pushkar/ABAGAIL
The library contains a number of interconnected Java packages that implement machine learning and artificial intelligence algorithms. These are artificial intelligence algorithms implemented for the kind of people that like to implement algorithms themselves.
Covers discrete optimization via hill climbing, simulated annealing, genetic algorithms, and MIMIC; supervised learning with neural networks, SVMs, and decision trees; and unsupervised methods including HMMs, clustering, and dimensionality reduction. The modular architecture allows mixing custom probability distributions, kernel functions, activation functions, and matrix decompositions alongside built-in implementations. Includes supporting utilities for graph algorithms, kd-tree indexing, and data preprocessing with PCA/ICA/LDA.
252 stars. No commits in the last 6 months.
Stars
252
Forks
480
Language
Java
License
BSD-3-Clause
Category
Last pushed
Jan 13, 2024
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/pushkar/ABAGAIL"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Related frameworks
yuce/pyswip
PySwip is a Python-Prolog interface that enables querying SWI-Prolog in your Python programs.
lab-v2/pyreason
An explainable inference software supporting annotated, real valued, graph based and temporal logic
TweetyProjectTeam/TweetyProject
TweetyProject is a collection of Java libraries that implement approaches to different areas of...
amrinderarora/ai
Classical AI algorithms. Cutting edge, since 1960s. Amrinder Arora
leoprover/scala-tptp-parser
A parser for the TPTP logic languages for automated theorem proving written in Scala