pchlenski/manify
A Python Library for Learning Non-Euclidean Representations
When your data doesn't fit neatly into flat, Euclidean spaces, this library helps you understand and model it better. You can feed in complex datasets like social networks or biological pathways and get back representations that capture their intricate, non-linear relationships. This is useful for researchers and data scientists working with graph-structured data or high-dimensional biological, social, or physical system data.
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Use this if you need to analyze data where standard machine learning models struggle because the underlying relationships are inherently curved or hierarchical, not flat.
Not ideal if your data relationships are well-described by simple linear distances, or if you prefer off-the-shelf Euclidean machine learning tools for simpler datasets.
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Jupyter Notebook
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MIT
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Last pushed
Aug 05, 2025
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