Daniel@0: function [dPsi, M, SO_time] = cuttingPlaneFull(k, X, W, Ypos, Yneg, batchSize, SAMPLES, ClassScores)
Daniel@0: %
Daniel@0: % [dPsi, M, SO_time] = cuttingPlaneFull(k, X, W, Yp, Yn, batchSize, SAMPLES)
Daniel@0: %
Daniel@0: %   k           = k parameter for the SO
Daniel@0: %   X           = d*n data matrix
Daniel@0: %   W           = d*d PSD metric
Daniel@0: %   Yp          = cell-array of relevant results for each point
Daniel@0: %   Yn          = cell-array of irrelevant results for each point
Daniel@0: %   batchSize   = number of points to use in the constraint batch
Daniel@0: %   SAMPLES     = indices of valid points to include in the batch
Daniel@0: %
Daniel@0: %   dPsi        = dPsi vector for this batch
Daniel@0: %   M           = mean loss on this batch
Daniel@0: %   SO_time     = time spent in separation oracle
Daniel@0: 
Daniel@0:     global SO PSI DISTANCE CPGRADIENT;
Daniel@0: 
Daniel@0:     [d,n,m] = size(X);
Daniel@0:     D       = DISTANCE(W, X);
Daniel@0: 
Daniel@0:     M       = 0;
Daniel@0:     S       = zeros(n);
Daniel@0:     dIndex  = sub2ind([n n], 1:n, 1:n);
Daniel@0: 
Daniel@0:     SO_time = 0;
Daniel@0: 
Daniel@0:     if isempty(ClassScores)
Daniel@0:         for i = 1:batchSize
Daniel@0:             if i > length(SAMPLES)
Daniel@0:                 break;
Daniel@0:             end
Daniel@0:             i = SAMPLES(i);
Daniel@0: 
Daniel@0:             if isempty(Ypos{i})
Daniel@0:                 continue;
Daniel@0:             end
Daniel@0:             if isempty(Yneg)
Daniel@0:                 % Construct a negative set 
Daniel@0:                 Ynegative = setdiff((1:n)', [i ; Ypos{i}]);
Daniel@0:             else
Daniel@0:                 Ynegative = Yneg{i};
Daniel@0:             end
Daniel@0:             SO_start        = tic();
Daniel@0:                 [yi, li]    =   SO(i, D, Ypos{i}, Ynegative, k);
Daniel@0:             SO_time         = SO_time + toc(SO_start);
Daniel@0: 
Daniel@0:             M               = M + li /batchSize;
Daniel@0:             snew            = PSI(i, yi', n, Ypos{i}, Ynegative);
Daniel@0:             S(i,:)          = S(i,:) + snew';
Daniel@0:             S(:,i)          = S(:,i) + snew;
Daniel@0:             S(dIndex)       = S(dIndex) - snew';
Daniel@0:         end
Daniel@0:     else
Daniel@0: 
Daniel@0:         % Do it class-wise for efficiency
Daniel@0:         for j = 1:length(ClassScores.classes)
Daniel@0:             c       = ClassScores.classes(j);
Daniel@0:             points  = find(ClassScores.Y == c);
Daniel@0: 
Daniel@0:             Yneg    = find(ClassScores.Yneg{j});
Daniel@0:             yp      = ClassScores.Ypos{j};
Daniel@0:             
Daniel@0:             if length(points) <= 1
Daniel@0:                 continue;
Daniel@0:             end
Daniel@0:             for x = 1:length(points)
Daniel@0:                 i           = points(x);
Daniel@0:                 yp(i)       = 0;
Daniel@0:                 Ypos        = find(yp);
Daniel@0:                 SO_start    = tic();
Daniel@0:                     [yi, li]    = SO(i, D, Ypos, Yneg, k);
Daniel@0:                 SO_time     = SO_time + toc(SO_start);
Daniel@0: 
Daniel@0:                 M           = M + li /batchSize;
Daniel@0:                 snew        = PSI(i, yi', n, Ypos, Yneg);
Daniel@0:                 S(i,:)      = S(i,:) + snew';
Daniel@0:                 S(:,i)      = S(:,i) + snew;
Daniel@0:                 S(dIndex)   = S(dIndex) - snew';
Daniel@0: 
Daniel@0:                 yp(i)       = 1;
Daniel@0:             end
Daniel@0:         end
Daniel@0:     end
Daniel@0: 
Daniel@0:     dPsi    = CPGRADIENT(X, S) / batchSize;
Daniel@0: 
Daniel@0: end