Functional programming is becoming increasingly widespread in industry. This trend is driven by the adoption of Scala as the main programming language for many applications. Scala fuses functional and object-oriented programming in a practical package. It interoperates seamlessly with both Java and Javascript. Scala is the implementation language of many important frameworks, including Apache Spark, Kafka, and Akka. It provides the core infrastructure for sites such as Twitter, Tumblr and also Coursera. In this course you will discover the elements of the functional programming style and learn how to apply them usefully in your daily programming tasks. You will also develop a solid foundation for reasoning about functional programs, by touching upon proofs of invariants and the tracing of execution symbolically. The course is hands on; most units introduce short programs that serve as illustrations of important concepts and invite you to play with them, modifying and improving them. The course is complemented by a series programming projects as homework assignments. Learning Outcomes. By the end of this course you will be able to: - understand the principles of functional programming, - write purely functional programs, using recursion, pattern matching, and higher-order functions, - combine functional programming with objects and classes, - design immutable data structures, - reason about properties of functions, - understand generic types for functional programs Recommended background: You should have at least one year programming experience. Proficiency with Java or C# is ideal, but experience with other languages such as C/C++, Python, Javascript or Ruby is also sufficient. You should have some familiarity using the command line.
Lecture 5.4 - Higher-Order List Functions
Loading...
From the course by École Polytechnique Fédérale de Lausanne
Functional Programming Principles in Scala
5398 ratings
École Polytechnique Fédérale de Lausanne
5398 ratings
Course 1 of 5 in the Specialization Functional Programming in Scala
From the lesson
Lists
This week we dive into Lists, the most commonly-used data structure in Scala.
Meet the Instructors
Martin OderskyProfessor
Computer Science
So far, All operations on lists were first order.
That means, the functions took lists of primitive types as arguments and returned
them as results. In this session, we're going to change
that. We're going to introduce higher order list
functions that work on lists and take another function as argument.
We will see that with just a handful of these higher order functions, we can
describe a great variety of different tasks.
The examples in the previous sessions have shown that functions of lists often have
very similar structure. In fact, we can identify several recurring
patterns, such as transforming each element in a list in a certain way,
Or retrieving from a list all elements that satisfy a given criterion, or maybe
combining the elements of a list using an operator.
And since we are in a functional language which allows programmers to write generic
functions using high auto-functions, we can apply the same techniques to functions
over the lists. So in this session, we are going to be
interested in higher order functions over the lists.
Our first common operation is to transform each element of a list, and then return
the list of results. So for instant to multiply each element of
a list by the same factor, you could write a function scale list which takes a list
of doubles as input and a factor and returns a list of double and what it does
is, well if the input list is nil, it just returns it unchanged, and otherwise it
multiplies the first element of the list by the factor, and it does a recursive
call of scale list with the rest of the list and factor.
So, obviously, that function would multiply each element of the list by the
same factor. That scheme can be generalized to a method
map on the list class which can apply in arbitrary operation to all elements of a
list. So here's a simple way to define map on
the abstract class list of t. We would say def map and map takes a
function from t to some. Other type, u, which could be the same as
type t, or it could be different. So, u is a type parameter of MAP.
And then it returns a list of u. And then the body of MAP is just the body
of scale lists, but now generalized. So, in the case of nil, we return the list
unchanged. If the list is non-nil, then we apply the
function, F to the first element X,
And we follow that with a recursive call of xs.mapf.
In fact the actual implementation of map N class list and this kind of standard
library is a bit more complicated for several reasons.
First, the actual definition is in fact terra cursive.
Where as this definition isn't you see after the call to map you still have a
call to cons. The second the actual implementation to
maps for arbituary collections not just lists.
But for understanding map list definition here we'll do very well.
So using map, we can now write scale list much more concisely, so much more
concisely that, that it's hardly worth writing a different function for it.
We would just say, scale list of XS and a factor S, map XS map, with the function
that takes an X and multiplies X by the factor.
So here's an exercise for you. Let's take a function square list that
squares each element of a list, then returns the result.
There are two possible ways to do that, either with pattern matching or using map.
I invite you to try both possible ways by filling in the three triple question marks
in the definition of square lists here and the definition of square lists down there.
So let's see how we would do that. In the paren.
Matching definition to take the squares of an empty list we would surely return the
empty list again. To take the squares of a list with a head
y and a tail ys, what do we do? Well, we start by taking the square of y
and we follow that by a recursive call of square list.
Of Y-S. So far so good.
I think by now we all know how to do these things cold.
But, let's see whether we can do it shorter using map.
Well, to use square list with map, what can we do?
Well, we map it by the function that takes an X, and returns X times X.
And that's it. So obviously the definition with map is
much shorter and I would, argue also clearer than the one that uses
paramatching and recursion. So here's another common operation on
lists, selecting all elements that satisfy a given condition.
For instance, you might want to select all elements from a list that are positives.
Here you have a function pause alps. It takes a list of int, gives you back a
list of int. And the para matching definition would
read as you see here. So for the empty list we can just return
it. If its non-empty and the first element is
in fact greater than zero it's positive, then we include it in the result list.
So we return the first element followed by.
Pause LM's of the rest of the list. And, otherwise, we just do pause LM's of
the rest of the list. So, the first element gets dropped, and we
just filter the remainder of the list with pause LM's.
Again, this pattern can be generalized to the method filter in the list class.
So here you see the definition of filter. It takes now a predicate that takes an
element of the list element type t, that gives you back a boolean, and it will
return a list of t's. The definition is an obvious
generalization of posElems that we've seen before.
So we match on the current list. If it's nil then we return to middle list.
And if it's not nil and the head of the list XX satisfies our predicate So P of X
is true. Then we return X followed by X dot filter
P. And otherwise just accept that filter P.
Again using filter we can write poseElems much more concisely.
We will just write excess filter x such that x is greater than zero.
Besides filter, there are also other methods that extract sub-lists a list
based on some predicate. You see the list of these methods here.
Rather than going through the list, I just wanted to show them in action in a new
worksheet. So what I'm going to do is I'm going to
take the data definitions of the previous worksheet and create a new worksheet.
Call this, call this list far. .
And we have our test data here. And what I'm, what we're gonna do is lets
say the first one would be numbs filter so all numbers greater than zero, so that
would have filtered out the -four here. If we do, nums filter not X, X greater
than zero, what do you expect to get? Right.
You'd expect to get just the negative number, -four.
The other. And as that was partitioned.
Partition is like filter and filter not in one go.
So, if you write that here. Then what you see is you get a pair of two
lists. The first list contains all those elements
for which the predicate is true. So that was the positive elements.
And the second list contains all those elements for which the predicates is
false. So you see the partition is just like
filter and filter not as a pair. However it will run in a single traversal
through the input list numbers, where as if you do first a filter and a filter not
you would get two traversals. The next two functions are a bit different
in that they look at a prefix and a suffix of a list.
So what I can do here is I can say, Nums TakeWhile.
X, X greater than zero. So what that gives me is the longest
prefix of the list, such that the predicate is true.
So here I would say okay two is greater than zero.
But then at minus four, I would stop, because minus four is not greater than
zero. So it will, any further elements will not
take part in take while. That's the main difference between take
while and filter. Filter will always.
Select all elements in the list that satisfy the criteria, whereas take while
will only take the longest prefix of the list.
The opposite of take while is drop while. So, let's write that.
So take while and drop while relate to each other such, just like take and drop
relate to each other. Drop while would then return the remainder
of the list, without the prefix taken by take while, so it would be the list that
starts with a negative element and then goes until the end of the input list.
And finally, where we had partition, that combined a filter and filter not, we also
have an operation that combines a take while and a drop while.
That operation is called span. So if we do that then what we will see is
that it will give us essentially the combination of a TakeWhile, that was a
list two and a DropWhile. But like Partition, it will only need a
single traversal, not two. Let's apply the function that we've seen
so far in an exercise. The task is to write a function pack that
packs consecutive duplicates of list elements into sublists.
So if you apply pack to this input list here, we would expect to get back a list
of lists, where the first sublist is formed from, from the three consecutive
A's here. The second sublist has just a single B.
The third sublist has the two consecutive C's.
And the final sublist has the trailing A here.
The idea is to use a template like this one here, we have a defined pack to be a
generic function over type lists of T, returns a list of lists of T, Obviously if
the list is empty, then that's what we would expect back.
So the only case to handle is really this case here.
If the list is nonempty, what do we do? I've already copied my input list data and
the template of the pack function, so the only case to fill in is when the list is
nonempty, consisting of a head, X, and a tail, XS.
Which of the six functions here would be applicable?
Well, what we wanna do is take off a leading sub list and then do something
with the rest of the list. So it's a combination of take while and
drop while and that's what span would give us.
So let's set up a para match first rest equals xs span and what should be the
predicate be. Well we say take elements as long as they
are equal to x, the leading elements of the list.
Once we have that we would say, first is already the sublist that will constitute
the first element of our list, and the other elements would be the result of a
recursive call of pack to the rest of the list.
And that gives us our function, pack, so let's apply pack to our data list.
Pack of data gives us. A list consisting of three As, one B, two
Cs, and an A. Just what we needed.
We're not done yet as a second exercise I would like you to use Peg to write a
second function and code that produces a run length encoding of a list.
Run length encodings are often used for compressions of images and other files.
The idea is to encode n consecutive duplicates of an element of the list as a
pair xn. So instead of writing the element n times
we just have a single entry pair of what the element was and how many times we have
written it. For instance.
Is in code of the list that we've seen before should give us.
A3, B1, C2 and again, A1. So, let's go again to the worksheet to
solve that. What we're interested in is a function in
code, and it should also be generic taking a list of T, and now it would return a
list of pairs of the element and the count which is an integer.
And what should the body of encode be? Well, it turns out that most of the work
has already been done by pack. Once we have a packed list.
All we need is, is a simple transformation to get to the run length encoding.
And that transformation will be applied to each element.
So the natural operation to use is a map. So what we do is we start with pack of
excess. And we then apply our map.
A pack of Xs lead to, here's a list of lists.
So if we apply our map then we have get each individual sub list as an argument.
Let's call that sub list Ys here and what do we do in the map, well, we return a
pair where the first element of the pair is the first element in the sub list and
the second element of the pair is the length of that sub list.
There we are with encode. All we need to do is encode of data.
To get a single test case. And we get what we expected, a 3B, one C2,
and A1.
Explore our Catalog
Join for free and get personalized recommendations, updates and offers.
Coursera provides universal access to the world’s best education,
partnering with top universities and organizations to offer courses online.
© 2018 Coursera Inc. All rights reserved.
