| 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 <list> |
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| 14 | #include <algorithm> |
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| 15 | |
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| 16 | using namespace bdm; |
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| 17 | using namespace std; |
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| 18 | using namespace itpp; |
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| 19 | |
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| 20 | const double max_range = 99999999999999999999999.0;//numeric_limits<double>::max()/10e-10; |
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| 21 | |
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| 22 | enum actions {MERGE, SPLIT}; |
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| 23 | |
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| 24 | class polyhedron; |
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| 25 | class vertex; |
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| 26 | |
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| 27 | /// A class describing a single polyhedron of the split complex. From a collection of such classes a Hasse diagram |
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| 28 | /// of the structure in the exponent of a Laplace-Inverse-Gamma density will be created. |
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| 29 | class polyhedron |
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| 30 | { |
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| 31 | /// A property having a value of 1 usually, with higher value only if the polyhedron arises as a coincidence of |
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| 32 | /// more than just the necessary number of conditions. For example if a newly created line passes through an already |
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| 33 | /// existing point, the points multiplicity will rise by 1. |
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| 34 | int multiplicity; |
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| 35 | |
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| 36 | int split_state; |
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| 37 | |
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| 38 | int merge_state; |
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| 39 | |
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| 40 | |
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| 41 | |
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| 42 | public: |
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| 43 | /// A list of polyhedrons parents within the Hasse diagram. |
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| 44 | list<polyhedron*> parents; |
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| 45 | |
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| 46 | /// A list of polyhedrons children withing the Hasse diagram. |
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| 47 | list<polyhedron*> children; |
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| 48 | |
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| 49 | /// All the vertices of the given polyhedron |
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| 50 | list<vertex*> vertices; |
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| 51 | |
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| 52 | /// A list used for storing children that lie in the positive region related to a certain condition |
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| 53 | list<polyhedron*> positivechildren; |
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| 54 | |
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| 55 | /// A list used for storing children that lie in the negative region related to a certain condition |
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| 56 | list<polyhedron*> negativechildren; |
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| 57 | |
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| 58 | /// Children intersecting the condition |
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| 59 | list<polyhedron*> neutralchildren; |
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| 60 | |
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| 61 | list<polyhedron*> totallyneutralgrandchildren; |
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| 62 | |
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| 63 | list<polyhedron*> totallyneutralchildren; |
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| 64 | |
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| 65 | bool totally_neutral; |
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| 66 | |
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| 67 | list<polyhedron*> mergechildren; |
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| 68 | |
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| 69 | polyhedron* positiveparent; |
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| 70 | |
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| 71 | polyhedron* negativeparent; |
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| 72 | |
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| 73 | polyhedron* next_poly; |
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| 74 | |
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| 75 | polyhedron* prev_poly; |
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| 76 | |
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| 77 | int message_counter; |
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| 78 | |
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| 79 | /// List of triangulation polyhedrons of the polyhedron given by their relative vertices. |
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| 80 | list<list<vertex*>> triangulations; |
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| 81 | |
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| 82 | /// A list of relative addresses serving for Hasse diagram construction. |
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| 83 | list<int> kids_rel_addresses; |
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| 84 | |
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| 85 | /// Default constructor |
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| 86 | polyhedron() |
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| 87 | { |
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| 88 | multiplicity = 1; |
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| 89 | |
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| 90 | message_counter = 0; |
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| 91 | |
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| 92 | totally_neutral = NULL; |
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| 93 | } |
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| 94 | |
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| 95 | /// Setter for raising multiplicity |
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| 96 | void raise_multiplicity() |
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| 97 | { |
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| 98 | multiplicity++; |
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| 99 | } |
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| 100 | |
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| 101 | /// Setter for lowering multiplicity |
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| 102 | void lower_multiplicity() |
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| 103 | { |
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| 104 | multiplicity--; |
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| 105 | } |
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| 106 | |
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| 107 | /// An obligatory operator, when the class is used within a C++ STL structure like a vector |
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| 108 | int operator==(polyhedron polyhedron2) |
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| 109 | { |
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| 110 | return true; |
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| 111 | } |
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| 112 | |
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| 113 | /// An obligatory operator, when the class is used within a C++ STL structure like a vector |
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| 114 | int operator<(polyhedron polyhedron2) |
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| 115 | { |
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| 116 | return false; |
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| 117 | } |
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| 118 | |
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| 119 | |
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| 120 | |
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| 121 | void set_state(double state_indicator, actions action) |
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| 122 | { |
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| 123 | switch(action) |
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| 124 | { |
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| 125 | case MERGE: |
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| 126 | merge_state = (int)sign(state_indicator); |
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| 127 | break; |
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| 128 | case SPLIT: |
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| 129 | split_state = (int)sign(state_indicator); |
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| 130 | break; |
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| 131 | } |
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| 132 | } |
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| 133 | |
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| 134 | int get_state(actions action) |
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| 135 | { |
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| 136 | switch(action) |
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| 137 | { |
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| 138 | case MERGE: |
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| 139 | return merge_state; |
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| 140 | break; |
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| 141 | case SPLIT: |
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| 142 | return split_state; |
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| 143 | break; |
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| 144 | } |
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| 145 | } |
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| 146 | |
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| 147 | int number_of_children() |
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| 148 | { |
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| 149 | return children.size(); |
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| 150 | } |
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| 151 | |
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| 152 | |
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| 153 | }; |
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| 154 | |
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| 155 | /// A class for representing 0-dimensional polyhedron - a vertex. It will be located in the bottom row of the Hasse |
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| 156 | /// diagram representing a complex of polyhedrons. It has its coordinates in the parameter space. |
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| 157 | class vertex : public polyhedron |
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| 158 | { |
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| 159 | /// A dynamic array representing coordinates of the vertex |
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| 160 | vec coordinates; |
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| 161 | |
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| 162 | |
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| 163 | |
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| 164 | public: |
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| 165 | |
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| 166 | |
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| 167 | |
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| 168 | /// Default constructor |
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| 169 | vertex(); |
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| 170 | |
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| 171 | /// Constructor of a vertex from a set of coordinates |
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| 172 | vertex(vec coordinates) |
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| 173 | { |
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| 174 | this->coordinates = coordinates; |
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| 175 | } |
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| 176 | |
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| 177 | /// A method that widens the set of coordinates of given vertex. It is used when a complex in a parameter |
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| 178 | /// space of certain dimension is established, but the dimension is not known when the vertex is created. |
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| 179 | void push_coordinate(double coordinate) |
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| 180 | { |
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| 181 | coordinates = concat(coordinates,coordinate); |
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| 182 | } |
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| 183 | |
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| 184 | /// A method obtaining the set of coordinates of a vertex. These coordinates are not obtained as a pointer |
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| 185 | /// (not given by reference), but a new copy is created (they are given by value). |
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| 186 | vec get_coordinates() |
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| 187 | { |
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| 188 | return coordinates; |
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| 189 | } |
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| 190 | |
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| 191 | |
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| 192 | }; |
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| 193 | |
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| 194 | /// A class representing a polyhedron in a top row of the complex. Such polyhedron has a condition that differitiates |
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| 195 | /// it from polyhedrons in other rows. |
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| 196 | class toprow : public polyhedron |
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| 197 | { |
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| 198 | |
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| 199 | public: |
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| 200 | /// A condition used for determining the function of a Laplace-Inverse-Gamma density resulting from Bayesian estimation |
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| 201 | vec condition; |
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| 202 | |
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| 203 | /// Default constructor |
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| 204 | toprow(){}; |
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| 205 | |
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| 206 | /// Constructor creating a toprow from the condition |
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| 207 | toprow(vec condition) |
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| 208 | { |
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| 209 | this->condition = condition; |
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| 210 | } |
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| 211 | |
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| 212 | }; |
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| 213 | |
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| 214 | class condition |
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| 215 | { |
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| 216 | public: |
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| 217 | vec value; |
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| 218 | |
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| 219 | int multiplicity; |
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| 220 | |
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| 221 | condition(vec value) |
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| 222 | { |
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| 223 | this->value = value; |
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| 224 | multiplicity = 1; |
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| 225 | } |
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| 226 | }; |
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| 227 | |
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| 228 | class c_statistic |
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| 229 | { |
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| 230 | polyhedron* end_poly; |
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| 231 | polyhedron* start_poly; |
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| 232 | |
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| 233 | public: |
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| 234 | vector<polyhedron*> rows; |
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| 235 | |
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| 236 | vector<polyhedron*> row_ends; |
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| 237 | |
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| 238 | c_statistic() |
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| 239 | { |
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| 240 | end_poly = new polyhedron(); |
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| 241 | start_poly = new polyhedron(); |
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| 242 | }; |
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| 243 | |
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| 244 | void append_polyhedron(int row, polyhedron* appended_start, polyhedron* appended_end) |
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| 245 | { |
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| 246 | if(row>((int)rows.size())-1) |
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| 247 | { |
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| 248 | if(row>rows.size()) |
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| 249 | { |
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| 250 | throw new exception("You are trying to append a polyhedron whose children are not in the statistic yet!"); |
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| 251 | return; |
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| 252 | } |
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| 253 | |
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| 254 | rows.push_back(end_poly); |
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| 255 | row_ends.push_back(end_poly); |
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| 256 | } |
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| 257 | |
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| 258 | // POSSIBLE FAILURE: the function is not checking if start and end are connected |
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| 259 | |
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| 260 | if(rows[row] != end_poly) |
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| 261 | { |
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| 262 | appended_start->prev_poly = row_ends[row]; |
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| 263 | row_ends[row]->next_poly = appended_start; |
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| 264 | |
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| 265 | } |
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| 266 | else if((row>0 && rows[row-1]!=end_poly)||row==0) |
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| 267 | { |
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| 268 | appended_start->prev_poly = start_poly; |
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| 269 | rows[row]= appended_start; |
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| 270 | } |
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| 271 | else |
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| 272 | { |
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| 273 | throw new exception("Wrong polyhedron insertion into statistic: missing intermediary polyhedron!"); |
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| 274 | } |
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| 275 | |
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| 276 | appended_end->next_poly = end_poly; |
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| 277 | row_ends[row] = appended_end; |
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| 278 | } |
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| 279 | |
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| 280 | void append_polyhedron(int row, polyhedron* appended_poly) |
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| 281 | { |
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| 282 | append_polyhedron(row,appended_poly,appended_poly); |
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| 283 | } |
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| 284 | |
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| 285 | void insert_polyhedron(int row, polyhedron* inserted_poly, polyhedron* following_poly) |
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| 286 | { |
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| 287 | if(following_poly != end_poly) |
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| 288 | { |
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| 289 | inserted_poly->next_poly = following_poly; |
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| 290 | inserted_poly->prev_poly = following_poly->prev_poly; |
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| 291 | |
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| 292 | if(following_poly->prev_poly == start_poly) |
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| 293 | { |
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| 294 | rows[row] = inserted_poly; |
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| 295 | } |
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| 296 | else |
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| 297 | { |
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| 298 | inserted_poly->prev_poly->next_poly = inserted_poly; |
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| 299 | } |
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| 300 | |
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| 301 | following_poly->prev_poly = inserted_poly; |
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| 302 | } |
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| 303 | else |
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| 304 | { |
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| 305 | this->append_polyhedron(row, inserted_poly); |
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| 306 | } |
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| 307 | |
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| 308 | } |
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| 309 | |
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| 310 | |
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| 311 | |
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| 312 | |
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| 313 | void delete_polyhedron(int row, polyhedron* deleted_poly) |
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| 314 | { |
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| 315 | if(deleted_poly->prev_poly != start_poly) |
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| 316 | { |
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| 317 | deleted_poly->prev_poly->next_poly = deleted_poly->next_poly; |
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| 318 | } |
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| 319 | else |
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| 320 | { |
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| 321 | rows[row] = deleted_poly->next_poly; |
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| 322 | } |
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| 323 | |
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| 324 | if(deleted_poly->next_poly!=end_poly) |
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| 325 | { |
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| 326 | deleted_poly->next_poly->prev_poly = deleted_poly->prev_poly; |
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| 327 | } |
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| 328 | else |
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| 329 | { |
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| 330 | row_ends[row] = deleted_poly->prev_poly; |
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| 331 | } |
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| 332 | |
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| 333 | |
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| 334 | |
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| 335 | deleted_poly->next_poly = NULL; |
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| 336 | deleted_poly->prev_poly = NULL; |
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| 337 | } |
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| 338 | |
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| 339 | int size() |
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| 340 | { |
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| 341 | return rows.size(); |
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| 342 | } |
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| 343 | |
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| 344 | polyhedron* get_end() |
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| 345 | { |
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| 346 | return end_poly; |
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| 347 | } |
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| 348 | |
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| 349 | polyhedron* get_start() |
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| 350 | { |
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| 351 | return start_poly; |
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| 352 | } |
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| 353 | |
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| 354 | int row_size(int row) |
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| 355 | { |
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| 356 | if(this->size()>row && row>=0) |
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| 357 | { |
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| 358 | int row_size = 0; |
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| 359 | |
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| 360 | for(polyhedron* row_poly = rows[row]; row_poly!=end_poly; row_poly=row_poly->next_poly) |
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| 361 | { |
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| 362 | row_size++; |
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| 363 | } |
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| 364 | |
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| 365 | return row_size; |
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| 366 | } |
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| 367 | else |
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| 368 | { |
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| 369 | throw new exception("There is no row to obtain size from!"); |
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| 370 | } |
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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 | //! Conditional(e) Multicriteria-Laplace-Inverse-Gamma distribution density |
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| 376 | class emlig // : eEF |
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| 377 | { |
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| 378 | |
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| 379 | /// A statistic in a form of a Hasse diagram representing a complex of convex polyhedrons obtained as a result |
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| 380 | /// of data update from Bayesian estimation or set by the user if this emlig is a prior density |
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| 381 | c_statistic statistic; |
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| 382 | |
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| 383 | vector<list<polyhedron*>> for_splitting; |
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| 384 | |
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| 385 | vector<list<polyhedron*>> for_merging; |
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| 386 | |
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| 387 | list<condition*> conditions; |
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| 388 | |
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| 389 | double normalization_factor; |
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| 390 | |
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| 391 | void alter_toprow_conditions(vec condition, bool should_be_added) |
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| 392 | { |
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| 393 | for(polyhedron* horiz_ref = statistic.rows[statistic.size()-1];horiz_ref!=statistic.get_end();horiz_ref=horiz_ref->next_poly) |
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| 394 | { |
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| 395 | double product = 0; |
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| 396 | |
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| 397 | list<vertex*>::iterator vertex_ref = horiz_ref->vertices.begin(); |
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| 398 | |
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| 399 | do |
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| 400 | { |
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| 401 | product = (*vertex_ref)->get_coordinates()*condition; |
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| 402 | } |
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| 403 | while(product == 0); |
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| 404 | |
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| 405 | if((product>0 && should_be_added)||(product<0 && !should_be_added)) |
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| 406 | { |
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| 407 | ((toprow*) horiz_ref)->condition += condition; |
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| 408 | } |
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| 409 | else |
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| 410 | { |
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| 411 | ((toprow*) horiz_ref)->condition -= condition; |
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| 412 | } |
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| 413 | } |
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| 414 | } |
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| 415 | |
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| 416 | |
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| 417 | |
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| 418 | void send_state_message(polyhedron* sender, vec toadd, vec toremove, int level) |
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| 419 | { |
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| 420 | |
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| 421 | bool shouldmerge = (toremove.size() != 0); |
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| 422 | bool shouldsplit = (toadd.size() != 0); |
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| 423 | |
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| 424 | if(shouldsplit||shouldmerge) |
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| 425 | { |
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| 426 | for(list<polyhedron*>::iterator parent_iterator = sender->parents.begin();parent_iterator!=sender->parents.end();parent_iterator++) |
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| 427 | { |
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| 428 | polyhedron* current_parent = *parent_iterator; |
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| 429 | |
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| 430 | current_parent->message_counter++; |
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| 431 | |
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| 432 | bool is_last = (current_parent->message_counter == current_parent->number_of_children()); |
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| 433 | |
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| 434 | if(shouldmerge) |
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| 435 | { |
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| 436 | int child_state = sender->get_state(MERGE); |
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| 437 | int parent_state = current_parent->get_state(MERGE); |
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| 438 | |
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| 439 | if(parent_state == 0) |
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| 440 | { |
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| 441 | current_parent->set_state(child_state, MERGE); |
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| 442 | |
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| 443 | if(child_state == 0) |
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| 444 | { |
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| 445 | current_parent->mergechildren.push_back(sender); |
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| 446 | } |
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| 447 | } |
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| 448 | else |
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| 449 | { |
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| 450 | if(child_state == 0) |
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| 451 | { |
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| 452 | if(parent_state > 0) |
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| 453 | { |
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| 454 | sender->positiveparent = current_parent; |
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| 455 | } |
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| 456 | else |
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| 457 | { |
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| 458 | sender->negativeparent = current_parent; |
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| 459 | } |
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| 460 | } |
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| 461 | } |
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| 462 | |
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| 463 | if(is_last) |
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| 464 | { |
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| 465 | if(parent_state > 0) |
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| 466 | { |
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| 467 | for(list<polyhedron*>::iterator merge_child = current_parent->mergechildren.begin(); merge_child != current_parent->mergechildren.end();merge_child++) |
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| 468 | { |
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| 469 | (*merge_child)->positiveparent = current_parent; |
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| 470 | } |
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| 471 | } |
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| 472 | |
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| 473 | if(parent_state < 0) |
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| 474 | { |
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| 475 | for(list<polyhedron*>::iterator merge_child = current_parent->mergechildren.begin(); merge_child != current_parent->mergechildren.end();merge_child++) |
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| 476 | { |
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| 477 | (*merge_child)->negativeparent = current_parent; |
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| 478 | } |
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| 479 | } |
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| 480 | |
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| 481 | if(parent_state == 0) |
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| 482 | { |
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| 483 | for_merging[level+1].push_back(current_parent); |
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| 484 | } |
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| 485 | |
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| 486 | current_parent->mergechildren.clear(); |
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| 487 | } |
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| 488 | |
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| 489 | |
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| 490 | } |
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| 491 | |
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| 492 | if(shouldsplit) |
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| 493 | { |
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| 494 | current_parent->totallyneutralgrandchildren.insert(current_parent->totallyneutralgrandchildren.end(),sender->totallyneutralchildren.begin(),sender->totallyneutralchildren.end()); |
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| 495 | |
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| 496 | switch(sender->get_state(SPLIT)) |
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| 497 | { |
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| 498 | case 1: |
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| 499 | current_parent->positivechildren.push_back(sender); |
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| 500 | break; |
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| 501 | case 0: |
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| 502 | current_parent->neutralchildren.push_back(sender); |
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| 503 | |
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| 504 | if(current_parent->totally_neutral == NULL) |
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| 505 | { |
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| 506 | current_parent->totally_neutral = sender->totally_neutral; |
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| 507 | } |
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| 508 | else |
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| 509 | { |
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| 510 | current_parent->totally_neutral = current_parent->totally_neutral && sender->totally_neutral; |
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| 511 | } |
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| 512 | |
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| 513 | if(sender->totally_neutral) |
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| 514 | { |
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| 515 | current_parent->totallyneutralchildren.push_back(sender); |
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| 516 | } |
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| 517 | |
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| 518 | break; |
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| 519 | case -1: |
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| 520 | current_parent->negativechildren.push_back(sender); |
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| 521 | break; |
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| 522 | } |
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| 523 | |
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| 524 | if(is_last) |
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| 525 | { |
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| 526 | unique(current_parent->totallyneutralgrandchildren.begin(),current_parent->totallyneutralgrandchildren.end()); |
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| 527 | |
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| 528 | if((current_parent->negativechildren.size()>0&¤t_parent->positivechildren.size()>0)|| |
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| 529 | (current_parent->neutralchildren.size()>0&¤t_parent->totally_neutral==false)) |
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| 530 | { |
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| 531 | |
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| 532 | for_splitting[level+1].push_back(current_parent); |
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| 533 | |
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| 534 | current_parent->set_state(0, SPLIT); |
|---|
| 535 | } |
|---|
| 536 | else |
|---|
| 537 | { |
|---|
| 538 | if(current_parent->negativechildren.size()>0) |
|---|
| 539 | { |
|---|
| 540 | current_parent->set_state(-1, SPLIT); |
|---|
| 541 | |
|---|
| 542 | ((toprow*)current_parent)->condition-=toadd; |
|---|
| 543 | } |
|---|
| 544 | else if(current_parent->positivechildren.size()>0) |
|---|
| 545 | { |
|---|
| 546 | current_parent->set_state(1, SPLIT); |
|---|
| 547 | |
|---|
| 548 | ((toprow*)current_parent)->condition+=toadd; |
|---|
| 549 | } |
|---|
| 550 | else |
|---|
| 551 | { |
|---|
| 552 | current_parent->raise_multiplicity(); |
|---|
| 553 | } |
|---|
| 554 | |
|---|
| 555 | current_parent->positivechildren.clear(); |
|---|
| 556 | current_parent->negativechildren.clear(); |
|---|
| 557 | current_parent->neutralchildren.clear(); |
|---|
| 558 | current_parent->totallyneutralchildren.clear(); |
|---|
| 559 | current_parent->totallyneutralgrandchildren.clear(); |
|---|
| 560 | current_parent->totally_neutral = NULL; |
|---|
| 561 | current_parent->kids_rel_addresses.clear(); |
|---|
| 562 | //current_parent->set_state(0, SPLIT); |
|---|
| 563 | } |
|---|
| 564 | } |
|---|
| 565 | } |
|---|
| 566 | |
|---|
| 567 | if(is_last) |
|---|
| 568 | { |
|---|
| 569 | send_state_message(current_parent,toadd,toremove,level+1); |
|---|
| 570 | } |
|---|
| 571 | |
|---|
| 572 | } |
|---|
| 573 | |
|---|
| 574 | } |
|---|
| 575 | } |
|---|
| 576 | |
|---|
| 577 | public: |
|---|
| 578 | |
|---|
| 579 | int number_of_parameters; |
|---|
| 580 | |
|---|
| 581 | /// A default constructor creates an emlig with predefined statistic representing only the range of the given |
|---|
| 582 | /// parametric space, where the number of parameters of the needed model is given as a parameter to the constructor. |
|---|
| 583 | emlig(int number_of_parameters) |
|---|
| 584 | { |
|---|
| 585 | this->number_of_parameters = number_of_parameters; |
|---|
| 586 | |
|---|
| 587 | create_statistic(number_of_parameters); |
|---|
| 588 | } |
|---|
| 589 | |
|---|
| 590 | /// A constructor for creating an emlig when the user wants to create the statistic by himself. The creation of a |
|---|
| 591 | /// statistic is needed outside the constructor. Used for a user defined prior distribution on the parameters. |
|---|
| 592 | emlig(c_statistic statistic) |
|---|
| 593 | { |
|---|
| 594 | this->statistic = statistic; |
|---|
| 595 | } |
|---|
| 596 | |
|---|
| 597 | void step_me() |
|---|
| 598 | { |
|---|
| 599 | for(int i = 0;i<statistic.size();i++) |
|---|
| 600 | { |
|---|
| 601 | for(polyhedron* horiz_ref = statistic.rows[i];horiz_ref!=statistic.get_end();horiz_ref=horiz_ref->next_poly) |
|---|
| 602 | { |
|---|
| 603 | char* string = "Checkpoint"; |
|---|
| 604 | } |
|---|
| 605 | } |
|---|
| 606 | } |
|---|
| 607 | |
|---|
| 608 | int statistic_rowsize(int row) |
|---|
| 609 | { |
|---|
| 610 | return statistic.row_size(row); |
|---|
| 611 | } |
|---|
| 612 | |
|---|
| 613 | void add_condition(vec toadd) |
|---|
| 614 | { |
|---|
| 615 | vec null_vector = ""; |
|---|
| 616 | |
|---|
| 617 | add_and_remove_condition(toadd, null_vector); |
|---|
| 618 | } |
|---|
| 619 | |
|---|
| 620 | |
|---|
| 621 | void remove_condition(vec toremove) |
|---|
| 622 | { |
|---|
| 623 | vec null_vector = ""; |
|---|
| 624 | |
|---|
| 625 | add_and_remove_condition(null_vector, toremove); |
|---|
| 626 | |
|---|
| 627 | } |
|---|
| 628 | |
|---|
| 629 | |
|---|
| 630 | void add_and_remove_condition(vec toadd, vec toremove) |
|---|
| 631 | { |
|---|
| 632 | bool should_remove = (toremove.size() != 0); |
|---|
| 633 | bool should_add = (toadd.size() != 0); |
|---|
| 634 | |
|---|
| 635 | for_splitting.clear(); |
|---|
| 636 | for_merging.clear(); |
|---|
| 637 | |
|---|
| 638 | for(int i = 0;i<statistic.size();i++) |
|---|
| 639 | { |
|---|
| 640 | list<polyhedron*> empty_split; |
|---|
| 641 | list<polyhedron*> empty_merge; |
|---|
| 642 | |
|---|
| 643 | for_splitting.push_back(empty_split); |
|---|
| 644 | for_merging.push_back(empty_merge); |
|---|
| 645 | } |
|---|
| 646 | |
|---|
| 647 | list<condition*>::iterator toremove_ref = conditions.end(); |
|---|
| 648 | bool condition_should_be_added = false; |
|---|
| 649 | |
|---|
| 650 | for(list<condition*>::iterator ref = conditions.begin();ref!=conditions.end();ref++) |
|---|
| 651 | { |
|---|
| 652 | if(should_remove) |
|---|
| 653 | { |
|---|
| 654 | if((*ref)->value == toremove) |
|---|
| 655 | { |
|---|
| 656 | if((*ref)->multiplicity>1) |
|---|
| 657 | { |
|---|
| 658 | (*ref)->multiplicity--; |
|---|
| 659 | |
|---|
| 660 | alter_toprow_conditions(toremove,false); |
|---|
| 661 | |
|---|
| 662 | should_remove = false; |
|---|
| 663 | } |
|---|
| 664 | else |
|---|
| 665 | { |
|---|
| 666 | toremove_ref = ref; |
|---|
| 667 | } |
|---|
| 668 | } |
|---|
| 669 | } |
|---|
| 670 | |
|---|
| 671 | if(should_add) |
|---|
| 672 | { |
|---|
| 673 | if((*ref)->value == toadd) |
|---|
| 674 | { |
|---|
| 675 | (*ref)->multiplicity++; |
|---|
| 676 | |
|---|
| 677 | alter_toprow_conditions(toadd,true); |
|---|
| 678 | |
|---|
| 679 | should_add = false; |
|---|
| 680 | } |
|---|
| 681 | else |
|---|
| 682 | { |
|---|
| 683 | condition_should_be_added = true; |
|---|
| 684 | } |
|---|
| 685 | } |
|---|
| 686 | } |
|---|
| 687 | |
|---|
| 688 | if(toremove_ref!=conditions.end()) |
|---|
| 689 | { |
|---|
| 690 | conditions.erase(toremove_ref); |
|---|
| 691 | } |
|---|
| 692 | |
|---|
| 693 | if(condition_should_be_added) |
|---|
| 694 | { |
|---|
| 695 | conditions.push_back(new condition(toadd)); |
|---|
| 696 | } |
|---|
| 697 | |
|---|
| 698 | |
|---|
| 699 | |
|---|
| 700 | for(polyhedron* horizontal_position = statistic.rows[0];horizontal_position!=statistic.get_end();horizontal_position=horizontal_position->next_poly) |
|---|
| 701 | { |
|---|
| 702 | vertex* current_vertex = (vertex*)horizontal_position; |
|---|
| 703 | |
|---|
| 704 | if(should_add||should_remove) |
|---|
| 705 | { |
|---|
| 706 | vec appended_vec = current_vertex->get_coordinates(); |
|---|
| 707 | appended_vec.ins(0,-1.0); |
|---|
| 708 | |
|---|
| 709 | if(should_add) |
|---|
| 710 | { |
|---|
| 711 | double local_condition = toadd*appended_vec; |
|---|
| 712 | |
|---|
| 713 | current_vertex->set_state(local_condition,SPLIT); |
|---|
| 714 | |
|---|
| 715 | if(local_condition == 0) |
|---|
| 716 | { |
|---|
| 717 | current_vertex->totally_neutral = true; |
|---|
| 718 | |
|---|
| 719 | current_vertex->raise_multiplicity(); |
|---|
| 720 | } |
|---|
| 721 | } |
|---|
| 722 | |
|---|
| 723 | if(should_remove) |
|---|
| 724 | { |
|---|
| 725 | double local_condition = toremove*appended_vec; |
|---|
| 726 | |
|---|
| 727 | current_vertex->set_state(local_condition,MERGE); |
|---|
| 728 | |
|---|
| 729 | if(local_condition == 0) |
|---|
| 730 | { |
|---|
| 731 | for_merging[0].push_back(current_vertex); |
|---|
| 732 | } |
|---|
| 733 | } |
|---|
| 734 | } |
|---|
| 735 | |
|---|
| 736 | send_state_message(current_vertex, toadd, toremove, 0); |
|---|
| 737 | |
|---|
| 738 | } |
|---|
| 739 | |
|---|
| 740 | if(should_add) |
|---|
| 741 | { |
|---|
| 742 | int k = 1; |
|---|
| 743 | |
|---|
| 744 | vector<list<polyhedron*>>::iterator beginning_ref = ++for_splitting.begin(); |
|---|
| 745 | |
|---|
| 746 | for(vector<list<polyhedron*>>::iterator vert_ref = beginning_ref;vert_ref<for_splitting.end();vert_ref++) |
|---|
| 747 | { |
|---|
| 748 | |
|---|
| 749 | for(list<polyhedron*>::reverse_iterator split_ref = vert_ref->rbegin();split_ref != vert_ref->rend();split_ref++) |
|---|
| 750 | { |
|---|
| 751 | polyhedron* new_totally_neutral_child; |
|---|
| 752 | |
|---|
| 753 | polyhedron* current_polyhedron = (*split_ref); |
|---|
| 754 | |
|---|
| 755 | if(vert_ref == beginning_ref) |
|---|
| 756 | { |
|---|
| 757 | vec coordinates1 = ((vertex*)(*(current_polyhedron->children.begin())))->get_coordinates(); |
|---|
| 758 | vec coordinates2 = ((vertex*)(*(current_polyhedron->children.begin()++)))->get_coordinates(); |
|---|
| 759 | coordinates2.ins(0,-1.0); |
|---|
| 760 | |
|---|
| 761 | double t = (-toadd*coordinates2)/(toadd(1,toadd.size()-1)*coordinates1)+1; |
|---|
| 762 | |
|---|
| 763 | vec new_coordinates = coordinates1*t+(coordinates2(1,coordinates2.size()-1)-coordinates1); |
|---|
| 764 | |
|---|
| 765 | vertex* neutral_vertex = new vertex(new_coordinates); |
|---|
| 766 | |
|---|
| 767 | new_totally_neutral_child = neutral_vertex; |
|---|
| 768 | } |
|---|
| 769 | else |
|---|
| 770 | { |
|---|
| 771 | toprow* neutral_toprow = new toprow(); |
|---|
| 772 | |
|---|
| 773 | new_totally_neutral_child = neutral_toprow; |
|---|
| 774 | } |
|---|
| 775 | |
|---|
| 776 | new_totally_neutral_child->children.insert(new_totally_neutral_child->children.end(), |
|---|
| 777 | current_polyhedron->totallyneutralgrandchildren.begin(), |
|---|
| 778 | current_polyhedron->totallyneutralgrandchildren.end()); |
|---|
| 779 | |
|---|
| 780 | toprow* positive_poly = new toprow(((toprow*)current_polyhedron)->condition+toadd); |
|---|
| 781 | toprow* negative_poly = new toprow(((toprow*)current_polyhedron)->condition-toadd); |
|---|
| 782 | |
|---|
| 783 | for(list<polyhedron*>::iterator parent_ref = current_polyhedron->parents.begin();parent_ref!=current_polyhedron->parents.end();parent_ref++) |
|---|
| 784 | { |
|---|
| 785 | (*parent_ref)->totallyneutralgrandchildren.push_back(new_totally_neutral_child); |
|---|
| 786 | |
|---|
| 787 | (*parent_ref)->neutralchildren.remove(current_polyhedron); |
|---|
| 788 | (*parent_ref)->positivechildren.push_back(positive_poly); |
|---|
| 789 | (*parent_ref)->negativechildren.push_back(negative_poly); |
|---|
| 790 | } |
|---|
| 791 | |
|---|
| 792 | positive_poly->parents.insert(positive_poly->parents.end(), |
|---|
| 793 | current_polyhedron->parents.begin(), |
|---|
| 794 | current_polyhedron->parents.end()); |
|---|
| 795 | |
|---|
| 796 | negative_poly->parents.insert(negative_poly->parents.end(), |
|---|
| 797 | current_polyhedron->parents.begin(), |
|---|
| 798 | current_polyhedron->parents.end()); |
|---|
| 799 | |
|---|
| 800 | positive_poly->children.push_back(new_totally_neutral_child); |
|---|
| 801 | negative_poly->children.push_back(new_totally_neutral_child); |
|---|
| 802 | |
|---|
| 803 | new_totally_neutral_child->parents.push_back(positive_poly); |
|---|
| 804 | new_totally_neutral_child->parents.push_back(negative_poly); |
|---|
| 805 | |
|---|
| 806 | for(list<polyhedron*>::iterator child_ref = current_polyhedron->positivechildren.begin();child_ref!=current_polyhedron->positivechildren.end();child_ref++) |
|---|
| 807 | { |
|---|
| 808 | (*child_ref)->parents.remove(current_polyhedron); |
|---|
| 809 | (*child_ref)->parents.push_back(positive_poly); |
|---|
| 810 | } |
|---|
| 811 | |
|---|
| 812 | positive_poly->children.insert(positive_poly->children.end(), |
|---|
| 813 | current_polyhedron->positivechildren.begin(), |
|---|
| 814 | current_polyhedron->positivechildren.end()); |
|---|
| 815 | |
|---|
| 816 | for(list<polyhedron*>::iterator child_ref = current_polyhedron->negativechildren.begin();child_ref!=current_polyhedron->negativechildren.end();child_ref++) |
|---|
| 817 | { |
|---|
| 818 | (*child_ref)->parents.remove(current_polyhedron); |
|---|
| 819 | (*child_ref)->parents.push_back(negative_poly); |
|---|
| 820 | } |
|---|
| 821 | |
|---|
| 822 | negative_poly->children.insert(negative_poly->children.end(), |
|---|
| 823 | current_polyhedron->negativechildren.begin(), |
|---|
| 824 | current_polyhedron->negativechildren.end()); |
|---|
| 825 | |
|---|
| 826 | statistic.append_polyhedron(k-1, new_totally_neutral_child); |
|---|
| 827 | |
|---|
| 828 | statistic.insert_polyhedron(k, positive_poly, current_polyhedron); |
|---|
| 829 | statistic.insert_polyhedron(k, negative_poly, current_polyhedron); |
|---|
| 830 | |
|---|
| 831 | statistic.delete_polyhedron(k, current_polyhedron); |
|---|
| 832 | |
|---|
| 833 | delete current_polyhedron; |
|---|
| 834 | } |
|---|
| 835 | |
|---|
| 836 | k++; |
|---|
| 837 | } |
|---|
| 838 | } |
|---|
| 839 | |
|---|
| 840 | /* |
|---|
| 841 | vector<int> sizevector; |
|---|
| 842 | for(int s = 0;s<statistic.size();s++) |
|---|
| 843 | { |
|---|
| 844 | sizevector.push_back(statistic.row_size(s)); |
|---|
| 845 | }*/ |
|---|
| 846 | } |
|---|
| 847 | |
|---|
| 848 | protected: |
|---|
| 849 | |
|---|
| 850 | /// A method for creating plain default statistic representing only the range of the parameter space. |
|---|
| 851 | void create_statistic(int number_of_parameters) |
|---|
| 852 | { |
|---|
| 853 | // An empty vector of coordinates. |
|---|
| 854 | vec origin_coord; |
|---|
| 855 | |
|---|
| 856 | // We create an origin - this point will have all the coordinates zero, but now it has an empty vector of coords. |
|---|
| 857 | vertex *origin = new vertex(origin_coord); |
|---|
| 858 | |
|---|
| 859 | // It has itself as a vertex. There will be a nice use for this when the vertices of its parents are searched in |
|---|
| 860 | // the recursive creation procedure below. |
|---|
| 861 | origin->vertices.push_back(origin); |
|---|
| 862 | |
|---|
| 863 | /* |
|---|
| 864 | // As a statistic, we have to create a vector of vectors of polyhedron pointers. It will then represent the Hasse |
|---|
| 865 | // diagram. First we create a vector of polyhedrons.. |
|---|
| 866 | list<polyhedron*> origin_vec; |
|---|
| 867 | |
|---|
| 868 | // ..we fill it with the origin.. |
|---|
| 869 | origin_vec.push_back(origin); |
|---|
| 870 | |
|---|
| 871 | // ..and we fill the statistic with the created vector. |
|---|
| 872 | statistic.push_back(origin_vec); |
|---|
| 873 | */ |
|---|
| 874 | |
|---|
| 875 | statistic = *(new c_statistic()); |
|---|
| 876 | |
|---|
| 877 | statistic.append_polyhedron(0, origin); |
|---|
| 878 | |
|---|
| 879 | // Now we have a statistic for a zero dimensional space. Regarding to how many dimensional space we need to |
|---|
| 880 | // describe, we have to widen the descriptional default statistic. We use an iterative procedure as follows: |
|---|
| 881 | for(int i=0;i<number_of_parameters;i++) |
|---|
| 882 | { |
|---|
| 883 | // We first will create two new vertices. These will be the borders of the parameter space in the dimension |
|---|
| 884 | // of newly added parameter. Therefore they will have all coordinates except the last one zero. We get the |
|---|
| 885 | // right amount of zero cooridnates by reading them from the origin |
|---|
| 886 | vec origin_coord = origin->get_coordinates(); |
|---|
| 887 | |
|---|
| 888 | // And we incorporate the nonzero coordinates into the new cooordinate vectors |
|---|
| 889 | vec origin_coord1 = concat(origin_coord,-max_range); |
|---|
| 890 | vec origin_coord2 = concat(origin_coord,max_range); |
|---|
| 891 | |
|---|
| 892 | |
|---|
| 893 | // Now we create the points |
|---|
| 894 | vertex *new_point1 = new vertex(origin_coord1); |
|---|
| 895 | vertex *new_point2 = new vertex(origin_coord2); |
|---|
| 896 | |
|---|
| 897 | new_point1->vertices.push_back(new_point1); |
|---|
| 898 | new_point2->vertices.push_back(new_point2); |
|---|
| 899 | |
|---|
| 900 | //********************************************************************************************************* |
|---|
| 901 | // The algorithm for recursive build of a new Hasse diagram representing the space structure from the old |
|---|
| 902 | // diagram works so that you create two copies of the old Hasse diagram, you shift them up one level (points |
|---|
| 903 | // will be segments, segments will be areas etc.) and you connect each one of the original copied polyhedrons |
|---|
| 904 | // with its offspring by a parent-child relation. Also each of the segments in the first (second) copy is |
|---|
| 905 | // connected to the first (second) newly created vertex by a parent-child relation. |
|---|
| 906 | //********************************************************************************************************* |
|---|
| 907 | |
|---|
| 908 | |
|---|
| 909 | /* |
|---|
| 910 | // Create the vectors of vectors of pointers to polyhedrons to hold the copies of the old Hasse diagram |
|---|
| 911 | vector<vector<polyhedron*>> new_statistic1; |
|---|
| 912 | vector<vector<polyhedron*>> new_statistic2; |
|---|
| 913 | */ |
|---|
| 914 | |
|---|
| 915 | c_statistic* new_statistic1 = new c_statistic(); |
|---|
| 916 | c_statistic* new_statistic2 = new c_statistic(); |
|---|
| 917 | |
|---|
| 918 | |
|---|
| 919 | // Copy the statistic by rows |
|---|
| 920 | for(int j=0;j<statistic.size();j++) |
|---|
| 921 | { |
|---|
| 922 | |
|---|
| 923 | |
|---|
| 924 | // an element counter |
|---|
| 925 | int element_number = 0; |
|---|
| 926 | |
|---|
| 927 | /* |
|---|
| 928 | vector<polyhedron*> supportnew_1; |
|---|
| 929 | vector<polyhedron*> supportnew_2; |
|---|
| 930 | |
|---|
| 931 | new_statistic1.push_back(supportnew_1); |
|---|
| 932 | new_statistic2.push_back(supportnew_2); |
|---|
| 933 | */ |
|---|
| 934 | |
|---|
| 935 | // for each polyhedron in the given row |
|---|
| 936 | for(polyhedron* horiz_ref = statistic.rows[j];horiz_ref!=statistic.get_end();horiz_ref=horiz_ref->next_poly) |
|---|
| 937 | { |
|---|
| 938 | // Append an extra zero coordinate to each of the vertices for the new dimension |
|---|
| 939 | // If vert_ref is at the first index => we loop through vertices |
|---|
| 940 | if(j == 0) |
|---|
| 941 | { |
|---|
| 942 | // cast the polyhedron pointer to a vertex pointer and push a zero to its vector of coordinates |
|---|
| 943 | ((vertex*) horiz_ref)->push_coordinate(0); |
|---|
| 944 | } |
|---|
| 945 | /* |
|---|
| 946 | else |
|---|
| 947 | { |
|---|
| 948 | ((toprow*) (*horiz_ref))->condition.ins(0,0); |
|---|
| 949 | }*/ |
|---|
| 950 | |
|---|
| 951 | // if it has parents |
|---|
| 952 | if(!horiz_ref->parents.empty()) |
|---|
| 953 | { |
|---|
| 954 | // save the relative address of this child in a vector kids_rel_addresses of all its parents. |
|---|
| 955 | // This information will later be used for copying the whole Hasse diagram with each of the |
|---|
| 956 | // relations contained within. |
|---|
| 957 | for(list<polyhedron*>::iterator parent_ref = horiz_ref->parents.begin();parent_ref != horiz_ref->parents.end();parent_ref++) |
|---|
| 958 | { |
|---|
| 959 | (*parent_ref)->kids_rel_addresses.push_back(element_number); |
|---|
| 960 | } |
|---|
| 961 | } |
|---|
| 962 | |
|---|
| 963 | // ************************************************************************************************** |
|---|
| 964 | // Here we begin creating a new polyhedron, which will be a copy of the old one. Each such polyhedron |
|---|
| 965 | // will be created as a toprow, but this information will be later forgotten and only the polyhedrons |
|---|
| 966 | // in the top row of the Hasse diagram will be considered toprow for later use. |
|---|
| 967 | // ************************************************************************************************** |
|---|
| 968 | |
|---|
| 969 | // First we create vectors specifying a toprow condition. In the case of a preconstructed statistic |
|---|
| 970 | // this condition will be a vector of zeros. There are two vectors, because we need two copies of |
|---|
| 971 | // the original Hasse diagram. |
|---|
| 972 | vec vec1(number_of_parameters+1); |
|---|
| 973 | vec1.zeros(); |
|---|
| 974 | |
|---|
| 975 | vec vec2(number_of_parameters+1); |
|---|
| 976 | vec2.zeros(); |
|---|
| 977 | |
|---|
| 978 | // We create a new toprow with the previously specified condition. |
|---|
| 979 | toprow* current_copy1 = new toprow(vec1); |
|---|
| 980 | toprow* current_copy2 = new toprow(vec2); |
|---|
| 981 | |
|---|
| 982 | // The vertices of the copies will be inherited, because there will be a parent/child relation |
|---|
| 983 | // between each polyhedron and its offspring (comming from the copy) and a parent has all the |
|---|
| 984 | // vertices of its child plus more. |
|---|
| 985 | for(list<vertex*>::iterator vertex_ref = horiz_ref->vertices.begin();vertex_ref!=horiz_ref->vertices.end();vertex_ref++) |
|---|
| 986 | { |
|---|
| 987 | current_copy1->vertices.push_back(*vertex_ref); |
|---|
| 988 | current_copy2->vertices.push_back(*vertex_ref); |
|---|
| 989 | } |
|---|
| 990 | |
|---|
| 991 | // The only new vertex of the offspring should be the newly created point. |
|---|
| 992 | current_copy1->vertices.push_back(new_point1); |
|---|
| 993 | current_copy2->vertices.push_back(new_point2); |
|---|
| 994 | |
|---|
| 995 | // This method guarantees that each polyhedron is already triangulated, therefore its triangulation |
|---|
| 996 | // is only one set of vertices and it is the set of all its vertices. |
|---|
| 997 | current_copy1->triangulations.push_back(current_copy1->vertices); |
|---|
| 998 | current_copy2->triangulations.push_back(current_copy2->vertices); |
|---|
| 999 | |
|---|
| 1000 | // Now we have copied the polyhedron and we have to copy all of its relations. Because we are copying |
|---|
| 1001 | // in the Hasse diagram from bottom up, we always have to copy the parent/child relations to all the |
|---|
| 1002 | // kids and when we do that and know the child, in the child we will remember the parent we came from. |
|---|
| 1003 | // This way all the parents/children relations are saved in both the parent and the child. |
|---|
| 1004 | if(!horiz_ref->kids_rel_addresses.empty()) |
|---|
| 1005 | { |
|---|
| 1006 | for(list<int>::iterator kid_ref = horiz_ref->kids_rel_addresses.begin();kid_ref!=horiz_ref->kids_rel_addresses.end();kid_ref++) |
|---|
| 1007 | { |
|---|
| 1008 | polyhedron* new_kid1 = new_statistic1->rows[j-1]; |
|---|
| 1009 | polyhedron* new_kid2 = new_statistic2->rows[j-1]; |
|---|
| 1010 | |
|---|
| 1011 | // THIS IS NOT EFFECTIVE: It could be improved by having the list indexed for new_statistic, but |
|---|
| 1012 | // not indexed for statistic. Hopefully this will not cause a big slowdown - happens only offline. |
|---|
| 1013 | if(*kid_ref) |
|---|
| 1014 | { |
|---|
| 1015 | for(int k = 1;k<=(*kid_ref);k++) |
|---|
| 1016 | { |
|---|
| 1017 | new_kid1=new_kid1->next_poly; |
|---|
| 1018 | new_kid2=new_kid2->next_poly; |
|---|
| 1019 | } |
|---|
| 1020 | } |
|---|
| 1021 | |
|---|
| 1022 | // find the child and save the relation to the parent |
|---|
| 1023 | current_copy1->children.push_back(new_kid1); |
|---|
| 1024 | current_copy2->children.push_back(new_kid2); |
|---|
| 1025 | |
|---|
| 1026 | // in the child save the parents' address |
|---|
| 1027 | new_kid1->parents.push_back(current_copy1); |
|---|
| 1028 | new_kid2->parents.push_back(current_copy2); |
|---|
| 1029 | } |
|---|
| 1030 | |
|---|
| 1031 | // Here we clear the parents kids_rel_addresses vector for later use (when we need to widen the |
|---|
| 1032 | // Hasse diagram again) |
|---|
| 1033 | horiz_ref->kids_rel_addresses.clear(); |
|---|
| 1034 | } |
|---|
| 1035 | // If there were no children previously, we are copying a polyhedron that has been a vertex before. |
|---|
| 1036 | // In this case it is a segment now and it will have a relation to its mother (copywise) and to the |
|---|
| 1037 | // newly created point. Here we create the connection to the new point, again from both sides. |
|---|
| 1038 | else |
|---|
| 1039 | { |
|---|
| 1040 | // Add the address of the new point in the former vertex |
|---|
| 1041 | current_copy1->children.push_back(new_point1); |
|---|
| 1042 | current_copy2->children.push_back(new_point2); |
|---|
| 1043 | |
|---|
| 1044 | // Add the address of the former vertex in the new point |
|---|
| 1045 | new_point1->parents.push_back(current_copy1); |
|---|
| 1046 | new_point2->parents.push_back(current_copy2); |
|---|
| 1047 | } |
|---|
| 1048 | |
|---|
| 1049 | // Save the mother in its offspring |
|---|
| 1050 | current_copy1->children.push_back(horiz_ref); |
|---|
| 1051 | current_copy2->children.push_back(horiz_ref); |
|---|
| 1052 | |
|---|
| 1053 | // Save the offspring in its mother |
|---|
| 1054 | horiz_ref->parents.push_back(current_copy1); |
|---|
| 1055 | horiz_ref->parents.push_back(current_copy2); |
|---|
| 1056 | |
|---|
| 1057 | |
|---|
| 1058 | // Add the copies into the relevant statistic. The statistic will later be appended to the previous |
|---|
| 1059 | // Hasse diagram |
|---|
| 1060 | new_statistic1->append_polyhedron(j,current_copy1); |
|---|
| 1061 | new_statistic2->append_polyhedron(j,current_copy2); |
|---|
| 1062 | |
|---|
| 1063 | // Raise the count in the vector of polyhedrons |
|---|
| 1064 | element_number++; |
|---|
| 1065 | |
|---|
| 1066 | } |
|---|
| 1067 | |
|---|
| 1068 | } |
|---|
| 1069 | |
|---|
| 1070 | /* |
|---|
| 1071 | statistic.begin()->push_back(new_point1); |
|---|
| 1072 | statistic.begin()->push_back(new_point2); |
|---|
| 1073 | */ |
|---|
| 1074 | |
|---|
| 1075 | statistic.append_polyhedron(0, new_point1); |
|---|
| 1076 | statistic.append_polyhedron(0, new_point2); |
|---|
| 1077 | |
|---|
| 1078 | // Merge the new statistics into the old one. This will either be the final statistic or we will |
|---|
| 1079 | // reenter the widening loop. |
|---|
| 1080 | for(int j=0;j<new_statistic1->size();j++) |
|---|
| 1081 | { |
|---|
| 1082 | /* |
|---|
| 1083 | if(j+1==statistic.size()) |
|---|
| 1084 | { |
|---|
| 1085 | list<polyhedron*> support; |
|---|
| 1086 | statistic.push_back(support); |
|---|
| 1087 | } |
|---|
| 1088 | |
|---|
| 1089 | (statistic.begin()+j+1)->insert((statistic.begin()+j+1)->end(),new_statistic1[j].begin(),new_statistic1[j].end()); |
|---|
| 1090 | (statistic.begin()+j+1)->insert((statistic.begin()+j+1)->end(),new_statistic2[j].begin(),new_statistic2[j].end()); |
|---|
| 1091 | */ |
|---|
| 1092 | statistic.append_polyhedron(j+1,new_statistic1->rows[j],new_statistic1->row_ends[j]); |
|---|
| 1093 | statistic.append_polyhedron(j+1,new_statistic2->rows[j],new_statistic2->row_ends[j]); |
|---|
| 1094 | } |
|---|
| 1095 | |
|---|
| 1096 | |
|---|
| 1097 | } |
|---|
| 1098 | |
|---|
| 1099 | /* |
|---|
| 1100 | vector<list<toprow*>> toprow_statistic; |
|---|
| 1101 | int line_count = 0; |
|---|
| 1102 | |
|---|
| 1103 | for(vector<list<polyhedron*>>::iterator polyhedron_ref = ++statistic.begin(); polyhedron_ref!=statistic.end();polyhedron_ref++) |
|---|
| 1104 | { |
|---|
| 1105 | list<toprow*> support_list; |
|---|
| 1106 | toprow_statistic.push_back(support_list); |
|---|
| 1107 | |
|---|
| 1108 | for(list<polyhedron*>::iterator polyhedron_ref2 = polyhedron_ref->begin(); polyhedron_ref2 != polyhedron_ref->end(); polyhedron_ref2++) |
|---|
| 1109 | { |
|---|
| 1110 | toprow* support_top = (toprow*)(*polyhedron_ref2); |
|---|
| 1111 | |
|---|
| 1112 | toprow_statistic[line_count].push_back(support_top); |
|---|
| 1113 | } |
|---|
| 1114 | |
|---|
| 1115 | line_count++; |
|---|
| 1116 | }*/ |
|---|
| 1117 | |
|---|
| 1118 | /* |
|---|
| 1119 | vector<int> sizevector; |
|---|
| 1120 | for(int s = 0;s<statistic.size();s++) |
|---|
| 1121 | { |
|---|
| 1122 | sizevector.push_back(statistic.row_size(s)); |
|---|
| 1123 | } |
|---|
| 1124 | */ |
|---|
| 1125 | |
|---|
| 1126 | } |
|---|
| 1127 | |
|---|
| 1128 | |
|---|
| 1129 | |
|---|
| 1130 | |
|---|
| 1131 | }; |
|---|
| 1132 | |
|---|
| 1133 | /* |
|---|
| 1134 | |
|---|
| 1135 | //! Robust Bayesian AR model for Multicriteria-Laplace-Inverse-Gamma density |
|---|
| 1136 | class RARX : public BM |
|---|
| 1137 | { |
|---|
| 1138 | private: |
|---|
| 1139 | |
|---|
| 1140 | emlig posterior; |
|---|
| 1141 | |
|---|
| 1142 | public: |
|---|
| 1143 | RARX():BM() |
|---|
| 1144 | { |
|---|
| 1145 | }; |
|---|
| 1146 | |
|---|
| 1147 | void bayes(const itpp::vec &yt, const itpp::vec &cond = empty_vec) |
|---|
| 1148 | { |
|---|
| 1149 | |
|---|
| 1150 | } |
|---|
| 1151 | |
|---|
| 1152 | };*/ |
|---|
| 1153 | |
|---|
| 1154 | |
|---|
| 1155 | |
|---|
| 1156 | #endif //TRAGE_H |
|---|