# finding the closest value

3 views (last 30 days)
jason on 8 Sep 2022
Commented: Walter Roberson on 8 Sep 2022
I am working with a very large data set, in which I need to pull numbers from 1 data set that corresponds with a different set. But, if no number from the first data table match the second, then it pulls the next highest number from the second data set.
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David Hill on 8 Sep 2022
Do all numbers in the first data set need a corresponding number from the other data set (either a match or the next largest)?
jason on 8 Sep 2022
Yes all the numbers must have a corresponding number

Walter Roberson on 8 Sep 2022
Sort each of the data sets. Then proceed iteratively matching points, first from one set and then the other. For A(1) find the first entry B(K) that is >= A(1) . Take that B(K) and scan through A values, A(2), A(3) and so on until you find A(J) > B(K) -- each one found during the search gets matched to B(K) . Then switch and search forward from A(J) looking for the next B entry to match; anything in-between in B is to be ignored.
The algorithm should not be difficult.
There is a vectorized way to proceed, but it would require memory proportional to numel(A) by numel(B) and you indicated that you have a "very large" data set, so it seems unlikely you would be wanting to use that technique.
There is another approach using interp1() with 'next', would probably look something similar to
sB = sort(B);
interp1(sB, sB, A, 'next')
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jason on 8 Sep 2022
How would you create it the vectorized way?
Walter Roberson on 8 Sep 2022
D = A(:).' - B(:);
D(D < 0) = inf;
Now you take min(D) along the first dimension, getting out the indices. You would then use indices to index B to find the actual value.
If your A were 30000 entries and your B were 5000 entries then this would require 12 gigabytes for D.
Vectorized does not always mean "most efficient": the above code compares every entry of A to every entry of B and has to scan all of the results to find the closest, which would take O(m*n) time. Whereas sorting A and B and proceeding incrementally like I describe would be O(m*log(m)) or O(n*log(n)) whichever is larger.

### More Answers (1)

David Hill on 8 Sep 2022
data3=data1;
S=sort(data2);
idx= find(~ismember(data1,data2));
for k=idx
f=find(S>data1(k),1);
data3(k)=S(f);
end

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