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/362216/2936035
m figure330.m
% figure330 - Compare 'octave band' PSD (SDF) estimates for vertical
% shear measurements via periodogram, multitaper PSD,
% Haar and D6 wavelet variance estimates.
%
% Usage:
% run figure330
%
%
www.eeworm.com/read/362216/2936128
m wvar_var_fd_sdf_acvs.m
function [wvar_var] = wvar_var_fd_sdf_acvs(method, wtfname, N, delta, sigma_squared, ...
cov_method)
% wvar_var_fd_sdf_acvs -- Calculate variance of wavelet
www.eeworm.com/read/362216/2936143
m modwt_cum_level_cum_wav_svar.m
function [clcwsvar] = modwt_cum_level_cum_wav_svar(cwsvar)
% modwt_cum_level_cum_wav_svar -- Calculate cumulative level of cumulative sample variance of MODWT wavelet coefficients.
%
% Usage:
% [clc
www.eeworm.com/read/472878/6859112
cpp gasdev.cpp
#include
#include "dist.h"
float NormalDist::gasdev(void)
// returns a normally distributed deviate with zero mean and unit
// variance, using ran1(idum) as the source of uniform deviates.
www.eeworm.com/read/249499/12491233
changes
$Id: CHANGES 151 2007-10-17 13:03:06Z bhm $
Changes in 2.1-0
================
New features:
-------------
- Jackknife variance estimation of regression coefficients are now available.
(Note that t
www.eeworm.com/read/189063/8493075
m da_pcavr.m
%
% da_pcavr
%
% Plots the variance of the individual prinipal
% components
%
w1=gcf;
da_front;
da_pcapb;
set(w1,'NumberTitle','off','Name','Principal Component Analysis');
drawnow;
fig
www.eeworm.com/read/289743/8529909
m fastmvu.m
function [mappedX, mapping] = fastmvu(X, no_dims, k, finetune, eig_impl)
%FAST_MVU Runs the Fast Maximum Variance Unfolding algorithm
%
% [mappedX, mapping] = fastmvu(X, no_dims, k, finetune)
%
% Co
www.eeworm.com/read/289119/8575024
m plsgacv.m
% PLSC
% Computation of Cross-Validated Explained Variance
% after predictors selection using genetic algorithms
% sintax:
% [best,exp_var_cv,mxi,sxi,myi,syi]=plsgacv(x,y,aut,ng,A,msca,ssca);
www.eeworm.com/read/388439/8609655
m da_pcavr.m
%
% da_pcavr
%
% Plots the variance of the individual prinipal
% components
%
w1=gcf;
da_front;
da_pcapb;
set(w1,'NumberTitle','off','Name','Principal Component Analysis');
drawnow;
fig
www.eeworm.com/read/288586/8620342
m plsgacv.m
% PLSC
% Computation of Cross-Validated Explained Variance
% after predictors selection using genetic algorithms
% sintax:
% [best,exp_var_cv,mxi,sxi,myi,syi]=plsgacv(x,y,aut,ng,A,msca,ssca);