TSIClient
tslearn
TSIClient | tslearn | |
---|---|---|
1 | 51 | |
6 | 2,780 | |
- | 0.5% | |
7.0 | 6.9 | |
7 months ago | 9 days ago | |
Python | Python | |
MIT License | BSD 2-clause "Simplified" 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.
TSIClient
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Hosting Python Packages in Azure DevOps
from setuptools import setup, find_packages with open('README.md') as f: long_description = f.read() setup( name = 'animalsounds', # How you named your package folder (TSIClient) packages = ['animalsounds'], # Chose the same as "name" version = '1.0.0', # Start with a small number and increase it with every change you make license='MIT', # Chose a license from here: https://help.github.com/articles/licensing-a-repository long_description=long_description, long_description_content_type='text/markdown', # This is important! author = 'Vivek Raja P S', # Type in your name author_email = '[email protected]', # Type in your E-Mail url = 'https://github.com/Vivek0712/azure-devops-pypackage', # Provide either the link to your github or to your website #download_url = 'https://github.com/RaaLabs/TSIClient/archive/v_0.7.tar.gz', # If you create releases through Github, then this is important keywords = ['Azure', 'DevOps', 'Python'], # Keywords that define your package best packages = find_packages("src", exclude=["test"]), classifiers=[ 'Development Status :: 3 - Alpha', # Chose either "3 - Alpha", "4 - Beta" or "5 - Production/Stable" as the current state of your package 'Intended Audience :: Developers', # Define that your audience are developers 'Topic :: Software Development :: Build Tools', 'License :: OSI Approved :: MIT License', # Again, pick a license 'Programming Language :: Python :: 3.5', 'Programming Language :: Python :: 3.6', 'Programming Language :: Python :: 3.9', ], )
tslearn
What are some alternatives?
pytorch-forecasting - Time series forecasting with PyTorch
sktime - A unified framework for machine learning with time series
azure-devops-pypackage
sktime-dl - DEPRECATED, now in sktime - companion package for deep learning based on TensorFlow
neural_prophet - NeuralProphet: A simple forecasting package
Time-series-classification-and-clustering-with-Reservoir-Computing - Library for implementing reservoir computing models (echo state networks) for multivariate time series classification and clustering.
scikit-hts - Hierarchical Time Series Forecasting with a familiar API
R-Time-Series-Task-View-Supplement - R Time series packages not included in CRAN Task View: Time Series Analysis
InceptionTime - InceptionTime: Finding AlexNet for Time Series Classification
Finance-Using-Python - This product helps to understand the stocks in visual manner and as well as it saves the record. And help to predict the data
atspy - AtsPy: Automated Time Series Models in Python (by @firmai)