comparison toolboxes/distance_learning/mlr/cuttingPlane/cuttingPlaneFull.m @ 0:cc4b1211e677 tip

initial commit to HG from
Changeset:
646 (e263d8a21543) added further path and more save "camirversion.m"

+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)
+%
+%   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
+%
+%   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)
+        for i = 1:batchSize
+            if i > length(SAMPLES)
+                break;
+            end
+            i = SAMPLES(i);
+
+            if isempty(Ypos{i})
+                continue;
+            end
+            if isempty(Yneg)
+                % Construct a negative set 
+                Ynegative = setdiff((1:n)', [i ; Ypos{i}]);
+            else
+                Ynegative = Yneg{i};
+            end
+            SO_start        = tic();
+                [yi, li]    =   SO(i, D, Ypos{i}, Ynegative, k);
+            SO_time         = SO_time + toc(SO_start);
+
+            M               = M + li /batchSize;
+            snew            = PSI(i, yi', n, Ypos{i}, Ynegative);
+            S(i,:)          = S(i,:) + snew';
+            S(:,i)          = S(:,i) + snew;
+            S(dIndex)       = S(dIndex) - snew';
+        end
+    else
+
+        % Do it class-wise for efficiency
+        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
+            for x = 1:length(points)
+                i           = points(x);
+                yp(i)       = 0;
+                Ypos        = find(yp);
+                SO_start    = tic();
+                    [yi, li]    = SO(i, D, Ypos, Yneg, k);
+                SO_time     = SO_time + toc(SO_start);
+
+                M           = M + li /batchSize;
+                snew        = PSI(i, yi', n, Ypos, Yneg);
+                S(i,:)      = S(i,:) + snew';
+                S(:,i)      = S(:,i) + snew;
+                S(dIndex)   = S(dIndex) - snew';
+
+                yp(i)       = 1;
+            end
+        end
+    end
+
+    dPsi    = CPGRADIENT(X, S) / batchSize;
+
+end