In the Shogun library, the PCA algorithm is implemented in the CPCA class. It has one primary configuration option – the number of target dimensions, which can be modified with the set_target_dim() method. After we make this configuration, we need to execute the fit() method for training purposes and then use the apply_to_feature_vector() method to transform an individual sample:

 void PCAReduction(Some<CDenseFeatures<DataType>> features,
                   const int target_dim) {
   auto pca = some<CPCA>();
   pca->set_target_dim(target_dim);
   pca->fit(features);
 
   auto feature_matrix = features->get_feature_matrix();
   for (index_t i = 0; i < features->get_num_vectors(); ++i) {
     auto vector = feature_matrix.get_column(i);
     auto new_vector = pca->apply_to_feature_vector(vector);
   }
 }

The following graph shows the result of applying Shogun PCA implementation to our data: