1 | /*! |
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2 | \file |
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3 | \brief Probability distributions for Exponential Family models. |
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4 | \author Vaclav Smidl. |
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5 | |
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6 | ----------------------------------- |
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7 | BDM++ - C++ library for Bayesian Decision Making under Uncertainty |
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8 | |
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9 | Using IT++ for numerical operations |
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10 | ----------------------------------- |
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11 | */ |
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12 | |
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13 | #ifndef EF_H |
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14 | #define EF_H |
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15 | |
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16 | |
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17 | #include "../shared_ptr.h" |
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18 | #include "../base/bdmbase.h" |
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19 | #include "../math/chmat.h" |
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20 | |
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21 | namespace bdm { |
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22 | |
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23 | |
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24 | //! Global Uniform_RNG |
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25 | extern Uniform_RNG UniRNG; |
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26 | //! Global Normal_RNG |
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27 | extern Normal_RNG NorRNG; |
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28 | //! Global Gamma_RNG |
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29 | extern Gamma_RNG GamRNG; |
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30 | |
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31 | /*! |
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32 | * \brief General conjugate exponential family posterior density. |
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33 | |
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34 | * More?... |
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35 | */ |
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36 | |
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37 | class eEF : public epdf { |
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38 | public: |
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39 | // eEF() :epdf() {}; |
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40 | //! default constructor |
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41 | eEF () : epdf () {}; |
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42 | //! logarithm of the normalizing constant, \f$\mathcal{I}\f$ |
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43 | virtual double lognc() const = 0; |
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44 | |
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45 | //!Evaluate normalized log-probability |
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46 | virtual double evallog_nn ( const vec &val ) const NOT_IMPLEMENTED(0); |
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47 | |
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48 | //!Evaluate normalized log-probability |
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49 | virtual double evallog ( const vec &val ) const { |
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50 | double tmp; |
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51 | tmp = evallog_nn ( val ) - lognc(); |
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52 | return tmp; |
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53 | } |
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54 | //!Evaluate normalized log-probability for many samples |
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55 | virtual vec evallog_mat ( const mat &Val ) const { |
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56 | vec x ( Val.cols() ); |
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57 | for ( int i = 0; i < Val.cols(); i++ ) { |
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58 | x ( i ) = evallog_nn ( Val.get_col ( i ) ) ; |
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59 | } |
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60 | return x - lognc(); |
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61 | } |
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62 | //!Evaluate normalized log-probability for many samples |
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63 | virtual vec evallog_mat ( const Array<vec> &Val ) const { |
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64 | vec x ( Val.length() ); |
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65 | for ( int i = 0; i < Val.length(); i++ ) { |
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66 | x ( i ) = evallog_nn ( Val ( i ) ) ; |
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67 | } |
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68 | return x - lognc(); |
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69 | } |
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70 | |
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71 | //!Power of the density, used e.g. to flatten the density |
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72 | virtual void pow ( double p ) NOT_IMPLEMENTED_VOID; |
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73 | }; |
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74 | |
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75 | |
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76 | //! Estimator for Exponential family |
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77 | class BMEF : public BM { |
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78 | protected: |
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79 | //! forgetting factor |
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80 | double frg; |
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81 | //! cached value of lognc() in the previous step (used in evaluation of \c ll ) |
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82 | double last_lognc; |
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83 | public: |
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84 | //! Default constructor (=empty constructor) |
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85 | BMEF ( double frg0 = 1.0 ) : BM (), frg ( frg0 ) {} |
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86 | //! Copy constructor |
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87 | BMEF ( const BMEF &B ) : BM ( B ), frg ( B.frg ), last_lognc ( B.last_lognc ) {} |
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88 | //!get statistics from another model |
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89 | virtual void set_statistics ( const BMEF* BM0 ) NOT_IMPLEMENTED_VOID; |
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90 | |
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91 | //! Weighted update of sufficient statistics (Bayes rule) |
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92 | virtual void bayes_weighted ( const vec &data, const vec &cond = empty_vec, const double w = 1.0 ) {}; |
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93 | //original Bayes |
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94 | void bayes ( const vec &yt, const vec &cond = empty_vec ); |
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95 | |
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96 | //!Flatten the posterior according to the given BMEF (of the same type!) |
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97 | virtual void flatten ( const BMEF * B ) NOT_IMPLEMENTED_VOID; |
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98 | |
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99 | double logpred ( const vec &yt ) const NOT_IMPLEMENTED(0); |
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100 | |
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101 | virtual epdf* epredictor() const NOT_IMPLEMENTED(NULL); |
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102 | |
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103 | virtual pdf* predictor() const NOT_IMPLEMENTED(NULL); |
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104 | |
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105 | void to_setting ( Setting &set ) const |
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106 | { |
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107 | BM::to_setting( set ); |
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108 | UI::save(frg, set, "frg"); |
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109 | // TODO DOPLNIT? ALE MOMENTALNE CHYBI FROM_SETTING PRO INSPIRACI |
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110 | } |
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111 | void from_setting( const Setting &set) { |
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112 | BM::from_setting(set); |
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113 | if ( !UI::get ( frg, set, "frg" ) ) |
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114 | frg = 1.0; |
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115 | |
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116 | } |
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117 | }; |
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118 | |
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119 | template<class sq_T, template <typename> class TEpdf> |
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120 | class mlnorm; |
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121 | |
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122 | /*! |
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123 | * \brief Gaussian density with positive definite (decomposed) covariance matrix. |
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124 | |
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125 | * More?... |
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126 | */ |
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127 | template<class sq_T> |
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128 | class enorm : public eEF { |
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129 | protected: |
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130 | //! mean value |
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131 | vec mu; |
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132 | //! Covariance matrix in decomposed form |
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133 | sq_T R; |
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134 | public: |
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135 | //!\name Constructors |
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136 | //!@{ |
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137 | |
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138 | enorm () : eEF (), mu (), R () {}; |
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139 | enorm ( const vec &mu, const sq_T &R ) { |
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140 | set_parameters ( mu, R ); |
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141 | } |
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142 | void set_parameters ( const vec &mu, const sq_T &R ); |
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143 | /*! Create Normal density |
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144 | \f[ f(rv) = N(\mu, R) \f] |
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145 | from structure |
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146 | \code |
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147 | class = 'enorm<ldmat>', (OR) 'enorm<chmat>', (OR) 'enorm<fsqmat>'; |
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148 | mu = []; // mean value |
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149 | R = []; // variance, square matrix of appropriate dimension |
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150 | \endcode |
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151 | */ |
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152 | void from_setting ( const Setting &root ); |
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153 | void to_setting ( Setting &root ) const ; |
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154 | |
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155 | void validate() { |
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156 | bdm_assert ( mu.length() == R.rows(), "mu and R parameters do not match" ); |
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157 | dim = mu.length(); |
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158 | } |
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159 | //!@} |
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160 | |
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161 | //! \name Mathematical operations |
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162 | //!@{ |
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163 | |
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164 | //! dupdate in exponential form (not really handy) |
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165 | void dupdate ( mat &v, double nu = 1.0 ); |
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166 | |
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167 | vec sample() const; |
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168 | |
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169 | double evallog_nn ( const vec &val ) const; |
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170 | double lognc () const; |
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171 | vec mean() const { |
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172 | return mu; |
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173 | } |
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174 | vec variance() const { |
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175 | return diag ( R.to_mat() ); |
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176 | } |
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177 | // mlnorm<sq_T>* condition ( const RV &rvn ) const ; <=========== fails to cmpile. Why? |
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178 | shared_ptr<pdf> condition ( const RV &rvn ) const; |
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179 | |
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180 | // target not typed to mlnorm<sq_T, enorm<sq_T> > & |
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181 | // because that doesn't compile (perhaps because we |
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182 | // haven't finished defining enorm yet), but the type |
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183 | // is required |
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184 | void condition ( const RV &rvn, pdf &target ) const; |
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185 | |
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186 | shared_ptr<epdf> marginal ( const RV &rvn ) const; |
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187 | void marginal ( const RV &rvn, enorm<sq_T> &target ) const; |
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188 | //!@} |
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189 | |
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190 | //! \name Access to attributes |
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191 | //!@{ |
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192 | |
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193 | vec& _mu() { |
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194 | return mu; |
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195 | } |
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196 | const vec& _mu() const { |
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197 | return mu; |
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198 | } |
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199 | void set_mu ( const vec mu0 ) { |
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200 | mu = mu0; |
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201 | } |
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202 | sq_T& _R() { |
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203 | return R; |
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204 | } |
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205 | const sq_T& _R() const { |
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206 | return R; |
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207 | } |
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208 | //!@} |
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209 | |
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210 | }; |
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211 | UIREGISTER2 ( enorm, chmat ); |
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212 | SHAREDPTR2 ( enorm, chmat ); |
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213 | UIREGISTER2 ( enorm, ldmat ); |
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214 | SHAREDPTR2 ( enorm, ldmat ); |
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215 | UIREGISTER2 ( enorm, fsqmat ); |
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216 | SHAREDPTR2 ( enorm, fsqmat ); |
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217 | |
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218 | |
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219 | /*! |
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220 | * \brief Gauss-inverse-Wishart density stored in LD form |
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221 | |
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222 | * For \f$p\f$-variate densities, given rv.count() should be \f$p\times\f$ V.rows(). |
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223 | * |
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224 | */ |
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225 | class egiw : public eEF { |
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226 | protected: |
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227 | //! Extended information matrix of sufficient statistics |
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228 | ldmat V; |
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229 | //! Number of data records (degrees of freedom) of sufficient statistics |
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230 | double nu; |
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231 | //! Dimension of the output |
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232 | int dimx; |
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233 | //! Dimension of the regressor |
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234 | int nPsi; |
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235 | public: |
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236 | //!\name Constructors |
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237 | //!@{ |
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238 | egiw() : eEF() {}; |
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239 | egiw ( int dimx0, ldmat V0, double nu0 = -1.0 ) : eEF() { |
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240 | set_parameters ( dimx0, V0, nu0 ); |
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241 | }; |
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242 | |
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243 | void set_parameters ( int dimx0, ldmat V0, double nu0 = -1.0 ); |
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244 | //!@} |
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245 | |
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246 | vec sample() const; |
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247 | mat sample_mat ( int n ) const; |
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248 | vec mean() const; |
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249 | vec variance() const; |
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250 | void sample_mat ( mat &Mi, chmat &Ri ) const; |
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251 | |
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252 | void factorize ( mat &M, ldmat &Vz, ldmat &Lam ) const; |
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253 | //! LS estimate of \f$\theta\f$ |
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254 | vec est_theta() const; |
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255 | |
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256 | //! Covariance of the LS estimate |
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257 | ldmat est_theta_cov() const; |
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258 | |
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259 | //! expected values of the linear coefficient and the covariance matrix are written to \c M and \c R , respectively |
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260 | void mean_mat ( mat &M, mat&R ) const; |
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261 | //! In this instance, val= [theta, r]. For multivariate instances, it is stored columnwise val = [theta_1 theta_2 ... r_1 r_2 ] |
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262 | double evallog_nn ( const vec &val ) const; |
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263 | double lognc () const; |
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264 | void pow ( double p ) { |
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265 | V *= p; |
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266 | nu *= p; |
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267 | }; |
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268 | |
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269 | //! \name Access attributes |
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270 | //!@{ |
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271 | |
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272 | ldmat& _V() { |
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273 | return V; |
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274 | } |
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275 | const ldmat& _V() const { |
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276 | return V; |
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277 | } |
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278 | double& _nu() { |
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279 | return nu; |
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280 | } |
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281 | const double& _nu() const { |
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282 | return nu; |
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283 | } |
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284 | const int & _dimx() const { |
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285 | return dimx; |
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286 | } |
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287 | |
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288 | /*! Create Gauss-inverse-Wishart density |
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289 | \f[ f(rv) = GiW(V,\nu) \f] |
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290 | from structure |
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291 | \code |
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292 | class = 'egiw'; |
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293 | V = []; // square matrix |
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294 | dV = []; // vector of diagonal of V (when V not given) |
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295 | nu = []; // scalar \nu ((almost) degrees of freedom) |
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296 | // when missing, it will be computed to obtain proper pdf |
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297 | dimx = []; // dimension of the wishart part |
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298 | rv = RV({'name'}) // description of RV |
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299 | rvc = RV({'name'}) // description of RV in condition |
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300 | \endcode |
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301 | */ |
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302 | |
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303 | void from_setting ( const Setting &set ) { |
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304 | epdf::from_setting ( set ); |
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305 | UI::get ( dimx, set, "dimx", UI::compulsory ); |
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306 | if ( !UI::get ( nu, set, "nu", UI::optional ) ) { |
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307 | nu = -1; |
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308 | } |
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309 | mat V; |
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310 | if ( !UI::get ( V, set, "V", UI::optional ) ) { |
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311 | vec dV; |
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312 | UI::get ( dV, set, "dV", UI::compulsory ); |
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313 | set_parameters ( dimx, ldmat ( dV ), nu ); |
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314 | |
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315 | } else { |
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316 | set_parameters ( dimx, V, nu ); |
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317 | } |
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318 | } |
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319 | |
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320 | void to_setting ( Setting& set ) const { |
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321 | epdf::to_setting ( set ); |
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322 | UI::save ( dimx, set, "dimx" ); |
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323 | UI::save ( V.to_mat(), set, "V" ); |
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324 | UI::save ( nu, set, "nu" ); |
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325 | }; |
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326 | |
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327 | void validate() { |
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328 | // check sizes, rvs etc. |
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329 | } |
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330 | void log_register ( bdm::logger& L, const string& prefix ); |
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331 | |
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332 | void log_write() const; |
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333 | //!@} |
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334 | }; |
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335 | UIREGISTER ( egiw ); |
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336 | SHAREDPTR ( egiw ); |
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337 | |
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338 | /*! \brief Dirichlet posterior density |
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339 | |
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340 | Continuous Dirichlet density of \f$n\f$-dimensional variable \f$x\f$ |
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341 | \f[ |
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342 | f(x|\beta) = \frac{\Gamma[\gamma]}{\prod_{i=1}^{n}\Gamma(\beta_i)} \prod_{i=1}^{n}x_i^{\beta_i-1} |
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343 | \f] |
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344 | where \f$\gamma=\sum_i \beta_i\f$. |
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345 | */ |
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346 | class eDirich: public eEF { |
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347 | protected: |
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348 | //!sufficient statistics |
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349 | vec beta; |
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350 | public: |
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351 | //!\name Constructors |
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352 | //!@{ |
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353 | |
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354 | eDirich () : eEF () {}; |
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355 | eDirich ( const eDirich &D0 ) : eEF () { |
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356 | set_parameters ( D0.beta ); |
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357 | }; |
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358 | eDirich ( const vec &beta0 ) { |
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359 | set_parameters ( beta0 ); |
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360 | }; |
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361 | void set_parameters ( const vec &beta0 ) { |
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362 | beta = beta0; |
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363 | dim = beta.length(); |
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364 | } |
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365 | //!@} |
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366 | |
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367 | //! using sampling procedure from wikipedia |
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368 | vec sample() const { |
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369 | vec y ( beta.length() ); |
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370 | for ( int i = 0; i < beta.length(); i++ ) { |
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371 | GamRNG.setup ( beta ( i ), 1 ); |
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372 | #pragma omp critical |
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373 | y ( i ) = GamRNG(); |
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374 | } |
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375 | return y / sum ( y ); |
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376 | } |
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377 | |
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378 | vec mean() const { |
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379 | return beta / sum ( beta ); |
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380 | }; |
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381 | vec variance() const { |
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382 | double gamma = sum ( beta ); |
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383 | return elem_mult ( beta, ( gamma - beta ) ) / ( gamma*gamma* ( gamma + 1 ) ); |
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384 | } |
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385 | //! In this instance, val is ... |
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386 | double evallog_nn ( const vec &val ) const { |
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387 | double tmp; |
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388 | tmp = ( beta - 1 ) * log ( val ); |
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389 | return tmp; |
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390 | } |
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391 | |
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392 | double lognc () const { |
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393 | double tmp; |
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394 | double gam = sum ( beta ); |
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395 | double lgb = 0.0; |
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396 | for ( int i = 0; i < beta.length(); i++ ) { |
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397 | lgb += lgamma ( beta ( i ) ); |
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398 | } |
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399 | tmp = lgb - lgamma ( gam ); |
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400 | return tmp; |
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401 | } |
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402 | |
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403 | //!access function |
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404 | vec& _beta() { |
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405 | return beta; |
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406 | } |
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407 | /*! configuration structure |
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408 | \code |
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409 | class = 'eDirich'; |
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410 | beta = []; //parametr beta |
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411 | \endcode |
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412 | */ |
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413 | void from_setting ( const Setting &set ) { |
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414 | epdf::from_setting ( set ); |
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415 | UI::get ( beta, set, "beta", UI::compulsory ); |
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416 | validate(); |
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417 | } |
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418 | void validate() { |
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419 | //check rv |
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420 | dim = beta.length(); |
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421 | } |
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422 | |
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423 | void to_setting ( Setting &set ) const |
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424 | { |
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425 | eEF::to_setting( set ); |
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426 | UI::save( beta, set, "beta" ); |
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427 | } |
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428 | }; |
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429 | UIREGISTER ( eDirich ); |
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430 | |
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431 | /*! Random Walk on Dirichlet |
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432 | Using simple assignment |
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433 | \f[ \beta = rvc / k + \beta_c \f] |
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434 | hence, mean value = rvc, variance = (k+1)*mean*mean; |
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435 | |
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436 | The greater k is, the greater is the variance of the random walk; |
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437 | |
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438 | \f$ \beta_c \f$ is used as regularizing element to avoid corner cases, i.e. when one element of rvc is zero. |
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439 | By default is it set to 0.1; |
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440 | */ |
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441 | |
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442 | class mDirich: public pdf_internal<eDirich> { |
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443 | protected: |
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444 | //! constant \f$ k \f$ of the random walk |
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445 | double k; |
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446 | //! cache of beta_i |
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447 | vec &_beta; |
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448 | //! stabilizing coefficient \f$ \beta_c \f$ |
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449 | vec betac; |
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450 | public: |
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451 | mDirich() : pdf_internal<eDirich>(), _beta ( iepdf._beta() ) {}; |
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452 | void condition ( const vec &val ) { |
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453 | _beta = val / k + betac; |
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454 | }; |
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455 | /*! Create Dirichlet random walk |
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456 | \f[ f(rv|rvc) = Di(rvc*k) \f] |
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457 | from structure |
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458 | \code |
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459 | class = 'mDirich'; |
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460 | k = 1; // multiplicative constant k |
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461 | --- optional --- |
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462 | rv = RV({'name'},size) // description of RV |
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463 | beta0 = []; // initial value of beta |
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464 | betac = []; // initial value of beta |
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465 | \endcode |
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466 | */ |
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467 | void from_setting ( const Setting &set ) { |
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468 | pdf::from_setting ( set ); // reads rv and rvc |
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469 | if ( _rv()._dsize() > 0 ) { |
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470 | rvc = _rv().copy_t ( -1 ); |
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471 | } |
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472 | vec beta0; |
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473 | if ( !UI::get ( beta0, set, "beta0", UI::optional ) ) { |
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474 | beta0 = ones ( _rv()._dsize() ); |
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475 | } |
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476 | if ( !UI::get ( betac, set, "betac", UI::optional ) ) { |
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477 | betac = 0.1 * ones ( _rv()._dsize() ); |
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478 | } |
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479 | _beta = beta0; |
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480 | |
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481 | UI::get ( k, set, "k", UI::compulsory ); |
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482 | validate(); |
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483 | } |
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484 | void validate() { |
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485 | pdf_internal<eDirich>::validate(); |
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486 | bdm_assert ( _beta.length() == betac.length(), "beta0 and betac are not compatible" ); |
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487 | if ( _rv()._dsize() > 0 ) { |
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488 | bdm_assert ( ( _rv()._dsize() == dimension() ) , "Size of rv does not match with beta" ); |
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489 | } |
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490 | dimc = _beta.length(); |
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491 | }; |
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492 | }; |
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493 | UIREGISTER ( mDirich ); |
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494 | |
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495 | //! \brief Estimator for Multinomial density |
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496 | class multiBM : public BMEF { |
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497 | protected: |
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498 | //! Conjugate prior and posterior |
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499 | eDirich est; |
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500 | //! Pointer inside est to sufficient statistics |
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501 | vec β |
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502 | public: |
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503 | //!Default constructor |
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504 | multiBM () : BMEF (), est (), beta ( est._beta() ) { |
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505 | if ( beta.length() > 0 ) { |
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506 | last_lognc = est.lognc(); |
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507 | } else { |
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508 | last_lognc = 0.0; |
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509 | } |
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510 | } |
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511 | //!Copy constructor |
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512 | multiBM ( const multiBM &B ) : BMEF ( B ), est ( B.est ), beta ( est._beta() ) {} |
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513 | //! Sets sufficient statistics to match that of givefrom mB0 |
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514 | void set_statistics ( const BM* mB0 ) { |
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515 | const multiBM* mB = dynamic_cast<const multiBM*> ( mB0 ); |
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516 | beta = mB->beta; |
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517 | } |
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518 | void bayes ( const vec &yt, const vec &cond = empty_vec ); |
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519 | |
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520 | double logpred ( const vec &yt ) const; |
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521 | |
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522 | void flatten ( const BMEF* B ); |
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523 | |
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524 | //! return correctly typed posterior (covariant return) |
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525 | const eDirich& posterior() const { |
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526 | return est; |
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527 | }; |
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528 | //! constructor function |
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529 | void set_parameters ( const vec &beta0 ) { |
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530 | est.set_parameters ( beta0 ); |
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531 | est.validate(); |
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532 | if ( evalll ) { |
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533 | last_lognc = est.lognc(); |
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534 | } |
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535 | } |
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536 | |
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537 | void to_setting ( Setting &set ) const { |
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538 | BMEF::to_setting ( set ); |
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539 | UI::save( &est, set, "prior" ); |
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540 | } |
---|
541 | }; |
---|
542 | UIREGISTER( multiBM ); |
---|
543 | |
---|
544 | /*! |
---|
545 | \brief Gamma posterior density |
---|
546 | |
---|
547 | Multivariate Gamma density as product of independent univariate densities. |
---|
548 | \f[ |
---|
549 | f(x|\alpha,\beta) = \prod f(x_i|\alpha_i,\beta_i) |
---|
550 | \f] |
---|
551 | */ |
---|
552 | |
---|
553 | class egamma : public eEF { |
---|
554 | protected: |
---|
555 | //! Vector \f$\alpha\f$ |
---|
556 | vec alpha; |
---|
557 | //! Vector \f$\beta\f$ |
---|
558 | vec beta; |
---|
559 | public : |
---|
560 | //! \name Constructors |
---|
561 | //!@{ |
---|
562 | egamma () : eEF (), alpha ( 0 ), beta ( 0 ) {}; |
---|
563 | egamma ( const vec &a, const vec &b ) { |
---|
564 | set_parameters ( a, b ); |
---|
565 | }; |
---|
566 | void set_parameters ( const vec &a, const vec &b ) { |
---|
567 | alpha = a, beta = b; |
---|
568 | dim = alpha.length(); |
---|
569 | }; |
---|
570 | //!@} |
---|
571 | |
---|
572 | vec sample() const; |
---|
573 | double evallog ( const vec &val ) const; |
---|
574 | double lognc () const; |
---|
575 | //! Returns pointer to internal alpha. Potentially dengerous: use with care! |
---|
576 | vec& _alpha() { |
---|
577 | return alpha; |
---|
578 | } |
---|
579 | //! Returns pointer to internal beta. Potentially dengerous: use with care! |
---|
580 | vec& _beta() { |
---|
581 | return beta; |
---|
582 | } |
---|
583 | vec mean() const { |
---|
584 | return elem_div ( alpha, beta ); |
---|
585 | } |
---|
586 | vec variance() const { |
---|
587 | return elem_div ( alpha, elem_mult ( beta, beta ) ); |
---|
588 | } |
---|
589 | |
---|
590 | /*! Create Gamma density |
---|
591 | \f[ f(rv|rvc) = \Gamma(\alpha, \beta) \f] |
---|
592 | from structure |
---|
593 | \code |
---|
594 | class = 'egamma'; |
---|
595 | alpha = [...]; // vector of alpha |
---|
596 | beta = [...]; // vector of beta |
---|
597 | rv = RV({'name'}) // description of RV |
---|
598 | \endcode |
---|
599 | */ |
---|
600 | void from_setting ( const Setting &set ) { |
---|
601 | epdf::from_setting ( set ); // reads rv |
---|
602 | UI::get ( alpha, set, "alpha", UI::compulsory ); |
---|
603 | UI::get ( beta, set, "beta", UI::compulsory ); |
---|
604 | validate(); |
---|
605 | } |
---|
606 | void validate() { |
---|
607 | bdm_assert ( alpha.length() == beta.length(), "parameters do not match" ); |
---|
608 | dim = alpha.length(); |
---|
609 | } |
---|
610 | }; |
---|
611 | UIREGISTER ( egamma ); |
---|
612 | SHAREDPTR ( egamma ); |
---|
613 | |
---|
614 | /*! |
---|
615 | \brief Inverse-Gamma posterior density |
---|
616 | |
---|
617 | Multivariate inverse-Gamma density as product of independent univariate densities. |
---|
618 | \f[ |
---|
619 | f(x|\alpha,\beta) = \prod f(x_i|\alpha_i,\beta_i) |
---|
620 | \f] |
---|
621 | |
---|
622 | Vector \f$\beta\f$ has different meaning (in fact it is 1/beta as used in definition of iG) |
---|
623 | |
---|
624 | Inverse Gamma can be converted to Gamma using \f[ |
---|
625 | x\sim iG(a,b) => 1/x\sim G(a,1/b) |
---|
626 | \f] |
---|
627 | This relation is used in sampling. |
---|
628 | */ |
---|
629 | |
---|
630 | class eigamma : public egamma { |
---|
631 | protected: |
---|
632 | public : |
---|
633 | //! \name Constructors |
---|
634 | //! All constructors are inherited |
---|
635 | //!@{ |
---|
636 | //!@} |
---|
637 | |
---|
638 | vec sample() const { |
---|
639 | return 1.0 / egamma::sample(); |
---|
640 | }; |
---|
641 | //! Returns poiter to alpha and beta. Potentially dangerous: use with care! |
---|
642 | vec mean() const { |
---|
643 | return elem_div ( beta, alpha - 1 ); |
---|
644 | } |
---|
645 | vec variance() const { |
---|
646 | vec mea = mean(); |
---|
647 | return elem_div ( elem_mult ( mea, mea ), alpha - 2 ); |
---|
648 | } |
---|
649 | }; |
---|
650 | /* |
---|
651 | //! Weighted mixture of epdfs with external owned components. |
---|
652 | class emix : public epdf { |
---|
653 | protected: |
---|
654 | int n; |
---|
655 | vec &w; |
---|
656 | Array<epdf*> Coms; |
---|
657 | public: |
---|
658 | //! Default constructor |
---|
659 | emix ( const RV &rv, vec &w0): epdf(rv), n(w0.length()), w(w0), Coms(n) {}; |
---|
660 | void set_parameters( int &i, double wi, epdf* ep){w(i)=wi;Coms(i)=ep;} |
---|
661 | vec mean(){vec pom; for(int i=0;i<n;i++){pom+=Coms(i)->mean()*w(i);} return pom;}; |
---|
662 | }; |
---|
663 | */ |
---|
664 | |
---|
665 | //! Uniform distributed density on a rectangular support |
---|
666 | |
---|
667 | class euni: public epdf { |
---|
668 | protected: |
---|
669 | //! lower bound on support |
---|
670 | vec low; |
---|
671 | //! upper bound on support |
---|
672 | vec high; |
---|
673 | //! internal |
---|
674 | vec distance; |
---|
675 | //! normalizing coefficients |
---|
676 | double nk; |
---|
677 | //! cache of log( \c nk ) |
---|
678 | double lnk; |
---|
679 | public: |
---|
680 | //! \name Constructors |
---|
681 | //!@{ |
---|
682 | euni () : epdf () {} |
---|
683 | euni ( const vec &low0, const vec &high0 ) { |
---|
684 | set_parameters ( low0, high0 ); |
---|
685 | } |
---|
686 | void set_parameters ( const vec &low0, const vec &high0 ) { |
---|
687 | distance = high0 - low0; |
---|
688 | low = low0; |
---|
689 | high = high0; |
---|
690 | nk = prod ( 1.0 / distance ); |
---|
691 | lnk = log ( nk ); |
---|
692 | dim = low.length(); |
---|
693 | } |
---|
694 | //!@} |
---|
695 | |
---|
696 | double evallog ( const vec &val ) const { |
---|
697 | if ( any ( val < low ) && any ( val > high ) ) { |
---|
698 | return -inf; |
---|
699 | } else return lnk; |
---|
700 | } |
---|
701 | vec sample() const { |
---|
702 | vec smp ( dim ); |
---|
703 | #pragma omp critical |
---|
704 | UniRNG.sample_vector ( dim , smp ); |
---|
705 | return low + elem_mult ( distance, smp ); |
---|
706 | } |
---|
707 | //! set values of \c low and \c high |
---|
708 | vec mean() const { |
---|
709 | return ( high - low ) / 2.0; |
---|
710 | } |
---|
711 | vec variance() const { |
---|
712 | return ( pow ( high, 2 ) + pow ( low, 2 ) + elem_mult ( high, low ) ) / 3.0; |
---|
713 | } |
---|
714 | /*! Create Uniform density |
---|
715 | \f[ f(rv) = U(low,high) \f] |
---|
716 | from structure |
---|
717 | \code |
---|
718 | class = 'euni' |
---|
719 | high = [...]; // vector of upper bounds |
---|
720 | low = [...]; // vector of lower bounds |
---|
721 | rv = RV({'name'}); // description of RV |
---|
722 | \endcode |
---|
723 | */ |
---|
724 | void from_setting ( const Setting &set ) { |
---|
725 | epdf::from_setting ( set ); // reads rv and rvc |
---|
726 | |
---|
727 | UI::get ( high, set, "high", UI::compulsory ); |
---|
728 | UI::get ( low, set, "low", UI::compulsory ); |
---|
729 | set_parameters ( low, high ); |
---|
730 | validate(); |
---|
731 | } |
---|
732 | void validate() { |
---|
733 | bdm_assert ( high.length() == low.length(), "Incompatible high and low vectors" ); |
---|
734 | dim = high.length(); |
---|
735 | bdm_assert ( min ( distance ) > 0.0, "bad support" ); |
---|
736 | } |
---|
737 | }; |
---|
738 | UIREGISTER ( euni ); |
---|
739 | |
---|
740 | //! Uniform density with conditional mean value |
---|
741 | class mguni : public pdf_internal<euni> { |
---|
742 | //! function of the mean value |
---|
743 | shared_ptr<fnc> mean; |
---|
744 | //! distance from mean to both sides |
---|
745 | vec delta; |
---|
746 | public: |
---|
747 | void condition ( const vec &cond ) { |
---|
748 | vec mea = mean->eval ( cond ); |
---|
749 | iepdf.set_parameters ( mea - delta, mea + delta ); |
---|
750 | } |
---|
751 | //! load from |
---|
752 | void from_setting ( const Setting &set ) { |
---|
753 | pdf::from_setting ( set ); //reads rv and rvc |
---|
754 | UI::get ( delta, set, "delta", UI::compulsory ); |
---|
755 | mean = UI::build<fnc> ( set, "mean", UI::compulsory ); |
---|
756 | |
---|
757 | iepdf.set_parameters ( -delta, delta ); |
---|
758 | dimc = mean->dimensionc(); |
---|
759 | validate(); |
---|
760 | } |
---|
761 | }; |
---|
762 | UIREGISTER ( mguni ); |
---|
763 | /*! |
---|
764 | \brief Normal distributed linear function with linear function of mean value; |
---|
765 | |
---|
766 | Mean value \f$ \mu=A*\mbox{rvc}+\mu_0 \f$. |
---|
767 | */ |
---|
768 | template < class sq_T, template <typename> class TEpdf = enorm > |
---|
769 | class mlnorm : public pdf_internal< TEpdf<sq_T> > { |
---|
770 | protected: |
---|
771 | //! Internal epdf that arise by conditioning on \c rvc |
---|
772 | mat A; |
---|
773 | //! Constant additive term |
---|
774 | vec mu_const; |
---|
775 | // vec& _mu; //cached epdf.mu; !!!!!! WHY NOT? |
---|
776 | public: |
---|
777 | //! \name Constructors |
---|
778 | //!@{ |
---|
779 | mlnorm() : pdf_internal< TEpdf<sq_T> >() {}; |
---|
780 | mlnorm ( const mat &A, const vec &mu0, const sq_T &R ) : pdf_internal< TEpdf<sq_T> >() { |
---|
781 | set_parameters ( A, mu0, R ); |
---|
782 | } |
---|
783 | |
---|
784 | //! Set \c A and \c R |
---|
785 | void set_parameters ( const mat &A0, const vec &mu0, const sq_T &R0 ) { |
---|
786 | this->iepdf.set_parameters ( zeros ( A0.rows() ), R0 ); |
---|
787 | A = A0; |
---|
788 | mu_const = mu0; |
---|
789 | this->dimc = A0.cols(); |
---|
790 | } |
---|
791 | //!@} |
---|
792 | //! Set value of \c rvc . Result of this operation is stored in \c epdf use function \c _ep to access it. |
---|
793 | void condition ( const vec &cond ) { |
---|
794 | this->iepdf._mu() = A * cond + mu_const; |
---|
795 | //R is already assigned; |
---|
796 | } |
---|
797 | |
---|
798 | //!access function |
---|
799 | const vec& _mu_const() const { |
---|
800 | return mu_const; |
---|
801 | } |
---|
802 | //!access function |
---|
803 | const mat& _A() const { |
---|
804 | return A; |
---|
805 | } |
---|
806 | //!access function |
---|
807 | mat _R() const { |
---|
808 | return this->iepdf._R().to_mat(); |
---|
809 | } |
---|
810 | //!access function |
---|
811 | sq_T __R() const { |
---|
812 | return this->iepdf._R(); |
---|
813 | } |
---|
814 | |
---|
815 | //! Debug stream |
---|
816 | template<typename sq_M> |
---|
817 | friend std::ostream &operator<< ( std::ostream &os, mlnorm<sq_M, enorm> &ml ); |
---|
818 | |
---|
819 | /*! Create Normal density with linear function of mean value |
---|
820 | \f[ f(rv|rvc) = N(A*rvc+const, R) \f] |
---|
821 | from structure |
---|
822 | \code |
---|
823 | class = 'mlnorm<ldmat>', (OR) 'mlnorm<chmat>', (OR) 'mlnorm<fsqmat>'; |
---|
824 | A = []; // matrix or vector of appropriate dimension |
---|
825 | const = []; // vector of constant term |
---|
826 | R = []; // square matrix of appropriate dimension |
---|
827 | \endcode |
---|
828 | */ |
---|
829 | void from_setting ( const Setting &set ) { |
---|
830 | pdf::from_setting ( set ); |
---|
831 | |
---|
832 | UI::get ( A, set, "A", UI::compulsory ); |
---|
833 | UI::get ( mu_const, set, "const", UI::compulsory ); |
---|
834 | mat R0; |
---|
835 | UI::get ( R0, set, "R", UI::compulsory ); |
---|
836 | set_parameters ( A, mu_const, R0 ); |
---|
837 | validate(); |
---|
838 | }; |
---|
839 | void to_setting (Setting &set) const { |
---|
840 | pdf::to_setting(set); |
---|
841 | UI::save ( A, set, "A"); |
---|
842 | UI::save ( mu_const, set, "const"); |
---|
843 | UI::save ( _R(), set, "R"); |
---|
844 | } |
---|
845 | void validate() { |
---|
846 | pdf_internal<TEpdf<sq_T> >::validate(); |
---|
847 | bdm_assert ( A.rows() == mu_const.length(), "mlnorm: A vs. mu mismatch" ); |
---|
848 | bdm_assert ( A.rows() == _R().rows(), "mlnorm: A vs. R mismatch" ); |
---|
849 | |
---|
850 | } |
---|
851 | }; |
---|
852 | UIREGISTER2 ( mlnorm, ldmat ); |
---|
853 | SHAREDPTR2 ( mlnorm, ldmat ); |
---|
854 | UIREGISTER2 ( mlnorm, fsqmat ); |
---|
855 | SHAREDPTR2 ( mlnorm, fsqmat ); |
---|
856 | UIREGISTER2 ( mlnorm, chmat ); |
---|
857 | SHAREDPTR2 ( mlnorm, chmat ); |
---|
858 | |
---|
859 | //! pdf with general function for mean value |
---|
860 | template<class sq_T> |
---|
861 | class mgnorm : public pdf_internal< enorm< sq_T > > { |
---|
862 | private: |
---|
863 | // vec μ WHY NOT? |
---|
864 | shared_ptr<fnc> g; |
---|
865 | |
---|
866 | public: |
---|
867 | //!default constructor |
---|
868 | mgnorm() : pdf_internal<enorm<sq_T> >() { } |
---|
869 | //!set mean function |
---|
870 | inline void set_parameters ( const shared_ptr<fnc> &g0, const sq_T &R0 ); |
---|
871 | inline void condition ( const vec &cond ); |
---|
872 | |
---|
873 | |
---|
874 | /*! Create Normal density with given function of mean value |
---|
875 | \f[ f(rv|rvc) = N( g(rvc), R) \f] |
---|
876 | from structure |
---|
877 | \code |
---|
878 | class = 'mgnorm'; |
---|
879 | g.class = 'fnc'; // function for mean value evolution |
---|
880 | g._fields_of_fnc = ...; |
---|
881 | |
---|
882 | R = [1, 0; // covariance matrix |
---|
883 | 0, 1]; |
---|
884 | --OR -- |
---|
885 | dR = [1, 1]; // diagonal of cavariance matrix |
---|
886 | |
---|
887 | rv = RV({'name'}) // description of RV |
---|
888 | rvc = RV({'name'}) // description of RV in condition |
---|
889 | \endcode |
---|
890 | */ |
---|
891 | |
---|
892 | void from_setting ( const Setting &set ) { |
---|
893 | pdf::from_setting ( set ); |
---|
894 | shared_ptr<fnc> g = UI::build<fnc> ( set, "g", UI::compulsory ); |
---|
895 | |
---|
896 | mat R; |
---|
897 | vec dR; |
---|
898 | if ( UI::get ( dR, set, "dR" ) ) |
---|
899 | R = diag ( dR ); |
---|
900 | else |
---|
901 | UI::get ( R, set, "R", UI::compulsory ); |
---|
902 | |
---|
903 | set_parameters ( g, R ); |
---|
904 | validate(); |
---|
905 | } |
---|
906 | void validate() { |
---|
907 | bdm_assert ( g->dimension() == this->dimension(), "incompatible function" ); |
---|
908 | } |
---|
909 | }; |
---|
910 | |
---|
911 | UIREGISTER2 ( mgnorm, chmat ); |
---|
912 | SHAREDPTR2 ( mgnorm, chmat ); |
---|
913 | |
---|
914 | |
---|
915 | /*! (Approximate) Student t density with linear function of mean value |
---|
916 | |
---|
917 | The internal epdf of this class is of the type of a Gaussian (enorm). |
---|
918 | However, each conditioning is trying to assure the best possible approximation by taking into account the zeta function. See [] for reference. |
---|
919 | |
---|
920 | Perhaps a moment-matching technique? |
---|
921 | */ |
---|
922 | class mlstudent : public mlnorm<ldmat, enorm> { |
---|
923 | protected: |
---|
924 | //! Variable \f$ \Lambda \f$ from theory |
---|
925 | ldmat Lambda; |
---|
926 | //! Reference to variable \f$ R \f$ |
---|
927 | ldmat &_R; |
---|
928 | //! Variable \f$ R_e \f$ |
---|
929 | ldmat Re; |
---|
930 | public: |
---|
931 | mlstudent () : mlnorm<ldmat, enorm> (), |
---|
932 | Lambda (), _R ( iepdf._R() ) {} |
---|
933 | //! constructor function |
---|
934 | void set_parameters ( const mat &A0, const vec &mu0, const ldmat &R0, const ldmat& Lambda0 ) { |
---|
935 | iepdf.set_parameters ( mu0, R0 );// was Lambda, why? |
---|
936 | A = A0; |
---|
937 | mu_const = mu0; |
---|
938 | Re = R0; |
---|
939 | Lambda = Lambda0; |
---|
940 | } |
---|
941 | |
---|
942 | void condition ( const vec &cond ); |
---|
943 | |
---|
944 | void validate() { |
---|
945 | bdm_assert ( A.rows() == mu_const.length(), "mlstudent: A vs. mu mismatch" ); |
---|
946 | bdm_assert ( _R.rows() == A.rows(), "mlstudent: A vs. R mismatch" ); |
---|
947 | |
---|
948 | } |
---|
949 | }; |
---|
950 | /*! |
---|
951 | \brief Gamma random walk |
---|
952 | |
---|
953 | Mean value, \f$\mu\f$, of this density is given by \c rvc . |
---|
954 | Standard deviation of the random walk is proportional to one \f$k\f$-th the mean. |
---|
955 | This is achieved by setting \f$\alpha=k\f$ and \f$\beta=k/\mu\f$. |
---|
956 | |
---|
957 | The standard deviation of the walk is then: \f$\mu/\sqrt(k)\f$. |
---|
958 | */ |
---|
959 | class mgamma : public pdf_internal<egamma> { |
---|
960 | protected: |
---|
961 | |
---|
962 | //! Constant \f$k\f$ |
---|
963 | double k; |
---|
964 | |
---|
965 | //! cache of iepdf.beta |
---|
966 | vec &_beta; |
---|
967 | |
---|
968 | public: |
---|
969 | //! Constructor |
---|
970 | mgamma() : pdf_internal<egamma>(), k ( 0 ), |
---|
971 | _beta ( iepdf._beta() ) { |
---|
972 | } |
---|
973 | |
---|
974 | //! Set value of \c k |
---|
975 | void set_parameters ( double k, const vec &beta0 ); |
---|
976 | |
---|
977 | void condition ( const vec &val ) { |
---|
978 | _beta = k / val; |
---|
979 | }; |
---|
980 | /*! Create Gamma density with conditional mean value |
---|
981 | \f[ f(rv|rvc) = \Gamma(k, k/rvc) \f] |
---|
982 | from structure |
---|
983 | \code |
---|
984 | class = 'mgamma'; |
---|
985 | beta = [...]; // vector of initial alpha |
---|
986 | k = 1.1; // multiplicative constant k |
---|
987 | rv = RV({'name'}) // description of RV |
---|
988 | rvc = RV({'name'}) // description of RV in condition |
---|
989 | \endcode |
---|
990 | */ |
---|
991 | void from_setting ( const Setting &set ) { |
---|
992 | pdf::from_setting ( set ); // reads rv and rvc |
---|
993 | vec betatmp; // ugly but necessary |
---|
994 | UI::get ( betatmp, set, "beta", UI::compulsory ); |
---|
995 | UI::get ( k, set, "k", UI::compulsory ); |
---|
996 | set_parameters ( k, betatmp ); |
---|
997 | validate(); |
---|
998 | } |
---|
999 | void validate() { |
---|
1000 | pdf_internal<egamma>::validate(); |
---|
1001 | |
---|
1002 | dim = _beta.length(); |
---|
1003 | dimc = _beta.length(); |
---|
1004 | } |
---|
1005 | }; |
---|
1006 | UIREGISTER ( mgamma ); |
---|
1007 | SHAREDPTR ( mgamma ); |
---|
1008 | |
---|
1009 | /*! |
---|
1010 | \brief Inverse-Gamma random walk |
---|
1011 | |
---|
1012 | Mean value, \f$ \mu \f$, of this density is given by \c rvc . |
---|
1013 | Standard deviation of the random walk is proportional to one \f$ k \f$-th the mean. |
---|
1014 | This is achieved by setting \f$ \alpha=\mu/k^2+2 \f$ and \f$ \beta=\mu(\alpha-1)\f$. |
---|
1015 | |
---|
1016 | The standard deviation of the walk is then: \f$ \mu/\sqrt(k)\f$. |
---|
1017 | */ |
---|
1018 | class migamma : public pdf_internal<eigamma> { |
---|
1019 | protected: |
---|
1020 | //! Constant \f$k\f$ |
---|
1021 | double k; |
---|
1022 | |
---|
1023 | //! cache of iepdf.alpha |
---|
1024 | vec &_alpha; |
---|
1025 | |
---|
1026 | //! cache of iepdf.beta |
---|
1027 | vec &_beta; |
---|
1028 | |
---|
1029 | public: |
---|
1030 | //! \name Constructors |
---|
1031 | //!@{ |
---|
1032 | migamma() : pdf_internal<eigamma>(), |
---|
1033 | k ( 0 ), |
---|
1034 | _alpha ( iepdf._alpha() ), |
---|
1035 | _beta ( iepdf._beta() ) { |
---|
1036 | } |
---|
1037 | |
---|
1038 | migamma ( const migamma &m ) : pdf_internal<eigamma>(), |
---|
1039 | k ( 0 ), |
---|
1040 | _alpha ( iepdf._alpha() ), |
---|
1041 | _beta ( iepdf._beta() ) { |
---|
1042 | } |
---|
1043 | //!@} |
---|
1044 | |
---|
1045 | //! Set value of \c k |
---|
1046 | void set_parameters ( int len, double k0 ) { |
---|
1047 | k = k0; |
---|
1048 | iepdf.set_parameters ( ( 1.0 / ( k*k ) + 2.0 ) *ones ( len ) /*alpha*/, ones ( len ) /*beta*/ ); |
---|
1049 | dimc = dimension(); |
---|
1050 | }; |
---|
1051 | void condition ( const vec &val ) { |
---|
1052 | _beta = elem_mult ( val, ( _alpha - 1.0 ) ); |
---|
1053 | }; |
---|
1054 | }; |
---|
1055 | |
---|
1056 | |
---|
1057 | /*! |
---|
1058 | \brief Gamma random walk around a fixed point |
---|
1059 | |
---|
1060 | Mean value, \f$\mu\f$, of this density is given by a geometric combination of \c rvc and given fixed point, \f$p\f$. \f$l\f$ is the coefficient of the geometric combimation |
---|
1061 | \f[ \mu = \mu_{t-1} ^{l} p^{1-l}\f] |
---|
1062 | |
---|
1063 | Standard deviation of the random walk is proportional to one \f$k\f$-th the mean. |
---|
1064 | This is achieved by setting \f$\alpha=k\f$ and \f$\beta=k/\mu\f$. |
---|
1065 | |
---|
1066 | The standard deviation of the walk is then: \f$\mu/\sqrt(k)\f$. |
---|
1067 | */ |
---|
1068 | class mgamma_fix : public mgamma { |
---|
1069 | protected: |
---|
1070 | //! parameter l |
---|
1071 | double l; |
---|
1072 | //! reference vector |
---|
1073 | vec refl; |
---|
1074 | public: |
---|
1075 | //! Constructor |
---|
1076 | mgamma_fix () : mgamma (), refl () {}; |
---|
1077 | //! Set value of \c k |
---|
1078 | void set_parameters ( double k0 , vec ref0, double l0 ) { |
---|
1079 | mgamma::set_parameters ( k0, ref0 ); |
---|
1080 | refl = pow ( ref0, 1.0 - l0 ); |
---|
1081 | l = l0; |
---|
1082 | dimc = dimension(); |
---|
1083 | }; |
---|
1084 | |
---|
1085 | void condition ( const vec &val ) { |
---|
1086 | vec mean = elem_mult ( refl, pow ( val, l ) ); |
---|
1087 | _beta = k / mean; |
---|
1088 | }; |
---|
1089 | }; |
---|
1090 | |
---|
1091 | |
---|
1092 | /*! |
---|
1093 | \brief Inverse-Gamma random walk around a fixed point |
---|
1094 | |
---|
1095 | Mean value, \f$\mu\f$, of this density is given by a geometric combination of \c rvc and given fixed point, \f$p\f$. \f$l\f$ is the coefficient of the geometric combimation |
---|
1096 | \f[ \mu = \mu_{t-1} ^{l} p^{1-l}\f] |
---|
1097 | |
---|
1098 | ==== Check == vv = |
---|
1099 | Standard deviation of the random walk is proportional to one \f$k\f$-th the mean. |
---|
1100 | This is achieved by setting \f$\alpha=k\f$ and \f$\beta=k/\mu\f$. |
---|
1101 | |
---|
1102 | The standard deviation of the walk is then: \f$\mu/\sqrt(k)\f$. |
---|
1103 | */ |
---|
1104 | class migamma_ref : public migamma { |
---|
1105 | protected: |
---|
1106 | //! parameter l |
---|
1107 | double l; |
---|
1108 | //! reference vector |
---|
1109 | vec refl; |
---|
1110 | public: |
---|
1111 | //! Constructor |
---|
1112 | migamma_ref () : migamma (), refl () {}; |
---|
1113 | |
---|
1114 | //! Set value of \c k |
---|
1115 | void set_parameters ( double k0 , vec ref0, double l0 ) { |
---|
1116 | migamma::set_parameters ( ref0.length(), k0 ); |
---|
1117 | refl = pow ( ref0, 1.0 - l0 ); |
---|
1118 | l = l0; |
---|
1119 | dimc = dimension(); |
---|
1120 | }; |
---|
1121 | |
---|
1122 | void condition ( const vec &val ) { |
---|
1123 | vec mean = elem_mult ( refl, pow ( val, l ) ); |
---|
1124 | migamma::condition ( mean ); |
---|
1125 | }; |
---|
1126 | |
---|
1127 | |
---|
1128 | /*! Create inverse-Gamma density with conditional mean value |
---|
1129 | \f[ f(rv|rvc) = i\Gamma(k, k/(rvc^l \circ ref^{(1-l)}) \f] |
---|
1130 | from structure |
---|
1131 | \code |
---|
1132 | class = 'migamma_ref'; |
---|
1133 | ref = [1e-5; 1e-5; 1e-2 1e-3]; // reference vector |
---|
1134 | l = 0.999; // constant l |
---|
1135 | k = 0.1; // constant k |
---|
1136 | rv = RV({'name'}) // description of RV |
---|
1137 | rvc = RV({'name'}) // description of RV in condition |
---|
1138 | \endcode |
---|
1139 | */ |
---|
1140 | void from_setting ( const Setting &set ); |
---|
1141 | |
---|
1142 | // TODO dodelat void to_setting( Setting &set ) const; |
---|
1143 | }; |
---|
1144 | |
---|
1145 | |
---|
1146 | UIREGISTER ( migamma_ref ); |
---|
1147 | SHAREDPTR ( migamma_ref ); |
---|
1148 | |
---|
1149 | /*! Log-Normal probability density |
---|
1150 | only allow diagonal covariances! |
---|
1151 | |
---|
1152 | Density of the form \f$ \log(x)\sim \mathcal{N}(\mu,\sigma^2) \f$ , i.e. |
---|
1153 | \f[ |
---|
1154 | x \sim \frac{1}{x\sigma\sqrt{2\pi}}\exp{-\frac{1}{2\sigma^2}(\log(x)-\mu)} |
---|
1155 | \f] |
---|
1156 | |
---|
1157 | Function from_setting loads mu and R in the same way as it does for enorm<>! |
---|
1158 | */ |
---|
1159 | class elognorm: public enorm<ldmat> { |
---|
1160 | public: |
---|
1161 | vec sample() const { |
---|
1162 | return exp ( enorm<ldmat>::sample() ); |
---|
1163 | }; |
---|
1164 | vec mean() const { |
---|
1165 | vec var = enorm<ldmat>::variance(); |
---|
1166 | return exp ( mu - 0.5*var ); |
---|
1167 | }; |
---|
1168 | |
---|
1169 | }; |
---|
1170 | |
---|
1171 | /*! |
---|
1172 | \brief Log-Normal random walk |
---|
1173 | |
---|
1174 | Mean value, \f$\mu\f$, is... |
---|
1175 | |
---|
1176 | */ |
---|
1177 | class mlognorm : public pdf_internal<elognorm> { |
---|
1178 | protected: |
---|
1179 | //! parameter 1/2*sigma^2 |
---|
1180 | double sig2; |
---|
1181 | |
---|
1182 | //! access |
---|
1183 | vec μ |
---|
1184 | public: |
---|
1185 | //! Constructor |
---|
1186 | mlognorm() : pdf_internal<elognorm>(), |
---|
1187 | sig2 ( 0 ), |
---|
1188 | mu ( iepdf._mu() ) { |
---|
1189 | } |
---|
1190 | |
---|
1191 | //! Set value of \c k |
---|
1192 | void set_parameters ( int size, double k ) { |
---|
1193 | sig2 = 0.5 * log ( k * k + 1 ); |
---|
1194 | iepdf.set_parameters ( zeros ( size ), 2*sig2*eye ( size ) ); |
---|
1195 | |
---|
1196 | dimc = size; |
---|
1197 | }; |
---|
1198 | |
---|
1199 | void condition ( const vec &val ) { |
---|
1200 | mu = log ( val ) - sig2;//elem_mult ( refl,pow ( val,l ) ); |
---|
1201 | }; |
---|
1202 | |
---|
1203 | /*! Create logNormal random Walk |
---|
1204 | \f[ f(rv|rvc) = log\mathcal{N}( \log(rvc)-0.5\log(k^2+1), k I) \f] |
---|
1205 | from structure |
---|
1206 | \code |
---|
1207 | class = 'mlognorm'; |
---|
1208 | k = 0.1; // "variance" k |
---|
1209 | mu0 = 0.1; // Initial value of mean |
---|
1210 | rv = RV({'name'}) // description of RV |
---|
1211 | rvc = RV({'name'}) // description of RV in condition |
---|
1212 | \endcode |
---|
1213 | */ |
---|
1214 | void from_setting ( const Setting &set ); |
---|
1215 | |
---|
1216 | // TODO dodelat void to_setting( Setting &set ) const; |
---|
1217 | |
---|
1218 | }; |
---|
1219 | |
---|
1220 | UIREGISTER ( mlognorm ); |
---|
1221 | SHAREDPTR ( mlognorm ); |
---|
1222 | |
---|
1223 | /*! inverse Wishart density defined on Choleski decomposition |
---|
1224 | |
---|
1225 | */ |
---|
1226 | class eWishartCh : public epdf { |
---|
1227 | protected: |
---|
1228 | //! Upper-Triagle of Choleski decomposition of \f$ \Psi \f$ |
---|
1229 | chmat Y; |
---|
1230 | //! dimension of matrix \f$ \Psi \f$ |
---|
1231 | int p; |
---|
1232 | //! degrees of freedom \f$ \nu \f$ |
---|
1233 | double delta; |
---|
1234 | public: |
---|
1235 | //! Set internal structures |
---|
1236 | void set_parameters ( const mat &Y0, const double delta0 ) { |
---|
1237 | Y = chmat ( Y0 ); |
---|
1238 | delta = delta0; |
---|
1239 | p = Y.rows(); |
---|
1240 | dim = p * p; |
---|
1241 | } |
---|
1242 | //! Set internal structures |
---|
1243 | void set_parameters ( const chmat &Y0, const double delta0 ) { |
---|
1244 | Y = Y0; |
---|
1245 | delta = delta0; |
---|
1246 | p = Y.rows(); |
---|
1247 | dim = p * p; |
---|
1248 | } |
---|
1249 | //! Sample matrix argument |
---|
1250 | mat sample_mat() const { |
---|
1251 | mat X = zeros ( p, p ); |
---|
1252 | |
---|
1253 | //sample diagonal |
---|
1254 | for ( int i = 0; i < p; i++ ) { |
---|
1255 | GamRNG.setup ( 0.5* ( delta - i ) , 0.5 ); // no +1 !! index if from 0 |
---|
1256 | #pragma omp critical |
---|
1257 | X ( i, i ) = sqrt ( GamRNG() ); |
---|
1258 | } |
---|
1259 | //do the rest |
---|
1260 | for ( int i = 0; i < p; i++ ) { |
---|
1261 | for ( int j = i + 1; j < p; j++ ) { |
---|
1262 | #pragma omp critical |
---|
1263 | X ( i, j ) = NorRNG.sample(); |
---|
1264 | } |
---|
1265 | } |
---|
1266 | return X*Y._Ch();// return upper triangular part of the decomposition |
---|
1267 | } |
---|
1268 | |
---|
1269 | vec sample () const { |
---|
1270 | return vec ( sample_mat()._data(), p*p ); |
---|
1271 | } |
---|
1272 | |
---|
1273 | virtual vec mean() const NOT_IMPLEMENTED(0); |
---|
1274 | |
---|
1275 | //! return expected variance (not covariance!) |
---|
1276 | virtual vec variance() const NOT_IMPLEMENTED(0); |
---|
1277 | |
---|
1278 | virtual double evallog ( const vec &val ) const NOT_IMPLEMENTED(0); |
---|
1279 | |
---|
1280 | //! fast access function y0 will be copied into Y.Ch. |
---|
1281 | void setY ( const mat &Ch0 ) { |
---|
1282 | copy_vector ( dim, Ch0._data(), Y._Ch()._data() ); |
---|
1283 | } |
---|
1284 | |
---|
1285 | //! fast access function y0 will be copied into Y.Ch. |
---|
1286 | void _setY ( const vec &ch0 ) { |
---|
1287 | copy_vector ( dim, ch0._data(), Y._Ch()._data() ); |
---|
1288 | } |
---|
1289 | |
---|
1290 | //! access function |
---|
1291 | const chmat& getY() const { |
---|
1292 | return Y; |
---|
1293 | } |
---|
1294 | }; |
---|
1295 | |
---|
1296 | //! Inverse Wishart on Choleski decomposition |
---|
1297 | /*! Being computed by conversion from `standard' Wishart |
---|
1298 | */ |
---|
1299 | class eiWishartCh: public epdf { |
---|
1300 | protected: |
---|
1301 | //! Internal instance of Wishart density |
---|
1302 | eWishartCh W; |
---|
1303 | //! size of Ch |
---|
1304 | int p; |
---|
1305 | //! parameter delta |
---|
1306 | double delta; |
---|
1307 | public: |
---|
1308 | //! constructor function |
---|
1309 | void set_parameters ( const mat &Y0, const double delta0 ) { |
---|
1310 | delta = delta0; |
---|
1311 | W.set_parameters ( inv ( Y0 ), delta0 ); |
---|
1312 | p = Y0.rows(); |
---|
1313 | } |
---|
1314 | |
---|
1315 | virtual void validate (){ |
---|
1316 | dim = W.dimension(); |
---|
1317 | } |
---|
1318 | |
---|
1319 | |
---|
1320 | vec sample() const { |
---|
1321 | mat iCh; |
---|
1322 | iCh = inv ( W.sample_mat() ); |
---|
1323 | return vec ( iCh._data(), dim ); |
---|
1324 | } |
---|
1325 | //! access function |
---|
1326 | void _setY ( const vec &y0 ) { |
---|
1327 | mat Ch ( p, p ); |
---|
1328 | mat iCh ( p, p ); |
---|
1329 | copy_vector ( dim, y0._data(), Ch._data() ); |
---|
1330 | |
---|
1331 | iCh = inv ( Ch ); |
---|
1332 | W.setY ( iCh ); |
---|
1333 | } |
---|
1334 | |
---|
1335 | virtual double evallog ( const vec &val ) const { |
---|
1336 | chmat X ( p ); |
---|
1337 | const chmat& Y = W.getY(); |
---|
1338 | |
---|
1339 | copy_vector ( p*p, val._data(), X._Ch()._data() ); |
---|
1340 | chmat iX ( p ); |
---|
1341 | X.inv ( iX ); |
---|
1342 | // compute |
---|
1343 | // \frac{ |\Psi|^{m/2}|X|^{-(m+p+1)/2}e^{-tr(\Psi X^{-1})/2} }{ 2^{mp/2}\Gamma_p(m/2)}, |
---|
1344 | mat M = Y.to_mat() * iX.to_mat(); |
---|
1345 | |
---|
1346 | double log1 = 0.5 * p * ( 2 * Y.logdet() ) - 0.5 * ( delta + p + 1 ) * ( 2 * X.logdet() ) - 0.5 * trace ( M ); |
---|
1347 | //Fixme! Multivariate gamma omitted!! it is ok for sampling, but not otherwise!! |
---|
1348 | |
---|
1349 | /* if (0) { |
---|
1350 | mat XX=X.to_mat(); |
---|
1351 | mat YY=Y.to_mat(); |
---|
1352 | |
---|
1353 | double log2 = 0.5*p*log(det(YY))-0.5*(delta+p+1)*log(det(XX))-0.5*trace(YY*inv(XX)); |
---|
1354 | cout << log1 << "," << log2 << endl; |
---|
1355 | }*/ |
---|
1356 | return log1; |
---|
1357 | }; |
---|
1358 | |
---|
1359 | virtual vec mean() const NOT_IMPLEMENTED(0); |
---|
1360 | |
---|
1361 | //! return expected variance (not covariance!) |
---|
1362 | virtual vec variance() const NOT_IMPLEMENTED(0); |
---|
1363 | }; |
---|
1364 | |
---|
1365 | //! Random Walk on inverse Wishart |
---|
1366 | class rwiWishartCh : public pdf_internal<eiWishartCh> { |
---|
1367 | protected: |
---|
1368 | //!square root of \f$ \nu-p-1 \f$ - needed for computation of \f$ \Psi \f$ from conditions |
---|
1369 | double sqd; |
---|
1370 | //!reference point for diagonal |
---|
1371 | vec refl; |
---|
1372 | //! power of the reference |
---|
1373 | double l; |
---|
1374 | //! dimension |
---|
1375 | int p; |
---|
1376 | |
---|
1377 | public: |
---|
1378 | rwiWishartCh() : sqd ( 0 ), l ( 0 ), p ( 0 ) {} |
---|
1379 | //! constructor function |
---|
1380 | void set_parameters ( int p0, double k, vec ref0, double l0 ) { |
---|
1381 | p = p0; |
---|
1382 | double delta = 2 / ( k * k ) + p + 3; |
---|
1383 | sqd = sqrt ( delta - p - 1 ); |
---|
1384 | l = l0; |
---|
1385 | refl = pow ( ref0, 1 - l ); |
---|
1386 | |
---|
1387 | iepdf.set_parameters ( eye ( p ), delta ); |
---|
1388 | dimc = iepdf.dimension(); |
---|
1389 | } |
---|
1390 | void condition ( const vec &c ) { |
---|
1391 | vec z = c; |
---|
1392 | int ri = 0; |
---|
1393 | for ( int i = 0; i < p*p; i += ( p + 1 ) ) {//trace diagonal element |
---|
1394 | z ( i ) = pow ( z ( i ), l ) * refl ( ri ); |
---|
1395 | ri++; |
---|
1396 | } |
---|
1397 | |
---|
1398 | iepdf._setY ( sqd*z ); |
---|
1399 | } |
---|
1400 | }; |
---|
1401 | |
---|
1402 | //! Switch between various resampling methods. |
---|
1403 | enum RESAMPLING_METHOD { MULTINOMIAL = 0, STRATIFIED = 1, SYSTEMATIC = 3 }; |
---|
1404 | /*! |
---|
1405 | \brief Weighted empirical density |
---|
1406 | |
---|
1407 | Used e.g. in particle filters. |
---|
1408 | */ |
---|
1409 | class eEmp: public epdf { |
---|
1410 | protected : |
---|
1411 | //! Number of particles |
---|
1412 | int n; |
---|
1413 | //! Sample weights \f$w\f$ |
---|
1414 | vec w; |
---|
1415 | //! Samples \f$x^{(i)}, i=1..n\f$ |
---|
1416 | Array<vec> samples; |
---|
1417 | public: |
---|
1418 | //! \name Constructors |
---|
1419 | //!@{ |
---|
1420 | eEmp () : epdf (), w (), samples () {}; |
---|
1421 | //! copy constructor |
---|
1422 | eEmp ( const eEmp &e ) : epdf ( e ), w ( e.w ), samples ( e.samples ) {}; |
---|
1423 | //!@} |
---|
1424 | |
---|
1425 | //! Set samples and weights |
---|
1426 | void set_statistics ( const vec &w0, const epdf &pdf0 ); |
---|
1427 | //! Set samples and weights |
---|
1428 | void set_statistics ( const epdf &pdf0 , int n ) { |
---|
1429 | set_statistics ( ones ( n ) / n, pdf0 ); |
---|
1430 | }; |
---|
1431 | //! Set sample |
---|
1432 | void set_samples ( const epdf* pdf0 ); |
---|
1433 | //! Set sample |
---|
1434 | void set_parameters ( int n0, bool copy = true ) { |
---|
1435 | n = n0; |
---|
1436 | w.set_size ( n0, copy ); |
---|
1437 | samples.set_size ( n0, copy ); |
---|
1438 | }; |
---|
1439 | //! Set samples |
---|
1440 | void set_parameters ( const Array<vec> &Av ) { |
---|
1441 | n = Av.size(); |
---|
1442 | w = 1 / n * ones ( n ); |
---|
1443 | samples = Av; |
---|
1444 | }; |
---|
1445 | virtual void validate (){ |
---|
1446 | bdm_assert (samples.length()==w.length(),"samples and weigths are of different lengths"); |
---|
1447 | n = w.length(); |
---|
1448 | if (n>0) |
---|
1449 | pdf::dim = samples ( 0 ).length(); |
---|
1450 | } |
---|
1451 | //! Potentially dangerous, use with care. |
---|
1452 | vec& _w() { |
---|
1453 | return w; |
---|
1454 | }; |
---|
1455 | //! Potentially dangerous, use with care. |
---|
1456 | const vec& _w() const { |
---|
1457 | return w; |
---|
1458 | }; |
---|
1459 | //! access function |
---|
1460 | Array<vec>& _samples() { |
---|
1461 | return samples; |
---|
1462 | }; |
---|
1463 | //! access function |
---|
1464 | const vec& _sample ( int i ) const { |
---|
1465 | return samples ( i ); |
---|
1466 | }; |
---|
1467 | //! access function |
---|
1468 | const Array<vec>& _samples() const { |
---|
1469 | return samples; |
---|
1470 | }; |
---|
1471 | //! Function performs resampling, i.e. removal of low-weight samples and duplication of high-weight samples such that the new samples represent the same density. |
---|
1472 | //! The vector with indeces of new samples is returned in variable \c index. |
---|
1473 | void resample ( ivec &index, RESAMPLING_METHOD method = SYSTEMATIC ); |
---|
1474 | |
---|
1475 | //! Resampling without returning index of new particles. |
---|
1476 | void resample ( RESAMPLING_METHOD method = SYSTEMATIC ) { |
---|
1477 | ivec ind; |
---|
1478 | resample ( ind, method ); |
---|
1479 | }; |
---|
1480 | |
---|
1481 | //! inherited operation : NOT implemented |
---|
1482 | vec sample() const NOT_IMPLEMENTED(0); |
---|
1483 | |
---|
1484 | //! inherited operation : NOT implemented |
---|
1485 | double evallog ( const vec &val ) const NOT_IMPLEMENTED(0); |
---|
1486 | |
---|
1487 | vec mean() const { |
---|
1488 | vec pom = zeros ( dim ); |
---|
1489 | for ( int i = 0; i < n; i++ ) { |
---|
1490 | pom += samples ( i ) * w ( i ); |
---|
1491 | } |
---|
1492 | return pom; |
---|
1493 | } |
---|
1494 | vec variance() const { |
---|
1495 | vec pom = zeros ( dim ); |
---|
1496 | for ( int i = 0; i < n; i++ ) { |
---|
1497 | pom += pow ( samples ( i ), 2 ) * w ( i ); |
---|
1498 | } |
---|
1499 | return pom - pow ( mean(), 2 ); |
---|
1500 | } |
---|
1501 | //! For this class, qbounds are minimum and maximum value of the population! |
---|
1502 | void qbounds ( vec &lb, vec &ub, double perc = 0.95 ) const; |
---|
1503 | |
---|
1504 | void to_setting ( Setting &set ) const { |
---|
1505 | epdf::to_setting( set ); |
---|
1506 | UI::save ( samples, set, "samples" ); |
---|
1507 | UI::save ( w, set, "w" ); |
---|
1508 | } |
---|
1509 | |
---|
1510 | void from_setting ( const Setting &set ) { |
---|
1511 | epdf::from_setting( set ); |
---|
1512 | |
---|
1513 | UI::get( samples, set, "samples", UI::compulsory ); |
---|
1514 | UI::get ( w, set, "w", UI::compulsory ); |
---|
1515 | validate(); |
---|
1516 | } |
---|
1517 | |
---|
1518 | }; |
---|
1519 | UIREGISTER(eEmp); |
---|
1520 | |
---|
1521 | |
---|
1522 | //////////////////////// |
---|
1523 | |
---|
1524 | template<class sq_T> |
---|
1525 | void enorm<sq_T>::set_parameters ( const vec &mu0, const sq_T &R0 ) { |
---|
1526 | //Fixme test dimensions of mu0 and R0; |
---|
1527 | mu = mu0; |
---|
1528 | R = R0; |
---|
1529 | validate(); |
---|
1530 | }; |
---|
1531 | |
---|
1532 | template<class sq_T> |
---|
1533 | void enorm<sq_T>::from_setting ( const Setting &set ) { |
---|
1534 | epdf::from_setting ( set ); //reads rv |
---|
1535 | |
---|
1536 | UI::get ( mu, set, "mu", UI::compulsory ); |
---|
1537 | mat Rtmp;// necessary for conversion |
---|
1538 | UI::get ( Rtmp, set, "R", UI::compulsory ); |
---|
1539 | R = Rtmp; // conversion |
---|
1540 | validate(); |
---|
1541 | } |
---|
1542 | |
---|
1543 | template<class sq_T> |
---|
1544 | void enorm<sq_T>::to_setting ( Setting &set ) const { |
---|
1545 | epdf::to_setting ( set ); //reads rv |
---|
1546 | UI::save ( mu, set, "mu"); |
---|
1547 | UI::save ( R.to_mat(), set, "R"); |
---|
1548 | } |
---|
1549 | |
---|
1550 | template<class sq_T> |
---|
1551 | void enorm<sq_T>::dupdate ( mat &v, double nu ) { |
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1552 | // |
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1553 | }; |
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1554 | |
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1555 | // template<class sq_T> |
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1556 | // void enorm<sq_T>::tupdate ( double phi, mat &vbar, double nubar ) { |
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1557 | // // |
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1558 | // }; |
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1559 | |
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1560 | template<class sq_T> |
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1561 | vec enorm<sq_T>::sample() const { |
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1562 | vec x ( dim ); |
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1563 | #pragma omp critical |
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1564 | NorRNG.sample_vector ( dim, x ); |
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1565 | vec smp = R.sqrt_mult ( x ); |
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1566 | |
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1567 | smp += mu; |
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1568 | return smp; |
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1569 | }; |
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1570 | |
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1571 | // template<class sq_T> |
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1572 | // double enorm<sq_T>::eval ( const vec &val ) const { |
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1573 | // double pdfl,e; |
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1574 | // pdfl = evallog ( val ); |
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1575 | // e = exp ( pdfl ); |
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1576 | // return e; |
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1577 | // }; |
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1578 | |
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1579 | template<class sq_T> |
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1580 | double enorm<sq_T>::evallog_nn ( const vec &val ) const { |
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1581 | // 1.83787706640935 = log(2pi) |
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1582 | double tmp = -0.5 * ( R.invqform ( mu - val ) );// - lognc(); |
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1583 | return tmp; |
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1584 | }; |
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1585 | |
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1586 | template<class sq_T> |
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1587 | inline double enorm<sq_T>::lognc () const { |
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1588 | // 1.83787706640935 = log(2pi) |
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1589 | double tmp = 0.5 * ( R.cols() * 1.83787706640935 + R.logdet() ); |
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1590 | return tmp; |
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1591 | }; |
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1592 | |
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1593 | |
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1594 | // template<class sq_T> |
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1595 | // vec mlnorm<sq_T>::samplecond (const vec &cond, double &lik ) { |
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1596 | // this->condition ( cond ); |
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1597 | // vec smp = epdf.sample(); |
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1598 | // lik = epdf.eval ( smp ); |
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1599 | // return smp; |
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1600 | // } |
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1601 | |
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1602 | // template<class sq_T> |
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1603 | // mat mlnorm<sq_T>::samplecond (const vec &cond, vec &lik, int n ) { |
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1604 | // int i; |
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1605 | // int dim = rv.count(); |
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1606 | // mat Smp ( dim,n ); |
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1607 | // vec smp ( dim ); |
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1608 | // this->condition ( cond ); |
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1609 | // |
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1610 | // for ( i=0; i<n; i++ ) { |
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1611 | // smp = epdf.sample(); |
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1612 | // lik ( i ) = epdf.eval ( smp ); |
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1613 | // Smp.set_col ( i ,smp ); |
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1614 | // } |
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1615 | // |
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1616 | // return Smp; |
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1617 | // } |
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1618 | |
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1619 | |
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1620 | template<class sq_T> |
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1621 | shared_ptr<epdf> enorm<sq_T>::marginal ( const RV &rvn ) const { |
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1622 | enorm<sq_T> *tmp = new enorm<sq_T> (); |
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1623 | shared_ptr<epdf> narrow ( tmp ); |
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1624 | marginal ( rvn, *tmp ); |
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1625 | return narrow; |
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1626 | } |
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1627 | |
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1628 | template<class sq_T> |
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1629 | void enorm<sq_T>::marginal ( const RV &rvn, enorm<sq_T> &target ) const { |
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1630 | bdm_assert ( isnamed(), "rv description is not assigned" ); |
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1631 | ivec irvn = rvn.dataind ( rv ); |
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1632 | |
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1633 | sq_T Rn ( R, irvn ); // select rows and columns of R |
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1634 | |
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1635 | target.set_rv ( rvn ); |
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1636 | target.set_parameters ( mu ( irvn ), Rn ); |
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1637 | } |
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1638 | |
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1639 | template<class sq_T> |
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1640 | shared_ptr<pdf> enorm<sq_T>::condition ( const RV &rvn ) const { |
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1641 | mlnorm<sq_T> *tmp = new mlnorm<sq_T> (); |
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1642 | shared_ptr<pdf> narrow ( tmp ); |
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1643 | condition ( rvn, *tmp ); |
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1644 | return narrow; |
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1645 | } |
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1646 | |
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1647 | template<class sq_T> |
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1648 | void enorm<sq_T>::condition ( const RV &rvn, pdf &target ) const { |
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1649 | typedef mlnorm<sq_T> TMlnorm; |
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1650 | |
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1651 | bdm_assert ( isnamed(), "rvs are not assigned" ); |
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1652 | TMlnorm &uptarget = dynamic_cast<TMlnorm &> ( target ); |
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1653 | |
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1654 | RV rvc = rv.subt ( rvn ); |
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1655 | bdm_assert ( ( rvc._dsize() + rvn._dsize() == rv._dsize() ), "wrong rvn" ); |
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1656 | //Permutation vector of the new R |
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1657 | ivec irvn = rvn.dataind ( rv ); |
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1658 | ivec irvc = rvc.dataind ( rv ); |
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1659 | ivec perm = concat ( irvn , irvc ); |
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1660 | sq_T Rn ( R, perm ); |
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1661 | |
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1662 | //fixme - could this be done in general for all sq_T? |
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1663 | mat S = Rn.to_mat(); |
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1664 | //fixme |
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1665 | int n = rvn._dsize() - 1; |
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1666 | int end = R.rows() - 1; |
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1667 | mat S11 = S.get ( 0, n, 0, n ); |
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1668 | mat S12 = S.get ( 0, n , rvn._dsize(), end ); |
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1669 | mat S22 = S.get ( rvn._dsize(), end, rvn._dsize(), end ); |
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1670 | |
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1671 | vec mu1 = mu ( irvn ); |
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1672 | vec mu2 = mu ( irvc ); |
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1673 | mat A = S12 * inv ( S22 ); |
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1674 | sq_T R_n ( S11 - A *S12.T() ); |
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1675 | |
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1676 | uptarget.set_rv ( rvn ); |
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1677 | uptarget.set_rvc ( rvc ); |
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1678 | uptarget.set_parameters ( A, mu1 - A*mu2, R_n ); |
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1679 | } |
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1680 | |
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1681 | //// |
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1682 | /////// |
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1683 | template<class sq_T> |
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1684 | void mgnorm<sq_T >::set_parameters ( const shared_ptr<fnc> &g0, const sq_T &R0 ) { |
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1685 | g = g0; |
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1686 | this->iepdf.set_parameters ( zeros ( g->dimension() ), R0 ); |
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1687 | } |
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1688 | |
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1689 | template<class sq_T> |
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1690 | void mgnorm<sq_T >::condition ( const vec &cond ) { |
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1691 | this->iepdf._mu() = g->eval ( cond ); |
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1692 | }; |
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1693 | |
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1694 | //! \todo unify this stuff with to_string() |
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1695 | template<class sq_T> |
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1696 | std::ostream &operator<< ( std::ostream &os, mlnorm<sq_T> &ml ) { |
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1697 | os << "A:" << ml.A << endl; |
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1698 | os << "mu:" << ml.mu_const << endl; |
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1699 | os << "R:" << ml._R() << endl; |
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1700 | return os; |
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1701 | }; |
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1702 | |
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1703 | } |
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1704 | #endif //EF_H |
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