List of eigenvalueDecomposition() Examples

Examples of eigenvalueDecomposition()

weka.core.Matrix.eigenvalueDecomposition()
Performs Eigenvalue Decomposition using Householder QR Factorization Matrix must be symmetrical. Eigenvectors are return in parameter V, as columns of the 2D array. (Real parts of) Eigenvalues are returned in parameter d. @param V double array in which the eigenvectors are returned @param d array in which the eigenvalues are returned @throws Exception if matrix is not symmetric

Examples of weka.core.Matrix.eigenvalueDecomposition()


    double [] d = new double[m_numAttribs]; 
    double [][] v = new double[m_numAttribs][m_numAttribs];


    Matrix corr = new Matrix(m_correlation);
    corr.eigenvalueDecomposition(v, d);
    m_eigenvectors = (double [][])v.clone();
    m_eigenvalues = (double [])d.clone();
    
    /*for (int i = 0; i < m_numAttribs; i++) {
      for (int j = 0; j < m_numAttribs; j++) {

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Examples of weka.core.Matrix.eigenvalueDecomposition()


    double [] d = new double[m_numAttribs]; 
    double [][] v = new double[m_numAttribs][m_numAttribs];


    Matrix corr = new Matrix(m_correlation);
    corr.eigenvalueDecomposition(v, d);
    m_eigenvectors = (double [][])v.clone();
    m_eigenvalues = (double [])d.clone();


    // any eigenvalues less than 0 are not worth anything --- change to 0
    for (int i = 0; i < m_eigenvalues.length; i++) {

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Examples of weka.core.Matrix.eigenvalueDecomposition()


    double [] d = new double[m_numAttribs]; 
    double [][] v = new double[m_numAttribs][m_numAttribs];


    Matrix corr = new Matrix(m_correlation);
    corr.eigenvalueDecomposition(v, d);
    m_eigenvectors = (double [][])v.clone();
    m_eigenvalues = (double [])d.clone();
    
    /*for (int i = 0; i < m_numAttribs; i++) {
      for (int j = 0; j < m_numAttribs; j++) {

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