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git-svn-id: svn://scm.gforge.inria.fr/svnroot/paradiseo@1813 331e1502-861f-0410-8da2-ba01fb791d7f
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jhumeau
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88 changed files with 2726 additions and 2720 deletions
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@ -46,42 +46,42 @@
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* To compute the autocorrelation function:
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* Perform a random walk based on the neighborhood,
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* The fitness values of solutions are collected during the random walk
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* The autocorrelation can be computed from the serie of fitness values
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*
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* The autocorrelation can be computed from the serie of fitness values
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*
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*/
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template <class Neighbor>
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class moAutocorrelationSampling : public moSampling<Neighbor>
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{
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public:
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typedef typename Neighbor::EOT EOT ;
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using moSampling<Neighbor>::localSearch;
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typedef typename Neighbor::EOT EOT ;
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/**
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* Default Constructor
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* @param _init initialisation method of the solution
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* @param _neighborhood neighborhood giving neighbor in random order
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* @param _fullEval Fitness function, full evaluation function
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* @param _eval neighbor evaluation, incremental evaluation function
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* @param _nbStep Number of steps of the random walk
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*/
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moAutocorrelationSampling(eoInit<EOT> & _init,
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moNeighborhood<Neighbor> & _neighborhood,
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eoEvalFunc<EOT>& _fullEval,
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moEval<Neighbor>& _eval,
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unsigned int _nbStep) :
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moSampling<Neighbor>(_init, * new moRandomWalk<Neighbor>(_neighborhood, _fullEval, _eval, _nbStep), fitnessStat){}
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using moSampling<Neighbor>::localSearch;
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/**
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* default destructor
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*/
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~moAutocorrelationSampling() {
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// delete the pointer on the local search which has been constructed in the constructor
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delete localSearch;
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}
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/**
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* Default Constructor
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* @param _init initialisation method of the solution
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* @param _neighborhood neighborhood giving neighbor in random order
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* @param _fullEval Fitness function, full evaluation function
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* @param _eval neighbor evaluation, incremental evaluation function
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* @param _nbStep Number of steps of the random walk
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*/
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moAutocorrelationSampling(eoInit<EOT> & _init,
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moNeighborhood<Neighbor> & _neighborhood,
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eoEvalFunc<EOT>& _fullEval,
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moEval<Neighbor>& _eval,
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unsigned int _nbStep) :
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moSampling<Neighbor>(_init, * new moRandomWalk<Neighbor>(_neighborhood, _fullEval, _eval, _nbStep), fitnessStat) {}
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/**
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* default destructor
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*/
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~moAutocorrelationSampling() {
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// delete the pointer on the local search which has been constructed in the constructor
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delete localSearch;
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}
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protected:
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moFitnessStat<EOT> fitnessStat;
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moFitnessStat<EOT> fitnessStat;
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};
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@ -42,40 +42,40 @@
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#include <sampling/moSampling.h>
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/**
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* To compute the density of states:
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* To compute the density of states:
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* Sample the fitness of random solution in the search space
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* The fitness values of solutions are collected during the random search
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*
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*
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*/
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template <class Neighbor>
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class moDensityOfStatesSampling : public moSampling<Neighbor>
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{
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public:
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typedef typename Neighbor::EOT EOT ;
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using moSampling<Neighbor>::localSearch;
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typedef typename Neighbor::EOT EOT ;
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/**
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* Default Constructor
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* @param _init initialisation method of the solution
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* @param _fullEval Fitness function, full evaluation function
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* @param _nbSol Number of solutions in the sample
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*/
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moDensityOfStatesSampling(eoInit<EOT> & _init,
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eoEvalFunc<EOT>& _fullEval,
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unsigned int _nbSol) :
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moSampling<Neighbor>(_init, * new moRandomSearch<Neighbor>(_init, _fullEval, _nbSol), fitnessStat){}
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using moSampling<Neighbor>::localSearch;
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/**
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* default destructor
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*/
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~moDensityOfStatesSampling() {
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// delete the pointer on the local search which has been constructed in the constructor
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delete localSearch;
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}
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/**
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* Default Constructor
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* @param _init initialisation method of the solution
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* @param _fullEval Fitness function, full evaluation function
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* @param _nbSol Number of solutions in the sample
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*/
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moDensityOfStatesSampling(eoInit<EOT> & _init,
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eoEvalFunc<EOT>& _fullEval,
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unsigned int _nbSol) :
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moSampling<Neighbor>(_init, * new moRandomSearch<Neighbor>(_init, _fullEval, _nbSol), fitnessStat) {}
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/**
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* default destructor
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*/
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~moDensityOfStatesSampling() {
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// delete the pointer on the local search which has been constructed in the constructor
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delete localSearch;
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}
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protected:
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moFitnessStat<EOT> fitnessStat;
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moFitnessStat<EOT> fitnessStat;
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};
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@ -45,7 +45,7 @@
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#include <sampling/moSampling.h>
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/**
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* To compute the fitness distance correlation:
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* To compute the fitness distance correlation:
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* Sample the fitness and the distance from a particular solution of random solution in the search space
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* The fitness values and distances of solutions are collected during the random search
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* Then the correlation between the fitness and the distance can be computed
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@ -55,40 +55,40 @@ template <class Neighbor>
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class moFDCsampling : public moSampling<Neighbor>
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{
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public:
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typedef typename Neighbor::EOT EOT ;
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using moSampling<Neighbor>::localSearch;
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typedef typename Neighbor::EOT EOT ;
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/**
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* Default Constructor
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* @param _init initialisation method of the solution
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* @param _fullEval a full evaluation function
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* @param _dist the distance function between solution
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* @param _refSol the reference solution to compute the distance (think of global optimum when possible)
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* @param _nbSol Number of solutions of the sample
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*/
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moFDCsampling(eoInit<EOT> & _init,
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eoEvalFunc<EOT>& _fullEval,
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eoDistance<EOT>& _dist,
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EOT& _refSol,
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unsigned int _nbSol) :
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moSampling<Neighbor>(_init, * new moRandomSearch<Neighbor>(_init, _fullEval, _nbSol), fitnessStat),
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distStat(_dist, _refSol)
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{
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add(distStat);
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}
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using moSampling<Neighbor>::localSearch;
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/**
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* default destructor
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*/
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~moFDCsampling() {
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// delete the pointer on the local search which has been constructed in the constructor
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delete localSearch;
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}
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/**
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* Default Constructor
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* @param _init initialisation method of the solution
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* @param _fullEval a full evaluation function
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* @param _dist the distance function between solution
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* @param _refSol the reference solution to compute the distance (think of global optimum when possible)
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* @param _nbSol Number of solutions of the sample
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*/
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moFDCsampling(eoInit<EOT> & _init,
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eoEvalFunc<EOT>& _fullEval,
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eoDistance<EOT>& _dist,
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EOT& _refSol,
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unsigned int _nbSol) :
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moSampling<Neighbor>(_init, * new moRandomSearch<Neighbor>(_init, _fullEval, _nbSol), fitnessStat),
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distStat(_dist, _refSol)
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{
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add(distStat);
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}
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/**
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* default destructor
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*/
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~moFDCsampling() {
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// delete the pointer on the local search which has been constructed in the constructor
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delete localSearch;
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}
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protected:
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moFitnessStat<EOT> fitnessStat;
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moDistanceStat<EOT> distStat;
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moFitnessStat<EOT> fitnessStat;
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moDistanceStat<EOT> distStat;
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};
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@ -45,8 +45,8 @@
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#include <sampling/moSampling.h>
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/**
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* To compute an estimation of the fitness cloud,
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* i.e. the scatter plot of solution fitness versus neighbor fitness:
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* To compute an estimation of the fitness cloud,
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* i.e. the scatter plot of solution fitness versus neighbor fitness:
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*
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* This class do nothing. See others mo(...)FitnessCloudSampling classes
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* with different fitness sampling methods
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@ -55,44 +55,44 @@ template <class Neighbor>
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class moFitnessCloudSampling : public moSampling<Neighbor>
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{
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public:
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typedef typename Neighbor::EOT EOT ;
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using moSampling<Neighbor>::localSearch;
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typedef typename Neighbor::EOT EOT ;
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/**
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* Default Constructor
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* @param _init initialisation method of the solution
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* @param _neighborhood neighborhood to get a neighbor
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* @param _fullEval Fitness function, full evaluation function
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* @param _eval neighbor evaluation, incremental evaluation function
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* @param _nbSol Number of solutions in the sample
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*/
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moFitnessCloudSampling(eoInit<EOT> & _init,
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moNeighborhood<Neighbor> & _neighborhood,
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eoEvalFunc<EOT>& _fullEval,
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moEval<Neighbor>& _eval,
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unsigned int _nbSol) :
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moSampling<Neighbor>(_init, * new moDummyLS<Neighbor>(_fullEval), fitnessStat),
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neighborhood(_neighborhood),
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fullEval(_fullEval),
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eval(_eval),
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nbSol(_nbSol)
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{}
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using moSampling<Neighbor>::localSearch;
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/**
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* default destructor
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*/
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~moFitnessCloudSampling() {
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// delete the pointer on the local search which has been constructed in the constructor
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delete localSearch;
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}
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/**
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* Default Constructor
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* @param _init initialisation method of the solution
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* @param _neighborhood neighborhood to get a neighbor
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* @param _fullEval Fitness function, full evaluation function
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* @param _eval neighbor evaluation, incremental evaluation function
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* @param _nbSol Number of solutions in the sample
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*/
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moFitnessCloudSampling(eoInit<EOT> & _init,
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moNeighborhood<Neighbor> & _neighborhood,
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eoEvalFunc<EOT>& _fullEval,
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moEval<Neighbor>& _eval,
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unsigned int _nbSol) :
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moSampling<Neighbor>(_init, * new moDummyLS<Neighbor>(_fullEval), fitnessStat),
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neighborhood(_neighborhood),
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fullEval(_fullEval),
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eval(_eval),
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nbSol(_nbSol)
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{}
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/**
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* default destructor
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*/
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~moFitnessCloudSampling() {
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// delete the pointer on the local search which has been constructed in the constructor
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delete localSearch;
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}
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protected:
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moNeighborhood<Neighbor> & neighborhood;
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eoEvalFunc<EOT>& fullEval;
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moEval<Neighbor>& eval;
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unsigned int nbSol;
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moFitnessStat<EOT> fitnessStat;
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moNeighborhood<Neighbor> & neighborhood;
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eoEvalFunc<EOT>& fullEval;
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moEval<Neighbor>& eval;
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unsigned int nbSol;
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moFitnessStat<EOT> fitnessStat;
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};
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@ -52,58 +52,58 @@
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* Perform a simple Hill-climber based on the neighborhood (adaptive walk),
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* The lengths of HC are collected and the final solution which are local optima
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* The adaptive walk is repeated several times
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*
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*
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*/
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template <class Neighbor>
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class moHillClimberSampling : public moSampling<Neighbor>
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{
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public:
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typedef typename Neighbor::EOT EOT ;
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using moSampling<Neighbor>::localSearch;
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typedef typename Neighbor::EOT EOT ;
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/**
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* Default Constructor
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* @param _init initialisation method of the solution
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* @param _neighborhood neighborhood giving neighbor in random order
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* @param _fullEval a full evaluation function
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* @param _eval an incremental evaluation of neighbors
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* @param _nbAdaptWalk Number of adaptive walks
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*/
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moHillClimberSampling(eoInit<EOT> & _init,
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moNeighborhood<Neighbor> & _neighborhood,
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eoEvalFunc<EOT>& _fullEval,
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moEval<Neighbor>& _eval,
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unsigned int _nbAdaptWalk) :
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moSampling<Neighbor>(initHC, * new moRandomSearch<Neighbor>(initHC, _fullEval, _nbAdaptWalk), copyStat),
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copyStat(lengthStat),
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checkpoint(trueCont),
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hc(_neighborhood, _fullEval, _eval, checkpoint),
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initHC(_init, hc)
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{
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// to count the number of step in the HC
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checkpoint.add(lengthStat);
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using moSampling<Neighbor>::localSearch;
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// add the solution into statistics
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add(solStat);
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}
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/**
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* Default Constructor
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* @param _init initialisation method of the solution
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* @param _neighborhood neighborhood giving neighbor in random order
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* @param _fullEval a full evaluation function
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* @param _eval an incremental evaluation of neighbors
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* @param _nbAdaptWalk Number of adaptive walks
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*/
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moHillClimberSampling(eoInit<EOT> & _init,
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moNeighborhood<Neighbor> & _neighborhood,
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eoEvalFunc<EOT>& _fullEval,
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moEval<Neighbor>& _eval,
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unsigned int _nbAdaptWalk) :
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moSampling<Neighbor>(initHC, * new moRandomSearch<Neighbor>(initHC, _fullEval, _nbAdaptWalk), copyStat),
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copyStat(lengthStat),
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checkpoint(trueCont),
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hc(_neighborhood, _fullEval, _eval, checkpoint),
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initHC(_init, hc)
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{
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// to count the number of step in the HC
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checkpoint.add(lengthStat);
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/**
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* default destructor
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*/
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~moHillClimberSampling() {
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// delete the pointer on the local search which has been constructed in the constructor
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delete localSearch;
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}
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// add the solution into statistics
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add(solStat);
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}
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/**
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* default destructor
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*/
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~moHillClimberSampling() {
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// delete the pointer on the local search which has been constructed in the constructor
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delete localSearch;
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}
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protected:
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moSolutionStat<EOT> solStat;
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moMinusOneCounterStat<EOT> lengthStat;
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moTrueContinuator<Neighbor> trueCont;
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moStatFromStat<EOT, unsigned int> copyStat;
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moCheckpoint<Neighbor> checkpoint;
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moSimpleHC<Neighbor> hc;
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moLocalSearchInit<Neighbor> initHC;
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moSolutionStat<EOT> solStat;
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moMinusOneCounterStat<EOT> lengthStat;
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moTrueContinuator<Neighbor> trueCont;
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moStatFromStat<EOT, unsigned int> copyStat;
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moCheckpoint<Neighbor> checkpoint;
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moSimpleHC<Neighbor> hc;
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moLocalSearchInit<Neighbor> initHC;
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};
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@ -44,8 +44,8 @@
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#include <sampling/moFitnessCloudSampling.h>
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/**
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* To compute an estimation of the fitness cloud,
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* i.e. the scatter plot of solution fitness versus neighbor fitness:
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* To compute an estimation of the fitness cloud,
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* i.e. the scatter plot of solution fitness versus neighbor fitness:
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*
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* Here solution are sampled with Metropolis-Hasting method
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*
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@ -53,59 +53,59 @@
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* and the best fitness of k random neighbor
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*
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* The values are collected during the Metropolis-Hasting walk
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*
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*
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*/
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template <class Neighbor>
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class moMHBestFitnessCloudSampling : public moFitnessCloudSampling<Neighbor>
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{
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public:
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typedef typename Neighbor::EOT EOT ;
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using moSampling<Neighbor>::localSearch;
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using moSampling<Neighbor>::checkpoint;
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using moSampling<Neighbor>::monitorVec;
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using moSampling<Neighbor>::continuator;
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using moFitnessCloudSampling<Neighbor>::fitnessStat;
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typedef typename Neighbor::EOT EOT ;
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/**
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* Default Constructor
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* @param _init initialisation method of the solution
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* @param _neighborhood neighborhood to get one random neighbor (supposed to be random neighborhood)
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* @param _fullEval Fitness function, full evaluation function
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* @param _eval neighbor evaluation, incremental evaluation function
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* @param _nbStep Number of step of the MH sampling
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*/
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moMHBestFitnessCloudSampling(eoInit<EOT> & _init,
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moNeighborhood<Neighbor> & _neighborhood,
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eoEvalFunc<EOT>& _fullEval,
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moEval<Neighbor>& _eval,
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unsigned int _nbStep) :
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moFitnessCloudSampling<Neighbor>(_init, _neighborhood, _fullEval, _eval, _nbStep),
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neighborBestStat(_neighborhood, _eval)
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{
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// delete the dummy local search
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delete localSearch;
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// Metropolis-Hasting sampling
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localSearch = new moMetropolisHasting<Neighbor>(_neighborhood, _fullEval, _eval, _nbStep);
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using moSampling<Neighbor>::localSearch;
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using moSampling<Neighbor>::checkpoint;
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using moSampling<Neighbor>::monitorVec;
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using moSampling<Neighbor>::continuator;
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using moFitnessCloudSampling<Neighbor>::fitnessStat;
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// delete the checkpoint with the wrong continuator
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delete checkpoint;
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/**
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* Default Constructor
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* @param _init initialisation method of the solution
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* @param _neighborhood neighborhood to get one random neighbor (supposed to be random neighborhood)
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* @param _fullEval Fitness function, full evaluation function
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* @param _eval neighbor evaluation, incremental evaluation function
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* @param _nbStep Number of step of the MH sampling
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*/
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moMHBestFitnessCloudSampling(eoInit<EOT> & _init,
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moNeighborhood<Neighbor> & _neighborhood,
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eoEvalFunc<EOT>& _fullEval,
|
||||
moEval<Neighbor>& _eval,
|
||||
unsigned int _nbStep) :
|
||||
moFitnessCloudSampling<Neighbor>(_init, _neighborhood, _fullEval, _eval, _nbStep),
|
||||
neighborBestStat(_neighborhood, _eval)
|
||||
{
|
||||
// delete the dummy local search
|
||||
delete localSearch;
|
||||
|
||||
// set the continuator
|
||||
continuator = localSearch->getContinuator();
|
||||
// Metropolis-Hasting sampling
|
||||
localSearch = new moMetropolisHasting<Neighbor>(_neighborhood, _fullEval, _eval, _nbStep);
|
||||
|
||||
// re-construction of the checkpoint
|
||||
checkpoint = new moCheckpoint<Neighbor>(*continuator);
|
||||
checkpoint->add(fitnessStat);
|
||||
checkpoint->add(*monitorVec[0]);
|
||||
// delete the checkpoint with the wrong continuator
|
||||
delete checkpoint;
|
||||
|
||||
// one random neighbor
|
||||
add(neighborBestStat);
|
||||
}
|
||||
// set the continuator
|
||||
continuator = localSearch->getContinuator();
|
||||
|
||||
// re-construction of the checkpoint
|
||||
checkpoint = new moCheckpoint<Neighbor>(*continuator);
|
||||
checkpoint->add(fitnessStat);
|
||||
checkpoint->add(*monitorVec[0]);
|
||||
|
||||
// one random neighbor
|
||||
add(neighborBestStat);
|
||||
}
|
||||
|
||||
protected:
|
||||
moNeighborBestStat< Neighbor > neighborBestStat;
|
||||
moNeighborBestStat< Neighbor > neighborBestStat;
|
||||
|
||||
};
|
||||
|
||||
|
|
|
|||
|
|
@ -44,8 +44,8 @@
|
|||
#include <sampling/moFitnessCloudSampling.h>
|
||||
|
||||
/**
|
||||
* To compute an estimation of the fitness cloud,
|
||||
* i.e. the scatter plot of solution fitness versus neighbor fitness:
|
||||
* To compute an estimation of the fitness cloud,
|
||||
* i.e. the scatter plot of solution fitness versus neighbor fitness:
|
||||
*
|
||||
* Here solution are sampled with Metropolis-Hasting method
|
||||
*
|
||||
|
|
@ -53,59 +53,59 @@
|
|||
* and the fitness of one random neighbor
|
||||
*
|
||||
* The values are collected during the Metropolis-Hasting walk
|
||||
*
|
||||
*
|
||||
*/
|
||||
template <class Neighbor>
|
||||
class moMHRndFitnessCloudSampling : public moFitnessCloudSampling<Neighbor>
|
||||
{
|
||||
public:
|
||||
typedef typename Neighbor::EOT EOT ;
|
||||
|
||||
using moSampling<Neighbor>::localSearch;
|
||||
using moSampling<Neighbor>::checkpoint;
|
||||
using moSampling<Neighbor>::monitorVec;
|
||||
using moSampling<Neighbor>::continuator;
|
||||
using moFitnessCloudSampling<Neighbor>::fitnessStat;
|
||||
typedef typename Neighbor::EOT EOT ;
|
||||
|
||||
/**
|
||||
* Default Constructor
|
||||
* @param _init initialisation method of the solution
|
||||
* @param _neighborhood neighborhood to get one random neighbor (supposed to be random neighborhood)
|
||||
* @param _fullEval Fitness function, full evaluation function
|
||||
* @param _eval neighbor evaluation, incremental evaluation function
|
||||
* @param _nbStep Number of step of the MH sampling
|
||||
*/
|
||||
moMHRndFitnessCloudSampling(eoInit<EOT> & _init,
|
||||
moNeighborhood<Neighbor> & _neighborhood,
|
||||
eoEvalFunc<EOT>& _fullEval,
|
||||
moEval<Neighbor>& _eval,
|
||||
unsigned int _nbStep) :
|
||||
moFitnessCloudSampling<Neighbor>(_init, _neighborhood, _fullEval, _eval, _nbStep),
|
||||
neighborFitnessStat(_neighborhood, _eval)
|
||||
{
|
||||
// delete the dummy local search
|
||||
delete localSearch;
|
||||
|
||||
// Metropolis-Hasting sampling
|
||||
localSearch = new moMetropolisHasting<Neighbor>(_neighborhood, _fullEval, _eval, _nbStep);
|
||||
using moSampling<Neighbor>::localSearch;
|
||||
using moSampling<Neighbor>::checkpoint;
|
||||
using moSampling<Neighbor>::monitorVec;
|
||||
using moSampling<Neighbor>::continuator;
|
||||
using moFitnessCloudSampling<Neighbor>::fitnessStat;
|
||||
|
||||
// delete the checkpoint with the wrong continuator
|
||||
delete checkpoint;
|
||||
/**
|
||||
* Default Constructor
|
||||
* @param _init initialisation method of the solution
|
||||
* @param _neighborhood neighborhood to get one random neighbor (supposed to be random neighborhood)
|
||||
* @param _fullEval Fitness function, full evaluation function
|
||||
* @param _eval neighbor evaluation, incremental evaluation function
|
||||
* @param _nbStep Number of step of the MH sampling
|
||||
*/
|
||||
moMHRndFitnessCloudSampling(eoInit<EOT> & _init,
|
||||
moNeighborhood<Neighbor> & _neighborhood,
|
||||
eoEvalFunc<EOT>& _fullEval,
|
||||
moEval<Neighbor>& _eval,
|
||||
unsigned int _nbStep) :
|
||||
moFitnessCloudSampling<Neighbor>(_init, _neighborhood, _fullEval, _eval, _nbStep),
|
||||
neighborFitnessStat(_neighborhood, _eval)
|
||||
{
|
||||
// delete the dummy local search
|
||||
delete localSearch;
|
||||
|
||||
// set the continuator
|
||||
continuator = localSearch->getContinuator();
|
||||
// Metropolis-Hasting sampling
|
||||
localSearch = new moMetropolisHasting<Neighbor>(_neighborhood, _fullEval, _eval, _nbStep);
|
||||
|
||||
// re-construction of the checkpoint
|
||||
checkpoint = new moCheckpoint<Neighbor>(*continuator);
|
||||
checkpoint->add(fitnessStat);
|
||||
checkpoint->add(*monitorVec[0]);
|
||||
// delete the checkpoint with the wrong continuator
|
||||
delete checkpoint;
|
||||
|
||||
// one random neighbor
|
||||
add(neighborFitnessStat);
|
||||
}
|
||||
// set the continuator
|
||||
continuator = localSearch->getContinuator();
|
||||
|
||||
// re-construction of the checkpoint
|
||||
checkpoint = new moCheckpoint<Neighbor>(*continuator);
|
||||
checkpoint->add(fitnessStat);
|
||||
checkpoint->add(*monitorVec[0]);
|
||||
|
||||
// one random neighbor
|
||||
add(neighborFitnessStat);
|
||||
}
|
||||
|
||||
protected:
|
||||
moNeighborFitnessStat< Neighbor > neighborFitnessStat;
|
||||
moNeighborFitnessStat< Neighbor > neighborFitnessStat;
|
||||
|
||||
};
|
||||
|
||||
|
|
|
|||
|
|
@ -46,78 +46,78 @@
|
|||
#include <sampling/moSampling.h>
|
||||
|
||||
/**
|
||||
* To compute the neutral degree:
|
||||
* To compute the neutral degree:
|
||||
* Sample the fitness of random solution in the search space (1er information)
|
||||
* and sample the neutral degree (2nd information), i.e. the number of neighbor solutions with the same fitness value
|
||||
* The values are collected during the random search
|
||||
*
|
||||
*
|
||||
*/
|
||||
template <class Neighbor>
|
||||
class moNeutralDegreeSampling : public moSampling<Neighbor>
|
||||
{
|
||||
public:
|
||||
typedef typename Neighbor::EOT EOT ;
|
||||
|
||||
using moSampling<Neighbor>::localSearch;
|
||||
typedef typename Neighbor::EOT EOT ;
|
||||
|
||||
/**
|
||||
* Default Constructor
|
||||
* @param _init initialisation method of the solution
|
||||
* @param _neighborhood neighborhood to compute the neutral degree
|
||||
* @param _fullEval Fitness function, full evaluation function
|
||||
* @param _eval neighbor evaluation, incremental evaluation function
|
||||
* @param _nbSol Number of solutions in the sample
|
||||
*/
|
||||
moNeutralDegreeSampling(eoInit<EOT> & _init,
|
||||
moNeighborhood<Neighbor> & _neighborhood,
|
||||
eoEvalFunc<EOT>& _fullEval,
|
||||
moEval<Neighbor>& _eval,
|
||||
unsigned int _nbSol) :
|
||||
moSampling<Neighbor>(_init, * new moRandomSearch<Neighbor>(_init, _fullEval, _nbSol), fitnessStat),
|
||||
neighborhoodStat(_neighborhood, _eval),
|
||||
ndStat(neighborhoodStat)
|
||||
{
|
||||
add(neighborhoodStat, false);
|
||||
add(ndStat);
|
||||
}
|
||||
using moSampling<Neighbor>::localSearch;
|
||||
|
||||
/**
|
||||
* Constructor with comparators
|
||||
* @param _init initialisation method of the solution
|
||||
* @param _neighborhood neighborhood to compute the neutral degree
|
||||
* @param _fullEval Fitness function, full evaluation function
|
||||
* @param _eval neighbor evaluation, incremental evaluation function
|
||||
* @param _neighborComparator a neighbor Comparator
|
||||
* @param _solNeighborComparator a comparator between a solution and a neighbor
|
||||
* @param _nbSol Number of solutions in the sample
|
||||
*/
|
||||
moNeutralDegreeSampling(eoInit<EOT> & _init,
|
||||
moNeighborhood<Neighbor> & _neighborhood,
|
||||
eoEvalFunc<EOT>& _fullEval,
|
||||
moEval<Neighbor>& _eval,
|
||||
moNeighborComparator<Neighbor>& _neighborComparator,
|
||||
moSolNeighborComparator<Neighbor>& _solNeighborComparator,
|
||||
unsigned int _nbSol) :
|
||||
moSampling<Neighbor>(_init, * new moRandomSearch<Neighbor>(_init, _fullEval, _nbSol), fitnessStat),
|
||||
neighborhoodStat(_neighborhood, _eval, _neighborComparator, _solNeighborComparator),
|
||||
ndStat(neighborhoodStat)
|
||||
{
|
||||
add(neighborhoodStat, false);
|
||||
add(ndStat);
|
||||
}
|
||||
/**
|
||||
* Default Constructor
|
||||
* @param _init initialisation method of the solution
|
||||
* @param _neighborhood neighborhood to compute the neutral degree
|
||||
* @param _fullEval Fitness function, full evaluation function
|
||||
* @param _eval neighbor evaluation, incremental evaluation function
|
||||
* @param _nbSol Number of solutions in the sample
|
||||
*/
|
||||
moNeutralDegreeSampling(eoInit<EOT> & _init,
|
||||
moNeighborhood<Neighbor> & _neighborhood,
|
||||
eoEvalFunc<EOT>& _fullEval,
|
||||
moEval<Neighbor>& _eval,
|
||||
unsigned int _nbSol) :
|
||||
moSampling<Neighbor>(_init, * new moRandomSearch<Neighbor>(_init, _fullEval, _nbSol), fitnessStat),
|
||||
neighborhoodStat(_neighborhood, _eval),
|
||||
ndStat(neighborhoodStat)
|
||||
{
|
||||
add(neighborhoodStat, false);
|
||||
add(ndStat);
|
||||
}
|
||||
|
||||
/**
|
||||
* default destructor
|
||||
*/
|
||||
~moNeutralDegreeSampling() {
|
||||
// delete the pointer on the local search which has been constructed in the constructor
|
||||
delete localSearch;
|
||||
}
|
||||
/**
|
||||
* Constructor with comparators
|
||||
* @param _init initialisation method of the solution
|
||||
* @param _neighborhood neighborhood to compute the neutral degree
|
||||
* @param _fullEval Fitness function, full evaluation function
|
||||
* @param _eval neighbor evaluation, incremental evaluation function
|
||||
* @param _neighborComparator a neighbor Comparator
|
||||
* @param _solNeighborComparator a comparator between a solution and a neighbor
|
||||
* @param _nbSol Number of solutions in the sample
|
||||
*/
|
||||
moNeutralDegreeSampling(eoInit<EOT> & _init,
|
||||
moNeighborhood<Neighbor> & _neighborhood,
|
||||
eoEvalFunc<EOT>& _fullEval,
|
||||
moEval<Neighbor>& _eval,
|
||||
moNeighborComparator<Neighbor>& _neighborComparator,
|
||||
moSolNeighborComparator<Neighbor>& _solNeighborComparator,
|
||||
unsigned int _nbSol) :
|
||||
moSampling<Neighbor>(_init, * new moRandomSearch<Neighbor>(_init, _fullEval, _nbSol), fitnessStat),
|
||||
neighborhoodStat(_neighborhood, _eval, _neighborComparator, _solNeighborComparator),
|
||||
ndStat(neighborhoodStat)
|
||||
{
|
||||
add(neighborhoodStat, false);
|
||||
add(ndStat);
|
||||
}
|
||||
|
||||
/**
|
||||
* default destructor
|
||||
*/
|
||||
~moNeutralDegreeSampling() {
|
||||
// delete the pointer on the local search which has been constructed in the constructor
|
||||
delete localSearch;
|
||||
}
|
||||
|
||||
protected:
|
||||
moFitnessStat<EOT> fitnessStat;
|
||||
moNeighborhoodStat< Neighbor > neighborhoodStat;
|
||||
moNeutralDegreeNeighborStat< Neighbor > ndStat;
|
||||
moFitnessStat<EOT> fitnessStat;
|
||||
moNeighborhoodStat< Neighbor > neighborhoodStat;
|
||||
moNeutralDegreeNeighborStat< Neighbor > ndStat;
|
||||
|
||||
};
|
||||
|
||||
|
|
|
|||
|
|
@ -58,8 +58,8 @@
|
|||
* To explore the evolvability of solutions in a neutral networks:
|
||||
* Perform a random neutral walk based on the neighborhood,
|
||||
* The measures of evolvability of solutions are collected during the random neutral walk
|
||||
* The distribution and autocorrelation can be computed from the serie of values
|
||||
*
|
||||
* The distribution and autocorrelation can be computed from the serie of values
|
||||
*
|
||||
* Informations collected:
|
||||
* - the current solution of the walk
|
||||
* - the distance from the starting solution
|
||||
|
|
@ -76,71 +76,71 @@ template <class Neighbor>
|
|||
class moNeutralWalkSampling : public moSampling<Neighbor>
|
||||
{
|
||||
public:
|
||||
typedef typename Neighbor::EOT EOT ;
|
||||
|
||||
using moSampling<Neighbor>::localSearch;
|
||||
typedef typename Neighbor::EOT EOT ;
|
||||
|
||||
/**
|
||||
* Default Constructor
|
||||
* @param _initSol the first the solution of the walk
|
||||
* @param _neighborhood neighborhood giving neighbor in random order
|
||||
* @param _fullEval Fitness function, full evaluation function
|
||||
* @param _eval neighbor evaluation, incremental evaluation function
|
||||
* @param _distance the distance to measure the distance from the initial solution
|
||||
* @param _nbStep Number of steps of the random walk
|
||||
*/
|
||||
moNeutralWalkSampling(EOT & _initSol,
|
||||
moNeighborhood<Neighbor> & _neighborhood,
|
||||
eoEvalFunc<EOT>& _fullEval,
|
||||
moEval<Neighbor>& _eval,
|
||||
eoDistance<EOT> & _distance,
|
||||
unsigned int _nbStep) :
|
||||
moSampling<Neighbor>(init, * new moRandomNeutralWalk<Neighbor>(_neighborhood, _fullEval, _eval, _nbStep), solutionStat),
|
||||
init(_initSol),
|
||||
distStat(_distance, _initSol),
|
||||
neighborhoodStat(_neighborhood, _eval),
|
||||
minStat(neighborhoodStat),
|
||||
averageStat(neighborhoodStat),
|
||||
stdStat(neighborhoodStat),
|
||||
maxStat(neighborhoodStat),
|
||||
nbSupStat(neighborhoodStat),
|
||||
nbInfStat(neighborhoodStat),
|
||||
sizeStat(neighborhoodStat),
|
||||
ndStat(neighborhoodStat)
|
||||
{
|
||||
add(neighborhoodStat, false);
|
||||
add(distStat);
|
||||
add(minStat);
|
||||
add(averageStat);
|
||||
add(stdStat);
|
||||
add(maxStat);
|
||||
add(sizeStat);
|
||||
add(nbInfStat);
|
||||
add(ndStat);
|
||||
add(nbSupStat);
|
||||
}
|
||||
using moSampling<Neighbor>::localSearch;
|
||||
|
||||
/**
|
||||
* default destructor
|
||||
*/
|
||||
~moNeutralWalkSampling() {
|
||||
// delete the pointer on the local search which has been constructed in the constructor
|
||||
delete localSearch;
|
||||
}
|
||||
/**
|
||||
* Default Constructor
|
||||
* @param _initSol the first the solution of the walk
|
||||
* @param _neighborhood neighborhood giving neighbor in random order
|
||||
* @param _fullEval Fitness function, full evaluation function
|
||||
* @param _eval neighbor evaluation, incremental evaluation function
|
||||
* @param _distance the distance to measure the distance from the initial solution
|
||||
* @param _nbStep Number of steps of the random walk
|
||||
*/
|
||||
moNeutralWalkSampling(EOT & _initSol,
|
||||
moNeighborhood<Neighbor> & _neighborhood,
|
||||
eoEvalFunc<EOT>& _fullEval,
|
||||
moEval<Neighbor>& _eval,
|
||||
eoDistance<EOT> & _distance,
|
||||
unsigned int _nbStep) :
|
||||
moSampling<Neighbor>(init, * new moRandomNeutralWalk<Neighbor>(_neighborhood, _fullEval, _eval, _nbStep), solutionStat),
|
||||
init(_initSol),
|
||||
distStat(_distance, _initSol),
|
||||
neighborhoodStat(_neighborhood, _eval),
|
||||
minStat(neighborhoodStat),
|
||||
averageStat(neighborhoodStat),
|
||||
stdStat(neighborhoodStat),
|
||||
maxStat(neighborhoodStat),
|
||||
nbSupStat(neighborhoodStat),
|
||||
nbInfStat(neighborhoodStat),
|
||||
sizeStat(neighborhoodStat),
|
||||
ndStat(neighborhoodStat)
|
||||
{
|
||||
add(neighborhoodStat, false);
|
||||
add(distStat);
|
||||
add(minStat);
|
||||
add(averageStat);
|
||||
add(stdStat);
|
||||
add(maxStat);
|
||||
add(sizeStat);
|
||||
add(nbInfStat);
|
||||
add(ndStat);
|
||||
add(nbSupStat);
|
||||
}
|
||||
|
||||
/**
|
||||
* default destructor
|
||||
*/
|
||||
~moNeutralWalkSampling() {
|
||||
// delete the pointer on the local search which has been constructed in the constructor
|
||||
delete localSearch;
|
||||
}
|
||||
|
||||
protected:
|
||||
moSolInit<EOT> init;
|
||||
moSolutionStat<EOT> solutionStat;
|
||||
moDistanceStat<EOT> distStat;
|
||||
moNeighborhoodStat< Neighbor > neighborhoodStat;
|
||||
moMinNeighborStat< Neighbor > minStat;
|
||||
moAverageFitnessNeighborStat< Neighbor > averageStat;
|
||||
moStdFitnessNeighborStat< Neighbor > stdStat;
|
||||
moMaxNeighborStat< Neighbor > maxStat;
|
||||
moNbSupNeighborStat< Neighbor > nbSupStat;
|
||||
moNbInfNeighborStat< Neighbor > nbInfStat;
|
||||
moSizeNeighborStat< Neighbor > sizeStat;
|
||||
moNeutralDegreeNeighborStat< Neighbor > ndStat;
|
||||
moSolInit<EOT> init;
|
||||
moSolutionStat<EOT> solutionStat;
|
||||
moDistanceStat<EOT> distStat;
|
||||
moNeighborhoodStat< Neighbor > neighborhoodStat;
|
||||
moMinNeighborStat< Neighbor > minStat;
|
||||
moAverageFitnessNeighborStat< Neighbor > averageStat;
|
||||
moStdFitnessNeighborStat< Neighbor > stdStat;
|
||||
moMaxNeighborStat< Neighbor > maxStat;
|
||||
moNbSupNeighborStat< Neighbor > nbSupStat;
|
||||
moNbInfNeighborStat< Neighbor > nbInfStat;
|
||||
moSizeNeighborStat< Neighbor > sizeStat;
|
||||
moNeutralDegreeNeighborStat< Neighbor > ndStat;
|
||||
|
||||
};
|
||||
|
||||
|
|
|
|||
|
|
@ -44,66 +44,66 @@
|
|||
#include <sampling/moFitnessCloudSampling.h>
|
||||
|
||||
/**
|
||||
* To compute an estimation of the fitness cloud,
|
||||
* i.e. the scatter plot of solution fitness versus neighbor fitness:
|
||||
* To compute an estimation of the fitness cloud,
|
||||
* i.e. the scatter plot of solution fitness versus neighbor fitness:
|
||||
*
|
||||
* Sample the fitness of random solution in the search space
|
||||
* and the best fitness of k random neighbor
|
||||
*
|
||||
* The values are collected during the random search
|
||||
*
|
||||
*
|
||||
*/
|
||||
template <class Neighbor>
|
||||
class moRndBestFitnessCloudSampling : public moFitnessCloudSampling<Neighbor>
|
||||
{
|
||||
public:
|
||||
typedef typename Neighbor::EOT EOT ;
|
||||
|
||||
using moSampling<Neighbor>::localSearch;
|
||||
using moSampling<Neighbor>::checkpoint;
|
||||
using moSampling<Neighbor>::monitorVec;
|
||||
using moSampling<Neighbor>::continuator;
|
||||
using moFitnessCloudSampling<Neighbor>::fitnessStat;
|
||||
typedef typename Neighbor::EOT EOT ;
|
||||
|
||||
/**
|
||||
* Default Constructor
|
||||
* @param _init initialisation method of the solution
|
||||
* @param _neighborhood neighborhood to get one random neighbor (supposed to be random neighborhood)
|
||||
* @param _fullEval Fitness function, full evaluation function
|
||||
* @param _eval neighbor evaluation, incremental evaluation function
|
||||
* @param _nbSol Number of solutions in the sample
|
||||
*/
|
||||
moRndBestFitnessCloudSampling(eoInit<EOT> & _init,
|
||||
moNeighborhood<Neighbor> & _neighborhood,
|
||||
eoEvalFunc<EOT>& _fullEval,
|
||||
moEval<Neighbor>& _eval,
|
||||
unsigned int _nbSol) :
|
||||
moFitnessCloudSampling<Neighbor>(_init, _neighborhood, _fullEval, _eval, _nbSol),
|
||||
neighborBestStat(_neighborhood, _eval)
|
||||
{
|
||||
// delete the dummy local search
|
||||
delete localSearch;
|
||||
|
||||
// random sampling
|
||||
localSearch = new moRandomSearch<Neighbor>(_init, _fullEval, _nbSol);
|
||||
using moSampling<Neighbor>::localSearch;
|
||||
using moSampling<Neighbor>::checkpoint;
|
||||
using moSampling<Neighbor>::monitorVec;
|
||||
using moSampling<Neighbor>::continuator;
|
||||
using moFitnessCloudSampling<Neighbor>::fitnessStat;
|
||||
|
||||
// delete the checkpoint with the wrong continuator
|
||||
delete checkpoint;
|
||||
/**
|
||||
* Default Constructor
|
||||
* @param _init initialisation method of the solution
|
||||
* @param _neighborhood neighborhood to get one random neighbor (supposed to be random neighborhood)
|
||||
* @param _fullEval Fitness function, full evaluation function
|
||||
* @param _eval neighbor evaluation, incremental evaluation function
|
||||
* @param _nbSol Number of solutions in the sample
|
||||
*/
|
||||
moRndBestFitnessCloudSampling(eoInit<EOT> & _init,
|
||||
moNeighborhood<Neighbor> & _neighborhood,
|
||||
eoEvalFunc<EOT>& _fullEval,
|
||||
moEval<Neighbor>& _eval,
|
||||
unsigned int _nbSol) :
|
||||
moFitnessCloudSampling<Neighbor>(_init, _neighborhood, _fullEval, _eval, _nbSol),
|
||||
neighborBestStat(_neighborhood, _eval)
|
||||
{
|
||||
// delete the dummy local search
|
||||
delete localSearch;
|
||||
|
||||
// set the continuator
|
||||
continuator = localSearch->getContinuator();
|
||||
// random sampling
|
||||
localSearch = new moRandomSearch<Neighbor>(_init, _fullEval, _nbSol);
|
||||
|
||||
// re-construction of the checkpoint
|
||||
checkpoint = new moCheckpoint<Neighbor>(*continuator);
|
||||
checkpoint->add(fitnessStat);
|
||||
checkpoint->add(*monitorVec[0]);
|
||||
// delete the checkpoint with the wrong continuator
|
||||
delete checkpoint;
|
||||
|
||||
// one random neighbor
|
||||
add(neighborBestStat);
|
||||
}
|
||||
// set the continuator
|
||||
continuator = localSearch->getContinuator();
|
||||
|
||||
// re-construction of the checkpoint
|
||||
checkpoint = new moCheckpoint<Neighbor>(*continuator);
|
||||
checkpoint->add(fitnessStat);
|
||||
checkpoint->add(*monitorVec[0]);
|
||||
|
||||
// one random neighbor
|
||||
add(neighborBestStat);
|
||||
}
|
||||
|
||||
protected:
|
||||
moNeighborBestStat< Neighbor > neighborBestStat;
|
||||
moNeighborBestStat< Neighbor > neighborBestStat;
|
||||
|
||||
};
|
||||
|
||||
|
|
|
|||
|
|
@ -44,66 +44,66 @@
|
|||
#include <sampling/moFitnessCloudSampling.h>
|
||||
|
||||
/**
|
||||
* To compute an estimation of the fitness cloud,
|
||||
* i.e. the scatter plot of solution fitness versus neighbor fitness:
|
||||
* To compute an estimation of the fitness cloud,
|
||||
* i.e. the scatter plot of solution fitness versus neighbor fitness:
|
||||
*
|
||||
* Sample the fitness of random solution in the search space
|
||||
* and the fitness of one random neighbor
|
||||
*
|
||||
* The values are collected during the random search
|
||||
*
|
||||
*
|
||||
*/
|
||||
template <class Neighbor>
|
||||
class moRndRndFitnessCloudSampling : public moFitnessCloudSampling<Neighbor>
|
||||
{
|
||||
public:
|
||||
typedef typename Neighbor::EOT EOT ;
|
||||
|
||||
using moSampling<Neighbor>::localSearch;
|
||||
using moSampling<Neighbor>::checkpoint;
|
||||
using moSampling<Neighbor>::monitorVec;
|
||||
using moSampling<Neighbor>::continuator;
|
||||
using moFitnessCloudSampling<Neighbor>::fitnessStat;
|
||||
typedef typename Neighbor::EOT EOT ;
|
||||
|
||||
/**
|
||||
* Default Constructor
|
||||
* @param _init initialisation method of the solution
|
||||
* @param _neighborhood neighborhood to get one random neighbor (supposed to be random neighborhood)
|
||||
* @param _fullEval Fitness function, full evaluation function
|
||||
* @param _eval neighbor evaluation, incremental evaluation function
|
||||
* @param _nbSol Number of solutions in the sample
|
||||
*/
|
||||
moRndRndFitnessCloudSampling(eoInit<EOT> & _init,
|
||||
moNeighborhood<Neighbor> & _neighborhood,
|
||||
eoEvalFunc<EOT>& _fullEval,
|
||||
moEval<Neighbor>& _eval,
|
||||
unsigned int _nbSol) :
|
||||
moFitnessCloudSampling<Neighbor>(_init, _neighborhood, _fullEval, _eval, _nbSol),
|
||||
neighborFitnessStat(_neighborhood, _eval)
|
||||
{
|
||||
// delete the dummy local search
|
||||
delete localSearch;
|
||||
|
||||
// random sampling
|
||||
localSearch = new moRandomSearch<Neighbor>(_init, _fullEval, _nbSol);
|
||||
using moSampling<Neighbor>::localSearch;
|
||||
using moSampling<Neighbor>::checkpoint;
|
||||
using moSampling<Neighbor>::monitorVec;
|
||||
using moSampling<Neighbor>::continuator;
|
||||
using moFitnessCloudSampling<Neighbor>::fitnessStat;
|
||||
|
||||
// delete the checkpoint with the wrong continuator
|
||||
delete checkpoint;
|
||||
/**
|
||||
* Default Constructor
|
||||
* @param _init initialisation method of the solution
|
||||
* @param _neighborhood neighborhood to get one random neighbor (supposed to be random neighborhood)
|
||||
* @param _fullEval Fitness function, full evaluation function
|
||||
* @param _eval neighbor evaluation, incremental evaluation function
|
||||
* @param _nbSol Number of solutions in the sample
|
||||
*/
|
||||
moRndRndFitnessCloudSampling(eoInit<EOT> & _init,
|
||||
moNeighborhood<Neighbor> & _neighborhood,
|
||||
eoEvalFunc<EOT>& _fullEval,
|
||||
moEval<Neighbor>& _eval,
|
||||
unsigned int _nbSol) :
|
||||
moFitnessCloudSampling<Neighbor>(_init, _neighborhood, _fullEval, _eval, _nbSol),
|
||||
neighborFitnessStat(_neighborhood, _eval)
|
||||
{
|
||||
// delete the dummy local search
|
||||
delete localSearch;
|
||||
|
||||
// set the continuator
|
||||
continuator = localSearch->getContinuator();
|
||||
// random sampling
|
||||
localSearch = new moRandomSearch<Neighbor>(_init, _fullEval, _nbSol);
|
||||
|
||||
// re-construction of the checkpoint
|
||||
checkpoint = new moCheckpoint<Neighbor>(*continuator);
|
||||
checkpoint->add(fitnessStat);
|
||||
checkpoint->add(*monitorVec[0]);
|
||||
// delete the checkpoint with the wrong continuator
|
||||
delete checkpoint;
|
||||
|
||||
// one random neighbor
|
||||
add(neighborFitnessStat);
|
||||
}
|
||||
// set the continuator
|
||||
continuator = localSearch->getContinuator();
|
||||
|
||||
// re-construction of the checkpoint
|
||||
checkpoint = new moCheckpoint<Neighbor>(*continuator);
|
||||
checkpoint->add(fitnessStat);
|
||||
checkpoint->add(*monitorVec[0]);
|
||||
|
||||
// one random neighbor
|
||||
add(neighborFitnessStat);
|
||||
}
|
||||
|
||||
protected:
|
||||
moNeighborFitnessStat< Neighbor > neighborFitnessStat;
|
||||
moNeighborFitnessStat< Neighbor > neighborFitnessStat;
|
||||
|
||||
};
|
||||
|
||||
|
|
|
|||
|
|
@ -51,159 +51,159 @@
|
|||
* To sample the search space:
|
||||
* A local search is used to sample the search space
|
||||
* Some statistics are computed at each step of the local search
|
||||
*
|
||||
*
|
||||
* Can be used to study the fitness landscape
|
||||
*/
|
||||
template <class Neighbor>
|
||||
class moSampling : public eoF<void>
|
||||
{
|
||||
public:
|
||||
typedef typename Neighbor::EOT EOT ;
|
||||
|
||||
/**
|
||||
* Default Constructor
|
||||
* @param _init initialisation method of the solution
|
||||
* @param _localSearch local search to sample the search space
|
||||
* @param _stat statistic to compute during the search
|
||||
* @param _monitoring the statistic is saved into the monitor if true
|
||||
*/
|
||||
template <class ValueType>
|
||||
moSampling(eoInit<EOT> & _init, moLocalSearch<Neighbor> & _localSearch, moStat<EOT,ValueType> & _stat, bool _monitoring = true) : init(_init), localSearch(&_localSearch), continuator(_localSearch.getContinuator())
|
||||
{
|
||||
checkpoint = new moCheckpoint<Neighbor>(*continuator);
|
||||
add(_stat, _monitoring);
|
||||
}
|
||||
typedef typename Neighbor::EOT EOT ;
|
||||
|
||||
/**
|
||||
* default destructor
|
||||
*/
|
||||
~moSampling() {
|
||||
// delete all monitors
|
||||
for(unsigned i = 0; i < monitorVec.size(); i++)
|
||||
delete monitorVec[i];
|
||||
|
||||
// delete the checkpoint
|
||||
delete checkpoint ;
|
||||
}
|
||||
|
||||
/**
|
||||
* Add a statistic
|
||||
* @param _stat another statistic to compute during the search
|
||||
* @param _monitoring the statistic is saved into the monitor if true
|
||||
*/
|
||||
template< class ValueType >
|
||||
void add(moStat<EOT, ValueType> & _stat, bool _monitoring = true) {
|
||||
checkpoint->add(_stat);
|
||||
|
||||
if (_monitoring) {
|
||||
moVectorMonitor<EOT> * monitor = new moVectorMonitor<EOT>(_stat);
|
||||
monitorVec.push_back(monitor);
|
||||
checkpoint->add(*monitor);
|
||||
/**
|
||||
* Default Constructor
|
||||
* @param _init initialisation method of the solution
|
||||
* @param _localSearch local search to sample the search space
|
||||
* @param _stat statistic to compute during the search
|
||||
* @param _monitoring the statistic is saved into the monitor if true
|
||||
*/
|
||||
template <class ValueType>
|
||||
moSampling(eoInit<EOT> & _init, moLocalSearch<Neighbor> & _localSearch, moStat<EOT,ValueType> & _stat, bool _monitoring = true) : init(_init), localSearch(&_localSearch), continuator(_localSearch.getContinuator())
|
||||
{
|
||||
checkpoint = new moCheckpoint<Neighbor>(*continuator);
|
||||
add(_stat, _monitoring);
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* To sample the search and get the statistics
|
||||
* the statistics are stored in the moVectorMonitor vector
|
||||
*/
|
||||
void operator()(void) {
|
||||
// clear all statistic vectors
|
||||
for(unsigned i = 0; i < monitorVec.size(); i++)
|
||||
monitorVec[i]->clear();
|
||||
/**
|
||||
* default destructor
|
||||
*/
|
||||
~moSampling() {
|
||||
// delete all monitors
|
||||
for (unsigned i = 0; i < monitorVec.size(); i++)
|
||||
delete monitorVec[i];
|
||||
|
||||
// change the checkpoint to compute the statistics
|
||||
localSearch->setContinuator(*checkpoint);
|
||||
|
||||
// the initial solution
|
||||
EOT solution;
|
||||
|
||||
// initialisation of the solution
|
||||
init(solution);
|
||||
|
||||
// compute the sampling
|
||||
(*localSearch)(solution);
|
||||
|
||||
// set back to initial continuator
|
||||
localSearch->setContinuator(*continuator);
|
||||
}
|
||||
|
||||
/**
|
||||
* to export the vectors of values into one file
|
||||
* @param _filename file name
|
||||
* @param _delim delimiter between statistics
|
||||
*/
|
||||
void fileExport(std::string _filename, std::string _delim = " ") {
|
||||
// create file
|
||||
std::ofstream os(_filename.c_str());
|
||||
|
||||
if (!os) {
|
||||
std::string str = "moSampling: Could not open " + _filename;
|
||||
throw std::runtime_error(str);
|
||||
// delete the checkpoint
|
||||
delete checkpoint ;
|
||||
}
|
||||
|
||||
// all vector have the same size
|
||||
unsigned vecSize = monitorVec[0]->size();
|
||||
|
||||
for(unsigned int i = 0; i < vecSize; i++) {
|
||||
os << monitorVec[0]->getValue(i);
|
||||
|
||||
for(unsigned int j = 1; j < monitorVec.size(); j++) {
|
||||
os << _delim.c_str() << monitorVec[j]->getValue(i);
|
||||
}
|
||||
|
||||
os << std::endl ;
|
||||
/**
|
||||
* Add a statistic
|
||||
* @param _stat another statistic to compute during the search
|
||||
* @param _monitoring the statistic is saved into the monitor if true
|
||||
*/
|
||||
template< class ValueType >
|
||||
void add(moStat<EOT, ValueType> & _stat, bool _monitoring = true) {
|
||||
checkpoint->add(_stat);
|
||||
|
||||
if (_monitoring) {
|
||||
moVectorMonitor<EOT> * monitor = new moVectorMonitor<EOT>(_stat);
|
||||
monitorVec.push_back(monitor);
|
||||
checkpoint->add(*monitor);
|
||||
}
|
||||
}
|
||||
|
||||
}
|
||||
|
||||
/**
|
||||
* to export one vector of values into a file
|
||||
* @param _col number of vector to print into file
|
||||
* @param _filename file name
|
||||
*/
|
||||
void fileExport(unsigned int _col, std::string _filename) {
|
||||
if (_col >= monitorVec.size()) {
|
||||
std::string str = "moSampling: Could not export into file the vector. The index does not exists (too large)";
|
||||
throw std::runtime_error(str);
|
||||
/**
|
||||
* To sample the search and get the statistics
|
||||
* the statistics are stored in the moVectorMonitor vector
|
||||
*/
|
||||
void operator()(void) {
|
||||
// clear all statistic vectors
|
||||
for (unsigned i = 0; i < monitorVec.size(); i++)
|
||||
monitorVec[i]->clear();
|
||||
|
||||
// change the checkpoint to compute the statistics
|
||||
localSearch->setContinuator(*checkpoint);
|
||||
|
||||
// the initial solution
|
||||
EOT solution;
|
||||
|
||||
// initialisation of the solution
|
||||
init(solution);
|
||||
|
||||
// compute the sampling
|
||||
(*localSearch)(solution);
|
||||
|
||||
// set back to initial continuator
|
||||
localSearch->setContinuator(*continuator);
|
||||
}
|
||||
|
||||
monitorVec[_col]->fileExport(_filename);
|
||||
}
|
||||
|
||||
/**
|
||||
* to get one vector of values
|
||||
* @param _numStat number of stattistics to get (in order of creation)
|
||||
* @return the vector of value (all values are converted in double)
|
||||
*/
|
||||
const std::vector<double> & getValues(unsigned int _numStat) {
|
||||
return monitorVec[_numStat]->getValues();
|
||||
}
|
||||
/**
|
||||
* to export the vectors of values into one file
|
||||
* @param _filename file name
|
||||
* @param _delim delimiter between statistics
|
||||
*/
|
||||
void fileExport(std::string _filename, std::string _delim = " ") {
|
||||
// create file
|
||||
std::ofstream os(_filename.c_str());
|
||||
|
||||
/**
|
||||
* to get one vector of solutions values
|
||||
* @param _numStat number of stattistics to get (in order of creation)
|
||||
* @return the vector of value (all values are converted in double)
|
||||
*/
|
||||
const std::vector<EOT> & getSolutions(unsigned int _numStat) {
|
||||
return monitorVec[_numStat]->getSolutions();
|
||||
}
|
||||
if (!os) {
|
||||
std::string str = "moSampling: Could not open " + _filename;
|
||||
throw std::runtime_error(str);
|
||||
}
|
||||
|
||||
// all vector have the same size
|
||||
unsigned vecSize = monitorVec[0]->size();
|
||||
|
||||
for (unsigned int i = 0; i < vecSize; i++) {
|
||||
os << monitorVec[0]->getValue(i);
|
||||
|
||||
for (unsigned int j = 1; j < monitorVec.size(); j++) {
|
||||
os << _delim.c_str() << monitorVec[j]->getValue(i);
|
||||
}
|
||||
|
||||
os << std::endl ;
|
||||
}
|
||||
|
||||
}
|
||||
|
||||
/**
|
||||
* to export one vector of values into a file
|
||||
* @param _col number of vector to print into file
|
||||
* @param _filename file name
|
||||
*/
|
||||
void fileExport(unsigned int _col, std::string _filename) {
|
||||
if (_col >= monitorVec.size()) {
|
||||
std::string str = "moSampling: Could not export into file the vector. The index does not exists (too large)";
|
||||
throw std::runtime_error(str);
|
||||
}
|
||||
|
||||
monitorVec[_col]->fileExport(_filename);
|
||||
}
|
||||
|
||||
/**
|
||||
* to get one vector of values
|
||||
* @param _numStat number of stattistics to get (in order of creation)
|
||||
* @return the vector of value (all values are converted in double)
|
||||
*/
|
||||
const std::vector<double> & getValues(unsigned int _numStat) {
|
||||
return monitorVec[_numStat]->getValues();
|
||||
}
|
||||
|
||||
/**
|
||||
* to get one vector of solutions values
|
||||
* @param _numStat number of stattistics to get (in order of creation)
|
||||
* @return the vector of value (all values are converted in double)
|
||||
*/
|
||||
const std::vector<EOT> & getSolutions(unsigned int _numStat) {
|
||||
return monitorVec[_numStat]->getSolutions();
|
||||
}
|
||||
|
||||
/**
|
||||
* @return name of the class
|
||||
*/
|
||||
virtual std::string className(void) const {
|
||||
return "moSampling";
|
||||
}
|
||||
|
||||
/**
|
||||
* @return name of the class
|
||||
*/
|
||||
virtual std::string className(void) const {
|
||||
return "moSampling";
|
||||
}
|
||||
|
||||
protected:
|
||||
eoInit<EOT> & init;
|
||||
moLocalSearch<Neighbor> * localSearch;
|
||||
eoInit<EOT> & init;
|
||||
moLocalSearch<Neighbor> * localSearch;
|
||||
|
||||
moContinuator<Neighbor> * continuator;
|
||||
moCheckpoint<Neighbor> * checkpoint;
|
||||
moContinuator<Neighbor> * continuator;
|
||||
moCheckpoint<Neighbor> * checkpoint;
|
||||
|
||||
// std::vector<moStatBase<EOT>*> statVec;
|
||||
std::vector< moVectorMonitor<EOT> *> monitorVec;
|
||||
// std::vector<moStatBase<EOT>*> statVec;
|
||||
std::vector< moVectorMonitor<EOT> *> monitorVec;
|
||||
|
||||
};
|
||||
|
||||
|
|
|
|||
|
|
@ -41,174 +41,174 @@
|
|||
|
||||
/**
|
||||
* Tools to compute some basic statistics
|
||||
*
|
||||
*
|
||||
* Remember it is better to use some statistic tool like R, etc.
|
||||
* But it could be usefull to have here in paradisEO
|
||||
*/
|
||||
class moStatistics
|
||||
class moStatistics
|
||||
{
|
||||
public:
|
||||
/**
|
||||
* Default Constructor
|
||||
*/
|
||||
moStatistics() { }
|
||||
/**
|
||||
* Default Constructor
|
||||
*/
|
||||
moStatistics() { }
|
||||
|
||||
/**
|
||||
* To compute min, max , average and standard deviation of a vector of double
|
||||
*
|
||||
* @param data vector of double
|
||||
* @param min to compute
|
||||
* @param max to compute
|
||||
* @param avg average to compute
|
||||
* @param std standard deviation to compute
|
||||
*/
|
||||
void basic(const std::vector<double> & data,
|
||||
double & min, double & max, double & avg, double & std) {
|
||||
|
||||
if (data.size() == 0) {
|
||||
min = 0.0;
|
||||
max = 0.0;
|
||||
avg = 0.0;
|
||||
std = 0.0;
|
||||
} else {
|
||||
unsigned int n = data.size();
|
||||
/**
|
||||
* To compute min, max , average and standard deviation of a vector of double
|
||||
*
|
||||
* @param data vector of double
|
||||
* @param min to compute
|
||||
* @param max to compute
|
||||
* @param avg average to compute
|
||||
* @param std standard deviation to compute
|
||||
*/
|
||||
void basic(const std::vector<double> & data,
|
||||
double & min, double & max, double & avg, double & std) {
|
||||
|
||||
min = data[0];
|
||||
max = data[0];
|
||||
avg = 0.0;
|
||||
std = 0.0;
|
||||
if (data.size() == 0) {
|
||||
min = 0.0;
|
||||
max = 0.0;
|
||||
avg = 0.0;
|
||||
std = 0.0;
|
||||
} else {
|
||||
unsigned int n = data.size();
|
||||
|
||||
double d;
|
||||
for(unsigned int i = 0; i < n; i++) {
|
||||
d = data[i];
|
||||
if (d < min)
|
||||
min = d;
|
||||
if (max < d)
|
||||
max = d;
|
||||
avg += d;
|
||||
std += d * d;
|
||||
}
|
||||
min = data[0];
|
||||
max = data[0];
|
||||
avg = 0.0;
|
||||
std = 0.0;
|
||||
|
||||
avg /= n;
|
||||
double d;
|
||||
for (unsigned int i = 0; i < n; i++) {
|
||||
d = data[i];
|
||||
if (d < min)
|
||||
min = d;
|
||||
if (max < d)
|
||||
max = d;
|
||||
avg += d;
|
||||
std += d * d;
|
||||
}
|
||||
|
||||
std = std / n - avg * avg ;
|
||||
if (std > 0)
|
||||
std = sqrt(std);
|
||||
avg /= n;
|
||||
|
||||
std = std / n - avg * avg ;
|
||||
if (std > 0)
|
||||
std = sqrt(std);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* To compute the distance between solutions
|
||||
*
|
||||
* @param data vector of solutions
|
||||
* @param distance method to compute the distance
|
||||
* @param matrix matrix of distances between solutions
|
||||
*/
|
||||
template <class EOT>
|
||||
void distances(const std::vector<EOT> & data, eoDistance<EOT> & distance,
|
||||
std::vector< std::vector<double> > & matrix) {
|
||||
if (data.size() == 0) {
|
||||
matrix.resize(0);
|
||||
} else {
|
||||
unsigned int n = data.size();
|
||||
/**
|
||||
* To compute the distance between solutions
|
||||
*
|
||||
* @param data vector of solutions
|
||||
* @param distance method to compute the distance
|
||||
* @param matrix matrix of distances between solutions
|
||||
*/
|
||||
template <class EOT>
|
||||
void distances(const std::vector<EOT> & data, eoDistance<EOT> & distance,
|
||||
std::vector< std::vector<double> > & matrix) {
|
||||
if (data.size() == 0) {
|
||||
matrix.resize(0);
|
||||
} else {
|
||||
unsigned int n = data.size();
|
||||
|
||||
matrix.resize(n);
|
||||
for(unsigned i = 0; i < n; i++)
|
||||
matrix[i].resize(n);
|
||||
matrix.resize(n);
|
||||
for (unsigned i = 0; i < n; i++)
|
||||
matrix[i].resize(n);
|
||||
|
||||
unsigned j;
|
||||
for(unsigned i = 0; i < n; i++) {
|
||||
matrix[i][i] = 0.0;
|
||||
for(j = 0; j < i; j++) {
|
||||
matrix[i][j] = distance(data[i], data[j]);
|
||||
matrix[j][i] = matrix[i][j];
|
||||
}
|
||||
}
|
||||
unsigned j;
|
||||
for (unsigned i = 0; i < n; i++) {
|
||||
matrix[i][i] = 0.0;
|
||||
for (j = 0; j < i; j++) {
|
||||
matrix[i][j] = distance(data[i], data[j]);
|
||||
matrix[j][i] = matrix[i][j];
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* To compute the autocorrelation and partial autocorrelation
|
||||
*
|
||||
* @param data vector of double
|
||||
* @param nbS number of correlation coefficients
|
||||
* @param rho autocorrelation coefficients
|
||||
* @param phi partial autocorrelation coefficients
|
||||
*/
|
||||
void autocorrelation(const std::vector<double> & data, unsigned int nbS,
|
||||
std::vector<double> & rho, std::vector<double> & phi) {
|
||||
if (data.size() == 0) {
|
||||
rho.resize(0);
|
||||
phi.resize(0);
|
||||
} else {
|
||||
unsigned int n = data.size();
|
||||
/**
|
||||
* To compute the autocorrelation and partial autocorrelation
|
||||
*
|
||||
* @param data vector of double
|
||||
* @param nbS number of correlation coefficients
|
||||
* @param rho autocorrelation coefficients
|
||||
* @param phi partial autocorrelation coefficients
|
||||
*/
|
||||
void autocorrelation(const std::vector<double> & data, unsigned int nbS,
|
||||
std::vector<double> & rho, std::vector<double> & phi) {
|
||||
if (data.size() == 0) {
|
||||
rho.resize(0);
|
||||
phi.resize(0);
|
||||
} else {
|
||||
unsigned int n = data.size();
|
||||
|
||||
double cov[nbS+1];
|
||||
double m[nbS+1];
|
||||
double sig[nbS+1];
|
||||
double cov[nbS+1];
|
||||
double m[nbS+1];
|
||||
double sig[nbS+1];
|
||||
|
||||
rho.resize(nbS+1);
|
||||
phi.resize(nbS+1);
|
||||
rho[0] = 1.0;
|
||||
phi[0] = 1.0; // ?
|
||||
|
||||
unsigned s, k;
|
||||
rho.resize(nbS+1);
|
||||
phi.resize(nbS+1);
|
||||
rho[0] = 1.0;
|
||||
phi[0] = 1.0; // ?
|
||||
|
||||
for(s = 0; s <= nbS; s++) {
|
||||
cov[s] = 0;
|
||||
m[s] = 0;
|
||||
sig[s] = 0;
|
||||
}
|
||||
unsigned s, k;
|
||||
|
||||
double m0, s0;
|
||||
unsigned j;
|
||||
for (s = 0; s <= nbS; s++) {
|
||||
cov[s] = 0;
|
||||
m[s] = 0;
|
||||
sig[s] = 0;
|
||||
}
|
||||
|
||||
k = 0;
|
||||
s = nbS;
|
||||
while (s > 0) {
|
||||
while (k + s < n) {
|
||||
for(j = 0; j <= s; j++) {
|
||||
m[j] += data[k+j];
|
||||
sig[j] += data[k+j] * data[k+j];
|
||||
cov[j] += data[k] * data[k+j];
|
||||
}
|
||||
k++;
|
||||
}
|
||||
|
||||
m[s] /= n - s;
|
||||
sig[s] = sig[s] / (n - s) - m[s] * m[s];
|
||||
if (sig[s] <= 0)
|
||||
sig[s] = 0;
|
||||
else
|
||||
sig[s] = sqrt(sig[s]);
|
||||
m0 = m[0] / (n - s);
|
||||
s0 = sqrt(sig[0] / (n - s) - m0 * m0);
|
||||
cov[s] = cov[s] / (n - s) - (m[0] / (n - s)) * m[s];
|
||||
rho[s] = cov[s] / (sig[s] * s0);
|
||||
s--;
|
||||
}
|
||||
double m0, s0;
|
||||
unsigned j;
|
||||
|
||||
double phi2[nbS+1][nbS+1];
|
||||
double tmp1, tmp2;
|
||||
k = 0;
|
||||
s = nbS;
|
||||
while (s > 0) {
|
||||
while (k + s < n) {
|
||||
for (j = 0; j <= s; j++) {
|
||||
m[j] += data[k+j];
|
||||
sig[j] += data[k+j] * data[k+j];
|
||||
cov[j] += data[k] * data[k+j];
|
||||
}
|
||||
k++;
|
||||
}
|
||||
|
||||
phi2[1][1] = rho[1];
|
||||
for(k = 2; k <= nbS; k++) {
|
||||
tmp1 = 0;
|
||||
tmp2 = 0;
|
||||
for(j = 1; j < k; j++) {
|
||||
tmp1 += phi2[k-1][j] * rho[k-j];
|
||||
tmp2 += phi2[k-1][j] * rho[j];
|
||||
}
|
||||
phi2[k][k] = (rho[k] - tmp1) / (1 - tmp2);
|
||||
for(j = 1; j < k; j++)
|
||||
phi2[k][j] = phi2[k-1][j] - phi2[k][k] * phi2[k-1][k-j];
|
||||
}
|
||||
|
||||
for(j = 1; j <= nbS; j++)
|
||||
phi[j] = phi2[j][j];
|
||||
|
||||
m[s] /= n - s;
|
||||
sig[s] = sig[s] / (n - s) - m[s] * m[s];
|
||||
if (sig[s] <= 0)
|
||||
sig[s] = 0;
|
||||
else
|
||||
sig[s] = sqrt(sig[s]);
|
||||
m0 = m[0] / (n - s);
|
||||
s0 = sqrt(sig[0] / (n - s) - m0 * m0);
|
||||
cov[s] = cov[s] / (n - s) - (m[0] / (n - s)) * m[s];
|
||||
rho[s] = cov[s] / (sig[s] * s0);
|
||||
s--;
|
||||
}
|
||||
|
||||
double phi2[nbS+1][nbS+1];
|
||||
double tmp1, tmp2;
|
||||
|
||||
phi2[1][1] = rho[1];
|
||||
for (k = 2; k <= nbS; k++) {
|
||||
tmp1 = 0;
|
||||
tmp2 = 0;
|
||||
for (j = 1; j < k; j++) {
|
||||
tmp1 += phi2[k-1][j] * rho[k-j];
|
||||
tmp2 += phi2[k-1][j] * rho[j];
|
||||
}
|
||||
phi2[k][k] = (rho[k] - tmp1) / (1 - tmp2);
|
||||
for (j = 1; j < k; j++)
|
||||
phi2[k][j] = phi2[k-1][j] - phi2[k][k] * phi2[k-1][k-j];
|
||||
}
|
||||
|
||||
for (j = 1; j <= nbS; j++)
|
||||
phi[j] = phi2[j][j];
|
||||
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
};
|
||||
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue