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sklearn.metrics.pairwise.manhattan_distances(X, Y=None, sum_over_features=True, size_threshold=500000000.0)
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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
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. Not supported for sparse matrix inputs.
size_threshold : int, default=5e8
Unused parameter.
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.]]...)
sklearn.metrics.pairwise.manhattan_distances()
2017-01-15 04:26:33
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