sklearn.metrics.pairwise.manhattan_distances¶
- sklearn.metrics.pairwise.manhattan_distances(X, Y=None, sum_over_features=True, size_threshold=500000000.0)¶
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.
size_threshold : int, default=5e8
Avoid creating temporary matrices bigger than size_threshold (in bytes). If the problem size gets too big, the implementation then breaks it down in smaller problems.
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.]]...)