Trivial particle filter with proposal density equal to parameter evolution model. More...
Trivial particle filter with proposal density equal to parameter evolution model.
Posterior density is represented by a weighted empirical density (eEmp ).
#include <particles.h>
Public Member Functions | |
| void | set_options (const string &opt) |
| virtual void | bayes_gensmp () |
| bayes I - generate samples and add their weights to lls | |
| virtual void | bayes_weights () |
| bayes II - compute weights of the | |
| virtual bool | do_resampling () |
| important part of particle filtering - decide if it is time to perform resampling | |
| void | bayes (const vec &dt) |
| Incremental Bayes rule. | |
| vec & | __w () |
| access function | |
| vec & | _lls () |
| access function | |
| RESAMPLING_METHOD | _resmethod () const |
| const eEmp & | posterior () const |
| access function | |
| void | from_setting (const Setting &set) |
| void | resmethod_from_set (const Setting &set) |
| auxiliary function reading parameter 'resmethod' from configuration file | |
| void | prior_from_set (const Setting &set) |
| load prior information from set and set internal structures accordingly | |
| void | validate () |
| This method TODO. | |
| void | resample (ivec &ind) |
| resample posterior density (from outside - see MPF) | |
| Array< vec > & | __samples () |
| virtual string | to_string () |
| This method returns a basic info about the current instance. | |
| virtual void | to_setting (Setting &set) const |
| This method save all the instance properties into the Setting structure. | |
Constructors | |
| PF () | |
| void | set_parameters (int n0, double res_th0=0.5, RESAMPLING_METHOD rm=SYSTEMATIC) |
| void | set_model (shared_ptr< mpdf > par0, shared_ptr< mpdf > obs0) |
| void | set_statistics (const vec w0, const epdf &epdf0) |
| void | set_statistics (const eEmp &epdf0) |
Constructors | |
| virtual BM * | _copy_ () const |
| Copy function required in vectors, Arrays of BM etc. Have to be DELETED manually! Prototype:. | |
Mathematical operations | |
| virtual void | bayesB (const mat &Dt) |
| Batch Bayes rule (columns of Dt are observations). | |
| virtual double | logpred (const vec &dt) const |
| vec | logpred_m (const mat &dt) const |
| Matrix version of logpred. | |
| virtual epdf * | epredictor () const |
Constructs a predictive density . | |
| virtual mpdf * | predictor () const |
Constructs conditional density of 1-step ahead predictor . | |
Access to attributes | |
| const RV & | _drv () const |
| void | set_drv (const RV &rv) |
| void | set_rv (const RV &rv) |
| double | _ll () const |
| void | set_evalll (bool evl0) |
Protected Attributes | |
| int | n |
| number of particles; | |
| eEmp | est |
| posterior density | |
| vec & | _w |
pointer into eEmp | |
| Array< vec > & | _samples |
pointer into eEmp | |
| shared_ptr< mpdf > | par |
| Parameter evolution model. | |
| shared_ptr< mpdf > | obs |
| Observation model. | |
| vec | lls |
| internal structure storing loglikelihood of predictions | |
| RESAMPLING_METHOD | resmethod |
| which resampling method will be used | |
| double | res_threshold |
| RV | drv |
| Random variable of the data (optional). | |
| double | ll |
| Logarithm of marginalized data likelihood. | |
| bool | evalll |
If true, the filter will compute likelihood of the data record and store it in ll . Set to false if you want to save computational time. | |
Options | |
| bool | opt_L_smp |
| Log all samples. | |
| bool | opt_L_wei |
| Log all samples. | |
Extension to conditional BM | |
This extension is useful e.g. in Marginalized Particle Filter (bdm::MPF). Alternatively, it can be used for automated connection to DS when the condition is observed | |
| const RV & | _rvc () const |
| access function | |
| virtual void | condition (const vec &val) |
Substitute val for rvc. | |
| RV | rvc |
| Name of extension variable. | |
Logging of results | |
|
| |
| virtual void | log_add (logger &L, const string &name="") |
| Add all logged variables to a logger. | |
| virtual void | logit (logger &L) |
Save results to the given logger, details of what is stored is configured by LIDs and options. | |
| ivec | LIDs |
| IDs of storages in loggers 4:[1=mean,2=lb,3=ub,4=ll]. | |
| ivec | LFlags |
| Flags for logging - same size as LIDs, each entry correspond to the same in LIDs. | |
| virtual BM* bdm::BM::_copy_ | ( | ) | const [inline, virtual, inherited] |
Copy function required in vectors, Arrays of BM etc. Have to be DELETED manually! Prototype:.
BM* _copy_() const {return new BM(*this);}
Reimplemented in bdm::ARX, bdm::ARXfrg, bdm::KalmanFull, bdm::KalmanCh, bdm::EKFCh, and bdm::BMEF.
| void bdm::PF::bayes | ( | const vec & | dt | ) | [virtual] |
Incremental Bayes rule.
| dt | vector of input data |
Implements bdm::BM.
References _samples, bayes_gensmp(), bayes_weights(), do_resampling(), est, lls, n, obs, bdm::eEmp::resample(), and resmethod.
| void bdm::PF::from_setting | ( | const Setting & | set | ) | [inline, virtual] |
configuration structure for basic PF
parameter_pdf = mpdf_class; // parameter evolution pdf observation_pdf = mpdf_class; // observation pdf prior = epdf_class; // prior probability density --- optional --- n = 10; // number of particles resmethod = 'systematic', or 'multinomial', or 'stratified' // resampling method res_threshold = 0.5; // resample when active particles drop below 50%
Reimplemented from bdm::BM.
References bdm::RV::add(), obs, par, prior_from_set(), and resmethod_from_set().
| virtual double bdm::BM::logpred | ( | const vec & | dt | ) | const [inline, virtual, inherited] |
Evaluates predictive log-likelihood of the given data record I.e. marginal likelihood of the data with the posterior integrated out.
Reimplemented in bdm::ARX, bdm::MixEF, and bdm::multiBM.
References bdm_error.
Referenced by bdm::BM::logpred_m().
| void bdm::PF::set_options | ( | const string & | opt | ) | [inline, virtual] |
Set posterior density by sampling from epdf0 Extends original BM::set_options by two more options:
Reimplemented from bdm::BM.
double bdm::PF::res_threshold [protected] |
resampling threshold; in this case its meaning is minimum ratio of active particles For example, for 0.5 resampling is performed when the numebr of active aprticles drops belo 50%.
Referenced by do_resampling(), and resmethod_from_set().
1.6.1