Import.
line source
% test for matrix/linfitsvd
%
% 17-11-2009 L Ferraioli
% CREATION
%
%
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% $Id: test_matrix_linfitsvd.m,v 1.3 2010/04/25 21:27:57 mauro Exp $
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%% Definitions
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pl_retrieve = plist('hostname', 'btlab.science.unitn.it', 'database', 'algo_testing');
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%%%%% Loading data %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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%% Loading parameters cells
fprintf('===== loading data... =====\n')
% define parameters names and values
parnames = { 'S11', 'S1D', 'SD1', 'SDD', ...
'TH', 'Th2', 'dH', ...
'dS11', 'dS1D', 'dSD1', 'dSDD', ...
'dh1', 'dh2', ...
'dsH', 'dsh1', 'dsh2', ...
'dx1', 'dx2', ...
'w0', 'w1', 'w2'};
exp3_nomvalues = {1 0 0 1 ...
0.35 0.28 0 ...
0 0 0 0, ...
0 0, ...
0 0 0, ...
0 0, ...
0.188 0 + 0.00114i 0 + 0.00141i ...
};
exp3_truevalues = {1 0 0 1 ...
0.35 0.28 0.0526103537824434 ...
0 0 -0.0001 0, ...
0.0753993274155418 0.0569786061730967, ...
0.0670701072189726 0.00576108053758739 -0.0846457231187905, ...
-0.0456546316180145 0.0395739758886965, ...
0.188 0 + 0.00114i 0 + 0.00141i ...
};
% set ordered used parameters
usedparams = {'dH','dsH','dS11','dS1D','dSD1','dSDD',...
'dh2','dsh2','dx1','dx2'};
%% get non-linear response model
% the model is non-linear in the parameter dependence
fprintf('===== Get TF Model... =====\n')
H = matrix(plist('built-in','mdc3_ifo2ifo_v2'));
%% load input signal
% those are the same of exp 1
fprintf('===== Loading input signals... =====\n')
oi1 = ao(pl_retrieve.pset('id', 1));
oid = ao(pl_retrieve.pset('id', 2));
%% load Coloring filters
fprintf('===== loading coloring filters... =====\n')
cf = matrix(pl_retrieve.pset('id', 26));
cf11 = cf.getObjectAtIndex(1, 1);
cf12 = cf.getObjectAtIndex(1, 2);
cf21 = cf.getObjectAtIndex(2, 1);
cf22 = cf.getObjectAtIndex(2, 2);
%% load Whitening filters
fprintf('===== loading whitening filters... =====\n')
WF = matrix(pl_retrieve.pset('id', 27));
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%%%%% Generating signals %%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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%% Get signals output of the system
fprintf('===== get true values... =====\n')
mod = copy(H, true);
% get response with true params
for ii = 1:numel(H.objs)
mod.objs(ii).setParams(parnames,exp3_truevalues);
end
plfft = plist('Npad',[]);
s11 = fftfilt(oi1,mod.objs(1,1),plfft);
s12 = fftfilt(oid,mod.objs(1,2),plfft);
s21 = fftfilt(oi1,mod.objs(2,1),plfft);
s22 = fftfilt(oid,mod.objs(2,2),plfft);
% get signals for exp 3.1
s1_exp_3_1 = s11;
s1_exp_3_1.setName;
sd_exp_3_1 = s21;
sd_exp_3_1.setName;
% get signals for exp 3.2
s1_exp_3_2 = s12;
s1_exp_3_2.setName;
sd_exp_3_2 = s22;
sd_exp_3_2.setName;
%% Adding noise to signals
fprintf('===== adding noise to signals... =====\n')
% get params
Nsecs = s1_exp_3_1.nsecs;
fs = s1_exp_3_1.fs;
plcf = plist('bank','parallel');
pl_noise = plist('tsfcn', 'randn(size(t))', 'fs', fs, 'nsecs', Nsecs);
% starting noise generation exp1.1
a1_exp_3_1 = ao(pl_noise);
a2_exp_3_1 = ao(pl_noise);
% coloring noise exp 1.1
na1_exp_3_1 = filter(a1_exp_3_1,cf11,plcf) + filter(a2_exp_3_1,cf12,plcf);
na2_exp_3_1 = filter(a1_exp_3_1,cf21,plcf) + filter(a2_exp_3_1,cf22,plcf);
% starting noise generation exp 1.2
a1_exp_3_2 = ao(pl_noise);
a2_exp_3_2 = ao(pl_noise);
% coloring noise exp 1.2
na1_exp_3_2 = filter(a1_exp_3_2,cf11,plcf) + filter(a2_exp_3_2,cf12,plcf);
na2_exp_3_2 = filter(a1_exp_3_2,cf21,plcf) + filter(a2_exp_3_2,cf22,plcf);
% adding noise to signals
o1_exp_3_1 = s1_exp_3_1 + na1_exp_3_1;
od_exp_3_1 = sd_exp_3_1 + na2_exp_3_1;
o1_exp_3_2 = s1_exp_3_2 + na1_exp_3_2;
od_exp_3_2 = sd_exp_3_2 + na2_exp_3_2;
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%%%%% Build input for linfitsvd %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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%% Build input objects
% empty ao
eao = ao();
%%
%%% Model %%%
% get response with nominal params
for ii = 1:numel(H.objs)
H.objs(ii).setParams(parnames,exp3_nomvalues);
end
%%% exp_3_1 %%%
os1 = matrix(o1_exp_3_1,od_exp_3_1,plist('shape',[2 1]));
is1 = matrix(oi1,eao,plist('shape',[2 1]));
%%% exp_3_2 %%%
os2 = matrix(o1_exp_3_2,od_exp_3_2,plist('shape',[2 1]));
is2 = matrix(eao,oid,plist('shape',[2 1]));
%%
%%% Input signals
iS = collection(is1,is2);
%%% Known parameters
vals = [0 0 0];
nms = {'dS11','dS1D','dSDD'};
errs = [1e-4 1e-3 1e-4];
kwnpar = pest(vals, nms, errs);
%% do fit
plfit = plist(...
'FitParams',usedparams,...
'Model',H,...
'Input',iS,...
'WhiteningFilter',WF,...
'Nloops',30,...
'Ncut',100,...
'KnownParams',kwnpar);
opars = linfitsvd(os1,os2,plfit);