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1. Select System Administration Synaptic Package Manager. You ll need to enter
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Figure 3-1. A Player with a user-defined data source
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The simplest distribution is always x; this is a distribution that always samples to the same value. Its expectation and support are easy to calculate. The expectation of a function H is just H applied to the value, and the support is a set containing the single value x. The next distribution defined is coinFlip, which is a distribution that models the ability to choose between two outcomes. Listing 9-8 shows how you can define a workflow builder for distribution objects. Listing 9-8. Defining a Builder for Probabilistic Modeling Using Computation Expressions let bind (dist:Distribution<'T>) (k: 'T -> Distribution<'U>) = { new Distribution<'U> with member d.Sample = (k dist.Sample).Sample member d.Support = Set.unionMany (dist.Support |> (fun d -> (k d).Support)) member d.Expectation H = dist.Expectation(fun x -> (k x).Expectation H) } type DistributionBuilder() = member x.Delay f = bind (always ()) f member x.Bind (d, f) = bind d f member x.Return v = always v member x.ReturnFrom vs = vs let dist = new DistributionBuilder() The types of these primitives are as follows: val bind: Distribution<'T> -> ('T -> Distribution<'U>) -> Distribution<'U> val dist: DistributionBuilder Listing 9-8 shows the all-important bind primitive; it combines two distributions, using the sample from the first to guide the sample from the second. The support and expectation are calculated by taking the support from the first and splaying it over the support of the second. The expectation is computed by using the first distribution to compute the expectation of a function derived from the second. These are standard results in probability theory and are the basic machinery you need to get going with some interesting modeling. Before you begin using workflow syntax, you define two derived functions to compute distributions. Listing 9-9 shows the additional derived operations for distribution objects that you use later in this example. Listing 9-9. Defining the Derived Operations for Probabilistic Modeling Using Computation Expressions let weightedCases (inp: ('T * float) list) = let rec coinFlips w l = match l with | [] -> failwith "no coinFlips" | [(d,_)] -> always d | (d,p)::rest -> coinFlip (p/(1.0-w)) (always d) (coinFlips (w+p) rest) coinFlips 0.0 inp let countedCases inp = let total = Seq.sumBy (fun (_,v) -> v) inp weightedCases (inp |> (fun (x,v) -> (x, float v / float total)))
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Note There is a great book that covers EDI mapping in BizTalk Server, titled Pro Mapping in BizTalk Server 2009
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CHAPTER 12: Securing a Wireless Network
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You need to define business rules using facts that an orchestration cannot provide. The facts may not be available in the orchestration, or you may need to reuse a fact across many instances of the orchestration.
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<servlet> <servlet-name>XmlRpcServlet</servlet-name> <servlet-class>org.apache.xmlrpc. webserver.XmlRpcServlet</servlet-class> <init-param> <param-name>enabledForExtensions</param-name> <param-value>true</param-value> <description> Sets, whether the servlet supports vendor extensions for XML-RPC. </description> </init-param> </servlet> <servlet-mapping> <servlet-name>XmlRpcServlet</servlet-name> <url-pattern>/xmlrpc</url-pattern>
Figure 3 34. Database Lookup functoid with multiple columns
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CHAPTER 9: Virtualization
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Figure 1-11. Completed Variables window
CHAPTER 6: Mass Deployment
You should always have a specification. Without one, you don t know exactly what you are building, and many an application goes hopelessly over budget or over deadline or both because insufficient time was spent specifying what the application actually needed to do. That is not to say that a specification is set in stone. No project ever emerges with quite the design that the architect had in mind when he started work. The specification should be changed as its inadequacies and misconceptions become clear. This is not a book on design, so the full-blown specification that I worked from in building the example application would be overkill. Moreover, it has some eccentric requirements, because they were bent by the need to illustrate architectural detail where the normal situation is reversed. Given these constraints, I have limited my specification to a couple of use case scenarios explaining how a typical user might interact with the site. For information on how a real specification should be put together, I recommend reading the four-part article Painless Functional Specifications by the always excellent Joel Spolsky in Joel on Software (Apress, 2007) and on his website at
Sometimes, you can see where Oracle is going by looking at hidden parameters and undocumented functions. This section describes a couple of features that you should not use on a production system but you might like to know about them so that you can understand what s happening when they hit you in a future release. There could be many ways in SQL to specify a single query, and as time passes the optimizer is enhanced to find new ways to convert safely and efficiently between texts that are structured very differently but produce the same result. Consider the following query (see intersect_join.sql in the online code suite): select n2 from t1 where n1 < 3 intersect select n2 from t2 where n1 < 2 ; In principle, provided you deal with the problems of checking equality between null columns, you could rewrite this as follows: select from where and and ; distinct t1.n2 t1, t2 t1.n1 < 3 t2.n1 < 2 t2.n2 = t1.n2
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