Probabilistic data structures for processing continuous, unbounded streams.
翻译 - 用于处理连续无界流的概率数据结构。
In-memory nucleotide sequence k-mer counting, filtering, graph traversal and more
JS implementation of probabilistic data structures: Bloom Filter (and its derived), HyperLogLog, Count-Min Sketch, Top-K and MinHash
C++ Implementations of sketch data structures with SIMD Parallelism, including Python bindings
Sketching Algorithms for Clojure (bloom filter, min-hash, hyper-loglog, count-min sketch)
Probabilistic data structures in python http://pyprobables.readthedocs.io/en/latest/index.html
A probabilistic data structures library for C#
A class library implementing probabilistic data structures in .NET
Pond is a high performance object-pooling library for Python
Count-Min Sketch Implementation in C
An implementation of Count-Min Sketch in Golang
#计算机科学#A compressed adaptive optimizer for training large-scale deep learning models using PyTorch
PHP client for RedisBloom module
🎛️ Use RedisBloom in PHP!
High performance approximate algorithms in Go (e.g. morris counter, count min, etc.)
an implementation of Count-Min Sketch, an approximate counting data structure for summarizing data streams, in golang
Thread-safe and persistent Golang implementations of probabilistic data structures: Bloom Filter, Cuckoo Filter, HyperLogLog, Count-Min Sketch and Top-K
#算法刷题#Implementation and experimental tests of various algorithms.