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annotate m-toolbox/test/test_matrix_linlsqsvd.m @ 33:5e7477b94d94 database-connection-manager

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author Daniele Nicolodi <nicolodi@science.unitn.it>
date Mon, 05 Dec 2011 16:20:06 +0100
parents f0afece42f48
children
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Daniele Nicolodi <nicolodi@science.unitn.it>
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1 % TEST_MATRIX_LINLSQSVD tests the linlsqsvd method of the AO class.
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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 % L Ferraioli 10-11-2010
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4 %
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Daniele Nicolodi <nicolodi@science.unitn.it>
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5 % $Id: test_matrix_linlsqsvd.m,v 1.1 2011/02/18 17:07:35 luigi Exp $
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6 %
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7 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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8
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Daniele Nicolodi <nicolodi@science.unitn.it>
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9 %% 1) Determine the coefficients of a linear combination of noises and
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Daniele Nicolodi <nicolodi@science.unitn.it>
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10 %% comapre with lscov:
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11 %
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Daniele Nicolodi <nicolodi@science.unitn.it>
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12 % Make some data
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13 fs = 10;
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14 nsecs = 10;
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Daniele Nicolodi <nicolodi@science.unitn.it>
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15 B1 = ao(plist('tsfcn', 'randn(size(t))', 'fs', fs, 'nsecs', nsecs, 'yunits', 'T'));
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16 B1.setName;
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17 B2 = ao(plist('tsfcn', 'randn(size(t))', 'fs', fs, 'nsecs', nsecs, 'yunits', 'T'));
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18 B2.setName;
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19 B3 = ao(plist('tsfcn', 'randn(size(t))', 'fs', fs, 'nsecs', nsecs, 'yunits', 'T'));
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20 B3.setName;
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21 B4 = ao(plist('tsfcn', 'randn(size(t))', 'fs', fs, 'nsecs', nsecs, 'yunits', 'T'));
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22 B4.setName;
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23
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24 C1 = matrix(B1,B2,plist('shape',[2,1]));
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25 C1.setName;
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Daniele Nicolodi <nicolodi@science.unitn.it>
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26 C2 = matrix(B3,B4,plist('shape',[2,1]));
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27 C2.setName;
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28
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29 C = matrix([B1 B3;B2 B4]);
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30 C.setName;
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31
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32 n1 = ao(plist('tsfcn', 'randn(size(t))', 'fs', fs, 'nsecs', nsecs, 'yunits', 'm'));
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33 n2 = ao(plist('tsfcn', 'randn(size(t))', 'fs', fs, 'nsecs', nsecs, 'yunits', 'm'));
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34
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35 n = matrix(n1,n2,plist('shape',[2,1]));
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36 n.setName;
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37
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38 a = [ao(1,plist('yunits','m/T')) ao(2,plist('yunits','m/T'))];
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39 A = matrix(a,plist('shape',[2,1]));
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40
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Daniele Nicolodi <nicolodi@science.unitn.it>
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41 % assign output values
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Daniele Nicolodi <nicolodi@science.unitn.it>
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42 y = C*A + n;
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Daniele Nicolodi <nicolodi@science.unitn.it>
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43
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44 %%% Get a fit with linlsqsvd
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Daniele Nicolodi <nicolodi@science.unitn.it>
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45 pobj1 = linlsqsvd(C1, C2, y);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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46
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47 % combine results
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48 for ii=1:numel(pobj1.y)
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Daniele Nicolodi <nicolodi@science.unitn.it>
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49 prs(ii) = ao(cdata(pobj1.y(ii)));
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Daniele Nicolodi <nicolodi@science.unitn.it>
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50 prs(ii).setYunits(pobj1.yunits(ii));
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Daniele Nicolodi <nicolodi@science.unitn.it>
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51 end
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Daniele Nicolodi <nicolodi@science.unitn.it>
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52 Pars = matrix(prs,plist('shape',[numel(prs),1]));
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Daniele Nicolodi <nicolodi@science.unitn.it>
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53 yfit1 = C*Pars;
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Daniele Nicolodi <nicolodi@science.unitn.it>
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54
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Daniele Nicolodi <nicolodi@science.unitn.it>
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55 %%% do linear combination: using eval
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Daniele Nicolodi <nicolodi@science.unitn.it>
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56 yfit2 = pobj1.eval;
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Daniele Nicolodi <nicolodi@science.unitn.it>
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57
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58 % Plot (compare data with fit)
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Daniele Nicolodi <nicolodi@science.unitn.it>
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59 iplot(y.objs(1), yfit1.objs(1), yfit2.objs(1))
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60 iplot(y.objs(2), yfit1.objs(2), yfit2.objs(2))
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61
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Daniele Nicolodi <nicolodi@science.unitn.it>
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62 %% 2) Determine the coefficients of a linear combination of noises:
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Daniele Nicolodi <nicolodi@science.unitn.it>
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63 %
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Daniele Nicolodi <nicolodi@science.unitn.it>
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64 % Make some data
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Daniele Nicolodi <nicolodi@science.unitn.it>
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65 fs = 10;
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Daniele Nicolodi <nicolodi@science.unitn.it>
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66 nsecs = 10;
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Daniele Nicolodi <nicolodi@science.unitn.it>
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67 x1 = ao(plist('tsfcn', 'randn(size(t))', 'fs', fs, 'nsecs', nsecs, 'yunits', 'T'));
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68 x1.setName;
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69 x2 = ao(plist('tsfcn', 'randn(size(t))', 'fs', fs, 'nsecs', nsecs, 'yunits', 'm'));
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70 x2.setName;
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71 x3 = ao(plist('tsfcn', 'randn(size(t))', 'fs', fs, 'nsecs', nsecs, 'yunits', 'T'));
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72 x3.setName;
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73 x4 = ao(plist('tsfcn', 'randn(size(t))', 'fs', fs, 'nsecs', nsecs, 'yunits', 'm'));
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74 x4.setName;
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75
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76 C1 = matrix(x1,x3,plist('shape',[2,1]));
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77 C1.setName;
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78 C2 = matrix(x2,x4,plist('shape',[2,1]));
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79 C2.setName;
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80
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81 C = matrix([x1 x2;x3 x4]);
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82 C.setName;
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83
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84 n1 = ao(plist('tsfcn', 'randn(size(t))', 'fs', fs, 'nsecs', nsecs, 'yunits', 'm'));
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85 n2 = ao(plist('tsfcn', 'randn(size(t))', 'fs', fs, 'nsecs', nsecs, 'yunits', 'm'));
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Daniele Nicolodi <nicolodi@science.unitn.it>
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86 n = matrix(n1,n2,plist('shape',[2,1]));
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87 n.setName;
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88
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89 a = [ao(1,plist('yunits','m/T')) ao(2,plist('yunits','m/m'))];
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90 A = matrix(a,plist('shape',[2,1]));
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91 A.setName;
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Daniele Nicolodi <nicolodi@science.unitn.it>
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92
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Daniele Nicolodi <nicolodi@science.unitn.it>
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93 y = C*A + n;
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Daniele Nicolodi <nicolodi@science.unitn.it>
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94
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Daniele Nicolodi <nicolodi@science.unitn.it>
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95 %%% Get a fit with linlsqsvd
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Daniele Nicolodi <nicolodi@science.unitn.it>
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96 pobj1 = linlsqsvd(C1, C2, y);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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97
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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 % combine results
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Daniele Nicolodi <nicolodi@science.unitn.it>
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100 for ii=1:numel(pobj1.y)
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Daniele Nicolodi <nicolodi@science.unitn.it>
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101 prs(ii) = ao(cdata(pobj1.y(ii)));
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Daniele Nicolodi <nicolodi@science.unitn.it>
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102 prs(ii).setYunits(pobj1.yunits(ii));
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Daniele Nicolodi <nicolodi@science.unitn.it>
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103 end
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Daniele Nicolodi <nicolodi@science.unitn.it>
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104 Pars = matrix(prs,plist('shape',[numel(prs),1]));
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Daniele Nicolodi <nicolodi@science.unitn.it>
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105 yfit1 = C*Pars;
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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 %%% do linear combination: using eval
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Daniele Nicolodi <nicolodi@science.unitn.it>
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108 yfit2 = pobj1.eval;
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Daniele Nicolodi <nicolodi@science.unitn.it>
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109
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Daniele Nicolodi <nicolodi@science.unitn.it>
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110 % Plot (compare data with fit)
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Daniele Nicolodi <nicolodi@science.unitn.it>
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111 iplot(y.objs(1), yfit1.objs(1), yfit2.objs(1))
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112 iplot(y.objs(2), yfit1.objs(2), yfit2.objs(2))
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113
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114