1 | // This file is a part of Framsticks SDK. http://www.framsticks.com/ |
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2 | // Copyright (C) 1999-2020 Maciej Komosinski and Szymon Ulatowski. |
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3 | // See LICENSE.txt for details. |
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4 | |
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5 | #include "measure-hungarian.h" |
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6 | |
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7 | const int SimilMeasureHungarian::iNOFactors = 4; |
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8 | |
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9 | #define FIELDSTRUCT SimilMeasureHungarian |
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10 | |
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11 | static ParamEntry simil_hungarian_paramtab[] = { |
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12 | { "Creature: Similarity: Graph optimal", 1, 7, "SimilMeasureHungarian", "Evaluates morphological dissimilarity using hungarian measure. More information:\nhttp://www.framsticks.com/bib/Komosinski-et-al-2001\nhttp://www.framsticks.com/bib/Komosinski-and-Kubiak-2011\nhttp://www.framsticks.com/bib/Komosinski-2016\nhttps://doi.org/10.1007/978-3-030-16692-2_8", }, |
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13 | { "simil_parts", 0, 0, "Weight of parts count", "f 0 100 0", FIELD(m_adFactors[0]), "Differing number of parts is also handled by the 'part degree' similarity component.", }, |
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14 | { "simil_partdeg", 0, 0, "Weight of parts' degree", "f 0 100 1", FIELD(m_adFactors[1]), "", }, |
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15 | { "simil_neuro", 0, 0, "Weight of neurons count", "f 0 100 0.1", FIELD(m_adFactors[2]), "", }, |
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16 | { "simil_partgeom", 0, 0, "Weight of parts' geometric distances", "f 0 100 0", FIELD(m_adFactors[3]), "", }, |
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17 | { "simil_fixedZaxis", 0, 0, "Fix 'z' (vertical) axis?", "d 0 1 0", FIELD(fixedZaxis), "", }, |
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18 | { "simil_weightedMDS", 0, 0, "Should weighted MDS be used?", "d 0 1 0", FIELD(wMDS), "If activated, weighted MDS with vertex (i.e., Part) degrees as weights is used for 3D alignment of body structure.", }, |
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19 | { "evaluateDistance", 0, PARAM_DONTSAVE | PARAM_USERHIDDEN, "Evaluate model dissimilarity", "p f(oGeno,oGeno)", PROCEDURE(p_evaldistance), "Calculates dissimilarity between two models created from Geno objects.", }, |
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20 | { 0, }, |
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21 | }; |
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22 | |
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23 | #undef FIELDSTRUCT |
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24 | |
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25 | SimilMeasureHungarian::SimilMeasureHungarian() : localpar(simil_hungarian_paramtab, this) |
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26 | { |
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27 | localpar.setDefault(); |
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28 | |
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29 | nSmaller = 0; |
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30 | nBigger = 0; |
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31 | |
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32 | for (int i = 0; i < 2; i++) |
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33 | { |
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34 | degrees[i] = nullptr; |
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35 | neurons[i] = nullptr; |
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36 | on_joint[i] = 0; |
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37 | anywhere[i] = 0; |
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38 | } |
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39 | |
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40 | assignment = nullptr; |
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41 | parts_distances = nullptr; |
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42 | temp_parts_distances = nullptr; |
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43 | |
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44 | save_matching = false; |
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45 | } |
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46 | |
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47 | void SimilMeasureHungarian::prepareData() |
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48 | { |
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49 | m_iSmaller = models[0]->getPartCount() <= models[1]->getPartCount() ? 0 : 1; |
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50 | nSmaller = models[m_iSmaller]->getPartCount(); |
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51 | nBigger = models[1 - m_iSmaller]->getPartCount(); |
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52 | |
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53 | for (int i = 0; i < 2; i++) |
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54 | { |
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55 | int size = models[i]->getPartCount(); |
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56 | degrees[i] = new int[size](); |
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57 | neurons[i] = new int[size](); |
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58 | } |
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59 | |
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60 | countDegrees(); |
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61 | countNeurons(); |
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62 | |
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63 | parts_distances = new double[nBigger*nBigger](); |
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64 | fillPartsDistances(parts_distances, nBigger, nSmaller, false); |
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65 | assignment = new int[nBigger](); |
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66 | |
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67 | if (save_matching) |
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68 | for (int i = 0; i < nBigger; i++) |
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69 | min_assignment.push_back(0); |
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70 | } |
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71 | |
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72 | void SimilMeasureHungarian::beforeTransformation() |
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73 | { |
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74 | temp_parts_distances = new double[nBigger*nBigger](); |
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75 | std::copy(parts_distances, parts_distances + nBigger * nBigger, temp_parts_distances); |
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76 | } |
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77 | |
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78 | double SimilMeasureHungarian::distanceForTransformation() |
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79 | { |
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80 | fillPartsDistances(temp_parts_distances, nBigger, nSmaller, true); |
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81 | std::fill_n(assignment, nBigger, 0); |
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82 | double distance = hungarian.Solve(temp_parts_distances, assignment, nBigger, nBigger); |
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83 | |
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84 | delete[] temp_parts_distances; |
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85 | return addNeuronsPartsDiff(distance); |
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86 | } |
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87 | |
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88 | double SimilMeasureHungarian::distanceWithoutAlignment() |
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89 | { |
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90 | double distance = hungarian.Solve(parts_distances, assignment, nBigger, nBigger); |
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91 | if (save_matching) |
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92 | copyMatching(); |
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93 | return addNeuronsPartsDiff(distance); |
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94 | } |
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95 | |
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96 | double SimilMeasureHungarian::addNeuronsPartsDiff(double dist) |
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97 | { |
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98 | //add difference in anywhere and onJoint neurons |
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99 | dist += m_adFactors[2] * (abs(on_joint[0] - on_joint[1]) + abs(anywhere[0] - anywhere[1])); |
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100 | //add difference in part numbers |
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101 | dist += (nBigger - nSmaller) * m_adFactors[0]; |
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102 | return dist; |
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103 | } |
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104 | |
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105 | void SimilMeasureHungarian::copyMatching() |
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106 | { |
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107 | min_assignment.clear(); |
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108 | min_assignment.insert(min_assignment.begin(), assignment, assignment + nBigger); |
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109 | } |
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110 | |
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111 | void SimilMeasureHungarian::cleanData() |
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112 | { |
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113 | for (int i = 0; i < 2; i++) |
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114 | { |
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115 | // delete degree and position arrays |
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116 | SAFEDELETEARRAY(degrees[i]); |
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117 | SAFEDELETEARRAY(neurons[i]); |
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118 | |
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119 | on_joint[i] = 0; |
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120 | anywhere[i] = 0; |
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121 | } |
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122 | |
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123 | delete[] assignment; |
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124 | delete[] parts_distances; |
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125 | |
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126 | if (save_matching) |
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127 | min_assignment.clear(); |
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128 | } |
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129 | |
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130 | void SimilMeasureHungarian::countDegrees() |
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131 | { |
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132 | Part *P1, *P2; |
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133 | int i, j, i1, i2; |
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134 | |
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135 | for (i = 0; i < 2; i++) |
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136 | { |
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137 | for (j = 0; j < models[i]->getJointCount(); j++) |
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138 | { |
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139 | Joint *J = models[i]->getJoint(j); |
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140 | |
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141 | P1 = J->part1; |
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142 | P2 = J->part2; |
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143 | |
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144 | i1 = models[i]->findPart(P1); |
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145 | i2 = models[i]->findPart(P2); |
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146 | |
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147 | degrees[i][i1]++; |
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148 | degrees[i][i2]++; |
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149 | } |
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150 | } |
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151 | } |
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152 | |
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153 | void SimilMeasureHungarian::countNeurons() |
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154 | { |
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155 | Part *P1; |
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156 | Joint *J1; |
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157 | int i, j, i2; |
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158 | |
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159 | for (i = 0; i < 2; i++) |
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160 | { |
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161 | for (j = 0; j < models[i]->getNeuroCount(); j++) |
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162 | { |
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163 | Neuro *N = models[i]->getNeuro(j); |
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164 | // count parts attached to neurons |
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165 | P1 = N->getPart(); |
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166 | if (P1) |
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167 | { |
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168 | i2 = models[i]->findPart(P1); |
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169 | neurons[i][i2]++; |
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170 | } |
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171 | else |
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172 | // count unattached neurons |
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173 | { |
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174 | J1 = N->getJoint(); |
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175 | if (J1) |
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176 | on_joint[i]++; |
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177 | else |
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178 | anywhere[i]++; |
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179 | } |
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180 | } |
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181 | } |
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182 | } |
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183 | |
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184 | void SimilMeasureHungarian::fillPartsDistances(double*& dist, int bigger, int smaller, bool geo) |
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185 | { |
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186 | for (int i = 0; i < bigger; i++) |
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187 | { |
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188 | for (int j = 0; j < bigger; j++) |
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189 | { |
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190 | // assign penalty for unassignment for vertex from bigger model |
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191 | if (j >= smaller) |
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192 | { |
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193 | if (geo) |
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194 | dist[i*bigger + j] += m_adFactors[3] * coordinates[1 - m_iSmaller][i].length(); |
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195 | else |
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196 | dist[i*bigger + j] = m_adFactors[1] * degrees[1 - m_iSmaller][i] + m_adFactors[2] * neurons[1 - m_iSmaller][i]; |
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197 | } |
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198 | // compute distance between parts |
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199 | else |
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200 | { |
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201 | if (geo){ |
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202 | dist[i*bigger + j] += m_adFactors[3] * coordinates[1 - m_iSmaller][i].distanceTo(coordinates[m_iSmaller][j]); |
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203 | } |
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204 | else |
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205 | dist[i*bigger + j] = m_adFactors[1] * abs(degrees[1 - m_iSmaller][i] - degrees[m_iSmaller][j]) |
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206 | + m_adFactors[2] * abs(neurons[1 - m_iSmaller][i] - neurons[m_iSmaller][j]); |
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207 | } |
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208 | } |
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209 | } |
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210 | } |
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211 | |
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212 | /** Returns number of factors involved in final distance computation. |
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213 | These factors include differences in numbers of parts, degrees, |
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214 | number of neurons. |
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215 | */ |
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216 | int SimilMeasureHungarian::getNOFactors() |
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217 | { |
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218 | return SimilMeasureHungarian::iNOFactors; |
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219 | } |
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220 | |
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221 | int SimilMeasureHungarian::setParams(std::vector<double> params) |
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222 | { |
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223 | int i = 0; |
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224 | for (i = 0; i < SimilMeasureHungarian::iNOFactors; i++) |
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225 | m_adFactors[i] = params.at(i); |
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226 | fixedZaxis = params.at(i); |
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227 | return 0; |
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228 | } |
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