| | 51 | }; |
| | 52 | |
| | 53 | void egiwmix::set_parameters ( const vec &w0, const Array<egiw*> &Coms0, bool copy ) { |
| | 54 | w = w0/sum ( w0 ); |
| | 55 | dim = Coms0(0)->dimension(); |
| | 56 | int i; |
| | 57 | for ( i=0;i<w.length();i++ ) { |
| | 58 | it_assert_debug ( dim== ( Coms0 ( i )->dimension() ),"Component sizes do not match!" ); |
| | 59 | } |
| | 60 | if ( copy ) { |
| | 61 | Coms.set_length(Coms0.length()); |
| | 62 | for ( i=0;i<w.length();i++ ) {it_error("Not imp..."); |
| | 63 | *Coms ( i ) =*Coms0 ( i );} |
| | 64 | destroyComs=true; |
| | 65 | } |
| | 66 | else { |
| | 67 | Coms = Coms0; |
| | 68 | destroyComs=false; |
| | 69 | } |
| | 70 | } |
| | 71 | |
| | 72 | vec egiwmix::sample() const { |
| | 73 | //Sample which component |
| | 74 | vec cumDist = cumsum ( w ); |
| | 75 | double u0; |
| | 76 | #pragma omp critical |
| | 77 | u0 = UniRNG.sample(); |
| | 78 | |
| | 79 | int i=0; |
| | 80 | while ( ( cumDist ( i ) <u0 ) && ( i< ( w.length()-1 ) ) ) {i++;} |
| | 81 | |
| | 82 | return Coms ( i )->sample(); |
| | 83 | } |
| | 84 | |
| | 85 | vec egiwmix::mean() const { |
| | 86 | int i; vec mu = zeros ( dim ); |
| | 87 | for ( i = 0;i < w.length();i++ ) {mu += w ( i ) * Coms ( i )->mean(); } |
| | 88 | return mu; |
| | 89 | } |
| | 90 | |
| | 91 | vec egiwmix::variance() const { |
| | 92 | // non-central moment |
| | 93 | vec mom2 = zeros ( dim ); |
| | 94 | for ( int i = 0;i < w.length();i++ ) { |
| | 95 | // pow is overloaded, we have to use another approach |
| | 96 | mom2 += w ( i ) * (Coms(i)->variance() + elem_mult ( Coms(i)->mean(), Coms(i)->mean() )); |
| | 97 | } |
| | 98 | // central moment |
| | 99 | // pow is overloaded, we have to use another approach |
| | 100 | return mom2 - elem_mult( mean(), mean() ); |
| | 101 | } |
| | 102 | |
| | 103 | emix* egiwmix::marginal(const RV &rv) const{ |
| | 104 | it_assert_debug(isnamed(), "rvs are not assigned"); |
| | 105 | |
| | 106 | Array<epdf*> Cn(Coms.length()); |
| | 107 | for(int i=0;i<Coms.length();i++){Cn(i)=Coms(i)->marginal(rv);} |
| | 108 | emix* tmp = new emix(); |
| | 109 | tmp->set_parameters(w,Cn,false); |
| | 110 | tmp->ownComs(); |
| | 111 | return tmp; |
| | 112 | } |
| | 113 | |
| | 114 | egiw* egiwmix::approx() { |
| | 115 | // NB: dimx == 1 !!! |
| | 116 | // The following code might look a bit spaghetti-like, |
| | 117 | // consult Dedecius, K. et al.: Partial forgetting in AR models. |
| | 118 | |
| | 119 | double sumVecCommon; // common part for many terms in eq. |
| | 120 | int len = w.length(); // no. of mix components |
| | 121 | int dimLS = Coms(1)->_V()._D().length() - 1; // dim of LS |
| | 122 | vec vecNu(len); // vector of dfms of components |
| | 123 | vec vecD(len); // vector of LS reminders of comps. |
| | 124 | vec vecCommon(len); // vector of common parts |
| | 125 | mat matVecsTheta; // matrix which rows are theta vects. |
| | 126 | |
| | 127 | // fill in the vectors vecNu, vecD and matVecsTheta |
| | 128 | for ( int i=0; i<len; i++ ) { |
| | 129 | vecNu.shift_left( Coms(i)->_nu() ); |
| | 130 | vecD.shift_left( Coms(i)->_V()._D()(0) ); |
| | 131 | matVecsTheta.append_row( Coms(i)->est_theta() ); |
| | 132 | } |
| | 133 | |
| | 134 | // calculate the common parts and their sum |
| | 135 | vecCommon = elem_mult ( w, elem_div(vecNu, vecD) ); |
| | 136 | sumVecCommon = sum(vecCommon); |
| | 137 | |
| | 138 | // LS estimator of theta |
| | 139 | vec aprEstTheta(dimLS); aprEstTheta.zeros(); |
| | 140 | for ( int i=0; i<len; i++ ) { |
| | 141 | aprEstTheta += matVecsTheta.get_row( i ) * vecCommon ( i ); |
| | 142 | } |
| | 143 | aprEstTheta /= sumVecCommon; |
| | 144 | |
| | 145 | |
| | 146 | // LS estimator of dfm |
| | 147 | double aprNu; |
| | 148 | double A = log( sumVecCommon ); // Term 'A' in equation |
| | 149 | |
| | 150 | for ( int i=0; i<len; i++ ) { |
| | 151 | A += w(i) * ( log( vecD(i) ) - psi( 0.5 * vecNu(i) ) ); |
| | 152 | } |
| | 153 | |
| | 154 | aprNu = ( 1 + sqrt(1 + 2 * (A - LOG2)/3 ) ) / ( 2 * (A - LOG2) ); |
| | 155 | |
| | 156 | |
| | 157 | // LS reminder (term D(0,0) in C-syntax) |
| | 158 | double aprD = aprNu / sumVecCommon; |
| | 159 | |
| | 160 | // Aproximation of cov |
| | 161 | // the following code is very numerically sensitive, thus |
| | 162 | // we have to eliminate decompositions etc. as much as possible |
| | 163 | mat aprC = zeros(dimLS, dimLS); |
| | 164 | for ( int i=0; i<len; i++ ) { |
| | 165 | aprC += Coms(i)->est_theta_cov().to_mat() * w(i); |
| | 166 | vec tmp = ( matVecsTheta.get_row(i) - aprEstTheta ); |
| | 167 | aprC += vecCommon(i) * outer_product( tmp, tmp); |
| | 168 | } |
| | 169 | |
| | 170 | // Construct GiW pdf :: BEGIN |
| | 171 | ldmat aprCinv ( inv(aprC) ); |
| | 172 | vec D = concat( aprD, aprCinv._D() ); |
| | 173 | mat L = eye(len+1); |
| | 174 | L.set_submatrix(1,0, aprCinv._L() * aprEstTheta); |
| | 175 | L.set_submatrix(1,1, aprCinv._L()); |
| | 176 | ldmat aprLD (L, D); |
| | 177 | |
| | 178 | egiw* aprgiw = new egiw(1, aprLD, aprNu); |
| | 179 | return aprgiw; |