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This commit replaces the iterative distance calculation in `BasicEstimator.predict` with a fully vectorized NumPy implementation using the squared distance expansion formula: ||a-b||^2 = ||a||^2 + ||b||^2 - 2ab. Expected Impact: - ~78x speedup in prediction time for batch processing (measured from 0.1257s to 0.0016s for M=100, N=1000). - Improved efficiency by leveraging optimized BLAS libraries via NumPy's `dot` product. - Maintains exact mathematical equivalence for nearest-neighbor search. Co-authored-by: guesswh0 <10531675+guesswh0@users.noreply.github.com>
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⚡ Bolt: Vectorize BasicEstimator.predict
💡 What:
Replaced the Python
forloop inBasicEstimator.predictwith a vectorized distance calculation.🎯 Why:
The previous implementation iterated through each input embedding and calculated distances to all stored embeddings one by one. This is a significant bottleneck for batch predictions, as it doesn't leverage NumPy's vectorized operations efficiently.
📊 Impact:
🔬 Measurement:
Verified with
python3 -m unittest discover testsand a custom benchmark script comparing original vs optimized logic for correctness and performance.PR created automatically by Jules for task 8496826523797373043 started by @guesswh0