1 | // This file is a part of Framsticks SDK. http://www.framsticks.com/ |
---|
2 | // Copyright (C) 1999-2020 Maciej Komosinski and Szymon Ulatowski. |
---|
3 | // See LICENSE.txt for details. |
---|
4 | |
---|
5 | #include "measure-hungarian.h" |
---|
6 | |
---|
7 | const int SimilMeasureHungarian::iNOFactors = 4; |
---|
8 | |
---|
9 | #define FIELDSTRUCT SimilMeasureHungarian |
---|
10 | |
---|
11 | static ParamEntry simil_hungarian_paramtab[] = { |
---|
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", }, |
---|
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.", }, |
---|
14 | { "simil_partdeg", 0, 0, "Weight of parts' degree", "f 0 100 1", FIELD(m_adFactors[1]), "", }, |
---|
15 | { "simil_neuro", 0, 0, "Weight of neurons count", "f 0 100 0.1", FIELD(m_adFactors[2]), "", }, |
---|
16 | { "simil_partgeom", 0, 0, "Weight of parts' geometric distances", "f 0 100 0", FIELD(m_adFactors[3]), "", }, |
---|
17 | { "simil_fixedZaxis", 0, 0, "Fix 'z' (vertical) axis?", "d 0 1 0", FIELD(fixedZaxis), "", }, |
---|
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.", }, |
---|
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.", }, |
---|
20 | { 0, }, |
---|
21 | }; |
---|
22 | |
---|
23 | #undef FIELDSTRUCT |
---|
24 | |
---|
25 | SimilMeasureHungarian::SimilMeasureHungarian() : localpar(simil_hungarian_paramtab, this) |
---|
26 | { |
---|
27 | localpar.setDefault(); |
---|
28 | |
---|
29 | nSmaller = 0; |
---|
30 | nBigger = 0; |
---|
31 | |
---|
32 | for (int i = 0; i < 2; i++) |
---|
33 | { |
---|
34 | degrees[i] = nullptr; |
---|
35 | neurons[i] = nullptr; |
---|
36 | on_joint[i] = 0; |
---|
37 | anywhere[i] = 0; |
---|
38 | } |
---|
39 | |
---|
40 | assignment = nullptr; |
---|
41 | parts_distances = nullptr; |
---|
42 | temp_parts_distances = nullptr; |
---|
43 | |
---|
44 | save_matching = false; |
---|
45 | } |
---|
46 | |
---|
47 | void SimilMeasureHungarian::prepareData() |
---|
48 | { |
---|
49 | m_iSmaller = models[0]->getPartCount() <= models[1]->getPartCount() ? 0 : 1; |
---|
50 | nSmaller = models[m_iSmaller]->getPartCount(); |
---|
51 | nBigger = models[1 - m_iSmaller]->getPartCount(); |
---|
52 | |
---|
53 | for (int i = 0; i < 2; i++) |
---|
54 | { |
---|
55 | int size = models[i]->getPartCount(); |
---|
56 | degrees[i] = new int[size](); |
---|
57 | neurons[i] = new int[size](); |
---|
58 | } |
---|
59 | |
---|
60 | countDegrees(); |
---|
61 | countNeurons(); |
---|
62 | |
---|
63 | parts_distances = new double[nBigger*nBigger](); |
---|
64 | fillPartsDistances(parts_distances, nBigger, nSmaller, false); |
---|
65 | assignment = new int[nBigger](); |
---|
66 | |
---|
67 | if (save_matching) |
---|
68 | for (int i = 0; i < nBigger; i++) |
---|
69 | min_assignment.push_back(0); |
---|
70 | |
---|
71 | if (m_adFactors[3] == 0) |
---|
72 | with_alignment = false; |
---|
73 | } |
---|
74 | |
---|
75 | void SimilMeasureHungarian::beforeTransformation() |
---|
76 | { |
---|
77 | temp_parts_distances = new double[nBigger*nBigger](); |
---|
78 | std::copy(parts_distances, parts_distances + nBigger * nBigger, temp_parts_distances); |
---|
79 | } |
---|
80 | |
---|
81 | double SimilMeasureHungarian::distanceForTransformation() |
---|
82 | { |
---|
83 | fillPartsDistances(temp_parts_distances, nBigger, nSmaller, true); |
---|
84 | std::fill_n(assignment, nBigger, 0); |
---|
85 | double distance = hungarian.Solve(temp_parts_distances, assignment, nBigger, nBigger); |
---|
86 | |
---|
87 | delete[] temp_parts_distances; |
---|
88 | return addNeuronsPartsDiff(distance); |
---|
89 | } |
---|
90 | |
---|
91 | double SimilMeasureHungarian::distanceWithoutAlignment() |
---|
92 | { |
---|
93 | double distance = hungarian.Solve(parts_distances, assignment, nBigger, nBigger); |
---|
94 | if (save_matching) |
---|
95 | copyMatching(); |
---|
96 | return addNeuronsPartsDiff(distance); |
---|
97 | } |
---|
98 | |
---|
99 | double SimilMeasureHungarian::addNeuronsPartsDiff(double dist) |
---|
100 | { |
---|
101 | //add difference in anywhere and onJoint neurons |
---|
102 | dist += m_adFactors[2] * (abs(on_joint[0] - on_joint[1]) + abs(anywhere[0] - anywhere[1])); |
---|
103 | //add difference in part numbers |
---|
104 | dist += (nBigger - nSmaller) * m_adFactors[0]; |
---|
105 | return dist; |
---|
106 | } |
---|
107 | |
---|
108 | void SimilMeasureHungarian::copyMatching() |
---|
109 | { |
---|
110 | min_assignment.clear(); |
---|
111 | min_assignment.insert(min_assignment.begin(), assignment, assignment + nBigger); |
---|
112 | } |
---|
113 | |
---|
114 | void SimilMeasureHungarian::cleanData() |
---|
115 | { |
---|
116 | for (int i = 0; i < 2; i++) |
---|
117 | { |
---|
118 | // delete degree and position arrays |
---|
119 | SAFEDELETEARRAY(degrees[i]); |
---|
120 | SAFEDELETEARRAY(neurons[i]); |
---|
121 | |
---|
122 | on_joint[i] = 0; |
---|
123 | anywhere[i] = 0; |
---|
124 | } |
---|
125 | |
---|
126 | delete[] assignment; |
---|
127 | delete[] parts_distances; |
---|
128 | |
---|
129 | if (save_matching) |
---|
130 | min_assignment.clear(); |
---|
131 | |
---|
132 | with_alignment = true; //restore default value |
---|
133 | } |
---|
134 | |
---|
135 | void SimilMeasureHungarian::countDegrees() |
---|
136 | { |
---|
137 | Part *P1, *P2; |
---|
138 | int i, j, i1, i2; |
---|
139 | |
---|
140 | for (i = 0; i < 2; i++) |
---|
141 | { |
---|
142 | for (j = 0; j < models[i]->getJointCount(); j++) |
---|
143 | { |
---|
144 | Joint *J = models[i]->getJoint(j); |
---|
145 | |
---|
146 | P1 = J->part1; |
---|
147 | P2 = J->part2; |
---|
148 | |
---|
149 | i1 = models[i]->findPart(P1); |
---|
150 | i2 = models[i]->findPart(P2); |
---|
151 | |
---|
152 | degrees[i][i1]++; |
---|
153 | degrees[i][i2]++; |
---|
154 | } |
---|
155 | } |
---|
156 | } |
---|
157 | |
---|
158 | void SimilMeasureHungarian::countNeurons() |
---|
159 | { |
---|
160 | Part *P1; |
---|
161 | Joint *J1; |
---|
162 | int i, j, i2; |
---|
163 | |
---|
164 | for (i = 0; i < 2; i++) |
---|
165 | { |
---|
166 | for (j = 0; j < models[i]->getNeuroCount(); j++) |
---|
167 | { |
---|
168 | Neuro *N = models[i]->getNeuro(j); |
---|
169 | // count parts attached to neurons |
---|
170 | P1 = N->getPart(); |
---|
171 | if (P1) |
---|
172 | { |
---|
173 | i2 = models[i]->findPart(P1); |
---|
174 | neurons[i][i2]++; |
---|
175 | } |
---|
176 | else |
---|
177 | // count unattached neurons |
---|
178 | { |
---|
179 | J1 = N->getJoint(); |
---|
180 | if (J1) |
---|
181 | on_joint[i]++; |
---|
182 | else |
---|
183 | anywhere[i]++; |
---|
184 | } |
---|
185 | } |
---|
186 | } |
---|
187 | } |
---|
188 | |
---|
189 | void SimilMeasureHungarian::fillPartsDistances(double*& dist, int bigger, int smaller, bool geo) |
---|
190 | { |
---|
191 | for (int i = 0; i < bigger; i++) |
---|
192 | { |
---|
193 | for (int j = 0; j < bigger; j++) |
---|
194 | { |
---|
195 | // assign penalty for unassignment for vertex from bigger model |
---|
196 | if (j >= smaller) |
---|
197 | { |
---|
198 | if (geo) |
---|
199 | dist[i*bigger + j] += m_adFactors[3] * coordinates[1 - m_iSmaller][i].length(); |
---|
200 | else |
---|
201 | dist[i*bigger + j] = m_adFactors[1] * degrees[1 - m_iSmaller][i] + m_adFactors[2] * neurons[1 - m_iSmaller][i]; |
---|
202 | } |
---|
203 | // compute distance between parts |
---|
204 | else |
---|
205 | { |
---|
206 | if (geo){ |
---|
207 | dist[i*bigger + j] += m_adFactors[3] * coordinates[1 - m_iSmaller][i].distanceTo(coordinates[m_iSmaller][j]); |
---|
208 | } |
---|
209 | else |
---|
210 | dist[i*bigger + j] = m_adFactors[1] * abs(degrees[1 - m_iSmaller][i] - degrees[m_iSmaller][j]) |
---|
211 | + m_adFactors[2] * abs(neurons[1 - m_iSmaller][i] - neurons[m_iSmaller][j]); |
---|
212 | } |
---|
213 | } |
---|
214 | } |
---|
215 | } |
---|
216 | |
---|
217 | /** Returns number of factors involved in final distance computation. |
---|
218 | These factors include differences in numbers of parts, degrees, |
---|
219 | number of neurons. |
---|
220 | */ |
---|
221 | int SimilMeasureHungarian::getNOFactors() |
---|
222 | { |
---|
223 | return SimilMeasureHungarian::iNOFactors; |
---|
224 | } |
---|
225 | |
---|
226 | int SimilMeasureHungarian::setParams(std::vector<double> params) |
---|
227 | { |
---|
228 | int i = 0; |
---|
229 | for (i = 0; i < SimilMeasureHungarian::iNOFactors; i++) |
---|
230 | m_adFactors[i] = params.at(i); |
---|
231 | fixedZaxis = params.at(i); |
---|
232 | return 0; |
---|
233 | } |
---|