📄 toon0554.m
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% toon0554 -- AutoSpline Reconstructions from Noisy Data
%
% Here we depict a reconstruction using penalized thin-plate splines,
% in which periodic thin-plate splines are used to smooth the data,
% with roughness penalty proportional to the energy stored in the
% second derivative. The constant of proportionality (or Lagrangian
% tuning parameter) is the empirical minimizer of the Stein Unbiased
% Estimate of Risk.
%
global yblocks ybumps yheavi yDoppler
global t
%
clf;
SplineInit
%
[xhat, c] = SplineUChoose(yblocks,2.);
versaplot(221,t,xhat,[],' 4 (a) AutoSpline[Blocks]',[],[])
%
[xhat,c] = SplineUChoose(ybumps,2.);
versaplot(222,t,xhat,[],' 4 (b) AutoSpline[Bumps]',[],[])
%
[xhat,c] = SplineUChoose(yheavi,2.);
versaplot(223,t,xhat,[],' 4 (c) AutoSpline[HeaviSine]',[],[])
%
[xhat,c] = SplineUChoose(yDoppler,2.);
versaplot(224,t,xhat,[],' 4 (d) AutoSpline[Doppler]',[],[])
%% Part of Wavelab Version 850% Built Tue Jan 3 13:20:43 EST 2006% This is Copyrighted Material% For Copying permissions see COPYING.m% Comments? e-mail wavelab@stat.stanford.edu
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