GNOLL
m2cgen
| GNOLL | m2cgen | |
|---|---|---|
| 5 | 8 | |
| 37 | 2,717 | |
| - | 0.6% | |
| 8.3 | 0.0 | |
| 6 days ago | 7 months ago | |
| Yacc | Python | |
| GNU General Public License v3.0 only | MIT License |
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.
GNOLL
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GNOLL: Dice Notation For Everywhere & Everyone
GNOLL is a dice notation library (used for rolling dice in board games, tabletop rpgs, etc) that is designed for more than once-off usage.
- GNOLL: An efficient grammar-based dice roller that supports a lot more dice notation than most python dice packages
- Show HN: Gnoll: an open source library for parsing a wide range of dice notation
- I created GNOLL, a software library you can include in your own project to help parse all sorts of dice notation
m2cgen
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How to use python ML script in tauri?
Check out: https://github.com/BayesWitnesses/m2cgen
- EleutherAI announces it has become a non-profit
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Redis as a Database — Data Migration With RedisOM, RedisGears and Redlock
Notice that I’m using random values to populate the Sentiment field. You might compute the values for your fields based on other fields or actually use an ML model to perform the transformation. E.g. you could make use of m2cgen to transform trained models to pure python code and load them in **RedisGears **to be executed in a *GearsBuilder *instance. Another option is to pull out the big guns and go straight to RedisAI.
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Why isn’t Go used in AI/ML?
I wish that it was more common for model outputs to be converted the way bayeswitness does with mc2gen https://github.com/BayesWitnesses/m2cgen
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Use your decision tree model in your Javascript project today with m2cgen
And that’s it! All the magic in just two lines of code. I would like to thank the authors of the m2cgen library and encourage you to try it out.
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We use Rust for an opensource malware detection engine. It's great at detecting ransomwares and we want to share results and ideas with you.
I forgot to update the README. We just replaced RNN with xgboost that has a better f1 and is very quick, as the decision trees are translated to plain rust using m2cgen.
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Is data science/engineering in Rust practical, does it provide any benefit over Python, and what are the best crates?
Probably, as many frameworks come with a Rust support (or there are wrappers). Some models, like decision tree, can also be automatically translated to plain Rust (in my company we use m2cgen to translate xgboost models to plain rust code).
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Flutter Machine Learning App
These repositories on GitHub are good start I think: https://github.com/BayesWitnesses/m2cgen and https://github.com/vickylance/dart_nn
What are some alternatives?
ranstax - Random item picker from named stacks of known size
TensorFlow.NET - .NET Standard bindings for Google's TensorFlow for developing, training and deploying Machine Learning models in C# and F#.
gnomodkit - Mod SDK & loader for Gnomoria. Obsoleted by Gnoll: https://github.com/Nefaro/gnoll
Synapses - A group of neural-network libraries for functional and mainstream languages
program - An open-source codebase for sharing programming solutions. Good collection of `good first issue`
R Provider - Access R packages from F#
entish - Entish is a declarative Datalog-like language for formal RPG rules
gorse - Gorse open source recommender system engine
RogueTraderGeneratorTools - Solar system generator for the Rogue Trader roleplaying game.
randomforest - Random Forest implementation in golang
slides - The source of the slides of Gabor Szabo shared on the Code Maven site
gago - :four_leaf_clover: Evolutionary optimization library for Go (genetic algorithm, partical swarm optimization, differential evolution)