Stephan Kulow
2011-04-14 07:40:16 +00:00
committed by Git OBS Bridge
commit 96235a67ab
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.osc

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version https://git-lfs.github.com/spec/v1
oid sha256:fd769448ec977a3626a135b02e0d5fcd41de2d9d14ade0632194d1ad801eeccd
size 7997

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-------------------------------------------------------------------
Thu Apr 14 07:40:09 UTC 2011 - coolo@opensuse.org
- initial package 0.04
* created by cpanspec 1.78.04

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#
# spec file for package perl-Algorithm-NaiveBayes (Version 0.04)
#
# Copyright (c) 2010 SUSE LINUX Products GmbH, Nuernberg, Germany.
#
# All modifications and additions to the file contributed by third parties
# remain the property of their copyright owners, unless otherwise agreed
# upon. The license for this file, and modifications and additions to the
# file, is the same license as for the pristine package itself (unless the
# license for the pristine package is not an Open Source License, in which
# case the license is the MIT License). An "Open Source License" is a
# license that conforms to the Open Source Definition (Version 1.9)
# published by the Open Source Initiative.
# Please submit bugfixes or comments via http://bugs.opensuse.org/
#
Name: perl-Algorithm-NaiveBayes
Version: 0.04
Release: 1
License: GPL+ or Artistic
%define cpan_name Algorithm-NaiveBayes
Summary: Bayesian prediction of categories
Url: http://search.cpan.org/dist/Algorithm-NaiveBayes/
Group: Development/Libraries/Perl
Source: http://www.cpan.org/authors/id/K/KW/KWILLIAMS/%{cpan_name}-%{version}.tar.gz
BuildArch: noarch
BuildRoot: %{_tmppath}/%{name}-%{version}-build
BuildRequires: perl
BuildRequires: perl(Module::Build)
BuildRequires: perl-macros
BuildRequires: perl(List::Util)
BuildRequires: perl(Module::Build)
Requires: perl(List::Util)
%{perl_requires}
%description
This module implements the classic "Naive Bayes" machine learning
algorithm. It is a well-studied probabilistic algorithm often used in
automatic text categorization. Compared to other algorithms (kNN, SVM,
Decision Trees), it's pretty fast and reasonably competitive in the quality
of its results.
A paper by Fabrizio Sebastiani provides a really good introduction to text
categorization: the
http://faure.iei.pi.cnr.it/~fabrizio/Publications/ACMCS02.pdf manpage
%prep
%setup -q -n %{cpan_name}-%{version}
%build
%{__perl} Build.PL installdirs=vendor
./Build build flags=%{?_smp_mflags}
%check
./Build test
%install
./Build install destdir=%{buildroot} create_packlist=0
%perl_gen_filelist
%clean
%{__rm} -rf %{buildroot}
%files -f %{name}.files
%defattr(-,root,root,755)
%doc %attr(644,-,-) Changes
%changelog