1 | |
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2 | /*! |
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3 | \file |
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4 | \brief Robust |
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5 | \author Vasek Smidl |
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6 | |
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7 | */ |
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8 | |
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9 | #include "estim/arx.h" |
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10 | #include "robustlib.h" |
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11 | #include <vector> |
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12 | #include <iostream> |
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13 | #include <fstream> |
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14 | //#include <itpp/itsignal.h> |
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15 | #include "windows.h" |
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16 | #include "ddeml.h" |
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17 | #include "stdio.h" |
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18 | |
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19 | //#include "DDEClient.h" |
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20 | //#include <conio.h> |
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21 | |
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22 | |
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23 | using namespace itpp; |
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24 | using namespace bdm; |
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25 | |
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26 | //const int emlig_size = 2; |
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27 | //const int utility_constant = 5; |
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28 | |
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29 | const int max_model_order = 1; |
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30 | const double apriorno = 0.01; |
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31 | const int max_window_size = 121; |
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32 | |
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33 | /* |
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34 | HDDEDATA CALLBACK DdeCallback( |
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35 | UINT uType, // Transaction type. |
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36 | UINT uFmt, // Clipboard data format. |
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37 | HCONV hconv, // Handle to the conversation. |
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38 | HSZ hsz1, // Handle to a string. |
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39 | HSZ hsz2, // Handle to a string. |
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40 | HDDEDATA hdata, // Handle to a global memory object. |
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41 | DWORD dwData1, // Transaction-specific data. |
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42 | DWORD dwData2) // Transaction-specific data. |
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43 | { |
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44 | return 0; |
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45 | } |
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46 | |
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47 | void DDERequest(DWORD idInst, HCONV hConv, char* szItem) |
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48 | { |
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49 | HSZ hszItem = DdeCreateStringHandle(idInst, szItem, 0); |
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50 | HDDEDATA hData = DdeClientTransaction(NULL,0,hConv,hszItem,CF_TEXT, |
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51 | XTYP_ADVSTART,TIMEOUT_ASYNC , NULL); //TIMEOUT_ASYNC |
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52 | if (hData==NULL) |
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53 | { |
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54 | printf("Request failed: %s\n", szItem); |
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55 | } |
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56 | |
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57 | if (hData==0) |
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58 | { |
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59 | printf("Request failed: %s\n", szItem); |
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60 | } |
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61 | } |
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62 | |
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63 | DWORD WINAPI ThrdFunc( LPVOID n ) |
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64 | { |
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65 | return 0; |
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66 | } |
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67 | */ |
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68 | |
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69 | class model |
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70 | { |
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71 | public: |
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72 | set<pair<int,int>> ar_components; |
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73 | |
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74 | // Best thing would be to inherit the two models from a single souce, this is planned, but now structurally |
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75 | // problematic. |
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76 | RARX* my_rarx; //vzmenovane parametre pre triedu model |
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77 | ARXwin* my_arx; |
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78 | |
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79 | bool has_constant; |
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80 | int window_size; //musi byt vacsia ako pocet krokov ak to nema ovplyvnit |
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81 | int predicted_channel; |
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82 | mat* data_matrix; |
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83 | vec predictions; |
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84 | char name[80]; |
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85 | |
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86 | model(set<pair<int,int>> ar_components, //funkcie treidz model-konstruktor |
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87 | bool robust, |
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88 | bool has_constant, |
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89 | int window_size, |
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90 | int predicted_channel, |
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91 | mat* data_matrix) |
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92 | { |
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93 | this->ar_components.insert(ar_components.begin(),ar_components.end()); |
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94 | |
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95 | strcpy(name,"M"); |
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96 | |
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97 | for(set<pair<int,int>>::iterator ar_ref = ar_components.begin();ar_ref!=ar_components.end();ar_ref++) |
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98 | { |
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99 | char buffer1[2]; |
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100 | char buffer2[2]; |
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101 | itoa((*ar_ref).first,buffer1,10); |
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102 | itoa((*ar_ref).second,buffer2,10); |
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103 | |
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104 | strcat(name,buffer1); |
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105 | strcat(name,buffer2); |
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106 | strcat(name,"_"); |
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107 | } |
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108 | |
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109 | this->has_constant = has_constant; |
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110 | |
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111 | if(has_constant) |
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112 | { |
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113 | strcat(name,"C"); |
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114 | } |
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115 | |
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116 | this->window_size = window_size; |
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117 | this->predicted_channel = predicted_channel; |
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118 | this->data_matrix = data_matrix; |
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119 | |
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120 | if(robust) |
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121 | { |
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122 | strcat(name,"R"); |
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123 | |
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124 | if(has_constant) |
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125 | { |
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126 | my_rarx = new RARX(ar_components.size()+1,window_size,true,sqrt(2*apriorno),sqrt(2*apriorno),ar_components.size()+4); |
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127 | my_arx = NULL; |
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128 | } |
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129 | else |
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130 | { |
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131 | my_rarx = new RARX(ar_components.size(),window_size,false,sqrt(2*apriorno),sqrt(2*apriorno),ar_components.size()+3); |
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132 | my_arx = NULL; |
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133 | } |
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134 | } |
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135 | else |
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136 | { |
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137 | my_rarx = NULL; |
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138 | my_arx = new ARXwin(); |
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139 | mat V0; |
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140 | |
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141 | if(has_constant) |
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142 | { |
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143 | V0 = apriorno * eye(ar_components.size()+2); //aj tu konst |
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144 | //V0(0,0) = 0; |
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145 | my_arx->set_constant(true); |
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146 | } |
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147 | else |
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148 | { |
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149 | |
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150 | V0 = apriorno * eye(ar_components.size()+1);//menit konstantu |
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151 | //V0(0,1) = -0.01; |
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152 | //V0(1,0) = -0.01; |
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153 | my_arx->set_constant(false); |
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154 | |
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155 | } |
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156 | |
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157 | my_arx->set_statistics(1, V0, V0.rows()+2); |
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158 | my_arx->set_parameters(window_size); |
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159 | my_arx->validate(); |
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160 | |
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161 | vec mean = my_arx->posterior().mean(); |
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162 | cout << mean << endl; |
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163 | } |
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164 | } |
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165 | |
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166 | void data_update(int time) //vlozime cas a ono vlozi do data_vector podmineky(conditions) a predikce, ktore pouzije do bayes |
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167 | { |
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168 | vec data_vector; |
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169 | for(set<pair<int,int>>::iterator ar_iterator = ar_components.begin();ar_iterator!=ar_components.end();ar_iterator++) |
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170 | { //ar?iterator ide len od 1 pod 2, alebo niekedy len 1 |
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171 | data_vector.ins(data_vector.size(),(*data_matrix).get(ar_iterator->first,time-ar_iterator->second)); |
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172 | // do data vector vlozi pre dany typ regresoru prislusne cisla z data_matrix. Ale ako? preco time-ar_iterator->second? |
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173 | } |
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174 | if(my_rarx!=NULL) |
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175 | { //pre robust priradi az tu do data_vector aj predikciu |
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176 | data_vector.ins(0,(*data_matrix).get(predicted_channel,time)); |
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177 | my_rarx->bayes(data_vector); |
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178 | } |
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179 | else |
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180 | { |
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181 | vec pred_vec;//tu sa predikcia zadava zvlast |
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182 | pred_vec.ins(0,(*data_matrix).get(predicted_channel,time)); |
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183 | my_arx->bayes(pred_vec,data_vector); |
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184 | } |
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185 | } |
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186 | |
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187 | pair<vec,vec> predict(int sample_size, int time, itpp::Laplace_RNG* LapRNG) //nerozumiem, ale vraj to netreba, nepouziva to |
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188 | { |
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189 | vec condition_vector; |
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190 | for(set<pair<int,int>>::iterator ar_iterator = ar_components.begin();ar_iterator!=ar_components.end();ar_iterator++) |
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191 | { |
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192 | condition_vector.ins(condition_vector.size(),(*data_matrix).get(ar_iterator->first,time-ar_iterator->second+1)); |
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193 | } |
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194 | |
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195 | if(my_rarx!=NULL) |
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196 | { |
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197 | pair<vec,mat> imp_samples = my_rarx->posterior->sample(sample_size,false); |
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198 | |
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199 | //cout << imp_samples.first << endl; |
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200 | |
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201 | vec sample_prediction; |
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202 | for(int t = 0;t<sample_size;t++) |
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203 | { |
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204 | vec lap_sample = condition_vector; |
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205 | |
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206 | if(has_constant) |
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207 | { |
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208 | lap_sample.ins(lap_sample.size(),1.0); |
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209 | } |
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210 | |
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211 | lap_sample.ins(lap_sample.size(),(*LapRNG)()); |
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212 | |
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213 | sample_prediction.ins(0,lap_sample*imp_samples.second.get_col(t)); |
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214 | } |
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215 | |
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216 | return pair<vec,vec>(imp_samples.first,sample_prediction); |
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217 | } |
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218 | else |
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219 | { |
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220 | mat samples = my_arx->posterior().sample_mat(sample_size); |
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221 | |
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222 | vec sample_prediction; |
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223 | for(int t = 0;t<sample_size;t++) |
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224 | { |
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225 | vec gau_sample = condition_vector; |
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226 | |
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227 | if(has_constant) |
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228 | { |
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229 | gau_sample.ins(gau_sample.size(),1.0); |
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230 | } |
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231 | |
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232 | gau_sample.ins(gau_sample.size(),randn()); |
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233 | |
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234 | sample_prediction.ins(0,gau_sample*samples.get_col(t)); |
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235 | } |
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236 | |
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237 | return pair<vec,vec>(ones(sample_prediction.size()),sample_prediction); |
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238 | } |
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239 | |
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240 | } |
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241 | |
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242 | |
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243 | static set<set<pair<int,int>>> possible_models_recurse(int max_order,int number_of_channels) |
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244 | { |
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245 | set<set<pair<int,int>>> created_model_types; |
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246 | |
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247 | if(max_order == 1)//ukoncovacia vetva |
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248 | { |
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249 | for(int channel = 0;channel<number_of_channels;channel++)//pre AR 1 model vytvori kombinace kanalov v prvom kroku poyadu |
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250 | { |
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251 | set<pair<int,int>> returned_type; |
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252 | returned_type.insert(pair<int,int>(channel,1)); //?? |
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253 | created_model_types.insert(returned_type); |
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254 | } |
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255 | |
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256 | return created_model_types; |
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257 | } |
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258 | else |
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259 | { |
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260 | created_model_types = possible_models_recurse(max_order-1,number_of_channels);//tu uz mame ulozene kombinace o jeden krok dozadu //rekuryivne volanie |
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261 | set<set<pair<int,int>>> returned_types; |
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262 | |
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263 | for(set<set<pair<int,int>>>::iterator model_ref = created_model_types.begin();model_ref!=created_model_types.end();model_ref++) |
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264 | { |
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265 | |
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266 | for(int order = 1; order<=max_order; order++) |
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267 | { |
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268 | for(int channel = 0;channel<number_of_channels;channel++) |
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269 | { |
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270 | set<pair<int,int>> returned_type; |
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271 | pair<int,int> new_pair = pair<int,int>(channel,order);//?? |
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272 | if(find((*model_ref).begin(),(*model_ref).end(),new_pair)==(*model_ref).end()) //?? |
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273 | { |
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274 | returned_type.insert((*model_ref).begin(),(*model_ref).end()); //co vlozi na zaciatok retuned_type? |
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275 | returned_type.insert(new_pair); |
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276 | |
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277 | |
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278 | returned_types.insert(returned_type); |
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279 | } |
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280 | } |
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281 | } |
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282 | } |
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283 | |
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284 | created_model_types.insert(returned_types.begin(),returned_types.end()); |
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285 | |
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286 | return created_model_types; |
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287 | } |
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288 | } |
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289 | }; |
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290 | |
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291 | |
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292 | |
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293 | |
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294 | int main ( int argc, char* argv[] ) |
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295 | { |
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296 | |
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297 | // EXPERIMENT: A moving window estimation and prediction of RARX is tested on data generated from |
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298 | // y_t=0.95*y_(t-1)+0.05*y_(t-2)+0.2*e_t, where e_t is normally, student(4) and cauchy distributed. It |
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299 | // can be compared to the classical setup. |
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300 | |
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301 | itpp::Laplace_RNG LapRNG = Laplace_RNG(); |
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302 | |
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303 | vector<vector<string>> strings; |
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304 | |
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305 | char* file_string = "C:\\results\\cauchy"; // "C:\\dataADClosePercDiff"; // |
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306 | |
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307 | char dfstring[80]; |
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308 | strcpy(dfstring,file_string); |
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309 | strcat(dfstring,".txt"); |
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310 | |
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311 | |
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312 | mat data_matrix; |
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313 | ifstream myfile(dfstring); |
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314 | if (myfile.is_open()) |
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315 | { |
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316 | string line; |
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317 | while(getline(myfile,line)) |
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318 | { |
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319 | vec data_vector; |
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320 | while(line.find(',') != string::npos) //zmenil som ciarku za medzeru |
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321 | { |
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322 | //line.erase(0,1); // toto som sem pridal |
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323 | int loc2 = line.find('\n'); |
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324 | int loc = line.find(','); |
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325 | data_vector.ins(data_vector.size(),atof(line.substr(0,loc).c_str())); |
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326 | line.erase(0,loc+1); |
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327 | } |
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328 | |
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329 | data_matrix.ins_row(data_matrix.rows(),data_vector); |
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330 | } |
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331 | |
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332 | myfile.close(); |
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333 | } |
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334 | else |
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335 | { |
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336 | cout << "Can't open data file!" << endl; |
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337 | } |
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338 | |
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339 | //konec nacitavania dat |
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340 | set<set<pair<int,int>>> model_types = model::possible_models_recurse(max_model_order,data_matrix.rows()); //volanie funkce kde robi kombinace modelov |
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341 | //to priradime do model_types, data_matrix.row urcuje pocet kanalov dat |
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342 | vector<model*> models; |
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343 | for(set<set<pair<int,int>>>::iterator model_type = model_types.begin();model_type!=model_types.end();model_type++) |
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344 | {// prechadza rozne typy kanalov, a poctu regresorov |
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345 | for(int window_size = max_window_size-1;window_size < max_window_size;window_size++) |
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346 | { |
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347 | //models.push_back(new model((*model_type),true,true,window_size,0,&data_matrix)); // to su len konstruktory, len inicializujeme rozne typy |
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348 | //models.push_back(new model((*model_type),false,true,window_size,0,&data_matrix)); |
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349 | models.push_back(new model((*model_type),true,false,window_size,0,&data_matrix)); |
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350 | models.push_back(new model((*model_type),false,false,window_size,0,&data_matrix)); |
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351 | } |
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352 | |
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353 | //set<pair<int,int>> empty_list; |
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354 | //models.push_back(new model(empty_list,false,true,100,0,&data_matrix)); |
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355 | } |
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356 | |
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357 | mat result_lognc; |
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358 | // mat result_preds; |
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359 | |
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360 | ofstream myfilew; |
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361 | //char fstring[80]; |
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362 | //strcpy(fstring,file_string); |
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363 | //strcat(fstring,"lognc.txt"); |
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364 | //strcat(fstring,"preds.txt"); |
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365 | |
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366 | for(int time = max_model_order;time<data_matrix.cols();time++) //time<data_matrix.cols() |
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367 | { |
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368 | if(time==max_window_size) |
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369 | { |
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370 | exit(1); |
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371 | } |
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372 | |
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373 | //pocet stlpcov data_matrix je pocet casovych krokov |
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374 | vec cur_res_lognc; |
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375 | // vec preds; |
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376 | vector<string> nazvy; |
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377 | for(vector<model*>::iterator model_ref = models.begin();model_ref!=models.end();model_ref++) |
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378 | {//posuvam s apo models, co je pole modelov urobene o cyklus vyssie. Teda som v case time a robim to tam pre vsetky typy modelov, kombinace regresorov |
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379 | (*model_ref)->data_update(time); //pozret sa preco je toto tu nutne |
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380 | |
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381 | cout << "Updated." << endl; |
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382 | //if (time = max_model_order) nazvy.push_back(models.model_ref]);// ako by som mohol dostat nazov modelu? |
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383 | |
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384 | if((*model_ref)->my_rarx!=NULL) //vklada normalizacni faktor do cur_res_lognc |
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385 | { |
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386 | cout << "Maxlik vertex:" << (*model_ref)->my_rarx->posterior->minimal_vertex->get_coordinates() << endl; |
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387 | cur_res_lognc.ins(cur_res_lognc.size(),(*model_ref)->my_rarx->posterior->_ll()); |
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388 | } |
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389 | else |
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390 | { |
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391 | cur_res_lognc.ins(cur_res_lognc.size(),(*model_ref)->my_arx->_ll()); |
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392 | } |
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393 | |
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394 | if(time == max_window_size-1) |
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395 | { |
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396 | //*********************** |
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397 | int sample_size = 100000; |
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398 | //*********************** |
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399 | |
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400 | pair<vec,mat> samples; |
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401 | if((*model_ref)->my_arx!=NULL) |
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402 | { |
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403 | mat samp_mat = (*model_ref)->my_arx->posterior().sample_mat(sample_size); |
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404 | samples = pair<vec,mat>(ones(samp_mat.cols()),samp_mat); |
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405 | } |
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406 | else |
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407 | { |
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408 | samples = (*model_ref)->my_rarx->posterior->sample(sample_size,true); |
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409 | } |
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410 | |
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411 | char fstring[80]; |
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412 | strcpy(fstring,file_string); |
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413 | strcat(fstring,(*model_ref)->name); |
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414 | strcat(fstring,".txt"); |
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415 | |
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416 | //cout << samples.first << endl; |
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417 | |
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418 | myfilew.open(fstring,ios::app); |
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419 | |
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420 | /* |
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421 | for(int i = 0;i<samples.first.size();i++) |
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422 | { |
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423 | myfilew << samples.first.get(i) << ","; |
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424 | } |
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425 | myfilew << endl; |
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426 | */ |
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427 | |
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428 | for(int j = 0;j<samples.second.rows()+1;j++) |
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429 | { |
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430 | for(int i = 0;i<samples.second.cols();i++) |
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431 | { |
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432 | if(j!=samples.second.rows()) |
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433 | { |
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434 | myfilew << samples.second.get(j,i) << ","; |
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435 | } |
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436 | /* |
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437 | else |
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438 | { |
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439 | myfilew << "0,"; |
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440 | } |
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441 | */ |
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442 | } |
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443 | myfilew << endl; |
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444 | } |
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445 | |
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446 | cout << "*************************************" << endl; |
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447 | |
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448 | myfilew.close(); |
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449 | } |
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450 | |
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451 | /* // PREDICTIONS |
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452 | pair<vec,vec> predictions = (*model_ref)->predict(500,time,&LapRNG); |
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453 | |
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454 | cout << predictions.first << endl << predictions.second << endl; |
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455 | |
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456 | double avg_prediction = (predictions.first*predictions.second)/(predictions.first*ones(predictions.first.size())); |
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457 | |
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458 | (*model_ref)->predictions.ins((*model_ref)->predictions.size(),avg_prediction); |
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459 | |
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460 | myfilew.open(fstring,ios::app); |
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461 | myfilew << avg_prediction << ","; |
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462 | myfilew.close(); |
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463 | */ |
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464 | |
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465 | //preds.ins(0,data_matrix.get(0,time+1)); |
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466 | } |
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467 | |
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468 | |
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469 | /* // REAL PRICE |
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470 | myfilew.open(fstring,ios::app); |
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471 | myfilew << data_matrix.get(0,time+1) << endl; |
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472 | myfilew.close(); |
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473 | */ |
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474 | |
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475 | result_lognc.ins_col(result_lognc.cols(),cur_res_lognc); |
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476 | // result_preds.ins_col(result_preds.cols(),preds); |
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477 | |
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478 | // cout << "Updated." << endl; |
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479 | |
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480 | /* |
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481 | myfilew.open(fstring,ios::app); |
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482 | |
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483 | // myfile << my_rarx->posterior->minimal_vertex->get_coordinates()[0]; |
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484 | |
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485 | if(time == max_model_order) |
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486 | { |
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487 | for(int i = 0;i<cur_res_lognc.size();i++) |
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488 | { |
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489 | for(set<pair<int,int>>::iterator ar_ref = models[i]->ar_components.begin();ar_ref != models[i]->ar_components.end();ar_ref++) |
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490 | { |
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491 | myfilew << (*ar_ref).second << (*ar_ref).first; |
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492 | } |
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493 | |
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494 | myfilew << "."; |
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495 | |
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496 | if(models[i]->my_arx == NULL) |
---|
497 | { |
---|
498 | myfilew << "1"; |
---|
499 | } |
---|
500 | else |
---|
501 | { |
---|
502 | myfilew << "0"; |
---|
503 | } |
---|
504 | |
---|
505 | if(models[i]->has_constant) |
---|
506 | { |
---|
507 | myfilew << "1"; |
---|
508 | } |
---|
509 | else |
---|
510 | { |
---|
511 | myfilew << "0"; |
---|
512 | } |
---|
513 | |
---|
514 | myfilew << ","; |
---|
515 | } |
---|
516 | |
---|
517 | myfilew << endl; |
---|
518 | } |
---|
519 | |
---|
520 | |
---|
521 | // for(int i = 0;i<cur_res_lognc.size();i++) |
---|
522 | // { |
---|
523 | // myfilew << cur_res_lognc[i] << ' ';//zmenil som ciarku ze medzeru |
---|
524 | // } |
---|
525 | |
---|
526 | |
---|
527 | myfilew << endl; |
---|
528 | |
---|
529 | myfilew.close(); |
---|
530 | */ |
---|
531 | |
---|
532 | } |
---|
533 | |
---|
534 | |
---|
535 | |
---|
536 | // EXPERIMENT: One step ahead price prediction. Comparison of classical and robust model using optimal trading |
---|
537 | // with maximization of logarithm of one-step ahead wealth. |
---|
538 | |
---|
539 | |
---|
540 | |
---|
541 | /* |
---|
542 | cout << "One experiment finished." << endl; |
---|
543 | |
---|
544 | ofstream myfile; |
---|
545 | myfile.open("c:\\robust_ar1.txt",ios::app); |
---|
546 | myfile << endl; |
---|
547 | myfile.close(); |
---|
548 | |
---|
549 | myfile.open("c:\\robust_ar2.txt",ios::app); |
---|
550 | myfile << endl; |
---|
551 | myfile.close();*/ |
---|
552 | |
---|
553 | |
---|
554 | //emlig* emlig1 = new emlig(emlig_size); |
---|
555 | |
---|
556 | //emlig1->step_me(0); |
---|
557 | //emlig* emlig2 = new emlig(emlig_size); |
---|
558 | |
---|
559 | /* |
---|
560 | emlig1->set_correction_factors(4); |
---|
561 | |
---|
562 | for(int j = 0;j<emlig1->correction_factors.size();j++) |
---|
563 | { |
---|
564 | for(set<my_ivec>::iterator vec_ref = emlig1->correction_factors[j].begin();vec_ref!=emlig1->correction_factors[j].end();vec_ref++) |
---|
565 | { |
---|
566 | cout << j << " "; |
---|
567 | |
---|
568 | for(int i=0;i<(*vec_ref).size();i++) |
---|
569 | { |
---|
570 | cout << (*vec_ref)[i]; |
---|
571 | } |
---|
572 | |
---|
573 | cout << endl; |
---|
574 | } |
---|
575 | }*/ |
---|
576 | |
---|
577 | /* |
---|
578 | vec condition5 = "1.0 1.0 1.01";//"-0.3 1.7 1.5"; |
---|
579 | |
---|
580 | emlig1->add_condition(condition5); |
---|
581 | //emlig1->step_me(0); |
---|
582 | |
---|
583 | |
---|
584 | vec condition1a = "-1.0 1.02 0.5"; |
---|
585 | //vec condition1b = "1.0 1.0 1.01"; |
---|
586 | emlig1->add_condition(condition1a); |
---|
587 | //emlig2->add_condition(condition1b); |
---|
588 | |
---|
589 | vec condition2a = "-0.3 1.7 1.5"; |
---|
590 | //vec condition2b = "-1.0 1.0 1.0"; |
---|
591 | emlig1->add_condition(condition2a); |
---|
592 | //emlig2->add_condition(condition2b); |
---|
593 | |
---|
594 | vec condition3a = "0.5 -1.01 1.0"; |
---|
595 | //vec condition3b = "0.5 -1.01 1.0"; |
---|
596 | |
---|
597 | emlig1->add_condition(condition3a); |
---|
598 | //emlig2->add_condition(condition3b); |
---|
599 | |
---|
600 | vec condition4a = "-0.5 -1.0 1.0"; |
---|
601 | //vec condition4b = "-0.5 -1.0 1.0"; |
---|
602 | |
---|
603 | emlig1->add_condition(condition4a); |
---|
604 | //cout << "************************************************" << endl; |
---|
605 | //emlig2->add_condition(condition4b); |
---|
606 | //cout << "************************************************" << endl; |
---|
607 | |
---|
608 | //cout << emlig1->minimal_vertex->get_coordinates(); |
---|
609 | |
---|
610 | //emlig1->remove_condition(condition3a); |
---|
611 | //emlig1->step_me(0); |
---|
612 | //emlig1->remove_condition(condition2a); |
---|
613 | //emlig1->remove_condition(condition1a); |
---|
614 | //emlig1->remove_condition(condition5); |
---|
615 | |
---|
616 | |
---|
617 | //emlig1->step_me(0); |
---|
618 | //emlig2->step_me(0); |
---|
619 | |
---|
620 | |
---|
621 | // DA SE POUZIT PRO VYPIS DO SOUBORU |
---|
622 | // emlig1->step_me(0); |
---|
623 | |
---|
624 | //emlig1->remove_condition(condition1); |
---|
625 | |
---|
626 | |
---|
627 | |
---|
628 | |
---|
629 | |
---|
630 | /* |
---|
631 | for(int i = 0;i<100;i++) |
---|
632 | { |
---|
633 | cout << endl << "Step:" << i << endl; |
---|
634 | |
---|
635 | double condition[emlig_size+1]; |
---|
636 | |
---|
637 | for(int k = 0;k<=emlig_size;k++) |
---|
638 | { |
---|
639 | condition[k] = (rand()-RAND_MAX/2)/1000.0; |
---|
640 | } |
---|
641 | |
---|
642 | |
---|
643 | vec* condition_vec = new vec(condition,emlig_size+1); |
---|
644 | emlig1->add_condition(*condition_vec); |
---|
645 | |
---|
646 | /* |
---|
647 | for(polyhedron* toprow_ref = emlig1->statistic.rows[emlig_size]; toprow_ref != emlig1->statistic.end_poly; toprow_ref = toprow_ref->next_poly) |
---|
648 | { |
---|
649 | cout << ((toprow*)toprow_ref)->probability << endl; |
---|
650 | } |
---|
651 | */ |
---|
652 | /* |
---|
653 | cout << emlig1->statistic_rowsize(emlig_size) << endl << endl; |
---|
654 | |
---|
655 | /* |
---|
656 | if(i-emlig1->number_of_parameters >= 0) |
---|
657 | { |
---|
658 | pause(30); |
---|
659 | } |
---|
660 | */ |
---|
661 | |
---|
662 | // emlig1->step_me(i); |
---|
663 | |
---|
664 | /* |
---|
665 | vector<int> sizevector; |
---|
666 | for(int s = 0;s<=emlig1->number_of_parameters;s++) |
---|
667 | { |
---|
668 | sizevector.push_back(emlig1->statistic_rowsize(s)); |
---|
669 | } |
---|
670 | */ |
---|
671 | //} |
---|
672 | |
---|
673 | |
---|
674 | |
---|
675 | |
---|
676 | /* |
---|
677 | emlig1->step_me(1); |
---|
678 | |
---|
679 | vec condition = "2.0 0.0 1.0"; |
---|
680 | |
---|
681 | emlig1->add_condition(condition); |
---|
682 | |
---|
683 | vector<int> sizevector; |
---|
684 | for(int s = 0;s<=emlig1->number_of_parameters;s++) |
---|
685 | { |
---|
686 | sizevector.push_back(emlig1->statistic_rowsize(s)); |
---|
687 | } |
---|
688 | |
---|
689 | emlig1->step_me(2); |
---|
690 | |
---|
691 | condition = "2.0 1.0 0.0"; |
---|
692 | |
---|
693 | emlig1->add_condition(condition); |
---|
694 | |
---|
695 | sizevector.clear(); |
---|
696 | for(int s = 0;s<=emlig1->number_of_parameters;s++) |
---|
697 | { |
---|
698 | sizevector.push_back(emlig1->statistic_rowsize(s)); |
---|
699 | } |
---|
700 | */ |
---|
701 | |
---|
702 | return 0; |
---|
703 | } |
---|
704 | |
---|
705 | |
---|