potential bug - the Matlab interface

[yscoffee]

I tried to run a very simple binary classification via the matlab interface of libsvm where

class A : [ 1, 1] class B : [-1,-1] and [ 1, -1 ]

but got wrong prediction results (compared to the python interface) cases [-1,-1] and [1,-1] are all wrong.

here is the sample code

N=500;
A_pts = repmat([1,1],N*2,1);
A_label = ones(size(A_pts,1),1);

B_pts = repmat([-1,-1],N,1);
B_pts = cat(1,B_pts, repmat([1,-1],N,1));    
B_label = -1*ones(size(B_pts,1),1);

x = [ A_pts ; B_pts ];
y = [ A_label ; B_label ];

svmmodel = svmtrain(x,y);
svmpredict(1,[1,1],svmmodel)
svmpredict(-1,[-1,-1],svmmodel) % wrong 
svmpredict(-1,[1,-1],svmmodel) % wrong 

output:

optimization finished, #iter = 500 nu = 0.500000 obj = -1000.000000, rho = -1.000000 nSV = 1000, nBSV = 1000 Total nSV = 1000

[cjlin1]

you should do svm_train(y,x) rather than x,y YS Wong writes:

I tried to run a very simple binary classification via the matlab interface of libsvm where

class A : [ 1, 1] class B : [-1,-1] and [ 1, -1 ]

but got wrong prediction results (compared to the python interface) cases [-1,-1] and [1,-1] are all wrong.

here is the sample code

N=500; A_pts = repmat([1,1],N*2,1); A_label = ones(size(A_pts,1),1);

B_pts = repmat([-1,-1],N,1); B_pts = cat(1,B_pts, repmat([1,-1],N,1));
B_label = -1*ones(size(B_pts,1),1);

x = [ A_pts ; B_pts ]; y = [ A_label ; B_label ];

svmmodel = svmtrain(x,y); svmpredict(1,[1,1],svmmodel) svmpredict(-1,[-1,-1],svmmodel) % wrong svmpredict(-1,[1,-1],svmmodel) % wrong

output:

optimization finished, #iter = 500
nu = 0.500000
obj = -1000.000000, rho = -1.000000
nSV = 1000, nBSV = 1000
Total nSV = 1000

— You are receiving this because you are subscribed to this thread. Reply to this email directly, view it on GitHub, or mute the thread.*

[yscoffee]

Thanks