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;
}
