public IModelDiscreteIterative <int, int> GenerateModelDiscreteIncremental(IDataSet <int, int> training_set)
{
//TODO check data context
ModelNearestNeighborList <int, int, int> model = new ModelNearestNeighborList <int, int, int>(training_set.DataContext, new FunctionDistanceHamming());
IList <Tuple <int[], int> > training_instances = new List <Tuple <int[], int> >();
for (int instance_index = 0; instance_index < training_set.InstanceCount; instance_index++)
{
training_instances.Add(new Tuple <int[], int>(training_set.GetInstanceFeatureData(instance_index), training_set.GetInstanceLabelData(instance_index)[0]));
}
model.Add(training_instances);
return(model);
}
public IModelDiscreteIterative <DomainType, LabelType> GenerateModelDiscrete(IDataSet <DomainType, LabelType> training_set)
{
ModelNearestNeighborList <DomainType, DistanceType, LabelType> model = new ModelNearestNeighborList <DomainType, DistanceType, LabelType>(
this.DataContext,
new List <Tuple <DomainType[], LabelType> >(this.list),
this.distance_function,
this.voting_system,
this.neighbor_count);
IList <Tuple <DomainType[], LabelType> > training_instances = new List <Tuple <DomainType[], LabelType> >();
for (int instance_index = 0; instance_index < training_set.InstanceCount; instance_index++)
{
training_instances.Add(new Tuple <DomainType[], LabelType>(training_set.GetInstanceFeatureData(instance_index), training_set.GetInstanceLabelData(instance_index)[0]));
}
model.Add(training_instances);
return(model);
}