nan_euclidean_distances#

sklearn.metrics.pairwise.nan_euclidean_distances(X, Y=None, *, squared=False, missing_values=nan, copy=True)[source]#

Calculate the euclidean distances in the presence of missing values.

Compute the euclidean distance between each pair of samples in X and Y, where Y=X is assumed if Y=None. When calculating the distance between a pair of samples, this formulation ignores feature coordinates with a missing value in either sample and scales up the weight of the remaining coordinates:

dist(x,y) = sqrt(weight * sq. distance from present coordinates)

where:

weight = Total # of coordinates / # of present coordinates

For example, the distance between [3, na, na, 6] and [1, na, 4, 5] is:

\[\sqrt{\frac{4}{2}((3-1)^2 + (6-5)^2)}\]

If all the coordinates are missing or if there are no common present coordinates then NaN is returned for that pair.

Read more in the User Guide.

Parameters:
Xarray-like of shape (n_samples_X, n_features)

An array where each row is a sample and each column is a feature.

Yarray-like 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.

squaredbool, default=False

Return squared Euclidean distances.

missing_valuesnp.nan, float or int, default=np.nan

Representation of missing value.

copybool, default=True

Make and use a deep copy of X and Y (if Y exists).

Returns:
distancesndarray of shape (n_samples_X, n_samples_Y)

Returns the distances between the row vectors of X and the row vectors of Y.

See also

paired_distances

Distances between pairs of elements of X and Y.

References

Examples

>>> from sklearn.metrics.pairwise import nan_euclidean_distances
>>> nan = float("NaN")
>>> X = [[0, 1], [1, nan]]
>>> nan_euclidean_distances(X, X) # distance between rows of X
array([[0.        , 1.41421356],
       [1.41421356, 0.        ]])

>>> # get distance to origin
>>> nan_euclidean_distances(X, [[0, 0]])
array([[1.        ],
       [1.41421356]])