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sklearn.metrics.pairwise.paired_distances(X, Y, metric='euclidean', **kwds)
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Computes the paired distances between X and Y.
Computes the distances between (X[0], Y[0]), (X[1], Y[1]), etc...
Read more in the User Guide.
Parameters: X : ndarray (n_samples, n_features)
Array 1 for distance computation.
Y : ndarray (n_samples, n_features)
Array 2 for distance computation.
metric : string or callable
The metric to use when calculating distance between instances in a feature array. If metric is a string, it must be one of the options specified in PAIRED_DISTANCES, including ?euclidean?, ?manhattan?, or ?cosine?. Alternatively, if metric is a callable function, it is called on each pair of instances (rows) and the resulting value recorded. The callable should take two arrays from X as input and return a value indicating the distance between them.
Returns: distances : ndarray (n_samples, )
See also
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pairwise_distances
- pairwise distances.
Examples
>>> from sklearn.metrics.pairwise import paired_distances >>> X = [[0, 1], [1, 1]] >>> Y = [[0, 1], [2, 1]] >>> paired_distances(X, Y) array([ 0., 1.])
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sklearn.metrics.pairwise.paired_distances()
2017-01-15 04:26:34
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