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CVS log for pkgsrc/math/libshorttext/distinfo

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Default branch: MAIN


Revision 1.2 / (download) - annotate - [select for diffs], Tue Nov 3 23:33:36 2015 UTC (3 years, 9 months ago) by agc
Branch: MAIN
CVS Tags: pkgsrc-2019Q2-base, pkgsrc-2019Q2, pkgsrc-2019Q1-base, pkgsrc-2019Q1, pkgsrc-2018Q4-base, pkgsrc-2018Q4, pkgsrc-2018Q3-base, pkgsrc-2018Q3, pkgsrc-2018Q2-base, pkgsrc-2018Q2, pkgsrc-2018Q1-base, pkgsrc-2018Q1, pkgsrc-2017Q4-base, pkgsrc-2017Q4, pkgsrc-2017Q3-base, pkgsrc-2017Q3, pkgsrc-2017Q2-base, pkgsrc-2017Q2, pkgsrc-2017Q1-base, pkgsrc-2017Q1, pkgsrc-2016Q4-base, pkgsrc-2016Q4, pkgsrc-2016Q3-base, pkgsrc-2016Q3, pkgsrc-2016Q2-base, pkgsrc-2016Q2, pkgsrc-2016Q1-base, pkgsrc-2016Q1, pkgsrc-2015Q4-base, pkgsrc-2015Q4, pkgsrc-, HEAD
Changes since 1.1: +2 -1 lines
Diff to previous 1.1 (colored)

Add SHA512 digests for distfiles for math category

Problems found locating distfiles:
	Package dfftpack: missing distfile dfftpack-20001209.tar.gz
	Package eispack: missing distfile eispack-20001130.tar.gz
	Package fftpack: missing distfile fftpack-20001130.tar.gz
	Package linpack: missing distfile linpack-20010510.tar.gz
	Package minpack: missing distfile minpack-20001130.tar.gz
	Package odepack: missing distfile odepack-20001130.tar.gz
	Package py-networkx: missing distfile networkx-1.10.tar.gz
	Package py-sympy: missing distfile sympy-0.7.6.1.tar.gz
	Package quadpack: missing distfile quadpack-20001130.tar.gz

Otherwise, existing SHA1 digests verified and found to be the same on
the machine holding the existing distfiles (morden).  All existing
SHA1 digests retained for now as an audit trail.

Revision 1.1.1.1 / (download) - annotate - [select for diffs] (vendor branch), Wed Oct 29 17:06:40 2014 UTC (4 years, 9 months ago) by cheusov
Branch: TNF
CVS Tags: pkgsrc-base, pkgsrc-2015Q3-base, pkgsrc-2015Q3, pkgsrc-2015Q2-base, pkgsrc-2015Q2, pkgsrc-2015Q1-base, pkgsrc-2015Q1, pkgsrc-2014Q4-base, pkgsrc-2014Q4
Changes since 1.1: +0 -0 lines
Diff to previous 1.1 (colored)

LibShortText is an open source tool for short-text classification and
analysis. It can handle the classification of, for example, titles,
questions, sentences, and short messages. Main features of
LibShortText include
  * It is more efficient than general text-mining packages. On a
    typical computer, processing and training 10 million short texts
    takes only around half an hour.
  * The fast training and testing is built upon the linear classifier
  * LIBLINEAR
  * Default options often work well without tedious tuning.
  * An interactive tool for error analysis is included. Based on the
    property that each short text contains few words, LibShortText
    provides details in predicting each text.


Revision 1.1 / (download) - annotate - [select for diffs], Wed Oct 29 17:06:40 2014 UTC (4 years, 9 months ago) by cheusov
Branch: MAIN

Initial revision

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