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Date:      Tue, 25 Jul 2006 13:13:59 +0800 (CST)
From:      Cheng-Lung Sung <clsung@FreeBSD.org>
To:        FreeBSD-gnats-submit@FreeBSD.org
Subject:   ports/100798: [NEW PORT] science/pcp: Machine learning program for pattern classification
Message-ID:  <20060725051359.6CD5C7E976@FreeBSD.csie.nctu.edu.tw>
Resent-Message-ID: <200607250520.k6P5KFBr020178@freefall.freebsd.org>

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>Number:         100798
>Category:       ports
>Synopsis:       [NEW PORT] science/pcp: Machine learning program for pattern classification
>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:   Tue Jul 25 05:20:15 GMT 2006
>Closed-Date:
>Last-Modified:
>Originator:     Cheng-Lung Sung
>Release:        FreeBSD 6.1-STABLE i386
>Organization:
FreeBSD @ Taiwan
>Environment:
System: FreeBSD FreeBSD.csie.nctu.edu.tw 6.1-STABLE FreeBSD 6.1-STABLE #9: Thu May 11 14:31:45 CST 2006
>Description:
PCP (Pattern Classification Program) is an open-source 
machine learning program for supervised classification 
of patterns (vectors of measurements). 

PCP implements the following algorithms and methods:

* Fisher's linear discriminant
* dimensionality reduction using Singular Value Decomposition
* Principal Component Analysis
* feature subset selection
* Bayes error estimation
* parametric classifiers (linear and quadratic)
* least-squares (pseudo-inverse) linear discriminant
* k-Nearest Neighbor (k-NN)
* neural networks (Multi-Layer Perceptron (MLP))
* Support Vector Machine (SVM) algorithm
* SVM, MLP and k-NN model selection
* cross-validation
* bagging (committee) classification

WWW:	http://pcp.sourceforge.net/

Generated with FreeBSD Port Tools 0.77
>How-To-Repeat:
>Fix:

--- pcp-2.2.shar begins here ---
# 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:
#
#	pcp
#	pcp/pkg-descr
#	pcp/Makefile
#	pcp/pkg-plist
#	pcp/distinfo
#
echo c - pcp
mkdir -p pcp > /dev/null 2>&1
echo x - pcp/pkg-descr
sed 's/^X//' >pcp/pkg-descr << 'END-of-pcp/pkg-descr'
XPCP (Pattern Classification Program) is an open-source 
Xmachine learning program for supervised classification 
Xof patterns (vectors of measurements). 
X
XPCP implements the following algorithms and methods:
X
X* Fisher's linear discriminant
X* dimensionality reduction using Singular Value Decomposition
X* Principal Component Analysis
X* feature subset selection
X* Bayes error estimation
X* parametric classifiers (linear and quadratic)
X* least-squares (pseudo-inverse) linear discriminant
X* k-Nearest Neighbor (k-NN)
X* neural networks (Multi-Layer Perceptron (MLP))
X* Support Vector Machine (SVM) algorithm
X* SVM, MLP and k-NN model selection
X* cross-validation
X* bagging (committee) classification
X
XWWW:	http://pcp.sourceforge.net/
END-of-pcp/pkg-descr
echo x - pcp/Makefile
sed 's/^X//' >pcp/Makefile << 'END-of-pcp/Makefile'
X# New ports collection makefile for:	pcp
X# Date created:		2006-07-25
X# Whom:			Cheng-Lung Sung <clsung@FreeBSD.org>
X#
X# $FreeBSD$
X#
X
XPORTNAME=	pcp
XPORTVERSION=	2.2
XCATEGORIES=	science math
XMASTER_SITES=	${MASTER_SITE_SOURCEFORGE}
XMASTER_SITE_SUBDIR=	${PORTNAME}
X
XMAINTAINER=	clsung@FreeBSD.org
XCOMMENT=	Machine learning program for pattern classification
X
XGNU_CONFIGURE=	yes
X
X.include <bsd.port.mk>
END-of-pcp/Makefile
echo x - pcp/pkg-plist
sed 's/^X//' >pcp/pkg-plist << 'END-of-pcp/pkg-plist'
X@comment $FreeBSD$
Xbin/pcp
END-of-pcp/pkg-plist
echo x - pcp/distinfo
sed 's/^X//' >pcp/distinfo << 'END-of-pcp/distinfo'
XMD5 (pcp-2.2.tar.gz) = 5be1ace0dbcd07dab47302807fa32c39
XSHA256 (pcp-2.2.tar.gz) = 928f2b7ea40de8ad5568f381a77b9379907a4966a56352cf5945a8664258d95a
XSIZE (pcp-2.2.tar.gz) = 2710238
END-of-pcp/distinfo
exit
--- pcp-2.2.shar ends here ---

>Release-Note:
>Audit-Trail:
>Unformatted:



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