:py:mod:`anomalib.models.components.sampling.k_center_greedy`
=============================================================

.. py:module:: anomalib.models.components.sampling.k_center_greedy

.. autoapi-nested-parse::

   This module comprises PatchCore Sampling Methods for the embedding.

   - k Center Greedy Method
       Returns points that minimizes the maximum distance of any point to a center.
       . https://arxiv.org/abs/1708.00489



Module Contents
---------------

Classes
~~~~~~~

.. autoapisummary::

   anomalib.models.components.sampling.k_center_greedy.KCenterGreedy




.. py:class:: KCenterGreedy(embedding: torch.Tensor, sampling_ratio: float)

   Implements k-center-greedy method.

   :param embedding: Embedding vector extracted from a CNN
   :type embedding: Tensor
   :param sampling_ratio: Ratio to choose coreset size from the embedding size.
   :type sampling_ratio: float

   .. rubric:: 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])

   .. py:method:: reset_distances() -> None

      Reset minimum distances.


   .. py:method:: update_distances(cluster_centers: List[int]) -> None

      Update min distances given cluster centers.

      :param cluster_centers: indices of cluster centers
      :type cluster_centers: List[int]


   .. py:method:: get_new_idx() -> int

      Get index value of a sample.

      Based on minimum distance of the cluster

      :returns: Sample index
      :rtype: int


   .. py:method:: select_coreset_idxs(selected_idxs: Optional[List[int]] = None) -> List[int]

      Greedily form a coreset to minimize the maximum distance of a cluster.

      :param selected_idxs: index of samples already selected. Defaults to an empty set.

      :returns: indices of samples selected to minimize distance to cluster centers


   .. py:method:: sample_coreset(selected_idxs: Optional[List[int]] = None) -> torch.Tensor

      Select coreset from the embedding.

      :param selected_idxs: index of samples already selected. Defaults to an empty set.

      :returns: Output coreset
      :rtype: Tensor

      .. rubric:: Example

      >>> embedding.shape
      torch.Size([219520, 1536])
      >>> sampler = KCenterGreedy(...)
      >>> coreset = sampler.sample_coreset()
      >>> coreset.shape
      torch.Size([219, 1536])