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/288527/8627006
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/287770/8670473
m mvdr.m
function [an,e] = mvdr(x,m,varargin)
% MVDR Minimum Variance Distortionless Response model
% A = MVDR(X,P) finds the coefficients, A=[ 1 A(2) ... A(P+1) ],
% of an Nth order MVDR all-pole model filter
www.eeworm.com/read/431310/8689481
m mvdrlizi_2.m
function [Pmv,theta]=MVDR(x,f0,d,Nbeam,DL)
% minimum variance distortionless response
% x: the signal received by the array
% d: distance of interelement.
% Nbeam: 波束数 DL: diagonal loading
% re
www.eeworm.com/read/282683/9074145
m fastmvu.m
function mappedX = fastmvu(X, no_dims, k, eig_impl);
%FAST_MVU Runs the Fast Maximum Variance Unfolding algorithm
%
% [mappedX, details] = fastmvu(X, no_dims, k)
%
% Computes a low dimensional embed
www.eeworm.com/read/376842/9303787
m dispeeof.m
% dispEEOF(CHP,EXPVAR,DT,NLAG,MOD) Display few EEOFs.
%
% => DISPLAY FEW EEOFs.
% CHP contains all the EEOFs as EOF*LAG*X*Y.
% EXPVAR is a matrix with the explained variance of each
% EEOFs in %. Thi
www.eeworm.com/read/178062/9420752
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);
www.eeworm.com/read/177981/9425147
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/164272/10120324
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/424101/10492522
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);