代码搜索：explaining
找到约 77 项符合「explaining」的源代码
代码结果 77
www.eeworm.com/read/378680/9219173
cpp nghtmlstaticctrl.cpp
// XHTMLStatic.cpp Version 1.0
//
// Author: Hans Dietrich
// hdietrich2@hotmail.com
//
// Thanks to Charles Petzold for explaining how GetTextExtentPoint32() works,
// in his excelle
www.eeworm.com/read/140779/5781135
f error.f
******************************************************************
*
* ERROR Version 45G
*
* $Log: error.f,v $
* Revision 1.2 1996/03/14 22:55:09 jaf
* Comments added explaining which of the local
www.eeworm.com/read/168433/5446857
f error.f
******************************************************************
*
* ERROR Version 45G
*
* $Log: error.f,v $
* Revision 1.2 1996/03/14 22:55:09 jaf
* Comments added explaining which of the local
www.eeworm.com/read/360995/10070180
m pca_dd.m
%PCA_DD Principal Component data description
%
% W = PCA_DD(A,FRACREJ,N)
%
% Traininig of a PCA, with N features (or explaining a fraction N of
% the variance).
%
% Default: N=0.9
% Copyright:
www.eeworm.com/read/451547/7462011
m pca_dd.m
%PCA_DD Principal Component data description
%
% W = PCA_DD(A,FRACREJ,N)
%
% Traininig of a PCA, with N features (or explaining a fraction N of
% the variance).
%
% Default: N=0.9
% Copyright:
www.eeworm.com/read/397111/8067391
m pca_dd.m
%PCA_DD Principal Component data description
%
% W = PCA_DD(A,FRACREJ,N)
%
% Traininig of a PCA, with N features (or explaining a fraction N of
% the variance).
%
% Default: N=0.9
% Copyright:
www.eeworm.com/read/493294/6400539
m pca_dd.m
%PCA_DD Principal Component data description
%
% W = PCA_DD(A,FRACREJ,N)
%
% Traininig of a PCA, with N features (or explaining a fraction N of
% the variance).
%
% Default: N=0.9
% Copyright:
www.eeworm.com/read/492400/6422326
m pca_dd.m
%PCA_DD Principal Component data description
%
% W = PCA_DD(A,FRACREJ,N)
%
% Traininig of a PCA, with N features (or explaining a fraction N of
% the variance).
%
% Default: N=0.9
% Copyright:
www.eeworm.com/read/400576/11573585
m pca_dd.m
%PCA_DD Principal Component data description
%
% W = PCA_DD(A,FRACREJ,N)
%
% Traininig of a PCA, with N features (or explaining a fraction N of
% the variance).
%
% Default: N=0.9
% Copyright:
www.eeworm.com/read/213240/15140071
m pca_dd.m
%PCA_DD Principal Component data description
%
% W = PCA_DD(A,FRACREJ,N)
%
% Traininig of a PCA, with N features (or explaining a fraction N of
% the variance).
%
% Default: N=0.9
% Copyright: