⚡ Bolt: vectorize BasicEstimator distance calculation#28
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💡 What: Vectorized the distance calculation in `BasicEstimator.predict` using the squared Euclidean distance expansion formula: ||a-b||^2 = ||a||^2 + ||b||^2 - 2ab. Also updated `BasicEstimator.fit` to pre-calculate the squared norms of fitted embeddings. 🎯 Why: The previous implementation used a Python loop over query embeddings and `np.linalg.norm` for each, which is inefficient for large batches and doesn't leverage optimized BLAS routines for matrix multiplication. 📊 Impact: - Reduces prediction time by ~10x for batches of 500 query embeddings against 2000 fitted embeddings. - Significant speedup even for single-query predictions by avoiding repeated norm calculations. 🔬 Measurement: Verified using a benchmark script comparing original loop-based logic vs vectorized logic. Correctness confirmed with existing unit tests. Backward compatibility maintained for older fitted models using `getattr`. Co-authored-by: guesswh0 <10531675+guesswh0@users.noreply.github.com>
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Vectorized
BasicEstimator.predictfor significantly faster face recognition.PR created automatically by Jules for task 10653179766961728889 started by @guesswh0