public override IEnumerable <Subset> GetSubsets(Batch batch, IRandom rand)
{
for (int i = 0; i < Size; i++)
{
yield return(FeatureSelector.SelectFeatures(batch.TrainInstances, rand));
}
}
public override IEnumerable <Subset> GetSubsets(Batch batch, IRandom rand)
{
for (int i = 0; i < Size; i++)
{
// REVIEW: Consider ways to reintroduce "balanced" samples.
var viewTrain = new BootstrapSampleTransform(Host, new BootstrapSampleTransform.Arguments(), Data.Data);
var dataTrain = RoleMappedData.Create(viewTrain, Data.Schema.GetColumnRoleNames());
yield return(FeatureSelector.SelectFeatures(dataTrain, rand));
}
}
public override IEnumerable <Subset> GetSubsets(Batch batch, Random rand)
{
string name = Data.Data.Schema.GetTempColumnName();
var args = new GenerateNumberTransform.Options();
args.Columns = new[] { new GenerateNumberTransform.Column()
{
Name = name
} };
args.Seed = (uint)rand.Next();
IDataTransform view = new GenerateNumberTransform(Host, args, Data.Data);
// REVIEW: This won't be very efficient when Size is large.
for (int i = 0; i < Size; i++)
{
var viewTrain = new RangeFilter(Host, new RangeFilter.Options()
{
Column = name, Min = (Double)i / Size, Max = (Double)(i + 1) / Size
}, view);
var dataTrain = new RoleMappedData(viewTrain, Data.Schema.GetColumnRoleNames());
yield return(FeatureSelector.SelectFeatures(dataTrain, rand));
}
}
