/*
* read the classifier from the given ioHandler
*/
bool svm::read(ioHandler& handler,const bool complete) {
bool b=true;
if (complete) {
b=handler.readBegin();
}
b = b && supervisedInstanceClassifier::read(handler,false);
if (b) {
b=b && lti::read(handler, "nClasses",nClasses);
b=b && lti::read(handler, "alpha",alpha);
delete trainData;
dmatrix* t=new dmatrix();
b=b && lti::read(handler, "vectors",*t);
trainData=t;
b=b && lti::read(handler, "idMap",idMap);
b=b && lti::read(handler, "rIdMap",rIdMap);
b=b && lti::read(handler, "srcIds",srcIds);
b=b && lti::read(handler, "bias", bias);
int n;
b=b && lti::read(handler, "nKernels", n);
//kernels.
b=b && handler.readBegin();
b=b && handler.trySymbol("kernels");
className cn;
std::string kname;
kernels.resize(n);
// always read the complete kernel vector, so we have the setup
// ready to go without the need for any subsequent
// initializations
for (int i=0; i<n; i++) {
b=b && lti::read(handler, "name", kname);
if (kname != "unknown") {
kernels[i]=getParameters().createKernel(kname);
b=b && kernels[i]->read(handler);
} else {
kernels[i]=0;
}
}
b=b && handler.readEnd();
b=b && lti::read(handler, "offset", offset);
b=b && lti::read(handler, "scale", scale);
rebuildTargets();
defineOutputTemplate();
}
if (complete) {
b=b && handler.readEnd();
}
return b;
}
