⚡ Bolt: Optimize DBSCAN and auto K-selection with squared distances#140
⚡ Bolt: Optimize DBSCAN and auto K-selection with squared distances#140teerthsharma wants to merge 1 commit into
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Replaced `libm::sqrt` with squared distance checks inside hot loops of `auto_k_selection` and `DBSCAN::region_query` in `crates/aether-core/src/ml/clustering.rs`. Also properly rejected negative/NaN thresholds and implemented an early loop break if `sum >= eps_sq`, keeping robust NaN handling. This significantly speeds up clustering algorithms by removing `sqrt` overhead. Co-authored-by: teerthsharma <78080953+teerthsharma@users.noreply.github.com>
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💡 What: Optimized hot spatial search loops in
auto_k_selectionandDBSCAN::region_queryby calculating the squared distance instead of callinglibm::sqrt.🎯 Why: Calculating distances over loops with
libm::sqrtcreates significant mathematical overhead, which is a known performance bottleneck. Applying squared thresholds drastically reduces this.📊 Impact: Reduces computational overhead in spatial neighbor checks for clustering, potentially providing measurable reductions in processing time, specifically removing
O(n^2)instances ofsqrt.🔬 Measurement: Verify by running the clustering test suite (
cargo test -p aether-core --offline --lib ml::clustering) and profiling theauto_k_selectionandDBSCAN::fitroutines with large point counts.PR created automatically by Jules for task 17799919632338192388 started by @teerthsharma