LibSVR.cpp
Go to the documentation of this file.00001
00002
00003
00004
00005
00006
00007
00008
00009
00010
00011 #include "regression/svr/LibSVR.h"
00012 #include "lib/io.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 free(model);
00036 }
00037
00038 bool CLibSVR::train(CFeatures* data)
00039 {
00040 ASSERT(kernel);
00041 ASSERT(labels && labels->get_num_labels());
00042
00043 if (data)
00044 {
00045 if (labels->get_num_labels() != data->get_num_vectors())
00046 SG_ERROR("Number of training vectors does not match number of labels\n");
00047 kernel->init(data, data);
00048 }
00049
00050 free(model);
00051
00052 struct svm_node* x_space;
00053
00054 problem.l=labels->get_num_labels();
00055 SG_INFO( "%d trainlabels\n", problem.l);
00056
00057 problem.y=new float64_t[problem.l];
00058 problem.x=new struct svm_node*[problem.l];
00059 x_space=new struct svm_node[2*problem.l];
00060
00061 for (int32_t i=0; i<problem.l; i++)
00062 {
00063 problem.y[i]=labels->get_label(i);
00064 problem.x[i]=&x_space[2*i];
00065 x_space[2*i].index=i;
00066 x_space[2*i+1].index=-1;
00067 }
00068
00069 int32_t weights_label[2]={-1,+1};
00070 float64_t weights[2]={1.0,get_C2()/get_C1()};
00071
00072 param.svm_type=EPSILON_SVR;
00073 param.kernel_type = LINEAR;
00074 param.degree = 3;
00075 param.gamma = 0;
00076 param.coef0 = 0;
00077 param.nu = 0.5;
00078 param.kernel=kernel;
00079 param.cache_size = kernel->get_cache_size();
00080 param.C = get_C1();
00081 param.eps = epsilon;
00082 param.p = tube_epsilon;
00083 param.shrinking = 1;
00084 param.nr_weight = 2;
00085 param.weight_label = weights_label;
00086 param.weight = weights;
00087 param.use_bias = get_bias_enabled();
00088
00089 const char* error_msg = svm_check_parameter(&problem,¶m);
00090
00091 if(error_msg)
00092 SG_ERROR("Error: %s\n",error_msg);
00093
00094 model = svm_train(&problem, ¶m);
00095
00096 if (model)
00097 {
00098 ASSERT(model->nr_class==2);
00099 ASSERT((model->l==0) || (model->l>0 && model->SV && model->sv_coef && model->sv_coef[0]));
00100
00101 int32_t num_sv=model->l;
00102
00103 create_new_model(num_sv);
00104
00105 CSVM::set_objective(model->objective);
00106
00107 set_bias(-model->rho[0]);
00108
00109 for (int32_t i=0; i<num_sv; i++)
00110 {
00111 set_support_vector(i, (model->SV[i])->index);
00112 set_alpha(i, model->sv_coef[0][i]);
00113 }
00114
00115 delete[] problem.x;
00116 delete[] problem.y;
00117 delete[] x_space;
00118
00119 svm_destroy_model(model);
00120 model=NULL;
00121 return true;
00122 }
00123 else
00124 return false;
00125 }