src.spectral_clustering.kmeans¶
- src.spectral_clustering.kmeans(X: torch.Tensor, num_clusters: int, init_x: Union[torch.Tensor, str] = 'plus', distance: str = 'euclidean', tol: float = 0.0001, device=device(type='cpu')) Tuple[torch.Tensor, torch.Tensor] [source]¶
Perform kmeans on given data matrix \(\mathbf X\).
- Parameters
X – \((n\times d)\) input data matrix. \(n\): number of samples. \(d\): feature dimension
num_clusters – (int) number of clusters
init_x – how to initiate x (provide a initial state of x or define a init method) [default: ‘plus’]
distance – distance [options: ‘euclidean’, ‘cosine’] [default: ‘euclidean’]
tol – convergence threshold [default: 0.0001]
device – computing device [default: cpu]
- Returns
cluster ids, cluster centers