mars.learn.metrics.pairwise.haversine_distances#
- mars.learn.metrics.pairwise.haversine_distances(X, Y=None)[源代码]#
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 distance 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.
\[D(x, y) = 2\arcsin[\sqrt{\sin^2((x1 - y1) / 2) + \cos(x1)\cos(y1)\sin^2((x2 - y2) / 2)}]\]- 参数
X (array_like, shape (n_samples_1, 2)) –
Y (array_like, shape (n_samples_2, 2), optional) –
- 返回
distance
- 返回类型
{Tensor}, shape (n_samples_1, n_samples_2)
提示
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.
实际案例
We want to calculate the distance between the Ezeiza Airport (Buenos Aires, Argentina) and the Charles de Gaulle Airport (Paris, France)
>>> from mars.learn.metrics.pairwise import haversine_distances >>> bsas = [-34.83333, -58.5166646] >>> paris = [49.0083899664, 2.53844117956] >>> result = haversine_distances([bsas, paris]) >>> (result * 6371000/1000).execute() # multiply by Earth radius to get kilometers array([[ 0. , 11279.45379464], [11279.45379464, 0. ]])