stargus
BrewPOTS
stargus | BrewPOTS | |
---|---|---|
3 | 2 | |
123 | 40 | |
0.8% | - | |
6.3 | 5.9 | |
2 months ago | 22 days ago | |
C++ | Jupyter Notebook | |
GNU General Public License v3.0 only | BSD 3-clause "New" or "Revised" License |
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stargus
- Reminder for SC1 Modders + Modding Noobs like me; Before Starcraft (SC1 + BW) turns 25 on the 31st of March = April 1st (the day of Rooster Teeth of RedVsBlue), what do you guys think of Stargus (Starcraft1 Stratagus) until a new version like 3.3.? for better customs until late 2023 possibly? (RePo)
- Reminder for SC1 Modders + Modding Noobs like me; Before Starcraft (SC1 + BW) turns 25 on the 31st of March = April 1st (the day of Rooster Teeth of RedVsBlue), what do you guys of Stargus (Starcraft1 Stratagus) until a new version like 3.3.? for better customs until late 2023 possibly? :)
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Stratagus engine, Wargus and War1gus games updated to 3.1.0
So, Stargus, the Starcraft-specific reimplementation, is currently unmaintained? Does it work?
BrewPOTS
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We're building PyPOTS: a Python toolbox for data mining on Partially-Observed Time Series
Due to all kinds of reasons like failures of collection sensors, communication errors, and unexpected malfunctions, missing values are common to see in time series from the real-world environment. No matter whether we like them or not, missing data makes partially-observed time series (POTS) a pervasive problem in open-world modeling and prevents advanced data analysis. Although this problem is important, the area of data mining on POTS still lacks a dedicated toolkit. PyPOTS is created to fill in this gap. PyPOTS (pronounced "Pie Pots") is the first (and so far the only) Python toolbox/library specifically designed for data mining and machine learning on partially-observed time series (POTS), namely, incomplete time series with missing values, A.K.A. irregularly-sampled time series, supporting tasks of imputation, classification, clustering, and forecasting on POTS datasets. It is born to become a handy toolbox that is going to make data mining on POTS easy rather than tedious, to help engineers and researchers focus more on the core problems in their hands rather than on how to deal with the missing parts in their data. PyPOTS will keep integrating classical and the latest state-of-the-art data mining algorithms for partially-observed multivariate time series. For sure, besides various algorithms, PyPOTS has unified APIs together with detailed documentation and interactive examples across algorithms as tutorials. Feedback, questions, and contributions are all very welcome! Website: https://pypots.com Paper link: https://arxiv.org/abs/2305.18811 GitHub repo: https://github.com/WenjieDu/PyPOTS Tutorials: https://github.com/WenjieDu/BrewPOTS Docs: https://docs.pypots.com
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We're building PyPOTS: an open-source Python toolbox for data mining on Partially-Observed Time Series
Tutorials: https://github.com/WenjieDu/BrewPOTS
What are some alternatives?
NVIDIA-vBIOS-VFIO-Patcher - A Python script to patch NVIDIA vBIOS dumps into a format compatible with VFIO passthrough
datawig - Imputation of missing values in tables.
wargus - Importer and scripts for Warcraft II: Tides of Darkness, the expansion Beyond the Dark Portal, and Aleonas Tales
PyPOTS - A Python toolbox/library for reality-centric machine/deep learning and data mining on partially-observed time series with PyTorch, including SOTA neural network models for science analysis tasks of imputation, classification, clustering, forecasting & anomaly detection on incomplete (irregularly-sampled) multivariate TS with NaN missing values
stratagus - The Stratagus strategy game engine
DataDrivenDynSyst - Scripts and notebooks to accompany the book Data-Driven Methods for Dynamic Systems
war1gus - Importer and scripts for Warcraft: Orcs and Humans
Deep_XF - Package towards building Explainable Forecasting and Nowcasting Models with State-of-the-art Deep Neural Networks and Dynamic Factor Model on Time Series data sets with single line of code. Also, provides utilify facility for time-series signal similarities matching, and removing noise from timeseries signals.
tsai - Time series Timeseries Deep Learning Machine Learning Python Pytorch fastai | State-of-the-art Deep Learning library for Time Series and Sequences in Pytorch / fastai