1 | /*! |
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2 | \file |
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3 | \brief Bayesian Filtering using stochastic sampling (Particle Filters) |
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4 | \author Vaclav Smidl. |
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5 | |
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6 | ----------------------------------- |
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7 | BDM++ - C++ library for Bayesian Decision Making under Uncertainty |
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8 | |
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9 | Using IT++ for numerical operations |
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10 | ----------------------------------- |
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11 | */ |
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12 | |
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13 | #ifndef PF_H |
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14 | #define PF_H |
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15 | |
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16 | #include <itpp/itbase.h> |
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17 | #include "libBM.h" |
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18 | #include "libDC.h" |
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19 | |
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20 | using namespace itpp; |
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21 | |
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22 | enum RESAMPLING_METHOD { MULTINOMIAL = 0, STRATIFIED = 1, SYSTEMATIC = 3 }; |
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23 | |
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24 | /*! |
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25 | * \brief A Particle Filter prototype |
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26 | |
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27 | Bayesian Filtering equations hold. |
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28 | */ |
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29 | class PF : public BM { |
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30 | protected: |
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31 | int n; //number of particles |
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32 | vec w; //particle weights |
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33 | Uniform_RNG URNG; //used for resampling |
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34 | |
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35 | public: |
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36 | //! Returns indexes of particles that should be resampled. The ordering MUST guarantee inplace replacement. (Important for MPF.) |
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37 | ivec resample(RESAMPLING_METHOD method = SYSTEMATIC); |
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38 | PF (vec w); |
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39 | //TODO remove or implement bayes()! |
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40 | void bayes(const vec &dt, bool evell){}; |
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41 | }; |
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42 | |
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43 | /*! |
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44 | * \brief Trivial particle filter with proposal density that is not conditioned on the data. |
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45 | |
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46 | |
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47 | */ |
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48 | |
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49 | class TrivialPF : public PF { |
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50 | Array<vec> ptcls; |
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51 | |
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52 | bool is_proposal; |
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53 | mpdf *prop; |
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54 | mpdf *par; |
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55 | mpdf *obs; |
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56 | |
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57 | public: |
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58 | TrivialPF(mpdf &par, mpdf &obs, mpdf &prop, int n0); |
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59 | TrivialPF(mpdf &par, mpdf &obs, int n0); |
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60 | void bayes(const vec &dt, bool evalll); |
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61 | }; |
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62 | |
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63 | class MPF : public TrivialPF { |
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64 | Array<BM> Bms; |
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65 | public: |
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66 | MPF(BM &B, mpdf &prop, mpdf &obs, mpdf &par); |
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67 | void bayes(vec &dt); |
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68 | }; |
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69 | |
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70 | #endif // KF_H |
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71 | |
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