c0fc28e455
Per `feedback_isv_for_adaptive_bounds`, the hardcoded `warmup_gate = (fold_step_counter / WARMUP_STEPS_FALLBACK).min(1.0)` ramp violated the rule: adaptive bounds in ISV, never hardcoded constants. The variance-driven k_aux/k_q sigmoid steepness already provides warmup behavior intrinsically: - High variance (cold-start, EMAs still moving) → k → K_MIN → flat sigmoid → gate ≈ 0.5 regardless of input. That IS the warmup. - Low variance (settled) → k → K_BASE → sharp sigmoid → gates respond correctly to driver signals. Adding a separate hardcoded step-counter multiplier on top was double-counting + tuning-driven (the 1000-step threshold had no principled basis). Removed entirely. Removed (per `feedback_no_partial_refactor`, all atomically): - `WARMUP_STEPS_FALLBACK` constant in `sp14_isv_slots.rs` - `warmup_gate: f32` parameter in `alpha_grad_compute_kernel.cu` - `gate1 * gate2 * warmup_gate` → `gate1 * gate2` in kernel - `warmup_gate` arg from `launch_sp14_alpha_grad_compute` - `fold_step_counter: usize` field on the trainer struct - `fold_step_counter = 0` reset in `reset_for_fold` - `fold_step_counter` init in trainer constructor - `let warmup_gate: f32 = 1.0;` and `.arg(&warmup_gate)` from B.4 oracle tests (4 launches: 2 in alpha_grad_schmitt_hysteresis, 20 in alpha_grad_adaptive_beta loop) Build: clean, 18 warnings (pre-existing baseline). Tests: cargo test --no-run on sp14_oracle_tests succeeds. Net result: EGF gate's warmup behavior now lives entirely in the variance-driven k_aux/k_q sigmoid steepness controller (ISV slots 388/var_aux, 389/var_q). No hardcoded step counter. Honors `feedback_isv_for_adaptive_bounds` and `pearl_controller_anchors_isv_driven`. 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;