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add distance

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git-svn-id: svn://scm.gforge.inria.fr/svnroot/paradiseo@374 331e1502-861f-0410-8da2-ba01fb791d7f
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liefooga 2007-06-26 12:10:04 +00:00
commit 792849c8bd
5 changed files with 243 additions and 187 deletions
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branches/paradiseo-moeo-1.0/src/distance/moeoDistance.h
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@ -13,9 +13,7 @@
#ifndef MOEODISTANCE_H_
#define MOEODISTANCE_H_
#include <math.h>
#include <eoFunctor.h>
#include <utils/eoRealBounds.h>
/**
* The base class for distance computation.
@ -25,203 +23,32 @@ class moeoDistance : public eoBF < const MOEOT &, const MOEOT &, const Type >
{
public:
/**
* Nothing to do
* @param _pop the population
*/
* Nothing to do
* @param _pop the population
*/
virtual void setup(const eoPop < MOEOT > & _pop)
{}
/**
* Nothing to do
* @param _min lower bound
* @param _max upper bound
* @param _obj the objective index
*/
virtual void setup(double _min, double _max, unsigned _obj)
virtual void setup(double _min, double _max, unsigned int _obj)
{}
/**
* Nothing to do
* @param _realInterval the eoRealInterval object
* @param _obj the objective index
*/
virtual void setup(eoRealInterval _realInterval, unsigned _obj)
* Nothing to do
* @param _realInterval the eoRealInterval object
* @param _obj the objective index
*/
virtual void setup(eoRealInterval _realInterval, unsigned int _obj)
{}
};
/**
* The base class for double distance computation with normalized objective values (i.e. between 0 and 1).
*/
template < class MOEOT , class Type = double >
class moeoNormalizedDistance : public moeoDistance < MOEOT , Type >
{
public:
/** the objective vector type of the solutions */
typedef typename MOEOT::ObjectiveVector ObjectiveVector;
/**
* Default ctr
*/
moeoNormalizedDistance()
{
bounds.resize(ObjectiveVector::Traits::nObjectives());
// initialize bounds in case someone does not want to use them
for (unsigned i=0; i<ObjectiveVector::Traits::nObjectives(); i++)
{
bounds[i] = eoRealInterval(0,1);
}
}
/**
* Returns a very small value that can be used to avoid extreme cases (where the min bound == the max bound)
*/
static double tiny()
{
return 1e-6;
}
/**
* Sets the lower and the upper bounds for every objective using extremes values for solutions contained in the population _pop
* @param _pop the population
*/
virtual void setup(const eoPop < MOEOT > & _pop)
{
double min, max;
for (unsigned i=0; i<ObjectiveVector::Traits::nObjectives(); i++)
{
min = _pop[0].objectiveVector()[i];
max = _pop[0].objectiveVector()[i];
for (unsigned j=1; j<_pop.size(); j++)
{
min = std::min(min, _pop[j].objectiveVector()[i]);
max = std::max(max, _pop[j].objectiveVector()[i]);
}
// setting of the bounds for the objective i
setup(min, max, i);
}
}
/**
* Sets the lower bound (_min) and the upper bound (_max) for the objective _obj
* @param _min lower bound
* @param _max upper bound
* @param _obj the objective index
*/
virtual void setup(double _min, double _max, unsigned _obj)
{
if (_min == _max)
{
_min -= tiny();
_max += tiny();
}
bounds[_obj] = eoRealInterval(_min, _max);
}
/**
* Sets the lower bound and the upper bound for the objective _obj using a eoRealInterval object
* @param _realInterval the eoRealInterval object
* @param _obj the objective index
*/
virtual void setup(eoRealInterval _realInterval, unsigned _obj)
{
bounds[_obj] = _realInterval;
}
protected:
/** the bounds for every objective (bounds[i] = bounds for the objective i) */
std::vector < eoRealInterval > bounds;
};
/**
* A class allowing to compute an euclidian distance between two solutions in the objective space with normalized objective values (i.e. between 0 and 1).
* A distance value then lies between 0 and sqrt(nObjectives).
*/
template < class MOEOT >
class moeoEuclideanDistance : public moeoNormalizedDistance < MOEOT >
{
public:
/** the objective vector type of the solutions */
typedef typename MOEOT::ObjectiveVector ObjectiveVector;
/**
* Returns the euclidian distance between _moeo1 and _moeo2 in the objective space
* @param _moeo1 the first solution
* @param _moeo2 the second solution
*/
const double operator()(const MOEOT & _moeo1, const MOEOT & _moeo2)
{
double result = 0.0;
double tmp1, tmp2;
for (unsigned i=0; i<ObjectiveVector::nObjectives(); i++)
{
tmp1 = (_moeo1.objectiveVector()[i] - bounds[i].minimum()) / bounds[i].range();
tmp2 = (_moeo2.objectiveVector()[i] - bounds[i].minimum()) / bounds[i].range();
result += (tmp1-tmp2) * (tmp1-tmp2);
}
return sqrt(result);
}
private:
/** the bounds for every objective */
using moeoNormalizedDistance < MOEOT > :: bounds;
};
/**
* A class allowing to compute the Manhattan distance between two solutions in the objective space normalized objective values (i.e. between 0 and 1).
* A distance value then lies between 0 and nObjectives.
*/
template < class MOEOT >
class moeoManhattanDistance : public moeoNormalizedDistance < MOEOT >
{
public:
/** the objective vector type of the solutions */
typedef typename MOEOT::ObjectiveVector ObjectiveVector;
/**
* Returns the Manhattan distance between _moeo1 and _moeo2 in the objective space
* @param _moeo1 the first solution
* @param _moeo2 the second solution
*/
const double operator()(const MOEOT & _moeo1, const MOEOT & _moeo2)
{
double result = 0.0;
double tmp1, tmp2;
for (unsigned i=0; i<ObjectiveVector::nObjectives(); i++)
{
tmp1 = (_moeo1.objectiveVector()[i] - bounds[i].minimum()) / bounds[i].range();
tmp2 = (_moeo2.objectiveVector()[i] - bounds[i].minimum()) / bounds[i].range();
result += fabs(tmp1-tmp2);
}
return result;
}
private:
/** the bounds for every objective */
using moeoNormalizedDistance < MOEOT > :: bounds;
};
#endif /*MOEODISTANCE_H_*/