clear, close all

addpath(genpath(pwd))

cd '/home/pan/Work/Hopkins155'; % cd to your Hopkins155 dir

file = dir;

idx = 0;

idx2 = 0;

idx3 = 0;

for i = 1:length(file)

if ( (file(i).isdir == 1) && ~strcmp(file(i).name,'.') && ~strcmp(file(i).name,'..') )

filepath = file(i).name;

eval(['cd ' filepath]);

f = dir;

foundValidData = false;

for j = 1:length(f)

if ( ~isempty(strfind(f(j).name,'_truth.mat')) )

ind = j;

foundValidData = true;

eval(['load ' f(ind).name]);

break

end

end

cd ..

if(foundValidData && max(s)<=3)

idx = idx+1;

n = max(s);

N = size(x,2);

F = size(x,3);

D = 3*F;

X = reshape(permute(x(1:3,:,:),[1 3 2]),D,N); % data matrix with dimension DxF

x1 = X(1:3,:);

xlast = X(end-2:end,:);

X = zeros(9,N);

for nn = 1:N

X(:,nn) = kron(x1(:,nn),xlast(:,nn));

end

X = repmat(X,30,1);

lambda1 = 0.23; lambda2 = 5.5; lambda3 = 1e5; kType = 'pol'; Affine = true; outlier = false;

param.alpha = 0.6;

param.a = 2;

param.b = 2; param.eta = 20;

Missrate = lowRankKernelSubspaceClustering(X,s,lambda1,lambda2, lambda3, kType, Affine, outlier, param);

disp([filepath ': ' num2str(100*Missrate) '%']);

missrate_tol(idx) = Missrate;

if(max(s) == 2)

idx2 = idx2+1;

missrate_two(idx2) = Missrate;

elseif(max(s) == 3)

idx3 = idx3+1;

missrate_three(idx3) = Missrate;

end

end

end

avgtol = mean(missrate_tol);

medtol = median(missrate_tol);

avgtwo = mean(missrate_two);

medtwo = median(missrate_two);

avgthree = mean(missrate_three);

medthree = median(missrate_three);

disp(['Mean of all: ' num2str(100*avgtol) '%' ', median of all: ' num2str(100*medtol) '%;']);

disp(['Mean of two: ' num2str(100*avgtwo) '%' ', median of two: ' num2str(100*medtwo) '%;']);

disp(['Mean of three: ' num2str(100*avgthree) '%' ', median of three: ' num2str(100*medthree) '%.']);