2e84fd35d4
Final SP5 Layer A producer. Per-branch C51 num_atoms derived from
per-branch ATOM_V_HALF (Pearl 1, Task A1). Threshold cascade:
v_half < 0.1 → 64 atoms (narrow Q, high resolution)
v_half < 1.0 → 32 atoms (moderate)
v_half ≥ 1.0 → 16 atoms (wide Q, modest resolution)
4 ISV slots [ATOM_NUM_ATOMS_BASE=274..278). Pearls A+D smooths the
discrete output during transitions; Layer B's atoms_update consumer
rounds to nearest valid count.
producer_step_scratch_buf grew 203 → 207. wiener_state_buf already
at 543 (sized at A1 for entire SP5 block).
StateResetRegistry: 1 new FoldReset entry (sp5_atom_num_atoms).
atoms_update consumer migration deferred to Layer B.
LAYER A COMPLETE. 8 producers + 3 auxiliary kernels (q_branch_stats,
grad_cosine_sim, q_skew_kurtosis) populating 110 ISV slots [174..286)
with 4 cross-fold-persistent slots carved out (Kelly).
Refs: SP5 spec 6e6e0fa11 line 1020
Co-Authored-By: Claude Opus 4.7 (1M context) <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;