void get_samples()
{
ASSERT(samples.size() == fetchers.size());
for (size_t i = 0; i < samples.size(); ++i)
{
CDenseFeatures<float64_t> *ptr = (CDenseFeatures<float64_t>*)fetchers[i]->fetch(samples[i].ptr);
ptr->get_feature_matrix().display_matrix();
}
}
int main(int, char*[])
{
init_shogun_with_defaults();
#ifdef HAVE_LAPACK // for CDataGenerator::generate_gaussian()
// initialize the random number generator with a fixed seed, for repeatability
CMath::init_random(10);
// Prepare the training data
const int num_features = 20;
const int num_classes = 4;
const int num_examples_per_class = 20;
SGMatrix<float64_t> X;
try
{
X = CDataGenerator::generate_gaussians(
num_examples_per_class,num_classes,num_features);
}
catch (ShogunException e)
{
// out of memory
SG_SPRINT(e.get_exception_string());
return 0;
}
CDenseFeatures<float64_t>* features = new CDenseFeatures<float64_t>(X);
// Create a deep autoencoder
CNeuralLayers* layers = new CNeuralLayers();
layers
->input(num_features)
->rectified_linear(10)->rectified_linear(5)->rectified_linear(10)
->linear(num_features);
CDeepAutoencoder* ae = new CDeepAutoencoder(layers->done());
// uncomment this line to enable info logging
// ae->io->set_loglevel(MSG_INFO);
// pre-train
ae->pt_epsilon.set_const(1e-6);
ae->pre_train(features);
// fine-tune
ae->train(features);
// reconstruct the data
CDenseFeatures<float64_t>* reconstructions = ae->reconstruct(features);
SGMatrix<float64_t> X_reconstructed = reconstructions->get_feature_matrix();
// find the average difference between the data and the reconstructions
float64_t avg_diff = 0;
int32_t N = X.num_rows*X.num_cols;
for (int32_t i=0; i<N; i++)
avg_diff += CMath::abs(X[i]-X_reconstructed[i])/CMath::abs(X[i]);
avg_diff /= N;
SG_SINFO("Average difference = %f %\n", avg_diff*100);
// Clean up
SG_UNREF(ae);
SG_UNREF(layers);
SG_UNREF(features);
SG_UNREF(reconstructions);
#endif
exit_shogun();
return 0;
}