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/449504/7501980
m normlt_rnd.m
function result = normlt_rnd(mu,sigma2,left)
% PURPOSE: compute random draws from a left-truncated normal
% distribution, with mean = mu, variance = sigma2
% ------------------------------
www.eeworm.com/read/441410/7670711
m fixing2.m
%FIXING2 Filter version of Examples 12.4 and 12.7.
% Shows the impact on introducing constraints
% as observations with zero variance
%Kai Borre 07-10-97
%Copyright (c) by Kai Borr
www.eeworm.com/read/199851/7818689
m orthexpanalysis2.m
function OrthExpAnalysis2
% 正交试验的极差分析Variance Analysis of Orthogonal experiment
%
% Author: HUANG Huajiang
% Copyright 2003 UNILAB Research Center,
% East China University of Science and T
www.eeworm.com/read/199851/7818691
m orthexpanalysis1.m
function OrthExpAnalysis1
% 正交试验的极差分析Variance Analysis of Orthogonal experiment
%
% Author: HUANG Huajiang
% Copyright 2003 UNILAB Research Center,
% East China University of Science and T
www.eeworm.com/read/197649/7982947
m dss.m
function z=dss(nt,dt)
% z=dss(nt,dt):
%
% Function to analyze the degree of stationarity of data nt(n,m) by
% calculating the variance for each n, where n specifies the number
% of f
www.eeworm.com/read/197108/8029196
m orthexpanalysis2.m
function OrthExpAnalysis2
% 正交试验的极差分析Variance Analysis of Orthogonal experiment
%
% Author: HUANG Huajiang
% Copyright 2003 UNILAB Research Center,
% East China University of Science and T
www.eeworm.com/read/197108/8029199
m orthexpanalysis1.m
function OrthExpAnalysis1
% 正交试验的极差分析Variance Analysis of Orthogonal experiment
%
% Author: HUANG Huajiang
% Copyright 2003 UNILAB Research Center,
% East China University of Science and T
www.eeworm.com/read/397111/8067242
m kwhiten.m
%KWHITEN Whiten the data in kernel space.
%
% W = kwhiten(A,DIM,KTYPE,PAR1)
%
% Apply a kernel PCA to dataset A and retain DIM dimensions, or a
% fraction DIM of the total variance. The data A
www.eeworm.com/read/397097/8069142
m kwhiten.m
%KWHITEN Whiten the data in kernel space.
%
% W = kwhiten(A,dim,ktype,par1)
%
% Apply a kernel PCA and retain dim dimensions, or a fraction dim of
% the total variance. The data is then rescal
www.eeworm.com/read/296017/8128585
m huffman.m
function C = huffman(D,W,varargin)
% HUFFMAN Huffman encoder.
% C = HUFFMAN(D,W) generates a static minimum-variance Huffman tree and
% corresponding codebook C for the source symbols with