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freebsd-ports/science/py-mlpy/Makefile

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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 13:02:20 -04:00
# New ports collection makefile for: py-mlpy
# Date created: 18 April, 2009
# Whom: Wen Heping <wenheping@gmail.com>
#
# $FreeBSD$
#
PORTNAME= mlpy
- Update to 2.2.1
2010-10-28 08:09:35 -04:00
PORTVERSION= 2.2.1
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 13:02:20 -04:00
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 08:05:06 -05:00
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 13:02:20 -04:00
COMMENT= High performance Python package for predictive modeling
BUILD_DEPENDS= ${PYTHON_SITELIBDIR}/numpy:${PORTSDIR}/math/py-numpy
RUN_DEPENDS= ${BUILD_DEPENDS}
- Bump PORTREVISION to chase the update of math/gsl
2010-03-28 21:12:27 -04:00
LIB_DEPENDS= gsl.15:${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 13:02:20 -04:00
CFLAGS+= -I${LOCALBASE}/include
LDFLAGS+= -L${LOCALBASE}/lib
MAKE_ENV+= CFLAGS="${CFLAGS}" LDFLAGS="${LDFLAGS}"
USE_PYTHON= yes
USE_PYDISTUTILS= yes
PYDISTUTILS_PKGNAME= MLPY
.include <bsd.port.mk>
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