/// <summary>
/// Calculates and returns width of a gaussian function </summary>
/// <param name="centroid"> </param>
/// <param name="nearestNeighbours"> </param>
/// <returns> </returns>
private double calculateSigma(KVector centroid, KVector[] nearestNeighbours)
{
double sigma = 0;
foreach (KVector nn in nearestNeighbours)
{
sigma += Math.Pow(centroid.distanceFrom(nn), 2);
}
sigma = Math.Sqrt(1 / ((double)nearestNeighbours.Length) * sigma);
return(sigma);
}
/// <summary>
/// http://en.wikipedia.org/wiki/Selection_algorithm </summary>
/// <param name="vector"> </param>
/// <param name="k"> </param>
/// <returns> </returns>
public virtual KVector[] getKNearestNeighbours(KVector vector, int k)
{
KVector[] nearestNeighbours = new KVector[k];
// calculate distances for entire dataset
foreach (KVector otherVector in dataSet)
{
double distance = vector.distanceFrom(otherVector);
otherVector.Distance = distance;
}
for (int i = 0; i < k; i++)
{
int minIndex = i;
KVector minVector = dataSet[i];
double minDistance = minVector.Distance;
for (int j = i + 1; j < dataSet.Count; j++)
{
if (dataSet[j].Distance <= minDistance)
{
minVector = dataSet[j];
minDistance = minVector.Distance;
minIndex = j;
}
}
// swap list[i] and list[minIndex]
KVector temp = dataSet[i];
dataSet[i] = dataSet[minIndex];
dataSet[minIndex] = temp;
nearestNeighbours[i] = dataSet[i];
}
// function select(list[1..n], k)
// for i from 1 to k
// minIndex = i
// minValue = list[i]
// for j from i+1 to n
// if list[j] < minValue
// minIndex = j
// minValue = list[j]
// swap list[i] and list[minIndex]
// return list[k]
return(nearestNeighbours);
}