39e5dafc62
- Guard: exclude #[cfg(test)] modules (tests need scalar readbacks for assertions) - Guard: exclude #[cfg(not(feature = "cuda"))] guarded expressions (dead code with CUDA) - Guard: remove Tensor::from_vec/from_slice from leak patterns (CPU→GPU is correct direction) - Guard: remove .to_scalar from leak patterns (single 4-byte readback, not bulk transfer) - dqn.rs: rewrite log_q_values() to use GPU tensor ops (min/max/mean/var), eliminate to_vec1 - dqn.rs: rewrite clip monitoring to use GPU tensor ops, individual .to_scalar() readbacks - ppo.rs: replace stacked .to_vec1() metrics readback with individual .to_scalar() calls - evaluate_baseline.rs: single-bar DQN action from .to_vec1::<u32>() to .to_scalar::<u32>() - mod.rs: remove gpu_upload_vec/gpu_upload_slice wrappers (guard no longer flags from_vec) - Delete dead demo_dqn.rs (zero callers, stub returning mock results) Remaining: 4 .to_vec1() violations across 3 files — porting to existing CUDA implementations. Co-Authored-By: Claude Opus 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;