## Tools and laziness

Here is a list of exercises related to project management, testing, and laziness.

### Packaging

The file Game.hs implements a small guessing game, all in one file. You can run it using runghc Game.hs on the terminal prompt. The goal of this first exercise is to turn this file into a proper Cabal project:

1. Initialize a project with one executable stanza.
2. Separate the pure part of the game (data type declarations and functions next and step) into a separate module, which should be imported by the Main module.
3. If you use Stack, initialize also the stack.yaml file.
4. Run the game using Cabal or Stack.

### Smooth permutations

In this assignment we want to build a library to generate smooth permutations. Given a list of integers xs and an integer d, a smooth permutation of xs with maximum distance d is a permutation in which the difference of any two consecutive elements is at most d. A naïve implementation just generates all the permutations of a list,

split []     = []
split (x:xs) = (x, xs) : [(y, x:ys) | (y, ys) <- split xs]

perms []     = [[]]
perms xs     = [(v:p) | (v, vs) <- split xs, p <- perms vs]


and then filters out those which are smooth,

smooth n (x:y:ys) = abs (y - x) < n && smooth n (y:ys)
smooth _ _        = True

smoothPerms :: Int -> [Int] -> [[Int]]
smoothPerms n xs = filter (smooth n) (perms xs)


Exercise 1: Packaging and documentation

1. Create a library smoothies which exports perms and smoothPerms from a module SmoothPermsSlow. You should be able to install the package by just running cabal install in it.
2. Document the exported functions using Haddock.

Exercise 2: Testsuite

1. Write a SmothPermsTest module with a comprehensive set of properties to check that smoothPerms works correctly.
2. Integrate your testsuite with Cabal using tasty (here is how you do so).

Exercise 3: Implementation with trees

The initial implementation of smoothPerms is very expensive. A better approach is to build a tree, for which it holds that each path from the root to a leaf corresponds to one of the possible permutations, next prune this tree such that only smooth paths are represented, and finally use this tree to generate all the smooth permutations from. Expose this new implementation in a new SmoothPermsTree module.

1. Define a data type PermTree to represented a permutation tree.
2. Define a function listToPermTree which maps a list onto this tree.
3. Define a function permTreeToPerms which generates all permutations represented by a tree.

At this point the perms functions given above should be the composition of listToPermTree and permTreeToPerms.

4. Define a function pruneSmooth, which leaves only smooth permutations in the tree.
5. Redefine the function smoothPerms.

Integrate this module in the testsuite you developed in the previous exercise.

Exercise 4: Unfolds

Recall the definition of unfoldr for lists,

unfoldr :: (s -> Maybe (a, s)) -> s -> [a]
unfoldr next x = case next x of
Nothing     -> []
Just (y, r) -> y : unfoldr next r


We can define an unfold function for binary trees as well:

data Tree a = Leaf a | Node (Tree a) (Tree a)
deriving Show

unfoldTree :: (s -> Either a (s, s)) -> s -> Tree a
unfoldTree next x = case next x of
Left  y      -> Leaf y
Right (l, r) -> Node (unfoldTree next l) (unfoldTree next r)


Define the following functions in a new module UnfoldUtils, which should not be exposed by your package. Define the functions using unfoldr or unfoldTree, as required.

1. iterate :: (a -> a) -> a -> [a]. The call iterate f x generates the infinite list [x, f x, f (f x), ...].
2. map :: (a -> b) -> [a] -> [b].
3. balanced :: Int -> Tree (), which generates a balanced binary tree of the given height.
4. sized :: Int -> Tree Int, which generates any tree with the given number of nodes. Each leaf in the returned tree should have a unique label.

Define a new module SmoothPermsUnfold with an unfoldPermTree function which generates a PermTree as defined in the previous exercise. Then use that unfoldPermTree to implement a new version of listToPermTree and smoothPerms.

(Optional) Write the following proofs as comments in the UnfoldUtils module.

1. Prove using induction and equational reasoning that the version of map you defined using unfoldr coincides with the definition of map by recursion.
2. We define the size of a binary tree as the number of internal nodes.

 size (Leaf _)   = 0
size (Node l r) = 1 + size l + size r


What is the size of a balanced tree as generated by balanced? Prove your result using induction and equational reasoning.

Exercise 5: Performance

1. Use the criterion package to make and run benchmarks for the given naïve solution and the implementations using trees and unfolds, in order to find out whether your solution really gives higher performance.
2. Use heap profiles to analyse and draw conclusions about the differences.

### Heap profiles

Exercise 1: Generate heap profiles for the following functions:

rev  = foldl (flip (:)) []
rev' = foldr (\x r -> r ++ [x]) []


by using them as function f in a main program as follows

main = print $f [1 .. 1000000]  (adapt the size of 1000000 according to the speed of your machine to get good results). Interpret and try to explain the results! Exercise 2: Do the same for conc xs ys = foldr (:) ys xs conc' = foldl (\k x -> k . (x:)) id  with main = print$ f [1 .. 1000000] [1 .. 1000000]


Exercise 3: Finally, have a look at

f1 = let xs == [1 .. 1000000] in if length xs > 0 then head xs else 0
f2 = if length [1 .. 1000000] > 0 then head [1 .. 1000000] else 0


### Forcing evaluation

Write a function

forceBoolList :: [Bool] -> r -> r


that completely forces a list of booleans without using seq. Note that pattern matching drives evaluation.

Explain why the function forceBoolList has the type as specified above and not

forceBoolList :: [Bool] -> [Bool]


and why seq is defined as it is, and

force :: a -> a
force a = seq a a


is useless.