static void Main(string[] args)
{
IKMeans _kMeans = new KMeans();
IDistance _distance = new EuclideanDistance();
Random rnd = new Random(0);
int objCount = 10;
int intrstsCount = 2;
int clusterCount = 2;
List <ClusterEntityDTO> clusters = new List <ClusterEntityDTO>();
for (int i = 0; i < objCount; i++)
{
ClusterEntityDTO dTO = new ClusterEntityDTO
{
Name = string.Format("{0:yyyyMMddHHmmss}", DateTime.Now),
Id = i
};
List <Interest> interests = new List <Interest>();
Console.WriteLine("___" + dTO.Name + "___");
for (int j = 0; j < intrstsCount; j++)
{
double rand = Math.Round(rnd.NextDouble() * 200.0);
Interest intr = new Interest
{
Name = string.Format("intr{0:yyyyMMddHHmmss}", DateTime.Now),
Weight = rand,
Weight2 = rand
};
Console.WriteLine("--- " + intr.Weight + "---");
interests.Add(intr);
}
Console.WriteLine("-------------------------------");
dTO.Interests = interests;
clusters.Add(dTO);
}
Console.WriteLine("-----------------------------------");
Console.WriteLine("-----------------------------------");
Console.WriteLine("-----------------------------------");
var result = _kMeans.Clustering(clusters, _distance, clusterCount);
for (int i = 0; i < objCount; i++)
{
Console.WriteLine("___" + result[i].Name + "___");
for (int j = 0; j < intrstsCount; j++)
{
Console.WriteLine("---" + result[i].Interests[j].Weight2 + "---");
}
if (i < objCount - 1 && result[i].CentroidId != result[i + 1].CentroidId)
{
Console.WriteLine("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%");
}
}
Console.ReadLine();
}
