(define (checksum-2 ls)
(if (null? ls)
0
(let ([n 0])
(+ (+ n 1))(* n (car ls))(checksum-2 (cdr ls)))))
Ok, I have this code, its suppose to, if I wrote it right, the number (n) should increase by one every time it goes through the list, so n (in reality) should be like 1 2 3 4, but I want n to be multiplied by the car of the list.
Everything loads, but when the answer is returned I get 0.
Thanks!
If you format your code differently, you might have an easier time seeing what is going on:
(define (checksum-2 ls)
(if (null? ls)
0
(let ([n 0])
(+ (+ n 1))
(* n (car ls))
(checksum-2 (cdr ls)))))
Inside the let form, the expressions are evaluated in sequence but you're not using the results for any of them (except the last one). The results of the addition and multiplication are simply discarded.
What you need to do in this case is define a new helper function that uses an accumulator and performs the recursive call. I'm going to guess this is homework or a learning exercise, so I'm not going to give away the complete answer.
UPDATE: As a demonstration of the sort of thing you might need to do, here is a similar function in Scheme to sum the integers from 1 to n:
(define (sum n)
(define (sum-helper n a)
(if (<= n 0)
a
(sum-helper (- n 1) (+ a n))))
(sum-helper n 0))
You should be able to use a similar framework to implement your checksum-2 function.
Related
How to I create a function called numDivisors. The function is defined as (numDivisors n listOfNums) and it counts the number of integers in the list that divide n without any remainder.
Example of the function call
(numDivisors 10 '(1 20 30 2 5 40 10 60))
returns 4 from (1 2 5 10)
Current code:
(define numDivisors
(lambda (x lst)
(cond
((null? lst) 0)
((eq? (remainder 10 (car lst)) 0) (+ 1 (numDivisors x (cdr lst))))
)
)
)
Your solution is close.
First you need to change eq? to =, which tests equality of numbers, and change (remainder 10 ...) to (remainder x ...), so that 10 is no longer hard-wired into your solution.
Second, you need to add a third clause to your cond to handle the case where the remainder is not 0. I'll let you think about that; given what you have done so far, I am confident you will figure it out.
And you should stack those three closing parentheses at the end of the last line of code instead of placing them on lines by themselves.
An experienced Scheme programmer would probably write:
(define (numDivisors n xs)
(define (divides? d n) (zero? (modulo n d)))
(length (filter (lambda (d) (divides? d n)) xs)))
If that makes no sense at all, you should probably wait a few weeks. I'm sure your instructor will soon have you writing code like that.
Example
(trace '((1 2 3) (4 5 6) (7 8 9))) should evaluate to 15 (1+5+9).
Hint: use map to obtain the smaller matrix on which trace can be applied recursively. The Matrix should be squared.
i tried to do it but i cant seem to do it, i tried to get the diagonals first.
define (diagonals m n)
(append
(for/list ([slice (in-range 1 (- (* 2 n) 2))])
(let ((z (if (< slice n) 0 (add1 (- slice n)))))
(for/list ([j (in-range z (add1 (- slice z)))])
(vector-ref (vector-ref m (sub1 (- n j))) (- slice j))))
is there any way to solve that question in a very simple recursive way using map.
i tried to solve it like that.
define (nth n l)
(if (or (> n (length l)) (< n 0))
(if (eq? n 0) (car l)
(nth (- n 1) (cdr l)))))
(+ (nth 3 '(3 4 5)) (nth 2 '(3 4 5)) (nth 3 '(3 4 5)))
but it didnt work too.
Although I don't think answering homework questions is a good idea in general, I can't resist this because it is an example of both what is so beautiful about Lisp programs and what can be so horrible.
What is so beautiful:
the recursive algorithm is almost identical to a mathematical proof by induction and it's just so pretty and clever;
What is so horrible:
matrices are not semantically nested lists and it's just this terrible pun to pretend they are (I'm not sure if my use of first & rest makes it better or worse);
it just conses like mad for no good reason at all;
I'm pretty sure its time complexity is n^2 when it could be n.
Of course Lisp programs do not have to be horrible in this way.
To compute the trace of a matrix:
if the matrix is null, then the trace is 0;
otherwise add the top left element to the trace of the matrix you get by removing the first row and column.
Or:
(define (awful-trace m)
(if (null? m)
;; the trace of the null matrix is 0
0
;; otherwise the trace is the top left element added to ...
(+ (first (first m))
;; the trace of the matrix without its first row and column which
;; we get by mapping rest over the rest of the matrix
(awful-trace (map rest (rest m))))))
And you may be tempted to think the following function is better, but it is just as awful in all the ways described above, while being harder to read for anyone not versed in the auxiliary-tail-recursive-function-with-an-accumulator trick:
(define (awful-trace/not-actually-better m)
(define (awful-loop mm tr)
(if (null? mm)
tr
(awful-loop (map rest (rest mm))
(+ tr (first (first mm))))))
(awful-loop m 0))
Try:
(apply + (map (lambda (index row) (list-ref row index))
'(0 1 2)
'((1 2 3) (4 5 6) (7 8 9))))
Of course, turn that into a function.
To handle matrices larger than 3x3, we need more indices.
Since map stops when it traverses the shortest of the lists, the (0 1 2) list can just be padded out by hand as large as ... your best guess at the the largest matrix you think you would ever represent with nested lists in Scheme before you graduate and never see this stuff again.
I am learning Scheme/Racket and am confused with recursion-vs-iteration concept.
The specific question is: Write a function that sums up a list of numbers.
Take the following code, for example:
(define (func list)
(define (dostuff list res)
(if (empty? list)
res
(dostuff (cdr list) (+ (car list) res))))
(dostuff list 0))
According to the instructor, this is a iterative solution. But I don't understand why. dostuff is calling itself within its implementation, so doesn't that automatically make it recursive?
Or am I missing some concept here?
It's "iterative" because it's tail-recursive. That is, it recurses only in tail position (i.e., the code returns to the caller immediately after the recursion, with no other work, so it's safe to just replace the current call frame with the recursive call's).
In languages like Scheme, which enforces "proper tail calls", tail recursion is effectively a goto. As Alexis's comment says, in Scheme, loops are written using tail recursion, since Scheme does not have a goto.
I recommend the MIT 6.001 lecture 1B of Structure and Interpretations whose the Prof. Gerald Jay Sussman made a graceful explanation about the differences of iteration vs recursion using Scheme (LISP-1).
The biggest difference behind that types of algorithms is linked with the concept of memory and space. Iterative procedures doesn't need know what happened on the previously statements. However, recursive procedures yes.
Think a little about each these algorithms for add:
;; iterative
(define (+ x y)
(if (= x 0)
y
(+ (1- x) (1+ y))))
;; recursive
(define (+ x y)
(if (= x 0)
y
(1+ (+ (1- x) y))))
All them do the same things, but the way of the procedures are executed are different.
If you expand the execution for the first using (+ 3 4) we have that flow:
(+ 3 4)
(+ 2 5)
(+ 1 6)
(+ 0 7)
7
Time=O(n), space=O(1)
However, for the second, see:
(+ 3 4)
(1+ (+ 2 4))
(1+ (1+ (+ 1 4)))
(1+ (1+ (1+ (+ 0 4))))
(1+ (1+ (1+ 4)))
(1+ (1+ 5))
(1+ 6)
7
time=O(n), space=O(n)
I am new to Racket, and I am trying to write a recursive function that takes a number n and returns the sum of the squares of the first n integers. For example, (this-function 3) returns 14 because 14 is 9 + 4 + 1 + 0.
I tried creating two separate functions, one that squares each number and returns a list of the squared numbers, and a second that sums up the list. The function the squares each number is:
(define (squared my-list)
(cond [(empty? my-list) empty]
[(zero? my-list) 0]
[else (cons (expt my-list 2)
(cons (squared (sub1 my-list)) empty))]))
which if I run (squared 3) returns (cons 9 (cons (cons 4 (cons (cons 1 (cons 0 empty)) empty)) empty)).
When I run the second function (the sum function):
(define (sum numbers)
(cond
[(empty? numbers) 0]
[else (+ (first numbers) (sum (rest numbers)))]))
and run (sum (squared 3)) I get an error message because (squared 3) returns an extra "cons" in the list.
How can I fix this?
Your logic in squared is a little bit off. I'll explain the issues clause-by-clause.
[(empty? my-list) empty]
This doesn't make any sense since my-list will never even be a list. In fact, my-list is poorly named. The parameter squared takes is a number, not a list. This clause can be completely removed.
[(zero? my-list) 0]
This is what the actual terminating case should be, but it shouldn't return 0. Remember, squared has to return a list, not a number. This case should return empty.
[else (cons (expt my-list 2)
(cons (squared (sub1 my-list)) empty))]))
Finally, this clause is far too complicated. You have the right idea—to cons the new number onto the rest of the list—but you're cons'ing too many times. Remember, the result of (squared (sub1 my-list)) is itself a list, and the second argument of cons is the rest of the list. You don't need the extra cons—you can just eliminate it completely.
Combining these changes, you get this, which does what you want:
(define (squared my-list)
(if (zero? my-list) empty
(cons (expt my-list 2)
(squared (sub1 my-list)))))
(I also replaced cond with if since cond is no longer necessary.)
That said, this code is not very Racket-y. You had a good idea to break up your function into two functions—in functional programming, functions should really only ever do one thing—but you can break this up further! Specifically, you can leverage Racket's built-in higher-order functions to make this type of thing extremely easy.
You can replace your entire squared function by appropriately combining map and range. For example, the following creates a list of the squares from 0–3.
(map (curryr expt 2) (range 4))
(You need to call (range 4) because the list generated by range goes from 0 to n-1.)
Next, you can easily sum a list using apply. To sum the above list, you'd do something like this:
(apply + (map (curryr expt 2) (range 4)))
That gives you the appropriate result of 14. Obviously, you could encapsulate this in its own function for clarity, but it's a lot clearer what the above code is doing once you learn Racket's functional constructs.
(However, I'm not sure if you're allowed to use those, since your question looks a lot like homework. Just noted for future reference and completeness.)
The most straightforward solution is to use the closed form:
(define (sum-of-squares n)
(* 1/6 n (+ n 1) (+ n n 1)))
Credit: WolframAlpha
one function for providing a list of squares and one function for summing up the list is not necessary.
This will do the trick, and is recursive as required.
(define (my-sq n)
(cond [(zero? n) 0]
[else
(+ (* n n) (my-sq (- n 1)))]))
(my-sq 3) -> 14
Does scheme have a function to call a function n times. I don't want map/for-each as the function doesn't have any arguments. Something along the lines of this :-
(define (call-n-times proc n)
(if (= 0 n)
'()
(cons (proc) (call-n-times proc (- n 1)))))
(call-n-times read 10)
SRFI 1 has a list-tabulate function that can build a list from calling a given function, with arguments 0 through (- n 1). However, it does not guarantee the order of execution (in fact, many implementations start from (- n 1) and go down), so it's not ideal for calling read with.
In Racket, you can do this:
(for/list ((i 10))
(read))
to call read 10 times and collect the result of each; and it would be done left-to-right. But since you tagged your question for Guile, we need to do something different.
Luckily, Guile has SRFI 42, which enables you to do:
(list-ec (: i 10)
(read))
Implementing tail-recursion modulo cons optimization by hand, to build the resulting list with O(1) extra space:
(define (iterate0-n proc n) ; iterate a 0-arguments procedure n times
(let ((res (list 1))) ; return a list of results in order
(let loop ((i n) (p res))
(if (< i 1)
(cdr res)
(begin
(set-cdr! p (list (proc)))
(loop (- i 1) (cdr p)))))))
This technique first (?) described in Friedman and Wise's TR19.