% Test that fancy routine matches the behavior of the obvious Matlab routine
% (when numerics aren't a problem) over a wide range of weird ND-array sizes
% (This is really a test of ndhelper.m)
sizes = {[1 3], [5 1], [10 3], [0], [0 9], [3 0], [3 1 4], [1 2 3], [1 1 1 4], [1 1 0 5]};
for ii = 1:numel(sizes)
    sz = sizes{ii};
    A = rand(sz);
    testclose(logsumexp2(A), log(sum(exp(A))));
    for dim = 1:length(sz)
        if ~testclose(logsumexp2(A, dim), log(sum(exp(A), dim)))
            sz
            size(logsumexp2(A, dim))
            %size(ls_logsumexp(A, dim)) % LS version crashes!
            size(log(sum(exp(A), dim)))
            keyboard
        end
    end
end


% Test that answers are ok when numerics are horrible by comparing to lightspeed LOGSUMEXP
A = [-Inf -Inf -3000 -3000 -Inf -600 -599 -10 5 1000 999 Inf Inf 1000  Inf NaN 5 2]';
lcs =  zeros(size(A));
lcs2 = zeros(size(A));
for ii = 1:length(A)
    lcs(ii) = logsumexp2(A(1:ii));
end
for ii = 1:length(A)
    lcs2(ii) = ls_logsumexp(A(1:ii));
end
% If using Tom Minka's logsumexp, it fails (or at least used to fail) to deal
% with NaNs correctly if there are Inf's in the array. I'll fix that here:
if ~all(isnan(lcs2(end-2:end)))
    fprintf('NOTE: lightspeed didn''t produce NaN''s that I think it should have so I ''fixed'' them\n');
    lcs2(end-2:end) = NaN;
end
testclose(lcs, lcs2);
fprintf('A warning (but not failure) on the previous line about NaN''s was expected.\n');

% Speed test from lightspeed. When I tried it, the new version is a bit faster,
% although it depends on the size of the input.
x = rand(1000,1)*1000; % Get a speed benefit for a large spread of inputs.
tic; for iter = 1:1000 logsumexp2(x); end; toc
tic; for iter = 1:1000 ls_logsumexp(x); end; toc

test_exit