sandlib vs nondeterminism

sandlib

By regularlambda

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nondeterminism

Nondeterministic computations in Haskell (by abarbu)

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sandlib		nondeterminism
	Project
-	Mentions	1
-	Stars	7
-	Growth	-
-	Activity	0.0
-	Latest Commit	almost 2 years ago
	Language	Haskell
BSD 3-clause "New" or "Revised" License	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.

nondeterminism

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

McCarthy's Ambiguous Operator
1 project | news.ycombinator.com | 12 Sep 2022

I found this after wondering why the main data structure in [this package](https://github.com/abarbu/nondeterminism-haskell) was called `Amb`. Seems like this operator can be used to generate a constraint-solving engine elegantly.

What are some alternatives?

When comparing sandlib and nondeterminism 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.

hnn - haskell neural network library

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.

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.

GoAIRatings - Estimate Go AI ratings by real games

keera-posture - Alleviate your back pain using Haskell and a webcam

heukarya - genetic programming in haskell

GA - Haskell module for working with genetic algorithms

HSGEP - Haskell Gene Expression Programming Library

HOpenCV - Haskell bindings for OpenCV