I'm trying to edit the current program I have
(define (sumofnumber n)
(if (= n 0)
1
(+ n (sumofnumber (modulo n 2 )))))
so that it returns the sum of an n number of positive squares. For example if you inputted in 3 the program would do 1+4+9 to get 14. I have tried using modulo and other methods but it always goes into an infinite loop.
The base case is incorrect (the square of zero is zero), and so is the recursive step (why are you taking the modulo?) and the actual operation (where are you squaring the value?). This is how the procedure should look like:
(define (sum-of-squares n)
(if (= n 0)
0
(+ (* n n)
(sum-of-squares (- n 1)))))
A definition using composition rather than recursion. Read the comments from bottom to top for the procedural logic:
(define (sum-of-squares n)
(foldl + ; sum the list
0
(map (lambda(x)(* x x)) ; square each number in list
(map (lambda(x)(+ x 1)) ; correct for range yielding 0...(n - 1)
(range n))))) ; get a list of numbers bounded by n
I provide this because you are well on your way to understanding the idiom of recursion. Composition is another of Racket's idioms worth exploring and often covered after recursion in educational contexts.
Sometimes I find composition easier to apply to a problem than recursion. Other times, I don't.
You're not squaring anything, so there's no reason to expect that to be a sum of squares.
Write down how you got 1 + 4 + 9 with n = 3 (^ is exponentiation):
1^2 + 2^2 + 3^2
This is
(sum-of-squares 2) + 3^2
or
(sum-of-squares (- 3 1)) + 3^2
that is,
(sum-of-squares (- n 1)) + n^2
Notice that modulo does not occur anywhere, nor do you add n to anything.
(And the square of 0 is 0 , not 1.)
You can break the problem into small chunks.
1. Create a list of numbers from 1 to n
2. Map a square function over list to square each number
3. Apply + to add all the numbers in squared list
(define (sum-of-number n)
(apply + (map (lambda (x) (* x x)) (sequence->list (in-range 1 (+ n 1))))))
> (sum-of-number 3)
14
This is the perfect opportunity for using the transducers technique.
Calculating the sum of a list is a fold. Map and filter are folds, too. Composing several folds together in a nested fashion, as in (sum...(filter...(map...sqr...))), leads to multiple (here, three) list traversals.
But when the nested folds are fused, their reducing functions combine in a nested fashion, giving us a one-traversal fold instead, with the one combined reducer function:
(define (((mapping f) kons) x acc) (kons (f x) acc)) ; the "mapping" transducer
(define (((filtering p) kons) x acc) (if (p x) (kons x acc) acc)) ; the "filtering" one
(define (sum-of-positive-squares n)
(foldl ((compose (mapping sqr) ; ((mapping sqr)
(filtering (lambda (x) (> x 0)))) ; ((filtering {> _ 0})
+) 0 (range (+ 1 n)))) ; +))
; > (sum-of-positive-squares 3)
; 14
Of course ((compose f g) x) is the same as (f (g x)). The combined / "composed" (pun intended) reducer function is created just by substituting the arguments into the definitions, as
((mapping sqr) ((filtering {> _ 0}) +))
=
( (lambda (kons)
(lambda (x acc) (kons (sqr x) acc)))
((filtering {> _ 0}) +))
=
(lambda (x acc)
( ((filtering {> _ 0}) +)
(sqr x) acc))
=
(lambda (x acc)
( ( (lambda (kons)
(lambda (x acc) (if ({> _ 0} x) (kons x acc) acc)))
+)
(sqr x) acc))
=
(lambda (x acc)
( (lambda (x acc) (if (> x 0) (+ x acc) acc))
(sqr x) acc))
=
(lambda (x acc)
(let ([x (sqr x)] [acc acc])
(if (> x 0) (+ x acc) acc)))
which looks almost as something a programmer would write. As an exercise,
((filtering {> _ 0}) ((mapping sqr) +))
=
( (lambda (kons)
(lambda (x acc) (if ({> _ 0} x) (kons x acc) acc)))
((mapping sqr) +))
=
(lambda (x acc)
(if (> x 0) (((mapping sqr) +) x acc) acc))
=
(lambda (x acc)
(if (> x 0) (+ (sqr x) acc) acc))
So instead of writing the fused reducer function definitions ourselves, which as every human activity is error-prone, we can compose these reducer functions from more atomic "transformations" nay transducers.
Works in DrRacket.
Related
I'm going through the exercises in [SICP][1] and am wondering if someone can explain the difference between these two seemingly equivalent functions that are giving different results! Is this because of rounding?? I'm thinking the order of functions shouldn't matter here but somehow it does? Can someone explain what's going on here and why it's different?
Details:
Exercise 1.45: ..saw that finding a fixed point of y => x/y does not
converge, and that this can be fixed by average damping. The same
method works for finding cube roots as fixed points of the
average-damped y => x/y^2. Unfortunately, the process does not work
for fourth roots—a single average damp is not enough to make a
fixed-point search for y => x/y^3 converge.
On the other hand, if we
average damp twice (i.e., use the average damp of the average damp of
y => x/y^3) the fixed-point search does converge. Do some experiments
to determine how many average damps are required to compute nth roots
as a fixed-point search based upon repeated average damping of y => x/y^(n-1).
Use this to implement a simple procedure for computing the roots
using fixed-point, average-damp, and the repeated procedure
of Exercise 1.43. Assume that any arithmetic operations you need are
available as primitives.
My answer (note order of repeat and average-damping):
(define (nth-root-me x n num-repetitions)
(fixed-point (repeat (average-damping (lambda (y)
(/ x (expt y (- n 1)))))
num-repetitions)
1.0))
I see an alternate web solution where repeat is called directly on average damp and then that function is called with the argument
(define (nth-root-web-solution x n num-repetitions)
(fixed-point
((repeat average-damping num-repetition)
(lambda (y) (/ x (expt y (- n 1)))))
1.0))
Now calling both of these, there seems to be a difference in the answers and I can't understand why! My understanding is the order of the functions shouldn't affect the output (they're associative right?), but clearly it is!
> (nth-root-me 10000 4 2)
>
> 10.050110705350287
>
> (nth-root-web-solution 10000 4 2)
>
> 10.0
I did more tests and it's always like this, my answer is close, but the other answer is almost always closer! Can someone explain what's going on? Why aren't these equivalent? My guess is the order of calling these functions is messing with it but they seem associative to me.
For example:
(repeat (average-damping (lambda (y) (/ x (expt y (- n 1)))))
num-repetitions)
vs
((repeat average-damping num-repetition)
(lambda (y) (/ x (expt y (- n 1)))))
Other Helper functions:
(define (fixed-point f first-guess)
(define (close-enough? v1 v2)
(< (abs (- v1 v2))
tolerance))
(let ((next-guess (f first-guess)))
(if (close-enough? next-guess first-guess)
next-guess
(fixed-point f next-guess))))
(define (average-damping f)
(lambda (x) (average x (f x))))
(define (repeat f k)
(define (repeat-helper f k acc)
(if (<= k 1)
acc
;; compose the original function with the modified one
(repeat-helper f (- k 1) (compose f acc))))
(repeat-helper f k f))
(define (compose f g)
(lambda (x)
(f (g x))))
You are asking why “two seemingly equivalent functions” produce a different result, but the two functions are in effect very different.
Let’s try to simplify the problem to see why they are different. The only difference between the two functions are the two expressions:
(repeat (average-damping (lambda (y) (/ x (expt y (- n 1)))))
num-repetitions)
((repeat average-damping num-repetition)
(lambda (y) (/ x (expt y (- n 1)))))
In order to simplify our discussion, we assume num-repetition equal to 2, and a simpler function then that lambda, for instance the following function:
(define (succ x) (+ x 1))
So the two different parts are now:
(repeat (average-damping succ) 2)
and
((repeat average-damping 2) succ)
Now, for the first expression, (average-damping succ) returns a numeric function that calculates the average between a parameter and its successor:
(define h (average-damping succ))
(h 3) ; => (3 + succ(3))/2 = (3 + 4)/2 = 3.5
So, the expression (repeat (average-damping succ) 2) is equivalent to:
(lambda (x) ((compose h h) x)
which is equivalent to:
(lambda (x) (h (h x))
Again, this is a numeric function and if we apply this function to 3, we have:
((lambda (x) (h (h x)) 3) ; => (h 3.5) => (3.5 + 4.5)/2 = 4
In the second case, instead, we have (repeat average-damping 2) that produces a completely different function:
(lambda (x) ((compose average-damping average-damping) x)
which is equivalent to:
(lambda (x) (average-damping (average-damping x)))
You can see that the result this time is a high-level function, not an integer one, that takes a function x and applies two times the average-damping function to it. Let’s verify this by applying this function to succ and then applying the result to the number 3:
(define g ((lambda (x) (average-damping (average-damping x))) succ))
(g 3) ; => 3.25
The difference in the result is not due to numeric approximation, but to a different computation: first (average-damping succ) returns the function h, which computes the average between the parameter and its successor; then (average-damping h) returns a new function that computes the average between the parameter and the result of the function h. Such a function, if passed a number like 3, first calculates the average between 3 and 4, which is 3.5, then calculates the average between 3 (again the parameter), and 3.5 (the previous result), producing 3.25.
The definition of repeat entails
((repeat f k) x) = (f (f (f (... (f x) ...))))
; 1 2 3 k
with k nested calls to f in total. Let's write this as
= ((f^k) x)
and also define
(define (foo n) (lambda (y) (/ x (expt y (- n 1)))))
; ((foo n) y) = (/ x (expt y (- n 1)))
Then we have
(nth-root-you x n k) = (fixed-point ((average-damping (foo n))^k) 1.0)
(nth-root-web x n k) = (fixed-point ((average-damping^k) (foo n)) 1.0)
So your version makes k steps with the once-average-damped (foo n) function on each iteration step performed by fixed-point; the web's uses the k-times-average-damped (foo n) as its iteration step. Notice that no matter how many times it is used, a once-average-damped function is still average-damped only once, and using it several times is probably only going to exacerbate a problem, not solve it.
For k == 1 the two resulting iteration step functions are of course equivalent.
In your case k == 2, and so
(your-step y) = ((average-damping (foo n))
((average-damping (foo n)) y)) ; and,
(web-step y) = ((average-damping (average-damping (foo n))) y)
Since
((average-damping f) y) = (average y (f y))
we have
(your-step y) = ((average-damping (foo n))
(average y ((foo n) y)))
= (let ((z (average y ((foo n) y))))
(average z ((foo n) z)))
(web-step y) = (average y ((average-damping (foo n)) y))
= (average y (average y ((foo n) y)))
= (+ (* 0.5 y) (* 0.5 (average y ((foo n) y))))
= (+ (* 0.75 y) (* 0.25 ((foo n) y)))
;; and in general:
;; = (2^k-1)/2^k * y + 1/2^k * ((foo n) y)
The difference is clear. Average damping is used to dampen the possibly erratic jumps of (foo n) at certain ys, and the higher the k the stronger the damping effect, as is clearly seen from the last formula.
I was wondering if this is the fastest possible version of this function.
(defun foo (x y)
(cond
;if x = 0, return y+1
((zp x) (+ 1 y))
;if y = 0, return foo on decrement x and 1
((zp y) (foo (- x 1) 1))
;else run foo on decrement x and y = (foo x (- y 1))
(t (foo (- x 1) (foo x (- y 1))))))
When I run this, I usually get stack overflow error, so I am trying to figure out a way to compute something like (foo 3 1000000) without using the computer.
From analyzing the function I think it is embedded foo in the recursive case that causes the overflow in (foo 3 1000000). But since you are decrementing y would the number of steps just equal y?
edit: removed lie from comments
12 years ago I wrote this:
(defun ackermann (m n)
(declare (fixnum m n) (optimize (speed 3) (safety 0)))
(let ((memo (make-hash-table :test #'equal))
(ncal 0) (nhit 0))
(labels ((ack (aa bb)
(incf ncal)
(cond ((zerop aa) (1+ bb))
((= 1 aa) (+ 2 bb))
((= 2 aa) (+ 3 (* 2 bb)))
((= 3 aa) (- (ash 1 (+ 3 bb)) 3))
((let* ((key (cons aa bb))
(val (gethash key memo)))
(cond (val (incf nhit) val)
(t (setq val (if (zerop bb)
(ack (1- aa) 1)
(ack (1- aa) (ack aa (1- bb)))))
(setf (gethash key memo) val)
val)))))))
(let ((ret (ack m n)))
(format t "A(~d,~d)=~:d (~:d calls, ~:d cache hits)~%"
m n ret ncal nhit)
(values ret memo)))))
As you can see, I am using an explicit formula for small a and memoization for larger a.
Note, however, that this function grows so fast that it makes little sense to try to compute the actual values; you will run out of atoms in the universe faster - memoization or not.
Conceptually speaking, stack overflows don't have anything to do with speed, but they concern space usage. For instance, consider the following implementations of length. The first will run into a stack overflow for long lists. The second will too, unless your Lisp implements tail call optimization. The third will not. All have the same time complexity (speed), though; they're linear in the length of the list.
(defun length1 (list)
(if (endp list)
0
(+ 1 (length1 (rest list)))))
(defun length2 (list)
(labels ((l2 (list len)
(if (endp list)
len
(l2 (rest list) (1+ len)))))
(l2 list 0)))
(defun length3 (list)
(do ((list list (rest list))
(len 0 (1+ len)))
((endp list) len)))
You can do something similar for your code, though you'll still have one recursive call that will contribute to stack space. Since this does appear to be the Ackermann function, I'm going to use zerop instead of zp and ack instead of foo. Thus, you could do:
(defun foo2 (x y)
(do () ((zp x) (+ 1 y))
(if (zp y)
(setf x (1- x)
y 1)
(psetf x (1- x)
y (foo x (1- y))))))
Since x is decreasing by 1 on each iteration, and the only conditional change is on y, you could simplify this as:
(defun ack2 (x y)
(do () ((zerop x) (1+ y))
(if (zerop y)
(setf x (1- x)
y 1)
(psetf x (1- x)
y (ack2 x (1- y))))))
Since y is the only thing that conditionally changes during iterations, you could further simplify this to:
(defun ack3 (x y)
(do ((x x (1- x))
(y y (if (zerop y) 1 (ack3 x (1- y)))))
((zerop x) (1+ y))))
This is an expensive function to compute, and this will get you a little bit farther, but you're still not going to get, e.g., to (ackN 3 1000000). All these definitions are available for easy copying and pasting from http://pastebin.com/mNA9TNTm.
Generally, memoization is your friend in this type of computation. Might not apply as it depends on the specific arguments in the recursion; but it is a useful approach to explore.
How do I make the substitution? I tried to trace but I don't really get what is going on...
the code:
(define (repeated f n)
(if (zero? n)
identity
(lambda (x) ((repeated f (- n 1)) (f x)))))
f is a function and n is an integer that gives the number of times we should apply f.
....can someone help me to interpret it. I know it returns several procedures and i want to believe that it goes f(f(f(x)))
okey i will re-ask this question but in different manner, because i didn't really get an answer last time. consider this code
(define (repeated f n)
(if (zero? n)
identity
(lambda (x) ((repeated f (- n 1)) (f x)))))
where n is a positive integer and f is an arbitrary function: how does scheme operate on this code lets say we give (repeated f 2). what will happen? this is what think:
(f 2)
(lambda (x) ((repeated f (- 2 1)) (f x))))
(f 1)
(lambda (x) ((lambda (x) ((repeated f (- 1 1)) (f x)))) (f x))))
(f 0)
(lambda (x) ((lambda (x) (identity (f x)))) (f x))))
> (lambda (x) ((lambda (x) (identity (f x)))) (f x))))
> (lambda (x) ((lambda (x) ((f x)))) (f x))))
here is were i get stuck first i want it to go (f(f(x)) but now i will get (lambda x ((f x) (f x)) , the parentheses is certaintly wrong , but i think you understand what i mean. What is wrong with my arguments on how the interpreter works
Your implementation actually delays the further recursion and return a procedure whose body will create copies of itself to fulfill the task at runtime.
Eg. (repeated double 4) ==> (lambda (x) ((repeated double (- 4 1)) (double x)))
So when calling it ((repeated double 4) 2) it runs ((repeated double (- 4 1)) (double 2)))
where the operand part evaluates to (lambda (x) ((repeated double (- 3 1)) (double x))) and so on making the closures at run time so the evaluation becomes equal to this, but in stages during runtime..
((lambda (x) ((lambda (x) ((lambda (x) ((lambda (x) ((lambda (x) (identity x)) (double x))) (double x))) (double x))) (double x))) 2)
A different way of writing the same functionality would be like this:
(define (repeat fun n)
(lambda (x)
(let repeat-loop ((n n)
(x x))
(if (<= n 0)
x
(repeat-loop (- n 1) (fun x))))))
(define (double x) (+ x x))
((repeat double 4) 2) ; ==> 32
You've got a function that takes a function f and an non-negative integer n and returns the function fn, i.e., f(f(f(…f(n)…). Depending on how you think of your recursion, this could be implemented straightforwardly in either of two ways. In both cases, if n is 0, then you just need a function that returns its argument, and that function is the identity function. (This is sort of by convention, in the same way that x0 = 1. It does make sense when it's considered in more depth, but that's probably out of scope for this question.)
How you handle the recursive case is where you have some options. The first option is to think of fn(x) as f(fn-1(x)), where you call f with the result of calling fn-1 with x:
(define (repeated f n)
(if (zero? n)
identity
(lambda (x)
(f ((repeated f (- n 1)) x)))))
The other option is to think of fn(x) as fn-1(f(x)) where _fn-1 gets called with the result of f(x).
(define (repeated f n)
(if (zero? n)
identity
(lambda (x)
((repeated f (- n 1)) (f x)))))
In either case, the important thing to note here is that in Scheme, a form like
(function-form arg-form-1 arg-form-2 ...)
is evaluated by evaluating function-form to produce a value function-value (which should be a function) and evaluating each arg-form-i to produce values arg-value-i, and then calling _function-value_ with the arg-values. Since (repeated ...) produces a function, it's suitable as a function-form:
(f ((repeated f (- n 1)) x))
; |--- f^{n-1} ------|
; |---- f^{n-1}(x) ------|
;|------f(f^{n-1}(x)) ------|
((repeated f (- n 1)) (f x))
; |--- f^{n-1} ------|
;|---- f^{n-1}(f(x))--------|
Based on Will Ness's comment, it's worth pointing out that while these are somewhat natural ways to decompose this problem (i.e., based on the equalities fn(x) = fn-1(f(x)) = f(fn-1(x))), it's not necessarily the most efficient. These solutions both require computing some intermediate function objects to represent fn-1 that require a fair amount of storage, and then some computation on top of that. Computing fn(x) directly is pretty straightforward and efficient with, e.g., repeat:
(define (repeat f n x)
(let rep ((n n) (x x))
(if (<= n 0)
x
(rep (- n 1) (f x)))))
A more efficient version of repeated, then, simply curries the x argument of repeat:
(define (repeated f n)
(lambda (x)
(repeat f n x)))
This should have better run time performance than either of the other implementations.
Danny. I think that if we work repeated with small values of n (0, 1 and 2) will be able to see how the function translates to f(f(f(...(x))). I assume that identity's implementation is (define (identity x) x) (i.e. returns its only parameter as is), and that the "then" part of the if should be (identity f).
(repeated f 0) ;should apply f only once, no repetition
-> (identity f)
-> f
(repeated f 1) ;expected result is f(f(x))
-> (lambda (x) ((repeated f 0) (f x)))
-> (lambda (x) (f (f x))) ;we already know that (repeated f 0) is f
(repeated f 2) ;expected result is f(f(f(x)))
-> (lambda (x) ((repeated f 1) (f x)))
-> (lambda (x) (f (f (f x)))) ; we already know that (repeated f 1) if f(f(x))
... and so on.
Equational reasoning would be very helpful here. Imagine lambda calculus-based language with Haskell-like syntax, practically a combinatory calculus.
Here, parentheses are used just for grouping of expressions (not for function calls, which have no syntax at all – just juxtaposition): f a b c is the same as ((f a) b) c, the same as Scheme's (((f a) b) c). Definitions like f a b = ... are equivalent to (define f (lambda (a) (lambda (b) ...))) (and shortcut for (lambda (a) ...) is (\a-> ...).
Scheme's syntax just obscures the picture here. I don't mean parentheses, but being forced to explicit lambdas instead of just equations and freely shifting the arguments around:
f a b = \c -> .... === f a b c = .... ; `\ ->` is for 'lambda'
Your code is then nearly equivalent to
repeated f n x ; (define (repeated f n)
| n <= 0 = x ; (if (zero? n) identity
| otherwise = repeated f (n-1) (f x) ; (lambda (x)
; ((repeated f (- n 1)) (f x)))))
(read | as "when"). So
repeated f 2 x = ; ((repeated f 2) x) = ((\x-> ((repeated f 1) (f x))) x)
= repeated f 1 (f x) ; = ((repeated f 1) (f x))
= repeated f 0 (f (f x)) ; = ((\y->((repeated f 0) (f y))) (f x))
= f (f x) ; = ((\z-> z) (f (f x)))
; = (f (f x))
The above reduction sequence leaves out the particulars of environment frames creation and chaining in Scheme, but it all works out pretty much intuitively. f is the same f, n-1 where n=2 is 1 no matter when we perform the subtraction, etc..
this is possibly much of an elementary question, but I'm having trouble with a procedure I have to write in Scheme. The procedure should return all the prime numbers less or equal to N (N is from input).
(define (isPrimeHelper x k)
(if (= x k) #t
(if (= (remainder x k) 0) #f
(isPrimeHelper x (+ k 1)))))
(define ( isPrime x )
(cond
(( = x 1 ) #t)
(( = x 2 ) #t)
( else (isPrimeHelper x 2 ) )))
(define (printPrimesUpTo n)
(define result '())
(define (helper x)
(if (= x (+ 1 n)) result
(if (isPrime x) (cons x result) ))
( helper (+ x 1)))
( helper 1 ))
My check for prime works, however the function printPrimesUpTo seem to loop forever. Basically the idea is to check whether a number is prime and put it in a result list.
Thanks :)
You have several things wrong, and your code is very non-idiomatic. First, the number 1 is not prime; in fact, is it neither prime nor composite. Second, the result variable isn't doing what you think it is. Third, your use of if is incorrect everywhere it appears; if is an expression, not a statement as in some other programming languages. And, as a matter of style, closing parentheses are stacked at the end of the line, and don't occupy a line of their own. You need to talk with your professor or teaching assistant to clear up some basic misconceptions about Scheme.
The best algorithm to find the primes less than n is the Sieve of Eratosthenes, invented about twenty-two centuries ago by a Greek mathematician who invented the leap day and a system of latitude and longitude, accurately measured the circumference of the Earth and the distance from Earth to Sun, and was chief librarian of Ptolemy's library at Alexandria. Here is a simple version of his algorithm:
(define (primes n)
(let ((bits (make-vector (+ n 1) #t)))
(let loop ((p 2) (ps '()))
(cond ((< n p) (reverse ps))
((vector-ref bits p)
(do ((i (+ p p) (+ i p))) ((< n i))
(vector-set! bits i #f))
(loop (+ p 1) (cons p ps)))
(else (loop (+ p 1) ps))))))
Called as (primes 50), that returns the list (2 3 5 7 11 13 17 19 23 29 31 37 41 43 47). It is much faster than testing numbers for primality by trial division, as you are attempting to do. If you must, here is a proper primality checker:
(define (prime? n)
(let loop ((d 2))
(cond ((< n (* d d)) #t)
((zero? (modulo n d)) #f)
(else (loop (+ d 1))))))
Improvements are possible for both algorithms. If you are interested, I modestly recommend this essay on my blog.
First, it is good style to express nested structure by indentation, so it is visually apparent; and also to put each of if's clauses, the consequent and the alternative, on its own line:
(define (isPrimeHelper x k)
(if (= x k)
#t ; consequent
(if (= (remainder x k) 0) ; alternative
;; ^^ indentation
#f ; consequent
(isPrimeHelper x (+ k 1))))) ; alternative
(define (printPrimesUpTo n)
(define result '())
(define (helper x)
(if (= x (+ 1 n))
result ; consequent
(if (isPrime x) ; alternative
(cons x result) )) ; no alternative!
;; ^^ indentation
( helper (+ x 1)))
( helper 1 ))
Now it is plainly seen that the last thing that your helper function does is to call itself with an incremented x value, always. There's no stopping conditions, i.e. this is an infinite loop.
Another thing is, calling (cons x result) does not alter result's value in any way. For that, you need to set it, like so: (set! result (cons x result)). You also need to put this expression in a begin group, as it is evaluated not for its value, but for its side-effect:
(define (helper x)
(if (= x (+ 1 n))
result
(begin
(if (isPrime x)
(set! result (cons x result)) ) ; no alternative!
(helper (+ x 1)) )))
Usually, the explicit use of set! is considered bad style. One standard way to express loops is as tail-recursive code using named let, usually with the canonical name "loop" (but it can be any name whatever):
(define (primesUpTo n)
(let loop ((x n)
(result '()))
(cond
((<= x 1) result) ; return the result
((isPrime x)
(loop (- x 1) (cons x result))) ; alter the result being built
(else (loop (- x 1) result))))) ; go on with the same result
which, in presence of tail-call optimization, is actually equivalent to the previous version.
The (if) expression in your (helper) function is not the tail expression of the function, and so is not returned, but control will always continue to (helper (+ x 1)) and recurse.
The more efficient prime?(from Sedgewick's "Algorithms"):
(define (prime? n)
(define (F n i) "helper"
(cond ((< n (* i i)) #t)
((zero? (remainder n i)) #f)
(else
(F n (+ i 1)))))
"primality test"
(cond ((< n 2) #f)
(else
(F n 2))))
You can do this much more nicely. I reformated your code:
(define (prime? x)
(define (prime-helper x k)
(cond ((= x k) #t)
((= (remainder x k) 0) #f)
(else
(prime-helper x (+ k 1)))))
(cond ((= x 1) #f)
((= x 2) #t)
(else
(prime-helper x 2))))
(define (primes-up-to n)
(define (helper x)
(cond ((= x 0) '())
((prime? x)
(cons x (helper (- x 1))))
(else
(helper (- x 1)))))
(reverse
(helper n)))
scheme#(guile-user)> (primes-up-to 20)
$1 = (2 3 5 7 11 13 17 19)
Please don’t write Scheme like C or Java – and have a look at these style rules for languages of the lisp-family for the sake of readability: Do not use camel-case, do not put parentheses on own lines, mark predicates with ?, take care of correct indentation, do not put additional whitespace within parentheses.
I've written the code for multiplicative inverse of modulo m. It works for most of the initial cases but not for some. The code is below:
(define (inverse x m)
(let loop ((x (modulo x m)) (a 1))
(cond ((zero? x) #f) ((= x 1) a)
(else (let ((q (- (quotient m x))))
(loop (+ m (* q x)) (modulo (* q a) m)))))))
For example it gives correct values for (inverse 5 11) -> 9 (inverse 9 11) -> 5 (inverse 7 11 ) - > 8 (inverse 8 12) -> #f but when i give (inverse 5 12) it produces #f while it should have been 5. Can you see where the bug is?
Thanks for any help.
The algorithm you quoted is Algorithm 9.4.4 from the book Prime Numbers by Richard Crandall and Carl Pomerance. In the text of the book they state that the algorithm works for both prime and composite moduli, but in the errata to their book they correctly state that the algorithm works always for prime moduli and mostly, but not always, for composite moduli. Hence the failure that you found.
Like you, I used Algorithm 9.4.4 and was mystified at some of my results until I discovered the problem.
Here's the modular inverse function that I use now, which works with both prime and composite moduli, as long as its two arguments are coprime to one another. It is essentially the extended Euclidean algorithm that #OscarLopez uses, but with some redundant calculations stripped out. If you like, you can change the function to return #f instead of throwing an error.
(define (inverse x m)
(let loop ((x x) (b m) (a 0) (u 1))
(if (zero? x)
(if (= b 1) (modulo a m)
(error 'inverse "must be coprime"))
(let* ((q (quotient b x)))
(loop (modulo b x) x u (- a (* u q)))))))
Does it have to be precisely that algorithm? if not, try this one, taken from wikibooks:
(define (egcd a b)
(if (zero? a)
(values b 0 1)
(let-values (((g y x) (egcd (modulo b a) a)))
(values g (- x (* (quotient b a) y)) y))))
(define (modinv a m)
(let-values (((g x y) (egcd a m)))
(if (not (= g 1))
#f
(modulo x m))))
It works as expected:
(modinv 5 11) ; 9
(modinv 9 11) ; 5
(modinv 7 11) ; 8
(modinv 8 12) ; #f
(modinv 5 12) ; 5
I think this is the Haskell code on that page translated directly into Scheme:
(define (inverse p q)
(cond ((= p 0) #f)
((= p 1) 1)
(else
(let ((recurse (inverse (mod q p) p)))
(and recurse
(let ((n (- p recurse)))
(div (+ (* n q) 1) p)))))))
It looks like you're trying to convert it from recursive to tail-recursive, which is why things don't match up so well.
These two functions below can help you as well.
Theory
Here’s how we find the multiplicative inverse d. We want e*d = 1(mod n), which means that ed + nk = 1 for some integer k. So we’ll write a procedure that solves the general equation ax + by = 1, where a and b are given, x and y are variables, and all of these values are integers. We’ll use this procedure to solve ed + nk = 1 for d and k. Then we can throw away k and simply return d.
>
(define (ax+by=1 a b)
(if (= b 0)
(cons 1 0)
(let* ((q (quotient a b))
(r (remainder a b))
(e (ax+by=1 b r))
(s (car e))
(t (cdr e)))
(cons t (- s (* q t))))))
This function is a general solution to an equation in form of ax+by=1 where a and b is given.The inverse-mod function simply uses this solution and returns the inverse.
(define inverse-mod (lambda (a m)
(if (not (= 1 (gcd a m)))
(display "**Error** No inverse exists.")
(if (> 0(car (ax+by=1 a m)))
(+ (car (ax+by=1 a m)) m)
(car (ax+by=1 a m))))))
Some test cases are :
(inverse-mod 5 11) ; -> 9 5*9 = 45 = 1 (mod 11)
(inverse-mod 9 11) ; -> 5
(inverse-mod 7 11) ; -> 8 7*8 = 56 = 1 (mod 11)
(inverse-mod 5 12) ; -> 5 5*5 = 25 = 1 (mod 12)
(inverse-mod 8 12) ; -> error no inverse exists