public int GetClosestCentroid(Point point)
{
int minIndex = 0;
double minDistance = PointMeasures.Distance(point, centroids[minIndex]);
for (int i = 0; i < centroids.Length; i++)
{
double distance = PointMeasures.Distance(point, centroids[i]);
if (minDistance > distance)
{
minIndex = i;
minDistance = distance;
}
}
return(minIndex);
}
/// <summary>
/// Updates the centroids by computing mean of each cluster
/// </summary>
private void updateCentroids()
{
// 3)
for (int j = 0; j < k; j++)
{
List <int> cluster = cluster_indices.ElementAt(j);
List <Point> cluster_points = new List <Point>();
for (int i = 0; i < cluster.Count; i++)
{
cluster_points.Add(data[cluster[i]]);
}
if (cluster_points.Count > 0)
{
centroids[j] = PointMeasures.Mean(cluster_points); // TODO cluster had no points
}
}
}
/// <summary>
/// Returns true if all centroids have been converged
/// </summary>
/// <param name="currentCentroids"></param>
/// <param name="previousCentroids"></param>
/// <returns></returns>
private bool isConverged(Point[] currentCentroids, Point[] previousCentroids)
{
// Array of flags determining convergances
// flag[i] = 1, means that that centroid has been converged
int size = currentCentroids.Length;
int[] flag = new int[size];
for (int i = 0; i < size; i++)
{
flag[i] = 0;
}
// compute the distances and check if something was changed
// according to the tolerance.
for (int i = 0; i < size; i++)
{
double distance = PointMeasures.Distance(currentCentroids[i], previousCentroids[i]);
if (distance <= distance_tolerance)
{
flag[i] = 1;
}
}
// Check if all centroids are converged
int flagSum = 0;
for (int i = 0; i < size; i++)
{
flagSum += flag[i];
}
if (flagSum == size && current_iter > 1)
{
return(true);
}
else
{
return(false);
}
}
/// <summary>
/// Group points to their closest centroids
/// </summary>
private void updateClusters()
{
cluster_indices = refreshIndicesList();
for (int i = 0; i < data.Count; i++)
{
Point point = data[i];
int min_index = 0;
double min = PointMeasures.Distance(point, centroids[min_index]);
// for every centroid
for (int j = 0; j < k; j++)
{
double distance = PointMeasures.Distance(point, centroids[j]);
if (min >= distance)
{
min = distance;
min_index = j;
}
}
cluster_indices.ElementAt(min_index).Add(i);
}
}