# mars.learn.metrics.pairwise.manhattan_distances#

mars.learn.metrics.pairwise.manhattan_distances(X, Y=None, sum_over_features=True)[source]#

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.

Parameters
• 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.

Returns

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.

Return type

Tensor

Examples

```>>> from mars.learn.metrics.pairwise import manhattan_distances
>>> manhattan_distances([], []).execute()
array([[0.]])
>>> manhattan_distances([], []).execute()
array([[1.]])
>>> manhattan_distances([], []).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.]])
```