63183bb6a4
Plan 4 Task 2c.3c.2. Additive only — no production callers (2c.3c.4 wires them). Two infrastructure additions for the GRN trunk backward chain: 1. launch_dw_only_no_bias on CublasBackwardSet: variant of launch_dw_only that skips the bias-grad kernel call. Linear_residual in h_s1 GRN block has no bias, so calling launch_dw_only with db=0u64 would segfault the bias-grad kernel. 2. saxpy_inplace on CublasGemmSet: y += alpha * x for element-wise gradient accumulation. h_s2 GRN's identity residual needs d_h_s1 += d_pre_ln_h_s2 after Linear_a_h_s2's backward overwrites d_h_s1 with d_x = d_linear_a @ W_a. Implementation reuses the existing dqn_saxpy_f32_kernel (already used by the experience collector's IQR/ensemble-variance Q-bonus paths) — no new kernel, no cuBLAS legacy-handle stream-binding work, kernel handle loaded once at CublasGemmSet::new from DQN_UTILITY_CUBIN. Both methods sit dead-code until 2c.3c.4's wire-up commit. 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;