[7] | 1 | /*! |
---|
| 2 | \file |
---|
| 3 | \brief Bayesian Filtering for linear Gaussian models (Kalman Filter) and extensions |
---|
| 4 | \author Vaclav Smidl. |
---|
| 5 | |
---|
| 6 | ----------------------------------- |
---|
| 7 | BDM++ - C++ library for Bayesian Decision Making under Uncertainty |
---|
| 8 | |
---|
| 9 | Using IT++ for numerical operations |
---|
| 10 | ----------------------------------- |
---|
| 11 | */ |
---|
| 12 | |
---|
| 13 | #ifndef KF_H |
---|
| 14 | #define KF_H |
---|
| 15 | |
---|
[262] | 16 | |
---|
[384] | 17 | #include "../math/functions.h" |
---|
| 18 | #include "../stat/exp_family.h" |
---|
[37] | 19 | #include "../math/chmat.h" |
---|
[384] | 20 | #include "../base/user_info.h" |
---|
[7] | 21 | |
---|
[583] | 22 | namespace bdm |
---|
| 23 | { |
---|
[7] | 24 | |
---|
[477] | 25 | /*! |
---|
[583] | 26 | * \brief Basic elements of linear state-space model |
---|
[32] | 27 | |
---|
[477] | 28 | Parameter evolution model:\f[ x_t = A x_{t-1} + B u_t + Q^{1/2} e_t \f] |
---|
| 29 | Observation model: \f[ y_t = C x_{t-1} + C u_t + Q^{1/2} w_t. \f] |
---|
| 30 | Where $e_t$ and $w_t$ are independent vectors Normal(0,1)-distributed disturbances. |
---|
[583] | 31 | */ |
---|
[477] | 32 | template<class sq_T> |
---|
[583] | 33 | class StateSpace |
---|
| 34 | { |
---|
| 35 | protected: |
---|
| 36 | //! Matrix A |
---|
| 37 | mat A; |
---|
| 38 | //! Matrix B |
---|
| 39 | mat B; |
---|
| 40 | //! Matrix C |
---|
| 41 | mat C; |
---|
| 42 | //! Matrix D |
---|
| 43 | mat D; |
---|
| 44 | //! Matrix Q in square-root form |
---|
| 45 | sq_T Q; |
---|
| 46 | //! Matrix R in square-root form |
---|
| 47 | sq_T R; |
---|
| 48 | public: |
---|
[679] | 49 | StateSpace() : A(), B(), C(), D(), Q(), R() {} |
---|
[660] | 50 | //!copy constructor |
---|
[679] | 51 | StateSpace(const StateSpace<sq_T> &S0) : A(S0.A), B(S0.B), C(S0.C), D(S0.D), Q(S0.Q), R(S0.R) {} |
---|
[660] | 52 | //! set all matrix parameters |
---|
[583] | 53 | void set_parameters (const mat &A0, const mat &B0, const mat &C0, const mat &D0, const sq_T &Q0, const sq_T &R0); |
---|
[660] | 54 | //! validation |
---|
[583] | 55 | void validate(); |
---|
| 56 | //! not virtual in this case |
---|
| 57 | void from_setting (const Setting &set) { |
---|
| 58 | UI::get (A, set, "A", UI::compulsory); |
---|
| 59 | UI::get (B, set, "B", UI::compulsory); |
---|
| 60 | UI::get (C, set, "C", UI::compulsory); |
---|
| 61 | UI::get (D, set, "D", UI::compulsory); |
---|
| 62 | mat Qtm, Rtm; |
---|
| 63 | if(!UI::get(Qtm, set, "Q", UI::optional)){ |
---|
| 64 | vec dq; |
---|
| 65 | UI::get(dq, set, "dQ", UI::compulsory); |
---|
| 66 | Qtm=diag(dq); |
---|
| 67 | } |
---|
| 68 | if(!UI::get(Rtm, set, "R", UI::optional)){ |
---|
| 69 | vec dr; |
---|
| 70 | UI::get(dr, set, "dQ", UI::compulsory); |
---|
| 71 | Rtm=diag(dr); |
---|
| 72 | } |
---|
| 73 | R=Rtm; // automatic conversion |
---|
| 74 | Q=Qtm; |
---|
| 75 | |
---|
| 76 | validate(); |
---|
| 77 | } |
---|
[586] | 78 | //! access function |
---|
[605] | 79 | const mat& _A() const {return A;} |
---|
[586] | 80 | //! access function |
---|
[605] | 81 | const mat& _B()const {return B;} |
---|
[586] | 82 | //! access function |
---|
[605] | 83 | const mat& _C()const {return C;} |
---|
[586] | 84 | //! access function |
---|
[605] | 85 | const mat& _D()const {return D;} |
---|
[586] | 86 | //! access function |
---|
[605] | 87 | const sq_T& _Q()const {return Q;} |
---|
[586] | 88 | //! access function |
---|
[605] | 89 | const sq_T& _R()const {return R;} |
---|
[583] | 90 | }; |
---|
[32] | 91 | |
---|
[583] | 92 | //! Common abstract base for Kalman filters |
---|
| 93 | template<class sq_T> |
---|
| 94 | class Kalman: public BM, public StateSpace<sq_T> |
---|
| 95 | { |
---|
| 96 | protected: |
---|
| 97 | //! id of output |
---|
| 98 | RV yrv; |
---|
| 99 | //! Kalman gain |
---|
| 100 | mat _K; |
---|
| 101 | //!posterior |
---|
[679] | 102 | enorm<sq_T> est; |
---|
[583] | 103 | //!marginal on data f(y|y) |
---|
| 104 | enorm<sq_T> fy; |
---|
| 105 | public: |
---|
[679] | 106 | Kalman<sq_T>() : BM(), StateSpace<sq_T>(), yrv(), _K(), est(){} |
---|
[660] | 107 | //! Copy constructor |
---|
[679] | 108 | Kalman<sq_T>(const Kalman<sq_T> &K0) : BM(K0), StateSpace<sq_T>(K0), yrv(K0.yrv), _K(K0._K), est(K0.est), fy(K0.fy){} |
---|
[660] | 109 | //!set statistics of the posterior |
---|
[679] | 110 | void set_statistics (const vec &mu0, const mat &P0) {est.set_parameters (mu0, P0); }; |
---|
[660] | 111 | //!set statistics of the posterior |
---|
[679] | 112 | void set_statistics (const vec &mu0, const sq_T &P0) {est.set_parameters (mu0, P0); }; |
---|
[660] | 113 | //! return correctly typed posterior (covariant return) |
---|
[679] | 114 | const enorm<sq_T>& posterior() const {return est;} |
---|
[583] | 115 | //! load basic elements of Kalman from structure |
---|
| 116 | void from_setting (const Setting &set) { |
---|
| 117 | StateSpace<sq_T>::from_setting(set); |
---|
| 118 | |
---|
| 119 | mat P0; vec mu0; |
---|
| 120 | UI::get(mu0, set, "mu0", UI::optional); |
---|
| 121 | UI::get(P0, set, "P0", UI::optional); |
---|
| 122 | set_statistics(mu0,P0); |
---|
| 123 | // Initial values |
---|
| 124 | UI::get (yrv, set, "yrv", UI::optional); |
---|
[679] | 125 | UI::get (rvc, set, "urv", UI::optional); |
---|
| 126 | set_yrv(concat(yrv,rvc)); |
---|
[583] | 127 | |
---|
| 128 | validate(); |
---|
| 129 | } |
---|
[660] | 130 | //! validate object |
---|
[583] | 131 | void validate() { |
---|
| 132 | StateSpace<sq_T>::validate(); |
---|
[679] | 133 | dimy = this->C.rows(); |
---|
| 134 | dimc = this->B.cols(); |
---|
| 135 | set_dim(this->A.rows()); |
---|
| 136 | |
---|
| 137 | bdm_assert(est.dimension(), "Statistics and model parameters mismatch"); |
---|
[583] | 138 | } |
---|
| 139 | }; |
---|
| 140 | /*! |
---|
| 141 | * \brief Basic Kalman filter with full matrices |
---|
| 142 | */ |
---|
[32] | 143 | |
---|
[583] | 144 | class KalmanFull : public Kalman<fsqmat> |
---|
| 145 | { |
---|
| 146 | public: |
---|
| 147 | //! For EKFfull; |
---|
| 148 | KalmanFull() :Kalman<fsqmat>(){}; |
---|
| 149 | //! Here dt = [yt;ut] of appropriate dimensions |
---|
[679] | 150 | void bayes (const vec &yt, const vec &cond=empty_vec); |
---|
[653] | 151 | BM* _copy_() const { |
---|
| 152 | KalmanFull* K = new KalmanFull; |
---|
| 153 | K->set_parameters (A, B, C, D, Q, R); |
---|
[679] | 154 | K->set_statistics (est._mu(), est._R()); |
---|
[653] | 155 | return K; |
---|
| 156 | } |
---|
[583] | 157 | }; |
---|
[586] | 158 | UIREGISTER(KalmanFull); |
---|
[32] | 159 | |
---|
| 160 | |
---|
[477] | 161 | /*! \brief Kalman filter in square root form |
---|
[271] | 162 | |
---|
[477] | 163 | Trivial example: |
---|
| 164 | \include kalman_simple.cpp |
---|
| 165 | |
---|
[583] | 166 | Complete constructor: |
---|
[477] | 167 | */ |
---|
[583] | 168 | class KalmanCh : public Kalman<chmat> |
---|
| 169 | { |
---|
| 170 | protected: |
---|
| 171 | //! @{ \name Internal storage - needs initialize() |
---|
| 172 | //! pre array (triangular matrix) |
---|
| 173 | mat preA; |
---|
| 174 | //! post array (triangular matrix) |
---|
| 175 | mat postA; |
---|
| 176 | //!@} |
---|
| 177 | public: |
---|
| 178 | //! copy constructor |
---|
| 179 | BM* _copy_() const { |
---|
| 180 | KalmanCh* K = new KalmanCh; |
---|
| 181 | K->set_parameters (A, B, C, D, Q, R); |
---|
[679] | 182 | K->set_statistics (est._mu(), est._R()); |
---|
[681] | 183 | K->validate(); |
---|
[583] | 184 | return K; |
---|
| 185 | } |
---|
| 186 | //! set parameters for adapt from Kalman |
---|
| 187 | void set_parameters (const mat &A0, const mat &B0, const mat &C0, const mat &D0, const chmat &Q0, const chmat &R0); |
---|
| 188 | //! initialize internal parametetrs |
---|
| 189 | void initialize(); |
---|
[37] | 190 | |
---|
[583] | 191 | /*!\brief Here dt = [yt;ut] of appropriate dimensions |
---|
[283] | 192 | |
---|
[583] | 193 | The following equality hold::\f[ |
---|
| 194 | \left[\begin{array}{cc} |
---|
| 195 | R^{0.5}\\ |
---|
| 196 | P_{t|t-1}^{0.5}C' & P_{t|t-1}^{0.5}CA'\\ |
---|
| 197 | & Q^{0.5}\end{array}\right]<\mathrm{orth.oper.}>=\left[\begin{array}{cc} |
---|
| 198 | R_{y}^{0.5} & KA'\\ |
---|
| 199 | & P_{t+1|t}^{0.5}\\ |
---|
| 200 | \\\end{array}\right]\f] |
---|
[283] | 201 | |
---|
[583] | 202 | Thus this object evaluates only predictors! Not filtering densities. |
---|
| 203 | */ |
---|
[679] | 204 | void bayes (const vec &yt, const vec &cond=empty_vec); |
---|
[675] | 205 | |
---|
[586] | 206 | void from_setting(const Setting &set){ |
---|
[583] | 207 | Kalman<chmat>::from_setting(set); |
---|
[679] | 208 | validate(); |
---|
| 209 | } |
---|
| 210 | void validate() { |
---|
| 211 | Kalman<chmat>::validate(); |
---|
[583] | 212 | initialize(); |
---|
| 213 | } |
---|
[477] | 214 | }; |
---|
[586] | 215 | UIREGISTER(KalmanCh); |
---|
[37] | 216 | |
---|
[477] | 217 | /*! |
---|
| 218 | \brief Extended Kalman Filter in full matrices |
---|
[62] | 219 | |
---|
[477] | 220 | An approximation of the exact Bayesian filter with Gaussian noices and non-linear evolutions of their mean. |
---|
| 221 | */ |
---|
[583] | 222 | class EKFfull : public KalmanFull |
---|
| 223 | { |
---|
| 224 | protected: |
---|
| 225 | //! Internal Model f(x,u) |
---|
| 226 | shared_ptr<diffbifn> pfxu; |
---|
[527] | 227 | |
---|
[583] | 228 | //! Observation Model h(x,u) |
---|
| 229 | shared_ptr<diffbifn> phxu; |
---|
[283] | 230 | |
---|
[583] | 231 | public: |
---|
| 232 | //! Default constructor |
---|
| 233 | EKFfull (); |
---|
[527] | 234 | |
---|
[583] | 235 | //! Set nonlinear functions for mean values and covariance matrices. |
---|
| 236 | void set_parameters (const shared_ptr<diffbifn> &pfxu, const shared_ptr<diffbifn> &phxu, const mat Q0, const mat R0); |
---|
[527] | 237 | |
---|
[583] | 238 | //! Here dt = [yt;ut] of appropriate dimensions |
---|
[679] | 239 | void bayes (const vec &yt, const vec &cond=empty_vec); |
---|
[583] | 240 | //! set estimates |
---|
| 241 | void set_statistics (const vec &mu0, const mat &P0) { |
---|
[679] | 242 | est.set_parameters (mu0, P0); |
---|
[583] | 243 | }; |
---|
[660] | 244 | //! access function |
---|
[583] | 245 | const mat _R() { |
---|
[679] | 246 | return est._R().to_mat(); |
---|
[583] | 247 | } |
---|
[686] | 248 | void from_setting (const Setting &set) { |
---|
| 249 | BM::from_setting(set); |
---|
[653] | 250 | shared_ptr<diffbifn> IM = UI::build<diffbifn> ( set, "IM", UI::compulsory ); |
---|
| 251 | shared_ptr<diffbifn> OM = UI::build<diffbifn> ( set, "OM", UI::compulsory ); |
---|
| 252 | |
---|
| 253 | //statistics |
---|
| 254 | int dim = IM->dimension(); |
---|
| 255 | vec mu0; |
---|
| 256 | if ( !UI::get ( mu0, set, "mu0" ) ) |
---|
| 257 | mu0 = zeros ( dim ); |
---|
| 258 | |
---|
| 259 | mat P0; |
---|
| 260 | vec dP0; |
---|
| 261 | if ( UI::get ( dP0, set, "dP0" ) ) |
---|
| 262 | P0 = diag ( dP0 ); |
---|
| 263 | else if ( !UI::get ( P0, set, "P0" ) ) |
---|
| 264 | P0 = eye ( dim ); |
---|
| 265 | |
---|
| 266 | set_statistics ( mu0, P0 ); |
---|
| 267 | |
---|
| 268 | //parameters |
---|
| 269 | vec dQ, dR; |
---|
| 270 | UI::get ( dQ, set, "dQ", UI::compulsory ); |
---|
| 271 | UI::get ( dR, set, "dR", UI::compulsory ); |
---|
| 272 | set_parameters ( IM, OM, diag ( dQ ), diag ( dR ) ); |
---|
[686] | 273 | |
---|
[653] | 274 | string options; |
---|
| 275 | if ( UI::get ( options, set, "options" ) ) |
---|
| 276 | set_options ( options ); |
---|
| 277 | // pfxu = UI::build<diffbifn>(set, "IM", UI::compulsory); |
---|
| 278 | // phxu = UI::build<diffbifn>(set, "OM", UI::compulsory); |
---|
| 279 | // |
---|
| 280 | // mat R0; |
---|
| 281 | // UI::get(R0, set, "R",UI::compulsory); |
---|
| 282 | // mat Q0; |
---|
| 283 | // UI::get(Q0, set, "Q",UI::compulsory); |
---|
| 284 | // |
---|
| 285 | // |
---|
| 286 | // mat P0; vec mu0; |
---|
| 287 | // UI::get(mu0, set, "mu0", UI::optional); |
---|
| 288 | // UI::get(P0, set, "P0", UI::optional); |
---|
| 289 | // set_statistics(mu0,P0); |
---|
| 290 | // // Initial values |
---|
| 291 | // UI::get (yrv, set, "yrv", UI::optional); |
---|
| 292 | // UI::get (urv, set, "urv", UI::optional); |
---|
| 293 | // set_drv(concat(yrv,urv)); |
---|
| 294 | // |
---|
| 295 | // // setup StateSpace |
---|
| 296 | // pfxu->dfdu_cond(mu0, zeros(pfxu->_dimu()), A,true); |
---|
| 297 | // phxu->dfdu_cond(mu0, zeros(pfxu->_dimu()), C,true); |
---|
| 298 | // |
---|
| 299 | validate(); |
---|
| 300 | } |
---|
| 301 | void validate() { |
---|
| 302 | // check stats and IM and OM |
---|
| 303 | } |
---|
[477] | 304 | }; |
---|
[586] | 305 | UIREGISTER(EKFfull); |
---|
[62] | 306 | |
---|
[586] | 307 | |
---|
[477] | 308 | /*! |
---|
| 309 | \brief Extended Kalman Filter in Square root |
---|
[37] | 310 | |
---|
[477] | 311 | An approximation of the exact Bayesian filter with Gaussian noices and non-linear evolutions of their mean. |
---|
| 312 | */ |
---|
[37] | 313 | |
---|
[583] | 314 | class EKFCh : public KalmanCh |
---|
| 315 | { |
---|
| 316 | protected: |
---|
| 317 | //! Internal Model f(x,u) |
---|
| 318 | shared_ptr<diffbifn> pfxu; |
---|
[527] | 319 | |
---|
[583] | 320 | //! Observation Model h(x,u) |
---|
| 321 | shared_ptr<diffbifn> phxu; |
---|
| 322 | public: |
---|
| 323 | //! copy constructor duplicated - calls different set_parameters |
---|
| 324 | BM* _copy_() const { |
---|
| 325 | EKFCh* E = new EKFCh; |
---|
| 326 | E->set_parameters (pfxu, phxu, Q, R); |
---|
[679] | 327 | E->set_statistics (est._mu(), est._R()); |
---|
[583] | 328 | return E; |
---|
| 329 | } |
---|
| 330 | //! Set nonlinear functions for mean values and covariance matrices. |
---|
| 331 | void set_parameters (const shared_ptr<diffbifn> &pfxu, const shared_ptr<diffbifn> &phxu, const chmat Q0, const chmat R0); |
---|
[527] | 332 | |
---|
[583] | 333 | //! Here dt = [yt;ut] of appropriate dimensions |
---|
[679] | 334 | void bayes (const vec &yt, const vec &cond=empty_vec); |
---|
[357] | 335 | |
---|
[583] | 336 | void from_setting (const Setting &set); |
---|
[357] | 337 | |
---|
[681] | 338 | void validate(){}; |
---|
[583] | 339 | // TODO dodelat void to_setting( Setting &set ) const; |
---|
[357] | 340 | |
---|
[477] | 341 | }; |
---|
[37] | 342 | |
---|
[583] | 343 | UIREGISTER (EKFCh); |
---|
| 344 | SHAREDPTR (EKFCh); |
---|
[357] | 345 | |
---|
| 346 | |
---|
[7] | 347 | //////// INstance |
---|
| 348 | |
---|
[477] | 349 | /*! \brief (Switching) Multiple Model |
---|
| 350 | The model runs several models in parallel and evaluates thier weights (fittness). |
---|
[62] | 351 | |
---|
[477] | 352 | The statistics of the resulting density are merged using (geometric?) combination. |
---|
[283] | 353 | |
---|
[477] | 354 | The next step is performed with the new statistics for all models. |
---|
| 355 | */ |
---|
[583] | 356 | class MultiModel: public BM |
---|
| 357 | { |
---|
| 358 | protected: |
---|
| 359 | //! List of models between which we switch |
---|
| 360 | Array<EKFCh*> Models; |
---|
| 361 | //! vector of model weights |
---|
| 362 | vec w; |
---|
| 363 | //! cache of model lls |
---|
| 364 | vec _lls; |
---|
| 365 | //! type of switching policy [1=maximum,2=...] |
---|
| 366 | int policy; |
---|
| 367 | //! internal statistics |
---|
| 368 | enorm<chmat> est; |
---|
| 369 | public: |
---|
[660] | 370 | //! set internal parameters |
---|
[583] | 371 | void set_parameters (Array<EKFCh*> A, int pol0 = 1) { |
---|
| 372 | Models = A;//TODO: test if evalll is set |
---|
| 373 | w.set_length (A.length()); |
---|
| 374 | _lls.set_length (A.length()); |
---|
| 375 | policy = pol0; |
---|
[357] | 376 | |
---|
[583] | 377 | est.set_rv (RV ("MM", A (0)->posterior().dimension(), 0)); |
---|
| 378 | est.set_parameters (A (0)->posterior().mean(), A (0)->posterior()._R()); |
---|
[477] | 379 | } |
---|
[679] | 380 | void bayes (const vec &yt, const vec &cond=empty_vec) { |
---|
[583] | 381 | int n = Models.length(); |
---|
| 382 | int i; |
---|
| 383 | for (i = 0; i < n; i++) { |
---|
[679] | 384 | Models (i)->bayes (yt); |
---|
[583] | 385 | _lls (i) = Models (i)->_ll(); |
---|
| 386 | } |
---|
| 387 | double mlls = max (_lls); |
---|
| 388 | w = exp (_lls - mlls); |
---|
| 389 | w /= sum (w); //normalization |
---|
| 390 | //set statistics |
---|
| 391 | switch (policy) { |
---|
| 392 | case 1: { |
---|
| 393 | int mi = max_index (w); |
---|
| 394 | const enorm<chmat> &st = Models (mi)->posterior() ; |
---|
| 395 | est.set_parameters (st.mean(), st._R()); |
---|
| 396 | } |
---|
| 397 | break; |
---|
| 398 | default: |
---|
| 399 | bdm_error ("unknown policy"); |
---|
| 400 | } |
---|
| 401 | // copy result to all models |
---|
| 402 | for (i = 0; i < n; i++) { |
---|
| 403 | Models (i)->set_statistics (est.mean(), est._R()); |
---|
| 404 | } |
---|
[477] | 405 | } |
---|
[660] | 406 | //! return correctly typed posterior (covariant return) |
---|
[583] | 407 | const enorm<chmat>& posterior() const { |
---|
| 408 | return est; |
---|
[477] | 409 | } |
---|
[357] | 410 | |
---|
[583] | 411 | void from_setting (const Setting &set); |
---|
[357] | 412 | |
---|
[583] | 413 | // TODO dodelat void to_setting( Setting &set ) const; |
---|
[338] | 414 | |
---|
[477] | 415 | }; |
---|
[338] | 416 | |
---|
[583] | 417 | UIREGISTER (MultiModel); |
---|
| 418 | SHAREDPTR (MultiModel); |
---|
[357] | 419 | |
---|
[586] | 420 | //! conversion of outer ARX model (mlnorm) to state space model |
---|
| 421 | /*! |
---|
| 422 | The model is constructed as: |
---|
| 423 | \f[ x_{t+1} = Ax_t + B u_t + R^{1/2} e_t, y_t=Cx_t+Du_t + R^{1/2}w_t, \f] |
---|
| 424 | For example, for: |
---|
[605] | 425 | Using Frobenius form, see []. |
---|
| 426 | |
---|
| 427 | For easier use in the future, indeces theta_in_A and theta_in_C are set. TODO - explain |
---|
[586] | 428 | */ |
---|
[605] | 429 | //template<class sq_T> |
---|
| 430 | class StateCanonical: public StateSpace<fsqmat>{ |
---|
| 431 | protected: |
---|
| 432 | //! remember connection from theta ->A |
---|
| 433 | datalink_part th2A; |
---|
| 434 | //! remember connection from theta ->C |
---|
| 435 | datalink_part th2C; |
---|
| 436 | //! remember connection from theta ->D |
---|
| 437 | datalink_part th2D; |
---|
| 438 | //!cached first row of A |
---|
| 439 | vec A1row; |
---|
| 440 | //!cached first row of C |
---|
| 441 | vec C1row; |
---|
| 442 | //!cached first row of D |
---|
| 443 | vec D1row; |
---|
| 444 | |
---|
| 445 | public: |
---|
| 446 | //! set up this object to match given mlnorm |
---|
[625] | 447 | void connect_mlnorm(const mlnorm<fsqmat> &ml){ |
---|
| 448 | //get ids of yrv |
---|
[605] | 449 | const RV &yrv = ml._rv(); |
---|
| 450 | //need to determine u_t - it is all in _rvc that is not in ml._rv() |
---|
| 451 | RV rgr0 = ml._rvc().remove_time(); |
---|
| 452 | RV urv = rgr0.subt(yrv); |
---|
[586] | 453 | |
---|
| 454 | //We can do only 1d now... :( |
---|
[620] | 455 | bdm_assert(yrv._dsize()==1, "Only for SISO so far..." ); |
---|
[605] | 456 | |
---|
| 457 | // create names for |
---|
[586] | 458 | RV xrv; //empty |
---|
| 459 | RV Crv; //empty |
---|
[605] | 460 | int td=ml._rvc().mint(); |
---|
| 461 | // assuming strictly proper function!!! |
---|
[586] | 462 | for (int t=-1;t>=td;t--){ |
---|
[605] | 463 | xrv.add(yrv.copy_t(t)); |
---|
| 464 | Crv.add(urv.copy_t(t)); |
---|
[586] | 465 | } |
---|
[679] | 466 | |
---|
[605] | 467 | // get mapp |
---|
| 468 | th2A.set_connection(xrv, ml._rvc()); |
---|
| 469 | th2C.set_connection(Crv, ml._rvc()); |
---|
| 470 | th2D.set_connection(urv, ml._rvc()); |
---|
| 471 | |
---|
| 472 | //set matrix sizes |
---|
[679] | 473 | this->A=zeros(xrv._dsize(),xrv._dsize()); |
---|
| 474 | for (int j=1; j<xrv._dsize(); j++){A(j,j-1)=1.0;} // off diagonal |
---|
| 475 | this->B=zeros(xrv._dsize(),1); |
---|
[605] | 476 | this->B(0) = 1.0; |
---|
[679] | 477 | this->C=zeros(1,xrv._dsize()); |
---|
[605] | 478 | this->D=zeros(1,urv._dsize()); |
---|
[679] | 479 | this->Q = zeros(xrv._dsize(),xrv._dsize()); |
---|
[605] | 480 | // R is set by update |
---|
[586] | 481 | |
---|
[605] | 482 | //set cache |
---|
| 483 | this->A1row = zeros(xrv._dsize()); |
---|
| 484 | this->C1row = zeros(xrv._dsize()); |
---|
| 485 | this->D1row = zeros(urv._dsize()); |
---|
| 486 | |
---|
| 487 | update_from(ml); |
---|
| 488 | validate(); |
---|
| 489 | }; |
---|
| 490 | //! fast function to update parameters from ml - not checked for compatibility!! |
---|
| 491 | void update_from(const mlnorm<fsqmat> &ml){ |
---|
| 492 | |
---|
[586] | 493 | vec theta = ml._A().get_row(0); // this |
---|
[605] | 494 | |
---|
| 495 | th2A.filldown(theta,A1row); |
---|
| 496 | th2C.filldown(theta,C1row); |
---|
| 497 | th2D.filldown(theta,D1row); |
---|
[586] | 498 | |
---|
[605] | 499 | R = ml._R(); |
---|
| 500 | |
---|
[586] | 501 | A.set_row(0,A1row); |
---|
[605] | 502 | C.set_row(0,C1row+D1row*A1row); |
---|
| 503 | D.set_row(0,D1row); |
---|
| 504 | |
---|
| 505 | } |
---|
| 506 | }; |
---|
[586] | 507 | |
---|
[583] | 508 | /////////// INSTANTIATION |
---|
[357] | 509 | |
---|
[477] | 510 | template<class sq_T> |
---|
[583] | 511 | void StateSpace<sq_T>::set_parameters (const mat &A0, const mat &B0, const mat &C0, const mat &D0, const sq_T &Q0, const sq_T &R0) |
---|
| 512 | { |
---|
[28] | 513 | |
---|
[583] | 514 | A = A0; |
---|
| 515 | B = B0; |
---|
| 516 | C = C0; |
---|
| 517 | D = D0; |
---|
[477] | 518 | R = R0; |
---|
| 519 | Q = Q0; |
---|
[583] | 520 | validate(); |
---|
[477] | 521 | } |
---|
[22] | 522 | |
---|
[477] | 523 | template<class sq_T> |
---|
[583] | 524 | void StateSpace<sq_T>::validate(){ |
---|
[679] | 525 | bdm_assert (A.cols() == A.rows(), "KalmanFull: A is not square"); |
---|
| 526 | bdm_assert (B.rows() == A.rows(), "KalmanFull: B is not compatible"); |
---|
| 527 | bdm_assert (C.cols() == A.rows(), "KalmanFull: C is not compatible"); |
---|
| 528 | bdm_assert ( (D.rows() == C.rows()) && (D.cols() == B.cols()), "KalmanFull: D is not compatible"); |
---|
| 529 | bdm_assert ( (Q.cols() == A.rows()) && (Q.rows() == A.rows()), "KalmanFull: Q is not compatible"); |
---|
| 530 | bdm_assert ( (R.cols() == C.rows()) && (R.rows() == C.rows()), "KalmanFull: R is not compatible"); |
---|
[583] | 531 | } |
---|
[22] | 532 | |
---|
[254] | 533 | } |
---|
[7] | 534 | #endif // KF_H |
---|
| 535 | |
---|