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/359009/10171219
m garchllfn.m
function [LLF , G , H , e , h] = garchllfn(Parameters , y , R , M , P , Q , X)
%GARCHLLFN Univariate GARCH process objective function (Gaussian innovations).
% Compute the log-likelihood objective
www.eeworm.com/read/359009/10171246
m garchsim.m
function [e , h , y] = garchsim(spec, nSamples, nPaths, seed, X)
%GARCHSIM Univariate GARCH process simulation.
% Given specifications for the conditional mean and variance of a univariate
% t
www.eeworm.com/read/358751/10180173
pas cowcycle.pas
{
ID:maigoak1
PROG:cowcycle
}
program cowcycle;
const
fs=56;rs=36;
var
fin,fout:text;
gear,ans:array[1..fs+rs]of byte;
f,r,f1,f2,r1,r2,i:byte;
minvar:real;
procedure update;
www.eeworm.com/read/162188/10328033
src ppc.src
/*
** princomp.src - Principle Components
**
** (C) Copyright 1996 Aptech Systems, Inc.
** All Rights Reserved.
**
** This Software Product is PROPRIETARY SOURCE CODE OF APTECH
** SYSTEMS,
www.eeworm.com/read/424281/10468081
c stat.c
#include
#include
int
main(void)
{
double data[5] = {17.2, 18.1, 16.5, 18.3, 12.6};
double mean, variance, largest, smallest;
mean = gsl_stats_mean(data, 1
www.eeworm.com/read/418695/10935163
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/466212/7041103
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/299984/7140541
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/463748/7176076
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/462318/7202831
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