Java tutorial
/* * Licensed to the Apache Software Foundation (ASF) under one * or more contributor license agreements. See the NOTICE file * distributed with this work for additional information * regarding copyright ownership. The ASF licenses this file * to you under the Apache License, Version 2.0 (the * "License"); you may not use this file except in compliance * with the License. You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, * software distributed under the License is distributed on an * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY * KIND, either express or implied. See the License for the * specific language governing permissions and limitations * under the License. */ package datafu.pig.hash.lsh.cosine; import org.apache.commons.math.linear.ArrayRealVector; import org.apache.commons.math.linear.RealVector; import org.apache.commons.math.random.RandomGenerator; import org.apache.commons.math.random.UnitSphereRandomVectorGenerator; import datafu.pig.hash.lsh.interfaces.LSH; /** * From wikipedia's article on {@link <a href="http://en.wikipedia.org/wiki/Locality-sensitive_hashing" target="_blank">Locality Sensitive Hashing</a>}: * <pre> * Locality-sensitive hashing (LSH) is a method of performing probabilistic dimension reduction of high-dimensional 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). * </pre> * * In particular, this implementation implements a locality sensitive hashing scheme which maps high-dimensional vectors which are * close together (with high probability) according to {@link <a href="http://en.wikipedia.org/wiki/Cosine_similarity" target="_blank">Cosine Similarity</a>} * into the same buckets. Each LSH maps a vector onto one side or the other of a random hyperplane, thereby producing a single * bit as the hash value. Multiple, independent, hashes can be run on the same input and aggregated together to form a more * broad domain than a single bit. * * For more information, see Charikar, Moses S.. (2002). "Similarity Estimation Techniques from Rounding Algorithms". Proceedings of the 34th Annual ACM Symposium on Theory of Computing 2002. * * */ public class HyperplaneLSH extends LSH { private RealVector r; /** * Locality sensitive hash that maps vectors onto 0,1 in such a way that colliding * vectors are "near" one another according to cosine similarity with high probability. * * <p> * Generally, multiple LSH are combined via repetition to increase the range of the hash function to the full set of longs. * This repetition is accomplished by wrapping instances of the LSH in a LSHFamily, which does the combination. * * The size of the hash family corresponds to the number of independent hashes you want to apply to the data. * In a k-near neighbors style of searching, this corresponds to the number of neighbors you want to find * (i.e. the number of vectors within a distance according to cosine similarity). */ public HyperplaneLSH(int dim, RandomGenerator rg) { super(dim, rg); UnitSphereRandomVectorGenerator generator = new UnitSphereRandomVectorGenerator(dim, rg); //compute our vector representing a hyperplane of dimension dim by taking a random vector //located on the unit sphere double[] normalVector = generator.nextVector(); r = new ArrayRealVector(normalVector); } /** * Compute which side of the hyperplane that the parameter is on. * * @param vector The vector to test. * @return one if the dot product with the hyperplane is positive, 0 if negative. */ public long apply(RealVector vector) { return r.dotProduct(vector) >= 0 ? 1 : 0; } }