oineus.bottleneck_matching

oineus.bottleneck_matching(dgm_1, dgm_2, *, delta=0.01, ignore_inf_points=True)[source]

Compute the optimal bottleneck matching between two persistence diagrams.

Parameters mirror wasserstein_matching() (without q and internal_p). delta=0.0 runs Hera’s exact algorithm. Essential-family cardinality mismatch (when ignore_inf_points=False) raises ValueError.

Examples

>>> import numpy as np, oineus
>>> m = oineus.bottleneck_matching(np.array([[0.0, 5.0]]),
...                                np.array([[1.0, 6.0]]), delta=0.0)
>>> m.distance
1.0
>>> len(m.longest.finite)
1
Parameters:
Return type:

BottleneckMatching