[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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[1379] | 30 | const double apriorno=0.005; |
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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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[1379] | 76 | ARXwin* 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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[1379] | 112 | my_arx = new ARXwin(); |
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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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[1379] | 119 | my_arx->set_constant(true); |
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[1361] | 120 | } |
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| 121 | else |
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| 122 | { |
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| 123 | |
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[1376] | 124 | V0 = apriorno * eye(ar_components.size()+1);//menit konstantu |
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[1370] | 125 | V0(0,0) = 1; |
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[1361] | 126 | my_arx->set_constant(false); |
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| 127 | |
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| 128 | } |
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| 129 | |
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[1379] | 130 | my_arx->set_statistics(1, V0, V0.rows()+1); |
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| 131 | my_arx->set_parameters(window_size); |
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[1361] | 132 | my_arx->validate(); |
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| 133 | } |
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[1358] | 134 | } |
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[1361] | 135 | |
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[1376] | 136 | 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] | 137 | { |
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[1376] | 138 | vec data_vector; |
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| 139 | for(set<pair<int,int>>::iterator ar_iterator = ar_components.begin();ar_iterator!=ar_components.end();ar_iterator++) |
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| 140 | { //ar?iterator ide len od 1 pod 2, alebo niekedy len 1 |
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| 141 | data_vector.ins(data_vector.size(),(*data_matrix).get(ar_iterator->first,time-ar_iterator->second)); |
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| 142 | // 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] | 143 | } |
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[1376] | 144 | if(my_rarx!=NULL) |
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| 145 | { //pre robusr priradi az tu do data_vector aj rpedikciu |
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| 146 | data_vector.ins(0,(*data_matrix).get(predicted_channel,time)); |
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| 147 | my_rarx->bayes(data_vector); |
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| 148 | } |
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[1358] | 149 | else |
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| 150 | { |
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[1376] | 151 | vec pred_vec;//tu sa predikcia zadava zvlast |
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| 152 | pred_vec.ins(0,(*data_matrix).get(predicted_channel,time)); |
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| 153 | my_arx->bayes(pred_vec,data_vector); |
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[1361] | 154 | } |
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| 155 | } |
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| 156 | |
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[1376] | 157 | 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] | 158 | { |
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[1376] | 159 | vec condition_vector; |
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| 160 | for(set<pair<int,int>>::iterator ar_iterator = ar_components.begin();ar_iterator!=ar_components.end();ar_iterator++) |
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[1367] | 161 | { |
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[1376] | 162 | condition_vector.ins(condition_vector.size(),(*data_matrix).get(ar_iterator->first,time-ar_iterator->second+1)); |
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| 163 | } |
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[1367] | 164 | |
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[1376] | 165 | if(my_rarx!=NULL) |
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| 166 | { |
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| 167 | pair<vec,mat> imp_samples = my_rarx->posterior->importance_sample(sample_size); |
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[1367] | 168 | |
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[1376] | 169 | //cout << imp_samples.first << endl; |
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| 170 | |
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| 171 | vec sample_prediction; |
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| 172 | for(int t = 0;t<sample_size;t++) |
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[1367] | 173 | { |
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[1376] | 174 | vec lap_sample = condition_vector; |
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[1367] | 175 | |
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[1376] | 176 | if(has_constant) |
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[1367] | 177 | { |
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[1376] | 178 | lap_sample.ins(lap_sample.size(),1.0); |
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[1367] | 179 | } |
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[1376] | 180 | |
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| 181 | lap_sample.ins(0,(*LapRNG)()); |
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[1367] | 182 | |
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[1376] | 183 | sample_prediction.ins(0,lap_sample*imp_samples.second.get_col(t)); |
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[1367] | 184 | } |
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| 185 | |
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[1376] | 186 | return pair<vec,vec>(imp_samples.first,sample_prediction); |
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| 187 | } |
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| 188 | else |
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| 189 | { |
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| 190 | mat samples = my_arx->posterior().sample_mat(sample_size); |
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| 191 | |
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| 192 | vec sample_prediction; |
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| 193 | for(int t = 0;t<sample_size;t++) |
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| 194 | { |
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| 195 | vec gau_sample = condition_vector; |
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[1367] | 196 | |
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[1376] | 197 | if(has_constant) |
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[1367] | 198 | { |
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[1376] | 199 | gau_sample.ins(gau_sample.size(),1.0); |
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[1367] | 200 | } |
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[1376] | 201 | |
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| 202 | gau_sample.ins(0,randn()); |
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[1367] | 203 | |
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[1376] | 204 | sample_prediction.ins(0,gau_sample*samples.get_col(t)); |
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[1367] | 205 | } |
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[1376] | 206 | |
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| 207 | return pair<vec,vec>(ones(sample_prediction.size()),sample_prediction); |
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[1367] | 208 | } |
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| 209 | |
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| 210 | } |
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| 211 | |
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| 212 | |
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[1376] | 213 | static set<set<pair<int,int>>> possible_models_recurse(int max_order,int number_of_channels) |
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[1361] | 214 | { |
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[1376] | 215 | set<set<pair<int,int>>> created_model_types; |
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[1361] | 216 | |
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[1376] | 217 | if(max_order == 1)//ukoncovacia vetva |
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[1361] | 218 | { |
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[1376] | 219 | 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] | 220 | { |
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[1376] | 221 | set<pair<int,int>> returned_type; |
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| 222 | returned_type.insert(pair<int,int>(channel,1)); //?? |
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| 223 | created_model_types.insert(returned_type); |
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[1358] | 224 | } |
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[1361] | 225 | |
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| 226 | return created_model_types; |
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| 227 | } |
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| 228 | else |
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| 229 | { |
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[1376] | 230 | 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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| 231 | set<set<pair<int,int>>> returned_types; |
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[1361] | 232 | |
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[1376] | 233 | 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] | 234 | { |
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| 235 | |
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| 236 | for(int order = 1; order<=max_order; order++) |
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[1358] | 237 | { |
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[1361] | 238 | for(int channel = 0;channel<number_of_channels;channel++) |
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| 239 | { |
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[1376] | 240 | set<pair<int,int>> returned_type; |
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| 241 | pair<int,int> new_pair = pair<int,int>(channel,order);//?? |
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| 242 | if(find((*model_ref).begin(),(*model_ref).end(),new_pair)==(*model_ref).end()) //?? |
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[1361] | 243 | { |
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[1376] | 244 | returned_type.insert((*model_ref).begin(),(*model_ref).end()); //co vlozi na zaciatok retuned_type? |
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| 245 | returned_type.insert(new_pair); |
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| 246 | |
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| 247 | |
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| 248 | returned_types.insert(returned_type); |
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[1361] | 249 | } |
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| 250 | } |
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[1358] | 251 | } |
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| 252 | } |
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[1361] | 253 | |
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[1376] | 254 | created_model_types.insert(returned_types.begin(),returned_types.end()); |
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[1361] | 255 | |
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| 256 | return created_model_types; |
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| 257 | } |
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[1358] | 258 | } |
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[1361] | 259 | }; |
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| 260 | |
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| 261 | |
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| 262 | |
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| 263 | |
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[1367] | 264 | int main ( int argc, char* argv[] ) { |
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[1358] | 265 | |
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[1367] | 266 | /* |
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[1376] | 267 | DWORD Id; |
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[1367] | 268 | HANDLE hThrd = CreateThread( NULL, 0, (LPTHREAD_START_ROUTINE)ThrdFunc, (LPVOID)1, 0, &Id); |
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[1376] | 269 | |
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| 270 | if ( !hThrd ) |
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[1367] | 271 | { |
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| 272 | cout<<"Error Creating Threads,,,,.exiting"<<endl; |
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| 273 | return -1; |
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| 274 | } |
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| 275 | Sleep ( 100 ); |
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[1376] | 276 | |
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[1361] | 277 | |
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[1376] | 278 | char szApp[] = "MT4"; |
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| 279 | char szTopic[] = "QUOTE"; |
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| 280 | char szItem1[] = "EURUSD"; |
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[1361] | 281 | |
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| 282 | //DDE Initialization |
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[1376] | 283 | DWORD idInst=0; |
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[1361] | 284 | UINT iReturn; |
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| 285 | iReturn = DdeInitialize(&idInst, (PFNCALLBACK)DdeCallback, |
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| 286 | APPCLASS_STANDARD | APPCMD_CLIENTONLY, 0 ); |
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| 287 | if (iReturn!=DMLERR_NO_ERROR) |
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| 288 | { |
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| 289 | printf("DDE Initialization Failed: 0x%04x\n", iReturn); |
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| 290 | Sleep(1500); |
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| 291 | return 0; |
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| 292 | } |
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| 293 | |
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| 294 | //DDE Connect to Server using given AppName and topic. |
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| 295 | HSZ hszApp, hszTopic; |
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| 296 | HCONV hConv; |
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| 297 | hszApp = DdeCreateStringHandle(idInst, szApp, 0); |
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| 298 | hszTopic = DdeCreateStringHandle(idInst, szTopic, 0); |
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| 299 | hConv = DdeConnect(idInst, hszApp, hszTopic, NULL); |
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[1367] | 300 | //DdeFreeStringHandle(idInst, hszApp); |
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| 301 | //DdeFreeStringHandle(idInst, hszTopic); |
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[1361] | 302 | if (hConv == NULL) |
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| 303 | { |
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| 304 | printf("DDE Connection Failed.\n"); |
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| 305 | Sleep(1500); DdeUninitialize(idInst); |
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| 306 | return 0; |
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| 307 | } |
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| 308 | |
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| 309 | //Execute commands/requests specific to the DDE Server. |
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| 310 | |
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[1376] | 311 | DDERequest(idInst, hConv, szItem1); |
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[1361] | 312 | |
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[1367] | 313 | while(1) |
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| 314 | { |
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[1376] | 315 | MSG msg; |
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| 316 | BOOL MsgReturn = GetMessage ( &msg , NULL , 0 , 0 ); |
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| 317 | |
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| 318 | if(MsgReturn) |
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[1367] | 319 | { |
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[1376] | 320 | TranslateMessage(&msg); |
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| 321 | DispatchMessage(&msg); |
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[1367] | 322 | } |
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| 323 | } |
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[1376] | 324 | |
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[1361] | 325 | //DDE Disconnect and Uninitialize. |
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[1367] | 326 | DdeDisconnect(hConv); |
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| 327 | DdeUninitialize(idInst); |
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[1376] | 328 | */ |
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[1361] | 329 | |
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[1376] | 330 | |
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[1361] | 331 | |
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[1376] | 332 | /* |
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| 333 | // 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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| 334 | // where e_t is normally, student(4) and cauchy distributed are tested using robust AR model, to obtain the |
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| 335 | // variance of location parameter estimators and compare it to the classical setup. |
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| 336 | vector<vector<vector<string>>> string_lists; |
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| 337 | string_lists.push_back(vector<vector<string>>()); |
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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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[1186] | 340 | |
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[1376] | 341 | char* file_strings[3] = {"c:\\ar_normal.txt", "c:\\ar_student.txt", "c:\\ar_cauchy.txt"}; |
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| 342 | |
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[1282] | 343 | |
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[1376] | 344 | for(int i = 0;i<3;i++) |
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| 345 | { |
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| 346 | ifstream myfile(file_strings[i]); |
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[1282] | 347 | if (myfile.is_open()) |
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[1376] | 348 | { |
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| 349 | while ( myfile.good() ) |
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| 350 | { |
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| 351 | string line; |
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| 352 | getline(myfile,line); |
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| 353 | |
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| 354 | vector<string> parsed_line; |
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[1282] | 355 | while(line.find(',') != string::npos) |
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| 356 | { |
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[1376] | 357 | int loc = line.find(','); |
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| 358 | parsed_line.push_back(line.substr(0,loc)); |
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[1282] | 359 | line.erase(0,loc+1); |
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[1376] | 360 | } |
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[1282] | 361 | |
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[1376] | 362 | string_lists[i].push_back(parsed_line); |
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| 363 | } |
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| 364 | myfile.close(); |
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[1282] | 365 | } |
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[1376] | 366 | } |
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[1282] | 367 | |
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[1376] | 368 | for(int j = 0;j<string_lists.size();j++) |
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| 369 | { |
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[1301] | 370 | |
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[1376] | 371 | for(int i = 0;i<string_lists[j].size()-1;i++) |
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| 372 | { |
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| 373 | vector<vec> conditions; |
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| 374 | //emlig* emliga = new emlig(2); |
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| 375 | RARX* my_rarx = new RARX(2,30); |
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[1301] | 376 | |
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[1376] | 377 | for(int k = 1;k<string_lists[j][i].size();k++) |
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[1282] | 378 | { |
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[1376] | 379 | vec condition; |
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| 380 | //condition.ins(0,1); |
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| 381 | condition.ins(0,string_lists[j][i][k]); |
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| 382 | conditions.push_back(condition); |
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[1282] | 383 | |
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[1376] | 384 | //cout << "orig:" << condition << endl; |
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| 385 | |
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| 386 | if(conditions.size()>1) |
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| 387 | { |
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| 388 | conditions[k-2].ins(0,string_lists[j][i][k]); |
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| 389 | |
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[1282] | 390 | } |
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[1376] | 391 | |
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| 392 | if(conditions.size()>2) |
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| 393 | { |
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| 394 | conditions[k-3].ins(0,string_lists[j][i][k]); |
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| 395 | |
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| 396 | //cout << "modi:" << conditions[k-3] << endl; |
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| 397 | |
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| 398 | my_rarx->bayes(conditions[k-3]); |
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| 399 | |
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| 400 | |
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| 401 | //if(k>5) |
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| 402 | //{ |
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| 403 | // cout << "MaxLik coords:" << emliga->minimal_vertex->get_coordinates() << endl; |
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| 404 | //} |
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| 405 | |
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| 406 | } |
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| 407 | |
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[1282] | 408 | } |
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| 409 | |
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[1376] | 410 | //emliga->step_me(0); |
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| 411 | /* |
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| 412 | ofstream myfile; |
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| 413 | myfile.open("c:\\robust_ar1.txt",ios::app); |
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| 414 | myfile << my_rarx->minimal_vertex->get_coordinates()[0] << ";"; |
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| 415 | myfile.close(); |
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| 416 | |
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| 417 | myfile.open("c:\\robust_ar2.txt",ios::app); |
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| 418 | myfile << emliga->minimal_vertex->get_coordinates()[1] << ";"; |
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| 419 | myfile.close(); |
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[1301] | 420 | |
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[1284] | 421 | |
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[1376] | 422 | cout << "MaxLik coords:" << emliga->minimal_vertex->get_coordinates() << endl; |
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| 423 | cout << "Step: " << i << endl; |
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| 424 | } |
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[1282] | 425 | |
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[1376] | 426 | cout << "One experiment finished." << endl; |
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[1284] | 427 | |
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[1376] | 428 | ofstream myfile; |
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| 429 | myfile.open("c:\\robust_ar1.txt",ios::app); |
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| 430 | myfile << endl; |
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| 431 | myfile.close(); |
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[1284] | 432 | |
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[1376] | 433 | myfile.open("c:\\robust_ar2.txt",ios::app); |
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| 434 | myfile << endl; |
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| 435 | myfile.close(); |
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| 436 | }*/ |
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| 437 | |
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| 438 | // EXPERIMENT: A moving window estimation and prediction of RARX is tested on data generated from |
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| 439 | // 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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| 440 | // can be compared to the classical setup. |
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| 441 | |
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| 442 | itpp::Laplace_RNG LapRNG = Laplace_RNG(); |
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[1301] | 443 | |
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[1376] | 444 | vector<vector<string>> strings; |
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[1301] | 445 | |
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[1379] | 446 | char* file_string = "c:\\ar_normal_single"; // "c:\\dataTYClosePercDiff"; // |
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[1301] | 447 | |
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[1376] | 448 | char dfstring[80]; |
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| 449 | strcpy(dfstring,file_string); |
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| 450 | strcat(dfstring,".txt"); |
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| 451 | |
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| 452 | |
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| 453 | mat data_matrix; |
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| 454 | ifstream myfile(dfstring); |
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| 455 | if (myfile.is_open()) |
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| 456 | { |
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| 457 | string line; |
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| 458 | while(getline(myfile,line)) |
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| 459 | { |
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| 460 | vec data_vector; |
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| 461 | while(line.find(',') != string::npos) //zmenil som ciarku za medzeru |
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| 462 | { |
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| 463 | line.erase(0,1); // toto som sem pridal |
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| 464 | int loc2 = line.find('\n'); |
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| 465 | int loc = line.find(','); |
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| 466 | data_vector.ins(data_vector.size(),atof(line.substr(0,loc).c_str())); |
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| 467 | line.erase(0,loc+1); |
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| 468 | } |
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[1301] | 469 | |
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[1376] | 470 | data_matrix.ins_row(data_matrix.rows(),data_vector); |
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| 471 | } |
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[1361] | 472 | |
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[1376] | 473 | myfile.close(); |
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| 474 | } |
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| 475 | else |
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| 476 | { |
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| 477 | cout << "Can't open data file!" << endl; |
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| 478 | } |
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| 479 | |
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| 480 | //konec nacitavania dat |
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| 481 | 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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| 482 | //to priradime do model_types, data_matrix.row urcuje pocet kanalov dat |
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| 483 | vector<model*> models; |
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| 484 | for(set<set<pair<int,int>>>::iterator model_type = model_types.begin();model_type!=model_types.end();model_type++) |
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| 485 | {// prechadza rozne typy kanalov, a poctu regresorov |
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[1379] | 486 | for(int window_size = 15;window_size < 16;window_size++) |
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[1376] | 487 | { |
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| 488 | 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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| 489 | models.push_back(new model((*model_type),false,true,window_size,0,&data_matrix)); |
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| 490 | models.push_back(new model((*model_type),true,false,window_size,0,&data_matrix)); |
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| 491 | models.push_back(new model((*model_type),false,false,window_size,0,&data_matrix)); |
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| 492 | } |
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[1365] | 493 | |
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[1379] | 494 | //set<pair<int,int>> empty_list; |
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| 495 | //models.push_back(new model(empty_list,false,true,100,0,&data_matrix)); |
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[1376] | 496 | } |
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| 497 | |
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| 498 | mat result_lognc; |
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| 499 | // mat result_preds; |
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| 500 | |
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| 501 | for(int time = max_model_order;time<data_matrix.cols();time++) //time<data_matrix.cols() |
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| 502 | { //pocet stlpcov data_matrix je pocet casovych krokov |
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| 503 | vec cur_res_lognc; |
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| 504 | // vec preds; |
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| 505 | vector<string> nazvy; |
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| 506 | for(vector<model*>::iterator model_ref = models.begin();model_ref!=models.end();model_ref++) |
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| 507 | {//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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| 508 | (*model_ref)->data_update(time); //pozret sa preco je toto tu nutne |
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[1379] | 509 | |
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| 510 | cout << "Updated." << endl; |
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[1376] | 511 | //if (time = max_model_order) nazvy.push_back(models.model_ref]);// ako by som mohol dostat nazov modelu? |
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| 512 | |
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| 513 | if((*model_ref)->my_rarx!=NULL) //vklada normalizacnz faktor do cur_res_lognc |
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| 514 | { |
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| 515 | cur_res_lognc.ins(cur_res_lognc.size(),(*model_ref)->my_rarx->posterior->log_nc); |
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[1361] | 516 | } |
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[1376] | 517 | else |
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| 518 | { |
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| 519 | cur_res_lognc.ins(cur_res_lognc.size(),(*model_ref)->my_arx->posterior().lognc()); |
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| 520 | } |
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[1367] | 521 | |
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[1376] | 522 | // pair<vec,vec> predictions = (*model_ref)->predict(200,time,&LapRNG); |
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[1367] | 523 | |
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[1376] | 524 | // preds.ins(preds.size(),(predictions.first*predictions.second)/(predictions.first*ones(predictions.first.size()))); |
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| 525 | // preds.ins(0,data_matrix.get(0,time+1)); |
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[1361] | 526 | |
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[1376] | 527 | } |
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[1367] | 528 | |
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[1376] | 529 | result_lognc.ins_col(result_lognc.cols(),cur_res_lognc); |
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| 530 | // result_preds.ins_col(result_preds.cols(),preds); |
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| 531 | |
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| 532 | // cout << "Updated." << endl; |
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| 533 | |
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[1361] | 534 | /* |
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[1301] | 535 | vector<vec> conditions; |
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| 536 | //emlig* emliga = new emlig(2); |
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[1358] | 537 | RARX* my_rarx = new RARX(2,10,false); |
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[1337] | 538 | |
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[1338] | 539 | |
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[1337] | 540 | mat V0 = 0.0001 * eye ( 3 ); |
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[1349] | 541 | ARX* my_arx = new ARX(0.85); |
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[1337] | 542 | my_arx->set_statistics ( 1, V0 ); //nu is default (set to have finite moments) |
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| 543 | my_arx->set_constant ( false ); |
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| 544 | my_arx->validate(); |
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[1338] | 545 | |
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[1301] | 546 | |
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[1338] | 547 | for(int k = 1;k<strings[j].size();k++) |
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[1301] | 548 | { |
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| 549 | vec condition; |
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| 550 | //condition.ins(0,1); |
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| 551 | condition.ins(0,strings[j][k]); |
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| 552 | conditions.push_back(condition); |
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| 553 | |
---|
| 554 | //cout << "orig:" << condition << endl; |
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| 555 | |
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| 556 | if(conditions.size()>1) |
---|
| 557 | { |
---|
| 558 | conditions[k-2].ins(0,strings[j][k]); |
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| 559 | |
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| 560 | } |
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| 561 | |
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| 562 | if(conditions.size()>2) |
---|
| 563 | { |
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| 564 | conditions[k-3].ins(0,strings[j][k]); |
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| 565 | |
---|
[1349] | 566 | // cout << "Condition:" << conditions[k-3] << endl; |
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[1301] | 567 | |
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| 568 | my_rarx->bayes(conditions[k-3]); |
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[1338] | 569 | //my_rarx->posterior->step_me(1); |
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[1337] | 570 | |
---|
| 571 | vec cond_vec; |
---|
| 572 | cond_vec.ins(0,conditions[k-3][0]); |
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| 573 | |
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[1338] | 574 | my_arx->bayes(cond_vec,conditions[k-3].right(2)); |
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[1301] | 575 | |
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[1361] | 576 | /* |
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[1346] | 577 | if(k>8) |
---|
[1301] | 578 | { |
---|
[1324] | 579 | //my_rarx->posterior->step_me(0); |
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| 580 | |
---|
[1346] | 581 | //mat samples = my_rarx->posterior->sample_mat(10); |
---|
[1343] | 582 | |
---|
[1346] | 583 | pair<vec,mat> imp_samples = my_rarx->posterior->importance_sample(1000); |
---|
[1343] | 584 | |
---|
[1346] | 585 | //cout << imp_samples.first << endl; |
---|
[1336] | 586 | |
---|
[1337] | 587 | vec sample_prediction; |
---|
[1358] | 588 | vec averaged_params = zeros(imp_samples.second.rows()); |
---|
[1346] | 589 | for(int t = 0;t<imp_samples.first.size();t++) |
---|
[1337] | 590 | { |
---|
| 591 | vec lap_sample = conditions[k-3].left(2); |
---|
[1346] | 592 | //lap_sample.ins(lap_sample.size(),1.0); |
---|
[1337] | 593 | |
---|
| 594 | lap_sample.ins(0,LapRNG()); |
---|
| 595 | |
---|
[1346] | 596 | sample_prediction.ins(0,lap_sample*imp_samples.second.get_col(t)); |
---|
[1358] | 597 | |
---|
| 598 | averaged_params += imp_samples.first[t]*imp_samples.second.get_col(t); |
---|
[1337] | 599 | } |
---|
| 600 | |
---|
[1358] | 601 | averaged_params = averaged_params*(1/(imp_samples.first*ones(imp_samples.first.size()))); |
---|
| 602 | |
---|
| 603 | // cout << "Averaged estimated parameters: " << averaged_params << endl; |
---|
[1338] | 604 | |
---|
[1358] | 605 | vec sample_pow = sample_prediction; |
---|
[1343] | 606 | |
---|
| 607 | // cout << sample_prediction << endl; |
---|
[1337] | 608 | vec poly_coefs; |
---|
[1346] | 609 | double prediction; |
---|
[1337] | 610 | bool stop_iteration = false; |
---|
[1343] | 611 | int en = 1; |
---|
[1337] | 612 | do |
---|
| 613 | { |
---|
[1346] | 614 | double poly_coef = imp_samples.first*sample_pow/(imp_samples.first*ones(imp_samples.first.size())); |
---|
[1337] | 615 | |
---|
[1346] | 616 | if(en==1) |
---|
| 617 | { |
---|
| 618 | prediction = poly_coef; |
---|
| 619 | } |
---|
| 620 | |
---|
[1343] | 621 | poly_coef = poly_coef*en*fact(utility_constant-2+en)/fact(utility_constant-2); |
---|
| 622 | |
---|
[1337] | 623 | if(abs(poly_coef)>numeric_limits<double>::epsilon()) |
---|
| 624 | { |
---|
| 625 | sample_pow = elem_mult(sample_pow,sample_prediction); |
---|
[1343] | 626 | poly_coefs.ins(0,pow(-1.0,en+1)*poly_coef); |
---|
[1337] | 627 | } |
---|
| 628 | else |
---|
| 629 | { |
---|
| 630 | stop_iteration = true; |
---|
| 631 | } |
---|
| 632 | |
---|
| 633 | en++; |
---|
[1343] | 634 | |
---|
| 635 | if(en>20) |
---|
| 636 | { |
---|
| 637 | stop_iteration = true; |
---|
| 638 | } |
---|
[1337] | 639 | } |
---|
| 640 | while(!stop_iteration); |
---|
| 641 | |
---|
[1343] | 642 | /* |
---|
| 643 | ofstream myfile_coef; |
---|
| 644 | |
---|
| 645 | myfile_coef.open("c:\\coefs.txt",ios::app); |
---|
| 646 | |
---|
| 647 | for(int t = 0;t<poly_coefs.size();t++) |
---|
| 648 | { |
---|
| 649 | myfile_coef << poly_coefs[t] << ","; |
---|
| 650 | } |
---|
| 651 | |
---|
| 652 | myfile_coef << endl; |
---|
| 653 | myfile_coef.close(); |
---|
| 654 | */ |
---|
| 655 | |
---|
[1349] | 656 | //cout << "Coefficients: " << poly_coefs << endl; |
---|
[1338] | 657 | |
---|
[1343] | 658 | /* |
---|
| 659 | vec bas_coef = vec("1.0 2.0 -8.0"); |
---|
| 660 | cout << "Coefs: " << bas_coef << endl; |
---|
| 661 | cvec actions2 = roots(bas_coef); |
---|
| 662 | cout << "Roots: " << actions2 << endl; |
---|
| 663 | */ |
---|
| 664 | |
---|
[1361] | 665 | /* |
---|
[1346] | 666 | |
---|
[1338] | 667 | cvec actions = roots(poly_coefs); |
---|
[1343] | 668 | |
---|
| 669 | |
---|
[1338] | 670 | bool is_max = false; |
---|
| 671 | for(int t = 0;t<actions.size();t++) |
---|
| 672 | { |
---|
[1343] | 673 | if(actions[t].imag() == 0) |
---|
[1338] | 674 | { |
---|
[1343] | 675 | double second_derivative = 0; |
---|
| 676 | for(int q = 1;q<poly_coefs.size();q++) |
---|
| 677 | { |
---|
| 678 | second_derivative+=poly_coefs[q]*pow(actions[t].real(),q-1)*q; |
---|
| 679 | } |
---|
| 680 | |
---|
| 681 | if(second_derivative<0) |
---|
| 682 | { |
---|
| 683 | cout << "Action:" << actions[t].real() << endl; |
---|
| 684 | |
---|
| 685 | is_max = true; |
---|
| 686 | } |
---|
[1338] | 687 | } |
---|
| 688 | } |
---|
[1301] | 689 | |
---|
[1338] | 690 | if(!is_max) |
---|
| 691 | { |
---|
| 692 | cout << "No maximum." << endl; |
---|
| 693 | } |
---|
| 694 | |
---|
| 695 | // cout << "MaxLik coords:" << my_rarx->posterior->minimal_vertex->get_coordinates() << endl; |
---|
| 696 | |
---|
[1346] | 697 | /* |
---|
[1337] | 698 | double prediction = 0; |
---|
| 699 | for(int s = 1;s<samples.rows();s++) |
---|
[1336] | 700 | { |
---|
| 701 | |
---|
[1346] | 702 | double avg_parameter = imp_samples.get_row(s)*ones(samples.cols())/samples.cols(); |
---|
[1337] | 703 | |
---|
| 704 | prediction += avg_parameter*conditions[k-3][s-1]; |
---|
| 705 | |
---|
[1336] | 706 | |
---|
[1337] | 707 | |
---|
| 708 | /* |
---|
[1336] | 709 | ofstream myfile; |
---|
| 710 | char fstring[80]; |
---|
| 711 | strcpy(fstring,file_strings[j]); |
---|
[1301] | 712 | |
---|
[1336] | 713 | char es[5]; |
---|
| 714 | strcat(fstring,itoa(s,es,10)); |
---|
| 715 | |
---|
| 716 | strcat(fstring,"_res.txt"); |
---|
| 717 | |
---|
| 718 | |
---|
| 719 | myfile.open(fstring,ios::app); |
---|
| 720 | |
---|
| 721 | //myfile << my_rarx->posterior->minimal_vertex->get_coordinates()[0]; |
---|
| 722 | myfile << avg_parameter; |
---|
| 723 | |
---|
| 724 | if(k!=strings[j].size()-1) |
---|
| 725 | { |
---|
| 726 | myfile << ","; |
---|
| 727 | } |
---|
| 728 | else |
---|
| 729 | { |
---|
| 730 | myfile << endl; |
---|
| 731 | } |
---|
| 732 | myfile.close(); |
---|
[1337] | 733 | */ |
---|
| 734 | |
---|
[1338] | 735 | |
---|
[1346] | 736 | //} |
---|
| 737 | |
---|
| 738 | // cout << "Prediction: "<< prediction << endl; |
---|
[1361] | 739 | /* |
---|
[1337] | 740 | enorm<ldmat>* pred_mat = my_arx->epredictor(conditions[k-3].left(2)); |
---|
| 741 | double prediction2 = pred_mat->mean()[0]; |
---|
[1361] | 742 | */ |
---|
[1337] | 743 | |
---|
[1365] | 744 | |
---|
[1376] | 745 | ofstream myfile; |
---|
| 746 | char fstring[80]; |
---|
| 747 | strcpy(fstring,file_string); |
---|
[1337] | 748 | |
---|
[1376] | 749 | strcat(fstring,"lognc.txt"); |
---|
| 750 | |
---|
| 751 | myfile.open(fstring,ios::app); |
---|
| 752 | |
---|
| 753 | // myfile << my_rarx->posterior->minimal_vertex->get_coordinates()[0]; |
---|
| 754 | |
---|
| 755 | if(time == max_model_order) |
---|
| 756 | { |
---|
| 757 | for(int i = 0;i<cur_res_lognc.size();i++) |
---|
| 758 | { |
---|
| 759 | for(set<pair<int,int>>::iterator ar_ref = models[i]->ar_components.begin();ar_ref != models[i]->ar_components.end();ar_ref++) |
---|
| 760 | { |
---|
| 761 | myfile << (*ar_ref).second << (*ar_ref).first; |
---|
| 762 | } |
---|
| 763 | |
---|
| 764 | myfile << "."; |
---|
| 765 | |
---|
| 766 | if(models[i]->my_arx == NULL) |
---|
| 767 | { |
---|
| 768 | myfile << "1"; |
---|
| 769 | } |
---|
| 770 | else |
---|
| 771 | { |
---|
| 772 | myfile << "0"; |
---|
| 773 | } |
---|
| 774 | |
---|
| 775 | if(models[i]->has_constant) |
---|
| 776 | { |
---|
| 777 | myfile << "1"; |
---|
| 778 | } |
---|
| 779 | else |
---|
| 780 | { |
---|
| 781 | myfile << "0"; |
---|
| 782 | } |
---|
| 783 | |
---|
| 784 | myfile << ","; |
---|
| 785 | } |
---|
| 786 | |
---|
| 787 | myfile << endl; |
---|
| 788 | } |
---|
| 789 | |
---|
| 790 | for(int i = 0;i<cur_res_lognc.size();i++) |
---|
| 791 | { |
---|
| 792 | myfile << cur_res_lognc[i] << ' ';//zmenil som ciarku ze medzeru |
---|
| 793 | } |
---|
| 794 | |
---|
| 795 | myfile << endl; |
---|
| 796 | |
---|
| 797 | myfile.close(); |
---|
| 798 | } |
---|
[1361] | 799 | /* |
---|
[1337] | 800 | myfile.open(f2string,ios::app); |
---|
| 801 | myfile << prediction2; |
---|
| 802 | |
---|
| 803 | if(k!=strings[j].size()-1) |
---|
| 804 | { |
---|
| 805 | myfile << ","; |
---|
| 806 | } |
---|
| 807 | else |
---|
| 808 | { |
---|
| 809 | myfile << endl; |
---|
| 810 | } |
---|
| 811 | myfile.close(); |
---|
[1361] | 812 | //*//* |
---|
[1337] | 813 | |
---|
[1319] | 814 | } |
---|
[1361] | 815 | } */ |
---|
[1301] | 816 | |
---|
| 817 | //emliga->step_me(0); |
---|
| 818 | /* |
---|
| 819 | ofstream myfile; |
---|
| 820 | myfile.open("c:\\robust_ar1.txt",ios::app); |
---|
| 821 | myfile << my_rarx->minimal_vertex->get_coordinates()[0] << ";"; |
---|
| 822 | myfile.close(); |
---|
| 823 | |
---|
| 824 | myfile.open("c:\\robust_ar2.txt",ios::app); |
---|
| 825 | myfile << emliga->minimal_vertex->get_coordinates()[1] << ";"; |
---|
| 826 | myfile.close(); |
---|
| 827 | |
---|
| 828 | |
---|
| 829 | cout << "MaxLik coords:" << emliga->minimal_vertex->get_coordinates() << endl; |
---|
| 830 | cout << "Step: " << i << endl;*/ |
---|
[1361] | 831 | //} |
---|
[1337] | 832 | |
---|
| 833 | |
---|
[1365] | 834 | //} |
---|
[1337] | 835 | |
---|
| 836 | |
---|
| 837 | // EXPERIMENT: One step ahead price prediction. Comparison of classical and robust model using optimal trading |
---|
| 838 | // with maximization of logarithm of one-step ahead wealth. |
---|
| 839 | |
---|
| 840 | |
---|
[1301] | 841 | |
---|
| 842 | /* |
---|
| 843 | cout << "One experiment finished." << endl; |
---|
| 844 | |
---|
| 845 | ofstream myfile; |
---|
| 846 | myfile.open("c:\\robust_ar1.txt",ios::app); |
---|
| 847 | myfile << endl; |
---|
| 848 | myfile.close(); |
---|
| 849 | |
---|
| 850 | myfile.open("c:\\robust_ar2.txt",ios::app); |
---|
| 851 | myfile << endl; |
---|
| 852 | myfile.close();*/ |
---|
[1300] | 853 | |
---|
[1301] | 854 | |
---|
| 855 | //emlig* emlig1 = new emlig(emlig_size); |
---|
| 856 | |
---|
| 857 | //emlig1->step_me(0); |
---|
| 858 | //emlig* emlig2 = new emlig(emlig_size); |
---|
[1300] | 859 | |
---|
[1267] | 860 | /* |
---|
| 861 | emlig1->set_correction_factors(4); |
---|
[1266] | 862 | |
---|
[1267] | 863 | for(int j = 0;j<emlig1->correction_factors.size();j++) |
---|
| 864 | { |
---|
| 865 | for(set<my_ivec>::iterator vec_ref = emlig1->correction_factors[j].begin();vec_ref!=emlig1->correction_factors[j].end();vec_ref++) |
---|
| 866 | { |
---|
[1268] | 867 | cout << j << " "; |
---|
| 868 | |
---|
[1267] | 869 | for(int i=0;i<(*vec_ref).size();i++) |
---|
| 870 | { |
---|
| 871 | cout << (*vec_ref)[i]; |
---|
| 872 | } |
---|
| 873 | |
---|
| 874 | cout << endl; |
---|
| 875 | } |
---|
[1268] | 876 | }*/ |
---|
| 877 | |
---|
[1301] | 878 | /* |
---|
[1300] | 879 | vec condition5 = "1.0 1.0 1.01";//"-0.3 1.7 1.5"; |
---|
| 880 | |
---|
[1299] | 881 | emlig1->add_condition(condition5); |
---|
[1301] | 882 | //emlig1->step_me(0); |
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| 883 | |
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| 884 | |
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| 885 | vec condition1a = "-1.0 1.02 0.5"; |
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[1300] | 886 | //vec condition1b = "1.0 1.0 1.01"; |
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[1301] | 887 | emlig1->add_condition(condition1a); |
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[1300] | 888 | //emlig2->add_condition(condition1b); |
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[1267] | 889 | |
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[1301] | 890 | vec condition2a = "-0.3 1.7 1.5"; |
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[1300] | 891 | //vec condition2b = "-1.0 1.0 1.0"; |
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[1301] | 892 | emlig1->add_condition(condition2a); |
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[1300] | 893 | //emlig2->add_condition(condition2b); |
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[1234] | 894 | |
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[1301] | 895 | vec condition3a = "0.5 -1.01 1.0"; |
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[1300] | 896 | //vec condition3b = "0.5 -1.01 1.0"; |
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[1280] | 897 | |
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[1301] | 898 | emlig1->add_condition(condition3a); |
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[1300] | 899 | //emlig2->add_condition(condition3b); |
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[1280] | 900 | |
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[1301] | 901 | vec condition4a = "-0.5 -1.0 1.0"; |
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[1300] | 902 | //vec condition4b = "-0.5 -1.0 1.0"; |
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| 903 | |
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[1301] | 904 | emlig1->add_condition(condition4a); |
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[1300] | 905 | //cout << "************************************************" << endl; |
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| 906 | //emlig2->add_condition(condition4b); |
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| 907 | //cout << "************************************************" << endl; |
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| 908 | |
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[1299] | 909 | //cout << emlig1->minimal_vertex->get_coordinates(); |
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[1300] | 910 | |
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[1301] | 911 | //emlig1->remove_condition(condition3a); |
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| 912 | //emlig1->step_me(0); |
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| 913 | //emlig1->remove_condition(condition2a); |
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| 914 | //emlig1->remove_condition(condition1a); |
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| 915 | //emlig1->remove_condition(condition5); |
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| 916 | |
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[1275] | 917 | |
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[1299] | 918 | //emlig1->step_me(0); |
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| 919 | //emlig2->step_me(0); |
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| 920 | |
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[1282] | 921 | |
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| 922 | // DA SE POUZIT PRO VYPIS DO SOUBORU |
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[1275] | 923 | // emlig1->step_me(0); |
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| 924 | |
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| 925 | //emlig1->remove_condition(condition1); |
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| 926 | |
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[1301] | 927 | |
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[1275] | 928 | |
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| 929 | |
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[1301] | 930 | |
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[1275] | 931 | /* |
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[1282] | 932 | for(int i = 0;i<100;i++) |
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[1219] | 933 | { |
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[1282] | 934 | cout << endl << "Step:" << i << endl; |
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[1208] | 935 | |
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[1268] | 936 | double condition[emlig_size+1]; |
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[1220] | 937 | |
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[1268] | 938 | for(int k = 0;k<=emlig_size;k++) |
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| 939 | { |
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[1272] | 940 | condition[k] = (rand()-RAND_MAX/2)/1000.0; |
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[1268] | 941 | } |
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| 942 | |
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[1216] | 943 | |
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[1268] | 944 | vec* condition_vec = new vec(condition,emlig_size+1); |
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[1219] | 945 | emlig1->add_condition(*condition_vec); |
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[1271] | 946 | |
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[1272] | 947 | /* |
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| 948 | 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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| 949 | { |
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| 950 | cout << ((toprow*)toprow_ref)->probability << endl; |
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| 951 | } |
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| 952 | */ |
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[1275] | 953 | /* |
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[1271] | 954 | cout << emlig1->statistic_rowsize(emlig_size) << endl << endl; |
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[1268] | 955 | |
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[1272] | 956 | /* |
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[1271] | 957 | if(i-emlig1->number_of_parameters >= 0) |
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| 958 | { |
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| 959 | pause(30); |
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| 960 | } |
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[1272] | 961 | */ |
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[1219] | 962 | |
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[1271] | 963 | // emlig1->step_me(i); |
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[1219] | 964 | |
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[1272] | 965 | /* |
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[1219] | 966 | vector<int> sizevector; |
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| 967 | for(int s = 0;s<=emlig1->number_of_parameters;s++) |
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| 968 | { |
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| 969 | sizevector.push_back(emlig1->statistic_rowsize(s)); |
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| 970 | } |
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[1272] | 971 | */ |
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[1275] | 972 | //} |
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| 973 | |
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[1219] | 974 | |
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| 975 | |
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| 976 | |
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| 977 | /* |
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| 978 | emlig1->step_me(1); |
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| 979 | |
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| 980 | vec condition = "2.0 0.0 1.0"; |
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| 981 | |
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[1208] | 982 | emlig1->add_condition(condition); |
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| 983 | |
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[1216] | 984 | vector<int> sizevector; |
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| 985 | for(int s = 0;s<=emlig1->number_of_parameters;s++) |
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| 986 | { |
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| 987 | sizevector.push_back(emlig1->statistic_rowsize(s)); |
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| 988 | } |
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| 989 | |
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[1219] | 990 | emlig1->step_me(2); |
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[1216] | 991 | |
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[1219] | 992 | condition = "2.0 1.0 0.0"; |
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[1216] | 993 | |
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| 994 | emlig1->add_condition(condition); |
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| 995 | |
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| 996 | sizevector.clear(); |
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| 997 | for(int s = 0;s<=emlig1->number_of_parameters;s++) |
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| 998 | { |
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| 999 | sizevector.push_back(emlig1->statistic_rowsize(s)); |
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| 1000 | } |
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[1219] | 1001 | */ |
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[1216] | 1002 | |
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[1376] | 1003 | return 0; |
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| 1004 | } |
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[976] | 1005 | |
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[1282] | 1006 | |
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