rbfnet.asv

RBF神经网络的程序, RBF神经网络的程序

function a = rbfnet(hyper) 
%=============================================================================
% Radial Basis Function Network object             
%=============================================================================  
% a=rbfnet(hyperParam) 
%
% Generates an rbfnet object with given hyperparameters. The used basis
% functions are exp(-gamma*||x-z_i||)  where gamma and z_i can be optimized
%
%
%   Hyperparameters (with defaults)
%   nr_of_centers=1      -- the number of centers to be used
%   init_with_kmeans=0   -- the centers are initialized with a kmeans 
%   fixgamma=0           -- the variance is fixed. model parameter gamma
%                           is used
%   maxiter=2000         -- number of maximal iterations
%   eps = 0              -- stopping criteria
% 
%   Model
%    centers               -- the centers used for basis functions
%    gamma                 -- estimated 1/2*variance^2 parameter
%    alpha                 -- final coefficients
%
% Methods:
%  train, test
%
% Example:
% % Estimate Sinc Function  with Rbfnet and Bagged Rbfnet
%t=[-5:0.29:5]';
%ttest=[-5:0.06:5]';
%y=sin(t)./t+1e-2*randn(length(t),1);
%ytest=sin(ttest)./ttest+1e-1*randn(length(ttest),1);
%
%d=data(t,y)
%dtest=data(ttest,ytest)
%
%rbn=rbfnet('nr_of_centers=3');
%rbn.fixgamma=0; rbn.gamma=1e-3; rbn.eps=1e-5; rbn.init_with_kmeans =0;
%rbn.maxiter=500; rbn.algorithm.use_signed_output=0;
%a0=bagging(rbn,'bags=3');
%a0.algorithm.use_signed_output=0;
%[r,a]=train(group({rbn,a0}),d)
%a1=a.child{1}; % this was the rbf net 
%r=test(a1,dtest);
%hold on;
%plot(ttest,ytest,'.')
%plot(ttest,r.X,'ro-'); 
%loss(r,'quadratic_loss')
%a2=a.child{2}; % this was the bagged rbf net
%r=test(a2,dtest); 
%plot(ttest,r.X,'b+-');  loss(r,'quadratic_loss')
%legend({'true','Rbf','Bagged RBF'})
%plot(a1.centers,a.centers*0,'rx')
%=============================================================================
% Reference : Neural Networks
% Author    : Chris Bishop
% Link      : -
%=============================================================================
   


  a.nr_of_centers=1;
  a.init_with_kmeans=0;
  a.fixgamma=0;
  a.maxiter=2000;
  a.eps=1e-5;
  
% model
  a.centers=[];
  a.gamma=[];
  a.alpha=[];
  
  p=algorithm('rbfnet');
  a= class(a,'rbfnet',p);

  a.algorithm.alias={'k','nr_of_centers'}; % kernel aliases
 
  if nargin==1,
    eval_hyper;
  end;