代码搜索：Variance
找到约 2,271 项符合「Variance」的源代码
代码结果 2,271
www.eeworm.com/read/423552/10550599
m mfbox_pre_varthreshold_run.m
function [X,mask,grid,timeline,params,private]=mfbox_pre_varthreshold_run(X,mask,grid,timeline,params,runflag,private)
% select voxels by variance
%
% Usage:
% [X,mask,grid,timeline,params,private]=m
www.eeworm.com/read/159601/10636786
m da_pcavr.m
%
% da_pcavr
%
% Plots the variance of the individual prinipal
% components
%
w1=gcf;
da_front;
da_pcapb;
set(w1,'NumberTitle','off','Name','Principal Component Analysis');
drawnow;
fig
www.eeworm.com/read/349916/10783452
m da_pcavr.m
%
% da_pcavr
%
% Plots the variance of the individual prinipal
% components
%
w1=gcf;
da_front;
da_pcapb;
set(w1,'NumberTitle','off','Name','Principal Component Analysis');
drawnow;
fig
www.eeworm.com/read/461294/7230015
m da_pcavr.m
%
% da_pcavr
%
% Plots the variance of the individual prinipal
% components
%
w1=gcf;
da_front;
da_pcapb;
set(w1,'NumberTitle','off','Name','Principal Component Analysis');
drawnow;
fig
www.eeworm.com/read/457219/7332188
m da_pcavr.m
%
% da_pcavr
%
% Plots the variance of the individual prinipal
% components
%
w1=gcf;
da_front;
da_pcapb;
set(w1,'NumberTitle','off','Name','Principal Component Analysis');
drawnow;
fig
www.eeworm.com/read/452217/7445372
m da_pcavr.m
%
% da_pcavr
%
% Plots the variance of the individual prinipal
% components
%
w1=gcf;
da_front;
da_pcapb;
set(w1,'NumberTitle','off','Name','Principal Component Analysis');
drawnow;
fig
www.eeworm.com/read/206731/7457164
m da_pcavr.m
%
% da_pcavr
%
% Plots the variance of the individual prinipal
% components
%
w1=gcf;
da_front;
da_pcapb;
set(w1,'NumberTitle','off','Name','Principal Component Analysis');
drawnow;
fig
www.eeworm.com/read/448935/7521650
m xwt.m
function varargout=xwt(x,y,varargin)
%% Cross wavelet transform
% Creates a figure of cross wavelet power in units of
% normalized variance.
%
% USAGE: [Wxy,period,scale,coi,sig95]=xwt(x,y,[,sett
www.eeworm.com/read/298649/7947875
m da_pcavr.m
%
% da_pcavr
%
% Plots the variance of the individual prinipal
% components
%
w1=gcf;
da_front;
da_pcapb;
set(w1,'NumberTitle','off','Name','Principal Component Analysis');
drawnow;
fig
www.eeworm.com/read/196569/8074624
m plsgacv.m
% PLSC
% Computation of Cross-Validated Explained Variance
% after predictors selection using genetic algorithms
% sintax:
% [best,exp_var_cv,mxi,sxi,myi,syi]=plsgacv(x,y,aut,ng,A,msca,ssca);