Learning f# using fparsec to parse some delimited data. I am using sepBy to get a list of results and filtering out the empty results. I don't want items that are empty but I do want to know what position the items were in. Is there a better way to get the index instead of List.mapi and later filtering out empties? The delimited data will have other delimited data inside (represents children).
type result = {value:string; index:int}
type parent = {results:result list; index:int}
let delims = "|^"
let pipeandcarrotdata = "|A^B^C||B|C"
let zipi res = List.mapi (fun i item -> res i item)
let zipPipes = zipi (fun i t -> {results = t; index = i})
let zipCarrots = zipi (fun i t -> {value = t; index = i})
let cleanlist predicate = List.filter (fun i -> predicate i)
let cleanPipes = cleanlist (fun t -> t.results.IsEmpty <> true)
let cleanCarrots = cleanlist (fun t -> t.value <> "")
let collect items = List.mapi (fun i item -> {value = item; index = i}) items
let cleanEmpties items = List.filter (fun item -> item.value <> "") items
let pCarrots = sepBy (manyChars (noneOf delims)) (pstring "^") |>> (zipCarrots >> cleanCarrots)
let pPipes = sepBy pCarrots (pstring "|") |>> (zipPipes >> cleanPipes)
test pPipes pipeandcarrotdata
Result:
[{results = [{value = "A";index = 0;}; {value = "B";index = 1;}; {value = "C";index = 2;}]; index = 1;};
{results = [{value = "B"; index = 0;}]; index = 3;};
{results = [{value = "C"; index = 0;}];index = 4;}]
Later I will need to support a more complex hierarchy and I would like to avoid writing 5-6 more functions like "cleanPipes" and "cleanCarrots" and then another 5-6 functions like "zipPipes" and "zipCarrots".
Related
How can I interpret property based test code?
I'm struggling to translate the instructions on the following snippet:
let myProperty = Prop.forAll fiveAndThrees <| fun number ->
let actual = transform number
let expected = "jbdhjsdhjdsjhsdglsdjlljh"
expected = actual
Check.QuickThrowOnFailure myProperty
Specifically, I'm struggling with the backwards pipeline operator (i.e. "<|").
Here's the test:
[<Fact>]
let ``FizzBuzz.transform returns FizzBuzz`` () =
let fiveAndThrees = Arb.generate<int> |> Gen.map ((*) (3 * 5))
|> Arb.fromGen
let myProperty = Prop.forAll fiveAndThrees <| fun number ->
let actual = transform number
let expected = "jbdhjsdhjdsjhsdglsdjlljh"
expected = actual
Check.QuickThrowOnFailure myProperty
Can someone please guide me step by step on how this code works?
Could this be rewritten using the forward pipe operator (i.e. "|>")?
This answer only covers why <| is used instead of |>.
Here are 5 examples that work toward making use of <| with a large function. The 6th example is to show how the code looks using |> instead of <|. The point is that with the 6th example using |> you have to look into the code to find the primary function funThatNeedsListAndFunc but with <| the primary function funThatNeedsListAndFunc is obvious. So you typically see <| when the last parameter is a function and you want to pass in the function after the primary function for easier comprehension. That's all; don't read more into than that.
After reading the Mark's blog I also learned that <| is useful to remove ( ) around fun. An example using ( ) is given as example 7.
let funThatNeedsListAndFunc list func =
func list
let func = List.sum
let list = Seq.toList { 0 .. 5}
let result1 = funThatNeedsListAndFunc list func
printfn "result1: %A" result1
let result2 = funThatNeedsListAndFunc list <| func
printfn "result2: %A" result2
let result3 = funThatNeedsListAndFunc list <| List.sum
printfn "result3: %A" result3
let result4 = funThatNeedsListAndFunc list <|
fun (list : 'a list) -> List.sum list
printfn "result4: %A" result4
let result5 = funThatNeedsListAndFunc list <|
fun (list : 'a list) ->
// This will be a long function
let a = 1
let b = 2
let c = a * b
let result = List.sum list
let d = "more useless lines"
let (e,f,g) = ("a", 15, 3.0)
result
printfn "result5: %A" result5
.
let result6 =
fun (list : 'a list) ->
// This will be a long function
let a = 1
let b = 2
let c = a * b
let result = List.sum list
let d = "more useless lines"
let (e,f,g) = ("a", 15, 3.0)
result
|> funThatNeedsListAndFunc list
printfn "result6: %A" result6
.
let result7 =
funThatNeedsListAndFunc list (fun (list : 'a list) ->
// This will be a long function
let a = 1
let b = 2
let c = a * b
let result = List.sum list
let d = "more useless lines"
let (e,f,g) = ("a", 15, 3.0)
result)
printfn "result7: %A" result7
I'm trying to return a list from a function, but I'm getting an error that says that an unit was expected instead. Also, I would like to know if this code appears to be structured correctly in general.
code:
let rec calculateVariants (attList: NewProductAttributeInfo list) (activeCount: int)
(currentList: (int * NewProductAttributeInfo) list) =
// group attribute list by category id
let attGrouped = attList |> List.groupBy (fun x -> x.AttributeCategoryId)
// define mutable list
let mutable stageList = currentList
// begin iteration
for catId,details in attGrouped do
for d in details do
if activeCount = 0
then stageList <- (activeCount,d) :: stageList
let groupLength = attGrouped.Length
if (activeCount + 1) <= groupLength
then
let selectCat,selectDetails = attGrouped.[activeCount + 1]
selectDetails
|> List.filter (fun x ->
stageList
|> List.exists (fun (x') ->
not(x' = (activeCount,x))))
|> (fun x ->
match x with
| [] -> ()
| head :: tail ->
stageList <- (activeCount, head) :: stageList
let currentCategory = activeCount + 1
calculateVariants attList currentCategory stageList
)
stageList // <-- error Unit expected
if .. then .. else should return the same type on both branches. If you omit else branch then compiler assuming that it returns unit. Add else branch returning list.
Edit:
Given your problem description, the easiest way would be something like this:
type NewProductAttributeInfo = {AttributeCategoryId: string; AttributeId: string}
let products = [ { AttributeCategoryId = "Size"; AttributeId = "S"};
{ AttributeCategoryId = "Mat"; AttributeId = "Linen" };
{ AttributeCategoryId = "Mat"; AttributeId = "Poliester" };
{ AttributeCategoryId = "Color"; AttributeId = "White" };
{ AttributeCategoryId = "Color"; AttributeId = "Green" };
{ AttributeCategoryId = "Mat"; AttributeId = "Linen" };
{ AttributeCategoryId = "Mat"; AttributeId = "Cotton" };
{ AttributeCategoryId = "Mat"; AttributeId = "Poliester" };
{ AttributeCategoryId = "Size"; AttributeId = "XL" } ]
let group list =
list
|> Set.ofList // Provides uniqueness of attribute combinations
|> Seq.groupBy (fun x -> x.AttributeCategoryId) // Grouping by CatId
|> List.ofSeq
let res = group products
Result:
val it : (string * seq<NewProductAttributeInfo>) list =
[("Color", seq [{AttributeCategoryId = "Color";
AttributeId = "Green";}; {AttributeCategoryId = "Color";
AttributeId "White";}]);
("Mat",
seq
[{AttributeCategoryId = "Mat";
AttributeId = "Cotton";}; {AttributeCategoryId = "Mat";
AttributeId = "Linen";};
{AttributeCategoryId = "Mat";
AttributeId = "Poliester";}]);
("Size", seq [{AttributeCategoryId = "Size";
AttributeId = "S";}; {AttributeCategoryId = "Size";
AttributeId = "XL";}])]
This is the solution that I came with. It works, but I'm sure it can be optimized quite a bit. I have a duplicate issue that is solved with the Set.ofList function externally after this code runs, which I'm still working on.
type NewProductAttributeInfo = {
AttributeId : string;
AttributeCategoryId : string
}
let rec private returnVariant (curIdx: int) (listLength: int)
(attList: (int * NewProductAttributeInfo * NewProductAttributeInfo) list)
(curList: NewProductAttributeInfo list) =
match curList with
| x when x.Length = listLength -> curList
| x ->
let attTup =
attList
|> List.filter (fun x' ->
let idx1,att1,att2' = x'
idx1 >= curIdx && not(curList
|> List.exists (fun x'' ->
x'' = att2'))
)
let idx1,att1,att2 = attTup |> List.head
let newList = curList # [att2]
returnVariant idx1 newList.Length attList newList
let rec calculateVariants (attList: NewProductAttributeInfo list)
(currentList: (int * NewProductAttributeInfo * NewProductAttributeInfo) list) =
// group attribute list by category id
let attGrouped = attList |> List.groupBy (fun x -> x.AttributeCategoryId)
let (firstGroupCatId,firstGroupDetails) = attGrouped.[0]
match currentList with
| [] ->
let rawVariants = [for nxt in 0 .. (attGrouped.Length - 1) do
if nxt > 0
then
// begin iteration
for d in firstGroupDetails do
let _,det = attGrouped.[nxt]
for det' in det do
yield (nxt, d, det')
]
calculateVariants attList rawVariants
| x ->
let groupLength = x |> List.groupBy (fun (idx,d0,nxtD) -> idx)
|> List.length |> ((+)1)
let sortedGroup = x |> List.sortBy (fun (x,y,z) -> x)
if groupLength > 2
then // below is the block that generates the duplicates
[for att in sortedGroup do
for attCompare in sortedGroup do
let idx1,att1,att2 = att
let idx2,attC1,attC2 = attCompare
if idx2 > idx1 && att2 <> attC2
then
let idString =
returnVariant idx2 groupLength x [att1; att2; attC2]
|> List.map (fun nl -> nl.AttributeId)
yield String.concat "," idString
]
else
[
for att in sortedGroup do
let idx1,att1,att2 = att
let idString =
returnVariant idx1 groupLength x [att1; att2]
|> List.map (fun nl -> nl.AttributeId)
yield String.concat "," idString
]
In the following code, the compiler gets error on index <- index + 1 with error
Error 3 The mutable variable 'index' is used in an invalid way. Mutable variables cannot be captured by closures. Consider eliminating this use of mutation or using a heap-allocated mutable reference cell via 'ref' and '!'. d:\Users....\Program.fs 11 22 ConsoleApplication2
However, it has been defined as mutable?
let rec iterateTupleMemberTypes (tupleArgTypes: System.Type[]) (columnNames: string[]) startingIndex =
seq {
let mutable index = startingIndex
for t in tupleArgTypes do
match t.IsGenericType with
| true -> iterateTupleMemberTypes (t.GetGenericArguments()) columnNames index |> ignore
| false ->
printfn "Name: %s Type: %A" (columnNames.[index]) t
index <- index + 1
yield (columnNames.[index]), t
} |> Map.ofSeq
let myFile = CsvProvider<"""d:\temp\sample.txt""">.GetSample()
let firstRow = myFile.Rows |> Seq.head
let tupleType = firstRow.GetType()
let tupleArgTypes = tupleType.GetGenericArguments()
let m = iterateTupleMemberTypes tupleArgTypes myFile.Headers.Value 0
An idiomatic version of this might look like the following:
#r #"..\packages\FSharp.Data.2.2.2\lib\net40\FSharp.Data.dll"
open FSharp.Data
open System
type SampleCsv = CsvProvider<"Sample.csv">
let sample = SampleCsv.GetSample()
let rec collectLeaves (typeTree : Type) =
seq {
match typeTree.IsGenericType with
| false -> yield typeTree.Name
| true -> yield! typeTree.GetGenericArguments() |> Seq.collect collectLeaves
}
let columnTypes = (sample.Rows |> Seq.head).GetType() |> collectLeaves
let columnDefinitions = columnTypes |> Seq.zip sample.Headers.Value |> Map.ofSeq
let getDefinitions (sample : SampleCsv) = (sample.Rows |> Seq.head).GetType() |> collectLeaves |> Seq.zip sample.Headers.Value |> Map.ofSeq
Personally, I wouldn't be concerned too much about the performance of Map vs Dictionary (and rather have the immutable Map) unless there are hundreds of columns.
The statement after it, let index = 0, shadows your definition of mutable variable index. Also, to make mutables work in sequences, you need refs. https://msdn.microsoft.com/en-us/library/dd233186.aspx
Suggested by #Ming-Tang, I changed the mutable variable to ref and it works now. However, is it a way not to use mutable/ref variable at all?
let rec iterateTupleMemberTypes (tupleArgTypes: System.Type[]) (columnNames: string[]) startingIndex =
seq {
let index = ref startingIndex
for t in tupleArgTypes do
match t.IsGenericType with
| true ->
yield! iterateTupleMemberTypes (t.GetGenericArguments()) columnNames !index
| false ->
printfn "Name: %s Type: %A" (columnNames.[!index]) t
yield (columnNames.[!index]), t
index := !index + 1
} |> dict
let myFile = CsvProvider<"""d:\temp\sample.txt""">.GetSample()
let firstRow = myFile.Rows |> Seq.head
let tupleType = firstRow.GetType()
let tupleArgTypes = tupleType.GetGenericArguments()
let m = iterateTupleMemberTypes tupleArgTypes myFile.Headers.Value 0
I am using Array.Parallel.map on a function but find that it is not executing at anywhere near full processor capacity. I am assuming this is because the function creates a lot of objects when running List.map and List.map2. Would this be causing a synchronization issue and is there a more appropriate way of doing this? At the moment the only way I can think of getting around this is by running each process as a separate executable using something like xargs under Linux.
I put together the script below to demonstrate the problem. It is a very basic data categorizer which relies on a field having a certain value as a rule to determine if this will predict a category:
open System
type CategoryAssessment =
{ fieldIndex: int
value: int
ruleAssessments: list<int> }
let InitAssessment categorizeFields rules =
let ruleAssessments = List.init (List.length rules) (fun x -> 0)
List.map (fun categorizeField ->
let fieldIndex, categoryValue = categorizeField
{ CategoryAssessment.fieldIndex = fieldIndex;
value = categoryValue;
ruleAssessments = ruleAssessments })
categorizeFields
let AssessCategory ruleMatches (row : int[]) categoryAssessment =
let fieldIndex = categoryAssessment.fieldIndex
let categoryValue = categoryAssessment.value
let categoryMatch = categoryValue = row.[fieldIndex]
let newRuleAssessments =
List.map2 (fun ruleAssessment ruleMatch ->
if ruleMatch = categoryMatch then
ruleAssessment + 1
else
ruleAssessment)
categoryAssessment.ruleAssessments
ruleMatches
{ categoryAssessment with ruleAssessments = newRuleAssessments }
let MatchRule (row : int[]) rule =
let fieldIndex, eqVal = rule
row.[fieldIndex] = eqVal
let Assess categorizeFields rules input =
printfn "START - Assess"
let d =
Array.fold (fun categoryAssessment row ->
let ruleMatches = List.map (MatchRule row) rules
List.map (AssessCategory ruleMatches row) categoryAssessment)
(InitAssessment categorizeFields rules)
input
printfn "END - Assess"
d
let JoinAssessments assessments =
let numAssessments = Array.length assessments
Array.fold (fun accAssessment assessment ->
List.map2 (fun accCategory category ->
let newRuleAssessments =
List.map2 (+)
accCategory.ruleAssessments
category.ruleAssessments
{ accCategory with
ruleAssessments = newRuleAssessments })
accAssessment
assessment)
assessments.[0]
assessments.[1..(numAssessments-1)]
let numRecords = 10000
let numFields = 20
let numSplits = 10
let numRules = 10000
let inputs = Array.create numSplits
[| for i in 1 .. (numRecords / numSplits) ->
[| for j in 1 .. numFields ->
(i % 10) + j |] |]
let categorizeFields = [ (1, 6); (2, 3); (2, 4); (3, 2) ]
let rules = [ for i in 1 .. numRules -> (i % numFields, i) ]
let assessments =
Array.Parallel.map (Assess categorizeFields rules) inputs
|> JoinAssessments
printfn "Assessments: %A" assessments
0
After a fair bit of investigation, the ultimate answer to my question seems to be to find a way of not creating lots of objects. The easiest change to do this is moving to using arrays instead of lists. I have written up my findings more fully in an article: Beware of Immutable Lists for F# Parallel Processing.
The above program when altered as follows, runs better between threads and runs much quicker even on a single thread. Further improvements can be made by making the ruleAssessments field mutable as demonstrated in the referenced article.
open System
type CategoryAssessment =
{ fieldIndex: int
value: int
ruleAssessments: int[] }
let InitAssessment categorizeFields rules =
let ruleAssessments = Array.create (Array.length rules) 0
Array.map (fun categorizeField ->
let fieldIndex, categoryValue = categorizeField
{ CategoryAssessment.fieldIndex = fieldIndex;
value = categoryValue;
ruleAssessments = ruleAssessments })
categorizeFields
let AssessCategory ruleMatches (row : int[]) categoryAssessment =
let fieldIndex = categoryAssessment.fieldIndex
let categoryValue = categoryAssessment.value
let categoryMatch = categoryValue = row.[fieldIndex]
let newRuleAssessments =
Array.map2 (fun ruleAssessment ruleMatch ->
if ruleMatch = categoryMatch then
ruleAssessment + 1
else
ruleAssessment)
categoryAssessment.ruleAssessments
ruleMatches
{ categoryAssessment with ruleAssessments = newRuleAssessments }
let MatchRule (row : int[]) rule =
let fieldIndex, eqVal = rule
row.[fieldIndex] = eqVal
let Assess categorizeFields rules input =
printfn "START - Assess"
let d =
Array.fold (fun categoryAssessment row ->
let ruleMatches = Array.map (MatchRule row) rules
Array.map (AssessCategory ruleMatches row) categoryAssessment)
(InitAssessment categorizeFields rules)
input
printfn "END - Assess"
d
let JoinAssessments assessments =
let numAssessments = Array.length assessments
Array.fold (fun accAssessment assessment ->
Array.map2 (fun accCategory category ->
let newRuleAssessments =
Array.map2 (+)
accCategory.ruleAssessments
category.ruleAssessments
{ accCategory with
ruleAssessments = newRuleAssessments })
accAssessment
assessment)
assessments.[0]
assessments.[1..(numAssessments-1)]
let numRecords = 10000
let numFields = 20
let numSplits = 10
let numRules = 10000
let inputs = Array.create numSplits
[| for i in 1 .. (numRecords / numSplits) ->
[| for j in 1 .. numFields ->
(i % 10) + j |] |]
let categorizeFields = [| (1, 6); (2, 3); (2, 4); (3, 2) |]
let rules = [| for i in 1 .. numRules -> (i % numFields, i) |]
let assessments =
Array.Parallel.map (Assess categorizeFields rules) inputs
|> JoinAssessments
printfn "Assessments: %A" assessments
0
This is a version of your program that doesn't require mutability and uses nearly all of the 4 cpus on my iMac.
To pull it off, it's driven by assessing each rule in parallel, not by processing records. That also required the input array to be transposed making it be fields by records.
open System
type CategoryAssessment =
{ fieldIndex: int
value: int
ruleAssessments: list<int> }
let MatchRule rVal fVal =
rVal = fVal
let AssessRule cMatches (inputs:int[][]) (rIndex, rVal) =
// printfn "START - Assess" // uses more cpu than the code itself
let matches = inputs.[rIndex] |>
Array.map2 (fun cVal fVal -> (MatchRule rVal fVal) = cVal) cMatches
let assessment = matches |>
Array.map ( fun v -> if v then 1 else 0 ) |>
Array.sum
// printfn "END - Assess"
assessment
let Assess categorizeFields rules (inputs:int[][]) =
categorizeFields |> List.map (fun (catIndex, catValue) ->
let catMatches = inputs.[catIndex] |> Array.map( fun v -> v = catValue )
let assessments = rules |> Array.Parallel.map
(AssessRule catMatches inputs)
|> Array.toList
{ CategoryAssessment.fieldIndex = catIndex;
value = catValue;
ruleAssessments = assessments }
)
let numRecords = 10000
let numFields = 20
let numRules = 10000
let inputs = [| for j in 1 .. numFields ->
[| for i in 1 .. numRecords -> (i % 10) + j |] |]
let categorizeFields = [ (1, 6); (2, 3); (2, 4); (3, 2) ]
let rules = [| for i in 1 .. numRules -> (i % numFields, i) |]
let assessments = Assess categorizeFields rules inputs
printfn "Assessments: %A" assessments
Assessing by rule allowed the summing of a single integer across all records for a given rule, avoiding mutable state and extra memory allocations.
I used a lot of array iteration to get the speed up but didn't remove all the lists.
I fear I changed the functionality while refactoring or made assumptions that can't be applied to your actual problem, however I do hope it's a useful example.
I have the following F# function which makes use of a ref variable to seed and keep track of a running total, something tells me this isn't in the spirit of fp or even particular clear on its own. I'd like some direction on the clearest (possible fp, but if an imperative approach is clearer I'd be open to that) way to express this in F#. Note that selectItem implements a random weighted selection algorithm.
type WeightedItem(id: int, weight: int) =
member self.id = id
member self.weight = weight
let selectItem (items: WeightedItem list) (rand:System.Random) =
let totalWeight = List.sumBy (fun (item: WeightedItem) -> item.weight) items
let selection = rand.Next(totalWeight) + 1
let runningWeight = ref 0
List.find
(fun (item: WeightedItem) ->
runningWeight := !runningWeight + item.weight
!runningWeight >= selection)
items
let items = [new WeightedItem(1,100); new WeightedItem(2,50); new WeightedItem(3,25)]
let selection = selectItem items (new System.Random())
Here is a version of the search algorithm using a recursive function. My F# is very rusty and I don't know what to return when we can't find anything:
let rec find list item total =
match list with
| h::t -> if h > total then h else find t item total+h
| [] -> 0 //<-- return some sort of default to say can't find the item
EDIT
Full code:
type WeightedItem(id: int, weight: int) =
member self.id = id
member self.weight = weight
let selectItem (items: WeightedItem list) (rand:System.Random) =
let totalWeight = List.sumBy (fun (item: WeightedItem) -> item.weight) items
let selection = rand.Next(totalWeight) + 1
let rec find runningWeight ((h:WeightedItem)::t) =
let newRunningWeight = runningWeight + h.weight
if newRunningWeight >= selection then
h
else
find newRunningWeight t
find 0 items
let items = [new WeightedItem(1,100)
new WeightedItem(2,50)
new WeightedItem(3,25)]
let selection = selectItem items (new System.Random())
Hm, here's one with Seq.scan, but it also feels very ugly...
type WeightedItem(id: int, weight: int) =
member self.id = id
member self.weight = weight
let selectItem (items: WeightedItem list) (rand:System.Random) =
let totalWeight = List.sumBy (fun (item: WeightedItem) -> item.weight) items
let selection = rand.Next(totalWeight) + 1
Seq.scan
(fun (runningWeight,found,itemO) (item: WeightedItem) ->
if not found then
let newRunningWeight = runningWeight + item.weight
newRunningWeight, newRunningWeight >= selection, Some(item)
else
(runningWeight,found,itemO))
(0,false,None)
items
|> Seq.find (fun (rw,f,i) -> f)
|> (fun (rw,f,i) -> i.Value)
let items = [new WeightedItem(1,100)
new WeightedItem(2,50)
new WeightedItem(3,25)]
let selection = selectItem items (new System.Random())
Igor's answer is probably the best one for items stored in a list in terms of efficiency, but since Brian's scan approach is representative of a recurrent sequence manipulation pattern, I suggest a slightly more compact variation :
let selectItem (items: WeightedItem list) (rand:System.Random) =
let totalWeight = List.sumBy (fun (item: WeightedItem) -> item.weight) items
let selection = rand.Next(totalWeight) + 1
items
|> Seq.scan (fun acc (item : WeightedItem) -> acc + item.weight) 0
|> Seq.skip 1 |> Seq.zip items
|> Seq.find (fun (i, rw) -> rw >= selection) |> fst
Use Seq.unfold to build an on-demand sequence that accumulates runningWeight and then search it for the first element that had a sufficiently large runningWeight using Seq.pick:
let gen = function
| _, [] -> None
| runningWeight, item::items ->
let runningWeight = runningWeight + item.weight
Some((if runningWeight >= selection then Some item else None), (runningWeight, items))
Seq.unfold gen (0, xs) |> Seq.pick id
Hm, here's one way to do it with a fold, but it feels inelegant and always traverses the whole list...
type WeightedItem(id: int, weight: int) =
member self.id = id
member self.weight = weight
let selectItem (items: WeightedItem list) (rand:System.Random) =
let totalWeight = List.sumBy (fun (item: WeightedItem) -> item.weight) items
let selection = rand.Next(totalWeight) + 1
List.fold
(fun (runningWeight,found) (item: WeightedItem) ->
if not found then
let newRunningWeight = runningWeight + item.weight
newRunningWeight, newRunningWeight >= selection
else
(runningWeight,found))
(0,false)
items
|> fst
let items = [new WeightedItem(1,100)
new WeightedItem(2,50)
new WeightedItem(3,25)]
let selection = selectItem items (new System.Random())
Hm, here's some mutables and a loop; still traverses the whole list though...
type WeightedItem(id: int, weight: int) =
member self.id = id
member self.weight = weight
let selectItem (items: WeightedItem list) (rand:System.Random) =
let totalWeight = List.sumBy (fun (item: WeightedItem) -> item.weight) items
let selection = rand.Next(totalWeight) + 1
let mutable runningWeight = 0
let mutable found = None
for item in items do
match found with
| None ->
runningWeight <- runningWeight + item.weight
if runningWeight >= selection then
found <- Some(item)
| _ -> ()
found.Value
let items = [new WeightedItem(1,100)
new WeightedItem(2,50)
new WeightedItem(3,25)]
let selection = selectItem items (new System.Random())
This is my favorite of the three. I look forward to the day that F# adds break. Of course you can call GetEnumerator and take full control, but that is ugly too.