Synthetic data and provenance

All three datasets are original generated teaching fixtures. There are no real people, customers, medical records or operational measurements. dlcourse/core.py::generate is the authoritative generator; python -m tools.export_data reproduces the CSVs and manifest. Training generates these tensors directly rather than reading mutable CSV files.

Each task has 600 row IDs and a deterministic random 60/20/20 train/validation/test split. IDs are local to a task. The same split permutation across tasks does not link the fictional observations semantically. Regression rows are IID. Classification features are independent conditional on fixed alternating class labels, with 300 examples of each class before random splitting. Grouped or time-dependent data needs a different design.

• regression.csv: row_id, split, x0, x1, target. Inputs are uniform on [-2,2]; target is 1.5x0 - 0.7x1 + 0.9*x0² plus Gaussian noise with standard deviation 0.15. All units are fictional arbitrary units.
• rings.csv: row_id, split, x0, x1, target. Alternating labels generate 300 examples of each class before splitting; random angle and noisy radii around 0.7 and 1.5 give concentric rings. Labels are 0 inner, 1 outer. Radius noise standard deviation is 0.12.
• bars.csv: row_id, split, x0…x63, target. Flattened 8×8 grayscale arrays; row-major order reconstructs [1,8,8]. Random bar row/column is an integer 1…6; noise standard deviation 0.15 precedes clipping to [0,1]. Labels are 0 horizontal, 1 vertical.

CSV uses nine significant digits for float32 export. Feature standardization for regression/rings uses only the 360 training rows. Bars use known clipping bounds rather than a fitted scaler. Test rows remain visible for reproducibility; this is a teaching fixture, not a secure hidden examination. manifest.json contains hashes and feature/target shapes.