From d2b133901a65244934eb642ec8e20c797efaf650 Mon Sep 17 00:00:00 2001
From: Bertrand <bertrand.horel@gmail.com>
Date: Fri, 19 Feb 2016 15:03:51 +0000
Subject: nettoyage du dépôt
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---
 stratified_sampling.hpp | 167 ------------------------------------------------
 1 file changed, 167 deletions(-)
 delete mode 100644 stratified_sampling.hpp

(limited to 'stratified_sampling.hpp')

diff --git a/stratified_sampling.hpp b/stratified_sampling.hpp
deleted file mode 100644
index 81080fc..0000000
--- a/stratified_sampling.hpp
+++ /dev/null
@@ -1,167 +0,0 @@
-#include <vector>
-#include <algorithm>
-#include <iostream>
-#include <gsl/gsl_rng.h>
-#include "rtnorm.hpp"
-
-using namespace std;
-
-template <typename T>
-struct var_alea {
-    typedef T result_type;
-    var_alea() : value(0) {};
-    var_alea(T value) : value(value) {};
-    virtual ~var_alea() {};
-    virtual T operator()() = 0;
-    T current() const { return value; };
-protected:
-    T value;
-};
-
-typedef var_alea<double> var_alea_real;
-
-struct gaussian_truncated : public var_alea_real
-{
-    gaussian_truncated(double a, double b, double mean=0, double sigma2=1, int seed=0)
-        :a(a), b(b), mean(mean), sigma2(sigma2), seed(seed) {
-        const gsl_rng_type* type = gsl_rng_default;
-        gen = gsl_rng_alloc(type);
-        gsl_rng_set(gen, seed);
-    };
-    gaussian_truncated(gaussian_truncated const &other)
-        :a(other.a),b(other.b), mean(other.mean),sigma2(other.sigma2){
-        gen = gsl_rng_clone(other.gen);
-    };
-
-    double operator()() {
-        pair<double, double> p = rtnorm(gen, a, b, mean, sigma2);
-        return value = p.first;
-    };
-
-    ~gaussian_truncated() { gsl_rng_free(gen); }
-private:
-    double a, b, mean, sigma2;
-    int seed;
-    gsl_rng *gen;
-};
-
-template <typename L>
-struct stratified_sampling {
-    stratified_sampling(vector<double> p, vector<L> X)
-        :p(p), X(X), mean(p.size(), 0), sigma2(p.size(), 0), I(p.size()){};
-    void draw(int N);
-    vector<double> get_mean() const;
-    vector<double> get_var() const;
-    void print_mean() const;
-    void print_sigma() const;
-    pair<double,double> estimator() const;
-private:
-		void update(int N);
-    vector<double> p;
-    vector<L> X;
-    vector<int> M;
-    vector<int> cumM;
-    vector<double> mean;
-    vector<double> sigma2;
-    const int I;
-};
-
-//actualisation du nombre de tirages à faire par strates
-template <typename L>
-void stratified_sampling<L>::update(int Nk) {
-    bool first_step = M.empty();
-    //reinitialistation du vecteur M du nombre de tirages par strates
-    if (first_step) {
-        M.resize(I,1);
-        cumM.resize(I,0);
-    }
-    else {
-        for(int i=0; i<I; i++){
-            cumM[i]=cumM[i]+M[i];
-            M[i]=1;
-        }
-    }
-
-    std::vector<double> m(I, 0); //le vecteur des m_i idéals
-
-    if (first_step) {
-        for (int i=0; i<I; i++) {
-            m[i] = (Nk-I)*p[i];
-        }
-    }
-    else {
-				//On remplit un vecter des écarts types à parti de notre vecteur de variance
-				std::vector<double> sigma(p.size(),0);
-				for (int i=0; i < I; i++) {
-					sigma[i]=sqrt(sigma2[i]);
-				}
-        double scal = std::inner_product(p.begin(), p.end(), sigma.begin(), (double) 0);
-        for (int i=0; i < I; i++) {
-            m[i] = (Nk-I)*p[i]*sigma[i]/scal;
-            //std::cout<<m[i]<<std::endl;
-        }
-    }
-    M[0]+=floor(m[0]);
-    double current = m[0];
-    for (int i=1; i<I-1; i++){
-        M[i] += floor(current+m[i]) - floor(current);
-        current += m[i];
-    }
-    M[I-1]+=Nk-I-floor(current);
-}
-
-template <typename L>
-void stratified_sampling<L>::draw(int N) {
-		update(N);
-    double m, s, oldmean;
-    for(int i=0;i<I;i++){
-        m=0;
-        s=0;
-        for(int j=0;j<M[i];j++){
-            m=m+X[i]();
-            s=s+X[i].current()*X[i].current();
-        }
-        oldmean=mean[i];
-        mean[i]=(mean[i]*cumM[i]+m)/(cumM[i]+M[i]);
-        sigma2[i]=((sigma2[i]+oldmean*oldmean)*cumM[i] + s)/(cumM[i]+M[i]) - mean[i]*mean[i];
-    }
-};
-
-template <typename L>
-vector<double> stratified_sampling<L>::get_mean() const {
-    return mean;
-};
-
-template <typename L>
-vector<double> stratified_sampling<L>::get_var() const {
-    return sigma2;
-};
-
-template <typename L>
-void stratified_sampling<L>::print_mean() const {
-	cout<<"les espérances :"<<endl;
-	    for(int i=0;i<I;i++){
-        cout<<mean[i]<<"\t";
-			}
-			cout<<endl;
-};
-
-template <typename L>
-void stratified_sampling<L>::print_sigma() const {
-	cout<<"les écarts types :"<<endl;
-	    for(int i=0;i<I;i++){
-        cout<<sqrt(sigma2[i])<<"\t";
-			}
-			cout<<endl;
-};
-
-template <typename L>
-pair<double,double> stratified_sampling<L>::estimator() const {
-	double est_mean = 0;
-	double est_std = 0;
-	for (int i=0; i<I; i++) {
-		est_mean += mean[i]*p[i];
-		est_std += sqrt(sigma2[i])*p[i];
-	}
-	return {est_mean, est_std};
-}
-- 
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