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
|
// This is mul/mbl/mbl_ar_process.h
#ifndef mbl_ar_process_h_
#define mbl_ar_process_h_
//:
// \file
// \brief Compute the parameters of a second order autoregressive process.
// \author Franck Bettinger
#include <vnl/vnl_matrix.h>
#include <vnl/vnl_vector.h>
#include <vsl/vsl_binary_io.h>
#include <vcl_vector.h>
#include <vcl_string.h>
#include <vcl_iosfwd.h>
class vnl_random;
//: Compute the parameters of a second order autoregressive process.
template<class T>
class mbl_ar_process
{
vnl_matrix<T> A_2,A_1,B_0;
vnl_vector<T> Xm; // mean
public:
//: Constructor
mbl_ar_process();
//: Destructor
~mbl_ar_process();
//: Version number for I/O
short version_no() const;
//: Name of the class
vcl_string is_a() const;
//: Does the name of the class match the argument?
bool is_class(vcl_string const& s) const;
//: Print class to os
void print_summary(vcl_ostream& os) const;
//: Save class to binary file stream
void b_write(vsl_b_ostream& bfs) const;
//: Load class from binary file stream
void b_read(vsl_b_istream& bfs);
//: Dynamic learning
void learn(vcl_vector<vnl_vector<T> >& data);
//: Learning using Burg's algorithm
void learn_burg(vcl_vector<vnl_vector<T> >& data);
//: Prediction
// of a vector given the two previous vectors
// \param rng Use an externally provided random number generator,
// rather than one statically local to the function.
vnl_vector<T> predict(vnl_vector<T>& Xm1, vnl_vector<T>& Xm2, vnl_random *rng=0);
};
//: Write to binary stream
template<class T>
void vsl_b_write(vsl_b_ostream& s, const mbl_ar_process<T>* p);
//: Read data from binary stream
template<class T>
void vsl_b_read(vsl_b_istream& s, mbl_ar_process<T>* & v);
//: Print class to os
template<class T>
void vsl_print_summary(vcl_ostream& os, const mbl_ar_process<T>* p);
#endif // mbl_ar_process_h_
|
