initial package

OBS-URL: https://build.opensuse.org/package/show/devel:languages:perl/perl-Algorithm-KMeans?expand=0&rev=1

.gitattributes

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+## Default LFS
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.bsp filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.gem filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.jar filter=lfs diff=lfs merge=lfs -text
+*.lz filter=lfs diff=lfs merge=lfs -text
+*.lzma filter=lfs diff=lfs merge=lfs -text
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+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text

.gitignore

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+.osc

Algorithm-KMeans-2.05.tar.gz

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a1d07298d499b9b4e2cd93f1bbd9289814ab461e69ecc35c796e495fe9b5a9a8
+size 57703

perl-Algorithm-KMeans.changes

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+-------------------------------------------------------------------
+Thu Jan 14 07:00:22 UTC 2016 - coolo@suse.com
+
+- initial package 2.05
+ * created by cpanspec 1.78.08
+

perl-Algorithm-KMeans.spec

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+#
+# spec file for package perl-Algorithm-KMeans
+#
+# 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-KMeans
+Version:        2.05
+Release:        0
+%define cpan_name Algorithm-KMeans
+Summary:        For Clustering Multidimensional Data
+License:        GPL-1.0+ or Artistic-1.0
+Group:          Development/Libraries/Perl
+Url:            http://search.cpan.org/dist/Algorithm-KMeans/
+Source0:        http://www.cpan.org/authors/id/A/AV/AVIKAK/%{cpan_name}-%{version}.tar.gz
+BuildArch:      noarch
+BuildRoot:      %{_tmppath}/%{name}-%{version}-build
+BuildRequires:  perl
+BuildRequires:  perl-macros
+BuildRequires:  perl(Graphics::GnuplotIF) >= 1.6
+BuildRequires:  perl(Math::GSL) >= 0.32
+BuildRequires:  perl(Math::Random) >= 0.71
+Requires:       perl(Graphics::GnuplotIF) >= 1.6
+Requires:       perl(Math::GSL) >= 0.32
+Requires:       perl(Math::Random) >= 0.71
+%{perl_requires}
+
+%description
+Clustering with K-Means takes place iteratively and involves two steps: 1)
+assignment of data samples to clusters on the basis of how far the data
+samples are from the cluster centers; and 2) Recalculation of the cluster
+centers (and cluster covariances if you are using the Mahalanobis distance
+metric for clustering).
+
+Obviously, before the two-step approach can proceed, we need to initialize
+the the cluster centers. How this initialization is carried out is
+important. The module gives you two very different ways for carrying out
+this initialization. One option, called the 'smart' option, consists of
+subjecting the data to principal components analysis to discover the
+direction of maximum variance in the data space. The data points are then
+projected on to this direction and a histogram constructed from the
+projections. Centers of the smoothed histogram are used to seed the
+clustering operation. The other option is to choose the cluster centers
+purely randomly. You get the first option if you set 'cluster_seeding' to
+'smart' in the constructor, and you get the second option if you set it to
+'random'.
+
+How to specify the number of clusters, 'K', is one of the most vexing
+issues in any approach to clustering. In some case, we can set 'K' on the
+basis of prior knowledge. But, more often than not, no such prior knowledge
+is available. When the programmer does not explicitly specify a value for
+'K', the approach taken in the current implementation is to try all
+possible values between 2 and some largest possible value that makes
+statistical sense. We then choose that value for 'K' which yields the best
+value for the QoC (Quality of Clustering) metric. It is generally believed
+that the largest value for 'K' should not exceed 'sqrt(N/2)' where 'N' is
+the number of data samples to be clustered.
+
+What to use for the QoC metric is obviously a critical issue unto itself.
+In the current implementation, the value of QoC is the ratio of the average
+radius of the clusters and the average distance between the cluster
+centers.
+
+Every iterative algorithm requires a stopping criterion. The criterion
+implemented here is that we stop iterations when there is no re-assignment
+of the data points during the assignment step.
+
+Ordinarily, the output produced by a K-Means clusterer will correspond to a
+local minimum for the QoC values, as opposed to a global minimum. The
+current implementation protects against that when the module constructor is
+called with the 'random' option for 'cluster_seeding' by trying different
+randomly selected initial cluster centers and then selecting the one that
+gives the best overall QoC value.
+
+A K-Means clusterer will generally produce good results if the overlap
+between the clusters is minimal and if each cluster exhibits variability
+that is uniform in all directions. When the data variability is different
+along the different directions in the data space, the results you obtain
+with a K-Means clusterer may be improved by first normalizing the data
+appropriately, as can be done in this module when you set the
+'do_variance_normalization' option in the constructor. However, as pointed
+out elsewhere in this documentation, such normalization may actually
+decrease the performance of the clusterer if the overall data variability
+along any dimension is more a result of separation between the means than a
+consequence of intra-cluster variability.
+
+%prep
+%setup -q -n %{cpan_name}-%{version}
+find . -type f ! -name \*.pl -print0 | xargs -0 chmod 644
+
+%build
+%{__perl} Makefile.PL INSTALLDIRS=vendor
+%{__make} %{?_smp_mflags}
+
+%check
+%{__make} test
+
+%install
+%perl_make_install
+%perl_process_packlist
+%perl_gen_filelist
+
+%files -f %{name}.files
+%defattr(-,root,root,755)
+%doc examples README
+
+%changelog