scib_rapids.utils.KMeans

scib_rapids.utils.KMeans#

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

CuPy/RAPIDS implementation of KMeans clustering.

Parameters:

  • n_clusters (int (default: 8)) – Number of clusters.

  • init (Literal['k-means++', 'random'] (default: 'k-means++')) – Cluster centroid initialization method: ‘k-means++’ or ‘random’.

  • n_init (int (default: 1)) – Number of times the k-means algorithm will be initialized.

  • max_iter (int (default: 300)) – Maximum number of iterations.

  • tol (float (default: 0.0001)) – Relative tolerance with regards to inertia to declare convergence.

  • seed (int (default: 0)) – Random seed.

Methods table#

fit(X)

Fit the model to the data.

Methods#

KMeans.fit(X)[source]#

Fit the model to the data.