`sklearn.metrics.pairwise`.manhattan_distances¶

sklearn.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:

Xarray-like of shape (n_samples_X, n_features): An array where each row is a sample and each column is a feature.
Yarray-like 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.
sum_over_featuresbool, 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:

Dndarray of shape (n_samples_X * n_samples_Y, n_features) or (n_samples_X, n_samples_Y): 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.

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.]])
>>> import numpy as np
>>> X = np.ones((1, 2))
>>> y = np.full((2, 2), 2.)
>>> manhattan_distances(X, y, sum_over_features=False)
array([[1., 1.],
       [1., 1.]])

sklearn.metrics.pairwise.manhattan_distances¶

`sklearn.metrics.pairwise`.manhattan_distances¶