diff --git a/Scripts/API/Football-Standings/.env b/Scripts/API/Football Standings/.env similarity index 100% rename from Scripts/API/Football-Standings/.env rename to Scripts/API/Football Standings/.env diff --git a/Scripts/API/Football-Standings/README.md b/Scripts/API/Football Standings/README.md similarity index 100% rename from Scripts/API/Football-Standings/README.md rename to Scripts/API/Football Standings/README.md diff --git a/Scripts/API/Football-Standings/main.py b/Scripts/API/Football Standings/main.py similarity index 100% rename from Scripts/API/Football-Standings/main.py rename to Scripts/API/Football Standings/main.py diff --git a/Scripts/API/Football-Standings/output.png b/Scripts/API/Football Standings/output.png similarity index 100% rename from Scripts/API/Football-Standings/output.png rename to Scripts/API/Football Standings/output.png diff --git a/Scripts/API/Football-Standings/requirements.txt b/Scripts/API/Football Standings/requirements.txt similarity index 100% rename from Scripts/API/Football-Standings/requirements.txt rename to Scripts/API/Football Standings/requirements.txt diff --git a/Scripts/API/GitHubSizeChecker/README.md b/Scripts/API/GitHub Size Checker/README.md similarity index 100% rename from Scripts/API/GitHubSizeChecker/README.md rename to Scripts/API/GitHub Size Checker/README.md diff --git a/Scripts/API/GitHubSizeChecker/Screenshot.png b/Scripts/API/GitHub Size Checker/Screenshot.png similarity index 100% rename from Scripts/API/GitHubSizeChecker/Screenshot.png rename to Scripts/API/GitHub Size Checker/Screenshot.png diff --git a/Scripts/API/GitHubSizeChecker/Script.py b/Scripts/API/GitHub Size Checker/Script.py similarity index 100% rename from Scripts/API/GitHubSizeChecker/Script.py rename to Scripts/API/GitHub Size Checker/Script.py diff --git a/Scripts/API/GitHubSizeChecker/requirements.txt b/Scripts/API/GitHub Size Checker/requirements.txt similarity index 100% rename from Scripts/API/GitHubSizeChecker/requirements.txt rename to Scripts/API/GitHub Size Checker/requirements.txt diff --git a/Scripts/API/Github_information/README.md b/Scripts/API/Github Information/README.md similarity index 100% rename from Scripts/API/Github_information/README.md rename to Scripts/API/Github Information/README.md diff --git a/Scripts/API/Github_information/Screenshot.png b/Scripts/API/Github Information/Screenshot.png similarity index 100% rename from Scripts/API/Github_information/Screenshot.png rename to Scripts/API/Github Information/Screenshot.png diff --git a/Scripts/API/Github_information/github_scraper.py b/Scripts/API/Github Information/github_scraper.py similarity index 100% rename from Scripts/API/Github_information/github_scraper.py rename to Scripts/API/Github Information/github_scraper.py diff --git a/Scripts/API/Github_information/requirements.txt b/Scripts/API/Github Information/requirements.txt similarity index 100% rename from Scripts/API/Github_information/requirements.txt rename to Scripts/API/Github Information/requirements.txt diff --git a/Scripts/API/Google-spreadsheet-share-and-retrieve/README.md b/Scripts/API/Google Spreadsheet/README.md similarity index 100% rename from Scripts/API/Google-spreadsheet-share-and-retrieve/README.md rename to Scripts/API/Google Spreadsheet/README.md diff --git a/Scripts/API/Google-spreadsheet-share-and-retrieve/create_sheet.py b/Scripts/API/Google Spreadsheet/create_sheet.py similarity index 100% rename from Scripts/API/Google-spreadsheet-share-and-retrieve/create_sheet.py rename to Scripts/API/Google Spreadsheet/create_sheet.py diff --git a/Scripts/API/Google-spreadsheet-share-and-retrieve/credentials.json b/Scripts/API/Google Spreadsheet/credentials.json similarity index 100% rename from Scripts/API/Google-spreadsheet-share-and-retrieve/credentials.json rename to Scripts/API/Google Spreadsheet/credentials.json diff --git a/Scripts/API/Google-spreadsheet-share-and-retrieve/get_sheet.py b/Scripts/API/Google Spreadsheet/get_sheet.py similarity index 100% rename from Scripts/API/Google-spreadsheet-share-and-retrieve/get_sheet.py rename to Scripts/API/Google Spreadsheet/get_sheet.py diff --git a/Scripts/API/Google-spreadsheet-share-and-retrieve/outputs/gmail-noti.JPG b/Scripts/API/Google Spreadsheet/outputs/gmail-noti.JPG similarity index 100% rename from Scripts/API/Google-spreadsheet-share-and-retrieve/outputs/gmail-noti.JPG rename to Scripts/API/Google Spreadsheet/outputs/gmail-noti.JPG diff --git a/Scripts/API/Google-spreadsheet-share-and-retrieve/outputs/google-sheets-credentials.JPG b/Scripts/API/Google Spreadsheet/outputs/google-sheets-credentials.JPG similarity index 100% rename from Scripts/API/Google-spreadsheet-share-and-retrieve/outputs/google-sheets-credentials.JPG rename to Scripts/API/Google Spreadsheet/outputs/google-sheets-credentials.JPG diff --git a/Scripts/API/Google-spreadsheet-share-and-retrieve/requirement.txt b/Scripts/API/Google Spreadsheet/requirement.txt similarity index 100% rename from Scripts/API/Google-spreadsheet-share-and-retrieve/requirement.txt rename to Scripts/API/Google Spreadsheet/requirement.txt diff --git a/Scripts/API/Google-spreadsheet-share-and-retrieve/sample.csv b/Scripts/API/Google Spreadsheet/sample.csv similarity index 100% rename from Scripts/API/Google-spreadsheet-share-and-retrieve/sample.csv rename to Scripts/API/Google Spreadsheet/sample.csv diff --git a/Scripts/API/Google-spreadsheet-share-and-retrieve/sample.json b/Scripts/API/Google Spreadsheet/sample.json similarity index 100% rename from Scripts/API/Google-spreadsheet-share-and-retrieve/sample.json rename to Scripts/API/Google Spreadsheet/sample.json diff --git a/Scripts/API/Google-Py Translator/README.md b/Scripts/API/Google Translator/README.md similarity index 100% rename from Scripts/API/Google-Py Translator/README.md rename to Scripts/API/Google Translator/README.md diff --git a/Scripts/API/Google-Py Translator/img.png b/Scripts/API/Google Translator/img.png similarity index 100% rename from Scripts/API/Google-Py Translator/img.png rename to Scripts/API/Google Translator/img.png diff --git a/Scripts/API/Google-Py Translator/main.py b/Scripts/API/Google Translator/main.py similarity index 100% rename from Scripts/API/Google-Py Translator/main.py rename to Scripts/API/Google Translator/main.py diff --git a/Scripts/API/Google-Py Translator/requirements.txt b/Scripts/API/Google Translator/requirements.txt similarity index 100% rename from Scripts/API/Google-Py Translator/requirements.txt rename to Scripts/API/Google Translator/requirements.txt diff --git a/Scripts/API/Random_Album_API/Procfile b/Scripts/API/Random Album API/Procfile similarity index 100% rename from Scripts/API/Random_Album_API/Procfile rename to Scripts/API/Random Album API/Procfile diff --git a/Scripts/API/Random_Album_API/README.md b/Scripts/API/Random Album API/README.md similarity index 100% rename from Scripts/API/Random_Album_API/README.md rename to Scripts/API/Random Album API/README.md diff --git a/Scripts/API/Random_Album_API/Random_Album_API/__init__.py b/Scripts/API/Random Album API/Random_Album_API/__init__.py similarity index 100% rename from Scripts/API/Random_Album_API/Random_Album_API/__init__.py rename to Scripts/API/Random Album API/Random_Album_API/__init__.py diff --git a/Scripts/API/Random_Album_API/Random_Album_API/__init__.pyc b/Scripts/API/Random Album API/Random_Album_API/__init__.pyc similarity index 100% rename from Scripts/API/Random_Album_API/Random_Album_API/__init__.pyc rename to Scripts/API/Random Album API/Random_Album_API/__init__.pyc diff --git a/Scripts/API/Random_Album_API/Random_Album_API/application.py b/Scripts/API/Random Album API/Random_Album_API/application.py similarity index 100% rename from Scripts/API/Random_Album_API/Random_Album_API/application.py rename to Scripts/API/Random Album API/Random_Album_API/application.py diff --git a/Scripts/API/Random_Album_API/Random_Album_API/application.pyc b/Scripts/API/Random Album API/Random_Album_API/application.pyc similarity index 100% rename from Scripts/API/Random_Album_API/Random_Album_API/application.pyc rename to Scripts/API/Random Album API/Random_Album_API/application.pyc diff --git a/Scripts/API/Random_Album_API/Random_Album_API/dataset/__init__.py b/Scripts/API/Random Album API/Random_Album_API/dataset/__init__.py similarity index 100% rename from Scripts/API/Random_Album_API/Random_Album_API/dataset/__init__.py rename to Scripts/API/Random Album API/Random_Album_API/dataset/__init__.py diff --git a/Scripts/API/Random_Album_API/Random_Album_API/dataset/albumlist.csv b/Scripts/API/Random Album API/Random_Album_API/dataset/albumlist.csv similarity index 100% rename from Scripts/API/Random_Album_API/Random_Album_API/dataset/albumlist.csv rename to Scripts/API/Random Album API/Random_Album_API/dataset/albumlist.csv diff --git a/Scripts/API/Random_Album_API/Random_Album_API/logics/__init__.py b/Scripts/API/Random Album API/Random_Album_API/logics/__init__.py similarity index 100% rename from Scripts/API/Random_Album_API/Random_Album_API/logics/__init__.py rename to Scripts/API/Random Album API/Random_Album_API/logics/__init__.py diff --git a/Scripts/API/Random_Album_API/Random_Album_API/logics/app_logic.py b/Scripts/API/Random Album API/Random_Album_API/logics/app_logic.py similarity index 100% rename from Scripts/API/Random_Album_API/Random_Album_API/logics/app_logic.py rename to Scripts/API/Random Album API/Random_Album_API/logics/app_logic.py diff --git a/Scripts/API/Random_Album_API/Random_Album_API/run_server.py b/Scripts/API/Random Album API/Random_Album_API/run_server.py similarity index 100% rename from Scripts/API/Random_Album_API/Random_Album_API/run_server.py rename to Scripts/API/Random Album API/Random_Album_API/run_server.py diff --git a/Scripts/API/Random_Album_API/Random_Album_API/shared_resources/__init__.py b/Scripts/API/Random Album API/Random_Album_API/shared_resources/__init__.py similarity index 100% rename from Scripts/API/Random_Album_API/Random_Album_API/shared_resources/__init__.py rename to Scripts/API/Random Album API/Random_Album_API/shared_resources/__init__.py diff --git a/Scripts/API/Random_Album_API/Random_Album_API/shared_resources/argument_check.py b/Scripts/API/Random Album API/Random_Album_API/shared_resources/argument_check.py similarity index 100% rename from Scripts/API/Random_Album_API/Random_Album_API/shared_resources/argument_check.py rename to Scripts/API/Random Album API/Random_Album_API/shared_resources/argument_check.py diff --git a/Scripts/API/Random_Album_API/Random_Album_API/shared_resources/exceptions.py b/Scripts/API/Random Album API/Random_Album_API/shared_resources/exceptions.py similarity index 100% rename from Scripts/API/Random_Album_API/Random_Album_API/shared_resources/exceptions.py rename to Scripts/API/Random Album API/Random_Album_API/shared_resources/exceptions.py diff --git a/Scripts/API/Random_Album_API/Random_Album_API/views/__init__.py b/Scripts/API/Random Album API/Random_Album_API/views/__init__.py similarity index 100% rename from Scripts/API/Random_Album_API/Random_Album_API/views/__init__.py rename to Scripts/API/Random Album API/Random_Album_API/views/__init__.py diff --git a/Scripts/API/Random_Album_API/Random_Album_API/views/api_view.py b/Scripts/API/Random Album API/Random_Album_API/views/api_view.py similarity index 100% rename from Scripts/API/Random_Album_API/Random_Album_API/views/api_view.py rename to Scripts/API/Random Album API/Random_Album_API/views/api_view.py diff --git a/Scripts/API/Random_Album_API/requirements.txt b/Scripts/API/Random Album API/requirements.txt similarity index 100% rename from Scripts/API/Random_Album_API/requirements.txt rename to Scripts/API/Random Album API/requirements.txt diff --git a/Scripts/API/Random_Album_API/runtime.txt b/Scripts/API/Random Album API/runtime.txt similarity index 100% rename from Scripts/API/Random_Album_API/runtime.txt rename to Scripts/API/Random Album API/runtime.txt diff --git a/Scripts/API/Random_Album_API/screenshots/all_albums-min.png b/Scripts/API/Random Album API/screenshots/all_albums-min.png similarity index 100% rename from Scripts/API/Random_Album_API/screenshots/all_albums-min.png rename to Scripts/API/Random Album API/screenshots/all_albums-min.png diff --git a/Scripts/API/Random_Album_API/screenshots/invalid_request-min.png b/Scripts/API/Random Album API/screenshots/invalid_request-min.png similarity index 100% rename from Scripts/API/Random_Album_API/screenshots/invalid_request-min.png rename to Scripts/API/Random Album API/screenshots/invalid_request-min.png diff --git a/Scripts/API/Random_Album_API/screenshots/random_album-min.png b/Scripts/API/Random Album API/screenshots/random_album-min.png similarity index 100% rename from Scripts/API/Random_Album_API/screenshots/random_album-min.png rename to Scripts/API/Random Album API/screenshots/random_album-min.png diff --git a/Scripts/API/Random_Joke/README.md b/Scripts/API/Random Joke/README.md similarity index 99% rename from Scripts/API/Random_Joke/README.md rename to Scripts/API/Random Joke/README.md index 2376f617b..1aed1361d 100644 --- a/Scripts/API/Random_Joke/README.md +++ b/Scripts/API/Random Joke/README.md @@ -9,7 +9,7 @@ - Make a virtual environment and activate it (suggested, can be skipped) - Get the code on your machine - Clone the repo `git clone https://github.com/Python-World/Python_and_the_Web.git` - - Move to this directory `cd Python_and_the_Web/Scripts/API/Random_Joke` + - Move to this directory `cd Python_and_the_Web/Scripts/API/Random Joke` - Install requirements `pip install -r requirements.txt` - Run it! `python joke.py` (on linux it's possible to run it with `./joke.py` too but you'll need to `chroot +x joke.py`) diff --git a/Scripts/API/Random_Joke/images/example1.png b/Scripts/API/Random Joke/images/example1.png similarity index 100% rename from Scripts/API/Random_Joke/images/example1.png rename to Scripts/API/Random Joke/images/example1.png diff --git a/Scripts/API/Random_Joke/images/example2.png b/Scripts/API/Random Joke/images/example2.png similarity index 100% rename from Scripts/API/Random_Joke/images/example2.png rename to Scripts/API/Random Joke/images/example2.png diff --git a/Scripts/API/Random_Joke/images/example3.png b/Scripts/API/Random Joke/images/example3.png similarity index 100% rename from Scripts/API/Random_Joke/images/example3.png rename to Scripts/API/Random Joke/images/example3.png diff --git a/Scripts/API/Random_Joke/joke.py b/Scripts/API/Random Joke/joke.py similarity index 100% rename from Scripts/API/Random_Joke/joke.py rename to Scripts/API/Random Joke/joke.py diff --git a/Scripts/API/Random_Joke/requirements.txt b/Scripts/API/Random Joke/requirements.txt similarity index 100% rename from Scripts/API/Random_Joke/requirements.txt rename to Scripts/API/Random Joke/requirements.txt diff --git a/Scripts/API/Random_Quote_Notification/README.md b/Scripts/API/Random Quote Notification/README.md similarity index 100% rename from Scripts/API/Random_Quote_Notification/README.md rename to Scripts/API/Random Quote Notification/README.md diff --git a/Scripts/API/Random_Quote_Notification/icon.ico b/Scripts/API/Random Quote Notification/icon.ico similarity index 100% rename from Scripts/API/Random_Quote_Notification/icon.ico rename to Scripts/API/Random Quote Notification/icon.ico diff --git a/Scripts/API/Random_Quote_Notification/main.py b/Scripts/API/Random Quote Notification/main.py similarity index 100% rename from Scripts/API/Random_Quote_Notification/main.py rename to Scripts/API/Random Quote Notification/main.py diff --git a/Scripts/API/Random_Quote_Notification/requirements.txt b/Scripts/API/Random Quote Notification/requirements.txt similarity index 100% rename from Scripts/API/Random_Quote_Notification/requirements.txt rename to Scripts/API/Random Quote Notification/requirements.txt diff --git a/Scripts/API/Random_Quote_Notification/result.PNG b/Scripts/API/Random Quote Notification/result.PNG similarity index 100% rename from Scripts/API/Random_Quote_Notification/result.PNG rename to Scripts/API/Random Quote Notification/result.PNG diff --git a/Scripts/API/twilio_sms/README.md b/Scripts/API/Twilio Sms/README.md similarity index 100% rename from Scripts/API/twilio_sms/README.md rename to Scripts/API/Twilio Sms/README.md diff --git a/Scripts/API/twilio_sms/output.png b/Scripts/API/Twilio Sms/output.png similarity index 100% rename from Scripts/API/twilio_sms/output.png rename to Scripts/API/Twilio Sms/output.png diff --git a/Scripts/API/twilio_sms/requirements.txt b/Scripts/API/Twilio Sms/requirements.txt similarity index 100% rename from Scripts/API/twilio_sms/requirements.txt rename to Scripts/API/Twilio Sms/requirements.txt diff --git a/Scripts/API/twilio_sms/twilio_sms.py b/Scripts/API/Twilio Sms/twilio_sms.py similarity index 100% rename from Scripts/API/twilio_sms/twilio_sms.py rename to Scripts/API/Twilio Sms/twilio_sms.py diff --git a/Scripts/API/Twitter-topic-modeling-and-sentiment-analysis/README.md b/Scripts/API/Twitter Sentiment Analysis/README.md similarity index 100% rename from Scripts/API/Twitter-topic-modeling-and-sentiment-analysis/README.md rename to Scripts/API/Twitter Sentiment Analysis/README.md diff --git a/Scripts/API/Twitter-topic-modeling-and-sentiment-analysis/__pycache__/btm_model.cpython-37.pyc b/Scripts/API/Twitter Sentiment Analysis/__pycache__/btm_model.cpython-37.pyc similarity index 100% rename from Scripts/API/Twitter-topic-modeling-and-sentiment-analysis/__pycache__/btm_model.cpython-37.pyc rename to Scripts/API/Twitter Sentiment Analysis/__pycache__/btm_model.cpython-37.pyc diff --git a/Scripts/API/Twitter-topic-modeling-and-sentiment-analysis/__pycache__/sentiment.cpython-37.pyc b/Scripts/API/Twitter Sentiment Analysis/__pycache__/sentiment.cpython-37.pyc similarity index 100% rename from Scripts/API/Twitter-topic-modeling-and-sentiment-analysis/__pycache__/sentiment.cpython-37.pyc rename to Scripts/API/Twitter Sentiment Analysis/__pycache__/sentiment.cpython-37.pyc diff --git a/Scripts/API/Twitter-topic-modeling-and-sentiment-analysis/__pycache__/text_cleaning.cpython-37.pyc b/Scripts/API/Twitter Sentiment Analysis/__pycache__/text_cleaning.cpython-37.pyc similarity index 100% rename from Scripts/API/Twitter-topic-modeling-and-sentiment-analysis/__pycache__/text_cleaning.cpython-37.pyc rename to Scripts/API/Twitter Sentiment Analysis/__pycache__/text_cleaning.cpython-37.pyc diff --git a/Scripts/API/Twitter-topic-modeling-and-sentiment-analysis/app.py b/Scripts/API/Twitter Sentiment Analysis/app.py similarity index 100% rename from Scripts/API/Twitter-topic-modeling-and-sentiment-analysis/app.py rename to Scripts/API/Twitter Sentiment Analysis/app.py diff --git a/Scripts/API/Twitter-topic-modeling-and-sentiment-analysis/btm_model.py b/Scripts/API/Twitter Sentiment Analysis/btm_model.py similarity index 100% rename from Scripts/API/Twitter-topic-modeling-and-sentiment-analysis/btm_model.py rename to Scripts/API/Twitter Sentiment Analysis/btm_model.py diff --git a/Scripts/API/Twitter-topic-modeling-and-sentiment-analysis/images/app-twitter-oauth.JPG b/Scripts/API/Twitter Sentiment Analysis/images/app-twitter-oauth.JPG similarity index 100% rename from Scripts/API/Twitter-topic-modeling-and-sentiment-analysis/images/app-twitter-oauth.JPG rename to Scripts/API/Twitter Sentiment Analysis/images/app-twitter-oauth.JPG diff --git a/Scripts/API/Twitter-topic-modeling-and-sentiment-analysis/images/twitter-app-dashboard.JPG b/Scripts/API/Twitter Sentiment Analysis/images/twitter-app-dashboard.JPG similarity index 100% rename from Scripts/API/Twitter-topic-modeling-and-sentiment-analysis/images/twitter-app-dashboard.JPG rename to Scripts/API/Twitter Sentiment Analysis/images/twitter-app-dashboard.JPG diff --git a/Scripts/API/Twitter-topic-modeling-and-sentiment-analysis/images/twitter-app-details.JPG b/Scripts/API/Twitter Sentiment Analysis/images/twitter-app-details.JPG similarity index 100% rename from Scripts/API/Twitter-topic-modeling-and-sentiment-analysis/images/twitter-app-details.JPG rename to Scripts/API/Twitter Sentiment Analysis/images/twitter-app-details.JPG diff --git a/Scripts/API/Twitter-topic-modeling-and-sentiment-analysis/requirements.txt b/Scripts/API/Twitter Sentiment Analysis/requirements.txt similarity index 100% rename from Scripts/API/Twitter-topic-modeling-and-sentiment-analysis/requirements.txt rename to Scripts/API/Twitter Sentiment Analysis/requirements.txt diff --git a/Scripts/API/Twitter-topic-modeling-and-sentiment-analysis/sentiment.py b/Scripts/API/Twitter Sentiment Analysis/sentiment.py similarity index 100% rename from Scripts/API/Twitter-topic-modeling-and-sentiment-analysis/sentiment.py rename to Scripts/API/Twitter Sentiment Analysis/sentiment.py diff --git a/Scripts/API/Twitter-topic-modeling-and-sentiment-analysis/templates/user_dash.html b/Scripts/API/Twitter Sentiment Analysis/templates/user_dash.html similarity index 100% rename from Scripts/API/Twitter-topic-modeling-and-sentiment-analysis/templates/user_dash.html rename to Scripts/API/Twitter Sentiment Analysis/templates/user_dash.html diff --git a/Scripts/API/Twitter-topic-modeling-and-sentiment-analysis/text_cleaning.py b/Scripts/API/Twitter Sentiment Analysis/text_cleaning.py similarity index 100% rename from Scripts/API/Twitter-topic-modeling-and-sentiment-analysis/text_cleaning.py rename to Scripts/API/Twitter Sentiment Analysis/text_cleaning.py diff --git a/Scripts/API/simple_api_requests/README.md b/Scripts/API/simple_api_requests/README.md deleted file mode 100644 index f84d05fa4..000000000 --- a/Scripts/API/simple_api_requests/README.md +++ /dev/null @@ -1,8 +0,0 @@ -SIMPLE REQUESTS API - - - -python3 main.py - -and open your browser navigate to #http://127.0.0.1:8000/ for see all rest data -or #http://127.0.0.1:8000/ for see only ID \ No newline at end of file diff --git a/Scripts/API/simple_api_requests/main.py b/Scripts/API/simple_api_requests/main.py deleted file mode 100644 index a0db3d7f5..000000000 --- a/Scripts/API/simple_api_requests/main.py +++ /dev/null @@ -1,28 +0,0 @@ -from flask import Flask, jsonify -import requests -import json - -app = Flask(__name__) - - -# GET ALL API DATA -# http://127.0.0.1:8000/ -@app.route("/") -def get_api(): - r = requests.get("https://jsonplaceholder.typicode.com/todos/") - data = json.dumps(r.json()) - return jsonify(json.loads(data)) - - -# GET API BY Id -# http://127.0.0.1:8000/1 -@app.route("/") -def get_api_by_id(id): - r = requests.get("https://jsonplaceholder.typicode.com/todos/" + str(id)) - data = json.dumps(r.json()) - return jsonify(json.loads(data)) - - -if __name__ == "__main__": - app.debug = True - app.run(host="0.0.0.0", port=8000) diff --git a/Scripts/API/simple_api_requests/requirements.txt b/Scripts/API/simple_api_requests/requirements.txt deleted file mode 100644 index aefc08b46..000000000 --- a/Scripts/API/simple_api_requests/requirements.txt +++ /dev/null @@ -1,2 +0,0 @@ -Flask==1.0.2 -requests==2.20.0 \ No newline at end of file diff --git a/Scripts/API/wikipedia/README.md b/Scripts/API/wikipedia/README.md index 2e39f03c5..7d56b7751 100644 --- a/Scripts/API/wikipedia/README.md +++ b/Scripts/API/wikipedia/README.md @@ -10,7 +10,7 @@ This script helps to fetch data from wikipedia API and provides it in a structur 3. OR you can use the parser by calling method ``` get_all_wiki(query) ``` ### Screenshot/GIF showing the sample use of the script -![Alt Screenshot](https://github.com/MNISAR/Python_and_the_Web/blob/master/Scripts/API/wikipedia/output.png "output") +![Alt Screenshot](https://github.com/MNISAR/Python_and_the_Web/blob/master/Scripts/API/Wikipedia/output.png "output") ## *Author Name* [MNISAR](https://www.github.com/MNISAR) \ No newline at end of file diff --git a/Scripts/Bots/DiscordBot/README.md b/Scripts/Bots/Discord/README.md similarity index 100% rename from Scripts/Bots/DiscordBot/README.md rename to Scripts/Bots/Discord/README.md diff --git a/Scripts/Bots/DiscordBot/Screen Shot 2020-10-01 at 10.58.16 PM.png b/Scripts/Bots/Discord/Screen Shot 2020-10-01 at 10.58.16 PM.png similarity index 100% rename from Scripts/Bots/DiscordBot/Screen Shot 2020-10-01 at 10.58.16 PM.png rename to Scripts/Bots/Discord/Screen Shot 2020-10-01 at 10.58.16 PM.png diff --git a/Scripts/Bots/DiscordBot/bot.py b/Scripts/Bots/Discord/bot.py similarity index 100% rename from Scripts/Bots/DiscordBot/bot.py rename to Scripts/Bots/Discord/bot.py diff --git a/Scripts/Bots/DiscordBot/requirements.txt b/Scripts/Bots/Discord/requirements.txt similarity index 100% rename from Scripts/Bots/DiscordBot/requirements.txt rename to Scripts/Bots/Discord/requirements.txt diff --git a/Scripts/Bots/Instagram_Bot/Instagram_bot.py b/Scripts/Bots/Instagram/Instagram_bot.py similarity index 100% rename from Scripts/Bots/Instagram_Bot/Instagram_bot.py rename to Scripts/Bots/Instagram/Instagram_bot.py diff --git a/Scripts/Bots/Instagram_Bot/LoginPage.png b/Scripts/Bots/Instagram/LoginPage.png similarity index 100% rename from Scripts/Bots/Instagram_Bot/LoginPage.png rename to Scripts/Bots/Instagram/LoginPage.png diff --git a/Scripts/Bots/Instagram_Bot/MainPage.PNG b/Scripts/Bots/Instagram/MainPage.PNG similarity index 100% rename from Scripts/Bots/Instagram_Bot/MainPage.PNG rename to 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from Scripts/Bots/Zoom_Meetings_Bot/trainer/computeraudio.png rename to Scripts/Bots/Zoom Meetings/trainer/computeraudio.png diff --git a/Scripts/Bots/Zoom_Meetings_Bot/trainer/finaljoin.png b/Scripts/Bots/Zoom Meetings/trainer/finaljoin.png similarity index 100% rename from Scripts/Bots/Zoom_Meetings_Bot/trainer/finaljoin.png rename to Scripts/Bots/Zoom Meetings/trainer/finaljoin.png diff --git a/Scripts/Bots/Zoom_Meetings_Bot/trainer/join1.png b/Scripts/Bots/Zoom Meetings/trainer/join1.png similarity index 100% rename from Scripts/Bots/Zoom_Meetings_Bot/trainer/join1.png rename to Scripts/Bots/Zoom Meetings/trainer/join1.png diff --git a/Scripts/Bots/Zoom_Meetings_Bot/trainer/join2.png b/Scripts/Bots/Zoom Meetings/trainer/join2.png similarity index 100% rename from Scripts/Bots/Zoom_Meetings_Bot/trainer/join2.png rename to Scripts/Bots/Zoom Meetings/trainer/join2.png diff --git a/Scripts/Bots/Zoom_Meetings_Bot/trainer/videooff.png b/Scripts/Bots/Zoom Meetings/trainer/videooff.png similarity index 100% rename from Scripts/Bots/Zoom_Meetings_Bot/trainer/videooff.png rename to Scripts/Bots/Zoom Meetings/trainer/videooff.png diff --git a/Scripts/Miscellaneous/Activation_Functions_from_scratch_in_Keras/src/Activation-Functions(GELU,SELU,ELU,LeakyReLU,PRELU).ipynb b/Scripts/Miscellaneous/Activation_Functions_from_scratch_in_Keras/src/Activation-Functions(GELU,SELU,ELU,LeakyReLU,PRELU).ipynb index 27e586254..4b516f971 100644 --- a/Scripts/Miscellaneous/Activation_Functions_from_scratch_in_Keras/src/Activation-Functions(GELU,SELU,ELU,LeakyReLU,PRELU).ipynb +++ b/Scripts/Miscellaneous/Activation_Functions_from_scratch_in_Keras/src/Activation-Functions(GELU,SELU,ELU,LeakyReLU,PRELU).ipynb @@ -1 +1,2399 @@ -{"nbformat":4,"nbformat_minor":0,"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":"3.6.8"},"colab":{"name":"Activation-Functions(GELU,SELU,ELU,LeakyReLU,PRELU).ipynb","provenance":[],"collapsed_sections":[]}},"cells":[{"cell_type":"markdown","metadata":{"id":"IVla6vrH5ScY","colab_type":"text"},"source":["# Custom Layers in Keras"]},{"cell_type":"markdown","metadata":{"id":"RYiQ_D825ScZ","colab_type":"text"},"source":["# Task 1: Importing Libraries"]},{"cell_type":"code","metadata":{"id":"zYY9HmiSS0Rv","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":120},"executionInfo":{"status":"ok","timestamp":1595982638698,"user_tz":-330,"elapsed":31262,"user":{"displayName":"Agrover112","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiMJACGAX3kCfRjB2hgzdG8w9zL1lAAKbPPMz0qLA=s64","userId":"09574164879083471944"}},"outputId":"65bff92a-e408-4ab5-b12a-148c5a501d15"},"source":["from google.colab import drive\n","drive.mount('/content/drive')"],"execution_count":1,"outputs":[{"output_type":"stream","text":["Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n","\n","Enter your authorization code:\n","··········\n","Mounted at /content/drive\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"VG_GYD1B2TrE","colab_type":"code","colab":{},"executionInfo":{"status":"ok","timestamp":1595982645364,"user_tz":-330,"elapsed":3345,"user":{"displayName":"Agrover112","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiMJACGAX3kCfRjB2hgzdG8w9zL1lAAKbPPMz0qLA=s64","userId":"09574164879083471944"}}},"source":["import os\n","os.chdir('/content/drive/My Drive/Colab Notebooks/src')"],"execution_count":2,"outputs":[]},{"cell_type":"code","metadata":{"id":"BHnBhBf8S9u9","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":33},"executionInfo":{"status":"ok","timestamp":1595982650881,"user_tz":-330,"elapsed":8846,"user":{"displayName":"Agrover112","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiMJACGAX3kCfRjB2hgzdG8w9zL1lAAKbPPMz0qLA=s64","userId":"09574164879083471944"}},"outputId":"5e471a5f-4616-46e3-e389-90d88ae6cb8b"},"source":["!ls"],"execution_count":3,"outputs":[{"output_type":"stream","text":["'Custom Activations using Layers - Complete.ipynb' utils\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"hIhyYtyaTCCt","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":33},"executionInfo":{"status":"ok","timestamp":1595964076958,"user_tz":-330,"elapsed":922,"user":{"displayName":"Agrover112","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiMJACGAX3kCfRjB2hgzdG8w9zL1lAAKbPPMz0qLA=s64","userId":"09574164879083471944"}},"outputId":"4bf85e55-469c-4827-fe89-909a841b165b"},"source":["#%cd \"./utils\""],"execution_count":12,"outputs":[{"output_type":"stream","text":["/content/drive/My Drive/Colab Notebooks/src/utils\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"EGhe8MIf4UOI","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":33},"executionInfo":{"status":"ok","timestamp":1595964217899,"user_tz":-330,"elapsed":2849,"user":{"displayName":"Agrover112","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiMJACGAX3kCfRjB2hgzdG8w9zL1lAAKbPPMz0qLA=s64","userId":"09574164879083471944"}},"outputId":"b83b4217-00b8-47bf-bad8-3a5d0b745765"},"source":["#!ls"],"execution_count":18,"outputs":[{"output_type":"stream","text":["Utils utils.py\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"hT6lcs952iXc","colab_type":"code","colab":{},"executionInfo":{"status":"ok","timestamp":1595982667751,"user_tz":-330,"elapsed":4552,"user":{"displayName":"Agrover112","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiMJACGAX3kCfRjB2hgzdG8w9zL1lAAKbPPMz0qLA=s64","userId":"09574164879083471944"}}},"source":["%run './utils/utils.py'"],"execution_count":4,"outputs":[]},{"cell_type":"code","metadata":{"id":"PaX7BQU15ScZ","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":33},"executionInfo":{"status":"ok","timestamp":1595982672373,"user_tz":-330,"elapsed":1183,"user":{"displayName":"Agrover112","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiMJACGAX3kCfRjB2hgzdG8w9zL1lAAKbPPMz0qLA=s64","userId":"09574164879083471944"}},"outputId":"2443cde6-069d-4925-cf72-b152a9d27fd8"},"source":["import tensorflow as tf\n","#import utils\n","import matplotlib.pyplot as plt\n","%matplotlib inline\n","\n","print('TensorFlow Version:', tf.__version__)"],"execution_count":5,"outputs":[{"output_type":"stream","text":["TensorFlow Version: 2.2.0\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"okH6xfqU5Scd","colab_type":"text"},"source":["# Task 2: Import and Visualize Dataset"]},{"cell_type":"code","metadata":{"id":"IX9JVAVt5Sce","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":322},"executionInfo":{"status":"ok","timestamp":1595982674797,"user_tz":-330,"elapsed":2644,"user":{"displayName":"Agrover112","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiMJACGAX3kCfRjB2hgzdG8w9zL1lAAKbPPMz0qLA=s64","userId":"09574164879083471944"}},"outputId":"6b22a4f8-3fa7-4321-b8bf-d2dd1e7858fc"},"source":["(x_train, y_train), (x_test, y_test) = load_data()\n","\n","plot_random_examples(x_train, y_train).show()"],"execution_count":6,"outputs":[{"output_type":"stream","text":["Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n","11493376/11490434 [==============================] - 0s 0us/step\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"tags":[]}}]},{"cell_type":"markdown","metadata":{"id":"c-Ao0bQN5Sch","colab_type":"text"},"source":[" # Creating a Custom Activations"]},{"cell_type":"markdown","metadata":{"id":"3WeVEPWjEoDH","colab_type":"text"},"source":["# One way to create activations by inheriting keras.layers.Layer class"]},{"cell_type":"markdown","metadata":{"id":"Nuo4vm3aKI0X","colab_type":"text"},"source":["Most parameters of activation functions are derived from SELU paper"]},{"cell_type":"code","metadata":{"id":"Z8I7oinq5Sci","colab_type":"code","colab":{},"executionInfo":{"status":"ok","timestamp":1595985710725,"user_tz":-330,"elapsed":770,"user":{"displayName":"Agrover112","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiMJACGAX3kCfRjB2hgzdG8w9zL1lAAKbPPMz0qLA=s64","userId":"09574164879083471944"}}},"source":["# For the absolute sake of simplicity I do not create a file and add these in them\n","\n","# LEAKY RELU\n","\"\"\"\n","Avoids dead relu problem,however no alpha value is learned :/\n","\"\"\"\n","\n","class leakyrelu(tf.keras.layers.Layer):\n"," def __init__(self, **kwargs):\n"," super(leakyrelu, self).__init__(**kwargs)\n"," \n"," def build(self, input_shape):\n"," #self.alpha = self.add_weight(name='minimum', shape=(1,),initializer='zeros',trainable=False)\n"," super(leakyrelu, self).build(input_shape)\n"," \n"," def call(self, x):\n"," alpha=tf.constant([0.01],shape=(1,),name='alpha') #chosing an arbitrary alpha value\n"," return tf.maximum(0., x) + alpha * tf.minimum(0., x)\n","\n","# PARAMETRIC RELU\n","\"\"\"\n","Like Leaky ReLu but alpha parameter is a learnable paramter :D\n","\"\"\"\n","\n","class ParametricRelu(tf.keras.layers.Layer):\n"," def __init__(self, **kwargs):\n"," super(ParametricRelu, self).__init__(**kwargs)\n"," \n"," def build(self, input_shape):\n"," self.alpha = self.add_weight(\n"," name='minimum', \n"," shape=(1,),\n"," initializer='zeros',\n"," trainable=True\n"," )\n"," super(ParametricRelu, self).build(input_shape)\n"," \n"," def call(self, x):\n"," return tf.maximum(0., x) + self.alpha * tf.minimum(0., x)\n","\n","# ELU\n","\n","class Elu(tf.keras.layers.Layer):\n"," def __init__(self,**kwargs):\n"," super(Elu, self).__init__(**kwargs)\n"," \n"," def build(self, input_shape):\n"," self.alpha = self.add_weight( name='minimum', shape=(1,),initializer='ones',trainable=True)\n"," super(Elu, self).build(input_shape)\n"," \n"," def call(self, x):\n"," return tf.maximum(0., x) + self.alpha * (tf.exp(tf.minimum(0., x))-1)\n","\n","\n","#SELU\n","\"\"\"\n","Scaled Elu\n","Note: Requires weight_init:'lecun_normal for self-normalization and AlphaDroput.\n","\"\"\"\n","\n","class sElu(tf.keras.layers.Layer):\n"," def __init__(self,**kwargs):\n"," super(sElu, self).__init__(**kwargs)\n"," \n"," def build(self, input_shape):\n"," #self.alpha = self.add_weight( name='minimum', shape=(1,),initializer='lecun_normal',trainable=True) \n"," super(sElu, self).build(input_shape)\n"," \n"," def call(self, x): #Values from original author\n"," l=tf.constant([1.0507009873554804934193349852946],name='lambda') #Scaling factors as derived by authors \n"," a=tf.constant([1.6732632423543772848170429916717],name='alpha') #Alpha as derived by authors\n","\n"," return l*(tf.maximum(0., x) + (a* tf.exp(tf.minimum(0., x)-a)))"],"execution_count":54,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"6tnlYfNE5Scl","colab_type":"text"},"source":["# Task 4: Creating the Model"]},{"cell_type":"code","metadata":{"id":"4zKiptyZ5Scn","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":251},"executionInfo":{"status":"ok","timestamp":1595985716037,"user_tz":-330,"elapsed":1061,"user":{"displayName":"Agrover112","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiMJACGAX3kCfRjB2hgzdG8w9zL1lAAKbPPMz0qLA=s64","userId":"09574164879083471944"}},"outputId":"a611f535-ec43-4e70-d2de-42dc003a237a"},"source":["def create_model(use):\n"," model = tf.keras.models.Sequential()\n"," if use=='selu':\n"," winit='lecun_normal' #As required by SELU authors\n"," else:\n"," winit='glorot_uniform'\n","\n"," model.add(tf.keras.layers.Dense(64, input_shape=(784,),kernel_initializer=winit))\n"," if use == 'relu':\n"," model.add(tf.keras.layers.ReLU())\n"," elif use== 'prelu':\n"," model.add(ParametricRelu())\n"," elif use== 'leakyrelu':\n"," model.add(leakyrelu())\n"," elif use== 'elu' :\n"," model.add(Elu())\n"," elif use== 'selu' :\n"," model.add(sElu())\n"," \n"," model.add(tf.keras.layers.Dense(10, activation='softmax',kernel_initializer=winit))\n"," model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n"," return model\n","\n","model = create_model(use='elu')\n","model.summary()"],"execution_count":55,"outputs":[{"output_type":"stream","text":["Model: \"sequential_11\"\n","_________________________________________________________________\n","Layer (type) Output Shape Param # \n","=================================================================\n","dense_22 (Dense) (None, 64) 50240 \n","_________________________________________________________________\n","elu_3 (Elu) (None, 64) 1 \n","_________________________________________________________________\n","dense_23 (Dense) (None, 10) 650 \n","=================================================================\n","Total params: 50,891\n","Trainable params: 50,891\n","Non-trainable params: 0\n","_________________________________________________________________\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"yNWrD33-_aCB","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":33},"executionInfo":{"status":"ok","timestamp":1595967574676,"user_tz":-330,"elapsed":951,"user":{"displayName":"Agrover112","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiMJACGAX3kCfRjB2hgzdG8w9zL1lAAKbPPMz0qLA=s64","userId":"09574164879083471944"}},"outputId":"292a8b0a-17ec-4f89-c6ce-52d5264f7c0c"},"source":["#model.layers[1].get_weights()"],"execution_count":73,"outputs":[{"output_type":"execute_result","data":{"text/plain":["[array([0.], dtype=float32)]"]},"metadata":{"tags":[]},"execution_count":73}]},{"cell_type":"markdown","metadata":{"id":"QpVxpls2JYJt","colab_type":"text"},"source":["# ELU"]},{"cell_type":"code","metadata":{"id":"g_5Hen4s5Scr","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":301},"executionInfo":{"status":"ok","timestamp":1595985748402,"user_tz":-330,"elapsed":28499,"user":{"displayName":"Agrover112","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiMJACGAX3kCfRjB2hgzdG8w9zL1lAAKbPPMz0qLA=s64","userId":"09574164879083471944"}},"outputId":"fdd6b0d8-5b90-4000-e3f6-4422371793af"},"source":["import time\n","start=time.time()\n","print('Initial alpha:', model.layers[1].get_weights())\n","\n","h = model.fit(\n"," x_train, y_train,\n"," validation_data=(x_test, y_test),\n"," epochs=7\n",")\n","\n","print('Final alpha:', model.layers[1].get_weights())\n","print(time.time()-start)"],"execution_count":56,"outputs":[{"output_type":"stream","text":["Initial alpha: [array([1.], dtype=float32)]\n","Epoch 1/7\n","1875/1875 [==============================] - 4s 2ms/step - loss: 0.3257 - accuracy: 0.9067 - val_loss: 0.2008 - val_accuracy: 0.9393\n","Epoch 2/7\n","1875/1875 [==============================] - 4s 2ms/step - loss: 0.1680 - accuracy: 0.9504 - val_loss: 0.1427 - val_accuracy: 0.9590\n","Epoch 3/7\n","1875/1875 [==============================] - 4s 2ms/step - loss: 0.1174 - accuracy: 0.9650 - val_loss: 0.1066 - val_accuracy: 0.9683\n","Epoch 4/7\n","1875/1875 [==============================] - 4s 2ms/step - loss: 0.0910 - accuracy: 0.9724 - val_loss: 0.1000 - val_accuracy: 0.9717\n","Epoch 5/7\n","1875/1875 [==============================] - 4s 2ms/step - loss: 0.0731 - accuracy: 0.9781 - val_loss: 0.0890 - val_accuracy: 0.9724\n","Epoch 6/7\n","1875/1875 [==============================] - 4s 2ms/step - loss: 0.0620 - accuracy: 0.9809 - val_loss: 0.0840 - val_accuracy: 0.9741\n","Epoch 7/7\n","1875/1875 [==============================] - 4s 2ms/step - loss: 0.0513 - accuracy: 0.9842 - val_loss: 0.0844 - val_accuracy: 0.9748\n","Final alpha: [array([1.0048169], dtype=float32)]\n","27.67535638809204\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"AquGwa2Z5Scu","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":279},"executionInfo":{"status":"ok","timestamp":1595985749101,"user_tz":-330,"elapsed":28995,"user":{"displayName":"Agrover112","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiMJACGAX3kCfRjB2hgzdG8w9zL1lAAKbPPMz0qLA=s64","userId":"09574164879083471944"}},"outputId":"6c8db259-8953-457f-d961-2d540ae5c2b2"},"source":["plot_results(h).show()"],"execution_count":57,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"BerqQKSqJcFB","colab_type":"text"},"source":["# SELU"]},{"cell_type":"code","metadata":{"id":"2CccDRmeLngA","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":251},"executionInfo":{"status":"ok","timestamp":1595985749103,"user_tz":-330,"elapsed":28588,"user":{"displayName":"Agrover112","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiMJACGAX3kCfRjB2hgzdG8w9zL1lAAKbPPMz0qLA=s64","userId":"09574164879083471944"}},"outputId":"91f185ae-83b5-4ba7-9e73-326ff47a12ac"},"source":["\n","model = create_model(use='selu')\n","model.summary()\n"],"execution_count":58,"outputs":[{"output_type":"stream","text":["Model: \"sequential_12\"\n","_________________________________________________________________\n","Layer (type) Output Shape Param # \n","=================================================================\n","dense_24 (Dense) (None, 64) 50240 \n","_________________________________________________________________\n","s_elu_3 (sElu) (None, 64) 0 \n","_________________________________________________________________\n","dense_25 (Dense) (None, 10) 650 \n","=================================================================\n","Total params: 50,890\n","Trainable params: 50,890\n","Non-trainable params: 0\n","_________________________________________________________________\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"50IcXmRcL392","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":301},"executionInfo":{"status":"ok","timestamp":1595985776790,"user_tz":-330,"elapsed":56079,"user":{"displayName":"Agrover112","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiMJACGAX3kCfRjB2hgzdG8w9zL1lAAKbPPMz0qLA=s64","userId":"09574164879083471944"}},"outputId":"67aab42d-ed12-418c-e3d8-83d3c08294ef"},"source":["import time\n","start=time.time()\n","print('Initial alpha:', model.layers[1].get_weights())\n","\n","h = model.fit(\n"," x_train, y_train,\n"," validation_data=(x_test, y_test),\n"," epochs=7\n",")\n","\n","print('Final alpha:', model.layers[1].get_weights())\n","print(time.time()-start)"],"execution_count":59,"outputs":[{"output_type":"stream","text":["Initial alpha: []\n","Epoch 1/7\n","1875/1875 [==============================] - 4s 2ms/step - loss: 0.3184 - accuracy: 0.9084 - val_loss: 0.1805 - val_accuracy: 0.9466\n","Epoch 2/7\n","1875/1875 [==============================] - 4s 2ms/step - loss: 0.1601 - accuracy: 0.9523 - val_loss: 0.1330 - val_accuracy: 0.9598\n","Epoch 3/7\n","1875/1875 [==============================] - 4s 2ms/step - loss: 0.1144 - accuracy: 0.9654 - val_loss: 0.1045 - val_accuracy: 0.9675\n","Epoch 4/7\n","1875/1875 [==============================] - 4s 2ms/step - loss: 0.0885 - accuracy: 0.9733 - val_loss: 0.0952 - val_accuracy: 0.9709\n","Epoch 5/7\n","1875/1875 [==============================] - 4s 2ms/step - loss: 0.0718 - accuracy: 0.9785 - val_loss: 0.0924 - val_accuracy: 0.9708\n","Epoch 6/7\n","1875/1875 [==============================] - 4s 2ms/step - loss: 0.0599 - accuracy: 0.9811 - val_loss: 0.0866 - val_accuracy: 0.9726\n","Epoch 7/7\n","1875/1875 [==============================] - 4s 2ms/step - loss: 0.0503 - accuracy: 0.9843 - val_loss: 0.0868 - val_accuracy: 0.9733\n","Final alpha: []\n","27.53625178337097\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"fJBRYu2tMGM9","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":279},"executionInfo":{"status":"ok","timestamp":1595985776791,"user_tz":-330,"elapsed":55940,"user":{"displayName":"Agrover112","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiMJACGAX3kCfRjB2hgzdG8w9zL1lAAKbPPMz0qLA=s64","userId":"09574164879083471944"}},"outputId":"5ede86e7-9dcc-4d19-d038-a1535d668c1e"},"source":["plot_results(h).show()"],"execution_count":60,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"tW3ci_-iJbya","colab_type":"text"},"source":["# PRELU"]},{"cell_type":"code","metadata":{"id":"91WmwFJQ5Scy","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":251},"executionInfo":{"status":"ok","timestamp":1595985776792,"user_tz":-330,"elapsed":55457,"user":{"displayName":"Agrover112","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiMJACGAX3kCfRjB2hgzdG8w9zL1lAAKbPPMz0qLA=s64","userId":"09574164879083471944"}},"outputId":"e6bd59d9-d9a9-44a0-e102-f5d3b4ca7754"},"source":["model = create_model(use='prelu')\n","model.summary()"],"execution_count":61,"outputs":[{"output_type":"stream","text":["Model: \"sequential_13\"\n","_________________________________________________________________\n","Layer (type) Output Shape Param # \n","=================================================================\n","dense_26 (Dense) (None, 64) 50240 \n","_________________________________________________________________\n","parametric_relu_1 (Parametri (None, 64) 1 \n","_________________________________________________________________\n","dense_27 (Dense) (None, 10) 650 \n","=================================================================\n","Total params: 50,891\n","Trainable params: 50,891\n","Non-trainable params: 0\n","_________________________________________________________________\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"YVsiHhql5Sc1","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":301},"executionInfo":{"status":"ok","timestamp":1595985804389,"user_tz":-330,"elapsed":82839,"user":{"displayName":"Agrover112","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiMJACGAX3kCfRjB2hgzdG8w9zL1lAAKbPPMz0qLA=s64","userId":"09574164879083471944"}},"outputId":"9fa04eb0-1b8e-49fd-f815-370119abbb75"},"source":["import time\n","start=time.time()\n","print('Initial alpha:', model.layers[1].get_weights())\n","h = model.fit(\n"," x_train, y_train,\n"," validation_data=(x_test, y_test),\n"," epochs=7\n",")\n","print('Final alpha:', model.layers[1].get_weights())\n","print(time.time()-start)"],"execution_count":62,"outputs":[{"output_type":"stream","text":["Initial alpha: [array([0.], dtype=float32)]\n","Epoch 1/7\n","1875/1875 [==============================] - 4s 2ms/step - loss: 0.2943 - accuracy: 0.9169 - val_loss: 0.1568 - val_accuracy: 0.9527\n","Epoch 2/7\n","1875/1875 [==============================] - 4s 2ms/step - loss: 0.1272 - accuracy: 0.9625 - val_loss: 0.1051 - val_accuracy: 0.9688\n","Epoch 3/7\n","1875/1875 [==============================] - 4s 2ms/step - loss: 0.0900 - accuracy: 0.9729 - val_loss: 0.1029 - val_accuracy: 0.9706\n","Epoch 4/7\n","1875/1875 [==============================] - 4s 2ms/step - loss: 0.0710 - accuracy: 0.9779 - val_loss: 0.0945 - val_accuracy: 0.9739\n","Epoch 5/7\n","1875/1875 [==============================] - 4s 2ms/step - loss: 0.0555 - accuracy: 0.9829 - val_loss: 0.0931 - val_accuracy: 0.9731\n","Epoch 6/7\n","1875/1875 [==============================] - 4s 2ms/step - loss: 0.0466 - accuracy: 0.9854 - val_loss: 0.0959 - val_accuracy: 0.9726\n","Epoch 7/7\n","1875/1875 [==============================] - 4s 2ms/step - loss: 0.0409 - accuracy: 0.9869 - val_loss: 0.0939 - val_accuracy: 0.9732\n","Final alpha: [array([-0.97849256], dtype=float32)]\n","27.185328006744385\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"coFyCBgY5Sc4","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":282},"executionInfo":{"status":"ok","timestamp":1595985804390,"user_tz":-330,"elapsed":82660,"user":{"displayName":"Agrover112","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiMJACGAX3kCfRjB2hgzdG8w9zL1lAAKbPPMz0qLA=s64","userId":"09574164879083471944"}},"outputId":"41bdc621-39a5-4118-9e1a-3690743807b9"},"source":["plot_results(h).show()"],"execution_count":63,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"wrsqurgALSXm","colab_type":"text"},"source":["# Leaky Relu"]},{"cell_type":"code","metadata":{"id":"pFnw6soPHMxg","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":251},"executionInfo":{"status":"ok","timestamp":1595985804391,"user_tz":-330,"elapsed":82135,"user":{"displayName":"Agrover112","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiMJACGAX3kCfRjB2hgzdG8w9zL1lAAKbPPMz0qLA=s64","userId":"09574164879083471944"}},"outputId":"cf51b769-357f-4d76-f69a-2543b200495a"},"source":["model = create_model(use='leakyrelu')\n","model.summary()"],"execution_count":64,"outputs":[{"output_type":"stream","text":["Model: \"sequential_14\"\n","_________________________________________________________________\n","Layer (type) Output Shape Param # \n","=================================================================\n","dense_28 (Dense) (None, 64) 50240 \n","_________________________________________________________________\n","leakyrelu_4 (leakyrelu) (None, 64) 0 \n","_________________________________________________________________\n","dense_29 (Dense) (None, 10) 650 \n","=================================================================\n","Total params: 50,890\n","Trainable params: 50,890\n","Non-trainable params: 0\n","_________________________________________________________________\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"8AfF5-kjHWpd","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":301},"executionInfo":{"status":"ok","timestamp":1595985831123,"user_tz":-330,"elapsed":108572,"user":{"displayName":"Agrover112","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiMJACGAX3kCfRjB2hgzdG8w9zL1lAAKbPPMz0qLA=s64","userId":"09574164879083471944"}},"outputId":"43d18ab6-a4b7-46f7-a075-a046b0fe38e5"},"source":["import time\n","start=time.time()\n","print('Initial alpha:', model.layers[1].get_weights())\n","h = model.fit(\n"," x_train, y_train,\n"," validation_data=(x_test, y_test),\n"," epochs=7\n",")\n","print('Final alpha:', model.layers[1].get_weights())\n","print(time.time()-start)"],"execution_count":65,"outputs":[{"output_type":"stream","text":["Initial alpha: []\n","Epoch 1/7\n","1875/1875 [==============================] - 4s 2ms/step - loss: 0.3022 - accuracy: 0.9158 - val_loss: 0.1655 - val_accuracy: 0.9521\n","Epoch 2/7\n","1875/1875 [==============================] - 4s 2ms/step - loss: 0.1455 - accuracy: 0.9579 - val_loss: 0.1265 - val_accuracy: 0.9616\n","Epoch 3/7\n","1875/1875 [==============================] - 4s 2ms/step - loss: 0.1060 - accuracy: 0.9687 - val_loss: 0.1046 - val_accuracy: 0.9685\n","Epoch 4/7\n","1875/1875 [==============================] - 4s 2ms/step - loss: 0.0842 - accuracy: 0.9754 - val_loss: 0.0961 - val_accuracy: 0.9714\n","Epoch 5/7\n","1875/1875 [==============================] - 4s 2ms/step - loss: 0.0696 - accuracy: 0.9792 - val_loss: 0.0926 - val_accuracy: 0.9710\n","Epoch 6/7\n","1875/1875 [==============================] - 4s 2ms/step - loss: 0.0594 - accuracy: 0.9825 - val_loss: 0.0804 - val_accuracy: 0.9752\n","Epoch 7/7\n","1875/1875 [==============================] - 4s 2ms/step - loss: 0.0496 - accuracy: 0.9854 - val_loss: 0.0823 - val_accuracy: 0.9744\n","Final alpha: []\n","26.97652316093445\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"3WsfrSC5H-7_","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":279},"executionInfo":{"status":"ok","timestamp":1595985832360,"user_tz":-330,"elapsed":109490,"user":{"displayName":"Agrover112","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiMJACGAX3kCfRjB2hgzdG8w9zL1lAAKbPPMz0qLA=s64","userId":"09574164879083471944"}},"outputId":"7510eea4-961f-4073-f5d5-5867e8b3c253"},"source":["plot_results(h).show()"],"execution_count":66,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"kpLwwA-fGQBh","colab_type":"text"},"source":["# Method 2 for creating activation functions"]},{"cell_type":"code","metadata":{"id":"sSQjWjkcH_d9","colab_type":"code","colab":{},"executionInfo":{"status":"ok","timestamp":1595985832362,"user_tz":-330,"elapsed":108847,"user":{"displayName":"Agrover112","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiMJACGAX3kCfRjB2hgzdG8w9zL1lAAKbPPMz0qLA=s64","userId":"09574164879083471944"}}},"source":["# Creating swish and GELU"],"execution_count":67,"outputs":[]},{"cell_type":"code","metadata":{"id":"f7DovSYOGVzJ","colab_type":"code","colab":{},"executionInfo":{"status":"ok","timestamp":1595986912332,"user_tz":-330,"elapsed":1017,"user":{"displayName":"Agrover112","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiMJACGAX3kCfRjB2hgzdG8w9zL1lAAKbPPMz0qLA=s64","userId":"09574164879083471944"}}},"source":["from keras.backend import sigmoid\n","from keras.layers import Activation\n","from keras import backend as K\n","from keras.utils.generic_utils import get_custom_objects\n","def swish(x, beta = 1):\n"," return (x * sigmoid(beta * x))\n","\n","def custom_gelu(x): #Used in BERT and GPT2 paper yet to read completely\n"," return 0.5 * x * (1 + tf.tanh(tf.sqrt(2 / np.pi) * (x + 0.044715 * tf.pow(x, 3))))\n","\n","\n","get_custom_objects().update({'custom_gelu': Activation(custom_gelu)})\n","get_custom_objects().update({'swish': Activation(swish)})\n"],"execution_count":86,"outputs":[]},{"cell_type":"code","metadata":{"id":"jkC7-MesIrJG","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":217},"executionInfo":{"status":"ok","timestamp":1595987340647,"user_tz":-330,"elapsed":1155,"user":{"displayName":"Agrover112","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiMJACGAX3kCfRjB2hgzdG8w9zL1lAAKbPPMz0qLA=s64","userId":"09574164879083471944"}},"outputId":"6cfcae1a-721a-48fb-de24-4023e6124d64"},"source":["def create_model(use):\n"," model = tf.keras.models.Sequential()\n"," if use=='gelu':\n"," model.add(tf.keras.layers.Dense(64, input_shape=(784,),activation=custom_gelu))\n"," else:\n"," model.add(tf.keras.layers.Dense(64, input_shape=(784,),activation=use)) \n"," model.add(tf.keras.layers.Dense(10, activation='softmax'))\n"," model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n"," return model\n","\n","model = create_model(use='gelu')\n","model.summary()"],"execution_count":99,"outputs":[{"output_type":"stream","text":["Model: \"sequential_31\"\n","_________________________________________________________________\n","Layer (type) Output Shape Param # \n","=================================================================\n","dense_54 (Dense) (None, 64) 50240 \n","_________________________________________________________________\n","dense_55 (Dense) (None, 10) 650 \n","=================================================================\n","Total params: 50,890\n","Trainable params: 50,890\n","Non-trainable params: 0\n","_________________________________________________________________\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"LW37FiSrQDxs","colab_type":"text"},"source":["# EDIT: layers[1].get_weights() prints first layers weights than the activation fucntion in previous case"]},{"cell_type":"code","metadata":{"id":"FJzOWqXSMi30","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"status":"ok","timestamp":1595987065103,"user_tz":-330,"elapsed":26899,"user":{"displayName":"Agrover112","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiMJACGAX3kCfRjB2hgzdG8w9zL1lAAKbPPMz0qLA=s64","userId":"09574164879083471944"}},"outputId":"876d7fe3-3d1a-4d2a-e7a9-b708c11a69ed"},"source":["import time\n","start=time.time()\n","print('Initial alpha:', model.layers[1].get_weights())\n","gh = model.fit(\n"," x_train, y_train,\n"," validation_data=(x_test, y_test),\n"," epochs=7\n",")\n","print('Final alpha:', model.layers[1].get_weights())\n","print(time.time()-start)"],"execution_count":92,"outputs":[{"output_type":"stream","text":["Initial alpha: [array([[ 0.12353632, 0.16423404, 0.10222274, 0.1256558 , -0.01892754,\n"," -0.02012911, 0.04866058, 0.12828976, -0.08458149, -0.14631438],\n"," [-0.18274339, 0.05565363, -0.1672585 , 0.27012166, 0.25833252,\n"," 0.14464161, 0.04973227, -0.16902003, -0.037247 , 0.19368479],\n"," [-0.1621024 , -0.1792361 , -0.13050628, -0.08876258, -0.07096566,\n"," -0.01914582, -0.23445225, -0.13684182, 0.1989077 , -0.02570578],\n"," [ 0.12473157, 0.1441727 , 0.09397429, 0.00332046, -0.18190944,\n"," 0.01261935, 0.04458568, 0.16517016, 0.1471281 , 0.1981079 ],\n"," [ 0.09285513, 0.07188725, -0.09558456, 0.274725 , 0.15193444,\n"," -0.15837169, -0.27295187, 0.04262924, -0.12887274, -0.18479514],\n"," [ 0.18666595, 0.19486734, -0.10955802, 0.2639028 , -0.05440684,\n"," 0.1187984 , 0.10627389, -0.0036768 , -0.05467792, -0.03288031],\n"," [-0.28256285, -0.01646656, 0.22653136, 0.14125109, 0.04477257,\n"," 0.18957227, -0.12007512, 0.11028606, -0.05040519, 0.17090565],\n"," [-0.07029289, -0.25333554, 0.26304957, 0.1184682 , -0.20027709,\n"," -0.05293474, 0.22399291, 0.04855311, -0.08478639, 0.0674209 ],\n"," [-0.03744619, 0.08813277, -0.00325006, 0.05665702, -0.25226253,\n"," -0.15875526, -0.20920554, 0.28402016, 0.16325998, 0.0451816 ],\n"," [-0.23873171, 0.23971388, -0.13957408, -0.09348366, -0.12637225,\n"," -0.2402848 , -0.05544731, 0.09038535, -0.08063239, 0.14313167],\n"," [ 0.02704713, 0.03723124, 0.07753408, -0.11149089, -0.11205675,\n"," 0.12067702, -0.04036833, -0.17216973, -0.2718422 , 0.07661545],\n"," [ 0.15340158, -0.02871212, -0.05914746, -0.1691258 , 0.19760653,\n"," 0.07027653, -0.15858269, -0.07719523, -0.24819945, 0.08997631],\n"," [ 0.0839386 , -0.04098694, -0.11869203, -0.03156623, 0.25113901,\n"," 0.22811094, -0.14236392, 0.10863847, 0.17532489, -0.02590632],\n"," [-0.04118179, -0.28281942, 0.25266996, 0.11430162, -0.10380402,\n"," -0.23941793, 0.10782734, 0.01313919, -0.13310677, 0.00274774],\n"," [ 0.2623755 , -0.17281556, 0.26839992, -0.28289232, 0.15371516,\n"," 0.17648354, 0.13412029, 0.25674364, 0.07614484, 0.25217316],\n"," [ 0.09696636, -0.2488767 , -0.14929838, 0.11973554, 0.09945437,\n"," 0.10234487, 0.11125267, -0.02682629, 0.20431268, -0.01868701],\n"," [ 0.2398974 , -0.22453779, 0.00181392, 0.15036017, 0.11731035,\n"," 0.13048673, 0.06268138, -0.1291726 , 0.18524647, -0.00345013],\n"," [ 0.23072919, 0.22656229, -0.0305545 , -0.2006329 , 0.15256426,\n"," 0.24350306, -0.00238964, -0.10016523, -0.07996708, 0.03127399],\n"," [ 0.15715683, 0.13723537, 0.2599326 , -0.00454673, -0.14765364,\n"," 0.01927587, -0.08839978, -0.24598016, 0.24590883, 0.04467174],\n"," [ 0.28469744, -0.19297448, -0.10297522, 0.11548486, -0.27860025,\n"," -0.03212348, 0.12768874, 0.15053445, -0.03746478, -0.01304054],\n"," [ 0.13235274, -0.05376115, -0.09554958, 0.16528022, -0.23063228,\n"," 0.05321336, -0.09569412, 0.04959235, -0.03146312, -0.1879291 ],\n"," [ 0.25544515, -0.07771601, 0.27834478, -0.09400016, 0.01559928,\n"," -0.13363984, 0.25343934, 0.06726781, 0.27582756, 0.25787637],\n"," [ 0.24614927, -0.17233023, -0.05539557, 0.07997304, 0.08181086,\n"," 0.08295244, 0.01330552, -0.14077578, 0.16088969, 0.18886304],\n"," [ 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-0.27451706, -0.0096046 , -0.17229173, 0.11372587,\n"," 0.16570196, 0.08970821, 0.12342274, 0.09351537, 0.11486232],\n"," [ 0.24118587, -0.15010566, 0.11712337, 0.11177421, -0.00830454,\n"," 0.01989925, 0.24000868, 0.18229899, 0.05130878, 0.07131067],\n"," [-0.16590717, -0.05283773, 0.2588742 , 0.20440745, -0.01442698,\n"," 0.26104942, 0.05893219, -0.12315626, -0.25597253, 0.18352267],\n"," [ 0.23686764, 0.12973064, -0.25767562, 0.16183057, -0.04931265,\n"," -0.0014129 , -0.03652439, 0.25875202, -0.26775396, 0.21114567],\n"," [-0.05129379, 0.2568433 , 0.09075764, -0.20865196, -0.20863254,\n"," -0.05773021, -0.05420792, 0.00864473, -0.20908794, 0.15594622]],\n"," dtype=float32), array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32)]\n","Epoch 1/7\n","1875/1875 [==============================] - 4s 2ms/step - loss: 0.3024 - accuracy: 0.9147 - val_loss: 0.1745 - val_accuracy: 0.9492\n","Epoch 2/7\n","1875/1875 [==============================] - 4s 2ms/step - loss: 0.1472 - accuracy: 0.9577 - val_loss: 0.1303 - val_accuracy: 0.9596\n","Epoch 3/7\n","1875/1875 [==============================] - 4s 2ms/step - loss: 0.1036 - accuracy: 0.9701 - val_loss: 0.1071 - val_accuracy: 0.9664\n","Epoch 4/7\n","1875/1875 [==============================] - 4s 2ms/step - loss: 0.0793 - accuracy: 0.9766 - val_loss: 0.0886 - val_accuracy: 0.9734\n","Epoch 5/7\n","1875/1875 [==============================] - 4s 2ms/step - loss: 0.0645 - accuracy: 0.9804 - val_loss: 0.0878 - val_accuracy: 0.9727\n","Epoch 6/7\n","1875/1875 [==============================] - 4s 2ms/step - loss: 0.0533 - accuracy: 0.9837 - val_loss: 0.0799 - val_accuracy: 0.9753\n","Epoch 7/7\n","1875/1875 [==============================] - 4s 2ms/step - loss: 0.0445 - accuracy: 0.9866 - val_loss: 0.0834 - val_accuracy: 0.9744\n","Final alpha: [array([[-5.51505834e-02, 3.73578846e-01, 2.18160808e-01,\n"," 1.89281136e-01, -3.09743397e-02, -1.95218548e-01,\n"," 1.10915758e-01, 7.23766506e-01, 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0.20067522,\n"," 0.19168544, 0.04727132, 0.03243769, -0.03037991, -0.17500848],\n"," dtype=float32)]\n","26.089535236358643\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"y2JMcbi1PY78","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":217},"executionInfo":{"status":"ok","timestamp":1595987330791,"user_tz":-330,"elapsed":1168,"user":{"displayName":"Agrover112","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiMJACGAX3kCfRjB2hgzdG8w9zL1lAAKbPPMz0qLA=s64","userId":"09574164879083471944"}},"outputId":"9449c445-0aeb-458f-fc95-a33ae933dc0d"},"source":["\n","model = create_model(use='swish')\n","model.summary()"],"execution_count":98,"outputs":[{"output_type":"stream","text":["Model: \"sequential_30\"\n","_________________________________________________________________\n","Layer (type) Output Shape Param # \n","=================================================================\n","dense_52 (Dense) (None, 64) 50240 \n","_________________________________________________________________\n","dense_53 (Dense) (None, 10) 650 \n","=================================================================\n","Total params: 50,890\n","Trainable params: 50,890\n","Non-trainable params: 0\n","_________________________________________________________________\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"a-QTLz6KPoAp","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"status":"ok","timestamp":1595987396895,"user_tz":-330,"elapsed":26910,"user":{"displayName":"Agrover112","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiMJACGAX3kCfRjB2hgzdG8w9zL1lAAKbPPMz0qLA=s64","userId":"09574164879083471944"}},"outputId":"30cc653e-df19-42fd-b372-e72d1378718a"},"source":["import time\n","start=time.time()\n","print('Initial alpha:', model.layers[1].get_weights())\n","sh = model.fit(\n"," x_train, y_train,\n"," validation_data=(x_test, y_test),\n"," epochs=7\n",")\n","print('Final alpha:', model.layers[1].get_weights())\n","print(time.time()-start)"],"execution_count":100,"outputs":[{"output_type":"stream","text":["Initial alpha: [array([[ 0.02084643, -0.16138217, 0.2829087 , 0.14652565, -0.18471095,\n"," -0.12087099, 0.16201237, -0.00325066, -0.06640942, 0.10206673],\n"," [ 0.2119534 , -0.2558481 , -0.01877779, 0.24446562, 0.14282861,\n"," -0.2140877 , -0.00336966, 0.1178225 , 0.06388301, -0.25829393],\n"," [-0.00031894, 0.06267378, -0.16316961, 0.00606263, 0.14348754,\n"," -0.06689693, 0.21690711, 0.05176529, -0.01930445, -0.1682317 ],\n"," [ 0.00494301, -0.2725569 , -0.2420508 , -0.11959603, -0.15769775,\n"," 0.21959278, -0.00450423, -0.22490624, -0.18821198, -0.03454033],\n"," [ 0.2697967 , 0.25885156, -0.06789531, 0.22188452, -0.06498179,\n"," -0.07024346, 0.26663217, 0.07309249, -0.1939106 , 0.20388949],\n"," [ 0.22402522, -0.06327668, 0.18563873, -0.01840398, 0.07488164,\n"," 0.06559807, 0.23722163, 0.0770376 , 0.27612635, 0.01703215],\n"," [ 0.10302907, -0.07591681, -0.05564269, 0.0057731 , 0.18928951,\n"," 0.03045416, -0.22046757, 0.07652864, 0.07531661, -0.1704296 ],\n"," [-0.24654289, 0.02591667, -0.08242914, 0.06724623, -0.02255946,\n"," -0.06534167, -0.03041071, -0.20521495, -0.09052193, -0.02395412],\n"," [ 0.03733844, 0.02324182, -0.03946812, -0.04012549, 0.11786377,\n"," -0.02232137, -0.13969547, -0.2249879 , -0.14508776, 0.10241473],\n"," [-0.12471099, 0.11376971, 0.26658633, 0.09771979, -0.1470983 ,\n"," -0.14927183, -0.21017852, 0.22384271, 0.26428553, 0.15002811],\n"," [-0.02170658, -0.09789501, 0.17826393, -0.17258665, -0.11967471,\n"," -0.11184216, 0.03065729, -0.06385903, 0.0995706 , 0.21970102],\n"," [ 0.00225344, -0.17406702, -0.01554048, 0.21980903, 0.0067156 ,\n"," -0.16654111, 0.27014586, -0.0758528 , -0.13568078, -0.04987341],\n"," [-0.01047686, -0.09353484, -0.22051713, -0.07676102, -0.22467649,\n"," -0.28033516, -0.01440892, -0.01701415, 0.10294256, 0.22294334],\n"," [-0.02973881, 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0.15758154]],\n"," dtype=float32), array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32)]\n","Epoch 1/7\n","1875/1875 [==============================] - 4s 2ms/step - loss: 0.3040 - accuracy: 0.9131 - val_loss: 0.1710 - val_accuracy: 0.9498\n","Epoch 2/7\n","1875/1875 [==============================] - 4s 2ms/step - loss: 0.1468 - accuracy: 0.9569 - val_loss: 0.1243 - val_accuracy: 0.9643\n","Epoch 3/7\n","1875/1875 [==============================] - 4s 2ms/step - loss: 0.1058 - accuracy: 0.9686 - val_loss: 0.1057 - val_accuracy: 0.9683\n","Epoch 4/7\n","1875/1875 [==============================] - 4s 2ms/step - loss: 0.0829 - accuracy: 0.9747 - val_loss: 0.0975 - val_accuracy: 0.9697\n","Epoch 5/7\n","1875/1875 [==============================] - 4s 2ms/step - loss: 0.0676 - accuracy: 0.9790 - val_loss: 0.0876 - val_accuracy: 0.9728\n","Epoch 6/7\n","1875/1875 [==============================] - 4s 2ms/step - loss: 0.0567 - accuracy: 0.9825 - val_loss: 0.0781 - val_accuracy: 0.9738\n","Epoch 7/7\n","1875/1875 [==============================] - 4s 2ms/step - loss: 0.0485 - accuracy: 0.9848 - val_loss: 0.0826 - val_accuracy: 0.9731\n","Final alpha: [array([[ 1.49731815e-01, -4.33901697e-01, 4.64810282e-01,\n"," 1.87838107e-01, -1.31817329e+00, -1.36781454e-01,\n"," -2.20852926e-01, 2.31495425e-01, -1.28978742e-02,\n"," 2.20830426e-01],\n"," [ 2.61248738e-01, -5.24092019e-01, 1.00676581e-01,\n"," 2.92181581e-01, 3.80533114e-02, -1.31717250e-01,\n"," -9.61486623e-02, 1.64519414e-01, 7.32505098e-02,\n"," -5.89757383e-01],\n"," [-3.05296451e-01, 6.39697552e-01, -3.87004822e-01,\n"," -6.18221819e-01, 2.71083862e-01, -1.89693332e-01,\n"," 2.91126400e-01, 4.93878633e-01, -2.62698948e-01,\n"," -1.83036327e-01],\n"," [-5.38862571e-02, -7.07252800e-01, -7.63772011e-01,\n"," -1.54142007e-01, -7.22303510e-01, 6.31452680e-01,\n"," 4.30042744e-01, -2.54842222e-01, -2.98031420e-01,\n"," -4.55763824e-02],\n"," [ 3.45255196e-01, 2.81854272e-01, -3.33732665e-01,\n"," 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"]},{"cell_type":"code","metadata":{"id":"sg0MrzuAP9q5","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":561},"executionInfo":{"status":"ok","timestamp":1595987647070,"user_tz":-330,"elapsed":2228,"user":{"displayName":"Agrover112","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiMJACGAX3kCfRjB2hgzdG8w9zL1lAAKbPPMz0qLA=s64","userId":"09574164879083471944"}},"outputId":"3631954e-c179-4d93-8bb1-eda40804117b"},"source":["plot_results(gh)\n","plot_results(sh)"],"execution_count":104,"outputs":[{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{"tags":[]},"execution_count":104},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAtcAAAEKCAYAAADZxnkxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOzdd3zV9fXH8dfJIoOZgKyQMGRDhkAQEFm2gqgMBZmCWBXr3latVqrVqrWO+kNsRYViQHBRhaIsEReELSB7hSUk7ASyzu+P7w1caICMe3Nzk/N8PHjk3u88F6+Xdz73M0RVMcYYY4wxxpRcgK8LMMYYY4wxprywcG2MMcYYY4yHWLg2xhhjjDHGQyxcG2OMMcYY4yEWro0xxhhjjPEQC9fGGGOMMcZ4iIVrY4wxAIhIbxHZICKbReTxAvbHisg8EVktIgtFJNpt319F5GfXn5tKt3JjjCk7LFwbY4xBRAKBt4A+QCtgqIi0OuewV4BJqhoHjANecJ3bF7gMSAA6Ag+LSNXSqt0YY8oSC9fGGGMAkoDNqrpVVbOAqUC/c45pBcx3PV7gtr8VsEhVc1T1BLAa6F0KNRtjTJkT5OsCPKVmzZrasGFDX5dhjDHFsmzZsoOqWsuHJdQHdrk9T8VphXa3ChgIvA4MAKqISJRr+zMi8jcgHOgBrLvYDe1z2xjjry70mV1uwnXDhg1JSUnxdRnGGFMsIrLD1zUUwsPAP0RkNLAI2A3kqupXItIB+B44APwA5BZ0ARG5HbgdICYmxj63jTF+6UKf2dYtxBhjDDhBuYHb82jXttNUdY+qDlTVROBJ17bDrp/Pq2qCqv4GEGBjQTdR1XdUtb2qtq9Vy5cN9cYY4x0Wro0xxgAsBZqKSCMRCQGGADPdDxCRmiKS/+/GH4CJru2Bru4hiEgcEAd8VWqVG2NMGVJuuoUYY4wpPlXNEZG7gTlAIDBRVdeKyDggRVVnAt2BF0REcbqF3OU6PRj4VkQAjgIjVDWntF+DMcaUBRaujTEXlJ2dTWpqKidPnvR1KeVCaGgo0dHRBAcH+7qU/6Gqs4BZ52x72u3xDGBGAeedxJkxpMTs/XZ+Zfm9Y4w5w8K1MeaCUlNTqVKlCg0bNsTVMmmKSVVJS0sjNTWVRo0a+bqcMsnebwWz944x/sP6XBtjLujkyZNERUVZ0PEAESEqKspaZS/A3m8Fs/eOMf7DwrUx5qIs6HiO/V1enP0dFcz+XozxDxW7W0hqCvzyJVz1jK8rMcYYY4wx3qQKJw/D4V1wZJfz8+QR6P6YR29TsVuudy+Hxa/CvjW+rsQYcx49evRgzpw5Z2177bXXuPPOO897Tvfu3QtcnOR8243J58n3G8DBgwcJDg7m7bff9midxpgC5OXB0T2w8ydYMwMW/x2+eBCmDIK3LocXGsBfG8KErjB1GPz3Mfj+DcgrcM2rYqvYLddtb4SvnoQVU6DPi76uxhhTgKFDhzJ16lSuvvrq09umTp3KSy+95MOqTHnl6ffb9OnTufzyy0lOTmbs2LGeKtOYiinnFBxJPdPqfGSX8/zwTtfj3ZCXffY5YTWgWjRENobG3aBaA6jewPlZrQFE1AQPd7mq2OE6PBJa9IXV0+A34yAoxNcVGWPOceONN/LUU0+RlZVFSEgI27dvZ8+ePXTt2pU777yTpUuXkpmZyY033sizzz5b5Ounp6czZswYtm7dSnh4OO+88w5xcXF888033HfffYDT13XRokUcP36cm266iaNHj5KTk8P48ePp2rWrp1+y8SFPv9+Sk5P529/+xrBhw0hNTSU6OhqASZMm8corryAixMXFMXnyZPbv38/YsWPZunUrAOPHj6dz585efb3GlCknj7hCc36A3nl2kD6+/5wTBKrUdcJy/fbQeoATpKvFuAJ0NFSqUuovo2KHa4CEEbD2U9g4G1r183U1xpRpz/5nLev2HPXoNVvVq8oz17U+7/7IyEiSkpKYPXs2/fr1Y+rUqQwePBgR4fnnnycyMpLc3Fx69erF6tWriYuLK9L9n3nmGRITE/nss8+YP38+N998MytXruSVV17hrbfeokuXLhw/fpzQ0FDeeecdrr76ap588klyc3PJyMgo6cs3F+Dv77ddu3axd+9ekpKSGDx4MNOmTeOhhx5i7dq1PPfcc3z//ffUrFmT9PR0AO699166devGp59+Sm5uLsePH/foazfGp/Ly4MSBAkKzW0v0qSNnnxNYyRWWo6Hpb9xCs6v1uUq9MtkwauG6SQ/nP86Kf1u4NqaMyv+qPj/svPvuuwB89NFHvPPOO+Tk5LB3717WrVtX5HC9ePFiPv74YwB69uxJWloaR48epUuXLjz44IMMHz6cgQMHEh0dTYcOHRgzZgzZ2dn079+fhIQEj79W43ueer9NmzaNwYMHAzBkyBDGjBnDQw89xPz58xk0aBA1a9YEnEAPMH/+fCZNmgRAYGAg1apV8+bLNMazcrLg6G63rhq74MhOt+4buyH31NnnVKp2JizHdnZ11YiG6jGuLhu1IMD/hgdauA4IhIShTqf3o3uhal1fV2RMmXWhFj9v6tevHw888ADLly8nIyODdu3asW3bNl555RWWLl1KjRo1GD16tEfnAH788cfp27cvs2bNokuXLsyZM4crr7ySRYsW8eWXXzJ69GgefPBBbr75Zo/d05zN399vycnJ7Nu3jylTpgCwZ88eNm3aVBovwRjvyjoBe1fDnuWwZ4UzQUT6VkDPPq5yHSc8142HFteeCc35XTZCy+cvkF4N1yLSG3gdCAT+paovnrN/LHAXkAscB25X1XWufX8AbnXtu1dVzx6+7UkJw+Hbv8GqZOj6oNduY4wpnsqVK9OjRw/GjBnD0KFDATh69CgRERFUq1aN/fv3M3v2bLp3717ka3ft2pUpU6bwxz/+kYULF1KzZk2qVq3Kli1baNu2LW3btmXp0qX88ssvhIWFER0dzW233capU6dYvny5hetyyBPvt40bN3L8+HF27959etszzzxDcnIyN9xwAwMGDODBBx8kKiqK9PR0IiMj6dWrF+PHj+f+++8/3S3EWq+Nz+Wcgn0/nwnSe1bAgV9A85z9VetDvURnkojqMa5uHK7wHFTJt7X7iNfCtYgEAm8BvwFSgaUiMjM/PLt8qKpvu46/HngV6C0irYAhQGugHjBXRJqpqmfnSskX1QRiOsPKKXDFAx4fNWqMKbmhQ4cyYMAApk6dCkB8fDyJiYm0aNGCBg0a0KVLl0Jdp2/fvgQHBwPQqVMnJkyYwJgxY4iLiyM8PJwPPvgAcKZfW7BgAQEBAbRu3Zo+ffowdepUXn75ZYKDg6lcufLpr/BN+VPS91tycjIDBgw4a9sNN9zATTfdxNNPP82TTz5Jt27dCAwMJDExkffff5/XX3+d22+/nXfffZfAwEDGjx9Pp06dvPYajfkfudlOcN6dH6SXw/51Z2bgCK8J9S+Dltc7gbpeIlSp7duayyBR1YsfVZwLi3QC/qSqV7ue/wFAVV84z/FDgZtVtc+5x4rIHNe1fjjf/dq3b68lmr92xRT4/PcwZg7EXF786xhTzqxfv56WLVv6uoxypaC/UxFZpqrtfVSSTxT0uW3vtwuzvx/jMXl5kLbpTLeOPStg32rIcXV3Cq12JkDXu8z5WS3aGiBdLvSZ7c1uIfWBXW7PU4GO5x4kIncBDwIhQE+3c38859z63inTpVU/mPWIM7DRwrUxxhhjygtVOLTtTLeO3Stg70rIcs1IExzh9Ivu8LszgTqysQXpYvL5gEZVfQt4S0SGAU8Bowp7rojcDtwOEBMTU7JCKlV25kdc+yn0+SuERJTsesYYY4wxpU3VWaXQfbDhnhXOst/gTG9Xpy3ED3W6eNRLhJrNnAkejEd4M1zvBhq4PY92bTufqcD4opyrqu8A74Dz9WJJigUgcQSs/Des+xwShpX4csYYY4wxXnX8wJn+0flh+sSvzj4JhNqtnG/n6yU6YbpWyzI5N3R54s1wvRRoKiKNcILxEOCsxCoiTVU1f16ivkD+45nAhyLyKs6AxqbAEi/W6oi5HCKbOF1DLFwbY4wxpizJPHyma8ee5bBnpTOHNAACtZrDpb3O9JGu0waCw3xackXktXCtqjkicjcwB2cqvomqulZExgEpqjoTuFtErgKygUO4uoS4jvsIWAfkAHd5baYQdyKQOBzmjYO0Lc4sIsYYU0EUYvrUWGAiUAtIB0aoaqpr30s4jSQBwNfAfeqtEfPGVASnjjsDDN1n7kjfemZ/jUbQIAk63uGE6bpxPlnq2/wvr/a5VtVZwKxztj3t9vi+C5z7PPC896o7j/ihMP85WPkh9Ppjqd/eGGN8oZDTp74CTFLVD0SkJ/ACMFJEOgNdgPzlChcD3YCFpVW/MX5N1QnO2xbBriVOmD64wW0u6Wiol+B0X62XCHUTIDzStzWb8/L5gMYyp2o9aNLLWVCmxxPWwd8YH0tLS6NXr14A7Nu3j8DAQGrVqgXAkiVLCAk5f9/BlJQUJk2axBtvvFHo+zVs2JCUlJTTS1NXIEnAZlXdCiAiU4F+ON8g5muFM7sTwALgM9djBUJxZn0SIBjYXwo1e81nn33GgAEDWL9+PS1atPB1OaY8OrbfCdNbF8K2b85074io5bREt+p3ZsBh5Ut8WqopGgvXBUkcDtNHw9YFcOlVvq7GmAotKiqKlStXAvCnP/2JypUr8/DDD5/en5OTQ1BQwR9l7du3p337CjV1dEkUZvrUVcBAnK4jA4AqIhKlqj+IyAJgL064/oeqri+Fmr0mOTmZK664guTkZJ599llfl2PKg5NHYPt3TpDe+g0ccP0vElodGnWFLvdB4+4QdalNgefnAnxdQJnU/BoIq+EsLGOMKXNGjx7N2LFj6dixI48++ihLliyhU6dOJCYm0rlzZzZs2ADAwoULufbaawEnmI8ZM4bu3bvTuHHjIrVmb9++nZ49exIXF0evXr3YuXMnANOnT6dNmzbEx8dz5ZVXArB27VqSkpJISEggLi6OTZs2XejS/uZhoJuIrMDp9rEbyBWRS4GWODM71Qd6ikjXgi4gIreLSIqIpBw4cKC06i6S48ePs3jxYt59993TKzTm5uby8MMP06ZNG+Li4njzzTcBWLp0KZ07dyY+Pp6kpCSOHTvmy9JNWZJ90mmZnvdn+NdV8NdGMHUoLPsAqtSBq/4Ety2AR7fCTf+GpNugZlML1uWAtVwXJKgStB0My96DjHTr12RMvtmPw741nr1mnbbQ58WLH3eO1NRUvv/+ewIDAzl69CjffvstQUFBzJ07lyeeeIKPP/74f8755ZdfWLBgAceOHaN58+bceeedp5dCv5B77rmHUaNGMWrUKCZOnMi9997LZ599xrhx45gzZw7169fn8GFnDtm3336b++67j+HDh5OVlUVurvfHYnvIRadAVdU9OC3XiEhl4AZVPSwitwE/qupx177ZQCfg23NvUqQpVH30fvv888/p3bs3zZo1IyoqimXLlrFkyRK2b9/OypUrCQoKIj09naysLG666SamTZtGhw4dOHr0KGFhNjNDhZWXC3tXuVqmF8LOH53VDiUQ6reDrg9Co27OIMSgSr6u1niRhevzSRwBSybAzx87v00aY8qUQYMGERjojIk4cuQIo0aNYtOmTYgI2dnZBZ7Tt29fKlWqRKVKlbjkkkvYv38/0dHRF73XDz/8wCeffALAyJEjefTRRwHo0qULo0ePZvDgwQwcOBCATp068fzzz5OamsrAgQNp2rSpJ15uaSjM9Kk1gXRVzQP+gDNzCMBO4DYReQGnW0g34LXSKtzTkpOTue8+Z7z9kCFDSE5OZtu2bYwdO/Z0F6TIyEjWrFlD3bp16dChAwBVq1b1Wc3GB1QhbbMTpLcuhO3fOl0/AC5pBe1ugcbdILYLhNp7oyKxcH0+deOcFo4Vky1cG5OvGC3M3hIRcWYV1T/+8Y/06NGDTz/9lO3bt9O9e/cCz6lU6UxrUWBgIDk5OSWq4e233+ann37iyy+/pF27dixbtoxhw4bRsWNHvvzyS6655homTJhAz549S3Sf0lDI6VO7Ay+IiAKLgLtcp88AegJrcAY3/ldV/1PionzwfktPT2f+/PmsWbMGESE3NxcROR2gTQV3dI/TXzq/3/SxPc72ajHQ8jpo3AMaXWkDECs4C9cXkjgSZj/qfC1Zp62vqzHGnMeRI0eoX78+AO+//77Hr9+5c2emTp3KyJEjmTJlCl27Ot2Jt2zZQseOHenYsSOzZ89m165dHDlyhMaNG3Pvvfeyc+dOVq9e7RfhGgo1feoMnCB97nm5wB1eL7AUzJgxg5EjRzJhwoTT27p160Z8fDwTJkygR48ep7uFNG/enL1797J06VI6dOjAsWPHCAsLO+8AW+OHMg/B9sVOkN66ENJcYyjCo5wQ3aib0zpdo5H1lTan2SfAhbQdBF895QxsLEMtdsaYsz366KOMGjWK5557jr59+5b4enFxcQQEOOO9Bw8ezJtvvsktt9zCyy+/TK1atXjvvfcAeOSRR9i0aROqSq9evYiPj+evf/0rkydPJjg4mDp16vDEE0+UuB5TepKTk3nsscfO2nbDDTewfv16YmJiiIuLIzg4mNtuu427776badOmcc8995CZmUlYWBhz586lcuXKPqrelFh2Juz84Uzr9N5VzlzTwREQ2xnajXJm9LikNQTYnBCmYFJeFtBq3769pqSkeP7CH41yRvs+tAGCzj+frjHl1fr162nZsqWvyyhXCvo7FZFlqlqh5g0s6HPb3m8XZn8/Hpab4yzYsm2hE6h3LYHcUxAQBNEdnCDdqJszINEygHFzoc9sa7m+mMQRsO4z2DjbmdDdGGOMMf5JFQ78cqabx47v4NRRZ1+dts4Yq8bdIaYTVLJvIEzxWLi+mCY9oUo9p2uIhWtjjDHGvxzeeaabx7ZFcNy1eGiNRtBmoBOmG14JEVG+rNKUAlXlUEY2O9Mz2JF2gp1pGRzJzOapa1t59D4Wri8mIBDih8B3r8HRvVC1rq8rMqbUqSpig3U8orx0xfMme78VzN47hZSbDZu+hk1fOYE6fauzPeISZ/Bh/iDE6jG+rdN4RU5uHnuPnGRHWoYTotOdEL0zPYOdaRkcO3X2LFF1q4XyeJ8WBAV6rg+9hevCSBwBi1+F1VPhigd8XY0xpSo0NJS0tDSioqIs8JSQqpKWlkZoaKivSymz7P1WMHvvFEL6Nlg+CVZOcVqnQ6pAwysg6XandbpWC5vRo5w4cSrndHjemX7C7XEGuw9lkpN35hfRkMAAoiPDiI0Mp31sDWKiIoiNDCcmKpwGNcIJCwn0eH0WrgsjqonT/2rFFOhyv/3PaSqU6OhoUlNTKatLVfub0NDQQi1cU1HZ++387L1TgJws2PCls6T41gUgAdD0amdWj0uvgsCLr8Bqyh5V5cCxU+xIzzgTnNNOsCM9g13pGRw8nnXW8dXDg4mJDKdt/WpcG1eXmMhwYiIjiI0Kp3bVUAIDSje3WbgurMQR8PldzkjimI6+rsaYUhMcHEyjRo18XYapIOz9ZgolbQssex9WfggZB6FaA+j+hPNvdbX6vq7OFMKpnFxSD2We7q7h3hK9Mz2Dk9l5p48NEKhbLYzYqHCualmbmKhwYiMjnBAdFU61sLL1S5SF68Jq1R9mPQor/23h2hhjjCltOadg/X+cUL39W5BAaN4H2o12Jh8I8PzX+6ZkjmRks8O920aa0wd6V3ome45k4j6MICw4kJjIcGKjIriyaS1iosJPP69fPYyQIP+ZV9zCdWFVqgytB8DPn0DvFyEk4uLnGGOMMaZkDmxwun2sSobMdKgeC72ehoThUKWOr6urEFSVrNw8TmblkZGdQ2ZWLpnZuZzMziUzK4/M7FzSjjvdOM60RJ/g6MmzBw/WrFyJmMgwkhpFurpuhBMb5bQ+16pcqdyMs7BwXRSJw52W63UzIWGor6sxxhhjyqfsTFj3uROqd34PAcHQoq/TSt2om62O6CYvTzmVk0dGVs7/BN7M7Fwys5xtGWcF4tzT+0+6Pc7IOnu/++O8QkxWExQgRNcIIyYqgvgG1ZyuG64W6JjIcCIqVYzYWTFepafEdILIxrDi3xaujTHGGE/bv87p9rF6Kpw84vyb+5txED8MKtcq1CWOZGazI+0Ex0/lgEKegqKoQp4qitMSq/n7CtqGnt4HrvPcjz/rmmeOR8+c52w/+3hnn3M9cDvOrbbs3LzTgTjTPQifJxC7900urACB8JAgQoMDCQsJICw4kLDgQEKDA4mMCCG8hvM4f3tYiNvzkEDCz3keFhxItbBg6lYL9eiUdv7KwnVRiDhfQ83/szNvZmRjX1dkjDHG+LesE7D2U6eVOnUJBIZAy+udVuqGV/zPDF2qyuGMbLannXD+HHS6IGx3dUU4lJHtm9fhIcGBclZoDT0nwIaGBBJ+nv1hwYFn7T83ADv7AwgJDCg3XTDKIgvXRRU/FBY874xQ7vmUr6sxxhhj/NPe1U4r9ZrpzhLkNZvB1X+BuCFoeCQHj2exY8eh06H59M+DZ/flFYF61cJoWDOcPm3r0jDKmYatWlgwIhAg4voJIAQIiLh+4uwT1+OAANdPyc/0FzjebVuAAPn34pzjA0A4U4f7NfKPP73PAm+5YOG6qKrVd0Ylr0yG7n+w0cnGGGNMYZ06Bj9/7LRS71mOBoZyMLYPa2oPICWvGTu2ZrJ96Tp2pGU43TpcAgSiaziD3/ol1Cc2KpxGNSOIjYqgQWQYlYLs32JTdli4Lo7EETB9NGxdCJf28nU1xhhjTJmUl6fsO5LJgU0/ErHm3zTYPZtKeRlsC4hlSt5oPjrZmaPrKsM6CArYRgPX7BEdGkbSMCqc2JoRNPTDqdhMxWbhujiaXwNhNZwlVi1cG2OMqcBy85Q9hzPZkZbBtrQT7DjodOE4cPBXEg5/zSCZT3zAdjI1hP9oZ76pfA2ZlyQSW7Myj0Q58xg3jIqgXnUbDGfKBwvXxRFUCdoOdvqKZR5ygrYxxvg5EekNvA4EAv9S1RfP2R8LTARqAenACFVNFZEewN/dDm0BDFHVz0qncuNtObl5pB7KZHuasyCI+89d6Rlk5+bP06YkBW1lTNgieuR8S6XAk6RXacGWluMIvewmBlxSmxtLeSlqY0qbheviShwOSybAmhmQdJuvqzHGmBIRkUDgLeA3QCqwVERmquo6t8NeASap6gci0hN4ARipqguABNd1IoHNwFel+gKMx63cdZh/LtrK2j1HSD2USY7bRMfhIYHERkXQvHYVftuqDk2rZnPZ4a+J3vYRwQfXAxGQeBO0G01kvUQibaCeqUAsXBdX3Xio3daZ89rCtTHG/yUBm1V1K4CITAX6Ae7huhXwoOvxAqCglukbgdmqmuHFWo0Xrdh5iNfnbWLhhgNUDw+my6U16RtXl4ZRETSsGUFs/mp6ADt/hGXvwLLPIOck1EuE616HNjdApSq+finG+ISF65JIHAH/fQz2/Qx12vi6GmOMKYn6wC6356lAx3OOWQUMxOk6MgCoIiJRqprmdswQ4FVvFmq8Y9kOJ1Qv2niAGuHBPNq7OTd3akjlc1fVy0iHH991Zvw4uAEqVXXWgGg3yml4MqaC82q4LkT/vQeB3wE5wAFgjKrucO3LBda4Dt2pqtd7s9ZiaTsIvnrKGdjY+wVfV2OMMd72MPAPERkNLAJ2A7n5O0WkLtAWmHO+C4jI7cDtADExMd6s1RRSyvZ0Xp+3iW83HSQyIoTH+7Rg5OWxZy9VrQrbFztjjdbPhNwsiO4A/d6C1gMgJMJn9RtT1ngtXBey/94KoL2qZojIncBLwE2ufZmqmuCt+jwiIgpaXAOrp8FVz0JQiK8rMsaY4toNNHB7Hu3adpqq7sFpuUZEKgM3qOpht0MGA5+q6nmXyFPVd4B3ANq3b6/nO85435Jt6bw+byPfbU6jZuUQnrimBSMujyU8xC0anDjoNCAt+wDSt0BoNWh3i9NKXbu174o3pgzzZsv1RfvvuQbB5PsRGOHFerwjYQSs+xw2/hdalb3GdWOMKaSlQFMRaYQTqocAw9wPEJGaQLqq5gF/wJk5xN1Q13ZThv24NY3X527ih61p1Kxciaf6tmR4x1jCQtwWYlF1ViKe/ShkHYeYTtDtUWjVD4LDfFe8MX7Am+G6MP333N0KzHZ7HioiKThdRl4ss1M6NekJVeo6v9lbuDbG+ClVzRGRu3G6dAQCE1V1rYiMA1JUdSbQHXhBRBSnW8hd+eeLSEOclu9vSrl0U0g/bEnj9Xkb+XFrOrWqnCdUA2Qehi8egLWfQMOucM0rcEkL3xRtjB8qEwMaRWQE0B7o5rY5VlV3i0hjYL6IrFHVLeec5/u+e4FBED8Evnsdju2DKnV8U4cxxpSQqs4CZp2z7Wm3xzOAGec5dztOo4opQ1SVH7ak8dq8TSzZls4lVSrx9LWtGNYxhtDgApYM3/EDfHIbHNsLvZ6BLvdBgC0tbkxReDNcX7T/HoCIXAU8CXRT1VP521V1t+vnVhFZCCQCZ4XrMtN3L2EELP47rJoKV9zvszKMMcYYcEL191vSeG3uRpZuP0TtqpX403WtGJJ0nlCdmwOLXoZFL0H1WBjzFUS3K/3CjSkHvBmuC9N/LxGYAPRW1V/dttcAMlT1lKuPXxecwY5lU81LocHlzpzXXe4DmyzfGGOMD6gqizcf5PW5m0jZcYg6VUMZ1681g9s3KDhUAxza4bRW7/oJ4ofBNS/ZHNXGlIDXwnUh+++9DFQGposTSPOn3GsJTBCRPCAAp8/1ugJvVFYkjoCZd0PqUmiQ5OtqjDHGVCCqyqJNB3l97kaW7zxM3Wqh/Ll/Gwa3j6ZS0AW6dayZ4fSvBrjhXWh7Y+kUbEw55tU+14Xov3fVec77HmeuVP/Rur8zqnrFZAvXxhhjSoWqsnDjAV6fu4mVuw5Tv3oYz/Vvw6CLhepTx2DWo7DqQ2jQEQb+E2rEll7hxpRjZWJAY7lQqYozkf7Pn0LvF21CfWOMMV6jqizccIDX5m1ilStU/2VAW25sF01IUMCFT05dBh/fCod3QLfH4cpHnMH5xhiPsP+bPClhuA1sp+UAACAASURBVDMl37qZkDDU19UYY4wpZ1SV+b/8yuvzNrE69QjRNcJ4cWBbBl5WiFCdlwvfvQYL/gJV6sHoWRDbqXQKN6YCsXDtSbGdIbKxE7AtXBtjjPEQVWXu+l95Y94m1uw+QoPIMF66IY4Bl9UnOPAioRrgyG749A7Y/i20HgjX/h3Cqnu/cGMqIAvXniQCCcNg/nOQvg0iG/m6ImOMMX5MVflq3X7emLeJtXuOEhsVzks3xjEgsZChGpxvU2feA7nZ0H88xA+1Wa2M8SIL154WPwzmP+8sG9vzSV9XY4wxxg/l5Tmh+vV5m1i/9ygNo8J5ZVA8/RPqEVTYUJ11AuY8Acveh3qJzmwgUU28WrcxxsK151Wr7yyJvvJD6P64rWxljDGm0PLylDlr9/H6vE38su8YjWtG8OrgeK6PL0KoBti72hm0eHATXPEAdH8CgkK8V7gx5jQL196QOAJm3ALbvnGCtjHGGHMBeXnK7J/38ca8TWzYf4zGtSJ47aYErouvR2BAEbpw5OXBT+Nh7p8gPApu/hwad/Na3caY/2Xh2huaXwOh1WHFFAvXxhhjzis3T5m1Zi9vzt/Exv3HaVIrgteHJHBtXBFDNcCx/fDZnbBlHrS4Fq5/E8IjvVO4Mea8LFx7Q3AoxA2GZR9A5iEIq+HriowxxpQhuXnKF6v38Ob8zWz+9ThNL6nMG0MT6du2btFDNcDGr5xgnXXCmQmk3S02aNEYH7Fw7S0Jw2HJO/Dzx9Dhd76uxhhjTBmQH6rfmLeJLQdO0Kx2Zf4xLJFr2tQloDihOvskfP00LJkAtdvCje9CreaeL9wYU2gWrr2lbrzzQbfi3xaujTGmgsvJzeM/rpbqrQdO0Lx2Ff5v+GX0bl2neKEa4Nf1MONW+HUtXP576PWM882pMcanLFx7iwgkDof/Pg7710Lt1r6uyBhjjA/sPpzJqIlL2PzrcVrUqcL44ZdxdUlCtSos/Rd89RRUqgLDP4amV3m2aGNMsRVhXh9TZG0HQ0CwM7DRGGNMhXMkI5vRE5ew/8hJ3h5xGbPu7UqftsXsAgJwIg2mDoNZD0PDrnDn9xasjSljLFx7U0QUNO8Dq6dBTpavqzHGmAsSkd4iskFENovI4wXsjxWReSKyWkQWiki0274YEflKRNaLyDoRaViatZdFJ7NzuW1yCtvTTjDh5nb0Lm6/6nxbFsD4zrB5LvT+KwyfDpUv8VzBxhiPsHDtbYkjIeMgbJrj60qMMea8RCQQeAvoA7QChopIq3MOewWYpKpxwDjgBbd9k4CXVbUlkAT86v2qy668POWh6atYsi2dVwbF07lJzeJfLCcLvvojTO4PYdXhtvlw+VibDcSYMsrCtbc16QmV61jXEGNMWZcEbFbVraqaBUwF+p1zTCtgvuvxgvz9rhAepKpfA6jqcVXNKJ2yy6bnZ63ny9V7+UOfFvRLqF/8Cx3cDO/+Br5/A9rfCrctgDptPVeoMcbjLFx7W2AQJAyFTV85E/wbY0zZVB/Y5fY81bXN3SpgoOvxAKCKiEQBzYDDIvKJiKwQkZddLeEV0r++3cq7i7cxunNDbr+ycfEuogrLJ8OErnB4Bwz5EK59FULCPVusMcbjLFyXhoQRoLmweqqvKzHGmJJ4GOgmIiuAbsBuIBdn5qmurv0dgMbA6IIuICK3i0iKiKQcOHCgVIouTV+u3svzs9bTu3Ud/nhtK6Q4XTcyD8H00TDzbohu7wxabNHX47UaY7zDwnVpqHkpNLjcmfNa1dfVGGNMQXYDDdyeR7u2naaqe1R1oKomAk+6th3GaeVe6epSkgN8BlxW0E1U9R1Vba+q7WvVquWN1+EzP21N44FpK2kXU4PXhiQUb6XF7d/B+Cvgly/gqmdh5OdQtZ7nizXGeI2F69KSOBwOboTUFF9XYowxBVkKNBWRRiISAgwBZrofICI1RST/340/ABPdzq0uIvlpuSewrhRqLjM27j/GbZNSiI4M4583tyc0uIi9YnKzYf5z8MG1EBQCt34NV9wPAfbPtDH+xv6vLS2tB0BwOKyY7OtKjDHmf7hanO8G5gDrgY9Uda2IjBOR612HdQc2iMhGoDbwvOvcXJwuIfNEZA0gwD9L+SX4zL4jJxk9cQmVggP54JYkakSEFO0C6dvgvT6w6GWIHwZ3fAv1C2z4N8b4AVuhsbRUqgKt+sPPn0DvF21QijGmzFHVWcCsc7Y97fZ4BjDjPOd+DcR5tcAy6OjJbEa/t4QjmdlMu6MTDSKL+Nm++iP44kGQALjxPWgz8OLnGGPKNGu5Lk2JwyHrGKyfefFjjTHGlGlZOXnc+e9lbP71OONHtKNN/WqFP/nkUfjkdvjkNqjTBu5cbMHamHLCwnVpiu0CNRo5AxuNMcb4LVXl0Rmr+G5zGi/eEMeVzYowOHPXUnj7ClgzA3o8CaO+gOox3ivWGFOqLFyXJhFIGA7bv3X62BljjPFLL83ZwGcr9/Dwb5txY7voi58AkJfr9KueeDWgcMts6Paosx6CMabcsHBd2hKGAgKrkn1diTHGmGKY/MN2xi/cwrCOMdzV49LCnXQkFT64zpkRpPUAGLsYYjp6tU5jjG9YuC5t1aKhSQ9Y+SHk5fm6GmNMOSQi17lNmWc8aM7afTw9cy1XtbyEcde3LtwiMScOwttdYe8qGDABbvgXhBahf7Yxxq/Yh68vJI6AI7tg2ze+rsQYUz7dBGwSkZdEpIWviykvlu1I597kFcRFV+eNoYkEBRbyn9BVyZCZDqO/gPghThdBY0y55dVwLSK9RWSDiGwWkccL2P+giKwTkdUiMk9EYt32jRKRTa4/o7xZZ6lr3tdptbCBjcYYL1DVEUAisAV4X0R+cC07XsXHpfmtLQeOc+sHKdStFsrEUe0JDylkP2lVWD4ZopOgXqJ3izTGlAleC9ciEgi8BfQBWgFDRaTVOYetANqrahzO3Kkvuc6NBJ4BOgJJwDMiUsNbtZa64FBoOxjW/wcyD/m6GmNMOaSqR3E+V6cCdYEBwHIRucenhfmhX4+dZNTEJQSK8MGYJKIqVyr8yalL4eAGuGyk9wo0xpQp3my5TgI2q+pWVc3C+YDv536Aqi5Q1QzX0x+B/CHXVwNfq2q6qh4CvgZ6e7HW0pc4HHJPwc8f+7oSY0w5IyLXi8inwEIgGEhS1T5APPCQL2vzNydO5XDr+ymkHc9i4ugOxEZFFO0CyydBcIQziNEYUyF4M1zXB3a5PU91bTufW4HZRTnX9TVnioikHDhwoITllrK6CVC7DayY4utKjDHlzw3A31W1raq+rKq/ArgaM271bWn+Izs3j99PWc66vUd5a3gi8Q2qF+0Cp47D2k+hzQBnlV5jTIVQJgY0isgIoD3wclHOU9V3VLW9qravVasIE/iXBflzXu9ZDvvX+boaY0z58idgSf4TEQkTkYYAqjrPNyX5F1XliU/W8M3GAzzXvw09W9Qu+kXWfgpZxyHxZs8XaIwps7wZrncDDdyeR7u2nUVErgKeBK5X1VNFOdfvxQ2GgGBYaa3XxhiPmg64z/WZ69pmCunvczcxfVkq9/a8lKFJxVw9ccVkqNkMGiR5tjhjTJnmzXC9FGgqIo1EJAQYAsx0P0BEEoEJOMH6V7ddc4DfikgN10DG37q2lS8RNaF5b1g1FXKzfV2NMab8CHKNdQHA9TjEh/X4leQlO3lj3iYGtYvmgd80K95FDmyAXT9B4kibes+YCsZr4VpVc4C7cULxeuAjVV0rIuNE5HrXYS8DlYHpIrJSRGa6zk0H/owT0JcC41zbyp/EkZBxEDaWv98djDE+c8DtcxYR6Qcc9GE9fmP+L/t56rOf6dasFn8Z2LZwi8QUZPkkCAiC+KGeLdAYU+YVcqLO4lHVWcCsc7Y97fb4qgucOxGY6L3qyogmvaByHadrSMtrfV2NMaZ8GAtMEZF/AIIzQNw6/l7Eql2HuWvKClrWrcL/Db+M4MIuEnOunCznG8lmvaGyn40HMsaUmFfDtSmEwCBnxa7v34Rj+6FKMQbNGGOMG1XdAlwuIpVdz4/7uKQyb0faCca8v5SoyiFMHN2BiEol+Odx43+dbyQvK1/rnxljCqdQv5aLSISIBLgeN3PNoRrs3dK879CJLKan7Lr4gd6WOAI0F1ZP83UlxphyQkT6Ar8HHhSRp0Xk6YudU1GlHT/FqIlLyFPlgzFJXFIltGQXXDEZqtSDS3t5pkBjjF8p7Hdei4BQEakPfAWMBN73VlGl5V+Lt/LIjNW8/c0W3xZSsyk06Ogsh67q21qMMX5PRN4GbgLuwekWMgiILcR5vUVkg4hsFpHHC9gfKyLzRGS1iCwUkWi3fbmusTOnx8/4g8ysXG79IIW9R07yr1HtaVKrcskueHQPbJ4LCcMgINAzRRpj/Ephw7W4Fh8YCPyfqg4CWnuvrNLxwFXNuC6+Hi/O/oX/W7jZt8UkDHeWyN29zLd1GGPKg86qejNwSFWfBToBF5z2QkQCgbeAPkArYKiItDrnsFeASaoaB4wDXnDbl6mqCa4/1+MHcnLzuCd5OatSD/P6kETaxUaW/KIrp4DmOd9IGmMqpEKHaxHpBAwHvnRt8/tfyYMCA/j74Hiuj6/HS//dwFsLfBiwWw+A4HDn60RjjCmZk66fGSJSD8gG6l7knCRgs6pudU3dNxXod84xrYD5rscLCtjvN1SVp2euZe76X3n2+tb0blOn5BfNy3O+gWzYFSIblfx6xhi/VNhwfT/wB+BT13R6jXE+WP1eUGAArw6Op39CPV6es4E3523yTSGhVaFVP/j5E8jK8E0Nxpjy4j8iUh1nutPlwHbgw4ucUx9nVpF8qa5t7lbhfIMJMACoIiJRruehIpIiIj+KSP+SFF8a/m/hFj78aSdjuzXh5k4NPXPRHYvh0Ha4zCZmMaYiK9RwaFX9BvgGwDWw8aCq3uvNwkpTUGAAfxucgIjwt683osC9vZqWfiGJI2BVMqz/D8TfVPr3N8b4Pddn9DxVPQx8LCJfAKGqesQDl38Y+IeIjMYZi7MbZ/VHgFhV3e1qfJkvImtcs5acW9/twO0AMTHFXPmwhD5elsrLczbQP6Eej17d3HMXXj4ZKlWDltd57prGGL9T2NlCPhSRqiISAfwMrBORR7xbWukKDBBeGRTPwMT6vPr1Rl6bu7H0i4jtAjUawsp/l/69jTHlgqrm4fSdzn9+qpDBejfQwO15tGub+7X3qOpAVU0EnnRtO+z6udv1cyuwEEg8T33vqGp7VW1fq1bpzwG9aOMBHvt4NV0ujeKlG+MJCPDQ6omZh2Dd5xA3CILDPHNNY4xfKmy3kFaqehToD8wGGuHMGFKuBAYILw+K54bLonlt7iZe/XojWpqzd4hAwgjYtsj5atEYY4pnnojcIEVbXnAp0FREGolICDAEOGvWDxGpmT8tK05XwYmu7TVEpFL+MUAXYF1JX4Sn/bz7CHf+exmXXlKZ8SPaERLkwUWK18yA3FPOqrvGmAqtsJ8swa55rfsDM1U1GyiXc8YFBggv3RjHoHbRvDFvE38v7YCdMBQQWJlcevc0xpQ3dwDTgVMiclREjonI0QudoKo5wN3AHGA98JFrjM04t6XUuwMbRGQjUBt43rW9JZAiIqtwxuO8qKplKlzvSs/glveXUi0smPdvSaJqqIeXalg+Ceq0hXoJnr2uMcbvFHYJqgk4A2JWAYtEJBa44Ae1PwsMEP56QxwBIrwxfzN5Cg/9thlFawQqpmrR0KQHrPwQuj0GAR5sWTHGVAiqWqWY580CZp2z7Wm3xzOAGQWc9z3Qtjj3LA2HM7IY/d4STmXnMuXOztSpVsJFYs61dxXsWw3XvOLZ6xpj/FJhBzS+AbzhtmmHiPTwTkllQ0CA8MLAtojAPxZsJk+VR65uXjoBO2E4fHwrbF8Ejbt7/37GmHJFRK4saLuqLirtWnztZHYuv/sghV3pmUy6NYlmtYv1e8eFLZ8MgZWg7Y2ev7Yxxu8UKlyLSDXgGSD/A/sbnAUEPDH6vMwKCBD+MqAtIsL/LdxCnsJjvUshYLe4FkKrOfOlNu7u3XsZY8oj9wHnoThzWC8DevqmHN/IzVMemLaSlB2H+MewRC5vHHXxk4oqOxPWfAStroewGp6/vjHG7xS2W8hEnFlCBruejwTe48x8p+VWQIDwfP82BAi8/c0WVJXH+7TwbsAODoW2g5xwnXkYwqp7717GmHJHVc+aC05EGgCv+agcn1BV/vzFOmb/vI+n+rbk2rh63rnR+i/g5BEbyGiMOa2wHXqbqOozrpW7trqW023szcLKkoAA4bn+bRh5eSwTFm3lL7PWe3+QY8JwyDkJP3/s3fsYYyqCVJxBhxXGP7/dyvvfb+fWKxrxu65e/OdqxSSoHuusymiMMRS+5TpTRK5Q1cUAItIFyPReWWWPiDCuX2sCBP757TbyFJ7q29J7Ldj1EuGS1rByCnS41Tv3MMaUSyLyJmdmdAoAEnBWaqwQPl+5m7/M+oW+cXV58hov/k6Rvs2ZOrXHUzb43BhzWmHD9VhgkqvvNcAhYJR3Siq7RIQ/Xd8aEeHdxdvIU+Xpa1t5J2CLQOJwmPME/LoeLqlQjU7GmJJJcXucAySr6ne+KqY0fb/lIA9PX0VSo0j+NsiDi8QUZOUUkABIGOa9exhj/E5hZwtZBcSLSFXX86Micj+w2pvFlUUiwjPXtUIE3vtuO6q4nnvhAzzuJvj6aafv9dXPX/x4Y4xxzABOqmougIgEiki4qmb4uC6v+mXfUe6YtIyGURH8c2R7QoMDvXezvFxYMQWa9IJq9b13H2OM3ynS91iqetS1UiPAg16oxy+ICE9f24pbr2jE+99v55mZa73TBzuiJjTrDaunQW62569vjCmv5gHua3CHAXN9VEup2Hskk9ETlxJeKZD3xyRRLdzDi8Sca/M8OLYHLrOBjMaYs5Wkk1gpTPhcdokIT/VtyW1dGzHphx08/bmXAnbiSDhxADZ95flrG2PKq1BVPZ7/xPU43If1eNWRzGxGT1zK8VM5vDc6ifrVwy5+UkmtmAThNaFZH+/fyxjjV0oSrsvl8udFISI8cU1L7riyMZN/3MEfP/+ZvDwP/7VcehVUru10DTHGmMI5ISKX5T8RkXaU00Hop3JyuWNyClsPHmfCyHa0qlfV+zc9fgA2zIb4IRAU4v37GWP8ygX7XIvIMQoO0cLZXzlWWCJyet7rt79xFpp5rl8bzw2iCQxyPsC//wcc2w9VanvmusaY8ux+YLqI7MH5vK4D3OTbkjwvL095ePpqftyazms3JdDl0pqlc+PVUyEvx+a2NsYU6ILhWlW9sE5s+SMiPNa7OQEC/7fQWWjm+f5tPRewE0bAd687fa+73OuZaxpjyi1VXSoiLYDmrk0bVLXcDdx48b+/8J9Ve3isdwv6J5bSoEJVZ7nz6CS4pEXp3NMY41dsYk4PEREeubo5d/e4lOQlu/jDJ2s810WkVjPng3zlFOeD3RhjLkBE7gIiVPVnVf0ZqCwiv/d1XZ703nfbeGfRVkZeHsvYbqW4plnqUji4wQYyGmPOy8K1B4kID/22Gff0vJRpKbt47OPVngvYicPhwC+we5lnrmeMKc9uU9XD+U9U9RBwmw/r8ajZa/Yy7ot1/LZV7dNrD5Sa5ZMgOAJaDyi9expj/IqFaw8TER78TTPu7dWU6ctSefTj1eR6ImC3HghBYTaw0RhTGIHiljhFJBAoFyPvlm5P575pK0lsUJ03hiYS6M1FYs516jis/RTaDIBK1mvSGFMwC9dekB+w77+qKTOWpfLIjFUlD9ihVaFVP/j5Y8gq1+tAGGNK7r/ANBHpJSK9gGRgto9rKrHNvx7jdx+kEF09jHdHdfDuIjEFWfspZB2HxJtL977GGL/i1XAtIr1FZIOIbBaRxwvYf6WILBeRHBG58Zx9uSKy0vVnpjfr9Jb7r2rGA1c145Plu3l4ugcCduIIOHUUfvnCMwUaY8qrx4D5wFjXnzWUgxmeggICaFa7Mh+MSaJGhA8a4ldMhprNoEFS6d/bGOM3vBauXV9DvgX0AVoBQ0Wk1TmH7QRGAx8WcIlMVU1w/bneW3V6231XNeWh3zTj0xW7efCjleTk5hX/YrFdoHqs8wFvjDHnoap5wE/AdiAJ6Amsv9h5hWgQiRWReSKyWkQWikj0OfurikiqiPzDM6/kbA1rRvDRHZ1oEOmD9XAObIBdPznT75VmH29jjN+54FR8JZQEbFbVrQAiMhXoB6zLP0BVt7v2lSBxln339GpKQIDw8pwNqMKrg+MJCizG7zUBAU7r9YLn4dAOqBHr+WKNMX5LRJoBQ11/DgLTAFS1RyHOzW8Q+Q2QCiwVkZmqus7tsFeASar6gYj0BF4A3KfN+DOwyBOv5QJ1evPy57d8EgQEQfxQ39zfGOM3vNktpD6wy+15qmtbYYWKSIqI/Cgi/T1bWum7q8elPNq7OTNX7eH+aSVowY4fCgisSvZofcaYcuEXnFbqa1X1ClV9E8gt5LmnG0RUNQvIbxBx1wqnuwnAAvf9rlUgawNflaD+siknC1ZNhWa9oXItX1djjCnjyvKAxlhVbQ8MA14TkSbnHiAit7sCeMqBAwdKv8Ii+n33S3m8Twu+WL2X+6auJLs4Abt6A2jcHVZMgdwcT5dojPFvA4G9wAIR+adrMGNhm3oL0yCyynUPgAFAFRGJEpEA4G/Aw8WuvCzb+F/IOAiX2UBGY8zFeTNc7wYauD2Pdm0rFFXd7fq5FVgIJBZwzDuq2l5V29eq5R+tCWO7NeGJa1rw5Zq93Dd1RfECdodb4chOeK83pG/zfJHGGL+kqp+p6hCgBU7L8v3AJSIyXkR+64FbPAx0E5EVQDecz/Rc4PfALFVNvdgF/K1RBHDGuVSpB016+boSY4wf8Ga4Xgo0FZFGIhICDAEKNeuHiNQQkUquxzWBLrj11fZ3t1/ZhKf6tmTWmn3cm1yMgN3yOrhxIhzYCG93db6utJUbjTEuqnpCVT9U1etwGjZW4MwgciEXbRBR1T2qOlBVE4EnXdsOA52Au0VkO06/7JtF5MXz1OZfjSJH98DmuZAwDAK9OUzJGFNeeC1cq2oOcDcwB2eU+kequlZExonI9QAi0kFEUoFBwAQRWes6vSWQIiKrcFpfXjxnUI3f+13Xxvzx2lbM/nkfd3+4nKycIgbsNjfAnYuhTlv49A74+Hdw8oh3ijXG+C1VPeQKtBdrdr1og4iI1HR1AQH4AzDRdY/hqhqjqg1xWrcnqer/zDbil1ZOAc1zBpMbY0whePXXcFWdBcw6Z9vTbo+X4rSOnHve90Bbb9ZWFtx6RSMCBJ79zzru/nA5/xh2GSFBRfh9p3oMjP4Cvn0VFr4Au5bAwHcgtpP3ijbGlEuqmiMi+Q0igcDE/AYRIEVVZwLdgRdERHFmBbnLZwWXhrw8Z1Xchl0hspGvqzHG+ImyPKCxQrilSyOevb41X63bz++nFKMFOyAQuj0Ct37lTNX3/jUw/3kb7GiMKTJVnaWqzVS1iao+79r2tCtYo6ozVLWp65jfqeqpAq7xvqreXdq1e8WOxXBouw1kNMYUiYXrMmBU54aM69eauev38/spyziVU9iZs9xEt4exiyFuCCx6yQY7GmNMSS2fDJWqOeNcjDGmkCxclxE3d2rIn/u3Ye76X7nz38uLF7ArVYEB422wozHGlFTmIVj3OcQNgmC/XzneGFOKLFyXISMvj+X5AW2Y/8uvjJ28jJPZxQjYYIMdjTGmpNbMgNxTznLnxhhTBBauy5jhHWN5YWBbFmw4wB0lCdj5gx17PAVrP4XxV8COHzxbrDHGlFfLJzkNFPUSfF2JMcbPWLgug4YmxfDiwLZ8s/EAt01KKX7AtsGOxhhTdHtXwb7VkGgDGY0xRWfhuowakhTDSzfEsXjzwZIFbLDBjsYYUxTLJ0NgJae/tTHGFJGF6zJscIcG/NUVsH/3QQqZWSUI2DbY0RhjLi47E9Z8BK2uh7Aavq7GGOOHLFyXcYPbN+DlG+P5bstBbv1gackCNthgR2OMuZD1XzifiTaQ0RhTTBau/cCN7aL526B4ftiaxpj3l5KRVcI+0/mDHXvaYEdjjDnLiklQPdZZldEYY4rBwrWfGHhZNK8OjuenbR4K2AGBcKUNdjTGmNPSt8G2RU6rdYD982iMKR779PAjAxKj+ftNCSzZls7oiUvZmZZR8ovaYEdjjHGsnAISAAnDfF2JMcaPWbj2M/0S6vPakERW7z5Mz78t5KnP1vDr0ZMlu6gNdjTGVHR5ubBiCjTpBdXq/397dx5dVXnvf/z9zckICQmQEIEwqaDiBIhYBxC0Tq1DtVqHOrT1Xq9WWzuoHddtr63LVu1gq7/24tCKotTSaql1qAMy9KqADCJQFAElgBJmwpDx+/tj75AQAkkg5+yccz6vtfbKPs959j7fzdIn3zznGaKORkSSmJLrJHTh8X2Ydvs4rhjVj0mzVjHm3qnc/cISNm2vPrgbtzTZcefmjglaRKQzW/YqbFsDIzSRUUQOjpLrJFXaLZeffu5YXvv2WD5zTG/GT1/OmHum8ttX36ey6iDGTTef7Pj70ZrsKCKpb94E6FIMQ86LOhIRSXJKrpNc/55d+OXlw3jpG2M45fCe/OLl9zj9nqk8MnPFwe3sqMmOIpIuKitg6Qtw/BWQmR11NCKS5JRcp4ghpQX87zUjefbmUzmqdzd+8txixt33OpNmfURtXf2B3VSTHUUkHbwzCeprtba1iHQIJdcpZli/Ip74j5N48j9OorRbLt/960LO+tV0/r5gDfX1BzBBUZMdRSSVuQfbnZeNgl5HRh2NiKQAJdcp6pTDi3nmq6fw0LUjyY5l8LWn5vHZ387ktX9/gh9IYnzM5+Gmf2myo0gKM7NzzWypmS0zs++28P4AM3vVzN4xs9fNrKxJ+Vwzm29mi8zsCrfpPwAAHbNJREFUxsRHf4DKZ8P6pZrIKCIdRsl1CjMzzhpayvO3jub+K4axo7qWr/xxDpf9/g3eWr6h/Tcs6qfJjiIpysxiwIPAecBQ4EozG9qs2n3ABHc/DrgTuDssXwuc7O7DgJOA75pZn8REfpDmToCsrnD0xVFHIiIpQsl1GohlGBcN68sr3zqduy4+hlWbdnD5+De59tFZLCzf0r6babKjSKoaBSxz9+XuXg1MAi5qVmco8Fp4PrXhfXevdveqsDyHZPndUlUZdBQcc3EwBE5EpAMkRwMoHSIrlsEXTxrAtNvH8f3PHMk75Zu54IGZfHXi2yxbt619N9NkR5FU0xdY1eR1eVjW1ALgkvD8YqDAzHoCmFk/M3snvMfP3X1NnOM9eIuegepKGH5t1JGISApRcp2GcrNi3DDmMGbcMY5bzxzMtKUVnP2r6dz25wWs2tiOLdU12VEk3dwGnG5m84DTgdVAHYC7rwqHixwOXGdmpS3dwMxuMLM5ZjanoqIiUXG3bN7jUDwE+o2KNg4RSSlKrtNYQW4W3zxrCNPvGMdXTh3ElAVrOOMXr/PjKYtYt60dW6prsqNIKlgN9Gvyuiws283d17j7Je4+HPhBWLa5eR3gXWB0Sx/i7uPdfaS7jywpKenI+NunYimseitYfs8sujhEJOUouRZ65ufww/OHMu32sVx6Qj8ef/NDTr/nde558d9s2VHTtptosqNIspsNDDazQWaWDVwBTGlawcyKzazh98b3gEfD8jIzywvPuwOnAUsTFvmBmDsBMjLh+CujjkREUoySa9mtd2Eed19yLK9+63TOPrqU3037gNH3vMaDU5exo7oNExb3mOwY02RHkSTi7rXALcBLwBLgaXdfZGZ3mtmFYbWxwFIzew8oBe4Ky48C3jKzBcA04D53X5jQB2iP2upgCNuQcyE/wt5zEUlJdkBrHndCI0eO9Dlz5kQdRkpZsnYrv/jnUl5Zso7i/BxuGXcYV57Un5zMWOsXV22DF74D8ydC2YlwyUPQY1D8gxZJUmb2truPjDqORIqs3V48BZ6+Bq56Goack/jPF5Gkt782Wz3Xsk9H9e7Gw9edyF9uOoXDe3Xlx39fzBn3TePpOata31I9pwA+9/802VFEOp95j0NBbzjszKgjEZEUFNfkug27fY0Jd/WqNbNLm713nZm9Hx7XxTNO2b8TBnTnqf/8FI9fP4qe+dncMfkdzvn1dJ5fuLb1LdX3mux4PaxflpjARUSa27oGlr0Cw74IscyooxGRFBS35LqNu319BHwJeLLZtT2AHxHs9DUK+FE4SUYiYmaMHlzC324+ld9ffQIZZnx14lwufHAm096r2P+W6k0nOy7+GzxwAjxyNrz9GOzamriHEBGZPxG8HoZfHXUkIpKi4tlz3epuX+6+0t3fAZqPMTgHeNndN7r7JuBl4Nw4xiptZGace8whvPiNMfzisuPZvKOG6x6dxeXj32TOyo37vrBhsuM33oVP/xh2bIS/fx1+cQT89b9gxXSob2WoiYjIwaivh3lPwMDRmgMiInETz+S6Lbt9HdS1nWozgjQTyzA+f0IZr317LHdedDTLK7Zz6e/f4Mt/mMW7q/ezpXq33nDaN+GW2XD9K3DcF2Dp8/DYBfCbYfD6z2DTh4l7EBFJHx/OhE0rYYR2ZBSR+EnqCY2dZjOCNJadmcG1Jw9k+h1j+c65RzL3o82c/9uZ3PzkXJZXVO77QjPodyJccD98e2mwmkj3gfD63XD/cUGyveBPUN2OHSNFRPZn7uOQUwhHXRB1JCKSwuI5m6PV3b5auXZss2tf75CoJC66ZGdy09jDuOqk/jw0fTmP/msFL777MZeOKOPrnx5M36K8fV+c3SXowT7uC7D5I5j/VDAu8pkb4PlucPTFwfjIshO1k5qIHJidm4I5HyOugaz9tEciIgcpnj3Xre72tR8vAWebWfdwIuPZYZl0coV5Wdx2zhFMu30c1548gGfmrWbcva9z598Xs76yqvUbFPWHsd+Br8+H656DIz8LC/8Mj5wFD5wIM34JW9fG/0FEJLUsnAx1VcF25yIicRTXTWTM7DPAr4EY8Ki732VmdwJz3H2KmZ0IPAN0B3YBH7v70eG1XwG+H97qLnf/w/4+S5vIdE6rN+/k/lfeY/Lb5eRmxbjulIEcX1ZESUEOvQpyKCnIITerlU1pqrYFW6rPmwir3gTLCNanHf5FOOIzkJmTmIcRiSNtIhNnvx8NONw4MzGfJyIpbX9ttnZolIT4oKKSX778Hv94Z+9e54KcTEoKcigOk+2S/Jw9ku+Go2fXHGIbPwiGjCyYBNvWQF53OPayYM3a3sdr2IgkLSXXcbR2AfzvGDjvXjjphvh/noikvP212VpBXxLisJJ8HrxqBHdeWMXaLbuoqKyiYluTI3y9ZM1Wpm+rYltV7V73yDDo0TWHkoIz6FV0Nqd2X8hplS8xZM4fyZw1nu3dj2TH0CvIGXEFBT0OwZRoiwgEExljOXDcZVFHIiJpQMm1JFTP/Bx65rc+jGNndR3rK6tYt62Kim279krCK7ZV8cdth3Fv5fV0qfsCF8Te4LIN0zj+Xz+meuZPeNlH8GruWSwrPJmeBV326AFv6BkvKcihOL8Nw1JEJHnV7ISFTwcrhORpLzIRiT8l19Ip5WXH6NejC/16dNlvPXdny84aKrZ9hoptVby2eiElH0zm1DX/4Ozq2Wze0J1Xto5lUs0YJu4obfEe3XIzmyTfuXsk3w1DU3rmZ2MYtfX11NQ6NfX11NY5NXX11NY7tXX1VNcFZbX19dTU+e7z6trGOjVN3q8J6zfcq7aunpp6pyasX9Psfs3rN3z27vJ9vC7IzaQ4P4fi/Gx6ds2huKDhZw7FXbMpLsihZ/izICdTPf6SWpY8B7u2aG1rEUkYJdeS1MyMoi7ZFHXJZnBpARw+Dk4fB3W/hvf/SdG8iVz6/t+5tP4Z6g89gcqjLqe873l8Up3LuhZ6xBeWb6ZiWxXbq+sSEn+GQWYsg6wMC37GMsiKGZkxIysjg8yYkZmRQVZmQx0jPyuTzLB+dqxJnfC6zIwMsjMzgjoZxtZdtayvrGJDZTUfVFQya2U1m3ZU09J0i+zMjD0T7vCbhuL87DBBD/7QKM7PoUfXbGIZSsSlk5s3AYoGBLsyiogkgJJrSU2xrGAZvyM/C5UV8M6fyJg/kW6v3sHQzP9m6JHnB6uNnHB6sDV7M9urgoS0IfluWEYwK5YRJsFBEpsZM7JiFpTvTnCDxLYhUc7aKwEOyzMyyIgoOa2tq2fj9mrWV1YHiff2KtZvq2Z9+HPD9uAPjn9/vI31lVXU1O2diZtBjy7Zu5PtPZPwvcs0/EYSbuMKWDEdxv0QMpJ6zzQRSSJKriX15ZfAKbfAyTfDmnnBaiML/wzvToZuZTDsShh2FfQ4dPclXXMy6ZqTyYCeXSMMPH4yYxn06pZLr265rdZ19z16v9dXVoVHmJiH5wvLN7O+sprKFiajAuTnZDYm4mHveGMveThsJT8YE98tT8NTpAPMnxgs3TnsqqgjEZE0ouRa0ocZ9B0RHGffBUv/EaydPf0+mH4vDDg1WNJv6EWQkx91tJ2GmVGYl0VhXhaHlbRef1dN3R6J+IbKaiqaJeYfbtjB2x9uYuM+hqdkxYxeBbn0Lcqjd1EufYrygqMwPC/MUwIu+1dfF/z/fdiZUNg36mhEJI0ouZb0lJULx3w+OLashgVPwfwn4W9fhedvh6M/FyTaA07R2tntlJsVo6x7F8q6738yKkBdvbNxe/XuYSkbtjcMw6nmk627WLN5J3M/2sTzC9fuNTSla3aMPkV59C7Ko29RLn0Kg/M+RUFSfkhhLjmZGorSHmZ2LnA/wcZfD7v7z5q9PwB4FCgBNgJXu3u5mQ0Dfgd0A+oINv76U0KDb27Zq8Fa+Of9rPW6IiIdSMm1SGFfGHMbjP42rHoL5j0R7Ag5fyJ0HxQk2cOuhMKyqCNNObEM270yC4fsu159vbO+sorVm3eyZvMu1m7ZyerNO1m7eRdrtuxk8ZotrK+s3uu64vwc+oSJd58w8W7aC16cnxPZuPfOxsxiwIPAWUA5MNvMprj74ibV7gMmuPtjZnYGcDdwDbADuNbd3zezPsDbZvaSu29O8GM0mjcBuhTDkPMiC0FE0pOSa5EGZtD/U8Fx3s9h8ZQgwZ76U5h6FxxyLAwaExz9T4bcblFHnDYyMmz3GPHh/Vuus6umjo+3BL3daxp+hufLKiqZ/n4FO5qtApMVM3oX5tG7sKUhKEEyXpCblYAn7BRGAcvcfTmAmU0CLgKaJtdDgW+F51OBZwHc/b2GCu6+xszWEfRuR5NcV1bA0hfgpBshMzuSEEQkfSm5FmlJdtdwouOVwYoD706G5dNg1kPwxgNgMegzHAaNDpLtfp+C7NaHQUj85GbFGFjclYHFLU9CdXe27qwNery3BIn36rAXfM3mnby1YiMfb91FXf2ew08KcjN3J9q7E++iXHoX5tG3KI/SbrlkZ6bEShR9gVVNXpcDJzWrswC4hGDoyMVAgZn1dPcNDRXMbBSQDXwQ33D3451JUF8Lw6+JLAQRSV9KrkVa02MQjLk9OGp2wqpZsHJGsMTX//0WZv4KMrKg7MQg2R44OjjPan0lDkkcM6OwSxaFXbIY2qflbx3q6p1123axZnNjz/faLbt2J+QLyrewcXt1s/tCSX4OfYqCZHvEgO5cf9qgRDxSFG4DHjCzLwHTgdUEY6wBMLPewOPAde5e39INzOwG4AaA/v338TXEwXAPtjsvOxF6Hdnx9xcRaYWSa5H2yMqDQ08PDoCqSlj1ZpBor5gRrDoy7eeQmQv9RgW92gPHBCuUxNJmeEHSimU0DBPJ44QBLW+VvbO6LuztbhiC0piEL/l4azKP4V4N9Gvyuiws283d1xD0XGNm+cDnG8ZVm1k34B/AD9z9zX19iLuPB8YDjBw5soW1Yg5S+WxYvxQu+E2H31pEpC2UXIscjJx8OPzTwQGwczN89EZjsv3aT4PyrK4w4OSgV3vQGOh9fIub10jnl5cd49CSfA4tSbnlGmcDg81sEEFSfQWwxwLRZlYMbAx7pb9HsHIIZpYNPEMw2XFyQqNubu6E4P+3Yy6JNAwRSV9KrkU6Ul4RHHFecABs3wAfzgwS7RXT4ZUfBeU5hcEyf4PGBENJeh2tHeQkUu5ea2a3AC8RLMX3qLsvMrM7gTnuPgUYC9xtZk4wLOTm8PIvAGOAnuGQEYAvufv8RD4DVZXBSj/HXAw5BQn9aBGRBkquReKpa89gU5qhFwWvt33SOF575Qx474WgPK8HDDytcTWS4iFaX1sSzt2fB55vVvbfTc4nA3v1TLv7E8ATcQ+wNYuegepKGH5t1JGISBpTci2SSAWlcOylwQGwpbyxV3vlDFgyJSjPLw2HkITDSLoPUrIt0pp5jwd/mPYbFXUkIpLGlFyLRKmwrHHJP3fYtCJItht6t98NOwm7lTUOIRk4Gor67f++IummYmmwCdRZP9EfoiISKSXXIp2FGfQ4NDhOuC5Itte/DyumhUNIXoQFTwZ1uw8Ke7VPD5LtgtJoYxeJ2twJkJEJx18ZdSQikuaUXIt0VmZQMiQ4Rv0n1NfDusWNvdqL/hYkFBB8FT5oTJBoDxwdjPUWSRe11bBgEgw5F/JLoo5GRNKckmuRZJGRAYccExyfugnq62DtgjDZngHzn4LZDwd1S49pXPav74hgDLe+KpdU9d6LsGM9jNBERhGJnpJrkWSVEQsS574j4NRboa4G1swLhpGsmAFv/wHe+l1Qt0tP6DU0SLpLhwZL//U6MtjmXSTZzXscCnrDYWdGHYmIiJJrkZQRywpWSeg3KtiqvbYKyufAxwvhk3eDISVzH4OaHeEFFmztXnp0kGyXhkf3gdrgRpLH1jWw7BU47ZsQ0680EYmeWiKRVJWZAwNPDY4G9fXBiiTrFsMnixqPJc8B4U7UmXnQ66igh7v0mLDH+2joWhzJY4js1/yJ4PUw/OqoIxERAZRci6SXjAzoeVhwHHVBY3n1DqhYAp8sDhPvd2HpCzCvyb4g+aWNiXbDUXwEZOUm/jlEIPhjcd4TwfyCHodGHY2ICKDkWkQAsrtA3xOCo4E7VK6DdQ093IuD81kPQV1VUMdiQaK+x9CSoVDYX9u5S/x9OBM2rYRxP4g6EhGR3ZRci0jLzIL1swtK4bAzGsvramHj8sZx3J8sgtVzg62nG2QXhENLwoS719Ag6c7rnvjnkNQ193HIKdzzWxgRkYjFNbk2s3OB+4EY8LC7/6zZ+znABOAEYANwubuvNLOBwBJgaVj1TXe/MZ6xikgbxTIb19/mksbyqm2wbknjOO51i2HRX4NVSxp069sk2Q4T756DITM74Y8hSW7nJlj8NxhxDWTlRR2NiMhucUuuzSwGPAicBZQDs81sirsvblLtemCTux9uZlcAPwcuD9/7wN2HxSs+EelgOQWNq5U0cA9Wc2gYx/1J2NP9wVSorwnqZGQFm+CUDm0yvGRokIhrbW7Zl4WTg+FJw6+JOhIRkT3Es+d6FLDM3ZcDmNkk4CKgaXJ9EfDj8Hwy8ICZfpuKpAwzKOwbHIPPaiyvrYYN74fJdji85MM3YOGfG+tkdQ2WBew+MFgysOG8+yAo6heshiLpa+4EOORY6KM+GBHpXOKZXPcFVjV5XQ6ctK867l5rZluAhn2bB5nZPGAr8EN3nxHHWEUkkTKzG4eFcFlj+c5NjUNLNnwQTFbbuBw+eA1qdza5gUFhWZhsDwgS7t1J+KBgbLf+Tk9daxfAx+/AefdGHYmIyF4664TGtUB/d99gZicAz5rZ0e6+tWklM7sBuAGgf//+EYQpIh0qrzsMOCU4mnKHyk9g44og4d60Mlive9NKeO+fsH3dnvVzurXQ6x3+LOynzUaS3dzHIZYDx13Wel0RkQSL52+Y1UC/Jq/LwrKW6pSbWSZQCGxwdweqANz9bTP7ABgCzGl6sbuPB8YDjBw50uPxECLSCZhBwSHBMeDkvd+v3g6bPmxMuBuS8HVL4L0Xoa66yb1iwbCSpgl30yQ8tzARTyQHqmYnLHw6WCFEq8+ISCcUz+R6NjDYzAYRJNFXAFc1qzMFuA54A7gUeM3d3cxKgI3uXmdmhwKDgeVxjFVEkll213BC5NC936uvg21r90y6G5LwJVNgx4Y96+f1aHmcd/eB0K1PSm8N34YVngYAjwIlwEbgancvD997EfgUMNPdz49bkEueg11bglVCREQ6obgl1+EY6luAlwga6kfdfZGZ3QnMcfcpwCPA42a2jKChviK8fAxwp5nVAPXAje6+MV6xikgKy4gF47MLy2DgaXu/v2vLnr3eDUn46rnBUm/1tY11Y9lQ1H/PhLtpEp7dNRFPFBdtXOHpPmCCuz9mZmcAdwMNWe69QBfgv+Ia6LwJUDQABo6J68eIiByouA48dPfngeeblf13k/Nd7DGbaXf5X4C/xDM2EREgGAbS+7jgaK6uFraWt9zrvWo2VG3Zs37XXjDkHLjogQQE3uHassLTUOBb4flU4NmGN9z9VTMbG9cIN66AFdNh3A+1A6iIdFqa1SMisi+xzMZe6UPH7v3+jo17T7AsTNrJ1W1Z4WkBwc5B9wMXAwVm1tPdm42tiZOanXD4WTCs+QhDEZHOQ8m1iMiB6tIjOPqOiDqSRLmNYD+CLwHTCebT1LXnBge1ylPpULh6cvuuERFJMH2vJiIi0IYVntx9jbtf4u7DgR+EZZvb8yHuPt7dR7r7yJKSkoONWUSk01FyLSIi0GSFJzPLJphgPqVpBTMrNrOG3xvfI1g5REREmlByLSIiuHst0LDC0xLg6YYVnszswrDaWGCpmb0HlAJ3NVxvZjOAPwNnmlm5mZ2T0AcQEekkNOZaRESANq3wNBlocdCzu4+Ob3QiIslBPdciIiIiIh1EybWIiIiISAdRci0iIiIi0kGUXIuIiIiIdBBz96hj6BBmVgF8eACXFgPrOzicREjWuCF5Y1fciZVucQ9w97Ra+FntdtJQ3ImluBOrw9vslEmuD5SZzXH3kVHH0V7JGjckb+yKO7EUt+xLsv4bK+7EUtyJpbgbaViIiIiIiEgHUXItIiIiItJBlFzD+KgDOEDJGjckb+yKO7EUt+xLsv4bK+7EUtyJpbhDaT/mWkRERESko6jnWkRERESkg6R1cm1m55rZUjNbZmbfjTqetjCzR81snZm9G3Us7WFm/cxsqpktNrNFZnZr1DG1hZnlmtksM1sQxv0/UcfUHmYWM7N5ZvZc1LG0lZmtNLOFZjbfzOZEHU97mFmRmU02s3+b2RIzOznqmFJJMrbZkJztttrsaCRjmw3J227Hq81O22EhZhYD3gPOAsqB2cCV7r440sBaYWZjgEpggrsfE3U8bWVmvYHe7j7XzAqAt4HPJcG/twFd3b3SzLKAmcCt7v5mxKG1iZl9CxgJdHP386OOpy3MbCUw0t2Tbr1UM3sMmOHuD5tZNtDF3TdHHVcqSNY2G5Kz3VabHY1kbLMhedvteLXZ6dxzPQpY5u7L3b0amARcFHFMrXL36cDGqONoL3df6+5zw/NtwBKgb7RRtc4DleHLrPBIir9IzawM+CzwcNSxpAMzKwTGAI8AuHu1EusOlZRtNiRnu602O/HUZidWPNvsdE6u+wKrmrwuJwkajlRgZgOB4cBb0UbSNuHXdPOBdcDL7p4UcQO/Bu4A6qMOpJ0c+KeZvW1mN0QdTDsMAiqAP4Rf6z5sZl2jDiqFqM2OiNrshEnWNhuSs92OW5udzsm1RMDM8oG/AN9w961Rx9MW7l7n7sOAMmCUmXX6r3XN7Hxgnbu/HXUsB+A0dx8BnAfcHH6lngwygRHA79x9OLAdSJpxwSItUZudGEneZkNytttxa7PTObleDfRr8rosLJM4Cce//QWY6O5/jTqe9gq/LpoKnBt1LG1wKnBhOA5uEnCGmT0RbUht4+6rw5/rgGcIhgMkg3KgvEkv2WSChls6htrsBFObnVBJ22ZD0rbbcWuz0zm5ng0MNrNB4SD2K4ApEceUssJJJo8AS9z9l1HH01ZmVmJmReF5HsFkqn9HG1Xr3P177l7m7gMJ/tt+zd2vjjisVplZ13DyFOHXc2cDSbHCgrt/DKwysyPCojOBTj35K8mozU4gtdmJlaxtNiRvux3PNjuzI26SjNy91sxuAV4CYsCj7r4o4rBaZWZPAWOBYjMrB37k7o9EG1WbnApcAywMx8IBfN/dn48wprboDTwWrlSQATzt7km1RFKSKQWeCX6vkwk86e4vRhtSu3wNmBgmf8uBL0ccT8pI1jYbkrbdVpstbZXM7XZc2uy0XYpPRERERKSjpfOwEBERERGRDqXkWkRERESkgyi5FhERERHpIEquRUREREQ6iJJrEREREZEOouRa0oKZ1ZnZ/CZHh+2cZ2YDzazTr+kpIpIs1GZLMkvbda4l7ewMt8MVEZHOT222JC31XEtaM7OVZnaPmS00s1lmdnhYPtDMXjOzd8zsVTPrH5aXmtkzZrYgPE4JbxUzs4fMbJGZ/TPcGQwz+7qZLQ7vMymixxQRSQlqsyUZKLmWdJHX7CvGy5u8t8XdjwUeAH4dlv0WeMzdjwMmAr8Jy38DTHP344ERQMMOcYOBB939aGAz8Pmw/LvA8PA+N8br4UREUozabEla2qFR0oKZVbp7fgvlK4Ez3H25mWUBH7t7TzNbD/R295qwfK27F5tZBVDm7lVN7jEQeNndB4evvwNkuftPzexFoBJ4FnjW3Svj/KgiIklPbbYkM/Vci4Dv47w9qpqc19E4n+GzwIMEPSazzUzzHEREDo7abOnUlFyLwOVNfr4Rnv8fcEV4/kVgRnj+KnATgJnFzKxwXzc1swygn7tPBb4DFAJ79cSIiEi7qM2WTk1/kUm6yDOz+U1ev+juDUs7dTezdwh6Mq4My74G/MHMbgcqgC+H5bcC483seoLejpuAtfv4zBjwRNiYG/Abd9/cYU8kIpK61GZL0tKYa0lr4fi9ke6+PupYRERk/9RmSzLQsBARERERkQ6inmsRERERkQ6inmsRERERkQ6i5FpEREREpIMouRYRERER6SBKrkVEREREOoiSaxERERGRDqLkWkRERESkg/x/w1yeW4gJWxYAAAAASUVORK5CYII=\n","text/plain":["
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\n","text/plain":["
"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"vBWsbYzPQYzR","colab_type":"code","colab":{}},"source":[""],"execution_count":null,"outputs":[]}]} \ No newline at end of file +{ + "nbformat": 4, + "nbformat_minor": 0, + "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": "3.6.8" + }, + "colab": { + "name": "Activation-Functions(GELU,SELU,ELU,LeakyReLU,PRELU).ipynb", + "provenance": [], + "collapsed_sections": [] + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "IVla6vrH5ScY", + "colab_type": "text" + }, + "source": [ + "# Custom Layers in Keras" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "RYiQ_D825ScZ", + "colab_type": "text" + }, + "source": [ + "# Task 1: Importing Libraries" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "zYY9HmiSS0Rv", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 120 + }, + "executionInfo": { + "status": "ok", + "timestamp": 1595982638698, + "user_tz": -330, + "elapsed": 31262, + "user": { + "displayName": "Agrover112", + "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiMJACGAX3kCfRjB2hgzdG8w9zL1lAAKbPPMz0qLA=s64", + "userId": "09574164879083471944" + } + }, + "outputId": "65bff92a-e408-4ab5-b12a-148c5a501d15" + }, + "source": [ + "from google.colab import drive\n", + "\n", + "drive.mount(\"/content/drive\")" + ], + "execution_count": 1, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n", + "\n", + "Enter your authorization code:\n", + "··········\n", + "Mounted at /content/drive\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "VG_GYD1B2TrE", + "colab_type": "code", + "colab": {}, + "executionInfo": { + "status": "ok", + "timestamp": 1595982645364, + "user_tz": -330, + "elapsed": 3345, + "user": { + "displayName": "Agrover112", + "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiMJACGAX3kCfRjB2hgzdG8w9zL1lAAKbPPMz0qLA=s64", + "userId": "09574164879083471944" + } + } + }, + "source": [ + "import os\n", + "\n", + "os.chdir(\"/content/drive/My Drive/Colab Notebooks/src\")" + ], + "execution_count": 2, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "BHnBhBf8S9u9", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 33 + }, + "executionInfo": { + "status": "ok", + "timestamp": 1595982650881, + "user_tz": -330, + "elapsed": 8846, + "user": { + "displayName": "Agrover112", + "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiMJACGAX3kCfRjB2hgzdG8w9zL1lAAKbPPMz0qLA=s64", + "userId": "09574164879083471944" + } + }, + "outputId": "5e471a5f-4616-46e3-e389-90d88ae6cb8b" + }, + "source": [ + "!ls" + ], + "execution_count": 3, + "outputs": [ + { + "output_type": "stream", + "text": [ + "'Custom Activations using Layers - Complete.ipynb' utils\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "hIhyYtyaTCCt", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 33 + }, + "executionInfo": { + "status": "ok", + "timestamp": 1595964076958, + "user_tz": -330, + "elapsed": 922, + "user": { + "displayName": "Agrover112", + "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiMJACGAX3kCfRjB2hgzdG8w9zL1lAAKbPPMz0qLA=s64", + "userId": "09574164879083471944" + } + }, + "outputId": "4bf85e55-469c-4827-fe89-909a841b165b" + }, + "source": [ + "# %cd \"./utils\"" + ], + "execution_count": 12, + "outputs": [ + { + "output_type": "stream", + "text": [ + "/content/drive/My Drive/Colab Notebooks/src/utils\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "EGhe8MIf4UOI", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 33 + }, + "executionInfo": { + "status": "ok", + "timestamp": 1595964217899, + "user_tz": -330, + "elapsed": 2849, + "user": { + "displayName": "Agrover112", + "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiMJACGAX3kCfRjB2hgzdG8w9zL1lAAKbPPMz0qLA=s64", + "userId": "09574164879083471944" + } + }, + "outputId": "b83b4217-00b8-47bf-bad8-3a5d0b745765" + }, + "source": [ + "#!ls" + ], + "execution_count": 18, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Utils utils.py\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "hT6lcs952iXc", + "colab_type": "code", + "colab": {}, + "executionInfo": { + "status": "ok", + "timestamp": 1595982667751, + "user_tz": -330, + "elapsed": 4552, + "user": { + "displayName": "Agrover112", + "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiMJACGAX3kCfRjB2hgzdG8w9zL1lAAKbPPMz0qLA=s64", + "userId": "09574164879083471944" + } + } + }, + "source": [ + "%run './utils/utils.py'" + ], + "execution_count": 4, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "PaX7BQU15ScZ", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 33 + }, + "executionInfo": { + "status": "ok", + "timestamp": 1595982672373, + "user_tz": -330, + "elapsed": 1183, + "user": { + "displayName": "Agrover112", + "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiMJACGAX3kCfRjB2hgzdG8w9zL1lAAKbPPMz0qLA=s64", + "userId": "09574164879083471944" + } + }, + "outputId": "2443cde6-069d-4925-cf72-b152a9d27fd8" + }, + "source": [ + "import tensorflow as tf\n", + "\n", + "# import utils\n", + "import matplotlib.pyplot as plt\n", + "\n", + "%matplotlib inline\n", + "\n", + "print(\"TensorFlow Version:\", tf.__version__)" + ], + "execution_count": 5, + "outputs": [ + { + "output_type": "stream", + "text": [ + "TensorFlow Version: 2.2.0\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "okH6xfqU5Scd", + "colab_type": "text" + }, + "source": [ + "# Task 2: Import and Visualize Dataset" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "IX9JVAVt5Sce", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 322 + }, + "executionInfo": { + "status": "ok", + "timestamp": 1595982674797, + "user_tz": -330, + "elapsed": 2644, + "user": { + "displayName": "Agrover112", + "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiMJACGAX3kCfRjB2hgzdG8w9zL1lAAKbPPMz0qLA=s64", + "userId": "09574164879083471944" + } + }, + "outputId": "6b22a4f8-3fa7-4321-b8bf-d2dd1e7858fc" + }, + "source": [ + "(x_train, y_train), (x_test, y_test) = load_data()\n", + "\n", + "plot_random_examples(x_train, y_train).show()" + ], + "execution_count": 6, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n", + "11493376/11490434 [==============================] - 0s 0us/step\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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+R0TqiUgvL+7Vi7ZVCMKLezfKucSniYg0FJEOXtyrE22rEBTvZlZ6QEQ+jLoR6ZD1yY8f830/5h8/Xyxz/j9meWeuJiKyy4/5//Rj/v+JyKsi0jniNiGYG0RkvR/zT/ox/98islpE7oy4TQiOdzOLeHGvpojcISIzom5LOmT9sJeI+j+STSJSR0Sm+DF/fcRNQnBXi8gerbxXRJpG1BYUzTYRedKLe5VF5JSI3C7nutiRmXg3s8tEEfm9iJSPuiHpkPU9PyIifsz/1o/5jUSkpog0Od/dDiBCfsz/UETGiMhyEfmriLwvIt9G2igA4sW9DiLytR/zN0XdlnRxIvn5Dz/mHxaRVSLSPuq2ILAvROQarVzz/K8hA/kxf6Yf82/yY34LEfmXiOyMuk0IjHczezQXkU5e3MuXc8OXrby4NyfaJoUr64e9vLiXIyJn/Jh/2It75USkjZz7v01kpg0i8j0v7l0v536w9hSRu6NtEoLy4l5VP+Z/7cW9a+XcfB8WI2Qu3s0s4cf8R0TkERERL+79VESG+jG/T6SNCpkLPT/VRWSVF/c+kHMv55t+zF8ccZsQ0PmJsfeLyN/k3CqE1/2Yvz3aVqEI/uzFvR0iskhEBp3vnUUG4t1EJuF4CwAA4BQXen4AAAAUkh8AAOAUkh8AAOAUkh8AAOAUkh8AAOAUkh8AAOAUkh8AAOAUkh8AAOAUkh8AAOAUkh8AAOAUkh8AAOAUkh8AAOAUkh8AAOAUkh8AAOAUkh8AAOAUkh8AAOAUkh8AAOAUkh8AAOAUkh8AAOAUkh8AAOAUkh8AAOAUkh8AAOAUkh8AAOAUkh8AAOAUkh8AAOAUkh8AAOAUkh8AAOAUkh8AAOAUkh8AAOAUkh8AAOAUkh8AAOAUkh8AAOAUkh8AAOAUkh8AAOAUkh8AAOAUkh8AAOAUkh8AAOAUkh8AAOAUkh8AAOCU0oW5uEqVKn6tWrXS1BRcTH5+vhw4cMAL4148y2iF+SxFeJ5R493MHjzL7LJp06YDvu/n2L9eqOSnVq1asnHjxvBahULJzc0N7V48y2iF+SxFeJ5R493MHjzL7OJ53ucF/TrDXgAAwCkkPwAAwCkkPwAAwCkkPwAAwCkkPwAAwCkkPwAAwCkkPwAAwCkkPwAAwCkkPwAAwCkkPwAAwCkkPwAAwCmFOtsrqEOHDqm4cuXKRl21atVU3Ldv35TvOXjwYBWXKVPGqLvkkktUXKFChZTviczx0EMPGeVjx46pOC8vz6grX758sbQJgPkuioh07txZxR06dDDq7PcYKC70/AAAAKeQ/AAAAKcUy7DXFVdcoeK77rrLqNuyZYuK58yZY9Tt27cv4T3Hjh2bsK5ixYoqfuCBB1T8+9//3riuXLlyCe+BkufUqVMqXr16tVG3efNmFT/xxBNGHcNe4Vu/fr2K//GPfxh127ZtS+ke+vu9cOFCo+6+++5Tce3atY263r17q7hq1aopfReKT+vWrY3ypk2bVGz/TGfYK3scOHBAxS+//LJR98knn6h47dq1Rt1XX32l4jvvvNOoe+aZZ1SsT2cJAz0/AADAKSQ/AADAKSQ/AADAKcUy5+fSSy9V8dy5cxNep4/9iZhjg/a8guXLl6t4w4YNRt3hw4dVHI/HVfzFF18Y1+njicz/Kfn0uQP6HB+k36xZs4zyww8/rOL9+/cbdb7vq9jzvJTub1+nzxlo166dUTdt2jQV5+bmGnX6FhiNGzdO6bsRro0bNxrlVP8OoOTZuXOnUZ48ebKKP/74Y6NOn9eTn58f6PumTJlilCdMmKBi5vwAAAAUAckPAABwSrEMe6XqqquuMsr6sjd7CZw+nGUve162bJmKZ8+ereIZM2YY11WvXr3A+wEQOX36tIpHjhxp1NlDXTp9OPmzzz5LWHfbbbepWB9GEzG7uFu0aGHU/fWvf1WxvX2Ffq3dRoa2gYKdPXtWxa+++qqKhwwZYlz35ZdfJryHftJCx44djbo6deok/Jw+HWXevHkXb2xI6PkBAABOIfkBAABOIfkBAABOKVFzfgqjVKkLedutt95q1OllfezRPlrj2WefVXGbNm2MultuuSWUdgKZaurUqSq2t4nQ2UvRf/Ob36h47969Rt26detUPHPmTBXXrFkz5Xa1b99exSdOnDDq+vfvr2L7HX7qqadUbL/vKJrXXnst6iagEOxjRu6//34VL1iwQMU5OTnGdfq/rT169DDq+vXrp2J9exsRkbffflvFixYtMuqefvppFdvvrD6PKGz0/AAAAKeQ/AAAAKdk7LBXqn7yk5+ouGfPnkadvlvl/PnzjTqGvTJLt27dVFylSpUIW5I93njjjZSu69y5c8I6ezhryZIlKv7Od74TrGGan/3sZ0b5ww8/VLG9PL979+4q/uc//2nUVapUqchtcdmKFSuibgIuYs2aNSrWh6hEzC0p7rnnHhUPHz7cuO76668P9N1Lly5VsX3i+5gxY1SsD5mnGz0/AADAKSQ/AADAKSQ/AADAKVk/50fXunVro6zP+UHJt3bt2oR1+lEJYZ/+6yr9dHY9tunz6i4mjHk+yTz22GMqHjFihFF35MgRFU+aNMmoi8ViaW1Xtnv99ddTuu6GG25Ic0vwH/qRFSLmvBt7C4rx48erWF/2bi9ZT0b/GaHPGxIxj5myj7GKCj0/AADAKSQ/AADAKRk77KV3sX311VdGnb6cdvv27Sq2l7cisyQb9irM0AtSo5/ErC+Ttf3lL38xyvXq1Utbmy5m165dKvY8z6jTy7Vr1y62NmWrHTt2qPjMmTMpfSbZtggIlz20qy8p79Spk1Fnn96eiL6jur0Vhl7WT4YXMbeS0HdajxI9PwAAwCkkPwAAwCkkPwAAwCkZM+fH3j5dXzo3Z86cIt9/+fLlRvlXv/qViu0xynQv1wVKgmeffVbF9qnuf/vb31T85JNPGnUPPvigisuVK5em1p2Tn59vlJOd1p6bm6virl27pqtJzli5cqWKv/nmm4TXXXHFFSpmqXvx0Y96saW6HchLL71klCdOnKjiDz74IOW2dOnSRcV9+vRJ+XPpRM8PAABwCskPAABwSsYMe40ePdoov/XWW6Hev3z58kZ55syZKl6/fr1Rp+8M3aJFi1DbAdPu3btV/N5770XYEvfou7vaw0T6sNepU6eMOn0Z7dy5c426ihUrFvhd9rCavqR23759Rt2GDRtUvGzZMqNO//vStGlTo05fiqsPxSC92rdvr2L7mSAaixcvNsrPP/+8ivUT3vWhbxHzXS9TpoxRp5+gYO+uXr9+/eCNTRN6fgAAgFNIfgAAgFMyZtgrDPaBatOmTVNxu3btjLp169apuH///kbdwIEDVWx37919991FbicuOH78uIrtnbxRfOx3oGzZsir+xS9+YdTpq4AaN25s1CUabvrXv/5llPXu9cOHDxeusefpO9qKiOTk5AS6DwqmT0VIdgguu69Hw34v9V3aP/roI6PuvvvuS+me119/vYr1FdciIrfcckthmxgpen4AAIBTSH4AAIBTSH4AAIBTMmbOz6OPPmqUr7nmGhX/6U9/Mur0ZXX63IT58+cnvIetZcuWKn7nnXeMugEDBqhYn/8jYp4c3atXr4T3BzJJ6dLmj4q+ffuqWJ8HIGK+O/YOzKnS543UqlXLqNPnA3399dcJ76HvKitiblnxve99L1C7cIH+s06Pk12H4mNvK/Db3/5WxanO8bENGjRIxZk2x8dGzw8AAHAKyQ8AAHBKxgx76btHioi0atVKxWPHjjXqLr/8chWfOXNGxUEPJK1WrZpRzsvLU3Hbtm2NOn154c0332zU2d33uDh7h1GUPAsXLjTKyYZDfvjDH6q4e/fuKr7xxhuN6ypVqqTihg0bGnX60nd7p1p9yHvJkiVGXY8ePVTcoUMHo27UqFGC5PQdskVEjh07FlFLEIS+c3NQ+s9jfSd3kcwbSqbnBwAAOIXkBwAAOIXkBwAAOCVj5vzY9LkE9rEV6fbd735XxXXq1DHqPv/8cxXbJ8/369cvnc3KSidPnizw1zt27GiUf/CDHxRHc3De22+/reIpU6YkvE4/GV7EPIX96quvDvTd+snw999/f8Lr7Ll6kydPVvGuXbuMup49e6q4Xr16gdqV7X73u98Z5UTvJkoG/fgmEZFZs2ap2J5/+tRTT6n4z3/+s4oXLFhgXKdvXdGmTRujbvv27SpOdIxNSULPDwAAcArJDwAAcErGDntFSV/qrp9gbdNPhhdh2CtMFSpUMMrsIlu8li5dquJ///vfCa+ztyoIOtQVxPe//32jrG89sXr1aqNu4sSJKraHC1A4devWNcr2EDWKx6ZNm4yyvgXM8uXLjTp9mbq+JcTUqVON6+69914V61M87O9r0aJFgBYXL3p+AACAU0h+AACAU0h+AACAU5jzk4IdO3YY5eeeey6lzzHWXXjHjx83ylu3bi3wuoEDBxZHc5DAZZddltJ1tWvXTnNLwmEvfUdwffv2NcrXXXddRC1xz7fffqvi9evXG3WNGzdWcapHUdg/Z/XjovRT4kVE1q5dq2Lm/AAAAJQwJD8AAMApJWrY6+DBg0ZZP71Z784T+e/llEW1Z88eo6wPddldf7t3707pnjfccEPRG+aYK6+80ijrp3Rv3ry5uJuDBNq3b6/iP/7xj0bd6dOnVTxgwACjTn+G9rNON9/3C4yRmP5zMdVT3D/66KN0NQcXof87uWXLFqNOP5kgVfYWIr1791bxpEmTjLo33nhDxY888kihv6u40fMDAACcQvIDAACcQvIDAACcUqLm/Njzbnr16qViff6PiHkq7ZAhQwJ937Zt21Ssn3grkvq8nqpVqxrl6dOnq7g4t/J3zdixY42yfnr4j370o+JujnOaNGmiYnvrh1/+8pcq/vTTT426J598UsX2XKGwHTlyxCgfOHBAxfZchi5duqS1LZnqyy+/VPGpU6dS+sycOXOMsj73ZPbs2UZdgwYNitA6FIY+Xycofam7Hov897te0tHzAwAAnELyAwAAnFKihr0aNWpklJ955hkV/+EPfzDq9N0ku3fvntZ22UtyH3zwQRX/+te/Nupq1KiR1ra4JlG3+KJFi4xy5cqVVfzSSy+ltU0w9e/f3yjrwxwvvPCCUTd+/HgV79u3T8XDhw83rrv22mtVXK5cuUDt6ty5s1HWt69o2bKlUXfbbbcF+o5spw8h20P8R48eLfAzN998s1F+9NFHVcwwV3Q2bNig4ptuusmoO3nypIpnzJiR8B76MKh9qnteXl5Rm1is6PkBAABOIfkBAABOIfkBAABOKVFzfmxt27ZVsT1Gr2+1fscddxh1+timzj7J9s4771Rxbm6uUdeqVSsVlypl5ogVKlRI1myESF+eqc/zadasmXHdvffeW2xtQnITJ05UcdeuXY26cePGqXj58uUqtpdH6/P/6tSpY9Tpc4zs09j17SvWrFlj1F111VUFtkNEpF69eoLkfvzjHxtl/c++efPmKl6xYoVxXdmyZdPbMKRkzJgxKrb//utzVffu3ZvS/apVq2aU7aNsSjp6fgAAgFNIfgAAgFNK9LCXTt/B1y6vX7++uJuDYqIPVaxatSrCliAIe7haL+vvrT3sNWXKFBW///77Rt28efMSfl/9+vVVPGLECKNO75avWbNmsmajAPYWEmwpUfKUKVNGxaNHjzbq9O0kzp49a9TpQ1369JB27doZ1+lTUeyd9O0tYUo6en4AAIBTSH4AAIBTSH4AAIBTMmbOD4Ds0rRp0wJjEZHJkycXd3OAjOd5noofeeQRo84uu46eHwAA4BSSHwAA4BSSHwAA4BSSHwAA4BSSHwAA4BSSHwAA4BSSHwAA4BSSHwAA4BSSHwAA4BTP9/3UL/a8/SLyefqag4u4zvf9nDBuxLOMXGjPUoTnWQLwbmYPnmV2KfB5Fir5AQAAyHQMewEAAKeQ/AAAAKeQ/AAAAKeUjroB6ebFvctEZI2IXCrnfr/z/Jgfi7ZVCMqLey+KSAcR+dqP+TdG3R4UjRf3BovIPSLii8hWEenvx/zT0bYKQfBuZpdsfzdd6Pn5RkRa+TG/oYg0EpH2XtxrFnGbENwsEWkfdSNQdF7cu1pEfisiuef/sbxEROxTP4wAAAF2SURBVHpG2yoUwSzh3cwKLrybWZ/8+DHf92P+8fPFMuf/Y4lbhvJj/hoRORR1OxCa0iJSzot7pUXkchHZF3F7EBDvZtbJ6ncz65MfEREv7l3ixb33ReRrEXnTj/nro24T4Do/5n8hIuNFZLeI/K+IHPFj/vJoWwXAhXfTieTHj/nf+jG/kYjUFJEmXtxjPBqImBf3KolIZxG5XkRqiMgVXtzrE22rALjwbjqR/PyHH/MPi8gqYVwaKAluE5HP/Ji/34/5Z0Rkvoj8OOI2AXDg3XRhtVeOiJzxY/5hL+6VE5E2IjIm4mYBONel3syLe5eLyCkRaS0iG6NtEgBx4N10oeenuois8uLeByKyQc7N+VkccZsQkBf3XhGRdSLyfS/u7fXi3i+jbhOCOT/3bp6IbJZzS2lLici0SBuFwHg3s4cL7yZnewEAAKe40PMDAACgkPwAAACnkPwAAACnkPwAAACnkPwAAACnkPwAAACnkPwAAACn/D+4r6yTyEuNPQAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "c-Ao0bQN5Sch", + "colab_type": "text" + }, + "source": [ + " # Creating a Custom Activations" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "3WeVEPWjEoDH", + "colab_type": "text" + }, + "source": [ + "# One way to create activations by inheriting keras.layers.Layer class" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Nuo4vm3aKI0X", + "colab_type": "text" + }, + "source": [ + "Most parameters of activation functions are derived from SELU paper" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "Z8I7oinq5Sci", + "colab_type": "code", + "colab": {}, + "executionInfo": { + "status": "ok", + "timestamp": 1595985710725, + "user_tz": -330, + "elapsed": 770, + "user": { + "displayName": "Agrover112", + "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiMJACGAX3kCfRjB2hgzdG8w9zL1lAAKbPPMz0qLA=s64", + "userId": "09574164879083471944" + } + } + }, + "source": [ + "# For the absolute sake of simplicity I do not create a file and add these in them\n", + "\n", + "# LEAKY RELU\n", + "\"\"\"\n", + "Avoids dead relu problem,however no alpha value is learned :/\n", + "\"\"\"\n", + "\n", + "\n", + "class leakyrelu(tf.keras.layers.Layer):\n", + " def __init__(self, **kwargs):\n", + " super(leakyrelu, self).__init__(**kwargs)\n", + "\n", + " def build(self, input_shape):\n", + " # self.alpha = self.add_weight(name='minimum', shape=(1,),initializer='zeros',trainable=False)\n", + " super(leakyrelu, self).build(input_shape)\n", + "\n", + " def call(self, x):\n", + " alpha = tf.constant(\n", + " [0.01], shape=(1,), name=\"alpha\"\n", + " ) # chosing an arbitrary alpha value\n", + " return tf.maximum(0.0, x) + alpha * tf.minimum(0.0, x)\n", + "\n", + "\n", + "# PARAMETRIC RELU\n", + "\"\"\"\n", + "Like Leaky ReLu but alpha parameter is a learnable paramter :D\n", + "\"\"\"\n", + "\n", + "\n", + "class ParametricRelu(tf.keras.layers.Layer):\n", + " def __init__(self, **kwargs):\n", + " super(ParametricRelu, self).__init__(**kwargs)\n", + "\n", + " def build(self, input_shape):\n", + " self.alpha = self.add_weight(\n", + " name=\"minimum\", shape=(1,), initializer=\"zeros\", trainable=True\n", + " )\n", + " super(ParametricRelu, self).build(input_shape)\n", + "\n", + " def call(self, x):\n", + " return tf.maximum(0.0, x) + self.alpha * tf.minimum(0.0, x)\n", + "\n", + "\n", + "# ELU\n", + "\n", + "\n", + "class Elu(tf.keras.layers.Layer):\n", + " def __init__(self, **kwargs):\n", + " super(Elu, self).__init__(**kwargs)\n", + "\n", + " def build(self, input_shape):\n", + " self.alpha = self.add_weight(\n", + " name=\"minimum\", shape=(1,), initializer=\"ones\", trainable=True\n", + " )\n", + " super(Elu, self).build(input_shape)\n", + "\n", + " def call(self, x):\n", + " return tf.maximum(0.0, x) + self.alpha * (\n", + " tf.exp(tf.minimum(0.0, x)) - 1\n", + " )\n", + "\n", + "\n", + "# SELU\n", + "\"\"\"\n", + "Scaled Elu\n", + "Note: Requires weight_init:'lecun_normal for self-normalization and AlphaDroput.\n", + "\"\"\"\n", + "\n", + "\n", + "class sElu(tf.keras.layers.Layer):\n", + " def __init__(self, **kwargs):\n", + " super(sElu, self).__init__(**kwargs)\n", + "\n", + " def build(self, input_shape):\n", + " # self.alpha = self.add_weight( name='minimum', shape=(1,),initializer='lecun_normal',trainable=True)\n", + " super(sElu, self).build(input_shape)\n", + "\n", + " def call(self, x): # Values from original author\n", + " l = tf.constant(\n", + " [1.0507009873554804934193349852946], name=\"lambda\"\n", + " ) # Scaling factors as derived by authors\n", + " a = tf.constant(\n", + " [1.6732632423543772848170429916717], name=\"alpha\"\n", + " ) # Alpha as derived by authors\n", + "\n", + " return l * (tf.maximum(0.0, x) + (a * tf.exp(tf.minimum(0.0, x) - a)))" + ], + "execution_count": 54, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "6tnlYfNE5Scl", + "colab_type": "text" + }, + "source": [ + "# Task 4: Creating the Model" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "4zKiptyZ5Scn", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 251 + }, + "executionInfo": { + "status": "ok", + "timestamp": 1595985716037, + "user_tz": -330, + "elapsed": 1061, + "user": { + "displayName": "Agrover112", + "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiMJACGAX3kCfRjB2hgzdG8w9zL1lAAKbPPMz0qLA=s64", + "userId": "09574164879083471944" + } + }, + "outputId": "a611f535-ec43-4e70-d2de-42dc003a237a" + }, + "source": [ + "def create_model(use):\n", + " model = tf.keras.models.Sequential()\n", + " if use == \"selu\":\n", + " winit = \"lecun_normal\" # As required by SELU authors\n", + " else:\n", + " winit = \"glorot_uniform\"\n", + "\n", + " model.add(\n", + " tf.keras.layers.Dense(64, input_shape=(784,), kernel_initializer=winit)\n", + " )\n", + " if use == \"relu\":\n", + " model.add(tf.keras.layers.ReLU())\n", + " elif use == \"prelu\":\n", + " model.add(ParametricRelu())\n", + " elif use == \"leakyrelu\":\n", + " model.add(leakyrelu())\n", + " elif use == \"elu\":\n", + " model.add(Elu())\n", + " elif use == \"selu\":\n", + " model.add(sElu())\n", + "\n", + " model.add(\n", + " tf.keras.layers.Dense(\n", + " 10, activation=\"softmax\", kernel_initializer=winit\n", + " )\n", + " )\n", + " model.compile(\n", + " loss=\"categorical_crossentropy\", optimizer=\"adam\", metrics=[\"accuracy\"]\n", + " )\n", + " return model\n", + "\n", + "\n", + "model = create_model(use=\"elu\")\n", + "model.summary()" + ], + "execution_count": 55, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Model: \"sequential_11\"\n", + "_________________________________________________________________\n", + "Layer (type) Output Shape Param # \n", + "=================================================================\n", + "dense_22 (Dense) (None, 64) 50240 \n", + "_________________________________________________________________\n", + "elu_3 (Elu) (None, 64) 1 \n", + "_________________________________________________________________\n", + "dense_23 (Dense) (None, 10) 650 \n", + "=================================================================\n", + "Total params: 50,891\n", + "Trainable params: 50,891\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "yNWrD33-_aCB", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 33 + }, + "executionInfo": { + "status": "ok", + "timestamp": 1595967574676, + "user_tz": -330, + "elapsed": 951, + "user": { + "displayName": "Agrover112", + "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiMJACGAX3kCfRjB2hgzdG8w9zL1lAAKbPPMz0qLA=s64", + "userId": "09574164879083471944" + } + }, + "outputId": "292a8b0a-17ec-4f89-c6ce-52d5264f7c0c" + }, + "source": [ + "# model.layers[1].get_weights()" + ], + "execution_count": 73, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "[array([0.], dtype=float32)]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 73 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "QpVxpls2JYJt", + "colab_type": "text" + }, + "source": [ + "# ELU" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "g_5Hen4s5Scr", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 301 + }, + "executionInfo": { + "status": "ok", + "timestamp": 1595985748402, + "user_tz": -330, + "elapsed": 28499, + "user": { + "displayName": "Agrover112", + "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiMJACGAX3kCfRjB2hgzdG8w9zL1lAAKbPPMz0qLA=s64", + "userId": "09574164879083471944" + } + }, + "outputId": "fdd6b0d8-5b90-4000-e3f6-4422371793af" + }, + "source": [ + "import time\n", + "\n", + "start = time.time()\n", + "print(\"Initial alpha:\", model.layers[1].get_weights())\n", + "\n", + "h = model.fit(x_train, y_train, validation_data=(x_test, y_test), epochs=7)\n", + "\n", + "print(\"Final alpha:\", model.layers[1].get_weights())\n", + "print(time.time() - start)" + ], + "execution_count": 56, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Initial alpha: [array([1.], dtype=float32)]\n", + "Epoch 1/7\n", + "1875/1875 [==============================] - 4s 2ms/step - loss: 0.3257 - accuracy: 0.9067 - val_loss: 0.2008 - val_accuracy: 0.9393\n", + "Epoch 2/7\n", + "1875/1875 [==============================] - 4s 2ms/step - loss: 0.1680 - accuracy: 0.9504 - val_loss: 0.1427 - val_accuracy: 0.9590\n", + "Epoch 3/7\n", + "1875/1875 [==============================] - 4s 2ms/step - loss: 0.1174 - accuracy: 0.9650 - val_loss: 0.1066 - val_accuracy: 0.9683\n", + "Epoch 4/7\n", + "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0910 - accuracy: 0.9724 - val_loss: 0.1000 - val_accuracy: 0.9717\n", + "Epoch 5/7\n", + "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0731 - accuracy: 0.9781 - val_loss: 0.0890 - val_accuracy: 0.9724\n", + "Epoch 6/7\n", + "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0620 - accuracy: 0.9809 - val_loss: 0.0840 - val_accuracy: 0.9741\n", + "Epoch 7/7\n", + "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0513 - accuracy: 0.9842 - val_loss: 0.0844 - val_accuracy: 0.9748\n", + "Final alpha: [array([1.0048169], dtype=float32)]\n", + "27.67535638809204\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "AquGwa2Z5Scu", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 279 + }, + "executionInfo": { + "status": "ok", + "timestamp": 1595985749101, + "user_tz": -330, + "elapsed": 28995, + "user": { + "displayName": "Agrover112", + "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiMJACGAX3kCfRjB2hgzdG8w9zL1lAAKbPPMz0qLA=s64", + "userId": "09574164879083471944" + } + }, + "outputId": "6c8db259-8953-457f-d961-2d540ae5c2b2" + }, + "source": [ + "plot_results(h).show()" + ], + "execution_count": 57, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "BerqQKSqJcFB", + "colab_type": "text" + }, + "source": [ + "# SELU" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "2CccDRmeLngA", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 251 + }, + "executionInfo": { + "status": "ok", + "timestamp": 1595985749103, + "user_tz": -330, + "elapsed": 28588, + "user": { + "displayName": "Agrover112", + "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiMJACGAX3kCfRjB2hgzdG8w9zL1lAAKbPPMz0qLA=s64", + "userId": "09574164879083471944" + } + }, + "outputId": "91f185ae-83b5-4ba7-9e73-326ff47a12ac" + }, + "source": [ + "model = create_model(use=\"selu\")\n", + "model.summary()" + ], + "execution_count": 58, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Model: \"sequential_12\"\n", + "_________________________________________________________________\n", + "Layer (type) Output Shape Param # \n", + "=================================================================\n", + "dense_24 (Dense) (None, 64) 50240 \n", + "_________________________________________________________________\n", + "s_elu_3 (sElu) (None, 64) 0 \n", + "_________________________________________________________________\n", + "dense_25 (Dense) (None, 10) 650 \n", + "=================================================================\n", + "Total params: 50,890\n", + "Trainable params: 50,890\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "50IcXmRcL392", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 301 + }, + "executionInfo": { + "status": "ok", + "timestamp": 1595985776790, + "user_tz": -330, + "elapsed": 56079, + "user": { + "displayName": "Agrover112", + "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiMJACGAX3kCfRjB2hgzdG8w9zL1lAAKbPPMz0qLA=s64", + "userId": "09574164879083471944" + } + }, + "outputId": "67aab42d-ed12-418c-e3d8-83d3c08294ef" + }, + "source": [ + "import time\n", + "\n", + "start = time.time()\n", + "print(\"Initial alpha:\", model.layers[1].get_weights())\n", + "\n", + "h = model.fit(x_train, y_train, validation_data=(x_test, y_test), epochs=7)\n", + "\n", + "print(\"Final alpha:\", model.layers[1].get_weights())\n", + "print(time.time() - start)" + ], + "execution_count": 59, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Initial alpha: []\n", + "Epoch 1/7\n", + "1875/1875 [==============================] - 4s 2ms/step - loss: 0.3184 - accuracy: 0.9084 - val_loss: 0.1805 - val_accuracy: 0.9466\n", + "Epoch 2/7\n", + "1875/1875 [==============================] - 4s 2ms/step - loss: 0.1601 - accuracy: 0.9523 - val_loss: 0.1330 - val_accuracy: 0.9598\n", + "Epoch 3/7\n", + "1875/1875 [==============================] - 4s 2ms/step - loss: 0.1144 - accuracy: 0.9654 - val_loss: 0.1045 - val_accuracy: 0.9675\n", + "Epoch 4/7\n", + "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0885 - accuracy: 0.9733 - val_loss: 0.0952 - val_accuracy: 0.9709\n", + "Epoch 5/7\n", + "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0718 - accuracy: 0.9785 - val_loss: 0.0924 - val_accuracy: 0.9708\n", + "Epoch 6/7\n", + "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0599 - accuracy: 0.9811 - val_loss: 0.0866 - val_accuracy: 0.9726\n", + "Epoch 7/7\n", + "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0503 - accuracy: 0.9843 - val_loss: 0.0868 - val_accuracy: 0.9733\n", + "Final alpha: []\n", + "27.53625178337097\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "fJBRYu2tMGM9", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 279 + }, + "executionInfo": { + "status": "ok", + "timestamp": 1595985776791, + "user_tz": -330, + "elapsed": 55940, + "user": { + "displayName": "Agrover112", + "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiMJACGAX3kCfRjB2hgzdG8w9zL1lAAKbPPMz0qLA=s64", + "userId": "09574164879083471944" + } + }, + "outputId": "5ede86e7-9dcc-4d19-d038-a1535d668c1e" + }, + "source": [ + "plot_results(h).show()" + ], + "execution_count": 60, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "tW3ci_-iJbya", + "colab_type": "text" + }, + "source": [ + "# PRELU" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "91WmwFJQ5Scy", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 251 + }, + "executionInfo": { + "status": "ok", + "timestamp": 1595985776792, + "user_tz": -330, + "elapsed": 55457, + "user": { + "displayName": "Agrover112", + "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiMJACGAX3kCfRjB2hgzdG8w9zL1lAAKbPPMz0qLA=s64", + "userId": "09574164879083471944" + } + }, + "outputId": "e6bd59d9-d9a9-44a0-e102-f5d3b4ca7754" + }, + "source": [ + "model = create_model(use=\"prelu\")\n", + "model.summary()" + ], + "execution_count": 61, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Model: \"sequential_13\"\n", + "_________________________________________________________________\n", + "Layer (type) Output Shape Param # \n", + "=================================================================\n", + "dense_26 (Dense) (None, 64) 50240 \n", + "_________________________________________________________________\n", + "parametric_relu_1 (Parametri (None, 64) 1 \n", + "_________________________________________________________________\n", + "dense_27 (Dense) (None, 10) 650 \n", + "=================================================================\n", + "Total params: 50,891\n", + "Trainable params: 50,891\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "YVsiHhql5Sc1", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 301 + }, + "executionInfo": { + "status": "ok", + "timestamp": 1595985804389, + "user_tz": -330, + "elapsed": 82839, + "user": { + "displayName": "Agrover112", + "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiMJACGAX3kCfRjB2hgzdG8w9zL1lAAKbPPMz0qLA=s64", + "userId": "09574164879083471944" + } + }, + "outputId": "9fa04eb0-1b8e-49fd-f815-370119abbb75" + }, + "source": [ + "import time\n", + "\n", + "start = time.time()\n", + "print(\"Initial alpha:\", model.layers[1].get_weights())\n", + "h = model.fit(x_train, y_train, validation_data=(x_test, y_test), epochs=7)\n", + "print(\"Final alpha:\", model.layers[1].get_weights())\n", + "print(time.time() - start)" + ], + "execution_count": 62, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Initial alpha: [array([0.], dtype=float32)]\n", + "Epoch 1/7\n", + "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2943 - accuracy: 0.9169 - val_loss: 0.1568 - val_accuracy: 0.9527\n", + "Epoch 2/7\n", + "1875/1875 [==============================] - 4s 2ms/step - loss: 0.1272 - accuracy: 0.9625 - val_loss: 0.1051 - val_accuracy: 0.9688\n", + "Epoch 3/7\n", + "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0900 - accuracy: 0.9729 - val_loss: 0.1029 - val_accuracy: 0.9706\n", + "Epoch 4/7\n", + "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0710 - accuracy: 0.9779 - val_loss: 0.0945 - val_accuracy: 0.9739\n", + "Epoch 5/7\n", + "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0555 - accuracy: 0.9829 - val_loss: 0.0931 - val_accuracy: 0.9731\n", + "Epoch 6/7\n", + "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0466 - accuracy: 0.9854 - val_loss: 0.0959 - val_accuracy: 0.9726\n", + "Epoch 7/7\n", + "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0409 - accuracy: 0.9869 - val_loss: 0.0939 - val_accuracy: 0.9732\n", + "Final alpha: [array([-0.97849256], dtype=float32)]\n", + "27.185328006744385\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "coFyCBgY5Sc4", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 282 + }, + "executionInfo": { + "status": "ok", + "timestamp": 1595985804390, + "user_tz": -330, + "elapsed": 82660, + "user": { + "displayName": "Agrover112", + "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiMJACGAX3kCfRjB2hgzdG8w9zL1lAAKbPPMz0qLA=s64", + "userId": "09574164879083471944" + } + }, + "outputId": "41bdc621-39a5-4118-9e1a-3690743807b9" + }, + "source": [ + "plot_results(h).show()" + ], + "execution_count": 63, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "wrsqurgALSXm", + "colab_type": "text" + }, + "source": [ + "# Leaky Relu" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "pFnw6soPHMxg", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 251 + }, + "executionInfo": { + "status": "ok", + "timestamp": 1595985804391, + "user_tz": -330, + "elapsed": 82135, + "user": { + "displayName": "Agrover112", + "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiMJACGAX3kCfRjB2hgzdG8w9zL1lAAKbPPMz0qLA=s64", + "userId": "09574164879083471944" + } + }, + "outputId": "cf51b769-357f-4d76-f69a-2543b200495a" + }, + "source": [ + "model = create_model(use=\"leakyrelu\")\n", + "model.summary()" + ], + "execution_count": 64, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Model: \"sequential_14\"\n", + "_________________________________________________________________\n", + "Layer (type) Output Shape Param # \n", + "=================================================================\n", + "dense_28 (Dense) (None, 64) 50240 \n", + "_________________________________________________________________\n", + "leakyrelu_4 (leakyrelu) (None, 64) 0 \n", + "_________________________________________________________________\n", + "dense_29 (Dense) (None, 10) 650 \n", + "=================================================================\n", + "Total params: 50,890\n", + "Trainable params: 50,890\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "8AfF5-kjHWpd", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 301 + }, + "executionInfo": { + "status": "ok", + "timestamp": 1595985831123, + "user_tz": -330, + "elapsed": 108572, + "user": { + "displayName": "Agrover112", + "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiMJACGAX3kCfRjB2hgzdG8w9zL1lAAKbPPMz0qLA=s64", + "userId": "09574164879083471944" + } + }, + "outputId": "43d18ab6-a4b7-46f7-a075-a046b0fe38e5" + }, + "source": [ + "import time\n", + "\n", + "start = time.time()\n", + "print(\"Initial alpha:\", model.layers[1].get_weights())\n", + "h = model.fit(x_train, y_train, validation_data=(x_test, y_test), epochs=7)\n", + "print(\"Final alpha:\", model.layers[1].get_weights())\n", + "print(time.time() - start)" + ], + "execution_count": 65, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Initial alpha: []\n", + "Epoch 1/7\n", + "1875/1875 [==============================] - 4s 2ms/step - loss: 0.3022 - accuracy: 0.9158 - val_loss: 0.1655 - val_accuracy: 0.9521\n", + "Epoch 2/7\n", + "1875/1875 [==============================] - 4s 2ms/step - loss: 0.1455 - accuracy: 0.9579 - val_loss: 0.1265 - val_accuracy: 0.9616\n", + "Epoch 3/7\n", + "1875/1875 [==============================] - 4s 2ms/step - loss: 0.1060 - accuracy: 0.9687 - val_loss: 0.1046 - val_accuracy: 0.9685\n", + "Epoch 4/7\n", + "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0842 - accuracy: 0.9754 - val_loss: 0.0961 - val_accuracy: 0.9714\n", + "Epoch 5/7\n", + "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0696 - accuracy: 0.9792 - val_loss: 0.0926 - val_accuracy: 0.9710\n", + "Epoch 6/7\n", + "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0594 - accuracy: 0.9825 - val_loss: 0.0804 - val_accuracy: 0.9752\n", + "Epoch 7/7\n", + "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0496 - accuracy: 0.9854 - val_loss: 0.0823 - val_accuracy: 0.9744\n", + "Final alpha: []\n", + "26.97652316093445\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "3WsfrSC5H-7_", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 279 + }, + "executionInfo": { + "status": "ok", + "timestamp": 1595985832360, + "user_tz": -330, + "elapsed": 109490, + "user": { + "displayName": "Agrover112", + "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiMJACGAX3kCfRjB2hgzdG8w9zL1lAAKbPPMz0qLA=s64", + "userId": "09574164879083471944" + } + }, + "outputId": "7510eea4-961f-4073-f5d5-5867e8b3c253" + }, + "source": [ + "plot_results(h).show()" + ], + "execution_count": 66, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "kpLwwA-fGQBh", + "colab_type": "text" + }, + "source": [ + "# Method 2 for creating activation functions" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "sSQjWjkcH_d9", + "colab_type": "code", + "colab": {}, + "executionInfo": { + "status": "ok", + "timestamp": 1595985832362, + "user_tz": -330, + "elapsed": 108847, + "user": { + "displayName": "Agrover112", + "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiMJACGAX3kCfRjB2hgzdG8w9zL1lAAKbPPMz0qLA=s64", + "userId": "09574164879083471944" + } + } + }, + "source": [ + "# Creating swish and GELU" + ], + "execution_count": 67, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "f7DovSYOGVzJ", + "colab_type": "code", + "colab": {}, + "executionInfo": { + "status": "ok", + "timestamp": 1595986912332, + "user_tz": -330, + "elapsed": 1017, + "user": { + "displayName": "Agrover112", + "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiMJACGAX3kCfRjB2hgzdG8w9zL1lAAKbPPMz0qLA=s64", + "userId": "09574164879083471944" + } + } + }, + "source": [ + "from keras.backend import sigmoid\n", + "from keras.layers import Activation\n", + "from keras import backend as K\n", + "from keras.utils.generic_utils import get_custom_objects\n", + "\n", + "\n", + "def swish(x, beta=1):\n", + " return x * sigmoid(beta * x)\n", + "\n", + "\n", + "def custom_gelu(x): # Used in BERT and GPT2 paper yet to read completely\n", + " return (\n", + " 0.5\n", + " * x\n", + " * (1 + tf.tanh(tf.sqrt(2 / np.pi) * (x + 0.044715 * tf.pow(x, 3))))\n", + " )\n", + "\n", + "\n", + "get_custom_objects().update({\"custom_gelu\": Activation(custom_gelu)})\n", + "get_custom_objects().update({\"swish\": Activation(swish)})" + ], + "execution_count": 86, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "jkC7-MesIrJG", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 217 + }, + "executionInfo": { + "status": "ok", + "timestamp": 1595987340647, + "user_tz": -330, + "elapsed": 1155, + "user": { + "displayName": "Agrover112", + "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiMJACGAX3kCfRjB2hgzdG8w9zL1lAAKbPPMz0qLA=s64", + "userId": "09574164879083471944" + } + }, + "outputId": "6cfcae1a-721a-48fb-de24-4023e6124d64" + }, + "source": [ + "def create_model(use):\n", + " model = tf.keras.models.Sequential()\n", + " if use == \"gelu\":\n", + " model.add(\n", + " tf.keras.layers.Dense(\n", + " 64, input_shape=(784,), activation=custom_gelu\n", + " )\n", + " )\n", + " else:\n", + " model.add(\n", + " tf.keras.layers.Dense(64, input_shape=(784,), activation=use)\n", + " )\n", + " model.add(tf.keras.layers.Dense(10, activation=\"softmax\"))\n", + " model.compile(\n", + " loss=\"categorical_crossentropy\", optimizer=\"adam\", metrics=[\"accuracy\"]\n", + " )\n", + " return model\n", + "\n", + "\n", + "model = create_model(use=\"gelu\")\n", + "model.summary()" + ], + "execution_count": 99, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Model: \"sequential_31\"\n", + "_________________________________________________________________\n", + "Layer (type) Output Shape Param # \n", + "=================================================================\n", + "dense_54 (Dense) (None, 64) 50240 \n", + "_________________________________________________________________\n", + "dense_55 (Dense) (None, 10) 650 \n", + "=================================================================\n", + "Total params: 50,890\n", + "Trainable params: 50,890\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "LW37FiSrQDxs", + "colab_type": "text" + }, + "source": [ + "# EDIT: layers[1].get_weights() prints first layers weights than the activation fucntion in previous case" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "FJzOWqXSMi30", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "executionInfo": { + "status": "ok", + "timestamp": 1595987065103, + "user_tz": -330, + "elapsed": 26899, + "user": { + "displayName": "Agrover112", + "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiMJACGAX3kCfRjB2hgzdG8w9zL1lAAKbPPMz0qLA=s64", + "userId": "09574164879083471944" + } + }, + "outputId": "876d7fe3-3d1a-4d2a-e7a9-b708c11a69ed" + }, + "source": [ + "import time\n", + "\n", + "start = time.time()\n", + "print(\"Initial alpha:\", model.layers[1].get_weights())\n", + "gh = model.fit(x_train, y_train, validation_data=(x_test, y_test), epochs=7)\n", + "print(\"Final alpha:\", model.layers[1].get_weights())\n", + "print(time.time() - start)" + ], + "execution_count": 92, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Initial alpha: [array([[ 0.12353632, 0.16423404, 0.10222274, 0.1256558 , -0.01892754,\n", + " -0.02012911, 0.04866058, 0.12828976, -0.08458149, -0.14631438],\n", + " [-0.18274339, 0.05565363, -0.1672585 , 0.27012166, 0.25833252,\n", + " 0.14464161, 0.04973227, -0.16902003, -0.037247 , 0.19368479],\n", + " [-0.1621024 , -0.1792361 , -0.13050628, -0.08876258, -0.07096566,\n", + " -0.01914582, -0.23445225, -0.13684182, 0.1989077 , -0.02570578],\n", + " [ 0.12473157, 0.1441727 , 0.09397429, 0.00332046, -0.18190944,\n", + " 0.01261935, 0.04458568, 0.16517016, 0.1471281 , 0.1981079 ],\n", + " [ 0.09285513, 0.07188725, -0.09558456, 0.274725 , 0.15193444,\n", + " -0.15837169, -0.27295187, 0.04262924, -0.12887274, -0.18479514],\n", + " [ 0.18666595, 0.19486734, -0.10955802, 0.2639028 , -0.05440684,\n", + " 0.1187984 , 0.10627389, -0.0036768 , -0.05467792, -0.03288031],\n", + " [-0.28256285, -0.01646656, 0.22653136, 0.14125109, 0.04477257,\n", + " 0.18957227, -0.12007512, 0.11028606, -0.05040519, 0.17090565],\n", + " [-0.07029289, -0.25333554, 0.26304957, 0.1184682 , -0.20027709,\n", + " -0.05293474, 0.22399291, 0.04855311, -0.08478639, 0.0674209 ],\n", + " [-0.03744619, 0.08813277, -0.00325006, 0.05665702, -0.25226253,\n", + " -0.15875526, -0.20920554, 0.28402016, 0.16325998, 0.0451816 ],\n", + " [-0.23873171, 0.23971388, -0.13957408, -0.09348366, -0.12637225,\n", + " -0.2402848 , -0.05544731, 0.09038535, -0.08063239, 0.14313167],\n", + " [ 0.02704713, 0.03723124, 0.07753408, -0.11149089, -0.11205675,\n", + " 0.12067702, -0.04036833, -0.17216973, -0.2718422 , 0.07661545],\n", + " [ 0.15340158, -0.02871212, -0.05914746, -0.1691258 , 0.19760653,\n", + " 0.07027653, -0.15858269, -0.07719523, -0.24819945, 0.08997631],\n", + " [ 0.0839386 , -0.04098694, -0.11869203, -0.03156623, 0.25113901,\n", + " 0.22811094, -0.14236392, 0.10863847, 0.17532489, -0.02590632],\n", + " [-0.04118179, -0.28281942, 0.25266996, 0.11430162, -0.10380402,\n", + " -0.23941793, 0.10782734, 0.01313919, -0.13310677, 0.00274774],\n", + " [ 0.2623755 , -0.17281556, 0.26839992, -0.28289232, 0.15371516,\n", + " 0.17648354, 0.13412029, 0.25674364, 0.07614484, 0.25217316],\n", + " [ 0.09696636, -0.2488767 , -0.14929838, 0.11973554, 0.09945437,\n", + " 0.10234487, 0.11125267, -0.02682629, 0.20431268, -0.01868701],\n", + " [ 0.2398974 , -0.22453779, 0.00181392, 0.15036017, 0.11731035,\n", + " 0.13048673, 0.06268138, -0.1291726 , 0.18524647, -0.00345013],\n", + " [ 0.23072919, 0.22656229, -0.0305545 , -0.2006329 , 0.15256426,\n", + " 0.24350306, -0.00238964, -0.10016523, -0.07996708, 0.03127399],\n", + " [ 0.15715683, 0.13723537, 0.2599326 , -0.00454673, -0.14765364,\n", + " 0.01927587, -0.08839978, -0.24598016, 0.24590883, 0.04467174],\n", + " [ 0.28469744, -0.19297448, -0.10297522, 0.11548486, -0.27860025,\n", + " -0.03212348, 0.12768874, 0.15053445, -0.03746478, -0.01304054],\n", + " [ 0.13235274, -0.05376115, -0.09554958, 0.16528022, -0.23063228,\n", + " 0.05321336, -0.09569412, 0.04959235, -0.03146312, -0.1879291 ],\n", + " [ 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0.16920772, 0.27216235],\n", + " [ 0.16291592, -0.2244496 , -0.27257034, 0.09977853, 0.20331573,\n", + " -0.03658576, -0.05307302, 0.10591719, 0.26752636, -0.10431881],\n", + " [ 0.12995964, 0.08694682, -0.22049072, -0.09516092, 0.21594968,\n", + " -0.27152082, -0.00124571, 0.15669298, -0.0420858 , 0.03831068],\n", + " [-0.24632156, -0.20851931, -0.13980335, 0.23447362, -0.26435447,\n", + " -0.23892105, -0.04201369, -0.00239655, 0.16152501, 0.02180237],\n", + " [ 0.00632393, -0.11826174, 0.07419047, -0.01519534, 0.01887146,\n", + " -0.0618487 , -0.03163657, -0.02932727, -0.19730756, -0.03857654],\n", + " [-0.10034473, -0.11145274, -0.03592378, -0.05071218, -0.17107406,\n", + " 0.11298302, -0.14756504, -0.23586705, -0.10561422, 0.09345698],\n", + " [-0.17992043, 0.21630242, 0.0180164 , -0.27457693, 0.00463527,\n", + " -0.07183838, -0.10347332, 0.18747059, 0.28024182, 0.04037765],\n", + " [-0.21773517, 0.01789156, 0.16346994, 0.20346692, -0.17154339,\n", + " 0.09933549, 0.21306312, -0.10411195, 0.02566174, 0.14130554],\n", + " [ 0.0747236 , -0.05265021, -0.06506631, -0.10799637, -0.08611858,\n", + " -0.19419253, 0.23787501, -0.11132707, 0.27980247, 0.1080716 ],\n", + " [ 0.00941235, 0.05866221, 0.01417884, -0.25543818, 0.2470217 ,\n", + " 0.2721658 , 0.17885253, 0.08106485, 0.12707469, 0.07814005],\n", + " [-0.01358196, 0.19676629, 0.02211621, -0.0294441 , 0.26413247,\n", + " -0.04763816, 0.07809713, 0.24337974, 0.04297426, 0.26430747],\n", + " [ 0.20576435, -0.15302908, -0.11213054, 0.0764477 , -0.06149025,\n", + " -0.04263128, -0.22780538, -0.0414826 , -0.101744 , 0.05337417],\n", + " [-0.13963859, -0.02920118, -0.07521382, -0.13908283, -0.22487792,\n", + " 0.09819964, 0.10910845, -0.21841136, -0.13357887, 0.15916416],\n", + " [-0.16049308, -0.00383851, -0.06512971, 0.2695265 , 0.23787859,\n", + " 0.2807723 , -0.02682561, 0.01771674, 0.13609001, 0.10129294],\n", + " [-0.03365234, 0.24610916, 0.06191421, 0.03744617, 0.14224485,\n", + " -0.15471728, -0.1860874 , 0.04854891, -0.17592685, -0.1490349 ],\n", + " [ 0.16308528, 0.27674606, -0.002285 , 0.14865357, 0.10734648,\n", + " -0.16728131, -0.1147573 , 0.13631615, 0.09566283, 0.18318635],\n", + " [ 0.27222297, 0.243103 , -0.1273102 , 0.24715272, -0.06220607,\n", + " -0.14920524, -0.17180428, 0.16067368, 0.00890642, 0.2521982 ],\n", + " [-0.05227757, -0.23358694, 0.22480187, 0.21437624, -0.24380282,\n", + " 0.17585734, -0.12364249, -0.01528162, 0.0499295 , 0.2150178 ],\n", + " [-0.00107393, -0.27451706, -0.0096046 , -0.17229173, 0.11372587,\n", + " 0.16570196, 0.08970821, 0.12342274, 0.09351537, 0.11486232],\n", + " [ 0.24118587, -0.15010566, 0.11712337, 0.11177421, -0.00830454,\n", + " 0.01989925, 0.24000868, 0.18229899, 0.05130878, 0.07131067],\n", + " [-0.16590717, -0.05283773, 0.2588742 , 0.20440745, -0.01442698,\n", + " 0.26104942, 0.05893219, -0.12315626, -0.25597253, 0.18352267],\n", + " [ 0.23686764, 0.12973064, -0.25767562, 0.16183057, -0.04931265,\n", + " -0.0014129 , -0.03652439, 0.25875202, -0.26775396, 0.21114567],\n", + " [-0.05129379, 0.2568433 , 0.09075764, -0.20865196, -0.20863254,\n", + " -0.05773021, -0.05420792, 0.00864473, -0.20908794, 0.15594622]],\n", + " dtype=float32), array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32)]\n", + "Epoch 1/7\n", + "1875/1875 [==============================] - 4s 2ms/step - loss: 0.3024 - accuracy: 0.9147 - val_loss: 0.1745 - val_accuracy: 0.9492\n", + "Epoch 2/7\n", + "1875/1875 [==============================] - 4s 2ms/step - loss: 0.1472 - accuracy: 0.9577 - val_loss: 0.1303 - val_accuracy: 0.9596\n", + "Epoch 3/7\n", + "1875/1875 [==============================] - 4s 2ms/step - loss: 0.1036 - accuracy: 0.9701 - val_loss: 0.1071 - val_accuracy: 0.9664\n", + "Epoch 4/7\n", + "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0793 - accuracy: 0.9766 - val_loss: 0.0886 - val_accuracy: 0.9734\n", + "Epoch 5/7\n", + "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0645 - accuracy: 0.9804 - val_loss: 0.0878 - val_accuracy: 0.9727\n", + "Epoch 6/7\n", + "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0533 - accuracy: 0.9837 - val_loss: 0.0799 - val_accuracy: 0.9753\n", + "Epoch 7/7\n", + "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0445 - accuracy: 0.9866 - val_loss: 0.0834 - val_accuracy: 0.9744\n", + "Final alpha: [array([[-5.51505834e-02, 3.73578846e-01, 2.18160808e-01,\n", + " 1.89281136e-01, -3.09743397e-02, -1.95218548e-01,\n", + " 1.10915758e-01, 7.23766506e-01, -3.90888751e-01,\n", + " -1.42245936e+00],\n", + " [-6.97991371e-01, 2.21806556e-01, -3.23866487e-01,\n", + " 4.13698912e-01, 3.30327451e-01, 1.76613569e-01,\n", + " 2.70528235e-02, -1.53197438e-01, -1.12837195e-01,\n", + " 1.60273433e-01],\n", + " [-7.14135051e-01, -3.84111077e-01, -3.25362712e-01,\n", + " 3.83123010e-02, 7.61731192e-02, 1.27703622e-01,\n", + " -8.66195798e-01, -1.82165936e-01, 2.32192695e-01,\n", + " 6.03046939e-02],\n", + " [ 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[-5.70870817e-01, 5.45312345e-01, 2.57630527e-01,\n", + " -2.77483702e-01, -1.31997645e-01, -2.61727273e-01,\n", + " -8.78111362e-01, 1.61465421e-01, -4.19722289e-01,\n", + " 2.35004306e-01]], dtype=float32), array([-0.28244337, 0.08475335, 0.07105463, -0.14020827, 0.20067522,\n", + " 0.19168544, 0.04727132, 0.03243769, -0.03037991, -0.17500848],\n", + " dtype=float32)]\n", + "26.089535236358643\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "y2JMcbi1PY78", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 217 + }, + "executionInfo": { + "status": "ok", + "timestamp": 1595987330791, + "user_tz": -330, + "elapsed": 1168, + "user": { + "displayName": "Agrover112", + "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiMJACGAX3kCfRjB2hgzdG8w9zL1lAAKbPPMz0qLA=s64", + "userId": "09574164879083471944" + } + }, + "outputId": "9449c445-0aeb-458f-fc95-a33ae933dc0d" + }, + "source": [ + "model = create_model(use=\"swish\")\n", + "model.summary()" + ], + "execution_count": 98, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Model: \"sequential_30\"\n", + "_________________________________________________________________\n", + "Layer (type) Output Shape Param # \n", + "=================================================================\n", + "dense_52 (Dense) (None, 64) 50240 \n", + "_________________________________________________________________\n", + "dense_53 (Dense) (None, 10) 650 \n", + "=================================================================\n", + "Total params: 50,890\n", + "Trainable params: 50,890\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "a-QTLz6KPoAp", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "executionInfo": { + "status": "ok", + "timestamp": 1595987396895, + "user_tz": -330, + "elapsed": 26910, + "user": { + "displayName": "Agrover112", + "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiMJACGAX3kCfRjB2hgzdG8w9zL1lAAKbPPMz0qLA=s64", + "userId": "09574164879083471944" + } + }, + "outputId": "30cc653e-df19-42fd-b372-e72d1378718a" + }, + "source": [ + "import time\n", + "\n", + "start = time.time()\n", + "print(\"Initial alpha:\", model.layers[1].get_weights())\n", + "sh = model.fit(x_train, y_train, validation_data=(x_test, y_test), epochs=7)\n", + "print(\"Final alpha:\", model.layers[1].get_weights())\n", + "print(time.time() - start)" + ], + "execution_count": 100, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Initial alpha: [array([[ 0.02084643, -0.16138217, 0.2829087 , 0.14652565, -0.18471095,\n", + " -0.12087099, 0.16201237, -0.00325066, -0.06640942, 0.10206673],\n", + " [ 0.2119534 , -0.2558481 , -0.01877779, 0.24446562, 0.14282861,\n", + " -0.2140877 , -0.00336966, 0.1178225 , 0.06388301, -0.25829393],\n", + " [-0.00031894, 0.06267378, -0.16316961, 0.00606263, 0.14348754,\n", + " -0.06689693, 0.21690711, 0.05176529, -0.01930445, -0.1682317 ],\n", + " [ 0.00494301, -0.2725569 , -0.2420508 , -0.11959603, -0.15769775,\n", + " 0.21959278, -0.00450423, -0.22490624, -0.18821198, -0.03454033],\n", + " [ 0.2697967 , 0.25885156, -0.06789531, 0.22188452, -0.06498179,\n", + " -0.07024346, 0.26663217, 0.07309249, -0.1939106 , 0.20388949],\n", + " [ 0.22402522, -0.06327668, 0.18563873, -0.01840398, 0.07488164,\n", + " 0.06559807, 0.23722163, 0.0770376 , 0.27612635, 0.01703215],\n", + " [ 0.10302907, -0.07591681, -0.05564269, 0.0057731 , 0.18928951,\n", + " 0.03045416, -0.22046757, 0.07652864, 0.07531661, -0.1704296 ],\n", + " [-0.24654289, 0.02591667, -0.08242914, 0.06724623, -0.02255946,\n", + " -0.06534167, -0.03041071, -0.20521495, -0.09052193, -0.02395412],\n", + " [ 0.03733844, 0.02324182, -0.03946812, -0.04012549, 0.11786377,\n", + " -0.02232137, -0.13969547, 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array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32)]\n", + "Epoch 1/7\n", + "1875/1875 [==============================] - 4s 2ms/step - loss: 0.3040 - accuracy: 0.9131 - val_loss: 0.1710 - val_accuracy: 0.9498\n", + "Epoch 2/7\n", + "1875/1875 [==============================] - 4s 2ms/step - loss: 0.1468 - accuracy: 0.9569 - val_loss: 0.1243 - val_accuracy: 0.9643\n", + "Epoch 3/7\n", + "1875/1875 [==============================] - 4s 2ms/step - loss: 0.1058 - accuracy: 0.9686 - val_loss: 0.1057 - val_accuracy: 0.9683\n", + "Epoch 4/7\n", + "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0829 - accuracy: 0.9747 - val_loss: 0.0975 - val_accuracy: 0.9697\n", + "Epoch 5/7\n", + "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0676 - accuracy: 0.9790 - val_loss: 0.0876 - val_accuracy: 0.9728\n", + "Epoch 6/7\n", + "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0567 - accuracy: 0.9825 - val_loss: 0.0781 - val_accuracy: 0.9738\n", + "Epoch 7/7\n", + "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0485 - accuracy: 0.9848 - val_loss: 0.0826 - val_accuracy: 0.9731\n", + "Final alpha: [array([[ 1.49731815e-01, -4.33901697e-01, 4.64810282e-01,\n", + " 1.87838107e-01, -1.31817329e+00, -1.36781454e-01,\n", + " -2.20852926e-01, 2.31495425e-01, -1.28978742e-02,\n", + " 2.20830426e-01],\n", + " [ 2.61248738e-01, -5.24092019e-01, 1.00676581e-01,\n", + " 2.92181581e-01, 3.80533114e-02, -1.31717250e-01,\n", + " -9.61486623e-02, 1.64519414e-01, 7.32505098e-02,\n", + " -5.89757383e-01],\n", + " [-3.05296451e-01, 6.39697552e-01, -3.87004822e-01,\n", + " -6.18221819e-01, 2.71083862e-01, -1.89693332e-01,\n", + " 2.91126400e-01, 4.93878633e-01, -2.62698948e-01,\n", + " -1.83036327e-01],\n", + " [-5.38862571e-02, -7.07252800e-01, -7.63772011e-01,\n", + " -1.54142007e-01, -7.22303510e-01, 6.31452680e-01,\n", + " 4.30042744e-01, -2.54842222e-01, -2.98031420e-01,\n", + " -4.55763824e-02],\n", + " [ 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+ ], + "execution_count": 104, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 104 + }, + { + "output_type": "display_data", + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [], + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "vBWsbYzPQYzR", + "colab_type": "code", + "colab": {} + }, + "source": [ + "" + ], + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file diff --git a/Scripts/Miscellaneous/Facial Expressions Detection/model_training.ipynb b/Scripts/Miscellaneous/Facial Expressions Detection/model_training.ipynb index 67524e5b1..72a4e1372 100644 --- a/Scripts/Miscellaneous/Facial Expressions Detection/model_training.ipynb +++ b/Scripts/Miscellaneous/Facial Expressions Detection/model_training.ipynb @@ -14,7 +14,13 @@ "outputs": [], "source": [ "from tensorflow.keras.models import Sequential\n", - "from tensorflow.keras.layers import Conv2D, MaxPooling2D, Dropout, Flatten, Dense\n", + "from tensorflow.keras.layers import (\n", + " Conv2D,\n", + " MaxPooling2D,\n", + " Dropout,\n", + " Flatten,\n", + " Dense,\n", + ")\n", "from tensorflow.keras.metrics import categorical_crossentropy\n", "from tensorflow.keras.preprocessing.image import ImageDataGenerator" ] @@ -42,11 +48,13 @@ "metadata": {}, "outputs": [], "source": [ - "train_data_generator = ImageDataGenerator(rescale=1./255,\n", - " rotation_range=20,\n", - " shear_range=0.2,\n", - " zoom_range=0.2,\n", - " horizontal_flip=True)" + "train_data_generator = ImageDataGenerator(\n", + " rescale=1.0 / 255,\n", + " rotation_range=20,\n", + " shear_range=0.2,\n", + " zoom_range=0.2,\n", + " horizontal_flip=True,\n", + ")" ] }, { @@ -55,7 +63,7 @@ "metadata": {}, "outputs": [], "source": [ - "test_data_generator = ImageDataGenerator(rescale=1./255)" + "test_data_generator = ImageDataGenerator(rescale=1.0 / 255)" ] }, { @@ -72,11 +80,13 @@ } ], "source": [ - "training_data = train_data_generator.flow_from_directory(directory=train_data_path, \n", - " target_size=(48, 48),\n", - " color_mode='grayscale',\n", - " class_mode='categorical',\n", - " batch_size=64)" + "training_data = train_data_generator.flow_from_directory(\n", + " directory=train_data_path,\n", + " target_size=(48, 48),\n", + " color_mode=\"grayscale\",\n", + " class_mode=\"categorical\",\n", + " batch_size=64,\n", + ")" ] }, { @@ -93,11 +103,13 @@ } ], "source": [ - "testing_data = test_data_generator.flow_from_directory(directory=test_data_path, \n", - " target_size=(48, 48),\n", - " color_mode='grayscale',\n", - " class_mode='categorical',\n", - " batch_size=64)" + "testing_data = test_data_generator.flow_from_directory(\n", + " directory=test_data_path,\n", + " target_size=(48, 48),\n", + " color_mode=\"grayscale\",\n", + " class_mode=\"categorical\",\n", + " batch_size=64,\n", + ")" ] }, { @@ -142,17 +154,19 @@ "metadata": {}, "outputs": [], "source": [ - "model.add(Conv2D(32, 3, input_shape=(48, 48, 1), activation='relu', padding='same'))\n", + "model.add(\n", + " Conv2D(32, 3, input_shape=(48, 48, 1), activation=\"relu\", padding=\"same\")\n", + ")\n", "model.add(MaxPooling2D((2, 2), strides=2))\n", - "model.add(Conv2D(64, 3, activation='relu', padding='same'))\n", + "model.add(Conv2D(64, 3, activation=\"relu\", padding=\"same\"))\n", "model.add(Dropout(0.2))\n", "model.add(MaxPooling2D((2, 2), strides=2))\n", - "model.add(Conv2D(64, 3, activation='relu', padding='same'))\n", + "model.add(Conv2D(64, 3, activation=\"relu\", padding=\"same\"))\n", "model.add(Dropout(0.2))\n", "model.add(MaxPooling2D((2, 2), strides=2))\n", - "model.add(Conv2D(128, 3, activation='relu', padding='same'))\n", + "model.add(Conv2D(128, 3, activation=\"relu\", padding=\"same\"))\n", "model.add(Flatten())\n", - "model.add(Dense(4, activation='softmax'))" + "model.add(Dense(4, activation=\"softmax\"))" ] }, { @@ -214,7 +228,9 @@ "metadata": {}, "outputs": [], "source": [ - "model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])" + "model.compile(\n", + " optimizer=\"adam\", loss=\"categorical_crossentropy\", metrics=[\"accuracy\"]\n", + ")" ] }, { @@ -868,8 +884,14 @@ } ], "source": [ - "model.fit_generator(training_data, validation_data=testing_data, epochs=300, steps_per_epoch=len(training_data), \n", - " validation_steps=len(testing_data), verbose=1)" + "model.fit_generator(\n", + " training_data,\n", + " validation_data=testing_data,\n", + " epochs=300,\n", + " steps_per_epoch=len(training_data),\n", + " validation_steps=len(testing_data),\n", + " verbose=1,\n", + ")" ] }, { diff --git a/Scripts/Miscellaneous/Fake_news_web/fakenews_AI.ipynb b/Scripts/Miscellaneous/Fake_news_web/fakenews_AI.ipynb index 4f9d4978e..797ad4340 100644 --- a/Scripts/Miscellaneous/Fake_news_web/fakenews_AI.ipynb +++ b/Scripts/Miscellaneous/Fake_news_web/fakenews_AI.ipynb @@ -6,21 +6,22 @@ "metadata": {}, "outputs": [], "source": [ - "from wordcloud import WordCloud, STOPWORDS \n", - "import matplotlib.pyplot as plt \n", + "from wordcloud import WordCloud, STOPWORDS\n", + "import matplotlib.pyplot as plt\n", "from sklearn import metrics\n", "from sklearn.metrics import confusion_matrix\n", + "\n", "%matplotlib inline\n", "import seaborn as sns\n", "\n", - "import numpy as np # linear algebra\n", - "import pandas as pd #data processing\n", + "import numpy as np # linear algebra\n", + "import pandas as pd # data processing\n", "\n", "import os\n", "import re\n", "import nltk\n", "\n", - "nltk.download('wordnet')" + "nltk.download(\"wordnet\")" ] }, { @@ -29,8 +30,8 @@ "metadata": {}, "outputs": [], "source": [ - "train=pd.read_csv('../fake-news/train.csv')\n", - "test=pd.read_csv('../fake-news/test.csv')" + "train = pd.read_csv(\"../fake-news/train.csv\")\n", + "test = pd.read_csv(\"../fake-news/test.csv\")" ] }, { @@ -58,7 +59,7 @@ "outputs": [], "source": [ "print(train.isnull().sum())\n", - "print('************')\n", + "print(\"************\")\n", "print(test.isnull().sum())" ] }, @@ -68,10 +69,10 @@ "metadata": {}, "outputs": [], "source": [ - "test=test.fillna(' ')\n", - "train=train.fillna(' ')\n", - "test['total']=test['title']+' '+test['author']+test['text']\n", - "train['total']=train['title']+' '+train['author']+train['text']" + "test = test.fillna(\" \")\n", + "train = train.fillna(\" \")\n", + "test[\"total\"] = test[\"title\"] + \" \" + test[\"author\"] + test[\"text\"]\n", + "train[\"total\"] = train[\"title\"] + \" \" + train[\"author\"] + train[\"text\"]" ] }, { @@ -80,8 +81,8 @@ "metadata": {}, "outputs": [], "source": [ - "#Downloading nltk data\n", - "nltk.download('punkt')" + "# Downloading nltk data\n", + "nltk.download(\"punkt\")" ] }, { @@ -93,7 +94,7 @@ "from nltk.corpus import stopwords\n", "from nltk.stem import WordNetLemmatizer\n", "\n", - "stop_words = stopwords.words('english')" + "stop_words = stopwords.words(\"english\")" ] }, { @@ -109,21 +110,23 @@ "metadata": {}, "outputs": [], "source": [ - "lemmatizer=WordNetLemmatizer()\n", - "for index,row in train.iterrows():\n", - " filter_sentence = ''\n", - " \n", - " sentence = row['total']\n", - " sentence = re.sub(r'[^\\w\\s]','',sentence) #cleaning\n", - " \n", - " words = nltk.word_tokenize(sentence) #tokenization\n", - " \n", - " words = [w for w in words if not w in stop_words] #stopwords removal\n", - " \n", + "lemmatizer = WordNetLemmatizer()\n", + "for index, row in train.iterrows():\n", + " filter_sentence = \"\"\n", + "\n", + " sentence = row[\"total\"]\n", + " sentence = re.sub(r\"[^\\w\\s]\", \"\", sentence) # cleaning\n", + "\n", + " words = nltk.word_tokenize(sentence) # tokenization\n", + "\n", + " words = [w for w in words if not w in stop_words] # stopwords removal\n", + "\n", " for word in words:\n", - " filter_sentence = filter_sentence + ' ' + str(lemmatizer.lemmatize(word)).lower()\n", - " \n", - " train.loc[index,'total'] = filter_sentence\n" + " filter_sentence = (\n", + " filter_sentence + \" \" + str(lemmatizer.lemmatize(word)).lower()\n", + " )\n", + "\n", + " train.loc[index, \"total\"] = filter_sentence" ] }, { @@ -132,7 +135,7 @@ "metadata": {}, "outputs": [], "source": [ - "train = train[['total','label']]" + "train = train[[\"total\", \"label\"]]" ] }, { @@ -159,8 +162,8 @@ "metadata": {}, "outputs": [], "source": [ - "X_train = train['total']\n", - "Y_train = train['label']" + "X_train = train[\"total\"]\n", + "Y_train = train[\"label\"]" ] }, { @@ -178,7 +181,7 @@ }, "outputs": [], "source": [ - "#Feature extraction using count vectorization and tfidf.\n", + "# Feature extraction using count vectorization and tfidf.\n", "count_vectorizer = CountVectorizer()\n", "count_vectorizer.fit_transform(X_train)\n", "freq_term_matrix = count_vectorizer.transform(X_train)\n", @@ -209,12 +212,15 @@ "metadata": {}, "outputs": [], "source": [ - "test_counts = count_vectorizer.transform(test['total'].values)\n", + "test_counts = count_vectorizer.transform(test[\"total\"].values)\n", "test_tfidf = tfidf.transform(test_counts)\n", "\n", - "#split in samples\n", + "# split in samples\n", "from sklearn.model_selection import train_test_split\n", - "X_train, X_test, y_train, y_test = train_test_split(tf_idf_matrix, Y_train, random_state=0)" + "\n", + "X_train, X_test, y_train, y_test = train_test_split(\n", + " tf_idf_matrix, Y_train, random_state=0\n", + ")" ] }, { @@ -223,7 +229,7 @@ "metadata": {}, "outputs": [], "source": [ - "print(X_train.shape,X_test.shape)" + "print(X_train.shape, X_test.shape)" ] }, { @@ -232,7 +238,7 @@ "metadata": {}, "outputs": [], "source": [ - "from sklearn.linear_model import LogisticRegression# Logistic Regression" + "from sklearn.linear_model import LogisticRegression # Logistic Regression" ] }, { @@ -244,11 +250,18 @@ "logreg = LogisticRegression(C=1e5)\n", "logreg.fit(X_train, y_train)\n", "pred = logreg.predict(X_test)\n", - "print('Accuracy of Logistic classifier on training set: {:.2f}'\n", - " .format(logreg.score(X_train, y_train)))\n", - "print('Accuracy of Logistic classifier on test set: {:.2f}'\n", - " .format(logreg.score(X_test, y_test)))\n", + "print(\n", + " \"Accuracy of Logistic classifier on training set: {:.2f}\".format(\n", + " logreg.score(X_train, y_train)\n", + " )\n", + ")\n", + "print(\n", + " \"Accuracy of Logistic classifier on test set: {:.2f}\".format(\n", + " logreg.score(X_test, y_test)\n", + " )\n", + ")\n", "from sklearn.naive_bayes import MultinomialNB\n", + "\n", "cm = confusion_matrix(y_test, pred)\n", "cm" ] @@ -271,10 +284,16 @@ "NB = MultinomialNB()\n", "NB.fit(X_train, y_train)\n", "pred = NB.predict(X_test)\n", - "print('Accuracy of NB classifier on training set: {:.2f}'\n", - " .format(NB.score(X_train, y_train)))\n", - "print('Accuracy of NB classifier on test set: {:.2f}'\n", - " .format(NB.score(X_test, y_test)))\n", + "print(\n", + " \"Accuracy of NB classifier on training set: {:.2f}\".format(\n", + " NB.score(X_train, y_train)\n", + " )\n", + ")\n", + "print(\n", + " \"Accuracy of NB classifier on test set: {:.2f}\".format(\n", + " NB.score(X_test, y_test)\n", + " )\n", + ")\n", "cm = confusion_matrix(y_test, pred)\n", "cm" ] @@ -303,11 +322,13 @@ "metadata": {}, "outputs": [], "source": [ - "pipeline = Pipeline([\n", - " ('vect', CountVectorizer()),\n", - " ('tfidf', TfidfTransformer(norm='l2')),\n", - " ('clf', linear_model.LogisticRegression(C=1e5)),\n", - "])" + "pipeline = Pipeline(\n", + " [\n", + " (\"vect\", CountVectorizer()),\n", + " (\"tfidf\", TfidfTransformer(norm=\"l2\")),\n", + " (\"clf\", linear_model.LogisticRegression(C=1e5)),\n", + " ]\n", + ")" ] }, { @@ -327,7 +348,11 @@ "metadata": {}, "outputs": [], "source": [ - "pipeline.predict([\"flynn hillary clinton big woman campus breitbart daniel j flynnever get feeling life circle roundabout rather head straight line toward intended destination hillary clinton remains big woman campus leafy liberal wellesley massachusetts everywhere else vote likely inauguration dress remainder day way miss havisham forever wore wedding dress speaking great expectations hillary rodham overflowed 48 year ago first addressed wellesley graduating class the president college informed gathered 1969 student needed debate far i could ascertain spokesman kind like democratic primary 2016 minus term unknown even seven sisters school i glad miss adams made clear i speaking today u 400 u miss rodham told classmate after appointing edger bergen charlie mccarthys mortimer snerds attendance bespectacled granny glass awarding matronly wisdom least john lennon wisdom took issue previous speaker despite becoming first win election seat u s senate since reconstruction edward brooke came criticism calling empathy goal protestors criticized tactic though clinton senior thesis saul alinsky lamented black power demagogue elitist arrogance repressive intolerance within new left similar word coming republican necessitated brief rebuttal trust rodham ironically observed 1969 one word i asked class rehearsal wanted say everyone came said talk trust talk lack trust u way feel others talk trust bust what say what say feeling permeates generation perhaps even understood distrusted the trust bust certainly busted clintons 2016 plan she certainly even understand people distrusted after whitewater travelgate vast conspiracy benghazi missing email clinton found distrusted voice friday there load compromising road broadening political horizon and distrust american people trump edged 48 percent 38 percent question immediately prior novembers election stood major reason closing horizon clinton described vanquisher supporter embracing lie con alternative fact assault truth reason she failed explain american people chose lie truth as history major among today know well people power invent fact attack question mark beginning end free society offered that hyperbole like many people emerge 1960s hillary clinton embarked upon long strange trip from high school goldwater girl wellesley college republican president democratic politician clinton drank time place gave degree more significantly went idealist cynic comparison two wellesley commencement address show way back lamented long leader viewed politics art possible challenge practice politics art making appears impossible possible now big woman campus odd woman white house wonder current station even possible why arent i 50 point ahead asked september in may asks isnt president the woman famously dubbed congenital liar bill safire concludes lie mind getting stood election day like finding jilted bride wedding day inspires dangerous delusion\"])" + "pipeline.predict(\n", + " [\n", + " \"flynn hillary clinton big woman campus breitbart daniel j flynnever get feeling life circle roundabout rather head straight line toward intended destination hillary clinton remains big woman campus leafy liberal wellesley massachusetts everywhere else vote likely inauguration dress remainder day way miss havisham forever wore wedding dress speaking great expectations hillary rodham overflowed 48 year ago first addressed wellesley graduating class the president college informed gathered 1969 student needed debate far i could ascertain spokesman kind like democratic primary 2016 minus term unknown even seven sisters school i glad miss adams made clear i speaking today u 400 u miss rodham told classmate after appointing edger bergen charlie mccarthys mortimer snerds attendance bespectacled granny glass awarding matronly wisdom least john lennon wisdom took issue previous speaker despite becoming first win election seat u s senate since reconstruction edward brooke came criticism calling empathy goal protestors criticized tactic though clinton senior thesis saul alinsky lamented black power demagogue elitist arrogance repressive intolerance within new left similar word coming republican necessitated brief rebuttal trust rodham ironically observed 1969 one word i asked class rehearsal wanted say everyone came said talk trust talk lack trust u way feel others talk trust bust what say what say feeling permeates generation perhaps even understood distrusted the trust bust certainly busted clintons 2016 plan she certainly even understand people distrusted after whitewater travelgate vast conspiracy benghazi missing email clinton found distrusted voice friday there load compromising road broadening political horizon and distrust american people trump edged 48 percent 38 percent question immediately prior novembers election stood major reason closing horizon clinton described vanquisher supporter embracing lie con alternative fact assault truth reason she failed explain american people chose lie truth as history major among today know well people power invent fact attack question mark beginning end free society offered that hyperbole like many people emerge 1960s hillary clinton embarked upon long strange trip from high school goldwater girl wellesley college republican president democratic politician clinton drank time place gave degree more significantly went idealist cynic comparison two wellesley commencement address show way back lamented long leader viewed politics art possible challenge practice politics art making appears impossible possible now big woman campus odd woman white house wonder current station even possible why arent i 50 point ahead asked september in may asks isnt president the woman famously dubbed congenital liar bill safire concludes lie mind getting stood election day like finding jilted bride wedding day inspires dangerous delusion\"\n", + " ]\n", + ")" ] }, { @@ -336,8 +361,8 @@ "metadata": {}, "outputs": [], "source": [ - "#saving the pipeline\n", - "filename = 'pipeline.sav'\n", + "# saving the pipeline\n", + "filename = \"pipeline.sav\"\n", "joblib.dump(pipeline, filename)" ] }, @@ -347,7 +372,7 @@ "metadata": {}, "outputs": [], "source": [ - "filename = 'pipeline.sav'" + "filename = \"pipeline.sav\"" ] }, { @@ -364,8 +389,12 @@ "outputs": [], "source": [ "loaded_model = joblib.load(filename)\n", - "result = loaded_model.predict([\"flynn hillary clinton big woman campus breitbart daniel j flynnever get feeling life circle roundabout rather head straight line toward intended destination hillary clinton remains big woman campus leafy liberal wellesley massachusetts everywhere else vote likely inauguration dress remainder day way miss havisham forever wore wedding dress speaking great expectations hillary rodham overflowed 48 year ago first addressed wellesley graduating class the president college informed gathered 1969 student needed debate far i could ascertain spokesman kind like democratic primary 2016 minus term unknown even seven sisters school i glad miss adams made clear i speaking today u 400 u miss rodham told classmate after appointing edger bergen charlie mccarthys mortimer snerds attendance bespectacled granny glass awarding matronly wisdom least john lennon wisdom took issue previous speaker despite becoming first win election seat u s senate since reconstruction edward brooke came criticism calling empathy goal protestors criticized tactic though clinton senior thesis saul alinsky lamented black power demagogue elitist arrogance repressive intolerance within new left similar word coming republican necessitated brief rebuttal trust rodham ironically observed 1969 one word i asked class rehearsal wanted say everyone came said talk trust talk lack trust u way feel others talk trust bust what say what say feeling permeates generation perhaps even understood distrusted the trust bust certainly busted clintons 2016 plan she certainly even understand people distrusted after whitewater travelgate vast conspiracy benghazi missing email clinton found distrusted voice friday there load compromising road broadening political horizon and distrust american people trump edged 48 percent 38 percent question immediately prior novembers election stood major reason closing horizon clinton described vanquisher supporter embracing lie con alternative fact assault truth reason she failed explain american people chose lie truth as history major among today know well people power invent fact attack question mark beginning end free society offered that hyperbole like many people emerge 1960s hillary clinton embarked upon long strange trip from high school goldwater girl wellesley college republican president democratic politician clinton drank time place gave degree more significantly went idealist cynic comparison two wellesley commencement address show way back lamented long leader viewed politics art possible challenge practice politics art making appears impossible possible now big woman campus odd woman white house wonder current station even possible why arent i 50 point ahead asked september in may asks isnt president the woman famously dubbed congenital liar bill safire concludes lie mind getting stood election day like finding jilted bride wedding day inspires dangerous delusion\"])\n", - "print(result) " + "result = loaded_model.predict(\n", + " [\n", + " \"flynn hillary clinton big woman campus breitbart daniel j flynnever get feeling life circle roundabout rather head straight line toward intended destination hillary clinton remains big woman campus leafy liberal wellesley massachusetts everywhere else vote likely inauguration dress remainder day way miss havisham forever wore wedding dress speaking great expectations hillary rodham overflowed 48 year ago first addressed wellesley graduating class the president college informed gathered 1969 student needed debate far i could ascertain spokesman kind like democratic primary 2016 minus term unknown even seven sisters school i glad miss adams made clear i speaking today u 400 u miss rodham told classmate after appointing edger bergen charlie mccarthys mortimer snerds attendance bespectacled granny glass awarding matronly wisdom least john lennon wisdom took issue previous speaker despite becoming first win election seat u s senate since reconstruction edward brooke came criticism calling empathy goal protestors criticized tactic though clinton senior thesis saul alinsky lamented black power demagogue elitist arrogance repressive intolerance within new left similar word coming republican necessitated brief rebuttal trust rodham ironically observed 1969 one word i asked class rehearsal wanted say everyone came said talk trust talk lack trust u way feel others talk trust bust what say what say feeling permeates generation perhaps even understood distrusted the trust bust certainly busted clintons 2016 plan she certainly even understand people distrusted after whitewater travelgate vast conspiracy benghazi missing email clinton found distrusted voice friday there load compromising road broadening political horizon and distrust american people trump edged 48 percent 38 percent question immediately prior novembers election stood major reason closing horizon clinton described vanquisher supporter embracing lie con alternative fact assault truth reason she failed explain american people chose lie truth as history major among today know well people power invent fact attack question mark beginning end free society offered that hyperbole like many people emerge 1960s hillary clinton embarked upon long strange trip from high school goldwater girl wellesley college republican president democratic politician clinton drank time place gave degree more significantly went idealist cynic comparison two wellesley commencement address show way back lamented long leader viewed politics art possible challenge practice politics art making appears impossible possible now big woman campus odd woman white house wonder current station even possible why arent i 50 point ahead asked september in may asks isnt president the woman famously dubbed congenital liar bill safire concludes lie mind getting stood election day like finding jilted bride wedding day inspires dangerous delusion\"\n", + " ]\n", + ")\n", + "print(result)" ] }, { diff --git a/Scripts/Miscellaneous/ReadingTextFromImage/ImageToText.ipynb b/Scripts/Miscellaneous/ReadingTextFromImage/ImageToText.ipynb index 0a0427a31..de235cd21 100644 --- a/Scripts/Miscellaneous/ReadingTextFromImage/ImageToText.ipynb +++ b/Scripts/Miscellaneous/ReadingTextFromImage/ImageToText.ipynb @@ -8,7 +8,10 @@ "source": [ "import cv2\n", "import pytesseract\n", - "pytesseract.pytesseract.tesseract_cmd = r'C:\\\\Program Files\\\\Tesseract-OCR\\\\tesseract.exe'" + "\n", + "pytesseract.pytesseract.tesseract_cmd = (\n", + " r\"C:\\\\Program Files\\\\Tesseract-OCR\\\\tesseract.exe\"\n", + ")" ] }, { @@ -29,7 +32,9 @@ } ], "source": [ - "img = cv2.imread(r'C:\\Users\\SUCHITRA\\Desktop\\Deep Learning\\ReadingText\\download.jpg')\n", + "img = cv2.imread(\n", + " r\"C:\\Users\\SUCHITRA\\Desktop\\Deep Learning\\ReadingText\\download.jpg\"\n", + ")\n", "text = pytesseract.image_to_string(img)\n", "print(text)" ] @@ -67,8 +72,9 @@ } ], "source": [ - "\n", - "image = cv2.imread(r'C:\\Users\\SUCHITRA\\Desktop\\Deep Learning\\ReadingText\\Image3.jpg')\n", + "image = cv2.imread(\n", + " r\"C:\\Users\\SUCHITRA\\Desktop\\Deep Learning\\ReadingText\\Image3.jpg\"\n", + ")\n", "text = pytesseract.image_to_string(image)\n", "print(text)" ] diff --git a/Scripts/Miscellaneous/Research_paper_latex_parser/parser.ipynb b/Scripts/Miscellaneous/Research_paper_latex_parser/parser.ipynb index 21d510103..9cd7124b7 100644 --- a/Scripts/Miscellaneous/Research_paper_latex_parser/parser.ipynb +++ b/Scripts/Miscellaneous/Research_paper_latex_parser/parser.ipynb @@ -1,494 +1,505 @@ { - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { - "colab": { - "name": "Copy of NLP Phase 2.ipynb", - "provenance": [], - "collapsed_sections": [], - "toc_visible": true - }, - "kernelspec": { - "name": "python3", - "display_name": "Python 3" - }, - "accelerator": "TPU" + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "Copy of NLP Phase 2.ipynb", + "provenance": [], + "collapsed_sections": [], + "toc_visible": true }, - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "69Of-VcYM5Em", - "colab_type": "text" - }, - "source": [ - "#Extracting text from LaTex file of any research paper" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "zOiPoXMvcB_E", - "colab_type": "text" - }, - "source": [ - "Importing the necessary libraries:" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "9nxilX8BcKKG", - "colab_type": "code", - "colab": {} - }, - "source": [ - "import re\n", - "import json " - ], - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "-3E-_8Z3Jq2U", - "colab_type": "text" - }, - "source": [ - "Reading the latex file:" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "sLQzklQTJu5n", - "colab_type": "code", - "colab": {} - }, - "source": [ - "data = open('p1.tex').read()" - ], - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "K-zLqWwXky2N", - "colab_type": "text" - }, - "source": [ - "Getting rid of all the unwanted tags before extraction of text:" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "PI591JMak3y9", - "colab_type": "code", - "colab": {} - }, - "source": [ - "def purge_images(data):\n", - " imgs = re.findall(r'begin{figure}(.*?)end{figure}', data,re.S)\n", - " start = \"\\\\begin{figure}\"\n", - " end = \"end{figure}\"\n", - " imgs = [start + img + end for img in imgs]\n", - " for img in imgs:\n", - " data = data.replace(img,\" \")\n", - " return data\n", - "\n", - "def purge_table(data):\n", - " tables = re.findall(r'begin{table}(.*?)end{table}', data,re.S)\n", - " start = \"\\\\begin{table}\"\n", - " end = \"end{table}\"\n", - " tables = [start + table + end for table in tables]\n", - " for table in tables:\n", - " data = data.replace(table,\" \")\n", - " return data\n", - "\n", - "def purge_equation(data):\n", - " equations = re.findall(r'begin{equation}(.*?)end{equation}', data,re.S)\n", - " start = \"\\\\begin{equation}\"\n", - " end = \"end{equation}\"\n", - " equations = [start + equation + end for equation in equations]\n", - " for equation in equations:\n", - " data = data.replace(equation,\" \")\n", - " return data\n", - "\n", - "data = purge_images(data)\n", - "data = purge_table(data)\n", - "data = purge_equation(data) " - ], - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "kFjDJ6JRWDc9", - "colab_type": "text" - }, - "source": [ - "Function to convert list to string since the findall function returns a list" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "kWqaRV4FVsRA", - "colab_type": "code", - "colab": {} - }, - "source": [ - "def listToString(s): \n", - " # initialize an empty string \n", - " str1 = \"\" \n", - " # traverse in the string \n", - " for ele in s: \n", - " str1 += ele \n", - " # return string \n", - " return str1 " - ], - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "hBi8McmScrUl", - "colab_type": "text" - }, - "source": [ - "Extracting the title:" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "fnHec6ObcwQT", - "colab_type": "code", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 34 - }, - "outputId": "68eb56a5-3fac-4999-919f-ee83dea5b5d0" - }, - "source": [ - "title = re.findall(r'title{(.*?)}',data,re.S)\n", - "title = listToString(title)\n", - "print(title)" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "text": [ - "A Sample Research Paper\n" - ], - "name": "stdout" - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "lcYbME2oomQg", - "colab_type": "text" - }, - "source": [ - "Extracting the authors:" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "CI4olibco6O0", - "colab_type": "code", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 34 - }, - "outputId": "4730937b-4c98-4bc0-cef3-d2be97f06fe0" - }, - "source": [ - "author = re.findall(r'author{(.*?)}',data,re.S)\n", - "author = listToString(author)\n", - "print(author)" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "text": [ - "I.M. Great and So.R. Yu\n" - ], - "name": "stdout" - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "msjnC9RKcOJC", - "colab_type": "text" - }, - "source": [ - "Extracting the abstract:" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "ewj1hcl-bCGe", - "colab_type": "code", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 88 - }, - "outputId": "b6f5bce6-769b-43cc-eb9d-ffac7e3784a5" - }, - "source": [ - "abstract = re.findall(r'\\\\begin{abstract}(.*?)\\\\end{abstract}', data, re.S)\n", - "abstract = listToString(abstract)\n", - "print(abstract)" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "text": [ - "\n", - "An abstract is a great convenience for the reader and is required by all journals. Lorem ipsum dolor sit amet, consectetur adipiscing elit. Nam convallis diam at lobortis dapibus. In id efficitur libero. Vestibulum vel ullamcorper neque. Orci varius natoque penatibus et magnis dis parturient montes, nascetur ridiculus mus. Quisque et felis commodo, rutrum erat at, sodales odio. Nam mi ipsum, imperdiet vitae augue non, convallis accumsan ante. Vivamus in lacus id nisi gravida condimentum vitae convallis tortor. In viverra congue sollicitudin. Quisque eget leo feugiat, tincidunt enim et, pretium orci. Duis condimentum maximus turpis at malesuada. Morbi laoreet metus felis, et varius odio consequat ac. Curabitur rutrum ac sapien ut ultrices. Donec et pretium elit. Pellentesque habitant morbi tristique senectus et netus et malesuada fames ac turpis egestas.\n", - "\n" - ], - "name": "stdout" - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "iVVbaGWEpl0C", - "colab_type": "text" - }, - "source": [ - "Extracting the introduction:" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "grtQQhm2po9b", - "colab_type": "code", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 156 - }, - "outputId": "c2e83fc6-f342-4f72-8379-5b1011909daa" - }, - "source": [ - "introduction = re.findall(r'\\\\section{Introduction}(.*?)\\\\',data,re.S)\n", - "introduction = listToString(introduction)\n", - "print(introduction)" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "text": [ - "\n", - "\n", - "Using latex is pretty easy if you have a sample document you can follow.Lorem ipsum dolor sit amet, consectetur adipiscing elit. Sed volutpat ornare odio et faucibus. Donec fringilla massa eget auctor viverra. Mauris a imperdiet est. Cras tincidunt nulla ut elit tristique ultricies. Phasellus nec orci vel mi suscipit maximus at vitae tortor. Vivamus sed libero vel lacus aliquam rhoncus. Ut in lacinia nunc. Nullam quis mauris leo. Phasellus vitae nisl condimentum quam congue volutpat. Quisque et dapibus ipsum. Curabitur fringilla pellentesque elit, non posuere purus malesuada id. Pellentesque rutrum vitae urna eu mattis.\n", - "\n", - "Maecenas ac congue massa. Quisque a sem turpis. Duis et diam ex. Suspendisse et enim interdum, sodales risus eu, ultrices est. Suspendisse eu odio enim. In vulputate odio porttitor tincidunt vestibulum. Praesent tincidunt ullamcorper purus, quis semper felis volutpat quis.\n", - "\n", - "\n" - ], - "name": "stdout" - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "FYxnWEPUJSwi", - "colab_type": "text" - }, - "source": [ - "Extracting the results:" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "YG_GrwJhJXZ-", - "colab_type": "code", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 207 - }, - "outputId": "04faf45b-b02a-4d1f-e03d-cec3dc55425e" - }, - "source": [ - "results = re.findall(r'\\\\section{Results}(.*?)\\\\',data,re.S)\n", - "results = listToString(results)\n", - "print(results)" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "text": [ - "\n", - "Including figures, tables, and equations is easy. Latex also permits easy reference to document elements (figures, tables, sections). Lorem ipsum dolor sit amet, consectetur adipiscing elit. Aliquam tincidunt lorem luctus eros dictum faucibus. Fusce euismod libero et erat pretium dapibus. Pellentesque faucibus hendrerit est, ac fringilla urna. In porta, ante eu dictum vestibulum, nisl nulla euismod purus, ac bibendum nibh ante vel elit. Fusce diam ante, tincidunt id eleifend a, hendrerit vitae tellus. Duis pretium urna ac vestibulum eleifend. Suspendisse potenti. Aliquam varius odio in pretium semper. Ut faucibus lobortis mauris vel sollicitudin. Nullam condimentum, lacus quis mattis pellentesque, massa nulla cursus nisi, aliquet eleifend est tellus ut libero.\n", - "\n", - " \n", - "\n", - " \n", - "\n", - " \n", - "\n", - "\n" - ], - "name": "stdout" - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "OCLM7tNVrOGP", - "colab_type": "text" - }, - "source": [ - "Extracting the conclusions:" - ] + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "accelerator": "TPU" + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "69Of-VcYM5Em", + "colab_type": "text" + }, + "source": [ + "#Extracting text from LaTex file of any research paper" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "zOiPoXMvcB_E", + "colab_type": "text" + }, + "source": [ + "Importing the necessary libraries:" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "9nxilX8BcKKG", + "colab_type": "code", + "colab": {} + }, + "source": [ + "import re\n", + "import json" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-3E-_8Z3Jq2U", + "colab_type": "text" + }, + "source": [ + "Reading the latex file:" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "sLQzklQTJu5n", + "colab_type": "code", + "colab": {} + }, + "source": [ + "data = open(\"p1.tex\").read()" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "K-zLqWwXky2N", + "colab_type": "text" + }, + "source": [ + "Getting rid of all the unwanted tags before extraction of text:" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "PI591JMak3y9", + "colab_type": "code", + "colab": {} + }, + "source": [ + "def purge_images(data):\n", + " imgs = re.findall(r\"begin{figure}(.*?)end{figure}\", data, re.S)\n", + " start = \"\\\\begin{figure}\"\n", + " end = \"end{figure}\"\n", + " imgs = [start + img + end for img in imgs]\n", + " for img in imgs:\n", + " data = data.replace(img, \" \")\n", + " return data\n", + "\n", + "\n", + "def purge_table(data):\n", + " tables = re.findall(r\"begin{table}(.*?)end{table}\", data, re.S)\n", + " start = \"\\\\begin{table}\"\n", + " end = \"end{table}\"\n", + " tables = [start + table + end for table in tables]\n", + " for table in tables:\n", + " data = data.replace(table, \" \")\n", + " return data\n", + "\n", + "\n", + "def purge_equation(data):\n", + " equations = re.findall(r\"begin{equation}(.*?)end{equation}\", data, re.S)\n", + " start = \"\\\\begin{equation}\"\n", + " end = \"end{equation}\"\n", + " equations = [start + equation + end for equation in equations]\n", + " for equation in equations:\n", + " data = data.replace(equation, \" \")\n", + " return data\n", + "\n", + "\n", + "data = purge_images(data)\n", + "data = purge_table(data)\n", + "data = purge_equation(data)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "kFjDJ6JRWDc9", + "colab_type": "text" + }, + "source": [ + "Function to convert list to string since the findall function returns a list" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "kWqaRV4FVsRA", + "colab_type": "code", + "colab": {} + }, + "source": [ + "def listToString(s):\n", + " # initialize an empty string\n", + " str1 = \"\"\n", + " # traverse in the string\n", + " for ele in s:\n", + " str1 += ele\n", + " # return string\n", + " return str1" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "hBi8McmScrUl", + "colab_type": "text" + }, + "source": [ + "Extracting the title:" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "fnHec6ObcwQT", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 }, + "outputId": "68eb56a5-3fac-4999-919f-ee83dea5b5d0" + }, + "source": [ + "title = re.findall(r\"title{(.*?)}\", data, re.S)\n", + "title = listToString(title)\n", + "print(title)" + ], + "execution_count": null, + "outputs": [ { - "cell_type": "code", - "metadata": { - "id": "Xc1qbvqgrLkB", - "colab_type": "code", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 122 - }, - "outputId": "956acbdb-228b-4ebc-c0ae-3821f5adc794" - }, - "source": [ - "conclusions = re.findall(r'\\\\section{Conclusions}(.*?)\\\\',data,re.S)\n", - "conclusions = listToString(conclusions)\n", - "print(conclusions)" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "text": [ - "\n", - "\n", - "Man, latex is great! Lorem ipsum dolor sit amet, consectetur adipiscing elit. Aliquam tincidunt lorem luctus eros dictum faucibus. Fusce euismod libero et erat pretium dapibus. Pellentesque faucibus hendrerit est, ac fringilla urna. In porta, ante eu dictum vestibulum, nisl nulla euismod purus, ac bibendum nibh ante vel elit. Fusce diam ante, tincidunt id eleifend a, hendrerit vitae tellus. Duis pretium urna ac vestibulum eleifend. Suspendisse potenti. Aliquam varius odio in pretium semper. Ut faucibus lobortis mauris vel sollicitudin. Nullam condimentum, lacus quis mattis pellentesque, massa nulla cursus nisi, aliquet eleifend est tellus ut libero.\n", - "\n", - "\n" - ], - "name": "stdout" - } - ] + "output_type": "stream", + "text": [ + "A Sample Research Paper\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "lcYbME2oomQg", + "colab_type": "text" + }, + "source": [ + "Extracting the authors:" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "CI4olibco6O0", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 }, + "outputId": "4730937b-4c98-4bc0-cef3-d2be97f06fe0" + }, + "source": [ + "author = re.findall(r\"author{(.*?)}\", data, re.S)\n", + "author = listToString(author)\n", + "print(author)" + ], + "execution_count": null, + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "id": "J_Lvs9FkMGEn", - "colab_type": "text" - }, - "source": [ - "Extracting the acknowledgments:" - ] + "output_type": "stream", + "text": [ + "I.M. Great and So.R. Yu\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "msjnC9RKcOJC", + "colab_type": "text" + }, + "source": [ + "Extracting the abstract:" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "ewj1hcl-bCGe", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 88 }, + "outputId": "b6f5bce6-769b-43cc-eb9d-ffac7e3784a5" + }, + "source": [ + "abstract = re.findall(r\"\\\\begin{abstract}(.*?)\\\\end{abstract}\", data, re.S)\n", + "abstract = listToString(abstract)\n", + "print(abstract)" + ], + "execution_count": null, + "outputs": [ { - "cell_type": "code", - "metadata": { - "id": "3hL78TPFffGH", - "colab_type": "code", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 105 - }, - "outputId": "95857884-8fc1-474a-ca0e-871b46175a3c" - }, - "source": [ - "acknowledgments = re.findall(r'\\\\acknowledgments(.*?)\\\\',data,re.S)\n", - "acknowledgments = listToString(acknowledgments)\n", - "print(acknowledgments)" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "text": [ - "\n", - "The author is grateful to Donald Knuth for inventing tex, and making publication quality typesetting a reality for scientists around the world.\n", - "\n", - "\n" - ], - "name": "stdout" - } - ] + "output_type": "stream", + "text": [ + "\n", + "An abstract is a great convenience for the reader and is required by all journals. Lorem ipsum dolor sit amet, consectetur adipiscing elit. Nam convallis diam at lobortis dapibus. In id efficitur libero. Vestibulum vel ullamcorper neque. Orci varius natoque penatibus et magnis dis parturient montes, nascetur ridiculus mus. Quisque et felis commodo, rutrum erat at, sodales odio. Nam mi ipsum, imperdiet vitae augue non, convallis accumsan ante. Vivamus in lacus id nisi gravida condimentum vitae convallis tortor. In viverra congue sollicitudin. Quisque eget leo feugiat, tincidunt enim et, pretium orci. Duis condimentum maximus turpis at malesuada. Morbi laoreet metus felis, et varius odio consequat ac. Curabitur rutrum ac sapien ut ultrices. Donec et pretium elit. Pellentesque habitant morbi tristique senectus et netus et malesuada fames ac turpis egestas.\n", + "\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "iVVbaGWEpl0C", + "colab_type": "text" + }, + "source": [ + "Extracting the introduction:" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "grtQQhm2po9b", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 156 }, + "outputId": "c2e83fc6-f342-4f72-8379-5b1011909daa" + }, + "source": [ + "introduction = re.findall(r\"\\\\section{Introduction}(.*?)\\\\\", data, re.S)\n", + "introduction = listToString(introduction)\n", + "print(introduction)" + ], + "execution_count": null, + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "id": "7yu6bbjIbhkr", - "colab_type": "text" - }, - "source": [ - "Creating a dictionary of all the extracted text:" - ] + "output_type": "stream", + "text": [ + "\n", + "\n", + "Using latex is pretty easy if you have a sample document you can follow.Lorem ipsum dolor sit amet, consectetur adipiscing elit. Sed volutpat ornare odio et faucibus. Donec fringilla massa eget auctor viverra. Mauris a imperdiet est. Cras tincidunt nulla ut elit tristique ultricies. Phasellus nec orci vel mi suscipit maximus at vitae tortor. Vivamus sed libero vel lacus aliquam rhoncus. Ut in lacinia nunc. Nullam quis mauris leo. Phasellus vitae nisl condimentum quam congue volutpat. Quisque et dapibus ipsum. Curabitur fringilla pellentesque elit, non posuere purus malesuada id. Pellentesque rutrum vitae urna eu mattis.\n", + "\n", + "Maecenas ac congue massa. Quisque a sem turpis. Duis et diam ex. Suspendisse et enim interdum, sodales risus eu, ultrices est. Suspendisse eu odio enim. In vulputate odio porttitor tincidunt vestibulum. Praesent tincidunt ullamcorper purus, quis semper felis volutpat quis.\n", + "\n", + "\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "FYxnWEPUJSwi", + "colab_type": "text" + }, + "source": [ + "Extracting the results:" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "YG_GrwJhJXZ-", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 207 }, + "outputId": "04faf45b-b02a-4d1f-e03d-cec3dc55425e" + }, + "source": [ + "results = re.findall(r\"\\\\section{Results}(.*?)\\\\\", data, re.S)\n", + "results = listToString(results)\n", + "print(results)" + ], + "execution_count": null, + "outputs": [ { - "cell_type": "code", - "metadata": { - "id": "ka6qb6bRM0Hx", - "colab_type": "code", - "colab": {} - }, - "source": [ - "a_dict = {}\n", - "\n", - "for variable in [\"title\", \"author\", \"abstract\",\"introduction\",\"results\",\"conclusions\",\"acknowledgments\"]:\n", - " a_dict[variable] = eval(variable)\n", - " dict_1.append(a_dict)\n", - "\n", - "print(a_dict)" - ], - "execution_count": null, - "outputs": [] + "output_type": "stream", + "text": [ + "\n", + "Including figures, tables, and equations is easy. Latex also permits easy reference to document elements (figures, tables, sections). Lorem ipsum dolor sit amet, consectetur adipiscing elit. Aliquam tincidunt lorem luctus eros dictum faucibus. Fusce euismod libero et erat pretium dapibus. Pellentesque faucibus hendrerit est, ac fringilla urna. In porta, ante eu dictum vestibulum, nisl nulla euismod purus, ac bibendum nibh ante vel elit. Fusce diam ante, tincidunt id eleifend a, hendrerit vitae tellus. Duis pretium urna ac vestibulum eleifend. Suspendisse potenti. Aliquam varius odio in pretium semper. Ut faucibus lobortis mauris vel sollicitudin. Nullam condimentum, lacus quis mattis pellentesque, massa nulla cursus nisi, aliquet eleifend est tellus ut libero.\n", + "\n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + "\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "OCLM7tNVrOGP", + "colab_type": "text" + }, + "source": [ + "Extracting the conclusions:" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "Xc1qbvqgrLkB", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 122 }, + "outputId": "956acbdb-228b-4ebc-c0ae-3821f5adc794" + }, + "source": [ + "conclusions = re.findall(r\"\\\\section{Conclusions}(.*?)\\\\\", data, re.S)\n", + "conclusions = listToString(conclusions)\n", + "print(conclusions)" + ], + "execution_count": null, + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "id": "uE4P_xlSbmlj", - "colab_type": "text" - }, - "source": [ - "Converting the dictionary into a JSON file:" - ] + "output_type": "stream", + "text": [ + "\n", + "\n", + "Man, latex is great! Lorem ipsum dolor sit amet, consectetur adipiscing elit. Aliquam tincidunt lorem luctus eros dictum faucibus. Fusce euismod libero et erat pretium dapibus. Pellentesque faucibus hendrerit est, ac fringilla urna. In porta, ante eu dictum vestibulum, nisl nulla euismod purus, ac bibendum nibh ante vel elit. Fusce diam ante, tincidunt id eleifend a, hendrerit vitae tellus. Duis pretium urna ac vestibulum eleifend. Suspendisse potenti. Aliquam varius odio in pretium semper. Ut faucibus lobortis mauris vel sollicitudin. Nullam condimentum, lacus quis mattis pellentesque, massa nulla cursus nisi, aliquet eleifend est tellus ut libero.\n", + "\n", + "\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "J_Lvs9FkMGEn", + "colab_type": "text" + }, + "source": [ + "Extracting the acknowledgments:" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "3hL78TPFffGH", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 105 }, + "outputId": "95857884-8fc1-474a-ca0e-871b46175a3c" + }, + "source": [ + "acknowledgments = re.findall(r\"\\\\acknowledgments(.*?)\\\\\", data, re.S)\n", + "acknowledgments = listToString(acknowledgments)\n", + "print(acknowledgments)" + ], + "execution_count": null, + "outputs": [ { - "cell_type": "code", - "metadata": { - "id": "nErVmQx_Z515", - "colab_type": "code", - "colab": {} - }, - "source": [ - "with open(\"extracted_data.json\", \"w\") as outfile: \n", - " json.dump(a_dict, outfile) " - ], - "execution_count": null, - "outputs": [] + "output_type": "stream", + "text": [ + "\n", + "The author is grateful to Donald Knuth for inventing tex, and making publication quality typesetting a reality for scientists around the world.\n", + "\n", + "\n" + ], + "name": "stdout" } - ] + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "7yu6bbjIbhkr", + "colab_type": "text" + }, + "source": [ + "Creating a dictionary of all the extracted text:" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "ka6qb6bRM0Hx", + "colab_type": "code", + "colab": {} + }, + "source": [ + "a_dict = {}\n", + "\n", + "for variable in [\n", + " \"title\",\n", + " \"author\",\n", + " \"abstract\",\n", + " \"introduction\",\n", + " \"results\",\n", + " \"conclusions\",\n", + " \"acknowledgments\",\n", + "]:\n", + " a_dict[variable] = eval(variable)\n", + " dict_1.append(a_dict)\n", + "\n", + "print(a_dict)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "uE4P_xlSbmlj", + "colab_type": "text" + }, + "source": [ + "Converting the dictionary into a JSON file:" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "nErVmQx_Z515", + "colab_type": "code", + "colab": {} + }, + "source": [ + "with open(\"extracted_data.json\", \"w\") as outfile:\n", + " json.dump(a_dict, outfile)" + ], + "execution_count": null, + "outputs": [] + } + ] } \ No newline at end of file diff --git a/Scripts/Miscellaneous/heart attack analysis/heart-attack-analysis-xg-boost.ipynb b/Scripts/Miscellaneous/heart attack analysis/heart-attack-analysis-xg-boost.ipynb index dab7e1440..f70037707 100644 --- a/Scripts/Miscellaneous/heart attack analysis/heart-attack-analysis-xg-boost.ipynb +++ b/Scripts/Miscellaneous/heart attack analysis/heart-attack-analysis-xg-boost.ipynb @@ -1 +1,2110 @@ -{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.7.10","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\nimport seaborn as sns\nimport plotly.express as px\nimport matplotlib.pyplot as plt\nimport missingno as msno\nimport plotly.graph_objects as go\n\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\nimport warnings\nwarnings.filterwarnings('ignore')\n\nplt.style.use('fivethirtyeight')\n%matplotlib inline\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n for filename in filenames:\n print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2021-08-24T17:50:38.074908Z","iopub.execute_input":"2021-08-24T17:50:38.075253Z","iopub.status.idle":"2021-08-24T17:50:41.104332Z","shell.execute_reply.started":"2021-08-24T17:50:38.075223Z","shell.execute_reply":"2021-08-24T17:50:41.103401Z"},"trusted":true},"execution_count":2,"outputs":[{"name":"stdout","text":"/kaggle/input/heart-attack-analysis-prediction-dataset/o2Saturation.csv\n/kaggle/input/heart-attack-analysis-prediction-dataset/heart.csv\n","output_type":"stream"}]},{"cell_type":"markdown","source":"## Reading the data","metadata":{}},{"cell_type":"code","source":"data=pd.read_csv('/kaggle/input/heart-attack-analysis-prediction-dataset/heart.csv')\ndata.head()","metadata":{"execution":{"iopub.status.busy":"2021-08-24T17:50:41.106138Z","iopub.execute_input":"2021-08-24T17:50:41.106792Z","iopub.status.idle":"2021-08-24T17:50:41.157251Z","shell.execute_reply.started":"2021-08-24T17:50:41.106741Z","shell.execute_reply":"2021-08-24T17:50:41.156071Z"},"trusted":true},"execution_count":3,"outputs":[{"execution_count":3,"output_type":"execute_result","data":{"text/plain":" age sex cp trtbps chol fbs restecg thalachh exng oldpeak slp \\\n0 63 1 3 145 233 1 0 150 0 2.3 0 \n1 37 1 2 130 250 0 1 187 0 3.5 0 \n2 41 0 1 130 204 0 0 172 0 1.4 2 \n3 56 1 1 120 236 0 1 178 0 0.8 2 \n4 57 0 0 120 354 0 1 163 1 0.6 2 \n\n caa thall output \n0 0 1 1 \n1 0 2 1 \n2 0 2 1 \n3 0 2 1 \n4 0 2 1 ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
agesexcptrtbpscholfbsrestecgthalachhexngoldpeakslpcaathalloutput
063131452331015002.30011
137121302500118703.50021
241011302040017201.42021
356111202360117800.82021
457001203540116310.62021
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"# Exploratory data Analysis","metadata":{}},{"cell_type":"markdown","source":"### Basic information about Data","metadata":{}},{"cell_type":"code","source":"print('There are {} data points and {} features in the data'.format(data.shape[0],data.shape[1]))","metadata":{"execution":{"iopub.status.busy":"2021-08-24T17:50:41.158928Z","iopub.execute_input":"2021-08-24T17:50:41.159265Z","iopub.status.idle":"2021-08-24T17:50:41.165647Z","shell.execute_reply.started":"2021-08-24T17:50:41.159235Z","shell.execute_reply":"2021-08-24T17:50:41.164523Z"},"trusted":true},"execution_count":4,"outputs":[{"name":"stdout","text":"There are 303 data points and 14 features in the data\n","output_type":"stream"}]},{"cell_type":"code","source":"data.info()","metadata":{"execution":{"iopub.status.busy":"2021-08-24T17:50:41.167558Z","iopub.execute_input":"2021-08-24T17:50:41.168076Z","iopub.status.idle":"2021-08-24T17:50:41.197775Z","shell.execute_reply.started":"2021-08-24T17:50:41.168040Z","shell.execute_reply":"2021-08-24T17:50:41.196295Z"},"trusted":true},"execution_count":5,"outputs":[{"name":"stdout","text":"\nRangeIndex: 303 entries, 0 to 302\nData columns (total 14 columns):\n # Column Non-Null Count Dtype \n--- ------ -------------- ----- \n 0 age 303 non-null int64 \n 1 sex 303 non-null int64 \n 2 cp 303 non-null int64 \n 3 trtbps 303 non-null int64 \n 4 chol 303 non-null int64 \n 5 fbs 303 non-null int64 \n 6 restecg 303 non-null int64 \n 7 thalachh 303 non-null int64 \n 8 exng 303 non-null int64 \n 9 oldpeak 303 non-null float64\n 10 slp 303 non-null int64 \n 11 caa 303 non-null int64 \n 12 thall 303 non-null int64 \n 13 output 303 non-null int64 \ndtypes: float64(1), int64(13)\nmemory usage: 33.3 KB\n","output_type":"stream"}]},{"cell_type":"code","source":"data.describe()","metadata":{"execution":{"iopub.status.busy":"2021-08-24T17:50:41.199238Z","iopub.execute_input":"2021-08-24T17:50:41.199601Z","iopub.status.idle":"2021-08-24T17:50:41.266325Z","shell.execute_reply.started":"2021-08-24T17:50:41.199551Z","shell.execute_reply":"2021-08-24T17:50:41.264847Z"},"trusted":true},"execution_count":6,"outputs":[{"execution_count":6,"output_type":"execute_result","data":{"text/plain":" age sex cp trtbps chol fbs \\\ncount 303.000000 303.000000 303.000000 303.000000 303.000000 303.000000 \nmean 54.366337 0.683168 0.966997 131.623762 246.264026 0.148515 \nstd 9.082101 0.466011 1.032052 17.538143 51.830751 0.356198 \nmin 29.000000 0.000000 0.000000 94.000000 126.000000 0.000000 \n25% 47.500000 0.000000 0.000000 120.000000 211.000000 0.000000 \n50% 55.000000 1.000000 1.000000 130.000000 240.000000 0.000000 \n75% 61.000000 1.000000 2.000000 140.000000 274.500000 0.000000 \nmax 77.000000 1.000000 3.000000 200.000000 564.000000 1.000000 \n\n restecg thalachh exng oldpeak slp caa \\\ncount 303.000000 303.000000 303.000000 303.000000 303.000000 303.000000 \nmean 0.528053 149.646865 0.326733 1.039604 1.399340 0.729373 \nstd 0.525860 22.905161 0.469794 1.161075 0.616226 1.022606 \nmin 0.000000 71.000000 0.000000 0.000000 0.000000 0.000000 \n25% 0.000000 133.500000 0.000000 0.000000 1.000000 0.000000 \n50% 1.000000 153.000000 0.000000 0.800000 1.000000 0.000000 \n75% 1.000000 166.000000 1.000000 1.600000 2.000000 1.000000 \nmax 2.000000 202.000000 1.000000 6.200000 2.000000 4.000000 \n\n thall output \ncount 303.000000 303.000000 \nmean 2.313531 0.544554 \nstd 0.612277 0.498835 \nmin 0.000000 0.000000 \n25% 2.000000 0.000000 \n50% 2.000000 1.000000 \n75% 3.000000 1.000000 \nmax 3.000000 1.000000 ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
agesexcptrtbpscholfbsrestecgthalachhexngoldpeakslpcaathalloutput
count303.000000303.000000303.000000303.000000303.000000303.000000303.000000303.000000303.000000303.000000303.000000303.000000303.000000303.000000
mean54.3663370.6831680.966997131.623762246.2640260.1485150.528053149.6468650.3267331.0396041.3993400.7293732.3135310.544554
std9.0821010.4660111.03205217.53814351.8307510.3561980.52586022.9051610.4697941.1610750.6162261.0226060.6122770.498835
min29.0000000.0000000.00000094.000000126.0000000.0000000.00000071.0000000.0000000.0000000.0000000.0000000.0000000.000000
25%47.5000000.0000000.000000120.000000211.0000000.0000000.000000133.5000000.0000000.0000001.0000000.0000002.0000000.000000
50%55.0000001.0000001.000000130.000000240.0000000.0000001.000000153.0000000.0000000.8000001.0000000.0000002.0000001.000000
75%61.0000001.0000002.000000140.000000274.5000000.0000001.000000166.0000001.0000001.6000002.0000001.0000003.0000001.000000
max77.0000001.0000003.000000200.000000564.0000001.0000002.000000202.0000001.0000006.2000002.0000004.0000003.0000001.000000
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"### checking for null values","metadata":{}},{"cell_type":"code","source":"\nmsno.bar(data)\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2021-08-24T17:50:41.267947Z","iopub.execute_input":"2021-08-24T17:50:41.268348Z","iopub.status.idle":"2021-08-24T17:50:42.260698Z","shell.execute_reply.started":"2021-08-24T17:50:41.268305Z","shell.execute_reply":"2021-08-24T17:50:42.259641Z"},"trusted":true},"execution_count":7,"outputs":[{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"sns.heatmap(data.isnull(),yticklabels=False,cbar=False,cmap='viridis')","metadata":{"execution":{"iopub.status.busy":"2021-08-24T17:50:42.262103Z","iopub.execute_input":"2021-08-24T17:50:42.262510Z","iopub.status.idle":"2021-08-24T17:50:42.444523Z","shell.execute_reply.started":"2021-08-24T17:50:42.262472Z","shell.execute_reply":"2021-08-24T17:50:42.443356Z"},"trusted":true},"execution_count":8,"outputs":[{"execution_count":8,"output_type":"execute_result","data":{"text/plain":""},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"There are no missing values present in the dataset","metadata":{}},{"cell_type":"markdown","source":"### Checking correlation","metadata":{}},{"cell_type":"code","source":"plt.figure(figsize = (15, 8))\n\nsns.heatmap(data.corr(), annot = True, linewidths = 1)\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2021-08-24T17:50:42.448228Z","iopub.execute_input":"2021-08-24T17:50:42.448692Z","iopub.status.idle":"2021-08-24T17:50:43.571416Z","shell.execute_reply.started":"2021-08-24T17:50:42.448638Z","shell.execute_reply":"2021-08-24T17:50:43.570496Z"},"trusted":true},"execution_count":9,"outputs":[{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"There are no correlated columns presebt in the data ","metadata":{}},{"cell_type":"markdown","source":"### Analysis of Features","metadata":{}},{"cell_type":"markdown","source":"### Age","metadata":{}},{"cell_type":"code","source":"plt.figure(figsize = (16, 7))\nsns.distplot(data['age'])\nplt.title('Distribution Plot of Ages\\n', fontsize = 20)\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2021-08-24T17:50:43.573210Z","iopub.execute_input":"2021-08-24T17:50:43.573693Z","iopub.status.idle":"2021-08-24T17:50:43.962739Z","shell.execute_reply.started":"2021-08-24T17:50:43.573636Z","shell.execute_reply":"2021-08-24T17:50:43.961890Z"},"trusted":true},"execution_count":10,"outputs":[{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"Age_18_25 = data.age[(data.age >= 18) & (data.age <= 25)]\nAge_26_35 = data.age[(data.age >= 26) & (data.age <= 35)]\nAge_36_45 = data.age[(data.age >= 36) & (data.age <= 45)]\nAge_46_55 = data.age[(data.age >= 46) & (data.age <= 55)]\nAge_56_65 = data.age[(data.age >= 56) & (data.age <= 65)]\nAge_66_75 = data.age[(data.age >= 66) & (data.age <= 75)]\nAge_75above = data.age[data.age >= 76]\nx_Age = [ '18-25','26-35', '36-45', '46-55', '56-65','66-75','75+']\ny_Age = [len(Age_18_25.values), len(Age_26_35.values), len(Age_36_45.values), len(Age_46_55.values), len(Age_56_65.values),\n len(Age_66_75.values), len(Age_75above.values)]\n\npx.bar(data_frame = data, x = x_Age, y = y_Age, color = x_Age, template = 'plotly_dark',\n labels={\n 'x': \"Age\",\n 'y': \"Number\",\n 'color':'Age group'\n \n },\n title = 'Number of patients per Age group')","metadata":{"execution":{"iopub.status.busy":"2021-08-24T17:50:43.964009Z","iopub.execute_input":"2021-08-24T17:50:43.964456Z","iopub.status.idle":"2021-08-24T17:50:45.569476Z","shell.execute_reply.started":"2021-08-24T17:50:43.964408Z","shell.execute_reply":"2021-08-24T17:50:45.568343Z"},"trusted":true},"execution_count":11,"outputs":[{"output_type":"display_data","data":{"text/html":" \n "},"metadata":{}},{"output_type":"display_data","data":{"text/html":"
"},"metadata":{}}]},{"cell_type":"markdown","source":"We can see the cases are more of age group from 56 to 65","metadata":{}},{"cell_type":"markdown","source":"### Gender","metadata":{}},{"cell_type":"code","source":"\npx.bar(data_frame = data, x = list(data.sex.value_counts().keys()), y = list(data.sex.value_counts()), \n color = list(data.sex.value_counts().keys()), template = 'plotly_dark',\n labels={\n 'x': \"Gender\",\n 'y': \"Number\",\n 'color':'Gender group'\n \n },\n title = 'Number of patients per Gender group')\n","metadata":{"execution":{"iopub.status.busy":"2021-08-24T17:50:45.571029Z","iopub.execute_input":"2021-08-24T17:50:45.571358Z","iopub.status.idle":"2021-08-24T17:50:45.672438Z","shell.execute_reply.started":"2021-08-24T17:50:45.571324Z","shell.execute_reply":"2021-08-24T17:50:45.671211Z"},"trusted":true},"execution_count":12,"outputs":[{"output_type":"display_data","data":{"text/html":"
"},"metadata":{}}]},{"cell_type":"markdown","source":"Assigning labels for one hot encoding","metadata":{}},{"cell_type":"code","source":"# since we don't know 0 is male or female and vice versa we are assigning with the same label \ndata['sex'] = data['sex'].map({0:\"0_gender\", 1: \"1_gender\"}) \ndata.head()","metadata":{"execution":{"iopub.status.busy":"2021-08-24T17:50:45.676611Z","iopub.execute_input":"2021-08-24T17:50:45.677195Z","iopub.status.idle":"2021-08-24T17:50:45.700136Z","shell.execute_reply.started":"2021-08-24T17:50:45.677143Z","shell.execute_reply":"2021-08-24T17:50:45.698873Z"},"trusted":true},"execution_count":13,"outputs":[{"execution_count":13,"output_type":"execute_result","data":{"text/plain":" age sex cp trtbps chol fbs restecg thalachh exng oldpeak \\\n0 63 1_gender 3 145 233 1 0 150 0 2.3 \n1 37 1_gender 2 130 250 0 1 187 0 3.5 \n2 41 0_gender 1 130 204 0 0 172 0 1.4 \n3 56 1_gender 1 120 236 0 1 178 0 0.8 \n4 57 0_gender 0 120 354 0 1 163 1 0.6 \n\n slp caa thall output \n0 0 0 1 1 \n1 0 0 2 1 \n2 2 0 2 1 \n3 2 0 2 1 \n4 2 0 2 1 ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
agesexcptrtbpscholfbsrestecgthalachhexngoldpeakslpcaathalloutput
0631_gender31452331015002.30011
1371_gender21302500118703.50021
2410_gender11302040017201.42021
3561_gender11202360117800.82021
4570_gender01203540116310.62021
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"### cp\nChest Pain type chest pain type","metadata":{}},{"cell_type":"code","source":"\npx.bar(data_frame = data, x = list(data.cp.value_counts().keys()), y = list(data.cp.value_counts()), \n color = list(data.cp.value_counts().keys()), template = 'plotly_dark',\n labels={\n 'x': \"Chest Pain intnsity\",\n 'y': \"Count\",\n 'color':'Chest Pain intnsity'\n \n },\n title = 'Number of patients per Chest Pain intnsity')\n","metadata":{"execution":{"iopub.status.busy":"2021-08-24T17:50:45.701850Z","iopub.execute_input":"2021-08-24T17:50:45.702224Z","iopub.status.idle":"2021-08-24T17:50:45.788604Z","shell.execute_reply.started":"2021-08-24T17:50:45.702174Z","shell.execute_reply":"2021-08-24T17:50:45.787314Z"},"trusted":true},"execution_count":14,"outputs":[{"output_type":"display_data","data":{"text/html":"
"},"metadata":{}}]},{"cell_type":"code","source":"\ncp_0_1 = data.cp[(data.cp == 0) & (data.output == 1)]\ncp_0_0 = data.cp[(data.cp == 0) & (data.output == 0)]\ncp_1_1 = data.cp[(data.cp == 1) & (data.output == 1)]\ncp_1_0 = data.cp[(data.cp == 1) & (data.output == 0)]\ncp_2_1 = data.cp[(data.cp == 2) & (data.output == 1)]\ncp_2_0 = data.cp[(data.cp == 2) & (data.output == 0)]\ncp_3_1 = data.cp[(data.cp == 3) & (data.output == 1)]\ncp_3_0 = data.cp[(data.cp == 3) & (data.output == 0)]\n\ny_cp_1 = [len(cp_0_1.values), len(cp_1_1.values), len(cp_2_1.values), len(cp_3_1.values)]\ny_cp_0 = [len(cp_0_0.values), len(cp_1_0.values), len(cp_2_0.values),len(cp_3_0.values)]\n\nfig = go.Figure()\nfig.add_trace(go.Bar(\n x=[0,1,2,3],\n y=y_cp_1,\n name='Heart Attack',\n marker_color='indianred'\n))\nfig.add_trace(go.Bar(\n x=[0,1,2,3],\n y=y_cp_0,\n name='Safe',\n marker_color='lightsalmon'\n))\n\n# Here we modify the tickangle of the xaxis, resulting in rotated labels.\nfig.update_layout(barmode='group', xaxis_tickangle=-45)\nfig.show()","metadata":{"execution":{"iopub.status.busy":"2021-08-24T17:50:45.790389Z","iopub.execute_input":"2021-08-24T17:50:45.790948Z","iopub.status.idle":"2021-08-24T17:50:45.825684Z","shell.execute_reply.started":"2021-08-24T17:50:45.790891Z","shell.execute_reply":"2021-08-24T17:50:45.824258Z"},"trusted":true},"execution_count":15,"outputs":[{"output_type":"display_data","data":{"text/html":"
"},"metadata":{}}]},{"cell_type":"markdown","source":"Though Chest pain is represented as numeric data but it is categorical in nature. We can convert the data to categorical to get dummies. LabelEncoding will not work here as we can see that there is not such relation among the categories that resembles an ordinal relationship.","metadata":{}},{"cell_type":"code","source":"data['cp'] = data['cp'].map({0:\"Intensity_0\", 1: \"Intensity_1\", 2: 'Intensity_2',3:'Intensity_3'}) \ndata.head()","metadata":{"execution":{"iopub.status.busy":"2021-08-24T17:50:45.827498Z","iopub.execute_input":"2021-08-24T17:50:45.827861Z","iopub.status.idle":"2021-08-24T17:50:45.855474Z","shell.execute_reply.started":"2021-08-24T17:50:45.827829Z","shell.execute_reply":"2021-08-24T17:50:45.853873Z"},"trusted":true},"execution_count":16,"outputs":[{"execution_count":16,"output_type":"execute_result","data":{"text/plain":" age sex cp trtbps chol fbs restecg thalachh exng \\\n0 63 1_gender Intensity_3 145 233 1 0 150 0 \n1 37 1_gender Intensity_2 130 250 0 1 187 0 \n2 41 0_gender Intensity_1 130 204 0 0 172 0 \n3 56 1_gender Intensity_1 120 236 0 1 178 0 \n4 57 0_gender Intensity_0 120 354 0 1 163 1 \n\n oldpeak slp caa thall output \n0 2.3 0 0 1 1 \n1 3.5 0 0 2 1 \n2 1.4 2 0 2 1 \n3 0.8 2 0 2 1 \n4 0.6 2 0 2 1 ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
agesexcptrtbpscholfbsrestecgthalachhexngoldpeakslpcaathalloutput
0631_genderIntensity_31452331015002.30011
1371_genderIntensity_21302500118703.50021
2410_genderIntensity_11302040017201.42021
3561_genderIntensity_11202360117800.82021
4570_genderIntensity_01203540116310.62021
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"### trtbps\nresting blood pressure (in mm Hg)","metadata":{}},{"cell_type":"code","source":"plt.figure(figsize = (16, 7))\nsns.distplot(data['trtbps'])\nplt.title('Distribution Plot of Resting blood pressure (in mm Hg)\\n', fontsize = 20)\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2021-08-24T17:50:45.857331Z","iopub.execute_input":"2021-08-24T17:50:45.857744Z","iopub.status.idle":"2021-08-24T17:50:46.138074Z","shell.execute_reply.started":"2021-08-24T17:50:45.857710Z","shell.execute_reply":"2021-08-24T17:50:46.136810Z"},"trusted":true},"execution_count":17,"outputs":[{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"### chol\ncholestoral in mg/dl fetched via BMI sensor","metadata":{}},{"cell_type":"code","source":"px.box(x = 'trtbps', data_frame = data, template = 'plotly_dark')","metadata":{"execution":{"iopub.status.busy":"2021-08-24T17:50:46.140799Z","iopub.execute_input":"2021-08-24T17:50:46.141265Z","iopub.status.idle":"2021-08-24T17:50:46.253118Z","shell.execute_reply.started":"2021-08-24T17:50:46.141223Z","shell.execute_reply":"2021-08-24T17:50:46.251780Z"},"trusted":true},"execution_count":18,"outputs":[{"output_type":"display_data","data":{"text/html":"
"},"metadata":{}}]},{"cell_type":"markdown","source":"### chol\ncholestoral in mg/dl fetched via BMI sensor","metadata":{}},{"cell_type":"code","source":"plt.figure(figsize = (16, 7))\nsns.distplot(data['chol'])\nplt.title('Distribution Plot of cholestoral in mg/dl\\n', fontsize = 20)\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2021-08-24T17:50:46.255249Z","iopub.execute_input":"2021-08-24T17:50:46.255722Z","iopub.status.idle":"2021-08-24T17:50:46.518442Z","shell.execute_reply.started":"2021-08-24T17:50:46.255670Z","shell.execute_reply":"2021-08-24T17:50:46.517205Z"},"trusted":true},"execution_count":19,"outputs":[{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"px.box(x = 'chol', data_frame = data, template = 'plotly_dark')","metadata":{"execution":{"iopub.status.busy":"2021-08-24T17:50:46.520438Z","iopub.execute_input":"2021-08-24T17:50:46.520820Z","iopub.status.idle":"2021-08-24T17:50:46.598670Z","shell.execute_reply.started":"2021-08-24T17:50:46.520782Z","shell.execute_reply":"2021-08-24T17:50:46.597348Z"},"trusted":true},"execution_count":20,"outputs":[{"output_type":"display_data","data":{"text/html":"
"},"metadata":{}}]},{"cell_type":"markdown","source":"lets see trtbps and chol has similar outliers ","metadata":{}},{"cell_type":"code","source":" data.chol[data.trtbps >= 171]","metadata":{"execution":{"iopub.status.busy":"2021-08-24T17:50:46.600272Z","iopub.execute_input":"2021-08-24T17:50:46.600615Z","iopub.status.idle":"2021-08-24T17:50:46.610604Z","shell.execute_reply.started":"2021-08-24T17:50:46.600559Z","shell.execute_reply":"2021-08-24T17:50:46.609116Z"},"trusted":true},"execution_count":21,"outputs":[{"execution_count":21,"output_type":"execute_result","data":{"text/plain":"8 199\n101 270\n110 325\n203 274\n223 288\n241 249\n248 283\n260 228\n266 327\nName: chol, dtype: int64"},"metadata":{}}]},{"cell_type":"markdown","source":"the values of trtbps outliers are well in range of cholestrol level","metadata":{}},{"cell_type":"markdown","source":"### fbs\n(fasting blood sugar > 120 mg/dl) (1 = true; 0 = false)","metadata":{}},{"cell_type":"code","source":"\npx.bar(data_frame = data, x = list(data.fbs.value_counts().keys()), y = list(data.fbs.value_counts()), \n color = list(data.fbs.value_counts().keys()), template = 'plotly_dark',\n labels={\n 'x': \"fasting blood sugar > 120 mg/dl\",\n 'y': \"Count\",\n 'color':'fasting blood sugar > 120 mg/dl'\n \n },\n title = 'Number of patients having fasting blood sugar > 120 mg/dl')\n","metadata":{"execution":{"iopub.status.busy":"2021-08-24T17:50:46.612405Z","iopub.execute_input":"2021-08-24T17:50:46.612784Z","iopub.status.idle":"2021-08-24T17:50:46.703375Z","shell.execute_reply.started":"2021-08-24T17:50:46.612750Z","shell.execute_reply":"2021-08-24T17:50:46.702342Z"},"trusted":true},"execution_count":22,"outputs":[{"output_type":"display_data","data":{"text/html":"
"},"metadata":{}}]},{"cell_type":"code","source":"import plotly.graph_objects as go\n\nfbs_0_1 = data.fbs[(data.fbs == 0) & (data.output == 1)]\nfbs_0_0 = data.fbs[(data.fbs == 0) & (data.output == 0)]\nfbs_1_1 = data.fbs[(data.fbs == 1) & (data.output == 1)]\nfbs_1_0 = data.fbs[(data.fbs == 1) & (data.output == 0)]\n\ny_fbs_1 = [len(fbs_0_1.values), len(fbs_1_1.values)]\ny_fbs_0 = [len(fbs_0_0.values), len(fbs_1_0.values)]\n\nfig = go.Figure()\nfig.add_trace(go.Bar(\n x=[0,1],\n y=y_fbs_1,\n name='Heart Attack',\n marker_color='indianred'\n))\nfig.add_trace(go.Bar(\n x=[0,1],\n y=y_fbs_0,\n name='Safe',\n marker_color='lightsalmon'\n))\n\n# Here we modify the tickangle of the xaxis, resulting in rotated labels.\nfig.update_layout(barmode='group', xaxis_tickangle=-45)\nfig.show()","metadata":{"execution":{"iopub.status.busy":"2021-08-24T17:50:46.704849Z","iopub.execute_input":"2021-08-24T17:50:46.705159Z","iopub.status.idle":"2021-08-24T17:50:46.734276Z","shell.execute_reply.started":"2021-08-24T17:50:46.705128Z","shell.execute_reply":"2021-08-24T17:50:46.732958Z"},"trusted":true},"execution_count":23,"outputs":[{"output_type":"display_data","data":{"text/html":"
"},"metadata":{}}]},{"cell_type":"markdown","source":"Variation in sugar level is not the sole cause of a heart attack","metadata":{}},{"cell_type":"markdown","source":"### restecg\nresting electrocardiographic results","metadata":{}},{"cell_type":"code","source":"\npx.bar(data_frame = data, x = list(data.restecg.value_counts().keys()), y = list(data.restecg.value_counts()), \n color = list(data.restecg.value_counts().keys()),\n labels={\n 'x': \"resting electrocardiographic results\",\n 'y': \"Count\",\n 'color':'resting electrocardiographic results'\n \n },\n title = 'Number of patients per resting electrocardiographic results')\n","metadata":{"execution":{"iopub.status.busy":"2021-08-24T17:50:46.737761Z","iopub.execute_input":"2021-08-24T17:50:46.738162Z","iopub.status.idle":"2021-08-24T17:50:46.843963Z","shell.execute_reply.started":"2021-08-24T17:50:46.738124Z","shell.execute_reply":"2021-08-24T17:50:46.842720Z"},"trusted":true},"execution_count":24,"outputs":[{"output_type":"display_data","data":{"text/html":"
"},"metadata":{}}]},{"cell_type":"code","source":"import plotly.graph_objects as go\n\nrestecg_0_1 = data.restecg[(data.restecg == 0) & (data.output == 1)]\nrestecg_0_0 = data.restecg[(data.restecg == 0) & (data.output == 0)]\nrestecg_1_1 = data.restecg[(data.restecg == 1) & (data.output == 1)]\nrestecg_1_0 = data.restecg[(data.restecg == 1) & (data.output == 0)]\nrestecg_2_1 = data.restecg[(data.restecg == 2) & (data.output == 1)]\nrestecg_2_0 = data.restecg[(data.restecg == 2) & (data.output == 0)]\n\ny_restecg_1 = [len(restecg_0_1.values), len(restecg_1_1.values), len(restecg_2_1.values)]\ny_restecg_0 = [len(restecg_0_0.values), len(restecg_1_0.values), len(restecg_2_0.values)]\n\nfig = go.Figure()\nfig.add_trace(go.Bar(\n x=[0,1,2],\n y=y_restecg_1,\n name='Heart Attack',\n marker_color='indianred'\n))\nfig.add_trace(go.Bar(\n x=[0,1,2],\n y=y_restecg_0,\n name='Safe',\n marker_color='lightsalmon'\n))\n\n# Here we modify the tickangle of the xaxis, resulting in rotated labels.\nfig.update_layout(barmode='group', xaxis_tickangle=-45)\nfig.show()","metadata":{"execution":{"iopub.status.busy":"2021-08-24T17:50:46.846248Z","iopub.execute_input":"2021-08-24T17:50:46.846644Z","iopub.status.idle":"2021-08-24T17:50:46.876938Z","shell.execute_reply.started":"2021-08-24T17:50:46.846609Z","shell.execute_reply":"2021-08-24T17:50:46.875449Z"},"trusted":true},"execution_count":25,"outputs":[{"output_type":"display_data","data":{"text/html":"
"},"metadata":{}}]},{"cell_type":"raw","source":"We can see that if the result is 1 we have more chances of survival compared to 0 and 2 \nsince we have least data points which has category of '2' it the results for label 2 are ambiguous. Thus it is better we use one hot encoding for the feature","metadata":{}},{"cell_type":"code","source":"data['restecg'] = data['restecg'].map({0:\"restecg_0\", 1: \"restecg_1\", 2: 'restecg_2'}) \ndata.head()","metadata":{"execution":{"iopub.status.busy":"2021-08-24T17:50:46.878693Z","iopub.execute_input":"2021-08-24T17:50:46.879177Z","iopub.status.idle":"2021-08-24T17:50:46.909257Z","shell.execute_reply.started":"2021-08-24T17:50:46.879145Z","shell.execute_reply":"2021-08-24T17:50:46.907704Z"},"trusted":true},"execution_count":26,"outputs":[{"execution_count":26,"output_type":"execute_result","data":{"text/plain":" age sex cp trtbps chol fbs restecg thalachh exng \\\n0 63 1_gender Intensity_3 145 233 1 restecg_0 150 0 \n1 37 1_gender Intensity_2 130 250 0 restecg_1 187 0 \n2 41 0_gender Intensity_1 130 204 0 restecg_0 172 0 \n3 56 1_gender Intensity_1 120 236 0 restecg_1 178 0 \n4 57 0_gender Intensity_0 120 354 0 restecg_1 163 1 \n\n oldpeak slp caa thall output \n0 2.3 0 0 1 1 \n1 3.5 0 0 2 1 \n2 1.4 2 0 2 1 \n3 0.8 2 0 2 1 \n4 0.6 2 0 2 1 ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
agesexcptrtbpscholfbsrestecgthalachhexngoldpeakslpcaathalloutput
0631_genderIntensity_31452331restecg_015002.30011
1371_genderIntensity_21302500restecg_118703.50021
2410_genderIntensity_11302040restecg_017201.42021
3561_genderIntensity_11202360restecg_117800.82021
4570_genderIntensity_01203540restecg_116310.62021
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"### thalachh\nmaximum heart rate achieved","metadata":{}},{"cell_type":"code","source":"plt.figure(figsize = (16, 7))\nsns.distplot(data['thalachh'])\nplt.title('Distribution Plot of maximum heart rate achieved\\n', fontsize = 20)\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2021-08-24T17:50:46.911126Z","iopub.execute_input":"2021-08-24T17:50:46.911798Z","iopub.status.idle":"2021-08-24T17:50:47.181288Z","shell.execute_reply.started":"2021-08-24T17:50:46.911727Z","shell.execute_reply":"2021-08-24T17:50:47.179948Z"},"trusted":true},"execution_count":27,"outputs":[{"output_type":"display_data","data":{"text/plain":"
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JJIdl5X/bjWOfflPm7zTUlFypDo8wcinZ9lu8FKXvS27/nyEgQ3OudSZiUyb6ae7X0pT7cj71c2/Y2twb8fqO5MfdUob3yb9O4gWTnn1prZY5JOMrM9/BZpl8hLztySYf+StF9ay4ze4pG2/xkGANswBgYA+MybYvG//afpF2a98scD+Ldz7oPyfombI2/Qsi5x9b/1QF8d6Hd5SHecf/96Ulm9fz8jw/oHZ9l/V9Pe/sT/urzPmGMyLDvG39drGZblBb8bxbuSpmWZzrBrWtHkY9iR90nq/vscl77ATxQcneG1cm28vC/3z2VIXpSqu1uM0pZF5P06XSJvBoP/lTdDxy8HMdbeZP1/MLO5yk3rpR31kryEzNHbWzHJXHktpZ7IsGxHm+7v6P/B9sz17/+dYVm2WHuLZUfer2z6G9vb8pIY+5rZ1AF4/b56QdJYv9tQf9zo319iZvvLm7Xk/gzjdLzg3/f1Pe069x3ljwuS7rh+xAhglCCBAQCS/BYUt8m7YFotb4T43taPmNn7MpQXqLsLSmvSoi3y+hYXDUjAqcrlTRGZHMfB8pr5Nkq6M2lRV//yj/tfjrvWn5G+jyRdTYn7M4jkX/z7/zWz4qTXKVb3AJI39GN/ufAXeX3Yf5p8cW1m4+UNRNm1Tpd6eb8e93ewzbvkzfZwkd+kP9kX5U2T+4hLncI012rl1e+DkgdW9ev/r+QlODL5mbx+7z9xzj0s75fpZyVdnqVP/mB7W15LoPn+OUCS5P+f/joH8ewwf/ycv0s62My+lekLoZnNMbPZSUWr5LWU2jdtvcvUjwEx0+zI+aIvVvn3xyUXmtmuyj41btZYdvD9GpDY/NZ7v5fX+uWPfmIvebuwmfVlEOn+6hpQ9M+ZEidmVpLhHCR5iZmt8gY9vdQvuzHDen+Vl5j5jpn1GFTUzAJmdlzXc38A2YflneOuSFt3vhj/AkAGdCEBMOokDS4WkPcr8l7yun+E5X3B/0gfZhEpkvSMmS2XN2Dfe/IGvztZ3gBlC5xzS5PWf1ReP+sH/EEZo5LecM7dPQCH9JSk/2dmh8n7MjhF3q/bAXnTEW7rquGce9F//WMkveQ3DZ4k6WxJDypzy4xH5Q3y92cz+5ekJkkNzrnfZli363Vu8S9APyhpsZndpe6xEWbLm3EjfZaGfPMzSafLax7+hj+2SbG8LkIT5X0Jf6ZrZedcs5m9KOloM/u7vNH74/LqQtbm1P52n5D0T0lPmtk/5SXRDpI3TsRGSZcPxgHuKOdcwsx+LW/cl7fM7D/y/n+Ol5fAe1zdrVQkSWZ2nrwvKS/K67rQNUDoRfJmP7jezF71ZxMZquPoNLNfyUtIvW5md8q7NjpZ3oCc/RlgMR9cIW+Gl+9JutjMnpE3vsBUeeelQ+RNcbzSX/+X8hIVz/gDETfKa4l1lKQ7JJ2/AzE8Lq9lw/+a2d7yW7k4536wY4e0zd2Slkv6spntI+/X+5mSzpJ0rzInTLYXS3/fr4GM7buSDpN37n3HzO6Rd26dIe///qvKnCTYYc65R83sKnktn6r9c9pKeWNS7CIvYfCMpNPStmvzz0uXyZs9ZYt/XOn732Jm58tLmr9gZo9KWizv3D9D3iCflUodKPazkp6X1xXzFElvyGvRcp689/XsgTl6ACOGy4O5XLlx48ZtKG7qnlO+6xaVN1/9q/KmdztNXv/gTNs+4Z0ytz0vkPQ1SffL+7LZLm/QthfkzcoQTtu+RNIfJK2VN+iak9dfOjm2J3qJ/UZ/nVlJZbO69iPvYvs/8i7QW+UlMk7Nsq8K/3hr/fdgkaRPJe8vwzZflrTUX9/JmzEl43uTVB6Qd7H7ih9Tq/9efzbT+9zbe5Dp+Lfzt37CX/+4Pq6/KvmYksoLJX3Df4/a5H3BeEbSRVn2M1feRfcWeV+cnKRL+xjDIfIu/DfJm1J3tV9npmZY9zh/39f0o/5n/fv24f3v8f7I+6L/ZXmzCbTJS7TcLO+LUMrfS94XuDp5v872+BvKSxI5eQnEcG/HmK2+ba+eyBuzo0edkNfK5ip5XYa63vefyEtWZTruS7P9XXv7u2zv/c+wftbX6e3vJS+RdIWk5+QlJKL+MT0qr0VPZdr6Z8k7bzX5f5+H5CU4M75+pvckQwwflZeUavP3kfHv1d86Le9L8N/lDX7ZJu/L8df8upjt/eg1lv6+XwMcW8h/7Zfkza7UNVPSdZLmbq/u9lav1Pv/yVGSbpeXoOuQd85ZKOkXkg7uZZuuz87fbOe9mCVvWtRqeZ+NW+W1drpZ0rkZ1p8rL2HW4L8Hz0s6M1sd5MaN2+i+mXNOAAAAAAAA+YwxMAAAAAAAQN4jgQEAAAAAAPIeCQwAAAAAAJD3SGAAAAAAAIC8RwIDAAAAAADkPRIYAAAAAAAg75HAAAAAAAAAeY8EBgAAAAAAyHskMAAAAAAAQN4jgQEAAAAAAPIeCQwAAAAAAJD3SGAAAAAAAIC8RwIDAAAAAADkPRIYAAAAAAAg75HAAAAAAAAAeY8EBgAAAAAAyHskMAAAAAAAQN4jgQEAAAAAAPIeCQwAAAAAAJD3SGAAAAAAAIC8RwIDAAAAAADkPRIYAAAAAAAg75HAAAAAAAAAeY8EBgAAAAAAyHskMAAAAAAAQN4jgQEAAAAAAPJeKNcB5EpjY6PLdQwAAAAAAKCn8vJySy+jBQYAAAAAAMh7JDAAAAAAAEDeI4ExAlRXV+c6BOQB6gEk6gG6URcgUQ/goR6gC3UB0vCuByQwAAAAAABA3iOBAQAAAAAA8h4JDAAAAAAAkPdIYAAAAAAAgLxHAgMAAAAAAOQ9EhgAAAAAACDvkcAAAAAAAAB5jwQGAAAAAADIeyQwAAAAAABA3iOBAQAAAAAA8h4JDAAAAAAAkPdIYAAAAAAAgLxHAgMAAAAAAOS9IUtgmNlpZrbMzJab2VUZlkfM7B/+8hfNbJZffrKZvWpmb/n3JyRtc5BfvtzMfm1m5pePM7OHzazavx87VMcJAAAAAAAG3pAkMMwsKOl3kk6XNE/SRWY2L221yyTVO+fmSrpW0o/98s2SznbO7SPpEkk3J23zB0mflFTl307zy6+S9KhzrkrSo/5zAAAAAAAwTIWG6HUOlbTcObdCkszsNknzJS1JWme+pGv8x3dI+q2ZmXPu9aR1FksqMrOIpHGSxjjnXvD3+TdJ50q639/Xcf42N0l6QtLXB/qgAAAA4LlxWUuuQ9hpl+5ekusQAAC9GKouJNMkrUl6vtYvy7iOcy4mqVFSZdo6H5D0mnMu6q+/Nss+JznnNviPN0qatLMHAAAAAAAAcmeoWmDsNDPbS163klP6s51zzpmZ622d6urqnQktL4yEY8DOox5Aoh6gG3UB0tDVg5ra4JC8zmCqDsRzHcKg4XyALtQFSPlbD6qqqnpdPlQJjHWSZiQ9n+6XZVpnrZmFJJVL2iJJZjZd0p2SPuacezdp/elZ9lljZlOccxvMbIqk2t6C296blO+qq6uH/TFg51EPIFEP0I26AGlo68GkxPDvQlJVNTK7kHA+QBfqAqThXQ+GqgvJy5KqzGy2mYUlXShpQdo6C+QN0ilJ50t6zG89USHpXklXOeee7VrZ7yKy1cwO92cf+Zik/2TY1yVJ5QAAAAAAYBgakgSGP6bFFZIelLRU0u3OucVm9j0zO8df7QZJlWa2XNKX1T1zyBWS5kr6tpkt9G8T/WWfkXS9pOWS3pU3gKck/UjSyWZWLekk/zkAAAAAABimhmwMDOfcfZLuSyv7dtLjdkkXZNjuB5J+kGWfr0jaO0P5Fkkn7mTIAAAAAAAgTwxVFxIAAAAAAIAdRgIDAAAAAADkPRIYAAAAAAAg75HAAAAAAAAAeY8EBgAAAAAAyHskMAAAAAAAQN4jgQEAAAAAAPIeCQwAAAAAAJD3SGAAAAAAAIC8RwIDAAAAAADkPRIYAAAAAAAg75HAAAAAAAAAeY8EBgAAAAAAyHskMAAAAAAAQN4jgQEAAAAAAPIeCQwAAAAAAJD3SGAAAAAAAIC8RwIDAAAAAADkPRIYAAAAAAAg75HAAAAAAAAAeY8EBgAAAAAAyHskMAAAAAAAQN4jgQEAAAAAAPIeCQwAAAAAAJD3SGAAAAAAAIC8RwIDAAAAAADkPRIYAAAAAAAg75HAAAAAAAAAeY8EBgAAAAAAyHskMAAAAAAAQN4jgQEAAAAAAPIeCQwAAAAAAJD3SGAAAAAAAIC8RwIDAAAAAADkPRIYAAAAAAAg74VyHQAAAAAgSbGEU21bQvXRhGLOKeGkhJPiTooEpcrCgMYXBlUYtFyHCgDIgSFLYJjZaZJ+JSko6Xrn3I/Slkck/U3SQZK2SPqQc26VmVVKukPSIZJudM5d4a9fJunppF1Ml/R/zrkvmtmlkn4qaZ2/7LfOuesH7eAAAADQLwnntKoprrcbOrWhNaGNrXFtaU8o0YdtSwtMEwoDmlka0l7jQtq1LKRQgKQGAIx0Q5LAMLOgpN9JOlnSWkkvm9kC59ySpNUuk1TvnJtrZhdK+rGkD0lql/QtSXv7N0mSc65J0v5Jr/GqpH8n7e8fXckOAAAA5F573Ont+k4tru/UkvqYWmJuh/bT3OnU3BnXyqa4ntwQVWFQ2qOiQHuNLdC+lQWK0EIDAEakoWqBcaik5c65FZJkZrdJmi8pOYExX9I1/uM7JP3WzMw51yLpGTObm23nZrabpIlKbZEBAACAPLCxNa6nNkT1yqYOdfSliUU/tcelhVs6tXBLp+5cZTpqclhHT46oLMxwbwAwkgxVAmOapDVJz9dKOizbOs65mJk1SqqUtLkP+79QXouL5DT+B8zsGEnvSPqSc25N5k0BAAAw0BLOaXF9TE9viOqdxliftxsbNk0sCioSNAVM224tnU6b2xPaEk0o0UvDjdaY00Nro3psXVSHTAzr+KkRTSwKDsARAQBybaQM4nmhpIuTnt8t6VbnXNTMLpd0k6QTsm1cXV09yOENvpFwDNh51ANI1AN0oy5AGrp6UFPrJQmck5a3BvTElqA2d/TeAqIw4LRrcUKzixMaH3aqDDtFttNoIuGkrTFpYzSgd1sCWt4aUGu8Z5eRmJOer+nQCzVRHVwe19GV8e3uuzoQ732FYYzzAbpQFyDlbz2oqqrqdflQJTDWSZqR9Hy6ugfYTF9nrZmFJJXLG8yzV2a2n6SQc+7VrjLnXPJ210v6SW/72N6blO+qq6uH/TFg51EPIFEP0I26AGlo68GkRItWbI3p7vfatLIpeyKgPGw6oDK8bfDN4A4MvjlF0u6SjpXX0mN1c1xvbunU8zUdaounNs9wMr3cGNI7bQU6b1aR9qsskFnm16yqKul3LMMB5wN0oS5AGt71YKgSGC9LqjKz2fISFRdK+nDaOgskXSLpeUnnS3osrUtINhdJujW5wMymOOc2+E/PkbR0J2IHAABAL1Y1xXT9281aVJe9q8jssqCOnhLRfuMKdihpkU3ATLPKQppVFtKpMwr1Qk2HntjQrvpo6mVkY4fTje+0ao+KkM7ftUjjC+lWAgDDzZAkMPwxLa6Q9KC8aVT/4pxbbGbfk/SKc26BpBsk3WxmyyXVyUtySJLMbJWkMZLCZnaupFOSZjD5oKQz0l7y82Z2jqSYv69LB+vYAAAARqtYwun3i5v1v6839Wj5IEkm6aAJBTpuSkTTSwf/sjMSNB07NaKjpoS1cHOn7lnd1iOR8XZDTD9d2KQPzS3WgePDgx4TAGDgDNkYGM65+yTdl1b27aTH7ZIuyLLtrF72u2uGsqslXb2jsQIAAKB3r23q0Beea9BbdZ0Zl+81NqQzZxZpasnQt3QImumgCWHtM65AD61t1+Pro0rOr0QT0t/eadXKrTHNn1Wk0AC2CAEADJ6RMognAAAAhkB7zOn7r23VH5Y0Z5wNZJfSoM7ZpUhzynN/mRkOms7apUgHTwjrnyta9e7W1LE5nt7Yofea47p0txKNK2TKVQDId7n/ZAEAAMCwsLS+U5c9Wacl9T3HuigOmebPKtShE8JZB8nMlcnFQV2xV6me3dihO1e1pbTGWN0c18/ebNKluxXnLkAAQJ+QwAAAAECvnHO64e0WffPlRrVnmGDkg7sWaZ9xBSoL528rBjPTUVMimlkW1I3LWlUXTWxb1hpz+tPSFh08Iaz370oiAwDyVf5+ygAAACDntrTHddGjdbryhZ7JixmlQd1xcqWuO3ZcXicvks0sDenK/Uq119jU3/HiTrrsyXr9aUlzjiIDAGzP8PikAQAAwJB7bVOHjl2wSQ+sae+x7IO7FumZ+RN10vTCHES2c4pDAV22R4nOnJkau5P09Rcb9f1XG+VchgE+AAA5RRcSAAAA9HDTshZ99YUGdSRSy8sKTD87okIfmjO8u1oEzHTy9EKNjQR0y/LWlAFJf/5ms2rbEvrlkRUKMkMJAOQNEhgAAADYpj3m9NUXGnRzdWuPZYdMKNCfjx2nWWUj5xLy4AlhlYRMf13WkpKsubm6VU7Sr99XoUCeDUoKAKMVXUgAAAAgSVrfEtfp92/KmLz47F6luu+MCSMqedFlz7EF+sxepRoXSb00/r/qVn39RbqTAEC+IIEBAAAALdzcoRPvqdXrmztTyktCpr8cO1Y/PLRcBSO4O8WsspAePHO8phUHU8r/vLRF17yylSQGAOQBEhgAAACj3N3vtemM+zdrQ2vqgBdzxgT1yFkTRs3UolXlBfrPaZWaWJR6ifyrRc36yRtNOYoKANCFBAYAAMAo5ZzTL99s0sWP1ak1ltrC4NQZhXrs7Inac2xBjqLLjbnlBbrr1PE9upP87+tN+u0ikhgAkEskMAAAAEahzoTT559t0DWvbu2x7LN7leqWE8apPDw6LxXnjS3Qv0+p1JhwapeZb768Vf9Z1ZajqAAAo/NTCQAAYBRr6UzoI49u6TFYZ8ikXx5ZoR8eWj7qpw/df3xYd5xcqZJQ6vtw+VN1enVTR46iAoDRjQQGAADAKLKlPa5zHtish9ZGU8rLw6Z/nVKpS3cvyVFk+efQiRHdcuI4Jecw2uPSRY9u0ermWO4CA4BRigQGAADAKLGqKaZT792sV9NmGplZGtTDZ07QsVMLcxRZ/jp2aqGuPbIipay2LaEPPbxFjR2JzBsBAAYFCQwAAIBRYFFdp069d5OWb01tObDPuAI9dOYE7VYxugbr7I+LdyvRl/YpTSlb2hDTxx+vUyzB9KoAMFRIYAAAAIxwr2zq0Jn3b1JNW2qLgWOnRHTv6eM1uTiYo8iGj28dNEbzZ6W2UHlsfVTffqUxRxEBwOhDAgMAAGAEe2pDVPMf2KzGjtSWAufvWqR/nlypMaN0ppH+Cpjpj0eP00HjU1uq/H5xi+5aycwkADAU+MQCAAAYoZ6pC+iChzerJZaavLh8zxJdd8xYhYOje6aR/ioKmW45sVLTS1JbrFzxTL2WNXRm2QoAMFBIYAAAAIxAd65s1ZVLI4rGU8u/tn+ZfnRYuQJG8mJHTCoO6sbjx6kg6Sq6Oeb0scfq1NzJoJ4AMJhIYAAAAIww/3i3VZc9Wa+4S01SfP/gMfrGAWNkJC92ysETwvrRYeUpZcsaY/r8sw1yjkE9AWCwkMAAAAAYQW5b3qr/erpeyZNjmKRfHlmhz+1TlrO4RppP7F6iD84pSin798o2/WlpS44iAoCRjwQGAADACHFLdUuP5EXQpD8dM1aX7l6Su8BGIDPTL4+s0LyxoZTyb77UqIWbO3IUFQCMbCQwAAAARoC/V7fos880KLkDQ1BONxw7Th+cU5yzuEay4lBANx9fqTEF3V1yYk76f0/Wq4XxMABgwIW2vwoAAAAG043Ldq7bwUu1Hbp1eWtK8iIg6ZzJMTV0JHZ6/8huTnlIvz1qrD72eN22suVbY/rGS4361fvG5jAyABh5aIEBAAAwjL22OUPywqRLdi/WHqW0AhgK58wq0iW7pbZyuemdVt39XluOIgKAkYkEBgAAwDD1Vl2n/u+dnsmLS3cr1n6V4ZzFNRr9z6HlqipPbdz8+Wfrtb4lnmULAEB/kcAAAAAYhpbWd+rGZS1KbmMRkHTJbsXal+TFkCspCOjPx4xVQdLVdX3U+YOqMrUqAAwEEhgAAADDzPLGmP6yrEXxtKlSP1JFy4tc2n98WN88cExK2ZMbovrd4uYcRQQAIwsJDAAAgGFkdVNMf17arPRJLj40p0gHTSB5kWuf27tUx0yJpJT94LWteqehM0cRAcDIQQIDAABgmKhpi+tPS1sUTUtevH92kQ6fFMm8EYZUwEx/OHqsKsLdU6tG49IVzzQonqArCQDsDKZRBQAAGAYaogn9cXGzWmKpX4LP3qWwxy/+2DEDOd3sWbsU6f+qW7c9f2lThy57sk7HTS0csNfI5NLdSwZ1/wCQS7TAAAAAyHMtnQn9cUmz6jtSkxcnTovoxGmD+4UYO+ag8QXaa2zqb4X3rm7XpjZmJQGAHUUCAwAAII91xJ2uf7tFG9tS+40cPjGss2aSvMhXZqYPzilWUbC7K0lnQrp1eSuzkgDADiKBAQAAkKfizummd1q0sin1V/u9x4V0wZwimVmWLZEPysMBnTe7KKVsRVNcT2/syFFEADC8kcAAAADIQ8453bmyTYvrYynlc8YE9bGqEgVJXgwLh0wo0LyKtK4k77VpSztdSQCgv4YsgWFmp5nZMjNbbmZXZVgeMbN/+MtfNLNZfnmlmT1uZs1m9tu0bZ7w97nQv03sbV8AAADDxRPro3om7Zf6qcUBXbZHicJBkhfDRVdXksJgd1lHQrpjRZscXUkAoF+GJIFhZkFJv5N0uqR5ki4ys3lpq10mqd45N1fStZJ+7Je3S/qWpCuz7P4jzrn9/VvtdvYFAACQ9xZu7tB/3mtPKasImy6fV6riEA1oh5uKSEDnzkrtSrK0IaY3tnTmKCIAGJ6G6hPwUEnLnXMrnHMdkm6TND9tnfmSbvIf3yHpRDMz51yLc+4ZeYmMvsq4rx0PHwAAYGis3BpLmX5TkgqD0qf2LFV5mOTFcHXYxLDmjAmmlP17ZZvaYrTCAIC+GqpPwWmS1iQ9X+uXZVzHOReT1Cipsg/7/qvffeRbSUmKHd0XAABAzmxuj+v6t1uU/J02YNLHdy/R1JJg9g2R98xMF+xarOTeP1s7ne5b3Za7oABgmAltf5W89hHn3DozK5P0L0kXS/pbf3dSXV094IENtZFwDNh51ANI1AN0oy4MHzW1QbXHpb+tLVBLLPX3pdMndKqio041tVk23u6+awYgQgwEk3RYRVDP1Xdfgj+9MapdQ82aWjgwLTGqA5kHB+V8gC7UBUj5Ww+qqqp6XT5UCYx1kmYkPZ/ul2VaZ62ZhSSVS9rS206dc+v8+yYzu0VeV5W/9Xdf23uT8l11dfWwPwbsPOoBJOoBulEXhpcJ8WZdv7RFWzpTZxw5dXpEJ8+s2OH91tTWaNLESTsZHQbSuZVOyxY2aUs04ZeYHq0v0pf2LR2QmWWqqkp6lHE+QBfqAqThXQ+GqgvJy5KqzGy2mYUlXShpQdo6CyRd4j8+X9Jjrpehmc0sZGbj/ccFks6StGhH9gUAAJBLd7/XriUNqcmLA8cX6LQZhTmKCIMlHDSdv2vqgJ5rW+J6ZkNHli0AAF2GpAWGcy5mZldIelBSUNJfnHOLzex7kl5xzi2QdIOkm81suaQ6eUkOSZKZrZI0RlLYzM6VdIqk9yQ96CcvgpIekfRnf5Os+wIAAMgnt1S36PH10ZSymaVBXTinWIxBPjLtObZAB1QW6PWkWUjuW9OmA8YXaAwDtQJAVkM2BoZz7j5J96WVfTvpcbukC7JsOyvLbg/Ksn7WfQEAAOSLF2ui+uJzDSll5WHTZXuUKBwkeTGSnTu7SEsbOtXuD1kRjUv3rG7Xh+cW5zYwAMhjpHgBAAByYH1LXBc/XqeORHdZQUC6bI8SpksdBcrDAZ0+I7UryUu1HXqvKZZlCwAAn44AAABDLBp3uuTxLaptS6SUXzS3WDNLh/skceiroyaHNbko9XL8XyvblGDoNgDIiAQGAADAELv6xUa9vKkzpezk6REdOD6co4iQC8GA6bzZqa0wVjfH9fImBvQEgExIYAAAAAyh/6tu0V+WtaSU7VkR0unMODIq7V5RoH3HFaSU3fNeu9pjtMIAgHQkMAAAAIbIws0d+srzDSlls8qC+mhVsQLMODJqzZ9VqIKkq/KmTqcH17bnLiAAyFMkMAAAAIbAlva4PvpYnaLx7rKioOnmEypVUsAl2WhWWRjU8VMjKWVPboiqpjWeZQsAGJ34tAQAABhkCef0qafqtbYl9Qvpr95XoX3Sug9gdDppWqEqwt2tcBJOuvu9thxGBAD5hwQGAADAIPvFm816dF00pezyPUv0wTnFOYoI+SYcNM2flTqg56L6mKobO7NsAQCjDwkMAACAQfT0hqj+5/WtKWWHTwzrB4eW5ygi5Kv9Kws0qyyYUrZgVTvTqgKAjwQGAADAIKlti+v/PVmnRNL3z8pIQDccN04FAQbtRCqznq0w1rTE9dpmWmEAgEQCAwAAYFDEE06ffLJeNW2JlPI/HTNW00qCWbbCaDe7LKT9K1PHRbn3vTZ1xGmFAQAkMAAAAAbBz95s0pMbUse9+Mq+pTppemGOIsJwcdYuhQomNdCp73B6Kq0uAcBoRAIDAABggD29IaofL2xKKTtyUlhXHzAmRxFhOBlfGNRRk1OnVX1kXbuaOxNZtgCA0YEEBgAAwACqa4/r8qdSx70YX+iNexFi3Av00SnTIypKaobRHpceXNOew4gAIPdIYAAAAAwQ55w+92yD1rem/lJ+3TFjNaWYcS/QdyUFAZ0yI7UVxrM1HdrUFs9RRACQeyQwAAAABshflrXo3tWpv5J/Ye9SnTCNcS/Qf0dPjqgy0n25nnDS/bTCADCKkcAAAAAYAEvqO/XfLzWmlB04vkD/fSDjXmDHhAKmM2amJr9e29yptS2xHEUEALkVynUAAAAAO+vGZS05ff2OuNMv3mxSe1Lr/khAOmNmoW5Z3pq7wDDsHTC+QI+uC6R0S7rvvXZ9al5pDqMCgNygBQYAAMBOWvBemza2pY57ccGcYo0vZNwL7JyAmc6cWZRStqQhphVbaYUBYPQhgQEAALATltR36pmNHSllB08o0METwjmKCCPNvLEhzS5LTYbd/V6bnHNZtgCAkYkEBgAAwA5q7kzo1rQuIpWRgM6fXZyjiDASmZnO3iW1FcbKpriW1NMKA8DoQgIDAABgBzjndNu7rWrq7P4VPCDp4t2KVRiy3AWGEWnXMSHNq0gdvu7e1W1K0AoDwChCAgMAAGAHvFDboUV1qb+AnzKjULPKGCMdg+OMXVJnJFnfmtDrmztzFA0ADD0SGAAAAP20qS2uO1e2pZTtUhrUydMjOYoIo8H0kpAOGF+QUvbAmnbFaYUBYJQggQEAANAPcef0f9Wt6kiadCQckD5aVayg0XUEg+uMGYUpF/Cb2hN6dROtMACMDiQwAAAA+uGRtVG91xxPKTtvdpEmFDFlKgbfhKKgDpmYOsPNQ2tphQFgdCCBAQAA0EdrmmN6cG17Stne40I6fCJTpmLonDI9okBSY5/N7Qm9UtuRfQMAGCFIYAAAAPRBZ8Lp79WtSiT90F1aYLpwTrGMriMYQpWFQR3WoxVGVPEErTAAjGwkMAAAAPrgvtXt2tiWSCm7cE6xSgu4nMLQO3laoYJJebMt0YRe2kQrDAAjG5+4AAAA2/Hu1pieWB9NKTt0Qlh7jyvIsgUwuMYVBnp0XXpoTbs64rTCADBykcAAAADoRTTudEt1q5K/Fo4Nm86bXZSzmABJOnl6aiuM+g6vmxMAjFQkMAAAAHrxn1Vt2hJN7TpyUVWxikKMe4HcqogEdOSk1FYYP3ujSVFaYQAYoUhgAAAAZLG0vlPP1aSOK3D05LB2K6frCPLDSdMLlZxLW9ca1y20wgAwQpHAAAAAyKA1ltBt76Z+EZxQGNDZu9B1BPmjPBzQkZNTW2Fc+1aTOpmRBMAIRAIDAAAgg3+taFNjR/eXQJP0kapihYN0HUF+OWFq6lgYq5vjuv1dWmEAGHlIYAAAAKRZuLlDr27uTCk7aVpEs8pCOYoIyK4i0nNGkl+82aQ4rTAAjDBDlsAws9PMbJmZLTezqzIsj5jZP/zlL5rZLL+80sweN7NmM/tt0vrFZnavmb1tZovN7EdJyy41s01mttC//b8hOUgAADDsbe1I6J8r2lLKphYHdOqMwhxFBGzfidMKFUhqhfHu1rj+vbIt+wYAMAwNSQLDzIKSfifpdEnzJF1kZvPSVrtMUr1zbq6kayX92C9vl/QtSVdm2PXPnHN7SDpA0vvM7PSkZf9wzu3v364fwMMBAAAjlHNO/3i3VS2x7l+ugyZ9tKpEoQBdR5C/xhUGdMiE1FYYP3+zSQlHKwwAI8dQtcA4VNJy59wK51yHpNskzU9bZ76km/zHd0g60czMOdfinHtGXiJjG+dcq3Pucf9xh6TXJE0fzIMAAAAj24u1HVpcH0spO31GoaaWBHMUEdB3J02LpLTCeLshprvfa8++AQAMM0OVwJgmaU3S87V+WcZ1nHMxSY2SKvuyczOrkHS2pEeTij9gZm+a2R1mNmMH4wYAAKPElva47lyV2uR+VllQJ0yL5CgioH8mFAV1/q6ps+T87I0m0QgDwEgx7EeiMrOQpFsl/do5t8IvvlvSrc65qJldLq9lxwnZ9lFdXT34gQ6ykXAM2HnUA0jUA3QbTXWhpnbnWkg4J92yrkDRePdvOyFzOnVsmzZtGt6zOdTU1uQ6BAyh88sT+qcK5eQ1xXirrlPP1Adko+h8gN6Nps8GZJev9aCqqqrX5UOVwFgnKbkVxHS/LNM6a/2kRLmkLX3Y93WSqp1zv+wqcM4lb3e9pJ/0toPtvUn5rrq6etgfA3Ye9QAS9QDdRltdmJRo2antn1jfrtXtqU3tz51VrD2nDO/WFzW1NZo0cVKuw8AQOmX3Es2vq9NdSa2J/rKmQB8/dIbMGMdltBttnw3IbDjXg6HqQvKypCozm21mYUkXSlqQts4CSZf4j8+X9JhzvTd4M7MfyEt0fDGtfErS03MkLd3x0AEAwEi2sTWue9LGCdi9PKT3TQ5n2QLIb1fuV5byfFFTUM9s7MhRNAAwcIYkgeGPaXGFpAflJRNud84tNrPvmdk5/mo3SKo0s+WSvixp21SrZrZK0i8kXWpma81snplNl/Tf8mY1eS1tutTP+1OrviHp85IuHfyjBAAAw0084fR/1a1KmnRERUHTRXOL+bUaw9be4wp6TPt77ZtNOYoGAAbOkI2B4Zy7T9J9aWXfTnrcLumCLNvOyrLbjFcWzrmrJV29Q4ECAIBR46G17VrbEk8pO3/XIlVEhqqRKjA4vrxPqR5c092y6LH1US3c3KH9x9OyCMDwxaczAAAYld5riunhtdGUsv0rC3Tg+IIcRQQMnMMmRXTEpNRkxS/fas5RNAAwMEhgAACAUacj7vT35a1KJJWNKTBdsGsRXUcwYnx539SxMP6zqk3LGztzFA0A7DwSGAAAYNS5Z3WbatsSKWUXzi1WSQGXRhg5TpoW0d7julsUOUm/XkQrDADDF5/SAABgVHmnoVNPbUidkeGISWHNG0vXEYwsZqYv7VOaUnbr8latTxv3BQCGCxIYAABg1GiNJXTL8taUsspIQOfOKspRRMDgmj+rSNMLu1sbdSak3y+mFQaA4YkEBgAAGDXuXNmmho7uOVNN0keqihUJMu4FRqZQwHTxtFhK2V+Xtag+msiyBQDkLxIYAABgVHhjS4de3pQ6gOHxUyPadcyQzSoP5MSZk2KaVNR92d8Sc7rh7ZYcRgQAO4YEBgAAGPEaogn94922lLIpxQGdMbMwRxEBQycSkD6zV+pYGNctbVZ7zGXZAgDyEwkMAAAwoiWc0y3LW9Wa9GUtaF7XkVCAriMYHS7dvURlBd31vbYtodtXtPayBQDkHxIYAABgRHtifVTvNKaOAXD6jEJNL6HrCEaP8nBAl+xWklL2u0XNSjhaYQAYPvqcwDCz+WbGJz0AABg21jTHdO/q9pSyqjEhnTAtkqOIgNz59LwShZIaHS1rjOnhtdHcBQQA/dSfFhjfk7TBzH5rZocNVkAAAAADIRp3+ts7rYon/cBcHDJ9pKpYAaPrCEaf6aUhnTc7dcrg3yxqylE0ANB/fU5gOOf2k3SSpDZJ/zKzZWb2TTObNVjBAQAA7Ki7VrVpU3vqVJEfmlOkigg9aDF6fTZtMM9nNnbo9c0dOYoGAPqnX5/gzrk3nHNflTRD0mclXSDpXTN7ysw+YmZcEQAAgJxbuLlDz9ekfik7fGJY+1WGcxQRkB/2Hx/WMVNSu1D9dlFzjqIBgP7pd8LBzOZI+rakP0gq9B//WdIVku4Y0OgAAAD6aXN7XLe9mzq7woTCQI+m88Bo9bm9U1th3LWqTe81xbKsDQD5oz+DeH7WzF6Q9JKkSZIuds7t7pz7oXPuZkknSjplkOIEAADYrljC6aZlrWqPd5cFTbp4t2JFgox7AUjSSdMi2qOie2z+uJP+uIRWGADyX39aYJwu6eeSpjrnPuOceyF5oXOuVdL7BzI4AACA/ljwXpvWtMRTys7ZpUgzS5lIDehiZroirRXG395pVUM0kWULAMgP/UlgPOGc+6dzLmWuJTP7ctdj59xDAxYZAABAP7y5pUNPbUgd92KfcQU6ZgrjXgDpLti1WJOKur8KtMScblzWksOIAGD7+pPA+HaW8m8ORCAAAAA7qq49oVuXt6WUjY2YLppbJGPKVKCHSNB0+bzUVhh/XNKsjuR5hwEgz2y3PaWZndC1rpkdLyn5KmBXSUweDQAAciYad7rxnRa1JX3xCph0yW4lKg4xQRqQzcd3L9HP32hSS8z739nYltAdK1r14aqSHEcGAJn1pUPoDf59RNJfksqdpI2SPjfQQQEAAPTV119o0Orm1HEvzp5ZqFlljHsB9GZsJKCPVhXrT0u7u478dlGzLppbTMslAHlpuz9LOOdmO+dmS/p712P/tqtz7kjn3IIhiBMAAKCHv73TohvfSZ0yda+xIR03NZKjiIDh5b/2KlUgKVexpCGmx9ZHs28AADnU53aVzrmPDWYgAAAA/fHqpg5d+XxDStn4woA+UsWvx0BfzSoLaf4uRSllv1nElKoA8lOvCQwzW5r0eI2Zrc50G/wwAQAAum1qi+tjj9WpI2nWx3BA+sTujHsB9Nfn0qZUfWJ9VG9u6ciyNgDkzvY6h34y6fFHBzMQAACAvoglnD7+RJ3WtaaOe3Hh3GJNLQnmKCpg+DpwQlhHTgrruZrupMVvFzfrumPG5TAqAOip1wSGc+6ZpMdPDn44AAAAvbv6xUY9szH11+HjpkR04PhwjiIChr/P7V2q52rqtj3/94o2ffvAmKaXMhgugPzR5zaWZvZlM9vff3y4331kpZkdMWjRAQAAJPnTkmb9+e2WlLKjJod19qzCHEUEjAynzihUVXl3siLmpOuWtvSyBQAMvf50Ev2SpJX+4/+V9AtJP5D0ywGOCQAAoIeH17br6pcaU8qmlwT11+PGKcigncBOCZjpir1Sx8K46Z0WNXcmsmwBAEOvPwmMcudco5mVSdpP0m+cczdI2n1wQgMAAPAsqe/UJ56oU8J1l5WGTLedVKkJRYx7AQyED84pVmWk++tBY4fTbctbe9kCAIZWfxIYa8zsSEkXSnrKORc3szGS4tvZDgAAYIfVtsX1oUe2qKmzO3sRMOmG48Zp73EFOYwMGFmKQqaP71GSUvaHJc1KOJdlCwAYWv1JYHxV0h2S/lvS9/2ysyS9NNBBAQAASFJLZ0IffnSL1jSn/l7yw0PKdeoMxr0ABtple5SoIOkbwrtb43p4bTR3AQFAkj4nMJxz9znnpjrnZjnnXvWL/ynpnMEJDQAAjGYdcaePPV6nVzZ1ppRftkeJPj2vJMtWAHbGlOKgzptdlFL2hyXNOYoGAFL1a14kMyuXN+ZFadqixwYsIgAAMOolnNNnnqnXo+tSf/k9fmpEPzqsXMagncCg+cy8Ut3+btu250+sj2pJfafmjaXLFoDc6nMCw8wulfQ7Sc2SkkfzcZJ2HdiwAADAaOWc01UvNuqOFW0p5fuOK9BNx49TQYDkBTCY9h8f1hGTwnq+pmNb2R8WN+s3R43NYVQA0L8xMH4o6Xzn3CTn3OykG8kLAAAwYH76RpOuW9qSUrZrWVB3nFKpMeH+XLoA2FH/NS+1wfXtK1q1uZ2x+wHkVn+uAkKSHhqsQAAAAP64pFn/83pTStnkooDuPHW8JjJdKjBkzpxZqJml3f9z0bj017dbetkCAAZffxIYP5b0TTPboZ8+zOw0M1tmZsvN7KoMyyNm9g9/+YtmNssvrzSzx82s2cx+m7bNQWb2lr/Nr83vEGtm48zsYTOr9u9p7wYAQJ7745JmXfViY0pZedj071PHa5eyfg3bBWAnBQOmT+2ZOlju9W+3qCPOlKoAcqc/yYgvSfqmpCYzW518296GZhaUN37G6ZLmSbrIzOalrXaZpHrn3FxJ18pLmEhSu6RvSboyw67/IOmTkqr822l++VWSHnXOVUl61H8OAADy1B8W90xeFAVNt59UycCBQI5cvFuJSkPdY87UtCX075VtvWwBAIOrPwmMj0o6SdIZki5Ou23PoZKWO+dWOOc6JN0maX7aOvMl3eQ/vkPSiWZmzrkW59wz8hIZ25jZFEljnHMvOOecpL9JOjfDvm5KKgcAAHnmD4ubdfVLqcmLwqB060njdNikSI6iAlAeDugjVcUpZb9f3Czv0hsAhl6f22M6557cideZJmlN0vO1kg7Lto5zLmZmjZIqJW3uZZ9r0/Y5zX88yTm3wX+8UdKkHQ8dAAAMlt8vbtY3MiQvbjupUsdNLcxRVAC6XD6vVNctbVFXyuLNuk49X9OhIyeTXAQw9PozjWpE0rclXSSp0jlXbmanSNrNOffb3rfOHeecM7Ne08TV1dVDFc6gGQnHgJ1HPYBEPUC3fK4LzknXrS7Q9WtSu4dEAk4/2zOqaS1r1J/wa2oZ4DObmtqaXIeAIVQdyDxTyM6cD44eF9ZTdd1fG3760kb9ZM+OXrZAPsvnzwYMnXytB1VVVb0u78+IWNfKa+HwEUn3+2WL/fLtJTDWSZqR9Hy6X5ZpnbVmFpJULmnLdvY5Pcs+a8xsinNug9/VpLa34Lb3JuW76urqYX8M2HnUA0jUA3TL57oQTzhd+UKD/rqmNaXca3kxfodaXkxKMDtCJjW1NZo0kYaoo0lVVUmPsp09H1xZGtVTD3Q3in6yLqSCydM0i8F1h518/mzA0BnO9aA/Z53zJM11zrWYWUKSnHPrzGzadraTpJclVZnZbHlJhgslfThtnQWSLpH0vKTzJT3meulg5ycntprZ4ZJelPQxSb9J29eP/Pv/9PEYAQDAIGqPOX3yqTrd/V7K0FYqDpluOXEc3UaAnXTjsp7JvJra4E4l+Zxzmloc0PrWhCQp4aQvPNug82YX7fA+e3Pp7j2TMAAg9W8Qzw6lJTzMbIJ6byUhyRvTQtIVkh6UtFTS7c65xWb2PTM7x1/tBkmVZrZc0peVNHOIma2S9AtJl5rZ2qQZTD4j6XpJyyW9q+6WIT+SdLKZVcsbePRH/ThOAAAwCBo7Ejr/4c09khdjI6b/nLpjLS8ADD4z07FTUse8eKE2qvYYg3kCGFr9aYHxT0k3mdmXpG2zgPxS3owi2+Wcu0/SfWll30563C7pgizbzspS/oqkvTOUb5F0Yl/iAgAAg29VU0wXPrJFbzfEUsqnFQf171MrtXsFU6UC+ezACWHdvbpdzZ1e0iIal17c1NEjsQEAg6k/LTC+IWmFpLckVUiqlrRB0ncHPiwAADBSPLMxqhPu3tQjebF7eUgPnjme5AUwDBQETO+bFE4pe3pDVAmmVAUwhPrTAmOupGWS/kdSUNJdzrm3BiUqAAAwIty0rEVfeb5B6S3ND55QoNtPqtS4QmYPAYaL902O6JF1UcX9/+fN7Qktro9pn3EkIQEMje22wDDPX+S1vPiGpLMlfVLS62b2VzOzQY4RAAAMM50Jp6tebNAXnuuZvHj/7CItOG08yQtgmBkTDujA8anJiifXR3MUDYDRqC9dSD4l6ThJhzvndnHOHeGcmynpCElHS7p8EOMDAADDzPqWuM6+f7P+uKTnrAffOKBMNxw7VsWh/vRiBZAvjp2aOubF8q0xrW2JZVkbAAZWX64eLpb0eefcy8mF/vMv+ssBAAD0xPp2HbOgVi/UdqSUF4dMNx0/Tl/bf4xovAkMX9NLQpo7JrUX+lPrO7KsDQADqy8JjHmSnsyy7El/OQAAGMXiCacfvb5V5z24RZvbEynLppcEdf8Z4zV/VlGOogMwkNJbYby6uUNbOxJZ1gaAgdOXBEbQOdeUaYFfThtQAABGsXUtcZ330Bb9aGGT0ucjOHFaRE+eM0H7VYYzbgtg+NlrbEiVke6vAHEnPbuRsTAADL6+zEJSYGbHS8rW3rM/M5kAAIAR5D+r2vSFZ+vV0JGaujB54118Zb8yBegyAowoATMdMyWiO1e1bSt7tqZDJ00vVEGA/3cAg6cvyYdaSX/ZznIAADCKNHUm9PUXGnXL8tYey8YXBnT9sWN13NTCHEQGYCgcNjGs+9a0KRr3njd3Or22uUOHTYz0viEA7ITtJjCcc7OGIA4AADBMPLUhqs8/W69VTfEey46ZEtEfjx6rqSVMkQqMZIUh0+ETI3pyQ3fXkSfXR3XohDAD9QIYNIxfAQAA+qQhmtAXnq3XOQ9s7pG8KAhI3z9kjO46tZLkBTBKHDMlnNLHfH1rQsu3MqUqgMHD+BUAAGC77nmvTVc+36CNbT1nGti9PKQ/HztW+zJQJzCqVBYGtc+4Ar1Z17mt7Mn1UVWVF+QwKgAjGQkMAACQVW1bXF97oVF3JQ3Wl+xTe5bouweXqyhEk3FgNDp2SiQlgbG4PqZNbXFNKKIlFoCBRxcSAADQg3NOt1S36NB/12RMXuxWHtIDZ4zXTw6vIHkBjGK7jglqelK3MSdvnBwAGAwkMAAAQIr3mmL6wENb9JlnGnpMjxoy6cp9y/TUORN1+CRmGwBGO/OnVE32Ym2HWmM9u5sBwM4igQEAACRJ8YTTHxY368i7avXY+p6/oO5fWaDHz5mobx40RoW0ugDgO3B8gcYUdJ8TOhLSCzUdOYwIwEhFAgMAAGhpfadOvW+Trn6pUS2x1FYXhUHp+weP0SNnTdA+4xicD0CqUMB01OTUVhhPb4gq7lyWLQBgx5DAAABgFOuIO/3o9a06ZkGtXtnU2WP50ZPDeu7cSfrcPmUKBWh1ASCzIyeHVZD0zaK+w+nNLT3PKQCwM5iFBACAUeqVTR36/DP1WtIQ67FsTNj0g0PKdXFVscxIXADoXWlBQAdPCOv5pK4jT6yP6oDxTK8MYOCQwAAAYJRp6Uzoh69v1R8WtyhTA+8zZxbqZ0dUaEox0yAC6Ltjp0RSEhjvNce1qimmWWV85QAwMDibAAAwijyxvl1feLZB7zXHeyybUBjQz46o0Dm7FNLqAkC/TS4Oao+KkN5OatX15PqoZu3OVw4AA4OzCQAAo0BDNKH/frlRf69uzbj8w3OL9cNDyzU2wvBYAHbcsVMiKQmMN7Z0qj6a4NwCYECQwAAAYIT7z6o2ffWFBtW2JXosm1ka1C+PrNAJ0wpzEBmAkWaPipAmFQVU459vEvJmJDlnVlFuAwMwIpDAAABgJ9y4rCXXIWRUUxtUYXuT/rWiTW/W9ZwJwCQdMyWiM2YWkrwAMGDMTMdOiej2FW3byp6rierUGYWKBOmaBmDnkMAAAGCEcc7pra0BPbqySW3xnsN0Ti4K6MK5xQysB2BQHDwhrHtXt6sl5p1/2uPSS7UdOnpKJMeRARju6IwGAMAIsrUjoRvebtE9tQU9khdBk06dHtGV+5WRvAAwaMJB05GTU6dPfXJDVAmXad4jAOg7rl4AABgBnHN6bXOn/rWyTa2xnl8SdikN6sI5xZpSwtSoAAbfUZMjemxdVF151M3tCS2pj2nvcQW5DQzAsEYCAwCAYa6pI6F/ZhnroiAgnTGzUMdOiSjA1KgAhkh5OKADxhfolU3d56Un10dJYADYKSQwAAAYxl7f3KE7VrRt62uebFZZUB+eW6yJRbS6ADD0jpsSSUlgVG+NaW1LTNNL+AoCYMcwBgYAAMNQc2dCNy5r0U3vtPZIXoRMOqEyps/vXUryAkDOTC8Nac6Y1HPQk+ujOYoGwEhA+hMAgGFmcV2nbn23Vc2dPVtdzCz1Wl1Y82a6jADIueOmFurdrd3TTb+2uVNn75LQmDC/owLoPxIYAAAMEx1xp/+satOzNR09lgVNOn1GoY6fFlHQTDXNOQgQANLsNTakykhAW6IJSVLcSc9sjOqMmUU5jgzAcETqEwCAYWBtc0w/e7MpY/JiRklQV+5bppOmFypIqwsAeSRgpmOmRlLKnt3Yoc4EU6oC6D8SGAAA5LGEc3psXbuufatZtW2JlGUBSafNKNQX9yllelQAeeuwiWEVJp2iWmJOr2zqmYwFgO0hgQEAQJ5qiCb0hyUtWvBeu+JpP1aOLwzo8/uU6rQZhQoGaHUBIH8VBk1HTEpthfH4+qgSjlYYAPqHMTAAAMhDb2zp0D/ebVNrhulRD5sY1vtnFykSHJjExY3LWra/EgDshGOmRPTk+qi62pHVtiW0pD6mvccV5DQuAMMLCQwAAPJINO7075VterG2Z/Pq4pDpg7sWaf/x4RxEBgA7bmwkoAMnFOiVTZ3byh5d104CA0C/DFkXEjM7zcyWmdlyM7sqw/KImf3DX/6imc1KWna1X77MzE71y3Y3s4VJt61m9kV/2TVmti5p2RlDdZwAAOyo9S1x/fzNpozJi6oxIX1tvzKSFwCGreOnFqY8X9kU18qmWI6iATAcDUkLDDMLSvqdpJMlrZX0spktcM4tSVrtMkn1zrm5ZnahpB9L+pCZzZN0oaS9JE2V9IiZ7eacWyZp/6T9r5N0Z9L+rnXO/WyQDw0AgJ3mnNPzNR26c1WbOlPH6VTQpDNnFuq4qREFmGEEwDA2rSSo3StCWtbQnbR4fF1Us/egUTiAvhmqFhiHSlrunFvhnOuQdJuk+WnrzJd0k//4Dkknmpn55bc556LOuZWSlvv7S3aipHedc+8N2hEAADAI2mNOf6tu1e0reiYvJhYF9KV9SnXCtEKSFwBGhBPSplR9q65TtW3xHEUDYLgZqnTnNElrkp6vlXRYtnWcczEza5RU6Ze/kLbttLRtL5R0a1rZFWb2MUmvSPqKc64+W3DV1dV9PIz8NRKOATuPegCJejDUamp3fPrSjVHTXRtDqu/s+XvCvmVxnTwhqoLWNtW07mhsNTscG0YO6gGk/KkH5U6aFC5QTYd33nOS7n+3QadN7G6VUR0goTGYuE6AlL/1oKqqqtflw769lpmFJZ0j6eqk4j9I+r68c+L3Jf1c0iey7WN7b1K+q66uHvbHgJ1HPYBEPciFSYn+z+DhnNMzGzt019q2HtOjhgPSBbsW65CJOzfWRU1tjSZNnLRT+8DwRz2AlH/14JRAh26u7s7MvtUU1Pt3G6uysJfUqKoqyVVoIx7XCZCGdz0Yqi4k6yTNSHo+3S/LuI6ZhSSVS9rSh21Pl/Sac25bWtk5V+OcizvnEpL+rJ5dTgAAyInWWEI3LmvVv1b2TF5MLQ7oK/uW7XTyAgDy2f7jCzQ20t0tLuakpzdGcxgRgOFiqBIYL0uqMrPZfouJCyUtSFtngaRL/MfnS3rMOef88gv9WUpmS6qS9FLSdhcprfuImU1JenqepEUDdiQAAOyg1U0x/fyNZr1R19lj2ZGTwvriPmWaVLzjXVIAYDgImum4KakzkjyzsUPR9KwuAKQZki4k/pgWV0h6UFJQ0l+cc4vN7HuSXnHOLZB0g6SbzWy5pDp5SQ75690uaYmkmKTPOufikmRmJfJmNrk87SV/Ymb7y+tCsirDcgAAhoxzTk9uiOru99p7tLqIBKUPzSnWgUyPCmAUOXxSWA+ubVdrzDsptsacXqzt0DFTItvZEsBoNmRjYDjn7pN0X1rZt5Met0u6IMu2P5T0wwzlLfIG+kwvv3hn4wUAYCC0dCZ06/JWLaqP9Vg2vSSoS3Yr1oQiWl0AGF0iQdP7Jof18NruriNPrI/qfZNJ5gLIbtgP4gkAQL5a2RTTTcta1NDRs1n00ZPDmj+rSKEA06MCGJ2OmRzR4+ui8hthqC6a0BtbenaxA4AuJDAAABhgzjk9vj6qe1a3K5GWuygMShfNLdZ+lfzKCGB0KwsHdMjEsJ6v6dhW9ti6qJxzMiO5C6CnoRrEEwCAUaE1ltANb7dowXs9kxczS4P66n5lJC8AwHfc1IiSUxVrW+J6akNH1vUBjG4kMAAAGCDvNcX00zeaMo53cfzUiD6/d6kqCxnvAgC6TCoKaq9xqY3Cf7OoKUfRAMh3dCEBAGAnOef01MYOLVjV1mOWkeKQ6cNzi7X3uILcBAcAee7EqYVaVNe87fkj66JaXNepvThvAkhDCwwAAHZCW8zpxmWtunNlz+TFzNKgrty3jOQFAPRi9piQZpeltk6jFQaATEhgAACwg97Y0qGfv9mkN+p6jpp/zJSwPr93qcYV8lELANtz/NRIyvM7VrRpbXPP7ngARjeuqgAA6CfnnP76dotOuXeTNrcnUpYVBqWP716s988uZopUAOijvccVaEJSwjfmpN8vae5lCwCjEQkMAAD6obkzoU89Va8vPd+gaDx12fSSoK5klhEA6LeAmU6YltoK48ZlrdrSHs+yBYDRiAQGAAB9tLiuU8ffvUn/XNHWY9n7JoX1hX1KNZ5ZRgBghxw8IawxBd0t11pjTn9c0pLDiADkGxIYAAD0wf9Vt+ikezapujG1T3YkIF1cVawL5hSrgC4jALDDCgLWYyyM65Y2a2tHIssWAEYbEhgAAPSiNZbQZ56u1xXPNKgtbZqReWND+vJ+ZTpoAl1GAGAgHDk5orGR7mRwY4fTDW/TCgOAhwQGAABZvNPQqZPu3qRblrf2WPbRqmI9ctYETSqiywgADJRI0PRf80pTyn63uFmtMVphACCBAQBARv98t1XH371JSxpSu4wUBU2/P6pCvz1qrIpDfIwCwED71J6lKksaC2Nze0I3v9MzkQxg9OHKCwCAJO0xpy8+W69PPlWvllhql5HdykN69OwJ+nBVSY6iA4CRryIS0GV7pJ5nf/1WszrSuvEBGH1IYAAA4FuxNaaT792kGzP80vfBXYv02NkTNG9sQQ4iA4DR5TN7lSp5Uqd1rXHd9i6tMIDRjgQGAACS/rOqTccuqNVbdZ0p5ZGg9KsjK/SnY8aqtICPTQAYChOLgrp4t9RWGL98s0nxBK0wgNGMKzEAwKgWjTt97YUGXfJ4nZo6Uy+Mdy0L6uEzJ+iS3UtkxhSpADCUPr93qUJJp94VTXH9a2Vb7gICkHMkMAAAo9aqpphOv2+Trlvac4q+c2cV6YlzJmrfSqZIBYBcmFEa0oVzi1PKfrKQVhjAaEYCAwAwKt232usy8trm1C4jBQHpp4eX66/HjdWYMB+TAJBLX9m3TMGkVhjLt8ZohQGMYlyZAQBGlc6E0zdfatSHH61TY0fqr3gzS4N66MwJ+uSepXQZAYA8MHtMz1YYP32DVhjAaBXKdQAAgNHrxmU9u24MpvpoQje906JVTfEey/YeF9KH5xbrjS2demNLZ4atAQC5cOW+Zbpteau6ZlGtbozp3yvbdMGc4t43BDDi0AIDADAqLKnv1M/eaOqRvAiYNH9WoS7bvUTFIT4WASDfzB4T0ofSkhU/oRUGMCpxpQYAGNHizune99p03dIWtcRSL3YrwqbP7V2q46cW0mUEAPLYlfuljoVR3RjTnasYCwMYbUhgAABGrMaOhH6/uFkPr4v2WLZnRUhX7lem2WX0pgSAfLfrmJA+mN4KgxlJgFGHBAYAYESqbuzUT99o0rtbU7uMmKSzZhbqk3uWqLSAj0EAGC6+mtYK4x1aYQCjDlduAIARxTmnR9a26/eLW9TcmfrL3JgC0xV7leqk6YUK0GUEAIaVTK0wfrywSTFaYQCjBgkMAMCI0RZzumFZi+5Z3a70y9ndykP66n5lmlNOlxEAGK7SW2FUN8Z027utuQsIwJAigQEAGBHWt8T18zebtKgullJukk6dHtGn55WoLMzHHgAMZ7uOCemiuamtMH70epOicVphAKMBV3IAgGHvpdoOXftWkza3J1LKi0OmT+1ZotNnFtFlBABGiK/tX6bkfPTalrhuWtaSu4AADBkSGACAYSuWcLr93VbdsrxVnam5C80oCerKfcu059iC3AQHABgUM0tD+vjuJSllP3uzSS3pHwQARhwSGACAYamuPaFfvdWs52o6eiw7YlJYn9+nVOMK+ZgDgJHoK/uVqTjU3bKuti2h65bSCgMY6biyAwAMO0vrO/WzN5u0piV1itSCgHTR3CJ9aE6xCgJ0GQGAkWpiUVCfnpfaCuNXbzWpIUorDGAkI4EBABg2Es7pgTXtum5pi1pjqQO2jS8M6Iv7lOmwiZEcRQcAGEqf37tMY8LdyeqGDqffLm7OYUQABhsJDADAsNDSmdCfl7bogTU9p0jde2xIX963VNNKgjmJDQAw9CoiAX1h77KUsj8sbtamtniWLQAMdyQwAAB5b01zTD97s0lLG3pOkXrWzEJ9Yo8SFYf4SAOA0ebyeSWakDTeUUvM6ccLm3IYEYDBNGRXe2Z2mpktM7PlZnZVhuURM/uHv/xFM5uVtOxqv3yZmZ2aVL7KzN4ys4Vm9kpS+Tgze9jMqv37sYN+gACAQfFSbYd+9Vaz6qOp7S5KQ6b/mleik6YXMkUqAIxSpQUBfWW/1FYYf13WourGzhxFBGAwDUkCw8yCkn4n6XRJ8yRdZGbz0la7TFK9c26upGsl/djfdp6kCyXtJek0Sb/399fleOfc/s65g5PKrpL0qHOuStKj/nMAwDASd053rmzTLctblTbchWaVBXXlfmXarYIpUgFgtPvE7iWaVdb99SDupGte2ZrDiAAMlqFqgXGopOXOuRXOuQ5Jt0man7bOfEk3+Y/vkHSimZlffptzLuqcWylpub+/3iTv6yZJ5+78IQAAhkpLZ0J/XNKiJzdEeyw7enJYV+xVqooIXUYAAFI4aPrOQWNSyu5d3a7nNvb8DAEwvIWG6HWmSVqT9HytpMOyreOci5lZo6RKv/yFtG2n+Y+dpIfMzEn6k3PuOr98knNug/94o6RJvQVXXV3dv6PJQyPhGLDzqAeQhlc9qKntOehmTdT0rw0FaoyldgsJmdPpE2PauzSqLZvp39wXNbU1uQ4BeYB6AGl41YPqQP8H4dzLSXuXRbSoqftz5WvP1Ogv+0ZFL8NUw+k6AYMnX+tBVVVVr8uHKoExWI5yzq0zs4mSHjazt51zTyWv4JxzfoIjq+29Sfmuurp62B8Ddh71ANLwqweTEi0pz1/f3KFb17WqI5G6XkXYdNkepZpROtw/toZOTW2NJk3sNX+PUYB6AGn41YOqqpId2u6n5VGdft/mbc8XNQW1uGCazptdPFChDXvD7ToBg2M414Ohan+7TtKMpOfT/bKM65hZSFK5pC29beuc67qvlXSnuruW1JjZFH9fUyTVDuCxAAAGWMI53fNem256p2fyYteyoL6ybxnJCwBAr46YFNFZMwtTyr776lZF473+lglgGBmqBMbLkqrMbLaZheUNyrkgbZ0Fki7xH58v6THnnPPLL/RnKZktqUrSS2ZWYmZlkmRmJZJOkbQow74ukfSfQTouAMBOao0ldP3SFj2yrmdf5fdNCusze5WqLMx4FwCA7bvm4DEKJXUZWdUU1w1vt2TfAMCwMiRXhM65mKQrJD0oaamk251zi83se2Z2jr/aDZIqzWy5pC/LnznEObdY0u2Slkh6QNJnnXNxeeNaPGNmb0h6SdK9zrkH/H39SNLJZlYt6ST/OQAgz2xsjevaN5u1pCGWUh406UNzinTBnGKFAnReBgD0zdzyAn1899QuKD9euFVb2vs/rgaA/DNk7XGdc/dJui+t7NtJj9slXZBl2x9K+mFa2QpJ+2VZf4ukE3cyZADAILp/dZuufatJ0bRryrIC08d3L9GuY+gyAgDov68fUKbb3m1VU6fXdaSxw+mHrzXpF0dW5DYwADuNNrkAgCGVcE4/XbhVH360rkfyYmapN94FyQsAwI4aXxjUV/crSyn767IWvbGlI0cRARgoJDAAAEOmuTOhSx6v0w9fb1L6kGqHTCjQ5/YuVUWEjyYAwM759LxSzU1KhjtJV73YKG+IPQDDFVeJAIAhsXJrTKfcs0l3v9eeUh6QdN6sIn14brEKGO8CADAAwkHT/xxanlL2fE2H/r2yLUcRARgIJDAAAIPu8XXtOv7u2h6DdZaETJ/eq0THTo3IjOQFAGDgnDKjUKdOj6SUfevlRrV0JrJsASDfkcAAAAwa55x+s6hJH3h4ixo6Upvt7j2uQF/et1S7lRfkKDoAwEj3P4dWqCDpG8/61oSufbM5dwEB2CkkMAAAg6It5nT50/X61stblUjrcvz+2UV68IzxqiwM5iY4AMCoMKc8pM/MK00p+/WiJq3cGsuyBYB8RgIDADDg1jTHdNp9m3T7u6l9jU3SNQeN0Q3HjlVJAR9BAIDBd+X+ZZpU1P2Z05GQrnyhgQE9gWGIq0cAwIB6bmNUx9+9SW9s6UwpHxM23X5ypb64bxnjXQAAhkxZQUDfPTh1QM9H10V1JwN6AsMOCQwAwIBwzumGt5t1zgObtbk9dYC03ctDeuysCTp5emGOogMAjGYfmlOk900Op5Rd9VKjGqIM6AkMJyQwAAA7LRp3+sJzDfrK842KpbXIPX1GoR4+a4LmMlgnACBHzEzXHpE6oGdtW0Lff21r7oIC0G8kMAAAO2Vja1xn379Zf3untceyr+1fpr+fOE5jwnzcAABya7eKAn1p37KUsr+83aKXaqM5ighAf3FFCQDYYa9s6tDxd9fqpU0dKeUlIdPfjh+nbxwwRgHGuwAA5Ikv71OmOWO6Z8Bykr74XIM606fLApCXSGAAAHbI36tbdMZ9m7ShNbX/8KyyoB4+a4LOmVWUo8gAAMisMGT6xRFjU8qW1Mf0+8XNOYoIQH+QwAAA9EtnwumrLzTos880qCNt7LMTpkb0+NkTNW8s410AAPLTsVMjunBOapL9f1/fquWNnVm2AJAvSGAAAPqspjWucx7YrD8vbemx7HN7l+r2kys1NsJHCwAgv/3g0HKNjXR3cWyPS599pkFxupIAeY2rTABAn7xUG9Vxd9fq+ZrU8S4Kg9J1x4zV9w8pVyjAeBcAgPw3vjCo/zm0IqXsxdoO/X4JXUmAfEYCAwDQK+ec/vp2i868f3OP8S5mlAb1wBkT9ME5xTmKDgCAHXPhnCKdOqMwpewHr23VOw10JQHyFQkMAEBW7TGnzz/boC8936DOtPEujpsa0RNnT9D+48O5CQ4AgJ1gZvrlkRWqCHe3HozGpc88U09XEiBPkcAAAGS0tjmmM+7fpJurW3ss+8Lepbrj5EpVFgYzbAkAwPAwpTionxxekVL2yqZO/ZZZSYC8RAIDANDDMxujOu7uTXptc2oz2pKQ6cbjxum7jHcBABghLti1SGfOTO1K8sPXtmppPV1JgHxDAgMAsI1zTr9Z1KT5D2zW5vbUPiO7lgX18FkTdO7soixbAwAw/JiZfnFERcqsJB0J6ZNP1as9RlcSIJ+QwAAASJIaogl9+NE6fevlrYqnXa+dOqNQj509UfPGFuQmOAAABtGk4qB+ltaVZFFdp77zSmNuAgKQEQkMAIBe29ShYxbU6v417T2WfX3/Mt164jhVRPjIAACMXO+fXaQPpLUy/NPSFj2wpi1HEQFIx9UoAIxizjn9cUmzTr1vk1Y3x1OWlYdNt544TlcfMEYBY7wLAMDIZmb6xZEVmlmaOkD1Z59u0IbWeJatAAwlEhgAMEo1diR0yeN1uurFxh5TpB44vkBPnTNRp89kvAsAwOhRHg7ohmPHKZiUt98STejyp5haFcgHJDAAYBRauLlDxy2o1YL3enYZuXzPEj1wxgTtUhbKQWQAAOTWIRPD+sYBY1LKntoQ1a8XMbUqkGskMABgFHHO6Ya3m3XKvZu0sim1OeyYAtNNx4/Tjw+vUDhIlxEAwOj1xX1KdfTkcErZD17bqmc3RnMUEQCJBAYAjBr10YQ+/kS9vvJ8ozrSuozsO65AT54zUfNn0WUEAIBgwPSnY8ZpXNIA1nEnffyJOsbDAHKIBAYAjAJPrm/X++6q0V2reo6kftkeJXrozAmaPYYuIwAAdJlaEtTvj65IKattS+iSx+rUkT7fOIAhQQIDAEaw9pjTf7/UqPkPbtH61tRmF6Uh0w3HjtXPj6hQYYguIwAApDttRpGu3K8speylTR36xkuNOYoIGN1IYADACLW4rlMn3FOr3y3uOejYvuMK9MQ5E/SBXYtzEBkAAMPH1fuX6cRpkZSy699u0S3VLTmKCBi9SGAAwAiTcE6/X9ysE+6p1ZL6WMoyk/SlfUr1yFkTNLe8IDcBAgAwjAQDpuuPHaddSoMp5V9+vkELN3fkKCpgdCKBAQAjSG3U9P6HtugbLzUqmjbG2PSSoO45fby+c3A5s4wAANAPYyMB/e2EcSpMymG0x6UPP7pF61sY1BMYKozYBgDD2I3LvOarzjkt3NKpfywvVHui5xRvB08o0AdmF6u6MabqxliP5QAA5Iuuz7Z89IHZxfr78tZtz9e3JnTKvZv0ub1LFfF/HLh095JchQeMeCQwAGCYa+xI6I4VbXqrrlNeJ5FuRUHTBXOKdOD4cOaNAQBAnx0yMaw1LTE9taG768jalrj+9k6LLtujRAGjhSMwmEhgAMAw5ZzT8zVR/WdVm9oztF6tGhPSh6uKNTZCb0EAAAbKubOKtKU9ocVJ40wtro/prlVtev9sBscGBtOQXdWa2WlmtszMlpvZVRmWR8zsH/7yF81sVtKyq/3yZWZ2ql82w8weN7MlZrbYzL6QtP41ZrbOzBb6tzOG5CABYIis3BrT/Ae36B/v9kxeBE06Z5dC/ddeJSQvAAAYYAEzfWy3Ek0vSR3U86kNHXpqQ89unAAGzpBc2ZpZUNLvJJ0uaZ6ki8xsXtpql0mqd87NlXStpB/7286TdKGkvSSdJun3/v5ikr7inJsn6XBJn03b57XOuf39232DeHgAMGTiCaffLmrSkXfVZrxImhpJ6Mr9ynTCtEKasQIAMEgiQdMn9yxRRTj1s/bOlW26a2VbjqICRr6h+mnuUEnLnXMrnHMdkm6TND9tnfmSbvIf3yHpRDMzv/w251zUObdS0nJJhzrnNjjnXpMk51yTpKWSpg3BsQBATiyu69Qp927SN1/eqra4S1kWDkjnzirUxdM7NaU4mGUPAABgoJSHA/rknqVKbuzoJH3yqTo9srY9Z3EBI9lQjYExTdKapOdrJR2WbR3nXMzMGiVV+uUvpG2bkqjwu5scIOnFpOIrzOxjkl6R11KjPltw1dXV/TmWvDQSjgE7j3owMrXHpRvXFujGtSHFXc9WFbOKEjp9YqcqQl6LjJramqEOEXmKugCJegAP9WBwhCTNnxTQPzeE5PyBtDsT0kce3azf7BXVAeWJ3AaYAdeLkPK3HlRVVfW6fNgP4mlmpZL+JemLzrmtfvEfJH1fXhL0+5J+LukT2faxvTcp31VXVw/7Y8DOox6MPM453bO6Xd94qVFrmnuO0lkeNp0xo1CHTgzL/O4iNbU1mjRx0lCHijxEXYBEPYCHejC4JkkKl3bo/6q7p1eNJkxfebtId582Xvvn0UxgXC9CGt71YKi6kKyTNCPp+XS/LOM6ZhaSVC5pS2/bmlmBvOTF351z/+5awTlX45yLO+cSkv4srwsLAAwb7zR06v0PbdHFj9VlTF6cs0uhXjxvkg6bFNmWvAAAALlx8ISwzp9dlFLW1On0/oe26O2GzhxFBYw8Q5XAeFlSlZnNNrOwvEE5F6Sts0DSJf7j8yU95pxzfvmF/iwlsyVVSXrJHx/jBklLnXO/SN6RmU1JenqepEUDfkQAMAiaOhP69suNOvKuWj2+vucgnROLArrp+HH62wmVmsxYFwAA5I2jpkR05szClLK6aEJn379Zi+tIYgADYUi6kPhjWlwh6UFJQUl/cc4tNrPvSXrFObdAXjLiZjNbLqlOXpJD/nq3S1oib+aRzzrn4mZ2lKSLJb1lZgv9l/qGP+PIT8xsf3ldSFZJunwojhMAdpRzTv9c0aZvv9yojW09+8sGTbp8Xom+vv8YlYeZGhUAgHx08vRCVZWH9Mu3mreVbWpP6KwHNunOU/KrOwkwHA3ZGBh+YuG+tLJvJz1ul3RBlm1/KOmHaWXPSMrYbto5d/HOxgsAQ+Wtuk597YUGPV/TkXH50ZPD+snhFdpzbMEQRwYAAPrrOweNUXOn0/Vvt2wrq486nfPgZv37lPE6eAJJDGBHDftBPAFguFrbHNP/vN6kW5e3ymVYPq04qB8cOkbnzipinAsAAIYJM9NPDy9XwKTrlnYnMbZ2OJ37wGbdfnKljpwcyWGEwPBFAgPAqHXjspbtrzQIWmMJPbouqqc2RNWZYXa1oEknTI3opOmFauxwuumd1p4rAQCAvGVm+vFh5SoKmn61qLs7SXPM6QMPbdFfjx+r02YU9bIHAJmQwACAIRJLOD29MaqH10bVGsvU5kKaVxHSebOLNKGIAToBABjOzEzXHDxGkZDpJwubtpW3xZ0+/Gidfn54hT6+R0kOIwSGHxIYADDIEs7ptc2dund1m+qjmRMXk4oCOmeXIu01jnEuAAAYKcxM3zhgjAqDpu+9unVbecJJX3q+QaubY/rWQWMUoKso0CckMABgkDjn9E5jTHe/1661LfGM64wpMJ0+s1CHTgwryMULAAAj0pf3LdOYAtPXXmxUIum3jGvfatbalrh+e9RYRYJcBwDbQwIDAAZBdWOnHljTrne3Zk5cRILSidMKdeyUCBcsAACMAv9vz1JNLQnqsifq1RbvzmL8c0Wb1rbEdeNx4zSpmC6kQG9IYADAANpe4iJo0vsmh3XK9EKVFgSGODoAAJBLZ8ws0j2nB/WhR7Zoc3v3SN7P13TouLtr9bfjK3XIRKZZBbIhgQEAA2B5Y0wPrGnX8q2xrOscML5AZ84s1PhCfl0BAGC0OmhCWI+cNUHnP7Ql5bphQ2tCZ9y/ST89vEKX7s7gnkAmJDAAYCe86ycuqntJXOxREdIZMws1s5RTLgAAkGaVhfTQmeP18Sfq9eSG6LbyzoT0xeca9NrmDv34sAoVhehmCiTjahoA+sk5p+rGmB5eF1V1Y++Ji1NnFGp2GadaAACQalxhUP86pVLff3WrfrWoOWXZ395p1Su1HfrzseOYoQxIwlU1APRRwjm9uaVTj66Lak2WWUUkafeKkE6bXqjZYzjFAgCA7EIB03cPKdcB48P67DP1aol1D+65pCGmE+6p1XcPLtfle5bImK0MIIEBANvTmXB6ubZDj62Ppgy4lW638pBOm1GoXUlcAACAfjh3dpF2qwjpo49u0Yqm7h9JonHpqhcb9cjadv3uqLHMUoJRj6tsAMiiLeb07MaontoQ1dZOl3U9EhcAAGBnzRtboCfOmaivvdCg295tS1n2yLqojrirVv9zaLk+NKeI1hgYtbjaBoA0DdGEntoQ1XM1UbVn7ymivceFdOI0xrgAAAADY0w4oD8eM04nT2/Vl55v0NaO7h9Q6qIJffrpev1zRauuPbKCwcExKlHrAUDewJyrmuJ6akNUb2zpVLaOIgGTDh4f1gnTIppMM04AADAIPrBrsQ6ZGNblT9Xr+ZqOlGWProvqiDtr9c0Dx+hTe5YoGKA1BkYPEhgARrVYwun1zZ16akPvA3OGA9IRkyI6bmpEYyOBIYwQAACMRjNLQ7r7tPH67aJm/Wjh1pRWoS0xp6tfatTfl7fqfw8t19FTIrkLFBhCJDAAjEobW+O6f3Wbnq3pUHMv41uUhkzHTInofZPDKikgcQEAAIZOKGD64r5lOnuXIn3+uXo9uzG1Ncaiuk6d/cBmzZ9VqO8dXK5d6NaKEY4aDmDUcM7ppdoO3fB2i+5c1abO7BOKaGJRQMdMiejQCWGFgzTNBAAAuTOn3GuNcfM7rfrWy409Bhf/z6p2PbCmXVfsVarP7V2mClqLYoQigQFgxNvSHtety1t18zutWtYYy7qeSZo3NqRjpkS0W3mIEb4BAEDeCJjpkt1LdMqMQn3n5UbdviJ1ppJoXPr5m826/u0WfX7vMl0+r0SltB7FCEMCA8CIlHBOT22I6qZlrbpnde+tLSJB6bCJYR09OaIJRQzMCQAA8teU4qCuO3acLtsjqqteatTrmztTljd2OH3/ta3645JmfWnfMl26e7GKQyQyMDKQwAAwoqxvieuW5a26+Z0WvdfcyxyokiYUet1EDpkYViHdRAAAwAC4cVnLkL3WxVXF2qOiQ/e8166mtG4lm9oT+sZLjfqf17ZuG8+ruT6oSYntx3fp7iWDFTKwU0hgABj2tnYkdO/qdv1rRaseWx9VIvuYnAqZdPrMQl2yW4lWN8cUoJsIAAAYpgJmOmxiRPtXhvXUhqgeWxdVWzz1Qqg55nTfmnY9sq5d+5UFdUZ5ghnVMGyRwAAwLLXHnB5c6yUtHlrbnjK1WCZzxgT1sd1KdOGcYk0q9rqJDOUvJAAAAIMlEjSdPL1Q75sc1hPro3pifVQdad1nOxLSy40hvfrqVu09rkBHTQmragxjfmF4IYEBYNiIJZye3BDVHSvadM97bT2aSqaLBKX5uxTpY7uX6H2TwnxAAwCAEa04FNAZM4t0zJSIHlsX1XM10R4/8iQkvVnXqTfrOjWpKKCjJkd00IQCxsnAsEACA0Bei8adnt4Q1b2r23T3e+3a3N7LaJy+eWNDumS3En1oTjHTiAEAgFGntCCgc2YV6eTphXp2Y1RPbYj2mHpVkmraEvrXyjb9Z1Wb9qks0KETwtq9gq+IyF/UTgB5p649rkfWRXXf6nY9uq7noFSZzCwN6gOzi/SBXYu111iaQwIAABSFTCdNL9SxUyN6ZVOHHl3Tos0dPX/ciTnp9c2den1zp8rDplVNcb1/dpH2qyzgmgp5hQQGgJyLJ5xe39KpR9Z6A0y9uqlT209ZSBOLAjp3VpHO37VIh0ygiwgAAEAmBQHTEZMimq0GNRdW6pmNUb1Z15lx4PPGDqdfL2rWrxc1a1ZZUOfOKtK5s0hmID+QwAAw5BLOaWl9TE9vjOrpDVE9uzGqho6+pCykMWHTObt4SYujJkcUCvBBCgAA0Bdm0tzykOaWh9TYkdDzNR16ubZDW6KZu+iuaorrl28165dvNWt6SVCnzSjUaTMKddTkiApDXINh6JHAADDo2mJOC7d06KXaDr1Y26EXa7J/UGYysSigM2YU6oyZRTp2akSRIB+YAAAAO6M8HNBpMwp1yvSIVm6N68VNHVq4uaPH7CVd1rbEdf3bLbr+7RaVhEzHTo3ohKkRHTc1ojnMZoIhQgIDwIBqjzktbejUG1s69caWDi3c0qlFdZ3q7Hu+QpK097gCnTwtojNmFumgCQUK8KEIAAAw4AJmmlMe0pzykD4wu0iL6jpVF03okXXtimaZpr4l5nTf6nbdt7pdkjS9JKjjpkZ01OSIjpgU1szSIAkNDAoSGAB2SHvMaWVTTO80xrS0vlNvN8T0dkOnljfGFOtbb5AUFWHTcVMLddL0iE6cVqgpxcGBDxoAAABZRYKmgyaEdenuJdrakdADa9p158o2Pb6+vcd0rMnWtsT1f9Wt+r/qVknStOKgjpgc1uETwzpgfFh7jS2gywkGBAkMAD0451QXTWhDa0IbW+Na3xrXxta41rbEtXJrTCub4lrXEu/TQJvZlBWYjpwc0dGTwzp6SkR7jy1QkPEsAAAA8sKYcEAfnFOsD84pVmssoac2RPXA6nY9uLZdG1p7b1q7rjWuO1a06Y4VbZKkkEl7ji3Q/pUF2n98gfavJKmBHUMCAxihnHOKOSkad2qNOTV2JNTY4d9Hkx775VuicW1sTWh9a1w1rfGs/R931C6lQR02MaxDJ4Z1yETvQ4sBOAEAAPJfcSig02YU6bQZRXLO6c26Tj2xPqon1kf1fE2019YZkjdN61t1nXqrrlM3V3tlIZP2GFug/SoLtEdFSFXlIe1WXqCZpUGuEZEVCQxgAMUTTu1xp2jcqT0u/96l3UsPrGlTLCHFEl6SIZaQYs4plpDi/n3X84xl29bt3kc0ViT3wnrFnPPLtVMtJHbG2IhpRklI00uCml4a1IySoMrC3XOOd80zDgAAgOHFzLRfZVj7VYb1hX3K1B5zerE2qqc3dOi5mqhe3dyRdeyMZDEnLarzxkpLFg5Ic8Z4M6XsVh5SVXmBqspD2qUsqMpIgLE1RjkSGBiVnPNaJbTEnJo7nZo6E2rp9B63xBJq6nrcmVBzp1NzzKnZf9yS9Li5szsx0RZzOzT2w8AxDWXKwiRVREwTCoOaXBzQlOKgJhcFNak4oOJQYLvbAwAAYPgrDJmOnVqoY6cWSvJ+wHt9c4eer+nQa5u9Ad3XNPcho+HrSEhLG2Ja2hDrsawoaJpeGvR+JCsJakbX49KQZpYGNbGI69CRjgQG8p5zTh0JecmEmFOLn0RITi60dCUb/MfN/uPmzp6JhxZ/m0ROkw35LxL0ptfybqYxBd7j8YXerbIwQPM+AAAApIgETYdPiujwSZFtZVva43pjS6cWbunUQj+psbofSY0ubXGn6saYqht7Jje6lIZM44sCmlgY9O8DGl8U1MTCgCYWBTU2YioPB1QR8a5txxQY47ANI0OWwDCz0yT9SlJQ0vXOuR+lLY9I+pukgyRtkfQh59wqf9nVki6TFJf0eefcg73t08xmS7pNUqWkVyVd7JzrGOxjHKmcc+r0uy10dnV7SEid27o/eOWdCa/rQkdS94m2pO4T7TH/PvkW07bHrX5SoiUpSdGVfMhty4bhKyApFJAKAqaikKko6N8nP/bvi0OmMWHvhD4mHFBhkBM5AAAAdl5lYVAnTAvqhGmF28rq/KTGkoaYqhs69U5jTMu3xlTbtnMDsTXHnJqb4lrV1PcESVlB1zWwbUtqFIcCKi7wrpFL/Gtl77FXXhQ0hYNSJGAqCJoiAe95OGCKBE0FAS+ZEw50r0eiZOcNSQLDzIKSfifpZElrJb1sZgucc0uSVrtMUr1zbq6ZXSjpx5I+ZGbzJF0oaS9JUyU9Yma7+dtk2+ePJV3rnLvNzP7o7/sPg3+kg++yJ+pU0xZXwnmdBZyTWtoiKlxWq4STEn5Z92PnPe8ql5RIKkveTzw9QZE0zgL6xiQVBk2RYNe9pd175RvbEgoFvMGLQgFLuQ9uey6FzLz7pHWC6cv8+/q6zZo8YUJKWYA+ggAAAMhD4wqDOn5aUMdPSy1viCZU3RjTO42d/n1MK7bGtLY5ruZB+lWzqdOpqTMutQzK7rcJmHf9HzTvmj7g34JmCgbklckU8B9763Svm75dQCYz7ztI12V/12OT9INDyrX/+PDgHtQQM+cG/9upmR0h6Rrn3Kn+86slyTn3v0nrPOiv87yZhSRtlDRB0lXJ63at52/WY5+SfiRpk6TJzrlY+mt3aWxs5Gs5AAAAAAB5qLy8vMevsUM1wsk0SWuSnq/1yzKu45yLSWqU1wUk27bZyislNfj7yPZaAAAAAABgGGGIVgAAAAAAkPeGahDPdZJmJD2f7pdlWmet34WkXN5gnr1tm6l8i6QKMwv5rTAyvVbG5igAAAAAACA/DVULjJclVZnZbDMLyxuUc0HaOgskXeI/Pl/SY84boGOBpAvNLOLPLlIl6aVs+/S3edzfh/x9/mcQjw0AAAAAAAyyIUlg+C0hrpD0oKSlkm53zi02s++Z2Tn+ajdIqjSz5ZK+rO7BOxdLul3SEkkPSPqscy6ebZ/+vr4u6cv+vir9fY8IZrbKzN4ys4Vm9opfNs7MHjazav9+bK7jxOAys939OtB122pmXzSza8xsXVL5GbmOFQPLzP5iZrVmtiipLOM5wDy/NrPlZvammR2Yu8gxkLLUg5+a2dv+3/pOM6vwy2eZWVvSeeGPOQscAy5LXcj6WWBmV/vnhGVmdmrmvWK4yVIP/pFUB1aZ2UK/nHPCCGVmM8zscTNbYmaLzewLfjnXCaNIL/VgRFwnDMksJBg4ZrZK0sHOuc1JZT+RVOec+5GZXSVprHPu67mKEUPLvGmK10k6TNLHJTU7536W26gwWMzsGEnNkv7mnNvbL8t4DvC/tHxO0hny6sevnHOH5Sp2DJws9eAUea0XY2b2Y0ny68EsSfd0rYeRJUtduEYZPgvMm5r+VkmHyp+aXtJuzrn4kAaNAZepHqQt/7mkRufc9zgnjFxmNkXSFOfca2ZWJulVSedKulRcJ4wavdSD6RoB1wkM4jkyzJd0k//4JnkVFKPHiZLedc69l+tAMPicc09JqksrznYOmC/vYtY5516QNz7QlCEJFIMqUz1wzj2UNAPXC/IuVDDCZTknZDNf0m3OuahzbqWk5fKSGRjmeqsHZmaSPigveYURzDm3wTn3mv+4SV4r9WniOmFUyVYPRsp1AgmM4cdJesjMXjWzT/llk5xzG/zHGyVNyk1oyJELlXpRcoXfNOwvRnei0SLbOaAvU1hjZPqEpPuTns82s9fN7EkzOzpXQWFIZfos4JwwOh0tqcY5V51UxjlhhPN/VT9A0oviOmHUSqsHyYbtdQIJjOHnKOfcgZJOl/RZv8ngNv4gpvQLGiXMG8D2HEn/9Iv+IGmOpP0lbZD089xEhlzhHAAz+29JMUl/94s2SJrpnDtA3hhTt5jZmFzFhyHBZwGSXaTUHzo4J4xwZlYq6V+Svuic25q8jOuE0SNbPRju1wkkMIYZ59w6/75W0p3ymn7WdDX38u9rcxchhtjpkl5zztVIknOuxh/kNiHpz6Jp8GiR7RzQlymsMYKY2aWSzpL0Ef8iVX53gS3+41clvStpt5wFiUHXy2cB54RRxsxCkt4v6R9dZZwTRjYzK5D3pfXvzrl/+8VcJ4wyWerBiLhOIIExjJhZiT8Qi8ysRNIpkhYpdQpapo0dXVJ+VUnrt3ievPqBkS/bOWCBpI/5o4wfLm8Atw2ZdoDhz8xOk/Q1Sec451qTyif4g/3KzHaVNx35itxEiaHQy2dBtqnpMXKdJOlt59zargLOCSOXP97JDZKWOud+kbSI64RRJFs9GCnXCaFcB4B+mSTpTq9OKiTpFufcA2b2sqTbzewySe/JG6gJI5yfxDpZ0uVJxT8xs/3lNQ1clbYMI4CZ3SrpOEnjzWytpO9I+pEynwPukzey+HJJrfJmqcEIkKUeXC0pIulh/3PiBefcpyUdI+l7ZtYpKSHp0865vg76iDyXpS4cl+mzwJ/Cvmtq+pj8qelzEDYGWKZ64Jy7QT3HyZI4J4xk75N0saS3zJ82V9I3xHXCaJOtHvxaI+A6gWlUAQAAAABA3qMLCQAAAAAAyHskMAAAAAAAQN4jgQEAAAAAAPIeCQwAAAAAAJD3SGAAAAAAAIC8RwIDAAAMGTObZWbOzHZoKnd/27k7GcONZvaDwXwNAAAw8EhgAACAQWVmq8zspFzHAQAAhjcSGAAAAAAAIO+RwAAAAIPGzG6WNFPS3WbWLOmD/qKPmNlqM9tsZv+dtP6hZva8mTWY2QYz+62ZhbPs+0wze93MtprZGjO7Jm35UWb2nL+vNWZ2adLisWZ2r5k1mdmLZjYnbfcnmVm1v+3vzMx29r0AAAA7hwQGAAAYNM65iyWtlnS2c65U0u3+oqMk7S7pREnfNrM9/fK4pC9JGi/pCH/5Z7LsvkXSxyRVSDpT0n+Z2bmSZGa7SLpf0m8kTZC0v6SFSdteKOm7ksZKWi7ph2n7PkvSIZL2lZd0ObUfhw0AAAYBCQwAAJAL33XOtTnn3pD0hqT9JMk596pz7gXnXMw5t0rSnyQdm2kHzrknnHNvOecSzrk3Jd2atO6HJT3inLvVOdfpnNvinFuYtPmdzrmXnHMxSX+Xl+BI9iPnXINzbrWkxzMsBwAAQ2yHRgAHAADYSRuTHrdKKpUkM9tN0i8kHSypWN61yquZdmBmh0n6kaS9JYUlRST90188Q9K7/X39fiwHAABDjBYYAABgsLl+rPsHSW9LqnLOjZH0DUnZxp+4RdICSTOcc+WS/pi07hpJ6eNaAACAYYwEBgAAGGw1knbt47plkrZKajazPST913bWrXPOtZvZofK6jXT5u/5/O3dsQ2AAhGH4u8QGBmEOtRhCSykKjSFMYDmR/ANQ/BIdISFXPE99xdVvLjc+4lxV1aSqplU1/2J3AKAJAQMA+LVjkl1VXZMs38xuM4aIIckpyfnF7DrJoaqGJPs8H4Tm8btikWST5JLxgefsu/UBgA7qdvvkqhMAAADg/1xgAAAAAO0JGAAAAEB7AgYAAADQnoABAAAAtCdgAAAAAO0JGAAAAEB7AgYAAADQnoABAAAAtCdgAAAAAO3dASSajRun82I4AAAAAElFTkSuQmCC\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"thalachh_50_85 = data.thalachh[(data.thalachh >= 50) & (data.thalachh <= 85)]\nthalachh_86_110 = data.thalachh[(data.thalachh >= 86) & (data.thalachh <= 110)]\nthalachh_111_135 = data.thalachh[(data.thalachh >= 111) & (data.thalachh <= 135)]\nthalachh_136_160 = data.thalachh[(data.thalachh >= 136) & (data.thalachh <= 160)]\nthalachh_161_185 = data.thalachh[(data.thalachh >= 161) & (data.thalachh <= 185)]\nthalachh_185above = data.thalachh[data.thalachh >= 186]\nx_thalachh = [ '50-85','86-110', '111-135', '136-160', '161-185','185+']\ny_thalachh = [len(thalachh_50_85.values), len(thalachh_86_110.values), len(thalachh_111_135.values), len(thalachh_136_160.values)\n , len(thalachh_161_185.values), len(thalachh_185above.values)]\n\npx.bar(data_frame = data, x = x_thalachh, y = y_thalachh, color = x_thalachh, template = 'plotly_dark',\n labels={\n 'x': \"maximum heart rate achieved\",\n 'y': \"Count\",\n 'color':'maximum heart rate achieved'\n \n })","metadata":{"execution":{"iopub.status.busy":"2021-08-24T17:50:47.183126Z","iopub.execute_input":"2021-08-24T17:50:47.183871Z","iopub.status.idle":"2021-08-24T17:50:47.301138Z","shell.execute_reply.started":"2021-08-24T17:50:47.183814Z","shell.execute_reply":"2021-08-24T17:50:47.300088Z"},"trusted":true},"execution_count":28,"outputs":[{"output_type":"display_data","data":{"text/html":"
"},"metadata":{}}]},{"cell_type":"code","source":"px.bar(data_frame = data, x = 'age', y = 'thalachh', color = 'age', template = 'plotly_dark',\n labels={\n 'x': \"Age\",\n 'y': \"maximum heart beat\",\n 'color':'Age'},\n title = 'Age to maximum heart beat(sum)')","metadata":{"execution":{"iopub.status.busy":"2021-08-24T17:50:47.302751Z","iopub.execute_input":"2021-08-24T17:50:47.303354Z","iopub.status.idle":"2021-08-24T17:50:47.381054Z","shell.execute_reply.started":"2021-08-24T17:50:47.303308Z","shell.execute_reply":"2021-08-24T17:50:47.380069Z"},"trusted":true},"execution_count":29,"outputs":[{"output_type":"display_data","data":{"text/html":"
"},"metadata":{}}]},{"cell_type":"code","source":"px.box(x = 'thalachh', data_frame = data, template = 'plotly_dark')","metadata":{"execution":{"iopub.status.busy":"2021-08-24T17:50:47.382459Z","iopub.execute_input":"2021-08-24T17:50:47.383049Z","iopub.status.idle":"2021-08-24T17:50:47.454247Z","shell.execute_reply.started":"2021-08-24T17:50:47.383006Z","shell.execute_reply":"2021-08-24T17:50:47.453044Z"},"trusted":true},"execution_count":30,"outputs":[{"output_type":"display_data","data":{"text/html":"
"},"metadata":{}}]},{"cell_type":"markdown","source":"### exng\nexercise induced angina (1 = yes; 0 = no)","metadata":{}},{"cell_type":"code","source":"\npx.bar(data_frame = data, x = list(data.exng.value_counts().keys()), y = list(data.exng.value_counts()), \n color = list(data.exng.value_counts().keys()), template = 'plotly_dark',\n labels={\n 'x': \"exercise induced angina\",\n 'y': \"Count\",\n 'color':'exercise induced angina'\n \n },\n title = 'Number of patients having exercise induced angina')\n","metadata":{"execution":{"iopub.status.busy":"2021-08-24T17:50:59.714687Z","iopub.execute_input":"2021-08-24T17:50:59.715274Z","iopub.status.idle":"2021-08-24T17:50:59.797785Z","shell.execute_reply.started":"2021-08-24T17:50:59.715236Z","shell.execute_reply":"2021-08-24T17:50:59.796554Z"},"trusted":true},"execution_count":31,"outputs":[{"output_type":"display_data","data":{"text/html":"
"},"metadata":{}}]},{"cell_type":"code","source":"import plotly.graph_objects as go\n\nexng_0_1 = data.exng[(data.exng == 0) & (data.output == 1)]\nexng_0_0 = data.exng[(data.exng == 0) & (data.output == 0)]\nexng_1_1 = data.exng[(data.exng == 1) & (data.output == 1)]\nexng_1_0 = data.exng[(data.exng == 1) & (data.output == 0)]\n\ny_exng_1 = [len(exng_0_1.values), len(exng_1_1.values)]\ny_exng_0 = [len(exng_0_0.values), len(exng_1_0.values)]\n\nfig = go.Figure()\nfig.add_trace(go.Bar(\n x=[0,1],\n y=y_exng_1,\n name='Heart Attack',\n marker_color='indianred'\n))\nfig.add_trace(go.Bar(\n x=[0,1],\n y=y_exng_0,\n name='Safe',\n marker_color='lightsalmon'\n))\n\n# Here we modify the tickangle of the xaxis, resulting in rotated labels.\nfig.update_layout(barmode='group', xaxis_tickangle=-45)\nfig.show()","metadata":{"execution":{"iopub.status.busy":"2021-08-24T17:51:00.387656Z","iopub.execute_input":"2021-08-24T17:51:00.388234Z","iopub.status.idle":"2021-08-24T17:51:00.412075Z","shell.execute_reply.started":"2021-08-24T17:51:00.388183Z","shell.execute_reply":"2021-08-24T17:51:00.411098Z"},"trusted":true},"execution_count":32,"outputs":[{"output_type":"display_data","data":{"text/html":"
"},"metadata":{}}]},{"cell_type":"markdown","source":"We can see that not getting exercise induced angina may have a greater chance of heart attack","metadata":{}},{"cell_type":"markdown","source":"### oldpeak\nPrevious peak","metadata":{}},{"cell_type":"code","source":"plt.figure(figsize = (16, 7))\nsns.distplot(data['oldpeak'])\nplt.title('Distribution Plot of Previous peak achieved\\n', fontsize = 20)\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2021-08-24T17:51:02.145990Z","iopub.execute_input":"2021-08-24T17:51:02.146632Z","iopub.status.idle":"2021-08-24T17:51:02.366983Z","shell.execute_reply.started":"2021-08-24T17:51:02.146546Z","shell.execute_reply":"2021-08-24T17:51:02.365646Z"},"trusted":true},"execution_count":33,"outputs":[{"output_type":"display_data","data":{"text/plain":"
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= 'oldpeak', data_frame = data, template = 'plotly_dark')","metadata":{"execution":{"iopub.status.busy":"2021-08-24T17:51:02.482004Z","iopub.execute_input":"2021-08-24T17:51:02.482607Z","iopub.status.idle":"2021-08-24T17:51:02.555813Z","shell.execute_reply.started":"2021-08-24T17:51:02.482541Z","shell.execute_reply":"2021-08-24T17:51:02.554713Z"},"trusted":true},"execution_count":34,"outputs":[{"output_type":"display_data","data":{"text/html":"
"},"metadata":{}}]},{"cell_type":"markdown","source":"### slp","metadata":{}},{"cell_type":"code","source":"\npx.bar(data_frame = data, x = list(data.slp.value_counts().keys()), y = list(data.slp.value_counts()), \n color = list(data.slp.value_counts().keys()), template = 'plotly_dark',\n labels={\n 'x': \"slp\",\n 'y': \"Count\",\n 'color':'slp'\n \n },\n title = 'slp plot')\n","metadata":{"execution":{"iopub.status.busy":"2021-08-24T17:51:03.090528Z","iopub.execute_input":"2021-08-24T17:51:03.091171Z","iopub.status.idle":"2021-08-24T17:51:03.176137Z","shell.execute_reply.started":"2021-08-24T17:51:03.091125Z","shell.execute_reply":"2021-08-24T17:51:03.174996Z"},"trusted":true},"execution_count":35,"outputs":[{"output_type":"display_data","data":{"text/html":"
"},"metadata":{}}]},{"cell_type":"code","source":"import plotly.graph_objects as go\n\nslp_0_1 = data.slp[(data.slp == 0) & (data.output == 1)]\nslp_0_0 = data.slp[(data.slp == 0) & (data.output == 0)]\nslp_1_1 = data.slp[(data.slp == 1) & (data.output == 1)]\nslp_1_0 = data.slp[(data.slp == 1) & (data.output == 0)]\nslp_2_1 = data.slp[(data.slp == 2) & (data.output == 1)]\nslp_2_0 = data.slp[(data.slp == 2) & (data.output == 0)]\n\ny_slp_1 = [len(slp_0_1.values), len(slp_1_1.values), len(slp_2_1.values)]\ny_slp_0 = [len(slp_0_0.values), len(slp_1_0.values), len(slp_2_0.values)]\n\nfig = go.Figure()\nfig.add_trace(go.Bar(\n x=[0,1,2],\n y=y_slp_1,\n name='Heart Attack',\n marker_color='indianred'\n))\nfig.add_trace(go.Bar(\n x=[0,1,2],\n y=y_slp_0,\n name='Safe',\n marker_color='lightsalmon'\n))\n\n# Here we modify the tickangle of the xaxis, resulting in rotated labels.\nfig.update_layout(barmode='group', xaxis_tickangle=-45)\nfig.show()","metadata":{"execution":{"iopub.status.busy":"2021-08-24T17:51:03.565664Z","iopub.execute_input":"2021-08-24T17:51:03.566237Z","iopub.status.idle":"2021-08-24T17:51:03.594014Z","shell.execute_reply.started":"2021-08-24T17:51:03.566201Z","shell.execute_reply":"2021-08-24T17:51:03.591703Z"},"trusted":true},"execution_count":36,"outputs":[{"output_type":"display_data","data":{"text/html":"
"},"metadata":{}}]},{"cell_type":"code","source":"data['slp'] = data['slp'].map({0:\"slp_0\", 1: \"slp_1\", 2: 'slp_2'}) \ndata.head()","metadata":{"execution":{"iopub.status.busy":"2021-08-24T17:51:04.165074Z","iopub.execute_input":"2021-08-24T17:51:04.165445Z","iopub.status.idle":"2021-08-24T17:51:04.187353Z","shell.execute_reply.started":"2021-08-24T17:51:04.165413Z","shell.execute_reply":"2021-08-24T17:51:04.186565Z"},"trusted":true},"execution_count":37,"outputs":[{"execution_count":37,"output_type":"execute_result","data":{"text/plain":" age sex cp trtbps chol fbs restecg thalachh exng \\\n0 63 1_gender Intensity_3 145 233 1 restecg_0 150 0 \n1 37 1_gender Intensity_2 130 250 0 restecg_1 187 0 \n2 41 0_gender Intensity_1 130 204 0 restecg_0 172 0 \n3 56 1_gender Intensity_1 120 236 0 restecg_1 178 0 \n4 57 0_gender Intensity_0 120 354 0 restecg_1 163 1 \n\n oldpeak slp caa thall output \n0 2.3 slp_0 0 1 1 \n1 3.5 slp_0 0 2 1 \n2 1.4 slp_2 0 2 1 \n3 0.8 slp_2 0 2 1 \n4 0.6 slp_2 0 2 1 ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
agesexcptrtbpscholfbsrestecgthalachhexngoldpeakslpcaathalloutput
0631_genderIntensity_31452331restecg_015002.3slp_0011
1371_genderIntensity_21302500restecg_118703.5slp_0021
2410_genderIntensity_11302040restecg_017201.4slp_2021
3561_genderIntensity_11202360restecg_117800.8slp_2021
4570_genderIntensity_01203540restecg_116310.6slp_2021
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"### caa","metadata":{}},{"cell_type":"code","source":"\npx.bar(data_frame = data, x = list(data.caa.value_counts().keys()), y = list(data.caa.value_counts()), \n color = list(data.caa.value_counts().keys()), template = 'plotly_dark',\n labels={\n 'x': \"caa\",\n 'y': \"Count\",\n 'color':'caa'\n \n },\n title = 'caa plot')\n","metadata":{"execution":{"iopub.status.busy":"2021-08-24T17:51:05.260930Z","iopub.execute_input":"2021-08-24T17:51:05.261477Z","iopub.status.idle":"2021-08-24T17:51:05.336171Z","shell.execute_reply.started":"2021-08-24T17:51:05.261416Z","shell.execute_reply":"2021-08-24T17:51:05.335209Z"},"trusted":true},"execution_count":38,"outputs":[{"output_type":"display_data","data":{"text/html":"
"},"metadata":{}}]},{"cell_type":"code","source":"import plotly.graph_objects as go\n\ncaa_0_1 = data.caa[(data.caa == 0) & (data.output == 1)]\ncaa_0_0 = data.caa[(data.caa == 0) & (data.output == 0)]\ncaa_1_1 = data.caa[(data.caa == 1) & (data.output == 1)]\ncaa_1_0 = data.caa[(data.caa == 1) & (data.output == 0)]\ncaa_2_1 = data.caa[(data.caa == 2) & (data.output == 1)]\ncaa_2_0 = data.caa[(data.caa == 2) & (data.output == 0)]\n\ncaa_3_1 = data.caa[(data.caa == 3) & (data.output == 1)]\ncaa_3_0 = data.caa[(data.caa == 3) & (data.output == 0)]\n\ncaa_4_1 = data.caa[(data.caa == 4) & (data.output == 1)]\ncaa_4_0 = data.caa[(data.caa == 4) & (data.output == 0)]\n\ny_caa_1 = [len(caa_0_1.values), len(caa_1_1.values), len(caa_2_1.values), len(caa_3_1.values), len(caa_4_1.values)]\ny_caa_0 = [len(caa_0_0.values), len(caa_1_0.values), len(caa_2_0.values), len(caa_3_0.values), len(caa_4_0.values)]\n\nfig = go.Figure()\nfig.add_trace(go.Bar(\n x=[0,1,2,3,4],\n y=y_caa_1,\n name='Heart Attack',\n marker_color='indianred'\n))\nfig.add_trace(go.Bar(\n x=[0,1,2,3,4],\n y=y_caa_0,\n name='Safe',\n marker_color='lightsalmon'\n))\n\n# Here we modify the tickangle of the xaxis, resulting in rotated labels.\nfig.update_layout(barmode='group', xaxis_tickangle=-45)\nfig.show()","metadata":{"execution":{"iopub.status.busy":"2021-08-24T17:51:05.785608Z","iopub.execute_input":"2021-08-24T17:51:05.786009Z","iopub.status.idle":"2021-08-24T17:51:05.822881Z","shell.execute_reply.started":"2021-08-24T17:51:05.785972Z","shell.execute_reply":"2021-08-24T17:51:05.821271Z"},"trusted":true},"execution_count":39,"outputs":[{"output_type":"display_data","data":{"text/html":"
"},"metadata":{}}]},{"cell_type":"code","source":"data['caa'] = data['caa'].map({0:\"caa_0\", 1: \"caa_1\", 2: 'caa_2', 3: 'caa_3', 4: 'caa_4'}) \ndata.head()","metadata":{"execution":{"iopub.status.busy":"2021-08-24T17:51:06.350241Z","iopub.execute_input":"2021-08-24T17:51:06.350697Z","iopub.status.idle":"2021-08-24T17:51:06.374248Z","shell.execute_reply.started":"2021-08-24T17:51:06.350657Z","shell.execute_reply":"2021-08-24T17:51:06.372939Z"},"trusted":true},"execution_count":40,"outputs":[{"execution_count":40,"output_type":"execute_result","data":{"text/plain":" age sex cp trtbps chol fbs restecg thalachh exng \\\n0 63 1_gender Intensity_3 145 233 1 restecg_0 150 0 \n1 37 1_gender Intensity_2 130 250 0 restecg_1 187 0 \n2 41 0_gender Intensity_1 130 204 0 restecg_0 172 0 \n3 56 1_gender Intensity_1 120 236 0 restecg_1 178 0 \n4 57 0_gender Intensity_0 120 354 0 restecg_1 163 1 \n\n oldpeak slp caa thall output \n0 2.3 slp_0 caa_0 1 1 \n1 3.5 slp_0 caa_0 2 1 \n2 1.4 slp_2 caa_0 2 1 \n3 0.8 slp_2 caa_0 2 1 \n4 0.6 slp_2 caa_0 2 1 ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
agesexcptrtbpscholfbsrestecgthalachhexngoldpeakslpcaathalloutput
0631_genderIntensity_31452331restecg_015002.3slp_0caa_011
1371_genderIntensity_21302500restecg_118703.5slp_0caa_021
2410_genderIntensity_11302040restecg_017201.4slp_2caa_021
3561_genderIntensity_11202360restecg_117800.8slp_2caa_021
4570_genderIntensity_01203540restecg_116310.6slp_2caa_021
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"### thall","metadata":{}},{"cell_type":"code","source":"\npx.bar(data_frame = data, x = list(data.thall.value_counts().keys()), y = list(data.thall.value_counts()), \n color = list(data.thall.value_counts().keys()), template = 'plotly_dark',\n labels={\n 'x': \"thall\",\n 'y': \"Count\",\n 'color':'thall'\n \n },\n title = 'caa plot',barmode='group')\n","metadata":{"execution":{"iopub.status.busy":"2021-08-24T17:51:07.226753Z","iopub.execute_input":"2021-08-24T17:51:07.227144Z","iopub.status.idle":"2021-08-24T17:51:07.308313Z","shell.execute_reply.started":"2021-08-24T17:51:07.227112Z","shell.execute_reply":"2021-08-24T17:51:07.307176Z"},"trusted":true},"execution_count":41,"outputs":[{"output_type":"display_data","data":{"text/html":"
"},"metadata":{}}]},{"cell_type":"code","source":"import plotly.graph_objects as go\n\nthall_0_1 = data.thall[(data.thall == 0) & (data.output == 1)]\nthall_0_0 = data.thall[(data.thall == 0) & (data.output == 0)]\nthall_1_1 = data.thall[(data.thall == 1) & (data.output == 1)]\nthall_1_0 = data.thall[(data.thall == 1) & (data.output == 0)]\nthall_2_1 = data.thall[(data.thall == 2) & (data.output == 1)]\nthall_2_0 = data.thall[(data.thall == 2) & (data.output == 0)]\nthall_3_1 = data.thall[(data.thall == 3) & (data.output == 1)]\nthall_3_0 = data.thall[(data.thall == 3) & (data.output == 0)]\n\ny_thall_1 = [len(thall_0_1.values), len(thall_1_1.values), len(thall_2_1.values), len(thall_3_1.values)]\ny_thall_0 = [len(thall_0_0.values), len(thall_1_0.values), len(thall_2_0.values), len(thall_3_0.values)]\n\nfig = go.Figure()\nfig.add_trace(go.Bar(\n x=[0,1,2,3],\n y=y_thall_1,\n name='Heart Attack',\n marker_color='indianred'\n))\nfig.add_trace(go.Bar(\n x=[0,1,2,3],\n y=y_thall_0,\n name='Safe',\n marker_color='lightsalmon'\n))\n\n# Here we modify the tickangle of the xaxis, resulting in rotated labels.\nfig.update_layout(barmode='group', xaxis_tickangle=-45)\nfig.show()","metadata":{"execution":{"iopub.status.busy":"2021-08-24T17:51:07.832719Z","iopub.execute_input":"2021-08-24T17:51:07.833127Z","iopub.status.idle":"2021-08-24T17:51:07.864557Z","shell.execute_reply.started":"2021-08-24T17:51:07.833095Z","shell.execute_reply":"2021-08-24T17:51:07.863067Z"},"trusted":true},"execution_count":42,"outputs":[{"output_type":"display_data","data":{"text/html":"
"},"metadata":{}}]},{"cell_type":"code","source":"data['thall'] = data['thall'].map({0:\"thall_0\", 1: \"thall_1\", 2: 'thall_2', 3: 'thall_3'}) \ndata.head()","metadata":{"execution":{"iopub.status.busy":"2021-08-24T17:51:14.268121Z","iopub.execute_input":"2021-08-24T17:51:14.268562Z","iopub.status.idle":"2021-08-24T17:51:14.291975Z","shell.execute_reply.started":"2021-08-24T17:51:14.268526Z","shell.execute_reply":"2021-08-24T17:51:14.291132Z"},"trusted":true},"execution_count":43,"outputs":[{"execution_count":43,"output_type":"execute_result","data":{"text/plain":" age sex cp trtbps chol fbs restecg thalachh exng \\\n0 63 1_gender Intensity_3 145 233 1 restecg_0 150 0 \n1 37 1_gender Intensity_2 130 250 0 restecg_1 187 0 \n2 41 0_gender Intensity_1 130 204 0 restecg_0 172 0 \n3 56 1_gender Intensity_1 120 236 0 restecg_1 178 0 \n4 57 0_gender Intensity_0 120 354 0 restecg_1 163 1 \n\n oldpeak slp caa thall output \n0 2.3 slp_0 caa_0 thall_1 1 \n1 3.5 slp_0 caa_0 thall_2 1 \n2 1.4 slp_2 caa_0 thall_2 1 \n3 0.8 slp_2 caa_0 thall_2 1 \n4 0.6 slp_2 caa_0 thall_2 1 ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
agesexcptrtbpscholfbsrestecgthalachhexngoldpeakslpcaathalloutput
0631_genderIntensity_31452331restecg_015002.3slp_0caa_0thall_11
1371_genderIntensity_21302500restecg_118703.5slp_0caa_0thall_21
2410_genderIntensity_11302040restecg_017201.4slp_2caa_0thall_21
3561_genderIntensity_11202360restecg_117800.8slp_2caa_0thall_21
4570_genderIntensity_01203540restecg_116310.6slp_2caa_0thall_21
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"### Feature engineering","metadata":{}},{"cell_type":"code","source":"data.head()","metadata":{"execution":{"iopub.status.busy":"2021-08-24T17:51:15.404868Z","iopub.execute_input":"2021-08-24T17:51:15.405450Z","iopub.status.idle":"2021-08-24T17:51:15.425204Z","shell.execute_reply.started":"2021-08-24T17:51:15.405413Z","shell.execute_reply":"2021-08-24T17:51:15.424037Z"},"trusted":true},"execution_count":44,"outputs":[{"execution_count":44,"output_type":"execute_result","data":{"text/plain":" age sex cp trtbps chol fbs restecg thalachh exng \\\n0 63 1_gender Intensity_3 145 233 1 restecg_0 150 0 \n1 37 1_gender Intensity_2 130 250 0 restecg_1 187 0 \n2 41 0_gender Intensity_1 130 204 0 restecg_0 172 0 \n3 56 1_gender Intensity_1 120 236 0 restecg_1 178 0 \n4 57 0_gender Intensity_0 120 354 0 restecg_1 163 1 \n\n oldpeak slp caa thall output \n0 2.3 slp_0 caa_0 thall_1 1 \n1 3.5 slp_0 caa_0 thall_2 1 \n2 1.4 slp_2 caa_0 thall_2 1 \n3 0.8 slp_2 caa_0 thall_2 1 \n4 0.6 slp_2 caa_0 thall_2 1 ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
agesexcptrtbpscholfbsrestecgthalachhexngoldpeakslpcaathalloutput
0631_genderIntensity_31452331restecg_015002.3slp_0caa_0thall_11
1371_genderIntensity_21302500restecg_118703.5slp_0caa_0thall_21
2410_genderIntensity_11302040restecg_017201.4slp_2caa_0thall_21
3561_genderIntensity_11202360restecg_117800.8slp_2caa_0thall_21
4570_genderIntensity_01203540restecg_116310.6slp_2caa_0thall_21
\n
"},"metadata":{}}]},{"cell_type":"markdown","source":"### One hot encoding","metadata":{}},{"cell_type":"code","source":"data=pd.get_dummies(data)\ndata.head()","metadata":{"execution":{"iopub.status.busy":"2021-08-24T17:51:43.662879Z","iopub.execute_input":"2021-08-24T17:51:43.663284Z","iopub.status.idle":"2021-08-24T17:51:43.703889Z","shell.execute_reply.started":"2021-08-24T17:51:43.663250Z","shell.execute_reply":"2021-08-24T17:51:43.702491Z"},"trusted":true},"execution_count":45,"outputs":[{"execution_count":45,"output_type":"execute_result","data":{"text/plain":" age trtbps chol fbs thalachh exng oldpeak output sex_0_gender \\\n0 63 145 233 1 150 0 2.3 1 0 \n1 37 130 250 0 187 0 3.5 1 0 \n2 41 130 204 0 172 0 1.4 1 1 \n3 56 120 236 0 178 0 0.8 1 0 \n4 57 120 354 0 163 1 0.6 1 1 \n\n sex_1_gender ... slp_slp_2 caa_caa_0 caa_caa_1 caa_caa_2 caa_caa_3 \\\n0 1 ... 0 1 0 0 0 \n1 1 ... 0 1 0 0 0 \n2 0 ... 1 1 0 0 0 \n3 1 ... 1 1 0 0 0 \n4 0 ... 1 1 0 0 0 \n\n caa_caa_4 thall_thall_0 thall_thall_1 thall_thall_2 thall_thall_3 \n0 0 0 1 0 0 \n1 0 0 0 1 0 \n2 0 0 0 1 0 \n3 0 0 0 1 0 \n4 0 0 0 1 0 \n\n[5 rows x 29 columns]","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
agetrtbpscholfbsthalachhexngoldpeakoutputsex_0_gendersex_1_gender...slp_slp_2caa_caa_0caa_caa_1caa_caa_2caa_caa_3caa_caa_4thall_thall_0thall_thall_1thall_thall_2thall_thall_3
063145233115002.3101...0100000100
137130250018703.5101...0100000010
241130204017201.4110...1100000010
356120236017800.8101...1100000010
457120354016310.6110...1100000010
\n

5 rows × 29 columns

\n
"},"metadata":{}}]},{"cell_type":"code","source":"X= data.drop(['output'],axis=1)\nY= data[\"output\"]","metadata":{"execution":{"iopub.status.busy":"2021-08-24T17:51:44.409492Z","iopub.execute_input":"2021-08-24T17:51:44.409935Z","iopub.status.idle":"2021-08-24T17:51:44.418220Z","shell.execute_reply.started":"2021-08-24T17:51:44.409899Z","shell.execute_reply":"2021-08-24T17:51:44.416782Z"},"trusted":true},"execution_count":46,"outputs":[]},{"cell_type":"code","source":"from sklearn.model_selection import train_test_split\n# split the data to train and test set\nx_train,x_test,y_train,y_test = train_test_split(X,Y,train_size=0.85,random_state=42)\n\n\nprint(\"training data shape:- {} labels {} \".format(x_train.shape[0],x_train.shape[1]))\nprint(\"testing data shape:- {} labels {} \".format(x_test.shape[0],x_test.shape[1]))","metadata":{"execution":{"iopub.status.busy":"2021-08-24T17:51:46.397641Z","iopub.execute_input":"2021-08-24T17:51:46.398020Z","iopub.status.idle":"2021-08-24T17:51:46.571787Z","shell.execute_reply.started":"2021-08-24T17:51:46.397986Z","shell.execute_reply":"2021-08-24T17:51:46.571031Z"},"trusted":true},"execution_count":47,"outputs":[{"name":"stdout","text":"training data shape:- 257 labels 28 \ntesting data shape:- 46 labels 28 \n","output_type":"stream"}]},{"cell_type":"code","source":"from xgboost import XGBClassifier\nfrom sklearn.metrics import r2_score\n\nxgb = XGBClassifier(colsample_bylevel= 0.9,\n colsample_bytree = 0.8, \n gamma=0.99,\n max_depth= 5,\n min_child_weight= 1,\n n_estimators= 8,\n nthread= 5,\n random_state= 0,\n )\nxgb.fit(x_train,y_train)","metadata":{"execution":{"iopub.status.busy":"2021-08-24T17:51:46.758774Z","iopub.execute_input":"2021-08-24T17:51:46.759413Z","iopub.status.idle":"2021-08-24T17:51:46.898773Z","shell.execute_reply.started":"2021-08-24T17:51:46.759379Z","shell.execute_reply":"2021-08-24T17:51:46.897333Z"},"trusted":true},"execution_count":48,"outputs":[{"name":"stdout","text":"[17:51:46] WARNING: ../src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n","output_type":"stream"},{"execution_count":48,"output_type":"execute_result","data":{"text/plain":"XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=0.9,\n colsample_bynode=1, colsample_bytree=0.8, gamma=0.99, gpu_id=-1,\n importance_type='gain', interaction_constraints='',\n learning_rate=0.300000012, max_delta_step=0, max_depth=5,\n min_child_weight=1, missing=nan, monotone_constraints='()',\n n_estimators=8, n_jobs=5, nthread=5, num_parallel_tree=1,\n random_state=0, reg_alpha=0, reg_lambda=1, scale_pos_weight=1,\n subsample=1, tree_method='exact', validate_parameters=1,\n verbosity=None)"},"metadata":{}}]},{"cell_type":"code","source":"print('Accuracy of XGBoost classifier on training set: {:.2f}'\n .format(xgb.score(x_train, y_train)))\nprint('Accuracy of XGBoost classifier on test set: {:.2f}'\n .format(xgb.score(x_test, y_test)))","metadata":{"execution":{"iopub.status.busy":"2021-08-24T17:51:47.196831Z","iopub.execute_input":"2021-08-24T17:51:47.197210Z","iopub.status.idle":"2021-08-24T17:51:47.210886Z","shell.execute_reply.started":"2021-08-24T17:51:47.197179Z","shell.execute_reply":"2021-08-24T17:51:47.209783Z"},"trusted":true},"execution_count":49,"outputs":[{"name":"stdout","text":"Accuracy of XGBoost classifier on training set: 0.95\nAccuracy of XGBoost classifier on test set: 0.80\n","output_type":"stream"}]},{"cell_type":"code","source":"from sklearn import metrics\n\ny_pred=xgb.predict(x_test)\nprint(\"Accuracy of XG Boost model is:\",\nmetrics.accuracy_score(y_test, y_pred)*100)","metadata":{"execution":{"iopub.status.busy":"2021-08-24T17:51:47.731717Z","iopub.execute_input":"2021-08-24T17:51:47.732172Z","iopub.status.idle":"2021-08-24T17:51:47.743918Z","shell.execute_reply.started":"2021-08-24T17:51:47.732130Z","shell.execute_reply":"2021-08-24T17:51:47.742668Z"},"trusted":true},"execution_count":50,"outputs":[{"name":"stdout","text":"Accuracy of XG Boost model is: 80.43478260869566\n","output_type":"stream"}]},{"cell_type":"code","source":"from sklearn.metrics import confusion_matrix\n\nconf_matrix = confusion_matrix(y_true=y_test, y_pred=y_pred)\nplt.figure(figsize = (15, 8))\nsns.set(font_scale=1.4) # for label size\nsns.heatmap(conf_matrix, annot=True, annot_kws={\"size\": 16},cbar=False, linewidths = 1) # font size\nplt.title(\"Test Confusion Matrix\")\nplt.xlabel(\"Predicted class\")\nplt.ylabel(\"Actual class\")\nplt.savefig('conf_test.png')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2021-08-24T17:51:48.291984Z","iopub.execute_input":"2021-08-24T17:51:48.292444Z","iopub.status.idle":"2021-08-24T17:51:48.527485Z","shell.execute_reply.started":"2021-08-24T17:51:48.292408Z","shell.execute_reply":"2021-08-24T17:51:48.526197Z"},"trusted":true},"execution_count":51,"outputs":[{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"from sklearn.metrics import confusion_matrix\ny_pred_t=xgb.predict(x_train)\nconf_matrix = confusion_matrix(y_true=y_train, y_pred=y_pred_t)\nplt.figure(figsize = (15, 8))\nsns.set(font_scale=1.4) # for label size\nsns.heatmap(conf_matrix, annot=True, annot_kws={\"size\": 16},cbar=False, linewidths = 1) # font size\nplt.title(\"Train Confusion Matrix\")\nplt.xlabel(\"Predicted class\")\nplt.ylabel(\"Actual class\")\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2021-08-24T17:51:48.818203Z","iopub.execute_input":"2021-08-24T17:51:48.818597Z","iopub.status.idle":"2021-08-24T17:51:49.011291Z","shell.execute_reply.started":"2021-08-24T17:51:48.818549Z","shell.execute_reply":"2021-08-24T17:51:49.009691Z"},"trusted":true},"execution_count":52,"outputs":[{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{}}]},{"cell_type":"code","source":"from sklearn.metrics import precision_score, recall_score, f1_score\nprint(\"For testing data\")\nprint('Precision: %.3f' % precision_score(y_test, y_pred,average='micro'))\nprint('Recall: %.3f' % recall_score(y_test, y_pred,average='micro'))\nprint('F1 Score: %.3f' % f1_score(y_test, y_pred,average='micro'))\n\nprint()\n\nprint(\"For training data\")\ny_pred_t=xgb.predict(x_train)\nprint('Precision: %.3f' % precision_score(y_train, y_pred_t,average='micro'))\nprint('Recall: %.3f' % recall_score(y_train, y_pred_t,average='micro'))\nprint('F1 Score: %.3f' % f1_score(y_train, y_pred_t,average='micro'))\n","metadata":{"execution":{"iopub.status.busy":"2021-08-24T17:51:49.480900Z","iopub.execute_input":"2021-08-24T17:51:49.481316Z","iopub.status.idle":"2021-08-24T17:51:49.510782Z","shell.execute_reply.started":"2021-08-24T17:51:49.481282Z","shell.execute_reply":"2021-08-24T17:51:49.509418Z"},"trusted":true},"execution_count":53,"outputs":[{"name":"stdout","text":"For testing data\nPrecision: 0.804\nRecall: 0.804\nF1 Score: 0.804\n\nFor training data\nPrecision: 0.949\nRecall: 0.949\nF1 Score: 0.949\n","output_type":"stream"}]},{"cell_type":"markdown","source":"For fine tuning our main aim should be to reduce true negative","metadata":{}}]} \ No newline at end of file +{ + "metadata": { + "kernelspec": { + "language": "python", + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.7.10", + "mimetype": "text/x-python", + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "pygments_lexer": "ipython3", + "nbconvert_exporter": "python", + "file_extension": ".py" + } + }, + "nbformat_minor": 4, + "nbformat": 4, + "cells": [ + { + "cell_type": "code", + "source": [ + "# This Python 3 environment comes with many helpful analytics libraries installed\n", + "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n", + "# For example, here's several helpful packages to load\n", + "import seaborn as sns\n", + "import plotly.express as px\n", + "import matplotlib.pyplot as plt\n", + "import missingno as msno\n", + "import plotly.graph_objects as go\n", + "\n", + "\n", + "import numpy as np # linear algebra\n", + "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n", + "\n", + "import warnings\n", + "\n", + "warnings.filterwarnings(\"ignore\")\n", + "\n", + "plt.style.use(\"fivethirtyeight\")\n", + "%matplotlib inline\n", + "# Input data files are available in the read-only \"../input/\" directory\n", + "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n", + "\n", + "import os\n", + "\n", + "for dirname, _, filenames in os.walk(\"/kaggle/input\"):\n", + " for filename in filenames:\n", + " print(os.path.join(dirname, filename))\n", + "\n", + "# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\"\n", + "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session" + ], + "metadata": { + "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5", + "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19", + "execution": { + "iopub.status.busy": "2021-08-24T17:50:38.074908Z", + "iopub.execute_input": "2021-08-24T17:50:38.075253Z", + "iopub.status.idle": "2021-08-24T17:50:41.104332Z", + "shell.execute_reply.started": "2021-08-24T17:50:38.075223Z", + "shell.execute_reply": "2021-08-24T17:50:41.103401Z" + }, + "trusted": true + }, + "execution_count": 2, + "outputs": [ + { + "name": "stdout", + "text": "/kaggle/input/heart-attack-analysis-prediction-dataset/o2Saturation.csv\n/kaggle/input/heart-attack-analysis-prediction-dataset/heart.csv\n", + "output_type": "stream" + } + ] + }, + { + "cell_type": "markdown", + "source": "## Reading the data", + "metadata": {} + }, + { + "cell_type": "code", + "source": [ + "data = pd.read_csv(\n", + " \"/kaggle/input/heart-attack-analysis-prediction-dataset/heart.csv\"\n", + ")\n", + "data.head()" + ], + "metadata": { + "execution": { + "iopub.status.busy": "2021-08-24T17:50:41.106138Z", + "iopub.execute_input": "2021-08-24T17:50:41.106792Z", + "iopub.status.idle": "2021-08-24T17:50:41.157251Z", + "shell.execute_reply.started": "2021-08-24T17:50:41.106741Z", + "shell.execute_reply": "2021-08-24T17:50:41.156071Z" + }, + "trusted": true + }, + "execution_count": 3, + "outputs": [ + { + "execution_count": 3, + "output_type": "execute_result", + "data": { + "text/plain": " age sex cp trtbps chol fbs restecg thalachh exng oldpeak slp \\\n0 63 1 3 145 233 1 0 150 0 2.3 0 \n1 37 1 2 130 250 0 1 187 0 3.5 0 \n2 41 0 1 130 204 0 0 172 0 1.4 2 \n3 56 1 1 120 236 0 1 178 0 0.8 2 \n4 57 0 0 120 354 0 1 163 1 0.6 2 \n\n caa thall output \n0 0 1 1 \n1 0 2 1 \n2 0 2 1 \n3 0 2 1 \n4 0 2 1 ", + "text/html": "
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agesexcptrtbpscholfbsrestecgthalachhexngoldpeakslpcaathalloutput
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" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": "# Exploratory data Analysis", + "metadata": {} + }, + { + "cell_type": "markdown", + "source": "### Basic information about Data", + "metadata": {} + }, + { + "cell_type": "code", + "source": [ + "print(\n", + " \"There are {} data points and {} features in the data\".format(\n", + " data.shape[0], data.shape[1]\n", + " )\n", + ")" + ], + "metadata": { + "execution": { + "iopub.status.busy": "2021-08-24T17:50:41.158928Z", + "iopub.execute_input": "2021-08-24T17:50:41.159265Z", + "iopub.status.idle": "2021-08-24T17:50:41.165647Z", + "shell.execute_reply.started": "2021-08-24T17:50:41.159235Z", + "shell.execute_reply": "2021-08-24T17:50:41.164523Z" + }, + "trusted": true + }, + "execution_count": 4, + "outputs": [ + { + "name": "stdout", + "text": "There are 303 data points and 14 features in the data\n", + "output_type": "stream" + } + ] + }, + { + "cell_type": "code", + "source": "data.info()", + "metadata": { + "execution": { + "iopub.status.busy": "2021-08-24T17:50:41.167558Z", + "iopub.execute_input": "2021-08-24T17:50:41.168076Z", + "iopub.status.idle": "2021-08-24T17:50:41.197775Z", + "shell.execute_reply.started": "2021-08-24T17:50:41.168040Z", + "shell.execute_reply": "2021-08-24T17:50:41.196295Z" + }, + "trusted": true + }, + "execution_count": 5, + "outputs": [ + { + "name": "stdout", + "text": "\nRangeIndex: 303 entries, 0 to 302\nData columns (total 14 columns):\n # Column Non-Null Count Dtype \n--- ------ -------------- ----- \n 0 age 303 non-null int64 \n 1 sex 303 non-null int64 \n 2 cp 303 non-null int64 \n 3 trtbps 303 non-null int64 \n 4 chol 303 non-null int64 \n 5 fbs 303 non-null int64 \n 6 restecg 303 non-null int64 \n 7 thalachh 303 non-null int64 \n 8 exng 303 non-null int64 \n 9 oldpeak 303 non-null float64\n 10 slp 303 non-null int64 \n 11 caa 303 non-null int64 \n 12 thall 303 non-null int64 \n 13 output 303 non-null int64 \ndtypes: float64(1), int64(13)\nmemory usage: 33.3 KB\n", + "output_type": "stream" + } + ] + }, + { + "cell_type": "code", + "source": "data.describe()", + "metadata": { + "execution": { + "iopub.status.busy": "2021-08-24T17:50:41.199238Z", + "iopub.execute_input": "2021-08-24T17:50:41.199601Z", + "iopub.status.idle": "2021-08-24T17:50:41.266325Z", + "shell.execute_reply.started": "2021-08-24T17:50:41.199551Z", + "shell.execute_reply": "2021-08-24T17:50:41.264847Z" + }, + "trusted": true + }, + "execution_count": 6, + "outputs": [ + { + "execution_count": 6, + "output_type": "execute_result", + "data": { + "text/plain": " age sex cp trtbps chol fbs \\\ncount 303.000000 303.000000 303.000000 303.000000 303.000000 303.000000 \nmean 54.366337 0.683168 0.966997 131.623762 246.264026 0.148515 \nstd 9.082101 0.466011 1.032052 17.538143 51.830751 0.356198 \nmin 29.000000 0.000000 0.000000 94.000000 126.000000 0.000000 \n25% 47.500000 0.000000 0.000000 120.000000 211.000000 0.000000 \n50% 55.000000 1.000000 1.000000 130.000000 240.000000 0.000000 \n75% 61.000000 1.000000 2.000000 140.000000 274.500000 0.000000 \nmax 77.000000 1.000000 3.000000 200.000000 564.000000 1.000000 \n\n restecg thalachh exng oldpeak slp caa \\\ncount 303.000000 303.000000 303.000000 303.000000 303.000000 303.000000 \nmean 0.528053 149.646865 0.326733 1.039604 1.399340 0.729373 \nstd 0.525860 22.905161 0.469794 1.161075 0.616226 1.022606 \nmin 0.000000 71.000000 0.000000 0.000000 0.000000 0.000000 \n25% 0.000000 133.500000 0.000000 0.000000 1.000000 0.000000 \n50% 1.000000 153.000000 0.000000 0.800000 1.000000 0.000000 \n75% 1.000000 166.000000 1.000000 1.600000 2.000000 1.000000 \nmax 2.000000 202.000000 1.000000 6.200000 2.000000 4.000000 \n\n thall output \ncount 303.000000 303.000000 \nmean 2.313531 0.544554 \nstd 0.612277 0.498835 \nmin 0.000000 0.000000 \n25% 2.000000 0.000000 \n50% 2.000000 1.000000 \n75% 3.000000 1.000000 \nmax 3.000000 1.000000 ", + "text/html": "
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agesexcptrtbpscholfbsrestecgthalachhexngoldpeakslpcaathalloutput
count303.000000303.000000303.000000303.000000303.000000303.000000303.000000303.000000303.000000303.000000303.000000303.000000303.000000303.000000
mean54.3663370.6831680.966997131.623762246.2640260.1485150.528053149.6468650.3267331.0396041.3993400.7293732.3135310.544554
std9.0821010.4660111.03205217.53814351.8307510.3561980.52586022.9051610.4697941.1610750.6162261.0226060.6122770.498835
min29.0000000.0000000.00000094.000000126.0000000.0000000.00000071.0000000.0000000.0000000.0000000.0000000.0000000.000000
25%47.5000000.0000000.000000120.000000211.0000000.0000000.000000133.5000000.0000000.0000001.0000000.0000002.0000000.000000
50%55.0000001.0000001.000000130.000000240.0000000.0000001.000000153.0000000.0000000.8000001.0000000.0000002.0000001.000000
75%61.0000001.0000002.000000140.000000274.5000000.0000001.000000166.0000001.0000001.6000002.0000001.0000003.0000001.000000
max77.0000001.0000003.000000200.000000564.0000001.0000002.000000202.0000001.0000006.2000002.0000004.0000003.0000001.000000
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" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": "### checking for null values", + "metadata": {} + }, + { + "cell_type": "code", + "source": [ + "msno.bar(data)\n", + "plt.show()" + ], + "metadata": { + "execution": { + "iopub.status.busy": "2021-08-24T17:50:41.267947Z", + "iopub.execute_input": "2021-08-24T17:50:41.268348Z", + "iopub.status.idle": "2021-08-24T17:50:42.260698Z", + "shell.execute_reply.started": "2021-08-24T17:50:41.268305Z", + "shell.execute_reply": "2021-08-24T17:50:42.259641Z" + }, + "trusted": true + }, + "execution_count": 7, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": "
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\n" 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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "markdown", + "source": "There are no missing values present in the dataset", + "metadata": {} + }, + { + "cell_type": "markdown", + "source": "### Checking correlation", + "metadata": {} + }, + { + "cell_type": "code", + "source": [ + "plt.figure(figsize=(15, 8))\n", + "\n", + "sns.heatmap(data.corr(), annot=True, linewidths=1)\n", + "plt.show()" + ], + "metadata": { + "execution": { + "iopub.status.busy": "2021-08-24T17:50:42.448228Z", + "iopub.execute_input": "2021-08-24T17:50:42.448692Z", + "iopub.status.idle": "2021-08-24T17:50:43.571416Z", + "shell.execute_reply.started": "2021-08-24T17:50:42.448638Z", + "shell.execute_reply": "2021-08-24T17:50:43.570496Z" + }, + "trusted": true + }, + "execution_count": 9, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": "
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Z0UXXDpkxSZg5abfpSkcbMjRl5AIVeamZmNhaYOnmCLKMzy/T6b1/Pk0m9lGXyS/g7Iw19Dm0mMGBy6nWsCahmwJ0llNZbBxtSSx2fCXGJmLrUH4dVFqZ06p7a66cuFS4zHdMb746upKRM8eydu4PlRqvoD9VptMIvCzLcivAC3hbkqSawAdAO6Aj0KhY2a+AL2VZbg0MAUQNrWRbp6+i/ageTPpzASYWZuTnqUfmareoh6pAxYK2E1nc+R26vNoH29o19Bztk1MYKjCzNueHgXM5sPAXhn03CYDAX4+QGpPE63/Op9eHo7l38TZyQdW521MWy37PY9KsAck//gaAUR0njOvVIcxnFGHeI1G288CsVVM9R/n32A7uitK9PrEr/9B3KDphUsMaj+UTCX53JcjP3t3aVgM7UdvdjcPf//n4wlXQc00bUNulFgf3HtF3KBXi7qYAsqIT8dk/H/ePR5N0/jZygXa9qzWwPZF/6LfT+HdUH9wV8xb1iF2xXd+h6FSbfh25uOc08r/olf9W/XwwbdqQ5B9/BzTnLLfahHqPJrTrKJTtWvzrzlkPKQwNsG/TkJNvfYf/wI+p1csLh05NkQwNaDimO3t8Z7PN8y2Sr0fQdFJ/fYf7xBQGCt7+ZjL71uzm/r2iG5v+6/byTpcJ/LJ4HYMnDdNjhHqmUlXOTxVRlV6E87YkSYM0/18bGA0ckWU5CUCSpK1AQ8367kCTYs+YWUmSZCHLsn6fJK5i2o/uQRvNs3mRwWFUKzZSVs3RVmv0DdSjdNWK3aGu5lSd1Dh1mfjQaH4cswgAO1dHGvl4AOAxoCM3jwSjyi8gIzGV8Au3qOXuRtK9+1SW1mN60Gq4DwBRl8K0RgCtHG1JjdOe6pEal6w1qmrlVJR7akwS1/ep73JGBYchq2SUtpZkJqWx/5MNhdu8sm0uiZrpg7qUfz8RQ8ei0Q5DBzvy4hJLlVO298R2/HAix0xDzssDwKJ7R7KDbyBnZgOQcewcph6NybpwVTfBlyM3Jgljp6IptMaO1cmNSSpVzrKTO06ThnJz6BxkzfThqiw7NhmzYnXR1Lk62X9j2pGhhRmtN07n5qItpFwIqYwQ/5aOo31pp2k/7gWHYl0sN2tHWx7Elt5nj9KgYzO6vzWIb1/4qHA6uL4NHzeEoaMGAHAl6DqONYtueDk41SCu2IgJgIdXc5q2aMT+c39gYGhAdTsb1mz7jnGDJ+o07uKyY7TrnZmTLVkljqeHZbJikpAMFBhZKslNSgPg8tyidq7rn/NID4sp/L1akzpIBgakXCr9YhZdyo1Nwti5WJvhVJ282NLtoFVnd5zeHsqNIVW7zfAe3ZMuI7oDcCc4BNti+8/GsTopf/PYAmjdryO/fKD/++f5cQkYFRuhN3S0I7/Mc5YHthOGc2/09GLnrA7a56yj5zHT4zkrKzYZZbGRd6WTLVkx2m16Zmwy5s62RceWlZKcpHQyY5K4f/pm4Qtuog8FY9vchTzN4zvpd9XXSBE7z9DkrX46yqiI75jedBuufpYy9NJtqhc7vqo7Vicpruw6+PriicTeiWHPT2Xf+Du58xivzh9f8QH/S8hVaCppZagSI42SJHmj7gi2l2W5BRAIPOoBCgXQTpZlD81PTdFhLO3U+gN85TeTr/xmctX/PK0Gdwagjmd9stMySz17lBafQk5aFnU86wPQanBnrvpfAMBc83yVJEk8/9YgTm88CEBKdAL1O6jvBBqZmVDHsz73Q6MrNa9z6w6w0m8WK/1mccP/PC2GqPOq5VmfnLQs0u9r55V+P4Wc9CxqafJqMaQzNw+o87rhfwHX9o0BqO7qiIGRIZlJaRiZGmOkeebArVMzVPkq4m9HVWpeZcm+fBOjus4Y1nQAI0Os/LqScVj7rZomjetRY94kot+cR0HSg8LleTH3MWvdHAwUYGiA0qs5uaH6n56aEXwbU1cnjGvXQDIyxHZAJ1IOnNUqY9bUlbqLJxLy8kLyEx+U80lVy4PAUMzdHDGrY49kZIDzwPbE7b/wRNtKRga0WjuZyK3HCt+oqm8n1vuzxG8GS/xmcNn/PF6DuwBQt5z241FqNnVh2MLX+PHVz0nXPDdcFWxe8ztDu41haLcxHNp7hP7D/ABwb9WU9LR0Eu5rX+xu+Xkbz7foR8/WgxjTfzzhYRF67TACJAeFYuHmiFJT72oNbE+Mv3a9i/G/QJ3/qdvJmn3bEn9CfRFuYGaMgVLdztXo0gw5v4C0W0XtXK1BHYjcflJHmZQvI+g2JiXajGR/7RfzKJu6UnfxG9weV/XbjID1+wtfXhPkf452g7sC4ObZgKy0zMc+u1iSYz1nlNXMCb14qxKi/XuyL9/CqK4zRppzlqVfV9IPlT5nOXz0NlETP9I6Z+XHxGuds8xaNydHj9NTE4PCsHR1xLy2PQojA+oOaEekv/a05yj/i7gN01xb9W1D3HH1uxBiAi5h3bg2BmbGSAYKarRvxINbUWTFJlGtYU1MbNXvw3Ds0pzU25V7zVQW/3V7ed/vPd73e49z/mfoMsQbgAaeDclMyyDlfukbni9MfRGlpTk/f/Sj1nJHF6fC//d83ouY8JiSmwrPiKoy0lgNSJZlOVOSpEaop6SaA10lSbIB0lBPQ72sKe8PTAI+B5AkyUOW5SBdBz1t7mLOBV4iJSWVbgNHMfGV0Qzp11PXYTyRG4cDec7Hg+lHlpGblcPWaasK172zZxFf+anfyvbHB2v438Ov3AgI4qbm2SOP/h1oP1p9V+rK/rOc3xoAwKl1/gz7fAKT/T8HCc5vPULsjQid5XX7UBANfDx4++hS8rJy2TG1KK8Jexay0m8WALvnrGHgkvEYmhoTEhDM7cPBAAT+GsCAz19nov9iCvLy2T5F/XpvczsrRq17H1mWSYtNZtt7evqqigIV8fO/o9YPC0ChIHWbP7khd6k+aTTZV26Tcfg0dtNeRaE0w+lL9Vsr82PiiX5zHun7j6Ns60HdHeqpjpnHL5ARoOc36wEUqIj4YDUNN84FhQGJW/4i+9Y9nKeOICM4hAcHzlF7zksYmJtSb6X6DZa5UfGEvLwQgOd+X4hp/ZoYmJvifu4HwqcuJ/VIkB4TUpMLVFyZuZY2m2ciGSiI3BRA+s1IGk4fSkrwHe7vv0A1DzdarZmMkbU5Dr4taThtGEe7TsO5f3ts2zXCyMaCWi+oO2eX3l5J6tW7es5K7frhQBr7eDDryFfkZeWwaVrRa/Cn7FnMEr8ZAPSd8SItB3TEyMyYD099y5kth9m/7Df6zRyJidKEsd+9C0ByVAI/vfaFPlIp19G/TtK5Wwf2nvmNrKxsPnin6M3Yvx1cx9BuYx6xtf7IBSqCZq2l46YZSAYK7m4KIO1mFI2nDyUlKIwY/4uE/xKA1/KJ+J5aSm5KBmfHfwOAiZ0VHTfNQFbJZMcmc26SdjtXq387Toz8TB9paStQETFnNc/9MhcUChK2HCxsMzKDQ0g5cI5aH4zFwNyU+qumAZATFU/IOPXsmEbbFqjbDKUpLc6v5s6Ub6tEmwFw+fBFmvt4suDIN+Rm5bJ22reF6z7c8zkf+6nzGTJjFG0HdMLYzJjPTq3k2JaD/LlM/VUWrft15Nyf+u/cA1Cg4v4nK6j143xQGPDgd39yQyI056xbZBw+g/20V1AoTXFepj4/58fEEzXxI9L2H0fZrgUuO1eADBnHz5NxWH/nLLlAxfnZP/P8L9ORDBSEbj7Cg1tRuE8bQmLwHaL8LxKy6Qgdvp5A/xNLyElJ58QbywHIfZDJ9VV76bXnY5Blog8FE30wCIDLS7fR4485qPIKyIhK4NS73+stR4DAQxfw9GnFV0dXkqv5yo2HPt3zJe/7vYetY3UGT/ofUSH3WLx7KQD71+3m0Oa/6DnWj+adWlCQV0BGajrfTf5KX6noXxWaSloZJLkKPDcjSZIJsB1wAW4C1sA81NNRpwFJqEceI2VZni1Jkh3wLdAYdcf3qCzLJd8RLOclhPGsMbJz432XEfoOo8J9Gr6JeXWr3mv4n8a8uxu51biXvsOocA2v7+N8rYH6DqPCeUVuZ7fDs3Vs9YnbxGSX4foOo8ItDd9MM4d2jy/4L3Ml7jTbHF/UdxgVbnDsL5yrOejxBf9FWkf9wWsuz96zW6vDt3KzUW99h1Hhnruxl43Oo/QdRoUaGb2BF+oO1HcYFW7L3e16+H6zipFz+2SldKpMGnSoEv8mVWKkUZblHKBUKyVJ0nlZlr+XJMkQ+AN1xxJZlhOAF3QapCAIgiAIgiAIQlnEM416NU+SpCDgCnAHTadREARBEARBEARB0I0qMdJYHlmWp+o7BkEQBEEQBEEQhEdSFeg7gkpVpTuNgiAIgiAIgiAIVZ6YnioIgiAIgiAIgiD8V4mRRkEQBEEQBEEQhKfxjH/lhhhpFARBEARBEARBEMolRhoFQRAEQRAEQRCexjP+TKPoNAqCIAiCIAiCIDwNMT1VEARBEARBEARB+K+SZFnWdwyV5ZlNTBAEQRAEQRCeQZK+A/insoP3VErfw7SFX5X4N3mmp6e+7zJC3yFUuE/DN5GXEKbvMCqckZ0bC+qO1HcYFWr23Y188ozlBPDB3Y2MrjtY32FUuPV3t7G25ih9h1GhXora8MzWwUV1n619BTDz7gb8HYbrO4wK5xu3mW2OL+o7jAo1OPYXPnsG6+D0uxv40/HZu3bqF7uJczUH6TuMCtU66g961+6t7zAq3N57e/UdglCOZ7rTKAiCIAiCIAiCUOnEi3AEQRAEQRAEQRCEcokX4QiCIAiCIAiCIAj/VWKkURAEQRAEQRAE4Wk849NTxUijIAiCIAiCIAiCUC4x0igIgiAIgiAIgvA0VAX6jqBSiU6jIAiCIAiCIAjC0xDTUwVBEARBEARBEIT/KjHSKAiCIAiCIAiC8DTEV24IgiAIgiAIgiAI/1VipLEc/eeO5TkfD/Kycvl16gqir4aXKlOzmSvDvpiAkakxNw8HsfOjnwFwalyHQQtewVhpSnJkPJvf/Zac9CwUhgYM/fR1nJu6YGBowIVtxwj4boeOM3u8OQuXcvTEWWxtrNm+YaW+w3ks33ljqOfTgrysXHZNXUXslfBSZRybudBvyQQMTY0IPRyM/7x1WuvbvuZH9zkjWeoxnqzkdEytlPT9/HWs6zpQkJPHrmnfE38rUkcZqfWcN4b6mrx2PiKvAZq8Qg4Hs1+TV5d3B+M5wofMxDQADn++hZDDwTQb2IH2r/ct3N6hcW1W95lD3LW7OsnpUUbPe4UWPi3Jycrh+6nLuXslTGu9sakxk1ZMo0YdB1QqFYF/nefXTzfoKdoiNb3dafPxaCSFgtubArj87Z9a6xXGhnT+agLVm7uSk5zGkTeWkx6ZULje3Lk6AwM+JWjJNq6u2gPA0NNfkpeejaxSocovYJffhzrN6aHKqIMKQwP6fvoqTs1cURgquPT7cU58t1OXadFj3mjq+XiQl5XDrqnfE1dOXn2WjMfI1JjQw0EcmLdea32b13rTbc5IlnlMICs5vXC5k7sbY/6Yy/ZJy7m551xlp1JKdZ8WNJo/FslAQeTGQ4R/o/1va9OuEc99MhaLJnW4PP5r4nad0VpvYGFGx2NfcH/veW7MWqPL0Etx8HHH/ZMxSAYKwjce5tby0seW1zdvYO3uSm5yOmfHf03mvQQkIwNafv4q1i1ckVUylz5YR8LJ6wBIRgZ4LByHXYfGoJK5ungL0bt1v5+6zRuNm6YO7i2nDjo0c8FvyXgMTY0JOxzEQU0d7DRlKPV7tERWyWQmprJ3yirS76dgW8+J3l+8jkNTF459sZVz3+/RcVZq9j4taKbZbxEbDxOyXLsO2rZrRLOPx2DZpA4XJ3xNzK6zAFg1rUvzT1/GyFKJXKDi9ld/EL3jtD5SKJOVtyd1Pn4FSaEgftNfxH67TWu9w+v9sR/RHTm/gPykVO5MXk5uVDwADTd8gHnL50g/d53bYxfoI/xHmvDRBFo/35qcrByWTF5C6JVQrfUmpibMWjkLp7pOqApUnPnrDGsWq9sHe2d7pnw5BQsrCxQGCtYsWsO5w7o/pqoE8Uzjf89z3h7YuTryufd7bJu1mkELXimz3KD5L7Nt5mo+934PO1dHnvNuAcCQxa+z99PNLOv1Plf3n6er5iLd3a8thsaGLOv1Pl/3nUXbF7thU8tOZ3k9qYF+PVi5dL6+w3gi9XxaYOvqyIquU9gz80d6zR9XZrneC15m94wfWNF1CraujtTT7CsASydbXDs350GxC/kObw0g7loEP/Sayc7JK+gxb3Sl51JcfU1e33adwu6ZP+JXTl5+C15m14wf+LaMvM78uJfVfrNY7TeLkMPBAFzZfrJw2Y73VpB8L75KdBhb+LTEwdWJqV3f5KeZKxk3//Uyy+35fgfvd3ubOX5TaejVCHdvTx1Hqk1SSLRdMJYDoz5ju890XAe2o1oDZ60yDUZ4k/sgg22dpnBt9T5azR6utb71vJFEafZPcfuGLWCn72y9dRgrqw426dMWQ2MjVvWcweo+c2j54vNU02E7WM+nBTaujqzsOoW9M3+k1/yXyizXc8E49s74gZVdp2Dj6oibt3vhurLaDFDXB++ZL3Dn2OXKTKF8ConGi1/m4ouLOdF5Ck6DOmLesKZWkayoRK68s4LYbSfK/Ij6M/5H8ukbuoj20RQSLRaN48SLn3GgyzRqDeqAZYlcXF70JjclA//2kwlZtZdmc0YA4DrqeQAO+szgxAuLaD53FEgSAI3eHUhOwgMOdJzCgS7TSDil+1zdNHVwddcp7J/5Iz3KqYO+C8axb8YPrNbUQVdNHTy7ajdre83iZ7/ZhB4MpMM7gwDITsng4Nz1nFutn84iAAqJ5ovGcebFTzncZSrOgzpgUaoOJhD4zkqi/tCugwVZOQRNWkFA12mcHrGYph+PwdBKqcvoy6dQUHfB69we9QlXfN6m+sBOmDaopVUk80oY13pP5WqP90jafZLac8YUrotZuZ2wd5bpOOgn09qnNc6uzrzS+RW+fv9r3lr4Vpnlfl/1O6/7vM5bvd+iSesmeHl7ATDi7REc23WMt3q/xeI3F/Pmgjd1Gb6gQ6LTWIamvq24sO0YABGBIZhZKrG0t9YqY2lvjYmlGRGBIQBc2HaMpr7qA8je1Yk7Z9R3NW8fv0Sz3m0AkAEjMxMUBgqMTI0pyM0nOy1LN0n9DV4ezalmZanvMJ5Iwx6tuPS7el9FB4ZgaqXEooa1VhmLGtYYW5gRrdlXl34/RkPfVoXre3w4mkOLNiHLcuEy+wY1CT95FYDE0Bisa9ljbmdVydkUKZ5X1CPyMrEwI6pYXs8Vy+txmvZvz7U/T1VYzE+jZY82HP89AIDQwFsorcypVsNGq0xudi7XT10BoCAvn/ArYdg6Vtd1qFrsPOuRFh5HekQ8qrwC7uw4TZ2e2vugjm9LQraq92X47rM4dWpatK5nK9Ij4km5GaXTuJ9EZdVBWZYxUpogPWwH8/LJ0WE72KBHK678fhyA6MBQTKzMMS+Rl7kmr+hA9d32K78fp6GmfQfo/uEoDi/arNVmAHi95MvNvefISEit3CTKUa1lfTLvxJJ19z5yXgGx209So5eXVpnse/GkX4tAVsmltrd0d8XYvhqJAZd0FXK5bD3rk3EnjswIdS6R20/hVOLYcurpRcSvmjq66wz2nZoBYNmwJvePq9vvnIRU8lIzsPFwA6DucG9uPhx9lWVyk9J0lFGR+j1acVVTB2MCQzEtpw4aW5gRo6mDV38/TgNNHcxNLzpejJQmhfUwMzGV2EthqPL099p/G8/6ZNyJLdxv0dtP4dhTuw5m3Usg7XoElKiDGWGxZNyJBSAnLpmchFRMquvuvPso5p4NyAmPISciDjkvn6Qdx7Hp2UarTNrJK6iycwHIuHALI6ei81Pa8cuo0qve9R5AO992HPz9IAA3Am9gYWWBTYnzb052DpdOqduF/Lx8Qi6HYOekvtknyzJKC3XnXmmpJDEuUYfRVzEqVeX8VBFVptMoSZK5JEm7JUkKliTpiiRJL0iS1EqSpCOSJF2QJGm/JElOkiRVkyTppiRJz2m22yRJ0msVGYuVgy0Poosq/YPYJKwcbbXLONryICapqExMIlYO6jJxtyNpomnc3f3aYa1pOC7vOUNeVg6zz65g5slvOLp6F1kPMioy9P8cS0dbUovtq9TYJCwdtBs7Swcb0mKL9lVaTBKWmv3ZsEcr0mKTuH89QmubuGsRPNerNQDOLdyoVtOucBtdeNK8UovllVosL4DWY3x5fd8i+n3+GqZl3K1t0q8dV3ZUjU6jjaMtSdFFozZJsYnYOpT/7620UuLZ3YurJ/Q0ovMwDkcbMqKL9kFGTBJKR5tyy8gFKnJTMzGxscBQaUKzN/sStFR7ihOoT8K+m2bQd+8nNBzpU7lJlKOy6uD1PWfJy8zhvXPf8vaprzj1/W6yddgOWjraaOWV9sR5qcs06NGStNjkUm2GhYMNDXt6cXH9wUqM/tFMHW3JLpZbdnQSJk/abkkSz80bza15+p/yDWDqZENWsVyyYpIwc7Itt4xcoCIvLRNjW0seXI3AqWcrJAMFyjr2WLu7YuZsi5GmDjaZPozn/RfQZvU7mOjwZuBDT1oHS5+3isp0njaMCae+osnADhxf+nvlB/2ESu637JhETJ1sHrFF2aw966EwMiQjPK4iw/vHjB1tyS12jsqNScToETct7UZ058Hhi7oI7alVd6xOQrHcEmISsHMsf/aHuZU5bbu3JehEEAAbvtyAz2Af1p9dz8c/f8yKD1dUdshVl+g06kwvIFqW5RayLDcD9gHfAENlWW4F/AQskGX5AfAWsFaSpOGAjSzLq/UWdRm2Tl9F+1E9mPTnAkwszMjPywegdot6qApULGg7kcWd36HLq32wrV1Dz9H+dxmaGtPhzf4cXfpbqXUnV/yJqZWSV/csxOulnsReDS/zznxVdWHDXyzv8h7f955F+v0UenwwUmu9s0c98rNydf6cZkVQGCiY+M1k/NfsIf5e1big+Cc8pgzm2up95GfmlFq3d9An/NlrDn+N+pxGL3XHoe1zeojw6ZRXB5096qFSqVjW5i2+6fQe7V/zw7q2vZ6jfTIP24xjZbQZ3eeO4vDizSD/e9qJ4mqP8yXhYCA5xW6G/lvd3RRAVnQiPvvn4/7xaJLO30YukJEMDVDWrE7i+Vsc8p1N0vnbNJ878vEfWAUd+3wrK9u/w7XtJ2k5toe+w6lQJjWs8fxmIkHvrvxXHk/VB3fFvEU9Ylds13coFU5hoOD95e+zc81OYiPUo8LeA7z5a+tfjG4zmg/Hfsi0ZdOQNNPBhWdLVXoRzmVgiSRJnwK7gGSgGXBAU/kMgBgAWZYPSJI0DPgWaFH2x/097Uf3oM0I9XMQkcFhVHMuuoNUzdFW664zqO+6Vyt217OaU3VS49Rl4kOj+XHMIgDsXB1p5OMBgMeAjtw8Eowqv4CMxFTCL9yilrsbSffuV0QK/xmtxvTAc7h69CX6UhhWxfaVlaMtaXHJWuXT4pK1Rj8snWxJi03Cpq4D1rXteXWvel9ZOdnyyu4FrBnwIRnxD9g17fvCbd48vozkiMrdT17/IK/iI+BWmrwArelxFzcdZvhPU7W2bdqvPVd2nqzwHP6O7mN64T1cfbETdikEW+eiO5u2jtVJiiv74vXlxW8QdyeG/T/t0kmcj5IZm4y5c9E+MHeyJTM2ucwymTFJSAYKjK2U5CSnY+9ZH5c+bfCaPRxjKyWySqYgJ48baw8UfkZ2YioRey9g51GPuDM3Kz0fXdTBZgM6EBpwCVV+AZmJqdy7cAtndzdS7sVXWl4tx3THQ5NXTIm8LJ84r2Rs6tagWm17Xt67sHD5uN3z+XnAXJzcXRnwjfpZIKWtJfV8WqDKV3Hb/0Kl5VVSdmwSpsVyM3W2JSf2yTqB1l4NsG7biNov+WJgboLC2JCCzGxuz99UWeE+UnZMMmbFcjFzsiWrRIf2YZkszbFlZKksnG56eW7RiGnXP+eRHhZDblIa+ZnZhS++ifrzNC4veld+MoDnmO64a+pgrKYOPpyUXl4dLH3e0i4DcG37SYauncqJL0vPWNCHkvvN1Kk62TGl4y6PoYUZbTdM58biLaRcDKmMEP+R3NgkjIudo4ydqpMXW3oaplVnd5zeHsqNIXOQc/N1GeLf0ndsX3qN6AXAreBb2BXLzc7JjoTYhDK3e+fTd4i+E832H7cXLuv5Qk/mjJ4DwI2LNzAyMcLK1ooHiQ8qL4EqSpb1NzVcF6pMp1GW5VuSJLUE/ID5wCHgqizL7UuWlSRJATQGMgEb4KmHS06tP8Cp9QcAaOTjSYexvgTvPEkdz/pkp2WSFp+iVT4tPoWctCzqeNYnIjCEVoM7c2LtfgDMq1uRkZiKJEk8/9YgTm9UT1dKiU6gfoemBP5xHCMzE+p41uf4T3ufNvT/nAvrDnBhnXpf1X/eA6+xvlzbeQpnz/rkpGWRfj9Fq3z6/RRy07Nw9qxPdGAI7kM6c27tfuJv3mNZq4mF5d48voyf+s0hKzkdEysleVk5qPIK8BjuQ8TZG1rPkVSG8+sOcL5YXq3H+nJ15ylqetYnu5y8ctKzqOlZn6hieYH6WbOH5Rv19CL+ZrFDRJJo0rctPw/9uFLzeZy/1u3jr3X7AGjxfCt6jO3N6Z3HqefZkMy0TB7cL32hMXTqCJSWSn6c/p2uwy1TQlAYVq6OWNS2JzM2CdcB7Tj6pnZs9/wvUn9YZ+IvhODSpw0xJ64BsHfwJ4VlPCYPJi8jmxtrD2BoZgIKifyMbAzNTHDu2ozgL7frJB9d1MHUqARcOjThsqYdrOnZgDM/7qvUvC6u+4uL6/4CoN7zHrQa20PTZtQjJy2TjBJ5ZWjycvasR3RgKM2GdOLCWn/ib0bydauilzy8cfxL1vb7gKzkdFZ0mly4vM8XrxNyKFCnHUaA1MBQlG6OmNWxJzsmCceBHbj0xjdPtO3licsL/9/5ha5YtXDTW4cRIDkoFAs3R5R17MmKSaLWwPacKxYjQIz/Ber8rzNJF25Ts29b4k+on2M0MDMGSaIgM4caXZoh5xeQditKs00g9h0aE3/iGvadm5F6SzfPEweu+4tATR10e96DlmN7cH3nKZweUQdz07Nw8qxHTGAoTYd04uJafwBsXBxI1kzbbODbkqTQGJ3k8CRSgkIxL1YHnQe252KJ/VYeycgArzWTubf1WOEbVauKjKDbmLg6YVy7BnmxSdgO6ETom19qlVE2daXu4je4Nepj8qt4h2nXz7vY9bP6xmvr51vT76V+HNlxhEaejchIyyC5jPPvmGljUFoqWTZtmdby+9H38ejkwV9b/6J2/doYmxr/JzuM/wVVptMoSZIzkCTL8gZJklKAiYC9JEntZVk+JUmSEdBQluWrwHvAdWAWsEZTJq+iYrlxOJDnfDyYfmQZuVk5bJ22qnDdO3sW8ZXfTAD++GAN/3v4lRsBQdwMCALAo38H2o/2BeDK/rOc3xoAwKl1/gz7fAKT/T8HCc5vPULsDe3nYqqCaXMXcy7wEikpqXQbOIqJr4xmSL+e+g6rTCGHgqjn48HEo0sLv3LjoVf3LOQHv1kA7Juzhr4PX58fEExoGW+rLM6uvjP9lkwAWSb+dhS7i4066kLIoSDq+3jw5tGl5Gu+7uCh1/YsZLUmr71z1tBf80r20IDgwjdUdps5AscmdZFlmQeR8eye9VPh9nXbNiI1OqlSR3b+ruBDF/DwackXR78jNyuH1VOLLjLm71nCHL8p2DhWZ8CkYUSFRPLJ7i8AOLBuL0c2/6WvsJELVJye8zM9fpmOpFAQsuUIKbei8Jg6hMTgO9w7cJHbm4/Q+esJDD6+hJyUdI485gLK1N6K5398FwDJwIA7208SpYcXk1RWHTy37gD9vxjPhAOfgiQRvPUI92/c01leoYeCqOfTgglHl5CXlcvuqUXH9st7FvCT32wA9s9ZS98lr6u/7uAJ2oyqQC5QcWPmGlpunoVkoCBq02EybkZSb/owUoPDiN9/ASsPNzzWTMHI2hx735bUmzaUk12n6Tv0UuQCFUGz1tJx0wwkAwV3NwWQdjOKxtOHkhIURoz/RcJ/CcBr+UR8Ty0lNyWDs+PVHWQTOys6bpqBrJLJjk3m3KSiZ6yuzN9E62/ewP2TMeQkpnLh3VXlhVBpwg4F4ebTgteOLiE/K5e9xerg2D0L+FlTBw/MWUtvTR28ExBMmKYOdpnxArZuTsgqmdSoBPw1X41ibl+NMX9+grGFGbJKhdfLvfix+/uVfsOzOLlAxZVZa2m3aSaSgYJ7mwJIvxnJc9OHkhJ0hzj/C1TzcKP1T5MxsjbHoUdLnps2jICu03Du357q7RphbGNB7Re6ABD0zkpSr+r/Dd8UqIiYs5rnfpkLCgUJWw6SfesezlNHkBkcQsqBc9T6YCwG5qbUX6U+nnKi4gkZp57J1GjbAkzr18RAaUqL86u5M+VbUo8E6TGhIucOnaP186356fhPZGdl8+WUos7w8n3LeavXW9g52jHi7RFE3I7gm73q4+zPtX+yf/N+fvjkB97+9G0GvToIWZZZOnmpvlLRvyr0/GFlkEq+/U1fJEnqCXwOqIA84A0gH/gaqIa6g7sMOApsB9rIspwmSdJSIE2W5bklPlJ+32WEboLXoU/DN5GXEPb4gv8yRnZuLKj773y2pDyz727kk2csJ4AP7m5kdN3B+g6jwq2/u421NUfpO4wK9VLUhme2Di6q+2ztK4CZdzfg7zD88QX/ZXzjNrPN8UV9h1GhBsf+wmfPYB2cfncDfzo+e9dO/WI3ca7mIH2HUaFaR/1B79q99R1Ghdt7b++/9oHIrMM/VEqnyszn1Srxb1JlRhplWd4P7C9jVZcyljUutt3kMtYLgiAIgiAIgiA8cyRJ6gV8hfqdLz/Isry4xPo6wM+AtabMDFmWn+pLXKtMp1EQBEEQBEEQBOFfSUfTUyVJMkD9MtAeqN/rck6SpJ2yLF8rVmwO8KssyyskSWoC7AFcnubvVqWv3BAEQRAEQRAEQRDK1wYIkWU5TJblXGAzMKBEGRl4+EW01YDop/2jYqRREARBEARBEAThacg6exFOTaD4G+QigbYlyswD/CVJmgSYA92f9o+KkUZBEARBEARBEISnoVJVzs8/MwJYK8tyLdRfZ7he85WF/5joNAqCIAiCIAiCIPw7RAG1i/1eS7OsuFeAXwFkWT4FmAJ2T/NHRadREARBEARBEAThaciqyvkp7RzQQJIkV0mSjIHhwM4SZSKAbgCSJDVG3Wl8qi/oFp1GQRAEQRAEQRCEfwFZlvOBt1B/VeF11G9JvSpJ0seSJPXXFJsCvCZJUjCwCXhJluWn+h5J8SIcQRAEQRAEQRCEp6Gjr9wA0Hzn4p4Syz4s9v/XgI4V+TfFSKMgCIIgCIIgCIJQLjHSKAiCIAiCIAiC8DR0ONKoD9JTTm+typ7ZxARBEARBEAThGSTpO4B/KmvX0krpe5j1nVwl/k2e6ZHGeXVH6juECjfv7kYWPIN5zb67kbyEMH2HUaGM7NxYW3OUvsOocC9FbeBDl2evDn4cvpFtji/qO4wKNTj2F9Y8g3VwXNQGQpv11HcYFa7elf00c2in7zAq3JW400x0+Z++w6hQ34X/ym6HEfoOo8L1idvEyy5D9R1Ghfsp/DfGuwzTdxgValX4VpKHees7jApnszVA3yEI5XimO42CIAiCIAiCIAiV7hmfnipehCMIgiAIgiAIgiCUS4w0CoIgCIIgCIIgPA352R5pFJ1GQRAEQRAEQRCEpyGmpwqCIAiCIAiCIAj/VWKkURAEQRAEQRAE4Wk849NTxUijIAiCIAiCIAiCUC4x0igIgiAIgiAIgvA0nvFnGkWnURAEQRAEQRAE4WmITuN/U+95Y2jg04K8rFy2T11FzJXwUmWcmrkwcMkEjEyNuH04mL3z1hWua/OSL21G90ClUnH7UBAHFm3CwMiAvgtfwdndDVmlYt9H6wk/fV1nOfnOG0M9TU67pq4itoycHJu50G/JBAxNjQg9HIx/sZwA2r7mR/c5I1nqMZ6s5HRMrZT0/fx1rOs6UJCTx65p3xN/K1JHGT25OQuXcvTEWWxtrNm+YaW+w3msmt7utPl4NJJCwe1NAVz+9k+t9QpjQzp/NYHqzV3JSU7jyBvLSY9MKFxv7lydgQGfErRkG1dX7QGgyWu9aDDCG2SZ5BuRnJj8PQU5ebpMC7+5RcfVH1NXEXM1vFQZp2YuDP5CXQdvHw5mz0fqOjhs+STs3JwAMLVSkp2ayQq/WbgP6EDH8X0Lt3doVJuVfecQe+2uTnJy8HHH/ZMxSAYKwjce5tby0vvK65s3sHZ3JTc5nbPjvybzXgKSkQEtP38V6xauyCqZSx+sI+GkdnvQ/ucpKOvW4KD3+zrJpbia3u601dTBW+XUwS7F6mBAGXVwkKYOXtHUQQBJIdFv7ydkxibz19glOsvnccw6emE3YwKSgQGpv+8l5cdftdZb/a8PVsP7gUqFKjOL+HlfkRcWoadoH23mgsl07tae7KwcZr/9Cdcv3yxVZuWmL7F3sMPAwICLZ4KYP+MLVCoVzzWpzwefv4/S3Izoe7G8/8aHZKRn6iGL0obNHUdTH0/ysnJYN/U77l29U6pM/6nDaTu4C2bVLJjcdEzh8nZDuzJo5mhS4pIAOPLzPk5uOaSz2Mtj79OCJvPV7ce9jYcJ/Wan1nrbdo1o8skYLJvUIXD818TuOguAVdO6NPvsZQwtlMgqFSHL/iBmx2l9pFCmF+e+THMfT3Kzcvlx6nIiythXg6eOoMPgriirmTOx6ejC5bbOdryy5C2UVuYoFAp++3QDlwMCdRl+uV6YO45mPi3Jzcph7dRvy6yDA6aOoN3gLiirWfBOsbwAWvVpT993/weyTOT1u/z4zle6Cr1chh5tUI57CxQG5BzcTc72X8osZ9S2CxZTPyb1/fEUhN3EoH4jlOOnFq7P3rqWvLPHdRW2oAfimcYyNPBpga2rI193ncKfM3+kz/xxZZbru+Bl/pzxA193nYKtqyP1vVsA4NK+CY16tGJF75l81+N9Tn6/G4CWI54HYEXPGawftRjfOSORJEknOdXT5LSi6xT2zPyRXuXk1HvBy+ye8QMrNDnV0+QEYOlki2vn5jwodmHY4a0BxF2L4IdeM9k5eQU95o0u62P1bqBfD1Yuna/vMJ6IpJBou2AsB0Z9xnaf6bgObEe1Bs5aZRqM8Cb3QQbbOk3h2up9tJo9XGt963kjiTocXPi70tGGxi/7ssvvA3Z0m4lkoMB1QDud5FMYs3cLqrs68pX3FHbO+pF+C8qug/3mv8yOmT/wlfcUqrs60kBTB7e+9Q0r/Gaxwm8W1/ae4/q+cwBc2nGycPnv760g5V68zjqMKCRaLBrHiRc/40CXadQa1AHLhjW1iri86E1uSgb+7ScTsmovzeaMAMB1lLo9OOgzgxMvLKL53FFQrD1w9mtNfka2bvIoQVJItFswFv9Rn/GHz3TcyqiDDUd4k/Mgg987TeHq6n14laiDbeaNJLJYHXyoyau9SLkdXanx/20KBfZz3iTmjTlE9H8NCz8fjNzqaBVJ232YyMETiBw6kZSftmI3fbyegn20zt3aU8e1Nn7thjFv6iI++Gx6meWmvDabIc+PZmDXF7GpbkPP/ur6+NHSWSyb/x2DvUdxcE8A494cpcvwy9XU25Maro7M836bjbO+Z/iCV8ssd+ngBT4dMKvMdRd2nWSR33QW+U2vEh1GFBJNF4/j7IufcqTzVJwHdcCiRPuRFZVA8Dsrid52Qmt5QVYOQW+t4GjXaZwdvpgmn4zB0Eqpy+jL1dzbEwdXJ2Z6T+LnWSsZs+D1MssFHTzPJwNmlFre760hnNt9ko/6TGPVpC8ZPf+1yg75iTTz9qSGqxMfeE9iw6xVjFxQdlyXDp5n0YCZpZbXcHGk18RBfD5kDh/5TubXj9dUdsiPp1CgfOUd0he8T+p7YzHu+DyKWnVLlzM1w8RvCPm3rhUuKoi4Q9r740mb9irpC6ajfH0KKAx0GHwVJMuV81NFiE5jGZ7r0Yrg348BEBkYgqmVEosa1lplLGpYY2JhRmRgCADBvx+jkW8rAFqP6sbx73ZSkJsPQEZiKgD2DWpy5+S1wmXZqRk4u7vqIiUa9mjFJU1O0Y/IydjCjGhNTpd+P0ZDTU4APT4czaFFm5CLVWD7BjUJP3kVgMTQGKxr2WNuZ1XJ2fx9Xh7NqWZlqe8wnoidZz3SwuNIj4hHlVfAnR2nqdOzlVaZOr4tCdmq3p/hu8/i1Klp0bqerUiPiCflZpTWNgpDAwxMjZEMFBiaGZMZm1z5yRTTyLcVQduKHVeWSizsrbXKWNhbY2JZdFwFbSs6ropr1qctl3aeLLXcvX97Lv95quKDL4etZ30y7sSRGXEfOa+AyO2ncCqxr5x6ehHxqzrvqF1nsO/UDADLhjW5f1x97OQkpJKXmoGNhxsABkoT6o/348ay7TrLpbiSdTDsH9TBtDLqoNLJllrdPLi9KaDSc/g7TJo/R15ENPmRsZCfT/reAMyfb69VRs4oGm1TmJlWqRN5cT69urBzq3pk99KFq1haWWBXo3qpcg9HDw0NDTAyNipMp269Opw/pR7VOXXkLD36+Ogm8Mdw9/XizLajAIQH3kZpaY5Vifbj4brU+BTdBvcPWbesT+adWLLuqtuP6O2ncOjlpVUm614CadcikFXa9S0jLJbMO7EA5MQlk5uQinH1qnHu9fRtzcltAQCEBd5GaamkWhn7KizwNg/K2FcyMmYW6g6wmZWSlDjdnqvK08K3Nae3HQHgTuBtzMqpg3fKqYOdhncnYN0+MlMzAEjTXBvqk0H9Rqhio1Ddj4H8fPJOHMLYq2OpcmbDXyF7xybkvNyihbk5oCoAQDI2rrJtolBx/hWdRkmSxkiSdEmSpGBJktZLkrRWkqSVkiSdlyTpliRJfR//KU/OytGW1OjEwt9TY5OwcrDRLuNgQ2psUlGZmCSsHG0BqO7qRJ02jXh1+0e8tGUOzu7qC8G4a3d5rkdLFAYKrGvb49zMFSvn0ifzymBZRk6WJXKydLAhrVhOaTFJWGpyatijFWmxSdy/rj0dK+5aBM/1ag2Acws3qtW0K9xG+GeUjjZkRBfth4yYJJSONuWWkQtU5KZmYmJjgaHShGZv9iVo6Tat8pmxyVxZuYdhZ7/ihcDl5KZmEn30SuUnU4yVgy0PSh5XJfKycrQhNabEceWgXZ/qtmlEesIDksLjSv2NZn3bcXmn7jqNpk42ZBXLKSsmCTMn23LLyAUq8tIyMba15MHVCJx6tkIyUKCsY4+1uytmzuptm7w/jJCVuynIytFZLsWVrIOZMUmY/4062LyMOgjQ9qNRnJ+/qdQFsL4Z1qhOfmx84e/5cQkY1rArVc5qeD/q7F1D9SmvkrDoO12G+MQcnOyJjbpf+HtczH0cnOzLLLtq8zKOXN1LRnoG/n+qR95Cb4bxfO8uAPj264ZjzRqVH/QTsHawJTm6aJZLcmwi1n/zXOPZuy2z937Oq99NxsZJN+feRzF11G4/sqMTMS1xnD2Jap71UBgZkllGm6gPNg7VSSqWV1JsEjaOT/7vvePLX2k/sDNfnFrFu2tmsXHuj5UR5t9m7WCrlVdKbCI2f6MOOrg54eDqzLTfPuH9PxbQtKtHJUT59yhs7VElFrV9qqR4pOra7YWBawMU1e3Jv1h6+rNB/cZYLV2D1ZI1ZK5eWtiJ/M9SqSrnp4qo8p1GSZKaAnOA52VZbgG8o1nlArQB+gArJUky1U+EpSkMFZhZm/PDwLkcWPgLw76bBEDgr0dIjUni9T/n0+vD0dy7eBu5oOpUhvIYmhrT4c3+HF36W6l1J1f8iamVklf3LMTrpZ7EXg2vcheE/yUeUwZzbfU+8jO1OxvG1ZTU6dmS39q9x5aWkzBSmuA2uPTdxH+D5v3bl9kxrOVRj7ysXO5XwWdqy3J3UwBZ0Yn47J+P+8ejSTp/G7lAplrTuli4OBC997y+Q/xHPKcM5moZdbBWdw+yElJJvByun8AqQOrmP4noPY7EpT9iM/5FfYfz1MYPfxcf974YGxvTtpN6hOuDdxcw/KUhbPFfi7mFkjzNjJl/u8t/XeCDTm+yoPc0bhy/xJglb+o7pAphUsMaj+UTCX535TMz0tO2fydO/BbA1PbjWTZuIa99OUlnj/JUJoWBATVcnVgyfB4/TPqKUYvGY1ZFphSXS5IwG/smWetWlLm6IOQ6qZPHkTpjPKaDRoKRsY4DFHTp3/AinOeBrbIsJwDIspykaTx+lWVZBdyWJCkMaAQE/dM/0npMD1oNV0/DiboUpjUCaOVoS2qJ6RGpccmFI4sAVk62hSOPqTFJXN+nvuCLCg5DVskobS3JTEpj/ycbCrd5ZdtcEjXTSypDqzE98NTkFF1GTmklckqLS9YaJbR0siUtNgmbug5Y17bn1b2L1Ns62fLK7gWsGfAhGfEP2DXt+8Jt3jy+jOSI+wj/XGZsMubORfvB3Mm21FTSh2UyY5KQDBQYWynJSU7H3rM+Ln3a4DV7OMZWSmSVTEFOHlkJD0iLiCcnKQ2Au3vPU8OrAWElnpOpaG1G96DVCM1xFRxGtZLHVYm8UmOTsXIqcVzFFY14KQwUNOnZmpX95pT6W836tedyGVNWK1N2TDJmxXIyc7Ilq9hIafEyWZp9ZWSpJFezHy7PLWoPuv45j/SwGOzaN8a6hRs9z32FwkCBiV01Om+bw7HBunsmt2QdVDrZkvGEddDOsz51i9VBNHVQ6WhDHd+W1Hq+BQYmRhhbmtHl6zc4+nbZFyO6lH8/EUPHorvrhg525N9PKLd8+t4A7D6YpIvQnsjwcUMYOmoAAFeCrmuNDjo41SAuJr68TcnNyeXwvqP49OrMqaNnuRNyl9dfUN+bretWmy49OlRu8I/QZXRPOo7oBsDd4FBsnO0A9Ut9bByrkxKb9IittWWkpBf+/4nNBxk0Q//PambHarcfps7Vyf4bjw0YWpjReuN0bi7aQsqFkMoI8Yk9P7oXXTT76k5wKLbF8rJ1tCU5NrG8TUvp/EI3lo5Vt3ehF29hZGKMha2lXqZzeo/uSacR3QEIDw7B1rk6oZp11o7VSf4bdTA5NpHwoNuo8gtIjLzP/Tsx1HBx4u6l0MdvXElUSfEoio0sKmztkYuNPGKmxKC2KxbzlqnXW9ti8f4C0j+dTUFY0Qu2VFERyNlZGNR21Vr+n1OFRgUrw7+h01iekrfUnuoW27l1Bzi37gAADZ73oM1YX67sPEUtz/rkpGWRfj9Fq3z6/RRy0rOo5VmfyMAQWgzpzNm1+wG44X8B1/aNCT91jequjhgYGZKZlIaRqTFIEnlZObh1aoYqX0X87aiSoVSYC+sOcEGTU/3nPfAa68u1nadwfkROuelZOHvWJzowBPchnTm3dj/xN++xrNXEwnJvHl/GT/3mkJWcjomVkrysHFR5BXgM9yHi7A1y07MqLaf/goSgMKxcHbGobU9mbBKuA9px9E3tqXD3/C9Sf1hn4i+E4NKnDTEn1M/K7h38SWEZj8mDycvI5sbaA9h51sO+ZX0MTI0pyM7FqVNTEoLDKj2Xs+sPcHa9ug429PGg7VhfLmuOq+y0LNJLPPeRHp9CTlrRceUxuDOnNccVgFunZiSERWtNDQeQJIlmfdry47CPKz2n4pKDQrFwc0RZx56smCRqDWzPuYnLtcrE+F+gzv86k3ThNjX7tiX+hPo5RgMzdXtQkJlDjS7NkPMLSLsVRdqtKO78/BcAytp2tF8/TacdRihdB90GtONIiToY8YR1MD8jm+tr1XXgwmL1G0kd2zem2QS/KtFhBMi5chOjOjUxrOlAflwiFr29iZu+WKuMUR1n8iLUL/BRdmlDXkTltd1/1+Y1v7N5ze8AdOnegREvD2PvHwdwb9WU9LR0Eu5rX7CbKc0wt1CScD8RAwMDuvToyIXTQQDY2tmQlJCMJEmMf28cv/78h67TKXR0/X6Orlcf/818POk6thfnd57AxbMBWWmZf+vZRSt768Ly7j28iA3V/4yEB4GhmLs5YlbHnuyYJJwHtifwjeWP3xCQjAxotXYykVuPFb5RVZ8Ord/HofX7AHD3aUm3sb05s/MEbp4NyEzLLPPZxfIkRSfQpGNzTvwWgFO9mhiZGOnt+b+A9fsJKKyDLfEZ24tzO0/g+g/qYLD/OVr378jJrQGY21hSw9WJhAj9TikuCLmJwqkWihqOqJISMOr4PBlfFTvfZGbw4JUBhb9azFtG1roVFITdVG+TEA+qAhR2Dhg410EVX3kDIf8Ksug06tsh4A9JkpbKspwoSdLD29/DJEn6GXAF3Hh4+7EC3D4URAMfD94+upS8rFx2TF1VuG7CnoWs9FO/mW33nDUMXDIeQ1NjQgKCua15U2DgrwEM+Px1JvovpiAvn+1T1F/xYG5nxah17yPLMmmxyWx7T3cXTCGHgqjn48FETU67iuX06p6F/KDJad+cNfRdMh4jU2NCA4IJLePth8XZ1Xem35IJIMvE345id7FRx6pk2tzFnAu8REpKKt0GjmLiK6MZ0q+nvsMqk1yg4vScn+nxy3QkhYKQLUdIuRWFx9QhJAbf4d6Bi9zefITOX09g8PEl5KSkc2Tioy80EgJDubv7LP33z0eVX0DS1bvc2nhYRxmp3TqsPq7ePaKug39MK6qDb+xZyApNHdz1wRoGfaGug7cDgrkdUFQHm/drz6UypqbWbduIBzFJJN8rf0SlMsgFKoJmraXjphlIBgrubgog7WYUjacPJSUojBj/i4T/EoDX8on4nlpKbkoGZ8d/A4CJnRUdN81AVslkxyZzblLV6EBBUR301dTB25o66Dl1CAkl6uAQTR0MeEwdrNIKVCQs/BanVQuRDBSk/uFPXuhdbN4cQ87VW2QGnMbqxf4o27VEzs9HlZrO/Vlf6DvqMh396ySdu3Vg75nfyMrK5oN3ii4Afzu4jqHdxqA0N2P5us8xNjFGUkicPXGxsHPoN6gHw8cNBeCvPQH8sWmXXvIo6crhQJr6tOSjI1+Tm5XL+mlFNzFm7vmMRX7qt8QOmjESrwGdMDYzZsGpFZzccojdy7biM643zbt7oSooIDMlnXVT9f9Mqlyg4srMtbTZrH6jdeSmANJvRtJw+lBSgu9wf/8Fqnm40WrNZIyszXHwbUnDacM42nUazv3bY9uuEUY2FtR6Qf0M6qW3V5J6VUdvjn6ES4cv4u7TksVHlpOblcNPxfbVvD2fM89vGgDDZoyi7YDOGJuZ8MWpVRzbcpAdy35ly/yfGbt4Ar6v9EWWZX6c+q2+UtFy5fBFmvt4Mv/IN+Rm5fLztKK45uz5nPmavAbPGEUbTR1cfGolx7ccZNeyrVw9EkSTzi2Ye+BL5AIVvy9arzUCrheqAjJ//AqL2Z+DQkHu4b2oIsMxfWEcBaE3yTtf/uwdw0bNMR34InJBAahUZP6wDDntgQ6DF3RNkv8Fc+AlSRoLTAMKgIdf1pMNeAFWwGRZlkue2eR5dUfqLkgdmXd3Iwuewbxm391IXkLlj3zpkpGdG2tr6n8KVEV7KWoDH7o8e3Xw4/CNbHP89z+nVtzg2F9Y8wzWwXFRGwhtVjVv+jyNelf208xBt1+FowtX4k4z0eV/+g6jQn0X/iu7HUboO4wK1yduEy+7DNV3GBXup/DfGO8yTN9hVKhV4VtJHuat7zAqnM3WgH/tA6xZ62ZWSqfKbMyiKvFv8m8YaUSW5Z+Bnx/+LknSWuAvWZYn6C0oQRAEQRAEQRCE/4B/RadREARBEARBEAShyvoXzN58Gv/KTqMsyy/pOwZBEARBEARBEATgmX97apX/nkZBEARBEARBEARBf/6VI42CIAiCIAiCIAhVhhhpFARBEARBEARBEP6rxEijIAiCIAiCIAjC05Cf7ZFG0WkUBEEQBEEQBEF4CrLq2X57qpieKgiCIAiCIAiCIJRLjDQKgiAIgiAIgiA8DfEiHEEQBEEQBEEQBOG/SpLlZ3b+7TObmCAIgiAIgiA8gyR9B/BPZa6YVCl9D+Ub31SJf5Nnenrqrca99B1ChWt4fR+f1B2p7zAq3Ad3N7K25ih9h1GhXoraQF5CmL7DqHBGdm7sdhih7zAqXJ+4TRxzHKrvMCpU59jfmOry7O2rL8I3sajus9VeAMy8u4GNzs9eXiOjN3DaebC+w6hQ7aK3MdlluL7DqHBLwzez3fFFfYdR4QbG/sKGZ+zYGhW9gbdcXtB3GBVuefgWfYcglOOZ7jQKgiAIgiAIgiBUumf87ami0ygIgiAIgiAIgvA0xItwBEEQBEEQBEEQhP8qMdIoCIIgCIIgCILwNMRIoyAIgiAIgiAIgvBfJUYaBUEQBEEQBEEQnsaz+zWGgOg0CoIgCIIgCIIgPB0xPVUQBEEQBEEQBEH4rxIjjYIgCIIgCIIgCE9DfE/jf5uyUytqzHoDFAoe/LaP5B9+1VpvPXYw1Yb2hAIVBUkpxM75kvzo+wDYTX0F865tQJLIPBlI/MIV+kihUM95Y6jv04K8rFx2Tl1F7JXwUmUcm7kwYMkEDE2NCDkczP556wDo8u5gPEf4kJmYBsDhz7cQcjiYZgM70P71voXbOzSuzeo+c4i7dlcnOdX0dqfNx6ORFApubwrg8rd/aq1XGBvS+asJVG/uSk5yGkfeWE56ZELhenPn6gwM+JSgJdu4umoPAE1e60WDEd4gyyTfiOTE5O8pyMnTST5/15yFSzl64iy2NtZs37BS3+E8MXufFjSZPwbJQMG9jYcJ/Wan1nrbdo1o8skYLJvUIXD818TuOguAVdO6NPvsZQwtlMgqFSHL/iBmx2l9pFAmGx8P3D4Zh2SgIHbjQSKXb9dab9WuMfU+Hod5k7rcmPAlCbuKYnf5YBS23VshSRLJRy8RNucnHUf/aAPmjqWxjwe5WblsmbqCqKvhpcr0mvo/vAZ3wayaObObjitc7tamEf0/HINTozpsnPQ1l/ae1WHk2nrMG009Hw/ysnLYNfV74sppB/ssGY+RqTGhh4M4MG+91vo2r/Wm25yRLPOYQFZyOm3H96HpgA4AKAwVVK9fk6883yD7QYYuUsLJ2x2vT9TtYMimAK4tL90Odvh6AraadvD4hOVkaNpB68a1afPpyxhZmoFKZq/fh6hy8qg7sD3NJvVHlmWy4lI4Oek7cpLSdZJPWap5e+LyyctICgX3N/1F9PI/tNZbtm2Cy8cvo2xcl9tvLCVp96nCdXVmj8a6WysAopZtJXHnCZ3G/jiD5o6lsY8nuVk5bCrn2Oo99QW8BndBWc2cmU1fKlze9RU/2g5/HlV+AelJaWyZvpLkqIRS2+tLDR93mn+ibuvvbjzM7RJ1s3q7RjT/eDRWTepwfsI3RO/SX9tQFidvd1oXO7aulnNsPbzGOFbi2GqrObZkzbElKSS6rHobC5cayAUqIg8EErRwiz5S0zJ07ks01dTB9VNXEHn1Tqky/aa+QJvBXVBWs2BK07Gl1nv0asOrK6fwWb+ZRFwO00XYgo5VyvRUSZKsJUma+CTrJEnyliRpV2XE8dQUCmp88CZRr88hvN/rWPXxxrheHa0iOddDiBj2NncHvkGa/3Hsp74CgKlHY8w8m3B3wBvc7T8B0+YNMWvtro8sAKjv0wJbV0e+7TqF3TN/xG/+uDLL+S14mV0zfuDbrlOwdXWknneLwnVnftzLar9ZrPabRcjhYACubD9ZuGzHeytIvhevsw6jpJBou2AsB0Z9xnaf6bgObEe1Bs5aZRqM8Cb3QQbbOk3h2up9tJo9XGt963kjidLkAqB0tKHxy77s8vuAHd1mIhkocB3QTif5/BMD/Xqwcul8fYfx9ygkmi4ex9kXP+VI56k4D+qARcOaWkWyohIIfmcl0du0L+4KsnIIemsFR7tO4+zwxTT5ZAyGVkpdRl8+hYJ6i17l6osLuNDlPewHdULZsJZWkZyoBG6+8y33/ziutdzS6zmsWjfios8ULnhPxtKjHtU6NNVl9I/UyNsDe1dHFnu/x2+zVjNkwStllrt28CJfDZhTanlydAJbpq4kcId+L9br+bTAxtWRlV2nsHfmj/Sa/1KZ5XouGMfeGT+wsusUbFwdcfMuarstnWxx7dycB8VuPp1ZtZuf/Gbzk99sAj79lYgz13XWYZQUEq0XjuXwyM/Y5T0dlwHtsCrRDtYb4U1uSgY7O07hxup9eM5Rt4OSgYIO37zB2Rlr2O0zgwNDFyDn5SMZKPD6eBR/DVvAnu6zSLkeQcNxvjrJp0wKBa4LX+PGyPkEe79D9QGdMWugfWzlRsUT+u43JPxxTGu5dbdWKJu7canHZK70eR+nCQMwsDDTZfSP1NjbAztXJxZ6v8vWWasZuuDVMstdO3iBZQNml1oedS2cL/vN4ove73Np7xn6zhxZ2SE/OYVEi0XjOPXiZxzsMo1agzpgWUZbf/GdlUT+cVJPQZZPUki0WTiWQyM/40/NsVXyGqO+5tja0XEK10scWx2/eYMzM9awq9ixBXBt5W7+7DKdPb6zqdG6Ic4++rs2BGiiad8/8n6HTbNWM7yc9v3ywYt8XkYdBDAxN8V7nB93Am9XZqhVn6yqnJ8qorKeabQGSnUaJUkyLG9dVWTq/hx5ETHkRcZCXj6pe45g/nx7rTJZZy8hZ+cAkB18A0MHu8J1kokxkpEhkrERkqEBBYnJOo2/uIY9WnHpd/XJNCowBFMrJRY1rLXKWNSwxsTCjKjAEAAu/X6M53xbPfHfaNq/Pdf+PPX4ghXEzrMeaeFxpEfEo8or4M6O09TpqR1vHd+WhGxV5x2++yxOnYouxOv0bEV6RDwpN6O0tlEYGmBgaoxkoMDQzJjMWP3tt8fx8mhONStLfYfxt1i3rE/mnViy7t5HzisgevspHHp5aZXJupdA2rUI5BJTPTLCYsm8EwtATlwyuQmpGFe30lnsj2LpWZ/sO7FkR9xHzssnfvsJbHu21iqTcy+ezOt3Sz8sL8soTIxQGBuiMDFEMjIkNz5Fd8E/RlPfVpzfpj6OIgJDMLVUYmlvXapcRGAIaWXEnRyZQMyNCGQ9v1muQY9WXPld3WGPDgzFxMoc8xLtoLmmHYwODAXgyu/HaehbVD+7fziKw4s2l5tLkwHtubZDd+1g9RLt4N0dp6ldoh2s1bMlYZp2MGLXWRw07aBT1+akXL9HyrUIAHKT09XHnCSBJGFoZgKAkYUZWXpsBy0865MdHkNORBxyXj6JO45j07ONVpmcyLKPLbOGtUg7fQ0KVKiycsi8Hk41H09dhv9IzXy9OL/tKAB3A0MwK+fYulvOsRVy6hp52bmaMrexdrStzHD/FhvP+qTfiSMzQt3WR24/hWOJupl5L4HU6/eq5AtESh5b4TtOU+sxx5bjY46tgqxc4k5eB0CVV0DS5XCUTvrdZ+6+rTmrqYPhgbcxszTHqow6GB54m9Ryzkt9p7zAgZU7yM/JrcRI/wVUcuX8lEGSpF6SJN2UJClEkqQZ5ZT5nyRJ1yRJuipJ0i9Pm15ldRoXA/UkSQqSJOmcJEnHJEnaCVwrse5zTXkrSZJ2a5JfKUmSAkCSpHRJkr7UJHtQkiR7zfK3Nf8IlyRJ2lxJOWBYozr5sfGFv+fHJWDkUL3c8tWG9CTj2HkAsoOuk3kmGLejv+B29Bcyjl8gN+xeZYX6WJaOtqRGJxb+nhqbhKWDjXYZBxtSY5OKysQkYVnsBNR6jC+v71tEv89fw7SM0Z0m/dpxRYcXS0pHGzKii+LNiElC6WhTbhm5QEVuaiYmNhYYKk1o9mZfgpZu0yqfGZvMlZV7GHb2K14IXE5uaibRR69UfjL/IaaONmQVq4vZ0YmYlthvT6KaZz0URoZkhsdVZHj/mImTLTnRRaNPuTGJmDzhxUDahVs8OHmVtsGraRu8muTDQWTdjnr8hjpSzcGWlGL77EFsEtWq0MXpk7J0tNFqB9OeuB1Ul2nQoyVpscncvx5R5ucbmhrj1tWdm3vPVUL0ZTNztCGzWDuYGZOEmdOj28G81ExMbC2wdHMEWcbnl+n03j+fJhP7qMvkF3B2xhr6HFrM4MDlVGtYk9BNATrLqSRjx+rkFttvuTGJGD/hsZV5LRxrH08UZsYY2lpi1aEZJs52j99QR6xKHFspT3Fstf2fD9cDgioosqdn5lSirY9JwkzPHaS/Q1nGsaUs49jKLOPYsnJzRJZlnv9lOn7Fjq3ijKyU1OzhSezxq5WbyGNYO9iQrFUHE//WzYdaTV2xcarO1cOBlRGeUAZJkgyAb4HeQBNghCRJTUqUaQDMBDrKstwUePdp/25ldRpnAKGyLHsA04CWwDuyLDcsvk6W5Wma8m2ASagTrwcM1iw3B85rkj0CzC32+Z6yLLsDEyoph7/Fst/zmDRrQPKPvwFgVMcJ43p1CPMZRZj3SJTtPDBrVXWmm/1dFzb8xfIu7/F971mk30+hxwfaU2CcPeqRn5VL/K1IPUX493hMGcy11fvIz8zRWm5cTUmdni35rd17bGk5CSOlCW6DO+opSqE8JjWs8Vg+keB3Vz4T34tk6uKIskFNzniO54zHeKw7NcOqbWN9hyUUY2hqTIc3+3Ns6W/llmnQ3ZPI87d0NjX1aSkMDbBv05CTb32H/8CPqdXLC4dOTZEMDWg4pjt7fGezzfMtkq9H0HRSf32H+488OBJM8sELNN25iPrfTSb9wi3kgqo3qvW0Wg3sRG13Nw5//+fjCwuVTjI0oEabhpx46zv2D/yY2r28CkchQT19tdN3b3Lzx/2kR8Q/4pOqNkmSGPLBaLYtWP/4wv8BskpVKT9laAOEyLIcJstyLrAZGFCizGvAt7IsJwPIsnz/afPT1YtwzsqyXPqpWu31YQCSJG0COgG/ASrg4RPCG4CHw0KXgI2SJG0HtldGwAD59xMxdLQv/N3QwY68uMRS5ZTtPbEdP5zIMdOQ89QvTLHo3pHs4BvImdkAZBw7h6lHY7Iu6O6OkteYHngO9wEg+lIYVs5Fo6RWjrakxWlPN0qLS8aq2N0lKydb0jR33DMSUguXX9x0mOE/TdXatmm/9lzZqdtnEjJjkzF3LorX3Mm21FTSh2UyY5KQDBQYWynJSU7H3rM+Ln3a4DV7OMZWSvW0kZw8shIekBYRT06S+oU/d/eep4ZXA8K2Va0XJ/ybZccmY1asLpo6Vyf7b0x9M7Qwo/XG6dxctIWUCyGVEeI/khOTpDWCYexUnZyYpEdsUaS6XxtSL9xGpWkvkg8FYuXVkNQz1ysl1ifRYXQP2o54HoB7wWFYF9tn1RxteRD7ZLnpW8sx3fHQtIMxJdpByyduB5OxqVuDarXteXnvwsLl43bP5+cBc8mIfwBA437tubZTd7MtALJik1EWaweVTrZkxZTdDmZp2kEjKyU5SelkxiRx//TNwhfcRB8Kxra5C3lpWQCk31VfY0TsPEOTt/rpKKPScmMTMS6234ydqpP7hMcWQPTXvxP99e8A1P/2XbLDois8xr+j42hf2hUeW6Fax5b1Pzi2GnRsRve3BvHtCx9RkJtfobE+jayYEm29k7oO/ltklnFsZZZxbCmLXWMUP7biyji2Ho4qtv38FdLuxHLjh/26S6iYLqN96TCiGwB3g0Ox0aqD1Ul5wjpoYmGKU8PavLP5QwCs7K0Z/8M0Vr36uXgZTuWqCRSfvhgJtC1RpiGAJEknAANgnizL+57mj+rqexofd9u15FBBeUMHD5f3QT0s2xI4p3lWssJlX76JUV1nDGs6gJEhVn5dyTis/aZGk8b1qDFvEtFvzqMg6UHh8ryY+5i1bg4GCjA0QOnVnNxQ3U5PPb/uQOFLam76n8d9SGcAanrWJzsti/T7KVrl0++nkJOeRU3P+gC4D+nMrQMXALSef2zU04v4m8VGFCWJJn3bclXHF0sJQWFYuTpiUdsehZEBrgPacc//olaZe/4XqT9MnbdLnzbEnLgGwN7Bn/Bbu/f4rd17XPthP5e+2cmNtQfIiErEvmV9DEyNAXDq1JSUKjRN8FnwIDAUczdHzOrYIxkZ4DywPXH7LzzRtpKRAa3WTiZy67HCN6pWFWlBIZi6OWFSpwaSkSH2AzuS5P9k0xRzohKo1r4JGCiQDA2o1r4pmXoetT+5/gBf+s3kS7+ZXPU/j9dg9XFUx7M+2WmZZT5fVRVdXPdX4UtqbvlfoNmQTgA4e9YjJy2TjBLtYIamHXT2rAdAsyGduH3gAvE3I/m61Zus6PQeKzq9R2pMEmv6zCnsMJpYmlGnXSNul2iDKltiUBiWro6Ya9rBugPaEVkihij/i7hp2sE6fdsQd1zdDsYEXMK6cW0MzNTPcNdo34gHt6LIik2iWsOamNiqn5d27NKc1Nv662ilB4Vg6uqESW31sVV9QCeSn/DYQqHA0MYCAGXjuigbu5ByJKjygn0CJ9b7s8RvBkv8ZnDZ/zxeg7sAUPcfHFs1m7owbOFr/Pjq56Qnpj5+Ax1KCQrFws0RpaatrzWwPbH+T9bWVwUljy2XMo6tyEccWzZlHFsALaYPxdjSjPMfbtBtQsUcXe/PYr/3Wez3Ppf8z9FGUwddPBuQlZZZ7rOLJWWnZTGj5WvM7TSJuZ0mER54+7/dYdThM41PwBBoAHgDI4DVkiRZP016lTXSmAaU93aOsta1kSTJFbgLvAB8r1muAIaiHnZ9ETiued6xtizLhyVJOg4MByyAlArNAKBARfz876j1wwJQKEjd5k9uyF2qTxpN9pXbZBw+jd20V1EozXD6Uv1GqfyYeKLfnEf6/uMo23pQd4d6+lzm8QtkBJyp8BCfVMihIOr7ePDm0aXka75y46HX9ixktd8sAPbOWUP/JeMxNDUmNCC48C2p3WaOwLFJXWRZ5kFkPLtnFX0dQN22jUiNTiLlnm6nWMgFKk7P+Zkev0xXvw57yxFSbkXhMXUIicF3uHfgIrc3H6Hz1xMYfHwJOSnpHJm4/JGfmRAYyt3dZ+m/fz6q/AKSrt7l1sbDOsro75s2dzHnAi+RkpJKt4GjmPjKaIb066nvsB5JLlBxZeZa2mxWv502clMA6TcjaTh9KCnBd7i//wLVPNxotWYyRtbmOPi2pOG0YRztOg3n/u2xbdcIIxsLar2gPsldenslqVd188beRypQETrrB5ptmoNkoCBu0yEyb0ZSd/oLpAWFkuR/HguPejT5aTqG1ubY9vCizrQXuNj1PRL+PI11x2a0OrwUkEk6FETSgapzcXX9cCCNfDyYcWQZeVk5bJlW1H68t2cRX/rNBKDPjBfxHNABIzNj5pxaztkth/Ff9ju13d0Yu2oyymrmNOnWEt/3hvGF77Ty/lylCT0URD2fFkw4uoS8rFx2T/2+cN3Lexbwk5+6Hd8/Zy19l7yOoakxYQHBhBZ7w3J5Gvb04s7Ry+Rl5Ty2bEWSC1Scn/0zz/8yHclAQejmIzy4FYX7NHU7GOV/kZBNR+jw9QT6n1C3gyfeULeDuQ8yub5qL732fAyyTPShYKIPBgFweek2evwxB1VeARlRCZx69/tHRFHJClSEz/6BRr98iGSg4P7mg2TduketacPJCA4l2f8c5i3q0/DH9zG0Nse6R2tqTX2BSz7vIhkZ0OSPBeqPScsiZNIyqELTU68fDqSxjwezjnxFXlYOm6YVfXXSlD2LWeKnfr9F3xkv0nJAR4zMjPnw1Lec2XKY/ct+o9/MkZgoTRj73bsAJEcl8NNrX+gjlVLkAhWXZq2lw6YZ6q/c2BRA2s0oGk0fSkpQGLH+F7H2cKPtT+9hZG2OY4+WNJo2lENdp+s7dEAd/7nZP9OtjGMrKfgOkZpjq+PXExigObaOlzi2emuOrahDwUQdDELpZEvzdwfy4HYUfv7qN5/fWnOAkF8C9Jbn1cOBNPXxZO6Rr8jLymXDtKKvh5ux51MW+70PwIAZI/HS1MFPTn3HqS2H2LOs/On6QqWKAmoX+72WZllxkcAZWZbzgDuSJN1C3Yn8xw/dS5X1NjvNW3rcgSwgTpblvmWs2wvsBj5G3ZmsDxwGJsqyrJIkKR11B9IXuI+6Q5miKVMNkIANsiwvLiME+VbjXpWSmz41vL6PT+pWoVdqV5AP7m5kbc1R+g6jQr0UtYG8hGfvbpuRnRu7HUboO4wK1yduE8cch+o7jArVOfY3pro8e/vqi/BNLKr7bLUXADPvbmCj87OX18joDZx2Hvz4gv8i7aK3Mdll+OML/sssDd/MdscX9R1GhRsY+wsbnrFja1T0Bt5yeUHfYVS45eFbJH3H8E9lzB9VKZ0q8zkbtP5NNDMsbwHdUHcWzwEvyrJ8tViZXsAIWZbHSpJkBwQCHrIsl37O7glV2jONsiyX2+qUsa7LI8pOLmNxp38alyAIgiAIgiAIQoX651NJ/xZZlvMlSXoL2I/6ecWfZFm+KknSx6hfILpTs85XkqRrQAEw7Wk6jKC7F+EIgiAIgiAIgiAIT0mW5T3AnhLLPiz2/zIwWfNTIap0p1GWZQt9xyAIgiAIgiAIgvBIZX89xjNDV29PFQRBEARBEARBEP6FqvRIoyAIgiAIgiAIQpWno2ca9UV0GgVBEARBEARBEJ6GLKanCoIgCIIgCIIgCP9RYqRREARBEARBEAThaTzj01PFSKMgCIIgCIIgCIJQLjHSKAiCIAiCIAiC8BTkZ/wrN0SnURAEQRAEQRAE4Wk849NTJVl+ZhN8ZhMTBEEQBEEQhGeQpO8A/qn09wdXSt/D4tNtVeLf5JkeaTxfa6C+Q6hwXpHbGV13sL7DqHDr727jQ5eR+g6jQn0cvpHdDiP0HUaF6xO3ibyEMH2HUeGM7Nz4rvYofYdRoSbe28BEl//pO4wK9134r3xV59naVwDvRGxgo/Ozl9fI6A2sq/ls5TUmagPfPGPtBcCkexvY5PxsnYsBRkRvZJvji/oOo0INjv2F2S7PVk4AC8J/0XcI/9wzPtIoXoQjCIIgCIIgCIIglOuZHmkUBEEQBEEQBEGodPKz/SIcMdIoCIIgCIIgCIIglEuMNAqCIAiCIAiCIDyNZ/yZRtFpFARBEARBEARBeAryM95pFNNTBUEQBEEQBEEQhHKJkUZBEARBEARBEISnIUYaBUEQBEEQBEEQhP8qMdIoCIIgCIIgCILwNFTP9lduiE7jY1h5e1Lno1fBQEHCpgPEfrtNa73Da/2xG9EDuaCA/MRUwqd8Q25UPAANNnyIuedzpJ+7RshLC/QR/hMbPe8VWvi0JCcrh++nLufulTCt9camxkxaMY0adRxQqVQE/nWeXz/doKdotfnNHUMDnxbkZeXyx9RVxFwNL1XGqZkLg7+YgKGpEbcPB7Pno3UADFs+CTs3JwBMrZRkp2aywm8W7gM60HF838LtHRrVZmXfOcReu6uTnIqz92lBk/ljkAwU3Nt4mNBvdmqtt23XiCafjMGySR0Cx39N7K6zAFg1rUuzz17G0EKJrFIRsuwPYnac1nn8/8SchUs5euIstjbWbN+wUt/hPFJtb3c6zRuNwkDBtU0BBH73p9Z6hbEh3ZdNwL65K9nJafhPXE5aZAIm1hb0WvU2NVq4cWPrUY59sK5wm77rp6OsUQ2FgQExZ29ydM5avT9gP2zuOJr6eJKXlcO6qd9x7+qdUmX6Tx1O28FdMKtmweSmYwqXtxvalUEzR5MSlwTAkZ/3cXLLIZ3F3vWj0bj4eJCflYP/lO+JvxJeqkyN5i70WDIeQ1Njwg8HcWTuegBMqpnj991bWNWyJzUynj0TvyHnQSYm1ZR0//x1rOvWID8nj7+mribxViQAHi/3pOkIbyRJ4sqmwwT9uL9S83Pydsfrk9FICgUhmwK4trx0Hezw9QRsm7uSk5zG8QnLyYhMAMC6cW3afPoyRpZmoJLZ6/chCkMDemz/oHB7pZMt4b+f4MJc3bb5zt7utP64KK8r35bOq9NXRXkdfUOdl3ktOwYEfEZqWAwA8RdDODNjjda2PmsmY1HHnj+7zdRZPsV1+Wg0dZ9X18m/JpddJ+2bu9B9qbpO3j0UxNGHddLanF7fvoVVbXtS78WzT1MnH6rRwo1h2+ey783lhO45p5N8nLzdaampg6GbArheRh1s9/Ub2DZ3ISc5nZMTviEjMoG6gzrQeGLRuda6cW329ZxDytW7uL8/DJdhnTGuZs5vDV7RSR4lOfi44/6J+vwbvvEwt8rIy+ubN7B2dyU3OZ2z478m814CkpEBLT9/FesWrsgqmUsfrCPh5HUAag5oR6N3BiIZKIg5cJGr8zfrIzX6zB3Dcz4e5GXl8vvUlUSXce3k3MyVIV+Mx8jUmJuHg9ituXZyalKX/gtexsjECFW+ip0frCEyOBTXdo0Z9f0UkiPvA3B13zkOf/2HLtPSLzE9VbckSVorSdLQv1HeRZKkK5USjEJBnfnjuTX6Y676TMJ2QGdMG9TSKpJ5NYzrflO41uNdknefpNbssYXrYlds5847yyoltIrUwqclDq5OTO36Jj/NXMm4+a+XWW7P9zt4v9vbzPGbSkOvRrh7e+o40tIaeLeguqsjX3lPYeesH+m3YFyZ5frNf5kdM3/gK+8pVHd1pIF3CwC2vvUNK/xmscJvFtf2nuP6PvUJ9tKOk4XLf39vBSn34vXSYUQh0XTxOM6++ClHOk/FeVAHLBrW1CqSFZVA8Dsrid52Qmt5QVYOQW+t4GjXaZwdvpgmn4zB0Eqpy+j/sYF+PVi5dL6+w3gsSSHRZf5Ydo/5jE3PT6fBgHbYNHDWKtN4uDc5KRls7DyF4B/20X7WcAAKcvI488VvnJz/S6nP3f/GN/zaczabu8/AtLol9fq21Uk+5Wnq7UkNV0fmeb/NxlnfM3zBq2WWu3TwAp8OmFXmugu7TrLIbzqL/KbrtMPo4tMCaxdHfu4yhYMzfuT5BS+VWc5nwTgOvv8DP3eZgrWLI3W93QHwerMf905c4+euU7l34hpeE/sB0PrNASRcu8vGnrPwf28lXT8aDUD1hrVoOsKbLf3msrHnLFy7eVKtrkOl5ScpJFovHMvhkZ+xy3s6LgPaYVWiDtYb4U1uSgY7O07hxup9eM5R10HJQEGHb97g7Iw17PaZwYGhC5Dz8snPyGZvj9mFPxmRCdzTUeejeF5tF4zl4KjP2OkzHZeB7ahWIq8GI7zJeZDB9k5TuL56H61mDy9cl3Y3jl2+s9nlO7tUh7FOby/yM7J1kkdZ6vq0wNrVkfWdp3Do/R/xXvhSmeV8Fo7j0PQfWN95CtauRXWy1cR+RJ64xvouU4k8cY1WmjoJ6n+3DjNfIOLoZV2kUvg3Wy18iYCRn7HHezp1B7THqoH2ecpNUwd3dZzCzdV7aTFnBAB3/zjJvh6z2NdjFqcmrSA9Ip6Uq+pzbdSBQPz9PtRZHqUoJFosGseJFz/jQJdp1BrUAcsS51+XF9V5+befTMiqvTTT5OU66nkADvrM4MQLi2g+dxRIEsY2FjT/4EWODVvAX12nY1rDGvtOTXWeWkNvD+xcHVnqPZnts36g/4KXyyw3YP7LbJ/5A0u9J2Pn6khDzbVTzxkjOPzVNpb7zeKvpb/Rc+aIwm3Cz91gud8slvvN+m91GP8DqlynsSox92hATngMuRFxyHn5JO04jrWv9sVb2skrqLJzAUi/eBNjp+pF605cQpWRpdOY/4mWPdpw/PcAAEIDb6G0MqdaDRutMrnZuVw/pe6bF+TlE34lDFvH6iU/Suca+bYiaNsxACIDQzC1VGJhb61VxsLeGhNLMyIDQwAI2naMRr6tSn1Wsz5tubTzZKnl7v3bc/nPUxUf/BOwblmfzDuxZN29j5xXQPT2Uzj08tIqk3UvgbRrEaVGojLCYsm8EwtATlwyuQmpGFe30lnsT8PLoznVrCz1HcZj1fCox4PwOFIj4lHlFRCy8zSuJeqWq29LbvymrqOhu89Ss6P6AiE/K4fYc7fIz8kr9bl56ep2Q2FogIGRIcj6vXvp7uvFmW1HAQgPvI3S0hyrEsfZw3Wp8Sm6De4x3Hxbcf334wDEBoZiYmWOsoa1VhllDWuMLcyIDQwF4Prvx6nXU32c1evRimua/Xftt2PU81Uvt21Qk3snrwGQHBqDZS07lHZW2DRwJi4wlPzsXOQCFVGnb1C/t/YxW5Gqe9YjLTyOdE0dvLvjNLV7atfBWj1bErZVnUPErrM4aC5Snbo2J+X6PVKuRQCQm5xeqh2xdHPE1M6K+2duVloOZSmZV3gZedX2bUmoJq+7u8/i+AQX34ZKE5q83ptLX22vjLCfSPE6GfeYOhlXrE66aeqkm28rrmvq5PXfjhUuB3Af50vo3nNkJabqIBM1W896pIfHkaHZVxE7TlOrVB1sxZ2t6jbk3q6y91Xdge2J2FF0rk28GEL2/ZRKjf1RbD3rk3EnjswI9fk3cvspnErk5dTTi4hf1fsiatcZ7Ds1A8CyYU3uH78KQE5CKnmpGdh4uGFetwbpd2LJTUwD4P7RK9Ts20aHWak19m1FoOba6Z7m2smyRJtuqbl2uqe5dgrcdozGvkV1zcTCDABTKzPS4pJ1E3hVp5Ir56eK0HunUZKkMZIkXZIkKViSpPWaxV0kSTopSVLYw1FHSe1zSZKuSJJ0WZKkFyo7NmMnW3JjEgp/z41NxNjJttzy9iO68+DwxcoOq8LZONqSFF2UZ1JsIrYO5eeptFLi2d2Lqyd0dyezPFYOtjyITiz8PTU2CStH7Q6vlaMNqTFJRWVikrAqkV/dNo1IT3hAUnhcqb/RrG87Lu/UT6fR1NGGrGL5ZUcnYloivydRzbMeCiNDMsvIT/jnzB1tSI8uqlvpMUmYl9g/xcvIBSpy0zIxtbF47Gf33TCdlwK/Izcjm9DdZys28L/J2sGW5GJtRHJsItaO5bcRZfHs3ZbZez/n1e8mY+OkuxtOFo42pMcUHUPpsUlYlNhHFo42pMcmlVlGaWdFpubCNfN+Cko79Y2XhOsR1NfcwHFo4YZVTTssnGxJvBmJc5vnMLW2wNDUGBefFlhUYr5mjjZkFquDmTFJmDlp56d0tCGjWB3MS83ExNYCSzdHkGV8fplO7/3zaTKxT6nPrzugHXd36n5ae/GYQZ2XssR+K557YV6aY8uijj1998/H97fZ1GjzXOE2HtOHcnXVXvKzcnWQRdnUbUKxOhlTTp0sdt7KKNa2lFcnzR1tqNfLi8vrDlZyBtqUjrZkFsunrDpYcl/lpmZibKvdDtbp34672/Vzri2LqZP2+TcrJgmzEteAxcvIBSry0jIxtrXkwdUInHq2QjJQoKxjj7W7K2bOtqTficOynhPK2nZIBgqce3lh5qz7G/BWDjY8KHZ8lXft9KBYHXwQk4SVg7rM7o/W0Wvmi0w7+Q29Z43E/7MtheXqtGzAW3sXMXbtdGqUGHEW/t30+kyjJElNgTlAB1mWEyRJsgWWAk5AJ6ARsBP4DRgMeAAtADvgnCRJR/URd1lsB3dF6V6fm0Nn6zuUSqUwUDDxm8n4r9lD/L1npwPSvH/7MjuGtTzqkZeVy33Ns0r/RiY1rPFYPpHgt1fofcRKeHK7Rn2GgYkR3b9+g5odmxJ5rHJm4evC5b8ucH7nCfJz8+n0YnfGLHmTr178WN9h/SMPj6Dz3/1J13mjeXHvAhJu3iP+6l1UBSqSQ6K5sGIXAze+T35mDvHX7iJX0ZcjKAwNsG/TkH1+H5KflUu3LTNJvBROnGaEBMBlQHtOTlqhxyj/vqz7KWxr8y45yenYNnfB56f32OkzA4u69ljWdeD8vI2Y17LTd5gV5mGz3nnuKE4s3PyvbOere9ajICuXBzf/vefa4u5uCsCygTM+++eTGZlA0vnbyAUyeQ8yCHx/DW1WvY2skkk6fwvzSpy+XlnajOrOnk/Wc3XfOZr1acugT19nzaiFRF8J5/OOb5ObmUNDbw9Gfj+FL30m6ztcnZH/hcfe36HvF+E8D2yVZTkBQJblJEmSALbLsqwCrkmS9PBo6gRskmW5AIiTJOkI0Bq4VFnB5cYkYexUdGIxdqxObrG7Lg9ZdnLHadJQbg6dg5ybX1nhVKjuY3rhPbwHAGGXQrB1LsrT1rE6SXGl8wR4efEbxN2JYf9Pu3QSZ1najO5BqxE+AEQFh1Gt2F06K0dbUmO1p0mkxiZjVezuoJWTLanF8lMYKGjSszUr+80p9bea9WvP5TKmrOpKdmyy1l1IU+fqZMc++TQQQwszWm+czs1FW0i5EFIZIf6nZcQmY+FcVLcsnGzJKLF/HpbJiE1CMlBgbKkkOzn9iT6/ICePcP+LuPq21HmnscvonnQc0Q2Au8Gh2DjbAeopijaO1UmJLbuNKEtGSlG+JzYfZNCMURUaa0nuY7rTTNNGxF0K0xrps3C0Jb3EPkqPTcai2Mhp8TKZCakoa1irR3RqWJOVoJ72l5uexYGp3xduM+7El6RGqF+CdnXLEa5uOQJAh+n/0xoxqmhZsckoi9VBpZMtWTHa+WXGJmPubEtWjLoOGlkpyUlKJzMmifunb5KTpN4/0YeCsW3uUthptG5SB8lAQdLl8EqLvzwPY35I6WRLZon99jD3zOJ5aY6tnFz1f5Muh5MWfh8rN0eqe7hR3d2Vwae/RDI0wLS6Fb5bZ+M/rPJfVNd8bHeaaurk/eAwLIq16xZO5dTJYuct82JtS6k6qZmKWsPdlV7fvgWAqa0ldX1aIBeoCNt/oVJzy4xNQlksn7Lq4MN99bAOGlspyU0qahfqDGjP3e36O9eWJTtG+/xr5qSOv6wyhceWpZLcJPXU08vFXhzV9c95pGtezBR74CKxB9Sz0lxGPY9coJubSm1H96C1pg5GBodRrdjxVd61U7VidbCaky2pmmmoLYd0KXwpzpXdZxi0+DUActKLHsm6FRBE//njUNpYkpmcVjlJVTVVaCppZdD79NRy5BT7f0lfQWQE38bU1Qnj2jWQjAyxHdCJlAPa08TMmrpSd/FEQl5eSH7iAz1F+vf9tW4fc/ymMMdvChf8z9JpiDcA9TwbkpmWyYP7pTsmQ6eOQGmpZMNHP+k4Wm1n1x8ofEnNDf/zeAzuDEAtz/pkp2WRXuKZqvT4FHLSsqjlWR8Aj8GdueFfdBJ169SMhLBoUktcBEuSRLM+bfX2PCPAg8BQzN0cMatjj2RkgPPA9sQ94QWAZGRAq7WTidx6rPCNqkLFuh8cRjUXRyxr26MwMqB+/3bcOaA9RT38wEUaDVXX0Xp92hB14tojP9NQaVL4fJNkoKBuNw+SQ2IqJf5HObp+f+GLay75n6Xt4C4AuHg2ICst8289u1j8+Uf3Hl7EhlbuaMKldX/xS+/Z/NJ7NqH7L9B4SCcAHD3rkZOWWTi176HM+ynkpmfh6FkPgMZDOhGmaSPCDlykiWb/NRnamdAD6uXGVkoURgYANB3hTdTZG+RqLpjMNM8OWzpXp14vL27sqLyL4cSgMCxdHTHX1MG6A9oR6a9dB6P8L+I2TJ1Dnb5tiDuuroMxAZewblwbAzNjJAMFNdo34sGtqMLtXAa2J3yHftq/h3lZaPJyGdCOeyXyuud/kXqavOr2aUOs5tgysbVEUqgvHSzq2GPl6kBaxH1urTvIb60msa3de+wb+DGpYTE66TACXP75Lzb3ms3mXrMJK1YnHTzrkfuIOulQRp28c+AijTV1svHQzoXL13WczM8d3uPnDu8RuucsAbPXVnqHESCpRB2sM6Adkf7afzfK/yKuw9RtSO2+bbRGs5Ek6vRry1091bXyJAeFYuHmiFJz/q01sD0xJfKK8b9Anf+p90XNvm2JP6HOy8DMGAOlCQA1ujRDzi8gTXNsmWimExtVM8ftpe6Ebzysk3zOrD9Q+IKa6/7n8dRcO9X2rE9OWhZpJdr0NM21U23NtZPn4M5c1+Sfej8Z13aNAXDr0JREzaMvFvbVCrev1aIekiT9dzqM/wH6Hmk8BPwhSdJSWZYTNdNTy3MMGC9J0s+ALdAFmAaYVlp0BSoiPlhNw41zQWFA4pa/yL51D+epI8gIDuHBgXPUnvMSBuam1Fs5HYDcqHhCXl4IwHO/L8S0fk0MzE1xP/cD4VOXk3okqNLC/aeCD13Aw6clXxz9jtysHFZPXV64bv6eJczxm4KNY3UGTBpGVEgkn+z+AoAD6/ZyZPNf+gobgFuHg2jg48G7R5aqv3Jj2qrCdW/sWcgKP/WbHHd9sIZBmtdG3w4I5nZAcGG55v3ac6mMqal12zbiQUwSyffiKz+RcsgFKq7MXEubzTORDBREbgog/WYkDacPJSX4Dvf3X6Cahxut1kzGyNocB9+WNJw2jKNdp+Hcvz227RphZGNBrRfUJ+tLb68k9aoe3gL7N02bu5hzgZdISUml28BRTHxlNEP69dR3WKXIBSqOffAz/TZMRzJQcGPLEZJvRdF6yhDiL90h/MBFrm8+QrdlExh5bAnZKekceLPo+Bp18kuMLc0wMDLEtacXf45cTHZyOn4/TcbA2BAUElEnr3N1g26fUSrpyuFAmvq05KMjX5Oblcv6ad8Vrpu55zMW+anbv0EzRuI1oBPGZsYsOLWCk1sOsXvZVnzG9aZ5dy9UBQVkpqSzbup35f2pChd+KAgXnxaMPbaE/KxcrdHBF/cu4Jfe6kcKDs9ZS48lr6u/3uBwMOGH1W3E+e/+xG/FJJq+0JXUqAT2vPENALb1nfFdOh5kSLwVyV/TVxd+bp9V72BqY4EqL5+AD34mNzWTyiIXqDg/+2ee/0VdB0M3H+HBrSjcpw0hMfgOUf4XCdl0hA5fT6D/iSXkpKRz4g11Hcx9kMn1VXvptedjkGWiDwUTfTCo8LPr9GtLwOjPKy32x+V1ds7PdP9luvorN7ao82oxVZ1X5IGL3N58hE5fT2Dg8SXkpqRzdKI6L4d2jfCYOgRVfgGySub0zDXkpmToJY+yhB8Kou7zLRhzfAl5WbkcnFJUJ4fvW8DmXuo6GTB7Ld2XFtXJu5o6eeHbP+m1YhJNhnclLTKBvRO/0UseD6nr4Fq8f3kfyUBB2OYjpN6Kovm0ISRp6mDopgDaf/0GfU8sITclgxNvFMVco10jMqOTyIjQPtd6zBlB3YEdMDQzZsD5bwjddJgrS7aV/POVmlfQrLV03DQDyUDB3U0BpN2MovH0oaQEhRHj/3/27js8iuIN4Ph37tIbKQQuoSWh9wRCbwklQOhFBaSI+pOiCAIiVVCqCDZQwUaRqoiIdJDea+gdAqR30uvt7487QhqCktyFOJ/nyQPZnb173+ze7M7O7NxZAtfsx3vxSPyOfUZ6XBInh+nyMi9tR4u1E1G0CqlhsZzKMcS73szBlKpdEYBrC38n8U6YwXJ65Pq+AKr5ejL2wOdkpKSxMce10zvb5rBYf+20edpP9FkwHBP9tdON/QEAbJr4A12mD0ZloiIzLYNNk34AoE7nJjQe2B5tVhYZqemsH2XcY9PgSnhPozD2+FshxBB0jb8s4Jx+8RZFUTbo1ycqimIjdONW5wOd0T1WMktRlPVCCDd9+Tp5Xlo5Xb6nIVIwKO+gTQyq1NvYYRS6n+9t5EO3V40dRqH6OHA1W8v2f3rBF0yX8LVkRN15esEXjGlpD76pULTDJg1t5INVjHR72dhhFLpvAn/hy4ola18BjL6/itWuJS+vV0NWsbJcycprcPAqFpWw+gJg1INVrHUtWedigP4hq9moGWDsMApV77A1THErWTkBzA5cY7QRhs8r/o0ORdKosvtxd7H4mxi7pxFFUVYAK/5mvY3+XwVd4/L9POsDgbwNRkmSJEmSJEmSJIPI+5VFJU1xfaZRkiRJkiRJkiRJKgaM3tMoSZIkSZIkSZL0QivhPY2y0ShJkiRJkiRJkvQ8iudX8hYaOTxVkiRJkiRJkiRJeiLZ0yhJkiRJkiRJkvQc5EQ4kiRJkiRJkiRJ0n+W7GmUJEmSJEmSJEl6HiW8p1E2GiVJkiRJkiRJkp6HnAhHkiRJkiRJkiRJ+q+SPY2SJEmSJEmSJEnPoaRPhCMUpcQmWGITkyRJkiRJkqQSSBg7gH8r9iWfIml7OPy6v1j8TUp0T+PWsv2NHUKh6xK+luXlBho7jEL3WvAqNmoGGDuMQtU7bA2HNH2NHUahaxW2gW8qlLxjcOSDVWRE3TF2GIXKtLQHU91K1ucKYFbgGr6sWPKOwdH3V7G0fMnLa1jQKhaXsDrjnQermFnpVWOHUeim3VvNrrL9jB1GofMLX8f8SiXrGJxwbxULSmA9OP7+KmOH8O+V8GcaS3SjUZIkSZIkSZIkqaiV9OGpciIcSZIkSZIkSZIk6YlkT6MkSZIkSZIkSdLzKOHDU2VPoyRJkiRJkiRJkvREsqdRkiRJkiRJkiTpOSiyp1GSJEmSJEmSJEkqDoQQnYQQ14UQt4QQE/+mXB8hhCKE8H7e95Q9jZIkSZIkSZIkSc/DQD2NQgg18DXQAQgCTgkhNiuKciVPOVtgNHCiMN5X9jRKkiRJkiRJkiQ9B0VbND8FaAzcUhTljqIo6cA6oEcB5WYCnwCphZGfbDRKkiRJkiRJkiS9GMoBD3L8HqRflk0I0QCooCjK1sJ6Uzk89SmcfetTa9ZghFrFg9X7uL1oc671jk1rUGvmYGxrVeTcsK8I23ISALvalagz/3VMbKxQtFpuffE7oX8cN0YKAJTzqUfjjwchVCpurt3Pxa//zLVeZWZCqy+H41TXnbTYBA6MWExiUFT2emtXJ3ru/4SAhRu5vHQbAH2Pf05GYiqKVos2M4st/h8aNCeAsr71qDdTt38CV+/jxuL8eXkvGoF9PXfSYxM5Oewrkh9EIUzVNPj0Tezru6NoFS5MW0nU0au5tm22YhxWlcrwl88HhkwpHwdfTzxmDkWoVYSt/ougxZtyrbdrWpPKHw/FulYlrg3/nKgtj48zt2kDcWzfECEEsQcvcGfqTwaO/rEKPvVoOWMQKrWKK2v3c+6b/Puq/RfDca7rTmpsArtGLiYhKApzexs6LX2XMvU9uPbrQQ5NW5m9TdefJ2BVphQqtZrQk9c5OHV5sf5y3alzPuPgkZM4OtizadUSY4fzVF2mD6aarycZKen8Nn4JoZcD85VxreNO7wXDMLUw48a+ALZ+pNs/mlqV6DH7dUzMTdFmatk8bRnB529nb1eungdvbfyIX0Yt4vL2k0WaR5uPBuHm60lmShq7xn1H5KX8eZSp60aHhcMwsTAjcF8AB6b/DIB5KWv8v3kHu/LOxAdFsm3kItIeJmNma0nHL0dg6+qEykTN2aXbuPLrQQBsXZ1oN/9NbF0cUYA/hnxKQo76tLBV8KlH848GIdQqrq3dT0AB9XvbL4ZTup7us7VHX7+Xa1WHJpNeQWVmgjY9k+Oz1hJyVDe6SWWqpuWsIbg0q4miVTg1/1fubjtVZDk8UtGnHq1m6HK5snY/ZwuoJzrkqCd26usJgIZvd6NmPx+ULC2Hpq/k/oGLAAw++jkZSalos7QoWVn80kV3rur4zTvYe7gAYG5nRVp8Mus7TSnyHPPqOGMwVXzrk5GSzubxSwkr4PjU1HGjx8LhmFiYcmvfeXbOeFwPNnrND+9BHVC0Wm7uDeCvuWsNGH1+Tr71qTFrCEKtImj1XgLzXDc5NK1B9ZlDsKlVkYvDviJ8S+6Rc2obS1ocWkDE9tNcm7zMkKEXqN2MQXj4epKRksb28d8RXsD+KVvHDX99/XFnXwB/zdDVHy3H9aVKhwYoWoXk6Hi2j1tKYkQctXo2p/HwrgghSE9KYdeU5URevW/QvNp+NAh3fb24fdx3RBSUV103OunzursvgL36erHN5P54tPdCm5FJ3L0Idoz/jrT4ZFQmajrOf5MyddxQqVVc3niYk3nqoxKtmEyEI4RQAZ8BrxXm6xbLnkYhxLtCiKtCiNVCiPFGC0QlqD1vKCcHfMKBVuNx7dUcm2q5GvKkBEdxfvQSQjYeybU8KyWNgHe+5WCb9znZbx61Zg7GxM7KkNFnEypBk9lD2D1wPpt8J+DesymlqrrmKlO1vw/pD5PY2HIcV77fQcMp/XKtbzTjVYL3nc/32jtems1mvylGaTCiEtSfO5QjA+azu/X7lO/VHNs8+8dtgA/pcUnsajaWW0u3U2dqfwDcB7YF4C/fiRx5ZS51pw8EIbK3c/VvRGZSofTmPx+Vispz3+TygNmcaf0ezr1aYlWtfK4iacFRXB/9NRG/H8613Na7OnaNanDWdxxnfMZi61mZUs1rGzL6bEIlaD1rCFsHz2dt2wlU7dEUhzzHYM1+PqTFJbG61TjO/7CDZpN1x2BWWgYnFmzg6Kw1+V5354hF/NJxCuvaT8TCyZbKXZsYJJ9/q6d/B5Z8NsvYYTyTaj6eOLlr+NxnLJsm/0D32a8XWK77rNfZNOkHPvcZi5O7hqo+9QHoNLE/e7/cyNf+k/nrsw10mtQ/exuhEnSc2J9bhy4WeR5uvvWxd9OwovU4/pr4I21nv1ZgOd/ZQ/nrgx9Y0Xoc9m4aKvnUA8D77W48OHKFFW3G8+DIFbxHdgOg/uAOxNwMZk2nKfz28mxaTRuAylQNgN/nwzm7dCs/t/uA9d0+JCUqvsjyEypBi1lD2DZoPr/4TqBKj6bY5/ls1ejnQ9rDJNa1HMfF73fQVP/ZSo1JYMfQhWxoP4l97y2l7VfDs7dp8G4PUqLiWd/6fX7x/YDQY7lvqhVVLm1mDeHPwfNZ03YC1QqoJ2rp64lV+nqiuT4Xh6quVO3elDXtPmDzoPm0mf0aQvW4Tv/95dms7zQlu8EIsHPkYtZ3msL6TlO4vf0Ud7YXfaM4ryq+9XF01/B1m3FsnfQj/rOGFljOf/brbJn4A1+3GYeju4bK+s9ZpWa1qNahId91nsSSDh9w7LtC61T4d1SCmvNe5+yAeRxpNQ6XXi2wznfdFM2l0d8Slue66ZEqE18m9vg1Q0T7VB6+9XFw1/B9m3HsnPQjHWa9VmA5v9lD2THxB75vMw4Hdw3u+vrj5NKtLO80mRX+U7j91zmaj+4FQNyDSNa+PItlHSdx9KtNdJxbcP1aVNx96+PgpuHH1uPYNfFHOjyhXmw/eyi7PviBH1uPw8HtcV6Bhy6yvMNEVnScTOzdUJq8rasXq3VpjNrMhBV+k/i5yzTqD2iLXfnShkrrvyQYqJDj9/L6ZY/YAnWA/UKIQKApsPl5J8Mplo1GYCS6hztvGjMI+wZVSL4bRsq9CJSMLEI2HaNsp9x/75QHUSRcuZ+vdyPpThjJd8MASAuPJT0qHjMnO4PFnlNpr8okBIaTeD8SbUYWd/84TsWODXOVqejXgFu/HgIgcOtJXFo+blxU7NiQxPuRxF0Ppjhx9KpC0t1wku/r9k/QpmO45MnLpaM393/R5RW85QTOLesAYFutHBGHLwOQFhVPRnwSDp4eAKitzKkyzJ9rX2wyXDJPYOtVhdS7YaTej0DJyCRy0xEcOzbKVSbtQSTJV++BNs8tLkVBZW6KyswElbkJwtSE9Mg4wwWfQxnPyjwMDCdefwze2nwcd7/c+8rdrwHXNuj21e2tJynXQncMZqakEXbqBplpGfleNyMxBQCViRq1qQkoxbeXEcDbsy6l7GyNHcYzqenXkICNuv0RdO4WFrZW2Djb5ypj42yPua0lQeduARCw8RC1/HR1pAKY21gCYGFnSXx4bPZ2TV/ryOXtJ0mKfljkeXj4NeTqb7obKmHnbmNuZ41Vmdx5WJWxx8zGkrBzup7Qq78dpnJHXR6VOzTkiv64vLLhEJWz81Mws9blZ2ptQWpcEtpMLY5VXVGZqLh/6BIAGclpZKamF1l+ZTwrEx8YTsKjz9Yfx3HL89ly82vADX39fmfrSVz19Xv05Xskh8cBEHs9CLWFGSoz3QCk6q+04dyjkRuKQmpsYpHl8EjZPPXEzc3H8ciTi0eOeuLW1pOU19cTHn4Nubn5ONr0TBIeRPIwMJyynpWf+b2rdG3CjT+OFV4yz6hah4Zc+E1/jjp3Cws7K2zyHJ82Zewxt7EkWP85u/DbIarr/y7eA9tx9JvNZKVnApAcXXQ3KJ5FqTzXTWGbjlImz3VT6oNIEgu4bgKwreeOmXMpovdfMFTIf6tKh4Zc1tcfoeduY2FnjXWe/WOtrz9C9fXH5d8OU1VfT6Trz1EAplbmKPpzVMiZm6TFJ+v+f/YWti6ORZ1KLlX8cudl/ox5VdHXi/cOXULJ0l1zhJy9jY1GH7+iy1OoVZhYmJGVkUl6Qgr/FQZ8pvEUUFUI4S6EMAP6Adld+oqiPFQUpbSiKG6KorgBx4HuiqKcfp78il2jUQixBPAAtgPvAfWFEMeEEDeFEP/Tl3ERQhwUQgQIIS4JIVoVRSwWGgdSQqKzf08NicZC4/CPX6eUV2VUpiYkB4YXZnjPzErjQFJITPbvSaExWOXJI2cZJUtLenwy5g42mFiZU+ftrgR8tjHf6yqKgt/aiXTdPpNqr/oWbRIFsHDJvX9SQmOwzFPx5iyjZGnJSEjGzNGWh5fv49KxIUKtwqqiM/b13LF01W1b64OXuLVkK1kpaYZL5gnMXRxJC3k8rC09NBrzZzy5JJy5wcOjl2ly/nuanP+e2H0BpNw0TsPfWuNAYo5jMDE0Bus8x2DOMkqWlvSEZCwcbJ762l1XTeC1c9+QnpTK7a1FO8zxv8S2rAMPc+yz+LAY7PLsMzuNA/Ghj8s8DI3BtqyuzLaPVtJp0gDeP7qITpNfZff89dmvW6tjI06u2mOALMBG40Bi6ON6IjEsBps8edhoHEgMiymwjFVpO5Ij4gBIjojDqrTu5t/55btxqOLKm6cX8+quuRyY8TMoCvbuLqTFJ9Nl6Wj6b5tFy8n9c/V4FTYrFwcSc+yDpLAYrF0K+GyF5q7f83623Ls0IupiINr0TMz0o2Iavd+X3ttn0X7JKCxLF/1NT2uNAwnPUE8kFFBP/O22ikL31RN5eetMag/If65ybVKdlKiHPDTCOdpW40h8jvNYfNjjz1B2mbIOxOc4PuNDY7DVX6Q7urtQsXENXt/0EYPXT8WlnodhAn8CC40jqbmum2Iw1zxjg0gIqs8YxI0Zq4ooun/OVuOQa/8kPGH/JOTYPwmhMdjmOG5bvf8Sw499Sa2ezTn82W/53qNePx/uGriRbKNxICE0d15PqxcLKgNQ95XW2fHf2HaSjOQ0RpxezLDjX3D6u22kPkwqoiyKH0M1GhVFyQTeAXYCV4FfFEW5LIT4WAjRvajyK3aNRkVRhgMhgC/wOVAPaAs0Az4UQrgCA4CdiqJ4AvWBAKME+wzMy9jjuXgk58csKfa9IAXxHNebK9/vIDM5fwNqe6+Z/NlpKnsGfkqN19pTtkl1I0T479xbu5+UkGh8d86i3seDiDl9EyVLoVTtSti4lSVk+3PdjCkWLNw0WFUtxwmvYZzwHIZ9yzrYNalp7LAK3ZaB81nh/Q5qM5Ps3knJ+BoPbM+2mT/zafNRbJv5M70+eQuALh8OZue8tdl33F80j6Ku1KYuUVfu8YP3O6zpNAWfjwdjZmOJykSFa6PqHJq9hnXdPqRURWdqvdTaqDE/jUO1cjSZ1I9DE3XPPKvUKmxcnQg7c4ONnacSfuYWTacNMHKU/95vfWbyi/9U/hz8KXWHtMc1z7mqao9mRullLAwqExUW9tb81HM6e+asoc83o4wd0r9WYagfUX+dIy3HTZCS4NCnv7Kk2WiubDpKgyEdcq2r2Kwm9V5pw/6564wU3fNp8k53tJlarv6uG2qs8fRAm6VlSaNRfN9iLN7/86dURWcjR1kyKYqyTVGUaoqiVFYUZbZ+2YeKomwuoKzP8/YywosxEc4fiqKkAClCiH3oppk9BfwkhDAFNimKElAUb5waFoulq1P27xauTqSGxf7NFrmZ2FjSaPUErs9dT9yZW0UR4jNJDovF2vXxnT5rF0eS8+TxqExyaAxCrcLMzoq02EScvarg1qUx3lP6YWZnhaJVyErL4Nry3dmvkRodz/3tZyjtWZnwE9cNlldqaO79Y+niSEqek82jMin6vExtrUiPSQDg4vTHdzPb/DmDxDuhlG5WE/v6HnQ89SUqtQrz0qVotXEqh3ob5zm0tNAYzF0fPw9g5uL0zCdUJ//GxJ+5iTZZ92xm7N5z2HlXI/5E0T+blFdSWCw2OY5BGxdHkvIcg4/KJIXpj0Fbq2ceEpeVlkHgrrO4+zUgSD8sUPrnmgzqgHd/XU9M8Pk7lMqxz+w0jsTn2WfxYbHY5ej5LuXiSIJ+GKpXn9bZk+Jc2nqCnvP+B0C5eu68skh3YWvlYEs1H0+0WVqu7iq8GzX1Brenjj6P8At3sHF5XE/YaBxJzJNHYljs4+FVecokR8VjVcZe18tYxj77+cRaL7Xh9Le64ZsP74UT/yASh8ouJIbGEHnlHvH3IwG4vesMGq8qsP5AoeWXU3JoLDY59oG1xpGk0AI+Wy6OJOWo3x99tqxdHPH7YQz7xiwh/l4EAKmxiWQkp3J3m26f3Nlyghr92hRJ/HnjtH2GesK2gHri77Z99G9KdDx3dpyhrGdlQvTnKqFWUblTI9b7Tyvq9LJ5D+6AVz/d8Rly4Q52Oc5jdprHn6FHEsJjsctxfNq5OGb3bMWHxnBth24/hZy/g6JVsHK0JVl/njO01LAYLHJdNzmSFvZs5yx776rYN6lBhdf8UFubozIzISs5lZuzDDuxj9fg9tTT758w/f55NEbH9gn7xzbH/rF1cSShgGvFK5uO0nf5eI58rhu55VyjAh0/eZMNQz4lNa7oh397Dm5Pvf6P87LNUS/aPkO9mLdM7b6tqNzOi1/6z81eVrNHcwIPXECbmUVydDzBp2+gqefBQ319WNI9YShpiVHsehoLkPd2tKIoykGgNbqHPpcLIQYXxRs/PHcbaw8NlhWdEaZqXHs2I3znmWfaVpiqabh8LEG/HsqeUdVYogLuYOeuwaaCMypTNe49mvJg19lcZR7sOkuVl3SjfN26NCb0iG4Gve29Z7Kh6XtsaPoeV37YyYVFm7m2fDcmluaYWFsAYGJpjmubOsRdDzJoXrEBt7Hx0GCl3z/lezYjdFfu/RO66wwVX9blVa5rEyKP6J5jVFuaobYyB6BM6zoomVkk3Ajm7oo9bPd8m52NRnOgx0ck3Ak1WoMRICHgFhYeLphXLIMwNcG5Zwtidj3bZA1pwVGUalYL1CqEiZpSzWqTfMOw++iRiPN3KOWmwVZ/DFbp3pS7u3Mfg4G7z1Kjr25fVe7SmOAjVwp6qWwmVubZz6YJtYpK7TyJvRVaJPH/V5z4eTdf+0/ma//JXNl1Gs/euv1R3qsKaQkpJOZ5JjYxMo60hBTKe1UBwLN3K67qP4PxEbG4N9X1bHs0r020fujfwlZjWNhyNAtbjuby9hP8OW1ZoTYYAS6s3MOazlNY03kKt3eeoWaflgBovCqTlpCcPdz0keSIONITU9B46Z6Bq9mnJXf0edzZfZZa+uOyVt9W3N6tW54QEkUFfc+2VWk7HCq78PB+BOHn72BuZ4Wlo+7Z1QrNaxNThMPCI87foZR7js9Wj6bcy/PZurf7LNX09btHl8aE6D9bZnZWdF4xjpNz1xN++maebc7h2ky3/8q1rE2sAYa2h+epJ6oWUE/czVFPVOnSmCB9Lnd3n6Vq96aozEywreBMKTcN4QG3MbE0xzTHuapC6zpE5zhXVWhVh9jbISQ9Y8OmMJxeuZvv/Sfzvf9kru86Tb0++nOUVxVSE1JIzHN8JkbEkZaYQjn956xen1bc0B+H13edwU2/nxzdNahNTYzWYASIP3cbqxzXTZqezYl4xuumiyMXc6jhOxxqNIobH60m5JdDBm8wApxbuYcV/lNY4T+Fm7vOUFtff7jo64+kPPsnSV9/uOjrj9p9WnJLv38c3Mpml6vq14CY27pzlK2rEz2XjmHre0uI1c9/UdQCVu5hZecprOw8hVs7/2Ve+nrRrU09Go/oyu9vfJbrme2EkGgq6ifcM7U0x7VBFaJvhRggO8kQXoSexh5CiLmANeADTBRCVAKCFEX5XghhDjQAVv7Na/wrSpaWS5OW03jdJN3U0Wv3k3g9iGoT+hJ3/i4RO89QytODhsvGYmpvTVm/BlR7/yUOtnkf1+7NcGxaA1MHG8q/ohuadOHdJcRfvlfYYT5THsenrqDDmgkIlYpb6w8QdyMYz/F9iD5/lwe7z3Jz3QFafTWc3ocXkhaXyIGRi//2NS2c7Wj74xgAhFrN3U1HCTbwmHwlS0vA5OW0WDsRoVZxb+1+Eq4HU3NCX+IC7hC66yyBa/bjvXgkfsc+Iz0uiZPDFgFgXtqOFmsnomgVUsNiOTXqW4PG/syytNye/AN11k5FqFWEr91L8vUgKk14hYSA28TsOo2NZ2Vq/TQBE3trHDt4U/H9Vzjb5j2i/jyOfYs6NNz3GaAQszeAmN3PdvIubEqWlkPTVtBt1QTd1wKsP0DsjWAajetD5IW7BO4+y9V1B2j3xXBePbSQ1LhEdr/9+BgcePRzzGwtUZua4N7Rmz9fnUdqbCL+P41FbWYCKkHw0atcXvWXUfJ7Vu9Pn8epcxeIi4unXc+BjHxjEH26dTR2WAW6sS+Aar6ejD3wOekpaWx8f2n2ure3zeFr/8kAbJ72E30WDNd95cb+89zYHwDAHxN/wH/6YFQmKjLTMvhj0g/GSIPAvQG4+dZnyKGFZKaks3v8d9nrBmyfzZrOuq9Y2Dd1OR0WvoWJhRn39p0nUD9b9Olv/sT/21HUfqUN8cFRbBuhq0NOfrWJDguH8equuSDg8Nz12b13h2evpffaSSAEERfvcmntviLLT8nScnjaCvxX6+r36/rPlvf4PkSev8u93We5tu4Avl8Op5++ft+jr99rv9YBO7eyNBjTiwZjdLM6bh3wCanR8ZyYs462X47A7KOBpEYnsH/sd38XRqHlcnDaCnro64kr6w8QcyOYxuP6EKGvJ66sO0CHL4Yz8JAul536eiLmRjA3t5zg1b2foM3UckD/9TtWznb4fz8G0J2rbvxxlPs5zlVVuzc16tDUW3sDqOLrydsHPyNT/5Ubj/xv2xy+13/Otk9dRnf9Vx/c3n+eW/rjM+CX/XT/9C2G7ZpHVkYmm8cZ96t8lCwt1yYto8G6yQi1iuC1+0i6HkTlCS8Rf/4OkTvPYOfpgeeycZjaW+Ps14DK7/flaJv3jRr3k9zZG4CHb33+d1BXf2zPUX8M2TabFf66+mP31OV01tcfd/ef545+/7Se+AqOHi4oWoX44Ch26b9CpMXoXlg62NBh5msAKFlZrOxmuFno7+wNwN23Pm8eWkhGSjo7cuQ1ePtsVurrxT0589p3nrv6vNrNHILazISXVk8EIOTcLfZMXsa5FbvptPAtXtszDyEEl345SNS1B/kDKKmUont+vTgQxfG5Ev30sN7oHvL0AKoCpYH5+obiEOB9IANIBAYrinI3z8soW8v2p6TpEr6W5eUGGjuMQvda8Co2al7cZ2YK0jtsDYc0fY0dRqFrFbaBbyqUvGNw5INVZETdMXYYhcq0tAdT3UrW5wpgVuAavqxY8o7B0fdXsbR8yctrWNAqFpewOuOdB6uYWelVY4dR6KbdW82usv2eXvAF4xe+jvmVStYxOOHeKhaUwHpw/P1VL2zLK6y1T5E0qjQH9xeLv0mx7GnUTw8LMOMJ61cAKwwVjyRJkiRJkiRJ0n9VsWw0SpIkSZIkSZIkvSgUbbHoECwyL8JEOJIkSZIkSZIkSZKRyJ5GSZIkSZIkSZKk51DSv3JDNholSZIkSZIkSZKeg1LCZ0+Vw1MlSZIkSZIkSZKkJ5I9jZIkSZIkSZIkSc+hpA9PlT2NkiRJkiRJkiRJ0hPJnkZJkiRJkiRJkqTnIL9yQ5IkSZIkSZIkSfrPkj2NkiRJkiRJkiRJz0FRjB1B0RJKyc2wxCYmSZIkSZIkSSXQCzvG816D9kXS9qh0dk+x+JuU6J7GsW79jB1CofsscB0zK71q7DAK3bR7q1lWbqCxwyhUQ4NXMd6tv7HDKHQLAtcy0u1lY4dR6L4J/IWpbgOMHUahmhW4hoyoO8YOo9CZlvYocfUF6OqMGSWwfp9xb3WJO29Nu7eaDS4lKyeAvqGrS+wx+HnFklVnvHd/FTvKlrzr3E7h64wdgvQEJbrRKEmSJEmSJEmSVNTkRDiSJEmSJEmSJEnSf5bsaZQkSZIkSZIkSXoOJXeaGB3ZaJQkSZIkSZIkSXoOcniqJEmSJEmSJEmS9J8lexolSZIkSZIkSZKeg6LInkZJkiRJkiRJkiTpP0r2NEqSJEmSJEmSJD0HRWvsCIqWbDRKkiRJkiRJkiQ9B20JH54qG43PoNf0IdT09SI9JY21478l+HJgvjKdx7+Cd+/WWJWyZlLt17KXt3nDnyb92qLNzCIxJoH1E5YQGxxluOBz6DhjMFV865ORks7m8UsJuxSYr4ymjhs9Fg7HxMKUW/vOs3PGSgBaj+mNV39fkqMTANj36Xpu7TuPykRN10/exKWOOyoTFRd+O8yRbzYbLKdyPvVo8vEghErFjbX7ufj1n7nWq8xMaP3lcJzqupMWm8D+EYtJDHr897d2daLX/k8IWLiRS0u3ZS8XKkG37TNJDotlz5CFBsvnSXpMH0JNX0/SU9JZ/4RjsNP4l/Hu3RrLUtZMqT00e7lH4xp0/3AwLjUqsnrUV1zYftKAkT/ZS9OHUtvXi4yUNFaO/4YHl+/mK9N9fD+a9G6NZSkbxtYenL28ad829Jo0iLjwGAAOrNjB0fV7DRZ7Xl2mD6aarycZKen8Nn4JoQXsH9c67vReMAxTCzNu7Atg60e6z5amViV6zH4dE3NTtJlaNk9bRvD529nblavnwVsbP+KXUYu4XEz2XU5T53zGwSMncXSwZ9OqJcYO56lKap3RecZgqurr903jlxJaQP3uUseNnguHY2phys1959mur9/7Lh5FaQ8XACzsrEiNT2aJ/2Qs7W14ecloytXzIGDDQbZ9uMKQKQFFdN4yVdNlzhu41vNA0WrZ+dHP3Dt+1WA5lfWth+fHgxBqFXfX7Of64vzHYKOvRuBQz4302ESOD1tEclAUwlRNw/lv4FBfF/f5aT8TeUwXd8s1E7AoY48wURN14jrnJi0DrWHn/y+KY9CjZR3aT+yH2tSErIxMds9Zw92jVwyZFj4fDcLd15OMlDR2jfuOiALyKlPXjY4Lh2FiYcbdfQHsn/4zAFW7NKbZe71xrOLK2u7TCb+gO89VbFWHlhNfyc7r0Oy1PDBwXo+U9q1PzVlDQK0iaPVe7i7KfQ3n0LQGNWYOwbZWRc4P+4rwLSdyrVfbWNLq0ALCt5/m6uRlhgxdMjCjPdMohBgjhLAy1vs/q5o+npR2d2GOzxh+nfw9fWe/WWC5K3+d4YseU/ItD74SyOfdJrOg8wdc2H6CrpNeLeqQC1TFtz6O7hq+bjOOrZN+xH/W0ALL+c9+nS0Tf+DrNuNwdNdQ2ad+9roTP27ne//JfO8/mVv7zgNQq0sTTMxMWdpxIt93mUqDAW0pVb60QXISKkHT2UPYNXA+v/tOwKNnU0pVdc1Vplp/H9IeJvFby3Fc/n4H3lP65VrfeMarBOlzyanWm52IuxlSpPE/qxo+nji7a5jn8x4bJn9Pn9lvFFjuyl9n+bLH1HzLY0OiWD9+Cef+OFLUoT6z2j5elHHXMMPnXVZP/o5+T/hcXfjrDJ/0mFzgujNbjjLXfwJz/ScYtcFYzccTJ3cNn/uMZdPkH+g++/UCy3Wf9TqbJv3A5z5jcXLXUFX/2eo0sT97v9zI1/6T+euzDXSa1D97G6ESdJzYn1uHLhokl3+jp38Hlnw2y9hhPJOSWmdU1dfvX7UZx5+TfqTLE+r3rrNf58+JP/CVvn6voj8GN7yziCX+k1niP5krO05xdccpADLTMti34Fd2zV5jsFxyKqrzVoP+bQFY2nEiqwbOo8PUV0EYqIdAJfCa8xqHX53PzjYTqNCzGbbVyuUq4tbfh/SHSexoPo4b322n7lRdneDxqi7u3W0ncuiVedSb8Tju428tYk/7yez2+QBzJ1vKd2timHz0iuoYTI5NYO3rC/i240Q2jV1Cr89HGCwnADff+ti7aVjWehx7Jv5I29mvFViu3eyh7P7gB5a1Hoe9mwY3n3oARF8P4s+3viToxPVc5VNiEvjj9YX87DeJne8tpdMXw4s6lYKpBLXmvc7pAfM43GocLr1aYJ3neEwNjubi6G8J3VjwNUTViS8Tc/yaIaIt9hRFFMlPcVFojUah809ebwxQ7BuNdfy8Ob3xIAD3zt3C0tYKW2f7fOXunbtFQmRcvuW3jl0hIzVdX+Ym9hrHogz3iap1aMiF3w4BEHzuFhZ2VtiUsc9VxqaMPeY2lgSfuwXAhd8OUd2v4d++rqIomFqZI9QqTC3MyMrIJC0hpUhyyKu0V2USAsNJvB+JNiOLO38cp2LH3PFW9GvArV91eQduPYlLy9qP13VsSML9SOKuB+faxsrFkfLtPLm5dn+R5/Asavs15PRGXQ73z93C4gnH4P0nHIOxQVGEXruPUoy+dbaenzcn9J+rwHM3sbK1xq6AnALP3SS+gJyKk5p+DQnQ758g/f6xyZOLjbM95raWBOk/WwEbD1HLzxsABTC3sQTAws6S+PDY7O2avtaRy9tPkhT9sOgT+Ze8PetSys7W2GE8k5JaZ1Tv0JDzv+U4Bv+mfn90DJ7/7RA1Cqjfa3dpwsXNRwHISEnj/ukbZKZlFG0CT1BU563SVcsRqO/VSY6OJzU+Cdd67oWfQAEcvSqTGBhO0v1IlIwsHvxxHNc8x6Brp4bc+0VXPwZvOUmZVrpj0LZaOSKO6OJOi44n42ESDvV1cWcm6s67wkSNytREV7EYUFEdg2GX75EQEQdAxI0gTC3MUJsZbpBcZb+GXP3tsC6Wc7cxt7PGOk9e1mXsMbOxJOycboTI1d8OU7mjrn6PuRVC7J3QfK8befkeSeFxAETfCMLEwHk9Yt+gCsl3w0i5F4GSkUXYpqOU7eSdq0zKg0gSr9wvsOfarp475s6liN5/wVAhS0b0XI1GIYSbEOK6EGIlcAmYJoQ4JYS4IIT4SF/GWgixVQhxXghxSQjxihDiXcAV2CeE2Kcv5yeEOCaEOCuE+FUIYaNf3kgIcVS//UkhhK0QwkoI8YsQ4ooQ4nchxAkhhPeT4nwedmUdiQuJzv49LiyGUv+y4dfkZV+u7g8opMj+GVuNI/E58ogPi8G2rEPuMmUdiA+LeVwmNAbbHLk2GuzHWzvm0u3T/2Fhp2vvX912kozkNN479TXvHvuSY99tJfVhUhFno2OlcSAp5HG8yaExWGscnlhGydKSHp+MuYMNJlbm1H27KwGfbcz3uk0+GsjpWWtRDDy050lK5TkGHz7HMVhc2Jd1JDbk8ZC/2LDof3xDxatzE6Zs/5Q3vxmLg4tTYYf4zGzLOvAwx3EYHxaDXZ7j0E7jQHzo4zIPQx9//rZ9tJJOkwbw/tFFdJr8Krvnr89+3VodG3Fy1R4DZPHfUFLrDLsC6ne7PPW7XQH1u12ez1ylxjVIinpITGB40Qb8jIrqvBV+5R7VOjRAqFXYV3DGpY47dq6GqUMsNY6kBD/OKSU0Bss8x6ClxoGUHMdgRnwyZo42PLxyD1c/XdxWFZyxr+eOVbnHcbdc+wHdLn5LZmIqQXmGEBY1QxyDtfwbE3opkKz0zEKO/slsNA4khD7OKzEsBps8+8tG40BijrwKKvN3qvo3IsLAeT1irnEkJcd+Sw2JwfxZz8VCUGPGIK7NWFVE0b14FK0okp/iojB6GqsC3wDvAeWAxoAn0FAI0RroBIQoilJfUZQ6wA5FUb4CQgBfRVF8hRClgalAe0VRGgCngbFCCDNgPTBaUZT6QHsgBRgJxCqKUguYBvz9bcVioGHPllSo58G+7/58euFi6MyqPSxu/R7fdZ5MYkQcHabphtm6elZGq9XyReN3WNTyPZr9zx/7Cs5GjvbpvMb15vL3O8hMTsu1vHx7T1Ki4om+GGicwKRncnHPGaa1fJvZnd/n2uELDF74trFD+tcaD2zPtpk/82nzUWyb+TO9PnkLgC4fDmbnvLXFqof4v+y/UGfU6d6Mi5uPGTuMQvOk81bALweID43hzT9n4ffhIB6cvYmSVfynPQxce4CU0Bja7ZiF58eDiD6dO+7D/T9hi+fbqMxNKJOjh/xF8qRj0LlqOdpP7Mefk340QlRFx6laOVpO6seeST8ZO5R/rOJQPyL/OkdajhuiUslWGH3h9xRFOS6EWAD4Aef0y23QNSgPAQuFEJ8AWxRFOVTAazQFagFHhG58vhlwDKgOhCqKcgpAUZR4ACFES+BL/bJLQohC7RdvMciPpvpnHh6cv419jjuQ9hpHHob9sw9I1RZ1aP9OL75+5SOD3knyHtwBr36+AIRcuJPrTqqdxpGEHMPgABLCY3Pd9bNzcSRBn2tSVHz28rNr99Hvp/EA1OnRnNv7L6DNzCI5Op4HZ27gWs+DuAeRRZbXI8lhsVi7Po7XysWRpLDYAsskh8Yg1CrM7KxIi02ktFcVKnVpjPeUfpjZWYFWISstAyuNAxX9GlC+bX3U5qaY2VrS+qsRHHz32yLPJ6fmgzrQJPsYvJPrGCz1L47B4qD1oI606N8OgHvnb+PgWhrQPefhoHEi7h/klBSXmP3/I+v+otfEgYUa69M0GdQB7/66z1bw+TuUynEc2mkcic9zHMaHxWLn8rhMKZfHnz+vPq2zJ8W5tPUEPef9D4By9dx5ZdEoAKwcbKnm44k2S8vVXaeLLrESriTVGY0Gd6Chvn4PLqB+j89Tv8cXUL/n7PVRqVXU7NSI77rmfy7akAxx3lKytOye+bh35LWN04m+G1b4yRQgJSwGyxy9g5YujqTkOQZTwmKxdHUkRX8MmtpZkR6jq/POT38ct+/m6STcyR23Ni2DkJ1ncO3YkIiDl4owE8Mdg3YaR/p99x6/j11C7P2Iokgll/qD21NHX7+HX7iDbY6RLDYaRxLz7K/EsFhscuRVUJmC2Ggc6fbdGHa+t4SH94o+r4KkhcVgmWO/Wbg6kvaM52J776o4NKlBxdf8UFubozIzISs5lRuz1hZVuMVeSb/HWxiNxkdjEQUwV1GUpXkLCCEaAP7ALCHEX4qifJy3CLBbUZT+ebarWwjx/WNHft7FkZ93AVDT14uWQzpybvNRKnlVITUhucDnxp6kXG03XprzP74bMpfE6Pinb1CITq/czemVuwGo0taTRkP8uLz5GOW8qpCakEKi/jmBRxIj4khLTKGcVxWCz92iXp9WnFq+E9A9i/CofI2O3kReDwIgPjgKt+a1uPj7YUwtzSnnVZUTP+4wSH5RAXewc9dgU8GZ5LAYPHo05cDb3+Qqc3/XWaq81IrIM7dw69KYUP3zINt7z8wu4zm2N5lJqVxdrvtbnZn3CwCaZjWpM9zf4A1GgKM/7+boz7p4avp60WKIHwGbj1LxXxyDxcXBn3dy8Gfd8VTH14s2QzpxevMR3LyqkpKQ/I+eXbRzts8uX6+DN2G3g4og4ic78fNuTuj3TzVfT5oO8ePC5mOU96pCWkIKiXlySYyMIy0hhfJeVQg6dwvP3q04vlxXx8RHxOLetCZ3j1/Fo3ltovXDsha2GpO9fe8Fw7j+1znZYHxOJanOOLVyN6f09XvVtp40HuLHpZzH4BPq90fHYP0+rTipr98BPFrWIep2SK6LeGMwxHnLxMIMIQQZKWm4t6yDNlNL1M3cz6kWldiAO9i4a7Cq4ExKWAwVejTl5Mivc5UJ3XmWSi+3JubMLcp1bUzE4csAqC3NAEFWShplWtdBm6Ul4UYwaitzTG0sSY2IQ6hVuLTzIupE0U9MYohj0MLOigHLxrPnk3U8OH2jyHMCOL9yD+dX6h4LcG/rSf0hHbi++Rgar8qkJySTlCevpIg40hNT0HhVJuzcbWr2aUmAvn5/EnM7K3ouH8fheesJOX2zqFJ5qofnbmPlocGyojOpoTFoejbnwohFz7TthZGLs/9f7pU22NX3+E83GIFiNZS0KBTmU7c7gZlCiNWKoiQKIcoBGfr3iFEUZZUQIg54NE1iAmALRAHHga+FEFUURbklhLBGN9T1OuAihGikKMopIYQtuuGpR4CX0T0TWQsossbl1X3nqOnryeQDX5KRksba9x9PKT9u2zwW+k8EoOvEATTo0QJTSzM+PPY1J9bvY+cXG+g26VXMrcwZ8s0YAGKDo/jpfwuKKtwnurU3gCq+nrx98DMy9VOXP/K/bXP43l83Q+X2qcvorp82+vb+89mzzbWb1B9NrUooisLDoEi2TtYNpTi1cjfdFwxj+O5PQAjO/3qAiGsPDJKTkqXl+NQV+K2ZgFCpuLn+AHE3gvEa34eo83d5sPssN9cdoNVXw+lzeCFpcYnsz1HJvSiu7jtHDV9PJh74goyUNNa//3jfvbdtLp/7TwKgy8QBePVojqmlGVOPLebk+n3s+uI3KtTzYMjSsViVsqZWuwb4vfcSC/zeN1Y6AFzad47avg346MBXpKek8/P7jy/cJ22bz1z/CQD0mvgq3j1aYmZpxuxj33J0/V62fvErvkM7U7e9N9qsLJLjElk5/psnvVWRu7EvgGq+now98DnpKWlszLF/3t42h6/1n63N036iz4Lhuq/c2H+eG/rnm/+Y+AP+0wejMlGRmZbBH5N+MEYa/9r70+dx6twF4uLiaddzICPfGESfbh2NHVaBSmqdcXNvAFV9PXn34GdkpKTzR476ffi2OSzRH4Nbpy6jp75+v7X/PDdzzAJbp1szLhUwLHDM4S8wt7VEbWpCDT9vfh40j0gDNbCK6rxlXdqOV1d+gKIoxIfF8sd7hrsxqGRpCZi8nFZrP0CoVQSuO0D8jWBqvd+H2PN3Cd11lrtr99N40Qg6HV1IelwSJ4brLuLNnexotVYXd0poLKdG6eI2sTKn+YqxqMxMESpB5JEr3Fn5l8FygqI7BhsP8cPRrSxt3u1Nm3d7A/DzoHkkGegm/N29Abj51mfooYVkpqSza/x32ete3T6b1Z11s+bvnbocv4VvYWJhRuC+8wTq86rc0Rvfjwdj6WhLj2Xjibxyj98Hzaf+kA7Yu5WlyeheNBndC4CNAz8hxcCdC0qWliuTluG9bjJCrSJo7T4SrwdRZcJLPDx/h8idZ7Dz9KDBsnGY2Fvj7NeAKu/35Ugb415DSMYhnud5GSGEG7ohp3X0v4/mcaMwERgIVAE+BbToGpEjFEU5LYQYBbyD7nlHXyFEW+ATwFy//VRFUTYLIRoBiwBLdA3G9ujmBVuBbkjrNcADeElRlJy3a5SxbrmnSi8JPgtcx8xKxvnajqI07d5qlpUz7BDDojY0eBXj3fo/veALZkHgWka6vWzsMArdN4G/MNVtgLHDKFSzAteQEXXH2GEUOtPSHiWuvgBdnTGjBNbvM+6tLnHnrWn3VrPBpWTlBNA3dHWJPQY/r1iy6oz37q9iR9mSd53bKXzdC9tdd8mja5EMUK1zZ0ux+Js8V0+joiiBQJ0cv3+J/lnDHG6j64XMu+0idI3BR7/vBRoVUO4Uumceswkh1MBARVFShRCVgT3AvX+diCRJkiRJkiRJklQgw38pTOGwQjc01RTd85AjFUVJN3JMkiRJkiRJkiT9BylKsegQLDIvZKNRUZQEoEi+l1GSJEmSJEmSJOmfKOmzpxbG9zRKkiRJkiRJkiRJJdQL2dMoSZIkSZIkSZJUXGhL+PBU2dMoSZIkSZIkSZIkPZHsaZQkSZIkSZIkSXoOciIcSZIkSZIkSZIk6YnkRDiSJEmSJEmSJEnSf5bsaZQkSZIkSZIkSXoOciIcSZIkSZIkSZIk6T9LKCV3AG6JTUySJEmSJEmSSqAXtrvuVLleRdL2aBT8e76/iRCiE/AloAZ+UBRlXp71Y4E3gUwgEnhdUZR7zxNHiR6eWqdsU2OHUOguhR9nbqWBxg6j0E26t4rbdToaO4xCVfnSzhK7r76sWPLyGn2/5OU1+v4qlpUrWTkBDA1eRUbUHWOHUehMS3vgpWlh7DAK3bmwIzR19TF2GIXqeMh+Iju0MXYYhc559wEGVOpl7DAK3Zp7vzPJbYCxwyhUcwPXENOr5B2Djr8fMHYIxZ4QQg18DXQAgoBTQojNiqJcyVHsHOCtKEqyEGIEMB945XneVw5PlSRJkiRJkiRJeg5aRRTJTwEaA7cURbmjKEo6sA7okbOAoij7FEVJ1v96HCj/vPnJRqMkSZIkSZIkSdJzUIropwDlgAc5fg/SL3uSN4Dt/yannEr08FRJkiRJkiRJkqT/IiHEQMAbeO6xzLLRKEmSJEmSJEmS9BwM+JUbwUCFHL+X1y/LRQjRHpgCtFEUJe1531QOT5UkSZIkSZIkSXoxnAKqCiHchRBmQD9gc84CQggvYCnQXVGUiMJ4U9nTKEmSJEmSJEmS9BwUA/U0KoqSKYR4B9iJ7is3flIU5bIQ4mPgtKIom4FPARvgVyEEwH1FUbo/z/vKRqMkSZIkSZIkSdJz0BrwvRRF2QZsy7Pswxz/b1/Y7ymHp0qSJEmSJEmSJElPJHsaJUmSJEmSJEmSnoOCwSbCMQrZaPyHJs0eS6t2zUhNSWPKuzO5evH6E8suWvkp5Su50qvNqwaM8Mk6zBhEZV9PMlLS2DL+O8IvBeYro6njRpeFwzC1MOP2vgB2z/g51/rG/+tMu6mv8oXncFJiE7OXu9TzYPDv09k0ajHXt50q6lSeyrKFN6UnDkeo1cT/tp24H3/Jtd7u5S7Y9esGWi3a5BQiZ3xJxp37Roq2YEWxv5oM60LtHs0BUJmocKpSji+9RpD6MKnI8mjz0SDcfD3JTElj17jviCwgjzJ13eiwcBgmFmYE7gvgwHRdHualrPH/5h3syjsTHxTJtpGLSHuYjHkpK9p/+hb2lcqQmZbBnvHfE30jCADP1ztSu78PQggurd1HwI87X5i8zGwt6fjlCGxdnVCZqDm7dBtXfj0IgK2rE+3mv4mtiyMK8MeQT0kIiiqS3ADK+dSjyceDECoVN9bu5+LXf+ZarzIzofWXw3Gq605abAL7RywmMUc81q5O9Nr/CQELN3Jp6eMRNEIl6LZ9JslhsewZsrDI4n9eU+d8xsEjJ3F0sGfTqiXGDucfmTBrDC3aNSM1JZXpo2dz7eKNfGUWr1mIc1kn1CYmnDt+nrmTFqLVaqlWuypT5r+PubkZWVlZzJm4gMvnrhohi/zGzhxFs7ZNSUtJZeZ787h+8Wa+Mp+vnk/pMo6oTdQEnLjIgslfoNU+HjQ2YNjLvDt9JB3r9OBhzENDhl8gU+/G2IwchVCpSNm+lZT1a3Ktt+jaHcvuvUCbhZKSQsLnC8i6fw9MTLAdMx6TatVBqyXxm0VkXAgwThIFGDzjDTx9G5KeksaS8YsIvHQn13ozCzNGf/s+ZStq0Gq1nN1zmnWf6OrHGo1rMWj661Ss4caiUQs5ue2YMVLI1m36YKr7epKeks6G8UsIuRyYr4xrHXdeWqA7F1/fF8CfH60EQFOzIr1mv4GZlTmxQVGsH/M1aYkpunU1KtBrzpuY21iiaLV83WMamWkZhkwNAFOvxli9MQpUKtL2bCV145qCyzVtje0HM3k4/i2ybl/HpL43VoPeAhNTyMwgecW3ZF48Z+DoJUP62+GpQgh7IcRI/f99hBBb/smLCyGWCyH6/tOg/u69hBCBQojS//Q1C0Ords2o6F4B/6YvMWP8XKbNn/DEsu39fUhOSjZgdH+vsm99HNw1LGkzju2TfqTTrNcKLNdx9lC2T/yBJW3G4eCuwcOnXvY6WxdH3FvV5WGeC1WhEvhMeoW7hy4WZQrPTqXCeerbhI6Yyv3u/8PG3xdTj4q5iiRs3UdQ7+EE9R1J3E+/UnrCMCMFW7Ci2l8nlm7lJ/8p/OQ/hf2f/ML9E1eLtMHo5lsfezcNK1qP46+JP9J2dsF5+M4eyl8f/MCK1uOwd9NQSZ+H99vdeHDkCivajOfBkSt4j+wGQKO3exB15R6rO05m13tLaPPRIACcqpWndn8f1nebzuqOk3Fv50WpSmVfmLzqD+5AzM1g1nSawm8vz6bVtAGoTNUA+H0+nLNLt/Jzuw9Y3+1DUqLiCz2vR4RK0HT2EHYNnM/vvhPw6NmUUlVdc5Wp1t+HtIdJ/NZyHJe/34H3lH651jee8SpB+87ne+1ab3Yi7mZIkcVeWHr6d2DJZ7OMHcY/1rJdMyp6lKdHs1eYNX4+kz8ZX2C5D96axivtXqNvm4E4ONnToZsvAGOmjeS7hT/Rr/1rfDv/B8ZMG2nI8J+oWdsmVHAvz0stXmXuhIVMmPtegeWmDJvBoA5vMsB3KA5OpWjbzSd7XRlXZxq38SY0KMxAUT+FSoXtqDE8nDyBmDeHYOHbDnXFSrmKpO3dQ+xbQ4kd/ibJv6zFZvjbAFj4dwUg9q2hxE0ch/WwkSCKRy+Hp28DNO6ujG0zkh8mfcvrswo+v2797g/GtxvFJP9xVPOuQX2fBgBEhUSyZNwijv5x0JBhF6i6jydO7hoW+Izl98k/0HP26wWW6znrdTZO+oEFPmNxctdQzac+AH3m/Y8dn6zly04TubzzFK3f0u03lVrFy5+/ze9TfuQLvwl8328WWRmZBssrm0qF1VtjSJg5gYfvDsGsZTtU5SvlL2dhiUXXvmRev5y9SIl/SMLsScSPGUrSV3OxGT3FgIEXT1qlaH6Ki6c902gPFI8zRjHg26k1m3/V3TG/cOYytnY2lC7jlK+cpZUlg4f3Z+nnywwd4hNV7dCQS78dBiDk3G3M7ayxLmOfq4x1GXvMbSwJOXcbgEu/Haaan3f2+vYfDmTf3HUoSu4j2Ps1P65vP0VSEV7E/hPmdauTcT+EzKAwyMwkcft+rNs2y1VGydGgV1lagFKMPpUU7f56pFaPZlz5o2jv4Hr4NeSqPo8wfR5WefKwKmOPmY0lYfo8rv52mModdXlU7tCQKxsOAXBlwyEq6/NzrFqOB0evABB7OxTb8qWxKm2HQ1VXws/dJjM1HSVLS/Dxa1Tp7E1hK6q8FBTMrC0BMLW2IDUuCW2mFseqrqhMVNw/dAmAjOQ0MlPTCz2vR0p7VSYhMJzE+5FoM7K488dxKnZsmKtMRb8G3PpVl0Pg1pO4tKz9eF3HhiTcjyTueu6vjbJycaR8O09urt1fZLEXFm/PupSyszV2GP9Ym44t2fLLDgAunr2MrZ1tgeeppERdHWhiosbEzIRHtYSiKFjbWgNgY2tNZFjR9Wb/E607tmDbBt2ogctnr2BTyganMo75yiXr81KbqDE1M81Vt4+Z8Q6LZy2FYlLdm1SvSVZIMNqwUMjMJHX/Xsyat8xVRkl+fK4SFpbZsZtUciM94KyuTFwcSlKirtexGGjYoTGHftsHwK1zN7Cys8a+jEOuMump6Vw5pqvPsjIyCbx0B0eN7jiNCorkwbV7aIvB1XJNv4ac26ir5x6cu4WFrRW2zva5ytg622Nua8mDc7cAOLfxELX0dXppdxfunrgGwK3DF6nduREAVVvVI+zafcKu6kY4JcclohghX5OqNdGGBqMN1x2D6Yf3Yta4Zb5yVgPeIPX3NSgZj887WXdvosRG6/5//y6Ymet6Hf/DtIgi+SkuntZonAdUFkIEoJ+6VQixQQhxTQixWujncBVCfCiEOCWEuCSE+O7R8pyeVEYIUUUIsUcIcV4IcVYIUVm/SYHvpTdKX/aiEKLGc/8VnlFZF2fCgh9/1Ul4aARlXZzzlRs18S1WfLuG1JTn/h7NQmOrcSA+JDr794SwGGzL5q7Ebcs6EB8Wk/17fGgMthpdmaodGpAQFkvE1dxDOG3KOlCtozdnf/6rCKP/Z0zKOJEZFpn9e2Z4FCZl8ndO2/XrRsXty3Aa9yZRc78xZIhPVVT76xETCzM82tTj+vaiHUpso3EgMfRxHolhMdhoHPKXyZFHzjJWpe1IjogDIDkiDqvSdgBEXb1PlU66k3LZ+h7YlSuNjYsj0deDcG1cHQt7G0wszHDzrY+NS/4L5uKa1/nlu3Go4sqbpxfz6q65HJjxMygK9u4upMUn02XpaPpvm0XLyf0RqqI7kVhpHEgKeRx7cmgM1nnyy1lGydKSHp+MuYMNJlbm1H27KwGfbcz3uk0+GsjpWWuNcnH0X1HGxZmwkNznqTIFnKcAvl77GX9d2kJyYjJ7/tRd5C/48EvGTBvJ9jMbeW/6OyyaUzyG5jprnIkIeVyvR4RE4qwpOK8v1sxn+4VNJCUms3fLAQBadWxBZFgkt67cNki8z0JVujRZkY/3lTYqEnXp/Ocqi+49cVyxBus3h5P4zZcAZN6+jXmzFqBSo9JoMKlaDZVzGYPF/nccNE7E5Dh/xYRF41A2fwP/ESs7Kxq09+bykQuGCO8fKVXWgbgcdeHDsBjs8tSFdhoH4kNzlAmNoZT+fB1+Myi7AVnXvyn2+vNRaQ8NKApDV07knS2zaT2sa1GnUiDhWJqsqBzHYHQkKqfcx6Daoyqq0mXIOHP8ia9j2qwNWXduQKbhh9dKhvO0RuNE4LaiKJ7A+4AXMAaoBXgALfTlFiuK0khRlDqAJVDQ0f+kMquBrxVFqQ80B0L1y5/0XgBRiqI0AL4FCh57YyTVa1elglt5/tp+wNihFBoTCzOav92dQ59tyLeu/fSB7Ju3rtj11D2L+HV/cr/zUKI/+xGHYQOMHU6h+bv99UjV9l4Enb5RpENTi8Kjo+z0N39ibmfNgO2zqT/Uj8jL99BmaYm9FcKZb7fQc/UH9Px5ApFX7qFoDTkJ9r/zKK9KbeoSdeUeP3i/w5pOU/D5eDBmNpaoTFS4NqrOodlrWNftQ0pVdKbWS62NGvOTeI3rzeXvd5CZnPumWfn2nqRExRN9MdA4gUn5vN1/LB3q98DMzIxGLXU9yS8N6cXC6Yvo3LA3C6Z/xfTPJhk5yn9uzIAJdPXqg5mZKd4tvTC3NOe1Ua/y3afFZ/TPP5G6eRMxQwaQ9MNSrAYM1i3bsY2syAgcvlmKzYhRZFy5DC9AXZeXSq3inUXj2LFsKxEPwo0dTqH7bcJ3NB3Ynnf+nI25jUX2EFSVWk2lRtVZP/prlvb9iNodG1G5ee2nvJoRCIHV0LdJXvbkG+vqCm5YDR5G0pLi+4y6oSiIIvkpLv7pRDgnFUUJAtD3ProBhwFfIcQEwApwBC4Df+bZNl8ZIcR+oJyiKL8DKIqSqn/tv3svgEe3sM8Avf9hDv9Iv6F96DuwBwCXAq6iKff4Tl5ZlzKEh0bmKu/pXZfa9Wuw89TvqE3UOJV2YNnGbxja2/CjfBsMbo9nP91zKqEX7mDn+rjHxVbjSEJ4bK7yCeGx2Gke3w20c3EkISwWh0plKFXBmde3z8lePnTrLFb0mI5LPXd6LHoHACtHWyr71kebqeXmrjNFnd4TZUZEY5LjDrRJ2dJkRjx5iFXi9v2UnjbKEKH9LUPsr6RI3cQPNbs148rmohmaWm9we+r01+URfuFOrp4+G40jiWG580gMi8UmRx45yyRHxWNVxl7XG1fGPvs5vvTEFHaP/y57m6FHPif+vu6zeHn9AS6v1920aT7hZRJz3AEu7nnVeqkNp7/VVZ0P74UT/yASh8ouJIbGEHnlXnaOt3edQeNVBdYXzc2p5LBYrF0fx27l4khSnvwelUkOjUGoVZjZWZEWm0hprypU6tIY7yn9MLOzAq1CVloGVhoHKvo1oHzb+qjNTTGztaT1VyM4+O63RZLDf8nLQ3vT+1XddzZfDriKxjX3eSoiz3kqp/S0dPbvPIRPp1acOHiKri93Zv7ULwDYvXkvHy6cWKSx/50+r/Wkx6u6+8tXA65RxvVxvV7G1ZnIsL/P6+DOI7Tq2JLoiBhcKrqwas+PADi7OLNi53e87j+CmMjCqR/+DW1UFOocvYOq0s5kRT35XJW2/y9sRr+nG/elzSJpydc8uu1n/8XXZAU9KNqA/0aHwZ3x7dcBgDsXbuGY4/zlqHEiNrzgv/Ob80YSdjeEHT/9oykzilTTQR1opK/rg87fwd7VkXv6daU0jsTnqQvjw2Kxc3lcX5ZyceSh/nwdeTuEnwbPA6C0u4bqvl6Arscy8OQ1kmMTALi+LwDXOu7cPnoZQ1JiolCXznEMOjmjjX58DApLK9QV3bGd9YVuvb0jtpPnkDBnMlm3ryOcnLGZOIukL+egDSv+z6pLz+efNhpz3jrOAkyEEBbAN4C3oigPhBAzAIucGz1LmWd5rwLW5V1e6NYt+411y34DoHX75vR//SW2/76beg1rk5iQSFREdK7y61dsZP0KXZvWtYILX69aYJQGI8DZlXs4u3IPAJXbetJwSAeubD6Gq1dl0hKSSdIPj3skKSKOtMQUXL0qE3LuNnX6tOTM8l1EXg/iq4ZvZ5cbcfhzlnebRkpsIt+2HJu9vMuCt7i195xRG4wAaZeuY1qxHCblypIZHo1NZx/CJ8zLVca0oisZ93UVnFXrxmTcDy7opQzKEPsLwNzWkopNa/DnmKK5WL+wcg8X9Hm4tfWk/pAO3Nh8DI0+j+Q8eSRHxJGemILGqzJh525Ts09Lzi/fBcCd3Wep1bcVp7/5k1p9W3F7t+7YMrOzIjMlDW1GFrX7+xB88hrp+hnpLJ3sSImOx9bVicqdvFnfc8YLk1dCSBQVWtQm5OR13TOalV14eD+CtIdJmNtZYeloS0pMAhWa1yb8Qu7ZCAtTVMAd7Nw12FRwJjksBo8eTTnwdu47zfd3naXKS62IPHMLty6NCT2ie8Z0e++Z2WU8x/YmMymVq8t3A3Bmnm4WY02zmtQZ7i8bjIXkl2Ub+WWZ7rzTsn0z+r3ehx2b9lC3QcHnKUsrS6xtrIiKiEatVtOyfXPOndBNWhQZFkXD5l6cOXqOxi0bcv+O8Roivy3fxG/LNwHQvF1TXhrai92b9lK7QS0S45OIjsjdELG0ssTKxpLoiBjUajUt2jcl4MRFbl+7i3+9Xtnlfj+xjtc6DzP67KmZ16+hLlcelUaDNioKC5+2xM+dmauMulw5soJ15yezJs3ICtbNEo25uW7im9RUTBt4o2Rl6WZVNZLdK7eze+V2ADzbNsRviD/HNh+milc1UhKSiYuIzbfNS+MHYGVrxfcTvjZ0uH/r+M+7Of6zrs6q7utJsyF+nN98jApeVUhNSCEhMi5X+YTIONISUqjgVYUH527h1bsVx/R1vbWTHUnR8Qgh8H2nFydW684hNw5coPWwrphamJGVkYl7k5oc/jHX97QbRObNa6hcyqMqo0EbE4VZy7Ykff74GFSSk4gb0iP7d9uZX5C8/Ftdg9HKBtsp80j+eSmZ1y4ZPPbi6MXr6/9nntbgSgCeNhPAo8ZflBDCBugL5B0XV2AZRVEShBBBQoieiqJsEkKYA+p/EL9BHdxzlFbtmrP9xAZSUlKZNvrxzHob/lpJ33aDjRjd37u9N4DKvvUZfnAhGSnpbM3RS/P6ttn85K+b9Wrn1OV0XfgWJhZm3Nl/ntsFzH5Y7GVpiZrzNS5L5yDUKuJ/30XG7Xs4vD2YtMs3SN5/HLsB3bFq2gAlMxNtfCIRkxcYO+pcinJ/Vevozd2DF8kwwDO3gXsDcPOtz5BDC8lMSc/VOzhg+2zWdNblsW/qcjro87i37zyB+jxOf/Mn/t+OovYrbYgPjmLbiEUAOFZxxe+zYaBA9I0g9kz4Pvt1uywdjYWDDdqMTPZPW0F6fOHPYlxUeZ38ahMdFg7j1V1zQcDhuetJ1Tf2D89eS++1k0AIIi7e5dLafYWe1yNKlpbjU1fgt2YCQqXi5voDxN0Ixmt8H6LO3+XB7rPcXHeAVl8Np8/hhaTFJbJ/5OIii8cY3p8+j1PnLhAXF0+7ngMZ+cYg+nTraOywnurwnmO0bNeMzcd/ITUllRlj5mSvW7dnOf3av4allQVfrPwEUzNTVCoVp4+cZcOKTQDMHP8J788cjYmJmrS0dGa9P99ImeR29K/jNG/XhA1HV5Oaksas9z7JXrdy9w8M7vAmllYWfLp8DmZmpgiVirNHz/H7ys1GjPoptFkkLv6CUnMXIFQqUnduI+teIFZDXifzxjXSjx3FokdvzLwaQlYm2oREEubPBUBl70CpuZ+CoqCNiiThk9lGTuaxgL1n8PRtyOcHvyUtJY2l4xdlr5uz7TMm+4/FUeNEr1EvEXwriNlbdcMad63cxv51e/CoV4X3vvsA61I2NGjfiL7v9WNCh9FGyeX6vgCq+3oy/sDnZKSkseH9pdnrRm2bwyL/yQD8Me0n+i4YjqmFGTf2n+f6/gAA6ndvTrNBuh7YSztPceZX3eiQ1PgkDv+wjbc3z0JRFK7vC+D6vgCD5gaANovk77/AdvoC3Vdu/LWNrAeBWPZ/ncxb18g4dfSJm5r790LtUg7Ll4dg+fIQABI+Go/yMM5AwUuGJp40s2J2ASHWAPWAFCBcUZSu+uWLgdOKoiwXQswC+gNhwA3gnqIoM4QQy4EtiqJs+JsyVYGlQGkgA3gJqAiMf8J7BaLrsYwSQngDCxRF8SkgdKVO2ab/+g9TXF0KP87cSgONHUahm3RvFbfrFP8Lsn+i8qWdJXZffVmx5OU1+n7Jy2v0/VUsK1eycgIYGryKjKii62k1FtPSHnhpWjy94AvmXNgRmrr6GDuMQnU8ZD+RHdoYO4xC57z7AAMq9Xp6wRfMmnu/M8mt5MxdADA3cA0xvUreMej4+4Hi8xDfP7SrbL8imeDDL3xdsfibPHVop6IoBX7KFEV5J8f/pwJTCyjz2jOUuQm0zbP4DrD/Ce/lluP/pwGfp+UgSZIkSZIkSZJUVEr68NSnzZ4qSZIkSZIkSZIk/YcV6SQykiRJkiRJkiRJJZ3saZQkSZIkSZIkSZL+s2RPoyRJkiRJkiRJ0nNQKBbz1RQZ2WiUJEmSJEmSJEl6DtqS3WaUw1MlSZIkSZIkSZKkJ5M9jZIkSZIkSZIkSc9BW8KHp8qeRkmSJEmSJEmSJOmJZE+jJEmSJEmSJEnSc1CMHUARk41GSZIkSZIkSZKk51DSv6dRKEqJbReX2MQkSZIkSZIkqQR6YR8M3KgZUCRtj95ha4rF36RE9zRu1AwwdgiFrnfYGnaV7WfsMAqdX/g66pRtauwwCtWl8OOsdh1o7DAK3ashq0psXkvLl6y8hgWtYkalV40dRqGbcW81XpoWxg6j0J0LO0JG1B1jh1HoTEt7cKtWR2OHUaiqXNlJt4pdjR1Gofvz/hb+1PQ3dhiFrlvYWk6V62XsMApVo+Df8SjtZewwCt2dqHPGDuFf04pi0bYrMnIiHEmSJEmSJEmSJOmJSnRPoyRJkiRJkiRJUlEr6c/FyZ5GSZIkSZIkSZIk6YlkT6MkSZIkSZIkSdJzKOmzp8pGoyRJkiRJkiRJ0nPQlux5cOTwVEmSJEmSJEmSJOnJZE+jJEmSJEmSJEnSc9C+uF8x+UxkT6MkSZIkSZIkSZL0RLKnUZIkSZIkSZIk6TmU9K/ckI3GApT1rUe9mYMRahWBq/dxY/GfudarzEzwXjQC+3rupMcmcnLYVyQ/iEKYqmnw6ZvY13dH0SpcmLaSqKNXMbG2oPUfH2Zvb+nixIPfDnPhw58NnRoATr71qTFrCEKtImj1XgIXbc613qFpDarPHIJNrYpcHPYV4VtO5FqvtrGkxaEFRGw/zbXJywwZ+lNNmj2WVu2akZqSxpR3Z3L14vV8ZZas/RznsqVRq9WcPRHArIkL0Gq1VK9VhWmffoCVtSUhD8L4YMSHJCUmGyELcPGph/fMQQiViltr93OlgGOw+VfDcazrTlpsAoeHLyYpKAoA+5oVaPzJ65jaWoJWYbv/h2jTMqjUsxl1RnVHURRSwuM4Ouob0mISX9icVCZqOmyalr29lYsjgb8d4cz0VQbLCaCCTz2afzQIoVZxbe1+Ar7On1fbL4ZTup47qbEJ7BmxmMSgKMq1qkOTSa+gMjNBm57J8VlrCTl6RbeNqZqWs4bg0qwmilbh1PxfubvtlEHzAug8YzBVfeuTkZLOpvFLCb0UmK+MSx03ei4cjqmFKTf3nWf7jJUA9F08itIeLgBY2FmRGp/MEv/JWNrb8PKS0ZSr50HAhoNs+3CFIVPKZ8KsMbRo14zUlFSmj57NtYs38pVZvGYhzmWdUJuYcO74eeZOWohWq6Va7apMmf8+5uZmZGVlMWfiAi6fu2qELJ7d1DmfcfDISRwd7Nm0aomxw3lmVi29KT1pOKjVxG/YTtwPv+Rabz+kN3Z9O6FkZpEV+5CIqZ+RGRIBgNPYN7Bq0xiA2G/XkLjjgMHj/ztvffQWDX29SUtJ48txX3D70u1c680tzPng24m4VNKg1Wo5ueckK+Y9/ty07NqS/u8NAEXh7pW7LHh3gaFTyMXZtz519NdQ91fv49bi3NcYjk1rUOfjwdjWqsjZ4V8RuuUkAHa1K1H3k9cxtbVCydJy88vfCfnjuDFSKJCdjxcVP34DoVIRuXYPYV9vzLW+7Fvdce7fHiUzi8yYeO6OXUx6cCQA1VZNw7pBdRJPXeXmkNnGCP9vfThnAj7tW5Caksr7o6Zz+cK1J5b9btUXVKhUjs6tXsq1/I2Rg5jy8VgaVvMlNiauiCMunkr6RDiy0ZiXSlB/7lAOvzyXlNBofHfMInTXWRJuBGcXcRvgQ3pcEruajaV8j2bUmdqfk8MW4T6wLQB/+U7EvLQdzVd/wL5OU8lMSmVv+8nZ2/vunE2wES4AAVAJas57nTMvzyY1JJqmO+cQufMMSTnySwmO5tLob3Eb0bXAl6gy8WVijz+5QjGWVu2aUdG9Av5NX6Jew9pMmz+BAZ3fyFdu3P+mZDcGP/9xLh27t2X7pj189NlkFny0iNPHztGrf1eGvj2QxZ98Z+g0ECpBozlD2NtvHsmhMXTa9jFBO88QfzMku0zl/rpjcHOLcVTq0RSvqf04PHwxQq2i+aIRHH13CXFX7mPmYIOSkYlQq/D+eCBbfD4gLSYRr6n9qDbUj4sLN/5NJMU7p8y0DLZ3mJK9facdM3lg4M+VUAlazBrC1gHzSAqNoffWjwncdYa4HHnV6OdD2sMk1rUcR+XuTWk6uR97Ri4mNSaBHUMXkhweh0P18nRZPYFV3u8C0ODdHqRExbO+9fsgBBb21gbNC6Cqb30c3TV81WYc5b2q0GXWUH7oOT1fua6zX+fPiT8QdO4Wr66YQBWf+tzaf54N7yzKLuM39VXS4nWfucy0DPYt+JUy1StQpnp5g+VTkJbtmlHRozw9mr1C3Qa1mfzJeAb7v5Wv3AdvTcuuMxb8MJsO3XzZ+cdfjJk2ku8W/sSRvcdp2a4ZY6aN5H+9Rxk6jX+kp38HBvTpzuSZxm1Y/CMqFc5T3yb4zUlkhkdRYf0ikvYdJ+P2/ewiaVdv8+ClUSipadi90hWncW8SPm4OVq0bY16rCg96j0CYmVJu+ackHTqFkmScG4J5NfT1xtXNlWGt36K6V3VGzB7J+B7j8pX7/buNXDx2ERNTE2atnU1Dn4ac2X8GFzdX+o58iQm93yfpYRKlnEoZIYscVIK6c4dy/OU5pIRG02rHbMJ2nSEx1zVGFOdGL6HyyC65Ns1KSSNg1Lck3Q3DvKwDrXfNJmLfBTLji8G+UqmoNPstbvSfQXpoNLW2zSdu10lSbwZlF0m+dIcrncejTU3HeXBHKkwdzO0RCwEIXbIJlaU5ZQZ2NFYGT+TTviVuHhVp27gHng3rMvPTyfTuOLjAsh27tCW5gM+Oi2tZWvk0JfhBaFGHKxmRfKYxD0evKiTdDSf5fgRKRhZBm47h0rFhrjIuHb25/8shAIK3nMC5ZR0AbKuVI+LwZQDSouLJiE/CwdMj17Y2HhrMS9sRbaRGV6kGVUi+G0bKPV1+YZuOUqaTd64yqQ8iSbxyH0Wbv6Pdtp47Zs6liN5/wVAhPzPfTq3Z/Os2AC6cuYytnQ2lyzjlK/fo4s/ERI2pmSmKPs1KlSty+tg5AI4dOEmHLr6GCTwPJ6/KJASGk3g/Em1GFvf+OE6FPMdg+Y4NuPOr7hi8v+UkZVvWBsClTV3irj4g7oruYio9NlG3H4UAITCxNAfA1MaSlLDYFzunHGw9NFiUtiPiRP6e5aJUxrMy8YHhJOjzuvXHcdz8cufl5teAG/q87mw9ias+r+jL90gOjwMg9noQagszVGa6+3jVX2nDuUc9sYpCaqzheoQfqd6hIed/08UddO4WFnZW2JSxz1XGpow95jaWBJ27BcD53w5RI0/+ALW7NOHi5qMAZKSkcf/0DTLTMoo2gWfQpmNLtvyyA4CLZy9ja2f71DrDxMwkewiSoihY2+oa9Da21kSGRRkk7ufh7VmXUna2xg7jH7GoW52M+yFkBoVBRiaJ2/dj07ZZrjIpJ8+jpKYBkHrhKiZlSwNgVqUiKacvQpYWJSWN9Bt3sW7lne89jKWpXxP2/rYXgOvnrmNtZ41DGYdcZdJS07h47CIAmRmZ3L50GycXXX4dB3Rk28qtJD1MAuBh9EMDRp+fg1cVku6GZV9DhWw6hqZj7r93yoMoEq7ehzz1eNKdMJLuhgGQFh5LWlQ85k52Bov971h7VSUtMJS0++EoGZnE/HEYh46Nc5VJOHoJbWo6AElnbmDq8rguSTh8EW1iikFjflbtO7fh91+2ABBw5iJ2pWxx1n9+crKytuSNEQNZvPCHfOumzhrPvI++RFFK+gDNv6ctop/iwuCNRiHEQCHESSFEgBBiqRCiiRDighDCQghhLYS4LISoI4TwEULsF0JsEEJcE0KsFkII/Wv465edEUJ8JYTYUljxWbg4kBISnf17SmgMli6OTyyjZGnJSEjGzNGWh5fv49KxIUKtwqqiM/b13LF0zb1t+Z7NCNp8rLDC/ccsNI6k5sgvNSQGc43j32yRgxBUnzGIGzMMO/zvWZV1cSYsOCL79/DQCMq6OBdYdum6LzhweTtJiUns+lN3wr59/Q5tO7cGwK9bOzTlyhR90AWw1DiQHBKT/XtyaAyWLrkvIqw0DiTpyyhZWjLikzF3tMHWQwOKgu+aCXTeOYta+ju5SmYWJycuo8veefQ+t5hS1cpxe+3+FzqnnCr1aMq9zYYfxmTl4kBi6OO8ksJisM6Tl7XmcRklS0t6fDIWDja5yrh3aUTUxUC06ZmY2VkB0Oj9vvTePov2S0ZhWdrwF052Gkfic9QV8WEx2JXNnZtdWQfiwx7nHx8ag12e+qRS4xokRT0kJjC8aAP+F8q4OBMWkrvOKPOEOuPrtZ/x16UtJCcms+fPfQAs+PBLxkwbyfYzG3lv+jssmvPiDPd8kajLOpERFpn9e2ZYFOoy+S9qH7Hr3YnkQ7pRB2nX7mDV0hthYY7K3g7LxvUx0RS8j43BSeNEVOjjmw3RYdE4afLfuHjE2s6axu0bc/5IAADl3F1x9SjHJxvn8+mmBTRo06CoQ/5bea+hUkOjschTJz4Le6/KqExNSCom9YaZxpH0kMf7KT00GtO/2U+l+7fn4b6zhgjtuWlcyhAaHJb9e1hIOBqX/Nc/YyeN5IdvfiYlJXfjt31nH8JCI7h2Of/QfqlkMWijUQhRE3gFaKEoiieQBVQHNgOzgPnAKkVRLuk38QLGALUAD6CFEMICWAp0VhSlIVBsav97a/eTEhKN785Z1Pt4EDGnb6Jk5b7rUr5nM4J+N16j8XlUGOpH1F/nSMtxkfyiGtZvDL71umJmZkaTlrq7oNPGzKbfa31Yv2s51jZWZKRnGjnKf05losa5cTWOvvMNu3p+TPlO3pRtWRthoqba4PZs85vCRq93iL16n9qjuhs73GfypJxycuvRjHsv6OfKoVo5mkzqx6GJPwGgUquwcXUi7MwNNnaeSviZWzSdNsDIUf57dbo346IRb5QVlrf7j6VD/R6YmZnRqKWuN/WlIb1YOH0RnRv2ZsH0r5j+2SQjRynZdGuLRZ2qxP60AYCUo2dJPnSK8ms+R7NgEqnnr6JkFad7989OpVbx/qL3+XPZZsLv6xpTahM1rm6uTH55EgtGfco7n4zC2s7ww9kLk3kZe7wWjSRgzBJ4AXuunHq3wbp+ZcK+3WTsUApNzTrVqOhWgV3b9uVabmFpwcgxr/PFvG+NFFnxohTRT3Fh6Gca2wENgVP6TkNLIAL4GDgFpALv5ih/UlGUIAAhRADgBiQCdxRFuasvsxbI/xDKv5QaGoul6+O7R5YujqTkaSQ9KpMSGoNQqzC1tSI9JgGAizkm4Wjz5wwS7zwe312qVkWEWk3chbsYS2pYDBY58rNwdSQt7NkagfbeVbFvUoMKr/mhtjZHZWZCVnIqN2etLapwn6rf0D70HdgDgEsBV3P1DpZ1KUN4aOSTNiU9LZ19Ow7i26kVxw6e5O6te7z1ymgAKnlUoHWH5kUb/BOkhMVilaOH2srFkZTQ3ENJk8NisXZ1fHwM2lmRFpNIcmgMEcevZ09wE7L3PI513chI0N0ZTLyn61W5v/kEtd7pZqCMiiancP1QcPtaFRFqFTEXAw2WT3bMobHY5BiJYK1xJClPXklhujJJ+rzM7Kyyh5tauzji98MY9o1ZQrx+36TGJpKRnMrdbacBuLPlBDX6tTFIPo0Gd6BhP92w7OALd7DLUVfYaRyJD8+dW3x4bK6eRTsXx1w9jyq1ipqdGvFd16lFHPmze3lob3q/qrthcjngKhrX3HVGxFPqjP07D+HTqRUnDp6i68udmT/1CwB2b97LhwsnFmns/1VZ4dGY5ugdNNGUJisi/1Bgy2ZeOL7Vn+Ah4yHj8fDn2KVriV2qO0+VnT+RjHtB+bY1JP/BXejYX/ds280LNynt8rjX1EnjRHRYdIHbvTNvFCGBIWz+8fHEMlGh0Vw/d52szCzCH4QTcjcEVzdXbl64WbRJPEHeaygLFydS89SJf8fExpImqyZwbd564s7eKooQ/5X0sBjMXB/vJzMXJzIK2E92rerh8m5frvWZilKMbzwPev1lXhnUG4ALAZdxKafJXqdxLUtYaESu8g0a1aeuZy0Ont2K2kSNU2lH1vzxPR9N/ITyFcux9cB6/bZl+HPvGnr6DSIqouDjuCQr6RPhGHp4qgBWKIriqf+prijKDMAJsAFsAYsc5dNy/D8LAzRyYwNuY+OhwaqiM8JUTfmezQjddSZXmdBdZ6j4cisAynVtQuQR3cWr2tIMtZXumbEyreugZGblmkCnfK/mBG06WtQp/K34c7ex8tBgqc9P07M5ETvPPH1D4OLIxRxq+A6HGo3ixkerCfnlkFEbjADrlv1G33aD6dtuMHu3H6D7S/4A1GtYm8SExHyVlqWVZfYzS2q1mtYdWnD31j0AHEvrhtAIIRj23lB+WfG7ATN5LDrgDrbuGqwrOKMyVVOpR1OCduUe5hK86yweL+mOwYpdGxN+WDfrZuj+C9jXrIDa0gyhVlGmWQ0e3ggmJSyGUtXKYe6oe5ZJ07purkloXsScHnHr2YzAP4zTkxVx/g6l3DXY6vOq0qMp93bnzuve7rNU0+fl0aUxIUd0eZnZWdF5xThOzl1P+OmbebY5h2uzmgCUa1mb2JvBGMKplbtZ4j+ZJf6TubbrNPX76OIu71WFtIQUEiPicpVPjIgjLTGF8l5VAKjfpxXXdz+uTzxa1iHqdkiuhqSx/bJsI/3av0a/9q+xb8dBur7cCYC6DZ6tzmjZvjmB+jojMiyKhs29AGjcsiH37zwwYCb/HamXrmNaqRwm5cqCqQk2nX1I2pd7OLpZzcqUmf4uoe9MJysmx3N9KhWqUrp6z6yaO2bV3Uk+8mznvKKybeVWRnd+l9Gd3+X4zmO07aObRK+6V3WSE5KJjcjfyBo4fiDWtlZ8P+P7XMuP7zxG3WZ1AbBzsMPV3ZWw+2H5tjeUuIDbWOe4xnDt2YywXc/29xamaryXjeXBr4eyZ1QtLpICbmLu7oJZhTIIUxMce7QkdlfuidesartTad4Ibg6dQ6aRny19mp9/+oWuvv3o6tuP3dv20etl3cSHng3rkhCfSGR47psyq5f9SrM6frRu0IWXuwzl7u17DOjxP65fvUXjmu1o3aALrRt0ISwkgm5tB/wnG4z/BYbuafwL+EMI8bmiKBFCCEd0DcVFwDTAHfgEeOdvXuM64CGEcFMUJRDdcNdCo2RpCZi8nBZrJyLUKu6t3U/C9WBqTuhLXMAdQnedJXDNfrwXj8Tv2GekxyVxcphulkDz0na0WDsRRauQGhbLqVG5u+vLd2/KkVfnF2a4/5iSpeXapGU0WDcZoVYRvHYfSdeDqDzhJeLP3yFy5xnsPD3wXDYOU3trnP0aUPn9vhxt875R434WB/ccpVW75mw/sYGUlFSmjZ6VvW7DXyvp224wVtaWLF75KWbmZgiV4OSRs9mNQ/9eHeg3tC8Ae7bt5/e1hfao7D+iZGk5PWUFbddMQKhV3F53gIc3gqn3fh+iz98leNdZbq09QPOvhtP9yELS4hI5MmIxAOkPk7m6dDudtn0MikLI3vOE/BUAwMXPNtLh96loM7JICo7i2BjDzQxbVDkBVOzWhP2DPjVYLnnzOjxtBf6rJyBUKq6vP0DsjWC8x/ch8vxd7u0+y7V1B/D9cjj9Duvy2jNSl1ft1zpg51aWBmN60WBMLwC2DviE1Oh4TsxZR9svR2D20UBSoxPYP9bws/je3BtAVV9P3j34GRkp6fwxfmn2uuHb5rDEXzcj9Napy+i5cBgmFmbc2n+em/vOZ5er060ZlwoYmjrm8BeY21qiNjWhhp83Pw+aR6SBGsY5Hd5zjJbtmrH5+C+kpqQyY8yc7HXr9iynX/vXsLSy4IuVn2BqZopKpeL0kbNsWLEJgJnjP+H9maMxMVGTlpbOrPeNW78/i/enz+PUuQvExcXTrudARr4xiD7dit+MjrlkaYmc/TWu389BqFTE/76L9Fv3cHxnMKmXb5C87zilx/8PYWWJ5nNdr3ZmSASh78xAmKgpv0o3g6U2MZnwDz6BYjQ89fTe03j7evPdoe91X7kx/ovsdV9u/4rRnd/FSePEK+/248HNB3yx7UsAtq7Ywq51uzh74CxerRvw9V/foM3Ssmz2MhLiEoyUja5OvDR5OU3XTkKoVTxYu5/E60FUn9CXuIC7hO86QylPDxr9NBZTe2vKdmhA9fdfYn+b93Ht3gynpjUwc7Chwiu6+QUCRi8h/vI9o+WTLUvL/anfU33NdFCpiFr/F6k3HuA6vj/J528Rt/sU5acNQW1tQZWluuultOBIbg2dC0CNjbOxqFIOtZUF9U9/z91xXxN/IMCICT22b/dhfNq3ZN+pzaSmpDLh3RnZ67bsW0dX337GC+4FU3xqlqIhDD3TkRDiFWASul7ODOAPoL6iKH2EEGrgqH69FhivKEpX/XaLgdOKoiwXQnQDPgWS0A1rtVUU5dU8b6Vs1Ly4zwE9Se+wNewqW/I+wH7h66hTtqmxwyhUl8KPs9p1oLHDKHSvhqwqsXktLV+y8hoWtIoZlfJWjS++GfdW46VpYewwCt25sCNkRN0xdhiFzrS0B7dqFfOG6T9U5cpOulUs+GupXmR/3t/Cn5r+xg6j0HULW8upcr2MHUahahT8Ox6lvYwdRqG7E3XuhR3k+X35gUXSqPpf0Kpi8Tcx+Pc0KoqyHlj/hHVZQJMci/bnWJez93Gfoig19LOpfg2cLoJQJUmSJEmSJEmSnqqk9zS+qN/T+D/9xDiXgVLoZlOVJEmSJEmSJEmSCpnBexoLg6IonwOfGzsOSZIkSZIkSZIkpVgMIi06L2SjUZIkSZIkSZIkqbiQw1MlSZIkSZIkSZKkYkEI0UkIcV0IcUsIke9LgoUQ5kKI9fr1J4QQbs/7nrLRKEmSJEmSJEmS9By0RfSTl/7bJr4GOgO1gP5CiFp5ir0BxCqKUgXdI32fPG9+stEoSZIkSZIkSZL0YmgM3FIU5Y6iKOnAOqBHnjI9gBX6/28A2um/deJfk41GSZIkSZIkSZKk56AU0U8BygEPcvwepF9WYBlFUTKBh4DT8+QnJ8KRJEmSJEmSJEl6DtoSPnuq7GmUJEmSJEmSJEl6MQQDFXL8Xl6/rMAyQggTdN9rH/08byobjZIkSZIkSZIkSc/BUBPhAKeAqkIIdyGEGdAP2JynzGZgiP7/fYG9iqI8YbTrsxHPuX1xVmITkyRJkiRJkqQS6IUd5Pl5xYFF0vZ47/6qfH8TIYQ/8AWgBn5SFGW2EOJj4LSiKJuFEBbAz4AXEAP0UxTlzvPEUaKfaTxVrpexQyh0jYJ/Z6NmgLHDKHS9w9Yw0u1lY4dRqL4J/IXjrr2NHUahaxqykZXlBho7jEI3OHgViyuUrLzeebCKmZVeNXYYhW7avdU0dfUxdhiF7njIfm7V6mjsMApdlSs7yYh6rmuVYse0tAcmZnnnnXjxZaYHc7ZC3kkYX3wNHvxR4q4JGwX/TuXSDYwdRqG7HXXW2CH8a0/oFSwSiqJsA7blWfZhjv+nAi8V5nuW6EajJEmSJEmSJElSUSvpQxzlM42SJEmSJEmSJEnSE8meRkmSJEmSJEmSpOcgv3JDkiRJkiRJkiRJ+s+SPY2SJEmSJEmSJEnPwZAT4RiD7GmUJEmSJEmSJEmSnkj2NEqSJEmSJEmSJD2Hkj57qmw0SpIkSZIkSZIkPQdtCW82yuGpkiRJkiRJkiRJ0hPJnsansPPxouLHbyBUKiLX7iHs64251pd9qzvO/dujZGaRGRPP3bGLSQ+OBKDaqmlYN6hO4qmr3Bwy2xjhP47Ttx71Zg5GqFUErt7HjcV/5lqvMjPBe9EI7Ou5kx6byMlhX5H8IAphqqbBp29iX98dRatwYdpKoo5eBUCYqvGcM5TSzWuCVuHyvPWEbD1ljPSyvTR9KLV9vchISWPl+G94cPluvjLdx/ejSe/WWJayYWztwdnLm/ZtQ69Jg4gLjwHgwIodHF2/12CxP0kpHy/cZr6OUKmIWLuHkMW/51pv26QWbh+/jlXNStwc8RkxW49lr6s4ZRD27RoCEPzFr0RvPmLQ2HNy9alHo48HIVQqbq3dz6Wv8x+DLb8cjmNdd9JiEzg4YjFJQVFYly9Nj/3zib8TCkDk2VucmLgs17a+y8ZiU9GZP9tNMkguFX3q0WrGIIRaxZW1+zn7Tf5cOnwxHOe67qTGJrBz5GISgqIAaPh2N2r280HJ0nJo+kruH7gIwOCjn5ORlIo2S4uSlcUvXT4EoOM372Dv4QKAuZ0VafHJrO80xSB5dpwxmCq+9clISWfz+KWEXQrMV0ZTx40eC4djYmHKrX3n2TljJQCtx/TGq78vydEJAOz7dD239p1HZaqmy5w3cK3ngaLVsvOjn7l3/KpB8inI2JmjaNa2KWkpqcx8bx7XL97MV+bz1fMpXcYRtYmagBMXWTD5C7Tax1MeDBj2Mu9OH0nHOj14GPPQkOHnY9XSm9KThoNaTfyG7cT98Euu9fZDemPXtxNKZhZZsQ+JmPoZmSERADiNfQOrNo0BiP12DYk7Dhg8/n9j6pzPOHjkJI4O9mxatcTY4fxjn3/2MZ07tSU5JYU33niPcwGX8pX5a/evaFzKkpKSCkBn//5ERkZToYIry378klL2dqjVKqZMmcv2HcY9b9n5eFF+xv9ArSJ67W7Cv/kt1/oy/+uOUz8/yMoiI/oh98cvIj04Esta7lSYMxy1jRVotYQt+pXYPw8bKYv8Ssr1YEE+nPM+Pu1bkpKSyoRR07l84doTyy5d9TkVK5Wjc6uXAXhv4gjad/ZBq9USHRXDhFHTiQiLMlToxUpJnwin0BqNQohERVFsCli+HNiiKMqGQnyv1wBvRVHeKazXLJBKRaXZb3Gj/wzSQ6OptW0+cbtOknozKLtI8qU7XOk8Hm1qOs6DO1Jh6mBuj1gIQOiSTagszSkzsGORhvlUKkH9uUM5/PJcUkKj8d0xi9BdZ0m4EZxdxG2AD+lxSexqNpbyPZpRZ2p/Tg5bhPvAtgD85TsR89J2NF/9Afs6TQVFocaYnqRFPWR3i3EgBGYO+Xa/QdX28aKMu4YZPu/i5lWVfrPf5NOe+S+uL/x1hv0rdjBj/1f51p3ZcpRfpv9kiHCfjUqF+5z/cbXfR6SHRlNn23xid54iJccxmB4cye0xi3AZ3iPXpvbtGmJV14MLHcaiMjOl1m8zidt7lqzEFENngVAJmswewu7+80gOjcF/28c82HWGhzdDsstU7e9D2sMkNrUch1v3pjSc0o+DIxYDkHAvnC1+BTeUKnb2JjMp1SB5gC6XNrOG8MeAeSSGxvDylo+5u/sMsTlyqdXPh7S4JFa1GkfV7k1pPrkfO0cuxqGqK1W7N2VNuw+wLutAz7UTWdV6PIpWN6Tl95dnkxqbmOv9do5cnP3/FtMGkB6fbJA8q/jWx9Fdw9dtxlHOqwr+s4byU8/p+cr5z36dLRN/IPjcLfqvmEBln/rc3n8egBM/buf4d9tylW/QX1enLO04ESsnOwasmMAP3aaBYvhhPc3aNqGCe3leavEqtRvUYsLc93ij68h85aYMm0Fyou7vPvf7j2jbzYc9f+guzMu4OtO4jTehQWEGjb1AKhXOU98m+M1JZIZHUWH9IpL2HSfj9v3sImlXb/PgpVEoqWnYvdIVp3FvEj5uDlatG2NeqwoPeo9AmJlSbvmnJB06hZJkmOPtefT078CAPt2ZPHOBsUP5xzp3akvVKu7UqNWSJo0b8PXiuTRv2a3AsoMHv8OZsxdyLZs8aTS/bviTpd+tpGbNqvz5x89UqdbUEKEXTKWiwqxh3BwwnYzQaKpvWcDD3SdJvfkgu0jypbtEdhmLkppO6UGdKDflNe6O/BRtShr3xnxBWmAopmUdqbF1IfEHzpEVn2S8fB4pKdeDBfBp3wI3j4q0bdwDz4Z1+fjTSfTpOKTAsn5d2pKcp074fvFKPp/3LQBD/tePUePfYtr4OUUet2R4cnjq37D2qkpaYChp98NRMjKJ+eMwDh0b5yqTcPQS2tR0AJLO3MDUxenxusMX0RrhAj0vR68qJN0NJ/l+BEpGFkGbjuHSsWGuMi4dvbn/yyEAgrecwLllHQBsq5Uj4vBlANKi4smIT8LB0wOASv18uL5os+4FFIX0mAQDZVSwen7enNh4EIDAczexsrXGztk+X7nAczeJj4wzbHD/ko1XFVJzHIPRBRyDaUGRJF+9B9rc97gsq5Un4fgVyNKiTUkj+WogpXy9DBl+NievyiQEhpN4PxJtRhaBfxynQp5jsIJfA27/qjsG7209iaZl7ae+romVObXe6syFLzcVRdgFKutZmYeB4cTrc7m5+Tgefrlz8fBrwLUNulxubT1J+Ra19csbcnPzcbTpmSQ8iORhYDhlPSs/83tX6dqEG38ce3rBQlCtQ0Mu/KavE87dwsLOCpsy9rnK2JSxx9zGkuBztwC48Nshquf5W+RVumo5Ao9eASA5Op7U+CRc67kXfgLPoHXHFmzbsBOAy2evYFPKBqcyjvnKPWowqk3UmJqZ5mrgjpnxDotnLS0WMyBY1K1Oxv0QMoPCICOTxO37sWnbLFeZlJPnUVLTAEi9cBWTsqUBMKtSkZTTFyFLi5KSRvqNu1i38jZ4Dv+Gt2ddStnZGjuMf6Vbt478vFp3T/3EybOUsi+FRlPmmbdXFLCz092wLWVnR2hoeJHE+aysPauSFhhGuv6cFbv5EKX8cp+zEo9dRHl03XT2OqYa3XVT2t0Q0gJ1I0oywmPIiH6IiaOdYRN4gpJyPViQ9p19+P2XLQAEnLmIXSlbnPX1Qk5W1pa8MeJVvl74Q67liYmPG/WWVpYoRrgBWFwoRfRTXPyrRqMQYqwQ4pL+Z0yedUIIsVgIcV0IsQcok2NdoBBivhDiohDipBCiin65sxDiNyHEKf1PC/3yxkKIY0KIc0KIo0KI6gXE0kVfJv8R/pzMNI6khzzuYk8Pjc6u3ApSun97Hu47W9hhPDcLFwdSQqKzf08JjcHSxfGJZZQsLRkJyZg52vLw8n1cOjZEqFVYVXTGvp47lq6OmNpZAVBrwku03TWbxt+Pxry0cSt3+7KOxObYX7Fh0dhr8l8A/h2vzk2Ysv1T3vxmLA4uT97XhmKmcSI9x75LD43GzOXZckq+Eoi9rxcqSzNMHG2xa14Hc9dC/5g8EyuNA0khMY9jC43BSuOQq4ylxoFkfRklS0tGfDLm+t5rm4rOdN05C78NUyjT+HE14DmhL5eXbiczJd0AWehYaxxIyJFLYmgM1nlyyVlGydKSnpCMhYPN32+rKHRfPZGXt86k9gDffO/r2qQ6KVEPeRhomItCW40j8TmOvfiwGGzL5s7TtqwD8WGP84kPjcE2x2eu0WA/3toxl26f/g8LfZ0RfuUe1To0QKhV2FdwxqWOO3auxvmsOWuciQiJzP49IiQSZ41zgWW/WDOf7Rc2kZSYzN4tumGbrTq2IDIskltXbhsk3qdRl3UiI+xxPplhUajLPPkzb9e7E8mHdI8UpF27g1VLb4SFOSp7Oywb18fkCX8LqfCUc9UQ9ODxKIXgoFDKuWoKLPvDD59x+tQupkwek73s45kLGTCgN4F3TvPn5pWMHjO1qEP+W6Yap1zXTRlPuW5y6teB+P1n8i238qyKytSEtHvFoAefknM9WJCyLmUICX58XgkLiUDjkv+z/96kkfz4zarsIdI5jZv8NofPb6NH3858oe91/C/SFtFPcfGPG41CiIbAUKAJ0BT4nxAiZ/dFL6A6UAsYDDTP8xIPFUWpCywGvtAv+xL4XFGURkAf4NFtjGtAK0VRvIAPgVz93UKIXsBEwF9RFKMOoHbq3Qbr+pUJ+3aTMcModPfW7iclJBrfnbOo9/EgYk7fRMlSECZqrMo5EX36Bnv9phBz+iZ1p79q7HCfy8U9Z5jW8m1md36fa4cvMHjh28YO6bk8PHCe2L/OUHvzXKp8M5bEMzdQsopT9fNsUiLi2Nh4DFs6TuX0R6tp9fVITG0scahdEdtKZXmw47SxQywUv/WZyS/+U/lz8KfUHdIe1ya575FV7dHMYL2MheHMqj0sbv0e33WeTGJEHB2m6eqHgF8OEB8aw5t/zsLvw0E8OHvzhTguxwyYQFevPpiZmeLd0gtzS3NeG/Uq33267OkbF0M23dpiUacqsT/perlSjp4l+dApyq/5HM2CSaSev/pC7Jf/ikFDRuHVoD0+vr1o2aIxAwf2BaDfKz1ZufJX3Dy86dZ9MMuXf4UQwsjRPhvHXm2wrleF8CW5n9M3KeOA2xfvETjuK6MMW39eJfF6sGadalR0K8+ubfsKXL9wzte0rO/PHxu2M+jNfgaOTjKUf/NMY0vgd0VRkgCEEBuBVjnWtwbWKoqSBYQIIfI+kb02x7+f6//fHqiVo6KzE0LYAKWAFUKIquh6aE1zvE5bwBvwUxQl/l/k8VTpYTGY5eiZMXNxIiMsOl85u1b1cHm3L9f6TEVJzyyKUJ5Lamgsljnu5Fu6OJISGlNgmZTQGIRahamtVfZw04vTV2WXa/PnDBLvhJIek0Bmcmr2xDfBfx7HbYBP0SeTR+tBHWnRvx0A987fxsG1NHAdAAeNE3FhMX+zdW5JcY+fJTuy7i96TRxYqLH+G+lh0Zjl2HdmLk6khz57TiFf/UbIV7pJCKp8PYbUOyFP2aJoJIfFYu36uAfKysWR5LDYXGVSwmKxcnUk+dExaGdFmv75vrR03b8xFwNJCIzAzkODk6cHTvXc6X38c4SJGgsnO/x+ncKul4p2koGksFhsc+Ri4+JIUp5cHpVJCtPlYmZrRWps4t9u++jflOh47uw4Q1nPyoSc0B3LQq2icqdGrPefVqS5eQ/ugFc/XS9nyIU7uXoA7TSOJITnzjMhPBa7HD2Ldi6OJOg/c0lRj6vls2v30e+n8YCu53X3zMd1ymsbpxN913C9CX1e60mPV7sCcDXgGmVcH99RL+PqTGSOnrq80tPSObjzCK06tiQ6IgaXii6s2vMjAM4uzqzY+R2v+48gJvLZP6OFKSs8GtMcvYMmmtJkReS/n2rZzAvHt/oTPGQ8ZGRkL49dupbYpbpTdNn5E8m4F5RvW+n5jRg+hDfe0N1EOX06gPIVXLPXlSvvQnBI/s9DiH5ZYmISa9dtopG3J6tWbWDo0H506ao7Vx0/cQYLc3NKl3YkMjL/tYohZIRF57puMn3CdZNty/poRr3EjZem5LpuUtlYUmX5NELmryL53A2DxPwsSsr14CMDX3+ZVwb1AuBiwGVcy5XlUX+vxrUMYaG560GvRvWo61mLA2e3oDZR41TakdV/fMerPd7KVe6PDdv5ad1XfPnJizchVWHQvhj3a/41YzzTqBTwfxXQVFEUT/1POUVREoGZwD5FUeoA3QCLHNveBmyBakUVaFLATczdXTCrUAZhaoJjj5bE7so9O6hVbXcqzRvBzaFzyIw27qx5TxIbcBsbDw1WFZ0RpmrK92xG6K7cw0FCd52h4su6tn+5rk2IPKJ7jlFtaYbayhyAMq3roGRmZU+gE7rrHM7NawLg3KoO8Tkm1jGUgz/vZK7/BOb6T+DCrpM06d0aADevqqQkJP+jZxdzPv9Yr4M3YbeNf8GUGHALC3cXzPXHoFMBx+ATLC2LhQAAd4hJREFUqVSY6Id3WtWshFVNN+IOBBRdsH8jOuAOtu4abCo4ozJV49ajKQ925R6682DXWSq/pDsGK3VpTNgR3XNv5o62CJWuJrap6Iyde1kS7kdwY+VfbGg4io1N32NHz4+JvxNa5A1GgPDzdyjlpsFWn0vV7k25uzt3Lnd3n6VGX10uVbo0Jkify93dZ6navSkqMxNsKzhTyk1DeMBtTCzNMbXWVW8mluZUaF2H6OuPj78KreoQezuEpH9wE+TfOL1yN9/7T+Z7/8lc33Waen30dYJXFVITUkiMiMtVPjEijrTEFMp5VQGgXp9W3Nitq1tyPv9Yo6M3kfp8TCzMMLXU1SnuLeugzdQSddNwdcdvyzcxuMObDO7wJgd2HMa/r25iitoNapEYn0R0RO6/saWVZfZzjmq1mhbtm/L/9u47PoqiDeD4b+7SG0koKQgkiEiR3ntCJ4p0kS5YUBSRXn1BKWJDFFRQlA5ioSkgHaQKCAHpNZQUSAghvd6+f9yRHookdzE+Xz75cLc7d3me7O7czs7s3NWL17h09goB1bvQpcGLdGnwIuGh4Qxo95rFGowAiSfPYV2uNFalPcDaCqcOfsTtPJiljE3lJyk1+W1C35pMWuaZXnU6dMWM9wXaVPTF5mlf4vflHDYoHt/X8xZTt15b6tZry/r1m+nXx9hr2KB+baLvRhMWditLeb1eT/HixqHhVlZWPPtsa06dMl5Qun4tmJb+TQGoVKkCdna2FmswAsQdv4CtT8Z5k9vzzbi79VCWMvZVfSk78w0uDZqe5bxJWVtR/tvx3P5lJ1Eb95s79PsqKueD9yz7/kc6+veio38vtmzcRZcXjBfSatapRkx0LOE3s15sWrHwZxo/044WtZ+j57ODCLp0Nb3B6FO+THq5Nh1acOlCkNnyEOb1T3oa9wCLlFIzAYVxOGq/TOv/AAYrpRZjvJ/RH1iRaX1PYKbp/3tjrbYAQ4GPAZRSNTVNC8TY03jvbOKlbHFcBUYDq5VSPTRNO/UPcrm/NAPXJn3L0ysmg05HxKrtJJ6/jveoXsQfv0jU1sM88e4A9I52VJg/GoCk4HAuDvwAgEqrp2NXoTR6BztqHPmWKyO/JNoCJ+1amoHACYtosnIcSq/j6spdxJwLpvKY7kQFXiZ0y1GCVuyi7twhtD0wi+SoOA4NngOAbQkXmqwch2bQSAy7w+GhGWPVT05bSb05b1B9an+Sbkfz1zvzzZ5bZid3HqOqf23e2/0FyQnJLB39Vfq68Rs/4oOAMQB0GdeHup2aYmNvw/QDX7N/1Q42zP4J/4EdqNa6Loa0NOKjYlky6qu8fpX5pBkImriASiv+h9LruPXDdhLOX+eJ0S8Sd/wSd7YcxrFGBSp+NxYrV0dc29TjiVE9OeH/DspaT5U1xkZUWkwCF4fOBgsNN9PSDByatJjWK8YYv3Jj1W7ung+mxqhu3D5+hRtbj3Lhh900/eJ1Ou/9lOSoWP4wzRrq0bASNUd1w5CahmbQODh+IclRlptNT0sz8Me7i+m0bIzxKzdW7SbyfDD1R3bj1okrBG09yukfdtNm9uv03fMpSVGxbH7TmEvk+WAu/PYnfXZ8iCHVwO5Ji9AMGg4lXQj49h0AlF7P+XX7ubYrY5bEp55vaPahqRd3BFLBvyZv/jGLVNNXbtzz6sYZfBswAYBNkxby/KeDsbKz4dKu41zcaZw5tdX4XnhWKYemady9Ec6GCcZZiR1LuNBnyVg0TSM67A7rhlvu/pf92w/SuFUDft6/nMSEJKYN/zB93ZKtC+jf5hXsHez4eNEMbGysUTodR/cfY82S9RaL+b7SDIRP/xLvb2egdDqi12wh+eJV3N/qT+Kp88TvPEiJUa+iHOzx/Mx471tqyC1C35qCstLzxDLjTI+G2Hhujv3QYvXFoxo9eSaHj50gKiqaVp37MuTlfnTrWPhmqczNxk3bad++JefO7CM+IYFXXhmRvu7I4S3UrdcWW1sbNm5YgbW1FXq9nu3b97Dgu+UAjB77PvO//phhw15F0zRefmW4pVIxSjNw/d1vqLBsCkqv47bpvMlrZG/iT1zk7tZDlJ44EJ2DPb7zjJ/JySERXB40HbfnmuDcoCpWbs4U72GcZfnqiC9IOJ3zq7PMroicD+Zm19a9+LVuyo7D60hMSGTs21PS1/26cyUd/Xvd9/Wj332b8hXKYTBoBN8I5d2Rhe8rRczFUKimrcl/6p/McqSUGgEMMj1doGna7HtfuaGMY0znAG2Aa0AK8L2maT8rpYKAVUAHIAnopWnaRdMkNl8ClTE2ZP/QNO11pVQjYDEQB2wA+mqa5pP5KzdM91MuBzpqmpZ5NgLtcOkuj5xbYVcveA2rPXtbOox81zVsBUN8XrB0GPnqq6AfOejd1dJh5LuGIatZUtryQ3fzW//gZcwtU7Tyeuv6MqaW+3ffa5ybd68up6G3n6XDyHcHQ3Zxscq/o7HzKCqc3kxKxGVLh5GvrEuUx8qmtKXDyHepycEcLdPpwQX/ZWpfX0dROyesF7yGJ0vUtnQY+e5SxNF/7SDPiT69C6TVOD1oRaH4m/yj72nUNG0WMCvbMifT/xpwv+9P/FjTtLHZXhuBsecx++85QNbhp5NMyxcBi0yPj2GcdEcIIYQQQgghRD77R41GIYQQQgghhBBG/44B/f+cWRuNmqb5mPP3CSGEEEIIIYR4PNLTKIQQQgghhBCPoahPhCONRiGEEEIIIYR4DEW7yWiZ72kUQgghhBBCCPEvIT2NQgghhBBCCPEYivpEONLTKIQQQgghhBAiT9LTKIQQQgghhBCPoahPhCM9jUIIIYQQQggh8iQ9jUIIIYQQQgjxGIp2PyMoTSuyKRbZxIQQQgghhCiClKUD+KeG+bxYIG2Pz4N+KBR/kyLd0/iqTw9Lh5Dvvg36iY/K9bV0GPluzNVlbPDoZekw8tWzN1cywudFS4eR72YF/cCcMkVvHxx6fRlTy/WxdBj56t2ry/nZq2jlBNA9dDnhbVpYOox8V3LrbjqWfc7SYeS7X6/9hpVNaUuHka9Sk4NJibhs6TDynXWJ8kzy6W3pMPLdtKAVjPEpWucYHwWtJLhRS0uHke9KH9hh6RBEHop0o1EIIYQQQgghCppWxAc5ykQ4QgghhBBCCCHyJD2NQgghhBBCCPEYDJYOoIBJo1EIIYQQQgghHoN8T6MQQgghhBBCiP8s6WkUQgghhBBCiMdQtPsZpadRCCGEEEIIIcR9SE+jEEIIIYQQQjyGon5PozQahRBCCCGEEOIxyOypghcnD6Saf22SE5JYOOpLrp26kqNM51G9aNS1OQ7FnBhatV/68hfeHUClRs8AYGNng3OJYgyr/pK5Qs+i1ZR+lPevSUpCEptGfcPNk0E5yng840PAp4OxsrPh8s5Atk9ZCkDTkd2p0KY2mkEj/nY0m0bOJ/ZWFO5PetHhk9fwqOrDnk9+4vA3G82cVYaS/jWoMq0/Sq/j+vKdXJqzPst694aVqDK1P85VynJs8BeE/XYIAJeq5Xjmo0FYOTmgGQxcnL2G0HUHLZFCnrpMHkBl/1okJySxctTXBJ8KylGmw6ie1O3aHIdijoyv+lL68hYvB9DgxZYYUtOIjYxh1Zh53AmOMF/wmTR/rx/lWtYkNSGJbSO+ITyXfbBkNR9azzLug1d3BPLHZOM+aOvqSPsv38KlTEmir4fz+5A5JN2NT39dqRrl6bF2Mr+/OZdLGw+bK6Uc2k3pTwX/GqQkJLN+1HzCcsnR8xkfOn36OlZ21lzceZzNU5akr6v3Ulvq9muDZjBwYUcg2z9YacboM3j4V6fm+/1Qeh1XVuzi3Nxfs6zX2VhR74s3cKvuQ/KdWA4OnkP8jQiUtZ46H72MW43yaAYDx99dSviBMwA0XTEGu1KuKCs9EX+e49j4hWCw3JVZ67r1cRoyFKXTkbBpAwmrVmRZb/fc89g/3wUMaWgJCcR89glp166ClRXO74zCquLTYDAQ+9UcUk4EWiaJXLz23mvU8a9LUkISn4+czaWTl7Kst7WzZezX4/Aq54nBYODQtkMsnrk4fX3T55rSa3hv0DSunL7CJ29/Yu4UcvXZrPfp0L4l8QkJvPzycI4FnsxRZvvWn/D08iAhIRGADgG9CA+/TZky3iz87nOKubqg1+uYOPEDNv2+w9wpPJJJM2bxx75DuLu5snbZPEuH80iendyfiv41SUlI5pdR8wjN5TPL+xlfun4yGGs7G87vDGTDe8Z60LNKOTpNH4SVrTWGVAPr311I8PFLOV5vac9PHkAlU44/5vG53G7UC9Tp2hz7Yo68W3Wg+YN8CLYN61HsnbdQeh1x6zcSuzTrZ45Dl444deuElmZAS0ggauYsUoOuovf0wOOHRaRcvQ5AyqnTRH002wIZCHP519zTqJTapZSqa+7f+4xfLUr5ejHRbyhLJ8ynz/RXcy13YvsRZnQan2P5j1MX837AaN4PGM2OxZs4+vufBR1yrsr718DN15NvW4xk8/jvaDPtpVzLtZ0+kN/HLeDbFiNx8/XE1686AIfmb2BR+wksDpjIpe3HaDysCwCJUXFsn7yUw99arrEIgE5RdeZADvX+kN3NRuHdpTFOFUtnKZIQHMHxYfMIWb0vy/K0hCQC3/qaP1qM5tCLM6kytT9WLg7mjP6+KvvVpISvFzP83uGnCd/SfforuZY7vf0vZneamGN58OkgPus4gU86jOXEpj95bnyfgg45V+X8a+Dq68nSZiPZMfY7/Ga8lGs5/xkD2TFmAUubjcTV15Nypn2wzpCO3Nh3mqXNR3Fj32nqDOmY/hqlUzQe35Nrf/xtjlTyVMG/Bu6+nnzZYiQbxn9HwLTcTxICpg/it3EL+LLFSNx9PXnSrwYA5RpVoWKbOnzTYTzz2ozlwDcbzBl+Bp2i1oyX2NvnIza3GEOZzo1wznY8+fTyI/luHL83Hsn5bzZRbVIvAMr3aQnA1pbj2NNzJtWn9AGlADj42hy2tZ7AVr+x2BZ35omODcybV2Y6Hc5D3+HuhDFEvjIAO/9W6MuWy1Ikacc27rw2kDuvv0L8jytxev1NAOwCngPgzmsDiRo3EsfBQ9JztLQ6/nXx9vFmcPPX+HLcXN6YPiTXcmu+Wc0bLd9gWIdhVK5bhTp+dQDw8vGm+5AejOk6mjdbv8m3731rzvDz1KF9S56q4EulKk15442xfDn3gzzL9u//FnXrtaVuvbaEh98GYML4Yfz086/Uq9+OPn2HMOeLGeYK/R/rHNCGebOmWTqMR1bRrybFfT35zG8Eaycs4Pnpg3It9/y0Qawdv4DP/EZQ3NeTp0z1YPtxvdjx+Wq+DJjA9lk/0358L3OG/1Aq+dWkhK8nH/kN55cJ39Jl+su5ljuz/ShzOk0yc3SPQKfDdeQwbo8Yx81eA3Fo0xIrn6z1YMLm7dzq+wrhA14jdtkqig17I31d6o0Qwge8RviA16TBCGgF9K+w+Nc0Gi2lZtt6HFy9G4DLxy7g4OxIsZKuOcpdPnaBu+FR932ves835dD6ffctU1AqtKnDqV/2AhB67BJ2Lo44lnLNUsaxlCs2TvaEHjNe0Tv1y16eamtspyfHJqSXs3awRdOMO3H87WjCTlzGkJJmhizy5lq7AvFXwki4egstJY2QtQfwaJ/1GkPC9QhiTl9Dy9azEXc5jPgrYQAk3bxDckQ0NsVdzBb7gzzTti5HVv8BwNVjF7F3dsA5l33w6rGLxOSyD148cJqUxGRTmQu4eroXZLh5Kt+2DmdM++DNY5ewdXHEIds+6GDaB2+a9sEzv+ylfLu6Ga//eY9x+c970pcDVB/YlkubDpNwO9oMmeStYps6nPjFGGPwsYvYuTjglC1Hp1Ku2DrZE3zsIgAnftnD022NJ+x1+7Zi/1frSUtOBYzHlyW413qS2KCbxF0LR0tJ4/q6g3i3q5OljHf7Olz90bhfBv92iFLNqgLgXLE0t/adBiDpdjQpd+Nwq+ELQKqpHlFWenTWVhadas7q6cqkhQRjCAuF1FQSd+3ApnHTLGW0+IyebGVnnx6vVTkfkgOPGstERaHFxRp7HQuBhm0bsOMXYw/auWPncHRxxK2UW5YySYlJ/H3AeIElNSWVSycvUdyrBADterdj45INxN2NA+Du7btmjD5vHTu2Y+nynwH489BRirkWw9Oz1EO/XtPAxcUJgGIuLoSG3iyQOPNT3ZrVKObibOkwHlnltnUIXG2sB28cu4idswNO2T6znEq6Yutszw1TPRi4eg9VTOcbGmDrZA+AnYs90TfvmC32h1WlbR2OmnK8dp/P5Wt5fC4XFjZVKpF6I5i0EGM9GL9tB3bNG2cpk6UetLczHkziP6lQNhqVUo5KqQ1KqeNKqZNKqZ7Z1scqpT5TSp1SSm1XSpUsqFjcPNyJDLmd/vxO2O1/dNLtXroEJcqU4uz+nMNpzMHZ043oTHnEhEXi7JH1RMLZw42YsMiMMqGROHtmlGk2ugevH/icKp0bs3fWLwUf9COw83QjIVN+iSG3sfN0u88rcles1pPorK2IDyo8JxQuHu5EZcotKiySYv+w4dfgBX/O7ArMp8gejaOnG7GZ8ogNjcQp2zZy8nQjNjRjH4wLjcTRVMahhAvxt6IAiL8VhUMJl/T3fbJ9Xf5esr2AM3gwZ0/3LMdZdB7HWXSm4yw6NBJn0/Z09/WibP1KDFr7Hv1XTcKrennzBJ6Nvac7CcEZeSSERmKfbVvZe7qREGLMQ0szkBIdj427E3dPX8W7bW2UXodDmZK4VvfFoXTx9Nc1XTmWjn9/TWpsIjd+s8zICwBdiRKkhd9Kf26ICEdfokSOcnbPd8Z98QocX3md2K8+ByD10iVsGzUBnR6dpydWT1VEV/LhGzAFqbhncSJCM4af3w67TXHP4nmWd3RxpH7r+hzfFwhAaV9vvMuX5sPVH/Hx2k+o3aJ2QYf8UEp7e3Ljekj68+AboZT29sy17IIFszhyeAsTJ7yTvuz9qZ/Su3dXgi4f4df1Sxj2TiHu/fmXc/Zw425IpjouLBKXbPWHi6cb0Znq+ruhGXXlxveW0H58b0bvn0P7CX3Y+tEq8wT+CIrl4+eyJelKliDtVkY9mHYrAn3JnKfUjt064fHTMlzefI2oWXPTl+u9PSm5eD4lvvoMmxrVzBJzYWYooJ/ColA2GoH2QIimaTU0TXsG+D3bekfgiKZpVYHdwGRzB/io6ndswtGNB9EMhWnzP5o9H//EvEbDOL12P7UHtLF0OPnOtpQrNecO4fg784rklbQ6nZtSpnp5dn7z64ML/wvc20TNJvdl34wfisQ201npsHN15PvOk9k2YwXdvhpq6ZAeWdDK3SSERtLq92nUfL8ft49cQEvLqPf29vqQ32q+ic7WilJNq1ow0oeTuH4tkQN6E7dgPg69+xuX/b6RtPBbuH01H6c3hpJy+hT8C+t2nV7H6Dmj+XXhem5eM14o01vp8fbxZsIL4/lk6Me89eFQHF0cLRzpw+s3YCi1arfGz78LTZvUp2/f7gC82LMzS5b8hE/5unR8vj+LFn2BKiRDikVW9fu2ZuPUpXzceCgbpy6ly4evWTqk/7y4X9Zxs0dfor/6BpeBfQFIux3Jzc69CB8wmLuff4XbexNRDoXn1h6R/wrrRDh/A58qpT4EftM0bU+2yt0A3Lv0tAxYnZ+/3K9fO5r3ag3AleMXcffOuErr5lmcqEy9BA+rXscmrHh3Qb7F+DBq9W9N9Rf9AQg7cRkX7+IEm9Y5e7oTk23IR8zNO+k9HgDOXu7EhOUcFnJ67X66LxrFvs/y9c/+WBLD7mCfaTvZeRcnMZfY82LlZE+95WM498Eqov66WBAhPpIm/drSsJfx3rDrxy/hmik3V0937j7iPvhUk2do/VYXvuz5XvrQR3OoNqA1VXsZ98Fbxy/jlCkPJy93YrNto9iwOzh5ZeyDjl7uxJnKxEdE41DK1djLWMo1fShqqeq+tP/yLQDs3J0p518DLc3A5c1/FWhu99Tt34ZapuMsxHSc3eOSx3Hmkuk4c/FyT+/hjw6N5OzvR4zvdfwymkHDwd2Z+MiYgk4ji4SwSOwz9Q7ae7mTkG1bJYTdwd7bnYTQSJReh7WLA8mRsQAcn7wsvZz/+snEXA7L8lpDUgohm//Cu10dbv1hmdEXhogI9Jl6B3UlSpIWkfcEUUm7tuM0bDh8DBjSiJv3JXGmda6zvyTtxvWCDfg+Avo/S7te7QC4cOICJbwyekyLexbndtjtXF/31syhhASFsP67jEnDIkJvc+7YOdJS07h5/SYhV0Lw9vHmwokLBZtELt54fQAvv2y8B/vIkUCeKOOdvq70E14Eh4TleE2IaVlsbBwrf1hLvbo1WbbsZwYOfJFnnzOe7B788y/sbG0pUcI9/Z5H8Xga9GtDXVNdH3z8MsW8M9Vxnu5EZ6s/osPu4JKpri/mlVFX1urWPH1SnJMb/qTzzNznkjC3Rv3a0CD9c/nyY38uFwaG8Aj0pTLqQX2pEqSFh+dZPmHrTlxHv2N8kpKCISXF+PDcBdKCQ7Aq+wQpZ88XZMiFWmG6/7AgFMqeRk3TzgO1MTYepyml/vegl+Tn79+1dHP65DWBWw7TsGsLAMrXeoqEmPgH3ruYneeT3jgUc+TSUfMeSMeWbGNxwEQWB0zkwpa/qNrNeL+OV60nSYqJJ8401O+euFtRJMcm4FXrSQCqdmvKxa3GE283H4/0ck+1rU3kpVDzJPGQ7h67hGN5T+zLlkRZ6/Hu3IibD9loUNZ66iwawY2f9qTPqGpp+5Zu4dOAcXwaMI6/txyhbtfmAJSrVYHEmPhHukeidFUfesx4le9e+ZhYM98j9/fibfzQfiI/tJ/I5c1/Udm0D3rUepLkmPj04ab3xJv2QQ/TPli5W1MubzFuxytbj1K5ezPj8u7N0pcvaTKCxY2Hs7jxcC5tPMSuiYvM1mAEOLJkK98GTODbgAmc23KE6t2MMZauVYHEmARis+UYeyuKpNgESteqAED1bs04bzrOzm35C59GlQFw9/VEb21l9gYjwJ3Ayzj5euJQxng8lenUkNBsf9PQzUcp94Jxvyz9XH1u7T0FgN7eBr29LQClmj+DIc1AzPlg9A622Jnu71R6HV6tahFzMQRLST13Fn3pJ9B5eoKVFXZ+LUk+kPWec33pjMl/bBo0Ii34hvGJrS3Y2QFgXbsuWlqacVZVC9m4ZAPDOrzNsA5vc3DzAVp2M57YPl3raeJj4rlzK+cFtL6j+uLo7MC3U7JOdHNw8wGqNTIOM3Nxc8Hb15uwazkbZ+bw9bzF6RParF+/mX59jL2GDerXJvpuNGFht7KU1+v1FC9uHOJoZWXFs8+25tSpcwBcvxZMS39j/VOpUgXs7GylwZiP/ly6lS8DJvBlwARObzlCza7GevCJWhVIikkgNttnVmx4FEkxCTxhqgdrdm3GGVOdHn3rDr4NjfVg+cZVuV1Ibhc5sHQrswPGMztgPKe2HKG2KceytSqQ8Iify4VF8pmzWJUpjd7LWA86tG5J4p4DWcron8ioB+2aNCT1urH7QedaDHTGZoTe2wurMk+QGlK4zg3NrTAMT1VKuSultiqlLpj+z3GvllKqplLqgOk2vxPZbwPMS6HsaVRKeQORmqYtU0pFAdmni9QB3YEfgN7A3oKK5e+dR6nmX4vpu+eQnJDMotFfpq/738aPeT9gNADdxvWlQaem2Njb8NGBeexZtZ1fZ/8EGHsZD/+6v6BCfCiXdwRS3r8Gr/7xKakJyWwa9U36ugEbp7M4wDjr5tZJi+jw6WtY2dlwZddxLu88DkDzcT1xL++FZtCIDo5gy4SFADiWLEb/X6di42SPZjBQd1B7vms9NsvEOeagpRk4OX4R9X8Yj9LruLFyF7HnblBxTHeijl/h1ua/KFazPHUWjsDa1RGPtrWpOLoHf7QYjffzjXBvWAlrNyee6Gk8CT7x9jyiT1nuJDCzMzuPUdm/JhN2f05KQhIrR2dMvT5y40w+DRgHwHPjelO7UxOs7W3434Ev+XPVTjbP/pmO4/tg62DLgK/eAeBOcATfv2r+KfSDdgRSrmUN+u/9lJSEZLaPzNgHX/x9Oj+0N+6DuyYuovUs4z54dedxrpr2wb++/JX2Xw+lyostiLkRwaYhc8yew4Nc3BFIBf+avPnHLFJNX7lxz6sbZ/BtwAQANk1ayPOmr7a5tOs4F005Bv64i+c/fo3BW2aSlpLK+pGWmWZfSzMQOGERzVaORel1BP2wm+jzwVQZ3Y07x68QuuUoV1buov6cN2i//1OSo+L483Xj9rAt7kKzlWPRNI2E0DscHvo1AFYOtjRePAKdjTVKpwjfd5rLlrwP1ZBG7NzZFPvgE5ROR+LmjaRdDcJhwCBSz58l+cB+7Dp1xaZWHUhLxRATS8xHxhk7da5uFPvgY9A0DBHhxHw43XJ5ZHNkxxHq+tflmz3fGr9yY9Ts9HWfb/qCYR3eprhncXq+/SLXL1xn9kbjfZobFv/Glh+2cHT3UWo1r82X27/CkGZg4fSFxESZ/8JFdhs3bad9+5acO7OP+IQEXnllRPq6I4e3ULdeW2xtbdi4YQXW1lbo9Xq2b9/Dgu+WAzB67PvM//pjhg17FU3TePmV4ZZK5aGNnjyTw8dOEBUVTavOfRnycj+6dWxn6bAe6PzOQCr612TE7s9ITkhi9eiMevDNjTP40lQPrn/3e7p98rrxKzd2Hee86X77deMWEDC5PzorHalJKawbb95RWg/j7M5jVPKvydjds0lOSOKnTDm+s/EDZgcYZ9MPGNebmp0aY21vw4QDczm8aidbZxeiOSHSDER9OocSsz8EnZ643zaReiUI51dfIuXMeRL37sepe2ds69VBS01Fi4nhztQPAbCpWR2XVweipaaCphH10Wdo0ZavKwTjgO2aps1USo0zPR+brUw80F/TtAumNtdfSqnNmqZF3e+NlVYI7wNSSrXDNAgISAHeAD4BRmmadkQpFQt8A7QFbgE9NU3L3p+uverTw4xRm8e3QT/xUbm+lg4j3425uowNHoVvWu3H8ezNlYzwedHSYeS7WUE/MKdM0dsHh15fxtRylvk6koLy7tXl/OxVtHIC6B66nPA2LSwdRr4ruXU3Hcs+Z+kw8t2v137Dyqb0gwv+i6QmB5MScdnSYeQ76xLlmeTT29Jh5LtpQSsY41O0zjE+ClpJcKOWlg4j35U+sONfe7Nxv3JdC6RRtfTq6of+myilzgF+mqaFKqW8gF2apt13em+l1HGgu6Zp970PoVD2NGqathnYnG2xX7YyIxBCCCGEEEIIAeChadq9ccJhgMf9Ciul6gM2wKUHvXGhbDQKIYQQQgghxL+FucZuKqW2Abl959DEzE80TdOUUnmGZeqJXAoM0DTtgbdP/isbjZqmOVk6BiGEEEIIIYQAMJip2ahpWuu81imlbiqlvDINT72VRzkXYAMwUdO0gw/zewvl7KlCCCGEEEIIIR7JemCA6fEAYF32AkopG2ANsETTtJ8f9o2l0SiEEEIIIYQQj0EroH+PaCbQRil1AWhteo5Sqq5S6t5UxC8AzYGXlFKBpp+aD3rjf+XwVCGEEEIIIYQQGTRNuw20ymX5EUxfYahp2jJg2aO+tzQahRBCCCGEEOIxPHAmmX85aTQKIYQQQgghxGMw10Q4liL3NAohhBBCCCGEyJP0NAohhBBCCCHEY/gHk9b8q0hPoxBCCCGEEEKIPClNK7Kt4iKbmBBCCCGEEEWQsnQA/1TXcs8XSNtj9dX1heJvUqSHp56r1MHSIeS7p89u4lfPXpYOI991DFvJIJ/ulg4jX30f9DNrPXtbOox81zlsBSu9+1g6jHzXK2Q5WzxetHQY+artzR+YUq7obaspV5fTu1wXS4eR71ZcXVNk6/ejZTpZOox8Vfv6Oib5FL36fVrQClIiLls6jHxnXaI82z16WjqMfNXq5iqqezaydBj57kTYAUuHIPJQpBuNQgghhBBCCFHQivDoTUAajUIIIYQQQgjxWOQrN4QQQgghhBBC/GdJT6MQQgghhBBCPAaDpQMoYNLTKIQQQgghhBAiT9LTKIQQQgghhBCPQSvi9zRKo1EIIYQQQgghHoNMhCOEEEIIIYQQ4j9LehqFEEIIIYQQ4jHI9zT+xzk0rYPHxNdBp+Puz78T+e1PWda7vdSFYt3bQ1oaqZF3CZv4GakhtwAoOWoQji3qg04Rv/8Yt6bPs0QKOZT0r8EzU/uj9DquLd/Jxbnrs6x3b1iJZ97vj3OVshx9/QtCfzsEgEvVclT7cBDWzg5oaQYufL6GkHUHLZFCnnpPHkQ1/1okJyTz3ai5XDt1JUeZrqN60bhrCxyKOTKkar/05e7eJXj507dwcHFEp9Px84fL+HvXMXOG/0Cl/KtTzbTtri7fyYW5v2ZZX7xhJaq93w+XKmU58vocQkzbrjDw8qtO7an9UDodl1bu4ky22HU2VjT84g3cq/mQdCeW/a/PIe5GBOW6NKbykOfSy7lWLsPv7SYRdeoq1cf2wKdHM2yKOfLzUy+bO6UcivvXoNK0ASi9jhvLdxA0J+ux5dawEk9PHYBTlbL8PfgLbv72Z5b1eid7muz5hFubjnB2wkJzhp6rDlP685R/DVISklk7aj6hJ4NylPF6xofOn76OtZ01F3YeZ9OUJQB0nzuUEuW9ALBzcSAxOp55ARMo3/QZWo97Eb21FWkpqWydsYIr+0+bM60s+k95mZr+dUhOSGLeqDkEnbycZb2NnQ3Dvh6NR1lPDAYDR7cd4YcPlwJQqX4V+k0eRNlKPswZ+imHNh6wRApZFNX63cWvFk9MeRX0Om6v3MrNr37Jsr7Uq89T/MW2kJZGyu27XBs1h+TgcOyr+FJmxuvonRzAYCBszk/c+XWvhbLI3bOT+1PRvyYpCcn8MmoeoaeCcpTxfsaXrp8MxtrOhvM7A9nwnvE486xSjk7TB2Fla40h1cD6dxcSfPySmTN4NJNmzOKPfYdwd3Nl7bLCcV70MNz9a1Bx2ksovY6Q5Tu4OmddlvWuDSvzlKl+PzX4c25lqt9bhqwk9sw1ABKDIzjR/2Ozxv4gY6cNp1mrxiQmJPLusKmc+ft8jjJfr/iMEh7F0VvpOXrwODPGf4LBYKBilQq8+9EYHBwdCLkeyrghk4mLjbdAFqKgyfDU+9Hp8Pjfm9x49V2uPDcY52f9sHmybJYiiWcucbX72wR1GkLs5r2UHDUIALtalbGvXYWgTkMI6vgGdtUqYl+/miWyyEqnqPbBQP7s/SE7m4/Cu0tjnCqWzlIkITiCY8PmEbxmX5blaQlJBA79ml0tRnOw10yqvt8fKxcHc0Z/X9X8auHh68V4v6EsnjCP/tNfy7Vc4PYjTO00Lsfyjm914/CG/bz37GjmD/2MftNeLeiQH41OUeODgRzo/RHbm4/miS6Ncc5l2x0dNo8ba/ZbKMjcKZ2izoyX2NXnIzb6jaFcp0a4PJU19vK9/EiOiuO3JiM59+0makzqBcDVNfv5vc0Efm8zgQNDvyb2WjhRp64CELz1GFsC/mf2fHKlU1SeOYijvWeyr9lIvLo0wTHH9rnNyWFfE7Z6X65vUWHcC9w5eNYc0T7QU/41cPf15IsWI/l1/Hc8O21gruWemz6IX8ct4IsWI3H39aSCXw0Afn5rDvMCJjAvYAKnfz/Mmd8PAxB/J4aVgz7h63bjWDtiHl0+e8NsOWVX0782nr7ejGgxhAXjv2bQtMG5ltvwzTpGtRrK+ICRVKxbiRp+tQGICAln3sg57F/3hznDzltRrd91OspMG8zF/u9xpuVbuHVqht1TZbIUiT95hbPPjuBM22FEbdxP6YkvAWBISOLqO7M503ooF/u9xxOTX0bv4miBJHJX0a8mxX09+cxvBGsnLOD56YNyLff8tEGsHb+Az/xGUNzXk6dMx1n7cb3Y8flqvgyYwPZZP9N+fC9zhv+PdA5ow7xZ0ywdxqPRKZ6eOYjA3h9wsNkIPHKp3xODIzgz7Ctu5lK/pyUmc6jVWA61GlvoGoxNWzWiXPkyPNeoB++PmsmkD8fkWm7UaxPp0ao/XVv0wb24K207tgRgyqzxzJ7+Nd38+7J9025eGtLXnOEXKoYC+ikspNF4H3bVK5JyLYSUG2GQkkrMxt04tWqYpUzCnyfQEpOMj4+fxdqzhHGFpqFsbVDWVigba5SVnrSIKDNnkJNbrQrEXQkj/tottJQ0QtYewLNd3SxlEq5HEHPmGhiydrPHXQ4j7koYAEk375AUEY1tcRezxf4gtdrWY//qXQBcPnYBB2cHipV0zVHu8rEL3A2PyrFcQ8PeyXiSZO/iQNTNOwUY7aNzq1WB2Cs307fdjbUH8GxXJ0uZ+OsRRJ+5DobCVM2Ae60niQ26Sdy1cAwpaVxbd5AnssX+RLs6XPnJePJ9/bdDeDatmuN9ynVuxLV1Gb05t49eJPFWVIHG/rCK1a5A/JUwEq4at0/Y2v2Uap/12Eq8Hk7s6WtohpxDWJyr+2JTshi3d50wV8j39XSbOhz/ZQ8AN45dxM7FAadSrlnKOJVyxdbJnhvHLgJw/Jc9VGpbJ/tbUfXZBvy93nghI+zUVWJM2+zW+RtY29mgt7HMoJc6beqz55edAFw8dh4HF0dcS7llKZOcmMzpAycBSEtJJejkZdw9iwMQcSOc62evYshle1pCUa3fHWs+RVJQGMnXbqKlpHJn/R6Kta2fpUzsgb/REpMBiDt6DmvTNkq6EkJSUCgAKTcjSbl9Fyv3wpEXQOW2dQhcnek4c3bAKdvnllNJV2ydM46zwNV7qNLWuF01wNbJHgA7F3uiC9nnVm7q1qxGMRdnS4fxSFxqVyDhyk0STfX7zbX7KdG+XpYyGfV74fr8fRD/ds359cdNAJw4egpnFydKlCqeo9y93kMrKz3WNtbpM4WWK1+Wvw4YR2Ud2H2I1s/5mSfwQkgroH+FRaFqNCql+iulTiiljiulliqlOiql/lRKHVNKbVNKeZjK1VdKHTAt36+Uerog4rHyKEFKaHj689SwCKw8ch5I9xTr3pbYP44AkBh4lvg/T/DknuU8uWc5cXuPknz5ekGE+UjsvNxICLmd/jwx9DZ2Xm73eUXuXGs9ic7airigm/kZ3mNx8yhOZKbcIsMicfPMe3tlt+6zH2nUuRmfHJjPOwsnsHzydwUR5j9mn2PbRWLv5W7BiB6eg6c78Zlijw+NxD7bfmfv6UZ8SCQAWpqB5Oh4bNydspQp+3xDrq61/BDA3Nh5upOYefuERGLr+ZDbRymentKP81OWFVB0j87F053oTPlEh0Xi4pF1m7l4uBEdFplRJjQSl2w5l6tfibiIu0TmUldUCahP6Mkg0pJT8zn6h+Pmmb3OuI2bR97bzMHFgdqt63JqX+Fo2GdXVOt3a8/iJIdEpD9PCb2d3ijMTfEX2xC9668cyx1qPoXO2oqkq2EFEuc/4ezhxt2QTMdQWCQuntmOM083okMzytwNjcTZdCxufG8J7cf3ZvT+ObSf0IetH60yT+D/Mdnr96SQ29h6PvyxpbO1pt7mGdTdOI0SHeo++AVmVMqrJGEhGcf6zdBwSnmVzLXs1ys/Y9fJjcTFxrP1V+MFt0vnruDfvjkAbTu2xNO7VMEHLSyi0DQalVJVgUlAS03TagDDgL1AQ03TagE/APf6zM8CzUzL/wfMsEDIWbh09MeuakXufGe8z8K6rBc25ctwya8fl1r0xaFhDezr5Ow5+TeyLeVKrTlDCHxnHhShm34bPN+UfT/vYlSjwcweOINXPxuKUsrSYQmT4rWeJC0hmbvnblg6lHxXZmBbIrYfIynTiWFR8czzjfh7fc6GfsmnStN63Iv8Or5wXZzJi06v4605I/l94QZuXS8cjamC8G+v3927tMCxegVuzluTZblVKTd8Zg8naOQX/8q88lK/b2s2Tl3Kx42HsnHqUrp8mPttGcKy9td5k8PtJnDyjS+o+P4A7Mt5WDqkf+SNXsNpWaMjNjbW1G9qHFXyv+HT6flSV37YvBBHJwdSLHQRsDAwoBXIT2FRmCbCaQn8pGlaBICmaZFKqWrAKqWUF2AD3JvVpBiwWCn1FMbRGdYFEVDqzQisM11tsfIsQerN2znKOTSqifvrL3K93xi0lBQAnFo3JvH4WbT4RADi/jiCfc3KJPx1qiBCfWiJoXew9864QmvnVZzE0IcfzmLlZE+DZWM4O3MVUUcvFkSIj6Rlv/Y079UKgCvHL+GeKTd3T3fuhOXcXnlp1rMVswYY77O4dPQ81rY2OLk7E3M7On+D/ocScmw7dxL+JY2M+LBIHDLF7uDlTkK2/S4h7A4O3saclF6HjYsDyZGx6evLdmrE1bWF617NzBLDIrHLvH283UkKe7jt41r3KVwbVKLMS23RO9qis7EiLT6RC9NWFlS4uarXvw11XvQHIPjEZVwy5ePi6Z5j6Fv0zTtZehZdvNyz9Dzq9Doqt6/HN89NyvI6F093XvxmOGtGzOPOtVsFkUqe2vTvgP+LbQC4fOJitjqjOHdu5r7NXpk5hLArIfz+/W9mifOfKGr1+z0pYbex8S6R/tzaqzgpudTtzk1r4Dm0B+d7TETLdOKqc7KnwqJ3CfloGfHHck7wYW4N+rWhbi/TcXb8MsW8Mx1Dnu5Eh2U7zsLu4JJpVEkxL3diTMdirW7N0yfFObnhTzrPLGT34hcR2et3W+/iJIU9/LF1r2zi1Vvc2X8a52o+JFy13MWnngO70a3P8wCcCjyDp3dGI9bDqyS3Mo2yyy45KZmdm/fg3745B/84TNDFq7z+4jsAlCtfhmatmxRo7MJyCk1PYx7mAHM1TasGDAbsTMunAjs1TXsG6Jhpeb5K/Ps81uW8sS7tAdZWOAe0IHZH1tnkbCs/icd7bxM85D3SIu+mL08NDce+XjXQ68BKj329aiQVguGpUYGXcCzviX3ZkihrPd6dGxG2Jecwntwoaz11F47g+k970mfcs7QdS39nSsBopgSM5tiWQzTu6gdA+VpPER8Tn+u9i3mJDImgShPjZEVeT5bG2ta60DQYwbjtnMp74mDadk88wraztMjAyzj7euJYpiQ6az1lOzXkRrbYg7ccxbeHcYhLmefqc3NvpgssSlG2YwOuriucQ1MBoo9dwiHTseXZuTG3Nj/c9vl7yFz21HmLPfWGcv695YT8uMfsDUaAw0u2pk9ec3bLEWp0awbAE7UqkBSTQGy2+0djb0WRFJvAE7UqAFCjWzPObc3IuXzTZ4i4FJKlIWnn4kDvhaPY9uEPXD9i/hP4rUs2MSFgBBMCRnBky58062Y8ea9QqyIJMfFE3cp5IthjVG8cnB1Y+t735g73kRS1+v2euOMXsPXxwqZMKZS1FW7PN+Pu1qwx2lf1pezMN7g0aDqptzM+i5W1FeW/Hc/tX3YStbFwXHT6c+lWvgyYwJcBEzi95Qg1u2Y7zrJ9bsWGR5EUk3Gc1ezajDOm7Rp96w6+DSsDUL5xVW4XkiHFRU2MqX63Mx1bHp0bE7H5yEO91qqYI8p037a1uzOu9Z8m7rxlR8ysWvgLL7QewAutB7Dj9z/o+EIHAKrXrkpMTBwRt7JelLF3sE+/z1Gv19OsdWOuXDROSOdewjhMVynFa8MH8tOSrL38/yWaphXIT2FRmHoadwBrlFKzNE27rZRyx9ijGGxaPyBT2czLXyqwiNIM3Jr6NU98Nw10eu7+soXki9coPrQfiSfPE7fzT0qOfhmdgx3esycAxsZi8JD3iNm8F4eGNfBZ/zVoELf3CHE7/3zALyx4WpqBkxMW0XDleJRex/WVu4g9d4Onx3QnKvAKN7f8RbGa5an3/QisXR3xaFObp0f3YFeL0Xg/34jiDSth4+ZEmZ7Gk/vAYfOINs1kaWkndh6lun9tZu6eS3JCEt+P/ip93ZSNHzMlYDQAPcb1pUGnZtjY2/LJgfnsWbWddbN/ZNW0xQyY+TptX34OTdP4btSXlkolV1qagRMTFtF45TjjV26s3EXMuWAqjelOVOBlwrYcxbVmeRp8PxxrV0c829Sm0uju7GiR+0xo5o79yMRF+K0Yi9LruPzDbqLPB1NtdDcij18heMtRLq3cRaMv3uC5fZ+SHBXHvjfmpL++VMNKxIdEEnct69XPmpN6Ua5zY6zsbeh0ZA6XVu7k5KerzZ0eYMzx7PiF1P5hAkqvI3jlTuLO3eDJMT2IPn6Z8M1/4VKzPDUXjsTa1ZGSbWvz5Oju7G8x2iLxPsiFHYE85V+Tt/+YRUpCMutGzU9f9/rGGcwLMNZ5GyYtpPOng7Gys+HiruNc2Hk8vdwzHRtxMtvQ1PoD2uLu40GLt7vS4u2uACztN5M4C1ygCdzxFzX96/DZH1+TlJDE/FEZ+9yMjbOYEDACd8/idBnag+CLN5i+4VMAtizZyK4ftlG+egWGfzMWx2JO1G5dj+7DX2RMm2Fmz+OeIlu/pxm4/u43VFg2BaXXcXvVdhLPX8drZG/iT1zk7tZDlJ44EJ2DPb7zjPVdckgElwdNx+25Jjg3qIqVmzPFexhne7w64gsSTuf8OiZLOL8zkIr+NRmx+zOSE5JYPTrjOHtz4wy+NB1n69/9nm6fvG78yo1dxzm/KxCAdeMWEDC5PzorHalJKawbv8ASaTyS0ZNncvjYCaKiomnVuS9DXu5Ht47tLB3WfWlpBs6N/55aP0wAvY7QlbuIO3eD8qb6PWLzXzjXfJLq6fV7HXxH9+DPFqNwfKo0lT55Fc2goXSKoDnriDsf/OBfaiZ7tu2nWavGbDj4E4kJSbz7TsbMtj9uW8wLrQdg72DHF0s+wsbGBp1OcWjfUX5abGwcdujchp4DuwGwfeMu1q4svKMxxONRhakFq5QaAIwG0oBjwBrgM+AOxkZlPU3T/JRSjYDFQBywAeiraZpPtrfTzlXqYK7Qzebps5v41bPwT6n9qDqGrWSQT3dLh5Gvvg/6mbWevS0dRr7rHLaCld59LB1GvusVspwtHi9aOox81fbmD0wpV/S21ZSry+ldroulw8h3K66uKbL1+9EynSwdRr6qfX0dk3yKXv0+LWgFKRGXH1zwX8a6RHm2e/S0dBj5qtXNVVT3bGTpMPLdibAD/9rJJPyfaFMgjaqdN7YWir9JYeppRNO0xRgbg5mty6XcAaBipkWTspcRQgghhBBCCHMoTF+PURAK+z2NQgghhBBCCCEsqFD1NAohhBBCCCHEv42hEN3yVxCkp1EIIYQQQgghRJ6kp1EIIYQQQgghHkPR7meURqMQQgghhBBCPBZDEW82yvBUIYQQQgghhBB5kp5GIYQQQgghhHgM0tMohBBCCCGEEOI/S3oahRBCCCGEEOIxaEX8Kzek0SiEEEIIIYQQj6GoD09VRbhVXGQTE0IIIYQQoghSlg7gn6rv3aJA2h6HQnYXir9Jke5pXO7d19Ih5Ls+Ics4XLqLpcPId/WC1zDYp4elw8hX84N+YlkR3Af7hixjtWdvS4eR77qGreCjckVre425uozPyhatnACGX1vGeJ+itw9+ELSiyNbvRS2vesFrGOPTy9Jh5LuPglay3aOnpcPId61uriIl4rKlw8hX1iXK09Dbz9Jh5LuDIbssHcI/phXx/iqZCEcIIYQQQgghRJ6KdE+jEEIIIYQQQhS0InzLHyA9jUIIIYQQQggh7kN6GoUQQgghhBDiMRT12VOl0SiEEEIIIYQQj0GGpwohhBBCCCGE+M+SnkYhhBBCCCGEeAxFfXiq9DQKIYQQQgghhMiTNBqFEEIIIYQQ4jFoBfTvUSil3JVSW5VSF0z/u92nrItS6oZSau7DvLcMT82Fl1916k7th9LpuLhyF6fn/pplvc7GisZfvI57NV+S7sSw9/W5xN2IAMC1chnqfzgIa2d7MGhsCvgfhqQUynVuxDNDn0fTNBJuRrF/6FckRcZaIj1c/GpR9v2XUTod4Su3Efbl6izrPV57npK9WqOlppEaGc2VEXNJDg4HoOKyd3Gs/TSxh89wYcB0S4R/Xz0nD+QZ/9okJySxaNSXXD91JUeZTqN60bBrcxyKOTGsar8s6+o824jn3nkBNI0bZ67y3bDPzRV6Fl5+1amXaR88lcc+WNy0D+7Jtg82MO2DmmkfVDpF8/lv4+RTCi3NwI2txwicscqsOXn4V6f61P4ovY6g5Ts5n0tOdee8gWt1X5LvxHJo8BfEX49AWeup/fEruNbwRTNonHh3CRH7zwBQulNDKg3rjNLrCN16lFPTfjBrTve0mtKP8v41SUlIYtOob7h5MihHGY9nfAj4dDBWdjZc3hnI9ilLAWg6sjsV2tRGM2jE345m08j5xN6KokrnxtR//TmUUiTHJbBl4iLCz1wza15+7/XD15TXlpHfcCuXvEpV86GdKa8rOwPZNdmY11PP1qfR8K64V/Bm5fOTuXnCeCyWbfYMTcf1RG9tRVpKKnumr+T6/tNmy6nj5P487V+T5IRkfh41j5BTOXPyfsaXHp8MxtrOhnM7A/n1vSUAeFYuS5fpL2PjYMudGxGseudLkmITjOsqlaHLjFewdbJHMxj4stO7pCalmC2ve4pq/V5U88ru+ckDqORfk5SEZH4c9TXBueyf7Ua9QJ2uzbEv5si7VQeaP8gHcPevQcVpL6H0OkKW7+DqnHVZ1rs2rMxTUwfgVKUspwZ/zq3f/kxf1zJkJbGmei4xOIIT/T82a+z/1KQZs/hj3yHc3VxZu2yepcN5ZCOmDqVRy4YkJSQydfhMzv19IUeZz5Z/RIlS7uit9AT++TefTJiNwWBIX9978Au8PXkI7Z7pxN3Iu+YMv9AwFI6JcMYB2zVNm6mUGmd6PjaPslOBPx72jS3S06iUclVKDTE99lNK/faIr1+klOpuerxLKVU332LTKerNGMDOPh/xm98YfDo1xOUp7yxlnuzlR3JUHOubjOTst79Ta9KLxtfqdTSe8waHxi1kg/84tnafjpaSitLrqPt+X7b1mM7G1hOIOnONigPb5lfIj0ano9z017jQdyon/d+meOem2D31RJYi8Scvc7rDKE61GU7khv2UmdQ/fV3ovLVcHjbbzEE/nGf8alHK14t3/YaybMJ8+kx/NddyJ7Yf4YNO43MsL+XjSfshXfi42yTeazuCH99fWNAh50rpFPVnDGBHn4/41bQPFsu2D1Yw7YPrmozkTLZ9sMmcN/hz3EJ+y7QPApyet4Ffm49hY9uJlKpXEW//6uZLSqeo8cFA9vX+iK3NR/NEl8Y4VyydpYhPb2NOWxqN4OL8TTwzqRcAvn1bArDdfxz7en5Atcl9QSls3Jyo9m5v9vSYzrYWY7Ar5UrJplXNl5NJef8auPl68m2LkWwe/x1tpr2Ua7m20wfy+7gFfNtiJG6+nvj6Gf/+h+ZvYFH7CSwOmMil7cdoPKwLAFHXw1n5wjQWthvP/i/W0u6DQeZKCQAf/xq4+niysPlIto37jpbTX8q1XKvpA9k6dgELm4/E1ccTH1Net8/d4NfXPufGn+eylE+IjGHdoE9Z2nY8m4fPp/3s1ws6lXRP+9WkuK8nn/iNYM2EBXSenvvftPO0Qawev4BP/EZQ3NeTin41AOg281V+/3Aln7cfx6nNh2n+2nMA6PQ6XvjsTdZM/I7Zbcfw7YvTSDMdd2ZVVOv3oppXNpX8alLC15OP/Ibzy4Rv6TL95VzLndl+lDmdJpk5uoekUzw9cxCBvT/gYLMReHRpgmO2uj4xOIIzw77i5up9OV6elpjMoVZjOdRq7L+mwQjQOaAN82ZNs3QY/0ijlg0o4/sEPZr04YMxnzLmg+G5lps4eAr92rxCb/+BuBUvRsuOfunrSnmXpH6LuoTeCDNT1OI+OgGLTY8XA51zK6SUqgN4AFse9o0tNTzVFRhiod99X8VrPUlM0E1ir4VjSEnj6rqDlGlXJ0uZJ9rV5vJPewC49tshPEwnql4tqhF15jpRp41XyZLvxKIZNFAKlMLK3hYAayd7EsLumDGrDI61niIpKJSkazfRUlKJXLcXt3b1s5SJ2X8SQ2IyAHF/ncfaq3jGur1/YzBdWS9sarStx8HVuwG4cuwC9s6OuJR0zVHuyrELRIdH5Vje9MXW7FryO/HRcQDE3I4uyHDzlH0fDFp3kCcesA96PmAfTEtI5qapd86Qkkbk30E4eLmbLSf3WhWIu3KT+Gu30FLSuLH2AF7ZcvJqV5drPxpzCv7tT0o2fQYA54qlubX3FABJEdGkRMfhVrM8juVKEXsljOTbMQDc+uMkpZ/Lui+bQ4U2dTj1y14AQo9dws7FEcdSrlnKOJZyxcbJntBjlwA49ctenmprvNaVnOl4snawTZ+yO+SvCyRFxxsfH72Isxm3F8CTbetwxpRX2LFL2N4nrzBTXmd+2cuT7Yx5RV4M4c7l0BzvG37qKnE3owC4ff4GVnY26G3MM+ilcts6HFtt3MeuH7uInbMDztnqCOeSrtg623P92EUAjq3eQxXTtirh68WVP88CcHHv31TtUA+Ap5pVJ+zsNcJMPSTxUaa638yKav1eVPPKrkrbOhw17Z/Xjl3EPpf98966mFw+wwoDl9oVSLhyk8Srxrr+5tr9lGhfL0uZxOvhxJ6+hpapl+rfrm7NahRzcbZ0GP9I83ZN2PjzZgBOHT2NUzEnipfK+XkTH2v8PNJb6bG2sYZMvWrvTHmLudPmU8TngXmgwjA8FfDQNO3eh28YxoZhFkopHfApMOpR3thSjcaZwJNKqUDgY8BJKfWzUuqsUmq5UkoBKKX+p5Q6rJQ6qZT65t7ygmTv6UZ8SGT68/jQSOy9sg4HdvB0I85URkszkBIdj627E87lPUHT8F8xhg6bp1FlyLPGMqlpHBq3kGd3zKTrsbkUq1iaSyt3FXQqubLxdCc5JCL9eXLobaw9i+dZvkSv1tzdedQcoT02Vw93IkNupz+PCruNm+fDn2h7lPfCw9eb0T9PZeya6VRtUbMAonwwh1z2QYdc9sH4XPZBl/KeaJpGyxVjCMi0D2Zm7eJA6Ta1CDM1xMzBzsuNhEzbJiE0EvtsjaDMZbQ0Aykx8di4O3P31DW82tVB6XU4lC2Ja3Vf7L3dib1yE+cnvXAoUwKl1+Hdvi723nnvywXF2dON6Ey5xYRF4uyRdXs5e7gRE5axTWNCI3H2zCjTbHQPXj/wOVU6N2bvrF9y/I7qL/pxZdeJAog+b06ebsSEZuQVGxaJk6dbjjKxmfLKrcz9PBVQj1sng0hLNk+vXDEPN6IyHVt3wyJxyRavi6cb0aGZyoRGUsy0PW9euJHegKwW0BBXU8OkhKnuH7hkHG/9Np3mg58r6FRyVVTr96KaV3bFPNyJyvIZFkmxR/gMKwzsPN1JzJRDUshtbB+hTtDZWlNv8wzqbpxGiQ75NohM3EdJz5LcCglPf34rJJySniVzLTt7xUdsOrGWuNh4dvxmvEjfrF0TwsPCuXj6klniFaCU2mZqG2X/6ZS5nGa8Cp1bq3MIsFHTtBuP8nst1WgcB1zSNK0mMBqoBbwDVAHKA01M5eZqmlZP07RnAHvAMp/ED0lnpadk/Yrsf+srtnR+nyfa18WjaVWUlZ6K/Vuzse1EVtd6iztnrlF16POWDveBindtgWONJwn7eq2lQzELnV5PKV8vPn1xCguGfk7fDwZj7+Jg6bAeibLSU6p+Rfa99RWbO79PmfZ103shwTh8telXb3Luu83EXgu/zzsVHldX7iIh5Db+m6dR/f1+RB65gJamkXI3jmNjF1J//ts0XzeZ+BvhaGn/zivXez7+iXmNhnF67X5qD2iTZV3ZRpWp3rMFuz6wzP2aBaV4xdI0Hf8i28Z/b+lQHtovY76hYd/WvPXrdGyd7NKHoOr0esrVe5pVw75kfvf3qNquHk82Nv9Q6UdRVOv3oprXf8X+Om9yuN0ETr7xBRXfH4B9uRydJMKC3uk9hudqdcPGxpq6TWtha2/LS0P78M3Hlrmdp7AxaFqB/GSnaVprTdOeyeVnHXBTKeUFYPr/Vi6hNgLeUkoFAZ8A/ZVSMx+UX2GZCOfQvdauqffRB9gL+CulxgAOgDtwCvg1j/fIFwlhd3Dwzriy5+DlTkJo1qGk8WF3cPR2JyE0EqXXYe3iQFJkLPGhkdw6eC59gpuQHcdxr+ZDSoxxWEzsVeN2u7b+T6q81bEg08hTclgkNt4l0p/beBUnJex2jnIuzarj9XZ3znabhGamXoB/wq9fO5r2ag1A0PGLuHsX5961LlfP4tzJ1AvyIHfCbhMUeAFDahq3b9zi1pVQSvl4cfWEea+exeeyD8bnsg86eLsTn8s+eDOXffBer2KDj18m5koYZxdsNl9CQGLonSy9gPZexuMntzLpx5WzA8mRxqGnf09ell6uxa9TiDUNewzbepSwrcYeBZ++Lc3WaKzVvzXVX/Q3xnDiMi7exQk2rXP2dCfmZtbtFXPzDs6ZegycvdyJyWWI+um1++m+aBT7PjNO8lGyUhnaffgKPw/4mMSogp84q0b/1jzTy5jXzROXcc40xM/J053YbDHHht3BKVNeuZXJjZOnOx2/eYfNw+dx92pun2f5p2G/NtQz5XTj+GVcvd25alpXzNOd6GzxRofdwSVTL3gxL3fumrZn+KUQvu9v/Fwt4evJ0/61AGOPZdChs8TfMe6v53YG4v2ML5f2m683H4pe/X5PUc0LoFG/NjToZbxv+/rxy7hmqiddPd25+wifYYVBYlgkdplysPUuTtIj3I5zr2zi1Vvc2X8a52o+JFy9me9x/td1e6kznfoY+2HOBJ6llHdGz2Ip75KEh+V9UTk5KZk/Nu+jWbum3L4ViVdZL5Zt+w6Akl4lWbz5GwYFvEFk+L9r380P/2AoaUFYDwzAOKpzALAuewFN0/rce6yUegmoq2nauAe9cWH5yo2kTI/TACullB3wFdBd07RqwLeAXUEHcjvwMs6+njiWKYnOWk+5Tg25sSXrMJfgLUcp36MZAGWfq8/NvcaZ/0J3ncC1chn09jYovY5SjSpx93wwCWGRFKtYGlt343h3z+bViL4QUtCp5Cou8AK2vl7YlCmFsrbCvVNT7mw5nKWMQ1Vfys18gwsDZ5B6u3DPgLVr6WamBYxmWsBoArccpmHXFgD41nqKhJj4XO9dzMvxLYep2NDYO+Do5kwpXy8irpn/wyr7PuiTyz544z77oFsu+yBAjTHdsXG258j/lmFudwIv4VTeE4eyJVHWep7o3IjQLX9lKRO65S/KvmDMqfRzDQjfZzzh1tvboHcw3g9cqvkzaKlpxJhysi3hAoB1MUfKv9SaoOU7zZLPsSXbWBwwkcUBE7mw5S+qdmsKgFetJ0mKiSfuVlSW8nG3okiOTcCr1pMAVO3WlItbjfm7+WRcSX+qbW0iLxkbxM7exek8/x02DJ/HnSvmmVzg+JJtLO8wkeUdJnJp819UNuXlWetJku+Tl6cpr8rdmnIp23bNztbFgc6LRrJ35ipCjuScoS+/HVy6lTkBE5gTMIHTW45Qq6txHytTqwKJMQk57g2LCY8iKSaBMrUqAFCrazPOmHJyLG7c35RS+L/VhT+XbwPg/O4TeDxdBms7G3R6Hb4NKnPrwiON+skXRa1+v6eo5gVwYOlWZgeMZ3bAeE5tOUJt0/5ZtlYFEmLiC+29i3mJOXYJh/Ke2Jnqeo/OjYnYfOShXmtVzBFlur/Z2t0Z1/pPE3fe/MfRf8Evi9bSv80r9G/zCrt/30tA93YAVK1dhdjoOG7fytrgs3ewT7/PUa/X06R1Q65evMals1cIqN6FLg1epEuDFwkPDWdAu9f+kw3GQmQm0EYpdQFobXqOUqquUmrB47yxpXoaY4AH3TF8r4EYoZRyAroDPxdoVBjvpToycTEtV4xB6XVc+mE3d88HU310N24fv0LwlqNcXLmbxl+8zvP7PiUpKpZ9bxi/3iT5bjxn5m+i/cb3QdMI2XGckO2BAPw9azVt1kzCkJJGXHAEB975pqBTyV2agWuTvuXpFZNBpyNi1XYSz1/He1Qv4o9fJGrrYZ54dwB6RzsqzB8NQFJwOBcHfgBApdXTsatQGr2DHTWOfMuVkV8SvTvQMrlkc3LnUar512La7jkkJySzePSX6esmbfyYaQHGfLqO60v9Tk2xsbdh5oF57F21nd9m/8Sp3YFUaVaDyVs/Q0sz8MsHS4kzQ+9OdlqagcMTF9Mql30w8vgVbpj2wSZfvE4n0z64N9s+2MG0DwbvOE7w9kAcvNyp9k5n7l4IJmCLcYa38wu3cnHFLrPlFDhhEU1WjkPpdVxduYuYc8FUHtOdqMDLhG45StCKXdSdO4S2B2aRHBXHocFzAGPDsMnKcWgGjcSwOxwe+nX6+1af2p9iVcsCcPbTNcReNv/MbZd3BFLevwav/vEpqQnJbBqVcWwP2DidxQETAdg6aREdPn3N+NUUu45zeedxAJqP64l7eS80g0Z0cARbJhiH+TQZ1gV7NyfaTH0JAC0tjSUd/2e2vK7sCMTHvwYD9xjz2pIprz6bprO8gzGvHZMW0daUV9DO4wSZ8nqyXV383++PvbsznRaOIvz0Vdb0+4gaA9rg6uNBg2FdaGCaKXZ13w9JMMPEU+d2BvK0f01G7f6MlIQkfh49P33d0I0zmBMwAYB1735P909ex9rOhvO7jnNuVyAANZ5vTKN+xuHDJzcf5q+fjPf0JEbHsXfBRt5cPw1N0zi3M5BzOwMLPJ8cimr9XlTzyubszmNU8q/J2N2zSU5I4qdM++c7Gz9gdoBx1u+Acb2p2akx1vY2TDgwl8OrdrJ1ds57oS1BSzNwbvz31PphAuh1hK7cRdy5G5Qf04Po45eJ2PwXzjWfpPrCkVi7OlKybR18R/fgzxajcHyqNJU+eRXNoKF0iqA564g7H/zgX1oIjJ48k8PHThAVFU2rzn0Z8nI/unVsZ+mwHsr+7Qdp3KoBP+9fTmJCEtOGf5i+bsnWBfRv8wr2DnZ8vGgGNjbWKJ2Oo/uPsWbJegtGXTgVhq/c0DTtNtAql+VHgFdyWb4IWPQw7600CyWolFoBVAcSgJuapj1nWj4XOKJp2iKl1DSgF8bZf84DVzVNm6KUWgT8pmnaz0qpXcAo0x8jM225d18zZWM+fUKWcbh0F0uHke/qBa9hsE8PS4eRr+YH/cSyIrgP9g1ZxmrP3pYOI991DVvBR+WK1vYac3UZn5UtWjkBDL+2jPE+RW8f/CBoRZGt34taXvWC1zDGp5elw8h3HwWtZLtHT0uHke9a3VxFSsRlS4eRr6xLlKeht5+lw8h3B0N2FfiklwWlYsm6BdKoOh9+pFD8TSx2T6Omabl+4mua9lamx5OAHF9GpGnaS5ke+xVAeEIIIYQQQgjxUArJPY0FprDc0yiEEEIIIYQQohAqLLOnCiGEEEIIIcS/UmG4p7EgSaNRCCGEEEIIIR6DDE8VQgghhBBCCPGfJT2NQgghhBBCCPEYNM1g6RAKlPQ0CiGEEEIIIYTIk/Q0CiGEEEIIIcRjMBTxexql0SiEEEIIIYQQj0Er4rOnyvBUIYQQQgghhBB5kp5GIYQQQgghhHgMRX14qirCXalFNjEhhBBCCCGKIGXpAP6pJ9yfKZC2x43Ik4Xib1Kkexp7luts6RDy3aqra+lQpoOlw8h3m65v4k4PP0uHka/cftrFWz49LR1GvpsbtIqJPr0tHUa+mx60gk/K9rV0GPlq1LVl/O7xoqXDyHftb/5AZJcWlg4j37mv2U35ErUsHUa+uxxxjCdL1LZ0GPnqUsRRghu1tHQY+a70gR1U92xk6TDy3YmwAzT09rN0GPnqYMguUiIuWzqMfGddorylQ/jHinBHHFDEG41CCCGEEEIIUdAMRbzRKBPhCCGEEEIIIYTIk/Q0CiGEEEIIIcRj0Ir4dCrS0yiEEEIIIYQQIk/S0yiEEEIIIYQQj6GoT4QjPY1CCCGEEEIIIfIkPY1CCCGEEEII8RgMRfyeRmk0CiGEEEIIIcRjkOGpQgghhBBCCCH+s6SnUQghhBBCCCEeg6GI9zRKo/EhvDTlFWr51yEpIYmvR33BlZOXs6y3sbNh+Ndj8CjricFg4K9th1n54VIAWvdpR7v+ARjSDCTGJ/DN+K8IvnDDEmlk8fp7r1OvZT2SEpL4dMSnXDp5Kct6WztbJsybgFc5LwxpBv7c9icLZy4EoKR3SUZ+NhInFyd0eh0LP1jI4Z2HLZFGFlY16+Mw8C3Q6UnavoGktStyLWfdoDlOo94neuxg0i6fQ1+hEg6DR6WvT/xpESmH9por7IfSffJLVPWvRXJCEktHfc2NU1dylOk4qif1uzbHoZgTI6sOyLG+Zvv6vDJvJB91HM+1vy/nWG8Oz07uz9P+NUlJSOaXUfMIORWUo4z3M750+2Qw1nY2nNsZyIb3lgDgVaUcz08fhLWtNYZUA+vfXciN45fwbViZvt+M5M6NWwCc+v0wO79YY860aPleP3z9a5KakMSmkd9w62RQjjIe1Xxo/+lgrOxsuLIzkB2TjXVEiwm9KN+6FoaUVKKu3uL3Ud+QFB2PzkpPu49eodQzPuj0Ok6t3suhL381a173lPCvQeVpA0Cv48byHVyZsz7LereGlag0dQDOVcpyfPAX3Pztzyzr9U72NNvzCTc3HeHMhIXmDP2+rGvVx+HloaDTkbRtA4mr86gzGjbHeexU7o56jbRL57CqUReHfq+BlTWkphC/+GtS/z5m5ujz9r8ZY/Br3YTEhERGD53MqRNn8yz7zbLZlClXmg7NemRZ/vKQfkx8fwR1KvpzJzKqgCN+OP+bMRq/1k1JSEhkzAPymr/sM8qWK02HZi8AMHzcG7Tu4IfBYOB2RCRjhk7mVliEuULPk23DehR75y2UXkfc+o3ELl2ZZb1Dl444deuElmZAS0ggauYsUoOuovf0wOOHRaRcvQ5AyqnTRH002wIZ5G7stOE0a9WYxIRE3h02lTN/n89R5usVn1HCozh6Kz1HDx5nxvhPMBgMVKxSgXc/GoODowMh10MZN2QycbHxFsgipxFTh9KoZUOSEhKZOnwm5/6+kKPMZ8s/okQpd/RWegL//JtPJszGYDCkr+89+AXenjyEds904m7kXXOG/8gmzZjFH/sO4e7mytpl8ywdjrAwiw5PVUq9pJTyfozX+yileudnTNnV9K+Dp68Xw1q8wbfjv+Llaa/nWu63b9YyotVbjA0YwdN1K1PTrzYA+9b9weh2wxgbMJz189bQf9Kgggz3odTzr4e3rzcvN3uZL8Z+wVsz3sq13C/zf+E1/9d4q8NbVKlXhbp+dQHo9XYv9vy2h7c6vMXMN2fy5vQ3zRl+7nQ6HF4eRuz0sUQPH4BNk5boniiXs5ydPbYB3Ug9fzp9Udq1K8SMHUzM6FeInT4Gh9dGgk5vxuDvr4pfTUr6evKe3zBWTviWF6e/nGu5v7cf5eNOE3NdZ+toh9/AAK4cy/kBZy4V/WpSwteTWX4jWDthAc9Pz/1Y6DRtEGvHL2CW3whK+HpS0a8GAO3G9WLn56uZGzCBbbN+pt34XumvCTp8lrkBE5gbMMHsDUZf/xq4+XjyXfORbBn3HW2mv5RrudbTB7Jl7AK+az4SNx9PfP2qAxC0528WtRnH4nYTuHMllAZvdgSg4rP10dtYsbjteJY++y41erfE5YkS5korg05RZeYgjvSeyd5mI/Hq0gTHiqWzFEkMvs3fw74mdPW+XN/iqXEvEHkw7xN8i9DpcHjtHWKmjuHu2wOwadoqzzrD7rnupJ47lb5Ii75LzPTxRL8zkLgvPsBpWO7HnSX4tW6KT/mytKzfiQkjpjH14wl5lm33bEvi43KejHt5e9DMryHB10MLMtRH4te6SXpeE0dM4/2Px+dZtm0ueX07dwnPtuhJR/9e7Nyyh6GjXivokB9Mp8N15DBujxjHzV4DcWjTEiufrPtgwubt3Or7CuEDXiN22SqKDXsjfV3qjRDCB7xG+IDXClWDsWmrRpQrX4bnGvXg/VEzmfThmFzLjXptIj1a9adriz64F3elbceWAEyZNZ7Z07+mm39ftm/azUtD+poz/Dw1atmAMr5P0KNJHz4Y8yljPhiea7mJg6fQr80r9PYfiFvxYrTs6Je+rpR3Seq3qEvojTAzRf14Oge0Yd6saZYO419D07QC+SksLH1P40vAP240Aj5AgTYa67Wpzx+/7ALgwrHzOLo44lrKLUuZ5MRkTh04CUBaSipXTl7C3bM4AAmxCenlbB3s0ArBzEoN2zZk+y/bATh77CxOLk64ZcspKTGJEwdOAJCaksrFvy9Swst4wqppGg5ODgA4ODtw++ZtM0afO32FShjCgjHcCoXUVFL27cCmbpMc5exffJnEdSvRUpIzFiYngSENAGVjA4XoAAWo3rYeh1b/AUDQsQvYOzviUtI1R7mgYxeIDo/K9T2eG9mTrfPWkZqUnOt6c6jctg7HVu8B4Pqxi9g5O+CcLQ/nkq7YOttz/dhFAI6t3kPltnXT19s62QNg52JPzM075gn8ASq0rcOpX4w906HHLmHr4ohjKdcsZRxLuWLjZE/oMWOP/qlf9lKhnTGvq3tOoqUZr0KHHL2Ek6e78UUaWDvYovQ6rOxsSEtJJTkmAXNzrV2B+CthJFy9hZaSRtja/Xi0r5ulTML1cGJPXwNDzmPHpbovtiWLcXvXCXOF/FCsnqqMITQYw01jnZG8dwc29ZvmKOfQ+2US16zIUmekXbmAdsdY76VduwI2tsZex0KgdYcWrPnxNwAC//obl2LOlPTIebHBwdGel9/oy9xPF+RYN2naKGa+93mhOllp3cHvEfLqw5fZ8oqNjUt/bO9gXyhys6lSidQbwaSFGPfB+G07sGveOEsZLT6j8avs7Qrd51Nu/Ns159cfNwFw4ugpnF2cKFGqeI5y93oPraz0WNtYp58flStflr8OGHvuD+w+ROvn/MwT+AM0b9eEjT9vBuDU0dM4FXOieCn3HOXiTXnpTXll3mbvTHmLudPmUwhOBR9K3ZrVKObibOkw/jUMaAXyU1jke6NRKTVCKXXS9POOqTfwZKb1o5RSU5RS3YG6wHKlVKBSyl4pFaSU+kgp9bdS6pBSqoLpNYtM5e+9R6zp4Uygmen1uV/yeUxunu7cDskYwnI77DbuHjkriXscXByp07oeJ/dlnCC17d+Bz/+YR5/xA1g0OecHtLkV9yxORKacIkIjKOGZdw+Go4sjDVo3IHBfIADLPluGf1d/lh5ayvuL3+fr/31d0CE/kM69JIbb4enPDZHhqOIls5TR+z6FrnhJUo8ezPF6fYXKuMxaiMunC4n/dlZ6I7IwcPVw405IRsM8Kuw2rp5574PZPVHVFzev4pzaadnhcy4ebtwNiUx/Hh0WiYtn1osVLp5u3A3NKHM3NBIXD2OZDe8tof343ozeP4cOE/qw5aNV6eXK1n6KtzZ9wIBFYyj1VNZesILm5OlGTGjG9okJi8QpW15Onm7EhkXetwxAtZ7NuWJqXJ3feIiU+CTeODKXwQdnc+SbjSTejcvxmoJm6+lOQqb9LzEkEtuH3f+UotKUfpydsqyAovvnlHsJ0iJupT833A5HVzxrPagv/xS6EqVI+StnnXGPdaMWpF0+D6kpBRbro/D0KkVocEYvRljITTy9SuUoN2L8EBZ8tZSEhKwXIlp38CMs9BZnT+UcTmhJHl6lCAm+mf48LOQWnl4lc5QbPn4I3321jISExBzrRk54k73HN9KpewdmzywEn1slS5B2K2MfTLsVgb5kzpwcu3XC46dluLz5GlGz5qYv13t7UnLxfEp89Rk2NaqZJeaHUcqrJGEhGdvqZmg4pXLZVgBfr/yMXSc3Ehcbz9ZfdwJw6dwV/Ns3B6Btx5Z4eufcfy2hpGdJboVknGfcCgmnpGfuec1e8RGbTqwlLjaeHb/tBqBZuyaEh4Vz8fSlXF8jRGGXr41GpVQdYCDQAGgIvArkPDMCNE37GTgC9NE0raamafc+ue5qmlYNmAvMfsCvHAfsMb3+s3xI4bHo9DrenjOC3xdu4Nb1jApzy5JNDGv+OitmLqHr0B73eYfCR6fXMXbuWNYvXE/YNeOJiF8nP7b9tI1+9fvxvwH/Y/Ts0SilLBzpAyiF/YA3SViS+4lC2sUzRI8YSPS4wdh16QPWNmYOsGAopej2bj9WT19q6VAeW/2+rdk4dSkfNx7KhqlL6fKhcXhZyMkgPm7yNnM7jOfAoi30+WakhSP9Zxq89TyGVANn1hiHeHrWLI8hzcC8ekP5tskI6r4aQLGyuZ+gFFZlB7YlfPsxkjJdCPjXUAqHgW8Sv/CrPIvoy/jg0H8wcfM+NWNgj6/yMxUp61OGLRt3ZlluZ2/HkHcGFYoG1T9hzOuJHHnd8+mML2laI4B1P2+i3ysvmjm6fy7ul3Xc7NGX6K++wWWgcahm2u1IbnbuRfiAwdz9/Cvc3puIcnCwcKSP7o1ew2lZoyM2NtbUb1oHgP8Nn07Pl7ryw+aFODo5kJKcauEoH907vcfwXK1u2NhYU7dpLWztbXlpaB+++bjw3NMt8l9RH56a3xPhNAXWaJoWB6CUWg00e8T3WJnpf4s0BNv270CrF9sCcOnEBYp7Z1x9Lu5ZnMibuZ8AvTZzCGFXQtn4fe6TVexfv4dXpg3O/4AfwnMDnqN9r/YAnD9+nhKZcirhVYKIPCYEGPbhMEKuhLD2u7Xpy9r1bMekfpMAOHv0LNa21ri4u3D3tuVu6DZEhqPL1LOocy+JlqnnEXsH9GV8cZoy27je1R2nsdOJ/XAiaZfPZbxP8DW0xAT0ZXyzLDe35v3a0rhXKwCuHr+Em3fG0B5Xz+JEhT3cSbitkx1eFcsw7If/AeBS0pXBC0Yz/5WPzTIZToN+bajXyx+AG8cvU8w7o4fKxdOd6LCsQ0yjw+5QzCujTDEvd6JNw1Brd2uePinOyQ1/0mXmqwAkZRoCfn5XIM9PG4iDmzPxd2IKJimgZv/WVDflFXbiMs5eGdvH2dOd2Gx5xYbdyRh2mkuZqt2b8WSrWvzY64P0ZZU7NSZo9wkMqWnE344m+Mh5PKuX5+61cMwpKSwS+0z7n523O0kPuf+51n0KtwaVKPtSW/SOtuhsrEiLT+T8tJUPfnEB0yIj0JfI6MHQFS+J4XZGPajsHdCX9cV52mzjeld3nCfMIGbGBNIunUMVL4nTuGnEfT4DQ1iIucPPot+gF+jZrysAJwJP4VXaM32dp7cHYaG3spSvXa8G1WpW4Y+jG9Bb6Slewp0V677lvXEf8kTZ0mzYvcr02lL8umMFndv2I+KW+W9D6DvoBXr26wLA34Gn8C7twV+mdZ7epQgLzXos1KpXnWo1q7D76G/peS1f9w19OmW9f3Hdz5v4/ocv+PxDy07sYQiPQF8qYx/UlypBWnjex3fC1p24jn7H+CQlBUOKsXc75dwF0oJDsCr7BClnLdND3HNgN7r1eR6AU4Fn8PT2SF/n4VWSW6F555WclMzOzXvwb9+cg38cJujiVV5/8R0AypUvQ7PWOW81MZduL3WmU5/nADgTeJZS3hnnGaW8SxIedv+8/ti8j2btmnL7ViReZb1Ytu07AEp6lWTx5m8YFPAGkeH/wotq4j/JHLOnupK1R9PuAeW1XB6n3nsPpZQOKNBuoC1LNrFliXE8fq2WdWg3IID96/fwVK2KxMfEEXUr571UPUf1xsHZkfljvsyy3NPHi7CgUNN71SU0yDITC/y2+Dd+W2y8H6Rey3p0fKkju9ftplKtSsTFxHEnl5z6j+6Pg7MDs0fPzrL8VsgtajatybaftlGmQhls7Gws2mAESLt4Dp3XE+hKeWKIjMC6SUviPs9083Z8HHdf7pT+1GnKbBKWfE3a5XPG10SEgyENXQkP9N5lMYRb9ib1P5Zu4Y+lWwCo6l+L5gPa8df6/fjUeoqEmPg8713MLjEmgXG1X01/PuyH/7Fm+jKzzZ7659Kt/Ll0KwBP+9ek4YC2nFh/gDK1KpAUk0BMtjxiwqNIikmgTK0KXD92kVpdm3FgkfHvEH3rDr4NK3Pl4BnKN67K7SBjb75TyWLEhhv3vydqPIlSqkAbjACBS7YRuGQbAOVb1qTWgDacXX8Ar1pPkhQTT9ytrHnF3YoiOTYBr1pPEnrsElW7NeWoKS+fFtWp/8Zz/NBjGqmJGffNxYTcpmzjqpxevQ9re1u8a1fgr+9+L9C8cnP32CUcyntiX7YkiaGReHZuzIk35jzUa08MyRhKV7pnC1xqlC8UDUaA1Atns9QZNk1bEvfZ1PT1WnwcUQMy6gznqbOJX/S1scHo4ITzxJnEL51P6tmTub29WS39/keWfv8jAP5tmtLv5Rf5dfXv1KxTjZjoWMJvZr0ouHzhTyxf+BMApct4sWDFF/TuZKwn6ldulV7uj6Mb6NS6j8VmT132/Y8sM+Xl16Yp/V/uya+rN+eZ14qFP7Ni4c/Avbw+T28w+pQvQ9Bl40yjbTq04NKFIPMlkofkM2exKlMavZcnaeEROLRuSeTk6VnK6J8oTdqNYADsmjQk9brxsc61GIboGDAY0Ht7YVXmCVJDLDdx0aqFv7Bq4S8ANGvdmF6DurNp7Vaq165KTExcjosO9g72ODo5EHHrNnq9nmatG3P0z+MAuJdwIzLiDkopXhs+kJ+WmHdys8x+WbSWXxatBaBxq4b0GNiFrWt3ULV2FWKj47h9K2uDz97BHgcne27fikSv19OkdUMC//ybS2evEFC9S3q5NX/+wEsdBhf62VPFo5Gv3Hg0e4BFSqmZgAK6YByu+rZSqjgQCzwH3DvziQGy32HbE+O9ij2BA6ZlQUAd4EfgecD6Pq/PV8d2/EUt/zp8/sc8kk1fuXHPhxs/Y2zAcNw9i9N16AsEX7zOzA2zANi8ZAM7fthGuwEBVGtag7SUNOKiY/lqxOcFGe5DObzjMPVa1uP7vd+TmJDIZyMzOnTn/j6Xt9q/RQnPEvR6uxfXLlxjzibjCeKvi35l8w+bWTB1AW9/+DZdXumCpmnMGjHLUqlkMKQR/93nOE38GHQ6knduwnAjCLueA0m7dI6UI/vzfKlVpWrYde6NlpYGBgPxC2ajxRSeivzUzmNU9a/F5N2fk5KQzLLRGUPHxm38kJkBYwHoNK4PdTs1wdrehqkHvuLAqh1snP2zpcLO4dzOQCr612TE7s9ISUhi9ej56eve2jiDuQHGWR7Xv/s93T55HSs7Gy7sOs75XYEArB23gGcn90dnpSM1KYW14433Bz/ToQH1+7bGkJZGSmIyq4Y+XIMmv1zeEYivfw1e2fMpKQnJ/D7qm/R1/TdNZ0kH48ya2yYtosOnr5m+cuM4V3YaT5BaTR2A3saKHsvHARBy7CLbJizk2OKttP/0NV7aNhOlFCd//IOIs9fNmhuAlmbg9PiF1P1hAkqv48bKncSeu0GFMT24e/wy4Zv/wqVmeWovHImVqyMl29amwuju7Gsx2uyxPhJDGvHfzsZ58ifGr9zYvpG060HY9xpE6sWzpBzOu86wDeiC3qs09i8MwP4F49fbxLw3Cu1ulJmCz9vOrXvxa92UnYfXk5iQyJi3p6Sv+23nDzzn/+8ZlpnZLlNeOw6vIzEhkbGZ8vp150o6+vfK+8XA6HffpnyFchgMGsE3Qnl35PT7ljeLNANRn86hxOwPQacn7rdNpF4JwvnVl0g5c57Evftx6t4Z23p10FJT0WJiuDP1QwBsalbH5dWBaKmpoGlEffQZWnTBXix7WHu27adZq8ZsOPgTiQlJvPtOxgXcH7ct5oXWA7B3sOOLJR9hY2ODTqc4tO8oPy02Ng47dG5Dz4HdANi+cRdrV/5mkTyy27/9II1bNeDn/ctJTEhi2vAP09ct2bqA/m1ewd7Bjo8XzcDGxhql03F0/zHWLFl/n3ct3EZPnsnhYyeIioqmVee+DHm5H906trN0WIVWYZjssiCp/B4rq5QaAdybS3+BpmmzlVJvA8OAYOAyEKRp2hSlVDdgBpAANALOAKuADkAS0EvTtItKKQ9gHWCPscH5pqZpTkopa2AzUBxYlO2+Rq1nuc75mlthsOrqWjqU6WDpMPLdpuubuNPDz9Jh5Cu3n3bxlk9PS4eR7+YGrWKiT4FOWmwR04NW8EnZwjG1e34ZdW0Zv3v8OxsJ99P+5g9Edmlh6TDynfua3ZQvUcvSYeS7yxHHeLJEbUuHka8uRRwluFFLS4eR70of2EF1z0aWDiPfnQg7QENvP0uHka8OhuwiJcIy37lckKxLlC/kk2TkzdHBp0BajXHxQYXib5Lvw1M1TZsFzMq27Avgi1zK/gL8cu+5aTKVjzVNG5ut3E2ME+vcM9a0PAUoerW2EEIIIYQQ4l+jqA9PtfT3NAohhBBCCCGEKMTMMRHOQ9M0zcfSMQghhBBCCCHEoyhMX49REKSnUQghhBBCCCFEngpVT6MQQgghhBBC/NsU9dlTpdEohBBCCCGEEI9BhqcKIYQQQgghhPjPkp5GIYQQQgghhHgM0tMohBBCCCGEEOI/S3oahRBCCCGEEOIxFO1+RlBFvStVCCGEEEIIIcQ/J8NThRBCCCGEEELkSRqNQgghhBBCCCHyJI1GIYQQQgghhBB5kkajEEIIIYQQQog8SaNRiEJEKeWqlBryMOuUUn5Kqd/MF13BUkotUkp1f4TyPkqpkwUZ0+NQSr2tlDqjlFqulBpl6XgKilLqHaWUQyGII/34+CfHxqPuf5lel+fvUkoFKaVKPOp7ClBKxeax/B9tpwf8rpeUUnPz8z3zg1Jql1KqrqXjEPlbv/wbtqvpmPB+jNf7KKV652dMwvKk0ShE4eIK5Gg0KqWs8lonCq0hQBvggqUDeRTK6FE+G94BLN5oRI4PIUTBceW/Vb+8BPzjRiPgA0ijsYiRRuNDUEqtVUr9pZQ6pZR6zbTsZaXUeaXUIaXUt/euUiqlSiqlflFKHTb9NLFs9HlTSjkqpTYopY4rpU4qpXoqpeoopXab8t2slPJSShVTSp1TSj1tet1KpdSrlo7/YSil+iulTphyXGq62jdPKXXEtP2es3SM2cwEnlRKBZr2nz1KqfXA6WzrPjaVdzFtw3OmvHRgvEqvlPrMtM9uV0qVNC1/Wyl12vQ3+cEiGZpk3zamxc2VUvuVUpczXZVVSqmPTfvo30qpnhYM+6EopeYB5YFNwHCghlLqgFLqwr1jx3Rs/WHanieVUs0sGK+PaR9aApwE3jXtfyeUUu+ZyuRWX7yN8cRip1Jqp6lcW1OuR5VSPymlnEzL65m27XFTvemslHJQSv1o2ifXKKX+fIwr8OnHB/Ax4KSU+lkpdVYZe3uVKY7/mXI7qZT65t7ybH+PXMsopSoopbaZcjiqlHrS9JJcf5fJUFPZv5VSlf5hbg9FKdXX9LcNVErNV0o1MG1DO9P2O6WUekYZe0p25fH3CTAt+0sp9YUyw2gGpdQI09/6pFLqnWzrlFJqrmn/3AaUyrQuSCn1kelve0gpVcG0PNfPYaVUfdO+ecy0Lz6dSyzPmsqYtYc4t+Mr2/pc6/TCTOX8/O1oOsaPmY4jD1O5B26XQiDf6hdLyX6cqWyjdZRSo5RSU5Txs7cusNxUl9jf51jL0vOvMkYIzASamV4/3Jx5igKkaZr8POAHcDf9b4/xhKo0EAS4A9bAHmCuqcwKoKnpcVngjKXjv09e3YBvMz0vBuwHSpqe9wS+Nz1uAxwAXgR+t3TsD5lfVeA8UOLedgQWAb9jvGDyFHADsLN0rJli9gFOmh77AXGAb/Z1mdYnYmyc6IGtQHfTOg3oY3r8v0z7Zwhga3rsWgi3zU+mbVMFuJhpP91qytEDuAZ4Zf97FLYfUx1RApgCHDfVHyWA6xgbWiOBiaayesDZwvudAWgItAW+AZRpW/wGNM+tvsicp+lxCeAPwNH0fKxp/7MBLgP1TMtdACtgFDDftOwZIBWo+xg5ZD527gJPmHI4QEa97J7pNUuBjqbHizIdP3mV+RPoYnpsh7GH9X6/KwgYano8BFhQgNuwMvArYG16/hXQH5gGfAJ8CYy/39/HlNN1MuqclcBvBbzv1QH+BhwBJ+AUUAuINa3vSsbx7w1EZdpOQWQcQ/3vxUoen8P39jvT49bAL6bHLwFzgS4YP8/dLHAM5vZ5vOve8UAedXph/SH3Ot6NjO8HfwX49H7bpTD9kL/1S/p2NWP8eR1nmc8pRgFTcovxPsdael6m5/eOWz8KuO6QH/P/WCEexttKqS6mx2WAfsBuTdMiAZRSPwEVTetbA1UyXVxyUUo5aZqW6/0ZFvY38KlS6kOMJ4Z3MJ64bTXFrwdCATRN26qU6oHxxKOGZcJ9ZC2BnzRNiwDQNC3SlNePmqYZgAtKqctAJSDQYlHe3yFN0648YP1lMPYAYzzx+xljA2CVqcwyYLXp8QmMVw/XAmsLIuCHlNe2WWvaNqfvXYXGmNNKTdPSgJtKqd1APYy5/Fus0zQtAUhQxh65+sBh4HullDXGvAMtGSBwVdO0g0qpTzA2HI+ZljthvMCyh0z1haZpe3J5j4YYG/z7TNvTBuMJ1dNAqKZphwE0TYsGUEo1BT43LTuplMrPbXpI07Qbpt8TiPGkby/gr5Qag7HB547x5OnXbK/NUUYptQsorWnaGlO8iab3vt/vgoxj7y+MDaCC0grjieFhU0z2wC3gfYz7WiLwdqbyucUcC1zOVOesBF4rwJjBeHyv0TQtzhTLaiBzr3tzMo7/EKXUjmyvX5np/89Mj3P9HMbYEFuslHoKYyPMOtP7tMTYu9L23v5pZlk+jzVN25OtkyqvOr2wyq2OrwasUkp5Yawb7u1n99suhdXj1C+W8KDj7GHkdqyJ/xBpND6AUsoP4wdQI03T4k0nDmcxXtXNjQ5oeO+EojDTNO28Uqo2EIDxavQO4JSmaY2yl1XGYY+VgXiMVwtvmDPWfKY94HlhEveA9Q+by73lz2I8CesITFRKVdM0LfUx4stvSZkeF5phPfkgx3bSNO0PpVRzjNtkkVJqlqZpSywQ2z339jUFfKBp2vzsBTLXF0qp7ZqmvZ+9CLBV07Re2V5XrSACfoDM+1IaYKWUssPYA1dX07TrSqkpGHvX0j1MmYf5Xbmsy748vylgsaZp47MsNJ6gO2E8EbcjYzvfL+Z/Ey2Xx7l+DivjbSQ7NU3ropTywdibcs8ljKM2KgJHCizaPGT/PFZKbX/QS8wQVn6bA8zSNG296dxqimn5VPLeLoXVP6pfChlXst6m9qBYczvWUu+9h+k80Sa/ghOFj9zT+GDFgDumBmMljFfSHYEWSik3ZZygpFum8luAofeeKKVqmjPYR6GMM2PFa5q2DOMY/QZASaVUI9N6a6VUVVPx4cAZjDc2LzT1jhR2O4AeSqniAEopd9PyHkopnTLej1QeOGepAHMRAzg/wrr6SilfU2Xdk4zeDR1w7z6D3sBeU5kymqbtxDhssBjGk0lLyGvb5GYP0FMppVfG+3iaA4fMEGN+6qSM95UVxzhs57BSqhxwU9O0b4EFQG1LBpjJZmCQyrgXsbRSqlQu9cW9eDPvlweBJpnud3FUSlXEeIx5KaXqmZY7m+rOfcALpmVVgMdpXN7v2Lnn3klRhCm/3GbhzLWMpmkxwA2lVGdTvLaqEMwam8l2oLtSqhQYjynTPjYfeBdYDnz4gPc4B5Q3nbiDsU4paHuAzsp4f6sjGUNE7/mDjOPfC/DP9vqemf4/YHqc1+dwMSDY9PilbO9zFeNn+ZJMn3tmc5/j654cdboZw/sncqvjM//9B2Qqe7/tUljkV/1iKbkdZ5uAUkqp4kopWyDzHA+55ZvbsRaEcYQDwPNk9BI/zN9L/Mv8W68smtPvwOtKqTMYP1APYqzcZmA8cY3E2PN411T+beBL0zArK4wfeK+bO+iHVA34WCllAFKANzBeNfpCKVUMY/yzlVKpGO8/qK9pWoxS6g9gEjDZQnE/FE3TTimlpgO7lVJpZAy3u4Zx27kArxemXmFN024rpfYp483pCcDNPNZtAjZgHHY2F6gA7ATWmIrHYWxQTsI4RK0nxuHGy0zbVgFfaJoWZZ7MsrrPtsnNGqARxvsCNWCMpmlhmU5s/w1OYNw+JYCpmqaFKKUGAKOVUikYhwX2t2SA92iatkUpVRk4YBoeFwv0xbiPZa8vwHj/4+9KqRBN0/yVUi8BK00nIQCTTL0oPYE5Sil7jPt2a4xX5RcrpU5jrEdPkVGXPmrceR47mcpEKaW+xXhvehjG4+dRyvQD5iul3jf9DXr8k1gLgqZpp03H+xbTBaIUYB2QomnaCqWUHtivlGqJcahjbu+RoIxfK/C7UiqOXP4+BRD3UaXUIjIuBC3QNO2YyhiauQbjUMfTGOvuA9news30eZsE3Ovhzutz+COM+9skjPVn9ljOKqX6AD8ppTpqmnYpn9J8GLl9Hn+SaX1udXqhlUcdPwXj3/YOxkalr6n4fbdLYZBf9Yul5HGcHTbVZYcwnteezfSSRcA8pVQCxs9fyP1Y+xZYp5Q6jvF8+d5IhhNAmmn5Ik3TZDhrEXDvhmTxiJTpPkXT1fI1GCeMWfOg1wnLMlWav2ma9rOlYylISqlYTdMs1YsoxEMxNWSsNU1LNPX8bwOe1jQt2cKh/Wdl+mxTGO9hv1BYT/iUUkEYhwJGWDqWgiZ1urCk/9KxJvImPY3/3BSlVGuMwxG2YNlJRYQQ4t/IAePXdVhj7P0eIg1Gi3vV1Atug7F3KMe9rUIIIf57pKdRCCGEEEIIIUSeZCIcIYQQQgghhBB5kkajEEIIIYQQQog8SaNRCCGEEEIIIUSepNEohBBCCCGEECJP0mgUQgghhBBCCJGn/wPiMELQPHvo1AAAAABJRU5ErkJggg==\n" + }, + "metadata": { + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "markdown", + "source": "There are no correlated columns presebt in the data ", + "metadata": {} + }, + { + "cell_type": "markdown", + "source": "### Analysis of Features", + "metadata": {} + }, + { + "cell_type": "markdown", + "source": "### Age", + "metadata": {} + }, + { + "cell_type": "code", + "source": [ + "plt.figure(figsize=(16, 7))\n", + "sns.distplot(data[\"age\"])\n", + "plt.title(\"Distribution Plot of Ages\\n\", fontsize=20)\n", + "plt.show()" + ], + "metadata": { + "execution": { + "iopub.status.busy": "2021-08-24T17:50:43.573210Z", + "iopub.execute_input": "2021-08-24T17:50:43.573693Z", + "iopub.status.idle": "2021-08-24T17:50:43.962739Z", + "shell.execute_reply.started": "2021-08-24T17:50:43.573636Z", + "shell.execute_reply": "2021-08-24T17:50:43.961890Z" + }, + "trusted": true + }, + "execution_count": 10, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": "
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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "code", + "source": [ + "Age_18_25 = data.age[(data.age >= 18) & (data.age <= 25)]\n", + "Age_26_35 = data.age[(data.age >= 26) & (data.age <= 35)]\n", + "Age_36_45 = data.age[(data.age >= 36) & (data.age <= 45)]\n", + "Age_46_55 = data.age[(data.age >= 46) & (data.age <= 55)]\n", + "Age_56_65 = data.age[(data.age >= 56) & (data.age <= 65)]\n", + "Age_66_75 = data.age[(data.age >= 66) & (data.age <= 75)]\n", + "Age_75above = data.age[data.age >= 76]\n", + "x_Age = [\"18-25\", \"26-35\", \"36-45\", \"46-55\", \"56-65\", \"66-75\", \"75+\"]\n", + "y_Age = [\n", + " len(Age_18_25.values),\n", + " len(Age_26_35.values),\n", + " len(Age_36_45.values),\n", + " len(Age_46_55.values),\n", + " len(Age_56_65.values),\n", + " len(Age_66_75.values),\n", + " len(Age_75above.values),\n", + "]\n", + "\n", + "px.bar(\n", + " data_frame=data,\n", + " x=x_Age,\n", + " y=y_Age,\n", + " color=x_Age,\n", + " template=\"plotly_dark\",\n", + " labels={\"x\": \"Age\", \"y\": \"Number\", \"color\": \"Age group\"},\n", + " title=\"Number of patients per Age group\",\n", + ")" + ], + "metadata": { + "execution": { + "iopub.status.busy": "2021-08-24T17:50:43.964009Z", + "iopub.execute_input": "2021-08-24T17:50:43.964456Z", + "iopub.status.idle": "2021-08-24T17:50:45.569476Z", + "shell.execute_reply.started": "2021-08-24T17:50:43.964408Z", + "shell.execute_reply": "2021-08-24T17:50:45.568343Z" + }, + "trusted": true + }, + "execution_count": 11, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/html": " \n " + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/html": "
" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": "We can see the cases are more of age group from 56 to 65", + "metadata": {} + }, + { + "cell_type": "markdown", + "source": "### Gender", + "metadata": {} + }, + { + "cell_type": "code", + "source": [ + "px.bar(\n", + " data_frame=data,\n", + " x=list(data.sex.value_counts().keys()),\n", + " y=list(data.sex.value_counts()),\n", + " color=list(data.sex.value_counts().keys()),\n", + " template=\"plotly_dark\",\n", + " labels={\"x\": \"Gender\", \"y\": \"Number\", \"color\": \"Gender group\"},\n", + " title=\"Number of patients per Gender group\",\n", + ")" + ], + "metadata": { + "execution": { + "iopub.status.busy": "2021-08-24T17:50:45.571029Z", + "iopub.execute_input": "2021-08-24T17:50:45.571358Z", + "iopub.status.idle": "2021-08-24T17:50:45.672438Z", + "shell.execute_reply.started": "2021-08-24T17:50:45.571324Z", + "shell.execute_reply": "2021-08-24T17:50:45.671211Z" + }, + "trusted": true + }, + "execution_count": 12, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/html": "
" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": "Assigning labels for one hot encoding", + "metadata": {} + }, + { + "cell_type": "code", + "source": [ + "# since we don't know 0 is male or female and vice versa we are assigning with the same label\n", + "data[\"sex\"] = data[\"sex\"].map({0: \"0_gender\", 1: \"1_gender\"})\n", + "data.head()" + ], + "metadata": { + "execution": { + "iopub.status.busy": "2021-08-24T17:50:45.676611Z", + "iopub.execute_input": "2021-08-24T17:50:45.677195Z", + "iopub.status.idle": "2021-08-24T17:50:45.700136Z", + "shell.execute_reply.started": "2021-08-24T17:50:45.677143Z", + "shell.execute_reply": "2021-08-24T17:50:45.698873Z" + }, + "trusted": true + }, + "execution_count": 13, + "outputs": [ + { + "execution_count": 13, + "output_type": "execute_result", + "data": { + "text/plain": " age sex cp trtbps chol fbs restecg thalachh exng oldpeak \\\n0 63 1_gender 3 145 233 1 0 150 0 2.3 \n1 37 1_gender 2 130 250 0 1 187 0 3.5 \n2 41 0_gender 1 130 204 0 0 172 0 1.4 \n3 56 1_gender 1 120 236 0 1 178 0 0.8 \n4 57 0_gender 0 120 354 0 1 163 1 0.6 \n\n slp caa thall output \n0 0 0 1 1 \n1 0 0 2 1 \n2 2 0 2 1 \n3 2 0 2 1 \n4 2 0 2 1 ", + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
agesexcptrtbpscholfbsrestecgthalachhexngoldpeakslpcaathalloutput
0631_gender31452331015002.30011
1371_gender21302500118703.50021
2410_gender11302040017201.42021
3561_gender11202360117800.82021
4570_gender01203540116310.62021
\n
" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": "### cp\nChest Pain type chest pain type", + "metadata": {} + }, + { + "cell_type": "code", + "source": [ + "px.bar(\n", + " data_frame=data,\n", + " x=list(data.cp.value_counts().keys()),\n", + " y=list(data.cp.value_counts()),\n", + " color=list(data.cp.value_counts().keys()),\n", + " template=\"plotly_dark\",\n", + " labels={\n", + " \"x\": \"Chest Pain intnsity\",\n", + " \"y\": \"Count\",\n", + " \"color\": \"Chest Pain intnsity\",\n", + " },\n", + " title=\"Number of patients per Chest Pain intnsity\",\n", + ")" + ], + "metadata": { + "execution": { + "iopub.status.busy": "2021-08-24T17:50:45.701850Z", + "iopub.execute_input": "2021-08-24T17:50:45.702224Z", + "iopub.status.idle": "2021-08-24T17:50:45.788604Z", + "shell.execute_reply.started": "2021-08-24T17:50:45.702174Z", + "shell.execute_reply": "2021-08-24T17:50:45.787314Z" + }, + "trusted": true + }, + "execution_count": 14, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/html": "
" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "cp_0_1 = data.cp[(data.cp == 0) & (data.output == 1)]\n", + "cp_0_0 = data.cp[(data.cp == 0) & (data.output == 0)]\n", + "cp_1_1 = data.cp[(data.cp == 1) & (data.output == 1)]\n", + "cp_1_0 = data.cp[(data.cp == 1) & (data.output == 0)]\n", + "cp_2_1 = data.cp[(data.cp == 2) & (data.output == 1)]\n", + "cp_2_0 = data.cp[(data.cp == 2) & (data.output == 0)]\n", + "cp_3_1 = data.cp[(data.cp == 3) & (data.output == 1)]\n", + "cp_3_0 = data.cp[(data.cp == 3) & (data.output == 0)]\n", + "\n", + "y_cp_1 = [\n", + " len(cp_0_1.values),\n", + " len(cp_1_1.values),\n", + " len(cp_2_1.values),\n", + " len(cp_3_1.values),\n", + "]\n", + "y_cp_0 = [\n", + " len(cp_0_0.values),\n", + " len(cp_1_0.values),\n", + " len(cp_2_0.values),\n", + " len(cp_3_0.values),\n", + "]\n", + "\n", + "fig = go.Figure()\n", + "fig.add_trace(\n", + " go.Bar(\n", + " x=[0, 1, 2, 3], y=y_cp_1, name=\"Heart Attack\", marker_color=\"indianred\"\n", + " )\n", + ")\n", + "fig.add_trace(\n", + " go.Bar(x=[0, 1, 2, 3], y=y_cp_0, name=\"Safe\", marker_color=\"lightsalmon\")\n", + ")\n", + "\n", + "# Here we modify the tickangle of the xaxis, resulting in rotated labels.\n", + "fig.update_layout(barmode=\"group\", xaxis_tickangle=-45)\n", + "fig.show()" + ], + "metadata": { + "execution": { + "iopub.status.busy": "2021-08-24T17:50:45.790389Z", + "iopub.execute_input": "2021-08-24T17:50:45.790948Z", + "iopub.status.idle": "2021-08-24T17:50:45.825684Z", + "shell.execute_reply.started": "2021-08-24T17:50:45.790891Z", + "shell.execute_reply": "2021-08-24T17:50:45.824258Z" + }, + "trusted": true + }, + "execution_count": 15, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/html": "
" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": "Though Chest pain is represented as numeric data but it is categorical in nature. We can convert the data to categorical to get dummies. LabelEncoding will not work here as we can see that there is not such relation among the categories that resembles an ordinal relationship.", + "metadata": {} + }, + { + "cell_type": "code", + "source": [ + "data[\"cp\"] = data[\"cp\"].map(\n", + " {0: \"Intensity_0\", 1: \"Intensity_1\", 2: \"Intensity_2\", 3: \"Intensity_3\"}\n", + ")\n", + "data.head()" + ], + "metadata": { + "execution": { + "iopub.status.busy": "2021-08-24T17:50:45.827498Z", + "iopub.execute_input": "2021-08-24T17:50:45.827861Z", + "iopub.status.idle": "2021-08-24T17:50:45.855474Z", + "shell.execute_reply.started": "2021-08-24T17:50:45.827829Z", + "shell.execute_reply": "2021-08-24T17:50:45.853873Z" + }, + "trusted": true + }, + "execution_count": 16, + "outputs": [ + { + "execution_count": 16, + "output_type": "execute_result", + "data": { + "text/plain": " age sex cp trtbps chol fbs restecg thalachh exng \\\n0 63 1_gender Intensity_3 145 233 1 0 150 0 \n1 37 1_gender Intensity_2 130 250 0 1 187 0 \n2 41 0_gender Intensity_1 130 204 0 0 172 0 \n3 56 1_gender Intensity_1 120 236 0 1 178 0 \n4 57 0_gender Intensity_0 120 354 0 1 163 1 \n\n oldpeak slp caa thall output \n0 2.3 0 0 1 1 \n1 3.5 0 0 2 1 \n2 1.4 2 0 2 1 \n3 0.8 2 0 2 1 \n4 0.6 2 0 2 1 ", + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
agesexcptrtbpscholfbsrestecgthalachhexngoldpeakslpcaathalloutput
0631_genderIntensity_31452331015002.30011
1371_genderIntensity_21302500118703.50021
2410_genderIntensity_11302040017201.42021
3561_genderIntensity_11202360117800.82021
4570_genderIntensity_01203540116310.62021
\n
" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": "### trtbps\nresting blood pressure (in mm Hg)", + "metadata": {} + }, + { + "cell_type": "code", + "source": [ + "plt.figure(figsize=(16, 7))\n", + "sns.distplot(data[\"trtbps\"])\n", + "plt.title(\n", + " \"Distribution Plot of Resting blood pressure (in mm Hg)\\n\", fontsize=20\n", + ")\n", + "plt.show()" + ], + "metadata": { + "execution": { + "iopub.status.busy": "2021-08-24T17:50:45.857331Z", + "iopub.execute_input": "2021-08-24T17:50:45.857744Z", + "iopub.status.idle": "2021-08-24T17:50:46.138074Z", + "shell.execute_reply.started": "2021-08-24T17:50:45.857710Z", + "shell.execute_reply": "2021-08-24T17:50:46.136810Z" + }, + "trusted": true + }, + "execution_count": 17, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": "
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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "markdown", + "source": "### chol\ncholestoral in mg/dl fetched via BMI sensor", + "metadata": {} + }, + { + "cell_type": "code", + "source": [ + "px.box(x=\"trtbps\", data_frame=data, template=\"plotly_dark\")" + ], + "metadata": { + "execution": { + "iopub.status.busy": "2021-08-24T17:50:46.140799Z", + "iopub.execute_input": "2021-08-24T17:50:46.141265Z", + "iopub.status.idle": "2021-08-24T17:50:46.253118Z", + "shell.execute_reply.started": "2021-08-24T17:50:46.141223Z", + "shell.execute_reply": "2021-08-24T17:50:46.251780Z" + }, + "trusted": true + }, + "execution_count": 18, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/html": "
" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": "### chol\ncholestoral in mg/dl fetched via BMI sensor", + "metadata": {} + }, + { + "cell_type": "code", + "source": [ + "plt.figure(figsize=(16, 7))\n", + "sns.distplot(data[\"chol\"])\n", + "plt.title(\"Distribution Plot of cholestoral in mg/dl\\n\", fontsize=20)\n", + "plt.show()" + ], + "metadata": { + "execution": { + "iopub.status.busy": "2021-08-24T17:50:46.255249Z", + "iopub.execute_input": "2021-08-24T17:50:46.255722Z", + "iopub.status.idle": "2021-08-24T17:50:46.518442Z", + "shell.execute_reply.started": "2021-08-24T17:50:46.255670Z", + "shell.execute_reply": "2021-08-24T17:50:46.517205Z" + }, + "trusted": true + }, + "execution_count": 19, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": "
", + "image/png": 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+ }, + "metadata": { + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "code", + "source": [ + "px.box(x=\"chol\", data_frame=data, template=\"plotly_dark\")" + ], + "metadata": { + "execution": { + "iopub.status.busy": "2021-08-24T17:50:46.520438Z", + "iopub.execute_input": "2021-08-24T17:50:46.520820Z", + "iopub.status.idle": "2021-08-24T17:50:46.598670Z", + "shell.execute_reply.started": "2021-08-24T17:50:46.520782Z", + "shell.execute_reply": "2021-08-24T17:50:46.597348Z" + }, + "trusted": true + }, + "execution_count": 20, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/html": "
" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": "lets see trtbps and chol has similar outliers ", + "metadata": {} + }, + { + "cell_type": "code", + "source": [ + "data.chol[data.trtbps >= 171]" + ], + "metadata": { + "execution": { + "iopub.status.busy": "2021-08-24T17:50:46.600272Z", + "iopub.execute_input": "2021-08-24T17:50:46.600615Z", + "iopub.status.idle": "2021-08-24T17:50:46.610604Z", + "shell.execute_reply.started": "2021-08-24T17:50:46.600559Z", + "shell.execute_reply": "2021-08-24T17:50:46.609116Z" + }, + "trusted": true + }, + "execution_count": 21, + "outputs": [ + { + "execution_count": 21, + "output_type": "execute_result", + "data": { + "text/plain": "8 199\n101 270\n110 325\n203 274\n223 288\n241 249\n248 283\n260 228\n266 327\nName: chol, dtype: int64" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": "the values of trtbps outliers are well in range of cholestrol level", + "metadata": {} + }, + { + "cell_type": "markdown", + "source": "### fbs\n(fasting blood sugar > 120 mg/dl) (1 = true; 0 = false)", + "metadata": {} + }, + { + "cell_type": "code", + "source": [ + "px.bar(\n", + " data_frame=data,\n", + " x=list(data.fbs.value_counts().keys()),\n", + " y=list(data.fbs.value_counts()),\n", + " color=list(data.fbs.value_counts().keys()),\n", + " template=\"plotly_dark\",\n", + " labels={\n", + " \"x\": \"fasting blood sugar > 120 mg/dl\",\n", + " \"y\": \"Count\",\n", + " \"color\": \"fasting blood sugar > 120 mg/dl\",\n", + " },\n", + " title=\"Number of patients having fasting blood sugar > 120 mg/dl\",\n", + ")" + ], + "metadata": { + "execution": { + "iopub.status.busy": "2021-08-24T17:50:46.612405Z", + "iopub.execute_input": "2021-08-24T17:50:46.612784Z", + "iopub.status.idle": "2021-08-24T17:50:46.703375Z", + "shell.execute_reply.started": "2021-08-24T17:50:46.612750Z", + "shell.execute_reply": "2021-08-24T17:50:46.702342Z" + }, + "trusted": true + }, + "execution_count": 22, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/html": "
" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "import plotly.graph_objects as go\n", + "\n", + "fbs_0_1 = data.fbs[(data.fbs == 0) & (data.output == 1)]\n", + "fbs_0_0 = data.fbs[(data.fbs == 0) & (data.output == 0)]\n", + "fbs_1_1 = data.fbs[(data.fbs == 1) & (data.output == 1)]\n", + "fbs_1_0 = data.fbs[(data.fbs == 1) & (data.output == 0)]\n", + "\n", + "y_fbs_1 = [len(fbs_0_1.values), len(fbs_1_1.values)]\n", + "y_fbs_0 = [len(fbs_0_0.values), len(fbs_1_0.values)]\n", + "\n", + "fig = go.Figure()\n", + "fig.add_trace(\n", + " go.Bar(x=[0, 1], y=y_fbs_1, name=\"Heart Attack\", marker_color=\"indianred\")\n", + ")\n", + "fig.add_trace(\n", + " go.Bar(x=[0, 1], y=y_fbs_0, name=\"Safe\", marker_color=\"lightsalmon\")\n", + ")\n", + "\n", + "# Here we modify the tickangle of the xaxis, resulting in rotated labels.\n", + "fig.update_layout(barmode=\"group\", xaxis_tickangle=-45)\n", + "fig.show()" + ], + "metadata": { + "execution": { + "iopub.status.busy": "2021-08-24T17:50:46.704849Z", + "iopub.execute_input": "2021-08-24T17:50:46.705159Z", + "iopub.status.idle": "2021-08-24T17:50:46.734276Z", + "shell.execute_reply.started": "2021-08-24T17:50:46.705128Z", + "shell.execute_reply": "2021-08-24T17:50:46.732958Z" + }, + "trusted": true + }, + "execution_count": 23, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/html": "
" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": "Variation in sugar level is not the sole cause of a heart attack", + "metadata": {} + }, + { + "cell_type": "markdown", + "source": "### restecg\nresting electrocardiographic results", + "metadata": {} + }, + { + "cell_type": "code", + "source": [ + "px.bar(\n", + " data_frame=data,\n", + " x=list(data.restecg.value_counts().keys()),\n", + " y=list(data.restecg.value_counts()),\n", + " color=list(data.restecg.value_counts().keys()),\n", + " labels={\n", + " \"x\": \"resting electrocardiographic results\",\n", + " \"y\": \"Count\",\n", + " \"color\": \"resting electrocardiographic results\",\n", + " },\n", + " title=\"Number of patients per resting electrocardiographic results\",\n", + ")" + ], + "metadata": { + "execution": { + "iopub.status.busy": "2021-08-24T17:50:46.737761Z", + "iopub.execute_input": "2021-08-24T17:50:46.738162Z", + "iopub.status.idle": "2021-08-24T17:50:46.843963Z", + "shell.execute_reply.started": "2021-08-24T17:50:46.738124Z", + "shell.execute_reply": "2021-08-24T17:50:46.842720Z" + }, + "trusted": true + }, + "execution_count": 24, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/html": "
" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "import plotly.graph_objects as go\n", + "\n", + "restecg_0_1 = data.restecg[(data.restecg == 0) & (data.output == 1)]\n", + "restecg_0_0 = data.restecg[(data.restecg == 0) & (data.output == 0)]\n", + "restecg_1_1 = data.restecg[(data.restecg == 1) & (data.output == 1)]\n", + "restecg_1_0 = data.restecg[(data.restecg == 1) & (data.output == 0)]\n", + "restecg_2_1 = data.restecg[(data.restecg == 2) & (data.output == 1)]\n", + "restecg_2_0 = data.restecg[(data.restecg == 2) & (data.output == 0)]\n", + "\n", + "y_restecg_1 = [\n", + " len(restecg_0_1.values),\n", + " len(restecg_1_1.values),\n", + " len(restecg_2_1.values),\n", + "]\n", + "y_restecg_0 = [\n", + " len(restecg_0_0.values),\n", + " len(restecg_1_0.values),\n", + " len(restecg_2_0.values),\n", + "]\n", + "\n", + "fig = go.Figure()\n", + "fig.add_trace(\n", + " go.Bar(\n", + " x=[0, 1, 2],\n", + " y=y_restecg_1,\n", + " name=\"Heart Attack\",\n", + " marker_color=\"indianred\",\n", + " )\n", + ")\n", + "fig.add_trace(\n", + " go.Bar(x=[0, 1, 2], y=y_restecg_0, name=\"Safe\", marker_color=\"lightsalmon\")\n", + ")\n", + "\n", + "# Here we modify the tickangle of the xaxis, resulting in rotated labels.\n", + "fig.update_layout(barmode=\"group\", xaxis_tickangle=-45)\n", + "fig.show()" + ], + "metadata": { + "execution": { + "iopub.status.busy": "2021-08-24T17:50:46.846248Z", + "iopub.execute_input": "2021-08-24T17:50:46.846644Z", + "iopub.status.idle": "2021-08-24T17:50:46.876938Z", + "shell.execute_reply.started": "2021-08-24T17:50:46.846609Z", + "shell.execute_reply": "2021-08-24T17:50:46.875449Z" + }, + "trusted": true + }, + "execution_count": 25, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/html": "
" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "raw", + "source": "We can see that if the result is 1 we have more chances of survival compared to 0 and 2 \nsince we have least data points which has category of '2' it the results for label 2 are ambiguous. Thus it is better we use one hot encoding for the feature", + "metadata": {} + }, + { + "cell_type": "code", + "source": [ + "data[\"restecg\"] = data[\"restecg\"].map(\n", + " {0: \"restecg_0\", 1: \"restecg_1\", 2: \"restecg_2\"}\n", + ")\n", + "data.head()" + ], + "metadata": { + "execution": { + "iopub.status.busy": "2021-08-24T17:50:46.878693Z", + "iopub.execute_input": "2021-08-24T17:50:46.879177Z", + "iopub.status.idle": "2021-08-24T17:50:46.909257Z", + "shell.execute_reply.started": "2021-08-24T17:50:46.879145Z", + "shell.execute_reply": "2021-08-24T17:50:46.907704Z" + }, + "trusted": true + }, + "execution_count": 26, + "outputs": [ + { + "execution_count": 26, + "output_type": "execute_result", + "data": { + "text/plain": " age sex cp trtbps chol fbs restecg thalachh exng \\\n0 63 1_gender Intensity_3 145 233 1 restecg_0 150 0 \n1 37 1_gender Intensity_2 130 250 0 restecg_1 187 0 \n2 41 0_gender Intensity_1 130 204 0 restecg_0 172 0 \n3 56 1_gender Intensity_1 120 236 0 restecg_1 178 0 \n4 57 0_gender Intensity_0 120 354 0 restecg_1 163 1 \n\n oldpeak slp caa thall output \n0 2.3 0 0 1 1 \n1 3.5 0 0 2 1 \n2 1.4 2 0 2 1 \n3 0.8 2 0 2 1 \n4 0.6 2 0 2 1 ", + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
agesexcptrtbpscholfbsrestecgthalachhexngoldpeakslpcaathalloutput
0631_genderIntensity_31452331restecg_015002.30011
1371_genderIntensity_21302500restecg_118703.50021
2410_genderIntensity_11302040restecg_017201.42021
3561_genderIntensity_11202360restecg_117800.82021
4570_genderIntensity_01203540restecg_116310.62021
\n
" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": "### thalachh\nmaximum heart rate achieved", + "metadata": {} + }, + { + "cell_type": "code", + "source": [ + "plt.figure(figsize=(16, 7))\n", + "sns.distplot(data[\"thalachh\"])\n", + "plt.title(\"Distribution Plot of maximum heart rate achieved\\n\", fontsize=20)\n", + "plt.show()" + ], + "metadata": { + "execution": { + "iopub.status.busy": "2021-08-24T17:50:46.911126Z", + "iopub.execute_input": "2021-08-24T17:50:46.911798Z", + "iopub.status.idle": "2021-08-24T17:50:47.181288Z", + "shell.execute_reply.started": "2021-08-24T17:50:46.911727Z", + "shell.execute_reply": "2021-08-24T17:50:47.179948Z" + }, + "trusted": true + }, + "execution_count": 27, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": "
", + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "code", + "source": [ + "thalachh_50_85 = data.thalachh[(data.thalachh >= 50) & (data.thalachh <= 85)]\n", + "thalachh_86_110 = data.thalachh[(data.thalachh >= 86) & (data.thalachh <= 110)]\n", + "thalachh_111_135 = data.thalachh[\n", + " (data.thalachh >= 111) & (data.thalachh <= 135)\n", + "]\n", + "thalachh_136_160 = data.thalachh[\n", + " (data.thalachh >= 136) & (data.thalachh <= 160)\n", + "]\n", + "thalachh_161_185 = data.thalachh[\n", + " (data.thalachh >= 161) & (data.thalachh <= 185)\n", + "]\n", + "thalachh_185above = data.thalachh[data.thalachh >= 186]\n", + "x_thalachh = [\"50-85\", \"86-110\", \"111-135\", \"136-160\", \"161-185\", \"185+\"]\n", + "y_thalachh = [\n", + " len(thalachh_50_85.values),\n", + " len(thalachh_86_110.values),\n", + " len(thalachh_111_135.values),\n", + " len(thalachh_136_160.values),\n", + " len(thalachh_161_185.values),\n", + " len(thalachh_185above.values),\n", + "]\n", + "\n", + "px.bar(\n", + " data_frame=data,\n", + " x=x_thalachh,\n", + " y=y_thalachh,\n", + " color=x_thalachh,\n", + " template=\"plotly_dark\",\n", + " labels={\n", + " \"x\": \"maximum heart rate achieved\",\n", + " \"y\": \"Count\",\n", + " \"color\": \"maximum heart rate achieved\",\n", + " },\n", + ")" + ], + "metadata": { + "execution": { + "iopub.status.busy": "2021-08-24T17:50:47.183126Z", + "iopub.execute_input": "2021-08-24T17:50:47.183871Z", + "iopub.status.idle": "2021-08-24T17:50:47.301138Z", + "shell.execute_reply.started": "2021-08-24T17:50:47.183814Z", + "shell.execute_reply": "2021-08-24T17:50:47.300088Z" + }, + "trusted": true + }, + "execution_count": 28, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/html": "
" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "px.bar(\n", + " data_frame=data,\n", + " x=\"age\",\n", + " y=\"thalachh\",\n", + " color=\"age\",\n", + " template=\"plotly_dark\",\n", + " labels={\"x\": \"Age\", \"y\": \"maximum heart beat\", \"color\": \"Age\"},\n", + " title=\"Age to maximum heart beat(sum)\",\n", + ")" + ], + "metadata": { + "execution": { + "iopub.status.busy": "2021-08-24T17:50:47.302751Z", + "iopub.execute_input": "2021-08-24T17:50:47.303354Z", + "iopub.status.idle": "2021-08-24T17:50:47.381054Z", + "shell.execute_reply.started": "2021-08-24T17:50:47.303308Z", + "shell.execute_reply": "2021-08-24T17:50:47.380069Z" + }, + "trusted": true + }, + "execution_count": 29, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/html": "
" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "px.box(x=\"thalachh\", data_frame=data, template=\"plotly_dark\")" + ], + "metadata": { + "execution": { + "iopub.status.busy": "2021-08-24T17:50:47.382459Z", + "iopub.execute_input": "2021-08-24T17:50:47.383049Z", + "iopub.status.idle": "2021-08-24T17:50:47.454247Z", + "shell.execute_reply.started": "2021-08-24T17:50:47.383006Z", + "shell.execute_reply": "2021-08-24T17:50:47.453044Z" + }, + "trusted": true + }, + "execution_count": 30, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/html": "
" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": "### exng\nexercise induced angina (1 = yes; 0 = no)", + "metadata": {} + }, + { + "cell_type": "code", + "source": [ + "px.bar(\n", + " data_frame=data,\n", + " x=list(data.exng.value_counts().keys()),\n", + " y=list(data.exng.value_counts()),\n", + " color=list(data.exng.value_counts().keys()),\n", + " template=\"plotly_dark\",\n", + " labels={\n", + " \"x\": \"exercise induced angina\",\n", + " \"y\": \"Count\",\n", + " \"color\": \"exercise induced angina\",\n", + " },\n", + " title=\"Number of patients having exercise induced angina\",\n", + ")" + ], + "metadata": { + "execution": { + "iopub.status.busy": "2021-08-24T17:50:59.714687Z", + "iopub.execute_input": "2021-08-24T17:50:59.715274Z", + "iopub.status.idle": "2021-08-24T17:50:59.797785Z", + "shell.execute_reply.started": "2021-08-24T17:50:59.715236Z", + "shell.execute_reply": "2021-08-24T17:50:59.796554Z" + }, + "trusted": true + }, + "execution_count": 31, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/html": "
" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "import plotly.graph_objects as go\n", + "\n", + "exng_0_1 = data.exng[(data.exng == 0) & (data.output == 1)]\n", + "exng_0_0 = data.exng[(data.exng == 0) & (data.output == 0)]\n", + "exng_1_1 = data.exng[(data.exng == 1) & (data.output == 1)]\n", + "exng_1_0 = data.exng[(data.exng == 1) & (data.output == 0)]\n", + "\n", + "y_exng_1 = [len(exng_0_1.values), len(exng_1_1.values)]\n", + "y_exng_0 = [len(exng_0_0.values), len(exng_1_0.values)]\n", + "\n", + "fig = go.Figure()\n", + "fig.add_trace(\n", + " go.Bar(x=[0, 1], y=y_exng_1, name=\"Heart Attack\", marker_color=\"indianred\")\n", + ")\n", + "fig.add_trace(\n", + " go.Bar(x=[0, 1], y=y_exng_0, name=\"Safe\", marker_color=\"lightsalmon\")\n", + ")\n", + "\n", + "# Here we modify the tickangle of the xaxis, resulting in rotated labels.\n", + "fig.update_layout(barmode=\"group\", xaxis_tickangle=-45)\n", + "fig.show()" + ], + "metadata": { + "execution": { + "iopub.status.busy": "2021-08-24T17:51:00.387656Z", + "iopub.execute_input": "2021-08-24T17:51:00.388234Z", + "iopub.status.idle": "2021-08-24T17:51:00.412075Z", + "shell.execute_reply.started": "2021-08-24T17:51:00.388183Z", + "shell.execute_reply": "2021-08-24T17:51:00.411098Z" + }, + "trusted": true + }, + "execution_count": 32, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/html": "
" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": "We can see that not getting exercise induced angina may have a greater chance of heart attack", + "metadata": {} + }, + { + "cell_type": "markdown", + "source": "### oldpeak\nPrevious peak", + "metadata": {} + }, + { + "cell_type": "code", + "source": [ + "plt.figure(figsize=(16, 7))\n", + "sns.distplot(data[\"oldpeak\"])\n", + "plt.title(\"Distribution Plot of Previous peak achieved\\n\", fontsize=20)\n", + "plt.show()" + ], + "metadata": { + "execution": { + "iopub.status.busy": "2021-08-24T17:51:02.145990Z", + "iopub.execute_input": "2021-08-24T17:51:02.146632Z", + "iopub.status.idle": "2021-08-24T17:51:02.366983Z", + "shell.execute_reply.started": "2021-08-24T17:51:02.146546Z", + "shell.execute_reply": "2021-08-24T17:51:02.365646Z" + }, + "trusted": true + }, + "execution_count": 33, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": "
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+ }, + "metadata": { + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "code", + "source": [ + "px.box(x=\"oldpeak\", data_frame=data, template=\"plotly_dark\")" + ], + "metadata": { + "execution": { + "iopub.status.busy": "2021-08-24T17:51:02.482004Z", + "iopub.execute_input": "2021-08-24T17:51:02.482607Z", + "iopub.status.idle": "2021-08-24T17:51:02.555813Z", + "shell.execute_reply.started": "2021-08-24T17:51:02.482541Z", + "shell.execute_reply": "2021-08-24T17:51:02.554713Z" + }, + "trusted": true + }, + "execution_count": 34, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/html": "
" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": "### slp", + "metadata": {} + }, + { + "cell_type": "code", + "source": [ + "px.bar(\n", + " data_frame=data,\n", + " x=list(data.slp.value_counts().keys()),\n", + " y=list(data.slp.value_counts()),\n", + " color=list(data.slp.value_counts().keys()),\n", + " template=\"plotly_dark\",\n", + " labels={\"x\": \"slp\", \"y\": \"Count\", \"color\": \"slp\"},\n", + " title=\"slp plot\",\n", + ")" + ], + "metadata": { + "execution": { + "iopub.status.busy": "2021-08-24T17:51:03.090528Z", + "iopub.execute_input": "2021-08-24T17:51:03.091171Z", + "iopub.status.idle": "2021-08-24T17:51:03.176137Z", + "shell.execute_reply.started": "2021-08-24T17:51:03.091125Z", + "shell.execute_reply": "2021-08-24T17:51:03.174996Z" + }, + "trusted": true + }, + "execution_count": 35, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/html": "
" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "import plotly.graph_objects as go\n", + "\n", + "slp_0_1 = data.slp[(data.slp == 0) & (data.output == 1)]\n", + "slp_0_0 = data.slp[(data.slp == 0) & (data.output == 0)]\n", + "slp_1_1 = data.slp[(data.slp == 1) & (data.output == 1)]\n", + "slp_1_0 = data.slp[(data.slp == 1) & (data.output == 0)]\n", + "slp_2_1 = data.slp[(data.slp == 2) & (data.output == 1)]\n", + "slp_2_0 = data.slp[(data.slp == 2) & (data.output == 0)]\n", + "\n", + "y_slp_1 = [len(slp_0_1.values), len(slp_1_1.values), len(slp_2_1.values)]\n", + "y_slp_0 = [len(slp_0_0.values), len(slp_1_0.values), len(slp_2_0.values)]\n", + "\n", + "fig = go.Figure()\n", + "fig.add_trace(\n", + " go.Bar(\n", + " x=[0, 1, 2], y=y_slp_1, name=\"Heart Attack\", marker_color=\"indianred\"\n", + " )\n", + ")\n", + "fig.add_trace(\n", + " go.Bar(x=[0, 1, 2], y=y_slp_0, name=\"Safe\", marker_color=\"lightsalmon\")\n", + ")\n", + "\n", + "# Here we modify the tickangle of the xaxis, resulting in rotated labels.\n", + "fig.update_layout(barmode=\"group\", xaxis_tickangle=-45)\n", + "fig.show()" + ], + "metadata": { + "execution": { + "iopub.status.busy": "2021-08-24T17:51:03.565664Z", + "iopub.execute_input": "2021-08-24T17:51:03.566237Z", + "iopub.status.idle": "2021-08-24T17:51:03.594014Z", + "shell.execute_reply.started": "2021-08-24T17:51:03.566201Z", + "shell.execute_reply": "2021-08-24T17:51:03.591703Z" + }, + "trusted": true + }, + "execution_count": 36, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/html": "
" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "data[\"slp\"] = data[\"slp\"].map({0: \"slp_0\", 1: \"slp_1\", 2: \"slp_2\"})\n", + "data.head()" + ], + "metadata": { + "execution": { + "iopub.status.busy": "2021-08-24T17:51:04.165074Z", + "iopub.execute_input": "2021-08-24T17:51:04.165445Z", + "iopub.status.idle": "2021-08-24T17:51:04.187353Z", + "shell.execute_reply.started": "2021-08-24T17:51:04.165413Z", + "shell.execute_reply": "2021-08-24T17:51:04.186565Z" + }, + "trusted": true + }, + "execution_count": 37, + "outputs": [ + { + "execution_count": 37, + "output_type": "execute_result", + "data": { + "text/plain": " age sex cp trtbps chol fbs restecg thalachh exng \\\n0 63 1_gender Intensity_3 145 233 1 restecg_0 150 0 \n1 37 1_gender Intensity_2 130 250 0 restecg_1 187 0 \n2 41 0_gender Intensity_1 130 204 0 restecg_0 172 0 \n3 56 1_gender Intensity_1 120 236 0 restecg_1 178 0 \n4 57 0_gender Intensity_0 120 354 0 restecg_1 163 1 \n\n oldpeak slp caa thall output \n0 2.3 slp_0 0 1 1 \n1 3.5 slp_0 0 2 1 \n2 1.4 slp_2 0 2 1 \n3 0.8 slp_2 0 2 1 \n4 0.6 slp_2 0 2 1 ", + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
agesexcptrtbpscholfbsrestecgthalachhexngoldpeakslpcaathalloutput
0631_genderIntensity_31452331restecg_015002.3slp_0011
1371_genderIntensity_21302500restecg_118703.5slp_0021
2410_genderIntensity_11302040restecg_017201.4slp_2021
3561_genderIntensity_11202360restecg_117800.8slp_2021
4570_genderIntensity_01203540restecg_116310.6slp_2021
\n
" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": "### caa", + "metadata": {} + }, + { + "cell_type": "code", + "source": [ + "px.bar(\n", + " data_frame=data,\n", + " x=list(data.caa.value_counts().keys()),\n", + " y=list(data.caa.value_counts()),\n", + " color=list(data.caa.value_counts().keys()),\n", + " template=\"plotly_dark\",\n", + " labels={\"x\": \"caa\", \"y\": \"Count\", \"color\": \"caa\"},\n", + " title=\"caa plot\",\n", + ")" + ], + "metadata": { + "execution": { + "iopub.status.busy": "2021-08-24T17:51:05.260930Z", + "iopub.execute_input": "2021-08-24T17:51:05.261477Z", + "iopub.status.idle": "2021-08-24T17:51:05.336171Z", + "shell.execute_reply.started": "2021-08-24T17:51:05.261416Z", + "shell.execute_reply": "2021-08-24T17:51:05.335209Z" + }, + "trusted": true + }, + "execution_count": 38, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/html": "
" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "import plotly.graph_objects as go\n", + "\n", + "caa_0_1 = data.caa[(data.caa == 0) & (data.output == 1)]\n", + "caa_0_0 = data.caa[(data.caa == 0) & (data.output == 0)]\n", + "caa_1_1 = data.caa[(data.caa == 1) & (data.output == 1)]\n", + "caa_1_0 = data.caa[(data.caa == 1) & (data.output == 0)]\n", + "caa_2_1 = data.caa[(data.caa == 2) & (data.output == 1)]\n", + "caa_2_0 = data.caa[(data.caa == 2) & (data.output == 0)]\n", + "\n", + "caa_3_1 = data.caa[(data.caa == 3) & (data.output == 1)]\n", + "caa_3_0 = data.caa[(data.caa == 3) & (data.output == 0)]\n", + "\n", + "caa_4_1 = data.caa[(data.caa == 4) & (data.output == 1)]\n", + "caa_4_0 = data.caa[(data.caa == 4) & (data.output == 0)]\n", + "\n", + "y_caa_1 = [\n", + " len(caa_0_1.values),\n", + " len(caa_1_1.values),\n", + " len(caa_2_1.values),\n", + " len(caa_3_1.values),\n", + " len(caa_4_1.values),\n", + "]\n", + "y_caa_0 = [\n", + " len(caa_0_0.values),\n", + " len(caa_1_0.values),\n", + " len(caa_2_0.values),\n", + " len(caa_3_0.values),\n", + " len(caa_4_0.values),\n", + "]\n", + "\n", + "fig = go.Figure()\n", + "fig.add_trace(\n", + " go.Bar(\n", + " x=[0, 1, 2, 3, 4],\n", + " y=y_caa_1,\n", + " name=\"Heart Attack\",\n", + " marker_color=\"indianred\",\n", + " )\n", + ")\n", + "fig.add_trace(\n", + " go.Bar(\n", + " x=[0, 1, 2, 3, 4], y=y_caa_0, name=\"Safe\", marker_color=\"lightsalmon\"\n", + " )\n", + ")\n", + "\n", + "# Here we modify the tickangle of the xaxis, resulting in rotated labels.\n", + "fig.update_layout(barmode=\"group\", xaxis_tickangle=-45)\n", + "fig.show()" + ], + "metadata": { + "execution": { + "iopub.status.busy": "2021-08-24T17:51:05.785608Z", + "iopub.execute_input": "2021-08-24T17:51:05.786009Z", + "iopub.status.idle": "2021-08-24T17:51:05.822881Z", + "shell.execute_reply.started": "2021-08-24T17:51:05.785972Z", + "shell.execute_reply": "2021-08-24T17:51:05.821271Z" + }, + "trusted": true + }, + "execution_count": 39, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/html": "
" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "data[\"caa\"] = data[\"caa\"].map(\n", + " {0: \"caa_0\", 1: \"caa_1\", 2: \"caa_2\", 3: \"caa_3\", 4: \"caa_4\"}\n", + ")\n", + "data.head()" + ], + "metadata": { + "execution": { + "iopub.status.busy": "2021-08-24T17:51:06.350241Z", + "iopub.execute_input": "2021-08-24T17:51:06.350697Z", + "iopub.status.idle": "2021-08-24T17:51:06.374248Z", + "shell.execute_reply.started": "2021-08-24T17:51:06.350657Z", + "shell.execute_reply": "2021-08-24T17:51:06.372939Z" + }, + "trusted": true + }, + "execution_count": 40, + "outputs": [ + { + "execution_count": 40, + "output_type": "execute_result", + "data": { + "text/plain": " age sex cp trtbps chol fbs restecg thalachh exng \\\n0 63 1_gender Intensity_3 145 233 1 restecg_0 150 0 \n1 37 1_gender Intensity_2 130 250 0 restecg_1 187 0 \n2 41 0_gender Intensity_1 130 204 0 restecg_0 172 0 \n3 56 1_gender Intensity_1 120 236 0 restecg_1 178 0 \n4 57 0_gender Intensity_0 120 354 0 restecg_1 163 1 \n\n oldpeak slp caa thall output \n0 2.3 slp_0 caa_0 1 1 \n1 3.5 slp_0 caa_0 2 1 \n2 1.4 slp_2 caa_0 2 1 \n3 0.8 slp_2 caa_0 2 1 \n4 0.6 slp_2 caa_0 2 1 ", + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
agesexcptrtbpscholfbsrestecgthalachhexngoldpeakslpcaathalloutput
0631_genderIntensity_31452331restecg_015002.3slp_0caa_011
1371_genderIntensity_21302500restecg_118703.5slp_0caa_021
2410_genderIntensity_11302040restecg_017201.4slp_2caa_021
3561_genderIntensity_11202360restecg_117800.8slp_2caa_021
4570_genderIntensity_01203540restecg_116310.6slp_2caa_021
\n
" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": "### thall", + "metadata": {} + }, + { + "cell_type": "code", + "source": [ + "px.bar(\n", + " data_frame=data,\n", + " x=list(data.thall.value_counts().keys()),\n", + " y=list(data.thall.value_counts()),\n", + " color=list(data.thall.value_counts().keys()),\n", + " template=\"plotly_dark\",\n", + " labels={\"x\": \"thall\", \"y\": \"Count\", \"color\": \"thall\"},\n", + " title=\"caa plot\",\n", + " barmode=\"group\",\n", + ")" + ], + "metadata": { + "execution": { + "iopub.status.busy": "2021-08-24T17:51:07.226753Z", + "iopub.execute_input": "2021-08-24T17:51:07.227144Z", + "iopub.status.idle": "2021-08-24T17:51:07.308313Z", + "shell.execute_reply.started": "2021-08-24T17:51:07.227112Z", + "shell.execute_reply": "2021-08-24T17:51:07.307176Z" + }, + "trusted": true + }, + "execution_count": 41, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/html": "
" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "import plotly.graph_objects as go\n", + "\n", + "thall_0_1 = data.thall[(data.thall == 0) & (data.output == 1)]\n", + "thall_0_0 = data.thall[(data.thall == 0) & (data.output == 0)]\n", + "thall_1_1 = data.thall[(data.thall == 1) & (data.output == 1)]\n", + "thall_1_0 = data.thall[(data.thall == 1) & (data.output == 0)]\n", + "thall_2_1 = data.thall[(data.thall == 2) & (data.output == 1)]\n", + "thall_2_0 = data.thall[(data.thall == 2) & (data.output == 0)]\n", + "thall_3_1 = data.thall[(data.thall == 3) & (data.output == 1)]\n", + "thall_3_0 = data.thall[(data.thall == 3) & (data.output == 0)]\n", + "\n", + "y_thall_1 = [\n", + " len(thall_0_1.values),\n", + " len(thall_1_1.values),\n", + " len(thall_2_1.values),\n", + " len(thall_3_1.values),\n", + "]\n", + "y_thall_0 = [\n", + " len(thall_0_0.values),\n", + " len(thall_1_0.values),\n", + " len(thall_2_0.values),\n", + " len(thall_3_0.values),\n", + "]\n", + "\n", + "fig = go.Figure()\n", + "fig.add_trace(\n", + " go.Bar(\n", + " x=[0, 1, 2, 3],\n", + " y=y_thall_1,\n", + " name=\"Heart Attack\",\n", + " marker_color=\"indianred\",\n", + " )\n", + ")\n", + "fig.add_trace(\n", + " go.Bar(\n", + " x=[0, 1, 2, 3], y=y_thall_0, name=\"Safe\", marker_color=\"lightsalmon\"\n", + " )\n", + ")\n", + "\n", + "# Here we modify the tickangle of the xaxis, resulting in rotated labels.\n", + "fig.update_layout(barmode=\"group\", xaxis_tickangle=-45)\n", + "fig.show()" + ], + "metadata": { + "execution": { + "iopub.status.busy": "2021-08-24T17:51:07.832719Z", + "iopub.execute_input": "2021-08-24T17:51:07.833127Z", + "iopub.status.idle": "2021-08-24T17:51:07.864557Z", + "shell.execute_reply.started": "2021-08-24T17:51:07.833095Z", + "shell.execute_reply": "2021-08-24T17:51:07.863067Z" + }, + "trusted": true + }, + "execution_count": 42, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/html": "
" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "data[\"thall\"] = data[\"thall\"].map(\n", + " {0: \"thall_0\", 1: \"thall_1\", 2: \"thall_2\", 3: \"thall_3\"}\n", + ")\n", + "data.head()" + ], + "metadata": { + "execution": { + "iopub.status.busy": "2021-08-24T17:51:14.268121Z", + "iopub.execute_input": "2021-08-24T17:51:14.268562Z", + "iopub.status.idle": "2021-08-24T17:51:14.291975Z", + "shell.execute_reply.started": "2021-08-24T17:51:14.268526Z", + "shell.execute_reply": "2021-08-24T17:51:14.291132Z" + }, + "trusted": true + }, + "execution_count": 43, + "outputs": [ + { + "execution_count": 43, + "output_type": "execute_result", + "data": { + "text/plain": " age sex cp trtbps chol fbs restecg thalachh exng \\\n0 63 1_gender Intensity_3 145 233 1 restecg_0 150 0 \n1 37 1_gender Intensity_2 130 250 0 restecg_1 187 0 \n2 41 0_gender Intensity_1 130 204 0 restecg_0 172 0 \n3 56 1_gender Intensity_1 120 236 0 restecg_1 178 0 \n4 57 0_gender Intensity_0 120 354 0 restecg_1 163 1 \n\n oldpeak slp caa thall output \n0 2.3 slp_0 caa_0 thall_1 1 \n1 3.5 slp_0 caa_0 thall_2 1 \n2 1.4 slp_2 caa_0 thall_2 1 \n3 0.8 slp_2 caa_0 thall_2 1 \n4 0.6 slp_2 caa_0 thall_2 1 ", + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
agesexcptrtbpscholfbsrestecgthalachhexngoldpeakslpcaathalloutput
0631_genderIntensity_31452331restecg_015002.3slp_0caa_0thall_11
1371_genderIntensity_21302500restecg_118703.5slp_0caa_0thall_21
2410_genderIntensity_11302040restecg_017201.4slp_2caa_0thall_21
3561_genderIntensity_11202360restecg_117800.8slp_2caa_0thall_21
4570_genderIntensity_01203540restecg_116310.6slp_2caa_0thall_21
\n
" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": "### Feature engineering", + "metadata": {} + }, + { + "cell_type": "code", + "source": "data.head()", + "metadata": { + "execution": { + "iopub.status.busy": "2021-08-24T17:51:15.404868Z", + "iopub.execute_input": "2021-08-24T17:51:15.405450Z", + "iopub.status.idle": "2021-08-24T17:51:15.425204Z", + "shell.execute_reply.started": "2021-08-24T17:51:15.405413Z", + "shell.execute_reply": "2021-08-24T17:51:15.424037Z" + }, + "trusted": true + }, + "execution_count": 44, + "outputs": [ + { + "execution_count": 44, + "output_type": "execute_result", + "data": { + "text/plain": " age sex cp trtbps chol fbs restecg thalachh exng \\\n0 63 1_gender Intensity_3 145 233 1 restecg_0 150 0 \n1 37 1_gender Intensity_2 130 250 0 restecg_1 187 0 \n2 41 0_gender Intensity_1 130 204 0 restecg_0 172 0 \n3 56 1_gender Intensity_1 120 236 0 restecg_1 178 0 \n4 57 0_gender Intensity_0 120 354 0 restecg_1 163 1 \n\n oldpeak slp caa thall output \n0 2.3 slp_0 caa_0 thall_1 1 \n1 3.5 slp_0 caa_0 thall_2 1 \n2 1.4 slp_2 caa_0 thall_2 1 \n3 0.8 slp_2 caa_0 thall_2 1 \n4 0.6 slp_2 caa_0 thall_2 1 ", + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
agesexcptrtbpscholfbsrestecgthalachhexngoldpeakslpcaathalloutput
0631_genderIntensity_31452331restecg_015002.3slp_0caa_0thall_11
1371_genderIntensity_21302500restecg_118703.5slp_0caa_0thall_21
2410_genderIntensity_11302040restecg_017201.4slp_2caa_0thall_21
3561_genderIntensity_11202360restecg_117800.8slp_2caa_0thall_21
4570_genderIntensity_01203540restecg_116310.6slp_2caa_0thall_21
\n
" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": "### One hot encoding", + "metadata": {} + }, + { + "cell_type": "code", + "source": [ + "data = pd.get_dummies(data)\n", + "data.head()" + ], + "metadata": { + "execution": { + "iopub.status.busy": "2021-08-24T17:51:43.662879Z", + "iopub.execute_input": "2021-08-24T17:51:43.663284Z", + "iopub.status.idle": "2021-08-24T17:51:43.703889Z", + "shell.execute_reply.started": "2021-08-24T17:51:43.663250Z", + "shell.execute_reply": "2021-08-24T17:51:43.702491Z" + }, + "trusted": true + }, + "execution_count": 45, + "outputs": [ + { + "execution_count": 45, + "output_type": "execute_result", + "data": { + "text/plain": " age trtbps chol fbs thalachh exng oldpeak output sex_0_gender \\\n0 63 145 233 1 150 0 2.3 1 0 \n1 37 130 250 0 187 0 3.5 1 0 \n2 41 130 204 0 172 0 1.4 1 1 \n3 56 120 236 0 178 0 0.8 1 0 \n4 57 120 354 0 163 1 0.6 1 1 \n\n sex_1_gender ... slp_slp_2 caa_caa_0 caa_caa_1 caa_caa_2 caa_caa_3 \\\n0 1 ... 0 1 0 0 0 \n1 1 ... 0 1 0 0 0 \n2 0 ... 1 1 0 0 0 \n3 1 ... 1 1 0 0 0 \n4 0 ... 1 1 0 0 0 \n\n caa_caa_4 thall_thall_0 thall_thall_1 thall_thall_2 thall_thall_3 \n0 0 0 1 0 0 \n1 0 0 0 1 0 \n2 0 0 0 1 0 \n3 0 0 0 1 0 \n4 0 0 0 1 0 \n\n[5 rows x 29 columns]", + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
agetrtbpscholfbsthalachhexngoldpeakoutputsex_0_gendersex_1_gender...slp_slp_2caa_caa_0caa_caa_1caa_caa_2caa_caa_3caa_caa_4thall_thall_0thall_thall_1thall_thall_2thall_thall_3
063145233115002.3101...0100000100
137130250018703.5101...0100000010
241130204017201.4110...1100000010
356120236017800.8101...1100000010
457120354016310.6110...1100000010
\n

5 rows × 29 columns

\n
" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "X = data.drop([\"output\"], axis=1)\n", + "Y = data[\"output\"]" + ], + "metadata": { + "execution": { + "iopub.status.busy": "2021-08-24T17:51:44.409492Z", + "iopub.execute_input": "2021-08-24T17:51:44.409935Z", + "iopub.status.idle": "2021-08-24T17:51:44.418220Z", + "shell.execute_reply.started": "2021-08-24T17:51:44.409899Z", + "shell.execute_reply": "2021-08-24T17:51:44.416782Z" + }, + "trusted": true + }, + "execution_count": 46, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "from sklearn.model_selection import train_test_split\n", + "\n", + "# split the data to train and test set\n", + "x_train, x_test, y_train, y_test = train_test_split(\n", + " X, Y, train_size=0.85, random_state=42\n", + ")\n", + "\n", + "\n", + "print(\n", + " \"training data shape:- {} labels {} \".format(\n", + " x_train.shape[0], x_train.shape[1]\n", + " )\n", + ")\n", + "print(\n", + " \"testing data shape:- {} labels {} \".format(\n", + " x_test.shape[0], x_test.shape[1]\n", + " )\n", + ")" + ], + "metadata": { + "execution": { + "iopub.status.busy": "2021-08-24T17:51:46.397641Z", + "iopub.execute_input": "2021-08-24T17:51:46.398020Z", + "iopub.status.idle": "2021-08-24T17:51:46.571787Z", + "shell.execute_reply.started": "2021-08-24T17:51:46.397986Z", + "shell.execute_reply": "2021-08-24T17:51:46.571031Z" + }, + "trusted": true + }, + "execution_count": 47, + "outputs": [ + { + "name": "stdout", + "text": "training data shape:- 257 labels 28 \ntesting data shape:- 46 labels 28 \n", + "output_type": "stream" + } + ] + }, + { + "cell_type": "code", + "source": [ + "from xgboost import XGBClassifier\n", + "from sklearn.metrics import r2_score\n", + "\n", + "xgb = XGBClassifier(\n", + " colsample_bylevel=0.9,\n", + " colsample_bytree=0.8,\n", + " gamma=0.99,\n", + " max_depth=5,\n", + " min_child_weight=1,\n", + " n_estimators=8,\n", + " nthread=5,\n", + " random_state=0,\n", + ")\n", + "xgb.fit(x_train, y_train)" + ], + "metadata": { + "execution": { + "iopub.status.busy": "2021-08-24T17:51:46.758774Z", + "iopub.execute_input": "2021-08-24T17:51:46.759413Z", + "iopub.status.idle": "2021-08-24T17:51:46.898773Z", + "shell.execute_reply.started": "2021-08-24T17:51:46.759379Z", + "shell.execute_reply": "2021-08-24T17:51:46.897333Z" + }, + "trusted": true + }, + "execution_count": 48, + "outputs": [ + { + "name": "stdout", + "text": "[17:51:46] WARNING: ../src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n", + "output_type": "stream" + }, + { + "execution_count": 48, + "output_type": "execute_result", + "data": { + "text/plain": "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=0.9,\n colsample_bynode=1, colsample_bytree=0.8, gamma=0.99, gpu_id=-1,\n importance_type='gain', interaction_constraints='',\n learning_rate=0.300000012, max_delta_step=0, max_depth=5,\n min_child_weight=1, missing=nan, monotone_constraints='()',\n n_estimators=8, n_jobs=5, nthread=5, num_parallel_tree=1,\n random_state=0, reg_alpha=0, reg_lambda=1, scale_pos_weight=1,\n subsample=1, tree_method='exact', validate_parameters=1,\n verbosity=None)" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "print(\n", + " \"Accuracy of XGBoost classifier on training set: {:.2f}\".format(\n", + " xgb.score(x_train, y_train)\n", + " )\n", + ")\n", + "print(\n", + " \"Accuracy of XGBoost classifier on test set: {:.2f}\".format(\n", + " xgb.score(x_test, y_test)\n", + " )\n", + ")" + ], + "metadata": { + "execution": { + "iopub.status.busy": "2021-08-24T17:51:47.196831Z", + "iopub.execute_input": "2021-08-24T17:51:47.197210Z", + "iopub.status.idle": "2021-08-24T17:51:47.210886Z", + "shell.execute_reply.started": "2021-08-24T17:51:47.197179Z", + "shell.execute_reply": "2021-08-24T17:51:47.209783Z" + }, + "trusted": true + }, + "execution_count": 49, + "outputs": [ + { + "name": "stdout", + "text": "Accuracy of XGBoost classifier on training set: 0.95\nAccuracy of XGBoost classifier on test set: 0.80\n", + "output_type": "stream" + } + ] + }, + { + "cell_type": "code", + "source": [ + "from sklearn import metrics\n", + "\n", + "y_pred = xgb.predict(x_test)\n", + "print(\n", + " \"Accuracy of XG Boost model is:\",\n", + " metrics.accuracy_score(y_test, y_pred) * 100,\n", + ")" + ], + "metadata": { + "execution": { + "iopub.status.busy": "2021-08-24T17:51:47.731717Z", + "iopub.execute_input": "2021-08-24T17:51:47.732172Z", + "iopub.status.idle": "2021-08-24T17:51:47.743918Z", + "shell.execute_reply.started": "2021-08-24T17:51:47.732130Z", + "shell.execute_reply": "2021-08-24T17:51:47.742668Z" + }, + "trusted": true + }, + "execution_count": 50, + "outputs": [ + { + "name": "stdout", + "text": "Accuracy of XG Boost model is: 80.43478260869566\n", + "output_type": "stream" + } + ] + }, + { + "cell_type": "code", + "source": [ + "from sklearn.metrics import confusion_matrix\n", + "\n", + "conf_matrix = confusion_matrix(y_true=y_test, y_pred=y_pred)\n", + "plt.figure(figsize=(15, 8))\n", + "sns.set(font_scale=1.4) # for label size\n", + "sns.heatmap(\n", + " conf_matrix, annot=True, annot_kws={\"size\": 16}, cbar=False, linewidths=1\n", + ") # font size\n", + "plt.title(\"Test Confusion Matrix\")\n", + "plt.xlabel(\"Predicted class\")\n", + "plt.ylabel(\"Actual class\")\n", + "plt.savefig(\"conf_test.png\")\n", + "plt.show()" + ], + "metadata": { + "execution": { + "iopub.status.busy": "2021-08-24T17:51:48.291984Z", + "iopub.execute_input": "2021-08-24T17:51:48.292444Z", + "iopub.status.idle": "2021-08-24T17:51:48.527485Z", + "shell.execute_reply.started": "2021-08-24T17:51:48.292408Z", + "shell.execute_reply": "2021-08-24T17:51:48.526197Z" + }, + "trusted": true + }, + "execution_count": 51, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": "
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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "code", + "source": [ + "from sklearn.metrics import confusion_matrix\n", + "\n", + "y_pred_t = xgb.predict(x_train)\n", + "conf_matrix = confusion_matrix(y_true=y_train, y_pred=y_pred_t)\n", + "plt.figure(figsize=(15, 8))\n", + "sns.set(font_scale=1.4) # for label size\n", + "sns.heatmap(\n", + " conf_matrix, annot=True, annot_kws={\"size\": 16}, cbar=False, linewidths=1\n", + ") # font size\n", + "plt.title(\"Train Confusion Matrix\")\n", + "plt.xlabel(\"Predicted class\")\n", + "plt.ylabel(\"Actual class\")\n", + "plt.show()" + ], + "metadata": { + "execution": { + "iopub.status.busy": "2021-08-24T17:51:48.818203Z", + "iopub.execute_input": "2021-08-24T17:51:48.818597Z", + "iopub.status.idle": "2021-08-24T17:51:49.011291Z", + "shell.execute_reply.started": "2021-08-24T17:51:48.818549Z", + "shell.execute_reply": "2021-08-24T17:51:49.009691Z" + }, + "trusted": true + }, + "execution_count": 52, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": "
", + "image/png": 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "from sklearn.metrics import precision_score, recall_score, f1_score\n", + "\n", + "print(\"For testing data\")\n", + "print(\"Precision: %.3f\" % precision_score(y_test, y_pred, average=\"micro\"))\n", + "print(\"Recall: %.3f\" % recall_score(y_test, y_pred, average=\"micro\"))\n", + "print(\"F1 Score: %.3f\" % f1_score(y_test, y_pred, average=\"micro\"))\n", + "\n", + "print()\n", + "\n", + "print(\"For training data\")\n", + "y_pred_t = xgb.predict(x_train)\n", + "print(\"Precision: %.3f\" % precision_score(y_train, y_pred_t, average=\"micro\"))\n", + "print(\"Recall: %.3f\" % recall_score(y_train, y_pred_t, average=\"micro\"))\n", + "print(\"F1 Score: %.3f\" % f1_score(y_train, y_pred_t, average=\"micro\"))" + ], + "metadata": { + "execution": { + "iopub.status.busy": "2021-08-24T17:51:49.480900Z", + "iopub.execute_input": "2021-08-24T17:51:49.481316Z", + "iopub.status.idle": "2021-08-24T17:51:49.510782Z", + "shell.execute_reply.started": "2021-08-24T17:51:49.481282Z", + "shell.execute_reply": "2021-08-24T17:51:49.509418Z" + }, + "trusted": true + }, + "execution_count": 53, + "outputs": [ + { + "name": "stdout", + "text": "For testing data\nPrecision: 0.804\nRecall: 0.804\nF1 Score: 0.804\n\nFor training data\nPrecision: 0.949\nRecall: 0.949\nF1 Score: 0.949\n", + "output_type": "stream" + } + ] + }, + { + "cell_type": "markdown", + "source": "For fine tuning our main aim should be to reduce true negative", + "metadata": {} + } + ] +} \ No newline at end of file