1 | /*! |
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2 | \file |
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3 | \brief Bayesian Models (bm) that use Bayes rule to learn from observations |
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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 BM_H |
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14 | #define BM_H |
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15 | |
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16 | #include <itpp/itbase.h> |
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17 | //#include <std> |
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18 | |
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19 | using namespace itpp; |
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20 | |
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21 | /*! |
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22 | * \brief Class representing variables, most often random variables |
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23 | |
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24 | * More?... |
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25 | */ |
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26 | |
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27 | class RV { |
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28 | protected: |
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29 | //! size = sum of sizes |
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30 | int size; |
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31 | //! len = number of individual rvs |
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32 | int len; |
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33 | //! Vector of unique IDs |
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34 | ivec ids; |
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35 | //! Vector of sizes |
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36 | ivec sizes; |
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37 | //! Vector of shifts from current time |
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38 | ivec times; |
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39 | //! Array of names |
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40 | Array<std::string> names; |
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41 | |
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42 | private: |
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43 | //! auxiliary function used in constructor |
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44 | void init ( ivec in_ids, Array<std::string> in_names, ivec in_sizes, ivec in_times); |
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45 | public: |
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46 | //! Full constructor which is called by the others |
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47 | RV ( ivec in_ids, Array<std::string> in_names, ivec in_sizes, ivec in_times); |
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48 | //! default constructor |
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49 | RV ( ivec ids ); |
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50 | //! Empty constructor will be set later |
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51 | RV (); |
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52 | |
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53 | //! Printing output e.g. for debugging. |
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54 | friend std::ostream &operator<< ( std::ostream &os, const RV &rv ); |
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55 | |
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56 | //! Return length (number of scalars) of the RV. |
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57 | int count() const {return size;} ; |
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58 | |
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59 | //TODO why not inline and later?? |
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60 | |
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61 | //! Find indexes of another rv in self |
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62 | ivec find ( RV rv2 ); |
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63 | //! Add (concat) another variable to the current one |
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64 | void add (const RV &rv2 ); |
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65 | //! Add (concat) another variable to the current one |
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66 | friend RV concat (const RV &rv1, const RV &rv2 ); |
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67 | //! Subtract another variable from the current one |
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68 | RV subt ( RV rv2 ); |
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69 | //! Select only variables at indeces ind |
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70 | RV subselect ( ivec ind ); |
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71 | //! Select only variables at indeces ind |
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72 | RV operator() ( ivec ind ); |
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73 | //! Generate new \c RV with \c time shifted by delta. |
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74 | void t ( int delta ); |
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75 | //! generate a list of indeces, i.e. which |
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76 | ivec indexlist(); |
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77 | |
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78 | //!access function |
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79 | Array<std::string>& _names(){return names;}; |
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80 | }; |
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81 | |
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82 | |
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83 | //! Class representing function $f(x)$ of variable $x$ represented by \c rv |
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84 | |
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85 | class fnc { |
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86 | protected: |
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87 | //! Length of the output vector |
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88 | int dimy; |
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89 | public: |
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90 | //! function evaluates numerical value of $f(x)$ at $x=cond$ |
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91 | virtual vec eval ( const vec &cond ) { |
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92 | return vec ( 0 ); |
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93 | }; |
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94 | |
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95 | //! access function |
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96 | int _dimy() const{return dimy;} |
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97 | |
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98 | //! Destructor for future use; |
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99 | virtual ~fnc() {}; |
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100 | }; |
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101 | |
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102 | |
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103 | //! Probability density function with numerical statistics, e.g. posterior density. |
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104 | |
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105 | class epdf { |
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106 | protected: |
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107 | //! Identified of the random variable |
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108 | RV rv; |
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109 | public: |
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110 | //!default constructor |
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111 | epdf() :rv ( ivec ( 0 ) ) {}; |
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112 | |
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113 | //!default constructor |
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114 | epdf ( const RV &rv0 ) :rv ( rv0 ) {}; |
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115 | |
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116 | //! Returns the required moment of the epdf |
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117 | // virtual vec moment ( const int order = 1 ); |
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118 | //! Returns a sample from the density, \f$x \sim epdf(rv)\f$ |
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119 | virtual vec sample () const =0; |
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120 | //! Compute probability of argument \c val |
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121 | virtual double eval ( const vec &val ) const {return exp(this->evalpdflog(val));}; |
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122 | |
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123 | //! Compute log-probability of argument \c val |
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124 | virtual double evalpdflog ( const vec &val ) const =0; |
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125 | |
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126 | //! return expected value |
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127 | virtual vec mean() const =0; |
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128 | |
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129 | //! Destructor for future use; |
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130 | virtual ~epdf() {}; |
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131 | //! access function |
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132 | RV _rv() const {return rv;} |
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133 | }; |
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134 | |
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135 | |
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136 | //! Conditional probability density, e.g. modeling some dependencies. |
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137 | //TODO Samplecond can be generalized |
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138 | |
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139 | class mpdf { |
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140 | protected: |
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141 | //! modeled random variable |
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142 | RV rv; |
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143 | //! random variable in condition |
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144 | RV rvc; |
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145 | //! pointer to internal epdf |
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146 | epdf* ep; |
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147 | public: |
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148 | |
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149 | //! Returns the required moment of the epdf |
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150 | // virtual fnc moment ( const int order = 1 ); |
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151 | //! Returns a sample from the density conditioned on \c cond, \f$x \sim epdf(rv|cond)\f$. \param cond is numeric value of \c rv \param ll is a return value of log-likelihood of the sample. |
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152 | virtual vec samplecond ( vec &cond, double &ll ) {this->condition(cond);vec temp= ep->sample();ll=ep->evalpdflog(temp);return temp;}; |
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153 | //! Update \c ep so that it represents this mpdf conditioned on \c rvc = cond |
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154 | virtual void condition ( const vec &cond ) {}; |
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155 | |
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156 | //! Shortcut for conditioning and evaluation of the internal epdf. In some cases, this operation can be implemented efficiently. |
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157 | virtual double evalcond (const vec &dt, const vec &cond ) {this->condition(cond);return ep->eval(dt);}; |
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158 | |
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159 | //! Destructor for future use; |
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160 | virtual ~mpdf() {}; |
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161 | |
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162 | //! Default constructor |
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163 | mpdf ( const RV &rv0, const RV &rvc0 ) :rv ( rv0 ),rvc ( rvc0 ) {}; |
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164 | //! access function |
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165 | RV _rvc(){return rvc;} |
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166 | //!access function |
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167 | epdf& _epdf(){return *ep;} |
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168 | }; |
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169 | |
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170 | /*! \brief Abstract class for discrete-time sources of data. |
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171 | |
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172 | The class abstracts operations of: (i) data aquisition, (ii) data-preprocessing, (iii) scaling of data, and (iv) data resampling from the task of estimation and control. |
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173 | Moreover, for controlled systems, it is able to receive the desired control action and perform it in the next step. (Or as soon as possible). |
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174 | |
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175 | */ |
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176 | |
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177 | class DS { |
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178 | protected: |
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179 | //!Observed variables, returned by \c getdata(). |
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180 | RV Drv; |
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181 | //!Action variables, accepted by \c write(). |
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182 | RV Urv; // |
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183 | public: |
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184 | //! Returns full vector of observed data |
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185 | void getdata ( vec &dt ); |
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186 | //! Returns data records at indeces. |
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187 | void getdata ( vec &dt, ivec &indeces ); |
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188 | //! Accepts action variable and schedule it for application. |
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189 | void write ( vec &ut ); |
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190 | //! Accepts action variables at specific indeces |
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191 | void write ( vec &ut, ivec &indeces ); |
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192 | /*! \brief Method that assigns random variables to the datasource. |
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193 | Typically, the datasource will be constructed without knowledge of random variables. This method will associate existing variables with RVs. |
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194 | |
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195 | (Inherited from m3k, may be deprecated soon). |
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196 | */ |
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197 | void linkrvs ( RV &drv, RV &urv ); |
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198 | |
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199 | //! Moves from $t$ to $t+1$, i.e. perfroms the actions and reads response of the system. |
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200 | void step(); |
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201 | |
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202 | }; |
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203 | |
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204 | /*! \brief Bayesian Model of the world, i.e. all uncertainty is modeled by probabilities. |
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205 | |
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206 | */ |
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207 | |
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208 | class BM { |
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209 | protected: |
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210 | //!Random variable of the posterior |
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211 | RV rv; |
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212 | //!Logarithm of marginalized data likelihood. |
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213 | double ll; |
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214 | //! If true, the filter will compute likelihood of the data record and store it in \c ll . Set to false if you want to save time. |
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215 | bool evalll; |
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216 | public: |
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217 | |
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218 | //!Default constructor |
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219 | BM(const RV &rv0) :rv(rv0), ll ( 0 ),evalll ( true ) {//Fixme: test rv |
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220 | }; |
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221 | |
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222 | /*! \brief Incremental Bayes rule |
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223 | @param dt vector of input data |
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224 | */ |
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225 | virtual void bayes ( const vec &dt ) = 0; |
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226 | //! Batch Bayes rule (columns of Dt are observations) |
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227 | void bayes ( mat Dt ); |
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228 | //! Returns a pointer to the epdf representing posterior density on parameters. Use with care! |
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229 | virtual epdf& _epdf()=0; |
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230 | |
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231 | //! Destructor for future use; |
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232 | virtual ~BM() {}; |
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233 | //!access function |
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234 | const RV& _rv() const {return rv;} |
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235 | //!access function |
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236 | double _ll() const {return ll;} |
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237 | }; |
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238 | |
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239 | /*! |
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240 | \brief Conditional Bayesian Filter |
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241 | |
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242 | Evaluates conditional filtering density $f(rv|rvc,data)$ for a given \c rvc which is specified in each step by calling function \c condition. |
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243 | |
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244 | This is an interface class used to assure that certain BM has operation \c condition . |
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245 | |
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246 | */ |
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247 | |
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248 | class BMcond { |
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249 | protected: |
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250 | //! Identificator of the conditioning variable |
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251 | RV rvc; |
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252 | public: |
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253 | //! Substitute \c val for \c rvc. |
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254 | virtual void condition ( const vec &val ) =0; |
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255 | //! Default constructor |
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256 | BMcond(RV &rv0):rvc(rv0){}; |
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257 | //! Destructor for future use |
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258 | virtual ~BMcond(){}; |
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259 | //! access function |
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260 | const RV& _rvc() const {return rvc;} |
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261 | }; |
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262 | |
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263 | #endif // BM_H |
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