Unleashing Creativity - Designing a Unique Random Number Generator

Unleashing Creativity - Designing a Unique Random Number Generator

Table of Contents

1. Introduction
2. Fixing the Bug in vector.h
3. Working on Random Numbers
4. Random Engine Class
   • Private Variables
   • Random Number Function
   • Constructor and Seed Function
5. Random Distribution Class
   • Constructor
   • Generating Random Numbers
6. Implementing Randomness in STD Library
7. Adjusting the Hashing Process
8. Testing the Random Number Generation
9. Conclusion

Introduction

In this episode of Making Your Own Standard Library, we will be exploring the topic of random numbers in C++. We will begin by fixing a bug in the vector.h file, followed by creating a random engine class and a random distribution class. These classes will allow us to generate and manipulate random numbers within specified ranges. By the end of this tutorial, you will have a better understanding of how to implement randomness in your own STD library.

Fixing the Bug in vector.h

Before diving into the world of random numbers, we need to address a bug in the vector.h file. The bug involves a const T pointer in the vector.h code. We will fix the bug by removing the 'const' keyword and replacing it with 'T'. This simple fix will enable us to assign values to elements of the vector, allowing for greater flexibility and unpredictability.

Working on Random Numbers

To introduce some unpredictability and change in our library, we have decided to focus on random numbers in this episode. Unlike traditional random number generators that only provide a random float between zero and one, we will be creating a more advanced system that allows users to specify their own random distributions. This feature will add a new level of control and customization to their random number generation.

Random Engine Class

The foundation of our random number system is the random engine class. This class will consist of private variables and functions that play crucial roles in generating random numbers. The first private variable is the unsigned long long hash Max, which helps determine the maximum number for our random engine. We will also need an unsigned long long hash number as our seed, which will change with each random call.

To enable the random engine to retrieve random numbers, we will create a private function called hash. This function utilizes various binary and arithmetic operations to hash our seed number, making it more unpredictable and random. The hash function will be used to generate our random number.

Next, we move on to the public section of the random engine class. Here, we will define the basic constructor, random engine. In this constructor, we initialize the hash number to a preset value or zero. Additionally, we provide a function called seed that allows users to manually seed the random engine by specifying a seed value.

To actually obtain random numbers from the random engine, we include a public function called random. This function changes the hash number using the hash function and returns a random double value. To ensure the random number falls between 0 and 1, we divide the hash number by the hash Max and return the result.

Random Distribution Class

In order to create custom random number distributions, we introduce the random distribution class. This class will be a template class that can work with different data types like int, float, or double. The template type is denoted as 't'.

The private section of the random distribution class includes variables such as 't Min' and 't Max', which represent the minimum and maximum values of our distribution range. We also have a 'double range' variable that calculates the range of our distribution.

In the public section, we define the constructor for the random distribution class. This constructor takes in the minimum and maximum values and assigns them to the respective variables. The range is then calculated using the maximum and minimum values.

To generate random numbers within the specified distribution range, we create a public function called 'random'. This function takes a random engine reference as its input and utilizes the random engine to retrieve a random number. The number is then multiplied by the range and casted to the template type 't'. Finally, the minimum value is added to the result to ensure the number falls within the desired distribution range.

Implementing Randomness in STD Library

With the random engine and random distribution classes in place, we can now integrate randomness into our STD library. By using a random engine object and specifying the desired range, we can obtain random numbers within that range.

To test the functionality of our random number generation, we create a random distribution object and initialize it with a range of 0 to 100. We then generate random numbers using the random function and the random engine object. By changing the seed value, we can observe different random numbers being generated within the specified range.

Adjusting the Hashing Process

Upon further analysis, it appears that our hashing process may be insufficient. To address this issue and improve the randomness of our generated numbers, we decide to increase the number of times the hash function is called. By adding multiple iterations of the hash function within a loop, we can enhance the unpredictability of our random numbers.

Testing the Random Number Generation

After updating the hashing process, we retest the random number generation functionality. The revised implementation now produces a wider range of random numbers, further increasing the unpredictability and uniqueness of each generated value. By adjusting the seed value, we can obtain different sets of random numbers within the desired range.

Conclusion

In this episode, we explored the topic of random numbers in C++ and learned how to implement randomness in our own STD library. We fixed a bug in the vector.h file and created a random engine class and a random distribution class. These classes allow users to generate and manipulate random numbers within specified ranges and distributions. By adjusting the seed value and range, users can customize the random numbers generated by the library. Stay tuned for the next episode, where we will dive into time management and explore ways to achieve completely random number generation.

Highlights

• Fixing a bug in the vector.h file
• Introducing the random engine class and random distribution class
• Generating random numbers within specified ranges and distributions
• Customizing the random number generation with seed values and range adjustments
• Enhancing randomness by iterating the hash function
• Testing and verifying the functionality of the random number generation
• Future exploration of time management for completely random number generation

FAQ

Q: Can I use different data types in the random distribution class? A: Yes, the random distribution class is designed to be a template class, allowing the use of different data types such as int, float, or double.

Q: Can I customize the range of my random numbers? A: Absolutely! With the random distribution class, you have the flexibility to specify a range for your random numbers. Simply provide the desired minimum and maximum values when creating a random distribution object.

Q: How can I ensure that my random numbers are truly random? A: While our current implementation provides a good level of randomness, completely random number generation can be achieved by utilizing time management. In our upcoming episode, we will explore this topic and demonstrate how to incorporate time as a seed for ultimate randomness.