Functions can be accepted as parameters and can also be produced as return types. Indeed, functions can be created inside the body of other functions. These inner functions are known as closures.
For instance, we can implement an equivalent of the standard library's
foreach function by taking a function
f as the first parameter.
function myforeach(f, xs) for x in xs f(x) end end
We can test that this function indeed works as we expect:
julia> myforeach(println, ["a", "b", "c"]) a b c
By taking a function as the first parameter, instead of a later parameter, we can use Julia's do block syntax. The do block syntax is just a convenient way to pass an anonymous function as the first argument to a function.
julia> myforeach([1, 2, 3]) do x println(x^x) end 1 4 27
Our implementation of
myforeach above is roughly equivalent to the built-in
foreach function. Many other built-in higher order functions also exist.
Higher-order functions are quite powerful. Sometimes, when working with higher-order functions, the exact operations being performed become unimportant and programs can become quite abstract. Combinators are examples of systems of highly abstract higher-order functions.
Two of the most fundamental higher-order functions included in the standard library are
filter. These functions are generic and can operate on any iterable. In particular, they are well-suited for computations on arrays.
Suppose we have a dataset of schools. Each school teaches a particular subject, has a number of classes, and an average number of students per class. We can model a school with the following immutable type:
immutable School subject::Symbol nclasses::Int nstudents::Int # average no. of students per class end
Our dataset of schools will be a
dataset = [School(:math, 3, 30), School(:math, 5, 20), School(:science, 10, 5)]
Suppose we wish to find the number of students in total enrolled in a math program. To do this, we require several steps:
A naïve (not most performant) solution would simply be to use those three higher-order functions directly.
function nmath(data) maths = filter(x -> x.subject === :math, data) students = map(x -> x.nclasses * x.nstudents, maths) reduce(+, 0, students) end
and we verify there are 190 math students in our dataset:
julia> nmath(dataset) 190
Functions exist to combine these functions and thus improve performance. For instance, we could have used the
mapreduce function to perform the mapping and reduction in one step, which would save time and memory.
reduce is only meaningful for associative operations like
+, but occasionally it is useful to perform a reduction with a non-associative operation. The higher-order functions
foldr are provided to force a particular reduction order.