kpriv/freebsd-ports
1
0
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity
freebsd-ports/science/py-mlpy/Makefile

28 lines
720 B
Makefile
Raw Normal View History

Add NO_STAGE all over the place in preparation for the staging support (cat: science)
2013-09-21 00:48:30 +02:00
# Created by: Wen Heping <wenheping@gmail.com>
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 19:02:20 +02:00
# $FreeBSD$
PORTNAME= mlpy
- Update to 2.2.2
2010-12-23 09:22:39 +01:00
PORTVERSION= 2.2.2
Update the default version of GCC used in the Ports Collection from GCC 4.6.4 to GCC 4.7.3. This entails updating the lang/gcc port as well as changing the default in Mk/bsd.default-versions.mk. Part II, Bump PORTREVISIONs. PR: 182136 Supported by: Christoph Moench-Tegeder <cmt@burggraben.net> (fixing many ports) Tested by: bdrewery (two -exp runs)
2014-03-10 21:55:20 +01:00
PORTREVISION= 4
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 19:02:20 +02:00
CATEGORIES= science python
MASTER_SITES= https://mlpy.fbk.eu/download/src/
PKGNAMEPREFIX= ${PYTHON_PKGNAMEPREFIX}
DISTNAME= MLPY-${PORTVERSION}
- Update to 2.1.0 - Update my mail address to FreeBSD
2009-12-01 14:05:06 +01:00
MAINTAINER= wen@FreeBSD.org
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 19:02:20 +02:00
COMMENT= High performance Python package for predictive modeling
- Update to 2.2.2
2010-12-23 09:22:39 +01:00
BUILD_DEPENDS= ${PYTHON_SITELIBDIR}/numpy:${PORTSDIR}/math/py-numpy \
cython>0:${PORTSDIR}/lang/cython
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 18:40:15 +01:00
RUN_DEPENDS:= ${BUILD_DEPENDS}
Update math/gsl to 1.16 and adjust some dependent ports
2013-08-26 18:06:37 +02:00
LIB_DEPENDS= gsl:${PORTSDIR}/math/gsl
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 19:02:20 +02:00
- Update to 2.2.2
2010-12-23 09:22:39 +01:00
CFLAGS+= -I${LOCALBASE}/include -I${PYTHON_SITELIBDIR}/numpy/core
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 19:02:20 +02:00
LDFLAGS+= -L${LOCALBASE}/lib
USE_PYTHON= yes
USE_PYDISTUTILS= yes
PYDISTUTILS_PKGNAME= MLPY
Add NO_STAGE all over the place in preparation for the staging support (cat: science)
2013-09-21 00:48:30 +02:00
NO_STAGE= yes
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 19:02:20 +02:00
.include <bsd.port.mk>
Reference in a new issue Copy permalink