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view m-toolbox/classes/@matrix/tdfit.m @ 17:7afc99ec5f04 database-connection-manager
Update ao_model_retrieve_in_timespan
author | Daniele Nicolodi <nicolodi@science.unitn.it> |
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date | Mon, 05 Dec 2011 16:20:06 +0100 |
parents | f0afece42f48 |
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% TDFIT fit a MATRIX of transfer function SMODELs to a matrix of input and output signals. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % % DESCRIPTION: TDFIT fits a MATRIX of transfer function SMODELs to a set of % input and output signals. It uses ao\tdfit as the core algorithm. % % % CALL: b = tdfit(outputs, pl) % % INPUTS: outputs - an array of MATRIXs representing the outputs of a system, % one per each experiment. % pl - parameter list (see below) % % OUTPUTs: b - a pest object containing the best-fit parameters, % goodness-of-fit reduced chi-squared, fit degree-of-freedom % covariance matrix and uncertainties. Additional % quantities, like the Information Matrix, are contained % within the procinfo. The best-fit model can be evaluated % from pest\eval. % % <a href="matlab:utils.helper.displayMethodInfo('matrix', 'tdfit')">Parameters Description</a> % % EXAMPLES: % % VERSION: $Id: tdfit.m,v 1.9 2011/04/08 08:56:31 hewitson Exp $ % % HISTORY: 09-02-2011 G. Congedo % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% function varargout = tdfit(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 ltpdauoh objects [mtxs, mtxs_invars] = utils.helper.collect_objects(varargin(:), 'matrix', in_names); [pl, invars] = utils.helper.collect_objects(varargin(:), 'plist'); % combine plists pl = parse(pl, getDefaultPlist()); if nargout == 0 error('### tdfit cannot be used as a modifier. Please give an output variable.'); end % combine plists pl = parse(pl, getDefaultPlist()); % Extract necessary parameters inputs = pl.find('inputs'); TFmodels = pl.find('models'); WhFlts = pl.find('WhFlts'); inNames = pl.find('innames'); outNames = pl.find('outnames'); P0 = pl.find('P0'); pnames = pl.find('pnames'); % Checks if ~isa(mtxs,'matrix') error('### Please, give the system outputs as an array of MATRIXs per each experiment.'); end if ~isa(inputs,'collection') && any(~isa(inputs.objs,'matrix')) error('### Please, give the system inputs as a COLLECTION of MATRIXs per each experiment.'); end if ~isa(TFmodels,'ssm') && ~isa(TFmodels,'matrix') && any(~isa(TFmodels.objs,'smodel')) error('### Please, give the system transfer functions as a MATRIX of SMODELs, or a single SSM model.'); end if ~isa(WhFlts,'matrix') && any(~isa(WhFlts.objs,'filterbank')) error('### Please, give the system inputs as a MATRIX of FILTERBANKs.'); end % Define constants Nexp = numel(mtxs); % Prepare objects for fit outputs2fit = prepare2fit(mtxs,'outputs',Nexp); inputs2fit = prepare2fit(inputs,'inputs',Nexp); WhFlts2fit = prepare2fit(WhFlts,'filters',Nexp); if ~isa(TFmodels,'ssm') models2fit = prepare2fit(TFmodels,'models',Nexp); else models2fit = TFmodels; end inNames2fit = prepare2fit(inNames,'inNames',Nexp); outNames2fit = prepare2fit(outNames,'outNames',Nexp); % fit plist fitpl = pl.pset('inputs', inputs2fit,... 'models', models2fit,... 'WhFlts', WhFlts2fit,... 'inNames', inNames2fit,... 'outNames', outNames2fit,... 'P0', P0,... 'pnames', pnames); % do fit params = tdfit(outputs2fit, fitpl); % Make output pest out = copy(params,1); % Set Name and History mdlname = char(TFmodels(1).name); for kk=2:numel(TFmodels) mdlname = strcat(mdlname,[',' char(TFmodels(kk).name)]); end out.name = sprintf('tdfit(%s)', mdlname); out.addHistory(getInfo('None'), pl, mtxs_invars(:), [mtxs(:).hist]); % Set outputs if nargout > 0 varargout{1} = out; end end %-------------------------------------------------------------------------- % Included Functions %-------------------------------------------------------------------------- function obj2fit = prepare2fit(obj,type,Nexp) switch type case 'outputs' obj2fit = obj(1).objs; case 'inputs' obj2fit = obj.objs{1}.objs; case 'filters' obj2fit = obj.objs; case 'inNames' obj2fit = obj; case 'outNames' obj2fit = obj; end if exist('obj2fit','var') if size(obj2fit)~=[numel(obj2fit),1] obj2fit = obj2fit'; end for ii=2:Nexp switch type case 'outputs' obj2cat = obj(ii).objs; case 'inputs' obj2cat = obj.objs{ii}.objs; case 'filters' obj2cat = obj.objs; case 'inNames' obj2cat = obj; case 'outNames' obj2cat = obj; end if size(obj2cat)~=[numel(obj2cat),1] obj2cat = obj2cat'; end obj2fit = [obj2fit;obj2cat]; end end if strcmp(type,'models') obj2fit = smodel(); % obj2fit.setXvar(); sz = size(obj.objs); obj2fit = repmat(obj2fit,Nexp*sz); for ii=1:Nexp obj2fit((1:sz(1))+(ii-1)*sz(1),(1:sz(2))+(ii-1)*sz(2)) = obj.objs; end 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, 'matrix', 'ltpda', utils.const.categories.sigproc, '$Id: tdfit.m,v 1.9 2011/04/08 08:56:31 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(); % Inputs p = param({'Inputs', 'A COLLECTION of MATRIXs, one per each experiment, containing the input A0s.'}, paramValue.EMPTY_DOUBLE); pl.append(p); % Models p = param({'Models', 'A MATRIX of transfer function SMODELs.'}, paramValue.EMPTY_DOUBLE); pl.append(p); % PadRatio p = param({'PadRatio', ['PadRatio is defined as the ratio between the number of zero-pad points '... 'and the data length.<br>'... 'Define how much to zero-pad data after the signal.<br>'... 'Being <tt>tdfit</tt> a fft-based algorithm, no zero-padding might bias the estimation, '... 'therefore it is strongly suggested to do that.']}, 1); pl.append(p); % Whitening Filters p = param({'WhFlts', 'A MATRIX of FILTERBANKs containing the whitening filters per each output AO.'}, paramValue.EMPTY_DOUBLE); pl.append(p); % Parameters p = param({'Pnames', 'A cell-array of parameter names to fit.'}, paramValue.EMPTY_CELL); pl.append(p); % P0 p = param({'P0', 'An array of starting guesses for the parameters.'}, paramValue.EMPTY_DOUBLE); pl.append(p); % LB p = param({'LB', ['Lower bounds for the parameters.<br>'... 'This improves convergency. Mandatory for Monte Carlo.']}, paramValue.EMPTY_DOUBLE); pl.append(p); % UB p = param({'UB', ['Upper bounds for the parameters.<br>'... 'This improves the convergency. Mandatory for Monte Carlo.']}, paramValue.EMPTY_DOUBLE); pl.append(p); % Algorithm p = param({'ALGORITHM', ['A string defining the fitting algorithm.<br>'... '<tt>fminunc</tt>, <tt>fmincon</tt> require ''Optimization Toolbox'' to be installed.<br>'... '<tt>patternsearch</tt>, <tt>ga</tt>, <tt>simulannealbnd</tt> require ''Genetic Algorithm and Direct Search'' to be installed.<br>']}, ... {1, {'fminsearch', 'fminunc', 'fmincon', 'patternsearch', 'ga', 'simulannealbnd'}, paramValue.SINGLE}); pl.append(p); % OPTSET p = param({'OPTSET', ['An optimisation structure to pass to the fitting algorithm.<br>'... 'See <tt>fminsearch</tt>, <tt>fminunc</tt>, <tt>fmincon</tt>, <tt>optimset</tt>, for details.<br>'... 'See <tt>patternsearch</tt>, <tt>psoptimset</tt>, for details.<br>'... 'See <tt>ga</tt>, <tt>gaoptimset</tt>, for details.<br>'... 'See <tt>simulannealbnd</tt>, <tt>saoptimset</tt>, for details.']}, paramValue.EMPTY_STRING); pl.append(p); % SymDiff p = param({'SymDiff', 'Use symbolic derivatives or not. Only for gradient-based algorithm or for LinUnc option.'}, paramValue.NO_YES); pl.append(p); % DiffOrder p = param({'DiffOrder', 'Symbolic derivative order. Only for SymDiff option.'}, {1, {1,2}, paramValue.SINGLE}); pl.append(p); % FitUnc p = param({'FitUnc', 'Fit parameter uncertainties or not.'}, paramValue.YES_NO); pl.append(p); % UncMtd p = param({'UncMtd', ['Choose the uncertainties estimation method.<br>'... 'For multi-channel fitting <tt>hessian</tt> is mandatory.']}, {1, {'hessian', 'jacobian'}, paramValue.SINGLE}); pl.append(p); % LinUnc p = param({'LinUnc', 'Force linear symbolic uncertainties.'}, paramValue.YES_NO); pl.append(p); % GradSearch p = param({'GradSearch', 'Do a preliminary gradient-based search using the BFGS Quasi-Newton method.'}, paramValue.NO_YES); pl.append(p); % MonteCarlo p = param({'MonteCarlo', ['Do a Monte Carlo search in the parameter space.<br>'... 'Useful when dealing with high multiplicity of local minima. May be computer-expensive.<br>'... 'Note that, if used, P0 will be ignored. It also requires to define LB and UB.']}, paramValue.NO_YES); pl.append(p); % Npoints p = param({'Npoints', 'Set the number of points in the parameter space to be extracted.'}, 100000); pl.append(p); % Noptims p = param({'Noptims', 'Set the number of optimizations to be performed after the Monte Carlo.'}, 10); pl.append(p); end % END