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/168118/9938087
m autoreject.m
function [cvpi,cvpj]=autoreject(cvpi,cvpj,nshots,dev,standard)
% Function used to reject some of the cross over point based on the standard
% deviation or a constant difference from the cross over po
www.eeworm.com/read/164272/10120333
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/160583/10517345
py anscombe.py
#!/usr/bin/env python
"""
Edward Tufte uses this example from Anscombe to show 4 datasets of x
and y that have the same mean, standard deviation, and regression
line, but which are qualitatively diffe
www.eeworm.com/read/159601/10636832
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/350563/10732010
txt galog.txt
generation best average standard
number value fitness deviation
1 33.8906 -126.0450 38.2966
2 33.8906 -134.4133 31.0527
3 33.8906 -139.1599 27.2726
4 33.8906 -141.3564 24.9666
5
www.eeworm.com/read/349916/10783483
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/274679/10858608
m gaussian.m
% gaussian - returns a 1d Gaussian kernel.
%
% kernel = gaussian(peak,sigma,maxhw)
% Returns a 1d Gaussian kernel with peak as the value
% at the maximum and sigma as the standard deviation (in
www.eeworm.com/read/299984/7140004
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/461294/7230023
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/460435/7250479
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