package svmwrap

Svmwrap can be used to train/test regressors using libsvm-tools.

(Scary) usage: usage: ./svmwrap -i : training set or DB to screen --feats : number of features [-o ]: predictions output file [-np ]: ncores [--kernel ] choose kernel type {Lin|RBF|Sig|Pol} [-c ]: fix C [-e ]: epsilon in the loss function of epsilon-SVR; (0 <= epsilon <= max_i(|y_i|)) [-g ]: fix gamma (for RBF and Sig kernels) [-r ]: fix r for the Sig kernel [--iwn]: turn ON instance-wise-normalization [--scale]: turn ON [0:1] scaling (NOT PRODUCTION READY) [--no-plot]: no gnuplot [{-n|--NxCV} ]: folds of cross validation [-q]: quiet [-v|--verbose]: equivalent to not specifying -q [--seed ]: fix random seed [-p ]: training set portion (in [0.0:1.0]) [--pairs]: read from .AP files (atom pairs; will offset feat. indexes by 1) [--train <train.liblin>]: training set (overrides -p) [--valid <valid.liblin>]: validation set (overrides -p) [--test <test.liblin>]: test set (overrides -p) [{-l|--load} ]: prod. mode; use trained models [{-s|--save} ]: train. mode; save trained models [-f]: force overwriting existing model file [--scan-c]: scan for best C [--scan-e ]: epsilon scan #steps for SVR [--scan-g]: scan for best gamma [--regr]: regression (SVR); also, implied by -e and --scan-e [--e-range ::]: specific range for e (semantic=start:nsteps:stop) [--c-range <float,float,...>] explicit scan range for C (example='0.01,0.02,0.03') [--g-range <float,float,...>] explicit range for gamma (example='0.01,0.02,0.03') [--r-range <float,float,...>] explicit range for r (example='0.01,0.02,0.03')