discrete-dists 1.2.1

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discrete dists

Simple utilities for defining complex distributions over discrete elements. Backed by a fast sum-tree implementation written in Rust.

Getting Started

pip install discrete-dists

The formal API behavior is documented in SEMANTICS.md.

When to use which distribution

• Uniform: sample evenly from a fixed interval [lo, hi).
• Proportional: sample discrete elements proportionally to per-element nonnegative weights.
• Categorical: convenience wrapper around Proportional using standard probability terminology.
• MixtureDistribution: blend multiple sub-distributions with mixture weights.

API

Uniform Distribution

A very simple wrapper over np.random.default_rng().integers, conforming to the Distribution API defined in this library. This wrapper additionally introduces importance sampling ratio calculations, sampling without replacement, and stratified sampling.

import numpy as np
from discrete_dists.uniform import Uniform

rng = np.random.default_rng(0)

u = Uniform(100)

# sampling
print(u.sample(rng, 10))
print(u.stratified_sample(rng, 10))
print(u.sample_without_replacement(rng, 10))

# importance sampling ratio
other = Uniform(10)

items = [0, 3, 8]
isrs = u.isr(other, items)

# out-of-support probabilities are zero
print(u.probs([-1, 0, 99, 100]))

# updating the support
u.update_single(150)

Proportional Distribution

Sample proportional to a list of values.

from discrete_dists.proportional import Proportional
from discrete_dists.categorical import Categorical

p = Proportional(5)

# set the values to sample proportional to
p.update(idxs=[0, 2], values=[1, 2])
# approximately 33% of values are 0, and 66% are 2
print(p.sample(rng, 10000))

p.update(idxs=[1], values=[2])
# approximately 20% are 0, 40% are 1, and 40% are 2
print(p.sample(rng, 10000))

# standard alias if you prefer probability terminology
c = Categorical(5)

Mixture Distribution

Mix together arbitrary distributions with arbitrary supports.

from discrete_dists.proportional import Proportional
from discrete_dists.uniform import Uniform
from discrete_dists.mixture import MixtureDistribution, SubDistribution

prop = Proportional(100)
m = MixtureDistribution([
    SubDistribution(d=prop, p=0.2),
    SubDistribution(d=Uniform(10), p=0.8),
])

prop.update(idxs=np.arange(100), values=100-np.arange(100))

print(m.sample(rng, 10000))

Mixtures ignore defunct children when computing probabilities and samples.

Edge cases and contracts

• probs() returns 0 outside support.
• sample_without_replacement() returns exactly n unique elements or raises ValueError.
• isr() may return inf or nan when supports do not overlap.
• Proportional.update_support() supports same-width shifts and widening that fully contains the old support.

Performance notes

• The Rust-backed sum tree powers Proportional/Categorical operations.
• Core update/query paths use a flat tree layout and release the GIL in hot paths.
• In the current test benchmark on this branch, SumTree update and sample paths improved substantially compared with the earlier layered implementation.