sklearn.metrics.pairwise
.haversine_distances¶
- sklearn.metrics.pairwise.haversine_distances(X, Y=None)[source]¶
Compute the Haversine distance between samples in X and Y.
The Haversine (or great circle) distance is the angular distance between two points on the surface of a sphere. The first coordinate of each point is assumed to be the latitude, the second is the longitude, given in radians. The dimension of the data must be 2.
\[D(x, y) = 2\arcsin[\sqrt{\sin^2((x1 - y1) / 2) + \cos(x1)\cos(y1)\sin^2((x2 - y2) / 2)}]\]- Parameters:
- X{array-like, sparse matrix} of shape (n_samples_X, 2)
A feature array.
- Y{array-like, sparse matrix} of shape (n_samples_Y, 2), default=None
An optional second feature array. If
None
, usesY=X
.
- Returns:
- distancendarray of shape (n_samples_X, n_samples_Y)
The distance matrix.
Notes
As the Earth is nearly spherical, the haversine formula provides a good approximation of the distance between two points of the Earth surface, with a less than 1% error on average.
Examples
We want to calculate the distance between the Ezeiza Airport (Buenos Aires, Argentina) and the Charles de Gaulle Airport (Paris, France).
>>> from sklearn.metrics.pairwise import haversine_distances >>> from math import radians >>> bsas = [-34.83333, -58.5166646] >>> paris = [49.0083899664, 2.53844117956] >>> bsas_in_radians = [radians(_) for _ in bsas] >>> paris_in_radians = [radians(_) for _ in paris] >>> result = haversine_distances([bsas_in_radians, paris_in_radians]) >>> result * 6371000/1000 # multiply by Earth radius to get kilometers array([[ 0. , 11099.54035582], [11099.54035582, 0. ]])