%grid of parameters
folds = 5;
[C,gamma] = meshgrid(-5:2:15, -15:2:3);
%# grid search, and cross-validation
cv_acc = zeros(numel(C),1);
d= 2;
for i=1:numel(C)
cv_acc(i) = svmtrain(TrainLabel,TrainVec, ...
sprintf('-c %f -g %f -v %d -t %d', 2^C(i), 2^gamma(i), folds,d));
end
%# pair (C,gamma) with best accuracy
[~,idx] = max(cv_acc);
%# contour plot of paramter selection
contour(C, gamma, reshape(cv_acc,size(C))), colorbar
hold on;
text(C(idx), gamma(idx), sprintf('Acc = %.2f %%',cv_acc(idx)), ...
'HorizontalAlign','left', 'VerticalAlign','top')
hold off
xlabel('log_2(C)'), ylabel('log_2(\gamma)'), title('Cross-Validation Accuracy')
%# now you can train you model using best_C and best_gamma
best_C = 2^C(idx); best_gamma = 2^gamma(idx); %# ...
这也执行网格搜索...但是使用 matlab...不使用 grid.py...也许这有帮助...