e41dbb7d8a
Adds a new HEALTH_DIAG[{epoch}]: pearl_egf_diag line immediately after
the aux_moe block in the per-epoch metrics section of training_loop.rs.
Reads all 13 SP14 ISV slots [383..396) — α_smoothed, α_raw, β, k_aux,
k_q, var_aux, var_q, var_α, q_dis_short, q_dis_long, gate1 state,
post_open_min, lockout — via the established read_isv_signal_at pattern,
giving forensic visibility into EGF pearl state each epoch.
gate1/gate2 sigmoid outputs are intentionally omitted: recomputing them
host-side would violate feedback_no_cpu_compute_strict; the sigmoid
inputs are sufficient for a reader to infer the output values.
docs/isv-slots.md updated (Invariant 7): records B.12 HEALTH_DIAG wire-up.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
ml
10-model ML ensemble for the Foxhunt HFT system, built on Candle v0.9.1.
Models
- DQN (Rainbow) — deep Q-network with prioritized replay, dueling heads, noisy nets
- PPO — proximal policy optimization with GAE, LSTM policies, clip-higher
- TFT — temporal fusion transformer for multi-horizon forecasting
- Mamba2 — state space model for sequence prediction
- Liquid Networks — biologically inspired networks for non-stationary data
- TLOB — transformer-based limit order book analysis
- KAN — Kolmogorov-Arnold networks
- xLSTM — extended LSTM architecture
- TGGN — temporal graph neural network
- Diffusion — diffusion-based generative model
Key Modules
ensemble— model ensemble coordination and confidence aggregationhyperopt— PSO-based hyperparameter optimization with per-model adapterstrainers— unified training loops (DQN, PPO, supervised)inference—InferenceAdaptertrait for predictioncheckpoint— model checkpointing and restorationevaluation— walk-forward evaluation pipeline
Usage
use ml::dqn::DQN;
use ml::ppo::PpoTrainer;