代码搜索：CCA
找到约 957 项符合「CCA」的源代码
代码结果 957
www.eeworm.com/read/289743/8530063
m cca.m
function [Z, ccaEigen, ccaDetails] = cca(X, Y, EDGES, OPTS)
%
% Function [Z, CCAEIGEN, CCADETAILS] = CCA(X, Y, EDGES, OPTS) computes a low
% dimensional embedding Z in R^d that maximally preserves ang
www.eeworm.com/read/386037/8770447
css cca.css
.cca_banner {
padding-bottom: 10px;
line-height: 0.92em !important;
background-repeat: no-repeat;
padding-left: 285px;
background-image: url(/images/cca/sf_banner.png);
color: #4F5458;
text-tra
www.eeworm.com/read/384512/8866222
m cca.m
function [P] = cca(D, P, epochs, Mdist, alpha0, lambda0)
%CCA Projects data vectors using Curvilinear Component Analysis.
%
% P = cca(D, P, epochs, [Dist], [alpha0], [lambda0])
%
% P = cca(D,2,10);
www.eeworm.com/read/282683/9074281
m cca.m
function [Z, ccaEigen, ccaDetails] = cca(X, Y, EDGES, OPTS)
%
% Function [Z, CCAEIGEN, CCADETAILS] = CCA(X, Y, EDGES, OPTS) computes a low
% dimensional embedding Z in R^d that maximally preserves ang
www.eeworm.com/read/379828/9174505
m cca.m
function [Z, ccaEigen, ccaDetails] = cca(X, Y, EDGES, OPTS)
%
% Function [Z, CCAEIGEN, CCADETAILS] = CCA(X, Y, EDGES, OPTS) computes a low
% dimensional embedding Z in R^d that maximally preserves ang
www.eeworm.com/read/163149/10173286
a51 cca.a51
; cca.a51 generated from: ee.c
; COMPILER INVOKED BY:
; d:\Keil\C51\BIN\C51.EXE ee.c OPTIMIZE(9,SPEED) BROWSE DEBUG OBJECTEXTEND CODE
$NOMOD51
NAME EE
P0 DATA 080H
P1 DATA 090H
P2
www.eeworm.com/read/100045/7083454
sh cca.sh
#!/bin/sh
##
## CCA -- Trivial Client CA management for testing purposes
## Copyright (c) 1998-2000 Ralf S. Engelschall, All Rights Reserved.
##
# external tools
openssl="/usr/local/ssl/bin/open
www.eeworm.com/read/456205/7354398
sh cca.sh
#!/bin/sh
##
## CCA -- Trivial Client CA management for testing purposes
## Copyright (c) 1998-2001 Ralf S. Engelschall, All Rights Reserved.
##
# external tools
openssl="/usr/local/ssl/bin/open
www.eeworm.com/read/441462/7670150
m cca.m
function [theta,phi,lambda] = cca(X,Y,N)
% [theta,phi,lambda] = cca(X,Y,N)
% [theta,phi,lambda] = cca(X,Y)
%
% Canonical Correlation Analysis (CCA) model construction
%
% Input parameter
www.eeworm.com/read/397115/8066756
m cca.m
function [P] = cca(D, P, epochs, Mdist, alpha0, lambda0)
%CCA Projects data vectors using Curvilinear Component Analysis.
%
% P = cca(D, P, epochs, [Dist], [alpha0], [lambda0])
%
% P = cca(D,2,10);