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); |
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535 | } |
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536 | else |
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537 | { |
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538 | if(current_parent->negativechildren.size()>0) |
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539 | { |
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540 | current_parent->set_state(-1, SPLIT); |
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541 | |
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542 | ((toprow*)current_parent)->condition-=toadd; |
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543 | } |
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544 | else if(current_parent->positivechildren.size()>0) |
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545 | { |
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546 | current_parent->set_state(1, SPLIT); |
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547 | |
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548 | ((toprow*)current_parent)->condition+=toadd; |
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549 | } |
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550 | else |
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551 | { |
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552 | current_parent->raise_multiplicity(); |
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553 | } |
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554 | |
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555 | current_parent->positivechildren.clear(); |
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556 | current_parent->negativechildren.clear(); |
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557 | current_parent->neutralchildren.clear(); |
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558 | current_parent->totallyneutralchildren.clear(); |
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559 | current_parent->totallyneutralgrandchildren.clear(); |
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560 | current_parent->totally_neutral = NULL; |
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561 | current_parent->kids_rel_addresses.clear(); |
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562 | //current_parent->set_state(0, SPLIT); |
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563 | } |
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564 | } |
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565 | } |
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566 | |
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567 | if(is_last) |
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568 | { |
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569 | send_state_message(current_parent,toadd,toremove,level+1); |
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570 | } |
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571 | |
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572 | } |
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573 | |
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574 | } |
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575 | } |
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576 | |
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577 | public: |
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578 | |
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579 | int number_of_parameters; |
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580 | |
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581 | /// A default constructor creates an emlig with predefined statistic representing only the range of the given |
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582 | /// parametric space, where the number of parameters of the needed model is given as a parameter to the constructor. |
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583 | emlig(int number_of_parameters) |
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584 | { |
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585 | this->number_of_parameters = number_of_parameters; |
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586 | |
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587 | create_statistic(number_of_parameters); |
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588 | } |
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589 | |
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590 | /// A constructor for creating an emlig when the user wants to create the statistic by himself. The creation of a |
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591 | /// statistic is needed outside the constructor. Used for a user defined prior distribution on the parameters. |
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592 | emlig(c_statistic statistic) |
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593 | { |
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594 | this->statistic = statistic; |
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595 | } |
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596 | |
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597 | void step_me() |
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598 | { |
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599 | for(int i = 0;i<statistic.size();i++) |
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600 | { |
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601 | for(polyhedron* horiz_ref = statistic.rows[i];horiz_ref!=statistic.get_end();horiz_ref=horiz_ref->next_poly) |
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602 | { |
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603 | char* string = "Checkpoint"; |
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604 | } |
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605 | } |
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606 | } |
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607 | |
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608 | 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 |
---|