constructw.m

concttuc one matrix using matlab

            
            if ~options.bSelfConnected
                for i=1:size(G,1)
                    G(i,i) = 0;
                end
            end
            
            W = sparse(G);
        case {lower('HeatKernel')}
            if options.k > 0
                G = zeros(nSmp*(options.k+1),3);
                idNow = 0;
                for i=1:nLabel
                    classIdx = find(options.gnd==Label(i));
                    D = EuDist2(fea(classIdx,:),[],0);
                    [dump idx] = sort(D,2); % sort each row
                    clear D;
                    idx = idx(:,1:options.k+1);
                    dump = dump(:,1:options.k+1);
                    dump = exp(-dump/(2*options.t^2));
                    
                    nSmpClass = length(classIdx)*(options.k+1);
                    G(idNow+1:nSmpClass+idNow,1) = repmat(classIdx,[options.k+1,1]);
                    G(idNow+1:nSmpClass+idNow,2) = classIdx(idx(:));
                    G(idNow+1:nSmpClass+idNow,3) = dump(:);
                    idNow = idNow+nSmpClass;
                    clear dump idx
                end
                G = sparse(G(:,1),G(:,2),G(:,3),nSmp,nSmp);
            else
                G = zeros(nSmp,nSmp);
                for i=1:nLabel
                    classIdx = find(options.gnd==Label(i));
                    D = EuDist2(fea(classIdx,:),[],0);
                    D = exp(-D/(2*options.t^2));
                    G(classIdx,classIdx) = D;
                end
            end
            
            if ~options.bSelfConnected
                for i=1:size(G,1)
                    G(i,i) = 0;
                end
            end

            W = sparse(max(G,G'));
        case {lower('Cosine')}
            if ~options.bNormalized
                [nSmp, nFea] = size(fea);
                if issparse(fea)
                    fea2 = fea';
                    feaNorm = sum(fea2.^2,1).^.5;
                    for i = 1:nSmp
                        fea2(:,i) = fea2(:,i) ./ max(1e-10,feaNorm(i));
                    end
                    fea = fea2';
                    clear fea2;
                else
                    feaNorm = sum(fea.^2,2).^.5;
                    for i = 1:nSmp
                        fea(i,:) = fea(i,:) ./ max(1e-12,feaNorm(i));
                    end
                end

            end

            if options.k > 0
                G = zeros(nSmp*(options.k+1),3);
                idNow = 0;
                for i=1:nLabel
                    classIdx = find(options.gnd==Label(i));
                    D = fea(classIdx,:)*fea(classIdx,:)';
                    [dump idx] = sort(-D,2); % sort each row
                    clear D;
                    idx = idx(:,1:options.k+1);
                    dump = -dump(:,1:options.k+1);
                    
                    nSmpClass = length(classIdx)*(options.k+1);
                    G(idNow+1:nSmpClass+idNow,1) = repmat(classIdx,[options.k+1,1]);
                    G(idNow+1:nSmpClass+idNow,2) = classIdx(idx(:));
                    G(idNow+1:nSmpClass+idNow,3) = dump(:);
                    idNow = idNow+nSmpClass;
                    clear dump idx
                end
                G = sparse(G(:,1),G(:,2),G(:,3),nSmp,nSmp);
            else
                G = zeros(nSmp,nSmp);
                for i=1:nLabel
                    classIdx = find(options.gnd==Label(i));
                    G(classIdx,classIdx) = fea(classIdx,:)*fea(classIdx,:)';
                end
            end

            if ~options.bSelfConnected
                for i=1:size(G,1)
                    G(i,i) = 0;
                end
            end

            W = sparse(max(G,G'));
        otherwise
            error('WeightMode does not exist!');
    end
    elapse = cputime - tmp_T;
    return;
end


if strcmpi(options.NeighborMode,'KNN') && (options.k > 0)
    if strcmpi(options.Metric,'Euclidean')
        G = zeros(nSmp*(options.k+1),3);
        for i = 1:ceil(nSmp/BlockSize)
            if i == ceil(nSmp/BlockSize)
                smpIdx = (i-1)*BlockSize+1:nSmp;
                dist = EuDist2(fea(smpIdx,:),fea,0);
                dist = full(dist);
                [dump idx] = sort(dist,2); % sort each row
                idx = idx(:,1:options.k+1);
                dump = dump(:,1:options.k+1);
                if ~bBinary
                    dump = exp(-dump/(2*options.t^2));
                end
                
                G((i-1)*BlockSize*(options.k+1)+1:nSmp*(options.k+1),1) = repmat(smpIdx',[options.k+1,1]);
                G((i-1)*BlockSize*(options.k+1)+1:nSmp*(options.k+1),2) = idx(:);
                G((i-1)*BlockSize*(options.k+1)+1:nSmp*(options.k+1),3) = dump(:);
            else
                smpIdx = (i-1)*BlockSize+1:i*BlockSize;
                dist = EuDist2(fea(smpIdx,:),fea,0);
                dist = full(dist);
                [dump idx] = sort(dist,2); % sort each row
                idx = idx(:,1:options.k+1);
                dump = dump(:,1:options.k+1);
                if ~bBinary
                    dump = exp(-dump/(2*options.t^2));
                end
                
                G((i-1)*BlockSize*(options.k+1)+1:i*BlockSize*(options.k+1),1) = repmat(smpIdx',[options.k+1,1]);
                G((i-1)*BlockSize*(options.k+1)+1:i*BlockSize*(options.k+1),2) = idx(:);
                G((i-1)*BlockSize*(options.k+1)+1:i*BlockSize*(options.k+1),3) = dump(:);
            end
        end

        W = sparse(G(:,1),G(:,2),G(:,3),nSmp,nSmp);
    else
        if ~options.bNormalized
            [nSmp, nFea] = size(fea);
            if issparse(fea)
                fea2 = fea';
                clear fea;
                for i = 1:nSmp
                    fea2(:,i) = fea2(:,i) ./ max(1e-10,sum(fea2(:,i).^2,1).^.5);
                end
                fea = fea2';
                clear fea2;
            else
                feaNorm = sum(fea.^2,2).^.5;
                for i = 1:nSmp
                    fea(i,:) = fea(i,:) ./ max(1e-12,feaNorm(i));
                end
            end
        end
        
        G = zeros(nSmp*(options.k+1),3);
        for i = 1:ceil(nSmp/BlockSize)
            if i == ceil(nSmp/BlockSize)
                smpIdx = (i-1)*BlockSize+1:nSmp;
                dist = fea(smpIdx,:)*fea';
                dist = full(dist);
                [dump idx] = sort(-dist,2); % sort each row
                idx = idx(:,1:options.k+1);
                dump = -dump(:,1:options.k+1);

                G((i-1)*BlockSize*(options.k+1)+1:nSmp*(options.k+1),1) = repmat(smpIdx',[options.k+1,1]);
                G((i-1)*BlockSize*(options.k+1)+1:nSmp*(options.k+1),2) = idx(:);
                G((i-1)*BlockSize*(options.k+1)+1:nSmp*(options.k+1),3) = dump(:);
            else
                smpIdx = (i-1)*BlockSize+1:i*BlockSize;
                dist = fea(smpIdx,:)*fea';
                dist = full(dist);
                [dump idx] = sort(-dist,2); % sort each row
                idx = idx(:,1:options.k+1);
                dump = -dump(:,1:options.k+1);

                G((i-1)*BlockSize*(options.k+1)+1:i*BlockSize*(options.k+1),1) = repmat(smpIdx',[options.k+1,1]);
                G((i-1)*BlockSize*(options.k+1)+1:i*BlockSize*(options.k+1),2) = idx(:);
                G((i-1)*BlockSize*(options.k+1)+1:i*BlockSize*(options.k+1),3) = dump(:);
            end
        end

        W = sparse(G(:,1),G(:,2),G(:,3),nSmp,nSmp);
    end
    
    if strcmpi(options.WeightMode,'Binary')
        W(find(W)) = 1;
    end
    
    if ~options.bSelfConnected
        for i=1:size(W,1)
            W(i,i) = 0;
        end
    end

    W = max(W,W');
    
    elapse = cputime - tmp_T;
    return;
end


% strcmpi(options.NeighborMode,'KNN') & (options.k == 0)
% Complete Graph

if strcmpi(options.Metric,'Euclidean')
    W = EuDist2(fea,[],0);
    W = exp(-W/(2*options.t^2));
else
    if ~options.bNormalized
%         feaNorm = sum(fea.^2,2).^.5;
%         fea = fea ./ repmat(max(1e-10,feaNorm),1,size(fea,2));
        [nSmp, nFea] = size(fea);
        if issparse(fea)
            fea2 = fea';
            feaNorm = sum(fea2.^2,1).^.5;
            for i = 1:nSmp
                fea2(:,i) = fea2(:,i) ./ max(1e-10,feaNorm(i));
            end
            fea = fea2';
            clear fea2;
        else
            feaNorm = sum(fea.^2,2).^.5;
            for i = 1:nSmp
                fea(i,:) = fea(i,:) ./ max(1e-12,feaNorm(i));
            end
        end
    end
    
    W = full(fea*fea');
end

if ~options.bSelfConnected
    for i=1:size(W,1)
        W(i,i) = 0;
    end
end

W = max(W,W');



elapse = cputime - tmp_T;