simopt.input_models

Simple input-model wrappers for common distributions.

Module Contents

simopt.input_models.P

simopt.input_models.R

class simopt.input_models.InputModel

Bases: Protocol[P, R]

Abstract base for input models used by simulations.

rng: random.Random | None = None

set_rng(rng: random.Random) → None

Attach a Python RNG to the input model.

Parameters:

rng (random.Random) – Random number generator to use for sampling.

unset_rng() → None

Detach any RNG currently attached to the input model.

abstractmethod random(*args: P, **kwargs: P) → R

Generate a random variate from the input model.

Returns:

A random variate from the input model.

Return type:

T

class simopt.input_models.Exp

Bases: InputModel

Exponential distribution wrapper.

random(lambda_: float) → float

Sample from Exp(lambda_).

Parameters:

lambda (float) – Rate parameter (lambda > 0).

Returns:

An exponential variate.

Return type:

float

class simopt.input_models.Gamma

Bases: InputModel

Gamma distribution wrapper.

random(alpha: float, beta: float) → float

Sample from Gamma(alpha, beta).

Parameters:

• alpha (float) – Shape parameter.

• beta (float) – Scale parameter.

Returns:

A gamma variate.

Return type:

float

class simopt.input_models.WeightedChoice

Bases: InputModel

Discrete weighted choice wrapper.

random(population: collections.abc.Sequence[object], weights: collections.abc.Sequence[float]) → object

Sample an element from population according to weights.

Parameters:

• population (Sequence) – Items to choose from.

• weights (Sequence[float]) – Nonnegative weights for each item.

Returns:

A randomly selected element from population.

Return type:

Any

class simopt.input_models.Poisson

Bases: InputModel

Poisson distribution wrapper.

random(lam: float) → int

Sample from Poisson(lam).

Parameters:

lam (float) – Mean rate parameter (lambda >= 0).

Returns:

A Poisson variate.

Return type:

int

class simopt.input_models.Beta

Bases: InputModel

Beta distribution wrapper.

random(alpha: float, beta: float) → float

Sample from Beta(alpha, beta).

Parameters:

• alpha (float) – Alpha (>= 0).

• beta (float) – Beta (>= 0).

Returns:

A beta variate in [0, 1].

Return type:

float

class simopt.input_models.Triangular

Bases: InputModel

Triangular distribution wrapper.

random(low: float, high: float, mode: float) → float

Sample from Triangular(low, high, mode).

Parameters:

• low (float) – Lower bound.

• high (float) – Upper bound.

• mode (float) – Mode of the distribution.

Returns:

A triangular variate.

Return type:

float

class simopt.input_models.Uniform

Bases: InputModel

Uniform distribution wrapper.

random(low: float, high: float) → float

Sample from Uniform(low, high).

Parameters:

• low (float) – Lower bound.

• high (float) – Upper bound.

Returns:

A uniform variate in [low, high].

Return type:

float

class simopt.input_models.Normal

Bases: InputModel

Normal distribution wrapper.

random(mu: float = 0.0, sigma: float = 1.0) → float

Sample from Normal(mu, sigma).

Parameters:

• mu (float) – Mean.

• sigma (float) – Standard deviation.

Returns:

A normal variate.

Return type:

float