代码搜索：decomposition
找到约 1,689 项符合「decomposition」的源代码
代码结果 1,689
www.eeworm.com/read/373460/2761916
m oaoksk.m
function [model] = oaoksk( data, labels, ker, arg, C, stop, tmax, verb)
% OAOKSK One-Agains-One multi-class decomposition solved by Kernel-SK.
%
% [model] = oaoksk( data, labels, ker, arg, C, stop, t
www.eeworm.com/read/362216/2936152
m modwt_wavelet_sample_variance.m
function svar = modwt_wavelet_sample_variance(WJ, VJ0, X)
% modwt_wavelet_sample_variance -- Compute sample variance from MODWT wavelet decomposition and orignal data series.
%
% Usage:
% s2 = wavel
www.eeworm.com/read/393873/8256392
m bivariate_emd_principle.m
%bivariate_EMD_principle.m
%shows principle of the bivariate EMD extension
%reproduces Fig. 1 in "Bivariate Empirical Mode Decomposition", G. Rilling,
%P. Flandrin, P. Goncalves and J. M. Lilly, IEEE
www.eeworm.com/read/367442/9747989
m~ oaoksk.m~
function [model] = oaoksk( data, labels, ker, arg, C, stop, tmax, verb)
% OAOKSK One-Agains-One multi-class decomposition solved by Kernel-SK.
%
% [model] = oaoksk( data, labels, ker, arg, C, stop, t
www.eeworm.com/read/367442/9748077
m oaoksk.m
function [model] = oaoksk( data, labels, ker, arg, C, stop, tmax, verb)
% OAOKSK One-Agains-One multi-class decomposition solved by Kernel-SK.
%
% [model] = oaoksk( data, labels, ker, arg, C, stop, t
www.eeworm.com/read/269755/11079315
cpp svd.cpp
/// \ingroup newmat
///@{
/// \file svd.cpp
/// Singular value decomposition.
// Copyright (C) 1991,2,3,4,5: R B Davies
// Updated 17 July, 1995
#define WANT_MATH
#include "include.h"
www.eeworm.com/read/333887/12656140
m bivariate_emd_principle.m
%bivariate_EMD_principle.m
%shows principle of the bivariate EMD extension
%reproduces Fig. 1 in "Bivariate Empirical Mode Decomposition", G. Rilling,
%P. Flandrin, P. Goncalves and J. M. Lilly, IEEE
www.eeworm.com/read/205036/15328849
m parafac_als.m
function [P,Uinit] = parafac_als(X,R,opts)
%PARAFAC_ALS Compute a PARAFAC decomposition of any type of tensor.
%
% P = PARAFAC_ALS(X,R) computes an estimate of the best rank-R
% PARAFAC model of a
www.eeworm.com/read/202129/15390372
m bivariate_emd_principle.m
%bivariate_EMD_principle.m
%shows principle of the bivariate EMD extension
%reproduces Fig. 1 in "Bivariate Empirical Mode Decomposition", G. Rilling,
%P. Flandrin, P. Goncalves and J. M. Lilly, IEEE
www.eeworm.com/read/200944/15419847
m fnipals1.m
function [T,P]=fnipals1(X,W)
%function [T,P]=fnipals1(X,W)
% T and P are found so that X = T*P'
% The decomposition uses the power method.
%
% X : The matrix to be decomposed into T and P