LibSVMMultiClass.cpp
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00011 #include "classifier/svm/LibSVMMultiClass.h"
00012 #include "lib/io.h"
00013
00014 using namespace shogun;
00015
00016 CLibSVMMultiClass::CLibSVMMultiClass(LIBSVM_SOLVER_TYPE st)
00017 : CMultiClassSVM(ONE_VS_ONE), model(NULL), solver_type(st)
00018 {
00019 }
00020
00021 CLibSVMMultiClass::CLibSVMMultiClass(float64_t C, CKernel* k, CLabels* lab)
00022 : CMultiClassSVM(ONE_VS_ONE, C, k, lab), model(NULL), solver_type(LIBSVM_C_SVC)
00023 {
00024 }
00025
00026 CLibSVMMultiClass::~CLibSVMMultiClass()
00027 {
00028
00029 }
00030
00031 bool CLibSVMMultiClass::train(CFeatures* data)
00032 {
00033 struct svm_node* x_space;
00034
00035 problem = svm_problem();
00036
00037 ASSERT(labels && labels->get_num_labels());
00038 int32_t num_classes = labels->get_num_classes();
00039 problem.l=labels->get_num_labels();
00040 SG_INFO( "%d trainlabels, %d classes\n", problem.l, num_classes);
00041
00042 if (data)
00043 {
00044 if (labels->get_num_labels() != data->get_num_vectors())
00045 SG_ERROR("Number of training vectors does not match number of labels\n");
00046 kernel->init(data, data);
00047 }
00048
00049 problem.y=new float64_t[problem.l];
00050 problem.x=new struct svm_node*[problem.l];
00051 problem.pv=new float64_t[problem.l];
00052 problem.C=new float64_t[problem.l];
00053
00054 x_space=new struct svm_node[2*problem.l];
00055
00056 for (int32_t i=0; i<problem.l; i++)
00057 {
00058 problem.pv[i]=-1.0;
00059 problem.y[i]=labels->get_label(i);
00060 problem.x[i]=&x_space[2*i];
00061 x_space[2*i].index=i;
00062 x_space[2*i+1].index=-1;
00063 }
00064
00065 ASSERT(kernel);
00066
00067 param.svm_type=solver_type;
00068 param.kernel_type = LINEAR;
00069 param.degree = 3;
00070 param.gamma = 0;
00071 param.coef0 = 0;
00072 param.nu = get_nu();
00073 param.kernel=kernel;
00074 param.cache_size = kernel->get_cache_size();
00075 param.C = get_C1();
00076 param.eps = epsilon;
00077 param.p = 0.1;
00078 param.shrinking = 1;
00079 param.nr_weight = 0;
00080 param.weight_label = NULL;
00081 param.weight = NULL;
00082 param.use_bias = get_bias_enabled();
00083
00084 const char* error_msg = svm_check_parameter(&problem,¶m);
00085
00086 if(error_msg)
00087 SG_ERROR("Error: %s\n",error_msg);
00088
00089 model = svm_train(&problem, ¶m);
00090
00091 if (model)
00092 {
00093 ASSERT(model->nr_class==num_classes);
00094 ASSERT((model->l==0) || (model->l>0 && model->SV && model->sv_coef));
00095 create_multiclass_svm(num_classes);
00096
00097 int32_t* offsets=new int32_t[num_classes];
00098 offsets[0]=0;
00099
00100 for (int32_t i=1; i<num_classes; i++)
00101 offsets[i] = offsets[i-1]+model->nSV[i-1];
00102
00103 int32_t s=0;
00104 for (int32_t i=0; i<num_classes; i++)
00105 {
00106 for (int32_t j=i+1; j<num_classes; j++)
00107 {
00108 int32_t k, l;
00109
00110 float64_t sgn=1;
00111 if (model->label[i]>model->label[j])
00112 sgn=-1;
00113
00114 int32_t num_sv=model->nSV[i]+model->nSV[j];
00115 float64_t bias=-model->rho[s];
00116
00117 ASSERT(num_sv>0);
00118 ASSERT(model->sv_coef[i] && model->sv_coef[j-1]);
00119
00120 CSVM* svm=new CSVM(num_sv);
00121
00122 svm->set_bias(sgn*bias);
00123
00124 int32_t sv_idx=0;
00125 for (k=0; k<model->nSV[i]; k++)
00126 {
00127 svm->set_support_vector(sv_idx, model->SV[offsets[i]+k]->index);
00128 svm->set_alpha(sv_idx, sgn*model->sv_coef[j-1][offsets[i]+k]);
00129 sv_idx++;
00130 }
00131
00132 for (k=0; k<model->nSV[j]; k++)
00133 {
00134 svm->set_support_vector(sv_idx, model->SV[offsets[j]+k]->index);
00135 svm->set_alpha(sv_idx, sgn*model->sv_coef[i][offsets[j]+k]);
00136 sv_idx++;
00137 }
00138
00139 int32_t idx=0;
00140
00141 if (sgn>0)
00142 {
00143 for (k=0; k<model->label[i]; k++)
00144 idx+=num_classes-k-1;
00145
00146 for (l=model->label[i]+1; l<model->label[j]; l++)
00147 idx++;
00148 }
00149 else
00150 {
00151 for (k=0; k<model->label[j]; k++)
00152 idx+=num_classes-k-1;
00153
00154 for (l=model->label[j]+1; l<model->label[i]; l++)
00155 idx++;
00156 }
00157
00158
00159
00160
00161
00162
00163
00164 SG_DEBUG("svm[%d] has %d sv (total: %d), b=%f label:(%d,%d) -> svm[%d]\n", s, num_sv, model->l, bias, model->label[i], model->label[j], idx);
00165
00166 set_svm(idx, svm);
00167 s++;
00168 }
00169 }
00170
00171 CSVM::set_objective(model->objective);
00172
00173 delete[] offsets;
00174 delete[] problem.x;
00175 delete[] problem.y;
00176 delete[] x_space;
00177
00178 svm_destroy_model(model);
00179 model=NULL;
00180
00181 return true;
00182 }
00183 else
00184 return false;
00185 }
00186