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/415086/11084405
m gauss.m
% Evaluates a multidimensional, isotropic Gaussian with mean m and
% standard deviation sigma at the data points in the columns of x
function g = gauss(x, m, sigma, normalize)
D = size(x, 1);
www.eeworm.com/read/202785/15373423
m standvec.m
function z = standvec(x)
%
% STANDVEC
%
% Standardises a vector x, ie. the mean of x is
% subtracted from all elements of x and the
% result is divided by the standard deviation of x.
%
% Call
www.eeworm.com/read/200388/15434354
m ismooth.m
%ISMOOTH Smooth with Gaussian kernel
%
% ims = ismooth(im, sigma)
%
% Smooths all planes of the input image im with a unit volume Gaussian
% function of standard deviation sigma.
%
% The resulting im
www.eeworm.com/read/200388/15434390
m kgauss.m
%KGAUSS Gaussian smoothing kernel
%
% k = kgauss(sigma)
% k = kgauss(sigma, w)
%
% Returns a unit volume Gaussian smoothing kernel. The Gaussian has
% a standard deviation of sigma, and the convolut
www.eeworm.com/read/192078/8408372
m lms5.m
%LMS5 Problem 2.1
%
% 'ifile.mat' - input file containing:
% K - iterations
% H - FIR channel
% Neq - equalizer order
% sigman - standard deviation of noise at channel ou
www.eeworm.com/read/189063/8493100
m da_lsqs.m
%
% da_lsqs
%
% Least squares regression entry point
%
%
% Clear the screen
%
da_front;
drawnow;
%
% Make sure that none of the variables have a zero
% standard deviation
%
s=std(dat
www.eeworm.com/read/388439/8609686
m da_lsqs.m
%
% da_lsqs
%
% Least squares regression entry point
%
%
% Clear the screen
%
da_front;
drawnow;
%
% Make sure that none of the variables have a zero
% standard deviation
%
s=std(dat
www.eeworm.com/read/288527/8627038
m da_lsqs.m
%
% da_lsqs
%
% Least squares regression entry point
%
%
% Clear the screen
%
da_front;
drawnow;
%
% Make sure that none of the variables have a zero
% standard deviation
%
s=std(dat
www.eeworm.com/read/386050/8767478
m rnnc.m
%RNNC Random Neural Net classifier
%
% W = RNNC(A,N,S)
%
% INPUT
% A Input dataset
% N Number of neurons in the hidden layer
% S Standard deviation of weights in an input layer (default: 1
www.eeworm.com/read/177981/9425159
m da_lsqs.m
%
% da_lsqs
%
% Least squares regression entry point
%
%
% Clear the screen
%
da_front;
drawnow;
%
% Make sure that none of the variables have a zero
% standard deviation
%
s=std(dat