Claude/traffic data diffusion model 01 p jfsg4 ss cnw svd hc aqt9ny #33
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- Create interactive configuration wizard for LD2011_2014 dataset - Support GPU selection (A10/A40) and missing rate configuration (20%-80%) - Auto-generate training configs and launch scripts - Add comprehensive Chinese comments and usage instructions
This commit adds a complete Implicit-Explicit Diffusion Model for traffic data imputation: Features: - TCN Implicit Feature Extractor: Multi-scale temporal feature extraction with dilated causal convolutions - S4 Explicit Feature Extractor: Long-term dependency modeling with Structured State Space Models - IE Diffusion Model: Integration of implicit and explicit features for enhanced imputation - Complete training and evaluation pipeline - Support for multiple missing rates (20%-80%) - Comprehensive Chinese comments on every line of code Components: 1. models/: Core model implementations (TCN, S4, IEDiffusion) 2. data/: Traffic data loader and preprocessing 3. utils/: Diffusion process utilities 4. train.py: Training script with multi-GPU support 5. evaluate.py: Evaluation script with MAE/RMSE metrics 6. configs/: Configuration files for different missing rates 7. run_all.sh: One-click script to run all experiments 8. README.md: Detailed documentation with: - Architecture design and explanation - Hyperparameter tuning guide - Code explanation for each module - Quick start guide Documentation: - Every line of code has detailed Chinese comments - Three-level hyperparameter tuning guide (beginner, intermediate, advanced) - Explanations of TCN dilation rates, S4 ABCD matrices, and diffusion parameters
- Fix: S4Layer skip connection must be added before bidirectional concatenation - Add: check_gpu.py script to detect available GPUs and provide usage suggestions - Fix resolves RuntimeError: The size of tensor a (512) must match the size of tensor b (256)
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