Mercurial > hg > ltpda
view m-toolbox/classes/@matrix/linlsqsvd.m @ 35:4be5f7a5316f database-connection-manager
Suppress output messages on preferences loading and saving
author | Daniele Nicolodi <nicolodi@science.unitn.it> |
---|---|
date | Mon, 05 Dec 2011 16:20:06 +0100 |
parents | f0afece42f48 |
children |
line wrap: on
line source
% LINLSQSVD Linear least squares with singular value decomposition %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % % DESCRIPTION: Linear least square problem with singular value % decomposition % % ALGORITHM: % It solves the problem % % Y = HX % % where X are the parameters, Y the measurements, and H the linear % equations relating the two. % It is able to perform linear identification of the parameters of a % multichannel systems. The results of different experiments on the same % system can be passed as input. The algorithm, thanks to the singular % value decomposition, extract the maximum amount of information from each % single channel and for each experiment. Total information is then % combined to get the final result. % % CALL: pars = linfitsvd(H1,...,HN,Y,pl); % % If the experiment is 1 then H1,...,HN and Y are aos. % If the experiments are M, then H1,...,HN and Y are Mx1 matrix objects % with the aos relating to the given experiment in the proper position. % % INPUT: % - Hi represent the columns of H % - Y represent the measurement set % % OUTPUT: % - pars: a pest object containing parameter estimation % % 09-11-2010 L Ferraioli % CREATION % % <a href="matlab:utils.helper.displayMethodInfo('matrix', 'linfitsvd')">Parameters Description</a> % % VERSION: $Id: linlsqsvd.m,v 1.9 2011/05/02 14:18:19 luigi Exp $ % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% function varargout = linlsqsvd(varargin) %%% LTPDA stufs and get data %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % 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.OMNAME, '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'); inhists = [mtxs(:).hist]; %%% combine plists pl = parse(pl, getDefaultPlist()); %%% collect inputs names argsname = mtxs(end).name; %%% get input params kwnpars = find(pl,'KnownParams'); sThreshold = find(pl,'sThreshold'); %%% do fit % if ~isempty(kwnpars) && isfield(kwnpars,'pos') % [a,Ca,Corra,Vu,bu,Cbu,Fbu,mse,dof,ppm] = utils.math.linlsqsvd(mtxs,kwnpars); % else % [a,Ca,Corra,Vu,bu,Cbu,Fbu,mse,dof,ppm] = utils.math.linlsqsvd(mtxs); % end if ~isempty(kwnpars) && isfield(kwnpars,'pos') if ~isempty(sThreshold) [a,Ca,Corra,Vu,bu,Cbu,Fbu,mse,dof,ppm] = utils.math.linlsqsvd(mtxs,sThreshold,kwnpars); else [a,Ca,Corra,Vu,bu,Cbu,Fbu,mse,dof,ppm] = utils.math.linlsqsvd(mtxs,kwnpars); end else if ~isempty(sThreshold) [a,Ca,Corra,Vu,bu,Cbu,Fbu,mse,dof,ppm] = utils.math.linlsqsvd(mtxs,sThreshold); else [a,Ca,Corra,Vu,bu,Cbu,Fbu,mse,dof,ppm] = utils.math.linlsqsvd(mtxs); end end fitparams = cell(1,numel(a)); nmstr = ''; for kk=1:numel(a) fitparams{kk} = sprintf('a%s',num2str(kk)); units{kk} = mtxs(end).objs(1).yunits / mtxs(kk).objs(1).yunits; units{kk}.simplify; if isempty(nmstr) nmstr = sprintf('%s*%s',fitparams{kk},mtxs(kk).name); else nmstr = [nmstr '+' sprintf('%s*%s',fitparams{kk},mtxs(kk).name)]; end end pe = pest(); pe.setY(a); pe.setDy(sqrt(diag(Ca))); pe.setCov(Ca); pe.setChi2(mse); pe.setNames(fitparams); pe.setDof(dof); pe.setYunits(units{:}); pe.name = nmstr; pe.setModels(mtxs(1:end-1)); % set History pe.addHistory(getInfo('None'), pl, [mtxs_invars(:)], [inhists(:)]); if nargout == 1 varargout{1} = pe; elseif nargout == 11 varargout{1} = pe; varargout{2} = a; varargout{3} = Ca; varargout{4} = Corra; varargout{5} = Vu; varargout{6} = bu; varargout{7} = Cbu; varargout{8} = Fbu; varargout{9} = mse; varargout{10} = dof; varargout{11} = ppm; else error('invalid number of outputs!') 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: linlsqsvd.m,v 1.9 2011/05/02 14:18:19 luigi Exp $', sets, pl); ii.setArgsmin(2); ii.setOutmin(1); % ii.setOutmax(1); 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(); p = param({'KnownParams', ['Known Parameters. A struct array with the fields:<ul>'... '<li> pos - a number indicating the corresponding position of the parameter (corresponding column of H)</li>'... '<li> value - the value for the parameter</li>'... '<li> err - the uncertainty associated to the parameter</li>'... '</ul>']}, paramValue.EMPTY_CELL); pl.append(p); p = param({'sThreshold',['Fix upper treshold for singular values.'... 'Singular values larger than the value will be ignored.'... 'This correspon to consider only parameters combinations with error lower then the value']},... paramValue.EMPTY_DOUBLE); pl.append(p); end