1 files changed, 36 insertions, 23 deletions
diff --git a/src/projet.cpp b/src/projet.cpp
index 9d3ac15..205e362 100644
--- a/src/projet.cpp
+++ b/src/projet.cpp
@@ -75,24 +75,37 @@ std::vector<double> normalize (std::vector<double> mu) {
 }
       
     
- void exemple2_stratified (int d){
+ vector <vector<double> > exemple2_stratified (int d){
      std::vector<double> mu(d);
-     mu = argmax(0.05, 1.0, 50, 0.1, 45, d); 
-     std::vector<double> u(d);
-     u = normalize(mu);
-     vector<double> q = quantile_norm(100, 1);
-     vector<double> p(100, 0.01);
-     asian_option A(0.05, 1.0, 50, 0.1, 45, true);
-     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)));
-     }
-     stratified_sampling<tilted_option> S(p, X);
-     S.draw(1000000);
-     cout<<"l'estimateur de la moyenne est :"<<S.estimator().first<<endl;
+     vector<int> K = {45, 50, 55};
+     vector<int> N = {100000, 400000, 500000};
+     vector< vector<double> > data(3);
+     for (int i=0; i<3; i++){
+        mu = argmax(0.05, 1.0, 50, 0.1, K[i], d); 
+        std::vector<double> u(d);
+        u = normalize(mu);
+        vector<double> q = quantile_norm(100, 1);
+        vector<double> p(100, 0.01);
+        asian_option A(0.05, 1.0, 50, 0.1, K[i], true);
+        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 j=1; j<100; i++) {
+            X.push_back(compose(G, multi_gaussian_truncated(q[j-1],q[j], u)));
+        }
+        stratified_sampling<tilted_option> S(p, X);
+        vector<double> r(3, 0);
+        for (int j=0; j<3; i++){
+            S.draw(N[j]);
+        }
+        r[0] = K[i];
+        r[1] = S.estimator().first;
+        r[2] = S.estimator().second;
+        data[i] = r;
+        for(int j=0; i<3; i++){cout<<data[i][j]<<endl;};
+    }
+    return data;
 }
 
 void exemple2_rqmc(int d) {
@@ -131,12 +144,12 @@ int make_table1(vector< vector<double> > data1, vector< vector<double> > data2)
 int main()
 {   
     init_alea(1);
-    cout<<gsl_cdf_gaussian_Pinv(0.975,1)<<endl;
-    cout<<"Stratified_sampling sur l'exemple 1 de la normale"<<endl;
-    vector< vector<double> > data1 = exemple1_stratified(); 
-    cout<<"Randomised quasi Monte-Carlo sur l'exemple 1 de la normale"<<endl;
-    vector< vector<double> > data2 = exemple1_rqmc();
-    make_table1(data1, data2);
+    //~ cout<<gsl_cdf_gaussian_Pinv(0.975,1)<<endl;
+    //~ cout<<"Stratified_sampling sur l'exemple 1 de la normale"<<endl;
+    //~ vector< vector<double> > data1 = exemple1_stratified(); 
+    //~ cout<<"Randomised quasi Monte-Carlo sur l'exemple 1 de la normale"<<endl;
+    //~ vector< vector<double> > data2 = exemple1_rqmc();
+    //~ make_table1(data1, data2);
     
     exemple2_stratified(16);
     return 0;