GMR.m added

GMR.m

+function [expData, expSigma, H] = GMR(model, DataIn, in, out)
+%Gaussian mixture regression (GMR)
+%Sylvain Calinon, Danilo Bruno, 2014
+
+nbData = size(DataIn,2);
+nbVarOut = length(out);
+
+MuTmp = zeros(nbVarOut,model.nbStates);
+expData = zeros(nbVarOut,nbData);
+expSigma = zeros(nbVarOut,nbVarOut,nbData);
+for t=1:nbData
+  %Compute activation weight
+  for i=1:model.nbStates
+    H(i,t) = model.Priors(i) * gaussPDF(DataIn(:,t), model.Mu(in,i), model.Sigma(in,in,i)); 
+  end
+  H(:,t) = H(:,t)/sum(H(:,t));
+  %Compute expected conditional means
+	for i=1:model.nbStates
+		MuTmp(:,i) = model.Mu(out,i) + model.Sigma(out,in,i)/model.Sigma(in,in,i) * (DataIn(:,t)-model.Mu(in,i));
+		expData(:,t) = expData(:,t) + H(i,t) * MuTmp(:,i);
+	end
+	%Compute expected conditional covariances
+	for i=1:model.nbStates
+		SigmaTmp = model.Sigma(out,out,i) - model.Sigma(out,in,i)/model.Sigma(in,in,i) * model.Sigma(in,out,i);
+		expSigma(:,:,t) = expSigma(:,:,t) + H(i,t) * (SigmaTmp + MuTmp(:,i)*MuTmp(:,i)');  
+	 	for j=1:model.nbStates
+		 	expSigma(:,:,t) = expSigma(:,:,t) - H(i,t)*H(j,t) * (MuTmp(:,i)*MuTmp(:,j)');  
+	 	end
+	end
+end
+