(svm)[matlab].txt

支持向量机(SVM)实现的分类算法源码[matlab]

支持向量机(SVM)实现的分类算法源码[matlab]

程序代码： (代码标记  [code]...[/code]  )

function [iter, optCond, time, w, gamma] = lsvm(A,D,nu,tol,maxIter,alpha, ...
perturb,normalize);
% LSVM Langrangian Support Vector Machine algorithm
%   LSVM solves a support vector machine problem using an iterative
%   algorithm inspired by an augmented Lagrangian formulation.
%
%   iters = lsvm(A,D)
%
%   where A is the data matrix, D is a diagonal matrix of 1s and -1s
%   indicating which class the points are in, and 'iters' is the number
%   of iterations the algorithm used.
%
%   All the following additional arguments are optional:
%
%   [iters, optCond, time, w, gamma] = ...
%     lsvm(A,D,nu,tol,maxIter,alpha,perturb,normalize)
%
%   optCond is the value of the optimality condition at termination.
%   time is the amount of time the algorithm took to terminate.
%   w is the vector of coefficients for the separating hyperplane.
%   gamma is the threshold scalar for the separating hyperplane.
%
%   On the right hand side, A and D are required. If the rest are not
%   specified, the following defaults will be used:
%     nu = 1/size(A,1), tol = 1e-5, maxIter = 100, alpha = 1.9/nu,
%       perturb = 0, normalize = 0
%
%   perturb indicates that all the data should be perturbed by a random
%   amount between 0 and the value given. perturb is recommended only
%   for highly degenerate cases such as the exclusive or.
%
%   normalize should be set to 1 if the data should be normalized before 
%   training.
%
%   The value -1 can be used for any of the entries (except A and D) to
%   specify that default values should be used.
%
%   Copyright (C) 2000 Olvi L. Mangasarian and David R. Musicant.
%   Version 1.0 Beta 1
%   This software is free for academic and research use only.
%   For commercial use, contact [e-mail].

 % If D is a vector, convert it to a diagonal matrix.
 [k,n] = size(D);
 if k==1 | n==1
   D=diag(D);
 end;

 % Check all components of D and verify that they are +-1
 checkall = diag(D)==1 | diag(D)==-1;
 if any(checkall==0)
   error('Error in D: classes must be all 1 or -1.');
 end;

 m = size(A,1);

 if ~exist('nu') | nu==-1
   nu = 1/m;
 end;
 if ~exist('tol') | tol==-1
   tol = 1e-5;
 end;
 if ~exist('maxIter') | maxIter==-1
   maxIter = 100;
 end;
 if ~exist('alpha') | alpha==-1
   alpha = 1.9/nu;
 end;
 if ~exist('normalize') | normalize==-1
   normalize = 0;
 end;
 if ~exist('perturb') | perturb==-1
   perturb = 0;
 end;
 
 % Do a sanity check on alpha
 if alpha > 2/nu,
   disp(sprintf('Alpha is larger than 2/nu. Algorithm may not converge.'));
 end;

 % Perturb if appropriate
 rand('seed',22);
 if perturb,
   A = A + rand(size(A))*perturb;
 end;
 
 % Normalize if appropriate
 if normalize,
   avg = mean(A);
   dev = std(A);
   if (isempty(find(dev==0)))
     A = (A - avg(ones(m,1),:))./dev(ones(m,1),:);
   else
     warning('Could not normalize matrix: at least one column is constant.');
   end;
 end;
 
 % Create matrix H
 [m,n] = size(A);
 e = ones(m,1);
 H = D*[A -e];
 iter = 0;
 time = cputime;
 
 % "K" is an intermediate matrix used often in SMW calclulations
 K = H*inv((speye(n+1)/nu+H'*H));
 
 % Choose initial value for x
 x = nu*(1-K*(H'*e));
 
 % y is the old value for x, used to check for termination
 y = x + 1;
 
 while iter < maxIter & norm(y-x)>tol
   % Intermediate calculation which is used twice
   z = (1+pl(((x/nu+H*(H'*x))-alpha*x)-1));
   y = x;
   % Calculate new value of x
   x=nu*(z-K*(H'*z));
   iter = iter + 1;
 end;
 
 % Determine outputs
 time = cputime - time;
 optCond = norm(x-y);
 w = A'*D*x;
 gamma = -e'*D*x;
 disp(sprintf('Running time (CPU secs) = %g',time));
 disp(sprintf('Number of iterations = %d',iter));
 disp(sprintf('Training accuracy = %g',sum(D*(A*w-gamma)>0)/m));
 
 return;
 
function pl = pl(x);
 %PLUS function : max{x,0}
 pl = (x+abs(x))/2;
 return;