b1ef312a40
Two new CUDA kernels land as SP5 Layer A additive producers feeding ISV[250..270)
with per-branch IQN quantile-τ schedules derived from Q-distribution skew.
q_skew_kurtosis_update: single-block 4-thread, reads save_q_online[B×13],
two-pass central moments → skew clamped [-3,+3] + ex_kurt clamped [-3,+30];
writes scratch[171..179). EPS_DIV=1e-12 (Invariant 1 anchor). No atomicAdd.
pearl_5_iqn_tau_update: single-block 4-thread, shifts symmetric default τ
{0.05,0.25,0.5,0.75,0.95} by skew×SKEW_SHIFT=0.05, clamps to [0.01,0.99];
writes scratch[179..199). SKEW_SHIFT and envelope are Invariant 1 anchors.
launch_sp5_pearl_5_iqn_tau fires both kernels + 20 apply_pearls_ad calls
(ALPHA_META=1e-3) → ISV[IQN_TAU_BASE=250..270). SP5_SCRATCH_TOTAL 171→199.
StateResetRegistry +1 FoldReset entry (sp5_iqn_tau, ISV[250..270)).
training_loop.rs wired after Pearl 4 with tracing::warn on error.
Two GPU-only unit tests (10+11): zero-skew symmetric default + left-skew
floor clamp. Module docstring updated A1-A5.
No consumer migration — Layer A additive only per spec.
cargo check -p ml --offline clean (11 pre-existing warnings, none new).
cargo test --no-run clean.
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;