See: Description
Class  Description 

AffineTransformation 
Affine transformations implemented using homogeneous coordinates.

Centroid 
Class to compute the centroid of some data.

CholeskyDecomposition 
Cholesky Decomposition.

CovarianceMatrix 
Class for computing covariance matrixes using stable mean and variance
computations.

EigenvalueDecomposition 
Eigenvalues and eigenvectors of a real matrix.

LinearEquationSystem 
Class for systems of linear equations.

LUDecomposition 
LU Decomposition.

ProjectedCentroid 
Centroid only using a subset of dimensions.

QRDecomposition 
QR Decomposition.

SingularValueDecomposition 
Singular Value Decomposition.

VMath 
Class providing basic vector mathematics, for lowlevel vectors stored as
double[] . 
Some content of this package is adapted from the Jama package.
Five fundamental matrix decompositions, which consist of pairs or triples of matrices, permutation vectors, and the like, produce results in five decomposition classes. These decompositions are accessed by the Matrix class to compute solutions of simultaneous linear equations, determinants, inverses and other matrix functions. The five decompositions are:
Solve a linear system \(Ax=b\) and compute the residual norm, \(bAx\).
double[][] matrix = { {1.,2.,3}, {4.,5.,6.}, {7.,8.,10.} };
double[] b = MathUtil.randomDoubleArray(3, new Random());
double[] x = VMath.solve(matrix, b);
double[] r = VMath.minusEquals(VMath.times(matrix, x), b);
double norm = VMath.euclideanLength(r);
The original Jamapackage has been developed by the MathWorks and NIST and can be found at math.nist.gov.
Here, for the adaption some classes and methods convenient for data mining applications within ELKI were added. Furthermore some erroneous comments were corrected and the codingstyle was subtly changed to a more Javatypical style.
Copyright © 2019 ELKI Development Team. License information.