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## Ninety-Nine F# Problems - Problems 90 - 94 - Miscellaneous problems]

These are F# solutions of Ninety-Nine Haskell Problems which are themselves translations of Ninety-Nine Lisp Problems and Ninety-Nine Prolog Problems. The solutions are hidden so you can try to solve them yourself.

### Ninety-Nine F# Problems - Problems 90 - 94 - Miscellaneous problems

1: /// Ninety-Nine F# Problems - Problems 90 - 94 2: /// 3: /// These are F# solutions of Ninety-Nine Haskell Problems 4: /// (http://www.haskell.org/haskellwiki/H-99:_Ninety-Nine_Haskell_Problems), 5: /// which are themselves translations of Ninety-Nine Lisp Problems 6: /// (http://www.ic.unicamp.br/~meidanis/courses/mc336/2006s2/funcional/L-99_Ninety-Nine_Lisp_Problems.html) 7: /// and Ninety-Nine Prolog Problems 8: /// (https://sites.google.com/site/prologsite/prolog-problems). 9: /// 10: /// If you would like to contribute a solution or fix any bugs, send 11: /// an email to paks at kitiara dot org with the subject "99 F# problems". 12: /// I'll try to update the problem as soon as possible. 13: /// 14: /// The problems have different levels of difficulty. Those marked with a single asterisk (*) 15: /// are easy. If you have successfully solved the preceeding problems you should be able to 16: /// solve them within a few (say 15) minutes. Problems marked with two asterisks (**) are of 17: /// intermediate difficulty. If you are a skilled F# programmer it shouldn't take you more than 18: /// 30-90 minutes to solve them. Problems marked with three asterisks (***) are more difficult. 19: /// You may need more time (i.e. a few hours or more) to find a good solution 20: /// 21: /// Though the problems number from 1 to 99, there are some gaps and some additions marked with 22: /// letters. There are actually only 88 problems. 23: ///

### (**) Problem 90 : Eight queens problem

1: /// This is a classical problem in computer science. The objective is to place eight queens on a 2: /// chessboard so that no two queens are attacking each other; i.e., no two queens are in the 3: /// same row, the same column, or on the same diagonal. 4: /// 5: /// Hint: Represent the positions of the queens as a list of numbers 1..N. Example: 6: /// [4,2,7,3,6,8,5,1] means that the queen in the first column is in row 4, the queen in the 7: /// second column is in row 2, etc. Use the generate-and-test paradigm. 8: /// 9: /// Example in F#: 10: /// 11: /// > queens 8 |> Seq.length;; 12: /// val it : int = 92 13: /// > queens 8 |> Seq.head;; 14: /// val it : int list = [1; 5; 8; 6; 3; 7; 2; 4] 15: /// > queens 20 |> Seq.head;; 16: /// val it : int list = 17: /// [1; 3; 5; 2; 4; 13; 15; 12; 18; 20; 17; 9; 16; 19; 8; 10; 7; 14; 6; 11] 18: 19: (Solution)

### (**) Problem 91 : Knight's tour

1: /// Another famous problem is this one: How can a knight jump on an NxN chessboard in such a way 2: /// that it visits every square exactly once? A set of solutions is given on the The_Knights_Tour 3: /// page. 4: /// 5: /// Hints: Represent the squares by pairs of their coordinates of the form X/Y, where both X and 6: /// Y are integers between 1 and N. (Note that '/' is just a convenient functor, not division!) 7: /// Define the relation jump(N,X/Y,U/V) to express the fact that a knight can jump from X/Y to U/V 8: /// on a NxN chessboard. And finally, represent the solution of our problem as a list of N*N knight 9: /// positions (the knight's tour). 10: /// 11: /// There are two variants of this problem: 12: /// 1. find a tour ending at a particular square 13: /// 2. find a circular tour, ending a knight's jump from the start (clearly it doesn't matter where 14: /// you start, so choose (1,1)) 15: /// 16: /// Example in F#: 17: /// 18: /// > knightsTour 8 (1,1) |> Seq.head;; 19: /// val it : (int * int) list = 20: /// [(4, 3); (6, 4); (5, 6); (4, 8); (3, 6); (5, 5); (6, 3); (4, 4); (2, 3); 21: /// (1, 5); (3, 4); (5, 3); (6, 5); (4, 6); (2, 7); (3, 5); (5, 4); (6, 6); 22: /// (4, 5); (2, 4); (1, 6); (2, 8); (4, 7); (6, 8); (8, 7); (7, 5); (8, 3); 23: /// (7, 1); (5, 2); (3, 1); (1, 2); (3, 3); (4, 1); (2, 2); (1, 4); (2, 6); 24: /// (1, 8); (3, 7); (5, 8); (7, 7); (8, 5); (7, 3); (8, 1); (6, 2); (7, 4); 25: /// (8, 2); (6, 1); (4, 2); (2, 1); (1, 3); (2, 5); (1, 7); (3, 8); (5, 7); 26: /// (7, 8); (8, 6); (6, 7); (8, 8); (7, 6); (8, 4); (7, 2); (5, 1); (3, 2); 27: /// (1, 1)] 28: /// 29: /// > endKnightsTour 8 (4,2);; 30: /// val it : (int * int) list = 31: /// [(4, 2); (2, 1); (1, 3); (3, 2); (1, 1); (2, 3); (1, 5); (2, 7); (4, 8); 32: /// (6, 7); (8, 8); (7, 6); (6, 8); (8, 7); (7, 5); (8, 3); (7, 1); (5, 2); 33: /// (3, 1); (1, 2); (2, 4); (1, 6); (2, 8); (4, 7); (2, 6); (1, 8); (3, 7); 34: /// (5, 8); (7, 7); (8, 5); (7, 3); (8, 1); (6, 2); (4, 1); (2, 2); (1, 4); 35: /// (3, 5); (5, 6); (4, 4); (2, 5); (1, 7); (3, 8); (5, 7); (7, 8); (8, 6); 36: /// (7, 4); (6, 6); (4, 5); (3, 3); (5, 4); (4, 6); (6, 5); (8, 4); (7, 2); 37: /// (6, 4); (4, 3); (5, 1); (6, 3); (8, 2); (6, 1); (5, 3); (3, 4); (5, 5); 38: /// (3, 6)] 39: /// 40: /// > closedKnightsTour 8;; 41: /// val it : (int * int) list = 42: /// [(2, 3); (4, 4); (6, 3); (5, 5); (4, 3); (6, 4); (5, 6); (4, 8); (3, 6); 43: /// (1, 5); (3, 4); (5, 3); (6, 5); (4, 6); (2, 7); (3, 5); (5, 4); (6, 6); 44: /// (4, 5); (2, 4); (1, 6); (2, 8); (4, 7); (6, 8); (8, 7); (7, 5); (8, 3); 45: /// (7, 1); (5, 2); (3, 1); (1, 2); (3, 3); (4, 1); (2, 2); (1, 4); (2, 6); 46: /// (1, 8); (3, 7); (5, 8); (7, 7); (8, 5); (7, 3); (8, 1); (6, 2); (7, 4); 47: /// (8, 2); (6, 1); (4, 2); (2, 1); (1, 3); (2, 5); (1, 7); (3, 8); (5, 7); 48: /// (7, 8); (8, 6); (6, 7); (8, 8); (7, 6); (8, 4); (7, 2); (5, 1); (3, 2); 49: /// (1, 1)] 50: 51: (Solution)

### (***) Problem 92 : Von Koch's conjecture

1: /// Several years ago I met a mathematician who was intrigued by a problem for which he didn't 2: /// know a solution. His name was Von Koch, and I don't know whether the problem has been 3: /// solved since. 4: /// 5: /// 6 6: /// (d) (e)---(f) (4) (1)---(7) 7: /// | | 1 | | 5 8: /// (a)---(b)---(c) (3)---(6)---(2) 9: /// | 2 | 3 4 10: /// (g) (5) 11: /// 12: /// Anyway the puzzle goes like this: Given a tree with N nodes (and hence N-1 edges). Find a 13: /// way to enumerate the nodes from 1 to N and, accordingly, the edges from 1 to N-1 in such 14: /// a way, that for each edge K the difference of its node numbers equals to K. The conjecture 15: /// is that this is always possible. 16: /// 17: /// For small trees the problem is easy to solve by hand. However, for larger trees, and 14 is 18: /// already very large, it is extremely difficult to find a solution. And remember, we don't 19: /// know for sure whether there is always a solution! 20: /// 21: /// Write a predicate that calculates a numbering scheme for a given tree. What is the solution 22: /// for the larger tree pictured below? 23: /// 24: /// (i) (g) (d)---(k) (p) 25: /// \ | | | 26: /// (a)---(c)---(e)---(q)---(n) 27: /// / | | | 28: /// (h) (b) (f) (m) 29: /// 30: /// Example in F#: 31: /// > vonKoch (['d';'a';'g';'b';'c';'e';'f'],[('d', 'a');('a', 'g');('a', 'b');('b', 'e'); 32: /// ('b', 'c');('e', 'f')]) |> Seq.head;; 33: /// 34: /// val it : int list * (int * int * int) list = 35: /// ([4; 3; 5; 6; 2; 1; 7], 36: /// [(4, 3, 1); (3, 5, 2); (3, 6, 3); (6, 1, 5); (6, 2, 4); (1, 7, 6)]) 37: /// 38: 39: (Solution)

### (***) Problem 93 : An arithmetic puzzle

1: /// Given a list of integer numbers, find a correct way of inserting arithmetic signs (operators) 2: /// such that the result is a correct equation. Example: With the list of numbers [2,3,5,7,11] we 3: /// can form the equations 2-3+5+7 = 11 or 2 = (3*5+7)/11 (and ten others!). 4: /// 5: /// Division should be interpreted as operating on rationals, and division by zero should be 6: /// avoided. 7: /// 8: /// Example in F#: 9: /// 10: /// > solutions [2;3;5;7;11] |> List.iter (printfn "%s");; 11: /// 2 = 3 - (5 + (7 - 11)) 12: /// 2 = 3 - ((5 + 7) - 11) 13: /// 2 = (3 - 5) - (7 - 11) 14: /// 2 = (3 - (5 + 7)) + 11 15: /// 2 = ((3 - 5) - 7) + 11 16: /// 2 = ((3 * 5) + 7) / 11 17: /// 2 * (3 - 5) = 7 - 11 18: /// 2 - (3 - (5 + 7)) = 11 19: /// 2 - ((3 - 5) - 7) = 11 20: /// (2 - 3) + (5 + 7) = 11 21: /// (2 - (3 - 5)) + 7 = 11 22: /// ((2 - 3) + 5) + 7 = 11 23: /// val it : unit = () 24: /// 25: 26: (Solution)

### (***) Problem 94 : Generate K-regular simple graphs with N nodes

1: /// In a K-regular graph all nodes have a degree of K; i.e. the number of edges incident in each 2: /// node is K. How many (non-isomorphic!) 3-regular graphs with 6 nodes are there? 3: 4: (Solution needed)

// instead of solving the problem for 8 queens lets solve if for N queens.

// To solve the problem we are going to start with an empty board and then we're going

// add queen to it for each row. Elimitating invalid solutions. To do that we need a function

// (invalidPosition) that detects if one queen is in conflict with another one. And another

// function (validSolution) that would test if the queen that we're adding is not in

// conflict with any queen already on the board.

// Also, the solution is going to return a a sequence of solutions instead of a list.

// That way we can get one solution realy fast if that is only what we care. For example

// getting all the solutions for a 20x20 board would take a long time, but finding

// the first solution only takes 5 seconds.

//

let queens n =

let invalidPosition (x1, y1) (x2, y2) = (x1 = x2) || (y1 = y2) || abs (x1 - x2) = abs (y1 - y2)

let validSolution (queen, board) = board |> Seq.exists (invalidPosition queen) |> not

// With the function "loop", we're going to move one column at time, placing queens

// on each row and creating new boards with only valid solutions.

let rec loop boards y =

if y = 0 then

boards

else

let boards' = boards

|> Seq.collect(fun board -> [1 .. n] |> Seq.map(fun x -> (x,y),board))

|> Seq.filter validSolution

|> Seq.map(fun (pos, xs) -> pos::xs)

loop boards' (y - 1)

loop (Seq.singleton([])) n |> Seq.map (List.rev >> List.map fst)

// To solve the problem we are going to start with an empty board and then we're going

// add queen to it for each row. Elimitating invalid solutions. To do that we need a function

// (invalidPosition) that detects if one queen is in conflict with another one. And another

// function (validSolution) that would test if the queen that we're adding is not in

// conflict with any queen already on the board.

// Also, the solution is going to return a a sequence of solutions instead of a list.

// That way we can get one solution realy fast if that is only what we care. For example

// getting all the solutions for a 20x20 board would take a long time, but finding

// the first solution only takes 5 seconds.

//

let queens n =

let invalidPosition (x1, y1) (x2, y2) = (x1 = x2) || (y1 = y2) || abs (x1 - x2) = abs (y1 - y2)

let validSolution (queen, board) = board |> Seq.exists (invalidPosition queen) |> not

// With the function "loop", we're going to move one column at time, placing queens

// on each row and creating new boards with only valid solutions.

let rec loop boards y =

if y = 0 then

boards

else

let boards' = boards

|> Seq.collect(fun board -> [1 .. n] |> Seq.map(fun x -> (x,y),board))

|> Seq.filter validSolution

|> Seq.map(fun (pos, xs) -> pos::xs)

loop boards' (y - 1)

loop (Seq.singleton([])) n |> Seq.map (List.rev >> List.map fst)

// Wikipedia has a nice article about this problem http://en.wikipedia.org/wiki/Knights_tour

//

// The way this algorithm works is like this. We create a set (board) with all the positions

// in the board that have not being used. Also we have a function (moves) that returns a

// list of posible moves from the current position. The variable 'validMoves' is the result of

// removing all the positions returned by 'moves' that are not in the set 'board' (positions

// that are still available). If validMoves is empty, that means that we can not move

// anymore. If at that time the board is empty, we have a solution! Otherwise we remove the

// current position from the board add the curent position to the tour and continue to one

// of the valid moves.

// Now, the trick to make the algorithm converge is to move first to the valid position

// that has the less options once we move (Warnsdorff's rule).

//

let moves n (x,y) =

[(x + 2, y + 1); (x + 2, y - 1); (x - 2, y + 1); (x - 2, y - 1); (x - 1, y + 2); (x - 1, y - 2); (x + 1, y + 2); (x + 1, y - 2) ]

|> List.filter(fun (x,y) -> x > 0 && x <= n && y > 0 && y <= n)

let knightsTours n start =

let board = [1 .. n] |> List.collect(fun x -> [1 .. n] |> List.map(fun y -> (x,y))) |> Set.ofList

let rec loop tour board = seq {

let validMoves = tour

|> List.head // the head of the tour is our current position

|> moves n

|> List.filter(fun p -> board |> Set.contains p)

match validMoves with

| [] -> if board |> Set.isEmpty then yield tour // we found a solution!

| _ ->

// the call to sortBy is what makes this algorithm converge fast.

// We want to go first to the position with the less options

// once we move (Warnsdorff's rule).

for p in validMoves |> List.sortBy(moves n >> List.length) do

yield! loop (p::tour) <| Set.remove p board

}

loop [start] <| Set.remove start board

let closedKnightsTour n =

let start = (1,1)

let finish = moves n start |> Set.ofList

let flip f a b = f b a

// lets find the first solution that ends in a position next to the start

knightsTours n start |> Seq.find(List.head >> flip Set.contains finish)

let endKnightsTour n finish =

// just find a tour that starts with finish and reverse it!

knightsTours n finish |> Seq.head |> List.rev

//

// The way this algorithm works is like this. We create a set (board) with all the positions

// in the board that have not being used. Also we have a function (moves) that returns a

// list of posible moves from the current position. The variable 'validMoves' is the result of

// removing all the positions returned by 'moves' that are not in the set 'board' (positions

// that are still available). If validMoves is empty, that means that we can not move

// anymore. If at that time the board is empty, we have a solution! Otherwise we remove the

// current position from the board add the curent position to the tour and continue to one

// of the valid moves.

// Now, the trick to make the algorithm converge is to move first to the valid position

// that has the less options once we move (Warnsdorff's rule).

//

let moves n (x,y) =

[(x + 2, y + 1); (x + 2, y - 1); (x - 2, y + 1); (x - 2, y - 1); (x - 1, y + 2); (x - 1, y - 2); (x + 1, y + 2); (x + 1, y - 2) ]

|> List.filter(fun (x,y) -> x > 0 && x <= n && y > 0 && y <= n)

let knightsTours n start =

let board = [1 .. n] |> List.collect(fun x -> [1 .. n] |> List.map(fun y -> (x,y))) |> Set.ofList

let rec loop tour board = seq {

let validMoves = tour

|> List.head // the head of the tour is our current position

|> moves n

|> List.filter(fun p -> board |> Set.contains p)

match validMoves with

| [] -> if board |> Set.isEmpty then yield tour // we found a solution!

| _ ->

// the call to sortBy is what makes this algorithm converge fast.

// We want to go first to the position with the less options

// once we move (Warnsdorff's rule).

for p in validMoves |> List.sortBy(moves n >> List.length) do

yield! loop (p::tour) <| Set.remove p board

}

loop [start] <| Set.remove start board

let closedKnightsTour n =

let start = (1,1)

let finish = moves n start |> Set.ofList

let flip f a b = f b a

// lets find the first solution that ends in a position next to the start

knightsTours n start |> Seq.find(List.head >> flip Set.contains finish)

let endKnightsTour n finish =

// just find a tour that starts with finish and reverse it!

knightsTours n finish |> Seq.head |> List.rev

// After some searching on the internet I couldn't find an algorithm for Graceful labeling.

// So I decided to go the brute force route. I knew this would work with the first the example

// but I wasn't sure if it would work for the second tree (a tree with 14 Nodes means that we have

// 14! (87,178,291,200) posible ways to tag the tree).

// Luckly, it did!!

// To represent the trees, I decided to use a tuple with a list of nodes and a list of tuples with the edges

type 'a Graph = 'a list * ('a * 'a) list

// Here are the two examples above using that representation.

let g = (['d';'a';'g';'b';'c';'e';'f'],[('d', 'a');('a', 'g');('a', 'b');('b', 'e');('b', 'c');('e', 'f')])

let g' = (['i';'h';'g';'a';'b';'d';'c';'f';'k';'e';'q';'m';'p';'n'],[('i', 'a');('h', 'a');('a', 'b');('a', 'g');('a', 'c');('c', 'f');('c','d');('d','k');('c','e');('e','q');('q','m');('q','n');('n','p')])

// Now I knew how to generate permutations in F# from this snippet: http://fssnip.net/48

// But the problem was, that implementation was using lists and it would not work to generate the

// 87 billion permutations for the 14 node tree. Then I remember the LazyList type in the F#

// Power Pack. Now I can generate the permutations in a lazy way and pray that a solution

// can be found fast.

// Here is the implemetation of using LazyList.

#if INTERACTIVE

#r "FSharp.PowerPack.dll"

#endif

open Microsoft.FSharp.Collections

// the term interleave x ys returns a list of all possible ways of inserting

// the element x into the list ys.

let rec interleave x = function

| LazyList.Nil -> LazyList.ofList [ LazyList.ofList [x]]

| LazyList.Cons(y,ys) -> LazyList.ofSeq (seq { yield LazyList.cons x (LazyList.cons y ys)

for zs in interleave x ys do

yield LazyList.cons y zs })

// the function perms returns a lazy list of all permutations of a list.

let rec perms = function

| LazyList.Nil -> LazyList.ofList [LazyList.empty]

| LazyList.Cons(x,xs) -> LazyList.concat ( LazyList.map (interleave x) (perms xs))

// Now with the problem of generating all the permutations solved.

// It's time to tackle the real problem.

let vonKoch (nodes, edges) =

// diff is used to compute the edge difference acording the the map m

let diff (m : Map<_, _>) (a,b) = abs <| m.[a] - m.[b]

let size = nodes |> List.length

let edgSize = edges |> List.length

match nodes with

| [] -> failwith "Empty graph!!"

| _ when size <> (edgSize + 1) -> // make sure that we have a valid tree

failwith "The tree doesn't have N - 1 egdes. Where N is the number of nodes"

| _ ->

seq {

for p in perms <| LazyList.ofList [1 .. size] do

let sol = LazyList.toList p

let m = sol |> List.zip nodes |> Map.ofList

// I'm using Set here to filter out any duplicates.

// It's faster than Seq.distinct

let count = edges |> List.map (diff m) |> Set.ofList |> Set.count

// if the number of distint differences is equal to the number

// of edges, we found a solution!

if count = edgSize then

yield (sol, edges |> List.map (fun ((a,b) as e) -> m.[a], m.[b], diff m e))

}

// So I decided to go the brute force route. I knew this would work with the first the example

// but I wasn't sure if it would work for the second tree (a tree with 14 Nodes means that we have

// 14! (87,178,291,200) posible ways to tag the tree).

// Luckly, it did!!

// To represent the trees, I decided to use a tuple with a list of nodes and a list of tuples with the edges

type 'a Graph = 'a list * ('a * 'a) list

// Here are the two examples above using that representation.

let g = (['d';'a';'g';'b';'c';'e';'f'],[('d', 'a');('a', 'g');('a', 'b');('b', 'e');('b', 'c');('e', 'f')])

let g' = (['i';'h';'g';'a';'b';'d';'c';'f';'k';'e';'q';'m';'p';'n'],[('i', 'a');('h', 'a');('a', 'b');('a', 'g');('a', 'c');('c', 'f');('c','d');('d','k');('c','e');('e','q');('q','m');('q','n');('n','p')])

// Now I knew how to generate permutations in F# from this snippet: http://fssnip.net/48

// But the problem was, that implementation was using lists and it would not work to generate the

// 87 billion permutations for the 14 node tree. Then I remember the LazyList type in the F#

// Power Pack. Now I can generate the permutations in a lazy way and pray that a solution

// can be found fast.

// Here is the implemetation of using LazyList.

#if INTERACTIVE

#r "FSharp.PowerPack.dll"

#endif

open Microsoft.FSharp.Collections

// the term interleave x ys returns a list of all possible ways of inserting

// the element x into the list ys.

let rec interleave x = function

| LazyList.Nil -> LazyList.ofList [ LazyList.ofList [x]]

| LazyList.Cons(y,ys) -> LazyList.ofSeq (seq { yield LazyList.cons x (LazyList.cons y ys)

for zs in interleave x ys do

yield LazyList.cons y zs })

// the function perms returns a lazy list of all permutations of a list.

let rec perms = function

| LazyList.Nil -> LazyList.ofList [LazyList.empty]

| LazyList.Cons(x,xs) -> LazyList.concat ( LazyList.map (interleave x) (perms xs))

// Now with the problem of generating all the permutations solved.

// It's time to tackle the real problem.

let vonKoch (nodes, edges) =

// diff is used to compute the edge difference acording the the map m

let diff (m : Map<_, _>) (a,b) = abs <| m.[a] - m.[b]

let size = nodes |> List.length

let edgSize = edges |> List.length

match nodes with

| [] -> failwith "Empty graph!!"

| _ when size <> (edgSize + 1) -> // make sure that we have a valid tree

failwith "The tree doesn't have N - 1 egdes. Where N is the number of nodes"

| _ ->

seq {

for p in perms <| LazyList.ofList [1 .. size] do

let sol = LazyList.toList p

let m = sol |> List.zip nodes |> Map.ofList

// I'm using Set here to filter out any duplicates.

// It's faster than Seq.distinct

let count = edges |> List.map (diff m) |> Set.ofList |> Set.count

// if the number of distint differences is equal to the number

// of edges, we found a solution!

if count = edgSize then

yield (sol, edges |> List.map (fun ((a,b) as e) -> m.[a], m.[b], diff m e))

}

// This is similar to "The countdow problem" on chapter 11 in the book

// Programming in Haskell by Graham Hutton

// First let's define our operations. The ToString override is there to help

// on printing the solutions later on.

type Op = Add | Sub | Mult | Div

with override op.ToString() =

match op with

| Add -> "+"

| Sub -> "-"

| Mult -> "*"

| Div -> "/"

// Here we define wich opertaions are valid.

// For Add or Sub there is no problem

// For Mult we dont want trivial mutiplications by 1. Although the

// problem statement is not clear if that is an issue.

// For Div we don't want division by 0, by 1 or fractions

let valid op x y =

match op with

| Add -> true

| Sub -> true

| Mult -> x <> 1 && y <> 1

| Div -> y <> 0 && y <> 1 && x % y = 0

// this is function applies the operation to the x and y arguments

let app op x y =

match op with

| Add -> x + y

| Sub -> x - y

| Mult -> x * y

| Div -> x / y

// Now, we define our expresions. This is how are we going to build the

// solutions

type Expr = Val of int | App of Op * Expr * Expr

// Just for fun, I implemented the fold function for our expresions.

// There was no need since we only use it once on the toString function.

let foldExpr fval fapp expr =

let rec loop expr cont =

match expr with

| Val n -> cont <| fval n

| App(op, l, r) -> loop l <| fun ls -> loop r <| fun rs -> cont <| fapp op ls rs

loop expr id

// Once we have fold over expresions impelmenting toString is a one-liner.

// The code after the fold is just to remove the outher parentesis.

let toString exp =

let str = exp |> foldExpr string (fun op l r -> "(" + l + " " + string op + " " + r + ")")

if str.StartsWith("(") then

str.Substring(1,str.Length - 2)

else

str

// The 'eval' function returns a sigleton list with the result of the evaluation.

// If the expresion is not valid, returns the empty list ([])

let rec eval = function

| Val n -> [n]

| App(op, l, r) ->

[for x in eval l do

for y in eval r do

if valid op x y then

yield app op x y]

// The function 'init', 'inits', 'tails' are here to help implement the

// function splits and came from haskell

// the function inits accepts a list and returns the list without its last item

let rec init = function

| [] -> failwith "empty list!"

| [_] -> []

| x::xs -> x :: init xs

// The function inits returns the list of all initial segments

// of a list , in order of increasing length.

// Example:

// > inits [1..4];;

// val it : int list list = [[]; [1]; [1; 2]; [1; 2; 3]; [1; 2; 3; 4]]

let rec inits = function

| [] -> [[]]

| x::xs -> [ yield []

for ys in inits xs do

yield x::ys]

// the function tails returns the list of initial segments

// of its argument list, shortest last

// Example:

// > tails [1..4];;

// val it : int list list = [[1; 2; 3; 4]; [2; 3; 4]; [3; 4]; [4]; []]

let rec tails = function

| [] -> [[]]

| x::xs as ls ->

[ yield ls

for ys in tails xs do

yield ys ]

// this is what drives the solution to this problem and

// came from the haskell solution.

// Here is an example of its use:

// > splits [1..4];;

// val it : (int list * int list) list =

// [([1], [2; 3; 4]); ([1; 2], [3; 4]); ([1; 2; 3], [4])]

// As you can see, it returs all the ways we can split a list.

let splits xs = List.tail (init (List.zip (inits xs) (tails xs)))

// Now that we're armed with all these functions, we're ready to tackle the real problem.

// The goal of the function expressions is to build all valid expressions and its value given a

// list of numbers. First we split the list in all posible ways (1). Then we take

// the left side of the split and build all the valid expresions (2). We do the same for the

// right side (3). Now we combine the two expresions with all the operators (4). If the operation

// is valid, we add it to the list of expressions (5,6).

let rec expressions = function

| [x] -> [(Val x, x)]

| xs -> [ for xsl, xsr in splits xs do (* 1 *)

for (expl, vall) in expressions xsl do (* 2 *)

for (expr, valr) in expressions xsr do (* 3 *)

for op in [Add; Sub; Mult; Div] do (* 4 *)

if valid op vall valr then (* 5 *)

yield (App (op, expl, expr) ,app op vall valr) (* 6 *)]

// Now that we have a way of generating valid expressions, it's time to

// generate the equaions. Again, we split the list of numbers (1). Then we generate the

// list of expressions from the left side of the split (2). Same with the right side (3).

// If both expressions have the same value, add it to our soutions (4,5).

let equations = function

| [] -> failwith "error: empty list"

| [_] -> failwith "error: singleton list"

| xs -> [for xsl, xsr in splits xs do (* 1 *)

for el, vl in expressions xsl do (* 2 *)

for er, vr in expressions xsr do (* 3 *)

if vl = vr then (* 4 *)

yield (el, er) (* 5 *)]

// Go thought the list of equations a pretty-print them.

let solutions = equations >> List.map(fun (exp1, exp2) -> toString exp1 + " = " + toString exp2)

// Programming in Haskell by Graham Hutton

// First let's define our operations. The ToString override is there to help

// on printing the solutions later on.

type Op = Add | Sub | Mult | Div

with override op.ToString() =

match op with

| Add -> "+"

| Sub -> "-"

| Mult -> "*"

| Div -> "/"

// Here we define wich opertaions are valid.

// For Add or Sub there is no problem

// For Mult we dont want trivial mutiplications by 1. Although the

// problem statement is not clear if that is an issue.

// For Div we don't want division by 0, by 1 or fractions

let valid op x y =

match op with

| Add -> true

| Sub -> true

| Mult -> x <> 1 && y <> 1

| Div -> y <> 0 && y <> 1 && x % y = 0

// this is function applies the operation to the x and y arguments

let app op x y =

match op with

| Add -> x + y

| Sub -> x - y

| Mult -> x * y

| Div -> x / y

// Now, we define our expresions. This is how are we going to build the

// solutions

type Expr = Val of int | App of Op * Expr * Expr

// Just for fun, I implemented the fold function for our expresions.

// There was no need since we only use it once on the toString function.

let foldExpr fval fapp expr =

let rec loop expr cont =

match expr with

| Val n -> cont <| fval n

| App(op, l, r) -> loop l <| fun ls -> loop r <| fun rs -> cont <| fapp op ls rs

loop expr id

// Once we have fold over expresions impelmenting toString is a one-liner.

// The code after the fold is just to remove the outher parentesis.

let toString exp =

let str = exp |> foldExpr string (fun op l r -> "(" + l + " " + string op + " " + r + ")")

if str.StartsWith("(") then

str.Substring(1,str.Length - 2)

else

str

// The 'eval' function returns a sigleton list with the result of the evaluation.

// If the expresion is not valid, returns the empty list ([])

let rec eval = function

| Val n -> [n]

| App(op, l, r) ->

[for x in eval l do

for y in eval r do

if valid op x y then

yield app op x y]

// The function 'init', 'inits', 'tails' are here to help implement the

// function splits and came from haskell

// the function inits accepts a list and returns the list without its last item

let rec init = function

| [] -> failwith "empty list!"

| [_] -> []

| x::xs -> x :: init xs

// The function inits returns the list of all initial segments

// of a list , in order of increasing length.

// Example:

// > inits [1..4];;

// val it : int list list = [[]; [1]; [1; 2]; [1; 2; 3]; [1; 2; 3; 4]]

let rec inits = function

| [] -> [[]]

| x::xs -> [ yield []

for ys in inits xs do

yield x::ys]

// the function tails returns the list of initial segments

// of its argument list, shortest last

// Example:

// > tails [1..4];;

// val it : int list list = [[1; 2; 3; 4]; [2; 3; 4]; [3; 4]; [4]; []]

let rec tails = function

| [] -> [[]]

| x::xs as ls ->

[ yield ls

for ys in tails xs do

yield ys ]

// this is what drives the solution to this problem and

// came from the haskell solution.

// Here is an example of its use:

// > splits [1..4];;

// val it : (int list * int list) list =

// [([1], [2; 3; 4]); ([1; 2], [3; 4]); ([1; 2; 3], [4])]

// As you can see, it returs all the ways we can split a list.

let splits xs = List.tail (init (List.zip (inits xs) (tails xs)))

// Now that we're armed with all these functions, we're ready to tackle the real problem.

// The goal of the function expressions is to build all valid expressions and its value given a

// list of numbers. First we split the list in all posible ways (1). Then we take

// the left side of the split and build all the valid expresions (2). We do the same for the

// right side (3). Now we combine the two expresions with all the operators (4). If the operation

// is valid, we add it to the list of expressions (5,6).

let rec expressions = function

| [x] -> [(Val x, x)]

| xs -> [ for xsl, xsr in splits xs do (* 1 *)

for (expl, vall) in expressions xsl do (* 2 *)

for (expr, valr) in expressions xsr do (* 3 *)

for op in [Add; Sub; Mult; Div] do (* 4 *)

if valid op vall valr then (* 5 *)

yield (App (op, expl, expr) ,app op vall valr) (* 6 *)]

// Now that we have a way of generating valid expressions, it's time to

// generate the equaions. Again, we split the list of numbers (1). Then we generate the

// list of expressions from the left side of the split (2). Same with the right side (3).

// If both expressions have the same value, add it to our soutions (4,5).

let equations = function

| [] -> failwith "error: empty list"

| [_] -> failwith "error: singleton list"

| xs -> [for xsl, xsr in splits xs do (* 1 *)

for el, vl in expressions xsl do (* 2 *)

for er, vr in expressions xsr do (* 3 *)

if vl = vr then (* 4 *)

yield (el, er) (* 5 *)]

// Go thought the list of equations a pretty-print them.

let solutions = equations >> List.map(fun (exp1, exp2) -> toString exp1 + " = " + toString exp2)

let solution94 = "your solution here!!"

### More information

Link: |
http://fssnip.net/aw |

Posted: |
2 years ago |

Author: |
Cesar Mendoza (website) |

Tags: |
Ninety-Nine F# Problems, tutorial, F# |