diff options
| -rw-r--r-- | src/projet.cpp | 12 | ||||
| -rw-r--r-- | src/rqmc.hpp | 69 | ||||
| -rw-r--r-- | src/stratified_sampling.hpp | 4 |
3 files changed, 26 insertions, 59 deletions
diff --git a/src/projet.cpp b/src/projet.cpp index 311c5bb..de75fd1 100644 --- a/src/projet.cpp +++ b/src/projet.cpp @@ -48,11 +48,12 @@ vector< vector<double> > exemple1_stratified() { vector< vector<double> > exemple1_rqmc(){ int I = 100; - vector<int> N = {3, 13, 113, 313}; //les N choisis pour que les NI soient égaux aux N de l'exemple 1 stratified_sampling + vector<int> N = {3, 13, 113, 313}; //les N choisis pour que les N*I 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(size_t i =0; i<N.size(); i++){ - data[i] = monte_carlo (I, quasi_mean<struct first, sobol> (N[i], 1, f)); + quasi_gaussian<sobol> QG(1); + data[i] = monte_carlo (I, mean(N[i], compose(f, QG))); } cout<<"moyenne"<<"\t\t"<<"sigma"<<"\t\t"<<"taille IC"<<endl; for(int i =0; i<3; i++){ @@ -112,7 +113,7 @@ vector <vector<double> > exemple2_stratified (int d, bool call = true){ } vector< vector<double> > exemple2_rqmc(int d, bool call = true) { - int N= 10000; + int N = 10000; double r = 0.05; double T = 1.0; double S0 = 50; @@ -122,7 +123,8 @@ vector< vector<double> > exemple2_rqmc(int d, bool call = true) { 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], call); - data[i] = monte_carlo(I, quasi_mean<asian_option, sobol> (N, d, A)); + quasi_gaussian<sobol> QG(d); + data[i] = monte_carlo(I, mean(N, compose(A, QG))); } return data; }; @@ -170,7 +172,7 @@ int main() init_alea(0); cout<<"comparaison stratified sampling/RQMC sur l'example 1\n"<<endl; make_table1(exemple1_stratified(), exemple1_rqmc()); - + vector< vector<double> > data1 = exemple2_stratified(16); vector< vector<double> > data2 = exemple2_rqmc(16); vector< vector<double> > data3 = exemple2_stratified(64); diff --git a/src/rqmc.hpp b/src/rqmc.hpp index 840e069..a150447 100644 --- a/src/rqmc.hpp +++ b/src/rqmc.hpp @@ -9,7 +9,7 @@ double frac_part(double x); //fonctions de compositions de monte_carlo.hpp template <class _Result> -struct generator +struct generator { typedef _Result result_type; }; @@ -18,9 +18,12 @@ template <typename Fct, typename VA> struct compose_t : public generator< typename Fct::result_type > { compose_t(Fct f, VA X) : f(f), X(X) {}; - typename Fct::result_type operator()() { - return f(X()); + typename Fct::result_type operator()() { + return f(X()); }; + void reseed() { + X.reseed(); + } private: Fct f; VA X; }; @@ -36,6 +39,7 @@ struct mean_t: public generator<double> { mean_t(int n, VA X): n(n), X(X) {}; double operator()(){ + X.reseed(); double sum = 0; for(int i=0; i<n; i++){ sum+=X(); @@ -45,7 +49,7 @@ struct mean_t: public generator<double> private : int n; VA X; }; - + template <typename VA> inline mean_t<VA> mean(int n, VA X){ @@ -74,7 +78,7 @@ std::vector<double> monte_carlo(int n, L X, double p=0.05) template <typename L, typename Fct> std::vector<double> monte_carlo(int n, Fct f, L X, double p=0.05) { - return monte_carlo(n, compose(f, X), p); + return monte_carlo(n, compose(f, X), p); }; //Les classes de quasi-mean @@ -84,8 +88,8 @@ struct quasi_gaussian : public var_alea<std::vector<double> > { quasi_gaussian (int d, double mean = 0, double std =1) : d(d), mean(mean), std(std), s(d), U(0,1), seed(d) { - for(int i=0; i<d; i++) {seed[i]=U();} - }; + reseed(); + }; std::vector<double> operator ()(){ std::vector<double> result = s(); for(int i=0;i<d; i++){ @@ -93,54 +97,15 @@ struct quasi_gaussian : public var_alea<std::vector<double> > } return value = result; } - private: + void reseed() { + for(int i=0; i<d; i++) { + seed[i]=U(); + } + } + private: int d; double mean, std; LDS s; uniform U; std::vector<double> seed; }; - - - -struct gaussian_d : public var_alea<std::vector<double> > -{ - gaussian_d(int d, double mean = 0, double std =1) - : d(d), mean(mean), std(std), G(mean, std) {}; - std::vector<double> operator ()(){ - std::vector<double> result(d); - for(int i=0;i<d; i++){ - result[i] = G(); - } - return value = result; - } - private: - int d; - double mean, std; - gaussian G; -}; - -template <typename Fct, typename LDS> -struct quasi_mean : public generator<typename Fct::result_type> -{ - quasi_mean(int n, int d, Fct f) : n(n), d(d), f(f), G(d) {}; - - typename Fct::result_type operator()() { - double sum =0; - for(int i=0; i<n; i++){ - sum += f(G()); - - } - return sum/n; - }; - - private: - int n, d; - Fct f; - quasi_gaussian<LDS> G; - }; - - - - - diff --git a/src/stratified_sampling.hpp b/src/stratified_sampling.hpp index 9175f75..25eef80 100644 --- a/src/stratified_sampling.hpp +++ b/src/stratified_sampling.hpp @@ -48,7 +48,7 @@ struct multi_gaussian_truncated : public var_alea<std::vector<double> > } return X; } - + void reseed() {}; private: double a, b; uniform V; @@ -124,7 +124,7 @@ void stratified_sampling<L>::update(int Nk) { template <typename L> void stratified_sampling<L>::draw(int N) { - update(N); + update(N); double m, s, oldmean; for(int i=0;i<I;i++){ m=0; |