modif argmax

2 files changed, 15 insertions, 12 deletions

diff --git a/src/opti.cpp b/src/opti.cpp
index 7f838e9..efe5643 100644
--- a/src/opti.cpp
+++ b/src/opti.cpp
@@ -34,7 +34,7 @@ std::vector<double> argmax(double r, double T, double S0, double V, double K, in
 
     opt.set_xtol_rel(1e-4);
 
-    std::vector<double> x(d,0);
+    std::vector<double> x(d,1);
     std::vector<double> g(0);
 
     double maxf;
diff --git a/src/projet.cpp b/src/projet.cpp
index 205e362..241ab0b 100644
--- a/src/projet.cpp
+++ b/src/projet.cpp
@@ -77,33 +77,37 @@ std::vector<double> normalize (std::vector<double> mu) {
     
  vector <vector<double> > exemple2_stratified (int d){
      std::vector<double> mu(d);
-     vector<int> K = {45, 50, 55};
+     vector<double> K = {45, 50, 55};
      vector<int> N = {100000, 400000, 500000};
      vector< vector<double> > data(3);
+     vector<double> q = quantile_norm(100, 1);
+     vector<double> p(100, 0.01);
+     double r = 0.05;
+     double T = 1.0;
+     double S0 = 50;
+     double V = 0.1;
+     typedef compose_t<exponential_tilt<asian_option>, multi_gaussian_truncated> tilted_option;
      for (int i=0; i<3; i++){
-        mu = argmax(0.05, 1.0, 50, 0.1, K[i], d); 
+        mu = argmax(r, T, S0, V, 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);
+        asian_option A(r, T, S0, V, 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++) {
+        for(int j=1; j<100; j++) {
             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++){
+        for (int j=0; j<3; j++){
             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;};
+        for(int j=0; j<3; j++){cout<<data[i][j]<<endl;};
     }
     return data;
 }
@@ -111,7 +115,6 @@ std::vector<double> normalize (std::vector<double> mu) {
 void exemple2_rqmc(int d) {
     asian_option A(0.05, 1.0, 50.0, 0.1, 45,true);
     int N= 10000;
-
     
     std::vector<double> result(3);
     result = monte_carlo(100, quasi_mean<asian_option, sobol> (N, d, A));
@@ -143,7 +146,7 @@ int make_table1(vector< vector<double> > data1, vector< vector<double> > data2)
 
 int main()
 {   
-    init_alea(1);
+    init_alea(2);
     //~ 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();