Strategy for initializing the parameters of a Gaussian mixture model.

  • random: Samples responsibilities of datapoints at random and subsequently initializes means and covariances from these.

  • kmeans: Runs K-Means via pycave.clustering.KMeans and uses the centroids as the initial component means. For computing the covariances, responsibilities are given as the one-hot cluster assignments.

  • kmeans++: Runs only the K-Means++ initialization procedure to sample means in a smart fashion. Might be more efficient than kmeans as it does not actually run clustering. For many clusters, this is, however, still slow.

alias of Literal['random', 'kmeans', 'kmeans++']