Changeset 1370 for applications/robust

Timestamp:

05/13/11 19:03:16 (14 years ago)

Author:

sindj

Message:

Pripojeni na realna data. Prvni real time system. JS

Files:

• applications/robust/main.cpp (modified) (16 diffs)

applications/robust/main.cpp

@@ -16 +16 @@
 #include "ddeml.h"
 #include "stdio.h"
+#include <time.h>
 
 //#include "DDEClient.h"
@@ -28 +29 @@
 
 const int max_model_order = 2;
-
-
-/*
+const int data_count      = 5;
+
+static vec avg_vec;
+
 HDDEDATA CALLBACK DdeCallback(
-        UINT uType,     // Transaction type.
-        UINT uFmt,      // Clipboard data format.
-        HCONV hconv,    // Handle to the conversation.
-        HSZ hsz1,       // Handle to a string.
-        HSZ hsz2,       // Handle to a string.
-        HDDEDATA hdata, // Handle to a global memory object.
-        DWORD dwData1,  // Transaction-specific data.
-        DWORD dwData2)  // Transaction-specific data.
+UINT uType,     // Transaction type.
+UINT uFmt,      // Clipboard data format.
+HCONV hconv,    // Handle to the conversation.
+HSZ hsz1,       // Handle to a string.
+HSZ hsz2,       // Handle to a string.
+HDDEDATA hdata, // Handle to a global memory object.
+DWORD dwData1,  // Transaction-specific data.
+DWORD dwData2)  // Transaction-specific data.
 {
-        return 0;
+        char  * pData;
+        DWORD   data_len;
+        
+        switch (uType)
+        {     
+        case XTYP_ADVDATA: 
+
+                if ( hdata == NULL )
+                        cout << "No data in the message." << endl;
+                else
+                {
+                        pData = (char*)DdeAccessData( hdata, &data_len );
+                                                
+                        avg_vec.ins(0,atof(pData));
+                        
+                        cout << pData << endl;
+
+                        DdeUnaccessData( hdata );
+                }
+        }               
+        return (HDDEDATA) NULL; 
+
 }
+
+
 
 void DDERequest(DWORD idInst, HCONV hConv, char* szItem)
@@ -64 +89 @@
     return 0;
 }
-*/
+
 
 class model
@@ -77 +102 @@
 
         bool has_constant;
+        int  order;
         int  window_size;
         int  predicted_channel;
+        int  number_of_updates;
+        double prior_lognc;
+        vec  lognc_history;
         mat* data_matrix;
         
@@ -85 +114 @@
                   bool has_constant, 
                   int window_size, 
-                  int predicted_channel,
+                  int predicted_channel,                  
                   mat* data_matrix)
         {
                 this->ar_components.insert(this->ar_components.begin(),ar_components.begin(),ar_components.end());
-                this->has_constant      = has_constant;
-                this->window_size       = window_size;
-                this->predicted_channel = predicted_channel;
-                this->data_matrix       = data_matrix;
+                this->has_constant        = has_constant;
+                this->window_size         = window_size;
+                this->predicted_channel   = predicted_channel;          
+                this->data_matrix         = data_matrix;
+                number_of_updates = 0;
+
+                order = 0;
+                for(list<pair<int,int>>::iterator ar_ref = ar_components.begin();ar_ref!=ar_components.end();ar_ref++)
+                {
+                        if((*ar_ref).second > order)
+                        {
+                                order = (*ar_ref).second;
+                        }
+                }
 
                 if(robust)
@@ -106 +145 @@
                                 my_arx  = NULL;
                         }
+
+                        prior_lognc = my_rarx->posterior->log_nc;
                 }
                 else
@@ -116 +157 @@
                         {                               
                                 V0  = 0.001 * eye(ar_components.size()+2);
-                                V0(0,0) = 0;
+                                V0(0,0) = 1;
                                 my_arx->set_constant(true);     
                                 
@@ -124 +165 @@
                                 
                                 V0  = 0.001 * eye(ar_components.size()+1);
-                                V0(0,0) = 0;
+                                V0(0,0) = 1;
                                 my_arx->set_constant(false);                            
                                 
@@ -132 +173 @@
                         my_arx->set_parameters(window_size);
                         my_arx->validate();
+
+                        prior_lognc = my_arx->posterior().lognc();
                 }
         }
@@ -137 +180 @@
         void data_update(int time)
         {
-                vec data_vector;
-                for(list<pair<int,int>>::iterator ar_iterator = ar_components.begin();ar_iterator!=ar_components.end();ar_iterator++)
-                {
-                        data_vector.ins(data_vector.size(),(*data_matrix).get(ar_iterator->first,time-ar_iterator->second));
-                }
-
-                if(my_rarx!=NULL)
-                {
-                        data_vector.ins(0,(*data_matrix).get(predicted_channel,time));
-                        my_rarx->bayes(data_vector);
+                if(time > order)
+                {
+                        vec data_vector;
+                        for(list<pair<int,int>>::iterator ar_iterator = ar_components.begin();ar_iterator!=ar_components.end();ar_iterator++)
+                        {
+                                data_vector.ins(data_vector.size(),(*data_matrix).get(ar_iterator->first,time-ar_iterator->second));
+                        }
+
+                        // cout << "Update cond: " << data_vector << endl;
+
+                        double cur_lognc;
+                        if(my_rarx!=NULL)
+                        {
+                                data_vector.ins(0,(*data_matrix).get(predicted_channel,time));
+                                my_rarx->bayes(data_vector);
+                                cur_lognc = my_rarx->posterior->log_nc;
+                        }
+                        else
+                        {
+                                vec pred_vec;
+                                pred_vec.ins(0,(*data_matrix).get(predicted_channel,time));
+                                my_arx->bayes(pred_vec,data_vector);
+                                cur_lognc = my_arx->posterior().lognc();
+                        }
+
+                        number_of_updates++;
+
+                        if(number_of_updates>window_size)
+                        {
+                                lognc_history.ins(lognc_history.size(),cur_lognc-prior_lognc);
+                        }
                 }
                 else
                 {
-                        vec pred_vec;
-                        pred_vec.ins(0,(*data_matrix).get(predicted_channel,time));
-                        my_arx->bayes(pred_vec,data_vector);
+                        lognc_history.ins(lognc_history.size(),0);
                 }
         }
@@ -158 +220 @@
         pair<vec,vec> predict(int sample_size, int time, itpp::Laplace_RNG* LapRNG)
         {
-                vec condition_vector;
-                for(list<pair<int,int>>::iterator ar_iterator = ar_components.begin();ar_iterator!=ar_components.end();ar_iterator++)
-                {
-                        condition_vector.ins(condition_vector.size(),(*data_matrix).get(ar_iterator->first,time-ar_iterator->second+1));
-                }
-
-                if(my_rarx!=NULL)
-                {
-                        pair<vec,mat> imp_samples = my_rarx->posterior->importance_sample(sample_size);
-
-                        //cout << imp_samples.first << endl;
+                if(time > order)
+                {
+                        vec condition_vector;
+                        for(list<pair<int,int>>::iterator ar_iterator = ar_components.begin();ar_iterator!=ar_components.end();ar_iterator++)
+                        {
+                                condition_vector.ins(condition_vector.size(),(*data_matrix).get(ar_iterator->first,time-ar_iterator->second+1));
+                        }
+
+                        // cout << "Prediction cond: " << condition_vector << endl;
+
+                        if(my_rarx!=NULL)
+                        {
+                                pair<vec,mat> imp_samples = my_rarx->posterior->importance_sample(sample_size);
+
+                                //cout << "Point estimate: " << (imp_samples.second*imp_samples.first)/(imp_samples.first*ones(imp_samples.first.size())) << endl;
+                                
+                                vec sample_prediction;                  
+                                for(int t = 0;t<sample_size;t++)
+                                {
+                                        vec lap_sample = condition_vector;
+                                        
+                                        if(has_constant)
+                                        {
+                                                lap_sample.ins(lap_sample.size(),1.0);
+                                        }
+                                        
+                                        lap_sample.ins(0,(*LapRNG)());
+
+                                        sample_prediction.ins(0,lap_sample*imp_samples.second.get_col(t));                              
+                                }
+
+                                return pair<vec,vec>(imp_samples.first,sample_prediction);
+                        }
+                        else
+                        {
+                                mat samples = my_arx->posterior().sample_mat(sample_size);
+
+                                //cout << "Point estimate: " << (samples*ones(samples.cols()))/samples.cols() << endl;
+
+                                // cout << samples.get_col(1) << endl;
+                                
+                                vec sample_prediction;
+                                for(int t = 0;t<sample_size;t++)
+                                {
+                                        vec gau_sample = condition_vector;
+                                        
+                                        if(has_constant)
+                                        {
+                                                gau_sample.ins(gau_sample.size(),1.0);
+                                        }
+                                        
+                                        gau_sample.ins(gau_sample.size(),randn());
+
+                                        sample_prediction.ins(0,gau_sample*samples.get_col(t));                         
+                                }
+
+                                return pair<vec,vec>(ones(sample_prediction.size()),sample_prediction);
+                        }
+                }
+                else
+                {
                         
-                        vec sample_prediction;                  
-                        for(int t = 0;t<sample_size;t++)
-                        {
-                                vec lap_sample = condition_vector;
-                                
-                                if(has_constant)
-                                {
-                                        lap_sample.ins(lap_sample.size(),1.0);
-                                }
-                                
-                                lap_sample.ins(0,(*LapRNG)());
-
-                                sample_prediction.ins(0,lap_sample*imp_samples.second.get_col(t));                              
-                        }
-
-                        return pair<vec,vec>(imp_samples.first,sample_prediction);
-                }
-                else
-                {
-                        mat samples = my_arx->posterior().sample_mat(sample_size);
-                        
-                        vec sample_prediction;
-                        for(int t = 0;t<sample_size;t++)
-                        {
-                                vec gau_sample = condition_vector;
-                                
-                                if(has_constant)
-                                {
-                                        gau_sample.ins(gau_sample.size(),1.0);
-                                }
-                                
-                                gau_sample.ins(0,randn());
-
-                                sample_prediction.ins(0,gau_sample*samples.get_col(t));                         
-                        }
-
-                        return pair<vec,vec>(ones(sample_prediction.size()),sample_prediction);
+                        return pair<vec,vec>(zeros(1),ones(1));
                 }
         
@@ -261 +337 @@
 
 
+
+
+
 int main ( int argc, char* argv[] ) {   
         
+        vec data_vec;
+        mat data_matrix;
+        DWORD Id;
+
+        char  szApp[]   = "MT4";
+        char  szTopic[] = "BID";        
+        char  szItem[]  = "EURUSD";
+        char* itRef     = &szItem[0];
+
+        char* file_string = "c:\\rtdata";
+
+        ofstream myfile;
+        char fstring[80];                                       
+        strcpy(fstring,file_string);
+        strcat(fstring,itRef);
+        strcat(fstring,"lognc.txt");
+
+        list<list<pair<int,int>>> model_types = model::possible_models_recurse(max_model_order,1);
+        
+        int max_window = 50;
+        int min_window = 1;
+
+        list<model*> models;
+        for(list<list<pair<int,int>>>::iterator model_type = model_types.begin();model_type!=model_types.end();model_type++)
+        {
+                for(int window_size = min_window;window_size < max_window;window_size++)
+                {                       
+                        //models.push_back(new model((*model_type),true,true,window_size,0,&data_matrix));
+                        models.push_back(new model((*model_type),false,true,window_size,0,&data_matrix));                       
+                        //models.push_back(new model((*model_type),true,false,window_size,0,&data_matrix));
+                        //models.push_back(new model((*model_type),false,false,window_size,0,&data_matrix));                    
+                }
+        }
+        
+        mat result_lognc;
+        mat result_preds;
+        
         /*
-        DWORD Id;
         HANDLE hThrd = CreateThread( NULL, 0, (LPTHREAD_START_ROUTINE)ThrdFunc, (LPVOID)1, 0, &Id);
-  
-        if ( !hThrd )
+        if ( !hThrd )
     {
         cout<<"Error Creating Threads,,,,.exiting"<<endl;
@@ -273 +387 @@
     }
     Sleep ( 100 );
-
-        
-        char szApp[] = "MT4";
-        char szTopic[] = "QUOTE";       
-        char szItem1[] = "EURUSD";      
+        */
+        
+                
 
         //DDE Initialization
-        DWORD idInst=0;
+        DWORD idInst = 0;
         UINT iReturn;
         iReturn = DdeInitialize(&idInst, (PFNCALLBACK)DdeCallback, 
@@ -308 +420 @@
         //Execute commands/requests specific to the DDE Server.
         
-        DDERequest(idInst, hConv, szItem1);             
-        
+        DDERequest(idInst, hConv, szItem);              
+        
+        itpp::Laplace_RNG LapRNG = Laplace_RNG();
+
+        // time_t current_time = time(0);       
+
         while(1)
         {
-                MSG    msg;
-                BOOL   MsgReturn = GetMessage ( &msg , NULL , 0 , 0 );
-            
-                if(MsgReturn)
-                {
-                        TranslateMessage(&msg);
-                        DispatchMessage(&msg);                  
-                }
-        }
-
+                while(avg_vec.size() < data_count)
+                {
+                        MSG    msg;
+                        BOOL   MsgReturn = GetMessage ( &msg , NULL , 0 , 0 );
+                    
+                        if(MsgReturn)
+                        {
+                                TranslateMessage(&msg);
+                                DispatchMessage(&msg);                  
+                        }
+                        Sleep(500);
+                }
+
+
+                data_vec.ins(data_vec.size(),avg_vec*ones(avg_vec.size())/avg_vec.size());
+                data_matrix = mat(data_vec).T();
+                avg_vec.del(0,avg_vec.size()-1);                                
+                                        
+                vector<double> test_lognc;
+                vec preds;
+
+                for(list<model*>::iterator model_ref = models.begin();model_ref!=models.end();model_ref++)
+                {
+                        (*model_ref)->data_update(data_vec.size()-1);                   
+
+                        if((*model_ref)->lognc_history.size()==0)
+                        {
+                                test_lognc.push_back(0);
+                        }
+                        else
+                        {
+                                test_lognc.push_back((ones((*model_ref)->lognc_history.size())*(*model_ref)->lognc_history)/(*model_ref)->lognc_history.size()*(max_window/(*model_ref)->window_size)); 
+                        }
+
+                        pair<vec,vec> predictions = (*model_ref)->predict(500,data_vec.size()-1,&LapRNG);
+
+                        preds.ins(preds.size(),(predictions.first*predictions.second)/(predictions.first*ones(predictions.first.size())));
+                        
+                }
+
+                //preds.ins(0,data_matrix.get(0,data_vec.size()));      
+                
+                result_preds.ins_col(result_preds.cols(),preds);                                                        
+
+                myfile.open(fstring,ios::app);
+                
+                // myfile << my_rarx->posterior->minimal_vertex->get_coordinates()[0];
+                
+                for(int i = 0;i<test_lognc.size();i++)
+                {
+                        myfile << test_lognc[i] << ',';
+                }
+
+                myfile << endl;                         
+                
+                myfile.close();
+                
+        }
+        
         //DDE Disconnect and Uninitialize.
         DdeDisconnect(hConv);
         DdeUninitialize(idInst);
-        */
-
-        
-
-        /*
-        // 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, 
-        // where e_t is normally, student(4) and cauchy distributed are tested using robust AR model, to obtain the 
-        // variance of location parameter estimators and compare it to the classical setup.
-        vector<vector<vector<string>>> string_lists;
-        string_lists.push_back(vector<vector<string>>());
-        string_lists.push_back(vector<vector<string>>());
-        string_lists.push_back(vector<vector<string>>());
-
-        char* file_strings[3] = {"c:\\ar_normal.txt", "c:\\ar_student.txt", "c:\\ar_cauchy.txt"};
-        
-
-        for(int i = 0;i<3;i++)
-        {       
-                ifstream myfile(file_strings[i]);
+
+        return 0;
+}
+
+                /*
+                // EXPERIMENT: A moving window estimation and prediction of RARX is tested on data generated from 
+                // 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
+                // can be compared to the classical setup.
+                
+                vector<vector<string>> strings;
+
+                char* file_string = "c:\\dataGCClosePercDiff"; 
+
+                char dfstring[80];
+                strcpy(dfstring,file_string);
+                strcat(dfstring,".txt");
+                        
+                
+                mat data_matrix;
+                ifstream myfile(dfstring);
                 if (myfile.is_open())
-                {
-                        while ( myfile.good() )
-                        {
-                                string line;
-                                getline(myfile,line);
-                                
-                                vector<string> parsed_line;
+                {               
+                        string line;
+                        while(getline(myfile,line))
+                        {                       
+                                vec data_vector;
                                 while(line.find(',') != string::npos)
                                 {
-                                        int loc = line.find(',');
-                                        parsed_line.push_back(line.substr(0,loc));
+                                        int loc2 = line.find('\n');
+                                        int loc  = line.find(',');
+                                        data_vector.ins(data_vector.size(),atof(line.substr(0,loc).c_str()));                           
                                         line.erase(0,loc+1);                                    
-                                }                               
-
-                                string_lists[i].push_back(parsed_line);
-                        }
-                        myfile.close();
-                }
-        }
-
-        for(int j = 0;j<string_lists.size();j++)
-        { 
-                
-                for(int i = 0;i<string_lists[j].size()-1;i++)
-                {
-                        vector<vec> conditions;
-                        //emlig* emliga = new emlig(2);
-                        RARX* my_rarx = new RARX(2,30);
-
-                        for(int k = 1;k<string_lists[j][i].size();k++)
-                        {
-                                vec condition;
-                                //condition.ins(0,1);                           
-                                condition.ins(0,string_lists[j][i][k]);                         
-                                conditions.push_back(condition);
-
-                                //cout << "orig:" << condition << endl;
-
-                                if(conditions.size()>1)
-                                {               
-                                        conditions[k-2].ins(0,string_lists[j][i][k]);
-                                        
                                 }
 
-                                if(conditions.size()>2)
+                                data_matrix.ins_row(data_matrix.rows(),data_vector);
+                        }               
+
+                        myfile.close(); 
+                }
+                else
+                {
+                        cout << "Can't open data file!" << endl;
+                }
+                */
+                
+                
+                // cout << "Updated." << endl;
+        
+                /*
+                // 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, 
+                // where e_t is normally, student(4) and cauchy distributed are tested using robust AR model, to obtain the 
+                // variance of location parameter estimators and compare it to the classical setup.
+                vector<vector<vector<string>>> string_lists;
+                string_lists.push_back(vector<vector<string>>());
+                string_lists.push_back(vector<vector<string>>());
+                string_lists.push_back(vector<vector<string>>());
+
+                char* file_strings[3] = {"c:\\ar_normal.txt", "c:\\ar_student.txt", "c:\\ar_cauchy.txt"};
+                
+
+                for(int i = 0;i<3;i++)
+                {       
+                        ifstream myfile(file_strings[i]);
+                        if (myfile.is_open())
+                        {
+                                while ( myfile.good() )
                                 {
-                                        conditions[k-3].ins(0,string_lists[j][i][k]);
-
-                                        //cout << "modi:" << conditions[k-3] << endl;
-
-                                        my_rarx->bayes(conditions[k-3]);
-
-                                        
-                                        //if(k>5)
-                                        //{
-                                        //      cout << "MaxLik coords:" << emliga->minimal_vertex->get_coordinates() << endl;
-                                        //}
-                                        
-                                }                               
+                                        string line;
+                                        getline(myfile,line);
+                                        
+                                        vector<string> parsed_line;
+                                        while(line.find(',') != string::npos)
+                                        {
+                                                int loc = line.find(',');
+                                                parsed_line.push_back(line.substr(0,loc));
+                                                line.erase(0,loc+1);                                    
+                                        }                               
+
+                                        string_lists[i].push_back(parsed_line);
+                                }
+                                myfile.close();
+                        }
+                }
+
+                for(int j = 0;j<string_lists.size();j++)
+                { 
+                        
+                        for(int i = 0;i<string_lists[j].size()-1;i++)
+                        {
+                                vector<vec> conditions;
+                                //emlig* emliga = new emlig(2);
+                                RARX* my_rarx = new RARX(2,30);
+
+                                for(int k = 1;k<string_lists[j][i].size();k++)
+                                {
+                                        vec condition;
+                                        //condition.ins(0,1);                           
+                                        condition.ins(0,string_lists[j][i][k]);                         
+                                        conditions.push_back(condition);
+
+                                        //cout << "orig:" << condition << endl;
+
+                                        if(conditions.size()>1)
+                                        {               
+                                                conditions[k-2].ins(0,string_lists[j][i][k]);
+                                                
+                                        }
+
+                                        if(conditions.size()>2)
+                                        {
+                                                conditions[k-3].ins(0,string_lists[j][i][k]);
+
+                                                //cout << "modi:" << conditions[k-3] << endl;
+
+                                                my_rarx->bayes(conditions[k-3]);
+
+                                                
+                                                //if(k>5)
+                                                //{
+                                                //      cout << "MaxLik coords:" << emliga->minimal_vertex->get_coordinates() << endl;
+                                                //}
+                                                
+                                        }                               
+                                        
+                                }
+
+                                //emliga->step_me(0);
+                                /*
+                                ofstream myfile;
+                                myfile.open("c:\\robust_ar1.txt",ios::app);
+                                myfile << my_rarx->minimal_vertex->get_coordinates()[0] << ";";
+                                myfile.close();
+
+                                myfile.open("c:\\robust_ar2.txt",ios::app);
+                                myfile << emliga->minimal_vertex->get_coordinates()[1] << ";";
+                                myfile.close();
                                 
-                        }
-
-                        //emliga->step_me(0);
-                        /*
+
+                                cout << "MaxLik coords:" << emliga->minimal_vertex->get_coordinates() << endl;
+                                cout << "Step: " << i << endl;
+                        }
+
+                        cout << "One experiment finished." << endl;
+
                         ofstream myfile;
                         myfile.open("c:\\robust_ar1.txt",ios::app);
-                        myfile << my_rarx->minimal_vertex->get_coordinates()[0] << ";";
+                        myfile << endl;
                         myfile.close();
 
                         myfile.open("c:\\robust_ar2.txt",ios::app);
-                        myfile << emliga->minimal_vertex->get_coordinates()[1] << ";";
+                        myfile << endl;
                         myfile.close();
-                        
-
-                        cout << "MaxLik coords:" << emliga->minimal_vertex->get_coordinates() << endl;
-                        cout << "Step: " << i << endl;
-                }
-
-                cout << "One experiment finished." << endl;
-
-                ofstream myfile;
-                myfile.open("c:\\robust_ar1.txt",ios::app);
-                myfile << endl;
-                myfile.close();
-
-                myfile.open("c:\\robust_ar2.txt",ios::app);
-                myfile << endl;
-                myfile.close();
-        }*/    
-        
-        // EXPERIMENT: A moving window estimation and prediction of RARX is tested on data generated from 
-    // 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
-    // can be compared to the classical setup.
-        
-        itpp::Laplace_RNG LapRNG = Laplace_RNG();       
-
-        vector<vector<string>> strings;
-
-        char* file_string = "c:\\dataADClosePercDiff"; 
-
-        char dfstring[80];
-        strcpy(dfstring,file_string);
-        strcat(dfstring,".txt");
-                
-        
-        mat data_matrix;
-        ifstream myfile(dfstring);
-        if (myfile.is_open())
-        {               
-                string line;
-                while(getline(myfile,line))
-                {                       
-                        vec data_vector;
-                        while(line.find(',') != string::npos)
-                        {
-                                int loc2 = line.find('\n');
-                                int loc  = line.find(',');
-                                data_vector.ins(data_vector.size(),atof(line.substr(0,loc).c_str()));                           
-                                line.erase(0,loc+1);                                    
-                        }
-
-                        data_matrix.ins_row(data_matrix.rows(),data_vector);
-                }               
-
-                myfile.close(); 
-        }
-        else
-        {
-                cout << "Can't open data file!" << endl;
-        }
-        
-
-        list<list<pair<int,int>>> model_types = model::possible_models_recurse(max_model_order,data_matrix.rows());
-        
-        list<model*> models;
-        for(list<list<pair<int,int>>>::iterator model_type = model_types.begin();model_type!=model_types.end();model_type++)
-        {
-                for(int window_size = 30;window_size < 31;window_size++)
-                {
-                        models.push_back(new model((*model_type),true,true,window_size,0,&data_matrix));
-                        models.push_back(new model((*model_type),false,true,window_size,0,&data_matrix));
-                        models.push_back(new model((*model_type),true,false,window_size,0,&data_matrix));
-                        models.push_back(new model((*model_type),false,false,window_size,0,&data_matrix));
-                }
-        }
-        
-        mat result_lognc;
-        mat result_preds;
-
-        for(int time = max_model_order;time<data_matrix.cols();time++) //time<data_matrix.cols()
-        {       
-                vec cur_res_lognc;
-                vec preds;
-
-                for(list<model*>::iterator model_ref = models.begin();model_ref!=models.end();model_ref++)
-                {
-                        (*model_ref)->data_update(time);                        
-
-                        if((*model_ref)->my_rarx!=NULL)
-                        {
-                                cur_res_lognc.ins(cur_res_lognc.size(),(*model_ref)->my_rarx->posterior->log_nc);
-                        }
-                        else
-                        {
-                                cur_res_lognc.ins(cur_res_lognc.size(),(*model_ref)->my_arx->posterior().lognc());
-                        }
-
-                        pair<vec,vec> predictions = (*model_ref)->predict(500,time,&LapRNG);
-
-                        preds.ins(preds.size(),(predictions.first*predictions.second)/(predictions.first*ones(predictions.first.size())));
-                }
-
-                preds.ins(0,data_matrix.get(0,time+1));
-
-                result_lognc.ins_col(result_lognc.cols(),cur_res_lognc);
-                result_preds.ins_col(result_preds.cols(),preds);
-
-                // cout << "Updated." << endl;
-        
+                }*/    
+
                 /*
                 vector<vec> conditions;
@@ -735 +855 @@
 
                                         
-                                        ofstream myfile;
-                                        char fstring[80];                                       
-                                        strcpy(fstring,file_string);
-                                        
-                                        strcat(fstring,"preds.txt");                                    
-
-                                        myfile.open(fstring,ios::app);
-                                        
-                                        // myfile << my_rarx->posterior->minimal_vertex->get_coordinates()[0];
-                                        
-                                        for(int i = 0;i<preds.size();i++)
-                                        {
-                                                myfile << preds[i] << ',';
-                                        }
-
-                                        myfile << endl;                         
-                                        
-                                        myfile.close();
-                        }
+                                        
                                         /*
                                         myfile.open(f2string,ios::app);
@@ -958 +1060 @@
         */
 
-        return 0;
-}
-
-
-
+        
+
+