--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/m-toolbox/classes/@ao/sDomainFit.m	Wed Nov 23 19:22:13 2011 +0100
@@ -0,0 +1,669 @@
+% sDomainFit performs a fitting loop to identify model order and
+% parameters.
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%
+% DESCRIPTION: sDomainFit fit a partial fraction model to frequency
+%     response data using  the function utils.math.vcfit.
+%
+%     The function performs a fitting loop to automatically identify model
+%     order and parameters in s-domain. Output is a s-domain model expanded
+%     in partial fractions:
+%
+%              r1              rN
+%     f(s) = ------- + ... + ------- + d
+%            s - p1          s - pN
+%
+%     The function attempt to perform first the identification of a model
+%     with d = 0, then if the operation do not succeed, it try the
+%     identification with d different from zero.
+%     %     Identification loop stop when the stop condition is reached.
+%     Stop criterion is based on three different approachs:
+%
+%     1) Mean Squared Error and variation
+%     Check if the normalized mean squared error is lower than the value specified in
+%     FITTOL and if the relative variation of the mean squared error is lower
+%     than the value specified in MSEVARTOL.
+%     E.g. FITTOL = 1e-3, MSEVARTOL = 1e-2 search for a fit with
+%     normalized magnitude error lower than 1e-3 and and MSE relative
+%     variation lower than 1e-2.
+%
+%     1) Log residuals difference and root mean squared error
+%     Log Residuals difference
+%     Check if the minimum of the logarithmic difference between data and
+%     residuals is larger than a specified value. ie. if the conditioning
+%     value is 2, the function ensures that the difference between data and
+%     residuals is at lest 2 order of magnitude lower than data itsleves.
+%     Root Mean Squared Error
+%     Check that the variation of the root mean squared error is lower than
+%     10^(-1*value).
+%
+%     2) Residuals spectral flatness and root mean squared error
+%     Residuals Spectral Flatness
+%     In case of a fit on noisy data, the residuals from a good fit are
+%     expected to be as much as possible similar to a white noise. This
+%     property can be used to test the accuracy of a fit procedure. In
+%     particular it can be tested that the spectral flatness coefficient of
+%     the residuals is larger than a certain qiantity sf such that 0<sf<1.
+%     Root Mean Squared Error
+%     Check that the variation of the root mean squared error is lower than
+%     10^(-1*value).
+%
+%     Both in the first and second approach the fitting loop stops when the
+%     two stopping conditions are satisfied.
+%     The output are AOs containing the frequency response of the fitted
+%     model, while the Model parameters are output as a parfrac model
+%     in the output AOs procinfo filed.
+%
+%     The function can also perform a single loop without taking care of
+%     the stop conditions. This happens when 'AutoSearch' parameter is
+%     setted to 'off'.
+%
+%     If you provide more than one AO as input, they will be fitted
+%     together with a common set of poles.
+%
+% CALL:         mod = sDomainFit(a, pl)
+%
+% INPUTS:      a  - input AOs to fit to. If you provide more than one AO as
+%                   input, they will be fitted together with a common set
+%                   of poles. Only frequency domain (fsdata) data can be
+%                   fitted. Each non fsdata object will be ignored. Input
+%                   objects must have the same number of elements.
+%              pl - parameter list (see below)
+%
+% OUTPUTS:
+%               mod - matrix of one parfrac object for each input AO.
+%                     Usseful fit information are stored in the procinfoi
+%                     field:
+%                     FIT_RESP  - model frequency response.
+%                     FIT_RESIDUALS - analysis object containing the fit
+%                     residuals.
+%                     FIT_MSE - analysis object containing the mean squared
+%                     error progression during the fitting loop.
+%
+%
+% Note: all the input objects are assumed to caontain the same X
+% (frequencies) values
+%
+%
+% EXAMPLES:
+%
+% 1) Fit to a frequency-series using Mean Squared Error and variation stop
+% criterion
+%
+%   % Create a frequency-series AO
+%   pl_data = plist('fsfcn', '0.01./(0.0001+f)', 'f1', 1e-5, 'f2', 5, 'nf', 1000);
+%   a = ao(pl_data);
+%
+%   % Fitting parameter list
+%   pl_fit = plist('AutoSearch','on',...
+%   'StartPoles',[],...
+%   'StartPolesOpt','clog',...
+%   'maxiter',5,...
+%   'minorder',2,...
+%   'maxorder',20,...
+%   'weights',[],...
+%   'CONDTYPE','MSE',...
+%   'FITTOL',1e-3,...
+%   'MSEVARTOL',1e-2,...
+%   'Plot','off',...
+%   'ForceStability','off',...
+%   'direct term','off',...
+%   'CheckProgress','off');
+%
+%   % Do fit
+%   b = sDomainFit(a, pl_fit);
+%
+% 2) Fit to a frequency-series using Log residuals difference and mean
+% squared error variation stop criterion
+%
+%   % Create a frequency-series AO
+%   pl_data = plist('fsfcn', '0.01./(0.0001+f)', 'f1', 1e-5, 'f2', 5, 'nf', 1000);
+%   a = ao(pl_data);
+%
+%   % Fitting parameter list
+%   pl_fit = plist('FS',[],...
+%   'AutoSearch','on',...
+%   'StartPoles',[],...
+%   'StartPolesOpt','clog',...
+%   'maxiter',5,...
+%   'minorder',2,...
+%   'maxorder',20,...
+%   'weights',[],...
+%   'weightparam','abs',...
+%   'CONDTYPE','RLD',...
+%   'FITTOL',1e-3,...
+%   'MSEVARTOL',1e-2,...
+%   'Plot','off',...
+%   'ForceStability','off',...
+%   'CheckProgress','off');
+%
+%   % Do fit
+%   b = sDomainFit(a, pl_fit);
+%
+% 3) Fit to a frequency-series using Residuals spectral flatness and mean
+% squared error variation stop criterion
+%
+%   % Create a frequency-series AO
+%   pl_data = plist('fsfcn', '0.01./(0.0001+f)', 'f1', 1e-5, 'f2', 5, 'nf', 1000);
+%   a = ao(pl_data);
+%
+%   % Fitting parameter list
+%   pl_fit = plist('FS',[],...
+%   'AutoSearch','on',...
+%   'StartPoles',[],...
+%   'StartPolesOpt','clog',...
+%   'maxiter',5,...
+%   'minorder',2,...
+%   'maxorder',20,...
+%   'weights',[],...
+%   'weightparam','abs',...
+%   'CONDTYPE','RSF',...
+%   'FITTOL',0.5,...
+%   'MSEVARTOL',1e-2,...
+%   'Plot','off',...
+%   'ForceStability','off',...
+%   'CheckProgress','off');
+%
+%   % Do fit
+%   b = sDomainFit(a, pl_fit);
+%
+%
+% <a href="matlab:utils.helper.displayMethodInfo('ao', 'sDomainFit')">Parameters Description</a>
+%
+% VERSION:     $Id: sDomainFit.m,v 1.32 2011/08/15 09:46:44 hewitson Exp $
+%
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+function varargout = sDomainFit(varargin)
+  
+  % Check if this is a call for parameters
+  if utils.helper.isinfocall(varargin{:})
+    varargout{1} = getInfo(varargin{3});
+    return
+  end
+  
+  import utils.const.*
+  utils.helper.msg(msg.PROC3, 'running %s/%s', mfilename('class'), mfilename);
+  
+  % Collect input variable names
+  in_names = cell(size(varargin));
+  for ii = 1:nargin,in_names{ii} = inputname(ii);end
+  
+  % Collect all AOs and plists
+  [as, ao_invars] = utils.helper.collect_objects(varargin(:), 'ao', in_names);
+  pl              = utils.helper.collect_objects(varargin(:), 'plist', in_names);
+  
+  if nargout == 0
+    error('### sDomainFit cannot be used as a modifier. Please give an output variable.');
+  end
+  
+  %%% Decide on a deep copy or a modify
+  bs = copy(as, nargout);
+  inhists = [as.hist];
+  
+  % combine plists
+  pl = parse(pl, getDefaultPlist());
+  
+  %%%%% Extract necessary parameters %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+  
+  extpoles = find(pl, 'StartPoles'); % Check if external poles are providied
+  spolesopt = 0;
+  if isempty(extpoles) % if no external poles set them internally
+    splopt = find(pl, 'StartPolesOpt');
+    switch lower(splopt)
+      case 'real'
+        spolesopt = 1;
+      case 'clog'
+        spolesopt = 2;
+      case 'clin'
+        spolesopt = 3;
+    end
+  end
+  
+  maxiter = find(pl, 'maxiter'); % set the maximum number of iterations
+  minorder = find(pl, 'minorder'); % set the minimum function order
+  maxoredr = find(pl, 'maxorder');% set the maximum function order
+  
+  extweights = find(pl, 'weights'); % check if external weights are provided
+  weightparam = 0;
+  if isempty(extweights) % set internally the weights on the basis of the input options
+    wtparam = find(pl, 'weightparam');
+    switch lower(wtparam)
+      case 'ones'
+        weightparam = 1;
+      case 'abs'
+        weightparam = 2;
+      case 'sqrt'
+        weightparam = 3;
+    end
+  end
+  
+  % decide to plot or not
+  plt = find(pl, 'plot');
+  switch lower(plt)
+    case 'on'
+      showplot = 1;
+    case 'off'
+      showplot = 0;
+  end
+  
+  % Make a decision between Fit conditioning type
+  condtype = find(pl, 'CONDTYPE');
+  condtype = upper(condtype);
+  switch condtype
+    case 'MSE'
+      ctp = 'chivar'; % use normalized mean squared error value and relative variation
+      lrscond = find(pl, 'FITTOL');
+      % give an error for strange values of lrscond
+      if lrscond<0
+        error('!!! Negative values for FITTOL are not allowed !!!')
+      end
+      % handling data
+      lrscond = -1*log10(lrscond);
+      % give a warning for strange values of lrscond
+      if lrscond<0
+        warning('You are searching for a MSE lower than %s', num2str(10^(-1*lrscond)))
+      end
+    case 'RLD'
+      ctp = 'lrsmse'; % use residuals log difference and MSE relative variation
+      lrscond = find(pl, 'FITTOL');
+      % give a warning for strange values of lrscond
+      if lrscond<0
+        error('!!! Negative values for FITTOL are not allowed !!!')
+      end
+      if lrscond<1
+        warning('You are searching for a frequency by frequency residuals log difference of %s', num2str(lrscond))
+      end
+    case 'RSF'
+      ctp = 'rftmse'; % use residuals spectral flatness and MSE relative variation
+      lrscond = find(pl, 'FITTOL');
+      % give a warning for strange values of lrscond
+      if lrscond<0 || lrscond>1
+        error('!!! Values <0 or >1 for FITTOL are not allowed when CONDTYPE is RSF !!!')
+      end
+  end
+  
+  % Tolerance for the MSE relative variation
+  msevar = find(pl, 'MSEVARTOL');
+  % handling data
+  msevar = -1*log10(msevar);
+  % give a warning for strange values of msevar
+  if msevar<0
+    warning('You are searching for MSE relative variation lower than %s', num2str(10^(-1*msevar)))
+  end
+  
+  % decide to stabilize or not the model
+  stab = find(pl, 'ForceStability');
+  switch lower(stab)
+    case 'on'
+      stabfit = 1;
+    case 'off'
+      stabfit = 0;
+  end
+  
+  % decide to fit with or whitout direct term
+  dtm = find(pl, 'direct term');
+  switch lower(dtm)
+    case 'on'
+      dterm = 1;
+    case 'off'
+      dterm = 0;
+  end
+  
+  % decide to disp or not the fitting progress in matlab command window
+  prg = find(pl, 'CheckProgress');
+  switch lower(prg)
+    case 'on'
+      spy = 1;
+    case 'off'
+      spy = 0;
+  end
+  
+  % decide to perform or not a full automatic model search
+  autos = find(pl, 'AutoSearch');
+  switch lower(autos)
+    case 'on'
+      fullauto = 1;
+    case 'off'
+      fullauto = 0;
+  end
+  
+  % extract delay
+  delay = find(pl, 'delay');
+  
+  %%%%% End Extract necessary parameters %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+  
+  
+  
+  %%%%% Fitting %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+  
+  % Fit parameters
+  params = struct('spolesopt',spolesopt,...
+    'extpoles', extpoles,...
+    'Nmaxiter',maxiter,...
+    'minorder',minorder,...
+    'maxorder',maxoredr,...
+    'weightparam',weightparam,...
+    'extweights', extweights,...
+    'plot',showplot,...
+    'ctp',ctp,...
+    'lrscond',lrscond,...
+    'msevar',msevar,...
+    'stabfit',stabfit,...
+    'dterm',dterm,...
+    'spy',spy,...
+    'fullauto',fullauto);
+  
+  %%% extracting elements from AOs
+  
+  % Finding the index of the first fsdata
+  for gg = 1:numel(bs)
+    if isa(bs(gg).data, 'fsdata')
+      prm = gg;
+      break
+    end
+  end
+  
+  y = zeros(length(bs(prm).data.getY),numel(bs)); % initialize input vector
+  k = numel(bs(prm).data.getY); % setting a comparison constant
+  idx = true(numel(bs),1); % initialize the control index
+  for jj=1:numel(bs)
+    % checking that AOs are fsdata and skipping non fsdata objects
+    if ~isa(bs(jj).data, 'fsdata')
+      % skipping data if non fsdata
+      warning('!!! %s expects ao/fsdata objects. Skipping AO %s', mfilename, ao_invars{jj});
+      idx(jj) = false; % set the corresponding value of the control index to false
+    else
+      % preparing data for fit
+      yt = bs(jj).data.getY;
+      if numel(yt)~=k
+        error('Input AOs must have the same number of elements')
+      end
+      if size(yt,2)>1 % wish to work with columns
+        y(:,jj) = yt.';
+      else
+        y(:,jj) = yt;
+      end
+    end
+  end
+  %%% extracting frequencies
+  % Note: all the objects are assumed to caontain the same X (frequencies) values
+  f = bs(prm).data.getX;
+  
+  % reshaping y to contain only Y from fsdata, subtract delay if given by
+  % user
+  if ~isempty(delay)
+    y = y(:,idx)./exp(-2*pi*1i*f*delay);
+  else
+    y = y(:,idx);
+  end
+  
+  % Fitting loop
+  [res,poles,dterm,mresp,rdl,mse] = utils.math.autocfit(y,f,params);
+  
+  %%%%% Building output AOs with model responses, model parameters are %%%%
+  
+  for kk = 1:numel(bs)
+    if idx(kk) % set the corresponding Y values of fitted data
+      
+      % if delay is input we return a pzmodel with the corresponding delay
+      if isempty(delay)
+        mdl(kk) = parfrac(plist('res', res(:,kk),'poles', poles, 'dir',...
+          dterm(:,kk), 'name', sprintf('fit(%s)', ao_invars{kk})));
+      else
+        mdl_aux = parfrac(plist('res', res(:,kk),'poles', poles, 'dir',...
+          dterm(:,kk), 'name', sprintf('fit(%s)', ao_invars{kk})));
+        mdl(kk) = pzmodel(mdl_aux);
+        mdl(kk).setDelay(delay);
+      end
+      
+      % Output also response, residuals and mse progression in the procinfo
+      
+      rsp = mresp(:,kk);
+      bs(kk).data.setY(rsp);
+      
+      % Set output AO name
+      bs(kk).name = sprintf('fit(%s)', ao_invars{kk});
+      
+      res_ao = copy(bs(kk),1);
+      trdl = rdl(:,kk);
+      res_ao.data.setY(trdl);
+      
+      % Set output AO name
+      res_ao.name = sprintf('fit_residuals(%s)', ao_invars{kk});
+      
+      d = cdata();
+      tmse = mse(:,kk);
+      d.setY(tmse);
+      mse_ao = ao(d);
+      
+      % Set output AO name
+      mse_ao.name = sprintf('fit_mse(%s)', ao_invars{kk});
+      
+      procpl = plist('fit_resp',bs(kk),...
+        'fit_residuals',res_ao,...
+        'fit_mse',mse_ao);
+      
+      mdl(kk).setProcinfo(procpl);
+      
+    else
+      mdl(kk) = parfrac();
+    end
+    
+  end
+  
+  % set output as matrix if multiple inputs
+  if numel(mdl) ~= 1
+    mmdl = matrix(mdl);
+  else
+    mmdl = mdl;
+  end
+  
+  mmdl.setName(sprintf('fit(%s)', ao_invars{:}));
+  
+  mmdl.addHistory(getInfo('None'), pl, [ao_invars(:)], [inhists(:)]);
+  
+  % ----- Set outputs -----
+  if nargout == 1
+    varargout{1} = mmdl;
+  else
+    % multiple output is not supported
+    error('### Multiple output is not supported ###')
+  end
+  
+end
+
+%--------------------------------------------------------------------------
+% Get Info Object
+%--------------------------------------------------------------------------
+function ii = getInfo(varargin)
+  if nargin == 1 && strcmpi(varargin{1}, 'None')
+    sets = {};
+    pl   = [];
+  else
+    sets = {'Default'};
+    pl   = getDefaultPlist;
+  end
+  % Build info object
+  ii = minfo(mfilename, 'ao', 'ltpda', utils.const.categories.sigproc, '$Id: sDomainFit.m,v 1.32 2011/08/15 09:46:44 hewitson Exp $', sets, pl);
+  ii.setModifier(false);
+end
+
+%--------------------------------------------------------------------------
+% Get Default Plist
+%--------------------------------------------------------------------------
+function plout = getDefaultPlist()
+  persistent pl;  
+  if exist('pl', 'var')==0 || isempty(pl)
+    pl = buildplist();
+  end
+  plout = pl;  
+end
+
+function pl = buildplist()
+  
+  pl = plist();
+  
+  % AutoSearch
+  p = param({'AutoSearch', ['''on'': Parform a full automatic search for the<br>'...
+    'transfer function order. The fitting<br>'...
+    'procedure will stop when stop conditions<br>'...
+    'defined are satisfied.<br>'...
+    '''off'': Perform a fitting loop as long as the<br>'...
+    'number of iteration reach ''maxiter''. The order<br>'...
+    'of the fitting function will be that<br>'...
+    'specified in ''minorder''.']}, ...
+    {1, {'on', 'off'}, paramValue.SINGLE});
+  pl.append(p);
+  
+  % StartPoles
+  p = param({'StartPoles', ['A vector of starting poles. Providing a fixed<br>'...
+    'set of starting poles fixes the function<br>'...
+    'order. If it is left empty starting poles are<br>'...
+    'internally assigned.']}, paramValue.EMPTY_DOUBLE);
+  pl.append(p);
+  
+  % StartPolesOpt
+  p = param({'StartPolesOpt', ['Define the characteristics of internally<br>'...
+    'assigned starting poles. Admitted values<br>'...
+    'are:<ul>'...
+    '<li>''real'' linear-spaced real poles</li>'...
+    '<li>''clog'' log-spaced complex poles</li>'...
+    '<li>''clin'' linear-spaced complex poles<li></ul>']}, ...
+    {2, {'real', 'clog', 'clin'}, paramValue.SINGLE});
+  pl.append(p);
+  
+  % MaxIter
+  p = param({'MaxIter', 'Maximum number of iterations in fit routine.'}, paramValue.DOUBLE_VALUE(50));
+  pl.append(p);
+  
+  % MinOrder
+  p = param({'MinOrder', 'Minimum order to fit with.'}, paramValue.DOUBLE_VALUE(2));
+  pl.append(p);
+  
+  % MaxOrder
+  p = param({'MaxOrder', 'Maximum order to fit with.'}, paramValue.DOUBLE_VALUE(20));
+  pl.append(p);
+  
+  % Weights
+  p = param({'Weights', ['A vector with the desired weights. If a single<br>'...
+    'Ao is input weights must be a Nx1 vector where<br>'...
+    'N is the number of elements in the input Ao. If<br>'...
+    'M Aos are passed as input, then weights must<br>'...
+    'be a NxM matrix. If it is leaved empty weights<br>'...
+    'are internally assigned basing on the input<br>'...
+    'parameters']}, paramValue.EMPTY_DOUBLE);
+  pl.append(p);
+  
+  % Weightparam
+  p = param({'weightparam', ['Specify the characteristics of the internally<br>'...
+    'assigned weights. Admitted values are:<ul>'...
+    '<li>''ones'' assigns weights equal to 1 to all data.<li>'...
+    '<li>''abs'' weights data with <tt>1./abs(y)</tt></li>'...
+    '<li>''sqrt'' weights data with <tt>1./sqrt(abs(y))</tt></li>']}, ...
+    {2, {'ones', 'abs', 'sqrt'}, paramValue.SINGLE});
+  pl.append(p);
+  
+  % CONDTYPE
+  p = param({'CONDTYPE', ['Fit conditioning type. Admitted values are:<ul>'...
+    '<li>''MSE'' Mean Squared Error and variation</li>'...
+    '<li>''RLD'' Log residuals difference and mean squared error variation<li>'...
+    '<li>''RSF'' Residuals spectral flatness and mean squared error variation<li></ul>']}, ...
+    {1, {'MSE', 'RLD', 'RSF'}, paramValue.SINGLE});
+  pl.append(p);
+  
+  % FITTOL
+  p = param({'FITTOL', 'Fit tolerance.'}, paramValue.DOUBLE_VALUE(1e-3));
+  pl.append(p);
+  
+  % MSEVARTOL
+  p = param({'MSEVARTOL', ['Mean Squared Error Variation - Check if the<br>'...
+    'realtive variation of the mean squared error is<br>'...
+    'smaller than the value specified. This<br>'...
+    'option is useful for finding the minimum of Chi-squared.']}, ...
+    paramValue.DOUBLE_VALUE(1e-2));
+  pl.append(p);
+  
+  % Plot
+  p = param({'Plot', 'Plot results of each fitting step.'}, {2, {'on', 'off'}, paramValue.SINGLE});
+  p.val.setValIndex(2);
+  pl.append(p);
+  
+  % ForceStability
+  p = param({'ForceStability', 'Force poles to be stable'}, ...
+    {2, {'on', 'off'}, paramValue.SINGLE});
+  pl.append(p);
+  
+  % direct term
+  p = param({'direct term', 'Fit with direct term.'}, {2, {'on', 'off'}, paramValue.SINGLE});
+  pl.append(p);
+  
+  % CheckProgress
+  p = param({'CheckProgress', 'Display the status of the fit iteration.'}, ...
+    {2, {'on', 'off'}, paramValue.SINGLE});
+  pl.append(p);
+  
+  % Delay
+  p = param({'delay', 'Innput a delay that will be subtracted from the fit.<br>'...
+    'The output is a pzmodel which includes the inputted delay.'},paramValue.EMPTY_DOUBLE);
+  pl.append(p);
+end
+% END
+
+
+% PARAMETERS:
+%             'AutoSearch'  - 'on': Parform a full automatic search for the
+%                             transfer function order. The fitting
+%                             procedure will stop when stop conditions
+%                             defined are satisfied. [Default]
+%                             'off': Perform a fitting loop as long as the
+%                             number of iteration reach 'maxiter'. The order
+%                             of the fitting function will be that
+%                             specified in 'minorder'.
+%             'StartPoles'  - A vector of starting poles. Providing a fixed
+%                             set of starting poles fixes the function
+%                             order. If it is left empty starting poles are
+%                             internally assigned. [Default []]
+%             'StartPolesOpt' - Define the characteristics of internally
+%                               assigned starting poles. Admitted values
+%                               are:
+%                               'real' linspaced real poles
+%                               'clog' logspaced complex poles [Default]
+%                               'clin' linspaced complex poles
+%             'maxiter'   - Maximum number of allowed iteration. [Deafult
+%                           50].
+%             'minorder'  - Minimum model function order. [Default 2]
+%             'maxorder'  - Maximum model function order. [Default 20]
+%             'weights'   - A vector with the desired weights. If a single
+%                           Ao is input weights must be a Nx1 vector where
+%                           N is the number of elements in the input Ao. If
+%                           M Aos are passed as input, then weights must
+%                           be a NxM matrix. If it is leaved empty weights
+%                           are internally assigned basing on the input
+%                           parameters. [Default []]
+%             'weightparam' - Specify the characteristics of the internally
+%                             assigned weights. Admitted values are:
+%                             'ones' assigns weights equal to 1 to all data.
+%                             'abs' weights data with 1./abs(y) [Default]
+%                             'sqrt' weights data with 1./sqrt(abs(y))
+%             'CONDTYPE'  - Fit conditioning type. Admitted values are:
+%                             - 'MSE' Mean Squared Error and variation
+%                             [Default]
+%                             - 'RLD' Log residuals difference and mean
+%                             squared error variation
+%                             - 'RSF' Residuals spectral flatness and mean
+%                             squared error variation
+%               'FITTOL'  - Fit tolerance [Default, 1e-3]
+%           'MSEVARTOL'   - This allow to check if the relative variation
+%                           of mean squared error is lower than the value
+%                           sepcified. [Default 1e-2]
+%             'Plot'        - Plot fit result: 'on' or 'off' [default]
+%             'ForceStability'  - Force poles to be stable, values are
+%                                 'on' or 'off'. [Default 'off']
+%             'direct term' - Fit with direct term if 'on', without if
+%                             'off'. [Default 'off']
+%             'CheckProgress' - Disply the status of the fit iteration.
+%                               Values are 'on and 'off'. [Default 'off']
+%
+%
+