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/431675/8661688
m pca.m
%PCA Principal Component Analysis
%
% [W,alf] = pca(A,n)
% [W,n] = pca(A,alf)
%
% A principal component analysis is performed on the joint
% covarianve matrix of the data in A. If A is a labeled da
www.eeworm.com/read/431628/8664555
m ip_07_10.m
% MATLAB script for Illustrative Problem 10, Chapter 7.
echo on
K=10;N=2*K;T=100;variance=1;
noise=sqrt(variance)*randn(1,N);
a=rand(1,36);
a=sign(a-0.5);
b=reshape(a,9,4);
% Generate the 16QAM
www.eeworm.com/read/287843/8665310
m ip_07_10.m
% MATLAB script for Illustrative Problem 10, Chapter 7.
clear
echo on
K=10;N=2*K;T=100;variance=1;
noise=sqrt(variance)*randn(1,N);
a=rand(1,36);
a=sign(a-0.5);
b=reshape(a,9,4);
% Generate th
www.eeworm.com/read/386050/8767417
m pcaklm.m
%PCAKLM Principal Component Analysis/Karhunen-Loeve Mapping
% (PCA or MCA of overall/mean covariance matrix)
%
% [W,FRAC] = PCAKLM(TYPE,A,N)
% [W,N] = PCAKLM(TYPE,A,FRAC)
%
% INPUT
% A
www.eeworm.com/read/386050/8768768
m var.m
%VAR Datafile overload
%
% [V,U] = VAR(A,W)
%
% Computes variance V and mean U in a single run for speed.
www.eeworm.com/read/386050/8768946
m klldc.m
%KLLDC Linear classifier built on the KL expansion of the common covariance matrix
%
% W = KLLDC(A,N)
% W = KLLDC(A,ALF)
%
% INPUT
% A Dataset
% N Number of significant eigenvectors
% AL
www.eeworm.com/read/429797/8788465
m estimatenoise.m
function noisevar = estimatenoise(X,varargin)
% estimatenoise: additive noise estimation from a time series
% usage: noisevar = estimatenoise(X)
% usage: noisevar = estimatenoise(X,dim)
% usage: n
www.eeworm.com/read/385301/8809676
m leastsqrrdm.m
function [a,b,variance,CombineXY,s] = leastSqrRdm(x,y)
%LEASTSQRRDM Least square computation; x is random;
% [a, b] = LEASTSQRRDM(X,Y) perform least square computation based on
% N-by-p data ma
www.eeworm.com/read/385301/8809685
asv leastsqr.asv
function [parameter, variance, cov, x0, s0] = leastqur(x,y)
%LEASTQUR Least square computation
% PARAMETER = LESTQUR(X, Y) performs least square computation on N-by-(P+1)
% data matrix X and N
www.eeworm.com/read/385301/8809686
m leastsqr.m
function [parameter, variance, cov] = leastqur(x,y)
%LEASTQUR Least square computation
% PARAMETER = LESTQUR(X, Y) performs least square computation on N-by-(P+1)
% data matrix X and N-by-1 ve