private static DocDetails GetAllDetails(List <DocumentVector> docCollection, DocumentVector seedPoint, DocDetails docDetails)
{
float[] Weights = new float[docCollection.Count];
float minD = float.MaxValue;
float Sum = 0;
int i = 0;
foreach (DocumentVector point in docCollection)
{
if (point == seedPoint) //Delta is 0
{
continue;
}
Weights[i] = KMeansPlus.GetEucliedeanDistance(point, seedPoint);
Sum += Weights[i];
if (Weights[i] < minD)
{
minD = Weights[i];
}
i++;
}
docDetails.SeedDocVect = seedPoint;
docDetails.Weights = Weights;
docDetails.Sum = Sum;
docDetails.MinD = minD;
return(docDetails);
}
public static DocDetails GetMinimalPointDistance(List <DocDetails> pds)
{
float minValue = float.MinValue;
List <DocDetails> sameDistValues = new List <DocDetails>();
foreach (DocDetails pd in pds)
{
if (pd.MinD < minValue)
{
sameDistValues.Clear();
minValue = pd.MinD;
sameDistValues.Add(pd);
}
if (pd.MinD == minValue)
{
if (!sameDistValues.Contains(pd))
{
sameDistValues.Add(pd);
}
}
}
if (sameDistValues.Count > 1)
{
return(sameDistValues[KMeansPlus.GetRandNumCrypto(0, sameDistValues.Count)]);
}
else
{
return(sameDistValues[0]);
}
}
private static int GetWeightedProbDist(float[] w, float s)
{
float p = KMeansPlus.GetRandNumCrypto();
float q = 0;
int i = -1;
while (q < p)
{
i++;
q += (w[i] / s);
}
return(i);
}
public static List <DocumentVector> GetSeedPoints2v(List <DocumentVector> docCollection, int k)
{
List <DocumentVector> seedPoints = new List <DocumentVector>(k);
DocDetails docDetails;
List <DocDetails> docDetailsList = new List <DocDetails>();
int index = 0;
int firstIndex = KMeansPlus.GenerateRandomNumber(0, docCollection.Count);
DocumentVector FirstPoint = docCollection[firstIndex];
seedPoints.Add(FirstPoint);
for (int i = 0; i < k - 1; i++)
{
if (seedPoints.Count >= 2)
{
DocDetails minpd = GetMinimalPointDistance(docDetailsList);
index = GetWeightedProbDist(minpd.Weights, minpd.Sum);
DocumentVector SubsequentPoint = docCollection[index];
docDetails = new DocDetails();
docDetails = GetAllDetails(docCollection, SubsequentPoint, docDetails);
docDetailsList.Add(docDetails);
}
else
{
docDetails = new DocDetails();
docDetails = GetAllDetails(docCollection, FirstPoint, docDetails);
docDetailsList.Add(docDetails);
index = GetWeightedProbDist(docDetails.Weights, docDetails.Sum);
DocumentVector SecondPoint = docCollection[index];
seedPoints.Add(SecondPoint);
docDetails = new DocDetails();
docDetails = GetAllDetails(docCollection, SecondPoint, docDetails);
docDetailsList.Add(docDetails);
}
}
return(seedPoints);
}
/*
* public static ClusterPoint GetKMeansPP(List<DocumentVector> allPoints, int k)
* {
* List<DocumentVector> seedPoints = AdditionalFunctionalityForSecondKMeansppimplementation.GetSeedPoints2v(allPoints, k);
*
* //ClusterPoint result = KMeansCalculation(allPoints, seedPoints, k);
*
* //return result;
* }
*/
private static DocumentVector KMeansCalculation(List <DocumentVector> docList, List <DocumentVector> seedPoints, int k)
{
DocumentVector cluster = new DocumentVector();
float[] Distances = new float[k];
float minD = float.MaxValue;
List <DocumentVector> sameDPoint = new List <DocumentVector>();
bool exit = true;
foreach (DocumentVector vectror in docList)
{
foreach (DocumentVector seedPoint in seedPoints)
{
float dist = KMeansPlus.GetEucliedeanDistance(vectror, seedPoint);
if (dist < minD)
{
sameDPoint.Clear();
minD = dist;
sameDPoint.Add(seedPoint);
}
if (dist == minD)
{
if (!sameDPoint.Contains(seedPoint))
{
sameDPoint.Add(seedPoint);
}
}
}
DocumentVector keyPoint;
if (sameDPoint.Count > 1)
{
int index = KMeansPlus.GetRandNumCrypto(0, sameDPoint.Count);
keyPoint = sameDPoint[index];
}
else
{
keyPoint = sameDPoint[0];
}
/*
* //Assign ensemble point to correct central point cluster
* if (!cluster.ClustersPoint.ContainsKey(keyPoint)) //New
* {
* List<Point> newCluster = new List<Point>();
* newCluster.Add(p);
* cluster.PC.Add(keyPoint, newCluster);
* }
* else
* { //Existing cluster centre
* cluster.PC[keyPoint].Add(p);
* }
*/
//Reset
sameDPoint.Clear();
minD = float.MaxValue;
}
if (exit)
{
return(cluster);
}
else
{
return(KMeansCalculation(docList, seedPoints, k));
}
}
