Essentia
2.1-beta6-dev
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#include <jama_svd.h>
Public Member Functions | |
SVD (const Array2D< Real > &Arg) | |
void | getU (Array2D< Real > &A) |
void | getV (Array2D< Real > &A) |
void | getSingularValues (Array1D< Real > &x) |
void | getS (Array2D< Real > &A) |
double | norm2 () |
double | cond () |
int | rank () |
Private Attributes | |
Array2D< Real > | U |
Array2D< Real > | V |
Array1D< Real > | s |
int | m |
int | n |
Singular Value Decomposition.
For an m-by-n matrix A with m >= n, the singular value decomposition is an m-by-n orthogonal matrix U, an n-by-n diagonal matrix S, and an n-by-n orthogonal matrix V so that A = U*S*V'.
The singular values, sigma[k] = S[k][k], are ordered so that sigma[0] >= sigma[1] >= ... >= sigma[n-1].
The singular value decompostion always exists, so the constructor will never fail. The matrix condition number and the effective numerical rank can be computed from this decomposition.
(Adapted from JAMA, a Java Matrix Library, developed by jointly by the Mathworks and NIST; see http://math.nist.gov/javanumerics/jama).
References Array2D< T >::copy(), Array2D< T >::dim1(), Array2D< T >::dim2(), and TNT::hypot().
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Two norm of condition number (max(S)/min(S))
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Return the diagonal matrix of singular values
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Return the one-dimensional array of singular values
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Two norm (max(S))
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Effective numerical matrix rank
References Array1D< T >::dim().
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