b4c28811ab
Single kernel parameterized by branch slice. Launcher loops 0..4, launching once per branch with distinct scratch slot (1..5). Phase 2 applies Pearls A+D host-side per branch, writing to ISV[ATOM_POS_BOUND[ branch]] + wiener_state. Same end-to-end pattern as Task A5. GPU test verifies per-branch independence: 4 scales of |N(0,1)| samples (1×, 10×, 100×, 1000×) → 4 distinct p99 values within 5% tolerance. No consumer wired yet. Behavior unchanged. cargo check --lib --tests clean. 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;