Expand description
The instance module provides concrete implementations of the DurationSampler trait.
These include various statistical distributions (e.g., ConstantSampler, UniformSampler,
ExponentialSampler, NormalSampler, PoissonSampler), as well as samplers for
empirical data (EmpiricalSampler), rotational patterns (RotateSampler),
and conditional choices (ChoiceSampler).
Structs§
- Choice
Sampler - A sampler that selects one of several sub-samplers based on a weighted distribution.
- Constant
Sampler - A sampler that consistently returns a constant value.
- Empirical
Sampler - A sampler that draws from a discrete empirical distribution.
- Exponential
Sampler - A sampler that draws from an exponential distribution with a specified lambda (rate).
- Mode
Sampler - A sampler that switches between different sub-samplers based on a TimeTrigger.
- Normal
Sampler - A sampler that draws from a normal (Gaussian) distribution.
- Poisson
Sampler - A sampler that draws from a Poisson distribution with a specified lambda (rate).
- Rotate
Sampler - A sampler that returns durations from a fixed list in a cyclic (rotating) order.
- Uniform
Sampler - A sampler that draws from a continuous uniform distribution within [lower_bound, higher_bound).
Traits§
- Time
Trigger - A trait to determine which sampler should be active based on the current simulation time.