# mikeizbicki/HLearn

Homomorphic machine learning

Repository: https://github.com/mikeizbicki/HLearn
Canonical: https://ross.abutalabs.com/products/hlearn
Language: Haskell
License: NOASSERTION
License Family: other
Last push: 2016-05-29T16:51:53+00:00

## Health v2 (maintenance only)
Score: 23/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 8, longevity 100
- inputs: {"age_days": 5160, "days_push": 3748, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1651, forks 135 (observed 2026-08-28T04:05:16.955594+00:00)

## What it is
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
- artifact type: library
- maturity: abandoned
- function: machine-learning, math
- domain: machine-learning
- platform: -
- tags: haskell, homomorphisms, algebraic-structures, research-project, nearest-neighbors, algorithms, linux, macos

## Member repositories
- mikeizbicki/HLearn (main) score 23

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:16.955594+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T03:45:02.769791+00:00, confidence not recorded.
  - readme: https://github.com/mikeizbicki/HLearn (fetched 2026-08-28T04:05:16.955594+00:00, sha c0729f4d69ea)
- Data as of 2026-08-30T08:39:29.467469+00:00.