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/332054/12782823
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/244937/12831097
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/330422/12892236
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 nois
www.eeworm.com/read/140062/13111948
cpp d9r1.cpp
#include "iostream.h"
#include "stdlib.h"
#include "math.h"
void main()
{
//program d9r1
//driver for routine fit
int i,mwt,npt = 100;
double spread = 0.5;
double x[101]
www.eeworm.com/read/241416/13145289
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/241416/13145305
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/241049/13174567
cpp d9r1.cpp
#include "iostream.h"
#include "stdlib.h"
#include "math.h"
void main()
{
//program d9r1
//driver for routine fit
int i,mwt,npt = 100;
double spread = 0.5;
double x[101]
www.eeworm.com/read/323953/13306633
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/321160/13411492
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/321160/13411512
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));