CMulticlassLabels* CRelaxedTree::apply_multiclass(CFeatures* data)
{
if (data != NULL)
{
CDenseFeatures<float64_t> *feats = dynamic_cast<CDenseFeatures<float64_t>*>(data);
REQUIRE(feats != NULL, ("Require non-NULL dense features of float64_t\n"))
set_features(feats);
}
// init kernels for all sub-machines
for (int32_t i=0; i<m_machines->get_num_elements(); i++)
{
CSVM *machine = (CSVM*)m_machines->get_element(i);
CKernel *kernel = machine->get_kernel();
CFeatures* lhs = kernel->get_lhs();
kernel->init(lhs, m_feats);
SG_UNREF(machine);
SG_UNREF(kernel);
SG_UNREF(lhs);
}
CMulticlassLabels *lab = new CMulticlassLabels(m_feats->get_num_vectors());
SG_REF(lab);
for (int32_t i=0; i < lab->get_num_labels(); ++i)
{
lab->set_int_label(i, int32_t(apply_one(i)));
}
return lab;
}
CMulticlassLabels* CConditionalProbabilityTree::apply_multiclass(CFeatures* data)
{
if (data)
{
if (data->get_feature_class() != C_STREAMING_DENSE)
SG_ERROR("Expected StreamingDenseFeatures\n")
if (data->get_feature_type() != F_SHORTREAL)
SG_ERROR("Expected float32_t feature type\n")
set_features(dynamic_cast<CStreamingDenseFeatures<float32_t>* >(data));
}
vector<int32_t> predicts;
m_feats->start_parser();
while (m_feats->get_next_example())
{
predicts.push_back(apply_multiclass_example(m_feats->get_vector()));
m_feats->release_example();
}
m_feats->end_parser();
CMulticlassLabels *labels = new CMulticlassLabels(predicts.size());
for (size_t i=0; i < predicts.size(); ++i)
labels->set_int_label(i, predicts[i]);
return 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()
{
init_shogun_with_defaults();
const char* train_file_name = "../data/7class_example4_train.dense";
const char* test_file_name = "../data/7class_example4_test.dense";
CStreamingAsciiFile* train_file = new CStreamingAsciiFile(train_file_name);
SG_REF(train_file);
CStreamingDenseFeatures<float32_t>* train_features = new CStreamingDenseFeatures<float32_t>(train_file, true, 1024);
SG_REF(train_features);
CRandomConditionalProbabilityTree *cpt = new CRandomConditionalProbabilityTree();
cpt->set_num_passes(1);
cpt->set_features(train_features);
cpt->train();
cpt->print_tree();
CStreamingAsciiFile* test_file = new CStreamingAsciiFile(test_file_name);
SG_REF(test_file);
CStreamingDenseFeatures<float32_t>* test_features = new CStreamingDenseFeatures<float32_t>(test_file, true, 1024);
SG_REF(test_features);
CMulticlassLabels *pred = cpt->apply_multiclass(test_features);
test_features->reset_stream();
SG_SPRINT("num_labels = %d\n", pred->get_num_labels());
SG_UNREF(test_features);
SG_UNREF(test_file);
test_file = new CStreamingAsciiFile(test_file_name);
SG_REF(test_file);
test_features = new CStreamingDenseFeatures<float32_t>(test_file, true, 1024);
SG_REF(test_features);
CMulticlassLabels *gnd = new CMulticlassLabels(pred->get_num_labels());
test_features->start_parser();
for (int32_t i=0; i < pred->get_num_labels(); ++i)
{
test_features->get_next_example();
gnd->set_int_label(i, test_features->get_label());
test_features->release_example();
}
test_features->end_parser();
int32_t n_correct = 0;
for (index_t i=0; i < pred->get_num_labels(); ++i)
{
if (pred->get_int_label(i) == gnd->get_int_label(i))
n_correct++;
//SG_SPRINT("%d-%d ", pred->get_int_label(i), gnd->get_int_label(i));
}
SG_SPRINT("\n");
SG_SPRINT("Multiclass Accuracy = %.2f%%\n", 100.0*n_correct / gnd->get_num_labels());
SG_UNREF(train_features);
SG_UNREF(test_features);
SG_UNREF(train_file);
SG_UNREF(test_file);
SG_UNREF(cpt);
SG_UNREF(pred);
exit_shogun();
return 0;
}
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;
}