public static IModelDiscrete <double, int> Read(BinaryReader reader)
{
IDataContext data_context = DataSet.DataContext.Read(reader);
ModelNearestNeighborKDTreeDefault model = new ModelNearestNeighborKDTreeDefault(data_context);
double[][] instance_features = ToolsCollection.ConvertToArrayArray(reader.ReadFloat64Array2D());
bool[][] instance_missings = ToolsCollection.ConvertToArrayArray(new bool[instance_features.GetLength(0), instance_features.GetLength(1)]);
int[][] instance_labels = ToolsCollection.ConvertToArrayArray(reader.ReadInt32Array2D());
IDataSet <double, int> training_set = new DataSet <double, int>(data_context, instance_features, instance_missings, instance_labels);
return(model.GenerateModelDiscrete(training_set));
}
public IModelDiscreteIterative <double, int> GenerateModelDiscreteIncremental(IDataSet <double, int> training_set)
{
ModelNearestNeighborKDTreeDefault model = new ModelNearestNeighborKDTreeDefault(training_set.DataContext);
IList <Tuple <double[], int> > training_instances = new List <Tuple <double[], int> >();
for (int instance_index = 0; instance_index < training_set.InstanceCount; instance_index++)
{
training_instances.Add(new Tuple <double[], int>(training_set.GetInstanceFeatureData(instance_index), training_set.GetInstanceLabelData(instance_index)[0]));
}
model.Add(training_instances);
return(model);
}
public static void Write(BinaryWriter writer, ModelNearestNeighborKDTreeDefault model)
{
model.DataContext.Write(writer);
IList <Tuple <double[], int> > list = model.Values();
double[,] instance_features = new double[list.Count, model.DataContext.FeatureCount];
int[] instance_labels = new int[list.Count];
for (int instance_index = 0; instance_index < list.Count; instance_index++)
{
instance_features.Set1DIndex1(instance_index, list[instance_index].Item1);
instance_labels[instance_index] = list[instance_index].Item2;
}
writer.Write(instance_features);
writer.Write(instance_labels);
}
