Metric
DirectedHausdorff
Directed Hausdorff distance between event points.
For detected time points \(A = (a_1, a_2, \ldots, a_n)\) and true time points \(B = (b_1, b_2, \ldots, b_m)\), the directed (unnormalized) Hausdorff distance is defined as:
\[d(A, B) = \max_i \left| a_i - b'_i \right|\]
where \(b'_i\) is the true event closest to \(a_i\), that is, \(b'_i = \arg \min_{b\in B} |a_i - b|\).
If X is provided, the time points are taken as the location indices in X. Otherwise, it is assumed that X has a RangeIndex.
Schnellstart
python
from sktime.performance_metrics.detection import DirectedHausdorff
estimator = DirectedHausdorff()