scib_metrics.utils.KMeans#

class scib_metrics.utils.KMeans(n_clusters=8, init='k-means++', n_init=1, max_iter=300, tol=0.0001, seed=0)[source]#

This implementation is limited to Euclidean distance.

Parameters:

n_clusters int (default: 8)

Number of clusters.

init Literal['k-means++', 'random'] (default: 'k-means++')

Cluster centroid initialization method. One of the following:

'k-means++': Sample initial cluster centroids based on an
empirical distribution of the points’ contributions to the overall inertia.
'random': Uniformly sample observations as initial centroids

n_init int (default: 1)

Number of times the k-means algorithm will be initialized.

max_iter int (default: 300)

Maximum number of iterations of the k-means algorithm for a single run.

tol float (default: 0.0001)

Relative tolerance with regards to inertia to declare convergence.

seed int | Array (default: 0)

Random seed.

Methods table#

Fit the model to the data.