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 6.2 - Combinatorial Search and For-Expressions
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来自 École Polytechnique Fédérale de Lausanne 的课程
Functional Programming Principles in Scala
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École Polytechnique Fédérale de Lausanne
6186 个评分
课程 1（共 5 门，Specialization Functional Programming in Scala）
从本节课中
Collections
After a deep-dive into Lists, this week we'll explore other data structures; vectors, maps, ranges, arrays, and more. We'll dive into Scala's powerful and flexible for-comprehensions for querying data.
与讲师见面
Martin OderskyProfessor
Computer Science
In this session, we are going to look at the question of how to handle
nested sequences in combinatorial search problems.
We're going to explore a handy new notation for
these kind of problems namely For expressions.
Higher order functions and
collections often replace loops in imperative languages.
Programs that use many nested loops then
can be expressed often with combinations of these higher order functions.
I will now show you that using an example.
So the task is that, we want to find all pairs of positive integers i and
j, such that j is less than i and
i is bounded by some positive integer n and i + j is prime.
So for instance, if n = 7, the pairs we want to find are 2 1, 3 2, 4 1, 4 3, and
so on, because the sum of i and j in each case is a prime number.
So in imperative programming language I probably would use two nested loops,
one for the Is one for the Js together with a test whether the sum of i plus j
is a prime number and the sum buffer to collect the results.
But what would be a purely functional way to achieve same thing?
So a natural functional way to do this would be to generate data structures
bit by bit until we have generated the data structure that we need for
the final result.
So the first data structure that we want to generate is the sequence
of all pairs of integers i, j.
Such that the j and the i are within the bounds that we have specified.
And then, once we have that sequence, we can filter it to keep only those pairs for
which the sum i+j is prime, and then we're are done.
So we're left with the problem how to generate the sequence of
pairs of integers.
And there the natural way to do this would be to first generate the integers i
between 1 and n, n excluded.
And then for each integer i, generate the list of pairs (i,
1), (i, 2) and so on until i, i minus 1.
So once we have that, we can put it into code.
The last bit can be achieved combining until and map.
So we will try it 1 until n that gives us a list of i from 1 to n excluded.
And then for each of these integers, we map it,
call it i to a range that goes from one until i, i excluded.
Call the index here, j and for each combination of i and
j, that we have produced that way, we return the pair i and j.
So, let's do that in the worksheet.
I have opened a new worksheet, call it pairs and
I want to try out to build the sequence of pairs.
The first thing to do is set the upper limit, let's say n = 7.
And then I copy this expression that I had on the slide.
So what did I get here?
Well, if I have a look at the right, then I get a vector of vectors,
and each element of the vector is indeed an i with the j less than the i and
both greater or equal to one, so I get the pairs that I wanted to produce.
You might ask, well, why did I get back a vector of vectors?
Well, if you look at the class hierarchy that we have seen in the last session,
then you will see that range is a subtype of Sequence.
Now the arrange that we started with, one until n,
got transformed with a map, and that map produced a sequence of pairs.
And of course, sequences of pairs are not element of range, so
I needed some other representation.
The representation I got was in fact a type that sits between sequence and
range, it's called an index sequence.
So this is actually a sequence that uses random access and
the prototypical default implementation of an index sequence is just a vector.
So, what the type inference had decided was to say, well I can't do with ranges,
ranges can't contain pairs, so I go up the hierarchy and
I take the next best type that happened to be an index sequence and the type
the economically implementation of that type, and that happened to be a vector.
So that's how you ended up with vectors of vectors.
But we have seen that's still not the right thing to do.
We want to generate just the single collection of pairs,
not a collection of vectors.
So what we need to do,
is we need to concatenate all the element vectors into one single list of pairs.
So let's see how that can be done.
We can combine all the subsequences in our sequence of vectors of
pairs using a foldRight with concatenation as the operation.
So if we have a vector of sequences,
that should And
what we do is we combine them all using ++, the concatenation operation.
And on the right hand side, you would have the empty vector.
So that would work, that would give us a single sequence of pairs.
There's also another method here that is useful.
This is called xss.flatten.
So, flatten does, essentially, the same thing as concat.
It takes a collection of collections, and returns a single collection,
containing all the elements in the sub-collections.
And if the collection is a sequence,
then the combination will be just the concatenation of these sub-collections.
So a simpler way to express what we want is our pair generating expression and
then we just follow that by .flatten.
We're not done yet because we can apply a useful law.
Remember the flat map function that we've seen in the last session?
It takes a collection value function f and
apply's it to each element of the excess, and then combines the result.
So, flatMap is, actually, exactly the same thing as mapping f.
So, that would give us a collection of collections, and then applying flatten,
and map, followed by flatten, is exactly the pattern that we've seen here.
So it's a map here followed by flatten.
So we can contract the two to just use a flatMap here.
So we would have one until n, flatMap the function
that goes through each interval from one to i and forms the pair.
So let's do that in the worksheet.
Let's replace the call to map here by flatMap.
And we would get what we want here in flat sequence of pairs.
Now what we still need to do is we need to filter that sequence
according to the criterion that the sum of the two elements of the pair is prime.
So let's do that.
Let's follow the expression with a filter, takes a pair, and
it asks whether the following is prime.
The pair, its first element plus the pair.
Second element.
And if you do that, then indeed we get the sequence we wanted,
sequence of pairs of elements whose sum is prime.
So here the sum is 5, 7, 7, 7, 11.
All prime numbers.
So this works, but it's sad to say that it makes most people's head hurt.
Is there a simpler way to organize this expression
that makes it more understandable?
Well, one thing we definitely could try to do is name the intermediate results.
So split out a large expression like that into several smaller ones.
That's generally always a good move.
But, it turns out, there's a more fundamental way, how we can express
problems, like this, in a more high level notation that's simpler to understand.
We can get to that next.
So, we've seen that high-order functions, such as map, flatMap, or
filter, provide powerful constructs for manipulating lists, and other collections.
But we've also seen that sometimes the level of obstruction required by these
functions make the program hard to understand.
In this case, there's a patient in Scala that can help.
It's called a For Expression.
So let's introduce For Expressions by an example.
We start with the class of person that we've seen before.
That person has a name, which is a string, and an age which is the Int.
To obtain the names of all persons over 20 years,
you could write for p left arrow, that's get taken
from persons if p.age greater 20, yield p.name.
And in fact that expression is equivalent to the expression persons filter,
such that the person p has an age greater than 20, and then map
the filter expression such that for every person p, we get the packets name.
The two expressions have about the same size but
I would argue that this one is simpler to read and understand what it produces.
For expressions, like this, are similar to for loops and imperative languages but
there is an important difference.
A for loop operates with a side effect that changes something, for
expression doesn't, instead the for expression produces a new result and
essentially each element of the result is produced by a yield expression here.
So if you look at the syntax of for expressions, then here it is.
So they are of the form for and then comes a sequence.
You have generators of filters in parentheses and then comes a yield.
And then comes the expression that produces the final result elements.
So the sequence s can consist of generators and filters.
A generator is of the form p left arrow e, where p is a variable or more
generally a whole pattern and e can be an expression whose value is a collection.
The idea is that we would let p range over all elements of the collection e.
And the filter is of the form, if f where the f is a boolean expression.
And the idea here is that the filter will bring more from consideration
all elements where f is false.
The sequence must always start with a generator, and if there's several
generators in the sequence, the last generator is very faster than the first.
While the first one steps through more slowly, and then for
each element of the first, the second generator can be transversed and so on.
There's a minor variation in the syntax.
We can also write braces around the sequence of generators and filters.
And then the generators and
filters can be written on multiple lines without requiring semi-colons.
If you write it with parenthesis, then, if you have several generators,
we need to put a semi-colon between them.
Now let's see this with two examples.
The first example is our
expression that gave us all pairs whose sum were prime numbers.
So, that could be expressed simply like what you see here.
We let i range from 1 until n.
Will a j range from 1 until i.
We demand that the sum of i + j must be a prime number,
and then we yield the pair of i and j.
I think, there's no contest which one is clearer.
So, this one is rather crystal clear, whereas,
the other one was quite convoluted.
The second example I want you to do as an exercise, remember scalarProduct
from last session so, that was the function that took two vectors
of doubles multiplied to corresponding elements and then, some of the result,
can you write a version of scalarProduct that makes use of a for expression?
So, let's see how we would solve this.
So, I write a for expression, for.
What I want to do is, I want to let x and
y range over the result of zipping
the two vectors, xs and ys.
And then, I want to return in each case the multiplication x and y.
So that would give me the multiplication of corresponding elements in xs and ys.
And then, all I have to do in the end is sum it up.
So from the sum of the whole vector.
