From a1a55eedff66329d3c0f71aead5e78df71ca6e37 Mon Sep 17 00:00:00 2001
From: Guillaume Horel <guillaume.horel@gmail.com>
Date: Wed, 20 Apr 2016 20:25:38 -0400
Subject: rend le code un peu plus beau

---
 src/projet.cpp | 86 +++++++++++++++++++++++++++++-----------------------------
 1 file changed, 43 insertions(+), 43 deletions(-)

diff --git a/src/projet.cpp b/src/projet.cpp
index 16aca2b..46952e2 100644
--- a/src/projet.cpp
+++ b/src/projet.cpp
@@ -13,18 +13,19 @@
 using namespace std;
 
 struct first:public std::unary_function<std::vector<double>, double>
-{ 
+{
     double operator()(std::vector<double> X){
         return X[0];
-        }
+    }
 };
 
 vector< vector<double> > exemple1_stratified() {
+    int I = 10; //le nombre de strates
     vector<double> q = quantile_norm(10, 1);
-    vector<double> p(10, 0.1);
+    vector<double> p(I, 1/(double)I);
     vector<gaussian_truncated> rvar;
     rvar.push_back(gaussian_truncated(GSL_NEGINF, q[0]));
-    for (int i=1; i<10; i++){
+    for (int i=1; i<I; i++){
         rvar.push_back(gaussian_truncated(q[i-1], q[i]));
     };
     vector<int> N = {300, 1000, 10000, 20000}; //notre tableau du nombre successif de tirages, qui correspondent aux 300, 1300, 11300 et 31300
@@ -33,7 +34,7 @@ vector< vector<double> > exemple1_stratified() {
     stratified_sampling<gaussian_truncated> S(p,rvar);
     cout<<"N"<<"\t"<<"moyenne"<<"\t\t"<<"sigma"<<"\t"<<"théorique"<<endl;
     vector<double> r(4,0);
-    for (int i=0; i<4; i++){
+    for (size_t i=0; i<N.size(); i++){
         S.draw(N[i]);
         r[0]= r[0] + N[i];
         r[1] = S.estimator().first;
@@ -50,8 +51,8 @@ vector< vector<double> > exemple1_rqmc(){
     vector<int> N = {3, 13, 113, 313}; //les N choisis pour que les NI soient égaux aux N de l'exemple 1 stratified_sampling
     first f; //comme quasi_gaussian retourne un vecteur, on doit composer avec f pour avoir le double QG()[0]
     vector< vector<double> > data (4);
-    for(int i =0; i<4; i++){
-        data[i] = monte_carlo (I,quasi_mean<struct first, sobol> (N[i], 1, f));
+    for(size_t i =0; i<N.size(); i++){
+        data[i] = monte_carlo (I, quasi_mean<struct first, sobol> (N[i], 1, f));
     }
     cout<<"moyenne"<<"\t\t"<<"sigma"<<"\t\t"<<"taille IC"<<endl;
     for(int i =0; i<3; i++){
@@ -61,46 +62,45 @@ vector< vector<double> > exemple1_rqmc(){
 };
 
 
-std::vector<double> normalize (std::vector<double> mu) { 
+std::vector<double> normalize (std::vector<double> mu) {
     int d = mu.size();
-    double norm_mu = 0; 
-    std::vector<double> u(d); 
-    for(int i=0; i<d; i++) { 
-        norm_mu += mu[i]*mu[i]; 
-        } 
+    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);
-        }
+    }
     return u;
 }
-      
-    
- vector <vector<double> > exemple2_stratified (int d){
-     std::vector<double> mu(d);
-     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(r, T, S0, V, K[i], d); 
-        std::vector<double> u(d);
-        u = normalize(mu);
+
+
+vector <vector<double> > exemple2_stratified (int d){
+    vector<double> K = {45, 50, 55};
+    vector<int> N = {100000, 400000, 500000};
+    int I = 100; //le nombre de strates
+    vector< vector<double> > data(3);
+    vector<double> q = quantile_norm(I, 1);
+    vector<double> p(I, 1/(double)I);
+    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 (size_t i=0; i < K.size(); i++){
+        vector<double> mu = argmax(r, T, S0, V, K[i], d);
+        std::vector<double> u = normalize(mu);
         asian_option A(r, T, S0, V, K[i], true);
         exponential_tilt<asian_option> G(mu, A);
         std::vector<tilted_option> X;
-        X.push_back(compose(G, multi_gaussian_truncated(GSL_NEGINF,q[0], u)));
-        for(int j=1; j<100; j++) {
-            X.push_back(compose(G, multi_gaussian_truncated(q[j-1],q[j], u)));
+        X.push_back(compose(G, multi_gaussian_truncated(GSL_NEGINF, q[0], u)));
+        for(int j=1; j < I; 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; j++){
+        for (size_t j=0; j < N.size(); j++){
             S.draw(N[j]);
         }
         r[0] = K[i];
@@ -120,9 +120,11 @@ vector< vector<double> > exemple2_rqmc(int d) {
     double V = 0.1;
     vector< vector<double> > data(3);
     vector<double> K = {45, 50, 55};
-    for(int i =0; i<3; i++){
+    int I=100;//la taille du vrai Monte-Carlo
+    for(int i =0; i<3; i++) {
         asian_option A(r, T, S0, V, K[i], true);
-        data[i] = monte_carlo(100, quasi_mean<asian_option, sobol> (N, d, A));}
+        data[i] = monte_carlo(I, quasi_mean<asian_option, sobol> (N, d, A));
+    }
     for(int i =0; i<3; i++){
         std::cout<<data[i][0]<<std::endl;
     }
@@ -146,18 +148,16 @@ int make_table1(vector< vector<double> > data1, vector< vector<double> > data2)
 
 
 int main()
-{   
+{
     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(); 
+    //~ 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_rqmc(16);
     exemple2_stratified(16);
     return 0;
-    
-}
-
 
+}
-- 
cgit v1.2.3-70-g09d2