if Gaussian_flag        Arg.type = 'combo-gaussian';        Arg.tag  = Noise.tag;        Arg.dim  = Noise.dim;        Arg.noiseSources = Noise.noiseSources;        NoiseDS = gennoiseds(Arg);    else        % Restructure NoiseDS data structure        NoiseDS.type = 'NoiseDS';        NoiseDS.ns_type = Noise.type;        NoiseDS.cov_type = Noise.cov_type;        NoiseDS.tag = Noise.tag;        NoiseDS.N = Noise.N;        NoiseDS.idxArr = Noise.idxArr;        NoiseDS.dim = Noise.dim;        NoiseDS.mu = Noise.mu;        NoiseDS.cov = Noise.cov;        NoiseDS.noiseSources = Noise.noiseSources;        NoiseDS.sample = @sample_combo;        NoiseDS.update = @update_combo;        NoiseDS.likelihood = @likelihood_combo;    end%===============================================================================================%--- Gaussian Mixture Modelcase 'gmm'    if isfield(ArgDS,'M')        Noise.M = ArgDS.M;    else        error(' [ gennoiseds::gmm ] Number of mixture components not specified.');    end    %-- assign noise source mean vector    if ~isfield(ArgDS,'mu')        Noise.mu = zeros(Noise.dim,Noise.M);    % default value    else        if (size(ArgDS.mu)==[Noise.dim Noise.M])            Noise.mu = ArgDS.mu;        else            error(' [ gennoiseds::gmm ] Centroid mean dimension error.');        end    end    %-- check for and assign cov_type    if isfield(ArgDS,'cov_type')        Noise.cov_type = ArgDS.cov_type;    else        warning(' [ gennoiseds::gmm ] Covariance type field .cov_type not assigned!. Assuming default value, ''full''');        Noise.cov_type = 'full';             % default cov_type    end    %-- assign mixing weights    if ~isfield(ArgDS,'weights')        Noise.weights = (1/Noise.M)*ones(1,Noise.M);    else        if (length(ArgDS.weights)==Noise.M)            Noise.weights = ArgDS.weights/(sum(ArgDS.weights));   % assign normalized weights        else            error(' [ gennoiseds::gmm ] Incorrect number of mixing weights (priors).');        end    end    %-- assign rest of noise source structure    switch (Noise.cov_type)    %.............................................................................................    case {'full','diag'}        %-- assign noise source covariance structure        if ~isfield(ArgDS,'cov')            Noise.cov = repmat(eye(Noise.dim),[1 1 Noise.M]);    % default value            warning(' [ gennoiseds::gmm ] Covariance field .cov not assigned!. Assuming default unity value.');        elseif ((Noise.M == 1) & (size(ArgDS.cov) == [Noise.dim Noise.dim])) | ...               ((Noise.M  > 1) & (size(ArgDS.cov) == [Noise.dim Noise.dim Noise.M]))            Noise.cov = ArgDS.cov;        else            error([' [ gennoiseds::gmm::full ] Noise source covariance matrix has incorrect dimensions.']);        end    %.............................................................................................    case {'sqrt','sqrt-diag'}        %-- assign noise source covariance structure        if ~isfield(ArgDS,'cov')            Noise.cov = repmat(eye(Noise.dim),[1 1 Noise.M]);    % default value            warning(' [ gennoiseds::gmm ] Covariance field .cov not assigned!. Assuming default unity value.');        elseif ((Noise.M == 1) & (size(ArgDS.cov) == [Noise.dim Noise.dim])) | ...               ((Noise.M  > 1) & (size(ArgDS.cov) == [Noise.dim Noise.dim Noise.M]))            Noise.cov = ArgDS.cov;        else            error([' [ gennoiseds::gmm::sqrt ] Noise source covariance matrix has incorrect dimensions.']);        end    %.............................................................................................    otherwise        error(' [ gennoiseds::gmm ] Unkown noise source cov_type.');    end    % Restructure NoiseDS data structure    NoiseDS.type = 'NoiseDS';    NoiseDS.ns_type = Noise.type;    NoiseDS.cov_type = Noise.cov_type;    NoiseDS.tag = Noise.tag;    NoiseDS.dim = Noise.dim;    NoiseDS.M = Noise.M;    NoiseDS.weights = Noise.weights;    NoiseDS.mu = Noise.mu;    NoiseDS.cov = Noise.cov;    NoiseDS.sample = @gmmsample;    NoiseDS.likelihood = @likelihood_gmm;%===============================================================================================otherwise  error([' [ gennoiseds ] Noise type ' type ' not supported.']);endNoiseDS.adaptMethod = [];%***********************************************************************************************%***                                                                                         ***%***                               SUB FUNCTION BLOCK                                        ***%***                                                                                         ***%***********************************************************************************************%===============================================================================================function NoiseDS = update_gaussian(NoiseDS)% Updates a Gaussian noise source%% This function is only a placeholder here since Gaussian noise sources are completely updated, i.e.% mean and covariance are set externally, i.e. there are no INTERNAL structure to update.%===============================================================================================function noise = sample_gaussian(NoiseDS, N)% Generate N samples of a noise source specified by the NoiseDS data structure    switch NoiseDS.cov_type    case 'full'        A = chol(NoiseDS.cov)';    case 'diag'        A = diag(sqrt(diag(NoiseDS.cov)));    case 'sqrt'        A = NoiseDS.cov;    case 'sqrt-diag'        A = NoiseDS.cov;    otherwise        error(' [ sample_gaussian ] Unknown cov_type.');    end    noise = A*randn(NoiseDS.dim,N) + cvecrep(NoiseDS.mu,N);%===============================================================================================function llh = likelihood_combo_gaussian(NoiseDS, noise, idxVec)% Calculates the likelihood of sample 'noise', given the noise model NoiseDS. If the optional index% vector 'idxVec' is specified, only those sub-noise sources are used. The 'noise' vector's dimension should% concur with the implied total dimensionality of 'idxVec'    if (nargin == 2)      idxVec = 1:NoiseDS.N;    end    numNS = length(idxVec);    [dim,nov] = size(noise);    idxArr = NoiseDS.idxArr;        % copy beginning/ending index array    llh = ones(1,nov);    % ... for each noise source    for j=1:numNS,        idx1 = idxArr(idxVec(j),1);        idx2 = idxArr(idxVec(j),2);        idxRange = idx1:idx2;        dim = idx2-idx1+1;        switch NoiseDS.cov_type        case 'full'            D  = det(NoiseDS.cov(idxRange,idxRange));            iP = inv(NoiseDS.cov(idxRange,idxRange));        case 'diag'            D = prod(diag(NoiseDS.cov(idxRange,idxRange)));            iP = diag(1./diag(NoiseDS.cov(idxRange,idxRange)));        case 'sqrt'            D = det(NoiseDS.cov(idxRange,idxRange))^2;            iS = inv(NoiseDS.cov(idxRange,idxRange));            iP = iS'*iS;        case 'sqrt-diag'            D = prod(diag(NoiseDS.cov(idxRange,idxRange)))^2;            iP = diag(1./(diag(NoiseDS.cov(idxRange,idxRange)).^2));        otherwise            error(' [ likelihood_gaussian ] Unkown cov_type.');        end        X = noise - cvecrep(NoiseDS.mu(idxRange),nov);        q = 1/sqrt((2*pi)^dim * D);        llh = llh .* (q * exp(-0.5*diag(X'*iP*X)'));    end    llh = llh + 1e-99; % needed to avoid 0 likelihood (cause ill conditioning)%===============================================================================================function llh = likelihood_combo(NoiseDS, noise, idxVec)% Calculates the likelihood of sample 'noise', given the noise model NoiseDS.% 'idxVec' is an optional index vector that can be used to indicate which of the N sub-noise sources should be used.% to calculate the likelihood... this also requires 'noise' to have the same dimension of the relevant sub-noise source.    if (nargin == 2)      idxVec = 1:NoiseDS.N;    end    numNS = length(idxVec);    [dim,nov] = size(noise);    idxArr = NoiseDS.idxArr;        % copy beginning/ending index array    llh = ones(1,nov);    % ... for each noise source    for j=1:numNS,        idx1 = idxArr(idxVec(j),1);        idx2 = idxArr(idxVec(j),2);        subNoiseDS = NoiseDS.noiseSources{idxVec(j)};        llh = llh .* feval(subNoiseDS.likelihood, subNoiseDS, noise(idx1:idx2,:));    end    llh = llh + 1e-99; % needed to avoid 0 likelihood (cause ill conditioning)%===============================================================================================function noise = sample_combo(NoiseDS, N)%  Generate N samples of a noise source specified by the NoiseDS data structure    noise=zeros(NoiseDS.dim,N);     % setup noise sample output buffer    idxArr = NoiseDS.idxArr;        % copy beginning/ending index array    % ... for each noise source    for j=1:NoiseDS.N        subNoiseDS = NoiseDS.noiseSources{j};        noise(idxArr(j,1):idxArr(j,2),:) = feval(subNoiseDS.sample, subNoiseDS, N);    end%===============================================================================================function noise = sample_combo_gaussian(NoiseDS, N)%  Generate N samples of a noise source specified by the NoiseDS data structure    noise=cvecrep(NoiseDS.mu,N);    % setup noise sample output buffer    idxArr = NoiseDS.idxArr;        % copy beginning/ending index array    num = NoiseDS.N;    for j=1:num        ind1 = idxArr(j,1);        ind2 = idxArr(j,2);        switch NoiseDS.cov_type        case 'full'            A = chol(NoiseDS.cov(ind1:ind2,ind1:ind2))';        case 'diag'            A = diag(sqrt(diag(NoiseDS.cov(ind1:ind2,ind1:ind2))));        case 'sqrt'            A = NoiseDS.cov(ind1:ind2,ind1:ind2);        case 'sqrt-diag'            A = NoiseDS.cov(ind1:ind2,ind1:ind2);        otherwise            error(' [ sample_gaussian ] Unkown cov_type.');        end        noise(ind1:ind2,:) = noise(ind1:ind2,:) + A*randn(ind2-ind1+1,N);    end%===============================================================================================function NoiseDS = update_combo_gaussian(NoiseDS)% Updates a 'combination Gaussian' noise source which has N Gaussian sub noise sources. The global mean and covariance% is updated externally and then this function is called to update the internal sub-noise source structure.%idxArr = NoiseDS.idxArr;for j=1:NoiseDS.N,    ind1 = idxArr(j,1);    ind2 = idxArr(j,2);    idxRange = ind1:ind2;    NoiseDS.noiseSources{j}.mu = NoiseDS.mu(idxRange,1);    NoiseDS.noiseSources{j}.cov = NoiseDS.cov(idxRange,idxRange);end%===============================================================================================function noise = sample_gamma(NoiseDS, N)% Generate N samples of a noise source specified by the NoiseDS data structure    alpha = NoiseDS.alpha;    beta = NoiseDS.beta;    if (alpha==1)        noise = -log(1-rand(1,N))*beta;        return    end    flag=0;    if (alpha<1)        flag=1;        alpha=alpha+1;    end    gamma=alpha-1;    eta=sqrt(2.0*alpha-1.0);    c=.5-atan(gamma/eta)/pi;    y(N)=0;    for k=1:N,        aux=-.5;        while(aux<0)            y(k)=-.5;            while(y(k)<=0)                u=rand(1,1);                y(k) = gamma + eta * tan(pi*(u-c)+c-.5);            end            v=-log(rand(1,1));            aux=v+log(1.0+((y(k)-gamma)/eta)^2)+gamma*log(y(k)/gamma)-y(k)+gamma;        end    end    if (flag==1)        noise = y.*beta.*(rand(1,N)).^(1.0/(alpha-1));    else        noise = y.*beta;    end%===============================================================================================function llh = likelihood_gamma(NoiseDS, noise)% Calculates the likelihood of sample 'noise', given the noise model NoiseDS.    llh = noise.^(NoiseDS.alpha-1) .* exp((-1/NoiseDS.beta)*noise);%===============================================================================================function llh = likelihood_gmm(NoiseDS, noise)% Calculates the likelihood of sample 'noise', given the noise model NoiseDS.[prior,likelihood,evidence] = gmmprobability(NoiseDS, noise);llh = evidence;