nia
04f4eef997
*: Revbump packages that use Python at runtime without a PKGNAME prefix
2022-06-30 11:18:01 +00:00
nia
414fc7869d
math: Replace RMD160 checksums with BLAKE2s checksums
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All checksums have been double-checked against existing RMD160 and
SHA512 hashes
2021-10-26 10:55:21 +00:00
nia
3c576fbd23
math: Remove SHA1 hashes for distfiles
2021-10-07 14:27:43 +00:00
nia
f6dd9d2f87
Revbump packages with a runtime Python dep but no version prefix.
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For the Python 3.8 default switch.
2020-12-04 20:44:57 +00:00
rillig
79ae9cc434
math: align variable assignments
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pkglint -Wall -F --only aligned -r
Manual correction in R/Makefile.extension for the MASTER_SITES
continuation line.
2019-11-02 16:16:18 +00:00
maya
f34a8c24a3
PKGREVISION bump for anything using python without a PYPKGPREFIX.
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This is a semi-manual PKGREVISION bump.
2019-04-25 07:32:34 +00:00
wiz
8409e46c44
libshorttext: follow redirects
2017-11-15 22:13:24 +00:00
agc
286ea2536c
Add SHA512 digests for distfiles for math category
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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.
2015-11-03 23:33:26 +00:00
cheusov
16af52c1ec
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.
2014-10-29 17:06:40 +00:00
