1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
|
/*!
@authors Andrei Novikov (pyclustering@yandex.ru)
@date 2014-2020
@copyright BSD-3-Clause
*/
#pragma once
#include <vector>
namespace pyclustering {
namespace clst {
/*!
@brief Sequence container to store within cluster errors (WCE) for each K-value.
*/
using wce_sequence = std::vector<double>;
/*!
@class elbow_data elbow_data.hpp pyclustering/cluster/elbow_data.hpp
@brief Elbow analysis result that contain information about optimal amount of clusters and
total within cluster errors (WCE) for each K-value.
*/
class elbow_data {
private:
std::size_t m_amount = 0;
wce_sequence m_wce = { };
public:
/*!
@brief Default constructor of the Elbow result.
*/
elbow_data() = default;
/*!
@brief Default desctructor of the Elbow result.
*/
~elbow_data() = default;
public:
/*!
@brief Returns constant reference to total within cluster errors (WCE) for each K-value.
@return Constant reference to total within cluster errors (WCE) for each K-value.
*/
const wce_sequence & get_wce() const { return m_wce; }
/*!
@brief Returns reference to total within cluster errors (WCE) for each K-value.
@return Reference to total within cluster errors (WCE) for each K-value.
*/
wce_sequence & get_wce() { return m_wce; }
/*!
@brief Set optimal amount of clusters.
@details The method is used by Elbow method to set the final analysis result.
*/
void set_amount(const std::size_t p_amount) { m_amount = p_amount; }
/*!
@brief Returns optimal amount of clusters.
@return Optimal amount of clusters.
*/
std::size_t get_amount() const { return m_amount; }
};
}
}
|
