Here is a list of all data types members with links to the data types they belong to:
- p -
- P
: gpmp::nt::RC5
- pack_buffer_A()
: gpmp::linalg::DGEMM
, gpmp::linalg::IGEMM
, gpmp::linalg::SGEMM
- pack_buffer_B()
: gpmp::linalg::DGEMM
, gpmp::linalg::IGEMM
, gpmp::linalg::SGEMM
- pack_micro_A()
: gpmp::linalg::DGEMM
, gpmp::linalg::IGEMM
, gpmp::linalg::SGEMM
- pack_micro_B()
: gpmp::linalg::DGEMM
, gpmp::linalg::IGEMM
, gpmp::linalg::SGEMM
- partial_corr()
: gpmp::stats::Describe
- partition_distinct()
: gpmp::Comb
- partitions()
: gpmp::Comb
- penalty_type
: gpmp::ml::SVC
- percentile()
: gpmp::stats::Describe
- perform_cross_validation()
: gpmp::ml::Kfold
- permutation()
: gpmp::Comb
- permutation_p_value()
: gpmp::stats::Resampling
- permutation_test()
: gpmp::stats::Resampling
- permutations()
: gpmp::Comb
- phi()
: gpmp::Comb
- Pivot()
: gpmp::stats::ProbDist
- PivotFunctionForConfidenceInterval()
: gpmp::stats::ProbDist
- planar_gen()
: gpmp::Graph
- poisson()
: gpmp::stats::CDF
, gpmp::stats::PDF
- pollard_rho()
: gpmp::Factorization
, pygpmp.nt.nt.Factorization
- pollard_rho_log()
: gpmp::Logarithms
, pygpmp.nt.nt.Logarithms
- power_iter()
: gpmp::linalg::Eigen
- power_rule()
: gpmp::Differential
, pygpmp.calculus.calculus.Differential
- powerMean()
: gpmp::ml::Stats
- ppmc()
: gpmp::stats::Describe
- predict()
: BNN
, gpmp::ml::BayesBernoulli
, gpmp::ml::BayesClf
, gpmp::ml::BayesGauss
, gpmp::ml::BayesMultiNom
, gpmp::ml::KNN
, gpmp::ml::LinearRegression
, gpmp::ml::LogReg
, gpmp::ml::SVC
, pygpmp.ml.ml.LinearRegression
- predict_proba()
: gpmp::ml::SVC
- PredictionInterval()
: gpmp::stats::ProbDist
- prelu()
: gpmp::ml::Activation
- prelu_derivative()
: gpmp::ml::Activation
- PRGA()
: gpmp::RC4
, pygpmp.nt.nt.RC4
- PrimaryMLP()
: gpmp::ml::PrimaryMLP
- print_mtx()
: gpmp::linalg::Matrix< Type >
- print_shape()
: gpmp::linalg::Matrix< Type >
- printData()
: gpmp::core::DataTable
- prior_log_likelihood()
: BNN
- prior_variance
: BNN
- product_rule()
: gpmp::Differential
, pygpmp.calculus.calculus.Differential
- prop_backwards()
: gpmp::ml::SecondaryMLP< T >
- prop_forwards()
: gpmp::ml::SecondaryMLP< T >
- prop_signal()
: gpmp::ml::PrimaryMLP
- proportion_z_test()
: gpmp::stats::HypothesisTest