/// <summary>
/// K Means uses coordinate descent and the euclidean distance between randomly selected centroids to cluster the data
/// </summary>
/// <param name="data">The list of vectors to cluster</param>
/// <param name="k">The number of clusters to find</param>
/// <param name="maxIterations">The maximum number of iterations</param>
/// <returns>A list of k clusters</returns>
public static IReadOnlyList <IReadOnlyList <IVector> > KMeans(this IReadOnlyList <IVector> data, int k, int maxIterations = 1000)
{
using (var clusterer = new KMeans(k, data, DistanceMetric.Euclidean)) {
clusterer.ClusterUntilConverged(maxIterations);
return(clusterer.Clusters);
}
}
/// <summary>
/// K Means uses coordinate descent and a distance metric between randomly selected centroids to cluster the data
/// </summary>
/// <param name="data">The list of vectors to cluster</param>
/// <param name="lap">Linear algebra provider</param>
/// <param name="k">The number of clusters to find</param>
/// <param name="maxIterations">The maximum number of iterations</param>
/// <param name="distanceMetric">Distance metric to use to compare centroids</param>
/// <returns>A list of k clusters</returns>
public static IReadOnlyList <IReadOnlyList <IVector> > KMeans(this IReadOnlyList <IVector> data, ILinearAlgebraProvider lap, int k, int maxIterations = 1000, DistanceMetric distanceMetric = DistanceMetric.Euclidean)
{
using (var clusterer = new KMeans(lap, k, data, distanceMetric)) {
clusterer.ClusterUntilConverged(maxIterations);
return(clusterer.Clusters);
}
}
