Vectorize a regression map calculation - matlab

Question

I compute the regression map of a time series A(t) on a field B(x,y,t) in the following way:
A=1:10; %time
B=rand(100,100,10); %x,y,time
rc=nan(size(B,1),size(B,2));
for ii=size(B,1)
for jj=1:size(B,2)
tmp = cov(A,squeeze(B(ii,jj,:))); %covariance matrix
rc(ii,jj) = tmp(1,2); %covariance A and B
end
end
rc = rc/var(A); %regression coefficient
Is there a way to vectorize/speed up code? Or maybe some built-in function that I did not know of to achieve the same result?

Answer 1

In order to vectorize this algorithm, you would have to "get your hands dirty" and compute the covariance yourself. If you take a look inside cov you'll see that it has many lines of input checking and very few lines of actual computation, to summarize the critical steps:
y = varargin{1};
x = x(:);
y = y(:);
x = [x y];
[m,~] = size(x);
denom = m - 1;
xc = x - sum(x,1)./m; % Remove mean
c = (xc' * xc) ./ denom;
To simplify the above somewhat:
x = [x(:) y(:)];
m = size(x,1);
xc = x - sum(x,1)./m;
c = (xc' * xc) ./ (m - 1);
Now this is something that is fairly straightforward to vectorize...
function q51466884
A = 1:10; %time
B = rand(200,200,10); %x,y,time
%% Test Equivalence:
assert( norm(sol1-sol2) < 1E-10);
%% Benchmark:
disp([timeit(#sol1), timeit(#sol2)]);
%%
function rc = sol1()
rc=nan(size(B,1),size(B,2));
for ii=1:size(B,1)
for jj=1:size(B,2)
tmp = cov(A,squeeze(B(ii,jj,:))); %covariance matrix
rc(ii,jj) = tmp(1,2); %covariance A and B
end
end
rc = rc/var(A); %regression coefficient
end
function rC = sol2()
m = numel(A);
rB = reshape(B,[],10).'; % reshape
% Center:
cA = A(:) - sum(A)./m;
cB = rB - sum(rB,1)./m;
% Multiply:
rC = reshape( (cA.' * cB) ./ (m-1), size(B(:,:,1)) ) ./ var(A);
end
end
I get these timings: [0.5381 0.0025] which means we saved two orders of magnitude in the runtime :)
Note that a big part of optimizing the algorithm is assuming you don't have any "strangeness" in your data, like NaN values etc. Take a look inside cov.m to see all the checks that we skipped.