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MOD.m
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MOD.m
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function [Dictionary,output] = MOD(Data,param)
% =========================================================================
% MOD algorithm
% =========================================================================
% Given for comparison reasons only. For detils please see the paper
% "Method of optimal directions for frame design", written by K. Engan,
% S.O. Aase, and J.H. Husfy, appeared in the IEEE International Conference
% on Acoustics, Speech, and Signal Processing, 1999.
% =========================================================================
% INPUT ARGUMENTS:
% Data an nXN matrix that contins N signals (Y), each of dimension n.
% param structure that includes all required
% parameters for the K-SVD execution.
% Required fields are:
% K, ... the number of dictionary elements to train
% numIteration,... number of iterations to perform.
% errorFlag... if =0, a fix number of coefficients is
% used for representation of each signal. If so, param.L must be
% specified as the number of representing atom. if =1, arbitrary number
% of atoms represent each signal, until a specific representation error
% is reached. If so, param.errorGoal must be specified as the allowed
% error.
% (optional, see errorFlag) L,... % maximum coefficients to use in OMP coefficient calculations.
% (optional, see errorFlag) errorGoal, ... % allowed representation error in representing each signal.
% InitializationMethod,... mehtod to initialize the dictionary, can
% be one of the following arguments:
% * 'DataElements' (initialization by the signals themselves), or:
% * 'GivenMatrix' (initialization by a given matrix param.initialDictionary).
% (optional, see InitializationMethod) initialDictionary,... % if the initialization method
% is 'GivenMatrix', this is the matrix that will be used.
% preserveDCAtom, ... =1 for a DC atom (in which all entries are equal) to be generated, and
% not changed throughout the training.
% (optional) TrueDictionary, ... % if specified, in each
% iteration the difference between this dictionary and the trained one
% is measured and displayed.
% displayProgress, ... if =1 progress information is displyed. If param.errorFlag==0,
% the average repersentation error (RMSE) is displayed, while if
% param.errorFlag==1, the average number of required coefficients for
% representation of each signal is displayed.
% =========================================================================
% OUTPUT ARGUMENTS:
% Dictionary The extracted dictionary of size nX(param.K).
% output Struct that contains information about the current run. It may include the following fields:
% CoefMatrix The final coefficients matrix (it should hold that Data equals approximately Dictionary*output.CoefMatrix.
% ratio If the true dictionary was defined (in
% synthetic experiments), this parameter holds a vector of length
% param.numIteration that includes the detection ratios in each
% iteration).
% totalerr The total representation error after each
% iteration (defined only if
% param.displayProgress=1 and
% param.errorFlag = 0)
% numCoef A vector of length param.numIteration that
% include the average number of coefficients required for representation
% of each signal (in each iteration) (defined only if
% param.displayProgress=1 and
% param.errorFlag = 1)
% =========================================================================
if (size(Data,2) < param.K)
disp('Size of data is smaller than the dictionary size. Trivial solution...');
Dictionary = Data(:,1:size(Data,2));
return;
elseif (strcmp(param.InitializationMethod,'DataElements'))
Dictionary(:,1:param.K) = Data(:,1:param.K);
elseif (strcmp(param.InitializationMethod,'GivenMatrix'))
Dictionary = param.initialDictionary;
end
%normalize the dictionary.
Dictionary = Dictionary*diag(1./sqrt(sum(Dictionary.*Dictionary)));
Dictionary = Dictionary.*repmat(sign(Dictionary(1,:)),size(Dictionary,1),1); % multiply in the sign of the first element.
K = size(Dictionary,2);
totalErr = zeros(1,param.numIteration);
if (size(param.TrueDictionary)==size(Dictionary))
displayErrorWithTrueDictionary = 1;
ErrorBetweenDictionaries = zeros(param.numIteration+1,1);
ratio = zeros(param.numIteration+1,1);
else
displayErrorWithTrueDictionary = 0;
end
numCoef = param.L;
for iterNum = 1:param.numIteration
% find the coefficients
if (param.errorFlag==0)
%CoefMatrix = mexOMPIterative2(Data, [FixedDictionaryElement,Dictionary],param.L);
CoefMatrix = OMP(Dictionary,Data, param.L);
else
%CoefMatrix = mexOMPerrIterative(Data, [FixedDictionaryElement,Dictionary],param.errorGoal);
CoefMatrix = OMPerr(Dictionary,Data, param.errorGoal);
param.L = 1;
end
% improve the dictionary
Dictionary = Data*CoefMatrix'*inv(CoefMatrix*CoefMatrix' + 1e-7*speye(size(CoefMatrix,1)));
sumDictElems = sum(abs(Dictionary));
zerosIdx = find(sumDictElems<eps);
Dictionary(:,zerosIdx) = randn(size(Dictionary,1),length(zerosIdx));
Dictionary = Dictionary*diag(1./sqrt(sum(Dictionary.*Dictionary)));
if (iterNum>1 & param.displayProgress)
if (param.errorFlag==0)
output.totalerr(iterNum-1) = sqrt(sum(sum((Data-Dictionary*CoefMatrix).^2))/prod(size(Data)));
disp(['Iteration ',num2str(iterNum),' Total error is: ',num2str(output.totalerr(iterNum-1))]);
else
output.numCoef(iterNum-1) = length(find(CoefMatrix))/size(Data,2);
disp(['Iteration ',num2str(iterNum),' Average number of coefficients: ',num2str(output.numCoef(iterNum-1))]);
end
end
if (displayErrorWithTrueDictionary )
[ratio(iterNum+1),ErrorBetweenDictionaries(iterNum+1)] = I_findDistanseBetweenDictionaries(param.TrueDictionary,Dictionary);
disp(strcat(['Iteration ', num2str(iterNum),' ratio of restored elements: ',num2str(ratio(iterNum+1))]));
output.ratio = ratio;
end
end
output.CoefMatrix = CoefMatrix;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% findDistanseBetweenDictionaries
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [ratio,totalDistances] = I_findDistanseBetweenDictionaries(original,new)
% first, all the column in oiginal starts with positive values.
catchCounter = 0;
totalDistances = 0;
for i = 1:size(new,2)
new(:,i) = sign(new(1,i))*new(:,i);
end
for i = 1:size(original,2)
d = sign(original(1,i))*original(:,i);
distances =sum ( (new-repmat(d,1,size(new,2))).^2);
[minValue,index] = min(distances);
errorOfElement = 1-abs(new(:,index)'*d);
totalDistances = totalDistances+errorOfElement;
catchCounter = catchCounter+(errorOfElement<0.01);
end
ratio = 100*catchCounter/size(original,2);