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

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 % EVALUATEMODEL evaluate a curvefit model.
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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 %
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Daniele Nicolodi <nicolodi@science.unitn.it>
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4 % DESCRIPTION: EVALUATEMODEL evaluate a curvefit model.
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Daniele Nicolodi <nicolodi@science.unitn.it>
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5 %
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Daniele Nicolodi <nicolodi@science.unitn.it>
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6 % CALL: b = evaluateModel(a, 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: a - input AO(s) containing parameter values. The parameter
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Daniele Nicolodi <nicolodi@science.unitn.it>
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9 % values are collected from the Y data of all input cdata
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Daniele Nicolodi <nicolodi@science.unitn.it>
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10 % AOs. The most common approach would be one AO per
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Daniele Nicolodi <nicolodi@science.unitn.it>
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11 % parameter, or a single AO with all parameters in.
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Daniele Nicolodi <nicolodi@science.unitn.it>
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12 % pl - parameter list (see below)
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13 %
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Daniele Nicolodi <nicolodi@science.unitn.it>
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14 % OUTPUTs: b - an AO containing the model evaluated at the give X
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Daniele Nicolodi <nicolodi@science.unitn.it>
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15 % values, with the given parameter values.
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16 %
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Daniele Nicolodi <nicolodi@science.unitn.it>
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17 % <a href="matlab:utils.helper.displayMethodInfo('ao', 'evaluateModel')">Parameters Description</a>
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18 %
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Daniele Nicolodi <nicolodi@science.unitn.it>
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19 % VERSION: $Id: evaluateModel.m,v 1.14 2011/04/08 08:56:13 hewitson Exp $
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20 %
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Daniele Nicolodi <nicolodi@science.unitn.it>
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21 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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22
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Daniele Nicolodi <nicolodi@science.unitn.it>
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23 function varargout = evaluateModel(varargin)
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24
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Daniele Nicolodi <nicolodi@science.unitn.it>
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25 % Check if this is a call for parameters
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Daniele Nicolodi <nicolodi@science.unitn.it>
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26 if utils.helper.isinfocall(varargin{:})
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Daniele Nicolodi <nicolodi@science.unitn.it>
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27 varargout{1} = getInfo(varargin{3});
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Daniele Nicolodi <nicolodi@science.unitn.it>
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28 return
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Daniele Nicolodi <nicolodi@science.unitn.it>
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29 end
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30
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Daniele Nicolodi <nicolodi@science.unitn.it>
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31 import utils.const.*
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Daniele Nicolodi <nicolodi@science.unitn.it>
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32 utils.helper.msg(msg.PROC3, 'running %s/%s', mfilename('class'), mfilename);
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33
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Daniele Nicolodi <nicolodi@science.unitn.it>
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34 warning(['The method ''ao/curvefit'' and ''ao/evaluateModel'' have been replaced by ''ao/xfit'' and ''pest/eval''.' ...
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35 'They are no longer maintained and will be removed from future releases of LTPDA Toolbox.']);
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36
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Daniele Nicolodi <nicolodi@science.unitn.it>
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37 % Collect input variable names
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Daniele Nicolodi <nicolodi@science.unitn.it>
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38 in_names = cell(size(varargin));
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Daniele Nicolodi <nicolodi@science.unitn.it>
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39 for ii = 1:nargin,in_names{ii} = inputname(ii);end
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40
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Daniele Nicolodi <nicolodi@science.unitn.it>
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41 % Collect all AOs and plists
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Daniele Nicolodi <nicolodi@science.unitn.it>
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42 [as, ao_invars] = utils.helper.collect_objects(varargin(:), 'ao', in_names);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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43 pl = utils.helper.collect_objects(varargin(:), 'plist', in_names);
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44
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45 %%% Decide on a deep copy or a modify
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Daniele Nicolodi <nicolodi@science.unitn.it>
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46 bs = copy(as, nargout);
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47
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48 % Each of the input AOs should be a cdata AO; from these we get the
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Daniele Nicolodi <nicolodi@science.unitn.it>
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49 % parameter values
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Daniele Nicolodi <nicolodi@science.unitn.it>
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50 P = [];
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51 for kk=1:numel(bs)
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Daniele Nicolodi <nicolodi@science.unitn.it>
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52 if isa(bs(kk).data, 'cdata')
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53 P = [P; bs(kk).data.y(:)];
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Daniele Nicolodi <nicolodi@science.unitn.it>
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54 else
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Daniele Nicolodi <nicolodi@science.unitn.it>
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55 warning('!!! AO %s is not a cdata AO. Not using for parameter values.', bs(kk).name);
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56 end
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57 end
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58
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59 % combine plists
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60 pl = parse(pl, getDefaultPlist());
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61
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62 % Extract necessary parameters
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63 targetFcn = find(pl, 'Function');
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Daniele Nicolodi <nicolodi@science.unitn.it>
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64 ADDP = find(pl, 'ADDP');
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Daniele Nicolodi <nicolodi@science.unitn.it>
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65 dtype = find(pl, 'dtype');
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Daniele Nicolodi <nicolodi@science.unitn.it>
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66 Xdata = find(pl, 'Xdata');
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67 if isa(Xdata, 'ao')
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68 Xdata = Xdata.x;
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69 end
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70
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71
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72 if ~iscell(ADDP)
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73 ADDP = {ADDP};
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74 end
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75
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Daniele Nicolodi <nicolodi@science.unitn.it>
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76 % Check parameters
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77 if isempty(targetFcn)
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78 error('### Please specify a target function');
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79 end
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80 if isempty(P)
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81 error('### Please give values for the parameters');
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82 end
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83
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84 % Make an anonymous function of the target function
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Daniele Nicolodi <nicolodi@science.unitn.it>
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85 cmd = sprintf('tfunc = @(P,Xdata,ADDP)(%s);', targetFcn);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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86 eval(cmd);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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87 % Evaluate function at best fit
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Daniele Nicolodi <nicolodi@science.unitn.it>
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88 Y = tfunc(P, Xdata, ADDP);
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89 if isa(Y, 'ao')
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Daniele Nicolodi <nicolodi@science.unitn.it>
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90 Y = Y.y;
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91 end
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92
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Daniele Nicolodi <nicolodi@science.unitn.it>
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93 % Make new output AO
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Daniele Nicolodi <nicolodi@science.unitn.it>
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94 switch lower(dtype)
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Daniele Nicolodi <nicolodi@science.unitn.it>
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95 case 'tsdata'
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96 out = ao(tsdata(Xdata,Y));
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97 out.data.setXunits('s');
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98 case 'fsdata'
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99 out = ao(fsdata(Xdata,Y));
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100 out.data.setXunits('Hz');
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Daniele Nicolodi <nicolodi@science.unitn.it>
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101 case 'xydata'
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Daniele Nicolodi <nicolodi@science.unitn.it>
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102 out = ao(xydata(Xdata,Y));
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Daniele Nicolodi <nicolodi@science.unitn.it>
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103 otherwise
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Daniele Nicolodi <nicolodi@science.unitn.it>
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104 error('### Unknown data type specified. Choose from xydata, fsdata, or tsdata');
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105 end
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106
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Daniele Nicolodi <nicolodi@science.unitn.it>
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107 % Set output AO name
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Daniele Nicolodi <nicolodi@science.unitn.it>
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108 name = sprintf('eval(%s,', targetFcn);
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109 for kk=1:numel(bs)
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110 name = [name bs(kk).name ','];
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111 end
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112 name = [name(1:end-1) ')'];
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113 out.name = name;
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Daniele Nicolodi <nicolodi@science.unitn.it>
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114 % Add history
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115 out.addHistory(getInfo('None'), pl, ao_invars, [bs(:).hist]);
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116
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Daniele Nicolodi <nicolodi@science.unitn.it>
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117 % Set outputs
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Daniele Nicolodi <nicolodi@science.unitn.it>
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118 if nargout > 0
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119 varargout{1} = out;
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120 end
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121 end
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122
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123
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124 %--------------------------------------------------------------------------
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Daniele Nicolodi <nicolodi@science.unitn.it>
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125 % Get Info Object
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126 %--------------------------------------------------------------------------
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127 function ii = getInfo(varargin)
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128 if nargin == 1 && strcmpi(varargin{1}, 'None')
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Daniele Nicolodi <nicolodi@science.unitn.it>
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129 sets = {};
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130 pl = [];
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131 else
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132 sets = {'Default'};
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133 pl = getDefaultPlist;
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134 end
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Daniele Nicolodi <nicolodi@science.unitn.it>
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135 % Build info object
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136 ii = minfo(mfilename, 'ao', 'ltpda', utils.const.categories.sigproc, '$Id: evaluateModel.m,v 1.14 2011/04/08 08:56:13 hewitson Exp $', sets, pl);
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137 end
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Daniele Nicolodi <nicolodi@science.unitn.it>
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138
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139 %--------------------------------------------------------------------------
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Daniele Nicolodi <nicolodi@science.unitn.it>
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140 % Get Default Plist
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Daniele Nicolodi <nicolodi@science.unitn.it>
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141 %--------------------------------------------------------------------------
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Daniele Nicolodi <nicolodi@science.unitn.it>
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142 function plout = getDefaultPlist()
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Daniele Nicolodi <nicolodi@science.unitn.it>
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143 persistent pl;
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Daniele Nicolodi <nicolodi@science.unitn.it>
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144 if exist('pl', 'var')==0 || isempty(pl)
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Daniele Nicolodi <nicolodi@science.unitn.it>
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145 pl = buildplist();
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146 end
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Daniele Nicolodi <nicolodi@science.unitn.it>
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147 plout = pl;
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148 end
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Daniele Nicolodi <nicolodi@science.unitn.it>
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149
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Daniele Nicolodi <nicolodi@science.unitn.it>
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150 function pl = buildplist()
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Daniele Nicolodi <nicolodi@science.unitn.it>
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151
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Daniele Nicolodi <nicolodi@science.unitn.it>
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152 pl = plist();
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153
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Daniele Nicolodi <nicolodi@science.unitn.it>
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154 % Function
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Daniele Nicolodi <nicolodi@science.unitn.it>
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155 p = param({'Function', ['The function to evaluate. <br>'...
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Daniele Nicolodi <nicolodi@science.unitn.it>
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156 'The function should be parameterized by the vector of '...
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Daniele Nicolodi <nicolodi@science.unitn.it>
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157 'parameters P, the cell-array ADDP, and the '...
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Daniele Nicolodi <nicolodi@science.unitn.it>
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158 'x-vector Xdata.'...
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Daniele Nicolodi <nicolodi@science.unitn.it>
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159 ]}, paramValue.EMPTY_STRING);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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160 pl.append(p);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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161
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Daniele Nicolodi <nicolodi@science.unitn.it>
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162 % ADDP
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Daniele Nicolodi <nicolodi@science.unitn.it>
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163 p = param({'ADDP', 'A cell-array of additional parameters to pass to the target function'}, ...
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Daniele Nicolodi <nicolodi@science.unitn.it>
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164 {1, {{}}, paramValue.OPTIONAL});
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Daniele Nicolodi <nicolodi@science.unitn.it>
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165 pl.append(p);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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166
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167 % DTYPE
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168 p = param({'dtype', 'The data type to interpret this model as.'}, {1, {'xydata', 'fsdata', 'tsdata'}, paramValue.SINGLE});
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169 pl.append(p);
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170
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171 % XDATA
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172 p = param({'Xdata', ['The X values to evaluate the model at.<br>'...
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173 'This can be a vector or an AO (from which the Xdata will '...
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174 'be extracted).']}, paramValue.EMPTY_DOUBLE);
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175 pl.append(p);
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176
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177
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178 end
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179 % END