Utilities for randomly sampling from statistical distributions
Simple Random Distribution Sampling
SRDS is mainly a wrapper around scipy's statistical functions (scipy.stats). It makes it easier to sample from parameterized distributions and provides tools that accelerate random sampling.
Truncation or rejection sampling
srds adds several classes that make it easier to utilize scipy statistical distributions.
To sample from a log-normal distribution with
σ = 0.25, but truncate at
from srds import ParameterizedDistribution as PDist, BoundRejectionSampler dist = PDist.lognorm(0.25) sampler = BoundRejectionSampler(dist, 0.5, 2) x = sampler.sample(10)
Fast sampling of single values
dist.rvs on a scipy statistical distribution is computationally expensive. This is problematic for code that
does something like:
# will be slow (calls dist.rvs 10000 times) for i in range(10000): x = dist.sample() # ...
srds provides a
BufferedSampler that draws a larger sample from a distribution, and subsequently returns from that
from srds import BufferedSampler dist = BufferedSampler(dist) # will be much faster! (calls dist.rvs only 10 times with a sample size of 1k) for i in range(10000): x = dist.sample() # ...
Sampling from populations
You can use the
PopulationSampler to draw from a discrete set, and also bias the sampling with weights.
from srds import PopulationSampler sampler = PopulationSampler(['a', 'b', 'c'], [8, 1, 1]) sampler.sample() # will return 'a' 8 out of 10 times on average sampler.sample(10) # returns a list containing items from ['a', 'b', 'c'] in random order
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