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/362500/9996097
m plsdemo.m
echo on
%PLSDEMO Demonstrates PLS and PCR functions
%
% This demonstration illustrates the use of the PLS and
% PCR functions in the PLS_Toolbox.
echo off
%Copyright Eigenvector Research, In
www.eeworm.com/read/166509/10017307
m perf.m
function [SIR,variance]=perf(C,A)
%[SIR,variance]=perf(C,A)
%
% computes the distance of matrices C and A
% ignoring permutation and scaling of columns
% used to evaluate the separation error
www.eeworm.com/read/359009/10171263
m garchfit.m
function [coefficients, errors, LLF, innovations, sigma, summary] = garchfit(spec , y , X)
%GARCHFIT Univariate GARCH process parameter estimation.
% Given an observed univariate return series, es
www.eeworm.com/read/271244/11001963
m dst.m
function z=dst(nt,dt,nzp)
% The function DST analyzes the degree of stationarity of data nt(n,m) by
% calculating the variance for each n, where n specifies the number
% of frequency values, an
www.eeworm.com/read/469123/6977818
m gprsrpp.m
function [mu, S2SR, S2PP] = gprSRPP(logtheta, covfunc, x, INDEX, y, xstar);
% gprSRPP - Carries out approximate Gaussian process regression prediction
% using the subset of regressors (SR) or project
www.eeworm.com/read/299984/7139939
m pca.m
%PCA Principal component analysis (PCA or MCA on overall covariance matrix)
%
% [W,FRAC] = PCA(A,N)
% [W,N] = PCA(A,FRAC)
%
% INPUT
% A Dataset
% N or FRAC Number of dimensions
www.eeworm.com/read/460435/7250414
m pca.m
%PCA Principal component analysis (PCA or MCA on overall covariance matrix)
%
% [W,FRAC] = PCA(A,N)
% [W,N] = PCA(A,FRAC)
%
% INPUT
% A Dataset
% N or FRAC Number of dimensions
www.eeworm.com/read/450608/7480077
m pca.m
%PCA Principal component analysis (PCA or MCA on overall covariance matrix)
%
% [W,FRAC] = PCA(A,N)
% [W,N] = PCA(A,FRAC)
%
% INPUT
% A Dataset
% N or FRAC Number of dimensions
www.eeworm.com/read/441245/7672616
m pca.m
%PCA Principal component analysis (PCA or MCA on overall covariance matrix)
%
% [W,FRAC] = PCA(A,N)
% [W,N] = PCA(A,FRAC)
%
% INPUT
% A Dataset
% N or FRAC Number of dimensions
www.eeworm.com/read/197649/7982922
m dst.m
function z=dst(nt,dt,nzp)
% The function DST analyzes the degree of stationarity of data nt(n,m) by
% calculating the variance for each n, where n specifies the number
% of frequency values, an