pairwise_distances#

cuml.metrics.pairwise_distances(
X,
Y=None,
metric='euclidean',
convert_dtype='deprecated',
**kwds,
)[source]#

Compute the distance matrix from a feature array X and optional Y.

This function takes either one or two feature arrays, and returns a distance matrix.

Parameters:
X{array-like, sparse matrix}, shape=(n_samples_X, n_features)

A feature array.

Y{array-like, sparse matrix}, shape=(n_samples_y, n_features), default=None

A second feature array. If None, Y=X will be used.

metricstr, default=”euclidean”

The metric to use when calculating distance between instances in a feature array. Valid options are:

  • Supports both dense and sparse data: [‘canberra’, ‘chebyshev’, ‘cityblock’, ‘cosine’, ‘euclidean’, ‘hellinger’, ‘l1’, ‘l2’, ‘manhattan’, ‘minkowski’, ‘sqeuclidean’].

  • Supports dense only: [‘correlation’, ‘hamming’, ‘jensenshannon’, ‘kldivergence’, ‘nan_euclidean’, ‘russellrao’].

  • Supports sparse only: [‘dice’, ‘inner_product’, ‘jaccard’].

convert_dtypebool, default=”deprecated”

Deprecated since version 26.08: convert_dtype was deprecated in version 26.08 and will be removed in version 26.10. cuML only copies input arrays when necessary (e.g. to unify dtypes), there is no reason to provide this keyword going forward.

**kwdsoptional keyword parameters

Any additional metric-specific parameters. For example, with metric="minkowski", passing p sets the norm used.

Returns:
Darray, shape=(n_samples_X, n_samples_X) or (n_samples_X, n_samples_Y)

A distance matrix D such that D_{i, j} is the distance between the ith and jth vectors of the given matrix X, if Y is None. If Y is not None, then D_{i, j} is the distance between the ith array from X and the jth array from Y.

Examples

>>> import cupy as cp
>>> from cuml.metrics import pairwise_distances

>>> X = cp.array([[0., 0., 0.], [1., 1., 1.]])
>>> Y = cp.array([[1., 0., 0.], [1., 1., 0.]])

>>> pairwise_distances(X, metric="sqeuclidean")
array([[0., 3.],
       [3., 0.]])

>>> pairwise_distances(X, Y, metric="sqeuclidean")
array([[1., 2.],
       [2., 1.]])
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