class_docstr: Implements k-center-greedy method.
  
  Args:
      embedding (Tensor): Embedding vector extracted from a CNN
      sampling_ratio (float): Ratio to choose coreset size from the embedding size.
  
  Example:
      >>> embedding.shape
      torch.Size([219520, 1536])
      >>> sampler = KCenterGreedy(embedding=embedding)
      >>> sampled_idxs = sampler.select_coreset_idxs()
      >>> coreset = embedding[sampled_idxs]
      >>> coreset.shape
      torch.Size([219, 1536])
class_docstr_tok: ["Implements","k-center-greedy","method",".","Args",":","embedding","(","Tensor",")",":","Embedding","vector","extracted","from","a","CNN","sampling_ratio","(","float",")",":","Ratio","to","choose","coreset","size","from","the","embedding","size",".","Example",":",">",">",">","embedding.shape","torch.Size","(","[","219520",",","1536","]",")",">",">",">","sampler","=","KCenterGreedy","(","embedding=embedding",")",">",">",">","sampled_idxs","=","sampler.select_coreset_idxs","(",")",">",">",">","coreset","=","embedding","[","sampled_idxs","]",">",">",">","coreset.shape","torch.Size","(","[","219",",","1536","]",")"]
docstr: Reset minimum distances.
docstr_tok: ["Reset","minimum","distances","."]
code: def reset_distances(self) -> None:
      """Reset minimum distances."""
      self.min_distances = None
  
file_path: anomalib/src/anomalib/models/components/sampling/k_center_greedy.py
imports: from __future__ import annotations
  import torch
  import torch.nn.functional
  from torch import Tensor
  from tqdm import tqdm
  from anomalib.models.components.dimensionality_reduction import SparseRandomProjection
label_desc: Use image node_id 2 for calling the KCenterGreedy obj's underlying member method code with example usage: obj.reset_distances() without return types