- s -

• sample_dist() : gpmp::ml::VariationalAutoEncoder
• save() : gpmp::ml::AutoEncoder
• scalar_add() : gpmp::linalg::Matrix< Type >
• scalar_mult() : gpmp::linalg::Matrix< Type >
• schur_decomp() : gpmp::linalg::Eigen
• score() : gpmp::ml::SVC
• SecondaryMLP() : gpmp::ml::SecondaryMLP< T >
• secondPartialDerivativeTest() : gpmp::NumericalAnalysis
• seed() : gpmp::core::rndm::LCG
• selu() : gpmp::ml::Activation
• selu_derivative() : gpmp::ml::Activation
• sensitivity() : gpmp::linalg::Eigen
• set() : gpmp::linalg::Tensor
• set_kernel() : gpmp::ml::SVC
• set_kernel_parameters() : gpmp::ml::SVC
• set_penalty() : gpmp::ml::SVC
• set_random_state() : gpmp::ml::SVC
• set_signal_in() : gpmp::ml::PrimaryMLP
• set_verbose() : gpmp::ml::SVC
• setLogDestination() : gpmp::core::Logger , pygpmp.core.core.Logger
• setLogFile() : gpmp::core::Logger , pygpmp.core.core.Logger
• setLogLevel() : gpmp::core::Logger , pygpmp.core.core.Logger
• sgeaxpy() : gpmp::linalg::SGEMM
• sgemm_macro_kernel() : gpmp::linalg::SGEMM
• sgemm_micro_kernel() : gpmp::linalg::SGEMM
• sgemm_nn() : gpmp::linalg::SGEMM
• sgescal() : gpmp::linalg::SGEMM
• show_data() : gpmp::ml::LinearRegression , pygpmp.ml.ml.LinearRegression
• sieve_of_eratosthenes() : gpmp::PrimalityGen , pygpmp.nt.nt.PrimalityGen
• sigmoid() : gpmp::ml::Activation , gpmp::ml::AutoEncoder , gpmp::ml::LogReg , gpmp::ml::SVC
• sigmoid_activ() : gpmp::ml::SecondaryMLP< T >
• sigmoid_deriv() : gpmp::ml::SecondaryMLP< T >
• sigmoid_derivative() : gpmp::ml::Activation
• silu() : gpmp::ml::Activation
• silu_derivative() : gpmp::ml::Activation
• simulate() : gpmp::ml::PrimaryMLP
• skewness() : gpmp::stats::Describe
• smht() : gpmp::ml::Activation
• smht_derivative() : gpmp::ml::Activation
• smoothed_bootstrap() : gpmp::stats::Resampling
• softmax() : gpmp::ml::Activation
• softmax_derivative() : gpmp::ml::Activation
• softplus() : gpmp::ml::Activation
• solovoy_strassen() : gpmp::PrimalityTest , pygpmp.nt.nt.PrimalityTest
• solve_cholesky() : gpmp::linalg::LinSys
• solve_gauss() : gpmp::linalg::LinSys
• solve_jacobi() : gpmp::linalg::LinSys
• solve_lu() : gpmp::linalg::LinSys
• sort() : gpmp::core::DataTable , pygpmp.core.core.DataTable
• SparseAutoEncoder() : gpmp::ml::SparseAutoEncoder
• spearmans_rho() : gpmp::stats::Describe
• split_data() : gpmp::ml::LinearRegression , pygpmp.ml.ml.LinearRegression
• split_indices() : gpmp::ml::Kfold
• sqr_err() : gpmp::linalg::Matrix< Type >
• SRT() : gpmp::EuclideanDivision
• standardDeviation() : gpmp::ml::Stats
• std_mtx_add() : gpmp::linalg::Mtx
• std_mtx_mult() : gpmp::linalg::Mtx
• std_mtx_sub() : gpmp::linalg::Mtx
• std_mtx_tpose() : gpmp::linalg::Mtx
• stdev() : gpmp::stats::Describe
• stirling_num() : gpmp::Comb
• stoch_gradientdesc() : gpmp::ml::Trainers
• stolarskyMean() : gpmp::ml::Stats
• store_hash() : gpmp::RC4 , pygpmp.nt.nt.RC4
• str_to_double() : gpmp::core::DataTable , pygpmp.core.core.DataTable
• str_to_int() : gpmp::core::DataTable , pygpmp.core.core.DataTable
• student_t() : gpmp::stats::CDF , gpmp::stats::PDF
• sub() : gpmp::linalg::Matrix< Type >
• subfactorial() : gpmp::Comb
• subsample() : gpmp::stats::Resampling
• subsequences() : gpmp::Comb
• sum() : gpmp::linalg::Matrix< Type >
• SVC() : gpmp::ml::SVC
• SVD() : gpmp::linalg::SVD