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; } } }