source: orange/source/orange/liblinear_interface.hpp @ 10948:c1bc9e5b584e

Revision 10948:c1bc9e5b584e, 2.6 KB checked in by Ales Erjavec <ales.erjavec@…>, 22 months ago (diff)

Fixed weight vector initialization when class values are missing from training data.

Fixes #1214.

Line 
1/*
2    This file is part of Orange.
3
4    Copyright 1996-2010 Faculty of Computer and Information Science, University of Ljubljana
5    Contact: janez.demsar@fri.uni-lj.si
6
7    Orange is free software: you can redistribute it and/or modify
8    it under the terms of the GNU General Public License as published by
9    the Free Software Foundation, either version 3 of the License, or
10    (at your option) any later version.
11
12    Orange is distributed in the hope that it will be useful,
13    but WITHOUT ANY WARRANTY; without even the implied warranty of
14    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
15    GNU General Public License for more details.
16
17    You should have received a copy of the GNU General Public License
18    along with Orange.  If not, see <http://www.gnu.org/licenses/>.
19*/
20
21#ifndef LINEAR_HPP
22#define LINEAR_HPP
23
24// LIBLINEAR header
25
26#include "linear.h"
27
28#include <map>
29#include "classify.hpp"
30#include "learn.hpp"
31#include "orange.hpp"
32#include "domain.hpp"
33#include "examplegen.hpp"
34#include "table.hpp"
35#include "examples.hpp"
36
37// Alternative model save/load routines (using iostream, needed for in memory serialization)
38int linear_save_model_alt(string &, model *);
39model *linear_load_model_alt(string &);
40
41WRAPPER(ExampleTable)
42
43class ORANGE_API TLinearLearner : public TLearner{
44public:
45    __REGISTER_CLASS
46   
47    CLASSCONSTANTS(Lossfunction1_old_) enum {L2_LR, L2Loss_SVM_Dual, L2Loss_SVM, L1Loss_SVM_Dual }; //For backwards compatibility with 1.4 version.
48    CLASSCONSTANTS(Lossfunction1) enum {L2R_LR, L2R_L2Loss_SVC_Dual, L2R_L2Loss_SVC, L2R_L1Loss_SVC_Dual, MCSVM_CS, L1R_L2Loss_SVC, L1R_LR, L2R_LR_Dual};
49    CLASSCONSTANTS(LIBLINEAR_VERSION: VERSION=180)
50   
51    int solver_type;    //P(&LinearLearner_Lossfunction1) Solver type (loss function1)
52    float eps;          //P Stopping criteria
53    float C;            //P Regularization parameter
54    float bias;         //P bias parameter (default -1.0 - no bias)
55
56    TLinearLearner();
57    PClassifier operator()(PExampleGenerator, const int & = 0);
58};
59
60class ORANGE_API TLinearClassifier : public TClassifierFD{
61public:
62    __REGISTER_CLASS
63    TLinearClassifier() {};
64    TLinearClassifier(const PVariable &var, PExampleTable examples, model *_model);
65    ~TLinearClassifier();
66
67    PDistribution classDistribution(const TExample &);
68    TValue operator()(const TExample&);
69
70    PFloatListList weights; //P Computed feature weights
71    PExampleTable examples; //P Examples used to train the classifier
72    float bias; //PR bias
73    model *getModel(){ return linmodel; }
74private:
75    model *linmodel;
76    double dbias;
77    int get_nr_values();
78};
79
80WRAPPER(LinearLearner)
81WRAPPER(LinearClassifier)
82
83#endif /* LINEAR_HPP */
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