org.openimaj.knn

Interface NearestNeighbours<DATA,DISTANCES,PAIR_TYPE>

Type Parameters:

DATA - The type of data

DISTANCES - The type of distances measured (usually an array type)

PAIR_TYPE - The type of distance-index pair returned by the search methods

All Known Subinterfaces:

IncrementalNearestNeighbours<DATA,DISTANCES,PAIR_TYPE>

All Known Implementing Classes:

ByteADCNearestNeighbours, ByteNearestNeighbours, ByteNearestNeighboursExact, ByteNearestNeighboursKDTree, ByteSDCNearestNeighbours, DoubleADCNearestNeighbours, DoubleNearestNeighbours, DoubleNearestNeighboursExact, DoubleNearestNeighboursKDTree, DoubleSDCNearestNeighbours, FloatADCNearestNeighbours, FloatNearestNeighbours, FloatNearestNeighboursExact, FloatNearestNeighboursKDTree, FloatSDCNearestNeighbours, IncrementalByteADCNearestNeighbours, IncrementalDoubleADCNearestNeighbours, IncrementalFloatADCNearestNeighbours, IncrementalIntADCNearestNeighbours, IncrementalLongADCNearestNeighbours, IncrementalShortADCNearestNeighbours, IntADCNearestNeighbours, IntNearestNeighbours, IntNearestNeighboursExact, IntNearestNeighboursKDTree, IntSDCNearestNeighbours, LongADCNearestNeighbours, LongNearestNeighbours, LongNearestNeighboursExact, LongNearestNeighboursKDTree, LongSDCNearestNeighbours, LSHNearestNeighbours, ObjectNearestNeighbours, ObjectNearestNeighboursExact, ShortADCNearestNeighbours, ShortNearestNeighbours, ShortNearestNeighboursExact, ShortNearestNeighboursKDTree, ShortSDCNearestNeighbours

public interface NearestNeighbours<DATA,DISTANCES,PAIR_TYPE>

Interface for k-nearest-neighbour calculations with some data.

Author:

Jonathon Hare (jsh2@ecs.soton.ac.uk), Sina Samangooei (ss@ecs.soton.ac.uk)

Method Summary

Modifier and Type	Method and Description
void	searchKNN(DATA[] qus, int K, int[][] indices, DISTANCES[] distances) Search for the K nearest neighbours to each of the N queries, and return the indices of each nearest neighbour and their respective distances.
List<PAIR_TYPE>	searchKNN(DATA query, int K) Search for the K nearest neighbours to the given query and return an ordered list of pairs containing the distance and index of each neighbour.
void	searchKNN(List<DATA> qus, int K, int[][] indices, DISTANCES[] distances) Search for the K nearest neighbours to each of the N queries, and return the indices of each nearest neighbour and their respective distances.
PAIR_TYPE	searchNN(DATA query) Search for the nearest neighbour to the given query and return a pair containing the distance and index of that neighbour.
void	searchNN(DATA[] qus, int[] indices, DISTANCES distances) Search for the nearest neighbour to each of the N queries, and return the index of each nearest neighbour and the respective distance.
void	searchNN(List<DATA> qus, int[] indices, DISTANCES distances) Search for the nearest neighbour to each of the N queries, and return the index of each nearest neighbour and the respective distance.
int	size() Get the size of the dataset

Method Detail

searchNN

void searchNN(DATA[] qus,
              int[] indices,
              DISTANCES distances)

Search for the nearest neighbour to each of the N queries, and return the index of each nearest neighbour and the respective distance.

For efficiency, to use this method, you need to pre-construct the arrays for storing the results outside of the method and pass them in as arguments.

If a nearest-neighbour cannot be determined, it will have an index value of -1

Parameters:

qus - An array of N query vectors

indices - The return N-dimensional array for holding the indices of the nearest neighbour of each respective query.

distances - The return N-dimensional array for holding the distances of the nearest neighbour to each respective query.

searchKNN

void searchKNN(DATA[] qus,
               int K,
               int[][] indices,
               DISTANCES[] distances)

Search for the K nearest neighbours to each of the N queries, and return the indices of each nearest neighbour and their respective distances.

For efficiency, to use this method, you need to pre-construct the arrays for storing the results outside of the method and pass them in as arguments.

If a k-th nearest-neighbour cannot be determined, it will have an index value of -1

Parameters:

qus - An array of N query vectors

K - the number of neighbours to find

indices - The return N*K-dimensional array for holding the indices of the K nearest neighbours of each respective query.

distances - The return N*K-dimensional array for holding the distances of the nearest neighbours of each respective query.

searchNN

void searchNN(List<DATA> qus,
              int[] indices,
              DISTANCES distances)

Search for the nearest neighbour to each of the N queries, and return the index of each nearest neighbour and the respective distance.

For efficiency, to use this method, you need to pre-construct the arrays for storing the results outside of the method and pass them in as arguments.

If a nearest-neighbour cannot be determined, it will have an index value of -1

Parameters:

qus - An array of N query vectors

indices - The return N-dimensional array for holding the indices of the nearest neighbour of each respective query.

distances - The return N-dimensional array for holding the distances of the nearest neighbour to each respective query.

searchKNN

void searchKNN(List<DATA> qus,
               int K,
               int[][] indices,
               DISTANCES[] distances)

Search for the K nearest neighbours to each of the N queries, and return the indices of each nearest neighbour and their respective distances.

For efficiency, to use this method, you need to pre-construct the arrays for storing the results outside of the method and pass them in as arguments.

If a k-th nearest-neighbour cannot be determined, it will have an index value of -1

Parameters:

qus - An array of N query vectors

K - the number of neighbours to find

indices - The return N*K-dimensional array for holding the indices of the K nearest neighbours of each respective query.

distances - The return N*K-dimensional array for holding the distances of the nearest neighbours of each respective query.

searchKNN

List<PAIR_TYPE> searchKNN(DATA query,
                          int K)

Search for the K nearest neighbours to the given query and return an ordered list of pairs containing the distance and index of each neighbour.

If k neighbours cannot be determined, then the resultant list might have fewer than k elements.

Parameters:

query - the query vector

K - the number of neighbours to search for

Returns:

the top K nearest neighbours ordered by increasing distance

searchNN

PAIR_TYPE searchNN(DATA query)

Search for the nearest neighbour to the given query and return a pair containing the distance and index of that neighbour.

If the nearest-neighbour cannot be determined null will be returned.

Parameters:

query - the query vector

Returns:

the distance and index of the nearest neighbour

size

int size()

Get the size of the dataset

Returns:

the dataset size