comparison m-toolbox/classes/@matrix/linlsqsvd.m @ 0:f0afece42f48

Import.
author Daniele Nicolodi <nicolodi@science.unitn.it>
date Wed, 23 Nov 2011 19:22:13 +0100
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1 % LINLSQSVD Linear least squares with singular value decomposition
2 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
3 %
4 % DESCRIPTION: Linear least square problem with singular value
5 % decomposition
6 %
7 % ALGORITHM: % It solves the problem
8 %
9 % Y = HX
10 %
11 % where X are the parameters, Y the measurements, and H the linear
12 % equations relating the two.
13 % It is able to perform linear identification of the parameters of a
14 % multichannel systems. The results of different experiments on the same
15 % system can be passed as input. The algorithm, thanks to the singular
16 % value decomposition, extract the maximum amount of information from each
17 % single channel and for each experiment. Total information is then
18 % combined to get the final result.
19 %
20 % CALL: pars = linfitsvd(H1,...,HN,Y,pl);
21 %
22 % If the experiment is 1 then H1,...,HN and Y are aos.
23 % If the experiments are M, then H1,...,HN and Y are Mx1 matrix objects
24 % with the aos relating to the given experiment in the proper position.
25 %
26 % INPUT:
27 % - Hi represent the columns of H
28 % - Y represent the measurement set
29 %
30 % OUTPUT:
31 % - pars: a pest object containing parameter estimation
32 %
33 % 09-11-2010 L Ferraioli
34 % CREATION
35 %
36 % <a href="matlab:utils.helper.displayMethodInfo('matrix', 'linfitsvd')">Parameters Description</a>
37 %
38 % VERSION: $Id: linlsqsvd.m,v 1.9 2011/05/02 14:18:19 luigi Exp $
39 %
40 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
41
42 function varargout = linlsqsvd(varargin)
43
44 %%% LTPDA stufs and get data %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
45
46 % Check if this is a call for parameters
47 if utils.helper.isinfocall(varargin{:})
48 varargout{1} = getInfo(varargin{3});
49 return
50 end
51
52 import utils.const.*
53 utils.helper.msg(msg.OMNAME, 'running %s/%s', mfilename('class'), mfilename);
54
55 % Collect input variable names
56 in_names = cell(size(varargin));
57 for ii = 1:nargin,in_names{ii} = inputname(ii);end
58
59 % Collect all ltpdauoh objects
60 [mtxs, mtxs_invars] = utils.helper.collect_objects(varargin(:), 'matrix', in_names);
61 [pl, invars] = utils.helper.collect_objects(varargin(:), 'plist');
62
63
64 inhists = [mtxs(:).hist];
65
66
67 %%% combine plists
68 pl = parse(pl, getDefaultPlist());
69
70 %%% collect inputs names
71 argsname = mtxs(end).name;
72
73 %%% get input params
74 kwnpars = find(pl,'KnownParams');
75 sThreshold = find(pl,'sThreshold');
76
77
78 %%% do fit
79 % if ~isempty(kwnpars) && isfield(kwnpars,'pos')
80 % [a,Ca,Corra,Vu,bu,Cbu,Fbu,mse,dof,ppm] = utils.math.linlsqsvd(mtxs,kwnpars);
81 % else
82 % [a,Ca,Corra,Vu,bu,Cbu,Fbu,mse,dof,ppm] = utils.math.linlsqsvd(mtxs);
83 % end
84
85 if ~isempty(kwnpars) && isfield(kwnpars,'pos')
86 if ~isempty(sThreshold)
87 [a,Ca,Corra,Vu,bu,Cbu,Fbu,mse,dof,ppm] = utils.math.linlsqsvd(mtxs,sThreshold,kwnpars);
88 else
89 [a,Ca,Corra,Vu,bu,Cbu,Fbu,mse,dof,ppm] = utils.math.linlsqsvd(mtxs,kwnpars);
90 end
91 else
92 if ~isempty(sThreshold)
93 [a,Ca,Corra,Vu,bu,Cbu,Fbu,mse,dof,ppm] = utils.math.linlsqsvd(mtxs,sThreshold);
94 else
95 [a,Ca,Corra,Vu,bu,Cbu,Fbu,mse,dof,ppm] = utils.math.linlsqsvd(mtxs);
96 end
97 end
98
99 fitparams = cell(1,numel(a));
100 nmstr = '';
101 for kk=1:numel(a)
102 fitparams{kk} = sprintf('a%s',num2str(kk));
103 units{kk} = mtxs(end).objs(1).yunits / mtxs(kk).objs(1).yunits;
104 units{kk}.simplify;
105 if isempty(nmstr)
106 nmstr = sprintf('%s*%s',fitparams{kk},mtxs(kk).name);
107 else
108 nmstr = [nmstr '+' sprintf('%s*%s',fitparams{kk},mtxs(kk).name)];
109 end
110 end
111
112 pe = pest();
113 pe.setY(a);
114 pe.setDy(sqrt(diag(Ca)));
115 pe.setCov(Ca);
116 pe.setChi2(mse);
117 pe.setNames(fitparams);
118 pe.setDof(dof);
119 pe.setYunits(units{:});
120 pe.name = nmstr;
121 pe.setModels(mtxs(1:end-1));
122
123 % set History
124 pe.addHistory(getInfo('None'), pl, [mtxs_invars(:)], [inhists(:)]);
125
126
127 if nargout == 1
128 varargout{1} = pe;
129 elseif nargout == 11
130 varargout{1} = pe;
131 varargout{2} = a;
132 varargout{3} = Ca;
133 varargout{4} = Corra;
134 varargout{5} = Vu;
135 varargout{6} = bu;
136 varargout{7} = Cbu;
137 varargout{8} = Fbu;
138 varargout{9} = mse;
139 varargout{10} = dof;
140 varargout{11} = ppm;
141 else
142 error('invalid number of outputs!')
143 end
144
145 end
146
147
148 %--------------------------------------------------------------------------
149 % Get Info Object
150 %--------------------------------------------------------------------------
151 function ii = getInfo(varargin)
152 if nargin == 1 && strcmpi(varargin{1}, 'None')
153 sets = {};
154 pl = [];
155 else
156 sets = {'Default'};
157 pl = getDefaultPlist;
158 end
159 % Build info object
160 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);
161 ii.setArgsmin(2);
162 ii.setOutmin(1);
163 % ii.setOutmax(1);
164 ii.setModifier(false);
165 end
166
167 %--------------------------------------------------------------------------
168 % Get Default Plist
169 %--------------------------------------------------------------------------
170 function plout = getDefaultPlist()
171 persistent pl;
172 if exist('pl', 'var')==0 || isempty(pl)
173 pl = buildplist();
174 end
175 plout = pl;
176 end
177
178 function pl = buildplist()
179
180 pl = plist();
181
182 p = param({'KnownParams', ['Known Parameters. A struct array with the fields:<ul>'...
183 '<li> pos - a number indicating the corresponding position of the parameter (corresponding column of H)</li>'...
184 '<li> value - the value for the parameter</li>'...
185 '<li> err - the uncertainty associated to the parameter</li>'...
186 '</ul>']}, paramValue.EMPTY_CELL);
187 pl.append(p);
188
189 p = param({'sThreshold',['Fix upper treshold for singular values.'...
190 'Singular values larger than the value will be ignored.'...
191 'This correspon to consider only parameters combinations with error lower then the value']},...
192 paramValue.EMPTY_DOUBLE);
193 pl.append(p);
194
195
196 end