yat
0.18pre

Principal Component Analysis. More...
#include <yat/utility/PCA.h>
Public Member Functions  
PCA (const Matrix &)  
PCA (Matrix &&M)  
const Vector &  eigenvalues (void) const 
Returns eigenvalues. More...  
const Matrix &  eigenvectors (void) const 
Get all eigenvectors in a Matrix. More...  
Matrix  projection (const Matrix &) const 
Principal Component Analysis.
Class performing PCA using SVD. This class assumes that the columns corresponds to the dimenension of the problem. That means if data has dimension NxM (M=columns) the number of principalaxes will equal M1. When projecting data into this space, all Nx1 vectors will have dimension Mx1. Hence the projection will have dimension MxM where each column is a point in the new space.

explicit 
Constructor taking the datamatrix as input. No rowcentering should have been performed and no products.

explicit 
Same as PCA(const Matrix&) but moves M rather than copy.
const Vector& theplu::yat::utility::PCA::eigenvalues  (  void  )  const 
Returns eigenvalues.
const Matrix& theplu::yat::utility::PCA::eigenvectors  (  void  )  const 
Get all eigenvectors in a Matrix.
This function will project data onto the new coordinatesystem where the axes are the calculated eigenvectors. This means that PCA must have been run before this function can be used! Output is presented as coordinates in the Ndimensional room spanned by the eigenvectors.