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elimination of conscutive regions (generalization: ones with zeros between)

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I need effectively eliminate (by zeroing) the consecutive "1's" between "-1's" and start/end of column at each column of matrix A, which now can be separated by any number of zeroes. The number of consecutive "1's" between "-1's" and start/end of column is > N. This is a non-trivial generalization of my previous Question.

Again, typical size(A) = [100000,1000].

See example:

A =
     1    -1     0
     0     1     1
     0     1     1
     1     1     0
     0     0     1
     1    -1     0
    -1     1     1
    -1     0    -1
     1     1     1
     0     1    -1

For N = 2 the expected result is

Aclean =
     0    -1     0
     0     0     0
     0     0     0
     0     0     0
     0     0     0
     0    -1     0
    -1     0     0
    -1     0    -1
     1     0     1
     0     0    -1

For N = 3 the expected result is

Aclean =
    1    -1     0
    0     1     0
    0     1     0
    1     1     0
    0     0     0
    1    -1     0
   -1     1     0
   -1     0    -1
    1     1     1
    0     1    -1

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Michal Kvasnicka
Michal Kvasnicka on 1 Oct 2018
I need to eliminate (zeroed) all "1's" which are located between "-1's" and start/end at each column of A separately. The number of eliminated "1's" between "-1's" is greater than user defined N.
So in my example, in 1st column of A are three "1's" and any number of "0's", separated by two "-1's" and then one "1's". For N = 2 I will eliminate only first 3 > (N=2) "1's". For N = 3 I do not eliminate any "1's" from 1st columns of A. Similarly for all columns.
Matt J
Matt J on 1 Oct 2018
which now can be separated by any number of zeroes
This is the definition of non-consecutive. Why, then, are you saying the eliminated 1's are to be consecutive?

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Accepted Answer

Bruno Luong
Bruno Luong on 1 Oct 2018
A = [1 -1 0;
0 1 1;
0 1 1;
1 1 0;
0 0 1;
1 -1 0;
-1 1 1;
-1 0 -1;
1 1 1;
0 1 -1];
N = 3;
[m,n] = size(A);
Aclean = A;
for j=1:n
Aj = [-1; A(:,j); -1];
i = find(Aj == -1);
c = histc(find(Aj==1),i);
b = c <= N;
im = i(b);
ip = i([false; b(1:end-1)]);
a = accumarray(im,1,[m+2,1])-accumarray(ip,1,[m+2,1]);
mask = cumsum(a);
mask(i) = 1;
Aclean(:,j) = Aclean(:,j).*mask(2:end-1);
end
Aclean

  3 Comments

Bruno Luong
Bruno Luong on 1 Oct 2018
Cleaner version
[m,n] = size(A);
Aclean = A;
for j=1:n
Aj = A(:,j);
im1 = find(Aj == -1);
i1 = find(Aj==1);
[c,loc] = histc(i1,[-Inf; im1; Inf]);
b = c > N;
Aclean(i1(b(loc)),j) = 0;
end
Michal Kvasnicka
Michal Kvasnicka on 2 Oct 2018
Is there any serious reason to still use old "histc" instead "histcounts"? Both functions has same performance and "histc" will be removed in future releases.

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More Answers (2)

Michal Kvasnicka
Michal Kvasnicka on 1 Oct 2018
Edited: Michal Kvasnicka on 1 Oct 2018
I am still looking for better (faster) solution. Any idea how to improve so far best solution:
A = [1 -1 0;
0 1 1;
0 1 1;
1 1 0;
0 0 1;
1 -1 0;
-1 1 1;
-1 0 -1;
1 1 1;
0 1 -1];
N = 3;
sep = A==-1;
sep(1,:) = true;
idx = cumsum(sep(:));
sep(1,:) = A(1,:)==-1;
num = accumarray(idx, A(:)==1);
iff = num <= N;
Aclean = reshape(sep(:)|iff(idx), size(A)) .* A;
Aclean
Big test matrix:
A = double(rand(100000,1000)>.4) - double(rand(100000,1000)>.65);
Bruno's code (N = 5): Elapsed time is 4.848747 seconds.
My code (N = 5): Elapsed time is 3.257089 seconds.

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Bruno Luong
Bruno Luong on 2 Oct 2018
That's true, somehow it's a 1D scanning problem.
I think we are close to the limit of MATLAB can do, if faster speed is still needed, then one should go to MEX programming route instead of torturing MATLAB to squeeze out the last once of speed.

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