open source pkg v1

pkg/OpenFace/model_training/CCNF/CCRF/lib/CalcSigmaCCRF.m

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+function [ SigmaInv] = CalcSigmaCCRF(alphas, betas, precalcBwithoutBeta )
+%CALCSIGMAPRF Summary of this function goes here
+%   Detailed explanation goes here
+% constructing the sigma
+ 
+    % the number of elements in a current sequence
+    n = size(precalcBwithoutBeta{1},1);
+
+    q1 = sum(alphas) * eye(n);
+ 
+    % the above code can be simplified by the following 2 lines of the
+    % inner loop, we want to do that for every beta however
+    K2 = numel(betas);
+
+    q2 = zeros([n,n]);
+
+    % calculating the q2 from the paper
+    for i=1:K2
+
+        % We're basically performing the following calculation, but use
+        % precalculated D - S instead of doing it every iteration
+%         S = Similarities(:,:,i);
+%         D =  diag(sum(S));
+%         q = betas(i) * D - betas(i) * S;
+%         q2s(:,:,i) = q;
+%         q2 = q2 + betas(i)*precalcQ2withoutBeta(:,:,i);
+        q2 = q2 + betas(i)*precalcBwithoutBeta{i};
+    end
+    % This is another alternative, does not seem to be faster
+%     q2old = sum(bsxfun(@times, precalcQ2withoutBeta, reshape(betas,[1,1,K2])),3);
+
+%     q2 = sum(q2s, 3);
+%     % An alternative way of calculating the above could be using bsxfun,
+%     but this seems to be actually slower than using it
+%     S = bsxfun(@times, Similarities, -reshape(betas,[1,1,K2]));
+% 
+%     % now need the diagonals
+%     d = sum(Similarities);
+% 
+%     I = repmat(eye(n), [1, 1, K2]);
+%     I = bsxfun(@times, I, reshape(betas,[1,1,K2]));
+%     D = bsxfun(@times, I, d);
+% 
+%     q2s = D + S;
+%     q2 = sum(q2s2,3);
+    
+    SigmaInv = 2 * (q1 + q2);
+
+end
+