By Don Syme, Adam Granicz, Antonio Cisternino
Specialist F# 3.0 is ready useful programming in a stunning language that places the facility and magnificence of data-rich useful programming into the palms builders. together with .NET, F# achieves unequalled degrees of programmer productiveness and software readability. F# is not just one other practical programming language. it is a general-purpose language perfect for fixing real-world improvement difficulties. F# seamlessly integrates useful, principal, object-oriented and question programming kinds so that you can flexibly and skillfully remedy any programming challenge.
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Extra info for Expert F# 3.0, 3rd Edition
Init int -> (int -> 'T) -> 'T list Returns a new list. The length of the new list is specified by the first parameter. The second parameter must be a generating function that maps list indexes to values. append 'T list -> 'T list -> 'T list Returns a new list containing the elements of the first list followed by the elements of the second list. filter ('T -> bool) -> 'T list -> 'T list Returns a new list containing only those elements of the original list where the function returns true. map ('T -> 'U) -> 'T list -> 'U list Creates a new list by applying a function to each element of the original list.
Org", 86 -> true | _ -> false;; warning FS0026: This rule will never be matched val urlFilter2 : url:string -> agent:int -> bool ■ Tip Use wildcard patterns with care. F# can often determine whether a match is exhaustive, and the use of wildcard patterns effectively disables this analysis for any particular pattern match. Sometimes it’s better to write out the extra cases of a match as explicit patterns, because you can then adjust your code when new kinds of input data are introduced. Guarding Rules and Combining Patterns Individual rules of a match can be guarded by a condition that is tested if the pattern itself succeeds.
For example: val add : 'Key -> 'Value -> Table<'Key,'Value> -> Table<'Key,'Value> Or for example: member Add : 'Key * 'Value -> Table<'Key,'Value> As discussed in Chapter 3, immutable data structures are also called functional or persistent. The latter name is used because the original table isn’t modified when adding an element. Well-crafted persistent data structures don’t duplicate the actual memory used to store the data structure every time an addition is made; instead, internal nodes can be shared between the old and new data structures.