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/431675/8662095
m meancov.m
%MEANCOV Means and covariance estimation from multiclass data
%
% [U,G] = meancov(A)
%
% Computation of a set of mean vectors U and a set of covariance
% matrices G of the classes in the dataset A
www.eeworm.com/read/284259/8952277
sc sr.sc
sR.sC package:sbgcop R Documentation
_C_o_m_p_u_t_e _R_e_g_r_e_s_s_i_o_n _P_a_r_a_m_e_t_e_r_s
_D_e_s_c_r_i_p_t_i_o_n:
www.eeworm.com/read/284258/8952366
rd sr.sc.rd
\name{sR.sC}
\alias{sR.sC}
\title{ Compute Regression Parameters }
\description{
Compute an array of regression parameters from an array
of correlation parameters.
}
\usage{
sR.sC(sC)
}
%- maybe als
www.eeworm.com/read/185152/9054976
m initdual.m
function x=initdual()
% Usage:
% x=initdual()
% Sets the global constant matrices and vectors
% and returns the standard starting point
% for Shell Dual Problem
global A B C D E
A= [ -16,
www.eeworm.com/read/379294/9201231
readme
This directory contains the routines that generate finite difference
matrices from a second order elliptic operator.
1) 5-pt and 7-pt matrices on rectangular regions discretizing
ell
www.eeworm.com/read/181830/9235653
m range.m
function Q = range(A)
% RANGE -- find range of a matrix
%
% Q = range(A)
%
% This does essentially the same thing as the Matlab system
% routine ORTH, except it uses the tolerance parameter
www.eeworm.com/read/181830/9235671
m norm.m
function n = norm(S,varargin)
% NORM -- norm function for symbolic variables
%
% n = norm(x,type)
%
% This is the same as the usual NORM function, but for vectors
% or matrices of symbolic con
www.eeworm.com/read/179705/9343804
todo
* Document Jacobi eigen function, in particular that it only works for
symmetric matrices.
www.eeworm.com/read/166510/10017291
m init4acdc.m
function A0=init4acdc(M,wix)
%this function finds an initial guess for
%the acdc algorithm by performing
%pre-whitening on one of the target
%matrices, and then finding the orthogonal
%diagonaliz
www.eeworm.com/read/166509/10017311
m tdsep2.m
function [C,D]=tdsep2(x,sel);
%blind separation using approximate joint diagonalization of
%time delayed correlation-matrices
%
%version 2.01, 2/14/99 by AZ
%usage: [C,D]=tdsep2(x,sel);
% input