open source pkg v1

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

@@ -0,0 +1,48 @@
+function logL = LogLikelihoodCCRF(y_coll, x_coll, alphas, betas,...
+                                  lambda_a,lambda_b, PrecalcBsFlat,...
+                                  SigmaInvs, ChDecomps, Sigmas)
+% Calculating the log likelihood of the CCRF with multi alpha and beta    
+
+Q = numel(y_coll);
+logL = 0;
+for q=1:Q
+    
+    yq = y_coll{q};
+    xq = x_coll{q};
+    
+    n = size(xq, 1);
+      
+    b = CalcbCCRF(alphas, xq);
+            
+    % constructing the sigma inverse
+    if(nargin < 11)
+        [SigmaInv] = CalcSigmaCCRFflat(alphas, betas, n, PrecalcBsFlat{q});
+        L = chol(SigmaInv);        
+        mu = SigmaInv \ b;
+    else
+        SigmaInv = SigmaInvs{q};
+        L = ChDecomps{q};
+        Sigma = Sigmas{q};        
+        mu = Sigma * b;
+    end    
+
+    % normalisation = 1/((2*pi)^(n/2)*sqrt(det(Sigma)));
+    % Removing the division by pi, as it is constant
+    % normalisation = 1/(sqrt(det(sigma)));
+    % flipping around determinant of SigmaInv, as det(inv(Sigma)) = inv(det(Sigma)  
+%     normalisation = log(sqrt(det(SigmaInv)));
+
+    % normalisation 2 using Cholesky decomposition
+    normalisation2 = sum(log(diag(L))); % no times 2 here as we calculate the square root of determinant
+
+    % probq = normalisation * exp(-0.5 * (y - mu)'*SigmaInv*(y-mu));
+    % applying a logarithm to this leads to
+%     logLq = log(normalisation) + (-0.5 * (yq - mu)'*SigmaInv*(yq-mu));
+    logLq = normalisation2 + (-0.5 * (yq - mu)'*SigmaInv*(yq-mu));
+  
+    logL = logL + logLq;
+    
+end
+
+% add regularisation term
+logL = logL -lambda_b * (betas'*betas)/2 - lambda_a * (alphas'*alphas)/2;