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/400577/11573366
m klm.m
%KLM Karhunen-Loeve Mapping (PCA or MCA of mean covariance matrix)
%
% [W,FRAC] = KLM(A,N)
% [W,N] = KLM(A,FRAC)
%
% INPUT
% A Dataset
% N or FRAC Number of dimensions (>= 1) or fr
www.eeworm.com/read/158443/11615562
c xtutest.c
/* Driver for routine tutest */
#include
#include
#define NRANSI
#include "nr.h"
#include "nrutil.h"
#define NPTS 5000
#define MPTS 1000
#define EPS 0.02
#define VAR1 1
www.eeworm.com/read/343002/11984575
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/343002/11984598
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/153777/12007229
c vartab.c
/*----------------------------------------------------------------------
File : vartab.c
Contents: variation table management
Author : Christian Borgelt
History : 14.09.2000 file created
www.eeworm.com/read/342008/12046933
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/342008/12047480
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/255755/12057289
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/255755/12058318
m klm.m
%KLM Karhunen-Loeve Mapping (PCA or MCA of mean covariance matrix)
%
% [W,FRAC] = KLM(A,N)
% [W,N] = KLM(A,FRAC)
%
% INPUT
% A Dataset
% N or FRAC Number of dimensions (>= 1) or fr
www.eeworm.com/read/255284/12090189
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