public IModelLabelIterative <double, double> GenerateModelLabelIterative(IDataSet <double, double> training_set)
{
//ModelNearestNeighborKDTreeDefault model = new ModelNearestNeighborKDTreeDefault(training_set.DataContext);
ModelNearestNeighborKDTree <double, double, double> model = new ModelNearestNeighborKDTree <double, double, double>("ModelKNNIneterval", training_set.DataContext, new AlgebraRealFloat64(), new FunctionDistanceEuclidean());
IList <Tuple <double[], double> > training_instances = new List <Tuple <double[], double> >();
for (int instance_index = 0; instance_index < training_set.InstanceCount; instance_index++)
{
training_instances.Add(new Tuple <double[], double>(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)
{
ModelNearestNeighborKDTree <DomainType, DistanceType, LabelType> model = new ModelNearestNeighborKDTree <DomainType, DistanceType, LabelType>(
this.DataContext,
this.kdtree.Copy(),
this.DistanceFunction,
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);
}