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/489084/6482584
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/476907/6754316
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/400577/11572630
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/400577/11573145
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/400577/11573199
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/154874/11921426
m ex2.m
dt=1;
t=zeros(100,1);
mean=zeros(100,1);
var=zeros(100,1);
for i=1:100
t(i)=i;
end
for j=1:100
r=randn(100,1);
w=zeros(100,1);
w(1)=w0;
for i=2:100
w(i)=w(i-1)+sqrt(dt)*r(i);
www.eeworm.com/read/343762/11928510
m y2res.m
function [R]=y2res(Y)
% Y2RES evaluates basic statistics of a data series
%
% R = y2res(y)
% several statistics are estimated from each column of y
%
% OUTPUT:
% R.N number of samples, NaNs ar
www.eeworm.com/read/343762/11928709
m hist2res.m
function [R]=hist2res(H,fun)
% Evaluates Histogram data
% [R]=hist2res(H)
%
% [y]=hist2res(H,fun)
% estimates fun-statistic
%
% fun 'mean' mean
% 'std' standard deviation
% 'var' variance
% 'sem' stan
www.eeworm.com/read/342008/12046772
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/152442/12113209
m gaussianmask.m
function M = gaussianMask(k,s)
% k: the scaling factor
% s: standard variance
R = ceil(3*s); % cutoff radius of the gaussian kernal
for i = -R:R,
for j = -R:R,
M(i+ R+1,j+R+1) =