[976] | 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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[1337] | 9 | #include "estim/arx.h" |
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[976] | 10 | #include "robustlib.h" |
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[1216] | 11 | #include <vector> |
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[1284] | 12 | #include <iostream> |
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[1282] | 13 | #include <fstream> |
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[1376] | 14 | //#include <itpp/itsignal.h> |
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[1361] | 15 | #include "windows.h" |
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| 16 | #include "ddeml.h" |
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| 17 | #include "stdio.h" |
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[1282] | 18 | |
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[1361] | 19 | //#include "DDEClient.h" |
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| 20 | //#include <conio.h> |
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[1358] | 21 | |
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[1361] | 22 | |
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[1208] | 23 | using namespace itpp; |
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[1337] | 24 | using namespace bdm; |
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[976] | 25 | |
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[1361] | 26 | //const int emlig_size = 2; |
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| 27 | //const int utility_constant = 5; |
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[1268] | 28 | |
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[1361] | 29 | const int max_model_order = 2; |
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[1376] | 30 | const double apriorno=0.02; |
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[1272] | 31 | |
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[1376] | 32 | /* |
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[1361] | 33 | HDDEDATA CALLBACK DdeCallback( |
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[1376] | 34 | UINT uType, // Transaction type. |
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| 35 | UINT uFmt, // Clipboard data format. |
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| 36 | HCONV hconv, // Handle to the conversation. |
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| 37 | HSZ hsz1, // Handle to a string. |
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| 38 | HSZ hsz2, // Handle to a string. |
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| 39 | HDDEDATA hdata, // Handle to a global memory object. |
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| 40 | DWORD dwData1, // Transaction-specific data. |
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| 41 | DWORD dwData2) // Transaction-specific data. |
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[1361] | 42 | { |
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[1376] | 43 | return 0; |
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[1361] | 44 | } |
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| 45 | |
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| 46 | void DDERequest(DWORD idInst, HCONV hConv, char* szItem) |
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| 47 | { |
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| 48 | HSZ hszItem = DdeCreateStringHandle(idInst, szItem, 0); |
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| 49 | HDDEDATA hData = DdeClientTransaction(NULL,0,hConv,hszItem,CF_TEXT, |
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[1365] | 50 | XTYP_ADVSTART,TIMEOUT_ASYNC , NULL); //TIMEOUT_ASYNC |
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[1361] | 51 | if (hData==NULL) |
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| 52 | { |
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| 53 | printf("Request failed: %s\n", szItem); |
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| 54 | } |
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| 55 | |
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| 56 | if (hData==0) |
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| 57 | { |
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| 58 | printf("Request failed: %s\n", szItem); |
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| 59 | } |
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| 60 | } |
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| 61 | |
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[1367] | 62 | DWORD WINAPI ThrdFunc( LPVOID n ) |
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| 63 | { |
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| 64 | return 0; |
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| 65 | } |
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[1376] | 66 | */ |
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[1367] | 67 | |
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[1361] | 68 | class model |
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| 69 | { |
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| 70 | public: |
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[1376] | 71 | set<pair<int,int>> ar_components; |
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[1358] | 72 | |
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[1361] | 73 | // Best thing would be to inherit the two models from a single souce, this is planned, but now structurally |
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| 74 | // problematic. |
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[1376] | 75 | RARX* my_rarx; //vzmenovane parametre pre triedu model |
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| 76 | ARX* my_arx; |
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[1361] | 77 | |
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| 78 | bool has_constant; |
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[1376] | 79 | int window_size; //musi byt vacsia ako pocet krokov ak to nema ovplyvnit |
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[1361] | 80 | int predicted_channel; |
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| 81 | mat* data_matrix; |
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| 82 | |
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[1376] | 83 | model(set<pair<int,int>> ar_components, //funkcie treidz model-konstruktor |
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[1361] | 84 | bool robust, |
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| 85 | bool has_constant, |
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| 86 | int window_size, |
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[1376] | 87 | int predicted_channel, |
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[1361] | 88 | mat* data_matrix) |
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[1358] | 89 | { |
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[1376] | 90 | this->ar_components.insert(ar_components.begin(),ar_components.end()); |
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| 91 | this->has_constant = has_constant; |
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| 92 | this->window_size = window_size; |
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| 93 | this->predicted_channel = predicted_channel; |
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| 94 | this->data_matrix = data_matrix; |
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[1361] | 95 | |
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| 96 | if(robust) |
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| 97 | { |
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| 98 | if(has_constant) |
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| 99 | { |
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| 100 | my_rarx = new RARX(ar_components.size()+1,window_size,true); |
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| 101 | my_arx = NULL; |
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| 102 | } |
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[1376] | 103 | else |
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[1361] | 104 | { |
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| 105 | my_rarx = new RARX(ar_components.size(),window_size,false); |
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| 106 | my_arx = NULL; |
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| 107 | } |
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| 108 | } |
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| 109 | else |
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| 110 | { |
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| 111 | my_rarx = NULL; |
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[1376] | 112 | my_arx = new ARX(); |
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[1361] | 113 | mat V0; |
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| 114 | |
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| 115 | if(has_constant) |
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| 116 | { |
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[1376] | 117 | V0 = apriorno * eye(ar_components.size()+2); //aj tu konst |
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[1370] | 118 | V0(0,0) = 1; |
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[1361] | 119 | my_arx->set_constant(true); |
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| 120 | |
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| 121 | } |
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| 122 | else |
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| 123 | { |
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| 124 | |
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[1376] | 125 | V0 = apriorno * eye(ar_components.size()+1);//menit konstantu |
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[1370] | 126 | V0(0,0) = 1; |
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[1361] | 127 | my_arx->set_constant(false); |
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| 128 | |
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| 129 | } |
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| 130 | |
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| 131 | my_arx->set_statistics(1, V0); |
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[1376] | 132 | //my_arx->set_parameters(window_size); |
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[1361] | 133 | my_arx->validate(); |
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| 134 | } |
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[1358] | 135 | } |
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[1361] | 136 | |
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[1376] | 137 | 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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[1358] | 138 | { |
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[1376] | 139 | vec data_vector; |
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| 140 | for(set<pair<int,int>>::iterator ar_iterator = ar_components.begin();ar_iterator!=ar_components.end();ar_iterator++) |
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| 141 | { //ar?iterator ide len od 1 pod 2, alebo niekedy len 1 |
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| 142 | data_vector.ins(data_vector.size(),(*data_matrix).get(ar_iterator->first,time-ar_iterator->second)); |
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| 143 | // do data vector vlozi pre dany typ regresoru prislusne cisla z data_matrix. Ale ako? preco time-ar_iterator->second? |
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[1358] | 144 | } |
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[1376] | 145 | if(my_rarx!=NULL) |
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| 146 | { //pre robusr priradi az tu do data_vector aj rpedikciu |
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| 147 | data_vector.ins(0,(*data_matrix).get(predicted_channel,time)); |
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| 148 | my_rarx->bayes(data_vector); |
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| 149 | } |
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[1358] | 150 | else |
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| 151 | { |
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[1376] | 152 | vec pred_vec;//tu sa predikcia zadava zvlast |
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| 153 | pred_vec.ins(0,(*data_matrix).get(predicted_channel,time)); |
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| 154 | my_arx->bayes(pred_vec,data_vector); |
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[1361] | 155 | } |
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| 156 | } |
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| 157 | |
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[1376] | 158 | pair<vec,vec> predict(int sample_size, int time, itpp::Laplace_RNG* LapRNG) //nerozumiem, ale vraj to netreba, nepouziva to |
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[1367] | 159 | { |
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[1376] | 160 | vec condition_vector; |
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| 161 | for(set<pair<int,int>>::iterator ar_iterator = ar_components.begin();ar_iterator!=ar_components.end();ar_iterator++) |
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[1367] | 162 | { |
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[1376] | 163 | condition_vector.ins(condition_vector.size(),(*data_matrix).get(ar_iterator->first,time-ar_iterator->second+1)); |
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| 164 | } |
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[1367] | 165 | |
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[1376] | 166 | if(my_rarx!=NULL) |
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| 167 | { |
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| 168 | pair<vec,mat> imp_samples = my_rarx->posterior->importance_sample(sample_size); |
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[1367] | 169 | |
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[1376] | 170 | //cout << imp_samples.first << endl; |
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| 171 | |
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| 172 | vec sample_prediction; |
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| 173 | for(int t = 0;t<sample_size;t++) |
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[1367] | 174 | { |
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[1376] | 175 | vec lap_sample = condition_vector; |
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[1367] | 176 | |
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[1376] | 177 | if(has_constant) |
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[1367] | 178 | { |
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[1376] | 179 | lap_sample.ins(lap_sample.size(),1.0); |
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[1367] | 180 | } |
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[1376] | 181 | |
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| 182 | lap_sample.ins(0,(*LapRNG)()); |
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[1367] | 183 | |
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[1376] | 184 | sample_prediction.ins(0,lap_sample*imp_samples.second.get_col(t)); |
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[1367] | 185 | } |
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| 186 | |
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[1376] | 187 | return pair<vec,vec>(imp_samples.first,sample_prediction); |
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| 188 | } |
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| 189 | else |
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| 190 | { |
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| 191 | mat samples = my_arx->posterior().sample_mat(sample_size); |
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| 192 | |
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| 193 | vec sample_prediction; |
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| 194 | for(int t = 0;t<sample_size;t++) |
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| 195 | { |
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| 196 | vec gau_sample = condition_vector; |
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[1367] | 197 | |
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[1376] | 198 | if(has_constant) |
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[1367] | 199 | { |
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[1376] | 200 | gau_sample.ins(gau_sample.size(),1.0); |
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[1367] | 201 | } |
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[1376] | 202 | |
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| 203 | gau_sample.ins(0,randn()); |
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[1367] | 204 | |
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[1376] | 205 | sample_prediction.ins(0,gau_sample*samples.get_col(t)); |
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[1367] | 206 | } |
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[1376] | 207 | |
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| 208 | return pair<vec,vec>(ones(sample_prediction.size()),sample_prediction); |
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[1367] | 209 | } |
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| 210 | |
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| 211 | } |
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| 212 | |
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| 213 | |
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[1376] | 214 | static set<set<pair<int,int>>> possible_models_recurse(int max_order,int number_of_channels) |
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[1361] | 215 | { |
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[1376] | 216 | set<set<pair<int,int>>> created_model_types; |
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[1361] | 217 | |
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[1376] | 218 | if(max_order == 1)//ukoncovacia vetva |
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[1361] | 219 | { |
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[1376] | 220 | for(int channel = 0;channel<number_of_channels;channel++)//pre AR 1 model vytvori kombinace kanalov v prvom kroku poyadu |
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[1358] | 221 | { |
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[1376] | 222 | set<pair<int,int>> returned_type; |
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| 223 | returned_type.insert(pair<int,int>(channel,1)); //?? |
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| 224 | created_model_types.insert(returned_type); |
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[1358] | 225 | } |
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[1361] | 226 | |
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| 227 | return created_model_types; |
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| 228 | } |
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| 229 | else |
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| 230 | { |
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[1376] | 231 | 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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| 232 | set<set<pair<int,int>>> returned_types; |
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[1361] | 233 | |
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[1376] | 234 | 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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[1361] | 235 | { |
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| 236 | |
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| 237 | for(int order = 1; order<=max_order; order++) |
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[1358] | 238 | { |
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[1361] | 239 | for(int channel = 0;channel<number_of_channels;channel++) |
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| 240 | { |
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[1376] | 241 | set<pair<int,int>> returned_type; |
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| 242 | pair<int,int> new_pair = pair<int,int>(channel,order);//?? |
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| 243 | if(find((*model_ref).begin(),(*model_ref).end(),new_pair)==(*model_ref).end()) //?? |
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[1361] | 244 | { |
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[1376] | 245 | returned_type.insert((*model_ref).begin(),(*model_ref).end()); //co vlozi na zaciatok retuned_type? |
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| 246 | returned_type.insert(new_pair); |
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| 247 | |
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| 248 | |
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| 249 | returned_types.insert(returned_type); |
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[1361] | 250 | } |
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| 251 | } |
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[1358] | 252 | } |
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| 253 | } |
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[1361] | 254 | |
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[1376] | 255 | created_model_types.insert(returned_types.begin(),returned_types.end()); |
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[1361] | 256 | |
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| 257 | return created_model_types; |
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| 258 | } |
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[1358] | 259 | } |
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[1361] | 260 | }; |
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| 261 | |
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| 262 | |
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| 263 | |
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| 264 | |
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[1367] | 265 | int main ( int argc, char* argv[] ) { |
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[1358] | 266 | |
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[1367] | 267 | /* |
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[1376] | 268 | DWORD Id; |
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[1367] | 269 | HANDLE hThrd = CreateThread( NULL, 0, (LPTHREAD_START_ROUTINE)ThrdFunc, (LPVOID)1, 0, &Id); |
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[1376] | 270 | |
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| 271 | if ( !hThrd ) |
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[1367] | 272 | { |
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| 273 | cout<<"Error Creating Threads,,,,.exiting"<<endl; |
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| 274 | return -1; |
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| 275 | } |
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| 276 | Sleep ( 100 ); |
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[1376] | 277 | |
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[1361] | 278 | |
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[1376] | 279 | char szApp[] = "MT4"; |
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| 280 | char szTopic[] = "QUOTE"; |
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| 281 | char szItem1[] = "EURUSD"; |
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[1361] | 282 | |
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| 283 | //DDE Initialization |
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[1376] | 284 | DWORD idInst=0; |
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[1361] | 285 | UINT iReturn; |
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| 286 | iReturn = DdeInitialize(&idInst, (PFNCALLBACK)DdeCallback, |
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| 287 | APPCLASS_STANDARD | APPCMD_CLIENTONLY, 0 ); |
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| 288 | if (iReturn!=DMLERR_NO_ERROR) |
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| 289 | { |
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| 290 | printf("DDE Initialization Failed: 0x%04x\n", iReturn); |
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| 291 | Sleep(1500); |
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| 292 | return 0; |
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| 293 | } |
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| 294 | |
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| 295 | //DDE Connect to Server using given AppName and topic. |
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| 296 | HSZ hszApp, hszTopic; |
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| 297 | HCONV hConv; |
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| 298 | hszApp = DdeCreateStringHandle(idInst, szApp, 0); |
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| 299 | hszTopic = DdeCreateStringHandle(idInst, szTopic, 0); |
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| 300 | hConv = DdeConnect(idInst, hszApp, hszTopic, NULL); |
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[1367] | 301 | //DdeFreeStringHandle(idInst, hszApp); |
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| 302 | //DdeFreeStringHandle(idInst, hszTopic); |
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[1361] | 303 | if (hConv == NULL) |
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| 304 | { |
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| 305 | printf("DDE Connection Failed.\n"); |
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| 306 | Sleep(1500); DdeUninitialize(idInst); |
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| 307 | return 0; |
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| 308 | } |
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| 309 | |
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| 310 | //Execute commands/requests specific to the DDE Server. |
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| 311 | |
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[1376] | 312 | DDERequest(idInst, hConv, szItem1); |
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[1361] | 313 | |
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[1367] | 314 | while(1) |
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| 315 | { |
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[1376] | 316 | MSG msg; |
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| 317 | BOOL MsgReturn = GetMessage ( &msg , NULL , 0 , 0 ); |
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| 318 | |
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| 319 | if(MsgReturn) |
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[1367] | 320 | { |
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[1376] | 321 | TranslateMessage(&msg); |
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| 322 | DispatchMessage(&msg); |
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[1367] | 323 | } |
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| 324 | } |
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[1376] | 325 | |
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[1361] | 326 | //DDE Disconnect and Uninitialize. |
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[1367] | 327 | DdeDisconnect(hConv); |
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| 328 | DdeUninitialize(idInst); |
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[1376] | 329 | */ |
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[1361] | 330 | |
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[1376] | 331 | |
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[1361] | 332 | |
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[1376] | 333 | /* |
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| 334 | // EXPERIMENT: 100 AR model generated time series of length of 30 from y_t=0.95*y_(t-1)+0.05*y_(t-2)+0.2*e_t, |
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| 335 | // where e_t is normally, student(4) and cauchy distributed are tested using robust AR model, to obtain the |
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| 336 | // variance of location parameter estimators and compare it to the classical setup. |
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| 337 | vector<vector<vector<string>>> string_lists; |
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| 338 | string_lists.push_back(vector<vector<string>>()); |
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| 339 | string_lists.push_back(vector<vector<string>>()); |
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| 340 | string_lists.push_back(vector<vector<string>>()); |
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[1186] | 341 | |
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[1376] | 342 | char* file_strings[3] = {"c:\\ar_normal.txt", "c:\\ar_student.txt", "c:\\ar_cauchy.txt"}; |
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| 343 | |
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[1282] | 344 | |
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[1376] | 345 | for(int i = 0;i<3;i++) |
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| 346 | { |
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| 347 | ifstream myfile(file_strings[i]); |
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[1282] | 348 | if (myfile.is_open()) |
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[1376] | 349 | { |
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| 350 | while ( myfile.good() ) |
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| 351 | { |
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| 352 | string line; |
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| 353 | getline(myfile,line); |
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| 354 | |
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| 355 | vector<string> parsed_line; |
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[1282] | 356 | while(line.find(',') != string::npos) |
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| 357 | { |
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[1376] | 358 | int loc = line.find(','); |
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| 359 | parsed_line.push_back(line.substr(0,loc)); |
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[1282] | 360 | line.erase(0,loc+1); |
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[1376] | 361 | } |
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[1282] | 362 | |
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[1376] | 363 | string_lists[i].push_back(parsed_line); |
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| 364 | } |
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| 365 | myfile.close(); |
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[1282] | 366 | } |
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[1376] | 367 | } |
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[1282] | 368 | |
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[1376] | 369 | for(int j = 0;j<string_lists.size();j++) |
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| 370 | { |
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[1301] | 371 | |
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[1376] | 372 | for(int i = 0;i<string_lists[j].size()-1;i++) |
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| 373 | { |
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| 374 | vector<vec> conditions; |
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| 375 | //emlig* emliga = new emlig(2); |
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| 376 | RARX* my_rarx = new RARX(2,30); |
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[1301] | 377 | |
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[1376] | 378 | for(int k = 1;k<string_lists[j][i].size();k++) |
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[1282] | 379 | { |
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[1376] | 380 | vec condition; |
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| 381 | //condition.ins(0,1); |
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| 382 | condition.ins(0,string_lists[j][i][k]); |
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| 383 | conditions.push_back(condition); |
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[1282] | 384 | |
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[1376] | 385 | //cout << "orig:" << condition << endl; |
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| 386 | |
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| 387 | if(conditions.size()>1) |
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| 388 | { |
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| 389 | conditions[k-2].ins(0,string_lists[j][i][k]); |
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| 390 | |
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[1282] | 391 | } |
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[1376] | 392 | |
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| 393 | if(conditions.size()>2) |
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| 394 | { |
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| 395 | conditions[k-3].ins(0,string_lists[j][i][k]); |
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| 396 | |
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| 397 | //cout << "modi:" << conditions[k-3] << endl; |
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| 398 | |
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| 399 | my_rarx->bayes(conditions[k-3]); |
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| 400 | |
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| 401 | |
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| 402 | //if(k>5) |
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| 403 | //{ |
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| 404 | // cout << "MaxLik coords:" << emliga->minimal_vertex->get_coordinates() << endl; |
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| 405 | //} |
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| 406 | |
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| 407 | } |
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| 408 | |
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[1282] | 409 | } |
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| 410 | |
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[1376] | 411 | //emliga->step_me(0); |
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| 412 | /* |
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| 413 | ofstream myfile; |
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| 414 | myfile.open("c:\\robust_ar1.txt",ios::app); |
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| 415 | myfile << my_rarx->minimal_vertex->get_coordinates()[0] << ";"; |
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| 416 | myfile.close(); |
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| 417 | |
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| 418 | myfile.open("c:\\robust_ar2.txt",ios::app); |
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| 419 | myfile << emliga->minimal_vertex->get_coordinates()[1] << ";"; |
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| 420 | myfile.close(); |
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[1301] | 421 | |
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[1284] | 422 | |
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[1376] | 423 | cout << "MaxLik coords:" << emliga->minimal_vertex->get_coordinates() << endl; |
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| 424 | cout << "Step: " << i << endl; |
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| 425 | } |
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[1282] | 426 | |
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[1376] | 427 | cout << "One experiment finished." << endl; |
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[1284] | 428 | |
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[1376] | 429 | ofstream myfile; |
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| 430 | myfile.open("c:\\robust_ar1.txt",ios::app); |
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| 431 | myfile << endl; |
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| 432 | myfile.close(); |
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[1284] | 433 | |
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[1376] | 434 | myfile.open("c:\\robust_ar2.txt",ios::app); |
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| 435 | myfile << endl; |
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| 436 | myfile.close(); |
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| 437 | }*/ |
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| 438 | |
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| 439 | // EXPERIMENT: A moving window estimation and prediction of RARX is tested on data generated from |
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| 440 | // 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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| 441 | // can be compared to the classical setup. |
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| 442 | |
---|
| 443 | itpp::Laplace_RNG LapRNG = Laplace_RNG(); |
---|
[1301] | 444 | |
---|
[1376] | 445 | vector<vector<string>> strings; |
---|
[1301] | 446 | |
---|
[1376] | 447 | char* file_string = "c:\\ar_cauchy_single"; |
---|
[1301] | 448 | |
---|
[1376] | 449 | char dfstring[80]; |
---|
| 450 | strcpy(dfstring,file_string); |
---|
| 451 | strcat(dfstring,".txt"); |
---|
| 452 | |
---|
| 453 | |
---|
| 454 | mat data_matrix; |
---|
| 455 | ifstream myfile(dfstring); |
---|
| 456 | if (myfile.is_open()) |
---|
| 457 | { |
---|
| 458 | string line; |
---|
| 459 | while(getline(myfile,line)) |
---|
| 460 | { |
---|
| 461 | vec data_vector; |
---|
| 462 | while(line.find(',') != string::npos) //zmenil som ciarku za medzeru |
---|
| 463 | { |
---|
| 464 | line.erase(0,1); // toto som sem pridal |
---|
| 465 | int loc2 = line.find('\n'); |
---|
| 466 | int loc = line.find(','); |
---|
| 467 | data_vector.ins(data_vector.size(),atof(line.substr(0,loc).c_str())); |
---|
| 468 | line.erase(0,loc+1); |
---|
| 469 | } |
---|
[1301] | 470 | |
---|
[1376] | 471 | data_matrix.ins_row(data_matrix.rows(),data_vector); |
---|
| 472 | } |
---|
[1361] | 473 | |
---|
[1376] | 474 | myfile.close(); |
---|
| 475 | } |
---|
| 476 | else |
---|
| 477 | { |
---|
| 478 | cout << "Can't open data file!" << endl; |
---|
| 479 | } |
---|
| 480 | |
---|
| 481 | //konec nacitavania dat |
---|
| 482 | set<set<pair<int,int>>> model_types = model::possible_models_recurse(max_model_order,data_matrix.rows()); //volanie funkce kde robi kombinace modelov |
---|
| 483 | //to priradime do model_types, data_matrix.row urcuje pocet kanalov dat |
---|
| 484 | vector<model*> models; |
---|
| 485 | for(set<set<pair<int,int>>>::iterator model_type = model_types.begin();model_type!=model_types.end();model_type++) |
---|
| 486 | {// prechadza rozne typy kanalov, a poctu regresorov |
---|
| 487 | for(int window_size = 100;window_size < 101;window_size++) |
---|
| 488 | { |
---|
| 489 | models.push_back(new model((*model_type),true,true,window_size,0,&data_matrix)); // to su len konstruktory, len inicializujeme rozne typy |
---|
| 490 | models.push_back(new model((*model_type),false,true,window_size,0,&data_matrix)); |
---|
| 491 | models.push_back(new model((*model_type),true,false,window_size,0,&data_matrix)); |
---|
| 492 | models.push_back(new model((*model_type),false,false,window_size,0,&data_matrix)); |
---|
| 493 | } |
---|
[1365] | 494 | |
---|
[1376] | 495 | set<pair<int,int>> empty_list; |
---|
| 496 | models.push_back(new model(empty_list,false,true,100,0,&data_matrix)); |
---|
| 497 | } |
---|
| 498 | |
---|
| 499 | mat result_lognc; |
---|
| 500 | // mat result_preds; |
---|
| 501 | |
---|
| 502 | for(int time = max_model_order;time<data_matrix.cols();time++) //time<data_matrix.cols() |
---|
| 503 | { //pocet stlpcov data_matrix je pocet casovych krokov |
---|
| 504 | vec cur_res_lognc; |
---|
| 505 | // vec preds; |
---|
| 506 | vector<string> nazvy; |
---|
| 507 | for(vector<model*>::iterator model_ref = models.begin();model_ref!=models.end();model_ref++) |
---|
| 508 | {//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 |
---|
| 509 | (*model_ref)->data_update(time); //pozret sa preco je toto tu nutne |
---|
| 510 | //if (time = max_model_order) nazvy.push_back(models.model_ref]);// ako by som mohol dostat nazov modelu? |
---|
| 511 | |
---|
| 512 | if((*model_ref)->my_rarx!=NULL) //vklada normalizacnz faktor do cur_res_lognc |
---|
| 513 | { |
---|
| 514 | cur_res_lognc.ins(cur_res_lognc.size(),(*model_ref)->my_rarx->posterior->log_nc); |
---|
[1361] | 515 | } |
---|
[1376] | 516 | else |
---|
| 517 | { |
---|
| 518 | cur_res_lognc.ins(cur_res_lognc.size(),(*model_ref)->my_arx->posterior().lognc()); |
---|
| 519 | } |
---|
[1367] | 520 | |
---|
[1376] | 521 | // pair<vec,vec> predictions = (*model_ref)->predict(200,time,&LapRNG); |
---|
[1367] | 522 | |
---|
[1376] | 523 | // preds.ins(preds.size(),(predictions.first*predictions.second)/(predictions.first*ones(predictions.first.size()))); |
---|
| 524 | // preds.ins(0,data_matrix.get(0,time+1)); |
---|
[1361] | 525 | |
---|
[1376] | 526 | } |
---|
[1367] | 527 | |
---|
[1376] | 528 | result_lognc.ins_col(result_lognc.cols(),cur_res_lognc); |
---|
| 529 | // result_preds.ins_col(result_preds.cols(),preds); |
---|
| 530 | |
---|
| 531 | // cout << "Updated." << endl; |
---|
| 532 | |
---|
[1361] | 533 | /* |
---|
[1301] | 534 | vector<vec> conditions; |
---|
| 535 | //emlig* emliga = new emlig(2); |
---|
[1358] | 536 | RARX* my_rarx = new RARX(2,10,false); |
---|
[1337] | 537 | |
---|
[1338] | 538 | |
---|
[1337] | 539 | mat V0 = 0.0001 * eye ( 3 ); |
---|
[1349] | 540 | ARX* my_arx = new ARX(0.85); |
---|
[1337] | 541 | my_arx->set_statistics ( 1, V0 ); //nu is default (set to have finite moments) |
---|
| 542 | my_arx->set_constant ( false ); |
---|
| 543 | my_arx->validate(); |
---|
[1338] | 544 | |
---|
[1301] | 545 | |
---|
[1338] | 546 | for(int k = 1;k<strings[j].size();k++) |
---|
[1301] | 547 | { |
---|
| 548 | vec condition; |
---|
| 549 | //condition.ins(0,1); |
---|
| 550 | condition.ins(0,strings[j][k]); |
---|
| 551 | conditions.push_back(condition); |
---|
| 552 | |
---|
| 553 | //cout << "orig:" << condition << endl; |
---|
| 554 | |
---|
| 555 | if(conditions.size()>1) |
---|
| 556 | { |
---|
| 557 | conditions[k-2].ins(0,strings[j][k]); |
---|
| 558 | |
---|
| 559 | } |
---|
| 560 | |
---|
| 561 | if(conditions.size()>2) |
---|
| 562 | { |
---|
| 563 | conditions[k-3].ins(0,strings[j][k]); |
---|
| 564 | |
---|
[1349] | 565 | // cout << "Condition:" << conditions[k-3] << endl; |
---|
[1301] | 566 | |
---|
| 567 | my_rarx->bayes(conditions[k-3]); |
---|
[1338] | 568 | //my_rarx->posterior->step_me(1); |
---|
[1337] | 569 | |
---|
| 570 | vec cond_vec; |
---|
| 571 | cond_vec.ins(0,conditions[k-3][0]); |
---|
| 572 | |
---|
[1338] | 573 | my_arx->bayes(cond_vec,conditions[k-3].right(2)); |
---|
[1301] | 574 | |
---|
[1361] | 575 | /* |
---|
[1346] | 576 | if(k>8) |
---|
[1301] | 577 | { |
---|
[1324] | 578 | //my_rarx->posterior->step_me(0); |
---|
| 579 | |
---|
[1346] | 580 | //mat samples = my_rarx->posterior->sample_mat(10); |
---|
[1343] | 581 | |
---|
[1346] | 582 | pair<vec,mat> imp_samples = my_rarx->posterior->importance_sample(1000); |
---|
[1343] | 583 | |
---|
[1346] | 584 | //cout << imp_samples.first << endl; |
---|
[1336] | 585 | |
---|
[1337] | 586 | vec sample_prediction; |
---|
[1358] | 587 | vec averaged_params = zeros(imp_samples.second.rows()); |
---|
[1346] | 588 | for(int t = 0;t<imp_samples.first.size();t++) |
---|
[1337] | 589 | { |
---|
| 590 | vec lap_sample = conditions[k-3].left(2); |
---|
[1346] | 591 | //lap_sample.ins(lap_sample.size(),1.0); |
---|
[1337] | 592 | |
---|
| 593 | lap_sample.ins(0,LapRNG()); |
---|
| 594 | |
---|
[1346] | 595 | sample_prediction.ins(0,lap_sample*imp_samples.second.get_col(t)); |
---|
[1358] | 596 | |
---|
| 597 | averaged_params += imp_samples.first[t]*imp_samples.second.get_col(t); |
---|
[1337] | 598 | } |
---|
| 599 | |
---|
[1358] | 600 | averaged_params = averaged_params*(1/(imp_samples.first*ones(imp_samples.first.size()))); |
---|
| 601 | |
---|
| 602 | // cout << "Averaged estimated parameters: " << averaged_params << endl; |
---|
[1338] | 603 | |
---|
[1358] | 604 | vec sample_pow = sample_prediction; |
---|
[1343] | 605 | |
---|
| 606 | // cout << sample_prediction << endl; |
---|
[1337] | 607 | vec poly_coefs; |
---|
[1346] | 608 | double prediction; |
---|
[1337] | 609 | bool stop_iteration = false; |
---|
[1343] | 610 | int en = 1; |
---|
[1337] | 611 | do |
---|
| 612 | { |
---|
[1346] | 613 | double poly_coef = imp_samples.first*sample_pow/(imp_samples.first*ones(imp_samples.first.size())); |
---|
[1337] | 614 | |
---|
[1346] | 615 | if(en==1) |
---|
| 616 | { |
---|
| 617 | prediction = poly_coef; |
---|
| 618 | } |
---|
| 619 | |
---|
[1343] | 620 | poly_coef = poly_coef*en*fact(utility_constant-2+en)/fact(utility_constant-2); |
---|
| 621 | |
---|
[1337] | 622 | if(abs(poly_coef)>numeric_limits<double>::epsilon()) |
---|
| 623 | { |
---|
| 624 | sample_pow = elem_mult(sample_pow,sample_prediction); |
---|
[1343] | 625 | poly_coefs.ins(0,pow(-1.0,en+1)*poly_coef); |
---|
[1337] | 626 | } |
---|
| 627 | else |
---|
| 628 | { |
---|
| 629 | stop_iteration = true; |
---|
| 630 | } |
---|
| 631 | |
---|
| 632 | en++; |
---|
[1343] | 633 | |
---|
| 634 | if(en>20) |
---|
| 635 | { |
---|
| 636 | stop_iteration = true; |
---|
| 637 | } |
---|
[1337] | 638 | } |
---|
| 639 | while(!stop_iteration); |
---|
| 640 | |
---|
[1343] | 641 | /* |
---|
| 642 | ofstream myfile_coef; |
---|
| 643 | |
---|
| 644 | myfile_coef.open("c:\\coefs.txt",ios::app); |
---|
| 645 | |
---|
| 646 | for(int t = 0;t<poly_coefs.size();t++) |
---|
| 647 | { |
---|
| 648 | myfile_coef << poly_coefs[t] << ","; |
---|
| 649 | } |
---|
| 650 | |
---|
| 651 | myfile_coef << endl; |
---|
| 652 | myfile_coef.close(); |
---|
| 653 | */ |
---|
| 654 | |
---|
[1349] | 655 | //cout << "Coefficients: " << poly_coefs << endl; |
---|
[1338] | 656 | |
---|
[1343] | 657 | /* |
---|
| 658 | vec bas_coef = vec("1.0 2.0 -8.0"); |
---|
| 659 | cout << "Coefs: " << bas_coef << endl; |
---|
| 660 | cvec actions2 = roots(bas_coef); |
---|
| 661 | cout << "Roots: " << actions2 << endl; |
---|
| 662 | */ |
---|
| 663 | |
---|
[1361] | 664 | /* |
---|
[1346] | 665 | |
---|
[1338] | 666 | cvec actions = roots(poly_coefs); |
---|
[1343] | 667 | |
---|
| 668 | |
---|
[1338] | 669 | bool is_max = false; |
---|
| 670 | for(int t = 0;t<actions.size();t++) |
---|
| 671 | { |
---|
[1343] | 672 | if(actions[t].imag() == 0) |
---|
[1338] | 673 | { |
---|
[1343] | 674 | double second_derivative = 0; |
---|
| 675 | for(int q = 1;q<poly_coefs.size();q++) |
---|
| 676 | { |
---|
| 677 | second_derivative+=poly_coefs[q]*pow(actions[t].real(),q-1)*q; |
---|
| 678 | } |
---|
| 679 | |
---|
| 680 | if(second_derivative<0) |
---|
| 681 | { |
---|
| 682 | cout << "Action:" << actions[t].real() << endl; |
---|
| 683 | |
---|
| 684 | is_max = true; |
---|
| 685 | } |
---|
[1338] | 686 | } |
---|
| 687 | } |
---|
[1301] | 688 | |
---|
[1338] | 689 | if(!is_max) |
---|
| 690 | { |
---|
| 691 | cout << "No maximum." << endl; |
---|
| 692 | } |
---|
| 693 | |
---|
| 694 | // cout << "MaxLik coords:" << my_rarx->posterior->minimal_vertex->get_coordinates() << endl; |
---|
| 695 | |
---|
[1346] | 696 | /* |
---|
[1337] | 697 | double prediction = 0; |
---|
| 698 | for(int s = 1;s<samples.rows();s++) |
---|
[1336] | 699 | { |
---|
| 700 | |
---|
[1346] | 701 | double avg_parameter = imp_samples.get_row(s)*ones(samples.cols())/samples.cols(); |
---|
[1337] | 702 | |
---|
| 703 | prediction += avg_parameter*conditions[k-3][s-1]; |
---|
| 704 | |
---|
[1336] | 705 | |
---|
[1337] | 706 | |
---|
| 707 | /* |
---|
[1336] | 708 | ofstream myfile; |
---|
| 709 | char fstring[80]; |
---|
| 710 | strcpy(fstring,file_strings[j]); |
---|
[1301] | 711 | |
---|
[1336] | 712 | char es[5]; |
---|
| 713 | strcat(fstring,itoa(s,es,10)); |
---|
| 714 | |
---|
| 715 | strcat(fstring,"_res.txt"); |
---|
| 716 | |
---|
| 717 | |
---|
| 718 | myfile.open(fstring,ios::app); |
---|
| 719 | |
---|
| 720 | //myfile << my_rarx->posterior->minimal_vertex->get_coordinates()[0]; |
---|
| 721 | myfile << avg_parameter; |
---|
| 722 | |
---|
| 723 | if(k!=strings[j].size()-1) |
---|
| 724 | { |
---|
| 725 | myfile << ","; |
---|
| 726 | } |
---|
| 727 | else |
---|
| 728 | { |
---|
| 729 | myfile << endl; |
---|
| 730 | } |
---|
| 731 | myfile.close(); |
---|
[1337] | 732 | */ |
---|
| 733 | |
---|
[1338] | 734 | |
---|
[1346] | 735 | //} |
---|
| 736 | |
---|
| 737 | // cout << "Prediction: "<< prediction << endl; |
---|
[1361] | 738 | /* |
---|
[1337] | 739 | enorm<ldmat>* pred_mat = my_arx->epredictor(conditions[k-3].left(2)); |
---|
| 740 | double prediction2 = pred_mat->mean()[0]; |
---|
[1361] | 741 | */ |
---|
[1337] | 742 | |
---|
[1365] | 743 | |
---|
[1376] | 744 | ofstream myfile; |
---|
| 745 | char fstring[80]; |
---|
| 746 | strcpy(fstring,file_string); |
---|
[1337] | 747 | |
---|
[1376] | 748 | strcat(fstring,"lognc.txt"); |
---|
| 749 | |
---|
| 750 | myfile.open(fstring,ios::app); |
---|
| 751 | |
---|
| 752 | // myfile << my_rarx->posterior->minimal_vertex->get_coordinates()[0]; |
---|
| 753 | |
---|
| 754 | if(time == max_model_order) |
---|
| 755 | { |
---|
| 756 | for(int i = 0;i<cur_res_lognc.size();i++) |
---|
| 757 | { |
---|
| 758 | for(set<pair<int,int>>::iterator ar_ref = models[i]->ar_components.begin();ar_ref != models[i]->ar_components.end();ar_ref++) |
---|
| 759 | { |
---|
| 760 | myfile << (*ar_ref).second << (*ar_ref).first; |
---|
| 761 | } |
---|
| 762 | |
---|
| 763 | myfile << "."; |
---|
| 764 | |
---|
| 765 | if(models[i]->my_arx == NULL) |
---|
| 766 | { |
---|
| 767 | myfile << "1"; |
---|
| 768 | } |
---|
| 769 | else |
---|
| 770 | { |
---|
| 771 | myfile << "0"; |
---|
| 772 | } |
---|
| 773 | |
---|
| 774 | if(models[i]->has_constant) |
---|
| 775 | { |
---|
| 776 | myfile << "1"; |
---|
| 777 | } |
---|
| 778 | else |
---|
| 779 | { |
---|
| 780 | myfile << "0"; |
---|
| 781 | } |
---|
| 782 | |
---|
| 783 | myfile << ","; |
---|
| 784 | } |
---|
| 785 | |
---|
| 786 | myfile << endl; |
---|
| 787 | } |
---|
| 788 | |
---|
| 789 | for(int i = 0;i<cur_res_lognc.size();i++) |
---|
| 790 | { |
---|
| 791 | myfile << cur_res_lognc[i] << ' ';//zmenil som ciarku ze medzeru |
---|
| 792 | } |
---|
| 793 | |
---|
| 794 | myfile << endl; |
---|
| 795 | |
---|
| 796 | myfile.close(); |
---|
| 797 | } |
---|
[1361] | 798 | /* |
---|
[1337] | 799 | myfile.open(f2string,ios::app); |
---|
| 800 | myfile << prediction2; |
---|
| 801 | |
---|
| 802 | if(k!=strings[j].size()-1) |
---|
| 803 | { |
---|
| 804 | myfile << ","; |
---|
| 805 | } |
---|
| 806 | else |
---|
| 807 | { |
---|
| 808 | myfile << endl; |
---|
| 809 | } |
---|
| 810 | myfile.close(); |
---|
[1361] | 811 | //*//* |
---|
[1337] | 812 | |
---|
[1319] | 813 | } |
---|
[1361] | 814 | } */ |
---|
[1301] | 815 | |
---|
| 816 | //emliga->step_me(0); |
---|
| 817 | /* |
---|
| 818 | ofstream myfile; |
---|
| 819 | myfile.open("c:\\robust_ar1.txt",ios::app); |
---|
| 820 | myfile << my_rarx->minimal_vertex->get_coordinates()[0] << ";"; |
---|
| 821 | myfile.close(); |
---|
| 822 | |
---|
| 823 | myfile.open("c:\\robust_ar2.txt",ios::app); |
---|
| 824 | myfile << emliga->minimal_vertex->get_coordinates()[1] << ";"; |
---|
| 825 | myfile.close(); |
---|
| 826 | |
---|
| 827 | |
---|
| 828 | cout << "MaxLik coords:" << emliga->minimal_vertex->get_coordinates() << endl; |
---|
| 829 | cout << "Step: " << i << endl;*/ |
---|
[1361] | 830 | //} |
---|
[1337] | 831 | |
---|
| 832 | |
---|
[1365] | 833 | //} |
---|
[1337] | 834 | |
---|
| 835 | |
---|
| 836 | // EXPERIMENT: One step ahead price prediction. Comparison of classical and robust model using optimal trading |
---|
| 837 | // with maximization of logarithm of one-step ahead wealth. |
---|
| 838 | |
---|
| 839 | |
---|
[1301] | 840 | |
---|
| 841 | /* |
---|
| 842 | cout << "One experiment finished." << endl; |
---|
| 843 | |
---|
| 844 | ofstream myfile; |
---|
| 845 | myfile.open("c:\\robust_ar1.txt",ios::app); |
---|
| 846 | myfile << endl; |
---|
| 847 | myfile.close(); |
---|
| 848 | |
---|
| 849 | myfile.open("c:\\robust_ar2.txt",ios::app); |
---|
| 850 | myfile << endl; |
---|
| 851 | myfile.close();*/ |
---|
[1300] | 852 | |
---|
[1301] | 853 | |
---|
| 854 | //emlig* emlig1 = new emlig(emlig_size); |
---|
| 855 | |
---|
| 856 | //emlig1->step_me(0); |
---|
| 857 | //emlig* emlig2 = new emlig(emlig_size); |
---|
[1300] | 858 | |
---|
[1267] | 859 | /* |
---|
| 860 | emlig1->set_correction_factors(4); |
---|
[1266] | 861 | |
---|
[1267] | 862 | for(int j = 0;j<emlig1->correction_factors.size();j++) |
---|
| 863 | { |
---|
| 864 | for(set<my_ivec>::iterator vec_ref = emlig1->correction_factors[j].begin();vec_ref!=emlig1->correction_factors[j].end();vec_ref++) |
---|
| 865 | { |
---|
[1268] | 866 | cout << j << " "; |
---|
| 867 | |
---|
[1267] | 868 | for(int i=0;i<(*vec_ref).size();i++) |
---|
| 869 | { |
---|
| 870 | cout << (*vec_ref)[i]; |
---|
| 871 | } |
---|
| 872 | |
---|
| 873 | cout << endl; |
---|
| 874 | } |
---|
[1268] | 875 | }*/ |
---|
| 876 | |
---|
[1301] | 877 | /* |
---|
[1300] | 878 | vec condition5 = "1.0 1.0 1.01";//"-0.3 1.7 1.5"; |
---|
| 879 | |
---|
[1299] | 880 | emlig1->add_condition(condition5); |
---|
[1301] | 881 | //emlig1->step_me(0); |
---|
| 882 | |
---|
| 883 | |
---|
| 884 | vec condition1a = "-1.0 1.02 0.5"; |
---|
[1300] | 885 | //vec condition1b = "1.0 1.0 1.01"; |
---|
[1301] | 886 | emlig1->add_condition(condition1a); |
---|
[1300] | 887 | //emlig2->add_condition(condition1b); |
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[1267] | 888 | |
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[1301] | 889 | vec condition2a = "-0.3 1.7 1.5"; |
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[1300] | 890 | //vec condition2b = "-1.0 1.0 1.0"; |
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[1301] | 891 | emlig1->add_condition(condition2a); |
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[1300] | 892 | //emlig2->add_condition(condition2b); |
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[1234] | 893 | |
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[1301] | 894 | vec condition3a = "0.5 -1.01 1.0"; |
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[1300] | 895 | //vec condition3b = "0.5 -1.01 1.0"; |
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[1280] | 896 | |
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[1301] | 897 | emlig1->add_condition(condition3a); |
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[1300] | 898 | //emlig2->add_condition(condition3b); |
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[1280] | 899 | |
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[1301] | 900 | vec condition4a = "-0.5 -1.0 1.0"; |
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[1300] | 901 | //vec condition4b = "-0.5 -1.0 1.0"; |
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| 902 | |
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[1301] | 903 | emlig1->add_condition(condition4a); |
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[1300] | 904 | //cout << "************************************************" << endl; |
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| 905 | //emlig2->add_condition(condition4b); |
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| 906 | //cout << "************************************************" << endl; |
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| 907 | |
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[1299] | 908 | //cout << emlig1->minimal_vertex->get_coordinates(); |
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[1300] | 909 | |
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[1301] | 910 | //emlig1->remove_condition(condition3a); |
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| 911 | //emlig1->step_me(0); |
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| 912 | //emlig1->remove_condition(condition2a); |
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| 913 | //emlig1->remove_condition(condition1a); |
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| 914 | //emlig1->remove_condition(condition5); |
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| 915 | |
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[1275] | 916 | |
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[1299] | 917 | //emlig1->step_me(0); |
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| 918 | //emlig2->step_me(0); |
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| 919 | |
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[1282] | 920 | |
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| 921 | // DA SE POUZIT PRO VYPIS DO SOUBORU |
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[1275] | 922 | // emlig1->step_me(0); |
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| 923 | |
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| 924 | //emlig1->remove_condition(condition1); |
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| 925 | |
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[1301] | 926 | |
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[1275] | 927 | |
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| 928 | |
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[1301] | 929 | |
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[1275] | 930 | /* |
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[1282] | 931 | for(int i = 0;i<100;i++) |
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[1219] | 932 | { |
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[1282] | 933 | cout << endl << "Step:" << i << endl; |
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[1208] | 934 | |
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[1268] | 935 | double condition[emlig_size+1]; |
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[1220] | 936 | |
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[1268] | 937 | for(int k = 0;k<=emlig_size;k++) |
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| 938 | { |
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[1272] | 939 | condition[k] = (rand()-RAND_MAX/2)/1000.0; |
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[1268] | 940 | } |
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| 941 | |
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[1216] | 942 | |
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[1268] | 943 | vec* condition_vec = new vec(condition,emlig_size+1); |
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[1219] | 944 | emlig1->add_condition(*condition_vec); |
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[1271] | 945 | |
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[1272] | 946 | /* |
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| 947 | for(polyhedron* toprow_ref = emlig1->statistic.rows[emlig_size]; toprow_ref != emlig1->statistic.end_poly; toprow_ref = toprow_ref->next_poly) |
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| 948 | { |
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| 949 | cout << ((toprow*)toprow_ref)->probability << endl; |
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| 950 | } |
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| 951 | */ |
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[1275] | 952 | /* |
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[1271] | 953 | cout << emlig1->statistic_rowsize(emlig_size) << endl << endl; |
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[1268] | 954 | |
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[1272] | 955 | /* |
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[1271] | 956 | if(i-emlig1->number_of_parameters >= 0) |
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| 957 | { |
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| 958 | pause(30); |
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| 959 | } |
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[1272] | 960 | */ |
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[1219] | 961 | |
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[1271] | 962 | // emlig1->step_me(i); |
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[1219] | 963 | |
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[1272] | 964 | /* |
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[1219] | 965 | vector<int> sizevector; |
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| 966 | for(int s = 0;s<=emlig1->number_of_parameters;s++) |
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| 967 | { |
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| 968 | sizevector.push_back(emlig1->statistic_rowsize(s)); |
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| 969 | } |
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[1272] | 970 | */ |
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[1275] | 971 | //} |
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| 972 | |
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[1219] | 973 | |
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| 974 | |
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| 975 | |
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| 976 | /* |
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| 977 | emlig1->step_me(1); |
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| 978 | |
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| 979 | vec condition = "2.0 0.0 1.0"; |
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| 980 | |
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[1208] | 981 | emlig1->add_condition(condition); |
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| 982 | |
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[1216] | 983 | vector<int> sizevector; |
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| 984 | for(int s = 0;s<=emlig1->number_of_parameters;s++) |
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| 985 | { |
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| 986 | sizevector.push_back(emlig1->statistic_rowsize(s)); |
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| 987 | } |
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| 988 | |
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[1219] | 989 | emlig1->step_me(2); |
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[1216] | 990 | |
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[1219] | 991 | condition = "2.0 1.0 0.0"; |
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[1216] | 992 | |
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| 993 | emlig1->add_condition(condition); |
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| 994 | |
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| 995 | sizevector.clear(); |
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| 996 | for(int s = 0;s<=emlig1->number_of_parameters;s++) |
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| 997 | { |
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| 998 | sizevector.push_back(emlig1->statistic_rowsize(s)); |
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| 999 | } |
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[1219] | 1000 | */ |
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[1216] | 1001 | |
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[1376] | 1002 | return 0; |
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| 1003 | } |
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[976] | 1004 | |
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[1282] | 1005 | |
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