mrandom

This package provides a random number generator. It is based on the Mersenne Twister algorithm. It includes both 32 and 64 bit versions of the algorithm. The generator has large period of 2^19937-1 mrandom is a Python package that provides a high-quality random number generator along with utility functions like sampling, shuffling, rolling dice, flipping coins, and drawing cards. It is built on top of the Mersenne Twister (MT19937) algorithm, which is one of the most reliable and widely used pseudorandom number generators. The Mersenne Twister is a pseudorandom number generator developed by Makoto Matsumoto and Takuji Nishimura in 1997. t is known for its exceptional speed and statistical randomness and is used in many languages and libraries, including Python’s built-in random module.

Why It’s Used? • Very long period: MT19937 has a period of 2^{19937} - 1 meaning its random sequence takes an extremely long time before repeating. • Fast and efficient: Capable of generating millions of random numbers per second. • Statistically strong randomness: The numbers are uniformly distributed across 623 dimensions, making it ideal for simulations, analytics, and sampling. • Reproducibility: Mersenne Twister is deterministic — given the same seed, it produces the same sequence. This is extremely useful for testing and debugging.

Features

• Can generate random numbers

• Can return a choice from a list

• Can shuffle a list

• Can flip a coin

• Can roll a die

• Can shuffle a deck

• Deterministic output using seeds

• Consistent API similar to Python’s built-in random module

• Utility functions for common probability tasks

• Extensible design

Installing the package

pip install mrandom

Importing the module

>>> import mrandom

# 1 = 32 bit, 2 = 64 bit algorithm
>>> random = mrandom.RandomGenerator(s)

Examples

Return a random number between 0 and 1

>>> print(random.rand())
0.9888888888888889

Return a random number between a and b (inclusive)

>>> print(random.randint(a, b))
1

Return a random number between a and b (exclusive)

>>> print(random.randrange(a, b))
12

Return k random numbers between a and b (inclusive)

>>> print(random.sample(a, b, k))
[1, 2, 3, 4, 5]

Return a random number from a list l

>>> print(random.choice(l))
21

Return shuffled list l

>>> print(random.shuffle(l))
[1, 3, 5, 4, 2]

Return sample of flipping k coins n times

>>> print(random.flip_coin(n, k))
[1, 0, 0, 1, 0]

Return sample of rolling k dices n times

>>> print(random.roll_dice(n, k))
[[1, 4], [1, 1]]

Return sample of picking a card from a deck of 52 cards n times

>>> print(random.shuffle_deck(n))
["Ace of Spades", "Ace of Hearts", "Ace of Clubs", "Ace of Diamonds", "Ace of Clubs"]

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Classes class mrandom.RandomGenerator RandomGenerator is a simple pseudo-random number generator based on the Mersenne Twister algorithm. It supports generating random numbers, picking items from lists, shuffling sequences, flipping coins, rolling dice, and drawing cards from a deck.

Parameters: seed (int, optional): The starting value for the generator. Use this when you want reproducible random results. If not provided, a default or time-based seed is used. version (int, optional): Selects the algorithm version. Use 1 for the 32-bit version, or 2 for the 64-bit version.

Methods

Methods	Description
rand()	Return a random number between 0 and 1
randint(a, b)	Return a random number between a and b (inclusive)
randrange(a, b)	Return a random number between a and b (exclusive)
sample(a, b, k)	Return k random numbers between a and b (inclusive)
choice(l)	Return a random number from a list l
shuffle(l)	Return shuffled list l
flip_coin(n, k)	Return sample of flipping k coins n times
roll_dice(n, k)	Return sample of rolling k dices n times
shuffle_deck(n)	Return sample of picking a card from a deck of 52 cards n times

Real-World Use Cases of Mersenne Twiste

1. Scientific Simulations: Many simulation environments rely on large sets of random numbers: • Physics simulations • Molecular dynamics • Weather and climate modeling • Population modeling in biology

2. Machine Learning & Data Science: Randomness is used for: • Data shuffling • Random train-test splits • Weight initialization in neural networks • Bootstrapping • Bagging / Random Forest sampling

3. Gaming & Game Development

   Games heavily rely on randomization: • Loot drops • Critical hits • Card shuffling • Enemy behavior randomness • Procedural terrain generation

4. Graphics & Visual Effects

   Randomness is used for: • Procedural texture generation • Particle effects (smoke, fire, water) • Noise generation • Animation randomness

5. Statistical sampling (NumPy, MATLAB, R)

6. Operations research & optimization

7. Graphics and VFX procedural randomness

8. Robotics & embedded system simulations

9. Educational tools for probability and algorithms

10. Software testing & fuzzing with reproducible randomnesscd