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freebsd-ports/math/vowpal_wabbit/Makefile

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New port: vowpal_wabbit. The Vowpal Wabbit (VW) project is a fast out-of-core learning system sponsored by Microsoft Research and (previously) Yahoo! Research. There are two ways to have a fast learning algorithm: (a) start with a slow algorithm and speed it up, or (b) build an intrinsically fast learning algorithm. This project is about approach (b), and it's reached a state where it may be useful to others as a platform for research and experimentation. There are several optimization algorithms available with the baseline being sparse gradient descent (GD) on a loss function (several are available). WWW: https://github.com/JohnLangford/vowpal_wabbit/wiki
2015-01-15 14:00:58 +01:00
PORTNAME= vowpal_wabbit
Update to version 7.10.
2015-04-05 14:45:24 +02:00
PORTVERSION= 7.10
*/*: bump all direct Boost cunsumers
2023-04-27 19:54:50 +02:00
PORTREVISION= 21
New port: vowpal_wabbit. The Vowpal Wabbit (VW) project is a fast out-of-core learning system sponsored by Microsoft Research and (previously) Yahoo! Research. There are two ways to have a fast learning algorithm: (a) start with a slow algorithm and speed it up, or (b) build an intrinsically fast learning algorithm. This project is about approach (b), and it's reached a state where it may be useful to others as a platform for research and experimentation. There are several optimization algorithms available with the baseline being sparse gradient descent (GD) on a loss function (several are available). WWW: https://github.com/JohnLangford/vowpal_wabbit/wiki
2015-01-15 14:00:58 +01:00
CATEGORIES= math
MAINTAINER= demon@FreeBSD.org
COMMENT= Fast out-of-core learning system
Add WWW entries to port Makefiles It has been common practice to have one or more URLs at the end of the ports' pkg-descr files, one per line and prefixed with "WWW:". These URLs should point at a project website or other relevant resources. Access to these URLs required processing of the pkg-descr files, and they have often become stale over time. If more than one such URL was present in a pkg-descr file, only the first one was tarnsfered into the port INDEX, but for many ports only the last line did contain the port specific URL to further information. There have been several proposals to make a project URL available as a macro in the ports' Makefiles, over time. This commit implements such a proposal and moves one of the WWW: entries of each pkg-descr file into the respective port's Makefile. A heuristic attempts to identify the most relevant URL in case there is more than one WWW: entry in some pkg-descr file. URLs that are not moved into the Makefile are prefixed with "See also:" instead of "WWW:" in the pkg-descr files in order to preserve them. There are 1256 ports that had no WWW: entries in pkg-descr files. These ports will not be touched in this commit. The portlint port has been adjusted to expect a WWW entry in each port Makefile, and to flag any remaining "WWW:" lines in pkg-descr files as deprecated. Approved by: portmgr (tcberner)
2022-09-07 23:06:12 +02:00
WWW= https://github.com/JohnLangford/vowpal_wabbit/wiki
New port: vowpal_wabbit. The Vowpal Wabbit (VW) project is a fast out-of-core learning system sponsored by Microsoft Research and (previously) Yahoo! Research. There are two ways to have a fast learning algorithm: (a) start with a slow algorithm and speed it up, or (b) build an intrinsically fast learning algorithm. This project is about approach (b), and it's reached a state where it may be useful to others as a platform for research and experimentation. There are several optimization algorithms available with the baseline being sparse gradient descent (GD) on a loss function (several are available). WWW: https://github.com/JohnLangford/vowpal_wabbit/wiki
2015-01-15 14:00:58 +01:00
Remove ${PORTSDIR}/ from dependencies, categories m, n, o, and p. With hat: portmgr Sponsored by: Absolight
2016-04-01 16:16:16 +02:00
LIB_DEPENDS= libboost_thread.so:devel/boost-libs
New port: vowpal_wabbit. The Vowpal Wabbit (VW) project is a fast out-of-core learning system sponsored by Microsoft Research and (previously) Yahoo! Research. There are two ways to have a fast learning algorithm: (a) start with a slow algorithm and speed it up, or (b) build an intrinsically fast learning algorithm. This project is about approach (b), and it's reached a state where it may be useful to others as a platform for research and experimentation. There are several optimization algorithms available with the baseline being sparse gradient descent (GD) on a loss function (several are available). WWW: https://github.com/JohnLangford/vowpal_wabbit/wiki
2015-01-15 14:00:58 +01:00
Needs compiler with C++11 support.
2015-04-19 09:14:16 +02:00
USES= compiler:c++11-lang libtool
New port: vowpal_wabbit. The Vowpal Wabbit (VW) project is a fast out-of-core learning system sponsored by Microsoft Research and (previously) Yahoo! Research. There are two ways to have a fast learning algorithm: (a) start with a slow algorithm and speed it up, or (b) build an intrinsically fast learning algorithm. This project is about approach (b), and it's reached a state where it may be useful to others as a platform for research and experimentation. There are several optimization algorithms available with the baseline being sparse gradient descent (GD) on a loss function (several are available). WWW: https://github.com/JohnLangford/vowpal_wabbit/wiki
2015-01-15 14:00:58 +01:00
GNU_CONFIGURE= yes
USE_LDCONFIG= yes
Replace most occurences of github.com in MASTER_SITES with USE_GITHUB usage. With hat: portmgr Sponsored by: Absolight
2015-05-18 19:07:45 +02:00
USE_GITHUB= yes
GH_ACCOUNT= JohnLangford
New port: vowpal_wabbit. The Vowpal Wabbit (VW) project is a fast out-of-core learning system sponsored by Microsoft Research and (previously) Yahoo! Research. There are two ways to have a fast learning algorithm: (a) start with a slow algorithm and speed it up, or (b) build an intrinsically fast learning algorithm. This project is about approach (b), and it's reached a state where it may be useful to others as a platform for research and experimentation. There are several optimization algorithms available with the baseline being sparse gradient descent (GD) on a loss function (several are available). WWW: https://github.com/JohnLangford/vowpal_wabbit/wiki
2015-01-15 14:00:58 +01:00
CONFIGURE_ARGS= --enable-shared=no --with-boost=${LOCALBASE}
.include <bsd.port.mk>
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