Hands-On Machine Learning with C++

t-SNE

In the Shogun library, the t-SNE algorithm is implemented in the CTDistributedStochasticNeighborEmbedding class. Objects of this class should be configured with the target number of dimensions and the set_target_dim() method. The fit() and transform() methods should be used for training and data dimensionality reduction, respectively:

 void TSNEReduction(Some<CDenseFeatures<DataType>> features,                    const int target_dim) {   auto tsne = some<CTDistributedStochasticNeighborEmbedding>();   tsne->set_target_dim(target_dim);   tsne->fit(features);    auto new_features =       static_cast<CDenseFeatures<DataType> *>(tsne->transform(features));    auto feature_matrix = new_features->get_feature_matrix(); for (index_t i = 0; i < new_features->get_num_vectors(); ...