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 | #include <itpp/itbase.h> |
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17 | #include "../math/libDC.h" |
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18 | #include "libBM.h" |
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19 | #include "../itpp_ext.h" |
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20 | //#include <std> |
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21 | |
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22 | using namespace itpp; |
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23 | |
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24 | |
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25 | //! Global Uniform_RNG |
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26 | extern Uniform_RNG UniRNG; |
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27 | //! Global Normal_RNG |
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28 | extern Normal_RNG NorRNG; |
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29 | //! Global Gamma_RNG |
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30 | extern Gamma_RNG GamRNG; |
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31 | |
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32 | /*! |
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33 | * \brief General conjugate exponential family posterior density. |
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34 | |
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35 | * More?... |
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36 | */ |
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37 | |
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38 | class eEF : public epdf { |
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39 | |
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40 | public: |
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41 | // eEF() :epdf() {}; |
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42 | //! default constructor |
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43 | eEF ( const RV &rv ) :epdf ( rv ) {}; |
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44 | //!TODO decide if it is really needed |
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45 | virtual void tupdate ( double phi, mat &vbar, double nubar ) {}; |
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46 | //!TODO decide if it is really needed |
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47 | virtual void dupdate ( mat &v,double nu=1.0 ) {}; |
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48 | }; |
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49 | |
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50 | /*! |
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51 | * \brief Exponential family model. |
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52 | |
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53 | * More?... |
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54 | */ |
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55 | |
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56 | class mEF : public mpdf { |
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57 | |
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58 | public: |
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59 | //! Default constructor |
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60 | mEF ( const RV &rv0, const RV &rvc0 ) :mpdf ( rv0,rvc0 ) {}; |
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61 | }; |
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62 | |
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63 | /*! |
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64 | * \brief Gaussian density with positive definite (decomposed) covariance matrix. |
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65 | |
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66 | * More?... |
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67 | */ |
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68 | template<class sq_T> |
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69 | |
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70 | class enorm : public eEF { |
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71 | protected: |
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72 | //! mean value |
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73 | vec mu; |
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74 | //! Covariance matrix in decomposed form |
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75 | sq_T R; |
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76 | //! Cache: _iR = inv(R); |
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77 | sq_T _iR; |
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78 | //! indicator if \c _iR is chached |
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79 | bool cached; |
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80 | //! dimension (redundant from rv.count() for easier coding ) |
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81 | int dim; |
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82 | public: |
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83 | // enorm() :eEF() {}; |
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84 | //!Default constructor |
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85 | enorm ( RV &rv ); |
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86 | //! Set mean value \c mu and covariance \c R |
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87 | void set_parameters ( const vec &mu,const sq_T &R ); |
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88 | //! tupdate in exponential form (not really handy) |
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89 | void tupdate ( double phi, mat &vbar, double nubar ); |
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90 | //! dupdate in exponential form (not really handy) |
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91 | void dupdate ( mat &v,double nu=1.0 ); |
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92 | |
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93 | vec sample() const; |
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94 | //! TODO is it used? |
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95 | mat sample ( int N ) const; |
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96 | double eval ( const vec &val ) const ; |
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97 | double evalpdflog ( const vec &val ) const; |
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98 | vec mean() const {return mu;} |
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99 | |
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100 | //Access methods |
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101 | //! returns a pointer to the internal mean value. Use with Care! |
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102 | vec* _mu() {return μ} |
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103 | |
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104 | //! access function |
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105 | void set_mu(const vec mu0) { mu=mu0;} |
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106 | |
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107 | //! returns pointers to the internal variance and its inverse. Use with Care! |
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108 | void _R ( sq_T* &pR, sq_T* &piR ) { |
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109 | pR=&R; |
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110 | piR=&_iR; |
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111 | } |
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112 | |
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113 | //! set cache as inconsistent |
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114 | void _cached ( bool what ) {cached=what;} |
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115 | //! access mthod |
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116 | mat getR () {return R.to_mat();} |
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117 | }; |
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118 | |
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119 | /*! |
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120 | \brief Gamma posterior density |
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121 | |
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122 | Multvariate Gamma density as product of independent univariate densities. |
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123 | \f[ |
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124 | f(x|\alpha,\beta) = \prod f(x_i|\alpha_i,\beta_i) |
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125 | \f] |
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126 | */ |
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127 | |
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128 | class egamma : public eEF { |
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129 | protected: |
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130 | //! Vector \f$\alpha\f$ |
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131 | vec alpha; |
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132 | //! Vector \f$\beta\f$ |
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133 | vec beta; |
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134 | public : |
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135 | //! Default constructor |
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136 | egamma ( const RV &rv ) :eEF ( rv ) {}; |
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137 | //! Sets parameters |
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138 | void set_parameters ( const vec &a, const vec &b ) {alpha=a,beta=b;}; |
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139 | vec sample() const; |
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140 | //! TODO: is it used anywhere? |
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141 | mat sample ( int N ) const; |
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142 | double evalpdflog ( const vec &val ) const; |
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143 | //! Returns poiter to alpha and beta. Potentially dengerous: use with care! |
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144 | void _param ( vec* &a, vec* &b ) {a=αb=β}; |
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145 | vec mean() const {vec pom ( alpha ); pom/=beta; return pom;} |
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146 | }; |
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147 | /* |
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148 | //! Weighted mixture of epdfs with external owned components. |
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149 | class emix : public epdf { |
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150 | protected: |
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151 | int n; |
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152 | vec &w; |
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153 | Array<epdf*> Coms; |
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154 | public: |
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155 | //! Default constructor |
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156 | emix ( const RV &rv, vec &w0): epdf(rv), n(w0.length()), w(w0), Coms(n) {}; |
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157 | void set_parameters( int &i, double wi, epdf* ep){w(i)=wi;Coms(i)=ep;} |
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158 | vec mean(){vec pom; for(int i=0;i<n;i++){pom+=Coms(i)->mean()*w(i);} return pom;}; |
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159 | vec sample() {it_error ( "Not implemented" );return 0;} |
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160 | }; |
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161 | */ |
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162 | |
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163 | //! Uniform distributed density on a rectangular support |
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164 | |
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165 | class euni: public epdf { |
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166 | protected: |
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167 | //! lower bound on support |
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168 | vec low; |
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169 | //! upper bound on support |
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170 | vec high; |
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171 | //! internal |
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172 | vec distance; |
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173 | //! normalizing coefficients |
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174 | double nk; |
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175 | //! cache of log( \c nk ) |
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176 | double lnk; |
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177 | public: |
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178 | //! Defualt constructor |
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179 | euni ( const RV rv ) :epdf ( rv ) {} |
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180 | double eval ( const vec &val ) const {return nk;} |
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181 | double evalpdflog ( const vec &val ) const {return lnk;} |
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182 | vec sample() const { |
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183 | vec smp ( rv.count() ); UniRNG.sample_vector ( rv.count(),smp ); |
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184 | return low+distance*smp; |
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185 | } |
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186 | //! set values of \c low and \c high |
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187 | void set_parameters ( const vec &low0, const vec &high0 ) { |
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188 | distance = high0-low0; |
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189 | it_assert_debug ( min ( distance ) >0.0,"bad support" ); |
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190 | low = low0; |
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191 | high = high0; |
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192 | nk = prod ( 1.0/distance ); |
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193 | lnk = log ( nk ); |
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194 | } |
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195 | vec mean() const {vec pom=high; pom-=low; pom/=2.0; return pom;} |
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196 | }; |
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197 | |
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198 | |
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199 | /*! |
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200 | \brief Normal distributed linear function with linear function of mean value; |
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201 | |
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202 | Mean value $mu=A*rvc$. |
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203 | */ |
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204 | template<class sq_T> |
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205 | class mlnorm : public mEF { |
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206 | //! Internal epdf that arise by conditioning on \c rvc |
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207 | enorm<sq_T> epdf; |
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208 | vec* _mu; //cached epdf.mu; |
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209 | mat A; |
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210 | public: |
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211 | //! Constructor |
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212 | mlnorm ( RV &rv,RV &rvc ); |
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213 | //! Set \c A and \c R |
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214 | void set_parameters ( const mat &A, const sq_T &R ); |
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215 | //!Generate one sample of the posterior |
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216 | vec samplecond ( vec &cond, double &lik ); |
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217 | //!Generate matrix of samples of the posterior |
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218 | mat samplecond ( vec &cond, vec &lik, int n ); |
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219 | //! Set value of \c rvc . Result of this operation is stored in \c epdf use function \c _ep to access it. |
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220 | void condition ( vec &cond ); |
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221 | }; |
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222 | |
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223 | /*! |
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224 | \brief Gamma random walk |
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225 | |
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226 | Mean value, \f$\mu\f$, of this density is given by \c rvc . |
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227 | Standard deviation of the random walk is proportional to one $k$-th the mean. |
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228 | This is achieved by setting \f$\alpha=k\f$ and \f$\beta=k/\mu\f$. |
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229 | |
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230 | The standard deviation of the walk is then: \f$\mu/\sqrt(k)\f$. |
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231 | */ |
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232 | class mgamma : public mEF { |
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233 | protected: |
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234 | //! Internal epdf that arise by conditioning on \c rvc |
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235 | egamma epdf; |
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236 | //! Constant $k$ |
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237 | double k; |
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238 | //! cache of epdf.beta |
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239 | vec* _beta; |
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240 | |
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241 | public: |
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242 | //! Constructor |
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243 | mgamma ( const RV &rv,const RV &rvc ); |
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244 | //! Set value of \c k |
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245 | void set_parameters ( double k ); |
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246 | //!Generate one sample of the posterior |
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247 | vec samplecond ( vec &cond, double &lik ); |
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248 | //!Generate matrix of samples of the posterior |
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249 | mat samplecond ( vec &cond, vec &lik, int n ); |
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250 | void condition ( const vec &val ) {*_beta=k/val;}; |
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251 | }; |
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252 | |
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253 | /*! |
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254 | \brief Gamma random walk around a fixed point |
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255 | |
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256 | Mean value, \f$\mu\f$, of this density is given by a geometric combination of \c rvc and given fixed point, $p$. $k$ is the coefficient of the geometric combimation |
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257 | \f[ \mu = \mu_{t-1} ^{l} p^{1-l}\f] |
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258 | |
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259 | Standard deviation of the random walk is proportional to one $k$-th the mean. |
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260 | This is achieved by setting \f$\alpha=k\f$ and \f$\beta=k/\mu\f$. |
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261 | |
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262 | The standard deviation of the walk is then: \f$\mu/\sqrt(k)\f$. |
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263 | */ |
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264 | class mgamma_fix : public mgamma { |
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265 | protected: |
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266 | double l; |
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267 | vec refl; |
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268 | public: |
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269 | //! Constructor |
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270 | mgamma_fix ( const RV &rv,const RV &rvc ) : mgamma ( rv,rvc ),refl ( rv.count() ) {}; |
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271 | //! Set value of \c k |
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272 | void set_parameters ( double k0 , vec ref0, double l0 ) { |
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273 | mgamma::set_parameters ( k0 ); |
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274 | refl=pow ( ref0,1.0-l0 );l=l0; |
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275 | }; |
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276 | |
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277 | void condition ( const vec &val ) {vec mean=elem_mult ( refl,pow ( val,l ) ); *_beta=k/mean;}; |
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278 | }; |
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279 | |
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280 | //! Switch between various resampling methods. |
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281 | enum RESAMPLING_METHOD { MULTINOMIAL = 0, STRATIFIED = 1, SYSTEMATIC = 3 }; |
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282 | /*! |
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283 | \brief Weighted empirical density |
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284 | |
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285 | Used e.g. in particle filters. |
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286 | */ |
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287 | class eEmp: public epdf { |
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288 | protected : |
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289 | //! Number of particles |
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290 | int n; |
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291 | //! Sample weights $w$ |
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292 | vec w; |
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293 | //! Samples \f$x^{(i)}, i=1..n\f$ |
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294 | Array<vec> samples; |
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295 | public: |
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296 | //! Default constructor |
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297 | eEmp ( const RV &rv0 ,int n0 ) :epdf ( rv0 ),n ( n0 ),w ( n ),samples ( n ) {}; |
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298 | //! Set sample |
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299 | void set_parameters ( const vec &w0, epdf* pdf0 ); |
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300 | //! Potentially dangerous, use with care. |
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301 | vec& _w() {return w;}; |
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302 | //! access function |
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303 | Array<vec>& _samples() {return samples;}; |
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304 | //! 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. |
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305 | ivec resample ( RESAMPLING_METHOD method = SYSTEMATIC ); |
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306 | //! inherited operation : NOT implemneted |
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307 | vec sample() const {it_error ( "Not implemented" );return 0;} |
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308 | //! inherited operation : NOT implemneted |
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309 | double evalpdflog ( const vec &val ) const {it_error ( "Not implemented" );return 0.0;} |
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310 | vec mean() const { |
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311 | vec pom=zeros ( rv.count() ); |
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312 | for ( int i=0;i<n;i++ ) {pom+=samples ( i ) *w ( i );} |
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313 | return pom; |
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314 | } |
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315 | }; |
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316 | |
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317 | |
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318 | //////////////////////// |
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319 | |
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320 | template<class sq_T> |
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321 | enorm<sq_T>::enorm ( RV &rv ) :eEF ( rv ), mu ( rv.count() ),R ( rv.count() ),_iR ( rv.count() ),cached ( false ),dim ( rv.count() ) {}; |
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322 | |
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323 | template<class sq_T> |
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324 | void enorm<sq_T>::set_parameters ( const vec &mu0, const sq_T &R0 ) { |
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325 | //Fixme test dimensions of mu0 and R0; |
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326 | mu = mu0; |
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327 | R = R0; |
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328 | if ( _iR.rows() !=R.rows() ) _iR=R; // memory allocation! |
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329 | R.inv ( _iR ); //update cache |
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330 | cached=true; |
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331 | }; |
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332 | |
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333 | template<class sq_T> |
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334 | void enorm<sq_T>::dupdate ( mat &v, double nu ) { |
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335 | // |
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336 | }; |
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337 | |
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338 | template<class sq_T> |
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339 | void enorm<sq_T>::tupdate ( double phi, mat &vbar, double nubar ) { |
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340 | // |
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341 | }; |
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342 | |
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343 | template<class sq_T> |
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344 | vec enorm<sq_T>::sample() const { |
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345 | vec x ( dim ); |
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346 | NorRNG.sample_vector ( dim,x ); |
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347 | vec smp = R.sqrt_mult ( x ); |
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348 | |
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349 | smp += mu; |
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350 | return smp; |
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351 | }; |
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352 | |
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353 | template<class sq_T> |
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354 | mat enorm<sq_T>::sample ( int N ) const { |
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355 | mat X ( dim,N ); |
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356 | vec x ( dim ); |
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357 | vec pom; |
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358 | int i; |
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359 | |
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360 | for ( i=0;i<N;i++ ) { |
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361 | NorRNG.sample_vector ( dim,x ); |
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362 | pom = R.sqrt_mult ( x ); |
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363 | pom +=mu; |
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364 | X.set_col ( i, pom ); |
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365 | } |
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366 | |
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367 | return X; |
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368 | }; |
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369 | |
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370 | template<class sq_T> |
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371 | double enorm<sq_T>::eval ( const vec &val ) const { |
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372 | double pdfl,e; |
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373 | pdfl = evalpdflog ( val ); |
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374 | e = exp ( pdfl ); |
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375 | return e; |
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376 | }; |
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377 | |
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378 | template<class sq_T> |
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379 | double enorm<sq_T>::evalpdflog ( const vec &val ) const { |
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380 | if ( !cached ) {it_error ( "this should not happen, see cached" );} |
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381 | |
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382 | // 1.83787706640935 = log(2pi) |
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383 | return -0.5* ( R.cols() * 1.83787706640935 +R.logdet() +_iR.qform ( mu-val ) ); |
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384 | }; |
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385 | |
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386 | |
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387 | template<class sq_T> |
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388 | mlnorm<sq_T>::mlnorm ( RV &rv0,RV &rvc0 ) :mEF ( rv0,rvc0 ),epdf ( rv ),A ( rv0.count(),rv0.count() ) { |
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389 | _mu = epdf._mu(); |
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390 | } |
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391 | |
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392 | template<class sq_T> |
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393 | void mlnorm<sq_T>::set_parameters ( const mat &A0, const sq_T &R0 ) { |
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394 | epdf.set_parameters ( zeros ( rv.count() ),R0 ); |
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395 | A = A0; |
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396 | } |
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397 | |
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398 | template<class sq_T> |
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399 | vec mlnorm<sq_T>::samplecond ( vec &cond, double &lik ) { |
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400 | this->condition ( cond ); |
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401 | vec smp = epdf.sample(); |
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402 | lik = epdf.eval ( smp ); |
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403 | return smp; |
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404 | } |
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405 | |
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406 | template<class sq_T> |
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407 | mat mlnorm<sq_T>::samplecond ( vec &cond, vec &lik, int n ) { |
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408 | int i; |
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409 | int dim = rv.count(); |
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410 | mat Smp ( dim,n ); |
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411 | vec smp ( dim ); |
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412 | this->condition ( cond ); |
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413 | |
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414 | for ( i=0; i<n; i++ ) { |
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415 | smp = epdf.sample(); |
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416 | lik ( i ) = epdf.eval ( smp ); |
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417 | Smp.set_col ( i ,smp ); |
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418 | } |
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419 | |
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420 | return Smp; |
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421 | } |
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422 | |
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423 | template<class sq_T> |
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424 | void mlnorm<sq_T>::condition ( vec &cond ) { |
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425 | *_mu = A*cond; |
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426 | //R is already assigned; |
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427 | } |
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428 | |
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429 | /////////// |
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430 | |
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431 | |
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432 | #endif //EF_H |
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