C# Cluster Example

using System;

using CenterSpace.NMath.Core;

namespace CenterSpace.NMath.Examples.CSharp
{
  /// <summary>
  /// A .NET example in C# showing how to perform a hierarchical cluster analysis on a data set.
  /// </summary>
  public class ClusterExample
  {
    static void Main( string[] args )
    {
      // Class ClusterAnalysis perform hierarchical cluster analysis. Instances 
      // are constructed from a matrix of data, where each row represents an object
      // to be clustered. This code clusters 8 random vectors of length 3:
      var data = new DoubleMatrix( 8, 3, new RandGenUniform() );

      Console.WriteLine();
      Console.WriteLine( "Data =" );
      Console.WriteLine( data.ToTabDelimited( "F5" ) );

      var ca = new ClusterAnalysis( data );

      // The N property gets the number of objects clustered:
      Console.WriteLine( "Number of objects clustered = " + ca.N );
      Console.WriteLine();

      // Distances between objects are computed using a Distance.Function delegate.
      // The default distance delegates is Distance.EuclideanFunction. Property
      // Distances gets the vector of distances between all possible object pairs,
      // computed using the current distance delegate. For n objects, the distance
      // vector is of length (n-1)(n/2), with distances arranged in the order:
      // (1,2), (1,3), ..., (1,n), (2,3), ..., (2,n), ..., ..., (n-1,n)
      Console.WriteLine( "Results using Euclidean distance and Single linkage..." );
      Console.WriteLine();
      Console.WriteLine( "Distances: " );
      Console.WriteLine( ca.Distances.ToString( "F3" ) );
      Console.WriteLine();

      // Distances between clusters of objects are computed using a Linkage.Function
      // delegate. The default linkage delegate is Linkage.SingleFunction.
      // The Linkages property gets the complete hierarchical linkage tree, computed
      // from Distances using the current linkage delegate. At each level in the tree,
      // Columns 1 and 2 contain the indices of the clusters linked to form the next
      // cluster. Column 3 contains the distances between the clusters.
      Console.WriteLine( "Linkages: " );
      Console.WriteLine( ca.Linkages.ToTabDelimited( "F3" ) );

      // The CopheneticDistances property gets the vector of cophenetic distances
      // between all possible object pairs. The cophenetic distance between two
      // objects is defined to be the intergroup distance when the objects are first
      // combined into a single cluster in the linkage tree. The correlation between
      // the original Distances and the CopheneticDistances is sometimes taken as a
      // measure of appropriateness of a cluster analysis relative to the original data:
      double r = StatsFunctions.Correlation( ca.Distances, ca.CopheneticDistances );
      Console.WriteLine( "Cophenetic correlation = " + r );
      Console.WriteLine();

      // Delegates are provided as static variables on class Distance for
      // euclidean, squared euclidean, city-block (Manhattan), maximum (Chebychev),
      // and power distance functions. Delegates are provided as static variables
      // on class Linkage for single, complete, unweighted average, weighted average,
      // centroid, median, and Wards linkage functions. You can also easily create 
      // your own distance or linkage functions. This code repeats the analysis using
      // different distance and linkage delegates:
      ca.Update( data, Distance.SquaredEuclideanFunction, Linkage.CompleteFunction );
      Console.WriteLine( "Results using Squared Euclidean distance and Complete linkage..." );
      Console.WriteLine();
      Console.WriteLine( "Distances:" );
      Console.WriteLine( ca.Distances.ToString( "F3" ) );
      Console.WriteLine();
      Console.WriteLine( "Linkages: " );
      Console.WriteLine( ca.Linkages.ToTabDelimited( "F3" ) );

      // The CutTree() method constructs a set of clusters by cutting the
      // hierarchical linkage tree either at the specified height, or into the
      // specified number of clusters. This code cuts the tree into 3 clusters:
      Console.WriteLine( "CutTree() into 3 clusters..." );
      ClusterSet cs = ca.CutTree( 3 );
      Console.WriteLine( "Cluster each object is assigned to: " + cs );

      // The indexer on ClusterSet gets the cluster to which a given object is
      // assigned.
      Console.WriteLine( "Object 0 is in cluster: " + cs[0] );
      Console.WriteLine( "Object 3 is in cluster: " + cs[3] );

      // The Cluster() method returns an array of integers identifying the objects
      // assigned to a given cluster.
      int[] objects = cs.Cluster( 1 );
      Console.Write( "Objects in cluster 1: " );
      for ( int i = 0; i < objects.Length; i++ )
      {
        Console.Write( objects[i] + " " );
      }
      Console.WriteLine();

      Console.WriteLine();
      Console.WriteLine( "Press Enter Key" );
      Console.Read();

    }  // Main

  }  // class

}  // namespace