org.apache.spark.mllib.linalg

Class EigenValueDecomposition

Object

org.apache.spark.mllib.linalg.EigenValueDecomposition

public class EigenValueDecomposition
extends Object

:: Experimental :: Compute eigen-decomposition.

Constructor Summary

Constructors

Constructor and Description
EigenValueDecomposition()

Method Summary

Methods

Modifier and Type	Method and Description
static scala.Tuple2<breeze.linalg.DenseVector<Object>,breeze.linalg.DenseMatrix<Object>>	symmetricEigs(scala.Function1<breeze.linalg.DenseVector<Object>,breeze.linalg.DenseVector<Object>> mul, int n, int k, double tol, int maxIterations) Compute the leading k eigenvalues and eigenvectors on a symmetric square matrix using ARPACK.

Methods inherited from class Object

equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

EigenValueDecomposition

public EigenValueDecomposition()

Method Detail

symmetricEigs

public static scala.Tuple2<breeze.linalg.DenseVector<Object>,breeze.linalg.DenseMatrix<Object>> symmetricEigs(scala.Function1<breeze.linalg.DenseVector<Object>,breeze.linalg.DenseVector<Object>> mul,
                                                                                              int n,
                                                                                              int k,
                                                                                              double tol,
                                                                                              int maxIterations)

Compute the leading k eigenvalues and eigenvectors on a symmetric square matrix using ARPACK. The caller needs to ensure that the input matrix is real symmetric. This function requires memory for n*(4*k+4) doubles.

Parameters:

mul - a function that multiplies the symmetric matrix with a DenseVector.

n - dimension of the square matrix (maximum Int.MaxValue).

k - number of leading eigenvalues required, 0 < k < n.

tol - tolerance of the eigs computation.

maxIterations - the maximum number of Arnoldi update iterations.

Returns:

a dense vector of eigenvalues in descending order and a dense matrix of eigenvectors (columns of the matrix).