1 | #include "arx.h" |
---|
2 | namespace bdm { |
---|
3 | |
---|
4 | void ARX::bayes ( const vec &dt, const double w ) { |
---|
5 | double lnc; |
---|
6 | |
---|
7 | if ( frg < 1.0 ) { |
---|
8 | est.pow ( frg ); |
---|
9 | if ( evalll ) { |
---|
10 | last_lognc = est.lognc(); |
---|
11 | } |
---|
12 | } |
---|
13 | V.opupdt ( dt, w ); |
---|
14 | nu += w; |
---|
15 | |
---|
16 | // log(sqrt(2*pi)) = 0.91893853320467 |
---|
17 | if ( evalll ) { |
---|
18 | lnc = est.lognc(); |
---|
19 | ll = lnc - last_lognc - 0.91893853320467; |
---|
20 | last_lognc = lnc; |
---|
21 | } |
---|
22 | } |
---|
23 | |
---|
24 | double ARX::logpred ( const vec &dt ) const { |
---|
25 | egiw pred ( est ); |
---|
26 | ldmat &V = pred._V(); |
---|
27 | double &nu = pred._nu(); |
---|
28 | |
---|
29 | double lll; |
---|
30 | |
---|
31 | if ( frg < 1.0 ) { |
---|
32 | pred.pow ( frg ); |
---|
33 | lll = pred.lognc(); |
---|
34 | } else//should be save: last_lognc is changed only by bayes; |
---|
35 | if ( evalll ) { |
---|
36 | lll = last_lognc; |
---|
37 | } else { |
---|
38 | lll = pred.lognc(); |
---|
39 | } |
---|
40 | |
---|
41 | V.opupdt ( dt, 1.0 ); |
---|
42 | nu += 1.0; |
---|
43 | // log(sqrt(2*pi)) = 0.91893853320467 |
---|
44 | return pred.lognc() - lll - 0.91893853320467; |
---|
45 | } |
---|
46 | |
---|
47 | ARX* ARX::_copy_ ( ) const { |
---|
48 | ARX* Tmp = new ARX ( *this ); |
---|
49 | return Tmp; |
---|
50 | } |
---|
51 | |
---|
52 | void ARX::set_statistics ( const BMEF* B0 ) { |
---|
53 | const ARX* A0 = dynamic_cast<const ARX*> ( B0 ); |
---|
54 | |
---|
55 | bdm_assert_debug ( V.rows() == A0->V.rows(), "ARX::set_statistics Statistics differ" ); |
---|
56 | set_statistics ( A0->dimx, A0->V, A0->nu ); |
---|
57 | } |
---|
58 | |
---|
59 | enorm<ldmat>* ARX::epredictor ( const vec &rgr ) const { |
---|
60 | int dim = dimx;//est.dimension(); |
---|
61 | mat mu ( dim, V.rows() - dim ); |
---|
62 | mat R ( dim, dim ); |
---|
63 | |
---|
64 | enorm<ldmat>* tmp; |
---|
65 | tmp = new enorm<ldmat> ( ); |
---|
66 | //TODO: too hackish |
---|
67 | if ( drv._dsize() > 0 ) { |
---|
68 | } |
---|
69 | |
---|
70 | est.mean_mat ( mu, R ); //mu = |
---|
71 | //correction for student-t -- TODO check if correct!! |
---|
72 | //R*=nu/(nu-2); |
---|
73 | mat p_mu = mu.T() * rgr; //the result is one column |
---|
74 | tmp->set_parameters ( p_mu.get_col ( 0 ), ldmat ( R ) ); |
---|
75 | return tmp; |
---|
76 | } |
---|
77 | |
---|
78 | mlnorm<ldmat>* ARX::predictor ( ) const { |
---|
79 | int dim = est.dimension(); |
---|
80 | int dif = V.rows() - dim ;///<----------- TODO |
---|
81 | bdm_assert_debug ( ( dif == 0 ) || ( dif == 1 ), "Give RVs do not match" ); |
---|
82 | |
---|
83 | mat mu ( dim, V.rows() - dim ); |
---|
84 | mat R ( dim, dim ); |
---|
85 | mlnorm<ldmat>* tmp; |
---|
86 | tmp = new mlnorm<ldmat> ( ); |
---|
87 | |
---|
88 | est.mean_mat ( mu, R ); //mu = |
---|
89 | mu = mu.T(); |
---|
90 | //correction for student-t -- TODO check if correct!! |
---|
91 | |
---|
92 | if ( dif == 0 ) { // no constant term |
---|
93 | tmp->set_parameters ( mu, zeros ( dim ), ldmat ( R ) ); |
---|
94 | } else { |
---|
95 | //Assume the constant term is the last one: |
---|
96 | tmp->set_parameters ( mu.get_cols ( 0, mu.cols() - 2 ), mu.get_col ( mu.cols() - 1 ), ldmat ( R ) ); |
---|
97 | } |
---|
98 | return tmp; |
---|
99 | } |
---|
100 | |
---|
101 | mlstudent* ARX::predictor_student ( ) const { |
---|
102 | int dim = est.dimension(); |
---|
103 | int dif = V.rows() - est.dimension();//-------------TODO |
---|
104 | bdm_assert_debug ( ( dif == 0 ) || ( dif == 1 ), "Give RVs do not match" ); |
---|
105 | |
---|
106 | mat mu ( dim, V.rows() - dim ); |
---|
107 | mat R ( dim, dim ); |
---|
108 | mlstudent* tmp; |
---|
109 | tmp = new mlstudent ( ); |
---|
110 | |
---|
111 | est.mean_mat ( mu, R ); // |
---|
112 | mu = mu.T(); |
---|
113 | |
---|
114 | int xdim = dimx; |
---|
115 | int end = V._L().rows() - 1; |
---|
116 | ldmat Lam ( V._L() ( xdim, end, xdim, end ), V._D() ( xdim, end ) ); //exp val of R |
---|
117 | |
---|
118 | |
---|
119 | if ( dif == 0 ) { // no constant term |
---|
120 | tmp->set_parameters ( mu, zeros ( xdim ), ldmat ( R ), Lam ); |
---|
121 | } else { |
---|
122 | //Assume the constant term is the last one: |
---|
123 | if ( mu.cols() > 1 ) { |
---|
124 | tmp->set_parameters ( mu.get_cols ( 0, mu.cols() - 2 ), mu.get_col ( mu.cols() - 1 ), ldmat ( R ), Lam ); |
---|
125 | } else { |
---|
126 | tmp->set_parameters ( mat ( dim, 0 ), mu.get_col ( mu.cols() - 1 ), ldmat ( R ), Lam ); |
---|
127 | } |
---|
128 | } |
---|
129 | return tmp; |
---|
130 | } |
---|
131 | |
---|
132 | |
---|
133 | |
---|
134 | /*! \brief Return the best structure |
---|
135 | @param Eg a copy of GiW density that is being examined |
---|
136 | @param Eg0 a copy of prior GiW density before estimation |
---|
137 | @param Egll likelihood of the current Eg |
---|
138 | @param indeces current indeces |
---|
139 | \return best likelihood in the structure below the given one |
---|
140 | */ |
---|
141 | double egiw_bestbelow ( egiw Eg, egiw Eg0, double Egll, ivec &indeces ) { //parameter Eg is a copy! |
---|
142 | ldmat Vo = Eg._V(); //copy |
---|
143 | ldmat Vo0 = Eg._V(); //copy |
---|
144 | ldmat& Vp = Eg._V(); // pointer into Eg |
---|
145 | ldmat& Vp0 = Eg._V(); // pointer into Eg |
---|
146 | int end = Vp.rows() - 1; |
---|
147 | int i; |
---|
148 | mat Li; |
---|
149 | mat Li0; |
---|
150 | double maxll = Egll; |
---|
151 | double tmpll = Egll; |
---|
152 | double belll = Egll; |
---|
153 | |
---|
154 | ivec tmpindeces; |
---|
155 | ivec maxindeces = indeces; |
---|
156 | |
---|
157 | |
---|
158 | cout << "bb:(" << indeces << ") ll=" << Egll << endl; |
---|
159 | |
---|
160 | //try to remove only one rv |
---|
161 | for ( i = 0; i < end; i++ ) { |
---|
162 | //copy original |
---|
163 | Li = Vo._L(); |
---|
164 | Li0 = Vo0._L(); |
---|
165 | //remove stuff |
---|
166 | Li.del_col ( i + 1 ); |
---|
167 | Li0.del_col ( i + 1 ); |
---|
168 | Vp.ldform ( Li, Vo._D() ); |
---|
169 | Vp0.ldform ( Li0, Vo0._D() ); |
---|
170 | tmpll = Eg.lognc() - Eg0.lognc(); // likelihood is difference of norm. coefs. |
---|
171 | |
---|
172 | cout << "i=(" << i << ") ll=" << tmpll << endl; |
---|
173 | |
---|
174 | // |
---|
175 | if ( tmpll > Egll ) { //increase of the likelihood |
---|
176 | tmpindeces = indeces; |
---|
177 | tmpindeces.del ( i ); |
---|
178 | //search for a better match in this substructure |
---|
179 | belll = egiw_bestbelow ( Eg, Eg0, tmpll, tmpindeces ); |
---|
180 | if ( belll > maxll ) { //better match found |
---|
181 | maxll = belll; |
---|
182 | maxindeces = tmpindeces; |
---|
183 | } |
---|
184 | } |
---|
185 | } |
---|
186 | indeces = maxindeces; |
---|
187 | return maxll; |
---|
188 | } |
---|
189 | |
---|
190 | ivec ARX::structure_est ( egiw est0 ) { |
---|
191 | ivec ind = linspace ( 1, est.dimension() - 1 ); |
---|
192 | egiw_bestbelow ( est, est0, est.lognc() - est0.lognc(), ind ); |
---|
193 | return ind; |
---|
194 | } |
---|
195 | |
---|
196 | |
---|
197 | |
---|
198 | ivec ARX::structure_est_LT ( egiw est0 ) { |
---|
199 | //some stuff with beliefs etc. |
---|
200 | // ivec ind = bdm::straux1(V,nu, est0._V(), est0._nu()); |
---|
201 | return ivec();//ind; |
---|
202 | } |
---|
203 | |
---|
204 | void ARX::from_setting ( const Setting &set ) { |
---|
205 | shared_ptr<RV> yrv = UI::build<RV> ( set, "y", UI::compulsory ); |
---|
206 | shared_ptr<RV> rrv = UI::build<RV> ( set, "rgr", UI::compulsory ); |
---|
207 | int ylen = yrv->_dsize(); |
---|
208 | int rgrlen = rrv->_dsize(); |
---|
209 | |
---|
210 | string opt; |
---|
211 | if ( UI::get(opt, set, "options", UI::optional) ) { |
---|
212 | BM::set_options(opt); |
---|
213 | } |
---|
214 | |
---|
215 | //init |
---|
216 | mat V0; |
---|
217 | vec dV0; |
---|
218 | if ( !UI::get ( dV0, set, "dV0" ) ) |
---|
219 | dV0 = concat ( 1e-3 * ones ( ylen ), 1e-5 * ones ( rgrlen ) ); |
---|
220 | V0 = diag ( dV0 ); |
---|
221 | |
---|
222 | double nu0; |
---|
223 | if ( !UI::get ( nu0, set, "nu0" ) ) |
---|
224 | nu0 = rgrlen + ylen + 2; |
---|
225 | |
---|
226 | double frg; |
---|
227 | if ( !UI::get ( frg, set, "frg" ) ) |
---|
228 | frg = 1.0; |
---|
229 | |
---|
230 | set_parameters ( frg ); |
---|
231 | set_statistics ( ylen, V0, nu0 ); |
---|
232 | set_drv ( concat ( *yrv, *rrv ) ); |
---|
233 | |
---|
234 | //name results (for logging) |
---|
235 | set_rv ( RV ( "{theta r }", vec_2 ( ylen*rgrlen, ylen*ylen ) ) ); |
---|
236 | |
---|
237 | } |
---|
238 | |
---|
239 | } |
---|