root/applications/robust/robustlib.h @ 1172

/*!
  \file
  \brief Robust Bayesian auto-regression model
  \author Jan Sindelar.
*/

#ifndef ROBUST_H
#define ROBUST_H

#include <stat/exp_family.h>
#include <limits>
#include <vector>
#include <algorithm>
        
using namespace bdm;
using namespace std;

const double max_range = numeric_limits<double>::max()/10e-5;

class polyhedron;
class vertex;

/// A class describing a single polyhedron of the split complex. From a collection of such classes a Hasse diagram
/// of the structure in the exponent of a Laplace-Inverse-Gamma density will be created.
class polyhedron
{
        /// A property having a value of 1 usually, with higher value only if the polyhedron arises as a coincidence of
        /// more than just the necessary number of conditions. For example if a newly created line passes through an already
        /// existing point, the points multiplicity will rise by 1.
        int multiplicity;       

public:
        /// A list of polyhedrons parents within the Hasse diagram.
        vector<polyhedron*> parents;

        /// A list of polyhedrons children withing the Hasse diagram.
        vector<polyhedron*> children;

        /// All the vertices of the given polyhedron
        vector<vertex*> vertices;

        /// A list used for storing children that lie in the positive region related to a certain condition
        vector<polyhedron*> positivechildren;

        /// A list used for storing children that lie in the negative region related to a certain condition
        vector<polyhedron*> negativechildren;

        /// Children intersecting the condition
        vector<polyhedron*> neutralchildren;

        /// List of triangulation polyhedrons of the polyhedron given by their relative vertices. 
        vector<vector<vertex*>> triangulations;

        /// A list of relative addresses serving for Hasse diagram construction.
        vector<int> kids_rel_addresses;

        /// Default constructor
        polyhedron()
        {
                multiplicity = 1;       
        }
        
        /// Setter for raising multiplicity
        void RaiseMultiplicity()
        {
                multiplicity++;
        }

        /// Setter for lowering multiplicity
        void LowerMultiplicity()
        {
                multiplicity--;
        }
        
        /// An obligatory operator, when the class is used within a C++ STL structure like a vector
        int operator==(polyhedron polyhedron2)
        {
                return true;
        }

        /// An obligatory operator, when the class is used within a C++ STL structure like a vector
        int operator<(polyhedron polyhedron2)
        {
                return false;
        }
};

/// A
class vertex : public polyhedron
{
        vector<double> coordinates;

public:

        vertex();

        vertex(vector<double> coordinates)
        {
                this->coordinates = coordinates;
        }

        void push_coordinate(double coordinate)
        {
                coordinates.push_back(coordinate);
        }

        vector<double> get_coordinates()
        {
                vector<double> returned_vec;

                copy(this->coordinates.begin(),this->coordinates.end(),returned_vec.begin());

                return returned_vec;
        }
};

class toprow : public polyhedron
{
        vector<double> condition;

public:

        toprow();

        toprow(vector<double> condition)
        {
                this->condition = condition;
        }

};




//! Conditional(e) Multicriteria-Laplace-Inverse-Gamma distribution density
class emlig : public eEF
{

        vector<vector<polyhedron*>> statistic;
        
public: 

        emlig(int number_of_parameters)
        {
                create_statistic(number_of_parameters);
        }

        emlig(vector<vector<polyhedron*>> statistic)
        {
                this->statistic = statistic;
        }



protected:

    void create_statistic(int number_of_parameters)
        {
                vector<double> origin_coord;    

                vertex *origin = new vertex(origin_coord);

                origin->vertices.push_back(origin);

                vector<polyhedron*> origin_vec;

                origin_vec.push_back(origin);

                statistic.push_back(origin_vec);

                for(int i=0;i<number_of_parameters;i++)
                {
                        vector<double> origin_coord1 = origin->get_coordinates();
                        vector<double> origin_coord2 = origin->get_coordinates();

                        origin->push_coordinate(0);

                        origin_coord1.push_back(max_range);
                        origin_coord2.push_back(-max_range);

                        vertex *new_point1 = new vertex(origin_coord1);
                        vertex *new_point2 = new vertex(origin_coord2); 

                        vector<vector<polyhedron*>> new_statistic1;
                        vector<vector<polyhedron*>> new_statistic2;

                        
                        for(int j=0;j<statistic.size();j++)
                        {
                                int element_number = 0;

                                for(vector<polyhedron*>::iterator horiz_ref = statistic[j].begin();horiz_ref<statistic[j].end();horiz_ref++)
                                {       
                                        if(!(*horiz_ref)->parents.empty())
                                        {
                                                for(vector<polyhedron*>::iterator parent_ref = (*horiz_ref)->parents.begin();parent_ref < (*horiz_ref)->parents.end();parent_ref++)
                                                {
                                                        (*parent_ref)->kids_rel_addresses.push_back(element_number);                                                    
                                                }                                               
                                        }

                                        vector<double> vec1(i+2,0);
                                        vector<double> vec2(i+2,0);

                                        toprow *current_copy1 = new toprow(vec1);
                                        toprow *current_copy2 = new toprow(vec2);                                       

                                        for(vector<vertex*>::iterator vert_ref = (*horiz_ref)->vertices.begin();vert_ref<(*horiz_ref)->vertices.end();vert_ref++)
                                        {
                                                current_copy1->vertices.push_back(*vert_ref);
                                                current_copy2->vertices.push_back(*vert_ref);                                           
                                        }
                                        

                                        current_copy1->vertices.push_back(new_point1);
                                        current_copy2->vertices.push_back(new_point2);

                                        current_copy1->triangulations.push_back(current_copy1->vertices);
                                        current_copy2->triangulations.push_back(current_copy2->vertices);

                                        

                                        if(!(*horiz_ref)->kids_rel_addresses.empty())
                                        {
                                                for(vector<int>::iterator kid_ref = (*horiz_ref)->kids_rel_addresses.begin();kid_ref<(*horiz_ref)->kids_rel_addresses.end();kid_ref++)
                                                {                                               
                                                        current_copy1->children.push_back(new_statistic1[i][(*kid_ref)]);
                                                        current_copy2->children.push_back(new_statistic2[i][(*kid_ref)]);

                                                        new_statistic1[i][(*kid_ref)]->parents.push_back(current_copy1);
                                                        new_statistic2[i][(*kid_ref)]->parents.push_back(current_copy2);
                                                }                                               

                                                (*horiz_ref)->kids_rel_addresses.clear();
                                        }
                                        else
                                        {
                                                current_copy1->children.push_back(new_point1);
                                                current_copy2->children.push_back(new_point2);

                                                new_point1->parents.push_back(current_copy1);
                                                new_point2->parents.push_back(current_copy2);
                                        }

                                        current_copy1->children.push_back(*horiz_ref);
                                        current_copy2->children.push_back(*horiz_ref);

                                        new_statistic1[i+1].push_back(current_copy1);
                                        new_statistic2[i+1].push_back(current_copy2);
                                        
                                        element_number++;
                                }                               
                        }                               
                        
                }
        }


        
        
};

//! Robust Bayesian AR model for Multicriteria-Laplace-Inverse-Gamma density
class RARX : public BMEF{
};


#endif //TRAGE_H