⚡ Bolt: vectorize BasicEstimator.predict#45
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Vectorized the Euclidean distance calculation in BasicEstimator.predict using the expansion formula ||a-b||² = ||a||² + ||b||² - 2a·b and pre-calculated squared norms in fit(). Impact: ~12x speedup (0.24s -> 0.02s for 500 queries vs 2000 embeddings). Backward compatibility included for loaded models. Co-authored-by: guesswh0 <10531675+guesswh0@users.noreply.github.com>
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💡 What: Vectorized the Euclidean distance calculation in$|a-b|^2 = |a|^2 + |b|^2 - 2a \cdot b$ and pre-calculated squared norms in
BasicEstimator.predictusing the expansion formulafit().🎯 Why: The original implementation used a Python loop over query embeddings, calling
np.linalg.normfor each, which is inefficient for large batches.📊 Impact: Measured ~12x speedup (reduced from ~0.24s to ~0.02s for 500 queries against 2000 fitted embeddings).
🔬 Measurement: Run large batch prediction tasks. Backward compatibility verified with tests for loading legacy model states.
PR created automatically by Jules for task 13177618012556756016 started by @guesswh0