org.apache.spark.mllib.linalg

Class EigenValueDecomposition

java.lang.Object

org.apache.spark.mllib.linalg.EigenValueDecomposition

public class EigenValueDecomposition
extends java.lang.Object

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<java.lang.Object>,breeze.linalg.DenseMatrix<java.lang.Object>>	symmetricEigs(scala.Function1<breeze.linalg.DenseVector<java.lang.Object>,breeze.linalg.DenseVector<java.lang.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 java.lang.Object

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

Constructor Detail

EigenValueDecomposition

public EigenValueDecomposition()

Method Detail

symmetricEigs

public static scala.Tuple2<breeze.linalg.DenseVector<java.lang.Object>,breeze.linalg.DenseMatrix<java.lang.Object>> symmetricEigs(scala.Function1<breeze.linalg.DenseVector<java.lang.Object>,breeze.linalg.DenseVector<java.lang.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).