I have the following:
enum SomeType {
VariantA(String),
VariantB(String, i32),
}
fn transform(x: SomeType) -> SomeType {
// very complicated transformation, reusing parts of x in order to produce result:
match x {
SomeType::VariantA(s) => SomeType::VariantB(s, 0),
SomeType::VariantB(s, i) => SomeType::VariantB(s, 2 * i),
}
}
fn main() {
let mut data = vec![
SomeType::VariantA("hello".to_string()),
SomeType::VariantA("bye".to_string()),
SomeType::VariantB("asdf".to_string(), 34),
];
}
I would now like to call transform on each element of data and store the resulting value back in data. I could do something like data.into_iter().map(transform).collect(), but this will allocate a new Vec. Is there a way to do this in-place, reusing the allocated memory of data? There once was Vec::map_in_place in Rust but it has been removed some time ago.
As a work-around, I've added a Dummy variant to SomeType and then do the following:
for x in &mut data {
let original = ::std::mem::replace(x, SomeType::Dummy);
*x = transform(original);
}
This does not feel right, and I have to deal with SomeType::Dummy everywhere else in the code, although it should never be visible outside of this loop. Is there a better way of doing this?
Your first problem is not map, it's transform.
transform takes ownership of its argument, while Vec has ownership of its arguments. Either one has to give, and poking a hole in the Vec would be a bad idea: what if transform panics?
The best fix, thus, is to change the signature of transform to:
fn transform(x: &mut SomeType) { ... }
then you can just do:
for x in &mut data { transform(x) }
Other solutions will be clunky, as they will need to deal with the fact that transform might panic.
No, it is not possible in general because the size of each element might change as the mapping is performed (fn transform(u8) -> u32).
Even when the sizes are the same, it's non-trivial.
In this case, you don't need to create a Dummy variant because creating an empty String is cheap; only 3 pointer-sized values and no heap allocation:
impl SomeType {
fn transform(&mut self) {
use SomeType::*;
let old = std::mem::replace(self, VariantA(String::new()));
// Note this line for the detailed explanation
*self = match old {
VariantA(s) => VariantB(s, 0),
VariantB(s, i) => VariantB(s, 2 * i),
};
}
}
for x in &mut data {
x.transform();
}
An alternate implementation that just replaces the String:
impl SomeType {
fn transform(&mut self) {
use SomeType::*;
*self = match self {
VariantA(s) => {
let s = std::mem::replace(s, String::new());
VariantB(s, 0)
}
VariantB(s, i) => {
let s = std::mem::replace(s, String::new());
VariantB(s, 2 * *i)
}
};
}
}
In general, yes, you have to create some dummy value to do this generically and with safe code. Many times, you can wrap your whole element in Option and call Option::take to achieve the same effect .
See also:
Change enum variant while moving the field to the new variant
Why is it so complicated?
See this proposed and now-closed RFC for lots of related discussion. My understanding of that RFC (and the complexities behind it) is that there's an time period where your value would have an undefined value, which is not safe. If a panic were to happen at that exact second, then when your value is dropped, you might trigger undefined behavior, a bad thing.
If your code were to panic at the commented line, then the value of self is a concrete, known value. If it were some unknown value, dropping that string would try to drop that unknown value, and we are back in C. This is the purpose of the Dummy value - to always have a known-good value stored.
You even hinted at this (emphasis mine):
I have to deal with SomeType::Dummy everywhere else in the code, although it should never be visible outside of this loop
That "should" is the problem. During a panic, that dummy value is visible.
See also:
How can I swap in a new value for a field in a mutable reference to a structure?
Temporarily move out of borrowed content
How do I move out of a struct field that is an Option?
The now-removed implementation of Vec::map_in_place spans almost 175 lines of code, most of having to deal with unsafe code and reasoning why it is actually safe! Some crates have re-implemented this concept and attempted to make it safe; you can see an example in Sebastian Redl's answer.
You can write a map_in_place in terms of the take_mut or replace_with crates:
fn map_in_place<T, F>(v: &mut [T], f: F)
where
F: Fn(T) -> T,
{
for e in v {
take_mut::take(e, f);
}
}
However, if this panics in the supplied function, the program aborts completely; you cannot recover from the panic.
Alternatively, you could supply a placeholder element that sits in the empty spot while the inner function executes:
use std::mem;
fn map_in_place_with_placeholder<T, F>(v: &mut [T], f: F, mut placeholder: T)
where
F: Fn(T) -> T,
{
for e in v {
let mut tmp = mem::replace(e, placeholder);
tmp = f(tmp);
placeholder = mem::replace(e, tmp);
}
}
If this panics, the placeholder you supplied will sit in the panicked slot.
Finally, you could produce the placeholder on-demand; basically replace take_mut::take with take_mut::take_or_recover in the first version.
I would like to have a way to describe logic/spec level structs that include abstract lists. Example 2.2.7 on page 27 of the ACSL Reference Manual suggests that there is a
way to do this and it is as follows:
//# type point = struct { real x; real y; };
//# type triangle = point[3];
//# logic point origin = { .x = 0.0 , .y = 0.0 };
/*# logic triangle t_iso = { [0] = origin,
# [1] = { .y = 2.0 , .x = 0.0 }
# [2] = { .x = 2.0 , .y = 0.0 }};
#*/
/*# logic point centroid(triangle t) = {
# .x = mean3(t[0].x,t[1].x,t[2].x);
# .y = mean3(t[0].y,t[1].y,t[2].y);
# };
#*/
//# type polygon = point[];
/*# logic perimeter(polygon p) =
# \sum(0,\length(p)-1,\lambda integer i;d(p[i],p[(i+1) % \length(p)])) ;
#*/
If I copy/paste this exact code into a text editor and try to run this code with the wp plugin with:
frama-c -wp -wp-rte -wp-prover alt-ergo shapes.c
I get an error:
[kernel:annot-error] shapes.c:1: Warning: unexpected token '{'
If I give up on trying to write logic/spec level declarations of struct types, but would still like to write logic/spec level expressions that instantiate structs defined in C as follows:
struct somestruct {
int x;
int y;
};
/*#
logic struct somestruct foo = { .x = 3, .y = 4 };
*/
I still get an error:
[kernel:annot-error] aggregate_err.c:7: Warning:
unsupported aggregated field construct. Ignoring global annotation
and not having a way to write particular values of structs as expressions in specifications leads to some fairly ugly specifications, so I am hoping that I am doing something wrong.
If I dig into the source of frama-C 20.0 to try to find the part of the parser-generator code for /*# type declarations, it looks like the syntax in Ex 2.2.7 is not really implemented. It looks like the syntax for type level declarations is line 799 of
frama-c-20.0-Calcium/src/kernel_internals/parsing/logic_parser.mly
(called type_spec)
And the parse rule for type level declarations of structs is:
| STRUCT exit_rt_type identifier_or_typename { LTstruct $3 }
which looks like it would support
//# type foo = struct c_struct;
but not something like what Ex 2.2.7 has as in:
//# type point = struct { real x; real y; };
Is there something else I should be doing to have better support for structs in ACSL/Frama-C? Thanks!
Not all ACSL constructions are supported by the current Frama-C implementation. With each Frama-C release comes an ACSL implementation manual, which describes the constructions that are not yet implemented. For Frama-C 20.0 Calcium, this can be found here. In this document, unsupported constructions appear in red in the relevant BNF rule. Note however that other parts of the manual are left untouched. Notably, the fact that an example is included in the implementation manual does not imply that it is expected to be successfully parsed by the current Frama-C version. In your case, these are the rules of figure 2.17 on page 57, which show that indeed records are not implemented.
As you have already discovered by yourselves, it is indeed possible to define a C struct (possibly ghost) and an ACSL type out of it. Of course, since the struct lives in the C world, its fields must have C types (ACSL types in ghost declarations is unsupported as well).
Similarly, you can simulate the absence of direct record definition by an update (the \with construction) of all the fields of an arbitrary record, as in the following example:
//# ghost struct c_s { float x; float y; };
//# type point = struct c_s;
//# axiomatic Arbitrary_point { logic point empty; }
//# logic point my_point = {{ empty \with .x = (float)1. } \with .y = (float)2.};
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.