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/212384/15156759
m mylocstat.m
function g=mylocstat(Iloc,M,D,E,k)
%MYLOCSTAT - perform single-back gray value
% imadjustation based on local statistics for sliding block operation.
% Motivation: try local statistics enhancement
www.eeworm.com/read/209599/15216907
java agentparam.java
package asm;
import java.awt.Frame;
/**
* Title: Artificial Stock Market
* Description: 人工模拟股市（来源：SFI的Swarm版本）的Java版本
* Copyright: Copyright (c) 2003
* Company: http://agents.yea
www.eeworm.com/read/209599/15216913
java agent.java
package asm;
import java.util.Vector;
/**
* Title: Artificial Stock Market
* Description: 人工模拟股市（来源：SFI的Swarm版本）的Java版本
* Copyright: Copyright (c) 2003
* Company: http://agents.ye
www.eeworm.com/read/206197/15298780
m rls.m
clear,clc
m=8; % sensors
n=2; % sources
theta=[-20 0]; % in angle
d=1/2;
www.eeworm.com/read/206197/15298781
m lms.m
clear,clc
m=8; % sensors
n=2; % sources
theta=[-20 0]; % in angle
d=1/2;
www.eeworm.com/read/167728/5453059
out stat.out
The dataset is 17.2, 18.1, 16.5, 18.3, 12.6
The sample mean is 16.54
The estimated variance is 4.2984
The largest value is 18.3
The smallest value is 12.6
www.eeworm.com/read/293183/8310274
m scalem.m
%SCALEM Compute scaling map
%
% W = scalem(A)
%
% W is a map that shifts the origin to the mean of the dataset A.
%
% W = scalem(A,'variance')
%
% The origin is shifted to the mean of A and the
www.eeworm.com/read/293183/8310793
m klm.m
%KLM Karhunen-Loeve Mapping (PCA of mean covariance matrix)
%
% [W,alf] = klm(A,n)
% [W,n] = klm(A,alf)
%
% The Karhunen-Loeve Mapping performs a principal component analysis
% (PCA) on the mean cl
www.eeworm.com/read/173932/9629479
m fig9_28.m
clear all
npts = 2000;
del = 1/2000;
t = 0:del:1;
inp = (1+.2 .* t + .1 .*t.^2) + cos(2. * pi * 2.5 .* t);
X0 = [1,.1,.01]';
% it is assumed that the measurement vector H=[1,0,0]
% this is the
www.eeworm.com/read/173932/9629481
m fig9_27.m
clear all
npts = 2000;
del = 1/2000;
t = 0:del:1;
inp = (1+.2 .* t + .1 .*t.^2);% + cos(2. * pi * 2.5 .* t);
X0 = [1,.1,.01]';
% it is assumed that the measurmeny vector H=[1,0,0]
% this is the