Book Image

Java Coding Problems - Second Edition

By : Anghel Leonard
Book Image

Java Coding Problems - Second Edition

By: Anghel Leonard

Overview of this book

The super-fast evolution of the JDK between versions 12 and 21 has made the learning curve of modern Java steeper, and increased the time needed to learn it. This book will make your learning journey quicker and increase your willingness to try Java’s new features by explaining the correct practices and decisions related to complexity, performance, readability, and more. Java Coding Problems takes you through Java’s latest features but doesn’t always advocate the use of new solutions — instead, it focuses on revealing the trade-offs involved in deciding what the best solution is for a certain problem. There are more than two hundred brand new and carefully selected problems in this second edition, chosen to highlight and cover the core everyday challenges of a Java programmer. Apart from providing a comprehensive compendium of problem solutions based on real-world examples, this book will also give you the confidence to answer questions relating to matching particular streams and methods to various problems. By the end of this book you will have gained a strong understanding of Java’s new features and have the confidence to develop and choose the right solutions to your problems.
Table of Contents (16 chapters)
1
Text Blocks, Locales, Numbers, and Math
Free Chapter
2
Objects, Immutability, Switch Expressions, and Pattern Matching
14
Other Books You May Enjoy
15
Index

33. Selecting a pseudo-random number generator

When we flip a coin or roll the dice, we say that we see “true” or “natural” randomness at work. Even so, there are tools that pretend they are capable of predicting the path of flipping a coin, rolling dice, or spinning a roulette wheel, especially if some contextual conditions are met.

Computers can generate random numbers using algorithms via the so-called random generators. Since algorithms are involved, the generated numbers are considered pseudo-random. This is known as “pseudo”-randomness. Obviously, pseudo-random numbers are also predictable. How so?

A pseudo-random generator starts its job by seeding data. This is the generator’s secret (the seed), and it represents a piece of data used as the starting point to generate pseudo-random numbers. If we know how the algorithm works and what the seed was, then the output is predictable. Without knowing the seed, the rate of predictability...