[DB] Importing KNNL library
This commit is contained in:
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: Sun 07 May 2006 13:51:04 CEST
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* Last modified: Wed 08 Aug 2007 18:21:59 CEST
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*/
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#ifndef GENERALIZED_TRAINING_WEIGHT_HPP_INCLUDED
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#define GENERALIZED_TRAINING_WEIGHT_HPP_INCLUDED
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#include "operators.hpp"
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#include "training_functional.hpp"
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/**
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* \file generalized_training_weight.hpp
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* \brief File contains template class Basic_generalized_training_weight and some other classes derived form that one.
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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_generalized_training_weight
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* \brief Template class for the generalized training functions.
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* \param Value_type is a type of values.
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* \param Iteration_type is a type of interation counter.
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* \param Network_function_type is a type of function that will return proper value
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* based on network topology.
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* \param Space_funtion_type is a type of function that will return proper value
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* based on space topology.
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* \param Network_topology is a type of function that computes distances between
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* neurons based on network topology.
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* \param Space_topology is a type of function that computes distance between
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* value and weight in proper topology.
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* \param Index_type is a type of index in the neural network container.
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*/
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template
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<
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typename Value_type,
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typename Iteration_type,
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typename Network_function_type,
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typename Space_function_type,
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typename Network_topology,
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typename Space_topology,
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typename Index_type
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>
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struct Basic_generalized_training_weight
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{
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typedef Network_function_type network_function_type;
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typedef Space_function_type space_function_type;
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typedef Value_type value_type;
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typedef Iteration_type iteration_type;
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typedef Index_type index_type;
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typedef Network_topology network_topology;
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typedef Space_topology space_topology;
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};
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/**
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* \class Classic_training_weight
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* \brief Template class for the generalize training functions that calculates
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* generalized weight classical way.
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* \param Value_type is a type of values.
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* \param Iteration_type is a type of interation counter.
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* \param Network_function_type is a type of function that will return proper value
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* based on network topology.
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* \param Space_funtion_type is a type of function that will return proper value
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* based on space topology.
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* \param Network_topology is a type of function that computes distances between
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* neurons based on network topology.
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* \param Space_topology is a type of function that computes distance between
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* value and weight in proper topology.
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* \param Index_type is a type of index in the neural network container.
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* \f[
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* y=n_f (n_t (c_1,c_2,v_1,v_2)) \cdot s_f (s_t (x,w))
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* \f]
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*/
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template
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<
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typename Value_type,
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typename Iteration_type,
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typename Network_function_type,
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typename Space_function_type,
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typename Network_topology,
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typename Space_topology,
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typename Index_type
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>
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class Classic_training_weight
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: public Basic_generalized_training_weight
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<
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Value_type,
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Iteration_type,
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Network_function_type,
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Space_function_type,
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Network_topology,
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Space_topology,
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Index_type
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>
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{
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public:
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/** Functor computes weight based on the result from network topology. */
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Network_function_type network_function;
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/** Functor computes weight based on the result from space topology. */
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Space_function_type space_function;
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/** Functor computes generalized distance in network. */
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Network_topology network_topology;
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/** Functor computes generalized distance in data space. */
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Space_topology space_topology;
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/**
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* Function computes generalized weight for training proces.
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* This weight is not the same as weights in neural network.
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* \param weight is a weight from neural network.
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* \param value is a input value (value that trains network).
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* \param iteration is a number of training steps could be
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* number of training data sample.
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* \param c_1 is a row number (position) in the network of the central neuron.
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* \param c_2 is a column number (position) in the network of the central neuron.
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* \param v_1 is a row number (position) in the network of the trained neuron.
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* \param v_2 is a column number (position) in the network of the trained neuron.
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* \f[
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* y=n_f (n_t (c_1,c_2,v_1,v_2)) \cdot s_f (s_t (x,w))
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* \f]
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* where x is value and w is neuron weight.
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*/
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typename Space_function_type::value_type operator()
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(
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Value_type const & weight,
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Value_type const & value,
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Iteration_type const & //iteration
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,
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Index_type const & c_1,
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Index_type const & c_2,
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Index_type const & v_1,
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Index_type const & v_2
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)
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{
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// calculate result
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return
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(
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(network_function) ( (network_topology) ( c_1, c_2, v_1, v_2 ) )
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* (space_function) ( (space_topology) ( value, weight ) )
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);
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}
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/**
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* Constructor.
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* \param n_f is a network functor.
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* \param s_f is a data space functor.
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* \param n_t is a network topology functor.
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* \param s_t is a space topology functor.
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*/
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Classic_training_weight
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(
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Network_function_type const & n_f,
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Space_function_type const & s_f,
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Network_topology const & n_t,
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Space_topology const & s_t
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)
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: Basic_generalized_training_weight
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<
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Value_type,
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Iteration_type,
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Network_function_type,
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Space_function_type,
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Network_topology,
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Space_topology,
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Index_type
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>(),
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network_function ( n_f ),
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space_function ( s_f ),
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network_topology ( n_t ),
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space_topology ( s_t )
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{}
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/** Copy constructor */
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template
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<
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typename Value_type_2,
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typename Iteration_type_2,
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typename Network_function_type_2,
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typename Space_function_type_2,
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typename Network_topology_2,
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typename Space_topology_2,
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typename Index_type_2
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>
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Classic_training_weight
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(
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const Classic_training_weight
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<
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Value_type_2,
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Iteration_type_2,
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Network_function_type_2,
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Space_function_type_2,
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Network_topology_2,
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Space_topology_2,
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Index_type_2
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>
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& classic_training_weight
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)
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: Basic_generalized_training_weight
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<
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Value_type_2,
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Iteration_type_2,
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Network_function_type_2,
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Space_function_type_2,
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Network_topology_2,
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Space_topology_2,
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Index_type_2
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>(),
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network_function ( classic_training_weight.network_function ),
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space_function ( classic_training_weight.space_function ),
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network_topology ( classic_training_weight.network_topology ),
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space_topology ( classic_training_weight.space_topology )
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{}
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};
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/**
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* \class Experimental_training_weight
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* \brief Template class for the generalize training functions that calculates
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* generalizeg weight in experimental way.
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* \param Value_type is a type of values.
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* \param Iteration_type is a type of interation counter.
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* \param Network_function_type is a type of function that will return proper value
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* based on network topology.
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* \param Space_funtion_type is a type of function that will return proper value
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* based on space topology.
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* \param Network_topology is a type of function that computes distances between
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* neurons based on network topology.
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* \param Space_topology is a type of function that computes distance between
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* value and weight in proper topology.
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* \param Index_type is a type of index in the neural network container.
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* \param Parameters_type is a type of the parameters for experimenntal training.
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* \param n_power is a power q_N
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* \param s_power is a power q_S
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* \f[
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* y= ( p_1 \cdot n_f (n_t (c_1,c_2,v_1,v_2)) - p_0 )^q_N \cdot s_f (s_t (x,w))^q_S
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* \f]
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*/
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template
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<
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typename Value_type,
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typename Iteration_type,
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typename Network_function_type,
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typename Space_function_type,
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typename Network_topology,
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typename Space_topology,
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typename Index_type,
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typename Parameter_type,
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::boost::int32_t n_power = 1,
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::boost::int32_t s_power = 1
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>
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class Experimental_training_weight
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: public Basic_generalized_training_weight
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<
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Value_type,
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Iteration_type,
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Network_function_type,
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Space_function_type,
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Network_topology,
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Space_topology,
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Index_type
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>
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{
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public:
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//::boost::int32_t const s_power;
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//::boost::int32_t const n_power;
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/** Scaling parameter. */
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Parameter_type parameter_1;
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/** Shifting parameter. */
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Parameter_type parameter_0;
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/** Functor computes weight based on the result from network topology. */
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Network_function_type network_function;
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/** Functor computes weight based on the result from space topology. */
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Space_function_type space_function;
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/** Functor computes generalized distance in network. */
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Network_topology network_topology;
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/** Functor computes generalized distance in data space. */
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Space_topology space_topology;
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/**
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* Constructor.
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* \param n_f is a network functor.
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* \param s_f is a data space functor.
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* \param n_t is a network topology functor.
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* \param s_t is a space topology functor.
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* \param parameter_0_ is a scailing parameter.
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* \param parameter_1_ is a shifting parameter.
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*/
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Experimental_training_weight
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(
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Network_function_type const & n_f,
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Space_function_type const & s_f,
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Network_topology const & n_t,
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Space_topology const & s_t,
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Parameter_type const & parameter_0_,
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Parameter_type const & parameter_1_//,
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// ::boost::int32_t const s_power_ = 1,
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// ::boost::int32_t const n_power_ = 1
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)
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: //s_power ( s_power_ ),
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//n_power ( n_power_ ),
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parameter_1 ( parameter_1_),
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parameter_0 ( parameter_0_),
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network_function ( n_f ),
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space_function ( s_f ),
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network_topology ( n_t ),
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space_topology ( s_t )
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{}
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/** Copy constructor. */
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template
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<
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typename Value_type_2,
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typename Iteration_type_2,
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typename Network_function_type_2,
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typename Space_function_type_2,
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typename Network_topology_2,
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typename Space_topology_2,
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typename Index_type_2,
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typename Parameter_type_2
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>
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Experimental_training_weight
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(
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const Experimental_training_weight
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<
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Value_type_2,
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Iteration_type_2,
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Network_function_type_2,
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Space_function_type_2,
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Network_topology_2,
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Space_topology_2,
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Index_type_2,
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Parameter_type_2
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>
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& experimental_training_weight
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)
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: Basic_generalized_training_weight
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<
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Value_type_2,
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Iteration_type_2,
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Network_function_type_2,
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Space_function_type_2,
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Network_topology_2,
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Space_topology_2,
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Index_type_2
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>(),
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parameter_1 ( experimental_training_weight.parameter_1_),
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parameter_0 ( experimental_training_weight.parameter_0_),
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network_function ( experimental_training_weight.n_f ),
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space_function ( experimental_training_weight.s_f ),
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network_topology ( experimental_training_weight.n_t ),
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space_topology ( experimental_training_weight.s_t )
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{}
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/**
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* Function computes generalized weight for training proces.
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* This weight is not the same as weights in neural network.
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* \param weight is a weight from neural network.
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* \param value is a input value (value that trains network).
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* \param iteration is a number of training steps could be
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* number of training data sample.
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* \param c_1 is a row number (position) in the network of the central neuron.
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* \param c_2 is a column number (position) in the network of the central neuron.
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* \param v_1 is a row number (position) in the network of the trained neuron.
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* \param v_2 is a column number (position) in the network of the trained neuron.
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* \f[
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* y= ( p_1 \cdot n_f (n_t (c_1,c_2,v_1,v_2)) - p_0 ) \cdot s_f (s_t (x,w))
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* \f]
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* where x is value, w is neuron weight, \f$p_1\f$ is scaling parameter, \f$p_0\f$ is shifting parameter.
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*/
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typename Space_function_type::value_type operator()
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(
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Value_type & weight,
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Value_type const & value,
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Iteration_type const & //iteration
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,
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Index_type const & c_1,
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Index_type const & c_2,
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Index_type const & v_1,
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Index_type const & v_2
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) const
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{
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//::operators::power < typename Space_function_type::value_type, ::boost::int32_t > power_v;
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// calculate result
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return
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(
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// power_v( parameter_1 * (network_function) ( (network_topology) ( c_1, c_2, v_1, v_2 ) ) - parameter_0, n_power )
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// * power_v( (space_function) ( (space_topology) ( value, weight ) ), s_power )
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::operators::static_power<typename Space_function_type::value_type,n_power>( parameter_1 * (network_function) ( (network_topology) ( c_1, c_2, v_1, v_2 ) ) - parameter_0 )
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* ::operators::static_power<typename Space_function_type::value_type,s_power>( (space_function) ( (space_topology) ( value, weight ) ) )
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);
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}
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};
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/*\@}*/
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} // namespace neural_net
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#endif // GENERALIZED_TRAINING_WEIGHT_HPP_INCLUDED
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Reference in New Issue
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