@Reference(type=Inproceedings, author={"Marius Muja","David G. Lowe"}, title="Fast Approximate Nearest Neighbors with Automatic Algorithm Configuration", year="2009", booktitle="International Conference on Computer Vision Theory and Application VISSAPP\'09)", pages={"331","340"}, publisher="INSTICC Press") public class FloatNearestNeighboursKDTree extends FloatNearestNeighbours
Implementation inspired by http://www.robots.ox.ac.uk/~vgg/software/fastann/
Modifier and Type  Class and Description 

static class 
FloatNearestNeighboursKDTree.Factory
NearestNeighboursFactory for producing
FloatNearestNeighboursKDTree s. 
Modifier and Type  Field and Description 

static int 
DEFAULT_NCHECKS
The default number of checks performed during search when in exact mode.

static int 
DEFAULT_NTREES
The default number of kdtrees when not in exact mode.

FloatKDTreeEnsemble 
kdt
The ensemble of KDTrees

int 
nchecks
The number of checks

Constructor and Description 

FloatNearestNeighboursKDTree(float[][] pnts,
int ntrees,
int nchecks)
Construct the FloatNearestNeighboursKDTree with the given options.

Modifier and Type  Method and Description 

int 
numDimensions()
Get the number of dimensions of each vector in the dataset

void 
searchKNN(float[][] qus,
int K,
int[][] argmins,
float[][] mins)
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[][] argmins,
float[][] mins)
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[] argmins,
float[] mins)
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[] argmins,
float[] mins)
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

distanceFunc, distanceFunc, distanceFunc, distanceFunc
public static final int DEFAULT_NCHECKS
public static final int DEFAULT_NTREES
public final FloatKDTreeEnsemble kdt
public final int nchecks
public FloatNearestNeighboursKDTree(float[][] pnts, int ntrees, int nchecks)
pnts
 the datantrees
 the number of treesnchecks
 the number of checks during searchpublic int numDimensions()
FloatNearestNeighbours
numDimensions
in class FloatNearestNeighbours
public int size()
NearestNeighbours
public void searchKNN(float[][] qus, int K, int[][] argmins, float[][] mins)
NearestNeighbours
For efficiency, to use this method, you need to preconstruct the arrays for storing the results outside of the method and pass them in as arguments.
If a kth nearestneighbour cannot be determined, it will have an index value of 1
qus
 An array of N query vectorsK
 the number of neighbours to findargmins
 The return N*Kdimensional array for holding the indices of
the K nearest neighbours of each respective query.mins
 The return N*Kdimensional array for holding the distances of
the nearest neighbours of each respective query.public void searchNN(float[][] qus, int[] argmins, float[] mins)
NearestNeighbours
For efficiency, to use this method, you need to preconstruct the arrays for storing the results outside of the method and pass them in as arguments.
If a nearestneighbour cannot be determined, it will have an index value of 1
qus
 An array of N query vectorsargmins
 The return Ndimensional array for holding the indices of the
nearest neighbour of each respective query.mins
 The return Ndimensional array for holding the distances of
the nearest neighbour to each respective query.public void searchKNN(List<float[]> qus, int K, int[][] argmins, float[][] mins)
NearestNeighbours
For efficiency, to use this method, you need to preconstruct the arrays for storing the results outside of the method and pass them in as arguments.
If a kth nearestneighbour cannot be determined, it will have an index value of 1
qus
 An array of N query vectorsK
 the number of neighbours to findargmins
 The return N*Kdimensional array for holding the indices of
the K nearest neighbours of each respective query.mins
 The return N*Kdimensional array for holding the distances of
the nearest neighbours of each respective query.public void searchNN(List<float[]> qus, int[] argmins, float[] mins)
NearestNeighbours
For efficiency, to use this method, you need to preconstruct the arrays for storing the results outside of the method and pass them in as arguments.
If a nearestneighbour cannot be determined, it will have an index value of 1
qus
 An array of N query vectorsargmins
 The return Ndimensional array for holding the indices of the
nearest neighbour of each respective query.mins
 The return Ndimensional array for holding the distances of
the nearest neighbour to each respective query.public List<IntFloatPair> searchKNN(float[] query, int K)
NearestNeighbours
If k neighbours cannot be determined, then the resultant list might have fewer than k elements.
query
 the query vectorK
 the number of neighbours to search forpublic IntFloatPair searchNN(float[] query)
NearestNeighbours
If the nearestneighbour cannot be determined null
will be
returned.
query
 the query vector