/www/py-webware-component/

Change cmake default behaviour to outsource. 2018-12-25T20:25:39+00:00 tcberner tcberner@FreeBSD.org 2018-12-25T20:25:39+00:00 49081780dbf920e309ccc96ab737f85e04f46c66 Ports that build out of source now simply can use "USES=cmake" instead of "USES=cmake:outsource". Ports that fail to build out of source now need to specify "USES=cmake:insource". I tried to only set insource where explictely needed. PR: 232038 Exp-run by: antoine

Ports that build out of source now simply can use "USES=cmake"
instead of "USES=cmake:outsource". Ports that fail to build
out of source now need to specify "USES=cmake:insource".

I tried to only set insource where explictely needed.

PR:		232038
Exp-run by:	antoine

Bump PORTREVISION for ports depending on the canonical version of GCC 2018-12-12T01:35:33+00:00 gerald gerald@FreeBSD.org 2018-12-12T01:35:33+00:00 97e78d860783dbeec5f72d4f8581d78b52f954e3 defined via Mk/bsd.default-versions.mk which has moved from GCC 7.4 t GCC 8.2 under most circumstances. This includes ports - with USE_GCC=yes or USE_GCC=any, - with USES=fortran, - using Mk/bsd.octave.mk which in turn features USES=fortran, and - with USES=compiler specifying openmp, nestedfct, c11, c++0x, c++11-lang, c++11-lib, c++14-lang, c++17-lang, or gcc-c++11-lib plus, as a double check, everything INDEX-11 showed depending on lang/gcc7. PR: 231590

defined via Mk/bsd.default-versions.mk which has moved from GCC 7.4 t
GCC 8.2 under most circumstances.

This includes ports
 - with USE_GCC=yes or USE_GCC=any,
 - with USES=fortran,
 - using Mk/bsd.octave.mk which in turn features USES=fortran, and
 - with USES=compiler specifying openmp, nestedfct, c11, c++0x, c++11-lang,
   c++11-lib, c++14-lang, c++17-lang, or gcc-c++11-lib
plus, as a double check, everything INDEX-11 showed depending on lang/gcc7.

PR:		231590

devel/boost-*: update to 1.69.0 2018-12-12T00:15:49+00:00 jbeich jbeich@FreeBSD.org 2018-12-12T00:15:49+00:00 9450dbc10a4ffaf3052b6ac70c9dca088a5b123c Changes: http://www.boost.org/users/history/version_1_69_0.html PR: 232525 Exp-run by: antoine Differential Revision: https://reviews.freebsd.org/D17645

Changes:	http://www.boost.org/users/history/version_1_69_0.html
PR:		232525
Exp-run by:	antoine
Differential Revision:	https://reviews.freebsd.org/D17645

- Update math/armadillo to 9.200.4 2018-11-11T13:32:31+00:00 danilo danilo@FreeBSD.org 2018-11-11T13:32:31+00:00 3904a802a068d41ed4f5e0fc47d48e4c4725fa91

- Update to 9.100.4 2018-08-15T20:48:56+00:00 danilo danilo@FreeBSD.org 2018-08-15T20:48:56+00:00 1849a4585a70efda08eff1526d0dc252f0feb69e

devel/boost-*: update to 1.68.0 2018-08-09T06:58:30+00:00 jbeich jbeich@FreeBSD.org 2018-08-09T06:58:30+00:00 28df167cc772d301930a6f0487e841a7b7295193 - Switch to C++14 for libboost_system to support C++14 consumers Changes: http://www.boost.org/users/history/version_1_68_0.html PR: 229569 Exp-run by: antoine Differential Revision: https://reviews.freebsd.org/D16165

- Switch to C++14 for libboost_system to support C++14 consumers

Changes:	http://www.boost.org/users/history/version_1_68_0.html
PR:		229569
Exp-run by:	antoine
Differential Revision:	https://reviews.freebsd.org/D16165

Update to 3.0.1 2018-05-19T16:34:16+00:00 krion krion@FreeBSD.org 2018-05-19T16:34:16+00:00 d74db187c201fc3149a34da6f8bbd4ce833928d5 PR: 228318 Submitted by: maintainer

PR:		228318
Submitted by:	maintainer

New port: math/mlpack 2018-05-06T14:35:51+00:00 pi pi@FreeBSD.org 2018-05-06T14:35:51+00:00 5491f6d3a8279ee09d22514ed0216c033106c583 mlpack is a fast, flexible machine learning library, written in C++, that aims to provide fast, extensible implementations of cutting-edge machine learning algorithms. mlpack provides these algorithms as simple command-line programs, Python bindings, and C++ classes which can then be integrated into larger-scale machine learning solutions. WWW: http://www.mlpack.org/ PR: 228012 Submitted by: robert.ayrapetyan@gmail.com

mlpack is a fast, flexible machine learning library, written in
C++, that aims to provide fast, extensible implementations of
cutting-edge machine learning algorithms. mlpack provides these
algorithms as simple command-line programs, Python bindings, and
C++ classes which can then be integrated into larger-scale machine
learning solutions.

WWW: http://www.mlpack.org/

PR:		228012
Submitted by:	robert.ayrapetyan@gmail.com