HaVSA alternatives and similar packages
Based on the "AI" category.
Alternatively, view HaVSA alternatives based on common mentions on social networks and blogs.
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tensor-safe
A Haskell framework to define valid deep learning models and export them to other frameworks like TensorFlow JS or Keras. -
moo
Genetic algorithm library for Haskell. Binary and continuous (real-coded) GAs. Binary GAs: binary and Gray encoding; point mutation; one-point, two-point, and uniform crossover. Continuous GAs: Gaussian mutation; BLX-α, UNDX, and SBX crossover. Selection operators: roulette, tournament, and stochastic universal sampling (SUS); with optional niching, ranking, and scaling. Replacement strategies: generational with elitism and steady state. Constrained optimization: random constrained initialization, death penalty, constrained selection without a penalty function. Multi-objective optimization: NSGA-II and constrained NSGA-II. -
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Etage
A general data-flow framework featuring nondeterminism, laziness and neurological pseudo-terminology.
InfluxDB - Power Real-Time Data Analytics at Scale
* Code Quality Rankings and insights are calculated and provided by Lumnify.
They vary from L1 to L5 with "L5" being the highest.
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README
HaVSA (Have-Saa) is a Haskell implementation of the Version Space Algebra Machine Learning technique described in Tessa Lau's PhD thesis (the link is for a journal version):
Tessa Lau, Steven Wolfman, Pedro Domingos, and Daniel S. Weld, Programming by Demonstration using Version Space Algebra, Machine Learning, 2003. (http://tlau.org/research/papers/mlj01-draft.pdf)
Documentation
- [VSIntro Introduction to Version Space Algebra]
Publications about Version Space Algebra
A number of Version Spaces papers that I've come across are listed here
Contributors
HaVSA was created by Rogan Creswick
Other Implementations
If you're looking for an implementation in Java, you should check out JVersionSpaces