# histc with split intervals

8 vues (au cours des 30 derniers jours)
Par le 18 Déc 2013
Modifié(e) : Oleg Komarov le 19 Déc 2013
Hello All,
I am stuck with this problem for the past few days! So, any help would be greatly appreciated.
THE PROBLEM:
---------------
I have a huge list of discontinuous intervals, about 80000 of them, spread over the range from 1 to 250 million. And I have a much larger set of numbers (a vector of about 200 million elements) spread over the same range, i.e., 1 to 250 million. As if this was not complex enough, some of these intervals may overlap, but most of them are expected to be discontinuous.
Now I want to do a histogram count of how many elements of the vector fall within each of the intervals. If these were simply continuous intervals (without gaps), then one could use histc. Even if the intervals were guaranteed to be always discontinuous (i.e., with gaps), I could have still used histc and thrown out the counts in the gaps. But since I do not have the guarantee that the intervals would always be discontinuous, I am stumped.
So far, I tried two ways of attacking this problem:
1) Simply loop through the intervals, and do sum(vector >= start & vector <= end) inside the for loop. This was hopelessly slow.
2) Use cellfun or arrayfun like this: cellfun(@(L,U) sum(histc(vector,[L U])), Strts, Ends); where Strts and Ends are cell arrays defining the intervals.
Although the second solution would take a few hours, since I need to do this operation for hundreds of large datasets, I cannot afford the time.
So, is there a better way?
Any help would be greatly appreciated!
Thanks!
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Sean de Wolski le 18 Déc 2013
Could you provide a (small!) example set of data with expected results?

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### Réponse acceptée

Par le 19 Déc 2013
Well, I tried both your suggestions, and a few other ways using the bsxfun function, but because of the large sizes I am dealing with, bsxfun ran out of memory even on a large-scale cluster environment. I think it tries to create these ridiculously large matrices.
So, the simplest possible technique that actually worked was:
BINVec = zeros(1,Siz_Including_All_Intvls,'uint8'); BINVec(vector) = 1; SumVec = cellfun(@(L,U) sum(BINVec(L:U)), strts, ends);
where strts and ends define the intervals in cell arrays.
This code was very fast and I had the results in a few minutes! Lesson I learnt: Sometimes, the most underestimated solution is the one that works best. Although the above struck me earlier, I simply dismissed the thought assuming this would be very slow.
Good day.
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Oleg Komarov le 19 Déc 2013
Modifié(e) : Oleg Komarov le 19 Déc 2013
Cellfun is a masked loop and a simple loop will be faster. Now I understand you question.
You problem is trivial and no need to loop:
cumbin = cumsum(BINVec);
counts = cumbin(ends) - cumbin(ends) + 1;

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### Plus de réponses (2)

Kelly Kearney le 18 Déc 2013
I'd set up separate matrices that hold all the sub-bins (i.e. set of all endpoints) as well as which intervals are composed of which subbins. Here's a small example:
% Example data
ni = 5;
intervals = sort(rand(2,ni), 1);
% The subbins
x = unique(intervals);
% Which bins are part of each interval?
isin = zeros(length(x), ni);
for ii = 1:ni
b1 = find(x == intervals(1,ii));
b2 = find(x == intervals(2,ii))-1;
isin(b1:b2,ii) = 1;
end
% Histogram counts
xpt = rand(100,1);
n = histc(xpt, x);
% And map back to intervals
nperint = sum(bsxfun(@times, isin, n));
I think this method should work with or without gaps, and with or without overlap.
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Oleg Komarov le 18 Déc 2013
Modifié(e) : Oleg Komarov le 18 Déc 2013
You could use bsxfun(). A simple example might clarify. Suppose you have sample inputs:
A = [3, 5
10, 20
21, 30];
B = [1, 2
2, 5
7, 21
9, 30];
Calculate for each interval in A (discontinuos) how many times the lower bound is >=, i.e. ge(), AND the upper bound is <=, i.e. le(). Finally sum per interval in B (overlapping):
lbFallsWithin = bsxfun(@ge, A(:,1)', B(:,1));
upFallsWithin = bsxfun(@le, A(:,2)', B(:,2));
sum(lbFallsWithin & upFallsWithin,2)
ans =
0
1
1
2
WARNING:
The problem is that even for B with 1e6 intervals, you will have a logical lbFallsWithin of 80000*1e6bytes~74.5 GB! (keep in mind you need two of those).
The good news, is that you could block process, i.e. apply the snippet above for partitions of B, where the size of the parition is, loosely speaking, the maximum constrained by your amount of memory.
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