代码搜索：optional
找到约 6,947 项符合「optional」的源代码
代码结果 6,947
www.eeworm.com/read/365849/9844583
m calc_x0.m
function v=calc_x0(obj,N);
% Calculates the stochastic initital state estimate, x0
%
% Syntax: (* = optional)
%
% x0 = calc_x0(model, N);
%
% In arguments:
%
% 1. model
% Model object
% 2.
www.eeworm.com/read/365849/9844622
m calc_x0.m
function v=calc_x0(obj,N);
% Calculates the stochastic initital state estimate, x0
%
% Syntax: (* = optional)
%
% x0 = calc_x0(model, N);
%
% In arguments:
%
% 1. model
% Model object
% 2.
www.eeworm.com/read/365849/9844671
m calc_x0.m
function v=calc_x0(obj,N);
% Calculates the stochastic initital state estimate, x0
%
% Syntax: (* = optional)
%
% x0 = calc_x0(model, N);
%
% In arguments:
%
% 1. model
% Model object
% 2.
www.eeworm.com/read/361001/10069713
asv mvu.asv
function [Y,details]=mvu(DD,K,varargin)
% [Y,details]=mvu(DD,K,pars)
%
% 参数介绍
% DD SQUARED distance matrix of the input vectors (e.g. euclidean
% distances)数据点的距离平方矩阵
%
% Optional:
%
% K number
www.eeworm.com/read/361001/10069727
m mvu.m
function [Y,details]=mvu(DD,K,varargin)
% [Y,details]=mvu(DD,K,pars)
%
% 参数介绍
% DD SQUARED distance matrix of the input vectors (e.g. euclidean
% distances)数据点的距离平方矩阵
%
% Optional:
%
% K number
www.eeworm.com/read/163913/10140061
m disfrst.m
function y = Disfrst(f,a,p)
% Computes discrete fractional sine transform
% of order a of vector f
% p (optional) is order of approximation, default N/2
% S-C Pei, M-H Yeh, IEEE Tr SP 49(6)2001, p
www.eeworm.com/read/354397/10359047
m disfrct.m
function y = Disfrct(f,a,p)
%
% Computes discrete fractional cosine transform
% of order a of vector f
% p (optional) is order of approximation, default N/2
% S-C Pei, M-H Yeh, IEEE Tr SP 49(6)2001, p
www.eeworm.com/read/275202/10829356
m gatext.m
function h=GAtext(v,s,c)
%h=GAtext(v,s,c): draw string s at tip of vector v in color c (optional).
% h is the text handle.
%
%See also gable.
% GABLE, Copyright (c) 1999, University of Amsterdam
% C
www.eeworm.com/read/417976/10969661
html jndi-dsml-ext.html
DSML v1 Service Provider for JNDI 1.2 Optional Package
www.eeworm.com/read/299984/7140543
m nu_svr.m
%NU_SVR Support Vector Classifier: NU algorithm
%
% [W,J,C] = NU_SVR(A,TYPE,PAR,C,SVR_TYPE,NU_EPS,MC,PD)
%
% INPUT
% A Dataset
% TYPE Type of the kernel (optional; default: 'p')
% PAR K