/// <summary>
/// Let data be a set of n elements, then co-membership
/// is an n by n matrix with (i,j)th element equal to 1
/// if i and j fall into the same cluster from the clusters set, 0 otherwise.
/// The clusters and data can come from different samples (of the same population)
/// </summary>
/// <param name="learningCluster"></param>
/// <param name="testingClusterPoints"></param>
/// <returns></returns>
private int[][] co_membership(KMeans learningCluster, List <Point> testingClusterPoints)
{
int len = testingClusterPoints.Count;
int[][] m = new int[len][];
for (int i = 0; i < len; i++)
{
m[i] = new int[len];
}
for (int i = 0; i < len; i++)
{
for (int j = 0; j < len; j++)
{
if (i != j)
{
for (int k = 0; k < learningCluster.K; k++)
{
int i_centroid = learningCluster.GetClosestCentroid(testingClusterPoints[i]);
int j_centroid = learningCluster.GetClosestCentroid(testingClusterPoints[j]);
if (i_centroid == j_centroid)
{
m[i][j] = 1;
break;
}
else
{
m[i][j] = 0;
}
}
}
}
}
return(m);
}
/// <summary>
/// Let data be a set of n elements, then co-membership
/// is an n by n matrix with (i,j)th element equal to 1
/// if i and j fall into the same cluster from the clusters set, 0 otherwise.
/// The clusters and data can come from different samples (of the same population)
/// </summary>
/// <param name="learningCluster"></param>
/// <param name="testingClusterPoints"></param>
/// <returns></returns>
private int[][] co_membership(KMeans learningCluster, List<Point> testingClusterPoints)
{
int len = testingClusterPoints.Count;
int[][] m = new int[len][];
for (int i = 0; i < len; i++)
{
m[i] = new int[len];
}
for (int i = 0; i < len; i++)
{
for (int j = 0; j < len; j++)
{
if (i != j)
{
for (int k = 0; k < learningCluster.K; k++)
{
int i_centroid = learningCluster.GetClosestCentroid(testingClusterPoints[i]);
int j_centroid = learningCluster.GetClosestCentroid(testingClusterPoints[j]);
if (i_centroid == j_centroid){
m[i][j] = 1;
break;
}
else
m[i][j] = 0;
}
}
}
}
return m;
}
