A = [1,2,5,1,6,2,8,2,9];
B = [3,4,2,3,7,4,5,4,8];
c =
0 0 2 0 0 0 0 0
0 0 0 3 0 0 0 0
0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0
0 1 0 0 0 0 0 0
0 0 0 0 0 0 1 0
0 0 0 0 0 0 0 0
0 0 0 0 1 0 0 0
0 0 0 0 0 0 0 1
looking at unique rows
pairs = unique([A(:), B(:)], 'rows')
pairs =
1 3
2 4
5 2
6 7
8 5
9 8
To find the count for row 1
>> c(1,3)
ans =
If your rows don't contain positive integers, you can still do the problem by converting it to indeces using unique. Unique can create a vector of the unique elements and also a vector of indeces to reconstruct the original vector based on these values. For example:
>> [bA, mA, nA] = unique(A)
bA =
1 2 5 6 8 9
mA =
4 8 3 5 7 9
nA =
1 2 3 1 4 2 5 2 6
bA(nA) gives you back the original vector but lets you operate in terms of something (indeces) that ensure you are using positive integers.
>> bA(nA)
ans =
1 2 5 1 6 2 8 2 9
Now to use accumarray:
[bA, mA, nA] = unique(A);
[bB, mB, nB] = unique(B);
accumarray([nA(:), nB(:)],1)
ans =
0 2 0 0 0 0
0 0 3 0 0 0
1 0 0 0 0 0
0 0 0 0 1 0
0 0 0 1 0 0
0 0 0 0 0 1
To find the rows again,
your pairs are now in terms of indeces:
>> pairs = unique([nA(:) nB(:)], 'rows')
pairs =
So, c(1,2) = 2
and the original pair is
>> [bA(1) bB(2)]
ans =
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