88eb7aa241
Step 2 of β migration: 5 new buffers sized to alloc_episodes (vs trainer's batch_size). AuxHeadsForwardOps instance is collector- owned and stream-bound to collector stream via AuxHeadsForwardOps::new(&stream). Param tensors shared with trainer via existing f32_weight_ptrs_from_base path. Buffer sizing: exp_aux_nb_hidden_buf [alloc_episodes × 32], exp_aux_nb_logits/softmax_buf [alloc_episodes × 2], exp_aux_nb_label_buf [alloc_episodes] i32, exp_aux_dir_acc_buf [6] mapped-pinned (post-B1.1a 6-float layout matches trainer). Compile clean; sp14_oracle_tests 2/2 non-GPU pass (7 GPU tests ignored on RTX 3050 Ti host). No aux forward yet — buffers allocated, ready for wire. 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;