Question

I have a function which does the following loop many, many times:

for cluster=1:max(bins), % bins is a list in the same format as kmeans() IDX output
    select=bins==cluster; % find group of values
    means(select,:)=repmat_fast_spec(meanOneIn(x(select,:)),sum(select),1); 
    % (*, above) for each point, write the mean of all points in x that 
    % share its label in bins to the equivalent row of means
    delta_x(select,:)=x(select,:)-(means(select,:)); 
    %subtract out the mean from each point
end

Noting that repmat_fast_spec and meanOneIn are stripped-down versions of repmat() and mean(), respectively, I’m wondering if there’s a way to do the assignment in the line labeled (*) that avoids repmat entirely.

Any other thoughts on how to squeeze performance out of this thing would also be welcome.

Answer 1

Here is a possible improvement to avoid REPMAT:

x = rand(20,4);
bins = randi(3,[20 1]);

d = zeros(size(x));
for i=1:max(bins)
    idx = (bins==i);
    d(idx,:) = bsxfun(@minus, x(idx,:), mean(x(idx,:)));
end

Another possibility:

x = rand(20,4);
bins = randi(3,[20 1]);

m = zeros(max(bins),size(x,2));
for i=1:max(bins)
    m(i,:) = mean( x(bins==i,:) );
end
dd = x - m(bins,:);