mars.learn.metrics.pairwise.
manhattan_distances
Compute the L1 distances between the vectors in X and Y.
With sum_over_features equal to False it returns the componentwise distances.
Read more in the User Guide.
X (array_like) – A tensor with shape (n_samples_X, n_features).
Y (array_like, optional) – A tensor with shape (n_samples_Y, n_features).
sum_over_features (bool, default=True) – If True the function returns the pairwise distance matrix else it returns the componentwise L1 pairwise-distances. Not supported for sparse matrix inputs.
D – If sum_over_features is False shape is (n_samples_X * n_samples_Y, n_features) and D contains the componentwise L1 pairwise-distances (ie. absolute difference), else shape is (n_samples_X, n_samples_Y) and D contains the pairwise L1 distances.
Tensor
Examples
>>> from mars.learn.metrics.pairwise import manhattan_distances >>> manhattan_distances([[3]], [[3]]).execute() array([[0.]]) >>> manhattan_distances([[3]], [[2]]).execute() array([[1.]]) >>> manhattan_distances([[2]], [[3]]).execute() array([[1.]]) >>> manhattan_distances([[1, 2], [3, 4]], [[1, 2], [0, 3]]).execute() array([[0., 2.], [4., 4.]]) >>> import mars.tensor as mt >>> X = mt.ones((1, 2)) >>> y = mt.full((2, 2), 2.) >>> manhattan_distances(X, y, sum_over_features=False).execute() array([[1., 1.], [1., 1.]])