/*************************************************************************
This function initializes temporaries needed for ensemble training.
*************************************************************************/
private static void initmlpetrnsession(mlpbase.multilayerperceptron individualnetwork,
mlptrainer trainer,
mlpetrnsession session)
{
int[] dummysubset = new int[0];
//
// Prepare network:
// * copy input network to Session.Network
// * re-initialize preprocessor and weights if RandomizeNetwork=True
//
mlpbase.mlpcopy(individualnetwork, session.network);
initmlptrnsessions(individualnetwork, true, trainer, session.mlpsessions);
apserv.ivectorsetlengthatleast(ref session.trnsubset, trainer.npoints);
apserv.ivectorsetlengthatleast(ref session.valsubset, trainer.npoints);
}
/*************************************************************************
This function initializes temporaries needed for training session.
*************************************************************************/
private static void initmlpetrnsessions(mlpbase.multilayerperceptron individualnetwork,
mlptrainer trainer,
alglib.smp.shared_pool sessions)
{
mlpetrnsession t = new mlpetrnsession();
if( !alglib.smp.ae_shared_pool_is_initialized(sessions) )
{
initmlpetrnsession(individualnetwork, trainer, t);
alglib.smp.ae_shared_pool_set_seed(sessions, t);
}
}
public override alglib.apobject make_copy()
{
mlpetrnsession _result = new mlpetrnsession();
_result.trnsubset = (int[])trnsubset.Clone();
_result.valsubset = (int[])valsubset.Clone();
_result.mlpsessions = (alglib.smp.shared_pool)mlpsessions.make_copy();
_result.mlprep = (mlpreport)mlprep.make_copy();
_result.network = (mlpbase.multilayerperceptron)network.make_copy();
return _result;
}
