generated from mathias/template-go-web
v0.3.0
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>
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mathias/template-go-web.
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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
Description
JEPA-based latent representation learning for FX trading risk management — research project
370 KiB
Languages
Python
76.4%
Go
18.4%
Shell
4.3%
templ
0.6%
Dockerfile
0.3%