b88fd62af2
- NoiseScheduler: precomputed cosine/linear alpha_bar schedules - Denoiser: FC network with sinusoidal time embedding + SiLU + residual - DDIMSampler: deterministic fast sampling (10 steps from 1000 timesteps) - DiffusionTrainableAdapter: UnifiedTrainable for unified training pipeline - Hyperopt adapter with ParameterSpace (9 params, batch ≤64 for 4GB GPU) - ModelType::Diffusion registered in common + coordinator - 41 tests passing (config=3, noise=7, denoiser=4, sampler=5, trainable=12, hyperopt=7) - OOM-safe: FC denoiser instead of U-Net, small hidden dims, conservative defaults Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
ml
Machine learning models for Foxhunt.
Models
- DQN (Rainbow) -- Deep Q-Network with prioritized experience replay, dueling heads, noisy nets, double Q-learning
- PPO -- Proximal Policy Optimization with GAE, LSTM policies, clip-higher option
- TFT -- Temporal Fusion Transformer for multi-horizon time series forecasting
- Mamba2 -- State space model for efficient sequence prediction
- Liquid Networks -- Biologically inspired neural networks for non-stationary data
- TLOB -- Transformer-based Limit Order Book analysis
- Flash Attention -- Optimized attention implementation
Training
Two paths per model:
- Standalone trainer -- direct training loop (e.g.,
DQN::train,PpoTrainer) - UnifiedTrainable adapter -- wraps models for the hyperopt pipeline (e.g.,
DQNTrainableAdapter,UnifiedTrainablePPO)
Inference
InferenceAdapterBridge connects models to the ensemble coordinator in adaptive-strategy. Each model exposes an InferenceAdapter trait for prediction.
Backend
- Candle v0.9.1 --
VarMap,AdamW,loss.backward(),GradStore,opt.step(&grads) - CUDA required for training -- tested on RTX 3050 Ti 4GB, max batch size 230
- CPU inference supported
Hyperopt
ArgminOptimizer (Particle Swarm Optimization) with per-model adapters:
DQN, PPO, ContinuousPPO, TFT, Mamba2. Uses ParameterSpace trait for continuous parameter mapping.
ModelType Enum
15 variants: CompactDQN, DistilledMicroNet, DQN, RainbowDQN, MAMBA, TFT, TGGN, LNN, TLOB, PPO, Transformer, Mamba, LiquidNet, TGNN, Ensemble.
Key Modules
dqn, ppo, tft, mamba, liquid, tlob, flash_attention, ensemble, evaluation, inference, trainers, hyperopt, checkpoint, preprocessing, data_loaders, features, model_factory, training_pipeline, regime_detection, stress_testing, validation, bridge, common, metrics.
Testing
SQLX_OFFLINE=true cargo test -p ml --lib # ~2009 tests