manhattan_distances#

sklearn.metrics.pairwise.manhattan_distances(X, Y=None)[source]#

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

Read more in the User Guide.

Parameters:

X{array-like, sparse matrix} of shape (n_samples_X, n_features): An array where each row is a sample and each column is a feature.
Y{array-like, sparse matrix} of shape (n_samples_Y, n_features), default=None: An array where each row is a sample and each column is a feature. If None, method uses Y=X.

Returns:

distancesndarray of shape (n_samples_X, n_samples_Y): Pairwise L1 distances.

Notes

When X and/or Y are CSR sparse matrices and they are not already in canonical format, this function modifies them in-place to make them canonical.

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.]])