Mercurial > hg > ltpda
comparison m-toolbox/test/test_ao_padded_sine_model.m @ 0:f0afece42f48
Import.
| author | Daniele Nicolodi <nicolodi@science.unitn.it> |
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| date | Wed, 23 Nov 2011 19:22:13 +0100 |
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| -1:000000000000 | 0:f0afece42f48 |
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| 1 clear all; | |
| 2 | |
| 3 %% | |
| 4 fs = 100; | |
| 5 nsecs = 20; | |
| 6 t = [0:1/fs:nsecs-1/fs]; | |
| 7 | |
| 8 a = ao(plist('built-in', 'padded_sine', ... | |
| 9 'f0', .1, ... | |
| 10 'Timebase', t, ... | |
| 11 'Tstart', 1.1, ... | |
| 12 'Ncycles', 2.1, ... | |
| 13 'phi', 90)) | |
| 14 | |
| 15 n = ao(plist('tsfcn', '0.1*randn(size(t))', 'fs', fs, 'nsecs', nsecs)); | |
| 16 | |
| 17 sw = a+n; | |
| 18 | |
| 19 sw.iplot | |
| 20 | |
| 21 %% Now try to fit this | |
| 22 | |
| 23 % [dc,A,f0,phi,Tstart,N] | |
| 24 af = ao(plist('built-in', 'padded_sine', ... | |
| 25 'Timebase', sw, ... | |
| 26 'f0', .1, ... | |
| 27 'P', [0 1 .11 90 1 2])); | |
| 28 | |
| 29 iplot(sw, af) | |
| 30 | |
| 31 %% Now fit with simplex | |
| 32 | |
| 33 opts = optimset('Display', 'iter', ... | |
| 34 'MaxFunEvals', 10000, ... | |
| 35 'MaxIter', 10000, ... | |
| 36 'TolX', 1e-15, ... | |
| 37 'TolFun', 1e-15, ... | |
| 38 'LargeScale', 'on'); | |
| 39 | |
| 40 r0 = [0 1 .11 90 1 2]; | |
| 41 | |
| 42 mksine = @(r)(ao(plist('built-in', 'padded_sine', ... | |
| 43 'Timebase', sw, ... | |
| 44 'P', r)).y); | |
| 45 | |
| 46 f = @(r)(sum((mksine(r)-sw.y).^2)); | |
| 47 | |
| 48 % Do a few iterative fits | |
| 49 % for kk=1:5 | |
| 50 x0 = fminsearch(f, r0, opts); | |
| 51 % end | |
| 52 | |
| 53 | |
| 54 %% | |
| 55 | |
| 56 afit = ao(plist('built-in', 'padded_sine', 'Timebase', sw, 'P', x0)); | |
| 57 | |
| 58 iplot(sw, afit) | |
| 59 | |
| 60 | |
| 61 |