1 | |
---|
2 | #include <itpp/base/bessel.h> |
---|
3 | #include "libEF.h" |
---|
4 | #include <math.h> |
---|
5 | |
---|
6 | namespace bdm{ |
---|
7 | |
---|
8 | Uniform_RNG UniRNG; |
---|
9 | Normal_RNG NorRNG; |
---|
10 | Gamma_RNG GamRNG; |
---|
11 | |
---|
12 | using std::cout; |
---|
13 | |
---|
14 | void BMEF::bayes ( const vec &dt ) {this->bayes ( dt,1.0 );}; |
---|
15 | |
---|
16 | vec egiw::sample() const { |
---|
17 | it_warning ( "Function not implemented" ); |
---|
18 | return vec_1 ( 0.0 ); |
---|
19 | } |
---|
20 | |
---|
21 | double egiw::evallog_nn ( const vec &val ) const { |
---|
22 | int vend = val.length()-1; |
---|
23 | |
---|
24 | if ( dimx==1 ) { //same as the following, just quicker. |
---|
25 | double r = val ( vend ); //last entry! |
---|
26 | vec Psi ( nPsi+dimx ); |
---|
27 | Psi ( 0 ) = -1.0; |
---|
28 | Psi.set_subvector ( 1,val ( 0,vend-1 ) ); // fill the rest |
---|
29 | |
---|
30 | double Vq=V.qform ( Psi ); |
---|
31 | return -0.5* ( nu*log ( r ) + Vq /r ); |
---|
32 | } |
---|
33 | else { |
---|
34 | mat Th= reshape ( val ( 0,nPsi*dimx-1 ),nPsi,dimx ); |
---|
35 | fsqmat R ( reshape ( val ( nPsi*dimx,vend ),dimx,dimx ) ); |
---|
36 | mat Tmp=concat_vertical ( -eye ( dimx ),Th ); |
---|
37 | fsqmat iR ( dimx ); |
---|
38 | R.inv ( iR ); |
---|
39 | |
---|
40 | return -0.5* ( nu*R.logdet() + trace ( iR.to_mat() *Tmp.T() *V.to_mat() *Tmp ) ); |
---|
41 | } |
---|
42 | } |
---|
43 | |
---|
44 | double egiw::lognc() const { |
---|
45 | const vec& D = V._D(); |
---|
46 | |
---|
47 | double m = nu - nPsi -dimx-1; |
---|
48 | #define log2 0.693147180559945286226763983 |
---|
49 | #define logpi 1.144729885849400163877476189 |
---|
50 | #define log2pi 1.83787706640935 |
---|
51 | #define Inf std::numeric_limits<double>::infinity() |
---|
52 | |
---|
53 | double nkG = 0.5* dimx* ( -nPsi *log2pi + sum ( log ( D ( dimx,D.length()-1 ) ) ) ); |
---|
54 | // temporary for lgamma in Wishart |
---|
55 | double lg=0; |
---|
56 | for ( int i =0; i<dimx;i++ ) {lg+=lgamma ( 0.5* ( m-i ) );} |
---|
57 | |
---|
58 | double nkW = 0.5* ( m*sum ( log ( D ( 0,dimx-1 ) ) ) ) \ |
---|
59 | - 0.5*dimx* ( m*log2 + 0.5* ( dimx-1 ) *log2pi ) - lg; |
---|
60 | |
---|
61 | it_assert_debug ( ( ( -nkG-nkW ) >-Inf ) && ( ( -nkG-nkW ) <Inf ), "ARX improper" ); |
---|
62 | return -nkG-nkW; |
---|
63 | } |
---|
64 | |
---|
65 | vec egiw::est_theta() const { |
---|
66 | if ( dimx==1 ) { |
---|
67 | const mat &L = V._L(); |
---|
68 | int end = L.rows() - 1; |
---|
69 | |
---|
70 | mat iLsub = ltuinv ( L ( dimx,end,dimx,end ) ); |
---|
71 | |
---|
72 | vec L0 = L.get_col(0); |
---|
73 | |
---|
74 | return iLsub * L0(1,end); |
---|
75 | } |
---|
76 | else { |
---|
77 | it_error("ERROR: est_theta() not implemented for dimx>1"); |
---|
78 | return 0; |
---|
79 | } |
---|
80 | } |
---|
81 | |
---|
82 | ldmat egiw::est_theta_cov() const { |
---|
83 | if ( dimx == 1 ) { |
---|
84 | const mat &L = V._L(); |
---|
85 | const vec &D = V._D(); |
---|
86 | int end = D.length() - 1; |
---|
87 | |
---|
88 | mat Lsub = L ( 1, end, 1, end); |
---|
89 | mat Dsub = diag(D(1, end)); |
---|
90 | |
---|
91 | return inv( transpose(Lsub) * Dsub * Lsub ); |
---|
92 | |
---|
93 | } |
---|
94 | else { |
---|
95 | it_error("ERROR: est_theta_cov() not implemented for dimx>1"); |
---|
96 | return 0; |
---|
97 | } |
---|
98 | |
---|
99 | } |
---|
100 | |
---|
101 | vec egiw::mean() const { |
---|
102 | |
---|
103 | if ( dimx==1 ) { |
---|
104 | const vec &D= V._D(); |
---|
105 | int end = D.length()-1; |
---|
106 | |
---|
107 | vec m ( dim ); |
---|
108 | m.set_subvector ( 0, est_theta() ); |
---|
109 | m ( end ) = D ( 0 ) / ( nu -nPsi -2*dimx -2 ); |
---|
110 | return m; |
---|
111 | } |
---|
112 | else { |
---|
113 | mat M; |
---|
114 | mat R; |
---|
115 | mean_mat ( M,R ); |
---|
116 | return cvectorize ( concat_vertical ( M,R ) ); |
---|
117 | } |
---|
118 | |
---|
119 | } |
---|
120 | |
---|
121 | vec egiw::variance() const { |
---|
122 | |
---|
123 | if ( dimx==1 ) { |
---|
124 | int l=V.rows(); |
---|
125 | const ldmat tmp(V,linspace(1,l-1)); |
---|
126 | ldmat itmp(l); |
---|
127 | tmp.inv(itmp); |
---|
128 | double cove = V._D() ( 0 ) / ( nu -nPsi -2*dimx -2 ); |
---|
129 | |
---|
130 | vec var(l); |
---|
131 | var.set_subvector(0,diag(itmp.to_mat())*cove); |
---|
132 | var(l-1)=cove*cove/( nu -nPsi -2*dimx -2 ); |
---|
133 | return var; |
---|
134 | } |
---|
135 | else {it_error("not implemented"); return vec(0);} |
---|
136 | } |
---|
137 | |
---|
138 | void egiw::mean_mat ( mat &M, mat&R ) const { |
---|
139 | const mat &L= V._L(); |
---|
140 | const vec &D= V._D(); |
---|
141 | int end = L.rows()-1; |
---|
142 | |
---|
143 | ldmat ldR ( L ( 0,dimx-1,0,dimx-1 ), D ( 0,dimx-1 ) / ( nu -nPsi -2*dimx -2 ) ); //exp val of R |
---|
144 | mat iLsub=ltuinv ( L ( dimx,end,dimx,end ) ); |
---|
145 | |
---|
146 | // set mean value |
---|
147 | mat Lpsi = L ( dimx,end,0,dimx-1 ); |
---|
148 | M= iLsub*Lpsi; |
---|
149 | R= ldR.to_mat() ; |
---|
150 | } |
---|
151 | |
---|
152 | vec egamma::sample() const { |
---|
153 | vec smp ( dim); |
---|
154 | int i; |
---|
155 | |
---|
156 | for ( i=0; i<dim; i++ ) { |
---|
157 | if ( beta ( i ) >std::numeric_limits<double>::epsilon() ) { |
---|
158 | GamRNG.setup ( alpha ( i ),beta ( i ) ); |
---|
159 | } |
---|
160 | else { |
---|
161 | GamRNG.setup ( alpha ( i ),std::numeric_limits<double>::epsilon() ); |
---|
162 | } |
---|
163 | #pragma omp critical |
---|
164 | smp ( i ) = GamRNG(); |
---|
165 | } |
---|
166 | |
---|
167 | return smp; |
---|
168 | } |
---|
169 | |
---|
170 | // mat egamma::sample ( int N ) const { |
---|
171 | // mat Smp ( rv.count(),N ); |
---|
172 | // int i,j; |
---|
173 | // |
---|
174 | // for ( i=0; i<rv.count(); i++ ) { |
---|
175 | // GamRNG.setup ( alpha ( i ),beta ( i ) ); |
---|
176 | // |
---|
177 | // for ( j=0; j<N; j++ ) { |
---|
178 | // Smp ( i,j ) = GamRNG(); |
---|
179 | // } |
---|
180 | // } |
---|
181 | // |
---|
182 | // return Smp; |
---|
183 | // } |
---|
184 | |
---|
185 | double egamma::evallog ( const vec &val ) const { |
---|
186 | double res = 0.0; //the rest will be added |
---|
187 | int i; |
---|
188 | |
---|
189 | for ( i=0; i<dim; i++ ) { |
---|
190 | res += ( alpha ( i ) - 1 ) *std::log ( val ( i ) ) - beta ( i ) *val ( i ); |
---|
191 | } |
---|
192 | double tmp=res-lognc();; |
---|
193 | it_assert_debug ( std::isfinite ( tmp ),"Infinite value" ); |
---|
194 | return tmp; |
---|
195 | } |
---|
196 | |
---|
197 | double egamma::lognc() const { |
---|
198 | double res = 0.0; //will be added |
---|
199 | int i; |
---|
200 | |
---|
201 | for ( i=0; i<dim; i++ ) { |
---|
202 | res += lgamma ( alpha ( i ) ) - alpha ( i ) *std::log ( beta ( i ) ) ; |
---|
203 | } |
---|
204 | |
---|
205 | return res; |
---|
206 | } |
---|
207 | |
---|
208 | //MGamma |
---|
209 | void mgamma::set_parameters ( double k0, const vec &beta0 ) { |
---|
210 | k=k0; |
---|
211 | ep = &epdf; |
---|
212 | epdf.set_parameters ( k*ones ( beta0.length()),beta0 ); |
---|
213 | dimc = ep->dimension(); |
---|
214 | }; |
---|
215 | |
---|
216 | ivec eEmp::resample (RESAMPLING_METHOD method) { |
---|
217 | ivec ind=zeros_i ( n ); |
---|
218 | ivec N_babies = zeros_i ( n ); |
---|
219 | vec cumDist = cumsum ( w ); |
---|
220 | vec u ( n ); |
---|
221 | int i,j,parent; |
---|
222 | double u0; |
---|
223 | |
---|
224 | switch ( method ) { |
---|
225 | case MULTINOMIAL: |
---|
226 | u ( n - 1 ) = pow ( UniRNG.sample(), 1.0 / n ); |
---|
227 | |
---|
228 | for ( i = n - 2;i >= 0;i-- ) { |
---|
229 | u ( i ) = u ( i + 1 ) * pow ( UniRNG.sample(), 1.0 / ( i + 1 ) ); |
---|
230 | } |
---|
231 | |
---|
232 | break; |
---|
233 | |
---|
234 | case STRATIFIED: |
---|
235 | |
---|
236 | for ( i = 0;i < n;i++ ) { |
---|
237 | u ( i ) = ( i + UniRNG.sample() ) / n; |
---|
238 | } |
---|
239 | |
---|
240 | break; |
---|
241 | |
---|
242 | case SYSTEMATIC: |
---|
243 | u0 = UniRNG.sample(); |
---|
244 | |
---|
245 | for ( i = 0;i < n;i++ ) { |
---|
246 | u ( i ) = ( i + u0 ) / n; |
---|
247 | } |
---|
248 | |
---|
249 | break; |
---|
250 | |
---|
251 | default: |
---|
252 | it_error ( "PF::resample(): Unknown resampling method" ); |
---|
253 | } |
---|
254 | |
---|
255 | // U is now full |
---|
256 | j = 0; |
---|
257 | |
---|
258 | for ( i = 0;i < n;i++ ) { |
---|
259 | while ( u ( i ) > cumDist ( j ) ) j++; |
---|
260 | |
---|
261 | N_babies ( j ) ++; |
---|
262 | } |
---|
263 | // We have assigned new babies for each Particle |
---|
264 | // Now, we fill the resulting index such that: |
---|
265 | // * particles with at least one baby should not move * |
---|
266 | // This assures that reassignment can be done inplace; |
---|
267 | |
---|
268 | // find the first parent; |
---|
269 | parent=0; while ( N_babies ( parent ) ==0 ) parent++; |
---|
270 | |
---|
271 | // Build index |
---|
272 | for ( i = 0;i < n;i++ ) { |
---|
273 | if ( N_babies ( i ) > 0 ) { |
---|
274 | ind ( i ) = i; |
---|
275 | N_babies ( i ) --; //this index was now replicated; |
---|
276 | } |
---|
277 | else { |
---|
278 | // test if the parent has been fully replicated |
---|
279 | // if yes, find the next one |
---|
280 | while ( ( N_babies ( parent ) ==0 ) || ( N_babies ( parent ) ==1 && parent>i ) ) parent++; |
---|
281 | |
---|
282 | // Replicate parent |
---|
283 | ind ( i ) = parent; |
---|
284 | |
---|
285 | N_babies ( parent ) --; //this index was now replicated; |
---|
286 | } |
---|
287 | |
---|
288 | } |
---|
289 | |
---|
290 | // copy the internals according to ind |
---|
291 | for ( i=0;i<n;i++ ) { |
---|
292 | if ( ind ( i ) !=i ) { |
---|
293 | samples ( i ) =samples ( ind ( i ) ); |
---|
294 | } |
---|
295 | w ( i ) = 1.0/n; |
---|
296 | } |
---|
297 | |
---|
298 | return ind; |
---|
299 | } |
---|
300 | |
---|
301 | void eEmp::set_statistics ( const vec &w0, const epdf* epdf0 ) { |
---|
302 | //it_assert_debug(rv==epdf0->rv(),"Wrong epdf0"); |
---|
303 | dim = epdf0->dimension(); |
---|
304 | w=w0; |
---|
305 | w/=sum ( w0 );//renormalize |
---|
306 | n=w.length(); |
---|
307 | samples.set_size ( n ); |
---|
308 | dim = epdf0->dimension(); |
---|
309 | |
---|
310 | for ( int i=0;i<n;i++ ) {samples ( i ) =epdf0->sample();} |
---|
311 | } |
---|
312 | |
---|
313 | void eEmp::set_samples ( const epdf* epdf0 ) { |
---|
314 | //it_assert_debug(rv==epdf0->rv(),"Wrong epdf0"); |
---|
315 | w=1; |
---|
316 | w/=sum ( w );//renormalize |
---|
317 | |
---|
318 | for ( int i=0;i<n;i++ ) {samples ( i ) =epdf0->sample();} |
---|
319 | } |
---|
320 | |
---|
321 | }; |
---|