Hands-On Machine Learning with C++

PCA

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); ...