private static void ProccessDataSetNominalPCA(IDataSet <int, int> training_set, IDataSet <int, int> test_set)
{
int cluster_count = 10;
int instance_count = training_set.InstanceCount;
ITemplateClusteringCentroid <int, IDataSet <int> > template_cluster = new TemplateClusteringKMeansNominal(cluster_count);
TemplateDimensionReductionPCADefault template_pca = new TemplateDimensionReductionPCADefault(3);
IDataSet <int> training_set_unlabeled = training_set;
IClusteringCentroid <int> clustering = template_cluster.Cluster(training_set_unlabeled);
double[][] transformed_data1 = new double[instance_count][];
for (int instance_index = 0; instance_index < instance_count; instance_index++)
{
transformed_data1[instance_index] = clustering.Transform(training_set_unlabeled.GetInstanceFeatureData(instance_index));
}
IDataSet <double> data_set_transformed1 = new DataSet <double, int>(transformed_data1);
ITransform transform = template_pca.GenerateTransform(data_set_transformed1);
double[][] transformed_data2 = new double[instance_count][];
int[][] label_data = training_set.LabelData;
Color[] instance_colors = new Color[instance_count];
Color[] label_colors = ToolsColor.GetPallete(training_set.DataContext.GetLabelDescriptor(0).ValueCount);
for (int instance_index = 0; instance_index < instance_count; instance_index++)
{
transformed_data2[instance_index] = transform.TransformForward(data_set_transformed1.GetInstanceFeatureData(instance_index));
instance_colors[instance_index] = label_colors[label_data[instance_index][0]];
}
RendererPoints <Matrix <double> > renderer = new RendererPoints <Matrix <double> >(new AlgebraLinearReal64MathNet(), 1000, 1000, (AngleRadian)0, (AngleRadian)0, 10);
BitmapFast bitmap = renderer.Render(new Tuple <double[][], Color[]>(transformed_data2, instance_colors));
bitmap.Bitmap.Save("test.png");
}
