量机(SVM)实现的分类算法源码[matlab].rar

				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 musicant@cs.wisc.edu.
				
				 % 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;