% generate data
tempdisk = strel('disk',922/2);
tempdisk = double(repmat((1+sqrt(-1)).*tempdisk.Neighborhood,[1 1 15]));
tempnoise = (rand(921,921,15)+sqrt(-1).*rand(921,921,15))./10;
tempim1 = double(imresize(mean(imread('cameraman.tif'),3),[921,921]));
tempim1 = repmat(tempim1./max(tempim1(:)),[1 1 15]);
tempim2 = double(rgb2hsv(imread('fabric.png')));
tempim2 = imresize(tempim2(:,:,1),[921,921]);
tempim2 = repmat(tempim2./max(tempim2(:)),[1 1 15]);
sin1 = repmat(permute(sin(2.*pi.*(0:14)./15),[1 3 2]),[921 921 1]);
cdat = (sin1.*tempim1.*exp(-sqrt(-1).*2.*pi.*sin1.*(tempim2.^2)).*tempdisk+tempnoise);

%this is what the mean data looks like
meanm = mean(abs(cdat),3);
meanp = angle(mean(cdat,3));
figure; subplot(1,2,1); imshow(meanm,[]); title('mean magnitude'); subplot(1,2,2); imshow(meanp,[]); title('mean phase')

%get all pixel vectors in a single matrix
cvect = reshape(permute(cdat, [3,1,2]), [15, 921*921]);
%k means and eigs dont accept complex, so convert to real here?
cvectT = [real(cvect);imag(cvect)]';

%lets say 10000 by 10000 matrices are still ok
nvox = 10000;
nslab = floor(length(cvectT)/nvox);
nrest = rem(length(cvectT), nvox);

% spectral clusetring according to http://ai.stanford.edu/~ang/papers/nips01-spectral.pdf
keig = 50;%how many eigenvectors needed? more is better
affinity_sigma = 1;% i dont understand how to calculate this either
tic
for islab = (nslab+1):-1:1;
    islab/nslab
    toc
    if islab>nslab
        voxrange = (1:nrest) + ((islab-1)*nvox);;
    else
        voxrange = (1:nvox) + ((islab-1)*nvox);
    end
    Aff = exp( -squareform(pdist(cvectT(voxrange,:))).^2/(2*affinity_sigma^2) ); % affinity matrix
    Dsq = sparse(size(Aff,1),size(Aff,2)); %degree matrix
    for idiag=1:size(Aff,1)
        Dsq(idiag,idiag) = sum(Aff(idiag,:))^(1/2);
    end
    Lap = Dsq * Aff * Dsq; %normalize affinity matrix
    [eigVectors(voxrange,1:keig), eigValues] = eigs(Lap, keig); %eigenvectors of normalized aff mat
    normEigVectors(voxrange,1:keig) = eigVectors(voxrange,1:keig)./repmat(sqrt(sum(abs(eigVectors(voxrange,1:keig)).^2,2)), [1 keig]); %normalize rows of eigen vectors, normr only works on real numbers
    [idx,C,sumd,D] = kmeans([real(normEigVectors(voxrange,1:keig)),imag(normEigVectors(voxrange,1:keig))], 5); %k means on normalized eigenvecotrs

    idxval(voxrange) = idx;
end
idxim = reshape(idxval, [1280, 1280]);
figure; imshow(idxim,[])
toc