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); }
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) { 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.matrix[num_vectors*num_feats + i] = vec[i]; num_vectors++; stream_features->release_example(); } stream_features->end_parser(); mat.num_cols = num_vectors; // Create features with the useful values from mat CDenseFeatures< float64_t >* features = new CDenseFeatures<float64_t>(mat); CMulticlassLabels* labels = new CMulticlassLabels(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 CMulticlassOneVsOneStrategy(), (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); exit_shogun(); return 0; }