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.KMeansand 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
kmeansas it does not actually run clustering. For many clusters, this is, however, still slow.
Literal['random', 'kmeans', 'kmeans++']