I'm trying to write a function that receives a vector of vectors of strings and returns all vectors concatenated together, i.e. it returns a vector of strings.
The best I could do so far has been the following:
fn concat_vecs(vecs: Vec<Vec<String>>) -> Vec<String> {
let vals : Vec<&String> = vecs.iter().flat_map(|x| x.into_iter()).collect();
vals.into_iter().map(|v: &String| v.to_owned()).collect()
}
However, I'm not happy with this result, because it seems I should be able to get Vec<String> from the first collect call, but somehow I am not able to figure out how to do it.
I am even more interested to figure out why exactly the return type of collect is Vec<&String>. I tried to deduce this from the API documentation and the source code, but despite my best efforts, I couldn't even understand the signatures of functions.
So let me try and trace the types of each expression:
- vecs.iter(): Iter<T=Vec<String>, Item=Vec<String>>
- vecs.iter().flat_map(): FlatMap<I=Iter<Vec<String>>, U=???, F=FnMut(Vec<String>) -> U, Item=U>
- vecs.iter().flat_map().collect(): (B=??? : FromIterator<U>)
- vals was declared as Vec<&String>, therefore
vals == vecs.iter().flat_map().collect(): (B=Vec<&String> : FromIterator<U>). Therefore U=&String.
I'm assuming above that the type inferencer is able to figure out that U=&String based on the type of vals. But if I give the expression the explicit types in the code, this compiles without error:
fn concat_vecs(vecs: Vec<Vec<String>>) -> Vec<String> {
let a: Iter<Vec<String>> = vecs.iter();
let b: FlatMap<Iter<Vec<String>>, Iter<String>, _> = a.flat_map(|x| x.into_iter());
let c = b.collect();
print_type_of(&c);
let vals : Vec<&String> = c;
vals.into_iter().map(|v: &String| v.to_owned()).collect()
}
Clearly, U=Iter<String>... Please help me clear up this mess.
EDIT: thanks to bluss' hint, I was able to achieve one collect as follows:
fn concat_vecs(vecs: Vec<Vec<String>>) -> Vec<String> {
vecs.into_iter().flat_map(|x| x.into_iter()).collect()
}
My understanding is that by using into_iter I transfer ownership of vecs to IntoIter and further down the call chain, which allows me to avoid copying the data inside the lambda call and therefore - magically - the type system gives me Vec<String> where it used to always give me Vec<&String> before. While it is certainly very cool to see how the high-level concept is reflected in the workings of the library, I wish I had any idea how this is achieved.
EDIT 2: After a laborious process of guesswork, looking at API docs and using this method to decipher the types, I got them fully annotated (disregarding the lifetimes):
fn concat_vecs(vecs: Vec<Vec<String>>) -> Vec<String> {
let a: Iter<Vec<String>> = vecs.iter();
let f : &Fn(&Vec<String>) -> Iter<String> = &|x: &Vec<String>| x.into_iter();
let b: FlatMap<Iter<Vec<String>>, Iter<String>, &Fn(&Vec<String>) -> Iter<String>> = a.flat_map(f);
let vals : Vec<&String> = b.collect();
vals.into_iter().map(|v: &String| v.to_owned()).collect()
}
I'd think about: why do you use iter() on the outer vec but into_iter() on the inner vecs? Using into_iter() is actually crucial, so that we don't have to copy first the inner vectors, then the strings inside, we just receive ownership of them.
We can actually write this just like a summation: concatenate the vectors two by two. Since we always reuse the allocation & contents of the same accumulation vector, this operation is linear time.
To minimize time spent growing and reallocating the vector, calculate the space needed up front.
fn concat_vecs(vecs: Vec<Vec<String>>) -> Vec<String> {
let size = vecs.iter().fold(0, |a, b| a + b.len());
vecs.into_iter().fold(Vec::with_capacity(size), |mut acc, v| {
acc.extend(v); acc
})
}
If you do want to clone all the contents, there's already a method for that, and you'd just use vecs.concat() /* -> Vec<String> */
The approach with .flat_map is fine, but if you don't want to clone the strings again you have to use .into_iter() on all levels: (x is Vec<String>).
vecs.into_iter().flat_map(|x| x.into_iter()).collect()
If instead you want to clone each string you can use this: (Changed .into_iter() to .iter() since x here is a &Vec<String> and both methods actually result in the same thing!)
vecs.iter().flat_map(|x| x.iter().map(Clone::clone)).collect()
I'm trying to learn Haskell, specifically Snap, Blaze HTML5 and Persist. I would like to take every row in a table, select a single column from it, and then concatenate the values into a single string.
I've previously worked with C#'s LINQ quite extensively and under Entity Framework I could do it like this:
String.Join(", ", dbContext.People.Select(p => p.Name));
This would compile down to SELECT Name FROM People, with C# then concatenating those rows into a string with ", " in between.
To try and get the concatenation part right, I put this together, which seems to work:
intercalate ", " $ map show [1..10]
(it counts 1-9, concatenates with ", " in between the items)
However, I can't get this to work with Database.Persist.Sqlite. I'm not sure I quite understand the syntax here in Haskell. To contact the DB and retrieve the rows, I have to call: (as far as I understand)
runSqlite "TestDB" $ selectList ([] :: [Filter Person]) [] 0 0
The problem is that I'm not sure how to get the list out of runSqlite. runSqlite doesn't return the type I'm after, so I can't use the return value of runSqlite. How would I do this?
Thank you for reading.
To clarify:
Snap requires that I define a function to return the HTML I wish to send back to the client making the HTTP request. This means that:
page = runSqlite "TestDB" $ do
{pull data from the DB)
Is no-go as I can't return the data via the runSqlite call, and as far as I know I can't have a variable in the page function which is set within the runSqlite do block. All examples I can find just write to IO in the runSqlite do block, which is not what needs to be done here.
The type of runSqlite is:
runSqlite :: (MonadBaseControl IO m, MonadIO m) => Text -> SqlPersistT (NoLoggingT (ResourceT m)) a -> m a
And the type of selectList is:
[Filter val] -> [SelectOpt val] -> m [Entity val]
So, you can actually, use the nice do notation of Monad, to extract it:
runSqlite "TestDB" $ do
myData <- selectList ([] :: [Filter Person]) [] 0 0
-- Now do stuff with myData
The <- thing gets the list out of the monad. I would suggest you to go through this chapter to get an idea of how Persistent is used. Note that the chapters in the book assume a basic Haskell understanding.
The issue is that I want to use the selectList outside of runSqlite as
I need to pass the concatenated string to a Blaze HTML5 tag builder:
body $ do p (concatenated list...)
For this case, just define a function that does your intended task:
myLogic :: [SqlColumnData] -> String -- Note that SqlColumnData is hypothetical
myLogic xs = undefined
And then just call them appropriately in your main function:
main = runSqlite "TestDB" $ do
myData <- selectList ([] :: [Filter Person]) [] 0 0
let string = myLogic myData
-- do any other remaining stuff
It hadn't clicked that if I didn't use a do block with runSqlite, the result of the last call in the statement was the return value of the statement - this makes total sense.
https://gist.github.com/egonSchiele/5400694
In this example (not mine) the readPosts function does exactly what I'm after and cleared up some Haskell syntax confusion.
Thank you for your help #Sibi.
I'm reading about functional programming and I've noticed that Pattern Matching is mentioned in many articles as one of the core features of functional languages.
Can someone explain for a Java/C++/JavaScript developer what does it mean?
Understanding pattern matching requires explaining three parts:
Algebraic data types.
What pattern matching is
Why its awesome.
Algebraic data types in a nutshell
ML-like functional languages allow you define simple data types called "disjoint unions" or "algebraic data types". These data structures are simple containers, and can be recursively defined. For example:
type 'a list =
| Nil
| Cons of 'a * 'a list
defines a stack-like data structure. Think of it as equivalent to this C#:
public abstract class List<T>
{
public class Nil : List<T> { }
public class Cons : List<T>
{
public readonly T Item1;
public readonly List<T> Item2;
public Cons(T item1, List<T> item2)
{
this.Item1 = item1;
this.Item2 = item2;
}
}
}
So, the Cons and Nil identifiers define simple a simple class, where the of x * y * z * ... defines a constructor and some data types. The parameters to the constructor are unnamed, they're identified by position and data type.
You create instances of your a list class as such:
let x = Cons(1, Cons(2, Cons(3, Cons(4, Nil))))
Which is the same as:
Stack<int> x = new Cons(1, new Cons(2, new Cons(3, new Cons(4, new Nil()))));
Pattern matching in a nutshell
Pattern matching is a kind of type-testing. So let's say we created a stack object like the one above, we can implement methods to peek and pop the stack as follows:
let peek s =
match s with
| Cons(hd, tl) -> hd
| Nil -> failwith "Empty stack"
let pop s =
match s with
| Cons(hd, tl) -> tl
| Nil -> failwith "Empty stack"
The methods above are equivalent (although not implemented as such) to the following C#:
public static T Peek<T>(Stack<T> s)
{
if (s is Stack<T>.Cons)
{
T hd = ((Stack<T>.Cons)s).Item1;
Stack<T> tl = ((Stack<T>.Cons)s).Item2;
return hd;
}
else if (s is Stack<T>.Nil)
throw new Exception("Empty stack");
else
throw new MatchFailureException();
}
public static Stack<T> Pop<T>(Stack<T> s)
{
if (s is Stack<T>.Cons)
{
T hd = ((Stack<T>.Cons)s).Item1;
Stack<T> tl = ((Stack<T>.Cons)s).Item2;
return tl;
}
else if (s is Stack<T>.Nil)
throw new Exception("Empty stack");
else
throw new MatchFailureException();
}
(Almost always, ML languages implement pattern matching without run-time type-tests or casts, so the C# code is somewhat deceptive. Let's brush implementation details aside with some hand-waving please :) )
Data structure decomposition in a nutshell
Ok, let's go back to the peek method:
let peek s =
match s with
| Cons(hd, tl) -> hd
| Nil -> failwith "Empty stack"
The trick is understanding that the hd and tl identifiers are variables (errm... since they're immutable, they're not really "variables", but "values" ;) ). If s has the type Cons, then we're going to pull out its values out of the constructor and bind them to variables named hd and tl.
Pattern matching is useful because it lets us decompose a data structure by its shape instead of its contents. So imagine if we define a binary tree as follows:
type 'a tree =
| Node of 'a tree * 'a * 'a tree
| Nil
We can define some tree rotations as follows:
let rotateLeft = function
| Node(a, p, Node(b, q, c)) -> Node(Node(a, p, b), q, c)
| x -> x
let rotateRight = function
| Node(Node(a, p, b), q, c) -> Node(a, p, Node(b, q, c))
| x -> x
(The let rotateRight = function constructor is syntax sugar for let rotateRight s = match s with ....)
So in addition to binding data structure to variables, we can also drill down into it. Let's say we have a node let x = Node(Nil, 1, Nil). If we call rotateLeft x, we test x against the first pattern, which fails to match because the right child has type Nil instead of Node. It'll move to the next pattern, x -> x, which will match any input and return it unmodified.
For comparison, we'd write the methods above in C# as:
public abstract class Tree<T>
{
public abstract U Match<U>(Func<U> nilFunc, Func<Tree<T>, T, Tree<T>, U> nodeFunc);
public class Nil : Tree<T>
{
public override U Match<U>(Func<U> nilFunc, Func<Tree<T>, T, Tree<T>, U> nodeFunc)
{
return nilFunc();
}
}
public class Node : Tree<T>
{
readonly Tree<T> Left;
readonly T Value;
readonly Tree<T> Right;
public Node(Tree<T> left, T value, Tree<T> right)
{
this.Left = left;
this.Value = value;
this.Right = right;
}
public override U Match<U>(Func<U> nilFunc, Func<Tree<T>, T, Tree<T>, U> nodeFunc)
{
return nodeFunc(Left, Value, Right);
}
}
public static Tree<T> RotateLeft(Tree<T> t)
{
return t.Match(
() => t,
(l, x, r) => r.Match(
() => t,
(rl, rx, rr) => new Node(new Node(l, x, rl), rx, rr))));
}
public static Tree<T> RotateRight(Tree<T> t)
{
return t.Match(
() => t,
(l, x, r) => l.Match(
() => t,
(ll, lx, lr) => new Node(ll, lx, new Node(lr, x, r))));
}
}
For seriously.
Pattern matching is awesome
You can implement something similar to pattern matching in C# using the visitor pattern, but its not nearly as flexible because you can't effectively decompose complex data structures. Moreover, if you are using pattern matching, the compiler will tell you if you left out a case. How awesome is that?
Think about how you'd implement similar functionality in C# or languages without pattern matching. Think about how you'd do it without test-tests and casts at runtime. Its certainly not hard, just cumbersome and bulky. And you don't have the compiler checking to make sure you've covered every case.
So pattern matching helps you decompose and navigate data structures in a very convenient, compact syntax, it enables the compiler to check the logic of your code, at least a little bit. It really is a killer feature.
Short answer: Pattern matching arises because functional languages treat the equals sign as an assertion of equivalence instead of assignment.
Long answer: Pattern matching is a form of dispatch based on the “shape” of the value that it's given. In a functional language, the datatypes that you define are usually what are known as discriminated unions or algebraic data types. For instance, what's a (linked) list? A linked list List of things of some type a is either the empty list Nil or some element of type a Consed onto a List a (a list of as). In Haskell (the functional language I'm most familiar with), we write this
data List a = Nil
| Cons a (List a)
All discriminated unions are defined this way: a single type has a fixed number of different ways to create it; the creators, like Nil and Cons here, are called constructors. This means that a value of the type List a could have been created with two different constructors—it could have two different shapes. So suppose we want to write a head function to get the first element of the list. In Haskell, we would write this as
-- `head` is a function from a `List a` to an `a`.
head :: List a -> a
-- An empty list has no first item, so we raise an error.
head Nil = error "empty list"
-- If we are given a `Cons`, we only want the first part; that's the list's head.
head (Cons h _) = h
Since List a values can be of two different kinds, we need to handle each one separately; this is the pattern matching. In head x, if x matches the pattern Nil, then we run the first case; if it matches the pattern Cons h _, we run the second.
Short answer, explained: I think one of the best ways to think about this behavior is by changing how you think of the equals sign. In the curly-bracket languages, by and large, = denotes assignment: a = b means “make a into b.” In a lot of functional languages, however, = denotes an assertion of equality: let Cons a (Cons b Nil) = frob x asserts that the thing on the left, Cons a (Cons b Nil), is equivalent to the thing on the right, frob x; in addition, all variables used on the left become visible. This is also what's happening with function arguments: we assert that the first argument looks like Nil, and if it doesn't, we keep checking.
It means that instead of writing
double f(int x, int y) {
if (y == 0) {
if (x == 0)
return NaN;
else if (x > 0)
return Infinity;
else
return -Infinity;
} else
return (double)x / y;
}
You can write
f(0, 0) = NaN;
f(x, 0) | x > 0 = Infinity;
| else = -Infinity;
f(x, y) = (double)x / y;
Hey, C++ supports pattern matching too.
static const int PositiveInfinity = -1;
static const int NegativeInfinity = -2;
static const int NaN = -3;
template <int x, int y> struct Divide {
enum { value = x / y };
};
template <bool x_gt_0> struct aux { enum { value = PositiveInfinity }; };
template <> struct aux<false> { enum { value = NegativeInfinity }; };
template <int x> struct Divide<x, 0> {
enum { value = aux<(x>0)>::value };
};
template <> struct Divide<0, 0> {
enum { value = NaN };
};
#include <cstdio>
int main () {
printf("%d %d %d %d\n", Divide<7,2>::value, Divide<1,0>::value, Divide<0,0>::value, Divide<-1,0>::value);
return 0;
};
Pattern matching is sort of like overloaded methods on steroids. The simplest case would be the same roughly the same as what you seen in java, arguments are a list of types with names. The correct method to call is based on the arguments passed in, and it doubles as an assignment of those arguments to the parameter name.
Patterns just go a step further, and can destructure the arguments passed in even further. It can also potentially use guards to actually match based on the value of the argument. To demonstrate, I'll pretend like JavaScript had pattern matching.
function foo(a,b,c){} //no pattern matching, just a list of arguments
function foo2([a],{prop1:d,prop2:e}, 35){} //invented pattern matching in JavaScript
In foo2, it expects a to be an array, it breaks apart the second argument, expecting an object with two props (prop1,prop2) and assigns the values of those properties to variables d and e, and then expects the third argument to be 35.
Unlike in JavaScript, languages with pattern matching usually allow multiple functions with the same name, but different patterns. In this way it is like method overloading. I'll give an example in erlang:
fibo(0) -> 0 ;
fibo(1) -> 1 ;
fibo(N) when N > 0 -> fibo(N-1) + fibo(N-2) .
Blur your eyes a little and you can imagine this in javascript. Something like this maybe:
function fibo(0){return 0;}
function fibo(1){return 1;}
function fibo(N) when N > 0 {return fibo(N-1) + fibo(N-2);}
Point being that when you call fibo, the implementation it uses is based on the arguments, but where Java is limited to types as the only means of overloading, pattern matching can do more.
Beyond function overloading as shown here, the same principle can be applied other places, such as case statements or destructuring assingments. JavaScript even has this in 1.7.
Pattern matching allows you to match a value (or an object) against some patterns to select a branch of the code. From the C++ point of view, it may sound a bit similar to the switch statement. In functional languages, pattern matching can be used for matching on standard primitive values such as integers. However, it is more useful for composed types.
First, let's demonstrate pattern matching on primitive values (using extended pseudo-C++ switch):
switch(num) {
case 1:
// runs this when num == 1
case n when n > 10:
// runs this when num > 10
case _:
// runs this for all other cases (underscore means 'match all')
}
The second use deals with functional data types such as tuples (which allow you to store multiple objects in a single value) and discriminated unions which allow you to create a type that can contain one of several options. This sounds a bit like enum except that each label can also carry some values. In a pseudo-C++ syntax:
enum Shape {
Rectangle of { int left, int top, int width, int height }
Circle of { int x, int y, int radius }
}
A value of type Shape can now contain either Rectangle with all the coordinates or a Circle with the center and the radius. Pattern matching allows you to write a function for working with the Shape type:
switch(shape) {
case Rectangle(l, t, w, h):
// declares variables l, t, w, h and assigns properties
// of the rectangle value to the new variables
case Circle(x, y, r):
// this branch is run for circles (properties are assigned to variables)
}
Finally, you can also use nested patterns that combine both of the features. For example, you could use Circle(0, 0, radius) to match for all shapes that have the center in the point [0, 0] and have any radius (the value of the radius will be assigned to the new variable radius).
This may sound a bit unfamiliar from the C++ point of view, but I hope that my pseudo-C++ make the explanation clear. Functional programming is based on quite different concepts, so it makes better sense in a functional language!
Pattern matching is where the interpreter for your language will pick a particular function based on the structure and content of the arguments you give it.
It is not only a functional language feature but is available for many different languages.
The first time I came across the idea was when I learned prolog where it is really central to the language.
e.g.
last([LastItem], LastItem).
last([Head|Tail], LastItem) :-
last(Tail, LastItem).
The above code will give the last item of a list. The input arg is the first and the result is the second.
If there is only one item in the list the interpreter will pick the first version and the second argument will be set to equal the first i.e. a value will be assigned to the result.
If the list has both a head and a tail the interpreter will pick the second version and recurse until it there is only one item left in the list.
For many people, picking up a new concept is easier if some easy examples are provided, so here we go:
Let's say you have a list of three integers, and wanted to add the first and the third element. Without pattern matching, you could do it like this (examples in Haskell):
Prelude> let is = [1,2,3]
Prelude> head is + is !! 2
4
Now, although this is a toy example, imagine we would like to bind the first and third integer to variables and sum them:
addFirstAndThird is =
let first = head is
third = is !! 3
in first + third
This extraction of values from a data structure is what pattern matching does. You basically "mirror" the structure of something, giving variables to bind for the places of interest:
addFirstAndThird [first,_,third] = first + third
When you call this function with [1,2,3] as its argument, [1,2,3] will be unified with [first,_,third], binding first to 1, third to 3 and discarding 2 (_ is a placeholder for things you don't care about).
Now, if you only wanted to match lists with 2 as the second element, you can do it like this:
addFirstAndThird [first,2,third] = first + third
This will only work for lists with 2 as their second element and throw an exception otherwise, because no definition for addFirstAndThird is given for non-matching lists.
Until now, we used pattern matching only for destructuring binding. Above that, you can give multiple definitions of the same function, where the first matching definition is used, thus, pattern matching is a little like "a switch statement on stereoids":
addFirstAndThird [first,2,third] = first + third
addFirstAndThird _ = 0
addFirstAndThird will happily add the first and third element of lists with 2 as their second element, and otherwise "fall through" and "return" 0. This "switch-like" functionality can not only be used in function definitions, e.g.:
Prelude> case [1,3,3] of [a,2,c] -> a+c; _ -> 0
0
Prelude> case [1,2,3] of [a,2,c] -> a+c; _ -> 0
4
Further, it is not restricted to lists, but can be used with other types as well, for example matching the Just and Nothing value constructors of the Maybe type in order to "unwrap" the value:
Prelude> case (Just 1) of (Just x) -> succ x; Nothing -> 0
2
Prelude> case Nothing of (Just x) -> succ x; Nothing -> 0
0
Sure, those were mere toy examples, and I did not even try to give a formal or exhaustive explanation, but they should suffice to grasp the basic concept.
You should start with the Wikipedia page that gives a pretty good explanation. Then, read the relevant chapter of the Haskell wikibook.
This is a nice definition from the above wikibook:
So pattern matching is a way of
assigning names to things (or binding
those names to those things), and
possibly breaking down expressions
into subexpressions at the same time
(as we did with the list in the
definition of map).
Here is a really short example that shows pattern matching usefulness:
Let's say you want to sort up an element in a list:
["Venice","Paris","New York","Amsterdam"]
to (I've sorted up "New York")
["Venice","New York","Paris","Amsterdam"]
in an more imperative language you would write:
function up(city, cities){
for(var i = 0; i < cities.length; i++){
if(cities[i] === city && i > 0){
var prev = cities[i-1];
cities[i-1] = city;
cities[i] = prev;
}
}
return cities;
}
In a functional language you would instead write:
let up list value =
match list with
| [] -> []
| previous::current::tail when current = value -> current::previous::tail
| current::tail -> current::(up tail value)
As you can see the pattern matched solution has less noise, you can clearly see what are the different cases and how easy it's to travel and de-structure our list.
I've written a more detailed blog post about it here.