[DB] Importing KNNL library
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emeric
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/*
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* Copyright (c) 2006, Seweryn Habdank-Wojewodzki
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* Copyright (c) 2006, Janusz Rybarski
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*
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* All rights reserved.
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*
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* Redistribution and use in source and binary forms,
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* with or without modification, are permitted provided
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* that the following conditions are met:
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*
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* Redistributions of source code must retain the above
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* copyright notice, this list of conditions and the
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* following disclaimer.
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*
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* Redistributions in binary form must reproduce the
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* above copyright notice, this list of conditions
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* and the following disclaimer in the documentation
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* and/or other materials provided with the distribution.
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*
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* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS
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* AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED
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* WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
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* WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
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* A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL
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* THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY
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* DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
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* CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
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* PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF
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* USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)
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* HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,
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* WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
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* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY
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* WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED
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* OF THE POSSIBILITY OF SUCH DAMAGE.
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*/
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/*
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* e-mail: habdank AT gmail DOT com
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* e-mail: janusz.rybarski AT gmail DOT com
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*
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* File created: Wed 03 May 2006 13:18:41 CEST
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* Last modified: Wed 08 Aug 2007 18:22:48 CEST
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*/
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#ifndef WTM_TOPOLOGY_HPP_INCLUDED
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#define WTM_TOPOLOGY_HPP_INCLUDED
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#include <cmath>
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#include "operators.hpp"
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/**
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* \file wtm_topology.hpp
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* \brief File contains template classes for setting up topology in the network.
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* \ingroup neural_net
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*/
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namespace neural_net
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{
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/**
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* \addtogroup neural_net
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*/
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/*\@{*/
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/**
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* \class Basic_topology
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* \brief Basic class for topologies.
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* \param Result_type type of result of the topology as a functor
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* that calculates didtance between two points
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* in particular case two neurons.
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* \param Index_type is a type is index that is used in neural network.
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*/
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template
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<
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typename Result_type,
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typename Index_type
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>
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class Basic_topology
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{
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public:
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typedef Result_type result_type;
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typedef Index_type value_type;
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};
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/**
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* \class City_topology
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* \brief Topology for neural network that calculates distance between two neurons.
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* \param Index_type is a type of index.
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* \f[
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* d (n,m) = |n_1-m_1| + |n_2-m_2|
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* \f]
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*/
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template < typename Index_type >
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class City_topology
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: public Basic_topology < Index_type, Index_type >
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{
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public:
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/**
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* Function claculates distance.
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* \param index_1_1 is first index of first neuron.
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* \param index_1_2 is second index of first neuron.
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* \param index_2_1 is first index of second neuron.
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* \param index_2_2 is second index of second neuron.
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* \f[
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* d (n,m) = |n_1-m_1| + |n_2-m_2|
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* \f]
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* where: n is first neuron, m is second neuron.
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*/
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inline Index_type operator()
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(
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Index_type const & index_1_1,
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Index_type const & index_1_2,
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Index_type const & index_2_1,
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Index_type const & index_2_2
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) const
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{
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return
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(
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::operators::abs ( index_1_1 - index_2_1 )
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+ ::operators::abs ( index_1_2 - index_2_2 )
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);
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}
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};
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/**
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* \class Max_topology
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* \brief Topology for neural network that calculates distance between two neurons.
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* \param Index_type is a type of index.
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* \f[
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* d (n,m) = \max ( |n_1-m_1|, |n_2-m_2| )
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* \f]
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*/
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template < typename Index_type >
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class Max_topology
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: public Basic_topology < Index_type, Index_type >
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{
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public:
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/**
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* Function claculates distance.
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* \param index_1_1 is first index of first neuron.
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* \param index_1_2 is second index of first neuron.
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* \param index_2_1 is first index of second neuron.
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* \param index_2_2 is second index of second neuron.
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* \f[
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* d (n,m) = \max ( |n_1-m_1|, |n_2-m_2| )
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* \f]
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* where: n is first neuron, m is second neuron.
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*/
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inline Index_type operator()
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(
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Index_type const & index_1_1,
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Index_type const & index_1_2,
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Index_type const & index_2_1,
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Index_type const & index_2_2
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) const
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{
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return
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(
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::std::max
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(
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::operators::abs ( index_1_1 - index_2_1 ),
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::operators::abs ( index_1_2 - index_2_2 )
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)
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);
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}
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};
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/**
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* \class Hexagonal_topology
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* \brief Topology for neural network that calculates distance between two neurons.
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* \param Index_type is a type of index.
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* \f{eqnarray*}
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* h_1 (x) & = & \frac{ x_1 + 1 }{2} + x_2\\
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* h_2 (x) & = & \frac{h_{off}}{2} + x_2 - x_1 / 2;\\
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* t_1 & = & \max ( h_1 (n), h_1 (m) ) - \min ( h_1 (n), h_1 (m) )\\
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* t_2 & = & \max ( h_2 (n), h_2 (m) ) - \min ( h_2 (n), h_2 (m) )\\
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* d (n,m) & = & \left\{
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* \begin{array}{ll}
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* \max (|t_1|,|t_2|) & if \; sign \; of \; t_1 \; and \; t_2 \; is \; the \; same\\
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* |t_1|+|t_2| & otherwise
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* \end{array} \right.
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* \f}
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*/
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template < typename Index_type >
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class Hexagonal_topology
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: public Basic_topology < Index_type, Index_type >
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{
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public:
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/**
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* Constructor.
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* \param hex_offset_ is an offset of the hexagonal topology.
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* This value have to be not less than number of rows in neuron
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* container counted from 0.
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*/
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inline explicit Hexagonal_topology ( Index_type const & hex_offset_ )
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: hex_offset ( hex_offset_ )
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{}
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/** Copy constructor. */
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template < typename Index_type_2 >
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inline Hexagonal_topology ( Hexagonal_topology < Index_type_2 > const & hex_topology )
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: Basic_topology < Index_type_2, Index_type_2 >(),
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hex_offset ( hex_topology.hex_offset )
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{}
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/**
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* Function calculates distance.
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* \param index_1_1 is first index of first neuron.
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* \param index_1_2 is second index of first neuron.
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* \param index_2_1 is first index of second neuron.
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* \param index_2_2 is second index of second neuron.
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* \f{eqnarray*}
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* h_1 (x) & = & \frac{ x_1 + 1 }{2} + x_2\\
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* h_2 (x) & = & \frac{h_{off}}{2} + x_2 - x_1 / 2;\\
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* t_1 & = & \max ( h_1 (n), h_1 (m) ) - \min ( h_1 (n), h_1 (m) )\\
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* t_2 & = & \max ( h_2 (n), h_2 (m) ) - \min ( h_2 (n), h_2 (m) )\\
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* d (n,m) & = & \left\{
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* \begin{array}{ll}
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* \max (|t_1|,|t_2|) & if \; sign \; of \; t_1 \; and \; t_2 \; is \; the \; same\\
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* |t_1|+|t_2| & otherwise
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* \end{array} \right.
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* \f}
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* where: n is firs neuron, m is second.
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*/
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Index_type operator()
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(
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Index_type const & index_1_1,
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Index_type const & index_1_2,
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Index_type const & index_2_1,
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Index_type const & index_2_2
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) const
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{
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Index_type hex_index_1_1;
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Index_type hex_index_1_2;
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Index_type hex_index_2_1;
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Index_type hex_index_2_2;
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Index_type tmp_hex_index_1;
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Index_type tmp_hex_index_2;
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// recalculate indexes to the better indexes used in hexagonal space
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hex_index_1_1 = ( index_1_1 + 1 ) / 2 + index_1_2;
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hex_index_1_2 = ( hex_offset / 2 + index_1_2 ) - index_1_1 / 2;
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hex_index_2_1 = ( index_2_1 + 1 ) / 2 + index_2_2;
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hex_index_2_2 = ( hex_offset / 2 + index_2_2 ) - index_2_1 / 2;
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// calculate difference between points in hexagonal space
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tmp_hex_index_1 = ::std::max ( hex_index_1_1, hex_index_2_1 )
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- ::std::min ( hex_index_1_1, hex_index_2_1 );
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tmp_hex_index_2 = ::std::max ( hex_index_1_2, hex_index_2_2 )
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- ::std::min ( hex_index_1_2, hex_index_2_2 );
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// here we have special algebra to calculate distance,
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// bacause of special basis in this space:
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// ( 1, 1 ); ( -1, -1 ) have distance 1 the same as
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// ( -1, 0 ); ( 0, -1 ); ( 1, 0 ); ( 0, 1 ).
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if ( tmp_hex_index_1 == 0 && tmp_hex_index_2 == 0 )
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{
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return static_cast < Index_type > ( 0 );
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}
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// check if values have the same direction if yes it means that we have to use
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// reasoning based on assumption that ( -1, -1 ) and ( 1, 1 ) have distance 1.
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if ( ( ( hex_index_1_1 > hex_index_2_1 ) && ( hex_index_1_2 > hex_index_2_2 ) )
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|| ( ( hex_index_1_1 < hex_index_2_1 ) && ( hex_index_1_2 < hex_index_2_2 ) )
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)
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{
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return ::std::max ( tmp_hex_index_1, tmp_hex_index_2 );
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}
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else
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{
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return ::operators::abs ( tmp_hex_index_1 ) + ::operators::abs ( tmp_hex_index_2 );
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}
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return static_cast < Index_type > ( 0 );
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}
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protected:
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Index_type hex_offset;
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};
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/*\@}*/
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} // namespace neural_net
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#endif // WTM_TOPOLOGY_HPP_INCLUDED
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