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. (by astanin)
opencv
Haskell binding to OpenCV-3.x (by LumiGuide)
| moo | opencv | |
|---|---|---|
| - | 1 | |
| 61 | 152 | |
| - | 0.0% | |
| 0.0 | 0.0 | |
| over 1 year ago | 7 months ago | |
| Haskell | Haskell | |
| BSD 3-clause "New" or "Revised" License | BSD 3-clause "New" or "Revised" License |
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.
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.
moo
Posts with mentions or reviews of moo.
We have used some of these posts to build our list of alternatives
and similar projects.
We haven't tracked posts mentioning moo yet.
Tracking mentions began in Dec 2020.
opencv
Posts with mentions or reviews of opencv.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2021-12-31.
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Will I suffer attempting to use Haskell in a company that mainly uses c++
Learn inline-c and inline-c-cpp really well. You will feel enabled if you can call the power of C++ from Haskell. You can find some examples in the opencv package.
What are some alternatives?
When comparing moo and opencv you can also consider the following projects:
nn - A tiny neural network 🧠
caer - High-performance Vision library in Python. Scale your research, not boilerplate.
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
hip - Haskell Image Processing Library
heukarya - genetic programming in haskell
csp - Constraint satisfaction problem (CSP) solvers for Haskell
CV - Haskell wrappers and utilities for OpenCV machine vision library
HSGEP - Haskell Gene Expression Programming Library