smile.neighbor

Class LSH<E>

java.lang.Object

smile.neighbor.LSH<E>

Type Parameters:

E - the type of data objects in the hash table.

All Implemented Interfaces:

KNNSearch<double[],E>, NearestNeighborSearch<double[],E>, RNNSearch<double[],E>

public class LSH<E>
extends Object
implements NearestNeighborSearch<double[],E>, KNNSearch<double[],E>, RNNSearch<double[],E>

Locality-Sensitive Hashing. LSH is an efficient algorithm for approximate nearest neighbor search in high dimensional spaces by performing probabilistic dimension reduction of data. The basic idea is to hash the input items so that similar items are mapped to the same buckets with high probability (the number of buckets being much smaller than the universe of possible input items).

References

1. Alexandr Andoni and Piotr Indyk. Near-Optimal Hashing Algorithms for Near Neighbor Problem in High Dimensions. FOCS, 2006.
2. Alexandr Andoni, Mayur Datar, Nicole Immorlica, Piotr Indyk, and Vahab Mirrokni. Locality-Sensitive Hashing Scheme Based on p-Stable Distributions. 2004.

Author:

Haifeng Li

See Also:

MPLSH

Constructor Summary

Constructors

Constructor and Description
LSH(double[][] keys, E[] data) Constructor.
LSH(double[][] keys, E[] data, double w) Constructor.
LSH(double[][] keys, E[] data, double w, int H) Constructor.
LSH(int d, int L, int k) Constructor.
LSH(int d, int L, int k, double w) Constructor.
LSH(int d, int L, int k, double w, int H) Constructor.

Method Summary

Methods

Modifier and Type	Method and Description
boolean	isIdenticalExcluded() Get whether if query object self be excluded from the neighborhood.
Neighbor<double[],E>[]	knn(double[] q, int k) Search the k nearest neighbors to the query.
Neighbor<double[],E>	nearest(double[] q) Search the nearest neighbor to the given sample.
void	put(double[] key, E value) Insert an item into the hash table.
void	range(double[] q, double radius, List<Neighbor<double[],E>> neighbors) Search the neighbors in the given radius of query object, i.e.
void	setIdenticalExcluded(boolean excluded) Set if exclude query object self from the neighborhood.
String	toString()

Methods inherited from class java.lang.Object

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

Constructor Detail

LSH

public LSH(double[][] keys,
   E[] data)

Constructor.

Parameters:

keys - the keys of data objects.

data - the data objects.

LSH

public LSH(double[][] keys,
   E[] data,
   double w)

Constructor.

Parameters:

keys - the keys of data objects.

data - the data objects.

w - the width of random projections. It should be sufficiently away from 0. But we should not choose an w value that is too large, which will increase the query time.

LSH

public LSH(double[][] keys,
   E[] data,
   double w,
   int H)

Constructor.

Parameters:

keys - the keys of data objects.

data - the data objects.

w - the width of random projections. It should be sufficiently away from 0. But we should not choose an w value that is too large, which will increase the query time.

H - the size of universal hash tables.

LSH

public LSH(int d,
   int L,
   int k)

Constructor.

Parameters:

d - the dimensionality of data.

L - the number of hash tables.

k - the number of random projection hash functions, which is usually set to log(N) where N is the dataset size.

LSH

public LSH(int d,
   int L,
   int k,
   double w)

Constructor.

Parameters:

d - the dimensionality of data.

L - the number of hash tables.

k - the number of random projection hash functions, which is usually set to log(N) where N is the dataset size.

w - the width of random projections. It should be sufficiently away from 0. But we should not choose an w value that is too large, which will increase the query time.

LSH

public LSH(int d,
   int L,
   int k,
   double w,
   int H)

Constructor.

Parameters:

d - the dimensionality of data.

L - the number of hash tables.

k - the number of random projection hash functions, which is usually set to log(N) where N is the dataset size.

w - the width of random projections. It should be sufficiently away from 0. But we should not choose an w value that is too large, which will increase the query time.

H - the size of universal hash tables.

Method Detail

toString

public String toString()

Overrides:

toString in class Object

isIdenticalExcluded

public boolean isIdenticalExcluded()

Get whether if query object self be excluded from the neighborhood.

setIdenticalExcluded

public void setIdenticalExcluded(boolean excluded)

Set if exclude query object self from the neighborhood.

put

public void put(double[] key,
       E value)

Insert an item into the hash table.

nearest

public Neighbor<double[],E> nearest(double[] q)

Description copied from interface: NearestNeighborSearch

Search the nearest neighbor to the given sample.

Specified by:

nearest in interface NearestNeighborSearch<double[],E>

Parameters:

q - the query key.

Returns:

the nearest neighbor

knn

public Neighbor<double[],E>[] knn(double[] q,
                         int k)

Description copied from interface: KNNSearch

Search the k nearest neighbors to the query.

Specified by:

knn in interface KNNSearch<double[],E>

Parameters:

q - the query key.

k - the number of nearest neighbors to search for.

range

public void range(double[] q,
         double radius,
         List<Neighbor<double[],E>> neighbors)

Description copied from interface: RNNSearch

Search the neighbors in the given radius of query object, i.e. d(q, v) ≤ radius.

Specified by:

range in interface RNNSearch<double[],E>

Parameters:

q - the query key.

radius - the radius of search range from target.

neighbors - the list to store found neighbors in the given range on output.