bool TestExtMisc::test_eval() {
try {
f_eval("sleep(5);");
} catch (const NotSupportedException& e) {
return Count(false);
}
return Count(true);
}
void PluginFit::calculateFitCurveData(double *X, double *Y)
{
if (d_gen_function) {
double X0 = d_x[0];
double step = (d_x[d_n-1]-X0)/(d_points-1);
for (int i=0; i<d_points; i++){
double x = X0+i*step;
X[i] = x;
Y[i]= f_eval(x, d_results);
}
} else {
for (int i=0; i<d_points; i++) {
double x = d_x[i];
X[i] = x;
Y[i]= f_eval(x, d_results);
}
}
}
//Fonction max trouve le maximum des noeuds fils
int f_max(Pion *etat, int profondeur, int joueur, int *la, int *ca, int *lb, int *cb)
{
int maximumVal = -INFINI;
if (profondeur == 0 || f_gagnant() != 0)
{
if (f_test_mouvement(etat, *la, *ca, *lb, *cb, joueur) == 0)
{
return f_eval(etat, joueur);
}
else
{
return -maximumVal;
}
}
for (int i = 0; i < NB_LIGNES; i++){
for (int j = 0; j < NB_COLONNES; j++){
for (int a = -1; a <= 1; a++){
for (int b = -1; b <= 1; b++){
Pion *nouveauJeu = f_raz_plateau();
f_copie_plateau(etat, nouveauJeu);
if (f_test_mouvement(nouveauJeu, i, j, i + a, j + b, joueur) == 0)
{
f_bouge_piece(nouveauJeu, i, j, i + a, j + b, joueur);
int min = f_min(nouveauJeu, profondeur - 1, joueur, la, ca, lb, cb);
if (min > maximumVal){
maximumVal = min;
*la = i;
*ca = j;
*lb = i + a;
*cb = j + b;
}
}
free(nouveauJeu);
}
}
}
}
return maximumVal;
}
void Tresidual<T>::jacobian( Tmatrix<T>& J, const Tvector<T>& x_k ) const
{
Tvector<T> new_state( ORDER, 0.0 ); // New state vector
Tvector<T> f_eval( ORDER, 0.0 ); // Function evaluation
Tvector<T> f_at_new_state( ORDER, 0.0); // Function evaluation at the new state
for ( std::size_t alpha = 0; alpha < ORDER; ++alpha ) // Row index
{
for ( std::size_t beta = 0; beta < ORDER; ++beta ) // Column index
{
Tvector<T> delta(ORDER,0.0); // Perturbation vector
delta[ beta ] += DELTA;
new_state = x_k + delta; // Perturb the known value
// Perurbed function evaluation
residual_function( f_at_new_state, new_state );
residual_function( f_eval, x_k );
// Approximate the entry in the Jacobian
J(alpha,beta) = ( f_at_new_state[ alpha ] - f_eval[ alpha ] ) / DELTA;
}
}
}
