**Disclaimer:** I am not a *mixologist*. This is not professional cocktail advice!

Sam Horvath-Hunt blogged about modelling cocktails as monoids. This is a really cool example of FP modelling that I want to expand on. (Lennart Kolmodin once wrote that the dance steps of Tango form a monoid.)

First, I will demonstrate my ignorance by assuming that cocktail recipes are free commutative monoids over ingredients:

- The order in which you add ingredients does not matter,
- If you add two cocktails together, you get another cocktail,
- The identity cocktail is the empty cocktail with no ingredients in it.

Second, I want to up the ante with a recursive cocktail recipe.

It comes from a sketch in the computer science student revue at DIKU (University of Copenhagen): Superdrinks (2002); credits go to Uffe Christensen, Uffe Friis Lichtenberg, Jonas Ussing, Niels H. Christensen, Torben Æ. Mogensen, Jørgen Elgaard Larsen who either co-wrote or enacted the sketch.

A superdrinks consists of:

- 1 part gin,
- 2 parts lemon,
- 3 parts superdrinks.

Now, according to the sketch there are plenty of bad ways to materialize this drink; one such is through approximation: take 1 part gin, 2 parts lemon and 3 parts of whatever is your current best approximation of superdrinks. Iterate this process enough times and you will have a gradually finer superdrinks.

Recursively,

```
superdrinks(n) = 1 × gin
+ 2 × lemon
+ 3 × superdrinks (n-1)
```

As for `superdrinks(0)`

, it could be water. It could be gin!

Experimenting a little,

```
superdrinks(1) = 1 × gin + 2 × lemon + 3 × superdrinks(0)
superdrinks(2) = 1 × gin + 2 × lemon + 3 × superdrinks(1)
= 1 × gin
+ 2 × lemon
+ 3 × (1 × gin + 2 × lemon + 3 × superdrinks(0))
= 4 × gin + 8 × lemon + 9 × superdrinks(0)
```

The relationship between the number of parts of each ingredient can be expressed in closed form eliminating recursion:

```
superdrinks(n) = (3ⁿ - 1)/2 × gin
+ (3ⁿ - 1) × lemon
+ 3ⁿ × superdrinks(0)
```

(You can find the closed form either by recognizing that the series *3 × 3 × ...* with *n* occurrences is *3ⁿ*, that there's always one less part lemon than *superdrinks(0)*, and that there's always half the amount of gin of that; or you can solve their recurrence relation; or you can expand the three number series using a function,

```
> let superdrinks (gin, lemon, super) = (1 + 3*gin, 2 + 3*lemon, 3*super)
> unzip3 $ take 6 $ iterate superdrinks (0,0,1)
([0,1,4,13,40,121],[0,2,8,26,80,242],[1,3,9,27,81,243])
```

It is time to get schwifty.

The following ingredients are enough to make gin-tonic and superdrinks:

```
data Ingredient = Gin | Tonic | Lemon
deriving (Eq, Ord, Show)
```

A cocktail is any set of ingredients and their multiplicity:

```
newtype Cocktail = Cocktail (Map Ingredient Natural)
deriving (Eq, Ord, Show)
emptyCocktail :: Cocktail
emptyCocktail = Cocktail Map.empty
```

For convenience,

```
parts :: Natural -> Ingredient -> Cocktail
parts n ingredient =
Cocktail (Map.singleton ingredient n)
combine :: Cocktail -> Cocktail -> Cocktail
combine (Cocktail c1) (Cocktail c2) =
Cocktail (Map.unionWith (+) c1 c2)
```

One consequence of this modelling is:

```
> let gintonic = combine (1 `parts` Gin) (2 `parts` Tonic)
> gintonic == combine gintonic gintonic
False
```

Since these are cocktail *recipes*, I'd like to normalize the quantities of each ingredient so that recipes don't eventually say "2 parts gin, 4 parts tonic" or "0 parts gin":

```
normalize :: Cocktail -> Cocktail
normalize (Cocktail ingredients) =
Cocktail . normalize' $ ingredients
where
scale = foldr1 gcd (Map.elems ingredients)
normalize' = Map.map (`div` scale) . Map.filter (/= 0)
```

*(Note that while foldr1 is partial, because of Haskell's non-strict semantics, it is never evaluated when ingredients is empty because it is used within Map.map zero times.)*

Demonstrating `normalize`

:

```
> normalize emptyCocktail
Cocktail (fromList [])
> normalize (0 `parts` Gin)
Cocktail (fromList [])
> normalize $ combine (2 `parts` Gin) (4 `parts` Tonic)
Cocktail (fromList [(Gin,1),(Tonic,2)])
```

It would be tempting to specialize the `Eq Cocktail`

instance to use `normalize`

so that `c == combine c c`

for all `c`

. But I don't like to do that because if you ever need to compare for structural equality, you can't, whereas equality under normalization can be achieved with:

```
> let (=~) = (==) `on` normalize
> (1 `parts` Gin) =~ (2 `parts` Gin)
True
```

It would also be tempting to add normalization to `combine`

so that the combination of two cocktails is a normalized cocktail. But since this blog post is about monoidal cocktails and `combine`

is the best candidate for a composition operator, such choice actually breaks the law of associativity:

```
> let norm_combine c1 c2 = normalize (combine c1 c2)
> gin1 `norm_combine` (gin1 `norm_combine` tonic1)
Cocktail (fromList [(Gin,2),(Tonic,1)])
> (gin1 `norm_combine` gin1) `norm_combine` tonic1
Cocktail (fromList [(Gin,1),(Tonic,1)])
```

So while I like the notion of normalizing cocktail recipes, making it a part of the `Semigroup Cocktail`

instance would probably be a bad idea, leaving the much simpler instances:

```
instance Semigroup Cocktail where
(<>) = combine
instance Monoid Cocktail where
mempty = emptyCocktail
```

As for superdrinks, the recipe can now be expressed as:

```
superdrinks :: Natural -> Cocktail -> Cocktail
superdrinks n base = mconcat
[ ((3^n - 1) `div` 2) `parts` Gin
, (3^n - 1) `parts` Lemon
, (3^n) `rounds` base
]
rounds :: Natural -> Cocktail -> Cocktail
rounds n = mconcat . genericReplicate n
```

Whether the *best* approximation is using a base of `mempty`

or, as the revue sketch suggests, `n `parts` Gin`

, is highly subjective. For a 5th approximation of superdrinks using pure gin as 0th approximation,

```
> normalize $ superdrinks 5 (1 `parts` Gin)
Cocktail (fromList [(Gin,182),(Lemon,121)])
```

Cheers!

## Discussion

Great post! We liked it so much we recorded a podcast about it :) haskellweekly.news/episode/26.html

Ha, I just realized.

Thanks for making new podcast episodes again. :)