static void Main(string[] args)
{
List <Vector> vectors = new List <Vector>();
//Read WineData.csv containing 32 rows and 100 columns
var lines = File.ReadAllLines("WineData.csv").Select(
line => line.Split(',').Select(float.Parse).ToList())
.ToList();
//Loop through all rows and columns
for (var i = 0; i < lines.Count; i++)
{
for (var j = 0; j < lines[i].Count; j++)
{
if (vectors.ElementAtOrDefault(j) == null)
{
vectors.Add(new Vector());
}
//Transpose the dataset, into a list of 100 vectors containing 32 points
vectors[j].AddPoint(lines[i][j]);
}
}
//Get the users' input
Console.Write("How many iterations would you like to make: ");
var iterations = int.Parse(Console.ReadLine());
Console.Write("How many clusters would you like to create: ");
var clusters = int.Parse(Console.ReadLine());
while (clusters > vectors.Count())
{
Console.Write("You can't create more clusters than datapoints, please select a value lower than " + vectors.Count() + ": ");
clusters = int.Parse(Console.ReadLine());
}
Console.WriteLine("*******************************************");
//Get the best value for clusters (amount) by minimizing the SSE
double[] SSEGroup = new double[10];
for (var i = 1; i < 11; i++)
{
var KMeansAlgorithmDebug = new KMeansAlgorithm(iterations, i, vectors, true);
KMeansAlgorithmDebug.MainLoop();
SSEGroup[i - 1] = KMeansAlgorithmDebug.SSE1;
}
Console.WriteLine("Best value for k (amount of clusters) after minimizing the SSE for k = 1 through 10:");
Console.WriteLine("k = " + Convert.ToInt32(Array.IndexOf(SSEGroup, SSEGroup.Min()) + 1) + ": " + SSEGroup.Min());
Console.WriteLine("Increasing k will almost always minimize the SSE, so it makes sense that k is high");
Console.WriteLine("*******************************************");
//Run the main K-Means Algorithm Loop
var KMeansAlgorithm = new KMeansAlgorithm(iterations, clusters, vectors, false);
KMeansAlgorithm.MainLoop();
}
static void Main(string[] args)
{
//const int MAX = 1000;
int numClusters = 0;
//int[] clustering = new int[MAX];
string fileName = "..\\..\\..\\..\\Datasets\\normal\\normal.txt";
double[][] normalData = DataSetManager.ParseDataToDouble(fileName, 2, "\t");
Console.WriteLine("========= Data Clustering of file normal.txt");
//KMeansAlgorithm.ShowData(normalData, 1, true, true);
Console.WriteLine("Enter number of clusters: ");
numClusters = Convert.ToInt32(Console.ReadLine());
Console.WriteLine("\nSetting numClusters to " + numClusters);
int[] clustering = KMeansAlgorithm.Cluster(normalData, numClusters); // this is it
Console.WriteLine("\nK-means clustering complete\n");
Console.WriteLine("Raw data by cluster:\n");
KMeansAlgorithm.ShowClustered(normalData, clustering, numClusters, 1);
//==================== the second file ===============================
fileName = "..\\..\\..\\..\\Datasets\\unbalance\\unbalance.txt";
double[][] unbalanceData = DataSetManager.ParseDataToDouble(fileName, 2, " ");
Console.WriteLine("========= Data Clustering of file unbalance.txt");
//KMeansAlgorithm.ShowData(normalData, 1, true, true);
Console.WriteLine("Enter number of clusters: ");
numClusters = Convert.ToInt32(Console.ReadLine());
Console.WriteLine("\nSetting numClusters to " + numClusters);
clustering = KMeansAlgorithm.Cluster(unbalanceData, numClusters); // this is it
Console.WriteLine("\nK-means clustering complete\n");
Console.WriteLine("Raw data by cluster:\n");
KMeansAlgorithm.ShowClustered(unbalanceData, clustering, numClusters, 1);
Console.ReadLine();
}