I have some code to find the maximum height and replace it with the associated name. There are separate lists for height and names, each the same length and non-empty.
I can solve this using structural recursion but have to change it into accumulative, and I am unsure how to do that. All the examples I have seen are confusing me. Is anybody able to turn the code into one using accumulative recursion?
(define (tallest names heights)
(cond
[(empty? names) heights]
[(> (first heights) (first (rest heights)))
(cons (first names) (tallest (rest (rest names)) (rest (rest heights))))]
[else (tallest (rest names) (rest heights))]))
First of all, your provided tallest function doesn't actually work (calling (tallest '(Bernie Raj Amy) (list 1.5 1.6 1.7)) fails with a contract error), but I see what you're getting at. What's the difference between structural recursion and accumulative recursion?
Well, structural recursion works by building a structure as a return value, in which one of the values inside that structure is the result of a recursive call to the same function. Take the recursive calculation of a factorial, for example. You might define it like this:
(define (factorial n)
(if (zero? n) 1
(* n (factorial (sub1 n)))))
Visualize how this program would execute for an input of, say, 4. Each call leaves a "hole" in the multiplication expression to be filled in by the result of a recursive subcall. Here's what that would look like, visualized, using _ to represent one of those "holes".
(* 4 _)
(* 3 _)
(* 2 _)
(* 1 _)
1
Notice how a large portion of the work is done only after the final case is reached. Much of the work must be done in the process of popping the calls off the stack as they return because each call depends on performing some additional operation on its subcall's result.
How is accumulative recursion different? Well, in accumulative recursion, we use an extra argument to the function called an accumulator. Rewriting the above factorial function to use an accumulator would make it look like this:
(define (factorial n acc)
(if (zero? n) acc
(factorial (sub1 n) (* acc n))))
Now if we wanted to find the factorial of 4, we'd have to call (factorial 4 1), providing a starting value for the accumulator (I'll address how to avoid that in a moment). If you think about the call stack now, it would look quite different.
Notice how there are no "holes" to be filled in—the result of the factorial function is either the accumulator or a direct call to itself. This is referred to as a tail call, and the recursive call to factorial is referred to as being in tail position.
This turns out to be helpful for a few reasons. First of all, some functions are just easier to express with accumulative recursion, though factorial probably isn't one of them. More importantly, however, Scheme requires that implementations provide proper tails calls (sometimes also called "tail call optimization"), which means that the call stack will not grow in depth when tail calls are made.
There is plenty of existing information about how tail calls work and why they're useful, so I won't repeat that here. What's important to understand is that accumulative recursion involves an accumulator argument, which usually causes the resulting function to be implemented with a tail call.
But what do we do about the extra parameter? Well, we can actually just make a "helper" function that will do the accumulative recursion, but we will provide a function that automatically fills in the base case.
(define (factorial n)
(define (factorial-helper n acc)
(if (zero? n) acc
(factorial-helper (sub1 n) (* acc n))))
(factorial-helper n 1))
This sort of idiom is common enough that Racket provides a "named let" form, which simplifies the above function to this:
(define (factorial n)
(let helper ([n n] [acc 1])
(if (zero? n) acc
(helper (sub1 n) (* acc n)))))
But that's just some syntactic sugar for the same idea.
Okay, so: how does any of this apply to your question? Well, actually, using accumulative recursion makes implementing your problem quite easy. Here's a breakdown of how you'd structure the algorithm:
Just like in your original example, you'd iterate through the list until you get empty. This will form your "base case".
Your accumulator will be simple—it will be the current maximum element you've found.
Upon each iteration, if you find an element greater than the current maximum, that becomes the new accumulator. Otherwise, the accumulator remains the same.
Putting these all together, and here's a simple implementation:
(define (tallest-helper names heights current-tallest)
(cond
[(empty? names)
(car current-tallest)]
[(> (first heights) (cdr current-tallest))
(tallest-helper (rest names) (rest heights)
(cons (first names) (first heights)))]
[else
(tallest-helper (rest names) (rest heights)
current-tallest)]))
This can be further improved in plenty of ways—wrapping it in a function that provides the accumulator's starting value, using named let, removing some of the repetition, etc.—but I'll leave that as an exercise for you.
Just remember: the accumulator is effectively your "working sum". It's your "running total". Understand that, and things should make sense.
Related
I wonder how do you, experienced lispers / functional programmers usually make decision what to use. Compare:
(define (my-map1 f lst)
(reverse
(let loop ([lst lst] [acc '()])
(if (empty? lst)
acc
(loop (cdr lst) (cons (f (car lst)) acc))))))
and
(define (my-map2 f lst)
(if (empty? lst)
'()
(cons (f (car lst)) (my-map2 f (cdr lst)))))
The problem can be described in the following way: whenever we have to traverse a list, should we collect results in accumulator, which preserves tail recursion, but requires list reversion in the end? Or should we use unoptimized recursion, but then we don't have to reverse anything?
It seems to me the first solution is always better. Indeed, there's additional complexity (O(n)) there. However, it uses much less memory, let alone calling a function isn't done instantly.
Yet I've seen different examples where the second approach was used. Either I'm missing something or these examples were only educational. Are there situations where unoptimized recursion is better?
When possible, I use higher-order functions like map which build a list under the hood. In Common Lisp I also tend to use loop a lot, which has a collect keyword for building list in a forward way (I also use the series library which also implements it transparently).
I sometimes use recursive functions that are not tail-recursive because they better express what I want and because the size of the list is going to be relatively small; in particular, when writing a macro, the code being manipulated is not usually very large.
For more complex problems I don't collect into lists, I generally accept a callback function that is being called for each solution. This ensures that the work is more clearly separated between how the data is produced and how it is used.
This approach is to me the most flexible of all, because no assumption is made about how the data should be processed or collected. But it also means that the callback function is likely to perform side-effects or non-local returns (see example below). I don't think it is particularly a problem as long the the scope of the side-effects is small (local to a function).
For example, if I want to have a function that generates all natural numbers between 0 and N-1, I write:
(defun range (n f)
(dotimes (i n)
(funcall f i)))
The implementation here iterates over all values from 0 below N and calls F with the value I.
If I wanted to collect them in a list, I'd write:
(defun range-list (N)
(let ((list nil))
(range N (lambda (v) (push v list)))
(nreverse list)))
But, I can also avoid the whole push/nreverse idiom by using a queue. A queue in Lisp can be implemented as a pair (first . last) that keeps track of the first and last cons cells of the underlying linked-list collection. This allows to append elements in constant time to the end, because there is no need to iterate over the list (see Implementing queues in Lisp by P. Norvig, 1991).
(defun queue ()
(let ((list (list nil)))
(cons list list)))
(defun qpush (queue element)
(setf (cdr queue)
(setf (cddr queue)
(list element))))
(defun qlist (queue)
(cdar queue))
And so, the alternative version of the function would be:
(defun range-list (n)
(let ((q (queue)))
(range N (lambda (v) (qpush q v)))
(qlist q)))
The generator/callback approach is also useful when you don't want to build all the elements; it is a bit like the lazy model of evaluation (e.g. Haskell) where you only use the items you need.
Imagine you want to use range to find the first empty slot in a vector, you could do this:
(defun empty-index (vector)
(block nil
(range (length vector)
(lambda (d)
(when (null (aref vector d))
(return d))))))
Here, the block of lexical name nil allows the anonymous function to call return to exit the block with a return value.
In other languages, the same behaviour is often reversed inside-out: we use iterator objects with a cursor and next operations. I tend to think it is simpler to write the iteration plainly and call a callback function, but this would be another interesting approach too.
Tail recursion with accumulator
Traverses the list twice
Constructs two lists
Constant stack space
Can crash with malloc errors
Naive recursion
Traverses list twice (once building up the stack, once tearing down the stack).
Constructs one list
Linear stack space
Can crash with stack overflow (unlikely in racket), or malloc errors
It seems to me the first solution is always better
Allocations are generally more time-expensive than extra stack frames, so I think the latter one will be faster (you'll have to benchmark it to know for sure though).
Are there situations where unoptimized recursion is better?
Yes, if you are creating a lazily evaluated structure, in haskell, you need the cons-cell as the evaluation boundary, and you can't lazily evaluate a tail recursive call.
Benchmarking is the only way to know for sure, racket has deep stack frames, so you should be able to get away with both versions.
The stdlib version is quite horrific, which shows that you can usually squeeze out some performance if you're willing to sacrifice readability.
Given two implementations of the same function, with the same O notation, I will choose the simpler version 95% of the time.
There are many ways to make recursion preserving iterative process.
I usually do continuation passing style directly. This is my "natural" way to do it.
One takes into account the type of the function. Sometimes you need to connect your function with the functions around it and depending on their type you can choose another way to do recursion.
You should start by solving "the little schemer" to gain a strong foundation about it. In the "little typer" you can discover another type of doing recursion, founded on other computational philosophy, used in languages like agda, coq.
In scheme you can write code that is actually haskell sometimes (you can write monadic code that would be generated by a haskell compiler as intermediate language). In that case the way to do recursion is also different that "usual" way, etc.
false dichotomy
You have other options available to you. Here we can preserve tail-recursion and map over the list with a single traversal. The technique used here is called continuation-passing style -
(define (map f lst (return identity))
(if (null? lst)
(return null)
(map f
(cdr lst)
(lambda (r) (return (cons (f (car lst)) r))))))
(define (square x)
(* x x))
(map square '(1 2 3 4))
'(1 4 9 16)
This question is tagged with racket, which has built-in support for delimited continuations. We can accomplish map using a single traversal, but this time without using recursion. Enjoy -
(require racket/control)
(define (yield x)
(shift return (cons x (return (void)))))
(define (map f lst)
(reset (begin
(for ((x lst))
(yield (f x)))
null)))
(define (square x)
(* x x))
(map square '(1 2 3 4))
'(1 4 9 16)
It's my intention that this post will show you the detriment of pigeonholing your mind into a particular construct. The beauty of Scheme/Racket, I have come to learn, is that any implementation you can dream of is available to you.
I would highly recommend Beautiful Racket by Matthew Butterick. This easy-to-approach and freely-available ebook shatters the glass ceiling in your mind and shows you how to think about your solutions in a language-oriented way.
I'm new to Racket and I'm trying to define a function that takes a list of divisors and a list of numbers to test, and applies direct recursion and a function 'drop-divisible' for each element in the list of divisors.
I defined a function drop-divisible which takes a number and a list of numbers, and returns a new list containing only those numbers not "non-trivially divisible" by the number. This function is not the problem, it works. So what I have trouble with is the function who is gonna call this function, and itself.
Here is what I've come up with. I can imagine that this is far from correct, but I have no idea what to do.
(define (sieve-with divisors testlist)
(if (null? divisors)
'()
(begin
(drop-divisible (first divisors) testlist)
(sieve-with (rest divisors) testlist))))
You need to use tail recursion:
(define (sieve-with divisors list)
(cond [(empty? divisors) list]
[else (sieve-with (rest divisors)
(drop-divisible (first divisors) list))]))
Also, stay away from begin as much as possible. Stick to the functional paradigm.
I present two naive implementations of foldr in racket
This first one lacks a proper tail call and is problematic for large values of xs
(define (foldr1 f y xs)
(if (empty? xs)
y
(f (car xs) (foldr1 f y (cdr xs)))))
(foldr1 list 0 '(1 2 3))
; => (1 (2 (3 0))
This second one uses an auxiliary function with a continuation to achieve a proper tail call making it safe for use with large values of xs
(define (foldr2 f y xs)
(define (aux k xs)
(if (empty? xs)
(k y)
(aux (lambda (rest) (k (f (car xs) rest))) (cdr xs))))
(aux identity xs))
(foldr2 list 0 '(1 2 3))
; => (1 (2 (3 0)))
Looking at racket/control I see that racket supports first-class continuations. I was wondering if it was possible/beneficial to express the second implementation of foldr using shift and reset. I was playing around with it for a little while and my brain just ended up turning inside out.
Please provide thorough explanation with any answer. I'm looking for big-picture understanding here.
Disclaimers:
The “problem” of foldr you are trying to solve is actually its main feature.
Fundamentally, you cannot process a list in reverse easily and the best you can do is reverse it first. Your solution with a lambda, in its essence, is no different from a recursion, it’s just that instead of accumulating recursive calls on the stack you are accumulating them explicitly in many lambdas, so the only gain is that instead of being limited by the stack size, you can go as deep as much memory you have, since lambdas are, likely, allocated on the heap, and the trade off is that you now perform dynamic memory allocations/deallocations for each “recursive call”.
Now, with that out of the way, to the actual answer.
Let’s try to implement foldr keeping in mind that we can work with continuations. Here is my first attempt:
(define (foldr3 f y xs)
(if (empty? xs)
y
(reset
(f (car xs) (shift k (k (foldr3 f y (cdr xs))))))))
; ^ Set a marker here.
; ^ Ok, so we want to call `f`.
; ^ But we don’t have a value to pass as the second argument yet.
; Let’s just pause the computation, wrap it into `k` to use later...
; And then resume it with the result of computing the fold over the tail.
If you look closely at this code, you will realise, that it is exactly the same as your foldr – even though we “pause” the computation, we then immediately resume it and pass the result of a recursive call to it, and this construction is, of course, not tail recursive.
Ok, then it looks like we need to make sure that we do not resume it immediately, but rather perform the recursive computation first, and then resume the paused computation with the recursive computation result. Let’s rework our function to accept a continuation and call it once it has actually computed the value that it needs.
(define (foldr4 f y xs)
(define (aux k xs)
(if (empty? xs)
(k y)
(reset
(k (f (car xs) (shift k2 (aux k2 (cdr xs))))))))
(reset (shift k (aux k xs))))
The logic here is similar to the previous version: in the non-trivial branch of the if we set a reset marker, and then start computing the expression as if we had everything we need; however, in reality, we do not have the result for the tail of the list yet, so we pause the computation, “package it” into k2, and perform a (this time tail-) recursive call saying “hey, when you’ve got your result, resume this paused computation”.
If you analyse how this code is executed, you’ll see that there is absolutely no magic in it and it works simply by “wrapping” continuations one into another while it traverses the list, and then, once it reaches the end, the continuations are “unwrapped” and executed in the reverse order one by one. In fact, this function does exactly the same as what foldr2 does – the difference is merely syntactical: instead of creating explicit lambdas, the reset/shift pattern allows us to start writing out the expression right away and then at some point say “hold on a second, I don’t have this value yet, let’s pause here and return later”... but under the hood it ends up creating the same closure that lambda would!
I believe, there is no way to do better than this with lists.
Another disclaimer: I don’t have a working Scheme/Racket interpreter with reset/shift implemented, so I didn’t test the functions.
I'm trying to reverse a list in scheme and I came up with to the following solution:
(define l (list 1 2 3 4))
(define (reverse lista)
(car (cons (reverse (cdr (cons 0 lista))) 0)))
(display (reverse l))
Although it works I don't really understand why it works.
In my head, it would evaluate to a series of nested cons until cons of () (which the cdr of a list with one element).
I guess I am not understanding the substitution model, could someone explain me why it works?
Obs:
It is supposed to work only in not nested lists.
Taken form SICP, exercise 2.18.
I know there are many similar questions, but as far as I saw, none presented
this solution.
Thank you
[As this happens quite often, I write the answer anyway]
Scheme implementations do have their builtin versions of reverse, map, append etc. as they are specified in RxRS (e.g. https://www.cs.indiana.edu/scheme-repository/R4RS/r4rs_8.html).
In the course of learning scheme (and actually any lisp dialect) it's really valuable to implement them anyway. The danger is, one's definition can collide with the built-in one (although e.g. scheme's define or lisp's label should shadow them). Therefore it's always worth to call this hand-made implementation with some other name, like "my-reverse", "my-append" etc. This way you will save yourself much confusion, like in the following:
(let ([append
(lambda (xs ys)
(if (null? xs)
ys
(cons (car xs) (append (cdr xs) ys))))])
(append '(hello) '(there!)))
-- this one seems to work, creating a false impression that "let" works the same as "letrec". But just change the name to "my-append" and it breaks, because at the moment of evaluating the lambda form, the symbol "my-append" is not yet bound to anything (unlike "append" which was defined as a builtin procedure).
Of course such let form will work in a language with dynamic scoping, but scheme is lexical (with the exception of "define"s), and the reason is referential transparency (but that's so far offtopic that I can only refer interested reader to one of the lambda papers http://repository.readscheme.org/ftp/papers/ai-lab-pubs/AIM-453.pdf).
This reads pretty much the same as the solutions in other languages:
if the list is empty, return an empty list. Otherwise ...
chop off the first element (CAR)
reverse the remainder of the list (CDR)
append (CONS) the first element to that reversal
return the result
Now ... given my understanding from LISP days, the code would look more like this:
(append (reverse (cdr lista)) (list (car lista)))
... which matches my description above.
There are several ways to do it. Here is another:
(define my-reverse
(lambda (lst)
(define helper
(lambda (lst result)
(if (null? lst)
result
(helper (cdr lst) (cons (car lst) result)))))
(helper lst '())))
I am self-studyinig SICP and having a hard time finding order of growth of recursive functions.
The following procedure list->tree converts an ordered list to a balanced search tree:
(define (list->tree elements)
(car (partial-tree elements (length elements))))
(define (partial-tree elts n)
(if (= n 0)
(cons '() elts)
(let ((left-size (quotient (- n 1) 2)))
(let ((left-result (partial-tree elts left-size)))
(let ((left-tree (car left-result))
(non-left-elts (cdr left-result))
(right-size (- n (+ left-size 1))))
(let ((this-entry (car non-left-elts))
(right-result (partial-tree (cdr non-left-elts)
right-size)))
(let ((right-tree (car right-result))
(remaining-elts (cdr right-result)))
(cons (make-tree this-entry left-tree right-tree)
remaining-elts))))))))
I have been looking at the solution online, and the following website I believe offers the best solution but I have trouble making sense of it:
jots-jottings.blogspot.com/2011/12/sicp-exercise-264-constructing-balanced.html
My understanding is that the procedure 'partial-tree' repeatedly calls three argument each time it is called - 'this-entry', 'left-tree', and 'right-tree' respectively. (and 'remaining-elts' only when it is necessary - either in very first 'partial-tree' call or whenever 'non-left-elts' is called)
this-entry calls : car, cdr, and cdr(left-result)
left-entry calls : car, cdr, and itself with its length halved each step
right-entry calls: car, itself with cdr(cdr(left-result)) as argument and length halved
'left-entry' would have base 2 log(n) steps, and all three argument calls 'left-entry' separately.
so it would have Ternary-tree-like structure and the total number of steps I thought would be similar to 3^log(n). but the solution says it only uses each index 1..n only once. But doesn't 'this-entry' for example reduce same index at every node separate to 'right-entry'?
I am confused..
Further, in part (a) the solution website states:
"in the non-terminating case partial-tree first calculates the number
of elements that should go into the left sub-tree of a balanced binary
tree of size n, then invokes partial-tree with the elements and that
value which both produces such a sub-tree and the list of elements not
in that sub-tree. It then takes the head of the unused elements as the
value for the current node"
I believe the procedure does this-entry before left-tree. Why am I wrong?
This is my very first book on CS and I have yet to come across Master Theorem.
It is mentioned in some solutions but hopefully I should be able to do the question without using it.
Thank you for reading and I look forward to your kind reply,
Chris
You need to understand how let forms work. In
(let ((left-tree (car left-result))
(non-left-elts (cdr left-result))
left-tree does not "call" anything. It is created as a new lexical variable, and assigned the value of (car left-result). The parentheses around it are just for grouping together the elements describing one variable introduced by a let form: the variable's name and its value:
(let ( ( left-tree (car left-result) )
;; ^^ ^^
( non-left-elts (cdr left-result) )
;; ^^ ^^
Here's how to understand how the recursive procedure works: don't.
Just don't try to understand how it works; instead analyze what it does, assuming that it does (for the smaller cases) what it's supposed to do.
Here, (partial-tree elts n) receives two arguments: the list of elements (to be put into tree, presumably) and the list's length. It returns
(cons (make-tree this-entry left-tree right-tree)
remaining-elts)
a cons pair of a tree - the result of conversion, and the remaining elements, which are supposed to be none left, in the topmost call, if the length argument was correct.
Now that we know what it's supposed to do, we look inside it. And indeed assuming the above what it does makes total sense: halve the number of elements, process the list, get the tree and the remaining list back (non-empty now), and then process what's left.
The this-entry is not a tree - it is an element that is housed in a tree's node:
(let ((this-entry (car non-left-elts))
Setting
(right-size (- n (+ left-size 1))
means that n == right-size + 1 + left-size. That's 1 element that goes into the node itself, the this-entry element.
And since each element goes directly into its node, once, the total running time of this algorithm is linear in the number of elements in the input list, with logarithmic stack space use.