu362rwbJkMuM1qpMuUKzA9Cda/CWh9++L6enu7A1wEqjbWlPdTXW7spbr8R7TsuKe4yf2RcmM94HTBlm6/8WtPcD0gJrAL20kUhXh3crDejg4UuQt/CUgBMLv7Uy0Dw0l7sV0WvdEbr7ffSrH0/Ia2QT/Rz5eFLu1OVsA4gOux/R5MsIzMQKmaljuow0PtQ2ILq73mIDwiafXGSGWwKPE74lqLY9TzcfMSPjGUR78OFHGHIVrAGaYI2NxBMCgQvemd5qX9qkPcpDmkrhzb8fngzyE8kS5ywA9jhMMyTJXl4+fzG3S+0s+DSt2oIpxzeA99tUPsT8cI0cKlDmcJEEi7iRzmfR0/dEtrw/qMIx3GA8dJOmp2ihq9irHKP+1gn85Y22txAAdHrNQbfIZZabEdoPEULkK3j9fbIVAp4hbplPilnochmekT/WMQnfCqI8UMs0MtLLGN+JqWoQImEsOJp9pR7o8uRsTyljdsKGZ6qcfHT3S+KVtcBekIxWOJH9UjPU/IcDpiyiNtCzpCBTtwf6Nx2kDPEwDJokvXR3CjFhyRuEp8AQezbQ5W3SQ+FCPokvjlCuGcJsARuGKAOtNxyWwbJIkhihYzJMZFEacyDAxEjIlIV+/8+SimOT9JGoYB7/DmZpmbC2RwKxERlKkz4jcE4q54LhlnyOb7s03sKzqShjQzZEgqgiorg15hfHFOpvvjyH6nQdSvxMCm0incZFscTS321BzuKS1hm8kY3yVSPkvQFsaZrNlNjG55xY51/yNaZsKbt3S/bL+LffS8bAuDFjLoFFiewAmNOJwJMcW9ZNwIgagFSz2/ecQsgKGJSM1y4BlosGOWjxgc0Tdsm6krJMoLpHHJ+uNe2NPnftDBM/1UnSrrRXOYOlG3F1U/9kpOXdf/aV3Xt+u6fkVEfltE/lFd1/+2iPyfIvKvP9vtd0Tk733sUrpcLpfL5XK5XC7XD6mfRhjo35CnJgRvy9N3dP7bn8I1XC6Xy+VyuVwul6tRP5Ew0LquvyoiX322/X0R+bUf5fggqiUZPl32Sju6vLjV0+XZPhIc+7B6ibCe/ZXr3208/06kS2378dT8v36o59qK9XqXuS5hvj0+Wm8Pu7o/UbFOpOX+yoGWo4P0s01U7kN9bnhvvb2qml1WWM6H24qnTAtdvt08doz/95VrWqYuynQtUUulczBN319one/N9XqzQpcz2RZL4A9ZodvzGcoH7KtA0Nzum9qHp1+Cu0ebU5iIpBcMNH0+YjG7BWyK4aMIX62vaX12t4ChoN8Y4rpCnRfnCP9aNf/tgGXOt+nco2UYXdvAPNAGPG+0hOPfobZZ/6aO735H75nNoNgPVdI5EPWZAkUsERC29a6Wu61NiWqUYCrmN+21kwnwIxxTAA9bHQNb2Ad+hQDdbKHjvn4fblwGlWsuUwFaIIP7WwJ8jNhCBPSIznEMvxQRCUtFqLbeVLSMgWzRheJH28A9l9f1WOItdBQTON8w6JEBedmBVrR46eX1dueiOeRwecS2aw7YlQ2Cx6A4+zp/lD0gYQsEzwGfYTBgeh8uRXd0oNQM0gOeUe2pYxuRGaMWJys6URF/iZfWnTK81P5J4YKUH+gADSe6D5E78iPEIyM4OcULOH9hfHbOdZ9yAFxt3hLyBzwlIAoDTCZcWaykRl+ZEMM9jCXgYXSPrFhPUEZ0iIuW2sZE8RgA2ztvDvo0ganoW6KOwQrYzrDdmSwdNeNRRNzoPBcD6yO6ZHAlho+2BSKPiZ/VzfsDyeL9tjzSRu0+asaERESS+4q2C47vnAJZRd2IovFxQCQ2Pm92aCUqSOezqoNQWVJVO3A1I35J5HQBp8pw41lPLHiB5xhd9dDvNVzbajrVYf4ILy02tv4crmPVkG6OzUgo7+E6xVfoEwQ3j+Go2SNYKCLA+YORPq/rmc4lwUD78CNhousDmj+v6aJGh7gMc0CFsddH+fB84jOGAaC/aPpprOS4XC6Xy+VyuVwu189N/iPH5XK5XC6Xy+VyXSn9RHC1j6swrGRr+HRZ9migS3Y3+uqyswSKdbdzst6m09rjXJ117i8UsfoX9v90vX0zxpKiiGyHugz3Dbmr51roud75p4pP9G7rMuQsUsZgG+jWzUSvUYJbIA72P36gRN+/+dLX19tzpHPmWP89zfXYnViXsM8q/fwfvvOGUAmQnk+99ni9TUTtE+mj9fZbou5074capvrN79zRc27rkmenq8vWZQkMpcAy/yNdeg9valsHR7osOn0VS8orHZIVglGltMvZKwSiJqdwJsqAPWCFlY41ZUrXNmwDHwmAkx30tb0Pujo+n8wVmbk3RyjgpZ6nGOqFV0coBDC5gGWYW/SGyB7LWuzDCXCg2zd29J6hzue6JN1NdLn9YDBt2l2+lx9qGRB0m2mVjcNbMUB9EBhapQwLtH9TYZhqRJqGu51oe/TvaD/wfjuNdGl/hkDHOkDYItE4huEiGDYHfmbC2NAlRZ/IJesiRnSbI6KxeAVOaNtAL3rNaB3bJQH6GfS1f+Y3FLEgGrI40PMTresBFU1ncJTb1/3p9mZc7SKLSGTbaASiIcwXZCgrkDAifuWrOt+wXRg4m1w0ozs8z4oYW9KMQIVwKSTetynidxXwuwIBusFttRLMdoHP0CiJiB/GQgKntaIHbHQPcyAwnBlc+gzeRXcsYEt0M+T5nx6P8sE+kE6PHHscn8sj3GMcG6jzagdjuyWEk85fxS0dw6SuiahFcKSkc14+sF9jiKKR6LH3Mdz8Qs6xWiYilzZtUTd5j7HtuM/8GkNZEUK50Dovce8xPDbMtG8YvCoiUsU61rsPEaYLPC4f4rxD/U6TIKA030JbvKzzk8GyUJ/sSMtkQkLRvjnmGIO+hjp24gUcvhaWg2X46uwNuM8yfBYBotObOjcmuA+7pzqIi91ruIBuRlMMdAyY/FDvfwZsEpPLEYIeHCsSHC2Ag5W234iTlbgH0gt8YaHjKp5pMQOKrzEhniG2xIPhSox9imMdCxzn7Ktops8YotLxPbiu7cBts7L1JFJbI6Q5GOM7B1D46kTPW02A+31M+UqOy+VyuVwul8vlulLyHzkul8vlcrlcLpfrSumFwNXisFpjap/f0+zQ17oaqvmF7nvr7Us4Fy1rXS6ki9rXpq+st8N9OBoFdkmtD6cxHr/XUf5k967iZ3QOq1vc0pJAl14j/I6kq9mXDrU+XxspJvfF7fdRNl1GPY4VQ3pzeaPxuquxDdRaAfHqv6F4TzfIsa1lJRJYVM2ORb2v6VL19E5L4BuwKiJqvT5c8eBSlsS6tDv9prq6ZTvNDmRPT8wL6ibDGm99VdGy5KH24f2/fFuvgezAxWtajh04+x31dHn1uKPbdMt70tVl2zpDP6CcQZ+cVPPYiRb27w7pJTEWBq7q8TmwvvBYG2Ca6zL3MkNQX2iRo/W1wNWs5rr/AKvo6QR9C3yEwXb5jZaAsA3XuYQGNKdaNwaDxtPmdgrR0dNLuMMAjyN6EoJK6lzANWrRjIlxTDGQdHGjbNwnXNn7hVjK5WcVAeH0kw0ZINuMJaaXcJQq4YKEMMNs0IwDrXab0ToyRsTvTHgmkMMuHJeIgInYNqsinlc/hxmmdBDWmvd1/86YGJt+vjjWC8xu6DbLkU4wvwNnIeoUz4Fq4LrEqthnIiLdM7iC7el+05twOcyI3wAVY6gxhn22rX1IBCwdM3AXDmSnQEWBHPL8PNaED6NveKyIxXdDILHJrBl3M+dCPdkPxJUscobr1s3t0j1rdi20bQcscVsDDDnORURSuCSyHDwvjFTN/d05J8aGOhAhxXhm4DQepZIPgOvhXih7GIcIzF1pdaToo30RMhxtmIOxvZf7OsdwbOTD5vs7LPXg5QFQVoR0x3MgmhirLEe209xXoOvNmOydAmMr9fydMSdfkcU+Q6eB7wF97V7C6fMG70ndZuAu28u4BUZ6TxIhzYZAtDDfLvab1wboghatgK5u7G5waRoV7je7BC53eX/qYDVzcQtOyc+7x3C6JSoNh1HeF9EKQeSYC3pwYCwxVxN1E7HBx5yLRXSwE69OD8DC/39/Ij8p+UqOy+VyuVwul8vlulLyHzkul8vlcrlcLpfrSumFwNXSsFw7qR2CYSGixdDPea3LZWMgHF+/VBewz+ypaxjRqGgjLTKRZuSK6qWKd80Weu1tcDw9BINW+O2YYY2UONhJpuu5RMP2I8WhtmEP9kqszhNvBeoSEoJ/SU7tcufx11G335DnqkS58xbbnOkres7lpy8jAAAgAElEQVT9P25eFodZj2Rw9Vri2AguTSGSyZav61o4ncaSy5bAP7HYFJGBbF+XwEPagmHl1KA0H+j+R29oPxx2FHs7SPXzUzrfRAiwQ7MHQAb3/omOnYvPA6vqKjtSt1dT+o8wrraAd8C1rdhcG28oXxIhFA73RoGL13DLI8Jy+Eda//kt7dt8S6eSvAXjDDaMrIgQGTQGIY4rNdYRRqoxxDTqoP1GwPKYl4jsQJOZi75ieYg9LpWglHqASgCL2ERJGIDJUMWC4Z4kSeDyVsH9jzgd7yuWjyhFhG3joAWCsDNqDidkOYkXEFUJ7PRp2pWoXL7VHEjHfmb9+Tmxh9VW8/7E/nhs/6F2BHEbukBRPHYzkLBGG9Atj/gRlcLYcIbg2xJubnQULIbNAboUcUKOMTpo0QmPTmbEljbHZ2TCPZvruVICyiA2JtCRnxsXPt3Ot5rHNkOcU4QMtyFQxh0NkwHvCxEbAsw5h/d9jfYwyCX2YdvbeZlYIo5FPZNxyxxoykZHrOYycwzKyt58vC95Hy+u0Z1NP4cpqxn3GZxE03HzWGC7sL2IpRG9It5nXEE5njEGie6K2ABmzjm8R5dA/Ew/41stvh4aVQXPw3umZZ6om+9/zn/EPuk82pLv/ux6OG/LHMD650CTLQLG/ZvnlSUdD3H/cC7lo5sufwYbxXm4f4S2ELHud3Q6tcG/cKFD+GpL/OmPJV/JcblcLpfL5XK5XFdK/iPH5XK5XC6Xy+VyXSn5jxyXy+VyuVwul8t1pfRCvJMTBLX0ngF9IwCPj0JNdP37F59db/+ZwYP1Nu2aP7X1eL393ammQ5fgukuxHGOFf9NeOgQV+OSP9B2YYke5wbMlmG8N7Jay5b0IKgM4+t0zhf5/E/Z8KUDTDL9HN98rWpetb98vevCX88b9+O4N2yMSWm0388IxkuM7IzCXYTNzmeNVmBwW1zmY1dUM787gvSJe9yPvqpDPb2FZL1/Tc/W2tSAJGHYB4zq/qfXpAjCltXKy+WLJh+UDnNq5wHjDOxjnXwLATtAWbUF+WUSkc67btAEuYTMa9PS8OaxBaXe+XGgbd2DZXVQtY7Ugsw075DcGjZ8XgxaKNid/a++93pm2N+0mJy8hpRkW0qxbjnKXM+1n1oYsdAKbXfO+Dd754fsSKcZ2tOR7NM39tjGtSP+JDsrkob6sMX9dJ4oCadTWrpYMu37eURd0We3Cmv4cVp20W+Vwwz3Sf6L/o3uiLy5dfFoBe75rwHs7ndo5Jl4hCZw8e8u7PrRoZZvxPQ8y5bR6pdJLjPltWN32tdOTkdathF3v4lBBfb6bsFk3iuXgOOmeo24z9EOqbWHeZ8D7OeTZycXznHy/oH8CK2+Uu3Ouc1UxoH2yHhwiZHyzrEynz/m+Udg8ljhW+X4G90nwflKGYPYwgx0u7eHR5x3Un3MM7+EU7wmad1PEWnmbZHvYovP9sRrvBiXz5nvavHuA93n4ToYErBuKgP6PYHvMd6l4ngLPJJa/3vi2xvdNurDF59jg44rvVlKrBec9vFeH9204Fsw7HzkbSTcjzDfWAhqnxHgJN76qxLRj5ruCeFeH726xP1nnlse12Z9lNXEULdMB+59tas654j288Z3T/BttiXe0jL06+nYJe3l+RzXvSvI905ZysL055mnpbCz1Wx7vrDPnFBGR9IFOAuWeTizRW/f08zP9gvP8t+N/PPlKjsvlcrlcLpfL5bpS8h85LpfL5XK5XC6X60rphcDV8iqSB/Ona9rpUNcwl7Wu8355+6319nmp66jLStdsH680vn4vVb9EYl9tqJeISA4maoZ1+O1fUvvm8/vqq7mzO19v7/YUjSD2RsqqizXCW93L9fbrd07W2w9zPf9poYjVuFLPPyJmFdc1d+2ab71AQjTWzFlPbk/QlosSCNCSeIZuT15C7VoSd1/+37Uf3vkr8C0k6gNbzZu/r321QMr4phUk7TCJHHGJmenI8+Nm6142X+es+Td/1WKJTNSLuFqbRXEAy+E6al5fjua2DDZFmFbZQPlyPebRhY6ZOAZKlAFjYdO31E1QvsM/gYX6NZ0yBo/1/JefQtlgV12vkNi9sNcqgI/QfnP4EDgdEpXPc9hUJ2Ad2sR+xkwXYezE6g5u8Mt01oxitl5qY619ehPY6ZYO0O4TvXg+0CV8YxXLyxEBgSW2sbEFDpIsmrGNdNw875UdjAsm0/NYYFzJxPIfxKOIWxDXMFgK8IZ4oeeKp8qM5DtI1O603LcxcEXgOdw/2NbzBLAzJVYRGXzKdmL3nsYZXHxB5+VYp32Da9AePAaKFWDuSkesg273zrQtVttAk2k/DiwrNJa5qD8wuXTSjKRtivhijpT3fKvZKpap6LQNZn0q4o44lhbFSIuw6e2QwZ5oS4w+6J3asd070/osDoDs4Vxm7sFjk+OeOK2x5m7Bwdq+WvDe4z22aVnedE6id+EGemUsxTF0OcZi4FT9E20XzqvdM+3EeAlkH7h4BfzOYlVA3bLmenJ8JpNmPKvc6H/i6d3HsBY2KDz6FnNMG75bGpQXmNW0+XP2M+/zjn51M88J7s9+o12zyMZ3FPQpv6+w3Pbeax4PLYkfrQgd1Yb6sQ72+QnMfKYNH0+sT30w1Zs0Xur/K4Co/SzkKzkul8vlcrlcLpfrSsl/5LhcLpfL5XK5XK4rpRcCV8vKWN67fBpfe4B0+Xmia97Esh7CrmUBrOzeTJGCt+6ru9oXfvX99XY/tEtqR5Fer4N1651EsZLzB3q9AG4ik5lyCAkcqzKDg2kTT0pdt/zHj15bb98cqgvFZ7Yf6jmRTE+saA7ro5VZ/xYrpKiHLVYhdFrLca68akYVghaDMC6FDu/rTqefZzwwlpFZbLiDPfr1Zrwp2nBQYxp37wkQGJRj613lKiYva18tD+B8hHLQIYzjjdvE++iQV5ZcttdzxkD9AuBtxRA4FPpgY3hK55IIQLPbkRQoH5zqom2kv3f0AFISrU6AQOCmN7TOdGKhw5VE6LcEmEdXx3A+3LDIQzn6D4BxbWsdxnfQxqxnoecKUuAMKyCBhm/Sze6F7l8m6E84OVUxxj+KHfCUbEfgOSIbmFEfaBXcvwyKkjVjWeznvT/VeaKCM1u2S44Hx+43p3QTE2PqunGcYqo96rLct4+M0DgZYQ54oAVnejlxqCjTcbI81rkxAjLDY838xkRxDis042pP25qIBZEU4nPE00REwjkQZMwr/VNgjSOtZ7aDeyBrRpF4P9OZjGI/iBljwH6At7E+xFs5Z4alnf8juOLxvN1TfQZOb+Ieo2MT5gAmsNP9jH2Vdonx6Od0puI0RCSH+AzvQzPuKvvgC1E3omsB3BlZZ7Y30UxTJpzfPPc4v7c8D4nDrfZ0fyKKMcb5quX+Txa2D5kib53tmsdAMiLODhfCSTMeSawzA7rIeqYT2/br8wBp5H3O83MMLnfts4HjjfMBnd2SKVBZ3gMs3wjPBjw/iSt2xpgPiZe3YHkdoq9jbdOyp6PEONAVtm4h2oNlMteLmucPqZrbmw91832tbN7fti/m6rC5PGGBPshotYh5+I+/Yy8yUCfWINq0x/3ZyVdyXC6Xy+VyuVwu15WS/8hxuVwul8vlcrlcV0ovBK4WSL3GvehqNgLe9Qejl9bbW8kG0/NM40yZkc/cAfZFd7W2hCdpR5TSXb1edqnXCMPmcxFRY8Amg0v/mWvvrreJnL3WfbLeHoBdeilRh7fHOdLVqE2nLKA7dFcjosTy0f2tg/XvdKT7m2A3Bphh+Tfbor2YqurSrQP4SB8o1UMgJlxqR7iciHVbs+iCbtcJA/mATMyId+jnC4QwLkvtk1XUjPEZoe0ZNpoPsUR+QFuV5tNUG9gT25V1K9GW4VA74vqhWuvMV3ovEa1cZAhJTfRY4pFUtsvARNRtG0gX8KE4aT5PuRFWW8Eha34T7oFwyNr5vpZv9GtwC0TQYXCm9axTLrHrtYjGcBnehE0CsSAuQBejeon+N05+YtQ90w/agi65zQDZot/i/LQC3jLXm8M6DgFDgXMcg/PyobZd/1z5oe65tiMRu2wLWMxGPfsPELjZhVMbXL7aMKN4piergCbbsMFmpCmBqw9RGioZI8R3rO1VASuhu1yxay0ckxxz4IQIne5DFIfoWhdYIrG07iXmvbx5TA4+0DZdXoM1EzEcYDLRRPdPTnWfaoj7IrDPhqpLdAsoUtFcT17b4GTV8z83uCcJVxPI2HxsiEcJ76kIyNDywHJ/xKDo/tcZ0yWwGTM09ZwxfFUH3/BdvWeSuXKg0+t6re5Fs7OdGTsr9j/2x/i0Lo9iRCdF1jlOmtHXCPNHCadKtnfnVO8T3s9ErRnom+K5bL5+mFRmloHubcDYNlE8ooJwXqRLYmcXweEV5/1mB7f4EgHNnKvQXkTJojnmcGDN4RKTIMfqkjaSKL9YBTn6YQtzDlFTzu+8Nj/H87Pu4Eap8FqACZzGd5QLHcN0Qas7ei8FBS0cMQ6neLBsYLBUNZu1/r+fpXwlx+VyuVwul8vlcl0p+Y8cl8vlcrlcLpfLdaX0QuBqEoiEzywxLpa6/JuCN/n1vXfW223o1Y2Oojp/93ufW29Xxy1OFRuiAxm3cwQaBnSLutClxgnCE+nAxeBOYmynKw00zYBAjfpafzqfzcAxbcG6pAemK4jt0mE00qXHEOvwNqwUbiXA+kJYlNz6v/R6j39N64y8UPNrmUjL/CaW5Le1rMxXDIFJMbCNmBBxIxGR7oX+e++bGi5Vp9rGy2t97M+wPS5Vc5lb+2qUKSYSA0tkezMMlO53FpPS7WJCfE4/r2P0wdiO1cU13abTEF3hYoRvDhNgOSjTYoWlffQt+9kEgwI/Y5gdnWvofCbxBsf0TKbXNnBKonwR0MreiZ5rcgfIDcPjSrTlQBsmOWdAHBEtve7wvu7f4RL+lGF5QOOARSSXRPT0nMRwnv4/OFa9+UjLeqCBxflAC7X9nvbh4gBlwvgJSjgF3dNzJuFNPecOyrp4PjJj3HGAdDL8kC5w/UcWFTZOPsbVCCgaUJ98AFSsy6BGBvjp/p0TpD5i0ginOif10dbRQjvCnH+ln68OdQ6rzbEb4/M6gnUXGx384fFB87PFuO0RgQLqw0DGiIgNcJjOKVFBWo3hnlxg0kxxnwM9i8awNRORclfnxniCeXmh1w5gyUcUjeOBDlwmuBXl23ofzqM7zfcPXd3oItmhaxbqQ9yG2KuIHW9EAoluEdmMcZ+0jWfibQGwpO6Jtl021HFl3L7oUrUKGveJp1r/FKgoEciib7FMPkM650CrIjhsom4B+jZGmyXAPSPsU/abwzbTc4wXOnMBe1wd6A0QL+FeBtyMrl7hRjAq8T1iugKMuPdo2bh/HTf/7T4e6ZipZ5gP8Z1BSvYb5luUO1xuWL1+KDqfmftz4/UKOpjhXq97+L42QVm5/wr3Z8s5a8wBEXGyjO8XYJ680HTTsKP9Vow33hH4BZWv5LhcLpfL5XK5XK4rJf+R43K5XC6Xy+Vyua6UXghcraqCtePTeK5Lvm8/OFpv3/nsRfOxWF/+xvmd9fZfuKvBRMS7ukEzVrOpiEgPMTBiSXBd63XgSoJ1eBO2CU6EmNDNnmJ2fzC+g330uvf7e+vtfkgkSc/f7dll1HKFgD2ieNLsiNGG6z34svZJvqPl7j3GMiqNRUhVEMViICVwKG4TgSOS1T+xuAhRqfMv7uu5iIoh2I3YAgPZ6HwUAw2a51qQTqRLuL0oxz56cIXwzGxLzz+8BxQv1/5fapGNo1q1YUzXO9FyM6yS6BpDMldwhSuJ01XNWA3vnwr7S0WMC85P93QZPRsgYRJo2GqhlagXcDca2TIwVJDuSvPrevzW+zqmTzM4YaW6HY+0zoN7qA9xjhHDOXEfAkOIJ3o/hxkcjsYI3kTfVp1mhEfEIirF7QPd74kiAMlYO35xQHQL5UZQXdUHuvfpV7A/wz1RBuN8hW0gU0Q16PbUI0pi0JsNF6QOXbqIK+lmCSzLIjrNmFSxA+wH5SD2Ue5p0JwJQMV5uF0DdSu7zX/bIyb39Bj8oyUkLwKKQ0c+Irs8Dx2uVkC3So5nINvxGGOSaBAcl+QUz8brh3rOHrCVMxt0Gj9QxLfaVSyPbc97kmMymZGbRX/SjSxpbmPiYCbcGPdhULKt8TmuZcJqV9bNkfslCINcXNd2bQtJ7DwBBgeHPOKORKaikd5YYd6Mq3VGRDGBgQJR4z3M+9OU7dw+3xkkGY+1HB0GTNJ17EyxpHJbOegYYZshsMa4q/WvUGeDg2GbbRoQv0T/hItmB7JoZr+Xmf6dAbUkBpYRIcT3r33FgMMprTFRJgT91gh+DvIWLHWp5wl6cEQjJsd94FImpR2fNZzKZDzVMnXxRSDGKxJ5Cwq+BK431DqzDvVEz19e6PfMEM/ueqXlrtrCRn+B5Ss5LpfL5XK5XC6X60rJf+S4XC6Xy+VyuVyuK6UXA1crQxmfP8UP0oEuOx7s61Ibnb/mFULrgNgMEBL6e++/sd5+5Q1NSNvE1fjvOSxxSqyrH+5qOXY6ukTI66VwCDstdPmf6NoK7mq3erp0TA3i5qBThlDOBZgUPDY+ffzYHDP5DSxP0i0O5VjWQBpMcKluZ59B+FmqS6GTQyyvcpUTSEJnqPWJ4+Zl+AwYUrnTvLR//mnLcfUfwaUIgZmVNr1Mbms9OxdETIDHwUVq8ZKOhR30/3ily9PEu2YZ8CGiVKC4Rq8yUE0/p3NP0IL3idg2SCZAIIAGLYHTnQ4U46EDGTkRfj5d2SC9Jg3vaR/m26gziJlkhD7M4NwzBVIx33CvAnEUwXVoeE/ngMe/CnwG+GGJgNa6q5+v9uAUBLojR8BmhPIRJaoSLVD3FDgDio3bRYKCuI3tN7qzMVQv2zlsPIZoEAMj08tm/Cq7BjyBuBZcyrpEQ2r0M5CE1YENwFyXH0gfnaXKnn1kEKdiqKRBaYAx0SGLTkbygYY3x5m2Ed3CiJUIcBYGPkZLzGdwUwpOFM8Kb7+GMhMfsjgQnaBCoDHlQNuS9wPvb7pBGhc+tD3DLfPtH/FRzNDODNjfheKQMVDEem7d1QKipgwVjJrvDSPUgWOvhKuZQRQxzhmqWvSasa9yG89hjLfkEvckxtom6sQ/3RY4F+/p0LiIaR+WAx0/xFeNgx3GYbEHN9gpQscXzc+xZEZ3NfQh8MPOmZazSq2jGmXqzfsB800y13FbjYEs1oqrGcQVDlzRTOucEvXCvGIcyBBI2TlH2xErY51R5qprx7/BCYl30SEMgdDEssIu7kkGXfZ0LNCBLAC6ZtAy7gN8rJ7Ze2ldTLiRRdeP9X/EG314qd/LJMKcCSyNgZtEy6SPcGDe0+wH1DPoYn6vFWutJhZfXe+ygdZdBflKjsvlcrlcLpfL5bpS8h85LpfL5XK5XC6X60rphcDVJKgl7j5d0ux0dMmuG+sy5+NMQ/R6YXMY07zQZcpPHym6dZorwxTZeEI5KxX7+KeTG+vtB9Od9fb5WJcL96/p8ucEbipVpkuv//Uff2W9/eufe3u9Pc50/7ce6nLmZ2/fb6wP9f2JIhzXerrUWIApYGiliMidoS5P/v7Z6+vtT25r2wxCXVYelVrPcaFLnp2utvdqqW38YZ+JWPeuumj+7ZzDXazG/vG3FBMKukB4gDBtuo4t9xGklzW77hCloKMaP6eDWwCHtIuJtkWRw7EMqFMHaGWAMMt82FwHYnyJrq7L3ne1HU8/aytqHGvQvQxKZYjh/AFSLxlCiPCz5QDBk0OgBHC5i6a6/+nndIl8+326bun56Wo2uyXYh6yXGNGBiOea3kobP+89Qjhuh4yWbpYYP12lVA0CxnIQXSRKlA8ZqknXoGYHrc54w3UMuFb3gaIH5RCobZfOZBwnGGPnKNNBs6MYMTHjtEY06gQOOmkzVkbUrQNHJIMhbQTtxXAtSy71GsRvSlNPusihrHs639IJzbiIvXtPjwVuk8BFzIRZXmKePFNcrf+BHrs61vs8nG9gT6yrcV1C2OKyOagwgUNWjnDHCPgQ8w/pzEYMKbqvg7jewjzJcqZASIGwVH/yXb0WkRcRkW2dJ3ht4kS898zcQyc0g/SRy4M7lAlYhPMZXBvpdhXApUqO9LlPMSDSjBERg27xPuGzgWMsqM1OjdeTlmMZ3BoUcCok0gWuN17gumxrjlvgXSEQsE2ki2hd+r0nuo37YdMNcV0+oHjSVuU5nP3odjbR70CcYyrT7s19Lis8M00i+MbFWewVrf1QN6JrQ8yNuO+N4xmxNzqZpbR0bcEDiYPNUX8ic3Apq+FkJr0NJJiIGsuHcxEnC4CQsqtK4HEhwj1Dnp9tVG88gJtUOa7mcrlcLpfL5XK5XC+0/EeOy+VyuVwul8vlulJ6IXC1OKpkb+cp0rHT1WXro64yPU+WurzO8Ey6rh0D4/pgquGZxMT6sUXd7gwU6XrnUkP75itdwry5r8uCH1zurreXC2ACOOeXP6+YQAdr/mGqe716TTGEdy70up8+VJSsE+mxdwdn6+1//EjdgVZAqTYXIwcIKH1tR49fgdE6KRQHeHuuCB3bbwEEirhO2akaP+f2CghY2MMSPtZdF6/okurwu0BP0FXBxqr70R/qkvH0JYSwYWk8hsPN9AYcrrab0bXuQ91nweVskkRwiMuWXDrHTjQ1w+o360Mq4vJVhF++ZyvaO9G2WR7Aacq4gqFPgIzk20QGdP/0HPgQ8AmWb/t72H+mZRr+0QMt2zW9F+5/BVhJC/4Qz+2/U7jFsd+Iz+x/Wwt1/58DVsB+ewxcC4hNCUqAWAXD9qIFguoQ+Gdcpo7h2IU2Iq4WboQLDr6reJQ8PtH9gKIFn3lZy3pbr1GyP4GipWfAJOD2VPfSxs/zG9onMZypgncUj63vKltYbMGVpyS6p/NF97F1Fqq/8af6D6AO8ZEGOdef1GtERP+I0gCxkIg3EHCYhdYhApKS0kGpAGYHF6PoWMsTnOrnXbRXWxCgiEi13Yy10YGKTk6LL93UMqFqvO+LLaC/y2bMkHhOPWh2wgsXQNHQXhGxlcWGu1pOhywcM4Mb5kIx1QTBvfGo2XWsJroTI2yQqBMQRQajGowJaFBS0L2rxTULYaYiIlWPFoi6yXml6jV/9anJEBKzA/ZjxgnmtBgoagT8LIbLX7iDgO7H6rBKZzJzfrgCyqs6pp5WAuWeaUGSxyNpUoV2DUdwI2NbjvS7TrCl7UpcjcGbxLj4F/NqCJQfmF3EEE6UYfOREcCRrIJzGpEulk+ILOIe4Liv6Fg2JCLfee7+ba5jQdSMtxElE26LSAgH1Go2k+eJ85hIS98CoeO2y1dyXC6Xy+VyuVwu1xWT/8hxuVwul8vlcrlcV0ovBK4WBPU6THMr0eXM/VSX3a53dJluVOiSL9Grr73/ynr7zqFiaNNMlyNHK7vkf7HSZfUYgZ5bPV0LXsI1pZvoUvICuFo51u1t1CFvcetgiCcDJsdwbNsWPc+wp4jAzaEuf54udOnz/oN9e41dhD72tQ0YOJqDpVihrHmp2ztv0r1Jl4InCLqMllqH429oG33wL2oZqhwYAY14+rp/tsulbT1nald85eyzOgY6IyAtGNGLfdRniwiQ7oNcVCnh2BUk2v9xouOi01UMYXquY4chb3tvImxxh0FwwLMQkGdCQjewPON8BIyFrmVEtObHTHYDckQXrAncu870PDvvYMzDjW7wHV3CLw/VBastqC5AeYjAxXOLdPVOtazpuY715bH27fIAeIJpp2YHpaCFOCq6zeGERZ9ORHArGms/d0ZaznxAlyU9P13aRCxOUwE/CbcU/SQe1wG6lw2bgy47D4EYIZCuAsa0vKuYqQnkZPbwdUW3iHYQaZu/oqgbw3YDW02Jbl7XUwE5Kk8U0UsH2p81tqlyT+cxOk2Ra41vqftlG9ImwE2ItlB1X9vLOHkFFpohohbkwMkYHlg0h/Cttpr/fkhHLBO4CqexCg50ER2XgP2ESzhb7iu2Y8JWUU8ZoO1EpGpxWqoQjBmtgLEB/eWcxEDGCvdSCPxudaDtEqac+OEoBxQtv65zTIyQUBMKSXc09oeICEJQqwT3Paqcs+1nwFRDzrfNIZRmDFfNTnOcGwOgVBUDcxFaWRwBvaIr5ls6j3zE/Y/C/cB7qUJbJAyPZPDklra3YF4hQldjTAoCigOEUxJX5Dg0YxL3SzAE0oh7R0RE0KecMytgeTJEPxC/pAMbsLTqXCfBoNMcgl0ToQ04x7Q8WICrhQgMreC0FsQbjql0WwOCKzX5w+c7oZmAUiKuV9Ah7ePIV3JcLpfL5XK5XC7XlZL/yHG5XC6Xy+VyuVxXSi8ErlbXgWTP8KgMvFEOHuR/ee9z6+3fuqkBm0OkIv6l17+13r7MdSl0BARskllcjWGaJ+eKaJRL2mIB6ejA+QUYU/+6uv2EWG/mteny9vbbinns4tiLJVC8UttiAFe4YaJ1XhS6FDrYtQ468ykwgcPmULDSYHNY8qYNEA4da6aocQqqBlrn+7/FdEbdtBltwLXYvtPmsMV6Y6TuvgnXsX0sExv8TLfTMdAtIkcRAhOBbk1vo9woX0hep8VFbHhP+6p3quNodp3Jo9K43X9s3f+KHty1JnCLuwXMcA9l6tCxiDih9kl6CXQPt8Pla7pP9wyoyh++ud7OvvxLWmw6lmHoVcSkQBcQyXpaDoTNAd0iPhIj6HT8snZo0QdyNmCfYPzAuCbvoc7AKrqX2l7EskoELPaeaJ+kW0Agcc7NDEE6PgVAMWriDcCyuqe6XUVwc4P7Gx2LQgbpAR8iolZ2dJ/FbUU+eu/pfMP2Kna1nPEcLmVAVbI9i5VEfXVkjKbaTtWn9AYq0LfEtZJzDBriR3BdqoFHZi9rIDLdq8IxznNL59VwgXA+uDUVB9oWDEWsN8IWiS7Rba+8juMR7nTouOoAACAASURBVFkh9DTbbZ4cZteBKGH8JAM9Px0V809onemCFqM8dL9LxrT80/NnuxaZ6ZwoJlMCM6uAk9G10QQoA4Gav4JQUQzJHsZzto1zYnz2ENYbAFGbvqTPwOINfY73T4B3nuBeWFqMq9zWNmYdFgdwQ0VZVzvab2z7Ak5oMXC17KaWdX4NmF0fiO8jjPkenBpJ65V6ntU++gBl3lpocO0mHsx7sXtL78Mp+oTtvVXqPkTfiKb2EYBbbGsbFwPt8w76P8OcQYdJExJLdK3PPoAL66H9XhZU2ja99/VVhfqW3g+Lazo20hHd7/AMDBXhj+9rf5bHev58F/gqnmnJiT68ogvl5evhRrDu+lrAL/ExsT8RkWIAV0U46xrkbsoHJ9A6OscRm6T7H9E1Ym9Vi5slA0M5zz1Qp98AgalBH/XPMefPNpw3c/td5uclX8lxuVwul8vlcrlcV0r+I8flcrlcLpfL5XJdKb0QuFpVB7J8FmpJHCiBndJv3/3GensODonuYG9OdGmX50lhP3Wtb226Iuz38FKXbRfA1ZKuHp9fANeY6j7LLbjJ3GjGzOiiNjxuDoFaItyzrPQ36PuirkkD4GqzXJcy5xOLkvSGut9OqngCA0qpaQ53IJRjchd9MgHeMmwOmwxKYB7cZw94UoglbGJvzHEjYbdR5OkNYC8IGGRoHbGx089hOZymKdheHnJpFxhfCdwCfRhEcBxCuU8/iyA0fN47fb6j2vmnN/rwtBkzNAGDJoUN5UNAaQgTqaLPbZQJ1e890e3JX/y8nqdAW0+107vndIaBe9slsIXc4mqLa0BOCq03cQ2LDOmxIeqWjtFXmNFY53TKBtf9O2cIP0RAYA4sbQEkZYUgWbrl9b/+rhgdalCqwQdWwISAYtUtf24iPlQCp2JIaNltdrmLZ3D+ArZSHMARibganNyIq/UeaxvRHUpkw1Gqq4N9ehsYSwHcAtcrUbfeO+qGKUDLwif6edhT3Ib4GMMTKwSjlsc6n1fAcFiHMMOxGzgQ92MfstwFUETTz1CBqSczLo9At86BJaKv6C7YuWSYo85tYQ4sFRifqU9lkRk6uBn3K9QtXjbXJ9sFckRUEvP4ak+vR7dA1q173nyflynGZLMZn+n/+Z1t8/84R3F8r4AQRjC1ItbcA7KcbyOgt6/YU4gQU9atBH7IuZQYbIE+Ty/h/IYy532Mrx0tHHFQEZEo034rh0BiicSRXEKbVQguLXA9noeIWgl3yoLHYs7g/J7gXqj5fQBOczw/5/ynBddNU6Zh89iji14Eh7TVPuahpY4Top90OTRIKOZb4sf5IYKBF3DOzDjfcr7Z+JpN5CzhtZvdA+kGWRNT5jMjxvMz4ryF71kM7iV+FjU/fEzwKgJT6UxHJK0ufoD7389RvpLjcrlcLpfL5XK5rpT8R47L5XK5XC6Xy+W6UnohcDURXZ0kfnWZ61r1djyQ54khnG9fqgvHYV/RsDS0QUnXehoQdWtPXTymA3WKyAo6KunyXJZp82EVXkLwRym3w+ZQ0dFE6/nGDeWE+kDdjjvqOPSNU3Uumi6xHBs/P0BqU3RUW8LNLUMAagdYQYWlfQYvRitgCJoDKJefQ+Aj8K4QjmUB1tRZAyJwRINERKYvNeNKLNMKrj45hk/RxfViIndYVsd2mQFdEqA3lwjOQxhqruY2xnVsccRyNrsvbYYtGrefHnEiYCUITS3nRFeIQ3Bpv27cpogC0AUqBuYRz7RsdCOjY9vywLB05hoMAzUIGVAcIg0BAmpbiEvj5hYhuLb/EOFsQBLKfvMUmEy0bHTvKxmMieJnf+YlW44J0BKE/gVAFRieOLvRvE/vvt73xjUI+0Q4//RVHXwcS50L7atorGUzOAM6js5sLGd6aZGE+Nvv6zGf1DZowzHptGTwxVGz41u91AEXP9b5udzFTWaCIbWfy/1mF6QE9eeQJIYlIhItgeKhPcy9AdSHDnlEt9gPES9Nig/jOcX4J65IhFDQjumpPt+IpITmAta9im5epp4Mz8yIfemx2TYcH8cIyh0CewJ+xHuG5zTzygJ4+JzBo81/h63RLtlG8Gr3Ao6JRLHS5nIQlcqHzfhVAUfO4fcQ0FnqsxuEt8yPEOiK8+RE91AHXotYrmCuogvY0/oA0cI9Q9yNbUk0swT2lQBrNegilMP9L4JrJ9uU9wVdMesu8VA9f7ajbZSO7ITehtkRYzOB2pgPTR14HiBknEuTCZCznMdijMBRjmilCX3loxTzajSzmGE41vFTHsCFE/1DZFeAsQVk+4Gm1sB0TdA6QoNNaC5d11pCk+08DPY7RLgtHdV+iADTn4d8JcflcrlcLpfL5XJdKfmPHJfL5XK5XC6Xy3Wl9MLgah9qBUyKbl+XiaIHvbA5ZOhbJxoE98aBMlMxnLw6kV0WpTvbaKnLwdOFXnvQhYME1u2zOZYIp1rus+vKRhE5Y/DoARC6ONKlU4Z70slrL9Ulzn6iy5R0QZsurDtQ8a66iUyOtT5pC+uzDQe2fkfLfX4AnCFtDsMsEYSWw+zm+P/WMp3/S83oUpLodtFiCESHmk3VzeZSxpnHVLnFXc04xM1wUq4cZ8BTLoiq6D7JFM5+CPAcv6InMmVG1Y6+gSVlEVkcAwPDcCMCwj7pnBIl0f2JmyyP8DkRR5RptQ9MBivSMEGTbAcuKzg/3aQClJlIo4jI8D6Qm3vqolXt6L2evPVAt9/QJFoiJgnqSZciokFcek8enGm5X0HYIt2K4LRG3KoNQ6LLmIh1Qotmza4zDHGk4x2d40xYJ8IJ6aIlwDCIfRAHKYiMoGzxiTYeg/oMMYNjDTInYgLw4ofah8khHQaBIp4jxBPnCoCclSe4mRieetjiLAQ0pNqHgxLwiRIYFrEQ1ocYioh1SyuApdAVLSZaxeKxy3HaaNWMgKWXeMagzwffU5y6QqhgOCGXif5hsB+RxpXFvTk+OY5ZDjHIKnAdztFoC4ODkaxkNjTmLeLEnVGz65yZn4tmHIZzrIhItIRT3X7zw8GYzdHdE7unI5yH42emc7QJxG4JoibGRZS3QntFKzwD2abAaYmQbqpGtxOJ441CJy+OMW5zzBtnS87v/eZyEB9bHGKsYix0TxCsDCQ42KDkiI0xsNc4HiLIm1gfyx0vm1E8upTR8a8GikVHyviCWJZummcAcTO2b2zbq4YjI10fAwZ0wlFNCtQBbm4mxBMK4LYYLGirimNDfDkiZsax0IOr2xRuwDXa8QUJ/PxB+lgrOUEQ7AZB8HeCIPhOEATfDoLgnw2CYD8Igv8jCIK3nv137/lncrlcLpfL5XK5XK6fjD4urvY3ReQf1HX9KRH5vIh8W0T+ExH5vbquPyEiv/fs3y6Xy+VyuVwul8v1M9GPjasFQbAjIr8pIv+OiEhd15mIZEEQ/BUR+a1nu/1tEfmqiPyNH/a8nViX1Ih6DaJV0+4m9PM3br2z3v72pQaDzjLFMBi2KSLyhWv3G8/VgftZgTDI6RMs+3MNt6NLeO+PdfFqq6Plnue6hHtyoa4aSaLLkY9GCP/qI6juOpZOA7ptwInpXesOtDzeQEueIzrPJUDouqdwstkBwrGNZUvjrKFlPflV3SfetA5rEvYJGLy5UZXBAzhnPUFIHpEGlOnhn21Gq4gqEI1IL7hs3RwESGcyLvkzRC9kuBgc4hgcR8zh4g0bBnr8/6qjFIPQFkfKNxGxKNknwCSScTN6YHA1U0/Un+2COiRThOQO6eSETTprbayu0+WMiBo/r68faPHQrgWQLuIzsZqRSf8JsCQ41GQv6TkZqtkBkkBcbbXT/LcgBs8WQxu2yDaLgVsESzjSdeGERnet2fPvE44FYkXpZfM8adzO0BYFAjOJiGRbSePnHwm8vKa4X32maFUyw9xl3MjgojUGHoh+DsbqtBbuaahqudPslkaUorytoY28J01AJJySyoG2o0G1xNaV7okduHfxXjJOfbzHsM0xTMwqggvS6kAxkWgOt7QVbiAE8gVjoCRAUjjNRYsN9yq4iEVz/X8xAifnt3V8Eq2ywc+6TeyHTA/p8lwfe9LySLfi9IQxFY+BjG1ghqxD9xzhk3R5Q1NWKz5L2N7oZzp8MaiROB0zVSecSzEP43OiW3TOJFpN1DNatKOiRPQ41onHhWgXYpqLIyDymPcZNkqc0ASXwlGPzmkh0LhoQUdFfMc4V+RyeQNuiSLm2R3A2StcIRgUc4nBawcIuiR+R2yM52/+6rLR/3SIQ1tjbjQ4IFG/DVwtBGoctAWD0vUSuFrNgM4WXI1ubLIifsoHZTPSRpQuGOp33Xquz8aa56Tb2xV0V7srIici8t8FQfCHQRD8N0EQDETkWl3XD5/t80hErrWeweVyuVwul8vlcrl+wvo4P3JiEfmiiPytuq5/WURmsoGm1U//vN/48y4Igr8eBMHXgyD4egnfcJfL5XK5XC6Xy+X6OPo47mr3ROReXddfe/bvvyNPf+Q8DoLgRl3XD4MguCEiT5oOruv6d0Xkd0VE+p+4WYcNKBM/e2emOMPL/fPGAhFR68XAQiosL9d2aZsObvNMlzkL2Hx1Uz1XZ0+XyVdjoGWX2pRLBIF1Y2JvWF5FMObyTJ0ugl6z89nZXJcOrw0V5+gBq1t9cWSOCZcWoVmXA3zTBOvZ41y35yugUdewbH0ON5CSy7/YjLgNLAttH2JpM8+5VN8c7BluOOt0RnDmumxePuVyNpGJeovBa8Aq4P6VoinpCsZQUgaS8vPhe7q0y+X5i9cVDapx51XNBkAiIrKEUx8D5ugcVyHctAI2GcPxz1yvwyAwbIfcxv44lsgHEQv2OVGQdIR2eWDHdkR8ogQCcKHMWb1FjE2PZejn8AH5Gewz1YLEJ3rPENeafUqt5lbH2tYRMAT2LR3ujNPaRoheArQkeE8d4uSmzlGdM+Iqeu+ZsV40ox4FAkBZT47/fFs7jihRn/gZMb49zGdAmIyTW/gDHJ6OFBVLzrSDij3Mb3Ajo7Nb3Ueo8aGep9pTvqnqIsBypPNw0NFjkxOMHexPFIS4SIE+77yrrnsf0XXF5hhoSuyHqA/nQ94PJsAQWGaxBZwWfVX16FKFeQufR5c6tms6MQFp2wzVpMsdkZ4Swa/ENBms2xkR45HninM1AzAjGEmyLTqXQJrSZuyHc0Sc2EKEU8VvYqBL8RJhykRf4WbGOYnjxCBHQIa6I/K4LSGceKaxHww2zCBNlI0BsMk5HL42ymTGNw322DS4BkNwGVZKMdC4SnR+4nxTdiwivy7rFPdFFDTuY661ieJBFZ8BdfP9YzDaFmSdDmx83phQa7gOEu+rgAdzrg+AkJo+AOomG1hmMNW5sd7BvI8ySa7zL8dMtYX9gbS1BXrWg95z95GS9zP6CuhauK+vYNQ5nqsD7Zvi0ePm8/+c9WOv5NR1/UhEPgiC4JPPPvrzIvKnIvK/isjvPPvsd0Tk732sErpcLpfL5XK5XC7Xj6CPm5Pz74nI/xAEQSoi3xeRf1ee/nD6n4Mg+Gsi8p6I/Bsf8xoul8vlcrlcLpfL9UPrY/3Iqev6j0TkVxv+15//0c4jUj5bu03g8NWLdFns89sfrLenQKxK2Ju8saNk3BnCz4hJ0TVMRCTFvytgbQUQqgwYTwVEK+ziXDd0+2Cgy5E7CNhcxAz61EPP5/r5zq4eS0zupS11LhqtdAmS4amzcyxNikj3ni6xnh6qe0kG/oht/HCC8NAZwvyAQDGo7eY/0SXVi09oHeigw1C0rMDCIZZF6RCXtDjuZDvBxr+1f1YISTTOP1yp7rW4qVRkjnRzdrtq/LwG3hVP4Tq3pTtNXtHy0B2HTjcW6asbPxcR6b2HfkdwJcPs6rAZN6BzWpgz8I07YZtNgXZMp23np7UQNklwgFDrnNmg03CJAEQ4XuV3tJ7xhd4PRGZqkBtsY6J/NQL86l6KfRjsSLc8PU/vmxomHC521ttFT+8j9hXD/ERE4sfKOwYDuNQAQyDeZNAlhgESZ4AzG92yQjhw5ft97IMydYGZYvKJEbYqr9/UMgwQGAp3uHAM+7oN1dt6bY6N5ATuZ0OdEAyiNoF7D53DELwYM4iUqAacguRMA0nrV27o+Ync5QwDBUo1sPNnOMe1L3UcVghNDUcIdUZ/JlOMN9yr6Yjsmm5WQFLoWhhh/BtHKDgr1XBUq5fA+LpwStpwxaNDFB0GOTcaZy8iqOd6DeJtabcZbzPXRRdyH47/9ALIGPscKGZ1gNDXDVS0xPPAOOy1YVlwV6vS538lqjFnbH1X7/PoZZQJ9WEoMVFU9kk6wlzIEEqgYXRmFBGRDlwCMSZ7580BtyYAE/1mnpkMzMQ9HAG/Iq5pLPzY1hxfKbFZnJOHlrYPjUjF4z5hG/M5RqdCg5OhHAz9FOOiyGMxDluYJ4PM5S2uY8UGiof/F83Rv3O4cNJRjfMbz4vnWJ00c6PGgY2ugAz9xOfBoiXck+0e44vM6oqHgbpcLpfL5XK5XC7Xiyb/keNyuVwul8vlcrmulD7uOzk/cU0zXWI/ixV/+Gb50nr7RleXiOnA9sFsr/HzbqRLkLsd61CyFesSYb8DNw1gTPMpmKsx3FoO9VxpqsuLtwYIcAROl4GlmS/JcTX/1qQrHBG1+yPFZ1Z0UIvscvzqQMs0xvWiEO5SsIsapFr/Saz7xydY5sU1zj9J5x+9budc95nc1c8DYlXonxBOc3TcaQvqFLEICFE2Lr0zeC2ZAHUwpnNwFhpiyXxLl3nDGE5uCG5ddoASZcB7FkQE9JRltwX7MsvxFsu7/DxCEhmMB4qFjkX5DlzBUGeLx+ECLX3CoNNsW8/TvUCQZJ+8Dc7JlXqEno4+gSBdEdn+PrCMd9WZZXHrznp7eaiuVkQSiE2uOE5Qt0moY7iPNuq9qwhgWNgyfahqV7E0Ol8ZHI5OfgM7lXKI1UOEWFbNWEY61nGVb/EiwCGKZicnImrcp0Ib0ZXIOM0BQ4hmCIVLiSICnztUJEfEulwFGVAPuhkSRdxG+CZQsfJAGVc6HCVvalhzfQ33AspQAyurbx5Jo4CoySNFEWM4sAX5hrMlUQ8gI8bljWF+QHEY6GrGTMS5gQgpkJk527HZOc8IDkeyo+3I4EBin5tlJQbIcFQb+tniZIUideHYRmeu5bE+u+IF56dmrIo4FJFOBu6yjTbbhegTwxrpeFYalzfeV3hOdJvdSemoR2SIoZ90SKOIrhmXrpLbOOAHBSwGLeOHYdQoK8NkayKKONai3DgnHMhYVvYhxwjdxWITWoqxzRBKYLkitm3olkfFaJuQ521rM86lxO9qpq+29MmJPjPMfE6UjMgdz7PcQLpSzDnAcYmoCQJQ6ZIYTvAFyQSGNj9XzL2KeZL4mZnniKWxPC2honXmuJrL5XK5XC6Xy+Vy/UzlP3JcLpfL5XK5XC7XlZL/yHG5XC6Xy+VyuVxXSi/EOzlVFcj8mWVxN1E+klbHn+idfOS4Td3oaYL2/bm+txLjnY9QLK+5Het7NXxXpSybf//1byqDPhspR1qcKXdcXNNjlwWslQFnp6jnku9twO66zLX+0//+lu7/V8FromydgeUj80Trk5ewXIbt9KLU8hV4B4hW2Xf/vtY529X3HJ58Ee/k0GJUu0HKHhLO0Q9Bi+3x8pgWzUhpf2z3p2UzOWLahO68o+2UgfmN523v56iiQ+2fNNXtAd7bKmGJXQzAqYZ8h0XLM7vVzNSz/NFqo55DvPeB91sKYMHFAO8FpNrGy0PdPx3B+nxF7hyDj+MfQdZRi6031RmhDKgmLcQ3k69zMPbVJ9S+mBbUfIdhcYz2BsLdU+d400ZBj+9L4Dx39d09cvSDD/S9hfBMB3F9rI2dzDE+0Ve9DzDoRUTAUfOdGf5Ziex4tk17U7D9eH+mPND3hPhegKnbsTbMche2xAu8e7av7yEFO1q2cKT1T99UK+bsdbViznfsDZPCppvvT9BCNnkP78DAQjo7wDttsNblO2bJ8b5+DttbCbV9I7xLU+zpPFyibBGS7xNw9HxfyLxrISLldR0nnPdy9FXR03eAaAPcf6g3zfIoxf7Nds18X6BG3cq+1p/voVSoW76ldePY6b+n74Zm13TsiIhkO1qH9BLvM4Dt70z4PoeWb3EDKeddxDNMOX9o3652tc6LQy334CHnYZxnq/k9lwrjnGbffL9GxL4bkm3Tflc3Od74Dpx5j23D+fdDpZf6sMsPaY+M9wRxXTMnYW7ju0q0DS/Rpvk23qXt2K9rm+8BfqjZNf2cNt2dh9ip4nyN6/H9Q/bJBO/k4NjOI/1usLxBe/24cX++t1fRcj7Z+L7F9wB3rLX7+nO8u8evdXyXZnWoc1280A7tfF/nJL5jUw20vSvMk/ETPA9gfU9b7o/U4cNjH5ybfxeHyNjgu2i0ued7P3gGFluw3c/4DGi+doTPOb/xPSHWv9zD8wDRBMEC7w7h/MGOvqMZJ/bZUDx6LC+CfCXH5XK5XC6Xy+VyXSn5jxyXy+VyuVwul8t1pfRC4GpSB1Kuni7vLjJd8pqlWOYHShUGzajTu1Nd2r/R1+XFBP7GvcimBuebEfMfXgM4Vb2A9bPociHxsGRXsbcKvM4k1/1nmdZn/FiXdl9//ZGWL4Z1MdZgu/+BLv2lc13u5DlXue3Orb4iE6OJLvmuiAS2YGwV2vudf1XRgwBui6nSEBJmzdsBrLjZLkGJz7HPtT8B3nUdNt7XLOoEylDSy2YLzPHLTPzWzwf3abGpn3N5/uIa0ELgakQaaX2NZpQeLLSXu7RGbcYWDN4FTExEJB03W7cWffom62YAXI1JyRzmpn8yWNribx4RkLbOCONlqPt0L7D8PcDfS3CtRAPhzf6borVo+o7yZ8U9tRC+/I/+7Hq7Aga3PIJFKcZF1OJuuTikp69uroBPlf3j9XY807J1TYI4jr1ucSCKaBETzLNdoKyDZhRv9qlDPXYOPCEC0gRch8gMxxjtc419KvARY4fa00IQQ+P5RSwyYhCoMVCXTjOuRcRz8J5iL/ku+gGWxsUQVqqwBo6BpxhrYdrkAmEikmIsnTdSw4lulV39fxn6KsyB6D3Q+ZZ9zv0DoksYe51L7ZOCuN4UcxXwEUHCfZQBz9nDmDoAlljYZ2YIjKnsALlB/xBRC9F+RdpcB6JeyRioNOrM+a3octziPFHU+HkKHC4CesR5W0SkxHmJinGe7SBegMhdHaDtcc8Qg6Ytc47xSav9eN78HYV24rTgD/gc3mq532r7fKeNfmcEG3DMjeb5izFTEX3j86dHC2GMkV4zTkt7fd4jfFaxf+IJvmOgDOUm6sVkjJj9Q5Sv+btbtKC9uG4aXBr2yBXmMBsXgGf3EBgs5iFjS417pOwDGdyxzwbOY3yGmNgB1JPjjUhcjesZO3oMPYPTsW8x1xnUrWoetwFt2vmcYCRCz9qAvyjylRyXy+VyuVwul8t1peQ/clwul8vlcrlcLteV0guCq4nIM5RnsdClvEugQQ/76pZ2q6fpswksUD65o5jLt87hCFS1/5Z7bedsvT1bwREpo+sOlvyw5r2a6z4r4G0nQ12e3Eqbran2bsAJ7kLr9sqBOnH0Y8UTrnd1/ykQOOJms6VlnXqpojE7W8rx9BIgcVjbnK90wfRDfFBEJGDzYfv6/6NuTKPXdDk3h/NZDFwphxsZ3dW4PX6ZSdR6bOfSLqPuf0fbdQXsJzDLx7p/DwYnRIPo3rMA9hQnQLFa8MiiQBvlQOvgasbl8t4TriNjkw4rpb0WHY7mB80YRx1j2ZpOc0wsN8HpWNoGQse+TdTUS2ZA97oXcG/bA8Z5oxkLIVZInOnpNfT4Pp2wtq+vt/NfUVfBEBRCiNsqUdJJeqfAshgCjXaJ4TSWTNlIbBc9ePQqUM89jBGcZ+t9i8HSjSiewe1ph8iVfr7aQfuxb9knl3pDZAdMkYdr0Jk2zPSO7kP0Inqizmn1QPepkcQtfSCqQBiYWC8iEgF945iJlsBB4KJUAY0yblcjnShCImrARwyGgv4sD9Xhh5gIU9cN6gWEIzrTOWxT9Vgr1Ge6eKW4cLTSzwu4QBHxI2bVJp6HiscY6Li5KyAmrGc64jgCPnZi65kAqa3gMLe6Bqcl4FCC5uvQXQwOXybxHs5PJMQ5j9OdMJw1Yz+cqzsXcIGjY9cGipdinJQdHdN0rkzHOFcJFIuoLRzPqGiifRIv9T4h+dkZAXWjkxtwX95LCTDLYq77pECFq00XuYpYI3BfzKsJrkFns5DPHGDunPeSsXb66pBOiECiMWfynjdzLx8sHCNLHRjpyra1cWrE/yuB3NEVkHMjj+0+1gdQACSamBXrHKIOdY9zCQYxpzA6luXEgPFdLLPPBs5jdIKLLnQOrHt4Tozh+jnGHG3mpBZ0reVzKgAeGqCsnP8qfn6h37/NedK08fOft3wlx+VyuVwul8vlcl0p+Y8cl8vlcrlcLpfLdaX0YuBqRSDxRfzh5lpzuFe91FfEohva5b8PdZkhXCvC0iFwI7pjbR4zHQPdAFq1d0eX50osEc9mcAFaYnm1BW+iu9jlpTrfdPu6LPz2Y3VT6nTgarWvXRVjLbgP9Czdtu5VROvoTteJ9bwx2qMLvG0RKxI3fFfPkylZJ09+BcFRNDQpiC0An8jwmxqIVdTV66728DkcvrgtIvL4S9r23ZNmBGL7bV3+ndzV9iY+UML5hY5dDIMllpYVz79liDEd/d3vrLeXv/Kqnh8uRsRChu+C7xOR5RFcXeAUVCVYho4w3uAEGM3ZfrpLjRVvgxUAueMQTuHwRKccOlNlO3ArGhAlwXku7N9U6M5HpyG6I3UudGx0z4AAzYAJwR2pe4oxfIygWzjLdIA5GMelU8UCSgRPMnh2tav7myDRDXegtvC4xLgDMdxSnqvo/ul6u7vQG5HBbrNPazgl3Z4ELktbR1oJOgtRRC/oiBYv7RyT3FPcQcStdQAAIABJREFUN3zQHP4W3H1pvZ1eAImlYxNQueShzrfVy+qYSfQiOsd9kgArmelAN25pdCu6UAyj+ODBeju+qZikiEgQA+MBTtcFAsXxQ/ym7Ot8QzQqniBgL29G1MIZOF3UjfUJgd4QpYkRPhzMcZ7QDrAaOGKE9ujQbY5OgubatKRkwXWT88fggdY534Lr1BLBo2daVro9dc4wh80QSGmwZNuO8WOEoO5onxIJpAtf1G2++dL7eh4ToDtTBKp7AgfPkkGNKBMDWu9rPSMglESSgkJDaOMLYIb5BtIFxy+OgS5czoixBY+V2Y52FfFM0N7RrNmSMhnn2AfbPwS6xbETLDD+MT4ZSCkiEi15HzfjdMTv2DYR9pcTcOrg7qs52ntL+zY51T6PWaYnmOdQzroATogyB104OIpVMsHchTmmXui4qkf6/CgKctq4D3GNqkTb45ycP8oxuO4K+yfNmFmdt9iTtqhe/RCp4T8H+UqOy+VyuVwul8vlulLyHzkul8vlcrlcLpfrSunFwNVE1mt68RmW10Zwk/pEszMEVUnzPksgRgwbFRH59JEiFteOsMwNRGmVA13J9PMewjaTLV3+u9bT5f8M6V8duFgQUVst9fw727qM2kFoZwoe7HvnirSxnMTTRERu7OqS52iheAKXT3fg2EIUi7heBkTHOGctm53MeIGuEjayvAGXOnBSzJliWBoVb5ggFbpSLxW6tMAYmN/W5ebFAZaqsTpLlzLWoRw1h7xFQCgruO6BjjQOXI9++1P6P+h8RrczkA3RDbtsT9GNKwIGGAIDjJZ0zUEIH1C8CqggO5RI2+7bCMA8Qajs63DK4uzBcFOidOjcenO2Qb8P7utS/WofAbo34DIDzJCuaDkctaa34MaF9mKoXrat+9MpanFTsQVTTGNZ1+ycx6DOjxwPTKT+lLrFMQwzWhDTxLE8LRGIDx7qNlCF8PVDeZ6IjETASoh8VINmd7VqM7SPLlfExq5rmGo90vmQQyCh6xDC+YrbB1o+OBFFY8VbAqIXxDbOFGsOdxTJqbsI4AMmEx0oDkenORGRGvhdAHQloNMSMS641gX7uI8xTqKRjvMSbmzEu4hDFfcVp4t2FVEM9zApw2WpOofzUR/nDzaejXTSA2YTjhfSJDMe6CJFF0q6VE3QxnSKKvEcojMX9ifeZJDDb72l19rX+odH2odPj9FR1j3XsR5fAAfis/gB8FWOb7Qr8cjyHhBHXDcowXJDximMmCEQterxiZ6zh8n6TPszSOx3lxAIIsdMOoITGq5XT+FgiGPNfYi5ziCkQBR579V7eo8FC0xcmEsCIl0zPMgREvyRb25E1GZ0SANCxXkZfVWe4z6E41eA61UzbYvokbZ9CYytvt/srke8qy5a2u4HoF7VZNL6/54rYGZE7qgfFRv7UbG0XzT5So7L5XK5XC6Xy+W6UvIfOS6Xy+VyuVwul+tK6cXA1SKRcvh0uTEAhrP1LhCj+vm4GoMxO5EuNfb6cB7Zss5sIRiis0sN8cznulS7fYClTdiJWNc23R4gxHO+xDnBYhXAwaoJrnWsS439RM+zl+qSbQxkKked89z+Zr0Eolai/aqW7WEXAZs9LRNdoAbvIwwRq6UlELASbldEXSQkk6GbXHUmnkOk68bv2yXex7+2JU1ieGION7IEYXM5kSNce+sDbdfFneaATbqu1Qs43sHJLB3pAUOgEBefRGgpAxZXxMdMdUwYXtBCRNVo1/wagvrotDaDM9klMBHQKcWWnuf8U1q33j5CO0+agyfn1xX1KluQuWRjlZ64Yz5E2yAwrnuh15vcAnZ6BGQAfc4wULo6MTAx32l2k+F1I7gj1SECBdFeDLotO/beo+NTdQMIGa8Bt6eQ6GPG8M3msFIiD0Su0jM4P20D++GYj4EAYXBntxS3MY5qKAOdlUREyieKetDVp8Y8GQwQMLkNRyilaQ2uUgy0velSFQGrqSZAZhBOF95RHNCE8MHRqDzRMkfbwG02QvtqIDA1UUGgTuFU74EK4XlEt6hgqpNmhH7IgbdFqcWS1uW+hB0htsM+EFIgLCExtsKiNxERP54LSFS9hTKd0y0QyFn0/L+T0gXLOOQt0N7Am0K6wuE+KjGGy8ca/B2H7WWIiNkRp7vA+EE9Bd8/OGYE7UdHrRptF6GsdMeKgA0S0apO1LGL/RaPEQoJFHXTpSvAM6eean2iEdBKIGTFAkgg6hAd6fxUMeiRDnYDnd/NGONYZf1RnhJtYR6mP4DaivbUYY6uY9VS68Bxb9qJuBZRW8yZQUfHfzlG+X4IXXW866rJV3JcLpfL5XK5XC7XlZL/yHG5XC6Xy+VyuVxXSi8GrlaKRLOnv7eqW3DQeU2XKfcSLJF+ZOH22T5Aur43bnZKWoQWBXh1S5eMB3BLm4dE0XQZdneo5bic6nIpg0Fne7okPcnhFJXp58WpLmFv3dbl0tOplrsHZ5Bd1O1ooEvB0wTn33BfoSsc8bg+Qj8ZBvr/s/dmsZqk6Z3XG/u3nvOdJU9mVta+dnvraaanvbAMuGFmQEjDBUIwg2ShkXwDQoIbuDOXICEhcTPCEggjoWFHBmm4AGMBHjHW9Hgw0+3udlXXlutZ8mzfGjsXmXWe3xP1RmV1VbWdPjx/yXJ0VMQb7x7fyecX/ydSFj8IVYMqyC7l+nzm/xuZDmkhs7s2PAYOhkSldBojnrW+qZ2P6LSl0DfM6Hw79F5P8rFFXY+/CYxrBMSiJ7lrgLYplIzEw0tSIbrAEemj81c3z+32j2RuHP6S4EQlEm62EyBkY7j25XBXQv2aDMlQQW6xmUMkWK3GqOu5Hxul01pyCfSKSUg70yXflrL4vGghnXD+lqyHzQ1/8lEmnKW73OqWNG54JGUml9JHxLiGD5CAbV8wU+JjEdwCszlRTKdU78r9IbCcGPcU2CfKCZOEAn1cADM8QWI7ig5nwOFSJj1FksdqG4kgL5F0D31BTCxEAlCFn3aerRJOwoGpeFsQMtYjewxeBSgex4dOezHQIyIpxG3KfcFYYyYMPQNWBBSGjktd9Eo5MzFxIZMNnkq5LZCreIE5xgSbW/6kkuEY9QDSFg6kzUR1qGbd44gGBymiPc4518KFTSFARLGQJNIBUaPCHBgc8aaSc0/aliDhLtE9zh2637V0u+pxjWouNG7UFsBUZ8Ca4eCnsKnKzwG3F8/GmBo6lt0QV8CgGnmvaeH+14c90QnQAYFsKz3OxCmDIfb6c7mnxp4RALNUfUkMlq5jwGBdixcz0LCaDoRcFw3tNv3vz89SfXb2zGv63MUo5X7G889p4krTVy+L5JhMJpPJZDKZTKZrJfsjx2QymUwmk8lkMl0rPR+4Wty6au9JWBGRfVcBYyrBFWWBP0kTk4HuDiTsmiJT4zTWYcoklP82yiR8TNexGvU4PpPwd4JEaEwMuqgEsaD7GRN3hmspc3FOlxA8d0uuWU0lhH28FBRmU8oQlqXOpBnH/oSbbE9Ry/3LHDhdjoSJwLI2O0SrgAwh4g/zN5fv4cGhH+9hMDud0xFNzlcj/fd4g4mi0LUelVO4wsFpDTlWlcNT23PcALn7BLF0zrkIUXGiaETj6Lq2Qb8wOWk91O0sR4KoDc7l/jlqG6BfQ/ZxAneljOgexwQPQ1/UfgMyV8Gx7uQbMm+rEfA5JAOt8Cw60DnnXIJ8jirpJzDLZA13JSTV42BxDKsR1g9ohmSOBJh3xZmpfkmSVla7ghIVM6nD4FTqsAGiWYw5B/W41RO0Z8NJBlzrBHvR63AmQ1HVACjnrRvShhduynmiYTuCFa1uwrGO02Iuk5XJFpNT4FBAleoBknxeaGQmQNJPurbV9yRZqQuRABWubalyZoKD20NBbLIGSS+ZPHJf0CAHDIeJW9sMrzcgTER7VELChcZfWiA6LXA3d1vGIaCLHNvz47tXhyGTcg6BnwFd4ouYbk9Er/oUTuR90CKxp8KhGu321uco1ZZI+rjuSbyI/o7uC7/Zbkk9lLMd8KsAeF99LPdGN5E8dk5Ey/+u/yzxnuBM2kkXuZqI3lTe6SphZA8e2PcsdyjtoZudwgk/D7p1Aec3XN9sOohVw0SscKfDPOScJIqmijntQcNC7LdMQsmxxfnn0nXsC6Bypusli+SYTCaTyWQymUymayX7I8dkMplMJpPJZDJdKz0fuFobuCB/EhqtkRRvsZFw6eJlQcDC2B+CXFYSav7gTHCGMTC0SapDvrdHEs4+XwpWkOcS5t3dljBvBeyH1xQb6cpLuADRmS1LJKScviYh+fWFXJ9OpK5D1HsQS4h4awAHugBJ6joJU9dL6Y908GzET7mIoSjSgbkYE7kYTlaK6cLpza4fnwqB0kXg24oZnL9AlRTTDq5GnIp1rYnTyfnxAyl3fQCkCYRJg/q5B0ikuicFRWO5proh53NapwFpq2DyV8OgqIn9eFe+p+f2+gZch2heg3vaXK7ZhEBDkAA0XhCtorMbykEXNwncvqY8D4QUdErLvkP7OaVYZ+ecK2aoB54Xr1vvMfHFAHP95f9F1vDFW0B30B6FXO0JApXvyaDk234cjo5/6dK/93RzFbdB4D8GHra6I8/mXE0u/M+oDiRxZXwkba5vyV5XbKOdNL5CX9DVjcsoXAOxAa4WlmBRjztoy1gWaTvEHo3zbkUOEg5ccBdjwr+ajlU4T7czhQkBsYroqEZGF4gaEbCArl5lJxko8KAGbkzxzrb3noDJCYFc1YdyTLe0kKgf6hFwvvTgNiyH7SEypJKz1v7kpJ8SHdLgOtaumDUYzmlwUQuYMBWoVwuHM/a3C4BEHgnqRZcuooXsU1XluidLcqceve50KDds/K6svQLSRbevL4LZfSImq+1Dxrrqfd5n9M2z7lX1+GmI69OwMtNPSRbJMZlMJpPJZDKZTNdK9keOyWQymUwmk8lkulZ6LnC1oHYunj/5e6vckvBqmPpDrXRaowawuJqNJLyewvprEOnQ7FkuiAGjp2nqD+FujSTkvYCTVdfZ7BNdbvxOa9TwAyRL+5bgFsTb6PZ2jIShmw0SfhZ6OCMgYQMkAE1jYHNgWui6phJ34pDIUN2DjJHcGpzIf1jMENon0pShDiNiFf4koc45Vw+QoJEEIqLeMcySVjf97WG5YQlEa9s/95oadQJm1/a0v2bizSGTNqIOmGpxrv/dQWFQQPE4jac/kHE//3m5P17B/Q1oYdu36vEsomgpjJgqIHcR6I9oTfc6Oqr5y3dO90F6Sac+InRA/5DPkG5u939VMK7BiZwfHckDygmSsqbSuKCSMSR+qZLSIuHl5cvoPMy18WE/bqESIw4xOUhyAoNLgOhlp0gkChSrfvf9q+Pozdfk3ksZoOFC9sBiR/YhuqvFJ+LkRDwpfICkjcDEXCdhpsvhqAT8TDlwwc0uGvqTShKrIa7liFzBIaw+kSTORHpiIm07Mi+CbTCXF3DvgqNa0Emm3AKtUsk06S6FerdrOHYhkWKI9lT37svziOsBdfo8rl4/qfNXOO7HsIi4sQ3E74iZ0V0rHBPRg10ipOd/D9LIpLJMWtmT6JQv689K7NiirgrfowMbz/+ESSKZYFMlzPyqREQt6DKx/j2nPjzynu9TtCP4bvXo8DOu/IpliJrpc4jJm92NJ/tq8OHvfe77LZJjMplMJpPJZDKZrpXsjxyTyWQymUwmk8l0rfRc4GptIC5P8QLhXxwv3sm6t31KdxcS1iIadrEWRGJTaCTh1T1JPJcD/WIgtYbL2fGRhHaDJZyMgLo8RpLQxWNgDlAAV6vpt8SxiMjYHPV+r9yXe1FOFJF50YhVCJyqBvfU9BzHSJoaAHUjGkRkKAaiRPQoBMGSwSlqwWg+cDUiYNkJMBfmJtR5+lyGXiiEStFOWHj2jf/++1fHj/76z14d15nfCS0C6kX3soo4GQmL3I86JUxuuujgBp8Ugyk//VCH8Pe+K65D9/+yJCGk49n8NaBeG/+YsE4xqJKwoKuZt3ouLOB2RswQiEkC46PFK0hgh/5NkU/ROecyJDdNlw2uA2ZTyPnLNwRvoVMbE8PSRawcy/8Yngi2st5PvNezj5j0lFgmk40SM2w6O2l6JJ3cbMEhCnUdHiJxY4j9DVhKPUCiyzUcvm7fkvIfCp7CXbK8LXtVjaSiww+AqAE3a5dAt6aC9FU3xU2smOl9uJxgnYTEFKUzk4V0VLQC6lQgweALgndFS9QJ/dWMgKK9cVvOZ9gzjsDTAofJX5X9M1pJe4JS6pnf1Hs1k/o2MeeYP9lvhHWy2fHvE1sfy7htRlLvfBs4GJ6VXcKd89Tfd3Tac3AmW74p78OuOyWd90aYhxVc9coJ8GLUiU6IXD997S+2/Mjp8Ej6YvJA2sP5Uo6lPSkwy+hS5sjmBT1uTKBL9JXJq9NTuT/fxbwK/WOencuzs4/kN8PmNXE2ZKJbivskk1qPHgBvxDwMz7BBYy9otnU7GyRHLmb+NqTneBmjrPhSnl3M5HdGgoSu9UyeV24hSSocNuMNfjOgDQH6Pd8DoohrBvelncs38BJ3eu9O8G5Q7/dLGRO6QcZLOV8PZf4kZ37Ec4N1z3qnuL4Fpsu+UEK/R3CU5B7ebQOTf0cbzAFg1BXuj5fALDm3cT3RZGKgEccK57mXRCdIRDwCWkxUEu+MIJe+ru7ec1R8SxJWt7inPutJPgtFwH0dkmBXW0/q1N77/PEZi+SYTCaTyWQymUymayX7I8dkMplMJpPJZDJdKz0XuFrQCjZTA41qxhJGW1ZwB0K8L8TxzZEwM++fSxg5ggsa3dGcc25dCboShCzXeY+jlPFShoIRns3RrQX+jgRiE8TStg2SiraNH2mqgXSVcFGrkTBVPcs5l+76w7NE4jbIhrlYSXiyAYqXrfzOXMoVDI+avSv/4eGvALcZA0MCrkcXOJWQEs+qOuZA5ZgJI/3JJ4ttOf/g137O2waFIqFM1sNh/ihWUB0zdCz/Yft9GeeL1+AaxMSeKJ6JSp1z7uQXBbMh6sG+Ue5yG+JDWEugNImqDI8xDpjORGyUix5pT1R18ZIclzvS5gqOcE3HmWt4JA8ZHPsRpWrEZHjOKyZZzWf+9dPEUvH1Phk1OSRmyb5mv8Sr1nt9vNGYYbUjBRBjiXJpxOIl2dNWB/5/bxqcAl37liBayRyIAZA+4n3llkySfEvqUH9D5hQxvmipk2F+IiYzJd7pnHOrnr7MgI+0cK8K4HJXbAEDRblxDne5CzCXfuNFV47pcIXLU+xzu3DQCmRC1ynmZ4dC4f7G9aoSq24TDQKa+wrqwX1sJGPONVkIWagw3WIqbSuBUNKBL5r58Zn5He43etwUTjRFglKaiuKWmgQM0b3If43aV6dwIZ3BjS7gmADJwTut4fIHJhfMsJ53NQ6k9vfE/+4K4ZJIvCusMIFwWAKVLMeSxJWYYTlmOXLvZo/4KcuUPSLf5jtMWOThCSZ9x4xMzSuglZyTAS4aP5KyGiRXZ8LprV3/2tjs+N1Jw0rK57zl/sk9I4aLZDUWbHTxgh5Dzp8UyLfCQ/f88yRZ+XHkBHOG83YzC73XD87leqKO6v0BKWS/xO/KRg8c5waxaM49XsNxCEu4hHI90JEU78/unPE+iy6n+0TC8R4GVkdsNlng/THSzpnFAZIAA6eLVoKphoeCfjaX8vudzo4qkXfxtGN+Amc+i+SYTCaTyWQymUymayX7I8dkMplMJpPJZDJdKz0XuFrrJHzY+mkT5QK2rhPvNd87FJwjBHpG1Kur7UxirDGxKTyvwv0xEnQWa4Q/gZC1cIUbP4LLzDZCtXAlatZw2HhNHEciuKMxMejmMZKoZcBWtskbOVcCm2t7iKsScd6wB8siroOoqMKkQpAuy5vAc+De1QBRC+HklqJt8124Bi2lX8Z39cQoga8NHgNdgwvb7F1xWjr8trjGZGc9SMIUyUBnmAt0sMMcUTgHnG4qDM/0j8+vji9fFYQyIBmEInd/oJGh068j2SuSiTKxaD0GrgT3t9F9fzJVlbgUOwBxi558u25wKs8dPpaxunwDzxoASQGuVg/1GBbo7wIoAcPzxA95HAOhJCZB17mg6Qnbk7ACFrB1V/7H5StAvZCLjJ03fgD8tNLh8/hcrOqCEs5EmZRL/IgYS8KciiiW+FFYyTGdqYjDxSuch1MW+3GzK/0e9ThZMZFqV1z3RKjGD2TToKPaxdtIZAwHLuIgIeZMgXpn5zI++bbUKaYrUQ5UEhgb5xrxmQh1JjLknE5QSxxXvX4UHof9Y4o1kMH56EJu5tpj0uDBsX8dEpNpsYfB7EqhZFSU6/mpUDzcz72LzpjEodQaQzklsLQQbpNNz17CeUhkrATqRMRsHUhF0wX2v47xKvEbIoh04WTiX44DHTDZZ0R36FS32cWxUKAuk21f1a/YkjI5J4kuss3EOOkU55xGndb7/rIUfojb6RKZ76JOp3ROQx8P/HOS46/aiTmifnBg/yxK/AbqzBG2ge/Hhu8QTCDt/ieXcN3mcPkjrsW20UmUmBn3jD5Hzl7UM+2sSdYbdQ2w7wU1k1TLNXx3FxPODTmfzv3Ym0I/FXKK8QfWzPZzjTSZdEANXLHd1d8U1LiOpHpb4rdST/LZAEmjIySUb5+6R7J/niWL5JhMJpPJZDKZTKZrJfsjx2QymUwmk8lkMl0rPRe4WuAk7MfQfjRGsifE/xrnD8m/vvf46vh4JaGzBOjaMNY4UAJsqoDLGV3LYHbiqo9RLhNAIlw4vo/jQ4kXHn0TLkMITVYz2m3guYhNElcL0S+8puvMFj+Q+HH1gpwvEJ5cwwWkrsg90K1Frt//Qzk+B6KkXHZSf78oQ4w+LtGfQ88lCx2qZ/JNjVzJdSffkLGiq0s58ofeRw/keAU0IoJ728E/kL7/6J/3O87UA3nWvb+8h/OoP0LQbNvZWxrFpGMT+5X1ZsLNVtVDjlUyVfRxvkvnFjm/A4e8y5eAbsG9jIn6VPi/8A9iWHYcnhBxXh5IgwYX/qRyxAQUMoFnpMiLGBOfOpSJuEai4BIhfyI52x9IZxwBpeNaIGKjGBPnXLyRuRf97h9cHSdff+vqeHgDjk1w0SK6NTyRcVjdZj3w6EjuHR1i3dJRjWsSc4/9y8SedSZ1KybkM5wScYgUiSujDbBeJKfTSTXl3sGZH+NRTkRw2uNYMaksnYWISxDFTIBzRJjzRD6c03sX9xgOtUJfe7Y0rjftrOTHZiNgQsopjM52Chv2Ow0FrX9tO6fXRoT5trrh/3dPurOprVuhS3DRwhxrkGS4wbgRQxqewnXwDlz3sIdxbBV+20H02h7npRa4NOva+PN166SNCokjgotrsO8rJJLrDcicTqQqxxtcw7HpSu2HPXNMjZXCxvxlBrX/PyhcCQhgtvJfT4dRhXcRlYVjW9ApRv1v1JvzIVKJW+U8k1pzL+WeweSWClHDmqeDZT7z/1Smw+boiAlA4Uw30yyecjHFQKTIlh6t6TjLdQUMNpKB4FzNzjsb2VVdgc3O+FvUv4fFC385UUmnNCQnXetNhglK+d/Cc3FRq5xfzUI+NQjWXEBPK1j23flpWSTHZDKZTCaTyWQyXSvZHzkmk8lkMplMJpPpWum5wNXaQNxlArhx1ZcSjrssEKdEuDREuO+HD25eHQ+HSC6IZy1CnThteyihMDqytQhPEwlrDqTcBsmixh9LSPL8a1LOxdtwEIETWjuU42ANDGMBXAlYWQ2MLVBOFahbpf9mbUcIF3bjwZ9cg+MKbmxB6Q83n70FjAvRyb6kcHTcaVZIZIW20TnPVX4kgQnVnHOumvjj2USiph9LAYOzGtfI+XxH6nT6DvAeuB0xSSgRNfZRcgl85kJqRkcTIiMcDrqOKVca59zmgP/L75ry6v8sbbv3q0hChmvohBf1uOYQBVjvSMV3fyA3L16U9bO6KfduvSv3nn8dyCWRjI0eQ4bJM6JOwG+YbK9BMl0mi8sQ/ie6NDqWfhk+El6vSQQlY4JSPovHiUTXXTHzO+M0HcPH5S2pIOgTF5wJTzd5H4l4b0s2SGIV1difqI5YAddJAQchui8R0UovgWFs+xPqDYDJNYnUUyUF7DyD2Fwx9TNAbc8/q/Ul2EtW7O8etzyIiIRKBNmDzCg3sVNdKN2s2E66FykEpsfBT5XJdUj8lIkq+VzMq9Eh9rPHQLlhrxaSOGZ/dTosg3Mc0Zgm5hjK9XSz5N7TxeB8Uqgf1jCxJ/ajSviI/iJKFAExKra7+wqO6X6F3xZsg3K6pFsec8wClUuAFcVLPBvuatyfBnBLJPpMTLkCTlkhITrR6rBD6LCfEqF71JxWaCUwpg1wJbYhXeI3CnHcnv2GGLRCxlgfbgV9LpddnBJ9Rpe3CmPIenDM+Q5gYmGVlBS4Jl0EieYSg6SDY5UDLcZaZcLLEMg2n+WcxuCCEX5PoZ3phbzUQqC/bQJXzbvEWvm7BL/74JAW5kDLGvmRFhb+MU8eYxCxsQRrLMqG3GhnU0a/Nufyo6iaz92z1ObyDN9W37bmrmYymUwmk8lkMpn+fyr7I8dkMplMJpPJZDJdKz0XuFrQSHK/Yh/YypaE7FJY3yi8CYdv3z66Ov7oTDL4qcSenWdfrIXRKeeIMcOJJR1L6Ky4AIuF0PviDYQCxwgvnkmZbehnLMJdKb9e08YE6B4dq4il1TpUr8rdQ8gTKF4Cp7ZBjOOJ1GONRF2T78N1CUgTnUWcwiTkePqRHJ+/A1yNCc+25Obt9+R4fQDXn9udvkNf0qmOIX2G50ffE+u05S+I1RzdnoixNHDKUa40wOySEyQqBKLGsHMF97IEiAhdvYhtMMlnV3SUi+Ag8+gXMceItBHbQFidTlsqGRkePb3v51B2/kg4gtGRTIbDbyMxLvqIeMbgRJc1PvInsdzs9jjZAA8kTqjdd+R/rPeQlHaN8Dwcd0ZIaFpl/n/z4VgRyyTGuNnT9yra093LAAAgAElEQVQc6J03URj2iQKOiXCnU4n6sDaIBnELzM6BUgDJaPp2d2AFMfq9BJJQD6WhRDXitXYK6ks8qDA2bl10NiTGRKwI40NURTs/sV+AiaxkU4o3Mj/bGI5dqNsArl5ddzXlVEgkipgM0TfVttZ/EaSSLHuv6HejKkey5odEcQtuYj2Fum6CQr9LJt+z2hEK5dD9jP2FPa0vsTDbVvYkNuxNJErnM02gKzcz5TzXPnvfU456iX+u8tnKPZJ9xJ8oRJoa/17Ceio3NjyLzpbOOVdKfmuVEFthWXTxBAIV83cGGpEBg7x8VQa3by9JVlLBNfZA0FC6f4FPJZf+a55cJ8fpnEiUHNIxkQg/508fHkvXOu69bE8LBzaOT7JsvNezzhH2zKyzDjk+ei6hTgtMJrZt2eMqhnuJnLXAsYMSCdgPBRlrsJfQdS5Y4EdGgglQwN13TetEPYgNsLS2+vxuaF+1vlQkJwiCfysIgu8HQfC9IAj+VhAEgyAIXguC4PeDIHgvCIL/OgiC9NklmUwmk8lkMplMJtNXoy/8R04QBHecc/+mc+5bbdv+nHvimP8vO+f+A+fcf9S27ZvOuTPn3N/4KipqMplMJpPJZDKZTJ9HXxZXi51zwyAISufcyDn30Dn3q865v/b0v/+Wc+7fc879zc8sJZBwcLgCMgEbteNdsXeZpnB3gD78vZevjrf+vLAxy42UMz/Trj9RitDjFHhXhPMRM0QhvkjHs0qHkuUBz04WN/r7Uqf5z0nbwlSeG+O4fCTXK5e2QR/04FyaSbiQ7QkRJ65rPwZHDIPOaYiEKhec4ZGfkwiJ2SG8mlxIP67uILQPPOcTnPHqngVDyXgGwtAKgXrn1tVxNfL/bT96RMceIhxwCUEf0S2MyUYphvkXr8pxUPoRhr1/qMu5eN1fV44Dk9CpXiK5EvtRDYUbob+ZSHJ4KOsivzHA9egX9JFyUMKUJJLinEaxJvcxP4EDMBFpk7LPpBziKjzfEAcaAL9aIwHwFhKaAiuJV8Tn/O59dHvrIjnjhzIpgwuE7WvgEFOxQaJLEd2IFMbSkwBXITOgAganvBeOdXAUpNNaBped9Ew6r5rKJMlCPYZ09urD0vhs4kB8+8RwUUvgRjR8JLxNvieTng5FdFSrtsBPQQppIv2COhcD3Tbt3uTfx+lgSHSLLqEtcTC6qGG9EcuiO1aJxKjKqRCUyAgYaHqOd1gtD+vmXibWSKkkuyRMuK7Ql8o9ku5nqB/nc4y9vg8r417FMokWUl2UipgZ55VCqolK0s0Ne9oGfU+cqos1fqL0wo8hsT1qg+4xvyPiS5SqK44VHcWaHlfBYiYDx72OSFO5RS4PdcI7Vs1DhX3J+ZroFhPuYk9mAtju/rnZ4b7iHwcilGoeVnxP+PceYp3Ex3gvMdi2Z072uTzy+ijXE6ZJuP9K5wSVf662KVxvN1hYFDAzOrDxd1awwjsJ/RLRLQ3IcruidR7KmYlfaHsBdK3QTOufJqJGfeFITtu2951z/6Fz7mP35I+bC+fc33fOnbdt+0nr7jnn7nzZSppMJpPJZDKZTCbT59WXwdV2nHN/1Tn3mnPuBefc2Dn3V36C+389CILvBkHw3Xq5fPYNJpPJZDKZTCaTyfQ59GVwtX/aOfdB27bHzjkXBMH/4Jz7R51zsyAI4qfRnBedc/d9N7dt+5vOud90zrnspZfaT8KNLVyTiF/RBaxGvJTJQPe/fXh1fLqQGHm+RoLEhW5yDVzNLZAM8kBCeOlEQoRbOxLCm0fCFUQPBaUgDhVMEasFrhVs5FnztxGy7HFgK5Goc/QADmRCYbmm0QhCDYSogLsaE4PSQadt/AhDiWyGe9+TMVnd6HF+ihi2Z1jYj/cQ+1Fhe9QtBNrgnHOT+3CFGvlxgLO3JTyfIPKqHJtIH9J16VjOs/2kEif30L9IwkgEjO3kyMbAWdJzOX/xRiehK/5nvPafL3bozPN5Erj5nVgChLaJjzCJWFhKgxa3aVGEusFZSqEtnYSZfPZmT/5jsgCmCXQpBqJI7CeFSw/RmO7zrsq/lLB6OZH2lFi3dUK8Te4dnMDhCu4+3aSI1RAY3MuS0VUlZ5vLpO7DqYhubXblvELDgFnxemJ/MRI+EkVUY7Ar59f7Uv8MSBsRRef0XCJKkhPRwTpWTlN0vRz521BNZBDZTqIqLfAeUBsuZzuJYRF1zPzr9slNckhch/sHpVwec8ylGA5+QNo4rzh/+KwCew/xLiZJVejJEkmwQ1kMYccdjck0ldMl6kccmegasZ8ESRvpqDV8jPk2o2sfEo/iPcF51PSMlZrnuD47c0pEjrhG+Z4hXq4SWcN1jgk2ozX3TJzHeigjznm0oce9TbW5B2njeok678CUkx0iiqWdtrgO5fp8B8enwAkLvg961knPGtGJm9GnmEdMvBnWek1xXXIM+Y5WKCI6repxl+O6jQruh0Cik77+8o8/1SoknPd2UPs5Ei3zHiDVQQNnSIwh3SPpzlmP+aIl9obyd8SOL7zkjwly7fidiCTQ7RTZgCu8D7Zlg6oP8aPpOdKXcVf72Dn3S0EQjIIgCJxz33HO/ZFz7nedc//i02t+zTn321+uiiaTyWQymUwmk8n0+fVlvsn5fefcf+ec+wPn3D98WtZvOuf+Hefcvx0EwXvOuT3n3H/6FdTTZDKZTCaTyWQymT6XvpS7Wtu2v+Gc+43O6fedc9/+MuWaTCaTyWQymUwm0xfVl7WQ/koUpI0LX3zy0cQAVsfToQCVN4diw5pFfmu6w/VUrsE3PPOhwMKXmbYYbcGFl4V8YxPie5YY37P0GToqe1t8bxNkwi8qghSMazyXYajAqTawq47xbc/qBdSH6XSTjsUmvrGpCrC2sVyXV/LsJJU+KzM5T6b6+JtyPLrv98Ak/3/5qjy3GvHDEDCu+PYqORYOlFxvNdI9f/zn5XhwDGYV7Dm/59js+W2AFVMMBnn1gpwvt9DfI6nr8a9gDM+kv2J8F0OWnRaj5HpzfGtRjXU7px/I8eoWv2EAzw3OPZlLf+cz2AbPpJwI3wMNYD+79b5Ult9nRLmsH36rwT4lj+3wXVkLVLg7huo7HvDPizv8qAn1xjMCfGMVgbWmTXU98LPc61vw6IXUveDI00vabeIGlZVbt42cf74ve04Ky83Fq3tSFJrMeZhPwa1X5MKlfjksroPHUg7neZ1KhciLkwnnd0FcO8z8Tbtm55xLF7Da3vGnp+d3WYls467FWuV3JdkF9kzMEXL6PKZ9bgRmvR5Ivys7aFpI99j4Otf5xgBtoL2tsqtlpvWIcwa282izylh/6q8Tv8OgRa/qlxS2r7CbVVbMHStizrECc4zt5PcMtM3Np7iGaD/qxO8k2RchvoXhNy9c/+p7BowVvwdTdsCd7znUNzDqfv+3Gvw1oSyHaYPMuYrn8bsiftvErBP8zqnG7wTOYc69Vtkhy3Hc+SaH7xBludxjj83n8V51zCVMS/CcfezfJ5nWgHOP9VHfWKlsHp3vi/C4GvOB3+eoywP//2B7lI3+DO8x9B3fJXisSvPAb8PYfkrNz6EGpmjbv96DPTT3KH4mw/UAf/lwg29yBv7viuoB9iqs+XCbH4GK4qWUGcPqutyXzSrAOyCAVzivd8655kh+XDR/iuZiX+abHJPJZDKZTCaTyWR67mR/5JhMJpPJZDKZTKZrpecCV4vCxs22nvAnP7/38Or8GyOxpPu54d2r4/Na4pwlYqE/TG9fHf/OvbevjlNYeN6agZdwzk1SYRJOYJNXN7BpLuUZqxVisnNhTCYfy/WrvyAsTZzIs2seA2OrziV0GC2ADjCcDVytHdOHEeHISz2cwU2Jb4e0jUbo9XIj7cnfFzvAAGF1WgITXaHlsMKvthn+lfMKrSPCkEpfNDGydLM5sUYSaEddoc9C3EP8iKgU7TAZIi6nQAn2pL8j2IynmZynNXmzBA5E/gEYUnqGMDKIqQZtqwcaB1reCb3/rQb6F05Qp4ycBC3LcXyOY9R1dUDOAcXAWllhKLi8mDHFtz+ET6TTOedWt6TcnR9JvYMRrNwRVSc2RlSuHvQ8j8gYkMsW5aRzeS7narGNeo798yWGfe70rkZo2WdEhdYHsJonTtdjg06MjWOl8BnUKZ8ByVn7610n/mcRsZjek33x4lXhbaqb+t/FiDFpNMZfP9q4EvXo2uN+ImIl2QVsX4H1bnZlkjQHOMb4E5NTtuS5npOUsm6lBTMtVzn1iJlhj1EYF/cYEiOPgeTAjp5jwrmQsj0rvEvGsGJfEo1ySsr+vy8xOZc0MSbOT+wBtMGuiBUp9IZMDnFSlKOyzqNMrBHSsZzzzmlba2J58YrzzY9fsS/U+wPIWbKUvutrJ+dzCbyPY8i0A3GPRTXb9qk1gkdXY/dMEUtke9hm7knse4X+AjOrhv5/J2cbuG8pJBbIcdUhiKPC3x9U39zj/GF7nH/Ie1MtsBzuN0SfA8w1JayXMg56LnJusyvlKjv+hmOFvW5HOp+oMPHAEvVL1LwCrrdDHB2/DTO8J4Fi1kP8volinEf9d/QmM5gixcpKVmz93gfuT1IWyTGZTCaTyWQymUzXSvZHjslkMplMJpPJZLpWei5wtTBs3TR7Es56cSjpi1/Pjq6O30rEqeFuICzJvJGQ2G//6Beujl85ELuaCDHIQceZ7eZQ0qXfv5ByL+dSbgv8pl0jE/qSGI+UORgK21AUCO0hW3zwSOLFIXEOICZl4kcpohEy5tJZZ6TDoi0c1dwxMA6gTjWcReopMtmugDMgezfxCZXxHed5TYFQvRJCpwkc9QL0O53GOkmDXQgcZPTI766V7yJUy2zXwBaIfTA8624DB8MABcD+2gvp08Gp9FEGpyRmAeccYQg/ZvbtRP+7Q7JAXc/hEHYTrj7AF6c7wgAsFzLHmko4ifQS7mrnmAspUQVp58UrMocZ2p7chUsfHL7iQyCHYT8OpJC4oX+O0bWMCEM59SNtgxOMD6b/4KzjPPhU8xdRVyI5Pa5ug8d0iqLbU8ddjZmmUS5RB2bmpptOCXSDlGZElKLHpavPRYyuUcwWT0yGmGk5oiUS6kBOqFtvutOhDZlyDutxR0IfrXfh6gMMh66A3G9YPzpINZjPg1P/uKVz6aT1rnaH43VEh5WLFvqDa4b4TDnB8y4wzxUqK8fcGzLMf43n+J2iuCcTjeK8c07jjhWd/dge9jHRNb6X+G70m+u5JmO/+B2+iNtwLbGP2H6+64LO0mbfUGwb12e09l/PNUbcj33Z+qezcqljv3C9EBPjvOV84Zrq7jE1kLOmx1GNz1buZ32uaJgLfB/wPc4yNzt+5I7lKHc15XzW4zTnPu0G+IkUgpn428Dx4TgEfC9zbaAfuT9FmKscK5bJe4OePam79oKWSBiejX5a79Etz49N0s2yz1GvOZdjOiEqpz01X1ghlENHTrrd4b3dXQvhvmzebSwvqcRwNZPJZDKZTCaTyWT64rI/ckwmk8lkMplMJtO10nOBqyVh7W4Mn9hUnIHp+Sjcvzp+KZEsdynigrNIkgz99Z/5e1fH/+Xf/otXx2//8odXx3uZTko0QQw7gQtbwISGpxKfHD2UcN7mBhLhIUHnLKJbGMKCDUPSck3MBI43kAhviHaOxVrl/BFjx4gRdtEgYFBE1FrEzOuFxG23fkDXDCmGuE2xC4cjPCqsGVbH9dtAvYYS843gsjMeyhicvYZY8ELqM/5QsxAqZIy6El1IgGUVSIap3IR6ksXVeHZN97OamdNQTo/TDd2UIiYJ7Un8NXykY7477wr3wjDxZh+haswr3t2UQA6RkFC56TBsrTAEuT5Z+PEROlNFwBvZZiIv2Ylu2/YHcDZDWe2WP5zPpH8Z8KMcyMTqNpxl4HwTHgIBGT8bjRucoXzldiXnZ9+/uDqevyXOhM45VwAzISZDPKqJpXEhrgFJ4VIkuuS4ET9rIzjcABnhvKKrmUoAysR2TLYJR5/RcZ/9lsYMm4iIqx/vUshV5Z9XCo/DJqMS+xV+ZyGOJx20OL8ihZX11MHpPlNrBs55DZzWlPvfPpPwyb2ckw7H3J+GmNvKWYoubcC1FFYGsV/iLpLF5JPAkoZYJ31OYAHeK8R98x2USRQNeFu86FnbfXOhh3btQ7W65RLR6Tp4fSJioMrNDuUShyKK2Icb9dWbrnvE5Iil0YWSCPWn5iewU2JW5Rb6kig0xoTzISz0vvwsZfJFgSuw7RGBU/1IpzX1vsU13PScnrsal8X8BKbLttGFTiXojf3XqySrPXMhXcv/2OwSx3Zecd/q4pP8ycZ3K88HKLcm7uinrtV8U78BiNZy3qL8XodMvAP4O0G994GQDx7jAU6775aDP70/NSySYzKZTCaTyWQyma6V7I8ck8lkMplMJpPJdK30XOBqTRu4VfWEPzpxE+8150PB2MaBxAITxBSniFNGbwoLsJXI+VmCDFTOub1E8LXdofy3CljSGVy0KjifZI/pgCFlli8AQwFCFjIuPpB6J/eBRt2UkF8MpIso3fiG1LPIcW+t/2ZtgKK1vfFzJM5iQjGGP1Ht8b1+N42r8wy1wqVsa1vYgRhI3ziVNp9dSJ0jOs11XNoY6lbx2R73mmTux2eIiTA5WYjkni3wASZSvf17cjx/GVVA++mOppyiNv7j7EyPU3IpjVjfTN0XFh1UcEy8i1gN6zF7XyqY78j4DI7lhuBnZX0Stwh6ED3nNHoQAwfgWloByxuc+ecw5wadiehKQ+yDIXYiCdm59HWFhKREcpYvyODm28Ln0PXHOeeyS2lsdirlsk7BNpwagUZVzl/XvgR2xOyCHrIsBN6VXkqjy7F0NhPnMcEkcYu6k/yvHJFFkUO6mSl8BHNgeCrPSC/gsFjJReUUexjGcPwjcdtcvb13dVygPXSRC9R+1uPctNITlG1L1nBbLPz3M1llSlejpZzPgB9yn2DbBriGY8L1Ukz8aNDoSPqRuElXayQhZD8Rp9zM/PdznxyeYKzG/us/C7nynSceTWyHzobFNtFKXZZO3MqHyCFRL46bQhwL/15CLJGIVkNUGhiSdqZCHfAsYm/ErImxxpvOvGv9c6MEQqbwYqB7FHEtvjPZFzUcwpSLYA+mrlwYe3AzYqN1B7lUSDmazblKJ0H+kmW9+/A47pl0PeVvA/Z3hPXP9xDxZY4z8ctMDHyfXKfWA/cDnFfvRtzcs49xf6PrHNuQLMmlIWE7xoF9p5BToMj8TRLHcj49xIvSORcspOLRvjgX9/wS/anJIjkmk8lkMplMJpPpWsn+yDGZTCaTyWQymUzXSs8FrlbUkbv3NBHnz+wfXp3fAVqWOAm1LVvBdmpYd5SI+b51cHx1vJ8JurbTwdWIuG2lcrwZSFz5As5UxR5CfvsIvAH72gGKtdpIXUskBs3uyvnN2/JcJhutcjk+D5EkE4haW3/G36mIYQZI6NlmEnqN4Xi2ehVhznN5xgCuWLf+joQkgxIORyPprwrIyOJrUp0skWcl6KNIZUNlPBaIgBhZOec64WyIuM74CM9AGPb4G+g/4AOln5R0bSr1YyLWh/8UEJOHcKNCclK6EjHsTOSDaEPUQWGKbamgQoNICUWfIwDcg20oRxcMw43/8Y+kDt98Q+q9kD69eB1zcogwNzBLJqQNPqOadYawN9AlJmi8eE36glhBNQL2MKE9jt8dJrsArjUBlkhk6Fh4i5NvCM+QzzBWQIm6KN3kA9lzFH61O8Q92NNuST1UIkEgDOlCyhk+kj2j2AVO25NUVfU9EQuVMFNuyHeQYHLEOdzv0qWQO+AtfPb2+1gErR/VCNFfyULmQphjcZ9jH6p2ca8f7yNWQtyMmJBydOqI5RJNZNuIkCk3LpXcEy5NxBXXfkwkWQKTY3JSJA0mYpbMmSha1kud6vfE5CEQSjpzTfwYy0DMTZWI3qTAcog+jx74+5VuiUSJiDcy0S/nV4p+2WzrthFpIu7HZ3BtqPPYPog0cb8enaCPYzptEYnGuurB7BRKxffWA9YBY9vBKTlRlKsoHOyI7g3Ose8RxQRlxDZzz0wvMP9X/nHrS9zKeUHsM8ZxUGv31KjH9ZBrd4L1wKSUdPVq4RzH/os3WFdwIeU7QDkvYp1zn1QJcInloW0qyXjnus1MCmD9uO6pkL+5sAcQlVOJrDf4DVSyv5ngG59OnMr+3AJFG4To31wGPSjhSHymubzqkfyWD48FLzZczWQymUwmk8lkMpm+hOyPHJPJZDKZTCaTyXSt9FzganURu/O7T2yuDscSFx4ie9F/u/n21fGfm358dRwhe9PvHr99dXy+ESzk9lBYp7LVYdEQsdDLQmxaji6FXbrxNQm1HZ3AugROYMmF/L1YIxFcmvq5qvxFaVv6kbANRFXoUlUBPQhihDLb/uAfw+dt7L+ObmHhCmgRHlEgueODf0KsrBiGr+B0QhexEOH2+ibcYMIehGFNbEXOd93VmFSPTi4MVedTGYfN60yaioIY2kU5+csYn5EcjwYS/18ghl0ugU3OgXwAvdj9gTzg/E0ZaCJtyjHGdRAVug7RKKX0/1tF0EPfRD1uLTxe/uPvXB1P/uDe1XH+9q2r4yGwjVNgGw7J/xqgkXWm1x7FcPtqKn1D7GF4LGVdvAEUDbga0cIGyV2JXG3/UPaYcE/WvMZHGNonJuT813fcgZpMKh6/L/tVOBYbvmCIJMDbftRpdARkCAhdMZM+ioBeEEtaYx8qgCENzjE/134cqg2I8Ul9ug5P7I8o9yc6Zf9xT8p3/W6BxFiIUsT3wUxNBCEcHMp4MuFssQeckgk8mbSQblIdVDRGe0K4HaV45xD1InLG9hNPybf618AnSoAl0mkwO4fzJvabeAn0LPK3La41/hLmfuyFc5qoFxGl8cMC1wCvHmLOw1UyVcmE/e8h1XdEbxRzKIcpnBB1Fk59Hdclj2vM2+wcGFPurx/ndgyXqgyJvPkO5DpRiGcP3sk6E3UjGsm16pxGh8stuAoCOaMTXHYm8yfawFUxI7oEFK3yr1uNmQGNwzqvU/+64F7FPk06qGifgyFxrQy4b4W9tMacJK7F9qhysDbUb6bI/17lNckpHEZ7EjR/yoUWe4ZKOHou+zsRZ2Jj4Uqe1yZAnDGG6sWPOkVLKT/GVhrOxWGYajf8QSTH9SV+fDU9mYg7ajabZ1/0U5JFckwmk8lkMplMJtO1kv2RYzKZTCaTyWQyma6VngtcLUxqN7nzxJmhglvashKc4Zdn718dj5EhKwQ68J2DH14d/2+HX786noBDGqlMYTqZ6MEQNiNi2OPuX0gioxBOVqiqy06BSZTSrSGcw+oKCAeSTcYrZmeUwxx1CFKpZ40knw5OVlGqw9k1UDRX97BLxLVQjyj3h1QVzoDwvHI9QTK71YvoL3RYhTJLYF/NkGgDQt6IkDqnXXeGx/7kXDUdtYDW5Tv+5GzFtlwznMqcGWYyZ7YGQIYwnutUGKPpXbhUIaHeyTfAIUHEgVSSU+fc9v8jLoGPvnPz6rjO6PIFpCcin+F3kVL4AJ+HsR3dlQ5vZ8IK0lEvUXiTzPmA7mqYX+VU/5sKsaGtv3f/6vj0H3tR6oflurztRwBCOOjUSOzWhv65lN8c4TzQjhNZfPmBXKOTitKZjO4+en1FK+nY+p2XcA+SjJ4VOA8nLKwBOnY1wP2YlFM57jApMRC9FmssPOpx7qn82A7LCTp4bK+jFJyc6FJUTmLveSaDjZbSdxXQxeqOJP2MzoEcXspxM+pkhvzkmoL7Ah3E4A7U2SPTS6kH51ueSp0CbKDEgTY7Ug+VbBL9She1oOV+izkJt7T4QvaeFuXHF8KflnvCCg+O5Dzn3aeEMWRCXK5pul1tdpGwuQcr4h5DvInuf3STys6QxBTXtD1YMxEe1tk552LOn7E/mayaA7kfV4oXKGcCZ0cgodkprslkXtD9jW6RqwMg7sDe+L6tBuh3JvE9Y4ZN58IVN28Z9xhrJlbzDZjVHDg29mWFigL1qsb47UKnQdSAmGGG9c99nu1JLgRhCmuNrjL5ZF+STaJYRDY5nsS+uHfxvBL6KFoW3kuCJvNeo/ah6RA3dOYw6hGf4bIc6BsROmJzdJXclk8qQj4DTmgtXILDOZzTziRbcb0QXC3alz3WrWV86stORtM/Q7JIjslkMplMJpPJZLpWsj9yTCaTyWQymUwm07XS84GrBa3L4ifhzcdLwUQ+PhRe6y996/tXx9PQ79TwKBes7N27gva8PJGY4CTWId8dJ6G6FfA4urO1QAnqFXALhJjzPWALid9RLU4Q/h1IOHL9dWARhf/vzkhlbcQh8Bmicc45V7c9iALuieDUVsOliuHzrQ/kGjqB0UElIHpBww1gQsS76k9Zjjx97pyhc4SpOyYexM+md4EvIhxONGZ5C7gBYuw05pl+KMf11+SBA4znMEYirB4TIKIdxIfodqSTqPmRAuecm//8DSlXT10UAHwggiNUTIwJOOFDuPKMgRACubp8W1wEEyQwHL0va2n96gxtQHVQByZb7RAJCgEqXtn3NccNHxP969muPivL6FPRtTB7LPtHDne1egi0pS8vJJ5FZy3XuT5YMwsf9owCiAqdcgJYErJYNI042eBIJkOJhLHEgdhmrjdiLzVwHuKKxLu49roJM4fHwPIGwHgeyzPWN4FuoX5ElIjNEtVQ+CnqGm44saR/62245dGZcNiD5GC/IIrpnHMRcJpmAsc/4DPpJS27/Hgo20YHKuS31ugW5md8LJhIdUPWJJE+sr9MmEr3peRUJ8Eub8h843wICrm/GgJTRdf07Rl9WDPbxiSUxBWzQ8FjN3fkudyrmRhWzdVSvxzCBbAfrL14Ludj9A3xoZrXs5ye+dn0OEbSga3teTdEKyS6hcPdWHIoqjYXuxrFBLHsskfyO0Y57OGa5FjmAOfG8Bjo3oqJHrkOgWii/dzPigzjW5YAACAASURBVJlgXMRPifeFcEsM13hWBx9j/UK4gtXb+F0WEGPDmKActZcQzT8Fjp1xD8QahmMZ9/OYiOsaL2W4kYXk8ZPP+Jld4zrc0xboG2CQ/H0QLLCmM9ryco9Bmeeyl/Q5pNUnYrvW1p0fXX9GZZEck8lkMplMJpPJdK1kf+SYTCaTyWQymUyma6XnAlerm9BdLJ6EYne3JAT3+kunV8eDoPzUfV1lyB55cHDhPZ8EOgSXIx7eIK4cAf0qCgmdTt6FCxKJCTgi1UiQNUbyyAjx0mAHDko5yiQNAtSrPYKjx4GERZsKriyFDp0PtoC0lEjuh3JLJAWLN3RLk3KYoJJJONevO1xPBxU5HyPP1GYlIVW61LVDv4sJ3YeazkzNd+V55w6uZeg/ohQjOkohzN0gceXZN2We3ACipoYEc4R4IJ28zt+QypZigKLdlIh29LJRzm1u+JMKtkDRFDWHORYBG6vheHbyC8B+EG1n4rk+d5zl24KQRkASiORwfpHoojOZczpZZUz8iNH2jEiCnCeKpXgQJAANsDZKuJSd/IIMyvSe7CvFDpAMTJcITlEsk+omcS1vCXKjEAMgIOWuoBcK98MziGuRw0gey8LK93a8deJ847FC1KAaCfWG96X8OpP+ijcdpAvITQ1kgihKnQmCrBPa+l0RmdguOYND2JCZWLGG4RymcEDgZkED2z3Wf4N13sGe6MZELCmGc1YMl6oGSQizC7wPsMdESADJdUUMMFpgfx8joSlcs7K74o4UrKSdEfpFoT2p3kDpXhX1uE7RFawAKkrcT2G6uX9txCvp1xRJTDnnlePUnO5oQIaAFkaYX67RczIAZpPel37KX5J1stmTcsf3pf8G74ubZTPDvD/HPATS5jAvUiRxTeDMtzmQ91NfwuHoSFyz2hcFA47PmLkZjl1Oz/v4Qn43KawPonuXC7FWgZBFc3wKAHyKfVpPgc1hvqVncq9yBAOKRzcyhXo57TxKXI0KN3wJ+LHWIOaLFu+PC3/SS1eiTCTGDMfYMzjHgIm1PI+EmS6CG12i99tmKfcHqF+LNcBntyXRVLof4j3BZNwYK+5h1QUc0nqSeLaVf+78WZZFckwmk8lkMplMJtO1kv2RYzKZTCaTyWQyma6Vngtcra0CV549CYEuMwmXzZFg8bgSZ5ldZIaMwHDc20iYd76We08LCf3FHZsuJgdtwHQQLTuYyfOOvunHw4jo7I4kbHuxlBBzUUh3t0cS8m3hfJXsAT0AkjS6JaHQ0yPpC5fDuWSj/2bNgfG0G7rJ4CLiBohmVxO5aI7HabIK4VXgQ5cwisrO5YYFksg1QJfWKLSZAgFTTk7dRJI4TshE4ZCIDpK/1YiM072HjTufy7gNB35UcnEp14QYh/UBUJURncYQUgdWpbDETkJXxaIRUWMSWGBpi400jtikQsCYSPWISRLlvHL1AapSIHFcBvcqYhjtGtsK8YxO09b7xLLkntExk+TRec6PMHDephdw2oL9ELFBjvMc6eySNZ8ll9fAHmPQHEQOu0aGly/L+h4dAzEAElZMgZuASiGWp5ItcjiJQ6BfiYPFMN9hPzZwX4qYSBTnKzglJcSzFnotKCcnzgHgUTHctSJMDTqNca3Hl/4Ee+5dQYkCJE8NJzK47ZSICZJqPhB82THR4EgWA931nHOuQSI9upkR0XNAjopdKUu5ttV+/JDuchXmBfEzB1wtWgOtWwKfQmI/7pJRjMlQ6fdeKiS4C3JaPfrrmsBFrhoBy4OL3uZm5r2+Aa7ENcOkjQpXQ3LTsPCjlXS7aoedRJJA3ELgV0QZw9rveNjCCYsJR/k87t1Evcb3MFfRnvSSSCfQSqBXrUoG7Met1LxzztUjLCaiTkXtPf+ppJSflAv3svbeIylnX9DkCogafwOopMzEPfEsutQpZIyY5Fy7/4VwF6M7mZqrxOk2GB+u762R9/reMpHotzkWp7Ggp++IpbXA3gKeX63VLe1a/newA9R4DmQRfdMiWSdd15i41M3lN6pC1zj+PYjadZdFckwmk8lkMplMJtO1kv2RYzKZTCaTyWQyma6VngtczUWti7efhBvHmYQdp4mEUUewgUqQVTECh/M1ZNH6cLJ3dUxELXKamVmBeyrAetBd7WAg4cJJKvU4WggmcbnwJw9lYlA6X+U3Au95JgwlhjROJUx5MUT4GxZve3+oQ6qP/5z8t8EpErgh6Wc5QzKvXboGwflqSTcyKV85hPUkgiMaFa78f1PHD4Cxpf6EpEHH9GPrQ3nIZlfKZficSNvuD6WA8zdl2hN1i4BZlVvA2xr0XeNvAxPDphdyvB4wto+2raWc4aFcs7qlx5COZC2cxpg0NvhQUILFTSSYA76YrNBO5i/bJZ4i5/NdugjyXri1ILEfE7c2Czz3ssdNyWksi8lRmehSJcbEXB89IormxxUHJ3LMuZBeYhyAg00+FrRlc0PQmxBIV7Tx96PCypxO+peewnUoZt9gzsylbeWEKBoTyxKzkuvpXjU+xHw5CbzXZPcE3SoPxAUuAQ6TPGQSSrnms5Rc+F0S6dRW3CDi6cdbuE6ID4V7gs80j8Fbwa3IAVdjMj+VXBBOVOHaj2c5p120Arqw0XVt1Mlw+0m5WCchk/WijyNgPG0IHGgE9yruGUjUSBck4inE9ZgskDiUc86FZ3Pvf+uiX1fPBipXjfGehMtZMpd60LWu3JIy6cyWPQJisxCEh20gbtaOtAPX1TVoi3POtUD86DRFpz6V6HMJPDAE/g2Mi85knFchEvoqhy8gh0EBZP11Yb+JRxJd41pVGNpSZ4PmPGa9iQcqdO1Y1kywJ2g/kasayJRCQuE0R6e1IOcmjvcTygxXwOGAXgV97mXOOXcGvJQOZDkTvWLN4NYAmGZwiPkD97NgDKaeCTNz/x5WX8p+GOLeXl9U7Nv1ouPq1oeNEXGDE1qz2fiu7hfbcw3d0n5SWSTHZDKZTCaTyWQyXSvZHzkmk8lkMplMJpPpWum5wNWCwLngKR42TCT8PQGuNoskNBt1uZenOioFq7hYSyjzMJYQcdHJKkmUoO5BkSpgbCWPa4R5V0Cd4EBVVHBTQtLP5gyJB+HWsgGu1Y4k1DgErtZUftesx9/o4BYz3HOJUH+PuxrrEQHj2P4xEsQh4Vk5RjuRDLEayXEO85D0sg/1kmPiQOFnmIEMzuQ/DuleBbcn1u/xz9LtScpJcn9nBCnQnUieFQNjDGO/cxoROE7V7KF/udH5K73UAfDZu1LuZocObujjfeADSMo6fAgntHNgZqA5iHHRUYyOWKx1ei6DFd8T95n5S6+gfOAFcA6M57ptyRxIF+ZVfIpElD96T86/88tXx/kOEymiDTCUitAX44dwVkIEX6FHSK43vos6ACsZ0YEMLlNM7Oicc8NHcEmEWxQTNEY5ESi5tx7A5ZHJN2m0Bgxl8MOHUqeX9q+O1zeJPclhcCmYUAq3nuLFXVyEMQSGwoShznUSwtJpCQ5p7kgwmSS5JfcyMeC5YDLNDpCeGZwxmbQPeEt9diZlom7hNhKyEmGigxhcnYi5OOfUQiGiQxQlvJSxTYETMgFkCHQxfgxnpQ0QbObXvISb0gDviRoXMSlgRUSVWJUfPXtSGAer570Hl7J4ibkKLKsGNhltsMaAt4VDJHcFuhmsmImYyQyxr8LhTjmcwV2OblXOOefoToe5pJConqS+AeZYi8SQDnMjBI6rxgd93xyBld16WYrhvnqJeqJuYQ5M8iEcvsY6GahCvyaCUIU5EoefylpvCvTfkZTrDgTtV/OiJ6kkHQ9buHqxHCJqAfYY5RqGfch1cMp2LX3TrLTz2tUzkCQz2JZxbrGuuDeEI+BxSU+CTc6lnnXRLHuSilKf5xrnXH18/OyLfkIZoqZlkRyTyWQymUwmk8l0rWR/5JhMJpPJZDKZTKZrpecCV3NB6+Kn6M8lkhmGgYQg75aCUryQEE+Q8Od+giShwIroXhY6jXRdVoJ03L/Y9lZvvCth3tOlhDxXC9R1AYcboERhCHecWMK/9RQo2RKuNEsgcIEcr3IJiw9GUp8KrkHNVIfgObj5zZ4QJjkJuHER+6F7WTUgxoJnAfuaPJRnnb0F3MBPGapyKkSUVeLRDl0wOJSwcrmNBJhIKkh0LQIZoJ5BRxh0WIyxiuFyR1ytRZK/EKhfKsYorhoTC0ED2BeYkpjCzjnnih7nsM40ltMZkhNuw2UH9UsWQNcCtoEFMTEm0DAks6tviEMPx5bPYr8PTnSlB+dwWjqTh+cvyjpsX/mW9xnphd9dLQdxRQyQ7mchjpmQUCcAxbwlJuT8qGj2WCfJLJBMM4OLWInz6SnGAWgQ3ezUuF0CAQEOUr5yQx6M8awG/n/DSm/CpSyNvNc0cLJi+9PTTjt3gcQB48rfxF6K43iJPZAOcezvAu6RY7jOzaSdyU0ps4YLWon1z0SnARPX1mwb5sIJFq7TDnZBC7dBuq4RXYP7WY4khEx6Wo0wt9X+BkdOONAxQWsyl/JjOnwtZJHVuzLpF68KwsQ17Jye9xwT7plM/NsiiyvLWt3G+Kvy4VpKp8oX5Xw2l7qOHiC5aeafkzVcw+K13FsP/Nc7p50Ks8dAKCNiWdKGYib1S9HfbDPHnOeTU2nD5u2fuTomvlwDa423/X03fwWJqEeCumUnGsvL3z6QOtEZUBHY2LtvyX7NxLVMSjzF/Gkxn4s9uCJO4JZ3KfO8nsi64hjS7S253MZ5oP+TzmcEWLsxkFjuRU3Tes9XW/hdtpH+I/1e7CAJcEasG/ubSizO3wnYnwtihnDGZYLVSM/PYk/GXeHs331X7nn9RTlGPwVwMyTuqpzZ6JAIDJLvjOoG8D46fj4GAglstr4l3x3Q1a+ZyR7T3T8Vyrgrc6+9LwlniUQ2cL9TSUy/pCySYzKZTCaTyWQyma6V7I8ck8lkMplMJpPJdK30fOBqbeCq8km4bWtbQlZ3xudXx0TUqBJc1Q8X4tyzNZByLnIJD56uwSo5516ficvIKPMngqKL2gAuZ8sYYVG4YuWldGsOR7W2QcgbSR7dBO4eQMaIrhGNa3OEPxnVi3SILxoAubqgExQcpTI44mz8mFEKF6zVTb9zWAl0i3jb7H247CC0u7op/bK4Q8RMyiQ+1P1z/O4/I+FWuqXROSteEUuCIxDdv1Du8Aj9AsQx6DnuE5+lkmRifCK4+zAJZ9MhL4ot2t/JIdsZLYETTpi0T54HAsgNkEcxWfkZwr7w/PxFoIEpkrGh3kTu6HxWdnDKEC6BbSjzgUhkCdwvA6LG561uAwfJaCMmh5evSEUGj+kW6B+HYluuz87ReQotlPPFTLsD0dnPOdlziK4sX0ZSuZoIjNxJZ65qCsTkDlzHkGCR6AkT99bYe5avgONjnUesM8YZSWijXM//eIM5NpH785m/X+O11C/B+kym2DO3OC8wtqB7ho+BZ/T8Ux1xMO5VTexfU9mexofyGRy1gLsRIeN2EBUyPnQ/5JgnIDoaGF5y3WfbMnDrfWKwcn7rY3nW8IHcfPmmjO3qRn8i3gTz3qGdRHnrAfcuJqiVa/r2W86L1QFwNbQ5x1yoBvLgcugfH85n7gXq+k6d6B5ZTGV8K8xp1pVzfb2PNn+G0+fVNS8Q0fO7jSpny0T+R75NBJD7mZS5uqX3GLpYJku8ozA+6xv6nqvnYX2yrlGOZLLAwVboC7qepgu+D+h46S9/dIiJDpRuM9NjqJKL92DabGey5j7EtSfXhPh5pxxAUSW+b9Teg3JUQvQeBH9w5kfSntQP65JIXPy21G/ETwSAnXMfitnfUk6EdvL9wfFUKDO6ProBFHElaCH3wngDRDFFUvNxJ+FwKYgbkdJkC66V+GESXwgeV310131VskiOyWQymUwmk8lkulayP3JMJpPJZDKZTCbTtdLzgasFrYuTJ/HJJUL+R2tJ5nZeC9oxZvZI6OWhcDg/OhXnkRjJHEeJdgc6Q+htuUGoDo5aDhQDk3s2SB5aTxkWlPNNSTcm8kbAU4CfhT00lELUesyeujfz2e20J94OZGh0SDxBLlndkvOz96Sc89cRzleJCuV/XL4E5yPmZUP5xKrG9wLv9XVnpk7vyz3rG8ABEDGl69bL/8n3r44f/qs/K9cDHzh/R8qcMelnD6LW1v6wM13KBirXF9ETuWHvu5I47tFf3OcNqg0ca4aniR8yl5tDIk4iM6NDJPZjCHsMLPNYnHzo0hShbekCrkxIlhevOTZ+xz7nnKuBkowfMqEr0DX0K5EOIkoqAWiOPr7Aw3rW1eIF4l3+BIHEAqYfIFEjXG/oVuWcRhLoFJRcECeUQZy/LJ1BlIKoA116iNtM7/uZCSIfRHiIpRE94VwjDrQZ+feFJ+X68UCFkgCloQujSoCaETEBSoS5RJSESNvkgUzKaCUNzfeB+uyh71COwjlq/W9+XGPrXeJacFRjHxd+pCsgWoq9USWQDf3Xc29kB89fhMthBkcslvMZboz8b6Usb4WoES2jQD7qdcjNR40tnoX3JHEgrm1iT2EPyquOu0SWwj3lOJ9xA0X9Bpz3RD/9zpPE29jm9QE7Qw75jqmHQOPQTiK69YD7NooM9P7EexTyzHclbhkC0w0a/x7Deysk/iVqrPCuETE29gvLkeMcjp/EqrrJlFkP7h/ss749nXigQsvwvumjzrnH9CGKdU+/s1+KkoiZvl/htej7Ysv/GYLqS+6TdJJlmUzcSnSz53rWmw6GRKjVHAGuq/C5VO+fypUVz6ZjZkgs/kdH7qchi+SYTCaTyWQymUymayX7I8dkMplMJpPJZDJdKz0fuFobuPZpTI7OZKtY4ov5p2LSTxQh3vf+SlCfcSqx0Iu18GaHZ5KUyDnnXn5B3NXWS4mrj6diL5bF/kSazFc0OJK/F1dIKKYizHTpIq4G160GoWom1+O94w+k/OVrEkcPY42tNBskjMuBDQFjYvLIxUtMwMWEV3J8/iZcMi79YVGGcDegr/IZmS7UE/XZ3NBh66t6dpzj6Mg2OPXHnonAPPxrgqgxSSTDtrvfQ0j1TX89lEJ/eJ7h362PkNgw8+MJJ78onbT3A9jaOecWd2RO0rWOYe4QofE1kmEqoYuWt2VeqHA+xi07A7q5hBvZpcy39YHUbXAm1yxv+tHKwVnH/a/wh7ozJJwMK+COwLhWN3m9lFkDjYhAtSr3IVSDzj/lFtzvlNMgjl8UvDUBrtdF3dKFNDza0OVQMMCwQLLJO9LffW5hTBJK5CqfSR8Nj2Tfy3elfOIfRIDUJoZJHMN1L6MjWKTbmaCdwxM4T96S9hBpoJsf59uaOBlNzlAnzh+usRhJG5N7sp/XI3HbrF9EwmUiczTO67RtdAT0bZvjgzkDFCsD+taXrFZhpnRsIqlCVGfBfRiV68FZhidw/Jt+RiJmusJhnSg3P+xRMbYl0uIV9lKuPeJQCtEa0fGyD8vEs4CGafyS67mzVzf+6xR+ptaAHI4P0X66tvXgyERuiWX1jZVyDcP7nehWhHdvi3dM0nE25B5AJJBIV9cN0Vcn9ncfxqgcH1FXztW+/I1MPq36rgeHdM65qGfcVV0VioVrSIHiecToifEpdAvHRMoV0oX3ON8rlJqrtb4mUuijfz9k8uJECGn1bIptq+GcRoyaayxGXlk6ubH9g2NZ6PUQyYCJJau5rdsZFkjiig0ufrzGPUgOnfs/Q/myskiOyWQymUwmk8lkulayP3JMJpPJZDKZTCbTtdLzgas555qn4e0skRjkViYx8hPExQ+QUa1B3PGdyeHV8UeXkoiITml7+3P13ApOaPt78t8SOLI1RCZQvyiWawqiWBvGNv2hZyJj4RDOUgu4lQAxI22wfB3xTiZLK7v2VX7kRiUJVIn+WJgcEhXLhAZRLjMMwTJEutnDFFMhYn8omKFwlSyvM1NJlhC3CPpQHHW9Hx+ZvyrHKeZF1PhD0p84AjrnXJPINZMH0n66yWSX6Bc4/Sis7pfAPDjnJnDOGh3J8dnbcv/mFhK6JnJN3WMhwzYTbVCYIVCnAfCxEggM20B0ohBTRIUzuFCH2rc+ogWVHBLvauD2stoHfqlNEv1Cmds/lr1kBZSqz2WHIXk6w6x3/aH9uoOrZefSBrqolVNJsEaXHrqOtUimGvXgQ8QZiMywrsQHCiRz4zxUGFMPwsKknUy055xT/0xWwWlps+dHK4eP/fsQn02npQbtjM+B0y4xRzI45O3Ie4KudgqxIJaJvugmxiWaR5c35XjFhHx0V9r2t3OIsXXAeFSiXFRjijWikj9iTMYPsAFiz8vO+1/vCvdT+yedETvvk6cimrrKmABWrknwmuX81HXwI0l9KBETVBM3izThqxA1IohMDNoSr6YTGtYYMSu1PlE/InAKCWVCRmJV6p3G+SXn+T5rQ//1zmmki+PJZ7D93KP69k+FAfYk5GQ/xnRORBuIovFZ2RzudRPupXojVm5eWD9MOKv2DyJqaAPHIUNS87InITjfV9x7wzXHn+sQLqx0I1MJaTvjht9HEXA1jhVdIqO89l5DhRu4dk5kgJqUfB+uR2J2fppAhDK5Lxx4tCMvdeXyR6e1FRlN54KVLMw2BTr90T05/ikhatQzIzlBEPxnQRAcBUHwPZzbDYLgfw2C4N2n/3/n6fkgCIL/OAiC94Ig+H+DIPhHfpqVN5lMJpPJZDKZTKauPg+u9p875/5K59y/65z7nbZt33LO/c7T/+2cc/+sc+6tp//36865v/nVVNNkMplMJpPJZDKZPp+e+UdO27b/p3PutHP6rzrnfuvp8W855/4FnP8v2if6u865WRAEt7+qyppMJpPJZDKZTCbTs/RFv8m52bbtw6fHj5xzN58e33HO3cV1956ee+g+Q23rXPX0e5Ia1pP8Xqbu81WFzkqxd61x72ING95j/c3DS29ISvrjh8LLhwMw33uoUw3+G89o8U1GMMY3NjVZaz9PSavneINvQcBKupU8Nz8gKyrXx8tORuTXhIkMHvv5ahe3/tMr2snK+cu35PrZD2GlqrJm065VzidzPx9N617yxIHirnXbyKrTWpn3zN4XGPj0HWFCyfXqbxLwXUkhbeCTyxjjr3yj5fD0a36L5vUK31jR8pK2sp0VuXzB/w2Iyhw+lEbzO7E6gIUs6keeX1kroy/KsTxs9FDKXLzg/56Ftr+sG8cj7nyTw296FBfM702Gco36FuBAxkHbe+IBaM/6Juzo+Vx8I0FrYLXdoNr5jB930KrTKa35/RDaQ86b1tyjc+GZi225N5nTP1XOj47BgpNB57PAe6cL/xrh9fzuoM7YNjDkhd4vojWYcvX9gBxzXvGbB36HkmFsB6f8Tob9hXm+4QcDgfd4AVtu2hvzG4G+b5Kc01as6rskZkWnBTvXD6yVm5TrTe7dwO6deyzXVXYp7UxhJ73BN1a0Xycv391LqOFj7BMYkxxZ1/ktibL0pZ0yvz2hXW/lvz4owfDj20jOK85PfjPIMtW3R3M9cPz2pMHDs1POExz22ElzrXItVdiT1PekffOK2x5t2lkmv43FN0b6W53+dqboA9riU/obWnx75aQRqz3/NxzKlpkW0iqrPa3i/VbcXP/qO7zOtyZMscHn0Wqbfaz6HuPJevB7sxTvPbahxnqmnTql9nCMIfdC7p9qfjnn4lXnxFVFcBhjM8U3OUHJzZsfRNKKmmsPY77yp0IJNv52uqqnnhwrlB9cLtVl1b37/vv/hPWl3dXatm3dp14Pz1YQBL8eBMF3gyD4bj1fPvsGk8lkMplMJpPJZPoc+qJ/5Bx+gqE9/f9HT8/fd869hOtefHruU2rb9jfbtv1W27bfiqZj3yUmk8lkMplMJpPJ9BPri+Jq/5Nz7tecc//+0///2zj/bwRB8F85537ROXcBrK1fdeCaxZM44WYIi9pMjm/CNlrdir/T7q3EwzIvpWlv3Di5Or7xEj08ndtFOtlRIrHQRSHcy+lCMLgGyNl0LHHl5B0p5+f3pMnDyO/VOETcNQMXcAruq0aMOEFs9rKSuhVgEppO5ucBnr15FbgWypqlEg8/eVuefZZLm3NknR8C1zt5WZgMYnkt6tHCTjuaM623HDYDhHlpaczwbefP8cff8ON0tG48D6XNsx/Lf7h8WdpTAytZ30I9ToVvaXdknFPgYFkm/buCtTKoNJdeyv84+DvS14ffkvJBPX4qM3nTg7Ipe0+gjA1QinAi9Stg13o0kesVSoK+Y6b1za7MC+Jto2NYqG8B71PZwTGHO1gJcRhiDMtb0pfrG8ycznLlmBa1zKgeFsAPYVfKTNaLO/6s64PHnKBySBQk35Hj4aFu2wiZ52nXyb4h0lDMpP8qlbGaVtG0JZYxJNJWzfxbOtGLCPVJz6UjY8wj2kETHwxS3U62QWX25mU99rPsV/Y9+4ioXHoGjkfdi/4FzqFsvTnPcVxM+/+dL4Y9bHrJPQoYHDDNWFn6+rEx2kwzjYDCg9cccyBzc5lTwRZwGNi+1kN5AK3pO68G1W6iYpwnAd51RNpKEt/K+l2OiR8pi3/ikUSxUAfaT6t0B0TXsP9VnSzwyvoXe0wGBKpCG7inDWD9XiITPNHFhlbMPSgV+wLZL3pR0XbkR7lpcd7Fp2ivz32iDyEcPpJKLV6RDuD+yXUY4b3K/lL24z1pJJTNMLcFnE4XsDHO9RjyOs4NZZVddib1J+cT/3muSYX4wmqevyWShUyefEcmYh9ayXXYxv46PKkf1q7Ci+V5RP7rUc+eTowNbSPqxnePsnjG+ARrOR8QUYPtc1Bg7yE2WaLOh/Lpx/OkZ/6REwTB33LO/ZPOuf0gCO45537DPfnj5r8JguBvOOc+cs79S08v/9vOuX/OOfeec27lnPvXfgp1NplMJpPJZDKZTKZePfOPnLZt/5We//Qdz7Wtc+5f/7KVMplMJpPJZDKZTKYvqi+Kq321CtwVQ7A6fC2epwAAIABJREFUEUzqowuJka5uS0x1m3FUaDuVcOwrO2dXx9/e+fDq+KCDvR3E8r/jUEJ7J7nEmP/vxatXx/ljqdPWy/K87YEc/+L2+1fHg0BCgZtW2rBq/GmgR2HhPT9A3PkC3M4x0ss/Wk/VPe/Ob1wdf+f2H0tdEZN9JRWU716xd3X8o9XNq+Pvn4oLeADWYzQSDkE5zSGauZ5LP9YjxMKBamTb0nflRtAouu+EHWOQcptZgBn+9VvCLG8BxQE+oLLFZ35EKQTmEGGO7E2kH/MNsgxXcAQ7ujp0ixcEM6QbWT0ARjHWSAKxMeJXdKoLwPLVOQq+JeOTbstxvCd9x7aVJTLWA9cbHMk2kSHrPDNwtz3mfUEPkuSccwncxehSs7gFBBPjs76DSYB6B3AkTC4QquflwALKEUP7uAZYzY3fl/3j7BcEg00v/dmhlfOTcxqPYvZu4Ee8Zr0DtzjUKTvzuxQReyLeQxcsOuRRRCyKLb8DH+sWrzFfSj2I6bl02vr2yPlEHEhlWsdGkSITOudScimDuLmF8lG/wUNZh0Ehe8n4ETcN4JSdefiJuuhao9yrUCfM2xroCeut0C3/Vq/cMKf3pa4KPxzRRRBudMCYApXhHc50dE7sTE/iXiHxG2wfbEPXVe/qvHLk456EOmEfJ1ajUB+gjlHp31dYJq8n/uNcBwOL/OtVuTn2mV3h3pr1qLnu4Rw25DU45jREVQuszz7zWO5VUaERKOU0RsQTY6WwOSDVHDciWnQdy7f9719ic9mFH3XMt/z7cAIUM1kSd9YvkD60LEDbiimQWr5n0PdERVOsW15DVLoPOY3XPZsG6xxyTvUwek4jqEHENQrUFHhkSzc71CnBXlzsw1k4JZqNDRdNaIfSMS1c2sKNPKwZwEkVSHB4Lp98tCuZYOEWflg55+rH3cwzfzr60u5qJpPJZDKZTCaTyfQ8yf7IMZlMJpPJZDKZTNdKzw2uFjxNvjnekvDX3ljiqMS4QsRmI8Tg6JQWgme4nZxfHd/CsXPO7YVyT4IY5hIc02aBDINIEnqxkPg06zSLpMwGceg5GJP//eRrV8c3B8JCzBJpM8t8kM+816yRveof/PErjhrOpC+Ju21HYmtzK76QZ5Q73no//ru3pFBEYTd3GApldjVchESVgymQKbiUbQ+lng+RhLNaSNum7+qp2obPDlUzTK4Sh5EIxPnhAylz/Y7UNUmkoEEsxyUQvQbYQojEpQd/IKHdu98BujcgbiF1yE71vzukMjyqW1WiPzillNtoEBzvikvhUNpdYJYTzBG0MweuRjRm9Aj45b6sEVCTCqXq5HhTIg7E5L1bd2VepUtp6OpNoDhDuaZeoa7nQAzomnTqRyOK0o9kLF/bujoeHst/GD/EuMU9eIJzrprQqQzuhEdkD1DWy3RXo4MbEgUD9VCuVnAgo9tZH04YYl7QQSyCW081kBvipfR1NcFkdc41GdBC9gcRPSIjwNKGJ3Avu2AyVMyrmTyPblcx2lxty/6cnQp+nCLBaj7rcRTkctFN04gWDzGp0wVwQowbcTAmdVbJTYESKcSK7oxz6fsS80ihYTXWBVA3jmcX6aoHtHnzY21EU4kZlcAm8x4kkuIeVmKf0MlDUTe8bpUzG/c/7B0qQa/TCCHxIz4v3nCuw+UQ65a4FufJ6AiYkCZ0RMohUI4VccZXJkluGMDSIbA7P3WCZznWjm/EV7kGgJ2jPXReDCqgkj1udNx7iHv2JUPtS7DZTQaqk6z6EVy67an+Jq5Ht8AeJzTuhzxP8bnKmY3OhFiHNRJFt51f2byH/UfslH1GxFntV0DOiLcRV2uBu9dTuPLuyh7LJKbxgvgt3PvgzNbCdc1tMLm3t52S4Womk8lkMplMJpPJ9NXL/sgxmUwmk8lkMplM10rPB64GLYHVrFdIyHlTXLemsAwJEb9bIM79f33wxtXxX/jGB73PI6IWoayKbmElszXCWQTJINOox6KlR8TpmAz0DdhxETEjVvZ7i7evjpkwlEkhnXNuk0hIku1MYfdC3K9EjDgHc5Tvwl0ph3PHoUwfJn0kFrB6U8Kc22PB5NhfTMLarOCChGcV2zqMTKex8QO/m8zoBI50ryHE2oOrMERM9y46xxFROz4Hq3AB5xok9nv8czJv6d5GRK1N+pOIKUQDw1vi0fkews370sc1cQP+c0ZLTAzzhxgowtZ0yqnflPXJxJgKBwR60qBtm33dNmJMk4dwbQOiQXehgI5qOG7pUMNkdoikq2R+6MfJPSSPBAIUwe2NY7B4AS56GMPhaWf94570AojjFGsGeAJxDaIaxQTz/BHqCjSKc4ZOaEQvFnekslyrQerHHIitEFGL59r9cXNTOBblqFT6x4QY1+CRMDblDnDKHsyO7SFi1CJRX/YxMLtTwYajO1I+sTeVhHKl95gUzm50Oav73NKARtHlERS1Qn2KqVyzuA0HQySrpQNVesa+h/MRE6Aimfb6prwPu+5oao0BP2OCxhbIDa8hlojc1R2HxR6UCPsz+0sjjXJ+vetPeDnAvQrj7JaLdipHyxQJlLGOWRaTlXLuKTe2HhyXe6BOdBp4z5c9yUAdHe5y3c468z+crmt0/OJGVmGtczyrjLiS/9ls/wYYKMeQ65z9S+yLyW277pzcD3kP165GSPFs7Pt9eBzRPZ1wG0hfX+LW0D9WAdYtseEuAsf7N3tY92f4XYb3D/dlzmcmhNaosN95sBkDb0OZIdHfiX9zI6IbMdkormkmzBLsXPiNr8t/+8MfeMv9k5BFckwmk8lkMplMJtO1kv2RYzKZTCaTyWQyma6Vng9crQ5ccPokTNaMESNM4CyDeOSm6diMPBWdxv7Smz+8Op4heeg40LjFADFJOrjtZcIYjPf8yUdXwOkihILpTFbj70ie38mkzP/jnqB1X3/nwdUxEbMbkTiwsZ6T2J881Dnn2vVPNrwR4r9sT7Txh4inH8qxStgFPGX9oowbE2nSOY7HLF8lEZvr0DwdeAZImEj3KpU4iwjEAdELKacaAivI5OEh6t0Q9ULyzGgtfTS+z3oCvelm5POoGulrlHMQu4lDi/ODgayZnK4+xBDQBh4TV2vg/jY6lnm4OpA2E0nIYFpYAb0oJ0SynFKrEC24JY2IUOF6uODUNTCWpRwP4RTUh5KMH8KZDdhTpJBLP3rAhH8K2xnqfy9K4FgTMtHhUuZVnUmHEJWiU1JfskU6C5VwhMpO8Vy0gWiMQiBRjnL1ugD2VgGdGOu9l9iYQjDRN8kF0D9iP0gwp5LfYZ4orALtCdfECdGgGvUupa+Hj+DsiASobH83KSRd5ZJzcEOtMFoKs+lzREK5HLegk+D4qkxut3BOpJsS+7cZJDhPtFoOFebjOvOBSQIj4jBynoloud+oZLVjdoAcqsSjnJOY29zrC6JxdFrDqy7BOkovddvYf+qngsKUuc+yzf49gOu5z4FLu3z68S4mz6RzWl/SZOJzSSchZR76kSuFJtPZTI053oc35GY1JsDOK5Tf9xqLen6KBBwPrm06OHaQQ44b9z3upVxLHCu2n/Oe9eb1MExVeTu5vxMfU+hq9uw4QReZYxLTCm1LVngXYazpwFZF/qzbbHPf+LBt7LsISBufpe4l9tZiv0n89XHOuRqfS/xpRlMskmMymUwmk8lkMpmuleyPHJPJZDKZTCaTyXSt9Hzgao24ZYUlksstmZxRQmR0HQt7EKv35vtXx9+e/vjquO5jWJxG4i5K4VJ2kZT03nsHV8eTO5J4bjoAGwTR/YwiWvcrd8T9rQ/LO2+kPmx/ijh/fAA7GOdcuZJwIcstwDqV6hhh2JahYCkzPWd7pL/zHTi0jHAJXVZ6+oLnwzVwqMKPTjjn3OQeno2kbTkSAzKES9yCrls83n5fyjx6Hc5kKdA1zLE4RUK5lCyEH4cbnKCeu3KeAeLhw89wV0NZOZOJDhDaJgbY40bWJ4WuAd2iw9f2hzIZsmNBeI6+JVn+iikxFDg3yXJ5UlfgE8XEn2yNTktMbtrCFY7/UkPHt9Exkk2ew7lmDVefEEk4kWxxs083MvQ1E9Bh4LY+0EhrPYBLIHAv4kQhHMxGh3Bgm5A98CN9wyMZh5gIHNrAdqZzoHFzvwObdg0CPqacgpzS4BR70VyuW970I8VMPlqPZH8aYC41cL7Kd+hmxz1Jyhkcku/DPveSJFCO1kAU0Ucsk25PzjkXzbE5tMR44EgJ5FBhUlirCUi3DOheHxqlkJEefIYqJ/KwwSPBrInbbW4MXJ8CrHvlroW5R7SO7mXKgQu4EvEjojF8NxBjYzksf/D4/2vvTEMsS8/7/rxnu0vd2rfu6e5ZNbI22xNJEcY2sfIlkZ0IJQSCRUhsx+AEZLIQEuzkg/PFEAhxIBAbHCzkgG3F4DjxB+MlJmAIUSxFCEmjxTOj6em9u/Zbdz/n3Dcfqrqe33vnnO7RTPdUdeX5g9CZW+ee8+7n3n5/9//X11s7WggGJ3K9EJlB8+i2yDDQcfV6GCC0gSOlHhJFY1kbe3pOc59OgNVYWvOg2pE1AR7e2KejYvhv0qwD59VkoXq+NnZ1POfzD3cbnbujDdZ7Ss9nfdjPfEYH7nJBCCvWwmDshAsLx1LRqq5PgKtxfGL+5DXvTYbVc4yYWND/WBuDwFQ+hwZYCw/qkTa2QeBKy2BQIpEoH90zQwc3XgdzYwJnXISV8hkT1IfBwMFPEHBN1JPIsRuho0VEFkO3tdOS7eSYTCaTyWQymUymcyX7kmMymUwmk8lkMpnOlc4EruamImnvaLtttIkt3HmgMdiPJKJGJdiPTOiIhe9ypa//Xkc3s8VU0a8v7145OfYtPedwG0loSscFynDNFHv+KxmC8GA58nLvkp6P/fwbmfJNLOcEXEQxChERB9yPKFqIrunxAMlWI+B03Ia++L8Uh+hf1u3IMAxSt04Hz1U7qtVp2ibfpmWjk5tIiCXR4SfAb4AeEDdp7QATguvSzge1Ph71mWIruEQYKB2+uO3M8D/iGeOVOtsTHM8SKQzSAz2TDIGAIDQ1L7FNTrwLgbY+AUpChDBAFKvRmLSHgEQEhzEUjf0f4AVwShIJ8bX5a1o54kCDC3gT+oG4WpkRjakOMU2GRMCAdOFejV2t23hFzxmuon0x5ema1LsSbs0vffH2yXFxTe32io+/pNdaoMtOtStW2gdy1wWmCxQtMCcEekDsi0p6NTZIxNWA1fH6DNgUEXGHuo5NVxRZLFoLJ8fESsaLwLtQz9Z3dvWaLR1M7Z6Oi8A5rEbFht63mKt+vGVburbHcIuLRzV2ZyKSr2rHp4dABQcM4oTb4qQaY+P5jW0tB5HGfEXHUjQGbpKHSE+V2EYMcZ1F8bgeBmGvPSBKl+GOhLd3rit/N7gIPJh4G8dMTbAhHfzqwnqJ8WX7WjaGHDb2w7olcKkqgSsFOCGeDUngKAb0F+VORnTtq3baCkJVuwhxxTpHJ8Skp+cErnZ0BR2wbuF4JsoWjYi+aZ8QrYvG+AwFp77GvtZnDs9DrgEpsTQUIybKm/D5Uf2sy/ar5xjLKRK2B+dMgE1NdNxmmJPTuNqRkc8unjMNXO6AAGIucF7RaSyYnwNtu7jNdSXEEoOATrpQ4n4O1y2WtD/p8hhncFtkWCfWMQaGRsBX5/CciPvocyDEEZ8TfDxhHPkb+pwr++GzIbl4QesgpyfbyTGZTCaTyWQymUznSvYlx2QymUwmk8lkMp0rnQlcTZxI2TjaJkzg0DMd6dZZEKpZ45BGl65hoduFgZuY1IcXhdfS+60vaxDndtQ5Oe60detwocZdbYTgpAGSzb6689TJ8UurirNcaSq2wdDPzVTTFr86ePrkmC5tbj/E1Rpb39132DoMMAYatft+5a+yfrWzCo9dDUoVXB98As+P4a5GRxcRkcU3sGVKBypUmUGSCcZM4OqFYECG2bkYCBxuzcBMhlMSzxitVAfnZQcIcAT25IFb8XURkaXXdFv58Gk6H+k5C6/p64dwtQpDVoFDAF0rCmxn1ziwLf+5YjV0uwrQDiAfrbv63iDAbxBePwyoBIYAl5Z4ovej45ufaB2ae0RXqkMv87nqsZfe1XlbAgsIcJYgqI5uZ/oqnclERHxb3azijnaq29M1w2E9KDa1npN54hOo2y5Qim1dkwShl9G8zs/Jhq5VRG/i3Z5efwgcbISydfWc6L3PnRwTZxARiQIXoDrEESgWg/CA+rgJnHkY7pnCpY7YykgHFjG5aKCvs6QeeIrLgQklQHsmIVTh9rWNi8t6j3gMVPbqtl4LfZ68R/nlwEUKDkqTVcXSGre0vbPrsOlC/acdvX60DzQkARrXwPMA7kizKJ6D2xzxRZYpQMWATaZ3Dk6OOxMdY/vv0+MAaaLrGtaJxo6Ot3xBe8svoc5c2+kyBfRm1rQzxbpUAqkdrwIhQjnYP63bQBkP4fjXxLrX19ejFQSCYwgTD6RTIR0VORYCtBp1Tg7wueIBtGIE3DEIAEVbEIGKgcG1t3C/Pa1/Oa/jrbWFaxKJTYkf6jkpy436cH4GTnZZ+FGU+KbraZl8G+3d1WtFmLvFAtZeOBsm2zrHpgtA02sw2GCdJAbf4AccrNVYw+hAFo1nXMfqxDUwQrui/gw4jsc12HGGcV6zJsdYY6e7+tkyWdB1jsHK0gQ2PsIzcwZRo4rbd2r/9m7KdnJMJpPJZDKZTCbTuZJ9yTGZTCaTyWQymUznSmcCV4smIp1rR8eHSkbItIUtNexJ0x2M6oNPSmZT6+ruDZ6IrmV0c9vpKm5S5Hrv3TuLJ8fDZd1SJKIWYY+Z1//girpS3BnpFuH3da6fHM9Fui14IdG94G/iOqzndD5EEkbLwESAhE1rHOZYvgzObqnu2sp4VfthtFbtohUIr+dAo3xNMGjShyPWoZ5z6fOvBufd/dQLJ8fLr2g7EdGYLGo/jJYZzoVt8mZNORLUP8YxXPuCMNBU77v8ivZDf5MBk9V4G5G2KDRiCf42dwuuYxsIXnyqGjPjWGWQpgCPYz9MSTygHN1ndfu/fRcOMLht4wAOZww5ZGjfjONO8572W3JYjXsGzjq59icdbgKXrl3MDbw3BdpQwFGrmAcmBkyGjlB0wOE45zFd0I4KBWwOCEC8rfM4PtD7jVc29b1wjmsgbDO9gWREogoJ8B7gZHQsCgMm0WANQF0LwOqATkw28Pp0Bjm8A7RKFJVj306bOg6zA/QDQjk9cYhtOK0tqlsacS0iWtEu0L24em1zQ2AycG8rgDFG49D+L0Jzs/3oqMR+9t+5dnLc+LC6YYY4bfV6U87DQWlH8RE/B3wMqI8rUAb0FfHD7EBREqJ0s3J72n50hRLBfEO5ee9pA0Gke3CaYsAgwzMxbx3CCek0F6MfArcr4EbUtBP2G9FXOt5xTQsCfrGAlFgbGKQYXB9t3LqtbZz2tBxcz/IlYIwBxgTkOnAzxBwGGudaMwG76BPiiw7Br3SPjDCuXA60sKFzjIGOCdcPjmEE4CZAP4mYBegV8Cm2nWBMum4YZB5gYBgnDnPdN4kyA+NKicHiM9GOrlUxcFyiWB7rYS3Km2HNJPbVV6fJiOvQQThuHf7muX4cAo9r67yf4nU/rn5OBmu6r/484BqYV6jD9PCw8vi7vf5Zle3kmEwmk8lkMplMpnMl+5JjMplMJpPJZDKZzpXOBK7mE5HJ0tF2WIxdS+/gqEZcbVqNq/EcumBlCOFMpXoL+kGaDLFNfADnilXFBxqp3oNubjF4LWJ2exOkRELXxqsnx3RXo9pIhZxL9Hj9wkFw3vY2XIfQHhE4mxjHKQNX8XrW1fcOLmCLHMUjCsBwztE6nLymNS4mEZ2PcE3sft/89HuC96x8E+hGXo3i0Flm4ape+OB53SanE1yKesLYLRhLbMe6XVviTckIoXtA44hYEftys5l9DMbD3wLcDdcqR5jSdH9DQCeDNMM6VJdv5Su6zb/3/csnx3QoYhDeZJFOdijDToh0MayRwZVpF5hAXH0tTyQOY2a4AjQKIZSNa1qHdKLb9v1n4SZDU69DHdADOtbVjM9ZOTjqeDieyZaiWNJRpCkYG6QMgdvQBScCAleuL+lxEwgc3KToFpfdYjvWPAKev6xlaxE3C52CiFMRFWPAnM/o2KWN1npZXSX9EA5CHaA0QFqiHqySgKeQs/QHmrzoekB40F4M+n2QY5Uv9B6Nb9/S11lnOio9peF3rW2gv/vEaREk2SFaiDqgLWRB28IBe/FAC4nJuTmMNSImM7hagOIMtF2TLeAqzyHsGeiSB37E+dm6DVwH+FS5gv5ECGWEoFcH/DLCWhCsjTgnGLczDlKs2zTRdiKixn6IiOyiPg7hjtKcQcUq7k1MjnhkBASQeF/cxzNsQjc2OAoCDYxmHPLYllzIG9sIjIRDWDDH0Id013N9Opk1K8+P9uCoxXlIl7+s2oHM72N+ko+OZz7TDVAOzMPggQU8zMMJzOF14WdIOkbC8VJy4J4IVC/3dL0VYrq+etFgOR0cyHxe44L2IA0GDz8nuPnDETKibrXY2zu4/lmV7eSYTCaTyWQymUymcyX7kmMymUwmk8lkMpnOlc4GruZE7mdaFh3gPQu6BckwUB7HNbxBnXvXrBgsOgK7tJ/rVv3mhmJgey3FAYocIaMl3cuw/Y3wUeJqB2O9/npLt1GvDhRXo8Mb38syH6Ccu9/S94pI4KLVBGdDFzUqR4IbHdgO3gsHum41JkSsgA5sRQcOMmwX4oel3muyhj7vwcXlZtifkwX8bYT3wL2HwYN5R4/HS9g+xwxY/nMt6yDA0ohKYhwySBM4WH+Tr+v12V7C7EMGT87sUhOfmCBklJhdBIwr2md4IJ3JUOcCW/hoe4cwUPbn1scUq+rc1gqVTVd5/rSG7CizsA/jmlC54QWdY8mQddD3ss7Eu/IO20iv2bqgTojJgaIQjR2ESsI1iK5bxA/THpyb4NI3m09MJ7DAdWgJTkbAaojSRDnHA3AVIBa+o23EcEK6t+WLF/W4DWx0VfEhhugxOJFBe+yDNzlO1bjuMNwvGs4wmPfVQhvhZQ8sKUBagKQU129o+Z5/Vs8BilhuaYJh0gIyByQnnqBNs/Df/Bxcl8o79/QPdH8DNhUBuaG7WAmsKN3VexMTireAGje1Xerc2Dh2SqAnMVyT3IpijL54AKZNzAb4EcN7kyHRIjpgVodVsnzJFhC9dR3/Ad6EdokP4F61i3bBmOdYo+uciARBts3b2occ68L2oNsV0WmMkwCJnABF6zKoEdfEGIkQYDlZ1b7NiAyyDAynxH05t0XCQEfOmZgOYahDAZQzaVavT57hjnRIWwLuuaflIPbk5nVdYZuyDCXcu/yeIsTJsxpwLiLi63A1CmvPdAiUdfrwnyRMDx+OnL0TvS1EzfRYZDs5JpPJZDKZTCaT6VzJvuSYTCaTyWQymUymc6WzgaslIqON4+3DBNxLDLcroFSLiW5lMsyTAZarTWy7PkA5sCy6i601dMv7q2PFPvwruiV74SN3T447mW7bHpS6rd6JdRuVjmUfWVFHnGtDxYGebWsCHd3VLmf6+mtjDQ5k/d1FbNmKiIO7Fs+jStSfTmssq1zQuo1W8L14DBcc4FrREOegD3OEU5bAAvp93Tpv3IOzDBG4lZkQwhG29+MwDO6+MGQC96oS5kgl3nrtk3qPBYSBlsDVhhM4C+0g/K2v54zVgCxwnVv7mo6v7tNauBLGR1EZ1pO5rXQ4Yn0my0C6lrFNjvYO8l+BQ5VA1xz6yjf0uHMb14fLzGgRYY64Ptu0CEwEQ/SmtYMgwXtAyBCYd/AhRW4yhMMyVzQmqQDSJUCp2I7AM+IhXNCAiTR2dMwPV7QSDHSFyaHM5hNP1hSDSg+BtwCTyS8qukP0LZ7AKemGoiFTuKgRjXEIFZyuAodDECuvGaH+vA5DK8sFbUiiV0FirEiAkHkgMHTXGj+j61sQpLkM56uuYiwO4ab+tiJnDC5NLuga6PHeANvZ3ND30h0IdWDd0p2ZZwYczyKiOERaUFYiOsT6ggBVuj8Ss8I5Ho5qso1gUGI4ebWzliDwzwMHE7haiYhMgS5NEXSYAKds7jJ8FM8ZOAfGPWBFdHxjG7UQQkgnTOJQdHsC6kX5nrK8jnUrwzFJF7H4HsJqgRaGwYs6vx3K7em6hesznDEKcE3Un657I4xJuHcFToAYX3WhmiyPiITjkO5k21pnliMIrtzXdSUCNlrCkTBqo8+BxQfIGcc2HMHiIUJ82aY1ONh0Zy98AevKFNeNiHIGc/q7c819VFia6ezLdnJMJpPJZDKZTCbTuZJ9yTGZTCaTyWQymUznSmcCV3PpVJKNoy3mZku3dpdauo2+nh2+6X0ioevYoNCt4AKvbxXqDDLrLJbD/eyN4drJ8dW+IhYrbd3+HrwE5Gik2/D7A93O/W/j7zs5/sGN10+O61zhXpxT555bY3WBKhB62gMDlALLa8Ranu95SvE5kdDB7HXUjW22FGN7msGgOF5c0HPoItc/RKhmTnc5FCIFrjassd3qwqGIjm1BV80EvtWFU/nq47XPffHk+OY//ZhehwGbQODGE7iUTWscjmjQA9oiOyAapK/vvr+6njwHJKaIiBRA61gf3ptYIpEzYd4XnNOiCdzV+gxtA96Eax48q+Wev6k3nr+uc/X2D8FBaAmYD13d+mE7ZofaCMldBK8BsYgxHxa/o/c+eBYoHkgNLAEB+UrXvQRYWrxNjEcRC7odRUB16HZFFzk6sImIxAjuI7pFlETW4ViE66Z9uB0hPFCAeE7bqHQbaBkQqKSvZaBLW+DkBdQnG2m7TxcV4Rlc0kHYBD0mIiJf1UDP+MpT+jrRGLR9TKc1OlmlwHiAG5XAalwK97dVZUJLhKRS8YV1vSYcoRgMynK6QYj7Frfu6LXgVOaWl6RScCYbbeh8iMfaxnPXFTv2DAwEbuMw9oSxBvE3AAAgAElEQVTBmwNgiTV19kB73AMcnoiosV19DVqWHKBtgBIR/WOIZTQPdGsOTl58L+cCcKsSfRUD3WOYI9vIzaM/JWxXB1e9IEA0wvrRhBsZ1iQiXQ6udcSnXBuBqQzGZB/CIS471PcSDfM7iuilQFpZl+lh+BkoKNNQ17F4Q8c9xw/F/o/gTEaMq+xibeS930rwJBzoeC+K424Wg53WhGHWXctkqpPt5JhMJpPJZDKZTKZzJfuSYzKZTCaTyWQymc6VzgSuJiInqMwI7iP3gAztrSo+QeSMCNjtPpxhEt12/VZP3dG2G+HW9lqq27A3Booh7I90G3owqd7ynaB8DIZ8flWRhK2Jbrd3J3rNV3cUH3vvmuJqTeBnCbA04mpXuxr62R0rCkB8TkRkOMZ2sN4uwN0WkT5JXO9aX3GQ0f/W14fvw3bxgbZLjHDGbB/BjheJ+qBw2J2ev6pt176nf5jMw+2pEeJAzHplkGIy0ve3X1dM4N5P/sWT485N4FQgDIa7WsD9Bdp0qVwMpO8QwaA4nQ5cxM9oWBfgaii/m3FXW/2aXuDgPToHcmTqEQkretonLgcm1Nfj5i6QK6Bb7J8MRFPaRx+CkprAXa2EG5snJ4ayuRlDGyJa5YpWKIJjU+uujrd7H1EEhi5q7MMSU9WPeQ7wKeBdo4s6P1vX4Tg1B2wH1WFYK/szHoX9lt4Jg/tOxEBP1J/lI5lZohzJjuI6goBBnyLoFo5tDJskeuTnyECqeJ2iow1ZMiRzGtYzel5D/AI3L4YYIqzXM6wXIZkOznE+0/Um7sMh7abiuH5Vz482dK2KdoDY0HWLONwuQggR2jqr+MXnTo6nHW3vfEnfw7o53G+0CDxyjPn2YUX6GLCZHM44Z1Vcs2yjTxpwBe0hzHMPKNUe2mIGW0o6cHAjxsWQ0Qv6rHRTBKNu6BjO59CHGBsp1k+uMWyv4iPPow5AVBEwSse/6LY+J30f9ewEFo4iK4q4crQWK1ruGAhd2dK2SXi/ZTgVdmvcWuFGV85rOcrLirvHcBR0cHOMca/8ij7TB/NantbrOlaJ7omE89g10L9wv3NwdksuXzo5Hr7vgt5vATjyt7Tt5BbmG50GgXvSpU42tA6jZ+GoiLUtuwm3QLglShL+e3vMOmAce/TJtEkHSJ0DxQLmZx+BywfAAzF+6JYXP6XtUq5qPaNDfQ5NsfZOm9p2wb0Oq4NkRSTAscsL2mYRUEa5p58hg5BV4qHAHR3GUoDj0tkOfeiJK+KZRAfL6RbKAAdChiwHbnezLocB1vrdud89StlOjslkMplMJpPJZDpXsi85JpPJZDKZTCaT6VzpTOBqvnCS7x5te0XzupXX6Iwrzy9rvpu1U33v3a5u2e32dautkYbMzPeu3j453hrqth1DH0c4LhhoWWIbHhjTZlMRuAIMEPGzdkO3kb+9pUjG+rxuQdI5TbAjPy602wa5lq17GG7bR9d0KzFf0W3iXqFbkj0kUQa43hC43nsQZraFQL4eHKhAWzCcMdvV9srhukUkp38Z2MaACA/qUoSYTDKoxnsm83B/+wHdCqYbWQK0KOlzG1XbdR+MUpTCgSrTQg0vIMzylvZD54a+t39RC7f2dX1vPgfULdVzmvvhtm7vCvphk/ienkMMrHFHyzRZrm5v9tXq1/Q/xkvado0DYjhwyEP7jpYYfoh+A6JG17lkGPbhZKF6+XE1qAv7MAgfJYmD14nWDS5kOB9OcIta1v0XgEkBISSu19ivDticxQynCzoXfQxcqUmejvdAQCexRri0FauKLYxXgUxkxA+BSsKBLZ7MIBP3XwdKw6DO8bKWc8rcxVbYZ35dy1S2gW7gWumeohv9Z4G6oF0mC8BkMFazfa1nBpyuBD42bejrSUpkTg8jIFkOTl450JbJ02BARaRoY+0eEKMFKjbUa6VdtN8KUCTMB84Z4l3NPTiTYSxN0UZsF4YBt7aBnKL/3aKuHflCiKtxHmf7OsaaNxUNGlwAxjXS8g1Xqp+/XFeaWNP6m+g3lJsulFkPbpZJG8d6zdYlBGZiuhFdFgnbgLhb3tFyZ11dD8aLWr7mItYJBvSOdHzGE0WaRqvaRmyXmDmvHJIgtDq3dLwVTWCcc3Rw1GeYzNYT1w2cPodAAudQt20t1GAT5V7nGqufAVqYGwHWjOOkr53eewahycA12VedptaHYyEY5xKuY9khUFacxznAe0zwbE3ws4MMnwlTOBUWzZkk5/vXWeD6qXUrWmgLrlU9IM5w9J3OoPacb+MVIP9DfE5dwzqJMTxe03sQc2d7cV1pbI8qX+ezZwJ8u8Azo7EOp1+ssfFl/bw6WdExku2E1rDRkLg02vvqdT0JGFu8APSzG4YXvxPZTo7JZDKZTCaTyWQ6V7IvOSaTyWQymUwmk+lc6UzgalI6SbpH22FFBiwA4WL7uW47LiTVGNt6S10y+hPdgusOdEutd3UxeM/ttuJhez1sSQJLi4CiFdtwk2gD6WlhixCc1d5Itx0PJ/rerXu6NddZ0m2+N95AkFehW5A3N7Xc6/NazxyubmUv7M5yRcsRg4GZgvVhMGgGPC6OwHoAtyiXiPsx3BKIQMRtfiApSYj0nJzfBD4G/INb+2UWbvmufk37bedDdKzSc4ItXGzP5hPiDAiTXQN+hbImCdAbHA8TbhcTXdIypDDl6T5NnKf6fKITIiKTTjXeMULdpnA2y1fgatUnTqbXpBPc7geIT+khkamlL6urUfS0OlkluH4J96ayVR2eOtuHwd+IHMFpySHENAyAxRgjGkJDrUPgQNt6zdEaXaowtmn2MwT2BTSS2CQxQ6INIiIZnAddAdetNToC4bp0CERwZ76sa8Z4WcdPDpSqAUyK/2oVXAflC9BNBIbGdObaRIDjlHNbAo1XtT6DDd5D6984AAKUVeMmcQ164R3XGH0GJAe6ZkZYMzhGinngI3D68U1de3tXiOTMjM/gPzGX0H5ET4gGEbMK8EP0eYBVYZzncE7LQSBnh9XjkI5lxIpGm3Dgm+k3lm+K+ZAvqCvWeAHju1M9BtgWbK/RMp5LmGOjNcxnjKsEz4kC6wcdNn3E4GI9LGfMT4kj8zxXU1Yec/3N20SAgPF0q130iChyrS7a1WtM1gX2RKQV9SEayTkiIpK3iE1qmYYrCDfFWxpqliUxzfxwDrG50Roc6IhcEX2GwybnczAucP0p18y56vqLhOOzACIbuKoye5XrB9p4krIPMXCxrhA/czXPwACTa7jKcwKMjdj0zNxLBtXIKkUsOML85rwKcDXUk69newiQDspH9L16rpZwjmP9y442MJHzpB9+/ozoBAe8ON5D6PYYYbfDxxP0ajs5JpPJZDKZTCaT6VzJvuSYTCaTyWQymUymc6Wzgas5EZ8eb7FNsFU/1H3bARzBUlftFPTanrojrc8p0rXSUgyjuXE3eE8WA/UAMlAWWo5iV7dt041h5flEmqjVhvJK7US5msbzutc6xR5sb07LSmTs0py6o90dIDiRWFkZbvnGQ61DJ9VtQYaBlthLPRgrDtJDsChxEAZ9BmKXcPt/jfxQtdsXcbghHMRczZa3iMjtH1SnnfZdIFrAQVI4IhFD4LY6nWwYsOmwb+24RR7Y+uB8oBdzt7WtYzjzsTyTDlEvfW/nWrhlu/+i9gkRACJaRAXLuLp8IU6or09CQ6kTDTEP0/fBdWwMp7l9FqI6MJeanbZjIFSNQyCBQD2IWbGv6CwU1JM0Jd2oApchZTUKBMoVyMikUxTxl6DfkCPpZiyeIoRvxlsaDJrQOQ4hm3kHrnVAErIewiCzauyFmFVzF6gfylDCXSxFH6a3NGyQYZ6Nfb3OdF1vNouV1CzFQfk4J+kqmAOBSnaqXesYmBkP6NYD/AxuaUWHKbF6yADYvFM9VmcdHOvqxjnAfiaymXUZglztGDmVavwswPUSV3lOhvkSYDUB6od7zTzpgwDdrBrFIQJDhIooVh3uSbdBLpksR46Mw84ttAWdCrFY8b0JcN+iVT8mWT4f5DtjzNBFLWc5qsvNdSVYh3EcINug6/lMy+CoyD4PHCVr8CmRsA14vwKII+9d1AS30vWSLo90o2P9gzEcuHqhbDNOmlpOPWbdZkOG2a7UrAtb1b3TXnV4dfDorgliDtwpsfbQwZLPgzqXT+KAXM9m65D0qufxNCXvpodE0Vhuvpd9O+uGeVImuF8y7JnPPWLWEdY8tiMD0d04/AzsRngWYU77oX6Gno4eD6JG2U6OyWQymUwmk8lkOleyLzkmk8lkMplMJpPpXMm+5JhMJpPJZDKZTKZzpbPzm5z7X7f4tYu/ecFvT6aAYiMAixfnNSX1+r6m5G7OH54c8zc4IiKXmvity7zaPXeRin6Ywop1jCbrKoRJU+vuBX1vB7/DKWBDebOrP4ZYa+vvcF5Y2D45bsDSeSnRc+4NtZxRNaIqIiJlB783AkgZzf7A5Vi9HNw6LLQ97JEL2D2T34xhd90EX59rUd/0u5oqxQjNDRLli/A8piWTESZHHeXVNwx+24HufPp31Sr52x9CYjF+b8PfT0lBK0h9mWnSi69pvx0+w9TkapZ994P4YYiINPfQ9mDHyX/TpptVZt2mKfuNv22ptlvN9Gckwe9QmrjBZJnp4Cg0rkMmOjhHZpKZ93QwJds6X/NFTchOMDZYtwRYb8DOs2rk68Eg8zcvPoqqz6c9J9liWJHPcteBlzWum3a14/IlWPwGnD8Yefz2IvzNCH5jtKNtl93T3yJO5/T6zV2dJMmhlsHv4Dc5BSbSpq5Pwe8I9kL7/riv15qmSCZHfWhlPX6u+pGTHeJ3Qj0tR2Bvyt/VLMHKn/a7/H3Slg4M2uHm87CcRjc1DsJnQ2B3O8+5zt+qVNvBptoNUjBe4AC/eYANOO1nm3t43uC3WoEtLeucVZchsJmmZbCEawnbvu53OB7dzteD+VBj71tyrg6rf8MRiL9BwPUjlIF1YyL87HUDG2jYi7MPuS4Fv6Xhb2wCK2pep8bWfsRz9IzZ3w9V3Tdsx+r2FRGZ1vzehM9l9kPwezqcz3WVc2+aakcHvxnCfVPY69fZw/N5wLW6uaP3mrXg5w9hg9+h4Ddw4yVYItc8c/jboDet0ccKfgPH3wxxfNKKG7bxE1hoY0hJdqDn+Blr+hI23RzfZWB3jecs7sE1Kprg94pBu9ASHr+z3GbWAj5bw6Kav+PkfIlGHFSIL9jWweMG4bPB9TGw0J/lu/A7HMp2ckwmk8lkMplMJtO5kn3JMZlMJpPJZDKZTOdKD8XVnHOfFZG/LiL3vPcfOn7t34rIJ0VkIiKvichPee/3j//28yLy0yJSisg/8t7/4UNL4UW3A2mHWVZv7dZpazB3cjzf1K2zJrCvLJrhnqC8jCuPuRe6/j90b3vne/WUcl63/Gh3PcX+Z/A60ne3B4ox0Ta6CcaAeNYAWNko1y6kZbSIyBRo2Qj+hnNA6FKwKJc6yiix/ju3tXwe2FMZpDTTtlDL0LqjZerBPpYoogCHo62oHxCpqGfduA0dj7Ddiq3gILGbXYsivfG3NvT1Akm8KGuAq0XVZZq/oe/tPq9tN1qBJSfLgGNaz4qIJLCdJj4xWtN+LxvVyFk0Ij7BJGM9J0hmx2V4zuI3dHs52VYOp/c+TUenVSnncASki9aeIiGWI+ir4fN63WmNhS7rw+uOl2gBqpefe02xrClso/vP6YAjlhVgIhgj7MMYFq7JbsjejDa131uw1ox7GFcNzF1iSUQvUP/WTbWjd/CJDS2HYXEN1Cvp61pCxKz44HP6+qGWLbCuBbYwXsfAEJFkrhppYR2Yik0EJAZKknW1fHmHKeq0scW9gMY0d7Q+MfAZgQUqUR3OqWC+TMI+JL5I+/cG8EBB2xAt7G+iPzG/aVFcYowRYyK6lmqXB9a1jd1q9CSa6PNtvKTIbTwM69bqAqdB2xCPy+er8c2oBvsJbWxxry3eG6gOcWSu1bD7nyzgGQDr6sYenuPr4XMvaG/MUf6TLq+b9Ik0VV+H9cnQdoMN/TwQYG9YV+MazDZHPxOhJvZF7I1jXiRco4hlFY1qxLHuvcQPi1ZceQ4ndDCHOWcc8DG0Xa3NdF/bkeNORKTAMtPY18FH++UQU+RYkmrVrGl8doVjh2gYkWXYxrfYwLRixvNjdroAZWYblE29OTHYuvkWXDKumVdTPjMxZ9h2OXG1svocdvNQPw/4Flj7Pf25iIiIzzGZ3Ontp7yVO39ORD4x89ofi8iHvPffJyJ/LiI/LyLinPuAiPy4iHzw+D2/7JybgS1NJpPJZDKZTCaT6fHpoV9yvPd/KiK7M6/9kff+/tfCL4jI5ePjT4nI5733Y+/96yLyqoh87BGW12QymUwmk8lkMpkeqEfhrvb3ReS/HB9fkqMvPfd14/i1hyo6dueaZkhUbysW0AK6lWDPM8LxxTndLuvnunc8ARvUy0OLpxhcxgDp9KORHhcTff+9H0GK65B7sno4AQNEV7gCe8cO991/TfGc3QV1NYrg6taZ1y1CIlPDIRPUZ7Y134KbGRU4sOE429VyL7yOcn+PvrfOKWeaVb8e8Cxw+1r7um6pEtsoZ65DLGkEl5UY28qdm3SmQlm5nY02WrqulRg8h633FNhLECNejVP2ngLCAMcUogN0gfNou6VXQ4eSg+f0WkyIH6uRlRTzQPQyJBm3iVbpWCUCQ3yCbcx7EZkaX1o8OW7fhHPcFbhxcdw9YAwSLaToFkWEKILjn9Qgd3QWInI3uaDoDpEMoiFNYBE50MrAoQh9GKATMzaHREAC3VX3xPT21slx95Pv0/Kh3ESr6ChGTCQCVlEsKys6WdBCBEhkEzjYHMYF2j0G3ta+AaxkMVw/6cZDEQ+NCqJixETonkgXLbJOwNVSIkaY26g/U7eJXqTAQoi5hAnsM4OVrlBIJud8SHfAIjGx/P1wzuM/JeIWc3e0jYt29ZikGxvnWzTEQL+lrpCytnxyGI8V32Z7iYg07ip2OrqkcyNYx4mdAr9pAKkN5kBcfX6AABFRBGaaon3Zz95xbcd4QTvOIr4cV81dIoTAQ3EBOs/N3VbEZryi8ydwraMbFdaGt+bGVoNujaoXSs5zImkiIWYYD/jcxPMHY4lrqY/hMIihkQyJK+FeUYLjauczV2AdAopL7C09hMtjD9h8J1wwuWZEeQ3uSOc9HCeHZKvwTrq0oS0471m3wGkMa0bgqIj+T4DHpnSwnHXFa9H2FG2zq5/xSmDASVdfLxbxWwCuV45rLNBkIMhc91yOgtMJNHAgxPUnM/aM988Z6vWLra3Kc05b7wiUc879KxEpROQ33sZ7f8Y59yXn3Jemvf7D32AymUwmk8lkMplMb0Fv+0uOc+4n5ciQ4O94f/L176aIXMFpl49fe5O897/qvf+o9/6jUWeu6hSTyWQymUwmk8lk+q71tnA159wnRORfiMiPeO8H+NPvichvOud+SUSeEpEXReTP3so174cSRj0t0iTX72B7F9RNaCFTRCDGdvYrO+snxwyzZIDndBz6ILz/hVsnx2M4lXm4n/mhvp7uMiRTr1O2dMuvD/czurnRsWw4BmbW0XMcHLscghobqZ6TItCUONw4C53jJhNsGYOTyIHvDcCT7Yz0y+bhEJjUgt5jsgB8gJlO1ZlokneIoaBNUU+Gje69F6gf0JZ0ZrOvtQVkAv3A7eaywfvhnMD5RY8PgKi5VLdnI7QxjwVoJd3lGIRGJGHpNb3mYJ3uaHrOcCPk8ohlsD3YyAz0TIDWBQaBcXX5gvBM3gxYRT6nJ7Vf2cFFia0orha0L9pl1h0oQEAmOqbpHJXPAxkBSdHYpxsTy8rq6OvdZ7SR21s6T4gwhJiQ3my0XO2dQqesuWu94G/FvN6PCFm2hxDLth4XoBCCID2M23QbS22AyuEkrJkxgzQzzgWMT6AkeZuuZsDYDjEXxiH2REcgYhmNQ44NnI8QypRBfYdYTLy2XYD0AG+hS13ZmUmZPTmpGj3Jl3SOMfyQDnSz76GmGdoV92aZOK447rkmBW5kwG3SHv9AV0Ss53044S0icflQF8q5N3R8ufEMbgKHKN67ibk3gJMe0TLim/Ov65gcPAXOku56QD/Z/1NgZUSA6FLHMZX2p5WvN7phPwUIFdaYubtEpeBcinFP7ImBjgxwZAJ3e4sYo44LuoEmcLabzGP8A0VlYO5wDahSn6zsDK7G4FI6ONZgfQmQNrZ3XONyFw/gwtjiGliNdxEBoyMa8U6u86xPYycMiAxwPz6WUO4WncM4V1GHYA2gG92uzhOf0WKUODrGWwrXOTqZ4TNjVONS9iYMlsGvvFZf2yAaads7HGc9OJvBJTQIsiZyxkDOFC6XuKaf4F4JPgMjHNoPcB3IzQTxnkW9FQvp3xKRj4vImnPuhoj8ghy5qTVE5I+PK/kF7/0/9N6/7Jz7bRH5hhxhbJ/x3tcZ+plMJpPJZDKZTCbTI9dDv+R47z9d8fKvPeD8XxSRX3wnhTKZTCaTyWQymUymt6tH4a72juWmIkn/aNtrsoJt9DndLmsjwDJ2M9t/x7q0qGGWr2+t6nXooJOFW9t0KitLbKsXepztAE9g8OKsbcaxkhqrsSDoM0P43b4yNu4p3Y5sNPWclZZiAcTNSmB105nyTMa67b07UtyPLm9ziWIPuwNlZkYD3Qpt7DEUTuvfgqlPUGUUg0FbvcsM9tPXyzaQGYaBYis0nnGfae7TKQZ/wGn5fDVmxLLS5YxmaVFKZITBXtUBWbwvr0nE4vAStoJrwtLGIE9ERDa+oO7te9+vzkls48BBJoabHxA1opVCAgLnhP3G4EHtxOELOq8C1xuWp2YsTGdWGyIQRLGivrok+svqPNhWYzIZrujFOGaIR6aHCNLc1k7JgOS4BeJwGNuv6OCO36MhsUVD50WwDEXhzxsZvFbAKWf83gv6Frg01eGUQRjm17+l11/WseA2tE/ydW2Mos15i+tjvLFvie3QXW28puvCbGAmMZEMmBHD/eiu1OjCFQ1YUb6KwGHgHNldcKpET4BbxHB18jfv6DmXL54cB1QmMRcEo/qZPkz3cW/iIHAmyi+qzWGAJQLvKkDT0S2L876FMcky5cuKgEVFNcYmdEpKq2396IIkEqKSvG56W+deE+gs1zGiWwyfbexzYdHDMtMy5Sg3nRCJj3Ee0nWOawnHLREmkRAnDFChYF2iU191QDhxxQwud3TEClwO4cKaAXcNxhvPQQBu3NU+57hItg719fkwiHeaVX98I2ZHpdd0AY3WddyWLV0zYrj2TZvabzEcD+Ng3eIzmvfVsRMRy2N9FnS+uElYZleDnEWYydmeflaK93SuTuf1uhyfEZBYN2ZQJcqBdUXqHMgwnoN5yGs26mxlRdyQNnyoZ1eRZ+JhAaSeVPc5X3dA4DzK5Jpw3dverTx/Cow1CPN8gnV6MaQmk8lkMplMJpPJ9BhkX3JMJpPJZDKZTCbTudKZwNXEaXgegyeTG7o9O32BW5bVuNrtrjo8XVzWbXe6kTXicFuUGBzl4cJWAKci3sPtdr4+n8ElA0wLcTI6rZVLcE4D0kaHuO/cXTs5jhM4qg106zTaDrdIiQf1geUQm+uDpWjBwe0wIdOlh8UcHV1wShC0pa+PlvX8havTyvOHqwytpCtR9X1FRJJbCPrDtv/woo6Z4Yped7zMbXW9DsO8JivoZ5q1AA8cAg9Mt7RNs65efwLkbPE1baTeJUVyOITDQEIJtP1RxbVC1zKclFbjkUIHO94D49aVdG+CYxnCVukAM1oBhgQnLwby5XAQYj1nw+yIegyvoNEiPSa6ESBXNQGLddgkHdISuCUyeJRBdaMXFFEjokVHMLqgDTdhjyYi7au6/iTAVaYIyRuvwpkLY5KhrKxn/OLzep0lRVwjuCARjSFKUsDVipiMA54SAQsZr+o8miwAKwxN5CTd0ntHQybuhmjNyb33MfhYVvQD61AuwiEMjkXTlq718S7TbTEYgGTI5c3K61O8voiIB3Li4VQX31TkJu7rnB5t6jHHOlEvBuByDWS5iw6cN4nqYB5ONhUxcqs6Fogisp7FhTCmIZg/mCfF87re0D2QCvDaVQTOEi+uCbFs4NmTMQCU7QsXsAbGS+AQSLS2Hz7DpxlwJc6BTjW+zH4gukYnsGgM/BTOiXTsytjPDN5EWTMEVTqii+1qbJaIGh2+RERKhkpi7gXuWiifXwTXC4wrCIplwOagOgCS81YwFlhPOiGy3ETUuBbG4xCnJBLqEyyI02pkc9qpXm/qEMVyDeHVDGulGxld0Jr4LIo6RL1x5fnCsNV8Bh/c02dD8EGD78H9BC5nro2xvTTDtt/Xvq5PgjWDmF20oriiR05lvK6fMwVugTICHtoFctivz7h0DQQiB+jfu+tFZjs5JpPJZDKZTCaT6VzJvuSYTCaTyWQymUymc6Uzgav5SKToHG3bebif5XBX6050+25jXrfLGkgm+2vPvHxy/AfX339y/PyyBhhuNEPe4t5It3Cn02rMxq9iO3wfQV1XdKuujSBOBnTeHeiW4jDX904QUNpY0K3ABq5D1I1BpcMDIBx93VJs3Q6/s/avaDm6h22p0nyGQMIap7Z8vhqBGa3SNYZuNVJ5fPBCjbsaHOvoTBYPGJwWlvvgOW3Lzh297nAZiBqQq8kCkAQiIIHrlB5P++DBaoJOsx4QoAMGquk5t35Y2511JlZFdITBlrMK31ONkjD4Nj4EooWySt0tgFzSpWz3A7pFTkQxQEYQKNe6q6+zPuVMGGj3abg3AWmhS1fvotaB1yIqyjKlqCfxvgABQzGCcEI4UxErIv7BvmKIaz4Xzr2DDykOMOkwuFPPaSLQlAGLaZ9InL5h+wcVuWJ7x7mOMQYv8py8jXpeASa32ag8JwceymDDxm6IRo42GGgKrHGpOgAyO9TxOX9dOy7vVD+K2K5E7krQHNmhmHUAAAk0SURBVM09rX97UzEU/hMeUSrPINUO+zzEKEbr2jZjhCDL9+o9GO5IhHSyqMcZ6JSlV7Wjx1ir8rYOUI5VhiAH6wenPx0l0W8JHCnzGdyXY5fjLQhTZrYnmobrUDyqxknrnC3phNh9hs5s+nprG26EWMOJhyYjPadohXWbu60FHK1UBzOHcwzjE2HknD/9TQZA6nWm1WZ2MlyBOylQ3v4FPJ8Wddyyf6bom7mbaeU5R+XT4/YWQ7qBsyN0evKeEKm9L65Jgw2936wb5n1lCKvtXSa+jLIR60W55+7QXRDnT0LcjOMwb1XPMa6ZLBPXCfZt4KTKuYTXs77O7QSucAxBH2AsNHeqg2c5Jrl+iszgkbgH16uyBt9lWXkPrmnJEItP4G5ah5FPK18vuPaiHelUSjW2wg9pxHfpyDj3un4GD0JT6U63vVd5j3Kv+vUHyXZyTCaTyWQymUwm07mSfckxmUwmk8lkMplM50pnAlcT58Wnx/tkYEmmcDg7+OUrJ8fjf65bVsTVLjX09R97+ht6ft2+q4hMpgjFysm3YPsvRZDgZUUsFtvqxJHBwW1npE42V6+tnxw7uGClDS13Weh92/O65cfQ0yXc69oQW8oo5+CpcDs7QHc2sJUKZ7dhode6exfbnBN8/13QupXYeo6G1QhUNK7eXi5avvL1cp5bltVIG7fwRUQSbEl3EYoWIETA7OpQD+J0IxiLzL2ubyjmGE4HBGSRmB2wCGyj873ZQQ0OSVzgqRAHinKMQ+B7RLSKAv0MrLGxoxde+6oWqr+p/X8ISoCb0GWLBUR4JMIcG9u6vZy9piGM01WdU3d+RN2aZl2tAuQKxwzrlCvoh2riMgilXbgGVx66AgKlmjbg0gQsglhZWrO1H2AldBaawQeJfhG54dgjrpYCGejcRF9dBOK6CIQBCBQxwMkC+goIUCAiCc3qucp51LyG9e8gdLIaXIJbEtojR1sG/UYMdq46HDdAy4hbAOMiJkY3x2QJa2OK/uxivW3SLQ7jZSZUkpgd6xCMGR3qwbzP54k7Et0j0sfxU42JEcNhfYjqEMPiOpkDYSln1k/eI+Z6jTbOYcjG+wX3xtojwVovlZoso13w3mRUPYbHQRvpdegil8+YTCV4LhFx5ZoZuBliDmAZkjitLgcRQrYjx2GAFaHtiTGyPwOcFu3IecR2EQnxsOYe6olzcqx7detn4IrHgN6Oqzo97CuaYjLcNa7unyYMD4MxP4MyB2s31yi0ZcT+BC7PNZp9W3f9wCWXnzliooV1deZzAutFzfgXCR1NuWYGCD/aI5x7/AxV/V6GGtOdMZg/OOZ16G6bI0w6WAvwOTlsxxA5pCsnn4fNBQRqI3A3xi9JogVwrZThaiaTyWQymUwmk+n/d9mXHJPJZDKZTCaTyXSu5Lz3Dz/rcRfCuS0ReUNE1kRk+5SLYzq/svFletyyMWZ6nLLxZXrcsjFmetx6p2PsGe/9+sNPOyNfcu7LOfcl7/1HT7scpvMpG1+mxy0bY6bHKRtfpsctG2Omx613c4wZrmYymUwmk8lkMpnOlexLjslkMplMJpPJZDpXOmtfcn71tAtgOtey8WV63LIxZnqcsvFletyyMWZ63HrXxtiZ+k2OyWQymUwmk8lkMr1TnbWdHJPJZDKZTCaTyWR6RzoTX3Kcc59wzn3bOfeqc+7nTrs8pvMh59xV59zXnHNfcc596fi1FefcHzvnXjn+/+XTLqfpyZFz7rPOuXvOua/jtcox5Y70H47Xta865z58eiU3PQmqGV//2jl383gd+4pz7sfwt58/Hl/fds791dMptelJkXPuinPufzrnvuGce9k594+PX7c1zPRI9IAxdirr2Kl/yXHOxSLyH0XkR0XkAyLyaefcB063VKZzpL/svX8JdoU/JyJ/4r1/UUT+5Pi/Taa3qs+JyCdmXqsbUz8qIi8e/+9nRORX3qUymp5cfU7ePL5ERP798Tr2kvf+90VEjp+TPy4iHzx+zy8fP09NpjoVIvLPvPcfEJEfEJHPHI8jW8NMj0p1Y0zkFNaxU/+SIyIfE5FXvfff8d5PROTzIvKpUy6T6fzqUyLy68fHvy4if+MUy2J6wuS9/1MR2Z15uW5MfUpE/rM/0hdEZMk5d/HdKanpSVTN+KrTp0Tk8977sff+dRF5VY6epyZTpbz3t733Xz4+PhSRb4rIJbE1zPSI9IAxVqfHuo6dhS85l0TkOv77hjy4QUymtyovIn/knPu/zrmfOX5t03t/+/j4johsnk7RTOdIdWPK1jbTo9LPHuNCnwVia+PL9LblnHtWRP6CiPwfsTXM9Bg0M8ZETmEdOwtfckymx6Uf9t5/WI623D/jnPtL/KM/shY0e0HTI5ONKdNj0K+IyAsi8pKI3BaRf3e6xTE96XLOdUTkd0Tkn3jvu/ybrWGmR6GKMXYq69hZ+JJzU0Su4L8vH79mMr0jee9vHv//PRH5XTnaAr17f7v9+P/vnV4JTedEdWPK1jbTO5b3/q73vvTeT0XkP4miHDa+TN+1nHOpHH34/A3v/X89ftnWMNMjU9UYO6117Cx8yfmiiLzonHvOOZfJ0Q+Qfu+Uy2R6wuWcm3POzd8/FpG/IiJfl6Ox9RPHp/2EiPz30ymh6Rypbkz9noj8vWOHoh8QkQMgISbTW9LMbyD+phytYyJH4+vHnXMN59xzcvTj8D97t8tnenLknHMi8msi8k3v/S/hT7aGmR6J6sbYaa1jyaO60NuV975wzv2siPyhiMQi8lnv/cunXCzTk69NEfndo/kmiYj8pvf+D5xzXxSR33bO/bSIvCEif/sUy2h6wuSc+y0R+biIrDnnbojIL4jIv5HqMfX7IvJjcvRDyoGI/NS7XmDTE6Wa8fVx59xLcoQQXRWRfyAi4r1/2Tn32yLyDTlyNPqM9748jXKbnhj9kIj8XRH5mnPuK8ev/UuxNcz06FQ3xj59GuuYO8IvTSaTyWQymUwmk+l86CzgaiaTyWQymUwmk8n0yGRfckwmk8lkMplMJtO5kn3JMZlMJpPJZDKZTOdK9iXHZDKZTCaTyWQynSvZlxyTyWQymUwmk8l0rmRfckwmk8lkMplMJtO5kn3JMZlMJpPJZDKZTOdK9iXHZDKZTCaTyWQynSv9P47gZQiKrZNoAAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "data_base = load_audio_file2(filename)\n", "print(data_base.shape)\n", "fig = plt.figure(figsize=(14, 8))\n", "plt.imshow(data_base)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python [conda env:tf_gpu]", "language": "python", "name": "conda-env-tf_gpu-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: Chapter12/data_augmentation.ipynb ================================================ { "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import cv2 # for image reading and processsing\n", "import numpy as np\n", "from glob import glob\n", "import matplotlib.pyplot as plt\n", "from sklearn.utils import shuffle\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "img_files = np.array(glob(\"Obama/*\"))" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "def distort_image(img, rot = 50, shift_px = 40):\n", " \"\"\"\n", " Function to introduce random distortion: brightness, flip, rotation, and shift \n", " \"\"\"\n", " rows, cols,_ = img.shape\n", " choice = np.random.randint(5)\n", " #print(choice)\n", " if choice == 0: # Randomly rotate 0-50 degreee\n", " rot *= np.random.random() \n", " M = cv2.getRotationMatrix2D((cols/2,rows/2), rot, 1)\n", " dst = cv2.warpAffine(img,M,(cols,rows))\n", " elif choice == 1: # Randomly change the intensity\n", " hsv = cv2.cvtColor(img, cv2.COLOR_RGB2HSV)\n", " ratio = 1.0 + 0.4 * (np.random.rand() - 0.5)\n", " hsv[:, :, 2] = hsv[:, :, 2] * ratio\n", " dst = cv2.cvtColor(hsv, cv2.COLOR_HSV2RGB)\n", " elif choice == 2: # Randomly shift the image in horizontal and vertical direction\n", " x_shift,y_shift = np.random.randint(-shift_px,shift_px,2)\n", " M = np.float32([[1,0,x_shift],[0,1,y_shift]])\n", " dst = cv2.warpAffine(img,M,(cols,rows))\n", " elif choice == 3: # Randomly flip the image\n", " dst = np.fliplr(img)\n", " else:\n", " dst = img\n", " \n", " return dst" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Visualizing Augmented images\n", "samples_per_row = 2\n", "num_rows = 5\n", "fig, ax = plt.subplots(num_rows,samples_per_row,figsize=(15,15))\n", "fig.subplots_adjust(hspace = .4, wspace=.2)\n", "ax = ax.ravel()\n", "\n", "n_train = len(img_files)\n", "\n", "for i in range(num_rows):\n", " #print(i)\n", " idX = np.random.randint(0, n_train)\n", " source = img_files[idX]\n", " image = cv2.imread(source)\n", " ax[2*i].imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB)) \n", " ax[2*i].set_title('Original Image')\n", " ax[2*i].axis('off')\n", " img_new = distort_image(image) # Distorted Images\n", " img_new = cv2.cvtColor(img_new, cv2.COLOR_BGR2RGB)\n", " #print(img_crop.shape)\n", " ax[2*i+1].imshow(img_new) \n", " ax[2*i+1].set_title('Augmented Image')\n", " ax[2*i+1].axis('off')\n", " " ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "# data generator\n", "def data_generator(samples, batch_size=32, validation_flag = False):\n", " \"\"\"\n", " Function to generate data after, it reads the image files, performs random distortions and finally \n", "\treturns a batch of training or validation data\n", " \"\"\"\n", " num_samples = len(samples)\n", " while True: # Loop forever so the generator never terminates\n", " shuffle(samples)\n", " for offset in range(0, num_samples, batch_size):\n", " batch_samples = samples[offset:offset+batch_size]\n", "\n", " images = []\n", " \n", " for batch_sample in batch_samples:\n", " if validation_flag: # The validation data consists only of center image and without distortions\n", " image = cv2.imread(batch_sample)\n", " images.append(image)\n", " continue\n", " else: # In training dataset we introduce distortions to augment it and improve performance\n", " image = cv2.imread(batch_sample)\n", " # Randomly augment the training dataset to reduce overfitting\n", " image = distort_image(image)\n", " images.append(image)\n", " \n", " \n", "\n", " # Convert the data into numpy arrays\n", " X_train = np.array(images)\n", " \n", " yield X_train \n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "train_generator = data_generator(img_files, batch_size=32)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python [conda env:tensorflow]", "language": "python", "name": "conda-env-tensorflow-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.6" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: Chapter12/text_preprocessing.ipynb ================================================ { "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "HARI SELDON-... bom In the 1 1,988th year of the Galactic Era; died 12,069. The dates are more commonly given In terms of the current Foundational Era as - 79 to the year 1 F.E. Born to middle-class parents on Flelicon, Arcturus sector (where his father, In a legend of doubtful authenticity, was a tobacco grower in the hydroponic plants of the planet), he early showed amazing ability in mathematics. Anecdotes concerning his ability are innumerable, and some are contradictory. At the age of two, he is said to have ... Undoubtedly his greatest contributions were in the field of psychohistory. Seldon found the field little more than a set of vague axioms; he left it a profound statistical science.... The best existing authority we have for the details of his life is the biography written by Gaal Dornick who. as a young man, met Seldon two years before the great mathematician's death. The story of the meeting ... \n" ] } ], "source": [ "f = open('foundation.txt')\n", "text = f.read()\n", "print(text)\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "HARI SELDON bom In the 1 1 988th year of the Galactic Era died 12 069 The dates are more commonly given In terms of the current Foundational Era as 79 to the year 1 F E Born to middle class parents on Flelicon Arcturus sector where his father In a legend of doubtful authenticity was a tobacco grower in the hydroponic plants of the planet he early showed amazing ability in mathematics Anecdotes concerning his ability are innumerable and some are contradictory At the age of two he is said to have Undoubtedly his greatest contributions were in the field of psychohistory Seldon found the field little more than a set of vague axioms he left it a profound statistical science The best existing authority we have for the details of his life is the biography written by Gaal Dornick who as a young man met Seldon two years before the great mathematician s death The story of the meeting \n" ] } ], "source": [ "# clean data\n", "import re\n", "# remove Punctuation\n", "text = re.sub(r\"[^a-zA-Z0-9]\", \" \", text) \n", "print(text)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "hari seldon bom in the 1 1 988th year of the galactic era died 12 069 the dates are more commonly given in terms of the current foundational era as 79 to the year 1 f e born to middle class parents on flelicon arcturus sector where his father in a legend of doubtful authenticity was a tobacco grower in the hydroponic plants of the planet he early showed amazing ability in mathematics anecdotes concerning his ability are innumerable and some are contradictory at the age of two he is said to have undoubtedly his greatest contributions were in the field of psychohistory seldon found the field little more than a set of vague axioms he left it a profound statistical science the best existing authority we have for the details of his life is the biography written by gaal dornick who as a young man met seldon two years before the great mathematician s death the story of the meeting \n" ] } ], "source": [ "# Normalize text\n", "# Convert to lowercase\n", "text = text.lower() \n", "print(text)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['hari', 'seldon', 'bom', 'in', 'the', '1', '1', '988th', 'year', 'of', 'the', 'galactic', 'era', 'died', '12', '069', 'the', 'dates', 'are', 'more', 'commonly', 'given', 'in', 'terms', 'of', 'the', 'current', 'foundational', 'era', 'as', '79', 'to', 'the', 'year', '1', 'f', 'e', 'born', 'to', 'middle', 'class', 'parents', 'on', 'flelicon', 'arcturus', 'sector', 'where', 'his', 'father', 'in', 'a', 'legend', 'of', 'doubtful', 'authenticity', 'was', 'a', 'tobacco', 'grower', 'in', 'the', 'hydroponic', 'plants', 'of', 'the', 'planet', 'he', 'early', 'showed', 'amazing', 'ability', 'in', 'mathematics', 'anecdotes', 'concerning', 'his', 'ability', 'are', 'innumerable', 'and', 'some', 'are', 'contradictory', 'at', 'the', 'age', 'of', 'two', 'he', 'is', 'said', 'to', 'have', 'undoubtedly', 'his', 'greatest', 'contributions', 'were', 'in', 'the', 'field', 'of', 'psychohistory', 'seldon', 'found', 'the', 'field', 'little', 'more', 'than', 'a', 'set', 'of', 'vague', 'axioms', 'he', 'left', 'it', 'a', 'profound', 'statistical', 'science', 'the', 'best', 'existing', 'authority', 'we', 'have', 'for', 'the', 'details', 'of', 'his', 'life', 'is', 'the', 'biography', 'written', 'by', 'gaal', 'dornick', 'who', 'as', 'a', 'young', 'man', 'met', 'seldon', 'two', 'years', 'before', 'the', 'great', 'mathematician', 's', 'death', 'the', 'story', 'of', 'the', 'meeting']\n" ] } ], "source": [ "# Tokenize the texts\n", "words = text.split()\n", "print(words)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "[nltk_data] Downloading package punkt to /Users/am/nltk_data...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "['hari', 'seldon', 'bom', 'in', 'the', '1', '1', '988th', 'year', 'of', 'the', 'galactic', 'era', 'died', '12', '069', 'the', 'dates', 'are', 'more', 'commonly', 'given', 'in', 'terms', 'of', 'the', 'current', 'foundational', 'era', 'as', '79', 'to', 'the', 'year', '1', 'f', 'e', 'born', 'to', 'middle', 'class', 'parents', 'on', 'flelicon', 'arcturus', 'sector', 'where', 'his', 'father', 'in', 'a', 'legend', 'of', 'doubtful', 'authenticity', 'was', 'a', 'tobacco', 'grower', 'in', 'the', 'hydroponic', 'plants', 'of', 'the', 'planet', 'he', 'early', 'showed', 'amazing', 'ability', 'in', 'mathematics', 'anecdotes', 'concerning', 'his', 'ability', 'are', 'innumerable', 'and', 'some', 'are', 'contradictory', 'at', 'the', 'age', 'of', 'two', 'he', 'is', 'said', 'to', 'have', 'undoubtedly', 'his', 'greatest', 'contributions', 'were', 'in', 'the', 'field', 'of', 'psychohistory', 'seldon', 'found', 'the', 'field', 'little', 'more', 'than', 'a', 'set', 'of', 'vague', 'axioms', 'he', 'left', 'it', 'a', 'profound', 'statistical', 'science', 'the', 'best', 'existing', 'authority', 'we', 'have', 'for', 'the', 'details', 'of', 'his', 'life', 'is', 'the', 'biography', 'written', 'by', 'gaal', 'dornick', 'who', 'as', 'a', 'young', 'man', 'met', 'seldon', 'two', 'years', 'before', 'the', 'great', 'mathematician', 's', 'death', 'the', 'story', 'of', 'the', 'meeting']\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[nltk_data] Package punkt is already up-to-date!\n" ] } ], "source": [ "import os\n", "import nltk\n", "nltk.download('punkt') \n", "from nltk.tokenize import word_tokenize\n", "\n", "# Split text into words using NLTK\n", "words_nltk = word_tokenize(text)\n", "print(words_nltk)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['i', 'me', 'my', 'myself', 'we', 'our', 'ours', 'ourselves', 'you', \"you're\", \"you've\", \"you'll\", \"you'd\", 'your', 'yours', 'yourself', 'yourselves', 'he', 'him', 'his', 'himself', 'she', \"she's\", 'her', 'hers', 'herself', 'it', \"it's\", 'its', 'itself', 'they', 'them', 'their', 'theirs', 'themselves', 'what', 'which', 'who', 'whom', 'this', 'that', \"that'll\", 'these', 'those', 'am', 'is', 'are', 'was', 'were', 'be', 'been', 'being', 'have', 'has', 'had', 'having', 'do', 'does', 'did', 'doing', 'a', 'an', 'the', 'and', 'but', 'if', 'or', 'because', 'as', 'until', 'while', 'of', 'at', 'by', 'for', 'with', 'about', 'against', 'between', 'into', 'through', 'during', 'before', 'after', 'above', 'below', 'to', 'from', 'up', 'down', 'in', 'out', 'on', 'off', 'over', 'under', 'again', 'further', 'then', 'once', 'here', 'there', 'when', 'where', 'why', 'how', 'all', 'any', 'both', 'each', 'few', 'more', 'most', 'other', 'some', 'such', 'no', 'nor', 'not', 'only', 'own', 'same', 'so', 'than', 'too', 'very', 's', 't', 'can', 'will', 'just', 'don', \"don't\", 'should', \"should've\", 'now', 'd', 'll', 'm', 'o', 're', 've', 'y', 'ain', 'aren', \"aren't\", 'couldn', \"couldn't\", 'didn', \"didn't\", 'doesn', \"doesn't\", 'hadn', \"hadn't\", 'hasn', \"hasn't\", 'haven', \"haven't\", 'isn', \"isn't\", 'ma', 'mightn', \"mightn't\", 'mustn', \"mustn't\", 'needn', \"needn't\", 'shan', \"shan't\", 'shouldn', \"shouldn't\", 'wasn', \"wasn't\", 'weren', \"weren't\", 'won', \"won't\", 'wouldn', \"wouldn't\"]\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[nltk_data] Downloading package stopwords to /Users/am/nltk_data...\n", "[nltk_data] Package stopwords is already up-to-date!\n" ] } ], "source": [ "# List stop words\n", "from nltk.corpus import stopwords\n", "nltk.download('stopwords')\n", "print(stopwords.words(\"english\"))" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['hari', 'seldon', 'bom', '1', '1', '988th', 'year', 'galactic', 'era', 'died', '12', '069', 'dates', 'commonly', 'given', 'terms', 'current', 'foundational', 'era', '79', 'year', '1', 'f', 'e', 'born', 'middle', 'class', 'parents', 'flelicon', 'arcturus', 'sector', 'father', 'legend', 'doubtful', 'authenticity', 'tobacco', 'grower', 'hydroponic', 'plants', 'planet', 'early', 'showed', 'amazing', 'ability', 'mathematics', 'anecdotes', 'concerning', 'ability', 'innumerable', 'contradictory', 'age', 'two', 'said', 'undoubtedly', 'greatest', 'contributions', 'field', 'psychohistory', 'seldon', 'found', 'field', 'little', 'set', 'vague', 'axioms', 'left', 'profound', 'statistical', 'science', 'best', 'existing', 'authority', 'details', 'life', 'biography', 'written', 'gaal', 'dornick', 'young', 'man', 'met', 'seldon', 'two', 'years', 'great', 'mathematician', 'death', 'story', 'meeting']\n" ] } ], "source": [ "#Remove stop words\n", "words = [w for w in words if w not in stopwords.words(\"english\")]\n", "print(words)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['hari', 'seldon', 'bom', '1', '1', '988th', 'year', 'galact', 'era', 'die', '12', '069', 'date', 'commonli', 'given', 'term', 'current', 'foundat', 'era', '79', 'year', '1', 'f', 'e', 'born', 'middl', 'class', 'parent', 'flelicon', 'arcturu', 'sector', 'father', 'legend', 'doubt', 'authent', 'tobacco', 'grower', 'hydropon', 'plant', 'planet', 'earli', 'show', 'amaz', 'abil', 'mathemat', 'anecdot', 'concern', 'abil', 'innumer', 'contradictori', 'age', 'two', 'said', 'undoubtedli', 'greatest', 'contribut', 'field', 'psychohistori', 'seldon', 'found', 'field', 'littl', 'set', 'vagu', 'axiom', 'left', 'profound', 'statist', 'scienc', 'best', 'exist', 'author', 'detail', 'life', 'biographi', 'written', 'gaal', 'dornick', 'young', 'man', 'met', 'seldon', 'two', 'year', 'great', 'mathematician', 'death', 'stori', 'meet']\n" ] } ], "source": [ "from nltk.stem.porter import PorterStemmer\n", "\n", "# Reduce words to their stems\n", "stemmed = [PorterStemmer().stem(w) for w in words]\n", "print(stemmed)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['hari', 'seldon', 'bom', '1', '1', '988th', 'year', 'galactic', 'era', 'died', '12', '069', 'date', 'commonly', 'given', 'term', 'current', 'foundational', 'era', '79', 'year', '1', 'f', 'e', 'born', 'middle', 'class', 'parent', 'flelicon', 'arcturus', 'sector', 'father', 'legend', 'doubtful', 'authenticity', 'tobacco', 'grower', 'hydroponic', 'plant', 'planet', 'early', 'showed', 'amazing', 'ability', 'mathematics', 'anecdote', 'concerning', 'ability', 'innumerable', 'contradictory', 'age', 'two', 'said', 'undoubtedly', 'greatest', 'contribution', 'field', 'psychohistory', 'seldon', 'found', 'field', 'little', 'set', 'vague', 'axiom', 'left', 'profound', 'statistical', 'science', 'best', 'existing', 'authority', 'detail', 'life', 'biography', 'written', 'gaal', 'dornick', 'young', 'man', 'met', 'seldon', 'two', 'year', 'great', 'mathematician', 'death', 'story', 'meeting']\n" ] } ], "source": [ "from nltk.stem.wordnet import WordNetLemmatizer\n", "\n", "# Reduce words to their root form\n", "lemmed = [WordNetLemmatizer().lemmatize(w) for w in words]\n", "print(lemmed)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python [conda env:tensorflow]", "language": "python", "name": "conda-env-tensorflow-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.6" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: Chapter12/time_series_data_preprocessing.ipynb ================================================ { "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "import datetime\n", "from pandas_datareader import DataReader\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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\n", "
" ], "text/plain": [ " High Low Open Close Volume Adj Close\n", "Date \n", "2009-12-31 30.478571 30.080000 30.447144 30.104286 88102700.0 20.159719\n", "2010-01-04 30.642857 30.340000 30.490000 30.572857 123432400.0 20.473503\n", "2010-01-05 30.798571 30.464285 30.657143 30.625713 150476200.0 20.508902\n", "2010-01-06 30.747143 30.107143 30.625713 30.138571 138040000.0 20.182680\n", "2010-01-07 30.285715 29.864286 30.250000 30.082857 119282800.0 20.145369" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Apple = DataReader(\"AAPL\", \"yahoo\", start=datetime.datetime(2010, 1, 1), end=datetime.datetime(2015,12,31)) \n", "Apple.head()\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "close = Apple['Adj Close']\n", "plt.figure(figsize= (10,10))\n", "close.plot()\n", "plt.ylabel(\"Apple stocj close price\")\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "moving_average = close.rolling(window=20).mean()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize= (10,10))\n", "close.plot(label='Adj Close')\n", "moving_average.plot(label='Moving Average Window 20')\n", "plt.legend(loc='best')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fod = close.diff()\n", "plt.figure(figsize= (10,10))\n", "fod.plot(label='First order difference')\n", "fod.rolling(window=40).mean().plot(label='Rolling Average')\n", "plt.legend(loc='best')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Autocorrelation\n", "plt.figure(figsize= (10,10))\n", "fod.plot(label='First order difference')\n", "fod.rolling(window=40).mean().plot(label='Rolling Average')\n", "fod.rolling(window=40).corr(fod.shift(5)).plot(label='Auto correlation')\n", "plt.legend(loc='best')\n", "plt.show()\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "# Normalization\n", "from sklearn.preprocessing import MinMaxScaler\n", "def normalize(data):\n", " x = data.values.reshape(-1,1)\n", " pre_process = MinMaxScaler()\n", " x_normalized = pre_process.fit_transform(x)\n", " return x_normalized\n", "\n", "x_norm = normalize(close)\n" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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CPDlpSewqLqeiyhNwK7yk3Plw+McX6yIe3KOuz90YYwJR3cqu7nPPTHEuOprYvyNv/mIcpw5p/oJw1Z8F8XHCr0/oBzQvkdjWwppV2jbtqZ9OOJyiquVujDGBKnFT/VbPlumQkcwn1x9Nbtv6c96bS1HapycDsKOojE6ZgY2zXffSd97bBfvK6Zad2sjZoWUtd2NMi7SvOrgn1bRRe3VIrzfNsTmqu3FUoUOGMwA7+W+zeWN+frMf67RHZzP0zg94Z9Fmvl6z64DLdKAsuBtjWqTqJGBpQUzw9YdTBtA1K4XeHdK9LXeA619b2OR9H5+1ut6+vfsruObF75jy1NdhX5DbgrsxpkV66MOVQHATfI3v254vbz6O1KT4WsE9EA+8vwKAw7pl+j0e7MWHmmLB3RgTVTbv2c+8dQWNnlPoswDH0O6Bpx1oDt9vBG2S6k+zbEhDqYKLy8O7fJ8Fd2NMVDly6iec/cRXbPOZeVLXkDs+AODofh38zm8PtpLyKj5bGdj1N0k+5TnBvdIVnCX+wsmCuzEmapRX1iy+sXVvw8G9mm/KgFC74p/zvLdfnrOhwWCf7dNN5Dtb5m+f1O+TDyWbCmmMiRpb9tbMDQ9kfvkRvXNCWZxaOmc50yFVlZveXIwIrL3/1HrnJfgsAtI1u2YK5fz19RcNCSVruRtjokb1ykpAQGuZ+ksWFirVwX13idPfrwofLNla7zzfvvWctGRvf/3l48N79b0Fd2NM1Fi8aa/39qtzNzZ4Xq8OaUw+gCtQD0b1VMZF+Xu8+658YX6986pTEACUVXpYetdJZKYkUGlTIY0xrdXU95Z7b89pZMZMZZXW6v4Ih+qgvXDj3lr7yyprD5TedcZh3ttF7qye5MT4eueFmgV3Y0zU2t1A10xllcfvAhvB9t9rxntvL9tSyB3Tl7C7pHaZtu0tY3uRM/h77shcerZP8x67YkIvADJTEti0p+kB4mCy4G6MiQobC0rq7fvnV+vYWFDC47NWs8cnqFZ6lMQwLGA9ODeLt68exxh3oY9pX65j2pfrALzPv2nPfopKnX72cX3a17p/nPvt4rgBnfhi9c6wToe02TLGmKgwZ63TDXPNsX04tn9Hzvr7l/zlo1XsK63kmdlrSU2M57JxzqDkruJy4sPULTO0ezYDumTyzdra3UTv/fIojn/oM/7+2Q987k6LTHUHeN/4+RHE+aQbzm2bSpVHKdxfEbZBYGu5G2OiQnX/9E/G96RTZs2l/8/MXgvArn1Oy/2B95dT5VEW5++t/yAh4u+DJNlNUPa5z3z3THeO+4hD2jG8R1vv/nnrnGmQj3y8KpTFrCWg4C4iJ4nIChFZLSI3+TneQ0Q+FZHvRGSRiJwS/KIaY2JZodu1kZGSQMeM+il2F2/ay6crtjNrhRNMd+4LPM/6wWosuPs63Ceg+6oe/N0VxjI3GdxFJB54DDgZGAicLyID65x2C/Cqqg4HpgCPB7ugxpjYVlRaQWpiPInxcSQlxNEurfaap5+t3MFl/5hLqTvrxBPGxerj/Kzo5G91pobSDf/qeGfhj6MP7RDcgjUikJb7aGC1qq5R1XLgZeCMOucoUJ0KLQvYHLwiGmNag8L9lWSm1gwD/mh4N+/t0e6AJsCOwrKwlguoN+3yhkmH1gvkb189rsH7p6c49dpfXuV34DgUAgnu3QDfqwny3X2+7gAuFJF8YAZwrb8HEpErRWSeiMyL5kWwjTHht7uknOzUmtZ6Gze74qCumbRrU7O/yF2kI4wNd++sl2pXTOhVK7hP/dFghnbPbvD+KYnOuXe9s5QJD3zK2p3FoSmoj0Bmy/gbkq77sp4PTFPVB0XkCOAFETlMVT217qT6FPAUwMiRI8N7uZYxJqrtKakgu01N0q0097L9Ko/Stk4XDYS3Wya+TrdMdWB/7tKRDMnNbjL3e93MlZv37K81Hz4UAmm55wPdfbZzqd/t8lPgVQBV/QpIAdpjjDEBePSTVcxZV1CrNdytrZNRcWthKe3S6i/I0TWM65MmNDCnfmL/TgEt6lF3QDYcn0uBBPe5QF8R6SkiSTgDptPrnLMBOA5ARAbgBHfrdzHGNMnjUf78gbOq0qi8mr71/p2dYbw9JRXkpNUOoENzs3jmkpFhK6PvgOq7141v5MzAVHo8TZ90kJoM7qpaCVwDzASW4cyKWSIid4nI6e5p1wNXiMhC4CXgUtVw9ogZY1qqDT4DjNcd19d7Oy+njfd2TnrtbpmfHd272cvgHYzqiTGj8toyqOvBr/xUXBb6K1UDukJVVWfgDJT67rvN5/ZSoOGhYmOMacBXa3YB8PpVR9Ta75s7pm4gz20bvi4ZgPg4pyzidwgyME9eNIKEOOGn/5xHcVnol9yz9APGmIi6+c3FAH4vXALo0zG9Xss9t20bv+eGSoY7c6ek4sCD8qRBndm7v4KhuVlkpIQ+9FpwN8ZE1Oie7ZiztoDu7eq3xuf+4XjaJMXjUSUjOcE7DbJtm/oDrKHU0U2HUHCQV5hmpSby9jUH32cfCAvuxpiIKq/0MLxHNuLnKtAOGTXdMQtvP5FtRaVs2r3f77mh1CnT+VZRUBK+9AEHyxKHGWMiZuW2IhZs3NP0iTgXEnXJSmWkz4yacOnofsiUVoR+lkuwWMvdGBMxa3bsA+CC0T0iXJLGtW2TxIS+7bnkiLxIFyVgFtyNMSGlquwrqyQjpX4/eUGxk+Z3Qt/wJdQ6EHFxwgs/HRPpYjSLdcsYY0LqhtcXMfiOD6io8uDxKOWVNV0bBcVOErC2fq5ANQfHgrsxJmTKKz28Pj8fgLJKDze+sYh+t7znPZ6/ez/t0pLq5V4xB8+CuzEmZF6ZV5NQ9pb/LOY1N9CXlDtTGguKy72DlSa4rM/dGBMSHo9y61vfe7ffWlCTb3DJ5kJmr9rJvrJKksO0pmhrY8HdGBMSs1fvbPDYOU985b09pmf4pza2BtYtY4wJiWVbCgG4dmIf777JQ7rUO89a7qFhwd0YExJ791cQHyf06Zju3ecvp0pmGPKstEYW3I0xQfP9pr3c9d+leDzK2p3FdM1Oocxn6mNCXP2Q071deJOAtRYW3I0xQXPF8/N47ou1rNtVzIKNexjWvW2t2TDVKxpdNi6P9m6mx+5hzvDYWtj3IWNM0Gwvci5K+mZtAVv2ljI0N4uj+3Xg9tMGogqnD+vKxoL9/Oq4fizfUsTOfbvokuU/1a85OBbcjTFBUVhaQZXHWYBtxdYiAHp1SENEuGxcT+951cvjVa8rGuYEj62GdcsYYw7aqm1F/Pjpb7zbb7gXKzW2iPVodwpkZ2u5h4S13I0xB2Xmkq387IX5tfZVL6rRq326v7sAcM2xfZg0qDOHds4IaflaK2u5G2MOykMfrPS7f2yvdiQlNBxi4uLEAnsIWXA3xjTb/PW7qaxypjh2qJMb5qFzh5IYL5w3qnskimZcFtyNMc2ybEshZ/39S/7stthnr97JyEPaeo8f0TuHJXeexJnDcyNVRIMFd2NMM63c5syEWbalkO837QVgaPds7/HOmSmNdseY8LB3wBjTLA9/6LTYyys9fOeufzplVHe6ZqXwx7MGh33xauOfzZYxxjTLul0lAHy1ZhdfrdlFbttU+nRM58ubj4twyYwva7kbYwK2v7yq3r6BXTKttR6FAgruInKSiKwQkdUiclMD55wrIktFZImIvBjcYhpjosFPps0Fai5AAqh0r0o10aXJ4C4i8cBjwMnAQOB8ERlY55y+wM3AOFUdBPwqBGU1xkTYV2t2AXD+6O48c7GTRuCwblmRLJJpQCB97qOB1aq6BkBEXgbOAJb6nHMF8Jiq7gZQ1e3BLqgxJrLKfVL3Duvelp7t0/joN0eRl5MWwVKZhgTSLdMN2Oizne/u89UP6CciX4jI1yJyUrAKaIyJDovdaY9H9s6hZ3snoPfpmEFCvA3dRaNAWu7+RkrqdrIlAH2BY4Bc4H8icpiq7qn1QCJXAlcC9OjRo9mFNcZEzncbdgPw8HnDIlwSE4hAPnLzAd/riHOBzX7OeVtVK1R1LbACJ9jXoqpPqepIVR3ZoUOHAy2zMSYClm4upHNmCp0yLYtjSxBIcJ8L9BWRniKSBEwBptc55y3gWAARaY/TTbMmmAU1xkTWhoISumZbYG8pmgzuqloJXAPMBJYBr6rqEhG5S0ROd0+bCewSkaXAp8ANqrorVIU2xoTXiq1FzFu/m8E2M6bFCOgKVVWdAcyos+82n9sK/Mb9McbEmFkrnAlwVx7dO8IlMYGyYW5jTKM8HuX+95YD0K2RlZVMdLHgboxp1CvzNjZ9kok6FtyNMY26+c3FAPziGOuSaUksuBtjAvK7k/pHugimGSy4G2MatHr7vkgXwRwgC+7GGL8qqzyc/MjnkS6GOUAW3I0xfq3dWUxFlZNpZIxPil/TMthKTMYYvwqKywG4aOwh3HbawCbONtHGWu7GGL927nOC+4/H9iDRMj+2OPaOGWP82rmvDID26ckRLok5EBbcjTF+7SgqI06gbZukSBfFHAAL7sYYvzbuLqFLVirxcbb4dUtkwd0Y41dpRRUZKTbnoqWy4G6M8avKo9Zqb8EsuBtj/Kr0KAkW3FssC+4mJm3es58PlmyNdDHCrqyyClVlyea9vLd4i99zVJUvVu9kwcZaSxyzvbCUvSUV3m1rubds1qFmYtKxf55FWaWH1646glF5rePqyk+Xb+eyaXMBSEmMo7TCw5I7J5GWXPvf/ONl27n8+XkkJ8Sx4p6TvftH3/cxALefNpDLxvWkssqCe0tmLXcTc3YXl1NW6QFoVa332at3em+XVjj1P+3R2TgLpdU/r/o1WralkK9+qFkV887/LgWs5d7SWcvdxJzlW4u8t3/YURzBkoTXy3M21Nu3Zkcxr83P53evL+LUwV348zlD+WFHTabHOWsLOPfJr+rdr7SiijnrCujdIS2kZTahYy13EzNUlYLics5/+mvvvk+Wb2fv/opG7hUbPB6luLzK77Hfvb4IgHcXb+Ged5eyr6zSe6xuYL/5ZCdne/9b3wda14djrLHgbmLGDa8v4vC7P6y3f+b3sd81s8fnA+yhc4cC0Dkzpd55//5mAyu3FpHb1v9aqEf16xCaApqws+BuYsKb3+bz+vx8v8d+98Yiqjzq91isKCh28sA8MmUYQ3KzAbhwbA+/5xaXV3HtxD7e7e9uPYHbJg9kxT0n0a9TRq1z37l2fIhKbELNgruJCb95dWG9ff071wSqXW7wi1XVGRxz0pLp0zGdRXecyJVH1V7zNMkns+MhOWmcNrQrAG3TkvjJ+J4kJ8QTHyd8fP3RAFx3XF8O65YVphqYYLPgbmLKYxcczm9P7McpgzvXanUWlVY2cq+Wrzr3ek66k+QrMyWRpIQ4PnEDdbfsVMb3be89Py0pgb9OGcaa+06p91i9O6Szbuqp/OaEfmEouQkVmy1jWryKKmdKX2piPKcO6VLr2NMXj+SK5+fx8IcrGdMrh4vGHhKJIoZMRZWHQ295j57tnVkt1cG9Ws/2aTxw1hCOG9CRm95c7N3fJjkeEUFspmPMspa7afG2FZYCzsU3dR3WLROAdxZt4da3vg9ruUJNVbno2W/waM2slrrpeUWEc0d1Jyc9uVYqgTZJ8WEtqwk/C+6mxdvjXjLfNq1+3vG6M0bK3Qt3YsH89bv5ek1BrX2NrZgUXyu425f2WBdQcBeRk0RkhYisFpGbGjnvbBFRERkZvCIa07gZbg6VDhn1VwySOv0OV/1rfljKFGrfb9rLLrefvdpjFxze6H2s5d66NPnxLSLxwGPACUA+MFdEpqvq0jrnZQDXAd+EoqDGNOR/q5zL6Qd0zvR7/Lrj+vLXj1cBzkVNLZ2qMvlvs73bt04eyDkjc8lMSWz0fvFxTlsuKzXR1kRtBQJ5h0cDq1V1jaqWAy8DZ/g5727gAaA0iOUzMayiysOny7dTWXXgXSWb9uxn8aa9XDuxD6kNtEZ/cUxvjo6hi3O2Fdae1jm8R3aTgR0gNcn5d2/oAiYTWwIJ7t2AjT7b+e4+LxEZDnRX1XcaeyARuVJE5onIvB07djS7sCa2THnqay6bNpe3F2w+4MfYWFAC0Gjmx5TEeHL89Me3VM99sbbW9vDu2QHO8yASAAAesElEQVTdr7/7zeb4AZ2CXiYTfQIZVfE3Wcp7uZ+IxAEPA5c29UCq+hTwFMDIkSNj+5JB06iKKg/z1+8GYOmWQs46wMepnt/d1HJw/XwuaKqs8pAQH0dJeSUDb5sJwKI7Tgyo9RsNSitqcsj8+Zyh9cYVGvLjMT0Y36c9ee0tGVhrEEjLPR/o7rOdC/g2tTKAw4BZIrIOGAtMt0FV05hZK2q+uS3ZvPeAH+e6l74DaLBLptoVE3pxxYSeACza5DzfGp+kWAfz7SGYyis9PPD+cjbv2Q/AvHUFeOqkTlizo5jB3bJYe/8pnD0iN+DHFhEL7K1IIMF9LtBXRHqKSBIwBZhefVBV96pqe1XNU9U84GvgdFWdF5ISm5hwxfPOn0dSQhyb95Syu7g8oGmKb8zP54rn5zHp4c/5bOUOKt3A16t9eqP3i48TrjuuLwA/evxLANburAnuO4uiIz3Bm9/m8/isHzhy6ic8Pms1Zz/xFW9+t8l7XFVZsa2Ivp3SA26xm9apyW4ZVa0UkWuAmUA88JyqLhGRu4B5qjq98UcwprbX5tUM4eTltGHltn0Mv/tDhnXP5q2rxzV4vz0l5Vz/Wk0OmUuemwPAbZMHkpTQdDslIyWRvh3TWbV9H6rKOje4xwm10uBG0i0+F1o98P4KAIp9yrZuVwk7isoY3qNt2MtmWpaA5kOp6gxV7aeqvVX1Xnffbf4Cu6oeY61205DySg83uPnFAXLbtvHerrumZ10N5RafXCflQGMmD3GSZf3149U8+OFKwAn6BzNjJ5ji/LTGPaos3LiH8koPL36zHoAJfdrXO88YX3aZmgkr366Q9OQEumbXvoK0osrT4Bxs3/t2zUph815n1m1HP3nLG7K+wHmMhz9a6d2XGB9HeVV0jO/npCcxpmc73vIZA/h42Xbv0ncAE/raoKhpml3JYMLqx88417j9/JjefH/npHq5UBZvqj+4WlRawWOfrmbu2gKSE+JYde/J/PJ4p/+8U2b9q1Ib8/OjnTS41Q3kW04dQFK81Gq5l1d66g1ihktxWSXZbZwA3y3bmY++antRrXNiPTe9CQ4L7iasdu5zBi5/6Q5uZtcJ7tWDneC01Bds3MPt05fwp5kreGXeRsb3aU9ifJz3asthAc7xrta3UwY926dRvWZ0747pbN5byms+C330u+U9fv3qgmbXLRj2V1TRJimeV352BF/cNJE4qcmdU+3SI/MiUjbTsli3jAmrzpkpdGubSkqiM3XxqL61+457tHP64PeUlHPsn2fVv3+W0wVTPcjoL59MU3y7d3znts9asZ0jeucAztTIR6YMb/ZjH4zySg8VVUpacs2/pUehzJ1FdFi3TP5x6egDqrNpfazlbsJKUfp2rJm22LdTBmcdXjNXe0NBCY/PWs3o+z72e/933SRh4/rk0Kt9GueO7O73vMYMya1ZXSgrtSa4X/qPuSzKP/A59werpNz5wEpN9D9n/51rJ1hgNwGz4G7CqqSsqt4FR2cOr5XNggfeX1FvzvtrVx0BwJMXjgCgT8cMPvntMd71Qpvj35eP8d7OTE3g8vE9vdvnPPFVsx8vWKov7PIXwJMDmOppjC/7izFho6oUl1eSVieXeL/OTkt+Qt/2jMqrP3/7q5snMiqvHeumnsqYXjkHXY6MlEQO7+F8KGSnJnH9iYf6Pe+qF8KbHvhXrzj9/L6LVP/U/eDxXdDamEBYn7sJm+LyKjzqLPHmq2NGCv/73bF0zkrhk+XbmbvOCaqvX3UEg3OzSE4Ifu7xJy8aya7iMu/FT0Nys+p1yby/ZCv7y6tITogjLi58V4P29JnmmOZ+yymLoUVGTHhYy92ExZc/7OSw250kXf5muHRv14bE+LhaGQ6H92gbksAOTtdHf5/878u2FPo9b8Bt73PfjGUhKYMv36tQfa+2nehmcBxvFy2ZZrLgbsLiwmdq1nAZ27PhrhXf/ub4MLaWL5/Qq9b2HT7rsb61YFPd04Pu0xX+FxEZ1j2b1feeHJTuKNO6WHA3YZHuM72vsS6OSCXDuvGk/qy9/xTW3Of8dPNJi9CvUwbLthTyi3/Pr5VutzEl5ZVMfW85OwJMSLZgg5N64fMbjq13LMFWTTIHwP5qTMhVVnkod68ArTszxp9BXTMj0g0hIsTFOT8piTX/GkO7Z3PyI/9jxuKtbHAXB2nKv7/ewBOf/cCoez9ie1EpFz83h0X5DefOeWb2WuIEeuS0afAcY5rDBlRNyP3nu02UVnh44sLDmTSoc5PnT79mPKqRvcQ+3ucbhG8qgk2799eazdKQtj4rP1363FyWbilk6ea9zLvlhHrnllU63wa62fJ3Jois5W5CSlW9WSAnDeocULdLfJxEvCvCNzFXlUdp485aeWb2mlofPI9+soqrXphf78No1baafDBL3cHanfvKa10dW+0vHzmLd991+mHBq4Bp9Sy4m5AacNv73tstaXGJrtmpLL1rEhnJCVRpTXD/YvUuFrpTJjft2c+fP1jJ+0u2ejNUVnvy8zV+H/fYP8+ql/jr77N+AODIPjZoaoLHgrsJGVWltMLpa3/1Z0dEuDTN1yYpgfh4ocqjlJRXMdRNW3D3O0763ZveqMlLv2tfzcBp3dzwN0w6lJX3nOzdrl5kRFW9j5XbNjVk0z5N62TB3YTM3HW7vbdHHNIyVw6KF6HSo+yvqGJkXjsA5q/fzacrtteaCbNrX7n39uod+wC8OXQuPuIQkhLieOXKsc7x7c7xv32ymmdnrwUgf/f+0FfGtCoW3E3IfLF6JwDTrxkX1jnrwaTAi99sQBXa+QySfrl6J8u3FnHMoR0A2FpY0y3z46edOf1TzxrM6ntPJsPNPDmmVw6XHplHcVklL3y1joc+rFkwxJhgs+BuQqawtIL05IQDSu4VLQqKy2ttV3evPP0/p8Xdv3MmWamJ/PH95Zz0l89Zu7PYm7J3QJfMegPDnTJTKCqr5Na3l3j3Hd4j23LHmKCzqZAmZIrLKklLjp1+5LG92tVbiDshTvB4lKKySvaUVPCnmcsZ3C2LhDihTVL9f6/DumXW2n7j50cw4pB2IS23aZ2s5W5CZmthGR0zAl/fNNr5m+1z5uHduOGkmqySMxZvZeW2ogYvRvLNq3PdxD4W2E3IWHA3IaGqrNmxr8VfcXnm8G4M6prJTSf3Z5jbvXRUP6effdEdJ9K7QzoXjT2k1pW3q7bvq5WUzFeGz8pP/bv4P8eYYLBuGRMSd/53Kfm793PdxL6RLspBefi8YfX2PXnhCFZsK/Iu0SciPHzeMA7JaeO9IKlXh7R696vWq30aa3YWM8CCuwkhC+4m6CqrPEz7ch0Apw3tGtnChEBqUrzftMW+edgbWw7vyYtGMHPJVvJa+LcaE90suJug+517cc/D5w2tt6ReLPNdYWq4n+BfrW+nDPoGkJ/GmINhfe4m6Ba7l+efMrhLhEsSXm18Psiy2yQ1cqYxoWctdxNU5ZUeumSnUlpZ1eoup68eLLVVk0w0CKjlLiInicgKEVktIjf5Of4bEVkqIotE5GMROST4RTXhVuVRKqoCX7tz695SfvPqAj5fuYONBa3vcvpBXTO5YdKh3Hfm4EgXxZimW+4iEg88BpwA5ANzRWS6qi71Oe07YKSqlojIz4EHgPNCUWATPr1/PwOAr28+js5Zjc9XX7WtiBMe/jwcxYpacXHC1cfalaYmOgTSch8NrFbVNapaDrwMnOF7gqp+qqrVS9R8DeQGt5gmHPaWVHDvu0vZVlg7fe2WvU23wr9ZW1Br++4zBgW1bMaY5gmkz70bsNFnOx8Y08j5PwXe83dARK4ErgTo0aNHgEU0waSqDeZVf/O7fJ7+31oqqpRbJ9csEP23T1Zz+fie5O/Zz7kju9e735erd3LLW9+Tl9OGT397TIvK225MrAqk5e7vP9XvGmgiciEwEviTv+Oq+pSqjlTVkR06dAi8lCYoKqs89Lx5Bo+4F9rUtXKbk4p22pfrWLxpr3f/J8u3c8Ez3/C71xexYVftNUS37N3PBc84WRCvndjXArsxUSKQlns+4NtcywU21z1JRI4H/gAcraqBLfluwmqO23Xy8EcrGZybycT+nWodr14ODuBfX6/3+xiFpRUAbCwoYdqX66jO5HvGsK786PCmF782xoRHIC33uUBfEekpIknAFGC67wkiMhx4EjhdVbcHv5gmGN74dpP39k+mzQNg2ZZCJjzwCXPXFbB8SyHnj+6BCHy+cof33ENy2vB/w5wrTa958Vv27q9gwgOf8uzstTz9v7WkJcXzl/OGWavdmCjSZMtdVStF5BpgJhAPPKeqS0TkLmCeqk7H6YZJB15z/8E3qOrpISy3OQALNu6mXVqSN0f5aX+bTXFZJRsL9nPOE18BMCqvLR8t28b2ojLi44Qld04iKT6OLYWlvLVgM+t2lfDq3I21HrdrdqoFdmOiTEAXManqDGBGnX23+dw+PsjlMkGmqmwvLOOsEblsKCjhk+Xba/WrVxveoy1t2ySyo6iMKo+SkuhciNQtO9V7zr0zlgFw1dG9eeKzH5g4oGN4KmGMCZilH2glVm7bR1FZJYd2ziAl0f/b/ugFw+nZPo1TB/tP9rXwthNrbf/6hL68c+14bjqpf9DLa4w5OBbco9D+8ioe/GAFxWWVlJRXoup3clKzfLLcGQo59tCOtdYCBfjT2UNYcc9JTB7iBPVrJ/ZhVF5bHplSO91tVptEhroJsbq3SyU5IZ7DumVZl4wxUchyy0SZguJyDr/7QwA8qkz7Yh2jerbjH5eOOuAg+sJX6/jj+8sB6JyVQvv02ulo26cn18oDExcnvHbVkX4f65Urx3L9awv5ybieB1QWY0x4WHCPAtsKS0mKj6NtWhLPf7XOu/+xT38AYNaKHYy9/2OO6JVDVmoid5w+iILici58dg7LthRyw6RDmdi/IwO6ZFJSXokgtVLtPjPbWcw5M8V5u/t0TAcgPk5ISYhrdGGJulIS43nsgsMPssbGmFCz4B5hVR5lzH0f07ZNIpdP6MVfPlpFenICacnxbCusuVxgW2EZby1wLi84tn9HtuwtZZk7L/1PM1fwp5krWHrXJMZN/YTdJRX8cN8pxMcJq7YVsaGghHF9cvjrlOEAnDq4C3o+TBrUmcR4sW4VY2KQ9blH2BOfOa3z3SUV/GnmCgB+dXxfslOdfvF7/u8wb0u72nUvfcc7i5xAPyqvrXf/wNtmsrvEucho5pKtgDO3XRX+dv7h5LjdMSLCaUO7kpQQZ4HdmBhlwT2CissqvQHd10/G9eQvU4ZxwsBOnD0il+P610w1nPqjwRSWVvLF6l1kt0nktauO5NtbT6j3GL/497c88dkP3g+PuoOoxpjYZsE9Ql78ZgMj7nEGTh88Z6h3//K7TyIuThjQJZOnLx5JSmI8h+Q4feJnj8jlvFHdvX3n1fvbpSVx5+mDaJ+ezMT+HTlhoJNWYOp7ziDqZePywlUtY0yUsD73EPjyh50M6prFjqJSbp++hDtPd7pW3l6wiV++vKDWuXECPzq8GyXllXTKTPFeNOTr/NHdmTSok7dbZf6tJ/D3WT9w/uiazJqXHJnHJUfmAfD9pr18uHSb99jvTxkQgloaY6KZBGMO9YEYOXKkzps3LyLPHUqfLt/OZdPmctm4PGat2MHancV+z5s8pAsXH5FHh4xkerYPfLZKoOasLeDcJ79iQJdM3vvlhKA/vjEmMkRkvqqObOo8a7kH2YKNewD4xxfr/B4/6/BcHjx3qN9jwTS6ZzsePGcoo3u2C/lzGWOijwX3INtdUl5r+9hDO/DpCifD4pu/OJJhudlhK8tZI2xBLGNaKwvuQeDxKMu2FtIhI5npCzczoW97fjKuJy/O2cDD5w3jxtcXMaZXOw7v0bbpBzPGmCCw4B4E//5mPbe+vcS7ffWxfRjbK4dj3SmMj/3Yrug0xoSXTYUMgtd9FsFol5bE2F45ESyNMcZYcK9n/voC73J0gXhn0WYWbtzD8QOcueUT+1tuc2NM5LW6bpknPvuBf3+znn9cOoo+HTNqHSurrOKsvzsrEj183lDOHN70gOQHS5z55PedeRg/GZ9Hv04ZTdzDGGNCr9W13Ke+t5yNBfs5/qHPeW3eRkorqigqreAP/1nMp8trln/99SsLvXnUP12xncv/OZdb3/qe9btq5q1/t2E3M5ds5fgBneiYmcKRvdvXS6drjDGR0Kpa7nUv2Lrh9UXMXLKN3Lap/PubDbw6z1kbtH/nDJZvLaLnzTM4f3QPXpqzwXufz1bu4PPfHcv6XcWc+fiXANxx+sDwVcIYYwLQqlruSzY7KXKP7F0z4PnRsm1M+3IdABVVSlJ8HJe6l/EDtQI7wIaCEgbfPpPPVjpz1y8f35Pctm1CW3BjjGmmVhPcK6s8TP7bbACG+LmQKM7NfHtknxzvUnK+1t5/CrdOdlroRWWV3Pb2EpIT4rjpZFs/1BgTfVpFt8z89QXc9c4y7/a5I3O9qXCr3f+jwdz4xmKuOro3A7pk8siUYfTpmM6qbfvYXVKOiPDT8T0ZndeO0x51PiTG9MohIb7VfD4aY1qQmA/us1ft5MJnvwHggbOGcPaIXOLcZnqbpHiyUxO5emIfzhvVg7MOz/UG6zOGdQNgUNesWo83ODeLR6YM46nP1/C4XZxkjIlSMZsV8u0Fm3j+q/XMX78bcPK6+F7+v73IWbc0u40tYmGMaTladVbIlduKauVNP2dEbr28Lh0zUsJdLGOMCZsWF9wrqjz88b3lrNq+j6zURA7rlsmVR/WudU51ut1rJ/ZhVF47jurXIQIlNcaYyAkouIvIScAjQDzwjKpOrXM8GXgeGAHsAs5T1XXBLarjkY9W8czstd7t6Qs3k5qUwEVjD0FV2V5UxjsLNzM0N4vrTzw0FEUwxpio12RwF5F44DHgBCAfmCsi01V1qc9pPwV2q2ofEZkC/BE4LxQFvvLoXnRvl8qNbyz27rv1re/Jy2nDDa8tYmthKQA329JyxphWLJB5fKOB1aq6RlXLgZeBM+qccwbwT/f268BxIiLBK2aNzJREzhvVg3VTT+WXx/X17r/o2TnewN62TaJlZjTGtGqBdMt0Azb6bOcDYxo6R1UrRWQvkAPsDEYhG3LV0b1Zua2I977fCsD/DevKqUO60r+zJe8yxrRugQR3fy3wuvMnAzkHEbkSuBKgR48eATx141KT4nn4vGFcNm4vXbJS6N7O0gAYYwwE1i2TD3T32c4FNjd0jogkAFlAvaToqvqUqo5U1ZEdOgRnBktKYjyje7azwG6MMT4CCe5zgb4i0lNEkoApwPQ650wHLnFvnw18opG6OsoYY0zT3TJuH/o1wEycqZDPqeoSEbkLmKeq04FngRdEZDVOi31KKAttjDGmcQHNc1fVGcCMOvtu87ldCpwT3KIZY4w5UJbS0BhjYpAFd2OMiUEW3I0xJgZFLOWviOwA1h/g3dsT4gukwsjqEp1ipS6xUg+wulQ7RFWbnEseseB+MERkXiD5jFsCq0t0ipW6xEo9wOrSXNYtY4wxMciCuzHGxKCWGtyfinQBgsjqEp1ipS6xUg+wujRLi+xzN8YY07iW2nI3xhjTCAvuxhgTgyy4G2NMDIra4O6u3UqolusLNxGJ2te6OWLl/YCav7GWTkSy3N8t/m9MRDq7v1v835mIDBKRlEg9f9T9MYjIOBH5J3CLiLRryXnhRWS0iFwHoKqeSJfnYIjIGBF5GrhRRIKz0kqEiMhIEXkBuE1Eeke6PAdCROJEJFNE3gH+Ci37b0xEhovIx8DdAC38/36IiMwG7sFZbjQioiq4i0gv4HHgU+AQ4G4ROTWypTowIvIr4D84H1Inu/taXEtRROJF5H6cqVtfAIcDt4tIp8iWrPncgPgo8CTwMdAFuENEWtwyXm4gLwISgW4ich60vNa7OB4Gngf+qapXRLpMQXAL8LqqnqmqmyAy30Si7Q9hBLBMVacB1wMLgMki0r3Re0Wn1cBk4OfAzQCqWtUCv27GARuAc9z35VfAWCA1koU6EG5A/AQ4zq3LAzhr/VZGslwHoT9OfpK/AD8WkQxV9bSkvzG3hZ4OfKeqzwOISO+W9iEF3sZDb2Cfqv7F3XeCiGTjLHQU1iAf0RdQRMaKSD+fXXOBXBHprqq7cVqKe4AzI1LAZvBTl3eBRe7vfdXdM7hvcjSrUxcP8JKqrhSRZFXdjLNmbvvIlTBwdd8XVX1TVfeIyAnAPJzW+30iMiBihQyAbz18AsRqoBxY6/5cIiI9or1Lw8//yvXAGBG5VUS+AP4ETBOREZEpYeB86+I2HrYDE0TkVBF5C/gtTrfZDe45YXtvIhLcRSRbRN4FPgTOFZF091ApMBs4191eASwFciI5MNEYP3VJqz6kqlXuKlUPAj8VkfaqGrWtRH/vi1uHPQCqWiYiGUBP6i+SHlUael98AuNu4AJVPQEowQmMUdfV5K8ePgFiJFCoqkuAJcDtwN9FJDEaW74NvSeqWgg8BpyF8y33fGALcFa0ju80Upci4B84YwfPqeok4BlgrIiMDWcZI/UHkIazJuu17u2j3P07gK+BwSIyWlWrgE3AODdIRiO/dakzuDULp17XgjPQGt4iBqxuXSb4OWcMsERVN4tIuoj0DWcBm6Gh90Xd3/Pc5SPBWUJyOE6QjzYN/a+A012WISKvAL8D5gMrVbUiSgdXG6yLqv4VOFZVP1fVMuAtnA+vaHxPoPH35R0gD2jrbs8DtgFlYSxf+IK7iFwsIkeLSKY7yPAU8CpOa320iHRzg/nXwHfAw26LfhCwIZoGvZqoyxgR6eqeJ+D0teOMnN8oInuBw6OlX7QZdalebzcb2Cgil+F0ow2LRLn9CbQufozAaSlGxbeqZtSjLdAB2Irz4fRz4NBo6mJqznvidsVWG4HT/VcV1gI3IoC6dANQ1UU43TDXiEh74ELgMGBXWMsbyi4gN4B1Bl7E6bv9AedT7pequtM9ZxxON8w8VX3B574PAbk4s2YuVtUVIStoAJpZl7mq+i93XxzQC+erWjnwK1VdHP4a1DjQurj7XwB+DPwTeNj9Q46Yg3hfMnG+hdyHExyvV9WV4a+B40D/V9yuvurj6UCSqhZEoApeB/GeJANHAH/G+bCN6HvilulgYthvcP73+wK/VtWl4Sx7yFruIhLvfgXOADap6nHAL4ACfDKiqeoXwDqcFkeW26cLziffT1V1TBQE9ubWpb9blzbu1+NC4DZVPS4KAvuB1CXTZ1zkXeBcVb0sCgL7gb4vKW4/rwL3qOppEQ7sB/q/kqaqO8WZrhqnqvuiILAf6HuS6nbHlBMF7wkcfAxT1YdwgvqkcAd2CEHL3f36fhfOrJAZQCZwtqpe4h4XnMG4Kar6mbsvHafb4kiclvpwd1ZGRAWpLiNUNT8Cxa/lIOsyDugBDFPVLREofi1BqkvE/8bsfyX63hOInfclqC13ETkaZ1CnLc40rbuBCuDY6kFE95PwLuAOn7ueivOJuBAYHOkXBYJal2gI7AdblwU4dYmGwB6sukQ6sNv/SpS9JxBb7wuqGrQfnNkVF/lsP44zyHMpMN/dF4fTh/UqkOfuOwM4KphlsbpYXaK5LrFSD6tLFNclyC9MGyAZiHe3fwzc795eAFzr3h6Jc2FMxF8Aq4vVxephdYnFugS1W0ZVS1S1TJ2pfwAn4MxdB7gMGCBOoqOXgG8herO/WV2sLqEUK/UAq0u01iWh6VOaT5wEWQp0Aqa7u4uA3+PM91yrbkIddT8Go5XVJTrFSl1ipR5gdYk2oZoK6cHJVrcTGOJ+0t0KeFR1dvWL0kJYXaJTrNQlVuoBVpfoEsK+q7E4L9BsnPnqEe+DsrpYXaLxJ1bqYXWJrp+QXaEqIrnARcBD6lyc0GJZXaJTrNQlVuoBVpdoEtL0A8YYYyIj6tKCGmOMOXgW3I0xJgZZcDfGmBhkwd0YY2KQBXfTaohIlYgsEJElIrJQRH4jTSxHJyJ5InJBuMpoTLBYcDetyX5VHaaqg3AuKz8FZ93RxuQBFtxNi2NTIU2rISL7VDXdZ7sXzlKB7XFycL+As8oOwDWq+qWIfA0MANbirD71V2AqcAxOgqnHVPXJsFXCmABZcDetRt3g7u7bDfTHyRviUdVScRb9fklVR4rIMcBvVXWye/6VQEdVvUecZeG+AM5R1bVhrYwxTQhJ4jBjWpDqjH6JwKMiMgxnUeZ+DZx/Ik6ukbPd7SycNTItuJuoYsHdtFput0wVsB2n730bMBRnLKq0obvh5PSeGZZCGnOAbEDVtEoi0gF4AnhUnb7JLGCLOguaX4SzfiY43TUZPnedCfxcRBLdx+knImkYE2Ws5W5ak1QRWYDTBVOJM4D6kHvsceANETkH+BQodvcvAipFZCEwDXgEZwbNt+4iDTuA/wtXBYwJlA2oGmNMDLJuGWOMiUEW3I0xJgZZcDfGmBhkwd0YY2KQBXdjjIlBFtyNMSYGWXA3xpgYZMHdGGNi0P8DuBGTEJbT7xIAAAAASUVORK5CYII=\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize= (20,20))\n", "pd.DataFrame(x_norm, index = close.index).plot()\n", "plt.title(\"Normalized Apple Stock prices\")\n", "plt.show()\n" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "# Create window from the normalized data\n", "def window_transform(series, window_size):\n", " X = []\n", " y = []\n", "\n", " # Take values from the given series and generate a sequence input/output pairs\n", " # x= y = s_n+1 and so on\n", " for i in range(len(series) - window_size):\n", " X.append(series[i:i+window_size])\n", " y.append(series[i+window_size])\n", "\n", " # reshape each \n", " X = np.asarray(X)\n", " X.shape = (np.shape(X)[0:2])\n", " y = np.asarray(y)\n", " y.shape = (len(y),1)\n", "\n", " return X,y\n", "\n", "window_size = 7\n", "X,y = window_transform(x_norm,window_size = window_size)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[0.01682023, 0.01977367, 0.02010685, 0.01703634, 0.01668515,\n", " 0.0179458 , 0.01626199],\n", " [0.01977367, 0.02010685, 0.01703634, 0.01668515, 0.0179458 ,\n", " 0.01626199, 0.01410988],\n", " [0.02010685, 0.01703634, 0.01668515, 0.0179458 , 0.01626199,\n", " 0.01410988, 0.01674818],\n", " [0.01703634, 0.01668515, 0.0179458 , 0.01626199, 0.01410988,\n", " 0.01674818, 0.01564966],\n", " [0.01668515, 0.0179458 , 0.01626199, 0.01410988, 0.01674818,\n", " 0.01564966, 0.01249811]])" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X [:5]" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[0.01410988],\n", " [0.01674818],\n", " [0.01564966],\n", " [0.01249811],\n", " [0.0207011 ]])" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y[:5]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python [conda env:tensorflow]", "language": "python", "name": "conda-env-tensorflow-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.6" } }, "nbformat": 4, "nbformat_minor": 2 } ================================================ FILE: LICENSE ================================================ MIT License Copyright (c) 2018 Packt Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. ================================================ FILE: README.md ================================================ # Hands-On Artificial Intelligence for IoT Book Name This is the code repository for [Hands-On Artificial Intelligence for IoT](https://www.packtpub.com/big-data-and-business-intelligence/hands-artificial-intelligence-iot?utm_source=github&utm_medium=repository&utm_campaign=9781788836067), published by Packt. **Expert machine learning and deep learning techniques for developing smarter IoT systems** ## What is this book about? There are many applications that use data science and analytics to gain insights from terabytes of data. These apps, however, do not address the challenge of continually discovering patterns for IoT data. In Hands-On Artificial Intelligence for IoT, we cover various aspects of artificial intelligence (AI) and its implementation to make your IoT solutions smarter. This book covers the following exciting features: * Apply different AI techniques including machine learning and deep learning using TensorFlow and Keras * Access and process data from various distributed sources * Perform supervised and unsupervised machine learning for IoT data * Implement distributed processing of IoT data over Apache Spark using the MLLib and H2O.ai platforms * Forecast time-series data using deep learning methods If you feel this book is for you, get your [copy](https://www.amazon.com/dp/1788836065) today! https://www.packtpub.com/ ## Instructions and Navigations All of the code is organized into folders. For example, Chapter02. The code will look like the following: ``` A = tf.placeholder(tf.float32, None, name='A') B = tf.placeholder(tf.float32, None, name='B') ``` **Following is what you need for this book:** If you are a data science professional or a machine learning developer looking to build smart systems for IoT, Hands-On Artificial Intelligence for IoT is for you. If you want to learn how popular artificial intelligence (AI) techniques can be used in the Internet of Things domain, this book will also be of benefit. A basic understanding of machine learning concepts will be required to get the best out of this book. With the following software and hardware list you can run all code files present in the book (Chapter 1-12). ### Software and Hardware List | Chapter | Software required | OS required | | -------- | ------------------------------------ | -----------------------------------| | 1 | TensorFlow1.x Python 3.5> Numpy 1.14>| Windows10 MacOS 10.x Ubuntu 16.04+ | | 2 | TensorFlow1.x Python 3.5> Numpy 1.14>| Windows10 MacOS 10.x Ubuntu 16.04+ | | | Keras OpenpyXL SQL | | | | HDFS H5py | | | | | | | 3-5,7,9-11| TensorFlow1.x Python 3.5> Numpy 1.14>| Windows10 MacOS 10.x Ubuntu 16.04+ | | | Keras Scikit Learn Matplotlib | | | | Pandas Scipy | | | | | | | 6 | TensorFlow1.x Python 3.5> Numpy 1.14>| MacOS 10.x Ubuntu 16.04+ | | | Keras Scikit Learn Matplotlib | | | | Pandas Open AI Gym Random | | | | | | | 8 | TensorFlow1.x Python 3.5> Numpy 1.14>| Ubuntu 16.04 | | | Keras Scikit Learn Matplotlib | | | | Scipy Pandas Kafka | | | | TensorFrames SparkDL PySpark | | | TensorFlowOnSpark | We also provide a PDF file that has color images of the screenshots/diagrams used in this book. [Click here to download it](http://www.packtpub.com/sites/default/files/downloads/9781788836067_ColorImages.pdf). ### Related products * Artificial Intelligence with Python [[Packt]](https://www.packtpub.com/big-data-and-business-intelligence/artificial-intelligence-python?utm_source=github&utm_medium=repository&utm_campaign=9781786464392) [[Amazon]](https://www.amazon.com/dp/178646439X) * Artificial Intelligence By Example [[Packt]](https://www.packtpub.com/big-data-and-business-intelligence/artificial-intelligence-example?utm_source=github&utm_medium=repository&utm_campaign=9781788990547) [[Amazon]](https://www.amazon.com/dp/1788990544) ## Get to Know the Author **Amita Kapoor** is an associate professor in the Department of Electronics, SRCASW, University of Delhi, and has been actively teaching neural networks and artificial intelligence for the last 20 years. She completed her master's in electronics in 1996 and her PhD in 2011. During her PhD she was awarded the prestigious DAAD fellowship to pursue part of her research at the Karlsruhe Institute of Technology, Karlsruhe, Germany. She was awarded the Best Presentation Award at the Photonics 2008 international conference. She is an active member of ACM, AAAI, IEEE, and INNS. She has co-authored two books. She has more than 40 publications in international journals and conferences. Her present research areas include machine learning, artificial intelligence, deep reinforcement learning, and robotics. ## Other books by the author * [TensorFlow 1.x Deep Learning Cookbook](https://www.packtpub.com/big-data-and-business-intelligence/tensorflow-1x-deep-learning-cookbook?utm_source=github&utm_medium=repository&utm_campaign=9781788293594) ### Suggestions and Feedback [Click here](https://docs.google.com/forms/d/e/1FAIpQLSdy7dATC6QmEL81FIUuymZ0Wy9vH1jHkvpY57OiMeKGqib_Ow/viewform) if you have any feedback or suggestions. ### Download a free PDF If you have already purchased a print or Kindle version of this book, you can get a DRM-free PDF version at no cost.
Simply click on the link to claim your free PDF.

https://packt.link/free-ebook/9781788836067