Synthetic Datasets¶
The synthetic module provides deterministic benchmark data for method development and regression testing.
Main entry points¶
mltsa.synthetic.make_1d_dataset(...)mltsa.synthetic.make_2d_dataset(...)mltsa.synthetic.load_dataset(path)mltsa.synthetic.SyntheticDataset
Typical workflow¶
from mltsa.synthetic import make_1d_dataset, load_dataset
dataset = make_1d_dataset(n_trajectories=32, n_steps=64, n_features=12)
dataset.save("synthetic.h5", overwrite=True)
restored = load_dataset("synthetic.h5")
rebuilt = restored.rebuild_exact()
more = restored.generate_more(8)
combined = restored.append(more)
What is stored¶
Synthetic datasets persist:
Xandyfeature names
generation parameters
system definition metadata
relevant feature indices
time-dependent relevance when available
per-trajectory seeds
latent trajectories when available
That metadata is enough to rebuild the dataset exactly and to generate more trajectories from the same system definition.