openskill.py
Pylearn2
openskill.py | Pylearn2 | |
---|---|---|
22 | 1 | |
244 | 2,754 | |
3.3% | 0.1% | |
7.3 | 0.0 | |
27 days ago | over 2 years ago | |
Jupyter Notebook | Python | |
MIT License | BSD 3-clause "New" or "Revised" License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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openskill.py
- Show HN: Predict team ranks in sports and video games with openskill.py
- Predict how teams will rank in sports/video games using our rating system.
- I made a project with the ability to predict ranks of teams in a sports/video game match.
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Openskill: A patent-free alternative to TrueSkill
Linked is the Javascript library. There are also ports of this to Python, Kotlin, Lua, and Elixir.
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Predicting Overwatch Match Outcomes with 90% Accuracy
The new benchmark code can be found here.
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openskill.py VS trueskill - a user suggested alternative
2 projects | 30 Jan 2022
- openskill.py can now predict the winners of any game with 90% accuracy and faster than TrueSkill
Pylearn2
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iNeural : Update (8.12.21)
It is developed by taking inspiration from libraries such as iNeural, FANN, pylearn2, EBLearn, Torch7. Written mostly in C++, iNeural also leverages the power of Python. The biggest reason for its development is that it needs very few dependencies. For this reason, it is expected to be suitable for working in systems with limited system requirements.
What are some alternatives?
trueskill - An implementation of the TrueSkill rating system for Python
Keras - Deep Learning for humans
Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
SciKit-Learn Laboratory - SciKit-Learn Laboratory (SKLL) makes it easy to run machine learning experiments.
karateclub - Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs (CIKM 2020)
tensorflow - An Open Source Machine Learning Framework for Everyone
Data Flow Facilitator for Machine Learning (dffml) - The easiest way to use Machine Learning. Mix and match underlying ML libraries and data set sources. Generate new datasets or modify existing ones with ease.
PyBrain
seqeval - A Python framework for sequence labeling evaluation(named-entity recognition, pos tagging, etc...)
scikit-learn - scikit-learn: machine learning in Python
LightFM - A Python implementation of LightFM, a hybrid recommendation algorithm.
xgboost - Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow