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Introduce a new KMedoids clusterer implementation (src/Clusterers/KMedoids.php) implementing Estimator, Learner, Online, Probabilistic, Verbose and Persistable interfaces. Adds full training/partial training, predict/proba, inertia/loss tracking, medoids/sizes accessors, serialization and parameter validation. Also add documentation (docs/clusterers/k-medoids.md), unit tests (tests/Clusterers/KMedoidsTest.php) and a benchmark (benchmarks/Clusterers/KMedoidsBench.php). Tests and code use a seeder, distance kernel, and basic logging; invalid inputs and untrained prediction are guarded by exceptions.
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Hey @chouaibcher this is really cool! I'll take a look ASAP although just to warn you I don't have much time for Rubix ML these days so please be patient with me. Thanks I'm looking forward to the review! |
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Introduce a new KMedoids clusterer implementation (src/Clusterers/KMedoids.php) + documentation (docs/clusterers/k-medoids.md),
unit tests (tests/Clusterers/KMedoidsTest.php) and
a benchmark (benchmarks/Clusterers/KMedoidsBench.php).