proj/gentoo: Initial commit

This commit represents a new era for Gentoo:
Storing the gentoo-x86 tree in Git, as converted from CVS.

This commit is the start of the NEW history.
Any historical data is intended to be grafted onto this point.

Creation process:
1. Take final CVS checkout snapshot
2. Remove ALL ChangeLog* files
3. Transform all Manifests to thin
4. Remove empty Manifests
5. Convert all stale $Header$/$Id$ CVS keywords to non-expanded Git $Id$
5.1. Do not touch files with -kb/-ko keyword flags.

Signed-off-by: Robin H. Johnson <robbat2@gentoo.org>
X-Thanks: Alec Warner <antarus@gentoo.org> - did the GSoC 2006 migration tests
X-Thanks: Robin H. Johnson <robbat2@gentoo.org> - infra guy, herding this project
X-Thanks: Nguyen Thai Ngoc Duy <pclouds@gentoo.org> - Former Gentoo developer, wrote Git features for the migration
X-Thanks: Brian Harring <ferringb@gentoo.org> - wrote much python to improve cvs2svn
X-Thanks: Rich Freeman <rich0@gentoo.org> - validation scripts
X-Thanks: Patrick Lauer <patrick@gentoo.org> - Gentoo dev, running new 2014 work in migration
X-Thanks: Michał Górny <mgorny@gentoo.org> - scripts, QA, nagging
X-Thanks: All of other Gentoo developers - many ideas and lots of paint on the bikeshed

1 files changed, 37 insertions, 0 deletions

diff --git a/sci-libs/shogun/metadata.xml b/sci-libs/shogun/metadata.xml
new file mode 100644
index 000000000000..d0c30a0c09fb
--- /dev/null
+++ b/sci-libs/shogun/metadata.xml
@@ -0,0 +1,37 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<!DOCTYPE pkgmetadata SYSTEM "http://www.gentoo.org/dtd/metadata.dtd">
+<pkgmetadata>
+  <herd>sci</herd>
+  <longdescription lang="en">
+  SHOGUN - is a new machine learning toolbox with focus on large
+  scale kernel methods and especially on Support Vector Machines
+  (SVM) with focus to bioinformatics. It provides a generic SVM
+  object interfacing to several different SVM implementations. Each
+  of the SVMs can be combined with a variety of the many kernels
+  implemented. It can deal with weighted linear combination of a
+  number of sub-kernels, each of which not necessarily working on the
+  same domain, where  an optimal sub-kernel weighting can be learned
+  using Multiple Kernel Learning.  Apart from SVM 2-class
+  classification and regression problems, a number of linear methods
+  like Linear Discriminant Analysis (LDA), Linear Programming Machine
+  (LPM), (Kernel) Perceptrons and also algorithms to train hidden
+  markov models are implemented. The input feature-objects can be
+  dense, sparse or strings and of type int/short/double/char and can
+  be converted into different feature types. Chains of preprocessors
+  (e.g. substracting the mean) can be attached to each feature object
+  allowing for on-the-fly pre-processing.
+</longdescription>
+  <use>
+    <flag name="arpack">Enable support for <pkg>sci-libs/arpack</pkg></flag>
+    <flag name="eigen">Enable support for matrix package <pkg>dev-cpp/eigen</pkg></flag>
+    <flag name="glpk">Enable support for <pkg>sci-mathematics/glpk</pkg></flag>
+    <flag name="json">Enable support for parsing JSON format</flag>
+    <flag name="lpsolve">Enable support for Linear Programming Solver</flag>
+    <flag name="nlopt">Enable support non linear optimization from
+  <pkg>sci-libs/nlopt</pkg></flag>
+    <flag name="R">Enable support for <pkg>dev-lang/R</pkg></flag>
+    <flag name="octave">Enable support for <pkg>sci-mathematics/octave</pkg></flag>
+    <flag name="snappy">Enable support for snappy compression</flag>
+    <flag name="superlu">Enable support for <pkg>sci-libs/superlu</pkg></flag>
+  </use>
+</pkgmetadata>