[976] | 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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[1171] | 11 | #include <limits> |
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[976] | 12 | |
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| 13 | using namespace bdm; |
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| 14 | using namespace std; |
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| 15 | |
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| 16 | #include <vector> |
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[1171] | 17 | |
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| 18 | public const max_range = numeric_limits<double>::max( )/10e-5.0; |
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| 19 | |
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| 20 | class polyhedron; |
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| 21 | class vertex; |
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| 22 | |
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| 23 | class polyhedron |
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[976] | 24 | { |
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[1171] | 25 | vector<&polyhedron> parents; |
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[976] | 26 | |
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[1171] | 27 | vector<&polyhedron> children; |
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| 28 | |
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| 29 | vector<&vertex> vertices; |
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| 30 | |
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| 31 | vector<&polyhedron> positivechildren; |
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| 32 | |
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| 33 | vector<&polyhedron> negativechildren; |
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| 34 | |
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| 35 | vector<&polyhedron> neutralchildren; |
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| 36 | |
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| 37 | vector<vector<&vertex>> triangulations; |
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| 38 | |
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| 39 | int multiplicity = 1; |
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| 40 | |
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[976] | 41 | public: |
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| 42 | |
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[1171] | 43 | vector<int> kids_rel_addresses; |
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| 44 | |
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| 45 | polyhedron() |
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| 46 | { |
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| 47 | parents = new vector<&polyhedron>(); |
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| 48 | children = new vector<&polyhedron>(); |
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| 49 | vertices = new vector<&vertex>(); |
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| 50 | |
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| 51 | positivechildren = new vector(&polyhedron)(); |
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| 52 | negativechildren = new vector(&polyhedron)(); |
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| 53 | neutralchildren = new vector(&polyhedron)(); |
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| 54 | |
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| 55 | triangulations = new vector<vector<&vertex>>(); |
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| 56 | |
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| 57 | kids_rel_addresses = new vector<int>(); |
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| 58 | |
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| 59 | } |
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| 60 | |
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| 61 | |
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| 62 | void RaiseMultiplicity() |
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| 63 | { |
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| 64 | multiplicity++; |
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| 65 | } |
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| 66 | |
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| 67 | void LowerMultiplicity() |
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| 68 | { |
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| 69 | multiplicity--; |
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| 70 | } |
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| 71 | |
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| 72 | |
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[976] | 73 | }; |
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| 74 | |
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[1171] | 75 | class vertex : polyhedron |
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[976] | 76 | { |
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[1171] | 77 | vector<double> coordinates; |
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[976] | 78 | |
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| 79 | public: |
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[1171] | 80 | |
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| 81 | vertex(); |
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| 82 | |
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| 83 | vertex(vector<double> coordinates) |
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| 84 | { |
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| 85 | this.coordinates = coordinates; |
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| 86 | } |
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| 87 | |
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| 88 | void push_coordinate(double coordinate) |
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| 89 | { |
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| 90 | coordinates.push_back(coordinate); |
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| 91 | } |
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| 92 | |
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| 93 | vector<double> get_coordinates() |
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| 94 | { |
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| 95 | return this.coordinates(); |
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| 96 | } |
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[976] | 97 | } |
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| 98 | |
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[1171] | 99 | class toprow : polyhedron |
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| 100 | { |
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| 101 | vector<double> condition; |
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[976] | 102 | |
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| 103 | public: |
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| 104 | |
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[1171] | 105 | toprow(); |
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[976] | 106 | |
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[1171] | 107 | toprow(double condition) |
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| 108 | { |
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| 109 | this.condition = condition; |
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| 110 | } |
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| 111 | |
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| 112 | } |
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| 113 | |
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| 114 | |
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| 115 | |
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| 116 | |
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[976] | 117 | //! Conditional(e) Multicriteria-Laplace-Inverse-Gamma distribution density |
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| 118 | class emlig : public eEF{ |
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| 119 | |
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[1171] | 120 | vector<vector<&polyhedron>> statistic; |
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| 121 | |
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| 122 | public: |
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[976] | 123 | |
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[1171] | 124 | emlig(int number_of_parameters) |
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| 125 | { |
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| 126 | create_statistic(int number_of_parameters); |
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| 127 | } |
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| 128 | |
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| 129 | emlig(vector<vector<&polyhedron>> statistic) |
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| 130 | { |
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| 131 | this.statistic = statistic; |
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| 132 | } |
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| 133 | |
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| 134 | |
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| 135 | |
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| 136 | protected: |
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| 137 | |
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| 138 | create_statistic(int number_of parameters) |
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| 139 | { |
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| 140 | vector<double> origin_coord = new vector<double>(); |
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| 141 | |
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| 142 | origin = new vertex(origin_coord); |
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| 143 | |
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| 144 | origin.vertices.push_back(origin); |
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| 145 | |
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| 146 | vector<&polyhedron> origin_vec = new vector(&polyhedron)(1,); |
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| 147 | |
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| 148 | statistic.push_back(origin_vec); |
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| 149 | |
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| 150 | for(int i=0;i++;i<number_of_parameters) |
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| 151 | { |
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| 152 | vertex new_point1 = new vertex(origin.get_coordinates().push_back(max_range)); |
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| 153 | vertex new_point2 = new vertex(origin.get_coordinates().push_back(-max_range)); |
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| 154 | |
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| 155 | vector(vector(&polyhedron)) new_statistic1 = new vector(vector(&polyhedron)); |
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| 156 | vector(vector(&polyhedron)) new_statistic2 = new vector(vector(&polyhedron)); |
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| 157 | |
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| 158 | |
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| 159 | for(int j=0;j++;j<statistic.size()) |
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| 160 | { |
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| 161 | vector<&polyhedron>::iterator horiz_ref = statistic[j].begin(); |
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| 162 | |
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| 163 | int element_number = 0; |
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| 164 | |
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| 165 | do |
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| 166 | { |
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| 167 | if(!horiz_ref.parents.empty()) |
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| 168 | { |
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| 169 | vector<&polyhedron>::iterator end_ref = horiz_ref.parents.end(); |
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| 170 | |
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| 171 | vector<&polyhedron>::iterator parent_ref = horiz_ref.parents.begin(); |
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| 172 | |
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| 173 | do |
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| 174 | { |
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| 175 | parent_ref.kids_rel_addresses.push_back(element_number); |
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| 176 | |
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| 177 | parent_ref++; |
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| 178 | } |
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| 179 | while(parent_ref!=end_ref) |
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| 180 | } |
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| 181 | |
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| 182 | toprow current_copy1 = new toprow(new vector<int>(i+2,0)); |
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| 183 | toprow current_copy2 = new toprow(new vector<int>(i+2,0)); |
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| 184 | |
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| 185 | vector<&vertex>::iterator vert_ref = horiz_ref.vertices.begin(); |
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| 186 | |
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| 187 | do |
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| 188 | { |
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| 189 | current_copy1.vertices.push_back(vert_ref); |
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| 190 | current_copy2.vertices.push_back(vert_ref); |
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| 191 | |
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| 192 | vert_ref++; |
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| 193 | } |
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| 194 | while(!vert_ref!=horiz_ref.vertices.end()) |
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| 195 | |
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| 196 | current_copy1.vertices.push_back(new_point1); |
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| 197 | current_copy2.vertices.push_back(new_point2); |
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| 198 | |
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| 199 | current_copy1.triangulations.push_back(current_copy1.vertices); |
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| 200 | current_copy2.triangulations.push_back(current_copy2.vertices); |
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| 201 | |
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| 202 | vector<int>::iterator kid_ref = horiz_ref.kids_rel_addresses.begin(); |
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| 203 | |
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| 204 | if(!horiz_ref.kids_rel_addresses.empty()) |
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| 205 | { |
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| 206 | do |
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| 207 | { |
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| 208 | current_copy1.children.push_back(new_statistic1[i,kid_ref]); |
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| 209 | current_copy2.children.push_back(new_statistic2[i,kid_ref]); |
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| 210 | |
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| 211 | new_statistic1[i,kid_ref].parents.push_back(current_copy1); |
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| 212 | new_statistic2[i,kid_ref].parents.push_back(current_copy2); |
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| 213 | |
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| 214 | kid_ref++; |
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| 215 | |
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| 216 | } |
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| 217 | while(kid_ref!=horiz_ref.kids_rel_addresses.end()) |
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| 218 | |
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| 219 | horiz_ref.kids_rel_addresses.clear(); |
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| 220 | } |
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| 221 | else |
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| 222 | { |
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| 223 | current_copy1.children.push_back(new_point1); |
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| 224 | current_copy2.childern.push_back(new_point2); |
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| 225 | |
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| 226 | new_point1.parents.push_back(current_copy1); |
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| 227 | new_point2.parents.push_back(current_copy2); |
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| 228 | } |
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| 229 | |
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| 230 | current_copy1.children.push_back(horiz_ref); |
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| 231 | current_copy2.children.push_back(horiz_ref); |
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| 232 | |
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| 233 | new_statistic1[i+1].push_back(current_copy1); |
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| 234 | new_statistic2[i+1].push_back(current_copy2); |
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| 235 | |
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| 236 | element_number++; |
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| 237 | } |
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| 238 | while(statistic[j].end() != horiz_ptr++) |
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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 | |
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[976] | 245 | |
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[1171] | 246 | |
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[976] | 247 | }; |
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| 248 | |
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| 249 | //! Robust Bayesian AR model for Multicriteria-Laplace-Inverse-Gamma density |
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| 250 | class RARX : public BMEF{ |
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| 251 | }; |
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| 252 | |
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| 253 | |
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| 254 | #endif //TRAGE_H |
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