1 | /*! |
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2 | \file |
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3 | \brief Common DataSources. |
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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 DATASOURCE_H |
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14 | #define DATASOURCE_H |
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15 | |
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16 | |
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17 | #include "../base/bdmbase.h" |
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18 | #include "../stat/exp_family.h" |
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19 | #include "../base/user_info.h" |
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20 | |
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21 | namespace bdm { |
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22 | /*! |
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23 | * \brief Memory storage of off-line data column-wise |
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24 | |
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25 | The data are stored in an internal matrix \c Data . Each column of Data corresponds to one discrete time observation \f$t\f$. Access to this matrix is via indices \c rowid and \c delays. |
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26 | |
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27 | The data can be loaded from a file. |
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28 | */ |
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29 | class MemDS : public DS { |
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30 | protected: |
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31 | //! internal matrix of data |
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32 | mat Data; |
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33 | //! active column in the Data matrix |
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34 | int time; |
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35 | //! vector of rows that are presented in Dt |
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36 | ivec rowid; |
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37 | //! vector of delays that are presented in Dt |
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38 | ivec delays; |
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39 | |
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40 | public: |
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41 | void getdata ( vec &dt ); |
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42 | void getdata ( vec &dt, const ivec &indeces ); |
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43 | void set_rvs ( RV &drv, RV &urv ); |
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44 | void write ( vec &ut ) { |
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45 | it_error ( "MemDS::write is not supported" ); |
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46 | } |
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47 | void write ( vec &ut, ivec &indices ) { |
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48 | it_error ( "MemDS::write is not supported" ); |
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49 | } |
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50 | void step(); |
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51 | //!Default constructor |
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52 | MemDS () {}; |
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53 | MemDS ( mat &Dat, ivec &rowid0, ivec &delays0 ); |
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54 | }; |
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55 | |
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56 | /*! Pseudovirtual class for reading data from files |
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57 | |
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58 | */ |
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59 | class FileDS: public MemDS { |
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60 | |
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61 | public: |
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62 | void getdata ( vec &dt ) { |
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63 | it_assert_debug ( dt.length() == Data.rows(), "" ); |
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64 | dt = Data.get_col ( time ); |
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65 | }; |
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66 | void getdata ( vec &dt, const ivec &indeces ) { |
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67 | it_assert_debug ( dt.length() == indeces.length(), "" ); |
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68 | vec tmp ( indeces.length() ); |
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69 | tmp = Data.get_col ( time ); |
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70 | dt = tmp ( indeces ); |
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71 | }; |
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72 | //! returns number of data in the file; |
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73 | int ndat() { |
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74 | return Data.cols(); |
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75 | } |
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76 | //! no sense to log this type |
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77 | void log_add ( logger &L ) {}; |
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78 | //! no sense to log this type |
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79 | void logit ( logger &L ) {}; |
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80 | }; |
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81 | |
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82 | /*! |
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83 | * \brief Read Data Matrix from an IT file |
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84 | |
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85 | The constructor creates an internal matrix \c Data from an IT++ file. The file is binary and can be made using the IT++ library or the Matlab/Octave function itsave. NB: the data are stored columnwise, i.e. each column contains the data for time \f$t\f$! |
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86 | |
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87 | */ |
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88 | class ITppFileDS: public FileDS { |
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89 | |
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90 | public: |
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91 | ITppFileDS ( const string &fname, const string &varname ) : FileDS() { |
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92 | it_file it ( fname ); |
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93 | it << Name ( varname ); |
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94 | it >> Data; |
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95 | time = 0; |
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96 | //rowid and delays are ignored |
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97 | }; |
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98 | |
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99 | ITppFileDS () : FileDS() { |
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100 | }; |
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101 | |
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102 | void from_setting ( const Setting &set ); |
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103 | |
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104 | // TODO dodelat void to_setting( Setting &set ) const; |
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105 | |
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106 | }; |
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107 | |
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108 | UIREGISTER ( ITppFileDS ); |
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109 | |
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110 | /*! |
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111 | * \brief CSV file data storage |
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112 | The constructor creates \c Data matrix from the records in a CSV file \c fname. The orientation can be of two types: |
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113 | 1. \c BY_COL which is default - the data are stored in columns; one column per time \f$t\f$, one row per data item. |
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114 | 2. \c BY_ROW if the data are stored the classical CSV style. Then each column stores the values for data item, for ex. \f$[y_{t} y_{t-1} ...]\f$, one row for each discrete time instant. |
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115 | |
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116 | */ |
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117 | class CsvFileDS: public FileDS { |
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118 | |
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119 | public: |
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120 | //! Constructor - create DS from a CSV file. |
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121 | CsvFileDS ( const string& fname, const string& orientation = "BY_COL" ); |
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122 | }; |
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123 | |
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124 | |
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125 | |
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126 | /*! |
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127 | \brief Generator of ARX data |
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128 | |
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129 | */ |
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130 | class ArxDS : public DS { |
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131 | protected: |
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132 | //! Rv of the regressor |
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133 | RV Rrv; |
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134 | //! History, ordered as \f$[y_t, u_t, y_{t-1 }, u_{t-1}, \ldots]\f$ |
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135 | vec H; |
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136 | //! (future) input |
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137 | vec U; |
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138 | //! temporary variable for regressor |
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139 | vec rgr; |
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140 | //! data link: H -> rgr |
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141 | datalink rgrlnk; |
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142 | //! model of Y - linear Gaussian |
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143 | mlnorm<chmat> model; |
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144 | //! options |
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145 | bool opt_L_theta; |
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146 | //! loggers |
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147 | int L_theta; |
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148 | int L_R; |
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149 | int dt_size; |
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150 | public: |
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151 | void getdata ( vec &dt ) { |
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152 | //it_assert_debug ( dt.length() ==Drv.count(),"ArxDS" ); |
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153 | dt = H; |
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154 | }; |
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155 | void getdata ( vec &dt, const ivec &indices ) { |
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156 | it_assert_debug ( dt.length() == indices.length(), "ArxDS" ); |
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157 | dt = H ( indices ); |
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158 | }; |
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159 | void write ( vec &ut ) { |
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160 | //it_assert_debug ( ut.length() ==Urv.count(),"ArxDS" ); |
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161 | U = ut; |
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162 | }; |
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163 | void write ( vec &ut, const ivec &indices ) { |
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164 | it_assert_debug ( ut.length() == indices.length(), "ArxDS" ); |
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165 | set_subvector ( U, indices, ut ); |
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166 | }; |
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167 | void step(); |
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168 | //!Default constructor |
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169 | ArxDS ( ) {}; |
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170 | //! Set parameters of the internal model, H is maximum time delay |
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171 | void set_parameters ( const mat &Th0, const vec mu0, const chmat &sqR0 ) { |
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172 | model.set_parameters ( Th0, mu0, sqR0 ); |
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173 | }; |
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174 | //! Set |
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175 | void set_drv ( const RV &yrv, const RV &urv, const RV &rrv ) { |
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176 | Rrv = rrv; |
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177 | Urv = urv; |
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178 | dt_size = yrv._dsize() + urv._dsize(); |
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179 | |
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180 | RV drv = concat ( yrv, urv ); |
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181 | Drv = drv; |
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182 | int td = rrv.mint(); |
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183 | H.set_size ( drv._dsize() * ( -td + 1 ) ); |
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184 | U.set_size ( Urv._dsize() ); |
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185 | for ( int i = -1; i >= td; i-- ) { |
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186 | drv.t ( -1 ); |
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187 | Drv.add ( drv ); //shift u1 |
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188 | } |
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189 | rgrlnk.set_connection ( rrv, Drv ); |
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190 | |
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191 | dtsize = Drv._dsize(); |
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192 | utsize = Urv._dsize(); |
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193 | } |
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194 | //! set options from a string |
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195 | void set_options ( const string &s ) { |
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196 | opt_L_theta = ( s.find ( "L_theta" ) != string::npos ); |
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197 | }; |
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198 | virtual void log_add ( logger &L ) { |
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199 | //DS::log_add ( L ); too long!! |
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200 | L_dt = L.add ( Drv ( 0, dt_size ), "" ); |
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201 | L_ut = L.add ( Urv, "" ); |
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202 | |
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203 | mat &A = model._A(); |
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204 | mat R = model._R(); |
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205 | if ( opt_L_theta ) { |
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206 | L_theta = L.add ( RV ( "{th }", vec_1 ( A.rows() * A.cols() ) ), "t" ); |
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207 | } |
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208 | if ( opt_L_theta ) { |
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209 | L_R = L.add ( RV ( "{R }", vec_1 ( R.rows() * R.cols() ) ), "r" ); |
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210 | } |
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211 | } |
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212 | virtual void logit ( logger &L ) { |
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213 | //DS::logit ( L ); |
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214 | L.logit ( L_dt, H.left ( dt_size ) ); |
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215 | L.logit ( L_ut, U ); |
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216 | |
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217 | mat &A = model._A(); |
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218 | mat R = model._R(); |
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219 | if ( opt_L_theta ) { |
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220 | L.logit ( L_theta, vec ( A._data(), A.rows() *A.cols() ) ); |
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221 | }; |
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222 | if ( opt_L_theta ) { |
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223 | L.logit ( L_R, vec ( R._data(), R.rows() *R.rows() ) ); |
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224 | }; |
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225 | } |
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226 | |
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227 | // TODO dokumentace - aktualizovat |
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228 | /*! UI for ArxDS using factorized description! |
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229 | |
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230 | The ArxDS is constructed from a structure with fields: |
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231 | \code |
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232 | system = { |
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233 | type = "ArxDS"; |
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234 | // description of y variables |
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235 | y = {type="rv"; names=["y", "u"];}; |
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236 | // description of u variable |
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237 | u = {type="rv"; names=[];} |
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238 | // description of regressor |
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239 | rgr = {type="rv"; |
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240 | names = ["y","y","y","u"]; |
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241 | times = [-1, -2, -3, -1]; |
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242 | } |
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243 | |
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244 | // theta |
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245 | theta = [0.8, -0.3, 0.4, 1.0, |
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246 | 0.0, 0.0, 0.0, 0.0]; |
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247 | // offset (optional) |
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248 | offset = [0.0, 0.0]; |
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249 | //variance |
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250 | r = [0.1, 0.0, |
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251 | 0.0, 1.0]; |
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252 | //options: L_theta = log value of theta, |
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253 | opt = "L_theta"; |
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254 | }; |
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255 | \endcode |
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256 | |
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257 | Result is ARX data source offering with full history as Drv. |
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258 | */ |
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259 | void from_setting ( const Setting &set ); |
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260 | |
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261 | // TODO dodelat void to_setting( Setting &set ) const; |
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262 | }; |
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263 | |
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264 | UIREGISTER ( ArxDS ); |
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265 | |
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266 | class stateDS : public DS { |
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267 | private: |
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268 | //!conditional pdf of the state evolution \f$ f(x_t|x_{t-1}) \f$ |
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269 | shared_ptr<mpdf> IM; |
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270 | |
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271 | //!conditional pdf of the observations \f$ f(d_t|x_t) \f$ |
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272 | shared_ptr<mpdf> OM; |
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273 | |
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274 | protected: |
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275 | //! result storage |
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276 | vec dt; |
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277 | //! state storage |
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278 | vec xt; |
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279 | //! input storage |
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280 | vec ut; |
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281 | //! Logger |
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282 | int L_xt; |
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283 | |
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284 | public: |
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285 | void getdata ( vec &dt0 ) { |
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286 | dt0 = dt; |
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287 | } |
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288 | void getdata ( vec &dt0, const ivec &indeces ) { |
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289 | dt0 = dt ( indeces ); |
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290 | } |
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291 | |
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292 | stateDS ( const shared_ptr<mpdf> &IM0, const shared_ptr<mpdf> &OM0, int usize ) : IM ( IM0 ), OM ( OM0 ), |
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293 | dt ( OM0->dimension() ), xt ( IM0->dimension() ), |
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294 | ut ( usize ), L_xt(0) { } |
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295 | |
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296 | stateDS() : L_xt(0) { } |
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297 | |
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298 | virtual void step() { |
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299 | xt = IM->samplecond ( concat ( xt, ut ) ); |
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300 | dt = OM->samplecond ( concat ( xt, ut ) ); |
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301 | } |
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302 | |
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303 | virtual void log_add ( logger &L ) { |
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304 | DS::log_add ( L ); |
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305 | L_xt = L.add ( IM->_rv(), "true" ); |
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306 | } |
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307 | virtual void logit ( logger &L ) { |
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308 | DS::logit ( L ); |
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309 | L.logit ( L_xt, xt ); |
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310 | } |
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311 | |
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312 | /*! UI for stateDS |
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313 | |
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314 | The DS is constructed from a structure with fields: |
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315 | \code |
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316 | system = { |
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317 | type = "stateDS"; |
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318 | //Internal model |
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319 | IM = { type = "mpdf"; //<-- valid offspring! e.g. "mlnorm" |
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320 | rv = { //description of x_t |
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321 | names=["name1",...]; |
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322 | sizes=[2,1]; // optional default=[1,1...]; |
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323 | times=[0,0]; // optional default=[0,0...]; |
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324 | } |
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325 | rvu= { //description of u_t |
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326 | //optional default=empty |
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327 | } |
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328 | |
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329 | // remaining fields depending on the chosen type |
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330 | }; |
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331 | //Observation model |
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332 | OM = { type = "mpdf-offspring"; |
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333 | rv = {}; //description of d_t |
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334 | rvu = {type="internal", path="system.IM.rvu"}; //description of u_t |
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335 | |
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336 | //remaining fields |
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337 | } |
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338 | }; |
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339 | \endcode |
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340 | */ |
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341 | void from_setting ( const Setting &set ); |
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342 | |
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343 | // TODO dodelat void to_setting( Setting &set ) const; |
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344 | |
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345 | }; |
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346 | |
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347 | UIREGISTER ( stateDS ); |
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348 | |
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349 | }; //namespace |
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350 | |
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351 | #endif // DS_H |
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