Date: Wed, 22 Apr 2009 23:02:23 GMT From: Wen Heping <wenheping@gmail.com> To: freebsd-gnats-submit@FreeBSD.org Subject: ports/133932: [NEW PORT]science/py-mlpy:High performance Python package for predictive modeling Message-ID: <200904222302.n3MN2NX2061385@www.freebsd.org> Resent-Message-ID: <200904222310.n3MNA16o085388@freefall.freebsd.org>
next in thread | raw e-mail | index | archive | help
>Number: 133932 >Category: ports >Synopsis: [NEW PORT]science/py-mlpy:High performance Python package for predictive modeling >Confidential: no >Severity: non-critical >Priority: low >Responsible: freebsd-ports-bugs >State: open >Quarter: >Keywords: >Date-Required: >Class: change-request >Submitter-Id: current-users >Arrival-Date: Wed Apr 22 23:10:00 UTC 2009 >Closed-Date: >Last-Modified: >Originator: Wen Heping >Release: FreeBSD 8.0-CURRENT >Organization: ChangAn Middle School >Environment: FreeBSD fb8.wenjing.com 8.0-CURRENT FreeBSD 8.0-CURRENT #0: Sun Mar 22 22:12:06 CST 2009 root@fb8.wenjing.com:/usr/obj/usr/src/sys/GENERIC i386 >Description: 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/ >How-To-Repeat: >Fix: Patch attached with submission follows: # This is a shell archive. Save it in a file, remove anything before # this line, and then unpack it by entering "sh file". Note, it may # create directories; files and directories will be owned by you and # have default permissions. # # This archive contains: # # py-mlpy # py-mlpy/pkg-plist # py-mlpy/pkg-descr # py-mlpy/distinfo # py-mlpy/Makefile # echo c - py-mlpy mkdir -p py-mlpy > /dev/null 2>&1 echo x - py-mlpy/pkg-plist sed 's/^X//' >py-mlpy/pkg-plist << '622c4faa7b3c4dd5b7f4aef20790b5d1' Xbin/irelief-sigma Xbin/srda-landscape Xbin/svm-landscape Xbin/fda-landscape Xbin/knn-landscape Xbin/pda-landscape Xbin/dlda-landscape Xbin/borda Xbin/canberra Xbin/canberraq X%%PYTHON_SITELIBDIR%%/mlpy/__init__.py X%%PYTHON_SITELIBDIR%%/mlpy/__init__.pyc X%%PYTHON_SITELIBDIR%%/mlpy/__init__.pyo X%%PYTHON_SITELIBDIR%%/mlpy/_bmetrics.py X%%PYTHON_SITELIBDIR%%/mlpy/_bmetrics.pyc X%%PYTHON_SITELIBDIR%%/mlpy/_bmetrics.pyo X%%PYTHON_SITELIBDIR%%/mlpy/_borda.py X%%PYTHON_SITELIBDIR%%/mlpy/_borda.pyc X%%PYTHON_SITELIBDIR%%/mlpy/_borda.pyo X%%PYTHON_SITELIBDIR%%/mlpy/_canberra.py X%%PYTHON_SITELIBDIR%%/mlpy/_canberra.pyc X%%PYTHON_SITELIBDIR%%/mlpy/_canberra.pyo X%%PYTHON_SITELIBDIR%%/mlpy/_ci.py X%%PYTHON_SITELIBDIR%%/mlpy/_ci.pyc X%%PYTHON_SITELIBDIR%%/mlpy/_ci.pyo X%%PYTHON_SITELIBDIR%%/mlpy/_cwt.py X%%PYTHON_SITELIBDIR%%/mlpy/_cwt.pyc X%%PYTHON_SITELIBDIR%%/mlpy/_cwt.pyo X%%PYTHON_SITELIBDIR%%/mlpy/_data.py X%%PYTHON_SITELIBDIR%%/mlpy/_data.pyc X%%PYTHON_SITELIBDIR%%/mlpy/_data.pyo X%%PYTHON_SITELIBDIR%%/mlpy/_dlda.py X%%PYTHON_SITELIBDIR%%/mlpy/_dlda.pyc X%%PYTHON_SITELIBDIR%%/mlpy/_dlda.pyo X%%PYTHON_SITELIBDIR%%/mlpy/_dwt.so X%%PYTHON_SITELIBDIR%%/mlpy/_dwtfs.py X%%PYTHON_SITELIBDIR%%/mlpy/_dwtfs.pyc X%%PYTHON_SITELIBDIR%%/mlpy/_dwtfs.pyo X%%PYTHON_SITELIBDIR%%/mlpy/_fda.py X%%PYTHON_SITELIBDIR%%/mlpy/_fda.pyc X%%PYTHON_SITELIBDIR%%/mlpy/_fda.pyo X%%PYTHON_SITELIBDIR%%/mlpy/_hcluster.py X%%PYTHON_SITELIBDIR%%/mlpy/_hcluster.pyc X%%PYTHON_SITELIBDIR%%/mlpy/_hcluster.pyo X%%PYTHON_SITELIBDIR%%/mlpy/_irelief.py X%%PYTHON_SITELIBDIR%%/mlpy/_irelief.pyc X%%PYTHON_SITELIBDIR%%/mlpy/_irelief.pyo X%%PYTHON_SITELIBDIR%%/mlpy/_knn.py X%%PYTHON_SITELIBDIR%%/mlpy/_knn.pyc X%%PYTHON_SITELIBDIR%%/mlpy/_knn.pyo X%%PYTHON_SITELIBDIR%%/mlpy/_pda.py X%%PYTHON_SITELIBDIR%%/mlpy/_pda.pyc X%%PYTHON_SITELIBDIR%%/mlpy/_pda.pyo X%%PYTHON_SITELIBDIR%%/mlpy/_ranking.py X%%PYTHON_SITELIBDIR%%/mlpy/_ranking.pyc X%%PYTHON_SITELIBDIR%%/mlpy/_ranking.pyo X%%PYTHON_SITELIBDIR%%/mlpy/_resampling.py X%%PYTHON_SITELIBDIR%%/mlpy/_resampling.pyc X%%PYTHON_SITELIBDIR%%/mlpy/_resampling.pyo X%%PYTHON_SITELIBDIR%%/mlpy/_srda.py X%%PYTHON_SITELIBDIR%%/mlpy/_srda.pyc X%%PYTHON_SITELIBDIR%%/mlpy/_srda.pyo X%%PYTHON_SITELIBDIR%%/mlpy/_svm.py X%%PYTHON_SITELIBDIR%%/mlpy/_svm.pyc X%%PYTHON_SITELIBDIR%%/mlpy/_svm.pyo X%%PYTHON_SITELIBDIR%%/mlpy/_wavelet.py X%%PYTHON_SITELIBDIR%%/mlpy/_wavelet.pyc X%%PYTHON_SITELIBDIR%%/mlpy/_wavelet.pyo X%%PYTHON_SITELIBDIR%%/mlpy/canberracore.so X%%PYTHON_SITELIBDIR%%/mlpy/cwb.so X%%PYTHON_SITELIBDIR%%/mlpy/gslpy.so X%%PYTHON_SITELIBDIR%%/mlpy/hccore.so X%%PYTHON_SITELIBDIR%%/mlpy/nncore.so X%%PYTHON_SITELIBDIR%%/mlpy/progressbar.py X%%PYTHON_SITELIBDIR%%/mlpy/progressbar.pyc X%%PYTHON_SITELIBDIR%%/mlpy/progressbar.pyo X%%PYTHON_SITELIBDIR%%/mlpy/svmcore.so X%%PYTHON_SITELIBDIR%%/mlpy/version.py X%%PYTHON_SITELIBDIR%%/mlpy/version.pyc X%%PYTHON_SITELIBDIR%%/mlpy/version.pyo X@dirrm %%PYTHON_SITELIBDIR%%/mlpy 622c4faa7b3c4dd5b7f4aef20790b5d1 echo x - py-mlpy/pkg-descr sed 's/^X//' >py-mlpy/pkg-descr << '6a3da4c2f97db5504206d492a246456f' XMachine Learning PY (mlpy) is a high-performance Python package for Xpredictive modeling. It makes extensive use of numpy (http://scipy.org) Xto provide fast N-dimensional array manipulation and easy integration of XC code. mlpy provides high level procedures that support, with few lines Xof code, the design of rich Data Analysis Protocols (DAPs) for Xpreprocessing, clustering, predictive classification and feature Xselection. Methods are available for feature weighting and ranking, data Xresampling, error evaluation and experiment landscaping.The package Xincludes tools to measure stability in sets of ranked feature lists. X XWWW: http://mlpy.fbk.eu/ 6a3da4c2f97db5504206d492a246456f echo x - py-mlpy/distinfo sed 's/^X//' >py-mlpy/distinfo << '97d00e5e4cd02f1cf497afb2b88e9001' XMD5 (MLPY-2.0.0.tar.gz) = 2f2b33f97849cba7d469926a7724e770 XSHA256 (MLPY-2.0.0.tar.gz) = f58fd590df0c22310cda4e1770a3ea4a195c552c8e33db01c168d2d10bcebf74 XSIZE (MLPY-2.0.0.tar.gz) = 118326 97d00e5e4cd02f1cf497afb2b88e9001 echo x - py-mlpy/Makefile sed 's/^X//' >py-mlpy/Makefile << '98353251af9745646ac4c359688bef48' X# New ports collection makefile for: py-mlpy X# Date created: 18 April, 2009 X# Whom: Wen Heping <wenheping@gmail.com> X# X# $FreeBSD$ X# X XPORTNAME= mlpy XPORTVERSION= 2.0.0 XCATEGORIES= science python XMASTER_SITES= https://mlpy.fbk.eu/download/src/ XPKGNAMEPREFIX= ${PYTHON_PKGNAMEPREFIX} XDISTNAME= MLPY-${PORTVERSION} X XMAINTAINER= wenheping@gmail.com XCOMMENT= High performance Python package for predictive modeling X XBUILD_DEPENDS= ${PYTHON_SITELIBDIR}/numpy:${PORTSDIR}/math/py-numpy XRUN_DEPENDS= ${BUILD_DEPENDS} XLIB_DEPENDS= gsl.13:${PORTSDIR}/math/gsl X XCFLAGS+= -I${LOCALBASE}/include XLDFLAGS+= -L${LOCALBASE}/lib XMAKE_ENV+= CFLAGS="${CFLAGS}" LDFLAGS="${LDFLAGS}" XUSE_PYTHON= yes XUSE_PYDISTUTILS= yes XPYDISTUTILS_PKGNAME= MLPY X X.include <bsd.port.mk> 98353251af9745646ac4c359688bef48 exit >Release-Note: >Audit-Trail: >Unformatted:
Want to link to this message? Use this URL: <https://mail-archive.FreeBSD.org/cgi/mid.cgi?200904222302.n3MN2NX2061385>