Превращение двоичной матрицы в вектор последнего ненулевого индекса быстрым, векторизованным способом
В «Расширенных настройках удаленных репозиториев» есть настройка «Хранить артефакты локально». Проверьте, выбран ли он.
2 ответа
As shown by Mr Fooz, for loops can be pretty fast now with newer versions of MATLAB. However, if you really want to have compact vectorized code, I would suggest trying this:
[B,I] = max(flipud(A));
I = size(A,1)-I+1;
This is faster than your CUMSUM-based answer, but still not quite as fast as Mr Fooz's looping options.
Two additional things to consider:
What results do you want to get for a column that has no ones in it at all? With the above option I gave you, I believe you will get an index of size(A,1) (i.e. the number of rows in A) in such a case. For your option, I believe you will get a 1 in such a case, while the nested-for-loops option from Mr Fooz will give you a 0.
The relative speed of these different options will likely vary based on the size of A and the number of non-zeroes you expect it to have.
Fast is what you should worry about, not necessarily full vectorization. Recent versions of Matlab are much smarter about handling loops efficiently. If there's a compact vectorized way of expressing something, it's usually faster, but loops should not (always) be feared like they used to be.
clc
A = rand(5000)>0.5;
A(1,find(sum(A,1)==0)) = 1; % make sure there is at least one match
% Slow because it is doing too much work
tic;[B,I1]=max(cumsum(A));toc
% Fast because FIND is fast and it runs the inner loop
tic;
I3=zeros(1,5000);
for i=1:5000
I3(i) = find(A(:,i),1,'last');
end
toc;
assert(all(I1==I3));
% Even faster because the JIT in Matlab is smart enough now
tic;
I2=zeros(1,5000);
for i=1:5000
I2(i) = 0;
for j=5000:-1:1
if A(j,i)
I2(i) = j;
break;
end
end
end
toc;
assert(all(I1==I2));
On R2008a, Windows, x64, the cumsum version takes 0.9 seconds. The loop and find version takes 0.02 seconds. The double loop version takes a mere 0.001 seconds.
EDIT: Which one is fastest depends on the actual data. The double-loop takes 0.05 seconds when you change the 0.5 to 0.999 (because it takes longer to hit the break; on average). cumsum and the loop&find implementation have more consistent speeds.
EDIT 2: gnovice's flipud solution is clever. Unfortunately, on my test machine it takes 0.1 seconds, so it's much faster than cumsum, but slower than the looped versions.