**1.**Introduction**1.1.**Well-Typed Programs Do Go Wrong**1.2.**Refinement Types**1.3.**Audience**1.4.**Getting Started**1.5.**Sample Code**2.**Logic & SMT**2.1.**Syntax**2.2.**Semantics**2.3.**Verification Conditions**2.4.**Examples: Propositions**2.5.**Examples: Arithmetic**2.6.**Examples: Uninterpreted Function**2.7.**Recap**3.**Refinement Types**3.1.**Defining Types**3.2.**Errors**3.3.**Subtyping**3.4.**Writing Specifications**3.5.**Refining Function Types: Pre-conditions**3.6.**Refining Function Types: Post-conditions**3.7.**Testing Values: Booleans and Propositions**3.8.**Putting It All Together**3.9.**Recap**4.**Polymorphism**4.1.**Specification: Vector Bounds**4.2.**Verification: Vector Lookup**4.3.**Inference: Our First Recursive Function**4.4.**Higher-Order Functions: Bottling Recursion in a`loop`

**4.5.**Refinements and Polymorphism**4.6.**Recap**5.**Refined Datatypes**6.**Boolean Measures**7.**Numeric Measures**7.1.**Wholemeal Programming**7.2.**Specifying List Dimensions**7.3.**Lists: Size Preserving API**7.4.**Lists: Size Reducing API**7.5.**Dimension Safe Vector API**7.6.**Dimension Safe Matrix API**7.7.**Recap**8.**Elemental Measures**8.1.**Talking about Sets**8.2.**Proving QuickCheck Style Properties**8.3.**Content-Aware List API**8.4.**Permutations**8.5.**Uniqueness**8.6.**Unique Zippers**8.7.**Recap**9.**Case Study: Okasaki's Lazy Queues**10.**Case Study: Associative Maps**10.1.**Specifying Maps**10.2.**Using Maps: Well Scoped Expressions**10.3.**Implementing Maps: Binary Search Trees**10.4.**Recap**11.**Case Study: Pointers & Bytes**11.1.**HeartBleeds in Haskell**11.2.**Low-level Pointer API**11.3.**A Refined Pointer API**11.4.**Assumptions vs Guarantees**11.5.**ByteString API**11.6.**Application API**11.7.**Recap: Types Against Overflows**12.**Case Study: AVL Trees

{-@ LIQUID "--short-names" @-}
{-@ LIQUID "--no-termination" @-}
module NumericMeasures where
import Prelude hiding (map, zipWith, zip, take, drop, reverse)
{-@ type TRUE = {v:Bool | v} @-}
{-@ die :: {v:_ | false} -> a @-}
die msg = error msg
take, drop, take' :: Int -> [a] -> [a]
txgo :: Int -> Int -> Vector (Vector a) -> Vector (Vector a)
quickSort :: (Ord a) => [a] -> [a]
size :: [a] -> Int
flatten :: Int -> Int -> Vector (Vector a) -> Vector a
{-@ invariant {v:[a] | 0 <= size v} @-}

Many of the programs we have seen so far, for example those in here, suffer from *indexitis*. This is a term coined by Richard Bird which describes a tendency to perform low-level manipulations to iterate over the indices into a collection, opening the door to various off-by-one errors. Such errors can be eliminated by instead programming at a higher level, using a wholemeal approach where the emphasis is on using aggregate operations, like `map`

, `fold`

and `reduce`

.

**Wholemeal programming is no panacea** as it still requires us to take care when operating on *different* collections; if these collections are *incompatible*, e.g. have the wrong dimensions, then we end up with a fate worse than a crash, a possibly meaningless result. Fortunately, LiquidHaskell can help. Lets see how we can use measures to specify dimensions and create a dimension-aware API for lists which can be used to implement wholemeal dimension-safe APIs.^{1}

Indexitis begone! As an example of wholemeal programming, lets write a small library that represents vectors as lists and matrices as nested vectors:

data Vector a = V { vDim :: Int
, vElts :: [a]
}
deriving (Eq)
data Matrix a = M { mRow :: Int
, mCol :: Int
, mElts :: Vector (Vector a)
}
deriving (Eq)

**The Dot Product** of two `Vector`

s can be easily computed using a fold:

dotProd :: (Num a) => Vector a -> Vector a -> a
dotProd vx vy = sum (prod xs ys)
where
prod = zipWith (\x y -> x * y)
xs = vElts vx
ys = vElts vy

**Matrix Multiplication** can similarly be expressed in a high-level, wholemeal fashion, by eschewing low level index manipulations in favor of a high-level *iterator* over the `Matrix`

elements:

matProd :: (Num a) => Matrix a -> Matrix a -> Matrix a
matProd (M rx _ xs) (M _ cy ys)
= M rx cy elts
where
elts = for xs $ \xi ->
for ys $ \yj ->
dotProd xi yj

**The Iteration** embodied by the `for`

combinator, is simply a `map`

over the elements of the vector.

```
for :: Vector a -> (a -> b) -> Vector b
for (V n xs) f = V n (map f xs)
```

**Wholemeal programming frees** us from having to fret about low-level index range manipulation, but is hardly a panacea. Instead, we must now think carefully about the *compatibility* of the various aggreates. For example,

`dotProd`

is only sensible on vectors of the same dimension; if one vector is shorter than another (i.e. has fewer elements) then we will won't get a run-time crash but instead will get some gibberish result that will be dreadfully hard to debug.`matProd`

is only well defined on matrices of compatible dimensions; the number of columns of`mx`

must equal the number of rows of`my`

. Otherwise, again, rather than an error, we will get the wrong output.^{2}

In order to start reasoning about dimensions, we need a way to represent the *dimension* of a list inside the refinement logic. ^{3}

**Measures** are ideal for this task. Previously we saw how we could lift Haskell functions up to the refinement logic. Lets write a measure to describe the length of a list: ^{4}

{-@ measure size @-}
{-@ size :: xs:[a] -> {v:Nat | v = size xs} @-}
size [] = 0
size (_:rs) = 1 + size rs

**Measures Refine Constructors** As with refined data definitions, the measures are translated into strengthened types for the type's constructors. For example, the `size`

measure is translated into:

```
data [a] where
[] :: {v: [a] | size v = 0}
(:) :: a -> xs:[a] -> {v:[a]|size v = 1 + size xs}
```

**Multiple Measures** may be defined for the same data type. For example, in addition to the `size`

measure, we can define a `notEmpty`

measure for the list type:

{-@ measure notEmpty @-}
notEmpty :: [a] -> Bool
notEmpty [] = False
notEmpty (_:_) = True

**We Compose Different Measures** simply by *conjoining* the refinements in the strengthened constructors. For example, the two measures for lists end up yielding the constructors:

```
data [a] where
[] :: {v: [a] | not (notEmpty v) && size v = 0}
(:) :: a
-> xs:[a]
-> {v:[a]| notEmpty v && size v = 1 + size xs}
```

This is a very significant advantage of using measures instead of indices as in DML or Agda, as *decouples property from structure*, which crucially enables the use of the same structure for many different purposes. That is, we need not know *a priori* what indices to bake into the structure, but can define a generic structure and refine it *a posteriori* as needed with new measures.

We are almost ready to begin creating a dimension aware API for lists; one last thing that is useful is a couple of aliases for describing lists of a given dimension.

**To make signatures symmetric** lets define an alias for plain old (unrefined) lists:

type List a = [a]

**A ListN** is a list with exactly `N`

elements, and a `ListX`

is a list whose size is the same as another list `X`

. Note that when defining refinement type aliases, we use uppercase variables like `N`

and `X`

to distinguish *value* parameters from the lowercase *type* parameters like `a`

.

{-@ type ListN a N = {v:List a | size v = N} @-}
{-@ type ListX a X = ListN a {size X} @-}

With the types and aliases firmly in our pockets, let us write dimension-aware variants of the usual list functions. The implementations are the same as in the standard library i.e. `Data.List`

, but the specifications are enriched with dimension information.

**Exercise: (Map): **

**map** yields a list with the same size as the input. Fix the specification of `map`

so that the `prop_map`

is verified.

{-@ map :: (a -> b) -> xs:List a -> List b @-}
map _ [] = []
map f (x:xs) = f x : map f xs
{-@ prop_map :: List a -> TRUE @-}
prop_map xs = size ys == size xs
where
ys = map id xs

**Exercise: (Reverse): **We can `reverse`

the elements of a list as shown below, using the tail recursive function `go`

. Fix the signature for `go`

so that LiquidHaskell can prove the specification for `reverse`

.

**Hint: **How big is the list returned by `go`

?

{-@ reverse :: xs:List a -> ListX a xs @-}
reverse xs = go [] xs
where
go acc [] = acc
go acc (x:xs) = go (x:acc) xs

**zipWith** requires both lists to have the *same* size, and produces a list with that same size. ^{5}

{-@ zipWith :: (a -> b -> c) -> xs:List a
-> ListX b xs
-> ListX c xs
@-}
zipWith f (a:as) (b:bs) = f a b : zipWith f as bs
zipWith _ [] [] = []
zipWith _ _ _ = die "no other cases"

**unsafeZip** The signature for `zipWith`

is quite severe -- it rules out the case where the zipping occurs only upto the shorter input. Here's a function that actually allows for that case, where the output type is the *shorter* of the two inputs:

{-@ zip :: as:[a] -> bs:[b] -> {v:[(a,b)] | Tinier v as bs} @-}
zip (a:as) (b:bs) = (a, b) : zip as bs
zip [] _ = []
zip _ [] = []

The output type uses the predicate `Tinier Xs Ys Zs`

which defines the length of `Xs`

to be the smaller of that of `Ys`

and `Zs`

.^{6}

{-@ predicate Tinier X Y Z = Min (size X) (size Y) (size Z) @-}
{-@ predicate Min X Y Z = (if Y < Z then X = Y else X = Z) @-}

**Exercise: (Zip Unless Empty): **In my experience, `zip`

as shown above is far too permissive and lets all sorts of bugs into my code. As middle ground, consider `zipOrNull`

below. Write a specification for `zipOrNull`

such that the code below is verified by LiquidHaskell.

zipOrNull :: [a] -> [b] -> [(a, b)]
zipOrNull [] _ = []
zipOrNull _ [] = []
zipOrNull xs ys = zipWith (,) xs ys
{-@ test1 :: {v: _ | size v = 2} @-}
test1 = zipOrNull [0, 1] [True, False]
{-@ test2 :: {v: _ | size v = 0} @-}
test2 = zipOrNull [] [True, False]
{-@ test3 :: {v: _ | size v = 0} @-}
test3 = zipOrNull ["cat", "dog"] []

**Hint: **Yes, the type is rather gross; it uses a bunch of disjunctions `||`

, conjunctions `&&`

and implications `=>`

.

Next, lets look at some functions that truncate lists, in one way or another.

**Take** lets us grab the first `k`

elements from a list:

{-@ take' :: n:Nat -> ListGE a n -> ListN a n @-}
take' 0 _ = []
take' n (x:xs) = x : take' (n-1) xs
take' _ _ = die "won't happen"

The alias `ListGE a n`

denotes lists whose length is at least `n`

:

{-@ type ListGE a N = {v:List a | N <= size v} @-}

**Exercise: (Drop): **`Drop`

is the yang to `take`

's yin: it returns the remainder after extracting the first `k`

elements. Write a suitable specification for it so that the below typechecks.

drop 0 xs = xs
drop n (_:xs) = drop (n-1) xs
drop _ _ = die "won't happen"
{-@ test4 :: ListN String 2 @-}
test4 = drop 1 ["cat", "dog", "mouse"]

**Exercise: (Take it easy): **The version `take'`

above is too restrictive; it insists that the list actually have at least `n`

elements. Modify the signature for the *real* `take`

function so that the code below is accepted by LiquidHaskell.

take 0 _ = []
take _ [] = []
take n (x:xs) = x : take (n-1) xs
{-@ test5 :: [ListN String 2] @-}
test5 = [ take 2 ["cat", "dog", "mouse"]
, take 20 ["cow", "goat"] ]

**The Partition** function breaks a list into two sub-lists of elements that either satisfy or fail a user supplied predicate.

partition :: (a -> Bool) -> [a] -> ([a], [a])
partition _ [] = ([], [])
partition f (x:xs)
| f x = (x:ys, zs)
| otherwise = (ys, x:zs)
where
(ys, zs) = partition f xs

We would like to specify that the *sum* of the output tuple's dimensions equal the input list's dimension. Lets write measures to access the elements of the output:

```
{-@ measure fst @-}
fst (x, _) = x
{-@ measure snd @-}
snd (_, y) = y
```

We can now refine the type of `partition`

as:

{-@ partition :: _ -> xs:_ -> {v:_ | Sum2 v (size xs)} @-}

where `Sum2 V N`

holds for a pair of lists dimensions add to `N`

:

{-@ predicate Sum2 X N = size (fst X) + size (snd X) = N @-}

**Exercise: (QuickSort): **Use `partition`

to implement `quickSort`

.

-- >> quickSort [1,4,3,2]
-- [1,2,3,4]
{-@ quickSort :: (Ord a) => xs:List a -> ListX a xs @-}
quickSort [] = []
quickSort (x:xs) = undefined
{-@ test10 :: ListN String 2 @-}
test10 = quickSort test4

We can use the dimension aware lists to create a safe vector API.

**Legal Vectors** are those whose `vDim`

field actually equals the size of the underlying list:

{-@ data Vector a = V { vDim :: Nat
, vElts :: ListN a vDim } @-}

When `vDim`

is used a selector function, it returns the `vDim`

field of `x`

.

{-@ vDim :: x:_ -> {v: Nat | v = vDim x} @-}

The refined data type prevents the creation of illegal vectors:

okVec = V 2 [10, 20] -- accepted by LH
badVec = V 2 [10, 20, 30] -- rejected by LH

As usual, it will be handy to have a few aliases.

-- | Non Empty Vectors
{-@ type VectorNE a = {v:Vector a | vDim v > 0} @-}
-- | Vectors of size N
{-@ type VectorN a N = {v:Vector a | vDim v = N} @-}
-- | Vectors of Size Equal to Another Vector X
{-@ type VectorX a X = VectorN a {vDim X} @-}

**To Create** a `Vector`

safely, we can start with the empty vector `vEmp`

and then add elements one-by-one with `vCons`

:

{-@ vEmp :: VectorN a 0 @-}
vEmp = V 0 []
{-@ vCons :: a -> x:Vector a -> VectorN a {vDim x + 1} @-}
vCons x (V n xs) = V (n+1) (x:xs)

**To Access** vectors at a low-level, we can use equivalents of *head* and *tail*, which only work on non-empty `Vector`

s:

{-@ vHd :: VectorNE a -> a @-}
vHd (V _ (x:_)) = x
vHd _ = die "nope"
{-@ vTl :: x:VectorNE a -> VectorN a {vDim x - 1} @-}
vTl (V n (_:xs)) = V (n-1) xs
vTl _ = die "nope"

**To Iterate** over a vector we can use the `for`

combinator:

{-@ for :: x:Vector a -> (a -> b) -> VectorX b x @-}
for (V n xs) f = V n (map f xs)

**Binary Pointwise Operations** should only be applied to *compatible* vectors, i.e. vectors with equal dimensions. We can write a generic binary pointwise operator:

{-@ vBin :: (a -> b -> c) -> x:Vector a
-> VectorX b x
-> VectorX c x
@-}
vBin op (V n xs) (V _ ys) = V n (zipWith op xs ys)

**The Dot Product** of two `Vector`

s can be now implemented in a wholemeal *and* dimension safe manner, as:

{-@ dotProduct :: (Num a) => x:Vector a -> VectorX a x -> a @-}
dotProduct x y = sum $ vElts $ vBin (*) x y

**Exercise: (Vector Constructor): **Complete the *specification* and *implementation* of `vecFromList`

which *creates* a `Vector`

from a plain list.

vecFromList :: [a] -> Vector a
vecFromList xs = undefined
test6 = dotProduct vx vy -- should be accepted by LH
where
vx = vecFromList [1,2,3]
vy = vecFromList [4,5,6]

**Exercise: (Flatten): **Write a function to `flatten`

a nested `Vector`

.

{-@ flatten :: n:Nat
-> m:Nat
-> VectorN (VectorN a m) n
-> VectorN a {m * n}
@-}
flatten = undefined

**The Cross Product** of two vectors can now be computed in a nice wholemeal style, by a nested iteration followed by a `flatten`

.

{-@ product :: xs:Vector _
-> ys:Vector _
-> VectorN _ {vDim xs * vDim ys}
@-}
product xs ys = flatten (vDim ys) (vDim xs) xys
where
xys = for ys $ \y ->
for xs $ \x ->
x * y

The same methods let us create a dimension safe Matrix API which ensures that only legal matrices are created and that operations are performed on compatible matrices.

**Legal Matrices** are those where the dimension of the outer vector equals the number of rows `mRow`

and the dimension of each inner vector is `mCol`

. We can specify legality in a refined data definition:

{-@ data Matrix a =
M { mRow :: Pos
, mCol :: Pos
, mElts :: VectorN (VectorN a mCol) mRow
}
@-}

Notice that we avoid disallow degenerate matrices by requiring the dimensions to be positive.

{-@ type Pos = {v:Int | 0 < v} @-}

It is convenient to have an alias for matrices of a given size:

{-@ type MatrixN a R C = {v:Matrix a | Dims v R C } @-}
{-@ predicate Dims M R C = mRow M = R && mCol M = C @-}

For example, we can use the above to write type:

{-@ ok23 :: MatrixN _ 2 3 @-}
ok23 = M 2 3 (V 2 [ V 3 [1, 2, 3]
, V 3 [4, 5, 6] ])

**Exercise: (Legal Matrix): **Modify the definitions of `bad1`

and `bad2`

so that they are legal matrices accepted by LiquidHaskell.

bad1 :: Matrix Int
bad1 = M 2 3 (V 2 [ V 3 [1, 2 ]
, V 3 [4, 5, 6]])
bad2 :: Matrix Int
bad2 = M 2 3 (V 2 [ V 2 [1, 2]
, V 2 [4, 5] ])

**Exercise: (Matrix Constructor): **Write a function to construct a `Matrix`

from a nested list.

matFromList :: [[a]] -> Maybe (Matrix a)
matFromList [] = Nothing
matFromList xss@(xs:_)
| ok = Just (M r c vs)
| otherwise = Nothing
where
r = size xss
c = size xs
ok = undefined
vs = undefined

**Exercise: (Refined Matrix Constructor): **Refine the specification for `matFromList`

so that the following is accepted by LiquidHaskell.

{-@ mat23 :: Maybe (MatrixN Integer 2 2) @-}
mat23 = matFromList [ [1, 2]
, [3, 4] ]

**Hint: **It is easy to specify the number of rows from `xss`

. How will you figure out the number of columns? A measure may be useful.

**Matrix Multiplication** Finally, lets implement matrix multiplication. You'd think we did it already, but in fact the implementation at the top of this chapter is all wrong (run it and see!) We cannot just multiply any two matrices: the number of *columns* of the first must equal to the *rows* of the second -- after which point the result comprises the `dotProduct`

of the rows of the first matrix with the columns of the second.

{-@ matProduct :: (Num a) => x:Matrix a
-> y:{Matrix a | mCol x = mRow y}
-> MatrixN a (mRow x) (mCol y)
@-}
matProduct (M rx _ xs) my@(M _ cy _)
= M rx cy elts
where
elts = for xs $ \xi ->
for ys' $ \yj ->
dotProduct xi yj
M _ _ ys' = transpose my

To iterate over the *columns* of the matrix `my`

we just `transpose`

it so the columns become rows.

-- >>> ok32 == transpose ok23
-- True
ok32 = M 3 2 (V 3 [ V 2 [1, 4]
, V 2 [2, 5]
, V 2 [3, 6] ])

**Exercise: (Matrix Transpose): **Use the `Vector`

API to complete the implementation of `txgo`

. For inspiration, you might look at the implementation of `Data.List.transpose`

from the prelude. Better still, don't.

{-@ transpose :: m:Matrix a -> MatrixN a (mCol m) (mRow m) @-}
transpose (M r c rows) = M c r $ txgo c r rows
{-@ txgo :: c:Nat -> r:Nat
-> VectorN (VectorN a c) r
-> VectorN (VectorN a r) c
@-}
txgo c r rows = undefined

**Hint: **As shown by `ok23`

and `ok32`

, `transpose`

works by stripping out the `head`

s of the input rows, to create the corresponding output rows.

In this chapter, we saw how to use measures to describe numeric properties of structures like lists (`Vector`

) and nested lists (`Matrix`

).

Measures are

*structurally recursive*functions, with a single equation per data constructor,Measures can be used to create refined data definitions that prevent the creation of illegal values,

Measures can then be used to enable safe wholemeal programming, via dimension-aware APIs that ensure that operators only apply to compatible values.

We can use numeric measures to encode various other properties of data structures. We will see examples ranging from high-level AVL trees, to low-level safe pointer arithmetic.

In a later chapter we will use this API to implement K-means clustering.↩

In fact, while the implementation of

`matProd`

breezes past GHC it is quite wrong!↩We could just use

`vDim`

, but that is a cheat as there is no guarantee that the field's value actually equals the size of the list!↩Recall that these must be inductively defined functions, with a single equation per data-constructor↩

As made explicit by the call to

`die`

, the input type*rules out*the case where one list is empty and the other is not, as in that case the former's length is zero while the latter's is not, and hence, different.↩In logic,

`if p then q else r`

is the same as`p => q && not p => r`

.↩