line source
+ − % 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