Dakota
Version 6.20
Explore and Predict with Confidence
|
namespace for new Dakota surrogates module More...
Classes | |
class | GaussianProcess |
The GaussianProcess constructs a Gaussian Process regressor surrogate given a matrix of data. More... | |
class | GP_Objective |
ROL objective function for the Gaussian Process (GP) surrogate. More... | |
class | Kernel |
Kernel functions for the Gaussian Process surrogate. More... | |
class | Matern32Kernel |
Stationary kernel with C^1 smooth realizations. More... | |
class | Matern52Kernel |
Stationary kernel with C^2 smooth realizations. More... | |
class | PolynomialRegression |
The PolynomialRegression class constructs a polynomial regressor using ordinary least squares. More... | |
class | Python |
The Python class constructs a surrogate via python and has it ready for Dakota use. More... | |
class | SquaredExponentialKernel |
Stationary kernel with C^\infty smooth realizations. More... | |
class | Surrogate |
Parent class for surrogate models. More... | |
Typedefs | |
using | RolVec = ROL::Vector< double > |
Dakota alias for ROL Vector. | |
using | RolStdVec = ROL::StdVector< double > |
Dakota alias for ROL StdVector. | |
using | SCALER_TYPE = util::DataScaler::SCALER_TYPE |
alias for util SCALER_TYPE enum | |
using | SOLVER_TYPE = util::LinearSolverBase::SOLVER_TYPE |
alias for util SOLVER_TYPE enum | |
Functions | |
void | compute_next_combination (int num_dims, int level, VectorXi &index, bool &extend, int &h, int &t) |
void | size_level_index_vector (int num_dims, int level, MatrixXi &indices) |
Compute a matrix of basis indices for given dimension and level. Each row of the matrix sums to level. More... | |
void | compute_hyperbolic_subdim_level_indices (int num_dims, int level, int num_active_dims, double p, MatrixXi &indices) |
Compute a matrix of indices for a submatrix (i.e. up to the active dimensions column) of indices produced by size_level_index_vector(num_dims, level, indices) where each feature has a component > 0 and respects the p-norm cutoff. More... | |
void | compute_hyperbolic_level_indices (int num_dims, int level, double p, MatrixXi &indices) |
Compute the hyperbolic cross indices for a given level. More... | |
void | compute_hyperbolic_indices (int num_dims, int level, double p, MatrixXi &indices) |
Compute the hyperbolic cross indices for all levels up to level. More... | |
void | compute_reduced_indices (int num_dims, int level, MatrixXi &indices) |
Compute the reduced indices for all levels up to level. More... | |
void | fd_check_gradient (Surrogate &surr, const MatrixXd &sample, MatrixXd &fd_error, const int num_steps=10) |
Perform a centered finite difference check of a Surrogate's gradient method. More... | |
void | fd_check_hessian (Surrogate &surr, const MatrixXd &sample, MatrixXd &fd_error, const int num_steps=10) |
Perform a centered finite difference check of a Surrogate's Hessian method. More... | |
std::vector< MatrixXd > | compute_cw_dists_squared (const std::vector< MatrixXd > &cw_dists) |
Compute a vector of component-wise squared distances from a vector of component-wise signed distances. More... | |
std::shared_ptr< Kernel > | kernel_factory (const std::string &kernel_type) |
Creates a derived Kernel class. More... | |
namespace for new Dakota surrogates module
void compute_next_combination | ( | int | num_dims, |
int | level, | ||
VectorXi & | index, | ||
bool & | extend, | ||
int & | h, | ||
int & | t | ||
) |
[in] | num_dims | Dimension of the feature space. |
[in] | level | Total order in each row of indices. Should be >= 1. |
[in,out] | index | Vector of ints that specifies the powers for each term in the basis. |
[in,out] | extend | Bool for whether to continue with the computation of basis indices. |
[in,out] | h | Working variable for basis enumeration. |
[in,out] | t | Working variable for basis enumeration. |
Referenced by size_level_index_vector().
void size_level_index_vector | ( | int | num_dims, |
int | level, | ||
MatrixXi & | indices | ||
) |
Compute a matrix of basis indices for given dimension and level. Each row of the matrix sums to level.
[in] | num_dims | Dimension of the feature space. |
[in] | level | Total order in each row of indices. Should be >= 1. |
[out] | indices | Matrix of indices - (num_terms by num_dims). |
References compute_next_combination(), and dakota::util::n_choose_k().
Referenced by compute_hyperbolic_level_indices(), and compute_hyperbolic_subdim_level_indices().
void compute_hyperbolic_subdim_level_indices | ( | int | num_dims, |
int | level, | ||
int | num_active_dims, | ||
double | p, | ||
MatrixXi & | indices | ||
) |
Compute a matrix of indices for a submatrix (i.e. up to the active dimensions column) of indices produced by size_level_index_vector(num_dims, level, indices) where each feature has a component > 0 and respects the p-norm cutoff.
[in] | num_dims | Dimension of the feature space. |
[in] | level | Total order in each row of indices. Should be >= 1. |
[in] | num_active_dims | The # of active features and end index of the submatrix. |
[in] | p | Real value for p-norm. |
[out] | indices | Matrix of indices - (num_terms by num_active_dims) |
References dakota::util::num_nonzeros(), dakota::util::p_norm(), dakota::silence_unused_args(), and size_level_index_vector().
Referenced by compute_hyperbolic_level_indices().
void compute_hyperbolic_level_indices | ( | int | num_dims, |
int | level, | ||
double | p, | ||
MatrixXi & | indices | ||
) |
Compute the hyperbolic cross indices for a given level.
[in] | num_dims | Dimension of the feature space. |
[in] | level | Total order in each column of indices. Should be >= 0. |
[in] | p | Real value for p-norm. |
[out] | indices | Matrix of indices - (num_dims by num_terms) |
References dakota::util::append_columns(), compute_hyperbolic_subdim_level_indices(), dakota::util::nonzero(), dakota::util::num_nonzeros(), and size_level_index_vector().
Referenced by compute_hyperbolic_indices().
void compute_hyperbolic_indices | ( | int | num_dims, |
int | level, | ||
double | p, | ||
MatrixXi & | indices | ||
) |
Compute the hyperbolic cross indices for all levels up to level.
[in] | num_dims | Dimension of the feature space. |
[in] | level | Highest level to compute basis indices for. |
[in] | p | Real value for p-norm. |
[out] | indices | Matrix of indices - (num_dims by num_terms). |
References dakota::util::append_columns(), and compute_hyperbolic_level_indices().
Referenced by PolynomialRegression::build().
void compute_reduced_indices | ( | int | num_dims, |
int | level, | ||
MatrixXi & | indices | ||
) |
Compute the reduced indices for all levels up to level.
[in] | num_dims | Dimension of the feature space. |
[in] | level | Highest level to compute basis indices for. |
[out] | indices | Matrix of indices - (num_dims by num_terms). |
References dakota::util::append_columns().
Referenced by PolynomialRegression::build().
void fd_check_gradient | ( | Surrogate & | surr, |
const MatrixXd & | sample, | ||
MatrixXd & | fd_error, | ||
const int | num_steps = 10 |
||
) |
Perform a centered finite difference check of a Surrogate's gradient method.
[in] | surr | Reference to a Surrogate. |
[in] | sample | Point to evaluate the gradient at - (1 by numVariables). |
[out] | fd_error | Matrix of finite difference error for each component of the gradient - (num_steps by numVariables). |
[in] | num_steps | Number of increments (N) for the finite difference. The increment vector h = 10**(-i), i = 1, ..., N. |
References Surrogate::dataScaler, DataScaler::get_scaler_features_scale_factors(), Surrogate::gradient(), and Surrogate::value().
void fd_check_hessian | ( | Surrogate & | surr, |
const MatrixXd & | sample, | ||
MatrixXd & | fd_error, | ||
const int | num_steps = 10 |
||
) |
Perform a centered finite difference check of a Surrogate's Hessian method.
[in] | surr | Reference to a Surrogate. |
[in] | sample | Point to evaluate the Hessian at - (1 by numVariables). |
[out] | fd_error | Matrix of finite difference error for each independent component of the Hessian. There are numVariables*(numVariables+1)/2 = numInd components - (num_steps by numInd). of the Hessian - (num_steps by numVariables). |
[in] | num_steps | Number of increments (N) for the finite difference. The increment vector h = 10**(-i), i = 1, ..., N. |
References Surrogate::dataScaler, DataScaler::get_scaler_features_scale_factors(), Surrogate::hessian(), and Surrogate::value().
Compute a vector of component-wise squared distances from a vector of component-wise signed distances.
[in] | dists2 | Vector of signed component-wise distances. |
Referenced by Matern32Kernel::compute_first_deriv_pred_gram(), Matern52Kernel::compute_first_deriv_pred_gram(), and Matern52Kernel::compute_second_deriv_pred_gram().
std::shared_ptr< Kernel > kernel_factory | ( | const std::string & | kernel_type | ) |
Creates a derived Kernel class.
[in] | kernel_type | Name of the kernel. |
Referenced by GaussianProcess::build(), and GaussianProcess::serialize().