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annotate m-toolbox/test/test_ao_xfit.m @ 15:ce3fbb7ebe71 database-connection-manager

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Remove broken functions from utils.jmysql
author Daniele Nicolodi <nicolodi@science.unitn.it>
date Mon, 05 Dec 2011 16:20:06 +0100
parents f0afece42f48
children
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0
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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1 % Tests for xfit
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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2 %
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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3 % $Id: test_ao_xfit.m,v 1.7 2011/05/12 07:58:57 mauro Exp $
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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4 %
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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5
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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6 %% Case 1: Fit with function in plist
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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7 % Fit to a frequency-series
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Daniele Nicolodi <nicolodi@science.unitn.it>
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8
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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9 % Create a frequency-series
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Daniele Nicolodi <nicolodi@science.unitn.it>
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10 datapl = plist('fsfcn', '0.01./(0.0001+f) + 5*abs(randn(size(f))) ', 'f1', 1e-5, 'f2', 5, 'nf', 1000, ...
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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11 'xunits', 'Hz', 'yunits', 'N/Hz');
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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12 data = ao(datapl);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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13 data.setName;
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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14
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Daniele Nicolodi <nicolodi@science.unitn.it>
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15 % Do fit
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Daniele Nicolodi <nicolodi@science.unitn.it>
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16 fitpl = plist('Function', 'P(1)./(P(2) + Xdata) + P(3)', ...
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Daniele Nicolodi <nicolodi@science.unitn.it>
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17 'P0', [0.1 0.01 1]);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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18 params = xfit(data, fitpl);
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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19
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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20 % Evaluate model
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Daniele Nicolodi <nicolodi@science.unitn.it>
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21 BestModel = eval(params, plist('type','fsdata','xdata',data,'xfield','x'));
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Daniele Nicolodi <nicolodi@science.unitn.it>
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22 BestModel.setName;
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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23
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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24 % Display results
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Daniele Nicolodi <nicolodi@science.unitn.it>
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25 iplot(data,BestModel)
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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>
parents:
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27 %% Case 2: Fit with function in plist
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Daniele Nicolodi <nicolodi@science.unitn.it>
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28
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Daniele Nicolodi <nicolodi@science.unitn.it>
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29 % Create a noisy sine-wave
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Daniele Nicolodi <nicolodi@science.unitn.it>
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30 fs = 10;
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Daniele Nicolodi <nicolodi@science.unitn.it>
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31 nsecs = 500;
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Daniele Nicolodi <nicolodi@science.unitn.it>
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32 datapl = plist('waveform', 'Sine wave', 'f', 0.01, 'A', 0.6, 'fs', fs, 'nsecs', nsecs, ...
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Daniele Nicolodi <nicolodi@science.unitn.it>
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33 'xunits', 's', 'yunits', 'm');
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Daniele Nicolodi <nicolodi@science.unitn.it>
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34 sw = ao(datapl);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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35 noise = ao(plist('tsfcn', '0.01*randn(size(t))', 'fs', fs, 'nsecs', nsecs));
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Daniele Nicolodi <nicolodi@science.unitn.it>
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36 data = sw+noise;
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Daniele Nicolodi <nicolodi@science.unitn.it>
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37 data.setName;
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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38
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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39 % Do fit
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Daniele Nicolodi <nicolodi@science.unitn.it>
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40 fitpl = plist('Function', 'P(1).*sin(2*pi*P(2).*Xdata + P(3))', ...
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Daniele Nicolodi <nicolodi@science.unitn.it>
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41 'P0', [1 0.01 0]);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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42 params = xfit(data, fitpl);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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43
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Daniele Nicolodi <nicolodi@science.unitn.it>
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44 % Evaluate model
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Daniele Nicolodi <nicolodi@science.unitn.it>
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45 BestModel = eval(params, plist('type','tsdata','xdata',data,'xfield','x'));
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Daniele Nicolodi <nicolodi@science.unitn.it>
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46 BestModel.setName;
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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47
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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48 % Display results
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Daniele Nicolodi <nicolodi@science.unitn.it>
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49 iplot(data,BestModel)
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Daniele Nicolodi <nicolodi@science.unitn.it>
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50
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Daniele Nicolodi <nicolodi@science.unitn.it>
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51 %% Case 3: Fit with smodel
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Daniele Nicolodi <nicolodi@science.unitn.it>
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52
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Daniele Nicolodi <nicolodi@science.unitn.it>
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53 % Fit an smodel of a straight line to some data
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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 % Create a noisy straight-line
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Daniele Nicolodi <nicolodi@science.unitn.it>
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56 datapl = plist('xyfcn', '2.33 + 0.1*x + 0.01*randn(size(x))', 'x', 0:0.1:10, ...
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Daniele Nicolodi <nicolodi@science.unitn.it>
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57 'xunits', 's', 'yunits', 'm');
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Daniele Nicolodi <nicolodi@science.unitn.it>
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58 data = ao(datapl);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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59 data.setName;
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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 % Model to fit
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Daniele Nicolodi <nicolodi@science.unitn.it>
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62 mdl = smodel('a + b*x');
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Daniele Nicolodi <nicolodi@science.unitn.it>
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63 mdl.setXvar('x');
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Daniele Nicolodi <nicolodi@science.unitn.it>
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64 mdl.setParams({'a', 'b'}, {1 2});
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Daniele Nicolodi <nicolodi@science.unitn.it>
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65
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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66 % Fit model
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Daniele Nicolodi <nicolodi@science.unitn.it>
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67 fitpl = plist('Function', mdl, 'P0', [1 1]);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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68 params = xfit(data, fitpl);
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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69
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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70 % Evaluate model
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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71 BestModel = eval(params,plist('xdata',data,'xfield','x'));
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Daniele Nicolodi <nicolodi@science.unitn.it>
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72 BestModel.setName;
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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73
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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74 % Display results
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Daniele Nicolodi <nicolodi@science.unitn.it>
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75 iplot(data,BestModel)
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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76
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Daniele Nicolodi <nicolodi@science.unitn.it>
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77 %% Case 4: Fit with smodel:
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Daniele Nicolodi <nicolodi@science.unitn.it>
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78 % Fit a chirp-sine firstly starting from an initial guess (quite close
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Daniele Nicolodi <nicolodi@science.unitn.it>
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79 % to the true values) (bad convergency) and secondly by a Monte Carlo
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Daniele Nicolodi <nicolodi@science.unitn.it>
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80 % search (good convergency)
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Daniele Nicolodi <nicolodi@science.unitn.it>
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81
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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82 % Create a noisy chirp-sine
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Daniele Nicolodi <nicolodi@science.unitn.it>
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83 fs = 10;
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Daniele Nicolodi <nicolodi@science.unitn.it>
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84 nsecs = 1000;
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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85
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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86 % Model to fit and generate signal
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Daniele Nicolodi <nicolodi@science.unitn.it>
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87 mdl = smodel(plist('name', 'chirp', 'expression', 'A.*sin(2*pi*(f + f0.*t).*t + p)', ...
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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88 'params', {'A','f','f0','p'}, 'xvar', 't', 'xunits', 's', 'yunits', 'm'));
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Daniele Nicolodi <nicolodi@science.unitn.it>
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89
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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90 % signal
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Daniele Nicolodi <nicolodi@science.unitn.it>
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91 s = mdl.setValues({10,1e-4,1e-5,0.3});
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Daniele Nicolodi <nicolodi@science.unitn.it>
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92 s.setXvals(0:1/fs:nsecs-1/fs);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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93 signal = s.eval;
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Daniele Nicolodi <nicolodi@science.unitn.it>
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94 signal.setName;
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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95
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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96 % noise
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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97 noise = ao(plist('tsfcn', '1*randn(size(t))', 'fs', fs, 'nsecs', nsecs));
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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98
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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99 % data
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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100 data = signal + noise;
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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101 data.setName;
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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102
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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103 % Fit model from the starting guess
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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104 fitpl_ig = plist('Function', mdl, 'P0',[8,9e-5,9e-6,0]);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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105 params_ig = xfit(data, fitpl_ig);
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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106
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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107 % Evaluate model
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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108 BestModel_ig = eval(params_ig,plist('xdata',data,'xfield','x'));
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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109 BestModel_ig.setName;
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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110
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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111 % Display results
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Daniele Nicolodi <nicolodi@science.unitn.it>
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112 iplot(data,BestModel_ig)
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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113
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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114 % Fit model by a Monte Carlo search
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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115 fitpl_mc = plist('Function', mdl, ...
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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116 'MonteCarlo', 'yes', 'Npoints', 1000, 'LB', [8,9e-5,9e-6,0], 'UB', [11,3e-4,2e-5,2*pi]);
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Daniele Nicolodi <nicolodi@science.unitn.it>
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117 params_mc = xfit(data, fitpl_mc);
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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118
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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119 % Evaluate model
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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120 BestModel_mc = eval(params_mc,plist('xdata',data,'xfield','x'));
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Daniele Nicolodi <nicolodi@science.unitn.it>
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121 BestModel_mc.setName;
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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122
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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123 % Display results
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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124 iplot(data,BestModel_mc)
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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125
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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126 %% Case 5: Fit multichannel with smodel
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Daniele Nicolodi <nicolodi@science.unitn.it>
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127
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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128 % Ch.1 data
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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129 datapl = plist('xyfcn', '0.1*x + 0.01*randn(size(x))', 'x', 0:0.1:10, 'name', 'channel 1', ...
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Daniele Nicolodi <nicolodi@science.unitn.it>
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130 'xunits', 'K', 'yunits', 'Pa');
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Daniele Nicolodi <nicolodi@science.unitn.it>
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131 a1 = ao(datapl);
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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132 % Ch.2 data
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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133 datapl = plist('xyfcn', '2.5*x + 0.1*sin(2*pi*x) + 0.01*randn(size(x))', 'x', 0:0.1:10, 'name', 'channel 2', ...
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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134 'xunits', 'K', 'yunits', 'T');
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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135 a2 = ao(datapl);
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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136
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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137 % Model to fit
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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138 mdl1 = smodel('a*x');
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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139 mdl1.setXvar('x');
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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140 mdl1.setParams({'a'}, {1});
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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141 mdl1.setXunits('K');
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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142 mdl1.setYunits('Pa');
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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143
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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144 mdl2 = smodel('b*x + a*sin(2*pi*x)');
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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145 mdl2.setXvar('x');
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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146 mdl2.setParams({'a','b'}, {1,2});
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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147 mdl2.setXunits('K');
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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148 mdl2.setYunits('T');
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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149
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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150 % Fit model
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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151 params = xfit(a1,a2, plist('Function', [mdl1,mdl2]));
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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152
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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153 % evaluate model
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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154 b = eval(params, plist('index',1,'xdata',a1,'xfield','x'));
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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155 b.setName('fit Ch.1');
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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156 r = a1-b;
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
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157 r.setName('residuals');
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
158 iplot(a1,b,r)
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
159
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
160 b = eval(params, plist('index',2,'xdata',a2,'xfield','x'));
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
161 b.setName('fit Ch.2');
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
162 r = a2-b;
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
163 r.setName('residuals');
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Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
164 iplot(a2,b,r)
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
165
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
166
f0afece42f48 Import.
Daniele Nicolodi <nicolodi@science.unitn.it>
parents:
diff changeset
167