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github-stars-by-topic
:star: Generate a list of your GitHub stars by topic - automatically!
https://github.com/lorey/github-stars-by-topic
Last synced: 2 days ago
JSON representation
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How it works
- 5000 instead of 60 requests per hour
- Term Frequency Inverse Document Frequency (tf-idf) - processing on the list to extract relevant keywords for each repo. This results in a list of repos with corresponding tf-idf weights. A high tf-idf value means the term is very relevant for this document, a low value means the term is irrelevant (i.e. not existing or too common). The benefit of tf-idf values over plain term frequencies is that it results in low weights for terms that are very common. Or how [Wikipedia](https://en.wikipedia.org/wiki/Tf%E2%80%93idf) puts it:
- Non-Negative Matrix Factorization (NMF) - relevant keywords. This gives us two results. Firstly, a list of topics defined by their most-important keywords (high value means more relevance for the topic):
- example for topic extraction in the scikit-learn documentation
- my personal assistant bot called Totally not Jarvis
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Dependencies
- install
- install
- install
- install
- scipy stack - idf vectors, etc.). [install](http://scikit-learn.org/stable/install.html)
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