[8] | 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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[262] | 16 | |
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[461] | 17 | #include "../shared_ptr.h" |
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[384] | 18 | #include "../base/bdmbase.h" |
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[262] | 19 | #include "../math/chmat.h" |
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[8] | 20 | |
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[737] | 21 | namespace bdm { |
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[8] | 22 | |
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[32] | 23 | |
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| 24 | //! Global Uniform_RNG |
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[488] | 25 | extern Uniform_RNG UniRNG; |
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[33] | 26 | //! Global Normal_RNG |
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[488] | 27 | extern Normal_RNG NorRNG; |
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[33] | 28 | //! Global Gamma_RNG |
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[488] | 29 | extern Gamma_RNG GamRNG; |
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[32] | 30 | |
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[488] | 31 | /*! |
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| 32 | * \brief General conjugate exponential family posterior density. |
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[8] | 33 | |
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[488] | 34 | * More?... |
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| 35 | */ |
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[28] | 36 | |
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[737] | 37 | class eEF : public epdf { |
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| 38 | public: |
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[32] | 39 | // eEF() :epdf() {}; |
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[737] | 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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[565] | 44 | |
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[737] | 45 | //!Evaluate normalized log-probability |
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[766] | 46 | virtual double evallog_nn ( const vec &val ) const NOT_IMPLEMENTED(0); |
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[565] | 47 | |
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[737] | 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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[488] | 59 | } |
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[737] | 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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[488] | 67 | } |
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[737] | 68 | return x - lognc(); |
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| 69 | } |
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[565] | 70 | |
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[737] | 71 | //!Power of the density, used e.g. to flatten the density |
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[766] | 72 | virtual void pow ( double p ) NOT_IMPLEMENTED_VOID; |
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[488] | 73 | }; |
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[8] | 74 | |
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[33] | 75 | |
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[170] | 76 | //! Estimator for Exponential family |
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[737] | 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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[766] | 89 | virtual void set_statistics ( const BMEF* BM0 ) NOT_IMPLEMENTED_VOID; |
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[565] | 90 | |
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[737] | 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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[565] | 95 | |
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[737] | 96 | //!Flatten the posterior according to the given BMEF (of the same type!) |
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[766] | 97 | virtual void flatten ( const BMEF * B ) NOT_IMPLEMENTED_VOID; |
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[198] | 98 | |
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[746] | 99 | |
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| 100 | void to_setting ( Setting &set ) const |
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| 101 | { |
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| 102 | BM::to_setting( set ); |
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[796] | 103 | UI::save(frg, set, "frg"); |
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[766] | 104 | // TODO DOPLNIT? ALE MOMENTALNE CHYBI FROM_SETTING PRO INSPIRACI |
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[746] | 105 | } |
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[796] | 106 | void from_setting( const Setting &set) { |
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| 107 | BM::from_setting(set); |
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| 108 | if ( !UI::get ( frg, set, "frg" ) ) |
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| 109 | frg = 1.0; |
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| 110 | |
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| 111 | } |
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[850] | 112 | |
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| 113 | void validate() { |
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| 114 | BM::validate(); |
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| 115 | } |
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| 116 | |
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[488] | 117 | }; |
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[170] | 118 | |
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[797] | 119 | /*! Dirac delta density with predefined transformation |
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| 120 | |
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| 121 | Density of the type:\f[ f(x_t | y_t) = \delta (x_t - g(y_t)) \f] |
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| 122 | where \f$ x_t \f$ is the \c rv, \f$ y_t \f$ is the \c rvc and g is a deterministic transformation of class fn. |
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| 123 | */ |
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| 124 | class mgdirac: public pdf{ |
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| 125 | protected: |
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| 126 | shared_ptr<fnc> g; |
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| 127 | public: |
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| 128 | vec samplecond(const vec &cond) { |
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| 129 | bdm_assert_debug(cond.length()==g->dimensionc(),"given cond in not compatible with g"); |
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| 130 | vec tmp = g->eval(cond); |
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| 131 | return tmp; |
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| 132 | } |
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| 133 | double evallogcond ( const vec &yt, const vec &cond ){ |
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| 134 | return std::numeric_limits< double >::max(); |
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| 135 | } |
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| 136 | void from_setting(const Setting& set){ |
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| 137 | pdf::from_setting(set); |
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| 138 | g=UI::build<fnc>(set,"g",UI::compulsory); |
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| 139 | validate(); |
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| 140 | } |
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| 141 | void to_setting(Setting &set) const{ |
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| 142 | pdf::to_setting(set); |
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| 143 | UI::save(g.get(), set, "g"); |
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| 144 | } |
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| 145 | void validate() { |
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| 146 | dim = g->dimension(); |
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| 147 | dimc = g->dimensionc(); |
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| 148 | } |
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| 149 | }; |
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| 150 | UIREGISTER(mgdirac); |
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| 151 | |
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| 152 | |
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[504] | 153 | template<class sq_T, template <typename> class TEpdf> |
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[488] | 154 | class mlnorm; |
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[178] | 155 | |
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[488] | 156 | /*! |
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| 157 | * \brief Gaussian density with positive definite (decomposed) covariance matrix. |
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[8] | 158 | |
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[488] | 159 | * More?... |
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| 160 | */ |
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| 161 | template<class sq_T> |
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[737] | 162 | class enorm : public eEF { |
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| 163 | protected: |
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| 164 | //! mean value |
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| 165 | vec mu; |
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| 166 | //! Covariance matrix in decomposed form |
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| 167 | sq_T R; |
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| 168 | public: |
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| 169 | //!\name Constructors |
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| 170 | //!@{ |
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[270] | 171 | |
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[737] | 172 | enorm () : eEF (), mu (), R () {}; |
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| 173 | enorm ( const vec &mu, const sq_T &R ) { |
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| 174 | set_parameters ( mu, R ); |
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| 175 | } |
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| 176 | void set_parameters ( const vec &mu, const sq_T &R ); |
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| 177 | /*! Create Normal density |
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| 178 | \f[ f(rv) = N(\mu, R) \f] |
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| 179 | from structure |
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| 180 | \code |
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| 181 | class = 'enorm<ldmat>', (OR) 'enorm<chmat>', (OR) 'enorm<fsqmat>'; |
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| 182 | mu = []; // mean value |
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| 183 | R = []; // variance, square matrix of appropriate dimension |
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| 184 | \endcode |
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| 185 | */ |
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| 186 | void from_setting ( const Setting &root ); |
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[773] | 187 | void to_setting ( Setting &root ) const ; |
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| 188 | |
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[737] | 189 | void validate() { |
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| 190 | bdm_assert ( mu.length() == R.rows(), "mu and R parameters do not match" ); |
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| 191 | dim = mu.length(); |
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| 192 | } |
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| 193 | //!@} |
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[270] | 194 | |
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[737] | 195 | //! \name Mathematical operations |
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| 196 | //!@{ |
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[270] | 197 | |
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[737] | 198 | //! dupdate in exponential form (not really handy) |
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| 199 | void dupdate ( mat &v, double nu = 1.0 ); |
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[28] | 200 | |
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[809] | 201 | //! evaluate bhattacharya distance |
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| 202 | double bhattacharyya(const enorm<sq_T> &e2){ |
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| 203 | bdm_assert(dim == e2.dimension(), "enorms of differnt dimensions"); |
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| 204 | sq_T P=R; |
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| 205 | P.add(e2._R()); |
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| 206 | |
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| 207 | double tmp = 0.125*P.invqform(mu - e2._mu()) + 0.5*(P.logdet() - 0.5*(R.logdet() + e2._R().logdet())); |
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| 208 | return tmp; |
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| 209 | } |
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| 210 | |
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[737] | 211 | vec sample() const; |
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[450] | 212 | |
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[737] | 213 | double evallog_nn ( const vec &val ) const; |
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| 214 | double lognc () const; |
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| 215 | vec mean() const { |
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| 216 | return mu; |
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| 217 | } |
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| 218 | vec variance() const { |
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| 219 | return diag ( R.to_mat() ); |
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| 220 | } |
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[802] | 221 | mat covariance() const { |
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| 222 | return R.to_mat(); |
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| 223 | } |
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| 224 | // mlnorm<sq_T>* condition ( const RV &rvn ) const ; <=========== fails to cmpile. Why? |
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[737] | 225 | shared_ptr<pdf> condition ( const RV &rvn ) const; |
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[504] | 226 | |
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[737] | 227 | // target not typed to mlnorm<sq_T, enorm<sq_T> > & |
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| 228 | // because that doesn't compile (perhaps because we |
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| 229 | // haven't finished defining enorm yet), but the type |
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| 230 | // is required |
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| 231 | void condition ( const RV &rvn, pdf &target ) const; |
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[504] | 232 | |
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[737] | 233 | shared_ptr<epdf> marginal ( const RV &rvn ) const; |
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| 234 | void marginal ( const RV &rvn, enorm<sq_T> &target ) const; |
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| 235 | //!@} |
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[270] | 236 | |
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[737] | 237 | //! \name Access to attributes |
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| 238 | //!@{ |
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[270] | 239 | |
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[737] | 240 | vec& _mu() { |
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| 241 | return mu; |
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| 242 | } |
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| 243 | const vec& _mu() const { |
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| 244 | return mu; |
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| 245 | } |
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| 246 | void set_mu ( const vec mu0 ) { |
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| 247 | mu = mu0; |
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| 248 | } |
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| 249 | sq_T& _R() { |
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| 250 | return R; |
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| 251 | } |
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| 252 | const sq_T& _R() const { |
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| 253 | return R; |
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| 254 | } |
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| 255 | //!@} |
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[28] | 256 | |
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[488] | 257 | }; |
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[737] | 258 | UIREGISTER2 ( enorm, chmat ); |
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[529] | 259 | SHAREDPTR2 ( enorm, chmat ); |
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[737] | 260 | UIREGISTER2 ( enorm, ldmat ); |
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[529] | 261 | SHAREDPTR2 ( enorm, ldmat ); |
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[737] | 262 | UIREGISTER2 ( enorm, fsqmat ); |
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[529] | 263 | SHAREDPTR2 ( enorm, fsqmat ); |
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[8] | 264 | |
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[887] | 265 | typedef enorm<ldmat> egauss; |
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| 266 | UIREGISTER(egauss); |
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| 267 | |
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| 268 | |
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[802] | 269 | //forward declaration |
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| 270 | class mstudent; |
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[388] | 271 | |
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[802] | 272 | /*! distribution of multivariate Student t density |
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| 273 | |
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| 274 | Based on article by Genest and Zidek, |
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| 275 | */ |
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| 276 | template<class sq_T> |
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| 277 | class estudent : public eEF{ |
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| 278 | protected: |
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| 279 | //! mena value |
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| 280 | vec mu; |
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| 281 | //! matrix H |
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| 282 | sq_T H; |
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| 283 | //! degrees of freedom |
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| 284 | double delta; |
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| 285 | public: |
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| 286 | double evallog_nn(const vec &val) const{ |
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| 287 | double tmp = -0.5*H.logdet() - 0.5*(delta + dim) * log(1+ H.invqform(val - mu)/delta); |
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| 288 | return tmp; |
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| 289 | } |
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| 290 | double lognc() const { |
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| 291 | //log(pi) = 1.14472988584940 |
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| 292 | double tmp = -lgamma(0.5*(delta+dim))+lgamma(0.5*delta) + 0.5*dim*(log(delta) + 1.14472988584940); |
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| 293 | return tmp; |
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| 294 | } |
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| 295 | void marginal (const RV &rvm, estudent<sq_T> &marg) const { |
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[896] | 296 | ivec ind = rvm.findself_ids(rv); // indices of rvm in rv |
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[802] | 297 | marg._mu() = mu(ind); |
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| 298 | marg._H() = sq_T(H,ind); |
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| 299 | marg._delta() = delta; |
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| 300 | marg.validate(); |
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| 301 | } |
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| 302 | shared_ptr<epdf> marginal(const RV &rvm) const { |
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| 303 | shared_ptr<estudent<sq_T> > tmp = new estudent<sq_T>; |
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| 304 | marginal(rvm, *tmp); |
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| 305 | return tmp; |
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| 306 | } |
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| 307 | vec sample() const NOT_IMPLEMENTED(vec(0)) |
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| 308 | |
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| 309 | vec mean() const {return mu;} |
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| 310 | mat covariance() const {return delta/(delta-2)*H.to_mat();} |
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| 311 | vec variance() const {return diag(covariance());} |
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| 312 | //! \name access |
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| 313 | //! @{ |
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| 314 | //! access function |
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| 315 | vec& _mu() {return mu;} |
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| 316 | //! access function |
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| 317 | sq_T& _H() {return H;} |
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| 318 | //! access function |
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| 319 | double& _delta() {return delta;} |
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| 320 | //!@} |
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| 321 | //! todo |
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| 322 | void from_setting(const Setting &set){ |
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| 323 | epdf::from_setting(set); |
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| 324 | mat H0; |
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| 325 | UI::get(H0,set, "H"); |
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| 326 | H= H0; // conversion!! |
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| 327 | UI::get(delta,set,"delta"); |
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| 328 | UI::get(mu,set,"mu"); |
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| 329 | } |
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| 330 | void to_setting(Setting &set) const{ |
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| 331 | epdf::to_setting(set); |
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| 332 | UI::save(H.to_mat(), set, "H"); |
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| 333 | UI::save(delta, set, "delta"); |
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| 334 | UI::save(mu, set, "mu"); |
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| 335 | } |
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| 336 | void validate() { |
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| 337 | dim = H.rows(); |
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| 338 | } |
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| 339 | }; |
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| 340 | UIREGISTER2(estudent,fsqmat); |
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| 341 | UIREGISTER2(estudent,ldmat); |
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| 342 | UIREGISTER2(estudent,chmat); |
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| 343 | |
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[488] | 344 | /*! |
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| 345 | * \brief Gauss-inverse-Wishart density stored in LD form |
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[96] | 346 | |
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[488] | 347 | * For \f$p\f$-variate densities, given rv.count() should be \f$p\times\f$ V.rows(). |
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| 348 | * |
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| 349 | */ |
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[737] | 350 | class egiw : public eEF { |
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[870] | 351 | //! \var log_level_enums tri |
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| 352 | //! TODO DOPLNIT |
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[850] | 353 | LOG_LEVEL(egiw,tri); |
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[870] | 354 | |
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[737] | 355 | protected: |
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| 356 | //! Extended information matrix of sufficient statistics |
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| 357 | ldmat V; |
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| 358 | //! Number of data records (degrees of freedom) of sufficient statistics |
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| 359 | double nu; |
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| 360 | //! Dimension of the output |
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| 361 | int dimx; |
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| 362 | //! Dimension of the regressor |
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| 363 | int nPsi; |
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| 364 | public: |
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| 365 | //!\name Constructors |
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| 366 | //!@{ |
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| 367 | egiw() : eEF() {}; |
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| 368 | egiw ( int dimx0, ldmat V0, double nu0 = -1.0 ) : eEF() { |
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| 369 | set_parameters ( dimx0, V0, nu0 ); |
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[878] | 370 | validate(); |
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[737] | 371 | }; |
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[270] | 372 | |
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[737] | 373 | void set_parameters ( int dimx0, ldmat V0, double nu0 = -1.0 ); |
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| 374 | //!@} |
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[96] | 375 | |
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[737] | 376 | vec sample() const; |
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| 377 | mat sample_mat ( int n ) const; |
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| 378 | vec mean() const; |
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| 379 | vec variance() const; |
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| 380 | void sample_mat ( mat &Mi, chmat &Ri ) const; |
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[330] | 381 | |
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[737] | 382 | void factorize ( mat &M, ldmat &Vz, ldmat &Lam ) const; |
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| 383 | //! LS estimate of \f$\theta\f$ |
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| 384 | vec est_theta() const; |
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[330] | 385 | |
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[737] | 386 | //! Covariance of the LS estimate |
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| 387 | ldmat est_theta_cov() const; |
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[96] | 388 | |
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[737] | 389 | //! expected values of the linear coefficient and the covariance matrix are written to \c M and \c R , respectively |
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| 390 | void mean_mat ( mat &M, mat&R ) const; |
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| 391 | //! 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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| 392 | double evallog_nn ( const vec &val ) const; |
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| 393 | double lognc () const; |
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| 394 | void pow ( double p ) { |
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| 395 | V *= p; |
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| 396 | nu *= p; |
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| 397 | }; |
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[270] | 398 | |
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[802] | 399 | //! marginal density (only student for now) |
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| 400 | shared_ptr<epdf> marginal(const RV &rvm) const { |
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| 401 | bdm_assert(dimx==1, "Not supported"); |
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| 402 | //TODO - this is too trivial!!! |
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| 403 | ivec ind = rvm.findself_ids(rv); |
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| 404 | if (min(ind)==0) { //assume it si |
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| 405 | shared_ptr<estudent<ldmat> > tmp = new estudent<ldmat>; |
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| 406 | mat M; |
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| 407 | ldmat Vz; |
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| 408 | ldmat Lam; |
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| 409 | factorize(M,Vz,Lam); |
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| 410 | |
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| 411 | tmp->_mu() = M.get_col(0); |
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| 412 | ldmat H; |
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| 413 | Vz.inv(H); |
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| 414 | H *=Lam._D()(0)/nu; |
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| 415 | tmp->_H() = H; |
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| 416 | tmp->_delta() = nu; |
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| 417 | tmp->validate(); |
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| 418 | return tmp; |
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| 419 | } |
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| 420 | return NULL; |
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| 421 | } |
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[737] | 422 | //! \name Access attributes |
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| 423 | //!@{ |
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| 424 | |
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| 425 | ldmat& _V() { |
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| 426 | return V; |
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| 427 | } |
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| 428 | const ldmat& _V() const { |
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| 429 | return V; |
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| 430 | } |
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| 431 | double& _nu() { |
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| 432 | return nu; |
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| 433 | } |
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| 434 | const double& _nu() const { |
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| 435 | return nu; |
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| 436 | } |
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| 437 | const int & _dimx() const { |
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| 438 | return dimx; |
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| 439 | } |
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| 440 | |
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| 441 | /*! Create Gauss-inverse-Wishart density |
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| 442 | \f[ f(rv) = GiW(V,\nu) \f] |
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| 443 | from structure |
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| 444 | \code |
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| 445 | class = 'egiw'; |
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| 446 | V = []; // square matrix |
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| 447 | dV = []; // vector of diagonal of V (when V not given) |
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| 448 | nu = []; // scalar \nu ((almost) degrees of freedom) |
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| 449 | // when missing, it will be computed to obtain proper pdf |
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| 450 | dimx = []; // dimension of the wishart part |
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| 451 | rv = RV({'name'}) // description of RV |
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| 452 | rvc = RV({'name'}) // description of RV in condition |
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[889] | 453 | log_level = 'tri'; // set the level of logged details |
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[737] | 454 | \endcode |
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[889] | 455 | |
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| 456 | \sa log_level_enums |
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[737] | 457 | */ |
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| 458 | void from_setting ( const Setting &set ) { |
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| 459 | epdf::from_setting ( set ); |
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| 460 | UI::get ( dimx, set, "dimx", UI::compulsory ); |
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| 461 | if ( !UI::get ( nu, set, "nu", UI::optional ) ) { |
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| 462 | nu = -1; |
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[488] | 463 | } |
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[737] | 464 | mat V; |
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| 465 | if ( !UI::get ( V, set, "V", UI::optional ) ) { |
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| 466 | vec dV; |
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| 467 | UI::get ( dV, set, "dV", UI::compulsory ); |
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| 468 | set_parameters ( dimx, ldmat ( dV ), nu ); |
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[878] | 469 | validate(); |
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[737] | 470 | |
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| 471 | } else { |
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| 472 | set_parameters ( dimx, V, nu ); |
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[878] | 473 | validate(); |
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[665] | 474 | } |
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[737] | 475 | } |
---|
| 476 | |
---|
| 477 | void to_setting ( Setting& set ) const { |
---|
| 478 | epdf::to_setting ( set ); |
---|
| 479 | UI::save ( dimx, set, "dimx" ); |
---|
| 480 | UI::save ( V.to_mat(), set, "V" ); |
---|
| 481 | UI::save ( nu, set, "nu" ); |
---|
| 482 | }; |
---|
| 483 | |
---|
| 484 | void validate() { |
---|
[878] | 485 | dim = dimx * ( dimx + nPsi ); |
---|
| 486 | |
---|
| 487 | // check sizes, rvs etc. |
---|
[802] | 488 | // also check if RV are meaningful!!! |
---|
| 489 | // meaningful = rv for theta and rv for r are split! |
---|
[737] | 490 | } |
---|
[739] | 491 | void log_register ( bdm::logger& L, const string& prefix ); |
---|
[737] | 492 | |
---|
[739] | 493 | void log_write() const; |
---|
[737] | 494 | //!@} |
---|
[488] | 495 | }; |
---|
[529] | 496 | UIREGISTER ( egiw ); |
---|
| 497 | SHAREDPTR ( egiw ); |
---|
[96] | 498 | |
---|
[488] | 499 | /*! \brief Dirichlet posterior density |
---|
[173] | 500 | |
---|
[488] | 501 | Continuous Dirichlet density of \f$n\f$-dimensional variable \f$x\f$ |
---|
| 502 | \f[ |
---|
| 503 | f(x|\beta) = \frac{\Gamma[\gamma]}{\prod_{i=1}^{n}\Gamma(\beta_i)} \prod_{i=1}^{n}x_i^{\beta_i-1} |
---|
| 504 | \f] |
---|
| 505 | where \f$\gamma=\sum_i \beta_i\f$. |
---|
| 506 | */ |
---|
[737] | 507 | class eDirich: public eEF { |
---|
| 508 | protected: |
---|
| 509 | //!sufficient statistics |
---|
| 510 | vec beta; |
---|
| 511 | public: |
---|
| 512 | //!\name Constructors |
---|
| 513 | //!@{ |
---|
[270] | 514 | |
---|
[737] | 515 | eDirich () : eEF () {}; |
---|
| 516 | eDirich ( const eDirich &D0 ) : eEF () { |
---|
| 517 | set_parameters ( D0.beta ); |
---|
[878] | 518 | validate(); |
---|
[737] | 519 | }; |
---|
| 520 | eDirich ( const vec &beta0 ) { |
---|
| 521 | set_parameters ( beta0 ); |
---|
[878] | 522 | validate(); |
---|
[737] | 523 | }; |
---|
| 524 | void set_parameters ( const vec &beta0 ) { |
---|
| 525 | beta = beta0; |
---|
| 526 | dim = beta.length(); |
---|
| 527 | } |
---|
| 528 | //!@} |
---|
[270] | 529 | |
---|
[737] | 530 | //! using sampling procedure from wikipedia |
---|
| 531 | vec sample() const { |
---|
| 532 | vec y ( beta.length() ); |
---|
| 533 | for ( int i = 0; i < beta.length(); i++ ) { |
---|
| 534 | GamRNG.setup ( beta ( i ), 1 ); |
---|
| 535 | #pragma omp critical |
---|
| 536 | y ( i ) = GamRNG(); |
---|
[565] | 537 | } |
---|
[737] | 538 | return y / sum ( y ); |
---|
| 539 | } |
---|
[565] | 540 | |
---|
[737] | 541 | vec mean() const { |
---|
| 542 | return beta / sum ( beta ); |
---|
| 543 | }; |
---|
| 544 | vec variance() const { |
---|
| 545 | double gamma = sum ( beta ); |
---|
| 546 | return elem_mult ( beta, ( gamma - beta ) ) / ( gamma*gamma* ( gamma + 1 ) ); |
---|
| 547 | } |
---|
| 548 | //! In this instance, val is ... |
---|
| 549 | double evallog_nn ( const vec &val ) const { |
---|
| 550 | double tmp; |
---|
| 551 | tmp = ( beta - 1 ) * log ( val ); |
---|
| 552 | return tmp; |
---|
| 553 | } |
---|
[565] | 554 | |
---|
[737] | 555 | double lognc () const { |
---|
| 556 | double tmp; |
---|
| 557 | double gam = sum ( beta ); |
---|
| 558 | double lgb = 0.0; |
---|
| 559 | for ( int i = 0; i < beta.length(); i++ ) { |
---|
| 560 | lgb += lgamma ( beta ( i ) ); |
---|
[565] | 561 | } |
---|
[737] | 562 | tmp = lgb - lgamma ( gam ); |
---|
| 563 | return tmp; |
---|
| 564 | } |
---|
[565] | 565 | |
---|
[737] | 566 | //!access function |
---|
| 567 | vec& _beta() { |
---|
| 568 | return beta; |
---|
| 569 | } |
---|
| 570 | /*! configuration structure |
---|
| 571 | \code |
---|
| 572 | class = 'eDirich'; |
---|
| 573 | beta = []; //parametr beta |
---|
| 574 | \endcode |
---|
| 575 | */ |
---|
| 576 | void from_setting ( const Setting &set ) { |
---|
| 577 | epdf::from_setting ( set ); |
---|
| 578 | UI::get ( beta, set, "beta", UI::compulsory ); |
---|
| 579 | validate(); |
---|
| 580 | } |
---|
| 581 | void validate() { |
---|
| 582 | //check rv |
---|
| 583 | dim = beta.length(); |
---|
| 584 | } |
---|
[746] | 585 | |
---|
| 586 | void to_setting ( Setting &set ) const |
---|
| 587 | { |
---|
| 588 | eEF::to_setting( set ); |
---|
| 589 | UI::save( beta, set, "beta" ); |
---|
| 590 | } |
---|
[488] | 591 | }; |
---|
[737] | 592 | UIREGISTER ( eDirich ); |
---|
[96] | 593 | |
---|
[637] | 594 | /*! Random Walk on Dirichlet |
---|
[737] | 595 | Using simple assignment |
---|
[637] | 596 | \f[ \beta = rvc / k + \beta_c \f] |
---|
| 597 | hence, mean value = rvc, variance = (k+1)*mean*mean; |
---|
[737] | 598 | |
---|
[637] | 599 | The greater k is, the greater is the variance of the random walk; |
---|
[737] | 600 | |
---|
[637] | 601 | \f$ \beta_c \f$ is used as regularizing element to avoid corner cases, i.e. when one element of rvc is zero. |
---|
| 602 | By default is it set to 0.1; |
---|
| 603 | */ |
---|
| 604 | |
---|
[693] | 605 | class mDirich: public pdf_internal<eDirich> { |
---|
[737] | 606 | protected: |
---|
| 607 | //! constant \f$ k \f$ of the random walk |
---|
| 608 | double k; |
---|
| 609 | //! cache of beta_i |
---|
| 610 | vec &_beta; |
---|
| 611 | //! stabilizing coefficient \f$ \beta_c \f$ |
---|
| 612 | vec betac; |
---|
| 613 | public: |
---|
| 614 | mDirich() : pdf_internal<eDirich>(), _beta ( iepdf._beta() ) {}; |
---|
| 615 | void condition ( const vec &val ) { |
---|
| 616 | _beta = val / k + betac; |
---|
| 617 | }; |
---|
| 618 | /*! Create Dirichlet random walk |
---|
| 619 | \f[ f(rv|rvc) = Di(rvc*k) \f] |
---|
| 620 | from structure |
---|
| 621 | \code |
---|
| 622 | class = 'mDirich'; |
---|
| 623 | k = 1; // multiplicative constant k |
---|
| 624 | --- optional --- |
---|
| 625 | rv = RV({'name'},size) // description of RV |
---|
| 626 | beta0 = []; // initial value of beta |
---|
| 627 | betac = []; // initial value of beta |
---|
| 628 | \endcode |
---|
| 629 | */ |
---|
| 630 | void from_setting ( const Setting &set ) { |
---|
| 631 | pdf::from_setting ( set ); // reads rv and rvc |
---|
| 632 | if ( _rv()._dsize() > 0 ) { |
---|
| 633 | rvc = _rv().copy_t ( -1 ); |
---|
[637] | 634 | } |
---|
[737] | 635 | vec beta0; |
---|
| 636 | if ( !UI::get ( beta0, set, "beta0", UI::optional ) ) { |
---|
| 637 | beta0 = ones ( _rv()._dsize() ); |
---|
| 638 | } |
---|
| 639 | if ( !UI::get ( betac, set, "betac", UI::optional ) ) { |
---|
| 640 | betac = 0.1 * ones ( _rv()._dsize() ); |
---|
| 641 | } |
---|
| 642 | _beta = beta0; |
---|
| 643 | |
---|
| 644 | UI::get ( k, set, "k", UI::compulsory ); |
---|
| 645 | validate(); |
---|
| 646 | } |
---|
| 647 | void validate() { |
---|
| 648 | pdf_internal<eDirich>::validate(); |
---|
| 649 | bdm_assert ( _beta.length() == betac.length(), "beta0 and betac are not compatible" ); |
---|
| 650 | if ( _rv()._dsize() > 0 ) { |
---|
| 651 | bdm_assert ( ( _rv()._dsize() == dimension() ) , "Size of rv does not match with beta" ); |
---|
| 652 | } |
---|
| 653 | dimc = _beta.length(); |
---|
| 654 | }; |
---|
[637] | 655 | }; |
---|
[737] | 656 | UIREGISTER ( mDirich ); |
---|
[637] | 657 | |
---|
[181] | 658 | //! \brief Estimator for Multinomial density |
---|
[737] | 659 | class multiBM : public BMEF { |
---|
| 660 | protected: |
---|
| 661 | //! Conjugate prior and posterior |
---|
| 662 | eDirich est; |
---|
| 663 | //! Pointer inside est to sufficient statistics |
---|
| 664 | vec β |
---|
| 665 | public: |
---|
| 666 | //!Default constructor |
---|
| 667 | multiBM () : BMEF (), est (), beta ( est._beta() ) { |
---|
| 668 | if ( beta.length() > 0 ) { |
---|
| 669 | last_lognc = est.lognc(); |
---|
| 670 | } else { |
---|
| 671 | last_lognc = 0.0; |
---|
[488] | 672 | } |
---|
[737] | 673 | } |
---|
| 674 | //!Copy constructor |
---|
| 675 | multiBM ( const multiBM &B ) : BMEF ( B ), est ( B.est ), beta ( est._beta() ) {} |
---|
| 676 | //! Sets sufficient statistics to match that of givefrom mB0 |
---|
| 677 | void set_statistics ( const BM* mB0 ) { |
---|
| 678 | const multiBM* mB = dynamic_cast<const multiBM*> ( mB0 ); |
---|
| 679 | beta = mB->beta; |
---|
| 680 | } |
---|
[739] | 681 | void bayes ( const vec &yt, const vec &cond = empty_vec ); |
---|
[176] | 682 | |
---|
[739] | 683 | double logpred ( const vec &yt ) const; |
---|
[170] | 684 | |
---|
[739] | 685 | void flatten ( const BMEF* B ); |
---|
| 686 | |
---|
[737] | 687 | //! return correctly typed posterior (covariant return) |
---|
| 688 | const eDirich& posterior() const { |
---|
| 689 | return est; |
---|
| 690 | }; |
---|
| 691 | //! constructor function |
---|
| 692 | void set_parameters ( const vec &beta0 ) { |
---|
| 693 | est.set_parameters ( beta0 ); |
---|
[741] | 694 | est.validate(); |
---|
[737] | 695 | if ( evalll ) { |
---|
| 696 | last_lognc = est.lognc(); |
---|
[488] | 697 | } |
---|
[737] | 698 | } |
---|
[746] | 699 | |
---|
[737] | 700 | void to_setting ( Setting &set ) const { |
---|
| 701 | BMEF::to_setting ( set ); |
---|
[746] | 702 | UI::save( &est, set, "prior" ); |
---|
[737] | 703 | } |
---|
[488] | 704 | }; |
---|
[746] | 705 | UIREGISTER( multiBM ); |
---|
[170] | 706 | |
---|
[488] | 707 | /*! |
---|
| 708 | \brief Gamma posterior density |
---|
[32] | 709 | |
---|
[488] | 710 | Multivariate Gamma density as product of independent univariate densities. |
---|
| 711 | \f[ |
---|
| 712 | f(x|\alpha,\beta) = \prod f(x_i|\alpha_i,\beta_i) |
---|
| 713 | \f] |
---|
| 714 | */ |
---|
[32] | 715 | |
---|
[737] | 716 | class egamma : public eEF { |
---|
| 717 | protected: |
---|
| 718 | //! Vector \f$\alpha\f$ |
---|
| 719 | vec alpha; |
---|
| 720 | //! Vector \f$\beta\f$ |
---|
| 721 | vec beta; |
---|
| 722 | public : |
---|
| 723 | //! \name Constructors |
---|
| 724 | //!@{ |
---|
| 725 | egamma () : eEF (), alpha ( 0 ), beta ( 0 ) {}; |
---|
| 726 | egamma ( const vec &a, const vec &b ) { |
---|
| 727 | set_parameters ( a, b ); |
---|
[878] | 728 | validate(); |
---|
[737] | 729 | }; |
---|
| 730 | void set_parameters ( const vec &a, const vec &b ) { |
---|
| 731 | alpha = a, beta = b; |
---|
| 732 | }; |
---|
| 733 | //!@} |
---|
[270] | 734 | |
---|
[737] | 735 | vec sample() const; |
---|
| 736 | double evallog ( const vec &val ) const; |
---|
| 737 | double lognc () const; |
---|
| 738 | //! Returns pointer to internal alpha. Potentially dengerous: use with care! |
---|
| 739 | vec& _alpha() { |
---|
| 740 | return alpha; |
---|
| 741 | } |
---|
| 742 | //! Returns pointer to internal beta. Potentially dengerous: use with care! |
---|
| 743 | vec& _beta() { |
---|
| 744 | return beta; |
---|
| 745 | } |
---|
| 746 | vec mean() const { |
---|
| 747 | return elem_div ( alpha, beta ); |
---|
| 748 | } |
---|
| 749 | vec variance() const { |
---|
| 750 | return elem_div ( alpha, elem_mult ( beta, beta ) ); |
---|
| 751 | } |
---|
[225] | 752 | |
---|
[737] | 753 | /*! Create Gamma density |
---|
| 754 | \f[ f(rv|rvc) = \Gamma(\alpha, \beta) \f] |
---|
| 755 | from structure |
---|
| 756 | \code |
---|
| 757 | class = 'egamma'; |
---|
| 758 | alpha = [...]; // vector of alpha |
---|
| 759 | beta = [...]; // vector of beta |
---|
| 760 | rv = RV({'name'}) // description of RV |
---|
| 761 | \endcode |
---|
| 762 | */ |
---|
| 763 | void from_setting ( const Setting &set ) { |
---|
| 764 | epdf::from_setting ( set ); // reads rv |
---|
| 765 | UI::get ( alpha, set, "alpha", UI::compulsory ); |
---|
| 766 | UI::get ( beta, set, "beta", UI::compulsory ); |
---|
| 767 | validate(); |
---|
| 768 | } |
---|
| 769 | void validate() { |
---|
| 770 | bdm_assert ( alpha.length() == beta.length(), "parameters do not match" ); |
---|
| 771 | dim = alpha.length(); |
---|
| 772 | } |
---|
[488] | 773 | }; |
---|
[737] | 774 | UIREGISTER ( egamma ); |
---|
[529] | 775 | SHAREDPTR ( egamma ); |
---|
| 776 | |
---|
[488] | 777 | /*! |
---|
| 778 | \brief Inverse-Gamma posterior density |
---|
[225] | 779 | |
---|
[488] | 780 | Multivariate inverse-Gamma density as product of independent univariate densities. |
---|
| 781 | \f[ |
---|
| 782 | f(x|\alpha,\beta) = \prod f(x_i|\alpha_i,\beta_i) |
---|
| 783 | \f] |
---|
[283] | 784 | |
---|
[488] | 785 | Vector \f$\beta\f$ has different meaning (in fact it is 1/beta as used in definition of iG) |
---|
[225] | 786 | |
---|
[488] | 787 | Inverse Gamma can be converted to Gamma using \f[ |
---|
| 788 | x\sim iG(a,b) => 1/x\sim G(a,1/b) |
---|
| 789 | \f] |
---|
| 790 | This relation is used in sampling. |
---|
| 791 | */ |
---|
[270] | 792 | |
---|
[737] | 793 | class eigamma : public egamma { |
---|
| 794 | protected: |
---|
| 795 | public : |
---|
| 796 | //! \name Constructors |
---|
| 797 | //! All constructors are inherited |
---|
| 798 | //!@{ |
---|
| 799 | //!@} |
---|
[32] | 800 | |
---|
[737] | 801 | vec sample() const { |
---|
| 802 | return 1.0 / egamma::sample(); |
---|
| 803 | }; |
---|
| 804 | //! Returns poiter to alpha and beta. Potentially dangerous: use with care! |
---|
| 805 | vec mean() const { |
---|
| 806 | return elem_div ( beta, alpha - 1 ); |
---|
| 807 | } |
---|
| 808 | vec variance() const { |
---|
| 809 | vec mea = mean(); |
---|
| 810 | return elem_div ( elem_mult ( mea, mea ), alpha - 2 ); |
---|
| 811 | } |
---|
[488] | 812 | }; |
---|
| 813 | /* |
---|
| 814 | //! Weighted mixture of epdfs with external owned components. |
---|
| 815 | class emix : public epdf { |
---|
| 816 | protected: |
---|
| 817 | int n; |
---|
| 818 | vec &w; |
---|
| 819 | Array<epdf*> Coms; |
---|
| 820 | public: |
---|
| 821 | //! Default constructor |
---|
| 822 | emix ( const RV &rv, vec &w0): epdf(rv), n(w0.length()), w(w0), Coms(n) {}; |
---|
| 823 | void set_parameters( int &i, double wi, epdf* ep){w(i)=wi;Coms(i)=ep;} |
---|
| 824 | vec mean(){vec pom; for(int i=0;i<n;i++){pom+=Coms(i)->mean()*w(i);} return pom;}; |
---|
| 825 | }; |
---|
| 826 | */ |
---|
| 827 | |
---|
[32] | 828 | //! Uniform distributed density on a rectangular support |
---|
| 829 | |
---|
[737] | 830 | class euni: public epdf { |
---|
| 831 | protected: |
---|
[32] | 832 | //! lower bound on support |
---|
[737] | 833 | vec low; |
---|
[32] | 834 | //! upper bound on support |
---|
[737] | 835 | vec high; |
---|
[32] | 836 | //! internal |
---|
[737] | 837 | vec distance; |
---|
[32] | 838 | //! normalizing coefficients |
---|
[737] | 839 | double nk; |
---|
[33] | 840 | //! cache of log( \c nk ) |
---|
[737] | 841 | double lnk; |
---|
| 842 | public: |
---|
| 843 | //! \name Constructors |
---|
| 844 | //!@{ |
---|
| 845 | euni () : epdf () {} |
---|
| 846 | euni ( const vec &low0, const vec &high0 ) { |
---|
| 847 | set_parameters ( low0, high0 ); |
---|
| 848 | } |
---|
| 849 | void set_parameters ( const vec &low0, const vec &high0 ) { |
---|
| 850 | distance = high0 - low0; |
---|
| 851 | low = low0; |
---|
| 852 | high = high0; |
---|
| 853 | nk = prod ( 1.0 / distance ); |
---|
| 854 | lnk = log ( nk ); |
---|
| 855 | } |
---|
| 856 | //!@} |
---|
[270] | 857 | |
---|
[737] | 858 | double evallog ( const vec &val ) const { |
---|
| 859 | if ( any ( val < low ) && any ( val > high ) ) { |
---|
| 860 | return -inf; |
---|
| 861 | } else return lnk; |
---|
| 862 | } |
---|
| 863 | vec sample() const { |
---|
| 864 | vec smp ( dim ); |
---|
[270] | 865 | #pragma omp critical |
---|
[737] | 866 | UniRNG.sample_vector ( dim , smp ); |
---|
| 867 | return low + elem_mult ( distance, smp ); |
---|
| 868 | } |
---|
| 869 | //! set values of \c low and \c high |
---|
| 870 | vec mean() const { |
---|
| 871 | return ( high - low ) / 2.0; |
---|
| 872 | } |
---|
| 873 | vec variance() const { |
---|
| 874 | return ( pow ( high, 2 ) + pow ( low, 2 ) + elem_mult ( high, low ) ) / 3.0; |
---|
| 875 | } |
---|
| 876 | /*! Create Uniform density |
---|
| 877 | \f[ f(rv) = U(low,high) \f] |
---|
| 878 | from structure |
---|
| 879 | \code |
---|
| 880 | class = 'euni' |
---|
| 881 | high = [...]; // vector of upper bounds |
---|
| 882 | low = [...]; // vector of lower bounds |
---|
| 883 | rv = RV({'name'}); // description of RV |
---|
| 884 | \endcode |
---|
| 885 | */ |
---|
| 886 | void from_setting ( const Setting &set ) { |
---|
| 887 | epdf::from_setting ( set ); // reads rv and rvc |
---|
[471] | 888 | |
---|
[737] | 889 | UI::get ( high, set, "high", UI::compulsory ); |
---|
| 890 | UI::get ( low, set, "low", UI::compulsory ); |
---|
| 891 | set_parameters ( low, high ); |
---|
| 892 | validate(); |
---|
| 893 | } |
---|
| 894 | void validate() { |
---|
| 895 | bdm_assert ( high.length() == low.length(), "Incompatible high and low vectors" ); |
---|
| 896 | dim = high.length(); |
---|
| 897 | bdm_assert ( min ( distance ) > 0.0, "bad support" ); |
---|
| 898 | } |
---|
[488] | 899 | }; |
---|
[737] | 900 | UIREGISTER ( euni ); |
---|
[32] | 901 | |
---|
[665] | 902 | //! Uniform density with conditional mean value |
---|
[737] | 903 | class mguni : public pdf_internal<euni> { |
---|
[665] | 904 | //! function of the mean value |
---|
| 905 | shared_ptr<fnc> mean; |
---|
| 906 | //! distance from mean to both sides |
---|
| 907 | vec delta; |
---|
[737] | 908 | public: |
---|
| 909 | void condition ( const vec &cond ) { |
---|
| 910 | vec mea = mean->eval ( cond ); |
---|
| 911 | iepdf.set_parameters ( mea - delta, mea + delta ); |
---|
| 912 | } |
---|
| 913 | //! load from |
---|
| 914 | void from_setting ( const Setting &set ) { |
---|
| 915 | pdf::from_setting ( set ); //reads rv and rvc |
---|
| 916 | UI::get ( delta, set, "delta", UI::compulsory ); |
---|
| 917 | mean = UI::build<fnc> ( set, "mean", UI::compulsory ); |
---|
| 918 | iepdf.set_parameters ( -delta, delta ); |
---|
[878] | 919 | } |
---|
| 920 | |
---|
| 921 | void validate(){ |
---|
[737] | 922 | dimc = mean->dimensionc(); |
---|
[878] | 923 | iepdf.validate(); |
---|
[737] | 924 | } |
---|
[878] | 925 | |
---|
[665] | 926 | }; |
---|
[737] | 927 | UIREGISTER ( mguni ); |
---|
[488] | 928 | /*! |
---|
| 929 | \brief Normal distributed linear function with linear function of mean value; |
---|
[32] | 930 | |
---|
[536] | 931 | Mean value \f$ \mu=A*\mbox{rvc}+\mu_0 \f$. |
---|
[488] | 932 | */ |
---|
| 933 | template < class sq_T, template <typename> class TEpdf = enorm > |
---|
[737] | 934 | class mlnorm : public pdf_internal< TEpdf<sq_T> > { |
---|
| 935 | protected: |
---|
| 936 | //! Internal epdf that arise by conditioning on \c rvc |
---|
| 937 | mat A; |
---|
| 938 | //! Constant additive term |
---|
| 939 | vec mu_const; |
---|
[487] | 940 | // vec& _mu; //cached epdf.mu; !!!!!! WHY NOT? |
---|
[737] | 941 | public: |
---|
| 942 | //! \name Constructors |
---|
| 943 | //!@{ |
---|
| 944 | mlnorm() : pdf_internal< TEpdf<sq_T> >() {}; |
---|
| 945 | mlnorm ( const mat &A, const vec &mu0, const sq_T &R ) : pdf_internal< TEpdf<sq_T> >() { |
---|
| 946 | set_parameters ( A, mu0, R ); |
---|
[878] | 947 | validate(); |
---|
[737] | 948 | } |
---|
[461] | 949 | |
---|
[737] | 950 | //! Set \c A and \c R |
---|
| 951 | void set_parameters ( const mat &A0, const vec &mu0, const sq_T &R0 ) { |
---|
| 952 | this->iepdf.set_parameters ( zeros ( A0.rows() ), R0 ); |
---|
| 953 | A = A0; |
---|
| 954 | mu_const = mu0; |
---|
| 955 | } |
---|
[878] | 956 | |
---|
[737] | 957 | //!@} |
---|
| 958 | //! Set value of \c rvc . Result of this operation is stored in \c epdf use function \c _ep to access it. |
---|
| 959 | void condition ( const vec &cond ) { |
---|
| 960 | this->iepdf._mu() = A * cond + mu_const; |
---|
[487] | 961 | //R is already assigned; |
---|
[737] | 962 | } |
---|
[198] | 963 | |
---|
[737] | 964 | //!access function |
---|
| 965 | const vec& _mu_const() const { |
---|
| 966 | return mu_const; |
---|
| 967 | } |
---|
| 968 | //!access function |
---|
| 969 | const mat& _A() const { |
---|
| 970 | return A; |
---|
| 971 | } |
---|
| 972 | //!access function |
---|
| 973 | mat _R() const { |
---|
| 974 | return this->iepdf._R().to_mat(); |
---|
| 975 | } |
---|
| 976 | //!access function |
---|
| 977 | sq_T __R() const { |
---|
| 978 | return this->iepdf._R(); |
---|
| 979 | } |
---|
[8] | 980 | |
---|
[737] | 981 | //! Debug stream |
---|
| 982 | template<typename sq_M> |
---|
| 983 | friend std::ostream &operator<< ( std::ostream &os, mlnorm<sq_M, enorm> &ml ); |
---|
[488] | 984 | |
---|
[737] | 985 | /*! Create Normal density with linear function of mean value |
---|
| 986 | \f[ f(rv|rvc) = N(A*rvc+const, R) \f] |
---|
| 987 | from structure |
---|
| 988 | \code |
---|
| 989 | class = 'mlnorm<ldmat>', (OR) 'mlnorm<chmat>', (OR) 'mlnorm<fsqmat>'; |
---|
| 990 | A = []; // matrix or vector of appropriate dimension |
---|
| 991 | const = []; // vector of constant term |
---|
| 992 | R = []; // square matrix of appropriate dimension |
---|
| 993 | \endcode |
---|
| 994 | */ |
---|
| 995 | void from_setting ( const Setting &set ) { |
---|
| 996 | pdf::from_setting ( set ); |
---|
| 997 | |
---|
| 998 | UI::get ( A, set, "A", UI::compulsory ); |
---|
| 999 | UI::get ( mu_const, set, "const", UI::compulsory ); |
---|
| 1000 | mat R0; |
---|
| 1001 | UI::get ( R0, set, "R", UI::compulsory ); |
---|
| 1002 | set_parameters ( A, mu_const, R0 ); |
---|
| 1003 | validate(); |
---|
| 1004 | }; |
---|
[763] | 1005 | void to_setting (Setting &set) const { |
---|
| 1006 | pdf::to_setting(set); |
---|
| 1007 | UI::save ( A, set, "A"); |
---|
| 1008 | UI::save ( mu_const, set, "const"); |
---|
| 1009 | UI::save ( _R(), set, "R"); |
---|
| 1010 | } |
---|
[737] | 1011 | void validate() { |
---|
| 1012 | pdf_internal<TEpdf<sq_T> >::validate(); |
---|
| 1013 | bdm_assert ( A.rows() == mu_const.length(), "mlnorm: A vs. mu mismatch" ); |
---|
| 1014 | bdm_assert ( A.rows() == _R().rows(), "mlnorm: A vs. R mismatch" ); |
---|
[878] | 1015 | this->dimc = A.cols(); |
---|
[737] | 1016 | |
---|
| 1017 | } |
---|
[488] | 1018 | }; |
---|
[737] | 1019 | UIREGISTER2 ( mlnorm, ldmat ); |
---|
[529] | 1020 | SHAREDPTR2 ( mlnorm, ldmat ); |
---|
[737] | 1021 | UIREGISTER2 ( mlnorm, fsqmat ); |
---|
[529] | 1022 | SHAREDPTR2 ( mlnorm, fsqmat ); |
---|
[737] | 1023 | UIREGISTER2 ( mlnorm, chmat ); |
---|
[529] | 1024 | SHAREDPTR2 ( mlnorm, chmat ); |
---|
[488] | 1025 | |
---|
[693] | 1026 | //! pdf with general function for mean value |
---|
[488] | 1027 | template<class sq_T> |
---|
[737] | 1028 | class mgnorm : public pdf_internal< enorm< sq_T > > { |
---|
| 1029 | private: |
---|
[487] | 1030 | // vec μ WHY NOT? |
---|
[737] | 1031 | shared_ptr<fnc> g; |
---|
[527] | 1032 | |
---|
[737] | 1033 | public: |
---|
| 1034 | //!default constructor |
---|
| 1035 | mgnorm() : pdf_internal<enorm<sq_T> >() { } |
---|
| 1036 | //!set mean function |
---|
| 1037 | inline void set_parameters ( const shared_ptr<fnc> &g0, const sq_T &R0 ); |
---|
| 1038 | inline void condition ( const vec &cond ); |
---|
[357] | 1039 | |
---|
| 1040 | |
---|
[737] | 1041 | /*! Create Normal density with given function of mean value |
---|
| 1042 | \f[ f(rv|rvc) = N( g(rvc), R) \f] |
---|
| 1043 | from structure |
---|
| 1044 | \code |
---|
| 1045 | class = 'mgnorm'; |
---|
| 1046 | g.class = 'fnc'; // function for mean value evolution |
---|
| 1047 | g._fields_of_fnc = ...; |
---|
[357] | 1048 | |
---|
[737] | 1049 | R = [1, 0; // covariance matrix |
---|
| 1050 | 0, 1]; |
---|
| 1051 | --OR -- |
---|
| 1052 | dR = [1, 1]; // diagonal of cavariance matrix |
---|
[357] | 1053 | |
---|
[737] | 1054 | rv = RV({'name'}) // description of RV |
---|
| 1055 | rvc = RV({'name'}) // description of RV in condition |
---|
| 1056 | \endcode |
---|
| 1057 | */ |
---|
[357] | 1058 | |
---|
[737] | 1059 | void from_setting ( const Setting &set ) { |
---|
| 1060 | pdf::from_setting ( set ); |
---|
| 1061 | shared_ptr<fnc> g = UI::build<fnc> ( set, "g", UI::compulsory ); |
---|
[357] | 1062 | |
---|
[737] | 1063 | mat R; |
---|
| 1064 | vec dR; |
---|
| 1065 | if ( UI::get ( dR, set, "dR" ) ) |
---|
| 1066 | R = diag ( dR ); |
---|
| 1067 | else |
---|
| 1068 | UI::get ( R, set, "R", UI::compulsory ); |
---|
[280] | 1069 | |
---|
[737] | 1070 | set_parameters ( g, R ); |
---|
| 1071 | validate(); |
---|
| 1072 | } |
---|
| 1073 | void validate() { |
---|
| 1074 | bdm_assert ( g->dimension() == this->dimension(), "incompatible function" ); |
---|
[878] | 1075 | this->dim = g->dimension(); |
---|
| 1076 | this->dimc = g->dimensionc(); |
---|
| 1077 | this->iepdf.validate(); |
---|
[737] | 1078 | } |
---|
[488] | 1079 | }; |
---|
[357] | 1080 | |
---|
[737] | 1081 | UIREGISTER2 ( mgnorm, chmat ); |
---|
[529] | 1082 | SHAREDPTR2 ( mgnorm, chmat ); |
---|
[357] | 1083 | |
---|
[262] | 1084 | |
---|
[488] | 1085 | /*! (Approximate) Student t density with linear function of mean value |
---|
[262] | 1086 | |
---|
[488] | 1087 | The internal epdf of this class is of the type of a Gaussian (enorm). |
---|
| 1088 | However, each conditioning is trying to assure the best possible approximation by taking into account the zeta function. See [] for reference. |
---|
[270] | 1089 | |
---|
[488] | 1090 | Perhaps a moment-matching technique? |
---|
| 1091 | */ |
---|
[737] | 1092 | class mlstudent : public mlnorm<ldmat, enorm> { |
---|
| 1093 | protected: |
---|
| 1094 | //! Variable \f$ \Lambda \f$ from theory |
---|
| 1095 | ldmat Lambda; |
---|
| 1096 | //! Reference to variable \f$ R \f$ |
---|
| 1097 | ldmat &_R; |
---|
| 1098 | //! Variable \f$ R_e \f$ |
---|
| 1099 | ldmat Re; |
---|
| 1100 | public: |
---|
| 1101 | mlstudent () : mlnorm<ldmat, enorm> (), |
---|
| 1102 | Lambda (), _R ( iepdf._R() ) {} |
---|
| 1103 | //! constructor function |
---|
| 1104 | void set_parameters ( const mat &A0, const vec &mu0, const ldmat &R0, const ldmat& Lambda0 ) { |
---|
| 1105 | iepdf.set_parameters ( mu0, R0 );// was Lambda, why? |
---|
| 1106 | A = A0; |
---|
| 1107 | mu_const = mu0; |
---|
| 1108 | Re = R0; |
---|
| 1109 | Lambda = Lambda0; |
---|
| 1110 | } |
---|
[294] | 1111 | |
---|
[739] | 1112 | void condition ( const vec &cond ); |
---|
| 1113 | |
---|
[737] | 1114 | void validate() { |
---|
| 1115 | bdm_assert ( A.rows() == mu_const.length(), "mlstudent: A vs. mu mismatch" ); |
---|
| 1116 | bdm_assert ( _R.rows() == A.rows(), "mlstudent: A vs. R mismatch" ); |
---|
| 1117 | |
---|
| 1118 | } |
---|
[488] | 1119 | }; |
---|
[811] | 1120 | |
---|
[488] | 1121 | /*! |
---|
| 1122 | \brief Gamma random walk |
---|
[198] | 1123 | |
---|
[488] | 1124 | Mean value, \f$\mu\f$, of this density is given by \c rvc . |
---|
| 1125 | Standard deviation of the random walk is proportional to one \f$k\f$-th the mean. |
---|
| 1126 | This is achieved by setting \f$\alpha=k\f$ and \f$\beta=k/\mu\f$. |
---|
[32] | 1127 | |
---|
[488] | 1128 | The standard deviation of the walk is then: \f$\mu/\sqrt(k)\f$. |
---|
| 1129 | */ |
---|
[737] | 1130 | class mgamma : public pdf_internal<egamma> { |
---|
| 1131 | protected: |
---|
[461] | 1132 | |
---|
[737] | 1133 | //! Constant \f$k\f$ |
---|
| 1134 | double k; |
---|
[461] | 1135 | |
---|
[737] | 1136 | //! cache of iepdf.beta |
---|
| 1137 | vec &_beta; |
---|
[32] | 1138 | |
---|
[737] | 1139 | public: |
---|
| 1140 | //! Constructor |
---|
| 1141 | mgamma() : pdf_internal<egamma>(), k ( 0 ), |
---|
| 1142 | _beta ( iepdf._beta() ) { |
---|
| 1143 | } |
---|
[461] | 1144 | |
---|
[737] | 1145 | //! Set value of \c k |
---|
| 1146 | void set_parameters ( double k, const vec &beta0 ); |
---|
[461] | 1147 | |
---|
[737] | 1148 | void condition ( const vec &val ) { |
---|
| 1149 | _beta = k / val; |
---|
| 1150 | }; |
---|
| 1151 | /*! Create Gamma density with conditional mean value |
---|
| 1152 | \f[ f(rv|rvc) = \Gamma(k, k/rvc) \f] |
---|
| 1153 | from structure |
---|
| 1154 | \code |
---|
| 1155 | class = 'mgamma'; |
---|
| 1156 | beta = [...]; // vector of initial alpha |
---|
| 1157 | k = 1.1; // multiplicative constant k |
---|
| 1158 | rv = RV({'name'}) // description of RV |
---|
| 1159 | rvc = RV({'name'}) // description of RV in condition |
---|
| 1160 | \endcode |
---|
| 1161 | */ |
---|
| 1162 | void from_setting ( const Setting &set ) { |
---|
| 1163 | pdf::from_setting ( set ); // reads rv and rvc |
---|
| 1164 | vec betatmp; // ugly but necessary |
---|
| 1165 | UI::get ( betatmp, set, "beta", UI::compulsory ); |
---|
| 1166 | UI::get ( k, set, "k", UI::compulsory ); |
---|
| 1167 | set_parameters ( k, betatmp ); |
---|
| 1168 | validate(); |
---|
| 1169 | } |
---|
| 1170 | void validate() { |
---|
| 1171 | pdf_internal<egamma>::validate(); |
---|
| 1172 | |
---|
| 1173 | dim = _beta.length(); |
---|
| 1174 | dimc = _beta.length(); |
---|
| 1175 | } |
---|
[488] | 1176 | }; |
---|
[737] | 1177 | UIREGISTER ( mgamma ); |
---|
| 1178 | SHAREDPTR ( mgamma ); |
---|
[32] | 1179 | |
---|
[488] | 1180 | /*! |
---|
| 1181 | \brief Inverse-Gamma random walk |
---|
[225] | 1182 | |
---|
[488] | 1183 | Mean value, \f$ \mu \f$, of this density is given by \c rvc . |
---|
| 1184 | Standard deviation of the random walk is proportional to one \f$ k \f$-th the mean. |
---|
| 1185 | This is achieved by setting \f$ \alpha=\mu/k^2+2 \f$ and \f$ \beta=\mu(\alpha-1)\f$. |
---|
[461] | 1186 | |
---|
[488] | 1187 | The standard deviation of the walk is then: \f$ \mu/\sqrt(k)\f$. |
---|
| 1188 | */ |
---|
[737] | 1189 | class migamma : public pdf_internal<eigamma> { |
---|
| 1190 | protected: |
---|
| 1191 | //! Constant \f$k\f$ |
---|
| 1192 | double k; |
---|
[461] | 1193 | |
---|
[737] | 1194 | //! cache of iepdf.alpha |
---|
| 1195 | vec &_alpha; |
---|
[225] | 1196 | |
---|
[737] | 1197 | //! cache of iepdf.beta |
---|
| 1198 | vec &_beta; |
---|
[461] | 1199 | |
---|
[737] | 1200 | public: |
---|
| 1201 | //! \name Constructors |
---|
| 1202 | //!@{ |
---|
| 1203 | migamma() : pdf_internal<eigamma>(), |
---|
| 1204 | k ( 0 ), |
---|
| 1205 | _alpha ( iepdf._alpha() ), |
---|
| 1206 | _beta ( iepdf._beta() ) { |
---|
| 1207 | } |
---|
[225] | 1208 | |
---|
[737] | 1209 | migamma ( const migamma &m ) : pdf_internal<eigamma>(), |
---|
| 1210 | k ( 0 ), |
---|
| 1211 | _alpha ( iepdf._alpha() ), |
---|
| 1212 | _beta ( iepdf._beta() ) { |
---|
| 1213 | } |
---|
| 1214 | //!@} |
---|
[225] | 1215 | |
---|
[737] | 1216 | //! Set value of \c k |
---|
| 1217 | void set_parameters ( int len, double k0 ) { |
---|
| 1218 | k = k0; |
---|
[878] | 1219 | iepdf.set_parameters ( ( 1.0 / ( k*k ) + 2.0 ) *ones ( len ) /*alpha*/, ones ( len ) /*beta*/ ); |
---|
| 1220 | }; |
---|
| 1221 | |
---|
| 1222 | void validate (){ |
---|
| 1223 | iepdf.validate(); |
---|
[737] | 1224 | dimc = dimension(); |
---|
[878] | 1225 | }; |
---|
| 1226 | |
---|
[737] | 1227 | void condition ( const vec &val ) { |
---|
| 1228 | _beta = elem_mult ( val, ( _alpha - 1.0 ) ); |
---|
| 1229 | }; |
---|
[488] | 1230 | }; |
---|
[357] | 1231 | |
---|
[60] | 1232 | |
---|
[488] | 1233 | /*! |
---|
| 1234 | \brief Gamma random walk around a fixed point |
---|
[60] | 1235 | |
---|
[488] | 1236 | 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 |
---|
| 1237 | \f[ \mu = \mu_{t-1} ^{l} p^{1-l}\f] |
---|
[60] | 1238 | |
---|
[488] | 1239 | Standard deviation of the random walk is proportional to one \f$k\f$-th the mean. |
---|
| 1240 | This is achieved by setting \f$\alpha=k\f$ and \f$\beta=k/\mu\f$. |
---|
[294] | 1241 | |
---|
[488] | 1242 | The standard deviation of the walk is then: \f$\mu/\sqrt(k)\f$. |
---|
| 1243 | */ |
---|
[737] | 1244 | class mgamma_fix : public mgamma { |
---|
| 1245 | protected: |
---|
| 1246 | //! parameter l |
---|
| 1247 | double l; |
---|
| 1248 | //! reference vector |
---|
| 1249 | vec refl; |
---|
| 1250 | public: |
---|
| 1251 | //! Constructor |
---|
| 1252 | mgamma_fix () : mgamma (), refl () {}; |
---|
| 1253 | //! Set value of \c k |
---|
| 1254 | void set_parameters ( double k0 , vec ref0, double l0 ) { |
---|
| 1255 | mgamma::set_parameters ( k0, ref0 ); |
---|
| 1256 | refl = pow ( ref0, 1.0 - l0 ); |
---|
[878] | 1257 | l = l0; |
---|
| 1258 | }; |
---|
| 1259 | |
---|
| 1260 | void validate (){ |
---|
| 1261 | mgamma::validate(); |
---|
[737] | 1262 | dimc = dimension(); |
---|
| 1263 | }; |
---|
[60] | 1264 | |
---|
[737] | 1265 | void condition ( const vec &val ) { |
---|
| 1266 | vec mean = elem_mult ( refl, pow ( val, l ) ); |
---|
| 1267 | _beta = k / mean; |
---|
| 1268 | }; |
---|
[488] | 1269 | }; |
---|
[60] | 1270 | |
---|
[225] | 1271 | |
---|
[488] | 1272 | /*! |
---|
| 1273 | \brief Inverse-Gamma random walk around a fixed point |
---|
[225] | 1274 | |
---|
[488] | 1275 | 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 |
---|
| 1276 | \f[ \mu = \mu_{t-1} ^{l} p^{1-l}\f] |
---|
[225] | 1277 | |
---|
[488] | 1278 | ==== Check == vv = |
---|
| 1279 | Standard deviation of the random walk is proportional to one \f$k\f$-th the mean. |
---|
| 1280 | This is achieved by setting \f$\alpha=k\f$ and \f$\beta=k/\mu\f$. |
---|
[225] | 1281 | |
---|
[488] | 1282 | The standard deviation of the walk is then: \f$\mu/\sqrt(k)\f$. |
---|
| 1283 | */ |
---|
[737] | 1284 | class migamma_ref : public migamma { |
---|
| 1285 | protected: |
---|
| 1286 | //! parameter l |
---|
| 1287 | double l; |
---|
| 1288 | //! reference vector |
---|
| 1289 | vec refl; |
---|
| 1290 | public: |
---|
| 1291 | //! Constructor |
---|
| 1292 | migamma_ref () : migamma (), refl () {}; |
---|
[766] | 1293 | |
---|
[737] | 1294 | //! Set value of \c k |
---|
| 1295 | void set_parameters ( double k0 , vec ref0, double l0 ) { |
---|
| 1296 | migamma::set_parameters ( ref0.length(), k0 ); |
---|
| 1297 | refl = pow ( ref0, 1.0 - l0 ); |
---|
| 1298 | l = l0; |
---|
[878] | 1299 | }; |
---|
| 1300 | |
---|
| 1301 | void validate(){ |
---|
| 1302 | migamma::validate(); |
---|
[737] | 1303 | dimc = dimension(); |
---|
| 1304 | }; |
---|
[878] | 1305 | |
---|
[737] | 1306 | void condition ( const vec &val ) { |
---|
| 1307 | vec mean = elem_mult ( refl, pow ( val, l ) ); |
---|
| 1308 | migamma::condition ( mean ); |
---|
| 1309 | }; |
---|
[357] | 1310 | |
---|
| 1311 | |
---|
[737] | 1312 | /*! Create inverse-Gamma density with conditional mean value |
---|
| 1313 | \f[ f(rv|rvc) = i\Gamma(k, k/(rvc^l \circ ref^{(1-l)}) \f] |
---|
| 1314 | from structure |
---|
| 1315 | \code |
---|
| 1316 | class = 'migamma_ref'; |
---|
| 1317 | ref = [1e-5; 1e-5; 1e-2 1e-3]; // reference vector |
---|
| 1318 | l = 0.999; // constant l |
---|
| 1319 | k = 0.1; // constant k |
---|
| 1320 | rv = RV({'name'}) // description of RV |
---|
| 1321 | rvc = RV({'name'}) // description of RV in condition |
---|
| 1322 | \endcode |
---|
| 1323 | */ |
---|
| 1324 | void from_setting ( const Setting &set ); |
---|
[225] | 1325 | |
---|
[737] | 1326 | // TODO dodelat void to_setting( Setting &set ) const; |
---|
[488] | 1327 | }; |
---|
[357] | 1328 | |
---|
| 1329 | |
---|
[737] | 1330 | UIREGISTER ( migamma_ref ); |
---|
| 1331 | SHAREDPTR ( migamma_ref ); |
---|
[294] | 1332 | |
---|
[488] | 1333 | /*! Log-Normal probability density |
---|
| 1334 | only allow diagonal covariances! |
---|
[294] | 1335 | |
---|
[488] | 1336 | Density of the form \f$ \log(x)\sim \mathcal{N}(\mu,\sigma^2) \f$ , i.e. |
---|
| 1337 | \f[ |
---|
| 1338 | x \sim \frac{1}{x\sigma\sqrt{2\pi}}\exp{-\frac{1}{2\sigma^2}(\log(x)-\mu)} |
---|
| 1339 | \f] |
---|
[294] | 1340 | |
---|
[621] | 1341 | Function from_setting loads mu and R in the same way as it does for enorm<>! |
---|
[488] | 1342 | */ |
---|
[737] | 1343 | class elognorm: public enorm<ldmat> { |
---|
| 1344 | public: |
---|
| 1345 | vec sample() const { |
---|
| 1346 | return exp ( enorm<ldmat>::sample() ); |
---|
| 1347 | }; |
---|
| 1348 | vec mean() const { |
---|
| 1349 | vec var = enorm<ldmat>::variance(); |
---|
| 1350 | return exp ( mu - 0.5*var ); |
---|
| 1351 | }; |
---|
[285] | 1352 | |
---|
[488] | 1353 | }; |
---|
[285] | 1354 | |
---|
[488] | 1355 | /*! |
---|
| 1356 | \brief Log-Normal random walk |
---|
[285] | 1357 | |
---|
[488] | 1358 | Mean value, \f$\mu\f$, is... |
---|
[285] | 1359 | |
---|
[488] | 1360 | */ |
---|
[737] | 1361 | class mlognorm : public pdf_internal<elognorm> { |
---|
| 1362 | protected: |
---|
| 1363 | //! parameter 1/2*sigma^2 |
---|
| 1364 | double sig2; |
---|
[461] | 1365 | |
---|
[737] | 1366 | //! access |
---|
| 1367 | vec μ |
---|
| 1368 | public: |
---|
| 1369 | //! Constructor |
---|
| 1370 | mlognorm() : pdf_internal<elognorm>(), |
---|
| 1371 | sig2 ( 0 ), |
---|
| 1372 | mu ( iepdf._mu() ) { |
---|
| 1373 | } |
---|
[285] | 1374 | |
---|
[737] | 1375 | //! Set value of \c k |
---|
| 1376 | void set_parameters ( int size, double k ) { |
---|
| 1377 | sig2 = 0.5 * log ( k * k + 1 ); |
---|
| 1378 | iepdf.set_parameters ( zeros ( size ), 2*sig2*eye ( size ) ); |
---|
| 1379 | }; |
---|
[878] | 1380 | |
---|
| 1381 | void validate(){ |
---|
| 1382 | iepdf.validate(); |
---|
| 1383 | dimc = iepdf.dimension(); |
---|
| 1384 | } |
---|
[357] | 1385 | |
---|
[737] | 1386 | void condition ( const vec &val ) { |
---|
| 1387 | mu = log ( val ) - sig2;//elem_mult ( refl,pow ( val,l ) ); |
---|
| 1388 | }; |
---|
[357] | 1389 | |
---|
[737] | 1390 | /*! Create logNormal random Walk |
---|
| 1391 | \f[ f(rv|rvc) = log\mathcal{N}( \log(rvc)-0.5\log(k^2+1), k I) \f] |
---|
| 1392 | from structure |
---|
| 1393 | \code |
---|
| 1394 | class = 'mlognorm'; |
---|
| 1395 | k = 0.1; // "variance" k |
---|
| 1396 | mu0 = 0.1; // Initial value of mean |
---|
| 1397 | rv = RV({'name'}) // description of RV |
---|
| 1398 | rvc = RV({'name'}) // description of RV in condition |
---|
| 1399 | \endcode |
---|
| 1400 | */ |
---|
| 1401 | void from_setting ( const Setting &set ); |
---|
[357] | 1402 | |
---|
[737] | 1403 | // TODO dodelat void to_setting( Setting &set ) const; |
---|
[285] | 1404 | |
---|
[488] | 1405 | }; |
---|
[294] | 1406 | |
---|
[737] | 1407 | UIREGISTER ( mlognorm ); |
---|
| 1408 | SHAREDPTR ( mlognorm ); |
---|
[294] | 1409 | |
---|
[488] | 1410 | /*! inverse Wishart density defined on Choleski decomposition |
---|
| 1411 | |
---|
| 1412 | */ |
---|
[737] | 1413 | class eWishartCh : public epdf { |
---|
| 1414 | protected: |
---|
| 1415 | //! Upper-Triagle of Choleski decomposition of \f$ \Psi \f$ |
---|
| 1416 | chmat Y; |
---|
| 1417 | //! dimension of matrix \f$ \Psi \f$ |
---|
| 1418 | int p; |
---|
| 1419 | //! degrees of freedom \f$ \nu \f$ |
---|
| 1420 | double delta; |
---|
| 1421 | public: |
---|
| 1422 | //! Set internal structures |
---|
| 1423 | void set_parameters ( const mat &Y0, const double delta0 ) { |
---|
| 1424 | Y = chmat ( Y0 ); |
---|
| 1425 | delta = delta0; |
---|
[878] | 1426 | p = Y.rows(); |
---|
[737] | 1427 | } |
---|
| 1428 | //! Set internal structures |
---|
| 1429 | void set_parameters ( const chmat &Y0, const double delta0 ) { |
---|
| 1430 | Y = Y0; |
---|
| 1431 | delta = delta0; |
---|
| 1432 | p = Y.rows(); |
---|
[878] | 1433 | } |
---|
| 1434 | |
---|
| 1435 | virtual void validate (){ |
---|
[737] | 1436 | dim = p * p; |
---|
| 1437 | } |
---|
[878] | 1438 | |
---|
[737] | 1439 | //! Sample matrix argument |
---|
| 1440 | mat sample_mat() const { |
---|
| 1441 | mat X = zeros ( p, p ); |
---|
[488] | 1442 | |
---|
[737] | 1443 | //sample diagonal |
---|
| 1444 | for ( int i = 0; i < p; i++ ) { |
---|
| 1445 | GamRNG.setup ( 0.5* ( delta - i ) , 0.5 ); // no +1 !! index if from 0 |
---|
[294] | 1446 | #pragma omp critical |
---|
[737] | 1447 | X ( i, i ) = sqrt ( GamRNG() ); |
---|
| 1448 | } |
---|
| 1449 | //do the rest |
---|
| 1450 | for ( int i = 0; i < p; i++ ) { |
---|
| 1451 | for ( int j = i + 1; j < p; j++ ) { |
---|
[294] | 1452 | #pragma omp critical |
---|
[737] | 1453 | X ( i, j ) = NorRNG.sample(); |
---|
[294] | 1454 | } |
---|
[488] | 1455 | } |
---|
[737] | 1456 | return X*Y._Ch();// return upper triangular part of the decomposition |
---|
| 1457 | } |
---|
[766] | 1458 | |
---|
[737] | 1459 | vec sample () const { |
---|
| 1460 | return vec ( sample_mat()._data(), p*p ); |
---|
| 1461 | } |
---|
[766] | 1462 | |
---|
| 1463 | virtual vec mean() const NOT_IMPLEMENTED(0); |
---|
| 1464 | |
---|
| 1465 | //! return expected variance (not covariance!) |
---|
| 1466 | virtual vec variance() const NOT_IMPLEMENTED(0); |
---|
| 1467 | |
---|
| 1468 | virtual double evallog ( const vec &val ) const NOT_IMPLEMENTED(0); |
---|
| 1469 | |
---|
[737] | 1470 | //! fast access function y0 will be copied into Y.Ch. |
---|
| 1471 | void setY ( const mat &Ch0 ) { |
---|
| 1472 | copy_vector ( dim, Ch0._data(), Y._Ch()._data() ); |
---|
| 1473 | } |
---|
[766] | 1474 | |
---|
[737] | 1475 | //! fast access function y0 will be copied into Y.Ch. |
---|
| 1476 | void _setY ( const vec &ch0 ) { |
---|
| 1477 | copy_vector ( dim, ch0._data(), Y._Ch()._data() ); |
---|
| 1478 | } |
---|
[766] | 1479 | |
---|
[737] | 1480 | //! access function |
---|
| 1481 | const chmat& getY() const { |
---|
| 1482 | return Y; |
---|
| 1483 | } |
---|
[488] | 1484 | }; |
---|
[294] | 1485 | |
---|
[536] | 1486 | //! Inverse Wishart on Choleski decomposition |
---|
| 1487 | /*! Being computed by conversion from `standard' Wishart |
---|
| 1488 | */ |
---|
[737] | 1489 | class eiWishartCh: public epdf { |
---|
| 1490 | protected: |
---|
| 1491 | //! Internal instance of Wishart density |
---|
| 1492 | eWishartCh W; |
---|
| 1493 | //! size of Ch |
---|
| 1494 | int p; |
---|
| 1495 | //! parameter delta |
---|
| 1496 | double delta; |
---|
| 1497 | public: |
---|
| 1498 | //! constructor function |
---|
| 1499 | void set_parameters ( const mat &Y0, const double delta0 ) { |
---|
| 1500 | delta = delta0; |
---|
| 1501 | W.set_parameters ( inv ( Y0 ), delta0 ); |
---|
| 1502 | p = Y0.rows(); |
---|
| 1503 | } |
---|
[750] | 1504 | |
---|
| 1505 | virtual void validate (){ |
---|
[878] | 1506 | W.validate(); |
---|
| 1507 | dim = W.dimension(); |
---|
[750] | 1508 | } |
---|
| 1509 | |
---|
| 1510 | |
---|
[737] | 1511 | vec sample() const { |
---|
| 1512 | mat iCh; |
---|
| 1513 | iCh = inv ( W.sample_mat() ); |
---|
| 1514 | return vec ( iCh._data(), dim ); |
---|
| 1515 | } |
---|
| 1516 | //! access function |
---|
| 1517 | void _setY ( const vec &y0 ) { |
---|
| 1518 | mat Ch ( p, p ); |
---|
| 1519 | mat iCh ( p, p ); |
---|
| 1520 | copy_vector ( dim, y0._data(), Ch._data() ); |
---|
[488] | 1521 | |
---|
[737] | 1522 | iCh = inv ( Ch ); |
---|
| 1523 | W.setY ( iCh ); |
---|
| 1524 | } |
---|
[766] | 1525 | |
---|
[737] | 1526 | virtual double evallog ( const vec &val ) const { |
---|
| 1527 | chmat X ( p ); |
---|
| 1528 | const chmat& Y = W.getY(); |
---|
[488] | 1529 | |
---|
[737] | 1530 | copy_vector ( p*p, val._data(), X._Ch()._data() ); |
---|
| 1531 | chmat iX ( p ); |
---|
| 1532 | X.inv ( iX ); |
---|
| 1533 | // compute |
---|
[294] | 1534 | // \frac{ |\Psi|^{m/2}|X|^{-(m+p+1)/2}e^{-tr(\Psi X^{-1})/2} }{ 2^{mp/2}\Gamma_p(m/2)}, |
---|
[737] | 1535 | mat M = Y.to_mat() * iX.to_mat(); |
---|
[285] | 1536 | |
---|
[737] | 1537 | double log1 = 0.5 * p * ( 2 * Y.logdet() ) - 0.5 * ( delta + p + 1 ) * ( 2 * X.logdet() ) - 0.5 * trace ( M ); |
---|
| 1538 | //Fixme! Multivariate gamma omitted!! it is ok for sampling, but not otherwise!! |
---|
[461] | 1539 | |
---|
[737] | 1540 | /* if (0) { |
---|
| 1541 | mat XX=X.to_mat(); |
---|
| 1542 | mat YY=Y.to_mat(); |
---|
[461] | 1543 | |
---|
[737] | 1544 | double log2 = 0.5*p*log(det(YY))-0.5*(delta+p+1)*log(det(XX))-0.5*trace(YY*inv(XX)); |
---|
| 1545 | cout << log1 << "," << log2 << endl; |
---|
| 1546 | }*/ |
---|
| 1547 | return log1; |
---|
| 1548 | }; |
---|
[285] | 1549 | |
---|
[766] | 1550 | virtual vec mean() const NOT_IMPLEMENTED(0); |
---|
| 1551 | |
---|
| 1552 | //! return expected variance (not covariance!) |
---|
| 1553 | virtual vec variance() const NOT_IMPLEMENTED(0); |
---|
[488] | 1554 | }; |
---|
| 1555 | |
---|
[536] | 1556 | //! Random Walk on inverse Wishart |
---|
[737] | 1557 | class rwiWishartCh : public pdf_internal<eiWishartCh> { |
---|
| 1558 | protected: |
---|
| 1559 | //!square root of \f$ \nu-p-1 \f$ - needed for computation of \f$ \Psi \f$ from conditions |
---|
| 1560 | double sqd; |
---|
| 1561 | //!reference point for diagonal |
---|
| 1562 | vec refl; |
---|
| 1563 | //! power of the reference |
---|
| 1564 | double l; |
---|
| 1565 | //! dimension |
---|
| 1566 | int p; |
---|
[488] | 1567 | |
---|
[737] | 1568 | public: |
---|
| 1569 | rwiWishartCh() : sqd ( 0 ), l ( 0 ), p ( 0 ) {} |
---|
| 1570 | //! constructor function |
---|
| 1571 | void set_parameters ( int p0, double k, vec ref0, double l0 ) { |
---|
| 1572 | p = p0; |
---|
| 1573 | double delta = 2 / ( k * k ) + p + 3; |
---|
| 1574 | sqd = sqrt ( delta - p - 1 ); |
---|
| 1575 | l = l0; |
---|
| 1576 | refl = pow ( ref0, 1 - l ); |
---|
| 1577 | iepdf.set_parameters ( eye ( p ), delta ); |
---|
[878] | 1578 | }; |
---|
| 1579 | |
---|
| 1580 | void validate(){ |
---|
| 1581 | iepdf.validate(); |
---|
[737] | 1582 | dimc = iepdf.dimension(); |
---|
| 1583 | } |
---|
[878] | 1584 | |
---|
[737] | 1585 | void condition ( const vec &c ) { |
---|
| 1586 | vec z = c; |
---|
| 1587 | int ri = 0; |
---|
| 1588 | for ( int i = 0; i < p*p; i += ( p + 1 ) ) {//trace diagonal element |
---|
| 1589 | z ( i ) = pow ( z ( i ), l ) * refl ( ri ); |
---|
| 1590 | ri++; |
---|
[488] | 1591 | } |
---|
[285] | 1592 | |
---|
[737] | 1593 | iepdf._setY ( sqd*z ); |
---|
| 1594 | } |
---|
[488] | 1595 | }; |
---|
| 1596 | |
---|
[32] | 1597 | //! Switch between various resampling methods. |
---|
[488] | 1598 | enum RESAMPLING_METHOD { MULTINOMIAL = 0, STRATIFIED = 1, SYSTEMATIC = 3 }; |
---|
[887] | 1599 | |
---|
| 1600 | //! Shortcut for multinomial.sample(int n). Various simplifications are allowed see RESAMPLING_METHOD |
---|
| 1601 | void resample(const vec &w, ivec &ind, RESAMPLING_METHOD=SYSTEMATIC); |
---|
| 1602 | |
---|
[488] | 1603 | /*! |
---|
| 1604 | \brief Weighted empirical density |
---|
[32] | 1605 | |
---|
[488] | 1606 | Used e.g. in particle filters. |
---|
| 1607 | */ |
---|
[737] | 1608 | class eEmp: public epdf { |
---|
| 1609 | protected : |
---|
| 1610 | //! Number of particles |
---|
| 1611 | int n; |
---|
| 1612 | //! Sample weights \f$w\f$ |
---|
| 1613 | vec w; |
---|
| 1614 | //! Samples \f$x^{(i)}, i=1..n\f$ |
---|
| 1615 | Array<vec> samples; |
---|
| 1616 | public: |
---|
| 1617 | //! \name Constructors |
---|
| 1618 | //!@{ |
---|
| 1619 | eEmp () : epdf (), w (), samples () {}; |
---|
| 1620 | //! copy constructor |
---|
| 1621 | eEmp ( const eEmp &e ) : epdf ( e ), w ( e.w ), samples ( e.samples ) {}; |
---|
| 1622 | //!@} |
---|
[280] | 1623 | |
---|
[737] | 1624 | //! Set samples and weights |
---|
| 1625 | void set_statistics ( const vec &w0, const epdf &pdf0 ); |
---|
| 1626 | //! Set samples and weights |
---|
| 1627 | void set_statistics ( const epdf &pdf0 , int n ) { |
---|
| 1628 | set_statistics ( ones ( n ) / n, pdf0 ); |
---|
| 1629 | }; |
---|
| 1630 | //! Set sample |
---|
| 1631 | void set_samples ( const epdf* pdf0 ); |
---|
| 1632 | //! Set sample |
---|
| 1633 | void set_parameters ( int n0, bool copy = true ) { |
---|
| 1634 | n = n0; |
---|
| 1635 | w.set_size ( n0, copy ); |
---|
| 1636 | samples.set_size ( n0, copy ); |
---|
| 1637 | }; |
---|
| 1638 | //! Set samples |
---|
| 1639 | void set_parameters ( const Array<vec> &Av ) { |
---|
| 1640 | n = Av.size(); |
---|
| 1641 | w = 1 / n * ones ( n ); |
---|
| 1642 | samples = Av; |
---|
| 1643 | }; |
---|
[750] | 1644 | virtual void validate (){ |
---|
[760] | 1645 | bdm_assert (samples.length()==w.length(),"samples and weigths are of different lengths"); |
---|
| 1646 | n = w.length(); |
---|
| 1647 | if (n>0) |
---|
[795] | 1648 | pdf::dim = samples ( 0 ).length(); |
---|
[750] | 1649 | } |
---|
[737] | 1650 | //! Potentially dangerous, use with care. |
---|
| 1651 | vec& _w() { |
---|
| 1652 | return w; |
---|
| 1653 | }; |
---|
| 1654 | //! Potentially dangerous, use with care. |
---|
| 1655 | const vec& _w() const { |
---|
| 1656 | return w; |
---|
| 1657 | }; |
---|
| 1658 | //! access function |
---|
| 1659 | Array<vec>& _samples() { |
---|
| 1660 | return samples; |
---|
| 1661 | }; |
---|
| 1662 | //! access function |
---|
| 1663 | const vec& _sample ( int i ) const { |
---|
| 1664 | return samples ( i ); |
---|
| 1665 | }; |
---|
| 1666 | //! access function |
---|
| 1667 | const Array<vec>& _samples() const { |
---|
| 1668 | return samples; |
---|
| 1669 | }; |
---|
| 1670 | //! 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. |
---|
[887] | 1671 | void resample ( RESAMPLING_METHOD method = SYSTEMATIC ); |
---|
[565] | 1672 | |
---|
[737] | 1673 | //! inherited operation : NOT implemented |
---|
[766] | 1674 | vec sample() const NOT_IMPLEMENTED(0); |
---|
[565] | 1675 | |
---|
[737] | 1676 | //! inherited operation : NOT implemented |
---|
[766] | 1677 | double evallog ( const vec &val ) const NOT_IMPLEMENTED(0); |
---|
[737] | 1678 | |
---|
| 1679 | vec mean() const { |
---|
| 1680 | vec pom = zeros ( dim ); |
---|
| 1681 | for ( int i = 0; i < n; i++ ) { |
---|
| 1682 | pom += samples ( i ) * w ( i ); |
---|
[488] | 1683 | } |
---|
[737] | 1684 | return pom; |
---|
| 1685 | } |
---|
| 1686 | vec variance() const { |
---|
| 1687 | vec pom = zeros ( dim ); |
---|
| 1688 | for ( int i = 0; i < n; i++ ) { |
---|
| 1689 | pom += pow ( samples ( i ), 2 ) * w ( i ); |
---|
[488] | 1690 | } |
---|
[737] | 1691 | return pom - pow ( mean(), 2 ); |
---|
| 1692 | } |
---|
| 1693 | //! For this class, qbounds are minimum and maximum value of the population! |
---|
[739] | 1694 | void qbounds ( vec &lb, vec &ub, double perc = 0.95 ) const; |
---|
[760] | 1695 | |
---|
| 1696 | void to_setting ( Setting &set ) const { |
---|
| 1697 | epdf::to_setting( set ); |
---|
| 1698 | UI::save ( samples, set, "samples" ); |
---|
| 1699 | UI::save ( w, set, "w" ); |
---|
| 1700 | } |
---|
| 1701 | |
---|
| 1702 | void from_setting ( const Setting &set ) { |
---|
| 1703 | epdf::from_setting( set ); |
---|
| 1704 | |
---|
| 1705 | UI::get( samples, set, "samples", UI::compulsory ); |
---|
| 1706 | UI::get ( w, set, "w", UI::compulsory ); |
---|
| 1707 | validate(); |
---|
| 1708 | } |
---|
| 1709 | |
---|
[488] | 1710 | }; |
---|
[760] | 1711 | UIREGISTER(eEmp); |
---|
[32] | 1712 | |
---|
| 1713 | |
---|
[8] | 1714 | //////////////////////// |
---|
| 1715 | |
---|
[488] | 1716 | template<class sq_T> |
---|
[737] | 1717 | void enorm<sq_T>::set_parameters ( const vec &mu0, const sq_T &R0 ) { |
---|
[28] | 1718 | //Fixme test dimensions of mu0 and R0; |
---|
[488] | 1719 | mu = mu0; |
---|
| 1720 | R = R0; |
---|
| 1721 | validate(); |
---|
| 1722 | }; |
---|
[8] | 1723 | |
---|
[488] | 1724 | template<class sq_T> |
---|
[737] | 1725 | void enorm<sq_T>::from_setting ( const Setting &set ) { |
---|
| 1726 | epdf::from_setting ( set ); //reads rv |
---|
[384] | 1727 | |
---|
[737] | 1728 | UI::get ( mu, set, "mu", UI::compulsory ); |
---|
[488] | 1729 | mat Rtmp;// necessary for conversion |
---|
[737] | 1730 | UI::get ( Rtmp, set, "R", UI::compulsory ); |
---|
[488] | 1731 | R = Rtmp; // conversion |
---|
| 1732 | validate(); |
---|
| 1733 | } |
---|
[8] | 1734 | |
---|
[488] | 1735 | template<class sq_T> |
---|
[773] | 1736 | void enorm<sq_T>::to_setting ( Setting &set ) const { |
---|
| 1737 | epdf::to_setting ( set ); //reads rv |
---|
| 1738 | UI::save ( mu, set, "mu"); |
---|
| 1739 | UI::save ( R.to_mat(), set, "R"); |
---|
| 1740 | } |
---|
| 1741 | |
---|
| 1742 | template<class sq_T> |
---|
[737] | 1743 | void enorm<sq_T>::dupdate ( mat &v, double nu ) { |
---|
[488] | 1744 | // |
---|
| 1745 | }; |
---|
| 1746 | |
---|
[178] | 1747 | // template<class sq_T> |
---|
| 1748 | // void enorm<sq_T>::tupdate ( double phi, mat &vbar, double nubar ) { |
---|
| 1749 | // // |
---|
| 1750 | // }; |
---|
[8] | 1751 | |
---|
[488] | 1752 | template<class sq_T> |
---|
[737] | 1753 | vec enorm<sq_T>::sample() const { |
---|
| 1754 | vec x ( dim ); |
---|
[270] | 1755 | #pragma omp critical |
---|
[737] | 1756 | NorRNG.sample_vector ( dim, x ); |
---|
| 1757 | vec smp = R.sqrt_mult ( x ); |
---|
[12] | 1758 | |
---|
[488] | 1759 | smp += mu; |
---|
| 1760 | return smp; |
---|
| 1761 | }; |
---|
[8] | 1762 | |
---|
[214] | 1763 | // template<class sq_T> |
---|
| 1764 | // double enorm<sq_T>::eval ( const vec &val ) const { |
---|
| 1765 | // double pdfl,e; |
---|
| 1766 | // pdfl = evallog ( val ); |
---|
| 1767 | // e = exp ( pdfl ); |
---|
| 1768 | // return e; |
---|
| 1769 | // }; |
---|
[8] | 1770 | |
---|
[488] | 1771 | template<class sq_T> |
---|
[737] | 1772 | double enorm<sq_T>::evallog_nn ( const vec &val ) const { |
---|
[488] | 1773 | // 1.83787706640935 = log(2pi) |
---|
[737] | 1774 | double tmp = -0.5 * ( R.invqform ( mu - val ) );// - lognc(); |
---|
[488] | 1775 | return tmp; |
---|
| 1776 | }; |
---|
[28] | 1777 | |
---|
[488] | 1778 | template<class sq_T> |
---|
[737] | 1779 | inline double enorm<sq_T>::lognc () const { |
---|
[488] | 1780 | // 1.83787706640935 = log(2pi) |
---|
[737] | 1781 | double tmp = 0.5 * ( R.cols() * 1.83787706640935 + R.logdet() ); |
---|
[488] | 1782 | return tmp; |
---|
| 1783 | }; |
---|
[28] | 1784 | |
---|
[8] | 1785 | |
---|
[192] | 1786 | // template<class sq_T> |
---|
| 1787 | // vec mlnorm<sq_T>::samplecond (const vec &cond, double &lik ) { |
---|
| 1788 | // this->condition ( cond ); |
---|
| 1789 | // vec smp = epdf.sample(); |
---|
| 1790 | // lik = epdf.eval ( smp ); |
---|
| 1791 | // return smp; |
---|
| 1792 | // } |
---|
[8] | 1793 | |
---|
[192] | 1794 | // template<class sq_T> |
---|
| 1795 | // mat mlnorm<sq_T>::samplecond (const vec &cond, vec &lik, int n ) { |
---|
| 1796 | // int i; |
---|
| 1797 | // int dim = rv.count(); |
---|
| 1798 | // mat Smp ( dim,n ); |
---|
| 1799 | // vec smp ( dim ); |
---|
| 1800 | // this->condition ( cond ); |
---|
[198] | 1801 | // |
---|
[192] | 1802 | // for ( i=0; i<n; i++ ) { |
---|
| 1803 | // smp = epdf.sample(); |
---|
| 1804 | // lik ( i ) = epdf.eval ( smp ); |
---|
| 1805 | // Smp.set_col ( i ,smp ); |
---|
| 1806 | // } |
---|
[198] | 1807 | // |
---|
[192] | 1808 | // return Smp; |
---|
| 1809 | // } |
---|
[28] | 1810 | |
---|
[8] | 1811 | |
---|
[488] | 1812 | template<class sq_T> |
---|
[737] | 1813 | shared_ptr<epdf> enorm<sq_T>::marginal ( const RV &rvn ) const { |
---|
[504] | 1814 | enorm<sq_T> *tmp = new enorm<sq_T> (); |
---|
[737] | 1815 | shared_ptr<epdf> narrow ( tmp ); |
---|
[504] | 1816 | marginal ( rvn, *tmp ); |
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| 1817 | return narrow; |
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| 1818 | } |
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| 1819 | |
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| 1820 | template<class sq_T> |
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[737] | 1821 | void enorm<sq_T>::marginal ( const RV &rvn, enorm<sq_T> &target ) const { |
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| 1822 | bdm_assert ( isnamed(), "rv description is not assigned" ); |
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| 1823 | ivec irvn = rvn.dataind ( rv ); |
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[178] | 1824 | |
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[737] | 1825 | sq_T Rn ( R, irvn ); // select rows and columns of R |
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[280] | 1826 | |
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[504] | 1827 | target.set_rv ( rvn ); |
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[737] | 1828 | target.set_parameters ( mu ( irvn ), Rn ); |
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[488] | 1829 | } |
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[178] | 1830 | |
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[488] | 1831 | template<class sq_T> |
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[737] | 1832 | shared_ptr<pdf> enorm<sq_T>::condition ( const RV &rvn ) const { |
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[504] | 1833 | mlnorm<sq_T> *tmp = new mlnorm<sq_T> (); |
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[737] | 1834 | shared_ptr<pdf> narrow ( tmp ); |
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[504] | 1835 | condition ( rvn, *tmp ); |
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| 1836 | return narrow; |
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| 1837 | } |
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[178] | 1838 | |
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[504] | 1839 | template<class sq_T> |
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[737] | 1840 | void enorm<sq_T>::condition ( const RV &rvn, pdf &target ) const { |
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[504] | 1841 | typedef mlnorm<sq_T> TMlnorm; |
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| 1842 | |
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[737] | 1843 | bdm_assert ( isnamed(), "rvs are not assigned" ); |
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| 1844 | TMlnorm &uptarget = dynamic_cast<TMlnorm &> ( target ); |
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[270] | 1845 | |
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[737] | 1846 | RV rvc = rv.subt ( rvn ); |
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| 1847 | bdm_assert ( ( rvc._dsize() + rvn._dsize() == rv._dsize() ), "wrong rvn" ); |
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[488] | 1848 | //Permutation vector of the new R |
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[737] | 1849 | ivec irvn = rvn.dataind ( rv ); |
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| 1850 | ivec irvc = rvc.dataind ( rv ); |
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| 1851 | ivec perm = concat ( irvn , irvc ); |
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| 1852 | sq_T Rn ( R, perm ); |
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[178] | 1853 | |
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[488] | 1854 | //fixme - could this be done in general for all sq_T? |
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| 1855 | mat S = Rn.to_mat(); |
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| 1856 | //fixme |
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| 1857 | int n = rvn._dsize() - 1; |
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| 1858 | int end = R.rows() - 1; |
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[737] | 1859 | mat S11 = S.get ( 0, n, 0, n ); |
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| 1860 | mat S12 = S.get ( 0, n , rvn._dsize(), end ); |
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| 1861 | mat S22 = S.get ( rvn._dsize(), end, rvn._dsize(), end ); |
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[178] | 1862 | |
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[737] | 1863 | vec mu1 = mu ( irvn ); |
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| 1864 | vec mu2 = mu ( irvc ); |
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| 1865 | mat A = S12 * inv ( S22 ); |
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| 1866 | sq_T R_n ( S11 - A *S12.T() ); |
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[178] | 1867 | |
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[737] | 1868 | uptarget.set_rv ( rvn ); |
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| 1869 | uptarget.set_rvc ( rvc ); |
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| 1870 | uptarget.set_parameters ( A, mu1 - A*mu2, R_n ); |
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[799] | 1871 | uptarget.validate(); |
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[488] | 1872 | } |
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[178] | 1873 | |
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[887] | 1874 | /*! Dirac delta function distribution */ |
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| 1875 | class dirac: public epdf{ |
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| 1876 | protected: |
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| 1877 | vec point; |
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| 1878 | public: |
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| 1879 | double evallog (const vec &dt) const {return -inf;} |
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| 1880 | vec mean () const {return point;} |
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| 1881 | vec variance () const {return pow(point,2);} |
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| 1882 | void qbounds ( vec &lb, vec &ub, double percentage = 0.95 ) const { lb = point; ub = point;} |
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| 1883 | //! access |
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| 1884 | const vec& _point() {return point;} |
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| 1885 | void set_point(const vec& p){point =p; dim=p.length();} |
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| 1886 | vec sample() const {return point;} |
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| 1887 | }; |
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| 1888 | |
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[488] | 1889 | //// |
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| 1890 | /////// |
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| 1891 | template<class sq_T> |
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[737] | 1892 | void mgnorm<sq_T >::set_parameters ( const shared_ptr<fnc> &g0, const sq_T &R0 ) { |
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[527] | 1893 | g = g0; |
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[737] | 1894 | this->iepdf.set_parameters ( zeros ( g->dimension() ), R0 ); |
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[527] | 1895 | } |
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| 1896 | |
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[488] | 1897 | template<class sq_T> |
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[737] | 1898 | void mgnorm<sq_T >::condition ( const vec &cond ) { |
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| 1899 | this->iepdf._mu() = g->eval ( cond ); |
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| 1900 | }; |
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[28] | 1901 | |
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[536] | 1902 | //! \todo unify this stuff with to_string() |
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[488] | 1903 | template<class sq_T> |
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[737] | 1904 | std::ostream &operator<< ( std::ostream &os, mlnorm<sq_T> &ml ) { |
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[488] | 1905 | os << "A:" << ml.A << endl; |
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| 1906 | os << "mu:" << ml.mu_const << endl; |
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| 1907 | os << "R:" << ml._R() << endl; |
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| 1908 | return os; |
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| 1909 | }; |
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[28] | 1910 | |
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[254] | 1911 | } |
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[8] | 1912 | #endif //EF_H |
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