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 <itpp/itbase.h> |
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12 | #include <itpp/base/random.h> |
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13 | #include <map> |
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14 | #include <limits> |
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15 | #include <vector> |
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16 | #include <list> |
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17 | #include <set> |
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18 | #include <algorithm> |
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19 | |
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20 | using namespace bdm; |
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21 | using namespace std; |
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22 | using namespace itpp; |
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23 | |
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24 | const double max_range = 5;//numeric_limits<double>::max()/10e-10; |
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25 | |
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26 | /// An enumeration of possible actions performed on the polyhedrons. We can merge them or split them. |
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27 | enum actions {MERGE, SPLIT}; |
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28 | |
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29 | // Forward declaration of polyhedron, vertex and emlig |
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30 | class polyhedron; |
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31 | class vertex; |
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32 | class emlig; |
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33 | |
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34 | /* |
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35 | class t_simplex |
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36 | { |
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37 | public: |
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38 | set<vertex*> minima; |
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39 | |
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40 | set<vertex*> simplex; |
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41 | |
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42 | t_simplex(vertex* origin_vertex) |
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43 | { |
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44 | simplex.insert(origin_vertex); |
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45 | minima.insert(origin_vertex); |
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46 | } |
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47 | };*/ |
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48 | |
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49 | /// A class representing a single condition that can be added to the emlig. A condition represents data entries in a statistical model. |
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50 | class condition |
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51 | { |
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52 | public: |
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53 | /// Value of the condition representing the data |
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54 | vec value; |
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55 | |
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56 | /// Mulitplicity of the given condition may represent multiple occurences of same data entry. |
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57 | int multiplicity; |
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58 | |
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59 | /// Default constructor of condition class takes the value of data entry and creates a condition with multiplicity 1 (first occurence of the data). |
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60 | condition(vec value) |
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61 | { |
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62 | this->value = value; |
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63 | multiplicity = 1; |
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64 | } |
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65 | }; |
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66 | |
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67 | class simplex |
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68 | { |
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69 | |
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70 | |
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71 | public: |
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72 | |
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73 | set<vertex*> vertices; |
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74 | |
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75 | double probability; |
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76 | |
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77 | vector<multimap<double,double>> positive_gamma_parameters; |
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78 | |
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79 | vector<multimap<double,double>> negative_gamma_parameters; |
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80 | |
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81 | double positive_gamma_sum; |
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82 | |
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83 | double negative_gamma_sum; |
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84 | |
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85 | double min_beta; |
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86 | |
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87 | |
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88 | simplex(set<vertex*> vertices) |
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89 | { |
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90 | this->vertices.insert(vertices.begin(),vertices.end()); |
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91 | probability = 0; |
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92 | } |
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93 | |
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94 | simplex(vertex* vertex) |
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95 | { |
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96 | this->vertices.insert(vertex); |
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97 | probability = 0; |
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98 | } |
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99 | |
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100 | void clear_gammas() |
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101 | { |
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102 | positive_gamma_parameters.clear(); |
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103 | negative_gamma_parameters.clear(); |
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104 | |
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105 | positive_gamma_sum = 0; |
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106 | negative_gamma_sum = 0; |
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107 | |
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108 | min_beta = numeric_limits<double>::max(); |
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109 | } |
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110 | |
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111 | void insert_gamma(int order, double weight, double beta) |
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112 | { |
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113 | if(weight>=0) |
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114 | { |
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115 | while(positive_gamma_parameters.size()<order+1) |
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116 | { |
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117 | multimap<double,double> map; |
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118 | positive_gamma_parameters.push_back(map); |
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119 | } |
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120 | |
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121 | positive_gamma_sum += weight; |
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122 | |
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123 | positive_gamma_parameters[order].insert(pair<double,double>(weight,beta)); |
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124 | } |
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125 | else |
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126 | { |
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127 | while(negative_gamma_parameters.size()<order+1) |
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128 | { |
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129 | multimap<double,double> map; |
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130 | negative_gamma_parameters.push_back(map); |
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131 | } |
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132 | |
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133 | negative_gamma_sum -= weight; |
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134 | |
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135 | negative_gamma_parameters[order].insert(pair<double,double>(-weight,beta)); |
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136 | } |
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137 | |
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138 | if(beta < min_beta) |
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139 | { |
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140 | min_beta = beta; |
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141 | } |
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142 | } |
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143 | }; |
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144 | |
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145 | |
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146 | /// A class describing a single polyhedron of the split complex. From a collection of such classes a Hasse diagram |
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147 | /// of the structure in the exponent of a Laplace-Inverse-Gamma density will be created. |
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148 | class polyhedron |
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149 | { |
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150 | /// A property having a value of 1 usually, with higher value only if the polyhedron arises as a coincidence of |
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151 | /// more than just the necessary number of conditions. For example if a newly created line passes through an already |
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152 | /// existing point, the points multiplicity will rise by 1. |
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153 | int multiplicity; |
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154 | |
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155 | /// A property representing the position of the polyhedron related to current condition with relation to which we |
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156 | /// are splitting the parameter space (new data has arrived). This property is setup within a classification procedure and |
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157 | /// is only valid while the new condition is being added. It has to be reset when new condition is added and new classification |
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158 | /// has to be performed. |
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159 | int split_state; |
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160 | |
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161 | /// A property representing the position of the polyhedron related to current condition with relation to which we |
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162 | /// are merging the parameter space (data is being deleted usually due to a moving window model which is more adaptive and |
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163 | /// steps in for the forgetting in a classical Gaussian AR model). This property is setup within a classification procedure and |
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164 | /// is only valid while the new condition is being removed. It has to be reset when new condition is removed and new classification |
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165 | /// has to be performed. |
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166 | int merge_state; |
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167 | |
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168 | |
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169 | |
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170 | public: |
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171 | /// A pointer to the multi-Laplace inverse gamma distribution this polyhedron belongs to. |
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172 | emlig* my_emlig; |
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173 | |
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174 | /// A list of polyhedrons parents within the Hasse diagram. |
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175 | list<polyhedron*> parents; |
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176 | |
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177 | /// A list of polyhedrons children withing the Hasse diagram. |
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178 | list<polyhedron*> children; |
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179 | |
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180 | /// All the vertices of the given polyhedron |
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181 | set<vertex*> vertices; |
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182 | |
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183 | /// The conditions that gave birth to the polyhedron. If some of them is removed, the polyhedron ceases to exist. |
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184 | set<condition*> parentconditions; |
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185 | |
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186 | /// A list used for storing children that lie in the positive region related to a certain condition |
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187 | list<polyhedron*> positivechildren; |
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188 | |
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189 | /// A list used for storing children that lie in the negative region related to a certain condition |
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190 | list<polyhedron*> negativechildren; |
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191 | |
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192 | /// Children intersecting the condition |
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193 | list<polyhedron*> neutralchildren; |
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194 | |
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195 | /// A set of grandchildren of the polyhedron that when new condition is added lie exactly on the condition hyperplane. These grandchildren |
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196 | /// behave differently from other grandchildren, when the polyhedron is split. New grandchild is not necessarily created on the crossection of |
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197 | /// the polyhedron and new condition. |
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198 | set<polyhedron*> totallyneutralgrandchildren; |
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199 | |
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200 | /// A set of children of the polyhedron that when new condition is added lie exactly on the condition hyperplane. These children |
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201 | /// behave differently from other children, when the polyhedron is split. New child is not necessarily created on the crossection of |
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202 | /// the polyhedron and new condition. |
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203 | set<polyhedron*> totallyneutralchildren; |
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204 | |
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205 | /// Reverse relation to the totallyneutralgrandchildren set is needed for merging of already existing polyhedrons to keep |
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206 | /// totallyneutralgrandchildren list up to date. |
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207 | set<polyhedron*> grandparents; |
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208 | |
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209 | /// Vertices of the polyhedron classified as positive related to an added condition. When the polyhderon is split by the new condition, |
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210 | /// these vertices will belong to the positive part of the splitted polyhedron. |
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211 | set<vertex*> positiveneutralvertices; |
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212 | |
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213 | /// Vertices of the polyhedron classified as negative related to an added condition. When the polyhderon is split by the new condition, |
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214 | /// these vertices will belong to the negative part of the splitted polyhedron. |
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215 | set<vertex*> negativeneutralvertices; |
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216 | |
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217 | /// A bool specifying if the polyhedron lies exactly on the newly added condition or not. |
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218 | bool totally_neutral; |
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219 | |
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220 | /// When two polyhedrons are merged, there always exists a child lying on the former border of the polyhedrons. This child manages the merge |
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221 | /// of the two polyhedrons. This property gives us the address of the mediator child. |
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222 | polyhedron* mergechild; |
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223 | |
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224 | /// If the polyhedron serves as a mergechild for two of its parents, we need to have the address of the parents to access them. This |
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225 | /// is the pointer to the positive parent being merged. |
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226 | polyhedron* positiveparent; |
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227 | |
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228 | /// If the polyhedron serves as a mergechild for two of its parents, we need to have the address of the parents to access them. This |
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229 | /// is the pointer to the negative parent being merged. |
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230 | polyhedron* negativeparent; |
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231 | |
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232 | /// Adressing withing the statistic. Next_poly is a pointer to the next polyhedron in the statistic on the same level (if this is a point, |
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233 | /// next_poly will be a point etc.). |
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234 | polyhedron* next_poly; |
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235 | |
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236 | /// Adressing withing the statistic. Prev_poly is a pointer to the previous polyhedron in the statistic on the same level (if this is a point, |
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237 | /// next_poly will be a point etc.). |
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238 | polyhedron* prev_poly; |
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239 | |
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240 | /// A property counting the number of messages obtained from children within a classification procedure of position of the polyhedron related |
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241 | /// an added/removed condition. If the message counter reaches the number of children, we know the polyhedrons' position has been fully classified. |
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242 | int message_counter; |
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243 | |
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244 | /// List of triangulation polyhedrons of the polyhedron given by their relative vertices. |
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245 | set<simplex*> triangulation; |
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246 | |
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247 | /// A list of relative addresses serving for Hasse diagram construction. |
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248 | list<int> kids_rel_addresses; |
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249 | |
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250 | /// Default constructor |
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251 | polyhedron() |
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252 | { |
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253 | multiplicity = 1; |
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254 | |
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255 | message_counter = 0; |
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256 | |
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257 | totally_neutral = NULL; |
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258 | |
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259 | mergechild = NULL; |
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260 | } |
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261 | |
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262 | /// Setter for raising multiplicity |
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263 | void raise_multiplicity() |
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264 | { |
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265 | multiplicity++; |
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266 | } |
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267 | |
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268 | /// Setter for lowering multiplicity |
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269 | void lower_multiplicity() |
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270 | { |
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271 | multiplicity--; |
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272 | } |
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273 | |
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274 | int get_multiplicity() |
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275 | { |
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276 | return multiplicity; |
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277 | } |
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278 | |
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279 | /// An obligatory operator, when the class is used within a C++ STL structure like a vector |
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280 | int operator==(polyhedron polyhedron2) |
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281 | { |
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282 | return true; |
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283 | } |
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284 | |
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285 | /// An obligatory operator, when the class is used within a C++ STL structure like a vector |
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286 | int operator<(polyhedron polyhedron2) |
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287 | { |
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288 | return false; |
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289 | } |
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290 | |
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291 | |
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292 | /// A setter of state of current polyhedron relative to the action specified in the argument. The three possible states of the |
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293 | /// polyhedron are -1 - NEGATIVE, 0 - NEUTRAL, 1 - POSITIVE. Neutral state means that either the state has been reset or the polyhedron is |
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294 | /// ready to be split/merged. |
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295 | int set_state(double state_indicator, actions action) |
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296 | { |
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297 | switch(action) |
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298 | { |
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299 | case MERGE: |
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300 | merge_state = (int)sign(state_indicator); |
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301 | return merge_state; |
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302 | case SPLIT: |
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303 | split_state = (int)sign(state_indicator); |
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304 | return split_state; |
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305 | } |
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306 | } |
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307 | |
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308 | /// A getter of state of current polyhedron relative to the action specified in the argument. The three possible states of the |
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309 | /// polyhedron are -1 - NEGATIVE, 0 - NEUTRAL, 1 - POSITIVE. Neutral state means that either the state has been reset or the polyhedron is |
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310 | /// ready to be split/merged. |
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311 | int get_state(actions action) |
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312 | { |
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313 | switch(action) |
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314 | { |
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315 | case MERGE: |
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316 | return merge_state; |
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317 | break; |
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318 | case SPLIT: |
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319 | return split_state; |
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320 | break; |
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321 | } |
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322 | } |
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323 | |
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324 | /// Method for obtaining the number of children of given polyhedron. |
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325 | int number_of_children() |
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326 | { |
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327 | return children.size(); |
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328 | } |
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329 | |
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330 | /// A method for triangulation of given polyhedron. |
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331 | double triangulate(bool should_integrate); |
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332 | }; |
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333 | |
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334 | |
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335 | /// A class for representing 0-dimensional polyhedron - a vertex. It will be located in the bottom row of the Hasse |
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336 | /// diagram representing a complex of polyhedrons. It has its coordinates in the parameter space. |
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337 | class vertex : public polyhedron |
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338 | { |
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339 | /// A dynamic array representing coordinates of the vertex |
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340 | vec coordinates; |
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341 | |
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342 | public: |
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343 | /// A property specifying the value of the density (ted nevim, jestli je to jakoby log nebo ne) above the vertex. |
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344 | double function_value; |
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345 | |
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346 | /// Default constructor |
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347 | vertex(); |
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348 | |
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349 | /// Constructor of a vertex from a set of coordinates |
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350 | vertex(vec coordinates) |
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351 | { |
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352 | this->coordinates = coordinates; |
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353 | |
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354 | vertices.insert(this); |
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355 | |
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356 | simplex* vert_simplex = new simplex(vertices); |
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357 | |
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358 | triangulation.insert(vert_simplex); |
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359 | } |
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360 | |
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361 | /// A method that widens the set of coordinates of given vertex. It is used when a complex in a parameter |
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362 | /// space of certain dimension is established, but the dimension is not known when the vertex is created. |
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363 | void push_coordinate(double coordinate) |
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364 | { |
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365 | coordinates = concat(coordinates,coordinate); |
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366 | } |
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367 | |
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368 | /// A method obtaining the set of coordinates of a vertex. These coordinates are not obtained as a pointer |
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369 | /// (not given by reference), but a new copy is created (they are given by value). |
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370 | vec get_coordinates() |
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371 | { |
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372 | return coordinates; |
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373 | } |
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374 | |
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375 | }; |
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376 | |
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377 | |
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378 | /// A class representing a polyhedron in a top row of the complex. Such polyhedron has a condition that differen tiates |
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379 | /// it from polyhedrons in other rows. |
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380 | class toprow : public polyhedron |
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381 | { |
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382 | |
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383 | public: |
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384 | double probability; |
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385 | |
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386 | vertex* minimal_vertex; |
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387 | |
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388 | /// A condition used for determining the function of a Laplace-Inverse-Gamma density resulting from Bayesian estimation |
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389 | vec condition_sum; |
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390 | |
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391 | int condition_order; |
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392 | |
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393 | /// Default constructor |
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394 | toprow(){}; |
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395 | |
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396 | /// Constructor creating a toprow from the condition |
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397 | toprow(condition *condition, int condition_order) |
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398 | { |
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399 | this->condition_sum = condition->value; |
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400 | this->condition_order = condition_order; |
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401 | } |
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402 | |
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403 | toprow(vec condition_sum, int condition_order) |
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404 | { |
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405 | this->condition_sum = condition_sum; |
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406 | this->condition_order = condition_order; |
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407 | } |
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408 | |
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409 | double integrate_simplex(simplex* simplex, char c); |
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410 | |
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411 | }; |
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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 | |
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419 | class c_statistic |
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420 | { |
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421 | |
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422 | public: |
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423 | polyhedron* end_poly; |
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424 | polyhedron* start_poly; |
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425 | |
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426 | vector<polyhedron*> rows; |
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427 | |
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428 | vector<polyhedron*> row_ends; |
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429 | |
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430 | c_statistic() |
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431 | { |
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432 | end_poly = new polyhedron(); |
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433 | start_poly = new polyhedron(); |
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434 | }; |
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435 | |
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436 | ~c_statistic() |
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437 | { |
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438 | delete end_poly; |
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439 | delete start_poly; |
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440 | } |
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441 | |
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442 | void append_polyhedron(int row, polyhedron* appended_start, polyhedron* appended_end) |
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443 | { |
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444 | if(row>((int)rows.size())-1) |
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445 | { |
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446 | if(row>rows.size()) |
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447 | { |
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448 | throw new exception("You are trying to append a polyhedron whose children are not in the statistic yet!"); |
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449 | return; |
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450 | } |
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451 | |
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452 | rows.push_back(end_poly); |
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453 | row_ends.push_back(end_poly); |
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454 | } |
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455 | |
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456 | // POSSIBLE FAILURE: the function is not checking if start and end are connected |
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457 | |
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458 | if(rows[row] != end_poly) |
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459 | { |
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460 | appended_start->prev_poly = row_ends[row]; |
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461 | row_ends[row]->next_poly = appended_start; |
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462 | |
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463 | } |
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464 | else if((row>0 && rows[row-1]!=end_poly)||row==0) |
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465 | { |
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466 | appended_start->prev_poly = start_poly; |
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467 | rows[row]= appended_start; |
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468 | } |
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469 | else |
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470 | { |
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471 | throw new exception("Wrong polyhedron insertion into statistic: missing intermediary polyhedron!"); |
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472 | } |
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473 | |
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474 | appended_end->next_poly = end_poly; |
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475 | row_ends[row] = appended_end; |
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476 | } |
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477 | |
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478 | void append_polyhedron(int row, polyhedron* appended_poly) |
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479 | { |
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480 | append_polyhedron(row,appended_poly,appended_poly); |
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481 | } |
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482 | |
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483 | void insert_polyhedron(int row, polyhedron* inserted_poly, polyhedron* following_poly) |
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484 | { |
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485 | if(following_poly != end_poly) |
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486 | { |
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487 | inserted_poly->next_poly = following_poly; |
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488 | inserted_poly->prev_poly = following_poly->prev_poly; |
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489 | |
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490 | if(following_poly->prev_poly == start_poly) |
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491 | { |
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492 | rows[row] = inserted_poly; |
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493 | } |
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494 | else |
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495 | { |
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496 | inserted_poly->prev_poly->next_poly = inserted_poly; |
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497 | } |
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498 | |
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499 | following_poly->prev_poly = inserted_poly; |
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500 | } |
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501 | else |
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502 | { |
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503 | this->append_polyhedron(row, inserted_poly); |
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504 | } |
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505 | |
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506 | } |
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507 | |
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508 | |
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509 | |
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510 | |
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511 | void delete_polyhedron(int row, polyhedron* deleted_poly) |
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512 | { |
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513 | if(deleted_poly->prev_poly != start_poly) |
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514 | { |
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515 | deleted_poly->prev_poly->next_poly = deleted_poly->next_poly; |
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516 | } |
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517 | else |
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518 | { |
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519 | rows[row] = deleted_poly->next_poly; |
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520 | } |
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521 | |
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522 | if(deleted_poly->next_poly!=end_poly) |
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523 | { |
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524 | deleted_poly->next_poly->prev_poly = deleted_poly->prev_poly; |
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525 | } |
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526 | else |
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527 | { |
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528 | row_ends[row] = deleted_poly->prev_poly; |
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529 | } |
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530 | |
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531 | |
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532 | |
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533 | deleted_poly->next_poly = NULL; |
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534 | deleted_poly->prev_poly = NULL; |
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535 | } |
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536 | |
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537 | int size() |
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538 | { |
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539 | return rows.size(); |
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540 | } |
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541 | |
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542 | polyhedron* get_end() |
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543 | { |
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544 | return end_poly; |
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545 | } |
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546 | |
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547 | polyhedron* get_start() |
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548 | { |
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549 | return start_poly; |
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550 | } |
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551 | |
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552 | int row_size(int row) |
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553 | { |
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554 | if(this->size()>row && row>=0) |
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555 | { |
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556 | int row_size = 0; |
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557 | |
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558 | for(polyhedron* row_poly = rows[row]; row_poly!=end_poly; row_poly=row_poly->next_poly) |
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559 | { |
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560 | row_size++; |
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561 | } |
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562 | |
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563 | return row_size; |
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564 | } |
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565 | else |
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566 | { |
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567 | throw new exception("There is no row to obtain size from!"); |
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568 | } |
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569 | } |
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570 | }; |
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571 | |
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572 | |
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573 | class my_ivec : public ivec |
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574 | { |
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575 | public: |
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576 | my_ivec():ivec(){}; |
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577 | |
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578 | my_ivec(ivec origin):ivec() |
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579 | { |
---|
580 | this->ins(0,origin); |
---|
581 | } |
---|
582 | |
---|
583 | bool operator>(const my_ivec &second) const |
---|
584 | { |
---|
585 | return max(*this)>max(second); |
---|
586 | |
---|
587 | /* |
---|
588 | int size1 = this->size(); |
---|
589 | int size2 = second.size(); |
---|
590 | |
---|
591 | int counter1 = 0; |
---|
592 | while(0==0) |
---|
593 | { |
---|
594 | if((*this)[counter1]==0) |
---|
595 | { |
---|
596 | size1--; |
---|
597 | } |
---|
598 | |
---|
599 | if((*this)[counter1]!=0) |
---|
600 | break; |
---|
601 | |
---|
602 | counter1++; |
---|
603 | } |
---|
604 | |
---|
605 | int counter2 = 0; |
---|
606 | while(0==0) |
---|
607 | { |
---|
608 | if(second[counter2]==0) |
---|
609 | { |
---|
610 | size2--; |
---|
611 | } |
---|
612 | |
---|
613 | if(second[counter2]!=0) |
---|
614 | break; |
---|
615 | |
---|
616 | counter2++; |
---|
617 | } |
---|
618 | |
---|
619 | if(size1!=size2) |
---|
620 | { |
---|
621 | return size1>size2; |
---|
622 | } |
---|
623 | else |
---|
624 | { |
---|
625 | for(int i = 0;i<size1;i++) |
---|
626 | { |
---|
627 | if((*this)[counter1+i]!=second[counter2+i]) |
---|
628 | { |
---|
629 | return (*this)[counter1+i]>second[counter2+i]; |
---|
630 | } |
---|
631 | } |
---|
632 | |
---|
633 | return false; |
---|
634 | }*/ |
---|
635 | } |
---|
636 | |
---|
637 | |
---|
638 | bool operator==(const my_ivec &second) const |
---|
639 | { |
---|
640 | return max(*this)==max(second); |
---|
641 | |
---|
642 | /* |
---|
643 | int size1 = this->size(); |
---|
644 | int size2 = second.size(); |
---|
645 | |
---|
646 | int counter = 0; |
---|
647 | while(0==0) |
---|
648 | { |
---|
649 | if((*this)[counter]==0) |
---|
650 | { |
---|
651 | size1--; |
---|
652 | } |
---|
653 | |
---|
654 | if((*this)[counter]!=0) |
---|
655 | break; |
---|
656 | |
---|
657 | counter++; |
---|
658 | } |
---|
659 | |
---|
660 | counter = 0; |
---|
661 | while(0==0) |
---|
662 | { |
---|
663 | if(second[counter]==0) |
---|
664 | { |
---|
665 | size2--; |
---|
666 | } |
---|
667 | |
---|
668 | if(second[counter]!=0) |
---|
669 | break; |
---|
670 | |
---|
671 | counter++; |
---|
672 | } |
---|
673 | |
---|
674 | if(size1!=size2) |
---|
675 | { |
---|
676 | return false; |
---|
677 | } |
---|
678 | else |
---|
679 | { |
---|
680 | for(int i=0;i<size1;i++) |
---|
681 | { |
---|
682 | if((*this)[size()-1-i]!=second[second.size()-1-i]) |
---|
683 | { |
---|
684 | return false; |
---|
685 | } |
---|
686 | } |
---|
687 | |
---|
688 | return true; |
---|
689 | }*/ |
---|
690 | } |
---|
691 | |
---|
692 | bool operator<(const my_ivec &second) const |
---|
693 | { |
---|
694 | return !(((*this)>second)||((*this)==second)); |
---|
695 | } |
---|
696 | |
---|
697 | bool operator!=(const my_ivec &second) const |
---|
698 | { |
---|
699 | return !((*this)==second); |
---|
700 | } |
---|
701 | |
---|
702 | bool operator<=(const my_ivec &second) const |
---|
703 | { |
---|
704 | return !((*this)>second); |
---|
705 | } |
---|
706 | |
---|
707 | bool operator>=(const my_ivec &second) const |
---|
708 | { |
---|
709 | return !((*this)<second); |
---|
710 | } |
---|
711 | |
---|
712 | my_ivec right(my_ivec original) |
---|
713 | { |
---|
714 | |
---|
715 | } |
---|
716 | }; |
---|
717 | |
---|
718 | |
---|
719 | |
---|
720 | |
---|
721 | |
---|
722 | |
---|
723 | |
---|
724 | //! Conditional(e) Multicriteria-Laplace-Inverse-Gamma distribution density |
---|
725 | class emlig // : eEF |
---|
726 | { |
---|
727 | |
---|
728 | /// A statistic in a form of a Hasse diagram representing a complex of convex polyhedrons obtained as a result |
---|
729 | /// of data update from Bayesian estimation or set by the user if this emlig is a prior density |
---|
730 | |
---|
731 | |
---|
732 | vector<list<polyhedron*>> for_splitting; |
---|
733 | |
---|
734 | vector<list<polyhedron*>> for_merging; |
---|
735 | |
---|
736 | list<condition*> conditions; |
---|
737 | |
---|
738 | double normalization_factor; |
---|
739 | |
---|
740 | int condition_order; |
---|
741 | |
---|
742 | double last_log_nc; |
---|
743 | |
---|
744 | |
---|
745 | |
---|
746 | void alter_toprow_conditions(condition *condition, bool should_be_added) |
---|
747 | { |
---|
748 | for(polyhedron* horiz_ref = statistic.rows[statistic.size()-1];horiz_ref!=statistic.get_end();horiz_ref=horiz_ref->next_poly) |
---|
749 | { |
---|
750 | set<vertex*>::iterator vertex_ref = horiz_ref->vertices.begin(); |
---|
751 | |
---|
752 | do |
---|
753 | { |
---|
754 | vertex_ref++; |
---|
755 | } |
---|
756 | while((*vertex_ref)->parentconditions.find(condition)==(*vertex_ref)->parentconditions.end()); |
---|
757 | |
---|
758 | double product = (*vertex_ref)->get_coordinates()*condition->value; |
---|
759 | |
---|
760 | if(should_be_added) |
---|
761 | { |
---|
762 | ((toprow*) horiz_ref)->condition_order++; |
---|
763 | |
---|
764 | if(product>0) |
---|
765 | { |
---|
766 | ((toprow*) horiz_ref)->condition_sum += condition->value; |
---|
767 | } |
---|
768 | else |
---|
769 | { |
---|
770 | ((toprow*) horiz_ref)->condition_sum -= condition->value; |
---|
771 | } |
---|
772 | } |
---|
773 | else |
---|
774 | { |
---|
775 | ((toprow*) horiz_ref)->condition_order--; |
---|
776 | |
---|
777 | if(product<0) |
---|
778 | { |
---|
779 | ((toprow*) horiz_ref)->condition_sum += condition->value; |
---|
780 | } |
---|
781 | else |
---|
782 | { |
---|
783 | ((toprow*) horiz_ref)->condition_sum -= condition->value; |
---|
784 | } |
---|
785 | } |
---|
786 | } |
---|
787 | } |
---|
788 | |
---|
789 | |
---|
790 | |
---|
791 | void send_state_message(polyhedron* sender, condition *toadd, condition *toremove, int level) |
---|
792 | { |
---|
793 | |
---|
794 | bool shouldmerge = (toremove != NULL); |
---|
795 | bool shouldsplit = (toadd != NULL); |
---|
796 | |
---|
797 | if(shouldsplit||shouldmerge) |
---|
798 | { |
---|
799 | for(list<polyhedron*>::iterator parent_iterator = sender->parents.begin();parent_iterator!=sender->parents.end();parent_iterator++) |
---|
800 | { |
---|
801 | polyhedron* current_parent = *parent_iterator; |
---|
802 | |
---|
803 | current_parent->message_counter++; |
---|
804 | |
---|
805 | bool is_last = (current_parent->message_counter == current_parent->number_of_children()); |
---|
806 | bool is_first = (current_parent->message_counter == 1); |
---|
807 | |
---|
808 | bool out_of_the_game = true; |
---|
809 | |
---|
810 | if(shouldmerge) |
---|
811 | { |
---|
812 | int child_state = sender->get_state(MERGE); |
---|
813 | int parent_state = current_parent->get_state(MERGE); |
---|
814 | |
---|
815 | if(parent_state == 0||is_first) |
---|
816 | { |
---|
817 | parent_state = current_parent->set_state(child_state, MERGE); |
---|
818 | } |
---|
819 | |
---|
820 | if(child_state == 0) |
---|
821 | { |
---|
822 | if(current_parent->mergechild == NULL) |
---|
823 | { |
---|
824 | current_parent->mergechild = sender; |
---|
825 | } |
---|
826 | } |
---|
827 | |
---|
828 | if(is_last) |
---|
829 | { |
---|
830 | if(level == number_of_parameters-1) |
---|
831 | { |
---|
832 | if(parent_state == 1) |
---|
833 | { |
---|
834 | ((toprow*)current_parent)->condition_sum-=toremove->value; |
---|
835 | } |
---|
836 | |
---|
837 | if(parent_state == -1) |
---|
838 | { |
---|
839 | ((toprow*)current_parent)->condition_sum+=toremove->value; |
---|
840 | } |
---|
841 | } |
---|
842 | |
---|
843 | ((toprow*)current_parent)->condition_order--; |
---|
844 | |
---|
845 | |
---|
846 | if(current_parent->mergechild != NULL) |
---|
847 | { |
---|
848 | out_of_the_game = false; |
---|
849 | |
---|
850 | if(current_parent->mergechild->get_multiplicity()==1) |
---|
851 | { |
---|
852 | if(parent_state > 0) |
---|
853 | { |
---|
854 | current_parent->mergechild->positiveparent = current_parent; |
---|
855 | } |
---|
856 | |
---|
857 | if(parent_state < 0) |
---|
858 | { |
---|
859 | current_parent->mergechild->negativeparent = current_parent; |
---|
860 | } |
---|
861 | } |
---|
862 | else |
---|
863 | { |
---|
864 | out_of_the_game = true; |
---|
865 | } |
---|
866 | } |
---|
867 | |
---|
868 | if(out_of_the_game) |
---|
869 | { |
---|
870 | //current_parent->set_state(0,MERGE); |
---|
871 | |
---|
872 | if((level == number_of_parameters - 1) && (!shouldsplit)) |
---|
873 | { |
---|
874 | toprow* cur_par_toprow = ((toprow*)current_parent); |
---|
875 | cur_par_toprow->probability = 0.0; |
---|
876 | |
---|
877 | //set<simplex*> new_triangulation; |
---|
878 | |
---|
879 | for(set<simplex*>::iterator s_ref = current_parent->triangulation.begin();s_ref!=current_parent->triangulation.end();s_ref++) |
---|
880 | { |
---|
881 | double cur_prob = cur_par_toprow->integrate_simplex((*s_ref),'C'); |
---|
882 | |
---|
883 | cur_par_toprow->probability += cur_prob; |
---|
884 | |
---|
885 | //new_triangulation.insert(pair<double,set<vertex*>>(cur_prob,(*t_ref).second)); |
---|
886 | } |
---|
887 | |
---|
888 | normalization_factor += cur_par_toprow->probability; |
---|
889 | |
---|
890 | //current_parent->triangulation.clear(); |
---|
891 | //current_parent->triangulation.insert(new_triangulation.begin(),new_triangulation.end()); |
---|
892 | } |
---|
893 | } |
---|
894 | |
---|
895 | if(parent_state == 0) |
---|
896 | { |
---|
897 | for_merging[level+1].push_back(current_parent); |
---|
898 | //current_parent->parentconditions.erase(toremove); |
---|
899 | } |
---|
900 | |
---|
901 | |
---|
902 | } |
---|
903 | } |
---|
904 | |
---|
905 | if(shouldsplit) |
---|
906 | { |
---|
907 | current_parent->totallyneutralgrandchildren.insert(sender->totallyneutralchildren.begin(),sender->totallyneutralchildren.end()); |
---|
908 | |
---|
909 | for(set<polyhedron*>::iterator tot_child_ref = sender->totallyneutralchildren.begin();tot_child_ref!=sender->totallyneutralchildren.end();tot_child_ref++) |
---|
910 | { |
---|
911 | (*tot_child_ref)->grandparents.insert(current_parent); |
---|
912 | } |
---|
913 | |
---|
914 | if(current_parent->totally_neutral == NULL) |
---|
915 | { |
---|
916 | current_parent->totally_neutral = sender->totally_neutral; |
---|
917 | } |
---|
918 | else |
---|
919 | { |
---|
920 | current_parent->totally_neutral = current_parent->totally_neutral && sender->totally_neutral; |
---|
921 | } |
---|
922 | |
---|
923 | switch(sender->get_state(SPLIT)) |
---|
924 | { |
---|
925 | case 1: |
---|
926 | current_parent->positivechildren.push_back(sender); |
---|
927 | current_parent->positiveneutralvertices.insert(sender->vertices.begin(),sender->vertices.end()); |
---|
928 | break; |
---|
929 | case 0: |
---|
930 | current_parent->neutralchildren.push_back(sender); |
---|
931 | |
---|
932 | if(level!=0) |
---|
933 | { |
---|
934 | current_parent->positiveneutralvertices.insert(sender->positiveneutralvertices.begin(),sender->positiveneutralvertices.end()); |
---|
935 | current_parent->negativeneutralvertices.insert(sender->negativeneutralvertices.begin(),sender->negativeneutralvertices.end()); |
---|
936 | } |
---|
937 | else |
---|
938 | { |
---|
939 | current_parent->positiveneutralvertices.insert(*sender->vertices.begin()); |
---|
940 | current_parent->negativeneutralvertices.insert(*sender->vertices.begin()); |
---|
941 | } |
---|
942 | |
---|
943 | if(sender->totally_neutral) |
---|
944 | { |
---|
945 | current_parent->totallyneutralchildren.insert(sender); |
---|
946 | } |
---|
947 | |
---|
948 | break; |
---|
949 | case -1: |
---|
950 | current_parent->negativechildren.push_back(sender); |
---|
951 | current_parent->negativeneutralvertices.insert(sender->vertices.begin(),sender->vertices.end()); |
---|
952 | break; |
---|
953 | } |
---|
954 | |
---|
955 | if(is_last) |
---|
956 | { |
---|
957 | |
---|
958 | if((current_parent->negativechildren.size()>0&¤t_parent->positivechildren.size()>0) |
---|
959 | ||(current_parent->neutralchildren.size()>0&¤t_parent->totallyneutralchildren.empty())) |
---|
960 | { |
---|
961 | for_splitting[level+1].push_back(current_parent); |
---|
962 | |
---|
963 | current_parent->set_state(0, SPLIT); |
---|
964 | } |
---|
965 | else |
---|
966 | { |
---|
967 | if(current_parent->negativechildren.size()>0) |
---|
968 | { |
---|
969 | current_parent->set_state(-1, SPLIT); |
---|
970 | |
---|
971 | if(level == number_of_parameters-1) |
---|
972 | { |
---|
973 | ((toprow*)current_parent)->condition_sum-=toadd->value; |
---|
974 | } |
---|
975 | |
---|
976 | } |
---|
977 | else if(current_parent->positivechildren.size()>0) |
---|
978 | { |
---|
979 | current_parent->set_state(1, SPLIT); |
---|
980 | |
---|
981 | if(level == number_of_parameters-1) |
---|
982 | { |
---|
983 | ((toprow*)current_parent)->condition_sum+=toadd->value; |
---|
984 | } |
---|
985 | } |
---|
986 | else |
---|
987 | { |
---|
988 | current_parent->raise_multiplicity(); |
---|
989 | current_parent->totally_neutral = true; |
---|
990 | current_parent->parentconditions.insert(toadd); |
---|
991 | } |
---|
992 | |
---|
993 | ((toprow*)current_parent)->condition_order++; |
---|
994 | |
---|
995 | if(level == number_of_parameters - 1 && current_parent->mergechild == NULL) |
---|
996 | { |
---|
997 | toprow* cur_par_toprow = ((toprow*)current_parent); |
---|
998 | cur_par_toprow->probability = 0.0; |
---|
999 | |
---|
1000 | //map<double,set<vertex*>> new_triangulation; |
---|
1001 | |
---|
1002 | for(set<simplex*>::iterator s_ref = current_parent->triangulation.begin();s_ref!=current_parent->triangulation.end();s_ref++) |
---|
1003 | { |
---|
1004 | double cur_prob = cur_par_toprow->integrate_simplex((*s_ref),'C'); |
---|
1005 | |
---|
1006 | cur_par_toprow->probability += cur_prob; |
---|
1007 | |
---|
1008 | //new_triangulation.insert(pair<double,set<vertex*>>(cur_prob,(*t_ref).second)); |
---|
1009 | } |
---|
1010 | |
---|
1011 | normalization_factor += cur_par_toprow->probability; |
---|
1012 | |
---|
1013 | //current_parent->triangulation.clear(); |
---|
1014 | //current_parent->triangulation.insert(new_triangulation.begin(),new_triangulation.end()); |
---|
1015 | } |
---|
1016 | |
---|
1017 | if(out_of_the_game) |
---|
1018 | { |
---|
1019 | current_parent->positivechildren.clear(); |
---|
1020 | current_parent->negativechildren.clear(); |
---|
1021 | current_parent->neutralchildren.clear(); |
---|
1022 | //current_parent->totallyneutralchildren.clear(); |
---|
1023 | current_parent->totallyneutralgrandchildren.clear(); |
---|
1024 | // current_parent->grandparents.clear(); |
---|
1025 | current_parent->positiveneutralvertices.clear(); |
---|
1026 | current_parent->negativeneutralvertices.clear(); |
---|
1027 | current_parent->totally_neutral = NULL; |
---|
1028 | current_parent->kids_rel_addresses.clear(); |
---|
1029 | } |
---|
1030 | } |
---|
1031 | } |
---|
1032 | } |
---|
1033 | |
---|
1034 | if(is_last) |
---|
1035 | { |
---|
1036 | current_parent->mergechild = NULL; |
---|
1037 | current_parent->message_counter = 0; |
---|
1038 | |
---|
1039 | send_state_message(current_parent,toadd,toremove,level+1); |
---|
1040 | } |
---|
1041 | |
---|
1042 | } |
---|
1043 | |
---|
1044 | sender->totallyneutralchildren.clear(); |
---|
1045 | } |
---|
1046 | } |
---|
1047 | |
---|
1048 | public: |
---|
1049 | c_statistic statistic; |
---|
1050 | |
---|
1051 | vertex* minimal_vertex; |
---|
1052 | |
---|
1053 | double min_ll; |
---|
1054 | |
---|
1055 | double log_nc; |
---|
1056 | |
---|
1057 | |
---|
1058 | |
---|
1059 | vector<multiset<my_ivec>> correction_factors; |
---|
1060 | |
---|
1061 | int number_of_parameters; |
---|
1062 | |
---|
1063 | /// A default constructor creates an emlig with predefined statistic representing only the range of the given |
---|
1064 | /// parametric space, where the number of parameters of the needed model is given as a parameter to the constructor. |
---|
1065 | emlig(int number_of_parameters, double soft_prior_parameter) |
---|
1066 | { |
---|
1067 | this->number_of_parameters = number_of_parameters; |
---|
1068 | |
---|
1069 | condition_order = number_of_parameters+2; |
---|
1070 | |
---|
1071 | create_statistic(number_of_parameters, soft_prior_parameter); |
---|
1072 | |
---|
1073 | //step_me(10); |
---|
1074 | |
---|
1075 | min_ll = numeric_limits<double>::max(); |
---|
1076 | |
---|
1077 | |
---|
1078 | double normalization_factor = 0; |
---|
1079 | int counter = 0; |
---|
1080 | for(polyhedron* top_ref = statistic.rows[number_of_parameters];top_ref!=statistic.get_end();top_ref=top_ref->next_poly) |
---|
1081 | { |
---|
1082 | counter++; |
---|
1083 | toprow* cur_toprow = (toprow*)top_ref; |
---|
1084 | |
---|
1085 | set<simplex*>::iterator cur_simplex = cur_toprow->triangulation.begin(); |
---|
1086 | normalization_factor += cur_toprow->integrate_simplex(*cur_simplex,'X'); |
---|
1087 | } |
---|
1088 | |
---|
1089 | log_nc = log(normalization_factor) + logfact(condition_order-number_of_parameters-2)-(condition_order-number_of_parameters-2)*2; |
---|
1090 | |
---|
1091 | /* |
---|
1092 | cout << "part1: " << log(normalization_factor) << endl; |
---|
1093 | cout << "part2: " << logfact(condition_order-number_of_parameters-2) << endl; |
---|
1094 | pause(1); |
---|
1095 | */ |
---|
1096 | |
---|
1097 | |
---|
1098 | } |
---|
1099 | |
---|
1100 | /// A constructor for creating an emlig when the user wants to create the statistic by himself. The creation of a |
---|
1101 | /// statistic is needed outside the constructor. Used for a user defined prior distribution on the parameters. |
---|
1102 | emlig(c_statistic statistic, int condition_order) |
---|
1103 | { |
---|
1104 | this->statistic = statistic; |
---|
1105 | |
---|
1106 | min_ll = numeric_limits<double>::max(); |
---|
1107 | |
---|
1108 | this->condition_order = condition_order; |
---|
1109 | } |
---|
1110 | |
---|
1111 | |
---|
1112 | void step_me(int marker) |
---|
1113 | { |
---|
1114 | |
---|
1115 | for(int i = 0;i<statistic.size();i++) |
---|
1116 | { |
---|
1117 | //int zero = 0; |
---|
1118 | //int one = 0; |
---|
1119 | //int two = 0; |
---|
1120 | |
---|
1121 | for(polyhedron* horiz_ref = statistic.rows[i];horiz_ref!=statistic.get_end();horiz_ref=horiz_ref->next_poly) |
---|
1122 | { |
---|
1123 | |
---|
1124 | |
---|
1125 | if(i==statistic.size()-1) |
---|
1126 | { |
---|
1127 | cout << ((toprow*)horiz_ref)->condition_sum << " " << ((toprow*)horiz_ref)->probability << endl; |
---|
1128 | cout << "Condition: " << ((toprow*)horiz_ref)->condition_sum << endl; |
---|
1129 | cout << "Order:" << ((toprow*)horiz_ref)->condition_order << endl; |
---|
1130 | } |
---|
1131 | |
---|
1132 | |
---|
1133 | // cout << "Stepped." << endl; |
---|
1134 | |
---|
1135 | if(marker==101) |
---|
1136 | { |
---|
1137 | if(!(*horiz_ref).negativechildren.empty()||!(*horiz_ref).positivechildren.empty()||!(*horiz_ref).neutralchildren.empty()||!(*horiz_ref).kids_rel_addresses.empty()||!(*horiz_ref).mergechild==NULL||!(*horiz_ref).negativeneutralvertices.empty()) |
---|
1138 | { |
---|
1139 | cout << "Cleaning error!" << endl; |
---|
1140 | } |
---|
1141 | |
---|
1142 | } |
---|
1143 | |
---|
1144 | for(set<simplex*>::iterator sim_ref = (*horiz_ref).triangulation.begin();sim_ref!=(*horiz_ref).triangulation.end();sim_ref++) |
---|
1145 | { |
---|
1146 | if((*sim_ref)->vertices.size()!=i+1) |
---|
1147 | { |
---|
1148 | cout << "Something is wrong." << endl; |
---|
1149 | } |
---|
1150 | } |
---|
1151 | |
---|
1152 | /* |
---|
1153 | if(i==0) |
---|
1154 | { |
---|
1155 | cout << ((vertex*)horiz_ref)->get_coordinates() << endl; |
---|
1156 | } |
---|
1157 | */ |
---|
1158 | |
---|
1159 | /* |
---|
1160 | char* string = "Checkpoint"; |
---|
1161 | |
---|
1162 | |
---|
1163 | if((*horiz_ref).parentconditions.size()==0) |
---|
1164 | { |
---|
1165 | zero++; |
---|
1166 | } |
---|
1167 | else if((*horiz_ref).parentconditions.size()==1) |
---|
1168 | { |
---|
1169 | one++; |
---|
1170 | } |
---|
1171 | else |
---|
1172 | { |
---|
1173 | two++; |
---|
1174 | } |
---|
1175 | */ |
---|
1176 | |
---|
1177 | } |
---|
1178 | } |
---|
1179 | |
---|
1180 | |
---|
1181 | /* |
---|
1182 | list<vec> table_entries; |
---|
1183 | for(polyhedron* horiz_ref = statistic.rows[statistic.size()-1];horiz_ref!=statistic.row_ends[statistic.size()-1];horiz_ref=horiz_ref->next_poly) |
---|
1184 | { |
---|
1185 | toprow *current_toprow = (toprow*)(horiz_ref); |
---|
1186 | for(list<set<vertex*>>::iterator tri_ref = current_toprow->triangulation.begin();tri_ref!=current_toprow->triangulation.end();tri_ref++) |
---|
1187 | { |
---|
1188 | for(set<vertex*>::iterator vert_ref = (*tri_ref).begin();vert_ref!=(*tri_ref).end();vert_ref++) |
---|
1189 | { |
---|
1190 | vec table_entry = vec(); |
---|
1191 | |
---|
1192 | table_entry.ins(0,(*vert_ref)->get_coordinates()*current_toprow->condition.get(1,current_toprow->condition.size()-1)-current_toprow->condition.get(0,0)); |
---|
1193 | |
---|
1194 | table_entry.ins(0,(*vert_ref)->get_coordinates()); |
---|
1195 | |
---|
1196 | table_entries.push_back(table_entry); |
---|
1197 | } |
---|
1198 | } |
---|
1199 | } |
---|
1200 | |
---|
1201 | unique(table_entries.begin(),table_entries.end()); |
---|
1202 | |
---|
1203 | |
---|
1204 | |
---|
1205 | for(list<vec>::iterator entry_ref = table_entries.begin();entry_ref!=table_entries.end();entry_ref++) |
---|
1206 | { |
---|
1207 | ofstream myfile; |
---|
1208 | myfile.open("robust_data.txt", ios::out | ios::app); |
---|
1209 | if (myfile.is_open()) |
---|
1210 | { |
---|
1211 | for(int i = 0;i<(*entry_ref).size();i++) |
---|
1212 | { |
---|
1213 | myfile << (*entry_ref)[i] << ";"; |
---|
1214 | } |
---|
1215 | myfile << endl; |
---|
1216 | |
---|
1217 | myfile.close(); |
---|
1218 | } |
---|
1219 | else |
---|
1220 | { |
---|
1221 | cout << "File problem." << endl; |
---|
1222 | } |
---|
1223 | } |
---|
1224 | */ |
---|
1225 | |
---|
1226 | |
---|
1227 | return; |
---|
1228 | } |
---|
1229 | |
---|
1230 | int statistic_rowsize(int row) |
---|
1231 | { |
---|
1232 | return statistic.row_size(row); |
---|
1233 | } |
---|
1234 | |
---|
1235 | void add_condition(vec toadd) |
---|
1236 | { |
---|
1237 | vec null_vector = ""; |
---|
1238 | |
---|
1239 | add_and_remove_condition(toadd, null_vector); |
---|
1240 | } |
---|
1241 | |
---|
1242 | |
---|
1243 | void remove_condition(vec toremove) |
---|
1244 | { |
---|
1245 | vec null_vector = ""; |
---|
1246 | |
---|
1247 | add_and_remove_condition(null_vector, toremove); |
---|
1248 | } |
---|
1249 | |
---|
1250 | void add_and_remove_condition(vec toadd, vec toremove) |
---|
1251 | { |
---|
1252 | |
---|
1253 | //step_me(0); |
---|
1254 | normalization_factor = 0; |
---|
1255 | min_ll = numeric_limits<double>::max(); |
---|
1256 | |
---|
1257 | bool should_remove = (toremove.size() != 0); |
---|
1258 | bool should_add = (toadd.size() != 0); |
---|
1259 | |
---|
1260 | if(should_remove) |
---|
1261 | { |
---|
1262 | condition_order--; |
---|
1263 | } |
---|
1264 | |
---|
1265 | if(should_add) |
---|
1266 | { |
---|
1267 | condition_order++; |
---|
1268 | } |
---|
1269 | |
---|
1270 | for_splitting.clear(); |
---|
1271 | for_merging.clear(); |
---|
1272 | |
---|
1273 | for(int i = 0;i<statistic.size();i++) |
---|
1274 | { |
---|
1275 | list<polyhedron*> empty_split; |
---|
1276 | list<polyhedron*> empty_merge; |
---|
1277 | |
---|
1278 | for_splitting.push_back(empty_split); |
---|
1279 | for_merging.push_back(empty_merge); |
---|
1280 | } |
---|
1281 | |
---|
1282 | list<condition*>::iterator toremove_ref = conditions.end(); |
---|
1283 | bool condition_should_be_added = should_add; |
---|
1284 | |
---|
1285 | for(list<condition*>::iterator ref = conditions.begin();ref!=conditions.end();ref++) |
---|
1286 | { |
---|
1287 | if(should_remove) |
---|
1288 | { |
---|
1289 | if((*ref)->value == toremove) |
---|
1290 | { |
---|
1291 | if((*ref)->multiplicity>1) |
---|
1292 | { |
---|
1293 | (*ref)->multiplicity--; |
---|
1294 | |
---|
1295 | alter_toprow_conditions(*ref,false); |
---|
1296 | |
---|
1297 | should_remove = false; |
---|
1298 | } |
---|
1299 | else |
---|
1300 | { |
---|
1301 | toremove_ref = ref; |
---|
1302 | } |
---|
1303 | } |
---|
1304 | } |
---|
1305 | |
---|
1306 | if(should_add) |
---|
1307 | { |
---|
1308 | if((*ref)->value == toadd) |
---|
1309 | { |
---|
1310 | (*ref)->multiplicity++; |
---|
1311 | |
---|
1312 | alter_toprow_conditions(*ref,true); |
---|
1313 | |
---|
1314 | should_add = false; |
---|
1315 | |
---|
1316 | condition_should_be_added = false; |
---|
1317 | } |
---|
1318 | } |
---|
1319 | } |
---|
1320 | |
---|
1321 | condition* condition_to_remove = NULL; |
---|
1322 | |
---|
1323 | if(toremove_ref!=conditions.end()) |
---|
1324 | { |
---|
1325 | condition_to_remove = *toremove_ref; |
---|
1326 | conditions.erase(toremove_ref); |
---|
1327 | } |
---|
1328 | |
---|
1329 | condition* condition_to_add = NULL; |
---|
1330 | |
---|
1331 | if(condition_should_be_added) |
---|
1332 | { |
---|
1333 | condition* new_condition = new condition(toadd); |
---|
1334 | |
---|
1335 | conditions.push_back(new_condition); |
---|
1336 | condition_to_add = new_condition; |
---|
1337 | } |
---|
1338 | |
---|
1339 | for(polyhedron* horizontal_position = statistic.rows[0];horizontal_position!=statistic.get_end();horizontal_position=horizontal_position->next_poly) |
---|
1340 | { |
---|
1341 | vertex* current_vertex = (vertex*)horizontal_position; |
---|
1342 | |
---|
1343 | if(should_add||should_remove) |
---|
1344 | { |
---|
1345 | vec appended_coords = current_vertex->get_coordinates(); |
---|
1346 | appended_coords.ins(0,-1.0); |
---|
1347 | |
---|
1348 | if(should_add) |
---|
1349 | { |
---|
1350 | double local_condition = 0;// = toadd*(appended_coords.first/=appended_coords.second); |
---|
1351 | |
---|
1352 | local_condition = appended_coords*toadd; |
---|
1353 | |
---|
1354 | // cout << "Vertex multiplicity: "<< current_vertex->get_multiplicity() << endl; |
---|
1355 | |
---|
1356 | current_vertex->set_state(local_condition,SPLIT); |
---|
1357 | |
---|
1358 | /// \TODO There should be a rounding error tolerance used here to insure we are not having too many points because of rounding error. |
---|
1359 | if(local_condition == 0) |
---|
1360 | { |
---|
1361 | cout << "Condition to add: " << toadd << endl; |
---|
1362 | cout << "Vertex coords: " << appended_coords << endl; |
---|
1363 | |
---|
1364 | current_vertex->totally_neutral = true; |
---|
1365 | |
---|
1366 | current_vertex->raise_multiplicity(); |
---|
1367 | current_vertex->parentconditions.insert(condition_to_add); |
---|
1368 | } |
---|
1369 | else |
---|
1370 | { |
---|
1371 | current_vertex->totally_neutral = false; |
---|
1372 | } |
---|
1373 | } |
---|
1374 | |
---|
1375 | if(should_remove) |
---|
1376 | { |
---|
1377 | set<condition*>::iterator cond_ref; |
---|
1378 | |
---|
1379 | for(cond_ref = current_vertex->parentconditions.begin();cond_ref!=current_vertex->parentconditions.end();cond_ref++) |
---|
1380 | { |
---|
1381 | if(*cond_ref == condition_to_remove) |
---|
1382 | { |
---|
1383 | break; |
---|
1384 | } |
---|
1385 | } |
---|
1386 | |
---|
1387 | if(cond_ref!=current_vertex->parentconditions.end()) |
---|
1388 | { |
---|
1389 | current_vertex->parentconditions.erase(cond_ref); |
---|
1390 | current_vertex->set_state(0,MERGE); |
---|
1391 | for_merging[0].push_back(current_vertex); |
---|
1392 | } |
---|
1393 | else |
---|
1394 | { |
---|
1395 | double local_condition = toremove*appended_coords; |
---|
1396 | current_vertex->set_state(local_condition,MERGE); |
---|
1397 | } |
---|
1398 | } |
---|
1399 | } |
---|
1400 | |
---|
1401 | send_state_message(current_vertex, condition_to_add, condition_to_remove, 0); |
---|
1402 | |
---|
1403 | } |
---|
1404 | |
---|
1405 | // step_me(1); |
---|
1406 | |
---|
1407 | if(should_remove) |
---|
1408 | { |
---|
1409 | /* |
---|
1410 | for(int i = 0;i<for_merging.size();i++) |
---|
1411 | { |
---|
1412 | for(list<polyhedron*>::iterator merge_ref = for_merging[i].begin();merge_ref!=for_merging[i].end();merge_ref++) |
---|
1413 | { |
---|
1414 | |
---|
1415 | for(list<polyhedron*>::iterator par_ref = (*merge_ref)->children.begin();par_ref!=(*merge_ref)->children.end();par_ref++) |
---|
1416 | { |
---|
1417 | if(find((*par_ref)->parents.begin(),(*par_ref)->parents.end(),(*merge_ref))==(*par_ref)->parents.end()) |
---|
1418 | { |
---|
1419 | cout << "Parent/child relations are not matched!" << endl; |
---|
1420 | } |
---|
1421 | } |
---|
1422 | |
---|
1423 | //cout << (*merge_ref)->get_state(MERGE) << ","; |
---|
1424 | } |
---|
1425 | |
---|
1426 | // cout << endl; |
---|
1427 | } |
---|
1428 | */ |
---|
1429 | |
---|
1430 | |
---|
1431 | |
---|
1432 | cout << "Merging." << endl; |
---|
1433 | |
---|
1434 | set<vertex*> vertices_to_be_reduced; |
---|
1435 | |
---|
1436 | int k = 1; |
---|
1437 | |
---|
1438 | for(vector<list<polyhedron*>>::iterator vert_ref = for_merging.begin();vert_ref<for_merging.end();vert_ref++) |
---|
1439 | { |
---|
1440 | for(list<polyhedron*>::reverse_iterator merge_ref = vert_ref->rbegin();merge_ref!=vert_ref->rend();merge_ref++) |
---|
1441 | { |
---|
1442 | if((*merge_ref)->get_multiplicity()>1) |
---|
1443 | { |
---|
1444 | (*merge_ref)->parentconditions.erase(condition_to_remove); |
---|
1445 | |
---|
1446 | if(k==1) |
---|
1447 | { |
---|
1448 | vertices_to_be_reduced.insert((vertex*)(*merge_ref)); |
---|
1449 | } |
---|
1450 | else |
---|
1451 | { |
---|
1452 | (*merge_ref)->lower_multiplicity(); |
---|
1453 | } |
---|
1454 | |
---|
1455 | if((*merge_ref)->get_state(SPLIT)!=0||(*merge_ref)->totally_neutral) |
---|
1456 | { |
---|
1457 | (*merge_ref)->positivechildren.clear(); |
---|
1458 | (*merge_ref)->negativechildren.clear(); |
---|
1459 | (*merge_ref)->neutralchildren.clear(); |
---|
1460 | (*merge_ref)->totallyneutralgrandchildren.clear(); |
---|
1461 | (*merge_ref)->positiveneutralvertices.clear(); |
---|
1462 | (*merge_ref)->negativeneutralvertices.clear(); |
---|
1463 | (*merge_ref)->totally_neutral = NULL; |
---|
1464 | (*merge_ref)->kids_rel_addresses.clear(); |
---|
1465 | } |
---|
1466 | } |
---|
1467 | else |
---|
1468 | { |
---|
1469 | bool will_be_split = false; |
---|
1470 | |
---|
1471 | toprow* current_positive = (toprow*)(*merge_ref)->positiveparent; |
---|
1472 | toprow* current_negative = (toprow*)(*merge_ref)->negativeparent; |
---|
1473 | |
---|
1474 | if(current_positive->totally_neutral!=current_negative->totally_neutral) |
---|
1475 | { |
---|
1476 | throw new exception("Both polyhedrons must be totally neutral if they should be merged!"); |
---|
1477 | } |
---|
1478 | |
---|
1479 | //current_positive->condition_sum -= toremove; |
---|
1480 | //current_positive->condition_order--; |
---|
1481 | |
---|
1482 | current_positive->parentconditions.erase(condition_to_remove); |
---|
1483 | |
---|
1484 | current_positive->children.insert(current_positive->children.end(),current_negative->children.begin(),current_negative->children.end()); |
---|
1485 | current_positive->children.remove(*merge_ref); |
---|
1486 | |
---|
1487 | for(list<polyhedron*>::iterator child_ref = current_negative->children.begin();child_ref!=current_negative->children.end();child_ref++) |
---|
1488 | { |
---|
1489 | (*child_ref)->parents.remove(current_negative); |
---|
1490 | (*child_ref)->parents.push_back(current_positive); |
---|
1491 | } |
---|
1492 | |
---|
1493 | // current_positive->parents.insert(current_positive->parents.begin(),current_negative->parents.begin(),current_negative->parents.end()); |
---|
1494 | // unique(current_positive->parents.begin(),current_positive->parents.end()); |
---|
1495 | |
---|
1496 | for(list<polyhedron*>::iterator parent_ref = current_negative->parents.begin();parent_ref!=current_negative->parents.end();parent_ref++) |
---|
1497 | { |
---|
1498 | (*parent_ref)->children.remove(current_negative); |
---|
1499 | |
---|
1500 | switch(current_negative->get_state(SPLIT)) |
---|
1501 | { |
---|
1502 | case -1: |
---|
1503 | (*parent_ref)->negativechildren.remove(current_negative); |
---|
1504 | break; |
---|
1505 | case 0: |
---|
1506 | (*parent_ref)->neutralchildren.remove(current_negative); |
---|
1507 | break; |
---|
1508 | case 1: |
---|
1509 | (*parent_ref)->positivechildren.remove(current_negative); |
---|
1510 | break; |
---|
1511 | } |
---|
1512 | //(*parent_ref)->children.push_back(current_positive); |
---|
1513 | } |
---|
1514 | |
---|
1515 | if(current_positive->get_state(SPLIT)!=0&¤t_negative->get_state(SPLIT)==0) |
---|
1516 | { |
---|
1517 | for(list<polyhedron*>::iterator parent_ref = current_positive->parents.begin();parent_ref!=current_positive->parents.end();parent_ref++) |
---|
1518 | { |
---|
1519 | if(current_positive->get_state(SPLIT)==1) |
---|
1520 | { |
---|
1521 | (*parent_ref)->positivechildren.remove(current_positive); |
---|
1522 | } |
---|
1523 | else |
---|
1524 | { |
---|
1525 | (*parent_ref)->negativechildren.remove(current_positive); |
---|
1526 | } |
---|
1527 | |
---|
1528 | (*parent_ref)->neutralchildren.push_back(current_positive); |
---|
1529 | } |
---|
1530 | |
---|
1531 | current_positive->set_state(0,SPLIT); |
---|
1532 | for_splitting[k].push_back(current_positive); |
---|
1533 | |
---|
1534 | will_be_split = true; |
---|
1535 | } |
---|
1536 | |
---|
1537 | if((current_positive->get_state(SPLIT)==0&&!current_positive->totally_neutral)||(current_negative->get_state(SPLIT)==0&&!current_negative->totally_neutral)) |
---|
1538 | { |
---|
1539 | current_positive->negativechildren.insert(current_positive->negativechildren.end(),current_negative->negativechildren.begin(),current_negative->negativechildren.end()); |
---|
1540 | |
---|
1541 | current_positive->positivechildren.insert(current_positive->positivechildren.end(),current_negative->positivechildren.begin(),current_negative->positivechildren.end()); |
---|
1542 | |
---|
1543 | current_positive->neutralchildren.insert(current_positive->neutralchildren.end(),current_negative->neutralchildren.begin(),current_negative->neutralchildren.end()); |
---|
1544 | |
---|
1545 | switch((*merge_ref)->get_state(SPLIT)) |
---|
1546 | { |
---|
1547 | case -1: |
---|
1548 | current_positive->negativechildren.remove(*merge_ref); |
---|
1549 | break; |
---|
1550 | case 0: |
---|
1551 | current_positive->neutralchildren.remove(*merge_ref); |
---|
1552 | break; |
---|
1553 | case 1: |
---|
1554 | current_positive->positivechildren.remove(*merge_ref); |
---|
1555 | break; |
---|
1556 | } |
---|
1557 | |
---|
1558 | /* |
---|
1559 | current_positive->totallyneutralchildren.insert(current_negative->totallyneutralchildren.begin(),current_negative->totallyneutralchildren.end()); |
---|
1560 | |
---|
1561 | current_positive->totallyneutralchildren.erase(*merge_ref); |
---|
1562 | */ |
---|
1563 | |
---|
1564 | current_positive->totallyneutralgrandchildren.insert(current_negative->totallyneutralgrandchildren.begin(),current_negative->totallyneutralgrandchildren.end()); |
---|
1565 | |
---|
1566 | current_positive->negativeneutralvertices.insert(current_negative->negativeneutralvertices.begin(),current_negative->negativeneutralvertices.end()); |
---|
1567 | current_positive->positiveneutralvertices.insert(current_negative->positiveneutralvertices.begin(),current_negative->positiveneutralvertices.end()); |
---|
1568 | |
---|
1569 | will_be_split = true; |
---|
1570 | } |
---|
1571 | else |
---|
1572 | { |
---|
1573 | current_positive->positivechildren.clear(); |
---|
1574 | current_positive->negativechildren.clear(); |
---|
1575 | current_positive->neutralchildren.clear(); |
---|
1576 | // current_positive->totallyneutralchildren.clear(); |
---|
1577 | current_positive->totallyneutralgrandchildren.clear(); |
---|
1578 | current_positive->positiveneutralvertices.clear(); |
---|
1579 | current_positive->negativeneutralvertices.clear(); |
---|
1580 | current_positive->totally_neutral = NULL; |
---|
1581 | current_positive->kids_rel_addresses.clear(); |
---|
1582 | } |
---|
1583 | |
---|
1584 | current_positive->vertices.insert(current_negative->vertices.begin(),current_negative->vertices.end()); |
---|
1585 | |
---|
1586 | |
---|
1587 | for(set<vertex*>::iterator vert_ref = (*merge_ref)->vertices.begin();vert_ref!=(*merge_ref)->vertices.end();vert_ref++) |
---|
1588 | { |
---|
1589 | if((*vert_ref)->get_multiplicity()==1) |
---|
1590 | { |
---|
1591 | current_positive->vertices.erase(*vert_ref); |
---|
1592 | |
---|
1593 | if(will_be_split) |
---|
1594 | { |
---|
1595 | current_positive->negativeneutralvertices.erase(*vert_ref); |
---|
1596 | current_positive->positiveneutralvertices.erase(*vert_ref); |
---|
1597 | } |
---|
1598 | } |
---|
1599 | } |
---|
1600 | |
---|
1601 | if(current_negative->get_state(SPLIT)==0&&!current_negative->totally_neutral) |
---|
1602 | { |
---|
1603 | for_splitting[k].remove(current_negative); |
---|
1604 | } |
---|
1605 | |
---|
1606 | |
---|
1607 | |
---|
1608 | if(current_positive->totally_neutral) |
---|
1609 | { |
---|
1610 | for(set<polyhedron*>::iterator grand_ref = current_negative->grandparents.begin();grand_ref!=current_negative->grandparents.end();grand_ref++) |
---|
1611 | { |
---|
1612 | (*grand_ref)->totallyneutralgrandchildren.erase(current_negative); |
---|
1613 | (*grand_ref)->totallyneutralgrandchildren.insert(current_positive); |
---|
1614 | } |
---|
1615 | } |
---|
1616 | |
---|
1617 | current_positive->grandparents.clear(); |
---|
1618 | |
---|
1619 | normalization_factor += current_positive->triangulate(k==for_splitting.size()-1 && !will_be_split); |
---|
1620 | |
---|
1621 | statistic.delete_polyhedron(k,current_negative); |
---|
1622 | |
---|
1623 | delete current_negative; |
---|
1624 | |
---|
1625 | for(list<polyhedron*>::iterator child_ref = (*merge_ref)->children.begin();child_ref!=(*merge_ref)->children.end();child_ref++) |
---|
1626 | { |
---|
1627 | (*child_ref)->parents.remove(*merge_ref); |
---|
1628 | } |
---|
1629 | |
---|
1630 | /* |
---|
1631 | for(list<polyhedron*>::iterator parent_ref = (*merge_ref)->parents.begin();parent_ref!=(*merge_ref)->parents.end();parent_ref++) |
---|
1632 | { |
---|
1633 | (*parent_ref)->positivechildren.remove(*merge_ref); |
---|
1634 | (*parent_ref)->negativechildren.remove(*merge_ref); |
---|
1635 | (*parent_ref)->neutralchildren.remove(*merge_ref); |
---|
1636 | (*parent_ref)->children.remove(*merge_ref); |
---|
1637 | } |
---|
1638 | */ |
---|
1639 | |
---|
1640 | for(set<polyhedron*>::iterator grand_ch_ref = (*merge_ref)->totallyneutralgrandchildren.begin();grand_ch_ref!=(*merge_ref)->totallyneutralgrandchildren.end();grand_ch_ref++) |
---|
1641 | { |
---|
1642 | (*grand_ch_ref)->grandparents.erase(*merge_ref); |
---|
1643 | } |
---|
1644 | |
---|
1645 | |
---|
1646 | for(set<polyhedron*>::iterator grand_p_ref = (*merge_ref)->grandparents.begin();grand_p_ref!=(*merge_ref)->grandparents.end();grand_p_ref++) |
---|
1647 | { |
---|
1648 | (*grand_p_ref)->totallyneutralgrandchildren.erase(*merge_ref); |
---|
1649 | } |
---|
1650 | |
---|
1651 | statistic.delete_polyhedron(k-1,*merge_ref); |
---|
1652 | |
---|
1653 | |
---|
1654 | |
---|
1655 | if(k==1) |
---|
1656 | { |
---|
1657 | vertices_to_be_reduced.insert((vertex*)(*merge_ref)); |
---|
1658 | for_splitting[k-1].remove(*merge_ref); |
---|
1659 | } |
---|
1660 | else |
---|
1661 | { |
---|
1662 | delete (*merge_ref); |
---|
1663 | for_splitting[k-1].remove(*merge_ref); |
---|
1664 | } |
---|
1665 | } |
---|
1666 | } |
---|
1667 | |
---|
1668 | k++; |
---|
1669 | |
---|
1670 | } |
---|
1671 | |
---|
1672 | for(set<vertex*>::iterator vert_ref = vertices_to_be_reduced.begin();vert_ref!=vertices_to_be_reduced.end();vert_ref++) |
---|
1673 | { |
---|
1674 | if((*vert_ref)->get_multiplicity()>1) |
---|
1675 | { |
---|
1676 | (*vert_ref)->lower_multiplicity(); |
---|
1677 | } |
---|
1678 | else |
---|
1679 | { |
---|
1680 | delete (*vert_ref); |
---|
1681 | } |
---|
1682 | } |
---|
1683 | |
---|
1684 | delete condition_to_remove; |
---|
1685 | } |
---|
1686 | |
---|
1687 | |
---|
1688 | vector<int> sizevector; |
---|
1689 | for(int s = 0;s<statistic.size();s++) |
---|
1690 | { |
---|
1691 | sizevector.push_back(statistic.row_size(s)); |
---|
1692 | cout << statistic.row_size(s) << ", "; |
---|
1693 | } |
---|
1694 | |
---|
1695 | |
---|
1696 | cout << endl; |
---|
1697 | |
---|
1698 | if(should_add) |
---|
1699 | { |
---|
1700 | cout << "Splitting." << endl; |
---|
1701 | |
---|
1702 | int k = 1; |
---|
1703 | int counter = 0; |
---|
1704 | |
---|
1705 | vector<list<polyhedron*>>::iterator beginning_ref = ++for_splitting.begin(); |
---|
1706 | |
---|
1707 | for(vector<list<polyhedron*>>::iterator vert_ref = beginning_ref;vert_ref<for_splitting.end();vert_ref++) |
---|
1708 | { |
---|
1709 | |
---|
1710 | for(list<polyhedron*>::reverse_iterator split_ref = vert_ref->rbegin();split_ref != vert_ref->rend();split_ref++) |
---|
1711 | { |
---|
1712 | counter++; |
---|
1713 | |
---|
1714 | polyhedron* new_totally_neutral_child; |
---|
1715 | |
---|
1716 | polyhedron* current_polyhedron = (*split_ref); |
---|
1717 | |
---|
1718 | if(vert_ref == beginning_ref) |
---|
1719 | { |
---|
1720 | vec coordinates1 = ((vertex*)(*(current_polyhedron->children.begin())))->get_coordinates(); |
---|
1721 | vec coordinates2 = ((vertex*)(*(++current_polyhedron->children.begin())))->get_coordinates(); |
---|
1722 | |
---|
1723 | vec extended_coord2 = coordinates2; |
---|
1724 | extended_coord2.ins(0,-1.0); |
---|
1725 | |
---|
1726 | double t = (-toadd*extended_coord2)/(toadd(1,toadd.size()-1)*(coordinates1-coordinates2)); |
---|
1727 | |
---|
1728 | vec new_coordinates = (1-t)*coordinates2+t*coordinates1; |
---|
1729 | |
---|
1730 | // cout << "c1:" << coordinates1 << endl << "c2:" << coordinates2 << endl << "nc:" << new_coordinates << endl; |
---|
1731 | |
---|
1732 | vertex* neutral_vertex = new vertex(new_coordinates); |
---|
1733 | |
---|
1734 | new_totally_neutral_child = neutral_vertex; |
---|
1735 | } |
---|
1736 | else |
---|
1737 | { |
---|
1738 | toprow* neutral_toprow = new toprow(); |
---|
1739 | |
---|
1740 | neutral_toprow->condition_sum = ((toprow*)current_polyhedron)->condition_sum; // tohle tu bylo driv: zeros(number_of_parameters+1); |
---|
1741 | neutral_toprow->condition_order = ((toprow*)current_polyhedron)->condition_order+1; |
---|
1742 | |
---|
1743 | new_totally_neutral_child = neutral_toprow; |
---|
1744 | } |
---|
1745 | |
---|
1746 | new_totally_neutral_child->parentconditions.insert(current_polyhedron->parentconditions.begin(),current_polyhedron->parentconditions.end()); |
---|
1747 | new_totally_neutral_child->parentconditions.insert(condition_to_add); |
---|
1748 | |
---|
1749 | new_totally_neutral_child->my_emlig = this; |
---|
1750 | |
---|
1751 | new_totally_neutral_child->children.insert(new_totally_neutral_child->children.end(), |
---|
1752 | current_polyhedron->totallyneutralgrandchildren.begin(), |
---|
1753 | current_polyhedron->totallyneutralgrandchildren.end()); |
---|
1754 | |
---|
1755 | |
---|
1756 | |
---|
1757 | // cout << ((toprow*)current_polyhedron)->condition << endl << toadd << endl; |
---|
1758 | vec cur_pos_condition = ((toprow*)current_polyhedron)->condition_sum; |
---|
1759 | vec cur_neg_condition = ((toprow*)current_polyhedron)->condition_sum; |
---|
1760 | |
---|
1761 | if(k == number_of_parameters) |
---|
1762 | { |
---|
1763 | cur_pos_condition = cur_pos_condition + toadd; |
---|
1764 | cur_neg_condition = cur_neg_condition - toadd; |
---|
1765 | } |
---|
1766 | |
---|
1767 | toprow* positive_poly = new toprow(cur_pos_condition, ((toprow*)current_polyhedron)->condition_order+1); |
---|
1768 | toprow* negative_poly = new toprow(cur_neg_condition, ((toprow*)current_polyhedron)->condition_order+1); |
---|
1769 | |
---|
1770 | positive_poly->my_emlig = this; |
---|
1771 | negative_poly->my_emlig = this; |
---|
1772 | |
---|
1773 | positive_poly->parentconditions.insert(current_polyhedron->parentconditions.begin(),current_polyhedron->parentconditions.end()); |
---|
1774 | negative_poly->parentconditions.insert(current_polyhedron->parentconditions.begin(),current_polyhedron->parentconditions.end()); |
---|
1775 | |
---|
1776 | for(set<polyhedron*>::iterator grand_ref = current_polyhedron->totallyneutralgrandchildren.begin(); grand_ref != current_polyhedron->totallyneutralgrandchildren.end();grand_ref++) |
---|
1777 | { |
---|
1778 | (*grand_ref)->parents.push_back(new_totally_neutral_child); |
---|
1779 | |
---|
1780 | // tohle tu nebylo. ma to tu byt? |
---|
1781 | //positive_poly->totallyneutralgrandchildren.insert(*grand_ref); |
---|
1782 | //negative_poly->totallyneutralgrandchildren.insert(*grand_ref); |
---|
1783 | |
---|
1784 | //(*grand_ref)->grandparents.insert(positive_poly); |
---|
1785 | //(*grand_ref)->grandparents.insert(negative_poly); |
---|
1786 | |
---|
1787 | new_totally_neutral_child->vertices.insert((*grand_ref)->vertices.begin(),(*grand_ref)->vertices.end()); |
---|
1788 | } |
---|
1789 | |
---|
1790 | positive_poly->children.push_back(new_totally_neutral_child); |
---|
1791 | negative_poly->children.push_back(new_totally_neutral_child); |
---|
1792 | |
---|
1793 | |
---|
1794 | for(list<polyhedron*>::iterator parent_ref = current_polyhedron->parents.begin();parent_ref!=current_polyhedron->parents.end();parent_ref++) |
---|
1795 | { |
---|
1796 | (*parent_ref)->totallyneutralgrandchildren.insert(new_totally_neutral_child); |
---|
1797 | // new_totally_neutral_child->grandparents.insert(*parent_ref); |
---|
1798 | |
---|
1799 | (*parent_ref)->neutralchildren.remove(current_polyhedron); |
---|
1800 | (*parent_ref)->children.remove(current_polyhedron); |
---|
1801 | |
---|
1802 | (*parent_ref)->children.push_back(positive_poly); |
---|
1803 | (*parent_ref)->children.push_back(negative_poly); |
---|
1804 | (*parent_ref)->positivechildren.push_back(positive_poly); |
---|
1805 | (*parent_ref)->negativechildren.push_back(negative_poly); |
---|
1806 | } |
---|
1807 | |
---|
1808 | positive_poly->parents.insert(positive_poly->parents.end(), |
---|
1809 | current_polyhedron->parents.begin(), |
---|
1810 | current_polyhedron->parents.end()); |
---|
1811 | |
---|
1812 | negative_poly->parents.insert(negative_poly->parents.end(), |
---|
1813 | current_polyhedron->parents.begin(), |
---|
1814 | current_polyhedron->parents.end()); |
---|
1815 | |
---|
1816 | |
---|
1817 | |
---|
1818 | new_totally_neutral_child->parents.push_back(positive_poly); |
---|
1819 | new_totally_neutral_child->parents.push_back(negative_poly); |
---|
1820 | |
---|
1821 | for(list<polyhedron*>::iterator child_ref = current_polyhedron->positivechildren.begin();child_ref!=current_polyhedron->positivechildren.end();child_ref++) |
---|
1822 | { |
---|
1823 | (*child_ref)->parents.remove(current_polyhedron); |
---|
1824 | (*child_ref)->parents.push_back(positive_poly); |
---|
1825 | } |
---|
1826 | |
---|
1827 | positive_poly->children.insert(positive_poly->children.end(), |
---|
1828 | current_polyhedron->positivechildren.begin(), |
---|
1829 | current_polyhedron->positivechildren.end()); |
---|
1830 | |
---|
1831 | for(list<polyhedron*>::iterator child_ref = current_polyhedron->negativechildren.begin();child_ref!=current_polyhedron->negativechildren.end();child_ref++) |
---|
1832 | { |
---|
1833 | (*child_ref)->parents.remove(current_polyhedron); |
---|
1834 | (*child_ref)->parents.push_back(negative_poly); |
---|
1835 | } |
---|
1836 | |
---|
1837 | negative_poly->children.insert(negative_poly->children.end(), |
---|
1838 | current_polyhedron->negativechildren.begin(), |
---|
1839 | current_polyhedron->negativechildren.end()); |
---|
1840 | |
---|
1841 | positive_poly->vertices.insert(current_polyhedron->positiveneutralvertices.begin(),current_polyhedron->positiveneutralvertices.end()); |
---|
1842 | positive_poly->vertices.insert(new_totally_neutral_child->vertices.begin(),new_totally_neutral_child->vertices.end()); |
---|
1843 | |
---|
1844 | negative_poly->vertices.insert(current_polyhedron->negativeneutralvertices.begin(),current_polyhedron->negativeneutralvertices.end()); |
---|
1845 | negative_poly->vertices.insert(new_totally_neutral_child->vertices.begin(),new_totally_neutral_child->vertices.end()); |
---|
1846 | |
---|
1847 | new_totally_neutral_child->triangulate(false); |
---|
1848 | |
---|
1849 | normalization_factor += positive_poly->triangulate(k==for_splitting.size()-1); |
---|
1850 | normalization_factor += negative_poly->triangulate(k==for_splitting.size()-1); |
---|
1851 | |
---|
1852 | statistic.append_polyhedron(k-1, new_totally_neutral_child); |
---|
1853 | |
---|
1854 | |
---|
1855 | |
---|
1856 | statistic.insert_polyhedron(k, positive_poly, current_polyhedron); |
---|
1857 | statistic.insert_polyhedron(k, negative_poly, current_polyhedron); |
---|
1858 | |
---|
1859 | statistic.delete_polyhedron(k, current_polyhedron); |
---|
1860 | |
---|
1861 | delete current_polyhedron; |
---|
1862 | } |
---|
1863 | |
---|
1864 | k++; |
---|
1865 | } |
---|
1866 | } |
---|
1867 | |
---|
1868 | /* |
---|
1869 | vector<int> sizevector; |
---|
1870 | //sizevector.clear(); |
---|
1871 | for(int s = 0;s<statistic.size();s++) |
---|
1872 | { |
---|
1873 | sizevector.push_back(statistic.row_size(s)); |
---|
1874 | cout << statistic.row_size(s) << ", "; |
---|
1875 | } |
---|
1876 | |
---|
1877 | cout << endl; |
---|
1878 | */ |
---|
1879 | |
---|
1880 | // cout << "Normalization factor: " << normalization_factor << endl; |
---|
1881 | |
---|
1882 | log_nc = log(normalization_factor) + logfact(condition_order-number_of_parameters-2)-(condition_order-number_of_parameters-2)*2; |
---|
1883 | |
---|
1884 | /* |
---|
1885 | cout << "part1: " << log(normalization_factor) << endl; |
---|
1886 | cout << "part2: " << logfact(condition_order-number_of_parameters-2) << endl; |
---|
1887 | pause(1); |
---|
1888 | */ |
---|
1889 | |
---|
1890 | |
---|
1891 | /* |
---|
1892 | for(polyhedron* topr_ref = statistic.rows[statistic.size()-1];topr_ref!=statistic.row_ends[statistic.size()-1]->next_poly;topr_ref=topr_ref->next_poly) |
---|
1893 | { |
---|
1894 | cout << ((toprow*)topr_ref)->condition << endl; |
---|
1895 | } |
---|
1896 | */ |
---|
1897 | |
---|
1898 | // step_me(101); |
---|
1899 | |
---|
1900 | } |
---|
1901 | |
---|
1902 | void set_correction_factors(int order) |
---|
1903 | { |
---|
1904 | for(int remaining_order = correction_factors.size();remaining_order<order;remaining_order++) |
---|
1905 | { |
---|
1906 | multiset<my_ivec> factor_templates; |
---|
1907 | multiset<my_ivec> final_factors; |
---|
1908 | |
---|
1909 | my_ivec orig_template = my_ivec(); |
---|
1910 | |
---|
1911 | for(int i = 1;i<number_of_parameters-remaining_order+1;i++) |
---|
1912 | { |
---|
1913 | bool in_cycle = false; |
---|
1914 | for(int j = 0;j<=remaining_order;j++) { |
---|
1915 | |
---|
1916 | multiset<my_ivec>::iterator fac_ref = factor_templates.begin(); |
---|
1917 | |
---|
1918 | do |
---|
1919 | { |
---|
1920 | my_ivec current_template; |
---|
1921 | if(!in_cycle) |
---|
1922 | { |
---|
1923 | current_template = orig_template; |
---|
1924 | in_cycle = true; |
---|
1925 | } |
---|
1926 | else |
---|
1927 | { |
---|
1928 | current_template = (*fac_ref); |
---|
1929 | fac_ref++; |
---|
1930 | } |
---|
1931 | |
---|
1932 | current_template.ins(current_template.size(),i); |
---|
1933 | |
---|
1934 | // cout << "template:" << current_template << endl; |
---|
1935 | |
---|
1936 | if(current_template.size()==remaining_order+1) |
---|
1937 | { |
---|
1938 | final_factors.insert(current_template); |
---|
1939 | } |
---|
1940 | else |
---|
1941 | { |
---|
1942 | factor_templates.insert(current_template); |
---|
1943 | } |
---|
1944 | } |
---|
1945 | while(fac_ref!=factor_templates.end()); |
---|
1946 | } |
---|
1947 | } |
---|
1948 | |
---|
1949 | correction_factors.push_back(final_factors); |
---|
1950 | |
---|
1951 | } |
---|
1952 | } |
---|
1953 | |
---|
1954 | pair<vec,simplex*> choose_simplex() |
---|
1955 | { |
---|
1956 | double rnumber = randu(); |
---|
1957 | |
---|
1958 | // cout << "RND:" << rnumber << endl; |
---|
1959 | |
---|
1960 | // This could be more efficient (log n), but map::upper_bound() doesn't let me dereference returned iterator |
---|
1961 | double prob_sum = 0; |
---|
1962 | toprow* sampled_toprow; |
---|
1963 | for(polyhedron* top_ref = statistic.rows[number_of_parameters];top_ref!=statistic.end_poly;top_ref=top_ref->next_poly) |
---|
1964 | { |
---|
1965 | // cout << "CDF:"<< (*top_ref).first << endl; |
---|
1966 | |
---|
1967 | toprow* current_toprow = ((toprow*)top_ref); |
---|
1968 | |
---|
1969 | prob_sum += current_toprow->probability; |
---|
1970 | |
---|
1971 | if(prob_sum >= rnumber*normalization_factor) |
---|
1972 | { |
---|
1973 | sampled_toprow = (toprow*)top_ref; |
---|
1974 | break; |
---|
1975 | } |
---|
1976 | else |
---|
1977 | { |
---|
1978 | if(top_ref->next_poly==statistic.end_poly) |
---|
1979 | { |
---|
1980 | cout << "Error."; |
---|
1981 | } |
---|
1982 | } |
---|
1983 | } |
---|
1984 | |
---|
1985 | //// cout << "Toprow/Count: " << toprow_count << "/" << ordered_toprows.size() << endl; |
---|
1986 | // cout << &sampled_toprow << ";"; |
---|
1987 | |
---|
1988 | rnumber = randu(); |
---|
1989 | |
---|
1990 | set<simplex*>::iterator s_ref; |
---|
1991 | prob_sum = 0; |
---|
1992 | for(s_ref = sampled_toprow->triangulation.begin();s_ref!=sampled_toprow->triangulation.end();s_ref++) |
---|
1993 | { |
---|
1994 | prob_sum += (*s_ref)->probability; |
---|
1995 | |
---|
1996 | if(prob_sum/sampled_toprow->probability >= rnumber) |
---|
1997 | break; |
---|
1998 | } |
---|
1999 | |
---|
2000 | return pair<vec,simplex*>(sampled_toprow->condition_sum,*s_ref); |
---|
2001 | } |
---|
2002 | |
---|
2003 | pair<double,double> choose_sigma(simplex* sampled_simplex) |
---|
2004 | { |
---|
2005 | double sigma = 0; |
---|
2006 | double pg_sum; |
---|
2007 | double ng_sum; |
---|
2008 | do |
---|
2009 | { |
---|
2010 | double rnumber = randu(); |
---|
2011 | |
---|
2012 | |
---|
2013 | double sum_g = 0; |
---|
2014 | for(int i = 0;i<sampled_simplex->positive_gamma_parameters.size();i++) |
---|
2015 | { |
---|
2016 | for(multimap<double,double>::iterator g_ref = sampled_simplex->positive_gamma_parameters[i].begin();g_ref != sampled_simplex->positive_gamma_parameters[i].end();g_ref++) |
---|
2017 | { |
---|
2018 | sum_g += (*g_ref).first/sampled_simplex->positive_gamma_sum; |
---|
2019 | |
---|
2020 | |
---|
2021 | if(sum_g>rnumber) |
---|
2022 | { |
---|
2023 | //itpp::Gamma_RNG* gamma = new itpp::Gamma_RNG(conditions.size()-number_of_parameters,1/(*g_ref).second); |
---|
2024 | //sigma = 1/(*gamma)(); |
---|
2025 | |
---|
2026 | GamRNG.setup(conditions.size()-number_of_parameters,(*g_ref).second); |
---|
2027 | |
---|
2028 | sigma = 1/GamRNG(); |
---|
2029 | |
---|
2030 | // cout << "Sigma mean: " << (*g_ref).second/(conditions.size()-number_of_parameters-1) << endl; |
---|
2031 | break; |
---|
2032 | } |
---|
2033 | } |
---|
2034 | |
---|
2035 | if(sigma!=0) |
---|
2036 | { |
---|
2037 | break; |
---|
2038 | } |
---|
2039 | } |
---|
2040 | |
---|
2041 | rnumber = randu(); |
---|
2042 | |
---|
2043 | pg_sum = 0; |
---|
2044 | for(vector<multimap<double,double>>::iterator v_ref = sampled_simplex->positive_gamma_parameters.begin();v_ref!=sampled_simplex->positive_gamma_parameters.end();v_ref++) |
---|
2045 | { |
---|
2046 | for(multimap<double,double>::iterator pg_ref = (*v_ref).begin();pg_ref!=(*v_ref).end();pg_ref++) |
---|
2047 | { |
---|
2048 | pg_sum += exp((sampled_simplex->min_beta-(*pg_ref).second)/sigma)*pow((*pg_ref).second/sigma,(int)conditions.size()-number_of_parameters-1)*(*pg_ref).second/fact(conditions.size()-number_of_parameters-1)*(*pg_ref).first; |
---|
2049 | } |
---|
2050 | } |
---|
2051 | |
---|
2052 | ng_sum = 0; |
---|
2053 | for(vector<multimap<double,double>>::iterator v_ref = sampled_simplex->negative_gamma_parameters.begin();v_ref!=sampled_simplex->negative_gamma_parameters.end();v_ref++) |
---|
2054 | { |
---|
2055 | for(multimap<double,double>::iterator ng_ref = (*v_ref).begin();ng_ref!=(*v_ref).end();ng_ref++) |
---|
2056 | { |
---|
2057 | ng_sum += exp((sampled_simplex->min_beta-(*ng_ref).second)/sigma)*pow((*ng_ref).second/sigma,(int)conditions.size()-number_of_parameters-1)*(*ng_ref).second/fact(conditions.size()-number_of_parameters-1)*(*ng_ref).first; |
---|
2058 | } |
---|
2059 | } |
---|
2060 | } |
---|
2061 | while(pg_sum-ng_sum<0); |
---|
2062 | |
---|
2063 | return pair<double,double>((pg_sum-ng_sum)/pg_sum,sigma); |
---|
2064 | } |
---|
2065 | |
---|
2066 | mat sample_mat(int n) |
---|
2067 | { |
---|
2068 | |
---|
2069 | /// \TODO tady je to spatne, tady nesmi byt conditions.size(), viz RARX.bayes() |
---|
2070 | if(conditions.size()-2-number_of_parameters>=0) |
---|
2071 | { |
---|
2072 | mat sample_mat; |
---|
2073 | map<double,toprow*> ordered_toprows; |
---|
2074 | double sum_a = 0; |
---|
2075 | |
---|
2076 | //cout << "Likelihoods of toprows:" << endl; |
---|
2077 | |
---|
2078 | for(polyhedron* top_ref = statistic.rows[number_of_parameters];top_ref!=statistic.end_poly;top_ref=top_ref->next_poly) |
---|
2079 | { |
---|
2080 | toprow* current_top = (toprow*)top_ref; |
---|
2081 | |
---|
2082 | sum_a+=current_top->probability; |
---|
2083 | /* |
---|
2084 | cout << current_top->probability << " "; |
---|
2085 | |
---|
2086 | for(set<vertex*>::iterator vert_ref = (*top_ref).vertices.begin();vert_ref!=(*top_ref).vertices.end();vert_ref++) |
---|
2087 | { |
---|
2088 | cout << round(100*(*vert_ref)->get_coordinates())/100 << " ; "; |
---|
2089 | } |
---|
2090 | */ |
---|
2091 | |
---|
2092 | // cout << endl; |
---|
2093 | ordered_toprows.insert(pair<double,toprow*>(sum_a,current_top)); |
---|
2094 | } |
---|
2095 | |
---|
2096 | // cout << "Sum N: " << normalization_factor << endl; |
---|
2097 | |
---|
2098 | while(sample_mat.cols()<n) |
---|
2099 | { |
---|
2100 | //// cout << "*************************************" << endl; |
---|
2101 | |
---|
2102 | |
---|
2103 | |
---|
2104 | double rnumber = randu()*sum_a; |
---|
2105 | |
---|
2106 | // cout << "RND:" << rnumber << endl; |
---|
2107 | |
---|
2108 | // This could be more efficient (log n), but map::upper_bound() doesn't let me dereference returned iterator |
---|
2109 | int toprow_count = 0; |
---|
2110 | toprow* sampled_toprow; |
---|
2111 | for(map<double,toprow*>::iterator top_ref = ordered_toprows.begin();top_ref!=ordered_toprows.end();top_ref++) |
---|
2112 | { |
---|
2113 | // cout << "CDF:"<< (*top_ref).first << endl; |
---|
2114 | toprow_count++; |
---|
2115 | |
---|
2116 | if((*top_ref).first >= rnumber) |
---|
2117 | { |
---|
2118 | sampled_toprow = (*top_ref).second; |
---|
2119 | break; |
---|
2120 | } |
---|
2121 | } |
---|
2122 | |
---|
2123 | //// cout << "Toprow/Count: " << toprow_count << "/" << ordered_toprows.size() << endl; |
---|
2124 | // cout << &sampled_toprow << ";"; |
---|
2125 | |
---|
2126 | rnumber = randu(); |
---|
2127 | |
---|
2128 | set<simplex*>::iterator s_ref; |
---|
2129 | double sum_b = 0; |
---|
2130 | int simplex_count = 0; |
---|
2131 | for(s_ref = sampled_toprow->triangulation.begin();s_ref!=sampled_toprow->triangulation.end();s_ref++) |
---|
2132 | { |
---|
2133 | simplex_count++; |
---|
2134 | |
---|
2135 | sum_b += (*s_ref)->probability; |
---|
2136 | |
---|
2137 | if(sum_b/sampled_toprow->probability >= rnumber) |
---|
2138 | break; |
---|
2139 | } |
---|
2140 | |
---|
2141 | //// cout << "Simplex/Count: " << simplex_count << "/" << sampled_toprow->triangulation.size() << endl; |
---|
2142 | //// cout << "Simplex factor: " << (*s_ref)->probability << endl; |
---|
2143 | //// cout << "Toprow factor: " << sampled_toprow->probability << endl; |
---|
2144 | //// cout << "Emlig factor: " << normalization_factor << endl; |
---|
2145 | // cout << &(*tri_ref) << endl; |
---|
2146 | |
---|
2147 | int number_of_runs = 0; |
---|
2148 | bool have_sigma = false; |
---|
2149 | double sigma = 0; |
---|
2150 | do |
---|
2151 | { |
---|
2152 | rnumber = randu(); |
---|
2153 | |
---|
2154 | double sum_g = 0; |
---|
2155 | for(int i = 0;i<(*s_ref)->positive_gamma_parameters.size();i++) |
---|
2156 | { |
---|
2157 | for(multimap<double,double>::iterator g_ref = (*s_ref)->positive_gamma_parameters[i].begin();g_ref != (*s_ref)->positive_gamma_parameters[i].end();g_ref++) |
---|
2158 | { |
---|
2159 | sum_g += (*g_ref).first/(*s_ref)->positive_gamma_sum; |
---|
2160 | |
---|
2161 | |
---|
2162 | if(sum_g>rnumber) |
---|
2163 | { |
---|
2164 | //itpp::Gamma_RNG* gamma = new itpp::Gamma_RNG(conditions.size()-number_of_parameters,1/(*g_ref).second); |
---|
2165 | //sigma = 1/(*gamma)(); |
---|
2166 | |
---|
2167 | GamRNG.setup(conditions.size()-number_of_parameters,(*g_ref).second); |
---|
2168 | |
---|
2169 | sigma = 1/GamRNG(); |
---|
2170 | |
---|
2171 | // cout << "Sigma mean: " << (*g_ref).second/(conditions.size()-number_of_parameters-1) << endl; |
---|
2172 | break; |
---|
2173 | } |
---|
2174 | } |
---|
2175 | |
---|
2176 | if(sigma!=0) |
---|
2177 | { |
---|
2178 | break; |
---|
2179 | } |
---|
2180 | } |
---|
2181 | |
---|
2182 | rnumber = randu(); |
---|
2183 | |
---|
2184 | double pg_sum = 0; |
---|
2185 | for(vector<multimap<double,double>>::iterator v_ref = (*s_ref)->positive_gamma_parameters.begin();v_ref!=(*s_ref)->positive_gamma_parameters.end();v_ref++) |
---|
2186 | { |
---|
2187 | for(multimap<double,double>::iterator pg_ref = (*v_ref).begin();pg_ref!=(*v_ref).end();pg_ref++) |
---|
2188 | { |
---|
2189 | pg_sum += exp(((*s_ref)->min_beta-(*pg_ref).second)/sigma)*pow((*pg_ref).second/sigma,(int)conditions.size()-number_of_parameters-1)*(*pg_ref).second/fact(conditions.size()-number_of_parameters-1)*(*pg_ref).first; |
---|
2190 | } |
---|
2191 | } |
---|
2192 | |
---|
2193 | double ng_sum = 0; |
---|
2194 | for(vector<multimap<double,double>>::iterator v_ref = (*s_ref)->negative_gamma_parameters.begin();v_ref!=(*s_ref)->negative_gamma_parameters.end();v_ref++) |
---|
2195 | { |
---|
2196 | for(multimap<double,double>::iterator ng_ref = (*v_ref).begin();ng_ref!=(*v_ref).end();ng_ref++) |
---|
2197 | { |
---|
2198 | ng_sum += exp(((*s_ref)->min_beta-(*ng_ref).second)/sigma)*pow((*ng_ref).second/sigma,(int)conditions.size()-number_of_parameters-1)*(*ng_ref).second/fact(conditions.size()-number_of_parameters-1)*(*ng_ref).first; |
---|
2199 | } |
---|
2200 | } |
---|
2201 | |
---|
2202 | if((pg_sum-ng_sum)/pg_sum>rnumber) |
---|
2203 | { |
---|
2204 | have_sigma = true; |
---|
2205 | } |
---|
2206 | |
---|
2207 | number_of_runs++; |
---|
2208 | } |
---|
2209 | while(!have_sigma); |
---|
2210 | |
---|
2211 | //// cout << "Sigma: " << sigma << endl; |
---|
2212 | //// cout << "Nr. of sigma runs: " << number_of_runs << endl; |
---|
2213 | |
---|
2214 | int dimension = (*s_ref)->vertices.size()-1; |
---|
2215 | |
---|
2216 | mat jacobian(dimension,dimension); |
---|
2217 | vec gradient = sampled_toprow->condition_sum.right(dimension); |
---|
2218 | |
---|
2219 | vertex* base_vert = *(*s_ref)->vertices.begin(); |
---|
2220 | |
---|
2221 | //// cout << "Base vertex coords(should be close to est. param.): " << base_vert->get_coordinates() << endl; |
---|
2222 | |
---|
2223 | int row_count = 0; |
---|
2224 | |
---|
2225 | for(set<vertex*>::iterator vert_ref = ++(*s_ref)->vertices.begin();vert_ref!=(*s_ref)->vertices.end();vert_ref++) |
---|
2226 | { |
---|
2227 | vec current_coords = (*vert_ref)->get_coordinates(); |
---|
2228 | |
---|
2229 | //// cout << "Coords of vertex[" << row_count << "]: " << current_coords << endl; |
---|
2230 | |
---|
2231 | vec relative_coords = current_coords-base_vert->get_coordinates(); |
---|
2232 | |
---|
2233 | jacobian.set_row(row_count,relative_coords); |
---|
2234 | |
---|
2235 | row_count++; |
---|
2236 | } |
---|
2237 | |
---|
2238 | //// cout << "Jacobian: " << jacobian << endl; |
---|
2239 | |
---|
2240 | //// cout << "Gradient before trafo:" << gradient << endl; |
---|
2241 | |
---|
2242 | gradient = jacobian*gradient; |
---|
2243 | |
---|
2244 | //// cout << "Gradient after trafo:" << gradient << endl; |
---|
2245 | |
---|
2246 | // vec normal_gradient = gradient/sqrt(gradient*gradient); |
---|
2247 | // cout << gradient << endl; |
---|
2248 | // cout << normal_gradient << endl; |
---|
2249 | // cout << sqrt(gradient*gradient) << endl; |
---|
2250 | |
---|
2251 | mat rotation_matrix = eye(dimension); |
---|
2252 | |
---|
2253 | |
---|
2254 | |
---|
2255 | for(int i = 1;i<dimension;i++) |
---|
2256 | { |
---|
2257 | vec x_axis = zeros(dimension); |
---|
2258 | x_axis.set(0,1); |
---|
2259 | |
---|
2260 | x_axis = rotation_matrix*x_axis; |
---|
2261 | |
---|
2262 | double t = abs(gradient[i]/gradient*x_axis); |
---|
2263 | |
---|
2264 | double sin_theta = sign(gradient[i])*t/sqrt(1+pow(t,2)); |
---|
2265 | double cos_theta = sign(gradient*x_axis)/sqrt(1+pow(t,2)); |
---|
2266 | |
---|
2267 | mat partial_rotation = eye(dimension); |
---|
2268 | |
---|
2269 | partial_rotation.set(0,0,cos_theta); |
---|
2270 | partial_rotation.set(i,i,cos_theta); |
---|
2271 | |
---|
2272 | partial_rotation.set(0,i,sin_theta); |
---|
2273 | partial_rotation.set(i,0,-sin_theta); |
---|
2274 | |
---|
2275 | rotation_matrix = rotation_matrix*partial_rotation; |
---|
2276 | |
---|
2277 | } |
---|
2278 | |
---|
2279 | // cout << rotation_matrix << endl; |
---|
2280 | |
---|
2281 | mat extended_rotation = rotation_matrix; |
---|
2282 | extended_rotation.ins_col(0,zeros(extended_rotation.rows())); |
---|
2283 | |
---|
2284 | //// cout << "Extended rotation: " << extended_rotation << endl; |
---|
2285 | |
---|
2286 | vec minima = itpp::min(extended_rotation,2); |
---|
2287 | vec maxima = itpp::max(extended_rotation,2); |
---|
2288 | |
---|
2289 | //// cout << "Minima: " << minima << endl; |
---|
2290 | //// cout << "Maxima: " << maxima << endl; |
---|
2291 | |
---|
2292 | vec sample_coordinates; |
---|
2293 | bool is_inside = true; |
---|
2294 | |
---|
2295 | vec new_sample; |
---|
2296 | sample_coordinates = new_sample; |
---|
2297 | |
---|
2298 | for(int j = 0;j<number_of_parameters;j++) |
---|
2299 | { |
---|
2300 | rnumber = randu(); |
---|
2301 | |
---|
2302 | double coordinate; |
---|
2303 | |
---|
2304 | if(j==0) |
---|
2305 | { |
---|
2306 | vec new_gradient = rotation_matrix*gradient; |
---|
2307 | |
---|
2308 | //// cout << "New gradient(should have only first component nonzero):" << new_gradient << endl; |
---|
2309 | |
---|
2310 | // cout << "Max: " << maxima[0] << " Min: " << minima[0] << " Grad:" << new_gradient[0] << endl; |
---|
2311 | |
---|
2312 | double log_bracket = 1-rnumber*(1-exp(new_gradient[0]/sigma*(minima[0]-maxima[0]))); |
---|
2313 | |
---|
2314 | coordinate = minima[0]-sigma/new_gradient[0]*log(log_bracket); |
---|
2315 | } |
---|
2316 | else |
---|
2317 | { |
---|
2318 | coordinate = minima[j]+rnumber*(maxima[j]-minima[j]); |
---|
2319 | } |
---|
2320 | |
---|
2321 | sample_coordinates.ins(j,coordinate); |
---|
2322 | } |
---|
2323 | |
---|
2324 | //// cout << "Sampled coordinates(gradient direction): " << sample_coordinates << endl; |
---|
2325 | |
---|
2326 | sample_coordinates = rotation_matrix.T()*sample_coordinates; |
---|
2327 | |
---|
2328 | //// cout << "Sampled coordinates(backrotated direction):" << sample_coordinates << endl; |
---|
2329 | |
---|
2330 | |
---|
2331 | for(int j = 0;j<sample_coordinates.size();j++) |
---|
2332 | { |
---|
2333 | if(sample_coordinates[j]<0) |
---|
2334 | { |
---|
2335 | is_inside = false; |
---|
2336 | } |
---|
2337 | } |
---|
2338 | |
---|
2339 | double above_criterion = ones(sample_coordinates.size())*sample_coordinates; |
---|
2340 | |
---|
2341 | if(above_criterion>1) |
---|
2342 | { |
---|
2343 | is_inside = false; |
---|
2344 | } |
---|
2345 | |
---|
2346 | if(is_inside) |
---|
2347 | { |
---|
2348 | sample_coordinates = jacobian.T()*sample_coordinates+(*base_vert).get_coordinates(); |
---|
2349 | |
---|
2350 | sample_coordinates.ins(0,sigma); |
---|
2351 | |
---|
2352 | //// cout << "Sampled coordinates(parameter space):" << sample_coordinates << endl; |
---|
2353 | |
---|
2354 | sample_mat.ins_col(0,sample_coordinates); |
---|
2355 | |
---|
2356 | // cout << sample_mat.cols() << ","; |
---|
2357 | } |
---|
2358 | |
---|
2359 | // cout << sampled_toprow->condition_sum.right(sampled_toprow->condition_sum.size()-1)*min_grad->get_coordinates()-sampled_toprow->condition_sum[0] << endl; |
---|
2360 | // cout << sampled_toprow->condition_sum.right(sampled_toprow->condition_sum.size()-1)*max_grad->get_coordinates()-sampled_toprow->condition_sum[0] << endl; |
---|
2361 | |
---|
2362 | |
---|
2363 | } |
---|
2364 | |
---|
2365 | cout << endl; |
---|
2366 | return sample_mat; |
---|
2367 | } |
---|
2368 | else |
---|
2369 | { |
---|
2370 | throw new exception("You are trying to sample from density that is not determined (parameters can't be integrated out)!"); |
---|
2371 | |
---|
2372 | return 0; |
---|
2373 | } |
---|
2374 | |
---|
2375 | |
---|
2376 | } |
---|
2377 | |
---|
2378 | pair<vec,mat> importance_sample(int n) |
---|
2379 | { |
---|
2380 | vec probabilities; |
---|
2381 | mat samples; |
---|
2382 | |
---|
2383 | for(int i = 0;i<n;i++) |
---|
2384 | { |
---|
2385 | pair<vec,simplex*> condition_and_simplex = choose_simplex(); |
---|
2386 | |
---|
2387 | pair<double,double> probability_and_sigma = choose_sigma(condition_and_simplex.second); |
---|
2388 | |
---|
2389 | int dimension = condition_and_simplex.second->vertices.size()-1; |
---|
2390 | |
---|
2391 | mat jacobian(dimension,dimension); |
---|
2392 | vec gradient = condition_and_simplex.first.right(dimension); |
---|
2393 | |
---|
2394 | vertex* base_vert = *condition_and_simplex.second->vertices.begin(); |
---|
2395 | |
---|
2396 | //// cout << "Base vertex coords(should be close to est. param.): " << base_vert->get_coordinates() << endl; |
---|
2397 | |
---|
2398 | int row_count = 0; |
---|
2399 | |
---|
2400 | for(set<vertex*>::iterator vert_ref = ++condition_and_simplex.second->vertices.begin();vert_ref!=condition_and_simplex.second->vertices.end();vert_ref++) |
---|
2401 | { |
---|
2402 | vec current_coords = (*vert_ref)->get_coordinates(); |
---|
2403 | |
---|
2404 | //// cout << "Coords of vertex[" << row_count << "]: " << current_coords << endl; |
---|
2405 | |
---|
2406 | vec relative_coords = current_coords-base_vert->get_coordinates(); |
---|
2407 | |
---|
2408 | jacobian.set_row(row_count,relative_coords); |
---|
2409 | |
---|
2410 | row_count++; |
---|
2411 | } |
---|
2412 | |
---|
2413 | //// cout << "Jacobian: " << jacobian << endl; |
---|
2414 | |
---|
2415 | /// \todo Is this correct? Are the random coordinates really jointly uniform? I don't know. |
---|
2416 | vec sample_coords; |
---|
2417 | double sampling_diff = 1; |
---|
2418 | for(int j = 0;j<number_of_parameters;j++) |
---|
2419 | { |
---|
2420 | double rnumber = randu()*sampling_diff; |
---|
2421 | |
---|
2422 | sample_coords.ins(0,rnumber); |
---|
2423 | |
---|
2424 | sampling_diff -= rnumber; |
---|
2425 | } |
---|
2426 | |
---|
2427 | sample_coords = jacobian.T()*sample_coords+(*base_vert).get_coordinates(); |
---|
2428 | |
---|
2429 | vec extended_coords = sample_coords; |
---|
2430 | extended_coords.ins(0,-1.0); |
---|
2431 | |
---|
2432 | double exponent = extended_coords*condition_and_simplex.first; |
---|
2433 | double sample_prob = 1/condition_and_simplex.second->probability/pow(probability_and_sigma.second,(int)conditions.size()-number_of_parameters)*exp((-1)/probability_and_sigma.second*exponent); |
---|
2434 | sample_prob *= probability_and_sigma.first; |
---|
2435 | |
---|
2436 | sample_coords.ins(0,probability_and_sigma.second); |
---|
2437 | |
---|
2438 | samples.ins_col(0,sample_coords); |
---|
2439 | probabilities.ins(0,sample_prob); |
---|
2440 | } |
---|
2441 | |
---|
2442 | return pair<vec,mat>(probabilities,samples); |
---|
2443 | } |
---|
2444 | |
---|
2445 | int logfact(int factor) |
---|
2446 | { |
---|
2447 | if(factor>0) |
---|
2448 | { |
---|
2449 | return factor+logfact(factor-1); |
---|
2450 | } |
---|
2451 | else |
---|
2452 | { |
---|
2453 | return 0; |
---|
2454 | } |
---|
2455 | } |
---|
2456 | protected: |
---|
2457 | |
---|
2458 | /// A method for creating plain default statistic representing only the range of the parameter space. |
---|
2459 | void create_statistic(int number_of_parameters, double soft_prior_parameter) |
---|
2460 | { |
---|
2461 | /* |
---|
2462 | for(int i = 0;i<number_of_parameters;i++) |
---|
2463 | { |
---|
2464 | vec condition_vec = zeros(number_of_parameters+1); |
---|
2465 | condition_vec[i+1] = 1; |
---|
2466 | |
---|
2467 | condition* new_condition = new condition(condition_vec); |
---|
2468 | |
---|
2469 | conditions.push_back(new_condition); |
---|
2470 | } |
---|
2471 | */ |
---|
2472 | |
---|
2473 | // An empty vector of coordinates. |
---|
2474 | vec origin_coord; |
---|
2475 | |
---|
2476 | // We create an origin - this point will have all the coordinates zero, but now it has an empty vector of coords. |
---|
2477 | vertex *origin = new vertex(origin_coord); |
---|
2478 | |
---|
2479 | origin->my_emlig = this; |
---|
2480 | |
---|
2481 | /* |
---|
2482 | // As a statistic, we have to create a vector of vectors of polyhedron pointers. It will then represent the Hasse |
---|
2483 | // diagram. First we create a vector of polyhedrons.. |
---|
2484 | list<polyhedron*> origin_vec; |
---|
2485 | |
---|
2486 | // ..we fill it with the origin.. |
---|
2487 | origin_vec.push_back(origin); |
---|
2488 | |
---|
2489 | // ..and we fill the statistic with the created vector. |
---|
2490 | statistic.push_back(origin_vec); |
---|
2491 | */ |
---|
2492 | |
---|
2493 | statistic = *(new c_statistic()); |
---|
2494 | |
---|
2495 | statistic.append_polyhedron(0, origin); |
---|
2496 | |
---|
2497 | // Now we have a statistic for a zero dimensional space. Regarding to how many dimensional space we need to |
---|
2498 | // describe, we have to widen the descriptional default statistic. We use an iterative procedure as follows: |
---|
2499 | for(int i=0;i<number_of_parameters;i++) |
---|
2500 | { |
---|
2501 | // We first will create two new vertices. These will be the borders of the parameter space in the dimension |
---|
2502 | // of newly added parameter. Therefore they will have all coordinates except the last one zero. We get the |
---|
2503 | // right amount of zero cooridnates by reading them from the origin |
---|
2504 | vec origin_coord = origin->get_coordinates(); |
---|
2505 | |
---|
2506 | |
---|
2507 | |
---|
2508 | // And we incorporate the nonzero coordinates into the new cooordinate vectors |
---|
2509 | vec origin_coord1 = concat(origin_coord,-max_range); |
---|
2510 | vec origin_coord2 = concat(origin_coord,max_range); |
---|
2511 | |
---|
2512 | |
---|
2513 | // Now we create the points |
---|
2514 | vertex* new_point1 = new vertex(origin_coord1); |
---|
2515 | vertex* new_point2 = new vertex(origin_coord2); |
---|
2516 | |
---|
2517 | new_point1->my_emlig = this; |
---|
2518 | new_point2->my_emlig = this; |
---|
2519 | |
---|
2520 | //********************************************************************************************************* |
---|
2521 | // The algorithm for recursive build of a new Hasse diagram representing the space structure from the old |
---|
2522 | // diagram works so that you create two copies of the old Hasse diagram, you shift them up one level (points |
---|
2523 | // will be segments, segments will be areas etc.) and you connect each one of the original copied polyhedrons |
---|
2524 | // with its offspring by a parent-child relation. Also each of the segments in the first (second) copy is |
---|
2525 | // connected to the first (second) newly created vertex by a parent-child relation. |
---|
2526 | //********************************************************************************************************* |
---|
2527 | |
---|
2528 | |
---|
2529 | /* |
---|
2530 | // Create the vectors of vectors of pointers to polyhedrons to hold the copies of the old Hasse diagram |
---|
2531 | vector<vector<polyhedron*>> new_statistic1; |
---|
2532 | vector<vector<polyhedron*>> new_statistic2; |
---|
2533 | */ |
---|
2534 | |
---|
2535 | c_statistic* new_statistic1 = new c_statistic(); |
---|
2536 | c_statistic* new_statistic2 = new c_statistic(); |
---|
2537 | |
---|
2538 | |
---|
2539 | // Copy the statistic by rows |
---|
2540 | for(int j=0;j<statistic.size();j++) |
---|
2541 | { |
---|
2542 | |
---|
2543 | |
---|
2544 | // an element counter |
---|
2545 | int element_number = 0; |
---|
2546 | |
---|
2547 | /* |
---|
2548 | vector<polyhedron*> supportnew_1; |
---|
2549 | vector<polyhedron*> supportnew_2; |
---|
2550 | |
---|
2551 | new_statistic1.push_back(supportnew_1); |
---|
2552 | new_statistic2.push_back(supportnew_2); |
---|
2553 | */ |
---|
2554 | |
---|
2555 | // for each polyhedron in the given row |
---|
2556 | for(polyhedron* horiz_ref = statistic.rows[j];horiz_ref!=statistic.get_end();horiz_ref=horiz_ref->next_poly) |
---|
2557 | { |
---|
2558 | // Append an extra zero coordinate to each of the vertices for the new dimension |
---|
2559 | // If vert_ref is at the first index => we loop through vertices |
---|
2560 | if(j == 0) |
---|
2561 | { |
---|
2562 | // cast the polyhedron pointer to a vertex pointer and push a zero to its vector of coordinates |
---|
2563 | ((vertex*) horiz_ref)->push_coordinate(0); |
---|
2564 | } |
---|
2565 | /* |
---|
2566 | else |
---|
2567 | { |
---|
2568 | ((toprow*) (*horiz_ref))->condition.ins(0,0); |
---|
2569 | }*/ |
---|
2570 | |
---|
2571 | // if it has parents |
---|
2572 | if(!horiz_ref->parents.empty()) |
---|
2573 | { |
---|
2574 | // save the relative address of this child in a vector kids_rel_addresses of all its parents. |
---|
2575 | // This information will later be used for copying the whole Hasse diagram with each of the |
---|
2576 | // relations contained within. |
---|
2577 | for(list<polyhedron*>::iterator parent_ref = horiz_ref->parents.begin();parent_ref != horiz_ref->parents.end();parent_ref++) |
---|
2578 | { |
---|
2579 | (*parent_ref)->kids_rel_addresses.push_back(element_number); |
---|
2580 | } |
---|
2581 | } |
---|
2582 | |
---|
2583 | // ************************************************************************************************** |
---|
2584 | // Here we begin creating a new polyhedron, which will be a copy of the old one. Each such polyhedron |
---|
2585 | // will be created as a toprow, but this information will be later forgotten and only the polyhedrons |
---|
2586 | // in the top row of the Hasse diagram will be considered toprow for later use. |
---|
2587 | // ************************************************************************************************** |
---|
2588 | |
---|
2589 | // First we create vectors specifying a toprow condition. In the case of a preconstructed statistic |
---|
2590 | // this condition will be a vector of zeros. There are two vectors, because we need two copies of |
---|
2591 | // the original Hasse diagram. |
---|
2592 | vec vec1; |
---|
2593 | vec vec2; |
---|
2594 | if(!horiz_ref->kids_rel_addresses.empty()) |
---|
2595 | { |
---|
2596 | vec1 = ((toprow*)horiz_ref)->condition_sum; |
---|
2597 | vec1.ins(vec1.size(),-soft_prior_parameter); |
---|
2598 | |
---|
2599 | vec2 = ((toprow*)horiz_ref)->condition_sum; |
---|
2600 | vec2.ins(vec2.size(),soft_prior_parameter); |
---|
2601 | } |
---|
2602 | else |
---|
2603 | { |
---|
2604 | vec1.ins(0,-soft_prior_parameter); |
---|
2605 | vec2.ins(0,soft_prior_parameter); |
---|
2606 | |
---|
2607 | vec1.ins(0,0); |
---|
2608 | vec2.ins(0,0); |
---|
2609 | } |
---|
2610 | |
---|
2611 | // cout << vec1 << endl; |
---|
2612 | // cout << vec2 << endl; |
---|
2613 | |
---|
2614 | |
---|
2615 | // We create a new toprow with the previously specified condition. |
---|
2616 | toprow* current_copy1 = new toprow(vec1, this->condition_order); |
---|
2617 | toprow* current_copy2 = new toprow(vec2, this->condition_order); |
---|
2618 | |
---|
2619 | current_copy1->my_emlig = this; |
---|
2620 | current_copy2->my_emlig = this; |
---|
2621 | |
---|
2622 | // The vertices of the copies will be inherited, because there will be a parent/child relation |
---|
2623 | // between each polyhedron and its offspring (comming from the copy) and a parent has all the |
---|
2624 | // vertices of its child plus more. |
---|
2625 | for(set<vertex*>::iterator vertex_ref = horiz_ref->vertices.begin();vertex_ref!=horiz_ref->vertices.end();vertex_ref++) |
---|
2626 | { |
---|
2627 | current_copy1->vertices.insert(*vertex_ref); |
---|
2628 | current_copy2->vertices.insert(*vertex_ref); |
---|
2629 | } |
---|
2630 | |
---|
2631 | // The only new vertex of the offspring should be the newly created point. |
---|
2632 | current_copy1->vertices.insert(new_point1); |
---|
2633 | current_copy2->vertices.insert(new_point2); |
---|
2634 | |
---|
2635 | // This method guarantees that each polyhedron is already triangulated, therefore its triangulation |
---|
2636 | // is only one set of vertices and it is the set of all its vertices. |
---|
2637 | simplex* t_simplex1 = new simplex(current_copy1->vertices); |
---|
2638 | simplex* t_simplex2 = new simplex(current_copy2->vertices); |
---|
2639 | |
---|
2640 | current_copy1->triangulation.insert(t_simplex1); |
---|
2641 | current_copy2->triangulation.insert(t_simplex2); |
---|
2642 | |
---|
2643 | // Now we have copied the polyhedron and we have to copy all of its relations. Because we are copying |
---|
2644 | // in the Hasse diagram from bottom up, we always have to copy the parent/child relations to all the |
---|
2645 | // kids and when we do that and know the child, in the child we will remember the parent we came from. |
---|
2646 | // This way all the parents/children relations are saved in both the parent and the child. |
---|
2647 | if(!horiz_ref->kids_rel_addresses.empty()) |
---|
2648 | { |
---|
2649 | for(list<int>::iterator kid_ref = horiz_ref->kids_rel_addresses.begin();kid_ref!=horiz_ref->kids_rel_addresses.end();kid_ref++) |
---|
2650 | { |
---|
2651 | polyhedron* new_kid1 = new_statistic1->rows[j-1]; |
---|
2652 | polyhedron* new_kid2 = new_statistic2->rows[j-1]; |
---|
2653 | |
---|
2654 | // THIS IS NOT EFFECTIVE: It could be improved by having the list indexed for new_statistic, but |
---|
2655 | // not indexed for statistic. Hopefully this will not cause a big slowdown - happens only offline. |
---|
2656 | if(*kid_ref) |
---|
2657 | { |
---|
2658 | for(int k = 1;k<=(*kid_ref);k++) |
---|
2659 | { |
---|
2660 | new_kid1=new_kid1->next_poly; |
---|
2661 | new_kid2=new_kid2->next_poly; |
---|
2662 | } |
---|
2663 | } |
---|
2664 | |
---|
2665 | // find the child and save the relation to the parent |
---|
2666 | current_copy1->children.push_back(new_kid1); |
---|
2667 | current_copy2->children.push_back(new_kid2); |
---|
2668 | |
---|
2669 | // in the child save the parents' address |
---|
2670 | new_kid1->parents.push_back(current_copy1); |
---|
2671 | new_kid2->parents.push_back(current_copy2); |
---|
2672 | } |
---|
2673 | |
---|
2674 | // Here we clear the parents kids_rel_addresses vector for later use (when we need to widen the |
---|
2675 | // Hasse diagram again) |
---|
2676 | horiz_ref->kids_rel_addresses.clear(); |
---|
2677 | } |
---|
2678 | // If there were no children previously, we are copying a polyhedron that has been a vertex before. |
---|
2679 | // In this case it is a segment now and it will have a relation to its mother (copywise) and to the |
---|
2680 | // newly created point. Here we create the connection to the new point, again from both sides. |
---|
2681 | else |
---|
2682 | { |
---|
2683 | // Add the address of the new point in the former vertex |
---|
2684 | current_copy1->children.push_back(new_point1); |
---|
2685 | current_copy2->children.push_back(new_point2); |
---|
2686 | |
---|
2687 | // Add the address of the former vertex in the new point |
---|
2688 | new_point1->parents.push_back(current_copy1); |
---|
2689 | new_point2->parents.push_back(current_copy2); |
---|
2690 | } |
---|
2691 | |
---|
2692 | // Save the mother in its offspring |
---|
2693 | current_copy1->children.push_back(horiz_ref); |
---|
2694 | current_copy2->children.push_back(horiz_ref); |
---|
2695 | |
---|
2696 | // Save the offspring in its mother |
---|
2697 | horiz_ref->parents.push_back(current_copy1); |
---|
2698 | horiz_ref->parents.push_back(current_copy2); |
---|
2699 | |
---|
2700 | |
---|
2701 | // Add the copies into the relevant statistic. The statistic will later be appended to the previous |
---|
2702 | // Hasse diagram |
---|
2703 | new_statistic1->append_polyhedron(j,current_copy1); |
---|
2704 | new_statistic2->append_polyhedron(j,current_copy2); |
---|
2705 | |
---|
2706 | // Raise the count in the vector of polyhedrons |
---|
2707 | element_number++; |
---|
2708 | |
---|
2709 | } |
---|
2710 | |
---|
2711 | } |
---|
2712 | |
---|
2713 | /* |
---|
2714 | statistic.begin()->push_back(new_point1); |
---|
2715 | statistic.begin()->push_back(new_point2); |
---|
2716 | */ |
---|
2717 | |
---|
2718 | statistic.append_polyhedron(0, new_point1); |
---|
2719 | statistic.append_polyhedron(0, new_point2); |
---|
2720 | |
---|
2721 | // Merge the new statistics into the old one. This will either be the final statistic or we will |
---|
2722 | // reenter the widening loop. |
---|
2723 | for(int j=0;j<new_statistic1->size();j++) |
---|
2724 | { |
---|
2725 | /* |
---|
2726 | if(j+1==statistic.size()) |
---|
2727 | { |
---|
2728 | list<polyhedron*> support; |
---|
2729 | statistic.push_back(support); |
---|
2730 | } |
---|
2731 | |
---|
2732 | (statistic.begin()+j+1)->insert((statistic.begin()+j+1)->end(),new_statistic1[j].begin(),new_statistic1[j].end()); |
---|
2733 | (statistic.begin()+j+1)->insert((statistic.begin()+j+1)->end(),new_statistic2[j].begin(),new_statistic2[j].end()); |
---|
2734 | */ |
---|
2735 | statistic.append_polyhedron(j+1,new_statistic1->rows[j],new_statistic1->row_ends[j]); |
---|
2736 | statistic.append_polyhedron(j+1,new_statistic2->rows[j],new_statistic2->row_ends[j]); |
---|
2737 | } |
---|
2738 | } |
---|
2739 | |
---|
2740 | /* |
---|
2741 | vector<list<toprow*>> toprow_statistic; |
---|
2742 | int line_count = 0; |
---|
2743 | |
---|
2744 | for(vector<list<polyhedron*>>::iterator polyhedron_ref = ++statistic.begin(); polyhedron_ref!=statistic.end();polyhedron_ref++) |
---|
2745 | { |
---|
2746 | list<toprow*> support_list; |
---|
2747 | toprow_statistic.push_back(support_list); |
---|
2748 | |
---|
2749 | for(list<polyhedron*>::iterator polyhedron_ref2 = polyhedron_ref->begin(); polyhedron_ref2 != polyhedron_ref->end(); polyhedron_ref2++) |
---|
2750 | { |
---|
2751 | toprow* support_top = (toprow*)(*polyhedron_ref2); |
---|
2752 | |
---|
2753 | toprow_statistic[line_count].push_back(support_top); |
---|
2754 | } |
---|
2755 | |
---|
2756 | line_count++; |
---|
2757 | }*/ |
---|
2758 | |
---|
2759 | /* |
---|
2760 | vector<int> sizevector; |
---|
2761 | for(int s = 0;s<statistic.size();s++) |
---|
2762 | { |
---|
2763 | sizevector.push_back(statistic.row_size(s)); |
---|
2764 | } |
---|
2765 | */ |
---|
2766 | |
---|
2767 | } |
---|
2768 | |
---|
2769 | }; |
---|
2770 | |
---|
2771 | |
---|
2772 | |
---|
2773 | //! Robust Bayesian AR model for Multicriteria-Laplace-Inverse-Gamma density |
---|
2774 | class RARX //: public BM |
---|
2775 | { |
---|
2776 | private: |
---|
2777 | bool has_constant; |
---|
2778 | |
---|
2779 | int window_size; |
---|
2780 | |
---|
2781 | list<vec> conditions; |
---|
2782 | |
---|
2783 | public: |
---|
2784 | emlig* posterior; |
---|
2785 | |
---|
2786 | RARX(int number_of_parameters, const int window_size, bool has_constant)//:BM() |
---|
2787 | { |
---|
2788 | this->has_constant = has_constant; |
---|
2789 | |
---|
2790 | posterior = new emlig(number_of_parameters,0.001); |
---|
2791 | |
---|
2792 | this->window_size = window_size; |
---|
2793 | }; |
---|
2794 | |
---|
2795 | void bayes(itpp::vec yt) |
---|
2796 | { |
---|
2797 | if(has_constant) |
---|
2798 | { |
---|
2799 | int c_size = yt.size(); |
---|
2800 | |
---|
2801 | yt.ins(c_size,1.0); |
---|
2802 | } |
---|
2803 | |
---|
2804 | if(yt.size() == posterior->number_of_parameters+1) |
---|
2805 | { |
---|
2806 | conditions.push_back(yt); |
---|
2807 | } |
---|
2808 | else |
---|
2809 | { |
---|
2810 | throw new exception("Wrong condition size for bayesian data update!"); |
---|
2811 | } |
---|
2812 | |
---|
2813 | //posterior->step_me(0); |
---|
2814 | |
---|
2815 | /// \TODO tohle je spatne, tady musi byt jiny vypocet poctu podminek, kdyby nejaka byla multiplicitni, tak tohle bude spatne |
---|
2816 | if(conditions.size()>window_size && window_size!=0) |
---|
2817 | { |
---|
2818 | posterior->add_and_remove_condition(yt,conditions.front()); |
---|
2819 | conditions.pop_front(); |
---|
2820 | |
---|
2821 | //posterior->step_me(1); |
---|
2822 | } |
---|
2823 | else |
---|
2824 | { |
---|
2825 | posterior->add_condition(yt); |
---|
2826 | } |
---|
2827 | |
---|
2828 | |
---|
2829 | |
---|
2830 | } |
---|
2831 | |
---|
2832 | }; |
---|
2833 | |
---|
2834 | |
---|
2835 | |
---|
2836 | #endif //TRAGE_H |
---|