File perl-Algorithm-SVMLight.spec of Package perl-Algorithm-SVMLight

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File perl-Algorithm-SVMLight.spec of Package perl-Algorithm-SVMLight

#
# spec file for package perl-Algorithm-SVMLight
#
# Copyright (c) 2016 SUSE LINUX 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-SVMLight
Version:        0.09
Release:        0
%define cpan_name Algorithm-SVMLight
Summary:        Perl interface to SVMLight Machine-Learning Package
License:        Artistic-1.0 or GPL-1.0+
Group:          Development/Libraries/Perl
Url:            http://search.cpan.org/dist/Algorithm-SVMLight/
Source0:        http://www.cpan.org/authors/id/K/KW/KWILLIAMS/%{cpan_name}-%{version}.tar.gz
BuildRoot:      %{_tmppath}/%{name}-%{version}-build
BuildRequires:  perl
BuildRequires:  perl-macros
BuildRequires:  perl(Module::Build) >= 0.210000
%{perl_requires}

%description
This module implements a perl interface to Thorsten Joachims' SVMLight
package:

SVMLight is an implementation of Vapnik's Support Vector Machine
    [Vapnik, 1995] for the problem of pattern recognition, for the problem
    of regression, and for the problem of learning a ranking function. The
    optimization algorithms used in SVMlight are described in [Joachims,
    2002a ]. [Joachims, 1999a]. The algorithm has scalable memory
    requirements and can handle problems with many thousands of support
    vectors efficiently.

-- http://svmlight.joachims.org/

Support Vector Machines in general, and SVMLight specifically, represent
some of the best-performing Machine Learning approaches in domains such as
text categorization, image recognition, bioinformatics string processing,
and others.

For efficiency reasons, the underlying SVMLight engine indexes features by
integers, not strings. Since features are commonly thought of by name (e.g.
the words in a document, or mnemonic representations of engineered
features), we provide in 'Algorithm::SVMLight' a simple mechanism for
mapping back and forth between feature names (strings) and feature indices
(integers). If you want to use this mechanism, use the 'add_instance()' and
'predict()' methods. If not, use the 'add_instance_i()' (or
'read_instances()') and 'predict_i()' methods.

%prep
%setup -q -n %{cpan_name}-%{version}

%build
%{__perl} Build.PL installdirs=vendor optimize="%{optflags}"
./Build build flags=%{?_smp_mflags}

%check
./Build test

%install
./Build install destdir=%{buildroot} create_packlist=0
%perl_gen_filelist

%files -f %{name}.files
%defattr(-,root,root,755)
%doc Changes README SVMLight.patch

%changelog

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