source: orange/source/orange/libsvm_interface.hpp @ 11650:ed138a0a0d5c

Revision 11650:ed138a0a0d5c, 6.0 KB checked in by Ales Erjavec <ales.erjavec@…>, 9 months ago (diff)

Fixed an error in libsvm interface (example_to_svm function).

The 'index' did not get incremented in the presence of missing values.
I also cleaned up some old parameters, ...

RevLine 
[8978]1/*
2 
3 Copyright (c) 2000-2010 Chih-Chung Chang and Chih-Jen Lin
4 All rights reserved.
5 
6 Redistribution and use in source and binary forms, with or without
7 modification, are permitted provided that the following conditions
8 are met:
9 
10 1. Redistributions of source code must retain the above copyright
11 notice, this list of conditions and the following disclaimer.
12 
13 2. Redistributions in binary form must reproduce the above copyright
14 notice, this list of conditions and the following disclaimer in the
15 documentation and/or other materials provided with the distribution.
16 
17 3. Neither name of copyright holders nor the names of its contributors
18 may be used to endorse or promote products derived from this software
19 without specific prior written permission.
20 
21 
22 THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
23 ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
24 LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
25 A PARTICULAR PURPOSE ARE DISCLAIMED.  IN NO EVENT SHALL THE REGENTS OR
26 CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
27 EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
28 PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
29 PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
30 LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
31 NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
32 SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
33 */
34
35
36#ifndef __SVM_HPP
37#define __SVM_HPP
38
39#include "table.hpp"
40
41#include "classify.hpp"
42#include "learn.hpp"
43#include "orange.hpp"
44#include "domain.hpp"
45#include "examplegen.hpp"
46#include "table.hpp"
47#include "examples.hpp"
48#include "distance.hpp"
49
50#include "libsvm/svm.h"
51
52svm_model *svm_load_model_alt(string& buffer);
53int svm_save_model_alt(string& buffer, const svm_model *model);
54
55WRAPPER(ExampleGenerator)
56WRAPPER(KernelFunc)
57WRAPPER(SVMLearner)
58WRAPPER(SVMClassifier)
59WRAPPER(ExampleTable)
60
61class ORANGE_API TKernelFunc: public TOrange{
62public:
63    __REGISTER_ABSTRACT_CLASS
64    virtual float operator()(const TExample &, const TExample &)=0;
65};
66
67WRAPPER(KernelFunc)
68
69
70class ORANGE_API TSVMLearner : public TLearner{
71public:
72    __REGISTER_CLASS
73
74  CLASSCONSTANTS(SVMType: C_SVC=C_SVC; Nu_SVC=NU_SVC; OneClass=ONE_CLASS; Epsilon_SVR=EPSILON_SVR; Nu_SVR=NU_SVR)
75  CLASSCONSTANTS(Kernel: Linear=LINEAR; Polynomial=POLY; RBF=RBF; Sigmoid=SIGMOID; Custom=PRECOMPUTED)
76  CLASSCONSTANTS(LIBSVM_VERSION: VERSION=LIBSVM_VERSION)
77
78    //parameters
79    int svm_type; //P(&SVMLearner_SVMType)  SVM type (C_SVC=0, NU_SVC, ONE_CLASS, EPSILON_SVR=3, NU_SVR=4)
80    int kernel_type; //P(&SVMLearner_Kernel)  kernel type (LINEAR=0, POLY, RBF, SIGMOID, CUSTOM=4)
81    float degree;   //P polynomial kernel degree
82    float gamma;    //P poly/rbf/sigm parameter
83    float coef0;    //P poly/sigm parameter
84    float cache_size; //P cache size in MB
85    float eps;  //P stopping criteria
86    float C;    //P for C_SVC and C_SVR
87    float nu;   //P for NU_SVC and ONE_CLASS
88    float p;    //P for C_SVR
89    int shrinking;  //P shrinking
90    int probability;    //P probability
91    bool verbose;       //P verbose
92
93    int nr_weight;      /* for C_SVC */
94    int *weight_label;  /* for C_SVC */
95    double* weight;     /* for C_SVC */
96
97    PKernelFunc kernelFunc; //P custom kernel function
98
99    TSVMLearner();
100    ~TSVMLearner();
101
102    PClassifier operator()(PExampleGenerator, const int & = 0);
103
104protected:
[11650]105    virtual svm_node* example_to_svm(const TExample &ex, svm_node* node, double last=0.0);
[9187]106    virtual svm_node* init_problem(svm_problem &problem, PExampleTable examples, int n_elements);
[8978]107    virtual int getNumOfElements(PExampleGenerator examples);
[11606]108    virtual TSVMClassifier* createClassifier(
[11607]109                PDomain domain, svm_model* model, PExampleTable supportVectors, PExampleTable examples);
[8978]110};
111
112class ORANGE_API TSVMLearnerSparse : public TSVMLearner{
113public:
114    __REGISTER_CLASS
115    bool useNonMeta; //P include non meta attributes in the learning process
116protected:
[11650]117    virtual svm_node* example_to_svm(const TExample &ex, svm_node* node, double last=0.0);
[8978]118    virtual int getNumOfElements(PExampleGenerator examples);
[11606]119    virtual TSVMClassifier* createClassifier(
[11607]120            PDomain domain, svm_model* model, PExampleTable supportVectors, PExampleTable examples);
[8978]121};
122
123
[11607]124class ORANGE_API TSVMClassifier : public TClassifierFD {
[8978]125public:
126    __REGISTER_CLASS
[11607]127    TSVMClassifier() {
[8978]128        this->model = NULL;
129    };
130
[11607]131    TSVMClassifier(PDomain, svm_model * model, PExampleTable supportVectors,
132            PKernelFunc kernelFunc=NULL, PExampleTable examples=NULL);
[11606]133
[8978]134    ~TSVMClassifier();
135
136    TValue operator()(const TExample&);
137    PDistribution classDistribution(const TExample &);
138
139    PFloatList getDecisionValues(const TExample &);
140
141    PIntList nSV; //P nSV
142    PFloatList rho; //P rho
143    PFloatListList coef; //P coef
144    PFloatList probA; //P probA - pairwise probability information
145    PFloatList probB; //P probB - pairwise probability information
146    PExampleTable supportVectors; //P support vectors
[11607]147
148    PExampleTable examples; //P training instances when svm_type == Custom
149    PKernelFunc kernelFunc; //P custom kernel function used when svm_type == Custom
[8978]150
[9021]151    int svm_type; //P(&SVMLearner_SVMType)  SVM type (C_SVC=0, NU_SVC, ONE_CLASS, EPSILON_SVR=3, NU_SVR=4)
152    int kernel_type; //P(&SVMLearner_Kernel)  kernel type (LINEAR=0, POLY, RBF, SIGMOID, CUSTOM=4)
[8978]153
154    svm_model* getModel() {return model;}
155
156protected:
[11650]157    virtual svm_node* example_to_svm(const TExample &ex, svm_node* node, double last=0.0);
[8978]158    virtual int getNumOfElements(const TExample& example);
159
160private:
161    svm_model *model;
162};
163
[11607]164class ORANGE_API TSVMClassifierSparse : public TSVMClassifier {
[8978]165public:
166    __REGISTER_CLASS
[11607]167    TSVMClassifierSparse() {};
168
169    TSVMClassifierSparse(
170            PDomain domain, svm_model * model, bool useNonMeta,
171            PExampleTable supportVectors,
172            PKernelFunc kernelFunc=NULL,
173            PExampleTable examples=NULL
174            ) : TSVMClassifier(domain, model, supportVectors, kernelFunc, examples) {
[11606]175        this->useNonMeta = useNonMeta;
[8978]176    }
[11606]177
[11607]178    bool useNonMeta; //PR include non meta attributes
[11606]179
[8978]180protected:
[11650]181    virtual svm_node* example_to_svm(const TExample &ex, svm_node* node, double last=0.0);
[8978]182    virtual int getNumOfElements(const TExample& example);
183};
184
185#endif
186
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