Random

Auto-generated from src/stdlib/random.mcrs — do not edit manually.

API

• next_lcg
• random_range
• random_bool
• pcg_next_lo
• pcg_next_hi
• pcg_output
• binomial_sample
• hypergeometric_sample

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next_lcg v1.0.0

Advance the LCG state by one step, returning the next pseudo-random int32. The returned value is also the new seed for the next call.

fn next_lcg(seed: int): int

Parameters

Parameter	Description
seed	Current LCG state (any non-zero integer to start)

Returns: Next pseudo-random int32 (also serves as next seed)

Example

let seed: int = 12345
seed = next_lcg(seed)
seed = next_lcg(seed)  // advance two steps

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random_range v1.0.0

Generate a pseudo-random integer in the range [lo, hi).

fn random_range(seed: int, lo: int, hi: int): int

Parameters

Parameter	Description
seed	LCG output value (result of next_lcg)
lo	Inclusive lower bound
hi	Exclusive upper bound (must be > lo)

Returns: Integer in [lo, hi)

Example

seed = next_lcg(seed)
let roll: int = random_range(seed, 1, 7)  // dice roll: 1-6

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random_bool v1.0.0

Generate a pseudo-random boolean (0 or 1) with equal probability.

fn random_bool(seed: int): int

Parameters

Parameter	Description
seed	LCG output value (result of next_lcg)

Returns: 0 or 1 with ~50% probability each

Example

seed = next_lcg(seed)
let coin: int = random_bool(seed)  // 0 or 1

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pcg_next_lo v1.0.0

Advance the PCG low-word state by one step. Call together with pcg_next_hi; use pcg_output to extract the random value.

fn pcg_next_lo(state_lo: int): int

Parameters

Parameter	Description
state_lo	Current low word of PCG state

Returns: New low word

Example

let lo: int = pcg_next_lo(state_lo)

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pcg_next_hi v1.0.0

Advance the PCG high-word state by one step.

fn pcg_next_hi(state_hi: int, state_lo: int): int

Parameters

Parameter	Description
state_hi	Current high word of PCG state
state_lo	Current low word of PCG state (previous, before pcg_next_lo)

Returns: New high word

Example

let hi: int = pcg_next_hi(state_hi, state_lo)

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pcg_output v1.0.0

Extract an output value from the PCG low word using XSH-RR permutation.

fn pcg_output(state_lo: int): int

Parameters

Parameter	Description
state_lo	Current low word of PCG state (after pcg_next_lo)

Returns: Pseudo-random output value (unsigned, any int)

Example

let rng: int = pcg_output(state_lo)

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binomial_sample v1.0.0

Simulate n Bernoulli trials and count the number of successes (binomial distribution).

fn binomial_sample(n: int, p_x10000: int, seed: int): int

Parameters

Parameter	Description
n	Number of trials
p_x10000	Success probability ×10000 (e.g. 5000 = 50%, 3000 = 30%)
seed	LCG seed (any non-zero integer)

Returns: Number of successes in [0, n]

Example

let hits: int = binomial_sample(10, 5000, 99999)  // ~5 successes on average

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hypergeometric_sample v1.0.0

Draw draws items without replacement from a population and count successes (hypergeometric distribution).

fn hypergeometric_sample(pop_size: int, success_states: int, draws: int, seed: int): int

Parameters

Parameter	Description
pop_size	Total population size
success_states	Number of "success" items in the population
draws	Number of items to draw
seed	LCG seed (any non-zero integer)

Returns: Number of successes drawn, in [0, min(draws, success_states)]

Example

// 52-card deck, 4 aces, draw 5 cards
let aces: int = hypergeometric_sample(52, 4, 5, 42)

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