root/applications/robust/main.cpp @ 1343

/*!
\file
\brief Robust
\author Vasek Smidl 

 */

#include "estim/arx.h"
#include "robustlib.h"
#include <vector>
#include <iostream>
#include <fstream>
#include <itpp/itsignal.h>

using namespace itpp;
using namespace bdm;

const int emlig_size = 2;
const int utility_constant = 2;


int main ( int argc, char* argv[] ) {
        
        itpp::Laplace_RNG LapRNG = Laplace_RNG();

        /*
        // 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() )
                        {
                                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();
                        

                        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.
        

        vector<vector<string>> strings;

        char* file_strings[3] = {"c:\\dataADClosePercDiff","c:\\ar_student_single","c:\\ar_cauchy_single"};

        for(int i = 0;i<3;i++)
        {                       
                char dfstring[80];
                strcpy(dfstring,file_strings[i]);
                strcat(dfstring,".txt");
                
                ifstream myfile(dfstring);
                if (myfile.is_open())
                {                       
                        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);                                    
                        }                               

                        strings.push_back(parsed_line);
                        
                        myfile.close();
                }
        }
        
        for(int j = 0;j<strings.size();j++)
        {               
                vector<vec> conditions;
                //emlig* emliga = new emlig(2);
                RARX* my_rarx = new RARX(3,30,true);
                
                
                mat V0 = 0.0001 * eye ( 3 );
                ARX* my_arx = new ARX(0.97);
                my_arx->set_statistics ( 1, V0 ); //nu is default (set to have finite moments)
                my_arx->set_constant ( false );
                my_arx->validate();
                

                for(int k = 1;k<strings[j].size();k++)
                {
                        vec condition;
                        //condition.ins(0,1);                           
                        condition.ins(0,strings[j][k]);                         
                        conditions.push_back(condition);

                        //cout << "orig:" << condition << endl;

                        if(conditions.size()>1)
                        {               
                                conditions[k-2].ins(0,strings[j][k]);
                                        
                        }

                        if(conditions.size()>2)
                        {
                                conditions[k-3].ins(0,strings[j][k]);

                                cout << "Condition:" << conditions[k-3] << endl;

                                my_rarx->bayes(conditions[k-3]);
                                //my_rarx->posterior->step_me(1);
                                
                                vec cond_vec;
                                cond_vec.ins(0,conditions[k-3][0]);
                                
                                my_arx->bayes(cond_vec,conditions[k-3].right(2));
                                        
                                
                                if(k>10)
                                {
                                        //my_rarx->posterior->step_me(0);

                                        mat samples = my_rarx->posterior->sample_mat(10);

                                        pair<vec,mat> imp_samples = my_rarx->posterior->importance_sample(20);

                                        cout << imp_samples.first << endl;
                                        
                                        vec sample_prediction;
                                        for(int t = 0;t<samples.cols();t++)
                                        {
                                                vec lap_sample = conditions[k-3].left(2);
                                                lap_sample.ins(lap_sample.size(),1.0);
                                                
                                                lap_sample.ins(0,LapRNG());

                                                sample_prediction.ins(0,lap_sample*samples.get_col(t));
                                        }

                                        
                                        vec sample_pow = sample_prediction;
                                        
                                        // cout << sample_prediction << endl;
                                        vec poly_coefs;
                                        bool stop_iteration = false;
                                        int en = 1;
                                        do
                                        {
                                                double poly_coef = ones(sample_pow.size())*sample_pow/sample_pow.size();

                                                poly_coef = poly_coef*en*fact(utility_constant-2+en)/fact(utility_constant-2);

                                                if(abs(poly_coef)>numeric_limits<double>::epsilon())
                                                {
                                                        sample_pow = elem_mult(sample_pow,sample_prediction);
                                                        poly_coefs.ins(0,pow(-1.0,en+1)*poly_coef);
                                                }
                                                else
                                                {
                                                        stop_iteration = true;
                                                }
                                                
                                                en++;

                                                if(en>20)
                                                {
                                                        stop_iteration = true;
                                                }
                                        }
                                        while(!stop_iteration);

                                        /*
                                        ofstream myfile_coef;                                           

                                        myfile_coef.open("c:\\coefs.txt",ios::app);
                                        
                                        for(int t = 0;t<poly_coefs.size();t++)
                                        {
                                                myfile_coef << poly_coefs[t] << ",";                                    
                                        }

                                        myfile_coef << endl;
                                        myfile_coef.close();
                                        */

                                        // cout << "Coefficients: " << poly_coefs << endl;
                                                                                
                                        /*
                                        vec bas_coef = vec("1.0 2.0 -8.0");
                                        cout << "Coefs: " << bas_coef << endl;
                                        cvec actions2 = roots(bas_coef);
                                        cout << "Roots: " << actions2 << endl;
                                        */
                                        
                                        cvec actions = roots(poly_coefs);
                                        

                                        bool is_max = false;
                                        for(int t = 0;t<actions.size();t++)
                                        {
                                                if(actions[t].imag() == 0)
                                                {
                                                        

                                                        double second_derivative = 0;
                                                        for(int q = 1;q<poly_coefs.size();q++)
                                                        {
                                                                second_derivative+=poly_coefs[q]*pow(actions[t].real(),q-1)*q;
                                                        }

                                                        if(second_derivative<0)
                                                        {
                                                                cout << "Action:" << actions[t].real() << endl;

                                                                is_max = true;
                                                        }
                                                }
                                        }

                                        if(!is_max)
                                        {
                                                cout << "No maximum." << endl;
                                        }

                                        // cout << "MaxLik coords:" << my_rarx->posterior->minimal_vertex->get_coordinates() << endl;

                                        double prediction = 0;
                                        for(int s = 1;s<samples.rows();s++)
                                        {
                                                
                                                double avg_parameter = samples.get_row(s)*ones(samples.cols())/samples.cols();

                                                prediction += avg_parameter*conditions[k-3][s-1];

                                                
                                                
                                                /*
                                                ofstream myfile;
                                                char fstring[80];
                                                strcpy(fstring,file_strings[j]);

                                                char es[5];
                                                strcat(fstring,itoa(s,es,10));

                                                strcat(fstring,"_res.txt");
                                                

                                                myfile.open(fstring,ios::app);
                                                
                                                //myfile << my_rarx->posterior->minimal_vertex->get_coordinates()[0];
                                                myfile << avg_parameter;
                                                
                                                if(k!=strings[j].size()-1)
                                                {
                                                        myfile << ",";
                                                }
                                                else
                                                {
                                                        myfile << endl;
                                                }
                                                myfile.close();
                                                */
                                        }

                                        cout << "Prediction: "<< prediction << endl;
                                        
                                        enorm<ldmat>* pred_mat = my_arx->epredictor(conditions[k-3].left(2));
                                        double prediction2 = pred_mat->mean()[0];
                                        

                                        ofstream myfile;
                                        char fstring[80];
                                        char f2string[80];
                                        strcpy(fstring,file_strings[j]);
                                        strcpy(f2string,fstring);

                                        strcat(fstring,"pred.txt");
                                        strcat(f2string,"2pred.txt");
                                        

                                        myfile.open(fstring,ios::app);
                                        
                                        // myfile << my_rarx->posterior->minimal_vertex->get_coordinates()[0];
                                        myfile << prediction;
                                        
                                        if(k!=strings[j].size()-1)
                                        {
                                                myfile << ",";
                                        }
                                        else
                                        {
                                                myfile << endl;
                                        }
                                        myfile.close();

                                        
                                        myfile.open(f2string,ios::app);
                                        myfile << prediction2;
                                        
                                        if(k!=strings[j].size()-1)
                                        {
                                                myfile << ",";
                                        }
                                        else
                                        {
                                                myfile << endl;
                                        }
                                        myfile.close();
                                        

                                }                                       
                        }       
                        
                        //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();
                        

                        cout << "MaxLik coords:" << emliga->minimal_vertex->get_coordinates() << endl;
                        cout << "Step: " << i << endl;*/
                }


        }


        // EXPERIMENT: One step ahead price prediction. Comparison of classical and robust model using optimal trading
    //             with maximization of logarithm of one-step ahead wealth.

        
                
                /*
                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();*/
        

        //emlig* emlig1 = new emlig(emlig_size);

        //emlig1->step_me(0);
        //emlig* emlig2 = new emlig(emlig_size);
        
        /*
        emlig1->set_correction_factors(4);

        for(int j = 0;j<emlig1->correction_factors.size();j++)
        {
                for(set<my_ivec>::iterator vec_ref = emlig1->correction_factors[j].begin();vec_ref!=emlig1->correction_factors[j].end();vec_ref++)
                {
                        cout << j << "    ";
                        
                        for(int i=0;i<(*vec_ref).size();i++)
                        {
                                cout << (*vec_ref)[i];
                        }

                        cout << endl;
                }
        }*/
        
        /*
    vec condition5 = "1.0 1.0 1.01";//"-0.3 1.7 1.5";

        emlig1->add_condition(condition5);
        //emlig1->step_me(0);


        vec condition1a = "-1.0 1.02 0.5";
        //vec condition1b = "1.0 1.0 1.01";
        emlig1->add_condition(condition1a);
        //emlig2->add_condition(condition1b);

        vec condition2a = "-0.3 1.7 1.5";
        //vec condition2b = "-1.0 1.0 1.0";
        emlig1->add_condition(condition2a);
        //emlig2->add_condition(condition2b);

        vec condition3a = "0.5 -1.01 1.0";
        //vec condition3b = "0.5 -1.01 1.0";

        emlig1->add_condition(condition3a);
        //emlig2->add_condition(condition3b);   

        vec condition4a = "-0.5 -1.0 1.0";
        //vec condition4b = "-0.5 -1.0 1.0";    

        emlig1->add_condition(condition4a);
        //cout << "************************************************" << endl;
        //emlig2->add_condition(condition4b);
        //cout << "************************************************" << endl;
        
        //cout << emlig1->minimal_vertex->get_coordinates();
        
        //emlig1->remove_condition(condition3a);
        //emlig1->step_me(0);
        //emlig1->remove_condition(condition2a);
        //emlig1->remove_condition(condition1a);
        //emlig1->remove_condition(condition5);
        

        //emlig1->step_me(0);
        //emlig2->step_me(0);
        

        // DA SE POUZIT PRO VYPIS DO SOUBORU
        // emlig1->step_me(0);

        //emlig1->remove_condition(condition1);
        
        

        
        
        /*
        for(int i = 0;i<100;i++)
        {
                cout << endl << "Step:" << i << endl;           

                double condition[emlig_size+1];         

                for(int k = 0;k<=emlig_size;k++)
                {
                        condition[k] = (rand()-RAND_MAX/2)/1000.0;              
                }
                        

                vec* condition_vec = new vec(condition,emlig_size+1);
                emlig1->add_condition(*condition_vec);

                /*
                for(polyhedron* toprow_ref = emlig1->statistic.rows[emlig_size]; toprow_ref != emlig1->statistic.end_poly; toprow_ref = toprow_ref->next_poly)
                {
                        cout << ((toprow*)toprow_ref)->probability << endl;
                }
                */
                /*
                cout << emlig1->statistic_rowsize(emlig_size) << endl << endl;
        
                /*
                if(i-emlig1->number_of_parameters >= 0)
                {
                        pause(30);
                }
                */

                // emlig1->step_me(i);
                
                /*
                vector<int> sizevector;
                for(int s = 0;s<=emlig1->number_of_parameters;s++)
                {
                        sizevector.push_back(emlig1->statistic_rowsize(s));
                }
                */
        //}
    


        
        /*
        emlig1->step_me(1);

        vec condition = "2.0 0.0 1.0";  

        emlig1->add_condition(condition);

        vector<int> sizevector;
        for(int s = 0;s<=emlig1->number_of_parameters;s++)
        {
                sizevector.push_back(emlig1->statistic_rowsize(s));
        }

        emlig1->step_me(2);

        condition = "2.0 1.0 0.0";

        emlig1->add_condition(condition);

        sizevector.clear();
        for(int s = 0;s<=emlig1->number_of_parameters;s++)
        {
                sizevector.push_back(emlig1->statistic_rowsize(s));
        }
        */

        return 0;
}