-
Notifications
You must be signed in to change notification settings - Fork 3
/
baselearner_track.h
98 lines (80 loc) · 3.46 KB
/
baselearner_track.h
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
// ========================================================================== //
// ___. __ //
// ____ ____ _____ ______\_ |__ ____ ____ _______/ |_ //
// _/ ___\/ _ \ / \\____ \| __ \ / _ \ / _ \/ ___/\ __\ //
// \ \__( <_> ) Y Y \ |_> > \_\ ( <_> | <_> )___ \ | | //
// \___ >____/|__|_| / __/|___ /\____/ \____/____ > |__| //
// \/ \/|__| \/ \/ //
// //
// ========================================================================== //
//
// Compboost is free software: you can redistribute it and/or modify
// it under the terms of the GNU General Public License as published by
// the Free Software Foundation, either version 3 of the License, or
// (at your option) any later version.
// Compboost is distributed in the hope that it will be useful,
// but WITHOUT ANY WARRANTY; without even the implied warranty of
// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
// GNU General Public License for more details.
// You should have received a copy of the GNU General Public License
// along with Compboost. If not, see <http://www.gnu.org/licenses/>.
//
// This file contains:
// -------------------
//
// Implementation of tracking all baselearners within the main algorithm.
// This is more convenient, multifunctional and (most important) it keeps
// the main algorithm cleaner.
//
// The idea is, that every returned baselearner from the optimizer ìs
// registered in a vector of baselearner (the track). Later on we can
// predict by using this vector.
//
// Written by:
// -----------
//
// Daniel Schalk
// Institut für Statistik
// Ludwig-Maximilians-Universität München
// Ludwigstraße 33
// D-80539 München
//
// https://www.compstat.statistik.uni-muenchen.de
//
// =========================================================================== #
#ifndef BASELEARNERTACK_H_
#define BASELEARNERTACK_H_
#include "baselearner.h"
#include "baselearner_list.h"
namespace blearnertrack
{
class BaselearnerTrack
{
private:
// Vector of selected baselearner:
std::vector<blearner::Baselearner*> blearner_vector;
// Parameter map. The first element contains the baselearner type and the
// second element the parameter. This one will be updated in every
// iteration:
std::map<std::string, arma::mat> my_parameter_map;
double learning_rate;
public:
BaselearnerTrack ();
BaselearnerTrack (double);
// Insert a baselearner into vector and update parameter:
void InsertBaselearner (blearner::Baselearner*);
// Return the vector of baselearner:
std::vector<blearner::Baselearner*> GetBaselearnerVector ();
// Return so far estimated parameter map:
std::map<std::string, arma::mat> GetParameterMap ();
// Clear the vector without deleting the data in the factory:
void ClearBaselearnerVector ();
// Estimate parameter for specific iteration:
std::map<std::string, arma::mat> GetEstimatedParameterForIteration (unsigned int);
// Returns a matrix of parameters for every iteration:
std::pair<std::vector<std::string>, arma::mat> GetParameterMatrix ();
// Destructor:
~BaselearnerTrack ();
};
} // namespace blearnertrack
#endif // BASELEARNERTRACK_H_