From 5708c93251625133598739f42ed106aac83bf18a Mon Sep 17 00:00:00 2001
From: mcarrier <mcarrier@636b058d-ea47-450e-bf9e-a15bfbe3eedb>
Date: Fri, 29 Dec 2017 23:39:32 +0000
Subject: git-svn-id:
 svn+ssh://scm.gforge.inria.fr/svnroot/gudhi/branches/kernels@3107
 636b058d-ea47-450e-bf9e-a15bfbe3eedb

Former-commit-id: d0f6606d7afde132a4d86203eeff80e97f35adce
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 src/Kernels/doc/Intro_kernels.h | 61 +++++++++++++++++++++++++++++++++++++++++
 1 file changed, 61 insertions(+)
 create mode 100644 src/Kernels/doc/Intro_kernels.h

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diff --git a/src/Kernels/doc/Intro_kernels.h b/src/Kernels/doc/Intro_kernels.h
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+/*    This file is part of the Gudhi Library. The Gudhi library
+ *    (Geometric Understanding in Higher Dimensions) is a generic C++
+ *    library for computational topology.
+ *
+ *    Author(s):       Mathieu Carriere
+ *
+ *    Copyright (C) 2017  INRIA
+ *
+ *    This program is free software: you can redistribute it and/or modify
+ *    it under the terms of the GNU General Public License as published by
+ *    the Free Software Foundation, either version 3 of the License, or
+ *    (at your option) any later version.
+ *
+ *    This program is distributed in the hope that it will be useful,
+ *    but WITHOUT ANY WARRANTY; without even the implied warranty of
+ *    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ *    GNU General Public License for more details.
+ *
+ *    You should have received a copy of the GNU General Public License
+ *    along with this program.  If not, see <http://www.gnu.org/licenses/>.
+ */
+
+#ifndef DOC_KERNEL_INTRO_KERNEL_H_
+#define DOC_KERNEL_INTRO_KERNEL_H_
+
+namespace Gudhi {
+
+namespace kernel {
+
+/**  \defgroup kernel Kernels
+ * 
+ * \author    Mathieu Carrière
+ * 
+ * @{
+ * 
+ * Kernels are generalized scalar products. They take the form of functions whose evaluations on pairs of persistence diagrams are equal
+ * to the scalar products of the images of the diagrams under some feature map into a (generally unknown and infinite dimensional)
+ * Hilbert space. Kernels are
+ * very useful to handle any type of data for algorithms that require at least a Hilbert structure, such as Principal Component Analysis
+ * or Support Vector Machines. In this package, we implement three kernels for persistence diagrams: the Persistence Scale Space kernel,
+ * the Persistence Weighted Gaussian kernel and the Sliced Wasserstein kernel.
+ *
+ *
+ * When launching:
+ *
+ * \code $> ./BasicEx
+ * \endcode
+ *
+ * the program output is:
+ *
+ *
+ * \copyright GNU General Public License v3.                         
+ * \verbatim  Contact: gudhi-users@lists.gforge.inria.fr \endverbatim
+ */
+/** @} */  // end defgroup kernel
+
+}  // namespace kernel
+
+}  // namespace Gudhi
+
+#endif  // DOC_KERNEL_INTRO_KERNEL_H_
-- 
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