[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" |
---|
[703] | 21 | //#include <../applications/pmsm/simulator_zdenek/ekf_example/pmsm_mod.h> |
---|
[7] | 22 | |
---|
[737] | 23 | namespace bdm { |
---|
[7] | 24 | |
---|
[477] | 25 | /*! |
---|
[583] | 26 | * \brief Basic elements of linear state-space model |
---|
[32] | 27 | |
---|
[723] | 28 | Parameter evolution model:\f[ x_{t+1} = A x_{t} + B u_t + Q^{1/2} e_t \f] |
---|
| 29 | Observation model: \f[ y_t = C x_{t} + C u_t + R^{1/2} w_t. \f] |
---|
| 30 | Where $e_t$ and $w_t$ are mutually independent vectors of Normal(0,1)-distributed disturbances. |
---|
[583] | 31 | */ |
---|
[477] | 32 | template<class sq_T> |
---|
[737] | 33 | class StateSpace { |
---|
| 34 | protected: |
---|
| 35 | //! Matrix A |
---|
| 36 | mat A; |
---|
| 37 | //! Matrix B |
---|
| 38 | mat B; |
---|
| 39 | //! Matrix C |
---|
| 40 | mat C; |
---|
| 41 | //! Matrix D |
---|
| 42 | mat D; |
---|
| 43 | //! Matrix Q in square-root form |
---|
| 44 | sq_T Q; |
---|
| 45 | //! Matrix R in square-root form |
---|
| 46 | sq_T R; |
---|
| 47 | public: |
---|
| 48 | StateSpace() : A(), B(), C(), D(), Q(), R() {} |
---|
| 49 | //!copy constructor |
---|
| 50 | StateSpace ( const StateSpace<sq_T> &S0 ) : A ( S0.A ), B ( S0.B ), C ( S0.C ), D ( S0.D ), Q ( S0.Q ), R ( S0.R ) {} |
---|
| 51 | //! set all matrix parameters |
---|
| 52 | void set_parameters ( const mat &A0, const mat &B0, const mat &C0, const mat &D0, const sq_T &Q0, const sq_T &R0 ); |
---|
| 53 | //! validation |
---|
| 54 | void validate(); |
---|
| 55 | //! not virtual in this case |
---|
| 56 | void from_setting ( const Setting &set ) { |
---|
| 57 | UI::get ( A, set, "A", UI::compulsory ); |
---|
| 58 | UI::get ( B, set, "B", UI::compulsory ); |
---|
| 59 | UI::get ( C, set, "C", UI::compulsory ); |
---|
| 60 | UI::get ( D, set, "D", UI::compulsory ); |
---|
| 61 | mat Qtm, Rtm; // full matrices |
---|
| 62 | if ( !UI::get ( Qtm, set, "Q", UI::optional ) ) { |
---|
| 63 | vec dq; |
---|
| 64 | UI::get ( dq, set, "dQ", UI::compulsory ); |
---|
| 65 | Qtm = diag ( dq ); |
---|
| 66 | } |
---|
| 67 | if ( !UI::get ( Rtm, set, "R", UI::optional ) ) { |
---|
| 68 | vec dr; |
---|
| 69 | UI::get ( dr, set, "dQ", UI::compulsory ); |
---|
| 70 | Rtm = diag ( dr ); |
---|
| 71 | } |
---|
| 72 | R = Rtm; // automatic conversion to square-root form |
---|
| 73 | Q = Qtm; |
---|
| 74 | |
---|
| 75 | validate(); |
---|
| 76 | } |
---|
| 77 | //! access function |
---|
| 78 | const mat& _A() const { |
---|
| 79 | return A; |
---|
| 80 | } |
---|
| 81 | //! access function |
---|
| 82 | const mat& _B() const { |
---|
| 83 | return B; |
---|
| 84 | } |
---|
| 85 | //! access function |
---|
| 86 | const mat& _C() const { |
---|
| 87 | return C; |
---|
| 88 | } |
---|
| 89 | //! access function |
---|
| 90 | const mat& _D() const { |
---|
| 91 | return D; |
---|
| 92 | } |
---|
| 93 | //! access function |
---|
| 94 | const sq_T& _Q() const { |
---|
| 95 | return Q; |
---|
| 96 | } |
---|
| 97 | //! access function |
---|
| 98 | const sq_T& _R() const { |
---|
| 99 | return R; |
---|
| 100 | } |
---|
[583] | 101 | }; |
---|
[32] | 102 | |
---|
[737] | 103 | //! Common abstract base for Kalman filters |
---|
[583] | 104 | template<class sq_T> |
---|
[737] | 105 | class Kalman: public BM, public StateSpace<sq_T> { |
---|
| 106 | protected: |
---|
| 107 | //! id of output |
---|
| 108 | RV yrv; |
---|
| 109 | //! Kalman gain |
---|
| 110 | mat _K; |
---|
| 111 | //!posterior |
---|
| 112 | enorm<sq_T> est; |
---|
| 113 | //!marginal on data f(y|y) |
---|
| 114 | enorm<sq_T> fy; |
---|
| 115 | public: |
---|
| 116 | Kalman<sq_T>() : BM(), StateSpace<sq_T>(), yrv(), _K(), est() {} |
---|
| 117 | //! Copy constructor |
---|
| 118 | 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 ) {} |
---|
| 119 | //!set statistics of the posterior |
---|
| 120 | void set_statistics ( const vec &mu0, const mat &P0 ) { |
---|
| 121 | est.set_parameters ( mu0, P0 ); |
---|
| 122 | }; |
---|
| 123 | //!set statistics of the posterior |
---|
| 124 | void set_statistics ( const vec &mu0, const sq_T &P0 ) { |
---|
| 125 | est.set_parameters ( mu0, P0 ); |
---|
| 126 | }; |
---|
| 127 | //! return correctly typed posterior (covariant return) |
---|
| 128 | const enorm<sq_T>& posterior() const { |
---|
| 129 | return est; |
---|
| 130 | } |
---|
| 131 | //! load basic elements of Kalman from structure |
---|
| 132 | void from_setting ( const Setting &set ) { |
---|
| 133 | StateSpace<sq_T>::from_setting ( set ); |
---|
| 134 | |
---|
| 135 | mat P0; |
---|
| 136 | vec mu0; |
---|
| 137 | UI::get ( mu0, set, "mu0", UI::optional ); |
---|
| 138 | UI::get ( P0, set, "P0", UI::optional ); |
---|
| 139 | set_statistics ( mu0, P0 ); |
---|
| 140 | // Initial values |
---|
| 141 | UI::get ( yrv, set, "yrv", UI::optional ); |
---|
| 142 | UI::get ( rvc, set, "urv", UI::optional ); |
---|
| 143 | set_yrv ( concat ( yrv, rvc ) ); |
---|
| 144 | |
---|
| 145 | validate(); |
---|
| 146 | } |
---|
| 147 | //! validate object |
---|
| 148 | void validate() { |
---|
| 149 | StateSpace<sq_T>::validate(); |
---|
| 150 | dimy = this->C.rows(); |
---|
| 151 | dimc = this->B.cols(); |
---|
| 152 | set_dim ( this->A.rows() ); |
---|
| 153 | |
---|
| 154 | bdm_assert ( est.dimension(), "Statistics and model parameters mismatch" ); |
---|
| 155 | } |
---|
[583] | 156 | }; |
---|
| 157 | /*! |
---|
| 158 | * \brief Basic Kalman filter with full matrices |
---|
| 159 | */ |
---|
[32] | 160 | |
---|
[737] | 161 | class KalmanFull : public Kalman<fsqmat> { |
---|
| 162 | public: |
---|
| 163 | //! For EKFfull; |
---|
| 164 | KalmanFull() : Kalman<fsqmat>() {}; |
---|
| 165 | //! Here dt = [yt;ut] of appropriate dimensions |
---|
| 166 | void bayes ( const vec &yt, const vec &cond = empty_vec ); |
---|
| 167 | BM* _copy_() const { |
---|
| 168 | KalmanFull* K = new KalmanFull; |
---|
| 169 | K->set_parameters ( A, B, C, D, Q, R ); |
---|
| 170 | K->set_statistics ( est._mu(), est._R() ); |
---|
| 171 | return K; |
---|
| 172 | } |
---|
[583] | 173 | }; |
---|
[737] | 174 | UIREGISTER ( KalmanFull ); |
---|
[32] | 175 | |
---|
| 176 | |
---|
[477] | 177 | /*! \brief Kalman filter in square root form |
---|
[271] | 178 | |
---|
[477] | 179 | Trivial example: |
---|
| 180 | \include kalman_simple.cpp |
---|
| 181 | |
---|
[583] | 182 | Complete constructor: |
---|
[477] | 183 | */ |
---|
[737] | 184 | class KalmanCh : public Kalman<chmat> { |
---|
| 185 | protected: |
---|
| 186 | //! @{ \name Internal storage - needs initialize() |
---|
| 187 | //! pre array (triangular matrix) |
---|
| 188 | mat preA; |
---|
| 189 | //! post array (triangular matrix) |
---|
| 190 | mat postA; |
---|
| 191 | //!@} |
---|
| 192 | public: |
---|
| 193 | //! copy constructor |
---|
| 194 | BM* _copy_() const { |
---|
| 195 | KalmanCh* K = new KalmanCh; |
---|
| 196 | K->set_parameters ( A, B, C, D, Q, R ); |
---|
| 197 | K->set_statistics ( est._mu(), est._R() ); |
---|
| 198 | K->validate(); |
---|
| 199 | return K; |
---|
| 200 | } |
---|
| 201 | //! set parameters for adapt from Kalman |
---|
| 202 | void set_parameters ( const mat &A0, const mat &B0, const mat &C0, const mat &D0, const chmat &Q0, const chmat &R0 ); |
---|
| 203 | //! initialize internal parametetrs |
---|
| 204 | void initialize(); |
---|
[37] | 205 | |
---|
[737] | 206 | /*!\brief Here dt = [yt;ut] of appropriate dimensions |
---|
[283] | 207 | |
---|
[737] | 208 | The following equality hold::\f[ |
---|
| 209 | \left[\begin{array}{cc} |
---|
| 210 | R^{0.5}\\ |
---|
| 211 | P_{t|t-1}^{0.5}C' & P_{t|t-1}^{0.5}CA'\\ |
---|
| 212 | & Q^{0.5}\end{array}\right]<\mathrm{orth.oper.}>=\left[\begin{array}{cc} |
---|
| 213 | R_{y}^{0.5} & KA'\\ |
---|
| 214 | & P_{t+1|t}^{0.5}\\ |
---|
| 215 | \\\end{array}\right]\f] |
---|
[283] | 216 | |
---|
[737] | 217 | Thus this object evaluates only predictors! Not filtering densities. |
---|
| 218 | */ |
---|
| 219 | void bayes ( const vec &yt, const vec &cond = empty_vec ); |
---|
[675] | 220 | |
---|
[737] | 221 | void from_setting ( const Setting &set ) { |
---|
| 222 | Kalman<chmat>::from_setting ( set ); |
---|
| 223 | validate(); |
---|
| 224 | } |
---|
| 225 | void validate() { |
---|
| 226 | Kalman<chmat>::validate(); |
---|
| 227 | initialize(); |
---|
| 228 | } |
---|
[477] | 229 | }; |
---|
[737] | 230 | UIREGISTER ( KalmanCh ); |
---|
[37] | 231 | |
---|
[477] | 232 | /*! |
---|
| 233 | \brief Extended Kalman Filter in full matrices |
---|
[62] | 234 | |
---|
[477] | 235 | An approximation of the exact Bayesian filter with Gaussian noices and non-linear evolutions of their mean. |
---|
| 236 | */ |
---|
[737] | 237 | class EKFfull : public KalmanFull { |
---|
| 238 | protected: |
---|
| 239 | //! Internal Model f(x,u) |
---|
| 240 | shared_ptr<diffbifn> pfxu; |
---|
[527] | 241 | |
---|
[737] | 242 | //! Observation Model h(x,u) |
---|
| 243 | shared_ptr<diffbifn> phxu; |
---|
[283] | 244 | |
---|
[737] | 245 | public: |
---|
| 246 | //! Default constructor |
---|
| 247 | EKFfull (); |
---|
[527] | 248 | |
---|
[737] | 249 | //! Set nonlinear functions for mean values and covariance matrices. |
---|
| 250 | void set_parameters ( const shared_ptr<diffbifn> &pfxu, const shared_ptr<diffbifn> &phxu, const mat Q0, const mat R0 ); |
---|
[527] | 251 | |
---|
[737] | 252 | //! Here dt = [yt;ut] of appropriate dimensions |
---|
| 253 | void bayes ( const vec &yt, const vec &cond = empty_vec ); |
---|
| 254 | //! set estimates |
---|
| 255 | void set_statistics ( const vec &mu0, const mat &P0 ) { |
---|
| 256 | est.set_parameters ( mu0, P0 ); |
---|
| 257 | }; |
---|
| 258 | //! access function |
---|
| 259 | const mat _R() { |
---|
| 260 | return est._R().to_mat(); |
---|
| 261 | } |
---|
| 262 | void from_setting ( const Setting &set ) { |
---|
| 263 | BM::from_setting ( set ); |
---|
| 264 | shared_ptr<diffbifn> IM = UI::build<diffbifn> ( set, "IM", UI::compulsory ); |
---|
| 265 | shared_ptr<diffbifn> OM = UI::build<diffbifn> ( set, "OM", UI::compulsory ); |
---|
| 266 | |
---|
| 267 | //statistics |
---|
| 268 | int dim = IM->dimension(); |
---|
| 269 | vec mu0; |
---|
| 270 | if ( !UI::get ( mu0, set, "mu0" ) ) |
---|
| 271 | mu0 = zeros ( dim ); |
---|
| 272 | |
---|
| 273 | mat P0; |
---|
| 274 | vec dP0; |
---|
| 275 | if ( UI::get ( dP0, set, "dP0" ) ) |
---|
| 276 | P0 = diag ( dP0 ); |
---|
| 277 | else if ( !UI::get ( P0, set, "P0" ) ) |
---|
| 278 | P0 = eye ( dim ); |
---|
| 279 | |
---|
| 280 | set_statistics ( mu0, P0 ); |
---|
| 281 | |
---|
| 282 | //parameters |
---|
| 283 | vec dQ, dR; |
---|
| 284 | UI::get ( dQ, set, "dQ", UI::compulsory ); |
---|
| 285 | UI::get ( dR, set, "dR", UI::compulsory ); |
---|
| 286 | set_parameters ( IM, OM, diag ( dQ ), diag ( dR ) ); |
---|
| 287 | |
---|
| 288 | string options; |
---|
| 289 | if ( UI::get ( options, set, "options" ) ) |
---|
| 290 | set_options ( options ); |
---|
[653] | 291 | // pfxu = UI::build<diffbifn>(set, "IM", UI::compulsory); |
---|
| 292 | // phxu = UI::build<diffbifn>(set, "OM", UI::compulsory); |
---|
[737] | 293 | // |
---|
[653] | 294 | // mat R0; |
---|
| 295 | // UI::get(R0, set, "R",UI::compulsory); |
---|
| 296 | // mat Q0; |
---|
| 297 | // UI::get(Q0, set, "Q",UI::compulsory); |
---|
[737] | 298 | // |
---|
| 299 | // |
---|
[653] | 300 | // mat P0; vec mu0; |
---|
| 301 | // UI::get(mu0, set, "mu0", UI::optional); |
---|
| 302 | // UI::get(P0, set, "P0", UI::optional); |
---|
| 303 | // set_statistics(mu0,P0); |
---|
| 304 | // // Initial values |
---|
| 305 | // UI::get (yrv, set, "yrv", UI::optional); |
---|
| 306 | // UI::get (urv, set, "urv", UI::optional); |
---|
| 307 | // set_drv(concat(yrv,urv)); |
---|
[737] | 308 | // |
---|
[653] | 309 | // // setup StateSpace |
---|
| 310 | // pfxu->dfdu_cond(mu0, zeros(pfxu->_dimu()), A,true); |
---|
| 311 | // phxu->dfdu_cond(mu0, zeros(pfxu->_dimu()), C,true); |
---|
[737] | 312 | // |
---|
| 313 | validate(); |
---|
| 314 | } |
---|
| 315 | void validate() { |
---|
| 316 | // check stats and IM and OM |
---|
| 317 | } |
---|
[477] | 318 | }; |
---|
[737] | 319 | UIREGISTER ( EKFfull ); |
---|
[62] | 320 | |
---|
[586] | 321 | |
---|
[477] | 322 | /*! |
---|
| 323 | \brief Extended Kalman Filter in Square root |
---|
[37] | 324 | |
---|
[477] | 325 | An approximation of the exact Bayesian filter with Gaussian noices and non-linear evolutions of their mean. |
---|
| 326 | */ |
---|
[37] | 327 | |
---|
[737] | 328 | class EKFCh : public KalmanCh { |
---|
| 329 | protected: |
---|
| 330 | //! Internal Model f(x,u) |
---|
| 331 | shared_ptr<diffbifn> pfxu; |
---|
[527] | 332 | |
---|
[737] | 333 | //! Observation Model h(x,u) |
---|
| 334 | shared_ptr<diffbifn> phxu; |
---|
| 335 | public: |
---|
| 336 | //! copy constructor duplicated - calls different set_parameters |
---|
| 337 | BM* _copy_() const { |
---|
| 338 | EKFCh* E = new EKFCh; |
---|
| 339 | E->set_parameters ( pfxu, phxu, Q, R ); |
---|
| 340 | E->set_statistics ( est._mu(), est._R() ); |
---|
| 341 | return E; |
---|
| 342 | } |
---|
| 343 | //! Set nonlinear functions for mean values and covariance matrices. |
---|
| 344 | void set_parameters ( const shared_ptr<diffbifn> &pfxu, const shared_ptr<diffbifn> &phxu, const chmat Q0, const chmat R0 ); |
---|
[527] | 345 | |
---|
[737] | 346 | //! Here dt = [yt;ut] of appropriate dimensions |
---|
| 347 | void bayes ( const vec &yt, const vec &cond = empty_vec ); |
---|
[357] | 348 | |
---|
[737] | 349 | void from_setting ( const Setting &set ); |
---|
[357] | 350 | |
---|
[737] | 351 | void validate() {}; |
---|
| 352 | // TODO dodelat void to_setting( Setting &set ) const; |
---|
[357] | 353 | |
---|
[477] | 354 | }; |
---|
[37] | 355 | |
---|
[737] | 356 | UIREGISTER ( EKFCh ); |
---|
| 357 | SHAREDPTR ( EKFCh ); |
---|
[357] | 358 | |
---|
| 359 | |
---|
[7] | 360 | //////// INstance |
---|
| 361 | |
---|
[477] | 362 | /*! \brief (Switching) Multiple Model |
---|
| 363 | The model runs several models in parallel and evaluates thier weights (fittness). |
---|
[62] | 364 | |
---|
[477] | 365 | The statistics of the resulting density are merged using (geometric?) combination. |
---|
[283] | 366 | |
---|
[477] | 367 | The next step is performed with the new statistics for all models. |
---|
| 368 | */ |
---|
[737] | 369 | class MultiModel: public BM { |
---|
| 370 | protected: |
---|
| 371 | //! List of models between which we switch |
---|
| 372 | Array<EKFCh*> Models; |
---|
| 373 | //! vector of model weights |
---|
| 374 | vec w; |
---|
| 375 | //! cache of model lls |
---|
| 376 | vec _lls; |
---|
| 377 | //! type of switching policy [1=maximum,2=...] |
---|
| 378 | int policy; |
---|
| 379 | //! internal statistics |
---|
| 380 | enorm<chmat> est; |
---|
| 381 | public: |
---|
| 382 | //! set internal parameters |
---|
| 383 | void set_parameters ( Array<EKFCh*> A, int pol0 = 1 ) { |
---|
| 384 | Models = A;//TODO: test if evalll is set |
---|
| 385 | w.set_length ( A.length() ); |
---|
| 386 | _lls.set_length ( A.length() ); |
---|
| 387 | policy = pol0; |
---|
[357] | 388 | |
---|
[737] | 389 | est.set_rv ( RV ( "MM", A ( 0 )->posterior().dimension(), 0 ) ); |
---|
| 390 | est.set_parameters ( A ( 0 )->posterior().mean(), A ( 0 )->posterior()._R() ); |
---|
| 391 | } |
---|
| 392 | void bayes ( const vec &yt, const vec &cond = empty_vec ) { |
---|
| 393 | int n = Models.length(); |
---|
| 394 | int i; |
---|
| 395 | for ( i = 0; i < n; i++ ) { |
---|
| 396 | Models ( i )->bayes ( yt ); |
---|
| 397 | _lls ( i ) = Models ( i )->_ll(); |
---|
[477] | 398 | } |
---|
[737] | 399 | double mlls = max ( _lls ); |
---|
| 400 | w = exp ( _lls - mlls ); |
---|
| 401 | w /= sum ( w ); //normalization |
---|
| 402 | //set statistics |
---|
| 403 | switch ( policy ) { |
---|
| 404 | case 1: { |
---|
| 405 | int mi = max_index ( w ); |
---|
| 406 | const enorm<chmat> &st = Models ( mi )->posterior() ; |
---|
| 407 | est.set_parameters ( st.mean(), st._R() ); |
---|
[477] | 408 | } |
---|
[737] | 409 | break; |
---|
| 410 | default: |
---|
| 411 | bdm_error ( "unknown policy" ); |
---|
[477] | 412 | } |
---|
[737] | 413 | // copy result to all models |
---|
| 414 | for ( i = 0; i < n; i++ ) { |
---|
| 415 | Models ( i )->set_statistics ( est.mean(), est._R() ); |
---|
| 416 | } |
---|
| 417 | } |
---|
| 418 | //! return correctly typed posterior (covariant return) |
---|
| 419 | const enorm<chmat>& posterior() const { |
---|
| 420 | return est; |
---|
| 421 | } |
---|
[357] | 422 | |
---|
[737] | 423 | void from_setting ( const Setting &set ); |
---|
[357] | 424 | |
---|
[737] | 425 | // TODO dodelat void to_setting( Setting &set ) const; |
---|
[338] | 426 | |
---|
[477] | 427 | }; |
---|
[338] | 428 | |
---|
[737] | 429 | UIREGISTER ( MultiModel ); |
---|
| 430 | SHAREDPTR ( MultiModel ); |
---|
[357] | 431 | |
---|
[737] | 432 | //! conversion of outer ARX model (mlnorm) to state space model |
---|
[586] | 433 | /*! |
---|
| 434 | The model is constructed as: |
---|
| 435 | \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] |
---|
| 436 | For example, for: |
---|
[605] | 437 | Using Frobenius form, see []. |
---|
| 438 | |
---|
| 439 | For easier use in the future, indeces theta_in_A and theta_in_C are set. TODO - explain |
---|
[586] | 440 | */ |
---|
[605] | 441 | //template<class sq_T> |
---|
[737] | 442 | class StateCanonical: public StateSpace<fsqmat> { |
---|
| 443 | protected: |
---|
| 444 | //! remember connection from theta ->A |
---|
| 445 | datalink_part th2A; |
---|
| 446 | //! remember connection from theta ->C |
---|
| 447 | datalink_part th2C; |
---|
| 448 | //! remember connection from theta ->D |
---|
| 449 | datalink_part th2D; |
---|
| 450 | //!cached first row of A |
---|
| 451 | vec A1row; |
---|
| 452 | //!cached first row of C |
---|
| 453 | vec C1row; |
---|
| 454 | //!cached first row of D |
---|
| 455 | vec D1row; |
---|
[605] | 456 | |
---|
[737] | 457 | public: |
---|
| 458 | //! set up this object to match given mlnorm |
---|
| 459 | void connect_mlnorm ( const mlnorm<fsqmat> &ml ) { |
---|
| 460 | //get ids of yrv |
---|
| 461 | const RV &yrv = ml._rv(); |
---|
| 462 | //need to determine u_t - it is all in _rvc that is not in ml._rv() |
---|
| 463 | RV rgr0 = ml._rvc().remove_time(); |
---|
| 464 | RV urv = rgr0.subt ( yrv ); |
---|
[605] | 465 | |
---|
[737] | 466 | //We can do only 1d now... :( |
---|
| 467 | bdm_assert ( yrv._dsize() == 1, "Only for SISO so far..." ); |
---|
[586] | 468 | |
---|
[737] | 469 | // create names for |
---|
| 470 | RV xrv; //empty |
---|
| 471 | RV Crv; //empty |
---|
| 472 | int td = ml._rvc().mint(); |
---|
| 473 | // assuming strictly proper function!!! |
---|
| 474 | for ( int t = -1; t >= td; t-- ) { |
---|
| 475 | xrv.add ( yrv.copy_t ( t ) ); |
---|
| 476 | Crv.add ( urv.copy_t ( t ) ); |
---|
| 477 | } |
---|
[605] | 478 | |
---|
[737] | 479 | // get mapp |
---|
| 480 | th2A.set_connection ( xrv, ml._rvc() ); |
---|
| 481 | th2C.set_connection ( Crv, ml._rvc() ); |
---|
| 482 | th2D.set_connection ( urv, ml._rvc() ); |
---|
| 483 | |
---|
| 484 | //set matrix sizes |
---|
| 485 | this->A = zeros ( xrv._dsize(), xrv._dsize() ); |
---|
| 486 | for ( int j = 1; j < xrv._dsize(); j++ ) { |
---|
| 487 | A ( j, j - 1 ) = 1.0; // off diagonal |
---|
[605] | 488 | } |
---|
[737] | 489 | this->B = zeros ( xrv._dsize(), 1 ); |
---|
| 490 | this->B ( 0 ) = 1.0; |
---|
| 491 | this->C = zeros ( 1, xrv._dsize() ); |
---|
| 492 | this->D = zeros ( 1, urv._dsize() ); |
---|
| 493 | this->Q = zeros ( xrv._dsize(), xrv._dsize() ); |
---|
| 494 | // R is set by update |
---|
| 495 | |
---|
| 496 | //set cache |
---|
| 497 | this->A1row = zeros ( xrv._dsize() ); |
---|
| 498 | this->C1row = zeros ( xrv._dsize() ); |
---|
| 499 | this->D1row = zeros ( urv._dsize() ); |
---|
| 500 | |
---|
| 501 | update_from ( ml ); |
---|
| 502 | validate(); |
---|
| 503 | }; |
---|
| 504 | //! fast function to update parameters from ml - not checked for compatibility!! |
---|
| 505 | void update_from ( const mlnorm<fsqmat> &ml ) { |
---|
| 506 | |
---|
| 507 | vec theta = ml._A().get_row ( 0 ); // this |
---|
| 508 | |
---|
| 509 | th2A.filldown ( theta, A1row ); |
---|
| 510 | th2C.filldown ( theta, C1row ); |
---|
| 511 | th2D.filldown ( theta, D1row ); |
---|
| 512 | |
---|
| 513 | R = ml._R(); |
---|
| 514 | |
---|
| 515 | A.set_row ( 0, A1row ); |
---|
| 516 | C.set_row ( 0, C1row + D1row ( 0 ) *A1row ); |
---|
| 517 | D.set_row ( 0, D1row ); |
---|
| 518 | |
---|
| 519 | } |
---|
[605] | 520 | }; |
---|
[703] | 521 | /*! |
---|
| 522 | State-Space representation of multivariate autoregressive model. |
---|
| 523 | The original model: |
---|
[737] | 524 | \f[ y_t = \theta [\ldots y_{t-k}, \ldots u_{t-l}, \ldots z_{t-m}]' + \Sigma^{-1/2} e_t \f] |
---|
[703] | 525 | where \f$ k,l,m \f$ are maximum delayes of corresponding variables in the regressor. |
---|
[586] | 526 | |
---|
[703] | 527 | The transformed state is: |
---|
| 528 | \f[ x_t = [y_{t} \ldots y_{t-k-1}, u_{t} \ldots u_{t-l-1}, z_{t} \ldots z_{t-m-1}]\f] |
---|
| 529 | |
---|
| 530 | The state accumulates all delayed values starting from time \f$ t \f$ . |
---|
| 531 | |
---|
| 532 | |
---|
| 533 | */ |
---|
[737] | 534 | class StateFromARX: public StateSpace<chmat> { |
---|
| 535 | protected: |
---|
| 536 | //! remember connection from theta ->A |
---|
| 537 | datalink_part th2A; |
---|
| 538 | //! remember connection from theta ->B |
---|
| 539 | datalink_part th2B; |
---|
| 540 | //!function adds n diagonal elements from given starting point r,c |
---|
| 541 | void diagonal_part ( mat &A, int r, int c, int n ) { |
---|
| 542 | for ( int i = 0; i < n; i++ ) { |
---|
| 543 | A ( r, c ) = 1.0; |
---|
| 544 | r++; |
---|
| 545 | c++; |
---|
| 546 | } |
---|
| 547 | }; |
---|
| 548 | //! similar to ARX.have_constant |
---|
| 549 | bool have_constant; |
---|
| 550 | public: |
---|
| 551 | //! set up this object to match given mlnorm |
---|
| 552 | //! Note that state-space and common mpdf use different meaning of \f$ _t \f$ in \f$ u_t \f$. |
---|
| 553 | //!While mlnorm typically assumes that \f$ u_t \rightarrow y_t \f$ in state space it is \f$ u_{t-1} \rightarrow y_t \f$ |
---|
| 554 | //! For consequences in notation of internal variable xt see arx2statespace_notes.lyx. |
---|
| 555 | void connect_mlnorm ( const mlnorm<chmat> &ml, RV &xrv, RV &urv ) { |
---|
[723] | 556 | |
---|
[737] | 557 | //get ids of yrv |
---|
| 558 | const RV &yrv = ml._rv(); |
---|
| 559 | //need to determine u_t - it is all in _rvc that is not in ml._rv() |
---|
| 560 | const RV &rgr = ml._rvc(); |
---|
| 561 | RV rgr0 = rgr.remove_time(); |
---|
| 562 | urv = rgr0.subt ( yrv ); |
---|
| 563 | |
---|
| 564 | // create names for state variables |
---|
| 565 | xrv = yrv; |
---|
| 566 | |
---|
| 567 | int y_multiplicity = -rgr.mint ( yrv ); |
---|
| 568 | int y_block_size = yrv.length() * ( y_multiplicity ); // current yt + all delayed yts |
---|
| 569 | for ( int m = 0; m < y_multiplicity - 1; m++ ) { // ========= -1 is important see arx2statespace_notes |
---|
| 570 | xrv.add ( yrv.copy_t ( -m - 1 ) ); //add delayed yt |
---|
| 571 | } |
---|
| 572 | //! temporary RV for connection to ml.rvc, since notation of xrv and ml.rvc does not match |
---|
| 573 | RV xrv_ml = xrv.copy_t ( -1 ); |
---|
| 574 | |
---|
| 575 | // add regressors |
---|
| 576 | ivec u_block_sizes ( urv.length() ); // no_blocks = yt + unique rgr |
---|
| 577 | for ( int r = 0; r < urv.length(); r++ ) { |
---|
| 578 | RV R = urv.subselect ( vec_1 ( r ) ); //non-delayed element of rgr |
---|
| 579 | int r_size = urv.size ( r ); |
---|
| 580 | int r_multiplicity = -rgr.mint ( R ); |
---|
| 581 | u_block_sizes ( r ) = r_size * r_multiplicity ; |
---|
| 582 | for ( int m = 0; m < r_multiplicity; m++ ) { |
---|
| 583 | xrv.add ( R.copy_t ( -m - 1 ) ); //add delayed yt |
---|
| 584 | xrv_ml.add ( R.copy_t ( -m - 1 ) ); //add delayed yt |
---|
[703] | 585 | } |
---|
[737] | 586 | } |
---|
| 587 | // add constant |
---|
| 588 | if ( any ( ml._mu_const() != 0.0 ) ) { |
---|
| 589 | have_constant = true; |
---|
| 590 | xrv.add ( RV ( "bdm_reserved_constant_one", 1 ) ); |
---|
| 591 | } else { |
---|
| 592 | have_constant = false; |
---|
| 593 | } |
---|
| 594 | |
---|
| 595 | |
---|
| 596 | // get mapp |
---|
| 597 | th2A.set_connection ( xrv_ml, ml._rvc() ); |
---|
| 598 | th2B.set_connection ( urv, ml._rvc() ); |
---|
| 599 | |
---|
| 600 | //set matrix sizes |
---|
| 601 | this->A = zeros ( xrv._dsize(), xrv._dsize() ); |
---|
| 602 | //create y block |
---|
| 603 | diagonal_part ( this->A, yrv._dsize(), 0, y_block_size - yrv._dsize() ); |
---|
| 604 | |
---|
| 605 | this->B = zeros ( xrv._dsize(), urv._dsize() ); |
---|
| 606 | //add diagonals for rgr |
---|
| 607 | int active_x = y_block_size; |
---|
| 608 | for ( int r = 0; r < urv.length(); r++ ) { |
---|
| 609 | diagonal_part ( this->A, active_x + urv.size ( r ), active_x, u_block_sizes ( r ) - urv.size ( r ) ); |
---|
| 610 | this->B.set_submatrix ( active_x, 0, eye ( urv.size ( r ) ) ); |
---|
| 611 | active_x += u_block_sizes ( r ); |
---|
| 612 | } |
---|
| 613 | this->C = zeros ( yrv._dsize(), xrv._dsize() ); |
---|
| 614 | this->C.set_submatrix ( 0, 0, eye ( yrv._dsize() ) ); |
---|
| 615 | this->D = zeros ( yrv._dsize(), urv._dsize() ); |
---|
| 616 | this->R.setCh ( zeros ( yrv._dsize(), yrv._dsize() ) ); |
---|
| 617 | this->Q.setCh ( zeros ( xrv._dsize(), xrv._dsize() ) ); |
---|
| 618 | // Q is set by update |
---|
| 619 | |
---|
| 620 | update_from ( ml ); |
---|
| 621 | validate(); |
---|
| 622 | }; |
---|
| 623 | //! fast function to update parameters from ml - not checked for compatibility!! |
---|
| 624 | void update_from ( const mlnorm<chmat> &ml ) { |
---|
| 625 | |
---|
| 626 | vec Arow = zeros ( A.cols() ); |
---|
| 627 | vec Brow = zeros ( B.cols() ); |
---|
| 628 | // ROW- WISE EVALUATION ===== |
---|
| 629 | for ( int i = 0; i < ml._rv()._dsize(); i++ ) { |
---|
| 630 | |
---|
| 631 | vec theta = ml._A().get_row ( i ); |
---|
| 632 | |
---|
| 633 | th2A.filldown ( theta, Arow ); |
---|
| 634 | if ( have_constant ) { |
---|
| 635 | // constant is always at the end no need for datalink |
---|
| 636 | Arow ( A.cols() - 1 ) = ml._mu_const() ( i ); |
---|
[703] | 637 | } |
---|
[737] | 638 | this->A.set_row ( i, Arow ); |
---|
[723] | 639 | |
---|
[737] | 640 | th2B.filldown ( theta, Brow ); |
---|
| 641 | this->B.set_row ( i, Brow ); |
---|
| 642 | } |
---|
| 643 | this->Q._Ch().set_submatrix ( 0, 0, ml.__R()._Ch() ); |
---|
| 644 | |
---|
| 645 | }; |
---|
| 646 | //! access function |
---|
| 647 | bool _have_constant() const { |
---|
| 648 | return have_constant; |
---|
| 649 | } |
---|
[703] | 650 | }; |
---|
| 651 | |
---|
[583] | 652 | /////////// INSTANTIATION |
---|
[357] | 653 | |
---|
[477] | 654 | template<class sq_T> |
---|
[737] | 655 | 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 ) { |
---|
[28] | 656 | |
---|
[583] | 657 | A = A0; |
---|
| 658 | B = B0; |
---|
| 659 | C = C0; |
---|
| 660 | D = D0; |
---|
[477] | 661 | R = R0; |
---|
| 662 | Q = Q0; |
---|
[583] | 663 | validate(); |
---|
[477] | 664 | } |
---|
[22] | 665 | |
---|
[477] | 666 | template<class sq_T> |
---|
[737] | 667 | void StateSpace<sq_T>::validate() { |
---|
| 668 | bdm_assert ( A.cols() == A.rows(), "KalmanFull: A is not square" ); |
---|
| 669 | bdm_assert ( B.rows() == A.rows(), "KalmanFull: B is not compatible" ); |
---|
| 670 | bdm_assert ( C.cols() == A.rows(), "KalmanFull: C is not compatible" ); |
---|
| 671 | bdm_assert ( ( D.rows() == C.rows() ) && ( D.cols() == B.cols() ), "KalmanFull: D is not compatible" ); |
---|
| 672 | bdm_assert ( ( Q.cols() == A.rows() ) && ( Q.rows() == A.rows() ), "KalmanFull: Q is not compatible" ); |
---|
| 673 | bdm_assert ( ( R.cols() == C.rows() ) && ( R.rows() == C.rows() ), "KalmanFull: R is not compatible" ); |
---|
[583] | 674 | } |
---|
[22] | 675 | |
---|
[254] | 676 | } |
---|
[7] | 677 | #endif // KF_H |
---|
| 678 | |
---|