Daniel@0: function [dPsi, M, SO_time] = cuttingPlaneRandom(k, X, W, Ypos, Yneg, batchSize, SAMPLES, ClassScores)
Daniel@0: %
Daniel@0: % [dPsi, M, SO_time] = cuttingPlaneRandom(k, X, W, Yp, Yn, batchSize, SAMPLES, ClassScores)
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: %   ClassScores = structure for synthetic constraints
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 SETDISTANCE CPGRADIENT;
Daniel@0: 
Daniel@0:     [d,n]   = size(X);
Daniel@0: 
Daniel@0: 
Daniel@0:     if length(SAMPLES) == n
Daniel@0:         % All samples are fair game (full data)
Daniel@0:         Batch   = randperm(n);
Daniel@0:         Batch   = Batch(1:batchSize);
Daniel@0:         D       = SETDISTANCE(X, W, Batch);
Daniel@0: 
Daniel@0:     else
Daniel@0:         Batch   = randperm(length(SAMPLES));
Daniel@0:         Batch   = SAMPLES(Batch(1:batchSize));
Daniel@0: 
Daniel@0:         Ito     = sparse(n,1);
Daniel@0: 
Daniel@0:         if isempty(ClassScores)
Daniel@0:             for i = Batch
Daniel@0:                 Ito(Ypos{i}) = 1;
Daniel@0:                 Ito(Yneg{i}) = 1;
Daniel@0:             end
Daniel@0:             D       = SETDISTANCE(X, W, Batch, find(Ito));
Daniel@0:         else
Daniel@0:             D       = SETDISTANCE(X, W, Batch, 1:n);
Daniel@0:         end
Daniel@0:     end
Daniel@0: 
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 = Batch
Daniel@0:             SO_start        = tic();
Daniel@0:                 [yi, li]    =   SO(i, D, Ypos{i}, Yneg{i}, 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}, Yneg{i});
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:         for j = 1:length(ClassScores.classes)
Daniel@0:             c       = ClassScores.classes(j);
Daniel@0:             points  = find(ClassScores.Y(Batch) == c);
Daniel@0:             if ~any(points)
Daniel@0:                 continue;
Daniel@0:             end
Daniel@0: 
Daniel@0:             Yneg    = find(ClassScores.Yneg{j});
Daniel@0:             yp      = ClassScores.Ypos{j};
Daniel@0: 
Daniel@0:             for x = 1:length(points)
Daniel@0:                 i               = Batch(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