void RandomSplitJobManager::RetrieveRandomSplit (int splitNum,
FeatureVectorListPtr& trainData,
FeatureVectorListPtr& testData
)
{
trainData = NULL;
testData = NULL;
if ((splitNum < 0) || (splitNum >= (int)splits->NumOfOrderings ()))
{
log.Level (-1) << endl << endl
<< "RandomSplitJobManager::RetrieveRandomSplit ***ERROR*** Invalid SplitNum[" << splitNum << "]" << endl
<< endl;
return;
}
const
FeatureVectorListPtr ordering = splits->Ordering (splitNum);
trainData = ordering->ManufactureEmptyList (false);
testData = ordering->ManufactureEmptyList (false);
MLClassList::const_iterator classIDX;
for (classIDX = mlClasses->begin (); classIDX != mlClasses->end (); classIDX++)
{
MLClassPtr ic = *classIDX;
FeatureVectorListPtr examplesThisClass = ordering->ExtractExamplesForAGivenClass (ic);
int numTrainExamplesNeeded = (int)(0.5 + (double)(examplesThisClass->QueueSize ()) * (double)splitFraction);
int numExamplesAddToTrainSet = 0;
FeatureVectorList::const_iterator idx;
for (idx = examplesThisClass->begin (); idx != examplesThisClass->end (); idx++)
{
FeatureVectorPtr example = *idx;
if (numExamplesAddToTrainSet < numTrainExamplesNeeded)
{
trainData->PushOnBack (example);
numExamplesAddToTrainSet++;
}
else
{
testData->PushOnBack (example);
}
}
}
} /* RetrieveRandomSplit */
