.AixLib.Utilities.Clustering.KMeans

Information

This function applies k-means clustering to n-dimentional data and returns the centroid of the clusters, the cluster labels for each sample, and the size of each cluster.

The returned length of the cluster_size vector is max(n_clusters, n_cluster_size).

Implementation

The seed for random number generation is constant. It can be changed by modifying the constant seed.

Interface

impure function KMeans
  extends Modelica.Icons.Function;
  input Real data[n_samples, n_features] "Data to be clustered";
  input Integer n_clusters "Number of clusters to be generated";
  input Integer n_samples "Number of samples";
  input Integer n_features "Number of features";
  input Real relTol = 1e-5 "Relative tolerance on cluster positions";
  input Integer max_iter = 500 "Maximum number of k-means iterations";
  input Integer n_init = 10 "Number of runs with randomized centroid seeds";
  input Integer n_cluster_size = 0 "Length of the cluster_size output vector";
  output Real centroids[n_clusters, n_features] "Centroids of the clusters";
  output Integer labels[n_samples] "Cluster label associated with each data point";
  output Integer cluster_size[max(n_clusters, n_cluster_size)] "Size of the clusters";
end KMeans;

Revisions

• February 1, 2023, by Michael Wetter:
  Added impure declaration which is needed for compliance with the Modelica Language Specification, and is required by Optimica.
• June 9, 2022 by Massimo Cimmino:
  First Implementation