From 906c2dee383a7eb72632bfbdf61916a576a5460d Mon Sep 17 00:00:00 2001
From: Bertrand <bertrand.horel@gmail.com>
Date: Sun, 21 Feb 2016 16:35:30 +0000
Subject: création de la struct f_mu
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---
 src/stratified_sampling.hpp | 33 ++++++++++++++++++++++++++++++---
 1 file changed, 30 insertions(+), 3 deletions(-)

(limited to 'src/stratified_sampling.hpp')

diff --git a/src/stratified_sampling.hpp b/src/stratified_sampling.hpp
index cebee8d..7573ef2 100644
--- a/src/stratified_sampling.hpp
+++ b/src/stratified_sampling.hpp
@@ -5,6 +5,7 @@
 #include "rtnorm.hpp"
 #include "var_alea.hpp"
 #include <gsl/gsl_cdf.h>
+#include "option.hpp"
 
 using namespace std;
 
@@ -45,14 +46,14 @@ struct multi_gaussian_truncated : public var_alea<std::vector<double> >
         double v = V();
         double Z = gsl_cdf_gaussian_Pinv(v,1);
         std::vector<double> Y(d);
-        double scal = 0;
         for(int i=0; i<d; i++){
             Y[i] = G();
         }
-        std::vector<double> X(d);
+        double scal = 0;
         for(int j=0; j<d; j++){
             scal += Y[j]*u[j];
         }
+        std::vector<double> X(d);
         for(int i=0; i<d; i++){
             X[i] = u[i]*Z + Y[i] - u[i]*scal;
         }
@@ -78,7 +79,7 @@ struct stratified_sampling {
     void print_sigma() const;
     pair<double,double> estimator() const;
 private:
-		void update(int N);
+    void update(int N);
     vector<double> p;
     vector<L> X;
     vector<int> M;
@@ -187,3 +188,29 @@ pair<double,double> stratified_sampling<L>::estimator() const {
 	}
 	return {est_mean, est_std};
 }
+
+struct f_mu : public std::unary_function<std::vector<double>, double> 
+{
+    f_mu(std::vector<double> mu, asian_option A) : mu(mu), A(A){
+        double norm_mu = 0;
+        for(int i=0; i<16; i++) {
+            norm_mu += mu[i]*mu[i];
+        }
+    };
+    
+    double operator()(std::vector<double> X) {
+        std::vector<double> Y(X.size());
+        double scal = 0;
+        for (unsigned int i=0; i<X.size(); i++){
+            Y[i] = X[i] + mu[i];
+            scal+=X[i]*mu[i];
+        }
+        return A(Y)*exp(-scal-0.5*norm_mu);
+    };
+        
+    private :
+        std::vector<double> mu;
+        asian_option A;
+        double norm_mu;
+    };
+
-- 
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