mikeizbicki/HLearn
Homomorphic machine learning observed · 2026-08-28
Health v2 · maintenance only
23/100
- Activity 0
- Release rhythm 8
- Longevity 100
Flags: no_license
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-03. Adoption (stars, forks) is never an input.
- gap_med: n/a
- age_days: 5160
- days_rel: n/a
- days_push: 3748
- n_releases_24m: 0
Adoption not part of the score
1651 stars · 135 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded
HLearn is a high-performance machine learning library written in Haskell that exploits algebraic structures like monoids and groups for fast, flexible training. It is also a research project exploring the ideal interface for machine learning, developed alongside the SubHask library.
Use cases
- fast nearest neighbor search in arbitrary metric spaces
- parallel and online batch training of models
- fast cross-validation using algebraic structures
- untraining data points from learned models
- research into functional programming interfaces for machine learning
When to choose
- you need very fast nearest neighbor search in Haskell
- you want to experiment with algebra-based machine learning interfaces
- you are researching monoid-based parallel/online training
When to avoid
- you need a maintained library with recent updates
- you want a broad ecosystem of models and tooling
- you prefer Python/R-style ML workflows
Facets
library · maturity abandoned
machine-learning math machine-learning haskell homomorphisms algebraic-structures research-project nearest-neighbors algorithms linux macos
1 source
- readme: https://github.com/mikeizbicki/HLearn · fetched 2026-08-28 · c0729f4d69ea
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| mikeizbicki/HLearn | main | 23 |
For agents
markdown · JSON · MCP: product_card(name="mikeizbicki/HLearn")
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem