Calculating Hamming weight efficiently in matlab

Question

Given a MATLAB uint32 to be interpreted as a bit string, what is an efficient and concise way of counting how many nonzero bits are in the string?

I have a working

Answer 1

num_ones=uint8(zeros(intmax('uint32')/2^6,1));
% one time load of array not implemented here
tic
for i=1:4096*4096
 %v=num_ones(rem(i,64)+1)+num_ones(floor(i/64)+1); % 1.24 sec
 v=num_ones(mod(i,64)+1)+num_ones(floor(i/64)+1); % 1.20 sec
end
toc
tic
num_ones=uint8(zeros(65536,1));
for i=0:65535
 num_ones(i+1)=length( find( bitget( i, 1:32 ) ) ) ;
end
toc
% 0.43 sec to load
% smaller array to initialize
% one time load of array
tic
for i=1:4096*4096
 v=num_ones(mod(i,65536)+1)+num_ones(floor(i/65536)+1); %  0.95 sec
 %v=num_ones(mod(i,65536)+1)+num_ones(bitshift(i,-16)+1); % 16 sec for 4K*1K
end
toc
%vectorized
tic
num_ones=uint8(zeros(65536,1));
for i=0:65535
 num_ones(i+1)=length( find( bitget( i, 1:32 ) ) ) ;
end % 0.43 sec
toc
vt=randi(2^32,[4096*4096,1])-1;
tic
v=num_ones(mod(vt,65536)+1)+num_ones(floor(vt/65536)+1); % 0.85 sec
toc

Answer 2

EDIT: NEW SOLUTION

It appears that you want to repeat the calculation for every element in a 4096-by-4096 array of UINT32 values. If this is what you are doing, I think the fastest way to do it in MATLAB is to use the fact that BITGET is designed to operate on matrices of values. The code would look like this:

numArray = ...your 4096-by-4096 matrix of uint32 values...
w = zeros(4096,4096,'uint32');
for iBit = 1:32,
  w = w+bitget(numArray,iBit);
end

If you want to make vectorized versions of some of the other algorithms, I believe BITAND is also designed to operate on matrices.

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The old solution...

The easiest way I can think of is to use the DEC2BIN function, which gives you the binary representation (as a string) of a non-negative integer:

w = sum(dec2bin(num) == '1');  % Sums up the ones in the string

It's slow, but it's easy. =)

Answer 3

A fast approach is counting the bits in each byte using a lookup table, then summing these values; indeed, it's one of the approaches suggested on the web page given in the question. The nice thing about this approach is that both lookup and sum are vectorizable operations in MATLAB, so you can vectorize this approach and compute the hamming weight / number of set bits of a large number of bit strings simultaneously, very quickly. This approach is implemented in the bitcount submission on the MATLAB File Exchange.