Nerwork neighbour

Dann 2000-03-19 04:08:00

Win9x下，怎样获得网上邻居中的各个计算机名？

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Dann 2000-03-25

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谢谢两位!

Jackzhu 2000-03-21

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BOOL GetNetNeighbour(LPNETRESOURCE lpNetRC)
{
HANDLE hEnum = 0;

DWORD dwResult = WNetOpenEnum(RESOURCE_GLOBALNET, RESOURCETYPE_DISK, 0, lpNetRC, &hEnum);
if(dwResult != NO_ERROR)
return FALSE;

LPNETRESOURCE lpnrLocal = 0;
DWORD dwBuffer = 16384;
DWORD dwEntries = 0xFFFFFFFF;

lpnrLocal = (LPNETRESOURCE)GlobalAlloc(GPTR, dwBuffer);
dwResult = WNetEnumResource(hEnum, &dwEntries, lpnrLocal, &dwBuffer);

if(dwResult == NO_ERROR)
{
for(DWORD i = 0; i < dwEntries; i++)
{
// 得到邻居lpnrLocal[i];
}
}

if(lpnrLocal)
GlobalFree((HGLOBAL)lpnrLocal);
WNetCloseEnum(hEnum);

return TRUE;
}

Dann 2000-03-21

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mach:
我想参考一下你的源码，zhouyamin@private.21cn.com

mach 2000-03-20

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to Dann:
WNetXXX既可以在win nt下也可以在win 95/98下使用
我自己做过一个这样的控件(就是用WNetXXX,for win95/98/nt),你要的话我可以把源码发给你.

kxy 2000-03-20

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本站点的软件发布，winpopup加强版，Open Source , VC++
中有.http://www.midatech.com/csdn/softs/softview.asp?id=35

SoftDIY 2000-03-20

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www.codeguru.com有做好的EnumNetConnection类

Dann 2000-03-20

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I make a mistake.
WNetXXX only can be used in WinNT.

csdn2000 2000-03-20

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Dann 2000-03-19

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WNetXXX only can be used in Win9x.

xenogear 2000-03-19

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you can use WNetXXX API function for enumerate network resource, look msdn for more detailed information. regards.

K nearest neighbour algorithm in python

The rules of the game ：Each individual occupies the vertex of a graph and derives a payoff, P, from interactions with adjacent individuals. A cooperator (blue) pays a cost, c, for each neighbour to receive a benefit, b. A defector (red) pays no cost and provides no benefit. The fitness of a player i

Data mining is the useful tool to discovering the knowledge from large data. Different methods & algorithms are available in data mining. Classification is most common method used for finding the mine rule from the large database. Decision tree method generally used for the Classification, because it is the simple hierarchical structure for the user understanding & decision making. Various data mining algorithms available for classification based on Artificial Neural Network, Nearest Neighbour Rule & Baysen classifiers but decision tree mining is simple one. ID3 and C4.5 algorithms have been introduced by J.R Quinlan which produce reasonable decision trees. The objective of this paper is to present these algorithms. At first we present the classical algorithm that is ID3, then highlights of this study we will discuss in more detail C4.5 this one is a natural extension of the ID3 algorithm. And we will make a comparison between these two algorithms and others algorithms such as C5.0 and CART.

Contents Foreword 7 1 Scope 8 2 References 8 3 Abbreviations and Definitions 9 3.1 Abbreviations 9 3.2 Definitions 9 4 Overall Architecture and Functional Split 10 4.1 Overall Architecture 10 4.2 Functional Split 11 4.3 Network Interfaces 13 4.3.1 NG Interface 13 4.3.1.1 NG User Plane 13 4.3.1.2 NG Control Plane 13 4.3.2 Xn Interface 14 4.3.2.1 Xn User Plane 14 4.3.2.2 Xn Control Plane 14 4.4 Radio Protocol Architecture 15 4.4.1 User Plane 15 4.4.2 Control Plane 15 4.5 Multi-RAT Dual Connectivity 16 5 Physical Layer 16 5.1 Waveform, numerology and frame structure 16 5.2 Downlink 17 5.2.1 Downlink transmission scheme 17 5.2.2 Physical-layer processing for physical downlink shared channel 17 5.2.3 Physical downlink control channels 18 5.2.4 Synchronization signal and PBCH 18 5.2.5 Physical layer procedures 19 5.2.5.1 Link adaptation 19 5.2.5.2 Power Control 19 5.2.5.3 Cell search 19 5.2.5.4 HARQ 19 5.3 Uplink 19 5.3.1 Uplink transmission scheme 19 5.3.2 Physical-layer processing for physical uplink shared channel 19 5.3.3 Physical uplink control channel 20 5.3.4 Random access 21 5.3.5 Physical layer procedures 21 5.3.5.1 Link adaptation 21 5.3.5.2 Uplink Power control 21 5.3.5.3 Uplink timing control 21 5.3.5.4 HARQ 21 5.4 Carrier aggregation 21 5.4.1 Carrier aggregation 21 5.4.2 Supplemental Uplink 22 5.5 Transport Channels 22 6 Layer 2 23 6.1 Overview 23 6.2 MAC Sublayer 24 6.2.1 Services and Functions 24 6.2.2 Logical Channels 25 6.2.3 Mapping to Transport Channels 25 6.2.4 HARQ 25 6.3 RLC Sublayer 25 6.3.1 Transmission Modes 25 6.3.2 Services and Functions 26 6.3.3 ARQ 26 6.4 PDCP Sublayer 26 6.4.1 Services and Functions 26 6.5 SDAP Sublayer 27 6.6 L2 Data Flow 27 6.7 Carrier Aggregation 27 6.8 Dual Connectivity 29 6.9 Supplementary Uplink 29 6.10 Bandwidth Adaptation 29 7 RRC 30 7.1 Services and Functions 30 7.2 Protocol States 31 7.3 System Information Handling 31 7.4 Access Control 32 7.5 UE Capability Retrieval framework 32 7.6 Transport of NAS Messages 33 7.7 Carrier Aggregation 33 7.8 Bandwidth Adaptation 33 8 NG Identities 33 8.1 UE Identities 33 8.2 Network Identities 33 9 Mobility and State Transitions 34 9.1 Overview 34 9.2 Intra-NR 34 9.2.1 Mobility in RRC_IDLE 34 9.2.1.1 Cell Selection 34 9.2.1.2 Cell Reselection 35 9.2.2 Mobility in RRC_INACTIVE 35 9.2.2.1 Overview 35 9.2.2.2 Cell Reselection 36 9.2.2.3 RAN-Based Notification Area 36 9.2.2.4 State Transitions 37 9.2.2.4.1 UE triggered transition from RRC_INACTIVE to RRC_CONNECTED 37 9.2.2.4.2 Network triggered transition from RRC_INACTIVE to RRC_CONNECTED 37 9.2.2.5 RNA update 38 9.2.3 Mobility in RRC_CONNECTED 38 9.2.3.1 Overview 38 9.2.3.2 Handover 39 9.2.3.2.1 C-Plane Handling 39 9.2.3.2.2 U-Plane Handling 41 9.2.4 Measurements 41 9.2.5 Paging 43 9.2.6 Random Access Procedure 43 9.2.7 Radio Link Failure 44 9.3 Inter RAT 44 9.3.1 Intra 5GC 44 9.3.1.1 Cell Reselection 44 9.3.1.2 Handover 45 9.3.1.3 Measurements 45 9.3.2 From 5GC to EPC 45 9.3.2.1 Cell Reselection 45 9.3.2.2 Handover 45 9.3.2.3 Measurements 45 9.3.2.4 Data Forwarding 45 9.4 Roaming and Access Restrictions 46 10 Scheduling 46 10.1 Basic Scheduler Operation 46 10.2 Downlink Scheduling 46 10.3 Uplink Scheduling 47 10.4 Measurements to Support Scheduler Operation 47 10.5 Rate Control 47 10.5.1 Downlink 47 10.5.2 Uplink 48 10.6 Activation/Deactivation Mechanism 48 11 UE Power Saving 48 12 QoS 49 13 Security 50 13.1 Overview and Principles 50 13.2 Security Termination Points 51 13.3 State Transitions and Mobility 51 14 UE Capabilities 51 15 Self-Configuration and Self-Optimisation 51 15.1 Definitions 51 15.2 UE Support for self-configuration and self-optimisation 51 15.3 Self-configuration 52 15.3.1 Dynamic configuration of the NG-C interface 52 15.3.1.1 Prerequisites 52 15.3.1.2 SCTP initialization 52 15.3.1.3 Application layer initialization 52 15.3.2 Dynamic Configuration of the Xn interface 52 15.3.2.1 Prerequisites 52 15.3.2.2 SCTP initialization 52 15.3.2.3 Application layer initialization 52 15.3.3 Automatic Neighbour Cell Relation Function 52 15.3.3.1 General 52 15.3.3.2 Intra-system – intra NR Automatic Neighbour Cell Relation Function 52 15.3.3.3 Intra-system – intra E-UTRA Automatic Neighbour Cell Relation Function 53 15.3.3.4 Intra-system – inter RAT Automatic Neighbour Cell Relation Function 53 15.3.3.5 Inter-system Automatic Neighbour Cell Relation Function 53 15.3.4 Xn-C TNL address discovery 53 16 Verticals Support 53 16.1 URLLC 53 16.1.1 Overview 53 16.1.2 LCP Restrictions 53 16.1.3 Packet Duplication 53 16.2 IMS Voice 54 16.3 Network Slicing 54 16.3.1 General Principles and Requirements 54 16.3.2 CN Instance and NW Slice Selection 55 16.3.2.1 CN-RAN interaction and internal RAN aspects 55 16.3.2.2 Radio Interface Aspects 55 16.3.3 Resource Isolation and Management 55 16.3.4 Signalling Aspects 56 16.3.4.1 General 56 16.3.4.2 CN Instance and NW Slice Selection 56 16.3.4.3 UE Context Handling 56 16.3.4.4 PDU Session Handling 57 16.3.4.5 Mobility 58 16.4 Public Warning System 59 Annex A (informative): QoS Handling in RAN 60 A.1 PDU Session Establishment 60 A.2 New QoS Flow without Explicit Signalling 60 A.3 New QoS Flow with NAS Reflective QoS and Explicit RRC Signalling 61 A.4 New QoS Flow with Explicit Signalling 62 A.5 Release of QoS Flow with Explicit Signalling 63 A.6 UE Initiated UL QoS Flow 64 Annex B (informative): Deployment Scenarios 66 B.1 Supplementary Uplink 66 Annex C (informative): Change history 67

此工具包涵盖了模式识别和机器学习领域的重要算法和很多新的算法，由这些领域资深专家编写，并有相关教材出版，绝对不负众望。此工具包包括以下内容： * PCA * Mixtures of probabilistic PCA * Gaussian mixture model with EM training algorithm * Linear and logistic regression with IRLS training algorithm * Multi-layer perceptron with linear, logistic and softmax outputs and appropriate error functions * Radial basis function (RBF) networks with both Gaussian and non-local basis functions * Optimisers, including quasi-Newton methods, conjugate gradients and scaled conjugate gradients * Multi-layer perceptron with Gaussian mixture outputs (mixture density networks) * Gaussian prior distributions over parameters for the MLP, RBF and GLM including multiple hyper-parameters * Laplace approximation framework for Bayesian inference (evidence procedure) * Automatic Relevance Determination for input selection * Markov chain Monte-Carlo including simple Metropolis and hybrid Monte-Carlo * K-nearest neighbour classifier * K-means clustering * Generative Topographic Map * Neuroscale topographic projection * Gaussian Processes * Hinton diagrams for network weights * Self-organising map

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