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refactor(metrics): extract classification helpers from Accuracy (#1222) - #1429
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michalharakal merged 2 commits intoOct 20, 2026
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October 20, 2026 14:29
- accuracy calls central "forEachPrediction" method for accumulation - helper functions are no longer private
…rt (SKaiNET-developers#1222) - Split forEachPrediction into forEachWithIndexTargets and forEachWithSoftTargets for clear separation between Int32 hard indices and FP32/FP16 soft targets. - Factor out duplicated sample traversal into iteratePredictions and class prediction resolution into predictClass. - Use inline and crossinline to maintain zero-allocation performance. - Remove obsolete DType imports from Accuracy.kt.
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CHANGELOG [0.62.0] - 2026-10-21 with the parity headline over #1363, #1364, #1368, #1369, #1426, #1427, #1428 and #1430, plus entries for the operator coverage matrix (#1370), the BitNet decode allocation and threading fixes (#1359, #1360, #1361, #1362), the ClassificationSupport extraction (#1429) and the toolchain bumps (Kotlin 2.4.21, kotest 6.2.6, Gradle 9.8.1). VERSION_NAME, CITATION, Antora version, README bom coordinate, What's New and contributors, Android tutorial coordinates; regenerated operator and kernel-support matrices; SKEEP-007 rollout step 3 marked shipped.
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Preparatory refactor for the Precision, Recall, and F1 metrics feature (DARC parent #1222).
Extracts internal classification helpers (argmax, shape validation, target-dispatch, and sample traversal) from
Accuracy.ktinto aninternal object ClassificationSupport. This avoids triple duplication across upcoming metrics and unblocks Lane 2's accumulator (#1226).Closes #1225
Refs #1222