- d -

• data_augmentation() : gpmp::ml::Regularize
• data_size() : gpmp::ml::LinearRegression , pygpmp.ml.ml.LinearRegression
• DataTable() : gpmp::core::DataTable
• datetime() : gpmp::core::DataTable , pygpmp.core.core.DataTable
• decoder() : gpmp::ml::VariationalAutoEncoder
• decompress() : gpmp::Graph
• decrypt() : gpmp::nt::RC5 , gpmp::nt::RedPike , RC6
• decrypt_block() : RC6
• DenoisingAutoEncoder() : gpmp::ml::DenoisingAutoEncoder
• describe() : gpmp::core::DataTable , pygpmp.core.core.DataTable
• determinant() : gpmp::linalg::Eigen , gpmp::linalg::LinSys
• dfs() : gpmp::Graph
• dfs_connected_components() : gpmp::Graph
• dfs_rec() : gpmp::Graph
• dgeaxpy() : gpmp::linalg::DGEMM
• dgemm_macro_kernel() : gpmp::linalg::DGEMM
• dgemm_micro_kernel() : gpmp::linalg::DGEMM
• dgemm_nn() : gpmp::linalg::DGEMM
• dgescal() : gpmp::linalg::DGEMM
• diag() : gpmp::linalg::Matrix< Type >
• diagonally_dominant() : gpmp::linalg::LinSys
• dijkstra() : gpmp::Graph
• dispatch() : gpmp::core::ThreadDispatch
• display() : gpmp::core::DataTable , gpmp::Differential , gpmp::linalg::LinSys , gpmp::linalg::Tensor , gpmp::ml::AutoEncoder , gpmp::ml::BayesBernoulli , gpmp::ml::BayesClf , gpmp::ml::BayesGauss , gpmp::ml::BayesMultiNom , pygpmp.calculus.calculus.Differential , pygpmp.core.core.DataTable
• display_mtx() : gpmp::linalg::LinSys
• divisors() : gpmp::Comb
• dot_product() : gpmp::ml::SVC , gpmp::optim::QuasiNewton
• draw() : Checkerboard
• drop() : gpmp::core::DataTable
• dropout_regularization() : gpmp::ml::Regularize
• dyck_words() : gpmp::Comb