[357] | 1 | #include <math.h> |
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[262] | 2 | |
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[32] | 3 | #include <itpp/base/bessel.h> |
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[384] | 4 | #include "exp_family.h" |
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[13] | 5 | |
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[477] | 6 | namespace bdm { |
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[13] | 7 | |
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[32] | 8 | Uniform_RNG UniRNG; |
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| 9 | Normal_RNG NorRNG; |
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| 10 | Gamma_RNG GamRNG; |
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| 11 | |
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[13] | 12 | using std::cout; |
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| 13 | |
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[737] | 14 | /////////// |
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[487] | 15 | |
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[737] | 16 | void BMEF::bayes ( const vec &yt, const vec &cond ) { |
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[1064] | 17 | this->bayes_weighted ( yt, cond, 1.0 ); |
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[477] | 18 | }; |
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[170] | 19 | |
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[737] | 20 | void egiw::set_parameters ( int dimx0, ldmat V0, double nu0 ) { |
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[1064] | 21 | dimx = dimx0; |
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| 22 | nPsi = V0.rows()-dimx; |
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| 23 | V = V0; |
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| 24 | if ( nu0 < 0 ) { |
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| 25 | nu = 0.1 + nPsi + 2 * dimx + 2; // +2 assures finite expected value of R |
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| 26 | // terms before that are sufficient for finite normalization |
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| 27 | } else { |
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| 28 | nu = nu0; |
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| 29 | } |
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[629] | 30 | } |
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| 31 | |
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[96] | 32 | vec egiw::sample() const { |
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[1064] | 33 | mat M; |
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| 34 | chmat R; |
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| 35 | sample_mat ( M, R ); |
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[737] | 36 | |
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[1064] | 37 | return concat ( cvectorize ( M ), cvectorize ( R.to_mat() ) ); |
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[96] | 38 | } |
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| 39 | |
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[737] | 40 | mat egiw::sample_mat ( int n ) const { |
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[1064] | 41 | // TODO - correct approach - convert to product of norm * Wishart |
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| 42 | mat M; |
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| 43 | ldmat Vz; |
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| 44 | ldmat Lam; |
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| 45 | factorize ( M, Vz, Lam ); |
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[737] | 46 | |
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[1064] | 47 | chmat ChLam ( Lam.to_mat() ); |
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| 48 | chmat iChLam; |
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| 49 | ChLam.inv ( iChLam ); |
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[737] | 50 | |
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[1064] | 51 | eWishartCh Omega; //inverse Wishart, result is R, |
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| 52 | Omega.set_parameters ( iChLam, nu - 2*nPsi - dimx ); // 2*nPsi is there to match numercial simulations - check if analytically correct |
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| 53 | Omega.validate(); |
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[737] | 54 | |
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[1064] | 55 | mat OmChi; |
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| 56 | mat Z ( M.rows(), M.cols() ); |
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[730] | 57 | |
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[1064] | 58 | mat Mi; |
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| 59 | mat RChiT; |
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| 60 | mat tmp ( dimension(), n ); |
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| 61 | M=M.T();// ugly hack == decide what to do with M. |
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| 62 | for ( int i = 0; i < n; i++ ) { |
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| 63 | OmChi = Omega.sample_mat(); |
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| 64 | RChiT = inv ( OmChi ); |
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| 65 | Z = randn ( M.rows(), M.cols() ); |
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| 66 | Mi = M + RChiT * Z * inv ( Vz._L().T() * diag ( sqrt ( Vz._D() ) ) ); |
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[737] | 67 | |
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[1064] | 68 | tmp.set_col ( i, concat ( cvectorize ( Mi ), cvectorize ( RChiT*RChiT.T() ) ) ); |
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| 69 | } |
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| 70 | return tmp; |
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[730] | 71 | } |
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| 72 | |
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[737] | 73 | void egiw::sample_mat ( mat &Mi, chmat &Ri ) const { |
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| 74 | |
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[1064] | 75 | // TODO - correct approach - convert to product of norm * Wishart |
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| 76 | mat M; |
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| 77 | ldmat Vz; |
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| 78 | ldmat Lam; |
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| 79 | factorize ( M, Vz, Lam ); |
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[737] | 80 | |
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[1064] | 81 | chmat Ch; |
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| 82 | Ch.setCh ( Lam._L() *diag ( sqrt ( Lam._D() ) ) ); |
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| 83 | chmat iCh; |
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| 84 | Ch.inv ( iCh ); |
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[737] | 85 | |
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[1064] | 86 | eWishartCh Omega; //inverse Wishart, result is R, |
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| 87 | Omega.set_parameters ( iCh, nu - 2*nPsi - dimx ); // 2*nPsi is there to match numercial simulations - check if analytically correct |
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| 88 | Omega.validate(); |
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[737] | 89 | |
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[1064] | 90 | chmat Omi; |
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| 91 | Omi.setCh ( Omega.sample_mat() ); |
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[737] | 92 | |
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[1064] | 93 | if (M._datasize()>0) { |
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| 94 | mat Z = randn ( M.rows(), M.cols() ); |
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| 95 | Mi = M + Omi._Ch() * Z * inv ( Vz._L() * diag ( sqrt ( Vz._D() ) ) ); |
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| 96 | } |
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| 97 | Omi.inv ( Ri ); |
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[725] | 98 | } |
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| 99 | |
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[211] | 100 | double egiw::evallog_nn ( const vec &val ) const { |
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[1064] | 101 | bdm_assert_debug(val.length()==dimx*(nPsi+dimx),"Incorrect cond in egiw::evallog_nn" ); |
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[168] | 102 | |
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[1064] | 103 | int vend = val.length() - 1; |
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[168] | 104 | |
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[1064] | 105 | if ( dimx == 1 ) { //same as the following, just quicker. |
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| 106 | double r = val ( vend ); //last entry! |
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| 107 | if ( r < 0 ) return -inf; |
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| 108 | vec Psi ( nPsi + dimx ); |
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| 109 | Psi ( 0 ) = -1.0; |
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| 110 | Psi.set_subvector ( 1, val ( 0, vend - 1 ) ); // fill the rest |
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[170] | 111 | |
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[1064] | 112 | double Vq = V.qform ( Psi ); |
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| 113 | return -0.5* ( nu*log ( r ) + Vq / r ); |
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| 114 | } else { |
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| 115 | mat Tmp; |
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| 116 | if (nPsi>0) { |
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| 117 | mat Th = reshape ( val ( 0, nPsi * dimx - 1 ), nPsi, dimx ); |
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| 118 | Tmp = concat_vertical ( -eye ( dimx ), Th ); |
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| 119 | } else { |
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| 120 | Tmp = -eye(dimx); |
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| 121 | } |
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| 122 | fsqmat R ( reshape ( val ( nPsi*dimx, vend ), dimx, dimx ) ); |
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| 123 | double ldetR = R.logdet(); |
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| 124 | if ( !std::isfinite(ldetR) ) return -inf; |
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| 125 | fsqmat iR ( dimx ); |
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| 126 | R.inv ( iR ); |
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| 127 | |
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| 128 | return -0.5* ( nu*ldetR + trace ( iR.to_mat() *Tmp.T() *V.to_mat() *Tmp ) ); |
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| 129 | } |
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[96] | 130 | } |
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| 131 | |
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[168] | 132 | double egiw::lognc() const { |
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[1064] | 133 | const vec& D = V._D(); |
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[96] | 134 | |
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[1064] | 135 | double m = nu - nPsi - dimx - 1; |
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[168] | 136 | #define log2 0.693147180559945286226763983 |
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| 137 | #define logpi 1.144729885849400163877476189 |
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| 138 | #define log2pi 1.83787706640935 |
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[178] | 139 | #define Inf std::numeric_limits<double>::infinity() |
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[168] | 140 | |
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[1064] | 141 | double nkG = 0.5 * dimx * ( -nPsi * log2pi + sum ( log ( D.mid ( dimx, nPsi ) ) ) ); |
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| 142 | // temporary for lgamma in Wishart |
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| 143 | double lg = 0; |
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| 144 | for ( int i = 0; i < dimx; i++ ) { |
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| 145 | lg += lgamma ( 0.5 * ( m - i ) ); |
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| 146 | } |
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[168] | 147 | |
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[1064] | 148 | double nkW = 0.5 * ( m * sum ( log ( D ( 0, dimx - 1 ) ) ) ) \ |
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| 149 | - 0.5 * dimx * ( m * log2 + 0.5 * ( dimx - 1 ) * log2pi ) - lg; |
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[168] | 150 | |
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[637] | 151 | // bdm_assert_debug ( ( ( -nkG - nkW ) > -Inf ) && ( ( -nkG - nkW ) < Inf ), "ARX improper" ); |
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[1064] | 152 | if ( -nkG - nkW == Inf ) { |
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| 153 | cout << "??" << endl; |
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| 154 | } |
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| 155 | return -nkG - nkW; |
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[96] | 156 | } |
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| 157 | |
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[330] | 158 | vec egiw::est_theta() const { |
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[1064] | 159 | if ( dimx == 1 ) { |
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| 160 | const mat &L = V._L(); |
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| 161 | int end = L.rows() - 1; |
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[330] | 162 | |
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[1064] | 163 | mat iLsub = ltuinv ( L ( dimx, end, dimx, end ) ); |
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[330] | 164 | |
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[1064] | 165 | vec L0 = L.get_col ( 0 ); |
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[330] | 166 | |
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[1064] | 167 | return iLsub * L0 ( 1, end ); |
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| 168 | } else { |
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| 169 | bdm_error ( "ERROR: est_theta() not implemented for dimx>1" ); |
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| 170 | return vec(); |
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| 171 | } |
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[330] | 172 | } |
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| 173 | |
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[737] | 174 | void egiw::factorize ( mat &M, ldmat &Vz, ldmat &Lam ) const { |
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[1064] | 175 | const mat &L = V._L(); |
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| 176 | const vec &D = V._D(); |
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| 177 | int end = L.rows() - 1; |
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[737] | 178 | |
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[1064] | 179 | Lam = ldmat ( L ( 0, dimx - 1, 0, dimx - 1 ), D ( 0, dimx - 1 ) ); //exp val of R |
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[737] | 180 | |
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[1064] | 181 | if (dimx<=end) { |
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| 182 | Vz = ldmat ( L ( dimx, end, dimx, end ), D ( dimx, end ) ); |
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| 183 | mat iLsub = ltuinv ( Vz._L() ); |
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| 184 | // set mean value |
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| 185 | mat Lpsi = L ( dimx, end, 0, dimx - 1 ); |
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| 186 | M = iLsub * Lpsi; |
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| 187 | } |
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| 188 | /* if ( 0 ) { // test with Peterka |
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| 189 | mat VF = V.to_mat(); |
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| 190 | mat Vf = VF ( 0, dimx - 1, 0, dimx - 1 ); |
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| 191 | mat Vzf = VF ( dimx, end, 0, dimx - 1 ); |
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| 192 | mat VZ = VF ( dimx, end, dimx, end ); |
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[737] | 193 | |
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[1064] | 194 | mat Lam2 = Vf - Vzf.T() * inv ( VZ ) * Vzf; |
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| 195 | }*/ |
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[725] | 196 | } |
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| 197 | |
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[330] | 198 | ldmat egiw::est_theta_cov() const { |
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[1064] | 199 | if ( dimx == 1 ) { |
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| 200 | const mat &L = V._L(); |
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| 201 | const vec &D = V._D(); |
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| 202 | int end = D.length() - 1; |
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[330] | 203 | |
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[1064] | 204 | mat Lsub = L ( 1, end, 1, end ); |
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[725] | 205 | // mat Dsub = diag ( D ( 1, end ) ); |
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[330] | 206 | |
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[1064] | 207 | ldmat LD ( inv ( Lsub ).T(), 1.0 / D ( 1, end ) ); |
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| 208 | return LD; |
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[330] | 209 | |
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[1064] | 210 | } else { |
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| 211 | bdm_error ( "ERROR: est_theta_cov() not implemented for dimx>1" ); |
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| 212 | return ldmat(); |
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| 213 | } |
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[330] | 214 | |
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| 215 | } |
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| 216 | |
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[96] | 217 | vec egiw::mean() const { |
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[168] | 218 | |
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[1064] | 219 | if ( dimx == 1 ) { |
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| 220 | const vec &D = V._D(); |
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| 221 | int end = D.length() - 1; |
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[170] | 222 | |
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[1064] | 223 | vec m ( dim ); |
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| 224 | m.set_subvector ( 0, est_theta() ); |
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| 225 | m ( end ) = D ( 0 ) / ( nu - nPsi - 2 * dimx - 2 ); |
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| 226 | return m; |
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| 227 | } else { |
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| 228 | mat M; |
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| 229 | mat R; |
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| 230 | mean_mat ( M, R ); |
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| 231 | return concat ( cvectorize ( M ), cvectorize ( R ) ); |
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| 232 | } |
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[170] | 233 | |
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| 234 | } |
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[262] | 235 | |
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| 236 | vec egiw::variance() const { |
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[1064] | 237 | int l = V.rows(); |
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| 238 | // cut out rest of lower-right part of V |
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| 239 | // invert it |
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| 240 | ldmat itmp; |
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| 241 | if (dimx<l) { |
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| 242 | const ldmat tmp ( V, linspace ( dimx, l - 1 ) ); |
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| 243 | tmp.inv ( itmp ); |
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| 244 | } |
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| 245 | // following Wikipedia notation |
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| 246 | // m=nu-nPsi-dimx-1, p=dimx |
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| 247 | double mp1p = nu - nPsi - 2 * dimx; // m-p+1 |
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| 248 | double mp1m = mp1p - 2; // m-p-1 |
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| 249 | |
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| 250 | if ( dimx == 1 ) { |
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| 251 | double cove = V._D() ( 0 ) / mp1m ; |
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| 252 | |
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| 253 | vec var ( l ); |
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| 254 | var.set_subvector ( 0, diag ( itmp.to_mat() ) *cove ); |
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| 255 | var ( l - 1 ) = cove * cove / ( mp1m - 2 ); |
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| 256 | return var; |
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| 257 | } else { |
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| 258 | ldmat Vll ( V, linspace ( 0, dimx - 1 ) ); // top-left part of V |
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| 259 | mat Y = Vll.to_mat(); |
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| 260 | mat varY ( Y.rows(), Y.cols() ); |
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| 261 | |
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| 262 | double denom = ( mp1p - 1 ) * mp1m * mp1m * ( mp1m - 2 ); // (m-p)(m-p-1)^2(m-p-3) |
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| 263 | |
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| 264 | int i, j; |
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| 265 | for ( i = 0; i < Y.rows(); i++ ) { |
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| 266 | for ( j = 0; j < Y.cols(); j++ ) { |
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| 267 | varY ( i, j ) = ( mp1p * Y ( i, j ) * Y ( i, j ) + mp1m * Y ( i, i ) * Y ( j, j ) ) / denom; |
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| 268 | } |
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| 269 | } |
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| 270 | vec mean_dR = diag ( Y ) / mp1m; // corresponds to cove |
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| 271 | vec var_th = diag ( itmp.to_mat() ); |
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| 272 | vec var_Th ( mean_dR.length() *var_th.length() ); |
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| 273 | // diagonal of diag(mean_dR) \kron diag(var_th) |
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| 274 | for ( int i = 0; i < mean_dR.length(); i++ ) { |
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| 275 | var_Th.set_subvector ( i*var_th.length(), var_th*mean_dR ( i ) ); |
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| 276 | } |
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| 277 | |
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| 278 | return concat ( var_Th, cvectorize ( varY ) ); |
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| 279 | } |
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[262] | 280 | } |
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| 281 | |
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[225] | 282 | void egiw::mean_mat ( mat &M, mat&R ) const { |
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[1064] | 283 | const mat &L = V._L(); |
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| 284 | const vec &D = V._D(); |
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| 285 | int end = L.rows() - 1; |
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[225] | 286 | |
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[1064] | 287 | ldmat ldR ( L ( 0, dimx - 1, 0, dimx - 1 ), D ( 0, dimx - 1 ) / ( nu - nPsi - 2*dimx - 2 ) ); //exp val of R |
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[225] | 288 | |
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[1064] | 289 | // set mean value |
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| 290 | if (dimx<=end) { |
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| 291 | mat iLsub = ltuinv ( L ( dimx, end, dimx, end ) ); |
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| 292 | mat Lpsi = L ( dimx, end, 0, dimx - 1 ); |
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| 293 | M = iLsub * Lpsi; |
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| 294 | } |
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| 295 | R = ldR.to_mat() ; |
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[96] | 296 | } |
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| 297 | |
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[739] | 298 | void egiw::log_register ( bdm::logger& L, const string& prefix ) { |
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[1064] | 299 | epdf::log_register ( L, prefix ); |
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| 300 | if ( log_level[logvartheta] ) { |
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| 301 | int th_dim = dim - dimx*dimx; // dimension - dimension of cov |
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| 302 | L.add_vector( log_level, logvartheta, RV ( th_dim ), prefix ); |
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| 303 | } |
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[739] | 304 | } |
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| 305 | |
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| 306 | void egiw::log_write() const { |
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[1064] | 307 | epdf::log_write(); |
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| 308 | if ( log_level[logvartheta] ) { |
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| 309 | mat M; |
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| 310 | ldmat Lam; |
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| 311 | ldmat Vz; |
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| 312 | factorize ( M, Vz, Lam ); |
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| 313 | if( log_level[logvartheta] ) |
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| 314 | log_level.store( logvartheta, cvectorize ( est_theta_cov().to_mat() ) ); |
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| 315 | } |
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[739] | 316 | } |
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| 317 | |
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[956] | 318 | void egiw::from_setting ( const Setting &set ) { |
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[1064] | 319 | epdf::from_setting ( set ); |
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| 320 | UI::get ( dimx, set, "dimx", UI::compulsory ); |
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| 321 | if ( !UI::get ( nu, set, "nu", UI::optional ) ) { |
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| 322 | nu = -1; |
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| 323 | } |
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| 324 | mat Vful; |
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| 325 | if (!UI::get(V, set, "V", UI::optional)) { |
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| 326 | if ( !UI::get ( Vful, set, "V", UI::optional ) ) { |
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| 327 | vec dV; |
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| 328 | UI::get ( dV, set, "dV", UI::compulsory ); |
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| 329 | set_parameters ( dimx, ldmat ( dV ), nu ); |
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[956] | 330 | |
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[1064] | 331 | } else { |
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| 332 | set_parameters ( dimx, Vful, nu ); |
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| 333 | } |
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| 334 | } |
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| 335 | } |
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[956] | 336 | |
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| 337 | void egiw::to_setting ( Setting& set ) const { |
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[1064] | 338 | epdf::to_setting ( set ); |
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| 339 | UI::save ( dimx, set, "dimx" ); |
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| 340 | UI::save ( V, set, "V" ); |
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| 341 | UI::save ( nu, set, "nu" ); |
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| 342 | }; |
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[956] | 343 | |
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| 344 | void egiw::validate() { |
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[1064] | 345 | eEF::validate(); |
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| 346 | nPsi = V.rows() - dimx; |
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| 347 | dim = dimx * ( dimx + nPsi ); |
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| 348 | |
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| 349 | if ( nu < 0 ) { |
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| 350 | nu = 0.1 + nPsi + 2 * dimx + 2; // +2 assures finite expected value of R |
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| 351 | // terms before that are sufficient for finite normalization |
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| 352 | } |
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| 353 | |
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| 354 | // check sizes, rvs etc. |
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| 355 | // also check if RV are meaningful!!! |
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| 356 | // meaningful = rv for theta and rv for r are split! |
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| 357 | } |
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[739] | 358 | void multiBM::bayes ( const vec &yt, const vec &cond ) { |
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[1064] | 359 | if ( frg < 1.0 ) { |
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| 360 | beta *= frg; |
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| 361 | last_lognc = est.lognc(); |
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| 362 | } |
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| 363 | beta += yt; |
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| 364 | if ( evalll ) { |
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| 365 | ll = est.lognc() - last_lognc; |
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| 366 | } |
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[739] | 367 | } |
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| 368 | |
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| 369 | double multiBM::logpred ( const vec &yt ) const { |
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[1064] | 370 | eDirich pred ( est ); |
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| 371 | vec &beta = pred._beta(); |
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[739] | 372 | |
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[1064] | 373 | double lll; |
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| 374 | if ( frg < 1.0 ) { |
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| 375 | beta *= frg; |
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| 376 | lll = pred.lognc(); |
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| 377 | } else if ( evalll ) { |
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| 378 | lll = last_lognc; |
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| 379 | } else { |
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| 380 | lll = pred.lognc(); |
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| 381 | } |
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[739] | 382 | |
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[1064] | 383 | beta += yt; |
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| 384 | return pred.lognc() - lll; |
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[739] | 385 | } |
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[1013] | 386 | void multiBM::flatten ( const BMEF* B, double weight=1.0 ) { |
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[1064] | 387 | const multiBM* E = dynamic_cast<const multiBM*> ( B ); |
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| 388 | // sum(beta) should be equal to sum(B.beta) |
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| 389 | const vec &Eb = E->beta;//const_cast<multiBM*> ( E )->_beta(); |
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| 390 | beta *= ( sum ( Eb ) / sum ( beta ) * weight); |
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| 391 | if ( evalll ) { |
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| 392 | last_lognc = est.lognc(); |
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| 393 | } |
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[739] | 394 | } |
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| 395 | |
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[32] | 396 | vec egamma::sample() const { |
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[1064] | 397 | vec smp ( dim ); |
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| 398 | int i; |
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[32] | 399 | |
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[1064] | 400 | for ( i = 0; i < dim; i++ ) { |
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| 401 | if ( beta ( i ) > std::numeric_limits<double>::epsilon() ) { |
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| 402 | GamRNG.setup ( alpha ( i ), beta ( i ) ); |
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| 403 | } else { |
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| 404 | GamRNG.setup ( alpha ( i ), std::numeric_limits<double>::epsilon() ); |
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| 405 | } |
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[168] | 406 | #pragma omp critical |
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[1064] | 407 | smp ( i ) = GamRNG(); |
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| 408 | } |
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[32] | 409 | |
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[1064] | 410 | return smp; |
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[32] | 411 | } |
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| 412 | |
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| 413 | |
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[211] | 414 | double egamma::evallog ( const vec &val ) const { |
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[1064] | 415 | double res = 0.0; //the rest will be added |
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| 416 | int i; |
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[96] | 417 | |
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[1064] | 418 | if ( any ( val <= 0. ) ) return -inf; |
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| 419 | if ( any ( beta <= 0. ) ) return -inf; |
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| 420 | for ( i = 0; i < dim; i++ ) { |
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| 421 | res += ( alpha ( i ) - 1 ) * std::log ( val ( i ) ) - beta ( i ) * val ( i ); |
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| 422 | } |
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| 423 | double tmp = res - lognc();; |
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| 424 | bdm_assert_debug ( std::isfinite ( tmp ), "Infinite value" ); |
---|
| 425 | return tmp; |
---|
[96] | 426 | } |
---|
| 427 | |
---|
| 428 | double egamma::lognc() const { |
---|
[1064] | 429 | double res = 0.0; //will be added |
---|
| 430 | int i; |
---|
[32] | 431 | |
---|
[1064] | 432 | for ( i = 0; i < dim; i++ ) { |
---|
| 433 | res += lgamma ( alpha ( i ) ) - alpha ( i ) * std::log ( beta ( i ) ) ; |
---|
| 434 | } |
---|
[32] | 435 | |
---|
[1064] | 436 | return res; |
---|
[32] | 437 | } |
---|
| 438 | |
---|
[956] | 439 | void egamma::from_setting ( const Setting &set ) { |
---|
[1064] | 440 | epdf::from_setting ( set ); // reads rv |
---|
| 441 | UI::get ( alpha, set, "alpha", UI::compulsory ); |
---|
| 442 | UI::get ( beta, set, "beta", UI::compulsory ); |
---|
[1063] | 443 | } |
---|
[1064] | 444 | |
---|
[956] | 445 | void egamma::to_setting ( Setting &set ) const |
---|
[1064] | 446 | { |
---|
| 447 | epdf::to_setting( set ); |
---|
| 448 | UI::save( alpha, set, "alpha" ); |
---|
| 449 | UI::save( beta, set, "beta" ); |
---|
| 450 | } |
---|
| 451 | |
---|
| 452 | |
---|
[956] | 453 | void egamma::validate() { |
---|
[1064] | 454 | eEF::validate(); |
---|
| 455 | bdm_assert ( alpha.length() == beta.length(), "parameters do not match" ); |
---|
| 456 | dim = alpha.length(); |
---|
| 457 | } |
---|
[956] | 458 | |
---|
[477] | 459 | void mgamma::set_parameters ( double k0, const vec &beta0 ) { |
---|
[1064] | 460 | k = k0; |
---|
| 461 | iepdf.set_parameters ( k * ones ( beta0.length() ), beta0 ); |
---|
[461] | 462 | } |
---|
[32] | 463 | |
---|
[956] | 464 | |
---|
| 465 | void mgamma::from_setting ( const Setting &set ) { |
---|
[1064] | 466 | pdf::from_setting ( set ); // reads rv and rvc |
---|
| 467 | vec betatmp; // ugly but necessary |
---|
| 468 | UI::get ( betatmp, set, "beta", UI::compulsory ); |
---|
| 469 | UI::get ( k, set, "k", UI::compulsory ); |
---|
| 470 | set_parameters ( k, betatmp ); |
---|
| 471 | } |
---|
[956] | 472 | |
---|
| 473 | void mgamma::to_setting (Setting &set) const { |
---|
[1064] | 474 | pdf::to_setting(set); |
---|
| 475 | UI::save( _beta, set, "beta"); |
---|
| 476 | UI::save( k, set, "k"); |
---|
[956] | 477 | |
---|
[1064] | 478 | } |
---|
[956] | 479 | |
---|
| 480 | void mgamma::validate() { |
---|
[1064] | 481 | pdf_internal<egamma>::validate(); |
---|
[956] | 482 | |
---|
[1064] | 483 | dim = _beta.length(); |
---|
| 484 | dimc = _beta.length(); |
---|
| 485 | } |
---|
[956] | 486 | |
---|
[887] | 487 | void eEmp::resample ( RESAMPLING_METHOD method ) { |
---|
[1064] | 488 | ivec ind = zeros_i ( n ); |
---|
| 489 | bdm::resample(w,ind,method); |
---|
| 490 | // copy the internals according to ind |
---|
| 491 | for (int i = 0; i < n; i++ ) { |
---|
| 492 | if ( ind ( i ) != i ) { |
---|
| 493 | samples ( i ) = samples ( ind ( i ) ); |
---|
| 494 | } |
---|
| 495 | w ( i ) = 1.0 / n; |
---|
| 496 | } |
---|
[887] | 497 | } |
---|
| 498 | |
---|
| 499 | void resample ( const vec &w, ivec &ind, RESAMPLING_METHOD method ) { |
---|
[1064] | 500 | int n = w.length(); |
---|
| 501 | ind = zeros_i ( n ); |
---|
| 502 | ivec N_babies = zeros_i ( n ); |
---|
| 503 | vec cumDist = cumsum ( w ); |
---|
| 504 | vec u ( n ); |
---|
| 505 | int i, j, parent; |
---|
| 506 | double u0; |
---|
| 507 | |
---|
| 508 | switch ( method ) { |
---|
| 509 | case MULTINOMIAL: |
---|
| 510 | u ( n - 1 ) = pow ( UniRNG.sample(), 1.0 / n ); |
---|
| 511 | |
---|
| 512 | for ( i = n - 2; i >= 0; i-- ) { |
---|
| 513 | u ( i ) = u ( i + 1 ) * pow ( UniRNG.sample(), 1.0 / ( i + 1 ) ); |
---|
| 514 | } |
---|
| 515 | |
---|
| 516 | break; |
---|
| 517 | |
---|
| 518 | case STRATIFIED: |
---|
| 519 | |
---|
| 520 | for ( i = 0; i < n; i++ ) { |
---|
| 521 | u ( i ) = ( i + UniRNG.sample() ) / n; |
---|
| 522 | } |
---|
| 523 | |
---|
| 524 | break; |
---|
| 525 | |
---|
| 526 | case SYSTEMATIC: |
---|
| 527 | u0 = UniRNG.sample(); |
---|
| 528 | |
---|
| 529 | for ( i = 0; i < n; i++ ) { |
---|
| 530 | u ( i ) = ( i + u0 ) / n; |
---|
| 531 | } |
---|
| 532 | |
---|
| 533 | break; |
---|
| 534 | |
---|
| 535 | default: |
---|
| 536 | bdm_error ( "PF::resample(): Unknown resampling method" ); |
---|
| 537 | } |
---|
| 538 | |
---|
| 539 | // U is now full |
---|
| 540 | j = 0; |
---|
| 541 | |
---|
| 542 | for ( i = 0; i < n; i++ ) { |
---|
| 543 | while ( u ( i ) > cumDist ( j ) ) j++; |
---|
| 544 | |
---|
| 545 | N_babies ( j ) ++; |
---|
| 546 | } |
---|
| 547 | // We have assigned new babies for each Particle |
---|
| 548 | // Now, we fill the resulting index such that: |
---|
| 549 | // * particles with at least one baby should not move * |
---|
| 550 | // This assures that reassignment can be done inplace; |
---|
| 551 | |
---|
| 552 | // find the first parent; |
---|
| 553 | parent = 0; |
---|
| 554 | while ( N_babies ( parent ) == 0 ) parent++; |
---|
| 555 | |
---|
| 556 | // Build index |
---|
| 557 | for ( i = 0; i < n; i++ ) { |
---|
| 558 | if ( N_babies ( i ) > 0 ) { |
---|
| 559 | ind ( i ) = i; |
---|
| 560 | N_babies ( i ) --; //this index was now replicated; |
---|
| 561 | } else { |
---|
| 562 | // test if the parent has been fully replicated |
---|
| 563 | // if yes, find the next one |
---|
| 564 | while ( ( N_babies ( parent ) == 0 ) || ( N_babies ( parent ) == 1 && parent > i ) ) parent++; |
---|
| 565 | |
---|
| 566 | // Replicate parent |
---|
| 567 | ind ( i ) = parent; |
---|
| 568 | |
---|
| 569 | N_babies ( parent ) --; //this index was now replicated; |
---|
| 570 | } |
---|
| 571 | |
---|
| 572 | } |
---|
[32] | 573 | } |
---|
| 574 | |
---|
[488] | 575 | void eEmp::set_statistics ( const vec &w0, const epdf &epdf0 ) { |
---|
[1064] | 576 | dim = epdf0.dimension(); |
---|
| 577 | w = w0; |
---|
| 578 | w /= sum ( w0 );//renormalize |
---|
| 579 | n = w.length(); |
---|
| 580 | samples.set_size ( n ); |
---|
[32] | 581 | |
---|
[1064] | 582 | for ( int i = 0; i < n; i++ ) { |
---|
| 583 | samples ( i ) = epdf0.sample(); |
---|
| 584 | } |
---|
[32] | 585 | } |
---|
[178] | 586 | |
---|
[225] | 587 | void eEmp::set_samples ( const epdf* epdf0 ) { |
---|
[1064] | 588 | w = 1; |
---|
| 589 | w /= sum ( w );//renormalize |
---|
[178] | 590 | |
---|
[1064] | 591 | for ( int i = 0; i < n; i++ ) { |
---|
| 592 | samples ( i ) = epdf0->sample(); |
---|
| 593 | } |
---|
[178] | 594 | } |
---|
| 595 | |
---|
[477] | 596 | void migamma_ref::from_setting ( const Setting &set ) { |
---|
[1064] | 597 | migamma::from_setting(set); |
---|
| 598 | vec ref; |
---|
| 599 | double k,l; |
---|
[957] | 600 | |
---|
[1064] | 601 | UI::get ( ref, set, "ref" , UI::compulsory ); |
---|
| 602 | UI::get( k, set, "k", UI::compulsory ); |
---|
| 603 | UI::get( l, set, "l", UI::compulsory ); |
---|
| 604 | set_parameters ( k, ref, l ); |
---|
[357] | 605 | } |
---|
[957] | 606 | |
---|
[956] | 607 | void migamma_ref::to_setting (Setting &set) const { |
---|
[1064] | 608 | migamma::to_setting(set); |
---|
| 609 | UI::save ( pow ( refl, 1/(1.0 - l) ), set, "ref"); |
---|
| 610 | UI::save(l,set,"l"); |
---|
| 611 | UI::save(k,set,"k"); |
---|
[956] | 612 | } |
---|
[957] | 613 | |
---|
[477] | 614 | void mlognorm::from_setting ( const Setting &set ) { |
---|
[1064] | 615 | pdf_internal<elognorm>::from_setting(set); |
---|
| 616 | vec mu0; |
---|
| 617 | double k; |
---|
| 618 | UI::get ( mu0, set, "mu0", UI::compulsory ); |
---|
| 619 | UI::get( k, set, "k", UI::compulsory ); |
---|
| 620 | set_parameters ( mu0.length(), k ); |
---|
| 621 | condition ( mu0 ); |
---|
[357] | 622 | } |
---|
| 623 | |
---|
[956] | 624 | void mlognorm::to_setting (Setting &set) const { |
---|
[1064] | 625 | pdf_internal<elognorm>::to_setting(set); |
---|
| 626 | UI::save ( exp(mu + sig2), set, "mu0"); |
---|
[957] | 627 | |
---|
[1064] | 628 | // inversion of sig2 = 0.5 * log ( k * k + 1 ); |
---|
| 629 | double k = sqrt( exp( 2 * sig2 ) - 1 ); |
---|
| 630 | UI::save(k,set,"k"); |
---|
[956] | 631 | } |
---|
| 632 | |
---|
| 633 | |
---|
[739] | 634 | void mlstudent::condition ( const vec &cond ) { |
---|
[1064] | 635 | if ( cond.length() > 0 ) { |
---|
| 636 | iepdf._mu() = A * cond + mu_const; |
---|
| 637 | } else { |
---|
| 638 | iepdf._mu() = mu_const; |
---|
| 639 | } |
---|
| 640 | double zeta; |
---|
| 641 | //ugly hack! |
---|
| 642 | if ( ( cond.length() + 1 ) == Lambda.rows() ) { |
---|
| 643 | zeta = Lambda.invqform ( concat ( cond, vec_1 ( 1.0 ) ) ); |
---|
| 644 | } else { |
---|
| 645 | zeta = Lambda.invqform ( cond ); |
---|
| 646 | } |
---|
| 647 | _R = Re; |
---|
| 648 | _R *= ( 1 + zeta );// / ( nu ); << nu is in Re!!!!!! |
---|
[739] | 649 | } |
---|
| 650 | |
---|
| 651 | void eEmp::qbounds ( vec &lb, vec &ub, double perc ) const { |
---|
[1064] | 652 | // lb in inf so than it will be pushed below; |
---|
| 653 | lb.set_size ( dim ); |
---|
| 654 | ub.set_size ( dim ); |
---|
| 655 | lb = std::numeric_limits<double>::infinity(); |
---|
| 656 | ub = -std::numeric_limits<double>::infinity(); |
---|
| 657 | int j; |
---|
| 658 | for ( int i = 0; i < n; i++ ) { |
---|
| 659 | for ( j = 0; j < dim; j++ ) { |
---|
| 660 | if ( samples ( i ) ( j ) < lb ( j ) ) { |
---|
| 661 | lb ( j ) = samples ( i ) ( j ); |
---|
| 662 | } |
---|
| 663 | if ( samples ( i ) ( j ) > ub ( j ) ) { |
---|
| 664 | ub ( j ) = samples ( i ) ( j ); |
---|
| 665 | } |
---|
| 666 | } |
---|
| 667 | } |
---|
[739] | 668 | } |
---|
| 669 | |
---|
[956] | 670 | void eEmp::to_setting ( Setting &set ) const { |
---|
[1064] | 671 | epdf::to_setting( set ); |
---|
| 672 | UI::save ( samples, set, "samples" ); |
---|
| 673 | UI::save ( w, set, "w" ); |
---|
| 674 | } |
---|
[739] | 675 | |
---|
[956] | 676 | void eEmp::from_setting ( const Setting &set ) { |
---|
[1064] | 677 | epdf::from_setting( set ); |
---|
| 678 | |
---|
| 679 | UI::get( samples, set, "samples", UI::compulsory ); |
---|
| 680 | UI::get ( w, set, "w", UI::compulsory ); |
---|
[1063] | 681 | } |
---|
[956] | 682 | |
---|
[1064] | 683 | void eEmp::validate () { |
---|
| 684 | epdf::validate(); |
---|
| 685 | bdm_assert (samples.length()==w.length(),"samples and weigths are of different lengths"); |
---|
| 686 | n = w.length(); |
---|
| 687 | if (n>0) |
---|
| 688 | pdf::dim = samples ( 0 ).length(); |
---|
[1063] | 689 | } |
---|
[1064] | 690 | |
---|
[1063] | 691 | void eDirich::from_setting ( const Setting &set ) { |
---|
[1064] | 692 | epdf::from_setting ( set ); |
---|
| 693 | UI::get ( beta, set, "beta", UI::compulsory ); |
---|
[1063] | 694 | } |
---|
| 695 | |
---|
[956] | 696 | void eDirich::validate() { |
---|
[1064] | 697 | //check rv |
---|
| 698 | eEF::validate(); |
---|
| 699 | dim = beta.length(); |
---|
| 700 | } |
---|
[956] | 701 | |
---|
| 702 | void eDirich::to_setting ( Setting &set ) const |
---|
[1064] | 703 | { |
---|
| 704 | eEF::to_setting( set ); |
---|
| 705 | UI::save( beta, set, "beta" ); |
---|
| 706 | } |
---|
[956] | 707 | |
---|
| 708 | void euni::from_setting ( const Setting &set ) { |
---|
[1064] | 709 | epdf::from_setting ( set ); // reads rv and rvc |
---|
[956] | 710 | |
---|
[1064] | 711 | UI::get ( high, set, "high", UI::compulsory ); |
---|
| 712 | UI::get ( low, set, "low", UI::compulsory ); |
---|
| 713 | set_parameters ( low, high ); |
---|
| 714 | |
---|
| 715 | } |
---|
| 716 | |
---|
[956] | 717 | void euni::to_setting (Setting &set) const { |
---|
[1064] | 718 | epdf::to_setting ( set ); |
---|
| 719 | UI::save ( high, set, "high" ); |
---|
| 720 | UI::save ( low, set, "low" ); |
---|
| 721 | } |
---|
| 722 | |
---|
[956] | 723 | void euni::validate() { |
---|
[1064] | 724 | epdf::validate(); |
---|
| 725 | bdm_assert ( high.length() == low.length(), "Incompatible high and low vectors" ); |
---|
| 726 | dim = high.length(); |
---|
| 727 | bdm_assert ( min ( distance ) > 0.0, "bad support" ); |
---|
| 728 | } |
---|
[956] | 729 | |
---|
[1064] | 730 | void mgdirac::from_setting(const Setting& set) { |
---|
| 731 | pdf::from_setting(set); |
---|
| 732 | g=UI::build<fnc>(set,"g",UI::compulsory); |
---|
| 733 | validate(); |
---|
| 734 | } |
---|
| 735 | void mgdirac::to_setting(Setting &set) const { |
---|
| 736 | pdf::to_setting(set); |
---|
| 737 | UI::save(g.get(), set, "g"); |
---|
| 738 | } |
---|
[956] | 739 | void mgdirac::validate() { |
---|
[1064] | 740 | pdf::validate(); |
---|
| 741 | dim = g->dimension(); |
---|
| 742 | dimc = g->dimensionc(); |
---|
| 743 | } |
---|
[956] | 744 | |
---|
| 745 | void mDirich::from_setting ( const Setting &set ) { |
---|
[1064] | 746 | pdf::from_setting ( set ); // reads rv and rvc |
---|
| 747 | if ( _rv()._dsize() > 0 ) { |
---|
| 748 | rvc = _rv().copy_t ( -1 ); |
---|
| 749 | } |
---|
| 750 | vec beta0; |
---|
| 751 | if ( !UI::get ( beta0, set, "beta0", UI::optional ) ) { |
---|
| 752 | beta0 = ones ( _rv()._dsize() ); |
---|
| 753 | } |
---|
| 754 | if ( !UI::get ( betac, set, "betac", UI::optional ) ) { |
---|
| 755 | betac = 0.1 * ones ( _rv()._dsize() ); |
---|
| 756 | } |
---|
| 757 | _beta = beta0; |
---|
[956] | 758 | |
---|
[1064] | 759 | UI::get ( k, set, "k", UI::compulsory ); |
---|
| 760 | } |
---|
[956] | 761 | |
---|
| 762 | void mDirich::to_setting (Setting &set) const { |
---|
[1064] | 763 | pdf::to_setting(set); |
---|
| 764 | UI::save( _beta, set, "beta0"); |
---|
| 765 | UI::save( betac, set, "betac"); |
---|
| 766 | UI::save ( k, set, "k" ); |
---|
| 767 | } |
---|
[956] | 768 | |
---|
| 769 | |
---|
| 770 | void mDirich::validate() { |
---|
[1064] | 771 | pdf_internal<eDirich>::validate(); |
---|
| 772 | bdm_assert ( _beta.length() == betac.length(), "beta0 and betac are not compatible" ); |
---|
| 773 | if ( _rv()._dsize() > 0 ) { |
---|
| 774 | bdm_assert ( ( _rv()._dsize() == dimension() ) , "Size of rv does not match with beta" ); |
---|
| 775 | } |
---|
| 776 | dimc = _beta.length(); |
---|
| 777 | } |
---|
[1033] | 778 | |
---|
| 779 | |
---|
| 780 | void mBeta::from_setting ( const Setting &set ) { |
---|
[1064] | 781 | pdf::from_setting ( set ); // reads rv and rvc |
---|
| 782 | if ( _rv()._dsize() > 0 ) { |
---|
| 783 | rvc = _rv().copy_t ( -1 ); |
---|
| 784 | } |
---|
| 785 | if ( !UI::get ( iepdf.beta, set, "beta", UI::optional ) ) { |
---|
| 786 | iepdf.beta = ones ( _rv()._dsize() ); |
---|
| 787 | } |
---|
| 788 | if ( !UI::get ( iepdf.alpha, set, "alpha", UI::optional ) ) { |
---|
| 789 | iepdf.alpha = ones ( _rv()._dsize() ); |
---|
| 790 | } |
---|
| 791 | if ( !UI::get ( betac, set, "betac", UI::optional ) ) { |
---|
| 792 | betac = 0.1 * ones ( _rv()._dsize() ); |
---|
| 793 | } |
---|
| 794 | |
---|
| 795 | UI::get ( k, set, "k", UI::compulsory ); |
---|
[1033] | 796 | } |
---|
| 797 | |
---|
| 798 | void mBeta::to_setting (Setting &set) const { |
---|
[1064] | 799 | pdf::to_setting(set); |
---|
| 800 | UI::save( iepdf.beta, set, "beta"); |
---|
| 801 | UI::save( betac, set, "betac"); |
---|
| 802 | UI::save ( k, set, "k" ); |
---|
[1033] | 803 | } |
---|
| 804 | |
---|
[1013] | 805 | } |
---|