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package de.lmu.ifi.dbs.elki.index.lsh.hashfunctions;
/*
This file is part of ELKI:
Environment for Developing KDD-Applications Supported by Index-Structures
Copyright (C) 2015
Ludwig-Maximilians-Universität München
Lehr- und Forschungseinheit für Datenbanksysteme
ELKI Development Team
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU Affero General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU Affero General Public License for more details.
You should have received a copy of the GNU Affero General Public License
along with this program. If not, see <http://www.gnu.org/licenses/>.
*/
import java.util.Random;
import de.lmu.ifi.dbs.elki.data.NumberVector;
import de.lmu.ifi.dbs.elki.math.linearalgebra.randomprojections.RandomProjectionFamily;
import de.lmu.ifi.dbs.elki.utilities.documentation.Reference;
/**
* LSH hash function for vector space data. Depending on the choice of random
* vectors, it can be appropriate for Manhattan and Euclidean distances.
*
* Reference:
* <p>
* M. Datar and N. Immorlica and P. Indyk and V. S. Mirrokni<br />
* Locality-sensitive hashing scheme based on p-stable distributions<br />
* Proc. 20th annual symposium on Computational geometry<br />
* </p>
*
* @author Erich Schubert
*/
@Reference(authors = "M. Datar and N. Immorlica and P. Indyk and V. S. Mirrokni", //
title = "Locality-sensitive hashing scheme based on p-stable distributions", //
booktitle = "Proc. 20th annual symposium on Computational geometry", //
url = "http://dx.doi.org/10.1145/997817.997857")
public class MultipleProjectionsLocalitySensitiveHashFunction implements LocalitySensitiveHashFunction<NumberVector> {
/**
* Projection matrix.
*/
RandomProjectionFamily.Projection projection;
/**
* Shift offset.
*/
double[] shift;
/**
* Scaling factor: inverse of width.
*/
double iwidth;
/**
* Random numbers for mixing the hash codes of the individual functions
*/
int[] randoms1;
/**
* Constructor.
*
* @param projection Projection vectors
* @param width Width of bins
* @param rnd Random number generator
*/
public MultipleProjectionsLocalitySensitiveHashFunction(RandomProjectionFamily.Projection projection, double width, Random rnd) {
super();
this.projection = projection;
this.iwidth = 1. / width;
// Generate random shifts:
final int num = projection.getOutputDimensionality();
this.shift = new double[num];
this.randoms1 = new int[num];
for(int i = 0; i < num; i++) {
shift[i] = rnd.nextDouble() * width;
// Produce a large random number; although 7FFFFFFF would likely be large
// enough, we try to stick to the suggested approach (which assumes
// unsigned integers).
randoms1[i] = (rnd.nextInt(0x10000D) << 16) + rnd.nextInt(0xFFFFD) + 1;
}
}
/**
* Bit mask for signed int to unsigned long conversion.
*/
private final static long MASK32 = 0xFFFFFFFFL;
@Override
public int hashObject(NumberVector vec) {
// Project the vector:
final double[] proj = projection.project(vec);
long t1sum = 0L;
for(int i = 0; i < shift.length; i++) {
int ai = (int) Math.floor((proj[i] + shift[i]) * iwidth);
t1sum += (randoms1[i] & MASK32) * ai; // unsigned math!
}
return fastModPrime(t1sum);
}
@Override
public int hashObject(NumberVector vec, double[] buf) {
// Project the vector:
projection.project(vec, buf);
long t1sum = 0L;
for(int i = 0; i < shift.length; i++) {
int ai = (int) Math.floor((buf[i] + shift[i]) * iwidth);
t1sum += (randoms1[i] & MASK32) * ai; // unsigned math!
}
return fastModPrime(t1sum);
}
/**
* Fast modulo operation for the largest unsigned integer prime.
*
* @param data Long input
* @return {@code data % (2^32 - 5)}.
*/
public static int fastModPrime(long data) {
// Mix high and low 32 bit:
int high = (int) (data >>> 32);
// Use fast multiplication with 5 for high:
int alpha = ((int) data) + (high << 2 + high);
// Note that in Java, PRIME will be negative.
if(alpha < 0 && alpha > -5) {
alpha = alpha + 5;
}
return alpha;
}
@Override
public int getNumberOfProjections() {
return this.projection.getOutputDimensionality();
}
}
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