代码搜索：Eigenvalues
找到约 1,100 项符合「Eigenvalues」的源代码
代码结果 1,100
www.eeworm.com/read/273525/4205987
mata eigenvalues.mata
*! version 1.0.1 04jan2005
version 9.0
mata:
complex vector eigenvalues(numeric matrix A, |cond, real scalar nobalance)
{
numeric matrix Acpy
complex vector evals
if (args()==1) con
www.eeworm.com/read/273525/4209134
mata _eigenvalues.mata
*! version 1.0.1 04jan2005
version 9.0
mata:
complex vector _eigenvalues(numeric matrix A, |cond, real scalar nobalance)
{
complex vector evals
if (args()==1) cond = .
_eigen_work(0
www.eeworm.com/read/273525/4207081
dlg matrix_eigenvalues.dlg
/*
matrix eigenvalues
*! VERSION 1.1.0 28mar2005
*/
VERSION 9.0
INCLUDE _std_medium
INCLUDE _ht250
INCLUDE header
HELP hlp1, view("help matrix eigenvalues")
RESET res1
DIAL
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hlp matrix_eigenvalues.hlp
{smcl}
{* 28mar2005}{...}
{cmd:help matrix eigenvalues} {right:dialog: {bf:{dialog matrix_eigenvalues:matrix eigenvalues}}}
{hline}
{title:Title}
{p2colset 5 31 33 2}{...}
{p2col :{hi:[P] m
www.eeworm.com/read/421044/10759059
pdf 11. chapter 10 - eigenvalues and singular values.pdf
www.eeworm.com/read/480713/6660074
pdf 11. chapter 10 - eigenvalues and singular values.pdf
www.eeworm.com/read/449504/7502350
m vprgen.m
function [v, d] = vprgen(a,b)
% PURPOSE: Eigenvalues of the problem max(x'ax / x'bx)
%--------------------------------------------------------------
% USAGE: [v, d] = vprgen(a,b)
% where: a =
www.eeworm.com/read/360770/10078943
m a141.m
%----------------------------------------------------------------
% Example a1.4.1: Eigenvalues and Eigenvectors
%---------------------------------------------------------------
www.eeworm.com/read/241323/13156451
m morse.m
% calculates the known analytical formula for the eigenvalues of the
% Morse oscillator, then finds the numerical values using Numerov integration
% and automatic search for eigenvalues.
%
www.eeworm.com/read/339930/12194666
m kpca.m
function [EigenVectors, EigenValues, ratio] = KPCA(K, NumOfPC);
%-------------------------------------------------------------------------%
% KPCA: kernel principal component analysis for dimension