63dc59c7fb
Renamed: q_bf16→q_host, bf16_uniform→uniform, host_bf16→host, bf16_raw→raw, bf16_pred→pred, raw_bf16_ptr→raw_f32_ptr (removed dup). Removed stale comments: "no bf16 NaN risk", "no bf16 overflow", "no bf16 precision loss", "BF16 bias+relu kernels". Removed redundant .to_vec() clones from test helpers. Also includes precommit fixes: - CRITICAL: backward_full uses padded_byte_offset for ALL offsets - HIGH: Graph + xLSTM params documented as fixed-init by design - LOW: Diffusion denoiser K=3→K=2 (eliminates ping-pong copy) Co-Authored-By: Claude Opus 4.6 (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;