sklearn.metrics.pairwise
.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.
New in version 0.22.
- 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 usesY=X
.- squaredbool, default=False
Return squared Euclidean distances.
- missing_valuesnp.nan 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 ofY
.
See also
paired_distances
Distances between pairs of elements of X and Y.
References
John K. Dixon, “Pattern Recognition with Partly Missing Data”, IEEE Transactions on Systems, Man, and Cybernetics, Volume: 9, Issue: 10, pp. 617 - 621, Oct. 1979. http://ieeexplore.ieee.org/abstract/document/4310090/
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]])