代码搜索：deviation
找到约 1,443 项符合「deviation」的源代码
代码结果 1,443
www.eeworm.com/read/459593/7273111
m gwn.m
function B=GWN(n,beta)
% GWN- Generation of Gaussian White Noise
% Usage
% B=GWN(n,beta)
% Inputs
% n size of datas
% beta standard deviation
% Outputs
% B resulting noise
www.eeworm.com/read/459593/7273125
m gwn2.m
function B=GWN2(n,beta)
% GWN2- Generation of 2-D Gaussian White NNoise
% Usage
% B=GWN2(n,beta)
% Inputs
% n size of datas
% beta standard deviation
% Outputs
% B resulting
www.eeworm.com/read/459173/7279190
m gngauss.m
function[gsrv1,gsrv2]=gngauss(m,sgma)
% m--mean, sgma--standard deviation
if nargin==0,
m=0;sgma=1;
elseif nargin==1,
sgma=m;m=0;
end;
u=rand;
z=sgma*(sqrt(2*log(1/(1-u)))); %a R
www.eeworm.com/read/459173/7279201
m gngauss.m
function[gsrv1,gsrv2]=gngauss(m,sgma)
% m--mean, sgma--standard deviation
if nargin==0,
m=0;sgma=1;
elseif nargin==1,
sgma=m;m=0;
end;
u=rand;
z=sgma*(sqrt(2*log(1/(1-u)))); %a R
www.eeworm.com/read/456354/7351329
m normal.m
function y=normal(x,m,s)
% FUNCTION y=NORMAL(x,m,s)
% Gaussian distribution
% m=mean
% s=standard deviation
y=(1/sqrt(2*pi*s^2))*exp(-((x-m).^2)/(2*s^2));
www.eeworm.com/read/456354/7351351
m normal.m
function y=normal(x,m,s)
% FUNCTION y=NORMAL(x,m,s)
% Gaussian distribution
% m=mean
% s=standard deviation
y=(1/sqrt(2*pi*s^2))*exp(-((x-m).^2)/(2*s^2));
www.eeworm.com/read/455119/7377590
m pca_error_plots_close.m
%
% David Gleich
% CS 152 - Neural Networks
% 12 December 2003
%
% initialize random number generator
rand('seed', 2);
k = 4;
% load PCA data
X = pcadata('close');
fprintf('Compu
www.eeworm.com/read/455119/7377593
m pca_error_plots_separated.m
%
% David Gleich
% CS 152 - Neural Networks
% 12 December 2003
%
% initialize random number generator
rand('seed', 2);
k = 4;
% load PCA data
A = pcadata('separated');
fprintf('Com
www.eeworm.com/read/445830/7589523
m normal.m
function y=normal(x,m,s)
% FUNCTION y=NORMAL(x,m,s)
% Gaussian distribution
% m=mean
% s=standard deviation
y=(1/sqrt(2*pi*s^2))*exp(-((x-m).^2)/(2*s^2));
www.eeworm.com/read/445823/7589625
m normal.m
function y=normal(x,m,s)
% FUNCTION y=NORMAL(x,m,s)
% Gaussian distribution
% m=mean
% s=standard deviation
y=(1/sqrt(2*pi*s^2))*exp(-((x-m).^2)/(2*s^2));