代码搜索：Matrices
找到约 3,616 项符合「Matrices」的源代码
代码结果 3,616
www.eeworm.com/read/156528/11794979
m shibbsr.m
function B = shibbsR(X,m)
%
% Developpment version
%发展版本
%================================================================
% Blind separation of real signals with SHIBBS. Version 1.5 Dec. 1997.
%用
www.eeworm.com/read/156528/11795323
m shibbsr.m
function B = shibbsR(X,m)
%
% Developpment version
%================================================================
% Blind separation of real signals with SHIBBS. Version 1.5 Dec. 1997.
%
% Usag
www.eeworm.com/read/343491/11944491
m matlabshibbsr.m
function B = shibbs(X,m)
%
% Developpment version
%================================================================
% Blind separation of real signals with SHIBBS. Version 1.5 Dec. 1997.
%
% Usage
www.eeworm.com/read/343489/11944533
txt readme.txt
readme.txt
As of now (20-jul-02) this directory
contains the following:
* The acdc algorithm for finding the
approximate general (non-orthogonal)
joint diagonalizer (in the direct Least
www.eeworm.com/read/343489/11944535
m acdc.m
function [A,Lam,Nit,Cls]=...
acdc(M,w,A0,Lam0);
%acdc: appoximate joint diagonalization
%(in the direct Least-Squares sense) of
%a set of Hermitian matrices, using the
%iterative AC-DC alg
www.eeworm.com/read/343489/11944539
m acdc_sym.m
function [A,Lam,Nit,Cls]=...
acdc_sym(M,w,A0,Lam0);
%acdc_sym: appoximate joint diagonalization
%(in the direct Least-Squares sense) of
%a set of symmetric matrices, using the
%iterative A
www.eeworm.com/read/256797/11971879
m meancov.m
%MEANCOV Estimation of the means and covariances from multiclass data
%
% [U,G] = MEANCOV(A,N)
%
% INPUT
% A Dataset
% N Normalization to use for calculating covariances: by M, the number
%
www.eeworm.com/read/255755/12057259
m gauss.m
%GAUSS Generation of a multivariate Gaussian dataset
%
% A = GAUSS(N,U,G,LABTYPE)
%
% INPUT
% N Array of number of objects to generate for each class
% U Dataset with means, labels a
www.eeworm.com/read/255755/12057883
m nbayesc.m
%NBAYESC Bayes Classifier for given normal densities
%
% W = NBAYESC(U,G)
%
% INPUT
% U Dataset of means of classes
% G Covariance matrices (optional; default: identity matrices)
%
% OUTP
www.eeworm.com/read/255755/12057897
m meancov.m
%MEANCOV Estimation of the means and covariances from multiclass data
%
% [U,G] = MEANCOV(A,N)
%
% INPUT
% A Dataset
% N Normalization to use for calculating covariances: by M, the number
%