High performance computation of mean 2D-Euclidian-distance - matlab

Question

Let's say I have a position matrix P with dimensions 10x2, where the first column contains x values and second column the corresponding y values. I want the mean of the lengths of the positions. The way I have done this so far is with the following code:
avg = sum( sqrt( P(:,1).^2 + P(:,2).^2))/10);
I've been told that the integral function integral2 is much faster and more precise for this task. How can I use integral2 to compute the mean value?

Answer 1

Just so this question doesn't remain unanswered:
function q42372466(DO_SUM)
if ~nargin % nargin == 0
DO_SUM = true;
end
% Generate some data:
P = rand(2E7,2);
% Correctness:
R{1} = m1(P);
R{2} = m2(P);
R{3} = m3(P);
R{4} = m4(P);
R{5} = m5(P);
R{6} = m6(P);
for ind1 = 2:numel(R)
assert(abs(R{1}-R{ind1}) < 1E-10);
end
% Benchmark:
t(1) = timeit(#()m1(P));
t(2) = timeit(#()m2(P));
t(3) = timeit(#()m3(P));
t(4) = timeit(#()m4(P));
t(5) = timeit(#()m5(P));
t(6) = timeit(#()m6(P));
% Print timings:
disp(t);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Original method:
function out = m1(P)
if DO_SUM
out = sum( sqrt( P(:,1).^2 + P(:,2).^2))/max(size(P));
else
out = mean( sqrt( P(:,1).^2 + P(:,2).^2));
end
end
% pdist2 method:
function out = m2(P)
if DO_SUM
out = sum(pdist2([0,0],P))/max(size(P));
else
out = mean(pdist2([0,0],P));
end
end
% Shortened method #1:
function out = m3(P)
if DO_SUM
out = sum(sqrt(sum(P.*P,2)))/max(size(P));
else
out = mean(sqrt(sum(P.*P,2)));
end
end
% Shortened method #2:
function out = m4(P)
if DO_SUM
out = sum(sqrt(sum(P.^2,2)))/max(size(P));
else
out = mean(sqrt(sum(P.^2,2)));
end
end
% hypot
function out = m5(P)
if DO_SUM
out = sum(hypot(P(:,1),P(:,2)))/max(size(P));
else
out = mean(hypot(P(:,1),P(:,2)));
end
end
% (a+b)^2 formula , Divakar's idea
function out = m6(P)
% Since a^2 + b^2 = (a+b)^2 - 2ab,
if DO_SUM
out = sum(sqrt(sum(P,2).^2 - 2*prod(P,2)))/max(size(P));
else
out = mean(sqrt(sum(P,2).^2 - 2*prod(P,2)));
end
end
end
Typical result on my R2016b + Win10 x64:
>> q42372466(0) % with mean()
0.1165 0.1971 0.2167 0.2161 0.1719 0.2375
>> q42372466(1) % with sum()
0.1156 0.1979 0.2233 0.2181 0.1610 0.2357
Which means that your method is actually the best of the above, by a considerable margin! (Honestly - didn't expect that!)