jgrusewski 39848a926b fix(eval): read use_noisy_nets from hyperopt params in evaluate_baseline ...

NoisyLinear layers have different parameter names (sigma_weight/sigma_bias)
than standard Linear layers. Hardcoding use_noisy_nets=false caused
checkpoint loading to silently fail when the training run used noisy nets,
resulting in empty folds in the eval report.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

2026-03-04 12:07:51 +01:00

..

baseline_common

fix(ml): wire spread_cost_bps into RL training — commission + spread = total tx cost

2026-03-01 22:10:43 +01:00

cuda_test.rs

chore: clean up examples, update ML binaries and risk tests

2026-02-27 01:33:18 +01:00

download_baseline.rs

chore: clean up examples, update ML binaries and risk tests

2026-02-27 01:33:18 +01:00

evaluate_baseline.rs

fix(eval): read use_noisy_nets from hyperopt params in evaluate_baseline

2026-03-04 12:07:51 +01:00

evaluate_supervised.rs

fix(observability): dedicated OTLP runtime for sync training binaries

2026-03-02 18:54:10 +01:00

hyperopt_baseline_rl.rs

fix(dqn): production hardening — LR scheduler, urgency costs, F32 traps, atomic IO

2026-03-03 23:04:53 +01:00

hyperopt_baseline_supervised.rs

feat(hyperopt): record elapsed_seconds metric in all hyperopt binaries

2026-03-03 00:10:02 +01:00

train_baseline_rl.rs

fix: pass reward-shaping params from hyperopt to walk-forward DQN

2026-03-04 06:24:26 +01:00

train_baseline_supervised.rs

feat(supervised): record learning rate and epoch duration metrics

2026-03-03 00:10:00 +01:00