Randomer 0.3.0

Randomer - Advanced Random Data Generation Library for C#

Randomer is a powerful and efficient library for C# developers that provides specialized functions for generating random data of various types. This library is built on Extension Methods that operate on the Random class.

🎯 Key Features

📋 Library Sections

1. Basic Extensions

Core functions for generating simple data types:

• Generate random boolean values
• Generate random bytes
• Select random elements from arrays
• Generate non-repeating sets

Example:

var rnd = new Random();

// Generate boolean with specific probability
bool result = rnd.GenerateBool(0.7f); // 70% chance of true

// Generate random byte
byte randomByte = rnd.GenerateByte();

// Select random element from array
int[] numbers = { 1, 2, 3, 4, 5 };
int random = rnd.GetOf(numbers);

// Select multiple elements without duplicates
int[] selected = rnd.GetOf(numbers, 3, allowDuplicates: false);

2. String Extensions

Specialized functions for working with strings and specific formats:

• Select random character from string
• Generate random strings with specific characters
• Generate MAC addresses
• Generate IPv4 addresses

Example:

var rnd = new Random();

// Select random character
char vowel = rnd.GetCharOf("aeiou");

// Generate random string with specific characters
char[] symbols = { 'a', 'b', 'c', '1', '2', '3' };
string randomStr = rnd.GenerateString(10, symbols);

// Generate random MAC address
string macAddress = rnd.GenerateMacAddress();
// Result: "A1:B2:C3:D4:E5:F6"

// Generate random IP address
string ipAddress = rnd.GenerateIPv4Address();
string ipWithConstraint = rnd.GenerateIPv4Address(192, 168); // Starting with 192.168

3. Math Extensions

Probability distribution functions:

• Uniform Continuous Distribution
• Exponential Distribution
• Normal Distribution
• Uniform Discrete Distribution
• Bernoulli Distribution
• Binomial Distribution
• Poisson Distribution

Example:

var rnd = new Random();

// Generate number with exponential distribution
double exponential = rnd.GeneratePDExponential(lambda: 0.2);

4. Other Extensions

Specialized functions for other data types:

• Generate random GUID
• Generate random date and time

Example:

var rnd = new Random();

// Generate GUID
Guid randomGuid = rnd.GenerateGuid();

// Generate date between two dates
var startDate = new DateTimeOffset(2024, 1, 1, 0, 0, 0, TimeSpan.Zero);
var endDate = new DateTimeOffset(2024, 12, 31, 23, 59, 59, TimeSpan.Zero);
DateTimeOffset randomDate = rnd.GenerateDateTime(startDate, endDate);

// Generate date in relative range (e.g., 30 days in the future)
DateTimeOffset futureDate = rnd.GenerateDateTime(TimeSpan.FromDays(30));

Important Note about GUID Generation:

While you can generate random GUIDs using the C# built-in library with Guid.NewGuid(), this library provides an alternative approach. If you need reproducible random data (same sequence of random values on every test run), you can create a Random instance with a fixed seed:

// With a seeded Random, you get reproducible randomness
var seededRnd = new Random(seed: 12345);

// Every time you run with the same seed, you get the same GUID
Guid reproducibleGuid = seededRnd.GenerateGuid();
// Output: Always the same value (e.g., "a1b2c3d4-e5f6-4g7h-8i9j-0k1l2m3n4o5p")

// This is useful for testing scenarios where you want:
// - Randomized data to test your algorithms
// - But the same data on every test run for consistency and reproducibility
// - Allowing you to debug issues with predictable test data

// Example: Running tests multiple times with the same seed
var testRnd = new Random(42);
var testGuid1 = testRnd.GenerateGuid();
var testGuid2 = testRnd.GenerateGuid();

// Reset to the same seed for the next test run
testRnd = new Random(42);
var testGuid1_Again = testRnd.GenerateGuid();
var testGuid2_Again = testRnd.GenerateGuid();

// testGuid1 == testGuid1_Again ✓
// testGuid2 == testGuid2_Again ✓

🛠️ Installation

NuGet Package Manager

Install-Package Randomer

.NET CLI

dotnet add package Randomer

📝 Notes

• All functions are thread-safe when using a single Random instance
• Comprehensive error handling for invalid inputs
• Optimized for high performance
• Compatible with AOT Compilation

📄 License

This project is licensed under the MIT License. You are free to use, modify, and distribute this library in your projects.

Note: For better understanding of each section, please review the code examples in the example/ folder in source project.