public static double[][] clustering(double[][] x, int k, kmeansType initType, out double error)
{
double[][] m = null;
Console.WriteLine("Initialization...");
switch (initType)
{
case kmeansType.Forgy: m = ForgyInit(x, k); break;
case kmeansType.kmeanspp: m = kmeansppinit(x, k); break;
case kmeansType.RandomPartition: m = RandomPartion(x, k); break;
}
int reassigned = 0;
int iters = 0;
int[] a = new int[x.Length];
do
{
reassigned = Assignment(x, m, ref a);
if (reassigned != 0)
{
m = Update(x, a, k);
}
Console.Write("\rIteration {0}, reassigned {1} ", iters++, reassigned);
}while (reassigned != 0);
error = Error(x, m, a);
return(m);
}
public static double[][] clustering(double[][] x, int k, kmeansType initType, out double error)
{
System.Diagnostics.Stopwatch sw = new System.Diagnostics.Stopwatch();
double[][] m = null;
//Console.WriteLine("Initialization...");
switch (initType)
{
case kmeansType.Forgy: m = ForgyInit(x, k); break;
case kmeansType.kmeanspp: m = kmeansppinit(x, k); break;
case kmeansType.RandomPartition: m = RandomPartion(x, k); break;
}
int confirmations = 0;
int[] a = new int[x.Length];
int reassigned = 0;
int iters = 0;
bool[] ntu = new bool[k];
DateTime start = DateTime.Now;
do
{
for (int i = 0; i < k; i++)
{
ntu[i] = true;
}
do
{
sw.Restart();
reassigned = Assignment(x, m, ref a, ref ntu);
if (reassigned != 0)
{
m = Update(x, m, a, ntu, k);
}
int unmoved = ntu.Count(z => !z);
_log.Info($"[{(DateTime.Now - start).TotalMilliseconds} ms] Iteration {iters++}, reassigned {reassigned} unmoved {unmoved} Time {sw.ElapsedMilliseconds} ms ");
if (reassigned != 0)
{
confirmations = 0;
}
}while (reassigned != 0);
confirmations++;
_log.Info($"CONFIRMATION {confirmations}");
} while (confirmations < 2);
error = Error(x, m, a);
return(m);
}
public static double[][] clustering(double[,] x, int k, int restarts, kmeansType initType)
{
double[][] tx = new double[x.GetLength(0)][];
for (int row = 0; row < x.GetLength(0); row++)
{
tx[row] = new double[x.GetLength(1)];
for (int i = 0; i < tx[row].Length; i++)
{
tx[row][i] = x[row, i];
}
}
return(clustering(tx, k, restarts, initType));
}
public static double[][] clustering(double[][] x, int k, int restarts, kmeansType initType)
{
double[][][] pool = new double[restarts][][];
double[] error = new double[restarts];
for (int r = 0; r < restarts; r++)
{
_log.Info($"Restart {r}");
double er = 0.0;
pool[r] = clustering(x, k, initType, out er);
error[r] = er;
_log.Info($"Error {er}");
}
double min = error.Min();
int opt = error.FindIndex(z => z == min);
return(pool[opt]);
}
public static double[][] clustering(double[][] x, int k, int restarts, kmeansType initType)
{
double[][][] pool = new double[restarts][][];
double[] error = new double[restarts];
for (int r = 0; r < restarts; r++)
{
Console.WriteLine("Restart {0}", r);
double er = 0.0;
pool[r] = clustering(x, k, initType, out er);
error[r] = er;
Console.WriteLine("Error {0}", er);
}
double min = error.Min();
int opt = error.FindIndex(z => z == min);
return(pool[opt]);
}