| 1 | /*! |
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| 2 | \file |
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| 3 | \brief Robust Bayesian auto-regression model |
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| 4 | \author Jan Sindelar. |
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| 5 | */ |
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| 6 | |
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| 7 | #ifndef ROBUST_H |
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| 8 | #define ROBUST_H |
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| 9 | |
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| 10 | #include <stat/exp_family.h> |
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| 11 | #include <limits> |
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| 12 | #include <vector> |
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| 13 | #include <algorithm> |
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| 14 | |
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| 15 | using namespace bdm; |
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| 16 | using namespace std; |
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| 17 | using namespace itpp; |
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| 18 | |
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| 19 | const double max_range = numeric_limits<double>::max()/10e-5; |
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| 20 | |
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| 21 | class polyhedron; |
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| 22 | class vertex; |
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| 23 | |
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| 24 | /// A class describing a single polyhedron of the split complex. From a collection of such classes a Hasse diagram |
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| 25 | /// of the structure in the exponent of a Laplace-Inverse-Gamma density will be created. |
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| 26 | class polyhedron |
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| 27 | { |
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| 28 | /// A property having a value of 1 usually, with higher value only if the polyhedron arises as a coincidence of |
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| 29 | /// more than just the necessary number of conditions. For example if a newly created line passes through an already |
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| 30 | /// existing point, the points multiplicity will rise by 1. |
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| 31 | int multiplicity; |
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| 32 | |
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| 33 | int split_state; |
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| 34 | |
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| 35 | int merge_state; |
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| 36 | |
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| 37 | |
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| 38 | |
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| 39 | public: |
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| 40 | /// A list of polyhedrons parents within the Hasse diagram. |
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| 41 | vector<polyhedron*> parents; |
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| 42 | |
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| 43 | /// A list of polyhedrons children withing the Hasse diagram. |
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| 44 | vector<polyhedron*> children; |
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| 45 | |
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| 46 | /// All the vertices of the given polyhedron |
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| 47 | vector<vertex*> vertices; |
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| 48 | |
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| 49 | /// A list used for storing children that lie in the positive region related to a certain condition |
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| 50 | vector<polyhedron*> positivechildren; |
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| 51 | |
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| 52 | /// A list used for storing children that lie in the negative region related to a certain condition |
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| 53 | vector<polyhedron*> negativechildren; |
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| 54 | |
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| 55 | /// Children intersecting the condition |
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| 56 | vector<polyhedron*> neutralchildren; |
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| 57 | |
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| 58 | vector<polyhedron*> mergechildren; |
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| 59 | |
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| 60 | polyhedron* positiveparent; |
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| 61 | |
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| 62 | polyhedron* negativeparent; |
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| 63 | |
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| 64 | int message_counter; |
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| 65 | |
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| 66 | /// List of triangulation polyhedrons of the polyhedron given by their relative vertices. |
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| 67 | vector<vector<vertex*>> triangulations; |
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| 68 | |
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| 69 | /// A list of relative addresses serving for Hasse diagram construction. |
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| 70 | vector<int> kids_rel_addresses; |
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| 71 | |
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| 72 | /// Default constructor |
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| 73 | polyhedron() |
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| 74 | { |
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| 75 | multiplicity = 1; |
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| 76 | |
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| 77 | message_counter = 0; |
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| 78 | } |
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| 79 | |
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| 80 | /// Setter for raising multiplicity |
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| 81 | void raise_multiplicity() |
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| 82 | { |
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| 83 | multiplicity++; |
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| 84 | } |
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| 85 | |
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| 86 | /// Setter for lowering multiplicity |
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| 87 | void lower_multiplicity() |
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| 88 | { |
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| 89 | multiplicity--; |
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| 90 | } |
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| 91 | |
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| 92 | /// An obligatory operator, when the class is used within a C++ STL structure like a vector |
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| 93 | int operator==(polyhedron polyhedron2) |
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| 94 | { |
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| 95 | return true; |
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| 96 | } |
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| 97 | |
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| 98 | /// An obligatory operator, when the class is used within a C++ STL structure like a vector |
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| 99 | int operator<(polyhedron polyhedron2) |
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| 100 | { |
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| 101 | return false; |
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| 102 | } |
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| 103 | |
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| 104 | void set_state(double state_indicator, actions action) |
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| 105 | { |
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| 106 | switch(action) |
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| 107 | { |
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| 108 | case MERGE: |
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| 109 | merge_state = (int)sign(state_indicator); |
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| 110 | break; |
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| 111 | case SPLIT: |
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| 112 | split_state = (int)sign(state_indicator); |
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| 113 | break; |
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| 114 | } |
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| 115 | } |
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| 116 | |
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| 117 | int get_state(actions action) |
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| 118 | { |
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| 119 | switch(action) |
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| 120 | { |
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| 121 | case MERGE: |
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| 122 | return merge_state; |
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| 123 | break; |
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| 124 | case SPLIT: |
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| 125 | return split_state; |
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| 126 | break; |
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| 127 | } |
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| 128 | } |
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| 129 | |
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| 130 | int number_of_children() |
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| 131 | { |
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| 132 | return children.size()+positivechildren.size()+negativechildren.size()+neutralchildren.size(); |
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| 133 | } |
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| 134 | |
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| 135 | void send_state_message(bool shouldsplit, bool shouldmerge) |
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| 136 | { |
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| 137 | if(shouldsplit||shouldmerge) |
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| 138 | { |
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| 139 | for(vector<polyhedron*>::iterator parent_iterator = parents.begin();parent_iterator<parents.end();parent_iterator++) |
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| 140 | { |
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| 141 | polyhedron* current_parent = *parent_iterator; |
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| 142 | |
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| 143 | current_parent->message_counter++; |
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| 144 | |
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| 145 | bool is_last = (current_parent->message_counter == current_parent->number_of_children()); |
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| 146 | |
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| 147 | if(shouldmerge) |
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| 148 | { |
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| 149 | int child_state = get_state(MERGE); |
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| 150 | int parent_state = current_parent->get_state(MERGE); |
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| 151 | |
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| 152 | if(parent_state == NULL || parent_state == 0) |
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| 153 | { |
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| 154 | current_parent->set_state(child_state, MERGE); |
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| 155 | |
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| 156 | if(child_state == 0) |
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| 157 | { |
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| 158 | current_parent->mergechildren.push_back(this); |
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| 159 | } |
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| 160 | } |
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| 161 | else |
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| 162 | { |
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| 163 | if(child_state == 0) |
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| 164 | { |
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| 165 | if(parent_state > 0) |
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| 166 | { |
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| 167 | positiveparent = current_parent; |
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| 168 | } |
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| 169 | else |
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| 170 | { |
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| 171 | negativeparent = current_parent; |
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| 172 | } |
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| 173 | } |
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| 174 | } |
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| 175 | |
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| 176 | if(is_last) |
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| 177 | { |
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| 178 | if(parent_state > 0) |
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| 179 | { |
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| 180 | for(vector<polyhedron*>::iterator merge_child = current_parent->mergechildren.begin(); merge_child < current_parent->mergechildren.end();merge_child++) |
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| 181 | { |
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| 182 | (*merge_child)->positiveparent = current_parent; |
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| 183 | } |
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| 184 | } |
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| 185 | |
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| 186 | if(parent_state < 0) |
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| 187 | { |
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| 188 | for(vector<polyhedron*>::iterator merge_child = current_parent->mergechildren.begin(); merge_child < current_parent->mergechildren.end();merge_child++) |
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| 189 | { |
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| 190 | (*merge_child)->negativeparent = current_parent; |
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| 191 | } |
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| 192 | } |
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| 193 | |
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| 194 | current_parent->mergechildren.clear(); |
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| 195 | } |
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| 196 | |
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| 197 | |
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| 198 | } |
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| 199 | |
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| 200 | } |
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| 201 | } |
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| 202 | } |
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| 203 | }; |
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| 204 | |
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| 205 | /// A class for representing 0-dimensional polyhedron - a vertex. It will be located in the bottom row of the Hasse |
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| 206 | /// diagram representing a complex of polyhedrons. It has its coordinates in the parameter space. |
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| 207 | class vertex : public polyhedron |
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| 208 | { |
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| 209 | /// A dynamic array representing coordinates of the vertex |
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| 210 | vec coordinates; |
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| 211 | |
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| 212 | enum actions {MERGE, SPLIT}; |
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| 213 | |
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| 214 | public: |
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| 215 | |
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| 216 | |
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| 217 | |
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| 218 | /// Default constructor |
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| 219 | vertex(); |
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| 220 | |
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| 221 | /// Constructor of a vertex from a set of coordinates |
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| 222 | vertex(vec coordinates) |
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| 223 | { |
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| 224 | this->coordinates = coordinates; |
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| 225 | } |
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| 226 | |
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| 227 | /// A method that widens the set of coordinates of given vertex. It is used when a complex in a parameter |
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| 228 | /// space of certain dimension is established, but the dimension is not known when the vertex is created. |
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| 229 | void push_coordinate(double coordinate) |
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| 230 | { |
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| 231 | coordinates = concat(coordinates,coordinate); |
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| 232 | } |
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| 233 | |
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| 234 | /// A method obtaining the set of coordinates of a vertex. These coordinates are not obtained as a pointer |
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| 235 | /// (not given by reference), but a new copy is created (they are given by value). |
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| 236 | vec get_coordinates() |
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| 237 | { |
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| 238 | return coordinates; |
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| 239 | } |
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| 240 | |
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| 241 | |
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| 242 | }; |
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| 243 | |
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| 244 | /// A class representing a polyhedron in a top row of the complex. Such polyhedron has a condition that differitiates |
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| 245 | /// it from polyhedrons in other rows. |
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| 246 | class toprow : public polyhedron |
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| 247 | { |
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| 248 | |
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| 249 | public: |
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| 250 | /// A condition used for determining the function of a Laplace-Inverse-Gamma density resulting from Bayesian estimation |
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| 251 | vec condition; |
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| 252 | |
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| 253 | /// Default constructor |
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| 254 | toprow(); |
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| 255 | |
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| 256 | /// Constructor creating a toprow from the condition |
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| 257 | toprow(vec condition) |
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| 258 | { |
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| 259 | this->condition = condition; |
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| 260 | } |
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| 261 | |
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| 262 | }; |
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| 263 | |
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| 264 | class condition |
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| 265 | { |
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| 266 | public: |
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| 267 | vec value; |
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| 268 | |
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| 269 | int multiplicity; |
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| 270 | |
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| 271 | condition(vec value) |
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| 272 | { |
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| 273 | this->value = value; |
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| 274 | multiplicity = 1; |
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| 275 | } |
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| 276 | } |
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| 277 | |
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| 278 | |
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| 279 | //! Conditional(e) Multicriteria-Laplace-Inverse-Gamma distribution density |
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| 280 | class emlig // : eEF |
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| 281 | { |
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| 282 | |
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| 283 | /// A statistic in a form of a Hasse diagram representing a complex of convex polyhedrons obtained as a result |
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| 284 | /// of data update from Bayesian estimation or set by the user if this emlig is a prior density |
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| 285 | vector<vector<polyhedron*>> statistic; |
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| 286 | |
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| 287 | vector<condition*> conditions; |
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| 288 | |
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| 289 | double normalization_factor; |
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| 290 | |
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| 291 | void alter_toprow_conditions(vec condition, bool should_be_added) |
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| 292 | { |
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| 293 | for(vector<polyhedron*>::iterator horiz_ref = statistic[statistic.size()-1].begin();horiz_ref<statistic[statistic.size()-1].end();horiz_ref++) |
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| 294 | { |
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| 295 | double product = 0; |
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| 296 | |
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| 297 | vector<vertex*>::iterator vertex_ref = (*horiz_ref)->vertices.begin(); |
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| 298 | |
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| 299 | do |
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| 300 | { |
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| 301 | product = (*vertex_ref)->coordinates*condition; |
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| 302 | } |
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| 303 | while(product == 0) |
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| 304 | |
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| 305 | if((product>0 && should_be_added)||(product<0 && !should_be_added)) |
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| 306 | { |
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| 307 | ((toprow*) (*horiz_ref))->condition += condition; |
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| 308 | } |
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| 309 | else |
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| 310 | { |
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| 311 | ((toprow*) (*horiz_ref))->condition -= condition; |
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| 312 | } |
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| 313 | } |
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| 314 | } |
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| 315 | |
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| 316 | public: |
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| 317 | |
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| 318 | /// A default constructor creates an emlig with predefined statistic representing only the range of the given |
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| 319 | /// parametric space, where the number of parameters of the needed model is given as a parameter to the constructor. |
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| 320 | emlig(int number_of_parameters) |
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| 321 | { |
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| 322 | create_statistic(number_of_parameters); |
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| 323 | } |
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| 324 | |
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| 325 | /// A constructor for creating an emlig when the user wants to create the statistic by himself. The creation of a |
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| 326 | /// statistic is needed outside the constructor. Used for a user defined prior distribution on the parameters. |
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| 327 | emlig(vector<vector<polyhedron*>> statistic) |
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| 328 | { |
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| 329 | this->statistic = statistic; |
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| 330 | } |
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| 331 | |
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| 332 | void add_and_remove_condition(vec toremove, vec toadd) |
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| 333 | { |
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| 334 | vector<condition*>::iterator toremove_ref = conditions.end(); |
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| 335 | bool condition_should_be_added = false; |
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| 336 | |
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| 337 | for(vector<condition*>::iterator ref = conditions.begin();ref<conditions.end();ref++) |
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| 338 | { |
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| 339 | if(toremove != NULL) |
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| 340 | { |
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| 341 | if((*ref)->value == toremove) |
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| 342 | { |
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| 343 | if(multiplicity>1) |
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| 344 | { |
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| 345 | multiplicity--; |
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| 346 | |
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| 347 | alter_toprow_conditions(toremove,false); |
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| 348 | |
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| 349 | toremove = NULL; |
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| 350 | } |
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| 351 | else |
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| 352 | { |
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| 353 | toremove_ref = ref; |
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| 354 | } |
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| 355 | } |
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| 356 | } |
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| 357 | |
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| 358 | if(toadd != NULL) |
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| 359 | { |
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| 360 | if((*iterator)->value == toadd) |
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| 361 | { |
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| 362 | (*iterator)->multiplicity++; |
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| 363 | |
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| 364 | alter_toprow_conditions(toadd,true); |
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| 365 | |
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| 366 | toadd = NULL; |
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| 367 | } |
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| 368 | else |
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| 369 | { |
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| 370 | condition_should_be_added = true; |
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| 371 | } |
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| 372 | } |
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| 373 | } |
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| 374 | |
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| 375 | if(toremove_ref!=conditions.end()) |
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| 376 | { |
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| 377 | conditions.erase(toremove_ref); |
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| 378 | } |
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| 379 | |
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| 380 | if(condition_should_be_added) |
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| 381 | { |
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| 382 | conditions.push_back(new condition(toadd)); |
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| 383 | } |
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| 384 | |
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| 385 | vector<vector<polyhedron*>> for_splitting; |
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| 386 | vector<vector<polyhedron*>> for_merging; |
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| 387 | |
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| 388 | for(vector<polyhedron*>::iterator horizontal_position = statistic[0].begin();horizontal_position<statistic[0].end();horizontal_position++) |
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| 389 | { |
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| 390 | vertex* current_vertex = (vertex*)horizontal_position; |
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| 391 | |
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| 392 | if(toadd != NULL) |
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| 393 | { |
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| 394 | current_vertex->set_state(toadd*current_vertex->coordinates,SPLIT); |
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| 395 | } |
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| 396 | |
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| 397 | if(toremove != NULL) |
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| 398 | { |
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| 399 | current_vertex->set_state(toremove*current_vertex->coordinates,MERGE); |
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| 400 | } |
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| 401 | |
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| 402 | current_vertex->send_state_message(toadd != NULL, toremove != NULL); |
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| 403 | } |
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| 404 | } |
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| 405 | |
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| 406 | protected: |
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| 407 | |
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| 408 | /// A method for creating plain default statistic representing only the range of the parameter space. |
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| 409 | void create_statistic(int number_of_parameters) |
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| 410 | { |
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| 411 | // An empty vector of coordinates. |
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| 412 | vec origin_coord; |
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| 413 | |
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| 414 | // We create an origin - this point will have all the coordinates zero, but now it has an empty vector of coords. |
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| 415 | vertex *origin = new vertex(origin_coord); |
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| 416 | |
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| 417 | // It has itself as a vertex. There will be a nice use for this when the vertices of its parents are searched in |
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| 418 | // the recursive creation procedure below. |
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| 419 | origin->vertices.push_back(origin); |
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| 420 | |
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| 421 | // As a statistic, we have to create a vector of vectors of polyhedron pointers. It will then represent the Hasse |
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| 422 | // diagram. First we create a vector of polyhedrons.. |
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| 423 | vector<polyhedron*> origin_vec; |
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| 424 | |
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| 425 | // ..we fill it with the origin.. |
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| 426 | origin_vec.push_back(origin); |
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| 427 | |
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| 428 | // ..and we fill the statistic with the created vector. |
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| 429 | statistic.push_back(origin_vec); |
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| 430 | |
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| 431 | // Now we have a statistic for a zero dimensional space. Regarding to how many dimensional space we need to |
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| 432 | // describe, we have to widen the descriptional default statistic. We use an iterative procedure as follows: |
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| 433 | for(int i=0;i<number_of_parameters;i++) |
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| 434 | { |
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| 435 | // We first will create two new vertices. These will be the borders of the parameter space in the dimension |
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| 436 | // of newly added parameter. Therefore they will have all coordinates except the last one zero. We get the |
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| 437 | // right amount of zero cooridnates by reading them from the origin |
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| 438 | vec origin_coord = origin->get_coordinates(); |
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| 439 | |
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| 440 | // And we incorporate the nonzero coordinates into the new cooordinate vectors |
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| 441 | vec origin_coord1 = concat(origin_coord,max_range); |
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| 442 | vec origin_coord2 = concat(origin_coord,-max_range); |
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| 443 | |
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| 444 | // Now we create the points |
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| 445 | vertex *new_point1 = new vertex(origin_coord1); |
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| 446 | vertex *new_point2 = new vertex(origin_coord2); |
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| 447 | |
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| 448 | //********************************************************************************************************* |
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| 449 | // The algorithm for recursive build of a new Hasse diagram representing the space structure from the old |
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| 450 | // diagram works so that you create two copies of the old Hasse diagram, you shift them up one level (points |
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| 451 | // will be segments, segments will be areas etc.) and you connect each one of the original copied polyhedrons |
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| 452 | // with its offspring by a parent-child relation. Also each of the segments in the first (second) copy is |
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| 453 | // connected to the first (second) newly created vertex by a parent-child relation. |
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| 454 | //********************************************************************************************************* |
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| 455 | |
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| 456 | |
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| 457 | // Create the vectors of vectors of pointers to polyhedrons to hold the copies of the old Hasse diagram |
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| 458 | vector<vector<polyhedron*>> new_statistic1; |
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| 459 | vector<vector<polyhedron*>> new_statistic2; |
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| 460 | |
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| 461 | // Copy the statistic by rows |
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| 462 | for(int j=0;j<statistic.size();j++) |
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| 463 | { |
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| 464 | // an element counter |
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| 465 | int element_number = 0; |
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| 466 | |
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| 467 | vector<polyhedron*> supportnew_1; |
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| 468 | vector<polyhedron*> supportnew_2; |
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| 469 | |
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| 470 | new_statistic1.push_back(supportnew_1); |
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| 471 | new_statistic2.push_back(supportnew_2); |
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| 472 | |
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| 473 | // for each polyhedron in the given row |
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| 474 | for(vector<polyhedron*>::iterator horiz_ref = statistic[j].begin();horiz_ref<statistic[j].end();horiz_ref++) |
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| 475 | { |
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| 476 | // Append an extra zero coordinate to each of the vertices for the new dimension |
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| 477 | // If j==0 => we loop through vertices |
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| 478 | if(j == 0) |
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| 479 | { |
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| 480 | // cast the polyhedron pointer to a vertex pointer and push a zero to its vector of coordinates |
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| 481 | ((vertex*) (*horiz_ref))->push_coordinate(0); |
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| 482 | } |
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| 483 | |
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| 484 | // if it has parents |
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| 485 | if(!(*horiz_ref)->parents.empty()) |
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| 486 | { |
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| 487 | // save the relative address of this child in a vector kids_rel_addresses of all its parents. |
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| 488 | // This information will later be used for copying the whole Hasse diagram with each of the |
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| 489 | // relations contained within. |
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| 490 | for(vector<polyhedron*>::iterator parent_ref = (*horiz_ref)->parents.begin();parent_ref < (*horiz_ref)->parents.end();parent_ref++) |
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| 491 | { |
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| 492 | (*parent_ref)->kids_rel_addresses.push_back(element_number); |
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| 493 | } |
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| 494 | } |
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| 495 | |
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| 496 | // ************************************************************************************************** |
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| 497 | // Here we begin creating a new polyhedron, which will be a copy of the old one. Each such polyhedron |
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| 498 | // will be created as a toprow, but this information will be later forgotten and only the polyhedrons |
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| 499 | // in the top row of the Hasse diagram will be considered toprow for later use. |
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| 500 | // ************************************************************************************************** |
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| 501 | |
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| 502 | // First we create vectors specifying a toprow condition. In the case of a preconstructed statistic |
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| 503 | // this condition will be a vector of zeros. There are two vectors, because we need two copies of |
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| 504 | // the original Hasse diagram. |
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| 505 | vec vec1(i+2); |
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| 506 | vec1.zeros(); |
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| 507 | |
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| 508 | vec vec2(i+2); |
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| 509 | vec2.zeros(); |
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| 510 | |
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| 511 | // We create a new toprow with the previously specified condition. |
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| 512 | toprow *current_copy1 = new toprow(vec1); |
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| 513 | toprow *current_copy2 = new toprow(vec2); |
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| 514 | |
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| 515 | // The vertices of the copies will be inherited, because there will be a parent/child relation |
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| 516 | // between each polyhedron and its offspring (comming from the copy) and a parent has all the |
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| 517 | // vertices of its child plus more. |
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| 518 | for(vector<vertex*>::iterator vert_ref = (*horiz_ref)->vertices.begin();vert_ref<(*horiz_ref)->vertices.end();vert_ref++) |
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| 519 | { |
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| 520 | current_copy1->vertices.push_back(*vert_ref); |
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| 521 | current_copy2->vertices.push_back(*vert_ref); |
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| 522 | } |
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| 523 | |
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| 524 | // The only new vertex of the offspring should be the newly created point. |
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| 525 | current_copy1->vertices.push_back(new_point1); |
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| 526 | current_copy2->vertices.push_back(new_point2); |
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| 527 | |
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| 528 | // This method guarantees that each polyhedron is already triangulated, therefore its triangulation |
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| 529 | // is only one set of vertices and it is the set of all its vertices. |
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| 530 | current_copy1->triangulations.push_back(current_copy1->vertices); |
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| 531 | current_copy2->triangulations.push_back(current_copy2->vertices); |
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| 532 | |
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| 533 | // Now we have copied the polyhedron and we have to copy all of its relations. Because we are copying |
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| 534 | // in the Hasse diagram from bottom up, we always have to copy the parent/child relations to all the |
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| 535 | // kids and when we do that and know the child, in the child we will remember the parent we came from. |
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| 536 | // This way all the parents/children relations are saved in both the parent and the child. |
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| 537 | if(!(*horiz_ref)->kids_rel_addresses.empty()) |
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| 538 | { |
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| 539 | for(vector<int>::iterator kid_ref = (*horiz_ref)->kids_rel_addresses.begin();kid_ref<(*horiz_ref)->kids_rel_addresses.end();kid_ref++) |
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| 540 | { |
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| 541 | // find the child and save the relation to the parent |
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| 542 | current_copy1->children.push_back(new_statistic1[j-1][(*kid_ref)]); |
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| 543 | current_copy2->children.push_back(new_statistic2[j-1][(*kid_ref)]); |
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| 544 | |
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| 545 | // in the child save the parents' address |
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| 546 | new_statistic1[j-1][(*kid_ref)]->parents.push_back(current_copy1); |
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| 547 | new_statistic2[j-1][(*kid_ref)]->parents.push_back(current_copy2); |
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| 548 | } |
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| 549 | |
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| 550 | // Here we clear the parents kids_rel_addresses vector for later use (when we need to widen the |
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| 551 | // Hasse diagram again) |
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| 552 | (*horiz_ref)->kids_rel_addresses.clear(); |
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| 553 | } |
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| 554 | // If there were no children previously, we are copying a polyhedron that has been a vertex before. |
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| 555 | // In this case it is a segment now and it will have a relation to its mother (copywise) and to the |
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| 556 | // newly created point. Here we create the connection to the new point, again from both sides. |
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| 557 | else |
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| 558 | { |
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| 559 | // Add the address of the new point in the former vertex |
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| 560 | current_copy1->children.push_back(new_point1); |
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| 561 | current_copy2->children.push_back(new_point2); |
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| 562 | |
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| 563 | // Add the address of the former vertex in the new point |
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| 564 | new_point1->parents.push_back(current_copy1); |
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| 565 | new_point2->parents.push_back(current_copy2); |
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| 566 | } |
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| 567 | |
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| 568 | // Save the mother in its offspring |
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| 569 | current_copy1->children.push_back(*horiz_ref); |
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| 570 | current_copy2->children.push_back(*horiz_ref); |
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| 571 | |
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| 572 | // Save the offspring in its mother |
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| 573 | (*horiz_ref)->parents.push_back(current_copy1); |
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| 574 | (*horiz_ref)->parents.push_back(current_copy2); |
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| 575 | |
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| 576 | |
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| 577 | // Add the copies into the relevant statistic. The statistic will later be appended to the previous |
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| 578 | // Hasse diagram |
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| 579 | new_statistic1[j].push_back(current_copy1); |
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| 580 | new_statistic2[j].push_back(current_copy2); |
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| 581 | |
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| 582 | // Raise the count in the vector of polyhedrons |
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| 583 | element_number++; |
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| 584 | |
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| 585 | } |
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| 586 | } |
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| 587 | |
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| 588 | statistic[0].push_back(new_point1); |
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| 589 | statistic[0].push_back(new_point2); |
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| 590 | |
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| 591 | // Merge the new statistics into the old one. This will either be the final statistic or we will |
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| 592 | // reenter the widening loop. |
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| 593 | for(int j=0;j<new_statistic1.size();j++) |
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| 594 | { |
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| 595 | if(j+1==statistic.size()) |
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| 596 | { |
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| 597 | vector<polyhedron*> support; |
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| 598 | statistic.push_back(support); |
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| 599 | } |
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| 600 | |
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| 601 | statistic[j+1].insert(statistic[j+1].end(),new_statistic1[j].begin(),new_statistic1[j].end()); |
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| 602 | statistic[j+1].insert(statistic[j+1].end(),new_statistic2[j].begin(),new_statistic2[j].end()); |
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| 603 | } |
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| 604 | } |
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| 605 | } |
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| 606 | |
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| 607 | |
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| 608 | |
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| 609 | |
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| 610 | }; |
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| 611 | |
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| 612 | //! Robust Bayesian AR model for Multicriteria-Laplace-Inverse-Gamma density |
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| 613 | class RARX : public BM |
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| 614 | { |
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| 615 | private: |
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| 616 | |
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| 617 | emlig posterior; |
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| 618 | |
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| 619 | public: |
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| 620 | RARX():BM() |
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| 621 | { |
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| 622 | }; |
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| 623 | |
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| 624 | void bayes(const itpp::vec &yt, const itpp::vec &cond = empty_vec) |
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| 625 | { |
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| 626 | |
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| 627 | } |
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| 628 | |
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| 629 | }; |
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| 630 | |
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| 631 | |
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| 632 | #endif //TRAGE_H |
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