[176] | 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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[262] | 16 | |
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[176] | 17 | #include "mixef.h" |
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| 18 | |
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[254] | 19 | namespace bdm{ |
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[176] | 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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[192] | 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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[198] | 37 | Array<datalink_m2e*> zdls; |
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| 38 | |
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[176] | 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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[205] | 45 | //! Projection to empirical density |
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| 46 | eEmp eSmp; |
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| 47 | |
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[176] | 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 ( getrv ( false ) ), |
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[205] | 52 | Mix ( Array<BMEF*> ( 0 ),vec ( 0 ) ), dls ( n ), rvzs ( n ), zdls ( n ), eSmp(rv,0) { |
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[198] | 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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[192] | 57 | for ( int i=0;i<n;i++ ) { |
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| 58 | //Establich connection between mpdfs and merger |
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[198] | 59 | dls ( i ) = new datalink_m2e ( mpdfs ( i )->_rv(), mpdfs ( i )->_rvc(), rv ); |
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[192] | 60 | // find out what is missing in each mpdf |
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[198] | 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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[192] | 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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[205] | 73 | void set_parameters ( double beta0, int Ns0, int Nc0 ) {beta=beta0;Ns=Ns0;Nc=Nc0;eSmp.set_n(Ns0,false);} |
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[192] | 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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[176] | 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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[192] | 80 | //!Create a mixture density using known proposal |
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[176] | 81 | void merge ( const epdf* g0 ); |
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[192] | 82 | //!Create a mixture density, make sure to call init() before the first call |
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[176] | 83 | void merge () {merge ( & ( Mix._epdf() ) );}; |
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| 84 | |
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[192] | 85 | //! Merge log-likelihood values |
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[176] | 86 | vec lognorm_merge ( mat &lW ); |
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[192] | 87 | //! sample from merged density |
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| 88 | //! weight w is a |
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| 89 | vec sample ( ) const { return Mix._epdf().sample();} |
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[211] | 90 | double evallog ( const vec &dt ) const { |
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[192] | 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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[205] | 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 ( rv.count() ); |
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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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[229] | 104 | mat covariance() const { |
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[205] | 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(rv.count(), rv.count()); |
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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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[229] | 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(rv.count()); |
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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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[192] | 128 | //! for future use |
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| 129 | virtual ~merger() { |
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[198] | 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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[192] | 133 | } |
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| 134 | }; |
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| 135 | |
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| 136 | //! Access function |
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[182] | 137 | MixEF& _Mix() {return Mix;} |
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[205] | 138 | //! Access function |
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| 139 | eEmp& _Smp() {return eSmp;} |
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[176] | 140 | }; |
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| 141 | |
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[254] | 142 | } |
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[176] | 143 | |
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| 144 | #endif // MER_H |
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