kemalandClaude Opus 5.5
f347090a2f
- simurg/train/clusters.py: vectorised per-locus θ mixture (AA/AB/BB + outlier component, θ-ordered bounds), cluster selection and merging, lone clusters homozygous, missing clusters imputed by cross-locus regression per assay type, cluster_sep and train (GenTrain-field) scores. - formats/egt.py: write_egt (EGT v3, data block 9, round-trips through the reader). - pipeline/train.py: normalised θ/R from GTC raw X/Y + transforms, sex-aware (X females, Y males, MT haploid), intensity-only loci as one R cluster. - scripts/train/concordance_chr.py: array vs 1000G 30x on one chromosome. Demo (36 GSA v4 PGx arrays, chr22 vs 1000G): trained 99.29 % / NRD 2.6 % vs vendor EGT 99.57 % / 1.55 %. Co-Authored-By: Claude Opus 5.5 <[email protected]> Claude-Session: https://claude.ai/code/session_01814GKVzZVL3FuF3DeiZXhv