X-Git-Url: https://git.creatis.insa-lyon.fr/pubgit/?p=CreaPhase.git;a=blobdiff_plain;f=octave_packages%2Foptim-1.2.0%2Fexpfit.m;fp=octave_packages%2Foptim-1.2.0%2Fexpfit.m;h=995cb89d59a2da2be4054a853ff7d49d3959f0f4;hp=0000000000000000000000000000000000000000;hb=c880e8788dfc484bf23ce13fa2787f2c6bca4863;hpb=1705066eceaaea976f010f669ce8e972f3734b05

diff --git a/octave_packages/optim-1.2.0/expfit.m b/octave_packages/optim-1.2.0/expfit.m
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+## Copyright (C) 2000 Gert Van den Eynde <na.gvandeneynde@na-net.ornl.gov>
+## Copyright (C) 2002 Rolf Fabian <fabian@tu-cottbus.de>
+##
+## This program is free software; you can redistribute it and/or modify it under
+## the terms of the GNU General Public License as published by the Free Software
+## Foundation; either version 3 of the License, or (at your option) any later
+## version.
+##
+## This program is distributed in the hope that it will be useful, but WITHOUT
+## ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or
+## FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more
+## details.
+##
+## You should have received a copy of the GNU General Public License along with
+## this program; if not, see <http://www.gnu.org/licenses/>.
+
+## USAGE  [alpha,c,rms] = expfit( deg, x1, h, y )
+##
+## Prony's method for non-linear exponential fitting
+##
+## Fit function:   \sum_1^{deg} c(i)*exp(alpha(i)*x)
+##
+## Elements of data vector y must correspond to
+## equidistant x-values starting at x1 with stepsize h
+##
+## The method is fully compatible with complex linear
+## coefficients c, complex nonlinear coefficients alpha
+## and complex input arguments y, x1, non-zero h .
+## Fit-order deg  must be a real positive integer.
+##
+## Returns linear coefficients c, nonlinear coefficients
+## alpha and root mean square error rms. This method is
+## known to be more stable than 'brute-force' non-linear
+## least squares fitting.
+##
+## Example
+##    x0 = 0; step = 0.05; xend = 5; x = x0:step:xend;
+##    y = 2*exp(1.3*x)-0.5*exp(2*x);
+##    error = (rand(1,length(y))-0.5)*1e-4;
+##    [alpha,c,rms] = expfit(2,x0,step,y+error)
+##
+##  alpha =
+##    2.0000
+##    1.3000
+##  c =
+##    -0.50000
+##     2.00000
+##  rms = 0.00028461
+##
+## The fit is very sensitive to the number of data points.
+## It doesn't perform very well for small data sets.
+## Theoretically, you need at least 2*deg data points, but
+## if there are errors on the data, you certainly need more.
+##
+## Be aware that this is a very (very,very) ill-posed problem.
+## By the way, this algorithm relies heavily on computing the
+## roots of a polynomial. I used 'roots.m', if there is
+## something better please use that code.
+##
+## Demo for a complex fit-function:
+## deg= 2; N= 20; x1= -(1+i), x= linspace(x1,1+i/2,N).';
+## h = x(2) - x(1)
+## y= (2+i)*exp( (-1-2i)*x ) + (-1+3i)*exp( (2+3i)*x );
+## A= 5e-2; y+= A*(randn(N,1)+randn(N,1)*i); % add complex noise
+## [alpha,c,rms]= expfit( deg, x1, h, y )
+
+function [a,c,rms] = expfit(deg,x1,h,y)
+
+% Check input
+if deg<1, error('expfit: deg must be >= 1');     end
+if  ~h,   error('expfit: vanishing stepsize h'); end
+
+if ( N=length( y=y(:) ) ) < 2*deg
+   error('expfit: less than %d samples',2*deg);
+end
+
+% Solve for polynomial coefficients
+A = hankel( y(1:N-deg),y(N-deg:N) );
+s = - A(:,1:deg) \ A(:,deg+1);
+
+% Compose polynomial
+p = flipud([s;1]);
+
+% Compute roots
+u = roots(p);
+
+% nonlinear coefficients
+a = log(u)/h;
+
+% Compose second matrix A(i,j) = u(j)^(i-1)
+A = ( ones(N,1) * u(:).' ) .^ ( [0:N-1]' * ones(1,deg) );
+
+% Solve linear system
+f = A\y;
+
+% linear coefficients
+c = f./exp( a*x1 );
+
+% Compute rms of y - approx
+% where approx(i) = sum_k ( c(k) * exp(x(i)*a(k)) )
+if nargout > 2
+   x = x1+h*[0:N-1];
+   approx = exp( x(:) * a(:).' ) * c(:);
+   rms = sqrt( sumsq(approx - y) );
+end
+
+endfunction
+
+%!demo	 % same as in help - part
+%! deg= 2; N= 20; x1= -(1+i), x= linspace(x1,1+i/2,N).';
+%! h = x(2) - x(1)
+%! y= (2+i)*exp( (-1-2i)*x ) + (-1+3i)*exp( (2+3i)*x );
+%! A= 5e-2; y+= A*(randn(N,1)+randn(N,1)*i); % add complex noise
+%! [alpha,c,rms]= expfit( deg, x1, h, y )
+
+%!demo	 % demo for stepsize with negative real part
+%! deg= 2; N= 20; x1= +3*(1+i), x= linspace(x1,1+i/3,N).';
+%! h = x(2) - x(1)
+%! y= (2+i)*exp( (-1-2i)*x ) + (-1+3i)*exp( (2+3i)*x );
+%! A= 5e-2; y+= A*(randn(N,1)+randn(N,1)*i); % add complex noise
+%! [alpha,c,rms]= expfit( deg, x1, h, y )
+
+%!demo
+%! x0 = 1.5; step = 0.05; xend = 5;
+%! a = [1.3, 2]';
+%! c = [2, -0.5]';
+%! v = 1e-4;
+%! 
+%! x = x0:step:xend;
+%! y = exp (x(:) * a(:).') * c(:);
+%! err = randn (size (y)) * v;
+%! plot (x, y + err);
+%! 
+%! [a_out, c_out, rms] = expfit (2, x0, step, y+err)
+
+