697bb586d0
Adds the second of the two Phase E.1 kernel smoke tests in
alpha_kernels.rs (companion to the munchausen_target smoke from
91d1a52b9). End-to-end exercises the alpha_kill_criteria_compute_kernel
launcher with synthetic inputs covering all 4 outputs:
q_values = [[1, 2, 3], [5, 5, 5]] → q_spread ≈ 0.2041
action_counts = [10, 30, 60] → action_entropy ≈ 0.8980
rollout_R=100, isv[547]=-5185,
isv[548]=4953 → return_vs_random ≈ 1.0670
q_init=50, q_early=55 → early_movement = 0.1000
ISV buffer is sized to 552 floats with slots 547/548 populated using the
committed Task 7c baseline values — this exercises the production
slot-indexing path through the kernel's
`isv[random_baseline_mean_slot]` / `isv[random_baseline_std_slot]` reads,
not just isolated kernel arithmetic.
Does NOT chain apply_pearls_ad_kernel afterward — the smoothing path is
canonical SP4 applicator territory already covered elsewhere. This test
isolates the kill-criteria producer arithmetic.
Tolerance 0.01 on all four observations; passes on RTX 3050 Ti in <2s
including kernel JIT.
`cargo test -p ml --lib alpha_kernels`: 3 pass (compile witness + both
GPU smokes). Audit doc docs/isv-slots.md updated per Invariant 7.
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;