ai4r

AI4R — Artificial Intelligence for Ruby

🎓 Welcome to AI4R

Current version: 2.0

Your Lightweight Lab for AI and Machine Learning in Ruby.

AI4R isn’t just another machine learning library. It’s a learning playground. No black boxes and no bulky dependencies—just clean, readable Ruby implementations of core AI algorithms so you can explore, modify and really understand how they work.

Installation

AI4R is distributed as a gem and requires Ruby 3.2 or later.

Install the gem using RubyGems:

gem install ai4r

Add the library to your code:

require 'ai4r'

Where to start?

• Beginner Track – Build core intuition for AI—step by step, in Ruby.
• Intermediate Track – From "I can run a model" to "I can tune, extend, and build smart stuff that actually works."
• Advanced Track – Time to stop following recipes and start writing your own.

🧭 What’s Inside?

A quick map to AI4R’s built‑in toolkits, grouped by type. Each folder comes with examples and benchmark runners so you can dive right in.

🤖 Transformers – Play with the Building Blocks of Modern LLMs

“Meet your future coworker / overlord.”

This is not a full GPT—but it is the core logic, stripped down and readable.
AI4R ships with a bite-sized, dependency-free Transformer implementation that supports:

• Encoder-only mode (like BERT)
• Decoder-only mode (like GPT)
• Seq2Seq mode (like T5)

📂 Code: lib/ai4r/neural_network/transformer.rb Docs: Transformer guide

💡 Try this:
Load up the transformer and walk through a simple forward pass.
Everything from attention weights to layer normalization is short enough to read and understand in one go.

🧠 Classifiers – Make Predictions

"What’s the most likely outcome?"

You’ll find in lib/ai4r/classifiers/:

• ZeroR, OneR – the simplest baselines
• LogisticRegression, SimpleLinearRegression
• SupportVectorMachine
• RandomForest, GradientBoosting
• MultilayerPerceptron

Docs: logistic_regression.md, random_forest.md

Try this: run compare_all.rb to benchmark classifiers on real datasets.

🔍 Clusterers – Find Hidden Patterns

"What belongs together?"

Includes in lib/ai4r/clusterers/:

• KMeans
• DBSCAN
• Hierarchical clustering variants

Docs: kmeans.md, dbscan.md

Try this: cluster the Iris dataset using both KMeans and DBSCAN.

🧬 Neural Networks – Learn From Data

"What if we build a brain?"

• Backpropagation – classic feedforward network
• Hopfield – associative memory model
• Transformer – a tiny GPT‑style block (encoder, decoder, seq2seq)

Code: lib/ai4r/neural_network/

Try this: open transformer.rb and trace each step—it’s short enough to grok in one sitting.

🔎 Search Algorithms – Explore Possibility Spaces

"What’s the best path?"

You’ll find in lib/ai4r/search/:

• BreadthFirst, DepthFirst, IterativeDeepening
• A*
• MonteCarloTreeSearch

Docs: search_algorithms.md

Try this: run A* and DFS on a maze and time the difference.

🧪 Genetic Algorithms – Evolve a Solution

"Let’s mutate our way to a better answer."

• Generic GA framework
• A Traveling Salesman Problem (TSP) chromosome

Code: lib/ai4r/genetic_algorithm/

Try this: tweak the mutation rate in the TSP example.

🧭 Reinforcement Learning – Learn by Doing

"Reward me, and I’ll improve."

• Q‑Learning
• Policy Iteration

Code: lib/ai4r/reinforcement/

Docs: docs/reinforcement_learning.md

Try this: run a grid‑world training loop and watch the agent build its own policy.

🕵️ Hidden Markov Models – Guess What’s Hidden

"You can’t see the states—but you can infer them."

Code: lib/ai4r/hmm/hidden_markov_model.rb

Docs: docs/hmm.md

Try this: model a weather prediction problem with hidden states and visible activities.

🧠 Self‑Organizing Maps – Compress Dimensions

"Can we project complex data onto a simpler map?"

• Kohonen‑style SOM

Code: lib/ai4r/som/

Try this: reduce high‑dimensional vectors into a 2D neuron grid and color it based on class.

🧪 Benchmarks: Experiment & Compare

Each algorithm family has a benchmark runner:

• bench/classifier/
• bench/clusterer/
• bench/search/

Shared tools in bench/common/ make it easy to run head‑to‑head comparisons, track runtime, accuracy and more, and output clean reports.

Docs: docs/benches_overview.md

Try this: run bench/search/astar_vs_dfs.rb and explain why A* usually wins.

git clone https://github.com/SergioFierens/ai4r
cd ai4r
bundle install
ruby bench/classifier/compare_all.rb

💬 Feedback?

This library is maintained for the joy of it (and perhaps a misplaced sense of duty to Ruby). You can do whatever you want with it—it’s unlicensed. If you build something cool or just find it useful, drop a note in the project's comments.