The streaming percentiles library is a cross-platform, multi-language
(C++ and JavaScript) implementation of a number of online (single-pass)
percentile algorithms. This version of the streaming percentiles library
adds support for copy construction, assignment, move construction, and
move assignment on all analytics classes.
This change allows you to put streaming analytics classes into STL containers,
such as:
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#include<vector>#include<stmpct/gk.hpp>std::vector<stmpct::gk<double>> algs;
algs.emplace_back(0.01);
algs.emplace_back(0.05);
for (auto&g : algs) {
for (int i =0; i <1000; ++i) {
g.insert(rand());
}
double p95 = g.quantile(0.95); // Approx. 95th percentile for current epsilon
}
It also allows you to efficiently move the state of an algorithm
from one object to another without copying, as in:
Versions 1.x and 2.x of the C++ library required all measurements to use the type double,
and usage of the algorithms looked something like this:
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#include<stmpct/gk.hpp>double epsilon =0.1;
stmpct::gk g(epsilon);
for (int i =0; i <1000; ++i)
g.insert(rand());
double p50 = g.quantile(0.5); // Approx. median
double p95 = g.quantile(0.95); // Approx. 95th percentile
For version 3.x of the library, I have modified the algorithms to be
parameterized by type so that they are not limited to double. The
algorithms now may be used by type which implements a few simple
requirements: it must be default constructible, copy constructible,
assignable, and implement operator<. This means that they can now
be used with float, int, long double, a custom number type, or
even std::string.
This is part 7/8 of my Calculating Percentiles on Streaming Data series.
In 2005, Graham Cormode, Flip Korn, S. Muthukrishnan, and Divesh Srivastava published a paper called Effective Computation of Biased Quantiles over Data Streams [CKMS05]. This paper took the Greenwald-Khanna algorithm [GK01] and made the following notable changes:
Generalized the algorithm to work with arbitrary targeted quantiles, where a targeted quantile is a combination of a quantile $\phi$ and a maximum allowable error $\epsilon$. The algorithm will maintain the summary data structure $\mathsf{S}$ such that it will maintain each targeted quantile with the appropriate error rate.
Simplified the Compress() operation by removing the concept of banding.
The paper also demonstrated that the new, more generalized algorithm could handle the following cases with ease:
