update metric
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@ -2,7 +2,7 @@
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//-----------------------------------------------------------------------------
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// moeoEntropyMetric.h
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// (c) OPAC Team (LIFL), Dolphin Project (INRIA), 2006
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// (c) OPAC Team (LIFL), Dolphin Project (INRIA), 2007
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/*
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This library...
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@ -16,164 +16,162 @@
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#include <metric/moeoMetric.h>
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/**
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* The entropy gives an idea of the diversity of a Pareto set relatively to another Pareto set
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*
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* The entropy gives an idea of the diversity of a Pareto set relatively to another
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* (Basseur, Seynhaeve, Talbi: 'Design of Multi-objective Evolutionary Algorithms: Application to the Flow-shop Scheduling Problem', in Proc. of the 2002 Congress on Evolutionary Computation, IEEE Press, pp. 1155-1156)
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*/
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template < class EOT > class moeoEntropyMetric:public moeoVectorVsVectorBM < EOT,
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double >
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template < class ObjectiveVector >
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class moeoEntropyMetric : public moeoVectorVsVectorBinaryMetric < ObjectiveVector, double >
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{
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public:
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/**
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* The fitness type of a solution
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*/
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typedef typename EOT::Fitness EOFitness;
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/**
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* Returns the entropy of the Pareto set '_set1' relatively to the Pareto set '_set2'
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* @param _set1 the first Pareto set
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* @param _set2 the second Pareto set
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*/
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double operator()(const std::vector < ObjectiveVector > & _set1, const std::vector < ObjectiveVector > & _set2) {
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// normalization
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std::vector< ObjectiveVector > set1 = _set1;
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std::vector< ObjectiveVector > set2= _set2;
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removeDominated (set1);
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removeDominated (set2);
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prenormalize (set1);
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normalize (set1);
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normalize (set2);
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/**
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* Returns the entropy of the Pareto set '_set1' relatively to the Pareto set '_set2'
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* @param _set1 the first Pareto set
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* @param _set2 the second Pareto set
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*/
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double operator () (const std::vector < EOFitness > &_set1,
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const std::vector < EOFitness > &_set2)
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{
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// normalization
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std::vector < EOFitness > set1 = _set1;
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std::vector < EOFitness > set2 = _set2;
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removeDominated (set1);
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removeDominated (set2);
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prenormalize (set1);
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normalize (set1);
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normalize (set2);
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// making of PO*
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std::vector< ObjectiveVector > star; // rotf :-)
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computeUnion (set1, set2, star);
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removeDominated (star);
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// making of PO*
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std::vector < EOFitness > star; // rotf :-)
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computeUnion (set1, set2, star);
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removeDominated (star);
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// making of PO1 U PO*
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std::vector< ObjectiveVector > union_set1_star; // rotf again ...
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computeUnion (set1, star, union_set1_star);
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// making of PO1 U PO*
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std::vector < EOFitness > union_set1_star; // rotf again ...
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computeUnion (set1, star, union_set1_star);
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unsigned C = union_set1_star.size();
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float omega=0;
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float entropy=0;
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unsigned C = union_set1_star.size ();
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float omega = 0;
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float entropy = 0;
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for (unsigned i = 0; i < C; i++)
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{
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unsigned N_i =
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howManyInNicheOf (union_set1_star, union_set1_star[i],
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star.size ());
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unsigned n_i =
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howManyInNicheOf (set1, union_set1_star[i], star.size ());
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if (n_i > 0)
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{
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omega += 1.0 / N_i;
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entropy +=
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(float) n_i / (N_i * C) * log (((float) n_i / C) / log (2.0));
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}
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}
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entropy /= -log (omega);
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entropy *= log (2.0);
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return entropy;
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}
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for (unsigned i=0 ; i<C ; i++) {
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unsigned N_i = howManyInNicheOf (union_set1_star, union_set1_star[i], star.size());
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unsigned n_i = howManyInNicheOf (set1, union_set1_star[i], star.size());
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if (n_i > 0) {
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omega += 1.0 / N_i;
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entropy += (float) n_i / (N_i * C) * log (((float) n_i / C) / log (2.0));
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}
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}
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entropy /= - log (omega);
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entropy *= log (2.0);
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return entropy;
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}
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private:
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std::vector < double >vect_min_val;
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std::vector < double >vect_max_val;
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/** vector of min values */
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std::vector<double> vect_min_val;
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/** vector of max values */
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std::vector<double> vect_max_val;
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void removeDominated (std::vector < EOFitness > &_f)
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{
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for (unsigned i = 0; i < _f.size (); i++)
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{
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bool dom = false;
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for (unsigned j = 0; j < _f.size (); j++)
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if (i != j && _f[j].dominates (_f[i]))
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{
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dom = true;
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break;
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}
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if (dom)
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{
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_f[i] = _f.back ();
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_f.pop_back ();
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i--;
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}
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}
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}
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void prenormalize (const std::vector < EOFitness > &_f)
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{
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vect_min_val.clear ();
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vect_max_val.clear ();
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/**
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* Removes the dominated individuals contained in _f
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* @param _f a Pareto set
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*/
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void removeDominated(std::vector < ObjectiveVector > & _f) {
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for (unsigned i=0 ; i<_f.size(); i++) {
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bool dom = false;
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for (unsigned j=0; j<_f.size(); j++)
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if (i != j && _f[j].dominates(_f[i])) {
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dom = true;
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break;
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}
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if (dom) {
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_f[i] = _f.back();
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_f.pop_back();
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i--;
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}
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}
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}
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for (unsigned char i = 0; i < EOFitness::fitness_traits::nObjectives ();
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i++)
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{
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float min_val = _f.front ()[i], max_val = min_val;
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for (unsigned j = 1; j < _f.size (); j++)
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{
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if (_f[j][i] < min_val)
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min_val = _f[j][i];
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if (_f[j][i] > max_val)
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max_val = _f[j][i];
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}
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vect_min_val.push_back (min_val);
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vect_max_val.push_back (max_val);
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}
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}
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void normalize (std::vector < EOFitness > &_f)
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{
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for (unsigned i = 0; i < EOFitness::fitness_traits::nObjectives (); i++)
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for (unsigned j = 0; j < _f.size (); j++)
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_f[j][i] =
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(_f[j][i] - vect_min_val[i]) / (vect_max_val[i] - vect_min_val[i]);
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}
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/**
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* Prenormalization
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* @param _f a Pareto set
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*/
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void prenormalize (const std::vector< ObjectiveVector > & _f) {
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vect_min_val.clear();
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vect_max_val.clear();
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void computeUnion (const std::vector < EOFitness > &_f1,
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const std::vector < EOFitness > &_f2,
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std::vector < EOFitness > &_f)
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{
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_f = _f1;
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for (unsigned i = 0; i < _f2.size (); i++)
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{
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bool b = false;
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for (unsigned j = 0; j < _f1.size (); j++)
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if (_f1[j] == _f2[i])
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{
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b = true;
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break;
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}
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if (!b)
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_f.push_back (_f2[i]);
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}
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}
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for (unsigned char i=0 ; i<ObjectiveVector::nObjectives(); i++) {
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float min_val = _f.front()[i], max_val = min_val;
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for (unsigned j=1 ; j<_f.size(); j++) {
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if (_f[j][i] < min_val)
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min_val = _f[j][i];
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if (_f[j][i]>max_val)
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max_val = _f[j][i];
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}
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vect_min_val.push_back(min_val);
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vect_max_val.push_back (max_val);
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}
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}
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unsigned howManyInNicheOf (const std::vector < EOFitness > &_f,
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const EOFitness & _s, unsigned _size)
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{
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unsigned n = 0;
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for (unsigned i = 0; i < _f.size (); i++)
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{
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if (euclidianDistance (_f[i], _s) < (_s.size () / (double) _size))
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n++;
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}
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return n;
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}
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double euclidianDistance (const EOFitness & _set1, const EOFitness & _to,
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unsigned _deg = 2)
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{
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double dist = 0;
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for (unsigned i = 0; i < _set1.size (); i++)
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dist += pow (fabs (_set1[i] - _to[i]), (int) _deg);
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return pow (dist, 1.0 / _deg);
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}
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/**
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* Normalization
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* @param _f a Pareto set
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*/
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void normalize (std::vector< ObjectiveVector > & _f) {
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for (unsigned i=0 ; i<ObjectiveVector::nObjectives(); i++)
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for (unsigned j=0; j<_f.size(); j++)
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_f[j][i] = (_f[j][i] - vect_min_val[i]) / (vect_max_val[i] - vect_min_val[i]);
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}
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/**
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* Computation of the union of _f1 and _f2 in _f
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* @param _f1 the first Pareto set
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* @param _f2 the second Pareto set
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* @param _f the final Pareto set
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*/
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void computeUnion(const std::vector< ObjectiveVector > & _f1, const std::vector< ObjectiveVector > & _f2, std::vector< ObjectiveVector > & _f) {
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_f = _f1 ;
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for (unsigned i=0; i<_f2.size(); i++) {
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bool b = false;
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for (unsigned j=0; j<_f1.size(); j ++)
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if (_f1[j] == _f2[i]) {
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b = true;
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break;
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}
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if (! b)
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_f.push_back(_f2[i]);
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}
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}
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/**
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* How many in niche
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*/
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unsigned howManyInNicheOf (const std::vector< ObjectiveVector > & _f, const ObjectiveVector & _s, unsigned _size) {
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unsigned n=0;
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for (unsigned i=0 ; i<_f.size(); i++) {
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if (euclidianDistance(_f[i], _s) < (_s.size() / (double) _size))
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n++;
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}
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return n;
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}
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/**
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* Euclidian distance
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*/
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double euclidianDistance (const ObjectiveVector & _set1, const ObjectiveVector & _to, unsigned _deg = 2) {
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double dist=0;
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for (unsigned i=0; i<_set1.size(); i++)
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dist += pow(fabs(_set1[i] - _to[i]), (int)_deg);
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return pow(dist, 1.0 / _deg);
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}
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};
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#endif /*MOEOENTROPYMETRIC_H_ */
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#endif /*MOEOENTROPYMETRIC_H_*/
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