{"push_id":2562855608,"size":2,"distinct_size":1,"ref":"refs/heads/master","head":"630fced386cb7581bbef53f1fbb972c9998e1eea","before":"edb3da69ce90f8f37605aa477ba84d6f11f918c1","commits":[{"sha":"3e8bcee596323707f55a5c361ed4e436f65aa03e","author":{"name":"Dean Ferreyra","email":"ba6f564cd70a52d664c374158c4dbcf519ddf158@octw.com"},"message":"Add BloomFilter64 to support large Bloom filters\n\nAdd BloomFilter64 that is like BloomFilter, but uses ulong to\nrepresent m and count, and uses Buckets64 and Hash128.\n\nAdd Buckets64 that is like Buckets, but uses ulong to represent count\nand uses multiple arrays to overcome the C# array size limit of\napproximately 2^31 elements and the default .NET limit of 2^31 bytes\nper object.\n\nAdd Hash128 that is like Hash, but provides 128 bits of hash material.\nAlso, add Hash128.ComputeHashAndSum() to improve performance by\navoiding the conversion of bytes to string, then string to bytes.\n\nAdd Utils.OptimalM64() that takes a ulong n and calculates a ulong m.\n\nAdd Utils.HashKernel128() and Utils.HashKernel128ReturnValue for\nworking with 128 bits of hash material.  In Utils.HashKernel128(),\navoid the use of Skip() and Take() to improve performance.\n\nAdd TestBloomFilter64.cs, TestBuckets64.cs, TestHash128.cs to test\nthe new classes.","distinct":false,"url":"https://api.github.com/repos/dferreyra/ProbabilisticDataStructures/commits/3e8bcee596323707f55a5c361ed4e436f65aa03e"},{"sha":"630fced386cb7581bbef53f1fbb972c9998e1eea","author":{"name":"Matthew Lorimor","email":"9f205da9b39195f1ab136a9464cfdbad9ff42267@gmail.com"},"message":"Merge pull request #16 from dferreyra/BloomFilter64\n\nAdd BloomFilter64 to support large Bloom filters","distinct":true,"url":"https://api.github.com/repos/dferreyra/ProbabilisticDataStructures/commits/630fced386cb7581bbef53f1fbb972c9998e1eea"}]}
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