DSSC.zip_Deep Clustering_Deep sparse_clustering_matlab_subspace

function res = evalClustering(Y, predY) % struct of clustering result % [1] acc % [2] nmi_max % [3] nmi_sqrt % [4] purity % [5] precision, 1 * c % [6] recall, 1 * c % [7] f1 % [8] confus, confusion matrix % if size(Y,2) ~= 1 Y = Y'; end; if size(predY,2) ~= 1 predY = predY'; end; n = length(Y); uY = unique(Y); nclass = length(uY); Y0 = zeros(n,1); if nclass ~= max(Y) for i = 1:nclass Y0(find(Y == uY(i))) = i; end; Y = Y0; end; uY = unique(predY); nclass = length(uY); predY0 = zeros(n,1); if nclass ~= max(predY) for i = 1:nclass predY0(find(predY == uY(i))) = i; end; predY = predY0; end; Lidx = unique(Y); classnum = length(Lidx); predLidx = unique(predY); pred_classnum = length(predLidx); % purity correnum = 0; for ci = 1:pred_classnum incluster = Y(find(predY == predLidx(ci))); inclunub = hist(incluster, 1:max(incluster)); if isempty(inclunub) inclunub=0;end; correnum = correnum + max(inclunub); end; purity = correnum/length(predY); res = bestMap(Y, predY); [confus,accuracy,numcorrect,precision,recall,f_measure] = compute_accuracy_F (Y,res,1:nclass); % NMI [nmi_max, nmi_sqrt] = MutualInfo(Y,res); res = struct('acc', accuracy, 'nmi_max', nmi_max, 'nmi_sqrt', nmi_sqrt', 'purity', purity, ... 'precision', precision, 'recall', recall, 'f1', f_measure, ... 'confus', confus); end function [newL2, c] = bestMap(L1,L2) %bestmap: permute labels of L2 match L1 as good as possible % [newL2] = bestMap(L1,L2); %=========== L1 = L1(:); L2 = L2(:); if size(L1) ~= size(L2) error('size(L1) must == size(L2)'); end L1 = L1 - min(L1) + 1; % min (L1) <- 1; L2 = L2 - min(L2) + 1; % min (L2) <- 1; %=========== make bipartition graph ============ nClass = max(max(L1), max(L2)); G = zeros(nClass); for i=1:nClass for j=1:nClass G(i,j) = length(find(L1 == i & L2 == j)); end end %=========== assign with hungarian method ====== [c,t] = hungarian(-G); newL2 = zeros(nClass,1); for i=1:nClass newL2(L2 == i) = c(i); end end function [nmi_max, nmi_sqrt] = MutualInfo(L1,L2) % mutual information %=========== L1 = L1(:); L2 = L2(:); if size(L1) ~= size(L2) error('size(L1) must == size(L2)'); end L1 = L1 - min(L1) + 1; % min (L1) <- 1; L2 = L2 - min(L2) + 1; % min (L2) <- 1; %=========== make bipartition graph ============ nClass = max(max(L1), max(L2)); G = zeros(nClass); for i=1:nClass for j=1:nClass G(i,j) = length(find(L1 == i & L2 == j))+eps; end end sumG = sum(G(:)); %=========== calculate MIhat P1 = sum(G,2); P1 = P1/sumG; P2 = sum(G,1); P2 = P2/sumG; H1 = sum(-P1.*log2(P1)); H2 = sum(-P2.*log2(P2)); P12 = G/sumG; PPP = P12./repmat(P2,nClass,1)./repmat(P1,1,nClass); PPP(abs(PPP) < 1e-12) = 1; MI = sum(P12(:) .* log2(PPP(:))); nmi_max = real(MI) / max(real(H1), real(H2)); nmi_sqrt = real(MI) / (real(sqrt(H1)) * real(sqrt(H2)) + eps); end function [C,T]=hungarian(A) %HUNGARIAN Solve the Assignment problem using the Hungarian method. % %[C,T]=hungarian(A) %A - a square cost matrix. %C - the optimal assignment. %T - the cost of the optimal assignment. %s.t. T = trace(A(C,:)) is minimized over all possible assignments. % Adapted from the FORTRAN IV code in Carpaneto and Toth, "Algorithm 548: % Solution of the assignment problem [H]", ACM Transactions on % Mathematical Software, 6(1):104-111, 1980. % v1.0 96-06-14. Niclas Borlin, [email protected]. % Department of Computing Science, Ume?University, % Sweden. % All standard disclaimers apply. % A substantial effort was put into this code. If you use it for a % publication or otherwise, please include an acknowledgement or at least % notify me by email. /Niclas [m,n]=size(A); if (m~=n) error('HUNGARIAN: Cost matrix must be square!'); end % Save original cost matrix. orig=A; % Reduce matrix. A=hminired(A); % Do an initial assignment. [A,C,U]=hminiass(A); % Repeat while we have unassigned rows. while (U(n+1)) % Start with no path, no unchecked zeros, and no unexplored rows. LR=zeros(1,n); LC=zeros(1,n); CH=zeros(1,n); RH=[zeros(1,n) -1]; % No labelled columns. SLC=[]; % Start path in first unassigned row. r=U(n+1); % Mark row with end-of-path label. LR(r)=-1; % Insert row first in labelled row set. SLR=r; % Repeat until we manage to find an assignable zero. while (1) % If there are free zeros in row r if (A(r,n+1)~=0) % ...get column of first free zero. l=-A(r,n+1); % If there are more free zeros in row r and row r in not % yet marked as unexplored.. if (A(r,l)~=0 & RH(r)==0) % Insert row r first in unexplored list. RH(r)=RH(n+1); RH(n+1)=r; % Mark in which column the next unexplored zero in this row % is. CH(r)=-A(r,l); end else % If all rows are explored.. if (RH(n+1)<=0) % Reduce matrix. [A,CH,RH]=hmreduce(A,CH,RH,LC,LR,SLC,SLR); end % Re-start with first unexplored row. r=RH(n+1); % Get column of next free zero in row r. l=CH(r); % Advance "column of next free zero". CH(r)=-A(r,l); % If this zero is last in the list.. if (A(r,l)==0) % ...remove row r from unexplored list. RH(n+1)=RH(r); RH(r)=0; end end % While the column l is labelled, i.e. in path. while (LC(l)~=0) % If row r is explored.. if (RH(r)==0) % If all rows are explored.. if (RH(n+1)<=0) % Reduce cost matrix. [A,CH,RH]=hmreduce(A,CH,RH,LC,LR,SLC,SLR); end % Re-start with first unexplored row. r=RH(n+1); end % Get column of next free zero in row r. l=CH(r); % Advance "column of next free zero". CH(r)=-A(r,l); % If this zero is last in list.. if(A(r,l)==0) % ...remove row r from unexplored list. RH(n+1)=RH(r); RH(r)=0; end end % If the column found is unassigned.. if (C(l)==0) % Flip all zeros along the path in LR,LC. [A,C,U]=hmflip(A,C,LC,LR,U,l,r); % ...and exit to continue with next unassigned row. break; else % ...else add zero to path. % Label column l with row r. LC(l)=r; % Add l to the set of labelled columns. SLC=[SLC l]; % Continue with the row assigned to column l. r=C(l); % Label row r with column l. LR(r)=l; % Add r to the set of labelled rows. SLR=[SLR r]; end end end % Calculate the total cost. T=sum(orig(logical(sparse(C,1:size(orig,2),1)))); end function A=hminired(A) %HMINIRED Initial reduction of cost matrix for the Hungarian method. % %B=assredin(A) %A - the unreduced cost matris. %B - the reduced cost matrix with linked zeros in each row. % v1.0 96-06-13. Niclas Borlin, [email protected]. [m,n]=size(A); % Subtract column-minimum values f