Do you have such tiny inputs in all cases? Then a linear search is faster than the smart sorting of intersect and the binary search:
X = [21 32 33 41 28 91 30];
Y = [32 67 41 91 100];
tic; for k = 1:1e5; [~, ind] = intersect(Y, X); end; toc
tic; for k = 1:1e5; ind = AinBidxLin(Y, X); end; toc
tic; for k = 1:1e5; ind = AinBidx(Y, X); end; toc
% Matlab R2018b:
% Elapsed time is 7.531878 seconds. % Intersect
% Elapsed time is 0.296715 seconds. % Linear search with loops
% Elapsed time is 0.323204 seconds. % ismembc-Mex
function Ai = AinBidxLin(A, B)
% INPUT: A, B: Vectors, numerical or CHAR. No NaNs.
% OUTPUT: Ai: Indices of elements of A which appear in B.
% Linear search, efficient for small inputs, e.g. < 20 elements.
% AUTHOR: Jan, Heidelberg, CC BY-SA 3.0
nA = numel(A);
M = false(1, nA);
on = true;
B = B(:);
for iA = 1:nA
if ~isempty(find(A(iA) == B, 1))
M(iA) = on;
end
end
Ai = find(M);
end
function Ai = AinBidx(A, B)
% INPUT: A, B: Vectors, numerical or CHAR. No NaNs.
% OUTPUT: Ai: Indices of elements of A which appear in B.
Ai = find(ismembc(A(:), sort(B(:))));
end
If the inputs are not tiny, use the ismembc appraoch. This is the core of intersect also, but unfortunately it is not documented. Maybe this compiled C-Mex function will be removed in future Matlab versions.
[EDITED] Some further tests let me prefer the ismembc approach for all inputs, even very small ones. Please try this by your own.
The set functions intersect, setdiff, union has been accelerated in modern Matlab versions. But at least at Matlab online, ismembc is still 10 times faster.
