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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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% SVD_FIT estimates parameters for a linear model using SVD % % DESCRIPTION: SVD_FIT estimates parameters for a linear model using SVD % % CALL: X = svd_fit([C1 C2 ... CN], Y, pl) % X = svd_fit(C1,C2,C3,...,CN, Y, pl) % % INPUTS: C1...CN - AOs defing the models to fit the measurement set to. % Y - AO which represents the measurement set % % Note: the length of the vectors in Ci and Y must be the same. % Note: the last input AO is taken as Y. % % pl - parameter list (see below) % % OUTPUTs: X - An AO with N elements with the fitting coefficients to y_i % OR % - a vector of N AOs each with one fitting coefficient to y_i % % The procinfo field of the output AOs is filled with the following key/value % pairs: % % 'STDX' - standard deviations of the parameters % 'MSE' - the mean-squared errors % 'COV' - the covariance matrix % % % PARAMETERS: % % <a href="matlab:utils.helper.displayMethodInfo('ao', 'svd_fit')">Parameters Description</a> % % VERSION: $Id: svd_fit.m,v 1.6 2011/04/08 08:56:12 hewitson Exp $ % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% function varargout = svd_fit(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 [A, ao_invars] = utils.helper.collect_objects(varargin(:), 'ao', in_names); pl = utils.helper.collect_objects(varargin(:), 'plist', in_names); if nargout == 0 error('### svd_fit can not be used as a modifier method. Please give one output'); end % combine plists pl = parse(pl, getDefaultPlist()); % Build matrices for fit C = A(1:end-1); Y = A(end); H = C.y; y = Y.y; [u,s,v] = svd(H,0); P = v/s*u'*y; f = zeros(length(H),1); %y = zeros(length(d),1); for kk = 1:length(P) f = f + P(kk).*H(:,kk); end MSE = sum(abs(y-f).^2)./length(y); a = H'*H; S = inv(a)*MSE; STDX = sqrt(diag(S)); % Build X if find(pl,'vector_out') for jj = 1:length(P) X(jj) = ao(P(jj)); X(jj).data.setYunits(Y.yunits/C(jj).yunits); X(jj).data.setDy(STDX(jj)); X(jj).name = sprintf('svd_fit(%s)', Y.name); X(jj).addHistory(getInfo('None'), pl, ao_invars, [A(:).hist]); % Set proc info X(jj).procinfo = plist('STDX', STDX(jj), 'MSE', MSE, 'COV', S); end else X = ao(P); X.data.setYunits(Y.yunits/C(1).yunits); X.data.setDy(STDX); X.name = sprintf('svd_fit(%s)', Y.name); X.addHistory(getInfo('None'), pl, ao_invars, [A(:).hist]); % Set proc info X.procinfo = plist('STDX', STDX, 'MSE', MSE, 'COV', S); end % Set outputs varargout{1} = X; 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.op, '$Id: svd_fit.m,v 1.6 2011/04/08 08:56:12 hewitson Exp $', sets, pl); 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(); % Vector out p = param({'vector_out','The estimated coefficients are output as a vector of AOs.'}, paramValue.TRUE_FALSE); pl.append(p); end % END