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/456354/7351419
m gngauss.m
function [gsrv1,gsrv2]=gngauss(m,sgma)
% [gsrv1,gsrv2]=gngauss(m,sgma)
% [gsrv1,gsrv2]=gngauss(sgma)
% [gsrv1,gsrv2]=gngauss
% GNGAUSS generates two independent Gaussian random variables with mean
%
www.eeworm.com/read/456354/7351455
m gngauss.m
function [gsrv1,gsrv2]=gngauss(m,sgma)
% [gsrv1,gsrv2]=gngauss(m,sgma)
% [gsrv1,gsrv2]=gngauss(sgma)
% [gsrv1,gsrv2]=gngauss
% GNGAUSS generates two independent Gaussian random variables with mean
%
www.eeworm.com/read/456187/7355237
java exercise5_20.java
public class Exercise5_20 {
public static void main(String args[]) {
// Students' answers to the questions
char[][] answers = {
{'A', 'B', 'A', 'C', 'C', 'D', 'E', 'E', 'A', 'D'},
www.eeworm.com/read/455115/7377899
m ridgesegment.m
% RIDGESEGMENT - Normalises fingerprint image and segments ridge region
%
% Function identifies ridge regions of a fingerprint image and returns a
% mask identifying this region. It also normalises t
www.eeworm.com/read/452222/7444736
m std.m
function dev = std(chr)
% STD - Standard deviation
% For vectors, STD(chr) returns the standard
% deviation of the population. For matrices, STD(chr) is a column vector
% containing the stan
www.eeworm.com/read/450608/7480114
m gendatl.m
%GENDATL Generation of Lithuanian classes
%
% A = GENDATL(N,S)
%
% INPUT
% N Number of objects per class (optional; default: [50 50])
% S Standard deviation for the data generation (optional; d
www.eeworm.com/read/445831/7589499
m gngauss.m
function [gsrv1,gsrv2]=gngauss(m,sgma)
% [gsrv1,gsrv2]=gngauss(m,sgma)
% [gsrv1,gsrv2]=gngauss(sgma)
% [gsrv1,gsrv2]=gngauss
% GNGAUSS generates two independent Gaussian random variables with me
www.eeworm.com/read/442852/7643447
m main_ikeda.m
%function [x,y]=ikeda(n,mu,x0,y0)
%Syntax: [x,y]=ikeda(n,mu,x0,y0)
%_____________________________________
%
% Simulation of the Ikeda map.
% x'=1+mu*(xcos(t)-ysin(t)
% y'=mu*(xsin(t)+ycos(
www.eeworm.com/read/441245/7672674
m gendatl.m
%GENDATL Generation of Lithuanian classes
%
% A = GENDATL(N,S)
%
% INPUT
% N Number of objects per class (optional; default: [50 50])
% S Standard deviation for the data generation (optional; d
www.eeworm.com/read/441245/7673399
m gendatsin.m
%GENREGSIN Generate sinusoidal regression data
%
% X = GENDATSIN(N,SIGMA)
%
% INPUT
% N Number of objects to generate
% SIGMA Standard deviation of the noise
%
% OUTPUT
% X Reg