Date: Sun, 14 Jun 2020 14:22:35 +0000 (UTC) From: Sunpoet Po-Chuan Hsieh <sunpoet@FreeBSD.org> To: ports-committers@freebsd.org, svn-ports-all@freebsd.org, svn-ports-head@freebsd.org Subject: svn commit: r538750 - head/science/liblinear Message-ID: <202006141422.05EEMZsI093776@repo.freebsd.org>
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Author: sunpoet Date: Sun Jun 14 14:22:35 2020 New Revision: 538750 URL: https://svnweb.freebsd.org/changeset/ports/538750 Log: Update to 2.30 - Update MASTER_SITES - Add my LOCAL to MASTER_SITES - Remove DOCS and OPTIMIZED_CFLAGS options - Update pkg-descr - Update WWW - Take maintainership Changes: https://github.com/cjlin1/liblinear/commits/master Modified: head/science/liblinear/Makefile head/science/liblinear/distinfo head/science/liblinear/pkg-descr Modified: head/science/liblinear/Makefile ============================================================================== --- head/science/liblinear/Makefile Sun Jun 14 14:22:30 2020 (r538749) +++ head/science/liblinear/Makefile Sun Jun 14 14:22:35 2020 (r538750) @@ -2,37 +2,20 @@ # $FreeBSD$ PORTNAME= liblinear -PORTVERSION= 2.11 +PORTVERSION= 2.30 CATEGORIES= science math -MASTER_SITES= http://www.csie.ntu.edu.tw/~cjlin/liblinear/ \ - http://www.csie.ntu.edu.tw/~cjlin/liblinear/oldfiles/ +MASTER_SITES= https://www.csie.ntu.edu.tw/~cjlin/liblinear/ \ + https://www.csie.ntu.edu.tw/~cjlin/liblinear/oldfiles/ -MAINTAINER= ports@FreeBSD.org +MAINTAINER= sunpoet@FreeBSD.org COMMENT= Library for Large Linear Classification LICENSE= BSD3CLAUSE LICENSE_FILE= ${WRKSRC}/COPYRIGHT -USES= zip +PLIST_FILES= bin/predict bin/train -PORTDOCS= COPYRIGHT README - -PLIST_FILES= bin/train bin/predict - -OPTIONS_DEFINE= OPTIMIZED_CFLAGS DOCS -OPTIONS_DEFAULT=OPTIMIZED_CFLAGS - -# same as LIBLINEAR itself -OPTIMIZED_CFLAGS_CFLAGS= -Wall -O3 - do-install: - ${INSTALL_PROGRAM} ${WRKSRC}/train ${STAGEDIR}${PREFIX}/bin - ${INSTALL_PROGRAM} ${WRKSRC}/predict ${STAGEDIR}${PREFIX}/bin - -do-install-DOCS-on: - @${MKDIR} ${STAGEDIR}${DOCSDIR} - for f in ${PORTDOCS}; do \ - ${INSTALL_DATA} ${WRKSRC}/$$f ${STAGEDIR}${DOCSDIR}; \ - done + ${INSTALL_PROGRAM} ${WRKSRC}/predict ${WRKSRC}/train ${STAGEDIR}${PREFIX}/bin .include <bsd.port.mk> Modified: head/science/liblinear/distinfo ============================================================================== --- head/science/liblinear/distinfo Sun Jun 14 14:22:30 2020 (r538749) +++ head/science/liblinear/distinfo Sun Jun 14 14:22:35 2020 (r538750) @@ -1,3 +1,3 @@ -TIMESTAMP = 1497777405 -SHA256 (liblinear-2.11.zip) = f1f263f4b22530f07d298a1c4812d675ed879e4f7d3801abc9a637f62a505ce0 -SIZE (liblinear-2.11.zip) = 522832 +TIMESTAMP = 1591281350 +SHA256 (liblinear-2.30.tar.gz) = 881c7039c6cf93119c781fb56263de91617b3eca8c3951f2c19a3797de95c6ac +SIZE (liblinear-2.30.tar.gz) = 526468 Modified: head/science/liblinear/pkg-descr ============================================================================== --- head/science/liblinear/pkg-descr Sun Jun 14 14:22:30 2020 (r538749) +++ head/science/liblinear/pkg-descr Sun Jun 14 14:22:35 2020 (r538750) @@ -1,14 +1,20 @@ LIBLINEAR is a linear classifier for data with millions of instances and -features. It supports L2-regularized classifiers (L2-loss linear SVM, -L1-loss linear SVM, and logistic regression), L1-regularized classifiers -(L2-loss linear SVM and logistic regression). +features. It supports: +- L2-regularized classifiers +- L2-loss linear SVM, L1-loss linear SVM, and logistic regression (LR) +- L1-regularized classifiers (after version 1.4) +- L2-loss linear SVM and logistic regression (LR) +- L2-regularized support vector regression (after version 1.9) +- L2-loss linear SVR and L1-loss linear SVR. -Main features of LIBLINEAR include - -- Same data format as LIBSVM and similar usage -- One-vs-the rest and Crammer & Singer multi-class classification -- Cross validation for model selection +Main features of LIBLINEAR include: +- Same data format as LIBSVM, our general-purpose SVM solver, and also similar + usage +- Multi-class classification: 1) one-vs-the rest, 2) Crammer & Singer +- Cross validation for model evaulation +- Automatic parameter selection - Probability estimates (logistic regression only) - Weights for unbalanced data +- MATLAB/Octave, Java, Python, Ruby interfaces -WWW: http://www.csie.ntu.edu.tw/~cjlin/liblinear/ +WWW: https://www.csie.ntu.edu.tw/~cjlin/liblinear/
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