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#include <iostream>
#include <vector>
#include <gsl/gsl_cdf.h>
#include <gsl/gsl_math.h>
#include "stratified_sampling.hpp"
#include <cmath>
#include <algorithm>
#include "opti.hpp"
#include "option.hpp"
#include "rqmc.hpp"
using namespace std;
struct first:public std::unary_function<std::vector<double>, double>
{
double operator()(std::vector<double> X){
return X[0];
}
};
vector< vector<double> > exemple1_stratified() {
vector<double> q = quantile_norm(10, 1);
vector<double> p(10, 0.1);
vector<gaussian_truncated> rvar;
rvar.push_back(gaussian_truncated(GSL_NEGINF, q[0]));
for (int i=1; i<10; i++){
rvar.push_back(gaussian_truncated(q[i-1], q[i]));
};
vector<int> N = {300, 1000, 10000, 20000}; //notre tableau du nombre successif de tirages, qui correspondent aux 300, 1300, 11300 et 31300
//de l'article de Etoré et Jourdain
vector< vector<double> > data (4);
stratified_sampling<gaussian_truncated> S(p,rvar);
cout<<"N"<<"\t"<<"moyenne"<<"\t\t"<<"sigma"<<"\t"<<"théorique"<<endl;
vector<double> r(4,0);
for (int i=0; i<4; i++){
S.draw(N[i]);
r[0]= r[0] + N[i];
r[1] = S.estimator().first;
r[2] = S.estimator().second;
r[3] = 0.1559335;
cout<<r[0]<<"\t"<<r[1]<<"\t"<<r[2]<<"\t"<<r[3]<<endl;
data[i] = r;
};
return data;
};
vector< vector<double> > exemple1_rqmc(){
int I = 100;
vector<int> N = {3, 13, 113, 313}; //les N choisis pour que les NI soient égaux aux N de l'exemple 1 stratified_sampling
first f; //comme quasi_gaussian retourne un vecteur, on doit composer avec f pour avoir le double QG()[0]
vector< vector<double> > data (4);
for(int i =0; i<4; i++){
data[i] = monte_carlo (I,quasi_mean<struct first, sobol> (N[i], 1, f));
}
cout<<"moyenne"<<"\t\t"<<"sigma"<<"\t\t"<<"taille IC"<<endl;
for(int i =0; i<3; i++){
cout<<data[i][0]<<"\t"<<data[i][1]<<"\t"<<data[i][2]<<endl;
}
return data;
};
void exemple2_stratified (){
int d= 16;
std::vector<double> mu(d);
mu = argmax(0.05, 1.0, 50, 0.1, 45, d);
double norm_mu = 0;
std::vector<double> u(d);
for(int i=0; i<d; i++) {
norm_mu += mu[i]*mu[i];
}
for(int i=0; i<d; i++) {
u[i] = mu[i]/sqrt(norm_mu);
}
vector<double> q = quantile_norm(100, 1);
vector<double> p(100, 0.01);
asian_option A(0.05, 1.0, 50, 0.1, d, 45);
exponential_tilt<asian_option> G(mu, A);
typedef compose_t<exponential_tilt<asian_option>, multi_gaussian_truncated> tilted_option;
std::vector<tilted_option> X;
X.push_back(compose(G, multi_gaussian_truncated(GSL_NEGINF,q[0], u)));
for(int i=1; i<100; i++) {
X.push_back(compose(G, multi_gaussian_truncated(q[i-1],q[i], u)));
}
for(int i=0; i<100; i=i+10){
std::cout<<X[i]()<<endl;
}
stratified_sampling<tilted_option> S(p, X);
S.draw(1000);
cout<<"l'estimateur de la moyenne est :"<<S.estimator().first<<endl;
}
void exemple2_rqmc() {
asian_option A(0.05, 1.0, 50.0, 0.1, 16, 45);
int N= 10000;
int d =16;
std::vector<double> result(3);
result = monte_carlo(100, quasi_mean<asian_option, sobol> (N, d, A));
for(int i =0; i<3; i++){
std::cout<<result[i]<<std::endl;
}
std::vector<double> result2(3);
result2 = monte_carlo(100, quasi_mean<asian_option, halton> (N, d, A));
for(int i =0; i<3; i++){
std::cout<<result2[i]<<std::endl;
}
};
int main()
{
init_alea(1);
cout<<"Stratified_sampling sur l'exemple 1 de la normale"<<endl;
exemple1_stratified();
cout<<"Randomised quasi Monte-Carlo sur l'exemple 1 de la normale"<<endl;
exemple1_rqmc();
return 0;
}
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