|
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
|
#ifndef GenericHouseholder_h
#define GenericHouseholder_h
/** \class GenericHouseholder
* Generic implementation of the QR decomposition of a matrix using Householder transformation
*
* \author Lorenzo Agostino, R.Ofierzynski, CERN
*/
#include <vector>
#include <iostream>
class GenericHouseholder {
public:
/// Default constructor
/// CAVEAT: use normalise = true only if you know what you're doing!
GenericHouseholder(bool normalise = false);
/// Destructor
~GenericHouseholder();
/// run the Householder Algorithm several times (nIter). Returns the final vector of calibration coefficients.
std::vector<float> iterate(const std::vector<std::vector<float> >& eventMatrix,
const std::vector<float>& energyVector,
const int nIter);
/// run the Householder Algorithm. Returns the vector of calibration coefficients.
std::vector<float> iterate(const std::vector<std::vector<float> >& eventMatrix,
const std::vector<float>& energyVector);
private:
/// make decomposition
/// input: m=number of events, n=number of channels, qr=event matrix
/// output: qr = new event matrix, alpha, pivot
/// returns a boolean value, true if decomposition worked, false if it didn't
bool decompose(const int m,
const int n,
std::vector<std::vector<float> >& qr,
std::vector<float>& alpha,
std::vector<int>& pivot);
/// Apply transformations to rhs
/// output: r = ?, y = solution
void solve(int m,
int n,
const std::vector<std::vector<float> >& qr,
const std::vector<float>& alpha,
const std::vector<int>& pivot,
std::vector<float>& r,
std::vector<float>& y);
bool normaliseFlag;
};
#endif
|