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/244800/12842910
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/244076/12892389
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/137229/13339002
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/264420/11315741
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/346459/11743189
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/154760/11928795
m matched.m
function matched(L, D, S)
%MATCHED Matched filter response using a pulse signal.
% MATCHED(L, D, S)
% L=number of samples D= delay of received pulse (in samples).
% S^2= variance of added Gaussi
www.eeworm.com/read/337002/12402458
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/124842/14534515
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/214740/15090332
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