Merge branch 'master' of gitlab.idiap.ch:rli/task-parameterized-tensor-gmm-with-lqr

init_tensorGMM_kmeans.m

+function model = init_tensorGMM_kmeans(Data, model)
+% Author:   Leonel Rozo, 2014
+%           http://programming-by-demonstration.org/LeonelRozo
+%
+
+diagRegularizationFactor = 1E-4;
+
+%Matricization/flattening of tensor
+DataAll = reshape(Data, size(Data,1)*size(Data,2), size(Data,3)); 
+
+%The function 'kmeans' below is from the Matlab Statistics Toolbox (see note above)
+[Data_id, Centers] = kmeans(DataAll', model.nbStates);
+
+% Setting means and covariance matrices
+Mu = Centers';
+Sigma = zeros(model.nbFrames*model.nbVar, model.nbFrames*model.nbVar, model.nbStates);
+for i = 1 : model.nbStates
+  idtmp = find(Data_id==i);
+  model.Priors(i) = length(idtmp);
+  Sigma(:,:,i) = cov(DataAll(:,idtmp)') + eye(size(DataAll,1))*diagRegularizationFactor;
+end
+model.Priors = model.Priors / sum(model.Priors);
+
+%Reshape GMM parameters into a tensor 
+for m = 1 : model.nbFrames
+  for i = 1 : model.nbStates
+    model.Mu(:,m,i) = Mu((m-1)*model.nbVar+1:m*model.nbVar,i); 
+    model.Sigma(:,:,m,i) = Sigma((m-1)*model.nbVar+1:m*model.nbVar,(m-1)*model.nbVar+1:m*model.nbVar,i); 
+  end
+end
+
+

kmeansClustering.m

+function [idList, Mu] = kmeansClustering(Data, nbStates)
+% Initialization of the model with k-means. 
+% Author:	Sylvain Calinon, Tohid Alizadeh, 2013
+%         http://programming-by-demonstration.org/
+
+%Criterion to stop the EM iterative update
+cumdist_threshold = 1e-10;
+maxIter = 100;
+
+%Initialization of the parameters
+[nbVar, nbData] = size(Data);
+cumdist_old = -realmax;
+nbStep = 0;
+
+idTmp = randperm(nbData);
+Mu = Data(:,idTmp(1:nbStates));
+
+%k-means iterations
+while 1
+  %E-step %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+  for i=1:nbStates
+    %Compute distances
+    distTmp(:,i) = sum((Data-repmat(Mu(:,i),1,nbData)).^2);
+  end
+  [vTmp,idList] = min(distTmp,[],2);
+  cumdist = sum(vTmp);
+  %M-step %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+  for i=1:nbStates
+    %Update the centers
+    Mu(:,i) = mean(Data(:,idList==i),2);
+  end
+  %Stopping criterion %%%%%%%%%%%%%%%%%%%%
+  if abs(cumdist-cumdist_old) < cumdist_threshold
+    break;
+  end
+  cumdist_old = cumdist;
+  nbStep = nbStep+1;
+%   if nbStep>maxIter
+%     disp(['Maximum number of iterations, ' num2str(maxIter) 'is reached']);
+%     break;
+%   end
+end
+
+%disp(['Kmeans stopped after ' num2str(nbStep) ' steps.']); 
+
+