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