In the tryapl.org Primer when you select the Del glyph (∇) it shows an example of a Double-Del (∇∇) where a dop (as I understand it, a user defined operator) can reference itself in a recursive fashion:
Double-Del Syntax: dop self-reference
pow←{ ⍝ power operator: apply ⍵⍵ times
⍵⍵=0:⍵ ⍝ ⍵⍵ is 0: finished
⍺⍺ ∇∇(⍵⍵-1)⍺⍺ ⍵ ⍝ otherwise: recurse
}
Can someone provide me with some examples of this particular dop in use so I can see how to utilize it? I see that a single ⍺ is not used in the body of this dop, only ⍺⍺; does that make this a monadic dop?
I have tried a number of different ways to use this operator in an expression after it's defined and can't seem to get anything but errors or instances where it appears the body/text of the dop is in an array as text, alongside what I was trying to pass to it.
Example usage
incr←{1+⍵} ⍝ define an increment function
(incr pow 3) 5 ⍝ apply it 3 times to 5
8
Lack of single ⍺
The operator has ⍵⍵ which alone makes it a dyadic operator. The lack of a single ⍺ means that, when given the two required operands, it derives a monadic function.
Troubleshooting
When an operator takes an array (as opposed to a function) operand adjacent to an argument, it is crucial to separate the operand from the argument. This can be done in any of three ways:
Name the derived function and then apply it:add3←incr pow 3
add3 5
Parenthesise the derived function:(incr pow 3) 5
Separate the operand from the argument using a function (often, the identity function ⊢ is used): incr pow 3 ⊢ 5
Failing this, the intended operand and argument will strand to a single array, which then becomes the operand, leaving the derived function with no argument that it can apply to:
incr pow (3 5)
The result is therefore the derived function; the text source you see reported.
Related
I am looking for different ways of writing for loops in Julia! I know this is a basic question but I'm wondering what some of the different options are and if there are advantages/disadvantages with respect to performance.
For loop
Pro: fully flexible has break and continue
Con: no return, must specify iterator at start
While loop
Pro: fully flexible has break and continue
Con: no return, if iterator must be handled manually
Label+goto
Please don't use this for loops
Generator comprehension/Vector comprehension
Pro: Has return value, continue is expressed with filter clause, comes in lazy (generator) and eager forms (vector), can create multidimensional return vale
Con: really ugly for anything long, no break
Broadcast
Pro: express transform of multiple input susictly, has return value with output structure matching what it should be. Can be expressed with just a dot and supports loop fusion.
Con: no break no contine. Writing body means writing a function. Wrapping things you want to broadcast as scalar in Ref is a bit ugly
Map/pmap/asyncmap
Written in do-block form
Pro: can easily change to run distributed or asynchronously, had a return value
Con: no break, no continue
foreach function
It is a lot like map but no return value. So save on allocating that.
Other than that same pros and cons
This is straight from the Julia docs:
The for loop makes common repeated evaluation idioms easier to write. Since counting up and down like the above while loop does is so common, it can be expressed more concisely with a for loop:
julia> for i = 1:5
println(i)
end
1
2
3
4
5
Here the 1:5 is a range object, representing the sequence of numbers 1, 2, 3, 4, 5. The for loop iterates through these values, assigning each one in turn to the variable i. One rather important distinction between the previous while loop form and the for loop form is the scope during which the variable is visible. If the variable i has not been introduced in another scope, in the for loop form, it is visible only inside of the for loop, and not outside/afterward. You'll either need a new interactive session instance or a different variable name to test this:
julia> for j = 1:5
println(j)
end
1
2
3
4
5
julia> j
ERROR: UndefVarError: j not defined
See Scope of Variables for a detailed explanation of the variable scope and how it works in Julia.
In general, the for loop construct can iterate over any container. In these cases, the alternative (but fully equivalent) keyword in or ∈ is typically used instead of =, since it makes the code read more clearly:
julia> for i in [1,4,0]
println(i)
end
1
4
0
julia> for s ∈ ["foo","bar","baz"]
println(s)
end
foo
bar
baz
Various types of iterable containers will be introduced and discussed in later sections of the manual (see, e.g., Multi-dimensional Arrays).
I'm trying to calculate the average of an integer array using the reduce function in one step. I can't do this:
say (reduce {($^a + $^b)}, <1 2 3>) / <1 2 3>.elems;
because it calculates the average in 2 separate pieces.
I need to do it like:
say reduce {($^a + $^b) / .elems}, <1 2 3>;
but it doesn't work of course.
How to do it in one step? (Using map or some other function is welcomed.)
TL;DR This answer starts with an idiomatic way to write equivalent code before discussing P6 flavored "tacit" programming and increasing brevity. I've also added "bonus" footnotes about the hyperoperation Håkon++ used in their first comment on your question.5
Perhaps not what you want, but an initial idiomatic solution
We'll start with a simple solution.1
P6 has built in routines2 that do what you're asking. Here's a way to do it using built in subs:
say { sum($_) / elems($_) }(<1 2 3>); # 2
And here it is using corresponding3 methods:
say { .sum / .elems }(<1 2 3>); # 2
What about "functional programming"?
First, let's replace .sum with an explicit reduction:
.reduce(&[+]) / .elems
When & is used at the start of an expression in P6 you know the expression refers to a Callable as a first class citizen.
A longhand way to refer to the infix + operator as a function value is &infix:<+>. The shorthand way is &[+].
As you clearly know, the reduce routine takes a binary operation as an argument and applies it to a list of values. In method form (invocant.reduce) the "invocant" is the list.
The above code calls two methods -- .reduce and .elems -- that have no explicit invocant. This is a form of "tacit" programming; methods written this way implicitly (or "tacitly") use $_ (aka "the topic" or simply "it") as their invocant.
Topicalizing (explicitly establishing what "it" is)
given binds a single value to $_ (aka "it") for a single statement or block.
(That's all given does. Many other keywords also topicalize but do something else too. For example, for binds a series of values to $_, not just one.)
Thus you could write:
say .reduce(&[+]) / .elems given <1 2 3>; # 2
Or:
$_ = <1 2 3>;
say .reduce(&[+]) / .elems; # 2
But given that your focus is FP, there's another way that you should know.
Blocks of code and "it"
First, wrap the code in a block:
{ .reduce(&[+]) / .elems }
The above is a Block, and thus a lambda. Lambdas without a signature get a default signature that accepts one optional argument.
Now we could again use given, for example:
say do { .reduce(&[+]) / .elems } given <1 2 3>; # 2
But we can also just use ordinary function call syntax:
say { .reduce(&[+]) / .elems }(<1 2 3>)
Because a postfix (...) calls the Callable on its left, and because in the above case one argument is passed in the parens to a block that expects one argument, the net result is the same as the do4 and the given in the prior line of code.
Brevity with built ins
Here's another way to write it:
<1 2 3>.&{.sum/.elems}.say; #2
This calls a block as if it were a method. Imo that's still eminently readable, especially if you know P6 basics.
Or you can start to get silly:
<1 2 3>.&{.sum/$_}.say; #2
This is still readable if you know P6. The / is a numeric (division) operator. Numeric operators coerce their operands to be numeric. In the above $_ is bound to <1 2 3> which is a list. And in Perls, a collection in numeric context is its number of elements.
Changing P6 to suit you
So far I've stuck with standard P6.
You can of course write subs or methods and name them using any Unicode letters. If you want single letter aliases for sum and elems then go ahead:
my (&s, &e) = &sum, &elems;
But you can also extend or change the language as you see fit. For example, you can create user defined operators:
#| LHS ⊛ RHS.
#| LHS is an arbitrary list of input values.
#| RHS is a list of reducer function, then functions to be reduced.
sub infix:<⊛> (#lhs, *#rhs (&reducer, *#fns where *.all ~~ Callable)) {
reduce &reducer, #fns».(#lhs)
}
say <1 2 3> ⊛ (&[/], &sum, &elems); # 2
I won't bother to explain this for now. (Feel free to ask questions in the comments.) My point is simply to highlight that you can introduce arbitrary (prefix, infix, circumfix, etc.) operators.
And if custom operators aren't enough you can change any of the rest of the syntax. cf "braid".
Footnotes
1 This is how I would normally write code to do the computation asked for in the question. #timotimo++'s comment nudged me to alter my presentation to start with that, and only then shift gears to focus on a more FPish solution.
2 In P6 all built in functions are referred to by the generic term "routine" and are instances of a sub-class of Routine -- typically a Sub or Method.
3 Not all built in sub routines have correspondingly named method routines. And vice-versa. Conversely, sometimes there are correspondingly named routines but they don't work exactly the same way (with the most common difference being whether or not the first argument to the sub is the same as the "invocant" in the method form.) In addition, you can call a subroutine as if it were a method using the syntax .&foo for a named Sub or .&{ ... } for an anonymous Block, or call a method foo in a way that looks rather like a subroutine call using the syntax foo invocant: or foo invocant: arg2, arg3 if it has arguments beyond the invocant.
4 If a block is used where it should obviously be invoked then it is. If it's not invoked then you can use an explicit do statement prefix to invoke it.
5 Håkon's first comment on your question used "hyperoperation". With just one easy to recognize and remember "metaop" (for unary operations) or a pair of them (for binary operations), hyperoperations distribute an operation to all the "leaves"6 of a data structure (for an unary) or create a new one based on pairing up the "leaves" of a pair of data structures (for binary operations). NB. Hyperoperations are done in parallel7.
6 What is a "leaf" for a hyperoperation is determined by a combination of the operation being applied (see the is nodal trait) and whether a particular element is Iterable.
7 Hyperoperation is applied in parallel, at least semantically. Hyperoperation assumes8 that the operations on the "leaves" have no mutually interfering side-effects -- that is to say, that any side effect when applying the operation to one "leaf" can safely be ignored in respect to applying the operation to any another "leaf".
8 By using a hyperoperation the developer is declaring that the assumption of no meaningful side-effects is correct. The compiler will act on the basis it is, but will not check that it is true. In the safety sense it's like a loop with a condition. The compiler will follow the dev's instructions, even if the result is an infinite loop.
Here is an example using given and the reduction meta operator:
given <1 2 3> { say ([+] $_)/$_.elems } ;
In Julia we can redefine the operator like ＋,× ,sin, cos and so on. But one interesting thing is that after the redefinition the order among all those amended operators still hold,like multiplication still goes before summation no matter how you redefine your own function,why?
This is simply a function of how operator precedence is defined for infix operators. You can find the precedence table in the manual. Precedence defines how Julia code is parsed into a sequence of nested function calls. You can manually specify precedence with parentheses or traditional function call syntax.
You can see this happening just by asking Julia to parse (but not execute) the code. It returns back a quoted expression, which is printed in a way that mimics the standard way of writing that code in Julia. There are a number of tools that allow you to inspect this quoted expression; one that's helpful to display the nesting of calls is Meta.show_sexpr.
julia> ex = parse("1 + 2 * 3")
:(1 + 2 * 3)
julia> Meta.show_sexpr(ex)
(:call, :+, 1, (:call, :*, 2, 3))
This is simply stating that it will multiply 2*3 before adding 1 to it. That is entirely based upon the nested structure of the parentheses. Whatever function is bound to * will get executed first, regardless of its definition. Now compare the results above with things like:
parse("(1 + 2) * 3")
parse("+(1, *(2, 3))")
parse("*(1, +(2, 3))")
As follow up to yesterday's question Erlang: choosing unique items from a list, using recursion
In Erlang, say I wanted choose all unique items from a given list, e.g.
List = [foo, bar, buzz, foo].
and I had used your code examples resulting in
NewList = [bar, buzz].
How would I further manipulate NewList in Erlang?
For example, say I not only wanted to choose all unique items from List, but also count the total number of characters of all resulting items from NewList?
In functional programming we have patterns that occur so frequently they deserve their own names and support functions. Two of the most widely used ones are map and fold (sometimes reduce). These two form basic building blocks for list manipulation, often obviating the need to write dedicated recursive functions.
Map
The map function iterates over a list in order, generating a new list where each element is the result of applying a function to the corresponding element in the original list. Here's how a typical map might be implemented:
map(Fun, [H|T]) -> % recursive case
[Fun(H)|map(Fun, T)];
map(_Fun, []) -> % base case
[].
This is a perfect introductory example to recursive functions; roughly speaking, the function clauses are either recursive cases (result in a call to iself with a smaller problem instance) or base cases (no recursive calls made).
So how do you use map? Notice that the first argument, Fun, is supposed to be a function. In Erlang, it's possible to declare anonymous functions (sometimes called lambdas) inline. For example, to square each number in a list, generating a list of squares:
map(fun(X) -> X*X end, [1,2,3]). % => [1,4,9]
This is an example of Higher-order programming.
Note that map is part of the Erlang standard library as lists:map/2.
Fold
Whereas map creates a 1:1 element mapping between one list and another, the purpose of fold is to apply some function to each element of a list while accumulating a single result, such as a sum. The right fold (it helps to think of it as "going to the right") might look like so:
foldr(Fun, Acc, [H|T]) -> % recursive case
foldr(Fun, Fun(H, Acc), T);
foldr(_Fun, Acc, []) -> % base case
Acc.
Using this function, we can sum the elements of a list:
foldr(fun(X, Sum) -> Sum + X, 0, [1,2,3,4,5]). %% => 15
Note that foldr and foldl are both part of the Erlang standard library, in the lists module.
While it may not be immediately obvious, a very large class of common list-manipulation problems can be solved using map and fold alone.
Thinking recursively
Writing recursive algorithms might seem daunting at first, but as you get used to it, it turns out to be quite natural. When encountering a problem, you should identify two things:
How can I decompose the problem into smaller instances? In order for recursion to be useful, the recursive call must take a smaller problem as its argument, or the function will never terminate.
What's the base case, i.e. the termination criterion?
As for 1), consider the problem of counting the elements of a list. How could this possibly be decomposed into smaller subproblems? Well, think of it this way: Given a non-empty list whose first element (head) is X and whose remainder (tail) is Y, its length is 1 + the length of Y. Since Y is smaller than the list [X|Y], we've successfully reduced the problem.
Continuing the list example, when do we stop? Well, eventually, the tail will be empty. We fall back to the base case, which is the definition that the length of the empty list is zero. You'll find that writing function clauses for the various cases is very much like writing definitions for a dictionary:
%% Definition:
%% The length of a list whose head is H and whose tail is T is
%% 1 + the length of T.
length([H|T]) ->
1 + length(T);
%% Definition: The length of the empty list ([]) is zero.
length([]) ->
0.
You could use a fold to recurse over the resulting list. For simplicity I turned your atoms into strings (you could do this with list_to_atom/1):
1> NewList = ["bar", "buzz"].
["bar","buzz"]
2> L = lists:foldl(fun (W, Acc) -> [{W, length(W)}|Acc] end, [], NewList).
[{"buzz",4},{"bar",3}]
This returns a proplist you can access like so:
3> proplists:get_value("buzz", L).
4
If you want to build the recursion yourself for didactic purposes instead of using lists:
count_char_in_list([], Count) ->
Count;
count_char_in_list([Head | Tail], Count) ->
count_char_in_list(Tail, Count + length(Head)). % a string is just a list of numbers
And then:
1> test:count_char_in_list(["bar", "buzz"], 0).
7
I understand what the concept of currying is, and know how to use it. These are not my questions, rather I am curious as to how this is actually implemented at some lower level than, say, Haskell code.
For example, when (+) 2 4 is curried, is a pointer to the 2 maintained until the 4 is passed in? Does Gandalf bend space-time? What is this magic?
Short answer: yes a pointer is maintained to the 2 until the 4 is passed in.
Longer than necessary answer:
Conceptually, you're supposed to think about Haskell being defined in terms of the lambda calculus and term rewriting. Lets say you have the following definition:
f x y = x + y
This definition for f comes out in lambda calculus as something like the following, where I've explicitly put parentheses around the lambda bodies:
\x -> (\y -> (x + y))
If you're not familiar with the lambda calculus, this basically says "a function of an argument x that returns (a function of an argument y that returns (x + y))". In the lambda calculus, when we apply a function like this to some value, we can replace the application of the function by a copy of the body of the function with the value substituted for the function's parameter.
So then the expression f 1 2 is evaluated by the following sequence of rewrites:
(\x -> (\y -> (x + y))) 1 2
(\y -> (1 + y)) 2 # substituted 1 for x
(1 + 2) # substituted 2 for y
3
So you can see here that if we'd only supplied a single argument to f, we would have stopped at \y -> (1 + y). So we've got a whole term that is just a function for adding 1 to something, entirely separate from our original term, which may still be in use somewhere (for other references to f).
The key point is that if we implement functions like this, every function has only one argument but some return functions (and some return functions which return functions which return ...). Every time we apply a function we create a new term that "hard-codes" the first argument into the body of the function (including the bodies of any functions this one returns). This is how you get currying and closures.
Now, that's not how Haskell is directly implemented, obviously. Once upon a time, Haskell (or possibly one of its predecessors; I'm not exactly sure on the history) was implemented by Graph reduction. This is a technique for doing something equivalent to the term reduction I described above, that automatically brings along lazy evaluation and a fair amount of data sharing.
In graph reduction, everything is references to nodes in a graph. I won't go into too much detail, but when the evaluation engine reduces the application of a function to a value, it copies the sub-graph corresponding to the body of the function, with the necessary substitution of the argument value for the function's parameter (but shares references to graph nodes where they are unaffected by the substitution). So essentially, yes partially applying a function creates a new structure in memory that has a reference to the supplied argument (i.e. "a pointer to the 2), and your program can pass around references to that structure (and even share it and apply it multiple times), until more arguments are supplied and it can actually be reduced. However it's not like it's just remembering the function and accumulating arguments until it gets all of them; the evaluation engine actually does some of the work each time it's applied to a new argument. In fact the graph reduction engine can't even tell the difference between an application that returns a function and still needs more arguments, and one that has just got its last argument.
I can't tell you much more about the current implementation of Haskell. I believe it's a distant mutant descendant of graph reduction, with loads of clever short-cuts and go-faster stripes. But I might be wrong about that; maybe they've found a completely different execution strategy that isn't anything at all like graph reduction anymore. But I'm 90% sure it'll still end up passing around data structures that hold on to references to the partial arguments, and it probably still does something equivalent to factoring in the arguments partially, as it seems pretty essential to how lazy evaluation works. I'm also fairly sure it'll do lots of optimisations and short cuts, so if you straightforwardly call a function of 5 arguments like f 1 2 3 4 5 it won't go through all the hassle of copying the body of f 5 times with successively more "hard-coding".
Try it out with GHC:
ghc -C Test.hs
This will generate C code in Test.hc
I wrote the following function:
f = (+) 16777217
And GHC generated this:
R1.p[1] = (W_)Hp-4;
*R1.p = (W_)&stg_IND_STATIC_info;
Sp[-2] = (W_)&stg_upd_frame_info;
Sp[-1] = (W_)Hp-4;
R1.w = (W_)&integerzmgmp_GHCziInteger_smallInteger_closure;
Sp[-3] = 0x1000001U;
Sp=Sp-3;
JMP_((W_)&stg_ap_n_fast);
The thing to remember is that in Haskell, partially applying is not an unusual case. There's technically no "last argument" to any function. As you can see here, Haskell is jumping to stg_ap_n_fast which will expect an argument to be available in Sp.
The stg here stands for "Spineless Tagless G-Machine". There is a really good paper on it, by Simon Peyton-Jones. If you're curious about how the Haskell runtime is implemented, go read that first.