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1 // SPDX-License-Identifier: GPL-3.0-or-later
2
3 #ifndef ML_KMEANS_H
4 #define ML_KMEANS_H
5
6 #include "ml_features.h"
7
8 #include <ctime>
9
10 typedef struct web_buffer BUFFER;
11
12 struct ml_kmeans_inlined_t;
13
14 struct ml_kmeans_t {
15 std::vector<DSample> cluster_centers;
16 calculated_number_t min_dist;
17 calculated_number_t max_dist;
18 time_t after;
19 time_t before;
20
21 ml_kmeans_t() : min_dist(0), max_dist(0), after(0), before(0)
22 {
23 }
24
25 explicit ml_kmeans_t(const ml_kmeans_inlined_t &inlined);
26 ml_kmeans_t &operator=(const ml_kmeans_inlined_t &inlined);
27 };
28
29 struct ml_kmeans_inlined_t {
30 std::array<DSample, 2> cluster_centers;
31 calculated_number_t min_dist;
32 calculated_number_t max_dist;
33 time_t after;
34 time_t before;
35
36 ml_kmeans_inlined_t() : min_dist(0), max_dist(0), after(0), before(0)
37 {
38 cluster_centers[0] = 0;
39 cluster_centers[1] = 0;
40 }
41
42 explicit ml_kmeans_inlined_t(const ml_kmeans_t &km) : min_dist(km.min_dist), max_dist(km.max_dist), after(km.after), before(km.before)
43 {
44 if (km.cluster_centers.size() == 2) {
45 cluster_centers[0] = km.cluster_centers[0];
46 cluster_centers[1] = km.cluster_centers[1];
47 }
48 else {
49 cluster_centers[0] = 0;
50 cluster_centers[1] = 0;
51 }
52 }
53
54 ml_kmeans_inlined_t &operator=(const ml_kmeans_t &km)
55 {
56 if (km.cluster_centers.size() == 2) {
57 cluster_centers[0] = km.cluster_centers[0];
58 cluster_centers[1] = km.cluster_centers[1];
59 }
60 else {
61 cluster_centers[0] = 0;
62 cluster_centers[1] = 0;
63 }
64 min_dist = km.min_dist;
65 max_dist = km.max_dist;
66 after = km.after;
67 before = km.before;
68 return *this;
69 }
70 };
71
72 inline ml_kmeans_t::ml_kmeans_t(const ml_kmeans_inlined_t &inlined_km)
73 {
74 cluster_centers.reserve(2);
75 cluster_centers.push_back(inlined_km.cluster_centers[0]);
76 cluster_centers.push_back(inlined_km.cluster_centers[1]);
77
78 min_dist = inlined_km.min_dist;
79 max_dist = inlined_km.max_dist;
80
81 after = inlined_km.after;
82 before = inlined_km.before;
83 }
84
85 inline ml_kmeans_t &ml_kmeans_t::operator=(const ml_kmeans_inlined_t &inlined_km)
86 {
87 cluster_centers.clear();
88 cluster_centers.reserve(2);
89 cluster_centers.push_back(inlined_km.cluster_centers[0]);
90 cluster_centers.push_back(inlined_km.cluster_centers[1]);
91
92 min_dist = inlined_km.min_dist;
93 max_dist = inlined_km.max_dist;
94
95 after = inlined_km.after;
96 before = inlined_km.before;
97 return *this;
98 }
99
100 void ml_kmeans_init(ml_kmeans_t *kmeans);
101
102 void ml_kmeans_train(ml_kmeans_t *kmeans, const std::vector<DSample> &preprocessed_features, unsigned max_iters, time_t after, time_t before);
103
104 calculated_number_t ml_kmeans_anomaly_score(const ml_kmeans_inlined_t *kmeans, const DSample &DS);
105
106 void ml_kmeans_serialize(const ml_kmeans_inlined_t *inlined_km, BUFFER *wb);
107
108 bool ml_kmeans_deserialize(ml_kmeans_inlined_t *inlined_km, struct json_object *root);
109
110 #endif /* ML_KMEANS_H */