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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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using | usv = Eigen::Matrix< float_t, dimu, 1 > |
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using | usvr = Eigen::Matrix< float_t, dimur, 1 > |
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virtual void | filter (const obs_sized_vec &data, const std::array< usv, numparts > &Us, const usvr &Uresamp, 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_crn () |
| virtual destructor
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static constexpr unsigned int | dim_obs = dimobs |
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static constexpr unsigned int | dim_state = dimstate |
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◆ filter()
template<typename float_t , size_t dimobs, size_t dimstate, size_t dimu, size_t dimur, size_t numparts>
virtual void pf::bases::pf_base_crn< float_t, dimobs, dimstate, dimu, dimur, numparts >::filter |
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const obs_sized_vec & |
data, |
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const std::array< usv, numparts > & |
Us, |
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const usvr & |
Uresamp, |
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const func_vec & |
fs = func_vec() |
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pure virtual |
the filtering function that must be defined
- Parameters
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data | the most recent observation |
filter | functions whose expected value approx. is computed at each time step |
◆ getLogCondLike()
template<typename float_t , size_t dimobs, size_t dimstate, size_t dimu, size_t dimur, size_t numparts>
virtual float_t pf::bases::pf_base_crn< float_t, dimobs, dimstate, dimu, dimur, numparts >::getLogCondLike |
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const |
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pure virtual |
the getter method that must be defined (for conditional log-likelihood)
- Returns
- log p(y_t | y_{1:t-1}) approximation
The documentation for this class was generated from the following file: