I'm going through an exercise to grab the 'leaves' of a nested list in scheme (from SICP). Here is the exercise input-output:
(define x (list (lis 1 2) (list 3 4)))
(fringe x)
; (1 2 3 4)
(fringe (list x x))
; (1 2 3 4 1 2 3 4)
Now, I've come up with two answers for this: one recursive and one iterative. Here are my two implementations below:
(define (fr lst)
(cond ((null? lst) '())
((not (pair? (car lst))) (cons (car lst) (fr (cdr lst))))
(else (append (fr (car lst)) (fr (cdr lst))))))
(define (add-element-to-list lst elem)
(if (null? lst)
(list elem)
(cons (car lst) (add-element-to-list (cdr lst) elem))))
(define (fringe lst)
(define L '())
(define (iter lst)
(if (not (pair? (car lst)))
(set! L (add-element-to-list L (car lst))) ; update the list if it's a leaf
(iter (car lst))) ; otherwise recurse
(if (not (null? (cdr lst))) (iter (cdr lst))) ; and if we have a cdr, recurse on that
L
)
(iter lst)
)
(fringe x)
(fr x)
(fr (list x x))
(fringe (list x x))
; (1 2 3 4)
; (1 2 3 4)
; (1 2 3 4 1 2 3 4)
; (1 2 3 4 1 2 3 4)
; OK
The problem for me is, this exercise took me forever to figure out with a ton of head-bashing along the way (and it's still difficult for me to 'get it' as I write this up). Here are a few things I struggled with and seeing if there are any suggestions on ways to deal with these issues in scheme:
I thought initially that there are two cases. The normal/scalar case and the nested case. However, it seems like there are actually three! There's the normal case, the nested case, and then the null case -- and inner-lists also have the null case! Is there a good general pattern or something to account for the null case? Is this something that comes up a lot?
In the iterative case, why do I have to return L at the end? Why doesn't (iter lst) just return that (i.e., if I removed the standalone-L at the bottom of the iter function).
Finally, is there a 'cleaner' way to implement the iterative case? It seems like I have so much code, where it could probably be improved on.
The reason there are three cases is that you are importing some scalar / vector distinction from some other language: Scheme doesn't have it and it is not helpful. Instead a list is a recursively-defined object: a list is either the empty list, or it is a pair of something and a list. That means there are two distinctions to make, not one: is an object a pair, and is an object the empty list:
(define (lyst? o)
(or (null? o)
(and (pair? o) (lyst? (cdr o)))))
That's completely different than a vector / scalar distinction. I don't know what language you're getting this from, but just think about how the maths of this would work: vectors are defined over some scalar field, and there is no vector which is also a scalar. But for lists there is a list which is not a pair. Just stop thinking about vectors and scalars: it is not a helpful way to think about lists, pairs and the empty list.
The iterative version is too horrible to think about: there's a reason why SICP hasn't introduced set! yet.
First of all it's not actually iterative: like most of the 'iterative' solutions to this problem on the net it looks as if it is, but it's not. The reason it's not is that the skeleton of the iter function looks like
if blah
recurse on the first element of the list
otherwise do something else
if other blah
iterate on the rest of the list
And the critical thing is that both (1) and (2) always happen, so the call into the car of the list is not a tail call: it's a fully-fledged recursive call.
That being said you can make this much better: the absolutely standard way of doing this sort of thing is to use an accumulator:
(define (fringe l)
(define (fringe-loop thing accum)
(cond
((null? thing)
;; we're at the end of the list or an element which is empty list
accum)
((pair? thing)
;; we need to look at both the first of the list and the rest of the list
;; Note that the order is rest then first which means the accumulator
;; comes back in a good order
(fringe-loop (car thing)
(fringe-loop (cdr thing) accum)))
(else
;; not a list at all: collect this "atomic" thing
(cons thing accum))))
(fringe-loop l '()))
Note that this builds the fringe (linear) list from the bottom up, which is the natural way of building linear lists with recursion. To achieve this it slightly deviously orders the way it looks at things so the results come out in the right order. Note also that this is also not iterative: it's recursive, because of the (fringe-loop ... (fringe-loop ...)) call. But this time that's much clearer.
The reason it's not iterative is that the process of searching a (tree-like, Lisp) list is not iterative: it's what SICP would call a 'recursive process' because (Lisp's tree-like) lists are recursively defined in both their first and rest field. Nothing you can do will make the process iterative.
But you can make the code to appear iterative at the implementation level by managing the stack explicitly thus turning it into a tail recursive version. The nature of the computational process doesn't change though:
(define (fringe l)
(define (fringe-loop thing accum stack)
(cond
((null? thing)
;; ignore the () sentinel or () element
(if (null? stack)
;; nothing more to do
accum
;; continue with the thing most recently put aside
(fringe-loop (car stack) accum (cdr stack))))
((pair? thing)
;; carry on to the right, remembering to look to the left later
(fringe-loop (cdr thing) accum (cons (car thing) stack)))
(else
;; we're going to collect this atomic thing but we also need
;; to check the stack
(if (null? stack)
;; we're done
(cons thing accum)
;; collect this and continue with what was put aside
(fringe-loop (car stack) (cons thing accum) (cdr stack))))))
(fringe-loop l '() '()))
Whether that's worth it depends on how expensive you think recursive calls are and whether there is any recursion limit. However the general trick of explicitly managing what you are going to do next is useful in general as it can make it much easier to control search order.
(Note, of course, that you can do a trick like this for any program at all!)
It's about types. Principled development follows types. Then it becomes easy.
Lisp is an untyped language. It's like assembler on steroids. There are no types, no constraints on what you're able to code.
There are no types enforced by the language, but still there are types, conceptually. We code to types, we handle types, we produce values to a given specs i.e. values of some types as needed for the pieces of bigger system to interface properly, for the functions we write to work together properly, etc. etc.
What is it we're building a fringe of? Is it a "list"?
What is a "list"? Is it
(define (list? ls)
(or (null? ls)
(and (pair? ls)
(list? (cdr ls)))))
Is this what we're building a fringe of? How come it says nothing about the car of the thing, are we to ignore anything that's in the car? Why, no, of course not. We're not transforming a list. We're actually transforming a tree:
(define (tree? ls)
(or (null? ls)
(and (pair? ls)
(tree? (car ls))
(tree? (cdr ls)))))
Is it really enough though to only be able to have ()s in it? Probably not.
Is it
(define (tree? ls)
(or (null? ls)
(not (pair? ls)) ;; (atom? ls) is what we mean
(and ;; (pair? ls)
(tree? (car ls))
(tree? (cdr ls)))))
It 1 a tree? Apparently it is, but let's put this aside for now.
What we have here, is a structured, principled way to see a piece of data as belonging to a certain type. Or as some say, data type.
So then we just follow the same skeleton of the data type definition / predicate, to write a function that is to process the values of said type in some specific way (this is the approach promoted by Sterling and Shapiro's "The Art of Prolog").
(define (tree-fringe ls)
So, what is it to produce? A list of atoms in its leaves, that's what.
(cond
((null? ls)
A () is already a list?.
ls)
((not (pair? ls)) ;; (atom? ls) is what we mean
(handle-atom-case ls))
Let's put this off for now. On to the next case,
(else
;; (tree? (car ls))
;; (tree? (cdr ls))
both car and cdr of ls are tree?s. How to handle them, we already know. It's
(let ((a (tree-fringe (car ls)))
(b (tree-fringe (cdr ls)))
and what do we do with the two pieces? We piece them together. First goes the fringe from the left, then from the right. Simple:
(append a b )))))
(define (handle-atom-case ls)
;; bad name, inline its code inside
;; the `tree-fringe` later, when we have it
And so, what type of data does append expect in both its arguments? A list?, again.
And this is what we must produce for an atomic "tree". Such "tree" is its own fringe. Except,
;; tree: 1 2
;; fringe: ( 1 ) ( 2 )
it must be a list?. It's actually quite simple to turn an atomic piece of data, any data, into a list? containing that piece of data.
........ )
And that was the only non-trivial thing we had to come up with here, to get to the solution.
Recursion is about breaking stuff apart into the sub-parts which are similar to the whole thing, transforming those with that same procedure we are trying to write, then combining the results in some simple and straightforward way.
If a tree? contains two smaller trees?, well, we've hit the jackpot -- we already know how to handle them!
And when we have structural data types, we already have the way to pick them apart. It is how they are defined anyway.
Maybe I'll address your second question later.
I'm doing the exercises from SICP (not homework) and exercise 2.20 introduces dotted-tail notation, which is where you use (define (f a . b) ...) to pass a variable number of arguments (which end up in a list b). This problem in particular wants a procedure which takes an integer a and returns a list of all arguments with parity equal to a's. The problem is not difficult; here is my solution:
(define (same-parity a . b); a is an int, b is any number of int arguments
(let ((parity (remainder a 2)))
(define (proc li)
(cond ((null? li) null)
; If parity of the head of the list is = parity of a,
((= (remainder (car li) 2) parity)
; keep it and check the rest of the list.
(cons (car li) (proc (cdr li))))
; Otherwise ignore it and check the rest of the list.
(else (proc (cdr li)))))
(cons a (proc b))))
My question is that I don't seem to be using the dotted-tail feature at all. I might as well have just accepted exactly two arguments, a number and a list; I'm effectively wrapping the algorithm in a procedure proc which does away with the dotted-tail thing.
Before I wrote this solution, I wanted to have a recursive call resembling
(same-parity a . (cdr b))
or something spiritually similar, but no matter how I tried it, I kept passing lists of lists or extra procedures or whatever. This could be because I don't know exactly what . does, only what I want it to do (the Racket docs didn't clear anything up either). To sum up,
Is my solution what was intended for this exercise, or is there a way to actually use the dot notation (which seems to be the point of the exercise) in the algorithm?
You can't use (same-parity a . (cdr b)) (since that would be read in as (same-parity a cdr b)), but you can use (apply same-parity a (cdr b)). That's how you "splat" a list into arguments.
However, the "inner procedure" approach you had is generally more efficient, as there is less list copying going on.
I need to write a good set function that checks whether its argument lst is a properly represented set, i.e. it is a list consisting only of integers, with no duplicates, and returns true #t or false #f. For example:
(good-set? (1 5 2)) => #t
(good-set? ()) => #t
(good-set? (1 5 5)) => #f
(good-set? (1 (5) 2)) => #f
so I have began writing the function as:
(define (good-set? lst)
so I don't know how to proceed after this. Can anybody help?
One option would be to use andmap and sets, as has been suggested by #soegaard:
(define (good-set? lst) ; it's a good set if:
(and (andmap integer? lst) ; all its elements are integers and
(= (length lst) ; the list's length equals the size
(set-count (list->set lst))))) ; of a set with the same elements
But if you can't use sets or other advanced procedures, then traverse the list and test if the current element is an integer and is not present somewhere else in the list (use member for this), repeating this test for each element until there are no more elements in the list. Here's the general idea, fill-in the blanks:
(define (good-set? lst)
(cond (<???> ; if the list is empty
<???>) ; then it's a good set
((or <???> ; if the 1st element is not an integer or
<???>) ; the 1st element is in the rest of the list
<???>) ; then it's NOT a good set
(else ; otherwise
(good-set? <???>)))) ; advance recursion
Sets are built into the Racket standard library: I would recommend not reimplementing them in terms of lists unless you really need to do something customized.
If we need to treat this as a homework assignment, I would recommend using a design methodology to systematically attack this problem. In this case, see something like How to Design Programs with regards to designing functions that work on lists. As a brief sketch, we'd systematically figure out:
What's the structure of the data I'm working with?
What tests cases do I consider? (including the base case)
What's the overall shape of the function?
What's the meaning of the natural recursion?
How do I combine the result of the natural recursion in order to compute a solution to the total?
For this, check if the first number is duplicated, if it is not, then recurse by checking the rest. As such:
(define (good-set? list)
(or (null? list) ; nothing left, good!
(let ((head (car list)))
(rest (cdr list)))
(and (number? head) ; a number
(not (member = head rest)) ; not in the rest
(good-set? rest))))) ; check the rest
If you need member, then
(define (member pred item list)
(and (not (null? list))
(or (pred item (car list))
(member pred item (cdr list)))))
If the predicate bit? returns #t if the items is 1 or 0
(define bit?
(lambda (item)
(if (equal? item 0)
#t
(if (equal? item 1)
#t
#f))))
how can i use bit? in a new procedure, all-bits? that returns #t if all items in the list are bits? I can't figure it out
this is what i tried.
(define all-bits?
(lambda (ls)
(cond
[(null? ls) #t]
[(equal? (car ls all-bits?)) bit?]
[else (all-bits? ls (cdr ls))]))
The 2nd of your cond conditions is entirely wrong. It is syntactically wrong; equal? takes two parameters, car only takes one and bit also requires one, yet you are passing 1, 2 and 0 parameters, respectively. It also makes no sense: your helper function bit? is designed to test a single item, yet you're not testing anything with it. Why is all-bits? in that line at all?
The 3rd cond line is syntactically wrong: again, you're passing two parameters to a function that only takes one.
You need to be saying 'Return true if the first atom is a bit and the second atom is a bit and (and so on, and so on). That structure simply isn't represented in your code.
I'd also argue that all-bits should return false for the empty list and true where you have a list with a single bit, but maybe (all-bits? '()) ==> #t was specified in the original course question.
To do it your way (true for the empty list), your cond should only have two statements;
(cond
((null? l) #t)
(else ("Return true if the first atom is a bit **and** if the second atom is a bit **and** the third (and so on, and so on, recursively)"))
To do it my way, it would look like this:
(cond
((null? l) #f)
((and (null? (cdr l)) ("something here to test that (car l) is a bit")) #t)
(else ("Return true if the first atom is a bit **and** if the second atom is a bit **and** the third (and so on, and so on, recursively)")).
Should be clear that the bits in quotes are not real code.
I haven't done your homework for you, but I hope all my comments have made it more clear what you have to do. Even if you try to do your version rather than mine, mine actually includes a big hint to how you should structure the final line.
If you haven't yet been shown how to use and, use nested if statements.
I have decided to learn some functional language and I hooked up with the lisp-scheme after all.
I am trying to make a function which checks if a list is sorted, either with lowest first getting higher or vice versa, and if it can be sorted it should return true else false.
This is my first code, working only if the list is increasing (or equal).
(define sorted?
(lambda (lst)
(cond ((empty? lst) #t)
(else (and (<= (car lst) (cadr lst))
(sorted? (cdr lst)))))))
clarification: something like (sorted? '(1 2 3 4 5)) and (sorted? '(5 4 3 2 1)) should return true, else if not sorted false of course.
How am I supposed to think when programming in a functional style? The syntax seems straight-forward but I'm not used to the logic.
Specific implementation
I'd take Óscar López's answer and go one step further:
(define sorted? (lambda (lst)
(letrec ((sorted-cmp
(lambda (lst cmp)
(cond ((or (empty? lst) (empty? (cdr lst)))
#t)
(else (and (cmp (car lst) (cadr lst))
(sorted-cmp (cdr lst) cmp)))))))
(or (sorted-cmp lst <=) (sorted-cmp lst >=)))))
The biggest difference between this version and his is that sorted? now defines Óscar's version as an internal helper function using letrec and calls it both ways.
Functional Thinking
You actually chose a good example for illustrating some aspects of how Scheme views the world, and your implementation was off to a very good start.
One important functional principle involved in the solution to this problem is that anything you could put (**here** more stuff '(1 2 3 4)), you can pass around as an argument to another function. That is, functions are first class in a functional programming language. So the fact that you were using <= in your comparison means that you can pass <= as a parameter to another function that makes a comparison accordingly. Óscar's answer is a great illustration of that point.
Another aspect of this problem that embodies another common functional pattern is a function that consists primarily of a (cond) block. In many functional programming languages (Haskell, ML, OCaml, F#, Mathematica), you get stronger pattern matching abilities than you get, by default in Scheme. So with (cond) in Scheme, you have to describe how to test for the pattern that you seek, but that's usually fairly straightforward (for example the (or (empty? lst) (empty? (cdr lst))) in this implementation.
One final functional programming pattern that I see as well-embodied in this problem is that many functional programming solutions are recursive. Recursion is why I had to use letrec instead of plain ol' let.
Almost anything you can do by operating on the first element (or 2 elements as in this case) and then repeating the operation on the tail (cdr) of the list you do that way. Imperative for- or while-style loops aren't impossible in Scheme (although they are pretty much impossible in pure functional languages such as Haskell), they're slightly out of place in Scheme under many circumstances. But Scheme's flexibility that allows you, as the developer, to make that decision enables important performance or maintainability optimizations in certain circumstances.
Continuing exploration
My first implementation of sorted? for my answer here was going to decide which comparison operator to pass to sorted-cmp based on what it saw in the list. I backed off on that when I spotted that a list could start with two equal numbers '(1 1 2 3 4 5). But as I think more about it, there's definitely a way to track whether you've decided a direction yet and, thus, only have one call required to sorted-cmp. You might consider exploring that next.
You almost got it right, look:
(define sorted?
(lambda (lst)
(cond ((or (empty? lst) (empty? (cdr lst)))
#t)
(else (and (<= (car lst) (cadr lst))
(sorted? (cdr lst)))))))
A little modification in the base case, and you're all set. It's necessary to stop when there's only one element left in the list, otherwise the cadr expression will throw an error.
For the second part of your question: If you want to check if it's sorted using a different criterion, simply pass the comparison function as an argument, like this:
(define sorted?
(lambda (lst cmp)
(cond ((or (empty? lst) (empty? (cdr lst)))
#t)
(else (and (cmp (car lst) (cadr lst))
(sorted? (cdr lst) cmp))))))
(sorted? '(1 2 3 4 5) <=)
> #t
(sorted? '(5 4 3 2 1) >=)
> #t
Now if you want to know if a list is sorted in either ascending order or in descending order:
(define lst '(1 2 3 4 5))
(or (sorted? lst >=) (sorted? lst <=))
> #t
As you can see, functional programming is about defining procedures as generic as possible and combining them to solve problems. The fact that you can pass functions around as parameters helps a great deal for implementing generic functions.
I'm going to take your question to mean, more specifically, "if I already program in an imperative language like C or Java, how do I adjust my thinking for functional programming?" Using your problem as an example, I'm going to spend my Saturday morning answering this question in long form. I'll trace the evolution of a functional programmer through three stages, each a successively higher plane of zen - 1) thinking iteratively; 2) thinking recursively; and 3) thinking lazily.
Part I - Thinking Iteratively
Let's say I'm programming in C and I can't or won't use recursion - perhaps the compiler does not optimize tail recursion, and a recursive solution would overflow the stack. So I start thinking about what state I need to maintain. I imagine a little machine crawling over the input. It remembers if it is searching for an increasing or a decreasing sequence. If it hasn't decided yet, it does so based on the current input, if it can. If it finds input headed in the wrong direction, it terminates with zigzag=true. If it reaches the end of the input, it terminates with zigzag=false.
int
zigzag(int *data, int n)
{
enum {unknown, increasing, decreasing} direction = unknown;
int i;
for (i = 1; i < n; ++i)
{
if (data[i] > data[i - 1]) {
if (direction == decreasing) return 1;
direction = increasing;
}
if (data[i] < data[i - 1]) {
if (direction == increasing) return 1;
direction = decreasing;
}
}
/* We've made it through the gauntlet, no zigzagging */
return 0;
}
This program is typical of C programs: it is efficient but it is difficult to prove that it will do the right thing. Even for this simple example, it's not immediately obvious that this can't get stuck in an infinite loop, or take a wrong turn in its logic somewhere. Of course, it gets worse for more complicated programs.
Part II - Thinking Recursively
I find that the key to writing readable programs in the spirit of functional languages (as opposed to just trying to morph an imperative solution into that language) is to focus on what the program should calculate rather than on how it should do it. If you can do that with enough precision - if you can write the problem out clearly - then most of the time in functional programming, you're almost at the solution!
So let's start by writing out the thing to be calculated in more detail. We want to know if a list zigzags (i.e. decreases at some point, and increases at another). Which lists meet this criterion? Well, a list zigzags if:
it is more than two elements long AND
it initially increases, but then decreases at some point OR
it initially decreases, but then increases at some point OR
its tail zigzags.
It's possible to translate the above statements, more or less directly, into a Scheme function:
(define (zigzag xs)
(and (> (length xs) 2)
(or (and (initially-increasing xs) (decreases xs))
(and (initially-decreasing xs) (increases xs))
(zigzag (cdr xs)))))
Now we need definitions of initially-increasing, initially-decreasing, decreases, and increases. The initially- functions are straightforward enough:
(define (initially-increasing xs)
(> (cadr xs) (car xs)))
(define (initially-decreasing xs)
(< (cadr xs) (car xs)))
What about decreases and increases? Well, a sequence decreases if it is of length greater than one, and the first element is greater than the second, or its tail decreases:
(define (decreases xs)
(letrec ((passes
(lambda (prev rest)
(cond ((null? rest) #f)
((< (car rest) prev)
#t)
(else (passes (car rest) (cdr rest)))))))
(passes (car xs) (cdr xs))))
We could write a similar increases function, but it's clear that only one change is needed: < must become >. Duplicating so much code should make you uneasy. Couldn't I ask the language to make me a function like decreases, but using > in that place instead? In functional languages, you can do exactly that, because functions can return other functions! So we can write a function that implements: "given a comparison operator, return a function that returns true if that comparison is true for any two successive elements of its argument."
(define (ever op)
(lambda (xs)
(letrec ((passes
(lambda (prev rest)
(cond ((null? rest) #f)
((op (car rest) prev)
#t)
(else (passes (car rest) (cdr rest)))))))
(passes (car xs) (cdr xs)))))
increases and decreases can now both be defined very simply:
(define decreases (ever <))
(define increases (ever >))
No more functions to implement - we're done. The advantage of this version over the C version is clear - it's much easier to reason that this program will do the right thing. Most of this program is quite trivial with all the complexity being pushed into the ever function, which is a quite general operation that would be useful in plenty of other contexts. I am sure by searching one could find a standard (and thus more trustworthy) implementation rather than this custom one.
Though an improvement, this program still isn't perfect. There's lots of custom recursion and it's not obvious at first that all of it is tail recursive (though it is). Also, the program retains faint echos of C in the form of multiple conditional branches and exit points. We can get an even clearer implementation with the help of lazy evaluation, and for that we're going to switch languages.
Part III - Thinking Lazily
Let's go back to the problem definition. It can actually be stated much more simply than it was in part II - "A sequence zigzags (i.e. is non-sorted) if it contains comparisons between adjacent elements that go in both directions". I can translate that sentence, more or less directly, into a line of Haskell:
zigzag xs = LT `elem` comparisons && GT `elem` comparisons
Now I need a way to derive comparisons, the list of comparisons of every member of xs with its successor. This is not hard to do and is perhaps best explained by example.
> xs
[1,1,1,2,3,4,5,3,9,9]
> zip xs (tail xs)
[(1,1),(1,1),(1,2),(2,3),(3,4),(4,5),(5,3),(3,9),(9,9)]
> map (\(x,y) -> compare x y) $ zip xs (tail xs)
[EQ,EQ,LT,LT,LT,LT,GT,LT,EQ]
That's all we need; these two lines are the complete implementation -
zigzag xs = LT `elem` comparisons && GT `elem` comparisons
where comparisons = map (\(x,y) -> compare x y) $ zip xs (tail xs)
and I'll note that this program makes just one pass through the list to test for both the increasing and decreasing cases.
By now, you have probably thought of an objection: isn't this approach wasteful? Isn't this going to search through the entire input list, when it only has to go as far as the first change of direction? Actually, no, it won't, because of lazy evaluation. In the example above, it calculated the entire comparisons list because it had to in order to print it out. But if it's going to pass the result to zigzag, it will only evaluate the comparisons list far enough to find one instance of GT and one of LT, and no further. To convince yourself of this, consider these cases:
> zigzag $ 2:[1..]
True
> zigzag 1:[9,8..]
True
The input in both cases is an infinite list ([2,1,2,3,4,5..] and [1,9,8,7,6,5...]). Try to print them out, and they will fill up the screen. But pass them to zigzag, and it will return very quickly, as soon as it finds the first change in direction.
A lot of the difficultly in reading code comes from following multiple branches of control flow. And a lot of those branches are really efforts to avoid calculating more than we need to. But much of the same thing can be achieved with lazy evaluation, allowing the program to be both shorter and truer to the original question.
Try this
(define sorted?
(lambda (l)
(cond ((null? l) #t)
(else (check-asc? (car l) (sorted? (cdr l))
(check-desc? (car l) (sorted? (cdr l))))))
(define check-asc?
(lambda (elt lst)
(cond ((null? lst) #t)
(else (or (< elt (car lst)) (= elt (car lst))) (check-asc? (car lst) (cdr lst))))))
(define check-desc?
(lambda (elt lst)
(cond ((null? lst) #t)
(else (or (< elt (car lst)) (= elt (car lst))) (check-desc? (car lst) (cdr lst))))))
I am a newbie myself. I haven't tested this code. Still struggling with recursion. Please tell me if it worked or what error it gave.
The previous answer i gave was really bad.
I ran the code in DrScheme and it gave errors.
However I have modified it. Here is a code that works:
(define sorted?
(lambda (l)
(cond ((null? l) #t)
(else (if (check-asc? (car l) (cdr l)) #t
(check-desc? (car l) (cdr l)))))))
(define check-asc?
(lambda (elt lst)
(cond ((null? lst) #t)
(else (if (or (< elt (car lst)) (= elt (car lst))) (check-asc? (car lst) (cdr lst))
#f)))))
(define check-desc?
(lambda (elt lst)
(cond ((null? lst) #t)
(else (if (or (> elt (car lst)) (= elt (car lst))) (check-desc? (car lst) (cdr lst))
#f)))))
Cases checked:
(sorted? '(5 4 3 2 1))
returns #t
(sorted? '(1 2 3 4 5))
returns #t
(sorted? '(1 2 3 5 4))
returns #f
(sorted? '())
returns #t
(sorted? '(1))
returns #t