void test()
{
/* dense features from matrix */
CAsciiFile* feature_file = new CAsciiFile(fname_feats);
SGMatrix<float64_t> mat=SGMatrix<float64_t>();
mat.load(feature_file);
SG_UNREF(feature_file);
CDenseFeatures<float64_t>* features=new CDenseFeatures<float64_t>(mat);
SG_REF(features);
/* labels from vector */
CAsciiFile* label_file = new CAsciiFile(fname_labels);
SGVector<float64_t> label_vec;
label_vec.load(label_file);
SG_UNREF(label_file);
CMulticlassLabels* labels=new CMulticlassLabels(label_vec);
SG_REF(labels);
// Create liblinear svm classifier with L2-regularized L2-loss
CLibLinear* svm = new CLibLinear(L2R_L2LOSS_SVC);
SG_REF(svm);
// Add some configuration to the svm
svm->set_epsilon(EPSILON);
svm->set_bias_enabled(true);
CECOCDiscriminantEncoder *encoder = new CECOCDiscriminantEncoder();
encoder->set_features(features);
encoder->set_labels(labels);
// Create a multiclass svm classifier that consists of several of the previous one
CLinearMulticlassMachine* mc_svm = new CLinearMulticlassMachine(
new CECOCStrategy(encoder, new CECOCHDDecoder()), (CDotFeatures*) features, svm, labels);
SG_REF(mc_svm);
// Train the multiclass machine using the data passed in the constructor
mc_svm->train();
// Classify the training examples and show the results
CMulticlassLabels* output = CLabelsFactory::to_multiclass(mc_svm->apply());
SGVector< int32_t > out_labels = output->get_int_labels();
SGVector< int32_t >::display_vector(out_labels.vector, out_labels.vlen);
// Free resources
SG_UNREF(mc_svm);
SG_UNREF(svm);
SG_UNREF(output);
SG_UNREF(features);
SG_UNREF(labels);
}
int main(int argc, char** argv)
{
int32_t num_vectors = 0;
int32_t num_feats = 2;
init_shogun_with_defaults();
// Prepare to read a file for the training data
char fname_feats[] = "../data/fm_train_real.dat";
char fname_labels[] = "../data/label_train_multiclass.dat";
CStreamingAsciiFile* ffeats_train = new CStreamingAsciiFile(fname_feats);
CStreamingAsciiFile* flabels_train = new CStreamingAsciiFile(fname_labels);
SG_REF(ffeats_train);
SG_REF(flabels_train);
CStreamingDenseFeatures< float64_t >* stream_features =
new CStreamingDenseFeatures< float64_t >(ffeats_train, false, 1024);
CStreamingDenseFeatures< float64_t >* stream_labels =
new CStreamingDenseFeatures< float64_t >(flabels_train, true, 1024);
SG_REF(stream_features);
SG_REF(stream_labels);
// Create a matrix with enough space to read all the feature vectors
SGMatrix< float64_t > mat = SGMatrix< float64_t >(num_feats, 1000);
// Read the values from the file and store them in mat
SGVector< float64_t > vec;
stream_features->start_parser();
while ( stream_features->get_next_example() )
{
vec = stream_features->get_vector();
for ( int32_t i = 0 ; i < num_feats ; ++i )
mat[num_vectors*num_feats + i] = vec[i];
num_vectors++;
stream_features->release_example();
}
stream_features->end_parser();
// Create features with the useful values from mat
CDenseFeatures< float64_t >* features = new CDenseFeatures< float64_t >(mat.matrix, num_feats, num_vectors);
CLabels* labels = new CLabels(num_vectors);
SG_REF(features);
SG_REF(labels);
// Read the labels from the file
int32_t idx = 0;
stream_labels->start_parser();
while ( stream_labels->get_next_example() )
{
labels->set_int_label( idx++, (int32_t)stream_labels->get_label() );
stream_labels->release_example();
}
stream_labels->end_parser();
// Create liblinear svm classifier with L2-regularized L2-loss
CLibLinear* svm = new CLibLinear(L2R_L2LOSS_SVC);
SG_REF(svm);
// Add some configuration to the svm
svm->set_epsilon(EPSILON);
svm->set_bias_enabled(true);
// Create a multiclass svm classifier that consists of several of the previous one
CLinearMulticlassMachine* mc_svm = new CLinearMulticlassMachine(
new CECOCStrategy(new CECOCRandomDenseEncoder(), new CECOCHDDecoder()), (CDotFeatures*) features, svm, labels);
SG_REF(mc_svm);
// Train the multiclass machine using the data passed in the constructor
mc_svm->train();
// Classify the training examples and show the results
CLabels* output = mc_svm->apply();
SGVector< int32_t > out_labels = output->get_int_labels();
CMath::display_vector(out_labels.vector, out_labels.vlen);
// Free resources
SG_UNREF(mc_svm);
SG_UNREF(svm);
SG_UNREF(output);
SG_UNREF(features);
SG_UNREF(labels);
//SG_UNREF(ffeats_train);
//SG_UNREF(flabels_train);
SG_UNREF(stream_features);
SG_UNREF(stream_labels);
exit_shogun();
return 0;
}
void test()
{
// Prepare to read a file for the training data
char fname_feats[] = "../data/fm_train_real.dat";
char fname_labels[] = "../data/label_train_multiclass.dat";
CStreamingAsciiFile* ffeats_train = new CStreamingAsciiFile(fname_feats);
CStreamingAsciiFile* flabels_train = new CStreamingAsciiFile(fname_labels);
SG_REF(ffeats_train);
SG_REF(flabels_train);
CStreamingDenseFeatures< float64_t >* stream_features =
new CStreamingDenseFeatures< float64_t >(ffeats_train, false, 1024);
CStreamingDenseFeatures< float64_t >* stream_labels =
new CStreamingDenseFeatures< float64_t >(flabels_train, true, 1024);
SG_REF(stream_features);
SG_REF(stream_labels);
stream_features->start_parser();
// Read the values from the file and store them in features
CDenseFeatures< float64_t >* features=
(CDenseFeatures< float64_t >*)
stream_features->get_streamed_features(1000);
stream_features->end_parser();
CMulticlassLabels* labels = new CMulticlassLabels(features->get_num_vectors());
SG_REF(features);
SG_REF(labels);
// Read the labels from the file
int32_t idx = 0;
stream_labels->start_parser();
while ( stream_labels->get_next_example() )
{
labels->set_int_label( idx++, (int32_t)stream_labels->get_label() );
stream_labels->release_example();
}
stream_labels->end_parser();
// Create liblinear svm classifier with L2-regularized L2-loss
CLibLinear* svm = new CLibLinear(L2R_L2LOSS_SVC);
SG_REF(svm);
// Add some configuration to the svm
svm->set_epsilon(EPSILON);
svm->set_bias_enabled(true);
CECOCDiscriminantEncoder *encoder = new CECOCDiscriminantEncoder();
encoder->set_features(features);
encoder->set_labels(labels);
// Create a multiclass svm classifier that consists of several of the previous one
CLinearMulticlassMachine* mc_svm = new CLinearMulticlassMachine(
new CECOCStrategy(encoder, new CECOCHDDecoder()), (CDotFeatures*) features, svm, labels);
SG_REF(mc_svm);
// Train the multiclass machine using the data passed in the constructor
mc_svm->train();
// Classify the training examples and show the results
CMulticlassLabels* output = CMulticlassLabels::obtain_from_generic(mc_svm->apply());
SGVector< int32_t > out_labels = output->get_int_labels();
SGVector< int32_t >::display_vector(out_labels.vector, out_labels.vlen);
// Free resources
SG_UNREF(mc_svm);
SG_UNREF(svm);
SG_UNREF(output);
SG_UNREF(features);
SG_UNREF(labels);
SG_UNREF(ffeats_train);
SG_UNREF(flabels_train);
SG_UNREF(stream_features);
SG_UNREF(stream_labels);
}
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;
}
