Dakota
Version 6.19
Explore and Predict with Confidence
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This is the complete list of members for PyPolyReg, including all inherited members.
basisIndices | PolynomialRegression | private |
build(const MatrixXd &samples, const MatrixXd &response) override | PolynomialRegression | virtual |
clone() const override | PolynomialRegression | inlinevirtual |
compute_basis_matrix(const MatrixXd &samples, MatrixXd &basis_matrix) const | PolynomialRegression | |
configOptions | Surrogate | protected |
cross_validate(const MatrixXd &samples, const MatrixXd &response, const StringArray &mnames, const int num_folds=5, const int seed=20) | Surrogate | |
dataScaler | Surrogate | |
default_options() override | PolynomialRegression | privatevirtual |
defaultConfigOptions | Surrogate | protected |
evaluate_metrics(const StringArray &mnames, const MatrixXd &points, const MatrixXd &ref_values) | Surrogate | |
get_num_terms() const | PolynomialRegression | |
get_options(ParameterList &options) | Surrogate | |
get_polynomial_coeffs() const | PolynomialRegression | |
get_polynomial_intercept() const | PolynomialRegression | |
gradient(const MatrixXd &eval_points, const int qoi) override | PolynomialRegression | virtual |
gradient(const MatrixXd &eval_points) | PolynomialRegression | inline |
hessian(const MatrixXd &eval_point, const int qoi) override | PolynomialRegression | virtual |
hessian(const MatrixXd &eval_point) | PolynomialRegression | inline |
linearSolver | PolynomialRegression | private |
load(const std::string &infile, const bool binary, SurrHandle &surr_in) | Surrogate | static |
load(const std::string &infile, const bool binary) | Surrogate | static |
load(const std::string &infile, const bool binary, DerivedSurr &surr_in) | Surrogate | |
numQOI | Surrogate | protected |
numSamples | Surrogate | protected |
numTerms | PolynomialRegression | private |
numVariables | Surrogate | protected |
polynomialCoeffs | PolynomialRegression | private |
polynomialIntercept | PolynomialRegression | private |
PolynomialRegression() | PolynomialRegression | |
PolynomialRegression(const ParameterList &options) | PolynomialRegression | |
PolynomialRegression(const std::string ¶m_list_yaml_filename) | PolynomialRegression | |
PolynomialRegression(const MatrixXd &samples, const MatrixXd &response, const ParameterList &options) | PolynomialRegression | |
PolynomialRegression(const MatrixXd &samples, const MatrixXd &response, const std::string ¶m_list_yaml_filename) | PolynomialRegression | |
print_options() | Surrogate | |
PyPolyReg() (defined in PyPolyReg) | PyPolyReg | inline |
PyPolyReg(const pybind11::dict &pydict) | PyPolyReg | inline |
PyPolyReg(const Eigen::MatrixXd &samples, const Eigen::MatrixXd &response, const pybind11::dict &pydict) | PyPolyReg | inline |
response_labels(const std::vector< std::string > &resp_labels) | Surrogate | |
response_labels() const | Surrogate | |
responseLabels | Surrogate | protected |
responseOffset | Surrogate | |
responseScaleFactor | Surrogate | |
save(const SurrHandle &surr_out, const std::string &outfile, const bool binary) | Surrogate | static |
save(const DerivedSurr &surr_out, const std::string &outfile, const bool binary) | Surrogate | |
serialize(Archive &archive, const unsigned int version) | PolynomialRegression | private |
set_options(const ParameterList &options) | Surrogate | |
set_polynomial_coeffs(const MatrixXd &coeffs) | PolynomialRegression | |
Surrogate() | Surrogate | |
Surrogate(const ParameterList ¶m_list) | Surrogate | |
Surrogate(const MatrixXd &samples, const MatrixXd &response, const ParameterList ¶m_list) | Surrogate | |
value(const Eigen::MatrixXd &eval_points) | PyPolyReg | inline |
dakota::surrogates::PolynomialRegression::value(const MatrixXd &eval_points, const int qoi) override | PolynomialRegression | virtual |
variable_labels(const std::vector< std::string > &var_labels) | Surrogate | |
variable_labels() const | Surrogate | |
variableLabels | Surrogate | protected |
verbosity | PolynomialRegression | private |
~PolynomialRegression() | PolynomialRegression | |
~Surrogate() | Surrogate | virtual |