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- Update to 1.1

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- LIBLR is renamed to LIBLINEAR
- Update pkg-descr
- Drop deprecated TARGETDIR variable
This commit is contained in:
Rong-En Fan 2007-07-28 07:09:40 +00:00
parent abc16cfaf6
commit a649a6476d
Notes: svn2git 2021-03-31 03:12:20 +00:00 
svn path=/head/; revision=196440
3 changed files with 19 additions and 17 deletions
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19
science/liblinear/Makefile
View File

@ -5,20 +5,21 @@
# $FreeBSD$
#
PORTNAME= liblr
PORTVERSION= 1.00
PORTNAME= liblinear
PORTVERSION= 1.10
CATEGORIES= science math
MASTER_SITES= http://www.csie.ntu.edu.tw/~cjlin/liblinear/oldfiles/
MASTER_SITES= http://www.csie.ntu.edu.tw/~cjlin/liblinear/ \
http://www.csie.ntu.edu.tw/~cjlin/liblinear/oldfiles/
DISTNAME= ${PORTNAME}-${PORTVERSION:C/0$//}
MAINTAINER= rafan@FreeBSD.org
COMMENT= A library for Large Regularized Logistic Regression
COMMENT= A library for Large Linear Classification
OPTIONS= OCFLAGS "Use optimized CFLAGS" On
USE_ZIP= yes
MAKE_ENV= CC="${CC}" CXXC="${CXX}"
MAKE_ENV= CC="${CC}" CXX="${CXX}"
TXT_DOCS= COPYRIGHT README
@ -26,18 +27,18 @@ TXT_DOCS= COPYRIGHT README
PORTDOCS= ${TXT_DOCS}
.endif
PLIST_FILES= bin/lr-train bin/lr-predict
PLIST_FILES= bin/train bin/predict
.include <bsd.port.pre.mk>
.if !defined(WITHOUT_OCFLAGS)
# same as LIBIR itself
# same as LIBLINEAR itself
CFLAGS= -Wall -O3
.endif
do-install:
${INSTALL_PROGRAM} ${WRKSRC}/lr-train ${TARGETDIR}/bin/
${INSTALL_PROGRAM} ${WRKSRC}/lr-predict ${TARGETDIR}/bin/
${INSTALL_PROGRAM} ${WRKSRC}/train ${PREFIX}/bin
${INSTALL_PROGRAM} ${WRKSRC}/predict ${PREFIX}/bin
post-install:
.if !defined(NOPORTDOCS)

6
science/liblinear/distinfo
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@ -1,3 +1,3 @@
MD5 (liblr-1.0.zip) = 6407b44f889c1465df341d5242f30480
SHA256 (liblr-1.0.zip) = 1435e9dd96f9723872dc624d0ea3a12b0b6ab5d7240f41765c3fd69677bcbed3
SIZE (liblr-1.0.zip) = 153199
MD5 (liblinear-1.1.zip) = 72d650983942c19b53e0ee683da4da64
SHA256 (liblinear-1.1.zip) = 6a2e221befad0f16b32971144fb243373cf4614689aeefd8c83765039b3e0e1d
SIZE (liblinear-1.1.zip) = 183990

11
science/liblinear/pkg-descr
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@ -1,16 +1,17 @@
LIBLR is a linear classifier for data with millions of instances and
features. It implement a trust region Newton method in
LIBLINEAR is a linear classifier for data with millions of instances and
features. It supports both logistic regression and L2-loss linear SVM using a
trust region Newton method in
C.-J. Lin, R. C. Weng, and S. S. Keerthi. Trust region Newton method
for large-scale regularized logistic regression. Technical report, 2007.
A short version appears in ICML 2007.
Main features of LIBLR include
Main features of LIBLINEAR include
Same data format as LIBSVM and similar usage
One-vs-the rest multi-class classification
Cross validation for model selection
Probability estimates
Probability estimates (logistic regression only)
Weights for unbalanced data
WWW: http://www.csie.ntu.edu.tw/~cjlin/liblr/
WWW: http://www.csie.ntu.edu.tw/~cjlin/liblinear/