on n'utilise plus rtnorm

1 files changed, 87 insertions, 0 deletions

diff --git a/src/projet.cpp b/src/projet.cpp
new file mode 100644
index 0000000..fb6df08
--- /dev/null
+++ b/src/projet.cpp
@@ -0,0 +1,87 @@
+#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; 
+}