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This commit improves the performance of the `BasicEstimator.predict` method by replacing the iterative loop over input embeddings with a vectorized implementation using the squared distance expansion formula: ||a-b||^2 = ||a||^2 + ||b||^2 - 2a.b.
This optimization significantly reduces the overhead when processing multiple embeddings or comparing against a large number of fitted faces, leveraging optimized BLAS libraries via NumPy.
Changes:
- In `face_engine/models/basic_estimator.py`:
- Updated `fit` to store `self.embeddings` as a NumPy array.
- Updated `predict` to use vectorized operations for distance calculation.
- Added handling for empty input in `predict`.
- Added `tests/verify_optimization.py` for verification in restricted environments.
- Updated `.jules/bolt.md` with critical learnings.
Co-authored-by: guesswh0 <10531675+guesswh0@users.noreply.github.com>
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Optimized
BasicEstimator.predictusing vectorized distance calculations.PR created automatically by Jules for task 13704188198849075104 started by @guesswh0