Optimize K-means - #433
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Pull request overview
This PR optimizes KMeans training by reusing per-batch centroid distance computations for both cluster assignment and inertia (loss) calculation, reducing redundant kernel distance evaluations during mini-batch updates.
Changes:
- Precompute a per-batch distance matrix and reuse it for both label assignment (argmin) and inertia accumulation.
- Refactor
predictSample()to use a newcentroidDistances()helper and updateinertia()to operate on distance rows. - Add a PhpBench storage XML benchmark output file.
Reviewed changes
Copilot reviewed 1 out of 1 changed files in this pull request and generated 1 comment.
| File | Description |
|---|---|
| src/Clusterers/KMeans.php | Reuses computed centroid distances for assignments and inertia; introduces centroidDistances() and updates inertia() signature. |
| .phpbench/storage/7ea/8/16/13527d6169bfd8433ae290209f045b245f19565d.xml | Adds a generated benchmark artifact containing environment metadata. |
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KMeans inertia() recomputes distances already computed in predictSample — src/Clusterers/KMeans.php:366. Reuse the per-batch distance matrix for both assignment and loss. ~2× epoch cost.