mars.learn.metrics.pairwise.euclidean_distances¶

mars.learn.metrics.pairwise.
euclidean_distances
(X, Y=None, Y_norm_squared=None, squared=False, X_norm_squared=None)[source]¶ Considering the rows of X (and Y=X) as vectors, compute the distance matrix between each pair of vectors.
For efficiency reasons, the euclidean distance between a pair of row vector x and y is computed as:
dist(x, y) = sqrt(dot(x, x)  2 * dot(x, y) + dot(y, y))
This formulation has two advantages over other ways of computing distances. First, it is computationally efficient when dealing with sparse data. Second, if one argument varies but the other remains unchanged, then dot(x, x) and/or dot(y, y) can be precomputed.
However, this is not the most precise way of doing this computation, and the distance matrix returned by this function may not be exactly symmetric as required by, e.g.,
scipy.spatial.distance
functions.Read more in the User Guide.
 Parameters
X ({arraylike, sparse matrix}, shape (n_samples_1, n_features)) –
Y ({arraylike, sparse matrix}, shape (n_samples_2, n_features)) –
Y_norm_squared (arraylike, shape (n_samples_2, ), optional) – Precomputed dotproducts of vectors in Y (e.g.,
(Y**2).sum(axis=1)
) May be ignored in some cases, see the note below.squared (boolean, optional) – Return squared Euclidean distances.
X_norm_squared (arraylike, shape = [n_samples_1], optional) – Precomputed dotproducts of vectors in X (e.g.,
(X**2).sum(axis=1)
) May be ignored in some cases, see the note below.
Notes
To achieve better accuracy, X_norm_squared and Y_norm_squared may be unused if they are passed as
float32
. Returns
distances
 Return type
tensor, shape (n_samples_1, n_samples_2)
Examples
>>> from mars.learn.metrics.pairwise import euclidean_distances >>> X = [[0, 1], [1, 1]] >>> # distance between rows of X >>> euclidean_distances(X, X).execute() array([[0., 1.], [1., 0.]]) >>> # get distance to origin >>> euclidean_distances(X, [[0, 0]]).execute() array([[1. ], [1.41421356]])
See also
paired_distances
distances betweens pairs of elements of X and Y.