libpysal.weights.min_threshold_distance¶
- libpysal.weights.min_threshold_distance(data, p=2)[source]¶
Get the maximum nearest neighbor distance.
- Parameters:
- data
array
(n,k) or KDTree where KDtree.data is array (n,k) n observations on k attributes
- p
python:float
Minkowski p-norm distance metric parameter: 1<=p<=infinity 2: Euclidean distance 1: Manhattan distance
- data
- Returns:
- nnd
python:float
maximum nearest neighbor distance between the n observations
- nnd
Examples
>>> from libpysal.weights.util import min_threshold_distance >>> import numpy as np >>> x, y = np.indices((5, 5)) >>> x.shape = (25, 1) >>> y.shape = (25, 1) >>> data = np.hstack([x, y]) >>> min_threshold_distance(data) 1.0