Dakota  Version 6.15 Explore and Predict with Confidence
ReducedBasis Class Reference

Public Member Functions

ReducedBasis ()
default constructor

void set_matrix (const RealMatrix &)

const RealMatrix & get_matrix ()

void center_matrix ()
center the matrix by scaling each column by its means

void update_svd (bool center_matrix_by_col_means=true)
ensure that the factorization is current, centering if requested

bool is_valid () const

const Real & get_singular_values_sum () const

const Real & get_eigen_values_sum () const

const RealVector & get_column_means ()

const RealVector & get_singular_values () const

RealVector get_singular_values (const TruncationCondition &) const

const RealMatrix & get_left_singular_vector () const
the num_observations n x num_observations n orthogonal matrix U; the left singular vectors are the first min(n,p) columns

const RealMatrix & get_right_singular_vector_transpose () const
the num_responses p x num_responses p orthogonal matrix V'; the right singular vectors are the first min(n,p) rows of V' (columns of V)

Private Attributes

RealMatrix matrix

RealMatrix workingMatrix

RealMatrix U_matrix

RealVector S_values

RealMatrix VT_matrix

RealVector column_means

bool col_means_computed

bool is_centered

bool is_valid_svd

Real singular_values_sum

Real eigen_values_sum

TruncationCondition * truncation

Detailed Description

The ReducedBasis class is used to ... (TODO - RWH)

Class to manage data-driven dimension reduction. The passed matrix with num_observations n rows and num_responses p columns contains realizations of a set of responses. The class optionally centers the matrix by the column means. Stores a singular value decomposition of the passsed data matrix X = U*S*V', which can also be used for PCA, where we seek an eigendecomposition of the covariance: X'*X = V*D*V^{-1} = V*S^2*V'

The documentation for this class was generated from the following files:
• ReducedBasis.hpp
• ReducedBasis.cpp