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. 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 5.3 - Implicit Parameters
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Из курса от партнера École Polytechnique Fédérale de Lausanne
Functional Programming Principles in Scala
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École Polytechnique Fédérale de Lausanne
5578 оценки
Курс 1 из 5 — Specialization Functional Programming in Scala
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Lists
This week we dive into Lists, the most commonly-used data structure in Scala.
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Martin OderskyProfessor
Computer Science
This session will be concerned with parameterization techniques.
The question is how can we make a definition, such as mergesort more general
so that they can be used not for a singular argument type.
We'll see in this session how parameterization by either functions or
objects helps in this respect. One shortcoming of the merge-sort function
we've seen last session was that it can only be applied to list of X.
But it makes sense to, apply the same function or similar function also to lists
of other element types such as strings or doubles or booleans and so on.
How could we achieve that? Well, the most straightforward way to do
that would be let's parameterize merge sort.
So instead of int I have a type parameter T now.
But that wouldn't work because the comparison function wouldn't be
well-defined. So let's have a look at that.
Here I have the merge sort that we have defined at the end of the last session.
Let's just replace the int by a T, and make T a type parameter.
And the same thing here for the merge, which takes the type parameter of the
enclosing function msort, and what do we see?
Well, we see one error here, which says that value less is not a member of type
parameter T. Of course, now that our at least elements
can have an arbitrary type. We can no longer be sure that there is, in fact, a
less than function defined on elements of this type.
So what can we do? Well, the next refinement of our idea
would be to parameterize merge with the necessary comparison function.
So let's see how that would work. We would have a polymorphic function msort
with a type parameter t. And in addition to the list that we pass
to msor we also pass a less than function with an lt here that takes two ts and
returns a Boolean and the contract for LT would be that it should return the result
of comparing its two elements with the less than.
Once we have that then we have to apply LT in two instances.
Let's see how we would do that in the work sheet, so I have here.
I pass an LT, which takes two T's, and returns a Boolean.
And then I can write here, instead of x less than y, I can write here LT of x and
y, in the merge function. That's good, but, I have another problem
here. For the sort here, of course, I have to
pass the correct LT function into the two recursive calls of msort.
And finally, for the numbers here, I have to pass the right version of LT into
numbers. So, one way to do that would be to say,
well, let's take an int X and a Y, which is also of int, type int, and return X
less than Y. And that would compile.
And if we run it, we get the same result as before.
What we can do now is we can also add a data of another type.
Let's call, let's say fruits. That would be a list of let's say, apple.
Pineapple, Orange, and banana.
And we can now apply msort to the fruits list.
All we have to do is pass the right comparison function.
So this one here would take two strings. And it will return the result of the Java
call x compared to y, and that, that must be less than zero.
So compare to is a method on Java string which returns minus one if the first
string is less than the second string, a zero if they're equal, and one if the
first one is greater than second one. So since we want minus one, we have to
test here less than zero. Let's run the worksheet, and we see that
indeed, our list of fruit has been sorted in lexical, graphical order.
In fact, we can simplify the, calls here further, because the types of the two
function values are not necessary. We can leave them out and the Scala
compiler will infer them. So we could also write something like
that, and, That would give us the same list obviously.
And we could also leave the types out in the second function values of the strings.
The reason why that works is that this kind of compile is figure out, it's able
to figure out that X and Y need to have type int by simply analyzing the call of M
sort of nums, because nums is a list of int.
It will therefore know that the type parameter of M sort must be int and that
will determine in turn the types of the function parameters here.
That discussion shows that it's usually advantages if you have several parameter
lists and one of them is a function value, to put the function value last.
Cuz then you have a better chance that the type's already inferred by the time the
compiler will type check the function value and that means you don't have to
write them explicitly. So, so far, we have parameterized the
merge sort function with our own less than operation, which is perfectly possible,
but on the other hand, we could also use a predefined class.
There is already a class in the standard library that represents orderings with
less than function, but also all the other, all ordering functions like greater
than, less than or equal, and so on. That class is called scala.math.ordering
of T. So the type parameter T tells us what's
the type of the elements that are compared in the ordering class.
Instead of parameterizing with the less than function directly, we could also
parameterize msort with ordering instead. So, wouldn't be much that we have to
change. So in the worksheet, we'll have a look at
it. So instead of less than, we would have a
paramenter ord of type ordering of t. We should import that first.
Import math dot ordering. And then instead of the less than call
here we would call the less than method of our ordering type.
And instead of passing less than along, we can pass order along.
And in our, actual calls, we can now use, the predefined orderings, but the first
one would be ordering.int. That's the ordering on integers that is
defined as a value in the ordering object, and for the other one, it would be
ordering.string. And the results are the same as before.
Unsurprisingly. So, there's one remaining problem in.
Passing a round piece LT or all values are rather cumbersome.
Would be much nicer if we could somehow synthesize the right comparison operation
directly just given the type T. And we can make it at least appear that
way by avoiding passing or implicit, explicitly making it an implicit
parameter. So the only thing here is that we write
implicit ord cut orderings. So all I do now is I write implicit in
front of the. What parameter.
Nothing changes so far, but what it means is that I can now leave out the actual,
parameter in a, in a call, and the compiler will synthesize one for me.
So I can leave out the ort here, and I can leave out the ort there.
And I can even leave out arch in the ordering.int or the ordering of string and
everything would still compile and run as before.
So I can get rid, get rid of the ordering of string as well and its still the same
thing. So now my program is just as concise and
nice as in the case of list of int's but it is fully parametric.
How did that happen? How, how could we achieve that?
Well, what actually happens is that when you write an implicit parameter, and you
don't write an actual argument that matches that parameter, the compiler will
figure out the right implicit to pass, based on the demanded type.
So what are the precise rules for that? So let's say a function takes an implicit
parameter of type T. The compiler will then search for an
implicit definition. So that's a parameter or a vowel or an
object that's marked implicit. And that has a type that's compatible with
T. And finally, it's visible at the point of
the function call. Or it's defined in the companion object
associated with T. So in our case.
Here we have the first word that we left out here that was visible at the point of
the function call, because the word here is visible where as the orderings that we
left out here, they were defined in the companion object of the ordering class and
therefore also qualified as implicit arguments.
So once the compiler has done that search, if it comes back with a single more
specific definition, that definition will t-, be taken as the actual agrument.
So the arg-, that will be the synthesized argument for the implicit parameter.
And otherwise, if the compiler finds nothing or if it finds several possible
candidate definitions, then it's an error. So let's test your understanding with a
simple quiz. Consider the line in the definition of
msort where you have the two recursive calls.
So that would be this line here. Which.
Definition of ordering is implicitly inserted by the compiler here and here.?
Is it ordering.int, or ordering.string, or the Ord parameter of the N sort function?
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