Datasets:
Stored trained heads
predict.py uses the Python standard library and the fixed example relu_head_example.json. heads/ contains all sixteen confirmation heads in the same exact-rational hinge format. Each is generated from its stored knot values, not retrained during packaging.
python models/predict.py -0.8 -0.2 0 0.2 0.8
In Python, use load_model(path) and predict(x, model) from models/predict.py to select another head. Rational segment checks establish head norm <=1, head Lipschitz <=2 and output Lipschitz <=1 for constant-tail extension over real inputs. Conditional error bounds concern only [-1,1] and use the reported floating numerical risk oracle; they are separate from exact regularity checks. The CLI is not the NumPy batch-throughput benchmark.