## Lab 3 — Extra Assignments

Just for fun. You can choose freely whether to do 0, 1 or more of these. Don't expect us to spend time grading these however. There are no perfect, pre-defined answers here.**X.**Perhaps if we picked the blank in a smarter way, the

**solve**function would go faster?

One idea is to always pick the blank spot where there are *as few
possibilities left*. For example, if we have a row with one or two blank
spots, it is probably a good idea to pick one of those blank spots, since it
will limit the consecutive search most, and it will lead to search to a state
with more digits filled in. (Such a way of changing a solving method is called a
*heuristic* -- there is no absoluate guarantee that the search will go faster, but
often it actually will.)

Does your **solve** function work faster now? Experiment with different
heuristics (for example: only look at rows and columns, and not at 3x3 blocks),
and see which one performs best. Can you solve some of the hard
Sudokus now?

Do not forget to add appropriate properties that test your functions.

**Y.**The solving method we have used in this lab assignment is very basic, and in some sense naive. The best known methods to solve problems like Sudoku is to also include the notion of

*propagation*. This is the way most humans actually solve a Sudoku.

A simple variant of propagation is the following. Suppose we have Sudoku with a row with precisely one blank, such as the 3rd row in the example below:

36..712.. .5....18. ..92.47.. 596.13428 4..5.2..9 27.46.... ..53.89.. .83....6. ..769..43Our current solution would go and pick blanks, and start searching recursively, without making use of the fact that we already know the value of that blank (namely 7 in this case); all the other values have been used by the other cells in the row.

Implement a function

that, given a Sudoku, finds out which rows, columns, and 3x3 blocks only have one blank in them, and then fills those blanks with the only possibly remaining value. It repeats doing this until all rows, columns and 3x3 blocks are either completely filled up, or contain two holes or more.propagate::Sudoku->Sudoku

Now, add this function at the appropriate place in your solve function. Does it work faster now?

For other, more powerful propagation, you can for example read the following webpage:

- sudoku.com, and click on "how to solve".

Do not forget to add appropriate properties that test your functions.

**Z.**Write a function that produces interesting Sudoku puzzles. For example, one could have a function

that every time we run it, would print a new, interesting Sudoku puzzle on the screen.createSudoku::IO()

One can discuss what an interesting Sudoku puzzle is. Here are three properties that an interesting Sudoku puzzle must have:

- There must be a solution
- There must not be two different solutions
- There must not be too many digits already visible

Do not forget to add appropriate properties that test your functions.

**Å.**Generalize the Sudokus that are dealt with in this assignment to other dimensions, for example 4x4 Sudokus, or 4x3 Sudokus. Do all dimensions make sense? Make sure your solution works in general, for all possible dimensions that make sense.

For inspiration, look here: Monster Sudokus. Or here: xkcd comics :-)

Do not forget to add appropriate properties that test your functions.

**Ä1.**The type we use to represent sudokus,

is too big, meaning that it permits values that are not proper sudoku puzzles. This is why we wrote the functiondataSudoku=Sudoku{rows::[[MaybeInt]] }

`isSudoku`

to check that a
sudoku is well-formed.
We could instead take advantage of Haskell's type system and define a
type that exactly captures the set of values we want to work with, making
the function `isSudoku`

superfluous (i.e. it would return

for **True***all* values in the type), and
eliminating the possibility of writing buggy functions that return
ill-formed sudokus.

There are two independent improvements we can make:

- Instead of using

, we can define a type**Int**

that represents the digits 1 to 9, and nothing else.**Digit** - Instead of using lists, we can define a type

that represents vectors of size 9, together with safe indexing and updating functions that prevent index-out-of-bounds errors.**Nine**`a`

**Ä2.**Using a more specific type to represent sudoku puzzles means that we can't reuse the type if we want to solve other variants of sudoku puzzles, like we did in asssigment

**Å**. And if we use different types for different puzzle variants, the types prevent us from applying the same solver function to puzzle of differents variants, even though, the solver function in principle can be reused unchanged.

However, to decouple the solver function from particular puzzle types, we can define a type class

and generalize your solver to work for any type in this class:

classPuzzlepwhere`...`

For this to work, the functions on puzzles that are used in the solver need to turned into methods in the class, e.g. functions likesolve::Puzzlep=>p->Maybep

`isOkay`

, `blanks`

,
`candidates`

.
A limitation of type classes, as originally defined in Haskell and
presented in this course, is that they allow only *one* type
(the type parameter in the class) to vary between different instances, but
the methods in the

class need to
refer to more than one type that might vary between different puzzle variants:
**Puzzle**

- the type of the puzzles themselves,
- the type of the digits (or other symbols) that appear in the puzzle,
- and possibly the type of the positions (indexes) in the puzzle
(that we called

), but we could reuse the type for digits here.**Pos**

**Ä1**.

` `**class** **Puzzle** `p` **where**
`candidates` **::** `p` **->** **Pos** **->** [**Int**]

...

To obtain a better solution we will need to use some Haskell language extensions available in GHC. There are two possibilities:

- use a
*multi-parameter type class*, allowing all the types that vary between instances to be parameters in the type class. (You probably also need to specify*functional dependencies*to avoid type ambiguities.)

...**class****Puzzle**`p``i``d`**|**`p`**->**`i``d`**where**`candidates`**::**`p`**->**`i`**->**[`d`] - use
*type families*, allowing type class definition to contain types (in addition to functions), that can be defined in different ways in different instances.**class****Puzzle**`p`**where****type****Index**`p`**type****Digit**`p`

...`candidates`**::**`p`**->****Index**`p`**->**[**Digit**`p`]

**Ö.**We have stated the

*soundness*of the

**solve**function as a property; every produced solution should be a real solution. The dual of soundness is

*completeness*. Completeness says that whenever there is a solution, the

**solve**function should also produce a solution. (Equivalently, if the

**solve**function says that there is no solution, then there really is no solution.)

If we define the following helper datatype:

Then, implement a propertydataSolvableSudoku=SolvableSudoku

that states that, for any solvale Sudoku,prop_SolveComplete::SolvableSudoku->Boolprop_SolveComplete(Solvablesud)=(...)

**solve**produces an answer.

Now, make the type SolvableSudoku an instance of Arbitrary:

Here, you need to think about how to generate arbitrary Sudokus that are guaranteed to be solvable!instanceArbitrarySolvableSudokuwherearbitrary=(...)

One idea is to start with an arbitrary solved Sudoku, and randomly blank out some of the digits. Implement this!