`sklearn.metrics.pairwise`.manhattan_distances¶

sklearn.metrics.pairwise.manhattan_distances(X, Y=None, *, sum_over_features='deprecated')[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, 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.
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.

Deprecated since version 1.2: sum_over_features was deprecated in version 1.2 and will be removed in 1.4.

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.]])

sklearn.metrics.pairwise.manhattan_distances¶

`sklearn.metrics.pairwise`.manhattan_distances¶