代码搜索：NetWork
找到约 10,000 项符合「NetWork」的源代码
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www.eeworm.com/read/278507/10530994
c icmpcode_v4.c
#include "trace.h"
const char *
icmpcode_v4(int code)
{
static char errbuf[100];
switch (code) {
case 0: return("network unreachable");
case 1: return("host unreachable");
case 2: return("pro
www.eeworm.com/read/423392/10563042
readme
WPA Supplicant
==============
Copyright (c) 2003-2008, Jouni Malinen and contributors
All Rights Reserved.
This program is dual-licensed under both the GPL version 2 and BSD
license. Eithe
www.eeworm.com/read/278064/10576936
m demopsonet.m
% demoPSOnet.m
% script to show a quick, uncomplicated demo of using trainpso for training
% a neural net
%
% tries to build a feedforward neural net to approximate a noisy increaing
% sin funct
www.eeworm.com/read/277779/10605175
m hardlims.m
function a = hardlims(n,b)
%HARDLIMS Symmetric hard limit transfer function.
%
% Syntax
%
% A = hardlims(N)
% info = hardlims(code)
%
% Description
%
% HARDLIMS is a transfer functio
www.eeworm.com/read/277779/10605204
m hardlim.m
function a = hardlim(n,b)
%HARDLIM Hard limit transfer function.
%
% Syntax
%
% A = hardlim(N)
% info = hardlim(code)
%
% Description
%
% HARDLIM is a transfer function. Transfer fu
www.eeworm.com/read/277779/10605241
m rbf_hybrid.m
function [w,y] = rbf_hybrid(P,T,c,sig);
%
% RBF_FIXED: Radial Basis Function Network with Fixed Centers Selected at Random
% (S. Haykin, pp. 299, 1999)
%
% function [w,y] = rbf_fixed(P,T,
www.eeworm.com/read/277779/10605262
m dist.m
function z = dist(w,p)
%DIST Euclidean distance weight function.
%
% Syntax
%
% Z = dist(W,P)
% df = dist('deriv')
% D = dist(pos)
%
% Description
%
% DIST is the Euclidean distance
www.eeworm.com/read/351797/10609689
m train.m
function net = train(net, tutor, varargin)
% TRAIN
%
% Train a max-win multi-class support vector classifier network using the
% specified tutor to train each component two-class network.
%
www.eeworm.com/read/351797/10609856
m train.m
function net = train(net, tutor, varargin)
% TRAIN
%
% Train a max-win multi-class support vector classifier network using the
% specified tutor to train each component two-class network.
%
www.eeworm.com/read/351797/10609866
m train.m
function net = train(net, tutor, varargin)
% TRAIN
%
% Train a dag-svm multi-class support vector classifier network using the
% specified tutor to train each component two-class network.
%