f91098e456
Address two code-quality review items on commit 6dcaf1a1c:
1. Convert file-level + Pearl-section comments from `//` to rustdoc
(`//!` module-level + `///` on first constant of each section).
Matches sp4_isv_slots.rs style; SP5 module now `cargo doc`-discoverable
as peer of SP4.
2. Replace spot-check assertions in slot_layout_no_overlaps_and_total_correct
with a HashSet enumerating every slot reachable through every accessor.
Asserts exactly 110 unique slots, min=174, max=285, the 2-slot carve-out
gap (278, 279) absent, and the set equals {174..278} ∪ {280..286}.
Test now actually verifies the no-overlaps invariant its name promised.
Also adds SP5 section to docs/isv-slots.md (Invariant 7 audit-doc update
required by pre-commit hook for cuda_pipeline component changes).
No constant values, accessor signatures, or fingerprint string changed.
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;