/// <summary>
/// Calculate cluster centroids using k-means++
/// </summary>
/// <param name="numClusters"></param>
/// <returns></returns>
public double[][] calcClusterCentroidsInit(int numClusters)
{
int numGenes = chromosomes[0].GetGenes().Length;
//Initialise array
double[][] centroidVectorsInit = new double[numClusters][];
List <int> centroidChromoIndexes = new List <int>();
// 1. Choose random initial centroid
//int rndCentroidChromo = Friends.GetRandomInt(0, chromosomes.Length);
int rndCentroidChromo = 0; // Just choose the first one to keep some continuity
centroidChromoIndexes.Add(rndCentroidChromo);
while (centroidChromoIndexes.Count < numClusters)
{
// 2. Calculate distance from each chromo to the nearest centroid that has already been chosen
//the distance to the nearest centroid for each chromosome
List <double> chromoDistances = new List <double>();
for (int i = 0; i < chromosomes.Length; i++)
{
//distances from one chromo to all of the already chosen centroids
List <double> distances = new List <double>();
for (int j = 0; j < centroidChromoIndexes.Count; j++)
{
int centroidIndex = centroidChromoIndexes[j];
distances.Add(Friends.calcDistance(chromosomes[i].GetGenes(), chromosomes[centroidIndex].GetGenes()));
}
chromoDistances.Add(distances.Min()); //if the chromosome compares to itself and is chosen as a centroid, the distance will be zero (fine as we choose the largest distance for all chromosomes afterwards)
}
//3. Choose next centroid furthest away from the already selected ones
int indexOfMaxDist = chromoDistances.IndexOf(chromoDistances.Max());
centroidChromoIndexes.Add(indexOfMaxDist);
}
//Update array
for (int i = 0; i < numClusters; i++)
{
centroidVectorsInit[i] = new double[numGenes];
for (int j = 0; j < numGenes; j++)
{
centroidVectorsInit[i][j] = chromosomes[centroidChromoIndexes[i]].GetGenes()[j];
}
}
return(centroidVectorsInit);
}
/// <summary>
/// d) Update chromosome representatives and cluster distances after k-means clustering
/// </summary>
/// <param name="numClusters"></param>
public void calcKMeansRepresentatives(int numClusters)
{
double[][] clusterMeanVectors = calcClusterMeans(numClusters);
//Initialise lists
List <double>[] distances = new List <double> [numClusters];
List <int>[] distanceIndexes = new List <int> [numClusters];
for (int i = 0; i < numClusters; i++)
{
distances[i] = new List <double>();
distanceIndexes[i] = new List <int>();
}
//Run through each chromosome
for (int i = 0; i < chromosomes.Length; i++)
{
distances[chromosomes[i].clusterId].Add(Friends.calcDistance(chromosomes[i].GetGenes(), clusterMeanVectors[chromosomes[i].clusterId]));
distanceIndexes[chromosomes[i].clusterId].Add(i);
}
//Find the chromosome in each cluster with the smallest distance to the cluster mean
int[] chromoRepresentatives = new int[numClusters];
for (int i = 0; i < numClusters; i++)
{
double minDist = distances[i].Min();
int indexOfMin = distances[i].IndexOf(minDist);
int chromoRepId = distanceIndexes[i][indexOfMin];
chromosomes[chromoRepId].isRepresentative = true;
chromoRepresentatives[i] = chromoRepId;
}
//Calculate the distance between a chromosome and the chromosome closest to the cluster mean
for (int i = 0; i < chromosomes.Length; i++)
{
int closestToMean = chromoRepresentatives[chromosomes[i].clusterId];
chromosomes[i].distToRepresentative = Friends.calcDistance(chromosomes[i].GetGenes(), chromosomes[closestToMean].GetGenes());
}
}
/// <summary>
/// c) Update clustering by calculating the distance between the chromosome genes and the mean vectors for each cluster
/// </summary>
/// <param name="numClusters"></param>
/// <param name="clusterMeanVectors"></param>
/// <returns></returns>
public bool updateClustering(int numClusters, double[][] clusterMeanVectors)
{
bool go = true;
bool hasChanged = false;
bool hasZeroMembers = false;
//Run through all the chromosomes and compare its genes to all the mean vectors. Store new temporary clusterIds
int[] tempClusterId = new int[chromosomes.Length];
for (int i = 0; i < chromosomes.Length; i++)
{
double[] distances = new double[numClusters];
//Run through the clusters in order to compare to each mean vector
for (int j = 0; j < numClusters; j++)
{
distances[j] = Friends.calcDistance(chromosomes[i].GetGenes(), clusterMeanVectors[j]);
}
int newClusterId = identifyClusterId(distances);
tempClusterId[i] = newClusterId;
if (chromosomes[i].clusterId != newClusterId)
{
hasChanged = true;
}
}
//Check that each new cluster contains at least one chromosome before changing the clusterId property
int[] tempClusterCounts = new int[numClusters];
for (int i = 0; i < numClusters; i++)
{
tempClusterCounts[i] = 0;
}
for (int i = 0; i < chromosomes.Length; i++)
{
tempClusterCounts[tempClusterId[i]]++;
}
for (int i = 0; i < tempClusterCounts.Length; i++)
{
if (tempClusterCounts[i] == 0)
{
hasZeroMembers = true;
}
}
//If no cluster has zero elements then update the chromosome clusterId property
if (!hasZeroMembers)
{
for (int i = 0; i < chromosomes.Length; i++)
{
chromosomes[i].clusterId = tempClusterId[i];
}
}
//Check whether the cluster loop shall continue or not
if (!hasChanged || hasZeroMembers)
{
go = false;
}
return(go);
}
