Question

I have a MATLAB routine with one rather obvious bottleneck. I’ve profiled the function, with the result that 2/3 of the computing time is used in the function levels:

The function levels takes a matrix of floats and splits each column into nLevels buckets, returning a matrix of the same size as the input, with each entry replaced by the number of the bucket it falls into.

To do this I use the quantile function to get the bucket limits, and a loop to assign the entries to buckets. Here’s my implementation:

function [Y q] = levels(X,nLevels)
% "Assign each of the elements of X to an integer-valued level"

p = linspace(0, 1.0, nLevels+1);

q = quantile(X,p);
if isvector(q)
    q=transpose(q);
end

Y = zeros(size(X));

for i = 1:nLevels
    % "The variables g and l indicate the entries that are respectively greater than
    % or less than the relevant bucket limits. The line Y(g & l) = i is assigning the
    % value i to any element that falls in this bucket."
    if i ~= nLevels % "The default; doesnt include upper bound"
        g = bsxfun(@ge,X,q(i,:));
        l = bsxfun(@lt,X,q(i+1,:));
    else            % "For the final level we include the upper bound"
        g = bsxfun(@ge,X,q(i,:));
        l = bsxfun(@le,X,q(i+1,:));
    end
    Y(g & l) = i;
end

Is there anything I can do to speed this up? Can the code be vectorized?

Answer 1

If I understand correctly, you want to know how many items fell in each bucket.
Use:

n = hist(Y,nbins)

Though I am not sure that it will help in the speedup. It is just cleaner this way.

Edit : Following the comment:

You can use the second output parameter of histc

[n,bin] = histc(…) also returns an index matrix bin. If x is a vector, n(k) = >sum(bin==k). bin is zero for out of range values. If x is an M-by-N matrix, then