int main(int argc, char ** argv)
{
init_shogun_with_defaults();
#ifdef USE_MOSEK
int32_t num_examples = 10;
int32_t example_length = 250;
int32_t num_features = 10;
int32_t num_noise_features = 2;
CHMSVMModel* model = CTwoStateModel::simulate_data(num_examples, example_length, num_features, num_noise_features);
CStructuredLabels* labels = model->get_labels();
CFeatures* features = model->get_features();
CPrimalMosekSOSVM* sosvm = new CPrimalMosekSOSVM(model, labels);
SG_REF(sosvm);
sosvm->train();
// sosvm->get_w().display_vector("w");
CStructuredLabels* out = CLabelsFactory::to_structured(sosvm->apply());
ASSERT( out->get_num_labels() == labels->get_num_labels() );
for ( int32_t i = 0 ; i < out->get_num_labels() ; ++i )
{
CSequence* pred_seq = CSequence::obtain_from_generic( out->get_label(i) );
CSequence* true_seq = CSequence::obtain_from_generic( labels->get_label(i) );
SG_UNREF(pred_seq);
SG_UNREF(true_seq);
}
SG_UNREF(out);
SG_UNREF(features); // because model->get_features() increased the count
SG_UNREF(labels); // because model->get_labels() increased the count
SG_UNREF(sosvm);
#endif /* USE_MOSEK */
exit_shogun();
return 0;
}
int main(int argc, char ** argv)
{
init_shogun_with_defaults();
SGVector< float64_t > labs(NUM_CLASSES*NUM_SAMPLES);
SGMatrix< float64_t > feats(DIMS, NUM_CLASSES*NUM_SAMPLES);
gen_rand_data(labs, feats);
//read_data(labs, feats);
// Create train labels
CMulticlassSOLabels* labels = new CMulticlassSOLabels(labs);
CMulticlassLabels* mlabels = new CMulticlassLabels(labs);
// Create train features
CDenseFeatures< float64_t >* features = new CDenseFeatures< float64_t >(feats);
// Create structured model
CMulticlassModel* model = new CMulticlassModel(features, labels);
// Create loss function
CHingeLoss* loss = new CHingeLoss();
// Create SO-SVM
CPrimalMosekSOSVM* sosvm = new CPrimalMosekSOSVM(model, loss, labels);
CDualLibQPBMSOSVM* bundle = new CDualLibQPBMSOSVM(model, loss, labels, 1000);
bundle->set_verbose(false);
SG_REF(sosvm);
SG_REF(bundle);
CTime start;
float64_t t1;
sosvm->train();
SG_SPRINT(">>>> PrimalMosekSOSVM trained in %9.4f\n", (t1 = start.cur_time_diff(false)));
bundle->train();
SG_SPRINT(">>>> BMRM trained in %9.4f\n", start.cur_time_diff(false)-t1);
CStructuredLabels* out = CStructuredLabels::obtain_from_generic(sosvm->apply());
CStructuredLabels* bout = CStructuredLabels::obtain_from_generic(bundle->apply());
// Create liblinear svm classifier with L2-regularized L2-loss
CLibLinear* svm = new CLibLinear(L2R_L2LOSS_SVC);
// Add some configuration to the svm
svm->set_epsilon(EPSILON);
svm->set_bias_enabled(false);
// Create a multiclass svm classifier that consists of several of the previous one
CLinearMulticlassMachine* mc_svm =
new CLinearMulticlassMachine( new CMulticlassOneVsRestStrategy(),
(CDotFeatures*) features, svm, mlabels);
SG_REF(mc_svm);
// Train the multiclass machine using the data passed in the constructor
mc_svm->train();
CMulticlassLabels* mout = CMulticlassLabels::obtain_from_generic(mc_svm->apply());
SGVector< float64_t > w = sosvm->get_w();
for ( int32_t i = 0 ; i < w.vlen ; ++i )
SG_SPRINT("%10f ", w[i]);
SG_SPRINT("\n\n");
for ( int32_t i = 0 ; i < NUM_CLASSES ; ++i )
{
CLinearMachine* lm = (CLinearMachine*) mc_svm->get_machine(i);
SGVector< float64_t > mw = lm->get_w();
for ( int32_t j = 0 ; j < mw.vlen ; ++j )
SG_SPRINT("%10f ", mw[j]);
SG_UNREF(lm); // because of CLinearMulticlassMachine::get_machine()
}
SG_SPRINT("\n");
CStructuredAccuracy* structured_evaluator = new CStructuredAccuracy();
CMulticlassAccuracy* multiclass_evaluator = new CMulticlassAccuracy();
SG_REF(structured_evaluator);
SG_REF(multiclass_evaluator);
SG_SPRINT("SO-SVM: %5.2f%\n", 100.0*structured_evaluator->evaluate(out, labels));
SG_SPRINT("BMRM: %5.2f%\n", 100.0*structured_evaluator->evaluate(bout, labels));
SG_SPRINT("MC: %5.2f%\n", 100.0*multiclass_evaluator->evaluate(mout, mlabels));
// Free memory
SG_UNREF(multiclass_evaluator);
SG_UNREF(structured_evaluator);
SG_UNREF(mout);
SG_UNREF(mc_svm);
SG_UNREF(bundle);
SG_UNREF(sosvm);
SG_UNREF(bout);
SG_UNREF(out);
exit_shogun();
return 0;
}
