# Summing values for duplicate rows and columns

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Ulrik William Nash am 10 Jul. 2017
I have a vector of rows, columns, and values that I will use to create a sparse matrix:
rows = [1 2 3 1]; columns = [1 1 2 1]; values = [10 50 25 90];
Notice the duplicates:
(1,1) 10 (1,1) 90
What I need is to eliminate (row,column) duplicates by summing the values corresponding to these duplicates for each.
The solution in the current example is:
rows = [1 2 3]; columns = [1 1 2]; values = [100 50 25];
What operation on the three initial vectors reduce them to the solution above?
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### Akzeptierte Antwort

Andrei Bobrov am 10 Jul. 2017
Bearbeitet: Andrei Bobrov am 10 Jul. 2017
A = accumarray([[1 2 3 1]',[1 1 2 1]'],[10 50 25 90]',[],[],[],1);
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Andrei Bobrov am 10 Jul. 2017
corrected
Ulrik William Nash am 10 Jul. 2017
Thank you Andrei, that was very instructive.

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### Weitere Antworten (1)

Walter Roberson am 10 Jul. 2017
sparse(row, columns, values) is defined to do exactly this kind of totals.
If for some reason you need the simplified outputs afterwards, you can
[r, c, v] = find() on the sparse matrix.
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Ulrik William Nash am 10 Jul. 2017
Hi Walter,
I think my explanation needs a little more background. I have coded a procedure for constructing a large transition matrix. I do this by sequentially amending the r, c, v vectors shown below:
r = [r i];
c = [c indx];
s = [s transition_probs(ii,1)];
Unfortunately, due to the underlying process, a given combination of indexes for r and c may carry numerous probabilities. So, once I have finished my procedure, I must sum the values corresponding to duplicate r,c combinations.
Walter Roberson am 10 Jul. 2017
result = sparse(r, c, s);
[summary_r, summary_c, summary_s] = find(result);

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