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/327870/13058825
input_sga_minspec
# Input file for the GA Toolbox
# Author: Kumara Sastry
# Date: April, 2006
#
#
# GA type: SGA or NSGA
#
SGA
#
# Number of decision variables
#
2
#
# For each decision variable, ent
www.eeworm.com/read/327870/13058834
input_sga_maxspec
# Input file for the GA Toolbox
# Author: Kumara Sastry
# Date: April, 2006
#
#
# GA type: SGA or NSGA
#
SGA
#
# Number of decision variables
#
2
#
# For each decision variable, ent
www.eeworm.com/read/140850/13059610
m train.m
function net = train(tutor, x, y, C, kernel, zeta, net)
% TRAIN
%
% Train a support vector classification network, using the sequential minimal
% optimisation algorithm.
%
% net = train(tut
www.eeworm.com/read/242302/13078370
txt 如何以一个模板为基础生成文件.txt
你 现 在 访 问 的 站 点 的 页 面 实 际 上 就 是 采 用 模 板 方 式 生 成 的 。 你 可 以 这 样 组 织 你 的 模 板 文 件 ：
...
...
然 后 使 用 Replace函 数 将 “ ” 替
www.eeworm.com/read/325084/13228204
h hookmanager.h
// Copyright Ric Vieler, 2006
// Support header for hookManager.c
#ifndef _HOOK_MANAGER_H_
#define _HOOK_MANAGER_H_
NTSTATUS HookKernel( void );
BOOL IsSameFile( PUNICODE_STRING shortString,
www.eeworm.com/read/324120/13284171
input_nsga_maxspec
# Input file for the GA Toolbox
# Author: Kumara Sastry
# Date: April, 2006
#
#
# GA type: SGA or NSGA
#
NSGA
#
# Number of decision variables
#
3
#
# For each decision variable, en
www.eeworm.com/read/324120/13284212
input_nsga_minspec
# Input file for the GA Toolbox
# Author: Kumara Sastry
# Date: April, 2006
#
#
# GA type: SGA or NSGA
#
NSGA
#
# Number of decision variables
#
3
#
# For each decision variable, en
www.eeworm.com/read/324120/13284286
input_sga_minspec
# Input file for the GA Toolbox
# Author: Kumara Sastry
# Date: April, 2006
#
#
# GA type: SGA or NSGA
#
SGA
#
# Number of decision variables
#
2
#
# For each decision variable, ent
www.eeworm.com/read/324120/13284311
input_sga_maxspec
# Input file for the GA Toolbox
# Author: Kumara Sastry
# Date: April, 2006
#
#
# GA type: SGA or NSGA
#
SGA
#
# Number of decision variables
#
2
#
# For each decision variable, ent