datdemo.m

数字通信第四版原书的例程

%DATDEMO Nonlinear data fitting demonstration.
echo on;  clc
%       This example demonstrates fitting a nonlinear function to a
%	set of data.  It is used to demonstrate many of the different
%       methods which can be used in the OPTIMIZATION TOOLBOX.

%	Consider the following data:

pause	% Strike any key to continue.

Data = ...
[ 0.0000    5.8955
0.1000    3.5639
0.2000    2.5173
0.3000    1.9790
0.4000    1.8990
0.5000    1.3938
0.6000    1.1359
0.7000    1.0096
0.8000    1.0343
0.9000    0.8435
1.0000    0.6856
1.1000    0.6100
1.2000    0.5392
1.3000    0.3946
1.4000    0.3903
1.5000    0.5474
1.6000    0.3459
1.7000    0.1370
1.8000    0.2211
1.9000    0.1704
2.0000    0.2636];

%	Let's plot this data.

pause	% Strike any key for plot.
t = Data(:,1);
y = Data(:,2);
clf
plot(t,y,'o'), title('Input data'), pause
clc
%	We would like to fit the function
%
%	  y =  c(1)*exp(-lam(1)*t) + c(2)*exp(-lam(2)*t)
%
%	to the data.  This function has 2 linear parameters
%	and 2 nonlinear parameters.

echo off
disp('Please Wait - Compiling Optimization Routines')

% test_long is a variable used for auto testing of this routine
if ~exist('test_long')  test_long = 0; end
if exist('method')~=1 method = 8; end
if ~length(method) method = 8; end
l = 2; 

while 1
	lam = [1 0]'; 
	if ~test_long
		clc
		disp(' ')
		disp('   Choose any of the following methods to perform the data fit')
		disp('')        
		disp('        UNCONSTRAINED:    1) Broyden-Fletcher-Golfarb-Shanno')
		disp('                          2) Davidon-Fletcher-Powell')
		disp('                          3) Steepest Descent')
		disp('                          4) Simplex Search')
		disp('         LEAST SQUARES:   5) Gauss-Newton  ')
		disp('                          6) Levenberg-Marquardt ')
		disp('         MINIMAX          7) Seq. Quadratic Progr.')
		disp('')
		disp('                          0) Quit')
		disp('')
		disp('Note: Options 1:6 perform a least squares fit')
		disp('      Option 7 minimizes the worst case error')
		disp('      Gauss-Newton is the fastest method')
	end

	
	if test_long 
		if l>=2 
		    method=method-1;
		    l = 0;
		end
	else 
		method=-1; 
	end
	while (method<0 | method>7)
		method = [];
		while ~length(method)
		    method = input('Select a method number: ');
		end
	end
	if (method == 0) 
		return
	end
	OPTIONS=0;
	if method==2, OPTIONS(6)=1;
	elseif method==3, OPTIONS(6)=2;
	elseif method==4, OPTIONS(5)=1;
	elseif method==5, OPTIONS(5)=1;
	end
	if test_long
		l = l + 1;  
	else 
		l = [];
	end
	if method~=4&method~=7
		disp('')
		disp('    Choose any of the following line search methods')
		disp('')
		disp('           1) Mixed Polynomial Interpolation')
		disp('           2) Cubic Interpolation')
		disp('')

		while ~length(l)
		    l = input('Select a line search number: ');
		end
		if l==2, OPTIONS(7)=1; end
	end

	disp('')
	OPTIONS(2)=1e-3;
	t0=clock;
	if method==5|method==6
		disp('[lam,OPTIONS]=leastsq(''fitfun2'', lam,OPTIONS,[],Data);')
		[lam,OPTIONS]=leastsq('fitfun2', lam,OPTIONS,[],Data);
	elseif method==4
		disp('[lam,OPTIONS]=fmins(''norm(fitfun2(x,P1))'',lam, OPTIONS,[],Data); ')
		[lam,OPTIONS]=fmins('norm(fitfun2(x,P1))',lam, OPTIONS,[],Data); 
	elseif method~=7;
		disp('[lam,OPTIONS]=fminu(''norm(fitfun2(x,P1))'',lam,OPTIONS,[],Data);')
		[lam,OPTIONS]=fminu('norm(fitfun2(x,P1))',lam,OPTIONS,[],Data);
	else
		OPTIONS(15)=length(t);
		disp('[lam,OPTIONS]=minimax(''f=fitfun2(x,P1); g=[];'', lam, OPTIONSR,[],[],[],Data);')
		[lam,OPTIONS]=minimax('f=fitfun2(x,P1); g=[];', lam, OPTIONS,[],[],[],Data);
	end
	if test_long
		if (method<=4) OPTIONS(8) = OPTIONS(8).^2; end
		if OPTIONS(8)-0.03*(method==7)-(method==3) > 0.15, error('Optimization Toolbox in datdemo'), end
	end
	execution_time=etime(clock, t0)
	disp('Strike any key for menu')
	pause
end