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#include <iostream>
#include <gsl/gsl_rng.h>
#include <vector>
#include <gsl/gsl_cdf.h>
#include <gsl/gsl_math.h>
#include "stratified_sampling.hpp"
#include <cmath>
#include <algorithm>
#include "opti.hpp"
using namespace std;
//--génération quantiles--
vector<double> quantile_norm(int n, double sigma){
vector<double> q(n);
for (int i=0; i<n; i++) {
q[i] = gsl_cdf_gaussian_Pinv ((double)(i+1)/n, sigma);
}
return q;
}
void exemple1() {
gsl_rng_env_setup();
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],0,1,0));
for (int i=1; i<10; i++){
rvar.push_back(gaussian_truncated(q[i-1], q[i],0,1,i));
};
stratified_sampling<gaussian_truncated> S(p,rvar);
S.draw(100);
double x = 1.64*S.estimator().second;
cout<<"l'estimateur de la moyenne est :"<<S.estimator().first<<endl;
cout<<"Son intervalle de confiance à 95% est :"<<"["<<S.estimator().first-(x/10)<<" ,"<<S.estimator().first+(x/10)<<"]"<<endl;
S.draw(1000);
x = 1.64*S.estimator().second;
cout<<"l'estimateur de la moyenne est :"<<S.estimator().first<<endl;
cout<<"Son intervalle de confiance à 95% est :"<<"["<<S.estimator().first-(x/sqrt(1100))<<" ,"<<S.estimator().first+(x/sqrt(1100))<<"]"<<endl;
S.draw(10000);
x = 1.64*S.estimator().second;
cout<<"l'estimateur de la moyenne est :"<<S.estimator().first<<endl;
cout<<"Son intervalle de confiance à 95% est :"<<"["<<S.estimator().first-(x/sqrt(11100))<<" ,"<<S.estimator().first+(x/sqrt(11100))<<"]"<<endl;
};
void exemple2 (){
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);
f_mu G(mu,A);
multi_gaussian_truncated MG(q[50],q[51], u);
for(int i=0; i<10; i++){
std::cout<<G(MG())<<std::endl;
}
}
int main()
{
exemple2();
return 0;
}
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