The Art of Randomness Randomized Algorithms in the Real World (Ronald T. Kneusel) (Z-Library)

THE ART OF RANDOMNESS Randomized Algorithms in the Real World by Ronald T. Kneusel San Francisco

THE ART OF RANDOMNESS. Copyright © 2024 by Ronald T. Kneusel. All rights reserved. No part of this work may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or by any information storage or retrieval system, without the prior written permission of the copyright owner and the publisher. First printing 28 27 26 25 24         1 2 3 4 5 ISBN-13: 978-1-7185-0324-3 (print) ISBN-13: 978-1-7185-0325-0 (ebook) Published by No Starch Press , Inc. 245 8th Street, San Francisco, CA 94103 phone: +1.415.863.9900 www.nostarch.com; info@nostarch.com Publisher: William Pollock Managing Editor: Jill Franklin Production Manager: Sabrina Plomitallo-González Production Editor: Sydney Cromwell Developmental Editors: Alex Freed and Eva Morrow ®

Cover Illustrator: Gina Redman Interior Design: Octopod Studios Technical Reviewer: Doug Couwenhoven Copyeditor: George Hale Proofreader: Audrey Doyle Library of Congress Cataloging-in-Publication Data Name: Kneusel, Ronald T., author. Title: The art of randomness : using randomized algorithms in the real world / Ron Kneusel. Includes bibliographical references and index. Identifiers: LCCN 2023029979 (print) | LCCN 2023029980 (ebook) | ISBN 9781718503243 (paperback) |  ISBN 9781718503250 (ebook) Subjects: LCSH: Algorithms. | Numbers, Random. | Python (Computer program language) Classification: LCC QA9.58 .K635 2024 (print) | LCC QA9.58 (ebook) | DDC 519.2/3- -dc23/eng/20231018 LC record available at https://lccn.loc.gov/2023029979 LC ebook record available at https://lccn.loc.gov/2023029980 For customer service inquiries, please contact info@nostarch.com. For information on distribution, bulk sales, corporate sales, or translations: sales@nostarch.com. For

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About the Author Ronald T. Kneusel has been working with machine learning in industry since 2003 and completed a PhD in machine learning at the University of Colorado, Boulder, in 2016. Ron has six other books: How AI Works: From Sorcery to Science (No Starch Press, 2023), Strange Code: Esoteric Languages That Make Programming Fun Again (No Starch Press, 2022), Practical Deep Learning: A Python-Based Introduction (No Starch Press, 2021), Math for Deep Learning: What You Need to Know to Understand Neural Networks (No Starch Press, 2021), Numbers and Computers (Springer, 2017), and Random Numbers and Computers (Springer, 2018).

About the Technical Reviewer Doug Couwenhoven is a research scientist with more than 30 years of experience developing algorithms for digital imaging applications. He has a BS in physics and an MS in electrical engineering and signal processing. He spent the first 24 years of his career working at a large imaging company developing software algorithms for digital photography, printing, and imaging systems, and holds 50 US patents in the field. In 2013, he joined an aerospace technology company, and is currently part of a research group there that focuses on developing deep learning and machine learning algorithms for remotely sensed data.

BRIEF CONTENTS Foreword Acknowledgments Introduction Chapter 1: The Nature of Randomness Chapter 2: Hiding Information Chapter 3: Simulate the Real World Chapter 4: Optimize the World Chapter 5: Swarm Optimization Chapter 6: Machine Learning Chapter 7: Art Chapter 8: Music Chapter 9: Audio Signals Chapter 10: Experimental Design

Chapter 11: Computer Science Algorithms Chapter 12: Sampling Resources Index

CONTENTS IN DETAIL FOREWORD ACKNOWLEDGMENTS INTRODUCTION 1 THE NATURE OF RANDOMNESS Probability and Randomness Discrete Distributions Continuous Distributions Testing for Randomness Truly Random Processes Flipping Coins Rolling Dice Using Voltage Random Physical Processes

Atmospheric Radio Frequency Noise Voyager Plasma and Charged Particle Data Radioactive Decay Deterministic Processes Pseudorandom Numbers Quasirandom Sequences Combining Deterministic and Truly Random Processes The Book’s Randomness Engine The RE Class RE Class Examples Summary 2 HIDING INFORMATION In Strings Fixed Offset

Random Offset In Random Data How Much Can You Hide? The steg_random.py Code In an Audio File A Quiet Live Performance The steg_audio.py Code In an Image File Defining Image Formats Using NumPy and PIL Hiding Bits in Pixels Hiding One Image in Another The steg_image.py Code Exercises Summary

3 SIMULATE THE REAL WORLD Introduction to Models Estimate Pi Using a Dartboard Simulating Random Darts Understanding the RE Class Output Implementing the Darts Model Birthday Paradox Simulating 100,000 Parties Testing the Birthday Model Implementing the Birthday Model Simulating Evolution Natural Selection Static World

Gradually Changing World Catastrophic World Genetic Drift Testing the Simulations Exercises Summary 4 OPTIMIZE THE WORLD Optimization with Randomness Fitting with Swarms Curves The curves.py Code The Optimization Algorithms Fitting Data Introducing Stacks and Postfix Notation

Mapping Code to Points Creating gp.py Evolving Fit Functions Exercises Summary 5 SWARM OPTIMIZATION Packing Circles in a Square The Swarm Search The Code Placing Cell Towers The Swarm Search The Code Enhancing Images The Enhancement Function

The Code Arranging a Grocery Store The Environment The Shoppers The Objective Function The Shopping Simulation Exercises Summary 6 MACHINE LEARNING Datasets Histology Slide Data Handwritten Digits Neural Networks Anatomy Analysis

Randomness Initialization Extreme Learning Machines Implementation Testing Reckless Swarm Optimizations Random Forests Decision Trees Additional Randomness Models Combined with Voting Exercises Summary 7 ART Creating Random Art