void properties::private_vector_ave_sd_calc(double_vector &v,double &ave,double &sd){
for (unsigned int i=0;i<v.size();i++)
ave+=v[i];
ave=ave/v.size();
if (v.size()>1){
for (unsigned int i=0; i<v.size(); i++)
sd+=pow(v[i]-ave,2);
sd=sqrt(sd/(v.size()-1));
}else
sd=0;
}
// Fonction evo_strat est une implementation de l'algorithme (1+1)-ES.
// implémenter une adaptation automatique de la variance, qui s'appelle la "regle du 1/5"
// Elle est entre autres decrite ici: http://web.cecs.pdx.edu/~greenwd/ecjrnl.pdf
// le parametre adaptive déclenche la regle du 1/5
// si le parametre fitness_sequence est non nul, alors la sequence des fitnesses est enregistrée dedans
double_vector evo_strat(double_vector v0,double (*eval)(double_vector*),int ntrials=1000,double sigma=1.0,bool adaptive=false,double_vector *fitness_sequence=NULL)
{
void add_noise_to_vector(double_vector &,double);
int t,s=v0.size();
double_vector v=v0;
double eval_v = (*eval)(&v);
int nb_success_mutations=0;
cout<<"s = " <<s<< " eval_v = "<<eval_v<<endl;
for (t=1; t <= ntrials; t++) {
double_vector v2=v;
double eval_v2;
add_noise_to_vector(v2, sigma);
eval_v2 = (*eval)(&v2);
if (eval_v2 > eval_v) {
v = v2;
eval_v = eval_v2;
nb_success_mutations++;
}
if (fitness_sequence) fitness_sequence->push_back(eval_v);
if (t % (10*s) == 0 && adaptive) {
double ps = nb_success_mutations/(10.0*s); /* percentage of success */
double c=0.85;
if (ps > 0.2) sigma /= c;
else sigma *= c;
nb_success_mutations = 0;
}
}
return v;
}
// Fonction cree les valeurs aleatoires de vecteur
void add_noise_to_vector(double_vector &v,double sigma)
{
int i, s=v.size();
for (i = 0; i < s; i++)
v[i] += normal_distribution()*sigma;
}
double_vector L0Smoothing_1D(double_vector data, int jump)
{
double lambda = 0.01, kappa = 2.0;
double_vector out(data.size());
Matrix SR(data.size(), 1);
for(int j = 0; j < 1; ++j)
{
for(int i = 0; i < data.size(); ++i)
{
SR(i, j) = data[i];
}
}
double betamax = 1;
Matrix fx(1, 2);
fx(0, 0) = 1;
fx(0, 1) = -1;
Matrix fy(2, 1);
fy(0, 0) = 1;
fy(1, 0) = -1;
Matrix sizeI2D(1, 2);
sizeI2D(0, 0) = data.size();
sizeI2D(0, 1) = 1;
Matrix2 otfFx = psf2otf(fx, sizeI2D);
Matrix2 otfFy = psf2otf(fy, sizeI2D);
Matrix otfFx2 = MatAbsPow2(otfFx);
Matrix otfFy2 = MatAbsPow2(otfFy);
Matrix2 Normin1R = fft2_1D(SR);
Matrix Denormin2 = otfFx2;
float beta = 2 * lambda;
while(beta < betamax)
{
float lb = lambda / beta;
Matrix Denormin = ZMatrix(data.size(), 1);
Denormin = beta * Denormin2;
MatAdd(Denormin, 1);
Matrix vR = Matdiff(SR, 1);
Matrix Pos2Sum = MatPow2(vR);
for(int j = 0; j < 1; ++j)
{
for(int i = 0; i < data.size(); ++i)
{
if(Pos2Sum(i, j) < lb)
{
vR(i, j) = 0;
}
}
}
Matrix Normin2R = Matdiffinv(vR, 1);
Matrix2 FSR = (Normin1R + fft2_1D(vR) * beta) / Denormin;
SR = ifft2_1D(FSR);
beta = beta * kappa;
printf(".");
for(uint i = 0; i < data.size(); ++i)
{
for(uint j = 0; j < 1; ++j)
{
out[i] = SR(i, j);
}
}
}
return out;
}
