tdigest-cffi 0.1.2

T-Digest CFFI

t-digest是一个数据结构 为了准确地在线累积基于排名的统计数据，例如 分位数和修剪平均数，由Ted Dunning设计。

t-摘要构造算法使用一维变量 k-means聚类以生成与 Q-文摘。这种t-摘要数据结构可用于估计分位数 或者计算其他等级统计。T-文摘比 q-digest是t-digest可以处理浮点值，而 q-摘要仅限于整数。只要稍加改动，T-消化就可以 处理任何有序集中具有类似于 卑鄙。由t-摘要产生的分位数估计的准确性可以是 数量级的精度高于 尽管t-digest存储在磁盘上时更紧凑。

这个包提供经过测试、性能良好、线程安全的python 3cffi 与最初为编写的Usman Masood改编的t-digest实现的绑定 redis-tdigest。

安装

可以使用pip或包含的^{tt2}安装此包$ 脚本：

# Using pip
pip install tdigest-cffi

# Using setup.py
python setup.py install

用法

fromtdigestimportTDigest,RawTDigest# Thread-safe instance with default compression factordigest=TDigest()# Raw instance with default compression factordigest=RawTDigest()# Thread-safe instance with a custom compression factordigest=TDigest(compression=500)# Digest compressioncompression=digest.compression# Digest weightweight=digest.weight# Centroid countcentroid_count=digest.centroid_count# Compression countcompression_count=digest.compression_count# Insertion with unit weightdigest.insert(1000)# Insertion with custom weightdigest.insert(1000,2)# 99th percentile calculationquantile=digest.quantile(0.99)percentile=digest.percentile(99)# Cumulative distribution functioncdf=digest.cdf(1000)# P(X <= 1000)# Centroid extractionforcentroidindigest.centroids():print(centroid.mean,centroid.weight)# Digest mergingother=TDigest()other.insert(42)digest.merge(other)

许可证

BSD 3-Clause License

Copyright (c) 2018, Phil Demetriou
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