A base class for the Sequential Important Sampling with Resampling (SISR).
More...
#include <sisr_filter.h>
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| SISRFilter (const unsigned int &rs=1) |
| The (one and only) constructor. More...
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virtual | ~SISRFilter () |
| The (virtual) destructor.
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float_t | getLogCondLike () const |
| Returns the most recent (log-) conditional likelihood. More...
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std::vector< Mat > | getExpectations () const |
| return all stored expectations (taken with respect to $p(x_t|y_{1:t})$ More...
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void | filter (const osv &data, const std::vector< std::function< const Mat(const ssv &)> > &fs=std::vector< std::function< const Mat(const ssv &)> >()) |
| updates filtering distribution on a new datapoint. Optionally stores expectations of functionals. More...
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virtual float_t | logMuEv (const ssv &x1)=0 |
| Calculate muEv or logmuEv. More...
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virtual ssv | q1Samp (const osv &y1)=0 |
| Samples from time 1 proposal. More...
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virtual float_t | logQ1Ev (const ssv &x1, const osv &y1)=0 |
| Calculate q1Ev or log q1Ev. More...
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virtual float_t | logGEv (const osv &yt, const ssv &xt)=0 |
| Calculate gEv or logGEv. More...
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virtual float_t | logFEv (const ssv &xt, const ssv &xtm1)=0 |
| Evaluates the state transition density. More...
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virtual ssv | qSamp (const ssv &xtm1, const osv &yt)=0 |
| Samples from the proposal/instrumental/importance density at time t. More...
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virtual float_t | logQEv (const ssv &xt, const ssv &xtm1, const osv &yt)=0 |
| Evaluates the proposal/instrumental/importance density/pmf. More...
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virtual void | filter (const obs_sized_vec &data, const func_vec &fs=func_vec())=0 |
| the filtering function that must be defined More...
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virtual float_t | getLogCondLike () const=0 |
| the getter method that must be defined (for conditional log-likelihood) More...
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virtual | ~pf_base () |
| virtual destructor
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using | ssv = Eigen::Matrix< float_t, dimx, 1 > |
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using | osv = Eigen::Matrix< float_t, dimy, 1 > |
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using | Mat = Eigen::Matrix< float_t, Eigen::Dynamic, Eigen::Dynamic > |
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using | arrayStates = std::array< ssv, nparts > |
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using | arrayfloat_t = std::array< float_t, nparts > |
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using | float_type = float_t |
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using | obs_sized_vec = Eigen::Matrix< float_t, dimobs, 1 > |
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using | state_sized_vec = Eigen::Matrix< float_t, dimstate, 1 > |
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using | dynamic_matrix = Eigen::Matrix< float_t, Eigen::Dynamic, Eigen::Dynamic > |
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using | func = std::function< const dynamic_matrix(const state_sized_vec &)> |
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using | func_vec = std::vector< func > |
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static constexpr unsigned int | dim_obs |
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static constexpr unsigned int | dim_state |
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template<size_t nparts, size_t dimx, size_t dimy, typename resamp_t, typename float_t, bool debug = false>
class pf::filters::SISRFilter< nparts, dimx, dimy, resamp_t, float_t, debug >
A base class for the Sequential Important Sampling with Resampling (SISR).
- Author
- taylor
◆ arrayfloat_t
template<size_t nparts, size_t dimx, size_t dimy, typename resamp_t , typename float_t , bool debug = false>
type alias for array of float_ts
◆ arrayStates
template<size_t nparts, size_t dimx, size_t dimy, typename resamp_t , typename float_t , bool debug = false>
type alias for linear algebra stuff
◆ Mat
template<size_t nparts, size_t dimx, size_t dimy, typename resamp_t , typename float_t , bool debug = false>
using pf::filters::SISRFilter< nparts, dimx, dimy, resamp_t, float_t, debug >::Mat = Eigen::Matrix<float_t,Eigen::Dynamic,Eigen::Dynamic> |
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private |
type alias for linear algebra stuff
◆ osv
template<size_t nparts, size_t dimx, size_t dimy, typename resamp_t , typename float_t , bool debug = false>
"obs size vector" type alias for linear algebra stuff
◆ ssv
template<size_t nparts, size_t dimx, size_t dimy, typename resamp_t , typename float_t , bool debug = false>
"state size vector" type alias for linear algebra stuff
◆ SISRFilter()
template<size_t nparts, size_t dimx, size_t dimy, typename resamp_t , typename float_t , bool debug>
The (one and only) constructor.
- Parameters
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rs | the resampling schedule (resample every rs time points). |
◆ filter()
template<size_t nparts, size_t dimx, size_t dimy, typename resamp_t , typename float_t , bool debug>
void pf::filters::SISRFilter< nparts, dimx, dimy, resamp_t, float_t, debug >::filter |
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const osv & |
data, |
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const std::vector< std::function< const Mat(const ssv &)> > & |
fs = std::vector<std::function<const Mat(const ssv&)> >() |
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updates filtering distribution on a new datapoint. Optionally stores expectations of functionals.
- Parameters
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data | the most recent data point |
fs | a vector of functions if you want to calculate expectations. |
◆ getExpectations()
template<size_t nparts, size_t dimx, size_t dimy, typename resamp_t , typename float_t , bool debug>
return all stored expectations (taken with respect to $p(x_t|y_{1:t})$
- Returns
- return a std::vector<Mat> of expectations. How many depends on how many callbacks you gave to
◆ getLogCondLike()
template<size_t nparts, size_t dimx, size_t dimy, typename resamp_t , typename float_t , bool debug>
Returns the most recent (log-) conditional likelihood.
- Returns
- log p(y_t | y_{1:t-1}) or log p(y_1)
◆ logFEv()
template<size_t nparts, size_t dimx, size_t dimy, typename resamp_t , typename float_t , bool debug = false>
Evaluates the state transition density.
- Parameters
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xt | the current state |
xtm1 | the previous state |
- Returns
- a float_t evaluaton of the log density/pmf
◆ logGEv()
template<size_t nparts, size_t dimx, size_t dimy, typename resamp_t , typename float_t , bool debug = false>
Calculate gEv or logGEv.
- Parameters
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yt | is a const Vec& describing the time t datum |
xt | is a const Vec& describing the time t state |
- Returns
- the density or log-density evaluation as a float_t
◆ logMuEv()
template<size_t nparts, size_t dimx, size_t dimy, typename resamp_t , typename float_t , bool debug = false>
Calculate muEv or logmuEv.
- Parameters
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x1 | is a const Vec& describing the state sample |
- Returns
- the density or log-density evaluation as a float_t
◆ logQ1Ev()
template<size_t nparts, size_t dimx, size_t dimy, typename resamp_t , typename float_t , bool debug = false>
Calculate q1Ev or log q1Ev.
- Parameters
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x1 | is a const Vec& describing the time 1 state sample |
y1 | is a const Vec& describing the time 1 datum |
- Returns
- the density or log-density evaluation as a float_t
◆ logQEv()
template<size_t nparts, size_t dimx, size_t dimy, typename resamp_t , typename float_t , bool debug = false>
Evaluates the proposal/instrumental/importance density/pmf.
- Parameters
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xt | current state |
xtm1 | previous state |
yt | current observation |
- Returns
- a float_t evaluation of the log density/pmf
◆ q1Samp()
template<size_t nparts, size_t dimx, size_t dimy, typename resamp_t , typename float_t , bool debug = false>
Samples from time 1 proposal.
- Parameters
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y1 | is a const Vec& representing the first observed datum |
- Returns
- the sample as a Vec
◆ qSamp()
template<size_t nparts, size_t dimx, size_t dimy, typename resamp_t , typename float_t , bool debug = false>
Samples from the proposal/instrumental/importance density at time t.
- Parameters
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xtm1 | the previous state sample |
yt | the current observation |
- Returns
- a state sample for the current time xt
The documentation for this class was generated from the following file: