root/library/tests/tutorial/arx_simple.cpp @ 1419

#include "estim/arx.h"
#include <vector>
#include <iostream>
#include <fstream>
using namespace bdm;

// estimation of AR(0) model
int main(){
        //data
        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() )
                        {
                                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();
                }
        }

    //prior
    mat V0 = 0.0001 * eye ( 3 );
    //V0 ( 0, 0 ) = 0.1; //

        for(int j = 0;j<string_lists.size();j++)
        { 
                
                for(int i = 0;i<string_lists[j].size();i++)
                {
                        ARX Ar;
                        Ar.set_statistics ( 1, V0 ); //nu is default (set to have finite moments)
                        Ar.set_constant ( false );
                        Ar.validate();
                        // forgetting is default: 1.0
                        
                        vector<vec> conditions;
                        for(int k = 1;k<string_lists[j][i].size();k++)
                        {
                                vec condition;                                                          
                                condition.ins(0,string_lists[j][i][k]);                         
                                conditions.push_back(condition);                                

                                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 << conditions[k-3] << endl;// << conditions[k-3].left(1) << conditions[k-3].right(2);

                                        Ar.bayes(conditions[k-3].left(1),conditions[k-3].right(2));                             
                                }
                        }

                        ofstream myfile;
                        myfile.open("c:\\classic_ar1.txt",ios::app);
                        myfile << Ar.posterior().mean()[0] << ";";
                        myfile.close();

                        myfile.open("c:\\classic_ar2.txt",ios::app);
                        myfile << Ar.posterior().mean()[1] << ";";
                        myfile.close();


                        
                }

                ofstream myfile;
                myfile.open("c:\\classic_ar1.txt",ios::app);
                myfile << endl;
                myfile.close();

                myfile.open("c:\\classic_ar2.txt",ios::app);
                myfile << endl;
                myfile.close();
        }
}