So i'm trying to solve the collatz function iteratively in scheme but my test cases keep showing up as
(define (collatz n)
(define (collatz-iter n counter)
(if (<= n 1)
1
(if (even? n) (collatz-iter (/ n 2) (+ counter 1))
(collatz-iter (+ (* n 3) 1) (+ counter 1))
)
)
)
)
However, my test cases keep resulting in "#[constant 13 #x2]". What did I write wrong, if anything?
You forgot to call collatz-iter. Also, it's not clear what do you intend to do with counter, you just increment it, but never actually use its value - your procedure will always return 1 (assuming that the Collatz conjecture is true, which seems quite possible).
I'm guessing you intended to return the counter, so here's how to fix your procedure:
(define (collatz n)
(define (collatz-iter n counter)
(if (<= n 1)
counter ; return the counter
(if (even? n)
(collatz-iter (/ n 2) (+ counter 1))
(collatz-iter (+ (* n 3) 1) (+ counter 1)))))
(collatz-iter n 1)) ; call collatz-iter
And this is how it works for the examples in wikipedia:
(collatz 6)
=> 9
(collatz 11)
=> 15
(collatz 27)
=> 112
So basically we're counting the length of the Collatz sequence for a given number.
You should indent your code properly. With proper formatting, it's
(define (collatz n)
(define (collatz-iter n counter)
(if (<= n 1)
1
(if (even? n)
(collatz-iter (/ n 2) (+ counter 1))
(collatz-iter (+ (* n 3) 1) (+ counter 1))))))
which clearly has no body forms to execute, just an internal definition. You need to add a call to collatz-iter, like this:
(define (collatz n)
(define (collatz-iter n counter)
(if (<= n 1)
1
(if (even? n)
(collatz-iter (/ n 2) (+ counter 1))
(collatz-iter (+ (* n 3) 1) (+ counter 1)))))
(collatz-iter n 1))
(I'm not sure what your initial counter value should be. I'm assuming 1 is reasonable, but perhaps it should be zero?) Better yet, since the body it just a call to collatz-iter, you can make this a named let, which is more like your original code:
(define (collatz n)
(let iter ((n n) (counter 1))
(if (<= n 1)
1
(if (even? n)
(iter (/ n 2) (+ counter 1))
(iter (+ (* n 3) 1) (+ counter 1))))))
It's sort of like combining the internal definition with the single call to the local function. Once you've done this, though, you'll see that it always returns 1, when it eventually gets to the base case (assuming the Collatz conjecture is true, of course). Fixing this, you'll end up with:
(define (collatz n)
(let iter ((n n) (counter 1))
(if (<= n 1)
counter
(if (even? n)
(iter (/ n 2) (+ counter 1))
(iter (+ (* n 3) 1) (+ counter 1))))))
When I try to run your code in Racket I get the error:
no expression after a sequence of internal definitions
This is telling us that the collatz function conatains the collatz-iter definition, but no expression to call it (other than the recursive calls in collatz-iter). That can be fixed by adding a call to (collatz-iter n 0) as the last line in collatz.
However, when you run the program it always returns 1. Not very interesting. If instead you change it to return the value of counter you can see how many steps it took for the sequence to reach 1.
(define (collatz n)
(define (collatz-iter n counter)
(if (<= n 1)
counter
(if (even? n) (collatz-iter (/ n 2) (+ counter 1))
(collatz-iter (+ (* n 3) 1) (+ counter 1))
)
)
)
(collatz-iter n 0)
)
We can check it against a few examples given on the Wikipedia Collatz conjecture article.
> (collatz 6)
8
> (collatz 11)
14
> (collatz 27)
111
>
Related
I am trying to calculate and approximation for the Euler number using a do loop in Scheme
Something is not quite right because nothing is displayed. Can someone help me to find the fix for the code below? Thanks.
(define (factorial n)
(cond
((= n 0)1)
((* n(factorial(- n 1))))))
; using a do loop, I want to calculate 1/0! + 1/1! + 2/2! + 3/3!...
(define (ei n)
(define sum 0)
(do ((i 0 (+ 1 i)))
((> i n))
(+ sum (/ 1.(factorial i)))))
(ei 6)
I expect a number close to 2.7
You need to update the sum variable and also return its value:
(define (factorial n)
(cond
((= n 0) 1)
((* n (factorial (- n 1))))))
(define (ei n)
(define sum 0)
(do ((i 0 (+ 1 i)))
((> i n))
(set! sum (+ sum (/ 1. (factorial i)))))
sum)
(ei 6)
This results in 2.7180555555555554.
Use the fact that a do loop can update multiple variables.
(define (factorial n)
(cond
((= n 0) 1)
((* n (factorial (- n 1))))))
(define (ei n)
(do ((i 0 (+ 1 i))
(sum 0.0 (+ sum (/ 1. (factorial i)))))
((> i n) sum)))
I've spent some time looking at the questions about this on here and throughout the internet but I can't really find anything that makes sense to me.
Basically I need help on realizing a function in scheme that evaluates Leibniz's formula when you give it a value k. The value you input lets the function know how many values in the series it should compute. This is what I have so far, I'm not sure what way I need to write this program to make it work. Thanks!
(define (fin-alt-series k)
(cond ((= k 1)4)
((> k 1)(+ (/ (expt -1 k) (+(* 2.0 k) 1.0)) (fin-alt-series (- k 1.0))))))
The base case is incorrect. And we can clean-up the code a bit:
(define (fin-alt-series k)
(cond ((= k 0) 1)
(else
(+ (/ (expt -1.0 k)
(+ (* 2 k) 1))
(fin-alt-series (- k 1))))))
Even better, we can rewrite the procedure to use tail recursion, it'll be faster this way:
(define (fin-alt-series k)
(let loop ((k k) (sum 0))
(if (< k 0)
sum
(loop (- k 1)
(+ sum (/ (expt -1.0 k) (+ (* 2 k) 1)))))))
For example:
(fin-alt-series 1000000)
=> 0.7853984133971936
(/ pi 4)
=> 0.7853981633974483
I have to make a function that finds the "cost" of a Fibonacci number. My Fibonacci code is
(define fib (lambda (n) (cond
((< n 0) 'Error)
((= n 0) 0)
((= n 1) 1)
(else (+ (fib (- n 1)) (fib (- n 2)))))))
Each + or - that is used to evaluate a fib number is worth $1. Each < or > is worth $0.01. For example, 1 is worth $0.01, 2 is worth $3.03, etc. I don't know how to count the number of +, -, <, and >. Do I need the fib code in my fibCost code?
I'm not sure whether or not you wanted the solution to include the original code or not. There are direct ways of computing the cost, but I think it's interesting to look at ways that are similar to instrumenting the existing code. That is, what can we change so that something very much like the original code will compute what we want?
First, we can replace the arithmetic operators with a bit of indirection. That is, instead of calling (+ x y), you can call (op + 100 x y), which increments the total-cost variable.
(define (fib n)
(let* ((total-cost 0)
(op (lambda (fn cost . args)
(set! total-cost (+ total-cost cost))
(apply fn args))))
(let fib ((n n))
(cond
((op < 1 n 0) 'error)
((= n 0) 1)
((= n 1) 1)
(else (op + 100
(fib (op - 100 n 1))
(fib (op - 100 n 2))))))
total-cost))
That doesn't let us keep the original code, though. We can do better by defining local versions of the arithmetic operators, and then using the original code:
(define (fib n)
(let* ((total-cost 0)
(op (lambda (fn cost)
(lambda args
(set! total-cost (+ total-cost cost))
(apply fn args))))
(< (op < 1))
(+ (op + 100))
(- (op - 100)))
(let fib ((n n))
(cond
((< n 0) 'error)
((= n 0) 1)
((= n 1) 1)
(else (+ (fib (- n 1))
(fib (- n 2))))))
total-cost))
> (fib 1)
1
> (fib 2)
303
> (fib 3)
605
> (fib 4)
1209
What's nice about this approach is that if you start using macros to do some source code manipulation, you could actually use this as a sort of poor-man's profiler, or tracing system. (I'd suggest sticking with the more robust tools provided by the implementation, of course, but there are times when a technique like this can be useful.)
Additionally, this doesn't even have to compute the Fibonnaci number anymore. It's still computed because we do (apply fn args), but if we remove that, then we never even call the original arithmetic operation.
The quick and dirty solution would be to define a counter variable each time the cost procedure is started, and update it with the corresponding value at each branch of the recursion. For example:
(define (fib-cost n)
(let ((counter 0)) ; counter initialized with 0 at the beginning
(let fib ((n n)) ; inner fibonacci procedure
; update counter with the corresponding cost
(set! counter (+ counter 0.01))
(when (> n 1)
(set! counter (+ counter 3)))
(cond ((< n 0) 'Error)
((= n 0) 0)
((= n 1) 1)
(else (+ (fib (- n 1)) (fib (- n 2))))))
counter)) ; return the counter at the end
Answering your second question - no, we don't need the whole fib code; given that we're not interested in the actual value of fibonacci, the above can be further simplified to just make the required calls and ignore the returned values:
(define (fib-cost n)
(let ((counter 0)) ; counter initialized with 0 at the beginning
(let fib ((n n)) ; inner fibonacci procedure
; update counter with the corresponding cost
(set! counter (+ counter 0.01))
(when (> n 1)
(fib (- n 1))
(fib (- n 2))
(set! counter (+ counter 3))))
counter)) ; return the counter at the end
You have +/- just anytime you call the code recursively, in the else Part. So, easily anytime you enter the else part, you should count 3 of them. One for f(n-1), one for f(n-2) and one for f(n-1) + f(n-2).
Just for fun, a solution using syntactic extensions (aka "macros").
Let's define the following:
(define-syntax-rule (define-cost newf oldf thiscost totalcost)
(define (newf . parms)
(set! totalcost (+ totalcost thiscost))
(apply oldf parms)))
Now we create procedures based on the original procedures you want to have a cost:
(define-cost +$ + 100 cost)
(define-cost -$ - 100 cost)
(define-cost <$ < 1 cost)
so using +$ will do an addition and increase a cost counter by 1, and so on.
Now we adapt your inititial procedure to use the newly defined ones:
(define fib
(lambda (n)
(cond
((<$ n 0) 'Error)
((= n 0) 0)
((= n 1) 1)
(else
(+$ (fib (-$ n 1)) (fib (-$ n 2)))))))
For convenience, we create a macro to return both the result of a procedure and its cost:
(define-syntax-rule (howmuch f . args)
(begin
(set! cost 0)
(cons (apply f 'args) cost)))
then a cost variable
(define cost #f)
and off we go
> (howmuch fib 1)
'(1 . 1)
> (howmuch fib 2)
'(1 . 303)
> (howmuch fib 10)
'(55 . 26577)
> (howmuch fib 1)
'(1 . 1)
How could I use this procedure:
(define (sum f n )
(if (= n 1)
(f 1)
(+ ( f n ) (sum f (- n 1)))))
in order to redefine the following one?
(define (zeno n)
(cond ((= n 1)
(/ 1 2))
((> n 1)
(+ (zeno (- n 1))
(/ 1 (expt 2 n))))))
Basically, I am trying to create another function called zeno-sec that uses the sum function written above.
The procedure sum accepts another procedure f and you have to find that f. If you look at the second procedure zeno you can spot a possible body of f in the second clause of cond, that is (/ 1 (expt 2 n)). So f will be (lambda (a) (/ 1 (expt 2 a))). Combining it with sum, the zeno-sec will look like:
(define (zeno-sec n)
(sum (lambda (a)
(/ 1 (expt 2 a)))
n))
Edit: Maybe some clarifications could help. If you look at the two procedures, sum and zeno, you can see they have very similar structure: a conditional form and a recursion. Also if you switch the places of the subexpressions in the last expressions you will notice that they are almost the same:
(+ (sum f (- n 1))
(f n))
and
(+ (zeno (- n 1))
(/ 1 (expt 2 n)))
See how the call (zeno (- n 1)) resembles the (sum f (- n 1)) and the (f n) becomes (/ 1 (expt 2 n)). I hope that makes some sense.
I am working through SICP. In exercise 1.28 about the Miller-Rabin test. I had this code, that I know is wrong because it does not follow the instrcuccions of the exercise.
(define (fast-prime? n times)
(define (even? x)
(= (remainder x 2) 0))
(define (miller-rabin-test n)
(try-it (+ 1 (random (- n 1)))))
(define (try-it a)
(= (expmod a (- n 1) n) 1))
(define (expmod base exp m)
(cond ((= exp 0) 1)
((even? exp)
(if (and (not (= exp (- m 1))) (= (remainder (square exp) m) 1))
0
(remainder (square (expmod base (/ exp 2) m)) m)))
(else
(remainder (* base (expmod base (- exp 1) m)) m))))
(cond ((= times 0) true)
((miller-rabin-test n) (fast-prime? n (- times 1)))
(else false)))
In it I test if the square of the exponent is congruent to 1 mod n. Which according
to what I have read, and other correct implementations I have seen is wrong. I should test
the entire number as in:
...
(square
(trivial-test (expmod base (/ exp 2) m) m))
...
The thing is that I have tested this, with many prime numbers and large Carmicheal numbers,
and it seems to give the correct answer, though a bit slower. I don't understand why this
seems to work.
Your version of the function "works" only because you are lucky. Try this experiment: evaluate (fast-prime? 561 3) a hundred times. Depending on the random witnesses that your function chooses, sometimes it will return true and sometimes it will return false. When I did that I got 12 true and 88 false, but you may get different results, depending on your random number generator.
> (let loop ((k 0) (t 0) (f 0))
(if (= k 100) (values t f)
(if (fast-prime? 561 3)
(loop (+ k 1) (+ t 1) f)
(loop (+ k 1) t (+ f 1)))))
12
88
I don't have SICP in front of me -- my copy is at home -- but I can tell you the right way to perform a Miller-Rabin primality test.
Your expmod function is incorrect; there is no reason to square the exponent. Here is a proper function to perform modular exponentiation:
(define (expm b e m) ; modular exponentiation
(let loop ((b b) (e e) (x 1))
(if (zero? e) x
(loop (modulo (* b b) m) (quotient e 2)
(if (odd? e) (modulo (* b x) m) x)))))
Then Gary Miller's strong pseudoprime test, which is a strong version of your try-it test for which there is a witness a that proves the compositeness of every composite n, looks like this:
(define (strong-pseudoprime? n a) ; strong pseudoprime base a
(let loop ((r 0) (s (- n 1)))
(if (even? s) (loop (+ r 1) (/ s 2))
(if (= (expm a s n) 1) #t
(let loop ((r r) (s s))
(cond ((zero? r) #f)
((= (expm a s n) (- n 1)) #t)
(else (loop (- r 1) (* s 2)))))))))
Assuming the Extended Riemann Hypothesis, testing every a from 2 to n-1 will prove (an actual, deterministic proof, not just a probabilistic estimate of primality) the primality of a prime n, or identify at least one a that is a witness to the compositeness of a composite n. Michael Rabin proved that if n is composite, at least three-quarters of the a from 2 to n-1 are witnesses to that compositeness, so testing k random bases demonstrates, but does not prove, the primality of a prime n to a probability of 4−k. Thus, this implementation of the Miller-Rabin primality test:
(define (prime? n k)
(let loop ((k k))
(cond ((zero? k) #t)
((not (strong-pseudoprime? n (random (+ 2 (- n 3))))) #f)
(else (loop (- k 1))))))
That always works properly:
> (let loop ((k 0) (t 0) (f 0))
(if (= k 100) (values t f)
(if (prime? 561 3)
(loop (+ k 1) (+ t 1) f)
(loop (+ k 1) t (+ f 1)))))
0
100
I know your purpose is to study SICP rather than to program primality tests, but if you're interested in programming with prime numbers, I modestly recommend this essay at my blog, which discusses the Miller-Rabin test, among other topics. You should also know there are better (faster, less likely to report erroneous result) primality tests available than randomized Miller-Rabin.
It seems to me, you still got correct answer, because in each iteration of expmod you check conditions for previous iteration. You could try to debug exp value using display function inside expmod. Really, your code is not very different from this one.