StandardModel<Two_scale>::StandardModel(const StandardModel<Two_scale>& s) : yu(s.yu), yd(s.yd), ye(s.ye), g(s.g) { setPars(numStandardModelPars); setMu(s.displayMu()); setLoops(s.displayLoops()); setThresholds(s.displayThresholds()); }
StandardModel<Two_scale>::StandardModel() : yu(3, 3), yd(3, 3), ye(3, 3), g(3) , precision(1.0e-3) { setPars(numStandardModelPars); setMu(0.0); setLoops(1); setThresholds(0); }
StandardModel<Two_scale>::StandardModel(const DoubleMatrix& SMu, const DoubleMatrix& SMd, const DoubleMatrix& SMe, const DoubleVector& g_) : yu(SMu), yd(SMd), ye(SMe), g(g_) { setPars(numStandardModelPars); setMu(0.0); setLoops(1); setThresholds(0); }
void Leapforg::setEquation(double l, double r, double mu, SourceTerm st, BoundaryCond initial, BoundaryCond lb, BoundaryCond rb) { setInterval(l, r); setMu(mu); setSourceTerm(st); setInitialCond(initial); setBoundaryCond(1.0, 0.0, lb, 1.0, 0.0, rb); }
void MssmSusy::setSusy(const MssmSusy & s) { setLoops(s.displayLoops()); setThresholds(s.displayThresholds()); setMu(s.displayMu()); setYukawaMatrix(YU, s.displayYukawaMatrix(YU)); setYukawaMatrix(YD, s.displayYukawaMatrix(YD)); setYukawaMatrix(YE, s.displayYukawaMatrix(YE)); setHvev(s.displayHvev()); setTanb(s.displayTanb()); setSusyMu(s.displaySusyMu()); setAllGauge(s.displayGauge()); }
const StandardModel<Two_scale>& StandardModel<Two_scale>::operator=(const StandardModel<Two_scale>& s) { if (this == &s) return *this; yu = s.yu; yd = s.yd; ye = s.ye; g = s.g; setMu(s.displayMu()); setLoops(s.displayLoops()); setThresholds(s.displayThresholds()); return *this; }
const QedQcd & QedQcd::operator=(const QedQcd & m) { if (this == &m) return *this; a = m.a; mf = m.mf; mbPole = m.mbPole; input = m.input; ckm = m.ckm; pmns = m.pmns; setLoops(m.displayLoops()); setThresholds(m.displayThresholds()); setMu(m.displayMu()); return *this; }
AdaptiveSO2CPGSynPlas::AdaptiveSO2CPGSynPlas(Neuron* perturbingNeuron) : ExtendedSO2CPG(perturbingNeuron){ setAlpha(1.01); setPhi(0.3); setMu(1.00); setGamma(0.02); setEpsilon(0.03); setBeta(0.00); // for a range of P from -1 to 1 setBetaDynamics (-1.0, 0.003, 0.0000); setGammaDynamics (-1.0, 0.003, 1.0000); setEpsilonDynamics(0.04, 0.003, 0.0001); }
//Does the actual calling of Runge Kutta: default precision is TOLERANCE defined in //def.h //Returns >0 if there's a problem with the running int RGE::callRK(double x1, double x2, DoubleVector & v, DoubleVector (*derivs)(double, const DoubleVector &), double eps) { double tol; if (eps < 0.0) tol = TOLERANCE; else if (eps < EPSTOL) tol = EPSTOL; else tol = eps; // x1 == x2 with high precision if (close(fabs(x1), fabs(x2), EPSTOL)) return 0; // RGE in terms of natural log of renormalisation scale double from = log(fabs(x1)); double to = log(fabs(x2)); double guess = (from - to) * 0.1; //first step size double hmin = (from - to) * tol * 1.0e-5; int err = integrateOdes(v, from, to, tol, guess, hmin, derivs, odeStepper); setMu(x2); return err; }
void SymmIsotropicElasticityTensor::setShearModulus(const Real k) { setMu(k); }
void set_scale(double scale) { setMu(scale); }
GM(const arma::vec & mu, const arma::mat & sigma) { setMu(mu); setSigma(sigma); }
//create gaussian model with the highest loglikelyhood for the given data GM(const arma::mat& data) { setMu(arma::conv_to<arma::vec>::from(arma::mean(data, 1))); setSigma(arma::cov(data.t())); }