bool CMulticlassLogisticRegression::train_machine(CFeatures* data) { if (data) set_features((CDotFeatures*)data); REQUIRE(m_features, "%s::train_machine(): No features attached!\n"); REQUIRE(m_labels, "%s::train_machine(): No labels attached!\n"); REQUIRE(m_labels->get_label_type()==LT_MULTICLASS, "%s::train_machine(): " "Attached labels are no multiclass labels\n"); REQUIRE(m_multiclass_strategy, "%s::train_machine(): No multiclass strategy" " attached!\n"); int32_t n_classes = ((CMulticlassLabels*)m_labels)->get_num_classes(); int32_t n_feats = m_features->get_dim_feature_space(); slep_options options = slep_options::default_options(); if (m_machines->get_num_elements()!=0) { SGMatrix<float64_t> all_w_old(n_feats, n_classes); SGVector<float64_t> all_c_old(n_classes); for (int32_t i=0; i<n_classes; i++) { CLinearMachine* machine = (CLinearMachine*)m_machines->get_element(i); SGVector<float64_t> w = machine->get_w(); for (int32_t j=0; j<n_feats; j++) all_w_old(j,i) = w[j]; all_c_old[i] = machine->get_bias(); SG_UNREF(machine); } options.last_result = new slep_result_t(all_w_old,all_c_old); m_machines->reset_array(); } options.tolerance = m_epsilon; options.max_iter = m_max_iter; slep_result_t result = slep_mc_plain_lr(m_features,(CMulticlassLabels*)m_labels,m_z,options); SGMatrix<float64_t> all_w = result.w; SGVector<float64_t> all_c = result.c; for (int32_t i=0; i<n_classes; i++) { SGVector<float64_t> w(n_feats); for (int32_t j=0; j<n_feats; j++) w[j] = all_w(j,i); float64_t c = all_c[i]; CLinearMachine* machine = new CLinearMachine(); machine->set_w(w); machine->set_bias(c); m_machines->push_back(machine); } return true; }