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#include <iostream>
#include <gsl/gsl_rng.h>
#include <vector>
#include "rtnorm.hpp"
#include <gsl/gsl_cdf.h>
#include <gsl/gsl_math.h>
#include <cmath>
#include <algorithm>
//--génération quantiles--
std::vector<double> quantile_norm(int n, double sigma){
std::vector<double> q(n);
for (int i=0; i<n; i++) {
q[i] = gsl_cdf_gaussian_Pinv ((double)(i+1)/n, sigma);
}
return q;
}
//--tirage de normale tronquée entre les quantile de taille 1/n i et i+1--
double quantile_truncate_normal (int i, int n, double mu,
double sigma, gsl_rng *gen) {
std::vector<double> q;
q = quantile_norm(n, sigma);
std::pair<double, double> p;
p = rtnorm (gen, q[i], q[i+1], mu, sigma);
return p.first;
}
std::pair<double, double> mean_var( std::vector<double> r){
std::pair<double, double> p;
for(auto &x: r){
p.first += x;
p.second += x*x;
}
p.first /= r.size();
p.second /= r.size();
p.second -= p.first * p.first;
return p;
}
//actualisation du nombre de tirages à faire par strates
std::vector<int> update_sampling (std::vector<double> p,
std::vector<double> sigma, int n) {
int I = p.size();
std::vector<int> M(I, 1); // notre vecteur final à retourner
std::vector<double> m(I, 0); //le vecteur des m_i idéals
if (sigma.empty()) {
for (int i=0; i<I; i++) {
m[i] = (Nk-I)*p[i];
}
}
else {
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; i++){
M[i] += floor(current+m[i]) - floor(current);
current += m[i];
}
return M;
}
int main()
{
//--- GSL random init ---
gsl_rng_env_setup(); // Read variable environnement
const gsl_rng_type* type = gsl_rng_default; // Default algorithm 'twister'
gsl_rng *gen = gsl_rng_alloc (type); // Rand generator allocation
std::vector<double> q;
q = quantile_norm(10, 1);
std::pair<double, double> p;
std::pair<double, double> mv;
//number of classes
int I = 10;
//number of samples
int N = 10000;
std::vector<double> r(N);
double a;
for (int i=0; i<I; i++){
if(i==0){
a = GSL_NEGINF;
}else{
a = q[i-1];
}
for(int j=0; j<N; j++){
p = rtnorm (gen, a, q[i], 0, 1);
r[j] = p.first;
}
mv = mean_var(r);
//std::cout<<"mean :"<<mv.first<<" var :"<<mv.second<<std::endl;
}
std::vector<int> k;
std::vector<double> z = {(double)1/3,(double)1/3,(double)1/3};
std::vector<double> sigma = {0.1, 0.4, 0.3};
k = update_sampling(z, sigma, 10000);
for (int j=0; j<k.size(); j++){
std::cout<<k[j]<<std::endl;
}
gsl_rng_free(gen);
return 0;
}
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