sandlib VS csp

Compare sandlib vs csp and see what are their differences.

sandlib

By regularlambda
AI

csp

Constraint satisfaction problem (CSP) solvers for Haskell (by abarbu)
AI
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sandlib csp
- -
- 15
- -
- 0.0
- about 6 years ago
Haskell
BSD 3-clause "New" or "Revised" License LicenseRef-LGPL
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

sandlib

Posts with mentions or reviews of sandlib. We have used some of these posts to build our list of alternatives and similar projects.

We haven't tracked posts mentioning sandlib yet.
Tracking mentions began in Dec 2020.

csp

Posts with mentions or reviews of csp. We have used some of these posts to build our list of alternatives and similar projects.

We haven't tracked posts mentioning csp yet.
Tracking mentions began in Dec 2020.

What are some alternatives?

When comparing sandlib and csp you can also consider the following projects:

tensor-safe - A Haskell framework to define valid deep learning models and export them to other frameworks like TensorFlow JS or Keras.

grenade - Deep Learning in Haskell

best_AI_papers_2021 - A curated list of the latest breakthroughs in AI (in 2021) by release date with a clear video explanation, link to a more in-depth article, and code.

opencv - Haskell binding to OpenCV-3.x

GoAIRatings - Estimate Go AI ratings by real games

GA - Haskell module for working with genetic algorithms

heukarya - genetic programming in haskell

hnn - haskell neural network library

HaVSA - HaVSA (Have-Saa) is a Haskell implementation of the Version Space Algebra Machine Learning technique described by Tessa Lau.

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.

simple-genetic-algorithm - Simple parallel genetic algorithm implementation in pure Haskell