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LibSVR.cpp
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1 /*
2  * This program is free software; you can redistribute it and/or modify
3  * it under the terms of the GNU General Public License as published by
4  * the Free Software Foundation; either version 3 of the License, or
5  * (at your option) any later version.
6  *
7  * Written (W) 1999-2009 Soeren Sonnenburg
8  * Copyright (C) 1999-2009 Fraunhofer Institute FIRST and Max-Planck-Society
9  */
10 
12 #include <shogun/io/SGIO.h>
13 
14 using namespace shogun;
15 
17 : CSVM()
18 {
19  model=NULL;
20 }
21 
23 : CSVM()
24 {
25  model=NULL;
26 
27  set_C(C,C);
28  set_tube_epsilon(eps);
29  set_labels(lab);
30  set_kernel(k);
31 }
32 
34 {
35  SG_FREE(model);
36 }
37 
39 {
40  return CT_LIBSVR;
41 }
42 
44 {
45  ASSERT(kernel);
47 
48  if (data)
49  {
50  if (labels->get_num_labels() != data->get_num_vectors())
51  SG_ERROR("Number of training vectors does not match number of labels\n");
52  kernel->init(data, data);
53  }
54 
55  SG_FREE(model);
56 
57  struct svm_node* x_space;
58 
60  SG_INFO( "%d trainlabels\n", problem.l);
61 
63  problem.x=SG_MALLOC(struct svm_node*, problem.l);
64  x_space=SG_MALLOC(struct svm_node, 2*problem.l);
65 
66  for (int32_t i=0; i<problem.l; i++)
67  {
68  problem.y[i]=labels->get_label(i);
69  problem.x[i]=&x_space[2*i];
70  x_space[2*i].index=i;
71  x_space[2*i+1].index=-1;
72  }
73 
74  int32_t weights_label[2]={-1,+1};
75  float64_t weights[2]={1.0,get_C2()/get_C1()};
76 
77  param.svm_type=EPSILON_SVR; // epsilon SVR
78  param.kernel_type = LINEAR;
79  param.degree = 3;
80  param.gamma = 0; // 1/k
81  param.coef0 = 0;
82  param.nu = 0.5;
83  param.kernel=kernel;
84  param.cache_size = kernel->get_cache_size();
85  param.max_train_time = max_train_time;
86  param.C = get_C1();
87  param.eps = epsilon;
88  param.p = tube_epsilon;
89  param.shrinking = 1;
90  param.nr_weight = 2;
91  param.weight_label = weights_label;
92  param.weight = weights;
93  param.use_bias = get_bias_enabled();
94 
95  const char* error_msg = svm_check_parameter(&problem,&param);
96 
97  if(error_msg)
98  SG_ERROR("Error: %s\n",error_msg);
99 
100  model = svm_train(&problem, &param);
101 
102  if (model)
103  {
104  ASSERT(model->nr_class==2);
105  ASSERT((model->l==0) || (model->l>0 && model->SV && model->sv_coef && model->sv_coef[0]));
106 
107  int32_t num_sv=model->l;
108 
109  create_new_model(num_sv);
110 
111  CSVM::set_objective(model->objective);
112 
113  set_bias(-model->rho[0]);
114 
115  for (int32_t i=0; i<num_sv; i++)
116  {
117  set_support_vector(i, (model->SV[i])->index);
118  set_alpha(i, model->sv_coef[0][i]);
119  }
120 
121  SG_FREE(problem.x);
122  SG_FREE(problem.y);
123  SG_FREE(x_space);
124 
125  svm_destroy_model(model);
126  model=NULL;
127  return true;
128  }
129  else
130  return false;
131 }

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