Package smile.neighbor

Interface KNNSearch<K,V>

Type Parameters:
K - the type of keys.
V - the type of associated objects.
All Known Implementing Classes:
CoverTree, KDTree, LinearSearch, LSH, MPLSH, MutableLSH, RandomProjectionForest, RandomProjectionTree
public interface KNNSearch<K,V>
Retrieves the top k nearest neighbors to the query. This technique is commonly used in predictive analytics to estimate or classify a point based on the consensus of its neighbors. K-nearest neighbor graphs are graphs in which every point is connected to its k nearest neighbors.

  • Method Summary

    Modifier and Type
    Method
    Description
    default Neighbor<K,V>
    nearest(K q)
    Returns the nearest neighbor.
    Neighbor<K,V>[]
    search(K q, int k)
    Retrieves the k nearest neighbors to the query key.

  • Method Details

    • nearest

      default Neighbor<K,V> nearest(K q)
      Returns the nearest neighbor. In machine learning, we often build a nearest neighbor search data structure, and then search with object in the same dataset. The object itself is of course the nearest one with distance 0. Since this is generally useless, we check the reference during the search and excludes the query object from the results.
      Parameters:
      q - the query key.
      Returns:
      the nearest neighbor

    • search

      Neighbor<K,V>[] search(K q, int k)
      Retrieves the k nearest neighbors to the query key.
      Parameters:
      q - the query key.
      k - the number of nearest neighbors to search for.
      Returns:
      the k nearest neighbors