I am using libsvm with precomputed kernels. I generated a precomputed kernel file for the example data set heart_scale and executed the function svmtrain(). It worked properly and the support vectors were identifed correctly, i.e. similar to standard kernels.
However, when I am trying to run svmpredict(), it gave different results for the precomputed model file. After digging through the code, I noticed that the svm_predict_values() function, requires the actual features of the support vectors, which is unavailable in precomputed mode. In precomputed mode, we only have the coefficient and index of each support vector, which is mistaken for its features by svmpredict().
Is this a issue or am I missing something.
(Please let me know how to run svmpredict() in precomputed mode.)