62 DAF(
bool useRefKalman =
true,
double deltaWeight = 1e-3,
double deltaPval = 1e-3);
81 void addProbCut(
const double prob_cut,
const int measDim);
89 void setBetas(
double b1,
double b2=-1,
double b3=-1.,
double b4=-1.,
double b5=-1.,
double b6=-1.,
double b7=-1.,
double b8=-1.,
double b9=-1.,
double b10=-1.);
virtual void setDebugLvl(unsigned int lvl=1)
Abstract base class for Kalman fitter and derived fitting algorithms.
unsigned int maxIterations_
Maximum number of iterations to attempt. Forward and backward are counted as one iteration.
virtual void setMaxFailedHits(int val)
Abstract base class for a track representation.
Determinstic Annealing Filter (DAF) implementation.
std::vector< double > betas_
void setBetas(double b1, double b2=-1, double b3=-1., double b4=-1., double b5=-1., double b6=-1., double b7=-1., double b8=-1., double b9=-1., double b10=-1.)
Configure the annealing scheme.
bool calcWeights(Track *trk, const AbsTrackRep *rep, double beta)
Calculate and set the weights for the next fitting pass. Return if convergence is met....
void setAnnealingScheme(double bStart, double bFinal, unsigned int nSteps)
Configure the annealing scheme.
DAF & operator=(genfit::DAF const &)
AbsKalmanFitter * getKalman() const
void addProbCut(const double prob_cut, const int measDim)
Set the probability cut for the weight calculation for the hits for a specific measurement dimensiona...
std::map< int, double > chi2Cuts_
void setConvergenceDeltaWeight(double delta)
If all weights change less than delta between two iterations, the fit is regarded as converged.
virtual void setMaxFailedHits(int val)
virtual void setDebugLvl(unsigned int lvl=1)
void setProbCut(const double prob_cut)
Set the probability cut for the weight calculation for the hits.
void setMaxIterations(unsigned int n)
Set the maximum number of iterations.
void processTrackWithRep(Track *tr, const AbsTrackRep *rep, bool resortHits=false)
Process a track using the DAF.
boost::scoped_ptr< AbsKalmanFitter > kalman_
Collection of TrackPoint objects, AbsTrackRep objects and FitStatus objects.