MinVarFunction.hh
1.9 KB
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
97
98
99
100
101
102
103
104
105
106
107
/**
* MinVarFunction.hh
*
* Created on: 09 nov. 2014
* Author: AKKA
*/
#ifndef MINVARFUNCTION_HH_
#define MINVARFUNCTION_HH_
#include <vector>
#include <iostream>
namespace AMDA {
namespace Statistic {
namespace MinVar {
template<typename Type>
class MinVarFunction
{
public:
MinVarFunction(void) : _dim(0), _nbDataProcessed(0)
{
}
bool pushVector(std::vector<Type> v)
{
if (_nbDataProcessed == 0)
{
//initialize
_dim = v.size();
reset();
}
//check dimension
if (v.size() != _dim)
return false;
//update sum of components
for (unsigned int i = 0; i < _dim; ++i)
_v[i] += v[i];
//update working matrix
for (unsigned int i = 0; i < _dim; ++i)
for (unsigned int j = 0; j < _dim; ++j)
_a[i][j] += v[i]*v[j];
++_nbDataProcessed;
return true;
}
bool computeMinVar(std::vector<std::vector<Type>>& result)
{
if (_nbDataProcessed == 0)
{
reset();
return false;
}
result.clear();
//compute min var
for (unsigned int i = 0; i < _dim; ++i)
{
result.push_back(_a[i]);
for (unsigned int j = 0; j < _dim; ++j)
result[i][j] = result[i][j] / _nbDataProcessed - (_v[i] / _nbDataProcessed) * (_v[j] / _nbDataProcessed);
}
//re-init for next computation
reset();
return true;
}
void reset(void)
{
_nbDataProcessed = 0;
//init working matrix
_a.clear();
std::vector<Type> An;
for (unsigned int i = 0; i < _dim; ++i)
An.push_back(0);
for (unsigned int i = 0; i < _dim; ++i)
_a.push_back(An);
//init working vector
_v.clear();
for (unsigned int i = 0; i < _dim; ++i)
_v.push_back(0);
}
private:
unsigned int _dim;
int _nbDataProcessed;
std::vector<std::vector<Type>> _a;
std::vector<Type> _v;
};
} /* namespace MinVar */
} /* namespace Statistic */
} /* AMDA */
#endif