Replacement for repmat in MATLAB

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-dow开发者_Go百科n 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.

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,:);

One obvious way to speed up calculation in MATLAB is to make a MEX file. You can compile C code and perform any operations you want. If you're searching for the fastest-possible performance, turning the operation into a custom MEX file would likely be the way to go.

You may be able to get some improvement by using ACCUMARRAY.

%# gather array sizes
[nPts,nDims] = size(x);
nBins = max(bins);

%# calculate means. Not sure whether it might be faster to loop over nDims
meansCell = accumarray(bins,1:nPts,[nBins,1],@(idx){mean(x(idx,:),1)},{NaN(1,nDims)});
means = cell2mat(meansCell);

%# subtract cluster means from x - this is how you can avoid repmat in your code, btw.
%# all you need is the array with cluster means. 
delta_x = x - means(bins,:);

First of all: format your code properly, surround any operator or assignment by whitespace. I find your code very hard to comprehend as it looks like a big blob of characters.

Next of all, you could follow the other responses and convert the code to C (mex) or Java, automatically or manually, but in my humble opinion this is a last resort. You should only do such things when your performance is not there yet by a small margin. On the other hand, your algorithm doesn't show obvious flaws.

But the first thing you should do when trying to improve performance: profile. Use the MATLAB profiler to determine which part of your code is causing your problems. How much would you need to improve this to meet your expectations? If you don't know: first determine this boundary, otherwise you will be looking for a needle in a hay stack which might not even be in there in the first place. MATLAB will never be the fastest kid on the block with respect to runtime, but it might be the fastest with respect to development time for certain kinds of operations. In that respect, it might prove useful to sacrifice the clarity of MATLAB over the execution speed of other languages (C or even Java). But in the same respect, you might as well code everything in assembler to squeeze all of the performance out of the code.

Another obvious way to speed up calculation in MATLAB is to make a Java library (similar to @aardvarkk's answer) since MATLAB is built on Java and has very good integration with user Java libraries.

Java's easier to interface and compile than C. It might be slower than C in some cases, but the just-in-time (JIT) compiler in the Java virtual machine generally speeds things up very well.