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/477455/6736078
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/477455/6736079
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/264046/11331524
m reconst.m
function yr=reconst0(y, Interv)
% RECONST Reconstructs a series with jumps at intervention points
%
% yr=reconst(y,Int)
%
% y: Time series (*)
% Int: Vector of variance intervention or jump poi
www.eeworm.com/read/400576/11573521
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/223154/14652252
m zscore.m
function i = zscore(i,DIM)
% ZSCORE removes the mean and normalizes the data
% to a variance of 1.
%
% z = zscore(x,DIM)
% calculates the z-score of x along dimension DIM
% it removes the
www.eeworm.com/read/213240/15140011
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/209559/4975208
c timer.c
/*
* linux/net/sunrpc/timer.c
*
* Estimate RPC request round trip time.
*
* Based on packet round-trip and variance estimator algorithms described
* in appendix A of "Congestion Avoidance and Co
www.eeworm.com/read/316872/3607088
c timer.c
/*
* linux/net/sunrpc/timer.c
*
* Estimate RPC request round trip time.
*
* Based on packet round-trip and variance estimator algorithms described
* in appendix A of "Congestion Avoidance and Co
www.eeworm.com/read/309003/3708354
m toon0552.m
% toon0552 -- Noisy Versions of Four Signals
%
% The four objects of Figure 1 with white noise superposed.
% The noise is normally distributed with variance 1.
%
global yblocks ybumps yheavi yD
www.eeworm.com/read/309003/3708623
m idfig03.m
% idfig03 -- Ideal Figure 03: Noisy Versions of Four Signals
%
% The four objects of Figure 1 with white noise superposed.
% The noise is normally distributed with variance 1.
%
%
global yblo