org.openimaj.knn

Class FloatNearestNeighboursExact

java.lang.Object

org.openimaj.knn.FloatNearestNeighbours

org.openimaj.knn.FloatNearestNeighboursExact

All Implemented Interfaces:

NearestNeighbours<float[],float[],IntFloatPair>

public class FloatNearestNeighboursExact
extends FloatNearestNeighbours

Exact (brute-force) k-nearest-neighbour implementation.

Author:

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

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	FloatNearestNeighboursExact.Factory NearestNeighboursFactory for producing FloatNearestNeighboursExacts.

Field Summary

Fields

Modifier and Type	Field and Description
protected FloatFVComparator	distance
protected float[][]	pnts

Constructor Summary

Constructors

Constructor and Description
FloatNearestNeighboursExact(float[][] pnts) Construct the FloatNearestNeighboursExact over the provided dataset and using Euclidean distance.
FloatNearestNeighboursExact(float[][] pnts, FloatFVComparator distance) Construct the FloatNearestNeighboursExact over the provided dataset with the given distance function.

Method Summary

Methods

Modifier and Type	Method and Description
float	computeDistance(float[] a, float[] b) Compute the distance between two vectors using the underlying distance comparison used by this class.
float[][]	getPoints() Get the underlying data points.
int	numDimensions() Get the number of dimensions of each vector in the dataset
void	searchKNN(float[][] qus, int K, int[][] indices, float[][] 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<IntFloatPair>	searchKNN(float[] 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<float[]> qus, int K, int[][] indices, float[][] 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.
IntFloatPair	searchNN(float[] query) Search for the nearest neighbour to the given query and return a pair containing the distance and index of that neighbour.
void	searchNN(float[][] qus, int[] indices, float[] 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<float[]> qus, int[] indices, float[] 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

Methods inherited from class org.openimaj.knn.FloatNearestNeighbours

distanceFunc, distanceFunc, distanceFunc, distanceFunc

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Field Detail

pnts

protected final float[][] pnts

distance

protected final FloatFVComparator distance

Constructor Detail

FloatNearestNeighboursExact

public FloatNearestNeighboursExact(float[][] pnts)

Construct the FloatNearestNeighboursExact over the provided dataset and using Euclidean distance.

Parameters:

pnts - the dataset

FloatNearestNeighboursExact

public FloatNearestNeighboursExact(float[][] pnts,
                           FloatFVComparator distance)

Construct the FloatNearestNeighboursExact over the provided dataset with the given distance function.

Note: If the distance function provides similarities rather than distances they are automatically inverted.

Parameters:

pnts - the dataset

distance - the distance function

Method Detail

searchNN

public void searchNN(float[][] qus,
            int[] indices,
            float[] distances)

Description copied from interface: NearestNeighbours

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

public void searchKNN(float[][] qus,
             int K,
             int[][] indices,
             float[][] distances)

Description copied from interface: NearestNeighbours

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

public void searchNN(List<float[]> qus,
            int[] indices,
            float[] distances)

Description copied from interface: NearestNeighbours

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

public void searchKNN(List<float[]> qus,
             int K,
             int[][] indices,
             float[][] distances)

Description copied from interface: NearestNeighbours

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

public List<IntFloatPair> searchKNN(float[] query,
                           int K)

Description copied from interface: NearestNeighbours

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

public IntFloatPair searchNN(float[] query)

Description copied from interface: NearestNeighbours

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

numDimensions

public int numDimensions()

Description copied from class: FloatNearestNeighbours

Get the number of dimensions of each vector in the dataset

Specified by:

numDimensions in class FloatNearestNeighbours

Returns:

the number of dimensions

size

public int size()

Description copied from interface: NearestNeighbours

Get the size of the dataset

Returns:

the dataset size

getPoints

public float[][] getPoints()

Get the underlying data points.

Returns:

the data points

computeDistance

public float computeDistance(float[] a,
                    float[] b)

Compute the distance between two vectors using the underlying distance comparison used by this class.

Parameters:

a - the first vector

b - the second vector

Returns:

the distance between the two vectors