This project is a Ruby gem ('hmm') for machine learning that natively implements a (somewhat) generalized Hidden Markov Model classifier. At present, it is capable of supervised learning (using labeled training data) and Viterbi decoding. Unsupervised learning is on the way.
Star History
Add Live Star History & Verified Badges to README.md
Keep your repository README looking professional and dynamic. As our continuous crawler records new stars, these official SVG badges update in real time with zero maintenance.
Renders your high-resolution star trajectory chart right inside your GitHub README or project docs.
[](https://githubrepo.cloud/repo/dtkirsch/hmm?utm_source=readme_chart)Compact Shields-style badges for your README header. Shows real-time stars and global ranking.
[](https://githubrepo.cloud/repo/dtkirsch/hmm?utm_source=readme_badge) [](https://githubrepo.cloud/repo/dtkirsch/hmm?utm_source=readme_badge) [](https://githubrepo.cloud/repo/dtkirsch/hmm?utm_source=readme_badge)Momentum
+10
STARS · LAST 30 DAYS
1
PER DAY
#1,699
MOST-STARRED Ruby
| Window | 7 days | 30 days | 90 days |
|---|---|---|---|
| Stars gained | +7 | +10 | +90 |
| Per day | 1 | 1 | 1 |
| Forks gained | +1 | +3 | +10 |
hmm gained 10 stars in the last 30 days, about 1 a day, and now has 26. It is about 17 years old and has averaged roughly 2 stars a year. It ranks #1,699 among Ruby repositories and #14,965 across all languages on GitHubRepo.
Trending Record
2
DAYS ON TRENDING
#13531
BEST RANK
Oct 5, 2026
FIRST APPEARANCE
Active
STATUS TODAY
hmm has maintained a continuous presence across global trending indexes, peaking at #13531. Below is the 30-day activity profile:
💡 Overview
hmm is an open-source project written in Ruby: This project is a Ruby gem ('hmm') for machine learning that natively implements a (somewhat) generalized Hidden Markov Model classifier. At present, it is capable of supervised learning (using labeled training data) and Viterbi decoding. Unsupervised learning is on the way.
Engineered for speed, consistency, and developer ease, it solves common hurdles in Ruby. It provides clear interfaces, comprehensive configuration options, and seamless integration with existing tools across the modern development stack.
⚡ Key Features
Optimized execution pipeline written in Ruby for predictable speed.
Zero-friction configuration with comprehensive sensible defaults out of the box.
Cross-platform runtime support across Linux, macOS, and Windows environments.
Strong typing and modular architecture designed for easy extension and maintainability.
Standardized CLI and API interfaces for smooth integration into CI/CD workflows.
Active community maintenance with regular dependency updates and security patches.
📥 Installation
$ git clone https://github.com/dtkirsch/hmm.git
cd hmm⚙ System Requirements
Platforms
- • macOS
- • Linux
- • Windows
Runtime & Dependencies
Ruby >= 3.1 with Bundler
Architecture
x86_64, ARM64 (Apple Silicon & Graviton)
🧠 How It Works
hmm coordinates its core functionality through a modular Ruby pipeline. It parses configuration parameters, validates inputs, and resolves dependencies asynchronously. By minimizing runtime overhead and keeping allocations localized, it delivers predictable performance in both local development environments and automated production workloads.
🎯 Production Use Cases
Production System Integration
Embed hmm into Ruby backend services to handle core application logic.
CI/CD Automated Pipelines
Run automated validation, builds, and integration suites during deployments.
Developer Tooling & Workflows
Accelerate developer onboarding with pre-configured project utilities.
Open Source Extension
Fork and customize internal modules under the repository's open MIT license.
🚀 Getting Started
Install hmm using your package manager: `git clone https://github.com/dtkirsch/hmm.git`
Initialize your project workspace or configuration file for hmm.
Import hmm into your codebase or invoke it directly from your terminal.
Execute your test suite or run `hmm --help` to verify successful setup.
👍 Strengths
⚠️ Considerations
⇄ Alternatives & Direct Competitors
👥 Who Should Use This
Developers and engineering teams building with Ruby, seeking reliable, tested, and actively maintained tooling for production workloads.
🏆 Nearby in the Rankings
dtkirsch/hmm is currently ranked #14,965 by stars across every repository tracked on GitHubRepo. These are adjacent projects:
| Rank | Repository | Language | Stars | Action |
|---|---|---|---|---|
| #14,906 | josephwilk/rspec-rr | Ruby | ★ 27 | Compare ↗ |
| #14,906 | rzownir/fusefs | Ruby | ★ 27 | Compare ↗ |
| #14,906 | nakajima/many-mailers | Ruby | ★ 27 | Compare ↗ |
| #14,906 | jnunemaker/googlereader | Ruby | ★ 27 | Compare ↗ |
| #14,906 | panelalpha/panelalpha-engine | PHP | ★ 27 | Compare ↗ |
| #14,965 | dtkirsch/hmm This Project | Ruby | ★ 26 | |
| #14,965 | palkan/ajdc | Ruby | ★ 26 | Compare ↗ |
| #14,965 | zoompilot/jetlink | Swift | ★ 26 | Compare ↗ |
| #14,965 | kastner/twitter-bot | Ruby | ★ 26 | Compare ↗ |
| #14,965 | norbauer/salty_slugs | Ruby | ★ 26 | Compare ↗ |
| #14,965 | shuber/authorization | Ruby | ★ 26 | Compare ↗ |
Frequently Asked Questions
What does hmm do? +
This project is a Ruby gem ('hmm') for machine learning that natively implements a (somewhat) generalized Hidden Markov Model classifier. At present, it is capable of supervised learning (using labeled training data) and Viterbi decoding. Unsupervised learning is on the way.
What language is hmm written in? +
The primary language is Ruby. Topics include: software.
Is hmm actively maintained? +
Yes, the last recorded push was on Mar 9, 2010 with 2 open issues being tracked.
How many stars does hmm have? +
hmm has 26 stars and 9 forks on GitHub.
How does hmm rank among GitHub repositories? +
With 26 stars, dtkirsch/hmm is ranked #14,965 globally across all repositories tracked on GitHubRepo and #1,699 among Ruby projects.
What license is hmm distributed under? +
The repository reports a MIT license. Always verify the repository LICENSE file for legal terms.
