BodduSriPavan-111/diemsim
A Python Library Implementing Dimension Insensitive Euclidean Metric (DIEM)
This library helps data scientists and machine learning engineers accurately compare complex, multi-dimensional data points, like embeddings or feature vectors, even when their dimensions vary significantly. It takes in two numerical vectors and outputs a single DIEM score, indicating their similarity. This is for professionals working with high-dimensional data, needing more robust comparison metrics than standard Euclidean distance or Cosine similarity.
No commits in the last 6 months. Available on PyPI.
Use this if you need a highly accurate and computationally efficient way to measure the similarity between complex, multi-dimensional data points where traditional metrics might fall short.
Not ideal if your data is low-dimensional, or if you primarily need a simple, interpretable distance metric where speed is not a critical factor.
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MIT
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Last pushed
Sep 01, 2025
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