Add distance option to get 2nd and 3rd (or kth) nearest record

[MaxGhenis]

Rather than only the current minimum.

From https://www.irs.gov/pub/irs-soi/07rppsweber.pdf see

With the distance-based algorithm, protection against reidentification is measured in terms of the number of PUF records that lie at least as close to a record from the population as the true match. The minimum protection that is sought is having at least two records that are at least as close to a record from the population as the true match, if the true match is in the PUF.

[MaxGhenis]

np.argpartition can do this: https://stackoverflow.com/a/34226816/1840471

[MaxGhenis]

Largely added but the distances aren't coming out in the right order:

print(nearest[nearest.dist1 > nearest.dist3].shape[0])  # 129
print(nearest[nearest.dist1 < nearest.dist3].shape[0])  # 756

One of these should be zero.

[MaxGhenis]

From numpy.argpartition documentation:

Element index to partition by. The k-th element will be in its final sorted position and all smaller elements will be moved before it and all larger elements behind it. The order all elements in the partitions is undefined. If provided with a sequence of k-th it will partition all of them into their sorted position at once.

So it needs to be re-sorted either within nearest_record_single or at the end (probably faster).