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| VESPUCCI_EXPORT bool | plsregress (arma::mat X, arma::mat Y, int components, arma::mat &X_loadings, arma::mat &Y_loadings, arma::mat &X_scores, arma::mat &Y_scores, arma::mat &coefficients, arma::mat &percent_variance, arma::mat &fitted) |
| | Vespucci::MathDimensionReduction::plsregress PLS Regression Using SIMPLS algorithm. This is essentially a line-for-line rewrite of plsregress from the Octave statistics package. Copyright (C) 2012 Fernando Damian Nieuwveldt fdnie.nosp@m.uwve.nosp@m.ldt@g.nosp@m.mail.nosp@m..com This is an implementation of the SIMPLS algorithm: Reference: SIMPLS: An alternative approach to partial least squares regression. Chemometrics and Intelligent Laboratory Systems (1993) More...
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| VESPUCCI_EXPORT bool | VCA (const arma::mat &R, arma::uword p, arma::uvec &indices, arma::mat &endmember_spectra, arma::mat &projected_data, arma::mat &fractional_abundances) |
| | Vespucci::Math::DimensionReduction::VCA Vertex Component Analysis. More...
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| VESPUCCI_EXPORT double | estimate_snr (const arma::mat &R, arma::vec r_m, arma::mat x) |
| | Vespucci::Math Estimates Signal-to-Noise ratio. [Nascimento2005]. More...
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| VESPUCCI_EXPORT size_t | HySime (const arma::mat &y, const arma::mat &n, arma::mat &Rn, arma::mat &Ek) |
| | Vespucci::Math::DimensionReduction::HySime. More...
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| VESPUCCI_EXPORT size_t | HySime (const arma::mat &y, arma::mat &subspace) |
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| VESPUCCI_EXPORT void | EstimateAdditiveNoise (arma::mat &noise, arma::mat &noise_correlation, const arma::mat &sample) |
| | Vespucci::Math::DimensionReduction::EstimateAdditiveNoise. More...
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| VESPUCCI_EXPORT bool | svds (const arma::mat &X, arma::uword k, arma::mat &U, arma::vec &s, arma::mat &V) |
| | Vespucci::Math::DimensionReduction::svds Finds a few largest singular values of the arma::matrix X. This is based on the Matlab/Octave function svds(), a truncated singular value decomposition. This is designed to only take the kinds of inputs Vespucci will need (It only works on arma::mat types, and only returns the k largest singular values (none of that sigma business). U and V tolerances are not defined currently due to difficulties with Armadillo's find() method A sparse arma::matrix [0 X; X.t() 0] is formed. The eigenvalues of this arma::matrix are then found using arma::eigs_sym, a wrapper for ARPACK. If X is square, it can be "reconstructed" using X = U*diamat(s)*V.t(). This "reconstruction" will have lower noise. More...
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A namespace for math functions relating to dimension reduction.
A namespace for math functions relating to transforms.
| bool Vespucci::Math::DimensionReduction::svds |
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const arma::mat & |
X, |
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arma::uword |
k, |
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arma::mat & |
U, |
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arma::vec & |
s, |
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arma::mat & |
V |
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) |
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Vespucci::Math::DimensionReduction::svds Finds a few largest singular values of the arma::matrix X. This is based on the Matlab/Octave function svds(), a truncated singular value decomposition. This is designed to only take the kinds of inputs Vespucci will need (It only works on arma::mat types, and only returns the k largest singular values (none of that sigma business). U and V tolerances are not defined currently due to difficulties with Armadillo's find() method A sparse arma::matrix [0 X; X.t() 0] is formed. The eigenvalues of this arma::matrix are then found using arma::eigs_sym, a wrapper for ARPACK. If X is square, it can be "reconstructed" using X = U*diamat(s)*V.t(). This "reconstruction" will have lower noise.
This version is different from the armadillo version because it handles dense matrices.
- Parameters
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| X | An m n arma::matrix. |
| k | The number of eigenvalues to be found. |
| U | An m k unitary arma::matrix |
| s | a vector with n |
| V | |
- Returns
- Whether or not algorithm converged.
Definition at line 24 of file svds.cpp.
1.8.11 
