I have asked an earlier question regarding how to set-up my function based on the requirements in the problem, and using already the code I had written, the problem now is that, no matter what, the output stays the same, no matter how much I change the value of the parameter k in first-value-k-or-higher x tol 1.
Here is my code based on the feedback in my other question:
(define (square x)
(* x x))
(define (first-value-k-or-higher x tol k)
(if (<= (abs(- x
(square (babylonian x k))))
tol)
k
(first-value-k-or-higher x tol (+ k 1))))
(define (terms-needed x tol)
(first-value-k-or-higher x tol 1))
Here is a couple example outputs if the function looks like the one I have above:
> (terms-needed 15 .001)
1
> (terms-needed 234 3)
1
> (terms-needed 23421 453)
1
>
If I change the function terms-needed x tol to something like this:
(define (square x)
(* x x))
(define (first-value-k-or-higher x tol k)
(if (<= (abs(- x
(square (babylonian x k))))
tol)
k
(first-value-k-or-higher x tol (+ k 1))))
(define (terms-needed x tol)
(first-value-k-or-higher x tol 100))
The new function will output:
> (terms-needed 15 .0001)
100
> (terms-needed 243 3)
100
>
Terms-needed is supposed to evaluate to the number of terms in the infinite sum needed to be within tol, that is, the smallest k such that the difference between x and (square (babylonian x k)) is less than tol. As I have mentioned the problem is I keep getting the same output of "1" no matter what I put down for the values of the parameters of terms-needed x tol. I also believe that is where the problem is coming from, because if I change (first-value-k-or-higher x tol 1)) to something like (first-value-k-or-higher x tol 2)) or any other value (first-value-k-or-higher x tol 2)) will output that value, for example with (first-value-k-or-higher x tol 2)) will output 2.
Here is the function babylonian x k that is needed to run the program first-value-k-or-higher x tol k which is needed to run terms-needed x tol:
(define (babylonian x k)
(if (>= x 1)
(if (= k 0)
(/ x 2)
(* (/ 1 2) (+ (expt x (/ 1 2)) (/ x (expt x (/ 1 2))))))
1)
)
The babylonian function is supposed to compute roots using the babylonian method and evaluates to the k^th approximation (Sk). The babylonian function passes all the tests as well.
Here is my earlier problem for more background information
No,
(define (babylonian x k)
(if (>= x 1)
(if (= k 0)
(/ x 2)
(* (/ 1 2) (+ (expt x (/ 1 2)) (/ x (expt x (/ 1 2))))))
1)
)
could not possibly be computing "the k^th approximation" of x, because there's no k in the alternative of the inner if expression.
Related
I am trying to Define a recursive procedure called (nDivide x y n) with three parameters x, y, and n. It returns the result of x divided by y n times. I have a function that divides
(define (Divide x y)
(/ x y)).
Now I am trying to use the Divide functions in to nDivide and I cant get it to work
There are basically two ways to do something n times:
Do it once, then do it n - 1 times;
Do it n - 1 times, then do it once.
You get different procedures depending on which path you choose:
; divide, then recurse
(define (nDivide x y n)
(if (zero? n)
x
(nDivide (Divide x y) y (- n 1))))
; recurse, then divide
(define (nDivide x y n)
(if (zero? n)
x
(Divide (nDivide x y (- n 1)) y)))
Recursive implementation (though why would anyone do this):
(define (nDivide x y n)
(if (= n 0)
x
(Divide (nDivide x y (- n 1)) y)))
Here is an iterative implementation of nDivide, which is more straightforward:
(define (nDivide x y n)
(if (= n 0)
x
(nDivide (Divide x y) y (- n 1))))
(define (Divide x y) (/ x y))
Example:
(nDivide 10000 3 4)
;Value: 10000/81
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 am having trouble where my code feels incomplete and plain wrong. For my function (terms-needed x tol) I am supposed to find the smallest k such that the difference between x and (square (babylonian x k)) is less than tol (tolerance). In other words we are supposed to measure how large k needs to be in the function (babylonian x k) to provide a good approximation of the square root.
As of right now I am getting an error of "application: not a procedure;" with my code
(define (square x)
(* x x))
(define (first-value-k-or-higher x tol k)
(if (<= (x)
(square (babylonian x k)) tol)
k)
(first-value-k-or-higher x tol (+ k 1))
)
(define (terms-needed x tol)
(first-value-k-or-higher x tol 1))
We are supposed to use a helper-function (first-value-k-or-higher x tol k) that evaluates to k if (square (bablyonian x k)) is within tol of the argument x, otherwise calls itself recursively
with larger k.
This is the function that is needed to make (terms-needed x tol) work:
(define (babylonian x k)
(if (>= x 1)
(if (= k 0)
(/ x 2)
(* (/ 1 2) (+ (expt x (/ 1 2)) (/ x (expt x (/ 1 2))))))
1)
)
Here is the full text, providing the full context on what the problem is supposed to be.
We will now measure how large k needs to be in the above function to provide a good approximation
of the square root. You will write a SCHEME function (terms-needed x tol) that will
evaluate to the number of terms in the infinite sum needed to be within tol, that is, the smallest
k such that the difference between x and (square (babylonian x k)) is less than tol.
Remark 2. At first glance, the problem of defining (terms-needed x tol) appears a little challenging,
because it’s not at all obvious how to express it in terms of a smaller problem. But you might
consider writing a helper function (first-value-k-or-higher x tol k) that evaluates to k if
(square (bablyonian x k)) is within tol of the argument x, otherwise calls itself recursively
with larger k.
You have several problems.
First, you have parentheses around x in
(if (<= (x)
That's causing the error you're seeing, because it's trying to call the function named x, but x names a number, not a function.
Second, you're not calculating the difference between x and (square (babylonian x k)). Instead, you gave 3 arguments to <=.
Third, you're not making the recursive call when the comparison fails. It's outside the if, so it's being done all the time (if you make use of an editor's automatic indentation feature, you might have noticed this problem yourself).
Fourth, you need to get the absolute value of the difference, not just the difference itself. Otherwise, if the difference is a large negative number, you'll consider it within the tolerance, which it shouldn't be.
(define (first-value-k-or-higher x tol k)
(if (<= (abs (- x (square (babylonian x k))))
tol)
k
(first-value-k-or-higher x tol (+ k 1))))
I am writing a function in Scheme that is supposed to take two integers, X and Y, and then recursively add X/Y + (X-1)/(Y-1) + ...until one of the numbers reaches 0.
For example, take 4 and 3:
4/3 + 3/2 + 2/1 = 29/6
Here is my function which is not working correctly:
(define changingFractions (lambda (X Y)
(cond
( ((> X 0) and (> Y 0)) (+ (/ X Y) (changingFunctions((- X 1) (- Y 1)))))
( ((= X 0) or (= Y 0)) 0)
)
))
EDIT: I have altered my code to fix the problem listed in the comments, as well as changing the location of or and and.
(define changingFractions (lambda (X Y)
(cond
( (and (> X 0) (> Y 0)) (+ (/ X Y) (changingFunctions (- X 1) (- Y 1) )))
( (or (= X 0) (= Y 0)) 0)
)
))
Unfortunately, I am still getting an error.
A couple of problems there:
You should define a function with the syntax (define (func-name arg1 arg2 ...) func-body), rather than assigning a lambda function to a variable.
The and and or are used like functions, by having them as the first element in a form ((and x y) rather than (x and y)). Not by having them between the arguments.
You have an extra set of parens around the function parameters for the recursive call, and you wrote changingFunctions when the name is changingFractions.
Not an error, but don't put closing parens on their own line.
The naming convention in Lisps is to use dashes, not camelcase (changing-fractions rather than changingFractions).
With those fixed:
(define (changing-fractions x y)
(cond
((and (> x 0) (> y 0)) (+ (/ x y) (changing-fractions (- x 1) (- y 1))))
((or (= x 0) (= y 0)) 0)))
But you could change the cond to an if to make it clearer:
(define (changing-fractions x y)
(if (and (> x 0) (> y 0))
(+ (/ x y) (changing-fractions (- x 1) (- y 1)))
0))
I personally like this implementation. It has a proper tail call unlike the other answers provided here.
(define (changing-fractions x y (z 0))
(cond ((zero? x) z)
((zero? y) z)
(else (changing-fractions (sub1 x) (sub1 y) (+ z (/ x y))))))
(changing-fractions 4 3) ; => 4 5/6
The trick is the optional z parameter that defaults to 0. Using this accumulator, we can iteratively build up the fractional sum each time changing-fractions recurses. Compare this to the additional stack frames that are added for each recursion in #jkliski's answer
; changing-fractions not in tail position...
(+ (/ x y) (changing-fractions (- x 1) (- y 1)))
Im making a scheme program that calculates
cos(x) = 1-(x^2/2!)+(x^4/4!)-(x^6/6!).......
whats the most efficient way to finish the program and how would you do the alternating addition and subtraction, thats what I used the modulo for but doesnt work for 0 and 1 (first 2 terms). x is the intial value of x and num is the number of terms
(define cosine-taylor
(lambda (x num)
(do ((i 0 (+ i 1)))
((= i num))
(if(= 0 (modulo i 2))
(+ x (/ (pow-tr2 x (* i 2)) (factorial (* 2 i))))
(- x (/ (pow-tr2 x (* i 2)) (factorial (* 2 i))))
))
x))
Your questions:
whats the most efficient way to finish the program? Assuming you want use the Taylor series expansion and simply sum up the terms n times, then your iterative approach is fine. I've refined it below; but your algorithm is fine. Others have pointed out possible loss of precision issues; see below for my approach.
how would you do the alternating addition and subtraction? Use another 'argument/local-variable' of odd?, a boolean, and have it alternate by using not. When odd? subtract when not odd? add.
(define (cosine-taylor x n)
(let computing ((result 1) (i 1) (odd? #t))
(if (> i n)
result
(computing ((if odd? - +) result (/ (expt x (* 2 i)) (factorial (* 2 i))))
(+ i 1)
(not odd?)))))
> (cos 1)
0.5403023058681398
> (cosine-taylor 1.0 100)
0.5403023058681397
Not bad?
The above is the Scheme-ish way of performing a 'do' loop. You should easily be able to see the correspondence to a do with three locals for i, result and odd?.
Regarding loss of numeric precision - if you really want to solve the precision problem, then convert x to an 'exact' number and do all computation using exact numbers. By doing that, you get a natural, Scheme-ly algorithm with 'perfect' precision.
> (cosine-taylor (exact 1.0) 100)
3982370694189213112257449588574354368421083585745317294214591570720658797345712348245607951726273112140707569917666955767676493702079041143086577901788489963764057368985531760218072253884896510810027045608931163026924711871107650567429563045077012372870953594171353825520131544591426035218450395194640007965562952702049286379961461862576998942257714483441812954797016455243/7370634274437294425723020690955000582197532501749282834530304049012705139844891055329946579551258167328758991952519989067828437291987262664130155373390933935639839787577227263900906438728247155340669759254710591512748889975965372460537609742126858908788049134631584753833888148637105832358427110829870831048811117978541096960000000000000000000000000000000000000000000000000
> (inexact (cosine-taylor (exact 1.0) 100))
0.5403023058681398
we should calculate the terms in iterative fashion to prevent the loss of precision from dividing very large numbers:
(define (cosine-taylor-term x)
(let ((t 1.0) (k 0))
(lambda (msg)
(case msg
((peek) t)
((pull)
(let ((p t))
(set! k (+ k 2))
(set! t (* (- t) (/ x (- k 1)) (/ x k)))
p))))))
Then it should be easy to build a function to produce an n-th term, or to sum the terms up until a term is smaller than a pre-set precision value:
(define t (cosine-taylor-term (atan 1)))
;Value: t
(reduce + 0 (map (lambda(x)(t 'pull)) '(1 2 3 4 5)))
;Value: .7071068056832942
(cos (atan 1))
;Value: .7071067811865476
(t 'peek)
;Value: -2.4611369504941985e-8
A few suggestions:
reduce your input modulo 2pi - most polynomial expansions converge very slowly with large numbers
Keep track of your factorials rather than computing them from scratch each time (once you have 4!, you get 5! by multiplying by 5, etc)
Similarly, all your powers are powers of x^2. Compute x^2 just once, then multiply the "x power so far" by this number (x2), rather than taking x to the n'th power
Here is some python code that implements this - it converges with very few terms (and you can control the precision with the while(abs(delta)>precision): statement)
from math import *
def myCos(x):
precision = 1e-5 # pick whatever you need
xr = (x+pi/2) % (2*pi)
if xr > pi:
sign = -1
else:
sign = 1
xr = (xr % pi) - pi/2
x2 = xr * xr
xp = 1
f = 1
c = 0
ans = 1
temp = 0
delta = 1
while(abs(delta) > precision):
c += 1
f *= c
c += 1
f *= c
xp *= x2
temp = xp / f
c += 1
f *= c
c += 1
f *= c
xp *= x2
delta = xp/f - temp
ans += delta
return sign * ans
Other than that I can't help you much as I am not familiar with scheme...
For your general enjoyment, here is a stream implementation. The stream returns an infinite sequence of taylor terms based on the provided func. The func is called with the current index.
(define (stream-taylor func)
(stream-map func (stream-from 0)))
(define (stream-cosine x)
(stream-taylor (lambda (n)
(if (zero? n)
1
(let ((odd? (= 1 (modulo n 2))))
;; Use `exact` if desired...
;; and see #WillNess above; save 'last'; use for next; avoid expt/factorial
((if odd? - +) (/ (expt x (* 2 n)) (factorial (* 2 n)))))))))
> (stream-fold + 0 (stream-take 10 (stream-cosine 1.0)))
0.5403023058681397
Here's the most streamlined function I could come up with.
It takes advantage of the fact that the every term is multiplied by (-x^2) and divided by (i+1)*(i+2) to come up with the text term.
It also takes advantage of the fact that we are computing factorials of 2, 4, 6. etc. So it increments the position counter by 2 and compares it with 2*N to stop iteration.
(define (cosine-taylor x num)
(let ((mult (* x x -1))
(twice-num (* 2 num)))
(define (helper iter prev-term prev-out)
(if (= iter twice-num)
(+ prev-term prev-out)
(helper (+ iter 2)
(/ (* prev-term mult) (+ iter 1) (+ iter 2))
(+ prev-term prev-out))))
(helper 0 1 0)))
Tested at repl.it.
Here are some answers:
(cosine-taylor 1.0 2)
=> 0.5416666666666666
(cosine-taylor 1.0 4)
=> 0.5403025793650793
(cosine-taylor 1.0 6)
=> 0.5403023058795627
(cosine-taylor 1.0 8)
=> 0.5403023058681398
(cosine-taylor 1.0 10)
=> 0.5403023058681397
(cosine-taylor 1.0 20)
=> 0.5403023058681397