public void ClusteringTest()
{
List <ClusteredData> datanew = new List <ClusteredData>();
EuclidianDistance distance = new EuclidianDistance();
K_Means k_means = new K_Means(2, 0.1, distance);
ClusterAnalysis clusterAnalysis = new ClusterAnalysis(k_means);
datanew.Add(new ClusteredData(0, 0));
datanew.Add(new ClusteredData(0, 20));
datanew.Add(new ClusteredData(20, 0));
datanew.Add(new ClusteredData(20, 20));
var list = clusterAnalysis.Clustering(datanew);
Assert.AreEqual(2, list.Count);
var flag = from value in k_means.FinishesCentroids.Values
where (value.X == 0 && value.Y == 0) ||
(value.X == 0 && value.Y == 20) ||
(value.X == 20 && value.Y == 20) ||
(value.X == 20 && value.Y == 0) ||
(value.X == 10 && value.Y == 20) ||
(value.X == 0 && value.Y == 10) ||
(value.X == 20 && value.Y == 10) ||
(value.X == 0 && value.Y == 10)
select value;
Assert.AreEqual(0.1, k_means.CoefficientTaboo);
Assert.AreEqual(2, k_means.CountOfClusters);
Assert.IsNotNull(flag);
}
static void Main(string[] args)
{
List <ClusteredData> datanew = new List <ClusteredData>();
EuclidianDistance distance = new EuclidianDistance();
K_Means k_means = new K_Means(2, 0.1, distance);
ClusterAnalysis clusterAnalysis = new ClusterAnalysis(k_means);
datanew.Add(new ClusteredData(0, 0));
datanew.Add(new ClusteredData(0, 20));
datanew.Add(new ClusteredData(20, 0));
datanew.Add(new ClusteredData(20, 20));
var list = clusterAnalysis.Clustering(datanew);
/*
* List<ClusteredData> datanew = new List<ClusteredData>();
* List<K_Means> k_meansList = new List<K_Means>();
* List<List<Cluster>> clustersList = new List<List<Cluster>>();
* System.Diagnostics.Stopwatch myStopwatch = new System.Diagnostics.Stopwatch();
* EuclidianDistance distance = new EuclidianDistance();
*
* datanew.Add(new ClusteredData(0,0));
* datanew.Add(new ClusteredData(0, 20));
* datanew.Add(new ClusteredData(20, 0));
* datanew.Add(new ClusteredData(20, 20));
*
* var ewrt = datanew.Select(Er => Er.X);
* var ety = ewrt.Average();
*
*
*
* var e = datanew.Average(er => er.X);
*
*
* myStopwatch.Start(); //запуск
* for (var i = 0; i < 1000; i++)
* {
* K_Means k_means = new K_Means(2, 0.1, distance);
* clustersList.Add(k_means.Clustering(datanew));
* k_meansList.Add(k_means);
* Thread.Sleep(10);
*
* var flag = k_means.FinishesCentroids.ContainsValue(new Centroid(0, 0));
* var flag1 = k_means.FinishesCentroids.ContainsValue(new Centroid(0, 20));
* var flag2 = k_means.FinishesCentroids.ContainsValue(new Centroid(20, 20));
* var flag3 = k_means.FinishesCentroids.ContainsValue(new Centroid(20, 0));
* }
* myStopwatch.Stop();
*
* var fulltime = myStopwatch.ElapsedMilliseconds;
* double Onetime = myStopwatch.ElapsedMilliseconds/1000.0;
*
* int a = 0;
* int b = 0;
* int c = 0;
* int d = 0;
*
* foreach (var list in clustersList)
* {
* a += list.Count(q => q.Data.Count == 3);
* b += list.Count(q => q.Data.Count == 2);
* c += list.Count(q => q.Data.Count == 1);
* d += list.Count(q => q.Data.Count == 0);
* }
* b = b / 2;
* var ads = new Cluster(1, new Centroid(1, 10));
* //ads.IntraClusterDistance();
*
* //// Поиск объектов класса Cluster в списке optimumClusters с максимальным внутрикластерным расcтоянием
* //var nonOptimumClusters = from list in clustersList // определяем каждый объект из clusters как cluster
* // where
* // select cluster.IntraClusterDistance();// выбираем объект
*
*/
}
