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include
shark
ObjectiveFunctions
Benchmarks
CigarDiscus.h
Go to the documentation of this file.
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/*!
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*
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*
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* \brief Convex quadratic benchmark function.
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*
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*
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*
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* \author -
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* \date -
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*
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*
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* \par Copyright 1995-2017 Shark Development Team
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*
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* <BR><HR>
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* This file is part of Shark.
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* <http://shark-ml.org/>
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*
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* Shark is free software: you can redistribute it and/or modify
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* it under the terms of the GNU Lesser General Public License as published
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* by the Free Software Foundation, either version 3 of the License, or
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* (at your option) any later version.
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*
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* Shark is distributed in the hope that it will be useful,
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* but WITHOUT ANY WARRANTY; without even the implied warranty of
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* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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* GNU Lesser General Public License for more details.
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*
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* You should have received a copy of the GNU Lesser General Public License
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* along with Shark. If not, see <http://www.gnu.org/licenses/>.
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*
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*/
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#ifndef SHARK_OBJECTIVEFUNCTIONS_BENCHMARK_CIGARDISCUS_H
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#define SHARK_OBJECTIVEFUNCTIONS_BENCHMARK_CIGARDISCUS_H
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#include <
shark/ObjectiveFunctions/AbstractObjectiveFunction.h
>
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#include <
shark/Core/Random.h
>
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namespace
shark
{
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/**
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* \brief Convex quadratic benchmark function.
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*/
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class
CigarDiscus
:
public
SingleObjectiveFunction
{
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public
:
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CigarDiscus
(std::size_t
numberOfVariables
= 5,
double
alpha
=1E-3) : m_alpha(
alpha
) {
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m_features
|=
CAN_PROPOSE_STARTING_POINT
;
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m_numberOfVariables =
numberOfVariables
;
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}
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/// \brief From INameable: return the class name.
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std::string
name
()
const
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{
return
"CigarDiscus"
; }
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std::size_t
numberOfVariables
()
const
{
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return
m_numberOfVariables;
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}
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bool
hasScalableDimensionality
()
const
{
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return
true
;
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}
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/// \brief Adjusts the number of variables if the function is scalable.
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/// \param [in] numberOfVariables The new dimension.
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void
setNumberOfVariables
( std::size_t
numberOfVariables
){
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m_numberOfVariables =
numberOfVariables
;
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}
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SearchPointType
proposeStartingPoint
()
const
{
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RealVector x(
numberOfVariables
());
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for
(std::size_t i = 0; i < x.size(); i++) {
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x(i) =
random::uni
(*
mep_rng
, 0, 1);
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}
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return
x;
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}
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double
eval
(
const
SearchPointType
&p)
const
{
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m_evaluationCounter
++;
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double
sum = m_alpha *
sqr
(p(0)) +
sqr
(p(p.size() - 1));
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double
alpha
= ::sqrt(m_alpha);
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for
(std::size_t i = 1; i < p.size()-1; i++)
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sum += alpha *
sqr
(p(i));
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return
sum;
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}
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double
alpha
()
const
{
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return
m_alpha;
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}
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void
setAlpha
(
double
alpha
) {
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m_alpha =
alpha
;
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}
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private
:
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double
m_alpha;
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std::size_t m_numberOfVariables;
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};
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}
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#endif