# Haskell numeric types: quick reference

### Num

`class Num a`

`(+), (*), (-), abs, signum, fromInteger, (negate | (-))`

### Real

`class (Num a, Ord a) => Real`

`toRational`

### Fractional

`class Num a => Fractional a`

`(/), recip, fromRational`

### Integral

`class (Real a, Enum a) => Interal a`

`quot, rem, div, mod, quotRem, divMod, toInteger`

### RealFrac

`class (Real a, Fractional a) => RealFrac a`

`properFraction, truncate, round, ceiling, floor`

### Floating

`class Fractional a => Floating a`

```
pi, exp, sqrt,
log, (**), logBase,
sin, tan, cos,
asin, atan, acos,
sinh, tanh, cosh,
asinh, atanh, acosh
```

### RealFloat

`class (RealFrac a, Floating a) => RealFloat a`

```
floatRadix, floatDigits, floatRange,
decodeFloat, encodeFloat, exponent,
significand, isNaN, isInfinite,
isDenormalized, isNegativeZero, isIEEE,
atan2
```

# Convert hs to lhs

# hs2lhs

## tl;dr Here is the code to convert hs to lhs

Often I decide to write a blog post based on some haskell code that I have already written in *normal* (`.hs`

) form. Had I known before writing the code that it would become a blog post I would have written it using the literate haskell (`.lhs`

) format. So I wrote this small program to convert `.hs`

to `.lhs`

Although the script is short (probably over golfed), it does demonstrate some nice haskell features.

### Overloaded Strings and Data.Text

The ghc `OverLoadedStrings`

language extension allows you to use string literals as text literals so you don’t have to convert `String`

to `Text`

.

```
> {-# LANGUAGE OverloadedStrings #-}
```

### Multi-way if-expressions

Multi-way if-expressions allow the use of the guard syntax we commonly see for top level functions in if statements:

```
if | cond1 -> expr1
| cond2 -> expr2
...
| condn -> exprn
```

```
> {-# LANGUAGE MultiWayIf #-}
```

```
> module Main where
>
> import Control.Applicative ((<$>), (<|>))
> import Data.Maybe (fromMaybe)
> import Data.Text (Text, stripStart, stripPrefix,
> isPrefixOf, isSuffixOf)
> import qualified Data.Text as T
> import qualified Data.Text.IO as T
> import System.Environment
```

In order to handle line breaks, we need to keep track of whether or not the last line parsed was a comments or code.

```
> data Tag = Comment | Code
```

### Applicative and Alternative

The core of the program is the `lhsLine`

function which converts each line in the `.hs`

file to a line in the `.lhs`

file and keeps track of the `Tag`

. The `stripPrefix`

function from `Data.Text`

returns the input text stripped of a prefix as a `Maybe`

value. It returns `Nothing`

if the prefix does not match beginning of the text. We use `fmap`

(`<$>`

) to pair this result with its `Tag`

inside the `Maybe`

and the `Alternative`

instance of `Maybe`

(`<|>`

) to choose the first `Just`

value (or `Nothing`

) if neither alternative matches.

```
> lhsLine :: Tag -> Text -> (Tag, Text)
> lhsLine w t = fromMaybe d c
> where
> d = if | t == T.empty -> (Code, "")
> | isPrefixOf "{-#" t &&
> isSuffixOf "#-}" t -> (Code, "> " `T.append` t)
> | otherwise -> (Code, s `T.append` t)
> s = case w of {Comment -> "\n> "; Code -> "> "}
> c = stripC "-- |" t <|> stripC "--" t
> stripC p t = (\x -> (Comment, stripStart x)) <$> stripPrefix p t
```

We could use the State monad but it would be overkill. Simply threading the state (`Tag`

) through as an argument is fine.

```
> lhs :: Tag -> [Text] -> [Text]
> lhs _ [] = []
> lhs c (t:ts) = t' : (lhs c' ts)
> where (c', t') = lhsLine c t
```

```
> main = do
> text <- T.readFile . head =<< getArgs
> let p = T.lines text
> mapM_ T.putStrLn (lhs Code p)
```

Give it a try!

# Solving the 15-Puzzle with Haskell and diagrams

Something I like to do when learning a new programming language is to code a familiar project. This lets me gauge my progress and make comparisons with languages I’m more comfortable with. One of my favorite “familiar” projects is a solver for the 15-puzzle. If you’re not familiar with the 15-puzzle, it’s a classic grid based game with 4 rows and 4 columns containing a total of 15 tiles. The tiles are labeled 1-15 and there is one blank space. The object is to put the tiles in ascending order by repeatedly sliding a tile into the blank space. See 15 puzzle.

There’s a reason the 15 puzzle has become a favorite coding exercise of mine. Its solution is a particularly nice example of the interplay between algorithms and data structures. In particular, we use the A* algorithm which relies on a priority queue.

While studying Haskell and working on the diagrams project I decided that I could take my 15 puzzle solution to the next level, by using *diagrams* to animate it.

We start by getting the imports out of the way.

```
> module Main where
>
> import Data.Array.Unboxed
> import Data.List (elemIndex)
> import Data.List.Split (chunksOf)
> import Data.Maybe (mapMaybe)
> import qualified Data.PQueue.Prio.Min as PQ
> import Diagrams.Prelude
> import Diagrams.Backend.Rasterific.CmdLine
> import System.Environment
```

## Creating the animated GIF

Next, lets write the *diagrams* code to draw and animate a solution assuming we have already solved a 15 puzzle. The solution takes the form `[Board]`

where `Board`

is a matrix of tiles. Each tile is a number between 1 and 15.

```
> type Board = UArray (Int, Int) Int
```

First we need to draw a single `Board`

, i.e convert it to a diagram. Our strategy is to map a function that draws each tile onto the board, then concatenate the tile diagrams into a diagram of the puzzle board. *diagrams* has built in functions for vertically and horizontally concatenating lists of diagrams so we start by converting the `Board`

to a list.

```
> fromBoard :: Board -> [[Int]]
> fromBoard b = [row i | i <- [1..n]]
> where
> row i = [b ! (i, j) | j <- [1..n]]
> n = dim b
```

The number of rows and columns in the puzzle is the upper bound of the array since we are using 1 (not 0) as the starting indices for our array.

```
> dim :: Board -> Int
> dim = snd . snd . bounds
```

Assuming we have a function `drawTile`

that makes a number into a tile diagrams we can now create a diagram from a game board.

```
> boardDia :: Board -> Diagram B R2
> boardDia b = bg gray
> . frame 0.1
> . vcat' (with & sep .~ 0.075)
> . map (hcat' (with & sep .~ 0.075))
> . (map . map) drawTile $ fromBoard b
```

Here is `drawTile`

```
> drawTile :: Int -> Diagram B R2
> drawTile 0 = square 1 # lw none
> drawTile s = text (show s)
> # fontSize (Local 1)
> # fc white
> # scale 0.5
> # bold
> <> roundedRect 1 1 0.2
> # fc darkred
```

Now we need to assemble a bunch of board diagrams into a GIF. All we need to do is pass a list of diagrams and delay times `[(Diagram B R2, Int)]`

to the `mainWith`

function, choose a .gif file extension when we run the program and *diagrams* will make an animated GIF.

```
> dias :: [Board] -> [Diagram B R2]
> dias bs = map boardDia bs
```

We show each board for 1 second and pause for 3 seconds before repeating the GIF loop.

```
> times :: Int -> [Int]
> times n = replicate (n-1) 100 ++ [300]
```

```
> gifs :: [Board] -> [(Diagram B R2, Int)]
> gifs bs = zip (dias bs) (times . length $ bs)
```

Here is an example main program that solves a puzzle read in from a text file. The format of the puzzle file has as first line a single integer representing the dimension of the puzzle and each additional line a string of integers representing a row of the starting puzzle board. For example the puzzle at the top of the post has file:

```
4
9 2 8 11
0 5 13 7
15 1 4 10
3 14 6 12
```

To run the program type the the following at the command line and then enter the path to your puzzle file: ./Puzzle -o my_solution.gif -w 300

Of course we still need to write, `solve`

, `mkGameState`

, and `boards`

.

```
> main = do
> putStrLn "Enter the name of the file containing the puzzle:"
> txt <- readFile =<< getLine
> let game = fromString txt
> ([n], brd) = case game of
> [] -> error "Invalid puzzle file"
> x:xs -> (x, concat xs)
> let p = solve . mkGameState n $ brd
> mainWith $ gifs (boards p)
```

```
> fromString :: String -> [[Int]]
> fromString s = (map . map) read ws
> where ws = map words (lines s)
```

## The A* algorithm

We are going to search for a solution using the A* algorithm. We will keep track of the state of the game in an algebraic data type called `GameState`

.

The game state includes the board, the number of moves up until this point and a previous state (unless this it the start state). We also cache the location of the blank tile and the manhattan distance to the goal state; so that we only need to calculate these things once.

Notice that `GameState`

recursively contains the game state that preceded it (wrapped in a `Maybe`

) , except for the start state whose `previous`

field will contain `Nothing`

. This will allow us recreate all of the intermediate boards from the final solved board so that we can animate the game. We use the `boards`

function to create the list containing each board from start to finish.

```
> data GameState = GameState
> { board :: Board
> , dist :: Int
> , blank :: (Int, Int)
> , moves :: Int
> , previous :: Maybe GameState
> } deriving (Show, Eq, Ord)
```

```
> boards :: GameState -> [Board]
> boards p = reverse $ brds p
> where
> brds q = case previous q of
> Nothing -> [board q]
> Just r -> board q : brds r
```

The possible moves.

```
> data Direction = North | East | South | West
```

We create a priority queue `Frontier`

whose priorities are the sum of the moves made so far to reach the game state and the manhattan distance to the goal state. This is an admissible heuristic function which guarantees that the solution we find will take the minimum number of moves. The initial `Frontier`

contains only the start state. Then we recusively pop the minimum game state from the queue and check to see if it is the goal, if it is we are done, if not we calculate the states reachable by a legal game move (`neighbors`

) and add them to the queue. Here’s the code.

```
> type Frontier = PQ.MinPQueue Int GameState
```

Manhattan distance of a tile with value `v`

at position `(i, j)`

, for a game of dimension `n`

.

```
> manhattan :: Int -> Int -> Int -> Int -> Int
> manhattan v n i j = if v == 0 then 0 else rowDist + colDist
> where
> rowDist = abs (i-1 - ((v-1) `div` n))
> colDist = abs (j-1 - ((v-1) `mod` n))
```

Manhattan distance of entire board.

```
> totalDist :: Board -> Int
> totalDist b = sum [manhattan (b ! (i, j)) n i j | i <- [1..n], j <- [1..n]]
> where n = dim b
```

Create a start state from a list of tiles.

```
> mkGameState :: Int -> [Int] -> GameState
> mkGameState n xs = GameState b d z 0 Nothing
> where
> b = listArray ((1, 1), (n, n)) xs
> d = totalDist b
> Just z' = elemIndex 0 xs
> z = (1 + z' `div` n, 1 + z' `mod` n)
```

Update the game state after switching the position of the blank and tile `(i, j)`

.

```
> update :: GameState -> Int -> Int -> GameState
> update p i j = p { board = b
> , dist = totalDist b
> , blank = (i, j)
> , moves = moves p + 1
> , previous = Just p }
> where
> b = b' // [(blank p, b' ! (i, j)), ((i, j), 0)]
> b' = board p
```

Find the the board that can be reached from the current state by moving in the specified direction, being careful not to move off the board.

```
> neighbor :: GameState -> Direction -> Maybe GameState
> neighbor p dir = case dir of
> North -> if i <= 1 then Nothing else Just $ update p (i-1) j
> East -> if j >= n then Nothing else Just $ update p i (j+1)
> South -> if i >= n then Nothing else Just $ update p (i+1) j
> West -> if j <= 1 then Nothing else Just $ update p i (j-1)
> where
> (i, j) = blank p
> n = dim . board $ p
```

All of the states that can be reached in one move from the current state.

```
> neighbors :: GameState -> [GameState]
> neighbors p = mapMaybe (neighbor p) [North, East, South, West]
```

Finally, solve the puzzle.

```
> solve :: GameState -> GameState
> solve p = go (PQ.fromList [(dist p, p)])
> where
> go fr = if dist puzzle == 0
> then puzzle
> else go fr2
> where
> -- Retrieve the game state with the lowest priority and > -- remove it from
> -- the frontier.
> ((_, puzzle), fr1) = PQ.deleteFindMin fr
>
> -- If the new board is the smae as the previous board then
> -- do not add it to the queue since it has already been > -- explored.
> ns = case previous puzzle of
> Nothing -> neighbors puzzle
> Just n -> filter (\x -> board x /= board n)
> (neighbors puzzle)
>
> -- The priority of a puzzle is the number of moves so far
> -- plus the manhattan distance.
> ps = zip [moves q + dist q | q <- ns] ns
> fr2 = foldr (uncurry PQ.insert) fr1 ps
```

You can find more puzzles in my github repo: puzzles. Happy solving !

# Monkey typing ABRACADABRA

Since I decided to call this blog **martingalemeasure** it seems only fitting that the first post should be about probability; martingales in particular. In my favorite introductory book on measure theoretical probability, “Probablity with Martingales” by David Williams, we find an exercise in Chapter 10, which I paraphrase here:

*Suppose a monkey is typing randomly at a typewriter whose only keys are the capital letters through of the english alphabet. What is the expected (average) time it will take for the monkey to type the word ?*

This is not an easy problem. In fact it’s not entirely obvious that the average time is even finte! Williams expects the reader to solve it using the beautiful theory of martingales and in particular *Doob’s optional-stopping theorem*. We will calculate the result below, but stop short of a proof. (There are many proofs of this result already on the web. A quick search for “monkey abracadabra” will provide several links).

The purpose of this post is to try to provide the intuition behind the result. We do this by comparing the expected time it takes for the monkey to type with the expected time it takes to type the first eleven letters of the alphabet .

## Simplification

It’s almost always a good idea when trying to solve a problem to first attack the simplest case you can think of that still embodies the essence of the problem. For our problem we can certainly benefit both from picking shorter strings, and by choosing a smaller alphabet. The important distinction between and is that the former starts to repeat at charcter 8, i.e has a repeated sub-string. So if we look at versus we have a much shorter word that has the repeated sub-string . Since we lose nothing by restricting the possible letters to one of ,, or ; we restrict our alphabet to these three letters.

## Solution

Ok, let’s use martingale theory to solve the simplified problem and then apply the result to the bigger problem. As is often the case in probability theory we will think in terms of gamblers making bets. Suppose that just before each keystroke made by the monkey, a new gambler shows up at a casino and employs the following betting strategy. She wagers one dollar that the monkey will type an , the first letter in the word. If she wins, the casino pays her fair odds and she receives dollars. She then bets the entire dollars that the next keystroke will be a , the second letter. She continues until either she has lost her intial dollar or the word is typed in full; in that case she wins dollars, for a profit of .

Each individaul bet is fair, that is, has an expected value of zero. Indeed, our gambler has a rd chance of tripling her wager, say for profit of and a rds chance of losing it. Therefore, her expected value is . Hence, the entire strategy is fair, the gambler on average will not make or lose any money. If we now consider the total amount won or lost by the casino at any point in time, then that too must have expectation zero, since each gambler is playing a fair game, so is the casino as a whole. I should probably mention that the stochastic process describing the profit of the casino is a martingale, which is why we can apply the necessary mathematical machinary we need to solve this problem.

Here comes the big trick, when we say that the casino has expected gains of zero at *any* time, we mean random times as well. Including random times like the first time the word is typed or the first time the word is typed (technically time is called a stopping time and needs to satisfies one of the sufficient conditions of Doob’s theorem). We are almost there, all we need to do is calculate the expected gain by the casino at times and and we will be done. Let’s do first. The first time the monkey types , all of the gamblers except the 3rd to last will have winnings of zero. The third to last will have winnings of . The total amount wagered will be . So in order for the game to be fair we must have,

Hence, by the linearity of expectation,

But what happens when we analyze the string instead? At time , not only has the third to last gambler 27 in winnings, but the last gambler has 3. Since she wagered one dollar on the event the monkey typed an A and the monkey did. Hence,

and

Wow, the average time for the monkey to type is actually longer than the average time to type .

## Back to ABRACADABRA

It should now be straight forward to calculate the expected time it takes for the monkey to type and . We simply compute how much money the casino owes the first time the monkey types the word. In the case of the only winner is the gambler who started 11 keystrokes ago. She grosses so that turns out to be the expected time. But in the case of the gambler who started playing 4 keystrokes ago has winnings of and the gambler who bet on the last keystroke won , hence for ,

## Intuition

I don’t know about you, but I find this result is counter-intuitive. I expected it would take the same time on average, or if anything shorter to type , since at the 8th keystroke the monkey types an A starting the sequence again. Here is where the simplified case using and can help us get a better grip on what is really going on. In these cases we can easily caculate some probabilities. Let’s look at all of the winning strings after 5 keystrokes. We use * to mean any character in the alphabet, in this case one of , , or . Here are the three events that represent a case where the monkey has typed the string in 5 keystrokes or less:

```
A B C * *
* A B C *
* * A B C
```

If we want to know the probability that the monkey typed in the first 5 strokes, we need to take the probability of the union of the three events above. By looking at the third character in each string, we can see that the three cases are mutually exclusive. Therefore, we can just add their probabilities. The probability of the first case is clearly so the probability of the union is . In other words the probability that the monkey types in 5 keystrokes or less is . On the other hand for the case we have events:

```
A B A * *
* A B A *
* * A B A
```

And again the probability of each event is , but we can no longer add the three events since they are no longer mutually exclusive. We can see that the first and third cases overlap, they both contain the sequence:

`A B A B A`

And that is the only string that they have in common. So by the inclusion – exclusion principle we need to subtract that case out to get the probability as Which is lower by rd.

And now it should be clear why the average time for the monkey to type a word is longer if the word has repeated sub-strings. *For any given time the probability of typing the string with repeated sub-strings is lower so it must take longer on average for the event to happen*.