Question

我想为C-SVM分类选择参数c和gamma 使用libsvm \ tools \ grid.py的RBF（径向基函数）内核，但我不知道它是如何可能的？我安装了libsvm和gnuplot以及python并在python中运行了grid.py，但它有错误并且没有显示结果。

Answer 1

%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 ...这可能有帮助......

Answer 2

您可以使用提供的matlab脚本而不是grid.py FAQ

问：我如何使用MATLAB接口进行参数选择？ http://www.csie.ntu.edu.tw/~cjlin/libsvm/faq.html#f803

bestcv = 0;
for log2c = -1:3,
  for log2g = -4:1,
    cmd = ['-v 5 -c ', num2str(2^log2c), ' -g ', num2str(2^log2g)];
    cv = svmtrain(heart_scale_label, heart_scale_inst, cmd);
    if (cv >= bestcv),
      bestcv = cv; bestc = 2^log2c; bestg = 2^log2g;
    end
    fprintf('%g %g %g (best c=%g, g=%g, rate=%g)\n', log2c, log2g, cv, bestc, bestg, bestcv);
  end
end

如何使用grid.py进行参数选择？

2 个答案: