Mercurial Hosting > traffic-intelligence
comparison trajectorymanagement/src/LCSMetric.h @ 1159:e1e7acef8eab
moved trajectory management library into Traffic Intelligence
author | Nicolas Saunier <nicolas.saunier@polymtl.ca> |
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date | Mon, 22 Feb 2021 22:09:35 -0500 |
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1158:7eb972942f22 | 1159:e1e7acef8eab |
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1 #ifndef LCSMETRIC_H_ | |
2 #define LCSMETRIC_H_ | |
3 | |
4 #include "Metric.h" | |
5 | |
6 /** | |
7 * LCSMetric class. | |
8 * | |
9 * The Longest Common Subsequence metric | |
10 * This class mesures : | |
11 * 1) the similarity (number of points in "common", ie relative to distance similarity threshold) ; | |
12 * 2) the normalized distance between two trajectories | |
13 */ | |
14 | |
15 template<typename Tr, typename To> | |
16 class LCSMetric: public Metric<Tr, To> | |
17 { | |
18 public: | |
19 /** | |
20 * Constructor. | |
21 */ | |
22 LCSMetric() : similarityThreshold(0.0), eps(1e-6) | |
23 { | |
24 } | |
25 | |
26 /** | |
27 * Set similarity threshold between two points. | |
28 * | |
29 * @param[in] similarityThreshold similarity threshold | |
30 */ | |
31 bool setSimilarityThreshold(double similarityThreshold) | |
32 { | |
33 if (similarityThreshold >= 0.0) | |
34 { | |
35 this->similarityThreshold = similarityThreshold; | |
36 return true; | |
37 } | |
38 return false; | |
39 } | |
40 | |
41 /** | |
42 * Set machine epsilon. | |
43 * | |
44 * @param[in] eps machine epsilon | |
45 */ | |
46 bool setEps(double eps) | |
47 { | |
48 if (eps >= 0.0) | |
49 { | |
50 this->eps = eps; | |
51 return true; | |
52 } | |
53 return false; | |
54 } | |
55 | |
56 /** | |
57 * Compute similarity between two trajectories. | |
58 * | |
59 * @param[in] a input trajectory | |
60 * @param[in] b input trajectory | |
61 * @param[out] result distance between two trajectories | |
62 */ | |
63 void distance(const Trajectory<Tr> *a, const Trajectory<Tr> *b, To &result, unsigned int nbOfPoints = std::numeric_limits<unsigned int>::max()) | |
64 { | |
65 result = To(0); | |
66 unsigned int LCSS = 0; | |
67 similarity(a,b, LCSS); | |
68 unsigned int min_size = min(a->size(),b->size()); | |
69 result = 1 - double(LCSS/min_size); | |
70 } | |
71 | |
72 /** | |
73 * Compute similarity between two trajectories. | |
74 * | |
75 * @param[in] a input trajectory | |
76 * @param[in] b input trajectory | |
77 * @param[out] result similarity between two trajectories | |
78 */ | |
79 void similarity(const Trajectory<Tr> *a, const Trajectory<Tr> *b, unsigned int &result) | |
80 { | |
81 unsigned int LCS[a->size() + 1][b->size() + 1]; | |
82 | |
83 { //initialisation | |
84 for (unsigned int i = 0; i <= a->size(); ++i) | |
85 { | |
86 LCS[i][0] = 0; | |
87 } | |
88 | |
89 for (unsigned int j = 0; j <= b->size(); ++j) | |
90 { | |
91 LCS[0][j] = 0; | |
92 } | |
93 } | |
94 | |
95 { //algorithm | |
96 for (unsigned int i = 1; i <= a->size(); ++i) | |
97 { | |
98 for (unsigned int j = 1; j <= b->size(); ++j) | |
99 { | |
100 cv::Point3_<typeof(static_cast<Tr>(a->getPoint(i-1)).x)> p(a->getPoint(i - 1) - b->getPoint(j - 1)); | |
101 double distance = sqrt(pow(p.x, 2) + pow(p.y, 2) + pow(p.z, 2)); // il faudrait généraliser | |
102 if (distance <= similarityThreshold + eps) | |
103 { //a[i] == b[j] | |
104 LCS[i][j] = LCS[i - 1][j - 1] + 1; | |
105 } | |
106 else | |
107 { | |
108 LCS[i][j] = std::max(LCS[i - 1][j], LCS[i][j - 1]); | |
109 } | |
110 } | |
111 } | |
112 } | |
113 | |
114 result = LCS[a->size()][b->size()]; | |
115 } | |
116 | |
117 private: | |
118 /** | |
119 * Similarity threshold between two points. | |
120 */ | |
121 double similarityThreshold; | |
122 | |
123 /** | |
124 * Machine epsilon. | |
125 */ | |
126 double eps; | |
127 | |
128 }; | |
129 | |
130 #endif /* LCSMETRIC_H_ */ |