public static int DoKMeansWithMinVariance(int numClusters, ref List <MyPoint> PList, ref List <ClusterCenterPoint> CList, double maxError, int maxIterations, bool minVariance)
{
double stddev = 0;
if (minVariance == true)
{
stddev = double.MaxValue;
}
else
{
stddev = double.MinValue;
}
List <MyPoint> PListBest = new List <MyPoint>();
List <ClusterCenterPoint> CListBest = new List <ClusterCenterPoint>();
int iter = 0;
for (int m = 0; m < 20; m++) //Pick best (most balanced) clustering after 20 clustering attempts
{
List <MyPoint> PListCopy = new List <MyPoint>();
foreach (MyPoint mp in PList)
{
PListCopy.Add((MyPoint)mp.Clone());
}
List <ClusterCenterPoint> CListCopy = null;
iter += KMeans.DoKMeans(numClusters, ref PListCopy, ref CListCopy, 0.01, 100, true); // true means do kmeansplusplus
// ----compute variance of cluster memberships
int[] CCount = new int[numClusters];
for (int i = 0; i < numClusters; i++)
{
CCount[i] = 0;
}
foreach (MyPoint mp in PListCopy)
{
CCount[mp.ClusterId] += 1;
}
double variance = 0;
for (int i = 0; i < numClusters; i++)
{
variance += (CCount[i] - (PListCopy.Count) / (double)numClusters) * (CCount[i] - (PListCopy.Count) / (double)numClusters);
}
double stddevCopy = Math.Sqrt(variance);
string out1 = "";
for (int n = 0; n < CCount.Length; n++)
{
out1 += "Cluster " + n.ToString() + " count = " + CCount[n].ToString() + "\n";
}
//MessageBox.Show("StdDev = " + stddevCopy.ToString() + " " + out1);
if (minVariance == true)
{
if (stddevCopy < stddev) // if it improves, copy data into best
{
stddev = stddevCopy;
PListBest.Clear();
foreach (MyPoint mp in PListCopy)
{
PListBest.Add((MyPoint)mp.Clone());
}
CListBest.Clear();
foreach (ClusterCenterPoint cp in CListCopy)
{
CListBest.Add((ClusterCenterPoint)cp.Clone());
}
}
}
else
{
if (stddevCopy > stddev) // if it improves, copy data into best
{
stddev = stddevCopy;
PListBest.Clear();
foreach (MyPoint mp in PListCopy)
{
PListBest.Add((MyPoint)mp.Clone());
}
CListBest.Clear();
foreach (ClusterCenterPoint cp in CListCopy)
{
CListBest.Add((ClusterCenterPoint)cp.Clone());
}
}
}
}
CList = CListBest;
PList = PListBest;
return(iter);
}
public static int MDoKMeansWithMinVariance(int aiNumClusters,
ref List <MyPoint> aoPList,
ref List <ClusterCenterPoint> aoCL,
double adMaxError, int aiMaxIterations, bool abMinVariance)
{
string koOut;
double kdStdDev = 0;
double kdStdDevCopy = 0;
double kdVariance = 0;
int kiIter = 0;
List <MyPoint> koPListBest = new List <MyPoint>( );
List <MyPoint> koPListCopy;
List <ClusterCenterPoint> koCListBest = new List <ClusterCenterPoint>( );
List <ClusterCenterPoint> koCListCopy;
int[] kiCCount = new int[aiNumClusters];
if (abMinVariance == true)
{
kdStdDev = double.MaxValue;
}
else
{
kdStdDev = double.MinValue;
}
for (int m = 0; m < 20; m++) // pick best i.e., most balanced clustering
{ // of 20 attempts at custering
koPListCopy = new List <MyPoint>( );
foreach (MyPoint koMP in aoPList)
{
koPListCopy.Add(( MyPoint )koMP.Clone( ));
}
koCListCopy = null;
kiIter += KMeans.MDoKMeans(aiNumClusters, ref koPListCopy, ref koCListCopy, 0.01, 100, true); // true means do kmeansplusplus
// ----compute variance of cluster memberships
for (int i = 0; i < aiNumClusters; i++)
{
kiCCount[i] = 0;
}
foreach (MyPoint koMP in koPListCopy)
{
kiCCount[koMP.ViClusterId] += 1;
}
kdVariance = 0;
for (int i = 0; i < aiNumClusters; i++)
{
kdVariance += (kiCCount[i] - (koPListCopy.Count / ( double )aiNumClusters)) *
(kiCCount[i] - (koPListCopy.Count / ( double )aiNumClusters));
}
kdStdDevCopy = Math.Sqrt(kdVariance);
koOut = "";
for (int n = 0; n < kiCCount.Length; n++)
{
koOut += "Cluster " + n.ToString( ) + " count = " + kiCCount[n].ToString() + "\n";
}
//MessageBox.Show( "StdDev = " + koStdDevCopy.ToString( ) + " " + koOut );
if (abMinVariance == true)
{
if (kdStdDevCopy < kdStdDev) // if it improves, copy data into best
{
kdStdDev = kdStdDevCopy;
koPListBest.Clear( );
foreach (MyPoint koMP in koPListCopy)
{
koPListBest.Add(( MyPoint )koMP.Clone( ));
}
koCListBest.Clear( );
foreach (ClusterCenterPoint koCP in koCListCopy)
{
koCListBest.Add(( ClusterCenterPoint )koCP.Clone( ));
}
}
}
else
{
if (kdStdDevCopy > kdStdDev) // if it improves, copy data into best
{
kdStdDev = kdStdDevCopy;
koPListBest.Clear( );
foreach (MyPoint koMP in koPListCopy)
{
koPListBest.Add(( MyPoint )koMP.Clone( ));
}
koCListBest.Clear( );
foreach (ClusterCenterPoint koCP in koCListCopy)
{
koCListBest.Add(( ClusterCenterPoint )koCP.Clone( ));
}
}
}
}
aoCL = koCListBest;
aoPList = koPListBest;
return(kiIter);
}
public static int DoKMeansWithMinVariance(int numClusters, ref List <MyPoint> PList, ref List <ClusterCenterPoint> CList, double maxError, int maxIterations, bool minVariance)
{
List <MyPoint> copy = new List <MyPoint>(PList);
object olock = new object();
double stddev = 0;
if (minVariance == true)
{
stddev = double.MaxValue;
}
else
{
stddev = double.MinValue;
}
List <MyPoint> PListBest = new List <MyPoint>();
List <ClusterCenterPoint> CListBest = new List <ClusterCenterPoint>();
int iter = 0;
Parallel.For(0, 20, (m) =>
//for (int m = 0; m < 20; m++) //Pick best (most balanced) clustering after 20 clustering attempts
{
List <MyPoint> PListCopy = new List <MyPoint>();
for (int i = 0; i < copy.Count; i++)
{
PListCopy.Add((MyPoint)copy[i].Clone());
}
//foreach (MyPoint mp in PList)
//PListCopy.Add((MyPoint)mp.Clone());
List <ClusterCenterPoint> CListCopy = null;
iter += KMeans.DoKMeans(numClusters, ref PListCopy, ref CListCopy, 0.01, 100, true); // true means do kmeansplusplus
// ----compute variance of cluster memberships
int[] CCount = new int[numClusters];
for (int i = 0; i < numClusters; i++)
{
CCount[i] = 0;
}
foreach (MyPoint mp in PListCopy)
{
CCount[mp.ClusterId] += 1;
}
double variance = 0;
for (int i = 0; i < numClusters; i++)
{
variance += (CCount[i] - (PListCopy.Count) / (double)numClusters) * (CCount[i] - (PListCopy.Count) / (double)numClusters);
}
double stddevCopy = Math.Sqrt(variance);
string out1 = "";
for (int n = 0; n < CCount.Length; n++)
{
out1 += "Cluster " + n.ToString() + " count = " + CCount[n].ToString() + "\n";
}
//MessageBox.Show("StdDev = " + stddevCopy.ToString() + " " + out1);
if (minVariance == true)
{
lock (olock) //Protect while updating stddev.
// Otherwise one thread X can obtain a X stddevcopy better than the actual one stop at the if validation
//then a new thread Y starts with Y stddevcopy better than actual one and X one, updates stddev and stops
//thread X starts again (does not need to pass if statement again) and updates stddevcopy which would be wrong since the best one was Y
{
if (stddevCopy < stddev) // if it improves, copy data into best
{
stddev = stddevCopy;
PListBest.Clear();
foreach (MyPoint mp in PListCopy)
{
PListBest.Add((MyPoint)mp.Clone());
}
CListBest.Clear();
foreach (ClusterCenterPoint cp in CListCopy)
{
CListBest.Add((ClusterCenterPoint)cp.Clone());
}
}
}
}
/*
* else
* {
* lock (olock)
* {
* if (stddevCopy > stddev) // if it improves, copy data into best
* {
* stddev = stddevCopy;
* PListBest.Clear();
* foreach (MyPoint mp in PListCopy)
* PListBest.Add((MyPoint)mp.Clone());
* CListBest.Clear();
* foreach (ClusterCenterPoint cp in CListCopy)
* CListBest.Add((ClusterCenterPoint)cp.Clone());
* }
* }
* }
*/
});
CList = CListBest;
PList = PListBest;
return(iter);
}
