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freebsd-ports/science/py-mlpy/Makefile

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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 to libmpc version 1.0.1 which brings the following fixes: - Switched to automake 1.11.6, see CVE-2012-3386. - #14669: Fixed extraction of CC from gmp.h. - Fixed case of intermediate zero real or imaginary part in mpc_fma, found by hydra with GMP_CHECK_RANDOMIZE=1346362345. This is on top of the following changes from version 1.0 - Licence change towards LGPLv3+ for the code and GFDLv1.3+ (with no invariant sections) for the documentation. - 100% of all lines are covered by tests - Renamed functions . mpc_mul_2exp to mpc_mul_2ui . mpc_div_2exp to mpc_div_2ui - 0^0, which returned (NaN,NaN) previously, now returns (1,+0). - Removed compatibility with K&R compilers, which was untestable due to lack of such compilers. - New functions . mpc_log10 . mpc_mul_2si, mpc_div_2si - Speed-ups . mpc_fma - Bug fixes . mpc_div and mpc_norm now return a value indicating the effective rounding direction, as the other functions. . mpc_mul, mpc_sqr and mpc_norm now return correct results even if there are over- or underflows during the computation. . mpc_asin, mpc_proj, mpc_sqr: Wrong result when input variable has infinite part and equals output variable is corrected. . mpc_fr_sub: Wrong return value for imaginary part is corrected. Convert to the new LIB_DEPENDS standard and remove hard-coded .so versions from a couple of dependent ports. Bump PORTREVISIONS of all dependent ports. PR: 183141 Approved by: portmgr (bdrewery)
2013-10-26 02:52:33 +02:00
PORTREVISION= 3
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>
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