8.16.4.2. sklearn.metrics.pairwise.manhattan_distances

sklearn.metrics.pairwise.manhattan_distances(X, Y=None, sum_over_features=True)

Compute the L1 distances between the vectors in X and Y.

With sum_over_features equal to False it returns the componentwise distances.

Parameters :

X : array_like

An array with shape (n_samples_X, n_features).

Y : array_like, optional

An array 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.

Returns :

D : array

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.

Examples

>>> from sklearn.metrics.pairwise import manhattan_distances
>>> manhattan_distances(3, 3)
array([[0]])
>>> manhattan_distances(3, 2)
array([[1]])
>>> manhattan_distances(2, 3)
array([[1]])
>>> manhattan_distances([[1, 2], [3, 4]], [[1, 2], [0, 3]])
array([[0, 2],
       [4, 4]])
>>> import numpy as np
>>> X = np.ones((1, 2))
>>> y = 2 * np.ones((2, 2))
>>> manhattan_distances(X, y, sum_over_features=False)
array([[ 1.,  1.],
       [ 1.,  1.]])