This is a pre-production deployment of Warehouse, however changes made here WILL affect the production instance of PyPI.
Latest Version Dependencies status unknown Test status unknown Test coverage unknown
Project Description
The purpose of this implementation is to make the inner workings of
the algorithm easy to understand and not to distract or confuse
the reader with highly optimized code.

The SPEAR algorithm takes a list of user activities on resources
as input, and returns ranked lists of users by expertise scores
and resources by quality scores, respectively.

You can also use this library to simulate the HITS algorithm of
Jon Kleinberg. Simply supply a credit score function C(x) = 1 to
the SPEAR algorithm (see documentation of

More information about the SPEAR algorithm is available at:
* "Telling Experts from Spammers: Expertise Ranking in Folksonomies"
Michael G. Noll, Ching-man Au Yeung, et al.
SIGIR 09: Proceedings of 32nd International ACM SIGIR Conference
on Research and Development in Information Retrieval, Boston, USA,
July 2009, pp. 612-619, ISBN 978-1-60558-483-6

The code is licensed to you under version 2 of the GNU General Public

Copyright 2009-2010 Michael G. Noll <http:""/>
Ching-man Au Yeung <http:""/>
Release History

Release History


This version

History Node

TODO: Figure out how to actually get changelog content.

Changelog content for this version goes here.

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Download Files

Download Files

TODO: Brief introduction on what you do with files - including link to relevant help section.

File Name & Checksum SHA256 Checksum Help Version File Type Upload Date
Spear-1.0.tar.gz (11.8 kB) Copy SHA256 Checksum SHA256 Source Jul 10, 2010

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