pso-svr代码(用于matlab)
function [nsv,beta,bias]=svr(X,Y,ker,C,loss,e)
% SVR Support Vector Regression
%
% Usage : [nsv beta bias]=svr(X,Y,ker,C,loss,e)
%
% Parameters : X – Training inputs
% Y – Training targets
% ker – kernel function
% C – upper bound ( non – separable case )
% loss – loss function
% e – insensitivity
% nsv – number of support vectors
% beta – Difference of Lagrange Multipliers
% bias – bias term
%
% Author : Steve Gunn ( srg@ecs . soton . ac .uk )
if (nargin<3|nargin>6) % check correct number of arguments
help svr
else
fprintf (‘Support Vector Regressing …\n’)
fprintf (‘______________________________\n’)
n = size (X ,1);
if(nargin<6) e =0.0;end
if(nargin<5) loss='eInsensitive';end
if(nargin<4) C=Inf;end
if(nargin<3) ker='linear';end
% Construct the Kernel matrix
fprintf (‘Constructing …\n’);
H = zeros (n,n);
for i =1: n
for j =1: n
H(i,j)=svkernel(ker,X(i ,:),X(j ,:));
end
end
% Set up the parameters for the Optimisation problem
switch lower ( loss )
case ‘ einsensitive ‘,
Hb = [ H -H ; – H H];
c = [( e* ones (n ,1) – Y ); ( e* ones (n ,1) + Y )];
vlb = zeros (2* n ,1); % Set the bounds : alphas >= 0
vub = C* ones (2* n ,1); % alphas <= C
x0 = zeros (2* n ,1); % The starting point is [0 0 0 0]
neqcstr = nobias ( ker ); % Set the number of equality constraints (1 or 0)
if neqcstr
A=ones(1,n)-ones(1,n); b = 0; % Set the constraint Ax = b
else
A = []; b = [];
end
case ‘ quadratic ‘,
Hb = H + eye (n )/(2* C);
c = – Y;
vlb = -1e30*ones(n ,1);
vub = 1e30 * ones (n ,1);
x0 = zeros (n ,1); % The starting point is [0 0 0 0]
neqcstr = nobias ( ker ); % Set the number of equality constraints (1 or 0)
if neqcstr
A = ones (1, n ); b = 0; % Set the constraint Ax = b
else
A = []; b = [];
end
otherwise , disp (‘ Error : Unknown Loss Function \n’);
end
% Add small amount of zero order regularisation to
% avoid problems when Hessian is badly conditioned .
% Rank is always less than or equal to n.
% Note that adding to much reg will peturb solution
Hb = Hb+1e-10* eye ( size (Hb ));
% Solve the Optimisation Problem
fprintf (‘ Optimising …\ n’);
st = cputime ;
[ alpha lambda how ] = qp(Hb , c, A, b, vlb , vub , x0 , neqcstr );
fprintf (‘ Execution time : %4.1 f seconds \n’, cputime – st );
fprintf (‘ Status : % s\n’, how );
switch lower ( loss )
case ‘ einsensitive ‘,
beta = alpha (1: n ) – alpha (n +1:2* n);
case ‘ quadratic ‘,
beta = alpha ;
end
fprintf (‘|w0 |^2 : % f\n’, beta’ *H* beta );
fprintf (‘ Sum beta : % f\n’, sum ( beta ));
% Compute the number of Support Vectors
epsilon = svtol ( abs ( beta ));
svi = find ( abs ( beta ) >epsilon );
nsv = length ( svi );
fprintf (‘ Support Vectors: % d (%3.1 f %%)\ n’, nsv ,100* nsv /n);
% Implicit bias , b0
bias = 0;
% Explicit bias , b0
if nobias ( ker ) ~= 0
switch lower ( loss )
case ‘
einsensitive ‘,
% find bias from average of support vectors with interpolation error e
% SVs with interpolation error e have alphas : 0 < a
发布者：全栈程序员-站长，转载请注明出处：https://javaforall.net/227558.html原文链接：https://javaforall.net
