mathiasandClaude Sonnet 4.6 e11e7d2524
CD / Build & Import (push) Failing after 7s
CD / Deploy via GitOps (push) Has been skipped
CD / Lint / Test / Vet (push) Successful in 4s
feat(eval): Go evaluation harness — LinearProbe, Silhouette, EffectiveRank (#4)
internal/eval: three pure-Go diagnostics on frozen embeddings:
  LinearProbe(emb, y, λ) → val_vol_r2 (OOS R², closed-form ridge, Cholesky)
  Silhouette(emb, labels) → mean silhouette (Euclidean, multi-label, errors on <2 classes)
  EffectiveRank(emb) → Roy effective rank (Jacobi eigenvalues → entropy → exp(H))

cmd/eval/main.go: CLI driver reading embeddings.json (exported by train.py with
EXPORT_EMBEDDINGS=1), standardises per-dim, dispatches to -metric flag.
task eval:probe / eval:silhouette / eval:collapse wired in Taskfile.

8/8 tests pass (red-green: perfect clusters, rank-1, full-rank, noise, constant
target, single-label error). Pure stdlib, no external deps.

Closes #4.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
2026-06-24 12:01:04 +02:00
2026-05-27 21:55:18 +00:00
2026-05-27 21:55:18 +00:00
2026-05-27 21:55:18 +00:00
2026-05-27 21:55:18 +00:00
2026-05-27 21:55:18 +00:00
2026-05-27 21:55:18 +00:00
2026-05-27 21:55:18 +00:00
2026-05-27 21:55:18 +00:00
2026-05-27 21:55:18 +00:00

hostexecutor

Generated from mathias/template-go-web.

Bootstrap

After creating from template, run:

go mod tidy       # regenerate go.sum with real module path
task generate     # generate templ files
task build        # build the binary
S
Description
JEPA-based latent representation learning for FX trading risk management — research project
Readme
370 KiB
Languages
Python 76.4%
Go 18.4%
Shell 4.3%
templ 0.6%
Dockerfile 0.3%