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00011 #include <shogun/regression/svr/LibSVR.h>
00012 #include <shogun/io/SGIO.h>
00013
00014 using namespace shogun;
00015
00016 CLibSVR::CLibSVR()
00017 : CSVM()
00018 {
00019 model=NULL;
00020 }
00021
00022 CLibSVR::CLibSVR(float64_t C, float64_t eps, CKernel* k, CLabels* lab)
00023 : CSVM()
00024 {
00025 model=NULL;
00026
00027 set_C(C,C);
00028 set_tube_epsilon(eps);
00029 set_labels(lab);
00030 set_kernel(k);
00031 }
00032
00033 CLibSVR::~CLibSVR()
00034 {
00035 SG_FREE(model);
00036 }
00037
00038 EClassifierType CLibSVR::get_classifier_type()
00039 {
00040 return CT_LIBSVR;
00041 }
00042
00043 bool CLibSVR::train_machine(CFeatures* data)
00044 {
00045 ASSERT(kernel);
00046 ASSERT(labels && labels->get_num_labels());
00047
00048 if (data)
00049 {
00050 if (labels->get_num_labels() != data->get_num_vectors())
00051 SG_ERROR("Number of training vectors does not match number of labels\n");
00052 kernel->init(data, data);
00053 }
00054
00055 SG_FREE(model);
00056
00057 struct svm_node* x_space;
00058
00059 problem.l=labels->get_num_labels();
00060 SG_INFO( "%d trainlabels\n", problem.l);
00061
00062 problem.y=SG_MALLOC(float64_t, problem.l);
00063 problem.x=SG_MALLOC(struct svm_node*, problem.l);
00064 x_space=SG_MALLOC(struct svm_node, 2*problem.l);
00065
00066 for (int32_t i=0; i<problem.l; i++)
00067 {
00068 problem.y[i]=labels->get_label(i);
00069 problem.x[i]=&x_space[2*i];
00070 x_space[2*i].index=i;
00071 x_space[2*i+1].index=-1;
00072 }
00073
00074 int32_t weights_label[2]={-1,+1};
00075 float64_t weights[2]={1.0,get_C2()/get_C1()};
00076
00077 param.svm_type=EPSILON_SVR;
00078 param.kernel_type = LINEAR;
00079 param.degree = 3;
00080 param.gamma = 0;
00081 param.coef0 = 0;
00082 param.nu = 0.5;
00083 param.kernel=kernel;
00084 param.cache_size = kernel->get_cache_size();
00085 param.max_train_time = max_train_time;
00086 param.C = get_C1();
00087 param.eps = epsilon;
00088 param.p = tube_epsilon;
00089 param.shrinking = 1;
00090 param.nr_weight = 2;
00091 param.weight_label = weights_label;
00092 param.weight = weights;
00093 param.use_bias = get_bias_enabled();
00094
00095 const char* error_msg = svm_check_parameter(&problem,¶m);
00096
00097 if(error_msg)
00098 SG_ERROR("Error: %s\n",error_msg);
00099
00100 model = svm_train(&problem, ¶m);
00101
00102 if (model)
00103 {
00104 ASSERT(model->nr_class==2);
00105 ASSERT((model->l==0) || (model->l>0 && model->SV && model->sv_coef && model->sv_coef[0]));
00106
00107 int32_t num_sv=model->l;
00108
00109 create_new_model(num_sv);
00110
00111 CSVM::set_objective(model->objective);
00112
00113 set_bias(-model->rho[0]);
00114
00115 for (int32_t i=0; i<num_sv; i++)
00116 {
00117 set_support_vector(i, (model->SV[i])->index);
00118 set_alpha(i, model->sv_coef[0][i]);
00119 }
00120
00121 SG_FREE(problem.x);
00122 SG_FREE(problem.y);
00123 SG_FREE(x_space);
00124
00125 svm_destroy_model(model);
00126 model=NULL;
00127 return true;
00128 }
00129 else
00130 return false;
00131 }