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ulcessvp |
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#ifndef SEQ_OPTIMIZE_TEMPLATE_H
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#define SEQ_OPTIMIZE_TEMPLATE_H
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#include <iostream>
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#include <cstdlib>
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#include <sys/time.h>
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ulcessvp |
16 |
#ifdef _OPENMP
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ulcessvp |
12 |
#include <omp.h>
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#endif
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class Siman {
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int _nvars;
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unsigned _seed;
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unsigned _seedM;
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unsigned _seedP;
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int _l;
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int _nt;
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int _NT;
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int _ns;
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int _NS;
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IntVector _param;
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DoubleVector* _x;
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DoubleVector* _lowerb;
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DoubleVector* _upperb;
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DoubleVector _vm;
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double _t;
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double _rt;
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IntVector _nacp;
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int _nacc; //The number of accepted function evaluations
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int _nrej; //The number of rejected function evaluations
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int _naccmet; //The number of metropolis accepted function evaluations
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double _nsdiv;
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int _tempcheck;
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double _simanneps;
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DoubleVector _fstar;
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double _lratio;
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double _uratio;
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double _cs;
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DoubleVector* _bestx;
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int _scale;
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int* _converge; //NN
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double* _score; //NN
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public:
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//Required Default constructor : Params()
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Siman(){
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_nvars = 0;
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_seed = 1234;
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_seedM = 1234;
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_seedP = 1234;
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_l = 0;
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_nt = 0;
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_NT = 0;
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_ns = 0;
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_NS = 0;
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_param = IntVector();
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_x = NULL;
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_lowerb = NULL;
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_upperb = NULL;
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_vm = DoubleVector();
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_t = 0.;
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_rt = 0.;
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_nacp = IntVector();
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_nacc = 0; //The number of accepted function evaluations
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_nrej = 0; //The number of rejected function evaluations
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_naccmet = 0; //The number of metropolis accepted function evaluations
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_nsdiv = 0;
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_tempcheck = 0;
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_simanneps = 0;
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_fstar = DoubleVector();
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_lratio = 0.;
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_uratio = 0.;
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_cs = 0.;
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_bestx = NULL;
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_scale = 0;
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_converge =NULL;
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_score = 0;
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}
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Siman(unsigned &seed, unsigned &seedM, unsigned &seedP, int &nvars, int &nt, int &ns, IntVector param, DoubleVector* x,
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DoubleVector* lowerb, DoubleVector* upperb, DoubleVector vm, double t, double &rt,
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double nsdiv, int &tempcheck, double &simanneps, DoubleVector fstar,
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double &lratio, double &uratio, double &cs, DoubleVector* bestx, int &scale, int* converge,
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double* score):
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_seed(seed), _seedM(seedM), _seedP(seedP), _l(0), _nvars(nvars), _nt(nt), _NT(0), _ns(ns), _NS(0), _t(t),
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_rt(rt), _nsdiv(nsdiv), _nacc(1), _tempcheck(tempcheck), _simanneps(simanneps), _lratio(lratio),
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_uratio(uratio), _cs(cs), _scale(scale){
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_param = IntVector(param);
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_x = x;
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_lowerb = lowerb;
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_upperb = upperb;
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_vm = DoubleVector(vm);
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_nacp = IntVector(_nvars, 0);
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_nrej = 0;
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_naccmet = 0;
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randomizeParams();
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_fstar = DoubleVector(fstar);
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_bestx = bestx;
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_converge = converge;
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_score = score;
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}
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//Required Copy constructor : Params(const Params& other)
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Siman(const Siman& other): _nvars(other._nvars), _seed(other._seed), _seedM(other._seedM), _seedP(other._seedP),
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_l(other._l), _nt(other._nt), _NT(other._NT), _ns(other._ns), _NS(other._NS), _param(IntVector(other._param)),
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_x(other._x), _lowerb(other._lowerb),_upperb(other._upperb), _vm(DoubleVector(other._vm)), _t(other._t),
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_rt(other._rt), _nacp(IntVector(other._nacp)), _nacc(other._nacc), _nrej(other._nrej),
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_naccmet(other._naccmet), _nsdiv(other._nsdiv), _tempcheck(other._tempcheck), _simanneps(other._simanneps),
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_fstar(DoubleVector(other._fstar)), _lratio(other._lratio), _uratio(other._uratio), _cs(other._cs),
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_bestx(other._bestx), _scale(other._scale), _converge(other._converge), _score(other._score){}
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//Required operator= : Params& operator= (const Params& other)
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Siman& operator= (const Siman& other){
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_nvars = other._nvars;
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_seed = other._seed;
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_seedM = other._seedM;
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_seedP = other._seedP;
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_l = other._l;
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_nt = other._nt;
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_NT = other._NT;
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_ns = other._ns;
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_NS = other._NS;
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_param = IntVector(other._param);
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_x = other._x;
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_lowerb = other._lowerb;
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_upperb = other._upperb;
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_vm = DoubleVector(other._vm);
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_t = other._t;
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_rt = other._rt;
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_nacp = IntVector(other._nacp);
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_nacc = other._nacc; //The number of accepted function evaluations
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_nrej = other._nrej; //The number of rejected function evaluations
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_naccmet = other._naccmet; //The number of metropolis accepted function evaluations
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_nsdiv = other._nsdiv;
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_tempcheck = other._tempcheck;
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_simanneps = other._simanneps;
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_fstar = DoubleVector(other._fstar);
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_lratio = other._lratio;
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_uratio = other._uratio;
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_cs = other._cs;
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_bestx = other._bestx;
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_scale = other._scale;
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_converge =other._converge;
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_score = other._score;
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return *this;
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}
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//Destructor : ~Params()
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~Siman(){
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}
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unsigned* getSeed(){return &_seed;}
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unsigned* getSeedM(){return &_seedM;}
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unsigned getSeedM_(){return _seedM;}
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void setSeedM(unsigned val){_seedM = val;}
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int getNvars(){return _nvars;}
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int getL(){return _l;}
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void incrementL() { _l = (_l +1) % _nvars;}
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IntVector& getParam(){return _param;}
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DoubleVector& getX(){return *_x;}
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void setX(DoubleVector* point) {_x=point;}
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DoubleVector& getLowerb(){return *_lowerb;}
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DoubleVector& getUpperb(){return *_upperb;}
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DoubleVector& getVm(){return _vm;}
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void setVm(DoubleVector vm){_vm = vm;}
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DoubleVector& getFstar(){return _fstar;}
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double getT(){return _t;}
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void setT(double val){_t = val;}
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void incrementNacc() {_nacc++;}
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void incrementNrej(){_nrej++;}
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void incrementNaccmet(){_naccmet++;}
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void incrementNacp(int i){_nacp[i]++;}
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void randomizeParams(){
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int rnumber, rcheck, i, rchange = 0;
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while (rchange < _nvars) {
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rnumber = rand_r(&_seedP) % _nvars;
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rcheck = 1;
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for (i = 0; i < rchange; i++)
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if ((_param)[i] == rnumber)
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rcheck = 0;
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if (rcheck) {
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(_param)[rchange] = rnumber;
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rchange++;
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}
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}
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}
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int getNs() const {return _ns;}
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int getNS() const {return _NS;}
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void setNS(int ns = 0) {_NS = ns;}
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void incrementNS() {_NS++;}
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int getNt() const {return _nt;}
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int getNT() const {return _NT;}
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void setNT(int nt) {_NT = nt;}
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void incrementNT() {_NT++;};
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double getSimanneps() const {return _simanneps;}
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int getTempcheck() const {return _tempcheck;}
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const int getNacp(int i) const {return _nacp[i];}
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void setNacp(int i, int val) {_nacp[i] = val;}
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double getCs() const {return _cs;}
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double getLratio() const {return _lratio;}
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double getUratio() const {return _uratio;}
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void setConverge(int val) {(*_converge) = val;}
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DoubleVector* getBestx() {return _bestx;}
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int getScale() const {return _scale;}
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double getRt() const {return _rt;}
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double getNsdiv() const {return _nsdiv;}
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int* getConverge() const {return _converge;}
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int getNacc() const {return _nacc;}
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int getNaccmet() const {return _naccmet;}
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int getNrej() const {return _nrej;}
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void setNrej(int nrej) {_nrej = nrej;};
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double* getScore() const {return _score;}
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};
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/* IMPROVEMENTS:
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- OpenMP parallel implementation added
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| 267 : |
ulcessvp |
16 |
- Can be compiled with OpenMP using macro _OPENMP
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| 268 : |
ulcessvp |
12 |
- Memory for items is dynamically reused in the parallel searches
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- Items whose evaluation has not begun when a new optimum is found are not evaluated (only possible in TBB search)
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*/
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/// Performs a reproducible optimization process, sequentially in seq_opt or in parallel in paral_opt
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template<class SEED_T, class PARAMS_T, class CONTROL_T, double Evaluator(
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const PARAMS_T&), void BuildNewParams(SEED_T&, PARAMS_T&)>
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class ReproducibleSearch {
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const SEED_T initialSeed_;
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const PARAMS_T initialParams_;
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const int maxIters_;
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SEED_T seed_;
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PARAMS_T p_;
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int iters_;
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double optimumValue_;
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double funcval_;
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double searchTime_;
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/// Stores an individual item to evaluate and the data to restore the search state
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/// if it is chosen as new optimum item
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struct ParalEvalItem_t {
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int order_;
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PARAMS_T p_;
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SEED_T seed_; ///< seed AFTER generation of p_
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double value_;
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ParalEvalItem_t(int order, const PARAMS_T& p, const SEED_T& seed,
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double value = 0.) :
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order_(order), p_(p), seed_(seed), value_(value) {
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}
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ParalEvalItem_t() :
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value_(0.) {
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}
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void evaluateItem() {
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value_ = Evaluator(p_);
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}
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void print() const {
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std::cout << order_ << ' ' << p_ << " v= " << value_ << std::endl;
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}
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};
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/// Internal storage for search items with a padding field to avoid false sharing
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struct ParalEvalItemStorage_t {
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| 317 : |
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ParalEvalItem_t pei_;
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| 318 : |
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char padding_[128];
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};
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public:
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ReproducibleSearch(const SEED_T& seed, const PARAMS_T& params, int max_iters = 200) :
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initialSeed_(seed), initialParams_(params), maxIters_(max_iters) {}
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| 325 : |
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// Set the problem in its initial state
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void reset() {
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seed_ = initialSeed_;
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p_ = initialParams_;
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std::cout << ": " << p_ << " v= " << Evaluator(p_) << std::endl;
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}
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//########################################################
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| 334 : |
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| 335 : |
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| 336 : |
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void seq_opt(double funcval) {
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| 337 : |
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struct timeval t0, t1, t;
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| 338 : |
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reset();
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| 339 : |
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| 340 : |
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CONTROL_T control_object;
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| 341 : |
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gettimeofday(&t0, NULL);
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| 342 : |
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| 343 : |
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seed_.setX(&p_);
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| 344 : |
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| 345 : |
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funcval_ = funcval;
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| 346 : |
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optimumValue_ = funcval_;
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| 347 : |
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bool quit = false;
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| 348 : |
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for (iters_ = 1; iters_ <= maxIters_; ++iters_) {
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| 349 : |
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PARAMS_T p = p_;
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| 350 : |
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| 351 : |
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BuildNewParams(seed_, p);
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| 352 : |
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| 353 : |
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const double prev_val = funcval_;
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| 354 : |
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const double val = Evaluator(p);
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| 355 : |
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| 356 : |
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if (control_object.mustAccept(prev_val, val, seed_, iters_)) {
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| 357 : |
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int i;
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| 358 : |
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for (i = 0; i < seed_.getNvars(); i++){
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| 359 : |
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p_[i] = p[i];
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| 360 : |
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}
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funcval_ = val;
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| 362 : |
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} else if ((*seed_.getConverge()) == -1) return;
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| 363 : |
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| 364 : |
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control_object.optimum(val, optimumValue_, iters_, p_, initialParams_, seed_);
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| 365 : |
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| 366 : |
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if ((quit=control_object.mustTerminate(optimumValue_, funcval_, seed_, iters_))) {
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| 367 : |
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break;
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| 368 : |
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}
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| 369 : |
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}
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| 370 : |
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| 371 : |
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control_object.printResult(quit, seed_, iters_);
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| 372 : |
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gettimeofday(&t1, NULL);
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| 373 : |
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timersub(&t1, &t0, &t);
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| 374 : |
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searchTime_ = t.tv_sec + t.tv_usec / 1000000.0;
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| 375 : |
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}
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| 376 : |
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| 377 : |
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void paral_opt_omp(double funcval, int nthreads, int paral_tokens = -1) {
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| 378 : |
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struct timeval t0, t1, t;
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| 379 : |
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| 380 : |
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reset();
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| 381 : |
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| 382 : |
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CONTROL_T control_object;
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| 383 : |
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| 384 : |
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gettimeofday(&t0, NULL);
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| 385 : |
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| 386 : |
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#if defined(_OPENMP)
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| 387 : |
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omp_set_num_threads(nthreads);
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| 388 : |
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#endif
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| 389 : |
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| 390 : |
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if (paral_tokens < 0) {
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| 391 : |
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paral_tokens = nthreads;
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| 392 : |
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}
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| 393 : |
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| 394 : |
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seed_.setX(&p_);
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| 395 : |
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funcval_ = funcval;
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| 396 : |
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optimumValue_ = funcval_;
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| 397 : |
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bool quit = false;
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| 398 : |
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| 399 : |
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ParalEvalItemStorage_t * const storage =
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| 400 : |
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new ParalEvalItemStorage_t[paral_tokens]; //Guarda los datos de los diferentes threads
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| 401 : |
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| 402 : |
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int old_paral_tokens = paral_tokens;
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| 403 : |
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for (iters_ = 1; iters_ <= maxIters_;) {
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| 404 : |
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| 405 : |
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if ((seed_.getL() + paral_tokens -1) >= seed_.getNvars())
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| 406 : |
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paral_tokens = seed_.getNvars() - seed_.getL();
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| 407 : |
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| 408 : |
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if ((iters_ + paral_tokens - 1) > maxIters_) {
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| 409 : |
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paral_tokens = maxIters_ - (iters_ - 1);
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| 410 : |
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}
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| 411 : |
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| 412 : |
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for (int i = 0; i < paral_tokens; i++) {
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| 413 : |
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storage[i].pei_.p_ = p_;
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| 414 : |
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BuildNewParams(seed_, storage[i].pei_.p_);
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| 415 : |
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storage[i].pei_.seed_ = seed_;
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| 416 : |
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seed_.incrementL();
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| 417 : |
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}
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| 418 : |
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const double prev_val = funcval_;
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| 419 : |
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| 420 : |
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#pragma omp parallel for
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| 421 : |
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for (int i = 0; i < paral_tokens; i++) {
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| 422 : |
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storage[i].pei_.evaluateItem();
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| 423 : |
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}
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| 424 : |
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| 425 : |
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int j, i=0; //Notice that we need it after the loop
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| 426 : |
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if (control_object.mustAccept(prev_val, storage[i].pei_.value_, storage[i].pei_.seed_, iters_+i)) {
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| 427 : |
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for (j = 0; j < seed_.getNvars(); j++)
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| 428 : |
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p_[j] = storage[i].pei_.p_[j];
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| 429 : |
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funcval_ = storage[i].pei_.value_;
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| 430 : |
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} else{
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| 431 : |
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if ((*storage[i].pei_.seed_.getConverge()) == -1) return;
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| 432 : |
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| 433 : |
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for (i = 1; i < paral_tokens; i++) {
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| 434 : |
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storage[i].pei_.seed_.setSeedM(storage[i-1].pei_.seed_.getSeedM_());
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| 435 : |
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if (control_object.mustAccept(prev_val, storage[i].pei_.value_, storage[i].pei_.seed_, iters_+i)) {
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| 436 : |
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for (j = 0; j < seed_.getNvars(); j++)
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| 437 : |
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p_[j] = storage[i].pei_.p_[j];
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| 438 : |
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funcval_ = storage[i].pei_.value_;
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| 439 : |
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break;
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| 440 : |
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} else if ((*storage[i].pei_.seed_.getConverge()) == -1) return;
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| 441 : |
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}
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| 442 : |
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}
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| 443 : |
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i = (i == paral_tokens) ? (i - 1) : i;
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| 444 : |
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| 445 : |
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seed_ = storage[i].pei_.seed_;
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| 446 : |
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seed_.setNrej(seed_.getNrej()+i);
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| 447 : |
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control_object.optimum(storage[i].pei_.value_, optimumValue_, iters_,
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| 448 : |
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storage[i].pei_.p_, initialParams_, seed_);
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| 449 : |
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| 450 : |
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iters_ += i+1;
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| 451 : |
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| 452 : |
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if ((quit=control_object.mustTerminate(optimumValue_, funcval_, seed_, iters_))) {
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| 453 : |
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break;
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| 454 : |
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}
|
| 455 : |
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paral_tokens = old_paral_tokens;
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| 456 : |
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}
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| 457 : |
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control_object.printResult(quit, seed_ ,iters_);
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| 458 : |
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| 459 : |
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gettimeofday(&t1, NULL);
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| 460 : |
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timersub(&t1, &t0, &t);
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| 461 : |
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searchTime_ = t.tv_sec + t.tv_usec / 1000000.0;
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| 462 : |
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| 463 : |
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delete[] storage;
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| 464 : |
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}
|
| 465 : |
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| 466 : |
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/// Only used by sequential search when verbose
|
| 467 : |
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void print(int iter, double val) const {
|
| 468 : |
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ParalEvalItem_t v(iter, p_, seed_, val);
|
| 469 : |
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v.print();
|
| 470 : |
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}
|
| 471 : |
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| 472 : |
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const PARAMS_T& getResult() const {
|
| 473 : |
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return p_;
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| 474 : |
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}
|
| 475 : |
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| 476 : |
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double getOptimumValue() const {
|
| 477 : |
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return funcval_;
|
| 478 : |
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}
|
| 479 : |
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| 480 : |
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double getSearchTime() const {
|
| 481 : |
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return searchTime_;
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| 482 : |
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}
|
| 483 : |
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| 484 : |
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int iterations() const {
|
| 485 : |
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return iters_;
|
| 486 : |
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}
|
| 487 : |
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| 488 : |
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};
|
| 489 : |
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| 490 : |
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#endif
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