on n'utilise plus rtnorm

1 files changed, 0 insertions, 87 deletions

diff --git a/src/main.cpp b/src/main.cpp
deleted file mode 100644
index fb6df08..0000000
--- a/src/main.cpp
+++ /dev/null
@@ -1,87 +0,0 @@
-#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);
-     std::vector<compose_t <f_mu, multi_gaussian_truncated> > 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<compose_t <f_mu, multi_gaussian_truncated> > S(p, X);
-     S.draw(1000);
-     cout<<"l'estimateur de la moyenne est :"<<S.estimator().first<<endl;
-     //~ compose_t <f_mu, multi_gaussian_truncated> X = compose(G,MG);
-     //~ for(int i=0; i<10; i++){
-        //~ std::cout<<X()<<std::endl;
-    //~ }
-}
-
-
-int main()
-{   
-    
-    exemple2();
-        
-    return 0; 
-}