Up to [cvs.NetBSD.org] / pkgsrc / math / libshorttext
Request diff between arbitrary revisions
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
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 126.96.36.199 / (download) - annotate - [select for diffs] (vendor branch), Wed Oct 29 17:06:40 2014 UTC (4 years, 9 months ago) by cheusov
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
This form allows you to request diff's between any two revisions of a file. You may select a symbolic revision name using the selection box or you may type in a numeric name using the type-in text box.