int main(int argc, char** argv)
{
init_shogun_with_defaults();
/* dense features from matrix */
CCSVFile* feature_file = new CCSVFile(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 */
CCSVFile* label_file = new CCSVFile(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);
SG_SPRINT("Performing ShareBoost on a %d-class problem\n", labels->get_num_classes());
// Create ShareBoost Machine
CShareBoost *machine = new CShareBoost(features, labels, 10);
SG_REF(machine);
machine->train();
SGVector<int32_t> activeset = machine->get_activeset();
SG_SPRINT("%d out of %d features are selected:\n", activeset.vlen, mat.num_rows);
for (int32_t i=0; i < activeset.vlen; ++i)
SG_SPRINT("activeset[%02d] = %d\n", i, activeset[i]);
CDenseSubsetFeatures<float64_t> *subset_fea = new CDenseSubsetFeatures<float64_t>(features, machine->get_activeset());
SG_REF(subset_fea);
CMulticlassLabels* output = CLabelsFactory::to_multiclass(machine->apply(subset_fea));
int32_t correct = 0;
for (int32_t i=0; i < output->get_num_labels(); ++i)
if (output->get_int_label(i) == labels->get_int_label(i))
correct++;
SG_SPRINT("Accuracy = %.4f\n", float64_t(correct)/labels->get_num_labels());
// Free resources
SG_UNREF(machine);
SG_UNREF(output);
SG_UNREF(subset_fea);
SG_UNREF(features);
SG_UNREF(labels);
exit_shogun();
return 0;
}
int main(int argc, char** argv)
{
int32_t num_vectors = 0;
int32_t num_feats = 0;
init_shogun_with_defaults();
const char*fname_train = "../data/7class_example4_train.dense";
CStreamingAsciiFile *train_file = new CStreamingAsciiFile(fname_train);
SG_REF(train_file);
CStreamingDenseFeatures<float64_t> *stream_features = new CStreamingDenseFeatures<float64_t>(train_file, true, 1024);
SG_REF(stream_features);
SGMatrix<float64_t> mat;
SGVector<float64_t> labvec(1000);
stream_features->start_parser();
SGVector< float64_t > vec;
while (stream_features->get_next_example())
{
vec = stream_features->get_vector();
if (num_feats == 0)
{
num_feats = vec.vlen;
mat = SGMatrix<float64_t>(num_feats, 1000);
}
std::copy(vec.vector, vec.vector+vec.vlen, mat.get_column_vector(num_vectors));
labvec[num_vectors] = stream_features->get_label();
num_vectors++;
stream_features->release_example();
}
stream_features->end_parser();
mat.num_cols = num_vectors;
labvec.vlen = num_vectors;
CMulticlassLabels* labels = new CMulticlassLabels(labvec);
SG_REF(labels);
// Create features with the useful values from mat
CDenseFeatures< float64_t >* features = new CDenseFeatures<float64_t>(mat);
SG_REF(features);
SG_SPRINT("Performing ShareBoost on a %d-class problem\n", labels->get_num_classes());
// Create ShareBoost Machine
CShareBoost *machine = new CShareBoost(features, labels, 10);
SG_REF(machine);
machine->train();
SGVector<int32_t> activeset = machine->get_activeset();
SG_SPRINT("%d out of %d features are selected:\n", activeset.vlen, mat.num_rows);
for (int32_t i=0; i < activeset.vlen; ++i)
SG_SPRINT("activeset[%02d] = %d\n", i, activeset[i]);
CDenseSubsetFeatures<float64_t> *subset_fea = new CDenseSubsetFeatures<float64_t>(features, machine->get_activeset());
SG_REF(subset_fea);
CMulticlassLabels* output = CMulticlassLabels::obtain_from_generic(machine->apply(subset_fea));
int32_t correct = 0;
for (int32_t i=0; i < output->get_num_labels(); ++i)
if (output->get_int_label(i) == labels->get_int_label(i))
correct++;
SG_SPRINT("Accuracy = %.4f\n", float64_t(correct)/labels->get_num_labels());
// Free resources
SG_UNREF(machine);
SG_UNREF(output);
SG_UNREF(subset_fea);
SG_UNREF(features);
SG_UNREF(labels);
SG_UNREF(train_file);
SG_UNREF(stream_features);
exit_shogun();
return 0;
}
