Name: perl-Statistics-MaxEntropy
Summary: Maximum Entropy Modeling and Feature Induction
Version: 0.9
Release: 1.2
License: Artistic/GPL
Group: Applications/CPAN
URL: http://search.cpan.org/dist/Statistics-MaxEntropy/
Description: This module is an implementation of the Generalised and Improved
Iterative Scaling (GIS, IIS) algorithms and the Feature
Induction (FI) algorithm as defined in (Darroch and Ratcliff
1972) and (Della Pietra et al. 1997). The purpose of the scaling
algorithms is to find the maximum entropy distribution given a
set of events and (optionally) an initial distribution. Also a
set of candidate features may be specified; then the FI
algorithm may be applied to find and add the candidate
feature(s) that give the largest `gain' in terms of Kullback
Leibler divergence when it is added to the current set of
features.
The build was succesful. The following RPMs were generated:
The build was succesful. The following RPMs were generated:
The spec file used by the build system currently has the following contents:
# $Id$ # Authority: dries # Upstream: Hugo WL ter Doest <terdoest$cs,utwente,nl> %define perl_vendorlib %(eval "`%{__perl} -V:installvendorlib`"; echo $installvendorlib) %define perl_vendorarch %(eval "`%{__perl} -V:installvendorarch`"; echo $installvendorarch) %define real_name Statistics-MaxEntropy Summary: Maximum Entropy Modeling and Feature Induction Name: perl-Statistics-MaxEntropy Version: 0.9 Release: 1.2%{?dist} License: Artistic/GPL Group: Applications/CPAN URL: http://search.cpan.org/dist/Statistics-MaxEntropy/ Source: http://www.cpan.org/modules/by-module/Statistics/Statistics-MaxEntropy-%{version}.tar.gz BuildRoot: %{_tmppath}/%{name}-%{version}-%{release}-root BuildArch: noarch BuildRequires: perl BuildRequires: perl(ExtUtils::MakeMaker) %description This module is an implementation of the Generalised and Improved Iterative Scaling (GIS, IIS) algorithms and the Feature Induction (FI) algorithm as defined in (Darroch and Ratcliff 1972) and (Della Pietra et al. 1997). The purpose of the scaling algorithms is to find the maximum entropy distribution given a set of events and (optionally) an initial distribution. Also a set of candidate features may be specified; then the FI algorithm may be applied to find and add the candidate feature(s) that give the largest `gain' in terms of Kullback Leibler divergence when it is added to the current set of features. %prep %setup -n %{real_name}-%{version} %build %{__perl} Makefile.PL INSTALLDIRS="vendor" PREFIX="%{buildroot}%{_prefix}" %{__make} %{?_smp_mflags} %install %{__rm} -rf %{buildroot} %{__make} pure_install ### Clean up buildroot find %{buildroot} -name .packlist -exec %{__rm} {} \; %clean %{__rm} -rf %{buildroot} %files %defattr(-, root, root, 0755) %doc Changes README %doc %{_mandir}/man?/* %{_bindir}/ME.wrapper.pl %{perl_vendorlib}/Statistics %{perl_vendorlib}/auto/Statistics %changelog * Wed Mar 22 2006 Dries Verachtert <dries@ulyssis.org> - 0.9-1.2 - Rebuild for Fedora Core 5. * Sat Apr 9 2005 Dries Verachtert <dries@ulyssis.org> - 0.9-1 - Initial package.