Today I have posted new article on CodeProject.com. This is a first article from series of articles about Server Management Objects (SMO) and programming tasks using SMO.
First part of this series is about creating and managing databases storage objects. Here is a table of contents:
private static string ExportTableToCsv(DataTable sourceTable) { StringBuilder sb = new StringBuilder(); for (int rowCount = 0; rowCount < sourceTable.Rows.Count; rowCount++) { for (int colCount = 0; colCount < sourceTable.Columns.Count; colCount++) { sb.Append(sourceTable.Rows[rowCount][colCount]); if (colCount != sourceTable.Columns.Count - 1) { sb.Append(","); } } if (rowCount != sourceTable.Rows.Count - 1) { sb.AppendLine(); } } return sb.ToString(); }
ServerConnection conn = new ServerConnection("SQLSERVERINSTANCE", "USERNAME", "PASSWORD"); try { Server srv = new Server(conn); Login newLogin = new Login(srv, "TestLogin"); newLogin.LoginType = Microsoft.SqlServer.Management.Smo.LoginType.SqlLogin; newLogin.Create("TestPassword"); newLogin.AddToRole("sysadmin"); } catch (Exception err) { Console.WriteLine(err.Message); }This short example connects to SQL Server instance and creates new login called TestLogin with sysadmin server role.
| (1) |  |
The activation function F can be linear so that we have a linear network, or nonlinear. In this example I decided to use threshold (signum) function:
| (2) |  |
Output of network in this case is either +1 or -1 depending on the input. If the total input (weighted sum of all inputs) is positive, then the pattern belongs to class +1, otherwise to class -1. Because of this behavior, we can use perceptron for classification tasks.
Lets consider we have a perceptron with 2 inputs and we want to separate input patterns into 2 classes. In this case the separation between the classes is straight line, given by equation:
| (3) |  |
When we set x0=-1 and mark w0=θ then we can rewrite equation (3) into form:
| (4) |  |
Here I will describe learning method for perceptron. Learning method of perceptron is iterative procedure that adjust the weights. A learning sample is presented to the network. For each weight the new value is computed by adding a correction to the old value. The threshold is updated in the same way:
| (5) |   |
where y is output of perceptron, d is desired output and γ is the learning parameter.
svcutil.exe http://[machinename]/FileTransferService.svc<behaviors> <serviceBehaviors> <behavior name="MyServiceBehavior"> <serviceMetadata httpGetEnabled="true" externalMetadataLocation="http://IPorDomainName/MyServices/FileTransferService.wsdl" /> <serviceDebug includeExceptionDetailInFaults="false" /> </behavior> </serviceBehaviors> </behaviors>
Server srv = new Server(@"INSTANCEA"); LinkedServer lsrv = new LinkedServer(srv, "INSTANCEB"); LinkedServerLogin login = new LinkedServerLogin(); login.Parent = lsrv; login.Name = "LOGIN_ON_INSTANCEA"; login.RemoteUser = "LOGIN_ON_INSTANCEB"; login.SetRemotePassword("PASSWORD"); login.Impersonate = false; lsrv.ProductName = "SQL Server"; lsrv.Create(); login.Create();
