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Commit Graph

11 Commits

Author SHA1 Message Date
Eitan Adler
c59a3834c4 At the moment 1385 ports use BUILD_DEPENDS= ${RUN_DEPENDS} and 450
ports use BUILD_DEPENDS:= ${RUN_DEPENDS}. This patch fixes ports that are
currently broken. This is a temporary measure until we organically stop using
:= or someone(s) spend a lot of time changing all the ports over.

Explicit duplication > := > = and this just moves ports one step to the left

Approved by:	portmgr
2012-01-21 17:40:15 +00:00
Dmitry Marakasov
6f6fbe4bdf - Add LDFLAGS to CONFIGURE_ENV and MAKE_ENV (as it was done with LDFLAGS)
- Fix all ports that add {CPP,LD}FLAGS to *_ENV to modify flags instead

PR:		157936
Submitted by:	myself
Exp-runs by:	pav
Approved by:	pav
2011-09-23 22:26:39 +00:00
Brendan Fabeny
4fd0a4b1c1 Update math/gsl to 1.15, and adjust PORTREVISION
and LIB_DEPENDS of dependent ports
2011-05-09 16:42:27 +00:00
Wen Heping
4fcf5e51c6 - Update to 2.2.2 2010-12-23 08:22:39 +00:00
Wen Heping
b37ffd07f3 - Fix run error because of lacking cpow() function in FreeBSD.
- Remove MD5

PR:		ports/153299
Submitted by:	Kawaguti Ginga <ginga-freebsd@ginganet.org>
2010-12-21 01:22:30 +00:00
Wen Heping
3c8946ba94 - Update to 2.2.1 2010-10-28 12:09:35 +00:00
Wen Heping
6905d4a0d8 - Bump PORTREVISION to chase the update of math/gsl 2010-03-29 01:12:27 +00:00
Max Brazhnikov
7cb3294da7 Chase math/gsl update,
bump PORTREVISION
2009-12-01 20:33:45 +00:00
Wen Heping
39490db5ad - Update to 2.1.0
- Update my mail address to FreeBSD
2009-12-01 13:05:06 +00:00
Martin Wilke
9735e6c7c7 - Update to 2.0.3
PR:		135210
Submitted by:	Wen Heping <wenheping@gmail.com> (maintainer)
2009-06-05 21:59:04 +00:00
Martin Wilke
64b23ee9d0 Machine Learning PY (mlpy) is a high-performance Python package for
predictive modeling. It makes extensive use of numpy (http://scipy.org)
to provide fast N-dimensional array manipulation and easy integration of
C code. mlpy provides high level procedures that support, with few lines
of code, the design of rich Data Analysis Protocols (DAPs) for
preprocessing, clustering, predictive classification and feature
selection. Methods are available for feature weighting and ranking, data
resampling, error evaluation and experiment landscaping.The package
includes tools to measure stability in sets of ranked feature lists.

WWW:	http://mlpy.fbk.eu/

PR:		ports/133932
Submitted by:	Wen Heping <wenheping at gmail.com>
2009-04-23 17:02:20 +00:00