1 | /*! |
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2 | \file |
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3 | \brief Mergers for combination of pdfs |
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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 MER_H |
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14 | #define MER_H |
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15 | |
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16 | |
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17 | #include "mixef.h" |
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18 | |
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19 | namespace bdm{ |
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20 | using std::string; |
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21 | |
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22 | /*! |
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23 | @brief Function for general combination of pdfs |
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24 | |
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25 | Mixtures of Gaussian densities are used internally. Switching to other densities should be trivial. |
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26 | */ |
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27 | |
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28 | class merger : public compositepdf, public epdf { |
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29 | protected: |
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30 | //!Internal mixture of EF models |
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31 | MixEF Mix; |
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32 | //! Data link for each mpdf in mpdfs |
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33 | Array<datalink_m2e*> dls; |
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34 | //! Array of rvs that are not modelled by mpdfs at all (aux) |
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35 | Array<RV> rvzs; |
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36 | //! Data Links of rv0 mpdfs - these will be conditioned the (rv,rvc) of mpdfs |
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37 | Array<datalink_m2e*> zdls; |
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38 | |
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39 | //!Number of samples used in approximation |
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40 | int Ns; |
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41 | //!Number of components in a mixture |
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42 | int Nc; |
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43 | //!Prior on the log-normal merging model |
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44 | double beta; |
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45 | //! Projection to empirical density |
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46 | eEmp eSmp; |
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47 | |
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48 | public: |
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49 | //!Default constructor |
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50 | merger ( const Array<mpdf*> &S ) : |
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51 | compositepdf ( S ), epdf ( ), |
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52 | Mix ( Array<BMEF*> ( 0 ),vec ( 0 ) ), dls ( n ), rvzs ( n ), zdls ( n ), eSmp() { |
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53 | RV ztmp; |
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54 | // Extend rv by rvc! |
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55 | RV rvc; setrvc ( rv,rvc ); |
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56 | rv.add ( rvc ); |
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57 | for ( int i=0;i<n;i++ ) { |
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58 | //Establich connection between mpdfs and merger |
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59 | dls ( i ) = new datalink_m2e ( mpdfs ( i )->_rv(), mpdfs ( i )->_rvc(), rv ); |
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60 | // find out what is missing in each mpdf |
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61 | ztmp= mpdfs ( i )->_rv(); |
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62 | ztmp.add ( mpdfs ( i )->_rvc() ); |
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63 | rvzs ( i ) =rv.subt ( ztmp ); |
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64 | zdls ( i ) = new datalink_m2e ( rvzs ( i ), ztmp, rv ) ; |
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65 | }; |
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66 | //Set Default values of parameters |
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67 | beta=2.0; |
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68 | Ns=100; |
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69 | Nc=10; |
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70 | Mix.set_method ( EM ); |
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71 | } |
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72 | //! Set internal parameters used in approximation |
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73 | void set_parameters ( double beta0, int Ns0, int Nc0 ) {beta=beta0;Ns=Ns0;Nc=Nc0;eSmp.set_parameters(Ns0,false);} |
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74 | //!Initialize the proposal density. This function must be called before merge()! |
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75 | void init() { ////////////// NOT FINISHED |
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76 | Array<vec> Smps ( n ); |
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77 | //Gibbs sampling |
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78 | for ( int i=0;i<n;i++ ) {Smps ( i ) =zeros ( 0 );} |
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79 | } |
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80 | //!Create a mixture density using known proposal |
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81 | void merge ( const epdf* g0 ); |
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82 | //!Create a mixture density, make sure to call init() before the first call |
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83 | void merge () {merge ( & ( Mix.posterior() ) );}; |
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84 | |
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85 | //! Merge log-likelihood values |
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86 | vec lognorm_merge ( mat &lW ); |
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87 | //! sample from merged density |
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88 | //! weight w is a |
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89 | vec sample ( ) const { return Mix.posterior().sample();} |
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90 | double evallog ( const vec &dt ) const { |
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91 | vec dtf=ones ( dt.length() +1 ); |
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92 | dtf.set_subvector ( 0,dt ); |
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93 | return Mix.logpred ( dtf ); |
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94 | } |
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95 | vec mean() const { |
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96 | const Vec<double> &w = eSmp._w(); |
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97 | const Array<vec> &S = eSmp._samples(); |
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98 | vec tmp=zeros ( dim); |
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99 | for ( int i=0; i<Ns; i++ ) { |
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100 | tmp+=w ( i ) *S ( i ); |
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101 | } |
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102 | return tmp; |
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103 | } |
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104 | mat covariance() const { |
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105 | const vec &w = eSmp._w(); |
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106 | const Array<vec> &S = eSmp._samples(); |
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107 | |
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108 | vec mea = mean(); |
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109 | |
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110 | cout << sum(w) << "," << w*w <<endl; |
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111 | |
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112 | mat Tmp=zeros(dim, dim); |
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113 | for ( int i=0; i<Ns; i++ ) { |
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114 | Tmp+=w ( i ) *outer_product(S ( i ), S(i)); |
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115 | } |
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116 | return Tmp-outer_product(mea,mea); |
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117 | } |
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118 | vec variance() const { |
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119 | const vec &w = eSmp._w(); |
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120 | const Array<vec> &S = eSmp._samples(); |
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121 | |
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122 | vec tmp=zeros(dim); |
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123 | for ( int i=0; i<Ns; i++ ) { |
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124 | tmp+=w ( i ) *pow(S ( i ),2); |
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125 | } |
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126 | return tmp-pow(mean(),2); |
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127 | } |
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128 | //! for future use |
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129 | virtual ~merger() { |
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130 | for ( int i=0; i<n; i++ ) { |
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131 | delete dls ( i ); |
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132 | delete zdls ( i ); |
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133 | } |
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134 | }; |
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135 | |
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136 | //! Access function |
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137 | MixEF& _Mix() {return Mix;} |
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138 | //! Access function |
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139 | eEmp& _Smp() {return eSmp;} |
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140 | }; |
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141 | |
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142 | } |
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143 | |
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144 | #endif // MER_H |
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