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Let s now add a new UDF to the library. What if you are working on an integration project and the requirements call for expressing dates in the Julian format The Julian conversion simply takes the day of the year (number of days elapsed since December 31 of the previous year) and adds the year to form a numeric value, like DDDYYYY. In this case, you need to add a function that takes a month, date, and year value and returns the date expressed as a Julian date. The function should be defined as longlong julian(int month, int day, int year); I kept the function simple and used three integers. The function could be implemented in any number of ways (e.g., accepting a date or string value). Now let s add the JULIAN function to the UDF library you just built.

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Workflows provide a fascinating way to embed a range of nontrivial, nonstandard computations into F#. To give you a feel for this, this section defines a probabilistic workflow. That is, instead of writing expressions to compute, say, integers, you instead write expressions that compute distributions of integers. This case study is based on a paper by Ramsey and Pfeffer from 2002.

Async.Run: Async<'a> -> 'a Async.Spawn: Async<unit> -> unit Async.SpawnChild: Async<unit> -> Async<unit>

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For the purposes of this section, you re interested in distributions over discrete domains characterized by three things: You want to be able to sample from a distribution (for example, sample an integer or a coin flip). You want to compute the support of a distribution: that is, a set of values where all elements outside the set have zero chance of being sampled. You want to compute the expectation of a function over the distribution. For example, you can compute the probability of selecting element A by evaluating the expectation of the function (fun x -> if x = A then 1.0 else 0.0).

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This is what makes creating your own UDF library so valuable Any time you encounter a need for a new function, you can just add it to the existing library without having to create a new project from scratch The process for adding a new UDF begins with adding the function declarations to the extern section of the UDF library source code and then implementing the functions You can then recompile the library and deploy it to the bin directory of your MySQL server installation Let s walk through that process with the JULIAN function Open the expert_udfcc file and add the function declarations Recall that you need definitions for the julian_init(), julian_deinit(), and julian() functions.

Runs an operation in the thread pool and waits for its result. Queues the asynchronous computation as an operation in the thread pool. Queues the asynchronous computation, initially as a work item in the thread pool, but inherits the cancellation handle from the current asynchronous computation.

You can model this notion of a distribution with abstract objects. Listing 9-7 shows the definition of a type of distribution values and an implementation of the basic primitives always and coinFlip, which help build distributions. Listing 9-7. Implementing Probabilistic Modeling Using Computation Expressions type Distribution<'T when 'T : comparison> = abstract Sample : 'T abstract Support : Set<'T> abstract Expectation: ('T -> float) -> float let always x = { new Distribution<'T> with member d.Sample = x member d.Support = Set.singleton x member d.Expectation(H) = H(x) } let rnd = System.Random() let coinFlip (p:float) (d1:Distribution<'T>) (d2:Distribution<'T>) = if p < 0.0 || p > 1.0 then failwith "invalid probability in coinFlip" { new Distribution<'T> with member d.Sample = if rnd.NextDouble() < p then d1.Sample else d2.Sample member d.Support = Set.union d1.Support d2.Support member d.Expectation(H) = p * d1.Expectation(H) + (1.0-p) * d2.Expectation(H) } The types of these primitives are as follows: type Distribution<'T> = abstract Expectation: ('T -> float) -> float abstract Sample : 'T abstract Support : Set<'T> val always: 'T -> Distribution<'T> val coinFlip : float -> Distribution<'T> -> Distribution<'T> -> Distribution<'T>

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