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Recursive Functions on Lists

Computing with lists

  • There are two approaches to working with lists:

    • Write functions to do what you want, using recursive definitions that traverse the list structure.
    • Write combinations of the standard list processing functions.
  • The second approach is preferred, but the standard list processing functions do need to be defined, and those definitions use the first approach (recursive definitions).
  • We’ll cover both methods.

Recursion on lists

  • A list is built from the empty list and the function . In Haskell, the function is actually written as the operator , in other words : is pronounced as cons.
  • Every list must be either
    • or
    • for some (the head of the list) and (the tail)

where is pronounced as

  • The recursive definition follows the structure of the data:
    • Base case of the recursion is .
    • Recursion (or induction) case is .

Some examples of recursion on lists

Recursive definition of length

The length of a list can be computed recursively as follows:

length :: [a] -> Int
length [] = 0                  # the base case
length (x:xs) = 1 + length xs  # recursion case

Recursive definition of filter

  • filter is given a predicate (a function that gives a Boolean result) and a list, and returns a list of the elements that satisfy the predicate.
filter :: (a->Bool) -> [a] -> [a]

Filtering is useful for the “generate and test” programming paradigm.

filter (<5) [3,9,2,12,6,4] -- > [3,2,4]

The library definition for filter is shown below. This relies on guards, which we will investigate properly next week.

filter :: (a -> Bool) -> [a] -> [a]
filter pred []    = []
filter pred (x:xs)
  | pred x         = x : filter pred xs
  | otherwise      = filter pred xs

Computations over lists

  • Many computatations that would be for/while loops in an imperative language are naturally expressed as list computations in a functional language.
  • There are some common cases:

    • Perform a computation on each element of a list:
    • Iterate over a list, from left to right:
    • Iterate over a list, from right to left:
  • It’s good practice to use these three functions when applicable
  • And there are some related functions that we’ll see later

Function composition

  • We can express a large computation by “chaining together” a sequence of functions that perform smaller computations
  1. Start with an argument of type
  2. Apply a function to it, getting an intermediate result of type
  3. Then apply a function to the intermediate result, getting the final result of type
  • The entire computation (first , then ) is written as .
  • This is traditional mathematical notation; just remember that in , the functions are used in right to left order.
  • Haskell uses . as the function composition operator

    (.) :: (b->c) -> (a->b) -> a -> c
    (f . g) x = f (g x)

Performing an operation on every element of a list: map

  • map applies a function to every element of a list

    map f [x0,x1,x2] -- > [f x0, f x1, f x2]

Composition of maps

  • map is one of the most commonly used tools in your functional toolkit
  • A common style is to define a set of simple computations using map, and to compose them.

    map f (map g xs) = map (f . g) xs

This theorem is frequently used, in both directions.

Recursive definition of map

map :: (a -> b) -> [a] -> [b]
map _ []     = []
map f (x:xs) = f x : map f xs

Folding a list (reduction)

  • An iteration over a list to produce a singleton value is called a fold
  • There are several variations: folding from the left, folding from the right, several variations having to do with “initialisation”, and some more advanced variations.
  • Folds may look tricky at first, but they are extremely powerful, and they are used a lot! And they aren’t actually very complicated.

Left fold: foldl

  • foldl is fold from the left
  • Think of it as an iteration across a list, going left to right.
  • A typical application is
  • The is an initial value
  • The argument is a list of values which we combine systematically using the supplied function
  • A useful intuition: think of the argument as an “accumulator”.
  • The function takes the current value of the accumulator and a list element, and gives the new value of the accumulator.

    foldl :: (b->a->b) -> b -> [a] -> b

Examples of foldl with function notation

Examples of foldl with infix notation

In this example, + denotes an arbitrary operator for f; it isn’t supposed to mean specifically addition.

foldl (+) z []          -- > z
foldl (+) z [x0]        -- > z + x0
foldl (+) z [x0,x1]     -- > (z + x0) + x1
foldl (+) z [x0,x1,x2]  -- > ((z + x0) + x1) + x2

Recursive definition of foldl

foldl        :: (b -> a -> b) -> b -> [a] -> b
foldl f z0 xs0 = lgo z0 xs0
                lgo z []     =  z
                lgo z (x:xs) = lgo (f z x) xs

Right fold: foldr

  • Similar to , but it works from right to left
foldr :: (a -> b -> b) -> b -> [a] -> b

Examples of foldr with function notation

Examples of foldr with operator notation

foldr (+) z []          -- > z
foldr (+) z [x0]        -- > x0 + z
foldr (+) z [x0,x1]     -- > x0 + (x1 + z)
foldr (+) z [x0,x1,x2]  -- > x0 + (x1 + (x2 + z))

Recursive definition of foldr

foldr            :: (a -> b -> b) -> b -> [a] -> b
foldr k z = go
            go []     = z
            go (y:ys) = y `k` go ys

Relationship between foldr and list structure

We have seen that a list [x0,x1,x2] can also be written as

    x0 :  x1 : x2 : []

Folding (:) over a list using the empty list [] as accumulator gives:

foldr (:)  [] [x0,x1,x2]
  -- >
  x0 :  x1 : x2 : []

This is identical to constructing the list using (:) and [] ! We can formalise this relationship as follows:

Some applications of folds

sum xs = foldr (+) 0 xs
product xs = foldr (*) 1 xs

We can actually “factor out” the that appears at the right of each side of the equation, and write:

sum      = foldr (+) 0
product  = foldr (*) 1

(This is sometimes called “point free” style because you’re programming solely with the functions; the data isn’t mentioned directly.)

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