comparison toolboxes/distance_learning/mlr/cuttingPlane/cuttingPlaneFull.m @ 0:e9a9cd732c1e tip

first hg version after svn

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