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Pure Functional Memoization

There are many computation problems that resonate through the ages. Important Problems. Problems that merit a capital P.

The Halting Problem…

P versus NP…

The Fibonacci sequence…

The Fibonacci sequence is a super-important computation Problem that has vexed mathematicians for generations. It’s so simple!

fibs 0 = 0 fibs 1 = 1 fibs n = (fibs $ n - 1) + (fibs $ n - 2)

Done! Right? Let’s fire up ghci and find out.

*Fibs> fibs 10 55

… looking good…

*Fibs> fibs 40

… still waiting…


… Well that sure took an unfortunate amount of time. Let’s try 1000!

*Fibs> fibs 1000 *** Exception: <<You died before this returned>>

To this day, science wonders what fibs(1000) is. Well today we solve this!


The traditional way to solve this is to use Memoization. In an imperative language, we’d create an array of size n, and prepopulate arr[0] = 0 and arr[1] = 1. Next we’d loop over 2 to n, and for each we’d set arr[i] = arr[i-1] + arr[i-2].

Unfortunately for us, this is Haskell. What to do… Suppose we had a map of the solutions for 0 to i, we could calculate the solution for i + 1 pretty easily right?

fibsImpl _ 0 = 0 fibsImpl _ 1 = 1 fibsImpl m i = (mo + mt) where mo = Map.findWithDefault undefined (i - 1) m mt = Map.findWithDefault undefined (i - 2) m

We return 0 and 1 for i = 0 and i = 1 as usual. Next we lookup n – 1 and n – 2 from the map and return their sum. This is all pretty standard. But where does the map come from?

It turns out that this is one of those times that laziness is our friend. Consider this code:

fibs' n = let m = fmap (fibsImpl m) (Map.fromList (zip [0..n] [0..n])) in Map.findWithDefault undefined n m

When I first saw this pattern (which I call the Wizard Pattern, because it was clearly invented by a wizard), I was completely baffled. We pass the thing we’re creating into the function that’s creating it? Unthinkable!

It turns out that this is just what we need. Because of laziness, the fmap returns immediately, and m points to an unevaluated thunk. So, for i = 0, and i = 1, fibsImpl will return 0 and 1 respectively, and the map will map 0 -> 0 and 1 -> 1. Next for i = 2, Haskell will attempt to lookup from the map. When it does this, it will be forced to evaluate the result of i = 0 and i = 1, and it will add 2 -> 1 to the map. This will continue all the way through i = n. Finally, this function looks up and returns the value of fibs n in linearish time. (As we all know, Map lookup isn’t constant time, but this is a lot better than the exponential time we had before)

So let’s try it out.

*Fibs> fibs' 1 1 *Fibs> fibs' 10 55 *Fibs> fibs' 40 102334155

… so far so good…

*Fibs> fibs' 100 354224848179261915075 *Fibs> fibs' 1000 43466557686937456435688527675040625802564660517371780402481729089536555417949051890403879840079255169295922593080322634775209689623239873322471161642996440906533187938298969649928516003704476137795166849228875 *Fibs> fibs' 10000 33644764876431783266621612005107543310302148460680063906564769974680081442166662368155595513633734025582065332680836159373734790483865268263040892463056431887354544369559827491606602099884183933864652731300088830269235673613135117579297437854413752130520504347701602264758318906527890855154366159582987279682987510631200575428783453215515103870818298969791613127856265033195487140214287532698187962046936097879900350962302291026368131493195275630227837628441540360584402572114334961180023091208287046088923962328835461505776583271252546093591128203925285393434620904245248929403901706233888991085841065183173360437470737908552631764325733993712871937587746897479926305837065742830161637408969178426378624212835258112820516370298089332099905707920064367426202389783111470054074998459250360633560933883831923386783056136435351892133279732908133732642652633989763922723407882928177953580570993691049175470808931841056146322338217465637321248226383092103297701648054726243842374862411453093812206564914032751086643394517512161526545361333111314042436854805106765843493523836959653428071768775328348234345557366719731392746273629108210679280784718035329131176778924659089938635459327894523777674406192240337638674004021330343297496902028328145933418826817683893072003634795623117103101291953169794607632737589253530772552375943788434504067715555779056450443016640119462580972216729758615026968443146952034614932291105970676243268515992834709891284706740862008587135016260312071903172086094081298321581077282076353186624611278245537208532365305775956430072517744315051539600905168603220349163222640885248852433158051534849622434848299380905070483482449327453732624567755879089187190803662058009594743150052402532709746995318770724376825907419939632265984147498193609285223945039707165443156421328157688908058783183404917434556270520223564846495196112460268313970975069382648706613264507665074611512677522748621598642530711298441182622661057163515069260029861704945425047491378115154139941550671256271197133252763631939606902895650288268608362241082050562430701794976171121233066073310059947366875 *Fibs> fibs' 100000 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Neat. Even that last one only took a few seconds to return!

Again, This Time In Reverse!

Today we’ll be doing Exercise 5 from The C Programming Language. Today’s exercises aren’t terribly challenging, but there is some good stuff in here. The problem is:

Modify the temperature conversion program to print the table in reverse order, that is, from 300 degrees to 0.

I bet you thought we were done with temperature conversion, didn’t you? I’m right there with you, but luckily for us, this is also the section that introduces the for loop, so this is much less tedious. The new program for modification:

#include <stdio.h> int main (int argc, char ** argv) { for (int fahr = 0; fahr <= 300; fahr = fahr + 20) { printf("%3d %6.1f\n", fahr, (5.0/9.0)*(fahr-32)); } }

I made a few minor modifications. I gave main the correct signature to keep gcc from complaining, and I also moved the initialization of fahr to the loop. As you may know this is not valid C, and the compiler will throw an error. You might also know that this was made to be valid C in the C 99 revision. To compile this, you just need to add the -std=c99 flag to your gcc call.

To complete this exercise, you only need to modify the loop declaration. Change this line…

for (int fahr = 0; fahr <= 300; fahr = fahr + 20)

…to this…

for (int fahr = 300; fahr >= 0; fahr = fahr - 20)

Pretty straight forward stuff here. fahr starts at 300 instead of 0, and the loop continues so long as it remains greater than 0. Instead of adding 20 each iteration, we subtract it. This can be compiled like so:

gcc -std=c99 -Wall ex5.c -o ex5

In Haskell

Unfortunately, the authors of The C Programming Language did not see fit to include a starting Haskell program for us to modify. This seems like a pretty serious oversight to me, but we’ll just have to make due. Luckily for us, we can use the temperature conversion program I wrote for the last entry in this series. This program handles conversions, and we can use it to produce a table of all conversions between 0 and 300 degrees.

While I’ve established that Haskell does in fact have loops, we won’t be using them here. Instead we’ll be using ranges, functors, and sequencing to solve this problem in a more functional way.

First, we need a list of every 20th number between 0 and 300:

[300.0, 280.0 .. 0.0]

The .. there basically says “You get the idea, do that.” If you put enough information into it so that the compiler can figure out the pattern, and start and end points, the compiler will fill the rest in. In this case, I gave the first two values, telling the compiler I want increments of 20, and I gave it the last value so it knows where to stop.

Unfortunately, as you recall, my conversion program needs members of the Temperature typeclass. Surely I don’t plan to make Double a member, right? Right. We need to map a function over this list to produce a list of Temperatures. But what function should we use?

Something that beginning Haskellers may not realize is that constructors are in fact functions!

Prelude> :t Just Just :: a -> Maybe a Prelude> :t Left Left :: a -> Either a b

…and of course…

*Main> :t Fahrenheit Fahrenheit :: Double -> Fahrenheit

That’s right, we can map Fahrenheit over a functor just like any other function!

map Fahrenheit [300.0, 280.0 .. 0.0]

This will produce a list of every 20th Fahrenheit temperature between 300 and 0. However, we aren’t done yet. We actually need a list of Conversions, because this type is required for our insertConv function. To get this we can map toConversion over this list:

map toConversion $ map Fahrenheit [300.0, 280.0 .. 0.0]

…but that’s starting to get a bit ugly, there must be a better way. Normally, I’m not terribly fond of function composition, in this case it will make our life easier.

Function Composition

Haskell provides an operator . that is used for function composition. Let’s take a look at its type:

Prelude> :t (.) (.) :: (b -> c) -> (a -> b) -> a -> c

The composition operator takes a function that takes some type b as an argument, and that returns some type c. It takes a second function that takes some type a as an argument, and that returns some type b. Finally, it returns some type c.

What does this do for us? Basically, it takes a function a -> b, and a function b -> c, and turns them into a function a -> c. Let’s see an example:

Prelude> :t show show :: Show a => a -> String

The function show takes a member of the Show typeclass and returns a String.

Prelude> :t putStr putStr :: String -> IO ()

The function putStr takes a String, and returns an IO action.

Prelude> :t putStr . show putStr . show :: Show a => a -> IO ()

When we compose the two functions, we get a function that takes a member of the Show typeclass, and returns an IO action.

Logically, calling putStr . show someShowable is the same as calling putStr $ show someShowable or putStr (show someShowable). However, putStr . show is a valid function, where putStr $ show and putStr (show) are compiler errors if you don’t give show an argument.

How does this help us?

*Main> :t Fahrenheit Fahrenheit :: Double -> Fahrenheit *Main> :t toConversion toConversion :: (Temperature a, Temperature b) => a -> (a, b) *Main> :t toConversion . Fahrenheit toConversion . Fahrenheit :: Temperature b => Double -> (Fahrenheit, b)

By composing toConversion and Fahrenheit, we end up with a function that takes a Double as an argument and returns a Conversion from Fahrenheit. We can then map this function over our list of Doubles:

map (toConversion . Fahrenheit) [300.0, 280.0 .. 0.0]

Next, we need to turn these Conversions into TableBuilders. This is a simple matter of composing insertConv:

map (insertConv . toConversion . Fahrenheit) [300.0, 280.0 .. 0.0]

The type of this new function is:

:t (insertConv . toConversion . Fahrenheit) (insertConv . toConversion . Fahrenheit) :: Double -> TableBuilder ()

…or at least it would be if the type inferrer could figure out the type of toConversion. Unfortunately for us, it can’t because the implementation is purposely ambiguous. We need to add a type signature to it:

(insertConv . (toConversion :: Fahrenheit -> (Fahrenheit, Celsius)) . Fahrenheit)


Now we are ready to create our table. This part is actually very easy. Thanks to the library function sequence_ we won’t even need a do block. What does this function do? Let’s look at its signature:

Prelude> :t sequence_ sequence_ :: Monad m => [m a] -> m () Prelude> :t sequence sequence :: Monad m => [m a] -> m [a]

There are two variations of this function. The first, sequence_ evaluates each monadic action, and ignores the results. Basically, suppose we have some function: func :: a -> Monad b

let mList = map func ["foo", "bar", "baz"] in sequence_ mList

…is equivalent to…

do func "foo" func "bar" func "baz" return ()

The second variation evaluates each monadic action, and returns a list of all the results. Suppose we have our same function: func :: a -> Monad b

let mList = map func ["foo", "bar", "baz"] in sequence mList

…is equivalent to…

do foo <- func "foo" bar <- func "bar" baz <- func "baz" return [foo, bar, baz]

If you have a list of monads, you can use sequence or sequence_ to have them all evaluated. Use sequence if you care about the resulting values, use sequence_ if you only care about the monad’s side effect. Which do we want? Let’s look at the signature of insertConv:

*Main> :t insertConv insertConv :: (Temperature a, Temperature b) => Conversion a b -> TableBuilder ()

If we were to use sequence, we’d get a resulting value with the type TableBuilder [()]. Since nobody has any use for a list of empty tuples, we’ll be using sequence_.

So, what does our main look like?

main = do temps <- return (map (insertConv . (toConversion :: Fahrenheit -> (Fahrenheit, Celsius)) . Fahrenheit) [300.0, 280.0 .. 0.0]) putStrLn $ prettyPrint $ buildTable $ do sequence_ temps

This produces the following output:

------------------------------ || Fahrenheit || Celsius || ------------------------------ || 300.0 |> 148.9 || || 280.0 |> 137.8 || || 260.0 |> 126.7 || || 240.0 |> 115.6 || || 220.0 |> 104.4 || || 200.0 |> 93.3 || || 180.0 |> 82.2 || || 160.0 |> 71.1 || || 140.0 |> 60.0 || || 120.0 |> 48.9 || || 100.0 |> 37.8 || || 80.0 |> 26.7 || || 60.0 |> 15.6 || || 40.0 |> 4.4 || || 20.0 |> -6.7 || || 0.0 |> -17.8 || ------------------------------

A Trip To The Magical Land Of Monads

Monads can be a tricky topic. On the surface, they’re not hard at all. Taking Maybe for instance:

Prelude> Just 1 Just 1 Prelude> Nothing Nothing

That couldn’t possibly be easier. Something is either Just [something], or it is Nothing. However Monad world is the magical world of gotchas, unenforceable rules, and magical syntax. I had been planning on writing a post on Monads, much like I did with Applicatives and Functors. While researching this topic, I’ve determined that I’m not qualified to speak on this subject yet.

I am qualified to discuss the usage of Monads. I feel that say you are going to “learn how to do Monads” is similar to saying you are going to “learn how to do data structures”. Data structures are similar to each other in that they serve a common purpose: to contain data. However, a Vector is nothing like a Linked List, which is nothing like a Hash Map.

Similarly, a Maybe is nothing like an IO, which is nothing like a Writer. While they are all Monads, they serve different purposes. Today, I’d like to lay some groundwork on the topic and talk about binding functions.

On Magical Syntax

Much like Functor and Applicative, Monad brings functions that allow you to use normal values with monadic values. Monad brings the >>= operator. This operator is called the bind operator. (this is important for monads in general, but I won’t get into this today. Just know that the operator’s name is bind) The signature for >>= is:

(>>=) :: m a -> (a -> m b) -> m b

As you can see, it takes a Monad that contains an a, a function that takes an a and returns a Monad b, and returns a Monad b. Basically, it calls the function on the value contained in the monad, and does whatever additional action is appropriate for the monad, and returns a monad containing the result. In short: it is fmap for Monads. Let’s take a look at a quick example for Maybe:

Prelude> Just 1 >>= (\a -> Just $ a + 5) Just 6

As you can see, we bind Just 1 to a function that takes a value, adds it to 5, and wraps the result in a Just, which results in Just 6. Bind will correctly handle Nothing as well:

Prelude> Nothing >>= (\a -> Just $ a + 5) Nothing

Still with me? Good, because things are about to get magical.

Do Notation

Monads are so special, they have their own magical syntax! When working with monads, you may use do notation. What does do notation look like? Let’s take a look at a sample function:

justAdd :: (Num a) => a -> a -> Maybe a justAdd a b = Just $ a + b

This function takes 2 numbers, adds them, and wraps them up in a Maybe. Nothing earth shattering here. Let’s take a look at how to work with these using Bind:

justAddDemoBind = justAdd 2 2 >>= (justAdd 4) justAddDemoBindNothing = Nothing >>= (justAdd 4)

I’ve defined 2 simple functions here. The first calls justAdd 2 2, which returns Just 4. The function (justAdd 4) is then applied to it using the bind operator, which will return Just 8. The second attempts to apply the function (justAdd 4) to Nothing. Since the bind operator is smart enough to handle this, the final result of this function is Nothing. Simple, really. Now, let’s see do notation in action:

justAddDemoDo = do first <- justAdd 2 2 justAdd 4 first justAddDemoDoNothing = do first <- Nothing justAdd 4 first

Looks like a completely different language, right? In fact, these two functions do the exact same thing as the previous two. The purpose of do notation is to make working with Monads easier. In practice, if your logic is non-trivial, you end up with hugely nested statements. To see what’s going on, let’s break justAddDemoDo down:

justAddDemoDo = do

In the first line, we open our do block.

first <- justAdd 2 2

In the second line, we call justAdd 2 2, and assign the result to the name first. Notice that <- operator? That works basically the same as it does in list comprehensions, it does the assigning.

justAdd 4 first

Finally, we add 4 to first, resulting in Just 8, which is the value returned from the function. It seems like we’re treating our monad contained in first as a regular value. This is part of the magic of do notation. In fact, if this line were written as:justAdd first 4 it would have worked.

Another very important thing to note is that the last line of a do block must be an expression that returns a Monad! GHCI will throw a fit if it’s not.

The Pervasiveness Of Do

As you can see, under the hood, do notation just uses the >>= operator. You can also see that it is much simpler and cleaner looking than using the bind operator. However, that doesn’t mean that do is better. Like many things, it comes down to personal choice.

When reading literature on Haskell, you should be prepared to interpret do blocks, and usage of the bind operator. Like any tool, do and bind are not always the correct one. Picking the correct tool for the job is part of being a programmer. Hopefully this tutorial gave you enough familiarity to be able to use both.

The Point Of Applicative Functors

A few weeks ago, we talked about Functors. In case you’ve forgotten, you might want to click that link and read the post, but long story short: Functors allow you to call functions that take “bare” values on “dressed up” values.

Functors had one big weakness though: you can only call a function that takes one argument. I justified this using function currying, but there is another way. Today, we’ll be talking about that other way: Applicative functors.

Let’s Recap

Functors use a function called fmap to call a function on a functor. This function is called on the value within the functor, and a new functor containing the result is returned. This looks like this:

Prelude> fmap show $ Just 1 Just "1"

I fmap‘d show over Just 1, which returns Just "1". Let’s try another one:

Prelude> fmap (+ 1) $ Just 1 Just 2

Here, we’ve used function currying to fmap (+ 2) over Just 1, which returns Just 2. This is all fine and good in a contrived situation such as adding 1 to 1, but what about the real world? As you probably know, you often don’t have some nice constants ready to use in your function. There must be a better way…

Applicatives To The Rescue

To put it in layman’s terms, Applicative Functors are the middle ground. Let’s take a look at another example:

Prelude> let example = fmap (+) $ Just 1 Prelude> :t example example :: Maybe (Integer -> Integer)

If we fmap (+) over Just 1, the result is a function that takes an Integer and returns an Integer, wrapped in a Maybe. To do anything useful with this, you need to find a way to call this function on something else. That’s what Applicative Functors do for us. Let’s take a look at part of the class definition of Applicative:

class (Functor f) => Applicative f where pure :: a -> f a (<*>) :: f (a -> b) -> f a -> f b

We have two functions here: pure, which takes a value and wraps it in a minimal Functor, and (<*>), which takes a functor that contains a function, and calls it on another functor which contains a value, returning a value. In a nutshell, this allows us to call multi-argument functions with functors. There’s one more function exported by Control.Applicative that bears mentioning: (<$>). (<$>) is basically an alias for fmap that allows us to use fmap as infix:

Prelude> fmap (+1) $ Just 2 Just 3 Prelude> (+1) <$> Just 2 Just 3

As you can see, they function identically. Feel free to use (<$>), just know that it requires you to import Control.Applicative. Anyways, with that out of the way, here’s how you use an applicative. For this example, I will be adding 2 to 2. But 2 will be wrapped in a Maybe requiring extra steps:

Prelude> (+) <$> Just 2 <*> pure 2 Just 4

Let’s talk about what just happened. First, I fmap‘d (+) over Just 2 using the (<$>) operator. This returns a function that takes an Integer and returns an integer wrapped in a Maybe. Next I used the (<*>) operator to call that function on pure 2, which returns Just 4.

You may be wondering why I used pure 2 instead of Just 2. Sure, Just 2 would have worked, but pure 2 would have worked for any type of Applicative. Let’s take a look at another example:

Prelude> (+) <$> [1,2,99,100] <*> pure 2 [3,4,101,102]

As you can see, this is the exact same expression, except I used a List instead of a Maybe. This is where pure comes in handy. It allows you to write more generic code. pure works for any type of Applicative, therefore you needn’t concern yourself with what kind of Applicative is being passed into your function. You can rest easy knowing that it will work regardless.

Hopefully That All Made Sense

Applicatives are a pretty abstract concept, and it can be difficult to visualize how they can be useful. This is a topic that I don’t feel is documented very well. It’s a topic I had trouble with for a while.

Since I suspect that most readers will find this post with a google search term like “what is the point of applicative functors”, hopefully I’ve shed some light on the topic for you.

What Functors Can Do For You

One of the major aspects of Functional Programming, its lack of side effects, can be one of its major stumbling blocks. Sure, taking a value as a parameter and using it without creating a variable is a simple matter for basic types, but what of more complicated structures? How does one go about unwrapping a structure, operating on its value, and re-wrapping the value in said structure without losing it’s data? The answer is by using a convoluted set of functions that take way too many arguments just to preserve the state. Or, you could use a functor.

So I Overload Operator()?

No, we aren’t talking about C++ Classes with an operator() implementation to make it look like a function. Functors in Haskell are a completely different animal. The easiest way to explain it is to look at an example. By way of example, let’s take a look at the Maybe monad:

data Maybe a = Nothing | Just a deriving (Eq, Ord)

As you can see, Maybe has two type constructors: Nothing or Just something. Maybe is used to represent a calculation that may have failed. If the calculation failed, Nothing is returned. If it succeeded, Just whatever is returned. This is all fine and good, but Just whatever isn’t the same thing as whatever; it is encapsulated in a maybe, and must be extracted if it is to be used.

How do you do that? Well, first you have to account for a Nothing value. Assuming you have a Just, you have to extract it, probably using a new function. Then you have to use the value. Afterwards, you probably want to put it back into the Maybe. Sounds like a lot of work, right? Let’s take a look at what Functor has to offer us:

class Functor f where fmap :: (a -> b) -> f a -> f b

So, a functor defines 1 function (actually it defines more, but they have default implementations and can be ignored in 99% of cases) fmap, which takes a function that takes 1 argument of type a and returns a result of type b, a Functor a, and returns a Functor b. Basically, this amounts to fmap takes a function, calls it on the passed in functor, and returns a functor with the resulting value.

Maybe This Is Helpful?

Let’s take a look at how this works with Maybe. Here is Maybe‘s Functor implementation:

instance Functor Maybe where fmap _ Nothing = Nothing fmap f (Just a) = Just (f a)

Basically, if fmap Nothing is called, Nothing is returned. Otherwise, the passed in function is called on the value contained in the Just, and a new Just is returned with the result. This is a much more elegant way to deal with structures, there is no checking required, fmap knows how to do the right thing.

A Bit Limiting

You may be thinking to yourself “Isn’t this a bit limiting? I can only fmap using a function that takes 1 argument!” You’d be right, if not for Haskell’s partial function application. Say we want to add 2 to the Int contained in a Just. If we didn’t have partial function application, we would have to do something ugly like this:

fmap (\a -> a + 2) (Just 2)

… or maybe something truly awful like this:

addTwo :: Int -> Int addTwo a = a + 2

Luckily for us, we live in a just and righteous world where we can just partially apply +:

fmap (+2) (Just 2)

While this is a trivial example, I feel it adequately illustrates the point; you can fmap a function that takes any amount of arguments if you partially apply it!

This leads me to an important point that most literature leaves out: you must apply arguments from left to right, the order of arguments is important! When you’re writing functions, think about how users might want to partially apply them. Take these two functions for example:

padString1 :: String -> Int -> String padString1 s 0 = s padString1 s n = padString1 (s ++ "-") (n - 1) padString2 :: Int -> String -> String padString2 0 s = s padString2 n s = padString2 (n - 1) (s ++ "-")

Both of these functions do the same thing: they take a String and an Int, and append n -‘s to the string. The difference is how they can be partially applied: padString1 has the String first, so it can only be partially applied with the String. Conversely, padString2 has the Int first, so it can only be partially applied with the Int. For this reason, when creating functions, take time to consider these issues. There’s probably a whole blog post to be written about not messing this up. Maybe when I feel qualified, I’ll write it! For now, just keep it in mind.

Sounds Familiar

You’ve heard this word before, right? Map… But where?

instance Functor [] where fmap = map

That’s right, Lists are Functors!. The fmap implementation for List just calls the map function that you’re probably familiar with. (if you need a refresher, it takes a function, a list, and returns a list with the function called on all members of the original list)

In fact, most structures in Haskell are Functors. All Monads in the standard library are Functors, so it’s a good bet that you can fmap them. Either way, judicious use of fmap and Functors can make your code much cleaner and more manageable.

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