# cdipaolo/goml

On-line Machine Learning in Go (and so much more)

Repository: https://github.com/cdipaolo/goml
Canonical: https://ross.abutalabs.com/products/goml
Language: Go
License: MIT
License Family: permissive
Topics: machine-learning
Last push: 2022-07-15T00:13:54+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 4085, "days_push": 1511, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1614, forks 133 (observed 2026-08-28T04:05:11.339161+00:00)

## What it is
goml is a machine learning library written entirely in Go, offering both traditional batch learning and online, stream-based learning via channels. It implements models like linear/logistic regression, softmax regression, perceptrons, and k-means clustering with comprehensive tests and documentation.

## Use cases
- train machine learning models in Go
- online learning from streaming data over channels
- classify text or data with logistic or softmax regression
- cluster data points with k-means
- build a perceptron classifier in a Go application

## When to choose
- you need pure-Go machine learning with no CGo or Python dependencies
- you want online/streaming model updates in a reactive Go application
- you need well-documented, simple classic ML models

## When to avoid
- you need deep learning, GPU acceleration, or modern transformer models
- you need actively maintained software with recent releases
- you need a broad ecosystem of tools beyond classic ML algorithms

## Facets
- artifact type: library
- maturity: maintenance
- function: machine-learning
- domain: machine-learning
- platform: go, cross-platform
- tags: online-learning, streaming-learning, perceptron, k-means, linear-regression, logistic-regression, natural-language-processing

## Member repositories
- cdipaolo/goml (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:11.339161+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 2026-08-30T03:50:20.546556+00:00, confidence not recorded.
  - readme: https://github.com/cdipaolo/goml (fetched 2026-08-28T04:05:11.339161+00:00, sha badbdaf824e5)
- Data as of 2026-08-30T08:39:29.467469+00:00.