diff options
Diffstat (limited to 'src')
| -rw-r--r-- | src/option.cpp | 26 | ||||
| -rw-r--r-- | src/projet.cpp | 51 | ||||
| -rw-r--r-- | src/rqmc.hpp | 8 |
3 files changed, 45 insertions, 40 deletions
diff --git a/src/option.cpp b/src/option.cpp index 918e735..c50a7bf 100644 --- a/src/option.cpp +++ b/src/option.cpp @@ -6,31 +6,7 @@ double pos (double x){ } -int main(){ - init_alea(1); - asian_option A(0.05, 1.0, 50.0, 0.1, 16, 45); - int N= 10000; - - int d =16; - - - std::vector<double> result(3); - result = monte_carlo(100, quasi_option<asian_option, sobol> (N, d, A)); - for(int i =0; i<3; i++){ - std::cout<<result[i]<<std::endl; - } - - std::vector<double> result2(3); - result2 = monte_carlo(100, quasi_option<asian_option, halton> (N, d, A)); - for(int i =0; i<3; i++){ - std::cout<<result2[i]<<std::endl; - } - - - - return 0; -} - + diff --git a/src/projet.cpp b/src/projet.cpp index 980ef98..57f6b91 100644 --- a/src/projet.cpp +++ b/src/projet.cpp @@ -9,8 +9,7 @@ using namespace std; - -void exemple1() { +void exemple1_stratified() { vector<double> q = quantile_norm(10, 1); vector<double> p(10, 0.1); vector<gaussian_truncated> rvar; @@ -35,7 +34,7 @@ void exemple1() { }; - void exemple2 (){ + void exemple2_stratified (){ int d= 16; std::vector<double> mu(d); mu = argmax(0.05, 1.0, 50, 0.1, 45, d); @@ -62,18 +61,48 @@ void exemple1() { 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; - //~ } } +void exemple2_rqmc() { + asian_option A(0.05, 1.0, 50.0, 0.1, 16, 45); + int N= 10000; + + int d =16; + + + std::vector<double> result(3); + result = quasi_mean(100, quasi_option<asian_option, sobol> (N, d, A)); + for(int i =0; i<3; i++){ + std::cout<<result[i]<<std::endl; + } + + std::vector<double> result2(3); + result2 = quasi_mean(100, quasi_option<asian_option, halton> (N, d, A)); + for(int i =0; i<3; i++){ + std::cout<<result2[i]<<std::endl; + } +}; + +struct first:public std::unary_function<std::vector<double>, double> +{ double operator()(std::vector<double> X){return X[0];} +}; + +void exemple1_rqmc(){ + int N = 100; + first f; //comme quasi_gaussian retourne un vecteur, on doit composer avec f pour avoir le double QG()[0] + std::vector<double> result(3); + result = quasi_mean (100,quasi_option<struct first, sobol> (N, 1, f)); + for(int i =0; i<3; i++){ + std::cout<<result[i]<<std::endl; + } +}; int main() { init_alea(1); - exemple1(); - //exemple2(); - - return 0; + //exemple2_rqmc(); + //exemple1_stratified(); + exemple1_rqmc(); + return 0; + } diff --git a/src/rqmc.hpp b/src/rqmc.hpp index 1b33713..03f7e81 100644 --- a/src/rqmc.hpp +++ b/src/rqmc.hpp @@ -56,7 +56,7 @@ mean(int n, VA X){ //Les classes de monte-Carlo template <typename L> -std::vector<double> monte_carlo(int n, L X) +std::vector<double> quasi_mean(int n, L X, double p=0.05) { std::vector<double> result(3,0); double x; @@ -67,14 +67,14 @@ std::vector<double> monte_carlo(int n, L X) } result[0] /= (double) n; result[1] = (result[1] - n*result[0]*result[0])/(double)(n-1); - result[2] = 1.96*sqrt(result[1]/(double) n); + result[2] = gsl_cdf_gaussian_Pinv(1-p/2,1)*sqrt(result[1]/(double) n); return result; }; template <typename L, typename Fct> -std::vector<double> monte_carlo(int n, Fct f, L X) +std::vector<double> quasi_mean(int n, Fct f, L X, double p=0.05) { - return monte_carlo(n, compose(f, X)); + return quasi_mean(n, compose(f, X), p); }; //Les classes de quasi-mean |