CatgirlIntelligenceAgency/code/libraries/easy-lsh
2024-02-27 21:22:21 +01:00
..
java/nu/marginalia/lsh (refac) Remove src/main from all source code paths. 2024-02-23 16:13:40 +01:00
test/nu/marginalia/lsh (refac) Remove src/main from all source code paths. 2024-02-23 16:13:40 +01:00
build.gradle (refac) Remove src/main from all source code paths. 2024-02-23 16:13:40 +01:00
readme.md (docs) Begin un-fucking the docs after refactoring 2024-02-27 21:22:21 +01:00

Easy LSH

This a simple Locality-Sensitive Hash for document deduplication. Hashes are compared using their hamming distance.

Central Classes

Demo

Consider statistical distribution only

var lsh1 = new EasyLSH();
lsh1.addUnordered("lorem");
lsh1.addUnordered("ipsum");
lsh1.addUnordered("dolor");
lsh1.addUnordered("sit");
lsh1.addUnordered("amet");

long hash1 = lsh1.get();

var lsh2 = new EasyLSH();
lsh2.addUnordered("amet");
lsh2.addUnordered("ipsum");
lsh2.addUnordered("lorem");
lsh2.addUnordered("dolor");
lsh2.addUnordered("SEAT");

long hash2 = lsh2.get();

System.out.println(EasyLSH.hammingDistance(lsh1, lsh2));
// 1 -- these are similar

Consider order as well as distribution

var lsh1 = new EasyLSH();
lsh1.addOrdered("lorem");
lsh1.addOrdered("ipsum");
lsh1.addOrdered("dolor");
lsh1.addOrdered("sit");
lsh1.addOrdered("amet");

long hash1 = lsh1.get();

var lsh2 = new EasyLSH();
lsh2.addOrdered("amet");
lsh2.addOrdered("ipsum");
lsh2.addOrdered("lorem");
lsh2.addOrdered("dolor");
lsh2.addOrdered("SEAT");


long hash2 = lsh2.get();

System.out.println(EasyLSH.hammingDistance(lsh1, lsh2));
// 5 -- these are not very similar

// note the value is relatively low because there are few words
// and there simply can't be very many differences
// it will approach 32 as documents grow larger