CITATION.cff

cff-version: 1.2.0
title: "Affirmative Sampling: Reference Implementation"
doi: 10.5281/zenodo.6601691
version: 1.0.0
date-released: 2022-03-28
type: software
message: "If you use this software, please cite it as below."
authors:
  - family-names: Lumbroso
    given-names: Jérémie
    email: lumbroso@cs.princeton.edu
    affiliation: Department of Computer Science, Princeton University
    orcid: http://orcid.org/0000-0002-5563-687X
  - family-names: Martínez
    given-names: Conrado
    email: conrado@cs.upc.edu
    affiliation: Department of Computer Science, Universitat Politècnica de Catalunya
    orcid: https://orcid.org/0000-0003-1302-9067
license: MIT
repository-code: "https://github.com/jlumbroso/affirmative-sampling"
abstract: >-
  Affirmative Sampling
  is a practical and efficient novel
  algorithm to obtain random samples of distinct elements from a data
  stream. Its most salient feature is that the size $S$ of the
  sample will, on expectation, grow with the (unknown) number $n$ of
  distinct elements in the data stream. As any distinct element has
  the same probability to be sampled, and the sample size is greater
  when the "diversity" (the number of distinct elements) is greater,
  the samples that Affirmative Sampling delivers are more
  representative than those produced by any scheme where the sample
  size is fixed a priori—hence its name. This repository contains
  a reference implementation, in Python, to illustrate how the algorithm
  works and to showcase some basic experimentation.