Mercurial > hg > ltpda

annotate m-toolbox/classes/@ao/svd_fit.m @ 0:f0afece42f48

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Import.
author Daniele Nicolodi <nicolodi@science.unitn.it>
date Wed, 23 Nov 2011 19:22:13 +0100
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Daniele Nicolodi <nicolodi@science.unitn.it>
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1 % SVD_FIT estimates parameters for a linear model using SVD
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Daniele Nicolodi <nicolodi@science.unitn.it>
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2 %
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Daniele Nicolodi <nicolodi@science.unitn.it>
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3 % DESCRIPTION: SVD_FIT estimates parameters for a linear model using SVD
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Daniele Nicolodi <nicolodi@science.unitn.it>
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4 %
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Daniele Nicolodi <nicolodi@science.unitn.it>
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5 % CALL: X = svd_fit([C1 C2 ... CN], Y, pl)
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Daniele Nicolodi <nicolodi@science.unitn.it>
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6 % X = svd_fit(C1,C2,C3,...,CN, Y, pl)
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Daniele Nicolodi <nicolodi@science.unitn.it>
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7 %
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Daniele Nicolodi <nicolodi@science.unitn.it>
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8 % INPUTS: C1...CN - AOs defing the models to fit the measurement set to.
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Daniele Nicolodi <nicolodi@science.unitn.it>
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9 % Y - AO which represents the measurement set
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Daniele Nicolodi <nicolodi@science.unitn.it>
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10 %
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Daniele Nicolodi <nicolodi@science.unitn.it>
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11 % Note: the length of the vectors in Ci and Y must be the same.
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Daniele Nicolodi <nicolodi@science.unitn.it>
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12 % Note: the last input AO is taken as Y.
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13 %
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Daniele Nicolodi <nicolodi@science.unitn.it>
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14 % pl - parameter list (see below)
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Daniele Nicolodi <nicolodi@science.unitn.it>
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15 %
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Daniele Nicolodi <nicolodi@science.unitn.it>
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16 % OUTPUTs: X - An AO with N elements with the fitting coefficients to y_i
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Daniele Nicolodi <nicolodi@science.unitn.it>
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17 % OR
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Daniele Nicolodi <nicolodi@science.unitn.it>
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18 % - a vector of N AOs each with one fitting coefficient to y_i
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Daniele Nicolodi <nicolodi@science.unitn.it>
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19 %
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Daniele Nicolodi <nicolodi@science.unitn.it>
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20 % The procinfo field of the output AOs is filled with the following key/value
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Daniele Nicolodi <nicolodi@science.unitn.it>
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21 % pairs:
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Daniele Nicolodi <nicolodi@science.unitn.it>
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22 %
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Daniele Nicolodi <nicolodi@science.unitn.it>
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23 % 'STDX' - standard deviations of the parameters
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Daniele Nicolodi <nicolodi@science.unitn.it>
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24 % 'MSE' - the mean-squared errors
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Daniele Nicolodi <nicolodi@science.unitn.it>
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25 % 'COV' - the covariance matrix
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Daniele Nicolodi <nicolodi@science.unitn.it>
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26 %
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Daniele Nicolodi <nicolodi@science.unitn.it>
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27 %
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Daniele Nicolodi <nicolodi@science.unitn.it>
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28 % PARAMETERS:
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Daniele Nicolodi <nicolodi@science.unitn.it>
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29 %
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Daniele Nicolodi <nicolodi@science.unitn.it>
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30 % <a href="matlab:utils.helper.displayMethodInfo('ao', 'svd_fit')">Parameters Description</a>
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Daniele Nicolodi <nicolodi@science.unitn.it>
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31 %
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Daniele Nicolodi <nicolodi@science.unitn.it>
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32 % VERSION: $Id: svd_fit.m,v 1.6 2011/04/08 08:56:12 hewitson Exp $
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Daniele Nicolodi <nicolodi@science.unitn.it>
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33 %
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Daniele Nicolodi <nicolodi@science.unitn.it>
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34 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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Daniele Nicolodi <nicolodi@science.unitn.it>
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35 function varargout = svd_fit(varargin)
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Daniele Nicolodi <nicolodi@science.unitn.it>
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36
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Daniele Nicolodi <nicolodi@science.unitn.it>
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37 % Check if this is a call for parameters
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Daniele Nicolodi <nicolodi@science.unitn.it>
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38 if utils.helper.isinfocall(varargin{:})
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Daniele Nicolodi <nicolodi@science.unitn.it>
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39 varargout{1} = getInfo(varargin{3});
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Daniele Nicolodi <nicolodi@science.unitn.it>
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40 return
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Daniele Nicolodi <nicolodi@science.unitn.it>
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41 end
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42
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Daniele Nicolodi <nicolodi@science.unitn.it>
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43 import utils.const.*
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Daniele Nicolodi <nicolodi@science.unitn.it>
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44 utils.helper.msg(msg.PROC3, 'running %s/%s', mfilename('class'), mfilename);
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45
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Daniele Nicolodi <nicolodi@science.unitn.it>
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46 % Collect input variable names
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Daniele Nicolodi <nicolodi@science.unitn.it>
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47 in_names = cell(size(varargin));
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Daniele Nicolodi <nicolodi@science.unitn.it>
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48 for ii = 1:nargin,in_names{ii} = inputname(ii);end
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49
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Daniele Nicolodi <nicolodi@science.unitn.it>
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50 % Collect all AOs and plists
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Daniele Nicolodi <nicolodi@science.unitn.it>
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51 [A, ao_invars] = utils.helper.collect_objects(varargin(:), 'ao', in_names);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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52 pl = utils.helper.collect_objects(varargin(:), 'plist', in_names);
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53
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Daniele Nicolodi <nicolodi@science.unitn.it>
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54 if nargout == 0
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Daniele Nicolodi <nicolodi@science.unitn.it>
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55 error('### svd_fit can not be used as a modifier method. Please give one output');
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Daniele Nicolodi <nicolodi@science.unitn.it>
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56 end
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Daniele Nicolodi <nicolodi@science.unitn.it>
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57
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Daniele Nicolodi <nicolodi@science.unitn.it>
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58 % combine plists
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Daniele Nicolodi <nicolodi@science.unitn.it>
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59 pl = parse(pl, getDefaultPlist());
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Daniele Nicolodi <nicolodi@science.unitn.it>
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60
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Daniele Nicolodi <nicolodi@science.unitn.it>
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61 % Build matrices for fit
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62
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Daniele Nicolodi <nicolodi@science.unitn.it>
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63 C = A(1:end-1);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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64 Y = A(end);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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65 H = C.y;
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Daniele Nicolodi <nicolodi@science.unitn.it>
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66 y = Y.y;
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Daniele Nicolodi <nicolodi@science.unitn.it>
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67 [u,s,v] = svd(H,0);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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68 P = v/s*u'*y;
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Daniele Nicolodi <nicolodi@science.unitn.it>
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69 f = zeros(length(H),1); %y = zeros(length(d),1);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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70 for kk = 1:length(P)
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Daniele Nicolodi <nicolodi@science.unitn.it>
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71 f = f + P(kk).*H(:,kk);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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72 end
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Daniele Nicolodi <nicolodi@science.unitn.it>
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73 MSE = sum(abs(y-f).^2)./length(y);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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74 a = H'*H;
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Daniele Nicolodi <nicolodi@science.unitn.it>
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75 S = inv(a)*MSE;
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Daniele Nicolodi <nicolodi@science.unitn.it>
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76 STDX = sqrt(diag(S));
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77
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Daniele Nicolodi <nicolodi@science.unitn.it>
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78 % Build X
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Daniele Nicolodi <nicolodi@science.unitn.it>
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79 if find(pl,'vector_out')
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Daniele Nicolodi <nicolodi@science.unitn.it>
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80 for jj = 1:length(P)
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Daniele Nicolodi <nicolodi@science.unitn.it>
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81 X(jj) = ao(P(jj));
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Daniele Nicolodi <nicolodi@science.unitn.it>
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82 X(jj).data.setYunits(Y.yunits/C(jj).yunits);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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83 X(jj).data.setDy(STDX(jj));
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Daniele Nicolodi <nicolodi@science.unitn.it>
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84 X(jj).name = sprintf('svd_fit(%s)', Y.name);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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85 X(jj).addHistory(getInfo('None'), pl, ao_invars, [A(:).hist]);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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86 % Set proc info
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Daniele Nicolodi <nicolodi@science.unitn.it>
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87 X(jj).procinfo = plist('STDX', STDX(jj), 'MSE', MSE, 'COV', S);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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88 end
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Daniele Nicolodi <nicolodi@science.unitn.it>
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89 else
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Daniele Nicolodi <nicolodi@science.unitn.it>
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90 X = ao(P);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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91 X.data.setYunits(Y.yunits/C(1).yunits);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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92 X.data.setDy(STDX);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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93 X.name = sprintf('svd_fit(%s)', Y.name);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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94 X.addHistory(getInfo('None'), pl, ao_invars, [A(:).hist]);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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95 % Set proc info
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Daniele Nicolodi <nicolodi@science.unitn.it>
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96 X.procinfo = plist('STDX', STDX, 'MSE', MSE, 'COV', S);
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97 end
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Daniele Nicolodi <nicolodi@science.unitn.it>
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98
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Daniele Nicolodi <nicolodi@science.unitn.it>
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99 % Set outputs
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Daniele Nicolodi <nicolodi@science.unitn.it>
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100 varargout{1} = X;
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Daniele Nicolodi <nicolodi@science.unitn.it>
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101
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102 end
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Daniele Nicolodi <nicolodi@science.unitn.it>
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103
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Daniele Nicolodi <nicolodi@science.unitn.it>
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104 %--------------------------------------------------------------------------
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Daniele Nicolodi <nicolodi@science.unitn.it>
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105 % Get Info Object
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Daniele Nicolodi <nicolodi@science.unitn.it>
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106 %--------------------------------------------------------------------------
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Daniele Nicolodi <nicolodi@science.unitn.it>
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107 function ii = getInfo(varargin)
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Daniele Nicolodi <nicolodi@science.unitn.it>
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108 if nargin == 1 && strcmpi(varargin{1}, 'None')
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Daniele Nicolodi <nicolodi@science.unitn.it>
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109 sets = {};
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Daniele Nicolodi <nicolodi@science.unitn.it>
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110 pl = [];
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111 else
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Daniele Nicolodi <nicolodi@science.unitn.it>
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112 sets = {'Default'};
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Daniele Nicolodi <nicolodi@science.unitn.it>
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113 pl = getDefaultPlist;
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114 end
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Daniele Nicolodi <nicolodi@science.unitn.it>
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115 % Build info object
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Daniele Nicolodi <nicolodi@science.unitn.it>
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116 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);
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117 end
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118
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119 %--------------------------------------------------------------------------
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Daniele Nicolodi <nicolodi@science.unitn.it>
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120 % Get Default Plist
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121 %--------------------------------------------------------------------------
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122 function plout = getDefaultPlist()
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Daniele Nicolodi <nicolodi@science.unitn.it>
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123 persistent pl;
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Daniele Nicolodi <nicolodi@science.unitn.it>
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124 if exist('pl', 'var')==0 || isempty(pl)
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Daniele Nicolodi <nicolodi@science.unitn.it>
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125 pl = buildplist();
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Daniele Nicolodi <nicolodi@science.unitn.it>
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126 end
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Daniele Nicolodi <nicolodi@science.unitn.it>
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127 plout = pl;
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128 end
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Daniele Nicolodi <nicolodi@science.unitn.it>
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129
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Daniele Nicolodi <nicolodi@science.unitn.it>
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130 function pl = buildplist()
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Daniele Nicolodi <nicolodi@science.unitn.it>
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131 pl = plist();
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Daniele Nicolodi <nicolodi@science.unitn.it>
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132
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Daniele Nicolodi <nicolodi@science.unitn.it>
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133 % Vector out
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Daniele Nicolodi <nicolodi@science.unitn.it>
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134 p = param({'vector_out','The estimated coefficients are output as a vector of AOs.'}, paramValue.TRUE_FALSE);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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135 pl.append(p);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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136
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Daniele Nicolodi <nicolodi@science.unitn.it>
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137 end
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Daniele Nicolodi <nicolodi@science.unitn.it>
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138 % END